From 80b8f5f7f7e7c75a7f3ce3fb118029de6d90e422 Mon Sep 17 00:00:00 2001 From: Tim Munday Date: Tue, 4 Sep 2018 11:45:59 +0100 Subject: [PATCH 01/77] NanBool introduced --- HARK/ConsumptionSaving/ConsAggShockModel.py | 2 +- .../ConsGenIncProcessModel.py | 26 +++- HARK/ConsumptionSaving/ConsIndShockModel.py | 45 +++++-- HARK/ConsumptionSaving/ConsMarkovModel.py | 18 ++- HARK/ConsumptionSaving/ConsMedModel.py | 16 ++- HARK/ConsumptionSaving/ConsPrefShockModel.py | 33 +++-- HARK/ConsumptionSaving/ConsRepAgentModel.py | 2 +- HARK/ConsumptionSaving/ConsumerParameters.py | 3 + HARK/ConsumptionSaving/RepAgentModel.py | 2 +- .../TractableBufferStockModel.py | 1 + HARK/interpolation.py | 125 +++++++++++++----- 11 files changed, 198 insertions(+), 75 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py index 026304dff..952ed5d47 100644 --- a/HARK/ConsumptionSaving/ConsAggShockModel.py +++ b/HARK/ConsumptionSaving/ConsAggShockModel.py @@ -83,7 +83,7 @@ def __init__(self,time_flow=True,**kwds): # Add consumer-type specific objects, copying to create independent versions self.time_vary = deepcopy(IndShockConsumerType.time_vary_) self.time_inv = deepcopy(IndShockConsumerType.time_inv_) - self.delFromTimeInv('Rfree','vFuncBool','CubicBool') + self.delFromTimeInv('Rfree','vFuncBool','CubicBool','NanBool') self.poststate_vars = IndShockConsumerType.poststate_vars_ self.solveOnePeriod = solveConsAggShock self.update() diff --git a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py index b81bedff7..578ead42b 100644 --- a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py +++ b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py @@ -261,7 +261,8 @@ class ConsGenIncProcessSolver(ConsIndShockSetup): to shocks). ''' def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool): + pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool, + NanBool): ''' Constructor for a new solver for a one period problem with idiosyncratic shocks to persistent and transitory income, with persistent income tracked @@ -300,17 +301,20 @@ def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, included in the reported solution. CubicBool: boolean An indicator for whether the solver should use cubic or linear interpolation. + NanBool: boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- None ''' self.assignParameters(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,pLvlNextFunc, - BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool) + BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool, NanBool) self.defUtilityFuncs() def assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool): + pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool,NanBool): ''' Assigns inputs as attributes of self for use by other methods @@ -347,13 +351,16 @@ def assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, included in the reported solution. CubicBool: boolean An indicator for whether the solver should use cubic or linear interpolation. + NanBool: boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- none ''' ConsIndShockSetup.assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - 0.0,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) # dummy value for PermGroFac + 0.0,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool,NanBool) # dummy value for PermGroFac self.pLvlNextFunc = pLvlNextFunc self.pLvlGrid = pLvlGrid @@ -599,7 +606,8 @@ def usePointsForInterpolation(self,cLvl,mLvl,pLvl,interpolator): cFuncNowUnc = interpolator(mLvl,pLvl,cLvl) # Combine the constrained and unconstrained functions into the true consumption function - cFuncNow = LowerEnvelope2D(cFuncNowUnc,self.cFuncNowCnst) + cFuncNow = LowerEnvelope2D(cFuncNowUnc,self.cFuncNowCnst, + NanBool=self.NanBool) # Make the marginal value function vPfuncNow = self.makevPfunc(cFuncNow) @@ -869,7 +877,7 @@ def solve(self): def solveConsGenIncProcess(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,pLvlNextFunc, - BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool): + BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool,NanBool): ''' Solves the one period problem of a consumer who experiences persistent and transitory shocks to his income. Unlike in ConsIndShock, consumers do not @@ -910,6 +918,9 @@ def solveConsGenIncProcess(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,pL included in the reported solution. CubicBool: boolean An indicator for whether the solver should use cubic or linear interpolation. + NanBool: boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- @@ -919,7 +930,8 @@ def solveConsGenIncProcess(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,pL marginal value function, bounding MPCs, and normalized human wealth. ''' solver = ConsGenIncProcessSolver(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool) + pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool, + NanBool) solver.prepareToSolve() # Do some preparatory work solution_now = solver.solve() # Solve the period return solution_now diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 4ffd9a6d5..be83845a9 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -531,7 +531,8 @@ class ConsIndShockSetup(ConsPerfForesightSolver): to income. Has methods to set up but not solve the one period problem. ''' def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): + PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, + NanBool): ''' Constructor for a new solver-setup for problems with income subject to permanent and transitory shocks. @@ -570,17 +571,22 @@ def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. + NanBool: boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- None ''' self.assignParameters(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) + PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, + NanBool) self.defUtilityFuncs() def assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): + PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, + NanBool): ''' Assigns period parameters as attributes of self for use by other methods @@ -618,6 +624,9 @@ def assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. + NanBool: boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- @@ -630,6 +639,7 @@ def assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, self.aXtraGrid = aXtraGrid self.vFuncBool = vFuncBool self.CubicBool = CubicBool + self.NanBool = NanBool def defUtilityFuncs(self): @@ -893,7 +903,8 @@ def usePointsForInterpolation(self,cNrm,mNrm,interpolator): cFuncNowUnc = interpolator(mNrm,cNrm) # Combine the constrained and unconstrained functions into the true consumption function - cFuncNow = LowerEnvelope(cFuncNowUnc,self.cFuncNowCnst) + cFuncNow = LowerEnvelope(cFuncNowUnc, self.cFuncNowCnst, + NanBool = self.NanBool) # Make the marginal value function and the marginal marginal value function vPfuncNow = MargValueFunc(cFuncNow,self.CRRA) @@ -1175,7 +1186,7 @@ def solve(self): def solveConsIndShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, - BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): + BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, NanBool): ''' Solves a single period consumption-saving problem with CRRA utility and risky income (subject to permanent and transitory shocks). Can generate a value @@ -1214,6 +1225,10 @@ def solveConsIndShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGro included in the reported solution. CubicBool: boolean Indicator for whether the solver should use cubic or linear interpolation. + NanBool: boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. + Returns ------- @@ -1229,10 +1244,10 @@ def solveConsIndShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGro if (not CubicBool) and (not vFuncBool): solver = ConsIndShockSolverBasic(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA, Rfree,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool, - CubicBool) + CubicBool, NanBool) else: # Use the "advanced" solver if either is requested solver = ConsIndShockSolver(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) + PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, NanBool) solver.prepareToSolve() # Do some preparatory work solution_now = solver.solve() # Solve the period return solution_now @@ -1251,7 +1266,7 @@ class ConsKinkedRsolver(ConsIndShockSolver): it terminates immediately if Rboro < Rsave, as this has a different solution. ''' def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA, - Rboro,Rsave,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): + Rboro,Rsave,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, NanBool): ''' Constructor for a new solver for problems with risky income and a different interest rate on borrowing and saving. @@ -1294,6 +1309,9 @@ def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. + NanBool: boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- @@ -1305,7 +1323,7 @@ def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA, # Initialize the solver. Most of the steps are exactly the same as in # the non-kinked-R basic case, so start with that. ConsIndShockSolver.__init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rboro, - PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) + PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool,NanBool) # Assign the interest rates as class attributes, to use them later. self.Rboro = Rboro @@ -1375,7 +1393,7 @@ def prepareToCalcEndOfPrdvP(self): def solveConsKinkedR(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rboro,Rsave, - PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): + PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool,NanBool): ''' Solves a single period consumption-saving problem with CRRA utility and risky income (subject to permanent and transitory shocks), and different interest @@ -1420,6 +1438,9 @@ def solveConsKinkedR(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rboro,Rsave, included in the reported solution. CubicBool: boolean Indicator for whether the solver should use cubic or linear interpolation. + NanBool: boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- @@ -1433,7 +1454,7 @@ def solveConsKinkedR(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rboro,Rsave, solver = ConsKinkedRsolver(solution_next,IncomeDstn,LivPrb, DiscFac,CRRA,Rboro,Rsave,PermGroFac,BoroCnstArt, - aXtraGrid,vFuncBool,CubicBool) + aXtraGrid,vFuncBool,CubicBool,NanBool) solver.prepareToSolve() solution = solver.solve() @@ -1739,7 +1760,7 @@ class IndShockConsumerType(PerfForesightConsumerType): for risk aversion, discount factor, the interest rate, the grid of end-of- period assets, and an artificial borrowing constraint. ''' - time_inv_ = PerfForesightConsumerType.time_inv_ + ['BoroCnstArt','vFuncBool','CubicBool'] + time_inv_ = PerfForesightConsumerType.time_inv_ + ['BoroCnstArt','vFuncBool','CubicBool', 'NanBool'] shock_vars_ = ['PermShkNow','TranShkNow'] def __init__(self,cycles=1,time_flow=True,**kwds): diff --git a/HARK/ConsumptionSaving/ConsMarkovModel.py b/HARK/ConsumptionSaving/ConsMarkovModel.py index 73affbaff..d088a47bf 100644 --- a/HARK/ConsumptionSaving/ConsMarkovModel.py +++ b/HARK/ConsumptionSaving/ConsMarkovModel.py @@ -38,7 +38,7 @@ class ConsMarkovSolver(ConsIndShockSolver): ''' def __init__(self,solution_next,IncomeDstn_list,LivPrb,DiscFac, CRRA,Rfree_list,PermGroFac_list,MrkvArray,BoroCnstArt, - aXtraGrid,vFuncBool,CubicBool): + aXtraGrid,vFuncBool,CubicBool,NanBool): ''' Constructor for a new solver for a one period problem with risky income and transitions between discrete Markov states. In the descriptions below, @@ -85,6 +85,9 @@ def __init__(self,solution_next,IncomeDstn_list,LivPrb,DiscFac, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. + NanBool: boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- @@ -92,7 +95,7 @@ def __init__(self,solution_next,IncomeDstn_list,LivPrb,DiscFac, ''' # Set basic attributes of the problem ConsIndShockSolver.assignParameters(self,solution_next,np.nan,LivPrb,DiscFac,CRRA,np.nan, - np.nan,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) + np.nan,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool,NanBool) self.defUtilityFuncs() # Set additional attributes specific to the Markov model @@ -477,7 +480,8 @@ def makeSolution(self,cNrm,mNrm): self.cFuncNowCnst = LinearInterp([self.mNrmMin_list[i], self.mNrmMin_list[i]+1.0], [0.0,1.0]) cFuncNowUnc = interpfunc(mNrm[i,:],cNrm[i,:]) - cFuncNow = LowerEnvelope(cFuncNowUnc,self.cFuncNowCnst) + cFuncNow = LowerEnvelope(cFuncNowUnc,self.cFuncNowCnst, + NanBool=self.NanBool) # Make the marginal value function and pack up the current-state-conditional solution vPfuncNow = MargValueFunc(cFuncNow,self.CRRA) @@ -596,7 +600,7 @@ def makevFunc(self,solution): def solveConsMarkov(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, - MrkvArray,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): + MrkvArray,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool,NanBool): ''' Solves a single period consumption-saving problem with risky income and stochastic transitions between discrete states, in a Markov fashion. Has @@ -647,6 +651,9 @@ def solveConsMarkov(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFa CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. + NanBool: boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- @@ -662,7 +669,8 @@ def solveConsMarkov(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFa when in the i=0 Markov state this period. ''' solver = ConsMarkovSolver(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - PermGroFac,MrkvArray,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) + PermGroFac,MrkvArray,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, + NanBool) solution_now = solver.solve() return solution_now diff --git a/HARK/ConsumptionSaving/ConsMedModel.py b/HARK/ConsumptionSaving/ConsMedModel.py index 13719c77c..8d06b5efb 100644 --- a/HARK/ConsumptionSaving/ConsMedModel.py +++ b/HARK/ConsumptionSaving/ConsMedModel.py @@ -762,7 +762,7 @@ class ConsMedShockSolver(ConsGenIncProcessSolver): shocks to "medical need"-- multiplicative utility shocks for a second good. ''' def __init__(self,solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CRRAmed,Rfree,MedPrice, - pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool): + pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool,NanBool): ''' Constructor for a new solver for a one period problem with idiosyncratic shocks to permanent and transitory income and shocks to medical need. @@ -808,13 +808,16 @@ def __init__(self,solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CRRAme CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. + NanBool: boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- None ''' ConsGenIncProcessSolver.__init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool) + pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool,NanBool) self.MedShkDstn = MedShkDstn self.MedPrice = MedPrice self.CRRAmed = CRRAmed @@ -997,7 +1000,7 @@ def usePointsForInterpolation(self,xLvl,mLvl,pLvl,MedShk,interpolator): # Construct the unconstrained total expenditure function xFuncNowUnc = interpolator(mLvl,pLvl,MedShk,xLvl) xFuncNowCnst = self.xFuncNowCnst - xFuncNow = LowerEnvelope3D(xFuncNowUnc,xFuncNowCnst) + xFuncNow = LowerEnvelope3D(xFuncNowUnc,xFuncNowCnst,NanBool=self.NanBool) # Transform the expenditure function into policy functions for consumption and medical care aug_factor = 2 @@ -1291,7 +1294,7 @@ def solve(self): def solveConsMedShock(solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CRRAmed,Rfree,MedPrice, - pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool): + pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool,NanBool): ''' Solve the one period problem for a consumer with shocks to permanent and transitory income as well as medical need shocks (as multiplicative shifters @@ -1340,6 +1343,9 @@ def solveConsMedShock(solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CR CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. + NanBool: boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- @@ -1350,7 +1356,7 @@ def solveConsMedShock(solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CR on (mLvl,pLvl), with MedShk integrated out. ''' solver = ConsMedShockSolver(solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CRRAmed,Rfree, - MedPrice,pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool) + MedPrice,pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool,NanBool) solver.prepareToSolve() # Do some preparatory work solution_now = solver.solve() # Solve the period return solution_now diff --git a/HARK/ConsumptionSaving/ConsPrefShockModel.py b/HARK/ConsumptionSaving/ConsPrefShockModel.py index f83032c68..47ebf9d13 100644 --- a/HARK/ConsumptionSaving/ConsPrefShockModel.py +++ b/HARK/ConsumptionSaving/ConsPrefShockModel.py @@ -220,7 +220,8 @@ class ConsPrefShockSolver(ConsIndShockSolver): each period. ''' def __init__(self,solution_next,IncomeDstn,PrefShkDstn,LivPrb,DiscFac,CRRA, - Rfree,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): + Rfree,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, + NanBool): ''' Constructor for a new solver for problems with risky income, a different interest rate on borrowing and saving, and multiplicative shocks to utility. @@ -262,13 +263,16 @@ def __init__(self,solution_next,IncomeDstn,PrefShkDstn,LivPrb,DiscFac,CRRA, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. + NanBool: boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- None ''' ConsIndShockSolver.__init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA, - Rfree,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) + Rfree,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool,NanBool) self.PrefShkPrbs = PrefShkDstn[0] self.PrefShkVals = PrefShkDstn[1] @@ -332,7 +336,7 @@ def usePointsForInterpolation(self,cNrm,mNrm,interpolator): MPCmin_j = self.MPCminNow*self.PrefShkVals[j]**(1.0/self.CRRA) cFunc_this_shock = LowerEnvelope(LinearInterp(mNrm[j,:],cNrm[j,:], intercept_limit=self.hNrmNow*MPCmin_j, - slope_limit=MPCmin_j),self.cFuncNowCnst) + slope_limit=MPCmin_j),self.cFuncNowCnst,NanBool=self.NanBool) cFunc_list.append(cFunc_this_shock) # Combine the list of consumption functions into a single interpolation @@ -394,7 +398,7 @@ def makevFunc(self,solution): def solveConsPrefShock(solution_next,IncomeDstn,PrefShkDstn, LivPrb,DiscFac,CRRA,Rfree,PermGroFac,BoroCnstArt, - aXtraGrid,vFuncBool,CubicBool): + aXtraGrid,vFuncBool,CubicBool,NanBool): ''' Solves a single period of a consumption-saving model with preference shocks to marginal utility. Problem is solved using the method of endogenous gridpoints. @@ -436,6 +440,9 @@ def solveConsPrefShock(solution_next,IncomeDstn,PrefShkDstn, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. + NanBool: boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- @@ -451,7 +458,7 @@ def solveConsPrefShock(solution_next,IncomeDstn,PrefShkDstn, ''' solver = ConsPrefShockSolver(solution_next,IncomeDstn,PrefShkDstn,LivPrb, DiscFac,CRRA,Rfree,PermGroFac,BoroCnstArt,aXtraGrid, - vFuncBool,CubicBool) + vFuncBool,CubicBool,NanBool) solver.prepareToSolve() solution = solver.solve() return solution @@ -465,7 +472,8 @@ class ConsKinkyPrefSolver(ConsPrefShockSolver,ConsKinkedRsolver): each period, and a different interest rate on saving vs borrowing. ''' def __init__(self,solution_next,IncomeDstn,PrefShkDstn,LivPrb,DiscFac,CRRA, - Rboro,Rsave,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): + Rboro,Rsave,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, + NanBool): ''' Constructor for a new solver for problems with risky income, a different interest rate on borrowing and saving, and multiplicative shocks to utility. @@ -511,20 +519,24 @@ def __init__(self,solution_next,IncomeDstn,PrefShkDstn,LivPrb,DiscFac,CRRA, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. + NanBool: boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- None ''' ConsKinkedRsolver.__init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA, - Rboro,Rsave,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) + Rboro,Rsave,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, + NanBool) self.PrefShkPrbs = PrefShkDstn[0] self.PrefShkVals = PrefShkDstn[1] def solveConsKinkyPref(solution_next,IncomeDstn,PrefShkDstn, LivPrb,DiscFac,CRRA,Rboro,Rsave,PermGroFac,BoroCnstArt, - aXtraGrid,vFuncBool,CubicBool): + aXtraGrid,vFuncBool,CubicBool,NanBool): ''' Solves a single period of a consumption-saving model with preference shocks to marginal utility and a different interest rate on saving vs borrowing. @@ -571,6 +583,9 @@ def solveConsKinkyPref(solution_next,IncomeDstn,PrefShkDstn, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. + NanBool: boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- @@ -586,7 +601,7 @@ def solveConsKinkyPref(solution_next,IncomeDstn,PrefShkDstn, ''' solver = ConsKinkyPrefSolver(solution_next,IncomeDstn,PrefShkDstn,LivPrb, DiscFac,CRRA,Rboro,Rsave,PermGroFac,BoroCnstArt, - aXtraGrid,vFuncBool,CubicBool) + aXtraGrid,vFuncBool,CubicBool,NanBool) solver.prepareToSolve() solution = solver.solve() return solution diff --git a/HARK/ConsumptionSaving/ConsRepAgentModel.py b/HARK/ConsumptionSaving/ConsRepAgentModel.py index 82d0179ae..e98bd149b 100644 --- a/HARK/ConsumptionSaving/ConsRepAgentModel.py +++ b/HARK/ConsumptionSaving/ConsRepAgentModel.py @@ -208,7 +208,7 @@ def __init__(self,time_flow=True,**kwds): IndShockConsumerType.__init__(self,cycles=0,time_flow=time_flow,**kwds) self.AgentCount = 1 # Hardcoded, because this is rep agent self.solveOnePeriod = solveConsRepAgent - self.delFromTimeInv('Rfree','BoroCnstArt','vFuncBool','CubicBool') + self.delFromTimeInv('Rfree','BoroCnstArt','vFuncBool','CubicBool','NanBool') def getStates(self): ''' diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index c63472541..d526bb3a5 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -68,6 +68,7 @@ BoroCnstArt = 0.0 # Artificial borrowing constraint; imposed minimum level of end-of period assets CubicBool = False # Use cubic spline interpolation when True, linear interpolation when False vFuncBool = False # Whether to calculate the value function during solution +NanBool = True # Whether to exclude NA's when calculating lower envelope # Make a dictionary to specify an idiosyncratic income shocks consumer init_idiosyncratic_shocks = { 'CRRA': CRRA, @@ -93,6 +94,7 @@ 'tax_rate':0.0, 'vFuncBool':vFuncBool, 'CubicBool':CubicBool, + 'NanBool':NanBool, 'T_retire':T_retire, 'aNrmInitMean' : aNrmInitMean, 'aNrmInitStd' : aNrmInitStd, @@ -185,6 +187,7 @@ del init_agg_shocks['Rfree'] # Interest factor is endogenous in agg shocks model del init_agg_shocks['CubicBool'] # Not supported yet for agg shocks model del init_agg_shocks['vFuncBool'] # Not supported yet for agg shocks model +del init_agg_shocks['NanBool'] # Not supported yet for agg shocks model init_agg_shocks['PermGroFac'] = [1.0] init_agg_shocks['MgridBase'] = MgridBase init_agg_shocks['aXtraCount'] = 24 diff --git a/HARK/ConsumptionSaving/RepAgentModel.py b/HARK/ConsumptionSaving/RepAgentModel.py index dbeea42af..f402f9d87 100644 --- a/HARK/ConsumptionSaving/RepAgentModel.py +++ b/HARK/ConsumptionSaving/RepAgentModel.py @@ -208,7 +208,7 @@ def __init__(self,time_flow=True,**kwds): IndShockConsumerType.__init__(self,cycles=0,time_flow=time_flow,**kwds) self.AgentCount = 1 # Hardcoded, because this is rep agent self.solveOnePeriod = solveConsRepAgent - self.delFromTimeInv('Rfree','BoroCnstArt','vFuncBool','CubicBool') + self.delFromTimeInv('Rfree','BoroCnstArt','vFuncBool','CubicBool', 'NanBool') def getStates(self): ''' diff --git a/HARK/ConsumptionSaving/TractableBufferStockModel.py b/HARK/ConsumptionSaving/TractableBufferStockModel.py index 88f78d578..043c3dac4 100644 --- a/HARK/ConsumptionSaving/TractableBufferStockModel.py +++ b/HARK/ConsumptionSaving/TractableBufferStockModel.py @@ -542,6 +542,7 @@ def main(): 'tax_rate':0.0, # Tax rate on labor income (irrelevant) 'vFuncBool':False, # Whether to calculate the value function 'CubicBool':True, # Whether to use cubic splines (False --> linear splines) + 'NanBool': True, # Whether to excludes NA's when calculating the lower envelope 'MrkvArray':[MrkvArray] # State transition probabilities } MarkovType = MarkovConsumerType(**init_consumer_objects) # Make a basic consumer type diff --git a/HARK/interpolation.py b/HARK/interpolation.py index ff138a813..48dc76634 100644 --- a/HARK/interpolation.py +++ b/HARK/interpolation.py @@ -1642,7 +1642,7 @@ class LowerEnvelope(HARKinterpolator1D): ''' distance_criteria = ['functions'] - def __init__(self,*functions): + def __init__(self, *functions, NanBool = True): ''' Constructor to make a new lower envelope iterpolation. @@ -1650,11 +1650,14 @@ def __init__(self,*functions): ---------- *functions : function Any number of real functions; often instances of HARKinterpolator1D - + NanBool : boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- new instance of LowerEnvelope ''' + self.NanBool = NanBool self.functions = [] for function in functions: self.functions.append(function) @@ -1665,14 +1668,25 @@ def _evaluate(self,x): Returns the level of the function at each value in x as the minimum among all of the functions. Only called internally by HARKinterpolator1D.__call__. ''' - if _isscalar(x): - y = np.nanmin([f(x) for f in self.functions]) + + if self.NanBool == True: + if _isscalar(x): + y = np.nanmin([f(x) for f in self.functions]) + else: + m = len(x) + fx = np.zeros((m,self.funcCount)) + for j in range(self.funcCount): + fx[:,j] = self.functions[j](x) + y = np.nanmin(fx,axis=1) else: - m = len(x) - fx = np.zeros((m,self.funcCount)) - for j in range(self.funcCount): - fx[:,j] = self.functions[j](x) - y = np.nanmin(fx,axis=1) + if _isscalar(x): + y = np.min([f(x) for f in self.functions]) + else: + m = len(x) + fx = np.zeros((m,self.funcCount)) + for j in range(self.funcCount): + fx[:,j] = self.functions[j](x) + y = np.min(fx,axis=1) return y def _der(self,x): @@ -1701,7 +1715,6 @@ def _evalAndDer(self,x): dydx[c] = self.functions[j].derivative(x[c]) return y,dydx - class UpperEnvelope(HARKinterpolator1D): ''' The upper envelope of a finite set of 1D functions, each of which can be of @@ -1710,7 +1723,7 @@ class UpperEnvelope(HARKinterpolator1D): ''' distance_criteria = ['functions'] - def __init__(self,*functions): + def __init__(self,*functions, NanBool=True): ''' Constructor to make a new upper envelope iterpolation. @@ -1718,11 +1731,15 @@ def __init__(self,*functions): ---------- *functions : function Any number of real functions; often instances of HARKinterpolator1D + NanBool : boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- new instance of UpperEnvelope ''' + self.NanBool = NanBool self.functions = [] for function in functions: self.functions.append(function) @@ -1733,14 +1750,24 @@ def _evaluate(self,x): Returns the level of the function at each value in x as the maximum among all of the functions. Only called internally by HARKinterpolator1D.__call__. ''' - if _isscalar(x): - y = np.nanmax([f(x) for f in self.functions]) + if self.NanBool == True: + if _isscalar(x): + y = np.nanmax([f(x) for f in self.functions]) + else: + m = len(x) + fx = np.zeros((m,self.funcCount)) + for j in range(self.funcCount): + fx[:,j] = self.functions[j](x) + y = np.nanmax(fx,axis=1) else: - m = len(x) - fx = np.zeros((m,self.funcCount)) - for j in range(self.funcCount): - fx[:,j] = self.functions[j](x) - y = np.nanmax(fx,axis=1) + if _isscalar(x): + y = np.max([f(x) for f in self.functions]) + else: + m = len(x) + fx = np.zeros((m,self.funcCount)) + for j in range(self.funcCount): + fx[:,j] = self.functions[j](x) + y = np.max(fx,axis=1) return y def _der(self,x): @@ -1778,7 +1805,7 @@ class LowerEnvelope2D(HARKinterpolator2D): ''' distance_criteria = ['functions'] - def __init__(self,*functions): + def __init__(self,*functions, NanBool = True): ''' Constructor to make a new lower envelope iterpolation. @@ -1786,11 +1813,15 @@ def __init__(self,*functions): ---------- *functions : function Any number of real functions; often instances of HARKinterpolator2D + NanBool : boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- new instance of LowerEnvelope2D ''' + self.NanBool = NanBool self.functions = [] for function in functions: self.functions.append(function) @@ -1802,14 +1833,25 @@ def _evaluate(self,x,y): among all of the functions. Only called internally by HARKinterpolator2D.__call__. ''' - if _isscalar(x): - f = np.nanmin([f(x,y) for f in self.functions]) + + if self.NanBool == True: + if _isscalar(x): + f = np.nanmin([f(x,y) for f in self.functions]) + else: + m = len(x) + temp = np.zeros((m,self.funcCount)) + for j in range(self.funcCount): + temp[:,j] = self.functions[j](x,y) + f = np.nanmin(temp,axis=1) else: - m = len(x) - temp = np.zeros((m,self.funcCount)) - for j in range(self.funcCount): - temp[:,j] = self.functions[j](x,y) - f = np.nanmin(temp,axis=1) + if _isscalar(x): + f = np.min([f(x,y) for f in self.functions]) + else: + m = len(x) + temp = np.zeros((m,self.funcCount)) + for j in range(self.funcCount): + temp[:,j] = self.functions[j](x,y) + f = np.min(temp,axis=1) return f def _derX(self,x,y): @@ -1856,7 +1898,7 @@ class LowerEnvelope3D(HARKinterpolator3D): ''' distance_criteria = ['functions'] - def __init__(self,*functions): + def __init__(self,*functions, NanBool = True): ''' Constructor to make a new lower envelope iterpolation. @@ -1864,11 +1906,15 @@ def __init__(self,*functions): ---------- *functions : function Any number of real functions; often instances of HARKinterpolator3D + NanBool : boolean + An indicator for whether the solver should exclude NA's when forming + the lower envelope. Returns ------- None ''' + self.NanBool = NanBool self.functions = [] for function in functions: self.functions.append(function) @@ -1880,14 +1926,25 @@ def _evaluate(self,x,y,z): among all of the functions. Only called internally by HARKinterpolator3D.__call__. ''' - if _isscalar(x): - f = np.nanmin([f(x,y,z) for f in self.functions]) + + if self.NanBool == True: + if _isscalar(x): + f = np.nanmin([f(x,y,z) for f in self.functions]) + else: + m = len(x) + temp = np.zeros((m,self.funcCount)) + for j in range(self.funcCount): + temp[:,j] = self.functions[j](x,y,z) + f = np.nanmin(temp,axis=1) else: - m = len(x) - temp = np.zeros((m,self.funcCount)) - for j in range(self.funcCount): - temp[:,j] = self.functions[j](x,y,z) - f = np.nanmin(temp,axis=1) + if _isscalar(x): + f = np.min([f(x,y,z) for f in self.functions]) + else: + m = len(x) + temp = np.zeros((m,self.funcCount)) + for j in range(self.funcCount): + temp[:,j] = self.functions[j](x,y,z) + f = np.min(temp,axis=1) return f def _derX(self,x,y,z): From 91c7a28005deca9f59382fc03bcf2c9e0261d45b Mon Sep 17 00:00:00 2001 From: Tim Munday Date: Fri, 7 Sep 2018 18:11:12 +0100 Subject: [PATCH 02/77] update after comments --- HARK/ConsumptionSaving/ConsAggShockModel.py | 2 +- .../ConsGenIncProcessModel.py | 26 +-- HARK/ConsumptionSaving/ConsIndShockModel.py | 45 ++--- HARK/ConsumptionSaving/ConsMarkovModel.py | 18 +- HARK/ConsumptionSaving/ConsMedModel.py | 18 +- HARK/ConsumptionSaving/ConsPrefShockModel.py | 35 +--- HARK/ConsumptionSaving/ConsRepAgentModel.py | 2 +- HARK/ConsumptionSaving/ConsumerParameters.py | 5 +- HARK/ConsumptionSaving/RepAgentModel.py | 2 +- .../TractableBufferStockModel.py | 1 - HARK/interpolation.py | 173 ++++++++---------- 11 files changed, 122 insertions(+), 205 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py index 952ed5d47..026304dff 100644 --- a/HARK/ConsumptionSaving/ConsAggShockModel.py +++ b/HARK/ConsumptionSaving/ConsAggShockModel.py @@ -83,7 +83,7 @@ def __init__(self,time_flow=True,**kwds): # Add consumer-type specific objects, copying to create independent versions self.time_vary = deepcopy(IndShockConsumerType.time_vary_) self.time_inv = deepcopy(IndShockConsumerType.time_inv_) - self.delFromTimeInv('Rfree','vFuncBool','CubicBool','NanBool') + self.delFromTimeInv('Rfree','vFuncBool','CubicBool') self.poststate_vars = IndShockConsumerType.poststate_vars_ self.solveOnePeriod = solveConsAggShock self.update() diff --git a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py index 578ead42b..b81bedff7 100644 --- a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py +++ b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py @@ -261,8 +261,7 @@ class ConsGenIncProcessSolver(ConsIndShockSetup): to shocks). ''' def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool, - NanBool): + pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool): ''' Constructor for a new solver for a one period problem with idiosyncratic shocks to persistent and transitory income, with persistent income tracked @@ -301,20 +300,17 @@ def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, included in the reported solution. CubicBool: boolean An indicator for whether the solver should use cubic or linear interpolation. - NanBool: boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns ------- None ''' self.assignParameters(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,pLvlNextFunc, - BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool, NanBool) + BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool) self.defUtilityFuncs() def assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool,NanBool): + pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool): ''' Assigns inputs as attributes of self for use by other methods @@ -351,16 +347,13 @@ def assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, included in the reported solution. CubicBool: boolean An indicator for whether the solver should use cubic or linear interpolation. - NanBool: boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns ------- none ''' ConsIndShockSetup.assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - 0.0,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool,NanBool) # dummy value for PermGroFac + 0.0,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) # dummy value for PermGroFac self.pLvlNextFunc = pLvlNextFunc self.pLvlGrid = pLvlGrid @@ -606,8 +599,7 @@ def usePointsForInterpolation(self,cLvl,mLvl,pLvl,interpolator): cFuncNowUnc = interpolator(mLvl,pLvl,cLvl) # Combine the constrained and unconstrained functions into the true consumption function - cFuncNow = LowerEnvelope2D(cFuncNowUnc,self.cFuncNowCnst, - NanBool=self.NanBool) + cFuncNow = LowerEnvelope2D(cFuncNowUnc,self.cFuncNowCnst) # Make the marginal value function vPfuncNow = self.makevPfunc(cFuncNow) @@ -877,7 +869,7 @@ def solve(self): def solveConsGenIncProcess(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,pLvlNextFunc, - BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool,NanBool): + BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool): ''' Solves the one period problem of a consumer who experiences persistent and transitory shocks to his income. Unlike in ConsIndShock, consumers do not @@ -918,9 +910,6 @@ def solveConsGenIncProcess(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,pL included in the reported solution. CubicBool: boolean An indicator for whether the solver should use cubic or linear interpolation. - NanBool: boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns ------- @@ -930,8 +919,7 @@ def solveConsGenIncProcess(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,pL marginal value function, bounding MPCs, and normalized human wealth. ''' solver = ConsGenIncProcessSolver(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool, - NanBool) + pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool) solver.prepareToSolve() # Do some preparatory work solution_now = solver.solve() # Solve the period return solution_now diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index be83845a9..2053772bb 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -531,8 +531,7 @@ class ConsIndShockSetup(ConsPerfForesightSolver): to income. Has methods to set up but not solve the one period problem. ''' def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, - NanBool): + PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): ''' Constructor for a new solver-setup for problems with income subject to permanent and transitory shocks. @@ -571,22 +570,17 @@ def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. - NanBool: boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns ------- None ''' self.assignParameters(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, - NanBool) + PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) self.defUtilityFuncs() def assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, - NanBool): + PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): ''' Assigns period parameters as attributes of self for use by other methods @@ -624,9 +618,6 @@ def assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. - NanBool: boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns ------- @@ -639,7 +630,6 @@ def assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, self.aXtraGrid = aXtraGrid self.vFuncBool = vFuncBool self.CubicBool = CubicBool - self.NanBool = NanBool def defUtilityFuncs(self): @@ -904,7 +894,7 @@ def usePointsForInterpolation(self,cNrm,mNrm,interpolator): # Combine the constrained and unconstrained functions into the true consumption function cFuncNow = LowerEnvelope(cFuncNowUnc, self.cFuncNowCnst, - NanBool = self.NanBool) + nan_bool = False) # Make the marginal value function and the marginal marginal value function vPfuncNow = MargValueFunc(cFuncNow,self.CRRA) @@ -1186,7 +1176,7 @@ def solve(self): def solveConsIndShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, - BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, NanBool): + BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): ''' Solves a single period consumption-saving problem with CRRA utility and risky income (subject to permanent and transitory shocks). Can generate a value @@ -1225,9 +1215,6 @@ def solveConsIndShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGro included in the reported solution. CubicBool: boolean Indicator for whether the solver should use cubic or linear interpolation. - NanBool: boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns @@ -1244,10 +1231,10 @@ def solveConsIndShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGro if (not CubicBool) and (not vFuncBool): solver = ConsIndShockSolverBasic(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA, Rfree,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool, - CubicBool, NanBool) + CubicBool) else: # Use the "advanced" solver if either is requested solver = ConsIndShockSolver(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, NanBool) + PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) solver.prepareToSolve() # Do some preparatory work solution_now = solver.solve() # Solve the period return solution_now @@ -1266,7 +1253,7 @@ class ConsKinkedRsolver(ConsIndShockSolver): it terminates immediately if Rboro < Rsave, as this has a different solution. ''' def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA, - Rboro,Rsave,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, NanBool): + Rboro,Rsave,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): ''' Constructor for a new solver for problems with risky income and a different interest rate on borrowing and saving. @@ -1309,9 +1296,6 @@ def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. - NanBool: boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns ------- @@ -1323,7 +1307,7 @@ def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA, # Initialize the solver. Most of the steps are exactly the same as in # the non-kinked-R basic case, so start with that. ConsIndShockSolver.__init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rboro, - PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool,NanBool) + PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) # Assign the interest rates as class attributes, to use them later. self.Rboro = Rboro @@ -1393,7 +1377,7 @@ def prepareToCalcEndOfPrdvP(self): def solveConsKinkedR(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rboro,Rsave, - PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool,NanBool): + PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): ''' Solves a single period consumption-saving problem with CRRA utility and risky income (subject to permanent and transitory shocks), and different interest @@ -1438,9 +1422,6 @@ def solveConsKinkedR(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rboro,Rsave, included in the reported solution. CubicBool: boolean Indicator for whether the solver should use cubic or linear interpolation. - NanBool: boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns ------- @@ -1454,7 +1435,7 @@ def solveConsKinkedR(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rboro,Rsave, solver = ConsKinkedRsolver(solution_next,IncomeDstn,LivPrb, DiscFac,CRRA,Rboro,Rsave,PermGroFac,BoroCnstArt, - aXtraGrid,vFuncBool,CubicBool,NanBool) + aXtraGrid,vFuncBool,CubicBool) solver.prepareToSolve() solution = solver.solve() @@ -1760,7 +1741,7 @@ class IndShockConsumerType(PerfForesightConsumerType): for risk aversion, discount factor, the interest rate, the grid of end-of- period assets, and an artificial borrowing constraint. ''' - time_inv_ = PerfForesightConsumerType.time_inv_ + ['BoroCnstArt','vFuncBool','CubicBool', 'NanBool'] + time_inv_ = PerfForesightConsumerType.time_inv_ + ['BoroCnstArt','vFuncBool','CubicBool'] shock_vars_ = ['PermShkNow','TranShkNow'] def __init__(self,cycles=1,time_flow=True,**kwds): @@ -2431,7 +2412,7 @@ def constructAssetsGrid(parameters): #################################################################################################### def main(): - from . import ConsumerParameters as Params + import ConsumerParameters as Params from HARK.utilities import plotFuncsDer, plotFuncs from time import clock mystr = lambda number : "{:.4f}".format(number) diff --git a/HARK/ConsumptionSaving/ConsMarkovModel.py b/HARK/ConsumptionSaving/ConsMarkovModel.py index d088a47bf..73affbaff 100644 --- a/HARK/ConsumptionSaving/ConsMarkovModel.py +++ b/HARK/ConsumptionSaving/ConsMarkovModel.py @@ -38,7 +38,7 @@ class ConsMarkovSolver(ConsIndShockSolver): ''' def __init__(self,solution_next,IncomeDstn_list,LivPrb,DiscFac, CRRA,Rfree_list,PermGroFac_list,MrkvArray,BoroCnstArt, - aXtraGrid,vFuncBool,CubicBool,NanBool): + aXtraGrid,vFuncBool,CubicBool): ''' Constructor for a new solver for a one period problem with risky income and transitions between discrete Markov states. In the descriptions below, @@ -85,9 +85,6 @@ def __init__(self,solution_next,IncomeDstn_list,LivPrb,DiscFac, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. - NanBool: boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns ------- @@ -95,7 +92,7 @@ def __init__(self,solution_next,IncomeDstn_list,LivPrb,DiscFac, ''' # Set basic attributes of the problem ConsIndShockSolver.assignParameters(self,solution_next,np.nan,LivPrb,DiscFac,CRRA,np.nan, - np.nan,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool,NanBool) + np.nan,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) self.defUtilityFuncs() # Set additional attributes specific to the Markov model @@ -480,8 +477,7 @@ def makeSolution(self,cNrm,mNrm): self.cFuncNowCnst = LinearInterp([self.mNrmMin_list[i], self.mNrmMin_list[i]+1.0], [0.0,1.0]) cFuncNowUnc = interpfunc(mNrm[i,:],cNrm[i,:]) - cFuncNow = LowerEnvelope(cFuncNowUnc,self.cFuncNowCnst, - NanBool=self.NanBool) + cFuncNow = LowerEnvelope(cFuncNowUnc,self.cFuncNowCnst) # Make the marginal value function and pack up the current-state-conditional solution vPfuncNow = MargValueFunc(cFuncNow,self.CRRA) @@ -600,7 +596,7 @@ def makevFunc(self,solution): def solveConsMarkov(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFac, - MrkvArray,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool,NanBool): + MrkvArray,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): ''' Solves a single period consumption-saving problem with risky income and stochastic transitions between discrete states, in a Markov fashion. Has @@ -651,9 +647,6 @@ def solveConsMarkov(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFa CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. - NanBool: boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns ------- @@ -669,8 +662,7 @@ def solveConsMarkov(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGroFa when in the i=0 Markov state this period. ''' solver = ConsMarkovSolver(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - PermGroFac,MrkvArray,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, - NanBool) + PermGroFac,MrkvArray,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) solution_now = solver.solve() return solution_now diff --git a/HARK/ConsumptionSaving/ConsMedModel.py b/HARK/ConsumptionSaving/ConsMedModel.py index 8d06b5efb..d89da643f 100644 --- a/HARK/ConsumptionSaving/ConsMedModel.py +++ b/HARK/ConsumptionSaving/ConsMedModel.py @@ -762,7 +762,7 @@ class ConsMedShockSolver(ConsGenIncProcessSolver): shocks to "medical need"-- multiplicative utility shocks for a second good. ''' def __init__(self,solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CRRAmed,Rfree,MedPrice, - pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool,NanBool): + pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool): ''' Constructor for a new solver for a one period problem with idiosyncratic shocks to permanent and transitory income and shocks to medical need. @@ -808,16 +808,13 @@ def __init__(self,solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CRRAme CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. - NanBool: boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. - + Returns ------- None ''' ConsGenIncProcessSolver.__init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool,NanBool) + pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool) self.MedShkDstn = MedShkDstn self.MedPrice = MedPrice self.CRRAmed = CRRAmed @@ -1000,7 +997,7 @@ def usePointsForInterpolation(self,xLvl,mLvl,pLvl,MedShk,interpolator): # Construct the unconstrained total expenditure function xFuncNowUnc = interpolator(mLvl,pLvl,MedShk,xLvl) xFuncNowCnst = self.xFuncNowCnst - xFuncNow = LowerEnvelope3D(xFuncNowUnc,xFuncNowCnst,NanBool=self.NanBool) + xFuncNow = LowerEnvelope3D(xFuncNowUnc,xFuncNowCnst) # Transform the expenditure function into policy functions for consumption and medical care aug_factor = 2 @@ -1294,7 +1291,7 @@ def solve(self): def solveConsMedShock(solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CRRAmed,Rfree,MedPrice, - pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool,NanBool): + pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool): ''' Solve the one period problem for a consumer with shocks to permanent and transitory income as well as medical need shocks (as multiplicative shifters @@ -1343,9 +1340,6 @@ def solveConsMedShock(solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CR CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. - NanBool: boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns ------- @@ -1356,7 +1350,7 @@ def solveConsMedShock(solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CR on (mLvl,pLvl), with MedShk integrated out. ''' solver = ConsMedShockSolver(solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CRRAmed,Rfree, - MedPrice,pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool,NanBool) + MedPrice,pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool) solver.prepareToSolve() # Do some preparatory work solution_now = solver.solve() # Solve the period return solution_now diff --git a/HARK/ConsumptionSaving/ConsPrefShockModel.py b/HARK/ConsumptionSaving/ConsPrefShockModel.py index 47ebf9d13..a311f05ed 100644 --- a/HARK/ConsumptionSaving/ConsPrefShockModel.py +++ b/HARK/ConsumptionSaving/ConsPrefShockModel.py @@ -220,8 +220,7 @@ class ConsPrefShockSolver(ConsIndShockSolver): each period. ''' def __init__(self,solution_next,IncomeDstn,PrefShkDstn,LivPrb,DiscFac,CRRA, - Rfree,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, - NanBool): + Rfree,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): ''' Constructor for a new solver for problems with risky income, a different interest rate on borrowing and saving, and multiplicative shocks to utility. @@ -263,16 +262,13 @@ def __init__(self,solution_next,IncomeDstn,PrefShkDstn,LivPrb,DiscFac,CRRA, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. - NanBool: boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns ------- None ''' ConsIndShockSolver.__init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA, - Rfree,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool,NanBool) + Rfree,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) self.PrefShkPrbs = PrefShkDstn[0] self.PrefShkVals = PrefShkDstn[1] @@ -336,7 +332,7 @@ def usePointsForInterpolation(self,cNrm,mNrm,interpolator): MPCmin_j = self.MPCminNow*self.PrefShkVals[j]**(1.0/self.CRRA) cFunc_this_shock = LowerEnvelope(LinearInterp(mNrm[j,:],cNrm[j,:], intercept_limit=self.hNrmNow*MPCmin_j, - slope_limit=MPCmin_j),self.cFuncNowCnst,NanBool=self.NanBool) + slope_limit=MPCmin_j),self.cFuncNowCnst) cFunc_list.append(cFunc_this_shock) # Combine the list of consumption functions into a single interpolation @@ -398,7 +394,7 @@ def makevFunc(self,solution): def solveConsPrefShock(solution_next,IncomeDstn,PrefShkDstn, LivPrb,DiscFac,CRRA,Rfree,PermGroFac,BoroCnstArt, - aXtraGrid,vFuncBool,CubicBool,NanBool): + aXtraGrid,vFuncBool,CubicBool): ''' Solves a single period of a consumption-saving model with preference shocks to marginal utility. Problem is solved using the method of endogenous gridpoints. @@ -440,10 +436,7 @@ def solveConsPrefShock(solution_next,IncomeDstn,PrefShkDstn, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. - NanBool: boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. - + Returns ------- solution: ConsumerSolution @@ -458,7 +451,7 @@ def solveConsPrefShock(solution_next,IncomeDstn,PrefShkDstn, ''' solver = ConsPrefShockSolver(solution_next,IncomeDstn,PrefShkDstn,LivPrb, DiscFac,CRRA,Rfree,PermGroFac,BoroCnstArt,aXtraGrid, - vFuncBool,CubicBool,NanBool) + vFuncBool,CubicBool) solver.prepareToSolve() solution = solver.solve() return solution @@ -472,8 +465,7 @@ class ConsKinkyPrefSolver(ConsPrefShockSolver,ConsKinkedRsolver): each period, and a different interest rate on saving vs borrowing. ''' def __init__(self,solution_next,IncomeDstn,PrefShkDstn,LivPrb,DiscFac,CRRA, - Rboro,Rsave,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, - NanBool): + Rboro,Rsave,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool): ''' Constructor for a new solver for problems with risky income, a different interest rate on borrowing and saving, and multiplicative shocks to utility. @@ -519,24 +511,20 @@ def __init__(self,solution_next,IncomeDstn,PrefShkDstn,LivPrb,DiscFac,CRRA, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. - NanBool: boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns ------- None ''' ConsKinkedRsolver.__init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA, - Rboro,Rsave,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool, - NanBool) + Rboro,Rsave,PermGroFac,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) self.PrefShkPrbs = PrefShkDstn[0] self.PrefShkVals = PrefShkDstn[1] def solveConsKinkyPref(solution_next,IncomeDstn,PrefShkDstn, LivPrb,DiscFac,CRRA,Rboro,Rsave,PermGroFac,BoroCnstArt, - aXtraGrid,vFuncBool,CubicBool,NanBool): + aXtraGrid,vFuncBool,CubicBool): ''' Solves a single period of a consumption-saving model with preference shocks to marginal utility and a different interest rate on saving vs borrowing. @@ -583,9 +571,6 @@ def solveConsKinkyPref(solution_next,IncomeDstn,PrefShkDstn, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. - NanBool: boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns ------- @@ -601,7 +586,7 @@ def solveConsKinkyPref(solution_next,IncomeDstn,PrefShkDstn, ''' solver = ConsKinkyPrefSolver(solution_next,IncomeDstn,PrefShkDstn,LivPrb, DiscFac,CRRA,Rboro,Rsave,PermGroFac,BoroCnstArt, - aXtraGrid,vFuncBool,CubicBool,NanBool) + aXtraGrid,vFuncBool,CubicBool) solver.prepareToSolve() solution = solver.solve() return solution diff --git a/HARK/ConsumptionSaving/ConsRepAgentModel.py b/HARK/ConsumptionSaving/ConsRepAgentModel.py index e98bd149b..82d0179ae 100644 --- a/HARK/ConsumptionSaving/ConsRepAgentModel.py +++ b/HARK/ConsumptionSaving/ConsRepAgentModel.py @@ -208,7 +208,7 @@ def __init__(self,time_flow=True,**kwds): IndShockConsumerType.__init__(self,cycles=0,time_flow=time_flow,**kwds) self.AgentCount = 1 # Hardcoded, because this is rep agent self.solveOnePeriod = solveConsRepAgent - self.delFromTimeInv('Rfree','BoroCnstArt','vFuncBool','CubicBool','NanBool') + self.delFromTimeInv('Rfree','BoroCnstArt','vFuncBool','CubicBool') def getStates(self): ''' diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index d526bb3a5..94127882d 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -67,8 +67,7 @@ # A few other parameters BoroCnstArt = 0.0 # Artificial borrowing constraint; imposed minimum level of end-of period assets CubicBool = False # Use cubic spline interpolation when True, linear interpolation when False -vFuncBool = False # Whether to calculate the value function during solution -NanBool = True # Whether to exclude NA's when calculating lower envelope +vFuncBool = False # Whether to calculate the value function during solution # Make a dictionary to specify an idiosyncratic income shocks consumer init_idiosyncratic_shocks = { 'CRRA': CRRA, @@ -94,7 +93,6 @@ 'tax_rate':0.0, 'vFuncBool':vFuncBool, 'CubicBool':CubicBool, - 'NanBool':NanBool, 'T_retire':T_retire, 'aNrmInitMean' : aNrmInitMean, 'aNrmInitStd' : aNrmInitStd, @@ -187,7 +185,6 @@ del init_agg_shocks['Rfree'] # Interest factor is endogenous in agg shocks model del init_agg_shocks['CubicBool'] # Not supported yet for agg shocks model del init_agg_shocks['vFuncBool'] # Not supported yet for agg shocks model -del init_agg_shocks['NanBool'] # Not supported yet for agg shocks model init_agg_shocks['PermGroFac'] = [1.0] init_agg_shocks['MgridBase'] = MgridBase init_agg_shocks['aXtraCount'] = 24 diff --git a/HARK/ConsumptionSaving/RepAgentModel.py b/HARK/ConsumptionSaving/RepAgentModel.py index f402f9d87..dbeea42af 100644 --- a/HARK/ConsumptionSaving/RepAgentModel.py +++ b/HARK/ConsumptionSaving/RepAgentModel.py @@ -208,7 +208,7 @@ def __init__(self,time_flow=True,**kwds): IndShockConsumerType.__init__(self,cycles=0,time_flow=time_flow,**kwds) self.AgentCount = 1 # Hardcoded, because this is rep agent self.solveOnePeriod = solveConsRepAgent - self.delFromTimeInv('Rfree','BoroCnstArt','vFuncBool','CubicBool', 'NanBool') + self.delFromTimeInv('Rfree','BoroCnstArt','vFuncBool','CubicBool') def getStates(self): ''' diff --git a/HARK/ConsumptionSaving/TractableBufferStockModel.py b/HARK/ConsumptionSaving/TractableBufferStockModel.py index 043c3dac4..88f78d578 100644 --- a/HARK/ConsumptionSaving/TractableBufferStockModel.py +++ b/HARK/ConsumptionSaving/TractableBufferStockModel.py @@ -542,7 +542,6 @@ def main(): 'tax_rate':0.0, # Tax rate on labor income (irrelevant) 'vFuncBool':False, # Whether to calculate the value function 'CubicBool':True, # Whether to use cubic splines (False --> linear splines) - 'NanBool': True, # Whether to excludes NA's when calculating the lower envelope 'MrkvArray':[MrkvArray] # State transition probabilities } MarkovType = MarkovConsumerType(**init_consumer_objects) # Make a basic consumer type diff --git a/HARK/interpolation.py b/HARK/interpolation.py index 48dc76634..45b69f6f7 100644 --- a/HARK/interpolation.py +++ b/HARK/interpolation.py @@ -1642,7 +1642,7 @@ class LowerEnvelope(HARKinterpolator1D): ''' distance_criteria = ['functions'] - def __init__(self, *functions, NanBool = True): + def __init__(self, *functions, nan_bool = True): ''' Constructor to make a new lower envelope iterpolation. @@ -1650,14 +1650,21 @@ def __init__(self, *functions, NanBool = True): ---------- *functions : function Any number of real functions; often instances of HARKinterpolator1D - NanBool : boolean + nan_bool : boolean An indicator for whether the solver should exclude NA's when forming the lower envelope. Returns ------- new instance of LowerEnvelope ''' - self.NanBool = NanBool + + if nan_bool: + self.compare = np.nanmin + self.argcompare = np.nanargmin + else: + self.compare = np.min + self.argcompare = np.argmin + self.functions = [] for function in functions: self.functions.append(function) @@ -1669,24 +1676,15 @@ def _evaluate(self,x): all of the functions. Only called internally by HARKinterpolator1D.__call__. ''' - if self.NanBool == True: - if _isscalar(x): - y = np.nanmin([f(x) for f in self.functions]) - else: - m = len(x) - fx = np.zeros((m,self.funcCount)) - for j in range(self.funcCount): - fx[:,j] = self.functions[j](x) - y = np.nanmin(fx,axis=1) + if _isscalar(x): + y = self.compare([f(x) for f in self.functions]) else: - if _isscalar(x): - y = np.min([f(x) for f in self.functions]) - else: - m = len(x) - fx = np.zeros((m,self.funcCount)) - for j in range(self.funcCount): - fx[:,j] = self.functions[j](x) - y = np.min(fx,axis=1) + m = len(x) + fx = np.zeros((m,self.funcCount)) + for j in range(self.funcCount): + fx[:,j] = self.functions[j](x) + y = self.compare(fx,axis=1) + return y def _der(self,x): @@ -1694,7 +1692,7 @@ def _der(self,x): Returns the first derivative of the function at each value in x. Only called internally by HARKinterpolator1D.derivative. ''' - y,dydx = self.eval_with_derivative(x) + y,dydx = self._evalAndDer(x) return dydx # Sadly, this is the fastest / most convenient way... def _evalAndDer(self,x): @@ -1706,8 +1704,7 @@ def _evalAndDer(self,x): fx = np.zeros((m,self.funcCount)) for j in range(self.funcCount): fx[:,j] = self.functions[j](x) - fx[np.isnan(fx)] = np.inf - i = np.argmin(fx,axis=1) + i = self.argcompare(fx,axis=1) y = fx[np.arange(m),i] dydx = np.zeros_like(y) for j in range(self.funcCount): @@ -1723,7 +1720,7 @@ class UpperEnvelope(HARKinterpolator1D): ''' distance_criteria = ['functions'] - def __init__(self,*functions, NanBool=True): + def __init__(self,*functions, nan_bool=True): ''' Constructor to make a new upper envelope iterpolation. @@ -1731,7 +1728,7 @@ def __init__(self,*functions, NanBool=True): ---------- *functions : function Any number of real functions; often instances of HARKinterpolator1D - NanBool : boolean + nan_bool : boolean An indicator for whether the solver should exclude NA's when forming the lower envelope. @@ -1739,7 +1736,13 @@ def __init__(self,*functions, NanBool=True): ------- new instance of UpperEnvelope ''' - self.NanBool = NanBool + if nan_bool: + self.compare = np.nanmax + self.argcompare = np.nanargmax + else: + self.compare = np.max + self.argcompare = np.argmax + self.functions = [] for function in functions: self.functions.append(function) @@ -1750,24 +1753,15 @@ def _evaluate(self,x): Returns the level of the function at each value in x as the maximum among all of the functions. Only called internally by HARKinterpolator1D.__call__. ''' - if self.NanBool == True: - if _isscalar(x): - y = np.nanmax([f(x) for f in self.functions]) - else: - m = len(x) - fx = np.zeros((m,self.funcCount)) - for j in range(self.funcCount): - fx[:,j] = self.functions[j](x) - y = np.nanmax(fx,axis=1) + if _isscalar(x): + y = self.compare([f(x) for f in self.functions]) else: - if _isscalar(x): - y = np.max([f(x) for f in self.functions]) - else: - m = len(x) - fx = np.zeros((m,self.funcCount)) - for j in range(self.funcCount): - fx[:,j] = self.functions[j](x) - y = np.max(fx,axis=1) + m = len(x) + fx = np.zeros((m,self.funcCount)) + for j in range(self.funcCount): + fx[:,j] = self.functions[j](x) + y = self.compare(fx,axis=1) + return y def _der(self,x): @@ -1775,7 +1769,7 @@ def _der(self,x): Returns the first derivative of the function at each value in x. Only called internally by HARKinterpolator1D.derivative. ''' - y,dydx = self.eval_with_derivative(x) + y,dydx = self._evalAndDer(x) return dydx # Sadly, this is the fastest / most convenient way... def _evalAndDer(self,x): @@ -1787,8 +1781,7 @@ def _evalAndDer(self,x): fx = np.zeros((m,self.funcCount)) for j in range(self.funcCount): fx[:,j] = self.functions[j](x) - fx[np.isnan(fx)] = np.inf - i = np.argmax(fx,axis=1) + i = self.argcompare(fx,axis=1) y = fx[np.arange(m),i] dydx = np.zeros_like(y) for j in range(self.funcCount): @@ -1805,7 +1798,7 @@ class LowerEnvelope2D(HARKinterpolator2D): ''' distance_criteria = ['functions'] - def __init__(self,*functions, NanBool = True): + def __init__(self,*functions, nan_bool = True): ''' Constructor to make a new lower envelope iterpolation. @@ -1813,7 +1806,7 @@ def __init__(self,*functions, NanBool = True): ---------- *functions : function Any number of real functions; often instances of HARKinterpolator2D - NanBool : boolean + nan_bool : boolean An indicator for whether the solver should exclude NA's when forming the lower envelope. @@ -1821,7 +1814,13 @@ def __init__(self,*functions, NanBool = True): ------- new instance of LowerEnvelope2D ''' - self.NanBool = NanBool + + if nan_bool: + self.compare = np.nanmin + self.argcompare = np.nanargmin + else: + self.compare = np.min + self.argcompare = np.argmin self.functions = [] for function in functions: self.functions.append(function) @@ -1834,24 +1833,15 @@ def _evaluate(self,x,y): HARKinterpolator2D.__call__. ''' - if self.NanBool == True: - if _isscalar(x): - f = np.nanmin([f(x,y) for f in self.functions]) - else: - m = len(x) - temp = np.zeros((m,self.funcCount)) - for j in range(self.funcCount): - temp[:,j] = self.functions[j](x,y) - f = np.nanmin(temp,axis=1) + if _isscalar(x): + f = self.compare([f(x,y) for f in self.functions]) else: - if _isscalar(x): - f = np.min([f(x,y) for f in self.functions]) - else: - m = len(x) - temp = np.zeros((m,self.funcCount)) - for j in range(self.funcCount): - temp[:,j] = self.functions[j](x,y) - f = np.min(temp,axis=1) + m = len(x) + temp = np.zeros((m,self.funcCount)) + for j in range(self.funcCount): + temp[:,j] = self.functions[j](x,y) + f = self.compare(temp,axis=1) + return f def _derX(self,x,y): @@ -1863,8 +1853,7 @@ def _derX(self,x,y): temp = np.zeros((m,self.funcCount)) for j in range(self.funcCount): temp[:,j] = self.functions[j](x,y) - temp[np.isnan(temp)] = np.inf - i = np.argmin(temp,axis=1) + i = self.argcompare(temp,axis=1) dfdx = np.zeros_like(x) for j in range(self.funcCount): c = i == j @@ -1880,8 +1869,7 @@ def _derY(self,x,y): temp = np.zeros((m,self.funcCount)) for j in range(self.funcCount): temp[:,j] = self.functions[j](x,y) - temp[np.isnan(temp)] = np.inf - i = np.argmin(temp,axis=1) + i = self.argcompare(temp,axis=1) y = temp[np.arange(m),i] dfdy = np.zeros_like(x) for j in range(self.funcCount): @@ -1898,7 +1886,7 @@ class LowerEnvelope3D(HARKinterpolator3D): ''' distance_criteria = ['functions'] - def __init__(self,*functions, NanBool = True): + def __init__(self,*functions, nan_bool = True): ''' Constructor to make a new lower envelope iterpolation. @@ -1906,7 +1894,7 @@ def __init__(self,*functions, NanBool = True): ---------- *functions : function Any number of real functions; often instances of HARKinterpolator3D - NanBool : boolean + nan_bool : boolean An indicator for whether the solver should exclude NA's when forming the lower envelope. @@ -1914,7 +1902,12 @@ def __init__(self,*functions, NanBool = True): ------- None ''' - self.NanBool = NanBool + if nan_bool: + self.compare = np.nanmin + self.argcompare = np.nanargmin + else: + self.compare = np.min + self.argcompare = np.argmin self.functions = [] for function in functions: self.functions.append(function) @@ -1927,24 +1920,15 @@ def _evaluate(self,x,y,z): HARKinterpolator3D.__call__. ''' - if self.NanBool == True: - if _isscalar(x): - f = np.nanmin([f(x,y,z) for f in self.functions]) - else: - m = len(x) - temp = np.zeros((m,self.funcCount)) - for j in range(self.funcCount): - temp[:,j] = self.functions[j](x,y,z) - f = np.nanmin(temp,axis=1) + if _isscalar(x): + f = self.compare([f(x,y,z) for f in self.functions]) else: - if _isscalar(x): - f = np.min([f(x,y,z) for f in self.functions]) - else: - m = len(x) - temp = np.zeros((m,self.funcCount)) - for j in range(self.funcCount): - temp[:,j] = self.functions[j](x,y,z) - f = np.min(temp,axis=1) + m = len(x) + temp = np.zeros((m,self.funcCount)) + for j in range(self.funcCount): + temp[:,j] = self.functions[j](x,y,z) + f = self.compare(temp,axis=1) + return f def _derX(self,x,y,z): @@ -1956,8 +1940,7 @@ def _derX(self,x,y,z): temp = np.zeros((m,self.funcCount)) for j in range(self.funcCount): temp[:,j] = self.functions[j](x,y,z) - temp[np.isnan(temp)] = np.inf - i = np.argmin(temp,axis=1) + i = self.argcompare(temp,axis=1) dfdx = np.zeros_like(x) for j in range(self.funcCount): c = i == j @@ -1973,8 +1956,7 @@ def _derY(self,x,y,z): temp = np.zeros((m,self.funcCount)) for j in range(self.funcCount): temp[:,j] = self.functions[j](x,y,z) - temp[np.isnan(temp)] = np.inf - i = np.argmin(temp,axis=1) + i = self.argcompare(temp,axis=1) y = temp[np.arange(m),i] dfdy = np.zeros_like(x) for j in range(self.funcCount): @@ -1991,8 +1973,7 @@ def _derZ(self,x,y,z): temp = np.zeros((m,self.funcCount)) for j in range(self.funcCount): temp[:,j] = self.functions[j](x,y,z) - temp[np.isnan(temp)] = np.inf - i = np.argmin(temp,axis=1) + i = self.argcompare(temp,axis=1) y = temp[np.arange(m),i] dfdz = np.zeros_like(x) for j in range(self.funcCount): From a7d8e112adbc36e5537e0ee7198d8b36c4e3f629 Mon Sep 17 00:00:00 2001 From: Tim Munday Date: Fri, 7 Sep 2018 18:19:02 +0100 Subject: [PATCH 03/77] update after comments --- HARK/ConsumptionSaving/ConsIndShockModel.py | 3 +-- HARK/ConsumptionSaving/ConsMedModel.py | 2 +- HARK/ConsumptionSaving/ConsPrefShockModel.py | 2 +- HARK/ConsumptionSaving/ConsumerParameters.py | 2 +- 4 files changed, 4 insertions(+), 5 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 2053772bb..76923542e 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1216,7 +1216,6 @@ def solveConsIndShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGro CubicBool: boolean Indicator for whether the solver should use cubic or linear interpolation. - Returns ------- solution_now : ConsumerSolution @@ -2412,7 +2411,7 @@ def constructAssetsGrid(parameters): #################################################################################################### def main(): - import ConsumerParameters as Params + from . import ConsumerParameters as Params from HARK.utilities import plotFuncsDer, plotFuncs from time import clock mystr = lambda number : "{:.4f}".format(number) diff --git a/HARK/ConsumptionSaving/ConsMedModel.py b/HARK/ConsumptionSaving/ConsMedModel.py index d89da643f..13719c77c 100644 --- a/HARK/ConsumptionSaving/ConsMedModel.py +++ b/HARK/ConsumptionSaving/ConsMedModel.py @@ -808,7 +808,7 @@ def __init__(self,solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CRRAme CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. - + Returns ------- None diff --git a/HARK/ConsumptionSaving/ConsPrefShockModel.py b/HARK/ConsumptionSaving/ConsPrefShockModel.py index a311f05ed..f83032c68 100644 --- a/HARK/ConsumptionSaving/ConsPrefShockModel.py +++ b/HARK/ConsumptionSaving/ConsPrefShockModel.py @@ -436,7 +436,7 @@ def solveConsPrefShock(solution_next,IncomeDstn,PrefShkDstn, CubicBool: boolean An indicator for whether the solver should use cubic or linear inter- polation. - + Returns ------- solution: ConsumerSolution diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index 94127882d..c63472541 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -67,7 +67,7 @@ # A few other parameters BoroCnstArt = 0.0 # Artificial borrowing constraint; imposed minimum level of end-of period assets CubicBool = False # Use cubic spline interpolation when True, linear interpolation when False -vFuncBool = False # Whether to calculate the value function during solution +vFuncBool = False # Whether to calculate the value function during solution # Make a dictionary to specify an idiosyncratic income shocks consumer init_idiosyncratic_shocks = { 'CRRA': CRRA, From 0759843323ddbede0410ec90cfc58c6d33211296 Mon Sep 17 00:00:00 2001 From: Christopher Llorracc Carroll <1320319+llorracc@users.noreply.github.com> Date: Sun, 27 Jan 2019 21:57:24 -0500 Subject: [PATCH 04/77] Restore fixes from discarded master (#219) --- HARK/ConsumptionSaving/ConsIndShockModel.py | 108 ++++++++++---------- 1 file changed, 54 insertions(+), 54 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 4ffd9a6d5..9ffaa3295 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1680,9 +1680,9 @@ def getPostStates(self): def checkConditions(self,verbose=False): ''' - This method checks whether the instance's type satisfies the growth impatiance condition - (GIC), return impatiance condition (RIC), absolute impatiance condition (AIC), weak return - impatiance condition (WRIC), finite human wealth condition (FHWC) and finite value of + This method checks whether the instance's type satisfies the growth impatience condition + (GIC), return impatience condition (RIC), absolute impatience condition (AIC), weak return + impatience condition (WRIC), finite human wealth condition (FHWC) and finite value of autarky condition (FVAC). These are the conditions that are sufficient for nondegenerate solutions under infinite horizon with a 1 period cycle. Depending on the model at hand, a different combination of these conditions must be satisfied. To check which conditions are @@ -1704,31 +1704,32 @@ def checkConditions(self,verbose=False): return #Evaluate and report on the return impatience condition - RIC=(self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA))/self.Rfree - if RIC<1: - print('The return impatiance factor value for the supplied parameter values satisfies the return impatiance condition.') + RIF=(self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA))/self.Rfree + if RIF<1: + print('The return impatience factor value for the supplied parameter values satisfies the return impatience condition.') else: - print('The given type violates the return impatience condition with the supplied parameter values. Therefore, a nondegenerate solution may not be available. See Table 3 in "Theoretical Foundations of Buffer Stock Saving" (Carroll, 2011) to check which conditions are sufficient for a nondegenerate solution.') + print('The given type violates the Return Impatience Condition with the supplied parameter values; the factor is %1.5f ' % (RIF)) if verbose: - print('The return impatiance factor value for the supplied parameter values is ' + str(RIC)) + print(' For more, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') #Evaluate and report on the absolute impatience condition - AIC=self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA) - if AIC<1: - print('The absolute impatiance factor value for the supplied parameter values satisfies the absolute impatiance condition.') + AIF=(self.LivPrb[0]*self.Rfree*self.DiscFac)**(1/self.CRRA) + if AIF<1: + print('The absolute impatience factor value for the supplied parameter values satisfies the absolute impatience condition.') else: - print('The given type violates the absolute impatience condition with the supplied parameter values. Therefore, a nondegenerate solution may not be available. See Table 3 in "Theoretical Foundations of Buffer Stock Saving" (Carroll, 2011) to check which conditions are sufficient for a nondegenerate solution.') + print('The given type violates the absolute impatience condition with the supplied parameter values; the AIF is %1.5f ' % (AIF)) if verbose: - print('The absolute impatiance factor value for the supplied parameter values is ' + str(AIC)) + print(' Therefore, the absolute amount of consumption is expected to grow over time') + print(' For more, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') #Evaluate and report on the finite human wealth condition - FHWC=self.PermGroFac[0]/self.Rfree - if FHWC<1: + FHWF=self.PermGroFac[0]/self.Rfree + if FHWF<1: print('The finite human wealth factor value for the supplied parameter values satisfies the finite human wealth condition.') else: - print('The given type violates the finite human wealth condition with the supplied parameter values. Therefore, a nondegenerate solution may not be available. See Table 3 in "Theoretical Foundations of Buffer Stock Saving" (Carroll, 2011) to check which conditions are sufficient for a nondegenerate solution.') + print('The given type violates the finite human wealth condition; the finite human wealth factor value %2.5f ' % (FHWF)) if verbose: - print('The finite human wealth factor value for the supplied parameter values is ' + str(FHWC)) + print(' For more, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') class IndShockConsumerType(PerfForesightConsumerType): @@ -1995,68 +1996,67 @@ def preSolve(self): def checkConditions(self,verbose=False): ''' - This method checks whether the instance's type satisfies the growth impatiance condition - (GIC), return impatiance condition (RIC), absolute impatiance condition (AIC), weak return - impatiance condition (WRIC), finite human wealth condition (FHWC) and finite value of + This method checks whether the instance's type satisfies the growth impatience condition + (GIC), return impatience condition (RIC), absolute impatience condition (AIC), weak return + impatience condition (WRIC), finite human wealth condition (FHWC) and finite value of autarky condition (FVAC). These are the conditions that are sufficient for nondegenerate solutions under infinite horizon with a 1 period cycle. Depending on the model at hand, a - different combination of these conditions must be satisfied. To check which conditions are - relevant to the model at hand, a reference to the relevant theoretical literature is made. + different combination of these conditions must be satisfied. (For an exposition of the + conditions, see http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/) Parameters ---------- verbose : boolean - Specifies different levels of verbosity of feedback. When false, it only reports whether the - instance's type fails to satisfy a particular condition. When true, it reports all results, i.e. + Specifies different levels of verbosity of feedback. When False, it only reports whether the + instance's type fails to satisfy a particular condition. When True, it reports all results, i.e. the factor values for all conditions. Returns ------- None ''' - PerfForesightConsumerType.checkConditions(self) + PerfForesightConsumerType.checkConditions(self,verbose) if self.cycles!=0 or self.T_cycle > 1: return - #Some initial conditions - exp_psi_inv=0 - exp_psi_to_one_minus_rho=0 - - #Get expected psi inverse - for i in range(len(self.PermShkDstn[1])): - exp_psi_inv=exp_psi_inv+(1.0/self.PermShkCount)*(self.PermShkDstn[1][i])**(-1) - - #Get expected psi to the power one minus CRRA - for i in range(len(self.PermShkDstn[1])): - exp_psi_to_one_minus_rho=exp_psi_to_one_minus_rho+(1.0/self.PermShkCount)*(self.PermShkDstn[1][i])**(1-self.CRRA) + AIF=self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA) + RIF=(self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA))/self.Rfree + EPermShkInv=np.dot(self.PermShkDstn[0][0],1/self.PermShkDstn[0][1]) + EPermShkValFunc=np.dot(self.PermShkDstn[0][0],self.PermShkDstn[0][1]**(1-self.CRRA)) + PermGroFacAdj=self.PermGroFac[0]*EPermShkInv + Thorn=self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA) + GIF=Thorn/PermGroFacAdj #Evaluate and report on the growth impatience condition - GIC=(self.LivPrb[0]*exp_psi_inv*(self.Rfree*self.DiscFac)**(1/self.CRRA))/self.PermGroFac[0] - if GIC<1: - print('The growth impatiance factor value for the supplied parameter values satisfies the growth impatiance condition.') + if GIF<1: + print('The growth impatience factor value for the supplied parameter values satisfies the growth impatience condition.') else: - print('The given type violates the growth impatience condition with the supplied parameter values. Therefore, a nondegenerate solution may not be available. See Table 3 in "Theoretical Foundations of Buffer Stock Saving" (Carroll, 2011) to check which conditions are sufficient for a nondegenerate solution.') + print('The given parameter values violate the growth impatience condition for this consumer type; the GIF is: %2.4f' % (GIF)) if verbose: - print('The growth impatiance factor value for the supplied parameter values is ' + str(GIC)) + print(' Therefore, a target level of wealth does not exist.') + print(' For more, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') #Evaluate and report on the weak return impatience condition - WRIC=(self.LivPrb[0]*(self.UnempPrb**(1/self.CRRA))*(self.Rfree*self.DiscFac)**(1/self.CRRA))/self.Rfree - if WRIC<1: - print('The weak return impatiance factor value for the supplied parameter values satisfies the weak return impatiance condition.') + WRIF=(self.LivPrb[0]*(self.UnempPrb**(1/self.CRRA))*(self.Rfree*self.DiscFac)**(1/self.CRRA))/self.Rfree + if WRIF<1: + print('The weak return impatience factor value for the supplied parameter values satisfies the weak return impatience condition.') else: - print('The given type violates the weak return impatience condition with the supplied parameter values. Therefore, a nondegenerate solution may not be available. See Table 3 in "Theoretical Foundations of Buffer Stock Saving" (Carroll, 2011) to check which conditions are sufficient for a nondegenerate solution.') + print('The given type violates the weak return impatience condition with the supplied parameter values. The WRIF is: %2.4f' % (WRIF)) if verbose: - print('The weak return impatiance factor value for the supplied parameter values is ' + str(WRIC)) + print(' Therefore, a nondegenerate solution is not available.') + print(' For more, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') #Evaluate and report on the finite value of autarky condition - FVAC=self.LivPrb[0]*self.DiscFac*exp_psi_to_one_minus_rho*(self.PermGroFac[0]**(1-self.CRRA)) + FVAC=self.LivPrb[0]*self.DiscFac*EPermShkValFunc*(self.PermGroFac[0]**(1-self.CRRA)) + if FVAC<1: print('The finite value of autarky factor value for the supplied parameter values satisfies the finite value of autarky condition.') else: - print('The given type violates the finite value of autarky condition with the supplied parameter values. Therefore, a nondegenerate solution may not be available. See Table 3 in "Theoretical Foundations of Buffer Stock Saving" (Carroll, 2011) to check which conditions are sufficient for a nondegenerate solution.') + print('The given type violates the finite value of autarky condition with the supplied parameter values. The FVAC is %2.4f' %(FVAC)) if verbose: - print('The finite value of autarky factor value for the supplied parameter values is ' + str(FVAC)) + print(' Therefore, a nondegenerate solution is not available.') + print(' For more, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') class KinkedRconsumerType(IndShockConsumerType): ''' @@ -2185,15 +2185,15 @@ def getRfree(self): def checkConditions(self,verbose=False): ''' - This method checks whether the instance's type satisfies the growth impatiance condition - (GIC), return impatiance condition (RIC), absolute impatiance condition (AIC), weak return - impatiance condition (WRIC), finite human wealth condition (FHWC) and finite value of + This method checks whether the instance's type satisfies the growth impatience condition + (GIC), return impatience condition (RIC), absolute impatience condition (AIC), weak return + impatience condition (WRIC), finite human wealth condition (FHWC) and finite value of autarky condition (FVAC). These are the conditions that are sufficient for nondegenerate - solutions under infinite horizon with a 1 period cycle. Depending on the model at hand, a + infinite horizon solutions with a 1 period cycle. Depending on the model at hand, a different combination of these conditions must be satisfied. To check which conditions are relevant to the model at hand, a reference to the relevant theoretical literature is made. - NOT YET IMPLEMENTED FOR THIS CLASS + SHOULD BE INHERITED FROM ConsIndShockModel Parameters ---------- From 73791b0a0e2ca248f35b226f3ecaae52152df0b7 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Fri, 1 Feb 2019 18:25:12 +0100 Subject: [PATCH 05/77] Improvements to checkConditions that were reverted, ref #175. (#205) * Improvements to checkConditions that were reverted, ref #175. * Fix the factor calculations according to the other reverted commit! * s/impatiance/impatience * Name the number the determines if FVAC is met FVAF instead of FVAC for Factor. * Don't print the table information twice. * More control over warnings and information about impatience conditions. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 105 +++++++++++--------- 1 file changed, 60 insertions(+), 45 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 9ffaa3295..782321fed 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -727,16 +727,16 @@ def defBoroCnst(self,BoroCnstArt): # Calculate the minimum allowable value of money resources in this period self.BoroCnstNat = (self.solution_next.mNrmMin - self.TranShkMinNext)*\ (self.PermGroFac*self.PermShkMinNext)/self.Rfree - - # Note: need to be sure to handle BoroCnstArt==None appropriately. + + # Note: need to be sure to handle BoroCnstArt==None appropriately. # In Py2, this would evaluate to 5.0: np.max([None, 5.0]). - # However in Py3, this raises a TypeError. Thus here we need to directly + # However in Py3, this raises a TypeError. Thus here we need to directly # address the situation in which BoroCnstArt == None: if BoroCnstArt is None: self.mNrmMinNow = self.BoroCnstNat else: self.mNrmMinNow = np.max([self.BoroCnstNat,BoroCnstArt]) - if self.BoroCnstNat < self.mNrmMinNow: + if self.BoroCnstNat < self.mNrmMinNow: self.MPCmaxEff = 1.0 # If actually constrained, MPC near limit is 1 else: self.MPCmaxEff = self.MPCmaxNow @@ -1461,7 +1461,7 @@ class PerfForesightConsumerType(AgentType): poststate_vars_ = ['aNrmNow','pLvlNow'] shock_vars_ = [] - def __init__(self,cycles=1,time_flow=True,**kwds): + def __init__(self,cycles=1, time_flow=True,verbose=False,quiet=False, **kwds): ''' Instantiate a new consumer type with given data. See ConsumerParameters.init_perfect_foresight for a dictionary of @@ -1487,6 +1487,8 @@ def __init__(self,cycles=1,time_flow=True,**kwds): self.time_inv = deepcopy(self.time_inv_) self.poststate_vars = deepcopy(self.poststate_vars_) self.shock_vars = deepcopy(self.shock_vars_) + self.verbose = verbose + self.quiet = quiet self.solveOnePeriod = solvePerfForesight # solver for perfect foresight model def updateSolutionTerminal(self): @@ -1678,7 +1680,7 @@ def getPostStates(self): self.aLvlNow = self.aNrmNow*self.pLvlNow # Useful in some cases to precalculate asset level return None - def checkConditions(self,verbose=False): + def checkConditions(self,verbose=False,verbose_reference=False,public_call=False): ''' This method checks whether the instance's type satisfies the growth impatience condition (GIC), return impatience condition (RIC), absolute impatience condition (AIC), weak return @@ -1691,8 +1693,8 @@ def checkConditions(self,verbose=False): Parameters ---------- verbose : boolean - Specifies different levels of verbosity of feedback. When false, it only reports whether the - instance's type fails to satisfy a particular condition. When true, it reports all results, i.e. + Specifies different levels of verbosity of feedback. When False, it only reports whether the + instance's type fails to satisfy a particular condition. When True, it reports all results, i.e. the factor values for all conditions. Returns @@ -1703,34 +1705,41 @@ def checkConditions(self,verbose=False): print('This method only checks for the conditions for infinite horizon models with a 1 period cycle') return + violated = False + #Evaluate and report on the return impatience condition - RIF=(self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA))/self.Rfree + + RIF = (self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA))/self.Rfree if RIF<1: - print('The return impatience factor value for the supplied parameter values satisfies the return impatience condition.') + if public_call: + print('The return impatience factor value for the supplied parameter values satisfies the return impatience condition.') else: + violated = True print('The given type violates the Return Impatience Condition with the supplied parameter values; the factor is %1.5f ' % (RIF)) - if verbose: - print(' For more, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') #Evaluate and report on the absolute impatience condition - AIF=(self.LivPrb[0]*self.Rfree*self.DiscFac)**(1/self.CRRA) + AIF = self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA) if AIF<1: - print('The absolute impatience factor value for the supplied parameter values satisfies the absolute impatience condition.') + if public_call: + print('The absolute impatience factor value for the supplied parameter values satisfies the absolute impatience condition.') else: print('The given type violates the absolute impatience condition with the supplied parameter values; the AIF is %1.5f ' % (AIF)) - if verbose: + if verbose: + violated = True print(' Therefore, the absolute amount of consumption is expected to grow over time') - print(' For more, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') #Evaluate and report on the finite human wealth condition - FHWF=self.PermGroFac[0]/self.Rfree + FHWF = self.PermGroFac[0]/self.Rfree if FHWF<1: - print('The finite human wealth factor value for the supplied parameter values satisfies the finite human wealth condition.') + if public_call: + print('The finite human wealth factor value for the supplied parameter values satisfies the finite human wealth condition.') else: print('The given type violates the finite human wealth condition; the finite human wealth factor value %2.5f ' % (FHWF)) - if verbose: - print(' For more, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') + violated = True + if verbose and violated and verbose_reference: + print('[!] For more information on the conditions, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') + return violated class IndShockConsumerType(PerfForesightConsumerType): ''' @@ -1743,7 +1752,7 @@ class IndShockConsumerType(PerfForesightConsumerType): time_inv_ = PerfForesightConsumerType.time_inv_ + ['BoroCnstArt','vFuncBool','CubicBool'] shock_vars_ = ['PermShkNow','TranShkNow'] - def __init__(self,cycles=1,time_flow=True,**kwds): + def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): ''' Instantiate a new ConsumerType with given data. See ConsumerParameters.init_idiosyncratic_shocks for a dictionary of @@ -1761,12 +1770,17 @@ def __init__(self,cycles=1,time_flow=True,**kwds): None ''' # Initialize a basic AgentType - PerfForesightConsumerType.__init__(self,cycles=cycles,time_flow=time_flow,**kwds) + PerfForesightConsumerType.__init__(self,cycles=cycles,time_flow=time_flow, + verbose=verbose,quiet=quiet, **kwds) # Add consumer-type specific objects, copying to create independent versions self.solveOnePeriod = solveConsIndShock # idiosyncratic shocks solver self.update() # Make assets grid, income process, terminal solution + if not self.quiet: + self.checkConditions(verbose=self.verbose, + public_call=False) + def updateIncomeProcess(self): ''' Updates this agent's income process based on his own attributes. The @@ -1994,7 +2008,7 @@ def preSolve(self): PerfForesightConsumerType.preSolve(self) self.updateSolutionTerminal() - def checkConditions(self,verbose=False): + def checkConditions(self,verbose=False,public_call=True): ''' This method checks whether the instance's type satisfies the growth impatience condition (GIC), return impatience condition (RIC), absolute impatience condition (AIC), weak return @@ -2015,48 +2029,50 @@ def checkConditions(self,verbose=False): ------- None ''' - PerfForesightConsumerType.checkConditions(self,verbose) + violated = PerfForesightConsumerType.checkConditions(self, verbose=verbose, verbose_reference=False) if self.cycles!=0 or self.T_cycle > 1: return - AIF=self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA) - RIF=(self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA))/self.Rfree EPermShkInv=np.dot(self.PermShkDstn[0][0],1/self.PermShkDstn[0][1]) - EPermShkValFunc=np.dot(self.PermShkDstn[0][0],self.PermShkDstn[0][1]**(1-self.CRRA)) PermGroFacAdj=self.PermGroFac[0]*EPermShkInv Thorn=self.LivPrb[0]*(self.Rfree*self.DiscFac)**(1/self.CRRA) GIF=Thorn/PermGroFacAdj - #Evaluate and report on the growth impatience condition if GIF<1: - print('The growth impatience factor value for the supplied parameter values satisfies the growth impatience condition.') + if public_call: + print('The growth impatience factor value for the supplied parameter values satisfies the growth impatience condition.') else: + violated = True print('The given parameter values violate the growth impatience condition for this consumer type; the GIF is: %2.4f' % (GIF)) - if verbose: + if verbose: print(' Therefore, a target level of wealth does not exist.') - print(' For more, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') #Evaluate and report on the weak return impatience condition WRIF=(self.LivPrb[0]*(self.UnempPrb**(1/self.CRRA))*(self.Rfree*self.DiscFac)**(1/self.CRRA))/self.Rfree if WRIF<1: - print('The weak return impatience factor value for the supplied parameter values satisfies the weak return impatience condition.') + if public_call: + print('The weak return impatience factor value for the supplied parameter values satisfies the weak return impatience condition.') else: + violated = True print('The given type violates the weak return impatience condition with the supplied parameter values. The WRIF is: %2.4f' % (WRIF)) - if verbose: + if verbose: print(' Therefore, a nondegenerate solution is not available.') - print(' For more, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') #Evaluate and report on the finite value of autarky condition - FVAC=self.LivPrb[0]*self.DiscFac*EPermShkValFunc*(self.PermGroFac[0]**(1-self.CRRA)) - - if FVAC<1: - print('The finite value of autarky factor value for the supplied parameter values satisfies the finite value of autarky condition.') + EPermShkValFunc=np.dot(self.PermShkDstn[0][0],self.PermShkDstn[0][1]**(1-self.CRRA)) + FVAF=self.LivPrb[0]*self.DiscFac*EPermShkValFunc*(self.PermGroFac[0]**(1-self.CRRA)) + if FVAF<1: + if public_call: + print('The finite value of autarky factor value for the supplied parameter values satisfies the finite value of autarky condition.') else: - print('The given type violates the finite value of autarky condition with the supplied parameter values. The FVAC is %2.4f' %(FVAC)) - if verbose: + print('The given type violates the finite value of autarky condition with the supplied parameter values. The FVAF is %2.4f' %(FVAF)) + violated = True + if verbose: print(' Therefore, a nondegenerate solution is not available.') - print(' For more, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') + + if verbose and violated: + print('\n[!] For more information on the conditions, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') class KinkedRconsumerType(IndShockConsumerType): ''' @@ -2147,7 +2163,7 @@ def makeEulerErrorFunc(self,mMax=100,approx_inc_dstn=True): Has option to use approximate income distribution stored in self.IncomeDstn or to use a (temporary) very dense approximation. - NOT YET IMPLEMENTED FOR THIS CLASS + SHOULD BE INHERITED FROM ConsIndShockModel Parameters ---------- @@ -2198,8 +2214,8 @@ def checkConditions(self,verbose=False): Parameters ---------- verbose : boolean - Specifies different levels of verbosity of feedback. When false, it only reports whether the - instance's type fails to satisfy a particular condition. When true, it reports all results, i.e. + Specifies different levels of verbosity of feedback. When False, it only reports whether the + instance's type fails to satisfy a particular condition. When True, it reports all results, i.e. the factor values for all conditions. Returns @@ -2557,4 +2573,3 @@ def main(): if __name__ == '__main__': main() - From 3c3b064c6e43f13d6d112f3f577f9a9ab4fddfed Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Fri, 15 Feb 2019 08:20:26 -0500 Subject: [PATCH 06/77] Fixed imports in model files (#224) * Fixed imports in model files All of the consumption-saving model files used a style of import that didn't work when the file was run directly (rather than called as a module), even though they have a __main__ block (and main() function). This has now been fixed. Also mostly removed extraneous file RepAgentModel.py, which seems to be an old name of ConsRepAgentModel.py. This file now simply imports all of ConsRepAgentModel and warns the user to use that instead. * import ConsumerParameters -> import HARK.ConsumptionSaving.ConsumerParameters --- HARK/ConsumptionSaving/ConsAggShockModel.py | 8 +- .../ConsGenIncProcessModel.py | 7 +- HARK/ConsumptionSaving/ConsIndShockModel.py | 2 +- HARK/ConsumptionSaving/ConsMarkovModel.py | 10 +- HARK/ConsumptionSaving/ConsMedModel.py | 11 +- HARK/ConsumptionSaving/ConsPrefShockModel.py | 10 +- HARK/ConsumptionSaving/ConsRepAgentModel.py | 4 +- HARK/ConsumptionSaving/RepAgentModel.py | 387 +----------------- .../TractableBufferStockModel.py | 2 +- 9 files changed, 39 insertions(+), 402 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py index 026304dff..cf9c8c72a 100644 --- a/HARK/ConsumptionSaving/ConsAggShockModel.py +++ b/HARK/ConsumptionSaving/ConsAggShockModel.py @@ -16,7 +16,7 @@ CRRAutility_invP, CRRAutility_inv, combineIndepDstns,\ approxMeanOneLognormal from HARK.simulation import drawDiscrete, drawUniform -from .ConsIndShockModel import ConsumerSolution, IndShockConsumerType +from HARK.ConsumptionSaving.ConsIndShockModel import ConsumerSolution, IndShockConsumerType from HARK import HARKobject, Market, AgentType from copy import deepcopy import matplotlib.pyplot as plt @@ -1753,7 +1753,7 @@ def __init__(self,AFunc): ############################################################################### def main(): - from . import ConsumerParameters as Params + import HARK.ConsumptionSaving.ConsumerParameters as Params from time import clock from HARK.utilities import plotFuncs mystr = lambda number : "{:.4f}".format(number) @@ -1793,6 +1793,7 @@ def main(): mMin = AggShockExample.solution[0].mNrmMin(M) c_at_this_M = AggShockExample.cFunc[0](m_grid+mMin,M*np.ones_like(m_grid)) plt.plot(m_grid+mMin,c_at_this_M) + plt.ylim(0.,None) plt.show() if solve_agg_shocks_market: @@ -1813,6 +1814,7 @@ def main(): mMin = AggShockExample.solution[0].mNrmMin(M) c_at_this_M = AggShockExample.cFunc[0](m_grid+mMin,M*np.ones_like(m_grid)) plt.plot(m_grid+mMin,c_at_this_M) + plt.ylim(0.,None) plt.show() ######### EXAMPLE IMPLEMENTATIONS OF AggShockMarkovConsumerType ########### @@ -1843,6 +1845,7 @@ def main(): mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M) c_at_this_M = AggShockMrkvExample.cFunc[0][i](m_grid+mMin,M*np.ones_like(m_grid)) plt.plot(m_grid+mMin,c_at_this_M) + plt.ylim(0.,None) plt.show() if solve_markov_market: @@ -1861,6 +1864,7 @@ def main(): mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M) c_at_this_M = AggShockMrkvExample.cFunc[0][i](m_grid+mMin,M*np.ones_like(m_grid)) plt.plot(m_grid+mMin,c_at_this_M) + plt.ylim(0.,None) plt.show() if solve_krusell_smith: diff --git a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py index b81bedff7..b81cf6042 100644 --- a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py +++ b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py @@ -17,7 +17,7 @@ CRRAutility_invP, CRRAutility_inv, CRRAutilityP_invP,\ getPercentiles from HARK.simulation import drawLognormal, drawDiscrete, drawUniform -from .ConsIndShockModel import ConsIndShockSetup, ConsumerSolution, IndShockConsumerType +from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSetup, ConsumerSolution, IndShockConsumerType utility = CRRAutility utilityP = CRRAutilityP @@ -1276,7 +1276,7 @@ def updatepLvlNextFunc(self): ############################################################################### def main(): - from . import ConsumerParameters as Params + import HARK.ConsumptionSaving.ConsumerParameters as Params from HARK.utilities import plotFuncs from time import clock import matplotlib.pyplot as plt @@ -1306,6 +1306,7 @@ def main(): C = ExplicitExample.solution[0].cFunc(M_temp,p*np.ones_like(M_temp)) plt.plot(M_temp,C) plt.xlim(0.,20.) + plt.ylim(0.,None) plt.xlabel('Market resource level mLvl') plt.ylabel('Consumption level cLvl') plt.show() @@ -1328,6 +1329,7 @@ def main(): C = ExplicitExample.solution[0].cFunc(M_temp,p*np.ones_like(M_temp)) plt.plot(M_temp/p,C/p) plt.xlim(0.,20.) + plt.ylim(0.,None) plt.xlabel('Normalized market resources mNrm') plt.ylabel('Normalized consumption cNrm') plt.show() @@ -1382,6 +1384,7 @@ def main(): C = PersistentExample.solution[0].cFunc(M_temp,p*np.ones_like(M_temp)) plt.plot(M_temp,C) plt.xlim(0.,20.) + plt.ylim(0.,None) plt.xlabel('Market resource level mLvl') plt.ylabel('Consumption level cLvl') plt.show() diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 782321fed..40ec41345 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -2426,7 +2426,7 @@ def constructAssetsGrid(parameters): #################################################################################################### def main(): - from . import ConsumerParameters as Params + import HARK.ConsumptionSaving.ConsumerParameters as Params from HARK.utilities import plotFuncsDer, plotFuncs from time import clock mystr = lambda number : "{:.4f}".format(number) diff --git a/HARK/ConsumptionSaving/ConsMarkovModel.py b/HARK/ConsumptionSaving/ConsMarkovModel.py index 73affbaff..74f196cfe 100644 --- a/HARK/ConsumptionSaving/ConsMarkovModel.py +++ b/HARK/ConsumptionSaving/ConsMarkovModel.py @@ -9,10 +9,8 @@ from builtins import range from copy import deepcopy import numpy as np -from .ConsIndShockModel import ConsIndShockSolver, ValueFunc, MargValueFunc, ConsumerSolution, IndShockConsumerType -from .ConsAggShockModel import AggShockConsumerType -from HARK.utilities import combineIndepDstns, warnings # Because of "patch" to warnings modules -from HARK import Market, HARKobject +from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSolver, ValueFunc, \ + MargValueFunc, ConsumerSolution, IndShockConsumerType from HARK.simulation import drawDiscrete, drawUniform from HARK.interpolation import CubicInterp, LowerEnvelope, LinearInterp from HARK.utilities import CRRAutility, CRRAutilityP, CRRAutilityPP, CRRAutilityP_inv, \ @@ -974,7 +972,7 @@ def makeEulerErrorFunc(self,mMax=100,approx_inc_dstn=True): ############################################################################### def main(): - from . import ConsumerParameters as Params + import HARK.ConsumptionSaving.ConsumerParameters as Params from HARK.utilities import plotFuncs from time import clock from copy import copy @@ -1026,7 +1024,7 @@ def main(): start_time = clock() SerialUnemploymentExample.solve() end_time = clock() - print('Solving a Markov consumer took ' + mystr(end_time-start_time) + ' seconds.') + print('Solving a Markov consumer with serially correlated unemployment took ' + mystr(end_time-start_time) + ' seconds.') print('Consumption functions for each discrete state:') plotFuncs(SerialUnemploymentExample.solution[0].cFunc,0,50) if SerialUnemploymentExample.vFuncBool: diff --git a/HARK/ConsumptionSaving/ConsMedModel.py b/HARK/ConsumptionSaving/ConsMedModel.py index 13719c77c..8744a898b 100644 --- a/HARK/ConsumptionSaving/ConsMedModel.py +++ b/HARK/ConsumptionSaving/ConsMedModel.py @@ -11,14 +11,13 @@ from HARK.utilities import approxLognormal, addDiscreteOutcomeConstantMean, CRRAutilityP_inv,\ CRRAutility, CRRAutility_inv, CRRAutility_invP, CRRAutilityPP,\ makeGridExpMult, NullFunc -from HARK.simulation import drawLognormal -from .ConsIndShockModel import ConsumerSolution +from HARK.ConsumptionSaving.ConsIndShockModel import ConsumerSolution from HARK.interpolation import BilinearInterpOnInterp1D, TrilinearInterp, BilinearInterp, CubicInterp,\ LinearInterp, LowerEnvelope3D, UpperEnvelope, LinearInterpOnInterp1D,\ VariableLowerBoundFunc3D -from .ConsGenIncProcessModel import ConsGenIncProcessSolver, PersistentShockConsumerType,\ - ValueFunc2D, MargValueFunc2D, MargMargValueFunc2D, \ - VariableLowerBoundFunc2D +from HARK.ConsumptionSaving.ConsGenIncProcessModel import ConsGenIncProcessSolver,\ + PersistentShockConsumerType, ValueFunc2D, MargValueFunc2D,\ + MargMargValueFunc2D, VariableLowerBoundFunc2D from copy import deepcopy utility_inv = CRRAutility_inv @@ -1359,7 +1358,7 @@ def solveConsMedShock(solution_next,IncomeDstn,MedShkDstn,LivPrb,DiscFac,CRRA,CR ############################################################################### def main(): - from . import ConsumerParameters as Params + import HARK.ConsumptionSaving.ConsumerParameters as Params from HARK.utilities import CRRAutility_inv from time import clock import matplotlib.pyplot as plt diff --git a/HARK/ConsumptionSaving/ConsPrefShockModel.py b/HARK/ConsumptionSaving/ConsPrefShockModel.py index f83032c68..18f27c626 100644 --- a/HARK/ConsumptionSaving/ConsPrefShockModel.py +++ b/HARK/ConsumptionSaving/ConsPrefShockModel.py @@ -12,7 +12,7 @@ from builtins import range import numpy as np from HARK.utilities import approxMeanOneLognormal -from .ConsIndShockModel import IndShockConsumerType, ConsumerSolution, ConsIndShockSolver, \ +from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, ConsumerSolution, ConsIndShockSolver, \ ValueFunc, MargValueFunc, KinkedRconsumerType, ConsKinkedRsolver from HARK.interpolation import LinearInterpOnInterp1D, LinearInterp, CubicInterp, LowerEnvelope @@ -594,7 +594,7 @@ def solveConsKinkyPref(solution_next,IncomeDstn,PrefShkDstn, ############################################################################### def main(): - from . import ConsumerParameters as Params + import HARK.ConsumptionSaving.ConsumerParameters as Params import matplotlib.pyplot as plt from HARK.utilities import plotFuncs from time import clock @@ -618,6 +618,8 @@ def main(): PrefShk = PrefShockExample.PrefShkDstn[0][1][j] c = PrefShockExample.solution[0].cFunc(m,PrefShk*np.ones_like(m)) plt.plot(m,c) + plt.xlim([0.,None]) + plt.ylim([0.,None]) plt.show() print('Consumption function (and MPC) when shock=1:') @@ -625,6 +627,8 @@ def main(): k = PrefShockExample.solution[0].cFunc.derivativeX(m,np.ones_like(m)) plt.plot(m,c) plt.plot(m,k) + plt.xlim([0.,None]) + plt.ylim([0.,None]) plt.show() if PrefShockExample.vFuncBool: @@ -657,6 +661,7 @@ def main(): PrefShk = KinkyPrefExample.PrefShkDstn[0][1][j] c = KinkyPrefExample.solution[0].cFunc(m,PrefShk*np.ones_like(m)) plt.plot(m,c) + plt.ylim([0.,None]) plt.show() print('Consumption function (and MPC) when shock=1:') @@ -664,6 +669,7 @@ def main(): k = KinkyPrefExample.solution[0].cFunc.derivativeX(m,np.ones_like(m)) plt.plot(m,c) plt.plot(m,k) + plt.ylim([0.,None]) plt.show() if KinkyPrefExample.vFuncBool: diff --git a/HARK/ConsumptionSaving/ConsRepAgentModel.py b/HARK/ConsumptionSaving/ConsRepAgentModel.py index 82d0179ae..2b9b935e4 100644 --- a/HARK/ConsumptionSaving/ConsRepAgentModel.py +++ b/HARK/ConsumptionSaving/ConsRepAgentModel.py @@ -11,7 +11,7 @@ import numpy as np from HARK.interpolation import LinearInterp from HARK.simulation import drawUniform, drawDiscrete -from .ConsIndShockModel import IndShockConsumerType, ConsumerSolution, MargValueFunc +from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType, ConsumerSolution, MargValueFunc def solveConsRepAgent(solution_next,DiscFac,CRRA,IncomeDstn,CapShare,DeprFac,PermGroFac,aXtraGrid): ''' @@ -331,7 +331,7 @@ def main(): from copy import deepcopy from time import clock from HARK.utilities import plotFuncs - from . import ConsumerParameters as Params + import HARK.ConsumptionSaving.ConsumerParameters as Params # Make a quick example dictionary RA_params = deepcopy(Params.init_idiosyncratic_shocks) diff --git a/HARK/ConsumptionSaving/RepAgentModel.py b/HARK/ConsumptionSaving/RepAgentModel.py index dbeea42af..6dbae740a 100644 --- a/HARK/ConsumptionSaving/RepAgentModel.py +++ b/HARK/ConsumptionSaving/RepAgentModel.py @@ -1,384 +1,11 @@ ''' -This module contains models for solving representative agent macroeconomic models. -This stands in contrast to all other model modules in HARK, which (unsurprisingly) -take a heterogeneous agents approach. In these models, all attributes are either -time invariant or exist on a short cycle. +This file appears to be an old version of what is now ConsRepAgentModel.py. +Its previous contents have been entirely removed and replaced with a universal +import from ConsRepAgentModel. Whenever a user imports from this file, they +will get a warning that they should import from ConsRepAgentModel instead. ''' -from __future__ import division, print_function -from __future__ import absolute_import -from builtins import str -from builtins import range -import numpy as np -from HARK.interpolation import LinearInterp -from HARK.simulation import drawUniform, drawDiscrete -from .ConsIndShockModel import IndShockConsumerType, ConsumerSolution, MargValueFunc -def solveConsRepAgent(solution_next,DiscFac,CRRA,IncomeDstn,CapShare,DeprFac,PermGroFac,aXtraGrid): - ''' - Solve one period of the simple representative agent consumption-saving model. - - Parameters - ---------- - solution_next : ConsumerSolution - Solution to the next period's problem (i.e. previous iteration). - DiscFac : float - Intertemporal discount factor for future utility. - CRRA : float - Coefficient of relative risk aversion. - IncomeDstn : [np.array] - A list containing three arrays of floats, representing a discrete - approximation to the income process between the period being solved - and the one immediately following (in solution_next). Order: event - probabilities, permanent shocks, transitory shocks. - CapShare : float - Capital's share of income in Cobb-Douglas production function. - DeprFac : float - Depreciation rate of capital. - PermGroFac : float - Expected permanent income growth factor at the end of this period. - aXtraGrid : np.array - Array of "extra" end-of-period asset values-- assets above the - absolute minimum acceptable level. In this model, the minimum acceptable - level is always zero. - - Returns - ------- - solution_now : ConsumerSolution - Solution to this period's problem (new iteration). - ''' - # Unpack next period's solution and the income distribution - vPfuncNext = solution_next.vPfunc - ShkPrbsNext = IncomeDstn[0] - PermShkValsNext = IncomeDstn[1] - TranShKValsNext = IncomeDstn[2] - - # Make tiled versions of end-of-period assets, shocks, and probabilities - aNrmNow = aXtraGrid - aNrmCount = aNrmNow.size - ShkCount = ShkPrbsNext.size - aNrm_tiled = np.tile(np.reshape(aNrmNow,(aNrmCount,1)),(1,ShkCount)) - - # Tile arrays of the income shocks and put them into useful shapes - PermShkVals_tiled = np.tile(np.reshape(PermShkValsNext,(1,ShkCount)),(aNrmCount,1)) - TranShkVals_tiled = np.tile(np.reshape(TranShKValsNext,(1,ShkCount)),(aNrmCount,1)) - ShkPrbs_tiled = np.tile(np.reshape(ShkPrbsNext,(1,ShkCount)),(aNrmCount,1)) - - # Calculate next period's capital-to-permanent-labor ratio under each combination - # of end-of-period assets and shock realization - kNrmNext = aNrm_tiled/(PermGroFac*PermShkVals_tiled) - - # Calculate next period's market resources - KtoLnext = kNrmNext/TranShkVals_tiled - RfreeNext = 1. - DeprFac + CapShare*KtoLnext**(CapShare-1.) - wRteNext = (1.-CapShare)*KtoLnext**CapShare - mNrmNext = RfreeNext*kNrmNext + wRteNext*TranShkVals_tiled - - # Calculate end-of-period marginal value of assets for the RA - vPnext = vPfuncNext(mNrmNext) - EndOfPrdvP = DiscFac*np.sum(RfreeNext*(PermGroFac*PermShkVals_tiled)**(-CRRA)*vPnext*ShkPrbs_tiled,axis=1) - - # Invert the first order condition to get consumption, then find endogenous gridpoints - cNrmNow = EndOfPrdvP**(-1./CRRA) - mNrmNow = aNrmNow + cNrmNow - - # Construct the consumption function and the marginal value function - cFuncNow = LinearInterp(np.insert(mNrmNow,0,0.0),np.insert(cNrmNow,0,0.0)) - vPfuncNow = MargValueFunc(cFuncNow,CRRA) - - # Construct and return the solution for this period - solution_now = ConsumerSolution(cFunc=cFuncNow,vPfunc=vPfuncNow) - return solution_now - - - -def solveConsRepAgentMarkov(solution_next,MrkvArray,DiscFac,CRRA,IncomeDstn,CapShare,DeprFac,PermGroFac,aXtraGrid): - ''' - Solve one period of the simple representative agent consumption-saving model. - This version supports a discrete Markov process. - - Parameters - ---------- - solution_next : ConsumerSolution - Solution to the next period's problem (i.e. previous iteration). - MrkvArray : np.array - Markov transition array between this period and next period. - DiscFac : float - Intertemporal discount factor for future utility. - CRRA : float - Coefficient of relative risk aversion. - IncomeDstn : [[np.array]] - A list of lists containing three arrays of floats, representing a discrete - approximation to the income process between the period being solved - and the one immediately following (in solution_next). Order: event - probabilities, permanent shocks, transitory shocks. - CapShare : float - Capital's share of income in Cobb-Douglas production function. - DeprFac : float - Depreciation rate of capital. - PermGroFac : [float] - Expected permanent income growth factor for each state we could be in - next period. - aXtraGrid : np.array - Array of "extra" end-of-period asset values-- assets above the - absolute minimum acceptable level. In this model, the minimum acceptable - level is always zero. - - Returns - ------- - solution_now : ConsumerSolution - Solution to this period's problem (new iteration). - ''' - # Define basic objects - StateCount = MrkvArray.shape[0] - aNrmNow = aXtraGrid - aNrmCount = aNrmNow.size - EndOfPrdvP_cond = np.zeros((StateCount,aNrmCount)) + np.nan - - # Loop over *next period* states, calculating conditional EndOfPrdvP - for j in range(StateCount): - # Define next-period-state conditional objects - vPfuncNext = solution_next.vPfunc[j] - ShkPrbsNext = IncomeDstn[j][0] - PermShkValsNext = IncomeDstn[j][1] - TranShKValsNext = IncomeDstn[j][2] - - # Make tiled versions of end-of-period assets, shocks, and probabilities - ShkCount = ShkPrbsNext.size - aNrm_tiled = np.tile(np.reshape(aNrmNow,(aNrmCount,1)),(1,ShkCount)) - - # Tile arrays of the income shocks and put them into useful shapes - PermShkVals_tiled = np.tile(np.reshape(PermShkValsNext,(1,ShkCount)),(aNrmCount,1)) - TranShkVals_tiled = np.tile(np.reshape(TranShKValsNext,(1,ShkCount)),(aNrmCount,1)) - ShkPrbs_tiled = np.tile(np.reshape(ShkPrbsNext,(1,ShkCount)),(aNrmCount,1)) - - # Calculate next period's capital-to-permanent-labor ratio under each combination - # of end-of-period assets and shock realization - kNrmNext = aNrm_tiled/(PermGroFac[j]*PermShkVals_tiled) - - # Calculate next period's market resources - KtoLnext = kNrmNext/TranShkVals_tiled - RfreeNext = 1. - DeprFac + CapShare*KtoLnext**(CapShare-1.) - wRteNext = (1.-CapShare)*KtoLnext**CapShare - mNrmNext = RfreeNext*kNrmNext + wRteNext*TranShkVals_tiled - - # Calculate end-of-period marginal value of assets for the RA - vPnext = vPfuncNext(mNrmNext) - EndOfPrdvP_cond[j,:] = DiscFac*np.sum(RfreeNext*(PermGroFac[j]*PermShkVals_tiled)**(-CRRA)*vPnext*ShkPrbs_tiled,axis=1) - - # Apply the Markov transition matrix to get unconditional end-of-period marginal value - EndOfPrdvP = np.dot(MrkvArray,EndOfPrdvP_cond) - - # Construct the consumption function and marginal value function for each discrete state - cFuncNow_list = [] - vPfuncNow_list = [] - for i in range(StateCount): - # Invert the first order condition to get consumption, then find endogenous gridpoints - cNrmNow = EndOfPrdvP[i,:]**(-1./CRRA) - mNrmNow = aNrmNow + cNrmNow - - # Construct the consumption function and the marginal value function - cFuncNow_list.append(LinearInterp(np.insert(mNrmNow,0,0.0),np.insert(cNrmNow,0,0.0))) - vPfuncNow_list.append(MargValueFunc(cFuncNow_list[-1],CRRA)) - - # Construct and return the solution for this period - solution_now = ConsumerSolution(cFunc=cFuncNow_list,vPfunc=vPfuncNow_list) - return solution_now - - - -class RepAgentConsumerType(IndShockConsumerType): - ''' - A class for representing representative agents with inelastic labor supply. - ''' - time_inv_ = IndShockConsumerType.time_inv_ + ['CapShare','DeprFac'] - - def __init__(self,time_flow=True,**kwds): - ''' - Make a new instance of a representative agent. - - Parameters - ---------- - time_flow : boolean - Whether time is currently "flowing" forward for this instance. - - Returns - ------- - None - ''' - IndShockConsumerType.__init__(self,cycles=0,time_flow=time_flow,**kwds) - self.AgentCount = 1 # Hardcoded, because this is rep agent - self.solveOnePeriod = solveConsRepAgent - self.delFromTimeInv('Rfree','BoroCnstArt','vFuncBool','CubicBool') - - def getStates(self): - ''' - Calculates updated values of normalized market resources and permanent income level. - Uses pLvlNow, aNrmNow, PermShkNow, TranShkNow. - - Parameters - ---------- - None - - Returns - ------- - None - ''' - pLvlPrev = self.pLvlNow - aNrmPrev = self.aNrmNow - - # Calculate new states: normalized market resources and permanent income level - self.pLvlNow = pLvlPrev*self.PermShkNow # Updated permanent income level - self.kNrmNow = aNrmPrev/self.PermShkNow - self.yNrmNow = self.kNrmNow**self.CapShare*self.TranShkNow**(1.-self.CapShare) - self.Rfree = 1. + self.CapShare*self.kNrmNow**(self.CapShare-1.)*self.TranShkNow**(1.-self.CapShare) - self.DeprFac - self.wRte = (1.-self.CapShare)*self.kNrmNow**self.CapShare*self.TranShkNow**(-self.CapShare) - self.mNrmNow = self.Rfree*self.kNrmNow + self.wRte*self.TranShkNow - - -class RepAgentMarkovConsumerType(RepAgentConsumerType): - ''' - A class for representing representative agents with inelastic labor supply - and a discrete MarkovState - ''' - time_inv_ = RepAgentConsumerType.time_inv_ + ['MrkvArray'] - - def __init__(self,time_flow=True,**kwds): - ''' - Make a new instance of a representative agent with Markov state. - - Parameters - ---------- - time_flow : boolean - Whether time is currently "flowing" forward for this instance. - - Returns - ------- - None - ''' - RepAgentConsumerType.__init__(self,time_flow=time_flow,**kwds) - self.solveOnePeriod = solveConsRepAgentMarkov - - def updateSolutionTerminal(self): - ''' - Update the terminal period solution. This method should be run when a - new AgentType is created or when CRRA changes. - - Parameters - ---------- - None - - Returns - ------- - None - ''' - RepAgentConsumerType.updateSolutionTerminal(self) - - # Make replicated terminal period solution - StateCount = self.MrkvArray.shape[0] - self.solution_terminal.cFunc = StateCount*[self.cFunc_terminal_] - self.solution_terminal.vPfunc = StateCount*[self.solution_terminal.vPfunc] - self.solution_terminal.mNrmMin = StateCount*[self.solution_terminal.mNrmMin] - - - def getShocks(self): - ''' - Draws a new Markov state and income shocks for the representative agent. - - Parameters - ---------- - None - - Returns - ------- - None - ''' - cutoffs = np.cumsum(self.MrkvArray[self.MrkvNow,:]) - MrkvDraw = drawUniform(N=1,seed=self.RNG.randint(0,2**31-1)) - self.MrkvNow = np.searchsorted(cutoffs,MrkvDraw) - - t = self.t_cycle[0] - i = self.MrkvNow[0] - IncomeDstnNow = self.IncomeDstn[t-1][i] # set current income distribution - PermGroFacNow = self.PermGroFac[t-1][i] # and permanent growth factor - Indices = np.arange(IncomeDstnNow[0].size) # just a list of integers - # Get random draws of income shocks from the discrete distribution - EventDraw = drawDiscrete(N=1,X=Indices,P=IncomeDstnNow[0],exact_match=False,seed=self.RNG.randint(0,2**31-1)) - PermShkNow = IncomeDstnNow[1][EventDraw]*PermGroFacNow # permanent "shock" includes expected growth - TranShkNow = IncomeDstnNow[2][EventDraw] - self.PermShkNow = np.array(PermShkNow) - self.TranShkNow = np.array(TranShkNow) - - - def getControls(self): - ''' - Calculates consumption for the representative agent using the consumption functions. - - Parameters - ---------- - None - - Returns - ------- - None - ''' - t = self.t_cycle[0] - i = self.MrkvNow[0] - self.cNrmNow = self.solution[t].cFunc[i](self.mNrmNow) - - -############################################################################### -def main(): - from copy import deepcopy - from time import clock - from HARK.utilities import plotFuncs - from . import ConsumerParameters as Params - - # Make a quick example dictionary - RA_params = deepcopy(Params.init_idiosyncratic_shocks) - RA_params['DeprFac'] = 0.05 - RA_params['CapShare'] = 0.36 - RA_params['UnempPrb'] = 0.0 - RA_params['LivPrb'] = [1.0] - - # Make and solve a rep agent model - RAexample = RepAgentConsumerType(**RA_params) - t_start = clock() - RAexample.solve() - t_end = clock() - print('Solving a representative agent problem took ' + str(t_end-t_start) + ' seconds.') - plotFuncs(RAexample.solution[0].cFunc,0,20) - - # Simulate the representative agent model - RAexample.T_sim = 2000 - RAexample.track_vars = ['cNrmNow','mNrmNow','Rfree','wRte'] - RAexample.initializeSim() - t_start = clock() - RAexample.simulate() - t_end = clock() - print('Simulating a representative agent for ' + str(RAexample.T_sim) + ' periods took ' + str(t_end-t_start) + ' seconds.') - - # Make and solve a Markov representative agent - RA_markov_params = deepcopy(RA_params) - RA_markov_params['PermGroFac'] = [[0.97,1.03]] - RA_markov_params['MrkvArray'] = np.array([[0.99,0.01],[0.01,0.99]]) - RA_markov_params['MrkvNow'] = 0 - RAmarkovExample = RepAgentMarkovConsumerType(**RA_markov_params) - RAmarkovExample.IncomeDstn[0] = 2*[RAmarkovExample.IncomeDstn[0]] - t_start = clock() - RAmarkovExample.solve() - t_end = clock() - print('Solving a two state representative agent problem took ' + str(t_end-t_start) + ' seconds.') - plotFuncs(RAmarkovExample.solution[0].cFunc,0,10) - - # Simulate the two state representative agent model - RAmarkovExample.T_sim = 2000 - RAmarkovExample.track_vars = ['cNrmNow','mNrmNow','Rfree','wRte','MrkvNow'] - RAmarkovExample.initializeSim() - t_start = clock() - RAmarkovExample.simulate() - t_end = clock() - print('Simulating a two state representative agent for ' + str(RAexample.T_sim) + ' periods took ' + str(t_end-t_start) + ' seconds.') - -if __name__ == '__main__': - main() +import warnings +from HARK.ConsumptionSaving.ConsRepAgentModel import * +warnings.warn('Please import from ConsRepAgentModel rather than RepAgentModel. This module will be removed in a future version of HARK.') \ No newline at end of file diff --git a/HARK/ConsumptionSaving/TractableBufferStockModel.py b/HARK/ConsumptionSaving/TractableBufferStockModel.py index 88f78d578..057b4c3d5 100644 --- a/HARK/ConsumptionSaving/TractableBufferStockModel.py +++ b/HARK/ConsumptionSaving/TractableBufferStockModel.py @@ -474,7 +474,7 @@ def main(): # contained in the HARK folder. Also import the ConsumptionSavingModel import numpy as np # numeric Python from HARK.utilities import plotFuncs # basic plotting tools - from .ConsMarkovModel import MarkovConsumerType # An alternative, much longer way to solve the TBS model + from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType # An alternative, much longer way to solve the TBS model from time import clock # timing utility do_simulation = True From 7afa2c18f67c7e534fa51abbb4a2f74540086296 Mon Sep 17 00:00:00 2001 From: Econ-ARK Team Date: Sat, 16 Feb 2019 11:40:35 -0500 Subject: [PATCH 07/77] Merge #224 did not bring in the final version (#225) * Fixed imports in model files All of the consumption-saving model files used a style of import that didn't work when the file was run directly (rather than called as a module), even though they have a __main__ block (and main() function). This has now been fixed. Also mostly removed extraneous file RepAgentModel.py, which seems to be an old name of ConsRepAgentModel.py. This file now simply imports all of ConsRepAgentModel and warns the user to use that instead. * import ConsumerParameters -> import HARK.ConsumptionSaving.ConsumerParameters From d3bf1f99d2be7e39605686f4e26c5db10e012c39 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Tue, 26 Feb 2019 13:57:23 +0100 Subject: [PATCH 08/77] Remove Chinese Growth and NonDurables since these now exist as DemARKs. (#229) --- .../ConsumptionSaving/Demos/Chinese_Growth.py | 297 ------------------ .../NonDurables_During_Great_Recession.py | 230 -------------- 2 files changed, 527 deletions(-) delete mode 100644 HARK/ConsumptionSaving/Demos/Chinese_Growth.py delete mode 100644 HARK/ConsumptionSaving/Demos/NonDurables_During_Great_Recession.py diff --git a/HARK/ConsumptionSaving/Demos/Chinese_Growth.py b/HARK/ConsumptionSaving/Demos/Chinese_Growth.py deleted file mode 100644 index ba95e8b21..000000000 --- a/HARK/ConsumptionSaving/Demos/Chinese_Growth.py +++ /dev/null @@ -1,297 +0,0 @@ -""" -China's high net saving rate (approximately 25%) is a puzzle for economists, particularly in -light of a consistently high income growth rate. - -If the last exercise made you worry that invoking difficult-to-measure "uncertainty" can explain -anything (e.g. "the stock market fell today because the risk aversion of the representative -agent increased"), the next exercise may reassure you. It is designed to show that there are -limits to the phenomena that can be explained by invoking uncertainty. - -It asks "what beliefs about uncertainty would Chinese consumers need to hold in order to generate a -saving rate of 25%, given the rapid pace of Chinese growth"? - -#################################################################################################### -#################################################################################################### - -The first step is to create the ConsumerType we want to solve the model for. - -Model set up: - * "Standard" infinite horizon consumption/savings model, with mortality and - permanent and temporary shocks to income - * Markov state that represents the state of the Chinese economy (to be detailed later) - * Ex-ante heterogeneity in consumers' discount factors - -In our experiment, consumers will live in a stationary, low-growth environment (intended to -approximate China before 1978). Then, unexpectedly, income growth will surge at the same time -that income uncertainty increases (intended to approximate the effect of economic reforms in China -since 1978.) Consumers believe the high-growth, high-uncertainty state is highly persistent, but -temporary. - -HARK's MarkovConsumerType will be a very convient way to run this experiment. So we need to -prepare the parameters to create that ConsumerType, and then create it. -""" -from __future__ import division, print_function -### First bring in default parameter values from cstwPMC. We will change these as necessary. - -# Now, bring in what we need from the cstwMPC parameters -from builtins import str -from builtins import range -import HARK.cstwMPC.SetupParamsCSTW as cstwParams - -# Initialize the cstwMPC parameters -from copy import deepcopy -init_China_parameters = deepcopy(cstwParams.init_infinite) - -### Now, change the parameters as necessary -import numpy as np - -# For a Markov model, we need a Markov transition array. Create that array. -# Remember, for this simple example, we just have a low-growth state, and a high-growth state -StateCount = 2 #number of Markov states -ProbGrowthEnds = (1./160.) #probability agents assign to the high-growth state ending -MrkvArray = np.array([[1.,0.],[ProbGrowthEnds,1.-ProbGrowthEnds]]) #Markov array -init_China_parameters['MrkvArray'] = [MrkvArray] #assign the Markov array as a parameter - -# One other parameter to change: the number of agents in simulation -# We want to increase this, because later on when we vastly increase the variance of the permanent -# income shock, things get wonky. -# It is important to note that we need to change this value here, before we have used the parameters -# to initialize the MarkovConsumerType. This is because this parameter is used during initialization. -# Other parameters that are not used during initialization can also be assigned here, -# by changing the appropriate value in the init_China_parameters_dictionary; however, -# they can also be changed later, by altering the appropriate attribute of the initialized -# MarkovConsumerType. -init_China_parameters['AgentCount'] = 10000 - -### Import and initialize the HARK ConsumerType we want -### Here, we bring in an agent making a consumption/savings decision every period, subject -### to transitory and permanent income shocks, AND a Markov shock -from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType -ChinaExample = MarkovConsumerType(**init_China_parameters) - -# Currently, Markov states can differ in their interest factor, permanent growth factor, -# survival probability, and income distribution. Each of these needs to be specifically set. -# Do that here, except income distribution. That will be done later, because we want to examine -# the effects of different income distributions. - -ChinaExample.assignParameters(PermGroFac = [np.array([1.,1.06 ** (.25)])], #needs to be a list, with 0th element of shape of shape (StateCount,) - Rfree = np.array(StateCount*[init_China_parameters['Rfree']]), #need to be an array, of shape (StateCount,) - LivPrb = [np.array(StateCount*[init_China_parameters['LivPrb']][0])], #needs to be a list, with 0th element of shape of shape (StateCount,) - cycles = 0) -ChinaExample.track_vars = ['aNrmNow','cNrmNow','pLvlNow'] # Names of variables to be tracked - -#################################################################################################### -#################################################################################################### -""" -Now, add in ex-ante heterogeneity in consumers' discount factors -""" - -# The cstwMPC parameters do not define a discount factor, since there is ex-ante heterogeneity -# in the discount factor. To prepare to create this ex-ante heterogeneity, first create -# the desired number of consumer types - -num_consumer_types = 7 # declare the number of types we want -ChineseConsumerTypes = [] # initialize an empty list - -for nn in range(num_consumer_types): - # Now create the types, and append them to the list ChineseConsumerTypes - newType = deepcopy(ChinaExample) - ChineseConsumerTypes.append(newType) - -## Now, generate the desired ex-ante heterogeneity, by giving the different consumer types -## each with their own discount factor - -# First, decide the discount factors to assign -from HARK.utilities import approxUniform - -bottomDiscFac = 0.9800 -topDiscFac = 0.9934 -DiscFac_list = approxUniform(N=num_consumer_types,bot=bottomDiscFac,top=topDiscFac)[1] - -# Now, assign the discount factors we want to the ChineseConsumerTypes -for j in range(num_consumer_types): - ChineseConsumerTypes[j].DiscFac = DiscFac_list[j] - -#################################################################################################### -#################################################################################################### -""" -Now, write the function to perform the experiment. - -Recall that all parameters have been assigned appropriately, except for the income process. -This is because we want to see how much uncertainty needs to accompany the high-growth state -to generate the desired high savings rate. - -Therefore, among other things, this function will have to initialize and assign -the appropriate income process. -""" - -# First create the income distribution in the low-growth state, which we will not change -from HARK.ConsumptionSaving.ConsIndShockModel import constructLognormalIncomeProcessUnemployment -import HARK.ConsumptionSaving.ConsumerParameters as IncomeParams - -LowGrowthIncomeDstn = constructLognormalIncomeProcessUnemployment(IncomeParams)[0][0] - -# Remember the standard deviation of the permanent income shock in the low-growth state for later -LowGrowth_PermShkStd = IncomeParams.PermShkStd - - - -def calcNatlSavingRate(PrmShkVar_multiplier,RNG_seed = 0): - """ - This function actually performs the experiment we want. - - Remember this experiment is: get consumers into the steady-state associated with the low-growth - regime. Then, give them an unanticipated shock that increases the income growth rate - and permanent income uncertainty at the same time. What happens to the path for - the national saving rate? Can an increase in permanent income uncertainty - explain the high Chinese saving rate since economic reforms began? - - The inputs are: - * PrmShkVar_multiplier, the number by which we want to multiply the variance - of the permanent shock in the low-growth state to get the variance of the - permanent shock in the high-growth state - * RNG_seed, an integer to seed the random number generator for simulations. This useful - because we are going to run this function for different values of PrmShkVar_multiplier, - and we may not necessarily want the simulated agents in each run to experience - the same (normalized) shocks. - """ - - # First, make a deepcopy of the ChineseConsumerTypes (each with their own discount factor), - # because we are going to alter them - ChineseConsumerTypesNew = deepcopy(ChineseConsumerTypes) - - # Set the uncertainty in the high-growth state to the desired amount, keeping in mind - # that PermShkStd is a list of length 1 - PrmShkStd_multiplier = PrmShkVar_multiplier ** .5 - IncomeParams.PermShkStd = [LowGrowth_PermShkStd[0] * PrmShkStd_multiplier] - - # Construct the appropriate income distributions - HighGrowthIncomeDstn = constructLognormalIncomeProcessUnemployment(IncomeParams)[0][0] - - # To calculate the national saving rate, we need national income and national consumption - # To get those, we are going to start national income and consumption at 0, and then - # loop through each agent type and see how much they contribute to income and consumption. - NatlIncome = 0. - NatlCons = 0. - - for ChineseConsumerTypeNew in ChineseConsumerTypesNew: - ### For each consumer type (i.e. each discount factor), calculate total income - ### and consumption - - # First give each ConsumerType their own random number seed - RNG_seed += 19 - ChineseConsumerTypeNew.seed = RNG_seed - - # Set the income distribution in each Markov state appropriately - ChineseConsumerTypeNew.IncomeDstn = [[LowGrowthIncomeDstn,HighGrowthIncomeDstn]] - - # Solve the problem for this ChineseConsumerTypeNew - ChineseConsumerTypeNew.solve() - - """ - Now we are ready to simulate. - - This case will be a bit different than most, because agents' *perceptions* of the probability - of changes in the Chinese economy will differ from the actual probability of changes. - Specifically, agents think there is a 0% chance of moving out of the low-growth state, and - that there is a (1./160) chance of moving out of the high-growth state. In reality, we - want the Chinese economy to reach the low growth steady state, and then move into the - high growth state with probability 1. Then we want it to persist in the high growth - state for 40 years. - """ - - ## Now, simulate 500 quarters to get to steady state, then 40 years of high growth - ChineseConsumerTypeNew.T_sim = 660 - - # Ordinarily, the simulate method for a MarkovConsumerType randomly draws Markov states - # according to the transition probabilities in MrkvArray *independently* for each simulated - # agent. In this case, however, we want the discrete state to be *perfectly coordinated* - # across agents-- it represents a macroeconomic state, not a microeconomic one! In fact, - # we don't want a random history at all, but rather a specific, predetermined history: 125 - # years of low growth, followed by 40 years of high growth. - - # To do this, we're going to "hack" our consumer type a bit. First, we set the attribute - # MrkvPrbsInit so that all of the initial Markov states are in the low growth state. Then - # we initialize the simulation and run it for 500 quarters. However, as we do not - # want the Markov state to change during this time, we change its MrkvArray to always be in - # the low growth state with probability 1. - - ChineseConsumerTypeNew.MrkvPrbsInit = np.array([1.0,0.0]) # All consumers born in low growth state - ChineseConsumerTypeNew.MrkvArray[0] = np.array([[1.0,0.0],[1.0,0.0]]) # Stay in low growth state - ChineseConsumerTypeNew.initializeSim() # Clear the history and make all newborn agents - ChineseConsumerTypeNew.simulate(500) # Simulate 500 quarders of data - - # Now we want the high growth state to occur for the next 160 periods. We change the initial - # Markov probabilities so that any agents born during this time (to replace an agent who - # died) is born in the high growth state. Moreover, we change the MrkvArray to *always* be - # in the high growth state with probability 1. Then we simulate 160 more quarters. - - ChineseConsumerTypeNew.MrkvPrbsInit = np.array([0.0,1.0]) # All consumers born in low growth state - ChineseConsumerTypeNew.MrkvArray[0] = np.array([[0.0,1.0],[0.0,1.0]]) # Stay in low growth state - ChineseConsumerTypeNew.simulate(160) # Simulate 160 quarders of data - - # Now, get the aggregate income and consumption of this ConsumerType over time - IncomeOfThisConsumerType = np.sum((ChineseConsumerTypeNew.aNrmNow_hist*ChineseConsumerTypeNew.pLvlNow_hist* - (ChineseConsumerTypeNew.Rfree[0] - 1.)) + - ChineseConsumerTypeNew.pLvlNow_hist, axis=1) - - ConsOfThisConsumerType = np.sum(ChineseConsumerTypeNew.cNrmNow_hist*ChineseConsumerTypeNew.pLvlNow_hist,axis=1) - - # Add the income and consumption of this ConsumerType to national income and consumption - NatlIncome += IncomeOfThisConsumerType - NatlCons += ConsOfThisConsumerType - - - # After looping through all the ConsumerTypes, calculate and return the path of the national - # saving rate - NatlSavingRate = (NatlIncome - NatlCons) / NatlIncome - - return NatlSavingRate - - -#################################################################################################### -#################################################################################################### -""" -Now we can use the function we just defined to calculate the path of the national saving rate -following the economic reforms, for a given value of the increase to the variance of permanent -income accompanying the reforms. We are going to graph this path for various values for this -increase. - -Remember, we want to see if any plausible value for this increase can explain the high -Chinese saving rate. -""" - -# Declare the number of periods before the reforms to plot in the graph -quarters_before_reform_to_plot = 5 - -# Declare the quarters we want to plot results for -quarters_to_plot = np.arange(-quarters_before_reform_to_plot ,160,1) - -# Create a list to hold the paths of the national saving rate -NatlSavingsRates = [] - -# Create a list of floats to multiply the variance of the permanent shock to income by -PermShkVarMultipliers = (1.,2.,4.,8.,11.) - -# Loop through the desired multipliers, then get the path of the national saving rate -# following economic reforms, assuming that the variance of the permanent income shock -# was multiplied by the given multiplier -index = 0 -for PermShkVarMultiplier in PermShkVarMultipliers: - NatlSavingsRates.append(calcNatlSavingRate(PermShkVarMultiplier,RNG_seed = index)[-160 - quarters_before_reform_to_plot :]) - index +=1 - -# We've calculated the path of the national saving rate as we wanted -# All that's left is to graph the results! -import pylab as plt -plt.ylabel('Natl Savings Rate') -plt.xlabel('Quarters Since Economic Reforms') -plt.plot(quarters_to_plot,NatlSavingsRates[0],label=str(PermShkVarMultipliers[0]) + ' x variance') -plt.plot(quarters_to_plot,NatlSavingsRates[1],label=str(PermShkVarMultipliers[1]) + ' x variance') -plt.plot(quarters_to_plot,NatlSavingsRates[2],label=str(PermShkVarMultipliers[2]) + ' x variance') -plt.plot(quarters_to_plot,NatlSavingsRates[3],label=str(PermShkVarMultipliers[3]) + ' x variance') -plt.plot(quarters_to_plot,NatlSavingsRates[4],label=str(PermShkVarMultipliers[4]) + ' x variance') -plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, - ncol=2, mode="expand", borderaxespad=0.) #put the legend on top -plt.show() - diff --git a/HARK/ConsumptionSaving/Demos/NonDurables_During_Great_Recession.py b/HARK/ConsumptionSaving/Demos/NonDurables_During_Great_Recession.py deleted file mode 100644 index e60eee070..000000000 --- a/HARK/ConsumptionSaving/Demos/NonDurables_During_Great_Recession.py +++ /dev/null @@ -1,230 +0,0 @@ -""" -At the onset of the Great Recession, there was a large drop (6.32%, according to FRED) in consumer -spending on non-durables. Some economists have proffered that this could be attributed to precautionary -motives-- a perceived increase in household income uncertainty induces more saving (less consumption) -to protect future consumption against bad income shocks. How large of an increase in the standard -deviation of (log) permanent income shocks would be necessary to see an 6.32% drop in consumption in -one quarter? What about transitory income shocks? How high would the perceived unemployment -probability have to be? - -#################################################################################################### -#################################################################################################### - -The first step is to create the ConsumerType we want to solve the model for. - -Model set up: - * "Standard" infinite horizon consumption/savings model, with mortality and - permanent and temporary shocks to income - * Ex-ante heterogeneity in consumers' discount factors - -With this basic setup, HARK's IndShockConsumerType is the appropriate ConsumerType. -So we need to prepare the parameters to create that ConsumerType, and then create it. -""" - -## Import some things from cstwMPC -from __future__ import division, print_function -from builtins import str -from builtins import range -import numpy as np -from copy import deepcopy - -# Now, bring in what we need from the cstwMPC parameters -import HARK.cstwMPC.SetupParamsCSTW as cstwParams -from HARK.utilities import approxUniform - -## Import the HARK ConsumerType we want -## Here, we bring in an agent making a consumption/savings decision every period, subject -## to transitory and permanent income shocks. -from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType - -# Now initialize a baseline consumer type, using default parameters from infinite horizon cstwMPC -BaselineType = IndShockConsumerType(**cstwParams.init_infinite) -BaselineType.AgentCount = 10000 # Assign the baseline consumer type to have many agents in simulation - -#################################################################################################### -#################################################################################################### -""" -Now, add in ex-ante heterogeneity in consumers' discount factors -""" - -# The cstwMPC parameters do not define a discount factor, since there is ex-ante heterogeneity -# in the discount factor. To prepare to create this ex-ante heterogeneity, first create -# the desired number of consumer types -num_consumer_types = 7 # declare the number of types we want -ConsumerTypes = [] # initialize an empty list - -for nn in range(num_consumer_types): - # Now create the types, and append them to the list ConsumerTypes - newType = deepcopy(BaselineType) - ConsumerTypes.append(newType) - ConsumerTypes[-1].seed = nn # give each consumer type a different RNG seed - -## Now, generate the desired ex-ante heterogeneity, by giving the different consumer types -## each their own discount factor - -# First, decide the discount factors to assign -bottomDiscFac = 0.9800 -topDiscFac = 0.9934 -DiscFac_list = approxUniform(N=num_consumer_types,bot=bottomDiscFac,top=topDiscFac)[1] - -# Now, assign the discount factors we want -for j in range(num_consumer_types): - ConsumerTypes[j].DiscFac = DiscFac_list[j] - -##################################################################################################### -##################################################################################################### -""" -Now, solve and simulate the model for each consumer type -""" - -for ConsumerType in ConsumerTypes: - ### First solve the problem for this ConsumerType. - ConsumerType.solve() - - ### Now simulate many periods to get to the stationary distribution - ConsumerType.T_sim = 1000 - ConsumerType.initializeSim() - ConsumerType.simulate() - -##################################################################################################### -##################################################################################################### -""" -Now, create functions to see how aggregate consumption changes after household income uncertainty -increases in various ways -""" - -# In order to see how consumption changes, we need to be able to calculate average consumption -# in the last period. Create a function do to that here. -def calcAvgC(ConsumerTypes): - """ - This function calculates average consumption in the economy in last simulated period, - averaging across ConsumerTypes. - """ - # Make arrays with all types' (normalized) consumption and permanent income level - cNrm = np.concatenate([ThisType.cNrmNow for ThisType in ConsumerTypes]) - pLvl = np.concatenate([ThisType.pLvlNow for ThisType in ConsumerTypes]) - - # Calculate and return average consumption level in the economy - avgC = np.mean(cNrm*pLvl) - return avgC - -# Now create a function to run the experiment we want -- change income uncertainty, and see -# how consumption changes -def cChangeAfterUncertaintyChange(ConsumerTypes,newVals,paramToChange): - """ - Function to calculate the change in average consumption after change(s) in income uncertainty - Inputs: - * consumerTypes, a list of consumer types - * newvals, a list of new values to use for the income parameters - * paramToChange, a string telling the function which part of the income process to change - """ - - # Initialize an empty list to hold the changes in consumption that happen after parameters change. - changesInConsumption = [] - - # Get average consumption before parameters change - oldAvgC = calcAvgC(ConsumerTypes) - - # Now loop through the new income parameter values to assign, first assigning them, and then - # solving and simulating another period with those values - for newVal in newVals: - if paramToChange in ["PermShkStd","TranShkStd"]: # These parameters are time-varying, and thus are contained in a list. - thisVal = [newVal] # We need to make sure that our updated values are *also* in a (one element) list. - else: - thisVal = newVal - - # Copy everything we have from the consumerTypes - ConsumerTypesNew = deepcopy(ConsumerTypes) - - for index,ConsumerTypeNew in enumerate(ConsumerTypesNew): - setattr(ConsumerTypeNew,paramToChange,thisVal) # Set the changed value of the parameter - - # Because we changed the income process, and the income process is created - # during initialization, we need to be sure to update the income process - ConsumerTypeNew.updateIncomeProcess() - - # Solve the new problem - ConsumerTypeNew.solve() - - # Initialize the new consumer type to have the same distribution of assets and permanent - # income as the stationary distribution we simulated above - ConsumerTypeNew.initializeSim() # Reset the tracked history - ConsumerTypeNew.aNrmNow = ConsumerTypes[index].aNrmNow # Set assets to stationary distribution - ConsumerTypeNew.pLvlNow = ConsumerTypes[index].pLvlNow # Set permanent income to stationary dstn - - # Simulate one more period, which changes the values in cNrm and pLvl for each agent type - ConsumerTypeNew.simOnePeriod() - - # Calculate the percent change in consumption, for this value newVal for the given parameter - newAvgC = calcAvgC(ConsumerTypesNew) - changeInConsumption = 100. * (newAvgC - oldAvgC) / oldAvgC - - # Append the change in consumption to the list changesInConsumption - changesInConsumption.append(changeInConsumption) - - # Return the list of changes in consumption - return changesInConsumption - -## Define functions that calculate the change in average consumption after income process changes -def cChangeAfterPrmShkChange(newVals): - return cChangeAfterUncertaintyChange(ConsumerTypes,newVals,"PermShkStd") - -def cChangeAfterTranShkChange(newVals): - return cChangeAfterUncertaintyChange(ConsumerTypes,newVals,"TranShkStd") - -def cChangeAfterUnempPrbChange(newVals): - return cChangeAfterUncertaintyChange(ConsumerTypes,newVals,"UnempPrb") - -## Now, plot the functions we want - -# Import a useful plotting function from HARK.utilities -from HARK.utilities import plotFuncs -import matplotlib.pyplot as plt # We need this module to change the y-axis on the graphs - -ratio_min = 1. # minimum number to multiply income parameter by -targetChangeInC = -6.32 # Source: FRED -num_points = 10 #number of parameter values to plot in graphs - -## First change the variance of the permanent income shock -perm_ratio_max = 5.0 #??? # Put whatever value in you want! maximum number to multiply std of perm income shock by - -perm_min = BaselineType.PermShkStd[0] * ratio_min -perm_max = BaselineType.PermShkStd[0] * perm_ratio_max - -plt.ylabel('% Change in Consumption') -plt.xlabel('Std. Dev. of Perm. Income Shock (Baseline = ' + str(round(BaselineType.PermShkStd[0],2)) + ')') -plt.title('Change in Cons. Following Increase in Perm. Income Uncertainty') -plt.ylim(-20.,5.) -plt.hlines(targetChangeInC,perm_min,perm_max) -plotFuncs([cChangeAfterPrmShkChange],perm_min,perm_max,N=num_points) - - -### Now change the variance of the temporary income shock -#temp_ratio_max = ??? # Put whatever value in you want! maximum number to multiply std dev of temp income shock by -# -#temp_min = BaselineType.TranShkStd[0] * ratio_min -#temp_max = BaselineType.TranShkStd[0] * temp_ratio_max -# -#plt.ylabel('% Change in Consumption') -#plt.xlabel('Std. Dev. of Temp. Income Shock (Baseline = ' + str(round(BaselineType.TranShkStd[0],2)) + ')') -#plt.title('Change in Cons. Following Increase in Temp. Income Uncertainty') -#plt.ylim(-20.,5.) -#plt.hlines(targetChangeInC,temp_min,temp_max) -#plotFuncs([cChangeAfterTranShkChange],temp_min,temp_max,N=num_points) -# -# -# -### Now change the probability of unemployment -#unemp_ratio_max = ??? # Put whatever value in you want! maximum number to multiply prob of unemployment by -# -#unemp_min = BaselineType.UnempPrb * ratio_min -#unemp_max = BaselineType.UnempPrb * unemp_ratio_max -# -#plt.ylabel('% Change in Consumption') -#plt.xlabel('Unemployment Prob. (Baseline = ' + str(round(BaselineType.UnempPrb,2)) + ')') -#plt.title('Change in Cons. Following Increase in Unemployment Prob.') -#plt.ylim(-20.,5.) -#plt.hlines(targetChangeInC,unemp_min,unemp_max) -#plotFuncs([cChangeAfterUnempPrbChange],unemp_min,unemp_max,N=num_points) -# -# From 43d4bce666867fc6ec0437b3c7b4585ab1da137f Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Tue, 26 Feb 2019 13:58:15 +0100 Subject: [PATCH 09/77] Add mentions of DemARK and REMARK in README.md (#230) --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 1bdeb099c..877dfacab 100644 --- a/README.md +++ b/README.md @@ -37,6 +37,10 @@ Online documentation: https://econ-ark.github.io/HARK User guide: /Documentation/HARKmanual.pdf (in the repository) +Demonstrations of HARK functionality: [DemARK](https://github.com/econ-ark/DemARK/) + +Replications and Explorations Made using the ARK : [REMARK](https://github.com/econ-ark/REMARK/) + ## II. QUICK START GUIDE From 414986016b63502c5b938a6398254ae069ec5dac Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Wed, 27 Feb 2019 13:57:14 +0100 Subject: [PATCH 10/77] Remove incorrect statement in updateIncomeProcess @mnwhite this seems to be wrong, right? I don't see any `self.constructIncomeProcess`-ish statements, so it appears that the mean one log-normal equiprobably version is hard-coded. Correct? I think we should remove the statement unless I missed something in the code, and if it's a *planned* feature, let's just open an issue. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 40ec41345..69fccbaa4 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1783,9 +1783,7 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): def updateIncomeProcess(self): ''' - Updates this agent's income process based on his own attributes. The - function that generates the discrete income process can be swapped out - for a different process. + Updates this agent's income process based on his own attributes. Parameters ---------- From 7e690d92183f3965ca411bceb3919fbc7ce9bfa3 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Thu, 28 Feb 2019 08:37:17 -0500 Subject: [PATCH 11/77] Add MPC to simulation in ConsGenIncProcessModel (#170) The MPC is calculated and stored as an attribute (MPCnow) in some models, but this was omitted in ConsGenIncProcessModel. As it turns out, this functionality is necessary for an exercise/notebook that is being prepared for NBER SI. --- HARK/ConsumptionSaving/ConsGenIncProcessModel.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py index b81cf6042..251455480 100644 --- a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py +++ b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py @@ -1169,10 +1169,13 @@ def getControls(self): None ''' cLvlNow = np.zeros(self.AgentCount) + np.nan + MPCnow = np.zeros(self.AgentCount) + np.nan for t in range(self.T_cycle): these = t == self.t_cycle cLvlNow[these] = self.solution[t].cFunc(self.mLvlNow[these],self.pLvlNow[these]) + MPCnow[these] = self.solution[t].cFunc.derivativeX(self.mLvlNow[these],self.pLvlNow[these]) self.cLvlNow = cLvlNow + self.MPCnow = MPCnow def getPostStates(self): From 2df2fc8d6028fbd4242614adebbf924ca2027a43 Mon Sep 17 00:00:00 2001 From: Shauna Date: Fri, 1 Mar 2019 16:27:54 -0500 Subject: [PATCH 12/77] Create CONTRIBUTING.md --- CONTRIBUTING.md | 38 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 38 insertions(+) create mode 100644 CONTRIBUTING.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 000000000..771ff4f95 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,38 @@ +# Contributing to Econ-ARK + +### Welcome! + +Thank you for considering contributing to Econ-ARK! We're a young project with a small but committed community that's hoping to grow while maintaining our friendly and responsive culture. Whether you're an economist or a technologist, a writer or a coder, an undergrad or a full professor, a professional or a hobbyist, there's a place for you in the Econ-ARK community. + +We're still creating our contribution infrastructure, so this document is a work in progress. If you have any questions please feel free to @ or otherwise reach out project manager [Shauna](https://github.com/shaunagm), or lead developers [Chris](https://github.com/llorracc) and [Matt](https://github.com/mnwhite). If you prefer to connect through email, you can send it to __econ-ark at jhuecon dot org__. + +### How to Contribute + +We're open to all kinds of contributions, from bug reports to help with our docs to suggestions on how to improve our code. The best way to figure out if the contribution you'd like to make is something we'd merge or otherwise accept, is to open up an issue in our issue tracker. Please create an issue rather than immediately submitting pull request, unless the change you'd like to make is so minor you won't mind if the pull request is rejected. For bigger contributions, we want to proactively talk things through so we don't end up wasting your time. + +While we're thrilled to receive all kinds of contributions, there are a few key areas we'd especially like help with: + +* porting existing heterogenous agent/agent based models into HARK +* collecting projects which use Econ-ARK (which we store in the [remark](https://github.com/econ-ark/REMARK) repository) +* creating demonstrations of how to use Econ-ARK (which we store in the [demark](https://github.com/econ-ark/DemARK) repository) +* expanding test coverage of our existing code + +If you'd like to help with those or any other kind of contribution, reach out to us and we'll help you do so. + +We don't currently have guidelines for opening issues or pull requests, so include as much information as seems relevant to you, and we'll ask you if we need to know more. + +### Responding to Issues & Pull Requests + +We're trying to get better at managing our open issues and pull requests. We've created a new set of goals for all issues and pull requests in our Econ-ARK repos: + +1. Initial response within one or two days. +2. Substantive response within two weeks. +3. Resolution of issue/pull request within three months. + +If you've been waiting on us for more than two weeks for any reason, please feel free to give us a nudge. Correspondingly, we ask that you respond to any questions or requests from us within two weeks as well, even if it's just to say, "Sorry, I can't get to this for a while yet". If we don't hear back from you, we may close your issue or pull request. If you want to re-open it, just ask - we're glad to do so. + +### Getting Started + +The [quick start guide](https://github.com/econ-ark/HARK#ii-quick-start-guide) in our README provides instructions for how to get started running HARK. This also serves as a setup guide for new contributors. If you run into any problems, please let us know by opening an issue in the issue tracker. + +Thanks again! We're so glad to have you in our community. From 39cc609ff9532a471d374f0c0b75a048c747b162 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Fri, 1 Mar 2019 22:07:34 -0500 Subject: [PATCH 13/77] Fix one line break One line break in a PR I merged in was invalid in Python 2.7, should now be fixed. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index d2e19dc79..a2c02630a 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -893,8 +893,7 @@ def usePointsForInterpolation(self,cNrm,mNrm,interpolator): cFuncNowUnc = interpolator(mNrm,cNrm) # Combine the constrained and unconstrained functions into the true consumption function - cFuncNow = LowerEnvelope(cFuncNowUnc, self.cFuncNowCnst, - nan_bool = False) + cFuncNow = LowerEnvelope(cFuncNowUnc, self.cFuncNowCnst, nan_bool = False) # Make the marginal value function and the marginal marginal value function vPfuncNow = MargValueFunc(cFuncNow,self.CRRA) From eb12e6ce50c9e0dd5e0f760d441604456ad601ca Mon Sep 17 00:00:00 2001 From: Christopher Llorracc Carroll <1320319+llorracc@users.noreply.github.com> Date: Sat, 2 Mar 2019 09:41:35 -0500 Subject: [PATCH 14/77] Apply minor language edits from @lloracc Co-Authored-By: shaunagm --- CONTRIBUTING.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 771ff4f95..1b489aee9 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -13,8 +13,8 @@ We're open to all kinds of contributions, from bug reports to help with our docs While we're thrilled to receive all kinds of contributions, there are a few key areas we'd especially like help with: * porting existing heterogenous agent/agent based models into HARK -* collecting projects which use Econ-ARK (which we store in the [remark](https://github.com/econ-ark/REMARK) repository) -* creating demonstrations of how to use Econ-ARK (which we store in the [demark](https://github.com/econ-ark/DemARK) repository) +* curating and expanding the collection of projects which use Econ-ARK (which we store in the [remark](https://github.com/econ-ark/REMARK) repository) +* creating demonstrations of how to use Econ-ARK (which we store in the [DemARK](https://github.com/econ-ark/DemARK) repository) * expanding test coverage of our existing code If you'd like to help with those or any other kind of contribution, reach out to us and we'll help you do so. From 6d6b2952038552f14f66cca29380af2be3d3785a Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Thu, 7 Mar 2019 09:07:02 -0500 Subject: [PATCH 15/77] Undo nan_bool merge (#236) * Revert "Fix one line break" This reverts commit 39cc609ff9532a471d374f0c0b75a048c747b162. * Revert "Merge pull request #193 from TimMunday/NanBool" This reverts commit 4d19ea9662618f267749b1c9be8a8edc24b81387, reversing changes made to 7e690d92183f3965ca411bceb3919fbc7ce9bfa3. --- HARK/ConsumptionSaving/ConsIndShockModel.py | 4 +- HARK/interpolation.py | 98 +++++++-------------- 2 files changed, 32 insertions(+), 70 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index a2c02630a..69fccbaa4 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -893,7 +893,7 @@ def usePointsForInterpolation(self,cNrm,mNrm,interpolator): cFuncNowUnc = interpolator(mNrm,cNrm) # Combine the constrained and unconstrained functions into the true consumption function - cFuncNow = LowerEnvelope(cFuncNowUnc, self.cFuncNowCnst, nan_bool = False) + cFuncNow = LowerEnvelope(cFuncNowUnc,self.cFuncNowCnst) # Make the marginal value function and the marginal marginal value function vPfuncNow = MargValueFunc(cFuncNow,self.CRRA) @@ -1214,7 +1214,7 @@ def solveConsIndShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,PermGro included in the reported solution. CubicBool: boolean Indicator for whether the solver should use cubic or linear interpolation. - + Returns ------- solution_now : ConsumerSolution diff --git a/HARK/interpolation.py b/HARK/interpolation.py index 97cca5043..c04cd3e11 100644 --- a/HARK/interpolation.py +++ b/HARK/interpolation.py @@ -1642,7 +1642,7 @@ class LowerEnvelope(HARKinterpolator1D): ''' distance_criteria = ['functions'] - def __init__(self, *functions, nan_bool = True): + def __init__(self,*functions): ''' Constructor to make a new lower envelope iterpolation. @@ -1650,21 +1650,11 @@ def __init__(self, *functions, nan_bool = True): ---------- *functions : function Any number of real functions; often instances of HARKinterpolator1D - nan_bool : boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. + Returns ------- new instance of LowerEnvelope ''' - - if nan_bool: - self.compare = np.nanmin - self.argcompare = np.nanargmin - else: - self.compare = np.min - self.argcompare = np.argmin - self.functions = [] for function in functions: self.functions.append(function) @@ -1675,16 +1665,14 @@ def _evaluate(self,x): Returns the level of the function at each value in x as the minimum among all of the functions. Only called internally by HARKinterpolator1D.__call__. ''' - if _isscalar(x): - y = self.compare([f(x) for f in self.functions]) + y = np.nanmin([f(x) for f in self.functions]) else: m = len(x) fx = np.zeros((m,self.funcCount)) for j in range(self.funcCount): fx[:,j] = self.functions[j](x) - y = self.compare(fx,axis=1) - + y = np.nanmin(fx,axis=1) return y def _der(self,x): @@ -1692,7 +1680,7 @@ def _der(self,x): Returns the first derivative of the function at each value in x. Only called internally by HARKinterpolator1D.derivative. ''' - y,dydx = self._evalAndDer(x) + y,dydx = self.eval_with_derivative(x) return dydx # Sadly, this is the fastest / most convenient way... def _evalAndDer(self,x): @@ -1704,7 +1692,8 @@ def _evalAndDer(self,x): fx = np.zeros((m,self.funcCount)) for j in range(self.funcCount): fx[:,j] = self.functions[j](x) - i = self.argcompare(fx,axis=1) + fx[np.isnan(fx)] = np.inf + i = np.argmin(fx,axis=1) y = fx[np.arange(m),i] dydx = np.zeros_like(y) for j in range(self.funcCount): @@ -1712,6 +1701,7 @@ def _evalAndDer(self,x): dydx[c] = self.functions[j].derivative(x[c]) return y,dydx + class UpperEnvelope(HARKinterpolator1D): ''' The upper envelope of a finite set of 1D functions, each of which can be of @@ -1720,7 +1710,7 @@ class UpperEnvelope(HARKinterpolator1D): ''' distance_criteria = ['functions'] - def __init__(self,*functions, nan_bool=True): + def __init__(self,*functions): ''' Constructor to make a new upper envelope iterpolation. @@ -1728,21 +1718,11 @@ def __init__(self,*functions, nan_bool=True): ---------- *functions : function Any number of real functions; often instances of HARKinterpolator1D - nan_bool : boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns ------- new instance of UpperEnvelope ''' - if nan_bool: - self.compare = np.nanmax - self.argcompare = np.nanargmax - else: - self.compare = np.max - self.argcompare = np.argmax - self.functions = [] for function in functions: self.functions.append(function) @@ -1754,14 +1734,13 @@ def _evaluate(self,x): all of the functions. Only called internally by HARKinterpolator1D.__call__. ''' if _isscalar(x): - y = self.compare([f(x) for f in self.functions]) + y = np.nanmax([f(x) for f in self.functions]) else: m = len(x) fx = np.zeros((m,self.funcCount)) for j in range(self.funcCount): fx[:,j] = self.functions[j](x) - y = self.compare(fx,axis=1) - + y = np.nanmax(fx,axis=1) return y def _der(self,x): @@ -1769,7 +1748,7 @@ def _der(self,x): Returns the first derivative of the function at each value in x. Only called internally by HARKinterpolator1D.derivative. ''' - y,dydx = self._evalAndDer(x) + y,dydx = self.eval_with_derivative(x) return dydx # Sadly, this is the fastest / most convenient way... def _evalAndDer(self,x): @@ -1781,7 +1760,8 @@ def _evalAndDer(self,x): fx = np.zeros((m,self.funcCount)) for j in range(self.funcCount): fx[:,j] = self.functions[j](x) - i = self.argcompare(fx,axis=1) + fx[np.isnan(fx)] = np.inf + i = np.argmax(fx,axis=1) y = fx[np.arange(m),i] dydx = np.zeros_like(y) for j in range(self.funcCount): @@ -1798,7 +1778,7 @@ class LowerEnvelope2D(HARKinterpolator2D): ''' distance_criteria = ['functions'] - def __init__(self,*functions, nan_bool = True): + def __init__(self,*functions): ''' Constructor to make a new lower envelope iterpolation. @@ -1806,21 +1786,11 @@ def __init__(self,*functions, nan_bool = True): ---------- *functions : function Any number of real functions; often instances of HARKinterpolator2D - nan_bool : boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns ------- new instance of LowerEnvelope2D ''' - - if nan_bool: - self.compare = np.nanmin - self.argcompare = np.nanargmin - else: - self.compare = np.min - self.argcompare = np.argmin self.functions = [] for function in functions: self.functions.append(function) @@ -1832,16 +1802,14 @@ def _evaluate(self,x,y): among all of the functions. Only called internally by HARKinterpolator2D.__call__. ''' - if _isscalar(x): - f = self.compare([f(x,y) for f in self.functions]) + f = np.nanmin([f(x,y) for f in self.functions]) else: m = len(x) temp = np.zeros((m,self.funcCount)) for j in range(self.funcCount): temp[:,j] = self.functions[j](x,y) - f = self.compare(temp,axis=1) - + f = np.nanmin(temp,axis=1) return f def _derX(self,x,y): @@ -1853,7 +1821,8 @@ def _derX(self,x,y): temp = np.zeros((m,self.funcCount)) for j in range(self.funcCount): temp[:,j] = self.functions[j](x,y) - i = self.argcompare(temp,axis=1) + temp[np.isnan(temp)] = np.inf + i = np.argmin(temp,axis=1) dfdx = np.zeros_like(x) for j in range(self.funcCount): c = i == j @@ -1869,7 +1838,8 @@ def _derY(self,x,y): temp = np.zeros((m,self.funcCount)) for j in range(self.funcCount): temp[:,j] = self.functions[j](x,y) - i = self.argcompare(temp,axis=1) + temp[np.isnan(temp)] = np.inf + i = np.argmin(temp,axis=1) y = temp[np.arange(m),i] dfdy = np.zeros_like(x) for j in range(self.funcCount): @@ -1886,7 +1856,7 @@ class LowerEnvelope3D(HARKinterpolator3D): ''' distance_criteria = ['functions'] - def __init__(self,*functions, nan_bool = True): + def __init__(self,*functions): ''' Constructor to make a new lower envelope iterpolation. @@ -1894,20 +1864,11 @@ def __init__(self,*functions, nan_bool = True): ---------- *functions : function Any number of real functions; often instances of HARKinterpolator3D - nan_bool : boolean - An indicator for whether the solver should exclude NA's when forming - the lower envelope. Returns ------- None ''' - if nan_bool: - self.compare = np.nanmin - self.argcompare = np.nanargmin - else: - self.compare = np.min - self.argcompare = np.argmin self.functions = [] for function in functions: self.functions.append(function) @@ -1919,16 +1880,14 @@ def _evaluate(self,x,y,z): among all of the functions. Only called internally by HARKinterpolator3D.__call__. ''' - if _isscalar(x): - f = self.compare([f(x,y,z) for f in self.functions]) + f = np.nanmin([f(x,y,z) for f in self.functions]) else: m = len(x) temp = np.zeros((m,self.funcCount)) for j in range(self.funcCount): temp[:,j] = self.functions[j](x,y,z) - f = self.compare(temp,axis=1) - + f = np.nanmin(temp,axis=1) return f def _derX(self,x,y,z): @@ -1940,7 +1899,8 @@ def _derX(self,x,y,z): temp = np.zeros((m,self.funcCount)) for j in range(self.funcCount): temp[:,j] = self.functions[j](x,y,z) - i = self.argcompare(temp,axis=1) + temp[np.isnan(temp)] = np.inf + i = np.argmin(temp,axis=1) dfdx = np.zeros_like(x) for j in range(self.funcCount): c = i == j @@ -1956,7 +1916,8 @@ def _derY(self,x,y,z): temp = np.zeros((m,self.funcCount)) for j in range(self.funcCount): temp[:,j] = self.functions[j](x,y,z) - i = self.argcompare(temp,axis=1) + temp[np.isnan(temp)] = np.inf + i = np.argmin(temp,axis=1) y = temp[np.arange(m),i] dfdy = np.zeros_like(x) for j in range(self.funcCount): @@ -1973,7 +1934,8 @@ def _derZ(self,x,y,z): temp = np.zeros((m,self.funcCount)) for j in range(self.funcCount): temp[:,j] = self.functions[j](x,y,z) - i = self.argcompare(temp,axis=1) + temp[np.isnan(temp)] = np.inf + i = np.argmin(temp,axis=1) y = temp[np.arange(m),i] dfdz = np.zeros_like(x) for j in range(self.funcCount): From 29d1202a1ed82b207337a800971a0983932a7faf Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Thu, 14 Mar 2019 13:16:42 +0100 Subject: [PATCH 16/77] set numpy floating point error level to ignore. --- HARK/core.py | 75 ++++++++++++++++++++++++++++------------------------ 1 file changed, 40 insertions(+), 35 deletions(-) diff --git a/HARK/core.py b/HARK/core.py index 1b339d29a..481ee5a49 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -21,6 +21,11 @@ from time import clock from .parallel import multiThreadCommands, multiThreadCommandsFake +# Ignore floating point "errors". Numpy calls it "errors", but really it's excep- +# tions with well-defined answers such as 1.0/0.0 that is np.inf, -1.0/0.0 that is +# -np.inf, np.inf/np.inf is np.nan and so on. +np.seterr(all='ignore') + def distanceMetric(thing_A,thing_B): ''' A "universal distance" metric that can be used as a default in many settings. @@ -932,12 +937,12 @@ def __init__(self,agents=[],sow_vars=[],reap_vars=[],const_vars=[],track_vars=[] self.act_T = act_T self.tolerance = tolerance self.max_loops = 1000 - - self.print_parallel_error_once = True - # Print the error associated with calling the parallel method + + self.print_parallel_error_once = True + # Print the error associated with calling the parallel method # "solveAgents" one time. If set to false, the error will never # print. See "solveAgents" for why this prints once or never. - + def solveAgents(self): ''' Solves the microeconomic problem for all AgentTypes in this market. @@ -956,7 +961,7 @@ def solveAgents(self): multiThreadCommands(self.agents,['solve()']) except Exception as err: if self.print_parallel_error_once: - # Set flag to False so this is only printed once. + # Set flag to False so this is only printed once. self.print_parallel_error_once = False print("**** WARNING: could not execute multiThreadCommands in HARK.core.Market.solveAgents(), so using the serial version instead. This will likely be slower. The multiTreadCommands() functions failed with the following error:", '\n ', sys.exc_info()[0], ':', err) #sys.exc_info()[0]) multiThreadCommandsFake(self.agents,['solve()']) @@ -1182,21 +1187,21 @@ def updateDynamics(self): # Define a function to run the copying: def copy_module(target_path, my_directory_full_path, my_module): ''' - Helper function for copy_module_to_local(). Provides the actual copy - functionality, with highly cautious safeguards against copying over - important things. - + Helper function for copy_module_to_local(). Provides the actual copy + functionality, with highly cautious safeguards against copying over + important things. + Parameters ---------- target_path : string String, file path to target location - + my_directory_full_path: string String, full pathname to this file's directory - + my_module : string String, name of the module to copy - + Returns ------- none @@ -1222,19 +1227,19 @@ def copy_module(target_path, my_directory_full_path, my_module): return def print_helper(): - + my_directory_full_path = os.path.dirname(os.path.realpath(__file__)) - + print(my_directory_full_path) def copy_module_to_local(full_module_name): ''' - This function contains simple code to copy a submodule to a location on - your hard drive, as specified by you. The purpose of this code is to provide - users with a simple way to access a *copy* of code that usually sits deep in - the Econ-ARK package structure, for purposes of tinkering and experimenting - directly. This is meant to be a simple way to explore HARK code. To interact - with the codebase under active development, please refer to the documentation + This function contains simple code to copy a submodule to a location on + your hard drive, as specified by you. The purpose of this code is to provide + users with a simple way to access a *copy* of code that usually sits deep in + the Econ-ARK package structure, for purposes of tinkering and experimenting + directly. This is meant to be a simple way to explore HARK code. To interact + with the codebase under active development, please refer to the documentation under github.com/econ-ark/HARK/ To execute, do the following on the Python command line: @@ -1242,7 +1247,7 @@ def copy_module_to_local(full_module_name): from HARK.core import copy_module_to_local copy_module_to_local("FULL-HARK-MODULE-NAME-HERE") - For example, if you want SolvingMicroDSOPs you would enter + For example, if you want SolvingMicroDSOPs you would enter from HARK.core import copy_module_to_local copy_module_to_local("HARK.SolvingMicroDSOPs") @@ -1257,7 +1262,7 @@ def copy_module_to_local(full_module_name): #my_directory_full_path = os.path.dirname(os.path.realpath(__file__)) hark_core_directory_full_path = os.path.dirname(os.path.realpath(__file__)) # From https://stackoverflow.com/a/5137509 - # Important note from that answer: + # Important note from that answer: # (Note that the incantation above won't work if you've already used os.chdir() to change your current working directory, since the value of the __file__ constant is relative to the current working directory and is not changed by an os.chdir() call.) # # NOTE: for this specific file that I am testing, the path should be: @@ -1278,7 +1283,7 @@ def copy_module_to_local(full_module_name): head_path, my_module = os.path.split(my_directory_full_path) home_directory_with_module = os.path.join(home_directory_RAW, my_module) - + print("\n\n\nmy_directory_full_path:",my_directory_full_path,'\n\n\n') # Interact with the user: @@ -1289,39 +1294,39 @@ def copy_module_to_local(full_module_name): # - If not, just copy there # - Quit - target_path = input("""You have invoked the 'replicate' process for the current module:\n """ + + target_path = input("""You have invoked the 'replicate' process for the current module:\n """ + my_module + """\nThe default copy location is your home directory:\n """+ - home_directory_with_module +"""\nPlease enter one of the three options in single quotes below, excluding the quotes: - + home_directory_with_module +"""\nPlease enter one of the three options in single quotes below, excluding the quotes: + 'q' or return/enter to quit the process 'y' to accept the default home directory: """+home_directory_with_module+""" 'n' to specify your own pathname\n\n""") - + if target_path == 'n' or target_path == 'N': target_path = input("""Please enter the full pathname to your target directory location: """) - + # Clean up: target_path = os.path.expanduser(target_path) target_path = os.path.expandvars(target_path) target_path = os.path.normpath(target_path) - + # Check to see if they included the module name; if not add it here: temp_head, temp_tail = os.path.split(target_path) if temp_tail != my_module: target_path = os.path.join(target_path, my_module) - + elif target_path == 'y' or target_path == 'Y': # Just using the default path: target_path = home_directory_with_module else: # Assume "quit" - return - - if target_path != 'q' and target_path != 'Q' or target_path == '': + return + + if target_path != 'q' and target_path != 'Q' or target_path == '': # Run the copy command: - copy_module(target_path, my_directory_full_path, my_module) - + copy_module(target_path, my_directory_full_path, my_module) + return From 7e033be84cb27109988ffa0cfac4897c2af3f23b Mon Sep 17 00:00:00 2001 From: llorracc Date: Sun, 17 Mar 2019 23:03:35 -0400 Subject: [PATCH 17/77] Delete stuff called old --- HARK/cstwMPC/SetupParamsCSTWold.py | 296 ---------- HARK/cstwMPC/cstwMPCold.py | 861 ----------------------------- 2 files changed, 1157 deletions(-) delete mode 100644 HARK/cstwMPC/SetupParamsCSTWold.py delete mode 100644 HARK/cstwMPC/cstwMPCold.py diff --git a/HARK/cstwMPC/SetupParamsCSTWold.py b/HARK/cstwMPC/SetupParamsCSTWold.py deleted file mode 100644 index 1183f724a..000000000 --- a/HARK/cstwMPC/SetupParamsCSTWold.py +++ /dev/null @@ -1,296 +0,0 @@ -''' -Loads parameters used in the cstwMPC estimations. -''' -import numpy as np -import csv -from copy import copy, deepcopy -import os - -# Choose percentiles of the data to match and which estimation to run -do_lifecycle = False # Use lifecycle model if True, perpetual youth if False -do_beta_dist = True # Do beta-dist version if True, beta-point if False -run_estimation = False # Runs the estimation if True -find_beta_vs_KY = False # Computes K/Y ratio for a wide range of beta; should have do_beta_dist = False -do_sensitivity = [False, False, False, False, False, False, False, False] # Choose which sensitivity analyses to run: rho, xi_sigma, psi_sigma, mu, urate, mortality, g, R -do_liquid = False # Matches liquid assets data when True, net worth data when False -do_tractable = False # Uses a "tractable consumer" rather than solving full model when True -do_agg_shocks = True # Solve the FBS aggregate shocks version of the model -SCF_data_file = 'SCFwealthDataReduced.txt' -percentiles_to_match = [0.2, 0.4, 0.6, 0.8] # Which points of the Lorenz curve to match in beta-dist (must be in (0,1)) -#percentiles_to_match = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] # Can use this line if you want to match more percentiles -if do_beta_dist: - pref_type_count = 7 # Number of discrete beta types in beta-dist -else: - pref_type_count = 1 # Just one beta type in beta-point - -# Set basic parameters for the lifecycle micro model -init_age = 24 # Starting age for agents -Rfree = 1.04**(0.25) # Quarterly interest factor -working_T = 41*4 # Number of working periods -retired_T = 55*4 # Number of retired periods -total_T = working_T+retired_T # Total number of periods -CRRA = 1.0 # Coefficient of relative risk aversion -DiscFac_guess = 0.99 # Initial starting point for discount factor -UnempPrb = 0.07 # Probability of unemployment while working -UnempPrbRet = 0.0005 # Probabulity of "unemployment" while retired -IncUnemp = 0.15 # Unemployment benefit replacement rate -IncUnempRet = 0.0 # Ditto when retired -P0_sigma = 0.4 # Standard deviation of initial permanent income -BoroCnstArt = 0.0 # Artificial borrowing constraint - -# Set grid sizes -PermShkCount = 5 # Number of points in permanent income shock grid -TranShkCount = 5 # Number of points in transitory income shock grid -aXtraMin = 0.00001 # Minimum end-of-period assets in grid -aXtraMax = 20 # Maximum end-of-period assets in grid -aXtraCount = 20 # Number of points in assets grid -exp_nest = 3 # Number of times to 'exponentially nest' when constructing assets grid -sim_pop_size = 2000 # Number of simulated agents per preference type -CubicBool = False # Whether to use cubic spline interpolation -vFuncBool = False # Whether to calculate the value function during solution - -# Set random seeds -a0_seed = 138 # Seed for initial wealth draws -P0_seed = 666 # Seed for initial permanent income draws - -# Define the paths of permanent and transitory shocks (from Sabelhaus and Song) -TranShkStd = (np.concatenate((np.linspace(0.1,0.12,17), 0.12*np.ones(17), np.linspace(0.12,0.075,61), np.linspace(0.074,0.007,68), np.zeros(retired_T+1)))*4)**0.5 -TranShkStd = np.ndarray.tolist(TranShkStd) -PermShkStd = np.concatenate((((0.00011342*(np.linspace(24,64.75,working_T-1)-47)**2 + 0.01)/(11.0/4.0))**0.5,np.zeros(retired_T+1))) -PermShkStd = np.ndarray.tolist(PermShkStd) - -# Import survival probabilities from SSA data -data_location = os.path.dirname(os.path.abspath(__file__)) -f = open(data_location + '/' + 'USactuarial.txt','r') -actuarial_reader = csv.reader(f,delimiter='\t') -raw_actuarial = list(actuarial_reader) -base_death_probs = [] -for j in range(len(raw_actuarial)): - base_death_probs += [float(raw_actuarial[j][4])] # This effectively assumes that everyone is a white woman -f.close - -# Import adjustments for education and apply them to the base mortality rates -f = open(data_location + '/' + 'EducMortAdj.txt','r') -adjustment_reader = csv.reader(f,delimiter=' ') -raw_adjustments = list(adjustment_reader) -d_death_probs = [] -h_death_probs = [] -c_death_probs = [] -for j in range(76): - d_death_probs += [base_death_probs[j + init_age]*float(raw_adjustments[j][1])] - h_death_probs += [base_death_probs[j + init_age]*float(raw_adjustments[j][2])] - c_death_probs += [base_death_probs[j + init_age]*float(raw_adjustments[j][3])] -for j in range(76,96): - d_death_probs += [base_death_probs[j + init_age]*float(raw_adjustments[75][1])] - h_death_probs += [base_death_probs[j + init_age]*float(raw_adjustments[75][2])] - c_death_probs += [base_death_probs[j + init_age]*float(raw_adjustments[75][3])] -LivPrb_d = [] -LivPrb_h = [] -LivPrb_c = [] -for j in range(len(d_death_probs)): # Convert annual mortality rates to quarterly survival rates - LivPrb_d += 4*[(1 - d_death_probs[j])**0.25] - LivPrb_h += 4*[(1 - h_death_probs[j])**0.25] - LivPrb_c += 4*[(1 - c_death_probs[j])**0.25] - -# Define permanent income growth rates for each education level (from Cagetti 2003) -PermGroFac_d_base = [5.2522391e-002, 5.0039782e-002, 4.7586132e-002, 4.5162424e-002, 4.2769638e-002, 4.0408757e-002, 3.8080763e-002, 3.5786635e-002, 3.3527358e-002, 3.1303911e-002, 2.9117277e-002, 2.6968437e-002, 2.4858374e-002, 2.2788068e-002, 2.0758501e-002, 1.8770655e-002, 1.6825511e-002, 1.4924052e-002, 1.3067258e-002, 1.1256112e-002, 9.4915947e-003, 7.7746883e-003, 6.1063742e-003, 4.4876340e-003, 2.9194495e-003, 1.4028022e-003, -6.1326258e-005, -1.4719542e-003, -2.8280999e-003, -4.1287819e-003, -5.3730185e-003, -6.5598280e-003, -7.6882288e-003, -8.7572392e-003, -9.7658777e-003, -1.0713163e-002, -1.1598112e-002, -1.2419745e-002, -1.3177079e-002, -1.3869133e-002, -4.3985368e-001, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003, -8.5623256e-003] -PermGroFac_h_base = [4.1102173e-002, 4.1194381e-002, 4.1117402e-002, 4.0878307e-002, 4.0484168e-002, 3.9942056e-002, 3.9259042e-002, 3.8442198e-002, 3.7498596e-002, 3.6435308e-002, 3.5259403e-002, 3.3977955e-002, 3.2598035e-002, 3.1126713e-002, 2.9571062e-002, 2.7938153e-002, 2.6235058e-002, 2.4468848e-002, 2.2646594e-002, 2.0775369e-002, 1.8862243e-002, 1.6914288e-002, 1.4938576e-002, 1.2942178e-002, 1.0932165e-002, 8.9156095e-003, 6.8995825e-003, 4.8911556e-003, 2.8974003e-003, 9.2538802e-004, -1.0178097e-003, -2.9251214e-003, -4.7894755e-003, -6.6038005e-003, -8.3610250e-003, -1.0054077e-002, -1.1675886e-002, -1.3219380e-002, -1.4677487e-002, -1.6043137e-002, -5.5864350e-001, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002, -1.0820465e-002] -PermGroFac_c_base = [3.9375106e-002, 3.9030288e-002, 3.8601230e-002, 3.8091011e-002, 3.7502710e-002, 3.6839406e-002, 3.6104179e-002, 3.5300107e-002, 3.4430270e-002, 3.3497746e-002, 3.2505614e-002, 3.1456953e-002, 3.0354843e-002, 2.9202363e-002, 2.8002591e-002, 2.6758606e-002, 2.5473489e-002, 2.4150316e-002, 2.2792168e-002, 2.1402124e-002, 1.9983263e-002, 1.8538663e-002, 1.7071404e-002, 1.5584565e-002, 1.4081224e-002, 1.2564462e-002, 1.1037356e-002, 9.5029859e-003, 7.9644308e-003, 6.4247695e-003, 4.8870812e-003, 3.3544449e-003, 1.8299396e-003, 3.1664424e-004, -1.1823620e-003, -2.6640003e-003, -4.1251914e-003, -5.5628564e-003, -6.9739162e-003, -8.3552918e-003, -6.8938447e-001, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004, -6.1023256e-004] -PermGroFac_d_base += 31*[PermGroFac_d_base[-1]] # Add 31 years of the same permanent income growth rate to the end of the sequence -PermGroFac_h_base += 31*[PermGroFac_h_base[-1]] -PermGroFac_c_base += 31*[PermGroFac_c_base[-1]] -PermGroFac_d_retire = PermGroFac_d_base[40] # Store the big shock to permanent income at retirement -PermGroFac_h_retire = PermGroFac_h_base[40] -PermGroFac_c_retire = PermGroFac_c_base[40] -PermGroFac_d_base[40] = PermGroFac_d_base[39] # Overwrite the "retirement drop" with the adjacent growth rate -PermGroFac_h_base[40] = PermGroFac_h_base[39] -PermGroFac_c_base[40] = PermGroFac_c_base[39] -PermGroFac_d = [] -PermGroFac_h = [] -PermGroFac_c = [] -for j in range(len(PermGroFac_d_base)): # Make sequences of quarterly permanent income growth factors from annual permanent income growth rates - PermGroFac_d += 4*[(1 + PermGroFac_d_base[j])**0.25] - PermGroFac_h += 4*[(1 + PermGroFac_h_base[j])**0.25] - PermGroFac_c += 4*[(1 + PermGroFac_c_base[j])**0.25] -PermGroFac_d[working_T-1] = 1 + PermGroFac_d_retire # Put the big shock at retirement back into the sequence -PermGroFac_h[working_T-1] = 1 + PermGroFac_h_retire -PermGroFac_c[working_T-1] = 1 + PermGroFac_c_retire - -# Set population macro parameters -pop_growth = 1.01**(0.25) # population growth rate -TFP_growth = 1.015**(0.25) # TFP growth rate -d_pct = 0.11 # proportion of HS dropouts -h_pct = 0.55 # proportion of HS graduates -c_pct = 0.34 # proportion of college graduates -P0_d = 5 # average initial permanent income, dropouts -P0_h = 7.5 # average initial permanent income, HS grads -P0_c = 12 # average initial permanent income, college grads -a0_values = [0.17, 0.5, 0.83] # initial wealth/income ratio values -a0_probs = [1.0/3.0, 1.0/3.0, 1.0/3.0] # ...and probabilities - -# Calculate the social security tax rate for the economy -d_income = np.concatenate((np.array([1]),np.cumprod(PermGroFac_d)))*P0_d -h_income = np.concatenate((np.array([1]),np.cumprod(PermGroFac_h)))*P0_h -c_income = np.concatenate((np.array([1]),np.cumprod(PermGroFac_c)))*P0_c -cohort_weight = pop_growth**np.array(np.arange(0,-(total_T+1),-1)) -econ_weight = TFP_growth**np.array(np.arange(0,-(total_T+1),-1)) -d_survival_cum = np.concatenate((np.array([1]),np.cumprod(LivPrb_d))) -h_survival_cum = np.concatenate((np.array([1]),np.cumprod(LivPrb_h))) -c_survival_cum = np.concatenate((np.array([1]),np.cumprod(LivPrb_c))) -total_income_working = (d_pct*d_income[0:working_T]*d_survival_cum[0:working_T] + h_pct*h_income[0:working_T]*h_survival_cum[0:working_T] + c_pct*c_income[0:working_T]*c_survival_cum[0:working_T])*cohort_weight[0:working_T]*econ_weight[0:working_T] -total_income_retired = (d_pct*d_income[working_T:total_T]*d_survival_cum[working_T:total_T] + h_pct*h_income[working_T:total_T]*h_survival_cum[working_T:total_T] + c_pct*c_income[working_T:total_T]*c_survival_cum[working_T:total_T])*cohort_weight[working_T:total_T]*econ_weight[working_T:total_T] -tax_rate_SS = np.sum(total_income_retired)/np.sum(total_income_working) -tax_rate_U = UnempPrb*IncUnemp -tax_rate = tax_rate_SS + tax_rate_U - -# Generate normalized weighting vectors for each age and education level -age_size_d = d_pct*cohort_weight*d_survival_cum -age_size_h = h_pct*cohort_weight*h_survival_cum -age_size_c = c_pct*cohort_weight*c_survival_cum -total_pop_size = sum(age_size_d) + sum(age_size_h) + sum(age_size_c) -age_weight_d = age_size_d/total_pop_size -age_weight_h = age_size_h/total_pop_size -age_weight_c = age_size_c/total_pop_size -age_weight_all = np.concatenate((age_weight_d,age_weight_h,age_weight_c)) -age_weight_short = np.concatenate((age_weight_d[0:total_T],age_weight_h[0:total_T],age_weight_c[0:total_T])) -total_output = np.sum(total_income_working)/total_pop_size - -# Set indiividual parameters for the infinite horizon model -l_bar = 10.0/9.0 # Labor supply per individual (constant) -PermGroFac_i = [1.000**0.25] # Permanent income growth factor (no perm growth) -beta_i = 0.99 # Default intertemporal discount factor -LivPrb_i = [1.0 - 1.0/160.0] # Survival probability -PermShkStd_i = [(0.01*4/11)**0.5] # Standard deviation of permanent shocks to income -TranShkStd_i = [(0.01*4)**0.5] # Standard deviation of transitory shocks to income -sim_periods = 1000 # Number of periods to simulate (idiosyncratic shocks model) -sim_periods_agg_shocks = 3000# Number of periods to simulate (aggregate shocks model) -Nagents_agg_shocks = 4800 # Number of agents to simulate (aggregate shocks model) -age_weight_i = LivPrb_i**np.arange(0,sim_periods,dtype=float) # Weight on each cohort, from youngest to oldest -total_pop_size_i = np.sum(age_weight_i) -age_weight_i = age_weight_i/total_pop_size_i # *Normalized* weight on each cohort -if not do_lifecycle: - age_weight_all = age_weight_i - age_weight_short = age_weight_i[0:sim_periods] - total_output = l_bar - -# Set aggregate parameters for the infinite horizon model -PermShkAggCount = 3 # Number of discrete permanent aggregate shocks -TranShkAggCount = 3 # Number of discrete transitory aggregate shocks -PermShkAggStd = np.sqrt(0.00004) # Standard deviation of permanent aggregate shocks -TranShkAggStd = np.sqrt(0.00001) # Standard deviation of transitory aggregate shocks -CapShare = 0.36 # Capital's share of output -DeprFac = 0.025 # Capital depreciation factor -CRRAPF = 1.0 # CRRA in perfect foresight calibration -DiscFacPF = 0.99 # Discount factor in perfect foresight calibration -slope_prev = 1.0 # Initial slope of kNextFunc (aggregate shocks model) -intercept_prev = 0.0 # Initial intercept of kNextFunc (aggregate shocks model) - -# Import the SCF wealth data -f = open(data_location + '/' + SCF_data_file,'r') -SCF_reader = csv.reader(f,delimiter='\t') -SCF_raw = list(SCF_reader) -SCF_wealth = np.zeros(len(SCF_raw)) + np.nan -SCF_weights = deepcopy(SCF_wealth) -for j in range(len(SCF_raw)): - SCF_wealth[j] = float(SCF_raw[j][0]) - SCF_weights[j] = float(SCF_raw[j][1]) - - -# Make dictionaries for lifecycle consumer types -init_dropout = {"CRRA":CRRA, - "Rfree":Rfree, - "PermGroFac":PermGroFac_d, - "BoroCnstArt":BoroCnstArt, - "CubicBool":CubicBool, - "vFuncBool":vFuncBool, - "PermShkStd":PermShkStd, - "PermShkCount":PermShkCount, - "TranShkStd":TranShkStd, - "TranShkCount":TranShkCount, - "T_total":total_T, - "UnempPrb":UnempPrb, - "UnempPrbRet":UnempPrbRet, - "T_retire":working_T-1, - "IncUnemp":IncUnemp, - "IncUnempRet":IncUnempRet, - "aXtraMin":aXtraMin, - "aXtraMax":aXtraMax, - "aXtraCount":aXtraCount, - "aXtraExtra":[], - "exp_nest":exp_nest, - "LivPrb":LivPrb_d, - "DiscFac":DiscFac_guess, # dummy value, will be overwritten - "tax_rate":tax_rate_SS, # for math reasons, only SS tax goes here - 'Nagents':sim_pop_size, - 'sim_periods':total_T+1, - } -init_highschool = copy(init_dropout) -init_highschool["PermGroFac"] = PermGroFac_h -init_highschool["LivPrb"] = LivPrb_h -adj_highschool = {"PermGroFac":PermGroFac_h,"LivPrb":LivPrb_h} -init_college = copy(init_dropout) -init_college["PermGroFac"] = PermGroFac_c -init_college["LivPrb"] = LivPrb_c -adj_college = {"PermGroFac":PermGroFac_c,"LivPrb":LivPrb_c} - -# Make a dictionary for the infinite horizon type -init_infinite = {"CRRA":CRRA, - "Rfree":1.01/LivPrb_i[0], - "PermGroFac":PermGroFac_i, - "BoroCnstArt":BoroCnstArt, - "CubicBool":CubicBool, - "vFuncBool":vFuncBool, - "PermShkStd":PermShkStd_i, - "PermShkCount":PermShkCount, - "TranShkStd":TranShkStd_i, - "TranShkCount":TranShkCount, - "UnempPrb":UnempPrb, - "IncUnemp":IncUnemp, - "UnempPrbRet":None, - "IncUnempRet":None, - "aXtraMin":aXtraMin, - "aXtraMax":aXtraMax, - "aXtraCount":aXtraCount, - "aXtraExtra":[None], - "aXtraNestFac":exp_nest, - "LivPrb":LivPrb_i, - "beta":beta_i, # dummy value, will be overwritten - "cycles":0, - "T_total":1, - "T_retire":0, - "tax_rate":0.0, - 'sim_periods':sim_periods, - 'Nagents':sim_pop_size, - 'l_bar':l_bar, - } - -# Make a dictionary for the aggregate shocks type -init_agg_shocks = deepcopy(init_infinite) -init_agg_shocks['Nagents'] = Nagents_agg_shocks -init_agg_shocks['sim_periods'] = sim_periods_agg_shocks -init_agg_shocks['tolerance'] = 0.0001 -init_agg_shocks['kGridBase'] = np.array([0.3,0.6,0.8,0.9,0.98,1.0,1.02,1.1,1.2,1.6]) - -# Make a dictionary for the aggrege shocks market -aggregate_params = {'PermShkAggCount': PermShkAggCount, - 'TranShkAggCount': TranShkAggCount, - 'PermShkAggStd': PermShkAggStd, - 'TranShkAggStd': TranShkAggStd, - 'DeprFac': DeprFac, - 'CapShare': CapShare, - 'CRRA': CRRAPF, - 'DiscFac': DiscFacPF, - 'LivPrb': LivPrb_i[0], - 'slope_prev': slope_prev, - 'intercept_prev': intercept_prev, - } - -beta_save = DiscFac_guess # Hacky way to save progress of estimation -diff_save = 1000000.0 # Hacky way to save progress of estimation - - -if __name__ == '__main__': - print("Sorry, SetupParamsCSTW doesn't actually do anything on its own.") - print("This module is imported by cstwMPC, providing data and calibrated") - print("parameters for the various estimations. Please see that module if") - print("you want more interesting output.") diff --git a/HARK/cstwMPC/cstwMPCold.py b/HARK/cstwMPC/cstwMPCold.py deleted file mode 100644 index adf79ffdf..000000000 --- a/HARK/cstwMPC/cstwMPCold.py +++ /dev/null @@ -1,861 +0,0 @@ -''' -Nearly all of the estimations for the paper "The Distribution of Wealth and the -Marginal Propensity to Consume", by Chris Carroll, Jiri Slacalek, Kiichi Tokuoka, -and Matthew White. The micro model is a very slightly altered version of -ConsIndShockModel; the macro model is ConsAggShockModel. See SetupParamsCSTW -for parameters and execution options. -''' - -import numpy as np -from copy import deepcopy -from time import time -from HARK.utilities import approxMeanOneLognormal, combineIndepDstns, approxUniform, calcWeightedAvg, \ - getPercentiles, getLorenzShares, calcSubpopAvg -from HARK.simulation import drawDiscrete, drawMeanOneLognormal -from HARK import AgentType -from HARK.parallel import multiThreadCommandsFake -import SetupParamsCSTW as Params -import HARK.ConsumptionSaving.ConsIndShockModel as Model -from HARK.ConsumptionSaving.ConsAggShockModel import CobbDouglasEconomy, AggShockConsumerType -from scipy.optimize import golden, brentq -import matplotlib.pyplot as plt -import csv - -# ================================================================= -# ====== Make an extension of the basic ConsumerType ============== -# ================================================================= - -class cstwMPCagent(Model.IndShockConsumerType): - ''' - A consumer type in the cstwMPC model; a slight modification of base ConsumerType. - ''' - def __init__(self,time_flow=True,**kwds): - ''' - Make a new consumer type for the cstwMPC model. - - Parameters - ---------- - time_flow : boolean - Indictator for whether time is "flowing" forward for this agent. - **kwds : keyword arguments - Any number of keyword arguments of the form key=value. Each value - will be assigned to the attribute named in self. - - Returns - ------- - new instance of cstwMPCagent - ''' - # Initialize a basic AgentType - AgentType.__init__(self,solution_terminal=deepcopy(Model.IndShockConsumerType.solution_terminal_), - time_flow=time_flow,pseudo_terminal=False,**kwds) - - # Add consumer-type specific objects, copying to create independent versions - self.time_vary = deepcopy(Model.IndShockConsumerType.time_vary_) - self.time_inv = deepcopy(Model.IndShockConsumerType.time_inv_) - self.solveOnePeriod = Model.solveConsIndShock - self.update() - - def simulateCSTW(self): - ''' - The simulation method for the no aggregate shocks version of the model. - Initializes the agent type, simulates a history of state and control - variables, and stores the wealth history in self.W_history and the - annualized MPC history in self.kappa_history. - - Parameters - ---------- - none - - Returns - ------- - none - ''' - self.initializeSim() - self.simConsHistory() - self.W_history = self.pHist*self.bHist/self.Rfree - if Params.do_lifecycle: - self.W_history = self.W_history*self.cohort_scale - self.kappa_history = 1.0 - (1.0 - self.MPChist)**4 - - def update(self): - ''' - Update the income process, the assets grid, and the terminal solution. - - Parameters - ---------- - none - - Returns - ------- - none - ''' - orig_flow = self.time_flow - if self.cycles == 0: # hacky fix for labor supply l_bar - self.updateIncomeProcessAlt() - else: - self.updateIncomeProcess() - self.updateAssetsGrid() - self.updateSolutionTerminal() - self.timeFwd() - self.resetRNG() - if self.cycles > 0: - self.IncomeDstn = Model.applyFlatIncomeTax(self.IncomeDstn, - tax_rate=self.tax_rate, - T_retire=self.T_retire, - unemployed_indices=range(0,(self.TranShkCount+1)* - self.PermShkCount,self.TranShkCount+1)) - self.makeIncShkHist() - if not orig_flow: - self.timeRev() - - def updateIncomeProcessAlt(self): - ''' - An alternative method for constructing the income process in the infinite - horizon model, where the labor supply l_bar creates a small oddity. - - Parameters - ---------- - none - - Returns - ------- - none - ''' - tax_rate = (self.IncUnemp*self.UnempPrb)/(self.l_bar*(1.0-self.UnempPrb)) - TranShkDstn = deepcopy(approxMeanOneLognormal(self.TranShkCount,sigma=self.TranShkStd[0],tail_N=0)) - TranShkDstn[0] = np.insert(TranShkDstn[0]*(1.0-self.UnempPrb),0,self.UnempPrb) - TranShkDstn[1] = np.insert(self.l_bar*TranShkDstn[1]*(1.0-tax_rate),0,self.IncUnemp) - PermShkDstn = approxMeanOneLognormal(self.PermShkCount,sigma=self.PermShkStd[0],tail_N=0) - self.IncomeDstn = [combineIndepDstns(PermShkDstn,TranShkDstn)] - self.TranShkDstn = TranShkDstn - self.PermShkDstn = PermShkDstn - self.addToTimeVary('IncomeDstn') - - -def assignBetaDistribution(type_list,DiscFac_list): - ''' - Assigns the discount factors in DiscFac_list to the types in type_list. If - there is heterogeneity beyond the discount factor, then the same DiscFac is - assigned to consecutive types. - - Parameters - ---------- - type_list : [cstwMPCagent] - The list of types that should be assigned discount factors. - DiscFac_list : [float] or np.array - List of discount factors to assign to the types. - - Returns - ------- - none - ''' - DiscFac_N = len(DiscFac_list) - type_N = len(type_list)/DiscFac_N - j = 0 - b = 0 - while j < len(type_list): - t = 0 - while t < type_N: - type_list[j](DiscFac = DiscFac_list[b]) - t += 1 - j += 1 - b += 1 - - -# ================================================================= -# ====== Make some data analysis and reporting tools ============== -# ================================================================= - -def calculateKYratioDifference(sim_wealth,weights,total_output,target_KY): - ''' - Calculates the absolute distance between the simulated capital-to-output - ratio and the true U.S. level. - - Parameters - ---------- - sim_wealth : numpy.array - Array with simulated wealth values. - weights : numpy.array - List of weights for each row of sim_wealth. - total_output : float - Denominator for the simulated K/Y ratio. - target_KY : float - Actual U.S. K/Y ratio to match. - - Returns - ------- - distance : float - Absolute distance between simulated and actual K/Y ratios. - ''' - sim_K = calcWeightedAvg(sim_wealth,weights)/(Params.l_bar) - sim_KY = sim_K/total_output - distance = (sim_KY - target_KY)**1.0 - return distance - - -def calculateLorenzDifference(sim_wealth,weights,percentiles,target_levels): - ''' - Calculates the sum of squared differences between the simulatedLorenz curve - at the specified percentile levels and the target Lorenz levels. - - Parameters - ---------- - sim_wealth : numpy.array - Array with simulated wealth values. - weights : numpy.array - List of weights for each row of sim_wealth. - percentiles : [float] - Points in the distribution of wealth to match. - target_levels : np.array - Actual U.S. Lorenz curve levels at the specified percentiles. - - Returns - ------- - distance : float - Sum of squared distances between simulated and target Lorenz curves. - ''' - sim_lorenz = getLorenzShares(sim_wealth,weights=weights,percentiles=percentiles) - distance = sum((100*sim_lorenz-100*target_levels)**2) - return distance - - -# Define the main simulation process for matching the K/Y ratio -def simulateKYratioDifference(DiscFac,nabla,N,type_list,weights,total_output,target): - ''' - Assigns a uniform distribution over DiscFac with width 2*nabla and N points, then - solves and simulates all agent types in type_list and compares the simuated - K/Y ratio to the target K/Y ratio. - - Parameters - ---------- - DiscFac : float - Center of the uniform distribution of discount factors. - nabla : float - Width of the uniform distribution of discount factors. - N : int - Number of discrete consumer types. - type_list : [cstwMPCagent] - List of agent types to solve and simulate after assigning discount factors. - weights : np.array - Age-conditional array of population weights. - total_output : float - Total output of the economy, denominator for the K/Y calculation. - target : float - Target level of capital-to-output ratio. - - Returns - ------- - my_diff : float - Difference between simulated and target capital-to-output ratios. - ''' - if type(DiscFac) in (list,np.ndarray,np.array): - DiscFac = DiscFac[0] - DiscFac_list = approxUniform(N,DiscFac-nabla,DiscFac+nabla)[1] # only take values, not probs - assignBetaDistribution(type_list,DiscFac_list) - multiThreadCommandsFake(type_list,beta_point_commands) - my_diff = calculateKYratioDifference(np.vstack((this_type.W_history for this_type in type_list)), - np.tile(weights/float(N),N),total_output,target) - return my_diff - - -mystr = lambda number : "{:.3f}".format(number) -''' -Truncates a float at exactly three decimal places when displaying as a string. -''' - -def makeCSTWresults(DiscFac,nabla,save_name=None): - ''' - Produces a variety of results for the cstwMPC paper (usually after estimating). - - Parameters - ---------- - DiscFac : float - Center of the uniform distribution of discount factors - nabla : float - Width of the uniform distribution of discount factors - save_name : string - Name to save the calculated results, for later use in producing figures - and tables, etc. - - Returns - ------- - none - ''' - DiscFac_list = approxUniform(N=Params.pref_type_count,bot=DiscFac-nabla,top=DiscFac+nabla)[1] - assignBetaDistribution(est_type_list,DiscFac_list) - multiThreadCommandsFake(est_type_list,beta_point_commands) - - lorenz_distance = np.sqrt(betaDistObjective(nabla)) - - makeCSTWstats(DiscFac,nabla,est_type_list,Params.age_weight_all,lorenz_distance,save_name) - - -def makeCSTWstats(DiscFac,nabla,this_type_list,age_weight,lorenz_distance=0.0,save_name=None): - ''' - Displays (and saves) a bunch of statistics. Separate from makeCSTWresults() - for compatibility with the aggregate shock model. - - Parameters - ---------- - DiscFac : float - Center of the uniform distribution of discount factors - nabla : float - Width of the uniform distribution of discount factors - this_type_list : [cstwMPCagent] - List of agent types in the economy. - age_weight : np.array - Age-conditional array of weights for the wealth data. - lorenz_distance : float - Distance between simulated and actual Lorenz curves, for display. - save_name : string - Name to save the calculated results, for later use in producing figures - and tables, etc. - - Returns - ------- - none - ''' - sim_length = this_type_list[0].sim_periods - sim_wealth = (np.vstack((this_type.W_history for this_type in this_type_list))).flatten() - sim_wealth_short = (np.vstack((this_type.W_history[0:sim_length,:] for this_type in this_type_list))).flatten() - sim_kappa = (np.vstack((this_type.kappa_history for this_type in this_type_list))).flatten() - sim_income = (np.vstack((this_type.pHist[0:sim_length,:]*np.asarray(this_type.TranShkHist[0:sim_length,:]) for this_type in this_type_list))).flatten() - sim_ratio = (np.vstack((this_type.W_history[0:sim_length,:]/this_type.pHist[0:sim_length,:] for this_type in this_type_list))).flatten() - if Params.do_lifecycle: - sim_unemp = (np.vstack((np.vstack((this_type.IncUnemp == this_type.TranShkHist[0:Params.working_T,:],np.zeros((Params.retired_T+1,this_type_list[0].Nagents),dtype=bool))) for this_type in this_type_list))).flatten() - sim_emp = (np.vstack((np.vstack((this_type.IncUnemp != this_type.TranShkHist[0:Params.working_T,:],np.zeros((Params.retired_T+1,this_type_list[0].Nagents),dtype=bool))) for this_type in this_type_list))).flatten() - sim_ret = (np.vstack((np.vstack((np.zeros((Params.working_T,this_type_list[0].Nagents),dtype=bool),np.ones((Params.retired_T+1,this_type_list[0].Nagents),dtype=bool))) for this_type in this_type_list))).flatten() - else: - sim_unemp = np.vstack((this_type.IncUnemp == this_type.TranShkHist[0:sim_length,:] for this_type in this_type_list)).flatten() - sim_emp = np.vstack((this_type.IncUnemp != this_type.TranShkHist[0:sim_length,:] for this_type in this_type_list)).flatten() - sim_ret = np.zeros(sim_emp.size,dtype=bool) - sim_weight_all = np.tile(np.repeat(age_weight,this_type_list[0].Nagents),Params.pref_type_count) - - if Params.do_beta_dist and Params.do_lifecycle: - kappa_mean_by_age_type = (np.mean(np.vstack((this_type.kappa_history for this_type in this_type_list)),axis=1)).reshape((Params.pref_type_count*3,DropoutType.T_total+1)) - kappa_mean_by_age_pref = np.zeros((Params.pref_type_count,DropoutType.T_total+1)) + np.nan - for j in range(Params.pref_type_count): - kappa_mean_by_age_pref[j,] = Params.d_pct*kappa_mean_by_age_type[3*j+0,] + Params.h_pct*kappa_mean_by_age_type[3*j+1,] + Params.c_pct*kappa_mean_by_age_type[3*j+2,] - kappa_mean_by_age = np.mean(kappa_mean_by_age_pref,axis=0) - kappa_lo_beta_by_age = kappa_mean_by_age_pref[0,:] - kappa_hi_beta_by_age = kappa_mean_by_age_pref[Params.pref_type_count-1,:] - - lorenz_fig_data = makeLorenzFig(Params.SCF_wealth,Params.SCF_weights,sim_wealth,sim_weight_all) - mpc_fig_data = makeMPCfig(sim_kappa,sim_weight_all) - - kappa_all = calcWeightedAvg(np.vstack((this_type.kappa_history for this_type in this_type_list)),np.tile(age_weight/float(Params.pref_type_count),Params.pref_type_count)) - kappa_unemp = np.sum(sim_kappa[sim_unemp]*sim_weight_all[sim_unemp])/np.sum(sim_weight_all[sim_unemp]) - kappa_emp = np.sum(sim_kappa[sim_emp]*sim_weight_all[sim_emp])/np.sum(sim_weight_all[sim_emp]) - kappa_ret = np.sum(sim_kappa[sim_ret]*sim_weight_all[sim_ret])/np.sum(sim_weight_all[sim_ret]) - - my_cutoffs = [(0.99,1),(0.9,1),(0.8,1),(0.6,0.8),(0.4,0.6),(0.2,0.4),(0.0,0.2)] - kappa_by_ratio_groups = calcSubpopAvg(sim_kappa,sim_ratio,my_cutoffs,sim_weight_all) - kappa_by_income_groups = calcSubpopAvg(sim_kappa,sim_income,my_cutoffs,sim_weight_all) - - quintile_points = getPercentiles(sim_wealth_short,weights=sim_weight_all,percentiles=[0.2, 0.4, 0.6, 0.8]) - wealth_quintiles = np.ones(sim_wealth_short.size,dtype=int) - wealth_quintiles[sim_wealth_short > quintile_points[0]] = 2 - wealth_quintiles[sim_wealth_short > quintile_points[1]] = 3 - wealth_quintiles[sim_wealth_short > quintile_points[2]] = 4 - wealth_quintiles[sim_wealth_short > quintile_points[3]] = 5 - MPC_cutoff = getPercentiles(sim_kappa,weights=sim_weight_all,percentiles=[2.0/3.0]) - these_quintiles = wealth_quintiles[sim_kappa > MPC_cutoff] - these_weights = sim_weight_all[sim_kappa > MPC_cutoff] - hand_to_mouth_total = np.sum(these_weights) - hand_to_mouth_pct = [] - for q in range(5): - hand_to_mouth_pct.append(np.sum(these_weights[these_quintiles == (q+1)])/hand_to_mouth_total) - - results_string = 'Estimate is DiscFac=' + str(DiscFac) + ', nabla=' + str(nabla) + '\n' - results_string += 'Lorenz distance is ' + str(lorenz_distance) + '\n' - results_string += 'Average MPC for all consumers is ' + mystr(kappa_all) + '\n' - results_string += 'Average MPC in the top percentile of W/Y is ' + mystr(kappa_by_ratio_groups[0]) + '\n' - results_string += 'Average MPC in the top decile of W/Y is ' + mystr(kappa_by_ratio_groups[1]) + '\n' - results_string += 'Average MPC in the top quintile of W/Y is ' + mystr(kappa_by_ratio_groups[2]) + '\n' - results_string += 'Average MPC in the second quintile of W/Y is ' + mystr(kappa_by_ratio_groups[3]) + '\n' - results_string += 'Average MPC in the middle quintile of W/Y is ' + mystr(kappa_by_ratio_groups[4]) + '\n' - results_string += 'Average MPC in the fourth quintile of W/Y is ' + mystr(kappa_by_ratio_groups[5]) + '\n' - results_string += 'Average MPC in the bottom quintile of W/Y is ' + mystr(kappa_by_ratio_groups[6]) + '\n' - results_string += 'Average MPC in the top percentile of y is ' + mystr(kappa_by_income_groups[0]) + '\n' - results_string += 'Average MPC in the top decile of y is ' + mystr(kappa_by_income_groups[1]) + '\n' - results_string += 'Average MPC in the top quintile of y is ' + mystr(kappa_by_income_groups[2]) + '\n' - results_string += 'Average MPC in the second quintile of y is ' + mystr(kappa_by_income_groups[3]) + '\n' - results_string += 'Average MPC in the middle quintile of y is ' + mystr(kappa_by_income_groups[4]) + '\n' - results_string += 'Average MPC in the fourth quintile of y is ' + mystr(kappa_by_income_groups[5]) + '\n' - results_string += 'Average MPC in the bottom quintile of y is ' + mystr(kappa_by_income_groups[6]) + '\n' - results_string += 'Average MPC for the employed is ' + mystr(kappa_emp) + '\n' - results_string += 'Average MPC for the unemployed is ' + mystr(kappa_unemp) + '\n' - results_string += 'Average MPC for the retired is ' + mystr(kappa_ret) + '\n' - results_string += 'Of the population with the 1/3 highest MPCs...' + '\n' - results_string += mystr(hand_to_mouth_pct[0]*100) + '% are in the bottom wealth quintile,' + '\n' - results_string += mystr(hand_to_mouth_pct[1]*100) + '% are in the second wealth quintile,' + '\n' - results_string += mystr(hand_to_mouth_pct[2]*100) + '% are in the third wealth quintile,' + '\n' - results_string += mystr(hand_to_mouth_pct[3]*100) + '% are in the fourth wealth quintile,' + '\n' - results_string += 'and ' + mystr(hand_to_mouth_pct[4]*100) + '% are in the top wealth quintile.' + '\n' - print(results_string) - - if save_name is not None: - with open('./Results/' + save_name + 'LorenzFig.txt','w') as f: - my_writer = csv.writer(f, delimiter='\t',) - for j in range(len(lorenz_fig_data[0])): - my_writer.writerow([lorenz_fig_data[0][j], lorenz_fig_data[1][j], lorenz_fig_data[2][j]]) - f.close() - with open('./Results/' + save_name + 'MPCfig.txt','w') as f: - my_writer = csv.writer(f, delimiter='\t') - for j in range(len(mpc_fig_data[0])): - my_writer.writerow([lorenz_fig_data[0][j], mpc_fig_data[1][j]]) - f.close() - if Params.do_beta_dist and Params.do_lifecycle: - with open('./Results/' + save_name + 'KappaByAge.txt','w') as f: - my_writer = csv.writer(f, delimiter='\t') - for j in range(len(kappa_mean_by_age)): - my_writer.writerow([kappa_mean_by_age[j], kappa_lo_beta_by_age[j], kappa_hi_beta_by_age[j]]) - f.close() - with open('./Results/' + save_name + 'Results.txt','w') as f: - f.write(results_string) - f.close() - - -def makeLorenzFig(real_wealth,real_weights,sim_wealth,sim_weights): - ''' - Produces a Lorenz curve for the distribution of wealth, comparing simulated - to actual data. A sub-function of makeCSTWresults(). - - Parameters - ---------- - real_wealth : np.array - Data on household wealth. - real_weights : np.array - Weighting array of the same size as real_wealth. - sim_wealth : np.array - Simulated wealth holdings of many households. - sim_weights :np.array - Weighting array of the same size as sim_wealth. - - Returns - ------- - these_percents : np.array - An array of percentiles of households, by wealth. - real_lorenz : np.array - Lorenz shares for real_wealth corresponding to these_percents. - sim_lorenz : np.array - Lorenz shares for sim_wealth corresponding to these_percents. - ''' - these_percents = np.linspace(0.0001,0.9999,201) - real_lorenz = getLorenzShares(real_wealth,weights=real_weights,percentiles=these_percents) - sim_lorenz = getLorenzShares(sim_wealth,weights=sim_weights,percentiles=these_percents) - plt.plot(100*these_percents,real_lorenz,'-k',linewidth=1.5) - plt.plot(100*these_percents,sim_lorenz,'--k',linewidth=1.5) - plt.xlabel('Wealth percentile',fontsize=14) - plt.ylabel('Cumulative wealth ownership',fontsize=14) - plt.title('Simulated vs Actual Lorenz Curves',fontsize=16) - plt.legend(('Actual','Simulated'),loc=2,fontsize=12) - plt.ylim(-0.01,1) - plt.show() - return (these_percents,real_lorenz,sim_lorenz) - - -def makeMPCfig(kappa,weights): - ''' - Plot the CDF of the marginal propensity to consume. A sub-function of makeCSTWresults(). - - Parameters - ---------- - kappa : np.array - Array of (annualized) marginal propensities to consume for the economy. - weights : np.array - Age-conditional weight array for the data in kappa. - - Returns - ------- - these_percents : np.array - Array of percentiles of the marginal propensity to consume. - kappa_percentiles : np.array - Array of MPCs corresponding to the percentiles in these_percents. - ''' - these_percents = np.linspace(0.0001,0.9999,201) - kappa_percentiles = getPercentiles(kappa,weights,percentiles=these_percents) - plt.plot(kappa_percentiles,these_percents,'-k',linewidth=1.5) - plt.xlabel('Marginal propensity to consume',fontsize=14) - plt.ylabel('Cumulative probability',fontsize=14) - plt.title('CDF of the MPC',fontsize=16) - plt.show() - return (these_percents,kappa_percentiles) - - -def calcKappaMean(DiscFac,nabla): - ''' - Calculates the average MPC for the given parameters. This is a very small - sub-function of sensitivityAnalysis. - - Parameters - ---------- - DiscFac : float - Center of the uniform distribution of discount factors - nabla : float - Width of the uniform distribution of discount factors - - Returns - ------- - kappa_all : float - Average marginal propensity to consume in the population. - ''' - DiscFac_list = approxUniform(N=Params.pref_type_count,bot=DiscFac-nabla,top=DiscFac+nabla)[1] - assignBetaDistribution(est_type_list,DiscFac_list) - multiThreadCommandsFake(est_type_list,beta_point_commands) - - kappa_all = calcWeightedAvg(np.vstack((this_type.kappa_history for this_type in est_type_list)), - np.tile(Params.age_weight_all/float(Params.pref_type_count), - Params.pref_type_count)) - return kappa_all - - -def sensitivityAnalysis(parameter,values,is_time_vary): - ''' - Perform a sensitivity analysis by varying a chosen parameter over given values - and re-estimating the model at each. Only works for perpetual youth version. - Saves numeric results in a file named SensitivityPARAMETER.txt. - - Parameters - ---------- - parameter : string - Name of an attribute/parameter of cstwMPCagent on which to perform a - sensitivity analysis. The attribute should be a single float. - values : [np.array] - Array of values that the parameter should take on in the analysis. - is_time_vary : boolean - Indicator for whether the parameter of analysis is time_varying (i.e. - is an element of cstwMPCagent.time_vary). While the sensitivity analysis - should only be used for the perpetual youth model, some parameters are - still considered "time varying" in the consumption-saving model and - are encapsulated in a (length=1) list. - - Returns - ------- - none - ''' - fit_list = [] - DiscFac_list = [] - nabla_list = [] - kappa_list = [] - for value in values: - print('Now estimating model with ' + parameter + ' = ' + str(value)) - Params.diff_save = 1000000.0 - old_value_storage = [] - for this_type in est_type_list: - old_value_storage.append(getattr(this_type,parameter)) - if is_time_vary: - setattr(this_type,parameter,[value]) - else: - setattr(this_type,parameter,value) - this_type.update() - output = golden(betaDistObjective,brack=bracket,tol=10**(-4),full_output=True) - nabla = output[0] - fit = output[1] - DiscFac = Params.DiscFac_save - kappa = calcKappaMean(DiscFac,nabla) - DiscFac_list.append(DiscFac) - nabla_list.append(nabla) - fit_list.append(fit) - kappa_list.append(kappa) - with open('./Results/Sensitivity' + parameter + '.txt','w') as f: - my_writer = csv.writer(f, delimiter='\t',) - for j in range(len(DiscFac_list)): - my_writer.writerow([values[j], kappa_list[j], DiscFac_list[j], nabla_list[j], fit_list[j]]) - f.close() - j = 0 - for this_type in est_type_list: - setattr(this_type,parameter,old_value_storage[j]) - this_type.update() - j += 1 - - -# Only run below this line if module is run rather than imported: -if __name__ == "__main__": - # ================================================================= - # ====== Make the list of consumer types for estimation =========== - #================================================================== - - # Set target Lorenz points and K/Y ratio (MOVE THIS TO SetupParams) - if Params.do_liquid: - lorenz_target = np.array([0.0, 0.004, 0.025,0.117]) - KY_target = 6.60 - else: # This is hacky until I can find the liquid wealth data and import it - lorenz_target = getLorenzShares(Params.SCF_wealth,weights=Params.SCF_weights,percentiles=Params.percentiles_to_match) - #lorenz_target = np.array([-0.002, 0.01, 0.053,0.171]) - KY_target = 10.26 - - # Make a vector of initial wealth-to-permanent income ratios - a_init = drawDiscrete(N=Params.sim_pop_size,P=Params.a0_probs,X=Params.a0_values,seed=Params.a0_seed) - - # Make the list of types for this run, whether infinite or lifecycle - if Params.do_lifecycle: - # Make cohort scaling array - cohort_scale = Params.TFP_growth**(-np.arange(Params.total_T+1)) - cohort_scale_array = np.tile(np.reshape(cohort_scale,(Params.total_T+1,1)),(1,Params.sim_pop_size)) - - # Make base consumer types for each education level - DropoutType = cstwMPCagent(**Params.init_dropout) - DropoutType.a_init = a_init - DropoutType.cohort_scale = cohort_scale_array - HighschoolType = deepcopy(DropoutType) - HighschoolType(**Params.adj_highschool) - CollegeType = deepcopy(DropoutType) - CollegeType(**Params.adj_college) - DropoutType.update() - HighschoolType.update() - CollegeType.update() - - # Make initial distributions of permanent income for each education level - p_init_base = drawMeanOneLognormal(N=Params.sim_pop_size, sigma=Params.P0_sigma, seed=Params.P0_seed) - DropoutType.p_init = Params.P0_d*p_init_base - HighschoolType.p_init = Params.P0_h*p_init_base - CollegeType.p_init = Params.P0_c*p_init_base - - # Set the type list for the lifecycle estimation - short_type_list = [DropoutType, HighschoolType, CollegeType] - spec_add = 'LC' - - else: - # Make the base infinite horizon type and assign income shocks - InfiniteType = cstwMPCagent(**Params.init_infinite) - InfiniteType.tolerance = 0.0001 - InfiniteType.a_init = 0*np.ones_like(a_init) - - # Make histories of permanent income levels for the infinite horizon type - p_init_base = np.ones(Params.sim_pop_size,dtype=float) - InfiniteType.p_init = p_init_base - - # Use a "tractable consumer" instead if desired. - # If you want this to work, you must edit TractableBufferStockModel slightly. - # See comments around line 34 in that module for instructions. - if Params.do_tractable: - from HARK.ConsumptionSaving.TractableBufferStockModel import TractableConsumerType - TractableInfType = TractableConsumerType(DiscFac=0.99, # will be overwritten - UnempPrb=1-InfiniteType.LivPrb[0], - Rfree=InfiniteType.Rfree, - PermGroFac=InfiniteType.PermGroFac[0], - CRRA=InfiniteType.CRRA, - sim_periods=InfiniteType.sim_periods, - IncUnemp=InfiniteType.IncUnemp, - Nagents=InfiniteType.Nagents) - TractableInfType.p_init = InfiniteType.p_init - TractableInfType.timeFwd() - TractableInfType.TranShkHist = InfiniteType.TranShkHist - TractableInfType.PermShkHist = InfiniteType.PermShkHist - TractableInfType.a_init = InfiniteType.a_init - - # Set the type list for the infinite horizon estimation - if Params.do_tractable: - short_type_list = [TractableInfType] - spec_add = 'TC' - else: - short_type_list = [InfiniteType] - spec_add = 'IH' - - # Expand the estimation type list if doing beta-dist - if Params.do_beta_dist: - long_type_list = [] - for j in range(Params.pref_type_count): - long_type_list += deepcopy(short_type_list) - est_type_list = long_type_list - else: - est_type_list = short_type_list - - if Params.do_liquid: - wealth_measure = 'Liquid' - else: - wealth_measure = 'NetWorth' - - - # ================================================================= - # ====== Define estimation objectives ============================= - #================================================================== - - # Set commands for the beta-point estimation - beta_point_commands = ['solve()','unpackcFunc()','timeFwd()','simulateCSTW()'] - - # Make the objective function for the beta-point estimation - betaPointObjective = lambda DiscFac : simulateKYratioDifference(DiscFac, - nabla=0, - N=1, - type_list=est_type_list, - weights=Params.age_weight_all, - total_output=Params.total_output, - target=KY_target) - - # Make the objective function for the beta-dist estimation - def betaDistObjective(nabla): - # Make the "intermediate objective function" for the beta-dist estimation - #print('Trying nabla=' + str(nabla)) - intermediateObjective = lambda DiscFac : simulateKYratioDifference(DiscFac, - nabla=nabla, - N=Params.pref_type_count, - type_list=est_type_list, - weights=Params.age_weight_all, - total_output=Params.total_output, - target=KY_target) - if Params.do_tractable: - top = 0.98 - else: - top = 0.998 - DiscFac_new = brentq(intermediateObjective,0.90,top,xtol=10**(-8)) - N=Params.pref_type_count - sim_wealth = (np.vstack((this_type.W_history for this_type in est_type_list))).flatten() - sim_weights = np.tile(np.repeat(Params.age_weight_all,Params.sim_pop_size),N) - my_diff = calculateLorenzDifference(sim_wealth,sim_weights,Params.percentiles_to_match,lorenz_target) - print('DiscFac=' + str(DiscFac_new) + ', nabla=' + str(nabla) + ', diff=' + str(my_diff)) - if my_diff < Params.diff_save: - Params.DiscFac_save = DiscFac_new - return my_diff - - - - # ================================================================= - # ========= Estimating the model ================================== - #================================================================== - - if Params.run_estimation: - # Estimate the model and time it - t_start = time() - if Params.do_beta_dist: - bracket = (0,0.015) # large nablas break IH version - nabla = golden(betaDistObjective,brack=bracket,tol=10**(-4)) - DiscFac = Params.DiscFac_save - spec_name = spec_add + 'betaDist' + wealth_measure - else: - nabla = 0 - if Params.do_tractable: - bot = 0.9 - top = 0.98 - else: - bot = 0.9 - top = 1.0 - DiscFac = brentq(betaPointObjective,bot,top,xtol=10**(-8)) - spec_name = spec_add + 'betaPoint' + wealth_measure - t_end = time() - print('Estimate is DiscFac=' + str(DiscFac) + ', nabla=' + str(nabla) + ', took ' + str(t_end-t_start) + ' seconds.') - #spec_name=None - makeCSTWresults(DiscFac,nabla,spec_name) - - - - # ================================================================= - # ========= Relationship between DiscFac and K/Y ratio =============== - #================================================================== - - if Params.find_beta_vs_KY: - t_start = time() - DiscFac_list = np.linspace(0.95,1.01,201) - KY_ratio_list = [] - for DiscFac in DiscFac_list: - KY_ratio_list.append(betaPointObjective(DiscFac) + KY_target) - KY_ratio_list = np.array(KY_ratio_list) - t_end = time() - plt.plot(DiscFac_list,KY_ratio_list,'-k',linewidth=1.5) - plt.xlabel(r'Discount factor $\beta$',fontsize=14) - plt.ylabel('Capital to output ratio',fontsize=14) - print('That took ' + str(t_end-t_start) + ' seconds.') - plt.show() - with open('./Results/' + spec_add + '_KYbyBeta' + '.txt','w') as f: - my_writer = csv.writer(f, delimiter='\t',) - for j in range(len(DiscFac_list)): - my_writer.writerow([DiscFac_list[j], KY_ratio_list[j]]) - f.close() - - - - # ================================================================= - # ========= Sensitivity analysis ================================== - #================================================================== - - # Sensitivity analysis only set up for infinite horizon model! - if Params.do_lifecycle: - bracket = (0,0.015) - else: - bracket = (0,0.015) # large nablas break IH version - spec_name = None - - if Params.do_sensitivity[0]: # coefficient of relative risk aversion sensitivity analysis - CRRA_list = np.linspace(0.5,4.0,15).tolist() #15 - sensitivityAnalysis('CRRA',CRRA_list,False) - - if Params.do_sensitivity[1]: # transitory income stdev sensitivity analysis - TranShkStd_list = [0.01] + np.linspace(0.05,0.8,16).tolist() #16 - sensitivityAnalysis('TranShkStd',TranShkStd_list,True) - - if Params.do_sensitivity[2]: # permanent income stdev sensitivity analysis - PermShkStd_list = np.linspace(0.02,0.18,17).tolist() #17 - sensitivityAnalysis('PermShkStd',PermShkStd_list,True) - - if Params.do_sensitivity[3]: # unemployment benefits sensitivity analysis - IncUnemp_list = np.linspace(0.0,0.8,17).tolist() #17 - sensitivityAnalysis('IncUnemp',IncUnemp_list,False) - - if Params.do_sensitivity[4]: # unemployment rate sensitivity analysis - UnempPrb_list = np.linspace(0.02,0.12,16).tolist() #16 - sensitivityAnalysis('UnempPrb',UnempPrb_list,False) - - if Params.do_sensitivity[5]: # mortality rate sensitivity analysis - LivPrb_list = 1.0 - np.linspace(0.003,0.0125,16).tolist() #16 - sensitivityAnalysis('LivPrb',LivPrb_list,True) - - if Params.do_sensitivity[6]: # permanent income growth rate sensitivity analysis - PermGroFac_list = np.linspace(0.00,0.04,17).tolist() #17 - sensitivityAnalysis('PermGroFac',PermGroFac_list,True) - - if Params.do_sensitivity[7]: # interest rate sensitivity analysis - Rfree_list = (np.linspace(1.0,1.04,17)/InfiniteType.survival_prob[0]).tolist() - sensitivityAnalysis('Rfree',Rfree_list,False) - - - # ======================================================================= - # ========= FBS aggregate shocks model ================================== - #======================================================================== - if Params.do_agg_shocks: - # These are the perpetual youth estimates in case we want to skip estimation (and we do) - beta_point_estimate = 0.989142 - beta_dist_estimate = 0.985773 - nabla_estimate = 0.0077 - - # Make a set of consumer types for the FBS aggregate shocks model - BaseAggShksType = AggShockConsumerType(**Params.init_agg_shocks) - agg_shocks_type_list = [] - for j in range(Params.pref_type_count): - new_type = deepcopy(BaseAggShksType) - new_type.seed = j - new_type.resetRNG() - new_type.makeIncShkHist() - agg_shocks_type_list.append(new_type) - if Params.do_beta_dist: - beta_agg = beta_dist_estimate - nabla_agg = nabla_estimate - else: - beta_agg = beta_point_estimate - nabla_agg = 0.0 - DiscFac_list_agg = approxUniform(N=Params.pref_type_count,bot=beta_agg-nabla_agg,top=beta_agg+nabla_agg)[1] - assignBetaDistribution(agg_shocks_type_list,DiscFac_list_agg) - - # Make a market for solving the FBS aggregate shocks model - agg_shocks_market = CobbDouglasEconomy(agents = agg_shocks_type_list, - act_T = Params.sim_periods_agg_shocks, - tolerance = 0.0001, - **Params.aggregate_params) - agg_shocks_market.makeAggShkHist() - - # Edit the consumer types so they have the right data - for this_type in agg_shocks_market.agents: - this_type.p_init = drawMeanOneLognormal(N=this_type.Nagents,sigma=0.9,seed=0) - this_type.getEconomyData(agg_shocks_market) - - # Solve the aggregate shocks version of the model - t_start = time() - agg_shocks_market.solve() - t_end = time() - print('Solving the aggregate shocks model took ' + str(t_end - t_start) + ' seconds.') - for this_type in agg_shocks_type_list: - this_type.W_history = this_type.pHist*this_type.bHist - this_type.kappa_history = 1.0 - (1.0 - this_type.MPChist)**4 - agg_shock_weights = np.concatenate((np.zeros(200),np.ones(Params.sim_periods_agg_shocks-200))) - agg_shock_weights = agg_shock_weights/np.sum(agg_shock_weights) - makeCSTWstats(beta_agg,nabla_agg,agg_shocks_type_list,agg_shock_weights) From 05f26a6bb74c53686896c3e02f8a629aed24bbb9 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Tue, 19 Mar 2019 12:35:54 +0100 Subject: [PATCH 18/77] Make calcChoiceProbs more accurate, and make simultaneous evalution faster (and more accurate). --- HARK/interpolation.py | 39 ++++++++++++++++++++++++++++++--------- 1 file changed, 30 insertions(+), 9 deletions(-) diff --git a/HARK/interpolation.py b/HARK/interpolation.py index c04cd3e11..7913a3986 100644 --- a/HARK/interpolation.py +++ b/HARK/interpolation.py @@ -3362,11 +3362,12 @@ def _derY(self,x,y): # Calculate the derivative with respect to x (and return it) dfdy = y_alpha*dfda + y_beta*dfdb return dfdy - + ############################################################################### ## Functions used in discrete choice models with T1EV taste shocks ############ ############################################################################### + def calcLogSumChoiceProbs(Vals, sigma): ''' Returns the final optimal value and choice probabilities given the choice @@ -3384,14 +3385,34 @@ def calcLogSumChoiceProbs(Vals, sigma): P : [numpy.array] A numpy.array that holds the discrete choice probabilities ''' + # Assumes that NaNs have been replaced by -numpy.inf or similar + if sigma == 0.0: + # We could construct a linear index here and use unravel_index. + Pflat = np.argmax(Vals, axis=0) + + V = np.zeros(Vals[0].shape) + Probs = np.zeros(Vals.shape) + for i in range(Vals.shape[0]): + optimalIndices = Pflat == i + V[optimalIndices] = Vals[i][optimalIndices] + Probs[i][optimalIndices] = 1 + return V, Probs + + # else we have a taste shock + maxV = np.max(Vals, axis=0) + + # calculate maxV+sigma*log(sum_i=1^J exp((V[i]-maxV))/sigma) + sumexp = np.sum(np.exp((Vals-maxV)/sigma), axis=0) + LogSumV = np.log(sumexp) + LogSumV = maxV + sigma*LogSumV - return calcLogSum(Vals, sigma), calcChoiceProbs(Vals, sigma) + Probs = np.exp((Vals-LogSumV)/sigma) + return LogSumV, Probs def calcChoiceProbs(Vals, sigma): ''' Returns the choice probabilities given the choice specific value functions `Vals`. Probabilities are degenerate if sigma == 0.0. - Parameters ---------- Vals : [numpy.array] @@ -3413,14 +3434,14 @@ def calcChoiceProbs(Vals, sigma): Probs[i][Pflat==i] = 1 return Probs - Probs = np.divide(np.exp((Vals-Vals[0])/sigma), np.sum(np.exp((Vals-Vals[0])/sigma), axis=0)) + maxV = np.max(Vals, axis=0) + Probs = np.divide(np.exp((Vals-maxV)/sigma), np.sum(np.exp((Vals-maxV)/sigma), axis=0)) return Probs def calcLogSum(Vals, sigma): ''' Returns the optimal value given the choice specific value functions Vals. - Parameters ---------- Vals : [numpy.array] @@ -3440,13 +3461,13 @@ def calcLogSum(Vals, sigma): return V # else we have a taste shock - maxV = Vals.max() + maxV = np.max(Vals, axis=0) # calculate maxV+sigma*log(sum_i=1^J exp((V[i]-maxV))/sigma) sumexp = np.sum(np.exp((Vals-maxV)/sigma), axis=0) - V = np.log(sumexp) - V = maxV + sigma*V - return V + LogSumV = np.log(sumexp) + LogSumV = maxV + sigma*LogSumV + return LogSumV def main(): print("Sorry, HARK.interpolation doesn't actually do much on its own.") From 960b633911e32a764c330918d2851f202a1061b3 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Tue, 19 Mar 2019 14:32:51 +0100 Subject: [PATCH 19/77] Move the seterr statement to solve and simulate and use error states instead, as these will automatically reset upon exit. --- HARK/core.py | 51 +++++++++++++++++++++++++++------------------------ 1 file changed, 27 insertions(+), 24 deletions(-) diff --git a/HARK/core.py b/HARK/core.py index 481ee5a49..95a5035f2 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -21,11 +21,6 @@ from time import clock from .parallel import multiThreadCommands, multiThreadCommandsFake -# Ignore floating point "errors". Numpy calls it "errors", but really it's excep- -# tions with well-defined answers such as 1.0/0.0 that is np.inf, -1.0/0.0 that is -# -np.inf, np.inf/np.inf is np.nan and so on. -np.seterr(all='ignore') - def distanceMetric(thing_A,thing_B): ''' A "universal distance" metric that can be used as a default in many settings. @@ -377,12 +372,16 @@ def solve(self,verbose=False): none ''' - self.preSolve() # Do pre-solution stuff - self.solution = solveAgent(self,verbose) # Solve the model by backward induction - if self.time_flow: # Put the solution in chronological order if this instance's time flow runs that way - self.solution.reverse() - self.addToTimeVary('solution') # Add solution to the list of time-varying attributes - self.postSolve() # Do post-solution stuff + # Ignore floating point "errors". Numpy calls it "errors", but really it's excep- + # tions with well-defined answers such as 1.0/0.0 that is np.inf, -1.0/0.0 that is + # -np.inf, np.inf/np.inf is np.nan and so on. + with np.errstate(divide='ignore', over='ignore', under='ignore', invalid='ignore'): + self.preSolve() # Do pre-solution stuff + self.solution = solveAgent(self,verbose) # Solve the model by backward induction + if self.time_flow: # Put the solution in chronological order if this instance's time flow runs that way + self.solution.reverse() + self.addToTimeVary('solution') # Add solution to the list of time-varying attributes + self.postSolve() # Do post-solution stuff def resetRNG(self): ''' @@ -685,19 +684,23 @@ def simulate(self,sim_periods=None): ------- None ''' - orig_time = self.time_flow - self.timeFwd() - if sim_periods is None: - sim_periods = self.T_sim - - for t in range(sim_periods): - self.simOnePeriod() - for var_name in self.track_vars: - exec('self.' + var_name + '_hist[self.t_sim,:] = self.' + var_name) - self.t_sim += 1 - - if not orig_time: - self.timeRev() + # Ignore floating point "errors". Numpy calls it "errors", but really it's excep- + # tions with well-defined answers such as 1.0/0.0 that is np.inf, -1.0/0.0 that is + # -np.inf, np.inf/np.inf is np.nan and so on. + with np.errstate(divide='ignore', over='ignore', under='ignore', invalid='ignore'): + orig_time = self.time_flow + self.timeFwd() + if sim_periods is None: + sim_periods = self.T_sim + + for t in range(sim_periods): + self.simOnePeriod() + for var_name in self.track_vars: + exec('self.' + var_name + '_hist[self.t_sim,:] = self.' + var_name) + self.t_sim += 1 + + if not orig_time: + self.timeRev() def clearHistory(self): ''' From ffb501242b59603bed860d4300d8261232dd7428 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Wed, 20 Mar 2019 04:00:41 -0400 Subject: [PATCH 20/77] Delete old demo files (#243) We have two folders inside of /ConsumptionSaving that contain model demos. To my knowledge, all of these have been turned into DemARK notebooks other than TryAlternativeParameters, which has no explanation or documentation. I'm not even sure when or why it was put into HARK. This commit removes these old files. --- .../TryAlternativeParameterValues.py | 42 ---- .../ConsIndShockModelDemos/__init__.py | 0 HARK/ConsumptionSaving/Demos/Fagereng_demo.py | 188 ---------------- .../Demos/MPC_credit_vs_MPC_income.py | 201 ------------------ HARK/ConsumptionSaving/Demos/__init__.py | 0 5 files changed, 431 deletions(-) delete mode 100644 HARK/ConsumptionSaving/ConsIndShockModelDemos/TryAlternativeParameterValues.py delete mode 100644 HARK/ConsumptionSaving/ConsIndShockModelDemos/__init__.py delete mode 100644 HARK/ConsumptionSaving/Demos/Fagereng_demo.py delete mode 100644 HARK/ConsumptionSaving/Demos/MPC_credit_vs_MPC_income.py delete mode 100644 HARK/ConsumptionSaving/Demos/__init__.py diff --git a/HARK/ConsumptionSaving/ConsIndShockModelDemos/TryAlternativeParameterValues.py b/HARK/ConsumptionSaving/ConsIndShockModelDemos/TryAlternativeParameterValues.py deleted file mode 100644 index a29e3e521..000000000 --- a/HARK/ConsumptionSaving/ConsIndShockModelDemos/TryAlternativeParameterValues.py +++ /dev/null @@ -1,42 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Thu Nov 9 09:40:49 2017 - -@author: ccarroll@llorracc.org -""" -from __future__ import division, print_function -from builtins import str -from builtins import range -import pylab # the plotting tools - -xPoints=100 # number of points at which to sample a function when plotting it using pylab -mMinVal = 0. # minimum value of the consumer's cash-on-hand to show in plots -mMaxVal = 5. # maximum value of the consumer's cash-on-hand to show in plots - -import HARK.ConsumptionSaving.ConsumerParameters as Params # Read in the database of parameters -my_dictionary = Params.init_idiosyncratic_shocks # Create a dictionary containing the default values of the parameters -import numpy as np # Get the suite of tools for doing numerical computation in python -from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType # Set up the tools for solving a consumer's problem - -# define a function that generates the plot -def perturbParameterToGetcPlotList(base_dictionary,param_name,param_min,param_max,N=20,time_vary=False): - param_vec = np.linspace(param_min,param_max,num=N,endpoint=True) # vector of alternative values of the parameter to examine - thisConsumer = IndShockConsumerType(**my_dictionary) # create an instance of the consumer type - thisConsumer.cycles = 0 # Make this type have an infinite horizon - x = np.linspace(mMinVal,mMaxVal,xPoints,endpoint=True) # Define a vector of x values that span the range from the minimum to the maximum values of m - - for j in range(N): # loop from the first to the last values of the parameter - if time_vary: # Some parameters are time-varying; others are not - setattr(thisConsumer,param_name,[param_vec[j]]) - else: - setattr(thisConsumer,param_name,param_vec[j]) - thisConsumer.update() # set up the preliminaries required to solve the problem - thisConsumer.solve() # solve the problem - y = thisConsumer.solution[0].cFunc(x) # Get the values of the consumption function at the points in the vector of x points - pylab.plot(x,y,label=str(round(param_vec[j],3))) # plot it and generate a label indicating the rounded value of the parameter - pylab.legend(loc='upper right') # put the legend in the upper right - return pylab # return the figure - -cPlot_by_DiscFac = perturbParameterToGetcPlotList(my_dictionary,'DiscFac',0.899,0.999,5,False) # create the figure -cPlot_by_DiscFac.show() # show it - diff --git a/HARK/ConsumptionSaving/ConsIndShockModelDemos/__init__.py b/HARK/ConsumptionSaving/ConsIndShockModelDemos/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/HARK/ConsumptionSaving/Demos/Fagereng_demo.py b/HARK/ConsumptionSaving/Demos/Fagereng_demo.py deleted file mode 100644 index ee24dd6d4..000000000 --- a/HARK/ConsumptionSaving/Demos/Fagereng_demo.py +++ /dev/null @@ -1,188 +0,0 @@ -''' -This module runs a quick and dirty structural estimation based on Table 9 of -"MPC Heterogeneity and Household Balance Sheets" by Fagereng, Holm, and Natvik. -Authors use Norweigian administrative data on income, household assets, and lottery -winnings to examine the MPC from transitory income shocks (lottery prizes). In -Table 9, they report estimated MPC broken down by quartiles of bank deposits and -prize size; this table is reproduced here as MPC_target_base. In this demo, we -use the Table 9 estimates as targets in a simple structural estimation, seeking -to minimize the sum of squared differences between simulated and estimated MPCs -by changing the (uniform) distribution of discount factors. Can their results -be rationalized by a simple one-asset consumption-saving model? This module -includes several options for estimating different specifications: - -TypeCount : Integer number of discount factors in discrete distribution; can be - set to 1 to turn off ex ante heterogeneity. -AdjFactor : Scaling factor for the target MPCs; user can try to fit estimated - MPCs scaled down by (e.g.) 50%. -T_kill : Maximum number of years the (perpetually young) agents are allowed - to live. Because this is quick and dirty, it's also the number of - periods to simulate. -Splurge : Amount of lottery prize that an individual will automatically spend - in a moment of irrational excitement, before coming to his senses - and behaving according to his consumption function. The patterns in - Table 9 can be fit much better when this is set around $700 --> 0.7. -do_secant : Boolean indicator for whether to use "secant MPC", which is average - MPC over the range of the prize. MNW believes authors' regressions - are estimating this rather than point MPC. When False, structural - estimation uses point MPC after receiving prize. NB: This is incom- - patible with Splurge > 0. -drop_corner : Boolean for whether to include target MPC in the top left corner, - which is greater than 1. Authors discuss reasons why the MPC - from a transitory shock *could* exceed 1. Option is included here - because this target tends to push the estimate around a bit. -''' -from __future__ import division, print_function -from builtins import str -from builtins import range -import numpy as np -from copy import deepcopy - -from HARK.utilities import approxUniform, getPercentiles -from HARK.parallel import multiThreadCommands -from HARK.estimation import minimizeNelderMead -from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType -from HARK.cstwMPC.SetupParamsCSTW import init_infinite # dictionary with most ConsumerType parameters - -TypeCount = 8 # Number of consumer types with heterogeneous discount factors -AdjFactor = 1.0 # Factor by which to scale all of Fagereng's MPCs in Table 9 -T_kill = 100 # Don't let agents live past this age -Splurge = 0.0 # Consumers automatically spend this amount of any lottery prize -do_secant = True # If True, calculate MPC by secant, else point MPC -drop_corner = False # If True, ignore upper left corner when calculating distance - -# Define the MPC targets from Table 9; element i,j is lottery quartile i, deposit quartile j -MPC_target_base = np.array([[1.047, 0.745, 0.720, 0.490], - [0.762, 0.640, 0.559, 0.437], - [0.663, 0.546, 0.390, 0.386], - [0.354, 0.325, 0.242, 0.216]]) -MPC_target = AdjFactor*MPC_target_base - -# Define the four lottery sizes, in thousands of USD; these are eyeballed centers/averages -lottery_size = np.array([1.625, 3.3741, 7.129, 40.0]) - -# Make an initialization dictionary on an annual basis -base_params = deepcopy(init_infinite) -base_params['LivPrb'] = [0.975] -base_params['Rfree'] = 1.04/base_params['LivPrb'][0] -base_params['PermShkStd'] = [0.1] -base_params['TranShkStd'] = [0.1] -base_params['T_age'] = T_kill # Kill off agents if they manage to achieve T_kill working years -base_params['AgentCount'] = 10000 -base_params['pLvlInitMean'] = np.log(23.72) # From Table 1, in thousands of USD -base_params['T_sim'] = T_kill # No point simulating past when agents would be killed off - -# Make several consumer types to be used during estimation -BaseType = IndShockConsumerType(**base_params) -EstTypeList = [] -for j in range(TypeCount): - EstTypeList.append(deepcopy(BaseType)) - EstTypeList[-1](seed = j) - -# Define the objective function -def FagerengObjFunc(center,spread,verbose=False): - ''' - Objective function for the quick and dirty structural estimation to fit - Fagereng, Holm, and Natvik's Table 9 results with a basic infinite horizon - consumption-saving model (with permanent and transitory income shocks). - - Parameters - ---------- - center : float - Center of the uniform distribution of discount factors. - spread : float - Width of the uniform distribution of discount factors. - verbose : bool - When True, print to screen MPC table for these parameters. When False, - print (center, spread, distance). - - Returns - ------- - distance : float - Euclidean distance between simulated MPCs and (adjusted) Table 9 MPCs. - ''' - # Give our consumer types the requested discount factor distribution - beta_set = approxUniform(N=TypeCount,bot=center-spread,top=center+spread)[1] - for j in range(TypeCount): - EstTypeList[j](DiscFac = beta_set[j]) - - # Solve and simulate all consumer types, then gather their wealth levels - multiThreadCommands(EstTypeList,['solve()','initializeSim()','simulate()','unpackcFunc()']) - WealthNow = np.concatenate([ThisType.aLvlNow for ThisType in EstTypeList]) - - # Get wealth quartile cutoffs and distribute them to each consumer type - quartile_cuts = getPercentiles(WealthNow,percentiles=[0.25,0.50,0.75]) - for ThisType in EstTypeList: - WealthQ = np.zeros(ThisType.AgentCount,dtype=int) - for n in range(3): - WealthQ[ThisType.aLvlNow > quartile_cuts[n]] += 1 - ThisType(WealthQ = WealthQ) - - # Keep track of MPC sets in lists of lists of arrays - MPC_set_list = [ [[],[],[],[]], - [[],[],[],[]], - [[],[],[],[]], - [[],[],[],[]] ] - - # Calculate the MPC for each of the four lottery sizes for all agents - for ThisType in EstTypeList: - ThisType.simulate(1) - c_base = ThisType.cNrmNow - MPC_this_type = np.zeros((ThisType.AgentCount,4)) - for k in range(4): # Get MPC for all agents of this type - Llvl = lottery_size[k] - Lnrm = Llvl/ThisType.pLvlNow - if do_secant: - SplurgeNrm = Splurge/ThisType.pLvlNow - mAdj = ThisType.mNrmNow + Lnrm - SplurgeNrm - cAdj = ThisType.cFunc[0](mAdj) + SplurgeNrm - MPC_this_type[:,k] = (cAdj - c_base)/Lnrm - else: - mAdj = ThisType.mNrmNow + Lnrm - MPC_this_type[:,k] = cAdj = ThisType.cFunc[0].derivative(mAdj) - - # Sort the MPCs into the proper MPC sets - for q in range(4): - these = ThisType.WealthQ == q - for k in range(4): - MPC_set_list[k][q].append(MPC_this_type[these,k]) - - # Calculate average within each MPC set - simulated_MPC_means = np.zeros((4,4)) - for k in range(4): - for q in range(4): - MPC_array = np.concatenate(MPC_set_list[k][q]) - simulated_MPC_means[k,q] = np.mean(MPC_array) - - # Calculate Euclidean distance between simulated MPC averages and Table 9 targets - diff = simulated_MPC_means - MPC_target - if drop_corner: - diff[0,0] = 0.0 - distance = np.sqrt(np.sum((diff)**2)) - if verbose: - print(simulated_MPC_means) - else: - print (center, spread, distance) - return distance - - -def main(): - guess = [0.92,0.03] - f_temp = lambda x : FagerengObjFunc(x[0],x[1]) - opt_params = minimizeNelderMead(f_temp, guess, verbose=True) - print('Finished estimating for scaling factor of ' + str(AdjFactor) + ' and "splurge amount" of $' + str(1000*Splurge)) - print('Optimal (beta,nabla) is ' + str(opt_params) + ', simulated MPCs are:') - dist = FagerengObjFunc(opt_params[0],opt_params[1],True) - print('Distance from Fagereng et al Table 9 is ' + str(dist)) - -# t_start = clock() -# X = FagerengObjFunc(0.814,0.122) -# t_end = clock() -# print('That took ' + str(t_end - t_start) + ' seconds.') -# print(X) - - -if __name__ == '__main__': - main() - - diff --git a/HARK/ConsumptionSaving/Demos/MPC_credit_vs_MPC_income.py b/HARK/ConsumptionSaving/Demos/MPC_credit_vs_MPC_income.py deleted file mode 100644 index 4e40c6337..000000000 --- a/HARK/ConsumptionSaving/Demos/MPC_credit_vs_MPC_income.py +++ /dev/null @@ -1,201 +0,0 @@ -""" -This is a HARK demo. - -The application here is to examine the Marginal Propensity to Consume (MPC) out of an increase in -a credit limit, and to compare it to the MPC out of temporary income. - -This demo is very heavily commented so that HARK newcomers can use it to figure out how HARK works. -It also does things, like import modules in the body of the code rather than at the top, that -are typically deprecated by Python programmers. This is all to make the code easier to read -and understand. - -There are many ways to use HARK, and this demo cannot show them all. -This demo demonstrates one great way to use HARK: import and solve a model for different parameter -values, to see how parameters affect the solution. - -#################################################################################################### -#################################################################################################### - -The first step is to create the ConsumerType we want to solve the model for. -""" -from __future__ import division, print_function - -## Import the HARK ConsumerType we want -## Here, we bring in an agent making a consumption/savings decision every period, subject -## to transitory and permanent income shocks. -from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType - -## Import the default parameter values -import HARK.ConsumptionSaving.ConsumerParameters as Params - -## Now, create an instance of the consumer type using the default parameter values -## We create the instance of the consumer type by calling IndShockConsumerType() -## We use the default parameter values by passing **Params.init_idiosyncratic_shocks as an argument -BaselineExample = IndShockConsumerType(**Params.init_idiosyncratic_shocks) - -## Note: we've created an instance of a very standard consumer type, and many assumptions go -## into making this kind of consumer. As with any structural model, these assumptions matter. -## For example, this consumer pays the same interest rate on -## debt as she earns on savings. If instead we wanted to solve the problem of a consumer -## who pays a higher interest rate on debt than she earns on savings, this would be really easy, -## since this is a model that is also solved in HARK. All we would have to do is import that model -## and instantiate an instance of that ConsumerType instead. As a homework assignment, we leave it -## to you to uncomment the two lines of code below, and see how the results change! -#from ConsIndShockModel import KinkedRconsumerType -#BaselineExample = KinkedRconsumerType(**Params.init_kinked_R) - - - -#################################################################################################### -#################################################################################################### - -""" -The next step is to change the values of parameters as we want. - -To see all the parameters used in the model, along with their default values, see -ConsumerParameters.py - -Parameter values are stored as attributes of the ConsumerType the values are used for. -For example, the risk-free interest rate Rfree is stored as BaselineExample.Rfree. -Because we created BaselineExample using the default parameters values. -at the moment BaselineExample.Rfree is set to the default value of Rfree (which, at the time -this demo was written, was 1.03). Therefore, to change the risk-free interest rate used in -BaselineExample to (say) 1.02, all we need to do is: - -BaselineExample.Rfree = 1.02 -""" - -## Change some parameter values -BaselineExample.Rfree = 1.02 #change the risk-free interest rate -BaselineExample.CRRA = 2. # change the coefficient of relative risk aversion -BaselineExample.BoroCnstArt = -.3 # change the artificial borrowing constraint -BaselineExample.DiscFac = .5 #chosen so that target debt-to-permanent-income_ratio is about .1 - # i.e. BaselineExample.solution[0].cFunc(.9) ROUGHLY = 1. - -## There is one more parameter value we need to change. This one is more complicated than the rest. -## We could solve the problem for a consumer with an infinite horizon of periods that (ex-ante) -## are all identical. We could also solve the problem for a consumer with a fininite lifecycle, -## or for a consumer who faces an infinite horizon of periods that cycle (e.g., a ski instructor -## facing an infinite series of winters, with lots of income, and summers, with very little income.) -## The way to differentiate is through the "cycles" attribute, which indicates how often the -## sequence of periods needs to be solved. The default value is 1, for a consumer with a finite -## lifecycle that is only experienced 1 time. A consumer who lived that life twice in a row, and -## then died, would have cycles = 2. But neither is what we want. Here, we need to set cycles = 0, -## to tell HARK that we are solving the model for an infinite horizon consumer. - - -## Note that another complication with the cycles attribute is that it does not come from -## Params.init_idiosyncratic_shocks. Instead it is a keyword argument to the __init__() method of -## IndShockConsumerType. -BaselineExample.cycles = 0 - - -#################################################################################################### -#################################################################################################### - -""" -Now, create another consumer to compare the BaselineExample to. -""" -# The easiest way to begin creating the comparison example is to just copy the baseline example. -# We can change the parameters we want to change later. -from copy import deepcopy -XtraCreditExample = deepcopy(BaselineExample) - - -# Now, change whatever parameters we want. -# Here, we want to see what happens if we give the consumer access to more credit. -# Remember, parameters are stored as attributes of the consumer they are used for. -# So, to give the consumer more credit, we just need to relax their borrowing constraint a bit. - -# Declare how much we want to increase credit by -credit_change = .001 - -# Now increase the consumer's credit limit. -# We do this by decreasing the artificial borrowing constraint. -XtraCreditExample.BoroCnstArt = BaselineExample.BoroCnstArt - credit_change - - - -#################################################################################################### -""" -Now we are ready to solve the consumers' problems. -In HARK, this is done by calling the solve() method of the ConsumerType. -""" - -### First solve the baseline example. -BaselineExample.solve() - -### Now solve the comparison example of the consumer with a bit more credit -XtraCreditExample.solve() - - - -#################################################################################################### -""" -Now that we have the solutions to the 2 different problems, we can compare them -""" - -## We are going to compare the consumption functions for the two different consumers. -## Policy functions (including consumption functions) in HARK are stored as attributes -## of the *solution* of the ConsumerType. The solution, in turn, is a list, indexed by the time -## period the solution is for. Since in this demo we are working with infinite-horizon models -## where every period is the same, there is only one time period and hence only one solution. -## e.g. BaselineExample.solution[0] is the solution for the BaselineExample. If BaselineExample -## had 10 time periods, we could access the 5th with BaselineExample.solution[4] (remember, Python -## counts from 0!) Therefore, the consumption function cFunc from the solution to the -## BaselineExample is BaselineExample.solution[0].cFunc - - -## First, declare useful functions to plot later - -def FirstDiffMPC_Income(x): - # Approximate the MPC out of income by giving the agent a tiny bit more income, - # and plotting the proportion of the change that is reflected in increased consumption - - # First, declare how much we want to increase income by - # Change income by the same amount we change credit, so that the two MPC - # approximations are comparable - income_change = credit_change - - # Now, calculate the approximate MPC out of income - return (BaselineExample.solution[0].cFunc(x + income_change) - - BaselineExample.solution[0].cFunc(x)) / income_change - - -def FirstDiffMPC_Credit(x): - # Approximate the MPC out of credit by plotting how much more of the increased credit the agent - # with higher credit spends - return (XtraCreditExample.solution[0].cFunc(x) - - BaselineExample.solution[0].cFunc(x)) / credit_change - - - -## Now, plot the functions we want - -# Import a useful plotting function from HARK.utilities -from HARK.utilities import plotFuncs -import pylab as plt # We need this module to change the y-axis on the graphs - - -# Declare the upper limit for the graph -x_max = 10. - - -# Note that plotFuncs takes four arguments: (1) a list of the arguments to plot, -# (2) the lower bound for the plots, (3) the upper bound for the plots, and (4) keywords to pass -# to the legend for the plot. - -# Plot the consumption functions to compare them -print('Consumption functions:') -plotFuncs([BaselineExample.solution[0].cFunc,XtraCreditExample.solution[0].cFunc], - BaselineExample.solution[0].mNrmMin,x_max, - legend_kwds = {'loc': 'upper left', 'labels': ["Baseline","XtraCredit"]}) - - -# Plot the MPCs to compare them -print('MPC out of Credit v MPC out of Income') -plt.ylim([0.,1.2]) -plotFuncs([FirstDiffMPC_Credit,FirstDiffMPC_Income], - BaselineExample.solution[0].mNrmMin,x_max, - legend_kwds = {'labels': ["MPC out of Credit","MPC out of Income"]}) - diff --git a/HARK/ConsumptionSaving/Demos/__init__.py b/HARK/ConsumptionSaving/Demos/__init__.py deleted file mode 100644 index e69de29bb..000000000 From 8e453dead4b6303e9fbd0632c8d845bed9adedcc Mon Sep 17 00:00:00 2001 From: Christopher Llorracc Carroll <1320319+llorracc@users.noreply.github.com> Date: Mon, 8 Apr 2019 21:06:15 -0400 Subject: [PATCH 21/77] Changed hardcoded updateAFunc parameters into proper parameters (#244) * Changed hardcoded updateAFunc parameters into proper parameters Four parameters that govern how CobbDouglasEconomy.updateAFunc works were defined locally, within the method, but are now attributes to be assigned at init (or at least before the user tries to solve): - update_weight --> DampingFac, now defined complementarily - verbose --> verbose - discard_periods --> T_discard - max_loops --> max_loops To prevent this from being a breaking change, init method writes old hardcoded values if omitted from passed inputs. This will be improved with a warning later. Untested, as it turns out Anaconda3 is incorrectly installed on this computer. * Tested de-hardcoded AFunc updating parameters Put new parameters in dictionaries in ConsumerParameters.py. Also added necessary lines to MarkovCobbDouglasEconomy and fixed one output description. --- HARK/ConsumptionSaving/ConsAggShockModel.py | 44 +++++++++++++------- HARK/ConsumptionSaving/ConsumerParameters.py | 10 ++++- 2 files changed, 37 insertions(+), 17 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py index cf9c8c72a..c055d2821 100644 --- a/HARK/ConsumptionSaving/ConsAggShockModel.py +++ b/HARK/ConsumptionSaving/ConsAggShockModel.py @@ -901,9 +901,20 @@ def __init__(self,agents=[],tolerance=0.0001,act_T=1000,**kwds): tolerance=tolerance, act_T=act_T) self.assignParameters(**kwds) - self.max_loops = 20 self.update() - + + # Use previously hardcoded values for AFunc updating if not passed + # as part of initialization dictionary. This is to prevent a last + # minute update to HARK before a release from having a breaking change. + if not hasattr(self,'DampingFac'): + self.DampingFac = 0.5 + if not hasattr(self,'max_loops'): + self.max_loops = 20 + if not hasattr(self,'T_discard'): + self.T_discard = 200 + if not hasattr(self,'verbose'): + self.verbose = True + def millRule(self,aLvlNow,pLvlNow): ''' @@ -998,11 +1009,11 @@ def reset(self): Parameters ---------- - none + None Returns ------- - none + None ''' self.Shk_idx = 0 Market.reset(self) @@ -1015,11 +1026,11 @@ def makeAggShkHist(self): Parameters ---------- - none + None Returns ------- - none + None ''' sim_periods = self.act_T Events = np.arange(self.AggShkDstn[0].size) # just a list of integers @@ -1085,18 +1096,18 @@ def calcAFunc(self,MaggNow,AaggNow): Parameters ---------- MaggNow : [float] - List of the history of the simulated aggregate market resources for an economy. + List of the history of the simulated aggregate market resources for an economy. AaggNow : [float] - List of the history of the simulated aggregate savings for an economy. + List of the history of the simulated aggregate savings for an economy. Returns ------- (unnamed) : CapDynamicRule Object containing a new savings rule ''' - verbose = True - discard_periods = 200 # Throw out the first T periods to allow the simulation to approach the SS - update_weight = 0.80 # Proportional weight to put on new function vs old function parameters + verbose = self.verbose + discard_periods = self.T_discard # Throw out the first T periods to allow the simulation to approach the SS + update_weight = 1. - self.DampingFac # Proportional weight to put on new function vs old function parameters total_periods = len(MaggNow) # Regress the log savings against log market resources @@ -1576,9 +1587,9 @@ def calcAFunc(self,MaggNow,AaggNow): (unnamed) : CapDynamicRule Object containing new saving rules for each Markov state. ''' - verbose = True - discard_periods = 200 # Throw out the first T periods to allow the simulation to approach the SS - update_weight = 0.8 # Proportional weight to put on new function vs old function parameters + verbose = self.verbose + discard_periods = self.T_discard # Throw out the first T periods to allow the simulation to approach the SS + update_weight = 1. - self.DampingFac # Proportional weight to put on new function vs old function parameters total_periods = len(MaggNow) # Trim the histories of M_t and A_t and convert them to logs @@ -1762,7 +1773,7 @@ def main(): solve_agg_shocks_market = True # Solve for the equilibrium aggregate saving rule in a CobbDouglasEconomy solve_markov_micro = False # Solve an AggShockMarkovConsumerType's microeconomic problem - solve_markov_market = False # Solve for the equilibrium aggregate saving rule in a CobbDouglasMarkovEconomy + solve_markov_market = True # Solve for the equilibrium aggregate saving rule in a CobbDouglasMarkovEconomy solve_krusell_smith = True # Solve a simple Krusell-Smith-style two state, two shock model solve_poly_state = False # Solve a CobbDouglasEconomy with many states, potentially utilizing the "state jumper" @@ -1827,6 +1838,7 @@ def main(): # Make a Cobb-Douglas economy for the agents MrkvEconomyExample = CobbDouglasMarkovEconomy(agents = [AggShockMrkvExample],**Params.init_mrkv_cobb_douglas) + MrkvEconomyExample.DampingFac = 0.2 # Turn down damping MrkvEconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks AggShockMrkvExample.getEconomyData(MrkvEconomyExample) # Have the consumers inherit relevant objects from the economy @@ -1856,7 +1868,7 @@ def main(): t_end = clock() print('Solving the "macroeconomic" aggregate shocks model took ' + str(t_end - t_start) + ' seconds.') - print('Aggregate savings as a function of aggregate market resources (for each macro state):') + print('Consumption function at each aggregate market resources-to-labor ratio gridpoint (for each macro state):') m_grid = np.linspace(0,10,200) AggShockMrkvExample.unpackcFunc() for i in range(2): diff --git a/HARK/ConsumptionSaving/ConsumerParameters.py b/HARK/ConsumptionSaving/ConsumerParameters.py index c63472541..dbeca20e0 100644 --- a/HARK/ConsumptionSaving/ConsumerParameters.py +++ b/HARK/ConsumptionSaving/ConsumerParameters.py @@ -179,6 +179,10 @@ CRRAPF = CRRA # Coefficient of relative risk aversion of perfect foresight calibration intercept_prev = 0.0 # Intercept of aggregate savings function slope_prev = 1.0 # Slope of aggregate savings function +verbose_cobb_douglas = True # Whether to print solution progress to screen while solving +T_discard = 200 # Number of simulated "burn in" periods to discard when updating AFunc +DampingFac = 0.5 # Damping factor when updating AFunc; puts DampingFac weight on old params, rest on new +max_loops = 20 # Maximum number of AFunc updating loops to allow # Make a dictionary to specify an aggregate shocks consumer init_agg_shocks = copy(init_idiosyncratic_shocks) @@ -205,7 +209,11 @@ 'AggregateL':1.0, 'act_T':1200, 'intercept_prev': intercept_prev, - 'slope_prev': slope_prev + 'slope_prev': slope_prev, + 'verbose': verbose_cobb_douglas, + 'T_discard': T_discard, + 'DampingFac': DampingFac, + 'max_loops': max_loops } # ----------------------------------------------------------------------------- From c51d60e21e3eb0c22f93f0670c176c66f5477641 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Thu, 11 Apr 2019 15:24:40 +0200 Subject: [PATCH 22/77] Remove old Testing-folder file because it's already covered by the tests run on Travis. (#245) --- ...{test_initial.py => test_HARKutilities.py} | 0 Testing/HARKutilities_UnitTests.py | 68 ------------------- 2 files changed, 68 deletions(-) rename HARK/tests/{test_initial.py => test_HARKutilities.py} (100%) delete mode 100644 Testing/HARKutilities_UnitTests.py diff --git a/HARK/tests/test_initial.py b/HARK/tests/test_HARKutilities.py similarity index 100% rename from HARK/tests/test_initial.py rename to HARK/tests/test_HARKutilities.py diff --git a/Testing/HARKutilities_UnitTests.py b/Testing/HARKutilities_UnitTests.py deleted file mode 100644 index 26bbd874a..000000000 --- a/Testing/HARKutilities_UnitTests.py +++ /dev/null @@ -1,68 +0,0 @@ -""" -This file implements unit tests to check HARK/utilities.py -""" -from __future__ import print_function, division -from __future__ import absolute_import - -from builtins import str -from builtins import zip -from builtins import range -from builtins import object - -import HARK.utilities - -# Bring in modules we need -import unittest -import numpy as np - -class testsForHARKutilities(unittest.TestCase): - - def setUp(self): - self.c_vals = np.linspace(.5,10.,20) - self.CRRA_vals = np.linspace(1.,10.,10) - - def first_diff_approx(self,func,x,delta,*args): - """ - Take the first (centered) difference approximation to the derivative of a function. - - """ - return (func(x+delta,*args) - func(x-delta,*args)) / (2. * delta) - - def derivative_func_comparison(self,deriv,func): - """ - This method computes the first difference approximation to the derivative of a function - "func" and the (supposedly) closed-form derivative of that function ("deriv") over a - grid. It then checks that these two things are "close enough." - """ - - # Loop through different values of consumption - for c in self.c_vals: - # Loop through different values of risk aversion - for CRRA in self.CRRA_vals: - - # Calculate the difference between the derivative of the function and the - # first difference approximation to that derivative. - diff = abs(deriv(c,CRRA) - self.first_diff_approx(func,c,.000001,CRRA)) - - # Make sure the derivative and its approximation are close - self.assertLess(diff,.01) - - def test_CRRAutilityP(self): - # Test the first derivative of the utility function - self.derivative_func_comparison(HARK.utilities.CRRAutilityP,HARK.utilities.CRRAutility) - - def test_CRRAutilityPP(self): - # Test the second derivative of the utility function - self.derivative_func_comparison(HARK.utilities.CRRAutilityPP,HARK.utilities.CRRAutilityP) - - def test_CRRAutilityPPP(self): - # Test the third derivative of the utility function - self.derivative_func_comparison(HARK.utilities.CRRAutilityPPP,HARK.utilities.CRRAutilityPP) - - def test_CRRAutilityPPPP(self): - # Test the fourth derivative of the utility function - self.derivative_func_comparison(HARK.utilities.CRRAutilityPPPP,HARK.utilities.CRRAutilityPPP) - -if __name__ == '__main__': - print('testing Harkutilities.py') - unittest.main() From dfa22bcaccc0a4c611f7f44cd15af371c3f50560 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Thu, 11 Apr 2019 20:43:43 +0200 Subject: [PATCH 23/77] [RFC] Include DCEGM main function in HARK (#226) * DCEGM * Add small test. * Fix test * dcegmIntervals -> dcegmSegments * Add convenience index in dcegmSegments instead. * Some cleanup. * Fix tests. * Fix tests. --- HARK/dcegm.py | 192 +++++++++++++++++++++++++++++++++++++++ HARK/tests/test_dcegm.py | 48 ++++++++++ 2 files changed, 240 insertions(+) create mode 100644 HARK/dcegm.py create mode 100644 HARK/tests/test_dcegm.py diff --git a/HARK/dcegm.py b/HARK/dcegm.py new file mode 100644 index 000000000..27b241576 --- /dev/null +++ b/HARK/dcegm.py @@ -0,0 +1,192 @@ +""" +Functions for working with the DCEGM algorithm. +""" +import numpy as np +from HARK.interpolation import LinearInterp + +def calcSegments(x, v): + """ + Find index vectors `rise` and `fall` such that `rise` holds the indeces `i` + such that x[i+1]>x[i] and `fall` holds indeces `j` such that either + - x[j+1] < x[j] or, + - x[j]>x[j-1] and v[j] x[i-1] # true if grid decreases on index decrement + val_fell = v[i] < v[i-1] # true if value rises on index decrement + + if (ip1_falls and i_rose) or (val_fell and i_rose): + + # we are in a region where the endogenous grid is decreasing or + # the value function rises by stepping back in the grid. + fall = np.append(fall, i) # add the index to the vector + + # We now iterate from the current index onwards until we find point + # where resources rises again. Unfortunately, we need to check + # each points, as there can be multiple spells of falling endogenous + # grids, so we cannot use bisection or some other fast algorithm. + k = i + while x[k+1] < x[k]: + k = k + 1 + # k now holds either the next index the starts a new rising + # region, or it holds the length of M, `m_len`. + + rise = np.append(rise, k) + + # Set the index to the point where resources again is rising + i = k + + i = i + 1 + + # Add the last index for convenience (then all segments are complete, as + # len(fall) == len(rise), and we can form them by range(rise[j], fall[j]+1). + fall = np.append(fall, len(v)-1) + + return rise, fall +# think! nanargmax makes everythign super ugly because numpy changed the wraning +# in all nan slices to a valueerror...it's nans, aaarghgghg +def calcMultilineEnvelope(M, C, V_T, commonM): + """ + Do the envelope step of the DCEGM algorithm. Takes in market ressources, + consumption levels, and inverse values from the EGM step. These represent + (m, c) pairs that solve the necessary first order conditions. This function + calculates the optimal (m, c, v_t) pairs on the commonM grid. + + Parameters + ---------- + M : np.array + market ressources from EGM step + C : np.array + consumption from EGM step + V_T : np.array + transformed values at the EGM grid + commonM : np.array + common grid to do upper envelope calculations on + + Returns + ------- + + + """ + m_len = len(commonM) + rise, fall = calcSegments(M, V_T) + + num_kinks = len(fall) # number of kinks / falling EGM grids + + # Use these segments to sequentially find upper envelopes. commonVARNAME + # means the VARNAME evaluated on the common grid with a cloumn for each kink + # discovered in calcSegments. This means that commonVARNAME is a matrix + # common grid length-by-number of segments to consider. In the end, we'll + # use nanargmax over the columns to pick out the best (transformed) values. + # This is why we fill the arrays with np.nan's. + commonV_T = np.empty((m_len, num_kinks)) + commonV_T[:] = np.nan + commonC = np.empty((m_len, num_kinks)) + commonC[:] = np.nan + + # Now, loop over all segments as defined by the "kinks" or the combination + # of "rise" and "fall" indeces. These (rise[j], fall[j]) pairs define regions + for j in range(num_kinks): + # Find points in the common grid that are in the range of the points in + # the interval defined by (rise[j], fall[j]). + below = M[rise[j]] >= commonM # boolean array of bad indeces below + above = M[fall[j]] <= commonM # boolen array of bad indeces above + in_range = below + above == 0 # pick out elements that are neither + + # create range of indeces in the input arrays + idxs = range(rise[j], fall[j]+1) + # grab ressource values at the relevant indeces + m_idx_j = M[idxs] + + # based in in_range, find the relevant ressource values to interpolate + m_eval = commonM[in_range] + + # re-interpolate to common grid + commonV_T[in_range,j] = LinearInterp(m_idx_j, V_T[idxs], lower_extrap=True)(m_eval) + commonC[in_range,j] = LinearInterp(m_idx_j, C[idxs], lower_extrap=True)(m_eval) # Interpolat econsumption also. May not be nesserary + # for each row in the commonV_T matrix, see if all entries are np.nan. This + # would mean that we have no valid value here, so we want to use this boolean + # vector to filter out irrelevant entries of commonV_T. + row_all_nan = np.array([np.all(np.isnan(row)) for row in commonV_T]) + # Now take the max of all these line segments. + idx_max = np.zeros(commonM.size, dtype = int) + idx_max[row_all_nan == False] = np.nanargmax(commonV_T[row_all_nan == False], axis=1) + + # prefix with upper for variable that are "upper enveloped" + upperV_T = np.zeros(m_len) + + # Set the non-nan rows to the maximum over columns + upperV_T[row_all_nan == False] = np.nanmax(commonV_T[row_all_nan == False, :], axis=1) + # Set the rest to nan + upperV_T[row_all_nan] = np.nan + + # Add the zero point in the bottom + if np.isnan(upperV_T[0]): + # in transformed space space, utility of zero-consumption (-inf) is 0.0 + upperV_T[0] = 0.0 + # commonM[0] is typically 0, so this is safe, but maybe it should be 0.0 + commonC[0] = commonM[0] + + # Extrapolate if NaNs are introduced due to the common grid + # going outside all the sub-line segments + IsNaN = np.isnan(upperV_T) + upperV_T[IsNaN] = LinearInterp(commonM[IsNaN == False], upperV_T[IsNaN == False])(commonM[IsNaN]) + + + LastBeforeNaN = np.append(np.diff(IsNaN)>0, 0) + LastId = LastBeforeNaN*idx_max # Find last id-number + idx_max[IsNaN] = LastId[IsNaN] + # Linear index used to get optimal consumption based on "id" from max + ncols = commonC.shape[1] + rowidx = np.cumsum(ncols*np.ones(len(commonM), dtype=int))-ncols + idx_linear = np.unravel_index(rowidx+idx_max, commonC.shape) + upperC = commonC[idx_linear] + upperC[IsNaN] = LinearInterp(commonM[IsNaN==0], upperC[IsNaN==0])(commonM[IsNaN]) + + # TODO calculate cross points of line segments to get the true vertical drops + + upperM = commonM.copy() # anticipate this TODO + + return upperM, upperC, upperV_T + +def main(): + print("Sorry, HARK.discontools doesn't actually do anything on its own.") + +if __name__ == '__main__': + main() diff --git a/HARK/tests/test_dcegm.py b/HARK/tests/test_dcegm.py new file mode 100644 index 000000000..40f3b526a --- /dev/null +++ b/HARK/tests/test_dcegm.py @@ -0,0 +1,48 @@ +""" +This file implements unit tests to check discrete choice functions +""" +from HARK import dcegm + +# Bring in modules we need +import unittest +import numpy as np + +class testsForDCEGM(unittest.TestCase): + + def setUp(self): + self.commonM = np.linspace(0,10.0,30) + self.m_in = np.array([1.0, 2.0, 3.0, 2.5, 2.0, 4.0, 5.0, 6.0]) + self.c_in = np.array([1.0, 2.0, 3.0, 2.5, 2.0, 4.0, 5.0, 6.0]) + self.v_in = np.array([0.5, 1.0, 1.5, 0.75, 0.5, 3.5, 5.0, 7.0]) + + def test_crossing(self): + # Test that the upper envelope has the approximate correct value + # where the two increasing segments with m_1 = [2, 3] and m_2 = [2.0, 4.0] + # is the correct value. + # + # Calculate the crossing by hand + slope_1 = (1.5 - 1.0)/(3.0 - 2.0) + slope_2 = (3.5 - 0.5)/(4.0 - 2.0) + m_cross = 2.0 + (0.5 - 1.0)/(slope_1 - slope_2) + + m_out, c_out, v_out = dcegm.calcMultilineEnvelope(self.m_in, self.c_in, self.v_in, self.commonM) + + m_idx = 0 + for m in m_out: + if m > m_cross: + break + m_idx += 1 + + # Just right of the cross, the second segment is optimal + true_v = 0.5 + (m_out[m_idx] - 2.0)*slope_2 + self.assertTrue(abs(v_out[m_idx] - true_v) < 1e-12) + + # also test that first elements are 0 etc + + # def test_crossing_in_grid(self): + # # include crossing m in common grid + # commonM_augmented = np.append(self.commonM, m_cross).sort() + # + # m_out, c_out, v_out = calcMultilineEnvelope(self.m_in, self.c_in, self.v_in, self.commonM) + # + # self.assertTrue( From 76699e0c512cdd59af89c80a4c49ae4088a86509 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Thu, 11 Apr 2019 22:35:03 +0200 Subject: [PATCH 24/77] Update some text in dcegm (#248) * Update some text in dcegm As per our discussion, these were some of the small things Matt had spotted. @shaunagm * Update dcegm.py --- HARK/dcegm.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/HARK/dcegm.py b/HARK/dcegm.py index 27b241576..a108e3081 100644 --- a/HARK/dcegm.py +++ b/HARK/dcegm.py @@ -1,5 +1,8 @@ """ -Functions for working with the DCEGM algorithm. +Functions for working with the discrete-continuous EGM (DCEGM) algorithm as +described in "The endogenous grid method for discrete-continuous dynamic +choice models with (or without) taste shocks" by Iskhakov et al. (2016) +[https://doi.org/10.3982/QE643 and ijrsDCEGM2017 in our Zotero] """ import numpy as np from HARK.interpolation import LinearInterp @@ -186,7 +189,7 @@ def calcMultilineEnvelope(M, C, V_T, commonM): return upperM, upperC, upperV_T def main(): - print("Sorry, HARK.discontools doesn't actually do anything on its own.") + print("Sorry, HARK.dcegm doesn't actually do anything on its own.") if __name__ == '__main__': main() From b22df42aba3b67275acc625be7135e36641c4ecd Mon Sep 17 00:00:00 2001 From: Shauna Gordon-McKeon Date: Fri, 12 Apr 2019 09:09:53 -0400 Subject: [PATCH 25/77] Prepare release 0.10.0.dev1 --- CHANGES.md | 42 ++++++++++++++++++++++++++++++++++++++++++ MANIFEST.in | 2 +- setup.py | 2 +- 3 files changed, 44 insertions(+), 2 deletions(-) create mode 100644 CHANGES.md diff --git a/CHANGES.md b/CHANGES.md new file mode 100644 index 000000000..481067e65 --- /dev/null +++ b/CHANGES.md @@ -0,0 +1,42 @@ +HARK +Version 0.10.0.dev1 +Release Notes + +# Introduction + +This document contains the release notes for the 0.10.dev1 version of HARK. HARK aims to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents. + +For more information on HARK, see [our Github organization](https://github.com/econ-ark). + +## Changes + +### 0.10.0.dev1 + +Release Date: 04-12-2019 + +#### Major Changes + +* Adds [tools](https://github.com/econ-ark/HARK/blob/master/HARK/dcegm.py) to solve problems that arise from the interaction of discrete and continuous variables, using the [DCEGM](https://github.com/econ-ark/DemARK/blob/master/notebooks/DCEGM-Upper-Envelope.ipynb) method of [Iskhakov et al.](https://onlinelibrary.wiley.com/doi/abs/10.3982/QE643), who apply the their discrete-continuous solution algorithm to the problem of optimal endogenous retirement; their results are replicated using our new tool [here](https://github.com/econ-ark/REMARK/blob/master/REMARKs/EndogenousRetirement/Endogenous-Retirement.ipynb). ([226](https://github.com/econ-ark/HARK/pull/226)) +* Parameters of ConsAggShockModel.CobbDouglasEconomy.updateAFunc and ConsAggShockModel.CobbDouglasMarkovEconomy.updateAFunc that govern damping and the number of discarded 'burn-in' periods were previously hardcoded, now proper instance-level parameters. ([244](https://github.com/econ-ark/HARK/pull/244)) +* Improve accuracy and performance of functions for evaluating the integrated value function and conditional choice probabilities for models with extreme value type I taste shocks. ([242](https://github.com/econ-ark/HARK/pull/242)) +* Add calcLogSum, calcChoiceProbs, calcLogSumChoiceProbs to HARK.interpolation. ([209](https://github.com/econ-ark/HARK/pull/209), [217](https://github.com/econ-ark/HARK/pull/217)) +* Create tool to produce an example "template" of a REMARK based on SolvingMicroDSOPs. ([176](https://github.com/econ-ark/HARK/pull/176)) + +#### Minor Changes + +* Moved old utilities tests. ([245](https://github.com/econ-ark/HARK/pull/245)) +* Deleted old files related to "cstwMPCold". ([239](https://github.com/econ-ark/HARK/pull/239)) +* Set numpy floating point error level to ignore. ([238](https://github.com/econ-ark/HARK/pull/238)) +* Fixed miscellaneous imports. ([212](https://github.com/econ-ark/HARK/pull/212), [224](https://github.com/econ-ark/HARK/pull/224), [225](https://github.com/econ-ark/HARK/pull/225)) +* Improve the tests of buffer stock model impatience conditions in IndShockConsumerType. ([219](https://github.com/econ-ark/HARK/pull/219)) +* Add basic support for Travis continuous integration testing. ([208](https://github.com/econ-ark/HARK/pull/208)) +* Add SciPy to requirements.txt. ([207](https://github.com/econ-ark/HARK/pull/207)) +* Fix indexing bug in bilinear interpolation. ([194](https://github.com/econ-ark/HARK/pull/194)) +* Update the build process to handle Python 2 and 3 compatibility. ([172](https://github.com/econ-ark/HARK/pull/172)) +* Add MPCnow attribute to ConsGenIncProcessModel. ([170](https://github.com/econ-ark/HARK/pull/170)) +* All standalone demo files have been removed. The content that was in these files can now be found in similarly named Jupyter notebooks in the DEMARK repository. Some of these notebooks are also linked from econ-ark.org. ([229](https://github.com/econ-ark/HARK/pull/229), [243](https://github.com/econ-ark/HARK/pull/243)) + +#### Other Notes + +* Not all changes from 0.9.1 may be listed in these release notes. If you are having trouble addressing a breaking change, please reach out to us. + diff --git a/MANIFEST.in b/MANIFEST.in index 8dd76ae48..492b95d00 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,4 +1,4 @@ -# Include the README +# Include the README, CHANGES, and CONTRIBUTING files include *.md # Include the license file diff --git a/setup.py b/setup.py index ac0472894..b9e3e2779 100644 --- a/setup.py +++ b/setup.py @@ -42,7 +42,7 @@ # For a discussion on single-sourcing the version across setup.py and the # project code, see # https://packaging.python.org/en/latest/single_source_version.html - version='0.9.1', # Required + version='0.10.0.dev1', # Required # This is a one-line description or tagline of what your project does. This # corresponds to the "Summary" metadata field: From 8c1fa3795cee4f6c641482b5f4f86288cfeaedd8 Mon Sep 17 00:00:00 2001 From: llorracc Date: Fri, 12 Apr 2019 22:13:40 -0400 Subject: [PATCH 26/77] Suppress annoying impatience check when not needed --- HARK/ConsumptionSaving/ConsIndShockModel.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 69fccbaa4..2e732ef61 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1702,9 +1702,10 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False None ''' if self.cycles!=0 or self.T_cycle > 1: - print('This method only checks for the conditions for infinite horizon models with a 1 period cycle') + if verbose = True: + print('This method only checks for the conditions for infinite horizon models with a 1 period cycle') return - + violated = False #Evaluate and report on the return impatience condition From 7de1a921b4c6046220fc1db6677df0d0df88adf5 Mon Sep 17 00:00:00 2001 From: llorracc Date: Thu, 18 Apr 2019 10:11:43 -0400 Subject: [PATCH 27/77] Fix verbosity check in ConsIndShockModel --- HARK/ConsumptionSaving/ConsIndShockModel.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 2e732ef61..c353e65df 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1702,7 +1702,7 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False None ''' if self.cycles!=0 or self.T_cycle > 1: - if verbose = True: + if verbose == True: print('This method only checks for the conditions for infinite horizon models with a 1 period cycle') return From 78a7ddd691edf430c7df6653098d1142a0b691bb Mon Sep 17 00:00:00 2001 From: Shauna Gordon-McKeon Date: Thu, 18 Apr 2019 13:18:12 -0400 Subject: [PATCH 28/77] Prepare release 0.10.0.dev2 --- CHANGES.md | 20 ++++++++++++++++++-- setup.py | 2 +- 2 files changed, 19 insertions(+), 3 deletions(-) diff --git a/CHANGES.md b/CHANGES.md index 481067e65..109b8eaf8 100644 --- a/CHANGES.md +++ b/CHANGES.md @@ -1,15 +1,31 @@ HARK -Version 0.10.0.dev1 +Version 0.10.0.dev2 Release Notes # Introduction -This document contains the release notes for the 0.10.dev1 version of HARK. HARK aims to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents. +This document contains the release notes for the 0.10.0.dev2 version of HARK. HARK aims to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents. For more information on HARK, see [our Github organization](https://github.com/econ-ark). ## Changes +### 0.10.0.dev2 + +Release Date: 04-18-2019 + +#### Major Changes + +None + +#### Minor Changes + +* Fix verbosity check in ConsIndShockModel. ([250](https://github.com/econ-ark/HARK/pull/250)) + +#### Other Changes + +None + ### 0.10.0.dev1 Release Date: 04-12-2019 diff --git a/setup.py b/setup.py index b9e3e2779..a7cd77c1f 100644 --- a/setup.py +++ b/setup.py @@ -42,7 +42,7 @@ # For a discussion on single-sourcing the version across setup.py and the # project code, see # https://packaging.python.org/en/latest/single_source_version.html - version='0.10.0.dev1', # Required + version='0.10.0.dev2', # Required # This is a one-line description or tagline of what your project does. This # corresponds to the "Summary" metadata field: From 8426824d005d1a0938e0db6c2f59a8a845cfd7e0 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Wed, 24 Apr 2019 09:50:52 +0200 Subject: [PATCH 29/77] Add a comment to the construction of aNrmNow fixes #253 --- HARK/ConsumptionSaving/ConsIndShockModel.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index c353e65df..5b557d464 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -793,6 +793,12 @@ def prepareToCalcEndOfPrdvP(self): aNrmNow : np.array A 1D array of end-of-period assets; also stored as attribute of self. ''' + + # We define aNrmNow all the way from BoroCnstNat up to max(self.aXtraGrid) + # even if BoroCnstNat < BoroCnstArt, so we can construct the consumption + # function as the lower envelope of the (by the artificial borrowing con- + # straint) uconstrained consumption function, and the artificially con- + # strained consumption function. aNrmNow = np.asarray(self.aXtraGrid) + self.BoroCnstNat ShkCount = self.TranShkValsNext.size aNrm_temp = np.tile(aNrmNow,(ShkCount,1)) From c31afaee638d7e30cdcc91b492cb605776dc9ff0 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Thu, 25 Apr 2019 11:26:37 +0200 Subject: [PATCH 30/77] Test initialization of IndShockConsumerType. --- HARK/tests/test_ConsIndShockInit.py | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 HARK/tests/test_ConsIndShockInit.py diff --git a/HARK/tests/test_ConsIndShockInit.py b/HARK/tests/test_ConsIndShockInit.py new file mode 100644 index 000000000..bcbc0cf05 --- /dev/null +++ b/HARK/tests/test_ConsIndShockInit.py @@ -0,0 +1,25 @@ +""" +This file tests whether ConsIndShockModel's are initialized correctly. +""" + + +# Bring in modules we need +import unittest +import numpy as np +import HARK.ConsumptionSaving.ConsumerParameters as Params +from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType +from HARK.utilities import plotFuncsDer, plotFuncs + + +class testsForConsIndShockModelInitialization(unittest.TestCase): + # We don't need a setUp method for the tests to run, but it's convenient + # if we want to test various things on the same model in different test_* + # methods. + def setUp(self): + + # Make and solve an idiosyncratic shocks consumer with a finite lifecycle + LifecycleExample = IndShockConsumerType(**Params.init_lifecycle) + self.model = LifecycleExample + + def test_LifecycleIncomeProcess(self): + self.assertEqual(len(self.model.IncomeDstn), self.model.T_cycle) From 34ab061780a50c7c10f07b116e415a02fa54686d Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Fri, 26 Apr 2019 16:14:40 +0200 Subject: [PATCH 31/77] Introduce approxNormal and approxLognormalGaussHermite and two helper functions for converting between location and scale in normal<->lognormal. --- HARK/utilities.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/HARK/utilities.py b/HARK/utilities.py index 83f8371b1..3f81cf3c0 100644 --- a/HARK/utilities.py +++ b/HARK/utilities.py @@ -12,6 +12,7 @@ import functools import warnings import numpy as np # Python's numeric library, abbreviated "np" +import math try: import matplotlib.pyplot as plt # Python's plotting library except ImportError: @@ -551,6 +552,27 @@ def approxMeanOneLognormal(N, sigma=1.0, **kwargs): pmf,X = approxLognormal(N=N, mu=mu_adj, sigma=sigma, **kwargs) return [pmf,X] +def approxNormal(N, mu=0.0, sigma=1.0): + x, w = np.polynomial.hermite.hermgauss(N) + # normalize w + pmf = w*np.pi**-0.5 + # correct x + X = 2.0**0.5*sigma*x + mu + return [pmf, X] + +def approxLogNormalGaussHermite(N, mu=0.0, sigma=1.0): + pmf, X = approxNormal(N) + return pmf, np.exp(X) + +def calcNormalStyleParsFromLognormal(avgLognormal, varLognormal): + avgNormal = math.log(avgLognormal/math.sqrt(1+varLognormal/avgLognormal**2)) + varNormal = math.sqrt(math.log(1+varLognormal/avgLognormal**2)) + return avgNormal, varNormal + +def calcLognormalStyleParsFromNormal(mu, sigma): + + + def approxBeta(N,a=1.0,b=1.0): ''' Calculate a discrete approximation to the beta distribution. May be quite From a6d71c5fa861dcef0c44d86bd4868dda3a8e05d5 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Fri, 26 Apr 2019 16:24:24 +0200 Subject: [PATCH 32/77] Add missing --- HARK/utilities.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/HARK/utilities.py b/HARK/utilities.py index 3f81cf3c0..41721ca17 100644 --- a/HARK/utilities.py +++ b/HARK/utilities.py @@ -569,9 +569,10 @@ def calcNormalStyleParsFromLognormal(avgLognormal, varLognormal): varNormal = math.sqrt(math.log(1+varLognormal/avgLognormal**2)) return avgNormal, varNormal -def calcLognormalStyleParsFromNormal(mu, sigma): - - +def calcLognormalStyleParsFromNormal(muNormal, varNormal): + avgLognormal = math.exp(muNormal+varNormal*0.5) + varLognormal = (math.exp(varNormal)-1)*math.exp(2*muNormal+varNormal) + return avgLognormal, varLognormal def approxBeta(N,a=1.0,b=1.0): ''' From 5c3282a57ab48b6567bb3dd50868677569001e29 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Fri, 26 Apr 2019 16:26:02 +0200 Subject: [PATCH 33/77] names. --- HARK/utilities.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/HARK/utilities.py b/HARK/utilities.py index 41721ca17..82ecfc8aa 100644 --- a/HARK/utilities.py +++ b/HARK/utilities.py @@ -564,12 +564,12 @@ def approxLogNormalGaussHermite(N, mu=0.0, sigma=1.0): pmf, X = approxNormal(N) return pmf, np.exp(X) -def calcNormalStyleParsFromLognormal(avgLognormal, varLognormal): +def calcNormalStyleParsFromLognormalPars(avgLognormal, varLognormal): avgNormal = math.log(avgLognormal/math.sqrt(1+varLognormal/avgLognormal**2)) varNormal = math.sqrt(math.log(1+varLognormal/avgLognormal**2)) return avgNormal, varNormal -def calcLognormalStyleParsFromNormal(muNormal, varNormal): +def calcLognormalStyleParsFromNormalPars(muNormal, varNormal): avgLognormal = math.exp(muNormal+varNormal*0.5) varLognormal = (math.exp(varNormal)-1)*math.exp(2*muNormal+varNormal) return avgLognormal, varLognormal From 057d90375323a865f3f170ee32636a81fb2b10ae Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Fri, 26 Apr 2019 19:28:38 +0200 Subject: [PATCH 34/77] Allow user to chose parameter specification as lognormal or normal parameters. --- HARK/utilities.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/HARK/utilities.py b/HARK/utilities.py index 82ecfc8aa..80cb096fe 100644 --- a/HARK/utilities.py +++ b/HARK/utilities.py @@ -560,8 +560,16 @@ def approxNormal(N, mu=0.0, sigma=1.0): X = 2.0**0.5*sigma*x + mu return [pmf, X] -def approxLogNormalGaussHermite(N, mu=0.0, sigma=1.0): - pmf, X = approxNormal(N) +def approxLognormalGaussHermite(N, mu=0.0, sigma=1.0, parametersAs='normal'): + if parametersAs == 'normal': + mu = mu + sigma = sigma + elif parametersAs == 'lognormal': + mu, sigma = calcNormalStyleParsFromLognormalPars(mu, sigma) + else: + # throw an error + return False + pmf, X = approxNormal(N, mu, sigma) return pmf, np.exp(X) def calcNormalStyleParsFromLognormalPars(avgLognormal, varLognormal): From b5c246b631b1b4ca5a49ba9af4d245c7b3faffae Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Fri, 26 Apr 2019 19:29:53 +0200 Subject: [PATCH 35/77] simpler mu, sigma no-op. --- HARK/utilities.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/HARK/utilities.py b/HARK/utilities.py index 80cb096fe..2e744c6ae 100644 --- a/HARK/utilities.py +++ b/HARK/utilities.py @@ -562,8 +562,7 @@ def approxNormal(N, mu=0.0, sigma=1.0): def approxLognormalGaussHermite(N, mu=0.0, sigma=1.0, parametersAs='normal'): if parametersAs == 'normal': - mu = mu - sigma = sigma + mu, sigma = mu, sigma elif parametersAs == 'lognormal': mu, sigma = calcNormalStyleParsFromLognormalPars(mu, sigma) else: From b96912c35c9d7173154ba9f6e027c9483656ba5a Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Fri, 26 Apr 2019 21:00:01 +0200 Subject: [PATCH 36/77] Use std instead of var. --- HARK/utilities.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/HARK/utilities.py b/HARK/utilities.py index 2e744c6ae..c0bc043bf 100644 --- a/HARK/utilities.py +++ b/HARK/utilities.py @@ -557,7 +557,7 @@ def approxNormal(N, mu=0.0, sigma=1.0): # normalize w pmf = w*np.pi**-0.5 # correct x - X = 2.0**0.5*sigma*x + mu + X = math.sqrt(2.0)*sigma*x + mu return [pmf, X] def approxLognormalGaussHermite(N, mu=0.0, sigma=1.0, parametersAs='normal'): @@ -571,15 +571,19 @@ def approxLognormalGaussHermite(N, mu=0.0, sigma=1.0, parametersAs='normal'): pmf, X = approxNormal(N, mu, sigma) return pmf, np.exp(X) -def calcNormalStyleParsFromLognormalPars(avgLognormal, varLognormal): +def calcNormalStyleParsFromLognormalPars(avgLognormal, stdLognormal): + varLognormal = stdLognormal**2 avgNormal = math.log(avgLognormal/math.sqrt(1+varLognormal/avgLognormal**2)) varNormal = math.sqrt(math.log(1+varLognormal/avgLognormal**2)) - return avgNormal, varNormal + stdNormal = math.sqrt(varNormal) + return avgNormal, stdNormal -def calcLognormalStyleParsFromNormalPars(muNormal, varNormal): +def calcLognormalStyleParsFromNormalPars(muNormal, stdNormal): + varNormal = stdNormal**2 avgLognormal = math.exp(muNormal+varNormal*0.5) varLognormal = (math.exp(varNormal)-1)*math.exp(2*muNormal+varNormal) - return avgLognormal, varLognormal + stdLognormal = math.sqrt(varLognormal) + return avgLognormal, stdLognormal def approxBeta(N,a=1.0,b=1.0): ''' From 9354c65911341c4d641f5b57895f8a777b90ea03 Mon Sep 17 00:00:00 2001 From: Christopher Llorracc Carroll <1320319+llorracc@users.noreply.github.com> Date: Fri, 26 Apr 2019 15:58:41 -0400 Subject: [PATCH 37/77] Partial update to README for PyCon (#257) * Partial update to README for PyCon * Further updates to README installation/getting started guide * Fix formatting of paragraphs within list * Minor formatting fixes and language tweaks --- README.md | 159 ++++++++++++++++++++++++++---------------------------- 1 file changed, 77 insertions(+), 82 deletions(-) diff --git a/README.md b/README.md index 877dfacab..f3a3b6dc3 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # Heterogeneous Agents Resources and toolKit (HARK) -pre-release 0.9.1 - 13 July, 2018 +pre-release 0.10.0.dev2 Click the Badge for Citation Info. [![DOI](https://zenodo.org/badge/50448254.svg)](https://zenodo.org/badge/latestdoi/50448254) @@ -44,84 +44,79 @@ Replications and Explorations Made using the ARK : [REMARK](https://github.com/e ## II. QUICK START GUIDE -This is going to be easy, friend. HARK is written in Python, specifically the -Anaconda distribution of Python. Follow these easy steps to get HARK going: - -1) Go to https://www.continuum.io/downloads and download Anaconda for your -operating system - -2) Install Anaconda, using the instructions provided on that page. Now you have -installed everything you need to run most of HARK. But you still need to get HARK -on your machine. - -3) To get HARK on your machine, you should know that HARK is managed with version -control software called "Git". HARK is hosted on a website called "GitHub" devoted -to hosting projects managed with Git. - - If you don't want to know more than that, you don't have to. Go to HARK's page -on GitHub (https://github.com/econ-ark/HARK), click the "Clone or download" button -in the upper right hand corner of the page, then click "Download ZIP". Unzip it -into an empty directory. Maybe call that directory /HARK ? The choice is yours. - - You can also clone HARK off GitHub using Git. This is slightly more difficult, -because it involves installing Git on your machine and learning a little about -how to use Git. We believe this is an investment worth making, but it is up to you. -To learn more about Git, read the documentation at https://git-scm.com/documentation -or visit many other great Git resources on the internet. - -4) Open Spyder, an interactive development environment (IDE) for Python -(specifically, iPython). On Windows, open a command prompt and type "spyder". -On Linux, open the command line and type "spyder". On Mac, open the command -line and type "spyder". - -5) Navigate to the directory where you put the HARK files. This can be done -within Spyder by doing "import os" and then using os.chdir() to change directories. -chdir works just like cd at a command prompt on most operating systems, except that -it takes a string as input: os.chdir('Music') moves to the Music subdirectory -of the current working directory. - -6) Run one of HARK's modules. You can either type "run MODULENAME" after navigating -to the correct directory (see step 5), or click the green arrow "run" button in -Spyder's toolbar after opening the module in the editor. Every module should -do *something* when run, but that something might not be very interesting in -some cases. For starters, check out /ConsumptionSavingModel/ConsIndShockModel.py -See section III below for a full list of modules that produce non-trivial output. - -7) The Python environment can be cleared or reset with ctrl+. Note that -this will also change the current working directory back to its default. -To change the default directory (the "global working directory"), see -Tools-->Preferences-->Global working directory; you might need to restart -Spyder for the change to take effect. - -8) Read the more complete documentation in [HARKmanual.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/HARKmanual.pdf). - -9) OPTIONAL: If you want to use HARK's multithreading capabilities, you will -need two Python packages that do not come automatically with Anaconda: joblib -and dill. Assuming you have the necessary permissions on your machine, the -easiest way to do this is through Anaconda. Go to the command line, and type -"conda install joblib" and then "conda install dill" (accept defaults if prompted). -If this doesn't work, but you have Git, you can just clone the packages directly -off GitHub. Go to the command line and navigate to the directory you want to put -these packages in. Then type "git clone https://github.com/joblib/joblib.git" -and then "git clone https://github.com/uqfoundation/dill". Joblib should work -after this, but there is one more step to get dill working. Navigate to dill's -directory in the command line, and then type "python setup.py build". Then you -should have joblib and dill working on your machine. - -Note: If you did not put joblib and dill in one of the paths in sys.path, you will -need to add the joblib and dill directories to sys.path. The easiest way to do this -is to open up Anaconda, and type: - -```python -import sys -sys.path.append('path_to_joblib_directory') -sys.path.append('path_to_dill_directory') -``` +HARK is an open source project written in Python. It's compatible with both Python +2 and 3, and with the Anaconda distribution of Python. + +The simplest way to install HARK is to use [pip](https://pip.pypa.io/en/stable/installing/). We recommend using a virtual environment such as [virtualenv]((https://virtualenv.pypa.io/en/latest/)), and using Python 3 rather than Python 2, but it should still work without a virtual environment and/or using Python 2. + +To install HARK with pip, type `pip install econ-ark`. + +### Using HARK with Anaconda + +Simply installing HARK with pip does not give you easy access to HARK's many graphical capabilities. One way to access these capabilities is by using Anaconda. + +1) Download Anaconda for your operating system and follow the installation instructions [at Anaconda.com](https://www.anaconda.com/distribution/#download-section). + +2) Open Spyder, an interactive development environment (IDE) for Python (specifically, iPython). You may be able to do this through Anaconda's graphical interface, or you can do so from the command line/prompt. To do so, simply open a command line/prompt and type `spyder`. + +3) Now it's time to install HARK. First, try typing `pip install econ-ark` into the iPython shell within Spyder. + + If that doesn't work for you, you will need to manually add HARK to your Spyder environment. To do this, you'll need to get the code from Github and import it into Spyder. To get the code from Github, you can either clone it or download a zipped file. + + To clone the file, type `git clone git@github.com:econ-ark/HARK.git` in your chosen repository ([more details here](https://git-scm.com/documentation)). + + To download the zipped file, go to [the HARK repository on GitHub](https://github.com/econ-ark/HARK). In the upper righthand corner is a button that says "clone or download". Click the "Download Zip" option and then unzip the contents into your chosen directory. + + Once you've got a copy of HARK in a directory, return to Spyder and navigate to that directorywhere you put HARK. This can be done within Spyder by doing `import os` and then using `os.chdi()` to change directories. chdir works just like cd at a command prompt on most operating systems, except that it takes a string as input: `os.chdir('Music')` moves to the Music subdirectory of the current working directory. + +6) Run one of HARK's modules. You can either type `run MODULENAME` after navigating to the correct directory (see step 5), or click the green arrow "run" button in Spyder's toolbar after opening the module in the editor. Every module should do *something* when run, but that something might not be very interesting in some cases. For starters, check out `/ConsumptionSavingModel/ConsIndShockModel.py`. See section III below for a full list of modules that produce non-trivial output. + +7) OPTIONAL: If you want to use HARK's multithreading capabilities, you will need two Python packages that do not come automatically with Anaconda: joblib and dill. Assuming you have the necessary permissions on your machine, the easiest way to do this is through Anaconda. Go to the command line, and type `conda install joblib` and `conda install dill` (accept defaults if prompted). If this doesn't work, but you have Git, you can just clone the packages directly off GitHub. Go to the command line and navigate to the directory you want to put these packages in. Then type `git clone https://github.com/joblib/joblib.git` and then `git clone https://github.com/uqfoundation/dill`. Joblib should work after this, but there is one more step to get dill working. Navigate to dill's directory in the command line, and then type `python setup.py build`. Then you should have joblib and dill working on your machine. + + Note: If you did not put joblib and dill in one of the paths in sys.path, you will need to add the joblib and dill directories to sys.path. The easiest way to do this is to open up Anaconda, and type: + + ```python + import sys + sys.path.append('path_to_joblib_directory') + sys.path.append('path_to_dill_directory') + ``` + +### Making changes to HARK + +If you want to make changes to HARK, you'll need to have access to the source files. Installing HARK via pip (either at the command line, or inside Spyder) makes it hard to access those files, so you'll need to download HARK again using git clone. + +1. Navigate to wherever you want to put the repository and type `git clone git@github.com:econ-ark/HARK.git` ([more details here](https://git-scm.com/documentation)). + +2. Then, create and activate a [virtual environment]([virtualenv]((https://virtualenv.pypa.io/en/latest/))). Install virtualenv if you need to and then type: + + `virtualenv venv + source venv/bin/activate` + + Once the virtualenv is activated, you should see `(venv)` in your command prompt. + +3. Finally, you can install HARK's requirements into the virtual environment with `pip install -r requirements.txt'. + +4. To check that everything has been set up correctly, run HARK's tests with `python -m unittest`. + +### Trouble with installation? + +We've done our best to give correct, thorough instructions on how to install HARK but we know this information may be inaccurate or incomplete. Please let us know if you run into trouble so we can update this guide! Here's a list of platforms and versions this guide has been verified for: + +| Installation Type | Platform | Python Version | Date Tested | Tested By | +| ------------- |:-------------:| -----:| -----:|-----:| +| basic pip install | Linux (16.04) | 3 | 04-24-2019 | @shaunagm | +| anaconda | Linux (16.04) | 3 | 04-24-2019 | @shaunagm | + +### Next steps + +To learn more about how to use HARK, check out our [user manual](https://github.com/econ-ark/HARK/blob/master/Documentation/HARKmanual.pdf). + +For help making changes to HARK, check out our [contributing guide](https://github.com/econ-ark/HARK/blob/Partial-Fix-to-Installation-README/CONTRIBUTING.md). + ## III. LIST OF FILES IN REPOSITORY -This section contains descriptions of every file included in the HARK -repository at the time of the beta release, categorized for convenience. +This section contains descriptions of the main files in the repo. Documentation files: * [README.md](https://github.com/econ-ark/HARK/blob/master/README.md): The file you are currently reading. @@ -133,29 +128,29 @@ Documentation files: * [Documentation/NARK.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/NARK.pdf): Variable naming conventions for HARK, plus concordance with LaTeX variable definitions. Still in development. Tool modules: -* HARKcore.py: +* HARK/core.py: Frameworks for "microeconomic" and "macroeconomic" models in HARK. We somewhat abuse those terms as shorthand; see the user guide for a description of what we mean. Every model in HARK extends the classes AgentType and Market in this module. Does nothing when run. -* HARKutilities.py: +* HARK/utilities.py: General purpose tools and utilities. Contains literal utility functions (in the economic sense), functions for making discrete approximations to continuous distributions, basic plotting functions for convenience, and a few unclassifiable things. Does nothing when run. -* HARKestimation.py: +* HARK/estimation.py: Functions for estimating models. As is, it only has a few wrapper functions for scipy.optimize optimization routines. Will be expanded in the future with more interesting things. Does nothing when run. -* HARKsimulation.py: +* HARK/simulation.py: Functions for generating simulated data. Functions in this module have names like drawUniform, generating (lists of) arrays of draws from various distributions. Does nothing when run. -* HARKinterpolation.py: +* HARK/interpolation.py: Classes for representing interpolated function approximations. Has 1D-4D interpolation methods, mostly based on linear or cubic spline interpolation. Will have ND methods in the future. Does nothing when run. -* HARKparallel.py: +* HARK/parallel.py: Early version of parallel processing in HARK. Works with instances of the AgentType class (or subclasses of it), distributing commands (as methods) to be run on a list of AgentTypes. Only works with local CPU. From e254669c285a6dd2d7ffce13130e144cef44c4f5 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Fri, 26 Apr 2019 22:40:56 +0200 Subject: [PATCH 38/77] Remove parametersAs and add tests. --- HARK/tests/test_approxDstns.py | 26 ++++++++++++++++++++++++++ HARK/utilities.py | 9 +-------- 2 files changed, 27 insertions(+), 8 deletions(-) create mode 100644 HARK/tests/test_approxDstns.py diff --git a/HARK/tests/test_approxDstns.py b/HARK/tests/test_approxDstns.py new file mode 100644 index 000000000..d6ed65ed1 --- /dev/null +++ b/HARK/tests/test_approxDstns.py @@ -0,0 +1,26 @@ +""" +This file implements unit tests apprixomate distributions. +""" + +# Bring in modules we need +import HARK.utilities as util +import unittest +import numpy as np + +class testsForDCEGM(unittest.TestCase): + def setUp(self): + # setup the parameters to loop over + self.muNormals = np.linspace(-3.0, 2.0, 50) + self.stdNormals = np.linspace(0.01, 2.0, 50) + + def test_mu_normal(self): + for muNormal in self.muNormals: + for stdNormal in self.stdNormals: + w, x = util.approxNormal(40, muNormal) + self.assertTrue(sum(w*x)-muNormal<1e-12) + + def test_mu_lognormal_from_normal(self): + for muNormal in muNormals: + for stdNormal in stdNormals: + w, x = util.approxLognormalGaussHermite(40, muNormal, stdNormal) + self.assertTrue(abs(sum(w*x)-util.calcLognormalStyleParsFromNormalPars(muNormal, stdNormal)[0])<1e-12) diff --git a/HARK/utilities.py b/HARK/utilities.py index c0bc043bf..81553a252 100644 --- a/HARK/utilities.py +++ b/HARK/utilities.py @@ -560,14 +560,7 @@ def approxNormal(N, mu=0.0, sigma=1.0): X = math.sqrt(2.0)*sigma*x + mu return [pmf, X] -def approxLognormalGaussHermite(N, mu=0.0, sigma=1.0, parametersAs='normal'): - if parametersAs == 'normal': - mu, sigma = mu, sigma - elif parametersAs == 'lognormal': - mu, sigma = calcNormalStyleParsFromLognormalPars(mu, sigma) - else: - # throw an error - return False +def approxLognormalGaussHermite(N, mu=0.0, sigma=1.0): pmf, X = approxNormal(N, mu, sigma) return pmf, np.exp(X) From 6f524cb3b7c4fc228abc2266a2e25c1267a76e3d Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Fri, 26 Apr 2019 23:16:34 +0200 Subject: [PATCH 39/77] add self to vars from setUp(). --- HARK/tests/test_approxDstns.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/HARK/tests/test_approxDstns.py b/HARK/tests/test_approxDstns.py index d6ed65ed1..940845c96 100644 --- a/HARK/tests/test_approxDstns.py +++ b/HARK/tests/test_approxDstns.py @@ -20,7 +20,7 @@ def test_mu_normal(self): self.assertTrue(sum(w*x)-muNormal<1e-12) def test_mu_lognormal_from_normal(self): - for muNormal in muNormals: - for stdNormal in stdNormals: + for muNormal in self.muNormals: + for stdNormal in self.stdNormals: w, x = util.approxLognormalGaussHermite(40, muNormal, stdNormal) self.assertTrue(abs(sum(w*x)-util.calcLognormalStyleParsFromNormalPars(muNormal, stdNormal)[0])<1e-12) From 3d184153a189e618a87c9540df1cd12044039cc5 Mon Sep 17 00:00:00 2001 From: Christopher Llorracc Carroll <1320319+llorracc@users.noreply.github.com> Date: Fri, 26 Apr 2019 19:32:49 -0400 Subject: [PATCH 40/77] Further updates to README.md (#260) --- README.md | 209 ++++++++++++++++++++++++++++++------------------------ 1 file changed, 115 insertions(+), 94 deletions(-) diff --git a/README.md b/README.md index f3a3b6dc3..9d4bc20c4 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # Heterogeneous Agents Resources and toolKit (HARK) -pre-release 0.10.0.dev2 +pre-release 0.10.0.dev2 Click the Badge for Citation Info. [![DOI](https://zenodo.org/badge/50448254.svg)](https://zenodo.org/badge/latestdoi/50448254) @@ -7,35 +7,30 @@ Click the Badge for Citation Info. Table of Contents: -* [I. Introduction](#i-introduction) -* [II. Quick start guide](#ii-quick-start-guide) -* [III. List of files in repository](#iii-list-of-files-in-repository) -* [IV. Warnings and disclaimers](#iv-warnings-and-disclaimers) -* [V. License Information](#v-license) +* [1. Introduction](#i-introduction) +* [2. Quick start guide](#ii-quick-start-guide) +* [3. List of files in repository](#iii-list-of-files-in-repository) +* [4. Warnings and disclaimers](#iv-warnings-and-disclaimers) +* [5. License Information](#v-license) ## I. INTRODUCTION -Welcome to HARK! We are tremendously excited you're here. HARK is -very much a work in progress, but we hope you find it valuable. We -*really* hope you find it so valuable that you decide to contribute -to it yourself. This document will tell you how to get HARK up and -running on your machine, and what you will find in HARK once you do. +Welcome to HARK! This document will tell you how to get HARK up and +running on your machine, and what you will find in HARK once you do. -If you have any comments on the code or documentation, we'd love to -hear from you! Our email addresses are: +If you have any comments on the code or documentation, or (even better) if you want to +contribute new content to HARK, we'd love to hear from you! +Our email addresses are: * Chris Carroll: ccarroll@llorracc.org * Matthew White: mnwhite@gmail.com -* Nathan Palmer: Nathan.Palmer@ofr.treasury.gov -* David Low: David.Low@cfpb.gov -* Alexander Kaufman: akaufman10@gmail.com GitHub repository: https://github.com/econ-ark/HARK Online documentation: https://econ-ark.github.io/HARK -User guide: /Documentation/HARKmanual.pdf (in the repository) +User guide: [Documentation/HARKmanual.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/HARKmanual.pdf) (in the [HARK repository](https://github.com/econ-ark/HARK)) Demonstrations of HARK functionality: [DemARK](https://github.com/econ-ark/DemARK/) @@ -45,58 +40,87 @@ Replications and Explorations Made using the ARK : [REMARK](https://github.com/e ## II. QUICK START GUIDE HARK is an open source project written in Python. It's compatible with both Python -2 and 3, and with the Anaconda distribution of Python. +2 and 3, and with the Anaconda distributions of python 2 and 3. But we recommend +using python 3; eventually support for python 2 will end. -The simplest way to install HARK is to use [pip](https://pip.pypa.io/en/stable/installing/). We recommend using a virtual environment such as [virtualenv]((https://virtualenv.pypa.io/en/latest/)), and using Python 3 rather than Python 2, but it should still work without a virtual environment and/or using Python 2. +### Installing HARK with pip -To install HARK with pip, type `pip install econ-ark`. +The simplest way to install HARK is to use [pip](https://pip.pypa.io/en/stable/installing/). +To install HARK with pip, at a command line type `pip install econ-ark`. + +If you are installing via pip, we recommend using a virtual environment such as [virtualenv](https://virtualenv.pypa.io/en/latest/). Creation of a virtual environment isolates the installation of `econ-ark` from the installations of any other python tools and packages. + +To install `virtualenv`, then to create an environment named `econ-ark`, and finally to activate that environment: + +``` +cd [directory where you want to store the econ-ark virtual environment] +pip install virtualenv +virtualenv econ-ark +source activate econ-ark +``` + +---- ### Using HARK with Anaconda -Simply installing HARK with pip does not give you easy access to HARK's many graphical capabilities. One way to access these capabilities is by using Anaconda. +Installing HARK with pip does not give you full access to HARK's many graphical capabilities. One way to access these capabilities is by using [Anaconda](https://anaconda.com/why-anaconda), which is a distribution of python along with many packages that are frequently used in scientific computing.. -1) Download Anaconda for your operating system and follow the installation instructions [at Anaconda.com](https://www.anaconda.com/distribution/#download-section). +1. Download Anaconda for your operating system and follow the installation instructions [at Anaconda.com](https://www.anaconda.com/distribution/#download-section). -2) Open Spyder, an interactive development environment (IDE) for Python (specifically, iPython). You may be able to do this through Anaconda's graphical interface, or you can do so from the command line/prompt. To do so, simply open a command line/prompt and type `spyder`. +1. Anaconda includes its own virtual environment system called `conda` which stores environments in a preset location (so you don't have to choose). So in order to create and activate an econ-ark virtual environment: +``` +conda create -n econ-ark anaconda +source activate econ-ark +``` +1. Open Spyder, an interactive development environment (IDE) for Python (specifically, iPython). You may be able to do this through Anaconda's graphical interface, or you can do so from the command line/prompt. To do so, simply open a command line/prompt and type `spyder`. -3) Now it's time to install HARK. First, try typing `pip install econ-ark` into the iPython shell within Spyder. +1. To verify that spyder has access to HARK try typing `pip install econ-ark` into the iPython shell within Spyder. If you have successfully installed HARK as above, you should see a lot of messages saying 'Requirement satisfied'. - If that doesn't work for you, you will need to manually add HARK to your Spyder environment. To do this, you'll need to get the code from Github and import it into Spyder. To get the code from Github, you can either clone it or download a zipped file. + * If that doesn't work, you will need to manually add HARK to your Spyder environment. To do this, you'll need to get the code from Github and import it into Spyder. To get the code from Github, you can either clone it or download a zipped file. - To clone the file, type `git clone git@github.com:econ-ark/HARK.git` in your chosen repository ([more details here](https://git-scm.com/documentation)). + * If you have `git` installed on the command line, type `git clone git@github.com:econ-ark/HARK.git` in your chosen directory ([more details here](https://git-scm.com/documentation)). - To download the zipped file, go to [the HARK repository on GitHub](https://github.com/econ-ark/HARK). In the upper righthand corner is a button that says "clone or download". Click the "Download Zip" option and then unzip the contents into your chosen directory. + * If you do not have `git` available on your computer, you can download the [GitHub Desktop app](https://desktop.github.com/) and use it to make a local clone - Once you've got a copy of HARK in a directory, return to Spyder and navigate to that directorywhere you put HARK. This can be done within Spyder by doing `import os` and then using `os.chdi()` to change directories. chdir works just like cd at a command prompt on most operating systems, except that it takes a string as input: `os.chdir('Music')` moves to the Music subdirectory of the current working directory. + * If you don't want to clone HARK, but just to download it, go to [the HARK repository on GitHub](https://github.com/econ-ark/HARK). In the upper righthand corner is a button that says "clone or download". Click the "Download Zip" option and then unzip the contents into your chosen directory. -6) Run one of HARK's modules. You can either type `run MODULENAME` after navigating to the correct directory (see step 5), or click the green arrow "run" button in Spyder's toolbar after opening the module in the editor. Every module should do *something* when run, but that something might not be very interesting in some cases. For starters, check out `/ConsumptionSavingModel/ConsIndShockModel.py`. See section III below for a full list of modules that produce non-trivial output. + Once you've got a copy of HARK in a directory, return to Spyder and navigate to that directory where you put HARK. This can be done within Spyder by doing `import os` and then using `os.chdir()` to change directories. `chdir` works just like cd at a command prompt on most operating systems, except that it takes a string as input: `os.chdir('Music')` moves to the Music subdirectory of the current working directory. -7) OPTIONAL: If you want to use HARK's multithreading capabilities, you will need two Python packages that do not come automatically with Anaconda: joblib and dill. Assuming you have the necessary permissions on your machine, the easiest way to do this is through Anaconda. Go to the command line, and type `conda install joblib` and `conda install dill` (accept defaults if prompted). If this doesn't work, but you have Git, you can just clone the packages directly off GitHub. Go to the command line and navigate to the directory you want to put these packages in. Then type `git clone https://github.com/joblib/joblib.git` and then `git clone https://github.com/uqfoundation/dill`. Joblib should work after this, but there is one more step to get dill working. Navigate to dill's directory in the command line, and then type `python setup.py build`. Then you should have joblib and dill working on your machine. +6) Most of the modules in HARK are just collections of tools. There are a few demonstration +applications that use the tools that you automatically get when you install HARK -- they are listed below in [Application Modules](#application-modules). A much larger set of uses of HARK can be found at two repositories: + * [DemARK](https://github.com/econ-ark/DemARK): Demonstrations of the use of HARK + * [REMARK](https://github.com/econ-ark/REMARK): Replications of existing papers made using HARK - Note: If you did not put joblib and dill in one of the paths in sys.path, you will need to add the joblib and dill directories to sys.path. The easiest way to do this is to open up Anaconda, and type: +You will want to obtain your own local copy of these repos using: +``` +git clone https://github.com/econ-ark/DemARK.git +``` +and similarly for the REMARK repo. Once you have downloaded them, you will find that each repo contains a `notebooks` directory that contains a number of [jupyter notebooks](https://jupyter.org/). If you have the jupyter notebook tool installed (it is installed as part of Anaconda), you should be able to launch the +jupyter notebook app from the command line with the command: - ```python - import sys - sys.path.append('path_to_joblib_directory') - sys.path.append('path_to_dill_directory') - ``` +``` +jupyter notebook +``` +and from there you can open the notebooks and execute them. ### Making changes to HARK -If you want to make changes to HARK, you'll need to have access to the source files. Installing HARK via pip (either at the command line, or inside Spyder) makes it hard to access those files, so you'll need to download HARK again using git clone. +If you want to make changes or contributions (yay!) to HARK, you'll need to have access to the source files. Installing HARK via pip (either at the command line, or inside Spyder) makes it hard to access those files (and it's a bad idea to mess with the original code anyway because you'll likely forget what changes you made). If you are adept at GitHub, you can [fork](https://help.github.com/en/articles/fork-a-repo) the repo. If you are less experienced, you should download a personal copy of HARK again using `git clone` (see above) or the GitHub Desktop app. 1. Navigate to wherever you want to put the repository and type `git clone git@github.com:econ-ark/HARK.git` ([more details here](https://git-scm.com/documentation)). 2. Then, create and activate a [virtual environment]([virtualenv]((https://virtualenv.pypa.io/en/latest/))). Install virtualenv if you need to and then type: - `virtualenv venv - source venv/bin/activate` +``` +virtualenv econ-ark +source econ-ark/bin/activate +``` - Once the virtualenv is activated, you should see `(venv)` in your command prompt. +3. Once the virtualenv is activated, you may see `(econ-ark)` in your command prompt (depending on how your machine is configured) -3. Finally, you can install HARK's requirements into the virtual environment with `pip install -r requirements.txt'. +3. Finally, you can install HARK's requirements into the virtual environment with `pip install -r requirements.txt`. -4. To check that everything has been set up correctly, run HARK's tests with `python -m unittest`. +4. To check that everything has been set up correctly, run HARK's tests with `python -m unittest`. ### Trouble with installation? @@ -104,8 +128,9 @@ We've done our best to give correct, thorough instructions on how to install HAR | Installation Type | Platform | Python Version | Date Tested | Tested By | | ------------- |:-------------:| -----:| -----:|-----:| -| basic pip install | Linux (16.04) | 3 | 04-24-2019 | @shaunagm | -| anaconda | Linux (16.04) | 3 | 04-24-2019 | @shaunagm | +| basic pip install | Linux (16.04) | 3 | 2019-04-24 | @shaunagm | +| anaconda | Linux (16.04) | 3 | 2019-04-24 | @shaunagm | +| basic pip install | MacOS 10.13.2 "High Sierra" | 2.7| 2019-04-26 | @llorracc | ### Next steps @@ -120,7 +145,7 @@ This section contains descriptions of the main files in the repo. Documentation files: * [README.md](https://github.com/econ-ark/HARK/blob/master/README.md): The file you are currently reading. -* [Documentation/HARKdoc.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/HARKdoc.pdf): A mini-user guide produced for a December 2015 workshop on HARK, unofficially representing the alpha version. Somewhat out of date. +* [Documentation/HARKdoc.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/HARKdoc.pdf): A mini-user guide produced for a December 2015 workshop on HARK, unofficially representing the alpha version. (Substantially out of date). * [Documentation/HARKmanual.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/HARKmanual.pdf): A user guide for HARK, written for the beta release at CEF 2016 in Bordeaux. Should contain 90% fewer lies relative to HARKdoc.pdf. * [Documentation/HARKmanual.tex](https://github.com/econ-ark/HARK/blob/master/Documentation/HARKmanual.tex): LaTeX source for the user guide. Open source code probably requires an open source manual as well. * [Documentation/ConsumptionSavingModels.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/ConsumptionSavingModels.pdf): Mathematical descriptions of the various consumption-saving models in HARK and how they map into the code. @@ -160,7 +185,7 @@ Tool modules: Model modules: * ConsumptionSavingModel/TractableBufferStockModel.py: - A "tractable" model of consumption and saving in which agents face one + * A "tractable" model of consumption and saving in which agents face one simple risk with constant probability: that they will become permanently unemployed and receive no further income. Unlike other models in HARK, this one is not solved by iterating on a sequence of one period problems. @@ -168,7 +193,7 @@ Model modules: the AgentType.solve framework. Solves an example of the model when run, then solves the same model again using MarkovConsumerType. * ConsumptionSavingModel/ConsIndShockModel.py: - Consumption-saving models with idiosyncratic shocks to income. Shocks + * Consumption-saving models with idiosyncratic shocks to income. Shocks are fully transitory or fully permanent. Solves perfect foresight model, a model with idiosyncratic income shocks, and a model with idiosyncratic income shocks and a different interest rate on borrowing vs saving. When @@ -176,14 +201,14 @@ Model modules: horizon problem, a ten period lifecycle model, a four period "cyclical" model, and versions with perfect foresight and "kinked R". * ConsumptionSavingModel/ConsPrefShockModel.py: - Consumption-saving models with idiosyncratic shocks to income and multi- + * Consumption-saving models with idiosyncratic shocks to income and multi- plicative shocks to utility. Currently has two models: one that extends the idiosyncratic shocks model, and another that extends the "kinked R" model. The second model has very little new code, and is created merely by merging the two "parent models" via multiple inheritance. When run, solves examples of the preference shock models. * ConsumptionSavingModel/ConsMarkovModel.py: - Consumption-saving models with a discrete state that evolves according to + * Consumption-saving models with a discrete state that evolves according to a Markov rule. Discrete states can vary by their income distribution, interest factor, and/or expected permanent income growth rate. When run, solves four example models: (1) A serially correlated unemployment model @@ -193,7 +218,7 @@ Model modules: income growth rate that is serially correlated. (4) A model with a time- varying interest factor that is serially correlated. * ConsumptionSavingModel/ConsAggShockModel.py: - Consumption-saving models with idiosyncratic and aggregate income shocks. + * Consumption-saving models with idiosyncratic and aggregate income shocks. Currently has a micro model with a basic solver (linear spline consumption function only, no value function), and a Cobb-Douglas economy for the agents to "live" in (as a "macroeconomy"). When run, solves an example of @@ -201,7 +226,7 @@ Model modules: problem to find an evolution rule for the capital-to-labor ratio that is justified by consumers' collective actions. * FashionVictim/FashionVictimModel.py: - A very serious model about choosing to dress as a jock or a punk. Used to + * A very serious model about choosing to dress as a jock or a punk. Used to demonstrate micro and macro framework concepts from HARKcore. It might be the simplest model possible for this purpose, or close to it. When run, the module solves the microeconomic problem of a "fashion victim" for an @@ -210,9 +235,10 @@ Model modules: for the evolution of the style distribution in the population that is justi- fied by fashion victims' collective actions. -Application modules: +Application modules: + * SolvingMicroDSOPs/StructEstimation.py: - Conducts a very simple structural estimation using the idiosyncratic shocks + * Conducts a very simple structural estimation using the idiosyncratic shocks model in ConsIndShocksModel. Estimates an adjustment factor to an age-varying sequence of discount factors (taken from Cagetti (2003)) and a coefficient of relative risk aversion that makes simulated agents' wealth profiles best @@ -221,50 +247,50 @@ Application modules: map of the objective function. Based on section 9 of Chris Carroll's lecture notes "Solving Microeconomic Dynamic Stochastic Optimization Problems". * cstwMPC/cstwMPC.py: - Conducts the estimations for the paper "The Distribution of Wealth and the + * Conducts the estimations for the paper "The Distribution of Wealth and the Marginal Propensity to Consume" by Carroll, Slacalek, Tokuoka, and White (2016). Runtime options are set in SetupParamsCSTW.py, specifying choices such as: perpetual youth vs lifecycle, beta-dist vs beta-point, liquid assets vs net worth, aggregate vs idiosyncratic shocks, etc. Uses ConsIndShockModel and ConsAggShockModel; can demonststrate HARK's "macro" framework on a real model. * cstwMPC/MakeCSTWfigs.py: - Makes various figures for the text of the cstwMPC paper. Requires many output + * Makes various figures for the text of the [cstwMPC](http://econ.jhu.edu/people/ccarroll/papers/cstwMPC) paper. Requires many output files produced by cstwMPC.py, from various specifications, which are not distributed with HARK. Has not been tested in quite some time. * cstwMPC/MakeCSTWfigsForSlides.py: - Makes various figures for the slides for the cstwMPC paper. Requires many + * Makes various figures for the slides for the cstwMPC paper. Requires many output files produced by cstwMPC.py, from various specifications, which are not distributed with HARK. Has not been tested in quite some time. Parameter and data modules: * ConsumptionSaving/ConsumerParameters.py: - Defines dictionaries with the minimal set of parameters needed to solve the + * Defines dictionaries with the minimal set of parameters needed to solve the models in ConsIndShockModel, ConsAggShockModel, ConsPrefShockModel, and ConsMarkovModel. These dictionaries are used to make examples when those modules are run. Does nothing when run itself. * SolvingMicroDSOPs/SetupSCFdata.py: - Imports 2004 SCF data for use by SolvingMicroDSOPs/StructEstimation.py. + * Imports 2004 SCF data for use by SolvingMicroDSOPs/StructEstimation.py. * cstwMPC/SetupParamsCSTW.py: - Loads calibrated model parameters for cstwMPC.py, chooses specification. + * Loads calibrated model parameters for cstwMPC.py, chooses specification. * FashionVictim/FashionVictimParams.py: - Example parameters for FashionVictimModel.py, loaded when that module is run. + * Example parameters for FashionVictimModel.py, loaded when that module is run. Test modules: * Testing/ComparisonTests.py: - Early version of unit testing for HARK, still in development. Compares + * Early version of unit testing for HARK, still in development. Compares the perfect foresight model solution to the idiosyncratic shocks model solution with shocks turned off; also compares the tractable buffer stock model solution to the same model solved using a "Markov" description. * Testing/ModelTesting.py: - Early version of unit testing for HARK, still in development. Defines a + * Early version of unit testing for HARK, still in development. Defines a few wrapper classes to run unit tests on subclasses of AgentType. * Testing/ModelTestingExample.py - An example of ModelTesting.py in action, using TractableBufferStockModel. + * An example of ModelTesting.py in action, using TractableBufferStockModel. * Testing/TBSunitTests.py: - Early version of unit testing for HARK, still in development. Runs a test + * Early version of unit testing for HARK, still in development. Runs a test on TractableBufferStockModel. * Testing/MultithreadDemo.py: - Demonstrates the multithreading functionality in HARKparallel.py. When + * Demonstrates the multithreading functionality in HARKparallel.py. When run, it solves oneexample consumption-saving model with idiosyncratic shocks to income, then solves *many* such models serially, varying the coefficient of relative risk aversion between rho=1 and rho=8, displaying @@ -274,75 +300,70 @@ Test modules: Data files: * SolvingMicroDSOPs/SCFdata.csv: - SCF 2004 data for use in SolvingMicroDSOPs/StructEstimation.py, loaded by + * SCF 2004 data for use in SolvingMicroDSOPs/StructEstimation.py, loaded by SolvingMicroDSOPs/EstimationParameters.py. * cstwMPC/SCFwealthDataReduced.txt: - SCF 2004 data with just net worth and data weights, for use by cstwMPC.py + * SCF 2004 data with just net worth and data weights, for use by cstwMPC.py * cstwMPC/USactuarial.txt: - U.S. mortality data from the Social Security Administration, for use by + * U.S. mortality data from the Social Security Administration, for use by cstwMPC.py when running a lifecycle specification. * cstwMPC/EducMortAdj.txt: - Mortality adjusters by education and age (columns by sex and race), for use + * Mortality adjusters by education and age (columns by sex and race), for use by cstwMPC.py when running a lifecycle specification. Taken from an appendix of PAPER. Other files that you don't need to worry about: -* */index.py: - A file used by Sphinx when generating html documentation for HARK. Users +* /index.py: + * A file used by Sphinx when generating html documentation for HARK. Users don't need to worry about it. Several copies are found throughout HARK. * .gitignore: - A file that tells git which files (or types of files) might be found in + * A file that tells git which files (or types of files) might be found in the repository directory tree, but should be ignored (not tracked) for the repo. Currently ignores compiled Python code, LaTex auxiliary files, etc. * LICENSE: - License text for HARK, Apache 2.0. Read it if you're a lawyer! + * License text for HARK, Apache 2.0. Read it if you're a lawyer! * SolvingMicroDSOPs/SMMcontour.png: - Contour plot of the objective function for SolvingMicroDSOPs/StructEstimation.py. + * Contour plot of the objective function for SolvingMicroDSOPs/StructEstimation.py. Generated when that module is run, along with a PDF version. * cstwMPC/Figures/placeholder.txt: - A placeholder file because git doesn't like empty folders, but cstwMPC.py + * A placeholder file because git doesn't like empty folders, but cstwMPC.py needs the /Figures directory to exist when it runs. * cstwMPC/Results/placeholder.txt: - A placeholder file because git doesn't like empty folders, but cstwMPC.py + * A placeholder file because git doesn't like empty folders, but cstwMPC.py needs the /Results directory to exist when it runs. * Documentation/conf.py: - A configuration file for producing html documentation with Sphinx, generated + * A configuration file for producing html documentation with Sphinx, generated by sphinx-quickstart. * Documentation/includeme.rst: - A very small file used by Sphinx to produce documentation. + * A very small file used by Sphinx to produce documentation. * Documentation/index.rst: - A list of modules to be included in HARK's Sphinx documentation. This should + * A list of modules to be included in HARK's Sphinx documentation. This should be edited if a new tool or model module is added to HARK. * Documentation/instructions.md: - A markdown file with instructions for how to set up and run Sphinx. You + * A markdown file with instructions for how to set up and run Sphinx. You don't need to read it. * Documentation/simple-steps-getting-sphinx-working.md: - Another markdown file with instructions for how to set up and run Sphinx. + * Another markdown file with instructions for how to set up and run Sphinx. * Documentation/make.bat: - A batch file for producing Sphinx documentation, generated by sphinx-quickstart. + * A batch file for producing Sphinx documentation, generated by sphinx-quickstart. * Documentation/Makefile: - Another Sphinx auxiliary file generated by sphinx-quickstart. + * Another Sphinx auxiliary file generated by sphinx-quickstart. * Documentation/econtex.sty: - LaTeX style file with notation definitions. + * LaTeX style file with notation definitions. * Documentation/econtex.cls: - LaTeX class file with document layout for the user manual. + * LaTeX class file with document layout for the user manual. * Documentation/econtexSetup.sty: - LaTeX style file with notation definitions. + * LaTeX style file with notation definitions. * Documentation/econtexShortcuts.sty: - LaTeX style file with notation definitions. + * LaTeX style file with notation definitions. * Documentation/UserGuidePic.pdf: - Image for the front cover of the user guide, showing the consumption + * Image for the front cover of the user guide, showing the consumption function for the KinkyPref model. ## IV. WARNINGS AND DISCLAIMERS -This is an early beta version of HARK. The code has not been -extensively tested as it should be. We hope it is useful, but -there are absolutely no guarantees (expressed or implied) that -it works or will do what you want. Use at your own risk. And -please, let us know if you find bugs by posting an issue to the -GitHub page! +This is a beta version of HARK. The code has not been extensively tested as it should be. We hope it is useful, but there are absolutely no guarantees (expressed or implied) that it works or will do what you want. Use at your own risk. And please, let us know if you find bugs by posting an issue to [the GitHub page](https://github.com/econ-ark/HARK)! ## V. License From 42c7880f022db29a8f1fe909d6e5047b16751cb6 Mon Sep 17 00:00:00 2001 From: Shauna Gordon-McKeon Date: Sat, 27 Apr 2019 13:18:48 -0400 Subject: [PATCH 41/77] Add Flake8 to requirements, setup, and travis Travis config commented out until code actually linted to a reasonable level. --- .travis.yml | 1 + requirements.txt | 1 + setup.cfg | 4 ++++ 3 files changed, 6 insertions(+) diff --git a/.travis.yml b/.travis.yml index 8355d4505..71237b781 100644 --- a/.travis.yml +++ b/.travis.yml @@ -7,3 +7,4 @@ install: - pip install -r requirements.txt script: - pytest HARK/tests + # - flake8 HARK diff --git a/requirements.txt b/requirements.txt index ea07a6bbb..f03da14e6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,3 +6,4 @@ future joblib dill scipy +flake8 diff --git a/setup.cfg b/setup.cfg index a1bb0d6cb..e20572e11 100644 --- a/setup.cfg +++ b/setup.cfg @@ -12,4 +12,8 @@ license_file = LICENSE # bdist_wheel from trying to make a universal wheel. For more see: # https://packaging.python.org/tutorials/distributing-packages/#wheels +[flake8] +exclude = .git +max-line-length = 119 + universal=1 From 90fc1b195f9e5cadfaf0d398cbe81734ea2ba65f Mon Sep 17 00:00:00 2001 From: Mridul Seth Date: Mon, 6 May 2019 10:27:46 -0400 Subject: [PATCH 42/77] add version dunder in init --- HARK/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/HARK/__init__.py b/HARK/__init__.py index c4d09cb22..34aeb9a30 100644 --- a/HARK/__init__.py +++ b/HARK/__init__.py @@ -1,2 +1,4 @@ from __future__ import absolute_import from .core import * + +__version__ = '0.10.0.dev2' From 00bbc3a969c7b4e1b383dfa50f8ebf56afdbb945 Mon Sep 17 00:00:00 2001 From: Alexandra Walker Date: Mon, 6 May 2019 09:39:04 -0500 Subject: [PATCH 43/77] updated readme with installation fixes --- README.md | 25 +++++++++++++++++-------- 1 file changed, 17 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 9d4bc20c4..46db63286 100644 --- a/README.md +++ b/README.md @@ -17,10 +17,10 @@ Table of Contents: ## I. INTRODUCTION Welcome to HARK! This document will tell you how to get HARK up and -running on your machine, and what you will find in HARK once you do. +running on your machine, and what you will find in HARK once you do. If you have any comments on the code or documentation, or (even better) if you want to -contribute new content to HARK, we'd love to hear from you! +contribute new content to HARK, we'd love to hear from you! Our email addresses are: * Chris Carroll: ccarroll@llorracc.org @@ -45,7 +45,7 @@ using python 3; eventually support for python 2 will end. ### Installing HARK with pip -The simplest way to install HARK is to use [pip](https://pip.pypa.io/en/stable/installing/). +The simplest way to install HARK is to use [pip](https://pip.pypa.io/en/stable/installing/). To install HARK with pip, at a command line type `pip install econ-ark`. @@ -76,9 +76,9 @@ source activate econ-ark 1. To verify that spyder has access to HARK try typing `pip install econ-ark` into the iPython shell within Spyder. If you have successfully installed HARK as above, you should see a lot of messages saying 'Requirement satisfied'. - * If that doesn't work, you will need to manually add HARK to your Spyder environment. To do this, you'll need to get the code from Github and import it into Spyder. To get the code from Github, you can either clone it or download a zipped file. + * If that doesn't work, you will need to manually add HARK to your Spyder environment. To do this, you'll need to get the code from Github and import it into Spyder. To get the code from Github, you can either clone it or download a zipped file. - * If you have `git` installed on the command line, type `git clone git@github.com:econ-ark/HARK.git` in your chosen directory ([more details here](https://git-scm.com/documentation)). + * If you have `git` installed on the command line, type `git clone git@github.com:econ-ark/HARK.git` in your chosen directory ([more details here](https://git-scm.com/documentation)). * If you do not have `git` available on your computer, you can download the [GitHub Desktop app](https://desktop.github.com/) and use it to make a local clone @@ -107,18 +107,27 @@ and from there you can open the notebooks and execute them. If you want to make changes or contributions (yay!) to HARK, you'll need to have access to the source files. Installing HARK via pip (either at the command line, or inside Spyder) makes it hard to access those files (and it's a bad idea to mess with the original code anyway because you'll likely forget what changes you made). If you are adept at GitHub, you can [fork](https://help.github.com/en/articles/fork-a-repo) the repo. If you are less experienced, you should download a personal copy of HARK again using `git clone` (see above) or the GitHub Desktop app. -1. Navigate to wherever you want to put the repository and type `git clone git@github.com:econ-ark/HARK.git` ([more details here](https://git-scm.com/documentation)). +1. Navigate to wherever you want to put the repository and type `git clone git@github.com:econ-ark/HARK.git` ([more details here](https://git-scm.com/documentation)). If you get a permission denied error, you may need to setup SSH for GitHub, or you can clone using HTTPS: 'git clone https://github.com/econ-ark/HARK.git'. -2. Then, create and activate a [virtual environment]([virtualenv]((https://virtualenv.pypa.io/en/latest/))). Install virtualenv if you need to and then type: +2. Then, create and activate a [virtual environment]([virtualenv]((https://virtualenv.pypa.io/en/latest/))). + +For Mac or Linux: + +Install virtualenv if you need to and then type: ``` virtualenv econ-ark source econ-ark/bin/activate ``` +For Windows: +``` +virtualenv econ-ark +econ-ark\\Scripts\\activate.bat +``` 3. Once the virtualenv is activated, you may see `(econ-ark)` in your command prompt (depending on how your machine is configured) -3. Finally, you can install HARK's requirements into the virtual environment with `pip install -r requirements.txt`. +3. Make sure to change to HARK directory, and install HARK's requirements into the virtual environment with `pip install -r requirements.txt`. 4. To check that everything has been set up correctly, run HARK's tests with `python -m unittest`. From e04770efc5051ce1148029f16d2e80f8672c4d39 Mon Sep 17 00:00:00 2001 From: Keith Blaha Date: Mon, 6 May 2019 08:44:48 -0700 Subject: [PATCH 44/77] Add vim swap files to .gitignore --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 50d4f7c4e..c9b038426 100644 --- a/.gitignore +++ b/.gitignore @@ -262,6 +262,9 @@ Web # Emacs automatic backup files *~ +# Vim swap files +*.swp + # Autogenerated equations eq From 6a4006d03978b368b880912e45d45e37c526f1d2 Mon Sep 17 00:00:00 2001 From: Tanya Schlusser Date: Mon, 6 May 2019 12:05:38 -0400 Subject: [PATCH 45/77] Modify hyperlinks to use relative links feature from GitHub --- README.md | 113 ++++++++++++++++++++++++++---------------------------- 1 file changed, 54 insertions(+), 59 deletions(-) diff --git a/README.md b/README.md index 9d4bc20c4..5646c5918 100644 --- a/README.md +++ b/README.md @@ -30,7 +30,7 @@ GitHub repository: https://github.com/econ-ark/HARK Online documentation: https://econ-ark.github.io/HARK -User guide: [Documentation/HARKmanual.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/HARKmanual.pdf) (in the [HARK repository](https://github.com/econ-ark/HARK)) +User guide: [Documentation/HARKmanual.pdf](Documentation/HARKmanual.pdf) (in the [HARK repository](https://github.com/econ-ark/HARK)) Demonstrations of HARK functionality: [DemARK](https://github.com/econ-ark/DemARK/) @@ -134,9 +134,9 @@ We've done our best to give correct, thorough instructions on how to install HAR ### Next steps -To learn more about how to use HARK, check out our [user manual](https://github.com/econ-ark/HARK/blob/master/Documentation/HARKmanual.pdf). +To learn more about how to use HARK, check out our [user manual](Documentation/HARKmanual.pdf). -For help making changes to HARK, check out our [contributing guide](https://github.com/econ-ark/HARK/blob/Partial-Fix-to-Installation-README/CONTRIBUTING.md). +For help making changes to HARK, check out our [contributing guide](CONTRIBUTING.md). ## III. LIST OF FILES IN REPOSITORY @@ -144,38 +144,38 @@ For help making changes to HARK, check out our [contributing guide](https://gith This section contains descriptions of the main files in the repo. Documentation files: -* [README.md](https://github.com/econ-ark/HARK/blob/master/README.md): The file you are currently reading. -* [Documentation/HARKdoc.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/HARKdoc.pdf): A mini-user guide produced for a December 2015 workshop on HARK, unofficially representing the alpha version. (Substantially out of date). -* [Documentation/HARKmanual.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/HARKmanual.pdf): A user guide for HARK, written for the beta release at CEF 2016 in Bordeaux. Should contain 90% fewer lies relative to HARKdoc.pdf. - * [Documentation/HARKmanual.tex](https://github.com/econ-ark/HARK/blob/master/Documentation/HARKmanual.tex): LaTeX source for the user guide. Open source code probably requires an open source manual as well. -* [Documentation/ConsumptionSavingModels.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/ConsumptionSavingModels.pdf): Mathematical descriptions of the various consumption-saving models in HARK and how they map into the code. - * [Documentation/ConsumptionSavingModels.tex](https://github.com/econ-ark/HARK/blob/master/Documentation/ConsumptionSavingModels.tex): LaTeX source for the "models" writeup. -* [Documentation/NARK.pdf](https://github.com/econ-ark/HARK/blob/master/Documentation/NARK.pdf): Variable naming conventions for HARK, plus concordance with LaTeX variable definitions. Still in development. +* [README.md](README.md): The file you are currently reading. +* [Documentation/HARKdoc.pdf](Documentation/HARKdoc.pdf): A mini-user guide produced for a December 2015 workshop on HARK, unofficially representing the alpha version. (Substantially out of date). +* [Documentation/HARKmanual.pdf](Documentation/HARKmanual.pdf): A user guide for HARK, written for the beta release at CEF 2016 in Bordeaux. Should contain 90% fewer lies relative to HARKdoc.pdf. + * [Documentation/HARKmanual.tex](Documentation/HARKmanual.tex): LaTeX source for the user guide. Open source code probably requires an open source manual as well. +* [Documentation/ConsumptionSavingModels.pdf](Documentation/ConsumptionSavingModels.pdf): Mathematical descriptions of the various consumption-saving models in HARK and how they map into the code. + * [Documentation/ConsumptionSavingModels.tex](Documentation/ConsumptionSavingModels.tex): LaTeX source for the "models" writeup. +* [Documentation/NARK.pdf](Documentation/NARK.pdf): Variable naming conventions for HARK, plus concordance with LaTeX variable definitions. Still in development. Tool modules: -* HARK/core.py: +* [HARK/core.py](HARK/core.py): Frameworks for "microeconomic" and "macroeconomic" models in HARK. We somewhat abuse those terms as shorthand; see the user guide for a description of what we mean. Every model in HARK extends the classes AgentType and Market in this module. Does nothing when run. -* HARK/utilities.py: +* [HARK/utilities.py](HARK/utilities.py): General purpose tools and utilities. Contains literal utility functions (in the economic sense), functions for making discrete approximations to continuous distributions, basic plotting functions for convenience, and a few unclassifiable things. Does nothing when run. -* HARK/estimation.py: +* [HARK/estimation.py](HARK/estimation.py): Functions for estimating models. As is, it only has a few wrapper functions for scipy.optimize optimization routines. Will be expanded in the future with more interesting things. Does nothing when run. -* HARK/simulation.py: +* [HARK/simulation.py](HARK/simulation.py): Functions for generating simulated data. Functions in this module have names like drawUniform, generating (lists of) arrays of draws from various distributions. Does nothing when run. -* HARK/interpolation.py: +* [HARK/interpolation.py](HARK/interpolation.py): Classes for representing interpolated function approximations. Has 1D-4D interpolation methods, mostly based on linear or cubic spline interpolation. Will have ND methods in the future. Does nothing when run. -* HARK/parallel.py: +* [HARK/parallel.py](HARK/parallel.py): Early version of parallel processing in HARK. Works with instances of the AgentType class (or subclasses of it), distributing commands (as methods) to be run on a list of AgentTypes. Only works with local CPU. @@ -184,7 +184,7 @@ Tool modules: when run. Model modules: -* ConsumptionSavingModel/TractableBufferStockModel.py: +* [ConsumptionSaving/TractableBufferStockModel.py](HARK/ConsumptionSaving/TractableBufferStockModel.py): * A "tractable" model of consumption and saving in which agents face one simple risk with constant probability: that they will become permanently unemployed and receive no further income. Unlike other models in HARK, @@ -192,7 +192,7 @@ Model modules: Instead, it uses a "backshooting" routine that has been shoehorned into the AgentType.solve framework. Solves an example of the model when run, then solves the same model again using MarkovConsumerType. -* ConsumptionSavingModel/ConsIndShockModel.py: +* [ConsumptionSaving/ConsIndShockModel.py](HARK/ConsumptionSaving/ConsIndShockModel.py): * Consumption-saving models with idiosyncratic shocks to income. Shocks are fully transitory or fully permanent. Solves perfect foresight model, a model with idiosyncratic income shocks, and a model with idiosyncratic @@ -200,14 +200,14 @@ Model modules: run, solves several examples of these models, including a standard infinite horizon problem, a ten period lifecycle model, a four period "cyclical" model, and versions with perfect foresight and "kinked R". -* ConsumptionSavingModel/ConsPrefShockModel.py: +* [ConsumptionSaving/ConsPrefShockModel.py](HARK/ConsumptionSaving/ConsPrefShockModel.py): * Consumption-saving models with idiosyncratic shocks to income and multi- plicative shocks to utility. Currently has two models: one that extends the idiosyncratic shocks model, and another that extends the "kinked R" model. The second model has very little new code, and is created merely by merging the two "parent models" via multiple inheritance. When run, solves examples of the preference shock models. -* ConsumptionSavingModel/ConsMarkovModel.py: +* [ConsumptionSaving/ConsMarkovModel.py](HARK/ConsumptionSaving/ConsMarkovModel.py): * Consumption-saving models with a discrete state that evolves according to a Markov rule. Discrete states can vary by their income distribution, interest factor, and/or expected permanent income growth rate. When run, @@ -217,7 +217,7 @@ Model modules: shocks for the next N periods. (3) A model with a time-varying permanent income growth rate that is serially correlated. (4) A model with a time- varying interest factor that is serially correlated. -* ConsumptionSavingModel/ConsAggShockModel.py: +* [ConsumptionSaving/ConsAggShockModel.py](HARK/ConsumptionSaving/ConsAggShockModel.py): * Consumption-saving models with idiosyncratic and aggregate income shocks. Currently has a micro model with a basic solver (linear spline consumption function only, no value function), and a Cobb-Douglas economy for the @@ -225,7 +225,7 @@ Model modules: the micro model in partial equilibrium, then solves the general equilibrium problem to find an evolution rule for the capital-to-labor ratio that is justified by consumers' collective actions. -* FashionVictim/FashionVictimModel.py: +* [FashionVictim/FashionVictimModel.py](HARK/FashionVictim/FashionVictimModel.py): * A very serious model about choosing to dress as a jock or a punk. Used to demonstrate micro and macro framework concepts from HARKcore. It might be the simplest model possible for this purpose, or close to it. When run, @@ -237,7 +237,7 @@ Model modules: Application modules: -* SolvingMicroDSOPs/StructEstimation.py: +* [SolvingMicroDSOPs/Code/StructEstimation.py](HARK/SolvingMicroDSOPs/Code/StructEstimation.py): * Conducts a very simple structural estimation using the idiosyncratic shocks model in ConsIndShocksModel. Estimates an adjustment factor to an age-varying sequence of discount factors (taken from Cagetti (2003)) and a coefficient @@ -246,50 +246,48 @@ Application modules: the calculation of standard errors by bootstrap and can construct a contour map of the objective function. Based on section 9 of Chris Carroll's lecture notes "Solving Microeconomic Dynamic Stochastic Optimization Problems". -* cstwMPC/cstwMPC.py: +* [cstwMPC/cstwMPC.py](HARK/cstwMPC/cstwMPC.py): * Conducts the estimations for the paper "The Distribution of Wealth and the Marginal Propensity to Consume" by Carroll, Slacalek, Tokuoka, and White (2016). Runtime options are set in SetupParamsCSTW.py, specifying choices such as: perpetual youth vs lifecycle, beta-dist vs beta-point, liquid assets vs net worth, aggregate vs idiosyncratic shocks, etc. Uses ConsIndShockModel and ConsAggShockModel; can demonststrate HARK's "macro" framework on a real model. -* cstwMPC/MakeCSTWfigs.py: +* [cstwMPC/MakeCSTWfigs.py](HARK/cstwMPC/MakeCSTWfigs.py): * Makes various figures for the text of the [cstwMPC](http://econ.jhu.edu/people/ccarroll/papers/cstwMPC) paper. Requires many output files produced by cstwMPC.py, from various specifications, which are not distributed with HARK. Has not been tested in quite some time. -* cstwMPC/MakeCSTWfigsForSlides.py: +* [cstwMPC/MakeCSTWfigsForSlides.py](HARK/cstwMPC/MakeCSTWfigsForSlides.py): * Makes various figures for the slides for the cstwMPC paper. Requires many output files produced by cstwMPC.py, from various specifications, which are not distributed with HARK. Has not been tested in quite some time. Parameter and data modules: -* ConsumptionSaving/ConsumerParameters.py: +* [ConsumptionSaving/ConsumerParameters.py](HARK/ConsumptionSaving/ConsumerParameters.py): * Defines dictionaries with the minimal set of parameters needed to solve the models in ConsIndShockModel, ConsAggShockModel, ConsPrefShockModel, and ConsMarkovModel. These dictionaries are used to make examples when those modules are run. Does nothing when run itself. -* SolvingMicroDSOPs/SetupSCFdata.py: +* [SolvingMicroDSOPs/SetupSCFdata.py](HARK/SolvingMicroDSOPs/Calibration/SetupSCFdata.py): * Imports 2004 SCF data for use by SolvingMicroDSOPs/StructEstimation.py. -* cstwMPC/SetupParamsCSTW.py: +* [cstwMPC/SetupParamsCSTW.py](HARK/cstwMPC/SetupParamsCSTW.py): * Loads calibrated model parameters for cstwMPC.py, chooses specification. -* FashionVictim/FashionVictimParams.py: +* [FashionVictim/FashionVictimParams.py](HARK/FashionVictim/FashionVictimParams.py): * Example parameters for FashionVictimModel.py, loaded when that module is run. Test modules: -* Testing/ComparisonTests.py: +* [Testing/Comparison_UnitTests.py](Testing/Comparison_UnitTests.py): * Early version of unit testing for HARK, still in development. Compares the perfect foresight model solution to the idiosyncratic shocks model solution with shocks turned off; also compares the tractable buffer stock model solution to the same model solved using a "Markov" description. -* Testing/ModelTesting.py: +* [Testing/ModelTesting.py](Testing/ModelTesting.py): * Early version of unit testing for HARK, still in development. Defines a few wrapper classes to run unit tests on subclasses of AgentType. -* Testing/ModelTestingExample.py - * An example of ModelTesting.py in action, using TractableBufferStockModel. -* Testing/TBSunitTests.py: +* [Testing/TractableBufferStockModel_UnitTests.py](Testing/TractableBufferStockModel_UnitTests.py) * Early version of unit testing for HARK, still in development. Runs a test on TractableBufferStockModel. -* Testing/MultithreadDemo.py: +* [Testing/MultithreadDemo.py](Testing/MultithreadDemo.py): * Demonstrates the multithreading functionality in HARKparallel.py. When run, it solves oneexample consumption-saving model with idiosyncratic shocks to income, then solves *many* such models serially, varying the @@ -299,15 +297,15 @@ Test modules: the results graphically along with the timing. Data files: -* SolvingMicroDSOPs/SCFdata.csv: +* [SolvingMicroDSOPs/Calibration/SCFdata.csv](HARK/SolvingMicroDSOPs/Calibration/SCFdata.csv): * SCF 2004 data for use in SolvingMicroDSOPs/StructEstimation.py, loaded by SolvingMicroDSOPs/EstimationParameters.py. -* cstwMPC/SCFwealthDataReduced.txt: +* [cstwMPC/SCFwealthDataReduced.txt](HARK/cstwMPC/SCFwealthDataReduced.txt): * SCF 2004 data with just net worth and data weights, for use by cstwMPC.py -* cstwMPC/USactuarial.txt: +* [cstwMPC/USactuarial.txt](HARK/cstwMPC/USactuarial.txt): * U.S. mortality data from the Social Security Administration, for use by cstwMPC.py when running a lifecycle specification. -* cstwMPC/EducMortAdj.txt: +* [cstwMPC/EducMortAdj.txt](HARK/cstwMPC/EducMortAdj.txt): * Mortality adjusters by education and age (columns by sex and race), for use by cstwMPC.py when running a lifecycle specification. Taken from an appendix of PAPER. @@ -316,47 +314,44 @@ Other files that you don't need to worry about: * /index.py: * A file used by Sphinx when generating html documentation for HARK. Users don't need to worry about it. Several copies are found throughout HARK. -* .gitignore: +* [.gitignore](.gitignore): * A file that tells git which files (or types of files) might be found in the repository directory tree, but should be ignored (not tracked) for the repo. Currently ignores compiled Python code, LaTex auxiliary files, etc. -* LICENSE: +* [LICENSE](LICENSE): * License text for HARK, Apache 2.0. Read it if you're a lawyer! -* SolvingMicroDSOPs/SMMcontour.png: +* [SolvingMicroDSOPs/Figures/SMMcontour.png](HARK/SolvingMicroDSOPs/Figures/SMMcontour.png): * Contour plot of the objective function for SolvingMicroDSOPs/StructEstimation.py. Generated when that module is run, along with a PDF version. -* cstwMPC/Figures/placeholder.txt: +* [cstwMPC/Figures/placeholder.txt](HARK/cstwMPC/Figures/placeholder.txt): * A placeholder file because git doesn't like empty folders, but cstwMPC.py needs the /Figures directory to exist when it runs. -* cstwMPC/Results/placeholder.txt: - * A placeholder file because git doesn't like empty folders, but cstwMPC.py - needs the /Results directory to exist when it runs. -* Documentation/conf.py: +* [Documentation/conf.py](Documentation/conf.py): * A configuration file for producing html documentation with Sphinx, generated by sphinx-quickstart. -* Documentation/includeme.rst: +* [Documentation/includeme.rst](Documentation/includeme.rst): * A very small file used by Sphinx to produce documentation. -* Documentation/index.rst: +* [Documentation/index.rst](Documentation/index.rst): * A list of modules to be included in HARK's Sphinx documentation. This should be edited if a new tool or model module is added to HARK. -* Documentation/instructions.md: +* [Documentation/instructions.md](Documentation/instructions.md): * A markdown file with instructions for how to set up and run Sphinx. You don't need to read it. -* Documentation/simple-steps-getting-sphinx-working.md: +* [Documentation/simple-steps-getting-sphinx-working.md](Documentation/simple-steps-getting-sphinx-working.md): * Another markdown file with instructions for how to set up and run Sphinx. -* Documentation/make.bat: +* [Documentation/make.bat](Documentation/make.bat): * A batch file for producing Sphinx documentation, generated by sphinx-quickstart. -* Documentation/Makefile: +* [Documentation/Makefile](Documentation/Makefile): * Another Sphinx auxiliary file generated by sphinx-quickstart. -* Documentation/econtex.sty: +* [Documentation/econtex.sty](Documentation/econtex.sty): * LaTeX style file with notation definitions. -* Documentation/econtex.cls: +* [Documentation/econtex.cls](Documentation/econtex.cls): * LaTeX class file with document layout for the user manual. -* Documentation/econtexSetup.sty: +* [Documentation/econtexSetup.sty](Documentation/econtexSetup.sty): * LaTeX style file with notation definitions. -* Documentation/econtexShortcuts.sty: +* [Documentation/econtexShortcuts.sty](Documentation/econtexShortcuts.sty): * LaTeX style file with notation definitions. -* Documentation/UserGuidePic.pdf: +* [Documentation/UserGuidePic.pdf](Documentation/UserGuidePic.pdf): * Image for the front cover of the user guide, showing the consumption function for the KinkyPref model. From d096d00c6bd6990ffe32f3ad34f3e7acbe4263a4 Mon Sep 17 00:00:00 2001 From: llorracc Date: Mon, 6 May 2019 13:37:58 -0400 Subject: [PATCH 46/77] Add "Learning HARK" section to README --- README.md | 41 +++++++++++++++++++++++++++++++++++++---- 1 file changed, 37 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index cd2423b6a..e6c515ffc 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,8 @@ Table of Contents: * [1. Introduction](#i-introduction) * [2. Quick start guide](#ii-quick-start-guide) + * [Installing](#Installing-HARK) + * [Learning HARK](#Learning-HARK) * [3. List of files in repository](#iii-list-of-files-in-repository) * [4. Warnings and disclaimers](#iv-warnings-and-disclaimers) * [5. License Information](#v-license) @@ -17,7 +19,8 @@ Table of Contents: ## I. INTRODUCTION Welcome to HARK! This document will tell you how to get HARK up and -running on your machine, and what you will find in HARK once you do. +running on your machine, how to get started using it, and give you an +overview of the main elements of the toolkit. If you have any comments on the code or documentation, or (even better) if you want to contribute new content to HARK, we'd love to hear from you! @@ -39,11 +42,12 @@ Replications and Explorations Made using the ARK : [REMARK](https://github.com/e ## II. QUICK START GUIDE +### Installing HARK HARK is an open source project written in Python. It's compatible with both Python 2 and 3, and with the Anaconda distributions of python 2 and 3. But we recommend using python 3; eventually support for python 2 will end. -### Installing HARK with pip +#### Installing HARK with pip The simplest way to install HARK is to use [pip](https://pip.pypa.io/en/stable/installing/). @@ -61,7 +65,7 @@ source activate econ-ark ``` ---- -### Using HARK with Anaconda +#### Using HARK with Anaconda Installing HARK with pip does not give you full access to HARK's many graphical capabilities. One way to access these capabilities is by using [Anaconda](https://anaconda.com/why-anaconda), which is a distribution of python along with many packages that are frequently used in scientific computing.. @@ -103,6 +107,35 @@ jupyter notebook ``` and from there you can open the notebooks and execute them. +#### Learning HARK + +We have a set of 30-second [Elevator Spiels](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#capsule-summaries-of-what-the-econ-ark-project-is) describing the project, tailored to people with several different kinds of background. + +The most broadly applicable advice is to go to [Econ-ARK](https://econ-ark.org) and click on "Notebooks", and choose [A Gentle Introduction to HARK](https://github.com/econ-ark/DemARK/blob/master/notebooks/Gentle-Intro-To-HARK.ipynb) which will launch as a [jupyter notebook](https://jupyter.org/). + +##### [For people with a technical/scientific/computing background but little economics background](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-people-with-a-technicalscientificcomputing-background-but-no-economics-background) + +* [A Gentle Introduction to HARK](https://github.com/econ-ark/DemARK/blob/master/notebooks/Gentle-Intro-To-HARK.ipynb) + +##### [For economists who have done some structural modeling](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-economists-who-have-done-some-structural-modeling) + +* A full replication of the [Iskhakov, Jørgensen, Rust, and Schjerning](https://onlinelibrary.wiley.com/doi/abs/10.3982/QE643) paper for solving the discrete-continuous retirement saving problem + * An informal discussion of the issues involved is [here](https://github.com/econ-ark/DemARK/blob/master/notebooks/DCEGM-Upper-Envelope.ipynb) (part of the [DemARK](https://github.com/econ-ark/DemARK) repo) + +* [Structural-Estimates-From-Empirical-MPCs](https://github.com/econ-ark/DemARK/blob/master/notebooks/Structural-Estimates-From-Empirical-MPCs-Fagereng-et-al.ipynb) is an example of the use of the toolkit in a discussion of a well known paper. (Yes, it is easy enough to use that you can estimate a structural model on somebody else's data in the limited time available for writing a discussion) + +##### [For economists who have not yet done any structural modeling but might be persuadable to start](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-economists-who-have-not-yet-done-any-structural-modeling-but-might-be-persuadable-to-start) + +* Start with [A Gentle Introduction to HARK](https://github.com/econ-ark/DemARK/blob/master/notebooks/Gentle-Intro-To-HARK.ipynb) to get your feet wet + +* A simple indirect inference/simulated method of moments structural estimation along the lines of Gourinchas and Parker's 2002 Econometrica paper or Cagetti's 2003 paper is performed by the [SolvingMicroDSOPs](https://github.com/econ-ark/REMARK/tree/master/REMARKs/SolvingMicroDSOPs) [REMARK](https://github.com/econ-ark/REMARK); this code implements the solution methods described in the corresponding section of [these lecture notes](http://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs/) + +##### [For Other Developers of Software for Computational Economics](https://github.com/econ-ark/PARK/blob/master/Elevator-Spiels.md#for-other-developers-of-software-for-computational-economics) + + +* Our workhorse module is [ConsIndShockModel.py](https://github.com/econ-ark/HARK/blob/master/HARK/ConsumptionSaving/ConsIndShockModel.py) + * which is explored and explained (a bit) in [this jupyter notebook](https://github.com/econ-ark/DemARK/blob/master/notebooks/ConsIndShockModel.ipynb) + ### Making changes to HARK If you want to make changes or contributions (yay!) to HARK, you'll need to have access to the source files. Installing HARK via pip (either at the command line, or inside Spyder) makes it hard to access those files (and it's a bad idea to mess with the original code anyway because you'll likely forget what changes you made). If you are adept at GitHub, you can [fork](https://help.github.com/en/articles/fork-a-repo) the repo. If you are less experienced, you should download a personal copy of HARK again using `git clone` (see above) or the GitHub Desktop app. @@ -201,7 +234,7 @@ Model modules: Instead, it uses a "backshooting" routine that has been shoehorned into the AgentType.solve framework. Solves an example of the model when run, then solves the same model again using MarkovConsumerType. -* [ConsumptionSaving/ConsIndShockModel.py](HARK/ConsumptionSaving/ConsIndShockModel.py): +* [ConsumptionSaving/ConsIndShockModel.py](HARK/ConsumptionSaving/ConsIndShockModel.py): * Consumption-saving models with idiosyncratic shocks to income. Shocks are fully transitory or fully permanent. Solves perfect foresight model, a model with idiosyncratic income shocks, and a model with idiosyncratic From 2a7ee8f54d99ba642a8c101996c52c0920e69558 Mon Sep 17 00:00:00 2001 From: Stephen Schroeder Date: Mon, 6 May 2019 14:43:47 -0400 Subject: [PATCH 47/77] linted Comparison and Model Tests --- .gitignore | 7 ++ Testing/Comparison_UnitTests.py | 142 +++++++++++++++----------------- Testing/ModelTesting.py | 122 ++++++++++----------------- 3 files changed, 115 insertions(+), 156 deletions(-) diff --git a/.gitignore b/.gitignore index c9b038426..aff6ecab7 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,10 @@ __pycache__/ *.py[cod] *$py.class +# Virtual Environments +venv/ +econ-arc/ + # C extensions *.so @@ -275,3 +279,6 @@ eq nate-notes/ *_region_*.* + +# VSCode +settings.json \ No newline at end of file diff --git a/Testing/Comparison_UnitTests.py b/Testing/Comparison_UnitTests.py index 9aa65d5f7..26f3940d8 100644 --- a/Testing/Comparison_UnitTests.py +++ b/Testing/Comparison_UnitTests.py @@ -1,6 +1,5 @@ """ This file implements unit tests for several of the ConsumptionSaving models in HARK. - These tests compare the output of different models in specific cases in which those models should yield the same output. The code will pass these tests if and only if the output is close "enough". @@ -8,17 +7,11 @@ from __future__ import print_function, division from __future__ import absolute_import -from builtins import str -from builtins import zip -from builtins import range -from builtins import object - # Bring in modules we need import unittest from copy import deepcopy import numpy as np - # Bring in the HARK models we want to test from HARK.ConsumptionSaving.ConsIndShockModel import solvePerfForesight, IndShockConsumerType from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType @@ -28,7 +21,6 @@ class Compare_PerfectForesight_and_Infinite(unittest.TestCase): """ Class to compare output of the perfect foresight and infinite horizon models. - When income uncertainty is removed from the infinite horizon model, it reduces in theory to the perfect foresight model. This class implements tests to make sure it reduces in practice to the perfect foresight model as well. @@ -42,20 +34,20 @@ def setUp(self): import HARK.ConsumptionSaving.ConsumerParameters as Params InfiniteType = IndShockConsumerType(**Params.init_idiosyncratic_shocks) - InfiniteType.assignParameters(LivPrb = [1.], - DiscFac = 0.955, - PermGroFac = [1.], - PermShkStd = [0.], - TempShkStd = [0.], - T_total = 1, T_retire = 0, BoroCnstArt = None, UnempPrb = 0., - cycles = 0) # This is what makes the type infinite horizon + InfiniteType.assignParameters(LivPrb=[1.], + DiscFac=0.955, + PermGroFac=[1.], + PermShkStd=[0.], + TempShkStd=[0.], + T_total=1, T_retire=0, BoroCnstArt=None, UnempPrb=0., + cycles=0 + ) # This is what makes the type infinite horizon InfiniteType.updateIncomeProcess() InfiniteType.solve() InfiniteType.timeFwd() InfiniteType.unpackcFunc() - # Make and solve a perfect foresight consumer type with the same parameters PerfectForesightType = deepcopy(InfiniteType) PerfectForesightType.solveOnePeriod = solvePerfForesight @@ -64,28 +56,25 @@ def setUp(self): PerfectForesightType.unpackcFunc() PerfectForesightType.timeFwd() - self.InfiniteType = InfiniteType + self.InfiniteType = InfiniteType self.PerfectForesightType = PerfectForesightType - def test_consumption(self): """" Now compare the consumption functions and make sure they are "close" """ - diffFunc = lambda m : self.PerfectForesightType.solution[0].cFunc(m) - \ - self.InfiniteType.cFunc[0](m) - points = np.arange(0.5,10.,.01) - difference = diffFunc(points) + def diffFunc(m): return self.PerfectForesightType.solution[0].cFunc(m) - \ + self.InfiniteType.cFunc[0](m) + points = np.arange(0.5, 10., .01) + difference = diffFunc(points) max_difference = np.max(np.abs(difference)) - self.assertLess(max_difference,0.01) - + self.assertLess(max_difference, 0.01) class Compare_TBS_and_Markov(unittest.TestCase): """ Class to compare output of the Tractable Buffer Stock and Markov models. - The only uncertainty in the TBS model is over when the agent will enter an absorbing state with 0 income. With the right transition arrays and income processes, this is just a special case of the Markov model. So with the right inputs, we should be able to solve the two @@ -93,73 +82,72 @@ class Compare_TBS_and_Markov(unittest.TestCase): """ def setUp(self): # Set up and solve TBS - base_primitives = {'UnempPrb' : .015, - 'DiscFac' : 0.9, - 'Rfree' : 1.1, - 'PermGroFac' : 1.05, - 'CRRA' : .95} - - + base_primitives = {'UnempPrb': .015, + 'DiscFac': 0.9, + 'Rfree': 1.1, + 'PermGroFac': 1.05, + 'CRRA': .95} TBSType = TractableConsumerType(**base_primitives) TBSType.solve() # Set up and solve Markov - MrkvArray = np.array([[1.0-base_primitives['UnempPrb'],base_primitives['UnempPrb']], - [0.0,1.0]]) - Markov_primitives = {"CRRA":base_primitives['CRRA'], - "Rfree":np.array(2*[base_primitives['Rfree']]), - "PermGroFac":[np.array(2*[base_primitives['PermGroFac']/ - (1.0-base_primitives['UnempPrb'])])], - "BoroCnstArt":None, - "PermShkStd":[0.0], - "PermShkCount":1, - "TranShkStd":[0.0], - "TranShkCount":1, - "T_total":1, - "UnempPrb":0.0, - "UnempPrbRet":0.0, - "T_retire":0, - "IncUnemp":0.0, - "IncUnempRet":0.0, - "aXtraMin":0.001, - "aXtraMax":TBSType.mUpperBnd, - "aXtraCount":48, - "aXtraExtra":[None], - "aXtraNestFac":3, - "LivPrb":[1.0], - "DiscFac":base_primitives['DiscFac'], - 'Nagents':1, - 'psi_seed':0, - 'xi_seed':0, - 'unemp_seed':0, - 'tax_rate':0.0, - 'vFuncBool':False, - 'CubicBool':True, - 'MrkvArray':MrkvArray - } - - MarkovType = MarkovConsumerType(**Markov_primitives) - MarkovType.cycles = 0 - employed_income_dist = [np.ones(1),np.ones(1),np.ones(1)] - unemployed_income_dist = [np.ones(1),np.ones(1),np.zeros(1)] - MarkovType.IncomeDstn = [[employed_income_dist,unemployed_income_dist]] + MrkvArray = np.array([[1.0-base_primitives['UnempPrb'], base_primitives['UnempPrb']], + [0.0, 1.0]]) + Markov_primitives = {"CRRA": base_primitives['CRRA'], + "Rfree": np.array(2*[base_primitives['Rfree']]), + "PermGroFac": [np.array(2*[base_primitives['PermGroFac'] / + (1.0-base_primitives['UnempPrb'])])], + "BoroCnstArt": None, + "PermShkStd": [0.0], + "PermShkCount": 1, + "TranShkStd": [0.0], + "TranShkCount": 1, + "T_total": 1, + "UnempPrb": 0.0, + "UnempPrbRet": 0.0, + "T_retire": 0, + "IncUnemp": 0.0, + "IncUnempRet": 0.0, + "aXtraMin": 0.001, + "aXtraMax": TBSType.mUpperBnd, + "aXtraCount": 48, + "aXtraExtra": [None], + "aXtraNestFac": 3, + "LivPrb": [1.0], + "DiscFac": base_primitives['DiscFac'], + 'Nagents': 1, + 'psi_seed': 0, + 'xi_seed': 0, + 'unemp_seed': 0, + 'tax_rate': 0.0, + 'vFuncBool': False, + 'CubicBool': True, + 'MrkvArray': MrkvArray + } + + MarkovType = MarkovConsumerType(**Markov_primitives) + MarkovType.cycles = 0 + employed_income_dist = [np.ones(1), np.ones(1), np.ones(1)] + unemployed_income_dist = [np.ones(1), np.ones(1), np.zeros(1)] + MarkovType.IncomeDstn = [[employed_income_dist, unemployed_income_dist]] MarkovType.solve() MarkovType.unpackcFunc() - self.TBSType = TBSType + self.TBSType = TBSType self.MarkovType = MarkovType def test_consumption(self): # Now compare the consumption functions and make sure they are "close" - diffFunc = lambda m : self.TBSType.solution[0].cFunc(m) - self.MarkovType.cFunc[0][0](m) - points = np.arange(0.1,10.,.01) - difference = diffFunc(points) + def diffFunc(m): return self.TBSType.solution[0].cFunc(m) - self.MarkovType.cFunc[0][0](m) + points = np.arange(0.1, 10., .01) + difference = diffFunc(points) max_difference = np.max(np.abs(difference)) - self.assertLess(max_difference,0.01) + self.assertLess(max_difference, 0.01) + if __name__ == '__main__': # Run all the tests - unittest.main() + unittest.main() \ No newline at end of file diff --git a/Testing/ModelTesting.py b/Testing/ModelTesting.py index 5b2b32115..1f4603ed5 100644 --- a/Testing/ModelTesting.py +++ b/Testing/ModelTesting.py @@ -25,46 +25,40 @@ class parameterCheck(object): parameters based on the original parameters. ''' - def __init__(self, model, base_primitives, multiplier = .1, N_param_values_in_range = 2): + def __init__(self, model, base_primitives, multiplier=.1, N_param_values_in_range=2): ''' Inputs ---------- model: an instance of AgentType with a working .solve() function - base_primitives: dictionary of input parameters for the the model - multiplier: coefficient that determines the range for each parameter within testing sets. the range for each parameter P is [P-P*multiplier,P+P*multiplier]. All testing parameters will be within this range - N_param_ values_in_range: number of different parameter values to test within the given range - ''' - self._model = model - self._base_primitives = base_primitives - self._multiplier = multiplier + self._model = model + self._base_primitives = base_primitives + self._multiplier = multiplier self.N_param_values_in_range = N_param_values_in_range - self.dict_of_min_max_and_N = self.makeParameterDictionary() - self._parametersToTest = self.findTestParameters() + self.dict_of_min_max_and_N = self.makeParameterDictionary() + self._parametersToTest = self.findTestParameters() - self.test_results = [] - self.validParams = [] - self.failedParams = [] + self.test_results = [] + self.validParams = [] + self.failedParams = [] def makeParameterDictionary(self): ''' Returns a dictionary that specifies the min, max, and number of values to check for each parameter - Inputs ---------- none - Returns ---------- dict_of_min_max_and_N: dictionary @@ -72,27 +66,24 @@ def makeParameterDictionary(self): consisting of (1) the minimum value of that parameter to ry, (2) the maximum value of that parameter to try, and (3) the number of values for that parameter to try ''' - dict_of_min_max_and_N = {key:(value-self._multiplier*value, # the min - value+self._multiplier*value, # the max - self.N_param_values_in_range) # number of param values to try - for key,value in self._base_primitives.items()} + dict_of_min_max_and_N = {key: (value-self._multiplier*value, # the min + value+self._multiplier*value, # the max + self.N_param_values_in_range) # number of param values to try + for key, value in self._base_primitives.items()} N_combinations = self.N_param_values_in_range**len(self._base_primitives) - print("There are " + str(N_combinations)+ " parameter combinations to test.") + print("There are " + str(N_combinations) + " parameter combinations to test.") return dict_of_min_max_and_N def findTestParameters(self): ''' This function creates sets (dictionaries) of parameters to test in the model - It returns a list of parameter sets (dictionaries) for testing - Inputs ---------- none - Returns ---------- parametersToTest: list @@ -102,25 +93,23 @@ def findTestParameters(self): 2nd test run should use a value of 1.02 for Rfree. ''' parameterLists = [] - keyOrder = [] - parametersToTest = [] - for key,value in list(self.dict_of_min_max_and_N.items()): + keyOrder = [] + parametersToTest = [] + for key, value in list(self.dict_of_min_max_and_N.items()): parameterRange = np.linspace(*value) parameterLists.append(parameterRange) keyOrder.append(key) for param_combination in itertools.product(*parameterLists): - parametersToTest.append(dict(list(zip(keyOrder,param_combination)))) + parametersToTest.append(dict(list(zip(keyOrder, param_combination)))) return parametersToTest def testParameters(self): ''' Runs the model on the test parameters and stores the error results. Also prints out the error messages that were thrown. - Inputs ---------- none - Returns ---------- None @@ -132,54 +121,47 @@ def testParameters(self): def narrowParameters(self): ''' this function needs to be able to identify the valid parameter space - then it can plug in those values to the makeParameterDictionary function and rerun the models - self._iterator = self.makeParameterDictionary() - parameterLists = [] for k,v in self._iterator.iteritems(): parameterRange = np.arange(*v) parameterLists.append(parameterRange) pairwise = list(all_pairs(parameterLists, previously_tested=self._testedParams)) print("Subsequent round of testing reduced to " + str(len(pairwise)) + " pairwise combinations of parameters") - self.runModel(pairwise) ''' raise NotImplementedError() - def runModel(self,parametersToTest): + def runModel(self, parametersToTest): ''' run the model using each set of test parameters. for each model, a new object (an instance of parameterInstanceCheck) records the results of the test. - Each result is places in the appropriate list (failedParams or validParams) - Inputs ---------- parametersToTest: list A list of dictionaries produced by findTestParameters. See the documentation for findTestParameters for more info. - Returns ---------- None ''' for i in range(len(parametersToTest)): - tempDict = dict(self._base_primitives) + tempDict = dict(self._base_primitives) tempParams = parametersToTest[i] - testData = parameterInstanceCheck(i,tempParams,tempDict) - Test = self._model(**tempParams) + testData = parameterInstanceCheck(i, tempParams, tempDict) + Test = self._model(**tempParams) print('Attempting to solve with parameter set ' + str(i)) try: Test.solve() - #TODO: Insert allowed exceptions here so they don't count as errors! + # TODO: Insert allowed exceptions here so they don't count as errors! except Exception as e: - testData.errorBoolean = True - testData.errorCode = str(e) - testData._tracebackText = sys.exc_info() + testData.errorBoolean = True + testData.errorCode = str(e) + testData._tracebackText = sys.exc_info() self.test_results.append(testData) for i in range(len(self.test_results)): @@ -191,11 +173,9 @@ def runModel(self,parametersToTest): def printErrors(self): ''' Print out the test numbers and error codes for all failed tests - Inputs ---------- none - Returns ---------- None @@ -205,22 +185,20 @@ def printErrors(self): print("test no " + str(i) + " failed with the following error code:") print(self.test_results[i].errorCode) - def printTestResults(self,test_number): + def printTestResults(self, test_number): """ This method prints out the results for a specific test. - Inputs ---------- test_number: int The number of the test to print results for - Returns ---------- None """ print("-----------------------------------------------------------------------") print("Showing specific results for test number " + str(test_number)) - #get a test result and find out more info + # get a test result and find out more info test = TBSCheck.test_results[test_number] print("the test number is : " + str(test.testNumber)) print("") @@ -236,53 +214,41 @@ class parameterInstanceCheck(object): ''' this class holds information for a single test of a model ''' - def __init__(self,testNumber,tested_primitives,original_primitives,errorBoolean=False, - errorCode=None,tracebackText=None): + def __init__(self, testNumber, tested_primitives, original_primitives, errorBoolean=False, + errorCode=None, tracebackText=None): ''' - Inputs ---------- testNumber: int The number of the test - - tested_primitives: dict the set of parameters that was tested - original_primitives: dict the original parameters that test parameters were constructed from - errorBoolean: boolean indicator of an error - errorCode: None or string text of the error (exception type included), if there is one. None otherwise. - tracebackText: full traceback, printable using the traceback.prin_excpetino function - ''' - self.testNumber = testNumber + self.testNumber = testNumber self.original_primitives = original_primitives - self.tested_primitives = tested_primitives - self.errorBoolean = errorBoolean - self.errorCode = errorCode - self._tracebackText = tracebackText - + self.tested_primitives = tested_primitives + self.errorBoolean = errorBoolean + self.errorCode = errorCode + self._tracebackText = tracebackText def traceback(self): ''' function that prints a traceback for an errror - Inputs ---------- none - Returns ---------- None - ''' try: traceback.print_exception(*self._tracebackText) @@ -299,18 +265,16 @@ def traceback(self): # Bring in the TractableBufferStockModel to test it import HARK.ConsumptionSaving.TractableBufferStockModel as Model - - base_primitives = {'UnempPrb' : .015, - 'DiscFac' : 0.9, - 'Rfree' : 1.1, - 'PermGroFac' : 1.05, - 'CRRA' : .95} + base_primitives = {'UnempPrb': .015, + 'DiscFac': 0.9, + 'Rfree': 1.1, + 'PermGroFac': 1.05, + 'CRRA': .95} # Assign a model and base parameters to be checked - TBSCheck = parameterCheck(Model.TractableConsumerType,base_primitives) + TBSCheck = parameterCheck(Model.TractableConsumerType, base_primitives) - #run the testing function. This runs the model multiple times + # run the testing function. This runs the model multiple times TBSCheck.testParameters() - TBSCheck.printTestResults(4) - + TBSCheck.printTestResults(4) \ No newline at end of file From f97de63ba4302f544eec836b3eba1811454151da Mon Sep 17 00:00:00 2001 From: Stephen Schroeder Date: Mon, 6 May 2019 14:47:01 -0400 Subject: [PATCH 48/77] changed econ-arc to econ-ark --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index aff6ecab7..f7720865b 100644 --- a/.gitignore +++ b/.gitignore @@ -5,7 +5,7 @@ __pycache__/ # Virtual Environments venv/ -econ-arc/ +econ-ark/ # C extensions *.so From 75f80a37ecd7ffe63e2b4515cc457348404e8d4a Mon Sep 17 00:00:00 2001 From: Stephen Schroeder Date: Mon, 6 May 2019 15:10:58 -0400 Subject: [PATCH 49/77] lint MultithreadDemo.py and TractableBuffer~.py --- Testing/MultithreadDemo.py | 53 ++++++------- .../TractableBufferStockModel_UnitTests.py | 74 +++++++++---------- 2 files changed, 63 insertions(+), 64 deletions(-) diff --git a/Testing/MultithreadDemo.py b/Testing/MultithreadDemo.py index 84ec61c73..68c6c542e 100644 --- a/Testing/MultithreadDemo.py +++ b/Testing/MultithreadDemo.py @@ -11,21 +11,22 @@ from __future__ import absolute_import from builtins import str -from builtins import zip from builtins import range -from builtins import object +import numpy as np -import HARK.ConsumptionSaving.ConsumerParameters as Params # Parameters for a consumer type -import HARK.ConsumptionSaving.ConsIndShockModel as Model # Consumption-saving model with idiosyncratic shocks -from HARK.utilities import plotFuncs, plotFuncsDer # Basic plotting tools +import HARK.ConsumptionSaving.ConsumerParameters as Params # Parameters for a consumer type +import HARK.ConsumptionSaving.ConsIndShockModel as Model # Consumption-saving model with idiosyncratic shocks +from HARK.utilities import plotFuncs, plotFuncsDer # Basic plotting tools from time import clock # Timing utility from copy import deepcopy # "Deep" copying for complex objects -from HARK.parallel import multiThreadCommandsFake, multiThreadCommands # Parallel processing -mystr = lambda number : "{:.4f}".format(number)# Format numbers as strings -import numpy as np # Numeric Python +from HARK.parallel import multiThreadCommandsFake, multiThreadCommands # Parallel processing -if __name__ == '__main__': # Parallel calls *must* be inside a call to __main__ + +def mystr(number): return "{:.4f}".format(number) # Format numbers as strings + + +if __name__ == '__main__': # Parallel calls *must* be inside a call to __main__ type_count = 32 # Number of values of CRRA to solve # Make the basic type that we'll use as a template. @@ -34,8 +35,8 @@ # single-threading (looping), due to overhead. BasicType = Model.IndShockConsumerType(**Params.init_idiosyncratic_shocks) BasicType.cycles = 0 - BasicType(aXtraMax = 100, aXtraCount = 64) - BasicType(vFuncBool = False, CubicBool = True) + BasicType(aXtraMax=100, aXtraCount=64) + BasicType(vFuncBool=False, CubicBool=True) BasicType.updateAssetsGrid() BasicType.timeFwd() @@ -46,33 +47,35 @@ print('Solving the basic consumer took ' + mystr(end_time-start_time) + ' seconds.') BasicType.unpackcFunc() print('Consumption function:') - plotFuncs(BasicType.cFunc[0],0,5) # plot consumption + plotFuncs(BasicType.cFunc[0], 0, 5) # plot consumption print('Marginal consumption function:') - plotFuncsDer(BasicType.cFunc[0],0,5) # plot MPC + plotFuncsDer(BasicType.cFunc[0], 0, 5) # plot MPC if BasicType.vFuncBool: print('Value function:') - plotFuncs(BasicType.solution[0].vFunc,0.2,5) + plotFuncs(BasicType.solution[0].vFunc, 0.2, 5) # Make many copies of the basic type, each with a different risk aversion - BasicType.vFuncBool = False # just in case it was set to True above + BasicType.vFuncBool = False # just in case it was set to True above my_agent_list = [] - CRRA_list = np.linspace(1,8,type_count) # All the values that CRRA will take on + CRRA_list = np.linspace(1, 8, type_count) # All the values that CRRA will take on for i in range(type_count): this_agent = deepcopy(BasicType) # Make a new copy of the basic type - this_agent.assignParameters(CRRA = CRRA_list[i]) # Give it a unique CRRA value - my_agent_list.append(this_agent) # Addd it to the list of agent types + this_agent.assignParameters(CRRA=CRRA_list[i]) # Give it a unique CRRA value + my_agent_list.append(this_agent) # Add it to the list of agent types # Make a list of commands to be run in parallel; these should be methods of ConsumerType - do_this_stuff = ['updateSolutionTerminal()','solve()','unpackcFunc()'] + do_this_stuff = ['updateSolutionTerminal()', 'solve()', 'unpackcFunc()'] # Solve the model for each type by looping over the types (not multithreading) start_time = clock() - multiThreadCommandsFake(my_agent_list, do_this_stuff) # Fake multithreading, just loops + multiThreadCommandsFake(my_agent_list, do_this_stuff) # Fake multithreading, just loops end_time = clock() - print('Solving ' + str(type_count) + ' types without multithreading took ' + mystr(end_time-start_time) + ' seconds.') + print('Solving ' + str(type_count) + + ' types without multithreading took ' + + mystr(end_time-start_time) + ' seconds.') # Plot the consumption functions for all types on one figure - plotFuncs([this_type.cFunc[0] for this_type in my_agent_list],0,5) + plotFuncs([this_type.cFunc[0] for this_type in my_agent_list], 0, 5) # Delete the solution for each type to make sure we're not just faking it for i in range(type_count): @@ -83,9 +86,9 @@ # And here's HARK's initial attempt at multithreading: start_time = clock() - multiThreadCommands(my_agent_list, do_this_stuff) # Actual multithreading + multiThreadCommands(my_agent_list, do_this_stuff) # Actual multithreading end_time = clock() - print('Solving ' + str(type_count) + ' types with multithreading took ' + mystr(end_time-start_time) + ' seconds.') + print('Solving ' + str(type_count) + ' types with multithreading took ' + mystr(end_time-start_time) + ' seconds.') # Plot the consumption functions for all types on one figure to see if it worked - plotFuncs([this_type.cFunc[0] for this_type in my_agent_list],0,5) + plotFuncs([this_type.cFunc[0] for this_type in my_agent_list], 0, 5) diff --git a/Testing/TractableBufferStockModel_UnitTests.py b/Testing/TractableBufferStockModel_UnitTests.py index 5a9af43e8..c53a9d3bc 100644 --- a/Testing/TractableBufferStockModel_UnitTests.py +++ b/Testing/TractableBufferStockModel_UnitTests.py @@ -7,57 +7,53 @@ from __future__ import print_function, division from __future__ import absolute_import -from builtins import str -from builtins import zip -from builtins import range -from builtins import object - import HARK.ConsumptionSaving.TractableBufferStockModel as Model import unittest + class FuncTest(unittest.TestCase): def setUp(self): - base_primitives = {'UnempPrb' : .015, - 'DiscFac' : 0.9, - 'Rfree' : 1.1, - 'PermGroFac' : 1.05, - 'CRRA' : .95} + base_primitives = {'UnempPrb': .015, + 'DiscFac': 0.9, + 'Rfree': 1.1, + 'PermGroFac': 1.05, + 'CRRA': .95} test_model = Model.TractableConsumerType(**base_primitives) test_model.solve() cNrm_list = [0.0, - 0.6170411710160961, - 0.7512931350607787, - 0.8242071925443384, - 0.8732633069358244, - 0.9090443048442146, - 0.9362584565290604, - 0.9574865107447327, - 0.9743235996720729, - 0.9878347049396029, - 0.9987694718922687, - 1.0499840337356576, - 1.0988370658458553, - 1.1079081119060201, - 1.1185500922622567, - 1.1309953859705277, - 1.1454986397022289, - 1.1623357560591763, - 1.1818022106863713, - 1.2042108062871855, - 1.2298890682784422, - 1.2591765689896088, - 1.2924225145436121, - 1.329983925942064, - 1.372224689976677, - 1.4195156568037894, - 1.4722358408529614, - 1.5307746658958221] - return test_model.solution[0].cNrm_list,cNrm_list + 0.6170411710160961, + 0.7512931350607787, + 0.8242071925443384, + 0.8732633069358244, + 0.9090443048442146, + 0.9362584565290604, + 0.9574865107447327, + 0.9743235996720729, + 0.9878347049396029, + 0.9987694718922687, + 1.0499840337356576, + 1.0988370658458553, + 1.1079081119060201, + 1.1185500922622567, + 1.1309953859705277, + 1.1454986397022289, + 1.1623357560591763, + 1.1818022106863713, + 1.2042108062871855, + 1.2298890682784422, + 1.2591765689896088, + 1.2924225145436121, + 1.329983925942064, + 1.372224689976677, + 1.4195156568037894, + 1.4722358408529614, + 1.5307746658958221] + return test_model.solution[0].cNrm_list, cNrm_list def test1(self): results = self.setUp() - self.assertEqual(results[0],results[1]) + self.assertEqual(results[0], results[1]) if __name__ == '__main__': From c170cab8d2c96bfe42f1c0f04da1de4b2f9edae7 Mon Sep 17 00:00:00 2001 From: rsaavy Date: Mon, 6 May 2019 15:46:50 -0400 Subject: [PATCH 50/77] Linted the Core.py file to make some changes --- HARK/core.py | 325 +++++++++++++++++++++++++++------------------------ 1 file changed, 170 insertions(+), 155 deletions(-) diff --git a/HARK/core.py b/HARK/core.py index 95a5035f2..5698122d2 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -21,7 +21,8 @@ from time import clock from .parallel import multiThreadCommands, multiThreadCommandsFake -def distanceMetric(thing_A,thing_B): + +def distanceMetric(thing_A, thing_B): ''' A "universal distance" metric that can be used as a default in many settings. @@ -42,12 +43,12 @@ def distanceMetric(thing_A,thing_B): typeB = type(thing_B) if typeA is list and typeB is list: - lenA = len(thing_A) # If both inputs are lists, then the distance between - lenB = len(thing_B) # them is the maximum distance between corresponding - if lenA == lenB: # elements in the lists. If they differ in length, - distance_temp = [] # the distance is the difference in lengths. + lenA = len(thing_A) # If both inputs are lists, then the distance between + lenB = len(thing_B) # them is the maximum distance between corresponding + if lenA == lenB: # elements in the lists. If they differ in length, + distance_temp = [] # the distance is the difference in lengths. for n in range(lenA): - distance_temp.append(distanceMetric(thing_A[n],thing_B[n])) + distance_temp.append(distanceMetric(thing_A[n], thing_B[n])) distance = max(distance_temp) else: distance = float(abs(lenA - lenB)) @@ -57,7 +58,7 @@ def distanceMetric(thing_A,thing_B): # If both inputs are array-like, return the maximum absolute difference b/w # corresponding elements (if same shape); return largest difference in dimensions # if shapes do not align. - elif hasattr(thing_A,'shape') and hasattr(thing_B,'shape'): + elif hasattr(thing_A, 'shape') and hasattr(thing_B, 'shape'): if thing_A.shape == thing_B.shape: distance = np.max(abs(thing_A - thing_B)) else: @@ -69,16 +70,17 @@ def distanceMetric(thing_A,thing_B): distance = 0.0 else: distance = thing_A.distance(thing_B) - else: # Failsafe: the inputs are very far apart + else: # Failsafe: the inputs are very far apart distance = 1000.0 return distance + class HARKobject(object): ''' A superclass for object classes in HARK. Comes with two useful methods: a generic/universal distance method and an attribute assignment method. ''' - def distance(self,other): + def distance(self, other): ''' A generic distance method, which requires the existence of an attribute called distance_criteria, giving a list of strings naming the attributes @@ -98,14 +100,14 @@ def distance(self,other): distance_list = [0.0] for attr_name in self.distance_criteria: try: - obj_A = getattr(self,attr_name) - obj_B = getattr(other,attr_name) - distance_list.append(distanceMetric(obj_A,obj_B)) - except: - distance_list.append(1000.0) # if either object lacks attribute, they are not the same + obj_A = getattr(self, attr_name) + obj_B = getattr(other, attr_name) + distance_list.append(distanceMetric(obj_A, obj_B)) + except AttributeError: + distance_list.append(1000.0) # if either object lacks attribute, they are not the same return max(distance_list) - def assignParameters(self,**kwds): + def assignParameters(self, **kwds): ''' Assign an arbitrary number of attributes to this agent. @@ -120,16 +122,16 @@ def assignParameters(self,**kwds): none ''' for key in kwds: - setattr(self,key,kwds[key]) + setattr(self, key, kwds[key]) - def __call__(self,**kwds): + def __call__(self, **kwds): ''' Assign an arbitrary number of attributes to this agent, as a convenience. See assignParameters. ''' self.assignParameters(**kwds) - def getAvg(self,varname,**kwds): + def getAvg(self, varname, **kwds): ''' Calculates the average of an attribute of this instance. Returns NaN if no such attribute. @@ -144,11 +146,12 @@ def getAvg(self,varname,**kwds): avg : float or np.array The average of this attribute. Might be an array if the axis keyword is passed. ''' - if hasattr(self,varname): - return np.mean(getattr(self,varname),**kwds) + if hasattr(self, varname): + return np.mean(getattr(self, varname), **kwds) else: return np.nan + class Solution(HARKobject): ''' A superclass for representing the "solution" to a single period problem in a @@ -158,6 +161,7 @@ class Solution(HARKobject): replacing each instance of Solution with HARKobject in the other modules. ''' + class AgentType(HARKobject): ''' A superclass for economic agents in the HARK framework. Each model should @@ -170,8 +174,8 @@ class AgentType(HARKobject): 'solveOnePeriod' should appear in exactly one of these lists, depending on whether the same solution method is used in all periods of the model. ''' - def __init__(self,solution_terminal=None,cycles=1,time_flow=False,pseudo_terminal=True, - tolerance=0.000001,seed=0,**kwds): + def __init__(self, solution_terminal=None, cycles=1, time_flow=False, pseudo_terminal=True, + tolerance=0.000001, seed=0, **kwds): ''' Initialize an instance of AgentType by setting attributes. @@ -211,18 +215,18 @@ def __init__(self,solution_terminal=None,cycles=1,time_flow=False,pseudo_termina ''' if solution_terminal is None: solution_terminal = NullFunc() - self.solution_terminal = solution_terminal - self.cycles = cycles - self.time_flow = time_flow - self.pseudo_terminal = pseudo_terminal - self.solveOnePeriod = NullFunc() - self.tolerance = tolerance - self.seed = seed - self.track_vars = [] - self.poststate_vars = [] - self.read_shocks = False - self.assignParameters(**kwds) - self.resetRNG() + self.solution_terminal = solution_terminal # NOQA + self.cycles = cycles # NOQA + self.time_flow = time_flow # NOQA + self.pseudo_terminal = pseudo_terminal # NOQA + self.solveOnePeriod = NullFunc() # NOQA + self.tolerance = tolerance # NOQA + self.seed = seed # NOQA + self.track_vars = [] # NOQA + self.poststate_vars = [] # NOQA + self.read_shocks = False # NOQA + self.assignParameters(**kwds) # NOQA + self.resetRNG() # NOQA def timeReport(self): ''' @@ -288,7 +292,7 @@ def timeRev(self): if self.time_flow: self.timeFlip() - def addToTimeVary(self,*params): + def addToTimeVary(self, *params): ''' Adds any number of parameters to time_vary for this instance. @@ -305,7 +309,7 @@ def addToTimeVary(self,*params): if param not in self.time_vary: self.time_vary.append(param) - def addToTimeInv(self,*params): + def addToTimeInv(self, *params): ''' Adds any number of parameters to time_inv for this instance. @@ -322,7 +326,7 @@ def addToTimeInv(self,*params): if param not in self.time_inv: self.time_inv.append(param) - def delFromTimeVary(self,*params): + def delFromTimeVary(self, *params): ''' Removes any number of parameters from time_vary for this instance. @@ -339,7 +343,7 @@ def delFromTimeVary(self,*params): if param in self.time_vary: self.time_vary.remove(param) - def delFromTimeInv(self,*params): + def delFromTimeInv(self, *params): ''' Removes any number of parameters from time_inv for this instance. @@ -356,7 +360,7 @@ def delFromTimeInv(self,*params): if param in self.time_inv: self.time_inv.remove(param) - def solve(self,verbose=False): + def solve(self, verbose=False): ''' Solve the model for this instance of an agent type by backward induction. Loops through the sequence of one period problems, passing the solution @@ -376,12 +380,12 @@ def solve(self,verbose=False): # tions with well-defined answers such as 1.0/0.0 that is np.inf, -1.0/0.0 that is # -np.inf, np.inf/np.inf is np.nan and so on. with np.errstate(divide='ignore', over='ignore', under='ignore', invalid='ignore'): - self.preSolve() # Do pre-solution stuff - self.solution = solveAgent(self,verbose) # Solve the model by backward induction - if self.time_flow: # Put the solution in chronological order if this instance's time flow runs that way + self.preSolve() # Do pre-solution stuff + self.solution = solveAgent(self, verbose) # Solve the model by backward induction + if self.time_flow: # Put the solution in chronological order if this instance's time flow runs that way self.solution.reverse() - self.addToTimeVary('solution') # Add solution to the list of time-varying attributes - self.postSolve() # Do post-solution stuff + self.addToTimeVary('solution') # Add solution to the list of time-varying attributes + self.postSolve() # Do post-solution stuff def resetRNG(self): ''' @@ -402,10 +406,9 @@ def checkElementsOfTimeVaryAreLists(self): A method to check that elements of time_vary are lists. """ for param in self.time_vary: - assert type(getattr(self,param))==list,param + ' is not a list, but should be' + \ + assert type(getattr(self, param)) == list, param + ' is not a list, but should be' + \ ' because it is in time_vary' - def preSolve(self): ''' A method that is run immediately before the model is solved, to check inputs or to prepare @@ -452,12 +455,13 @@ def initializeSim(self): ''' self.resetRNG() self.t_sim = 0 - all_agents = np.ones(self.AgentCount,dtype=bool) + all_agents = np.ones(self.AgentCount, dtype=bool) blank_array = np.zeros(self.AgentCount) for var_name in self.poststate_vars: - exec('self.' + var_name + ' = copy(blank_array)') - self.t_age = np.zeros(self.AgentCount,dtype=int) # Number of periods since agent entry - self.t_cycle = np.zeros(self.AgentCount,dtype=int) # Which cycle period each agent is on + setattr(self, var_name, copy(blank_array)) + # exec('self.' + var_name + ' = copy(blank_array)') + self.t_age = np.zeros(self.AgentCount, dtype=int) # Number of periods since agent entry + self.t_cycle = np.zeros(self.AgentCount, dtype=int) # Which cycle period each agent is on self.simBirth(all_agents) self.clearHistory() return None @@ -478,18 +482,18 @@ def simOnePeriod(self): None ''' self.getMortality() # Replace some agents with "newborns" - if self.read_shocks: # If shock histories have been pre-specified, use those + if self.read_shocks: # If shock histories have been pre-specified, use those self.readShocks() else: # Otherwise, draw shocks as usual according to subclass-specific method self.getShocks() - self.getStates() # Determine each agent's state at decision time + self.getStates() # Determine each agent's state at decision time self.getControls() # Determine each agent's choice or control variables based on states - self.getPostStates() # Determine each agent's post-decision / end-of-period states using states and controls + self.getPostStates() # Determine each agent's post-decision / end-of-period states using states and controls # Advance time for all agents - self.t_age = self.t_age + 1 # Age all consumers by one period - self.t_cycle = self.t_cycle + 1 # Age all consumers within their cycle - self.t_cycle[self.t_cycle == self.T_cycle] = 0 # Resetting to zero for those who have reached the end + self.t_age = self.t_age + 1 # Age all consumers by one period + self.t_cycle = self.t_cycle + 1 # Age all consumers within their cycle + self.t_cycle[self.t_cycle == self.T_cycle] = 0 # Resetting to zero for those who have reached the end def makeShockHistory(self): ''' @@ -515,7 +519,7 @@ def makeShockHistory(self): # Make blank history arrays for each shock variable for var_name in self.shock_vars: - setattr(self,var_name+'_hist',np.zeros((self.T_sim,self.AgentCount))+np.nan) + setattr(self, var_name+'_hist', np.zeros((self.T_sim, self.AgentCount)) + np.nan) # Make and store the history of shocks for each period for t in range(self.T_sim): @@ -524,9 +528,9 @@ def makeShockHistory(self): for var_name in self.shock_vars: exec('self.' + var_name + '_hist[self.t_sim,:] = self.' + var_name) self.t_sim += 1 - self.t_age = self.t_age + 1 # Age all consumers by one period - self.t_cycle = self.t_cycle + 1 # Age all consumers within their cycle - self.t_cycle[self.t_cycle == self.T_cycle] = 0 # Resetting to zero for those who have reached the end + self.t_age = self.t_age + 1 # Age all consumers by one period + self.t_cycle = self.t_cycle + 1 # Age all consumers within their cycle + self.t_cycle[self.t_cycle == self.T_cycle] = 0 # Resetting to zero for those who have reached the end # Restore the flow of time and flag that shocks can be read rather than simulated self.read_shocks = True @@ -570,10 +574,10 @@ def simDeath(self): Boolean array of size self.AgentCount indicating which agents die and are replaced. ''' print('AgentType subclass must define method simDeath!') - who_dies = np.ones(self.AgentCount,dtype=bool) + who_dies = np.ones(self.AgentCount, dtype=bool) return who_dies - def simBirth(self,which_agents): + def simBirth(self, which_agents): ''' Makes new agents for the simulation. Takes a boolean array as an input, indicating which agent indices are to be "born". Does nothing by default, must be overwritten by a subclass. @@ -622,7 +626,7 @@ def readShocks(self): None ''' for var_name in self.shock_vars: - setattr(self,var_name,getattr(self,var_name+'_hist')[self.t_sim,:]) + setattr(self, var_name, getattr(self, var_name + '_hist')[self.t_sim, :]) def getStates(self): ''' @@ -671,7 +675,7 @@ def getPostStates(self): ''' return None - def simulate(self,sim_periods=None): + def simulate(self, sim_periods=None): ''' Simulates this agent type for a given number of periods (defaults to self.T_sim if no input). Records histories of attributes named in self.track_vars in attributes named varname_hist. @@ -718,7 +722,7 @@ def clearHistory(self): exec('self.' + var_name + '_hist = np.zeros((self.T_sim,self.AgentCount)) + np.nan') -def solveAgent(agent,verbose): +def solveAgent(agent, verbose): ''' Solve the dynamic model for one agent type. This function iterates on "cycles" of an agent's model either a given number of times or until solution convergence @@ -742,8 +746,8 @@ def solveAgent(agent,verbose): agent.timeRev() # Check to see whether this is an (in)finite horizon problem - cycles_left = agent.cycles - infinite_horizon = cycles_left == 0 + cycles_left = agent.cycles # NOQA + infinite_horizon = cycles_left == 0 # NOQA # Initialize the solution, which includes the terminal solution if it's not a pseudo-terminal period solution = [] @@ -751,15 +755,15 @@ def solveAgent(agent,verbose): solution.append(deepcopy(agent.solution_terminal)) # Initialize the process, then loop over cycles - solution_last = agent.solution_terminal - go = True - completed_cycles = 0 - max_cycles = 5000 # escape clause + solution_last = agent.solution_terminal # NOQA + go = True # NOQA + completed_cycles = 0 # NOQA + max_cycles = 5000 # NOQA - escape clause if verbose: t_last = clock() while go: # Solve a cycle of the model, recording it if horizon is finite - solution_cycle = solveOneCycle(agent,solution_last) + solution_cycle = solveOneCycle(agent, solution_last) if not infinite_horizon: solution += solution_cycle @@ -769,7 +773,7 @@ def solveAgent(agent,verbose): if completed_cycles > 0: solution_distance = solution_now.distance(solution_last) go = (solution_distance > agent.tolerance and completed_cycles < max_cycles) - else: # Assume solution does not converge after only one cycle + else: # Assume solution does not converge after only one cycle solution_distance = 100.0 go = True else: @@ -784,16 +788,16 @@ def solveAgent(agent,verbose): if verbose: t_now = clock() if infinite_horizon: - print('Finished cycle #' + str(completed_cycles) + ' in ' + str(t_now-t_last) +\ - ' seconds, solution distance = ' + str(solution_distance)) + print('Finished cycle #' + str(completed_cycles) + ' in ' + str(t_now-t_last) + + ' seconds, solution distance = ' + str(solution_distance)) else: - print('Finished cycle #' + str(completed_cycles) + ' of ' + str(agent.cycles) +\ - ' in ' + str(t_now-t_last) + ' seconds.') + print('Finished cycle #' + str(completed_cycles) + ' of ' + str(agent.cycles) + + ' in ' + str(t_now-t_last) + ' seconds.') t_last = t_now # Record the last cycle if horizon is infinite (solution is still empty!) if infinite_horizon: - solution = solution_cycle # PseudoTerminal=False impossible for infinite horizon + solution = solution_cycle # PseudoTerminal=False impossible for infinite horizon # Restore the direction of time to its original orientation, then return the solution if original_time_flow: @@ -801,7 +805,7 @@ def solveAgent(agent,verbose): return solution -def solveOneCycle(agent,solution_last): +def solveOneCycle(agent, solution_last): ''' Solve one "cycle" of the dynamic model for one agent type. This function iterates over the periods within an agent's cycle, updating the time-varying @@ -826,7 +830,7 @@ def solveOneCycle(agent,solution_last): # Calculate number of periods per cycle, defaults to 1 if all variables are time invariant if len(agent.time_vary) > 0: name = agent.time_vary[0] - T = len(eval('agent.' + name)) + T = len(eval('agent.' + name)) else: T = 1 @@ -834,12 +838,12 @@ def solveOneCycle(agent,solution_last): always_same_solver = 'solveOnePeriod' not in agent.time_vary if always_same_solver: solveOnePeriod = agent.solveOnePeriod - these_args = getArgNames(solveOnePeriod) + these_args = getArgNames(solveOnePeriod) # Construct a dictionary to be passed to the solver time_inv_string = '' for name in agent.time_inv: - time_inv_string += ' \'' + name + '\' : agent.' +name + ',' + time_inv_string += ' \'' + name + '\' : agent.' + name + ',' time_vary_string = '' for name in agent.time_vary: time_vary_string += ' \'' + name + '\' : None,' @@ -847,7 +851,7 @@ def solveOneCycle(agent,solution_last): # Initialize the solution for this cycle, then iterate on periods solution_cycle = [] - solution_next = solution_last + solution_next = solution_last for t in range(T): # Update which single period solver to use (if it depends on time) if not always_same_solver: @@ -872,8 +876,8 @@ def solveOneCycle(agent,solution_last): return solution_cycle -#======================================================================== -#======================================================================== +# ======================================================================== +# ======================================================================== class Market(HARKobject): ''' @@ -881,8 +885,8 @@ class Market(HARKobject): dynamic general equilibrium models to solve the "macroeconomic" model as a layer on top of the "microeconomic" models of one or more AgentTypes. ''' - def __init__(self,agents=[],sow_vars=[],reap_vars=[],const_vars=[],track_vars=[],dyn_vars=[], - millRule=None,calcDynamics=None,act_T=1000,tolerance=0.000001): + def __init__(self, agents=[], sow_vars=[], reap_vars=[], const_vars=[], rack_vars=[], dyn_vars=[], + millRule=None, calcDynamics=None, act_T=1000, tolerance=0.000001): ''' Make a new instance of the Market class. @@ -927,24 +931,24 @@ def __init__(self,agents=[],sow_vars=[],reap_vars=[],const_vars=[],track_vars=[] ------- None ''' - self.agents = agents - self.reap_vars = reap_vars - self.sow_vars = sow_vars - self.const_vars = const_vars - self.track_vars = track_vars - self.dyn_vars = dyn_vars - if millRule is not None: # To prevent overwriting of method-based millRules + self.agents = agents # NOQA + self.reap_vars = reap_vars # NOQA + self.sow_vars = sow_vars # NOQA + self.const_vars = const_vars # NOQA + self.track_vars = track_vars # NOQA + self.dyn_vars = dyn_vars # NOQA + if millRule is not None: # To prevent overwriting of method-based millRules self.millRule = millRule - if calcDynamics is not None: # Ditto for calcDynamics + if calcDynamics is not None: # Ditto for calcDynamics self.calcDynamics = calcDynamics - self.act_T = act_T - self.tolerance = tolerance - self.max_loops = 1000 + self.act_T = act_T # NOQA + self.tolerance = tolerance # NOQA + self.max_loops = 1000 # NOQA self.print_parallel_error_once = True - # Print the error associated with calling the parallel method - # "solveAgents" one time. If set to false, the error will never - # print. See "solveAgents" for why this prints once or never. + # Print the error associated with calling the parallel method + # "solveAgents" one time. If set to false, the error will never + # print. See "solveAgents" for why this prints once or never. def solveAgents(self): ''' @@ -958,17 +962,19 @@ def solveAgents(self): ------- None ''' - #for this_type in self.agents: - # this_type.solve() + # for this_type in self.agents: + # this_type.solve() try: - multiThreadCommands(self.agents,['solve()']) + multiThreadCommands(self.agents, ['solve()']) except Exception as err: if self.print_parallel_error_once: # Set flag to False so this is only printed once. self.print_parallel_error_once = False - print("**** WARNING: could not execute multiThreadCommands in HARK.core.Market.solveAgents(), so using the serial version instead. This will likely be slower. The multiTreadCommands() functions failed with the following error:", '\n ', sys.exc_info()[0], ':', err) #sys.exc_info()[0]) - multiThreadCommandsFake(self.agents,['solve()']) - + print("**** WARNING: could not execute multiThreadCommands in HARK.core.Market.solveAgents() ", + "so using the serial version instead. This will likely be slower. " + "The multiTreadCommands() functions failed with the following error:", '\n', + sys.exc_info()[0], ':', err) # sys.exc_info()[0]) + multiThreadCommandsFake(self.agents, ['solve()']) def solve(self): ''' @@ -984,15 +990,15 @@ def solve(self): ------- None ''' - go = True - max_loops = self.max_loops # Failsafe against infinite solution loop + go = True + max_loops = self.max_loops # Failsafe against infinite solution loop completed_loops = 0 - old_dynamics = None + old_dynamics = None - while go: # Loop until the dynamic process converges or we hit the loop cap - self.solveAgents() # Solve each AgentType's micro problem - self.makeHistory() # "Run" the model while tracking aggregate variables - new_dynamics = self.updateDynamics() # Find a new aggregate dynamic rule + while go: # Loop until the dynamic process converges or we hit the loop cap + self.solveAgents() # Solve each AgentType's micro problem + self.makeHistory() # "Run" the model while tracking aggregate variables + new_dynamics = self.updateDynamics() # Find a new aggregate dynamic rule # Check to see if the dynamic rule has converged (if this is not the first loop) if completed_loops > 0: @@ -1001,11 +1007,11 @@ def solve(self): distance = 1000000.0 # Move to the next loop if the terminal conditions are not met - old_dynamics = new_dynamics + old_dynamics = new_dynamics completed_loops += 1 - go = distance >= self.tolerance and completed_loops < max_loops + go = distance >= self.tolerance and completed_loops < max_loops - self.dynamics = new_dynamics # Store the final dynamic rule in self + self.dynamics = new_dynamics # Store the final dynamic rule in self def reap(self): ''' @@ -1023,8 +1029,8 @@ def reap(self): for var_name in self.reap_vars: harvest = [] for this_type in self.agents: - harvest.append(getattr(this_type,var_name)) - setattr(self,var_name,harvest) + harvest.append(getattr(this_type, var_name)) + setattr(self, var_name, harvest) def sow(self): ''' @@ -1040,9 +1046,9 @@ def sow(self): none ''' for var_name in self.sow_vars: - this_seed = getattr(self,var_name) + this_seed = getattr(self, var_name) for this_type in self.agents: - setattr(this_type,var_name,this_seed) + setattr(this_type, var_name, this_seed) def mill(self): ''' @@ -1070,8 +1076,8 @@ def mill(self): product = self.millRule(**mill_dict) for j in range(len(self.sow_vars)): this_var = self.sow_vars[j] - this_product = getattr(product,this_var) - setattr(self,this_var,this_product) + this_product = getattr(product, this_var) + setattr(self, this_var, this_product) def cultivate(self): ''' @@ -1104,12 +1110,12 @@ def reset(self): ------- none ''' - for var_name in self.track_vars: # Reset the history of tracked variables - setattr(self,var_name + '_hist',[]) - for var_name in self.sow_vars: # Set the sow variables to their initial levels - initial_val = getattr(self,var_name + '_init') - setattr(self,var_name,initial_val) - for this_type in self.agents: # Reset each AgentType in the market + for var_name in self.track_vars: # Reset the history of tracked variables + setattr(self, var_name + '_hist', []) + for var_name in self.sow_vars: # Set the sow variables to their initial levels + initial_val = getattr(self, var_name + '_init') + setattr(self, var_name, initial_val) + for this_type in self.agents: # Reset each AgentType in the market this_type.reset() def store(self): @@ -1126,8 +1132,8 @@ def store(self): none ''' for var_name in self.track_vars: - value_now = getattr(self,var_name) - getattr(self,var_name + '_hist').append(value_now) + value_now = getattr(self, var_name) + getattr(self, var_name + '_hist').append(value_now) def makeHistory(self): ''' @@ -1142,13 +1148,13 @@ def makeHistory(self): ------- none ''' - self.reset() # Initialize the state of the market + self.reset() # Initialize the state of the market for t in range(self.act_T): - self.sow() # Distribute aggregated information/state to agents - self.cultivate() # Agents take action - self.reap() # Collect individual data from agents - self.mill() # Process individual data into aggregate data - self.store() # Record variables of interest + self.sow() # Distribute aggregated information/state to agents + self.cultivate() # Agents take action + self.reap() # Collect individual data from agents + self.mill() # Process individual data into aggregate data + self.store() # Record variables of interest def updateDynamics(self): ''' @@ -1175,11 +1181,11 @@ def updateDynamics(self): update_dict = eval('{' + history_vars_string + '}') # Calculate a new dynamic rule and distribute it to the agents in agent_list - dynamics = self.calcDynamics(**update_dict) # User-defined dynamics calculator + dynamics = self.calcDynamics(**update_dict) # User-defined dynamics calculator for var_name in self.dyn_vars: - this_obj = getattr(dynamics,var_name) + this_obj = getattr(dynamics, var_name) for this_type in self.agents: - setattr(this_type,var_name,this_obj) + setattr(this_type, var_name, this_obj) return dynamics @@ -1214,27 +1220,34 @@ def copy_module(target_path, my_directory_full_path, my_module): print("Goodbye!") return elif target_path == os.path.expanduser("~") or os.path.normpath(target_path) == os.path.expanduser("~"): - print("You have indicated that the target location is "+target_path+" -- that is, you want to wipe out your home directory with the contents of "+my_module+". My programming does not allow me to do that.\n\nGoodbye!") + print("You have indicated that the target location is " + target_path + + " -- that is, you want to wipe out your home directory with the contents of " + my_module + + ". My programming does not allow me to do that.\n\nGoodbye!") return elif os.path.exists(target_path): - print("There is already a file or directory at the location "+target_path+". For safety reasons this code does not overwrite existing files.\nPlease remove the file at "+target_path+" and try again.") + print("There is already a file or directory at the location " + target_path + + ". For safety reasons this code does not overwrite existing files.\n Please remove the file at " + + target_path + + " and try again.") return else: - user_input = input("""You have indicated you want to copy module:\n """+ my_module - + """\nto:\n """+ target_path +"""\nIs that correct? Please indicate: y / [n]\n\n""") + user_input = input("""You have indicated you want to copy module:\n """ + my_module + + """\nto:\n """ + target_path + """\nIs that correct? Please indicate: y / [n]\n\n""") if user_input == 'y' or user_input == 'Y': - #print("copy_tree(",my_directory_full_path,",", target_path,")") + # print("copy_tree(",my_directory_full_path,",", target_path,")") copy_tree(my_directory_full_path, target_path) else: print("Goodbye!") return + def print_helper(): my_directory_full_path = os.path.dirname(os.path.realpath(__file__)) print(my_directory_full_path) + def copy_module_to_local(full_module_name): ''' This function contains simple code to copy a submodule to a location on @@ -1259,14 +1272,17 @@ def copy_module_to_local(full_module_name): # Find a default directory -- user home directory: home_directory_RAW = os.path.expanduser("~") - # Thanks to https://stackoverflow.com/a/4028943 + # Thanks to https://stackoverflow.com/a/4028943 # Find the directory of the HARK.core module: - #my_directory_full_path = os.path.dirname(os.path.realpath(__file__)) + # my_directory_full_path = os.path.dirname(os.path.realpath(__file__)) hark_core_directory_full_path = os.path.dirname(os.path.realpath(__file__)) # From https://stackoverflow.com/a/5137509 # Important note from that answer: - # (Note that the incantation above won't work if you've already used os.chdir() to change your current working directory, since the value of the __file__ constant is relative to the current working directory and is not changed by an os.chdir() call.) + # (Note that the incantation above won't work if you've already used os.chdir() + # to change your current working directory, + # since the value of the __file__ constant is relative to the current working directory and is not changed by an + # os.chdir() call.) # # NOTE: for this specific file that I am testing, the path should be: # '/home/npalmer/anaconda3/envs/py3fresh/lib/python3.6/site-packages/HARK/SolvingMicroDSOPs/---example-file--- @@ -1274,7 +1290,9 @@ def copy_module_to_local(full_module_name): # Split out the name of the module. Break if proper format is not followed: all_module_names_list = full_module_name.split('.') # Assume put in at correct format if all_module_names_list[0] != "HARK": - print("\nWarning: the module name does not start with 'HARK'. Instead it is: '"+all_module_names_list[0]+"' -- please format the full namespace of the module you want. For example, 'HARK.SolvingMicroDSOPs'") + print("\nWarning: the module name does not start with 'HARK'. Instead it is: '" + + all_module_names_list[0]+"' --please format the full namespace of the module you want. \n" + "For example, 'HARK.SolvingMicroDSOPs'") print("\nGoodbye!") return @@ -1287,7 +1305,7 @@ def copy_module_to_local(full_module_name): home_directory_with_module = os.path.join(home_directory_RAW, my_module) - print("\n\n\nmy_directory_full_path:",my_directory_full_path,'\n\n\n') + print("\n\n\nmy_directory_full_path:", my_directory_full_path, '\n\n\n') # Interact with the user: # - Ask the user for the target place to copy the directory @@ -1298,14 +1316,13 @@ def copy_module_to_local(full_module_name): # - Quit target_path = input("""You have invoked the 'replicate' process for the current module:\n """ + - my_module + """\nThe default copy location is your home directory:\n """+ - home_directory_with_module +"""\nPlease enter one of the three options in single quotes below, excluding the quotes: + my_module + """\nThe default copy location is your home directory:\n """ + + home_directory_with_module + """\nPlease enter one of the three options in single quotes below, excluding the quotes: 'q' or return/enter to quit the process 'y' to accept the default home directory: """+home_directory_with_module+""" 'n' to specify your own pathname\n\n""") - if target_path == 'n' or target_path == 'N': target_path = input("""Please enter the full pathname to your target directory location: """) @@ -1333,8 +1350,6 @@ def copy_module_to_local(full_module_name): return - - def main(): print("Sorry, HARK.core doesn't actually do anything on its own.") print("To see some examples of its frameworks in action, try running a model module.") From 22f05cf37d6037fdde291f5017f32e6f203e4e5c Mon Sep 17 00:00:00 2001 From: Stephen Schroeder Date: Mon, 6 May 2019 16:33:05 -0400 Subject: [PATCH 51/77] lint HARK\test directory and ConsAggShockModel.py --- HARK/ConsumptionSaving/ConsAggShockModel.py | 720 ++++++++++---------- HARK/tests/test_HARKutilities.py | 28 +- HARK/tests/test_dcegm.py | 3 +- 3 files changed, 379 insertions(+), 372 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py index c055d2821..bdc6b0266 100644 --- a/HARK/ConsumptionSaving/ConsAggShockModel.py +++ b/HARK/ConsumptionSaving/ConsAggShockModel.py @@ -21,21 +21,22 @@ from copy import deepcopy import matplotlib.pyplot as plt -utility = CRRAutility -utilityP = CRRAutilityP -utilityPP = CRRAutilityPP +utility = CRRAutility +utilityP = CRRAutilityP +utilityPP = CRRAutilityPP utilityP_inv = CRRAutilityP_inv utility_invP = CRRAutility_invP -utility_inv = CRRAutility_inv +utility_inv = CRRAutility_inv + class MargValueFunc2D(HARKobject): ''' A class for representing a marginal value function in models where the standard envelope condition of dvdm(m,M) = u'(c(m,M)) holds (with CRRA utility). ''' - distance_criteria = ['cFunc','CRRA'] + distance_criteria = ['cFunc', 'CRRA'] - def __init__(self,cFunc,CRRA): + def __init__(self, cFunc, CRRA): ''' Constructor for a new marginal value function object. @@ -57,11 +58,12 @@ def __init__(self,cFunc,CRRA): self.cFunc = deepcopy(cFunc) self.CRRA = CRRA - def __call__(self,m,M): - return utilityP(self.cFunc(m,M),gam=self.CRRA) + def __call__(self, m, M): + return utilityP(self.cFunc(m, M), gam=self.CRRA) ############################################################################### + class AggShockConsumerType(IndShockConsumerType): ''' A class to represent consumers who face idiosyncratic (transitory and per- @@ -72,18 +74,18 @@ class AggShockConsumerType(IndShockConsumerType): evolves over time and take aggregate shocks into account when making their decision about how much to consume. ''' - def __init__(self,time_flow=True,**kwds): + def __init__(self, time_flow=True, **kwds): ''' Make a new instance of AggShockConsumerType, an extension of IndShockConsumerType. Sets appropriate solver and input lists. ''' - AgentType.__init__(self,solution_terminal=deepcopy(IndShockConsumerType.solution_terminal_), - time_flow=time_flow,pseudo_terminal=False,**kwds) + AgentType.__init__(self, solution_terminal=deepcopy(IndShockConsumerType.solution_terminal_), + time_flow=time_flow, pseudo_terminal=False, **kwds) # Add consumer-type specific objects, copying to create independent versions self.time_vary = deepcopy(IndShockConsumerType.time_vary_) self.time_inv = deepcopy(IndShockConsumerType.time_inv_) - self.delFromTimeInv('Rfree','vFuncBool','CubicBool') + self.delFromTimeInv('Rfree', 'vFuncBool', 'CubicBool') self.poststate_vars = IndShockConsumerType.poststate_vars_ self.solveOnePeriod = solveConsAggShock self.update() @@ -101,7 +103,7 @@ def reset(self): None ''' self.initializeSim() - self.aLvlNow = self.kInit*np.ones(self.AgentCount) # Start simulation near SS + self.aLvlNow = self.kInit*np.ones(self.AgentCount) # Start simulation near SS self.aNrmNow = self.aLvlNow/self.pLvlNow def updateSolutionTerminal(self): @@ -117,12 +119,14 @@ def updateSolutionTerminal(self): ------- None ''' - cFunc_terminal = BilinearInterp(np.array([[0.0,0.0],[1.0,1.0]]),np.array([0.0,1.0]),np.array([0.0,1.0])) - vPfunc_terminal = MargValueFunc2D(cFunc_terminal,self.CRRA) + cFunc_terminal = BilinearInterp(np.array([[0.0, 0.0], [1.0, 1.0]]), np.array([0.0, 1.0]), np.array([0.0, 1.0])) + vPfunc_terminal = MargValueFunc2D(cFunc_terminal, self.CRRA) mNrmMin_terminal = ConstantFunction(0) - self.solution_terminal = ConsumerSolution(cFunc=cFunc_terminal,vPfunc=vPfunc_terminal,mNrmMin=mNrmMin_terminal) + self.solution_terminal = ConsumerSolution(cFunc=cFunc_terminal, + vPfunc=vPfunc_terminal, + mNrmMin=mNrmMin_terminal) - def getEconomyData(self,Economy): + def getEconomyData(self, Economy): ''' Imports economy-determined objects into self from a Market. Instances of AggShockConsumerType "live" in some macroeconomy that has @@ -142,20 +146,19 @@ def getEconomyData(self,Economy): ------- None ''' - self.T_sim = Economy.act_T # Need to be able to track as many periods as economy runs - self.kInit = Economy.kSS # Initialize simulation assets to steady state - self.aNrmInitMean = np.log(0.00000001) # Initialize newborn assets to nearly zero - self.Mgrid = Economy.MSS*self.MgridBase # Aggregate market resources grid adjusted around SS capital ratio - self.AFunc = Economy.AFunc # Next period's aggregate savings function - self.Rfunc = Economy.Rfunc # Interest factor as function of capital ratio - self.wFunc = Economy.wFunc # Wage rate as function of capital ratio - self.DeprFac = Economy.DeprFac # Rate of capital depreciation - self.PermGroFacAgg = Economy.PermGroFacAgg # Aggregate permanent productivity growth - self.addAggShkDstn(Economy.AggShkDstn) # Combine idiosyncratic and aggregate shocks into one dstn - self.addToTimeInv('Mgrid','AFunc','Rfunc', 'wFunc','DeprFac','PermGroFacAgg') + self.T_sim = Economy.act_T # Need to be able to track as many periods as economy runs + self.kInit = Economy.kSS # Initialize simulation assets to steady state + self.aNrmInitMean = np.log(0.00000001) # Initialize newborn assets to nearly zero + self.Mgrid = Economy.MSS*self.MgridBase # Aggregate market resources grid adjusted around SS capital ratio + self.AFunc = Economy.AFunc # Next period's aggregate savings function + self.Rfunc = Economy.Rfunc # Interest factor as function of capital ratio + self.wFunc = Economy.wFunc # Wage rate as function of capital ratio + self.DeprFac = Economy.DeprFac # Rate of capital depreciation + self.PermGroFacAgg = Economy.PermGroFacAgg # Aggregate permanent productivity growth + self.addAggShkDstn(Economy.AggShkDstn) # Combine idiosyncratic and aggregate shocks into one dstn + self.addToTimeInv('Mgrid', 'AFunc', 'Rfunc', 'wFunc', 'DeprFac', 'PermGroFacAgg') - - def addAggShkDstn(self,AggShkDstn): + def addAggShkDstn(self, AggShkDstn): ''' Updates attribute IncomeDstn by combining idiosyncratic shocks with aggregate shocks. @@ -174,10 +177,9 @@ def addAggShkDstn(self,AggShkDstn): self.IncomeDstn = self.IncomeDstnWithoutAggShocks else: self.IncomeDstnWithoutAggShocks = self.IncomeDstn - self.IncomeDstn = [combineIndepDstns(self.IncomeDstn[t],AggShkDstn) for t in range(self.T_cycle)] - + self.IncomeDstn = [combineIndepDstns(self.IncomeDstn[t], AggShkDstn) for t in range(self.T_cycle)] - def simBirth(self,which_agents): + def simBirth(self, which_agents): ''' Makes new consumers for the given indices. Initialized variables include aNrm and pLvl, as well as time variables t_age and t_cycle. Normalized assets and permanent income levels @@ -192,8 +194,8 @@ def simBirth(self,which_agents): ------- None ''' - IndShockConsumerType.simBirth(self,which_agents) - if hasattr(self,'aLvlNow'): + IndShockConsumerType.simBirth(self, which_agents) + if hasattr(self, 'aLvlNow'): self.aLvlNow[which_agents] = self.aNrmNow[which_agents]*self.pLvlNow[which_agents] else: self.aLvlNow = self.aNrmNow*self.pLvlNow @@ -223,7 +225,7 @@ def simDeath(self): # Just select a random set of agents to die how_many_die = int(round(self.AgentCount*(1.0-self.LivPrb[0]))) - base_bool = np.zeros(self.AgentCount,dtype=bool) + base_bool = np.zeros(self.AgentCount, dtype=bool) base_bool[0:how_many_die] = True who_dies = self.RNG.permutation(base_bool) if self.T_age is not None: @@ -267,7 +269,7 @@ def getShocks(self): ------- None ''' - IndShockConsumerType.getShocks(self) # Update idiosyncratic shocks + IndShockConsumerType.getShocks(self) # Update idiosyncratic shocks self.TranShkNow = self.TranShkNow*self.TranShkAggNow*self.wRteNow self.PermShkNow = self.PermShkNow*self.PermShkAggNow @@ -288,13 +290,14 @@ def getControls(self): MaggNow = self.getMaggNow() for t in range(self.T_cycle): these = t == self.t_cycle - cNrmNow[these] = self.solution[t].cFunc(self.mNrmNow[these],MaggNow[these]) - MPCnow[these] = self.solution[t].cFunc.derivativeX(self.mNrmNow[these],MaggNow[these]) # Marginal propensity to consume + cNrmNow[these] = self.solution[t].cFunc(self.mNrmNow[these], MaggNow[these]) + MPCnow[these] = self.solution[t].cFunc.derivativeX(self.mNrmNow[these], + MaggNow[these]) # Marginal propensity to consume self.cNrmNow = cNrmNow self.MPCnow = MPCnow return None - def getMaggNow(self): # This function exists to be overwritten in StickyE model + def getMaggNow(self): # This function exists to be overwritten in StickyE model return self.MaggNow*np.ones(self.AgentCount) def marketAction(self): @@ -333,7 +336,7 @@ def calcBoundingValues(self): ''' raise NotImplementedError() - def makeEulerErrorFunc(self,mMax=100,approx_inc_dstn=True): + def makeEulerErrorFunc(self, mMax=100, approx_inc_dstn=True): ''' Creates a "normalized Euler error" function for this instance, mapping from market resources to "consumption error per dollar of consumption." @@ -360,8 +363,6 @@ def makeEulerErrorFunc(self,mMax=100,approx_inc_dstn=True): raise NotImplementedError() - - class AggShockMarkovConsumerType(AggShockConsumerType): ''' A class for representing ex ante heterogeneous "types" of consumers who @@ -369,13 +370,12 @@ class AggShockMarkovConsumerType(AggShockConsumerType): permanent and transitory), who lives in an environment where the macroeconomic state is subject to Markov-style discrete state evolution. ''' - def __init__(self,**kwds): - AggShockConsumerType.__init__(self,**kwds) + def __init__(self, **kwds): + AggShockConsumerType.__init__(self, **kwds) self.addToTimeInv('MrkvArray') self.solveOnePeriod = solveConsAggMarkov - - def addAggShkDstn(self,AggShkDstn): + def addAggShkDstn(self, AggShkDstn): ''' Variation on AggShockConsumerType.addAggShkDstn that handles the Markov state. AggShkDstn is a list of aggregate productivity shock distributions @@ -389,10 +389,9 @@ def addAggShkDstn(self,AggShkDstn): IncomeDstnOut = [] N = self.MrkvArray.shape[0] for t in range(self.T_cycle): - IncomeDstnOut.append([combineIndepDstns(self.IncomeDstn[t][n],AggShkDstn[n]) for n in range(N)]) + IncomeDstnOut.append([combineIndepDstns(self.IncomeDstn[t][n], AggShkDstn[n]) for n in range(N)]) self.IncomeDstn = IncomeDstnOut - def updateSolutionTerminal(self): ''' Update the terminal period solution. This method should be run when a @@ -410,8 +409,8 @@ def updateSolutionTerminal(self): # Make replicated terminal period solution StateCount = self.MrkvArray.shape[0] - self.solution_terminal.cFunc = StateCount*[self.solution_terminal.cFunc] - self.solution_terminal.vPfunc = StateCount*[self.solution_terminal.vPfunc] + self.solution_terminal.cFunc = StateCount*[self.solution_terminal.cFunc] + self.solution_terminal.vPfunc = StateCount*[self.solution_terminal.vPfunc] self.solution_terminal.mNrmMin = StateCount*[self.solution_terminal.mNrmMin] def getShocks(self): @@ -430,19 +429,21 @@ def getShocks(self): ------- None ''' - PermShkNow = np.zeros(self.AgentCount) # Initialize shock arrays + PermShkNow = np.zeros(self.AgentCount) # Initialize shock arrays TranShkNow = np.zeros(self.AgentCount) newborn = self.t_age == 0 for t in range(self.T_cycle): these = t == self.t_cycle N = np.sum(these) if N > 0: - IncomeDstnNow = self.IncomeDstn[t-1][self.MrkvNow] # set current income distribution - PermGroFacNow = self.PermGroFac[t-1] # and permanent growth factor - Indices = np.arange(IncomeDstnNow[0].size) # just a list of integers + IncomeDstnNow = self.IncomeDstn[t-1][self.MrkvNow] # set current income distribution + PermGroFacNow = self.PermGroFac[t-1] # and permanent growth factor + Indices = np.arange(IncomeDstnNow[0].size) # just a list of integers # Get random draws of income shocks from the discrete distribution - EventDraws = drawDiscrete(N,X=Indices,P=IncomeDstnNow[0],exact_match=True,seed=self.RNG.randint(0,2**31-1)) - PermShkNow[these] = IncomeDstnNow[1][EventDraws]*PermGroFacNow # permanent "shock" includes expected growth + EventDraws = drawDiscrete(N, X=Indices, P=IncomeDstnNow[0], + exact_match=True, seed=self.RNG.randint(0, 2**31-1)) + # permanent "shock" includes expected growth + PermShkNow[these] = IncomeDstnNow[1][EventDraws]*PermGroFacNow TranShkNow[these] = IncomeDstnNow[2][EventDraws] # That procedure used the *last* period in the sequence for newborns, but that's not right @@ -450,23 +451,24 @@ def getShocks(self): N = np.sum(newborn) if N > 0: these = newborn - IncomeDstnNow = self.IncomeDstn[0][self.MrkvNow] # set current income distribution - PermGroFacNow = self.PermGroFac[0] # and permanent growth factor - Indices = np.arange(IncomeDstnNow[0].size) # just a list of integers + IncomeDstnNow = self.IncomeDstn[0][self.MrkvNow] # set current income distribution + PermGroFacNow = self.PermGroFac[0] # and permanent growth factor + Indices = np.arange(IncomeDstnNow[0].size) # just a list of integers # Get random draws of income shocks from the discrete distribution - EventDraws = drawDiscrete(N,X=Indices,P=IncomeDstnNow[0],exact_match=False,seed=self.RNG.randint(0,2**31-1)) - PermShkNow[these] = IncomeDstnNow[1][EventDraws]*PermGroFacNow # permanent "shock" includes expected growth + EventDraws = drawDiscrete(N, X=Indices, P=IncomeDstnNow[0], + exact_match=False, seed=self.RNG.randint(0, 2**31-1)) + # permanent "shock" includes expected growth + PermShkNow[these] = IncomeDstnNow[1][EventDraws]*PermGroFacNow TranShkNow[these] = IncomeDstnNow[2][EventDraws] # PermShkNow[newborn] = 1.0 # TranShkNow[newborn] = 1.0 # Store the shocks in self - self.EmpNow = np.ones(self.AgentCount,dtype=bool) + self.EmpNow = np.ones(self.AgentCount, dtype=bool) self.EmpNow[TranShkNow == self.IncUnemp] = False self.TranShkNow = TranShkNow*self.TranShkAggNow*self.wRteNow self.PermShkNow = PermShkNow*self.PermShkAggNow - def getControls(self): ''' Calculates consumption for each consumer of this type using the consumption functions. @@ -488,29 +490,30 @@ def getControls(self): MrkvNow = self.getMrkvNow() StateCount = self.MrkvArray.shape[0] - MrkvBoolArray = np.zeros((StateCount,self.AgentCount),dtype=bool) + MrkvBoolArray = np.zeros((StateCount, self.AgentCount), dtype=bool) for i in range(StateCount): - MrkvBoolArray[i,:] = i == MrkvNow + MrkvBoolArray[i, :] = i == MrkvNow for t in range(self.T_cycle): these = t == self.t_cycle for i in range(StateCount): - those = np.logical_and(these,MrkvBoolArray[i,:]) - cNrmNow[those] = self.solution[t].cFunc[i](self.mNrmNow[those],MaggNow[those]) - MPCnow[those] = self.solution[t].cFunc[i].derivativeX(self.mNrmNow[those],MaggNow[those]) # Marginal propensity to consume + those = np.logical_and(these, MrkvBoolArray[i, :]) + cNrmNow[those] = self.solution[t].cFunc[i](self.mNrmNow[those], MaggNow[those]) + # Marginal propensity to consume + MPCnow[those] = self.solution[t].cFunc[i].derivativeX(self.mNrmNow[those], MaggNow[those]) self.cNrmNow = cNrmNow self.MPCnow = MPCnow return None - def getMrkvNow(self): # This function exists to be overwritten in StickyE model - return self.MrkvNow*np.ones(self.AgentCount,dtype=int) + def getMrkvNow(self): # This function exists to be overwritten in StickyE model + return self.MrkvNow*np.ones(self.AgentCount, dtype=int) ############################################################################### -def solveConsAggShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,PermGroFac, - PermGroFacAgg,aXtraGrid,BoroCnstArt,Mgrid,AFunc,Rfunc,wFunc,DeprFac): +def solveConsAggShock(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, PermGroFac, + PermGroFacAgg, aXtraGrid, BoroCnstArt, Mgrid, AFunc, Rfunc, wFunc, DeprFac): ''' Solve one period of a consumption-saving problem with idiosyncratic and aggregate shocks (transitory and permanent). This is a basic solver that @@ -566,7 +569,7 @@ def solveConsAggShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,PermGroFac, mNrmMinNext = solution_next.mNrmMin # Unpack the income shocks - ShkPrbsNext = IncomeDstn[0] + ShkPrbsNext = IncomeDstn[0] PermShkValsNext = IncomeDstn[1] TranShkValsNext = IncomeDstn[2] PermShkAggValsNext = IncomeDstn[3] @@ -577,84 +580,86 @@ def solveConsAggShock(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,PermGroFac, aNrmNow = aXtraGrid aCount = aNrmNow.size Mcount = Mgrid.size - aXtra_tiled = np.tile(np.reshape(aNrmNow,(1,aCount,1)),(Mcount,1,ShkCount)) + aXtra_tiled = np.tile(np.reshape(aNrmNow, (1, aCount, 1)), (Mcount, 1, ShkCount)) # Make tiled versions of the income shocks # Dimension order: Mnow, aNow, Shk - ShkPrbsNext_tiled = np.tile(np.reshape(ShkPrbsNext,(1,1,ShkCount)),(Mcount,aCount,1)) - PermShkValsNext_tiled = np.tile(np.reshape(PermShkValsNext,(1,1,ShkCount)),(Mcount,aCount,1)) - TranShkValsNext_tiled = np.tile(np.reshape(TranShkValsNext,(1,1,ShkCount)),(Mcount,aCount,1)) - PermShkAggValsNext_tiled = np.tile(np.reshape(PermShkAggValsNext,(1,1,ShkCount)),(Mcount,aCount,1)) - TranShkAggValsNext_tiled = np.tile(np.reshape(TranShkAggValsNext,(1,1,ShkCount)),(Mcount,aCount,1)) + ShkPrbsNext_tiled = np.tile(np.reshape(ShkPrbsNext, (1, 1, ShkCount)), (Mcount, aCount, 1)) + PermShkValsNext_tiled = np.tile(np.reshape(PermShkValsNext, (1, 1, ShkCount)), (Mcount, aCount, 1)) + TranShkValsNext_tiled = np.tile(np.reshape(TranShkValsNext, (1, 1, ShkCount)), (Mcount, aCount, 1)) + PermShkAggValsNext_tiled = np.tile(np.reshape(PermShkAggValsNext, (1, 1, ShkCount)), (Mcount, aCount, 1)) + TranShkAggValsNext_tiled = np.tile(np.reshape(TranShkAggValsNext, (1, 1, ShkCount)), (Mcount, aCount, 1)) # Calculate returns to capital and labor in the next period - AaggNow_tiled = np.tile(np.reshape(AFunc(Mgrid),(Mcount,1,1)),(1,aCount,ShkCount)) - kNext_array = AaggNow_tiled/(PermGroFacAgg*PermShkAggValsNext_tiled) # Next period's aggregate capital to labor ratio - kNextEff_array = kNext_array/TranShkAggValsNext_tiled # Same thing, but account for *transitory* shock - R_array = Rfunc(kNextEff_array) # Interest factor on aggregate assets - Reff_array = R_array/LivPrb # Effective interest factor on individual assets *for survivors* - wEff_array = wFunc(kNextEff_array)*TranShkAggValsNext_tiled # Effective wage rate (accounts for labor supply) - PermShkTotal_array = PermGroFac*PermGroFacAgg*PermShkValsNext_tiled*PermShkAggValsNext_tiled # total / combined permanent shock - Mnext_array = kNext_array*R_array + wEff_array # next period's aggregate market resources + AaggNow_tiled = np.tile(np.reshape(AFunc(Mgrid), (Mcount, 1, 1)), (1, aCount, ShkCount)) + kNext_array = AaggNow_tiled/(PermGroFacAgg*PermShkAggValsNext_tiled) # Next period's aggregate capital/labor ratio + kNextEff_array = kNext_array/TranShkAggValsNext_tiled # Same thing, but account for *transitory* shock + R_array = Rfunc(kNextEff_array) # Interest factor on aggregate assets + Reff_array = R_array/LivPrb # Effective interest factor on individual assets *for survivors* + wEff_array = wFunc(kNextEff_array)*TranShkAggValsNext_tiled # Effective wage rate (accounts for labor supply) + PermShkTotal_array = PermGroFac * PermGroFacAgg *\ + PermShkValsNext_tiled * PermShkAggValsNext_tiled # total / combined permanent shock + Mnext_array = kNext_array*R_array + wEff_array # next period's aggregate market resources # Find the natural borrowing constraint for each value of M in the Mgrid. # There is likely a faster way to do this, but someone needs to do the math: # is aNrmMin determined by getting the worst shock of all four types? - aNrmMin_candidates = PermGroFac*PermGroFacAgg*PermShkValsNext_tiled[:,0,:]*PermShkAggValsNext_tiled[:,0,:]/Reff_array[:,0,:]*\ - (mNrmMinNext(Mnext_array[:,0,:]) - wEff_array[:,0,:]*TranShkValsNext_tiled[:,0,:]) - aNrmMin_vec = np.max(aNrmMin_candidates,axis=1) + aNrmMin_candidates = PermGroFac*PermGroFacAgg*PermShkValsNext_tiled[:, 0, :] * \ + PermShkAggValsNext_tiled[:, 0, :]/Reff_array[:, 0, :] * \ + (mNrmMinNext(Mnext_array[:, 0, :]) - wEff_array[:, 0, :] * + TranShkValsNext_tiled[:, 0, :]) + aNrmMin_vec = np.max(aNrmMin_candidates, axis=1) BoroCnstNat_vec = aNrmMin_vec - aNrmMin_tiled = np.tile(np.reshape(aNrmMin_vec,(Mcount,1,1)),(1,aCount,ShkCount)) + aNrmMin_tiled = np.tile(np.reshape(aNrmMin_vec, (Mcount, 1, 1)), (1, aCount, ShkCount)) aNrmNow_tiled = aNrmMin_tiled + aXtra_tiled # Calculate market resources next period (and a constant array of capital-to-labor ratio) mNrmNext_array = Reff_array*aNrmNow_tiled/PermShkTotal_array + TranShkValsNext_tiled*wEff_array # Find marginal value next period at every income shock realization and every aggregate market resource gridpoint - vPnext_array = Reff_array*PermShkTotal_array**(-CRRA)*vPfuncNext(mNrmNext_array,Mnext_array) + vPnext_array = Reff_array*PermShkTotal_array**(-CRRA)*vPfuncNext(mNrmNext_array, Mnext_array) # Calculate expectated marginal value at the end of the period at every asset gridpoint - EndOfPrdvP = DiscFac*LivPrb*np.sum(vPnext_array*ShkPrbsNext_tiled,axis=2) + EndOfPrdvP = DiscFac*LivPrb*np.sum(vPnext_array*ShkPrbsNext_tiled, axis=2) # Calculate optimal consumption from each asset gridpoint cNrmNow = EndOfPrdvP**(-1.0/CRRA) - mNrmNow = aNrmNow_tiled[:,:,0] + cNrmNow + mNrmNow = aNrmNow_tiled[:, :, 0] + cNrmNow # Loop through the values in Mgrid and make a linear consumption function for each cFuncBaseByM_list = [] for j in range(Mcount): - c_temp = np.insert(cNrmNow[j,:],0,0.0) # Add point at bottom - m_temp = np.insert(mNrmNow[j,:] - BoroCnstNat_vec[j],0,0.0) - cFuncBaseByM_list.append(LinearInterp(m_temp,c_temp)) + c_temp = np.insert(cNrmNow[j, :], 0, 0.0) # Add point at bottom + m_temp = np.insert(mNrmNow[j, :] - BoroCnstNat_vec[j], 0, 0.0) + cFuncBaseByM_list.append(LinearInterp(m_temp, c_temp)) # Add the M-specific consumption function to the list # Construct the overall unconstrained consumption function by combining the M-specific functions - BoroCnstNat = LinearInterp(np.insert(Mgrid,0,0.0),np.insert(BoroCnstNat_vec,0,0.0)) - cFuncBase = LinearInterpOnInterp1D(cFuncBaseByM_list,Mgrid) - cFuncUnc = VariableLowerBoundFunc2D(cFuncBase,BoroCnstNat) + BoroCnstNat = LinearInterp(np.insert(Mgrid, 0, 0.0), np.insert(BoroCnstNat_vec, 0, 0.0)) + cFuncBase = LinearInterpOnInterp1D(cFuncBaseByM_list, Mgrid) + cFuncUnc = VariableLowerBoundFunc2D(cFuncBase, BoroCnstNat) # Make the constrained consumption function and combine it with the unconstrained component - cFuncCnst = BilinearInterp(np.array([[0.0,0.0],[1.0,1.0]]), - np.array([BoroCnstArt,BoroCnstArt+1.0]),np.array([0.0,1.0])) - cFuncNow = LowerEnvelope2D(cFuncUnc,cFuncCnst) + cFuncCnst = BilinearInterp(np.array([[0.0, 0.0], [1.0, 1.0]]), + np.array([BoroCnstArt, BoroCnstArt+1.0]), np.array([0.0, 1.0])) + cFuncNow = LowerEnvelope2D(cFuncUnc, cFuncCnst) # Make the minimum m function as the greater of the natural and artificial constraints - mNrmMinNow = UpperEnvelope(BoroCnstNat,ConstantFunction(BoroCnstArt)) + mNrmMinNow = UpperEnvelope(BoroCnstNat, ConstantFunction(BoroCnstArt)) # Construct the marginal value function using the envelope condition - vPfuncNow = MargValueFunc2D(cFuncNow,CRRA) + vPfuncNow = MargValueFunc2D(cFuncNow, CRRA) # Pack up and return the solution - solution_now = ConsumerSolution(cFunc=cFuncNow,vPfunc=vPfuncNow,mNrmMin=mNrmMinNow) + solution_now = ConsumerSolution(cFunc=cFuncNow, vPfunc=vPfuncNow, mNrmMin=mNrmMinNow) return solution_now ############################################################################### - -def solveConsAggMarkov(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,MrkvArray, - PermGroFac,PermGroFacAgg,aXtraGrid,BoroCnstArt,Mgrid, - AFunc,Rfunc,wFunc,DeprFac): +def solveConsAggMarkov(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, MrkvArray, + PermGroFac, PermGroFacAgg, aXtraGrid, BoroCnstArt, Mgrid, + AFunc, Rfunc, wFunc, DeprFac): ''' Solve one period of a consumption-saving problem with idiosyncratic and aggregate shocks (transitory and permanent). Moreover, the macroeconomic @@ -728,21 +733,21 @@ def solveConsAggMarkov(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,MrkvArray, mNrmMinNext = solution_next.mNrmMin[j] # Unpack the income shocks - ShkPrbsNext = IncomeDstn[j][0] + ShkPrbsNext = IncomeDstn[j][0] PermShkValsNext = IncomeDstn[j][1] TranShkValsNext = IncomeDstn[j][2] PermShkAggValsNext = IncomeDstn[j][3] TranShkAggValsNext = IncomeDstn[j][4] ShkCount = ShkPrbsNext.size - aXtra_tiled = np.tile(np.reshape(aXtraGrid,(1,aCount,1)),(Mcount,1,ShkCount)) + aXtra_tiled = np.tile(np.reshape(aXtraGrid, (1, aCount, 1)), (Mcount, 1, ShkCount)) # Make tiled versions of the income shocks # Dimension order: Mnow, aNow, Shk - ShkPrbsNext_tiled = np.tile(np.reshape(ShkPrbsNext,(1,1,ShkCount)),(Mcount,aCount,1)) - PermShkValsNext_tiled = np.tile(np.reshape(PermShkValsNext,(1,1,ShkCount)),(Mcount,aCount,1)) - TranShkValsNext_tiled = np.tile(np.reshape(TranShkValsNext,(1,1,ShkCount)),(Mcount,aCount,1)) - PermShkAggValsNext_tiled = np.tile(np.reshape(PermShkAggValsNext,(1,1,ShkCount)),(Mcount,aCount,1)) - TranShkAggValsNext_tiled = np.tile(np.reshape(TranShkAggValsNext,(1,1,ShkCount)),(Mcount,aCount,1)) + ShkPrbsNext_tiled = np.tile(np.reshape(ShkPrbsNext, (1, 1, ShkCount)), (Mcount, aCount, 1)) + PermShkValsNext_tiled = np.tile(np.reshape(PermShkValsNext, (1, 1, ShkCount)), (Mcount, aCount, 1)) + TranShkValsNext_tiled = np.tile(np.reshape(TranShkValsNext, (1, 1, ShkCount)), (Mcount, aCount, 1)) + PermShkAggValsNext_tiled = np.tile(np.reshape(PermShkAggValsNext, (1, 1, ShkCount)), (Mcount, aCount, 1)) + TranShkAggValsNext_tiled = np.tile(np.reshape(TranShkAggValsNext, (1, 1, ShkCount)), (Mcount, aCount, 1)) # Make a tiled grid of end-of-period aggregate assets. These lines use # next prd state j's aggregate saving rule to get a relevant set of Aagg, @@ -757,47 +762,52 @@ def solveConsAggMarkov(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,MrkvArray, # conditional marginal value functions are constructed is not relevant # to the values at which it will actually be evaluated. AaggGrid = AFunc[j](Mgrid) - AaggNow_tiled = np.tile(np.reshape(AaggGrid,(Mcount,1,1)),(1,aCount,ShkCount)) + AaggNow_tiled = np.tile(np.reshape(AaggGrid, (Mcount, 1, 1)), (1, aCount, ShkCount)) # Calculate returns to capital and labor in the next period - kNext_array = AaggNow_tiled/(PermGroFacAgg[j]*PermShkAggValsNext_tiled) # Next period's aggregate capital to labor ratio - kNextEff_array = kNext_array/TranShkAggValsNext_tiled # Same thing, but account for *transitory* shock - R_array = Rfunc(kNextEff_array) # Interest factor on aggregate assets - Reff_array = R_array/LivPrb # Effective interest factor on individual assets *for survivors* - wEff_array = wFunc(kNextEff_array)*TranShkAggValsNext_tiled # Effective wage rate (accounts for labor supply) - PermShkTotal_array = PermGroFac*PermGroFacAgg[j]*PermShkValsNext_tiled*PermShkAggValsNext_tiled # total / combined permanent shock - Mnext_array = kNext_array*R_array + wEff_array # next period's aggregate market resources + kNext_array = AaggNow_tiled/(PermGroFacAgg[j] * + PermShkAggValsNext_tiled) # Next period's aggregate capital to labor ratio + kNextEff_array = kNext_array/TranShkAggValsNext_tiled # Same thing, but account for *transitory* shock + R_array = Rfunc(kNextEff_array) # Interest factor on aggregate assets + Reff_array = R_array/LivPrb # Effective interest factor on individual assets *for survivors* + wEff_array = wFunc(kNextEff_array)*TranShkAggValsNext_tiled # Effective wage rate (accounts for labor supply) + PermShkTotal_array = PermGroFac*PermGroFacAgg[j] * \ + PermShkValsNext_tiled*PermShkAggValsNext_tiled # total / combined permanent shock + Mnext_array = kNext_array*R_array + wEff_array # next period's aggregate market resources # Find the natural borrowing constraint for each value of M in the Mgrid. # There is likely a faster way to do this, but someone needs to do the math: # is aNrmMin determined by getting the worst shock of all four types? - aNrmMin_candidates = PermGroFac*PermGroFacAgg[j]*PermShkValsNext_tiled[:,0,:]*PermShkAggValsNext_tiled[:,0,:]/Reff_array[:,0,:]*\ - (mNrmMinNext(Mnext_array[:,0,:]) - wEff_array[:,0,:]*TranShkValsNext_tiled[:,0,:]) - aNrmMin_vec = np.max(aNrmMin_candidates,axis=1) + aNrmMin_candidates = PermGroFac*PermGroFacAgg[j]*PermShkValsNext_tiled[:, 0, :] * \ + PermShkAggValsNext_tiled[:, 0, :]/Reff_array[:, 0, :] * \ + (mNrmMinNext(Mnext_array[:, 0, :]) - wEff_array[:, 0, :]*TranShkValsNext_tiled[:, 0, :]) + aNrmMin_vec = np.max(aNrmMin_candidates, axis=1) BoroCnstNat_vec = aNrmMin_vec - aNrmMin_tiled = np.tile(np.reshape(aNrmMin_vec,(Mcount,1,1)),(1,aCount,ShkCount)) + aNrmMin_tiled = np.tile(np.reshape(aNrmMin_vec, (Mcount, 1, 1)), (1, aCount, ShkCount)) aNrmNow_tiled = aNrmMin_tiled + aXtra_tiled # Calculate market resources next period (and a constant array of capital-to-labor ratio) mNrmNext_array = Reff_array*aNrmNow_tiled/PermShkTotal_array + TranShkValsNext_tiled*wEff_array - # Find marginal value next period at every income shock realization and every aggregate market resource gridpoint - vPnext_array = Reff_array*PermShkTotal_array**(-CRRA)*vPfuncNext(mNrmNext_array,Mnext_array) + # Find marginal value next period at every income shock + # realization and every aggregate market resource gridpoint + vPnext_array = Reff_array*PermShkTotal_array**(-CRRA)*vPfuncNext(mNrmNext_array, Mnext_array) # Calculate expectated marginal value at the end of the period at every asset gridpoint - EndOfPrdvP = DiscFac*LivPrb*np.sum(vPnext_array*ShkPrbsNext_tiled,axis=2) + EndOfPrdvP = DiscFac*LivPrb*np.sum(vPnext_array*ShkPrbsNext_tiled, axis=2) # Make the conditional end-of-period marginal value function - BoroCnstNat = LinearInterp(np.insert(AaggGrid,0,0.0),np.insert(BoroCnstNat_vec,0,0.0)) - EndOfPrdvPnvrs = np.concatenate((np.zeros((Mcount,1)),EndOfPrdvP**(-1./CRRA)),axis=1) - EndOfPrdvPnvrsFunc_base = BilinearInterp(np.transpose(EndOfPrdvPnvrs),np.insert(aXtraGrid,0,0.0),AaggGrid) - EndOfPrdvPnvrsFunc = VariableLowerBoundFunc2D(EndOfPrdvPnvrsFunc_base,BoroCnstNat) - EndOfPrdvPfunc_cond.append(MargValueFunc2D(EndOfPrdvPnvrsFunc,CRRA)) + BoroCnstNat = LinearInterp(np.insert(AaggGrid, 0, 0.0), np.insert(BoroCnstNat_vec, 0, 0.0)) + EndOfPrdvPnvrs = np.concatenate((np.zeros((Mcount, 1)), EndOfPrdvP**(-1./CRRA)), axis=1) + EndOfPrdvPnvrsFunc_base = BilinearInterp(np.transpose(EndOfPrdvPnvrs), np.insert(aXtraGrid, 0, 0.0), AaggGrid) + EndOfPrdvPnvrsFunc = VariableLowerBoundFunc2D(EndOfPrdvPnvrsFunc_base, BoroCnstNat) + EndOfPrdvPfunc_cond.append(MargValueFunc2D(EndOfPrdvPnvrsFunc, CRRA)) BoroCnstNat_cond.append(BoroCnstNat) # Prepare some objects that are the same across all current states - aXtra_tiled = np.tile(np.reshape(aXtraGrid,(1,aCount)),(Mcount,1)) - cFuncCnst = BilinearInterp(np.array([[0.0,0.0],[1.0,1.0]]),np.array([BoroCnstArt,BoroCnstArt+1.0]),np.array([0.0,1.0])) + aXtra_tiled = np.tile(np.reshape(aXtraGrid, (1, aCount)), (Mcount, 1)) + cFuncCnst = BilinearInterp(np.array([[0.0, 0.0], [1.0, 1.0]]), + np.array([BoroCnstArt, BoroCnstArt+1.0]), np.array([0.0, 1.0])) # Now loop through *this* period's discrete states, calculating end-of-period # marginal value (weighting across state transitions), then construct consumption @@ -808,24 +818,24 @@ def solveConsAggMarkov(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,MrkvArray, for i in range(StateCount): # Find natural borrowing constraint for this state by Aagg AaggNow = AFunc[i](Mgrid) - aNrmMin_candidates = np.zeros((StateCount,Mcount)) + np.nan + aNrmMin_candidates = np.zeros((StateCount, Mcount)) + np.nan for j in range(StateCount): - if MrkvArray[i,j] > 0.: # Irrelevant if transition is impossible - aNrmMin_candidates[j,:] = BoroCnstNat_cond[j](AaggNow) - aNrmMin_vec = np.nanmax(aNrmMin_candidates,axis=0) + if MrkvArray[i, j] > 0.: # Irrelevant if transition is impossible + aNrmMin_candidates[j, :] = BoroCnstNat_cond[j](AaggNow) + aNrmMin_vec = np.nanmax(aNrmMin_candidates, axis=0) BoroCnstNat_vec = aNrmMin_vec # Make tiled grids of aNrm and Aagg - aNrmMin_tiled = np.tile(np.reshape(aNrmMin_vec,(Mcount,1)),(1,aCount)) + aNrmMin_tiled = np.tile(np.reshape(aNrmMin_vec, (Mcount, 1)), (1, aCount)) aNrmNow_tiled = aNrmMin_tiled + aXtra_tiled - AaggNow_tiled = np.tile(np.reshape(AaggNow,(Mcount,1)),(1,aCount)) + AaggNow_tiled = np.tile(np.reshape(AaggNow, (Mcount, 1)), (1, aCount)) # Loop through feasible transitions and calculate end-of-period marginal value - EndOfPrdvP = np.zeros((Mcount,aCount)) + EndOfPrdvP = np.zeros((Mcount, aCount)) for j in range(StateCount): - if MrkvArray[i,j] > 0.: - temp = EndOfPrdvPfunc_cond[j](aNrmNow_tiled,AaggNow_tiled) - EndOfPrdvP += MrkvArray[i,j]*temp + if MrkvArray[i, j] > 0.: + temp = EndOfPrdvPfunc_cond[j](aNrmNow_tiled, AaggNow_tiled) + EndOfPrdvP += MrkvArray[i, j]*temp # Calculate consumption and the endogenous mNrm gridpoints for this state cNrmNow = EndOfPrdvP**(-1./CRRA) @@ -834,34 +844,33 @@ def solveConsAggMarkov(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,MrkvArray, # Loop through the values in Mgrid and make a piecewise linear consumption function for each cFuncBaseByM_list = [] for n in range(Mcount): - c_temp = np.insert(cNrmNow[n,:],0,0.0) # Add point at bottom - m_temp = np.insert(mNrmNow[n,:] - BoroCnstNat_vec[n],0,0.0) - cFuncBaseByM_list.append(LinearInterp(m_temp,c_temp)) + c_temp = np.insert(cNrmNow[n, :], 0, 0.0) # Add point at bottom + m_temp = np.insert(mNrmNow[n, :] - BoroCnstNat_vec[n], 0, 0.0) + cFuncBaseByM_list.append(LinearInterp(m_temp, c_temp)) # Add the M-specific consumption function to the list # Construct the unconstrained consumption function by combining the M-specific functions - BoroCnstNat = LinearInterp(np.insert(Mgrid,0,0.0),np.insert(BoroCnstNat_vec,0,0.0)) - cFuncBase = LinearInterpOnInterp1D(cFuncBaseByM_list,Mgrid) - cFuncUnc = VariableLowerBoundFunc2D(cFuncBase,BoroCnstNat) + BoroCnstNat = LinearInterp(np.insert(Mgrid, 0, 0.0), np.insert(BoroCnstNat_vec, 0, 0.0)) + cFuncBase = LinearInterpOnInterp1D(cFuncBaseByM_list, Mgrid) + cFuncUnc = VariableLowerBoundFunc2D(cFuncBase, BoroCnstNat) # Combine the constrained consumption function with unconstrained component - cFuncNow.append(LowerEnvelope2D(cFuncUnc,cFuncCnst)) + cFuncNow.append(LowerEnvelope2D(cFuncUnc, cFuncCnst)) # Make the minimum m function as the greater of the natural and artificial constraints - mNrmMinNow.append(UpperEnvelope(BoroCnstNat,ConstantFunction(BoroCnstArt))) + mNrmMinNow.append(UpperEnvelope(BoroCnstNat, ConstantFunction(BoroCnstArt))) # Construct the marginal value function using the envelope condition - vPfuncNow.append(MargValueFunc2D(cFuncNow[-1],CRRA)) + vPfuncNow.append(MargValueFunc2D(cFuncNow[-1], CRRA)) # Pack up and return the solution - solution_now = ConsumerSolution(cFunc=cFuncNow,vPfunc=vPfuncNow,mNrmMin=mNrmMinNow) + solution_now = ConsumerSolution(cFunc=cFuncNow, vPfunc=vPfuncNow, mNrmMin=mNrmMinNow) return solution_now ############################################################################### - class CobbDouglasEconomy(Market): ''' A class to represent an economy with a Cobb-Douglas aggregate production @@ -873,7 +882,7 @@ class CobbDouglasEconomy(Market): Note: The current implementation assumes a constant labor supply, but this will be generalized in the future. ''' - def __init__(self,agents=[],tolerance=0.0001,act_T=1000,**kwds): + def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds): ''' Make a new instance of CobbDouglasEconomy by filling in attributes specific to this kind of market. @@ -893,46 +902,46 @@ def __init__(self,agents=[],tolerance=0.0001,act_T=1000,**kwds): ------- None ''' - Market.__init__(self,agents=agents, - sow_vars=['MaggNow','AaggNow','RfreeNow','wRteNow','PermShkAggNow','TranShkAggNow','KtoLnow'], - reap_vars=['aLvlNow','pLvlNow'], - track_vars=['MaggNow','AaggNow'], - dyn_vars=['AFunc'], - tolerance=tolerance, - act_T=act_T) + Market.__init__(self, agents=agents, + sow_vars=['MaggNow', 'AaggNow', 'RfreeNow', + 'wRteNow', 'PermShkAggNow', 'TranShkAggNow', 'KtoLnow'], + reap_vars=['aLvlNow', 'pLvlNow'], + track_vars=['MaggNow', 'AaggNow'], + dyn_vars=['AFunc'], + tolerance=tolerance, + act_T=act_T) self.assignParameters(**kwds) self.update() - + # Use previously hardcoded values for AFunc updating if not passed # as part of initialization dictionary. This is to prevent a last # minute update to HARK before a release from having a breaking change. - if not hasattr(self,'DampingFac'): + if not hasattr(self, 'DampingFac'): self.DampingFac = 0.5 - if not hasattr(self,'max_loops'): + if not hasattr(self, 'max_loops'): self.max_loops = 20 - if not hasattr(self,'T_discard'): + if not hasattr(self, 'T_discard'): self.T_discard = 200 - if not hasattr(self,'verbose'): + if not hasattr(self, 'verbose'): self.verbose = True - - def millRule(self,aLvlNow,pLvlNow): + def millRule(self, aLvlNow, pLvlNow): ''' Function to calculate the capital to labor ratio, interest factor, and wage rate based on each agent's current state. Just calls calcRandW(). See documentation for calcRandW for more information. ''' - return self.calcRandW(aLvlNow,pLvlNow) + return self.calcRandW(aLvlNow, pLvlNow) - def calcDynamics(self,MaggNow,AaggNow): + def calcDynamics(self, MaggNow, AaggNow): ''' Calculates a new dynamic rule for the economy: end of period savings as a function of aggregate market resources. Just calls calcAFunc(). See documentation for calcAFunc for more information. ''' - return self.calcAFunc(MaggNow,AaggNow) + return self.calcAFunc(MaggNow, AaggNow) def update(self): ''' @@ -948,14 +957,15 @@ def update(self): ------- None ''' - self.kSS = ((self.getPermGroFacAggLR()**(self.CRRA)/self.DiscFac - (1.0-self.DeprFac))/self.CapShare)**(1.0/(self.CapShare-1.0)) + self.kSS = ((self.getPermGroFacAggLR() ** + (self.CRRA)/self.DiscFac - (1.0-self.DeprFac))/self.CapShare)**(1.0/(self.CapShare-1.0)) self.KtoYSS = self.kSS**(1.0-self.CapShare) self.wRteSS = (1.0-self.CapShare)*self.kSS**(self.CapShare) self.RfreeSS = (1.0 + self.CapShare*self.kSS**(self.CapShare-1.0) - self.DeprFac) self.MSS = self.kSS*self.RfreeSS + self.wRteSS - self.convertKtoY = lambda KtoY : KtoY**(1.0/(1.0 - self.CapShare)) # converts K/Y to K/L - self.Rfunc = lambda k : (1.0 + self.CapShare*k**(self.CapShare-1.0) - self.DeprFac) - self.wFunc = lambda k : ((1.0-self.CapShare)*k**(self.CapShare)) + self.convertKtoY = lambda KtoY: KtoY**(1.0/(1.0 - self.CapShare)) # converts K/Y to K/L + self.Rfunc = lambda k: (1.0 + self.CapShare*k**(self.CapShare-1.0) - self.DeprFac) + self.wFunc = lambda k: ((1.0-self.CapShare)*k**(self.CapShare)) self.KtoLnow_init = self.kSS self.MaggNow_init = self.kSS self.AaggNow_init = self.kSS @@ -964,8 +974,7 @@ def update(self): self.PermShkAggNow_init = 1.0 self.TranShkAggNow_init = 1.0 self.makeAggShkDstn() - self.AFunc = AggregateSavingRule(self.intercept_prev,self.slope_prev) - + self.AFunc = AggregateSavingRule(self.intercept_prev, self.slope_prev) def getPermGroFacAggLR(self): ''' @@ -984,7 +993,6 @@ def getPermGroFacAggLR(self): ''' return self.PermGroFacAgg - def makeAggShkDstn(self): ''' Creates the attributes TranShkAggDstn, PermShkAggDstn, and AggShkDstn. @@ -998,9 +1006,9 @@ def makeAggShkDstn(self): ------- None ''' - self.TranShkAggDstn = approxMeanOneLognormal(sigma=self.TranShkAggStd,N=self.TranShkAggCount) - self.PermShkAggDstn = approxMeanOneLognormal(sigma=self.PermShkAggStd,N=self.PermShkAggCount) - self.AggShkDstn = combineIndepDstns(self.PermShkAggDstn,self.TranShkAggDstn) + self.TranShkAggDstn = approxMeanOneLognormal(sigma=self.TranShkAggStd, N=self.TranShkAggCount) + self.PermShkAggDstn = approxMeanOneLognormal(sigma=self.PermShkAggStd, N=self.PermShkAggCount) + self.AggShkDstn = combineIndepDstns(self.PermShkAggDstn, self.TranShkAggDstn) def reset(self): ''' @@ -1033,8 +1041,8 @@ def makeAggShkHist(self): None ''' sim_periods = self.act_T - Events = np.arange(self.AggShkDstn[0].size) # just a list of integers - EventDraws = drawDiscrete(N=sim_periods,P=self.AggShkDstn[0],X=Events,seed=0) + Events = np.arange(self.AggShkDstn[0].size) # just a list of integers + EventDraws = drawDiscrete(N=sim_periods, P=self.AggShkDstn[0], X=Events, seed=0) PermShkAggHist = self.AggShkDstn[1][EventDraws] TranShkAggHist = self.AggShkDstn[2][EventDraws] @@ -1042,7 +1050,7 @@ def makeAggShkHist(self): self.PermShkAggHist = PermShkAggHist*self.PermGroFacAgg self.TranShkAggHist = TranShkAggHist - def calcRandW(self,aLvlNow,pLvlNow): + def calcRandW(self, aLvlNow, pLvlNow): ''' Calculates the interest factor and wage rate this period using each agent's capital stock to get the aggregate capital ratio. @@ -1061,9 +1069,9 @@ def calcRandW(self,aLvlNow,pLvlNow): aggregate permanent and transitory shocks. ''' # Calculate aggregate savings - AaggPrev = np.mean(np.array(aLvlNow))/np.mean(pLvlNow) # End-of-period savings from last period + AaggPrev = np.mean(np.array(aLvlNow))/np.mean(pLvlNow) # End-of-period savings from last period # Calculate aggregate capital this period - AggregateK = np.mean(np.array(aLvlNow)) # ...becomes capital today + AggregateK = np.mean(np.array(aLvlNow)) # ...becomes capital today # This version uses end-of-period assets and # permanent income to calculate aggregate capital, unlike the Mathematica # version, which first applies the idiosyncratic permanent income shocks @@ -1080,15 +1088,15 @@ def calcRandW(self,aLvlNow,pLvlNow): KtoLnow = AggregateK/AggregateL self.KtoYnow = KtoLnow**(1.0-self.CapShare) RfreeNow = self.Rfunc(KtoLnow/TranShkAggNow) - wRteNow = self.wFunc(KtoLnow/TranShkAggNow) - MaggNow = KtoLnow*RfreeNow + wRteNow*TranShkAggNow + wRteNow = self.wFunc(KtoLnow/TranShkAggNow) + MaggNow = KtoLnow*RfreeNow + wRteNow*TranShkAggNow self.KtoLnow = KtoLnow # Need to store this as it is a sow variable # Package the results into an object and return it - AggVarsNow = CobbDouglasAggVars(MaggNow,AaggPrev,KtoLnow,RfreeNow,wRteNow,PermShkAggNow,TranShkAggNow) + AggVarsNow = CobbDouglasAggVars(MaggNow, AaggPrev, KtoLnow, RfreeNow, wRteNow, PermShkAggNow, TranShkAggNow) return AggVarsNow - def calcAFunc(self,MaggNow,AaggNow): + def calcAFunc(self, MaggNow, AaggNow): ''' Calculate a new aggregate savings rule based on the history of the aggregate savings and aggregate market resources from a simulation. @@ -1106,20 +1114,20 @@ def calcAFunc(self,MaggNow,AaggNow): Object containing a new savings rule ''' verbose = self.verbose - discard_periods = self.T_discard # Throw out the first T periods to allow the simulation to approach the SS + discard_periods = self.T_discard # Throw out the first T periods to allow the simulation to approach the SS update_weight = 1. - self.DampingFac # Proportional weight to put on new function vs old function parameters total_periods = len(MaggNow) # Regress the log savings against log market resources - logAagg = np.log(AaggNow[discard_periods:total_periods]) + logAagg = np.log(AaggNow[discard_periods:total_periods]) logMagg = np.log(MaggNow[discard_periods-1:total_periods-1]) - slope, intercept, r_value, p_value, std_err = stats.linregress(logMagg,logAagg) + slope, intercept, r_value, p_value, std_err = stats.linregress(logMagg, logAagg) # Make a new aggregate savings rule by combining the new regression parameters # with the previous guess intercept = update_weight*intercept + (1.0-update_weight)*self.intercept_prev slope = update_weight*slope + (1.0-update_weight)*self.slope_prev - AFunc = AggregateSavingRule(intercept,slope) # Make a new next-period capital function + AFunc = AggregateSavingRule(intercept, slope) # Make a new next-period capital function # Save the new values as "previous" values for the next iteration self.intercept_prev = intercept @@ -1128,9 +1136,9 @@ def calcAFunc(self,MaggNow,AaggNow): # Plot aggregate resources vs aggregate savings for this run and print the new parameters if verbose: print('intercept=' + str(intercept) + ', slope=' + str(slope) + ', r-sq=' + str(r_value**2)) - #plot_start = discard_periods - #plt.plot(logMagg[plot_start:],logAagg[plot_start:],'.k') - #plt.show() + # plot_start = discard_periods + # plt.plot(logMagg[plot_start:],logAagg[plot_start:],'.k') + # plt.show() return AggShocksDynamicRule(AFunc) @@ -1141,7 +1149,7 @@ class SmallOpenEconomy(Market): exogenously determined by some "global" rate. However, the economy is still subject to aggregate productivity shocks. ''' - def __init__(self,agents=[],tolerance=0.0001,act_T=1000,**kwds): + def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds): ''' Make a new instance of SmallOpenEconomy by filling in attributes specific to this kind of market. @@ -1160,13 +1168,15 @@ def __init__(self,agents=[],tolerance=0.0001,act_T=1000,**kwds): ------- None ''' - Market.__init__(self,agents=agents, - sow_vars=['MaggNow','AaggNow','RfreeNow','wRteNow','PermShkAggNow','TranShkAggNow','KtoLnow'], - reap_vars=[], - track_vars=['MaggNow','AaggNow'], - dyn_vars=[], - tolerance=tolerance, - act_T=act_T) + Market.__init__(self, + agents=agents, + sow_vars=['MaggNow', 'AaggNow', 'RfreeNow', 'wRteNow', + 'PermShkAggNow', 'TranShkAggNow', 'KtoLnow'], + reap_vars=[], + track_vars=['MaggNow', 'AaggNow'], + dyn_vars=[], + tolerance=tolerance, + act_T=act_T) self.assignParameters(**kwds) self.update() @@ -1210,9 +1220,9 @@ def makeAggShkDstn(self): ------- None ''' - self.TranShkAggDstn = approxMeanOneLognormal(sigma=self.TranShkAggStd,N=self.TranShkAggCount) - self.PermShkAggDstn = approxMeanOneLognormal(sigma=self.PermShkAggStd,N=self.PermShkAggCount) - self.AggShkDstn = combineIndepDstns(self.PermShkAggDstn,self.TranShkAggDstn) + self.TranShkAggDstn = approxMeanOneLognormal(sigma=self.TranShkAggStd, N=self.TranShkAggCount) + self.PermShkAggDstn = approxMeanOneLognormal(sigma=self.PermShkAggStd, N=self.PermShkAggCount) + self.AggShkDstn = combineIndepDstns(self.PermShkAggDstn, self.TranShkAggDstn) def millRule(self): ''' @@ -1223,7 +1233,7 @@ def millRule(self): ''' return self.getAggShocks() - def calcDynamics(self,KtoLnow): + def calcDynamics(self, KtoLnow): ''' Calculates a new dynamic rule for the economy, which is just an empty object. There is no "dynamic rule" for a small open economy, because K/L does not generate w and R. @@ -1262,8 +1272,8 @@ def makeAggShkHist(self): None ''' sim_periods = self.act_T - Events = np.arange(self.AggShkDstn[0].size) # just a list of integers - EventDraws = drawDiscrete(N=sim_periods,P=self.AggShkDstn[0],X=Events,seed=0) + Events = np.arange(self.AggShkDstn[0].size) # just a list of integers + EventDraws = drawDiscrete(N=sim_periods, P=self.AggShkDstn[0], X=Events, seed=0) PermShkAggHist = self.AggShkDstn[1][EventDraws] TranShkAggHist = self.AggShkDstn[2][EventDraws] @@ -1293,7 +1303,7 @@ def getAggShocks(self): # Factor prices are constant RfreeNow = self.Rfunc(1.0/PermShkAggNow) - wRteNow = self.wFunc(1.0/PermShkAggNow) + wRteNow = self.wFunc(1.0/PermShkAggNow) # Aggregates are irrelavent AaggNow = 1.0 @@ -1301,7 +1311,7 @@ def getAggShocks(self): KtoLnow = 1.0/PermShkAggNow # Package the results into an object and return it - AggVarsNow = CobbDouglasAggVars(MaggNow,AaggNow,KtoLnow,RfreeNow,wRteNow,PermShkAggNow,TranShkAggNow) + AggVarsNow = CobbDouglasAggVars(MaggNow, AaggNow, KtoLnow, RfreeNow, wRteNow, PermShkAggNow, TranShkAggNow) return AggVarsNow @@ -1316,7 +1326,7 @@ class CobbDouglasMarkovEconomy(CobbDouglasEconomy): productivity growth factor can vary over time. ''' - def __init__(self,agents=[],tolerance=0.0001,act_T=1000,**kwds): + def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds): ''' Make a new instance of CobbDouglasMarkovEconomy by filling in attributes specific to this kind of market. @@ -1336,10 +1346,9 @@ def __init__(self,agents=[],tolerance=0.0001,act_T=1000,**kwds): ------- None ''' - CobbDouglasEconomy.__init__(self,agents=agents,tolerance=tolerance,act_T=act_T,**kwds) + CobbDouglasEconomy.__init__(self, agents=agents, tolerance=tolerance, act_T=act_T, **kwds) self.sow_vars.append('MrkvNow') - def update(self): ''' Use primitive parameters (and perfect foresight calibrations) to make @@ -1358,10 +1367,9 @@ def update(self): StateCount = self.MrkvArray.shape[0] AFunc_all = [] for i in range(StateCount): - AFunc_all.append(AggregateSavingRule(self.intercept_prev[i],self.slope_prev[i])) + AFunc_all.append(AggregateSavingRule(self.intercept_prev[i], self.slope_prev[i])) self.AFunc = AFunc_all - def getPermGroFacAggLR(self): ''' Calculates and returns the long run permanent income growth factor. This @@ -1380,14 +1388,13 @@ def getPermGroFacAggLR(self): # Find the long run distribution of Markov states w, v = np.linalg.eig(np.transpose(self.MrkvArray)) idx = (np.abs(w-1.0)).argmin() - x = v[:,idx].astype(float) + x = v[:, idx].astype(float) LR_dstn = (x/np.sum(x)) # Return the weighted average of aggregate permanent income growth factors - PermGroFacAggLR = np.dot(LR_dstn,np.array(self.PermGroFacAgg)) + PermGroFacAggLR = np.dot(LR_dstn, np.array(self.PermGroFacAgg)) return PermGroFacAggLR - def makeAggShkDstn(self): ''' Creates the attributes TranShkAggDstn, PermShkAggDstn, and AggShkDstn. @@ -1408,15 +1415,14 @@ def makeAggShkDstn(self): StateCount = self.MrkvArray.shape[0] for i in range(StateCount): - TranShkAggDstn.append(approxMeanOneLognormal(sigma=self.TranShkAggStd[i],N=self.TranShkAggCount)) - PermShkAggDstn.append(approxMeanOneLognormal(sigma=self.PermShkAggStd[i],N=self.PermShkAggCount)) - AggShkDstn.append(combineIndepDstns(PermShkAggDstn[-1],TranShkAggDstn[-1])) + TranShkAggDstn.append(approxMeanOneLognormal(sigma=self.TranShkAggStd[i], N=self.TranShkAggCount)) + PermShkAggDstn.append(approxMeanOneLognormal(sigma=self.PermShkAggStd[i], N=self.PermShkAggCount)) + AggShkDstn.append(combineIndepDstns(PermShkAggDstn[-1], TranShkAggDstn[-1])) self.TranShkAggDstn = TranShkAggDstn self.PermShkAggDstn = PermShkAggDstn self.AggShkDstn = AggShkDstn - def makeAggShkHist(self): ''' Make simulated histories of aggregate transitory and permanent shocks. @@ -1432,19 +1438,19 @@ def makeAggShkHist(self): ------- None ''' - self.makeMrkvHist() # Make a (pseudo)random sequence of Markov states + self.makeMrkvHist() # Make a (pseudo)random sequence of Markov states sim_periods = self.act_T # For each Markov state in each simulated period, draw the aggregate shocks # that would occur in that state in that period StateCount = self.MrkvArray.shape[0] - PermShkAggHistAll = np.zeros((StateCount,sim_periods)) - TranShkAggHistAll = np.zeros((StateCount,sim_periods)) + PermShkAggHistAll = np.zeros((StateCount, sim_periods)) + TranShkAggHistAll = np.zeros((StateCount, sim_periods)) for i in range(StateCount): - Events = np.arange(self.AggShkDstn[i][0].size) # just a list of integers - EventDraws = drawDiscrete(N=sim_periods,P=self.AggShkDstn[i][0],X=Events,seed=0) - PermShkAggHistAll[i,:] = self.AggShkDstn[i][1][EventDraws] - TranShkAggHistAll[i,:] = self.AggShkDstn[i][2][EventDraws] + Events = np.arange(self.AggShkDstn[i][0].size) # just a list of integers + EventDraws = drawDiscrete(N=sim_periods, P=self.AggShkDstn[i][0], X=Events, seed=0) + PermShkAggHistAll[i, :] = self.AggShkDstn[i][1][EventDraws] + TranShkAggHistAll[i, :] = self.AggShkDstn[i][2][EventDraws] # Select the actual history of aggregate shocks based on the sequence # of Markov states that the economy experiences @@ -1452,14 +1458,13 @@ def makeAggShkHist(self): TranShkAggHist = np.zeros(sim_periods) for i in range(StateCount): these = i == self.MrkvNow_hist - PermShkAggHist[these] = PermShkAggHistAll[i,these]*self.PermGroFacAgg[i] - TranShkAggHist[these] = TranShkAggHistAll[i,these] + PermShkAggHist[these] = PermShkAggHistAll[i, these]*self.PermGroFacAgg[i] + TranShkAggHist[these] = TranShkAggHistAll[i, these] # Store the histories self.PermShkAggHist = PermShkAggHist self.TranShkAggHist = TranShkAggHist - def makeMrkvHist(self): ''' Makes a history of macroeconomic Markov states, stored in the attribute @@ -1476,32 +1481,32 @@ def makeMrkvHist(self): ------- None ''' - if hasattr(self,'loops_max'): + if hasattr(self, 'loops_max'): loops_max = self.loops_max - else: # Maximum number of loops; final act_T never exceeds act_T*loops_max - loops_max = 10 + else: # Maximum number of loops; final act_T never exceeds act_T*loops_max + loops_max = 10 - state_T_min = 50 # Choose minimum number of periods in each state for a valid Markov sequence - logit_scale = 0.2 # Scaling factor on logit choice shocks when jumping to a new state + state_T_min = 50 # Choose minimum number of periods in each state for a valid Markov sequence + logit_scale = 0.2 # Scaling factor on logit choice shocks when jumping to a new state # Values close to zero make the most underrepresented states very likely to visit, while # large values of logit_scale make any state very likely to be jumped to. # Reset act_T to the level actually specified by the user - if hasattr(self,'act_T_orig'): + if hasattr(self, 'act_T_orig'): act_T = self.act_T_orig - else: # Or store it for the first time + else: # Or store it for the first time self.act_T_orig = self.act_T act_T = self.act_T # Find the long run distribution of Markov states w, v = np.linalg.eig(np.transpose(self.MrkvArray)) idx = (np.abs(w-1.0)).argmin() - x = v[:,idx].astype(float) + x = v[:, idx].astype(float) LR_dstn = (x/np.sum(x)) # Initialize the Markov history and set up transitions - MrkvNow_hist = np.zeros(self.act_T_orig,dtype=int) - cutoffs = np.cumsum(self.MrkvArray,axis=1) + MrkvNow_hist = np.zeros(self.act_T_orig, dtype=int) + cutoffs = np.cumsum(self.MrkvArray, axis=1) loops = 0 go = True MrkvNow = self.MrkvNow_init @@ -1510,35 +1515,35 @@ def makeMrkvHist(self): # Add histories until each state has been visited at least state_T_min times while go: - draws = drawUniform(N=self.act_T_orig,seed=loops) - for s in range(draws.size): # Add act_T_orig more periods + draws = drawUniform(N=self.act_T_orig, seed=loops) + for s in range(draws.size): # Add act_T_orig more periods MrkvNow_hist[t] = MrkvNow - MrkvNow = np.searchsorted(cutoffs[MrkvNow,:],draws[s]) + MrkvNow = np.searchsorted(cutoffs[MrkvNow, :], draws[s]) t += 1 # Calculate the empirical distribution state_T = np.zeros(StateCount) for i in range(StateCount): - state_T[i] = np.sum(MrkvNow_hist==i) + state_T[i] = np.sum(MrkvNow_hist == i) # Check whether each state has been visited state_T_min times if np.all(state_T >= state_T_min): - go = False # If so, terminate the loop + go = False # If so, terminate the loop continue # Choose an underrepresented state to "jump" to - if np.any(state_T == 0): # If any states have *never* been visited, randomly choose one of those + if np.any(state_T == 0): # If any states have *never* been visited, randomly choose one of those never_visited = np.where(np.array(state_T == 0))[0] MrkvNow = np.random.choice(never_visited) - else: # Otherwise, use logit choice probabilities to visit an underrepresented state - emp_dstn = state_T/act_T - ratios = LR_dstn/emp_dstn + else: # Otherwise, use logit choice probabilities to visit an underrepresented state + emp_dstn = state_T/act_T + ratios = LR_dstn/emp_dstn ratios_adj = ratios - np.max(ratios) ratios_exp = np.exp(ratios_adj/logit_scale) ratios_sum = np.sum(ratios_exp) jump_probs = ratios_exp/ratios_sum - cum_probs = np.cumsum(jump_probs) - MrkvNow = np.searchsorted(cum_probs,draws[-1]) + cum_probs = np.cumsum(jump_probs) + MrkvNow = np.searchsorted(cum_probs, draws[-1]) loops += 1 # Make the Markov state history longer by act_T_orig periods @@ -1546,16 +1551,15 @@ def makeMrkvHist(self): go = False print('makeMrkvHist reached maximum number of loops without generating a valid sequence!') else: - MrkvNow_new = np.zeros(self.act_T_orig,dtype=int) - MrkvNow_hist = np.concatenate((MrkvNow_hist,MrkvNow_new)) + MrkvNow_new = np.zeros(self.act_T_orig, dtype=int) + MrkvNow_hist = np.concatenate((MrkvNow_hist, MrkvNow_new)) act_T += self.act_T_orig # Store the results as attributes of self self.MrkvNow_hist = MrkvNow_hist self.act_T = act_T - - def millRule(self,aLvlNow,pLvlNow): + def millRule(self, aLvlNow, pLvlNow): ''' Function to calculate the capital to labor ratio, interest factor, and wage rate based on each agent's current state. Just calls calcRandW() @@ -1564,12 +1568,11 @@ def millRule(self,aLvlNow,pLvlNow): See documentation for calcRandW for more information. ''' MrkvNow = self.MrkvNow_hist[self.Shk_idx] - temp = self.calcRandW(aLvlNow,pLvlNow) - temp(MrkvNow = MrkvNow) + temp = self.calcRandW(aLvlNow, pLvlNow) + temp(MrkvNow=MrkvNow) return temp - - def calcAFunc(self,MaggNow,AaggNow): + def calcAFunc(self, MaggNow, AaggNow): ''' Calculate a new aggregate savings rule based on the history of the aggregate savings and aggregate market resources from a simulation. @@ -1588,29 +1591,29 @@ def calcAFunc(self,MaggNow,AaggNow): Object containing new saving rules for each Markov state. ''' verbose = self.verbose - discard_periods = self.T_discard # Throw out the first T periods to allow the simulation to approach the SS + discard_periods = self.T_discard # Throw out the first T periods to allow the simulation to approach the SS update_weight = 1. - self.DampingFac # Proportional weight to put on new function vs old function parameters total_periods = len(MaggNow) # Trim the histories of M_t and A_t and convert them to logs - logAagg = np.log(AaggNow[discard_periods:total_periods]) - logMagg = np.log(MaggNow[discard_periods-1:total_periods-1]) - MrkvHist = self.MrkvNow_hist[discard_periods-1:total_periods-1] + logAagg = np.log(AaggNow[discard_periods:total_periods]) + logMagg = np.log(MaggNow[discard_periods-1:total_periods-1]) + MrkvHist = self.MrkvNow_hist[discard_periods-1:total_periods-1] # For each Markov state, regress A_t on M_t and update the saving rule AFunc_list = [] rSq_list = [] for i in range(self.MrkvArray.shape[0]): these = i == MrkvHist - slope, intercept, r_value, p_value, std_err = stats.linregress(logMagg[these],logAagg[these]) - #if verbose: + slope, intercept, r_value, p_value, std_err = stats.linregress(logMagg[these], logAagg[these]) + # if verbose: # plt.plot(logMagg[these],logAagg[these],'.') # Make a new aggregate savings rule by combining the new regression parameters # with the previous guess intercept = update_weight*intercept + (1.0-update_weight)*self.intercept_prev[i] slope = update_weight*slope + (1.0-update_weight)*self.slope_prev[i] - AFunc_list.append(AggregateSavingRule(intercept,slope)) # Make a new next-period capital function + AFunc_list.append(AggregateSavingRule(intercept, slope)) # Make a new next-period capital function rSq_list.append(r_value**2) # Save the new values as "previous" values for the next iteration @@ -1619,21 +1622,22 @@ def calcAFunc(self,MaggNow,AaggNow): # Plot aggregate resources vs aggregate savings for this run and print the new parameters if verbose: - print('intercept=' + str(self.intercept_prev) + ', slope=' + str(self.slope_prev) + ', r-sq=' + str(rSq_list)) - #plt.show() + print('intercept=' + str(self.intercept_prev) + + ', slope=' + str(self.slope_prev) + ', r-sq=' + str(rSq_list)) + # plt.show() return AggShocksDynamicRule(AFunc_list) -class SmallOpenMarkovEconomy(CobbDouglasMarkovEconomy,SmallOpenEconomy): +class SmallOpenMarkovEconomy(CobbDouglasMarkovEconomy, SmallOpenEconomy): ''' A class for representing a small open economy, where the wage rate and interest rate are exogenously determined by some "global" rate. However, the economy is still subject to aggregate productivity shocks. This version supports a discrete Markov state. All methods in this class inherit from the two parent classes. ''' - def __init__(self,agents=[],tolerance=0.0001,act_T=1000,**kwds): - CobbDouglasMarkovEconomy.__init__(self,agents=agents,tolerance=tolerance,act_T=act_T,**kwds) + def __init__(self, agents=[], tolerance=0.0001, act_T=1000, **kwds): + CobbDouglasMarkovEconomy.__init__(self, agents=agents, tolerance=tolerance, act_T=act_T, **kwds) self.reap_vars = [] self.dyn_vars = [] @@ -1647,11 +1651,11 @@ def makeAggShkDstn(self): def millRule(self): MrkvNow = self.MrkvNow_hist[self.Shk_idx] - temp = SmallOpenEconomy.getAggShocks(self) - temp(MrkvNow = MrkvNow) + temp = SmallOpenEconomy.getAggShocks(self) + temp(MrkvNow=MrkvNow) return temp - def calcDynamics(self,KtoLnow): + def calcDynamics(self, KtoLnow): return HARKobject() def makeAggShkHist(self): @@ -1665,7 +1669,7 @@ class CobbDouglasAggVars(HARKobject): the interest factor, the wage rate, and the aggregate permanent and tran- sitory shocks. ''' - def __init__(self,MaggNow,AaggNow,KtoLnow,RfreeNow,wRteNow,PermShkAggNow,TranShkAggNow): + def __init__(self, MaggNow, AaggNow, KtoLnow, RfreeNow, wRteNow, PermShkAggNow, TranShkAggNow): ''' Make a new instance of CobbDouglasAggVars. @@ -1691,20 +1695,21 @@ def __init__(self,MaggNow,AaggNow,KtoLnow,RfreeNow,wRteNow,PermShkAggNow,TranShk ------- None ''' - self.MaggNow = MaggNow - self.AaggNow = AaggNow - self.KtoLnow = KtoLnow - self.RfreeNow = RfreeNow - self.wRteNow = wRteNow + self.MaggNow = MaggNow + self.AaggNow = AaggNow + self.KtoLnow = KtoLnow + self.RfreeNow = RfreeNow + self.wRteNow = wRteNow self.PermShkAggNow = PermShkAggNow self.TranShkAggNow = TranShkAggNow + class AggregateSavingRule(HARKobject): ''' A class to represent agent beliefs about aggregate saving at the end of this period (AaggNow) as a function of (normalized) aggregate market resources at the beginning of the period (MaggNow). ''' - def __init__(self,intercept,slope): + def __init__(self, intercept, slope): ''' Make a new instance of CapitalEvoRule. @@ -1719,11 +1724,11 @@ def __init__(self,intercept,slope): ------- new instance of CapitalEvoRule ''' - self.intercept = intercept - self.slope = slope - self.distance_criteria = ['slope','intercept'] + self.intercept = intercept + self.slope = slope + self.distance_criteria = ['slope', 'intercept'] - def __call__(self,Mnow): + def __call__(self, Mnow): ''' Evaluates aggregate savings as a function of the aggregate market resources this period. @@ -1744,7 +1749,7 @@ class AggShocksDynamicRule(HARKobject): ''' Just a container class for passing the dynamic rule in the aggregate shocks model to agents. ''' - def __init__(self,AFunc): + def __init__(self, AFunc): ''' Make a new instance of CapDynamicRule. @@ -1767,17 +1772,19 @@ def main(): import HARK.ConsumptionSaving.ConsumerParameters as Params from time import clock from HARK.utilities import plotFuncs - mystr = lambda number : "{:.4f}".format(number) - solve_agg_shocks_micro = False # Solve an AggShockConsumerType's microeconomic problem - solve_agg_shocks_market = True # Solve for the equilibrium aggregate saving rule in a CobbDouglasEconomy + def mystr(number): + "{:.4f}".format(number) + + solve_agg_shocks_micro = False # Solve an AggShockConsumerType's microeconomic problem + solve_agg_shocks_market = True # Solve for the equilibrium aggregate saving rule in a CobbDouglasEconomy - solve_markov_micro = False # Solve an AggShockMarkovConsumerType's microeconomic problem - solve_markov_market = True # Solve for the equilibrium aggregate saving rule in a CobbDouglasMarkovEconomy - solve_krusell_smith = True # Solve a simple Krusell-Smith-style two state, two shock model - solve_poly_state = False # Solve a CobbDouglasEconomy with many states, potentially utilizing the "state jumper" + solve_markov_micro = False # Solve an AggShockMarkovConsumerType's microeconomic problem + solve_markov_market = True # Solve for the equilibrium aggregate saving rule in a CobbDouglasMarkovEconomy + solve_krusell_smith = True # Solve a simple Krusell-Smith-style two state, two shock model + solve_poly_state = False # Solve a CobbDouglasEconomy with many states, potentially utilizing the "state jumper" - ################ EXAMPLE IMPLEMENTATIONS OF AggShockConsumerType ########## + # EXAMPLE IMPLEMENTATIONS OF AggShockConsumerType ### if solve_agg_shocks_micro or solve_agg_shocks_market: # Make an aggregate shocks consumer type @@ -1785,8 +1792,8 @@ def main(): AggShockExample.cycles = 0 # Make a Cobb-Douglas economy for the agents - EconomyExample = CobbDouglasEconomy(agents = [AggShockExample],**Params.init_cobb_douglas) - EconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks + EconomyExample = CobbDouglasEconomy(agents=[AggShockExample], **Params.init_cobb_douglas) + EconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks # Have the consumers inherit relevant objects from the economy AggShockExample.getEconomyData(EconomyExample) @@ -1798,13 +1805,13 @@ def main(): t_end = clock() print('Solving an aggregate shocks consumer took ' + mystr(t_end-t_start) + ' seconds.') print('Consumption function at each aggregate market resources-to-labor ratio gridpoint:') - m_grid = np.linspace(0,10,200) + m_grid = np.linspace(0, 10, 200) AggShockExample.unpackcFunc() for M in AggShockExample.Mgrid.tolist(): mMin = AggShockExample.solution[0].mNrmMin(M) - c_at_this_M = AggShockExample.cFunc[0](m_grid+mMin,M*np.ones_like(m_grid)) - plt.plot(m_grid+mMin,c_at_this_M) - plt.ylim(0.,None) + c_at_this_M = AggShockExample.cFunc[0](m_grid+mMin, M*np.ones_like(m_grid)) + plt.plot(m_grid+mMin, c_at_this_M) + plt.ylim(0., None) plt.show() if solve_agg_shocks_market: @@ -1816,19 +1823,19 @@ def main(): print('Solving the "macroeconomic" aggregate shocks model took ' + str(t_end - t_start) + ' seconds.') print('Aggregate savings as a function of aggregate market resources:') - plotFuncs(EconomyExample.AFunc,0,2*EconomyExample.kSS) + plotFuncs(EconomyExample.AFunc, 0, 2*EconomyExample.kSS) print('Consumption function at each aggregate market resources gridpoint (in general equilibrium):') AggShockExample.unpackcFunc() - m_grid = np.linspace(0,10,200) + m_grid = np.linspace(0, 10, 200) AggShockExample.unpackcFunc() for M in AggShockExample.Mgrid.tolist(): mMin = AggShockExample.solution[0].mNrmMin(M) - c_at_this_M = AggShockExample.cFunc[0](m_grid+mMin,M*np.ones_like(m_grid)) - plt.plot(m_grid+mMin,c_at_this_M) - plt.ylim(0.,None) + c_at_this_M = AggShockExample.cFunc[0](m_grid+mMin, M*np.ones_like(m_grid)) + plt.plot(m_grid+mMin, c_at_this_M) + plt.ylim(0., None) plt.show() - ######### EXAMPLE IMPLEMENTATIONS OF AggShockMarkovConsumerType ########### + # EXAMPLE IMPLEMENTATIONS OF AggShockMarkovConsumerType # if solve_markov_micro or solve_markov_market or solve_krusell_smith: # Make a Markov aggregate shocks consumer type @@ -1837,10 +1844,11 @@ def main(): AggShockMrkvExample.cycles = 0 # Make a Cobb-Douglas economy for the agents - MrkvEconomyExample = CobbDouglasMarkovEconomy(agents = [AggShockMrkvExample],**Params.init_mrkv_cobb_douglas) - MrkvEconomyExample.DampingFac = 0.2 # Turn down damping - MrkvEconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks - AggShockMrkvExample.getEconomyData(MrkvEconomyExample) # Have the consumers inherit relevant objects from the economy + MrkvEconomyExample = CobbDouglasMarkovEconomy(agents=[AggShockMrkvExample], **Params.init_mrkv_cobb_douglas) + MrkvEconomyExample.DampingFac = 0.2 # Turn down damping + MrkvEconomyExample.makeAggShkHist() # Simulate a history of aggregate shocks + AggShockMrkvExample.getEconomyData( + MrkvEconomyExample) # Have the consumers inherit relevant objects from the economy if solve_markov_micro: # Solve the microeconomic model for the Markov aggregate shocks example type (and display results) @@ -1849,15 +1857,16 @@ def main(): t_end = clock() print('Solving an aggregate shocks Markov consumer took ' + mystr(t_end-t_start) + ' seconds.') - print('Consumption function at each aggregate market resources-to-labor ratio gridpoint (for each macro state):') - m_grid = np.linspace(0,10,200) + print('Consumption function at each aggregate market \ + resources-to-labor ratio gridpoint (for each macro state):') + m_grid = np.linspace(0, 10, 200) AggShockMrkvExample.unpackcFunc() for i in range(2): for M in AggShockMrkvExample.Mgrid.tolist(): mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M) - c_at_this_M = AggShockMrkvExample.cFunc[0][i](m_grid+mMin,M*np.ones_like(m_grid)) - plt.plot(m_grid+mMin,c_at_this_M) - plt.ylim(0.,None) + c_at_this_M = AggShockMrkvExample.cFunc[0][i](m_grid+mMin, M*np.ones_like(m_grid)) + plt.plot(m_grid+mMin, c_at_this_M) + plt.ylim(0., None) plt.show() if solve_markov_market: @@ -1868,30 +1877,31 @@ def main(): t_end = clock() print('Solving the "macroeconomic" aggregate shocks model took ' + str(t_end - t_start) + ' seconds.') - print('Consumption function at each aggregate market resources-to-labor ratio gridpoint (for each macro state):') - m_grid = np.linspace(0,10,200) + print('Consumption function at each aggregate market \ + resources-to-labor ratio gridpoint (for each macro state):') + m_grid = np.linspace(0, 10, 200) AggShockMrkvExample.unpackcFunc() for i in range(2): for M in AggShockMrkvExample.Mgrid.tolist(): mMin = AggShockMrkvExample.solution[0].mNrmMin[i](M) - c_at_this_M = AggShockMrkvExample.cFunc[0][i](m_grid+mMin,M*np.ones_like(m_grid)) - plt.plot(m_grid+mMin,c_at_this_M) - plt.ylim(0.,None) + c_at_this_M = AggShockMrkvExample.cFunc[0][i](m_grid+mMin, M*np.ones_like(m_grid)) + plt.plot(m_grid+mMin, c_at_this_M) + plt.ylim(0., None) plt.show() if solve_krusell_smith: # Make a Krusell-Smith agent type # NOTE: These agents aren't exactly like KS, as they don't have serially correlated unemployment KSexampleType = deepcopy(AggShockMrkvExample) - KSexampleType.IncomeDstn[0] = [[np.array([0.96,0.04]),np.array([1.0,1.0]),np.array([1.0/0.96,0.0])], - [np.array([0.90,0.10]),np.array([1.0,1.0]),np.array([1.0/0.90,0.0])]] + KSexampleType.IncomeDstn[0] = [[np.array([0.96, 0.04]), np.array([1.0, 1.0]), np.array([1.0/0.96, 0.0])], + [np.array([0.90, 0.10]), np.array([1.0, 1.0]), np.array([1.0/0.90, 0.0])]] # Make a KS economy KSeconomy = deepcopy(MrkvEconomyExample) KSeconomy.agents = [KSexampleType] - KSeconomy.AggShkDstn = [[np.array([1.0]),np.array([1.0]),np.array([1.05])], - [np.array([1.0]),np.array([1.0]),np.array([0.95])]] - KSeconomy.PermGroFacAgg = [1.0,1.0] + KSeconomy.AggShkDstn = [[np.array([1.0]), np.array([1.0]), np.array([1.05])], + [np.array([1.0]), np.array([1.0]), np.array([0.95])]] + KSeconomy.PermGroFacAgg = [1.0, 1.0] KSexampleType.getEconomyData(KSeconomy) KSeconomy.makeAggShkHist() @@ -1902,24 +1912,23 @@ def main(): t_end = clock() print('Solving the Krusell-Smith model took ' + str(t_end - t_start) + ' seconds.') - if solve_poly_state: - StateCount = 15 # Number of Markov states - GrowthAvg = 1.01 # Average permanent income growth factor - GrowthWidth = 0.02 # PermGroFacAgg deviates from PermGroFacAgg in this range - Persistence = 0.90 # Probability of staying in the same Markov state - PermGroFacAgg = np.linspace(GrowthAvg-GrowthWidth,GrowthAvg+GrowthWidth,num=StateCount) + StateCount = 15 # Number of Markov states + GrowthAvg = 1.01 # Average permanent income growth factor + GrowthWidth = 0.02 # PermGroFacAgg deviates from PermGroFacAgg in this range + Persistence = 0.90 # Probability of staying in the same Markov state + PermGroFacAgg = np.linspace(GrowthAvg-GrowthWidth, GrowthAvg+GrowthWidth, num=StateCount) # Make the Markov array with chosen states and persistence - PolyMrkvArray = np.zeros((StateCount,StateCount)) + PolyMrkvArray = np.zeros((StateCount, StateCount)) for i in range(StateCount): for j in range(StateCount): - if i==j: - PolyMrkvArray[i,j] = Persistence - elif (i==(j-1)) or (i==(j+1)): - PolyMrkvArray[i,j] = 0.5*(1.0 - Persistence) - PolyMrkvArray[0,0] += 0.5*(1.0 - Persistence) - PolyMrkvArray[StateCount-1,StateCount-1] += 0.5*(1.0 - Persistence) + if i == j: + PolyMrkvArray[i, j] = Persistence + elif (i == (j-1)) or (i == (j+1)): + PolyMrkvArray[i, j] = 0.5*(1.0 - Persistence) + PolyMrkvArray[0, 0] += 0.5*(1.0 - Persistence) + PolyMrkvArray[StateCount-1, StateCount-1] += 0.5*(1.0 - Persistence) # Make a consumer type to inhabit the economy PolyStateExample = AggShockMarkovConsumerType(**Params.init_agg_mrkv_shocks) @@ -1929,7 +1938,7 @@ def main(): PolyStateExample.cycles = 0 # Make a Cobb-Douglas economy for the agents - PolyStateEconomy = CobbDouglasMarkovEconomy(agents = [PolyStateExample],**Params.init_mrkv_cobb_douglas) + PolyStateEconomy = CobbDouglasMarkovEconomy(agents=[PolyStateExample], **Params.init_mrkv_cobb_douglas) PolyStateEconomy.MrkvArray = PolyMrkvArray PolyStateEconomy.PermGroFacAgg = PermGroFacAgg PolyStateEconomy.PermShkAggStd = StateCount*[0.006] @@ -1938,8 +1947,9 @@ def main(): PolyStateEconomy.intercept_prev = StateCount*[0.0] PolyStateEconomy.update() PolyStateEconomy.makeAggShkDstn() - PolyStateEconomy.makeAggShkHist() # Simulate a history of aggregate shocks - PolyStateExample.getEconomyData(PolyStateEconomy) # Have the consumers inherit relevant objects from the economy + PolyStateEconomy.makeAggShkHist() # Simulate a history of aggregate shocks + PolyStateExample.getEconomyData( + PolyStateEconomy) # Have the consumers inherit relevant objects from the economy # Solve the many state model t_start = clock() @@ -1948,6 +1958,6 @@ def main(): t_end = clock() print('Solving a model with ' + str(StateCount) + ' states took ' + str(t_end - t_start) + ' seconds.') + if __name__ == '__main__': main() - diff --git a/HARK/tests/test_HARKutilities.py b/HARK/tests/test_HARKutilities.py index 59ff06760..7624ee064 100644 --- a/HARK/tests/test_HARKutilities.py +++ b/HARK/tests/test_HARKutilities.py @@ -4,31 +4,27 @@ from __future__ import print_function, division from __future__ import absolute_import -from builtins import str -from builtins import zip -from builtins import range -from builtins import object - import HARK.utilities # Bring in modules we need import unittest import numpy as np + class testsForHARKutilities(unittest.TestCase): def setUp(self): - self.c_vals = np.linspace(.5,10.,20) - self.CRRA_vals = np.linspace(1.,10.,10) + self.c_vals = np.linspace(.5, 10., 20) + self.CRRA_vals = np.linspace(1., 10., 10) - def first_diff_approx(self,func,x,delta,*args): + def first_diff_approx(self, func, x, delta, *args): """ Take the first (centered) difference approximation to the derivative of a function. """ - return (func(x+delta,*args) - func(x-delta,*args)) / (2. * delta) + return (func(x+delta, *args) - func(x-delta, *args)) / (2. * delta) - def derivative_func_comparison(self,deriv,func): + def derivative_func_comparison(self, deriv, func): """ This method computes the first difference approximation to the derivative of a function "func" and the (supposedly) closed-form derivative of that function ("deriv") over a @@ -42,23 +38,23 @@ def derivative_func_comparison(self,deriv,func): # Calculate the difference between the derivative of the function and the # first difference approximation to that derivative. - diff = abs(deriv(c,CRRA) - self.first_diff_approx(func,c,.000001,CRRA)) + diff = abs(deriv(c, CRRA) - self.first_diff_approx(func, c, .000001, CRRA)) # Make sure the derivative and its approximation are close - self.assertLess(diff,.01) + self.assertLess(diff, .01) def test_CRRAutilityP(self): # Test the first derivative of the utility function - self.derivative_func_comparison(HARK.utilities.CRRAutilityP,HARK.utilities.CRRAutility) + self.derivative_func_comparison(HARK.utilities.CRRAutilityP, HARK.utilities.CRRAutility) def test_CRRAutilityPP(self): # Test the second derivative of the utility function - self.derivative_func_comparison(HARK.utilities.CRRAutilityPP,HARK.utilities.CRRAutilityP) + self.derivative_func_comparison(HARK.utilities.CRRAutilityPP, HARK.utilities.CRRAutilityP) def test_CRRAutilityPPP(self): # Test the third derivative of the utility function - self.derivative_func_comparison(HARK.utilities.CRRAutilityPPP,HARK.utilities.CRRAutilityPP) + self.derivative_func_comparison(HARK.utilities.CRRAutilityPPP, HARK.utilities.CRRAutilityPP) def test_CRRAutilityPPPP(self): # Test the fourth derivative of the utility function - self.derivative_func_comparison(HARK.utilities.CRRAutilityPPPP,HARK.utilities.CRRAutilityPPP) + self.derivative_func_comparison(HARK.utilities.CRRAutilityPPPP, HARK.utilities.CRRAutilityPPP) diff --git a/HARK/tests/test_dcegm.py b/HARK/tests/test_dcegm.py index 40f3b526a..ee4962bca 100644 --- a/HARK/tests/test_dcegm.py +++ b/HARK/tests/test_dcegm.py @@ -7,10 +7,11 @@ import unittest import numpy as np + class testsForDCEGM(unittest.TestCase): def setUp(self): - self.commonM = np.linspace(0,10.0,30) + self.commonM = np.linspace(0, 10.0, 30) self.m_in = np.array([1.0, 2.0, 3.0, 2.5, 2.0, 4.0, 5.0, 6.0]) self.c_in = np.array([1.0, 2.0, 3.0, 2.5, 2.0, 4.0, 5.0, 6.0]) self.v_in = np.array([0.5, 1.0, 1.5, 0.75, 0.5, 3.5, 5.0, 7.0]) From 3f5e928c9af4ce24e38fddecda25ede56dffd0d7 Mon Sep 17 00:00:00 2001 From: rsaavy Date: Mon, 6 May 2019 16:44:36 -0400 Subject: [PATCH 52/77] Simple lint edits commits for dcegm.py --- HARK/dcegm.py | 45 ++++++++++++++++++++++++--------------------- 1 file changed, 24 insertions(+), 21 deletions(-) diff --git a/HARK/dcegm.py b/HARK/dcegm.py index a108e3081..773d95f13 100644 --- a/HARK/dcegm.py +++ b/HARK/dcegm.py @@ -7,6 +7,7 @@ import numpy as np from HARK.interpolation import LinearInterp + def calcSegments(x, v): """ Find index vectors `rise` and `fall` such that `rise` holds the indeces `i` @@ -43,22 +44,22 @@ def calcSegments(x, v): # # `fall` is a vector of indeces that represent the first elements in all # of the falling segments (the curve can potentially fold several times) - fall = np.empty(0, dtype=int) # initialize with empty and then add the last point below while-loop + fall = np.empty(0, dtype=int) # initialize with empty and then add the last point below while-loop - rise = np.array([0]) # Initialize such thatthe lowest point is the first grid point - i = 1 # Initialize + rise = np.array([0]) # Initialize such thatthe lowest point is the first grid point + i = 1 # Initialize while i <= len(x) - 2: # Check if the next (`ip1` stands for i plus 1) grid point is below the # current one, such that the line is folding back. - ip1_falls = x[i+1] < x[i] # true if grid decreases on index increment - i_rose = x[i] > x[i-1] # true if grid decreases on index decrement - val_fell = v[i] < v[i-1] # true if value rises on index decrement + ip1_falls = x[i+1] < x[i] # true if grid decreases on index increment + i_rose = x[i] > x[i-1] # true if grid decreases on index decrement + val_fell = v[i] < v[i-1] # true if value rises on index decrement if (ip1_falls and i_rose) or (val_fell and i_rose): # we are in a region where the endogenous grid is decreasing or # the value function rises by stepping back in the grid. - fall = np.append(fall, i) # add the index to the vector + fall = np.append(fall, i) # add the index to the vector # We now iterate from the current index onwards until we find point # where resources rises again. Unfortunately, we need to check @@ -84,6 +85,8 @@ def calcSegments(x, v): return rise, fall # think! nanargmax makes everythign super ugly because numpy changed the wraning # in all nan slices to a valueerror...it's nans, aaarghgghg + + def calcMultilineEnvelope(M, C, V_T, commonM): """ Do the envelope step of the DCEGM algorithm. Takes in market ressources, @@ -110,7 +113,7 @@ def calcMultilineEnvelope(M, C, V_T, commonM): m_len = len(commonM) rise, fall = calcSegments(M, V_T) - num_kinks = len(fall) # number of kinks / falling EGM grids + num_kinks = len(fall) # number of kinks / falling EGM grids # Use these segments to sequentially find upper envelopes. commonVARNAME # means the VARNAME evaluated on the common grid with a cloumn for each kink @@ -128,9 +131,9 @@ def calcMultilineEnvelope(M, C, V_T, commonM): for j in range(num_kinks): # Find points in the common grid that are in the range of the points in # the interval defined by (rise[j], fall[j]). - below = M[rise[j]] >= commonM # boolean array of bad indeces below - above = M[fall[j]] <= commonM # boolen array of bad indeces above - in_range = below + above == 0 # pick out elements that are neither + below = M[rise[j]] >= commonM # boolean array of bad indeces below + above = M[fall[j]] <= commonM # boolen array of bad indeces above + in_range = below + above == 0 # pick out elements that are neither # create range of indeces in the input arrays idxs = range(rise[j], fall[j]+1) @@ -141,14 +144,14 @@ def calcMultilineEnvelope(M, C, V_T, commonM): m_eval = commonM[in_range] # re-interpolate to common grid - commonV_T[in_range,j] = LinearInterp(m_idx_j, V_T[idxs], lower_extrap=True)(m_eval) - commonC[in_range,j] = LinearInterp(m_idx_j, C[idxs], lower_extrap=True)(m_eval) # Interpolat econsumption also. May not be nesserary + commonV_T[in_range, j] = LinearInterp(m_idx_j, V_T[idxs], lower_extrap=True)(m_eval) # NOQA + commonC[in_range, j] = LinearInterp(m_idx_j, C[idxs], lower_extrap=True)(m_eval) # NOQA Interpolat econsumption also. May not be nesserary # for each row in the commonV_T matrix, see if all entries are np.nan. This # would mean that we have no valid value here, so we want to use this boolean # vector to filter out irrelevant entries of commonV_T. row_all_nan = np.array([np.all(np.isnan(row)) for row in commonV_T]) # Now take the max of all these line segments. - idx_max = np.zeros(commonM.size, dtype = int) + idx_max = np.zeros(commonM.size, dtype=int) idx_max[row_all_nan == False] = np.nanargmax(commonV_T[row_all_nan == False], axis=1) # prefix with upper for variable that are "upper enveloped" @@ -164,32 +167,32 @@ def calcMultilineEnvelope(M, C, V_T, commonM): # in transformed space space, utility of zero-consumption (-inf) is 0.0 upperV_T[0] = 0.0 # commonM[0] is typically 0, so this is safe, but maybe it should be 0.0 - commonC[0] = commonM[0] + commonC[0] = commonM[0] # Extrapolate if NaNs are introduced due to the common grid # going outside all the sub-line segments IsNaN = np.isnan(upperV_T) upperV_T[IsNaN] = LinearInterp(commonM[IsNaN == False], upperV_T[IsNaN == False])(commonM[IsNaN]) - - - LastBeforeNaN = np.append(np.diff(IsNaN)>0, 0) - LastId = LastBeforeNaN*idx_max # Find last id-number + LastBeforeNaN = np.append(np.diff(IsNaN) > 0, 0) + LastId = LastBeforeNaN*idx_max # Find last id-number idx_max[IsNaN] = LastId[IsNaN] # Linear index used to get optimal consumption based on "id" from max ncols = commonC.shape[1] rowidx = np.cumsum(ncols*np.ones(len(commonM), dtype=int))-ncols idx_linear = np.unravel_index(rowidx+idx_max, commonC.shape) upperC = commonC[idx_linear] - upperC[IsNaN] = LinearInterp(commonM[IsNaN==0], upperC[IsNaN==0])(commonM[IsNaN]) + upperC[IsNaN] = LinearInterp(commonM[IsNaN == 0], upperC[IsNaN == 0])(commonM[IsNaN]) # TODO calculate cross points of line segments to get the true vertical drops - upperM = commonM.copy() # anticipate this TODO + upperM = commonM.copy() # anticipate this TODO return upperM, upperC, upperV_T + def main(): print("Sorry, HARK.dcegm doesn't actually do anything on its own.") + if __name__ == '__main__': main() From cd96404c402b358e6a37614e3b865001a25a1d18 Mon Sep 17 00:00:00 2001 From: Stephen Schroeder Date: Tue, 7 May 2019 14:28:06 -0400 Subject: [PATCH 53/77] Lint and fix lambda function --- HARK/ConsumptionSaving/ConsAggShockModel.py | 3 +- .../ConsGenIncProcessModel.py | 524 +++++++++--------- 2 files changed, 278 insertions(+), 249 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py index bdc6b0266..b0c6e4245 100644 --- a/HARK/ConsumptionSaving/ConsAggShockModel.py +++ b/HARK/ConsumptionSaving/ConsAggShockModel.py @@ -1773,8 +1773,7 @@ def main(): from time import clock from HARK.utilities import plotFuncs - def mystr(number): - "{:.4f}".format(number) + def mystr(number): return "{:.4f}".format(number) solve_agg_shocks_micro = False # Solve an AggShockConsumerType's microeconomic problem solve_agg_shocks_market = True # Solve for the equilibrium aggregate saving rule in a CobbDouglasEconomy diff --git a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py index 251455480..03bac79cb 100644 --- a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py +++ b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py @@ -19,23 +19,24 @@ from HARK.simulation import drawLognormal, drawDiscrete, drawUniform from HARK.ConsumptionSaving.ConsIndShockModel import ConsIndShockSetup, ConsumerSolution, IndShockConsumerType -utility = CRRAutility -utilityP = CRRAutilityP -utilityPP = CRRAutilityPP -utilityP_inv = CRRAutilityP_inv -utility_invP = CRRAutility_invP -utility_inv = CRRAutility_inv +utility = CRRAutility +utilityP = CRRAutilityP +utilityPP = CRRAutilityPP +utilityP_inv = CRRAutilityP_inv +utility_invP = CRRAutility_invP +utility_inv = CRRAutility_inv utilityP_invP = CRRAutilityP_invP + class ValueFunc2D(HARKobject): ''' A class for representing a value function in a model where persistent income is explicitly included as a state variable. The underlying interpolation is in the space of (m,p) --> u_inv(v); this class "re-curves" to the value function. ''' - distance_criteria = ['func','CRRA'] + distance_criteria = ['func', 'CRRA'] - def __init__(self,vFuncNvrs,CRRA): + def __init__(self, vFuncNvrs, CRRA): ''' Constructor for a new value function object. @@ -55,7 +56,7 @@ def __init__(self,vFuncNvrs,CRRA): self.func = deepcopy(vFuncNvrs) self.CRRA = CRRA - def __call__(self,m,p): + def __call__(self, m, p): ''' Evaluate the value function at given levels of market resources m and persistent income p. @@ -73,7 +74,7 @@ def __call__(self,m,p): Lifetime value of beginning this period with market resources m and persistent income p; has same size as inputs m and p. ''' - return utility(self.func(m,p),gam=self.CRRA) + return utility(self.func(m, p), gam=self.CRRA) class MargValueFunc2D(HARKobject): @@ -83,9 +84,9 @@ class MargValueFunc2D(HARKobject): This is copied from ConsAggShockModel, with the second state variable re- labeled as persistent income p. ''' - distance_criteria = ['cFunc','CRRA'] + distance_criteria = ['cFunc', 'CRRA'] - def __init__(self,cFunc,CRRA): + def __init__(self, cFunc, CRRA): ''' Constructor for a new marginal value function object. @@ -107,7 +108,7 @@ def __init__(self,cFunc,CRRA): self.cFunc = deepcopy(cFunc) self.CRRA = CRRA - def __call__(self,m,p): + def __call__(self, m, p): ''' Evaluate the marginal value function at given levels of market resources m and persistent income p. @@ -126,9 +127,9 @@ def __call__(self,m,p): market resources m and persistent income p; has same size as inputs m and p. ''' - return utilityP(self.cFunc(m,p),gam=self.CRRA) + return utilityP(self.cFunc(m, p), gam=self.CRRA) - def derivativeX(self,m,p): + def derivativeX(self, m, p): ''' Evaluate the first derivative with respect to market resources of the marginal value function at given levels of market resources m and per- @@ -148,9 +149,9 @@ def derivativeX(self,m,p): with market resources m and persistent income p; has same size as inputs m and p. ''' - c = self.cFunc(m,p) - MPC = self.cFunc.derivativeX(m,p) - return MPC*utilityPP(c,gam=self.CRRA) + c = self.cFunc(m, p) + MPC = self.cFunc.derivativeX(m, p) + return MPC*utilityPP(c, gam=self.CRRA) class MargMargValueFunc2D(HARKobject): @@ -158,9 +159,9 @@ class MargMargValueFunc2D(HARKobject): A class for representing a marginal marginal value function in models where the standard envelope condition of v'(m,p) = u'(c(m,p)) holds (with CRRA utility). ''' - distance_criteria = ['cFunc','CRRA'] + distance_criteria = ['cFunc', 'CRRA'] - def __init__(self,cFunc,CRRA): + def __init__(self, cFunc, CRRA): ''' Constructor for a new marginal marginal value function object. @@ -182,7 +183,7 @@ def __init__(self,cFunc,CRRA): self.cFunc = deepcopy(cFunc) self.CRRA = CRRA - def __call__(self,m,p): + def __call__(self, m, p): ''' Evaluate the marginal marginal value function at given levels of market resources m and persistent income p. @@ -200,16 +201,16 @@ def __call__(self,m,p): Marginal marginal value of beginning this period with market resources m and persistent income p; has same size as inputs. ''' - c = self.cFunc(m,p) - MPC = self.cFunc.derivativeX(m,p) - return MPC*utilityPP(c,gam=self.CRRA) + c = self.cFunc(m, p) + MPC = self.cFunc.derivativeX(m, p) + return MPC*utilityPP(c, gam=self.CRRA) class pLvlFuncAR1(HARKobject): ''' A class for representing AR1-style persistent income growth functions. ''' - def __init__(self,pLogMean,PermGroFac,Corr): + def __init__(self, pLogMean, PermGroFac, Corr): ''' Make a new pLvlFuncAR1 instance. @@ -230,7 +231,7 @@ def __init__(self,pLogMean,PermGroFac,Corr): self.LogGroFac = np.log(PermGroFac) self.Corr = Corr - def __call__(self,pLvlNow): + def __call__(self, pLvlNow): ''' Returns expected persistent income level next period as a function of this period's persistent income level. @@ -260,8 +261,8 @@ class ConsGenIncProcessSolver(ConsIndShockSetup): current persistent income into expected next period persistent income (subject to shocks). ''' - def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool): + def __init__(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, + pLvlNextFunc, BoroCnstArt, aXtraGrid, pLvlGrid, vFuncBool, CubicBool): ''' Constructor for a new solver for a one period problem with idiosyncratic shocks to persistent and transitory income, with persistent income tracked @@ -305,12 +306,12 @@ def __init__(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, ------- None ''' - self.assignParameters(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,pLvlNextFunc, - BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool) + self.assignParameters(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, pLvlNextFunc, + BoroCnstArt, aXtraGrid, pLvlGrid, vFuncBool, CubicBool) self.defUtilityFuncs() - def assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool): + def assignParameters(self, solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, + pLvlNextFunc, BoroCnstArt, aXtraGrid, pLvlGrid, vFuncBool, CubicBool): ''' Assigns inputs as attributes of self for use by other methods @@ -352,12 +353,14 @@ def assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, ------- none ''' - ConsIndShockSetup.assignParameters(self,solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - 0.0,BoroCnstArt,aXtraGrid,vFuncBool,CubicBool) # dummy value for PermGroFac + ConsIndShockSetup.assignParameters(self, solution_next, IncomeDstn, + LivPrb, DiscFac, CRRA, Rfree, + 0.0, BoroCnstArt, aXtraGrid, + vFuncBool, CubicBool) # dummy value for PermGroFac self.pLvlNextFunc = pLvlNextFunc self.pLvlGrid = pLvlGrid - def setAndUpdateValues(self,solution_next,IncomeDstn,LivPrb,DiscFac): + def setAndUpdateValues(self, solution_next, IncomeDstn, LivPrb, DiscFac): ''' Unpacks some of the inputs (and calculates simple objects based on them), storing the results in self for use by other methods. These include: @@ -386,18 +389,21 @@ def setAndUpdateValues(self,solution_next,IncomeDstn,LivPrb,DiscFac): None ''' # Run basic version of this method - ConsIndShockSetup.setAndUpdateValues(self,solution_next,IncomeDstn,LivPrb,DiscFac) + ConsIndShockSetup.setAndUpdateValues(self, solution_next, IncomeDstn, LivPrb, DiscFac) self.mLvlMinNext = solution_next.mLvlMin # Replace normalized human wealth (scalar) with human wealth level as function of persistent income self.hNrmNow = 0.0 - pLvlCount = self.pLvlGrid.size - IncShkCount = self.PermShkValsNext.size - pLvlNext = np.tile(self.pLvlNextFunc(self.pLvlGrid),(IncShkCount,1))*np.tile(self.PermShkValsNext,(pLvlCount,1)).transpose() - hLvlGrid = 1.0/self.Rfree*np.sum((np.tile(self.TranShkValsNext,(pLvlCount,1)).transpose()*pLvlNext + solution_next.hLvl(pLvlNext))*np.tile(self.ShkPrbsNext,(pLvlCount,1)).transpose(),axis=0) - self.hLvlNow = LinearInterp(np.insert(self.pLvlGrid,0,0.0),np.insert(hLvlGrid,0,0.0)) + pLvlCount = self.pLvlGrid.size + IncShkCount = self.PermShkValsNext.size + pLvlNext = np.tile(self.pLvlNextFunc(self.pLvlGrid), (IncShkCount, 1))*np.tile(self.PermShkValsNext, + (pLvlCount, 1)).transpose() + hLvlGrid = 1.0/self.Rfree*np.sum((np.tile(self.TranShkValsNext, (pLvlCount, 1)) + .transpose()*pLvlNext + solution_next.hLvl(pLvlNext)) * + np.tile(self.ShkPrbsNext, (pLvlCount, 1)).transpose(), axis=0) + self.hLvlNow = LinearInterp(np.insert(self.pLvlGrid, 0, 0.0), np.insert(hLvlGrid, 0, 0.0)) - def defBoroCnst(self,BoroCnstArt): + def defBoroCnst(self, BoroCnstArt): ''' Defines the constrained portion of the consumption function as cFuncNowCnst, an attribute of self. @@ -417,26 +423,27 @@ def defBoroCnst(self,BoroCnstArt): # Make temporary grids of income shocks and next period income values ShkCount = self.TranShkValsNext.size pLvlCount = self.pLvlGrid.size - PermShkVals_temp = np.tile(np.reshape(self.PermShkValsNext,(1,ShkCount)),(pLvlCount,1)) - TranShkVals_temp = np.tile(np.reshape(self.TranShkValsNext,(1,ShkCount)),(pLvlCount,1)) - pLvlNext_temp = np.tile(np.reshape(self.pLvlNextFunc(self.pLvlGrid),(pLvlCount,1)),(1,ShkCount))*PermShkVals_temp + PermShkVals_temp = np.tile(np.reshape(self.PermShkValsNext, (1, ShkCount)), (pLvlCount, 1)) + TranShkVals_temp = np.tile(np.reshape(self.TranShkValsNext, (1, ShkCount)), (pLvlCount, 1)) + pLvlNext_temp = np.tile(np.reshape(self.pLvlNextFunc(self.pLvlGrid), + (pLvlCount, 1)), (1, ShkCount))*PermShkVals_temp # Find the natural borrowing constraint for each persistent income level aLvlMin_candidates = (self.mLvlMinNext(pLvlNext_temp) - TranShkVals_temp*pLvlNext_temp)/self.Rfree - aLvlMinNow = np.max(aLvlMin_candidates,axis=1) - self.BoroCnstNat = LinearInterp(np.insert(self.pLvlGrid,0,0.0),np.insert(aLvlMinNow,0,0.0)) + aLvlMinNow = np.max(aLvlMin_candidates, axis=1) + self.BoroCnstNat = LinearInterp(np.insert(self.pLvlGrid, 0, 0.0), np.insert(aLvlMinNow, 0, 0.0)) # Define the minimum allowable mLvl by pLvl as the greater of the natural and artificial borrowing constraints if self.BoroCnstArt is not None: - self.BoroCnstArt = LinearInterp(np.array([0.0,1.0]),np.array([0.0,self.BoroCnstArt])) - self.mLvlMinNow = UpperEnvelope(self.BoroCnstArt,self.BoroCnstNat) + self.BoroCnstArt = LinearInterp(np.array([0.0, 1.0]), np.array([0.0, self.BoroCnstArt])) + self.mLvlMinNow = UpperEnvelope(self.BoroCnstArt, self.BoroCnstNat) else: self.mLvlMinNow = self.BoroCnstNat # Define the constrained consumption function as "consume all" shifted by mLvlMin - cFuncNowCnstBase = BilinearInterp(np.array([[0.,0.],[1.,1.]]),np.array([0.0,1.0]),np.array([0.0,1.0])) - self.cFuncNowCnst = VariableLowerBoundFunc2D(cFuncNowCnstBase,self.mLvlMinNow) - + cFuncNowCnstBase = BilinearInterp(np.array([[0., 0.], [1., 1.]]), + np.array([0.0, 1.0]), np.array([0.0, 1.0])) + self.cFuncNowCnst = VariableLowerBoundFunc2D(cFuncNowCnstBase, self.mLvlMinNow) def prepareToCalcEndOfPrdvP(self): ''' @@ -456,31 +463,31 @@ def prepareToCalcEndOfPrdvP(self): pLvlNow : np.array 2D array of persistent income levels this period. ''' - ShkCount = self.TranShkValsNext.size - pLvlCount = self.pLvlGrid.size - aNrmCount = self.aXtraGrid.size - pLvlNow = np.tile(self.pLvlGrid,(aNrmCount,1)).transpose() - aLvlNow = np.tile(self.aXtraGrid,(pLvlCount,1))*pLvlNow + self.BoroCnstNat(pLvlNow) - pLvlNow_tiled = np.tile(pLvlNow,(ShkCount,1,1)) - aLvlNow_tiled = np.tile(aLvlNow,(ShkCount,1,1)) # shape = (ShkCount,pLvlCount,aNrmCount) + ShkCount = self.TranShkValsNext.size + pLvlCount = self.pLvlGrid.size + aNrmCount = self.aXtraGrid.size + pLvlNow = np.tile(self.pLvlGrid, (aNrmCount, 1)).transpose() + aLvlNow = np.tile(self.aXtraGrid, (pLvlCount, 1))*pLvlNow + self.BoroCnstNat(pLvlNow) + pLvlNow_tiled = np.tile(pLvlNow, (ShkCount, 1, 1)) + aLvlNow_tiled = np.tile(aLvlNow, (ShkCount, 1, 1)) # shape = (ShkCount,pLvlCount,aNrmCount) if self.pLvlGrid[0] == 0.0: # aLvl turns out badly if pLvl is 0 at bottom - aLvlNow[0,:] = self.aXtraGrid - aLvlNow_tiled[:,0,:] = np.tile(self.aXtraGrid,(ShkCount,1)) + aLvlNow[0, :] = self.aXtraGrid + aLvlNow_tiled[:, 0, :] = np.tile(self.aXtraGrid, (ShkCount, 1)) # Tile arrays of the income shocks and put them into useful shapes - PermShkVals_tiled = np.transpose(np.tile(self.PermShkValsNext,(aNrmCount,pLvlCount,1)),(2,1,0)) - TranShkVals_tiled = np.transpose(np.tile(self.TranShkValsNext,(aNrmCount,pLvlCount,1)),(2,1,0)) - ShkPrbs_tiled = np.transpose(np.tile(self.ShkPrbsNext,(aNrmCount,pLvlCount,1)),(2,1,0)) + PermShkVals_tiled = np.transpose(np.tile(self.PermShkValsNext, (aNrmCount, pLvlCount, 1)), (2, 1, 0)) + TranShkVals_tiled = np.transpose(np.tile(self.TranShkValsNext, (aNrmCount, pLvlCount, 1)), (2, 1, 0)) + ShkPrbs_tiled = np.transpose(np.tile(self.ShkPrbsNext, (aNrmCount, pLvlCount, 1)), (2, 1, 0)) # Get cash on hand next period pLvlNext = self.pLvlNextFunc(pLvlNow_tiled)*PermShkVals_tiled mLvlNext = self.Rfree*aLvlNow_tiled + pLvlNext*TranShkVals_tiled # Store and report the results - self.ShkPrbs_temp = ShkPrbs_tiled - self.pLvlNext = pLvlNext - self.mLvlNext = mLvlNext - self.aLvlNow = aLvlNow + self.ShkPrbs_temp = ShkPrbs_tiled + self.pLvlNext = pLvlNext + self.mLvlNext = mLvlNext + self.aLvlNow = aLvlNow return aLvlNow, pLvlNow def calcEndOfPrdvP(self): @@ -499,10 +506,11 @@ def calcEndOfPrdvP(self): EndOfPrdVP : np.array A 2D array of end-of-period marginal value of assets. ''' - EndOfPrdvP = self.DiscFacEff*self.Rfree*np.sum(self.vPfuncNext(self.mLvlNext,self.pLvlNext)*self.ShkPrbs_temp,axis=0) + EndOfPrdvP = self.DiscFacEff*self.Rfree*np.sum(self.vPfuncNext(self.mLvlNext, self.pLvlNext) * + self.ShkPrbs_temp, axis=0) return EndOfPrdvP - def makeEndOfPrdvFunc(self,EndOfPrdvP): + def makeEndOfPrdvFunc(self, EndOfPrdvP): ''' Construct the end-of-period value function for this period, storing it as an attribute of self for use by other methods. @@ -517,30 +525,36 @@ def makeEndOfPrdvFunc(self,EndOfPrdvP): ------- none ''' - vLvlNext = self.vFuncNext(self.mLvlNext,self.pLvlNext) # value in many possible future states - EndOfPrdv = self.DiscFacEff*np.sum(vLvlNext*self.ShkPrbs_temp,axis=0) # expected value, averaging across states - EndOfPrdvNvrs = self.uinv(EndOfPrdv) # value transformed through inverse utility - EndOfPrdvNvrsP = EndOfPrdvP*self.uinvP(EndOfPrdv) + vLvlNext = self.vFuncNext(self.mLvlNext, self.pLvlNext) # value in many possible future states + EndOfPrdv = self.DiscFacEff*np.sum( + vLvlNext*self.ShkPrbs_temp, axis=0) # expected value, averaging across states + EndOfPrdvNvrs = self.uinv(EndOfPrdv) # value transformed through inverse utility + EndOfPrdvNvrsP = EndOfPrdvP*self.uinvP(EndOfPrdv) # Add points at mLvl=zero - EndOfPrdvNvrs = np.concatenate((np.zeros((self.pLvlGrid.size,1)),EndOfPrdvNvrs),axis=1) - if hasattr(self,'MedShkDstn'): - EndOfPrdvNvrsP = np.concatenate((np.zeros((self.pLvlGrid.size,1)),EndOfPrdvNvrsP),axis=1) + EndOfPrdvNvrs = np.concatenate((np.zeros((self.pLvlGrid.size, 1)), EndOfPrdvNvrs), axis=1) + if hasattr(self, 'MedShkDstn'): + EndOfPrdvNvrsP = np.concatenate((np.zeros((self.pLvlGrid.size, 1)), EndOfPrdvNvrsP), axis=1) else: - EndOfPrdvNvrsP = np.concatenate((np.reshape(EndOfPrdvNvrsP[:,0],(self.pLvlGrid.size,1)),EndOfPrdvNvrsP),axis=1) # This is a very good approximation, vNvrsPP = 0 at the asset minimum - aLvl_temp = np.concatenate((np.reshape(self.BoroCnstNat(self.pLvlGrid),(self.pLvlGrid.size,1)),self.aLvlNow),axis=1) + EndOfPrdvNvrsP = np.concatenate((np.reshape(EndOfPrdvNvrsP[:, 0], + (self.pLvlGrid.size, 1)), + EndOfPrdvNvrsP), axis=1) + # This is a very good approximation, vNvrsPP = 0 at the asset minimum + aLvl_temp = np.concatenate((np.reshape(self.BoroCnstNat(self.pLvlGrid), + (self.pLvlGrid.size, 1)), self.aLvlNow), axis=1) # Make an end-of-period value function for each persistent income level in the grid EndOfPrdvNvrsFunc_list = [] for p in range(self.pLvlGrid.size): - EndOfPrdvNvrsFunc_list.append(CubicInterp(aLvl_temp[p,:]-self.BoroCnstNat(self.pLvlGrid[p]),EndOfPrdvNvrs[p,:],EndOfPrdvNvrsP[p,:])) - EndOfPrdvNvrsFuncBase = LinearInterpOnInterp1D(EndOfPrdvNvrsFunc_list,self.pLvlGrid) + EndOfPrdvNvrsFunc_list.append(CubicInterp(aLvl_temp[p, :]-self.BoroCnstNat(self.pLvlGrid[p]), + EndOfPrdvNvrs[p, :], EndOfPrdvNvrsP[p, :])) + EndOfPrdvNvrsFuncBase = LinearInterpOnInterp1D(EndOfPrdvNvrsFunc_list, self.pLvlGrid) # Re-adjust the combined end-of-period value function to account for the natural borrowing constraint shifter - EndOfPrdvNvrsFunc = VariableLowerBoundFunc2D(EndOfPrdvNvrsFuncBase,self.BoroCnstNat) - self.EndOfPrdvFunc = ValueFunc2D(EndOfPrdvNvrsFunc,self.CRRA) + EndOfPrdvNvrsFunc = VariableLowerBoundFunc2D(EndOfPrdvNvrsFuncBase, self.BoroCnstNat) + self.EndOfPrdvFunc = ValueFunc2D(EndOfPrdvNvrsFunc, self.CRRA) - def getPointsForInterpolation(self,EndOfPrdvP,aLvlNow): + def getPointsForInterpolation(self, EndOfPrdvP, aLvlNow): ''' Finds endogenous interpolation points (c,m) for the consumption function. @@ -563,17 +577,18 @@ def getPointsForInterpolation(self,EndOfPrdvP,aLvlNow): mLvlNow = cLvlNow + aLvlNow # Limiting consumption is zero as m approaches mNrmMin - c_for_interpolation = np.concatenate((np.zeros((self.pLvlGrid.size,1)),cLvlNow),axis=-1) - m_for_interpolation = np.concatenate((self.BoroCnstNat(np.reshape(self.pLvlGrid,(self.pLvlGrid.size,1))),mLvlNow),axis=-1) + c_for_interpolation = np.concatenate((np.zeros((self.pLvlGrid.size, 1)), cLvlNow), axis=-1) + m_for_interpolation = np.concatenate((self.BoroCnstNat(np.reshape(self.pLvlGrid, + (self.pLvlGrid.size, 1))), mLvlNow), axis=-1) # Limiting consumption is MPCmin*mLvl as p approaches 0 - m_temp = np.reshape(m_for_interpolation[0,:],(1,m_for_interpolation.shape[1])) - m_for_interpolation = np.concatenate((m_temp,m_for_interpolation),axis=0) - c_for_interpolation = np.concatenate((self.MPCminNow*m_temp,c_for_interpolation),axis=0) + m_temp = np.reshape(m_for_interpolation[0, :], (1, m_for_interpolation.shape[1])) + m_for_interpolation = np.concatenate((m_temp, m_for_interpolation), axis=0) + c_for_interpolation = np.concatenate((self.MPCminNow*m_temp, c_for_interpolation), axis=0) return c_for_interpolation, m_for_interpolation - def usePointsForInterpolation(self,cLvl,mLvl,pLvl,interpolator): + def usePointsForInterpolation(self, cLvl, mLvl, pLvl, interpolator): ''' Constructs a basic solution for this period, including the consumption function and marginal value function. @@ -596,10 +611,10 @@ def usePointsForInterpolation(self,cLvl,mLvl,pLvl,interpolator): consumption function, marginal value function, and minimum m. ''' # Construct the unconstrained consumption function - cFuncNowUnc = interpolator(mLvl,pLvl,cLvl) + cFuncNowUnc = interpolator(mLvl, pLvl, cLvl) # Combine the constrained and unconstrained functions into the true consumption function - cFuncNow = LowerEnvelope2D(cFuncNowUnc,self.cFuncNowCnst) + cFuncNow = LowerEnvelope2D(cFuncNowUnc, self.cFuncNowCnst) # Make the marginal value function vPfuncNow = self.makevPfunc(cFuncNow) @@ -608,7 +623,7 @@ def usePointsForInterpolation(self,cLvl,mLvl,pLvl,interpolator): solution_now = ConsumerSolution(cFunc=cFuncNow, vPfunc=vPfuncNow, mNrmMin=0.0) return solution_now - def makevPfunc(self,cFunc): + def makevPfunc(self, cFunc): ''' Constructs the marginal value function for this period. @@ -623,10 +638,10 @@ def makevPfunc(self,cFunc): vPfunc : function Marginal value (of market resources) function for this period. ''' - vPfunc = MargValueFunc2D(cFunc,self.CRRA) + vPfunc = MargValueFunc2D(cFunc, self.CRRA) return vPfunc - def makevFunc(self,solution): + def makevFunc(self, solution): ''' Creates the value function for this period, defined over market resources m and persistent income p. self must have the attribute EndOfPrdvFunc in @@ -648,42 +663,45 @@ def makevFunc(self,solution): pSize = self.pLvlGrid.size # Compute expected value and marginal value on a grid of market resources - pLvl_temp = np.tile(self.pLvlGrid,(mSize,1)) # Tile pLvl across m values - mLvl_temp = np.tile(self.mLvlMinNow(self.pLvlGrid),(mSize,1)) + np.tile(np.reshape(self.aXtraGrid,(mSize,1)),(1,pSize))*pLvl_temp - cLvlNow = solution.cFunc(mLvl_temp,pLvl_temp) - aLvlNow = mLvl_temp - cLvlNow - vNow = self.u(cLvlNow) + self.EndOfPrdvFunc(aLvlNow,pLvl_temp) - vPnow = self.uP(cLvlNow) + pLvl_temp = np.tile(self.pLvlGrid, (mSize, 1)) # Tile pLvl across m values + mLvl_temp = np.tile(self.mLvlMinNow(self.pLvlGrid), (mSize, 1)) +\ + np.tile(np.reshape(self.aXtraGrid, (mSize, 1)), (1, pSize))*pLvl_temp + cLvlNow = solution.cFunc(mLvl_temp, pLvl_temp) + aLvlNow = mLvl_temp - cLvlNow + vNow = self.u(cLvlNow) + self.EndOfPrdvFunc(aLvlNow, pLvl_temp) + vPnow = self.uP(cLvlNow) # Calculate pseudo-inverse value and its first derivative (wrt mLvl) - vNvrs = self.uinv(vNow) # value transformed through inverse utility - vNvrsP = vPnow*self.uinvP(vNow) + vNvrs = self.uinv(vNow) # value transformed through inverse utility + vNvrsP = vPnow*self.uinvP(vNow) # Add data at the lower bound of m - mLvl_temp = np.concatenate((np.reshape(self.mLvlMinNow(self.pLvlGrid),(1,pSize)),mLvl_temp),axis=0) - vNvrs = np.concatenate((np.zeros((1,pSize)),vNvrs),axis=0) - vNvrsP = np.concatenate((np.reshape(vNvrsP[0,:],(1,vNvrsP.shape[1])),vNvrsP),axis=0) + mLvl_temp = np.concatenate((np.reshape(self.mLvlMinNow(self.pLvlGrid), (1, pSize)), mLvl_temp), axis=0) + vNvrs = np.concatenate((np.zeros((1, pSize)), vNvrs), axis=0) + vNvrsP = np.concatenate((np.reshape(vNvrsP[0, :], (1, vNvrsP.shape[1])), vNvrsP), axis=0) # Add data at the lower bound of p - MPCminNvrs = self.MPCminNow**(-self.CRRA/(1.0-self.CRRA)) - m_temp = np.reshape(mLvl_temp[:,0],(mSize+1,1)) - mLvl_temp = np.concatenate((m_temp,mLvl_temp),axis=1) - vNvrs = np.concatenate((MPCminNvrs*m_temp,vNvrs),axis=1) - vNvrsP = np.concatenate((MPCminNvrs*np.ones((mSize+1,1)),vNvrsP),axis=1) + MPCminNvrs = self.MPCminNow**(-self.CRRA/(1.0-self.CRRA)) + m_temp = np.reshape(mLvl_temp[:, 0], (mSize+1, 1)) + mLvl_temp = np.concatenate((m_temp, mLvl_temp), axis=1) + vNvrs = np.concatenate((MPCminNvrs*m_temp, vNvrs), axis=1) + vNvrsP = np.concatenate((MPCminNvrs*np.ones((mSize+1, 1)), vNvrsP), axis=1) # Construct the pseudo-inverse value function vNvrsFunc_list = [] for j in range(pSize+1): - pLvl = np.insert(self.pLvlGrid,0,0.0)[j] - vNvrsFunc_list.append(CubicInterp(mLvl_temp[:,j]-self.mLvlMinNow(pLvl),vNvrs[:,j],vNvrsP[:,j],MPCminNvrs*self.hLvlNow(pLvl),MPCminNvrs)) - vNvrsFuncBase = LinearInterpOnInterp1D(vNvrsFunc_list,np.insert(self.pLvlGrid,0,0.0)) # Value function "shifted" - vNvrsFuncNow = VariableLowerBoundFunc2D(vNvrsFuncBase,self.mLvlMinNow) + pLvl = np.insert(self.pLvlGrid, 0, 0.0)[j] + vNvrsFunc_list.append(CubicInterp(mLvl_temp[:, j]-self.mLvlMinNow(pLvl), + vNvrs[:, j], vNvrsP[:, j], MPCminNvrs*self.hLvlNow(pLvl), MPCminNvrs)) + vNvrsFuncBase = LinearInterpOnInterp1D(vNvrsFunc_list, + np.insert(self.pLvlGrid, 0, 0.0)) # Value function "shifted" + vNvrsFuncNow = VariableLowerBoundFunc2D(vNvrsFuncBase, self.mLvlMinNow) # "Re-curve" the pseudo-inverse value function into the value function - vFuncNow = ValueFunc2D(vNvrsFuncNow,self.CRRA) + vFuncNow = ValueFunc2D(vNvrsFuncNow, self.CRRA) return vFuncNow - def makeBasicSolution(self,EndOfPrdvP,aLvl,pLvl,interpolator): + def makeBasicSolution(self, EndOfPrdvP, aLvl, pLvl, interpolator): ''' Given end of period assets and end of period marginal value, construct the basic solution for this period. @@ -707,14 +725,13 @@ def makeBasicSolution(self,EndOfPrdvP,aLvl,pLvl,interpolator): The solution to this period's consumption-saving problem, with a consumption function, marginal value function, and minimum m. ''' - cLvl,mLvl = self.getPointsForInterpolation(EndOfPrdvP,aLvl) - pLvl_temp = np.concatenate((np.reshape(self.pLvlGrid,(self.pLvlGrid.size,1)),pLvl),axis=-1) - pLvl_temp = np.concatenate((np.zeros((1,mLvl.shape[1])),pLvl_temp)) - solution_now = self.usePointsForInterpolation(cLvl,mLvl,pLvl_temp,interpolator) + cLvl, mLvl = self.getPointsForInterpolation(EndOfPrdvP, aLvl) + pLvl_temp = np.concatenate((np.reshape(self.pLvlGrid, (self.pLvlGrid.size, 1)), pLvl), axis=-1) + pLvl_temp = np.concatenate((np.zeros((1, mLvl.shape[1])), pLvl_temp)) + solution_now = self.usePointsForInterpolation(cLvl, mLvl, pLvl_temp, interpolator) return solution_now - - def makeLinearcFunc(self,mLvl,pLvl,cLvl): + def makeLinearcFunc(self, mLvl, pLvl, cLvl): ''' Makes a quasi-bilinear interpolation to represent the (unconstrained) consumption function. @@ -733,21 +750,24 @@ def makeLinearcFunc(self,mLvl,pLvl,cLvl): cFuncUnc : LinearInterp The unconstrained consumption function for this period. ''' - cFunc_by_pLvl_list = [] # list of consumption functions for each pLvl + cFunc_by_pLvl_list = [] # list of consumption functions for each pLvl for j in range(pLvl.shape[0]): - pLvl_j = pLvl[j,0] - m_temp = mLvl[j,:] - self.BoroCnstNat(pLvl_j) - c_temp = cLvl[j,:] # Make a linear consumption function for this pLvl + pLvl_j = pLvl[j, 0] + m_temp = mLvl[j, :] - self.BoroCnstNat(pLvl_j) + c_temp = cLvl[j, :] # Make a linear consumption function for this pLvl if pLvl_j > 0: - cFunc_by_pLvl_list.append(LinearInterp(m_temp,c_temp,lower_extrap=True,slope_limit=self.MPCminNow,intercept_limit=self.MPCminNow*self.hLvlNow(pLvl_j))) + cFunc_by_pLvl_list.append(LinearInterp(m_temp, c_temp, lower_extrap=True, + slope_limit=self.MPCminNow, + intercept_limit=self.MPCminNow*self.hLvlNow(pLvl_j))) else: - cFunc_by_pLvl_list.append(LinearInterp(m_temp,c_temp,lower_extrap=True)) - pLvl_list = pLvl[:,0] - cFuncUncBase = LinearInterpOnInterp1D(cFunc_by_pLvl_list,pLvl_list) # Combine all linear cFuncs - cFuncUnc = VariableLowerBoundFunc2D(cFuncUncBase,self.BoroCnstNat) # Re-adjust for natural borrowing constraint (as lower bound) + cFunc_by_pLvl_list.append(LinearInterp(m_temp, c_temp, lower_extrap=True)) + pLvl_list = pLvl[:, 0] + cFuncUncBase = LinearInterpOnInterp1D(cFunc_by_pLvl_list, pLvl_list) # Combine all linear cFuncs + cFuncUnc = VariableLowerBoundFunc2D( + cFuncUncBase, self.BoroCnstNat) # Re-adjust for natural borrowing constraint (as lower bound) return cFuncUnc - def makeCubiccFunc(self,mLvl,pLvl,cLvl): + def makeCubiccFunc(self, mLvl, pLvl, cLvl): ''' Makes a quasi-cubic spline interpolation of the unconstrained consumption function for this period. Function is cubic splines with respect to mLvl, @@ -768,29 +788,35 @@ def makeCubiccFunc(self,mLvl,pLvl,cLvl): The unconstrained consumption function for this period. ''' # Calculate the MPC at each gridpoint - EndOfPrdvPP = self.DiscFacEff*self.Rfree*self.Rfree*np.sum(self.vPPfuncNext(self.mLvlNext,self.pLvlNext)*self.ShkPrbs_temp,axis=0) - dcda = EndOfPrdvPP/self.uPP(np.array(cLvl[1:,1:])) - MPC = dcda/(dcda+1.) - MPC = np.concatenate((np.reshape(MPC[:,0],(MPC.shape[0],1)),MPC),axis=1) # Stick an extra MPC value at bottom; MPCmax doesn't work - MPC = np.concatenate((self.MPCminNow*np.ones((1,self.aXtraGrid.size+1)),MPC),axis=0) + EndOfPrdvPP = self.DiscFacEff*self.Rfree*self.Rfree*np.sum( + self.vPPfuncNext(self.mLvlNext, self.pLvlNext)*self.ShkPrbs_temp, axis=0) + dcda = EndOfPrdvPP/self.uPP(np.array(cLvl[1:, 1:])) + MPC = dcda/(dcda+1.) + MPC = np.concatenate((np.reshape(MPC[:, 0], + (MPC.shape[0], 1)), MPC), axis=1) + # Stick an extra MPC value at bottom; MPCmax doesn't work + MPC = np.concatenate((self.MPCminNow*np.ones((1, self.aXtraGrid.size+1)), MPC), axis=0) # Make cubic consumption function with respect to mLvl for each persistent income level - cFunc_by_pLvl_list = [] # list of consumption functions for each pLvl + cFunc_by_pLvl_list = [] # list of consumption functions for each pLvl for j in range(pLvl.shape[0]): - pLvl_j = pLvl[j,0] - m_temp = mLvl[j,:] - self.BoroCnstNat(pLvl_j) - c_temp = cLvl[j,:] # Make a cubic consumption function for this pLvl - MPC_temp = MPC[j,:] + pLvl_j = pLvl[j, 0] + m_temp = mLvl[j, :] - self.BoroCnstNat(pLvl_j) + c_temp = cLvl[j, :] # Make a cubic consumption function for this pLvl + MPC_temp = MPC[j, :] if pLvl_j > 0: - cFunc_by_pLvl_list.append(CubicInterp(m_temp,c_temp,MPC_temp,lower_extrap=True,slope_limit=self.MPCminNow,intercept_limit=self.MPCminNow*self.hLvlNow(pLvl_j))) - else: # When pLvl=0, cFunc is linear - cFunc_by_pLvl_list.append(LinearInterp(m_temp,c_temp,lower_extrap=True)) - pLvl_list = pLvl[:,0] - cFuncUncBase = LinearInterpOnInterp1D(cFunc_by_pLvl_list,pLvl_list) # Combine all linear cFuncs - cFuncUnc = VariableLowerBoundFunc2D(cFuncUncBase,self.BoroCnstNat) # Re-adjust for lower bound of natural borrowing constraint + cFunc_by_pLvl_list.append(CubicInterp( + m_temp, c_temp, MPC_temp, lower_extrap=True, + slope_limit=self.MPCminNow, intercept_limit=self.MPCminNow*self.hLvlNow(pLvl_j))) + else: # When pLvl=0, cFunc is linear + cFunc_by_pLvl_list.append(LinearInterp(m_temp, c_temp, lower_extrap=True)) + pLvl_list = pLvl[:, 0] + cFuncUncBase = LinearInterpOnInterp1D(cFunc_by_pLvl_list, pLvl_list) # Combine all linear cFuncs + cFuncUnc = VariableLowerBoundFunc2D(cFuncUncBase, self.BoroCnstNat) + # Re-adjust for lower bound of natural borrowing constraint return cFuncUnc - def addMPCandHumanWealth(self,solution): + def addMPCandHumanWealth(self, solution): ''' Take a solution and add human wealth and the bounding MPCs to it. @@ -805,14 +831,14 @@ def addMPCandHumanWealth(self,solution): The solution to this period's consumption-saving problem, but now with human wealth and the bounding MPCs. ''' - solution.hNrm = 0.0 # Can't have None or setAndUpdateValues breaks, should fix - solution.hLvl = self.hLvlNow - solution.mLvlMin= self.mLvlMinNow + solution.hNrm = 0.0 # Can't have None or setAndUpdateValues breaks, should fix + solution.hLvl = self.hLvlNow + solution.mLvlMin = self.mLvlMinNow solution.MPCmin = self.MPCminNow - solution.MPCmax = 0.0 # MPCmax is actually a function in this model + solution.MPCmax = 0.0 # MPCmax is actually a function in this model return solution - def addvPPfunc(self,solution): + def addvPPfunc(self, solution): ''' Adds the marginal marginal value function to an existing solution, so that the next solver can evaluate vPP and thus use cubic interpolation. @@ -829,8 +855,8 @@ def addvPPfunc(self,solution): The same solution passed as input, but with the marginal marginal value function for this period added as the attribute vPPfunc. ''' - vPPfuncNow = MargMargValueFunc2D(solution.cFunc,self.CRRA) - solution.vPPfunc = vPPfuncNow + vPPfuncNow = MargMargValueFunc2D(solution.cFunc, self.CRRA) + solution.vPPfunc = vPPfuncNow return solution def solve(self): @@ -851,7 +877,7 @@ def solve(self): tion of persistent income. Might also include a value function and marginal marginal value function, depending on options selected. ''' - aLvl,pLvl = self.prepareToCalcEndOfPrdvP() + aLvl, pLvl = self.prepareToCalcEndOfPrdvP() EndOfPrdvP = self.calcEndOfPrdvP() if self.vFuncBool: self.makeEndOfPrdvFunc(EndOfPrdvP) @@ -859,8 +885,8 @@ def solve(self): interpolator = self.makeCubiccFunc else: interpolator = self.makeLinearcFunc - solution = self.makeBasicSolution(EndOfPrdvP,aLvl,pLvl,interpolator) - solution = self.addMPCandHumanWealth(solution) + solution = self.makeBasicSolution(EndOfPrdvP, aLvl, pLvl, interpolator) + solution = self.addMPCandHumanWealth(solution) if self.vFuncBool: solution.vFunc = self.makevFunc(solution) if self.CubicBool: @@ -868,8 +894,8 @@ def solve(self): return solution -def solveConsGenIncProcess(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,pLvlNextFunc, - BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool): +def solveConsGenIncProcess(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, pLvlNextFunc, + BoroCnstArt, aXtraGrid, pLvlGrid, vFuncBool, CubicBool): ''' Solves the one period problem of a consumer who experiences persistent and transitory shocks to his income. Unlike in ConsIndShock, consumers do not @@ -918,10 +944,10 @@ def solveConsGenIncProcess(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree,pL function (defined over market resources and persistent income), a marginal value function, bounding MPCs, and normalized human wealth. ''' - solver = ConsGenIncProcessSolver(solution_next,IncomeDstn,LivPrb,DiscFac,CRRA,Rfree, - pLvlNextFunc,BoroCnstArt,aXtraGrid,pLvlGrid,vFuncBool,CubicBool) + solver = ConsGenIncProcessSolver(solution_next, IncomeDstn, LivPrb, DiscFac, CRRA, Rfree, + pLvlNextFunc, BoroCnstArt, aXtraGrid, pLvlGrid, vFuncBool, CubicBool) solver.prepareToSolve() # Do some preparatory work - solution_now = solver.solve() # Solve the period + solution_now = solver.solve() # Solve the period return solution_now @@ -935,11 +961,11 @@ class GenIncProcessConsumerType(IndShockConsumerType): values for risk aversion, discount factor, the interest rate, the grid of end-of-period assets, and an artificial borrowing constraint. ''' - cFunc_terminal_ = BilinearInterp(np.array([[0.0,0.0],[1.0,1.0]]),np.array([0.0,1.0]),np.array([0.0,1.0])) - solution_terminal_ = ConsumerSolution(cFunc = cFunc_terminal_, mNrmMin=0.0, hNrm=0.0, MPCmin=1.0, MPCmax=1.0) - poststate_vars_ = ['aLvlNow','pLvlNow'] + cFunc_terminal_ = BilinearInterp(np.array([[0.0, 0.0], [1.0, 1.0]]), np.array([0.0, 1.0]), np.array([0.0, 1.0])) + solution_terminal_ = ConsumerSolution(cFunc=cFunc_terminal_, mNrmMin=0.0, hNrm=0.0, MPCmin=1.0, MPCmax=1.0) + poststate_vars_ = ['aLvlNow', 'pLvlNow'] - def __init__(self,cycles=1,time_flow=True,**kwds): + def __init__(self, cycles=1, time_flow=True, **kwds): ''' Instantiate a new ConsumerType with given data. See ConsumerParameters.init_explicit_perm_inc for a dictionary of the @@ -957,8 +983,8 @@ def __init__(self,cycles=1,time_flow=True,**kwds): None ''' # Initialize a basic ConsumerType - IndShockConsumerType.__init__(self,cycles=cycles,time_flow=time_flow,**kwds) - self.solveOnePeriod = solveConsGenIncProcess # idiosyncratic shocks solver with explicit persistent income + IndShockConsumerType.__init__(self, cycles=cycles, time_flow=time_flow, **kwds) + self.solveOnePeriod = solveConsGenIncProcess # idiosyncratic shocks solver with explicit persistent income def update(self): ''' @@ -990,13 +1016,14 @@ def updateSolutionTerminal(self): ------- None ''' - self.solution_terminal.vFunc = ValueFunc2D(self.cFunc_terminal_,self.CRRA) - self.solution_terminal.vPfunc = MargValueFunc2D(self.cFunc_terminal_,self.CRRA) - self.solution_terminal.vPPfunc = MargMargValueFunc2D(self.cFunc_terminal_,self.CRRA) - self.solution_terminal.hNrm = 0.0 # Don't track normalized human wealth - self.solution_terminal.hLvl = lambda p : np.zeros_like(p) # But do track absolute human wealth by persistent income - self.solution_terminal.mLvlMin = lambda p : np.zeros_like(p) # And minimum allowable market resources by perm inc - + self.solution_terminal.vFunc = ValueFunc2D(self.cFunc_terminal_, self.CRRA) + self.solution_terminal.vPfunc = MargValueFunc2D(self.cFunc_terminal_, self.CRRA) + self.solution_terminal.vPPfunc = MargMargValueFunc2D(self.cFunc_terminal_, self.CRRA) + self.solution_terminal.hNrm = 0.0 # Don't track normalized human wealth + self.solution_terminal.hLvl = lambda p: np.zeros_like(p) + # But do track absolute human wealth by persistent income + self.solution_terminal.mLvlMin = lambda p: np.zeros_like(p) + # And minimum allowable market resources by perm inc def updatepLvlNextFunc(self): ''' @@ -1012,11 +1039,10 @@ def updatepLvlNextFunc(self): ------- None ''' - pLvlNextFuncBasic = LinearInterp(np.array([0.,1.]),np.array([0.,1.])) + pLvlNextFuncBasic = LinearInterp(np.array([0., 1.]), np.array([0., 1.])) self.pLvlNextFunc = self.T_cycle*[pLvlNextFuncBasic] self.addToTimeVary('pLvlNextFunc') - def installRetirementFunc(self): ''' Installs a special pLvlNextFunc representing retirement in the correct @@ -1033,12 +1059,11 @@ def installRetirementFunc(self): ------- None ''' - if (not hasattr(self,'pLvlNextFuncRet')) or self.T_retire == 0: + if (not hasattr(self, 'pLvlNextFuncRet')) or self.T_retire == 0: return t = self.T_retire self.pLvlNextFunc[t] = self.pLvlNextFuncRet - def updatepLvlGrid(self): ''' Update the grid of persistent income levels. Currently only works for @@ -1063,38 +1088,41 @@ def updatepLvlGrid(self): # Simulate the distribution of persistent income levels by t_cycle in a lifecycle model if self.cycles == 1: - pLvlNow = drawLognormal(self.AgentCount,mu=self.pLvlInitMean,sigma=self.pLvlInitStd,seed=31382) - pLvlGrid = [] # empty list of time-varying persistent income grids + pLvlNow = drawLognormal(self.AgentCount, mu=self.pLvlInitMean, sigma=self.pLvlInitStd, seed=31382) + pLvlGrid = [] # empty list of time-varying persistent income grids # Calculate distribution of persistent income in each period of lifecycle for t in range(len(self.PermShkStd)): if t > 0: - PermShkNow = drawDiscrete(N=self.AgentCount,P=self.PermShkDstn[t-1][0],X=self.PermShkDstn[t-1][1],exact_match=False,seed=t) + PermShkNow = drawDiscrete(N=self.AgentCount, P=self.PermShkDstn[t-1][0], + X=self.PermShkDstn[t-1][1], exact_match=False, seed=t) pLvlNow = self.pLvlNextFunc[t-1](pLvlNow)*PermShkNow - pLvlGrid.append(getPercentiles(pLvlNow,percentiles=self.pLvlPctiles)) + pLvlGrid.append(getPercentiles(pLvlNow, percentiles=self.pLvlPctiles)) # Calculate "stationary" distribution in infinite horizon (might vary across periods of cycle) elif self.cycles == 0: - T_long = 1000 # Number of periods to simulate to get to "stationary" distribution - pLvlNow = drawLognormal(self.AgentCount,mu=self.pLvlInitMean,sigma=self.pLvlInitStd,seed=31382) - t_cycle = np.zeros(self.AgentCount,dtype=int) + T_long = 1000 # Number of periods to simulate to get to "stationary" distribution + pLvlNow = drawLognormal(self.AgentCount, mu=self.pLvlInitMean, sigma=self.pLvlInitStd, seed=31382) + t_cycle = np.zeros(self.AgentCount, dtype=int) for t in range(T_long): - LivPrb = LivPrbAll[t_cycle] # Determine who dies and replace them with newborns - draws = drawUniform(self.AgentCount,seed=t) + LivPrb = LivPrbAll[t_cycle] # Determine who dies and replace them with newborns + draws = drawUniform(self.AgentCount, seed=t) who_dies = draws > LivPrb - pLvlNow[who_dies] = drawLognormal(np.sum(who_dies),mu=self.pLvlInitMean,sigma=self.pLvlInitStd,seed=t+92615) + pLvlNow[who_dies] = drawLognormal(np.sum(who_dies), mu=self.pLvlInitMean, + sigma=self.pLvlInitStd, seed=t+92615) t_cycle[who_dies] = 0 - for j in range(self.T_cycle): # Update persistent income + for j in range(self.T_cycle): # Update persistent income these = t_cycle == j - PermShkTemp = drawDiscrete(N=np.sum(these),P=self.PermShkDstn[j][0],X=self.PermShkDstn[j][1],exact_match=False,seed=t+13*j) + PermShkTemp = drawDiscrete(N=np.sum(these), P=self.PermShkDstn[j][0], + X=self.PermShkDstn[j][1], exact_match=False, seed=t+13*j) pLvlNow[these] = self.pLvlNextFunc[j](pLvlNow[these])*PermShkTemp t_cycle = t_cycle + 1 t_cycle[t_cycle == self.T_cycle] = 0 # We now have a "long run stationary distribution", extract percentiles - pLvlGrid = [] # empty list of time-varying persistent income grids + pLvlGrid = [] # empty list of time-varying persistent income grids for t in range(self.T_cycle): these = t_cycle == t - pLvlGrid.append(getPercentiles(pLvlNow[these],percentiles=self.pLvlPctiles)) + pLvlGrid.append(getPercentiles(pLvlNow[these], percentiles=self.pLvlPctiles)) # Throw an error if cycles>1 else: @@ -1106,7 +1134,7 @@ def updatepLvlGrid(self): if not orig_time: self.timeRev() - def simBirth(self,which_agents): + def simBirth(self, which_agents): ''' Makes new consumers for the given indices. Initialized variables include aNrm and pLvl, as well as time variables t_age and t_cycle. Normalized assets and persistent income levels @@ -1122,13 +1150,14 @@ def simBirth(self,which_agents): None ''' # Get and store states for newly born agents - N = np.sum(which_agents) # Number of new consumers to make - aNrmNow_new = drawLognormal(N,mu=self.aNrmInitMean,sigma=self.aNrmInitStd,seed=self.RNG.randint(0,2**31-1)) - self.pLvlNow[which_agents] = drawLognormal(N,mu=self.pLvlInitMean,sigma=self.pLvlInitStd,seed=self.RNG.randint(0,2**31-1)) + N = np.sum(which_agents) # Number of new consumers to make + aNrmNow_new = drawLognormal(N, mu=self.aNrmInitMean, sigma=self.aNrmInitStd, + seed=self.RNG.randint(0, 2**31-1)) + self.pLvlNow[which_agents] = drawLognormal(N, mu=self.pLvlInitMean, sigma=self.pLvlInitStd, + seed=self.RNG.randint(0, 2**31-1)) self.aLvlNow[which_agents] = aNrmNow_new*self.pLvlNow[which_agents] - self.t_age[which_agents] = 0 # How many periods since each agent was born - self.t_cycle[which_agents] = 0 # Which period of the cycle each agent is currently in - + self.t_age[which_agents] = 0 # How many periods since each agent was born + self.t_cycle[which_agents] = 0 # Which period of the cycle each agent is currently in def getStates(self): ''' @@ -1153,8 +1182,7 @@ def getStates(self): pLvlNow[these] = self.pLvlNextFunc[t-1](self.pLvlNow[these])*self.PermShkNow[these] self.pLvlNow = pLvlNow # Updated persistent income level self.bLvlNow = RfreeNow*aLvlPrev # Bank balances before labor income - self.mLvlNow = self.bLvlNow + self.TranShkNow*self.pLvlNow # Market resources after income - + self.mLvlNow = self.bLvlNow + self.TranShkNow*self.pLvlNow # Market resources after income def getControls(self): ''' @@ -1172,11 +1200,10 @@ def getControls(self): MPCnow = np.zeros(self.AgentCount) + np.nan for t in range(self.T_cycle): these = t == self.t_cycle - cLvlNow[these] = self.solution[t].cFunc(self.mLvlNow[these],self.pLvlNow[these]) - MPCnow[these] = self.solution[t].cFunc.derivativeX(self.mLvlNow[these],self.pLvlNow[these]) + cLvlNow[these] = self.solution[t].cFunc(self.mLvlNow[these], self.pLvlNow[these]) + MPCnow[these] = self.solution[t].cFunc.derivativeX(self.mLvlNow[these], self.pLvlNow[these]) self.cLvlNow = cLvlNow - self.MPCnow = MPCnow - + self.MPCnow = MPCnow def getPostStates(self): ''' @@ -1226,7 +1253,7 @@ def updatepLvlNextFunc(self): pLvlNextFunc = [] for t in range(self.T_cycle): - pLvlNextFunc.append(LinearInterp(np.array([0.,1.]),np.array([0.,self.PermGroFac[t]]))) + pLvlNextFunc.append(LinearInterp(np.array([0., 1.]), np.array([0., self.PermGroFac[t]]))) self.pLvlNextFunc = pLvlNextFunc self.addToTimeVary('pLvlNextFunc') @@ -1264,10 +1291,10 @@ def updatepLvlNextFunc(self): self.timeFwd() pLvlNextFunc = [] - pLogMean = self.pLvlInitMean # Initial mean (log) persistent income + pLogMean = self.pLvlInitMean # Initial mean (log) persistent income for t in range(self.T_cycle): - pLvlNextFunc.append(pLvlFuncAR1(pLogMean,self.PermGroFac[t],self.PrstIncCorr)) + pLvlNextFunc.append(pLvlFuncAR1(pLogMean, self.PermGroFac[t], self.PrstIncCorr)) pLogMean += np.log(self.PermGroFac[t]) self.pLvlNextFunc = pLvlNextFunc @@ -1283,14 +1310,15 @@ def main(): from HARK.utilities import plotFuncs from time import clock import matplotlib.pyplot as plt - mystr = lambda number : "{:.4f}".format(number) + def mystr(number): return "{:.4f}".format(number) do_simulation = False # Display information about the pLvlGrid used in these examples print('The infinite horizon examples presented here use a grid of persistent income levels (pLvlGrid)') print('based on percentiles of the long run distribution of pLvl for the given parameters. These percentiles') - print('are specified in the attribute pLvlPctiles. Here, the lowest percentile is ' + str(Params.init_explicit_perm_inc['pLvlPctiles'][0]*100) + ' and the highest') + print('are specified in the attribute pLvlPctiles. Here, the lowest percentile is ' + + str(Params.init_explicit_perm_inc['pLvlPctiles'][0]*100) + ' and the highest') print('percentile is ' + str(Params.init_explicit_perm_inc['pLvlPctiles'][-1]*100) + '.\n') # Make and solve an example "explicit permanent income" consumer with idiosyncratic shocks @@ -1303,13 +1331,13 @@ def main(): # Plot the consumption function at various permanent income levels print('Consumption function by pLvl for explicit permanent income consumer:') pLvlGrid = ExplicitExample.pLvlGrid[0] - mLvlGrid = np.linspace(0,20,300) + mLvlGrid = np.linspace(0, 20, 300) for p in pLvlGrid: M_temp = mLvlGrid + ExplicitExample.solution[0].mLvlMin(p) - C = ExplicitExample.solution[0].cFunc(M_temp,p*np.ones_like(M_temp)) - plt.plot(M_temp,C) - plt.xlim(0.,20.) - plt.ylim(0.,None) + C = ExplicitExample.solution[0].cFunc(M_temp, p*np.ones_like(M_temp)) + plt.plot(M_temp, C) + plt.xlim(0., 20.) + plt.ylim(0., None) plt.xlabel('Market resource level mLvl') plt.ylabel('Consumption level cLvl') plt.show() @@ -1319,26 +1347,27 @@ def main(): t_start = clock() NormalizedExample.solve() t_end = clock() - print('Solving the equivalent problem with permanent income normalized out took ' + mystr(t_end-t_start) + ' seconds.') + print('Solving the equivalent problem with permanent income normalized out took ' + + mystr(t_end-t_start) + ' seconds.') # Show that the normalized consumption function for the "explicit permanent income" consumer # is almost identical for every permanent income level (and the same as the normalized problem's # cFunc), but is less accurate due to extrapolation outside the bounds of pLvlGrid. print('Normalized consumption function by pLvl for explicit permanent income consumer:') pLvlGrid = ExplicitExample.pLvlGrid[0] - mNrmGrid = np.linspace(0,20,300) + mNrmGrid = np.linspace(0, 20, 300) for p in pLvlGrid: M_temp = mNrmGrid*p + ExplicitExample.solution[0].mLvlMin(p) - C = ExplicitExample.solution[0].cFunc(M_temp,p*np.ones_like(M_temp)) - plt.plot(M_temp/p,C/p) - plt.xlim(0.,20.) - plt.ylim(0.,None) + C = ExplicitExample.solution[0].cFunc(M_temp, p*np.ones_like(M_temp)) + plt.plot(M_temp/p, C/p) + plt.xlim(0., 20.) + plt.ylim(0., None) plt.xlabel('Normalized market resources mNrm') plt.ylabel('Normalized consumption cNrm') plt.show() print('Consumption function for normalized problem (without explicit permanent income):') mNrmMin = NormalizedExample.solution[0].mNrmMin - plotFuncs(NormalizedExample.solution[0].cFunc,mNrmMin,mNrmMin+20.) + plotFuncs(NormalizedExample.solution[0].cFunc, mNrmMin, mNrmMin+20) print('The "explicit permanent income" solution deviates from the solution to the normalized problem because') print('of errors from extrapolating beyond the bounds of the pLvlGrid. The error is largest for pLvl values') @@ -1346,13 +1375,13 @@ def main(): # Plot the value function at various permanent income levels if ExplicitExample.vFuncBool: - pGrid = np.linspace(0.1,3.0,24) - M = np.linspace(0.001,5,300) + pGrid = np.linspace(0.1, 3.0, 24) + M = np.linspace(0.001, 5, 300) for p in pGrid: M_temp = M+ExplicitExample.solution[0].mLvlMin(p) - C = ExplicitExample.solution[0].vFunc(M_temp,p*np.ones_like(M_temp)) - plt.plot(M_temp,C) - plt.ylim([-200,0]) + C = ExplicitExample.solution[0].vFunc(M_temp, p*np.ones_like(M_temp)) + plt.plot(M_temp, C) + plt.ylim([-200, 0]) plt.xlabel('Market resource level mLvl') plt.ylabel('Value v') plt.show() @@ -1360,11 +1389,11 @@ def main(): # Simulate some data if do_simulation: ExplicitExample.T_sim = 500 - ExplicitExample.track_vars = ['mLvlNow','cLvlNow','pLvlNow'] - ExplicitExample.makeShockHistory() # This is optional + ExplicitExample.track_vars = ['mLvlNow', 'cLvlNow', 'pLvlNow'] + ExplicitExample.makeShockHistory() # This is optional ExplicitExample.initializeSim() ExplicitExample.simulate() - plt.plot(np.mean(ExplicitExample.mLvlNow_hist,axis=1)) + plt.plot(np.mean(ExplicitExample.mLvlNow_hist, axis=1)) plt.xlabel('Simulated time period') plt.ylabel('Average market resources mLvl') plt.show() @@ -1379,15 +1408,16 @@ def main(): print('Solving a persistent income shocks consumer took ' + mystr(t_end-t_start) + ' seconds.') # Plot the consumption function at various levels of persistent income pLvl - print('Consumption function by persistent income level pLvl for a consumer with AR1 coefficient of ' + str(PersistentExample.PrstIncCorr) + ':') + print('Consumption function by persistent income level pLvl for a consumer with AR1 coefficient of ' + + str(PersistentExample.PrstIncCorr) + ':') pLvlGrid = PersistentExample.pLvlGrid[0] - mLvlGrid = np.linspace(0,20,300) + mLvlGrid = np.linspace(0, 20, 300) for p in pLvlGrid: M_temp = mLvlGrid + PersistentExample.solution[0].mLvlMin(p) - C = PersistentExample.solution[0].cFunc(M_temp,p*np.ones_like(M_temp)) - plt.plot(M_temp,C) - plt.xlim(0.,20.) - plt.ylim(0.,None) + C = PersistentExample.solution[0].cFunc(M_temp, p*np.ones_like(M_temp)) + plt.plot(M_temp, C) + plt.xlim(0., 20.) + plt.ylim(0., None) plt.xlabel('Market resource level mLvl') plt.ylabel('Consumption level cLvl') plt.show() @@ -1395,12 +1425,12 @@ def main(): # Plot the value function at various persistent income levels if PersistentExample.vFuncBool: pGrid = PersistentExample.pLvlGrid[0] - M = np.linspace(0.001,5,300) + M = np.linspace(0.001, 5, 300) for p in pGrid: M_temp = M+PersistentExample.solution[0].mLvlMin(p) - C = PersistentExample.solution[0].vFunc(M_temp,p*np.ones_like(M_temp)) - plt.plot(M_temp,C) - plt.ylim([-200,0]) + C = PersistentExample.solution[0].vFunc(M_temp, p*np.ones_like(M_temp)) + plt.plot(M_temp, C) + plt.ylim([-200, 0]) plt.xlabel('Market resource level mLvl') plt.ylabel('Value v') plt.show() @@ -1408,14 +1438,14 @@ def main(): # Simulate some data if do_simulation: PersistentExample.T_sim = 500 - PersistentExample.track_vars = ['mLvlNow','cLvlNow','pLvlNow'] + PersistentExample.track_vars = ['mLvlNow', 'cLvlNow', 'pLvlNow'] PersistentExample.initializeSim() PersistentExample.simulate() - plt.plot(np.mean(PersistentExample.mLvlNow_hist,axis=1)) + plt.plot(np.mean(PersistentExample.mLvlNow_hist, axis=1)) plt.xlabel('Simulated time period') plt.ylabel('Average market resources mLvl') plt.show() + if __name__ == '__main__': main() - From 01fccad63f9a8e397d35f7354947e7c9c8406c68 Mon Sep 17 00:00:00 2001 From: Keith Blaha Date: Mon, 6 May 2019 13:28:02 -0700 Subject: [PATCH 54/77] Add gentle intro to HARK notebook with test --- .travis.yml | 2 +- Examples/Gentle-Intro-To-HARK.ipynb | 540 ++++++++++++++++++++ Examples/__init__.py | 0 Examples/tests/test_gentle_intro_to_hark.py | 43 ++ Examples/util.py | 55 ++ requirements.txt | 1 + setup.py | 3 +- 7 files changed, 642 insertions(+), 2 deletions(-) create mode 100644 Examples/Gentle-Intro-To-HARK.ipynb create mode 100644 Examples/__init__.py create mode 100644 Examples/tests/test_gentle_intro_to_hark.py create mode 100644 Examples/util.py diff --git a/.travis.yml b/.travis.yml index 71237b781..8f0e9509a 100644 --- a/.travis.yml +++ b/.travis.yml @@ -6,5 +6,5 @@ cache: pip install: - pip install -r requirements.txt script: - - pytest HARK/tests + - pytest # - flake8 HARK diff --git a/Examples/Gentle-Intro-To-HARK.ipynb b/Examples/Gentle-Intro-To-HARK.ipynb new file mode 100644 index 000000000..2a56d9fc3 --- /dev/null +++ b/Examples/Gentle-Intro-To-HARK.ipynb @@ -0,0 +1,540 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A Gentle Introduction to HARK\n", + "\n", + "This notebook provides a simple, hands-on tutorial for first time HARK users -- and potentially first time Python users. It does not go \"into the weeds\" - we have hidden some code cells that do boring things that you don't need to digest on your first experience with HARK. Our aim is to convey a feel for how the toolkit works.\n", + "\n", + "For readers for whom this is your very first experience with Python, we have put important Python concepts in $\\textbf{boldface}$. For those for whom this is the first time they have used a Jupyter notebook, we have put Jupyter instructions in $\\textit{italics}.$ Only cursory definitions (if any) are provided here. If you want to learn more, there are many online Python and Jupyter tutorials." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [], + "source": [ + "# This cell has a bit of initial setup. You can click the triangle to the left to expand it.\n", + "# Click the \"Run\" button immediately above the notebook in order to execute the contents of any cell\n", + "# WARNING: Each cell in the notebook relies upon results generated by previous cells\n", + "# The most common problem beginners have is to execute a cell before all its predecessors\n", + "# If you do this, you can restart the kernel (see the \"Kernel\" menu above) and start over\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# The first step is to be able to bring things in from different directories\n", + "import os\n", + "import sys\n", + "module_path = os.path.abspath(os.path.join('..'))\n", + "if module_path not in sys.path:\n", + " sys.path.append(module_path)\n", + "\n", + "from copy import deepcopy\n", + "from time import clock\n", + "\n", + "import numpy as np\n", + "\n", + "import HARK\n", + "from Examples.util import log_progress\n", + "from HARK.utilities import plotFuncs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Your First HARK Model: Perfect Foresight\n", + "\n", + "$$\\newcommand{\\CRRA}{\\rho}\\newcommand{\\DiscFac}{\\beta}$$\n", + "We start with almost the simplest possible consumption model: A consumer with CRRA utility \n", + "\n", + "\\begin{equation}\n", + "U(C) = \\frac{C^{1-\\CRRA}}{1-\\rho}\n", + "\\end{equation}\n", + "\n", + "has perfect foresight about everything except the (stochastic) date of death, which occurs with constant probability implying a \"survival probability\" $\\newcommand{\\LivPrb}{\\aleph}\\LivPrb < 1$. Permanent labor income $P_t$ grows from period to period by a factor $\\Gamma_t$. At the beginning of each period $t$, the consumer has some amount of market resources $M_t$ (which includes both market wealth and currrent income) and must choose how much of those resources to consume $C_t$ and how much to retain in a riskless asset $A_t$ which will earn return factor $R$. The agent's flow of utility $U(C_t)$ from consumption is geometrically discounted by factor $\\beta$. Between periods, the agent dies with probability $\\mathsf{D}_t$, ending his problem.\n", + "\n", + "The agent's problem can be written in Bellman form as:\n", + "\n", + "\\begin{eqnarray*}\n", + "V_t(M_t,P_t) &=& \\max_{C_t}~U(C_t) + \\beta \\aleph V_{t+1}(M_{t+1},P_{t+1}), \\\\\n", + "& s.t. & \\\\\n", + "%A_t &=& M_t - C_t, \\\\\n", + "M_{t+1} &=& R (M_{t}-C_{t}) + Y_{t+1}, \\\\\n", + "P_{t+1} &=& \\Gamma_{t+1} P_t, \\\\\n", + "\\end{eqnarray*}\n", + "\n", + "A particular perfect foresight agent's problem can be characterized by values of risk aversion $\\rho$, discount factor $\\beta$, and return factor $R$, along with sequences of income growth factors $\\{ \\Gamma_t \\}$ and survival probabilities $\\{\\mathsf{\\aleph}_t\\}$. To keep things simple, let's forget about \"sequences\" of income growth and mortality, and just think about an $\\textit{infinite horizon}$ consumer with constant income growth and survival probability.\n", + "\n", + "## Representing Agents in HARK\n", + "\n", + "HARK represents agents solving this type of problem as $\\textbf{instances}$ of the $\\textbf{class}$ $\\texttt{PerfForesightConsumerType}$, a $\\textbf{subclass}$ of $\\texttt{AgentType}$. To make agents of this class, we must import the class itself into our workspace. (Run the cell below in order to do this)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The $\\texttt{PerfForesightConsumerType}$ class contains within itself the python code that constructs the solution for the perfect foresight model we are studying here, as specifically articulated in [these lecture notes](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/). \n", + "\n", + "To create an instance of $\\texttt{PerfForesightConsumerType}$, we simply call the class as if it were a function, passing as arguments the specific parameter values we want it to have. In the hidden cell below, we define a $\\textbf{dictionary}$ named $\\texttt{PF_dictionary}$ with these parameter values:\n", + "\n", + "| Param | Description | Code | Value |\n", + "| :---: | --- | --- | :---: |\n", + "| $\\rho$ | Relative risk aversion | $\\texttt{CRRA}$ | 2.5 |\n", + "| $\\beta$ | Discount factor | $\\texttt{DiscFac}$ | 0.96 |\n", + "| $R$ | Risk free interest factor | $\\texttt{Rfree}$ | 1.03 |\n", + "| $\\newcommand{\\LivFac}{\\aleph}\\LivFac$ | Survival probability | $\\texttt{LivPrb}$ | 0.98 |\n", + "| $\\Gamma$ | Income growth factor | $\\texttt{PermGroFac}$ | 1.01 |\n", + "\n", + "\n", + "For now, don't worry about the specifics of dictionaries. All you need to know is that a dictionary lets us pass many arguments wrapped up in one simple data structure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [], + "source": [ + "# This cell defines a parameter dictionary. You can expand it if you want to see what that looks like.\n", + "PF_dictionary = {\n", + " 'CRRA' : 2.5,\n", + " 'DiscFac' : 0.96,\n", + " 'Rfree' : 1.03,\n", + " 'LivPrb' : [0.98],\n", + " 'PermGroFac' : [1.01],\n", + " 'T_cycle' : 1,\n", + " 'cycles' : 0,\n", + " 'AgentCount' : 10000\n", + "}\n", + "\n", + "# To those curious enough to open this hidden cell, you might notice that we defined\n", + "# a few extra parameters in that dictionary: T_cycle, cycles, and AgentCount. Don't\n", + "# worry about these for now." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make an $\\textbf{object}$ named $\\texttt{PFexample}$ which is an $\\textbf{instance}$ of the $\\texttt{PerfForesightConsumerType}$ class. The object $\\texttt{PFexample}$ will bundle together the abstract mathematical description of the solution embodied in $\\texttt{PerfForesightConsumerType}$, and the specific set of parameter values defined in $\\texttt{PF_dictionary}$. Such a bundle is created passing $\\texttt{PF_dictionary}$ to the class $\\texttt{PerfForesightConsumerType}$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "PFexample = PerfForesightConsumerType(**PF_dictionary) \n", + "# the asterisks ** basically say \"here come some arguments\" to PerfForesightConsumerType" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In $\\texttt{PFexample}$, we now have _defined_ the problem of a particular infinite horizon perfect foresight consumer who knows how to solve this problem. \n", + "\n", + "## Solving an Agent's Problem\n", + "\n", + "To tell the agent actually to solve the problem, we call the agent's $\\texttt{solve}$ $\\textbf{method}$. (A $\\textbf{method}$ is essentially a function that an object runs that affects the object's own internal characteristics -- in this case, the method adds the consumption function to the contents of $\\texttt{PFexample}$.)\n", + "\n", + "The cell below calls the $\\texttt{solve}$ method for $\\texttt{PFexample}$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "PFexample.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Running the $\\texttt{solve}$ method creates the $\\textbf{attribute}$ of $\\texttt{PFexample}$ named $\\texttt{solution}$. In fact, every subclass of $\\texttt{AgentType}$ works the same way: The class definition contains the abstract algorithm that knows how to solve the model, but to obtain the particular solution for a specific instance (paramterization/configuration), that instance must be instructed to $\\texttt{solve()}$ its problem. \n", + "\n", + "The $\\texttt{solution}$ attribute is always a $\\textit{list}$ of solutions to a single period of the problem. In the case of an infinite horizon model like the one here, there is just one element in that list -- the solution to all periods of the infinite horizon problem. The consumption function stored as the first element (element 0) of the solution list can be retrieved by:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "PFexample.solution[0].cFunc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the results proven in the associated [the lecture notes](http://econ.jhu.edu/people/ccarroll/public/lecturenotes/consumption/PerfForesightCRRA/) is that, for the specific problem defined above, there is a solution in which the _ratio_ $c = C/P$ is a linear function of the _ratio_ of market resources to permanent income, $m = M/P$. \n", + "\n", + "This is why $\\texttt{cFunc}$ can be represented by a linear interpolation. It can be plotted between an $m$ ratio of 0 and 10 using the command below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mPlotTop=10\n", + "plotFuncs(PFexample.solution[0].cFunc,0.,mPlotTop)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The figure illustrates one of the surprising features of the perfect foresight model: A person with zero money should be spending at a rate more than double their income (that is, $\\texttt{cFunc}(0.) \\approx 2.08$ - the intersection on the vertical axis). How can this be?\n", + "\n", + "The answer is that we have not incorporated any constraint that would prevent the agent from borrowing against the entire PDV of future earnings-- human wealth. How much is that? What's the minimum value of $m_t$ where the consumption function is defined? We can check by retrieving the $\\texttt{hNrm}$ **attribute** of the solution, which calculates the value of human wealth normalized by permanent income:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "humanWealth = PFexample.solution[0].hNrm\n", + "mMinimum = PFexample.solution[0].mNrmMin\n", + "print(\"This agent's human wealth is \" + str(humanWealth) + ' times his current income level.')\n", + "print(\"This agent's consumption function is defined (consumption is positive) down to m_t = \" + str(mMinimum))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yikes! Let's take a look at the bottom of the consumption function. In the cell below, set the bounds of the $\\texttt{plotFuncs}$ function to display down to the lowest defined value of the consumption function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# YOUR FIRST HANDS-ON EXERCISE!\n", + "# Fill in the value for \"mPlotBottom\" to plot the consumption function from the point where it is zero.\n", + "# plotFuncs(PFexample.solution[0].cFunc,mPlotBottom,mPlotTop)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Changing Agent Parameters\n", + "\n", + "Suppose you wanted to change one (or more) of the parameters of the agent's problem and see what that does. We want to compare consumption functions before and after we change parameters, so let's make a new instance of $\\texttt{PerfForesightConsumerType}$ by copying $\\texttt{PFexample}$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "NewExample = deepcopy(PFexample)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Python, you can set an $\\textbf{attribute}$ of an object just like any other variable. For example, we could make the new agent less patient:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "NewExample.DiscFac = 0.90\n", + "NewExample.solve()\n", + "mPlotBottom = mMinimum\n", + "plotFuncs([PFexample.solution[0].cFunc,NewExample.solution[0].cFunc],mPlotBottom,mPlotTop)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(Note that you can pass a **list** of functions to $\\texttt{plotFuncs}$ as the first argument rather than just a single function. Lists are written inside of [square brackets].)\n", + "\n", + "Let's try to deal with the \"problem\" of massive human wealth by making another consumer who has essentially no future income. We can virtually eliminate human wealth by making the permanent income growth factor $\\textit{very}$ small.\n", + "\n", + "In $\\texttt{PFexample}$, the agent's income grew by 1 percent per period -- his $\\texttt{PermGroFac}$ took the value 1.01. What if our new agent had a growth factor of 0.01 -- his income $\\textit{shrinks}$ by 99 percent each period? In the cell below, set $\\texttt{NewExample}$'s discount factor back to its original value, then set its $\\texttt{PermGroFac}$ attribute so that the growth factor is 0.01 each period.\n", + "\n", + "Important: Recall that the model at the top of this document said that an agent's problem is characterized by a sequence of income growth factors, but we tabled that concept. Because $\\texttt{PerfForesightConsumerType}$ treats $\\texttt{PermGroFac}$ as a $\\textit{time-varying}$ attribute, it must be specified as a $\\textbf{list}$ (with a single element in this case)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Revert NewExample's discount factor and make his future income minuscule\n", + "# print(\"your lines here\")\n", + "\n", + "# Compare the old and new consumption functions\n", + "plotFuncs([PFexample.solution[0].cFunc,NewExample.solution[0].cFunc],0.,10.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now $\\texttt{NewExample}$'s consumption function has the same slope (MPC) as $\\texttt{PFexample}$, but it emanates from (almost) zero-- he has basically no future income to borrow against!\n", + "\n", + "If you'd like, use the cell above to alter $\\texttt{NewExample}$'s other attributes (relative risk aversion, etc) and see how the consumption function changes. However, keep in mind that \\textit{no solution exists} for some combinations of parameters. HARK should let you know if this is the case if you try to solve such a model.\n", + "\n", + "\n", + "## Your Second HARK Model: Adding Income Shocks\n", + "\n", + "Linear consumption functions are pretty boring, and you'd be justified in feeling unimpressed if all HARK could do was plot some lines. Let's look at another model that adds two important layers of complexity: income shocks and (artificial) borrowing constraints.\n", + "\n", + "Specifically, our new type of consumer receives two income shocks at the beginning of each period: a completely transitory shock $\\theta_t$ and a completely permanent shock $\\psi_t$. Moreover, lenders will not let the agent borrow money such that his ratio of end-of-period assets $A_t$ to permanent income $P_t$ is less than $\\underline{a}$. As with the perfect foresight problem, this model can be framed in terms of $\\textit{normalized}$ variables, e.g. $m_t \\equiv M_t/P_t$. (See [here](http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/) for all the theory).\n", + "\n", + "\\begin{eqnarray*}\n", + "v_t(m_t) &=& \\max_{c_t} ~ U(c_t) ~ + \\phantom{\\LivFac} \\beta \\mathbb{E} [(\\Gamma_{t+1}\\psi_{t+1})^{1-\\rho} v_{t+1}(m_{t+1}) ], \\\\\n", + "a_t &=& m_t - c_t, \\\\\n", + "a_t &\\geq& \\underline{a}, \\\\\n", + "m_{t+1} &=& R/(\\Gamma_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}, \\\\\n", + "\\mathbb{E}[\\psi]=\\mathbb{E}[\\theta] &=& 1, \\\\\n", + "u(c) &=& \\frac{c^{1-\\rho}}{1-\\rho}.\n", + "\\end{eqnarray*}\n", + "\n", + "HARK represents agents with this kind of problem as instances of the class $\\texttt{IndShockConsumerType}$. To create an $\\texttt{IndShockConsumerType}$, we must specify the same set of parameters as for a $\\texttt{PerfForesightConsumerType}$, as well as an artificial borrowing constraint $\\underline{a}$ and a sequence of income shock joint. It's easy enough to pick a borrowing constraint -- say, zero -- but how would we specify the distributions of the shocks? Can't the joint distribution of permanent and transitory shocks be just about anything?\n", + "\n", + "$\\textit{Yes}$, and HARK can handle whatever correlation structure a user might care to specify. However, the default behavior of $\\texttt{IndShockConsumerType}$ is that the distribution of permanent income shocks is mean one lognormal, and the distribution of transitory shocks is mean one lognormal augmented with a point mass representing unemployment. The distributions are independent of each other by default, and are approximated with $N$ point equiprobable distributions.\n", + "\n", + "Let's make an infinite horizon instance of $\\texttt{IndShockConsumerType}$ with the same parameters as our original perfect foresight agent, plus the extra parameters to specify the income shock distribution and the artificial borrowing constraint. As before, we'll make a dictionary:\n", + "\n", + "\n", + "| Param | Description | Code | Value |\n", + "| :---: | --- | --- | :---: |\n", + "| $\\underline{a}$ | Artificial borrowing constraint | $\\texttt{BoroCnstArt}$ | 0.0 |\n", + "| $\\sigma_\\psi$ | Underlying stdev of permanent income shocks | $\\texttt{PermShkStd}$ | 0.1 |\n", + "| $\\sigma_\\theta$ | Underlying stdev of transitory income shocks | $\\texttt{TranShkStd}$ | 0.1 |\n", + "| $N_\\psi$ | Number of discrete permanent income shocks | $\\texttt{PermShkCount}$ | 7 |\n", + "| $N_\\theta$ | Number of discrete transitory income shocks | $\\texttt{TranShkCount}$ | 7 |\n", + "| $\\mho$ | Unemployment probability | $\\texttt{UnempPrb}$ | 0.05 |\n", + "| $\\underline{\\theta}$ | Transitory shock when unemployed | $\\texttt{IncUnemp}$ | 0.3 |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [ + 2 + ] + }, + "outputs": [], + "source": [ + "# This cell defines a parameter dictionary for making an instance of IndShockConsumerType.\n", + "\n", + "IndShockDictionary = {\n", + " 'CRRA': 2.5, # The dictionary includes our original parameters...\n", + " 'Rfree': 1.03,\n", + " 'DiscFac': 0.96,\n", + " 'LivPrb': [0.98],\n", + " 'PermGroFac': [1.01],\n", + " 'PermShkStd': [0.1], # ... and the new parameters for constructing the income process. \n", + " 'PermShkCount': 7,\n", + " 'TranShkStd': [0.1],\n", + " 'TranShkCount': 7,\n", + " 'UnempPrb': 0.05,\n", + " 'IncUnemp': 0.3,\n", + " 'BoroCnstArt': 0.0,\n", + " 'aXtraMin': 0.001, # aXtra parameters specify how to construct the grid of assets.\n", + " 'aXtraMax': 50., # Don't worry about these for now\n", + " 'aXtraNestFac': 3,\n", + " 'aXtraCount': 48,\n", + " 'aXtraExtra': [None],\n", + " 'vFuncBool': False, # These booleans indicate whether the value function should be calculated\n", + " 'CubicBool': False, # and whether to use cubic spline interpolation. You can ignore them.\n", + " 'aNrmInitMean' : -10.,\n", + " 'aNrmInitStd' : 0.0, # These parameters specify the (log) distribution of normalized assets\n", + " 'pLvlInitMean' : 0.0, # and permanent income for agents at \"birth\". They are only relevant in\n", + " 'pLvlInitStd' : 0.0, # simulation and you don't need to worry about them.\n", + " 'PermGroFacAgg' : 1.0,\n", + " 'T_retire': 0, # What's this about retirement? ConsIndShock is set up to be able to\n", + " 'UnempPrbRet': 0.0, # handle lifecycle models as well as infinite horizon problems. Swapping\n", + " 'IncUnempRet': 0.0, # out the structure of the income process is easy, but ignore for now.\n", + " 'T_age' : None,\n", + " 'T_cycle' : 1,\n", + " 'cycles' : 0,\n", + " 'AgentCount': 10000,\n", + " 'tax_rate':0.0,\n", + "}\n", + " \n", + "# Hey, there's a lot of parameters we didn't tell you about! Yes, but you don't need to\n", + "# think about them for now." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before, we need to import the relevant subclass of $\\texttt{AgentType}$ into our workspace, then create an instance by passing the dictionary to the class as if the class were a function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType\n", + "IndShockExample = IndShockConsumerType(**IndShockDictionary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can solve our new agent's problem just like before, using the $\\texttt{solve}$ method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "IndShockExample.solve()\n", + "plotFuncs(IndShockExample.solution[0].cFunc,0.,10.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Changing Constructed Attributes\n", + "\n", + "In the parameter dictionary above, we chose values for HARK to use when constructing its numeric representation of $F_t$, the joint distribution of permanent and transitory income shocks. When $\\texttt{IndShockExample}$ was created, those parameters ($\\texttt{TranShkStd}$, etc) were used by the $\\textbf{constructor}$ or $\\textbf{initialization}$ method of $\\texttt{IndShockConsumerType}$ to construct an attribute called $\\texttt{IncomeDstn}$.\n", + "\n", + "Suppose you were interested in changing (say) the amount of permanent income risk. From the section above, you might think that you could simply change the attribute $\\texttt{TranShkStd}$, solve the model again, and it would work.\n", + "\n", + "That's $\\textit{almost}$ true-- there's one extra step. $\\texttt{TranShkStd}$ is a primitive input, but it's not the thing you $\\textit{actually}$ want to change. Changing $\\texttt{TranShkStd}$ doesn't actually update the income distribution... unless you tell it to (just like changing an agent's preferences does not change the consumption function that was stored for the old set of parameters -- until you invoke the $\\texttt{solve}$ method again). In the cell below, we invoke the method $\\texttt{updateIncomeProcess}$ so HARK knows to reconstruct the attribute $\\texttt{IncomeDstn}$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "OtherExample = deepcopy(IndShockExample) # Make a copy so we can compare consumption functions\n", + "OtherExample.PermShkStd = [0.2] # Double permanent income risk (note that it's a one element list)\n", + "OtherExample.updateIncomeProcess() # Call the method to reconstruct the representation of F_t\n", + "OtherExample.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the cell below, use your blossoming HARK skills to plot the consumption function for $\\texttt{IndShockExample}$ and $\\texttt{OtherExample}$ on the same figure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use the line(s) below to plot the consumptions functions against each other\n" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,py", + "metadata_filter": { + "cells": "collapsed" + } + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/Examples/__init__.py b/Examples/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/Examples/tests/test_gentle_intro_to_hark.py b/Examples/tests/test_gentle_intro_to_hark.py new file mode 100644 index 000000000..3a2d6c574 --- /dev/null +++ b/Examples/tests/test_gentle_intro_to_hark.py @@ -0,0 +1,43 @@ +''' +Tests that the gentle intro to HARK notebook runs correctly +''' +from __future__ import print_function, division +from __future__ import absolute_import + +from builtins import str +from builtins import zip +from builtins import range +from builtins import object + +import os +import sys + +import nbformat +import unittest +from nbconvert.preprocessors import ExecutePreprocessor + +class TestGentleIntroToHark(unittest.TestCase): + + def test_notebook_runs(self): + # we only test that the notebook works in python3 + if sys.version_info[0] < 3: + return + + test_path = os.path.dirname(os.path.realpath(__file__)) + nb_path = os.path.join(test_path, '..', 'Gentle-Intro-To-HARK.ipynb') + with open(nb_path) as nb_f: + nb = nbformat.read(nb_f, as_version=nbformat.NO_CONVERT) + + ep = ExecutePreprocessor(timeout=60, kernel_name='python3') + ep.allow_errors = True + # this actually runs the notebook + ep.preprocess(nb, {}) + + errors = [] + for cell in nb.cells: + if 'outputs' in cell: + for output in cell['outputs']: + if output.output_type == 'error': + errors.append(output) + + self.assertFalse(errors) diff --git a/Examples/util.py b/Examples/util.py new file mode 100644 index 000000000..876172940 --- /dev/null +++ b/Examples/util.py @@ -0,0 +1,55 @@ +from ipywidgets import IntProgress, HTML, VBox +from IPython.display import display + +def log_progress(sequence, every=None, size=None, name='Items'): + is_iterator = False + if size is None: + try: + size = len(sequence) + except TypeError: + is_iterator = True + if size is not None: + if every is None: + if size <= 200: + every = 1 + else: + every = int(size / 200) # every 0.5% + else: + assert every is not None, 'sequence is iterator, set every' + + if is_iterator: + progress = IntProgress(min=0, max=1, value=1) + progress.bar_style = 'info' + else: + progress = IntProgress(min=0, max=size, value=0) + label = HTML() + box = VBox(children=[label, progress]) + display(box) + + index = 0 + try: + for index, record in enumerate(sequence, 1): + if index == 1 or index % every == 0: + if is_iterator: + label.value = '{name}: {index} / ?'.format( + name=name, + index=index + ) + else: + progress.value = index + label.value = u'{name}: {index} / {size}'.format( + name=name, + index=index, + size=size + ) + yield record + except: + progress.bar_style = 'danger' + raise + else: + progress.bar_style = 'success' + progress.value = index + label.value = "{name}: {index}".format( + name=name, + index=str(index or '?') + ) diff --git a/requirements.txt b/requirements.txt index f03da14e6..16d71076a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,3 +7,4 @@ joblib dill scipy flake8 +jupyter diff --git a/setup.py b/setup.py index a7cd77c1f..b27c4d9a7 100644 --- a/setup.py +++ b/setup.py @@ -152,7 +152,8 @@ 'numpydoc', 'dill', 'joblib', - 'future'], # Optional + 'future', # Optional + 'jupyter'], python_requires='>=2.7', From 12d6a5f080656e971619e31185e85b93081b9b63 Mon Sep 17 00:00:00 2001 From: Keith Blaha Date: Wed, 8 May 2019 13:47:03 -0700 Subject: [PATCH 55/77] Add non_empty argument validator --- HARK/tests/test_validators.py | 38 +++++++++++++++++++++++++++++++++++ HARK/validators.py | 35 ++++++++++++++++++++++++++++++++ requirements.txt | 1 + setup.py | 3 ++- 4 files changed, 76 insertions(+), 1 deletion(-) create mode 100644 HARK/tests/test_validators.py create mode 100644 HARK/validators.py diff --git a/HARK/tests/test_validators.py b/HARK/tests/test_validators.py new file mode 100644 index 000000000..375cd0740 --- /dev/null +++ b/HARK/tests/test_validators.py @@ -0,0 +1,38 @@ +import unittest + +from HARK.validators import non_empty + +class ValidatorsTests(unittest.TestCase): + ''' + Tests for validator decorators which validate function arguments + ''' + + def test_non_empty(self): + @non_empty('list_a') + def foo(list_a, list_b): + pass + + try: + foo([1], []) + except Exception: + self.fail() + with self.assertRaisesRegexp( + TypeError, + 'Expected non-empty argument for parameter list_a', + ): + foo([], [1]) + + @non_empty('list_a', 'list_b') + def foo(list_a, list_b): + pass + + with self.assertRaisesRegexp( + TypeError, + 'Expected non-empty argument for parameter list_b', + ): + foo([1], []) + with self.assertRaisesRegexp( + TypeError, + 'Expected non-empty argument for parameter list_a', + ): + foo([], [1]) diff --git a/HARK/validators.py b/HARK/validators.py new file mode 100644 index 000000000..fd467c6fd --- /dev/null +++ b/HARK/validators.py @@ -0,0 +1,35 @@ +''' +Decorators which can be used for validating arguments passed into decorated functions +''' + +from __future__ import print_function + +import sys +from functools import wraps + +if sys.version_info[0] < 3: + from funcsigs import signature +else: + from inspect import signature + + +def non_empty(*parameter_names): + ''' + Enforces arguments to parameters passed in have len > 0 + ''' + + def _decorator(f): + sig = signature(f) + # TODO - add validation that parameter names are in signature + + @wraps(f) + def _inner(*args, **kwargs): + bindings = sig.bind(*args, **kwargs) + for parameter_name in parameter_names: + if not len(bindings.arguments[parameter_name]): + raise TypeError( + 'Expected non-empty argument for parameter {}'.format(parameter_name) + ) + return f(*args, **kwargs) + return _inner + return _decorator diff --git a/requirements.txt b/requirements.txt index f03da14e6..e630586d1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,3 +7,4 @@ joblib dill scipy flake8 +funcsigs diff --git a/setup.py b/setup.py index a7cd77c1f..8da908e79 100644 --- a/setup.py +++ b/setup.py @@ -152,7 +152,8 @@ 'numpydoc', 'dill', 'joblib', - 'future'], # Optional + 'future', # Optional + 'funcsigs'], python_requires='>=2.7', From 11660fd978e1acca4d7e91397bc210ed9d8f1da4 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Sat, 11 May 2019 12:38:11 -0400 Subject: [PATCH 56/77] Moved checkConditions out of init (#284) IndShockConsumerType.__init__ was calling its checkConditions method, but this caused many subclasses to fail, as their checkConditions method either throws a notImplementedError or runs into an attribute error. CDC wants checkConditions called automatically, so this is now done in preSolve(). All subclasses have had a trivial preSolve method added, which only calls updateSolutionTerminal. --- HARK/ConsumptionSaving/ConsAggShockModel.py | 3 +++ HARK/ConsumptionSaving/ConsGenIncProcessModel.py | 3 +++ HARK/ConsumptionSaving/ConsIndShockModel.py | 16 +++++++++++----- HARK/ConsumptionSaving/ConsMarkovModel.py | 7 +++---- HARK/ConsumptionSaving/ConsMedModel.py | 3 +++ HARK/ConsumptionSaving/ConsPrefShockModel.py | 6 ++++++ HARK/ConsumptionSaving/ConsRepAgentModel.py | 6 ++++++ 7 files changed, 35 insertions(+), 9 deletions(-) diff --git a/HARK/ConsumptionSaving/ConsAggShockModel.py b/HARK/ConsumptionSaving/ConsAggShockModel.py index b0c6e4245..9f54707fd 100644 --- a/HARK/ConsumptionSaving/ConsAggShockModel.py +++ b/HARK/ConsumptionSaving/ConsAggShockModel.py @@ -105,6 +105,9 @@ def reset(self): self.initializeSim() self.aLvlNow = self.kInit*np.ones(self.AgentCount) # Start simulation near SS self.aNrmNow = self.aLvlNow/self.pLvlNow + + def preSolve(self): + self.updateSolutionTerminal() def updateSolutionTerminal(self): ''' diff --git a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py index 03bac79cb..d2a5f3c58 100644 --- a/HARK/ConsumptionSaving/ConsGenIncProcessModel.py +++ b/HARK/ConsumptionSaving/ConsGenIncProcessModel.py @@ -985,6 +985,9 @@ def __init__(self, cycles=1, time_flow=True, **kwds): # Initialize a basic ConsumerType IndShockConsumerType.__init__(self, cycles=cycles, time_flow=time_flow, **kwds) self.solveOnePeriod = solveConsGenIncProcess # idiosyncratic shocks solver with explicit persistent income + + def preSolve(self): + self.updateSolutionTerminal() def update(self): ''' diff --git a/HARK/ConsumptionSaving/ConsIndShockModel.py b/HARK/ConsumptionSaving/ConsIndShockModel.py index 5b557d464..b8b7f17ef 100644 --- a/HARK/ConsumptionSaving/ConsIndShockModel.py +++ b/HARK/ConsumptionSaving/ConsIndShockModel.py @@ -1496,6 +1496,10 @@ def __init__(self,cycles=1, time_flow=True,verbose=False,quiet=False, **kwds): self.verbose = verbose self.quiet = quiet self.solveOnePeriod = solvePerfForesight # solver for perfect foresight model + + + def preSolve(self): + self.updateSolutionTerminal() def updateSolutionTerminal(self): ''' @@ -1541,7 +1545,6 @@ def initializeSim(self): AgentType.initializeSim(self) - def simBirth(self,which_agents): ''' Makes new consumers for the given indices. Initialized variables include aNrm and pLvl, as @@ -1748,6 +1751,7 @@ def checkConditions(self,verbose=False,verbose_reference=False,public_call=False return violated + class IndShockConsumerType(PerfForesightConsumerType): ''' A consumer type with idiosyncratic shocks to permanent and transitory income. @@ -1784,9 +1788,6 @@ def __init__(self,cycles=1,time_flow=True,verbose=False,quiet=False,**kwds): self.solveOnePeriod = solveConsIndShock # idiosyncratic shocks solver self.update() # Make assets grid, income process, terminal solution - if not self.quiet: - self.checkConditions(verbose=self.verbose, - public_call=False) def updateIncomeProcess(self): ''' @@ -2010,8 +2011,9 @@ def makeEulerErrorFunc(self,mMax=100,approx_inc_dstn=True): self.eulerErrorFunc = eulerErrorFunc def preSolve(self): - PerfForesightConsumerType.preSolve(self) self.updateSolutionTerminal() + if not self.quiet: + self.checkConditions(verbose=self.verbose,public_call=False) def checkConditions(self,verbose=False,public_call=True): ''' @@ -2079,6 +2081,7 @@ def checkConditions(self,verbose=False,public_call=True): if verbose and violated: print('\n[!] For more information on the conditions, see Table 3 in "Theoretical Foundations of Buffer Stock Saving" at http://econ.jhu.edu/people/ccarroll/papers/BufferStockTheory/') + class KinkedRconsumerType(IndShockConsumerType): ''' A consumer type that faces idiosyncratic shocks to income and has a different @@ -2113,6 +2116,9 @@ def __init__(self,cycles=1,time_flow=True,**kwds): # Add consumer-type specific objects, copying to create independent versions self.solveOnePeriod = solveConsKinkedR # kinked R solver self.update() # Make assets grid, income process, terminal solution + + def preSolve(self): + self.updateSolutionTerminal() def calcBoundingValues(self): ''' diff --git a/HARK/ConsumptionSaving/ConsMarkovModel.py b/HARK/ConsumptionSaving/ConsMarkovModel.py index 74f196cfe..b3682220e 100644 --- a/HARK/ConsumptionSaving/ConsMarkovModel.py +++ b/HARK/ConsumptionSaving/ConsMarkovModel.py @@ -723,9 +723,8 @@ def checkMarkovInputs(self): def preSolve(self): """ - Do preSolve stuff inherited from IndShockConsumerType, then check to make sure that the - inputs that are specific to MarkovConsumerType are of the right shape (if arrays) or length - (if lists). + Check to make sure that the inputs that are specific to MarkovConsumerType + are of the right shape (if arrays) or length (if lists). Parameters ---------- @@ -735,7 +734,7 @@ def preSolve(self): ------- None """ - IndShockConsumerType.preSolve(self) + self.updateSolutionTerminal() self.checkMarkovInputs() def updateSolutionTerminal(self): diff --git a/HARK/ConsumptionSaving/ConsMedModel.py b/HARK/ConsumptionSaving/ConsMedModel.py index 8744a898b..75b2502ad 100644 --- a/HARK/ConsumptionSaving/ConsMedModel.py +++ b/HARK/ConsumptionSaving/ConsMedModel.py @@ -531,6 +531,9 @@ def __init__(self,cycles=1,time_flow=True,**kwds): self.solveOnePeriod = solveConsMedShock # Choose correct solver self.addToTimeInv('CRRAmed') self.addToTimeVary('MedPrice') + + def preSolve(self): + self.updateSolutionTerminal() def update(self): ''' diff --git a/HARK/ConsumptionSaving/ConsPrefShockModel.py b/HARK/ConsumptionSaving/ConsPrefShockModel.py index 18f27c626..636c8c3dd 100644 --- a/HARK/ConsumptionSaving/ConsPrefShockModel.py +++ b/HARK/ConsumptionSaving/ConsPrefShockModel.py @@ -43,6 +43,9 @@ def __init__(self,cycles=1,time_flow=True,**kwds): ''' IndShockConsumerType.__init__(self,cycles=cycles,time_flow=time_flow,**kwds) self.solveOnePeriod = solveConsPrefShock # Choose correct solver + + def preSolve(self): + self.updateSolutionTerminal() def update(self): ''' @@ -207,6 +210,9 @@ def __init__(self,cycles=1,time_flow=True,**kwds): self.solveOnePeriod = solveConsKinkyPref # Choose correct solver self.addToTimeInv('Rboro','Rsave') self.delFromTimeInv('Rfree') + + def preSolve(self): + self.updateSolutionTerminal() def getRfree(self): # Specify which getRfree to use return KinkedRconsumerType.getRfree(self) diff --git a/HARK/ConsumptionSaving/ConsRepAgentModel.py b/HARK/ConsumptionSaving/ConsRepAgentModel.py index 2b9b935e4..1f58c2c3b 100644 --- a/HARK/ConsumptionSaving/ConsRepAgentModel.py +++ b/HARK/ConsumptionSaving/ConsRepAgentModel.py @@ -209,6 +209,9 @@ def __init__(self,time_flow=True,**kwds): self.AgentCount = 1 # Hardcoded, because this is rep agent self.solveOnePeriod = solveConsRepAgent self.delFromTimeInv('Rfree','BoroCnstArt','vFuncBool','CubicBool') + + def preSolve(self): + self.updateSolutionTerminal() def getStates(self): ''' @@ -257,6 +260,9 @@ def __init__(self,time_flow=True,**kwds): ''' RepAgentConsumerType.__init__(self,time_flow=time_flow,**kwds) self.solveOnePeriod = solveConsRepAgentMarkov + + def preSolve(self): + self.updateSolutionTerminal() def updateSolutionTerminal(self): ''' From b292afd995a17a6b99359859653fd36777d2299e Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Tue, 14 May 2019 09:13:00 -0400 Subject: [PATCH 57/77] Fix typo in Market.__init__ (#286) Delinting at PyCon created a typo in the argument list for Market, now fixed. --- HARK/core.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HARK/core.py b/HARK/core.py index 5698122d2..55b7136d3 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -885,7 +885,7 @@ class Market(HARKobject): dynamic general equilibrium models to solve the "macroeconomic" model as a layer on top of the "microeconomic" models of one or more AgentTypes. ''' - def __init__(self, agents=[], sow_vars=[], reap_vars=[], const_vars=[], rack_vars=[], dyn_vars=[], + def __init__(self, agents=[], sow_vars=[], reap_vars=[], const_vars=[], track_vars=[], dyn_vars=[], millRule=None, calcDynamics=None, act_T=1000, tolerance=0.000001): ''' Make a new instance of the Market class. From ec8e4ed1c80dd9e24d9844e2e097d19fbe58b5a9 Mon Sep 17 00:00:00 2001 From: llorracc Date: Wed, 15 May 2019 15:09:30 -0400 Subject: [PATCH 58/77] Fixes multithreading problem from ipython kernel --- HARK/core.py | 425 ++++++++++++++++++++++------------------------- HARK/parallel.py | 4 +- 2 files changed, 205 insertions(+), 224 deletions(-) diff --git a/HARK/core.py b/HARK/core.py index 55b7136d3..0cc6ee4cb 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -21,8 +21,7 @@ from time import clock from .parallel import multiThreadCommands, multiThreadCommandsFake - -def distanceMetric(thing_A, thing_B): +def distanceMetric(thing_A,thing_B): ''' A "universal distance" metric that can be used as a default in many settings. @@ -43,12 +42,12 @@ def distanceMetric(thing_A, thing_B): typeB = type(thing_B) if typeA is list and typeB is list: - lenA = len(thing_A) # If both inputs are lists, then the distance between - lenB = len(thing_B) # them is the maximum distance between corresponding - if lenA == lenB: # elements in the lists. If they differ in length, - distance_temp = [] # the distance is the difference in lengths. + lenA = len(thing_A) # If both inputs are lists, then the distance between + lenB = len(thing_B) # them is the maximum distance between corresponding + if lenA == lenB: # elements in the lists. If they differ in length, + distance_temp = [] # the distance is the difference in lengths. for n in range(lenA): - distance_temp.append(distanceMetric(thing_A[n], thing_B[n])) + distance_temp.append(distanceMetric(thing_A[n],thing_B[n])) distance = max(distance_temp) else: distance = float(abs(lenA - lenB)) @@ -58,7 +57,7 @@ def distanceMetric(thing_A, thing_B): # If both inputs are array-like, return the maximum absolute difference b/w # corresponding elements (if same shape); return largest difference in dimensions # if shapes do not align. - elif hasattr(thing_A, 'shape') and hasattr(thing_B, 'shape'): + elif hasattr(thing_A,'shape') and hasattr(thing_B,'shape'): if thing_A.shape == thing_B.shape: distance = np.max(abs(thing_A - thing_B)) else: @@ -70,17 +69,16 @@ def distanceMetric(thing_A, thing_B): distance = 0.0 else: distance = thing_A.distance(thing_B) - else: # Failsafe: the inputs are very far apart + else: # Failsafe: the inputs are very far apart distance = 1000.0 return distance - class HARKobject(object): ''' A superclass for object classes in HARK. Comes with two useful methods: a generic/universal distance method and an attribute assignment method. ''' - def distance(self, other): + def distance(self,other): ''' A generic distance method, which requires the existence of an attribute called distance_criteria, giving a list of strings naming the attributes @@ -100,14 +98,14 @@ def distance(self, other): distance_list = [0.0] for attr_name in self.distance_criteria: try: - obj_A = getattr(self, attr_name) - obj_B = getattr(other, attr_name) - distance_list.append(distanceMetric(obj_A, obj_B)) - except AttributeError: - distance_list.append(1000.0) # if either object lacks attribute, they are not the same + obj_A = getattr(self,attr_name) + obj_B = getattr(other,attr_name) + distance_list.append(distanceMetric(obj_A,obj_B)) + except: + distance_list.append(1000.0) # if either object lacks attribute, they are not the same return max(distance_list) - def assignParameters(self, **kwds): + def assignParameters(self,**kwds): ''' Assign an arbitrary number of attributes to this agent. @@ -122,16 +120,16 @@ def assignParameters(self, **kwds): none ''' for key in kwds: - setattr(self, key, kwds[key]) + setattr(self,key,kwds[key]) - def __call__(self, **kwds): + def __call__(self,**kwds): ''' Assign an arbitrary number of attributes to this agent, as a convenience. See assignParameters. ''' self.assignParameters(**kwds) - def getAvg(self, varname, **kwds): + def getAvg(self,varname,**kwds): ''' Calculates the average of an attribute of this instance. Returns NaN if no such attribute. @@ -146,12 +144,11 @@ def getAvg(self, varname, **kwds): avg : float or np.array The average of this attribute. Might be an array if the axis keyword is passed. ''' - if hasattr(self, varname): - return np.mean(getattr(self, varname), **kwds) + if hasattr(self,varname): + return np.mean(getattr(self,varname),**kwds) else: return np.nan - class Solution(HARKobject): ''' A superclass for representing the "solution" to a single period problem in a @@ -161,7 +158,6 @@ class Solution(HARKobject): replacing each instance of Solution with HARKobject in the other modules. ''' - class AgentType(HARKobject): ''' A superclass for economic agents in the HARK framework. Each model should @@ -174,8 +170,8 @@ class AgentType(HARKobject): 'solveOnePeriod' should appear in exactly one of these lists, depending on whether the same solution method is used in all periods of the model. ''' - def __init__(self, solution_terminal=None, cycles=1, time_flow=False, pseudo_terminal=True, - tolerance=0.000001, seed=0, **kwds): + def __init__(self,solution_terminal=None,cycles=1,time_flow=False,pseudo_terminal=True, + tolerance=0.000001,seed=0,**kwds): ''' Initialize an instance of AgentType by setting attributes. @@ -215,18 +211,18 @@ def __init__(self, solution_terminal=None, cycles=1, time_flow=False, pseudo_ter ''' if solution_terminal is None: solution_terminal = NullFunc() - self.solution_terminal = solution_terminal # NOQA - self.cycles = cycles # NOQA - self.time_flow = time_flow # NOQA - self.pseudo_terminal = pseudo_terminal # NOQA - self.solveOnePeriod = NullFunc() # NOQA - self.tolerance = tolerance # NOQA - self.seed = seed # NOQA - self.track_vars = [] # NOQA - self.poststate_vars = [] # NOQA - self.read_shocks = False # NOQA - self.assignParameters(**kwds) # NOQA - self.resetRNG() # NOQA + self.solution_terminal = solution_terminal + self.cycles = cycles + self.time_flow = time_flow + self.pseudo_terminal = pseudo_terminal + self.solveOnePeriod = NullFunc() + self.tolerance = tolerance + self.seed = seed + self.track_vars = [] + self.poststate_vars = [] + self.read_shocks = False + self.assignParameters(**kwds) + self.resetRNG() def timeReport(self): ''' @@ -292,7 +288,7 @@ def timeRev(self): if self.time_flow: self.timeFlip() - def addToTimeVary(self, *params): + def addToTimeVary(self,*params): ''' Adds any number of parameters to time_vary for this instance. @@ -309,7 +305,7 @@ def addToTimeVary(self, *params): if param not in self.time_vary: self.time_vary.append(param) - def addToTimeInv(self, *params): + def addToTimeInv(self,*params): ''' Adds any number of parameters to time_inv for this instance. @@ -326,7 +322,7 @@ def addToTimeInv(self, *params): if param not in self.time_inv: self.time_inv.append(param) - def delFromTimeVary(self, *params): + def delFromTimeVary(self,*params): ''' Removes any number of parameters from time_vary for this instance. @@ -343,7 +339,7 @@ def delFromTimeVary(self, *params): if param in self.time_vary: self.time_vary.remove(param) - def delFromTimeInv(self, *params): + def delFromTimeInv(self,*params): ''' Removes any number of parameters from time_inv for this instance. @@ -360,7 +356,7 @@ def delFromTimeInv(self, *params): if param in self.time_inv: self.time_inv.remove(param) - def solve(self, verbose=False): + def solve(self,verbose=False): ''' Solve the model for this instance of an agent type by backward induction. Loops through the sequence of one period problems, passing the solution @@ -376,16 +372,12 @@ def solve(self, verbose=False): none ''' - # Ignore floating point "errors". Numpy calls it "errors", but really it's excep- - # tions with well-defined answers such as 1.0/0.0 that is np.inf, -1.0/0.0 that is - # -np.inf, np.inf/np.inf is np.nan and so on. - with np.errstate(divide='ignore', over='ignore', under='ignore', invalid='ignore'): - self.preSolve() # Do pre-solution stuff - self.solution = solveAgent(self, verbose) # Solve the model by backward induction - if self.time_flow: # Put the solution in chronological order if this instance's time flow runs that way - self.solution.reverse() - self.addToTimeVary('solution') # Add solution to the list of time-varying attributes - self.postSolve() # Do post-solution stuff + self.preSolve() # Do pre-solution stuff + self.solution = solveAgent(self,verbose) # Solve the model by backward induction + if self.time_flow: # Put the solution in chronological order if this instance's time flow runs that way + self.solution.reverse() + self.addToTimeVary('solution') # Add solution to the list of time-varying attributes + self.postSolve() # Do post-solution stuff def resetRNG(self): ''' @@ -406,9 +398,10 @@ def checkElementsOfTimeVaryAreLists(self): A method to check that elements of time_vary are lists. """ for param in self.time_vary: - assert type(getattr(self, param)) == list, param + ' is not a list, but should be' + \ + assert type(getattr(self,param))==list,param + ' is not a list, but should be' + \ ' because it is in time_vary' + def preSolve(self): ''' A method that is run immediately before the model is solved, to check inputs or to prepare @@ -455,13 +448,12 @@ def initializeSim(self): ''' self.resetRNG() self.t_sim = 0 - all_agents = np.ones(self.AgentCount, dtype=bool) + all_agents = np.ones(self.AgentCount,dtype=bool) blank_array = np.zeros(self.AgentCount) for var_name in self.poststate_vars: - setattr(self, var_name, copy(blank_array)) - # exec('self.' + var_name + ' = copy(blank_array)') - self.t_age = np.zeros(self.AgentCount, dtype=int) # Number of periods since agent entry - self.t_cycle = np.zeros(self.AgentCount, dtype=int) # Which cycle period each agent is on + exec('self.' + var_name + ' = copy(blank_array)') + self.t_age = np.zeros(self.AgentCount,dtype=int) # Number of periods since agent entry + self.t_cycle = np.zeros(self.AgentCount,dtype=int) # Which cycle period each agent is on self.simBirth(all_agents) self.clearHistory() return None @@ -482,18 +474,18 @@ def simOnePeriod(self): None ''' self.getMortality() # Replace some agents with "newborns" - if self.read_shocks: # If shock histories have been pre-specified, use those + if self.read_shocks: # If shock histories have been pre-specified, use those self.readShocks() else: # Otherwise, draw shocks as usual according to subclass-specific method self.getShocks() - self.getStates() # Determine each agent's state at decision time + self.getStates() # Determine each agent's state at decision time self.getControls() # Determine each agent's choice or control variables based on states - self.getPostStates() # Determine each agent's post-decision / end-of-period states using states and controls + self.getPostStates() # Determine each agent's post-decision / end-of-period states using states and controls # Advance time for all agents - self.t_age = self.t_age + 1 # Age all consumers by one period - self.t_cycle = self.t_cycle + 1 # Age all consumers within their cycle - self.t_cycle[self.t_cycle == self.T_cycle] = 0 # Resetting to zero for those who have reached the end + self.t_age = self.t_age + 1 # Age all consumers by one period + self.t_cycle = self.t_cycle + 1 # Age all consumers within their cycle + self.t_cycle[self.t_cycle == self.T_cycle] = 0 # Resetting to zero for those who have reached the end def makeShockHistory(self): ''' @@ -519,7 +511,7 @@ def makeShockHistory(self): # Make blank history arrays for each shock variable for var_name in self.shock_vars: - setattr(self, var_name+'_hist', np.zeros((self.T_sim, self.AgentCount)) + np.nan) + setattr(self,var_name+'_hist',np.zeros((self.T_sim,self.AgentCount))+np.nan) # Make and store the history of shocks for each period for t in range(self.T_sim): @@ -528,9 +520,9 @@ def makeShockHistory(self): for var_name in self.shock_vars: exec('self.' + var_name + '_hist[self.t_sim,:] = self.' + var_name) self.t_sim += 1 - self.t_age = self.t_age + 1 # Age all consumers by one period - self.t_cycle = self.t_cycle + 1 # Age all consumers within their cycle - self.t_cycle[self.t_cycle == self.T_cycle] = 0 # Resetting to zero for those who have reached the end + self.t_age = self.t_age + 1 # Age all consumers by one period + self.t_cycle = self.t_cycle + 1 # Age all consumers within their cycle + self.t_cycle[self.t_cycle == self.T_cycle] = 0 # Resetting to zero for those who have reached the end # Restore the flow of time and flag that shocks can be read rather than simulated self.read_shocks = True @@ -574,10 +566,10 @@ def simDeath(self): Boolean array of size self.AgentCount indicating which agents die and are replaced. ''' print('AgentType subclass must define method simDeath!') - who_dies = np.ones(self.AgentCount, dtype=bool) + who_dies = np.ones(self.AgentCount,dtype=bool) return who_dies - def simBirth(self, which_agents): + def simBirth(self,which_agents): ''' Makes new agents for the simulation. Takes a boolean array as an input, indicating which agent indices are to be "born". Does nothing by default, must be overwritten by a subclass. @@ -626,7 +618,7 @@ def readShocks(self): None ''' for var_name in self.shock_vars: - setattr(self, var_name, getattr(self, var_name + '_hist')[self.t_sim, :]) + setattr(self,var_name,getattr(self,var_name+'_hist')[self.t_sim,:]) def getStates(self): ''' @@ -675,7 +667,7 @@ def getPostStates(self): ''' return None - def simulate(self, sim_periods=None): + def simulate(self,sim_periods=None): ''' Simulates this agent type for a given number of periods (defaults to self.T_sim if no input). Records histories of attributes named in self.track_vars in attributes named varname_hist. @@ -688,23 +680,19 @@ def simulate(self, sim_periods=None): ------- None ''' - # Ignore floating point "errors". Numpy calls it "errors", but really it's excep- - # tions with well-defined answers such as 1.0/0.0 that is np.inf, -1.0/0.0 that is - # -np.inf, np.inf/np.inf is np.nan and so on. - with np.errstate(divide='ignore', over='ignore', under='ignore', invalid='ignore'): - orig_time = self.time_flow - self.timeFwd() - if sim_periods is None: - sim_periods = self.T_sim + orig_time = self.time_flow + self.timeFwd() + if sim_periods is None: + sim_periods = self.T_sim - for t in range(sim_periods): - self.simOnePeriod() - for var_name in self.track_vars: - exec('self.' + var_name + '_hist[self.t_sim,:] = self.' + var_name) - self.t_sim += 1 + for t in range(sim_periods): + self.simOnePeriod() + for var_name in self.track_vars: + exec('self.' + var_name + '_hist[self.t_sim,:] = self.' + var_name) + self.t_sim += 1 - if not orig_time: - self.timeRev() + if not orig_time: + self.timeRev() def clearHistory(self): ''' @@ -722,7 +710,7 @@ def clearHistory(self): exec('self.' + var_name + '_hist = np.zeros((self.T_sim,self.AgentCount)) + np.nan') -def solveAgent(agent, verbose): +def solveAgent(agent,verbose): ''' Solve the dynamic model for one agent type. This function iterates on "cycles" of an agent's model either a given number of times or until solution convergence @@ -746,8 +734,8 @@ def solveAgent(agent, verbose): agent.timeRev() # Check to see whether this is an (in)finite horizon problem - cycles_left = agent.cycles # NOQA - infinite_horizon = cycles_left == 0 # NOQA + cycles_left = agent.cycles + infinite_horizon = cycles_left == 0 # Initialize the solution, which includes the terminal solution if it's not a pseudo-terminal period solution = [] @@ -755,15 +743,15 @@ def solveAgent(agent, verbose): solution.append(deepcopy(agent.solution_terminal)) # Initialize the process, then loop over cycles - solution_last = agent.solution_terminal # NOQA - go = True # NOQA - completed_cycles = 0 # NOQA - max_cycles = 5000 # NOQA - escape clause + solution_last = agent.solution_terminal + go = True + completed_cycles = 0 + max_cycles = 5000 # escape clause if verbose: t_last = clock() while go: # Solve a cycle of the model, recording it if horizon is finite - solution_cycle = solveOneCycle(agent, solution_last) + solution_cycle = solveOneCycle(agent,solution_last) if not infinite_horizon: solution += solution_cycle @@ -773,7 +761,7 @@ def solveAgent(agent, verbose): if completed_cycles > 0: solution_distance = solution_now.distance(solution_last) go = (solution_distance > agent.tolerance and completed_cycles < max_cycles) - else: # Assume solution does not converge after only one cycle + else: # Assume solution does not converge after only one cycle solution_distance = 100.0 go = True else: @@ -788,16 +776,16 @@ def solveAgent(agent, verbose): if verbose: t_now = clock() if infinite_horizon: - print('Finished cycle #' + str(completed_cycles) + ' in ' + str(t_now-t_last) + - ' seconds, solution distance = ' + str(solution_distance)) + print('Finished cycle #' + str(completed_cycles) + ' in ' + str(t_now-t_last) +\ + ' seconds, solution distance = ' + str(solution_distance)) else: - print('Finished cycle #' + str(completed_cycles) + ' of ' + str(agent.cycles) + - ' in ' + str(t_now-t_last) + ' seconds.') + print('Finished cycle #' + str(completed_cycles) + ' of ' + str(agent.cycles) +\ + ' in ' + str(t_now-t_last) + ' seconds.') t_last = t_now # Record the last cycle if horizon is infinite (solution is still empty!) if infinite_horizon: - solution = solution_cycle # PseudoTerminal=False impossible for infinite horizon + solution = solution_cycle # PseudoTerminal=False impossible for infinite horizon # Restore the direction of time to its original orientation, then return the solution if original_time_flow: @@ -805,7 +793,7 @@ def solveAgent(agent, verbose): return solution -def solveOneCycle(agent, solution_last): +def solveOneCycle(agent,solution_last): ''' Solve one "cycle" of the dynamic model for one agent type. This function iterates over the periods within an agent's cycle, updating the time-varying @@ -830,7 +818,7 @@ def solveOneCycle(agent, solution_last): # Calculate number of periods per cycle, defaults to 1 if all variables are time invariant if len(agent.time_vary) > 0: name = agent.time_vary[0] - T = len(eval('agent.' + name)) + T = len(eval('agent.' + name)) else: T = 1 @@ -838,12 +826,12 @@ def solveOneCycle(agent, solution_last): always_same_solver = 'solveOnePeriod' not in agent.time_vary if always_same_solver: solveOnePeriod = agent.solveOnePeriod - these_args = getArgNames(solveOnePeriod) + these_args = getArgNames(solveOnePeriod) # Construct a dictionary to be passed to the solver time_inv_string = '' for name in agent.time_inv: - time_inv_string += ' \'' + name + '\' : agent.' + name + ',' + time_inv_string += ' \'' + name + '\' : agent.' +name + ',' time_vary_string = '' for name in agent.time_vary: time_vary_string += ' \'' + name + '\' : None,' @@ -851,7 +839,7 @@ def solveOneCycle(agent, solution_last): # Initialize the solution for this cycle, then iterate on periods solution_cycle = [] - solution_next = solution_last + solution_next = solution_last for t in range(T): # Update which single period solver to use (if it depends on time) if not always_same_solver: @@ -876,8 +864,8 @@ def solveOneCycle(agent, solution_last): return solution_cycle -# ======================================================================== -# ======================================================================== +#======================================================================== +#======================================================================== class Market(HARKobject): ''' @@ -885,8 +873,8 @@ class Market(HARKobject): dynamic general equilibrium models to solve the "macroeconomic" model as a layer on top of the "microeconomic" models of one or more AgentTypes. ''' - def __init__(self, agents=[], sow_vars=[], reap_vars=[], const_vars=[], track_vars=[], dyn_vars=[], - millRule=None, calcDynamics=None, act_T=1000, tolerance=0.000001): + def __init__(self,agents=[],sow_vars=[],reap_vars=[],const_vars=[],track_vars=[],dyn_vars=[], + millRule=None,calcDynamics=None,act_T=1000,tolerance=0.000001): ''' Make a new instance of the Market class. @@ -931,25 +919,25 @@ def __init__(self, agents=[], sow_vars=[], reap_vars=[], const_vars=[], track_va ------- None ''' - self.agents = agents # NOQA - self.reap_vars = reap_vars # NOQA - self.sow_vars = sow_vars # NOQA - self.const_vars = const_vars # NOQA - self.track_vars = track_vars # NOQA - self.dyn_vars = dyn_vars # NOQA - if millRule is not None: # To prevent overwriting of method-based millRules + self.agents = agents + self.reap_vars = reap_vars + self.sow_vars = sow_vars + self.const_vars = const_vars + self.track_vars = track_vars + self.dyn_vars = dyn_vars + if millRule is not None: # To prevent overwriting of method-based millRules self.millRule = millRule - if calcDynamics is not None: # Ditto for calcDynamics + if calcDynamics is not None: # Ditto for calcDynamics self.calcDynamics = calcDynamics - self.act_T = act_T # NOQA - self.tolerance = tolerance # NOQA - self.max_loops = 1000 # NOQA - - self.print_parallel_error_once = True - # Print the error associated with calling the parallel method - # "solveAgents" one time. If set to false, the error will never - # print. See "solveAgents" for why this prints once or never. - + self.act_T = act_T + self.tolerance = tolerance + self.max_loops = 1000 + + self.print_parallel_error_once = True + # Print the error associated with calling the parallel method + # "solveAgents" one time. If set to false, the error will never + # print. See "solveAgents" for why this prints once or never. + def solveAgents(self): ''' Solves the microeconomic problem for all AgentTypes in this market. @@ -962,19 +950,21 @@ def solveAgents(self): ------- None ''' - # for this_type in self.agents: - # this_type.solve() + #for this_type in self.agents: + # this_type.solve() try: - multiThreadCommands(self.agents, ['solve()']) + multiThreadCommands(self.agents,['solve()']) except Exception as err: + # import pdb; pdb.set_trace() if self.print_parallel_error_once: - # Set flag to False so this is only printed once. + # Set flag to False so this is only printed once. self.print_parallel_error_once = False - print("**** WARNING: could not execute multiThreadCommands in HARK.core.Market.solveAgents() ", - "so using the serial version instead. This will likely be slower. " - "The multiTreadCommands() functions failed with the following error:", '\n', - sys.exc_info()[0], ':', err) # sys.exc_info()[0]) - multiThreadCommandsFake(self.agents, ['solve()']) + print("**** WARNING: could not execute multiThreadCommands in HARK.core.Market.solveAgents(), so using the serial version instead." + + "This will likely be slower. " + + "The multiTreadCommands() functions failed with the following error:", + '\n ', sys.exc_info()[0], ':', err) #sys.exc_info()[0]) + multiThreadCommandsFake(self.agents,['solve()']) + def solve(self): ''' @@ -990,15 +980,15 @@ def solve(self): ------- None ''' - go = True - max_loops = self.max_loops # Failsafe against infinite solution loop + go = True + max_loops = self.max_loops # Failsafe against infinite solution loop completed_loops = 0 - old_dynamics = None + old_dynamics = None - while go: # Loop until the dynamic process converges or we hit the loop cap - self.solveAgents() # Solve each AgentType's micro problem - self.makeHistory() # "Run" the model while tracking aggregate variables - new_dynamics = self.updateDynamics() # Find a new aggregate dynamic rule + while go: # Loop until the dynamic process converges or we hit the loop cap + self.solveAgents() # Solve each AgentType's micro problem + self.makeHistory() # "Run" the model while tracking aggregate variables + new_dynamics = self.updateDynamics() # Find a new aggregate dynamic rule # Check to see if the dynamic rule has converged (if this is not the first loop) if completed_loops > 0: @@ -1007,11 +997,11 @@ def solve(self): distance = 1000000.0 # Move to the next loop if the terminal conditions are not met - old_dynamics = new_dynamics + old_dynamics = new_dynamics completed_loops += 1 - go = distance >= self.tolerance and completed_loops < max_loops + go = distance >= self.tolerance and completed_loops < max_loops - self.dynamics = new_dynamics # Store the final dynamic rule in self + self.dynamics = new_dynamics # Store the final dynamic rule in self def reap(self): ''' @@ -1029,8 +1019,8 @@ def reap(self): for var_name in self.reap_vars: harvest = [] for this_type in self.agents: - harvest.append(getattr(this_type, var_name)) - setattr(self, var_name, harvest) + harvest.append(getattr(this_type,var_name)) + setattr(self,var_name,harvest) def sow(self): ''' @@ -1046,9 +1036,9 @@ def sow(self): none ''' for var_name in self.sow_vars: - this_seed = getattr(self, var_name) + this_seed = getattr(self,var_name) for this_type in self.agents: - setattr(this_type, var_name, this_seed) + setattr(this_type,var_name,this_seed) def mill(self): ''' @@ -1076,8 +1066,8 @@ def mill(self): product = self.millRule(**mill_dict) for j in range(len(self.sow_vars)): this_var = self.sow_vars[j] - this_product = getattr(product, this_var) - setattr(self, this_var, this_product) + this_product = getattr(product,this_var) + setattr(self,this_var,this_product) def cultivate(self): ''' @@ -1110,12 +1100,12 @@ def reset(self): ------- none ''' - for var_name in self.track_vars: # Reset the history of tracked variables - setattr(self, var_name + '_hist', []) - for var_name in self.sow_vars: # Set the sow variables to their initial levels - initial_val = getattr(self, var_name + '_init') - setattr(self, var_name, initial_val) - for this_type in self.agents: # Reset each AgentType in the market + for var_name in self.track_vars: # Reset the history of tracked variables + setattr(self,var_name + '_hist',[]) + for var_name in self.sow_vars: # Set the sow variables to their initial levels + initial_val = getattr(self,var_name + '_init') + setattr(self,var_name,initial_val) + for this_type in self.agents: # Reset each AgentType in the market this_type.reset() def store(self): @@ -1132,8 +1122,8 @@ def store(self): none ''' for var_name in self.track_vars: - value_now = getattr(self, var_name) - getattr(self, var_name + '_hist').append(value_now) + value_now = getattr(self,var_name) + getattr(self,var_name + '_hist').append(value_now) def makeHistory(self): ''' @@ -1148,13 +1138,13 @@ def makeHistory(self): ------- none ''' - self.reset() # Initialize the state of the market + self.reset() # Initialize the state of the market for t in range(self.act_T): - self.sow() # Distribute aggregated information/state to agents - self.cultivate() # Agents take action - self.reap() # Collect individual data from agents - self.mill() # Process individual data into aggregate data - self.store() # Record variables of interest + self.sow() # Distribute aggregated information/state to agents + self.cultivate() # Agents take action + self.reap() # Collect individual data from agents + self.mill() # Process individual data into aggregate data + self.store() # Record variables of interest def updateDynamics(self): ''' @@ -1181,11 +1171,11 @@ def updateDynamics(self): update_dict = eval('{' + history_vars_string + '}') # Calculate a new dynamic rule and distribute it to the agents in agent_list - dynamics = self.calcDynamics(**update_dict) # User-defined dynamics calculator + dynamics = self.calcDynamics(**update_dict) # User-defined dynamics calculator for var_name in self.dyn_vars: - this_obj = getattr(dynamics, var_name) + this_obj = getattr(dynamics,var_name) for this_type in self.agents: - setattr(this_type, var_name, this_obj) + setattr(this_type,var_name,this_obj) return dynamics @@ -1196,21 +1186,21 @@ def updateDynamics(self): # Define a function to run the copying: def copy_module(target_path, my_directory_full_path, my_module): ''' - Helper function for copy_module_to_local(). Provides the actual copy - functionality, with highly cautious safeguards against copying over - important things. - + Helper function for copy_module_to_local(). Provides the actual copy + functionality, with highly cautious safeguards against copying over + important things. + Parameters ---------- target_path : string String, file path to target location - + my_directory_full_path: string String, full pathname to this file's directory - + my_module : string String, name of the module to copy - + Returns ------- none @@ -1220,42 +1210,35 @@ def copy_module(target_path, my_directory_full_path, my_module): print("Goodbye!") return elif target_path == os.path.expanduser("~") or os.path.normpath(target_path) == os.path.expanduser("~"): - print("You have indicated that the target location is " + target_path + - " -- that is, you want to wipe out your home directory with the contents of " + my_module + - ". My programming does not allow me to do that.\n\nGoodbye!") + print("You have indicated that the target location is "+target_path+" -- that is, you want to wipe out your home directory with the contents of "+my_module+". My programming does not allow me to do that.\n\nGoodbye!") return elif os.path.exists(target_path): - print("There is already a file or directory at the location " + target_path + - ". For safety reasons this code does not overwrite existing files.\n Please remove the file at " - + target_path + - " and try again.") + print("There is already a file or directory at the location "+target_path+". For safety reasons this code does not overwrite existing files.\nPlease remove the file at "+target_path+" and try again.") return else: - user_input = input("""You have indicated you want to copy module:\n """ + my_module - + """\nto:\n """ + target_path + """\nIs that correct? Please indicate: y / [n]\n\n""") + user_input = input("""You have indicated you want to copy module:\n """+ my_module + + """\nto:\n """+ target_path +"""\nIs that correct? Please indicate: y / [n]\n\n""") if user_input == 'y' or user_input == 'Y': - # print("copy_tree(",my_directory_full_path,",", target_path,")") + #print("copy_tree(",my_directory_full_path,",", target_path,")") copy_tree(my_directory_full_path, target_path) else: print("Goodbye!") return - def print_helper(): - + my_directory_full_path = os.path.dirname(os.path.realpath(__file__)) - + print(my_directory_full_path) - def copy_module_to_local(full_module_name): ''' - This function contains simple code to copy a submodule to a location on - your hard drive, as specified by you. The purpose of this code is to provide - users with a simple way to access a *copy* of code that usually sits deep in - the Econ-ARK package structure, for purposes of tinkering and experimenting - directly. This is meant to be a simple way to explore HARK code. To interact - with the codebase under active development, please refer to the documentation + This function contains simple code to copy a submodule to a location on + your hard drive, as specified by you. The purpose of this code is to provide + users with a simple way to access a *copy* of code that usually sits deep in + the Econ-ARK package structure, for purposes of tinkering and experimenting + directly. This is meant to be a simple way to explore HARK code. To interact + with the codebase under active development, please refer to the documentation under github.com/econ-ark/HARK/ To execute, do the following on the Python command line: @@ -1263,7 +1246,7 @@ def copy_module_to_local(full_module_name): from HARK.core import copy_module_to_local copy_module_to_local("FULL-HARK-MODULE-NAME-HERE") - For example, if you want SolvingMicroDSOPs you would enter + For example, if you want SolvingMicroDSOPs you would enter from HARK.core import copy_module_to_local copy_module_to_local("HARK.SolvingMicroDSOPs") @@ -1272,17 +1255,14 @@ def copy_module_to_local(full_module_name): # Find a default directory -- user home directory: home_directory_RAW = os.path.expanduser("~") - # Thanks to https://stackoverflow.com/a/4028943 + # Thanks to https://stackoverflow.com/a/4028943 # Find the directory of the HARK.core module: - # my_directory_full_path = os.path.dirname(os.path.realpath(__file__)) + #my_directory_full_path = os.path.dirname(os.path.realpath(__file__)) hark_core_directory_full_path = os.path.dirname(os.path.realpath(__file__)) # From https://stackoverflow.com/a/5137509 - # Important note from that answer: - # (Note that the incantation above won't work if you've already used os.chdir() - # to change your current working directory, - # since the value of the __file__ constant is relative to the current working directory and is not changed by an - # os.chdir() call.) + # Important note from that answer: + # (Note that the incantation above won't work if you've already used os.chdir() to change your current working directory, since the value of the __file__ constant is relative to the current working directory and is not changed by an os.chdir() call.) # # NOTE: for this specific file that I am testing, the path should be: # '/home/npalmer/anaconda3/envs/py3fresh/lib/python3.6/site-packages/HARK/SolvingMicroDSOPs/---example-file--- @@ -1290,9 +1270,7 @@ def copy_module_to_local(full_module_name): # Split out the name of the module. Break if proper format is not followed: all_module_names_list = full_module_name.split('.') # Assume put in at correct format if all_module_names_list[0] != "HARK": - print("\nWarning: the module name does not start with 'HARK'. Instead it is: '" - + all_module_names_list[0]+"' --please format the full namespace of the module you want. \n" - "For example, 'HARK.SolvingMicroDSOPs'") + print("\nWarning: the module name does not start with 'HARK'. Instead it is: '"+all_module_names_list[0]+"' -- please format the full namespace of the module you want. For example, 'HARK.SolvingMicroDSOPs'") print("\nGoodbye!") return @@ -1304,8 +1282,8 @@ def copy_module_to_local(full_module_name): head_path, my_module = os.path.split(my_directory_full_path) home_directory_with_module = os.path.join(home_directory_RAW, my_module) - - print("\n\n\nmy_directory_full_path:", my_directory_full_path, '\n\n\n') + + print("\n\n\nmy_directory_full_path:",my_directory_full_path,'\n\n\n') # Interact with the user: # - Ask the user for the target place to copy the directory @@ -1315,41 +1293,44 @@ def copy_module_to_local(full_module_name): # - If not, just copy there # - Quit - target_path = input("""You have invoked the 'replicate' process for the current module:\n """ + - my_module + """\nThe default copy location is your home directory:\n """ + - home_directory_with_module + """\nPlease enter one of the three options in single quotes below, excluding the quotes: - + target_path = input("""You have invoked the 'replicate' process for the current module:\n """ + + my_module + """\nThe default copy location is your home directory:\n """+ + home_directory_with_module +"""\nPlease enter one of the three options in single quotes below, excluding the quotes: + 'q' or return/enter to quit the process 'y' to accept the default home directory: """+home_directory_with_module+""" 'n' to specify your own pathname\n\n""") + if target_path == 'n' or target_path == 'N': target_path = input("""Please enter the full pathname to your target directory location: """) - + # Clean up: target_path = os.path.expanduser(target_path) target_path = os.path.expandvars(target_path) target_path = os.path.normpath(target_path) - + # Check to see if they included the module name; if not add it here: temp_head, temp_tail = os.path.split(target_path) if temp_tail != my_module: target_path = os.path.join(target_path, my_module) - + elif target_path == 'y' or target_path == 'Y': # Just using the default path: target_path = home_directory_with_module else: # Assume "quit" - return - - if target_path != 'q' and target_path != 'Q' or target_path == '': + return + + if target_path != 'q' and target_path != 'Q' or target_path == '': # Run the copy command: - copy_module(target_path, my_directory_full_path, my_module) - + copy_module(target_path, my_directory_full_path, my_module) + return + + def main(): print("Sorry, HARK.core doesn't actually do anything on its own.") print("To see some examples of its frameworks in action, try running a model module.") diff --git a/HARK/parallel.py b/HARK/parallel.py index 9e84b8ef5..602117300 100644 --- a/HARK/parallel.py +++ b/HARK/parallel.py @@ -84,13 +84,13 @@ def multiThreadCommands(agent_list,command_list,num_jobs=None): multiThreadCommandsFake(agent_list,command_list) return None - # Default umber of parallel jobs is the smaller of number of AgentTypes in + # Default number of parallel jobs is the smaller of number of AgentTypes in # the input and the number of available cores. if num_jobs is None: num_jobs = min(len(agent_list),multiprocessing.cpu_count()) # Send each command in command_list to each of the types in agent_list to be run - agent_list_out = Parallel(n_jobs=num_jobs)(delayed(runCommands)(*args) for args in zip(agent_list, len(agent_list)*[command_list])) + agent_list_out = Parallel(backend='multiprocessing',n_jobs=num_jobs)(delayed(runCommands)(*args) for args in zip(agent_list, len(agent_list)*[command_list])) # Replace the original types with the output from the parallel call for j in range(len(agent_list)): From 3f4da42c68da105cfdda382b9067e884a8b93cf7 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Wed, 15 May 2019 17:41:59 -0400 Subject: [PATCH 59/77] Revert HARK.core to previous commit Somehow the non-delinted version of HARK.core (from before PyCon) ended up in this branch. This commit simply reverts it. --- HARK/core.py | 425 +++++++++++++++++++++++++++------------------------ 1 file changed, 222 insertions(+), 203 deletions(-) diff --git a/HARK/core.py b/HARK/core.py index 0cc6ee4cb..55b7136d3 100644 --- a/HARK/core.py +++ b/HARK/core.py @@ -21,7 +21,8 @@ from time import clock from .parallel import multiThreadCommands, multiThreadCommandsFake -def distanceMetric(thing_A,thing_B): + +def distanceMetric(thing_A, thing_B): ''' A "universal distance" metric that can be used as a default in many settings. @@ -42,12 +43,12 @@ def distanceMetric(thing_A,thing_B): typeB = type(thing_B) if typeA is list and typeB is list: - lenA = len(thing_A) # If both inputs are lists, then the distance between - lenB = len(thing_B) # them is the maximum distance between corresponding - if lenA == lenB: # elements in the lists. If they differ in length, - distance_temp = [] # the distance is the difference in lengths. + lenA = len(thing_A) # If both inputs are lists, then the distance between + lenB = len(thing_B) # them is the maximum distance between corresponding + if lenA == lenB: # elements in the lists. If they differ in length, + distance_temp = [] # the distance is the difference in lengths. for n in range(lenA): - distance_temp.append(distanceMetric(thing_A[n],thing_B[n])) + distance_temp.append(distanceMetric(thing_A[n], thing_B[n])) distance = max(distance_temp) else: distance = float(abs(lenA - lenB)) @@ -57,7 +58,7 @@ def distanceMetric(thing_A,thing_B): # If both inputs are array-like, return the maximum absolute difference b/w # corresponding elements (if same shape); return largest difference in dimensions # if shapes do not align. - elif hasattr(thing_A,'shape') and hasattr(thing_B,'shape'): + elif hasattr(thing_A, 'shape') and hasattr(thing_B, 'shape'): if thing_A.shape == thing_B.shape: distance = np.max(abs(thing_A - thing_B)) else: @@ -69,16 +70,17 @@ def distanceMetric(thing_A,thing_B): distance = 0.0 else: distance = thing_A.distance(thing_B) - else: # Failsafe: the inputs are very far apart + else: # Failsafe: the inputs are very far apart distance = 1000.0 return distance + class HARKobject(object): ''' A superclass for object classes in HARK. Comes with two useful methods: a generic/universal distance method and an attribute assignment method. ''' - def distance(self,other): + def distance(self, other): ''' A generic distance method, which requires the existence of an attribute called distance_criteria, giving a list of strings naming the attributes @@ -98,14 +100,14 @@ def distance(self,other): distance_list = [0.0] for attr_name in self.distance_criteria: try: - obj_A = getattr(self,attr_name) - obj_B = getattr(other,attr_name) - distance_list.append(distanceMetric(obj_A,obj_B)) - except: - distance_list.append(1000.0) # if either object lacks attribute, they are not the same + obj_A = getattr(self, attr_name) + obj_B = getattr(other, attr_name) + distance_list.append(distanceMetric(obj_A, obj_B)) + except AttributeError: + distance_list.append(1000.0) # if either object lacks attribute, they are not the same return max(distance_list) - def assignParameters(self,**kwds): + def assignParameters(self, **kwds): ''' Assign an arbitrary number of attributes to this agent. @@ -120,16 +122,16 @@ def assignParameters(self,**kwds): none ''' for key in kwds: - setattr(self,key,kwds[key]) + setattr(self, key, kwds[key]) - def __call__(self,**kwds): + def __call__(self, **kwds): ''' Assign an arbitrary number of attributes to this agent, as a convenience. See assignParameters. ''' self.assignParameters(**kwds) - def getAvg(self,varname,**kwds): + def getAvg(self, varname, **kwds): ''' Calculates the average of an attribute of this instance. Returns NaN if no such attribute. @@ -144,11 +146,12 @@ def getAvg(self,varname,**kwds): avg : float or np.array The average of this attribute. Might be an array if the axis keyword is passed. ''' - if hasattr(self,varname): - return np.mean(getattr(self,varname),**kwds) + if hasattr(self, varname): + return np.mean(getattr(self, varname), **kwds) else: return np.nan + class Solution(HARKobject): ''' A superclass for representing the "solution" to a single period problem in a @@ -158,6 +161,7 @@ class Solution(HARKobject): replacing each instance of Solution with HARKobject in the other modules. ''' + class AgentType(HARKobject): ''' A superclass for economic agents in the HARK framework. Each model should @@ -170,8 +174,8 @@ class AgentType(HARKobject): 'solveOnePeriod' should appear in exactly one of these lists, depending on whether the same solution method is used in all periods of the model. ''' - def __init__(self,solution_terminal=None,cycles=1,time_flow=False,pseudo_terminal=True, - tolerance=0.000001,seed=0,**kwds): + def __init__(self, solution_terminal=None, cycles=1, time_flow=False, pseudo_terminal=True, + tolerance=0.000001, seed=0, **kwds): ''' Initialize an instance of AgentType by setting attributes. @@ -211,18 +215,18 @@ def __init__(self,solution_terminal=None,cycles=1,time_flow=False,pseudo_termina ''' if solution_terminal is None: solution_terminal = NullFunc() - self.solution_terminal = solution_terminal - self.cycles = cycles - self.time_flow = time_flow - self.pseudo_terminal = pseudo_terminal - self.solveOnePeriod = NullFunc() - self.tolerance = tolerance - self.seed = seed - self.track_vars = [] - self.poststate_vars = [] - self.read_shocks = False - self.assignParameters(**kwds) - self.resetRNG() + self.solution_terminal = solution_terminal # NOQA + self.cycles = cycles # NOQA + self.time_flow = time_flow # NOQA + self.pseudo_terminal = pseudo_terminal # NOQA + self.solveOnePeriod = NullFunc() # NOQA + self.tolerance = tolerance # NOQA + self.seed = seed # NOQA + self.track_vars = [] # NOQA + self.poststate_vars = [] # NOQA + self.read_shocks = False # NOQA + self.assignParameters(**kwds) # NOQA + self.resetRNG() # NOQA def timeReport(self): ''' @@ -288,7 +292,7 @@ def timeRev(self): if self.time_flow: self.timeFlip() - def addToTimeVary(self,*params): + def addToTimeVary(self, *params): ''' Adds any number of parameters to time_vary for this instance. @@ -305,7 +309,7 @@ def addToTimeVary(self,*params): if param not in self.time_vary: self.time_vary.append(param) - def addToTimeInv(self,*params): + def addToTimeInv(self, *params): ''' Adds any number of parameters to time_inv for this instance. @@ -322,7 +326,7 @@ def addToTimeInv(self,*params): if param not in self.time_inv: self.time_inv.append(param) - def delFromTimeVary(self,*params): + def delFromTimeVary(self, *params): ''' Removes any number of parameters from time_vary for this instance. @@ -339,7 +343,7 @@ def delFromTimeVary(self,*params): if param in self.time_vary: self.time_vary.remove(param) - def delFromTimeInv(self,*params): + def delFromTimeInv(self, *params): ''' Removes any number of parameters from time_inv for this instance. @@ -356,7 +360,7 @@ def delFromTimeInv(self,*params): if param in self.time_inv: self.time_inv.remove(param) - def solve(self,verbose=False): + def solve(self, verbose=False): ''' Solve the model for this instance of an agent type by backward induction. Loops through the sequence of one period problems, passing the solution @@ -372,12 +376,16 @@ def solve(self,verbose=False): none ''' - self.preSolve() # Do pre-solution stuff - self.solution = solveAgent(self,verbose) # Solve the model by backward induction - if self.time_flow: # Put the solution in chronological order if this instance's time flow runs that way - self.solution.reverse() - self.addToTimeVary('solution') # Add solution to the list of time-varying attributes - self.postSolve() # Do post-solution stuff + # Ignore floating point "errors". Numpy calls it "errors", but really it's excep- + # tions with well-defined answers such as 1.0/0.0 that is np.inf, -1.0/0.0 that is + # -np.inf, np.inf/np.inf is np.nan and so on. + with np.errstate(divide='ignore', over='ignore', under='ignore', invalid='ignore'): + self.preSolve() # Do pre-solution stuff + self.solution = solveAgent(self, verbose) # Solve the model by backward induction + if self.time_flow: # Put the solution in chronological order if this instance's time flow runs that way + self.solution.reverse() + self.addToTimeVary('solution') # Add solution to the list of time-varying attributes + self.postSolve() # Do post-solution stuff def resetRNG(self): ''' @@ -398,10 +406,9 @@ def checkElementsOfTimeVaryAreLists(self): A method to check that elements of time_vary are lists. """ for param in self.time_vary: - assert type(getattr(self,param))==list,param + ' is not a list, but should be' + \ + assert type(getattr(self, param)) == list, param + ' is not a list, but should be' + \ ' because it is in time_vary' - def preSolve(self): ''' A method that is run immediately before the model is solved, to check inputs or to prepare @@ -448,12 +455,13 @@ def initializeSim(self): ''' self.resetRNG() self.t_sim = 0 - all_agents = np.ones(self.AgentCount,dtype=bool) + all_agents = np.ones(self.AgentCount, dtype=bool) blank_array = np.zeros(self.AgentCount) for var_name in self.poststate_vars: - exec('self.' + var_name + ' = copy(blank_array)') - self.t_age = np.zeros(self.AgentCount,dtype=int) # Number of periods since agent entry - self.t_cycle = np.zeros(self.AgentCount,dtype=int) # Which cycle period each agent is on + setattr(self, var_name, copy(blank_array)) + # exec('self.' + var_name + ' = copy(blank_array)') + self.t_age = np.zeros(self.AgentCount, dtype=int) # Number of periods since agent entry + self.t_cycle = np.zeros(self.AgentCount, dtype=int) # Which cycle period each agent is on self.simBirth(all_agents) self.clearHistory() return None @@ -474,18 +482,18 @@ def simOnePeriod(self): None ''' self.getMortality() # Replace some agents with "newborns" - if self.read_shocks: # If shock histories have been pre-specified, use those + if self.read_shocks: # If shock histories have been pre-specified, use those self.readShocks() else: # Otherwise, draw shocks as usual according to subclass-specific method self.getShocks() - self.getStates() # Determine each agent's state at decision time + self.getStates() # Determine each agent's state at decision time self.getControls() # Determine each agent's choice or control variables based on states - self.getPostStates() # Determine each agent's post-decision / end-of-period states using states and controls + self.getPostStates() # Determine each agent's post-decision / end-of-period states using states and controls # Advance time for all agents - self.t_age = self.t_age + 1 # Age all consumers by one period - self.t_cycle = self.t_cycle + 1 # Age all consumers within their cycle - self.t_cycle[self.t_cycle == self.T_cycle] = 0 # Resetting to zero for those who have reached the end + self.t_age = self.t_age + 1 # Age all consumers by one period + self.t_cycle = self.t_cycle + 1 # Age all consumers within their cycle + self.t_cycle[self.t_cycle == self.T_cycle] = 0 # Resetting to zero for those who have reached the end def makeShockHistory(self): ''' @@ -511,7 +519,7 @@ def makeShockHistory(self): # Make blank history arrays for each shock variable for var_name in self.shock_vars: - setattr(self,var_name+'_hist',np.zeros((self.T_sim,self.AgentCount))+np.nan) + setattr(self, var_name+'_hist', np.zeros((self.T_sim, self.AgentCount)) + np.nan) # Make and store the history of shocks for each period for t in range(self.T_sim): @@ -520,9 +528,9 @@ def makeShockHistory(self): for var_name in self.shock_vars: exec('self.' + var_name + '_hist[self.t_sim,:] = self.' + var_name) self.t_sim += 1 - self.t_age = self.t_age + 1 # Age all consumers by one period - self.t_cycle = self.t_cycle + 1 # Age all consumers within their cycle - self.t_cycle[self.t_cycle == self.T_cycle] = 0 # Resetting to zero for those who have reached the end + self.t_age = self.t_age + 1 # Age all consumers by one period + self.t_cycle = self.t_cycle + 1 # Age all consumers within their cycle + self.t_cycle[self.t_cycle == self.T_cycle] = 0 # Resetting to zero for those who have reached the end # Restore the flow of time and flag that shocks can be read rather than simulated self.read_shocks = True @@ -566,10 +574,10 @@ def simDeath(self): Boolean array of size self.AgentCount indicating which agents die and are replaced. ''' print('AgentType subclass must define method simDeath!') - who_dies = np.ones(self.AgentCount,dtype=bool) + who_dies = np.ones(self.AgentCount, dtype=bool) return who_dies - def simBirth(self,which_agents): + def simBirth(self, which_agents): ''' Makes new agents for the simulation. Takes a boolean array as an input, indicating which agent indices are to be "born". Does nothing by default, must be overwritten by a subclass. @@ -618,7 +626,7 @@ def readShocks(self): None ''' for var_name in self.shock_vars: - setattr(self,var_name,getattr(self,var_name+'_hist')[self.t_sim,:]) + setattr(self, var_name, getattr(self, var_name + '_hist')[self.t_sim, :]) def getStates(self): ''' @@ -667,7 +675,7 @@ def getPostStates(self): ''' return None - def simulate(self,sim_periods=None): + def simulate(self, sim_periods=None): ''' Simulates this agent type for a given number of periods (defaults to self.T_sim if no input). Records histories of attributes named in self.track_vars in attributes named varname_hist. @@ -680,19 +688,23 @@ def simulate(self,sim_periods=None): ------- None ''' - orig_time = self.time_flow - self.timeFwd() - if sim_periods is None: - sim_periods = self.T_sim + # Ignore floating point "errors". Numpy calls it "errors", but really it's excep- + # tions with well-defined answers such as 1.0/0.0 that is np.inf, -1.0/0.0 that is + # -np.inf, np.inf/np.inf is np.nan and so on. + with np.errstate(divide='ignore', over='ignore', under='ignore', invalid='ignore'): + orig_time = self.time_flow + self.timeFwd() + if sim_periods is None: + sim_periods = self.T_sim - for t in range(sim_periods): - self.simOnePeriod() - for var_name in self.track_vars: - exec('self.' + var_name + '_hist[self.t_sim,:] = self.' + var_name) - self.t_sim += 1 + for t in range(sim_periods): + self.simOnePeriod() + for var_name in self.track_vars: + exec('self.' + var_name + '_hist[self.t_sim,:] = self.' + var_name) + self.t_sim += 1 - if not orig_time: - self.timeRev() + if not orig_time: + self.timeRev() def clearHistory(self): ''' @@ -710,7 +722,7 @@ def clearHistory(self): exec('self.' + var_name + '_hist = np.zeros((self.T_sim,self.AgentCount)) + np.nan') -def solveAgent(agent,verbose): +def solveAgent(agent, verbose): ''' Solve the dynamic model for one agent type. This function iterates on "cycles" of an agent's model either a given number of times or until solution convergence @@ -734,8 +746,8 @@ def solveAgent(agent,verbose): agent.timeRev() # Check to see whether this is an (in)finite horizon problem - cycles_left = agent.cycles - infinite_horizon = cycles_left == 0 + cycles_left = agent.cycles # NOQA + infinite_horizon = cycles_left == 0 # NOQA # Initialize the solution, which includes the terminal solution if it's not a pseudo-terminal period solution = [] @@ -743,15 +755,15 @@ def solveAgent(agent,verbose): solution.append(deepcopy(agent.solution_terminal)) # Initialize the process, then loop over cycles - solution_last = agent.solution_terminal - go = True - completed_cycles = 0 - max_cycles = 5000 # escape clause + solution_last = agent.solution_terminal # NOQA + go = True # NOQA + completed_cycles = 0 # NOQA + max_cycles = 5000 # NOQA - escape clause if verbose: t_last = clock() while go: # Solve a cycle of the model, recording it if horizon is finite - solution_cycle = solveOneCycle(agent,solution_last) + solution_cycle = solveOneCycle(agent, solution_last) if not infinite_horizon: solution += solution_cycle @@ -761,7 +773,7 @@ def solveAgent(agent,verbose): if completed_cycles > 0: solution_distance = solution_now.distance(solution_last) go = (solution_distance > agent.tolerance and completed_cycles < max_cycles) - else: # Assume solution does not converge after only one cycle + else: # Assume solution does not converge after only one cycle solution_distance = 100.0 go = True else: @@ -776,16 +788,16 @@ def solveAgent(agent,verbose): if verbose: t_now = clock() if infinite_horizon: - print('Finished cycle #' + str(completed_cycles) + ' in ' + str(t_now-t_last) +\ - ' seconds, solution distance = ' + str(solution_distance)) + print('Finished cycle #' + str(completed_cycles) + ' in ' + str(t_now-t_last) + + ' seconds, solution distance = ' + str(solution_distance)) else: - print('Finished cycle #' + str(completed_cycles) + ' of ' + str(agent.cycles) +\ - ' in ' + str(t_now-t_last) + ' seconds.') + print('Finished cycle #' + str(completed_cycles) + ' of ' + str(agent.cycles) + + ' in ' + str(t_now-t_last) + ' seconds.') t_last = t_now # Record the last cycle if horizon is infinite (solution is still empty!) if infinite_horizon: - solution = solution_cycle # PseudoTerminal=False impossible for infinite horizon + solution = solution_cycle # PseudoTerminal=False impossible for infinite horizon # Restore the direction of time to its original orientation, then return the solution if original_time_flow: @@ -793,7 +805,7 @@ def solveAgent(agent,verbose): return solution -def solveOneCycle(agent,solution_last): +def solveOneCycle(agent, solution_last): ''' Solve one "cycle" of the dynamic model for one agent type. This function iterates over the periods within an agent's cycle, updating the time-varying @@ -818,7 +830,7 @@ def solveOneCycle(agent,solution_last): # Calculate number of periods per cycle, defaults to 1 if all variables are time invariant if len(agent.time_vary) > 0: name = agent.time_vary[0] - T = len(eval('agent.' + name)) + T = len(eval('agent.' + name)) else: T = 1 @@ -826,12 +838,12 @@ def solveOneCycle(agent,solution_last): always_same_solver = 'solveOnePeriod' not in agent.time_vary if always_same_solver: solveOnePeriod = agent.solveOnePeriod - these_args = getArgNames(solveOnePeriod) + these_args = getArgNames(solveOnePeriod) # Construct a dictionary to be passed to the solver time_inv_string = '' for name in agent.time_inv: - time_inv_string += ' \'' + name + '\' : agent.' +name + ',' + time_inv_string += ' \'' + name + '\' : agent.' + name + ',' time_vary_string = '' for name in agent.time_vary: time_vary_string += ' \'' + name + '\' : None,' @@ -839,7 +851,7 @@ def solveOneCycle(agent,solution_last): # Initialize the solution for this cycle, then iterate on periods solution_cycle = [] - solution_next = solution_last + solution_next = solution_last for t in range(T): # Update which single period solver to use (if it depends on time) if not always_same_solver: @@ -864,8 +876,8 @@ def solveOneCycle(agent,solution_last): return solution_cycle -#======================================================================== -#======================================================================== +# ======================================================================== +# ======================================================================== class Market(HARKobject): ''' @@ -873,8 +885,8 @@ class Market(HARKobject): dynamic general equilibrium models to solve the "macroeconomic" model as a layer on top of the "microeconomic" models of one or more AgentTypes. ''' - def __init__(self,agents=[],sow_vars=[],reap_vars=[],const_vars=[],track_vars=[],dyn_vars=[], - millRule=None,calcDynamics=None,act_T=1000,tolerance=0.000001): + def __init__(self, agents=[], sow_vars=[], reap_vars=[], const_vars=[], track_vars=[], dyn_vars=[], + millRule=None, calcDynamics=None, act_T=1000, tolerance=0.000001): ''' Make a new instance of the Market class. @@ -919,25 +931,25 @@ def __init__(self,agents=[],sow_vars=[],reap_vars=[],const_vars=[],track_vars=[] ------- None ''' - self.agents = agents - self.reap_vars = reap_vars - self.sow_vars = sow_vars - self.const_vars = const_vars - self.track_vars = track_vars - self.dyn_vars = dyn_vars - if millRule is not None: # To prevent overwriting of method-based millRules + self.agents = agents # NOQA + self.reap_vars = reap_vars # NOQA + self.sow_vars = sow_vars # NOQA + self.const_vars = const_vars # NOQA + self.track_vars = track_vars # NOQA + self.dyn_vars = dyn_vars # NOQA + if millRule is not None: # To prevent overwriting of method-based millRules self.millRule = millRule - if calcDynamics is not None: # Ditto for calcDynamics + if calcDynamics is not None: # Ditto for calcDynamics self.calcDynamics = calcDynamics - self.act_T = act_T - self.tolerance = tolerance - self.max_loops = 1000 - - self.print_parallel_error_once = True - # Print the error associated with calling the parallel method - # "solveAgents" one time. If set to false, the error will never - # print. See "solveAgents" for why this prints once or never. - + self.act_T = act_T # NOQA + self.tolerance = tolerance # NOQA + self.max_loops = 1000 # NOQA + + self.print_parallel_error_once = True + # Print the error associated with calling the parallel method + # "solveAgents" one time. If set to false, the error will never + # print. See "solveAgents" for why this prints once or never. + def solveAgents(self): ''' Solves the microeconomic problem for all AgentTypes in this market. @@ -950,21 +962,19 @@ def solveAgents(self): ------- None ''' - #for this_type in self.agents: - # this_type.solve() + # for this_type in self.agents: + # this_type.solve() try: - multiThreadCommands(self.agents,['solve()']) + multiThreadCommands(self.agents, ['solve()']) except Exception as err: - # import pdb; pdb.set_trace() if self.print_parallel_error_once: - # Set flag to False so this is only printed once. + # Set flag to False so this is only printed once. self.print_parallel_error_once = False - print("**** WARNING: could not execute multiThreadCommands in HARK.core.Market.solveAgents(), so using the serial version instead." + - "This will likely be slower. " + - "The multiTreadCommands() functions failed with the following error:", - '\n ', sys.exc_info()[0], ':', err) #sys.exc_info()[0]) - multiThreadCommandsFake(self.agents,['solve()']) - + print("**** WARNING: could not execute multiThreadCommands in HARK.core.Market.solveAgents() ", + "so using the serial version instead. This will likely be slower. " + "The multiTreadCommands() functions failed with the following error:", '\n', + sys.exc_info()[0], ':', err) # sys.exc_info()[0]) + multiThreadCommandsFake(self.agents, ['solve()']) def solve(self): ''' @@ -980,15 +990,15 @@ def solve(self): ------- None ''' - go = True - max_loops = self.max_loops # Failsafe against infinite solution loop + go = True + max_loops = self.max_loops # Failsafe against infinite solution loop completed_loops = 0 - old_dynamics = None + old_dynamics = None - while go: # Loop until the dynamic process converges or we hit the loop cap - self.solveAgents() # Solve each AgentType's micro problem - self.makeHistory() # "Run" the model while tracking aggregate variables - new_dynamics = self.updateDynamics() # Find a new aggregate dynamic rule + while go: # Loop until the dynamic process converges or we hit the loop cap + self.solveAgents() # Solve each AgentType's micro problem + self.makeHistory() # "Run" the model while tracking aggregate variables + new_dynamics = self.updateDynamics() # Find a new aggregate dynamic rule # Check to see if the dynamic rule has converged (if this is not the first loop) if completed_loops > 0: @@ -997,11 +1007,11 @@ def solve(self): distance = 1000000.0 # Move to the next loop if the terminal conditions are not met - old_dynamics = new_dynamics + old_dynamics = new_dynamics completed_loops += 1 - go = distance >= self.tolerance and completed_loops < max_loops + go = distance >= self.tolerance and completed_loops < max_loops - self.dynamics = new_dynamics # Store the final dynamic rule in self + self.dynamics = new_dynamics # Store the final dynamic rule in self def reap(self): ''' @@ -1019,8 +1029,8 @@ def reap(self): for var_name in self.reap_vars: harvest = [] for this_type in self.agents: - harvest.append(getattr(this_type,var_name)) - setattr(self,var_name,harvest) + harvest.append(getattr(this_type, var_name)) + setattr(self, var_name, harvest) def sow(self): ''' @@ -1036,9 +1046,9 @@ def sow(self): none ''' for var_name in self.sow_vars: - this_seed = getattr(self,var_name) + this_seed = getattr(self, var_name) for this_type in self.agents: - setattr(this_type,var_name,this_seed) + setattr(this_type, var_name, this_seed) def mill(self): ''' @@ -1066,8 +1076,8 @@ def mill(self): product = self.millRule(**mill_dict) for j in range(len(self.sow_vars)): this_var = self.sow_vars[j] - this_product = getattr(product,this_var) - setattr(self,this_var,this_product) + this_product = getattr(product, this_var) + setattr(self, this_var, this_product) def cultivate(self): ''' @@ -1100,12 +1110,12 @@ def reset(self): ------- none ''' - for var_name in self.track_vars: # Reset the history of tracked variables - setattr(self,var_name + '_hist',[]) - for var_name in self.sow_vars: # Set the sow variables to their initial levels - initial_val = getattr(self,var_name + '_init') - setattr(self,var_name,initial_val) - for this_type in self.agents: # Reset each AgentType in the market + for var_name in self.track_vars: # Reset the history of tracked variables + setattr(self, var_name + '_hist', []) + for var_name in self.sow_vars: # Set the sow variables to their initial levels + initial_val = getattr(self, var_name + '_init') + setattr(self, var_name, initial_val) + for this_type in self.agents: # Reset each AgentType in the market this_type.reset() def store(self): @@ -1122,8 +1132,8 @@ def store(self): none ''' for var_name in self.track_vars: - value_now = getattr(self,var_name) - getattr(self,var_name + '_hist').append(value_now) + value_now = getattr(self, var_name) + getattr(self, var_name + '_hist').append(value_now) def makeHistory(self): ''' @@ -1138,13 +1148,13 @@ def makeHistory(self): ------- none ''' - self.reset() # Initialize the state of the market + self.reset() # Initialize the state of the market for t in range(self.act_T): - self.sow() # Distribute aggregated information/state to agents - self.cultivate() # Agents take action - self.reap() # Collect individual data from agents - self.mill() # Process individual data into aggregate data - self.store() # Record variables of interest + self.sow() # Distribute aggregated information/state to agents + self.cultivate() # Agents take action + self.reap() # Collect individual data from agents + self.mill() # Process individual data into aggregate data + self.store() # Record variables of interest def updateDynamics(self): ''' @@ -1171,11 +1181,11 @@ def updateDynamics(self): update_dict = eval('{' + history_vars_string + '}') # Calculate a new dynamic rule and distribute it to the agents in agent_list - dynamics = self.calcDynamics(**update_dict) # User-defined dynamics calculator + dynamics = self.calcDynamics(**update_dict) # User-defined dynamics calculator for var_name in self.dyn_vars: - this_obj = getattr(dynamics,var_name) + this_obj = getattr(dynamics, var_name) for this_type in self.agents: - setattr(this_type,var_name,this_obj) + setattr(this_type, var_name, this_obj) return dynamics @@ -1186,21 +1196,21 @@ def updateDynamics(self): # Define a function to run the copying: def copy_module(target_path, my_directory_full_path, my_module): ''' - Helper function for copy_module_to_local(). Provides the actual copy - functionality, with highly cautious safeguards against copying over - important things. - + Helper function for copy_module_to_local(). Provides the actual copy + functionality, with highly cautious safeguards against copying over + important things. + Parameters ---------- target_path : string String, file path to target location - + my_directory_full_path: string String, full pathname to this file's directory - + my_module : string String, name of the module to copy - + Returns ------- none @@ -1210,35 +1220,42 @@ def copy_module(target_path, my_directory_full_path, my_module): print("Goodbye!") return elif target_path == os.path.expanduser("~") or os.path.normpath(target_path) == os.path.expanduser("~"): - print("You have indicated that the target location is "+target_path+" -- that is, you want to wipe out your home directory with the contents of "+my_module+". My programming does not allow me to do that.\n\nGoodbye!") + print("You have indicated that the target location is " + target_path + + " -- that is, you want to wipe out your home directory with the contents of " + my_module + + ". My programming does not allow me to do that.\n\nGoodbye!") return elif os.path.exists(target_path): - print("There is already a file or directory at the location "+target_path+". For safety reasons this code does not overwrite existing files.\nPlease remove the file at "+target_path+" and try again.") + print("There is already a file or directory at the location " + target_path + + ". For safety reasons this code does not overwrite existing files.\n Please remove the file at " + + target_path + + " and try again.") return else: - user_input = input("""You have indicated you want to copy module:\n """+ my_module - + """\nto:\n """+ target_path +"""\nIs that correct? Please indicate: y / [n]\n\n""") + user_input = input("""You have indicated you want to copy module:\n """ + my_module + + """\nto:\n """ + target_path + """\nIs that correct? Please indicate: y / [n]\n\n""") if user_input == 'y' or user_input == 'Y': - #print("copy_tree(",my_directory_full_path,",", target_path,")") + # print("copy_tree(",my_directory_full_path,",", target_path,")") copy_tree(my_directory_full_path, target_path) else: print("Goodbye!") return + def print_helper(): - + my_directory_full_path = os.path.dirname(os.path.realpath(__file__)) - + print(my_directory_full_path) + def copy_module_to_local(full_module_name): ''' - This function contains simple code to copy a submodule to a location on - your hard drive, as specified by you. The purpose of this code is to provide - users with a simple way to access a *copy* of code that usually sits deep in - the Econ-ARK package structure, for purposes of tinkering and experimenting - directly. This is meant to be a simple way to explore HARK code. To interact - with the codebase under active development, please refer to the documentation + This function contains simple code to copy a submodule to a location on + your hard drive, as specified by you. The purpose of this code is to provide + users with a simple way to access a *copy* of code that usually sits deep in + the Econ-ARK package structure, for purposes of tinkering and experimenting + directly. This is meant to be a simple way to explore HARK code. To interact + with the codebase under active development, please refer to the documentation under github.com/econ-ark/HARK/ To execute, do the following on the Python command line: @@ -1246,7 +1263,7 @@ def copy_module_to_local(full_module_name): from HARK.core import copy_module_to_local copy_module_to_local("FULL-HARK-MODULE-NAME-HERE") - For example, if you want SolvingMicroDSOPs you would enter + For example, if you want SolvingMicroDSOPs you would enter from HARK.core import copy_module_to_local copy_module_to_local("HARK.SolvingMicroDSOPs") @@ -1255,14 +1272,17 @@ def copy_module_to_local(full_module_name): # Find a default directory -- user home directory: home_directory_RAW = os.path.expanduser("~") - # Thanks to https://stackoverflow.com/a/4028943 + # Thanks to https://stackoverflow.com/a/4028943 # Find the directory of the HARK.core module: - #my_directory_full_path = os.path.dirname(os.path.realpath(__file__)) + # my_directory_full_path = os.path.dirname(os.path.realpath(__file__)) hark_core_directory_full_path = os.path.dirname(os.path.realpath(__file__)) # From https://stackoverflow.com/a/5137509 - # Important note from that answer: - # (Note that the incantation above won't work if you've already used os.chdir() to change your current working directory, since the value of the __file__ constant is relative to the current working directory and is not changed by an os.chdir() call.) + # Important note from that answer: + # (Note that the incantation above won't work if you've already used os.chdir() + # to change your current working directory, + # since the value of the __file__ constant is relative to the current working directory and is not changed by an + # os.chdir() call.) # # NOTE: for this specific file that I am testing, the path should be: # '/home/npalmer/anaconda3/envs/py3fresh/lib/python3.6/site-packages/HARK/SolvingMicroDSOPs/---example-file--- @@ -1270,7 +1290,9 @@ def copy_module_to_local(full_module_name): # Split out the name of the module. Break if proper format is not followed: all_module_names_list = full_module_name.split('.') # Assume put in at correct format if all_module_names_list[0] != "HARK": - print("\nWarning: the module name does not start with 'HARK'. Instead it is: '"+all_module_names_list[0]+"' -- please format the full namespace of the module you want. For example, 'HARK.SolvingMicroDSOPs'") + print("\nWarning: the module name does not start with 'HARK'. Instead it is: '" + + all_module_names_list[0]+"' --please format the full namespace of the module you want. \n" + "For example, 'HARK.SolvingMicroDSOPs'") print("\nGoodbye!") return @@ -1282,8 +1304,8 @@ def copy_module_to_local(full_module_name): head_path, my_module = os.path.split(my_directory_full_path) home_directory_with_module = os.path.join(home_directory_RAW, my_module) - - print("\n\n\nmy_directory_full_path:",my_directory_full_path,'\n\n\n') + + print("\n\n\nmy_directory_full_path:", my_directory_full_path, '\n\n\n') # Interact with the user: # - Ask the user for the target place to copy the directory @@ -1293,42 +1315,39 @@ def copy_module_to_local(full_module_name): # - If not, just copy there # - Quit - target_path = input("""You have invoked the 'replicate' process for the current module:\n """ + - my_module + """\nThe default copy location is your home directory:\n """+ - home_directory_with_module +"""\nPlease enter one of the three options in single quotes below, excluding the quotes: - + target_path = input("""You have invoked the 'replicate' process for the current module:\n """ + + my_module + """\nThe default copy location is your home directory:\n """ + + home_directory_with_module + """\nPlease enter one of the three options in single quotes below, excluding the quotes: + 'q' or return/enter to quit the process 'y' to accept the default home directory: """+home_directory_with_module+""" 'n' to specify your own pathname\n\n""") - if target_path == 'n' or target_path == 'N': target_path = input("""Please enter the full pathname to your target directory location: """) - + # Clean up: target_path = os.path.expanduser(target_path) target_path = os.path.expandvars(target_path) target_path = os.path.normpath(target_path) - + # Check to see if they included the module name; if not add it here: temp_head, temp_tail = os.path.split(target_path) if temp_tail != my_module: target_path = os.path.join(target_path, my_module) - + elif target_path == 'y' or target_path == 'Y': # Just using the default path: target_path = home_directory_with_module else: # Assume "quit" - return - - if target_path != 'q' and target_path != 'Q' or target_path == '': - # Run the copy command: - copy_module(target_path, my_directory_full_path, my_module) - - return + return + if target_path != 'q' and target_path != 'Q' or target_path == '': + # Run the copy command: + copy_module(target_path, my_directory_full_path, my_module) + return def main(): From df434c6d32ed25ef23bb8dc56da504ca1845c81b Mon Sep 17 00:00:00 2001 From: Shauna Gordon-McKeon Date: Sat, 18 May 2019 19:10:07 -0400 Subject: [PATCH 60/77] Prepare release 0.10.0.dev3 --- CHANGES.md | 21 +++++++++++++++++++-- HARK/__init__.py | 2 +- setup.py | 2 +- 3 files changed, 21 insertions(+), 4 deletions(-) diff --git a/CHANGES.md b/CHANGES.md index 109b8eaf8..7e7b82cd8 100644 --- a/CHANGES.md +++ b/CHANGES.md @@ -1,15 +1,32 @@ HARK -Version 0.10.0.dev2 +Version 0.10.0.dev3 Release Notes # Introduction -This document contains the release notes for the 0.10.0.dev2 version of HARK. HARK aims to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents. +This document contains the release notes for the 0.10.0.dev3 version of HARK. HARK aims to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents. For more information on HARK, see [our Github organization](https://github.com/econ-ark). ## Changes +### 0.10.0.dev3 + +Release Date: 05-18-2019 + +#### Major Changes +- Fixes multithreading problems by using Parallels(backend='multiprocessing'). ([287](https://github.com/econ-ark/HARK/pull/287)) +- Fixes bug caused by misapplication of check_conditions. ([284](https://github.com/econ-ark/HARK/pull/284)) +- Adds functions to calculate quadrature nodes and weights for numerically evaluating expectations in the presence of (log-)normally distributed random variables. ([258](https://github.com/econ-ark/HARK/pull/258)) + +#### Minor Changes +- Adds method decorator which validates that arguments passed in are not empty. ([282](https://github.com/econ-ark/HARK/pull/282) +- Lints a variety of files. These PRs include some additional/related minor changes, like replacing an `exec` function, removing some lambdas, adding some files to .gitignore, etc. ([274](https://github.com/econ-ark/HARK/pull/274), [276](https://github.com/econ-ark/HARK/pull/276), [277](https://github.com/econ-ark/HARK/pull/277), [278](https://github.com/econ-ark/HARK/pull/278), [281](https://github.com/econ-ark/HARK/pull/281)) +- Adds vim swp files to gitignore. ([269](https://github.com/econ-ark/HARK/pull/269)) +- Adds version dunder in init. ([265](https://github.com/econ-ark/HARK/pull/265)) +- Adds flake8 to requirements.txt and config. ([261](https://github.com/econ-ark/HARK/pull/261)) +- Adds some unit tests for IndShockConsumerType. ([256](https://github.com/econ-ark/HARK/pull/256)) + ### 0.10.0.dev2 Release Date: 04-18-2019 diff --git a/HARK/__init__.py b/HARK/__init__.py index 34aeb9a30..88e5cff09 100644 --- a/HARK/__init__.py +++ b/HARK/__init__.py @@ -1,4 +1,4 @@ from __future__ import absolute_import from .core import * -__version__ = '0.10.0.dev2' +__version__ = '0.10.0.dev3' diff --git a/setup.py b/setup.py index 8da908e79..037f0d316 100644 --- a/setup.py +++ b/setup.py @@ -42,7 +42,7 @@ # For a discussion on single-sourcing the version across setup.py and the # project code, see # https://packaging.python.org/en/latest/single_source_version.html - version='0.10.0.dev2', # Required + version='0.10.0.dev3', # Required # This is a one-line description or tagline of what your project does. This # corresponds to the "Summary" metadata field: From b136d8e130c533f960f9bb052d69061031c7e31f Mon Sep 17 00:00:00 2001 From: Shauna Gordon-McKeon Date: Sat, 18 May 2019 19:36:37 -0400 Subject: [PATCH 61/77] Remove invalid characters that break pypi uploading --- CHANGES.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGES.md b/CHANGES.md index 7e7b82cd8..f468750ab 100644 --- a/CHANGES.md +++ b/CHANGES.md @@ -21,7 +21,7 @@ Release Date: 05-18-2019 #### Minor Changes - Adds method decorator which validates that arguments passed in are not empty. ([282](https://github.com/econ-ark/HARK/pull/282) -- Lints a variety of files. These PRs include some additional/related minor changes, like replacing an `exec` function, removing some lambdas, adding some files to .gitignore, etc. ([274](https://github.com/econ-ark/HARK/pull/274), [276](https://github.com/econ-ark/HARK/pull/276), [277](https://github.com/econ-ark/HARK/pull/277), [278](https://github.com/econ-ark/HARK/pull/278), [281](https://github.com/econ-ark/HARK/pull/281)) +- Lints a variety of files. These PRs include some additional/related minor changes, like replacing an exec function, removing some lambdas, adding some files to .gitignore, etc. ([274](https://github.com/econ-ark/HARK/pull/274), [276](https://github.com/econ-ark/HARK/pull/276), [277](https://github.com/econ-ark/HARK/pull/277), [278](https://github.com/econ-ark/HARK/pull/278), [281](https://github.com/econ-ark/HARK/pull/281)) - Adds vim swp files to gitignore. ([269](https://github.com/econ-ark/HARK/pull/269)) - Adds version dunder in init. ([265](https://github.com/econ-ark/HARK/pull/265)) - Adds flake8 to requirements.txt and config. ([261](https://github.com/econ-ark/HARK/pull/261)) From 038996ce8d695f475b7f861c88f80360a6c53dc1 Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Mon, 20 May 2019 14:33:25 +0200 Subject: [PATCH 62/77] Move two test files from Testing to HARK/tests. (#255) * Move two test files from Testing to HARK/tests. * Remove MultithreadedDemo.py * Use assertAlmostEqual in the reference test in file test_TractableBufferStockModel.py. * Fix inputs for MarkovConsumerType. Lacked T_cycle, LivPrb didn't match the number of states, and MrkvArray wasn't an array although it was time-varying. * Compare vectors properly. * Compare vectors properly. * Update test_modelcomparisons.py * Update test_modelcomparisons.py * Update test_modelcomparisons.py * Fix MrkvArray --- .../tests/test_TractableBufferStockModel.py | 13 ++- .../tests/test_modelcomparisons.py | 12 +-- Testing/MultithreadDemo.py | 94 ------------------- 3 files changed, 11 insertions(+), 108 deletions(-) rename Testing/TractableBufferStockModel_UnitTests.py => HARK/tests/test_TractableBufferStockModel.py (85%) rename Testing/Comparison_UnitTests.py => HARK/tests/test_modelcomparisons.py (94%) delete mode 100644 Testing/MultithreadDemo.py diff --git a/Testing/TractableBufferStockModel_UnitTests.py b/HARK/tests/test_TractableBufferStockModel.py similarity index 85% rename from Testing/TractableBufferStockModel_UnitTests.py rename to HARK/tests/test_TractableBufferStockModel.py index c53a9d3bc..234e9250c 100644 --- a/Testing/TractableBufferStockModel_UnitTests.py +++ b/HARK/tests/test_TractableBufferStockModel.py @@ -4,9 +4,8 @@ @author: kaufmana """ -from __future__ import print_function, division -from __future__ import absolute_import +import numpy as np import HARK.ConsumptionSaving.TractableBufferStockModel as Model import unittest @@ -21,7 +20,7 @@ def setUp(self): 'CRRA': .95} test_model = Model.TractableConsumerType(**base_primitives) test_model.solve() - cNrm_list = [0.0, + cNrm_list = np.array([0.0, 0.6170411710160961, 0.7512931350607787, 0.8242071925443384, @@ -48,12 +47,12 @@ def setUp(self): 1.372224689976677, 1.4195156568037894, 1.4722358408529614, - 1.5307746658958221] - return test_model.solution[0].cNrm_list, cNrm_list + 1.5307746658958221]) + return np.array(test_model.solution[0].cNrm_list), cNrm_list - def test1(self): + def test_equalityOfSolutions(self): results = self.setUp() - self.assertEqual(results[0], results[1]) + self.assertTrue(np.allclose(results[0], results[1], atol=1e-08)) if __name__ == '__main__': diff --git a/Testing/Comparison_UnitTests.py b/HARK/tests/test_modelcomparisons.py similarity index 94% rename from Testing/Comparison_UnitTests.py rename to HARK/tests/test_modelcomparisons.py index 26f3940d8..d8b3f6ee7 100644 --- a/Testing/Comparison_UnitTests.py +++ b/HARK/tests/test_modelcomparisons.py @@ -4,8 +4,6 @@ should yield the same output. The code will pass these tests if and only if the output is close "enough". """ -from __future__ import print_function, division -from __future__ import absolute_import # Bring in modules we need import unittest @@ -91,8 +89,7 @@ def setUp(self): TBSType.solve() # Set up and solve Markov - MrkvArray = np.array([[1.0-base_primitives['UnempPrb'], base_primitives['UnempPrb']], - [0.0, 1.0]]) + MrkvArray = [np.array([[1.0-base_primitives['UnempPrb'], base_primitives['UnempPrb']],[0.0, 1.0]])] Markov_primitives = {"CRRA": base_primitives['CRRA'], "Rfree": np.array(2*[base_primitives['Rfree']]), "PermGroFac": [np.array(2*[base_primitives['PermGroFac'] / @@ -113,7 +110,7 @@ def setUp(self): "aXtraCount": 48, "aXtraExtra": [None], "aXtraNestFac": 3, - "LivPrb": [1.0], + "LivPrb":[np.array([1.0,1.0]),], "DiscFac": base_primitives['DiscFac'], 'Nagents': 1, 'psi_seed': 0, @@ -122,7 +119,8 @@ def setUp(self): 'tax_rate': 0.0, 'vFuncBool': False, 'CubicBool': True, - 'MrkvArray': MrkvArray + 'MrkvArray': MrkvArray, + 'T_cycle':1 } MarkovType = MarkovConsumerType(**Markov_primitives) @@ -150,4 +148,4 @@ def diffFunc(m): return self.TBSType.solution[0].cFunc(m) - self.MarkovType.cFun if __name__ == '__main__': # Run all the tests - unittest.main() \ No newline at end of file + unittest.main() diff --git a/Testing/MultithreadDemo.py b/Testing/MultithreadDemo.py deleted file mode 100644 index 68c6c542e..000000000 --- a/Testing/MultithreadDemo.py +++ /dev/null @@ -1,94 +0,0 @@ -''' -A demonstration of parallel processing in HARK using HARK.parallel. -A benchmark consumption-saving model is solved for individuals whose CRRA varies -between 1 and 8. The infinite horizon model is solved serially and then in -parallel. Note that HARK.parallel will not work "out of the box", as Anaconda -does not include two packages needed for it; see HARK/parallel.py. When given a -sufficiently large amount of work for each thread to do, the maximum speedup -factor seems to be around P/2, where P is the number of processors. -''' -from __future__ import print_function, division -from __future__ import absolute_import - -from builtins import str -from builtins import range -import numpy as np - - -import HARK.ConsumptionSaving.ConsumerParameters as Params # Parameters for a consumer type -import HARK.ConsumptionSaving.ConsIndShockModel as Model # Consumption-saving model with idiosyncratic shocks -from HARK.utilities import plotFuncs, plotFuncsDer # Basic plotting tools -from time import clock # Timing utility -from copy import deepcopy # "Deep" copying for complex objects -from HARK.parallel import multiThreadCommandsFake, multiThreadCommands # Parallel processing - - -def mystr(number): return "{:.4f}".format(number) # Format numbers as strings - - -if __name__ == '__main__': # Parallel calls *must* be inside a call to __main__ - type_count = 32 # Number of values of CRRA to solve - - # Make the basic type that we'll use as a template. - # The basic type has an artificially dense assets grid, as the problem to be - # solved must be sufficiently large for multithreading to be faster than - # single-threading (looping), due to overhead. - BasicType = Model.IndShockConsumerType(**Params.init_idiosyncratic_shocks) - BasicType.cycles = 0 - BasicType(aXtraMax=100, aXtraCount=64) - BasicType(vFuncBool=False, CubicBool=True) - BasicType.updateAssetsGrid() - BasicType.timeFwd() - - # Solve the basic type and plot the results, to make sure things are working - start_time = clock() - BasicType.solve() - end_time = clock() - print('Solving the basic consumer took ' + mystr(end_time-start_time) + ' seconds.') - BasicType.unpackcFunc() - print('Consumption function:') - plotFuncs(BasicType.cFunc[0], 0, 5) # plot consumption - print('Marginal consumption function:') - plotFuncsDer(BasicType.cFunc[0], 0, 5) # plot MPC - if BasicType.vFuncBool: - print('Value function:') - plotFuncs(BasicType.solution[0].vFunc, 0.2, 5) - - # Make many copies of the basic type, each with a different risk aversion - BasicType.vFuncBool = False # just in case it was set to True above - my_agent_list = [] - CRRA_list = np.linspace(1, 8, type_count) # All the values that CRRA will take on - for i in range(type_count): - this_agent = deepcopy(BasicType) # Make a new copy of the basic type - this_agent.assignParameters(CRRA=CRRA_list[i]) # Give it a unique CRRA value - my_agent_list.append(this_agent) # Add it to the list of agent types - - # Make a list of commands to be run in parallel; these should be methods of ConsumerType - do_this_stuff = ['updateSolutionTerminal()', 'solve()', 'unpackcFunc()'] - - # Solve the model for each type by looping over the types (not multithreading) - start_time = clock() - multiThreadCommandsFake(my_agent_list, do_this_stuff) # Fake multithreading, just loops - end_time = clock() - print('Solving ' + str(type_count) - + ' types without multithreading took ' - + mystr(end_time-start_time) + ' seconds.') - - # Plot the consumption functions for all types on one figure - plotFuncs([this_type.cFunc[0] for this_type in my_agent_list], 0, 5) - - # Delete the solution for each type to make sure we're not just faking it - for i in range(type_count): - my_agent_list[i].solution = None - my_agent_list[i].cFunc = None - my_agent_list[i].time_vary.remove('solution') - my_agent_list[i].time_vary.remove('cFunc') - - # And here's HARK's initial attempt at multithreading: - start_time = clock() - multiThreadCommands(my_agent_list, do_this_stuff) # Actual multithreading - end_time = clock() - print('Solving ' + str(type_count) + ' types with multithreading took ' + mystr(end_time-start_time) + ' seconds.') - - # Plot the consumption functions for all types on one figure to see if it worked - plotFuncs([this_type.cFunc[0] for this_type in my_agent_list], 0, 5) From ba6e7ccab570daba69d89cd2b823b1c8748a543b Mon Sep 17 00:00:00 2001 From: Patrick Kofod Mogensen Date: Thu, 23 May 2019 13:50:56 +0200 Subject: [PATCH 63/77] Change init tests. --- HARK/tests/test_ConsIndShockInit.py | 25 ----------- HARK/tests/test_modelInits.py | 66 +++++++++++++++++++++++++++++ 2 files changed, 66 insertions(+), 25 deletions(-) delete mode 100644 HARK/tests/test_ConsIndShockInit.py create mode 100644 HARK/tests/test_modelInits.py diff --git a/HARK/tests/test_ConsIndShockInit.py b/HARK/tests/test_ConsIndShockInit.py deleted file mode 100644 index bcbc0cf05..000000000 --- a/HARK/tests/test_ConsIndShockInit.py +++ /dev/null @@ -1,25 +0,0 @@ -""" -This file tests whether ConsIndShockModel's are initialized correctly. -""" - - -# Bring in modules we need -import unittest -import numpy as np -import HARK.ConsumptionSaving.ConsumerParameters as Params -from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType -from HARK.utilities import plotFuncsDer, plotFuncs - - -class testsForConsIndShockModelInitialization(unittest.TestCase): - # We don't need a setUp method for the tests to run, but it's convenient - # if we want to test various things on the same model in different test_* - # methods. - def setUp(self): - - # Make and solve an idiosyncratic shocks consumer with a finite lifecycle - LifecycleExample = IndShockConsumerType(**Params.init_lifecycle) - self.model = LifecycleExample - - def test_LifecycleIncomeProcess(self): - self.assertEqual(len(self.model.IncomeDstn), self.model.T_cycle) diff --git a/HARK/tests/test_modelInits.py b/HARK/tests/test_modelInits.py new file mode 100644 index 000000000..4d4d9b2e2 --- /dev/null +++ b/HARK/tests/test_modelInits.py @@ -0,0 +1,66 @@ +""" +This file tests whether HARK's models are initialized correctly. +""" + + +# Bring in modules we need +import unittest +import numpy as np +import HARK.ConsumptionSaving.ConsumerParameters as Params +from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType +from HARK.ConsumptionSaving.ConsIndShockModel import KinkedRconsumerType +from HARK.ConsumptionSaving.ConsIndShockModel import IndShockConsumerType +from HARK.ConsumptionSaving.ConsMarkovModel import MarkovConsumerType +from HARK.utilities import plotFuncsDer, plotFuncs +from copy import copy + +class testInitialization(unittest.TestCase): + # We don't need a setUp method for the tests to run, but it's convenient + # if we want to test various things on the same model in different test_* + # methods. + def test_PerfForesightConsumerType(self): + try: + model = PerfForesightConsumerType(**Params.init_perfect_foresight) + except: + self.fail("PerfForesightConsumerType failed to initialize with Params.init_perfect_foresight.") + + def test_IndShockConsumerType(self): + try: + model = IndShockConsumerType(**Params.init_lifecycle) + except: + self.fail("IndShockConsumerType failed to initialize with Params.init_lifecycle.") + + def test_KinkedRconsumerType(self): + try: + model = KinkedRconsumerType(**Params.init_kinked_R) + except: + self.fail("KinkedRconsumerType failed to initialize with Params.init_kinked_R.") + + def test_MarkovConsumerType(self): + try: + unemp_length = 5 # Averange length of unemployment spell + urate_good = 0.05 # Unemployment rate when economy is in good state + urate_bad = 0.12 # Unemployment rate when economy is in bad state + bust_prob = 0.01 # Probability of economy switching from good to bad + recession_length = 20 # Averange length of bad state + p_reemploy =1.0/unemp_length + p_unemploy_good = p_reemploy*urate_good/(1-urate_good) + p_unemploy_bad = p_reemploy*urate_bad/(1-urate_bad) + boom_prob = 1.0/recession_length + MrkvArray = np.array([[(1-p_unemploy_good)*(1-bust_prob),p_unemploy_good*(1-bust_prob), + (1-p_unemploy_good)*bust_prob,p_unemploy_good*bust_prob], + [p_reemploy*(1-bust_prob),(1-p_reemploy)*(1-bust_prob), + p_reemploy*bust_prob,(1-p_reemploy)*bust_prob], + [(1-p_unemploy_bad)*boom_prob,p_unemploy_bad*boom_prob, + (1-p_unemploy_bad)*(1-boom_prob),p_unemploy_bad*(1-boom_prob)], + [p_reemploy*boom_prob,(1-p_reemploy)*boom_prob, + p_reemploy*(1-boom_prob),(1-p_reemploy)*(1-boom_prob)]]) + + # Make a consumer with serially correlated unemployment, subject to boom and bust cycles + init_serial_unemployment = copy(Params.init_idiosyncratic_shocks) + init_serial_unemployment['MrkvArray'] = [MrkvArray] + init_serial_unemployment['UnempPrb'] = 0 # to make income distribution when employed + init_serial_unemployment['global_markov'] = False + SerialUnemploymentExample = MarkovConsumerType(**init_serial_unemployment) + except: + self.fail("MarkovConsumerType failed to initialize with boom/bust unemployment.") From 3ceb86c9246ed04b738e36de8cea28b7669a5f78 Mon Sep 17 00:00:00 2001 From: Tao Wang Date: Sat, 25 May 2019 19:32:28 -0400 Subject: [PATCH 64/77] add the edited notebook to the correct branch --- HARK/BayerLuetticke/TwoAsset.ipynb | 39 +++++++++++++++++------------- 1 file changed, 22 insertions(+), 17 deletions(-) diff --git a/HARK/BayerLuetticke/TwoAsset.ipynb b/HARK/BayerLuetticke/TwoAsset.ipynb index e28102495..1b408e11e 100644 --- a/HARK/BayerLuetticke/TwoAsset.ipynb +++ b/HARK/BayerLuetticke/TwoAsset.ipynb @@ -530,12 +530,12 @@ "\n", "\n", "#### Households \n", - "- Maximizing discounted felicity\n", - " - Consumption $c$ \n", - " - CRRA coefficent: $\\xi$\n", - " - EOS of CES consumption bundle: $\\eta$\n", - " - Disutility from work in GHH form: \n", - " - Frisch elasticity $\\gamma$\n", + "- Maximizing discounted sum of felicity \n", + " - Felicity takes [GHH](https://en.wikipedia.org/wiki/Greenwood–Hercowitz–Huffman_preferences) form: $u(c,n) = U(c-G(h,n))$ where $h$ is individual productivity and $n$ is labor supply. In a nutshell, it is a type of non-separable utility between consumption and labor supply such that there is no labor supply effect by wealth and uncertainty. This is used to simplify computation but not necessary for the model.\n", + " - Consumption $c$ with elasticity of substitution across goods in CES form: $\\eta$\n", + " - $U$ takes CRRA form with coefficent of risk aversion: $\\xi$\n", + " - Frisch elasticity of labor supply $\\gamma$\n", + " - Discount factor $\\beta$\n", "- Two assets:\n", " - Liquid nominal bonds $b$, greater than lower bound $\\underline b$\n", " - Borrowing constraint due to a wedge between borrowing and saving rate: $R^b(b<0)=R^B(b>0)+\\bar R$ \n", @@ -544,7 +544,7 @@ " - If nontrading, receive divident $r$ and depreciates by $\\tau$\n", "- Idiosyncratic labor productivity $h$: \n", " - $h = 0$ for entreprener, only receive profits $\\Pi$\n", - " - $h = 1$ for labor, evolves according to an autoregression process, \n", + " - $h = 1$ for worker, evolves according to an AR(1) with time varying volatility \n", " - $\\rho_h$ persistence parameter\n", " - $\\epsilon^h$: idiosyncratic risk \n", "\n", @@ -556,25 +556,30 @@ "- Reseller \n", " - Rotemberg price setting: quadratic adjustment cost scalled by $\\frac{\\eta}{2\\kappa}$\n", " - Constant discount factor $\\beta$\n", - " - Investment subject to Tobin-Q adjustment cost $\\phi$ \n", - "- Aggregate risks $\\Omega$ include \n", - " - TFP $Z$, AR(1) process with persistence of $\\rho^Z$ and shock $\\epsilon^Z$ \n", - " - Uncertainty \n", - " - Monetary policy\n", - "- Central bank\n", + " - Investment subject to adjustment cost scaled by $\\phi$ \n", + " \n", + "#### Monetary and fiscal policy rules \n", + "- Central bank set norminal rate on bond\n", " - Taylor rule on nominal saving rate $R^B$: reacting deviation of inflation from target by $\\theta_R$ \n", " - $\\rho_R$: policy innertia\n", " - $\\epsilon^R$: monetary policy shocks\n", - "- Government \n", + "- Government responds to business conditions and stablize the debt\n", " - Government spending $G$ \n", - " - Tax $T$ \n", + " - Labor income tax $T$ \n", " - $\\rho_G$: intensity of repaying government debt: $\\rho_G=1$ implies roll-over \n", + " \n", + "#### Other \n", + "- Aggregate risks $\\Omega$ include \n", + " - TFP $Z$, AR(1) process with persistence of $\\rho^Z$ and shock $\\epsilon^Z$ \n", + " - Uncertainty: idiosyncratic productivity risks governed by transition probability matrix $P_H$. (Called aggregate risks although it is idiosyncratic productivity) \n", + " - Monetary policy: $\\epsilon_R$\n", + "\n", "\n", "#### Taking stock\n", "\n", "- Individual state variables: $b$, $k$ and $h$, the joint distribution of individual states $\\Theta$\n", "- Individual control variables: $c$, $n$, $b'$, $k'$ \n", - "- Optimal policy for adjust and non-adjust cases are $c^*_a$, $n^*_a$ $k^*_a$ and $b^*_a$ and $c^*_n$, $n^*_n$ and $b^*_n$, respetively \n" + "- Optimal policy for adjust and non-adjust cases are $c^*_a$, $n^*_a$ $k^*_a$ and $b^*_a$ and $c^*_n$, $n^*_n$ and $b^*_n$, respectively \n" ] }, { @@ -2268,7 +2273,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.1" }, "varInspector": { "cols": { From b2baafdebd1eb9df3f62d7626660794a4e0c8300 Mon Sep 17 00:00:00 2001 From: Tao Wang Date: Mon, 27 May 2019 15:09:40 -0400 Subject: [PATCH 65/77] correcting typo and one bug in codes --- HARK/BayerLuetticke/TwoAsset.ipynb | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/HARK/BayerLuetticke/TwoAsset.ipynb b/HARK/BayerLuetticke/TwoAsset.ipynb index 1b408e11e..3255ce46e 100644 --- a/HARK/BayerLuetticke/TwoAsset.ipynb +++ b/HARK/BayerLuetticke/TwoAsset.ipynb @@ -197,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "code_folding": [ 0 @@ -283,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "code_folding": [ 0 @@ -338,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "code_folding": [] }, @@ -414,8 +414,8 @@ " for j in range(self.mpar['nk']-1):\n", " Gamma_state[bb+np.arange(0,self.mpar['nk'],1), bb+j-1] = -np.squeeze(Xss[bb+np.arange(0,self.mpar['nk'],1)])\n", " Gamma_state[bb+j,bb-1+j] = 1. - Xss[bb+j] \n", - " Gamma_state[bb+j,bb-1+j] = Gamma_state[bb+j,bb-1+j] - \n", - " np.sum(Gamma_state[bb+np.arange(0,self.mpar['nk']),bb-1+j])\n", + " Gamma_state[bb+j,bb-1+j] = (Gamma_state[bb+j,bb-1+j] - \n", + " np.sum(Gamma_state[bb+np.arange(0,self.mpar['nk']),bb-1+j]))\n", " bb = self.mpar['nm'] + self.mpar['nk']\n", "\n", " for j in range(self.mpar['nh']-2): # Question: Why -2? Some other symmetry/adding-up condition?\n", @@ -531,7 +531,7 @@ "\n", "#### Households \n", "- Maximizing discounted sum of felicity \n", - " - Felicity takes [GHH](https://en.wikipedia.org/wiki/Greenwood–Hercowitz–Huffman_preferences) form: $u(c,n) = U(c-G(h,n))$ where $h$ is individual productivity and $n$ is labor supply. In a nutshell, it is a type of non-separable utility between consumption and labor supply such that there is no labor supply effect by wealth and uncertainty. This is used to simplify computation but not necessary for the model.\n", + " - Felicity takes [GHH](https://en.wikipedia.org/wiki/Greenwood–Hercowitz–Huffman_preferences) form: $u(c,n) = U(c-G(h,n))$, where $h$ is individual productivity and $n$ is labor supply. In a nutshell, it is a type of non-separable utility between consumption and labor supply such that there is no labor supply effect by wealth and uncertainty. This is used to simplify computation but not necessary for the model.\n", " - Consumption $c$ with elasticity of substitution across goods in CES form: $\\eta$\n", " - $U$ takes CRRA form with coefficent of risk aversion: $\\xi$\n", " - Frisch elasticity of labor supply $\\gamma$\n", @@ -543,7 +543,7 @@ " - Trading of illiquid assets is subject to a friction governed by $v$, the fraction of agents who can trade\n", " - If nontrading, receive divident $r$ and depreciates by $\\tau$\n", "- Idiosyncratic labor productivity $h$: \n", - " - $h = 0$ for entreprener, only receive profits $\\Pi$\n", + " - $h = 0$ for entrepreneur, only receive profits $\\Pi$\n", " - $h = 1$ for worker, evolves according to an AR(1) with time varying volatility \n", " - $\\rho_h$ persistence parameter\n", " - $\\epsilon^h$: idiosyncratic risk \n", @@ -584,7 +584,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "code_folding": [] }, From 01e21e1acfe80ce8636e6e6f1abe39b89fc00105 Mon Sep 17 00:00:00 2001 From: Shauna Gordon-McKeon Date: Thu, 30 May 2019 11:20:15 -0400 Subject: [PATCH 66/77] Prep for regular release 0.10.1 --- CHANGES.md | 10 ++++++++-- HARK/__init__.py | 2 +- README.md | 2 +- setup.py | 2 +- 4 files changed, 11 insertions(+), 5 deletions(-) diff --git a/CHANGES.md b/CHANGES.md index f468750ab..8eae65f02 100644 --- a/CHANGES.md +++ b/CHANGES.md @@ -1,15 +1,21 @@ HARK -Version 0.10.0.dev3 +Version 0.10.1 Release Notes # Introduction -This document contains the release notes for the 0.10.0.dev3 version of HARK. HARK aims to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents. +This document contains the release notes for the 0.10.1 version of HARK. HARK aims to produce an open source repository of highly modular, easily interoperable code for solving, simulating, and estimating dynamic economic models with heterogeneous agents. For more information on HARK, see [our Github organization](https://github.com/econ-ark). ## Changes +### 0.10.1 + +Release Date: 05-30-2019 + +No changes from 0.10.0.dev3. + ### 0.10.0.dev3 Release Date: 05-18-2019 diff --git a/HARK/__init__.py b/HARK/__init__.py index 88e5cff09..b36e35f84 100644 --- a/HARK/__init__.py +++ b/HARK/__init__.py @@ -1,4 +1,4 @@ from __future__ import absolute_import from .core import * -__version__ = '0.10.0.dev3' +__version__ = '0.10.1' diff --git a/README.md b/README.md index e6c515ffc..2aa4a0486 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # Heterogeneous Agents Resources and toolKit (HARK) -pre-release 0.10.0.dev2 +pre-release 0.10.1 Click the Badge for Citation Info. [![DOI](https://zenodo.org/badge/50448254.svg)](https://zenodo.org/badge/latestdoi/50448254) diff --git a/setup.py b/setup.py index 037f0d316..36d42f608 100644 --- a/setup.py +++ b/setup.py @@ -42,7 +42,7 @@ # For a discussion on single-sourcing the version across setup.py and the # project code, see # https://packaging.python.org/en/latest/single_source_version.html - version='0.10.0.dev3', # Required + version='0.10.1', # Required # This is a one-line description or tagline of what your project does. This # corresponds to the "Summary" metadata field: From 292c0a9fe4d081d0ce7cfbbb04ea903f64ffd704 Mon Sep 17 00:00:00 2001 From: Tao Wang Date: Fri, 31 May 2019 08:48:16 -0400 Subject: [PATCH 67/77] Drafted a new notebook explaining details of dimension reduction. --- .../DCT-Copula-Illustration.ipynb | 755 ++++++++++++++++++ 1 file changed, 755 insertions(+) create mode 100644 HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb diff --git a/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb b/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb new file mode 100644 index 000000000..befcb8205 --- /dev/null +++ b/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb @@ -0,0 +1,755 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dimension Reduction in [Bayer and Luetticke (2018)](https://cepr.org/active/publications/discussion_papers/dp.php?dpno=13071)\n", + "\n", + "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/econ-ark/HARK/BayerLuetticke?filepath=notebooks%2FHARK%2FBayerLuetticke%2FTwoAsset.ipynb)\n", + "\n", + "- Based on original slides by Christian Bayer and Ralph Luetticke \n", + "- Original Jupyter notebook by Seungcheol Lee \n", + "- Further edits by Chris Carrol Tao Wang \n", + "\n", + "This is an accompany to the [main notebook](TwoAsset.ipynb) illustrating dimension reduction in Bayer/Luetticke algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "code_folding": [ + 0, + 6, + 17 + ] + }, + "outputs": [], + "source": [ + "# Setup stuff\n", + "\n", + "# This is a jupytext paired notebook that autogenerates a corresponding .py file\n", + "# which can be executed from a terminal command line via \"ipython [name].py\"\n", + "# But a terminal does not permit inline figures, so we need to test jupyter vs terminal\n", + "# Google \"how can I check if code is executed in the ipython notebook\"\n", + "def in_ipynb():\n", + " try:\n", + " if str(type(get_ipython())) == \"\":\n", + " return True\n", + " else:\n", + " return False\n", + " except NameError:\n", + " return False\n", + "\n", + "# Determine whether to make the figures inline (for spyder or jupyter)\n", + "# vs whatever is the automatic setting that will apply if run from the terminal\n", + "if in_ipynb():\n", + " # %matplotlib inline generates a syntax error when run from the shell\n", + " # so do this instead\n", + " get_ipython().run_line_magic('matplotlib', 'inline') \n", + "else:\n", + " get_ipython().run_line_magic('matplotlib', 'auto') \n", + " \n", + "# The tools for navigating the filesystem\n", + "import sys\n", + "import os\n", + "\n", + "# Find pathname to this file:\n", + "my_file_path = os.path.dirname(os.path.abspath(\"TwoAsset.ipynb\"))\n", + "\n", + "# Relative directory for pickled code\n", + "code_dir = os.path.join(my_file_path, \"BayerLuetticke_code/TwoAssetCode\") \n", + "\n", + "sys.path.insert(0, code_dir)\n", + "sys.path.insert(0, my_file_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [], + "source": [ + "## Change working folder and load Stationary equilibrium (StE)\n", + "\n", + "import pickle\n", + "os.chdir(code_dir) # Go to the directory with pickled code\n", + "\n", + "## EX3SS_20.p is the information in the stationary equilibrium (20: the number of illiquid and liquid weath grids )\n", + "EX3SS=pickle.load(open(\"EX3SS_20.p\", \"rb\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is stored in the StE?\n", + "It includes following attributes:['par', 'mpar', 'grid', 'Output', 'targets', 'Vm', 'Vk', 'joint_distr', 'Copula', 'c_n_guess', 'c_a_guess', 'psi_guess', 'm_n_star', 'm_a_star', 'cap_a_star', 'mutil_c_n', 'mutil_c_a', 'mutil_c', 'P_H']\n", + "Grids of state variables:\n", + "30 grids for liquid assets;\n", + "30 grids for iliquid assets;\n", + "4 grids for individual productivity.\n", + "Therefore, the joint distribution across different states has dimension of (30, 30, 4)\n", + "Copula is computed from the joint distribution in StE \n", + " and will be used to transform the marginals back to joint distributions. \n", + "It includes two parts: grids and value:,\n", + " grids with dimension of (3600, 3), where the first element is total number of grids, and the second element is number of states,\n", + " and values with dimension of (3600,), \n", + " each entry of which is the probability of the three state variables below the grids.\n", + "The dimension of value function for capital Vk is (30, 30, 4)\n", + "Also, the dimension of policy function c_n should be equal to the dimension of state variables (30, 30, 4)\n", + "The same for policy under adjustment c_a:(30, 30, 4)\n" + ] + } + ], + "source": [ + "### Print content stored in StE \n", + "print('What is stored in the StE?')\n", + "print('It includes following attributes:'+str(list(EX3SS.keys())))\n", + "print('Grids of state variables:')\n", + "print(str(len(EX3SS['grid']['m']))+' grids for liquid assets;')\n", + "print(str(len(EX3SS['grid']['k']))+' grids for iliquid assets;')\n", + "print(str(len(EX3SS['grid']['h']))+' grids for individual productivity.')\n", + "print('Therefore, the joint distribution across different states has dimension of '+str(EX3SS['joint_distr'].shape))\n", + "print('Copula is computed from the joint distribution in StE \\\n", + " \\n and will be used to transform the marginals back to joint distributions. ')\n", + "print('It includes two parts: grids and value:'+ \\\n", + " ',\\n grids with dimension of '+str(EX3SS['Copula']['grid'].shape) + \\\n", + " ', where the first element is total number of grids' + \\\n", + " ', and the second element is number of states' + \\\n", + " ',\\n and values with dimension of '+str(EX3SS['Copula']['value'].shape) + \\\n", + " ', \\n each entry of which is the probability of the three state variables below the grids.')\n", + "print('The dimension of value function for capital Vk is '+ str(EX3SS['Vk'].shape))\n", + "\n", + "print('Also, the dimension of policy function c_n should be equal to the dimension of state variables '\\\n", + " + str(EX3SS['mutil_c_n'].shape))\n", + "print('The same for policy under adjustment c_a:'+ str(EX3SS['mutil_c_a'].shape))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dimension Reduction via discrete cosine transformation and fixed copula\n", + "\n", + "#### The dimensions of what?\n", + "\n", + "As printed out above, there have two high-dimension objects (note they are seperate things)\n", + "\n", + "- Value/policy functions (3600 =30 $\\times$ 30 $\\times$ 4 grids).\n", + "- Distribution(histograms) of individual states. (3600 =30 $\\times$ 30 $\\times$ 4 grids to characterize the joint distributions of individual states.) \n", + "\n", + "#### Intuitively, how?\n", + "\n", + "- When one aggregate shock is introduced to StE, the marginal utility/value associated with the 3600 grids of states do not change by equal degree. If at certain grids they do not change much, we can assume they remains the same as in StE. Therefore, 3600 grids can be reduced to a much smaller number of grids. This is the discrete consine transformation(DCT) idea. See [here](https://en.wikipedia.org/wiki/Discrete_cosine_transform) for Wikipedia page on DCT. \n", + "\n", + "- For the distribution of states, if we assume that the rank order correlation (e.g. the correlation of where you are in the distribution of liquid assets position and iliquid assets)remains the same after the aggregate shocks are introduced to StE, we just need to work with marginal distributions of each state instead of joint distributions. This reduces 3600 $\\times$ 3 to 30+30+4=64. This is the fixed copula idea. See [here](https://en.wikipedia.org/wiki/Copula_(probability_theory)) for Wikipedia page on copula.\n", + "\n", + "#### More accurately, how?\n", + "1. Use compression techniques as in video encoding\n", + " * Apply a discrete cosine transformation (DCT) to all value/policy functions\n", + " * Use Chebychev polynomials on roots grid \n", + " * Define a reference \"frame\": the steady-state equilibrium (StE)\n", + " * Represent fluctuations as differences from this reference frame\n", + " * Assume all coefficients of the DCT from the StE that are close to zero do not change when there is an aggregate shock (small things stay small and unchanged)\n", + " \n", + "2. Assume no changes in the rank correlation structure of $\\mu$ \n", + " * Calculate the Copula, $\\bar{C}$ of $\\mu$ in the StE\n", + " * Perturb only the marginal distributions\n", + " * Use fixed Copula to calculate an approximate joint distribution from marginals\n", + "\n", + "\n", + "The approach follows the insight of KS in that it uses the fact that some moments of the distribution do not matter for aggregate dynamics" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [], + "source": [ + "## Import necessary libraries\n", + "\n", + "from __future__ import print_function\n", + "import sys \n", + "sys.path.insert(0,'../')\n", + "\n", + "import numpy as np\n", + "from numpy.linalg import matrix_rank\n", + "import scipy as sc\n", + "from scipy.stats import norm \n", + "from scipy.interpolate import interp1d, interp2d, griddata, RegularGridInterpolator, interpn\n", + "import multiprocessing as mp\n", + "from multiprocessing import Pool, cpu_count, Process\n", + "from math import ceil\n", + "import math as mt\n", + "from scipy import sparse as sp # used to work with sparse matrices\n", + "from scipy import linalg #linear algebra \n", + "from math import log, cos, pi, sqrt\n", + "import time\n", + "from SharedFunc3 import Transition, ExTransitions, GenWeight, MakeGridkm, Tauchen, Fastroot\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as mpatches\n", + "import scipy.io #scipy input and output\n", + "import scipy.fftpack as sf # scipy discrete fourier transforms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Details\n", + "1) Apply compression techniques from video encoding\n", + " * Let $\\bar{\\Theta} = dct(\\bar{v})$ be the coefficients obtained from the DCT of the value function in StE\n", + " * Define an index set $\\mathop{I}$ that contains the x percent largest (i.e. most important) elements from $\\bar{\\Theta}$\n", + " * Let $\\theta$ be a sparse vector with non-zero entries only for elements $i \\in \\mathop{I}$\n", + " * Define \n", + " \\begin{equation}\n", + " \\tilde{\\Theta}(\\theta_t)=\\left\\{\n", + " \\begin{array}{@{}ll@{}}\n", + " \\bar{\\Theta}(i)+\\theta_t(i), & i \\in \\mathop{I} \\\\\n", + " \\bar{\\Theta}(i), & \\text{else}\n", + " \\end{array}\\right.\n", + " \\end{equation}\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2) Decoding\n", + " * Now we reconstruct $\\tilde{v}_t=\\tilde{v}(\\theta_t)=dct^{-1}(\\tilde{\\Theta}(\\theta_i))$\n", + " * idct is the inverse dct that goes from the $\\theta$ vector to the corresponding values\n", + " * This means that in the StE the reduction step adds no addtional approximation error:\n", + " * Remember that $\\tilde{v}(0)=\\bar{v}$ by construction\n", + " * Yet, it allows to reduce the number of derivatives that need to be calculated from the outset.\n", + " \n", + "3) The histogram is recovered the same way\n", + " * $\\mu_t$ is approximated as $\\bar{C}(\\bar{\\mu_t}^1,...,\\bar{\\mu_t}^n)$, where $n$ is the dimensionality of the idiosyncratic states\n", + " * The StE distribution is obtained when $\\mu = \\bar{C}(\\bar{\\mu}^1,...,\\bar{\\mu}^n)$\n", + " * Typically prices are only influenced through the marginal distributions\n", + " * The approach ensures that changes in the mass of one, say wealth, state are distributed in a sensible way across the other dimension\n", + " * The implied distributions look \"similar\" to the StE one (different in (Reiter, 2009))\n", + "\n", + "4) Too many equations\n", + " * The system\n", + " \\begin{align}\n", + " F(\\{d\\mu_t^1,...,d\\mu_t^n\\}, S_t, \\{d\\mu_{t+1}^1,...,d\\mu_{t+1}^n\\}, S_{t+1}, \\theta_t, P_t, \\theta_{t+1}, P_{t+1})\n", + " &= \\begin{bmatrix}\n", + " d\\bar{C}(\\bar{\\mu}_t^1,...,\\bar{\\mu}_t^n) - d\\bar{C}(\\bar{\\mu}_t^1,...,\\bar{\\mu}_t^n)\\Pi_{h_t} \\\\\n", + " dct[idct(\\tilde{\\Theta(\\theta_t)}) - (\\bar{u}_{h_t} + \\beta \\Pi_{h_t}idct(\\tilde{\\Theta(\\theta_{t+1})}] \\\\\n", + " S_{t+1} - H(S_t,d\\mu_t) \\\\\n", + " \\Phi(h_t,d\\mu_t,P_t,S_t) \\\\\n", + " \\end{bmatrix}\n", + " \\end{align}\n", + " has too many equations\n", + " * Uses only difference in marginals and the differences on $\\mathop{I}$ " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "## State reduction and discrete cosine transformation\n", + "\n", + "class StateReduc_Dct:\n", + " \n", + " def __init__(self, par, mpar, grid, Output, targets, Vm, Vk, \n", + " joint_distr, Copula, c_n_guess, c_a_guess, psi_guess,\n", + " m_n_star, m_a_star, cap_a_star, mutil_c_n, mutil_c_a,mutil_c, P_H):\n", + " \n", + " self.par = par # Parameters of the theoretical model\n", + " self.mpar = mpar # Parameters of the numerical representation\n", + " self.grid = grid # Discrete grid\n", + " self.Output = Output # Results of the calculations\n", + " self.targets = targets # Like, debt-to-GDP ratio or other desiderata\n", + " self.Vm = Vm # Marginal value from liquid cash-on-hand\n", + " self.Vk = Vk # Marginal value of capital\n", + " self.joint_distr = joint_distr # Multidimensional histogram\n", + " self.Copula = Copula # Encodes rank correlation structure of distribution\n", + " self.mutil_c = mutil_c # Marginal utility of consumption\n", + " self.P_H = P_H # Transition matrix for macro states (not including distribution)\n", + " \n", + " \n", + " def StateReduc(self):\n", + " \"\"\"\n", + " input\n", + " -----\n", + " self: dict, stored results from a StE \n", + " \n", + " output\n", + " ------\n", + " Newly generated\n", + " ===============\n", + " X_ss: ndarray, stacked states, including \n", + " Y_ss: ndarray, controls \n", + " Gamma_state: ndarray, marginal distributions of individual states \n", + " grid: ndarray, discrete grids\n", + " targets: ndarray, debt-to-GDP ratio or other desiderata\n", + " P_H: transition probability of\n", + " indexMUdct: ndarray, indices selected after dct operation on marginal utility of consumption\n", + " indexVKdct: ndarray, indices selected after dct operation on marginal value of capital\n", + " State: ndarray, dimension equal to reduced states\n", + " State_m: ndarray, dimension equal to reduced states\n", + " Contr: ndarray, dimension equal to reduced controls\n", + " Contr_m: ndarray, dimension equal to reduced controls\n", + " \n", + " Passed down from the model\n", + " ==========================\n", + " Copula: dict, grids and values\n", + " joint_distr: ndarray, nk x nm x nh\n", + " Output: dict, outputs from the model \n", + " par: dict, parameters of the theoretical model\n", + " mpar:dict, parameters of the numerical representation\n", + " aggrshock: string, type of aggregate shock used to purturb the StE \n", + " \"\"\"\n", + " \n", + " # Inverse of CRRA on x for utility and marginal utility\n", + " invutil = lambda x : ((1-self.par['xi'])*x)**(1./(1-self.par['xi'])) \n", + " invmutil = lambda x : (1./x)**(1./self.par['xi']) \n", + " \n", + " # X=States\n", + " # Marg dist of liquid assets summing over pty and illiquid assets k\n", + " Xss=np.asmatrix(np.concatenate((np.sum(np.sum(self.joint_distr.copy(),axis=1),axis =1), \n", + " np.transpose(np.sum(np.sum(self.joint_distr.copy(),axis=0),axis=1)),# marg dist k\n", + " np.sum(np.sum(self.joint_distr.copy(),axis=1),axis=0), # marg dist pty (\\approx income)\n", + " [np.log(self.par['RB'])],[ 0.]))).T # Given the constant interest rate\n", + " \n", + " # Y=\"controls\" (according to this literature's odd terminology)\n", + " # c = invmarg(marg(c)), so first bit gets consumption policy function\n", + " Yss=np.asmatrix(np.concatenate((invmutil(self.mutil_c.copy().flatten(order = 'F')),\\\n", + " invmutil(self.Vk.copy().flatten(order = 'F')),\n", + " [np.log(self.par['Q'])], # Question: Price of the illiquid asset, right?\n", + " [ np.log(self.par['PI'])], # Inflation\n", + " [ np.log(self.Output)], \n", + " [np.log(self.par['G'])], # Gov spending\n", + " [np.log(self.par['W'])], # Wage\n", + " [np.log(self.par['R'])], # Nominal R\n", + " [np.log(self.par['PROFITS'])], \n", + " [np.log(self.par['N'])], # Hours worked\n", + " [np.log(self.targets['T'])], # Taxes\n", + " [np.log(self.grid['K'])], # Kapital\n", + " [np.log(self.targets['B'])]))).T # Government debt\n", + " \n", + " # Mapping for Histogram\n", + " # Gamma_state matrix reduced set of states\n", + " # nm = number of gridpoints for liquid assets\n", + " # nk = number of gridpoints for illiquid assets\n", + " # nh = number of gridpoints for human capital (pty)\n", + " Gamma_state = np.zeros( # Create zero matrix of size [nm + nk + nh,nm + nk + nh - 4]\n", + " (self.mpar['nm']+self.mpar['nk']+self.mpar['nh'],\n", + " self.mpar['nm']+self.mpar['nk']+self.mpar['nh'] - 4)) \n", + " # Question: Why 4? 4 = 3+1, 3: sum to 1 for m, k, h and 1: for entrepreneurs \n", + "\n", + " # Impose adding-up conditions: \n", + " # In each of the block matrices, probabilities must add to 1\n", + " \n", + " for j in range(self.mpar['nm']-1): # np.squeeze reduces one-dimensional matrix to vector\n", + " Gamma_state[0:self.mpar['nm'],j] = -np.squeeze(Xss[0:self.mpar['nm']])\n", + " Gamma_state[j,j]=1. - Xss[j] # \n", + " Gamma_state[j,j]=Gamma_state[j,j] - np.sum(Gamma_state[0:self.mpar['nm'],j])\n", + " bb = self.mpar['nm'] # Question: bb='bottom base'? because bb shorter to type than self.mpar['nm'] everywhere\n", + "\n", + " for j in range(self.mpar['nk']-1):\n", + " Gamma_state[bb+np.arange(0,self.mpar['nk'],1), bb+j-1] = -np.squeeze(Xss[bb+np.arange(0,self.mpar['nk'],1)])\n", + " Gamma_state[bb+j,bb-1+j] = 1. - Xss[bb+j] \n", + " Gamma_state[bb+j,bb-1+j] = (Gamma_state[bb+j,bb-1+j] - \n", + " np.sum(Gamma_state[bb+np.arange(0,self.mpar['nk']),bb-1+j]))\n", + " bb = self.mpar['nm'] + self.mpar['nk']\n", + "\n", + " for j in range(self.mpar['nh']-2): \n", + " # Question: Why -2? 1 for h sum to 1 and 1 for entrepreneur Some other symmetry/adding-up condition.\n", + " Gamma_state[bb+np.arange(0,self.mpar['nh']-1,1), bb+j-2] = -np.squeeze(Xss[bb+np.arange(0,self.mpar['nh']-1,1)])\n", + " Gamma_state[bb+j,bb-2+j] = 1. - Xss[bb+j]\n", + " Gamma_state[bb+j,bb-2+j] = Gamma_state[bb+j,bb-2+j] - np.sum(Gamma_state[bb+np.arange(0,self.mpar['nh']-1,1),bb-2+j])\n", + "\n", + " # Number of other state variables not including the gridded -- here, just the interest rate \n", + " self.mpar['os'] = len(Xss) - (self.mpar['nm']+self.mpar['nk']+self.mpar['nh'])\n", + " # For each gridpoint there are two \"regular\" controls: consumption and illiquid saving\n", + " # Counts the number of \"other\" controls (PROFITS, Q, etc)\n", + " self.mpar['oc'] = len(Yss) - 2*(self.mpar['nm']*self.mpar['nk']*self.mpar['nh'])\n", + " \n", + " aggrshock = self.par['aggrshock']\n", + " accuracy = self.par['accuracy']\n", + " \n", + " # Do the dct on the steady state marginal utility\n", + " # Returns an array of indices for the used basis vectors\n", + " indexMUdct = self.do_dct(invmutil(self.mutil_c.copy().flatten(order='F')),\n", + " self.mpar,accuracy)\n", + "\n", + " # Do the dct on the steady state marginal value of capital\n", + " # Returns an array of indices for the used basis vectors\n", + " indexVKdct = self.do_dct(invmutil(self.Vk.copy()),self.mpar,accuracy)\n", + " \n", + " # Calculate the numbers of states and controls\n", + " aux = np.shape(Gamma_state)\n", + " self.mpar['numstates'] = np.int64(aux[1] + self.mpar['os'])\n", + " self.mpar['numcontrols'] = np.int64(len(indexMUdct) + \n", + " len(indexVKdct) + \n", + " self.mpar['oc'])\n", + " \n", + " # Size of the reduced matrices to be used in the Fsys\n", + " # Set to zero because in steady state they are zero\n", + " State = np.zeros((self.mpar['numstates'],1))\n", + " State_m = State\n", + " Contr = np.zeros((self.mpar['numcontrols'],1))\n", + " Contr_m = Contr\n", + " \n", + " return {'Xss': Xss, 'Yss':Yss, 'Gamma_state': Gamma_state, \n", + " 'par':self.par, 'mpar':self.mpar, 'aggrshock':aggrshock,\n", + " 'Copula':self.Copula,'grid':self.grid,'targets':self.targets,'P_H':self.P_H, \n", + " 'joint_distr': self.joint_distr, 'Output': self.Output, 'indexMUdct':indexMUdct, 'indexVKdct':indexVKdct,\n", + " 'State':State, 'State_m':State_m, 'Contr':Contr, 'Contr_m':Contr_m}\n", + "\n", + " # Discrete cosine transformation magic happens here\n", + " # sf is scipy.fftpack tool\n", + " def do_dct(self, obj, mpar, level):\n", + " \"\"\"\n", + " input\n", + " -----\n", + " obj: ndarray nm x nk x nh \n", + " dimension of states before dct \n", + " mpar: dict\n", + " parameters in the numerical representaion of the model, e.g. nm, nk and nh\n", + " level: float \n", + " accuracy level for dct \n", + " output\n", + " ------\n", + " index_reduced: ndarray n_dct x 1 \n", + " an array of indices that select the needed grids after dct\n", + " \n", + " \"\"\"\n", + " obj = np.reshape(obj.copy(),(mpar['nm'],mpar['nk'],mpar['nh']),order='F')\n", + " X1 = sf.dct(obj,norm='ortho',axis=0) # dct is operated along three dimensions axis=0/1/2\n", + " X2 = sf.dct(X1.copy(),norm='ortho',axis=1)\n", + " X3 = sf.dct(X2.copy(),norm='ortho',axis=2)\n", + "\n", + " # Pick the coefficients that are big\n", + " XX = X3.flatten(order='F') \n", + " ind = np.argsort(abs(XX.copy()))[::-1]\n", + " # i will \n", + " i = 1 \n", + " # Sort from smallest (=best) to biggest (=worst)\n", + " # and count how many are 'good enough to keep'\n", + " while linalg.norm(XX[ind[:i]].copy())/linalg.norm(XX) < level:\n", + " i += 1 \n", + " \n", + " needed = i # Question:Isn't this counting the ones that are NOT needed?\n", + " \n", + " index_reduced = np.sort(ind[:i]) # Retrieve the good \n", + " \n", + " return index_reduced" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [], + "source": [ + "## Choose an aggregate shock to perturb(one of three shocks: MP, TFP, Uncertainty)\n", + "\n", + "EX3SS['par']['aggrshock'] = 'MP'\n", + "EX3SS['par']['rhoS'] = 0.0 # Persistence of variance\n", + "EX3SS['par']['sigmaS'] = 0.001 # STD of variance shocks\n", + "\n", + "#EX3SS['par']['aggrshock'] = 'TFP'\n", + "#EX3SS['par']['rhoS'] = 0.95\n", + "#EX3SS['par']['sigmaS'] = 0.0075\n", + " \n", + "#EX3SS['par']['aggrshock'] = 'Uncertainty'\n", + "#EX3SS['par']['rhoS'] = 0.84 # Persistence of variance\n", + "#EX3SS['par']['sigmaS'] = 0.54 # STD of variance shocks" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "## Choose an accuracy of approximation with DCT\n", + "### Determines number of basis functions chosen -- enough to match this accuracy\n", + "### EX3SS is precomputed steady-state pulled in above\n", + "EX3SS['par']['accuracy'] = 0.99999 " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "## Implement state reduction and DCT\n", + "### Do state reduction on steady state\n", + "EX3SR=StateReduc_Dct(**EX3SS) # Takes StE result as input and get ready to invoke state reduction operation\n", + "SR=EX3SR.StateReduc() # StateReduc is operated " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What are the results from the state reduction?\n", + "Newly added attributes after the operation include \n", + "{'indexMUdct', 'State', 'Contr', 'Xss', 'Contr_m', 'aggrshock', 'Yss', 'State_m', 'indexVKdct', 'Gamma_state'}\n", + "\n", + "\n", + "The dimension of policy function is reduced to 154 from (30, 30, 4)\n", + "The dimension of value function is reduced to 94 from (30, 30, 4)\n", + "The total number of control variables is 259=154+94+ # of other macro controls\n", + "\n", + "\n", + "After marginalizing the joint-distribution, \n", + " the dimension of states including exogenous state, is 66\n", + "Dimension of gamma_state is (64, 60). It simply stacks all grids of different \n", + " state variables regardless of their joint distributions. \n", + " This is due to the assumption of the rank order remains the same.\n", + "The total number of state variables is 62=60+ # of other states\n" + ] + } + ], + "source": [ + "print('What are the results from the state reduction?')\n", + "print('Newly added attributes after the operation include \\n'+str(set(SR.keys())-set(EX3SS.keys())))\n", + "\n", + "print('\\n')\n", + "\n", + "print('The dimension of policy function is reduced to '+str(SR['indexMUdct'].shape[0]) \\\n", + " +' from '+str(EX3SS['mutil_c'].shape))\n", + "print('The dimension of value function is reduced to '+str(SR['indexVKdct'].shape[0]) \\\n", + " + ' from ' + str(EX3SS['Vk'].shape))\n", + "print('The total number of control variables is '+str(SR['Contr'].shape[0])+'='+str(SR['indexMUdct'].shape[0]) + \\\n", + " '+'+str(SR['indexVKdct'].shape[0])+'+ # of other macro controls')\n", + "print('\\n')\n", + "print('After marginalizing the joint distribution, \\\n", + " \\n the dimension of states including exogenous state, is '+str(SR['Xss'].shape[0]))\n", + "print('Dimension of gamma_state is '+str(SR['Gamma_state'].shape)+\\\n", + " '. It simply stacks all grids of different\\\n", + " \\n state variables regardless of their joint distributions.\\\n", + " \\n This is due to the assumption of the rank order remains the same.')\n", + "print('The total number of state variables is '+str(SR['State'].shape[0]) + '='+\\\n", + " str(SR['Gamma_state'].shape[1])+'+ # of other states')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Summary: what do we achieve after the transformation?\n", + "\n", + "- Via DCT, the dimension of policy function and value functions are reduced both from 3600 to 154 and 94, respectively.\n", + "- Via fixed copula operation and marginalizing the joint-distribution, the dimension of gamma_state is 64 now, (excluding exogeous states like interest rate)." + ] + } + ], + "metadata": { + "cite2c": { + "citations": { + "6202365/L5GBWHBM": { + "author": [ + { + "family": "Reiter", + "given": "Michael" + } + ], + "container-title": "Journal of Economic Dynamics and Control", + "id": "undefined", + "issue": "1", + "issued": { + "month": 1, + "year": 2010 + }, + "note": "Citation Key: reiterBackward", + "page": "28-35", + "page-first": "28", + "title": "Solving the Incomplete Markets Model with Aggregate Uncertainty by Backward Induction", + "type": "article-journal", + "volume": "34" + }, + "6202365/UKUXJHCN": { + "author": [ + { + "family": "Reiter", + "given": "Michael" + } + ], + "id": "6202365/UKUXJHCN", + "note": "Citation Key: reiter2002recursive \nbibtex*[publisher=Citeseer]", + "title": "Recursive computation of heterogeneous agent models", + "type": "article-journal" + }, + "6202365/VPUXICUR": { + "author": [ + { + "family": "Krusell", + "given": "Per" + }, + { + "family": "Smith", + "given": "Anthony A." + } + ], + "container-title": "Journal of Political Economy", + "id": "6202365/VPUXICUR", + "issue": "5", + "issued": { + "year": 1998 + }, + "page": "867–896", + "page-first": "867", + "title": "Income and Wealth Heterogeneity in the Macroeconomy", + "type": "article-journal", + "volume": "106" + }, + "6202365/WN76AW6Q": { + "author": [ + { + "family": "SeHyoun Ahn, Greg Kaplan, Benjamin Moll, Thomas Winberry", + "given": "" + }, + { + "family": "Wolf", + "given": "Christian" + } + ], + "editor": [ + { + "family": "Parker", + "given": "Jonathan" + }, + { + "family": "Martin S. Eichenbaum", + "given": "Organizers" + } + ], + "id": "6202365/WN76AW6Q", + "issued": { + "year": 2017 + }, + "note": "Citation Key: akmwwInequality \nbibtex*[booktitle=NBER Macroeconomics Annual;publisher=MIT Press;location=Cambridge, MA]", + "title": "When Inequality Matters for Macro and Macro Matters for Inequality", + "type": "article-journal", + "volume": "32" + }, + "undefined": { + "author": [ + { + "family": "Reiter", + "given": "Michael" + } + ], + "container-title": "Journal of Economic Dynamics and Control", + "id": "undefined", + "issue": "1", + "issued": { + "month": 1, + "year": 2010 + }, + "note": "Citation Key: reiterBackward", + "page": "28-35", + "page-first": "28", + "title": "Solving the Incomplete Markets Model with Aggregate Uncertainty by Backward Induction", + "type": "article-journal", + "volume": "34" + } + } + }, + "jupytext": { + "formats": "ipynb,py:light", + "text_representation": { + "extension": ".py", + "format_name": "light", + "format_version": "1.3", + "jupytext_version": "0.8.3" + } + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From a184a28491afe70fddeb6cc8fe2efb266b5f672e Mon Sep 17 00:00:00 2001 From: Shauna Date: Fri, 31 May 2019 11:01:17 -0400 Subject: [PATCH 68/77] Fix minor error in conda install instructions (#298) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2aa4a0486..86667d5eb 100644 --- a/README.md +++ b/README.md @@ -74,7 +74,7 @@ Installing HARK with pip does not give you full access to HARK's many graphical 1. Anaconda includes its own virtual environment system called `conda` which stores environments in a preset location (so you don't have to choose). So in order to create and activate an econ-ark virtual environment: ``` conda create -n econ-ark anaconda -source activate econ-ark +conda activate econ-ark ``` 1. Open Spyder, an interactive development environment (IDE) for Python (specifically, iPython). You may be able to do this through Anaconda's graphical interface, or you can do so from the command line/prompt. To do so, simply open a command line/prompt and type `spyder`. From b1ec374754acb0b01d4136a818d0eaa3622f7d54 Mon Sep 17 00:00:00 2001 From: llorracc Date: Fri, 31 May 2019 15:23:22 -0400 Subject: [PATCH 69/77] CDC edits with Tao on Zoom --- .../DCT-Copula-Illustration.ipynb | 192 ++++--- .../BayerLuetticke/DCT-Copula-Illustration.py | 493 ++++++++++++++++++ 2 files changed, 625 insertions(+), 60 deletions(-) create mode 100644 HARK/BayerLuetticke/DCT-Copula-Illustration.py diff --git a/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb b/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb index befcb8205..e127b8be0 100644 --- a/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb +++ b/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb @@ -10,17 +10,16 @@ "\n", "- Based on original slides by Christian Bayer and Ralph Luetticke \n", "- Original Jupyter notebook by Seungcheol Lee \n", - "- Further edits by Chris Carrol Tao Wang \n", + "- Further edits by Chris Carroll, Tao Wang \n", "\n", "This is an accompany to the [main notebook](TwoAsset.ipynb) illustrating dimension reduction in Bayer/Luetticke algorithm." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { "code_folding": [ - 0, 6, 17 ] @@ -67,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": { "code_folding": [ 0 @@ -75,85 +74,142 @@ }, "outputs": [], "source": [ - "## Change working folder and load Stationary equilibrium (StE)\n", + "# Change working folder and load Stationary equilibrium (StE)\n", "\n", "import pickle\n", "os.chdir(code_dir) # Go to the directory with pickled code\n", "\n", - "## EX3SS_20.p is the information in the stationary equilibrium (20: the number of illiquid and liquid weath grids )\n", + "## EX3SS_20.p is the information in the stationary equilibrium \n", + "## (20: the number of illiquid and liquid weath gridpoints)\n", + "### The comments above are original, but it seems that there are 30 not 20 points now\n", + "\n", "EX3SS=pickle.load(open(\"EX3SS_20.p\", \"rb\"))" ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": 9, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "What is stored in the StE?\n", - "It includes following attributes:['par', 'mpar', 'grid', 'Output', 'targets', 'Vm', 'Vk', 'joint_distr', 'Copula', 'c_n_guess', 'c_a_guess', 'psi_guess', 'm_n_star', 'm_a_star', 'cap_a_star', 'mutil_c_n', 'mutil_c_a', 'mutil_c', 'P_H']\n", "Grids of state variables:\n", - "30 grids for liquid assets;\n", - "30 grids for iliquid assets;\n", - "4 grids for individual productivity.\n", + "30 gridpoints for liquid assets;\n", + "30 gridpoints for illiquid assets;\n", + "4 gridpoints for individual productivity.\n", "Therefore, the joint distribution across different states has dimension of (30, 30, 4)\n", - "Copula is computed from the joint distribution in StE \n", - " and will be used to transform the marginals back to joint distributions. \n", - "It includes two parts: grids and value:,\n", - " grids with dimension of (3600, 3), where the first element is total number of grids, and the second element is number of states,\n", - " and values with dimension of (3600,), \n", - " each entry of which is the probability of the three state variables below the grids.\n", - "The dimension of value function for capital Vk is (30, 30, 4)\n", - "Also, the dimension of policy function c_n should be equal to the dimension of state variables (30, 30, 4)\n", - "The same for policy under adjustment c_a:(30, 30, 4)\n" + "The dimension of the value function for capital Vk is (30, 30, 4)\n", + "These happen to be the same size, but need not be\n", + "c_n is the policy function for a nonadjuster\n", + "c_a is the policy function for an adjuster\n", + "c_n dimensions happens to be equal to the dimensions of the grid of state variables (30, 30, 4)\n", + "The same is true for the policy function under adjustment c_a:(30, 30, 4)\n" ] } ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "### Print content stored in StE \n", - "print('What is stored in the StE?')\n", - "print('It includes following attributes:'+str(list(EX3SS.keys())))\n", - "print('Grids of state variables:')\n", - "print(str(len(EX3SS['grid']['m']))+' grids for liquid assets;')\n", - "print(str(len(EX3SS['grid']['k']))+' grids for iliquid assets;')\n", - "print(str(len(EX3SS['grid']['h']))+' grids for individual productivity.')\n", - "print('Therefore, the joint distribution across different states has dimension of '+str(EX3SS['joint_distr'].shape))\n", - "print('Copula is computed from the joint distribution in StE \\\n", - " \\n and will be used to transform the marginals back to joint distributions. ')\n", - "print('It includes two parts: grids and value:'+ \\\n", - " ',\\n grids with dimension of '+str(EX3SS['Copula']['grid'].shape) + \\\n", - " ', where the first element is total number of grids' + \\\n", - " ', and the second element is number of states' + \\\n", - " ',\\n and values with dimension of '+str(EX3SS['Copula']['value'].shape) + \\\n", - " ', \\n each entry of which is the probability of the three state variables below the grids.')\n", - "print('The dimension of value function for capital Vk is '+ str(EX3SS['Vk'].shape))\n", + "### Dimension Reduction via discrete cosine transformation and a fixed copula\n", + "\n", + "#### What is it whose dimension needs to be reduced?\n", "\n", - "print('Also, the dimension of policy function c_n should be equal to the dimension of state variables '\\\n", - " + str(EX3SS['mutil_c_n'].shape))\n", - "print('The same for policy under adjustment c_a:'+ str(EX3SS['mutil_c_a'].shape))\n" + "1. Policy and value functions\n", + "1. The distribution of agents across states\n", + "\n", + "Grids are constructed for values of the state variables:\n", + " * liquid ($nm$ points), illiquid assets ($nk$), and idiosyncratic pty ($nh$)\n", + "\n", + "So there are $nm \\times nk \\times nh$ potential combinations\n", + "\n", + "In principle, functions are represented by specifying their values at each specified combination of gridpoints and interpolating for intermediate values\n", + " * In practice, for technical reasons, interpolation is not necessary here\n", + "\n", + "There are two kinds of functions:\n", + "1. Policy functions and marginal value functions\n", + " * At each of the gridpoints, there is a number\n", + " * This is value for the value function\n", + " * This is consumption for the consumption function\n", + " * $c_n$ is the consumption function for the nonadjuster\n", + " * $c_a$ is the consumption function for the adjuster\n", + "1. The distribution (=\"histograms\") of agents across states\n", + " * In principle, distributions need not be computed at the same gridpoints used to represent the value and policy functions\n", + " * In practice, the same grids are used" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c_n is of dimension: (30, 30, 4)\n", + "c_a is of dimension: (30, 30, 4)\n", + "Vk is of dimension:(30, 30, 4)\n", + "Vm is of dimension:(30, 30, 4)\n", + "For convenience, these are all constructed from the same exogenous grids:\n", + "30 gridpoints for liquid assets;\n", + "30 gridpoints for illiquid assets;\n", + "4 gridpoints for individual productivity.\n", + "\n", + "Therefore, the joint distribution across different is of size: \n", + "30 * 30 * 4 = 3600\n" + ] + } + ], + "source": [ + "# Recover dimensions of the marginal value and consumption functions\n", + "\n", + "print('c_n is of dimension: ' + str(EX3SS['mutil_c_n'].shape))\n", + "print('c_a is of dimension: ' + str(EX3SS['mutil_c_a'].shape))\n", + "\n", + "print('Vk is of dimension:' + str(EX3SS['Vk'].shape))\n", + "print('Vm is of dimension:' + str(EX3SS['Vm'].shape))\n", + "\n", + "print('For convenience, these are all constructed from the same exogenous grids:')\n", + "print(str(len(EX3SS['grid']['m']))+' gridpoints for liquid assets;')\n", + "print(str(len(EX3SS['grid']['k']))+' gridpoints for illiquid assets;')\n", + "print(str(len(EX3SS['grid']['h']))+' gridpoints for individual productivity.')\n", + "print('')\n", + "print('Therefore, the joint distribution across different is of size: ')\n", + "print(str(EX3SS['mpar']['nm'])+\n", + " ' * '+str(EX3SS['mpar']['nk'])+\n", + " ' * '+str(EX3SS['mpar']['nh'])+\n", + " ' = '+ str(EX3SS['mpar']['nm']*EX3SS['mpar']['nk']*EX3SS['mpar']['nh']))\n", + " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Dimension Reduction via discrete cosine transformation and fixed copula\n", + "#### Intuitively, how does the reduction work?\n", + "\n", + "- The first step is to find an efficient \"compressed\" representation of the function (e.g., the consumption function). The analogy to image compression is that nearby pixels are likely to have identical or very similar colors, so we need only to find an efficient way to represent the way in which the colors change from one pixel to another. Similarly, consumption at a given point is likely to be close to consumption at a nearby point, so a function that captures that similarity efficiently can preserve most of the information without keeping all of the points.\n", "\n", - "#### The dimensions of what?\n", + "- We will be using the discrete consine transformation (DCT), which is commonly used in image compression. See [here](https://en.wikipedia.org/wiki/Discrete_cosine_transform) for the Wikipedia page on DCT. \n", "\n", - "As printed out above, there have two high-dimension objects (note they are seperate things)\n", + "- The other tool we use is the \"copula,\" which allows us to represent the distribution of people across idiosyncratic states efficiently\n", + " * The crucial assumption behind the copula is that what aggregate shocks do is to squeeze or distort the steady state distribution, but leave the rank structure of the distribution the same. Think of representing a balloon by a set of points on its surface; the copula assumption is effectively that when something happens to the balloon (more air is put in it, or it is squeezed on one side, say), we can represent what happens by thinking about how the relationship between points is distorted, rather than having to reconstruct the shape of the balloon with a completely independent set of new points. Which points are close to which other points does not change, but the distances between them can change. If the distances between them change in a particularly simple way, you can represent what has happened with a small amount of information. For example, if the balloon is perfectly spherical, then adding a given amount of air might increase the distances between adjacent points by 5 percent. (See the video illustration here)\n", "\n", - "- Value/policy functions (3600 =30 $\\times$ 30 $\\times$ 4 grids).\n", - "- Distribution(histograms) of individual states. (3600 =30 $\\times$ 30 $\\times$ 4 grids to characterize the joint distributions of individual states.) \n", + "- In the context of this model, the assumption is that the rank order correlation (e.g. the correlation of where you are in the distribution of liquid assets and illiquid assets) remains the same after the aggregate shocks are introduced to StE\n", "\n", - "#### Intuitively, how?\n", + "- In this case we just need to represent how the marginal distributions of each state change, instead of the full joint distributions. \n", "\n", - "- When one aggregate shock is introduced to StE, the marginal utility/value associated with the 3600 grids of states do not change by equal degree. If at certain grids they do not change much, we can assume they remains the same as in StE. Therefore, 3600 grids can be reduced to a much smaller number of grids. This is the discrete consine transformation(DCT) idea. See [here](https://en.wikipedia.org/wiki/Discrete_cosine_transform) for Wikipedia page on DCT. \n", + "- This reduces 3600 $\\times$ 3 to 30+30+4=64. See [here](https://en.wikipedia.org/wiki/Copula_(probability_theory)) for the Wikipedia page on copula.\n", "\n", - "- For the distribution of states, if we assume that the rank order correlation (e.g. the correlation of where you are in the distribution of liquid assets position and iliquid assets)remains the same after the aggregate shocks are introduced to StE, we just need to work with marginal distributions of each state instead of joint distributions. This reduces 3600 $\\times$ 3 to 30+30+4=64. This is the fixed copula idea. See [here](https://en.wikipedia.org/wiki/Copula_(probability_theory)) for Wikipedia page on copula.\n", + "(Eliminate or rewrite intuitively the stuff below)\n", "\n", "#### More accurately, how?\n", "1. Use compression techniques as in video encoding\n", @@ -169,7 +225,27 @@ " * Use fixed Copula to calculate an approximate joint distribution from marginals\n", "\n", "\n", - "The approach follows the insight of KS in that it uses the fact that some moments of the distribution do not matter for aggregate dynamics" + "The approach follows the insight of KS in that it uses the fact that some moments of the distribution do not matter for aggregate dynamics\n", + "\n", + "The copula is computed from the joint distribution of states in StE and will be used to transform the marginals back to joint distributions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "# Get some specs about the copula, which is precomputed in the EX3SS object\n", + "\n", + "print('The copula consists of two parts: gridpoints and values at those gridpoints:'+ \\\n", + " ',\\n gridpoints with dimension of '+str(EX3SS['Copula']['grid'].shape) + \\\n", + " ', where the first element is total number of gridpoints' + \\\n", + " ', and the second element is number of states' + \\\n", + " ',\\n and values with dimension of '+str(EX3SS['Copula']['value'].shape) + \\\n", + " ', \\n each entry of which is the probability of the three state variables below the grids.')" ] }, { @@ -516,7 +592,9 @@ { "cell_type": "code", "execution_count": 13, - "metadata": {}, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [ { "name": "stdout", @@ -561,7 +639,7 @@ " \\n state variables regardless of their joint distributions.\\\n", " \\n This is due to the assumption of the rank order remains the same.')\n", "print('The total number of state variables is '+str(SR['State'].shape[0]) + '='+\\\n", - " str(SR['Gamma_state'].shape[1])+'+ # of other states')\n" + " str(SR['Gamma_state'].shape[1])+'+ # of other states')" ] }, { @@ -688,13 +766,7 @@ } }, "jupytext": { - "formats": "ipynb,py:light", - "text_representation": { - "extension": ".py", - "format_name": "light", - "format_version": "1.3", - "jupytext_version": "0.8.3" - } + "formats": "ipynb,py:percent" }, "kernelspec": { "display_name": "Python 3", @@ -711,7 +783,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.7" }, "varInspector": { "cols": { diff --git a/HARK/BayerLuetticke/DCT-Copula-Illustration.py b/HARK/BayerLuetticke/DCT-Copula-Illustration.py new file mode 100644 index 000000000..1c63fcec1 --- /dev/null +++ b/HARK/BayerLuetticke/DCT-Copula-Illustration.py @@ -0,0 +1,493 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.2' +# jupytext_version: 1.1.3 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# %% [markdown] +# # Dimension Reduction in [Bayer and Luetticke (2018)](https://cepr.org/active/publications/discussion_papers/dp.php?dpno=13071) +# +# [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/econ-ark/HARK/BayerLuetticke?filepath=notebooks%2FHARK%2FBayerLuetticke%2FTwoAsset.ipynb) +# +# - Based on original slides by Christian Bayer and Ralph Luetticke +# - Original Jupyter notebook by Seungcheol Lee +# - Further edits by Chris Carroll, Tao Wang +# +# This is an accompany to the [main notebook](TwoAsset.ipynb) illustrating dimension reduction in Bayer/Luetticke algorithm. + +# %% {"code_folding": [6, 17]} +# Setup stuff + +# This is a jupytext paired notebook that autogenerates a corresponding .py file +# which can be executed from a terminal command line via "ipython [name].py" +# But a terminal does not permit inline figures, so we need to test jupyter vs terminal +# Google "how can I check if code is executed in the ipython notebook" +def in_ipynb(): + try: + if str(type(get_ipython())) == "": + return True + else: + return False + except NameError: + return False + +# Determine whether to make the figures inline (for spyder or jupyter) +# vs whatever is the automatic setting that will apply if run from the terminal +if in_ipynb(): + # %matplotlib inline generates a syntax error when run from the shell + # so do this instead + get_ipython().run_line_magic('matplotlib', 'inline') +else: + get_ipython().run_line_magic('matplotlib', 'auto') + +# The tools for navigating the filesystem +import sys +import os + +# Find pathname to this file: +my_file_path = os.path.dirname(os.path.abspath("TwoAsset.ipynb")) + +# Relative directory for pickled code +code_dir = os.path.join(my_file_path, "BayerLuetticke_code/TwoAssetCode") + +sys.path.insert(0, code_dir) +sys.path.insert(0, my_file_path) + +# %% {"code_folding": [0]} +# Change working folder and load Stationary equilibrium (StE) + +import pickle +os.chdir(code_dir) # Go to the directory with pickled code + +## EX3SS_20.p is the information in the stationary equilibrium +## (20: the number of illiquid and liquid weath gridpoints) +### The comments above are original, but it seems that there are 30 not 20 points now + +EX3SS=pickle.load(open("EX3SS_20.p", "rb")) + +# %% + + +# %% [markdown] +# ### Dimension Reduction via discrete cosine transformation and a fixed copula +# +# #### What is it whose dimension needs to be reduced? +# +# 1. Policy and value functions +# 1. The distribution of agents across states +# +# Grids are constructed for values of the state variables: +# * liquid ($nm$ points), illiquid assets ($nk$), and idiosyncratic pty ($nh$) +# +# So there are $nm \times nk \times nh$ potential combinations +# +# In principle, functions are represented by specifying their values at each specified combination of gridpoints and interpolating for intermediate values +# * In practice, for technical reasons, interpolation is not necessary here +# +# There are two kinds of functions: +# 1. Policy functions and marginal value functions +# * At each of the gridpoints, there is a number +# * This is value for the value function +# * This is consumption for the consumption function +# * $c_n$ is the consumption function for the nonadjuster +# * $c_a$ is the consumption function for the adjuster +# 1. The distribution (="histograms") of agents across states +# * In principle, distributions need not be computed at the same gridpoints used to represent the value and policy functions +# * In practice, the same grids are used + +# %% +# Recover dimensions of the marginal value and consumption functions + +print('c_n is of dimension: ' + str(EX3SS['mutil_c_n'].shape)) +print('c_a is of dimension: ' + str(EX3SS['mutil_c_a'].shape)) + +print('Vk is of dimension:' + str(EX3SS['Vk'].shape)) +print('Vm is of dimension:' + str(EX3SS['Vm'].shape)) + +print('For convenience, these are all constructed from the same exogenous grids:') +print(str(len(EX3SS['grid']['m']))+' gridpoints for liquid assets;') +print(str(len(EX3SS['grid']['k']))+' gridpoints for illiquid assets;') +print(str(len(EX3SS['grid']['h']))+' gridpoints for individual productivity.') +print('') +print('Therefore, the joint distribution across different is of size: ') +print(str(EX3SS['mpar']['nm'])+ + ' * '+str(EX3SS['mpar']['nk'])+ + ' * '+str(EX3SS['mpar']['nh'])+ + ' = '+ str(EX3SS['mpar']['nm']*EX3SS['mpar']['nk']*EX3SS['mpar']['nh'])) + + + +# %% [markdown] +# #### Intuitively, how does the reduction work? +# +# - The first step is to find an efficient "compressed" representation of the function (e.g., the consumption function). The analogy to image compression is that nearby pixels are likely to have identical or very similar colors, so we need only to find an efficient way to represent the way in which the colors change from one pixel to another. Similarly, consumption at a given point is likely to be close to consumption at a nearby point, so a function that captures that similarity efficiently can preserve most of the information without keeping all of the points. +# +# - We will be using the discrete consine transformation (DCT), which is commonly used in image compression. See [here](https://en.wikipedia.org/wiki/Discrete_cosine_transform) for the Wikipedia page on DCT. +# +# - The other tool we use is the "copula," which allows us to represent the distribution of people across idiosyncratic states efficiently +# * The crucial assumption behind the copula is that what aggregate shocks do is to squeeze or distort the steady state distribution, but leave the rank structure of the distribution the same. Think of representing a balloon by a set of points on its surface; the copula assumption is effectively that when something happens to the balloon (more air is put in it, or it is squeezed on one side, say), we can represent what happens by thinking about how the relationship between points is distorted, rather than having to reconstruct the shape of the balloon with a completely independent set of new points. Which points are close to which other points does not change, but the distances between them can change. If the distances between them change in a particularly simple way, you can represent what has happened with a small amount of information. For example, if the balloon is perfectly spherical, then adding a given amount of air might increase the distances between adjacent points by 5 percent. (See the video illustration here) +# +# - In the context of this model, the assumption is that the rank order correlation (e.g. the correlation of where you are in the distribution of liquid assets and illiquid assets) remains the same after the aggregate shocks are introduced to StE +# +# - In this case we just need to represent how the marginal distributions of each state change, instead of the full joint distributions. +# +# - This reduces 3600 $\times$ 3 to 30+30+4=64. See [here](https://en.wikipedia.org/wiki/Copula_(probability_theory)) for the Wikipedia page on copula. +# +# (Eliminate or rewrite intuitively the stuff below) +# +# #### More accurately, how? +# 1. Use compression techniques as in video encoding +# * Apply a discrete cosine transformation (DCT) to all value/policy functions +# * Use Chebychev polynomials on roots grid +# * Define a reference "frame": the steady-state equilibrium (StE) +# * Represent fluctuations as differences from this reference frame +# * Assume all coefficients of the DCT from the StE that are close to zero do not change when there is an aggregate shock (small things stay small and unchanged) +# +# 2. Assume no changes in the rank correlation structure of $\mu$ +# * Calculate the Copula, $\bar{C}$ of $\mu$ in the StE +# * Perturb only the marginal distributions +# * Use fixed Copula to calculate an approximate joint distribution from marginals +# +# +# The approach follows the insight of KS in that it uses the fact that some moments of the distribution do not matter for aggregate dynamics +# +# The copula is computed from the joint distribution of states in StE and will be used to transform the marginals back to joint distributions. + +# %% +# Get some specs about the copula, which is precomputed in the EX3SS object + +print('The copula consists of two parts: gridpoints and values at those gridpoints:'+ \ + ',\n gridpoints with dimension of '+str(EX3SS['Copula']['grid'].shape) + \ + ', where the first element is total number of gridpoints' + \ + ', and the second element is number of states' + \ + ',\n and values with dimension of '+str(EX3SS['Copula']['value'].shape) + \ + ', \n each entry of which is the probability of the three state variables below the grids.') + + +# %% {"code_folding": [0]} +## Import necessary libraries + +from __future__ import print_function +import sys +sys.path.insert(0,'../') + +import numpy as np +from numpy.linalg import matrix_rank +import scipy as sc +from scipy.stats import norm +from scipy.interpolate import interp1d, interp2d, griddata, RegularGridInterpolator, interpn +import multiprocessing as mp +from multiprocessing import Pool, cpu_count, Process +from math import ceil +import math as mt +from scipy import sparse as sp # used to work with sparse matrices +from scipy import linalg #linear algebra +from math import log, cos, pi, sqrt +import time +from SharedFunc3 import Transition, ExTransitions, GenWeight, MakeGridkm, Tauchen, Fastroot +import matplotlib.pyplot as plt +import matplotlib.patches as mpatches +import scipy.io #scipy input and output +import scipy.fftpack as sf # scipy discrete fourier transforms + + +# %% [markdown] +# #### Details +# 1) Apply compression techniques from video encoding +# * Let $\bar{\Theta} = dct(\bar{v})$ be the coefficients obtained from the DCT of the value function in StE +# * Define an index set $\mathop{I}$ that contains the x percent largest (i.e. most important) elements from $\bar{\Theta}$ +# * Let $\theta$ be a sparse vector with non-zero entries only for elements $i \in \mathop{I}$ +# * Define +# \begin{equation} +# \tilde{\Theta}(\theta_t)=\left\{ +# \begin{array}{@{}ll@{}} +# \bar{\Theta}(i)+\theta_t(i), & i \in \mathop{I} \\ +# \bar{\Theta}(i), & \text{else} +# \end{array}\right. +# \end{equation} +# + +# %% [markdown] +# 2) Decoding +# * Now we reconstruct $\tilde{v}_t=\tilde{v}(\theta_t)=dct^{-1}(\tilde{\Theta}(\theta_i))$ +# * idct is the inverse dct that goes from the $\theta$ vector to the corresponding values +# * This means that in the StE the reduction step adds no addtional approximation error: +# * Remember that $\tilde{v}(0)=\bar{v}$ by construction +# * Yet, it allows to reduce the number of derivatives that need to be calculated from the outset. +# +# 3) The histogram is recovered the same way +# * $\mu_t$ is approximated as $\bar{C}(\bar{\mu_t}^1,...,\bar{\mu_t}^n)$, where $n$ is the dimensionality of the idiosyncratic states +# * The StE distribution is obtained when $\mu = \bar{C}(\bar{\mu}^1,...,\bar{\mu}^n)$ +# * Typically prices are only influenced through the marginal distributions +# * The approach ensures that changes in the mass of one, say wealth, state are distributed in a sensible way across the other dimension +# * The implied distributions look "similar" to the StE one (different in (Reiter, 2009)) +# +# 4) Too many equations +# * The system +# \begin{align} +# F(\{d\mu_t^1,...,d\mu_t^n\}, S_t, \{d\mu_{t+1}^1,...,d\mu_{t+1}^n\}, S_{t+1}, \theta_t, P_t, \theta_{t+1}, P_{t+1}) +# &= \begin{bmatrix} +# d\bar{C}(\bar{\mu}_t^1,...,\bar{\mu}_t^n) - d\bar{C}(\bar{\mu}_t^1,...,\bar{\mu}_t^n)\Pi_{h_t} \\ +# dct[idct(\tilde{\Theta(\theta_t)}) - (\bar{u}_{h_t} + \beta \Pi_{h_t}idct(\tilde{\Theta(\theta_{t+1})}] \\ +# S_{t+1} - H(S_t,d\mu_t) \\ +# \Phi(h_t,d\mu_t,P_t,S_t) \\ +# \end{bmatrix} +# \end{align} +# has too many equations +# * Uses only difference in marginals and the differences on $\mathop{I}$ + +# %% {"code_folding": []} +## State reduction and discrete cosine transformation + +class StateReduc_Dct: + + def __init__(self, par, mpar, grid, Output, targets, Vm, Vk, + joint_distr, Copula, c_n_guess, c_a_guess, psi_guess, + m_n_star, m_a_star, cap_a_star, mutil_c_n, mutil_c_a,mutil_c, P_H): + + self.par = par # Parameters of the theoretical model + self.mpar = mpar # Parameters of the numerical representation + self.grid = grid # Discrete grid + self.Output = Output # Results of the calculations + self.targets = targets # Like, debt-to-GDP ratio or other desiderata + self.Vm = Vm # Marginal value from liquid cash-on-hand + self.Vk = Vk # Marginal value of capital + self.joint_distr = joint_distr # Multidimensional histogram + self.Copula = Copula # Encodes rank correlation structure of distribution + self.mutil_c = mutil_c # Marginal utility of consumption + self.P_H = P_H # Transition matrix for macro states (not including distribution) + + + def StateReduc(self): + """ + input + ----- + self: dict, stored results from a StE + + output + ------ + Newly generated + =============== + X_ss: ndarray, stacked states, including + Y_ss: ndarray, controls + Gamma_state: ndarray, marginal distributions of individual states + grid: ndarray, discrete grids + targets: ndarray, debt-to-GDP ratio or other desiderata + P_H: transition probability of + indexMUdct: ndarray, indices selected after dct operation on marginal utility of consumption + indexVKdct: ndarray, indices selected after dct operation on marginal value of capital + State: ndarray, dimension equal to reduced states + State_m: ndarray, dimension equal to reduced states + Contr: ndarray, dimension equal to reduced controls + Contr_m: ndarray, dimension equal to reduced controls + + Passed down from the model + ========================== + Copula: dict, grids and values + joint_distr: ndarray, nk x nm x nh + Output: dict, outputs from the model + par: dict, parameters of the theoretical model + mpar:dict, parameters of the numerical representation + aggrshock: string, type of aggregate shock used to purturb the StE + """ + + # Inverse of CRRA on x for utility and marginal utility + invutil = lambda x : ((1-self.par['xi'])*x)**(1./(1-self.par['xi'])) + invmutil = lambda x : (1./x)**(1./self.par['xi']) + + # X=States + # Marg dist of liquid assets summing over pty and illiquid assets k + Xss=np.asmatrix(np.concatenate((np.sum(np.sum(self.joint_distr.copy(),axis=1),axis =1), + np.transpose(np.sum(np.sum(self.joint_distr.copy(),axis=0),axis=1)),# marg dist k + np.sum(np.sum(self.joint_distr.copy(),axis=1),axis=0), # marg dist pty (\approx income) + [np.log(self.par['RB'])],[ 0.]))).T # Given the constant interest rate + + # Y="controls" (according to this literature's odd terminology) + # c = invmarg(marg(c)), so first bit gets consumption policy function + Yss=np.asmatrix(np.concatenate((invmutil(self.mutil_c.copy().flatten(order = 'F')),\ + invmutil(self.Vk.copy().flatten(order = 'F')), + [np.log(self.par['Q'])], # Question: Price of the illiquid asset, right? + [ np.log(self.par['PI'])], # Inflation + [ np.log(self.Output)], + [np.log(self.par['G'])], # Gov spending + [np.log(self.par['W'])], # Wage + [np.log(self.par['R'])], # Nominal R + [np.log(self.par['PROFITS'])], + [np.log(self.par['N'])], # Hours worked + [np.log(self.targets['T'])], # Taxes + [np.log(self.grid['K'])], # Kapital + [np.log(self.targets['B'])]))).T # Government debt + + # Mapping for Histogram + # Gamma_state matrix reduced set of states + # nm = number of gridpoints for liquid assets + # nk = number of gridpoints for illiquid assets + # nh = number of gridpoints for human capital (pty) + Gamma_state = np.zeros( # Create zero matrix of size [nm + nk + nh,nm + nk + nh - 4] + (self.mpar['nm']+self.mpar['nk']+self.mpar['nh'], + self.mpar['nm']+self.mpar['nk']+self.mpar['nh'] - 4)) + # Question: Why 4? 4 = 3+1, 3: sum to 1 for m, k, h and 1: for entrepreneurs + + # Impose adding-up conditions: + # In each of the block matrices, probabilities must add to 1 + + for j in range(self.mpar['nm']-1): # np.squeeze reduces one-dimensional matrix to vector + Gamma_state[0:self.mpar['nm'],j] = -np.squeeze(Xss[0:self.mpar['nm']]) + Gamma_state[j,j]=1. - Xss[j] # + Gamma_state[j,j]=Gamma_state[j,j] - np.sum(Gamma_state[0:self.mpar['nm'],j]) + bb = self.mpar['nm'] # Question: bb='bottom base'? because bb shorter to type than self.mpar['nm'] everywhere + + for j in range(self.mpar['nk']-1): + Gamma_state[bb+np.arange(0,self.mpar['nk'],1), bb+j-1] = -np.squeeze(Xss[bb+np.arange(0,self.mpar['nk'],1)]) + Gamma_state[bb+j,bb-1+j] = 1. - Xss[bb+j] + Gamma_state[bb+j,bb-1+j] = (Gamma_state[bb+j,bb-1+j] - + np.sum(Gamma_state[bb+np.arange(0,self.mpar['nk']),bb-1+j])) + bb = self.mpar['nm'] + self.mpar['nk'] + + for j in range(self.mpar['nh']-2): + # Question: Why -2? 1 for h sum to 1 and 1 for entrepreneur Some other symmetry/adding-up condition. + Gamma_state[bb+np.arange(0,self.mpar['nh']-1,1), bb+j-2] = -np.squeeze(Xss[bb+np.arange(0,self.mpar['nh']-1,1)]) + Gamma_state[bb+j,bb-2+j] = 1. - Xss[bb+j] + Gamma_state[bb+j,bb-2+j] = Gamma_state[bb+j,bb-2+j] - np.sum(Gamma_state[bb+np.arange(0,self.mpar['nh']-1,1),bb-2+j]) + + # Number of other state variables not including the gridded -- here, just the interest rate + self.mpar['os'] = len(Xss) - (self.mpar['nm']+self.mpar['nk']+self.mpar['nh']) + # For each gridpoint there are two "regular" controls: consumption and illiquid saving + # Counts the number of "other" controls (PROFITS, Q, etc) + self.mpar['oc'] = len(Yss) - 2*(self.mpar['nm']*self.mpar['nk']*self.mpar['nh']) + + aggrshock = self.par['aggrshock'] + accuracy = self.par['accuracy'] + + # Do the dct on the steady state marginal utility + # Returns an array of indices for the used basis vectors + indexMUdct = self.do_dct(invmutil(self.mutil_c.copy().flatten(order='F')), + self.mpar,accuracy) + + # Do the dct on the steady state marginal value of capital + # Returns an array of indices for the used basis vectors + indexVKdct = self.do_dct(invmutil(self.Vk.copy()),self.mpar,accuracy) + + # Calculate the numbers of states and controls + aux = np.shape(Gamma_state) + self.mpar['numstates'] = np.int64(aux[1] + self.mpar['os']) + self.mpar['numcontrols'] = np.int64(len(indexMUdct) + + len(indexVKdct) + + self.mpar['oc']) + + # Size of the reduced matrices to be used in the Fsys + # Set to zero because in steady state they are zero + State = np.zeros((self.mpar['numstates'],1)) + State_m = State + Contr = np.zeros((self.mpar['numcontrols'],1)) + Contr_m = Contr + + return {'Xss': Xss, 'Yss':Yss, 'Gamma_state': Gamma_state, + 'par':self.par, 'mpar':self.mpar, 'aggrshock':aggrshock, + 'Copula':self.Copula,'grid':self.grid,'targets':self.targets,'P_H':self.P_H, + 'joint_distr': self.joint_distr, 'Output': self.Output, 'indexMUdct':indexMUdct, 'indexVKdct':indexVKdct, + 'State':State, 'State_m':State_m, 'Contr':Contr, 'Contr_m':Contr_m} + + # Discrete cosine transformation magic happens here + # sf is scipy.fftpack tool + def do_dct(self, obj, mpar, level): + """ + input + ----- + obj: ndarray nm x nk x nh + dimension of states before dct + mpar: dict + parameters in the numerical representaion of the model, e.g. nm, nk and nh + level: float + accuracy level for dct + output + ------ + index_reduced: ndarray n_dct x 1 + an array of indices that select the needed grids after dct + + """ + obj = np.reshape(obj.copy(),(mpar['nm'],mpar['nk'],mpar['nh']),order='F') + X1 = sf.dct(obj,norm='ortho',axis=0) # dct is operated along three dimensions axis=0/1/2 + X2 = sf.dct(X1.copy(),norm='ortho',axis=1) + X3 = sf.dct(X2.copy(),norm='ortho',axis=2) + + # Pick the coefficients that are big + XX = X3.flatten(order='F') + ind = np.argsort(abs(XX.copy()))[::-1] + # i will + i = 1 + # Sort from smallest (=best) to biggest (=worst) + # and count how many are 'good enough to keep' + while linalg.norm(XX[ind[:i]].copy())/linalg.norm(XX) < level: + i += 1 + + needed = i # Question:Isn't this counting the ones that are NOT needed? + + index_reduced = np.sort(ind[:i]) # Retrieve the good + + return index_reduced + +# %% {"code_folding": [0]} +## Choose an aggregate shock to perturb(one of three shocks: MP, TFP, Uncertainty) + +EX3SS['par']['aggrshock'] = 'MP' +EX3SS['par']['rhoS'] = 0.0 # Persistence of variance +EX3SS['par']['sigmaS'] = 0.001 # STD of variance shocks + +#EX3SS['par']['aggrshock'] = 'TFP' +#EX3SS['par']['rhoS'] = 0.95 +#EX3SS['par']['sigmaS'] = 0.0075 + +#EX3SS['par']['aggrshock'] = 'Uncertainty' +#EX3SS['par']['rhoS'] = 0.84 # Persistence of variance +#EX3SS['par']['sigmaS'] = 0.54 # STD of variance shocks + +# %% {"code_folding": []} +## Choose an accuracy of approximation with DCT +### Determines number of basis functions chosen -- enough to match this accuracy +### EX3SS is precomputed steady-state pulled in above +EX3SS['par']['accuracy'] = 0.99999 + +# %% {"code_folding": []} +## Implement state reduction and DCT +### Do state reduction on steady state +EX3SR=StateReduc_Dct(**EX3SS) # Takes StE result as input and get ready to invoke state reduction operation +SR=EX3SR.StateReduc() # StateReduc is operated + +# %% +print('What are the results from the state reduction?') +print('Newly added attributes after the operation include \n'+str(set(SR.keys())-set(EX3SS.keys()))) + +print('\n') + +print('The dimension of policy function is reduced to '+str(SR['indexMUdct'].shape[0]) \ + +' from '+str(EX3SS['mutil_c'].shape)) +print('The dimension of value function is reduced to '+str(SR['indexVKdct'].shape[0]) \ + + ' from ' + str(EX3SS['Vk'].shape)) +print('The total number of control variables is '+str(SR['Contr'].shape[0])+'='+str(SR['indexMUdct'].shape[0]) + \ + '+'+str(SR['indexVKdct'].shape[0])+'+ # of other macro controls') +print('\n') +print('After marginalizing the joint distribution, \ + \n the dimension of states including exogenous state, is '+str(SR['Xss'].shape[0])) +print('Dimension of gamma_state is '+str(SR['Gamma_state'].shape)+\ + '. It simply stacks all grids of different\ + \n state variables regardless of their joint distributions.\ + \n This is due to the assumption of the rank order remains the same.') +print('The total number of state variables is '+str(SR['State'].shape[0]) + '='+\ + str(SR['Gamma_state'].shape[1])+'+ # of other states') + + +# %% [markdown] +# #### Summary: what do we achieve after the transformation? +# +# - Via DCT, the dimension of policy function and value functions are reduced both from 3600 to 154 and 94, respectively. +# - Via fixed copula operation and marginalizing the joint-distribution, the dimension of gamma_state is 64 now, (excluding exogeous states like interest rate). From fc137737d2d876734caf9cfcf723f3aa32e9e2ca Mon Sep 17 00:00:00 2001 From: Tao Wang Date: Wed, 5 Jun 2019 21:56:19 -0400 Subject: [PATCH 70/77] further edits to DCT-Copula-Illustration notebook, inclduing 2d and 3d graphs --- .../DCT-Copula-Illustration.ipynb | 374 +++++++++++------- .../BayerLuetticke/DCT-Copula-Illustration.py | 212 ++++++---- HARK/BayerLuetticke/TwoAsset.ipynb | 73 ++-- HARK/BayerLuetticke/TwoAsset.py | 121 +----- 4 files changed, 408 insertions(+), 372 deletions(-) diff --git a/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb b/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb index e127b8be0..49379ab3b 100644 --- a/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb +++ b/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb @@ -8,20 +8,23 @@ "\n", "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/econ-ark/HARK/BayerLuetticke?filepath=notebooks%2FHARK%2FBayerLuetticke%2FTwoAsset.ipynb)\n", "\n", + "\n", + "This is an accompany to the [main notebook](TwoAsset.ipynb) illustrating dimension reduction in Bayer/Luetticke algorithm.\n", + "\n", "- Based on original slides by Christian Bayer and Ralph Luetticke \n", "- Original Jupyter notebook by Seungcheol Lee \n", - "- Further edits by Chris Carroll, Tao Wang \n", - "\n", - "This is an accompany to the [main notebook](TwoAsset.ipynb) illustrating dimension reduction in Bayer/Luetticke algorithm." + "- Further edits by Chris Carroll, Tao Wang \n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "code_folding": [ + 0, 6, - 17 + 17, + 21 ] }, "outputs": [], @@ -66,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": { "code_folding": [ 0 @@ -86,34 +89,6 @@ "EX3SS=pickle.load(open(\"EX3SS_20.p\", \"rb\"))" ] }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "What is stored in the StE?\n", - "Grids of state variables:\n", - "30 gridpoints for liquid assets;\n", - "30 gridpoints for illiquid assets;\n", - "4 gridpoints for individual productivity.\n", - "Therefore, the joint distribution across different states has dimension of (30, 30, 4)\n", - "The dimension of the value function for capital Vk is (30, 30, 4)\n", - "These happen to be the same size, but need not be\n", - "c_n is the policy function for a nonadjuster\n", - "c_a is the policy function for an adjuster\n", - "c_n dimensions happens to be equal to the dimensions of the grid of state variables (30, 30, 4)\n", - "The same is true for the policy function under adjustment c_a:(30, 30, 4)\n" - ] - } - ], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -147,8 +122,12 @@ }, { "cell_type": "code", - "execution_count": 47, - "metadata": {}, + "execution_count": 3, + "metadata": { + "code_folding": [ + 0 + ] + }, "outputs": [ { "name": "stdout", @@ -196,47 +175,45 @@ "source": [ "#### Intuitively, how does the reduction work?\n", "\n", + "##### Reducing the dimension of policy/value functions\n", "- The first step is to find an efficient \"compressed\" representation of the function (e.g., the consumption function). The analogy to image compression is that nearby pixels are likely to have identical or very similar colors, so we need only to find an efficient way to represent the way in which the colors change from one pixel to another. Similarly, consumption at a given point is likely to be close to consumption at a nearby point, so a function that captures that similarity efficiently can preserve most of the information without keeping all of the points.\n", "\n", - "- We will be using the discrete consine transformation (DCT), which is commonly used in image compression. See [here](https://en.wikipedia.org/wiki/Discrete_cosine_transform) for the Wikipedia page on DCT. \n", + "- We will be using the discrete cosine transformation (DCT), which is commonly used in image compression. See [here](https://en.wikipedia.org/wiki/Discrete_cosine_transform) for the Wikipedia page on DCT. \n", + "\n", + "##### Reducing the dimension of joint distribution\n", "\n", "- The other tool we use is the \"copula,\" which allows us to represent the distribution of people across idiosyncratic states efficiently\n", - " * The crucial assumption behind the copula is that what aggregate shocks do is to squeeze or distort the steady state distribution, but leave the rank structure of the distribution the same. Think of representing a balloon by a set of points on its surface; the copula assumption is effectively that when something happens to the balloon (more air is put in it, or it is squeezed on one side, say), we can represent what happens by thinking about how the relationship between points is distorted, rather than having to reconstruct the shape of the balloon with a completely independent set of new points. Which points are close to which other points does not change, but the distances between them can change. If the distances between them change in a particularly simple way, you can represent what has happened with a small amount of information. For example, if the balloon is perfectly spherical, then adding a given amount of air might increase the distances between adjacent points by 5 percent. (See the video illustration here)\n", + " * In general, a multivariate joint distribution is not uniquely determined by marginal distributions only. A copula, to put it simply, characterizes the correlation across variables and it combined with marginal distributions determine the unique joint distribution. \n", + " * The crucial assumption of fixed copula is that what aggregate shocks do is to squeeze or distort the steady state distribution, but leave the rank structure of the distribution the same. Think of representing a balloon by a set of points on its surface; the copula assumption is effectively that when something happens to the balloon (more air is put in it, or it is squeezed on one side, say), we can represent what happens by thinking about how the relationship between points is distorted, rather than having to reconstruct the shape of the balloon with a completely independent set of new points. Which points are close to which other points does not change, but the distances between them can change. If the distances between them change in a particularly simple way, you can represent what has happened with a small amount of information. For example, if the balloon is perfectly spherical, then adding a given amount of air might increase the distances between adjacent points by 5 percent. (See the video illustration here)\n", "\n", "- In the context of this model, the assumption is that the rank order correlation (e.g. the correlation of where you are in the distribution of liquid assets and illiquid assets) remains the same after the aggregate shocks are introduced to StE\n", "\n", "- In this case we just need to represent how the marginal distributions of each state change, instead of the full joint distributions. \n", "\n", - "- This reduces 3600 $\\times$ 3 to 30+30+4=64. See [here](https://en.wikipedia.org/wiki/Copula_(probability_theory)) for the Wikipedia page on copula.\n", - "\n", - "(Eliminate or rewrite intuitively the stuff below)\n", - "\n", - "#### More accurately, how?\n", - "1. Use compression techniques as in video encoding\n", - " * Apply a discrete cosine transformation (DCT) to all value/policy functions\n", - " * Use Chebychev polynomials on roots grid \n", - " * Define a reference \"frame\": the steady-state equilibrium (StE)\n", - " * Represent fluctuations as differences from this reference frame\n", - " * Assume all coefficients of the DCT from the StE that are close to zero do not change when there is an aggregate shock (small things stay small and unchanged)\n", - " \n", - "2. Assume no changes in the rank correlation structure of $\\mu$ \n", - " * Calculate the Copula, $\\bar{C}$ of $\\mu$ in the StE\n", - " * Perturb only the marginal distributions\n", - " * Use fixed Copula to calculate an approximate joint distribution from marginals\n", - "\n", - "\n", - "The approach follows the insight of KS in that it uses the fact that some moments of the distribution do not matter for aggregate dynamics\n", - "\n", - "The copula is computed from the joint distribution of states in StE and will be used to transform the marginals back to joint distributions." + "- This reduces 3600 to 30+30+4=64. See [here](https://en.wikipedia.org/wiki/Copula_(probability_theory)) for the Wikipedia page on copula. The copula is computed from the joint distribution of states in StE and will be used to transform the marginals back to joint distributions." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { + "code_folding": [ + 0 + ], "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The copula consists of two parts: gridpoints and values at those gridpoints:,\n", + " gridpoints with dimension of (3600, 3), where the first element is total number of gridpoints, and the second element is number of states,\n", + " and values with dimension of (3600,), \n", + " each entry of which is the probability of the three state variables below the grids.\n" + ] + } + ], "source": [ "# Get some specs about the copula, which is precomputed in the EX3SS object\n", "\n", @@ -250,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": { "code_folding": [ 0 @@ -281,67 +258,19 @@ "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "import scipy.io #scipy input and output\n", - "import scipy.fftpack as sf # scipy discrete fourier transforms" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Details\n", - "1) Apply compression techniques from video encoding\n", - " * Let $\\bar{\\Theta} = dct(\\bar{v})$ be the coefficients obtained from the DCT of the value function in StE\n", - " * Define an index set $\\mathop{I}$ that contains the x percent largest (i.e. most important) elements from $\\bar{\\Theta}$\n", - " * Let $\\theta$ be a sparse vector with non-zero entries only for elements $i \\in \\mathop{I}$\n", - " * Define \n", - " \\begin{equation}\n", - " \\tilde{\\Theta}(\\theta_t)=\\left\\{\n", - " \\begin{array}{@{}ll@{}}\n", - " \\bar{\\Theta}(i)+\\theta_t(i), & i \\in \\mathop{I} \\\\\n", - " \\bar{\\Theta}(i), & \\text{else}\n", - " \\end{array}\\right.\n", - " \\end{equation}\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2) Decoding\n", - " * Now we reconstruct $\\tilde{v}_t=\\tilde{v}(\\theta_t)=dct^{-1}(\\tilde{\\Theta}(\\theta_i))$\n", - " * idct is the inverse dct that goes from the $\\theta$ vector to the corresponding values\n", - " * This means that in the StE the reduction step adds no addtional approximation error:\n", - " * Remember that $\\tilde{v}(0)=\\bar{v}$ by construction\n", - " * Yet, it allows to reduce the number of derivatives that need to be calculated from the outset.\n", - " \n", - "3) The histogram is recovered the same way\n", - " * $\\mu_t$ is approximated as $\\bar{C}(\\bar{\\mu_t}^1,...,\\bar{\\mu_t}^n)$, where $n$ is the dimensionality of the idiosyncratic states\n", - " * The StE distribution is obtained when $\\mu = \\bar{C}(\\bar{\\mu}^1,...,\\bar{\\mu}^n)$\n", - " * Typically prices are only influenced through the marginal distributions\n", - " * The approach ensures that changes in the mass of one, say wealth, state are distributed in a sensible way across the other dimension\n", - " * The implied distributions look \"similar\" to the StE one (different in (Reiter, 2009))\n", - "\n", - "4) Too many equations\n", - " * The system\n", - " \\begin{align}\n", - " F(\\{d\\mu_t^1,...,d\\mu_t^n\\}, S_t, \\{d\\mu_{t+1}^1,...,d\\mu_{t+1}^n\\}, S_{t+1}, \\theta_t, P_t, \\theta_{t+1}, P_{t+1})\n", - " &= \\begin{bmatrix}\n", - " d\\bar{C}(\\bar{\\mu}_t^1,...,\\bar{\\mu}_t^n) - d\\bar{C}(\\bar{\\mu}_t^1,...,\\bar{\\mu}_t^n)\\Pi_{h_t} \\\\\n", - " dct[idct(\\tilde{\\Theta(\\theta_t)}) - (\\bar{u}_{h_t} + \\beta \\Pi_{h_t}idct(\\tilde{\\Theta(\\theta_{t+1})}] \\\\\n", - " S_{t+1} - H(S_t,d\\mu_t) \\\\\n", - " \\Phi(h_t,d\\mu_t,P_t,S_t) \\\\\n", - " \\end{bmatrix}\n", - " \\end{align}\n", - " has too many equations\n", - " * Uses only difference in marginals and the differences on $\\mathop{I}$ " + "import scipy.fftpack as sf # scipy discrete fourier transforms\n", + "\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "from matplotlib.ticker import LinearLocator, FormatStrFormatter" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": { - "code_folding": [] + "code_folding": [ + 0 + ] }, "outputs": [], "source": [ @@ -389,7 +318,7 @@ " Contr: ndarray, dimension equal to reduced controls\n", " Contr_m: ndarray, dimension equal to reduced controls\n", " \n", - " Passed down from the model\n", + " Passed down from the input\n", " ==========================\n", " Copula: dict, grids and values\n", " joint_distr: ndarray, nk x nm x nh\n", @@ -538,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": { "code_folding": [ 0 @@ -563,9 +492,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": { - "code_folding": [] + "code_folding": [ + 0 + ] }, "outputs": [], "source": [ @@ -577,9 +508,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": { - "code_folding": [] + "code_folding": [ + 0 + ] }, "outputs": [], "source": [ @@ -591,8 +524,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": { + "code_folding": [ + 5, + 7, + 9, + 12, + 14, + 18 + ], "lines_to_next_cell": 2 }, "outputs": [ @@ -601,8 +542,6 @@ "output_type": "stream", "text": [ "What are the results from the state reduction?\n", - "Newly added attributes after the operation include \n", - "{'indexMUdct', 'State', 'Contr', 'Xss', 'Contr_m', 'aggrshock', 'Yss', 'State_m', 'indexVKdct', 'Gamma_state'}\n", "\n", "\n", "The dimension of policy function is reduced to 154 from (30, 30, 4)\n", @@ -610,7 +549,7 @@ "The total number of control variables is 259=154+94+ # of other macro controls\n", "\n", "\n", - "After marginalizing the joint-distribution, \n", + "After marginalizing the joint distribution, \n", " the dimension of states including exogenous state, is 66\n", "Dimension of gamma_state is (64, 60). It simply stacks all grids of different \n", " state variables regardless of their joint distributions. \n", @@ -621,7 +560,7 @@ ], "source": [ "print('What are the results from the state reduction?')\n", - "print('Newly added attributes after the operation include \\n'+str(set(SR.keys())-set(EX3SS.keys())))\n", + "#print('Newly added attributes after the operation include \\n'+str(set(SR.keys())-set(EX3SS.keys())))\n", "\n", "print('\\n')\n", "\n", @@ -646,10 +585,181 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Summary: what do we achieve after the transformation?\n", + "### Graphical Illustration\n", + "\n", + "#### Policy/value functions\n", + "\n", + "- Taking marginal utility as an example, one can plot its values at different grid points in both 2-dimensional and 3-dimensional spaces before and after dimension reduction. \n", + " - 2-dimensional graph: marginal utility at different grid points of a state variable fixing the values of other two state variables. \n", + " - For example, how the reduction works for liquid assets for given level of illiquid assets holding and productivity. \n", + "\n", + " - 3-dimensional graph: marginal utility at different grids points at grid points of liquid and illiquid assets with only value of productivity fixed. \n", + " - There is limitations at 1-dimensional graph, as we do not know ex ante at what grid points the dimension is reduced. So the 3-dimensional graph gives us a more complete picture. \n", + " - In this context, as we only have 4 grid points for productivity, we can fix an arbitrary one of the 4 grids and focus on how the number of grids is reduced for liquid and illiquid assets.\n", + " \n", + "#### Marginal distributions\n", + "\n", + "- We can also graphically show marginal distributions versus joint distribution. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "code_folding": [ + 0 + ], + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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XX9bIkSP1zW9+U2eccYZeffXVFvv+4x//qNNOO03nnXeeHnnkkfj6vXv36uqrr9aYMWNUWlqq3/zmN7r11lu1b98+lZWVadq0aZKkSy65RA888ECfX8dcM2tCSZvpdCpL8pnGAuSnBOQnIETITXEZnZu6W0Vm4q27Z7+eqWvy8juW+V1PbvHyO5a1OVuPzNajs195ee5S21teXq/3/+677/rYsWP91FNP9WuvvdZXrlwZf6y9s1+jR4/2sWPH+tixY33u3Lnu7n7llVf6Y489Ft9u9+7d8fvTp0+PP/b973/f77zzTndve/brySef9G984xvxM1wXX3yxr1q1ynfs2OFm5s8++2yb2Pft2+dFRUX+0ksv+QcffOBf/vKX42e/vvOd7/j1118f3/att95y97Znvw4fPuz5+fk9fNUisvXMvHN2HlHkpwjyU3hu5Ca4k5ticjE35fyVQylyNn56RbHmLa/T9IriNmfrkUOiI0TqqKMiy0cd1ecRIo855hitXbtWCxcuVEFBgb761a9q0aJFHW6/YsUKbdiwQRs2bNC3v/1tSZEzaAUFBS22qaio0JgxY7R8+XK9+OKLXcaxbNkyLVu2TOXl5TrjjDO0ZcsWbdu2TZI0ZMgQjR8/vs3fbNmyRcOGDdOpp54qM9P06dPjjz311FO67rrr4ssf/ehH291vv379NGDAAL377rtdxgigE+SnFshPQEiQm1rI9NyU8/McSpGmpItrdmrO5OFaXLNT40sGUyDmqsJC6bjjpP37pby8yL/HHdfnESL79euniRMnauLEiRozZozuu+8+zZgxo9t/f+SRR2p/dM67/fv365vf/KZqa2t18skn6/bbb48/1hl312233aZrrrmmxfqXX35ZRx99dId/Z2YdPl9Hj7V24MAB5eXldWtbAB0gP7VBfgJCgNzURibnppy/clhd36zZS9ZrflW5bpwyQvOryjV7yXpGBMxlb7whzZolPfdc5N/XX+/T023dujV+lkmSNmzYoCFDhkiSjj322G6dFRo5cqTq6uokKZ7M8vPztXfvXi1durTdv2n93J/97Gd17733au/evZKk1157TW+++Wan+z3ttNO0Y8cO1dfXS5IefPDB+GNTpkzR/Pnz48tvv/22JKl///46dOhQfP3u3btVUFCg/v37d3mcALpAfoojPwEhQm6Ky/TclPPF4caGPZpfVR6/UlhZkq/5VeXa2LAn4MgQmEcekX76U2ns2Mi/CR2Je2Pv3r266qqrdPrpp6u0tFSbNm3S7bffLkmaOXOmLrzwwhadqidNmhQfjvnKK6+UJF188cVauXKlJOn444/XN77xDY0ZM0aXXHKJzjrrrHb3O3jwYJ177rkaPXq0brnlFk2ZMkVVVVU655xzNGbMGF166aVdJte8vDwtXLhQF198sc4777x4Ypakf/7nf9bbb7+t0aNHa+zYsVqxYkX8mEpLS+OdqlesWKGLLrqoV68dgFbIT3HkJyBEyE1xGZ+buts5MRNv3elUzXDx2a9XwzGH0Lnnnutvv/120GH02Be/+EXfsmVLr/42Wwd88G7kp3huShganNyUfchPwSI/9e63E7IfuSlYQeamnL9yWFo0qEUz0lgz09KiQQFHBrR01113aefOnUGH0SMHDx7UJZdcohEjRgQdSsaJ5abGm78rrVmjxptuIzchtMhPAMKI3NRzOT8gTawZ6ewl6zW9oliLa3a2aGYKhEVFRUXQIfTYgAED4s070DOVo0/WuoTO8oVLFmmdFkk/zpP27QsuMKAd5CcAYURu6rmcv3IoMZVFLohcUUcmyfn3LDo0+KGBkZHKDg3M6/PQ4AinnP+sZyDeM+QCPueZJxnvGcWh2k5lwUil2SUvL0+7d+8myWUQd9fu3btze3j5wkI1+gD1O3hAhwcMVL+DB9To/fs8NDjChfyUechPyAXkpsyTrNyU881KE6eyqCzJ1/iSwS2WkfmKiorU0NCgpqamoENBD+Tl5amoqCjoMAJTXd+sfRu2SVUzVHjL9Wq88z+0ad1L2lHfTG7KIuSnzJTr+QnZj9yUmZKRm3K+OOxsKgt+gGWH/v37a9iwYUGHAfTIxoY9Kv3doyqM5qHCxfdqR30zuSnLkJ8AhBG5KXflfHEIAGE0a0JJm3WVJfkUhgAAIGVyvs8hU1kAAAAAAFcOmcoCAAAAAMSVQ0lMZQEAAAAAFIdiKgsAAAAAyPniMHEqixunjIg3MaVABAAAmc7MRpjZhoTbO2Z2Q9BxAQinnC8OO5vKAgCCsmBVfeQkVWOjNGGC9Prrqq5v1oJV9UGHBiCDuPtWdy9z9zJJZ0p6T9JvAw4LQEiFojg0s5PNbIWZbTazF83s+na2MTObZ2Z1ZrbRzM5Ixr5nTShp08ewsiS/3WHkASBdYiMpN978XWnNGjXedBsjKQPoqwsk1bv7K0EHAiCcwjJa6WFJN7n7OjM7VtJaM/uTu29K2OZCSadGbxWS7on+CwBZp3L0yVq3f398uXDJIq3TIunHedK+fcEFBiCTXSbpwdYrzWympJmSVFxcnO6YAIRIKK4cunuju6+L3n9X0mZJJ7Xa7AuS7veI5yQdb2aFfd13vOlWAppuAQjc9u1SVZUODcyTpMi/06ZJO3YEHBiATGRmAyR9XtLDrR9z94XuPs7dxxUUFKQ/OAChEYriMJGZDZVULqmm1UMnSXo1YblBbQtImdlMM6s1s9qmpqYu9xdruhUrEGMD1NB0C0CgCgvV6APU7+ABHR4wUP0OHlCj95dOOCHoyABkpgslrXP3N4IOBEB4hao4NLNjJP1G0g3u/k7rh9v5E2+zoodnv2ID0Mxesl5zl22Nj1zKXIcAglRd36xNG7bpjaoZOuIvNXqjaoY2rd/GSMoAeutytdOkFAAShaXPocysvyKF4QPu/kg7mzRIOjlhuUjSrmTsu7IkX9MrijVveZ3mTB5OYQggcBsb9qj0d4+qMJqPChffqx31zdrYsIccBaBHzOwoSZ+RdE3QsQAIt1AUh2Zmkn4uabO7z+1gs8ckzTazXykyEM0ed29Mxv6r65u1uGan5kwersU1OzW+ZDA/vgAEqr0RkytL8slNAHrM3d+TNDjoOACEXyiKQ0nnSrpC0t/MbEN03XclFUuSuy+Q9ISkiyTVKTJHz9XJ2HGsj2GsKen4ksE0LQUAAACQc0JRHLr7GrXfpzBxG5d0XbL3vbFhT4tCMNYHkaZbAAAAAHJJKIrDINF0CwAAAABCNlopAAAAACAYFIcAAAAAAIrDBavq28wbVl3frAWr6gOKCECYmNnJZrbCzDab2Ytmdn0725iZzTOzOjPbaGZnBBErAABAX+R8cVhaNEizl6yPF4ix0UtLiwYFHBmAkDgs6SZ3HylpvKTrzOz0VttcKOnU6G2mpHv6ulNOXAEAgHTL+eIwNjrp7CXrNXfZVqaxANCCuze6+7ro/XclbZZ0UqvNviDpfo94TtLxZlbYl/1y4goAAKRbzo9WKkUKxOkVxZq3vE5zJg+nMATQLjMbKqlcUk2rh06S9GrCckN0XWOrv5+pyJVFFRcXd7qvxBNX0yuKtbhmJyeuAABASuX8lUMpckZ+cc1OzZk8XItrdrZpygUAZnaMpN9IusHd32n9cDt/4m1WuC9093HuPq6goKDLfSaeuJpeUUxhCAAAUirni8NYU635VeW6ccqI+Jl6CkQAMWbWX5HC8AF3f6SdTRoknZywXCRpV1/3y4krAACQTjlfHG5s2NOiqVasKdfGhj0BRwYgDMzMJP1c0mZ3n9vBZo9JujI6aul4SXvcvbGDbbuFE1cAACDdcr7P4awJJW3WVZbk03wLQMy5kq6Q9Dcz2xBd911JxZLk7gskPSHpIkl1kt6TdHVfd9rZiSvyEwAASIWcLw4BoDPuvkbt9ylM3MYlXZfM/XLiCgAApFvONysFAAAAAFAcAgAAAABEcagFq+rbDPBQXd+sBavqA4oIAAAAANIv54vD0qJBLUYAjI0QWFo0KODIAAAAACB9cn5AmtgIgLOXrNf0imItrtnZYoRAAAAAAMgFOX/lUIoUiNMrijVveZ2mVxRTGAIAAADIORSHijQlXVyzU3MmD9fimp1MMg0gFOgTDQAA0inni8NYH8P5VeW6ccqIeBNTCkQAQaNPNIC+MrPjzWypmW0xs81mdk7QMQEIr5zvc7ixYU+LPoaxPogbG/bQvBRAoOgTDSAJ/kPSH939UjMbIOmooAMCEF45XxzOmlDSZl1lST4/vgCEQmKf6DmTh5ObAHSbmR0n6XxJMyTJ3Q9KOhhkTADCLeeblQJAmNEnGkAfnCKpSdIvzGy9mf3MzI4OOigA4UVxCAAhRZ9oAH10hKQzJN3j7uWS/lfSra03MrOZZlZrZrVNTU3pjhFAiFAcAkBIddYnGgC6oUFSg7vXRJeXKlIstuDuC919nLuPKygoSGuAAMIl5/scAkBY0ScaQF+4++tm9qqZjXD3rZIukLQp6LgAhBfFIQAAQPb6lqQHoiOVbpd0dcDxAAixnG9WyiTTAAAgW7n7hmiT0VJ3v8Td3w46JgDhlfPFIZNMAwAAAADNSplkGgAAAADElUNJLSeZnl5RTGEIAAAAIOdQHIpJpgGEE32iAQBAOuV8ccgk0wDCij7RAAAgnXK+z2Fnk0zTvBRAkOgTDQAA0inni0MmmQYQZol9oudMHk5uAgAAKZPzzUoBIMzoEw0AANKF4hAAQoo+0QAAIJ0oDgEgpDrrEw0AAJBsOd/nEADCij7RAAAgnbhyCAAAAACgOAQAAAAAUBxqwar6NoM7VNc3a8Gq+oAiAgAAAID0y/nisLRoUIvR/2KjA5YWDQo4MgAAAABIn1AUh2Z2r5m9aWYvdPD4RDPbY2YborfvJWvflSX5+uyoT+iaX67V3GVb48PGS+LqIQAAAICcEYriUNIiSVO72OZpdy+L3u5I5s7/z9gTdej9DzRveZ2mVxRLElcPAQAAAOSUUBSH7r5a0ltBxtC/30eU1/8jWvj0dl3zy7Ut5hYDgCDQJxoAAKRTKIrDbjrHzJ43sz+Y2ahkPWmsj+F/XXGmZn7qFO0/9IEOvf9Bsp4eAHqNPtEAACCdMqU4XCdpiLuPlfT/SXq0ow3NbKaZ1ZpZbVNTU5dPvLFhT7yP4eKanZozebj69/uIfvf8riSFDgC9U1mSr/lV5Zq9ZH2LPtG0agAAAKmQEcWhu7/j7nuj95+Q1N/M2v115O4L3X2cu48rKCjo8rlnTSiRpPiPrhunjNB/XXGmnnzxjTbNuQAg3SpL8jW9ojjeJ5rCEAAApEpGFIdmdoKZWfT+2YrEvTtZz79w9XZdO/GU+I+uypJ8XTvxFC1cvT1ZuwCAXqmub463alhcs5OTVgAAIGWOCDoASTKzByVNlJRvZg2Svi+pvyS5+wJJl0q61swOS9on6TJ392Ttf+b5p2j2kvUadeIgVZbkq7q+Wfes3B5vbgoAQYj1MYw1JR1fMpimpQB6zMxelvSupPclHXb3ccFGBCCsQlEcuvvlXTw+X9L8VO0/sV/P9IpiLa7ZyY8vAIGL9YlObNUwv6pcGxv2kJ8A9NQkd6fpAYBOhaI4DIPEfj1zJg/nhxcASZKZ3Svpc5LedPfR7Tw+UdL/SNoRXfVIsuZijfWJTlRZkk9+AgAAKZERfQ7TgX49ADqwSNLULrZ52t3LorekFIYAkEQuaZmZrTWzma0f7OlI7wCyF8WhWvbruXHKiHgTUwpEAO6+WtJbQccBAH1wrrufIelCSdeZ2fmJD/Z0pHcA2YviUJ336wGAbjjHzJ43sz+Y2aiONuLsPIAguPuu6L9vSvqtpLODjQhAWNHnUPTrAdAn6yQNcfe9ZnaRpEclndrehu6+UNJCSRo3blzSRlwGgI6Y2dGSPuLu70bvT5FE83cA7eLKIQD0gbu/4+57o/efkNTfzDizBCAsPiFpjZk9L+kvkn7v7n8MOCYAIcWVQwDoAzM7QdIb7u5mdrYiJ912BxwWAEiS3H27pLFBxwEgM3DlUNKCVfVtBp+prm/WglX1AUUEICzM7EFJz0oaYWYNZvY1M5tlZrOim1wq6YXoWfl5ki5zd5qMAgCAjENxKKm0aFCL0Uljo5eWFg3Y+5/uAAAgAElEQVQKODIAQXP3y9290N37u3uRu//c3Re4+4Lo4/PdfZS7j3X38e5enax9c+IKAACkE8WhPhyddPaS9Zq7bGt8WgsGpAEQJE5cAQCAdKLPYVRlSb6mVxRr3vI6zZk8nMIQQOAST1xNryjW4pqdnLgCAAApw5XDqOr6Zi2u2ak5k4drcc3ONk25ACAIiSeuplcUUxgCAICUoTjUh0215leV68YpI+Jn6ikQAQSNE1cAACBdKA4lbWzY06KpVqwp18aGPQFHBiCXceIKAACkE30OJc2aUNJmXWVJPs23AASqsxNX5CcAAJBsFIcAEFKcuAIAAOlEs1IAAAAAAMWhxETTAAAAAEBxKCaaBgAAAAD6HIqJpgEAAACAK4dRTDQNAACAMIp3gWpslCZMkF5/nS5QSAmKwygmmgYAAEAYxbpANd78XWnNGjXedBtdoJASSWtWambflPSwuzcl6znTJXGi6cqSfI0vGdxiGUBmy+T8BADkMFSOPlnr9u+PLxcuWaR1WiT9OE/aty+4wJB1knnlcE5i0jKzj5vZbUl8/pTpbKJpAFkhY/MToykDUAbnMCTJ9u1SVZUODcyTpMi/06ZJO3YEHBiyTafFoZn9zMy+Y2aF3XiuvYkL7v6mpEv7Ely6zJpQ0uYKYWVJfrsTUAMIh1zJT4ymDGSnXMlhSJLCQjX6APU7eECHBwxUv4MH1Oj9pRNOCDoyZJmurhyulnSWpL+a2eNm9iUz69/Bts+b2cxW647qc4QA0L6cyE+JoynPXbaVJu9A9siJHIbkqK5v1qYN2/RG1Qwd8ZcavVE1Q5vWb2OMDCRdp30O3f1+SfdLkpmNkHS+pNsl/d92Nr9F0uNm9hVJ1ZJGS1qfzGABICaX8lPiaMpzJg+nMASyQC7lMPTdxoY9Kv3doyqM5v/CxfdqR32zNjbs4f8EJFW3B6Rx962Stnby+Ftmdq6kiySVSXpc0uI+R5gmC1bVq7RoUIsvWHX0S0fzUiDcsj0/tR5NeXzJYH4MAFkk23MY+q6936KVJfn8X4Ck63ZxaGZ/kvS3hNuL7t5ieCR3d0m/j94ySqxfT6y5VuIIpgDCLZvzE6MpA9kvm3MYgMzSk6ksXpF0rqSZirRz/8DMtuvDRPaCuy9NfojpkdivZ3pFsRbX7OTHF5A5sjY/dTaaMvkJyBopzWFm1k9SraTX3P1zSYgXQJbqSbPSr0uSmZmkUyWVSpoo6cuSpkrqJykjf3zF0K8HyEzZnJ9oSgRkvzTksOslbZZ0XJ8CBZD1ejzPoUe85O5L3X22pFGS1kr6dNKjS7PW/XoYAQrILNmcnwBkv1TkMDMrknSxpJ8lKUwAWazHxWFr7t6syCha3+t7OMFJ7Ndz45QR8SamFIhA5sqW/AQgNyUph90t6TuSPuhoAzObaWa1Zlbb1NTUh10ByHTdLg7N7GwzO7qDh7dJOic5IQWjs349AMIt2/MTgOyWqhxmZp+T9Ka7r+1sO3df6O7j3H1cQUFBb3YFIEv0ZECa5xTpIP2yIp2jN0p6QVKjpCpJbyU9ujSiXw+Q0bI6PwHIeqnKYedK+ryZXSQpT9JxZrbY3af3PWQA2agnxeEwRTpIj43eLpdUIskk7ZX0taRHBwDdQ34CkMlSksPc/TZJt0mSmU2UdDOFIYDO9GS00lcUGWr5d7F1ZnakpHxJje5+OPnhAUDXyE8AMhk5DEBY9OTKYRvRCVpfTVIsAJA05CcAmSzZOczdV0pamaznA5Cd+jxaKQAAAAAg81EcRi1YVd9m2orq+mYtWFUfUEQAQG4CAADpQ3EYVVo0qMW8hrF5D0uLBgUcGYBcRm4CAADp0qc+h9kkNq/h7CXrNb2iWItrdraY9xAAgkBuAgAA6cKVwwSVJfmaXlGsecvrNL2imB9fAEKB3AQAANKB4jBBdX2zFtfs1JzJw7W4Zmebfj4AEARyEwAASIfQFIdmdq+ZvWlmL3TwuJnZPDOrM7ONZnZGMvcf68czv6pcN04ZEW/GxY8wAEEiNwEAgHQJTXEoaZGkqZ08fqGkU6O3mZLuSebONzbsadGPJ9bPZ2PDnmTuBgB6hNwEAADSJTQD0rj7ajMb2skmX5B0v7u7pOfM7HgzK3T3xmTsf9aEkjbrKkvy6dsDIFDkJgAAkC5hunLYlZMkvZqw3BBd14KZzTSzWjOrbWpqSltwAAAAAJDJMqk4tHbWeZsV7gvdfZy7jysoKEhDWAAAAACQ+TKpOGyQdHLCcpGkXQHFAiBHBD1YFgAAQLpkUnH4mKQroz/Exkvak6z+hgDQiUUKcLAsAACAdAlNcWhmD0p6VtIIM2sws6+Z2SwzmxXd5AlJ2yXVSfpvSd9MdgwLVtW3GR6+ur5ZC1bVJ3tXADKEu6+W9FYnm8QHy3L35yQdb2aF6YkOAAAgecI0WunlXTzukq5LZQylRYPi84lVluS3mF8MADrQ0WBZtGwAAAAZJTTFYRjE5g+bvWS9plcUa3HNzhbziwFAO7o1WJYUGU1ZkaanKi4uTmVMAAAAPRaaZqVhUVmSr+kVxZq3vE7TK4opDAF0pduDZfV2NGWavAMAgHSgOGylur5Zi2t2as7k4Vpcs7PNDzIAaCXlg2XFmrzH8lGsyXtp0aBk7gYAAOQ4mpUmSOxjWFmSr/Elg1ssA8g90cGyJkrKN7MGSd+X1F+S3H2BIoNlXaTIYFnvSbo62THQ5B0AAKQDxWGCjQ17Wvzgiv0g29iwhx9hQI4Kw2BZUssm73MmDycnAQCApKM4TDBrQkmbdZUl+fwIAxC41k3ex5cMJjcBAICkos8hAIRcYpP3G6eMiDcxpU80gK6YWZ6Z/cXMnjezF83sB0HHBCC8KA4BIOQ6a/IOAF04IGmyu4+VVCZpanTwLABog2alABByNHkH0FvRftF7o4v9o7d252IFAK4cAgAAZDEz62dmGyS9KelP7l7T6vGZZlZrZrVNTU3BBAkgFCgOW2GyaQAAkE3c/X13L5NUJOlsMxvd6vGF7j7O3ccVFBQEEySAUKA4bIXJpgEAQDZy979LWilpasChAAgp+hy2wmTTAAAgW5hZgaRD7v53MztS0qcl/STgsACEFMVhO5hsGgAAZIlCSfeZWT9FWoz92t0fDzgmACFFcdgOJpsGAADZwN03SioPOg4AmYE+h60w2TQAAACAXERx2AqTTQMAAADIRTQrbYXJpgGE0YJV9SotGtQiF1XXN2tjw5528xYAAEBPceUQADIA0+wAAIBU48ohAGQAptkBAACpxpVDAMgQidPsTK8opjAEAABJRXEIABmi9TQ7jKIMAACSieIQADIA0+wAAIBUozjswIJV9W1+dFXXN2vBqvqAIgKQy5hmBwAApBrFYQcYGRBAmMyaUNKmj2FlST7TWAAAgKRhtNIOMDIgAAAAgFzClcNOMDIgAAAAgFxBcdgJRgYEAAAAkCsoDjvAyIAAAAAAcgnFYQcYGRAAAABAWMRnU2hslCZMkF5/PemzKTAgTQfaGwGwsiSffocAAAAA0i42m8LvtyxR4Zo1arzpNs0+rUrzq8qTtg+KQwDIEAtW1au0aFCLk1TV9c3a2LCHKS0AAMhylaNP1rr9++PLhUsWaZ0WST/Ok/btS8o+aFYKABmC+VcBAMhh27dLVVU6NDBPkiL/Tpsm7diRtF1w5RAAMgTzrwIAkMMKC9XoA/SJgwd0eMBA9Tt4QI3eX4UnnJC0XXDlEAAyCPOvAgCQm6rrm7Vpwza9UTVDR/ylRm9UzdCm9duSOpsCVw4BIIO0nn91fMlgCkQAAHLAxoY9Kv3doyqM/r9fuPhe7YiOPZCs3wIUh11gAAgAYZE4/2plSb7GlwxusQwAALJXOmZToFlpFxgAAkBYMP8qgJ4ws5PNbIWZbTazF83s+qBjAhBuXDnsAgNAAAgL5l8F0EOHJd3k7uvM7FhJa83sT+6+KejAAIQTVw67gQEgAABApnH3RndfF73/rqTNkk4KNioAYUZx2A2tB4BI5ohAAAAAqWZmQyWVS6pp57GZZlZrZrVNTU3pDg1AiFAcdiFxAIgbp4yINzGlQAQAAJnAzI6R9BtJN7j7O60fd/eF7j7O3ccVFBSkP0AAoUFx2AUGgAAAAJnKzPorUhg+4O6PBB0PgHALTXFoZlPNbKuZ1ZnZre08PsPMmsxsQ/T29XTENWtCSZs+hpUl+UxjAQAAQs3MTNLPJW1297lBxwMg/EIxWqmZ9ZP0U0mfkdQg6a9m9lg7o2k95O6z0x4gAABA5jlX0hWS/mZmG6LrvuvuTwQYE4AQC0VxKOlsSXXuvl2SzOxXkr4giaGWAaCVBavqVVo0qEWrhur6Zm1s2EOrBgBx7r5GkgUdB4DMEZZmpSdJejVhuUHtD7X8JTPbaGZLzezk9p6IEbcAJFMYm7yXFg1qMTBWbOCs0qJBqd41AADIYmEpDts7q+Wtln8naai7l0p6StJ97T0RI24BSJaEJu8XSjpd0uVmdno7mz7k7mXR289SHVdsYKzZS9Zr7rKt8RGVmYMVAAD0RViKwwZJiVcCiyTtStzA3Xe7+4Ho4n9LOjNNsQHIXfEm7+5+UFKsyXvgKkvyNb2iWPOW12l6RTGFIQAA6LOwFId/lXSqmQ0zswGSLpP0WOIGZlaYsPh5SZvTGJ8WrKpvM7dhdX2zFqyqT2cYANIraU3epeQ2e6+ub9bimp2aM3m4FtfsZO5VAADQZ6EoDt39sKTZkp5UpOj7tbu/aGZ3mNnno5vNMbMXzex5SXMkzUhnjPTxAXJS0pq8S8lr9h7LP/OrynXjlBHxJqYUiAAAoC/CMlqposMqP9Fq3fcS7t8m6bZ0xxWT2MdnekWxFtfspI8PkP261eQ9YfG/Jf0k1UFtbNjTIv/E8tPGhj3kJAAA0GuhKQ4zQWIfnzmTh/MjDMh+8Sbvkl5TpMl7VeIGZlbo7o3RxbQ0eW9vuorKknxyEgAA6JNQNCvNFPTxAXJLJjR5BwAASBauHHZTYh+fypJ8jS8ZzPDxQA4Ie5N3AACAZOHKYTd11scHAAAAADIdVw67iT4+AAAAALIZVw4BIEMx/yoAAEgmikMAyFDMvwoAAJKJZqUAkKGYfxUAACQTVw4BIIMlzr86vaKYwhAAAPQaxSEAZDDmXwUAAMlCcdhLDAQBIGiJ86/eOGVEvIkpBSIAAOgNisNeYiAIAEFj/lUAAJBMDEjTSwwEASBozL8KAACSiSuHfcBAEAAAAACyBcVhHzAQBAAAAIBsQXHYSwwEAQAAws7M7jWzN83shaBjARB+FIe9xEAQAMKC0ZMBdGKRpKlBBwEgM1Ac9tKsCSVt+hhWluS3O0AEAKQSoycD6Ii7r5b0VtBxAMgMjFYKABmO0ZMBAEAycOUQALIAoycD6C0zm2lmtWZW29TUFHQ4AAJEcQgAWYDRkwH0lrsvdPdx7j6uoKAg6HAABIjiMMkYGAJAujF6MgAASAaKwyRjYAgA6cboyQA6YmYPSnpW0ggzazCzrwUdE4DwYkCaJGNgCADp1t4oyZUl+eQdAHL3y4OOAUDm4MphCjAwBICg0LQdAAD0FsVhCjAwBICg0LQdAAD0Fs1KkyxxYIjKknyNLxncYhkAUomm7QAAoLe4cphkDAwBIGg0bQcAAL3BlcMkY2AIAEFr3bR9fMlgchAAAOgSVw4BIIsw5yEAAOgtisM0YyRBAKlE03YAANBbFIdpxkiCAFJp1oSSeGEYOxlVWZIfb/LOySgAANARisM0SxxJcO6yrYxkCiBlOBkFAAB6ggFpApA4kuCcycMpDAGkBNNaAACAnuDKYQBajyTIQBEAUoVpLQAAQHdRHKYZIwkCSCdORgEAgO6iOEyzno4kyOimAHqrvZNRX1tUq/9+ur7NduQUAABAcZhmiSMJxiSOJNgaA0oA6K32TkbdOOVUzV22jZwCAADaYECakGNACQC91d5Jp298qkSjThxETgEAAG1w5TADMKAEgGRqL6fQhB0AAFAcZoBkDSjBjz8AUvs5hSbsAACA4jDkkjm6KT/+AHSUUyTF789dtjW+DVcVAQDIHRSHIdfT0U07k9h/sfWPv1TihyUQHp3llI6asHfnxBLfcwAAMl9oikMzm2pmW82szsxubefxgWb2UPTxGjMbmv4o06+no5t2JYj+i9lyxZIfv8mVSa9nNuWnznJKR03Yu3Niqaff896+/8n83KTyMxjE5zuM36kwxtQdmRo3slf8M9nYKE2YIL3+Op9JpEQoikMz6yfpp5IulHS6pMvN7PRWm31N0tvuPlzSv0v6SXqjzA5BTIgd1BXLZMuWIjcsMuX1zJX81FUT9q5OLPX0e97b9z+Zn5tUfgaD+HyH8TsVxpi6I1PjRvaKfSYbb/6utGaNGm+6jc8kUsPdA79JOkfSkwnLt0m6rdU2T0o6J3r/CEnNkqyz5z3zzDMdH3qmrsnL71jmz9Q1tbucanc9ucWH/NPjfteTW9Kyv1SIvWZ3Pbklra9dturN6ymp1slPSXfPyro2r/8zdU1+z8q6+P3uvFc9+Z739vuUzO9hKr/TQeSLMOaoMMbUHZmQn1JxC1tuQlRenrvU9paXF3RkyAA9yU2huHIo6SRJryYsN0TXtbuNux+WtEfS4LRElyWS2X+xp4K4YpkKTCuSXBnyeiYtP5nZTDOrNbPapqamFIXbO101N+3OwFg9/Z739v1P5ucmlZ/BID7fYfxOhTGm7sjUuFvrqlk8MsT27VJVlQ4NzJOkyL/Tpkk7dgQcGLJNWIpDa2ed92KbUP/4Clqy+y92VzJHXA1athS5YZEhr2fS8pO7L3T3ce4+rqCgICnBpUN3Tiz15nve2/c/mZ+bVH4Gg/h8h/E7FcaYuiNT407UzWbxyASFhWr0Aep38IAODxiofgcPqNH7SyecEHRkyDbdvcSYyptypNlWruqquVqmCLpZbrbp7espmpWGUk+/5719/5P5PUzldzqIfBHGHBXGmLojU/JTV7fu5K/Wt2zLTdnimbomf2rkub5r2tXuGzb4rmlX+1Mjzw39dwnh0JPcZJHtg2VmR0h6SdIFkl6T9FdJVe7+YsI210ka4+6zzOwySf/g7l/p7HnHjRvntbW1KYwcuWTBqnqVFg1qcfW1ur5ZGxv2pPzqazbq7etpZmvdfVw6Yozuj/yUAr19/5P5PUzldzqIfBHGHBXGmLojU/JTV8zsUklT3f3r0eUrJFW4++xW282UNFOSiouLz3zllVfSHis6l6nfJYRDT3JTKIpDSTKziyTdLamfpHvd/Udmdocile5jZpYn6ZeSyiW9Jekyd9/e2XPm+o8vIBsF8eOL/ASgO0JYHH5Z0mdbFYdnu/u3OvobchOQfXqSm45IdTDd5e5PSHqi1brvJdzfL+nL6Y4LAMhPADJUg6STE5aLJO0KKBYAGSAsA9IAAAAguf4q6VQzG2ZmAyRdJumxgGMCEGKhuXIIAACA5HH3w2Y2W5FBs2LN4l/s4s8A5DCKQwAAgCzVXrN4AOgIzUoBAAAAABSHAAAAAACKQwAAAACAKA4BAAAAAJLM3YOOIWXMrEnSKz38s3xJzSkIJwyy+dgkji+T9eTYhrh7QSqDSYce5qdsfu8lji/TcXwfyvj81EFuCsN7HIYYpHDEQQwfCkMcYYhB6jyObuemrC4Oe8PMat19XNBxpEI2H5vE8WWybD62ZMj214fjy2wcX/YLw2sQhhjCEgcxhCuOMMSQzDhoVgoAAAAAoDgEAAAAAFActmdh0AGkUDYfm8TxZbJsPrZkyPbXh+PLbBxf9gvDaxCGGKRwxEEMHwpDHGGIQUpSHPQ5BAAAAABw5RAAAAAAQHEYZ2ZTzWyrmdWZ2a1Bx9MbZnavmb1pZi8krPuYmf3JzLZF//1odL2Z2bzo8W40szOCi7xrZnayma0ws81m9qKZXR9dny3Hl2dmfzGz56PH94Po+mFmVhM9vofMbEB0/cDocl308aFBxt8dZtbPzNab2ePR5aw5tlTJkryU1d/dmGz+fJvZ8Wa21My2RN/Hc7Lp/TOzb0c/my+Y2YPRfJw17193dZVvzOzfzWxD9PaSmf09oDiKozllffQzdlEAMQwxsz9H97/SzIpSEEOb33StHk/Ld60bcZxmZs+a2QEzuzmgGKZFX4ONZlZtZmMDiOEL0f1vMLNaMzsv2TF0J46E7c4ys/fN7NIe78Tdc/4mqZ+kekmnSBog6XlJpwcdVy+O43xJZ0h6IWHd/yPp1uj9WyX9JHr/Ikl/kGSSxkuqCTr+Lo6tUNIZ0fvHSnpJ0ulZdHwm6Zjo/f6SaqJx/1rSZdH1CyRdG73/TUkLovcvk/RQ0MfQjWO8UdISSY9Hl7Pm2FL0emVLXsrq727CcWbt51vSfZK+Hr0/QNLx2fL+STpJ0g5JRya8bzOy6f3r5uvQo3wj6VuS7g0iDkX6VcXej9MlvRxADA9Luip6f7KkX6bgtWjzm67V42n5rnUjjo9LOkvSjyTdHFAMlZI+Gr1/YSpei27EcIw+7K5XKmlLEK9FdJt+kpZLekLSpT3dB1cOI86WVOfu2939oKRfSfpCwDH1mLuvlvRWq9VfUOQ/dkX/vSRh/f0e8Zyk482sMD2R9py7N7r7uuj9dyVtVuQ/9Ww5Pnf3vdHF/tGbK/KfztLo+tbHFzvupZIuMDNLU7g9Fj2rerGkn0WXTVlybCmULXkpq7+7UnZ/vs3sOEV+jPxcktz9oLv/XVn0/kk6QtKRZnaEpKMkNSpL3r8e6Gm+uVzSgwHF4ZKOi94fJGlXADGcLunP0fsr2nm8zzr4TZcoLd+1ruJw9zfd/a+SDiV73z2Iodrd344uPicp6VdyuxHDXo9WZpKOVuRzmnTd+FxIkZM3v5H0Zm/2QXEYcZKkVxOWG6LrssEn3L1RivxIU+QMj5TBxxxtxlOuyNW1rDk+izRL26DIl/lPipy5/Lu7H45ukngM8eOLPr5H0uD0Rtwjd0v6jqQPosuDlT3HlioZ9xnuSrZ+d5Xdn+9TJDVJ+kW0Gd/PzOxoZcn75+6vSfp/Je1UpCjcI2mtsuf9665uv29mNkTSMEWuTAQRx+2SpptZgyJXRr4VQAzPS/pS9P4XJR1rZun+HGTUdy2NvqbIFdW0M7MvmtkWSb+X9I8BxXCSIp/JBb19DorDiPbO+mX7MK4ZecxmdowiZ0NucPd3Otu0nXWhPj53f9/dyxQ543W2pJHtbRb9N2OOz8w+J+lNd1+buLqdTTPu2FIsq16HbP3u5sDn+whFmjDd4+7lkv5XkWakHcmo47NIX8kvKFLsnKjIGf8L29k0U9+/7urJcV0maam7vx9QHJdLWuTuRYo0rfylmSXz92x3YrhZ0gQzWy9pgqTXJB1u81epla2fxV4zs0mKFIf/FMT+3f237n6aIi0NfhhEDIqcrPynvnw/j0hiMJmsQdLJCctFSn4zhaC8YWaF7t4YbW4Qu8ScccdsZv0V+XH5gLs/El2dNccX4+5/N7OVivQhON7MjoieoU48htjxNUSbQg1S180MgnKupM9HBw3IU6Q50N3KjmNLpYz9DLeW5d/dbP98N0hqcPea6PJSRYrDbHn/Pi1ph7s3SZKZPaJI/6Vsef+6qyfv22WSrgswjq9JmipJ7v6smeVJylcvm9D1JgZ33yXpH6T4ia8vufueJO2/uzLtu5ZSZlaqSNP+C919d5CxuPtqMysxs3x3b07z7sdJ+lW0tXu+pIvM7LC7P9rdJ+DKYcRfJZ1qkdHJBiiS+B4LOKZkeUzSVdH7V0n6n4T1V0ZHuxovaU+siVAYRft0/FzSZnefm/BQthxfgZkdH71/pCI/WDYr0pchNtJU6+OLHfelkpYntHUPFXe/zd2L3H2oIt+t5e4+TVlwbCmWFXkp27+72f75dvfXJb1qZiOiqy6QtElZ8v4p0px0vJkdFf2sxo4vK96/HuhWvol+Dj4q6dkA49ipyPskMxupyEmZpnTGYGb5CVcrb5N0bxL3312Z9l1LGTMrlvSIpCvc/aWAYhge639skZFjB0hKe5Hq7sPcfWj0/6Slkr7Zk8Iw9iTcPD7q00uK9PP6v0HH08tjeFCRPhOHFDmj9DVF+kL8WdK26L8fi25rkn4aPd6/SRoXdPxdHNt5ijSX2ChpQ/R2URYdX6mk9dHje0HS96LrT5H0F0l1ioyONjC6Pi+6XBd9/JSgj6GbxzlRH47mmFXHlqLXKxvyUlZ/d1sda1Z+viWVSaqNvoePKlIcZM37J+kHkrZEc+8vJQ3MpvevB69Dm3wj6Q5Jn0/Y5nZJ/xZkHIoMBvOMIv3+NkiaEkAMl0Y/+y8pcrVqYApiaO833SxJs6KPp+W71o04Toiuf0fS36P3j0tzDD+T9LY+/D+mNoDX4Z8kvRjd/7OSzgvi/Wi17SL1YrTS2JCrAAAAAIAcRrNSAAAAAADFIQAAAACA4hAAAAAAIIpDAAAAAIAoDgEAAAAAojgEACDpzGyRmdUGHQcAJCI3oSsUhwAAAAAAikMAAAAAAMUhMkysOYSZXWxmm8zsPTP7vZl9zMyGm9kKM/vf6DalQccLAJJkZgPM7BEz22lmw4OOB0BuM7PPmNnG6G+mNWY2KuiYEA4Uh8hExZLukPTPkmZKqpS0UNKvordLJR0h6VdmZkEFCQCSZGZ5kn4raaykT7l7XcAhAchtxZLulPQjSZdL+rikX/ObCVLkBzSQaT4m6Rx3r5ek6BXCWyRd5e73R9eZpN9LOk3S5qACBZDbzOwoSY9JKpJ0vru/FnBIAPAxSee6+zZJMrOPKHICa4SkLUEGhuBx5RCZ6OVYYRgVOwu/vJ11J6UnJABo42hJf5T0CUkTKAwBhMTLscIwalP036IggkG4UBwiE/291fLBdtbH1uWlPhwAaNeJijR7f8Td3wg6GACI6uh3FL+ZQHEIAECKbJN0taR/NrNrgw4GAICu0OcQAIAUcU99dzMAAACXSURBVPdfmtkxkuab2bvuvjjomAAA6AjFIQAAKeTu90QLxF+Y2V53fzTomAAAaA/FIQAAKebud5rZsYpMsfN/3P1PQccEAEBr5u5BxwAAAAAACBgD0gAAAAAAKA4BAAAAABSHAAAAAABRHAIAAAAARHEIAAAAABDFIQAAAABAFIcAAAAAAFEcAgAAAABEcQgAAAAAkPT/AzqxJavMb6/yAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "## Graphical illustration\n", + "\n", + "### In 2D, we can look at how the number of grid points of \n", + "### one state is redcued at given grid values of other states. \n", + "\n", + "mgrid_fix = EX3SS['mpar']['nm']//11 # \"//\" is for floor division unambiguously \n", + "kgrid_fix = EX3SS['mpar']['nk']//11\n", + "hgrid_fix = EX3SS['mpar']['nh']//2\n", + "\n", + "\n", + "mut_StE = EX3SS['mutil_c']\n", + "dim_StE = mut_StE.shape\n", + "mgrid = EX3SS['grid']['m']\n", + "kgrid = EX3SS['grid']['k']\n", + "hgrid = EX3SS['grid']['h']\n", + "\n", + "mut_rdc_idx = np.unravel_index(SR['indexMUdct'],dim_StE,order='F')\n", + "\n", + "mgrid_rdc = mut_rdc_idx[0][(mut_rdc_idx[1]==kgrid_fix) & (mut_rdc_idx[2]==hgrid_fix)]\n", + "kgrid_rdc = mut_rdc_idx[1][(mut_rdc_idx[0]==mgrid_fix) & (mut_rdc_idx[2]==hgrid_fix)]\n", + "hgrid_rdc = mut_rdc_idx[2][(mut_rdc_idx[0]==mgrid_fix) & (mut_rdc_idx[1]==kgrid_fix)]\n", + "\n", + "## compare marginal utility before and after dct \n", + "plt.figure(figsize=(15,5))\n", + "plt.title('Marginal utility of consumption at grid points of states')\n", + "\n", + "plt.subplot(1,3,1)\n", + "plt.plot(mgrid,mut_StE[:,kgrid_fix,hgrid_fix],'x',label='StE(before dct)')\n", + "plt.plot(mgrid[mgrid_rdc],mut_StE[mgrid_rdc,kgrid_fix,hgrid_fix],'r*',label='StE(after dct)')\n", + "\n", + "plt.xlabel('m',size=15)\n", + "plt.ylabel(r'$u_c^\\prime$',size=15)\n", + "plt.legend()\n", + "\n", + "plt.subplot(1,3,2)\n", + "plt.plot(kgrid,mut_StE[mgrid_fix,:,hgrid_fix],'x',label='StE(before dct)')\n", + "plt.plot(kgrid[kgrid_rdc],mut_StE[mgrid_fix,kgrid_rdc,hgrid_fix],'r*',label='StE(after dct)')\n", + "plt.xlabel('k',size=15)\n", + "plt.ylabel(r'$u_c^\\prime$',size=15)\n", + "plt.legend()\n", + "\n", + "plt.subplot(1,3,3)\n", + "plt.plot(hgrid,mut_StE[mgrid_fix,kgrid_fix,:],'x',label='StE(before dct)')\n", + "plt.plot(hgrid[hgrid_rdc],mut_StE[mgrid_fix,kgrid_fix,hgrid_rdc],'r*',label='StE(after dct)')\n", + "plt.xlabel('h',size=15)\n", + "plt.ylabel(r'$u_c^\\prime$',size=15)\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "## 3D scatter plots of all grids and grids after dct\n", + "\n", + "## full grids \n", + "mmgrid,kkgrid = np.meshgrid(mgrid,kgrid)\n", + "\n", + "## rdc grids \n", + "\n", + "fig = plt.figure(figsize=(10,10))\n", + "fig.suptitle('Marginal utility at grid points of m and k(for different h)',fontsize=(13))\n", + "for hgrid_id in range(EX3SS['mpar']['nh']):\n", + " ## prepare the grids \n", + " hgrid_fix=hgrid_id\n", + " fix_bool = mut_rdc_idx[2]==hgrid_fix # for a fixed h grid value \n", + " rdc_id = (mut_rdc_idx[0][fix_bool], mut_rdc_idx[1][fix_bool],mut_rdc_idx[2][fix_bool])\n", + " mmgrid_rdc = mmgrid[rdc_id[0]].T[0]\n", + " kkgrid_rdc = kkgrid[rdc_id[1]].T[0]\n", + " mut_rdc= mut_StE[rdc_id]\n", + " \n", + " ## plots \n", + " ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d')\n", + " ax.scatter(mmgrid,kkgrid,mut_StE[:,:,hgrid_fix],label='StE(before dct)')\n", + " ax.scatter(mmgrid_rdc,kkgrid_rdc,mut_rdc,c='red',label='StE(after dct)')\n", + " ax.set_xlabel('m')\n", + " ax.set_ylabel('k')\n", + " ax.set_zlabel(r'$u^\\prime_c$')\n", + " ax.set_title(r'$h({})$'.format(hgrid_fix))\n", + " #ax.set_xlim(0, 200)\n", + " #ax.set_ylim(0, 400)\n", + " ax.view_init(40, 160)\n", + " ax.legend(loc=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Observation\n", + "\n", + "- For a given grid value of productivity, the remaining grid points after DCT to represent the whole m-k surface concentrate on low values of k and m. The reason, to put it simply, is that the slopes of the surface of marginal utility are very steep around this area. \n", + "- For different grid values of productivity(4 sub plots), the numbers of grid points operation differ. From the lowest to highest values of productivity, there are 78, 33, 25 and 18 grid points, respectively. They add up to the total number of grids 154 after DCT operation, as we print out above for marginal utility function. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Summary: what do we achieve after the transformation?\n", "\n", "- Via DCT, the dimension of policy function and value functions are reduced both from 3600 to 154 and 94, respectively.\n", - "- Via fixed copula operation and marginalizing the joint-distribution, the dimension of gamma_state is 64 now, (excluding exogeous states like interest rate)." + "- Via marginalizing the joint distribution with the fixed copula assumption, the marginal distribution is of dimension 64 compared to its joint distribution of a dimension of 3600.\n", + "\n", + "\n" ] } ], @@ -783,7 +893,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.3" }, "varInspector": { "cols": { diff --git a/HARK/BayerLuetticke/DCT-Copula-Illustration.py b/HARK/BayerLuetticke/DCT-Copula-Illustration.py index 1c63fcec1..c07623923 100644 --- a/HARK/BayerLuetticke/DCT-Copula-Illustration.py +++ b/HARK/BayerLuetticke/DCT-Copula-Illustration.py @@ -18,13 +18,15 @@ # # [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/econ-ark/HARK/BayerLuetticke?filepath=notebooks%2FHARK%2FBayerLuetticke%2FTwoAsset.ipynb) # +# +# This is an accompany to the [main notebook](TwoAsset.ipynb) illustrating dimension reduction in Bayer/Luetticke algorithm. +# # - Based on original slides by Christian Bayer and Ralph Luetticke # - Original Jupyter notebook by Seungcheol Lee # - Further edits by Chris Carroll, Tao Wang # -# This is an accompany to the [main notebook](TwoAsset.ipynb) illustrating dimension reduction in Bayer/Luetticke algorithm. -# %% {"code_folding": [6, 17]} +# %% {"code_folding": [0, 6, 17, 21]} # Setup stuff # This is a jupytext paired notebook that autogenerates a corresponding .py file @@ -74,9 +76,6 @@ def in_ipynb(): EX3SS=pickle.load(open("EX3SS_20.p", "rb")) -# %% - - # %% [markdown] # ### Dimension Reduction via discrete cosine transformation and a fixed copula # @@ -104,7 +103,7 @@ def in_ipynb(): # * In principle, distributions need not be computed at the same gridpoints used to represent the value and policy functions # * In practice, the same grids are used -# %% +# %% {"code_folding": [0]} # Recover dimensions of the marginal value and consumption functions print('c_n is of dimension: ' + str(EX3SS['mutil_c_n'].shape)) @@ -129,40 +128,24 @@ def in_ipynb(): # %% [markdown] # #### Intuitively, how does the reduction work? # +# ##### Reducing the dimension of policy/value functions # - The first step is to find an efficient "compressed" representation of the function (e.g., the consumption function). The analogy to image compression is that nearby pixels are likely to have identical or very similar colors, so we need only to find an efficient way to represent the way in which the colors change from one pixel to another. Similarly, consumption at a given point is likely to be close to consumption at a nearby point, so a function that captures that similarity efficiently can preserve most of the information without keeping all of the points. # -# - We will be using the discrete consine transformation (DCT), which is commonly used in image compression. See [here](https://en.wikipedia.org/wiki/Discrete_cosine_transform) for the Wikipedia page on DCT. +# - We will be using the discrete cosine transformation (DCT), which is commonly used in image compression. See [here](https://en.wikipedia.org/wiki/Discrete_cosine_transform) for the Wikipedia page on DCT. +# +# ##### Reducing the dimension of joint distribution # # - The other tool we use is the "copula," which allows us to represent the distribution of people across idiosyncratic states efficiently -# * The crucial assumption behind the copula is that what aggregate shocks do is to squeeze or distort the steady state distribution, but leave the rank structure of the distribution the same. Think of representing a balloon by a set of points on its surface; the copula assumption is effectively that when something happens to the balloon (more air is put in it, or it is squeezed on one side, say), we can represent what happens by thinking about how the relationship between points is distorted, rather than having to reconstruct the shape of the balloon with a completely independent set of new points. Which points are close to which other points does not change, but the distances between them can change. If the distances between them change in a particularly simple way, you can represent what has happened with a small amount of information. For example, if the balloon is perfectly spherical, then adding a given amount of air might increase the distances between adjacent points by 5 percent. (See the video illustration here) +# * In general, a multivariate joint distribution is not uniquely determined by marginal distributions only. A copula, to put it simply, characterizes the correlation across variables and it combined with marginal distributions determine the unique joint distribution. +# * The crucial assumption of fixed copula is that what aggregate shocks do is to squeeze or distort the steady state distribution, but leave the rank structure of the distribution the same. Think of representing a balloon by a set of points on its surface; the copula assumption is effectively that when something happens to the balloon (more air is put in it, or it is squeezed on one side, say), we can represent what happens by thinking about how the relationship between points is distorted, rather than having to reconstruct the shape of the balloon with a completely independent set of new points. Which points are close to which other points does not change, but the distances between them can change. If the distances between them change in a particularly simple way, you can represent what has happened with a small amount of information. For example, if the balloon is perfectly spherical, then adding a given amount of air might increase the distances between adjacent points by 5 percent. (See the video illustration here) # # - In the context of this model, the assumption is that the rank order correlation (e.g. the correlation of where you are in the distribution of liquid assets and illiquid assets) remains the same after the aggregate shocks are introduced to StE # # - In this case we just need to represent how the marginal distributions of each state change, instead of the full joint distributions. # -# - This reduces 3600 $\times$ 3 to 30+30+4=64. See [here](https://en.wikipedia.org/wiki/Copula_(probability_theory)) for the Wikipedia page on copula. -# -# (Eliminate or rewrite intuitively the stuff below) -# -# #### More accurately, how? -# 1. Use compression techniques as in video encoding -# * Apply a discrete cosine transformation (DCT) to all value/policy functions -# * Use Chebychev polynomials on roots grid -# * Define a reference "frame": the steady-state equilibrium (StE) -# * Represent fluctuations as differences from this reference frame -# * Assume all coefficients of the DCT from the StE that are close to zero do not change when there is an aggregate shock (small things stay small and unchanged) -# -# 2. Assume no changes in the rank correlation structure of $\mu$ -# * Calculate the Copula, $\bar{C}$ of $\mu$ in the StE -# * Perturb only the marginal distributions -# * Use fixed Copula to calculate an approximate joint distribution from marginals -# -# -# The approach follows the insight of KS in that it uses the fact that some moments of the distribution do not matter for aggregate dynamics -# -# The copula is computed from the joint distribution of states in StE and will be used to transform the marginals back to joint distributions. +# - This reduces 3600 to 30+30+4=64. See [here](https://en.wikipedia.org/wiki/Copula_(probability_theory)) for the Wikipedia page on copula. The copula is computed from the joint distribution of states in StE and will be used to transform the marginals back to joint distributions. -# %% +# %% {"code_folding": [0]} # Get some specs about the copula, which is precomputed in the EX3SS object print('The copula consists of two parts: gridpoints and values at those gridpoints:'+ \ @@ -199,53 +182,11 @@ def in_ipynb(): import scipy.io #scipy input and output import scipy.fftpack as sf # scipy discrete fourier transforms +from mpl_toolkits.mplot3d import Axes3D +from matplotlib.ticker import LinearLocator, FormatStrFormatter -# %% [markdown] -# #### Details -# 1) Apply compression techniques from video encoding -# * Let $\bar{\Theta} = dct(\bar{v})$ be the coefficients obtained from the DCT of the value function in StE -# * Define an index set $\mathop{I}$ that contains the x percent largest (i.e. most important) elements from $\bar{\Theta}$ -# * Let $\theta$ be a sparse vector with non-zero entries only for elements $i \in \mathop{I}$ -# * Define -# \begin{equation} -# \tilde{\Theta}(\theta_t)=\left\{ -# \begin{array}{@{}ll@{}} -# \bar{\Theta}(i)+\theta_t(i), & i \in \mathop{I} \\ -# \bar{\Theta}(i), & \text{else} -# \end{array}\right. -# \end{equation} -# -# %% [markdown] -# 2) Decoding -# * Now we reconstruct $\tilde{v}_t=\tilde{v}(\theta_t)=dct^{-1}(\tilde{\Theta}(\theta_i))$ -# * idct is the inverse dct that goes from the $\theta$ vector to the corresponding values -# * This means that in the StE the reduction step adds no addtional approximation error: -# * Remember that $\tilde{v}(0)=\bar{v}$ by construction -# * Yet, it allows to reduce the number of derivatives that need to be calculated from the outset. -# -# 3) The histogram is recovered the same way -# * $\mu_t$ is approximated as $\bar{C}(\bar{\mu_t}^1,...,\bar{\mu_t}^n)$, where $n$ is the dimensionality of the idiosyncratic states -# * The StE distribution is obtained when $\mu = \bar{C}(\bar{\mu}^1,...,\bar{\mu}^n)$ -# * Typically prices are only influenced through the marginal distributions -# * The approach ensures that changes in the mass of one, say wealth, state are distributed in a sensible way across the other dimension -# * The implied distributions look "similar" to the StE one (different in (Reiter, 2009)) -# -# 4) Too many equations -# * The system -# \begin{align} -# F(\{d\mu_t^1,...,d\mu_t^n\}, S_t, \{d\mu_{t+1}^1,...,d\mu_{t+1}^n\}, S_{t+1}, \theta_t, P_t, \theta_{t+1}, P_{t+1}) -# &= \begin{bmatrix} -# d\bar{C}(\bar{\mu}_t^1,...,\bar{\mu}_t^n) - d\bar{C}(\bar{\mu}_t^1,...,\bar{\mu}_t^n)\Pi_{h_t} \\ -# dct[idct(\tilde{\Theta(\theta_t)}) - (\bar{u}_{h_t} + \beta \Pi_{h_t}idct(\tilde{\Theta(\theta_{t+1})}] \\ -# S_{t+1} - H(S_t,d\mu_t) \\ -# \Phi(h_t,d\mu_t,P_t,S_t) \\ -# \end{bmatrix} -# \end{align} -# has too many equations -# * Uses only difference in marginals and the differences on $\mathop{I}$ - -# %% {"code_folding": []} +# %% {"code_folding": [0]} ## State reduction and discrete cosine transformation class StateReduc_Dct: @@ -290,7 +231,7 @@ def StateReduc(self): Contr: ndarray, dimension equal to reduced controls Contr_m: ndarray, dimension equal to reduced controls - Passed down from the model + Passed down from the input ========================== Copula: dict, grids and values joint_distr: ndarray, nk x nm x nh @@ -451,21 +392,21 @@ def do_dct(self, obj, mpar, level): #EX3SS['par']['rhoS'] = 0.84 # Persistence of variance #EX3SS['par']['sigmaS'] = 0.54 # STD of variance shocks -# %% {"code_folding": []} +# %% {"code_folding": [0]} ## Choose an accuracy of approximation with DCT ### Determines number of basis functions chosen -- enough to match this accuracy ### EX3SS is precomputed steady-state pulled in above EX3SS['par']['accuracy'] = 0.99999 -# %% {"code_folding": []} +# %% {"code_folding": [0]} ## Implement state reduction and DCT ### Do state reduction on steady state EX3SR=StateReduc_Dct(**EX3SS) # Takes StE result as input and get ready to invoke state reduction operation SR=EX3SR.StateReduc() # StateReduc is operated -# %% +# %% {"code_folding": [5, 7, 9, 12, 14, 18]} print('What are the results from the state reduction?') -print('Newly added attributes after the operation include \n'+str(set(SR.keys())-set(EX3SS.keys()))) +#print('Newly added attributes after the operation include \n'+str(set(SR.keys())-set(EX3SS.keys()))) print('\n') @@ -487,7 +428,114 @@ def do_dct(self, obj, mpar, level): # %% [markdown] -# #### Summary: what do we achieve after the transformation? +# ### Graphical Illustration +# +# #### Policy/value functions +# +# - Taking marginal utility as an example, one can plot its values at different grid points in both 2-dimensional and 3-dimensional spaces before and after dimension reduction. +# - 2-dimensional graph: marginal utility at different grid points of a state variable fixing the values of other two state variables. +# - For example, how the reduction works for liquid assets for given level of illiquid assets holding and productivity. +# +# - 3-dimensional graph: marginal utility at different grids points at grid points of liquid and illiquid assets with only value of productivity fixed. +# - There is limitations at 1-dimensional graph, as we do not know ex ante at what grid points the dimension is reduced. So the 3-dimensional graph gives us a more complete picture. +# - In this context, as we only have 4 grid points for productivity, we can fix an arbitrary one of the 4 grids and focus on how the number of grids is reduced for liquid and illiquid assets. +# +# #### Marginal distributions +# +# - We can also graphically show marginal distributions versus joint distribution. + +# %% {"code_folding": [0]} +## Graphical illustration + +### In 2D, we can look at how the number of grid points of +### one state is redcued at given grid values of other states. + +mgrid_fix = EX3SS['mpar']['nm']//11 # "//" is for floor division unambiguously +kgrid_fix = EX3SS['mpar']['nk']//11 +hgrid_fix = EX3SS['mpar']['nh']//2 + + +mut_StE = EX3SS['mutil_c'] +dim_StE = mut_StE.shape +mgrid = EX3SS['grid']['m'] +kgrid = EX3SS['grid']['k'] +hgrid = EX3SS['grid']['h'] + +mut_rdc_idx = np.unravel_index(SR['indexMUdct'],dim_StE,order='F') + +mgrid_rdc = mut_rdc_idx[0][(mut_rdc_idx[1]==kgrid_fix) & (mut_rdc_idx[2]==hgrid_fix)] +kgrid_rdc = mut_rdc_idx[1][(mut_rdc_idx[0]==mgrid_fix) & (mut_rdc_idx[2]==hgrid_fix)] +hgrid_rdc = mut_rdc_idx[2][(mut_rdc_idx[0]==mgrid_fix) & (mut_rdc_idx[1]==kgrid_fix)] + +## compare marginal utility before and after dct +plt.figure(figsize=(15,5)) +plt.title('Marginal utility of consumption at grid points of states') + +plt.subplot(1,3,1) +plt.plot(mgrid,mut_StE[:,kgrid_fix,hgrid_fix],'x',label='StE(before dct)') +plt.plot(mgrid[mgrid_rdc],mut_StE[mgrid_rdc,kgrid_fix,hgrid_fix],'r*',label='StE(after dct)') + +plt.xlabel('m',size=15) +plt.ylabel(r'$u_c^\prime$',size=15) +plt.legend() + +plt.subplot(1,3,2) +plt.plot(kgrid,mut_StE[mgrid_fix,:,hgrid_fix],'x',label='StE(before dct)') +plt.plot(kgrid[kgrid_rdc],mut_StE[mgrid_fix,kgrid_rdc,hgrid_fix],'r*',label='StE(after dct)') +plt.xlabel('k',size=15) +plt.ylabel(r'$u_c^\prime$',size=15) +plt.legend() + +plt.subplot(1,3,3) +plt.plot(hgrid,mut_StE[mgrid_fix,kgrid_fix,:],'x',label='StE(before dct)') +plt.plot(hgrid[hgrid_rdc],mut_StE[mgrid_fix,kgrid_fix,hgrid_rdc],'r*',label='StE(after dct)') +plt.xlabel('h',size=15) +plt.ylabel(r'$u_c^\prime$',size=15) +plt.legend() + +# %% {"code_folding": [0]} +## 3D scatter plots of all grids and grids after dct + +## full grids +mmgrid,kkgrid = np.meshgrid(mgrid,kgrid) + +## rdc grids + +fig = plt.figure(figsize=(10,10)) +fig.suptitle('Marginal utility at grid points of m and k(for different h)',fontsize=(13)) +for hgrid_id in range(EX3SS['mpar']['nh']): + ## prepare the grids + hgrid_fix=hgrid_id + fix_bool = mut_rdc_idx[2]==hgrid_fix # for a fixed h grid value + rdc_id = (mut_rdc_idx[0][fix_bool], mut_rdc_idx[1][fix_bool],mut_rdc_idx[2][fix_bool]) + mmgrid_rdc = mmgrid[rdc_id[0]].T[0] + kkgrid_rdc = kkgrid[rdc_id[1]].T[0] + mut_rdc= mut_StE[rdc_id] + + ## plots + ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d') + ax.scatter(mmgrid,kkgrid,mut_StE[:,:,hgrid_fix],label='StE(before dct)') + ax.scatter(mmgrid_rdc,kkgrid_rdc,mut_rdc,c='red',label='StE(after dct)') + ax.set_xlabel('m') + ax.set_ylabel('k') + ax.set_zlabel(r'$u^\prime_c$') + ax.set_title(r'$h({})$'.format(hgrid_fix)) + #ax.set_xlim(0, 200) + #ax.set_ylim(0, 400) + ax.view_init(40, 160) + ax.legend(loc=10) + +# %% [markdown] +# #### Observation +# +# - For a given grid value of productivity, the remaining grid points after DCT to represent the whole m-k surface concentrate on low values of k and m. The reason, to put it simply, is that the slopes of the surface of marginal utility are very steep around this area. +# - For different grid values of productivity(4 sub plots), the numbers of grid points operation differ. From the lowest to highest values of productivity, there are 78, 33, 25 and 18 grid points, respectively. They add up to the total number of grids 154 after DCT operation, as we print out above for marginal utility function. + +# %% [markdown] +# ### Summary: what do we achieve after the transformation? # # - Via DCT, the dimension of policy function and value functions are reduced both from 3600 to 154 and 94, respectively. -# - Via fixed copula operation and marginalizing the joint-distribution, the dimension of gamma_state is 64 now, (excluding exogeous states like interest rate). +# - Via marginalizing the joint distribution with the fixed copula assumption, the marginal distribution is of dimension 64 compared to its joint distribution of a dimension of 3600. +# +# +# diff --git a/HARK/BayerLuetticke/TwoAsset.ipynb b/HARK/BayerLuetticke/TwoAsset.ipynb index 3255ce46e..22395cac9 100644 --- a/HARK/BayerLuetticke/TwoAsset.ipynb +++ b/HARK/BayerLuetticke/TwoAsset.ipynb @@ -287,7 +287,9 @@ "metadata": { "code_folding": [ 0 - ] + ], + "lines_to_end_of_cell_marker": 0, + "lines_to_next_cell": 1 }, "outputs": [], "source": [ @@ -340,7 +342,9 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "code_folding": [] + "code_folding": [], + "lines_to_end_of_cell_marker": 0, + "lines_to_next_cell": 1 }, "outputs": [], "source": [ @@ -414,8 +418,8 @@ " for j in range(self.mpar['nk']-1):\n", " Gamma_state[bb+np.arange(0,self.mpar['nk'],1), bb+j-1] = -np.squeeze(Xss[bb+np.arange(0,self.mpar['nk'],1)])\n", " Gamma_state[bb+j,bb-1+j] = 1. - Xss[bb+j] \n", - " Gamma_state[bb+j,bb-1+j] = (Gamma_state[bb+j,bb-1+j] - \n", - " np.sum(Gamma_state[bb+np.arange(0,self.mpar['nk']),bb-1+j]))\n", + " Gamma_state[bb+j,bb-1+j] = Gamma_state[bb+j,bb-1+j] - \n", + " np.sum(Gamma_state[bb+np.arange(0,self.mpar['nk']),bb-1+j])\n", " bb = self.mpar['nm'] + self.mpar['nk']\n", "\n", " for j in range(self.mpar['nh']-2): # Question: Why -2? Some other symmetry/adding-up condition?\n", @@ -530,12 +534,12 @@ "\n", "\n", "#### Households \n", - "- Maximizing discounted sum of felicity \n", - " - Felicity takes [GHH](https://en.wikipedia.org/wiki/Greenwood–Hercowitz–Huffman_preferences) form: $u(c,n) = U(c-G(h,n))$, where $h$ is individual productivity and $n$ is labor supply. In a nutshell, it is a type of non-separable utility between consumption and labor supply such that there is no labor supply effect by wealth and uncertainty. This is used to simplify computation but not necessary for the model.\n", - " - Consumption $c$ with elasticity of substitution across goods in CES form: $\\eta$\n", - " - $U$ takes CRRA form with coefficent of risk aversion: $\\xi$\n", - " - Frisch elasticity of labor supply $\\gamma$\n", - " - Discount factor $\\beta$\n", + "- Maximizing discounted felicity\n", + " - Consumption $c$ \n", + " - CRRA coefficent: $\\xi$\n", + " - EOS of CES consumption bundle: $\\eta$\n", + " - Disutility from work in GHH form: \n", + " - Frisch elasticity $\\gamma$\n", "- Two assets:\n", " - Liquid nominal bonds $b$, greater than lower bound $\\underline b$\n", " - Borrowing constraint due to a wedge between borrowing and saving rate: $R^b(b<0)=R^B(b>0)+\\bar R$ \n", @@ -543,8 +547,8 @@ " - Trading of illiquid assets is subject to a friction governed by $v$, the fraction of agents who can trade\n", " - If nontrading, receive divident $r$ and depreciates by $\\tau$\n", "- Idiosyncratic labor productivity $h$: \n", - " - $h = 0$ for entrepreneur, only receive profits $\\Pi$\n", - " - $h = 1$ for worker, evolves according to an AR(1) with time varying volatility \n", + " - $h = 0$ for entreprener, only receive profits $\\Pi$\n", + " - $h = 1$ for labor, evolves according to an autoregression process, \n", " - $\\rho_h$ persistence parameter\n", " - $\\epsilon^h$: idiosyncratic risk \n", "\n", @@ -556,30 +560,25 @@ "- Reseller \n", " - Rotemberg price setting: quadratic adjustment cost scalled by $\\frac{\\eta}{2\\kappa}$\n", " - Constant discount factor $\\beta$\n", - " - Investment subject to adjustment cost scaled by $\\phi$ \n", - " \n", - "#### Monetary and fiscal policy rules \n", - "- Central bank set norminal rate on bond\n", + " - Investment subject to Tobin-Q adjustment cost $\\phi$ \n", + "- Aggregate risks $\\Omega$ include \n", + " - TFP $Z$, AR(1) process with persistence of $\\rho^Z$ and shock $\\epsilon^Z$ \n", + " - Uncertainty \n", + " - Monetary policy\n", + "- Central bank\n", " - Taylor rule on nominal saving rate $R^B$: reacting deviation of inflation from target by $\\theta_R$ \n", " - $\\rho_R$: policy innertia\n", " - $\\epsilon^R$: monetary policy shocks\n", - "- Government responds to business conditions and stablize the debt\n", + "- Government \n", " - Government spending $G$ \n", - " - Labor income tax $T$ \n", + " - Tax $T$ \n", " - $\\rho_G$: intensity of repaying government debt: $\\rho_G=1$ implies roll-over \n", - " \n", - "#### Other \n", - "- Aggregate risks $\\Omega$ include \n", - " - TFP $Z$, AR(1) process with persistence of $\\rho^Z$ and shock $\\epsilon^Z$ \n", - " - Uncertainty: idiosyncratic productivity risks governed by transition probability matrix $P_H$. (Called aggregate risks although it is idiosyncratic productivity) \n", - " - Monetary policy: $\\epsilon_R$\n", - "\n", "\n", "#### Taking stock\n", "\n", "- Individual state variables: $b$, $k$ and $h$, the joint distribution of individual states $\\Theta$\n", "- Individual control variables: $c$, $n$, $b'$, $k'$ \n", - "- Optimal policy for adjust and non-adjust cases are $c^*_a$, $n^*_a$ $k^*_a$ and $b^*_a$ and $c^*_n$, $n^*_n$ and $b^*_n$, respectively \n" + "- Optimal policy for adjust and non-adjust cases are $c^*_a$, $n^*_a$ $k^*_a$ and $b^*_a$ and $c^*_n$, $n^*_n$ and $b^*_n$, respetively \n" ] }, { @@ -1177,7 +1176,7 @@ " Difference = (LHS-RHS)\n", " \n", " return {'Difference':Difference, 'LHS':LHS, 'RHS':RHS, 'JD_new': JD_new, 'c_a_star':c_a_star, 'm_a_star':m_a_star,\n", - " 'k_a_star':k_a_star,'c_n_star':c_n_star,'m_n_star':m_n_star,'P':P}\n" + " 'k_a_star':k_a_star,'c_n_star':c_n_star,'m_n_star':m_n_star,'P':P}" ] }, { @@ -1186,7 +1185,8 @@ "metadata": { "code_folding": [ 0 - ] + ], + "lines_to_next_cell": 2 }, "outputs": [], "source": [ @@ -1357,7 +1357,7 @@ " k_a_star[k_a_star.copy()>grid['k'][-1]] = grid['k'][-1]\n", " m_a_star[m_a_star.copy()>grid['m'][-1]] = grid['m'][-1] \n", " \n", - " return {'c_a_star': c_a_star, 'm_a_star': m_a_star, 'k_a_star': k_a_star,'c_n_star': c_n_star, 'm_n_star': m_n_star}\n" + " return {'c_a_star': c_a_star, 'm_a_star': m_a_star, 'k_a_star': k_a_star,'c_n_star': c_n_star, 'm_n_star': m_n_star}" ] }, { @@ -1403,7 +1403,8 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "code_folding": [] + "code_folding": [], + "lines_to_next_cell": 1 }, "outputs": [], "source": [ @@ -1612,7 +1613,7 @@ " plt.plot(range(0,mpar['maxlag']),np.zeros((mpar['maxlag'])),'k--' )\n", " plt.xlabel('Quarter')\n", " plt.ylabel('Percent') \n", - " f_N.show()\n" + " f_N.show()" ] }, { @@ -2250,13 +2251,7 @@ } }, "jupytext": { - "formats": "ipynb,py:light", - "text_representation": { - "extension": ".py", - "format_name": "light", - "format_version": "1.3", - "jupytext_version": "0.8.3" - } + "formats": "ipynb,py:light" }, "kernelspec": { "display_name": "Python 3", @@ -2273,7 +2268,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.7.3" }, "varInspector": { "cols": { diff --git a/HARK/BayerLuetticke/TwoAsset.py b/HARK/BayerLuetticke/TwoAsset.py index 2d8b7cf41..8e6022d6e 100644 --- a/HARK/BayerLuetticke/TwoAsset.py +++ b/HARK/BayerLuetticke/TwoAsset.py @@ -1,133 +1,16 @@ # --- # jupyter: -# cite2c: -# citations: -# 6202365/L5GBWHBM: -# author: -# - family: Reiter -# given: Michael -# container-title: Journal of Economic Dynamics and Control -# id: undefined -# issue: '1' -# issued: -# month: 1 -# year: 2010 -# note: 'Citation Key: reiterBackward' -# page: 28-35 -# page-first: '28' -# title: Solving the Incomplete Markets Model with Aggregate Uncertainty by -# Backward Induction -# type: article-journal -# volume: '34' -# 6202365/UKUXJHCN: -# author: -# - family: Reiter -# given: Michael -# id: 6202365/UKUXJHCN -# note: "Citation Key: reiter2002recursive \nbibtex*[publisher=Citeseer]" -# title: Recursive computation of heterogeneous agent models -# type: article-journal -# 6202365/VPUXICUR: -# author: -# - family: Krusell -# given: Per -# - family: Smith -# given: Anthony A. -# container-title: Journal of Political Economy -# id: 6202365/VPUXICUR -# issue: '5' -# issued: -# year: 1998 -# page: "867\u2013896" -# page-first: '867' -# title: Income and Wealth Heterogeneity in the Macroeconomy -# type: article-journal -# volume: '106' -# 6202365/WN76AW6Q: -# author: -# - family: SeHyoun Ahn, Greg Kaplan, Benjamin Moll, Thomas Winberry -# given: '' -# - family: Wolf -# given: Christian -# editor: -# - family: Parker -# given: Jonathan -# - family: Martin S. Eichenbaum -# given: Organizers -# id: 6202365/WN76AW6Q -# issued: -# year: 2017 -# note: "Citation Key: akmwwInequality \nbibtex*[booktitle=NBER Macroeconomics\ -# \ Annual;publisher=MIT Press;location=Cambridge, MA]" -# title: When Inequality Matters for Macro and Macro Matters for Inequality -# type: article-journal -# volume: '32' -# undefined: -# author: -# - family: Reiter -# given: Michael -# container-title: Journal of Economic Dynamics and Control -# id: undefined -# issue: '1' -# issued: -# month: 1 -# year: 2010 -# note: 'Citation Key: reiterBackward' -# page: 28-35 -# page-first: '28' -# title: Solving the Incomplete Markets Model with Aggregate Uncertainty by -# Backward Induction -# type: article-journal -# volume: '34' # jupytext: # formats: ipynb,py:light # text_representation: # extension: .py # format_name: light -# format_version: '1.3' -# jupytext_version: 0.8.3 +# format_version: '1.4' +# jupytext_version: 1.1.3 # kernelspec: # display_name: Python 3 # language: python # name: python3 -# language_info: -# codemirror_mode: -# name: ipython -# version: 3 -# file_extension: .py -# mimetype: text/x-python -# name: python -# nbconvert_exporter: python -# pygments_lexer: ipython3 -# version: 3.6.7 -# varInspector: -# cols: -# lenName: 16 -# lenType: 16 -# lenVar: 40 -# kernels_config: -# python: -# delete_cmd_postfix: '' -# delete_cmd_prefix: 'del ' -# library: var_list.py -# varRefreshCmd: print(var_dic_list()) -# r: -# delete_cmd_postfix: ') ' -# delete_cmd_prefix: rm( -# library: var_list.r -# varRefreshCmd: 'cat(var_dic_list()) ' -# types_to_exclude: -# - module -# - function -# - builtin_function_or_method -# - instance -# - _Feature -# window_display: false -# widgets: -# application/vnd.jupyter.widget-state+json: -# state: {} -# version_major: 2 -# version_minor: 0 # --- # # [Bayer and Luetticke (2018)](https://cepr.org/active/publications/discussion_papers/dp.php?dpno=13071) From 4dbdd433960e0eb5e65447d0ca9d17fd12b09a90 Mon Sep 17 00:00:00 2001 From: Tao Wang Date: Thu, 6 Jun 2019 16:43:40 -0400 Subject: [PATCH 71/77] plotting consumption function for adjusters and nonadjusters --- .../DCT-Copula-Illustration.ipynb | 193 ++++++++++++------ .../BayerLuetticke/DCT-Copula-Illustration.py | 125 +++++++++--- 2 files changed, 229 insertions(+), 89 deletions(-) diff --git a/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb b/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb index 49379ab3b..81c142d8a 100644 --- a/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb +++ b/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb @@ -611,22 +611,73 @@ ], "scrolled": true }, + "outputs": [], + "source": [ + "## Graphical illustration\n", + "\n", + "### In 2D, we can look at how the number of grid points of \n", + "### one state is redcued at given grid values of other states. \n", + "\n", + "mgrid_fix = EX3SS['mpar']['nm']//11 # \"//\" is for floor division unambiguously \n", + "kgrid_fix = EX3SS['mpar']['nk']//11\n", + "hgrid_fix = EX3SS['mpar']['nh']//2\n", + "\n", + "\n", + "xi = EX3SS['par']['xi']\n", + "\n", + "invmutil = lambda x : (1./x)**(1./xi) \n", + "\n", + "### convert marginal utilities back to consumption function\n", + "mut_StE = EX3SS['mutil_c']\n", + "mut_n_StE = EX3SS['mutil_c_n'] # marginal utility of non-adjusters\n", + "mut_a_StE = EX3SS['mutil_c_a'] # marginal utility of adjusters \n", + "\n", + "c_StE = invmutil(mut_StE)\n", + "cn_StE = invmutil(mut_n_StE)\n", + "ca_StE = invmutil(mut_a_StE)\n", + "\n", + "\n", + "### grid values \n", + "dim_StE = mut_StE.shape\n", + "mgrid = EX3SS['grid']['m']\n", + "kgrid = EX3SS['grid']['k']\n", + "hgrid = EX3SS['grid']['h']\n", + "\n", + "## indexMUdct is one dimension, needs to be unraveled to 3 dimensions\n", + "\n", + "mut_rdc_idx = np.unravel_index(SR['indexMUdct'],dim_StE,order='F')\n", + "\n", + "## these are filtered indices for the fixed grids of other two states \n", + "\n", + "mgrid_rdc = mut_rdc_idx[0][(mut_rdc_idx[1]==kgrid_fix) & (mut_rdc_idx[2]==hgrid_fix)]\n", + "kgrid_rdc = mut_rdc_idx[1][(mut_rdc_idx[0]==mgrid_fix) & (mut_rdc_idx[2]==hgrid_fix)]\n", + "hgrid_rdc = mut_rdc_idx[2][(mut_rdc_idx[0]==mgrid_fix) & (mut_rdc_idx[1]==kgrid_fix)]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "code_folding": [ + 0 + ] + }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": { @@ -636,69 +687,87 @@ } ], "source": [ - "## Graphical illustration\n", - "\n", - "### In 2D, we can look at how the number of grid points of \n", - "### one state is redcued at given grid values of other states. \n", - "\n", - "mgrid_fix = EX3SS['mpar']['nm']//11 # \"//\" is for floor division unambiguously \n", - "kgrid_fix = EX3SS['mpar']['nk']//11\n", - "hgrid_fix = EX3SS['mpar']['nh']//2\n", + "## 2D graph: compare consumption function before and after dct \n", + "fig=plt.figure(figsize=(15,8))\n", + "fig.suptitle('Consumption at grid points of states')\n", "\n", + "## for non-adjusters \n", "\n", - "mut_StE = EX3SS['mutil_c']\n", - "dim_StE = mut_StE.shape\n", - "mgrid = EX3SS['grid']['m']\n", - "kgrid = EX3SS['grid']['k']\n", - "hgrid = EX3SS['grid']['h']\n", + "#c_n(m)\n", + "plt.subplot(2,3,1)\n", + "plt.plot(mgrid,cn_StE[:,kgrid_fix,hgrid_fix],'x',label='StE(before dct)')\n", + "plt.plot(mgrid[mgrid_rdc],cn_StE[mgrid_rdc,kgrid_fix,hgrid_fix],'r*',label='StE(after dct)')\n", + "plt.xlabel('m',size=15)\n", + "plt.ylabel(r'$c_n(m)$',size=15)\n", + "plt.legend()\n", "\n", - "mut_rdc_idx = np.unravel_index(SR['indexMUdct'],dim_StE,order='F')\n", + "## c_n(k)\n", + "plt.subplot(2,3,2)\n", + "plt.plot(kgrid,cn_StE[mgrid_fix,:,hgrid_fix],'x',label='StE(before dct)')\n", + "plt.plot(kgrid[kgrid_rdc],cn_StE[mgrid_fix,kgrid_rdc,hgrid_fix],'r*',label='StE(after dct)')\n", + "plt.xlabel('k',size=15)\n", + "plt.ylabel(r'$c_n(k)$',size=15)\n", + "plt.legend()\n", "\n", - "mgrid_rdc = mut_rdc_idx[0][(mut_rdc_idx[1]==kgrid_fix) & (mut_rdc_idx[2]==hgrid_fix)]\n", - "kgrid_rdc = mut_rdc_idx[1][(mut_rdc_idx[0]==mgrid_fix) & (mut_rdc_idx[2]==hgrid_fix)]\n", - "hgrid_rdc = mut_rdc_idx[2][(mut_rdc_idx[0]==mgrid_fix) & (mut_rdc_idx[1]==kgrid_fix)]\n", + "## c_n(h)\n", "\n", - "## compare marginal utility before and after dct \n", - "plt.figure(figsize=(15,5))\n", - "plt.title('Marginal utility of consumption at grid points of states')\n", + "plt.subplot(2,3,3)\n", + "plt.plot(hgrid,cn_StE[mgrid_fix,kgrid_fix,:],'x',label='StE(before dct)')\n", + "plt.plot(hgrid[hgrid_rdc],cn_StE[mgrid_fix,kgrid_fix,hgrid_rdc],'r*',label='StE(after dct)')\n", + "plt.xlabel('h',size=15)\n", + "plt.ylabel(r'$c_n(h)$',size=15)\n", + "plt.legend()\n", "\n", - "plt.subplot(1,3,1)\n", - "plt.plot(mgrid,mut_StE[:,kgrid_fix,hgrid_fix],'x',label='StE(before dct)')\n", - "plt.plot(mgrid[mgrid_rdc],mut_StE[mgrid_rdc,kgrid_fix,hgrid_fix],'r*',label='StE(after dct)')\n", "\n", + "### for adjusters \n", + "## c_a(m)\n", + "plt.subplot(2,3,4)\n", + "plt.plot(mgrid,ca_StE[:,kgrid_fix,hgrid_fix],'x',label='StE(before dct)')\n", + "plt.plot(mgrid[mgrid_rdc],ca_StE[mgrid_rdc,kgrid_fix,hgrid_fix],'r*',label='StE(after dct)')\n", "plt.xlabel('m',size=15)\n", - "plt.ylabel(r'$u_c^\\prime$',size=15)\n", + "plt.ylabel(r'$c_a(m)$',size=15)\n", "plt.legend()\n", "\n", - "plt.subplot(1,3,2)\n", - "plt.plot(kgrid,mut_StE[mgrid_fix,:,hgrid_fix],'x',label='StE(before dct)')\n", - "plt.plot(kgrid[kgrid_rdc],mut_StE[mgrid_fix,kgrid_rdc,hgrid_fix],'r*',label='StE(after dct)')\n", + "## c_a(k)\n", + "plt.subplot(2,3,5)\n", + "plt.plot(kgrid,ca_StE[mgrid_fix,:,hgrid_fix],'x',label='StE(before dct)')\n", + "plt.plot(kgrid[kgrid_rdc],ca_StE[mgrid_fix,kgrid_rdc,hgrid_fix],'r*',label='StE(after dct)')\n", "plt.xlabel('k',size=15)\n", - "plt.ylabel(r'$u_c^\\prime$',size=15)\n", + "plt.ylabel(r'$c_a(k)$',size=15)\n", "plt.legend()\n", "\n", - "plt.subplot(1,3,3)\n", - "plt.plot(hgrid,mut_StE[mgrid_fix,kgrid_fix,:],'x',label='StE(before dct)')\n", - "plt.plot(hgrid[hgrid_rdc],mut_StE[mgrid_fix,kgrid_fix,hgrid_rdc],'r*',label='StE(after dct)')\n", + "\n", + "## c_a(h)\n", + "plt.subplot(2,3,6)\n", + "plt.plot(hgrid,ca_StE[mgrid_fix,kgrid_fix,:],'x',label='StE(before dct)')\n", + "plt.plot(hgrid[hgrid_rdc],ca_StE[mgrid_fix,kgrid_fix,hgrid_rdc],'r*',label='StE(after dct)')\n", "plt.xlabel('h',size=15)\n", - "plt.ylabel(r'$u_c^\\prime$',size=15)\n", + "plt.ylabel(r'$c_a(h)$',size=15)\n", "plt.legend()" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { - "code_folding": [ - 0 - ] + "code_folding": [] }, "outputs": [ { "data": { - "image/png": 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\n", 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" + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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iMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMoW9yP1yU0pBRESFiK0ugIWxnSIi2np52ymObBARERERkSkYbBARERERkSkYbBARERERkSkYbBARERERkSkYbBARERERkSkYbBARERERkSkYbBARERERkSkYbBARERERkSkYbBARERERkSkYbBARERERkSkYbBARERERkSkYbNC21dvbixdffDHnfX/8x3+Mv/iLv9B1njvvvBMvvfSSkUUjIiJiO0UEBhu0TS0tLeHy5cu48cYbN9w3NzeHL3/5y3j44YfV2xYXF3HffffB4/Ggv78fX/va19T7PvKRj+Cxxx7blHITEdHOUGo79fnPfx633347nE4nHnjggYzj2U7RdsZgg7al06dPw+/3w+12b7jvySefxD333IO6ujr1tve///1wOByYnZ3FV7/6Vbzvfe9Te4nuvfde/PjHP8aVK1c2rfxERFTdSm2nfD4fHnnkETz44IMbjmc7RdsZgw3alk6dOoXBwUF88IMfRHt7O3w+H77//e8DAL73ve/hF37hF9Rjw+Ewvv3tb+MTn/gEvF4vXve61+Hee+/F3/3d3wEAXC4XDh48iGeffXZLXgsREVWfUtopAHjb296Gt771rWhtbd1wLrZTtJ0x2KBt6dSpU3jhhRdwzz33YHZ2Fg8//DA+85nPAEj1Jt1www3qsRcuXEBNTQ12796t3nbrrbdmzH/ds2cPTp48uXkvgIiIqlop7ZQebKdou2KwQdvS6dOn8bGPfQx33303bDYb9u7dq963vLyM+vp69fdQKITGxsaMxzc2NiIYDKq/19fXY3l52fyCExHRjlBKO6UH2ynarhhs0LYjpcSZM2fw5je/Wb3tzJkzakXe3NycEUh4vV6srKxknGNlZSWjog8Gg2hqajK55EREtBOU2k7pwXaKtisGG7TtjI2NAQCuu+469bahoSHs378fAHDLLbfgwoUL6n27d+9GPB7H8PCwetvJkydx0003qb+fPXsWt956q9lFJyKiHaDUdkoPtlO0XTHYoG3n1KlTuPnmmyGEUG8bGhpSK+F77rkHP/nJT9T7PB4P3va2t+Gxxx5DOBzGz3/+czz11FP4rd/6LQDA+vo6jh8/jje+8Y2b+0KIiKgqldpOAUA8HkckEkEikUAikUAkEkE8HgfAdoq2NwYbtO2cPn06o3dnYWEBMzMz2LdvHwDgXe96F5555hmsra2px/zlX/4l1tbW0NHRgfvvvx9/9Vd/pY5sfPe738XrX/96+Hy+zX0hRERUlcpppz75yU+irq4On/70p/GVr3wFdXV1+OQnPwmA7RRtb0JKWej+gncSWdWf/MmfoKOjA7//+79f9NhXvepV+NKXvqQ2AkQWJIofsmOxnaJtie0UVZm87RSDDSIi62OwkR/bKSKirZe3neI0KiIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDaItIqVEMpnc6mIQERHlxXaKKmXf6gIQVTsppRpYJJNJxONxJBIJJBIJSCnh9XpRU1MDm42xPxERbb7sdkppoxKJBJLJJNxuN2pra9lOUVkYbBAZpFBFnU0IASEEEokE4vE44vE4bDYbamtr1fuIiIiMlN1OadurbNp2SjnWZrPBbrfDZrOxnSLdGGwQlSC790cZqVD+DwBXrlyBEAJdXV0QQuStlJXjbTabes719XXYbDbU1NSgpqaGlTkREZVE205JKXN2fi0uLiIUCqGvr69gO6VQAo9kMoloNAohBOx2O9sp0oXBBlEO2QGFtqKWUmYcq1TC2spaCIGamhrdz6ecQ2kkzp49i/7+frjdblbmRES0Qb4putp2Skqpti+5gopy2ykAGBkZQXt7OxoaGmC329lOUV4MNmhH01bU0WgU8Xhc7b3RHpNdWZtFqaxXVlYgpUQsFkM8HkdNTQ0rcyKiHUjbTsViMUSjUdhstoJTdM2cjqucNxwOo7m5WZ0KrIx0MK+DsjHYoKpXKPFNO0oxOzuLWCyG3t7eiuajZo98lMtms6lTrFiZExFVLz1TdAFgaWkJCwsLuP766zclb0J57nzPk6ud0naOsYOMAAYbVEXyTX0qlPimrQyVituIC/lKK1htwKIdumYyORHR9lXpFF2ls6nSdqqUTrF87Uy+dkp5Tcw/JAWDDdp2skcpQqEQAMBut2cck2+O6naRXWZtZc5kciIi68pup1ZXVxGPx+FwODKO2awpurkY0SlWqJ3STgVmMvnOxmCDLKmUxLfLly/D4/Ggvb3dEhWZEdOoCp0juzIfGhrCvn37UFtby8qciGiT6J2iCwBzc3OIx+Po7e3dEXW0drEUKSVeeuklDAwMwO12M/9wB2KwQVuqnDW/s4d0tZWaVegpSyKRQCwWy3lfrh6jfM+hjOzEYjHEYjHY7XZW5kREBql0im727VZgVKeYntcjhEA4HGb+4Q7GYINMly/xbXZ2Fi0tLWplVQ1Tn3KJRqMIh8NYXV1V/1VWEwEAl8sFv9+P5ubmsp9DORcrcyKi8uQapZibm0N9fb26RGw1tVO5yp5IJDLaq3A4jGg0it7eXuzataui9kR5z5hMvvMw2CDDaIOK7E2EciW+TUxMoK2tzTIXw5X09EgpEYlEsLq6iuXlZcTjcUxPTyORSKC2thYejwdutxvt7e1wu91wOBxqIl08HsfY2BguXLgAv9+P9vZ23T1G2ZhMTkSUX6l7U0xPT+P666/PyLWoBvF4HGtra5iens7oBKupqYHb7YbH40FjYyO6uroAAAsLCzh06BC6u7vR29uL2traktop7bFMJt95GGxQyfLNUS205neugMKKFUqxMiWTSaytrak9Pqurq1hdXYWUEi6XC263G0IINDc3o7e3NyNpPZvSsDU2NmL//v1YXV3F+Pg4RkZGEIvFKgp+mExORDuZEVN0lfuMsBV1rrYTTDtaoeSU1NTUwOv1ZnSCZZdTCciuu+46+P1+TE9P4+jRo2htbUUikSgr2FAwmXznYLBBOeVLfAsGg1hfX0dTU1PG8dUwpKwVj8c3VNCRSARCCLjdbrXnR6mktcHUxMQEXC5XwUAjF7fbjb179yIajeLnP/85Dh8+jF27dqGnpwe1tbVlvY7syvzll19Gf38/PB4PK3Mi2tbyTdGNRCK4du0aWltb1eOsMPXJqD2Ysmk7wZT2am1tDclkUu0E83g88Pl8aoL2zMwMotEoenp6dD9PTU0N+vr60NPTg9nZWUxPT+P8+fO47rrrUF9fX/Tx+d737GTykZERtLW1obGxkfmHVYLBxg5XauJbdiVuBUoFVSqlJ0WpoK9evYr19XVcuXJlw1Cyz+eDy+XSVenlK0s8HkMiEYPT6S74eIfDAZfLhdtvvx2XL1/G0aNH0dbWhv7+frhcrpJfJ5CZTK68biaTE9F2UOoU3Wg0ivn5eXR0dFT83OW2L2aIx+PqqPrFixczOsHq6urg8Xjg8XjQ1ta2oRNMr0Qihnh8HQ6HJ2+7YLPZ0N3djStXrqCzsxPnz5+HEELNPSy2J0chQgh1mWDmH1YPBhs7hJ6pT3p6f4y8KN2sSlwZSs5O0tbmU3g8HtTX16OpqQkDAwMVv87sx6+vL2Fx8W8BBFFX91Y0Nd1ctMx2ux39/f3o7e3F7OwshoaG4PV64ff74fV6yyqXlLLgjq+szIloq1TzFN1S5FtURJkGm0wmS+4Ey5ar7Y3Fwpif/1sIMYfa2jegtfV1Rc/T3NwMn8+HlZUVjI2NYXh4GAMDA+jo6MgoV6n5HbnaKeYfbl8MNqpIoTW/L126hN7eXvXYSoaUrdLLky2ZTKo5FLnyKZQk7V27dqlDyVpXrlxBPB43pRKLxaYgxAKABsRiQwBuLvo+KuVQepG6urqwsLCAs2fPoqampqwVrPQk6SlBBytzIjJaoXZqcnJyw7SectopK41G5JOdT5GrE0xZVMTj8agX2YuLi1hcXERbW1vFZch+T+PxqxBiBkK0Ih5/EUDhYEPbnjQ0NODWW2/F2toaxsfHcfHiRfT29sLn86GmpsaQZHLmH25fDDa2oXIS365evYqBgYGKn9voP+xyGgRtPkU4HMb8/DySySQuXbqUkU/R0dGBuro6S/TWu1y9WF9vRzK5gvr6g2WdQwiBtrY2tLW1qb1IFy5cUJPJy/1ssvM6otEohBBM0iOispWzN8Xs7Cz6+/u3oLT5VRq4KPkUKysrWF1dRTAYLKkTbDM5nd1wOHoQj8/C47m7rHPU1dVhz549iEajuHTpEg4dOgSfz4dkMslk8h2MwYZF5Up8K3fqE2DsihpG9RgVKpNy0ZudpB2LxVBTU6NW0ErPvtfrVZfo22q5Kkq7vQmtrb8HIA6gvLwLLaUXaXV1FYcPH8ahQ4fQ19cHn89XMLgqFJRk33716lVIKdHR0cG8DiLKyagpuoA12ym9ii0qkkwm4XA40NfXV3Y+hdlsNhdaWh4CEAPgLHp8ofbE4XBkrGAVCoVw/vx5+P1+XbmHepPJFxcXEQ6H0dPTw3bKwhhsbLFCa35PTEygr68vo6Le6tU0jCal3LCKhjKU7HA4MlZ9GhgYyLnWeTgc3ibvhx1G/8m53W41mfzSpUt4/vnn4fP51HXQs5WyM/nq6qr6nWSSHtHOVWjq08TEBHp7ezdMe6m2dkr5V7uoiDK6rnSCFVpUZHZ2Fuvr62Xn2xktf1tgg55AQ1HsM1ZWsJqcnERTUxNOnDgBt9sNv9+fdwWrUpLJo9EogsEg2ymLY7CxScpJfJubm4Pf79+C0uZXbo+Rkk+hraSDwSCGhoZQV1enVtJNTU1lDSVbfX6u2bS9SFNTU+o66AMDAxm9SEYk6XHHV6LqVM4U3cXFRfT19ak7bFtBpSMb2nyK1dVVjI2NIRqNblhUROkEU/Ip9Jy3mpTyeoQQ6OrqQldXFxYXFwuuYMVk8urDYMNA+db8Vv6vPW4zh5SNVKwSV5bm0wYWkUgENpstY2m+jo4ORKNR3HzzzWXvIWFVW9mg1NTUZKxgdeLECXg8HnUFq3IqcYA7vhJVCzOm6FrtIlpvmfQsKmKz2dDe3o62trYtzaewqlIT9wFZIom1AAAgAElEQVSgtbUVra2tGbmHAwMD6OzsVD87JpNXF/7llKFQRa1MfVJU65Dy+vr6hqBC2bdBGaVobm5GT08PnE5nztdeLe9HLlv92rQrWC0uLuLcuXMQQiAWi+k+B5P0iLavzZqia8VgI1u+fAqlE6zQJq1nz56Fx+OpukDDiM+s0nNkr2A1OjqK3t5eJpNXoer66zFYqYlvQgjMz89bbupTuZR8Cm0lvbKygvX1dUQiETVJu7OzE263O2c+RSFWa6SsVBajCCEyepGOHj2KY8eOYSDHOujZ9CSTK59hLBbD1NQUdu3axSQ9ok201VN0ja7Hy11ZT5tPce3aNYRCIQwNDW3Ip2hqaqpof4pqUunrr2QVRC3tClaTk5NYXFzEpUuXEAgEis58KLWdmp6eRldXFxwOx47//DfTjg82CiW+5dqZtNpGKQAgkUioSdpKULG2tgYpZUavT0tLC+LxOGZmZrBnz56tLrYpjPhcS53HulkaGhrgdrtx8803Y2JiYsM66Nn0NiTK38XY2Bg6OzuZpEdkMCtP0d3sYEPJpyi0qIjT6YTD4cBNN92kO5/CbFYog9U5HA4MDg5icXERtbW1au5hf38/6urqcj6m1HZqYmICra2tSCaT3Mx2E+2YYCNf4lswGMTa2hpaW1vVY7UjFVtdQRjVcwAAsVhsw1CyMqdR2Z+ivr4enZ2defenCAaDhpQFqO7K18qvze12b1gHvbu7G319fRm9SOV895hMTlS+fFN0I5EIFhcX0dnZqR5rlc4vI4MN7euoZJPWaDSKpaWlkkfb85XJqNdnpdHzUur3fMcZeX2SraenB36/H7Ozszh58mTeFaxKLYMSZAghuJntJqq6YCMajSIej6O2tlbX1KdYLIZr166ho6NjC0udW6mJUsAr+1NkL80XDodx+vTpjP0pCuVTFCuTUYw4FyuH8mSvg57di1RuQ8JkcqLC4vE4otGo7nYqkUhgcXERPp/PsDIYdaFYaZugzadYX1/H6dOn1U4w7ch6KZu0Wm2KrlUZ9fmbQfnud3V1obOzM+8KVuVcI2k7k5UAn5vZmqvqgo0nnngCa2treOCBB3Qlvik9sVZUqMJMJpM5h5KTySScTqdaQXd2dsLj8eDEiRO47bbbNvkVbB6rfobbgbIOurKCldKLZLPZ0NDQUPZ5maRHlNu//uu/4rnnnsPHPvYxAMVHKYxup8q5SCt0rlz5IVrafIpCm7Ta7XYMDg7C4/FYpn6wSjkUVmrrzCpL9nczO/dwfHxcXcGqlGRy7fm0/9deayntlHZUnipXdcGG0+lEMBjUvXKEzWYrWlFuFSGE2uujDSjW1tYAIGMouaWlBW632/S1zo0eMrdSxWkUM4eWzZTdi3TmzBn1byl7HfRSz6v8q1x0RKNRAKnEwO34XhFVwuFwIJFI6K6vjW6nlADBiLnq2npcTz5FoU1aFxYWSh5tL1YmI1itnbJSnWlGWQq93w0NDbjlllvUFaxmZ2fR3NyMXbt2VXT9k91OxeNxxGIxSCnhdrst9Z5vR1UZbCgXMnpY5YJXm0+hVNDBYBCnTp1SA4r6+np0dXXB5XIxoYkAmNMIKr1IXV1dqKurw9TUFIaHh9Hf36+ug17JuYUQCIfDuHDhAm699VYmk9OO43Q6sb6+rvt4o4ONSkdKsvMpRkZG1GW1XS5X2Zu0GtUe88KwOCM6xTZrZCMXZQUrp9OJ5eXlvLmH5dBOXzx27Bhe9apXMf+wQjs+2NjMkQ1lf4rsJG1laokSVLS2tqKvrw9nz57F3r17DUlyM0q19xhtR2ZVfFJKeDwe9PX1bVgHPd8KVqWcW5kywh1faacpNdgwut7Vez49m7TW1taiu7sb7e3tFXcYWDEZ2yodklZl1siG3vPabDZ0dnaiq6tLzT1saWnBwMBA3hWsSimH0k4xmbwyVRdsOByOLR/ZSCQS6qZ32lU0svMpurq64Ha780bhVswnMWvlkWqymZ+Zmc+lrfCz10GvtBcpX5Ied3ylncDhcJS0waYZIxvK+ZRFRbI7wbSbtCqLiuzatWvD/hTr6+uWG223YoDAuky/cnYQz5d7mGsFq1LLkZ1/yGTy0lVdsLGZIxuJRGJDBR0Oh3H8+HG1gvZ4PGhtbS0rn0JP4l0ptmsuwXak531OJpMVN4hmfqa5zq2sgz4wMJDRi9Tf31/RuZlMTjvJVoxsaDdpXVtbw/DwMKLRqJpPoYysd3R0lLRJ607I47PaaIsRrFSWXEoNNrSP0+YeXrhwAQDUFaxKkevcyr/adorJ5MVVZbBh9FxYZRUNbWCh3Z/C4/Go+RTr6+s4cOCAIYnaZlTiVkq82ynzc5X5zdpliNfW1tRgsqOjAwMDA3A6nSWfe7ODDUV2L9KpU6ewtraGYDCoqxcpX3Jqrspc6WFlZU7VwszcQm19o/ybvUlrTU0NOjs70dLSojufwoiybda5OAKvT77Xpu1IDQaDSCQS6iphVpOvndKuYBUMBjE2NoYLFy6oSd96OwTzjdhpO8iUqcDMP8yv6oKNcqdRKfkU2UGFNp9CGaXo6+vLu9W9kVOfrFqJW5EVemm0SZNXrlzB1NSUGlRkB6Uul0v93i0vL+P48eNoamqC3++veJ6pUfRUyEovUltbGw4fPqz2Ig0MDKClpSXv4/WeW/neBoNBzM3NIRAIsDKnba/UdioXPfkUXq835/4UL730UkmJ24Wwndq+kskkgsFgRkeY8h1S2qzGxkYkk0mcOXMGLpcLgUCg7GlJZtDTltTX16srWB06dAjPP/88ent7i65gVWo7FYlEMD4+jhtuuIHJ5FmqLtgo1mOUTCbVoWTlj2tlZQXHjh2D0+lUh5K7u7sL5lPkY+TcWitW4oA1Luy3UqGRCrfbjVgshsbGRrWRz1fZxONxCCHg8/nQ3d2Nq1ev4uTJk/B4PEgkErrKslUjG7nU1tbi4MGDai/S8PAwBgYGcq5gVerutfF4HMFgkMnkVBX0jsBrN2mNRqM4f/48VldXM/IpPB5PyZu0VnunmNGsWKZSaEcqtNc9NTU1aGxshNfrRWNjI3w+34acHGWzyb6+PnVjPZvNhsHBwS18Ra8opS2pq6uDy+XC7bffjsnJSRw+fBhdXV15cw+VBHE9lO/uysoKN7PNYdOCjQcffBBPP/00Ojo6cObMGQDA29/+dpw/fx4AsLy8jKamJpw4cWLDYwcGBlBfX6/Oi3vhhRfyPo9Sia+srKhzVLW9PgAydiVtaWnB6uoq7rjjDkNep5UrXisOT1upEs8uS7GgQjtSoQ0qLly4oC75qJcQAp2dnejo6MD8/DxmZmZw4sQJDA4OFuxFskqwoa2Utb1I4+PjuHjx4oZepFLX+E8mk+qIBpPJabvL7hTT5lNoFxXR5lModUQp+RT5WLUet1qbAGyvUZJcQYV2pMLr9aKpqQk+nw+zs7Ooq6tDZ2en7vO3tLSgpaUF165dw8WLFxEOh7G4uFjRPkyVKqcNzM49PHbsGJqbmzGQtYJVqRsGKu2a0k4x//AVmxZsPPDAA/jABz6Ad73rXept3/zmN9X/f/jDH0ZjY2Pex//4xz9GW1tbzvvi8TieeOIJnD17FsePH8fIyAjuvvtufO5zn0N3dzcaGxvR3d2ds5fZ6IrNqhXvTv2C66EEFSsrK4hGo1haWtIVVJhBCIH29nZ1yVmlFykQCKCpqcm0582lnBVBtJQVrGKxGC5duqSuYNXb21tyA6E9nsnkZBazO8UikQiGh4dx8uRJrKys4L777sM73/lO9PX1ZexPkWuTVuW5jWDVdgrY/qMIm0GZ/hQKhdTgQgkqlCnfTU1N2LVrV97RrkrqycbGRtx222342c9+hsnJSQwPDyMQCKCtrW3T699KOtyKrWBVaTul/Mtk8k0MNu666y6Mj4/nvE9Kib//+7/Hj370o7LOXVNTg2QyiTe/+c14xzvegccffxxf//rXdT3W6A+82qdRWbHnSa9iIxWJRAIulwu9vb0VBRVGjTZoe5FGR0cRj8cRCAQyciGsMrJRqAeotrZW7UW6fPkyXnjhBTidzpJyU3INZzOZnIxmZqcYkApcGhoacOONN0JKiT/90z/dkr2UrNxOWc1W5pEoIxXaoCIUCiGZTCIej+sKKvIx4jXV1NTg1ltvRTgcxtjYGEZGRuD3+yve/LUURi18o13BSulc6OjoKGtkI9f5d3oyuSVyNn72s5+hs7MT119/fc77hRD4lV/5FQgh8PDDD+M973nPhvvf+973AgBmZ2crTryrhBUDBCPPZcUyZUskElhbW0MoFFKnJOgZqZiengaAkqY/bYbGxkYcOHAAoVAIo6OjaoXe3t5e8uhDKSod2chWU1OD3t5e9PT04Pz585iZmUEsFoPf70dDQ0PBxxYbzs5Vma+vr6OhoWHHVOZUOTM7xQDgqaeeUv//rW99C/v37y/7XJUwsu61auBiRflemzaoyE72zx6pCAaDWFlZMSRnwqiAwOPxYN++fVhbW8PY2BhGR0fR39+P7u5u0+tfI9tAITJXsDp//jxWVlYwMzOjK4AqVpZC7VS15x9aItj4+te/jvvvvz/v/T//+c/h8/lw9epVvPGNb8SNN96Iu+66K+expW6WZDSrXoxXYyWuVNDBYBDRaBTz8/MZ81PdbnfBKXTbjdfrxS233ILV1VWMjo7i4sWL8Pl8pj2f0cGGQgiBxsZG1NbWorm5GcPDw5BSwu/3513BSu/5lQo7mUxiaGgIr371q7njKxmi0k4xK1H+Row6F9spfZLJJFZWVjJG15XcMyWoKJbsHwqFLFuP1dXVYe/evVhfX8f4+DgOHTqk5uuZxax2qr6+HoODg5icnMTS0hJGR0fR09NTcAUrvbmI2qDjxRdfxGte85qqzz/c8mAjHo/jH/7hH3D8+PG8xygXVB0dHbjvvvtw9OjRvMFGqftsGM2qvTxWHNkA9PW2F0t6i8fjcLlc6rznrf5DrXRYt9B7okwXUpZfHh4extLSEqampuDz+QpWdJXMPzXyWOV4m82mThXTrmDV39+Pzs7OjNdSakK5cn7looo7vlKljOwUA8rrVTZq2iRXo9KvnDIlEomM9kr5UervclYQM1opr6nU8jmdTvT19aGrqwtzc3M4dOhQxvQhI5W7kIkeyWQStbW1eXMPs6c/lvr3qQQd2Xkd1dhObXmw8YMf/AA33ngjenp6ct4fDoeRTCZRX1+PcDiMZ599Fo899lje8211sGF0xWv0DuJWkv2HVCyoUNb8zl6eT5n+ZJX9Kcw0OzuDiYmfArDj+ut/EYODg4hGo1hdXc3oRcrV82J2sFFqJa49XlnBSlmnfHR0NOO1VBJsaP8md3qSHpXH6E4xoPT6WOnIsuqGsVY712bIFVTkG6kIBoMIBoMIBAJbXWyVWfXftWvXcPbszwDEMTj4WvT19eH555/H4cOH0d3dnXep2XJsRqcYkDv3MHsFq1LbKcVOSCbftGDj/vvvx3PPPYf5+Xn09PTg8ccfx7vf/W584xvf2NBbdPnyZTz00EN45plnMDs7i/vuuw9AqsJ/xzvegV/91V/N+zzKhclWsWrFa9SX1YgyKUHF2toapqamEIvFdAUVO0WhCjGZPI6enn+BlEAs1gWncz9qamqwe/du+P1+XLp0CYcPH4bP50Nvb29GL5LVRjZyHe9yuXDjjTciFothcnIShw4dQldXl7qnhl7ZlX52Za7tZauWypzMY3SnWDms2pFl1SlZRkokEoZMf9op1tfPw+f7Dmw2IBJxoqXlV+ByuXDHHXdgenoaR48eRXt7OwYGBipeHKGUjq5Sl7LN1U5pcw+zV7AyKlm9GpPJNy3YyLc61JNPPrnhNp/Ph2eeeQYAEAgEcPLkyZKeaysvHIycRmXVoW695yk2UpFIJODxeNDe3p4VVMRhgUG3spm5QlRHRw1WVjyw2ZKor7djbe2Vz0Lpeenv78fU1BSOHDmCzs5O9Pf3o7a21tRgo5xKvFDFWVtbi0AggP7+fly+fBkXL16E2+1GR0eHrgT+QuXJVZlre5AYeOxcm9UpBpTeTu2EtgXY2hH4XCMVwWAQdrsdjY2Nlpj+tB20tCRw7ZoDiYRAU1NCbUuUpWZ7enoyRgf8fj9cLldZz1XK96WSkY1sQryygtXS0hIuXLiA9fV1eDwe3c9TqOy52qntvJnt9r2iM5hRF4hWHtkwa/m+cqc/nTt3Dk1NTZrpT0mEw19DIvECamt/GXV1v1ZxebejQt9Fu/11aG0NA3BAylsBxDcca7fbMTAwgL6+PrUXqa2tDbt27bLMyIbe6SBKL1IymUQoFMLp06fhcrmKrmClZzhbW5knEglMT0+jubkZ9fX1TCbfoTazU6xUVg0QrDgCX0y+6U81NTUbdmW/fPky6uvr0d7erj4+Gv0RVld/ALv938Hp1NdOWa0+KXVRj1LY7Tejvf0uAFEkk69FPJ55v81mU5OtZ2ZmMDQ0hPr6egQCgZJXgzS7nSp2vBBCzT2cmJjA5OQkjhw5goGBgaIrWOkpj7adSiaTuHz5MlwuF5qamrZVXgeDDbxSWTLYKEwJKmKxmLp7qJHTn2KxBYTDP8X6eieczqfhcr0JQlQ+P9nKCvW+51YPKf+D+puUwbzH2mw2Ne9hZmYGJ0+eRCwWw9ramq78lq2YRlXo+MbGRuzduxfLy8sYHh5GMpncsO+I9ni9Q85KZb6wsACPx8NkctoUyvSjUr6n1T71yehRklKCikKb32nLlExGce3atxCJNMDlehrNza+F3d6sqzxWnCKmR+nl9iKZfGUkUMpY3ve2u7tbTSQ/ffo06urqkEgkSiqbWbmFpbZTTqcT3d3d8Pl8mJiYwMWLF4vmUZbaTi0tLamjbNspmZzBBkqv9AuphtWocm0kpA0qlAu/SnMqssskRAMikT44HJewtrYPgP4/QitV4pWWpdTHF3v/bTYbfD4fWlpa8OKLL+LEiRPwer0IBALweDwFy2Fmj1G5Cd/Nzc04ePAgQqEQxsbGcOHCBbUXSTlnqdO6lMco82KrNUmPrMPhcCAajeqePmL0NKp4dndzmawQbMTj8Q2j6+FwGCdOnNAdVBQqU+bvtVhb64XTOY5IpAuAtfZlsqJi7YMQAh0dHWhvb8fi4iJmZ2cxNDSEQCBQcBNNPecu91igvHZKCIG6ujo197DQClblXHdKKTe0U9thM1sGG9g5w9PZ5yq0kZDb7YbX60VTU9OGoOLYsWMFd8ktl93uRHv7hxAOz6KlpfQpP1ZiRJKYHqW+bqfTidtuuw3z8/M4c+YMnE4nBgcHUV9fn/PcVukxylUpe71e3HzzzYhEIpiYmFDXQe/p6SmrEtc+Jtd8WYfDYchKQETAKysn6g02tlPbYta5cgUV+UYqTp06hYMHDxpSruwytrV9ANeuXURzcz/sdqfhz7EZrNZmAq9squfxeOD3+zEyMgIpJQYHB9HcnHv0aLMTxIudX1uWYitYldspth3bKQYbqI7RiEISiQSi0SgWFhYwNzdX1koaWmbOz62r86CubmuWBjQzsTvf85Vye75jS+3VEUKgvb1d7UU6f/48bDYbAoEAmpqaMh6j99xmV+KFjne5XLjhhhsQCAQwOTmJw4cPo7GxseTvaa5GS1uZW7XHiLYnp9NZ0ga0RrdTVmzzAKgXTnqDikLTn4yQ6/W53fVwu7dm93cjmZWzka2cdqqpqQkHDx7EysoKRkdHMTw8jEAggNbW1oxzmZ0gbsTx+VawKrY3Vr7nKNROWVXVBhulJj8Z9SFtZYNQaM1vJeJta2uz1EoaVvvjsMp7YsawcK5jlcS2a9euYXR0FPF4XM2DMKvMQOnBiZ6RCmUFq4GBAQwPD2N6ehovv/wyBgYGDFvBisgoDoejpD2hjByBt8pofnZQMT8/j0QigampqYygQpl+wr/B7anUdkqroaEB+/fvV6fNjoyMwO/3o6OjQ/3u6b1gN2shE+3xhcoiROYKViMjIwiFQlhYWMiZe5jvObZjO1WVwYYyF9bp1De8ud1GNkrZSEgJKoaHh9HW1pZ3KLLUclF+m90LbkRg0tjYiAMHDiAYDGJ0dBQjIyOIxWIlLeFXag+QWdOubDYbWltbIaVES0sLTp8+DafTCb/fX3D+r1F5W0R6lLoBrVVHI/Scq9hIhdfrRXNzM2w2G1wul7pBohVYvce4XJv9miptp5Rps2traxgbG8PFixcxMDBQUsfVZoxs6GlDhEitYLV3716cO3cO09PTGB4eVnMPCz3ndm2nqjLYcDqdJQUbRle8RjUIUkqsra3hypUrZW8kpC2X1SpMK5bJCirpBar0vPX19bj11lvVHckPHz4Mv99vyBJ+lRxfTqJeTU0NOjo60NHRgaWlJVy8eBGJRAJ+v3/DUHw5z0FUCYfDsaXTqMwINvQGFflGKlZXV6u2M8uo12Vkm2lGR1K+c5RybKHnq6urw969exGJRDA+Po5r167h8uXL6O3tLVp/b0ZuYSk7o0sp4XQ61SBKzwpW27WdqspgQxnZ0GurK/F8FXQikYDdbofL5ap4IyFe2G8vmzWNKh+32426ujocOHAAY2NjGB0dRX9/P7q7u3NWdOVU4puZUN7c3Izm5maEQiGMj49jeHgY/f396OrqyljBajtW4rQ9lTOyYZXpvkqbFQqFsLS0hOXlZSwtLWUEFS0tLSVPf6r2dsrMfEcrM6OdcrlcuPHGG7G0tIT19XUcOnQIu3btQm9vb96pT5u1GlU558+1glVXVxf6+vpKWsHKqt+Nqgw2Sq3EjZ4Lm68SLzXpbX5+HqFQCH19fRWXy6qVuBFlstof12YufWv2lC2Xy4U9e/YgGo1ifHwchw4dytnrUk4OhpmVfr7ze71e7Nu3T13BamxsTN1cCrDed4mqVznt1GZ3immDCqXtUtosZXRd2Vxz7969Ff/9WLGdsmKZthszl6cFgOuuuw6BQEC9SPf5fOjr64PdnnmJu9mrUek5Pvv8elaw2o6dYlUbbJQysmH0XNhEIoGVlZWKVtJQzmX0Kh9WYuSF3XZ7bclkEuFwGE6nM+fSl2Zc9FYSmDgcDuzevRt+vx+XLl3C4cOH4fP50NvbC7vdvmWrfBQ6vlCFrF3BampqCocPH0Y0GkU0Gs3oRdJiIEJGKnU1KjPzLLRBhbIMenZQkW+kIhgMIhQKGfL3YWRbTIVV2lFVaqdYKcppG+x2OwKBAPr7+zE1NYUjR46go6MD/f39ap1utXaqUOCgXcHq6tWrOHXqFFwuF6LRKIMNq9isaVS5en3C4TCklFhfXy86P7UYoxsX2nxSSkQiEYTD4YyGHEhd8EYiETQ3NyMQCKhBh9lL31ZC6XXRVuidnZ2w2+0l9+hY4fja2lr4/X709/fjpz/9KV544QU0NTXpXsGKqFzlrEZV6YV4PB5HOBzGwsIClpeXceLECUSj0YygorW1VZ26oae+sHKnmFEjv1brzDJbPB5X26tgMAibzYbrrrtO954wuWxWO1VTU4P+/n709vbi8uXLOHbsGFpbWzEwMLApIxVGTycWQqCzs1PNPVQ25lVWjdwu13ZVGWwYPRdWqaCVn0K9PsFgENeuXcPg4GDFr2OzVwzZbFYsU7mkTO3kqVTQFy9exPr6OpLJJFwuFzweD7xeL9ra2uB2u2Gz2RCLxWCz2bCysoKhoSE0NjYiEAhs6tK35bLb7RgYGEBfXx+mp6dx8eJFeL1edHZ26lqYYTN6jEo53mazweFw4DWveQ3m5uZ0r2BFVC4zczay26xwOJwRVNjtdjgcDuzZs6fiJWWt2immlMuIqV3VKplMqiNT2bMwvF4vPB4P2tvbEY1G8eKLL6K5uRl+vx8ul8vUdqpUuc5ts9nQ09MDn8+HmZkZvPjii3A4HPB4PLrPa6V2SlnByuPxYPfu3ZiYmFBzDzs7Oy0/2lG1wUY5IxvFKmg9vT7KyIYRqj3Y2K6U74lSQYdCIcTjcdTW1sLr9cJms6GrqwstLS1F1+jW9lpcvXoVQ0NDcLvdSCQSusuzFcGGwmazobe3F4lEAqFQCMePH0dTUxP8fj/q6uoMK0s5PUylrAqiEELkXcHK5/NV9YUHbS4jNvUrt81SLir1rthYCNsp60smk1hbW8tot5aXl7GwsICGhoaCszDi8TiEEOjr68Ps7KwadAwMDJRUhq1qp2w2G3w+H7q7u3HhwgVcuXIFsVgMgUAAXq/X0LKYPRKilKmxsRG33HKLuiLX6OiomktpVVUZbBSbRpVdQc/Pz6tLkJU7lKywasXLSrx0iURCnR6nVNC5en38fn/Ghe3p06fh9Xp1bQakfLe0QcelS5cwMjKCl19+WddFu16xWAyRSMSUBDNlT4ubbroJs7OzOHHiBOrr6+H3+3P2JJm9ykepq13lol3BamZmxlJr/9P2V8o0qng8jkgkgtXVVfWisZLpT0bnKVqxnTLyXNul7VSmcGtHKpQOUCVftL6+Hl1dXZicnERnZyeampp0nVuIVzakm52dxdDQECKRCCKRSNHpVaW8f8lkEolEwvCgQwiBhoYG2O12NDY24uWXX4bD4UAgEFAXOchVFiu3U8qKXLFYDJOTkzhy5AjuuuuukjYi3CybGmw8+OCDePrpp9HR0YEzZ84AAD7+8Y/ji1/8Itrb2wEAn/rUp3DPPfdseOy//Mu/4IMf/CASiQQeeughfPSjH837PMrwdCQSwfr6etFen5qaGtTV1aG7u7vi17jVy+huxrmsWKZKSCmxurqKYDCIWCyGhYUFrK2twWazZawNvxk7rwsh0NbWhsXFRbS2tuLEiRNoaGhAIBDIGXTordwSiQROnjyJhYUFCCFw4MABQ8utlEPbIM3Pz+PMmTNwuVwIBAKor68vudwKo1ajKofX68X111/PUQ0yVK5pVIVGKmw2m7phZTkdYVpW2UHczHMZxai/e6NfWzQaVb8jSnCRSOIkTpQAACAASURBVCTgdDrVzrDW1lZ12m6u8pRDqePb2tpw6NChDdOrcimlvn/55Zdx4cIFOBwO3HLLLYYvJGOz2dDW1oa2tjYsLS1heHgYABAIBDZserxd2qna2loEAgH4/X5LBhrAJgcbDzzwAD7wgQ/gXe96V8btH/rQh/CRj3wk7+MSiQTe//734/vf/z56enpwxx134N5778XevXvVY6ampvDss8/ipZdewlNPPYVvf/vbOHjwIP7oj/6oaK/PTpj6ZMVK3CilDM8qwae250fp9YnH46irq0Nvby/q6urKrgSMWOXDZrNlTK/KF3Tofa719QgaGn6C/v4xLC4uQMr9hlfi2vMJIdDe3o729nYsLi7i/PnzsNlsGBwcRGNj45au8pHv/ETA5nSKKRuRzc3NYWRkBG95y1uKjlTMzc0hFAqhpaWl4tdo1bbFqufaStkrhS0tLSEajWJ5eVnNBezu7lZzcUpRaRvgcDhw5513ZkyvyhV06K2/U6/1CPbtexmh0Aqi0RsNmeqXrxzNzc04ePAgrl27htHRUYyMjGQkXpc68mB2O1WMlTvENjXYuOuuuzA+Pl7y444ePaquowwAv/Ebv4GnnnoqI9hQ9qS45557IITAbbfdhje/+c26zr9Z+2yUyqqrfFjxC5392rTJ2vl6fZRkK+WPfXJyEna73RKrEOWaXjU3N1d0pCOfuroQrrtuBPPzErfccqroZ1jqd6VQJdvS0oKWlha1Qo/H41hfXzc9eDCzkaDqZWan2IsvvogHH3wQDQ0NsNls6O7uxp133qkrUdvI9sCq7ZQVz2XklLNCkslkRlARDocRiUQ2BKBerxeRSAR+v7+i5zPqvck1vUrJ29MGHXrqV7s9gX37DmN+PoS+vjAcjgcqLqNWvnahsbERBw4cQCgUUoMOv9+PRCJhqXaqGCu3YZbI2fj85z+PL3/5y7j99tvx2c9+dsNQ1vT0NHp7e9Xfe3p6cOTIkYxj9u/fj/379wMAjh07VnKCeDwer+AVvMKKlaVyLqNY6fXF43F1DvPw8DBCoRBisRhqa2vVCrrcXp+tkus9URKX29vbM4IOr9er67MVohEeTz+czsvweg+gWNtpxsiDUqEHg0EcPXoUx48fx+DgINra2nQFP2auRrVdd2Ul45nZKbZ//34MDQ1BCIGvfvWrmJiYwJve9CZd57dygGDkxXg1jEYUIqVU2yylM2xtbQ0A1LyKxsZG+Hw+uFyuDXXP3Nzcppc5V/2X/TkVCjr0f6Y1aGrqR03NRbS2+pFIlL7IRyHF2hGv14tbbrkFq6urGBsbw8LCApqamuDxeHS1AeW0U9vluqRSW/4q3/e+9+HRRx+FEAKPPvooPvzhD+OJJ57IOCbfxVc+5WzqZ6ULaLPOtZ03SyrU6yNEakOf1tbWjA18toreCqfQZ5vv8dlBx7lz51BbW4vu7u4iIx0uBIPvxerqGLze1xUtW6Ey5FLKxX19fT3cbjf27duH8fFxtReps7Mz7zmsuCoI7SxGdIppv2PV0k4Znf9hlK2eRiWlVPMqQqEQFhcXEQqFsLS0hLq6OnUKVHt7O+rq6rZd/ZOvTs4VdLhcLp0X1Xasrr4Hi4tH0NT0yzD6EjWZTOrKaXC73bjpppuwtraGYDCIQ4cOoa+vDz6fr+DntJW5hVa35cFGZ2en+v/f/d3fxa/92q9tOKanpweTk5Pq71NTUwVXhtmsTf1ysWqDsF1GSbS9PkpgUazXZ2ZmBtFo1JC5zJtJGYLOpudzUoKOWCyGxcVFXdOrkkk3YrE+AMWDsVIrwXKmLXk8Huzbtw+RSARjY2MYHR1Ff38/uru7K05oLLU8DDaoEDM6xba6ndoJbZ5R5ylWplgstiFZOx6Pq/s6aIOKG264oaLyGLnQi9mLnShBx8jICCYnJ3H27NmCieQAkEw2YXV1LwDj2/NSX7MQAtdffz1sNhvGx8dx6NAh9PT0oKenJ2fQYvZqVNt51G/Lg40rV66oq0B95zvfwb59+zYcc8cdd2B4eBhjY2PYtWsXvvGNb+BrX/ta3nO6XC4sLy/rLgPnwpbGiPNIKZFIJBAMBtUdtsPhMJLJZEavT0dHx7bs9alEqRVWfX099u3bh7m5OZw8eRJerxeDg4Mbgo5Szmt2AjfwysWAy+XCnj17sL6+jomJCRw6dEhdM7zclTWMnkZFO5sZnWLl7gdlBKuOIFg1T1GhLIeevQme3W5Xp+12dnbC4/Fs2OdnaWkJq6urhpRjM3vD872Heut8IVKb0cXjcTQ3N2dsYJsr6DAzCCq3XXM4HNi9ezf8fj8uXbqEQ4cOYdeuXejt7c0YsTF7ZGM75xZuarBx//3347nnnsP8/Dx6enrw+OOP47nnnsOJEycghMDAwAC+8IUvAAAuX76Mhx56CM888wzsdjs+//nP4+6770YikcCDDz6Im266Ke/zbHUlbsXhaaB4xSvERdhsQ0gmb4OUgaLn0vulV3p9tBV0PB5HIpFQ96ro6emB2+227LJtpdjMCkG75Kx2elWuoMNqwUY2p9OZUaEfPnwYPp8vY2qKXpxGRUYyo1Nsq6dRWZG+17gMm20YUvZAyvzL1VfyGrWb4M3NzWFtbQ1XrlzZsBx6rk3wCpXHSj3TRiWI630uZbNb7fSqXEGH2cFGJQuH1NbWYnBwEP39/ZiamsLhw4fR1dWlrhq31dN9rfp3DWxysPH1r399w23vfve7cx7r8/nwzDPPqL/fc889OZcazKXU4Wmr9swYOdRdPHBZRzj8FwgGQ6iv/wnc7j8HkHvJuXxf6EQikbE+fCgUQjQaVXt9vF5vRq/PxMQEXC5XRq9hOaz8B1aOSoKCfEFHIBCwfLChyK7Qjxw5gmg0qib+6y3PVq4KQtvXZnWKlbKpH2Bsp5iRNnuUJB7/K6yuXoDT2Qyn888A5N8Futi5lOXQtSsXKqMPygi7y+WC2+3G4OBg1bU1lbyeUq5ztO1DrpwObdBhpZGNfBf3drsdAwMD6Ovrw/T0NI4dO4a2tjbE43FTO7m2c6fYlk+jMsNWjmxYdZQEKFw5xGIJLC9H4HYnsbwcQW1tEoWu65QVNLR5FTabTR1KbmlpKanXp1JW6jEyQqXvmTbomJ+fx6lTp2Cz2XTvFruVwYZCW6H/9Kc/xdGjR9HW1oaBgYGia6+XM42q0PHVdpFB+W1Wp9hWjmxYlZ7XOD8/DptNIBK5ioaGIFyu3MFG9rm0ydpKh5ieTfCUnEDWARuVmv+Q/XuuoKO5udm099roaUs2m02d8jszM4OJiQmcO3cOfr9f19L0W70vx2ZisIGdsZtqsS+03V6H5eXfxNWrL6O2di+6u1M9DEo+hVJBr66uIhwOY2JiAl6vF/X19ejq6ip7E7xqbDwrfU3l9hjlIkRqc722tjacO3cOV65cQTQaRSAQKLifiJUqQZvNBofDgVe/+tW4cuUKjh8/XnTH2p3UY0Tb01Z2illZsfpvYeE+2O2HEYkMorm5bcP9yi7skUgE4+PjiEQiGcuhl7MJXjW2U5UqtZ3KJzvoGB4eRk1NDSKRSMFE8lLLoBxvRgK3zWaDz+fD2NgYWlpacOLECXVGgcfjyfu4ndROVWWwUc40KiuORmzmuWKxGDo69mFpaRei0SiOHz+OZDIJl8ulVtBtbW1wu90YGhrCnj17Ks6vqOaeIiNGJvTQWxkKIVBfX69ODTh16pRaGeYKOqwwspHNZrNh165d8Pl8ak9YfX09/H7/hgqdq1GR1W3ldF+r0lOH7N79BiwvH4Tb7cb6+joWFhZyboInpURTUxNaW1srWg69WtupSutso6flKkFHTU0NJicniyaSl1qGco8vhTZwmp+fx5kzZ+B0OjE4OIj6+voNx5ezCeB2/T5WZbDhdDq3bC6s1Vf5UHp9tHNU4/E4amtr1aHkzs5OuN3ugr0+Vm70pJQIBoNwOBxFe0asyKgeo1zH2mw2daRDmV6VK+iQUmJ1dRUzMzPo6OgoWiFuZiWYq0J3uVwIBAJqhc7VqMjqtrKdsqpcbV6x5dC9Xm/OTfBeeuklNDQ0GLLvklnJ1MoO1dVe95TaPni9Xhw4cKBgIrly3ng8juXlZTQ2NhZ9jkpWTSz1Me3t7Whvb8fi4iLOnz8Pm82GQCCQMZXZSjMIzFa1wUY19BhVUq5kMpmRqL28vIy1tTUsLy9nrPs9MDBQcmVs1EWlWe/7pUsvIhL5FmKxVvj9D8Pj2dijYCYjplFVMhdWz3m106vm5+dx+vRpNQnS7XZjYWEBIyMXMDc3gz179hVdG97spL5c8lXog4ODO2p4mranctqpag42pJSIxWKIRCK4dOmSZZZDN6teW1q6ivHxb0GIOuze/f/C7c4/3caKzKrztSssKp1KV69ezRl0hEIhjI6OYnFxEbt378aePXuKnnuz6/mWlha0tLTg2rVruHjxIpLJJAKBAJqbm7ka1Xa3lZslGUnPF0fp9dEmva2urqpL9Hk8HjQ3N8Pr9WJlZQW7d+82pFxWCs6yy1Nb+4/weoch5cuIxf5/9t48PpK7vPN/V/V9qtWSuqXWrZY00njuGY8Njr0cHpvTjs2PeG1Yx9iwrAMhhCTEyQaCeXljvOyy+9vsBvPbkGCWXQNOAg4GbIjX5jBzamY096H7vq9Wt6Q+qn5/iCpaUnerj2qpNdbn9ZqXLanqW09XVz3P97k+z1uA29JaR8vPtNHUt+keu1pRJXM69PppbrnlBRwOmdnZjwGb62yst/Zqha5M6nW73WnJte1sbGOjkY2dKiS9mwuSDcHT6XRqf0WudOiFZqdgpY0JBr9Hbe0rSBIEg1VYrUc2TZaNuFYuDIterxePx7PG6Zifn6K5+f9SXh5hfPz9wPrOxmZtyIuKijhw4ACBQICuri6uX7/+psrA35DORqbp6UJUSquhUPStZtOQZRmLxbKiBCpRs/b09HRBe71aoqSknoWFi+j1dqzW8ozOLYR7tBGZjURrxDsdo6OvU1ISxGAoprm5T3OZM0EmaysK/Re/+AUDAwN0dHTQ0NBAaWlpyjW2csRoG1sTN6KdWo3VQ/DWo0MPhUJ0dnaqM00KAfm67263lYUFAZ0OrNbU7Hr5QiH1bKx3bCKno6Skj9raHkTRzu7dF9ZdO9PNfT7gcDjYu3cvwWCQEydOcObMGRoaGvB6vevKtu1sFBhMJhORSCTt4wstsxGJRFTlvLi4SFtbm0rRpyhot9udUdSnEBvX86XETaYPYbHsQZZdyLJf8/ULCVoyV8FvnA6T6Z3EYr9AEBbo7a2goiKUkr0qEyWY6XeejYLV6XSqQu/u7qajo4P6+vqkCn0rN95tY2si0zIq0N7p1eq5l2V5TaZCoUPPZAheofY85gNW6/2/1qlW4K2bLU5KbMT3pSAdhkXF6RgZMSCKDux2HQZD6qxGOmtvJJQxAa2trQwMDNDV1UVtbS0VFRVJ7V0hOEvZ4oZ0NrbKsCRlCF581CcSiaDX69VMhcFgYO/evWlT9CVDITob+YMJSTq0aVcvNJaPbCBJPoaHP0trayPRaFQtr0pG5ZeJHBvJwGGz2di1axeLi4t0d3cnVehbOWK0ja0JnU5HLBbbtOsrPSCZlCklG4IXCoXo7u7GZrPlRIf+5rJTNuADGZ9VKJ9J60BXpscKgkBx8S6uXv0UVVU2zp2DoqJLKWdcFJKzAcvyWK1WWltbWVpaoqenh6NHj1JdXU1VVdUam7QZPSda4YZ0NgqtQVySJHU+RXzGQqfTqVGfkpISamtr1zRrDw0N5exoQOEq3kKUKZ9QnoX5+XkCgQBOp5Oqqqo1CnAj09PJjgUrguCitBRKSkqYnJzkwoULCZ2OfDpIWjgCZrNZVei9vb2qQq+srESn0yFJUtrTybexDS2w2Zue9XpAkg3BU+jQ44fgtbW1sWvXrpxlKkQ7Vagy5QotNt750vmZrBuLlVJScoBbb5UZGxvj7NmzFBUVJXQ6Cs3ZiLdtJpOJHTt20NDQoNqoyspKqqqq1D3gVg6KbTsbaKdMZHl5CF40GqWnp0edsg2ozdqJKPo2AoUYMSqkl15rJIoABoNB4Dd0jQ6Hg9nZWQYGBvD7/ZSVlWV1bzfCMREEgdLS0hVOh8Viwe/3q5z2uTSqayX3ejCZTDQ3N1NfX09fXx/Hjh3D5/OpZYrb2MabBavp0OP1VDZD8LTavBaandIKW8HeybJMOBxmfn5e/RcMBlW9WVRUtOLYTJBZoCt9eeMZFuN7OhI5HfnMDGTzrCV6ZwwGA42NjdTV1dHf38+xY8eoqKigpqZmS/cW3pDORqZlVJl+QcoLuTrqo1D0xWIxLBYLZWVlG0rRlwpaK95CUuKbjfgmSOVfW1sbJpNJLYerrq7GZrOteBYikQjl5eXEYjE6Ozvp7u7G7/dnkYFID1o16SVyOqLRaEZlVJvNwGEwGPD7/dTW1tLf309fXx8lJSWUlpYmzHAUshLfxjbSQXxWVbFdp0+fVpu1bTZbTnTohehsaIUb0d7JsqzS4it2KxqNqnZLGeSr1+tZWlqio6MDURRpbGzE4XAUrJ1K5HQ4nU4aGhrymtnQ2k7p9Xrq6+upqalhcHCQEydOYDKZKC4u1uwaG4kb0tkwGAxEo1FN1kpG0Wc0GtWoT2VlJTabTa19PXnyJB6Pp+A2KDeqEt+oCJaSuVKeBSVzJQiC+iyUlJQwPT3NoUOH0lY8FouFXbt2EQwG6ezsZG5ubkUEaT2ZNqPkarXTMTo6yoULF9RMh1ZyZHN8JlAU+tLSEpIkceLECUpLS6mrq9vOdGxjS0KhQ19vCN7c3BwtLS1J69szQaGShhRaBn4znBbFbsUHwxYWFlhcXEQQBFwuF2VlZdTX1ycMtITDYRwOBwcPHmR6eprLly9jMpnwer0ZybDRdiqR07G0tMTS0lJaz3ym31W+7JROp6OmpoaqqiouXLhAf38/4XCY+vr6LTW0+IZ0NrL5wmV5eep0fKZiaWlpRdQnnqJvvesXWm1gIbJ8FFqaOx7xk9aVf/H1yspQxESZq2yjGzabjT179tDd3c3g4CBtbW00NjamdDw2y9lQ0Nvby5UrV1hYWGDfvn1ryqsSoVAH7nm9XlpaWhgeHqatrY3i4uItp9C38eZBfMlLfLN2ukPwhoeHNZNFS11+o5ZRbQSi0eiaEiil0kLJVij0+EpwaL2NtyAIqi0oLi7m5ptvZnJykqtXryJJEgsLC+uukUn5ktZ2ShAEDAYDkiQxNzfHxYsXcblcNDQ0pJR7M3oLU0EURZxOJ263G71ez5kzZ3A4HDQ0NKRkiiwU3JDORiooL8fqqE8oFKK/vz9tir5UKEQlV4gyFQKUKOD8/DyTk5NEo1H6+vrQ6XQrZpf4/f60G/Vzvc8Wi4WKigpKS0vXpK5zgdZKXJIk+vv/nl272rh2rRqb7S4OHz7M1NRUSqcj35mNbBx95RxRFKmsrMTn8zE6Oqoq9NbW1i2h0LdxY0LJsIfDYa5evboiwx5fqnkj0KEXIgrNdipUw/FOxeLi4gomy3T6bLKBktXW6XR0dnZy9uxZdfOeLBucaRmV1o7JuXM/oKTkDWZmXDQ2/hHAivKqRE5HoTkb8Jt7o0xWHx8f5/z581gsFhoaGnC5XHm9fi7YcGfj0Ucf5aWXXsLj8XDhwvIQlj/5kz/hBz/4AUajEb/fz9///d8nvGl1dXU4HA50Oh16vZ5Tp04lvY4sy1y/fh1RFNHr9WrUB1CjPvEUfadOnWLnzp2afMaN2djH0Ol+hCj2Eo3eiyxXb5hMWzWzocwviVfQShTQbrdjMpkoKSmhurp6w1g6Eh2nKLmioiI1dX3lyhWMRiONjY1Zs0BlIld6EaMou3b9jPl5Pa2tpzAa5xGEZYYat9ud1OnItxLPRumvPkcQBFWhT0xMFOwmaBtbH/Hvw3pD8ARheQ5OQ0NDzuxpWlK+p9LlgtCJKPYQi+0DSrJeR0uZMl1nM7G6YXt2dhZJkpifn1dL4iorKzGZTBkHZXKFQts6MjJCW1sbJSUl1NfXJ+z52cwM/M6dLyLLI7jdEazWj+BwtOLxeBgfH0/qdBRSua+CeDslCAIej4eysjKmpqa4fPkyTU1NGZW3bSQ23Nl45JFH+OQnP8nDDz+s/u7IkSM8/fTT6PV6/vRP/5Snn36aZ555JuH5r732GqWlpQn/9u1vf5vXXnuNixcv0tfXx6c+9Skef/xxDh8+rFL0bUQ5xkbM7RCELkKhf2JhQcDpDCKKf7rO8W+ezEZ8I6RitJWSOCWVvLrPBqCvry/rbJbWiJehuLiYQ4cOqZt3m82mpr83s0lPEAy43YdwOi8zNVWFXu+K+5uwwum4ePEiZrM5qya9bJyTTL/DZA6KsrnbpsV9cyHfQbFoNEpHRweyLPPHf/zHtLa2smfPHkRRVMt2E2XYT58+jdPpLDg69ORrTSHL/43FxSBG40lk+YkCkKkwIUnSmrlb4XAYg8Gg2q3q6mqKi4sJh8PU1tbmfE0tgmqCIFBRUYHX62VoaIiTJ0/i9Xqpq6tTn9PNDIoBeDz1RCLzzM9HsVqL1Gsom/VETkc286Dyvb9MJJNia0tKSjTPYmmJDZfsjjvuoKenZ8Xv7rrrLvX/b731Vv7hH/4hq7W9Xi+PPfYYO3fu5I477uDHP/5x2udqqZTy0eS2+oWamYHFxSX0+gh9fTJ1dRsnU6EocaVmORAIMDs7y8WLF9XsVSZTawsNie5t/OZdUYxFRUUZbay1jxgJyPIX0es76OoapaxsbQp9tdNx6dIlRFHMaJDYRij9rUwpuA3tkc+g2Be/+EVefPFFmpqaWFhYYPfu3bztbW+jtrZ23edso7IRWq0VDi8yOzuKLEvAAB7P+usUGrTOkCSil1XsltJn43a7qampSZghUI7dbKy+J6IoUlVVhc/nY2BggOPHj+Pz+aipqQHyT32bGn+GyXSMS5cC7N5dvuIvyZyORPOvUmEjpntvZTtVcG7Q3/3d3/HAAw8k/JsgCNx1110IgsDHP/5x/u2//bcr/v72t789p2trlQbLh0FYLZfBUMnFix9EFKdxOteflr3VG8STTVs3Go1qqVxtbW1O2SutHc5crpeKYUNRjKOjo1y8eJFYLIbNZluXrjI/zeRWZHkPsdivUh4V73T09fXR1dVFe3s7fr8fu92umdygTRnVNt7cyGdQ7HOf+xyf//znAbjzzjt5//vfT0lJ6vIiBVo6CBvhuMRixXR334XDMcL8/J51nQ24sRrE4+3W1NQUMzMzzM3NraBFz7TqYqPtVCo5Ep0viiI1NTVUVlaqs4wsFkva5T35sVOlSNL7CAZ/lZZtHR8f5+LFi4TD4bQa4DOVWzk+U2xlO1VQzsZ/+A//Ab1ez4c+9KGEf3/jjTfw+XyMjY1x5MgRWlpauOOOOzS5tjJNVYtN+UZEjKxWK3v33sXCwkLavMv5Lu3SAqtp+oLBIKFQSC0vULi/46etT0xMMDc3t+6mNR0UQmQgnWdH6SmYmJjAZDKpdMv19fVJU6n5Yq7KBIIg4HQ68Xq9eL1eLl26hMlkSul0ZNOzoVUZ1Ta2kQi5BMXin02TyUQkEkn7uoWc2Ugkl8Vioa7ufUxOTtLSUqXJtQoRyehlRVFUS6DcbjeCIHDTTTflfD0tdHO+HTGdTkd9fT3V1dWcPn2a69evI8syPp8vpa7Nt51avzx42emw2WycP39+3UZyBdlk4N9MdqpgnI3nnnuOl156iVdffTXpF+Dz+QDweDzcd999nDhxIqmzodPpiEajadewFWqZUaq1lNredNfRClp9vlgsxsLCAoODg2voZVfT9BWCE6A1kn2mTJWQx+PB7/erqWtl2ujqZ38znI2pqQGWlmbweneqSlJRmEqd6eTkZEqnYzuzsY1CgpZBsWwG0Bais6EE6xLB4/HgSSelESeXFshXBj4VvawSEEtkt5Ry3xsF6eplvV6P2+2msrKSYDDIsWPHqK+vp7y8POH5m2GnlO/S4/GssANms5m9e/embCRXsBlEJlsJBeFsvPzyyzzzzDP87Gc/S0ovqTAHORwOgsEgP/nJT9RUdCKYTCa1KTgdKBGjTGrJk6EQHRetZcoEsiwTCoVWUPUtLi4iSZLatF1eXp4Xmr5Ch1aGNT513d/fz/Hjx6msrKSmpkZVThutxKemzhMO/zsMhgW6uz+G3/94wrXXczo2omdjI5r7trH1oXVQzGQyEQ6H075+qk19pijULEmh2E6FJn96eprZ2VnOnTvH4uKiSotut9vzRi97I8JgMNDc3ExtbS1dXV309PTQ0NCwZgDyRtup+flJBgc/gcMxwOXLj3DTTR9dsfZ6jeTZypIPO1XIQdkNf0MefPBBXn/9dSYmJqiqquLJJ5/k6aefZmlpiSNHjgDL9bDPPvssQ0NDfPSjH+VHP/oRo6Oj3HfffcByZOGhhx7iXe96V9LrKEo83ch/oSpxreTKR9N6IoTD4TX835IkqQ3bTqeTiooKzGYzY2NjLCwsUFlZqZlcuSAcDtPX18fU1BTNzc2aOJ7ZIBdlq9PpqKuro6qqit7eXo4ePao6IRutxGOxCxgMQaJRMxbLL4FlZyOZ85DM6dgoNqpCVtTb2HzkKyiWqbNRiA5CIfRH5ILVwxGDwSCyLGOxWNDr9epGOVN62XxAlmWmp6cxmUxrNuqZrpOPno1kxyowmUy0traysLBAZ2cn3d3dNDY2UlJSkrRHVQsZkiEcPkNp6TWiUQtu9z8Cy85GIjr0eKejvb0du92+ghlyIzIbm/38ZYsNdzaef/75Nb977LHHEh7r8/n40Y9+BEBDQwPt7e1pX8doNGakxAs1PV2omY1YLLYilbyaps9msyWkl129TiFhePgyTufLLCwUMTz8caqq6jZbpHWRTNnq9Xr8fj81NTX09PRw9OhRdDpd2s+AFpH+4uJ3EAh8G7N5Er3+N+/4egZCy3GewwAAIABJREFUcToU7vBoNJqU2ScR8pGeLrRndRv5xUYFxbIpoyo025KPtbRaZ7VMq2nRE9HLVlVVrRiOODs7y8jICGazWRO5csXIyDDj4z9lZsbE0tJ91NTkToGbLXJxCiwWC7t27SIUCtHR0UFXVxdNTU0b7my4XLuZny9Bp5tFr/8NyVCytZM5HS6Xa0OCYls1A3/D5v6UMqp0oXU2QkvHRau1sjEGiWj6pqammJqawul0bll62USoqTmKw3EenU5EEH4LqNsUObRszjYYDDQ1NVFbW8vx48c5d+4cjY2NeL3elNfINmoVD73eS3Hx94EY8aom3bXdbjdut5vLly8zMjJCMBiksbFRc/YqBVv52d2GttiooNhmZjYKNUuiBWRZJhaLMTc3pzZuZ0Ivmw9opV+s1hMcOPASOp2OUKgM2BxnI1OK2mSwWq3s2bOH+fl5rl+/ztzcXFrsT8q6ud5XUfTidH4XQZhAlv0r1l4vABXvdFy9ehWdTkd1dXXa7FXbPRs3ADJ1Ngo5yqMlx3cqxNP0Kf+i0Sgmk0lV0CUlJRgMBsrKytJmwUqFQjJQRUWV2Gx2TCYLUMFmiqZ1ZMdoNOJwOKipqWFkZERNXZeWlua5SU9gtZpZ7o2KAKOA59fHJIfFYlHrYy9fvozBYEjpdGxlhbyNNxe27dRaZLpOMrslSRIWiwWHw5HTUN9C60fxenXo9SYMBgMuV/aOkhYb9UzOX+9Yu93O/v37aW9vZ3BwkMnJSRobG3E4HEnP0a7XrhhZXrmfSTfzoDgdsViMsbExNdPR0NCQtNwyk/VXn7NVbZsmzkY4HEav1xfUTTAajTcMpaDWzkYqmj7FqSgrK6O+vj7h5ORUMimZkHSyHFqmy7VAIPAudLpqzOYqJGm/Jmtmg0wjRpk4BWazmZ07dxIKhejs7KSrq0utl81l3cyMziwezx8jy7MIwmPAR9JaX8l0KOVVBoMBv9+/xhhprZC3Mx43BgrVTm33bKSHTO1Wf38/er2e8vLy9RffQlhaejt6/QQ2WwnRaPISPa2QytZnska6etRoNFJdXY0gCFy5cgWj0UhjY2PC/tt8PnOyHMPt/p9Eo5cRxX+HKN67zvEyTqeTPXv2MD4+zrlz51I6HdtsVBkgFovx6quvcuLECUwmE4cPH+aWW24piNrG7YjRMuJp+hYXF2lra8uZXjbVcf3938RofJHFxf3U1Pw5orgxTdZa3G9J0rOwcCtOZ3rDh/KFzDfvmTsFVquV3bt3Mz8/v6Je1uVyZSxDpvJGIhdZXOxhbs6GxfIdiopSOxurFWy803HlypU1Tsc2s9Q24lHodirT3kItbUuhOi7xdkvJWmhJiz47e42pqW+j1++gqup31l2jkDLwsmxnaupD2O01myxHfmyEcmxxcTE333wzk5OTXLhwAZvNpjZjZ7NupohErmEw/ISJCStW61/hdK7vbCRir0rmdOSjjKqQA2M5ORvf+MY3+MM//EN27dpFOBzmy1/+Mg8//DBf+MIXNBmwlgu2Wi2sTvdjdLrXiMVuJxZ7v/r7dJW4QtMXr6BX0/QZDAb27t2bM01fMplkWcJq/S6y7MLh+BWLiz1Yrf4EK2T22TYSG/myavHZc40u2e129u3bRyAQUIcuKU16mUy0zURpTk1VEIl4cLsnuXbtMDffnPr4ZAo2mdOxlRk7tqE9Ct1ObWZvYTQazegcQRhCFH+JLLcgSXvifp+dLk9Eix4MBtVSlFxo0VPJFAr9J4qKBpDlXxEItOJ07kl4nLLOjYh0N+obXYGQiBrd7XartLNFRUX4/X5MJlNeA0uhkAODwYbDscDISBNOZ+rj12OvWu10ZGunturzmNWuU3kYvvzlL/PCCy9w9913A3DlyhXuu+8+6urqePzxxzeNOhSyY6PavGzEArHYi0xMmHG5XkIQ3g7Yk66ViqbPbrdTVFREZWXlGpq+oaGhvPKBC4KITrcfSTqFIPgwmyvydq0bGfktYUp8rMPh4MCBA8zMzHDt2jUWFhbSbpzMVIaiIh9XrvwJsdgShw69JU62q8AksnwL8Bvdsd76itMxPT3NlStXiEajuN3utOXZxo2JG9FObXYZlU73/xGJjKPX/wL4AlCS9lrr2S2FFv3cuXMcPHgwuw8Vh1QyGQx2IIogGDCZ0qPH38ZKaEEiku668Rv3kZER2tra1P7RfO1prFYPFy8+gV4/TENDfLnaEhAAVjIkpsNeNTExoTodbrf7TZWBz+lbmpiYYM+e5YhAJBKhpaWFl19+mQ984AM88MADlJWVaSJkNtjMiFGmSjwWM9Dfb8ZuH6C3t4LKSgMGw7KnHI1GmZiYYGxsLC2avo1Aqs9XXPyXxGI9iKIPQUjeHLUNbaC1Y+JyuTh06BDt7e309fUxMzNDY2Ojpo1ugiCwZ8/eFfNVBOEiev0ngCix2INI0idWyJ2OUlbS7levXlVnuCTq6djGmwuFbqc2KyiWjc0bHZ1BkgYRBCtFRRJKS1+8XNnQy8ZjIyK3bvdfEAz+FIOhAZMpefZdkaeQMvCFJMtGB8UEQaCiogKv18vw8DDXrl2jqKgIn8+XF6ejrm4Pfv99cb+ZQa//KIIwSiz2GJL0iPoXSZIS9rjGy15WVkZpaSkTExNcuXIFURQJhUIp7Wu6KPSMR06ZDZ/PR29vLxUVFRgMBqLRKLW1terGeKsp8c0roxLo7X0f4fAAS0tFBALXWFxcBJbrjRX2p42k6VsPyRWeAZ2uKa01Ck2JbzQikQgmk2nN7/NVh5rJvTabzeogwHPnzuFwOPD7/Qnr3DOVN3FZ1BCx2AKSpEOn61xzfCbrWywW6urqsNvtXLlyBb1en5LVZL37UuhKfBuJsRXslNlsZm5uLu3jNzuzcfXqnTidXczOutmzx4wkLTE/P8/s7CyTk5P09vYCv6GXzYYWXcuynWT3ShTdOBwPaHKdTOQppLVytb35JDJZ71hRFKmsrCQcDhMIBDh27Bg+n4/a2tqkgddMP28iOyUIV4nFBgmHLZjNLwGPZCT38hrLTockSQwPD6/bSH6jICtnQ/kCfv/3f5/h4WGVfUgURRYXFxEEIaONfj6QTXp6I8qoktH0Wa1FWCxOPB4PpaWlKk3f9evXKS0t1YRmVitsb74yg9JPEwgEVkT6RFFEFEWam5vXlP3ks/EuXZkFQaC0tJTS0lLGxsY4c+YMLpeLhoaGFU5Sps5GouPHxloYG6vGYpkmFruLpiYZQfi/CMICUJ2xM2MwGNRMh1Jelczp2MoMH9tIjhvRTm1Wua9it1yuBrq6luW+dOkSZrNZ7adwOp3U1tZq8i7ls/F3M1FowbX17vHS0hKzs7O4XK6Egc58BcUyWbe0tJSbbrqJvr4+jh49SnV1NdXV1WueQy3s1NSUj/l5Aw7HMJ2d76ClBWAcQZhBkoSMnn1ZlnE4HOzdu3dFedWN6nTklHf62Mc+tuJnURQZGxvj05/+NF7vMqPPZimNzS6jisViKxq242n6lFRyKnrZ+LUKUUEVEu94IRmlaDRKNBplYGCAQCBAMBhEkiSsVuuaSF8kEkGSJLq6uuju7qapqQmn07mpEaN4KIpTEAS8Xi8ej4fh4WHa2tooLS1Vn91slPhqpTw1FeLEifei0+morDTQ3Pwv6PV/AUgUF78HUfxM1uvHOx3K4KV4p+NG3dhsYxmFbqc2s2dj9Vrr0cs6HA5uu+02lXBEwcDAgBo80QJazYDQyr4Umg3WGskCYkajEYvFQm9vLx6Ph/r6erVcKZ92KhMIgoBOp6O+vp7q6mp6e3s5evQotbW1+Hw+9ZnMtJk80fHBoMCrr34EvV7C5fLS0tKFwfAYsERR0T3EYqmZFeOhBLlWl1fdqE6HJkVus7OzzMzMEAgEEEWRhx56SI18bpYR38ha2EgksiJbMT09TSwWY2ZmJmeaPi3LuwoNWj4bG20MZFlmaWlphXJeWFhAp9MRjUYRBIHKykpsNlvKfhqr1cq+ffuYnZ3l6tWrmEymjCex5ysLkug6Pp+P8vJyhoaGOHHiBOXl5ZSUlGSkxBOVRdXW1tLV1UUoFGLv3r0Iws+AKCAiinPodOMIwgCyvJP1hgAmK7sqLi7m0KFDa5wOk8m0ndl4E+BGsFNaOhuKYzEwMJAzvazWGZdt5A8KrfDS0hJXr14lFAohy/KagJjybij9NmNjYxw/fpzKykpqamryTn2bDlbrer1ej9/vp6amhu7ubo4dO0ZdXR0VFRVZlfuuPt7n87Fz501MT09z6NAhRPEUEESWLVgsZwiFHgVixBOcpPs5b3SnIydnY35+nldffZUf/vCHnD9/nqmpKUwmE+Xl5bS2tnL48GHuvPNONXq0kTCZTIRCobSPT0eJr6aXVV5YvV6P3W7HZrNRUVGB0+kkEolQW1ub68fQtLxLK7wZIj3xkCRJNcaKcxGNRgmFQkiSRGNjo8r/LQgCJ0+eXNH8nAqKsikqKuLmm29mYmKC8+fPEwwGKS4uXrdHJ1/RpVSlRaIoUlVVhc/no7+/n3PnzqHX64nFYmkRFSSSw2w28+53vzvu+u8FeoF5ZmZaKC//XXS6CLHY765oHs9UdljrdCjnbOPGRCHbqY0oo0pEL6v0BMqyTHFxcdb0svFyFdrMjkLLwG80FGdycnKSiYkJBEFgaWlJpcOH5c2zw+FYV2+Lokh1dbXa/3T06FGcTmdeyDe0cEwMBgPNzc1qEKunpyfj/ViizIYoihw+fFj9WZLeQiy2E+hncvL9+Hx/iMFwjVjsj5Ckf51y/WR2KpnTUV9fn3K9QnfSs3Y22tra+OxnP8vAwAB33XUXf/AHf0BFRQWxWIz+/n5++ctf8tWvfpWf/OQnPPXUU1RXV2sp97owmUxMT0+nffxqZanQ9Cn/4r1/m82WlF4WlusctaoFLsSN/Y2sxKPRKFNTUys434E1E2rn5+c5efJ/Y7FM0tMTpqZGm0mupaWlVFZWIkkSp06dwuPxUFdXl3QTsBk0uQpEUaS2thaHw8HVq1c5duwY1dXVVFVVpdzsp9PwPTUV4pVXlqNR+/YN/bp3w4Iovg6YkeVWZPmtCc/NhL3q0KFDjIyMcOnSJc6cOYPf78e5HqH6NrYMtoKd0jKzEQ6H18ysiC/jVOhlzWYzgUCA4eHhtAMjqbBZvSQbtU6hI1lATK/X09FxBZ1uDperkXe8453q5zl16hROp3NdXRn/+XU6HQ0NDVRVVdHe3s7k5CQ2m43S0tKU92mzsiAmk4nW1lYWFxe5du0ac3NzjI+PrysvrM8uBTA+vsSPf/wOJEmitXWW2torgAtR/DtkuQVZLgMSv1/r2anVTsf58+dZWFjQjL1qo5Gxs6F4Y9/4xjd46KGHeOyxxxIe97u/+7sAPPnkkxmxbWiFdJW48pLOzc0RCoUYHx8nEomsoOmrrq7GZrOlXWqR2CDIQATIjE2qEJ0NrbCZSlyWZTVLpSjnQCCAwWDA7Xav+73rdEMcPvw8Ol2YublBQBtnA5bvi9vtprm5mf7+fo4fP05VVZUmTW/pIhMGKFEUcTqd7Nixg56enoT1svFIxxno6OhgYWEBQRC4fr2MnTsPIgh9wDw63X8FzEQi/xE4DKzkyc+Uvcput1NSUkJtbS3Xrl1Dp9OtcDq2wmZjGyuxlexUJr2Fij1Yj17WZrOtW8ZZiNkIrde6EW2nklXv6+tLGRAzGAzMzY3j8XwBl2uC/v49CMKdWV1ztQ40Go2Ul5cTDocZGRmhu7ub5uZmXC5XwvM3u+TKbDbT3NzM4uIiw8PDdHd309jYmHIeUzprT05OEolE0Ol0DA1ZkeVSYBqoQK9/DDASiXwLWEuvLElSWplExelwuVwcP36cc+fOqdPUt5LTkbGzoWwS/vqv/xpAbSJaDaWk4i//8i9zFDE7KA24CpQa+/iITygUQhAE9Qszm800NjbmTC+7VlkuYTB8FVHsJhq9n1jsX+Ww1uajEGVKhVgstsIoK7XJq4cgDg8PY7PZ8Hg8667pcoEs2wiHbbhcemIx7eRVlJySOaisrKSnp2dF/enqWs90kc9eEIXtKb5etr6+nvLy8hVrpaPEq6qquHz5MgB2ezlLS/8dMKHXf4BltTWCwfB7gI9I5KtAs3pupuxSyvHKjBFlsKHidBQSE9w20sNWslOpgmKyLK/Isk9PTxMIBJiZmUlIOpHp+71aj4viGXS6s0SjtyPLjTmtlQsKLSOxGfIkCogtLS0hSRJGoxGn07luINTpnMdqnWdmxkVra9+a9XOBLMsYjUb8fj+BQIDr168jCAJNTU1qmVY8CqEP0WAwsGfPHubn5+no6KCrq4umpiaKioqyWru2tpaOjg5CoRBebxWzs89RVBRBr3+CZTs1hcHwAWR5P9HofwN+U3aWaVBMlmVMJhOHDh1SMx1Wq3XLOB05zdn41re+RV9fH295y1soKyvDarXicDhwOp1qc1Eiw//oo4/y0ksv4fF4uHDhAgBTU1M88MAD9PT0UFdXx3e/+92ERv65557jqaeeAuAv/uIv1MhUPObn5+nu7ubSpUt86lOf4pFHHiEajWIymVYwQVksFlW28fFx5ufnNZljsVrxCsIQ4fAlQiE7DscrwNZ2NkA75au1gVJ4t+PL3+IZwFLVJmfWPLYTvf5RbLarRCIf1uwzJEL8Jr6zs5Pe3l4aGxspLS3N27ORiSJcrZSNRiM7duxgaWlJrZdtaGjA4/Go3/l6a/t8Pj74wQ8CcPHiRSYmJpBlmaqqZ9Dpvoko/hPgQBDOYDC8F0m6j1js3wNFGbOOrNZR8U7H9evXKSkpYceOHWmvt43CQK52aiMQn4FPRotuNBpX6C+lNCRXrM1szKDXf4NoVIfBcJlw+EtAevekEBvEC7GMKhUlfjoBMZPJxMjICNFolIqKijSuWI3VejsOxxkikQeIRlf+VQvGLwCHw8GBAweYnp7m4sWL2Gw2Ghsb1blMhcCwGH+s3W5n3759zM3NrXCS4ntQ0tEJFouF973vfQC0t7fT3n6NUCjEb/3WY7hc/xFBmAb0iOJP0evfhiQ9gST9P4CQtZ1KVF5ltVqTOk2FgpycjWAwyPPPP89XvvIVTCYTNTU1VFZWUl5ezu/93u+xc+fOhA/CI488wic/+Ukefvhh9Xdf+tKXeOc738kTTzzBl770Jb70pS/xzDPPrDhvamqKJ598klOnTiEIAgcPHuSee+5RnZLx8XHuuusurFYrxcXFiKLIe9/7Xnbt2rWuE6Ely8fqtYJBJ5OTYDb3MjT0r2hKb+YdUJjORiGUlcSXEUxMTDA3N6c2fsY7lFarNScGpuQQiEZTN4Bli2QK1Gg00traSigUoqOjg56eHpX5aqNkSIRkSlnZFC0sLNDZ2ammrmOxWFpKVonWTE9Pc+7cOQAOHDjA/v1fRJZb0OmeBPQIwgw63dcQxX8kGn0GSWrOmeIQlp2OgwcPpr3ONgoLudqpfAXFZFmmt7eXc+fO8eqrr3L27FkOHDjAf/kv/wWPx6Pqrrq6ujV2a35+nqmpKU3uz2ryEUnSMzo6Ryw2jShW43an/w4VahlVoSG+wkIJiq0OiHm9Xvx+v0YTsfWEw18AwsDaAbK5INF3VFxczOHDhxkfH+fMmTO43W4aGhryRn2bC3MVgNPp5ODBg+osJqPRSGNjIzabLeMS5ampKTo6OhBFkdde8/D+97+MKP5XdLr/CQQQBB16/aNI0rNEo3+DJGVGFb3azq52OsbGxm48Z0P5wB//+Mf5+Mc/DkBXVxdvvPEGX/va13jxxRe566672LlzZ8Iv7I477qCnp2fF71588UVef/11YLmO9m1ve9saZ+OVV17hyJEjap3dkSNHePnll3nwwQeB5ebatrY2RFHk5z//Od/5zne4++670/pM+VWWFq5evR+dLozF4tlUZ6PQ+MvTQSQSWRHxCQaDK6j6HA4Her2eluUJOxsiU76RSg6r1cqePXsIBAIcP36c9vZ2mpqasNlsSc/RWoZ4rPdMWSwWdu3apTpJk5OTlJaWps1ksri4qBoKhfRBkh5Ckt6DXv9hRPFngIQgDGEw/BtuuqmShYX/DdyS1vrrZXHSYdjaRuEhVzuVr6CYLMt89rOfpaWlhebmZjo6OvjWt76Vdv12vuxUOKzn3Ll3UVY2z9iYm9tvT49dLt9ybdV1VvfVzM7OEgwGuXz5smq3VldY5AcCyRyNXOxdMr0vCAIej4eysjIGBwc5ceIEkiSlHczdjP4OZRbT5OQkFy5cUIOUmdwfvV6vOvBKGZkk/QGyfCt6/WcQhOWAhSgexWg8REXFERYX/9+0109mpxSno9DtlCZzNgAaGhpoaGjg7W9/Oy+88ILKcJHulzU6OqqmBSsqKhgbG1tzzODg4Aq2kKqqKgYHB9Wf4x+OzR7qF6+crFYre/ceZHZ2Fp/Pl/FaWstVKBtqWBnFUKj64qM+i4uLKrWw3W5P2PQ4PT296ZOAtUS6hs3hcGC1WqmpqeHChQvY7Xb8fr+aus4FuZRRJYPiJJ06dYrh4WGGh4fTSv26XC4sFgtLS0vs3buXkZERiouLMZlcRKP/jCh+Fb3+3wPLdJ5m8wBm879CksxEoyNA6vuxPUH8zYNM7FS+gmKiKPLd734XWLZpP/zhD9OOYufTTplMJrzevQwODlJXV5fR5qUQnY2NhDK7QrFbqwNixcXFlJaWMjAwwK5duzZb3A2BIAhUVVVRUVHBr371K06fPk19fX1S4hAFm9lMXlJSgtvtZnx8nAsXLhAOh7Hb7WnZVJvNxp133kk4HMZisfDDH/4Qn8/Hvn23EIm8iE73p+h0LwASIOF2/wj4EdHo/UjS/0lL/q1sp3JyNkKhEIFAAIvFgtlsxmg0UlVVxfnz55mYmODgwYNIkqSZx5VIASV7eDZyqN9qJDIIZWVllJWVZbxWISpxLRwgpT45GAxy7do1AoHAioFSDodDpWgsJOdoI5CpQ+h2uzl8+DBjY2OcPn16xXTvjZAhU3lFUaS1tZVIJJK0XjYeer2eO+64A1mWeemllxgbG8PhcHD//fej1+uRpE8QifwWev37EYSxuOssYjC4iEQWU8qznrPxZnv+bjRoaae0CIrFIxvq23zZKUEQaGlpySpDXKh2SusMfHxAbPUwV8VuJWMBy2TuVyoUiiOWrt7X6XRYLBZaWloYGhri2LFj+P1+tYcv23XzdaySmfF6vZhMJk6fPk1JSQn19fUpy/FlWcbr9WI2m3n++edZWFhgZGSEyspKPB4PsdjXkaR3YjB8ElhQz9Pr/wlZdhCJBFLKtdWDYlk5GwqDx1e/+lW+9rWvceTIESorK2lpacFms3Hx4sWMGyq9Xi/Dw8NUVFQwPDyckBGoqqpKjSoBDAwM8La3vS3hepkOS8pnxKhQ1oLVmYQI09N/Awxhsz2OyVSX1TrrIVHTtiAIGI1GJElaQdW3jewgCAJer5eysjJ1urfP56OmpiYrZz+fzoZyfKp62USQJInR0VFMJhNzc3MsLCyoDoos7yUS6UCvfw+i+Ev1nHTE2upKfBuJkQ87lQ4yCYplM9SvUO2UloMxV8sVDncQifRhsdyKKKbHvJPr51No8aenp5mfn+f06dNEo9GsJqxrja0WAFGYq5qbm6mpqVF7DpuamtbQz+bT2chUz5eWluL3+xkaGuLkyZPq3KtEe5V4O+JwOJifn0ev1xMOh9VxCrL8IcLh2zEYbkMQxtVzBSHC8niE5J9lq9uprJwNZfPywAMP4PV6aW9v56c//SnPPvssAwMD3H777Tz00EMrjl0P99xzD8899xxPPPEEzz33HPfee++aY+6++27+/M//XK3b/slPfsLTTz+dcL1sMhvaKEsZvb4Dg2EMuCnn1bR2guIxN/c6gvB9ZFlkdva/4fF8Jat1FMiyrNaoKs6Fwv3ucDjUeQZWqxVRFJmbm2NoaIji4mKi0SgzMzNpTTPdKpBlmdOnT9PV1UVra2va6fNcSt2U6d4VFRX09vZy7NgxlT43E2jRIJ5q7fjjE9XLNjY2YrFYVpyn0+m4+eabaW9vp6amhvPnz9PQ0EB5efmvjzASjf4LExP34fP9+Neyrb+Z3OpKfBuJkQ87pXVQrJDKfXOBlhmX1XItLfURCv0+sMjCwi2Uln5Jk+vEI55eOBAIqFkIm82GxWLBaDSye/funAJihewgjI+Pc/36daqrq9cMtkz2vWZqp5RjzWYzu3btYn5+nuvXr6szOuIz2/lwNrKhmlXYnyorK6moqGBgYIATJ05QUVFBTU3NivLHeFnuvPNO+vr66Ojo4OWXX8Zut3P//ff/OjNSQyTSTzTahNXaH3fF9YcMbmU7lVMZVVVVFR/+8If58Id/Q/359a9/ndnZWUpKSpKe9+CDD/L6668zMTFBVVUVTz75JE888QS/8zu/w9e//nVqamp44YUXgOUpl88++yx/+7d/i9vt5nOf+xw333wzAJ///OeTDmXJRolroSx1utex2b5NZeU8glCHLGfQDZ4A+cySyLILWdYhCFGi0eTfV6J1YrEYMzMzK9LJ8ZNq0+V+l2WZWCzGqVP/B4PhArL8Fg4e/O2CVszpYmEhhMHwN7zrXRe5evUQ0eh/1YhhZH0ok16rq6vp6uri6NGjRCKRtJVzPjMbyZR+fL3s2bNnKSoqoqGhYcUxe/fu5aabbuJb3/oWkUiEq1ev8qEPfWhFeruz8wmKi19Ap5OB9TcHW12JbyM1srVTiaB1UGz1PKj1oF1p0BgWy99QXz+AIHwRWc6slzBfcilrxWNxcRRZXkIQLMRifUnOSk8mWU48uyJ+iG9tba0aEINlR2R2dhaDwUBX1zUGBo7j8exhx449GdupQimBiockSZw9+218vjNcvdpEaemn1wR6kiEXhim73c7+/fuZmZnh8uXLmM1mmpqaNr2MSsFqOyWKospk19/fz7Fjx9Rhuzpd1vBXAAAgAElEQVSdboUdUYYIvvHGGxiNRpUgIL6U/uLFf6C5uRub7RUkaX0HeqvbKc13Po899hhf+cpXeOqpp/irv/orNZUdj+effz7hua+++uqa3x06dIi//du/VX9+9NFHefTRR9eVY7PS07FYP/PzAZaWAlit4whC4TobRUWHmJz8ItHoOGVlRxKek4iqLxBYri1cWlrCbrdTUVGB3W7POCOhvMhLSxM0N/8dRmOUhYUzyPL7EYT0H81CdUzM5gDNzWcJBs3s2XMMnS5KOq+clk38BoOBHTt2sLi4yBtvvMGJEydobm5ed1DdRpRRJUI8k8no6CinT59mcXFxzVA25TmOxWJMTk6u4JxfNhIi6c4I2OqNd9vIHOnYqY0IimWq37XKbIji6V+TJwQQxVPEYvfktF4+7ZTDsZ/BwfcCV7BaE0+CT4RYLMbS0hKDg4Przq5IR3/FYlHgixw40MP0dBmLi9/EYkmPUa+QIQgSt9zyPKIYora2HVH8CLC+s6EVRa3L5eLmm29mYmKCs2fPJtT32ayby7GpjtfpdNTV1VFVVaVWD9TU1CQMou3fv58TJ04Qi8X4wQ9+wDvf+U5qa2uBZTsly3chSb+dtjxbubcwJ2ejs7OT8+fPs3v3bvR6PUajkYqKCgYHB+nt7QU2z5PfrMa7zs4dSNIpZmeLWVz0kGH1yhrkU4kLgkBp6VvVn5Ua1fh0cvwwRIfDgdfrZW5ujqWlJfWlyRUWiwm93kwoFMTlsiKKmb80hRgxEsUSnM49OBxdwAEikfR5zrVWHGazGYvFwk033aSmrlM1Zec7YrTe5l4QBMrLy/F6vfzsZz9bUy/7nve8hzNnztDT08NLL73Ebbfdxs6dO7OWJ5WjXOhKfBupka2d2oigGORnsvJ6mJrysLg4ydJSiFismCy4S9bIlS87JYp6qqv/KOnxspx4mKuyhsvlSjnMNR0sb/RilJf3MzfnoLh4AqNxnviJ0FsVgiBQUlJGJDKGXm8nGk0vq6Gcmw7W08kKfWtpaSk///nPOXnyJOXl5dTW1qb8zjbTTun1evx+PzU1NfT09DA/P8/w8DA+n0+9zr59+7DZbLz22msAtLW1rXA2Mp2zsZVtUVZvnnKTzp07xwc+8AE8Hg8NDQ0qi8Xp06f5yEc+ArBpEcPNqoXV68u5dOndTE9PU1ub+4CVfCnxSCSyQjkHg0FguUZV6a1I1gg1Pz+vmUzLCsCF2fw0VusxYrE7keUbo2cDDITD/wNR7EOS6lmvJlNBPh2n1alri8WSsD9iszIbq6EQCdx6660qZ7tSL1teXk5vby+SJNHZ2UlLS4uqb7R2frax9bAV7BRsTqBkZqaEjo6HmJ6eYvduT0E7G/FYPbsiEAgQiUQwGo1qX6AyzHVubo6RkRGqqqpylmf5vyZsto9it7+ALH8QWS5f58ytAh3R6FfQ619Hkg4D6e1bMv2+02WCMhgM3HrrrfT19XH8+HGqq6upqqpK+I4Wgp0yGAw0NTUxOjpKIBDg6NGjNDQ04PV6VSfKYDCoZee/+tWveOtb35pxRn2r26mchvrdd999SJLE0NAQp06d4uzZs/T09KwYhLRZN2ezamFramowmUxcu3YtK6pbreWKr1ENhUJcvnyZSCSCXq9XlXN1dTU2m23Dv6v4F1mS9iNJ+zf0+hsDK5KUHZVkPrE6dV1cXExDQ8OK1HUmjXeZRgwz/XyiKFJdXY3P56O/v5/jx49TUlJCcXExAwMDDA4O8vrrr/OOd7wjo3Vh6yvxbSTGVrBTm4WKigqmpuqYnZWpqanJeb18OBvRaHRNQGz17Iqampq0ym20giz/G2T5w6QbONoqkGU/0ag/w3PyMxUclt9HpVSpu7ubY8eOrdjAZyNDNkQmmdgpURRpaWlhcXGRzs5Oenp68Pv9lJaW8u53v5vvfe97mM1mzpw5w/79+7PKbGxlPZV1GVU0GmVqagqTyURZWRn33HMP99yTW92nlsi0LCqXzIYgjCAIk0hSM4JgwOPxMDAwkNVaa9dOX4nHYrEVDdura1T1ej11dXW4XK6cNrNa0/FuYyU26t7Gp66Hh4dXpK4zwUb2PMTXy/b19eFwONSBS9evX+ctb3lLxmtudSW+jeQodDsFm1OmZzQa2b9/P9FoNO1m4FTIxSbEz64IBAJMTEwwOTm5IluRbHZFvmRKJGPcypqs+WZCtn2Ier2epqYmampq1A18U1OTSuyw2eW+iWA2m7npppsIhUJ0dnbS1dWlOh29vb3odDpefvllKioq3lQZ+IydDeULO3r0KA8//DC33HILt956K5/+9KeJRCLodDoEIbMx7/lCJoomW8UkCOMYjV8GFonF3kI0+mF1rVgMZmchSW9g1nIpNarxTduhUAhRFFfwf/v9/hUR52AwmHYz3EZAO2aVwvg8mSISidDT06MyoCifQ8sG8XQgCAI+n4/y8nI1dR2JRNJWbvmUN9nzodfraWhowOfzMTk5ydDQEAaDgZ/+9KeUlpZmdI2trsS3sRY3qp3KHhJ6/f9CpztDNPrbxGJv03T1dHV5LBZThywqAbHVsyuKi4vxeDwZM4VlK1M662iBQnjWssX09DQGgwGXy6X+biPtlMlkYufOnQSDwRV0uYVQRpUMVquV3bt3Mz8/T0dHB2VlZYyNjRGNRunu7sbhcGSc2UhFvVzoz1fGzobygW6//XZefvll/uiP/ogrV64Ay1HHrWq0s/+iZllYmGZxUcRq7Vux1sCAwKlTOu6/P0q2yyuOxcjIyLo1qut9Bi2V742Y2djoTX5n5/PU1f0PQiEXQ0PPUlm5e8OunQjxqeuf//znSVPXq5FvZyPV2mazmTvvvJPvf//7BAIBrl27htFozGt6fRuFjxvVTmULQRhDFE+wtFSCyfTPG+JspJpd4XA4kg5znZubK/iNU7bYinazt/ciweBfE4sZqa7+C6qqGtY/KQG0+E5tNhv79u1jdnaWa9euEQgEWFxc1Jy5Kpvjk8Fut6sy9/b2Mjo6il6vp6urizvvvDPtdba6ncqJjWrHjh289NJLQGHSR26EwpqbK6Onpx67fZRAYDd79sDcHPz856WUlOiYmxP4znf0NDVJHDyYukwrUY1qNBpFkiScTmfONar5SytvIx3EZ6MCgQAlJf+EKEZwuYZZXDwGLDsb+di8Z/J96fV6zGYzBw8eTJi6TrR2vt61dBSs1+uloqKCYDCIKIoqB7rf76esrGxd2bY6peA2UqPQ7ZROp0tIvaslolEno6MCgnCeaPQW4piic4bSF6jUqqczuyIVCi0odqMG19KFxfItamvfQJZhamoH8KfAxgfn4lFUVMShQ4f45S9/ycWLFykqKsLv92MyJWd83KgyqmQoKiri7rvv5vnnn1dnt/T19aXdM1WIuisT5DxnQ+lz2Mo3IRcIgo6RkVuRJEndjDkcUF0dYnJSwG6XMRqhufk3jsbqGtX5+XkWFxfR6/WqclZqVBcWFujr69OEZrYQ08o3ovOjfL+KUxH//TocDhwOB6WlpYjifej1/51o1IQg7NrUDEEiJEtdO53OnNdOF+msLQgCBw4coKOjg0gkwtzcHH6/n7GxMbq7u2lsbExZkrHVKQW3sT4K2U4pzIlWqzVv11hagvb2d1NcDNPTesrLs3tno9GoSo+u6DZJkjCbzcRisYxnVyTCm31zv5mQZVktc1O+X5dLAAT0epGKipUb483Wm3q9nkOHDjE+Pk5bW5tKj56IsCTfDeLpoLy8nLKyMoaGhojFYrz66qscOnQIv9+/7vu/1e1Uzs5GISrvjYAgjKHTvUxRURUHDuxnbi6gDhYTBLBaY8zMgNksEQotMjc3wfDw2hpVh8NBRUUFZrM54YOkteLdjvRoi3jlHF8ucPXqVdWxSPb9hsMPY7W+i3DYTH//NN3dJ9mxY0feMhvZrrk6da1Q/SnKMZ9KMF0DUV1dTVFREZOTk0QiEdrb2zly5AjBYJCOjg66urpobGxMOMxwq6ent7E+Cvn7VWZCZeJsZEYf3YfVasHnq2N4eBi/vyGtbF+i2RXxfYEVFRXq7IpYLMbZs2cz7pdKLO92RiIVtAzQzc3NrXAcY7EYVqsVh8NBSUkJtbW1yPLniEabGBqaRac7QF3dchauEO6tEu2vqKjA6/UyMDDA8ePH1cne8e99NpmBfNi1Xbt2MTg4CMDo6CjBYJD29naKiopoaGhQCU9WY6vbqZydjcXFxaQ3B36zEdkKHllmpSbfRhQ7geOUltZQUtJIOBxmamqKQCCAKM5TW3uSoqIYgUAxkmRIWqOaCpkozGj0X4hGz2E03o8o1q35eyE+qIWgsNKFMvQwPuqjKOd4Ksb29nb27t2bxooCslyPySSya1clgUCAq1evqmUINptNM9m1cGCU1PXk5CTnzp3D6XTi9/vzmt7NRO7W1lZ+8YtfAMvDk3bs2EFNTQ179+4lEAhw/fp1urq6aGpqWpGd2epKfBvro5DtlNFozGgArSAI6w6iVKDT/Ry9/ruAgV27/pCbbrppzTGxWIyFhYUVjkUkElGHuabTF5jZxj6IIAwhy7VA4pLgQsp4F6LTkulzGo1GV5TwBoNBQqEQg4ODSQllFITD4HD8Hk6noE7Mrq+vL4hIe7x9EEWRmpoafD4fvb29HD16lPr6epX1aTOITBJhx44dvPLKK+rPb7zxBp/5zGcYHR3l9OnTuN3uNTT0sPXtVE7OxuzsLE899RT79u2joaGBsrIyRFHE4XDgdrs3vRFPUcpayyDLMjMzMktLPciykZGRLoLBKQwGg9q07XaLHD68J+drp6vowuEOFha+RCwmsbh4Fpfrm0llzxWFVo6VD8QrZ8UAw2+aG1Mp52zhcDg4dOgQbW3fIhh8ikCgiYqK/4zRqE15hVb3u6SkBLfbzejoKG1tbQiCkDNzTDJk8v7GOxsAr7zyCh/72MeA5Xt74MABNTuj0+loamrCbrdveSW+jdQodDuVzQDadPWvIHSyuCih080hCKOEwxUrqNFDoRBtbW0rhrnW1tZm3BeYrk2Q5SVCoU8Ri/Wg0+3HZvsyq6lkb1T2p42SR8lIxTNV6nQ6tZJCmavV1tZGa2tr2uuKokh9fT2VlZV0dnYyMjKAKP4LktREaem78/iJUmP1fVUme1dXV9PZ2Ulvby+NjY15dY4ycWQsFgu7du3i/PnzwDIr5cTEBOXl5Xi9XpWGXikJU4LT69mpQnveVyOnnZIgCFy8eJHvf//7TE9PE41Gue222/B6vfh8PiorK/F6vXi9Xm677TatZE4bSno6VURrPSg1qvGbTkmKMDVVid1+B+Gwk5aWXezcuZIzub+/XxMDlu78j8VFiVgMDAaJuTmIY6hTcaP0SHR2dnLuXDtVVdUcOnQo55dMUc6zs7NMT0/T1dW1QjlXVlZit9s3bENSXv5ViooGgOu0tzfg8fzOmpRwptBa0QqCQHl5OR6Ph5MnT3L58mWCwSA1NTWa3qdMlLjb7aa6upr+/n4AJicn1yhoJTszNTXFpUuXMJvNRCKRbWfjBsZWsVPpQgmipYLSN9bbW41O9xoLC2ZmZyexWs+tmF0RCoXYu3evpkGTVFhaGiMc7iIadaPXn8ZiiSCKKx2bdD5fOthse7e4uEh3dzcul0stsdYSq3s/A4GA2pifKVNlJjAajbS2tiLL/4mqqp8gyyIDA89QVfWvNbuGFlDkDIVCdHR0MDMzQ2VlZV6ulWnW5MiRI6qzAfCrX/2Ke++9dwUN/eDgICdOnFBnX231oFhOGsbpdPKjH/0IgFdffZWHHnoIp9OJx+Ph+PHjjIyMANDc3LwpStxoNLK0tJSWsyHLMpIkMT4+rjoVCwsL6qZTqVF1OGYxm59lZmaGtrbbkWUPTqczb15lugrT4Wiiq+vTLCxcwuO5N6e10pFJK2Qjz/T0/+Duu3/G0FADs7PPIorplaWtp5x1Oh1lZWVUVlZuapRgft6Fy9WPJOmorj7I/HxEZVfyeDwFFcFQargbGxuZmZlZk7pejUy/70wV7Fvf+la+853vqD/39PTQ0LCWqtHtdnPzzTczOTnJmTNnuHr1Kk1NTQl1RSHd721kjq1gpzJxNlZnNmKxmNq0rei2WCxCWdk1JGmczs57AAt79+6iqqoq5VrZIt13xGCooK/vNoqKTjM29j5crrW6u9Det2zlaWt7Cbv9x3R2VmA2fwar1ZbT4EOlN3BsbIylpSUGBwdX9H76fL4NnaNlMo3+uvRQYmzsHHNze9ZlhNoMWK1W9uzZw6VLlxgdHSUQCNDU1KRpmXKmdmp15lDRQQpEUaS6uhqfz0d/fz/Hjx8vuPciU2gSznj55Zf5xCc+wb333suFCxf45je/qaZ+BgcH6erq0uIyGSNZxEipvY9XztFoVKUjczqdeL1eLBbLmi9Yp/sFEMTl0nPokIgk3arpQ7sa6ToIgiDg978PeF/Oa2klUzrrZIPdu48SCFiorOzEYJggFFobNUrEqhGJRFI25nd1dSVt1E8XWtyXqanPEI2ewGrdQV3d2ykvF6iurub69ev09PSwY8cOXC5XRtfKd71qfOq6q6tLTV2XlpauuG6+uc7Ly8tX/Hz9+vWEzgYsP3+lpaVqBPDMmTO4XC4aGhoKzmBuI3cUsp1Kt4wqHA4TjUbp7+9naWmJUCiEIAhqGZTSF2gyXcZgOEo0KuFwwOzs3Xi93jXraeVspAudTk99/V8SCATw+xMH6QrNTmWLpqb/g93eiSwLRCLvBg6ldZ4kSSv2Jqsbt202G263WxOGylwQDH6c6en/jiB42LXrs0xNLXLq1CnKy8upq6vLK5VzNjAYDDQ2NqLX67lw4QJ2ux2/359T5YuCbOyaQnkNJL1XOp2Oul/Pvjp27Bjt7e3U1dVRWVm55bIcOTsb//zP/8zjjz/OM888w4c//GGefvppfvu3f5t//Md/xGg0UllZmTJ1dfXqVR544AH1566uLr74xS/y6U9/Wv3d66+/zr333kt9fT0A999/P5///OfXlc1kMjEyMsLk5CRWq1WdXQGsqFFV6uLa2tqor69P+ZLMzxcTDI4gSSIWSysOR3JHQ4sNnpYKc7OVr1aw2d6H3f5TYBeRSA2StKRGehQlHa+cFcWc7XySjYbR6MLv/8MVStBieZF9+/4XodD7OX9er/YcpPt8bdQsDKPRSEtLCwsLC3R0dKh0ucrk2Xxzna9+vhcXF9M6RymjGRkZoa2tjZKSEurr67fMM7ON1MjVTuUTiYJiSrAkvr9CGV4WDocxGAyUl5cnnV0hyxAKhYjFYlRVVVNdvSvhtbUqWcoEBoMBt9ud9O+F5mxkqze9Xh+xWD86nRkoIZEqStS4DajVFIl6AxUmo82GTteAw/FdioqKACgvB48Hpqdf4Nw5N17v25NmuDcDiu1xu90cPnyYsbExTp8+TWlpacbEPauRjZ2qqKhgYGAAQRDW7Z3R6/VYrVaampoYGRnh6NGj1NXV4fP5Cub+roecnA1JkvjoRz/KN77xDd7znvcgyzJ/9md/xgc/+EGefvppnnzySSD1BmPHjh2cPXsWWE4HV1ZWct9996057vbbb1cHM6XC6dOn+d73vkd7eztvvPEGjz/+OA8++CD333+/2hiV7KFQ+iOSe5n/gk73vwmHjXR23ofHY2LHjsRyaMV+UIjUt7FYjLGxLmw2PZWV69MoJkM2ny0ajTI9/XGWlt7J9LSR+fkLxGIxZFmmuLg4L43bmUCL+7v2uZlHlj/P/LyMxfKfOXjwKBMTEufOnWNhYYFIJLKuotzoWRgWi4Xdu3erLFCCIKhlSvnkOpckaUWkON3GdeUaCoXi0NAQJ0+epLy8nNbW1i2j0LexFlrYqXwGxQRB4MyZMwwNDdHS0qLOrlBY7oqKiqiqqsJoNCIIApcuXaKkpAS73Z5kxRlCoWMMDVmZm6vFZPKzc2fyaxdaAEpLmWKxGNFodJPswRcwGl9Clv3EYi1EIvOEw2F6enpSNm5vlYh1ou9IEB7B5bpCcbGdzs5ajh/vo6mpKac1tUL8+y0IAl6vl7KyMrU3wufzUVNTk1VGJlNGRkmSaGhoYMeOHQiCwL59+9I6x2g00tTURG1tLV1dXRw9epSGhoaEWctCQ05v4P/P3nuHR3aXd9+fU6Zqika99+bd9XbtrnshtrFD3ChxN3kIxvELgYS84Q3xw0vCk/CGEgiBGJz4iuHBTxzHJmDAgI0b9hbVXW3f1TZJK61WXRqNNOXMOe8f2hmPpOlFZa3vdfnySjrnd34zc+a+z12+31sURX76059y9dVXBz9ITdP48Y9/zDXXXBM04vHi9ddfp7a2NqXyoKZpNDc388lPfpIvfOEL/Pmf/zlNTU1xnRvLyElSG5KUj9F4GoNhOuoHvNKyM4G10oELF15g/fpnUNUshoa+RWHhlrSsuxDRVTUqKCubKylPT08zMDBAeXl5RvaRKBKpNsSzhtcrMjUlYjY7GR3NIitLJi/PRnZ2Nnv27KG1tZXS0tKo5OxEHtoTvd+irR1QgRofH+fIkSMYjcaEMqnJZIyampo4c+YMer2eK6+8Mu5zAxBFkbKyMkpKShgdHV0LNFY50uGn0p0Ua29v52tf+xrd3d2MjIywbt067rzzznmzKyIhlk+Q5Z9jMrVhtw8xMrIJg8EUda2lrmzEg/S0o/YA3+HkSTMlJU+SnV2S+sbiwHvcQA2n8/pL3MBWJEkKtvJmgri9HFi4/+np04iiiiBMUlxspbS0nhMnTgSrdJED5DksdVIslBsRkPUNVAwSQaICLKqqotPpaG5uTuichR0EbrebM2fOcO7cOa699toV17oWipTD/VADDnM3n8Fg4Jlnngm+OfF+CM8//zz3339/2L/t3buXTZs2UVJSwje+8Y2weuEA27ZtY9u2bcBcdjVR4l0kwysIPczMnERRjjI728yGDR/CbA4j+bRgrVQ//JXYRpWX14rfL2E0uvD5jgHJBRuB+yJgnEMH4y2FqsZCrJQM38J9iKKeV199HIejm4mJWj7yEQOS9A9I0l7y8z9EQ8Mfce7cOfbt2xfMcix8nxINNtLNq3A4HOzYsYOBgQGOHTvGiRMnqKmpSXtFRlVVysvLueGGG5BlOaU2KFEUKSgoSPr8NawcpNNPpSMpVllZyVe+8hXq6ur4m7/5GzZs2MDtt8cnHxpdoVBDVcdRlCGMRjPl5Y3k50fOLC81ZyMepMtPKcp/UFvbhSRJOJ0vkJ39udgnJYhwQ10XcgMDxG2fz8exY8cW8cpWK8J9Rrt3P0h9/W/p729ky5YazOYLbNvWzZ49Bo4cOYLVao1KIs90sBEpcSVJEjU1NZSVlXH27Fn27duHz+eLez/J+MxEK1jhEm9Go5F169bh9XpXdKABKQYbgUg13JscOtTs3Xff5eqrr4765nq9Xl5++WW++tWvLvrb1q1b6enpwWKx8Morr3D33XfT3d0dc38BNap4Ec3IyfLPGR/3ommlnD+/Ab3eT7SBr8vdLxpprXTsyWz+GILwd+j1JVgsNyV0rqqqwSzHxMQEk5OTtLW1LauqRihWSqYpdB+yLHPbbQ/S29tLc3M5ev0RJOlZVFWgtva7SNIfU1tbS1lZGadOnaKnpydIIg9guYONwGvKycnB4XCQlZVFa2srxcXFVFZWRjSUiVY2AscnMo15DZc30umnID1Jsfz8fPLz84Hk1KgiBRuS9C4u1z68Xic9PdvJz78iakB/ObdRZWdXomlz62Vnp06mjkXcDgx1fb/wvMLZ/Z07P8uJEx+kuroUq1VFp/soguBi0yYrRmMrFy8ORSWRL3VlYyH0ej2NjY3MzMywd+9e2traqK+vx+FwRD0vGT+V6OuMdo3VcM+lFGx86lOf4r777mPr1q1YLBZMJhM6nS44pKSnp4eWlhbefvtttm3bhskUuZz7q1/9iq1bt4ZtTQqd9nvHHXfwxBNPMDIyQl5eXtT9JapfHtmIj6Jpe7Hb9zM8XIwklQRJUZGQfiPuRRBeAXxo2h8AiSsopC8Aupbe3m+zbt0mot1CAUnG0KxPoBc5QNyemZlh69atKe5n5TjMdBjLcK8lNzc3yD/QtFm8XlDVGVyucgyGgwhCFwbDB1m/fj3T09OcOHECSZJoaGgIPnhnMtiI19AGji0rK6O4uJje3l727dtHZWUlJSUli9ZZiozRGi5vpNNPZSIpluhQv2j2ThTbkeVBpqf1eDzhyePzj89EZWMaQXCjadH9cySky56bTPdz9qyX+vom4NqEzg1H3A4MP0xlqOtK8VOZgsPhYNeuXZd+Oo+iTOH1qsjyKILgp6hIpqBgF729ffNalkJtfCaFTOJd22g0YjabWbduHSdPngwKnURqA1sqPxXpGislSRoNKQUbjz76KJ///OeRZZnt27dTXl6OLMvBHvqenh5EUeTJJ5+MasAB/uM//iNitmhwcDDYGtLa2oqqqnERP5MZlhTOGEhSC15vH9PTxTid9ZSWNseMJEVVRThwAHFyEhwO1I0bIQU5Tbf7FbzebwEaOp0Lk+njCa+RXh6JkdDbR1GUeUGFy+UKSjJarVaKiooWGWefz5cxZQ1N05iensZkMi0bWTxZxDJcw8NGXn/9PrKzLzA1VcqDD96PIMyiqs+jKL/EYrGwbds2RkdH6erqwuFwJNQOlEzrUjKBjCRJVFdXzytdL5wlshQZo2hYDUZ8DdGRTj+VqaSYz+eL+/VESooJQi9ebyuK4kRVc7BYbg5WTyIh/ZyNAXy+vwCmkaRPI4q3JrxC+pJHEk7ndiB6MivADQz4rnDE7enp6YT66zOJTGb/07kPp9NOZ+d1VFcfpqurmXvueRhRbMPvv42qqu9RUlLC6dOn6e3tpaGhgdzc3GWvbIQeG5ghFeAcHj16FLPZTF1d3SK53GQr8O8npPQUduutt3Lo0CHeeecdXnrpJV5//XUmJrunT9IAACAASURBVCbIyclhy5YtPPLII9x8880x15mZmeG1117jBz/4QfB33//+9wF4/PHHefHFF3nqqaeQZRmTycTzzz8f102TaBtVJCMuiv+NIAxiMmmMjNxGQ0PsqoK9qwvd9DRCfj5CXx/ixYsot98OSfbVjY9PYTarCAKMjIyTDBc6XUbc5/Phdrs5d+4cTqczOPwwwK9YCaoae/e+w9jYbgShiltv/UhKsnYrDaqqMjFRyOhoHgUFw4CCpkkIwnkk6fNoWhOq+sfk5uaya9cuBgYGOHz4MDqdLi4jl8lZGOGur9PpaGhooKKigtOnT3Pu3Dnq6+vJycnJuFTuGi5/pMtPQeaSYvFINAcQOSm2F0E4hChqTE9vR6+3x/zupLsqPDnZjqb14/UaEMWfk5e3fMHGwnXeI26/1wbldrvncQPz8vKWlLg9PDxMe3s7eXl5bNu2LW7btdTBRjLXEwSB48d3cfjwdiyWcUTxVTTNjiT9ClXdh15fGJzwffLkyYgDWNOFRP1U6LEOh4Pm5maGh4fZv38/OTk586TRM5mgu1yQcspX0zSuu+46rrvuuoh/h+g3q9lsZnR0dN7vHn/88eC/P/3pT/PpT3864b2lo41KEE4iCHsRxVm83gJgU8z+PWZnMfX14V+/HtlkQrPZEPr7YWIC4pTiXAiz+UOcOjUA+Kis/MOYx0dCIkY8lLgdapxlWcbn82EymSgoKAg7/DAT+0kExcXfYNu208zMZOF0Xk1OztIMQIr39UQ7LpbhKiws5IYbbqC/vx+9fgOKUoQgvI0oXkAUX0QQdCiKhKrejSDkUFpaislk4vjx4+zdu5fa2tqwJPJ4r5/K8dGONRqNrF+/HpfLRXd3N2fPnsVutycUKCZanr7c2xrWMId0+KlMJcUMBgOTk5Nxv5bwSTEFQXgOv9+Doujp76+nuTm2ok66Kxs+3zrc7jyMxinGxq4nRlEnIlL9Xmqahsvlwuv1curUqSBx22AwYLVaww51XQ6cOPFjmptfYnS0mMHBb1BSsryD+hJBLLtvsVj4/d//ffr6+nC5pvD5DiGKLUAlOt1DgIzP9xxm83Y2b97M+Pg4x48fx+OZm5uV7sGqqfopQRAoKCggPz8/KI0e4Bwmo5r4fkuKpRxshCoKhRqIUNWP5UI62qh0uh8hikNIksbIiIOcnDgib0GYM+KXpkMCXGKqxb2XhbDbc9i06S/QNC3pLH20zyJA3A4tJyuKgtFoxGq1YrfbKS0tDWbhTp06lbK2cybvjaqqizideqxWN5I0k7HrhEO86hVARIMTa40rrriC6upqurq6+PGPrUxMXMvDD798ybmPI8v/L5r2bXy+54F1iKKI3W6nrq6O06dP09PTQ0NDQ9jAebmCjQCysrLYvHkzk5OTHDx4EFmWg2pksbBWzl5DOKTDT2UyKZZIG1W4AEEUOxDFo8jyNIKQiyRtjmsycro5Gzk5NQwMfJvx8RmqqqqTWiPRyoaqqkFuYChx22QyoarqiiZub978SwRhgqqqMfz+bmD1BBsQ+3tTVlZGWVkZ77zzDs8++0H8/mbuuONnlJdrCIITWX4ITduJojyFw+Fg8+bN7N+/P65J5MlItMdr66P5BUEQKC0tpaioKMg5zMnJSUgN6v3od9LWzC5cesBeSUi9jUpBkp4D3MiyxMWLTVxxRRyydUYjs/X1OAYGEPLywOVCq66G7MhSufEgVe5BwIj7/f5FqhqapgWJ26FT1SNhpWeEdbovUFj4L/j91+D1Niz3dsIiWmUhHgR4KZOTk+h0Ol5++YM8+ujtiOJ3Lq1/Br3+Zvz+R9C0zxGQ+1y3bh3T09PB0nVDQwNZWVnz1s0U2S0RI2u32ykvL2d2dpauri6ys7Opra2N+tCQjN75+83ov5+xUv1UohX4xUmxpxHFMWQZhoeLKS6O70E/nW1Ugfe1rCy1eUfRPp9IE7fDEbc1TaO9vT3uwZ6ZRrj3Ojv7GuCXCIIZRWlkhbvVeUjET3m9XlyuWWTZxjvvXMMDD7yNpvUAM4jiK8jyzfj93wS2YjQa2bx5c/BBPhyJPNn9pjMpFso5PHz4MBMTE9jt9nmcw0h4P3ILVxdzNkGkWtkQhO8hinOZLFX1MzKyBavVGtdaMxs3MtvQgG52FrKzURsaYBkeanw+X9A4Dw0N4fF4uHDhQtA4FxcXY7FYEorK03ljZypoUZR7UZR7M7J2NKTbcceCzWbDarUyNTVFTc1Opqe3YDLlotd/EZAAJ5L0L+Tl/TcTE18D5kYJB4hvo6OjHDp0CLvdHnyQTybYSOTYRO8fm81GU1MTFy5coK2tjcLCQqqqqsIG38m0Ua0FG2tYTiSjRjU/KeZHln/GXCsV9PfXsWlTfFXndAcb6eRaxCJul5WVYbFYVvX3V1GeRBTvQNNK0LSVMZR2IaJ9pvHacpPJRFNTE6dPn6aq6sMcPvxHVFR8F7v9p4ALUTyBKN6O0fgAovhxRFEMBhkLSeSh+1oKIZNY0Ol0FBcXYzabGRkZmcc5jLaX1XzfJoPLOtgwGo3BzEc8WFjZMJm+PO/vDse6+NeSZXxVVaix+B1pQqhxDhjo2dlZZFkOGufc3FwkSaKioiKla6XTqaQDK0n6FlJ/XYlkjGRZ5oEHHsDj8XDw4EGeeeYZrFYr9923F4vlQwjCCUBDkvpobLwfVb0bRfkxga9+bm4uOTk5wQf5kpIS8vLyEn4NmZLVDR24VlJSQlFREX19fbS0tFBWVkZ5efk8o51u9arVkDFaw+pGMmpUiqIEf9bpHgTmkmqCAJOTm+KeMxN9QGBiSNYOLyRuj42NMTs7y9TUVErE7dXx3dWhqjuXexNRkY4KvCAI3Hzzzdx888288cYbtLS0IEmN3H//k+Tn/z2CMASAwfBDtmz5CfA8mnYzer1+EYm8sbERi8WSVGIpWYJ4PMcHZnS4XK55crnhEtTvR27hZR1spD4s6b0PeGLCktDE2FQegEXxLeAlNO16NO0ji/6uaRqzs7PzyskBQlXAOBcWFi4ibg8ODib0fqxh+RAv70MQBCRJwmw2c+TIESRJwuVyMTjop7KyHUn6FJL0fwjcy6L4U/R6C17v/w18JXitkpISCgsL6enp4cCBA+h0uozIECZDpAvdgyiKVFZWUlpaGpyaXl1dTVFRUTDju9Y7u4bVhFTbfXW63wb/rWlgs20Kd1pYpJao8SCKh9G0UjStKK61NC38xO1Q4nZWVhaTk5M0NKyc9tfVEbgsLeL1DwuPGx8fv9TOLTA4eAMOx05k+X4E4SwAOt0UcAeqWoOitAFZmM3mIIn8yJEjWCwWqqsT4wQlI30bL0KDk6ysLLZs2cLExATHjx/HYDBQX18/T1Y70WBmpcgdp4LLOthIdVjSq69+iJtu+jkej56nn36cJ56InxCdvBH3MzPzdSYmBCyWExiN1+L3+7lw4ULQQCuKgslkwmKxYLfbKSsrQ6/XJ30zKoqCKIoJye4l8toURQk+FKeyTrowNTVFS8sbmExWdu68fslkcQMTaJ1OJ1NTU7jd7pjl1mhY+N5deeWVtLa2YjabOXfuHACVlc+gadchy08A7z2g6PVfR1HaUNVfB38nSRI1NTVkZ2dz5MgRWltbI5LIk0W6pGxlWaauro7y8nLOnDkTLF0n4yTWgo01LCdS9VNTU1dgt3cBcPJkHaWlpUmvlQhU9Vt4PG8jy3Yk6buL1opE3DabzVgslojE7fHx8aT2c7lB0zT6+voAKC8vX7UPmwtt/o033sibb76J3+/n9ddfp6srj3vueRez+cFLidY5iOIZ9PpcvN5vAHPCCw6Hgx07dnDx4kU6Ozvx+/34/f64EkyJ+IZ08Bazs7PZvn07IyMjHDhwYB7n8P0oZHLZBxupDEtqbd1Ka+t7A4ESyZgmo/Lh9/txOqcYGckiJ+ciIyMmBgaOoShzUrN5eXlUV1enpEa1sGQ+PNyKonwZRbHicPwjFktsR5WIg+rv7+P48R8CWTQ3f2Le4KvlwrlzT3PDDT/A6zVw/vw/U119U9rWDhip0OnpU1NTuFwuNE0LtrQFMvE9PT309PTQ2NgYd+tDKEIN4o4dO9i0aRPPPfccXV1dHD58mIceegi7/eMMDlZRWHgHgvDe5y/LbxGu0CXLMg6Hg6qqqogk8mSR7tK3wWAIltlPnTrFxMQE5QkMobkcjPgaVjdS9VM/+MFHufLKAmZmzBw9uoXPfCa25G2ktRLB+HgHqqoiyxcQxbMoikB/f3+wJQrCE7djIZx/8fsVBgZ+DmiUlNyJJKX30UVRFI4dO4amaaxbt25FDII9caINv/8bqKqAx/NX1NdvXpLrBpQpp6amgiT8qqqqRXLpyWbbc3Nz+chHPsKzzz6LIAgMDw9z/vwkNTW/RlG+jNn8/807Xq//C7ze91TeBEGgqKiI7Oxs2tra4iaRZ7KyEel4QRDIz88nLy8v2KpcVFSEXq9fCzYuJyTaRrXwYbyqqiqYIb7hhhsSunYs/XKfz7do4nZgYuVvfvMliov3UVNTw1VX3UR7e3taMhvhzvf7n8ViGUEULzAz8ysslj9O6RoL4XY/zdVX/xRNg9HRUmy2j6V1/WRQXd2CpgkYjTPk5h4H5oKNZDN8AXUvp9PJ5OQkk5OTdHZ2BgOL0tJSsrKy5gWrqqqiKApbtmwJTvrOycmhtrY27uuGM54GgyF43/n9fnp7e1m/fj2Kso6jR9tZt25rUIFZVa+MuC68RyIfGxtbRCJPFplSizKbzWzcuJHDhw9z4cIFJicnqa+vjxkgXQ5GfA2rG4m2US18GNfpjLS3XwPMJcQSSUYlU9kIcAOPHv0AhYWvMjpah6q6EQQJURTD2rtEEM4+XLz4Y7Kz/xmAwcERSksfS2rtSDh37l3y87+MIGicPv1FGhtvS+v6ycBuf4GCgjYAhoZeBNIfbISrPqmqitlsxmazUVBQgCiKXLhwgb6+PpqamuZxEJJpowqgqqqKw4cP4/V6eeONNxAEgdzcP6e3t4qmpsfDrLQYZrOZK6+8kjNnztDS0kJ9fX1E9bFEfE8yfira/b6Qc3jq1CkcDscizmG09aMdtxqqXpd1sJFoeXoh8e7ee+9leHgYs9mccFY3YMQXEredTmdwMF7gYbSyshKz2czAgMT4uMDQkExJyUaMRnC71eBa6Qg2FjoWk2k7gtCKphnIylqf9DqRUFLiATRkWaOgYH7gt1xtVDbbJ5Hlv0TTstDpbp8nNxjrPQ4NLAIVC0EQ5lUsvF4vW7ZsiXs/gUnf58+fp6WlBZ/PF/fnHe6YO++8k3fffZfe3l7efvttxsfHaWxsRBD0+Hxu4BkEQULTPh52zYVZmpycHHbu3DmPRF5RUZHUw0QmZXXhvWnkgiBw+PBhLBYLtbW1EWcOrAUba1hupDp89iMf+QgvvfQSgiDw0Y9+NKFrR7PBmqbh8XgWDXXV6XRYLBYqK+9gaupaamsLKCgo4NChQ8Ehr6kg3J50utFLVVkBvX4kpfXDweF4F7N5CE0TkKS3gfQHG4n6u8LCekRRhyAIFBbWpXz9hYGFy+Wio6MjZvXJ6/WyYcOGSwHmUSwWS7BlNR5Esvk33HAD2dnZvPPOO7jdbt58803uvfdeZma24/WeQ6drBFR8vt8uXvQSBEFAr9fT1NQUlkQezz4S2XO04+PxIwHOod/vZ3x8fBHnMBIuBz912QcbqQ71y8/Pj/v8AHE7oKjh9Xo5d+7cPPJbOOJ2AP39Ai+9pEOWNd55R0d/v0pZmZq2h/Jw69jtf4SibEcQspCk+LPq8cJs/lMkaRawoSh3pX39ZKBpt+LzXQXogMiDr8JpuocGFuXl5WRlZc0zAh6PJymjIAgC5eXlFBUV8c4779DS0kJDQ0NUPkeke6KgoICGhgb6+/tRFIXTp09TUVERcs99Iqqee7h1Q0nkAf3z6upqiouLE3mZSWWMkiHSBVS2Ar29kVoQL4eM0RpWN5IJNkK/ozk5OXzyk59M6tqBwCUe4nasidvpUrYK56dycv4Ip3MAQfCTk5Pca42G3NwbEYRXmJs39YFFf1+OpJgo/g9kOR+Q0LTfT+jcUH5g6CytQGBRVFTE5OQkzc3NMdcKvHa73c6OHTsYHBykra0Nv9+f0vsiCAJVVVXs2bOHmZkZZmdneeutt6irqwOK8PkmY77G0HsxHIm8rq5u3iTyRCob6VQ1XAhRFCkuLiY3Nzc4ZDdWVWYt2FjBSF2NKjJCswSBL3VgaqnVasVkMpGbmxuXzKwgDCLLz3DttdmcPv04ra1mZBnuukshOzv9+uULfossb0zDOuGhaWUoyj9H+XumXlcszJejUxQFt9vN0NAQFy5cYGZmBkEQgo42XGCRCeh0OoxGI1deeSUnTpwI6ouH43NEy77U19dz4sQJzpw5w+TkJG+++WbcrYDRDGdgkFFpaWlQ/zy0GhgLmZQrhPlGOdDbW1BQQH9/P62trYuqMpeDyscaVjeSaaNK5aE+1HcNDQ3hcrkYGhqKSdyeu3YfoKJpi5UZM+mnJCmH7OxvJrxW/Pu5FngeQVDRtKqEr5MZ6MLOilrMZ4nOD4w0SysZuycIAsXFxRQUFPDuu+/S3t5OQ0ND1KRsNBubnZ3NXXfdxQsvvIBOp+P06dNxJ7AirRtKIg+dRJ4I0iVkEu14QXhvyO7MzAzd3d1BudyF3NbLwU9d1sFGqiofAYS2zgQy3IEsgcViIT8/f1HWtL+/P+7rer0/ZmLiDWRZZXr6Vh56qJFTp0TGxgSqq7W0thutpHWW68ujKMq8tjaXy4UkSXg8e6io+C9cro1s3vyP6HTJcxNSRVZWVnDoXldXF7m5udTU1MwrcUczQAaDgeuvv54LFy7g8XgYGRlhZGSEmpqauK4fy3AG9M9dLhd79+6ls7OTxsbGmO2GS2HEFx4viiLl5eWUlJTQ09Mzj1B4OWSM1rC6kWobVTSEVmenp6cXEbcdDkcwAxwLgtAB/P2lhMEXFs2HyGxSLPPQtNTmTy0F5tpvRpmc/A2nTxdjMDTNq7aXlJQkPKQ3GUiShNFoZP369Zw5c4aenh6ampoWtS5BbJtfXFxMUVERvb29CIJAW1sbO3bsiOknoq0bmmgKVOITaU9Opo0qFb9mNpvZtGkTU1NTnDx5ElmW5yUZLwc/ddkHG4mqfPh8PsbGxoLGOZS4HYnsGw6JZJ8GBiSys/14vfAHf3CW4uJ6tm9/79xklK0i7SkdWE0RdigRf2pqKjiFNlCxCPBlRFHE6fw4ZrMT6GNi4i5yc29J+HrpzkDk5uayc+fOIJ9joepGtGvl5eXR1NREZ2cnoijS0tLCjh07Yl4zkaxpVtacBnp1dTWHDh3CZrNRV1cXkUQ+p14Tv9lJpo0qklEOSPuWlZVx9uxZ9u3bR05OzopQnlnD+xfpaPeFud760KTYwonb4XzX2NgYY2NjcV13YqIdTRtG00DTWsjJyUywAelLiq0mX7UQC5Oc09PTlwKLr3PVVS1omsTw8L9SUnLzsu3RaDSyceNGJiYmOHLkCFarNaz9j/Y5iKLI7bffzrPPPhtUMjt+/DhXXHFF1GvH42tDJ5G/++677Nu3b9Ek8nDItDRtJD9ls9nYvn07o6OjHDx4EJvNRm1t7VqwsdIRrTwdSn4LzfwEboI5Atx7D6KJYiHZPBok6cN0dcmoqonNmxc/DK60jNFyZZ5iIRBYhDpbWZaDgw6rq6ujTqH1+XIRhElUVU9WVvzyqZmGKIpUVFRQXFzM6dOnaWlpobGxMeZ5giCwfft2jhw5wuzsLKqq0tXVxaZN0Yd+JdrqBHOl6507dwb7eYuLi6msrFwUlKdLUjAS4glOApNe3W43XV1deDwecnJy0jpPZA1riBfJtFH5fD6Gh4fDEretVmvcE7cTseVjY1sQhLcQBA2PZwsL6WQr0b+sRD8VDoHAIlRuNrRiUVpaisViQRRFJiYGABBFP1lZA0ldLx3vS+jDfnZ2Njt27AiKiJSVlQWVluIJCrKzs2lqaqKrqwtN03jjjTdobGyMavsTSezp9XqMRiObNm2KSiJPZu1kjo/lp0I5hx0dHVgslqhJsdUQVK+YYKOqqgqr1YokSciyTHt7+7y/a5rGZz/7WV555RXMZjPPPvssW7dujbDaHAJtVH6/n9nZ2XlKDF6vF6PROE9FyOPxcPHiRerr61N+PfEZTA1wU1ZWQU7OJxBFMaxqTqo9uontaXXA5/MFDfPExARTU1McOnQoWLGI19mGYmLiG6jqbuz2qzEamzK4++Sg0+loamrC5XJx/PjxIEcoGrKzs9mwYQPt7e2oqsqePXvYsGFDXJW5eBB6PwX6eQOTyENJ5IH1Mq1GlUgGyGg0UlJSgtvt5uzZs5w9e5b6+vqEpR3XsIZUIElSVEWohcRtj8cTTKzEQ9yOhkR8QlnZFo4c+QKBWRSprBVrT5czIgmPBJJisfiBs7Ofx2z+KlCHzXbP0m5+AUI/q4CISEFBQbByHIlvGA67du3i0KFDQQ7K0aNH2bBhQ8Tjk+kiiIdEnszamfBToa1gx48fZ3BwELPZTEVFxaqscqyYYAPgzTffJC8vL+zffvWrX9Hd3U13dzctLS38yZ/8CS0tLWGP7erqoqOjg3379nHs2DG2b9/ON77xDWpra4PaxgtvLpgzAul4qId4+mq9yPJXEYQjqOrHMJs/EvHIlZYxSmfQElhrZGQkOBV9IRZKB8/OzqLT6YKBhcViYXBwMKphigeqmo0oPoxOF5qy+xHwDnMTTKNXBGBpiFxZWVls27YtODm7u7ub6urqiJmPHTt20NHRgaZpTE9Pc/r0aRoaGiKun0jrUrjXK4riIhJ5QGkjE5WKhftJdH2TyUR9fT3j4+McO3YMk8lEXV1dyhKea7j8kImkWAButxuXyzWv4h5u4jbA0aNH4+ZfRUM8/A9BOIcoHkIQtrB5c+RZDyvVvyznOqGBxeTkJE6nkwMHDqQkPKIo2xgd/ekCIvU0gnASTVtHNIXFdCLSZyTLMvX19ZSVlXHixAm8Xm9cry87O5vCwkIGBgZQVZVXX32VK664ImJiLBVfG4lEHrhWptWoEvFToiiSm5uLLMv4/X727t27KIm3GrCigo1o+NnPfsYjjzyCIAjs2rWLiYkJLly4EFa54D//8z8pKCjgwx/+MAcPHuS1116L6xqZeIiOBFXtZWLiXaamzDgcz2EyZSbYGB//NV7vfyJJt6PXf3BFVjYOHHiXmZkXcLsdbNz4GQRBmDeTJDSwCOi4h37JApmhdMPn60KWv4QgKPj9bUjSgbRfIxXY7XaKi4vR6/W0tLRENEBWq5WcnBxGRkbw+/387Gc/47Of/WzU4XypBBsBhJLIT548SU9PT1BKM16kuzwd7viAsIPD4aC5uZmRkREOHDiAw+GgsbEx4oyONbw/ka6kWEdHB3v27GH//v2cP3+e6667jr/6q7+iubk56sRtn8+XtqRYbN/iRhC+hNs9gl5fCDzNnGR4MmtFhqb5mZ09gV5fhiAYVqSfmpqaYvfudzAYjFxzzbWLEpaRhEdCpdLdbjfbt29P6740zY3PdxOyfBGPpx6T6bdA8uTqUATus2hE7EgwmUxs3ryZgYEBjh07xrFjx6irq4s6dHLDhg0MDMy1hnm9Xl588UX+8A//MKXXEAnhSOQBfsdSqVElcrwsy9TU1FBeXh6Uy62rq4toi1YaVkywIQgCt956K4Ig8KlPfYrHHps/IbS/v5/y8vf66MvKyujv7w8bbPz93/89MPfl/+u//uu495AunXCIbXinp+1MTOTgcIxw6tRWrgw/zDmutSLB759Fkr6M0Sggikfw+bavqIyRx+NBURTy8r7Nhg378fslDh8uxuG4NdjaFk97QKbawyYnfeTkgCRpTEwIZGen/RIpIZAdqaysDPI5+vr6aGxsJHvBZuvq6hgZmRuG5ff7+cUvfsG99y6WVYTUKxsLkZWVxZYtWxgfH6erqwu3243dbg9bXQy3l0wT9Ra2AuTn55OXl8fAwAA+n28t2FhD3EgkKdbR0YHRaOSJJ56gs7OTd955J65rpEswBGLbTkXx4HQOXrIJA9jtCpKU/mBjaOgLmM1vMTWVi8HwoxUZbJw58wI33PAtvF4D5879AwUF2xcFFuGERwLw+/0ZSYrNzJzDbL6IougwmU6gKDPIcmJDiKNBEISw+473M7LZbOTm5mK1WmltbaW8vJzy8vKwa5aUlMx7Djt79ix+vz9sdSNdXQQBEnmgEr9v3z6ys7Mj8jnCIdPBSej6gSTe7Ows3d3dnDt3jg0bNizy+SsNKybY2L17NyUlJQwNDXHLLbfQ1NTE9ddfH/x7pEFj0SDLckLBw9JVNmaw2fo4ceLjHD48Tm1t9DJ7svsSBBlFsaDXj6EoFkTRiKa5E14nHBLZj6bNTVEPcCwCfcd6vR6/309RkQFBkNDrdWzdWo8opn+4YDLIzt7Mvn2fRpIOYrU+THa2gk73Q2AGn+8TQPjZF0uFcAZoenqaEydOIMvyvKx8RUUFra2twe/DyZMn41o3FhIxmg6Hg6KiIiRJClu6TnV9SF/vrCAIlJaWRs3CreH9h3QmxULPTeSeTWdSLPZaWRw+fBe5uccZHb2Sq66K/H1IxX8aDC0oihWdbhSP5xSaFl+f/1LA5/OhKAqVlW8gCD7M5lkE4S2GhyuDgUVWVtaytbQYjXWcO9dMeXkn3d13UFdnBmYQhBk0LbNZ73hlZEVRpKysjKKiIs6cORNVFWrLli10dHQEf25tbeWqq64Ku2463/MAJ3JmZob9+/czNjYWlIaOhUwnxcIdbzKZ2LhxI06nc1UoKq6YHZaUlABz04/vueceWltb5wUbZWVl9PX1BX8+f/588Jx0YWmMuIYs/x1+/zF27SrA6/0mkhQ9C5GsERdFHUbj07hcb2M270LTbMBEwusE4PP5OHv25WKfkwAAIABJREFUFEajOSqhMdBzHBpYhE6iLSkpwWAwIAhzmtpm89fQ6Z5C0yrw+W5Ken/phizLXH31XwSNmiz/CJ3u7y79PInX+2TY8+I1gOkwlAvXsFgsbNu2jeHhYTo7OykoKKC6uhpRFNm0aRP79+8PHjs8PBx2GFMiRjwZIl1ubi61tbWLSteRsmeZbqNajWS7NSwPMpEUSxTpXC+abxHFY5jN/8GOHZWcOvUYGzaUxVTESTbY8Hgewmx+BpdrI9nZG9G07qTWSRWRFA0VRcFg+AMsluOIooGmpgdR1ci8t6WEJMmUl/8nk5OT1NRkI4o9GI33IAjTeL1fRlEeXHTOUleOAvdsYH7EzMwMJ06cCM7nCCWR19TUzAs2Dh48uCTBRgBms5mSkhIURYlKIk9lL+mshFit1lWRFFsRwYbL5UJVVaxWKy6Xi1dffZUvfelL84658847+e53v8t9991HS0tLsF89nViaNiqViYlOXC4Bk+kUZvMU8F6wIQwMIF4iHapbt6KVlaVkxM3mGszmOSJhYBhhsjh16oeUl38Ht9uKx/NnaNoO3G73POPs9XrnBRalpaXo9fqoXyxNK8Pr/buk95UuRDIAgd+Njw9ht89VhiYnB7HZRgA/mla4lNsMItpnmZ+fT25ubvCB3mKx0NTUNC/Y6Ozs5Lbbbgu7biaDDUEQ5umfx8p0LZcRT/Taa7j8sRKSYulENN+iqt9hbKwfo7GTDRuuR9OiS0On4qcKCz+Fpn0SqzV+yfhIGBsborv7O4CM13tjxONCFQ0DwiMBqfSFioZtbW3Y7fejqh/A79cBK0smW5bloO10u18HhlBVAb//35HlxcEGLJ1tC3dPmM1mtmzZwtjYGF1dXeTk5FBbWxu2Mh1tfEGmXoOmacGZIZFI5AuPz3RlYzUEFNGwIoKNixcvcs89cxJuiqLwwAMP8MEPfpDvf//7ADz++OPccccdvPLKK9TV1WE2m/n3f//3tO9jadqo+ujrqyMn5wKnTm2lsdFKIFkk9Peje/ppuHQzS+3t+B57bEWofGiaRn7+b9A0MJvHsFgO0tZWgdFoxGq1YrfbKSsri6sPf7Xi+PFdCMJ16HQeoIKrr74G0HC7v4ffn/gAwFQRy9iGPtB3dnYyNTU17++RhnllMthYaGT1en2wdB2P/nk8WKtsrCETyHRSbCmU7BYiGv+jt1fCbp9iYsKALMfmrKWarBME8dL/U/N3TudTbN78EpoGR4/6gRvDKhqGBhbhhEfCQdMKkt7XUqG3t5bycjNG4yxdXVu46qonEcVBPJ4vsRxT0qPd1zk5OezatSs4tNZut2M2mykuLubChQsAEb8/ifqpRBCokEcjkYdeO9MV9cvBT62IYKOmpoaurq5Fv3/88ceD/xYEge9973sZ3Uem26gEYQhJepK6ulEuXKgkK+thzOb3qhpieztIElpR0dzxQ0OILS0IGzcuabChaRqzs7PzjLPP5yM//2pstm7AgShuiWsadTzXWi1obNzIL395Nz6fjwceOAq4AQFZ/gmgx+/fjqYtbcYonmvp9Xry8/MXGatoA43iNWzpKh9H0j/PNC4HI76GpUEmk2J6vT5YFV5KCEK4GU4asvwi2dnD9PVtYHBwJ7t2xQ6YVkJSDCA7W2TudA2j0Utra2tMRcNYWE3zQ4qLt/Nf//VFvF4Xd9+toNN9B/BjMPhxu9OfpI2FWD5CEATKy8spKiqiq6uLkZERrr/+el5//XUEQeCWW8In8paiAh9AOBJ5aCV+ubiFqwkrIthYKch0ZcPjGcTj6cXj0WOzuaioCDMFOvSGEgS4dNNnyogvDCympqZQFAWTyYTVag1qu8/JpO5AEB5E04z4fCdS3k88GB8fZ9++5xFFA9dc87GUMt6pwmazcf/99wPgcrXgdL6AKCqYTK9jNL6Gql6By/X8su0vGjRNw2w2s337djo7Oy9VqvLDKn0kShBPZ5tTqP55R0dHcChnrCGEyeJyMOJrWBpkMikWGEC7HMHGQp8gCMN4vc/h90vU1fVQXv6/yMqKrW60HMHGQn6g2+3GaLyJsrIp9PosXK6baG5uzviDvqZp9Pb2oihKkCMX6bh0XCsaLBYLDz30cVRVRVV/idvtZa7dtwezeRc+3wMoyhNLWkWL51o6nY6ioiJmZ2cZHx9n+/btUYcCZkrIJNraoSTy0Er8GrcwNtaCjRBklnjnQZb/N6o6iarmcOTI73H11fPPUbduRWpvRxgenvuF243a3IwQx7BBRbmI0/nXl9QyvoJeH75cqigKg4ODQeMcObAIj0AZeakqEoOD/8YttzyFpgmcOydRX//oomMyJX0bDfv3+zh48M/IzR3ioYeeRa/XI0n7yM39AOPjjwHrM76HZDI7N998M3V1dRiNRtxuN/v27aOmpoaioqLgWomum8nJqW+//XZMEnkqWI7WlTWsYSEMBgM+n2/JrxuuAu92X8DrvYhOJzI4WEN5eXxzceK1w37/OVR1HJ1uM5HmQYRbJ1xgodPpsNlsYaTSr710Vltce08VZ88exuP5EjqdhyNH/oIrr7w5o9eLRw5ekiSOHKlkYOBe7PZxrr/+twiCGb3+6yiKBVlOfShkPEhUudJgMFBfX8/o6ChdXV3k5uZSU1OzSJxgKdt9F2JhJd7lcuHz+eJOiqUr+AlgNfiw90WwsVy9sKFGXNPO4/e3MjxciMmkUVd3+6JztPJyfJ/8JGJrK4Km4d+xA62yEvHs2Zhf2PHx5zCZOgCBiYl/JT//b5mZmVnUCuXz+fB4POTm5lJVVbXiSUfl5acRBA1R9FNc3Bf7hCVCSUkJhw8fZny8mImJD5Kf/zYgI4p91Nd/Eb//Al7v/wNkJiMPid/XocTsAEpKSuju7qa3t5empibsdvuSlqejQRRFDAYDzc3NYUvX6cDlkDFaw+qHXq+PSITNJMIlxczmbzM7K11K8HyMior4vh/hW7LmY2ZmP37/EwiCj+npR3E4PhP2OFVVGR4eXiSVHmiFincG01LBbP4ZVVVzCkrDw88BmQ024kVBQSH79m0AfFx99QFk2YkkTWM2/w2bNmUDP884jyNZf5Kbm8vOnTuDfI6FCaeV4KcClfjf/e53dHZ2UlhYGJFEvhBrlY3LDDqdDp/PFzVbH4pMtVGp6kuI4gWqqnx0dd3D+vWLJUcBtIoK/BXzv/yxMkaqqjI7W4heLwAa588bOXeuDbPZjNVqDQYWmqZx9OhRKisr0/IalwI2258iSYcAA5L0MCuF4lFTU8PHPvYxFEXh5ZcdqGoFH//4v2IwTKNpKpL0j0jSr3G7n1sWUt5CRLp/9Ho969evx+l0cvz4cYxGI3q9PmLpOty6mVSLgvCl64aGhoQmkUfCmhrVGlYCAm1U8SKdfmr+ugN4vR0YDAIuVwHNzdfG/R2Ip7Lhch3BZHKjaTo8nnYiSaUHfrdQKj2Z15eOZGOsdQoL1yFJc5PPCwoyX9WOF0VFRdx3333Mzs7y4osyJSXvct11v8VgcJOVdQ64Go/nSXy+TxFr6niySPReXciVqKiooLi4mFOnTgWH1jocjhURbAT2q9Pp2LVrV1xy7slgLdhYBTAYDHi93riDjXRhvuGdQZZfwu32MznpQNPiN+AL11JVdV7F4vRpLw6HG4tlE9PTX8Ro1LFhwwfR6xf3/vp8vlVFyAZQ1XWo6u7l3kZY5OTkMDw8zMTEBKJYwI9+9AkeffQtdLouwIckdZGVtRm3+2/x+f4vPB5PUGpxcnISVVVpbGxMOlOfTmNrtVrZvn07w8PDHDlyBLfbTX5+fswMTTJGPFmjGShdT0xMcPToUbKysqivr0+pz/1yMOJrWP3Q6/UJtVGl6yF6IUTxGVwuCb3eQ1/fTtavj38SdaxgQ9M0JOl6RkZeRqcbY2DgFnp728LOYGpra6OmJj1tPkvh8wThbgTBgii60bTFXQvLCZvNhizLXLzoZ3j4akwmF9dc04qq+hDFKQyGLyLLLzI7+wKQk5E9pOqndDrdoqG1Vqs17raldLdRhUMsEnkquBz81GUfbATK00tNLA5to5Kk11CUIUwmLy5XMUVFV8c4ew6qquJyuXA6nYyNjXH+/HlUVcVsNmOz2XA48nnjjQIeftjPhg0qsCHqesvBbYiFZJ2lpmlMT09jMpnSvKPE4HA4cDgcjI6OAg389Kf13HjjcxQU7AH8gILR+EUE4R84cuRZsrJqsNvtwaFBvb29wRamRF9LujM7giBQUFDAxMQEHo+Hffv2UVtbS2FhYcRzM02MC4fs7Gx27NjB0NAQHR0dFBQUxF26DrefSEZ8raqxhqVCopWNzAQbo6jqb7Dbh3A6bVgsVyWUpAv1L5qmRZzBZLF8FZPJwsaN2RknxKfz/Yn+fov4/bem7Vrphslk4oorruD48eMcOHAnfX2NfPjD/45erwAKktSKxVLH7Oy38PkeZnp6OpgU83g8NDQ04HAkN1sknX4qdGhtQLWwrKwspu1PN0ciGiKRyFN5Bl0LNlYBApWNpUao4VWUH2A0TqEoMoODDTQ05C06PhBYTE1NBb/omqaRlZWFpmnYbDYqKyuDJKnnnpPZv1+it1fg6adFCgs1nnjCS7QgOp7Mk6IoKfE4xsfHOXr0MIWFxRmVLj1y5N8oKPg3ensbKC39p4xdJxZkWeaee+7h6NGj7N69m6EhP729H+KjH62lsvLZ4HEGwyS7dt2D1/t7eL0/we/3I8syW7ZsYWRkhP3791NYWJhwi1umyshFRUU0NDTM43PYbLZFxyWjRpWIrG4kCIJAYWEh+fn59PX10dLSQkVF4u1ql4MRX8PqR6LBRiCZlc57VxB+gyT14/eDy5WD1botrvMCgcXU1BRTU1OMjY3h8/lWxAymaD7P4/Egy3La++tDMT4+ytGjz2M0VrB58x1JrZEOCILATTfdxPr163nppZfo6yvnqac+x2c+8wKieOrSUQom02fQ6f6c48dfxWYrpKSkBE3TOH36NAaDgYaGhoQ/x0zMw8jPz6e6uvqSWuU+qqurKS4uTltSLJUKfAChlfh4J5FHwuXgp9aCjQzhPSN3FqOxHUHwIwgibvctiKI4b3Lp9PR0MLCw2WwUFRVRV1cXNILnz59HFMV5agw33qjQ1taNzeZkYqKJe+81kROjAhrN8KqqyrFjX8Ni+R2zsx+mqekTSb3unp6/ZceOnzM0VMH4+HM4HOmd8h5ATc23MZmmycsb5OLFd4HqjFxnIRRFCUoEO51OZmZmkGV53hAgVdW4cOEx8vM/iNl837zz9frf4vXOnxqfl5dHTk4OPT09tLS0oChKXMYxUZWPRAMTg8HAhg0bmJqa4sSJE5hMpkVtS8moUaVT6UoURSorKykpKeH06dO4XC5GRkbIy1sc0Ee6xloFYw3LjUTVqDJRpRaEp5EkL7KsMTi4jg0bChcdE2kGk9FoRBRFjEYj9fX1CyoiPgRh9JKSYeoPTE7nOMPDnTgcG3A4Fu8xHpw40cnIyLN4vYXs3PlncXPUEsXo6F9w1VVv4PdLDAxkoWmp88yShaZp6HQ6RFG8xIsx8E//9AkeffQlcnLag8fJso8dO25ienoYVdWhKArbtm1jaGiI9vZ2SktLE07sZCoplpeXx7p16+bxObLDTJ9cjgp8AIFKfOgk8kS/u5cDt/CyDzYCw5LiRbo+tPfK3F9Hlt0AzM7KjI3l0tHRgcViwWq1UlxcjMViiZpdCedY7PYDaNo5jEYRt3uWoqKriGfrkW7y2dleamr+N6BDVb+Nqj6MKEYuoUd6n5qa3sDjMZCf34fP1w1kJtgQxSrgIIJgwG6vZ3g4/bKRfr9/3vwRl8uFJEnB/uLq6mrMZnPwvcjLy+O1117D5XLxu9/9Drv9DygvP4zFsrC9bYzQYGPu9YhUV1dTVFTEnj176OzspKmpKaq+faYIcguPtdlsbN++PehsiouLqaqqCk4fzhTxLhGDr9PpqK2tZWJigv7+/mDpOh4S+Wow1Gu4vJGoGlU6B9ACCEI3ev1BRFFBUXR4vdcBzOMHRp/BBCMjI0xNTc0LNDTNy8zMJxDFE/j9N2CxfD2lfaqqn8nJj1Nc3M3UVDFe78vo9ZFtZKSgzGz+O3btOoDfLzI6ugWzOXrVIdngrqTkFJoGsqxgt/czONiU8BrJILSNLZAY8/l8mEwmbrrpJvbu3cv4+Dhut0pb21e48caXMRiemreGLD+F1/unwHuV5Ly8PM6cOcO+fftQFCXuvSSy70T9lF6vZ926dUxPT3P8+HH0ej0NDQ0Yjcak1k3m+FgIlXPv7e3F5XLR398fN4k8HZWW5cZlH2wkWp6G5G80v98fbIFyOp24XC5Mph8Hg4DJSRvXXXdDwmW0cJKCPp+B229/jebmdrq6ric7e1dc60SCyeTA680CnIhiMaIYvZUqkgExGu9CFP8bqECv3xRzT8nCaPw/CMKrwDpEsRo4mdJ6qqri8XgYHh5mcHCQ6elpBEEIBhaVlZWYzeaoX/iqqiosFgszMzN4vV52797NXXfdBYyRlZWHIKj4/WVAFXNDlha/hwaDAbPZTE1NDQcPHiQnJ4fa2tpFGuOQuUnf4Y4NbVs6d+4ce/fuDbbJZTLYSLRqIssymzZtYnJykmPHjmE2m4NzRdawhpWKZNuo0gVJ+p9IkoIggKKIjI8X09bWltAMpnAP5G53H5p2FJcrB6PxjUuk5OTbdDXNQ35+NzMzVuz2QTRtiFhV7XB2tqBAQNNAkgTy8jL3GGQyfRlZ/jx+fxVW652k6qciIVR8ZGpqCo/HE/Wzm5iYYO/evfh8Pvbv30929iNs3FiOyfTF4DGKcvei60iSRH19PaWlpezZs4cDBw7Q2NgYlW+4VEmxUD7HQhna5WijCocAiby/vx+n0xk3iTydlZblwlqwsQABIx6rjzMQWAS+4C6XC0EQghWL0tJSsrO/PW8g+NBQLQ0NiffrhTPi2dlNNDV9iOHhGjZvvpecnNjZg2g3qyjaMRiex+9vR5avJ3kZvC+jaf8DTctnYfY+HJJvBbCjaR+99G83oDE+/i0EYT9G419iNEYmy4cS7wMcGU3T8Pv95ObmUlpaisViScrY3Hjjjfz85z9nZmaGixcv0trayg033IDTOYamaZceEHxB4xcYBBS4VuD3DoeDXbt2BfkICwfvBY5NBOkw+KIoUlNTQ2lpKd3d3UxMTCRMHMykXGHgfbTb7TQ3NzM0NERnZ2eQRB4uaFvDGpYbibb7ptJGpWnavIrFzMwEBsMrwaTY7KyRa665NmHBinB70unKuHhxPXl5RxgcvJHa2tS+f5Jkxut9CJvtBbze2zAaq2LuKRyMxq8jSd9G02rx+z+Q0p6iX/8D+P0HLv009974fG6mp3uw2+sQxcRFLQLPHmfPnsXpdDI7OxtU9LLZbJSWlqLX66Pazo0bN3LmzBkuXryIz+djz5491NR8Ap/vQxgMf4vb/aeoaingRxAEvF4vsiwH7avZbMZsNlNaWhrkG0aanL5UwQa8J3KSl5cXlKGtqanBZDItWxvVQgT2nQiJ/HIQMrnsPW+ykoKhUBRlUcUiNOtdXl5OVlbWopthbMwd/LemwdGj99HQkPhrCLcnQRCoqrodSJ/MniDUxD1VNJoqh6ZVpW1P8UIUO3A4voko+nE6jwJzPagBxxrKkfH7/UF+TGgb28mTJ8nPzw9LhI4XWVlZQek7RVHo6uoKBgpz+xQRBCGYbfH7/fj9fnQ6XTDADby3giBQUVFBUVER3d3d9PX1ccUVV8xrDVoqI74QAT7HmTNn6Ovr4/DhwynL0C5EMpWN0OPDkcgrKyspLS2N671YLUZ8Dasfibb7xlvZ0DQtmFgJtX+hM5h0uufmteD291dRU5O4yl+4CrwsGygqepqJiWEqK4vS8p2ay77/FSZTfN/hcEGZplWjKPELi6SDIzO3xiyzs1eTmztIf/81FBf/V9RzwnEEA59fUVFR0sMNBUGgsrKSsbEx3G43LpeL119/ndtuu42ZmR8giiKSJAT9ld/vx+v1zvNTMEfUzs3Npaenh71791JfX09BQUFS70/o3uJBrKRY1aVZF4GkWLSW5ETWDndsIghNisVLIl8jiK8CJCMpOD4+HiTBzczMIIpisGIRKbAIh927P4DNto/8/GF+97vr2LbtpqReQ6hjUZQ30bR/QFUb0Ov/AUFYHunXlSah6/fPfR6CoOH1igwMdON0OlEUJehY8/PzqampSWt2W1VVNE0LVi00TSM3N5fR0VHGxsZQFIW9e/fysY99LGy1LOCgvV5vxGpaYPDe5OQkR48exWq1Ul9fnzGDmMi6RqOR8vJyzGYz7e3tlJSUUFlZmRbDmC5SXyiJPNBvXFdXR35++MGaa1jDUiNZ6dtQLJzBFBpY2Gy2oILPQrXBY8fqWLcORBFUFd5991GSGXER4HBd2g2i2IWm2TAaaykqKk98wahYnYkASTqDzTaEz6entHQ3qqoReC3ROII2m428vDzMZjO9vb0YDAYKC+Mnx4f6qUCSC+YqHO3t7aiqyqlTp2hubqakpCTiGgE/FepDA3zD4uJiTpw4QV9f3zy+4XL6qYDvPH/+PKdOneLQoUNxKWol8nCfDj8VSiLv6OgIO4l8tSe/3tfBRiBrEFqxcLvdjIyMkJ2dHVeffjSUl1fx7LOfAuZ0rq+5JjmDG+pYpqe/hao60et34/Xuxmr9vaTWTBXvEeATa3MZGRkhKysrZfUPTXtv8uz4+DiTkzW0t38Cq/Us8HHy8mw0NHQiSVkoyh2kQwVlocEOhSAIyLKM3W5n3bp1QVUkgAsXLtDb20t19eLeYlEUg8Fk4H4MBC4L7zu73c6OHTsYGBigpaWFrKysuB1OJkvZoihSVFQU5HOEPsynYiCT0UaPdrxOp6OxsZHZ2Vm6u7vp6ekJOt01rGE5kUwbVWi1fXp6OlixTTSx4nIV8IMfPMa6dUdoadnF7bcn11YU6qdmZ59GVZ9BFGUk6bvo9fHJ6KYb6VTtSsc6qlrH9HQ5NlsPg4O34PX2Bz+/RDiC0exqIDEZ8FehCPgbnU7Hxo0bOXnyJHq9HrfbjaqqvPHGGzz00EOL1gz1U36/n4mJCVRVnWejjUYjmzZtYmxsjIMHD5Kbm0tNTc2StlFFgslkorCwkJycHDo6OigqKqKysjJigi/T3MJwxy8kkYdOIr8csCKCjb6+Ph555BEGBwcRRZHHHnuMz372s/OOeeutt7jrrruCD2z33nsvX/rSl2KuHWijmpmZmTdkaGZmZp6yUGVlJVlZWRw+fJiqqqq0DIszmUx87nOfw+fzpdReEmowx8aayM9/A6/XBJSmvMelRGfny5hMz3LqVDHr138Vm80e97lerzeo4+50OnG73UEdd6vVyszMDJs3/03IGV9Flr/LXNboq8DDCe01HoMdKDEvNBwOh4NbbrmFs2fPMjU1hd/v5+23354XbASCpcnJyeDr8nq9wT7YcCVrmLsXSktLKSwspKOjg1OnTmE2m8PK/YViKQy+JEnU1tYG+Rw9PT00NTXFpQiV6j4g/j5bk8nExo0bmZycpLW1lUOHDlFfX79GIl/DssFgMDAxMRH2b6Ecs8CD6czMDLOzs+Tm5lJQUBBRRCIe5Obm4nZfz+7dFTQ1NYVNisSDUD81OdnBXGLbzfT0UfLzV0+w4XQ6aWt7C6PRyo4d1yLLcspJk0Arr9utcfz4PyEI05jNxWRn99PU9KdIkhu//0cIQmPCawPBakXoPgPtupH8lM1mY9u2bczOzrJv3z5gLjE2Ojo6j7AcIJ8HfJXH48FoNEb0Uzk5OezcuTPYuupwOOK2rZlOigXaant6eqIOrc3UPkL3EgnhJpHHq/y1krEigg1ZlvnmN7/J1q1bcTqdbNu2jVtuuYV169bNO+66667jF7/4Rcz1XC5XUD70Jz/5CU6nky1btvDkk09itVqD5chILRfpVPkQRTHlPvb3qghDFBd/hp6e6zGZyqmoaAKvFyRp7r84MTrayezs/0RRsiko+DZmc+LtJMlUNiorv0Vu7hlUVWR09BZstnvCHufz+eb1qc7OzqLT6bDZbFitVkpKSjAYDMFrezwehoaG5q0xNHSQkpK5L+jg4EGKilTmAo/w+w0EFT6fbx7HJ5bBjgRBmBui9PLLL6NpGsPDw+zevZuioqJLTmcuWLLZbMEqWkAtJJAxCi1ZL7yuLMvk5eUhSRLd3d0Yjcao5eGlzC4ZjUauvPLKYNuXxWIJo70fG+nKGEWCzWbDbDZTWFhIZ2dnsM1kjUS+hnDIZFIsUNlQFGXRHAtN04IVi8LCQmprazl37hw5OTnkxBquFAcEQeCOO+5I+b4PfbD3eh9CUQbxeOzk5t6c1HpzlfCDAOTlbVyyNpKzZ7/H9df/AJ/PQF/fv1BdHf/+Y3EE5wa5bgs+nI+P/yU63VlAYHz8f2G3R+dwxOOnAv/FA0EQuOqqq+jo6Aiu98Mf/pC77747mJTV6/XY7XZsNhtlZWXzAgdFUfB6vcFqSeC6gdbV4uJi9u/fz8jICPn5+TETT0vhpwJtXwE+R2A+RyhXM9OVjXjWDp1EvmfPHjo6OsKSyFdLe9WK8KrFxcUUF8/NY7BarVxxxRX09/cvCjbiRW9vL7/+9a/Ztm0bt99+O9XV1Tz44INxnZvOsmu6IAgCOl0LHs9TSJJMff23kXyN6P71XxHb2hAkCd+HP4z/934P4rjxfL5vkZd3FkFQmZz8CWbzp+LeS6iRS/R9cjgKEIQzSJJMXl5R8Pfj4+PB4CIwJC/Qp1pQUJCwkgRAf/8juN0D+Hz/P3tnHt/YXZ3979UuS5ZsSZYtr5J3z745M5MGEggpgZawJM0bCKEB2r4kTVlKtwCloaUFWkJZQoC8bWkKgYQEaICEQEIgCUlmsWf1zNjjbTzed0nWLl3d9w/PvSNZki157GQmzPMyJPhMAAAgAElEQVT55JOPx1dXv3st/c495zznebQYjfWYTE1IUinh8I8RxaoUZajU9ZUyMDCAx+PB6XTm5SybDYlEAr/fn8GNfumll7juuutoamrCZDItO9wmJ72yJK/sdJu6qUmShNFoTPPAkA2XcrVp88Fabfgy7WtiYoKDBw9SVVW1brMjsLpNX61WK+olIyMjihN5VVXVJT+Qdxlri7UuiskCEp2dnTz66KMMDg7yk5/8hC9/+ctKYpFq7pqKbMPYq8VaxbzF8yQQhMeprQ3i9/8HWq1l1SyB8fFHKSn5R0BgYuIeXK4bV7mmwq7N43kJEDAYQthsx4HFZGPpeSTpvHN6qg/JcjOCExMTaX/PublGbLbF309NNWG3/wBQk0i8g2RSSItT8n5otVo5ffo0KpVK8WlYzV4lz4j4/X7KysoYGxtT/v3gwYNcf/31WCyWZfdg2dBWjlNLi2M6nY6qqioCgQAnT57EYrHQ2NiYERtT7+krFadSTWu7u7spKipKEzlZr85GoXFKVv5qaGi4YCfyVxMXRbKRijNnznD48GF2796d8buXX36ZrVu3UllZyRe/+EU2btyY9RxtbW3ce++9AMzNzRXEy17rzsZaYFEN4rdEImFUKpFQ6Fkqf92Net8+knV1SPE42u9/H8nlIrkpt+SrDJ2uBehEkjQYjQ15ryMQ8NPdfTcm0yQ+39uB7QVex1cRxQfx+ysYH7cSCBwgFAoxPT2d1STvQrB165s5fdpzrsLytyQSYVSqEPC/JJO3AKVpylCwmPTa7XZOnz7NxMQEra2tKwbKZDKpbNhy0FGpVFgsFiwWCxs3bqSrq0s5fmxsjHA4nNeA8lKe7FLVKlj8bMiqS6mGSy0tLWmt8PXaxGVvi1wQBAGXy4XT6eTMmTMEg0GmpqbymudYzczGajd9lUpFbW1t2hB5W1uboiJ2GZex1kUxr9fLfffdx86dO3nTm97E3Nwcn/rUp/J6bfow9oVhrWKeIAjo9c8TCv0XkiShVk9hNH501edSqztQqcRzazwI5J9sDA8PMTc3TDxuKPg+FRf/GVrt3yBJRqzWP1T+PZX2urCwQCwWU6i8+fiQZENNze3s31+EKIa44go/Gs3HEQQQxUlE8U8BMuKUw+GgpKSE/v5+Dh8+TGtra07JVBkyFS91/YvXuljUu/766/mv//ov5fizZ89y8uRJpTux3L66NE5lo1YVFRXR0tLC6OgoBw4cUGYRsp13veJUrlhisVhob29XHL4rKysL+sys9WxhLuQaIr9UimIXVbIRCAS48cYb+fKXv5whP7pjxw6GhoYwm808+eSTvOMd76C3t3fFcxoMBvx+f95rWOvORqEPQNkgCEmCwUas1iMkEgbi8StRnXySZFnZYidDp0PSahEGBiCPZMNmu5tI5ArU6lIMhva81xEMPsXWrb9ApUpiNnuRpHfnPDaZTCoDjLKXhSAImM1vx2KxUFNTjMlkorOzk+bV6AHneM9UZaiGhsVE6vjxBjZufBlJEtBqv4zJ9K/EYvcgin+WcQ6dTsemTZuYm5vj6NGjlJeXK+pKsoykzF+VKQ5ms/ncNdVk+HO4XK60ZOP06dPccccd9Pb2MjIyQktLy4rD8vL5llKrln5OUw2Xuru7lfaw0Whcdy7sSpDnOcbHx5mYmODs2bMrBslXorORjZ7W3NxMTU0NkUgkxysv43cda1EUczgcfPvb3wbgqaee4plnnsn7/S/WzkYoFFBoInNz0xSgNppxLovlfSST+5AkgZKSP877tV7vKEbjLWzYMEVX19XA1wp89z8kFNrDwkIUny/GwsIx/H4/fX19aXSi1VaWU+OUIAhs3/5/APD57sRgWBSzCYefwWqdQRTfTzYpeY1GQ0tLC36/n1OnTlFSUkJ9fb0iqS5TueT/RFFU4lRlZSXFxcUZHTOLxZL2rLRjxw4GBwcZGxujpaVlRQqUnHSIokg8HieRSKDVapXPliAIVFdXU15eTl9fHyMjI7S2tmK1np/dLMTfYi1jmjycLYucFFIUW6/ZwuXWmTpE3tDQQF1d3arO90riokk24vE4N954I7feeivvete7Mn6fmny89a1v5c4772RmZgaHw7HseV9NZ9bVzDVkg1b773g8vwDKiEa/RGVlC5KzA9WJE0gmE0gSQiwGefN31RgMv1/wOmw217mHMxG9Pn2DWMpTlR/CZYNDk8mUk5Y0Pj7K6GgXbvcOHI785kdSN2xRFIlEIkQiEYU3KrdyBUHgzJk3cfCghfr6Pq699llAQKv9AoIwRiLxMSBzUL20tJTNmzfT39/PCy+8oJgkFRUVYbVaqaiooLm5eUWqlfyaUCgEoIgFbNq0ifn5eY4fP47D4cC9ROZuKbJRqxKJRNaHhKKiInbs2MH09LRiuPRqbeLZrmPLli2KrnhxcTGNjY1ZK4LrPbOx3PFGo3HFauFl/G5iPYpihapRrWVnY62SDZVqjpISL1NTG/D5yqis/NMLWpNO14Za/XzBr1WrezCZZojFDDQ3H1jx2nLNCMpV/4qKCnp6emhra1NiSyEVdfn/8qxFKBRSZg5T5wF7em7A5+tFp4tQX78PrfZlNJpfEIk8C2QvSFksFjZt2sTg4CAvvPCCkgDJcscOh4P6+vqctKVU7Nq1i2effVb52e/309raysLCAt3d3VgslrzOpVarlWRYjlOpsU2r1dLW1qac12g00tzcrMSAVzNOpRbFJicn8xI5We84le3zmzpELnepLnZcFMmGJEl88IMfpK2tjb/8y7/MeszExISiGnDgwAGSyeSKFu+wOlO/i61iFI8/Ryymw2icwmz2IwgCiT/6I3SDgwgjIwiShLh5M+IVV6zBqnNDq70KleqrxGJ9nD3bwtxcH8FgMG0AbjmecfZri6BS3UR7+1kGBzdhs/0844u4kjKUwWDA7XYrSmJLnbavueYa9u0zYDRuJxp9EfCiVofRar+OSrWfePyrRCK1GYobRqNR2bDHx8fR6/Vpm2K+qKmpoaenJ+PfS0tLaW9vZ3h4mIMHD9LQ0JCTWiXzg+X1+Xw+4vE45eXliKKY9X6nGi55vV5mZmbyktFbzy6IDLklPD4+rsxzLJ01WWuVjwtZ+2VcBqxfUWy9TP1eyXPF4/9CSclv0Wj0uFwfXtHdeyWsNnYWF19BMlmP2XyW8fE/ILVmIBv0yomF7GUhi4/kmhEUBIFDh76HXv8U4fBe9u798LJxamlxRy4WtbS0cPLkSaqrq6murk47Ztu2N/H00xKlpUN4PJ8nkYiiVp/CaKwnFvsSovge4vF4moKh7B5usVhobm5menoaSZJobm4ueFamtbU1LdmQ11ZcXMyuXbsYGxujo6Mja4xNhTzH4vP58Pl8xGIxmpqaFLUs+b7J55Vn+mpqai6qoliqyMlyRbFXM05ptdoV95aLBRdFsvHiiy/yne98h82bN7Nt2zYA/uVf/oWzZ88C8KEPfYjHHnuMb3zjG2g0GoxGIw8//HBef+BXs2Ikb+KrHTRexEvodCHM5nmGhnZTUbFIOZKcTqL33INqaAhJp0PyeCALdz4SgW9/W+Ctb/08avUkyWT+HY3sA3A2ioquIR5fwGaz0djYeEEqJnr9NOXlw0SjBurru4jFQkBRmpSf/GVbTnK2srKSsrIyent7GR8fp7W1VaEnmUwmrr32Wnp6evjGNz7Ctm2/5fWv/w06XRiV6iW02j0MDt5NPP52rFZrhuIGLNKhpqen6ezspKamJm8XaoCmpqa0ZCM1OZBVO8rLy+nt7WV0dJSWlhbUanVa8hOJRJTkx2634/F4UKvVK6pWycobk5OTTE1NKfdmOTfVV2pITxAEKisrKS8vZ3BwkH379tHU1KQkXOul8pF6/KXCd72MVx/rWRRbC1O/1WJtziURCo1hMokkkyLBYJDVKElLUhJRTFzgmsyoVD9DFL1MTo4RCEwQi8UIBoMIgqB0LArx0RLFGDt3fhaDIUw8vo9g8J2YTDU5JWdTO+up53c6ndjtdgYGBujo6EirmpvNZt75zncyMjLC008fZsuWDsrLx9Bqo2i1dzA39zC9vZ/AYrFhsVhwuVwZ7uGVlZVZKcD5wGQy0dDQQH9/P9XV1WmfW0FYlFx3Op309fUp1Cq9Xq8kPj6fT0l+rFYrJSUl1NbWotfrFWrVotjNedUqeaavrKyMgYEBpqensdvtecmlF/LQvtrCUjaRk6VFsVc7Tl0qBbOLItm46qqrVtxY7rrrLu66666Cz30x0KguBMHg54lEdMRiViyW92MypfgpmM0kc/CBJQm6u1UMDKj4yU9mqK09Q23tMDbbOHB9luPPm+QtHYCzWCwZA3BHjhyhpKTkguUSRdFFMrmdoqKjRCJvRpL0yuYtt6vzlfLTarVs2LABr9fL8ePHcTqdVFdXK3MWs7OzxGLQ0bGHoiI1e/b8ApVKRK1O0NZ2D6L4MrHY94HM6oUgCDidTmw2W9ZAsRxkOdVEIpHVfV4URcLhMBaLhenpaV566SV0Oh3l5eVYrVaqqqrSpH5TsZRalSvpEASBTZs2sbCwkGa4lO3vt57D5Nn+jmq1msbGRqqrq+np6eHs2bO0tLS8KjMbqbhUNvHLeGVwsRXF1kp7fy3ilCh+h+LiESBKb+9baWraW/A5YrFZ/P73YDKNoVa/G9ic92vlAWg5dgUCASRJIpFIYDabqampybr35guVSkCng2RSQKMR0OvVSpwqVHJWnq2TaURWqxWPx0M4HMbn8zE5OcmxY9s5c6aW22//f+h0C0iSioqKZykvP0U0+kMkKfeco81m44orruDMmTMcPHiQ5uZmSktL87rOd73rXSQSiaxxQRRFQqEQJpOJSCTCgQMH0Gg0lJWVUVJSQnl5eU7lyKXUqqVxSp6VW1hYYGRkhNnZWZqbm5f151jPIe5UpIqcZCuKvdpx6lLBRZFsrCdWQ6Nay87GhZ2rH72+B6vVx8xMJYJQnfcrEwn4n//RcvCgCoMB/v3fP8yePfu57bZfA+dN8uTkItUkT67uLzcAt5r7JNOgUpM5g8HEwMC9uN1W1OoyVKr0LlChX8pgMEggEMBsNjM8PMzAwAB2ux2Hw8HmzZtpamri2LFjPPeclqkpE2972/+iVscACbX6KYxGF+HwU0D2wfnUTVHmsa5kqGUwGLj99tsZHR3F7XYTCATSVEHkapvValUUsIaHh5mYmMBms61oiJSPGoi8IZaWlrJnzx7FcKm+vj6jJb5eGuMrnVd2oPV6vXR1daFWqwvyESi0Pf1a2cQv45XBehbFCqVRrXWcutACWzD4Y7TaMLGYkcrK7RiNy4teZMPCwvOYTCOIop7KyieQpL/Kelw2L4tkMql4kbhcLsxmM2q1mu7u7rz8HVKROg8o32OjsZizZz9HTc2vgXeg19cAhcWn1PXLJo1ms5mpqSmGh4cpKSnB6XTS1NSEx+Ph+PHjfPObFm688UHq6oYACUEYw2D4PeLxvyGR+Puc76FSqZS9vaenh7Gxsby9jmTxkVxiKFarlcbGRoqKihgfH2d4eJjS0tIVJeqzxSk54ZDvo1qtprW1lWAwSGdnJ5WVlTm7M+shZLIcchXF1luN6rUSp17zycar3dm4kHMFg/8PvT6O31/M6OgG2tpa836tVgt33x3lAx8wEAqVU1wc5r3vHWJq6hZ8vgNpJnkVFRUZ7Vgo/EGys/NpQqFB2treid3uyNiwAaVTIVeENm/ezMjICIcPn6W1ddEJe2bmAGr1+8+t4UHs9l1Z3285xY2qqipaW1uJRqN0d3ezsLBAeXm5IlMoCAInT24hEqnn5pvvQ6UKnDtzBKPxGkSxjljsZM7rlfmmo6OjHDx4kPr6epxOZ8ZDezgcTmsxd3V1pa1RDopLIfNiT58+rVCr8k06shktpW7MgiBQW1tLRUWFYmrU1tamBORXUus8G0pKSti9ezcnTpzg7NmzaDQaampqVvwsFkpZfK1s4pdx6WM1nY2LpQMvSYfQ6QYwmWaYmqpEFPNXOEyFybSTeNyMRhNgfv5qLBZJ2UNTO+6yl4XFYsnqZbHStSUSCYLBIBaLJS1G5ZqzkGctJiasHDzoPidbvnj/u7sfRhRnaWh4PwZDZoKVSkWW40Dq+svLy5V5hp6eHubn53E6nej1emprazl27BgPP3wbb33rz9iy5dC5sybRaj+PRvNNIpEjQG5RlaKiIrZt28bU1FROCrDMalg6DyjPYS4nhlJdXa1Qq+Q4tZKwRmrSkUgk0qhVcoyQ6WZnzpxh3759NDc3Z8wmrOfMxnJYWhTT6XR5O6TD7y7d93KysQQXDxc2hF7/BEVFfvR6DX7/rrw+cIlEQqn2nDgRQxDKefOb59i/v4JI5E50ugF27Vr5XN3dj6BWf5dQaA+bN9+dlZYjX1symWRs7CCbNt2FVhtjYOC3lJQsanYvN2ch/97tduN0OhVlCovlK1RVTQESY2NfAx7M2LBjsVheihtFRUVs376diYkJOjo68Hg8bN68mWAwyMGDB+nrK+JrX/sof/EXD6BSnXchV6uH0OsdRKMzOe+RLOXndDoVKdvy8nJl45aHzK1Wa0GqIDIMBgNbtmxhdnaWI0eO5M3BzWa0lG2z1el0bNy4MW0Irqmp6RWnUWWDIAhKMhyLxXIGnNWefzXHX8ZlrBde7aLYhcS8UOj7lJRMEI0amZ5upKUlf++mVOj1NYjiT5ifP8PsrMjCQheiKGI0GikuLsZut+N2u9P20Hg8zvBwH1arA5tt+UFZeT/s7PxH7PYT9PTcyPbt7wGWn7OQUVVVhcPhoKenh/HxcYqLO2lquhtBgKGhLjyebxCLxdIGuGXGgExFrqury9pd0Gg0bNmyhenpaQ4dOkRNTQ1ut5trr72Wp59+mp/+9B3MzJTxxjf+QnmNIHgxGt2Ew38O/GvO6xaERR8mu91Of38/Bw4coLKykng8rgyZy2ssKSnJucZc0Ol0bNiwQYkjpaWlCnV4OaRKumeLU7IyVGVlJT09PYqUuzyL+WolGzLkolh3dzfj4+OYTKa8i2K/i3HqNZ9sFEqjulg28Xj8l5jNY4BEMGhGo9mZcYwoihnKGiqViuLiYoqLi9my5Vm++tUfo9H8Ph/60J+j05XR2Tm04ntLkkRNzT+h18dIJrtZWHg7Vuuif0dqi1keTAawWIbR6WKIooqamm50Ol1BXxA5KRgfH2d62oPLpUaSwOttZGjoZUVxw2q1UlNTU5DGucy5tNvtygD59u3bOXLkyLngoOGhh77Ae97zz6jVfcrrVKpwznPKSZ0cWOThw/7+fkpLS9MG1C8Udrud0tJShoaGOHDgAE1NTSsOncr3fmFhAa/XSzQaRRTFrAFAHoIbGxvjwIEDBX1f1nPDlyRJ4TfLreuhoaGc1bPf1fb0ZVz6eLXpvquPeQmKiv4XUQSVKs70dDutrfl9x2Uqr9/vIxDoJRwuwmCwUlxchlY7R2NjY5oHQzb093+GhoaH8fnK8PkexWpdpBqnum7HYjHl3gYCx9m792HUapFgcACd7vaC9gC9Xq8kBZOTnajVIpIEBkM3L7/8MjqdTjF0XW7WLhfKysqw2Wz09/fT2dlJW1sbDoeDqakpfvvbvczMVHDzzQ+mvcZo/DrhcPZkI9Ul3OfzEQwGARgcHFQUlsxm85o8iFutVtrb2xkZGcnZ7V8K+d4Hg0G8Xi8LCwuIopi2NxuNRrZt26YU3ZxOJx6PZ90GxAv5XgmCgM1mQ6PREIvF2L9/P01NTa9oUexSmS18zScbr2Zn40I2cUH4N9TqOJIkMDPjRq02pc1YnDfJW/SyWDoAJ4phYrF7EUU1avX9SNINCMIiz3Sl61ukOFkRhDEEwYjBUJwRCOVOhDx8ZjD8AZL0EHp9L4nEp/L+MomimNaxCIVCqFQ3cPjwYmu8ru4GmppOkEx+FrgevT7TiC9fyJX8ubk5urq6uPLKK3nhhRcAGB4e5cCBB7niihtQq2cBSCZLz/0/qSR1Mn9VpTrvEt7Q0KA4nyeTSYaGhjh27BjNzc0FzRwsB1lVKpValTpAl0orkxMgSZIU5ZVt27aRSCQUF/JsnaqqqirKy8t5/vnnOXDgAK2trZSUlGRbjoL15M0mk0mliikHnPn5ebq6uhTecGqVs9D29FrweC/jMtYCl2pnQxR/RFHRLIIgEQgUAVuyHrfUyyIUCikP5nr9v9PU9CzRqBOT6cdotaUEg8G8usDV1b8gHtdhscywsHCAWCz94dbhcNDf36/IipeVlQNqJCmB0Vic9/c/mUymPbQHAgE0mhuYnT2KVruARvMP7NmzB5hGEKzA6gz/YLGiL88Fnjp1iu3bt/Piiy+ysLBAT089P/jBP3LzzZ/OeJ08Z5HaWYHzLuFut1t5RpAkidHRUbq6upR7sxYPrYIgUFNTk6GuKKsfpkq4y+uU6c9Wq5XNmzej0WiIx+MZccput7Nnzx7FzK6Qz+16xym1Wk19fX3GPEc21cf1lsq9WPE7kWxcalxYURynuLiLxc+jxMjITqLRRZUGuWKynEkegEqlIxotxWCYIRYzYzRalTXlgty1WKyAf4dE4meoVFegVldlUKFkabxTp04xPT19TgL3JySTkOt7sfShXU6Y5OHo+vp6TCbTuTVewfT0NKdOnaK9/d0YjUGSyd8SiVyBwbCt4HuaClmtY3Bw8NyMyAyJRILnnnsOj+c4en030WgXExNX4fcfTNsMq6urKS7OHaRSk4LUwbzVus0uhdFoZOvWrYyPj9PR0aFoqcu0MqvVitPpzCpJnI9qlUajwWAwsGnTJk6dOoXBYKC5uTnn+gvZCFeTDCw9vrS0lN27dytdmJqaGqqrq5Xv7VpKEF4qFaPLuPRxqdJ9BeFf0WgSJJMCU1O16PWlaVRev99PKBRCo9EoD71LvSwCgZdJJMzo9dOEQl1Yra9bdk2pcUoQ3ole/yDJZBkWy5WoVOnKUAaDgdLSUrq7u5mamjon1/rfCMI+JOlmsr2FJElKnJKTo9TCTW1tLWaz+dxe8/t4vV4GBnoIh++iru4HRKPFwPPodPkLumSDPBc4PDyMy+XC5/MBcPq0wG9+82OuvvqdCAJMTLyb3t4OZRbEarXicrmWNZ1NpQDLxStZnGQtIBf2ZmZmOHLkCDqdDrVaneFflY1avFyckmnXLpeLl156ia6uLtra2lacEykkNlxInEotih0/fpySkhIaGhoyimKFzha+FmLRaz7ZeDXNkvLZxOWKdGrVp6Hhb7FapXO/B5fr/czOhtiwYUMB761Cp/suPt+vMZuvRKu1pK0pmzKU/PtFKT8PWu1Hlv2CGo1Gtm/frpj9pNJ75Ovy+XzMzs6mJRYWi2XFh3ZYbCmXlpYSiWjQ6SQkSSAYjGE0diNJHi6keqRSqWhoaMBqtfI///M/ijrGo48+Snt7OxbLGykrsxY8ZyFD3nSmpqY4dOhQVhOnfLG0+xMMBtHpdDidTqLRKIFAgJaWlhW7KLlUq7JxlM1mM7t27WJycpKOjo6s+uJQ2Ea4VhKBqV2YgYEB9u/fT3Nz8+8sF/YyLn3I0tj54mIoisEMZvNpBAFUKonR0SsIhUIcPXpUofJ6PB6l65sLfv+N2Gzfxe9voLR0u7ImyK5gKP9erVaj1f4davWfIElWVKrstFWdTseWLVuUIWmPp4Xy8tcr150q4iFX2uXh6JUe2mGRu9/e3k4k8m6SSdBqA0xP/xKn8wOF3tAMyGIepaWlTE1NMTc3RzKZ5IUXjgHP4nA4sFqtbNliKdhsFhbvzaZNm1btzZEKufsj38dAIIBaraasrIx4PI7X66WxsTEvatXSOKVWqxWJYVhMzs1ms6LaZbPZllWFXE+6b7Y4IhfFRkdHlaJYTU2Nwn4o5JnitRKnXvPJRqGdjfWkUcktRDmxWDTJS6Rl+h6Pm5KSA8prkkmorGxgZuZY3u87P/8djMYvEovV47A/iBAWEONx5JXIknupHhaplYNCIAiLxmxms5nu7m5Onz6d5ikRjY6h138Nnc6Mx/NVbLbygs6v0WiYmvpPTp9+AK+3lde//m/Qak8jSfXE478G8v/SJhKJDMqWPNx2/PhxAObn55VBwLXAUhOnlpaWNJfhpUhthcuUrdzdn0WEw2F6enoUatVKXRT5753LaEmGIAhUVFQohkvyQ33qvMh6DYjnc25ZhjgUCnH69Gl8Pt+KtK8LWc9lXMZ6QRCEgh5wLlTpMBX5Ji6pXhZ+vx+P592KO7cggMVyC5IksnNn5nxhjjMSDv8Gh+MGJOnD2Gx6BOG8f8jCwoLyAJ2qYCj/fB4u8rl1TqeToqIienp66O/vR6/XE4/HlQH0ublpDAYDO3a0F2zEq1KpmJq6AY/nuwQCZgIBNRUVfw5cSzKZ6TS/HOQ5i9SHdq1WS3t7O7/85S+Vmcnp6Wl2795d0LlzQe72y3OBK1GAV6Lt1tXVZfiaRKNRent7FUPAlWYalyYdMgVY/tskk0lKSkrSpNw9Hg8ulyvju/RqxCm5e1RRUcHAwIAicvK7Olv4O5FsvFpcWFEU8Xq9zM3NpZnkyfKrqSZ5MpLJQNrG6fVa0OnUeSdAix/MB4jF1BjnTqL6m9swzKiRzGZiH/sYDQ0N9Pb2KhWM863sOYaG/gO12kVz863LfrhzKW44HA6SyaRCq3I6nYyM/ANVVR1IksDY2H9js/1twfexvv4NwBtIJOYxGL5CNKpGre5Gkp5GpdqMJNVkvQ9L/SzkOYtUnXBBEEgkEvT29hKJRAD44Q9/yPve9741aynLQ86BQIDu7m6Ki4uVKozMX02VRTSZTFitViorKykuLl4x8MldlOnpaQ4fPozL5cpLFSOX0VKu9VdVVdHd3c3w8DCtra0YDIZXvGKUDbK8Y2dnJ/39/fh8vozWda7zF/pQcRmXsV4opMgl8+7XAtkKbKleEKlUIrniX1lZid0+kHI8NIx6XOEAACAASURBVDZu49ChQ0tPnxXJZJK5uc9gNj+KKKqJxe6nqGgPkpRUqDI9PT2EQiEaGhrS9gG527HSXiKrLcn/hUKhNHfryclJ6urqzqkdPcr27X+NIEicPv152treU8AdXERd3b8zO/tRjEY7dXVbEYQ5kslHSSbbUKnasr4mlbIlF5cApbjk8XjSikvj4+McO7ZYeDx16hS7du2ioqKi4LVmQyoFuLu7m7GxMZqbmxV2SOqcRTQaXZG2uxR6vZ5NmzYpFCOHw4Hb7V5xD05VrZK7HPJ7yUl6qpT7yMhImpQ7rH9nY7lrSC2K9fT0EAgEqKurK+j8l5ONSwCvlFlSLBZL25jD4TCiKCpfxpVM8mRIkhGfrwirNYQowk9/+mluuin7mrKZDwEsLLRjL30KwxdCaHwCQm0tQiCA8YtfRH3//bS3tyuV9g0bNmAymZid/TiNjb8mmVQxOWnB5Xo7kN4NkFWX4vEo0WgvDscmWlu3ZXh01NXV0dPTw8TEBB6P3MkQsNsvbFNMJk0cP76NzZsPMz9fis32ftRqHdHoTwgEmtOqLMlkUqmy1NTUpHBsM6HRaNizZw+/+c1vAPD7/fz85z9n586deT205wuDwYDH42FkZITnn38erVarJBbLySLmC1nJpBDn2KXVo2g0qnBml153UVERO3bsUOQZy8vL15VGVejxWq2WpqYm/H4/Bw4coLa2dlnqWqHt7Mu4jIsFa9nZEASBSCTCxMSEEsNkKlFxcTHl5eUrGpdOT9soKsr+wCWvU45XMkTxBJKkQqWKE42epqTk9cqeo9Pp2LVrF2fPnuXgwYO0trZitVqZmTlOMPgxkkkdpaX3U1LiPneuTNUltVqtiHhkc7euq6ujr6/vXIHmF+h0i8Umh+MXQOHJxqIy0eJ6wmERnU4AkiwsfBybrY1E4jNEIuq04pJ8n/MtLr3+9a9Xkg2Ahx56iLe//e14PJ41K5zodDrcbjdjY2O8+OKLqNVqZY1yPL2QGcTS0tI01Sp5QH05LI1TkUgEURTTPk/ynIjf70+Tctdqtes6IJ7v8bLi5tGjRxkaGiIcDq/4vYLLalSXDNZD+napskY4HEar1SoPt7JJ3pkzZzCbzSt+kVKhVqt5/PFvcfbsSdRqC3feuai+JPNWl27Y8poX28wSfv+dOJ0vEhi5Cuv0JKo6z+JBxcWwsIAwOorKZqOxsRGfz0dXVxcVFRWYTHMsOpQmiUTO0NXVpXAu5euSVZf6+/+QurqjLCyUEo//EqPRlbYenU7H5s2bzw1430xraxXFxbUYje/N+z5kg06nw+//HF//eifvec9/IknziGIMUbyFcPh6otGP5l1lWYpt27YpyQZAf38/W7ZsoaOjg9bW1mWpT9mQrbMiBz+n04nb7WZ4eJhYLIbL5VozmVxZm9zlcinUquUG1LNVrKxWa1Y1EBllZWWK4dLc3Bzz8/N5rX+9zYzkTV9uXff39yut62xSwa+VitFlvDZQyHdjtZ2NpVReWRlKq9UqlE/3Ei+LXPj5z6/jTW96hkCgmCef/Bw33ZRfnFpcuxed7mqCwWkikUqczndlpXHW1dXhcDgU/waD4cu4XL2AxNDQ1xgb+6BCNZVFPGTVpcHBA8zOfpa5uSZ27vy7jPur0WhobW1lfn6e0dFdVFQ8g0olYLPdVvB9XYrOzk9jtf4Ah2OW2tp9SNI+JiammZ7+M8zmSux2Ox6Pp+Bih9FoxGazMTc3ByzuYaOjo8zMzKxK/XA52m5paSnV1dVMTEzg9/sVU9y1gEqlora2VlGtGhkZWXZAXe5SyeuMRCIUFRUpNODURMtisaRJudfW1q7rgHihxxsMBqqqqgiHw+zfv5+6uroMo8Wl538txKnXfLJRaKdiacUokUhkeFnID43FxcUZyhoX8t4y3va2twFvUzTCBUFAFEV8Ph8WiyVDGUpGKNSNXv9bQqFiispeQF20HSkUgqIiSCQWB0DOcdoXH8zUDA01kUyeJpm8kYaGKMlkJVrtH1JbW5azG1BX10U8rsNs9pFInAVcGcfA4kNpScnr6O2tIBqN0tYWx2BQMzT0a/z+Zygru4WKis3L3gu5s5LasWhv38P+/SO84Q3/hdEYwmCYoKrquyQSUUTxc6zmY63VamltbaW7u1v5t2g0yoYNGzKoT0uRa8hQDn65OitWqxWv16u0lD0ez5ptKjK1SO5CVFVVUVlZmaEGJstPpvqX5KNapVKpqK+vZ2ZmhqmpKSYmJmhtbc0q9Zd6n9ZyZmMpUjdljUZDS0uL0rqWpQhTk6KV1nOpVIwu47WBQmlU+XQ2otFoWmKRajJXUlJCbW0ts7OzJBIJamoy6ajLoaXlfh544Oe4XC5uuOENSlFvdnaW0tJSZaB3aZySpCQ+3y0YjcMkEjbKyv4753C3fE+qqqoYHR1Fry/GbhcAAa22AaezCrPZnLWqX1T013g8fSQSzzE7247L9Zas71FaWorF8mcMDu5kYcFPY+NeTKbzFKelswfZsFTEQ6Vqw+v9FEbj/YjiAGq1iNv9ODU1zxIKPYNOt7xP0nK46aabeOCBB5SfOzs7ueOOOxT1Q5n6lA2roe1arVYWFhbo7u5Wio6FFvNyQaZWzc3NcezYMRwOB7W1tYTDYSWxkIuecpxyuVwKhXcptUr+O6WKiPT19TE5OanQ51bCenfg5ThVU1OjzHPI85DZksXL0revQYiiSCgUIhAIcPLkScWobenQU74frHyTjXQpv/PnFoTzrqZbt26lu7sbm82W86FUFHvRaOKYTDNMTe3E/JGPob33XpifR0wk8L71rYyFQvgPHiQaTTI/b+Mb32jkC1/YRH19M2fOtFBRUUFtbe2y1yhJ/xeT6QFisZ0UFW1f9tq0Wi0bNmxQDHnKyrTU1NxGbW2cQOBRJKkbQTjPyVyqZZ7aCpcH/ARB4KmnprjvvnJuu+0buFwTqFQRtNrvIQi/IZH4EZC/cpeMP/iDP0hLNl544QU2btzIzp07GR0dVVq+JSUlaYlFJBJZtVO4rGRy9uzZvA378oGcAImiSElJCWfOnKGvr4/S0lLKysoyuMCpyKYGIn8Os1UfN27cSCAQ4NixY9jtdurr63MmZevZ2chF/dq+fTuzs7McPXo0TbXktSIpeBm/e8gWW1JnFOTEQqfTKfGrsrIyq8lcoXFKTnJsNhu33nqrwptPjVNer5empqasSYAoRtBohkkkilCr5wiHJzGZFk3aotFo2jxgqqR3Q0MDavU/MDjYhMlko7b2ZlSq3NQhi0VNMqlCEKC01LDstS36JFyhdPvLysrwer9Jbe2T9PdfzZYtX0ubHUj1s1hOxOO558KMjyfZs+d5iorCaDQ+zOa9TE//PRbLx1a1/yw1OpRd1rdu3aoobtXV1VFWVqYMmvt8PsLhsDKzUihtV5bhHRsby9uwLx/InbZYLEZJSQljY2OcOXOGkpISnE7nEpnhdMifudTimEajSVOtkrtXoVCIkZER5ufnVxRReSXjlFarpaWlhWAwqBTFmpub04pir5U49TuRbGT7Q6Uqa8ia4IDSxltqkrcaZKs+rSw5m10Zqri4mJ07dzI0NKTMWqRqSycS8wjC3SQSWgRBQq3+F3odKsJ/9mcIo6OonU4MbW04LBbq6uq54w4TZ84I+HyzfPjDC9TX2/n2t9sZHh5QnEtzVal1uk8gSX9dUAvYbrdTUlJCX9/TaDRxJEnCbPYxNjbCwkIobc7CarUuu8kAXHvttbhcLp54QmTPnh+yYUMXGk0CrXYYjWY3odBnUak+kvf6IFOJS3YzXVhYIJlMYjQaOXHiBIIgUF5ejs1mW5VLbLb3dbvdlJeXp1WnCuHFxuNxJaj4fD5lgE/mKzc1NRGNRunp6cHn8+UVKFKTjkQikVW1St6YbTYbu3fvZmRkhP3791NfX09FRUXae6x3Z2O588uGUPL66urqEEXxNVExuozXBuSH/nw+83JFd2hoSKFCaTQapeMuU3nzOddycWrpPKD8gJdLGUqj0bB9+3alOJPNGDQc/hZ6fZhkMszAwE3o9Un8/sMZXZfUh+Hz/k8aLJY7GRoa4uDBDtra2nJSXE2m/yKR+HfU6q1otdeseB/gvAt2X99Rtm//byRJwG5/jLNnbycSsaR1rWXPq1ydFYA9e65m3z49+/dbeMMbfgAkUavjVFT8PbOzT5BMPoLZXLjyYXl5OZOTk8rPMm03FothNps5ffo0PT09CuU128xKoZC7BWVlZQWpSqUilakgJ0AGgyGtUCeKIqdPn2Z2dhaHw7HiHr2SahUsfi4bGxsJhULLSrnD6pKHC41TJpOJHTt2KEWx1KLda2W28KJKNp566ik+8pGPIIoif/Inf8Lf/d3fpf0+Go3yvve9j87OTux2O4888ghut3vF80qSRGdnJwaDAY1Gk6Gs4XK5lCpMOBymr69vzbiJoiiSSCQK2rBzQVaLkDmsTqeTqqoq/H4/Xu+vqK+fQpIE5ufLCQZjlJQ4qX7d6xSX6VTcc0+c228PYLUOolJJfOQjn0Kv/8+MWY7cXY7CPvyRSAS/349K1cDg4E04HL9ldPQmioqSOJ1qPJ5K9Pr8TZC0Wi1bt26lo6ODn/3sHcTjWnbs6EQQkghCEpPpE0Sj/4EoHkAQ8lOVWkyAzAQCAeXfOjo6lASooaEBs9nM3Nwcvb29mEymC040UpGqKrWcN0c2PXP5QcNqtVJVVZX1by4/CMjVr3y9P1IrekupVakPRzIPV3Y5l3m48ndpvWc28jHpS21dj4+PU1RUtCadpMu4jAuFLBm+9MFCFMU0k7lgMIhKpSIej6PT6fLyslgJcpxa+h1KnbNYSofKBVny02azcfLkSaxWK3V1dQSDQbzeeZzO+/H5rOj1EVSqN2E2m6msrMyZHCUSUYaH/5jS0uN4vX+C2/0x3G43ZWVlyixHfX19lrXVo9F8raD7IM+wqVRGotFidLoF4nEDomgsuGsNizShq6++mu9/f4wHHijjAx/4FgbD4iC63b6PRGIDg4P/TWXl9XnvdZIkceWVV/LjH/8YWJxlPHDggELbraurY+PGjeccx3swGo2Ul5evWZySB7FlVamysjLcbnfG+lM7QPI8yEpD+7AY27ds2VKw90cu1So5TqlUqgwp92z+VOvt8L1cnLLb7WlFO7fbjSiKa2YK/GpCWKF9ujbaenlAFEWam5t5+umnqa6upr29ne9///tpRnb3338/x44d45vf/CYPP/wwP/7xj3nkkUeynu+ZZ57hySefpKOjgxMnTrBz504+9KEP0d7ejtlszsk5jEajdHd3s3Xr1oLWn1oJkj9M8/PznD17lra2NoxGY0EbdjYsVdyQDX6cTicVFXdiNg8gCCKTkx+jpuavlz3X6KjA7bcv0NLyJL29DXzhC19l27YH065nYGAAr9dLa2sr4XAYo9GY06kzFovR2flPaDR9WK1/jt3epqwztX0rPxCr1WoGBgYIBJ5i27bPIggSc3NfxmYrTAVkenqaX/7yl0xOTtLe/iLXXfdUhua6z3c3Ot2nMl6b2rL3+XzEYjHGx8fp7+8HFo3t7rjjjqzvK4pi2v1Zq+R06fnn5ubweDwkk8m0eRA5AbJaravqwMnnn5+fp6WlJaM1nwty9QgWqQcdHR3s3r076/v7fD6F59vY2MjMzAzhcJj6+vq83uvw4cMFVc7279/Prl278lZlOXLkCNFoFJ1Ol/V9dDrdxdS+vmgWchHiFYtTsH5Fsauuuorvfve7igS3XEiQKTryfzJFp6Ojg/b29oLWnq1jEQgE6O3tpaWlRTFavZA4tZQOOzc3RyKRwOFwYLcfxeX6OFptnKmpBkymZzAYlufRz809T1HR7YiiFkkSMJvPU10lSWJoaIjJyclluxyp8Pv9zMzM4HK50mYYZLNUOUZZLAG02t8yPl7PyIgau/1XaDQzVFR8AqPRWdA9mZ+f5+c//zmBQDfvf/8DWCwLab+fmHgjkvSdrDMFsVgsK203FosRjUbZunVrzutOJpOcPXuWiYkJWlpaVlQnLBSp53e73QiCkHUexGq1rmjiu9L5C6EYy9SqZDKJRqOhq6srY48PhUJ0d3ejUqkUKXdY/FuNj4/nbaJ86tQpKioq8r63R44coampadn5RlhkK8jzJrW1tTnj5qUSpy6aZOPll1/mnnvu4Re/+AUAn/vc5wC4++67lWPe/OY3c88997B3714SiQQVFRVMT09nvdG/+tWvANixYwfXXnstzz33XF7riMVinDx5km3btuU8JleLGc53LeQN2+/3093dTW1tbVazmeXeIxsvVK5eWCwWTCYTCwsL9PY+zfbtd2EwhIlETMzNPUZZ2ZXLnj8Ugv5+qKu7nxMn/LS13aLICKbC5/Nx+PADbNnybYJBGybTdxV5PzhfdRsZ+V9aWv4OtVpkfNzNwsJ3lE17ufbtyMj7qKv76TkfjtdRUfF4XvcnFX19fTzxxBPEYjEaGk5z663fy3LMl9Hr36koRMmGfvJGaLFYFJOnp556Cq/Xy/XXX7+iklggEODUqVMpnOILkx9cqroRCoWIx+MUFRXhdrspLS1d05ZqMBiku7sbo9FIY2Nj3hzeSCTC/Pw8vb29XHXVVTnXJEmSwsO1WCyK62s+6OzsZOPGjVm7NNnw8ssvs2fPnry/Y8eOHcPj8RCLxTh9+nTGvMmlsolfxqVbFDtz5gzPPvssHR0dPPbYY5SWlvLBD36Qt73tbcr3JdcD2sGDB5dNNnJJzgJpCYUgCIRCIU6ePEl5efmKM3upkNWMUuXR5Q6xvP+bzWbl/PX1f0VJSS+iqGFq6g5qaj6x4nvE45NEo9eh0QQIBrdjt/8w45hgMMjx48eJx09is1XQ2vrmtL1YphlNTY2TSNxFTU0vhw+/GYfjY0qlfbl5zLNn76O29jOoVElGR6/E5fppXvcnFVNTU3z/+98nmQzwwQ/+B+XlU2m/lyQ4fHg/TqdLif3BYDCta221WlfVTQ+Hw3R3d6PT6Whubr7gGCIL1qTOV6Z22ux2+wXJuC9FJBKhp6cHgJaWlrxjQjQaZX5+noGBAbZv3561WDo9PU1vby8VFRW43W7m5+eZnp6mtbU1r/c4ceIEVVVVeRvKHj58eFnlraXo6upiYWGBoqIimpubM153qcSpi4ZGNTo6mqaGUV1dzf79+3Meo9FosFqtCq9vKa699tpVrWMpf3WlDXulFnNJSQm7du2ip6eH6elp2traMr6EqW6c8n+pvNDl9LcXjX+ex2AIIUkCPl8ZDsfeZa9x0X30C9jtzxII/Al7996Z81ir1crGjT/FbPZjNvsYHf0Bkch7lbao7BpqtcofeIGSEi2VlS3LrkGGXn8bsdjTCEKSsbHdOBwfRqt1Ikl/A+S3WTU0NLBjxw4OHDhAf38z999/F3feeV/aMQ7H13j55Saqq6vTDP2WQqvVnlMDyw9ms5ldu3alDZDnK3WcKo+7nOoGoHRcPB7PmrbEZa7o5OQknZ2d1NTUZMjwpdK2UlWsiouLaW1tVWiC2VSrZJ6v0+nkyJEjzM3NYbPZ8uqkvBLqVYIgKPMcsgvtSlKEl/G7iwMHDtDY2KhUGW+55RYef/zxtGTj8ccf55577gEWlYPuuuuunJ/NU6dO4ff7ufXWW+nv7+e+++5blUmbHKeyCY3kE6fkfay/v59Dhw5lTfLlYd7UKnsikUibDcslO242m9myxYxG04vRGCQe16HX//6K1yVJSaJRAb3+54hiHzZbdsdsk8mEzfZr6uruB6C7+++x2d6WEU8NhtM0NZ0gmVSxe/cTaLXfWnENAAZDQl4RkjTP3NzsOfpN/nuE0+nkTW96E0899RTf+tYd3Hrr/9DQMKj8XhCguflGnn/+m7hcLtxuN2azeU32IZmiOzk5SUdHB263O2OmLhdyyeNmM8idmpqiv7+fRCKRF0U3XxgMBrZu3aoIzWSjVi1H25JFdVLFTmSkSrnv27ev4Pi6ljSqbJBNAZPJJIcPH8bpdK6pr8orhYsm2cjWYVn6B8nnmGxQq9WIopjXHydVTm2pMlQuKb+VoNFo2LhxI1NTU8oXXaPRKBu2rLhhsVgoKysrUFpOoqTkMVSqJJIkMDy8lWh0YtmNJBA4SXn5A0iSimTyk0jSTYoiVOp9kGlGorgNGESSNExNlaLXT1FbW0tzc3PKPW1DFOcRxaMUFf1N3vemrOw6otFeJEmiqelD6PVPIkkqAoEiTKa/XPnqz63T7XZz+PBhotEoMzMO7r33Hj7+8XuU4/T6r7N79y5OnTpFPB6nsbFxzToEMke5rKyMnp4exsfHaWlpSeNZLg3UsqGT3KlaaSC+srIyYzBvpTZsIeuvqKjA4XDQ39/PgQMHKCsrU7osqbStbAEw1RBwqRqIDK1WS0VFBZFIhNOnT2M0GpeVaJTPu54P/KlBQhAEpfso34Orrrrq8gD5ZaRhrYtib3nLW3jLWxYlWYuKivI2oJWLX9nilEajSeuw5wuVSkVTUxPz8/McOXJEmf2S96xoNKoM89psNtxud0HV63D4AcrLA0gSzM/biUScyxYIJCnJ6Oi7KSvbx9TUDiorH0MQMmN4NBrF7/djNB5EpRIRBAnYz+joDtxud9qchSS5kCQLavUCicSOvNdeVnYnMzOnUanGMRpvw2LZhiSJhMMPU1T0+rzOEY1GcTgc2Gw2ZmZmeOihP+amm77Hhg2nlWOKitRceeWV9PT0MDg4mBFHLgTyPm+32+nr62N8fJzW1tYM+qgc9+XnE7mzno/xoOzVIpsGt7S0FOxRtRzsdjulpaUMDQ2xb98+KioqlC5LIpFYVm5+OUl3Wcq9srKSo0ePEo1Gqampuaj8o+x2O3a7neHhYfbt24fH48Hlym47cDHiokk2qqurGR4eVn4eGRmhsrIy6zHV1dUkEgl8Pl9eJjY6nU5R50lFNmUoefMbGRnB7XZnfXAqBDLfUt6wAeVhy+12U1tbe0GbSTj8BKWlPgQBRFGgrOz9zM3NMTU1lbWLAqDX24nHdWg0YaL+MoSidPqO3+9XVCIWW8yfRK2+BY3GRXt7Hf39/QwMDCxRrBJQq9M7JIGAj4GBRykqaqGx8XU5r0GvX2xtTk+HsFoFJAmCwf/FZDqMINyDJDUox6bKO6aqWVgsFq655hqefvrpcxUO+PWvf8XevUkkaQNQgtEI27dvZ2Jigo6ODhoaGnA6C+PeLge9Xs+WLVuYmZnh0KFDlJaWnjMi9KepbqzW0EmWEfZ6vXR1deFwOJTP6GqRTR1Eq9UyNjaGyWRi06ZNK7Z781EDARRFr8bGRiYnJzl48CDV1dU5XdoL7VQUimybvuy3Eo/HLycal5GB9SyKyXFqKXIpGBqNRgYHB9O6Cav9zGbzMxoYGECj0eDxeNZAdU/Can0ElSpJMing811JPB7l0KFDbNiwIeseE4vNUla2n2jUgsNxmHB4BJ2uKm3/T6fD/gUwSDJppLX1UwwPC/T399PW1qbstYJgRxCeR6XqRaXamffqBUGHw7HYNZmcvB29fgFJgunpz2Iy/TcOR/qziuy7kUqHldd57bXX8thjjyGKIo899h7e+94fUF9/EklSE42+rMQRWSiktraWysrKNdsLtVotbW1teL1ejh07hsViwWg0Kj5i8jqXKoLlC7VaTVNTE4FAgO7ubsxm86qMdlMhz6vK9zMYDKLVahkfH0en0+U1N5lN0n1pnDIYDLjdbqampjh69KgiCrBcjF0LNarlkHp+lUpFXV0dLpeLvr4+hoeH2bt37yWhVnXRJBvt7e309vYyODhIVVUVDz/8MN/7Xjr3/oYbbuDBBx9k7969PPbYY7zxjW/M64+s1+uJRCKKYVkqUitA8gdg165dDAwMcPToUTZu3Jg3t26psU+qHOFSWszo6CiDg4O0tbVdULKhVv+lMhCdSKgoL38dlZUapqen6ezszHigjsV8TE19Ev1sOZYvBCgehFDRdQx98IOod+1S1LkMBgOBQIBgMIDD4UQQypEkUKmgqakpL8Wq6enb2bTpZURRzeTkDykv37PstSQSn+HAgb/HYvGzYUMXgnCMYLCbqan/wOtVZ9CMssk7dnd3MzQ0BMBLL73E9u1/nnZ/BUHA5XJht9uVLkRra+sF/Q1k86eldCiv16twvB0Ox5oFC9mbY3h4mIMHD+Y9OJfKr/Z6vRnt8FR1EEmSmJiY4MiRI3kHu+XUQOT3lzX581EFWW/n1OXOv5Z848t47WA9i2JynBJFMes84FIFw61btzI8PMyRI0cypNCXgzxnl6pml8vPaHJykoGBAfR6fd48+WyIRp/CavUjCLAozf5u3O4mvF4vR48epbq6OoO6qFZbmJ3dhNXaxexsI0NDI6jVk1npO4toBA4rr3e7yaFY5SSZPB8TR0eP4vV+kljMTlPTVzCbl+feh0K/RyLxBIIgUV09gNG4kbNnbyMa/TjBYDBtX5XFMZbSdq+55hplrvS7372ZG264gZaWdOpxWVkZpaWl9PX1cejQIdra2gqSmV0Kma69lA61sLDA7OwsjY2NbN68ec3ilNlsZufOnYyPj3Pw4MG8KcCyR1Rqd+U8XTvdxwRgZmaGEydOKM8ihapWqVSqNEl3SZIoKipi48aNShehoaEh59rXm0aVLU7pdDo2bNigPGNeCrhoBsQBnnzyST760Y8iiiIf+MAH+OQnP8mnP/1pdu3axQ033EAkEuG2227j8OHD2Gw2Hn744byUbTweD5/4xCe48cYbC5KcnZ+fp7u7G4/Hk8GjTeXbywPcKpVK2WBWGjiDRTWEEydOYLfbcWeRjlsZ8xiN5+Vi5+ZKMBpHlZ9jsRinTp0imUxit9sJBoPEYt+mpfl7mO4IEj9TjMb1e6gjEVSxGNEHH4Rzrf75+UESiRuxWmcYHPwILS0fz3j3M2e6GBz8BSrVZnbt+r0MWk8oFSqCdQAAIABJREFUtAGDYQYQ8Hr/DZvtfTmvRN5gZmdnmZ//EVu3/iMaTRyVSkKSDExO3ovV+t4V75Hf7+db3zrPw21sbOSd73xnzuNnZmbo7e3NOquQC8u5sC5V3ZCdV0tKSlaskKwG8uCcIAg0NzenPRDIMo7yWqPRaMY6V1pPIpGgv78fv99fUEt8qRqIWq1maGgIvV6f8XCWSxXkpZdeYu/evXlvzC+99BJXXrm8MEIq9u/fz86dO7Nu1oIgXGwJx+UBktx4xeJUIpGgubmZX/3qV1RVVdHe3s73vvc9Nm7cqBzz9a9/nePHjysD4j/60Y/4wQ9+sOK529vbue666/irv/qrjAHu5fa9hYUFTp48SWVlZQZPPtVPSi6EyA9u8kP7Smp20WiUEydOYDKZaGxsXNUelkjUUVw8Ayx24EMhr/K9E0WRnp4eQqEQTqfz3APxIB7PJzGb5xgbuw2H46NYLKVZ1zk09BLB4FEqK99FSUl5xu9XUqwaG7ueqqoOkkkVo6N/S3V1ZqxLPdfinvsMavUBtmz5BoIgIggq+vv/GEH4INXVrSveI0mSuO+++xTlMY1Gw4c//OGcr/N6vfT09CgD/Pk8K6Tu/7Laoiz3L4uiyO8n78EGg4GmpqY1r5THYjH6+vqIRCIZ1K2loiipJrnyOld6oBZFkaGhIaanp2lqasoruYf0OCUXxyYnJ4lEIoqQSTQapbe3l3A4TFtbW0ZSL3fn8k3GC41rR48eVWT3s+Eik8W9+NWo1hNDQ0N86EMfoqmpic985jMFm6WdOnUKURRxOBzKxp2quCEbKa2mCptMJjlz5gyzs7Ns3LixoMqF1/sWXK7nlZ+PHLmW2tpH0trh8Xhc8Q9xu91otd/GqX8A4/8JErG5MRoXVbeEyUni//zPJHfu5NAhFb/5zUv8xV/8EaKoIhJxUlJyMO29g8FZEonfw2TyMTNTyZkz9+FyVaZ1OcLhRzAY7iYabUCv/zFgUnTk5Y0wlQ8sz60Eg0FGR7/Cjh0vUlU1zOJtVTE19UcYjd9YcSN86KGHGBsbU36++eabqaury3m8/EAdCARobW1NS5qytcNlGV95I1zpoVSSJEZGRhgdHV0zh/ClmJqaore3V+Gpyiom8jplFZPVQtZsN5vNNDQ05B2MUqVyR0ZGMJvNOQdgZVWQ8vJyPB4P+/btKyh5KDTZePnll3PK9l5ONi4pvCaKYlNTU3z0ox9FEAS+9KUvFSSnLRuhBYNBKioqlDgliqJSYJDj1GqSBXkPGxsby1ti9jxmMBrP77+hkI5YbCJDdlyj0RAKhc4Z5T1HSclnEUUtkYgTu/23Wc/s9fag070VjSbG9HQrVVVPZxxz/PhDJJOPEo1eA1yJzWZL8+Xw+W7Ban0GSVITCHyB4uLbldcuR9uNxyfxeG7GZAqgUiURBB0LCxvp67uHhoYrV9xvDx06pHQ3YNGo733vy12QSyaTDA4OMjs7S2tra9rfIFUW3+v1KjSjQvZ/uZt95syZNRcikTE/P8+pU6cwGo1otdoMtoLVas3bjDIbwuEwPT09qNXqgoxxU+PU9PQ0oijiXiJX7fV6lcJhKi2so6ODzZs35/1ehcaplSTgLycbFxmSySRf+cpXeOSRR/jmN79Jc3Nz1uNyKW6oVCol23W5XGveuvL5fJw6dSprOzkXjhy5h717/+3cuuFnP/sRdrtTCSypD8LRaJSTJx+kpeVzaImie7sKTcluhCIziCLC5CRTX/gWnd5GHn9czdGjMe6++09xu3uprLyW4uJPp713JNKNTvcG4nE1Ol2cRGKAM2fG8Pl8Ge7joigyPz+FKL6D8vJ+jh17O7HYHWm+G6lVAUmSOHXqFGNjT3DddV/AYAgjSSoEIUkg4GF+/lEcjrac92VsbIyHHnpI+Vmn0/GRj6zsJu71ejl58iQmkwmNRqNo3Mv30mq1XpB5ltyFUKlUBTuEZztXatVKfqiIx+NEo1Ha2tryluLLF5IkMT4+ztDQkMIbXelexONxvF4vXq+X6elpmpubKSsry5mYy8n3xMQE8Xicq6++Ou/1FbqJL1dhupxsXFJ4zcQpgO985zt86Utf4qtf/So7d+aeK5DjlPyfPPgaDoepra2lurp6zSvUwWCQEydOKEZu+eyFk5Ofx+3+J+XnI0c2Ewzen7avynthPB6nu/sUWu23aGr6EaKoY3b2/+JyZZfI9fufxWC4nWRSQJIMGI09ab+Px8Mkky3odBESCS3h8IvMzbGkyzFPPP5loJxQ6N2MjBwhFHqOcHg7RmNlzgdhSZL49a+fwGL5Dldc8Uv0+hiSpEKSzBw69I+Ulv7BsntkMpnk3nvvTfu3j33sYys+W8idLK1Wq8xapNKM5G7VauNUPB6nt7eXSOT/s/fe8W2V5/v/+2ha8pL3jh0nXhkEshgJBcIuLVBaIEBLIKFAGSUto4yW0Q9QKJQdAvlSyihlFgqlgULZ2Xt6JY73tixZli1rnfP7w3lOJFty5NVfA75eL15E1jo60nnu577v676uPtUjbKQYOGwuuiui4ybiwVijvb2dqqoqtdt3uEKwmFkSsrfhZgkVRaGxsZHa2lry8vLIzMxky5YtHH300RFfa8ONU0NJwB9Jceo7k2wI7Nixg2XLlnHVVVdx+eWXqz4dYsHu6+tTB3kHbthdLhd79uxRB3zHOuv3+/1qu27atGlBm9HAASnBs9VoNNjtz1BUtJsdO5Zx3nk3DHlMTU23kZ7+Mh6PAdeHk0j/fwfbcrKM78c/pvTka/nlTUYaG2X0+j50OiO3397Cj3+cxODfkIzbfQ1RUZ/S13cFRuO9wKENe0xMDHq9HofDAYDZvJsZM/oDhqJIaDSthz0fGzduZNOmL1i2bAVJSZ2H3lnWU1n5MBkZV4a90J555hlcLpd6O9QiHk51w+/309vby7Rp08bcBAn6F8L9+/dHTN0aqrtisViIi4sLWuicTicVFRWYzeYxVd0S8Hq9aidIGIHBoZkQu90eJD0YSNsyGAxB1KpwQaCvr481a9aQkJAQMVd5JMlGuMcfSYv4BL59caqqqoolS5Zw1llnsXz5cqxWq8qvDzRKDaTEiHjh8XjYu3cvJpOJgoKCMaduigq7zWZj2rRpQdfmQEM/p9NJb28bZ511CVot+P1QU1NJZmZW2Nfv7PwIk+k6wE9PTxbJyeuHOBo/PT3Xoddvxuf7DWbzxUH3KooHWS5Eo3Ehy3pgK1ptGk6nkz179hAVFaUqbsmyjNksUVh4IUZjHz09cZhMpWi1Q68Du3dvBW5n7tz1SJJycIZSR2Pj1dTWXkpJSUlYis1XX33Fpk2b1NvTp0/n+9//ftBjAk39BAtAJABOp5PCwsIxFToRsNlsVFZWRkzdCpRIF92VQC+r+Pj4oDU1kAI8lqpbAn6/n5qaGjo6OigsLFRj+cDZFYfDETS7KPZ8iqIMmjsUEAlZd3c3Pp+PY489NuIC9HDj1ObNm5k1a1bIeHQkxanvVLLh9/v5+uuvWbt2LX/+85/xeDxkZ2fz3HPPkZCQoC7YQ23+Ahfa4QyPDwft7e1UVFSQlJSEoih0d/e7jY6mcuHxtOH1LiQ2tgNZ1rJly+Mk9eQwGZBSUlBKSkCS+Oc/u7njjh4kSeHUUzfxxBPnDfm6AzfsNpuN6Oho9Ho9Pp+PadOmERcXh8fTCszHYHBhtc4mPv52/P4KDIbLkKTQXESv18tXX31FaelGFi/+K7m5dUH3d3Yeg8Pxd1JTUwediz179vDRRx+pt0877TQmT54cpGYx1ELY09NDWVkZsbGxI+YpDwVB3eru7qa4uFjlY4ZbCAO/+0i6K4Et8bxhaKoPB52dnZSXl6PVatFqtXi9XqKjo7FYLEM6xga2rEOpVgmsW7eO4uJi9VoINNwbCEVRWL9+/ZglG2Jo8H8IE8lGeHyr4hT0U/zWrVvHiy++SGdnJykpKaxYsYKsrCxVPWio6zmQ9jRt2rRhUbIihd1uZ+/evVgsFjQaDd3d3ciyHDQPImida9d+xYED31BcfAbz5s0f8nVbWn5HVtYKvF49jY0zcDieCmlE19S0lq6uf5GQcC7p6eHFR2R5I17vS/T2nkZn59FBG3ZZllUufmJiIi7XAczm+ciyhFar4PFUodMN7Qnk8XhYvXo1iYmvcsYZ/z4ovdsPr3cK69Y9SWZmQdjC0iOPPKL+Oy4ujsWLFwcVFQea+gUmLn19fZSXl6teDGO98RSdZmFyJ/yRBAMksLs+0u6KKL5lZWWRk5Mz5nHK4XCos6sGg0G1GgikQoeKQQPjVKjZqe7ubjZs2EBGRkbE53+4ycamTZuYPXv2ET9b+J1LNm677TZmz57NvHnz2L59Ow8++CCPPfYYxx8/tBHeQAj+nqCTjBSBF62oXvj9fkwmE729vURFRQ3qcowEHR2vkJFxI4oi0d2diMFQTmNji6rGJBaRJ5+sYd++DRQU7Gfbttm8/PKhKovP56O5uYG6utdxu5OIipqh8lfj4+Pp7PyMnJw78HiMuFyvYDYXUV5erqpE+P2deDwVyHIPsbGXAjJdXfOJi/sozFH3v+fzzz9PX18PP/7xm5SUlA96zIYNH1NcPF89R0Id6rnnnlMfk5yczHHHHRfkvh7Jhr2xsZH6+noKCgpC6uSPFp2dnZSVlWEwGFTToUgWwkjh9XrZv38/vb29g+ZRhgPR9hZdi8AgKMsyVquV/Pz8iJOaUIN5AxdysSjLskx9fT0NDQ1hVUFkWWbjxo3Duo4nko1vDb5VcQrgnnvuIS8vj3nz5tHU1MQtt9zC3XffPajqfTg4nU727t1LRkbGqDdyA+nFYq3q6+tDkiRmzJgxKrUkAL+/F7+/BEnyotH46e7+AJdrEjU1NRQWFqrzbm53Fz7fXIzGXvr6ojEat6PXRx98jdAyqYFrf1XVRyiKh2nTLsTr9QYpVnV334rZ/D69vUtwu+swmzfi8dxFYuLFQx06q1atIjFxB5dd9ioaTbDqZXX13TQ2nhbUpRWiKCtWrFAfl5KSwrx584I27IfrKCiKQltbGwcOHBi3WQuHw0FpaSkajQadTqd6rojCUiRD3EPB7/dz4MABbDYbRUVFEZm+hsJAA0KHw6HOhEiSNCRFaqhjEwpSgapVAmvXrmXy5MlUV1eTk5Nz2OtsuMnGhg0bmDdvXsh9wESycQShrq6Oyy+/nIULF3LbbbcN64Lx+XyUl/dvfouKiiLanAS2RAVty2QyBbXwDhkQ9XPk6+rqKC4uHhUHv6trDklJ+wCJ2tqfkJX1Z6C/gl9aWkpiYiKTJ0/Gaj2AXr+MuLgq2touw+e7UaXE9KttPczUqetRFA2trS+QnX2W+h5tbWeRmroFSYLGxiVkZv5JXUS6urrUlntT00qysn6HVuvD4UglLu7X+P2LUJTQczQ2m42XXnoJn8/HGWes5rjjNgXdL8sa/vOf1cTGxqr62WazmU8++UR9zAknnMCCBQtGdO5Eu1cMnY304h7KMVxIZBYVFY3LAHlXVxcVFRXq93y4BMbj8aiJxUDDpMCKpUBgUlNUVBSxDOdQScfARdnj8VBZWYnL5Rqkq+7z+di6dSvHHhvaYTgUJpKNbw2+9XGqo6ODpUuXkpWVxQMPPDAsGVq/369em5EWrkINRoejbcGh6vTUqVNHxcG3Wl8jLe06NBoFhyMJjWYbBkMCfX19lJaWYjabKSgowO/vRJLmIMv9ztttbZ/S3S2rtF1JchAV9SyyHEN+/uPExR0qFFVVrWTy5PsAhaqq6ykouDukYpXN9m8SEn6KRuPD4zGj0zWGOep+dHV18eqrrxITU83VV69Cqw1OOFyu41m79j61KyViv9/vZ/369cTGxrJ48eIRFxa9Xi+VlZV4PB6Ki4tHzLoItWEXSpuKotDZ2TmkFOxoICjA0dHREQmReL3eoO7KQMXFuLi4oDjl9/uprq6ms7OTwsLCiPdUkcQpwVbo7OykuLg4LAX7uzpb+J1PNqB/o3L//ffzxRdfsGrVqiCH2EgghmYHJgQDfTcGVljEYNzhLliXy6W2qwNVNCKFy1VJbOw8dDofsqyhqektkpPPVu+XZZn9+/fT1vYpc+Y8giT52bnzGqKjz1ePU1BiHI7jiI7u97Ho6vo9iYk/V1/H6XyG6Oj7AQ19fX/BZDpTva+rq4vy8nIyMjJITo6nsfESLJY60tI60Gg8gBmfbx8QeoHctGkTX331FQALF37BokVfqfcpCuzcuY2enh5kWVbpbWvWrGH9+n6TpGXLlo3acVtozkdKSwolkTvUhv2/0RIX1IqpU6eqnZpArq1IgoaimA0Fh8NBRUWFqoceafIeiloVblEWvyWhYa/X6/F6vezYsYN58+ZFeDYmko1vEb4TcUpRFFasWMErr7zCypUrKSkJL5IRCkLme2BCEHj9h/LdiI+PPyxtCw5Jrev1egoLC0dU6e7qmk1qaiWyrKGm5hyysl5X71MUhZqaGurr6zGZdhMf/yUWSys22/lERf1YPV6tVkt9/WKys79AUSQaG68nJ+d36uu0t99AauobKApYrYtISjokSyyKbwkJCaSkuIiLOxWNRsblisNgiMPnOwOD4WHCXY779u3jH//4B9HRDpYvfxytNvinuWPHNlwuF263e9zobZ2dnVRWVpKZmRlRNyucRG44iXSPx8O+fftGndSEQ6AQSWCsHehnNXAmcCDFbCj09PRQUVGB0WikoKAg4vgWilo1UDVRULANBkPIWZTv6mzhRLIRgG+++YYbbriB2267bUhvhlDo7e1l165dGI1GDAaDqmQkOIyRUnfCQVRe2trahmXgBNDWdhm5uf8AwOUy0ddXQ0/PoVkLMRRvNj/HlNz3kTZCT+s04hf+FWWAJ4LX+zWStByfLxej8RUkKXixlKS9gAlFyaem5iU0mtfw+xczefKyoC5HSUkJWm0P0dHFSJKMVutHkhLxev8Pv/9y9TMHmvt88sknqsPuaad9yAknbAGgr+9FFKW/xS0W2qysLLKzs3G73UPOBQwX4ZQ6ROvebrfjcDjo6ekZtkSuQFtbG1VVVRErPg0Xosvh9XrR6XSDuLYxMTGjes9A+tlw2voDq0ebN28O240KVAXJzc0lOTmZPXv2MHfu3IiPcyLZ+NbgOxWndu/ezZVXXsnll1/OsmXLhnWtut1udu/ejSRJmEwm1XcjsAgSCXUnHBRFoampifr6+mF34z2eRmJiig+6jGuorV2F0XhmkEmu0WhEq91McfH9SJJMa+sicnJeHfRaNtt1WCx/R1EknM67iYu7Tr1PlquQ5QuRJC+K8lc0mpl0dnYGDQaLLkdGhhVJ2kFu7mNIkg9F0eLxPIxOdylwiA4VOL+wfv162tvbAT+/+93/Efj1uFw9QD/Xv6ysjOTk5BF6bA0NEWvtdnuQN0SowtJwJXIFRKwVFL2x/gw9PT2Ul5er37ssy2H9rEaCQPpZdnb2II+aoRBIrdqyZcugOKUoiirlnpmZSW5ubtiO/eHwbYlTE8nGANhsNq6++mpiY2N5+OGHQ1bDxSY4kA4lpEd9Ph99fX3MnDlzWAlBpBCyd5FycPuHohLQ630A1NZOobb2z4MGzvr6rHS2XsrkxzfAZuhzZ2CMScf35JMoRx/Nzp29yPIjaLVGioqWYzQeqiD0b7JtZGUdkphzuzsxGIrRan34/Trc7j2sXJlJXp7CokX9g8WpqanU1DzI3Lmfk5DQgUbT71De0XEllZWX4Xb7VO+N+Ph4XC5XkKv8UUcdxZlnnslA+P1+1YhuoAzvWEAMX+/fvx+j0ag6/Qae09FI5EJ/t23//v309PSMetYiUMkkMAkSDsGiAjbWwUIYOblcroioVaLCJqRy9Xo9M2fODDnPISDoWzabDa1WO0Gj+m7iOxenXC4Xv/71r2lubuaZZ54JaWKmKIoq4CHWACHiIGawpk+fPuYS2eL4htONl2WZ9vZLyM39EEkCj8fA2rUfYLGkqMWaQ67mz5KY+ACg0NY2ldjYD4M8J/rXYycezzNIUjwGwzVA+GJTefkvKCx8l8bGQtLS/o3B0J9EBHY5UlPPxmKxYjB48PlM+Hy5VFe/TldXj+q9ERhPn3/+ebUC/otfPEtSkh63ez1wiCIry7JqRDfQN2MsoCgKVquV8vJytQovBvjHqrAU6P0xmlmLUBRj4RMljPaE3PJ4iLUI0Z9IPoOgGNpsNnXGVpjDDvydC9pWW1sbRUVFJCYmfmeFTCaSjRBQFIU///nPrFy5khUrVpCUlITVaiU6OjpIySJQHjfwCxeeGZMmTRqXyrTYTDudziDnSqFkFJgEGY3/4rjjHlWfW1Z2JXl5zwx6zcbGB8jc8wRR9/bRl5CIVjcLV2sruvR03K//g9NOa+bRR3/Bscduor7+JvLybgegu7sFt/tMkpObKS29nGnTHj14jE40mqnodG4qKkrY+cU7LL8ri9RYF7+9V2HeKXrs9nJaW1txuao555w7MJlcgIIkafH55uH1vgsEX/ivv/46DQ0N6u3LLrtskCO1gKDbDMd1NRRCOZyazWZiY2Pp6elR1UzGoyUuuhBCbnmozzAShRAhDzjaYBHJZ0hISGDy5MlqN8XpdA4aOBdyviLIRKJaBf2c8d27d5OamhoRBe1w6lWCl/s/hIlkIzy+k3EK4L333uPee+/lj3/8I8XFxTQ2NhIXF4fD4VDnLAJpu4HXhdPppLS0VF0fxzpOCdpTe3s706dPVwsm4URRFi48Hb2+f86htTWDuLj9g16zp6cOt/sHJCc30NU1Ca/3OaqqtCQlJZGXl4fD0UhHx8/R6VyYTE+Slna0+tzq6k3Y7WvJyvoRqal5Aa+agixLSBLY7W+QkLBIvUfMu1RXbyEp6UOOP/4jJElGp/Pj9yficLyL0Th30LkLpP0CzJo1izPOOCPkeRJJjUjMRrqZ9vv9QWt/oAGhSDqHmiUYDUQXQpi+Hm7tDFSxHEgxtlgsg7prQiSkubk5iAI8lhDzIiaTialTp6pdrt7eXjVOBQ6cizhlMBjUOCUSjoFx2uVyqfO9LpdrItkIge/kIl5eXs6HH37IZ599xvr168nOzuaqq67i/PP7Zxgi4QWK4XFFUSguLh6XH0RrayuVlZXExcXh9/uDXLhFgDGZ4gm87puatpCQEMz1lWU/7e1nkrNuA9IfwZ2Ug9FQguL1cvPuy/k66zxaW31kZVWTmdnEPfdsobj4DgA6Ol4nMfFXKAp4vWaiog6or+tyfYPb/TovPbSEu54+BT1uQMKPlgduW8+pP+mnnNXX13PgwBouuOCvZGY2qm1nRTHhdn+Aohy60Do7O/nzn/+s3tZqtfz6178Oe45E5aWzszOonRwO4XihgYvLwDmb7u5uysvL1c30eOja19XV0dLSQmFhoVrFDOW/EVhhExv2SCCChdDmH+vfq8fj4cCBA7S0tKht+piYGPWcDpxfCYTP50OWZXVhDfU4p9PJ/v37SU9Pp6qq6rCKI36/n82bN3PccaElMyeSjSMK38k4VVtby/vvv8+XX37J559/Tnp6OhdffDFLliyJ2IlZzOs5nU6mT58+Lm7EQnFPqDAFelmJOOX1PkdS0u3qcyorTyMn5/1Br9XSspKkpPvx+3X09qaTmLhRlWe1Wq2kpLzNpEkvI0nQ3LyQzMy/A9Dd3YhefwJ6vZvOzgySk7ep58bpPJ3o6J34/THYbH/Bbv8Sj+cEXK4EfD4f0dHRmEwmGhsbyMj4LTNn7gRk+pcWHV7vA/h8Nww6rwMN+66//vqwal2KolBfX09TU1NE9LNAiXS73U53d3eQV0SoOZve3l51jR8P/yVBoaurq2PKlCmq98dw/TeGQqA3R2Fh4bCEEiKB1+ultraWxsZGtRs01MB5IMQ8RzjVKujfs+3atYv8/PyIuzQTyca3GN988w3V1dXMnz+f3Nxc7rzzTioqKnj22WeHbZ4jvA6KiopGVVEIJednMBiIiYmhu7sbnU7H9OnTB/3w9PpoNdmQZXC7ewa9dmfnV8THX4S23I/xajdK3AIkYxS0tbF1xs+4cvvVuFw2ZFni17/+gKVLf4RGYzl4XI3AiWg0vfT0nE9MzHODOgEly5fyqy138hqXAXCv5j5+c2UT3qeeVj/b9u3b2br1P5x33qvk5dUE8Vy93qX4fE+rt1etWkVXV5d6+5JLLiE7O3vI8yc4sgM7BKEqLCPhhSqKQl1dHc3NzUEJwVhBURRVbllRFDQazaDgMlrqVqA3x2jmRcKZ+4mKVUdHBz6fj6KioojpYYeTynU4HNTW1jJz5ky1Ld7R0aG2rgfC6/Wyfft25s8Prfk/kWwcUfhOxqkdO3awZcsW5s+fT1FREY8++igfffQRq1atIi8vb1ivZbVaqaysDNokjgThhs1jY2Pp7e3F7/czY8aMQUPFGk0sRuMh9aaqqo/IzPzegNf2YbWeRFbWLvx+HS0tN5GScq96v8PhoK7uCUpKVgLQ3X0FFssfAOjt3UZU1NkoSr9/hqI0I8vKweO04vNtobMzmmOO+QUGgxu324TPtwez+VCnV1EU1qxZg8ezirPP/keQ2pTfvwCPZzVwaM344osv2LJli3o7NjaWa6+9dsjz19vbS1lZGdHR0UydOlVdg0IZ+w01xB0OgcPXo/2uw0EU38S8ZGB3XcSA0XbROjo62L9/f8QO4aEQLmET51MkRkVFRRFT3A4XpzweDzt27CAlJYWmpiYKCgoO+x1MJBvfMaxevZrbb7+d+++/n9NOO21Yzx0uf3Wg9Jww9QtUBxl4wQqlpIGbq507/8DcufcjSbB+/R+ZM+f6Qe/X0HAZU6Z8AEDXW5nEPzMJenuRjz+enjvu4aQftKHR+OjujuXWW2u5+OI5GAwGuro6aWsrxWJJQ5abaG9PxGbbS0bGc3g8ycTEPExKSibx31vI/NLXyKGeFtKYRD2vf/8KPG/XAId66JX5AAAgAElEQVQ2m19//TVbtqzn/PPfZvr0sgHnBPr6OoB+/5FAbfKEhASuuuqqw34Pgt/f3t5OdHQ0Ho9nxBWWcHC5XJSVlREVFTWqDoHP5wsKLi6XS6XuybJMa2sreXl540LTEw7hYqE9XDcoUH7QbrervPChEja73U5lZWXEUrwC4QwBu7q6aGhoYPr06epjRSVPq9VSVFQUVAUTg7LhBsonko0jChNx6iA2bNjAtddey/Lly7nooouG9VyPx0NpaSlGo5HCwsLDXpOhaLsDZwIG0mFEUpOfn09aWpr69+7uSaSmWoHwRTGHYxfx8afg9fZXnH2+HRiNh16joeE/+HwPoih6PJ7vMWnSLzGZYg8eq5+enmswmb6ksXExzc0/pK+vj9jYWDIyMg5WrDuJjj6afiov2O33YjTOxWAI3uh98803dHa+zEUXvYFGE/jT0+JyfQHMUf8SaNgHcOuttw55TuHQcHdzczPR0dGqkEdgdX20FX2Px0NFRQWyLFNcXDzijlagMErgEH98fDwajYaWlpZxM+sLlLGNhAIsYqo4VkGHFklQqIRNUKvMZvOwukGBcUqY3mo0Gvr6+ti7dy9z5syhr6+PyspKvF7vkHOZE8nGdxAtLS1cccUVlJSUcM899wxrYyr4qx0dHUyfPj2onTqQvxpqsxbJZkxokYuqiHiO+I5DXew+Xy9abS4mUy8+n47GxrtJS/11/4qv1dLXJ/PGG7fzox+9y9q1C4iOvgqNBkwmHbm5V5OS0sKBAzPQ6V7GYrHgcp1HRsZOZFlDY+NtZGXdhv6ee3A98zKxfe0oSPSYzET9qQ/XZT9Cr78ZRTlKPZ5//etflJaWctZZHzJ//pZBxyvUPA63iA9UsnI4HGqFJSoqivb2dhISEpgyZcqY054COwSRVI/CuYaLxNJisQyiRIyVWd9QCOXNEZgIi2pQoPygxWKJOHAJl2Nh1peSkjIiNRC9Xk9XVxfNzc1MmzZt0GOFKkh6erqq/CKSwtmzZ4d8/Ylk44jCRJwKQFdXF9deey16vZ5HH310WEIlQuWtoaGBadOmBVV0AyVSHQ5HSNpuJNeM1+ulrKwMjUaj+lNZrU3I8kmYzX3U1f2NkpITBz2vsfGn5OV9gFYr09paQFzcViTpUCJjsx1DfHwTiqKlpuZBmpqmEB0dTXf3PrTab4ATyMlZQHx8PHb7V2Rm3oAsa7BaXyQr61QA2truJibmbUAiJqb1oAnuP4iJWRh0jj799FPa2lazdOkLg+RtPZ6T8fv/BcBTTz2lKihC6GRj4Jyd3+8nNjYWs9mM1WolKioqYh+v4UL4o0yaNInMzMzDutKLYxWqiyKmCjrswO56YEIwHkPwcIgCHJgQiPgfOGshYqo41khohuJzi3geyXkKRCC1SqfT4fF4KC8v55hjjlEfY7PZKC8vJykpaZBc/LdptnAi2RgmZFnmscce4+9//zvPP/88U6dOHdbzOzs7KS0tJTY2Vr0ghhriGy4E91NsvA43tNzc/DSTJ99+8LNp6empQa/v74z4fD727VtOScnrKIqGAwfOxeG4npiYWByOLcyefRM+nx6DwY3f34RGY8Bq/QkpKV8gyxpstkcxGk/BIMVjWn4bujffRJF8KDeB9AcFBQ2KYsbr/RBF6fdHcLvdvPjiizidTk466TNOOukboN+4SVEOJRtPPPEEXq8X6M/ub7rppqAFWxgmBVaDBl7EgiM7WopbOAgTOr/fH1RZFwZ+AwfOh6qwhIPdbqeiokJV6hhrRSmR1LS1tanKW9HR0eqCPVr5QQjWbS8qKorYhTiwZe1wONS5nHCPrampoaWlhYKCAsxmM/v27ePoo48O+XidTjfmSegoMZFshMdEnBoARVF4+eWXeeqpp3j66aeDNjeRwOFwsGfPHkwmE1qtVqXtBiYWo62uCzpPJLRTWfYiSVmYTD3IskRt7a1kZNwD9G9ordZ29PqzsVga8Pv17N37B8zmE+jq6qSkZDFmcy8ejxFZ3obJlExLy4/IzPwKSVJoaDiXjIyXg96vp6eQ+PhWQMLvj8HnuwSt9o+Iy9Dr9fLqq6/i9e7nxhufDmHg5wQk6urqePPNNwEoKCjghz/84aA5u0CJ9Pj4+KCkInCjO1rDxHAQZnROpzOocCVmAsWGXQycj8Q13Ol0Ul5eTmxsbEQD5CP5DDU1NTQ2NqpxymQyBR3raNdzcZ4cDscgU9mhEBin3G43NTU1g+KO2I/U19eTn5+veot8m2YLJ5KNEWLr1q38/Oc/55prruGnP/1pyEw3UM7N4XCoLtyxsbEql3XGjBnjYsrS09PD3r171U1ouEy8p2cKycktANhsSXR0rFOpWxqNhqNn/Qjdex70a330xZ2P/sY/YI9K56uvrJx88jlYLFXYbKdisbwBgKI46O5+Eq02i66uDWRnv4PLFYeirCPKmEFXl519+z/k2GNvRKfzHWxDm/B6b8PnuxWQaGpq4rXXXgPgnHPeZc6cXQdf20xfX7vKnd2wYQMAycnJTJ8+fZC5TyTVB1HhFlWRsb5wRfW+urpadYsVA+eBnYDRyg8KD5bCwsIRJ04DlTcOucb3e8S0t7er1cixHsyD/gpPZWWlqj0/VHAI7AYJCcKsrCxyc3ORJClsAiSMEz0eDzqdLmxnYyLZOKIwEafCYN++fSxZsoQf/vCH3HjjjSGvi3C03djYWPr6+vD5fMycOXPMzdsAlVYSGxvL1KlTw163bW2vMGnSLwDw+7VUVn5MX1+C6hiekPAI+flr0Gr9dHb+lqSkmw4+1olGk48sS2g04PFswmjMpbv7FWJjbwEkbLbHsNub0OkmkZ19EZIkUVv7JvHxvyMtrQlZ1iBJ4PMtRpYfA/o7RXa7nRdeeAGtto877vgDkhRYFOtCUbT09vbS1NRER0cHkiSNeM5OVMQlSaKoqGjM9wzCc6KyslLdrB9u4Hwk7yH8l0aTOIViLUA/zTwmJgabzYbb7R4X2Xvon0mpqKhQlbeG6jiJbpCQc7fb7SQnJ6uMioG/d1F4E4wFk8nEtm3bvhWzhRPJxijgdDq58cYb6e3t5bHHHqO1tRW9Xq9yA/1+vyrnFhcXN6gKHG7OYqwgy7Jq7CNctQX6+vqw2drIzz9UCS4tPRmtdqVasW5oeIUpq29E94KMX6dD8k7GnZjN06c9xGvvRfHvf59JS8v1uFyn4Pc3k5b2eyRJIirqRZKSSvB6czAYelEUDe3tD5CScikQg8fj4fPPb+S00/5BdLTzoKqHBr//e3g8fweieOutt6it7XcqlyQ/s2fnEB9fDKA6nDocDvR6PbNnzx5VizlQRaOwsJCkpKTDPykMAucXAgf5YmNj6e7uVp1jx2MRFDMKkc6LhOuwBFaDBi6GVqtVpSSNRk44HAJdzgW1CoKrbHa7PWQ3SDx/KDUQgfr6eiorK5k0aVJIucmJZOOIwkScGgIej4ff/va37Ny5k5UrV9Lb24vP50OSpIhou52dnVRUVIzbQLEQ12hpaRnUjRfULZPpOFJS+oti3d2xNDRsUNcoSZJwuYoxm234/XpstgdISbkSRVGoqvoIo/ENUlNLaW09HZttMampCSiKj8xMBUnS0dBwE9nZa1AUiZaWZ0lPv/Dgccn09EwhIaETnc6HLEvI8lR8vn+iKDlA//xGf9HLz113/R9aLdhssygtfUalmQV210e7pgjD1+GYpYZCuPmFuLg4ent71S7HeEihu91uKisrkWU5osJVoOqi3W4Pml8U/w08r6EowGOJwEH7QDGVQF8ru92uKkSKmCr8rQ4n6S5k+6Ojo+np6QnrHzWRbIwzHn/8cV544QUkSWLmzJn85S9/obm5mcWLF9PZ2cns2bN59dVXMRgMuN1uLr/8crZu3UpSUhJvvvnmsJU6wuHLL7/km2++4b333lPl3u6++26OOuqoQd4b4SAqO3FxcUyZMmXMN2/Qv0EsKysjLi4OWZZV6taUKReSnt6iPq6m5hXS0n6s3u5on0/2uXtRosEjZ1DlP43Fu39LV0w8mug+tFo/J5+8maef/gG1tYvJz/8UgPr6H5CV9QpNTdeTnf06Ho8Zg6EPkHA6X8NsPoOOjg7WrXuWc8996qC/Rj8UJZqOjn/S1JTA++8HSx+efvrpeDweZsyYETHVZjgQVW+9Xk9hYeFhv79wMrkDOyyBEIvIeNGeFEWhtbWV6upq8vLy1HbsUMobgXMhkUB4cwi1p/EwBevq6qKyspK+vj5VglDwbUPNsAgcTg1EwG6309DQcDCp7p8ZCQzeE8nGEYWJODUENm3axOeff86HH35IaWkpeXl53HLLLSxYsCBi2q7H46GsrAy9Xk9RUdG4XBvhqVuxHHXUIRpYXd0cUlK+Vm83NDxHVtbviIrqw2bLIipqExpNHM3Nn5CaugRJUqivX0hu7jtUVv6LvLyrMRg87Nv3WwoKbqKrax6JifuQZS2trT9CpzuOhIQlSJKe0tI1mEy3UVS0O4AqZaCv7yUcjkV0dnbyzjvvIMuHaFQnnXQSsiwzffr0cfFe8nq9QYPFh1u3D7f2h2IC9PT0UFZWpnacxuP7FopSWVlZqnt3uLmQwFmLSDss4QpXY4menh4qKipwOp3qdRTIWgjXuYokTgkn+3379lFSUkJWVtag15pINsYRjY2NLFy4kNLSUkwmExdddBHf//73Wb16NRdccAGLFy/m2muvZdasWfziF7/g2WefZdeuXTz33HO88cYbvPfeeyqHcrR4/vnnSUxMZP78+ciyzJIlS1i0aBE333zzsC5O8aNqa2sLMj8aCUK1xCVJIiYmht7eXiRJYsaMGRiNRqKiooMkZnt7HUhS/3Hb7XtIsHyPqBPcKDHgYxZabTxv7JvD7e6bkaL9FBZWsmJFPTk5F9PZ+QCJiU+gKFBdvZSYmPOIikrDaIzGbr+GrKwvkSTo7p5MdPSd+P0XUlFRyRdfvM5VVz1HbGx30Oew28/jzTfPorW1Vf3bggULmD59OmVlZarr9XgYUYnN+sBKXuCA5MCKoMViGdIrIhCBtKfxqh4JE6Genh6ioqLwer0jngsJB7HQRkVFqSZII0EoHXaj0YjFYkGr1dLc3Dxs99hANZBQRkudnZ20trZSUlKiztYIc8aYmJiJZOPIwkScGgKCkjp//nzi4+NZunQpeXl5/N///d+wVIhEB7i+vn7Q8PhwEUrJSlEUYmJicLvdeL1eZs6cidlsxuksICWlSX3uvn13k539G/V2W9tJ5ORsxefT09R0Bmlpbx78+3MkJ9+DJCk4ndnExm6jpeUOMjOfP9jFKCQx8Ws8ni1A/7B4XFwVINHcfD7p6f1+Tnv3biMq6gamTdsVpD5ltZ6M1focFRUVbN++Xf27Xq/nqquuoqysTK2sj1cRUXRnA4eWhfS86FqMRiZXiAUUFBSMquMfDkIVy263Yzab8Xg8I54LCQeh+qQoyqgowAONaLu7u1U1S4PBQHNzMxaLZVgzKSJOKYqixpzA34qQQjabzeqsSOB+4UiKU0dksnHcccexc+dO4uLiOP/887nxxhu57LLLaGlpQafTsX79eu69917+/e9/c+aZZ3Lvvfdy/PHH4/P5SE9Pp729fcw3qdDfmrzvvvtYs2YNq1atIisra1jPdzgclJaWkpOTE7HiQaBPhMPhCNpUhlpYRBt26tSpZGTkIYr3vb0GJMmmPq6u7moKC1+Dx0H5fxqImonk8fBq3yk8IN1E3pQa2tsz+eabfGprX8dofAxZTiUh4Wc0Nn5DUdGbyLKGzs636eqqZsqUWzAY3CiKhCzraGj4KTU1F9LSYqWmZg/XXvssCQldQZ9NlqO4//5DRk+SJHHLLbeo0oBdXV1MmzZt3LocpaWleDweTCYTLpcrSH7QYrGMmjc7XNfVcAjksIpqkOgE6PV62traSEtLG/dOSqTeHB6PR12w7Xa7qr4STod9NO6x4YyWOjo6VNd0AdF1iouLi6hi+F/GRLIRHhNxahhQFIWnnnqKv/3tb6xcuZLi4uJhPV/MA6ampqozUodDKJ8IEaeEpHvg+mez2aioqCA3N5e8vKkELltOZydarfHgv2uIizsGnc4LaLBa/0ls7Ek4nW04HD8kO3sfbnccsvwiBsMptLV9Rmrq5Wg0Purqbqa5uZ7o6ASKin5HW9vd5OQ8j0bjx+cz0N1dTG3tA3R16WlsbKCgYBXHH79+0Gdzuap55JHgAfNbb71VNWNta2ujpKRk3LocZWVlOJ1OtaB4uO76cCE6/jqdjsLCwlHFPTG/IH4HgQqRbW1tJCYmjotCJByiAGdkZAxp+CowUNLd4/GoRrShXM4D6diBrIJIEE7S3el0UlVVxaxZs1T/EpPJpH4PE8nGOOPJJ5/krrvuwmQyccYZZ/Dkk09y3HHHsX//fqCfj3322WezZ88eZsyYwccff6yavk2ZMoWNGzeOi929wFdffcUvf/lL7rjjDs4999xhPdfv91NRUYHX62XatGlBVJ5wxn7D9YkQeuo+XwPFxdfjdkcTH78dne5QlUuWUzGbe0AG6zMlWHYei5yczPp5VaQftZesrEY+/vgmvv/9u+jtnUxMjANF0dLR8Tp6/S0kJNQAEmVlP0FRfkRDg4fqqg2YzU6uuvpF/H4DijIXt/st3nzzYxoa6rj++qdISrIDhwbtHnjgbvz+Qxd0oHSgMLkbiy5HKHO/mJgYtFotVquV/Pz8cfG0CByaKygoiOh36ff7g45VcFjDKW8IJab29vZxoz0Jbw7B9RVym4FUM7vdjtPpRKfTBXFYh+Meu2/fPmRZprCwMOKB1VAt646ODrq6uigoKAh6rPg+LBbLuFTyRoGJZCM8JuLUCLBz506WLl3K0qVLueKKK4a1tsmyrCrzTJ8+PWhDG8rYTxRqBMUkkg2wz+c7qLT3Y3Jz6wGw2WKIijrU7a6r+yWFhf0diL4+M7Jch0Zjorl5FWlpdwMKPT3JxMbupaLiQQoKnqKrKxG9/m90dq4kN/edg+fiEjSaS0hIuJnMzCoMBjeS1K9Kpde/jCyX8NZbb5GV9QZnnPGp+v4iTj333NO0tVnVvy9fvlyN3U6nc8y6HAO762ID3C8fbCU7O5tJkyaNS5IqZkwj3UiHm18I3K8MVIhsaGigsbFx2EWlSBGOAjyQauZwONSkTcSqSLuAgbL0kfhUCYSKU06nUzWrFcfZ2tpKVVUV2dnZTJ48+Yjx2fifIntFApvNxvvvv091dTUWi4ULL7yQjz76aNDjxIUQKpkajwsxECeddBJffPEFV111FZ999hl/+MMfIq6+a7Vapk2bRmtrK5s2bSI1NRWfz0d3d7fKXYyPjyc/P3/ETpwGg4FZs2bR2JjMnj1vUlJSEpRotLZ+SF7eQVMlLbh/cjveq3+CzbaFecYzMfzTAx0S5xw9n1de7WPLlgdZseI6fD4dVquHvr5FWCwv4/FEUVz8d1qa1rDv1+ez4qvr8aMh9e1W5rywnazcdeh0szj//H+xcmUzK1Ys59ZbH8Bk8iK+toyMHBoaGkN+DovFwrx58zhw4ABbt26NuMsRirYjkjaLxUJubm7QBtjr9bJv3z6VdjOWFW9JksjOziYlJYXy8nKam5uD1EYC1SwGKm/Ex8eTnp5+WOUtjUajyukFKm+N5SKl1+spLi6ms7OTnTt3YjAY0Gg0ajCMj49n0qRJEVPNQiEqKoqZM2eq75GWlkZubu5hX09QqET1yOPx4PV6Q54z8X38jy3gEzjCcCTEqVmzZvHNN9+wfPlyfvazn/H0009HrGSn0WgoKCjAarWqcyaSJKnCKKJTmZubO6gCHCl0Oh3Tp0+npeVLPvvsPszmGI4+OthfKTv7TZUKbLUWk5RkQlEUfL53MJud+Hw6HI45Bx/7ChqNH4ulg337PsJgaEaj6Z+1SEiopLPzr2i1L1NT80emTn0Xg8FDTEw5inIiPt9bnH/++Tz/fCtOZwwXXPCeegySBBdemMiKFYeSjcBNdExMDHPmzKG2tpYtW7ZE3OWQZZmenh517Q9M2uLj48nJyQnaAIuO/9atW8dFiSktLY3ExET27dtHS0uLqpYkMHDWItDgcerUqYdV3pIkiZycHFJSUqisrFRl6UdqOBgKWq1Wnd8oK+s3Dtbr9UGdtszMTIqLi0ccp/R6PSUlJTgcDsrKytT92uGYC4FxSsjkejyeoHMmSRLp6ekkJydTXV1NS0sLOTk5IzrO/zaOuGTjP//5D5MnT1aHfS644ALWrVuH3W7H5/Oh0+loaGggMzMTgOzsbOrr68nOzlbVd8ZD+WkgEhMT+fvf/86qVas466yzePbZZ5kxY0bYx4eqWJjNZtra2oiLi+OYY44Z0w2Q2FQlJiayd+/eoKqL2fwr9XGKAgkJPwKgq+05Mu/tg71Q6prG33pq+Dg+mw5O5d577yEnZxLnnPM70tKqaW/Pw+0+mUmT/sya3y7gqq8eRoOMhMKFn73L87/6OVe8+wpgJTHxOC666Je8/noijzxyF5de+hdyc5uR5ac54YQFvPXWWwAhL36tVktBQQF2u51du3aF7HIMXAQDg2EkSZter2fatGlYrVZ27NhBdnZ2yGGt0cBoNDJr1ixaWlrYtGlT0DC/UN5ITU2loKBgxG1Ts9nM7NmzaWlpYcuWLeTn55OamjrizxFonBQ4eJicnIzP58Nut1NYWDjmg3liTqquro5NmzZFzCcWlTabzUZHRwdZWVnIsjwufOoJfLdxpMQps9nMqlWreOeddzjnnHN49NFHwxqIwaF5gIF+RjabDaPRyMyZM8ecfpienk5CwhOUlpZSXl6uroEdHd+Qk+NUH+f3Xwb0U6vy8jbT12dEo1Gw2a5l//5/k5urJzbWj9cbhU5XhMfTidXahSRJTJq0lby8LTQ1baOn5/+xbVsUc+b8Fb3eB7jRas9Do/kFP/jBtbz7rgefT+aii95Xi2Jm8xlcdtl8KioqmDVr1qA1VaPRqL+HcF2OQHppYHc90kKNiIVdXV3s2bNHpbmN5fomYmFHRwfbtm1TE0kxZxcfH09ycjL5+fkj3q9ERUVx1FFH0d7ezrZt28jJyRl1vA2Unw0cOpckCavVSl5e3rDM+iJBXFwcc+fOpbGxkc2bN0esIKYoijpz09HRQUJCgiqZL6DT6SgoKPhfGw4fEkccjWrjxo0sXbqUzZs3YzKZuOKKK5g7dy5ff/01P/7xj9XBu6OOOorrrruOFStWsHv3bnXw7t1331U3r/8tlJaWcuWVV3LxxRdzzTXX4HK56OvrU+klQskgUNNaZPNjOTweDoJmY7VaKSjIIyMjT73P65VwOjvo7OwgY8/RmH/jAguUO47mqtoXOODJQJvux2Ry8eCDezj33Evx+7VotX6am1eQkHALsRfaues/D/An5WYArudZ7p99F+YtrqDj6OqK5cknfwX0L47Lli0jMTGRLVu2UF1dzcKFC8nIyAj7Ofx+P/v378dms5GUlERvb2+QaZLFYolYJSwcfD4f+/fvp6enh5KSklHNi4RT3oiJicHlcqEoyiC3+bFCoJHewApVOISTIBQc1oHzQULiUJgajpdOf6jhv4H65mKOZeDvIJwayOGkc/9/wASNKjwm4tQYoL6+nssvv5wTTjiB3/zmN/j9fpxOp0ovEd5QgXFKdFWFFGhdXR0lJSXjInox0N1cozlGlcMF6OxsorvbTUfHrRxzzLtIkozdnkxr6xcYjZeTmVmKLGtobf09JtPTxMW1I8s6mpt/TXb2w+h0XhRFg9OZyu7dv8Xp/Iozz3wryLBPlrNZufJ6rFY3en0vs2dvJSrqeubPPzPizxFoMJqSkqLuBUZCiR7qPaqrq7FarWMyL+J2u4MSISHr7/V6VTn38XAH9/v9QUZ6kVCSBHNBHK8QSQlc+wM36YIC3NPTMyza03Dg8XjYv38/LpcryDgRgouhYo4lUC7ZYDCgKErIODUxszHOuOeee3jzzTfR6XQcc8wxvPDCCzQ2NqqSglOnTqWpqUltSZWVlWGxWMjJycFkMtHW1kZeXh5vvfUWCQkJKIrCTTfdxOrVqzGbzbz00kthzb6GC0VR2L9/P2vWrOGJJ57AZrNhNpt54oknmDJlCvHx8RHRocTw+HhU1QPfo6bmlxx77Nvq31pa0jlw4B30+rXMbf4N0u2ABfy+o3i15QRua/o9UpLCOed8yi231DFp0h+IinLR3Z1GdHQbbrcZ+SojP/37C/QqZvT4UJB4Zs5NdD4dx7HHbhp0HL///V2AnujoaK677rqwxxtus240Gunu7iYjI4P8/Pxx2TSOZF5EzNyIRWWgBne4IcmMjIxx4+F2dnZSWVkZ0jcj1CIYKOsXqQTheHtzQP/Ad0VFBWazGY1GE3RuRYAJtSgHDuZptVpVDWQi2TiiMBGnxgB1dXWsW7eOp556irq6OkwmE/fddx9z5swhPj4+Igpkb28ve/fuVY05x2PN6unpYffunZxyyunq32QZ1q37mri4WPLyvkdcXDeyDHV1PyMu7mEkaRaxsZ14vUZstsdJSPgVkiSjKBINDT/HYNhKQkIZZnMXkqTgciVQUXEzFRX7uPjil9Bqg39iH3xwFjt2HHJ1DpwlDAWxlgZu1qOionA6nSQlJVFYWDgum0an00lpaSmJiYkRx8JwNOPAObvAgl13d3dQt2a8ZJErKipISEgY9B7hBEfE8UZKNxfeHAkJCSG9l8YCdrudsrIyDAYDer0+qCMkjjdUtyKcVO6RFKeOyGRjOPD7/WRlZbFx40ZWrFhBYmIit99+Ow899BA2m42HH36Y1atX8/TTT7N69Wo2btzITTfdxMaNG8fsGJYtW0ZRURHHHnssLS0tPPjggzz44IOccsopw/4slZWVaiVhtGpIgUNRYh5AlstYtOh69TFWaxVmczpW61Fka6vg+yD3SWCcyYqGE/EV65l8ywHeeusmpmY06X0AACAASURBVE5dw513PgBocLliiI52oCgSttJiDGfXYemzI6HgMMURu66Xv629mEmTavne99YAh4btDhyYxF//uhQIXsQDjd3EYHS4zbqoiHR3d4+6AxEOgiNrt9tVydRAhFPeEMcbiXus3++nurqazs7OcVM0EeaPbW1tJCUl4Xa7I14Eh/MeYkh9NC7nAl6vV+1aBFIORDW2uLh4WAPeA9VAoqKijphFfAITcWoscPPNN5OQkMCxxx6Lz+fjjjvu4JZbbuGCCy4Y1usMZSY7EoSaW/P7K1m06Br1MQ5HDHp9Ky0tHzF58k8OHoeE1fopVusHTJ78PDqdj46OKbS2nkRh4SuAhMORRVxcIyBRW/sD8vI+wGh0H6RHaaiqup7PPvPw85//P3W+Q+Cf/zyL7dv7E46f//zn6rBx4GC0qKyLtXTgZl3IoLe3t4/r+l5XV0dra2tIqfVAcZRI1AFDQRgzipnD0a7vQ71HQ0MDSUlJ6iyrXq9XY5TFYhm1wW9DQwMNDQ2jcjkXEJRIEavEDKOgSk2dOnVY5oyyLKuGnEdanPrWJxuffPIJ9913H2vXrqWoqIgvv/ySjIwMmpubOfnkk6moqOCaa67h5JNP5pJLLgEIetx4oLm5mSVLlnDUUUfxu9/9btgXh5CvHa7bdTh3a2GYI2gwzc1vIUlPUVW1jIKCc0hJScZkiu0fxNsDrtsSkDrS0c8tR7Io7K6fyb90d7PikxN4/vmrycurJS3NRmJiCxqNjCxL+K06Ov6aRGZGM5wJHKQjb9t2NF1dMZxyyhr1ONevn8+nn34fgEsvvRSHwxEk5yoW7Egq62OpWBUOouskjkl0LYRXRCjljeFCyN5ZLJYxqbqIGaHAzXpUVBQ9PT2q9Ot4DEkLl3Oj0UhBQUFECbNIigNnQ7Rardq1GEg5cLlcVFZWIkkShYWFEXHIFUWhq6uLjRs3smHDBmbNmsWll146qs86xphINsJjIk6NA+x2O9dccw0mk4lHHnlk2BRem81GeXk5kydPJj09PeLnCXdrsT6JuZBAaolGoxAVZUGr7S9Q7d79BVOnzqOzcw7Z2RUAeDwGvN52urtnkJ5ejyxraGg4n5SUj+n/yUh0dWWTnHwAAJcrFqu1gMzMbRgMPvV4rNajeOWVk1m+/Ckk6VBRrLfXwKOP3gnAokWLiImJoaurC1mWg+JUJJv1sVSsCoeenh5KS0uJiooiLi5OVQoLpG+NdrPucrkoKysjKiqKgoKCUceQQM8QsVkXUvRRUVFMmzZtTAfIBYbrcg6Hp+5aLJagYxVUZrfbTVFRUUTXl/BR27x5Mxs2bCAtLY0bbrhhVJ91jPHdTTaWLl3K7NmzueGGG7BYLNjtdvW+hIQEbDYbP/jBD7j99ttZuHAhAKeeeioPP/wwc+fOHbfjkmWZRx55hPfff59Vq1aRn58/rOe73W727t1LTEwMU6dOHbQ4ybIc5G49EvlBoeGt093LnDn/Vv9eW/sQ/vr3yL9xIzarhVNbP6PePwl9nAefTs+8eZv5xz/OOzi74UOjUdBqZSr2FFJVOZnvX/DvAe+j4fPPT2DRonVYrUWsWnWxet95551HSkpKWBpMJBiPLkeooXPoD5RiKHo8ZHLr6+tpamqisLAw4gFSsUAFmhGJ38LAzbrgXtfW1g4yNRzLz9HW1saBAwcGGVJB6A6W2WwOmg2JJBgLh9pQ9C1R7Vu/fj0bN25k27Zt6PV65s+fz4IFCzj55JPH5bOPAhPJRnhMxKlxgqIovPjiizz77LM8/fTTHH300cN6vtfrpby8HEmSKC4uHlRwGWhC63A40Gg0Q7pbC/j9Hmy2zcTFzaaqqga3281xxy1Q1alaW6fg9f6FzMyTAAVZ1rJ//x0UFPwBjUbG5TLj82kwmfqQJJn+n5GWpqZppKWVYTR61PfyeAw899wyfvnLlerf1q1bwH/+czoajYbzzjuPhISEURWVxqPLEaqoJEkSbreb/Pz8caFkK4pCS0sLNTU1w4ohoZzONRpNkHt44J5FFF1DxZCxwlAU4MDZkEBp38NRdwfCbrdTWVkZkoYmpG7Xr1/P+vXr2bJlC7IsM2fOHBYsWMBJJ530v6ZG9d1MNjweD5mZmezdu5e0tLSwi/g555zDHXfcEbSI//GPf2TOnDnjfoybN2/m6quv5vrrrx92JVW0FVtbW5kyZYrasgsc4BIL9kglRxVFwWSKCXIadziaiXk1E80jCiRDWfdRnHrgYxQNJE618dZbP6GkpBxF0QAKih90t8r87NlXqJKn8OUPT8bwuhcGdNefeOI6HI5UdDodPp+P2NhYrrnmmjFbREba5QjncB1q6FxUqATvczwqVMIdXHQHBlaPAn8Hdrtd7WCJBTuSzbpw1fb5fONmcOfz+aiqqqKrq4u0tDS1nS8qg+J4I50NCQW/309tbS133303CxcuxOfzsXHjRiorK8nOzuaEE05g4cKFzJ8/f1zEF8YQE8lGeEzEqXFGRUUFS5Ys4YILLuC6664b1romNp+igAEM8okINKEd6ZpZVXUhM2asVm8fOHA5Xm8bRUUfA9DbG4PNlkFaWr9LuMORSGxs/3n2eAwYjX3odH78fg3d3YmYTF0Yjd6g99i5swS7PZGGhhxOP/0JdDrdYaXHh4uRdjlCSeXq9fqQQ+eiA2EymcZN1UjEECEQMjCGhCsqibgaidO5iCGikDgea7jw5mhvbyc9PV1liIg9ikguIqFFh4Ogbz3wwANql2Pjxo2UlpaSnJysxqnjjjtOVdH6H8V3M9l4//33WbFiBZ988gnA/0x7eiC6u7u5/vrr8Xq9PP7444dVdQhUBRKLitvtJiEhgZycnEEDXKNFVFS0mmzIMlRWPkjxJ3fCY0AyfNJ5Nr9qeoTzUt7nL76lTJpUy9q1J6DVyni9et689iJueGkFfnRIKEjI/Ob4h7hr7R+C3sfpjOKxx27nxBNPJCMjg8zMzDGn8kTS5QilvDGcgbNA/mpxcfG4mOiJAF5dXU12djY6nU6tDAa2bkfrICsqO2NFQxto8ieqV263m/j4+Ihb1od7D4fDwcaNG1m/fj2bNm2it7eXjo4OkpKSeOihhzjppJP+11Q8Dof/2ejyP4CJOPVfgNvt5s4776S0tJRnn32WtLS0IR8fWKQRHVW3201MTAy5ubnDMkqLBEZjdJDTeEdHBfHxM9Dr+xOGhobZpKbuxmDwoigSvb0moqN7kWUJn0+HViujKAo6Xf9shs+nRZalIEoVQGlpIe+8cynJyclccskl41KIiaTLMVAu3+v1DqvAGNjFjtRQdiTo6OhQuwOCZhwoODIWRSUx3J2UlEReXt6o1/ZQ1F1JklRLgoGKUqN5jy1btrBhwwY2bNhAR0cHvb29aDQaHnzwQc4555wjSt6Wb5Op33Dw+uuvqwszwLnnnsvLL7/M7bffzssvv8x5552n/v2ZZ55h8eLFbNy4kfj4+P/aAg4QGxvLK6+8wmuvvcbZZ5/NE088wbx584DBQ9wDzf2EWY4sy+zbt4+GhoYxl6A7cOAX5OevRFFg166XyM39JZwFrAS6YKH0OS/qr+SB7t8So+tm374CzjlnNWec8W9uvvlxLnK+zX80Z/CufAE+tJyoWc+NrmcGvU9LS/rB/7egKArR0dFjvgBqtVoKCwtVX46MjAwsFktQ4iaUN0RlabgJjyRJ5Obmqprqguo2FpvbgYpWADU1Neh0OqZMmTLmqiZJSUlYLBaqq6vZvHkzxcXFw/p9Cf61WLTFZmOgdryQtdy+ffuw6VuBlKgNGzawbds2jEajSolavny56nj74YcfsmHDBhYtWjSS0zGBCYw5jpQ4ZTQa+dOf/sTHH3/M+eefz+9//3tOP71fESpQGfDQEPehQePJkycTHR2NoihUV1dTV1fH9OnTx/T4PB4dUVH9iYEsw86dr3LqqYc6E253NAZD/22fT4vB4Mbn06LRyGi1frRaGVnun8eQJNDp+qmxfj8ELqlFRZVA/6ze9u3bI3bUHg4CfTmEmlRKSoq69g80+Js0adKwBWMkSSIzM5OkpCTKy8tpaWmhqKhoTAp8A9kAonIvSRKTJ09mypQpY7qJjo+PZ+7cudTX17N58+Zh0YxhaOpudna22nETtKZdu3YNm74lCoQDKVFz585lwYIFXHnlleTm5iJJEmvWrOGDDz5Qr/1vA761nY3e3l5ycnI4cOCAqr5gtVq56KKLqKurY9KkSbz99ttoNBqWLVvG559/Tk9PDzk5OaxatYqHHnqImpqa/6r0IMCOHTu47LLLyM3NxWazsWzZMqZPnx40HDfUZrK9vZ39+/cPe3g8UrhcNhITsw8eLMgPg+Yz6PGZ+ZXvcT7oPQ+NwY8+xcurr17GiSeuRboTrvnTc7zhu4QoqY+jpF18eu7p8G7/y8gyVFdP4rXX+hWoJk+ezA9/+ENVIq6wsHDMFqaBMnm9vb0AqnNpTEzMmAaNQG344S6AMFh+NpBiFCg/a7VaVWpQdnb2uLRZnU4n5eXlxMbGhg0WgQNyXV1d6lC/ON7DVQED6VvhvDm8Xi+7du1S5y0qKyvJyclRW83z5s37X6dEjQQTnY3wmIhT/+U4deDAARYvXkxcXBxut5tzzz2XE044IYiyM9SaLSitubm5Y5Yw9fRUYzQei0aj4PFsR6OZQ3y8M+B+E9HR/d5ObrdepUj5/RJaraImGaEQeF9ZWQFvv30Zc+bM4cQTT1TXq5KSklErRAoIqk4gJUpRFLKyskhLSxsxLTocAufo8vPzD9u1GohQsyGhFK26urooLy8fF8NBAZfLRUVFBTqdjsLCwpDfyUD2wnCpu4H0rXD+H36/n9LSUrUIVlpaSkpKypFEiRoJvps0qkiwZMkS/j/27js8yiptA/g9k4H0TgIJgRQC6Qk9EUIXRbCLiCJFaaKrqGvBDwusi4AiKyugoOyyNtDVXXEFQQQpCySEFiCJIRACgTRIz6RNOd8f7Ps6k8ZMMkPa/bsurl0z7R2YnGeec57znBEjRmDOnDmora1FZWUl3nnnnVZpPThp0iSUl5djyJAhuHLlCoqLi7F+/Xr5lFlT1dTUIDU1FQ4ODhabUQekGeSnEBb25e+vdUQJ28f1gAPwXdmDmJ+zESqhRYmNK77+5hHcd98PEEXAvqjRyCgIRpY+ALc5JeCeUz8Cgb8/9+rVL6Ci4kawvfvuuxEWFmZ0UFRzvqgbbpKXZoMaOjjpVnSsqq6uRlpaWqP7LAyv1zDAmHMgoeEBSNaqX5WSp+zsbPTp0wd2dnZyclH38CRXV9dmf/aKiopw+vRpHDx4EHPnzpWTi6NHj6K4uBhRUVEYPnw44uPjERYW1t5KopqjQ0UkC2OcusWt3NPS0jB48GCUlJQgKysLH3zwAUJDQ816Ho1Gg/T0G52jLDWjDkid5Qrh4+Mv/0yvBxr6XttUgtGQL76YAq12AsLCwhAdHS3/XJrka05TjYYaeNjY2BiNo7a2tvKZGZ6enlbrWGW4zyI0NLTBMjdzGo40RCoRKygoaLAVryXUbULi4uJiVLrb1Lkh5pA6Ue7cuRPz589Henq6XBKVl5eH0NBQOU7FxMS0t5Ko5mCy0ZCysjLExMQgMzPT6Atma9XM6vV6owFk7969eOGFF/D6669j0qRJZj2X1LkoNzcXERERzToV03AVQJqtsLe/jLi4J35/nROA4mEADsDPZbdjcc4yXNIFoKKLExxd1HB3L8HWd6bgwpPBeF/9R1wWvbHGfiGiVp5B6B/S5ed5992X0bWrD+Li4hAdHW3071FdXY3U1FQ4Ojo2mTw1NLsiley4ubk1uWpxK87lMKyRDQ4OljeCStcsXa80CDZ3lUWqX5UO1rJUUDKcbSsuLpaX8v38/ODl5WXy4UlNkQKRtNSclpaG1NRUjB8/Ho888gji4+PN6kvegXS6N2wGxqlWjFMnT57E7NmzMXfuXMyYMcPs301pTGzu/jbDlu5Se1Rb2ywMHz5Hvk9trQJduzb8MZESDlMSj19+GYOEhDH44x//WO+22tpa/Pbbb1AqlU0mT421oDelgcetOJcD+D15ksqBDUuMDK9XiqvNiTFqtRppaWlNrpQ3h2HpblFRESoqKqBQKNCzZ094e3tbZFVIiuVHjhzB4cOHkZKSgtOnT+O2227D448/jvj4eKsdxNvGdc49GzeTmZkJLy8vPPHEE0hOTsagQYOwZs0a5OfnywOzj48PCgoKAABXr141ajPm5+eHq1evWmwQr/sLMHbsWOzduxezZ8/Gnj17sGzZMpMPSFIoFOjduzc8PDyQkpICHx+fJmft627mM1wFcHNzg7+///9mK+KQn+8CrXYtystVCI3aDYQCOA3cUfAL+ugzcbfiRyi0AiUl7njooX+ibKsr5lV8glp0hRJ6zKr6B1at/qOcbBw92h/V1Y5QqfSIiYmpd212dnYYMGAArly5gqSkJISFhcHFxaXeKoDh7Mrv12uauns5evbsadFyJGk2SNqLcvbsWSgUCvTo0QMeHh7NqrltjFS/eunSJSQlJSEkJMTsIC6EQFVVlVHPcMPZNul6pQ3kKpWqWSspGo0GycnJcklURkaGXBI1ffp0DBkyBAUFBXjuuefg6OhoVr9+oo6grcepAQMG4ODBg3juueewd+9erFmzxqzxRto3l5KSIp8Q3diXQcMGEw2tAvTs2RN2dnYQIhZa7RyoVDeSiMYSDeD3BMOUoT49PaTRa+vatSuio6ORl5eHY8eOyavxdVv7Stfr6uoqX6+p6u7lsPQqhzTuazQauLi44Ny5c0hPT0f37t3h6elp9vU2xdHREYMGDcLVq1eRlJTU7E3qTZXu+vr6ws7OTi7fUiqVzZp4lUqiDh8+jISEBKSlpcHb2xvDhg3DAw88gJUrV6KyshIvv/wyqqur4e/vf/Mn7WQ69crGsWPHEBcXh0OHDiE2NhYLFy6Ei4sLPvzwwzbVelAIgfXr12Pz5s346KOPEB4ebtbjdTodMjIyUFVVhYiICHTt2tVoM59hxyXDThY3+6KtVgegW7drQAWAOQB+BKoc7eBXeAVaoUKFcIaHZxEGOyRhwNUTWK1/CXooMV35GTb1nwMcu/E8H388DwUFvggMDMTkyZMbfC1p1eL69evIy8uDUqmEh4eH0SqApQZcS6xyNLQxuu7s1bVr1+QaWW9vb6vMgqjVavz222/yqlBjs0fSqbfS9VZVVcHe3t6oZ3hjf7+GJ6mHhoY2OttmeHCeVBJVUlJiUkmUEAJCiLZ2Wuqt1OmmyMzAOIXWj1MA8PXXX2P58uVYvXo14uLizHqsXq9HVlYWCgsLERkZCXt7+wY7Ljk6OhqtVjc+JgjodLnIzv4rwsI+bPF7+/nnkUhIGItp06Y1WtostR0vLCxETk4OAMDNzQ3u7u5mnRFkCkuschh2tqw77kt7REtLS+X9gNY4lwO4kTCkp6fLk36NTbwZlhqbW7orNRKRNsI3dsq5NDFo2CUqLy8PYWFhcpyKjo5uMpYyTtXXqVc2pM20sbGxAIDJkydjxYoV6N69O3Jzc+XlaakG08/PD9nZ2fLjr1y5YvZ+iuZQKBR45plnMGrUKDzxxBN4/PHHMWfOHJN/6aUZ9JycHBw6dEiehXZ1dW12xyUA0Go3AbgXcAIwEcAhoGvXWrxatBKb9E8iH3oUFbrjmqMXEvWxcIAaNbDFf/XxONezL/ohA0IABQU+6NKli1Fnk7p7LaRVC09PTwQFBSE3NxcFBQUICAiw+FKyuaschieHSkGxodmVurp37w4PDw+kp6fL9Z2WPg3V0dERAwcORE5ODpKSkhAcHAwvLy+5RE66Zp1OJ19vv379zGpDaGNjg759+8qnnJeWlmLo0KFwcnJCVlaW0cF5dnZ2iI2NRXx8PF588UWTS6IUCkVnXJImajdxCgAeeeQRxMXFYcaMGRg1ahRefvlls/ZTdevWDRqNBgkJCVCpVPLJ4YarqaZTwMbGF1275pn/RhoghC3i4+Plv8ubHULXq1cvFBUVITs7W+4eaUnNWeWo23DEsLNlY+O+h4cHhgwZgvPnz+PEiRNWKTO2s7NDTEwM8vPzcfz4cbnDl5S8Sdcrtfd1c3NDUFCQWaW7SqUSAQEB6N69O3777TecPHkSkZGR8PLykkuipC5RADB48GDEx8dj9uzZZpVEdeJEo0mdemUDAEaMGIFPP/0UISEhWLJkCdRqNYAbLT+ljXdFRUV49913sX37dqxdu1beePfcc8/h6NGjt/R6q6qq8NJLLyE7Oxvr1q2r13GqbgtCaUCRVi0cHBxw8eJFODg4oG/fvhboR12DgoJvEWAzDxgH4DIALeCrv4p8dIeAEgrc6FuuggZKAAro8I7PYjx/dQ3y8z2xYcOzuOeee+SzIgwPempq9kraMOfl5YWAgACrfBltaJXDcBWgtLS02SeHGpJ6kUvdWSzdEUutVuP69evIzs6W94ZIs22WOpeltrYWp06dwpdffokdO3agS5cuiIiIwLBhwzB8+HAMHTrUKnthOglmWo1jnGpjcUqr1eLtt9/G/v37sXHjRvj5+dW7j3SIp/RlUjqIVhrzpYQpNDS0ReOTEFooFN6ws6sxazO4Ib0e+POf38Ltt4+Hq6srSktLUV1dbdIhdFVVVUhNTZX3JlijmUVDqxw3azjSnNPOpWYqPj4+Ft+TIJVwFRYW4vLly3I1gBSn3NzcLFJqrNVqkZKSgm+//RZff/01lEol+vbtK69axMXFwdnZmZNbzcMN4o05deqU3OEjKCgIf//736HX6+u1HvTw8IAQAn/4wx+wc+dOODg44O9//zsGDx7cKte9bds2vPnmm1i6dCmEEFAoFOjevbv8xbepFoRSz+ucnByEh4e3eGVAoymFi4svcAbAUAAKYEH1OvwLD6EI7tChC9xRiGJ4AFBgDPZiY4956JOTiXXrnkJhYQ9MmDAB3bt3N/ugJ71ej4sXL6KoqAjh4eFW6cBUU1ODq1ev4vLly1CpVFCpVEZt8lpycqghrVYrl7uFhYWZvD+nLp1OZxTEq6urjUq4qqurkZmZ2aLExrAk6vDhwzh69ChKS0sRHR2N4cOHo1+/fvjoo49gb2+PzZs3c+BuOf4FNo5xqo3GqQMHDuDZZ5/FSy+9BE9PT1RUVKB3795Qq9VGewJdXFwa/CKZl5eHrKysJsteTCWEDg4OzTuDqrzcHp9++meMGTNGjqvmnBxu2LBF2nNoabW1tcjPz8fFixehUCigUqmMTri2RAMP4EbMzczMRHFxMcLCwpq1B0J6nrqTdoalu1L5d0sSG2miLSkpSS6Jys/Pl0uioqOj8dVXXyE7Oxs//PCDxQ8R7oSYbLSUVK5jY2MDlUqFY8eOoaioCI888sgt73O+Z88efP/99zh8+DAuX76M8PBwLFiwAOPGjTPri29FRYVJm8dNUV19AcAouI8tBs4BNdVd4CGKUQV7GH/+BBQQeMfxNbxa/h7+9KclGDVqFIYOHdrs1wYgbwBr6XvR6/VyWz/DdrnSzFVRURHUajXCw8ObnQzcTFFREc6dO2fyJvWGlsalgCidxVGXRqPB+fPnUVVVhdDQ0JuuOEj11HVLouLi4hAfH4/hw4c3uO9E2pNCLcZko3GMU//TluLU0aNH8fXXX+PQoUO4cOECgoOD8cQTT+D+++8364tvVVUVUlJS5NKZlpSpaLXecHZWm/241asXIjZ2cov/ftRqtXxIX0s2djfVftbFxQXl5eUoKiqyaseq8vJypKWlmdz1sKnSXcMzowzpdDp5MtGU9yKEQE5OjryR+9ixY1AqlfLBefHx8Q1+P2CcshgmGy0VEBCAY8eOGXVLeOWVV1qlz/m+ffugUCgwePBg2NnZYeXKldi+fTs2btyIwMDAmz+BAenkcbVajYiIiBbtGVCr0+GZORCKEYDQAgvEOmzEU6j7+bNDJRapVuKZ3NVYt+5lzJs3zyL1rIYlT6YmA3UPT5JqQptqlystJVu6Y1VD76XumRnSfhbDZKglS+PFxcVytxE/Pz95Zqe2trZelyh/f3+jg/NYEnVLMdloHOPU/7SlOJWUlITi4mIMHToUrq6u+PTTT/Hxxx9j3bp1RmdUmEIIgaysLFy/fh0REREtGntyc/+KoKDXzHrMn/60BC+99JLFVgakkqfw8HCTVgakvQtSrDKlXa5UZmzpFuiNvZfQ0FB5xaaxZEhKLMwt3ZX2A7q7uxvt3ZFKoqSD89LS0tCjRw85TsXGxrIk6tZistFSDQ3irdXnvCGJiYmYP38+Fi5ciEceecTsx0snUEsbiE3R8ABoj9i3hwM7gQR9LEbiILT1+hAIvID30Wt1FoBgzJ8/3+zrbYr0BbpXr17w9fWVBxppQ580ANZt52pOCVdzEpvmKCoqQlpamrzBvLa21uhkVkuceK7T6bB9+3YsXboUcXFxyMzMNCqJkrpEceNbq2K0bBzj1P+09TiVlpaGWbNmYcqUKXjqqafMHrtKS0uRlpaG3r17m1wCKnVcksb9G2WqM+Djk2PSa1ZUdMF3323ElClTzLrWm5FWBqTTtA3jlGE717KyMigUCqPValPbz96qczlKS0uRkpKCLl26wMbGxqyzQ0yl1+tx9OhRzJ8/HyNGjMDly5dRUFAg7wuMj49HVFRUZzg4ry1jstFSgYGBcHd3h0KhwPz58zFv3jz5UDaJ1Hrw7rvvxqJFi4xaD65cudLqdbNlZWVYsGABFAoF3n//fbMHltraWqSmpsLW1hb9+vUz2sjWUOcNaQCUBhRpAKw6txMeQx4CtEB35OIautd7rbvwI2KXnMDDDz+MgICAFr3vhmi1WqSnp0OtVsPd3R0VFRXyhj5pdsUSA6AlVzmaCjI1NTVy6+Lm1shKpH0u0qrFyZMnYWdnh4iICBw6dEj+vHLlok1hstE4xqn/aQ9xqrq6Gq+++irOnz+PD2ugAAAAIABJREFU9evXmzy5JZHGdp1Oh7CwMKMZ8rqdAcvKyuSOS1Kcksp1ysvD4O19+aav98UXU3D//Z9abWP3+fPnUVRUBE9PT1RWVsp7Fwz3s7T0tS29ytFYVyspsQsPD2/WAY2GhBC4evWqXBJ1/PhxKJVKxMTEICkpCeHh4Vi7dm2L9/KQRbH1bUsdOnQIvr6+KCgowPjx4xEaGtrofRtK4G7FMp6Liwu+/PJLfPbZZ5gwYQI+/PBDs2pMu3btipiYGPmQHT8/P7nPuWHnDV9f30Y7bwCAfb8JqLxnNBz+vQ8vYSVexep698mGH2JxApWVldDpdBboilU/yEjvKTc3FwEBAS3el9IQNzc3DBkyBBcuXMCJEyfMWuWQDlKUkgvDrlY9evSol/CVl5c36yAnqUuUlFycP38eAQEBGDZsGGbMmIG1a9fK16zT6bBx40ZUVVUx2SBqZ9pDnLKzs8OaNWuwfft23HPPPVi2bBnGjRtn8uNVKhUiIiKQn58vxym9Xl+vM6C3t3eTHRedndOQmxsDH5/zTb5eZmY4zp49i7CwMIt0Q6qpqTFqk67T6WBnZ4e8vDz4+voiKirK4qvITk5O8kGvx44dM2uVo6nS3W7dutU7/buyshJpaWk3PduprqZKoiZPnoz33ntPXskXQuCrr75CUVERk412gisbzbBkyRI4OTnhk08+aTPL03WdP38eM2fOxKRJk/Dcc881OXgZnhYtfVHX6/Wora2Fh4cH+vTpY9a5CwCAoiLYBfpDp1WgKzT1blahAp999SMGDx6M7OxshIWFmbVvo6kv6tJysxRkNBoN0tPTodfr682EWdLNVjmkxE26ZukgRWmlxZTN/dKyeEFBAUJDQ+v9nQkhUFxcjMTERCQkJCAxMRHl5eVGJVGhoaEsiWp/uLLROMapBrSHOJWbm4tZs2YhIiICb775ZpNf5htq667T6aDRaODs7Iy+ffs2q+OSfDhtg68J7Nq1A2FhYbhw4YLZp1xL7WcNz4ySGo5IqxZSPNLpdDh//rzcgMRSJ3XXdbNVDsOzLUpKSkzax1iXtCpx5coV9O3bt8EW/RUVFUhKSpInwVgS1SGwjKol1Go19Ho9nJ2doVarMX78eLz55pvYs2dPm+1zDtz4cvvGG2/g5MmT+Oijj+Qg0lBrVGnVQupmYWNjIy/xVlRUNGvw67JwIVSffgoldKj/GRS4fr0Y9vb2qKysRGpqKtzd3RudsZd6stftZCENgKZ8Uc/Pz0dmZqZZ+1LMJQWM8vJyBAYGyqstht1CpISoJbNkarUaR44cwfbt2/HEE08gOTkZiYmJOHXqFOzt7Y26RHl5eXGDHG782wwePBg9e/bEjz/+iIsXL2Lq1KkoKirCwIED8fnnn6Nr166oqanBjBkzcPz4cXh6euLrr7+2SqmfmfgP2DjGKbTfOKXX6/H+++/jX//6FzZu3Ig+ffoAuPH7Kk0oGa5aGJYXqVQqefP4tWvXEBER0bz258IZ9g76ej/W6YCamgooFArU1NQgNTUVdnZ29VadJRqNxmjVojlf1KVuhObsSzGX4aRVUFAQtFptvdJdKSFqSdOY6upqnDp1Cp9//jkWLFiA1NRUo5KoIUOGYPjw4RgxYoTVTidvbzpqnGKyYYLMzEw88MADAG5k/Y899hgWL16MwsLCen3OXV1dMWjQIJSUlMjtB1UqFWpra1vlgyKEwBdffIHFixcjMjIS+fn5WLVqVb3NZk39kkubx/v06SOfUmuS2lrYu7tDCS2A+gmEWl1ldJ1St5GwsDAIIYw6WRj2ZG/JIXS1tbVIS0uDSqVCSEiIxWZODDchlpSUQK1WQ6PRwMPDA/7+/nBxcbHIaoJhSdSRI0dw7do1pKen4+GHH8a0adMwdOhQq82ItXerV6/GsWPHUFZWhh9//BFTpkzBgw8+iKlTp+Kpp55CTEwMFixYgPXr1+P06dP4+OOPsXXrVvz73//G119/3dqXzyjcOMYptO84BQA7duzAM888g9DQUFy5ckU+Jd3U84zKysqQmpparzGIKRRfb4XtzNn1Dv07caIPwkKTId1gOGMvHTYojfnl5eWwsbGRr7clh9BJ+1K0Wq3FyreA+hUBFRUVqK2thYuLCwICAuDm5maRvSlarRZnz56V41ROTg7S0tIwYcIEzJ49G7GxsRZpbtIRddQ4xWTDwtrSB+XcuXOYPHkyAgMDER0djaSkJPj7+2P58uVmfyGVvqR36dIFISEhJg9IqilTMHP7fdiKOXVuUUOtvvHlW2o/W1paiuvXr6OiogJOTk7w8fGBu7s7HB0dLVr2I4RAbm4uLl26hJCQEHh4eJj9HFLdbd0NctJskJ2dXb2VIXM7VjVUElVWVoaYmBh5NigkJASXL1/G/Pnz8d5775ndUrKzuHLlCmbOnInFixdj9erV+M9//gMvLy/k5eVBpVLhyJEjWLJkCXbt2oU777wTS5YswW233QatVosePXrg2rVrrR0YGZUbxzhlprYUp0pLSzFy5Ej4+vpi0KBBOHv2LLp27Yo1a9aY3RJdp9MhPT0dGo0G4eHhpk9KVVfjHr+d+EU9zejHzz67GO++Oh/w9IRWq5UnlIqKilBaWgp7e3v4+vrC3d3dIg1H6rp27RrOnz+PoKAgdO9ev9HKzRiW7paWlkKr1dYr3RVCtKhjlRAC5eXlcklUQkICrl+/jsjISKOSqMLCQjz77LOYNWsW7rrrLrPfS2fQkeMUkw0LamsfFOnf1rCl3tq1a/H555/j448/bnLzYGPPJ83qhIeHm3wKqr2jI5QwXqLWQIVTSYkNtp9VqVQ4f/48KisrER4e3qJl3KZUV1cjNTVV3sjWWAJluEHOcKXFsGd4Uyskpnaskk5mlWpYzSmJkk6Rp4ZNnjwZr732GsrLy7Fq1Sps3rwZcXFxOH/+xubQ7Oxs3HXXXTh79iwiIyOxc+dO+Pn5AQD69OmDxMREs2q1rYD/uI1jnDJDW4tTwI2xz/CL+pYtW7By5Up88MEHzTrwtaCgABcuXDBrMsnO0RE1lYDhPFy+0gU5R39BacWNUirDrlZdu3bFpUuXUFhYiPDw8OaVb5mgtrYWv/32G5RKJUJCQhpNoAxbuzendNfUjlVCCFy5cgWHDx9GYmIijh8/DhsbG6OSqKZWlhirGteR4xR331jQ888/j3fffRfl5eUAbpQfubm5yV9E/fz8cPXqVQDA1atX0atXLwCQB4TCwkKLflDq/kIrFAo8++yzGDVqFJ588knMmjULTzzxhMm/+AqFAn5+fnB3d0dKSkq9/uAN0el0KB83Dtijw41SKjUAJwjo4evr2+hm5ZCQEBQWFuLEiRPNntW5GTs7OwwYMABXrlxBUlKSvEndcINcaWkpampq4OTkBDc3N/j7+5u9/Ct1rDp//jyOHj0Kd3d39OvXDzU1NTh16hQSEhKQkJCACxcuIDAwEMOGDcOsWbPMKonqiIN3dXU1Ro4ciZqaGmi1WkyePBlLly7FrFmzsH//fnnWc/Pmzejfv3+jJyL/+OOP8Pb2xqBBg7Bv3z4ATXfiaa0uPUS3QluLUwDqxYBHH30UcXFxmDFjBm6//Xa8+OKLZpX3eHt7w8XFBSkpKSgsLESfPn2aXHXQ6/W4tmIFeji8hNxcD3h6lmHRotfxGD5Hdx8f9DVoOGIoKCgInp6eOHPmDPz8/Kyy76Br166Ijo5GXl4ejh07Jm9Sr1u6a9ja3c/Pz+yVFqljVVZWFpKSkuDg4ICoqChotVqcOXNGXrVIT0+Hr68vhg0bhocfftioS5QpOtpYyjhlGiYbFtKePijR0dE4cOAAXnjhBTz++OP48MMPzSolcnR0xODBg+V2rxEREfKX4rrnRAgh4Pruu0j7y8vw+eJTOKECFwPHQHNWjZt14fb09MTgwYORlpaGa9euNTmr0xLdunWDXq9HcnIyAMDW1lZetejZs6dF9kAolUp4eXkhISEB7777Luzs7GBra4v+/ftj+PDhWLFiBUJCQtglyoCtrS327t0LJycnaDQaxMfHy8vv7733HiZPnmx0/59++gkZGRnIyMhAYmIiFixYgMTERBw6dAg//PADduzYgerqapSVleH5559HSUkJtFotVCoVrly5Al9fXwA3vmxlZ2fDz89PTjybU2pH1Na0pzgVGBiIvXv3YsmSJbjvvvuwceNG+XfUFHZ2dhg4cKDc7tVw83iDDUfGjUPi8x+g2scJNijDFPyEsLIzwE2SHFdXVwwZMgQZGRk4deqU1Vbj3d3dodVqkZaWBr1eL7efdXNzQ2ho6E33XppCoVCgW7duSE5OxpIlS+RD+qKiojBs2DC88cYbiIyMtMqZI+0V45RpmGxYSHv7oDg4OGDDhg347rvvMGnSJLz33nvy4U6mUCqV6NOnD65evYrExETY29vL/cJdXV3h7e1t3GN7wzJgwzLUADA9XABdunRBdHQ0cnNzcfz4cYSEhLSor7a0QU4KNGq1Wm6ZGxkZiZKSEly7dk0+S6Qlr3PhwgWjkihHR0fExcVhxYoV+PXXX3HhwgWsWrWqtZc92yyFQiEfYKjRaKDRaJoMptu2bcOMGTOgUCgQFxeHkpIS5ObmYvny5Vi+fDkAYN++fVi1ahW+/PJLPPzww/j2228xdepU/OMf/8B9990HALj33nvxj3/8A7fddhu+/fZbjB07tk3PGBGZqr3FqS5dumDZsmXYt28fHnroISxevBh33323yY9XKBTw9/eHra0tjh8/DltbW+j1ernhiIeHBwIDA3+fxFoWDixbgGoAUWZcp42NDUJDQ3H9+nWcOHHC/GYqdTRVuhsWFobKykrk5OSgR48eLYqHTZVEvfXWWzhz5gwOHDiAZcuWITAwsNmv05ExTpmGezasQPqg/Pjjj3j44Yfx0EMPyRvvoqOj8fTTT2PdunU4c+aMvPHuX//6F7755ptWud4rV65gxowZiI2NxWuvvdbo/oPa2lp58DM8J8LZ2RnXrl2DnZ0dQkNDrTbrUV1djZSUFDg7OyM4ONikVQDDVoRSQHV2dpZnhBrqyy7Vrnp5ecHf39+k16mpqcHJkyflkqjMzEwEBQVh2LBhGD58OIYMGVJvhSQpKQn9+/e32rkfHYFOp8OgQYNw/vx5PPPMM1i5ciVmzZqFI0eOwNbWFuPGjcOKFStga2tr0onIhr+bmZmZckvBAQMG4IsvvoCtrS2qq6sxffp0nDx5Eh4eHti6dSuCgoJa669A0najSOtjnGqG9hanioqKMHv2bHh5eWH58uWNNtyQGo5I435tba0cp0pKSiCEQHh4uMU6PNXVnI6HTZXuNtYyt6qqCqmpqXB2dkafPn1MirsajUbuElW3JCo+Ph6xsbH1YuKZM2cQEBDQosm3jo5xSsYN4rdSe/yg6HQ6vPPOO/j555+xceNG9OzZEzk5OVAqlXKLPJVKZdTWz/BLshACOTk5zTqgzxxCCFy+fBn5+fn1OmdIG+SkQVvafN6cVoR6vR4XL15EUVFRvc1/QggUFRXJiUViYiLUarVcEhUfH49+/fqxJMqCSkpK8MADD+DDDz+Ep6cnevTogdraWsybNw99+vTBm2++iUmTJuG1114zGsTfffddDBo0qJWv3iKYbDSOcaoZ2mOcEkJgw4YN2LRpE9avX4+wsDBcvXoVSqVSPpC2bsORuiVN0ubxfv361TtszpLXmZubi8uXLyM0NBRubm5Gt0nlxlIXRsOzLVxdXU0u3RVCIDs7G7m5ufUOeZW6RB09elReYS8sLDTqEsWSKMtinOIG8Vtq9OjRGD16NIAbG8jqHpRkuKHI1tYWd911F4KCglr18JaKigrExsYiKysLI0aMgLOzM+6//3489dRT6N27N5ycnJr88qxQKNCzZ0+4ubkZdbSw9LKetCzu6emJlJQUOZiUlpaiqqpKPpywqc3nppDKxLy8vLBixQpotVqEhITg6NGjSE5OlkuiRowYgUWLFqFbt25tegnzVmpsw1xLPt9ubm4YPXo0du7ciZdeegnAjVrZJ554AqtWrQLwe8mHxLAchIiMtcc4VVlZidDQUAwfPhx33XUXHB0dMXr0aCxatMjkMd/b2xuurq7y5nFTV8nNoVAo5Ja4KSkpsLOzg7OzM0pLS41Kd729vdG3b99mf+FXKBTo3bs3PD098cknnyAjIwO33XYbjh8/jhMnTsDGxgZDhw7F8OHD8Yc//MHs80c6MsapW4tTr61A2lCUnJyMU6dOYefOnUhISMCrr76KF154ARkZGXB3d8emTZsAAJs2bYK7uzvOnz+PF154Aa+++qrFr+mf//wndu3ahYkTJ+LgwYOIjY1FYWEh3N3dzTqQztHREYMGDYJOp8OJEydQXV1tsWusra1FQUEBzp07h7S0NPkcitzcXPTu3RtxcXGIiYmRDydqbgCpqalBQkICPvjgA8ybNw+7d+/GgQMH8Je//AWTJk3CkSNH8N///herVq3C/fffzxO667DU5/vatWsoKSkBcKNk4JdffkFoaChyc3MB3Ji5+/777xEZGQngRg3rZ599BiEEEhIS4OrqCh8fn1b4GyBq/9pinNq1axe+++47xMbG4tChQ7j77rtRVFQER0dHs8Z8W1tbDBgwALa2tjh27BgqKiosdo0ajUY+HyMlJQUajQYVFRXIzs5Gjx49EBcXhwEDBiAwMBAeHh7NTjQ0Gg1OnDiBdevW4amnnsKWLVuQmpqKt99+G0OHDsWvv/6KhIQE/PWvf8UjjzzCE7rrYJy6xYQQTf2hOvz9/cXnn39usedTq9ViwIABIiEhQXh6egqNRiOEEOLw4cPijjvuEEIIcccdd4jDhw8LIYTQaDTC09NT6PV6i11DQ/R6vfjb3/4mBgwYIP773/8KtVpt9p8rV66IX375RWRmZpr92IqKCpGfny/S09NFYmKi2LNnjzhw4IBITk4Wly5dEiUlJUavs2fPHnH+/HlRUVFh9utcvnxZfPPNN+LFF18Uw4cPF/379xezZs0Sn3zyiUhLSxM6nU4IIcTBgwfF3Llzrfr33tG05POdnJws+vfvL6KiokRERIRYunSpEEKIMWPGiMjISBERESGmTZsmysvLhRA3PrNPP/20CAoKEpGRkSIpKakV3rHV3Gys7sx/qI7OEqeEEGLbtm0iKipKbN++vVlxKi8vT+zdu1f89ttvzYofBQUFIiMjQyQlJYk9e/aIffv2iVOnTomLFy+K4uLieq+TlpbWrNfJyckR33//vXj11VfF6NGjRXR0tJg2bZpYt26dSE5OFlqtVgghRHJyspg6daoct+jmGKcsptFxmmVUraTuhqI+ffq0eq9zQwqFAk888QSGDx+OmTNn4v7778czzzxj1mqBu7u73Lr2+vXrTW6W0+l08l6LkpIS1NTUyLNVNyvjkl4nPT1dPgW1sU3X0qne0n4LqSTqtttuw8iRI5ssiYqPjzerY1dnZonPd3R0NE6ePFnvuffu3dvgayoUCqxbt85K74io82nrcQq4MVM8ePBgzJw5E7/++itef/11s5puODs7Y/DgwcjIyEBycnKTm8f1er3R2RbmlO5Kr3PhwgWcPHkS4eHhje7N0Ov1uHz5srzX4sSJE1CpVHJJ1HPPPQcfH58G41R0dDS2bNli8vvvzBinbh0mG63ExsYGp06dkjcUpaWl1btPW+h13q9fP+zfvx//93//h8mTJ+Ojjz4y64C9Ll26ICoqCrm5uTh27Ji8ebzueRwA5NNZfX19zT7bQqVSISIiAvn5+UhKSoJOp8OIESNQXV1t1CXq4sWL6NOnD4YNG4Y5c+ZgyJAhVjuhvD3Jzs7GjBkzkJeXB6VSiXnz5mHhwoVYsmQJPvnkE3h5eQEA3nnnHUycOBEAsHz5cmzatAk2Njb461//ijvvvFN+vvby+SaixrWX32NfX1/s2rUL7777LiZOnIgNGzaYtZFdal177do1HD9+XN48LnVgNDqP439xql+/frC3tzfrPdrY2KBfv34oKirCyZMnUV1djTFjxkCj0eD06dNycpGeng4/Pz8MGzYMU6dOxerVq+X2qp0Z41T7xWSjBSorK/Hoo49Cq9Xim2++MepYZCppQ1FCQkKb7XXetWtXrFq1Crt27cL999+PpUuX4o477jD58UIIODk5wcvLCydPnoRCoYCzs7O8Qc7oPI4WEELAxsYGeXl5eO+996BWq+Hk5ISBAwdi+PDhWLVqFfr27csuUQ1QqVR4//33MXDgQJSXl2PQoEEYP348AOCFF16QN7tJUlNTsXXrVqSkpCAnJwe33347zp07V6/+uD18vok6ss4Sp5RKJRYtWoSxY8di+vTpePbZZzF16lSTHy+EgL29PXx8fJCSkgK9Xi+3n613HkcLSHGqrKwM7733HhYuXCjvIRk2bBjeeustREREsEtUAxin2i9+62qmvLw8jBo1Cr6+vvjhhx/MGsAb2lAUFhaGMWPG4NtvvwWABg9vAdCqh7fceeed2L17Nz755BMsWrQINTU1Dd5Po9Hg+vXruHDhAo4fP46jR4/i8uXLsLW1xcCBA+Hr6wu9Xg8fHx94eHg0O9HQ6/VIT0/HP/7xDyxYsADDhw/HtGnTcPr0aSxduhTz58+HSqXCa6+9htmzZ/OE7ib4+Phg4MCBAG4s90stJRuzbds2TJ06Fba2tggMDERwcLDczaa9fr6JOprOGKeGDh2KAwcO4Ndff8XcuXNRXl7e4P10Oh2KioqQmZmJEydOyOcjKZVKxMTEIDAwEDqdDj169EC3bt2anWjo9XpkZWVhy5YteP755zFq1Cg8+OCD2LdvH55//nm8/PLL6NKlCxYuXIinn34a0dHRTDQawTjVfnFloxlSU1Px5ptvYv78+c3quJGbm4uZM2dCp9NBr9djypQpuPvuuxEeHo6pU6fi9ddfx4ABA3DHHXdgzJgxyMnJQW5uLr777jsEBgbi448/xvjx45GVlYWAgAB88803cHd3hxACCxcuxI4dO+Dg4IDNmzfLv5iW4u3tjR9//BFr1qzBXXfdhfXr10MIgbKyMri4uKCsrAxKpVLuGe7n51evTMnFxQXFxcVITk5GQEAAevToYdJrV1dXy0EhISEBWVlZcknU3LlzGyyJuu+++8wq+yIgKysLJ0+elDu+rF27Fp999hkGDx6M999/H+7u7rh69Sri4uLkxxjWtpr6+Z49ezYAYPbs2Zg+fTqCg4PlXv5E1DKdOU45Ozvj888/xxdffIEJEyZgzZo18PDwQF5eHjw8PEwq3ZVOGE9JSYGvry/8/PxM+nJZtyTq3LlzcknUo48+ir/85S/1kr67776bX1zNxDjVvvBQPzMFBASguroa3bp1Q0JCglXrKHNzc5Gbm2u0ZPj9999j8+bN8PDwwKJFi7BixQoUFxdj5cqV2LFjBz788EPs2LEDiYmJWLhwIRITEy1+XadOncLevXuxY8cOHD9+HH369MG8efMwadIkuLi4mDwro9Fo8Ntvv0GhUCA0NNRohUMIgevXr8uJxdGjR1FZWYmBAwfKBxKxJMryKioqMGrUKCxevBgPPvgg8vPz5Q3zb7zxBnJzc/G3v/0NzzzzDG677TY8/vjjAG4MxBMnTsRDDz3Uyu+gw+I3kcYxTtXBOAWkp6dj9+7d+Pnnn3HgwAH4+/tj+vTpmDp1KlxcXExeUdfpdMjIyEBVVRUiIiKMNo8LIVBaWmp0cF5xcTGio6PlOBUeHs6VCgtjnGqzGo1T/KbWDCtWrEBUVBRuv/12FBcXW+11Glsy3LZtG2bOnAkAmDlzJr7//nsAN5YMZ8yYAYVCgbi4OJSUlMi9ni3p2LFjcHd3x/r165GdnY3+/ftj165dUCgUZg2q0uZxT09PzJs3D1999ZVRSdTjjz+O48ePY8yYMdi2bRtOnDiBTZs2sSTqf7KzszFmzBiEhYUhIiICa9asAQAUFRVh/Pjx6Nu3L8aPHy9/RoUQeO655xAcHIzo6GicOHHC6Pk0Gg0eeughTJs2DQ8++CAAoHv37rCxsYFSqcTcuXPlJejOejARUXvR2ePU6dOn0aVLFyxbtgz5+fmYOHEidu7ciZqaGrNKd6XN47169cJLL72EjRs34quvvsLChQsxcuRIPPTQQ9i3bx8GDx6MLVu24NSpU/j888+xYMECREVFdfpEg3GKAPCcDXNJ/ct1Op2YM2eOiI6OFnl5eVZ/3YsXL4pevXqJ0tJS4erqanSbm5ubEEKISZMmiYMHD8o/Hzt27C3r4fzNN9+I6Oho8csvv5jUN7ywsFDs3r1bvP3222LSpEli8ODBwsfHR4wfP17s27dPVFdX35Lrbs9ycnLE8ePHhRBClJWVib59+4qUlBTx8ssvi+XLlwshhFi+fLl45ZVXhBBCbN++XUyYMEHo9Xpx5MgRMXToUPm59Hq9mD59uli4cGG915CsXr1aPPLII0IIIc6ePSuio6NFdXW1yMzMFIGBgXKfd7KK1j7Loi3/oToYpxr2yy+/iKioKPHPf/7TpDhVUlIi9u/fL1asWCEeeOABMWDAANG7d28RGxsrfvrpJ1FRUXFLrrs9Y5zqVHjOhqUplUp88sknePHFFzFy5Ejs3r0bvXv3tsprVVRU4KGHHsIHH3wAFxeXRu8nWrE128MPP4zY2FjMmDED8fHxeOWVV+TZI9FASVRVVZVcErV69WoEBwdDr9fjnXfewenTpzFq1Khbct3tmY+Pj3zyaN0ZxX379gG4MaM4evRorFy5stEZRR8fHxw6dAiff/45oqKi0L9/fwA32gdKM3UKhQIBAQHYsGEDACAiIgJTpkxBeHg4VCoV1q1b1+ln8IjaGsYpY+PGjcPevXvx5JNPYs+ePVi2bJm8V0P8ryQqMTERR44cwdGjR41KopYuXYrw8HAolUp89NFH+O9//4sJEybckutuzxinCABXNtq62tpacccdd4iPalAiAAAgAElEQVT3339f/lm/fv3kTD4nJ0f069dPCCHEvHnzxFdffdXg/W4VjUYjlixZIuLi4sTbb78tpk+fLmJiYsSIESPEK6+8In744Qdx/fr1W3KybGfS1mcUqcVae/WgLf+hVtbe4pRerxdr164VAwYMEH/605/E7NmzxYABA8Rtt90mFi5cKL755huRk5PDOGVhjFMdXqPjdOcuem/jhBCYPXs2wsLC8OKLL8o/N2zBVrc122effQYhBBISEuDq6irPKNwqKpUKb731FubNm4esrCw89dRTSExMxIEDB7By5Urcc8898PT07LSdN5588kl4e3sjMjJS/tmSJUvQs2dP9O/fH/3798eOHTvk25YvX47g4GCEhIRg165dDT5ne5hRJKKOqT3GKYVCgWeeeQYrVqxAamoqHn/8cRw8eBCHDx/GBx98gIcffrjRE7o7A8YpsrimMpFbnBFRHQcPHhQARFRUlIiJiRExMTFi+/bt4vr162Ls2LEiODhYjB07VhQWFgohbszWPP300yIoKEhERkZyNqAN2r9/vzh+/LiIiIiQf/bWW2+J9957r959U1JSjOpNg4KC6tWbtrcZRWq21l49aMt/qBUxTnU8jFPUTFzZaI/i4+MhhMDp06dx6tQpnDp1ChMnToSnpyf27NmDjIwM7NmzBx4eHnjyySfRvXt37N+/HxcuXMCZM2cQFBTUrG4PZD0jR440+dTRpg4kAtrnjCIRdSyMUx0P4xRZGpONDmLWrFnYuXOn0c9WrFiBcePGISMjA+PGjcOKFSsAAD/99BMyMjKQkZGBjRs3YsGCBa1xyWRg7dq1iI6OxpNPPikH26tXr6JXr17yfQwPJAIgb5bbu3ev0dL2okWLsHv3bvTt2xe7d+/GokWLAAATJ05EUFAQgoODMXfuXKxfv/7Wvkki6tQYp9o3xilqLnaj6iBGjhyJrKwso581p9sD3XoLFizAG2+8IR9I9Mc//hF/+9vfblq7Ks0oNmTPnj0NPnbdunWWu3AiIjMwTrVfjFPUElzZ6MDy8/PlgdnHxwcFBQUAbj4TQcYa2izX3AOJGsIDiYios2KcsgzGKWrLmGx0QjebiSBj1l76Nzw999///rccLO69915s3boVNTU1uHjxIjIyMjB06FALvjMioraJcco8jFPUlrGMqgPr3r27vOycm5sLb29vAJyJMJcll/4fffRR7Nu3D9evX4efnx+WLl2Kffv28UAiIuqUGKcsg3GK2jImGx2Y1O1h0aJF9bo9rF27FlOnTkViYiK7PTSDuUv/0n23bNlS77lmz57d6OssXrwYixcvtuSlExG1GYxT1sM4RW0Fk40OoqGZiEWLFmHKlCnYtGkTevfujX/+858AbnR72LFjB4KDg+Hg4IC///3vrXz11hUQEABnZ2fY2NhApVLh2LFjKCoqwiOPPIKsrCwEBATgm2++gbu7e4tfi0v/REQNY5xqHOMUdWRMNjqIhmYiAPO7PezcuRMLFy6ETqfDnDlz5HZ07d2vv/6Kbt26yf8t1bIuWrQIK1aswIoVK7By5UqTn49L/0RE5mGcahrjFHVU3CBOMp1Oh2eeeQY//fQTUlNTsWXLFqSmprb2ZVnFtm3bMHPmTAA3alm///57sx7f3g4kCggIwJ///GeMGTMGTk5OiIqKwunTp7FlyxYEBwfD1dUVc+bMgVarbe1LJSJqFOOU6RinqM1o6nhxqx9sTm3K4cOHxR133CH/9zvvvCPeeeedVrwiywgICBADBgwQAwcOFBs2bBBCCOHq6mp0Hzc3t0YfP3XqVNGjRw+hUqlEz549xaeffiquX78uxo4dK4KDg8XYsWNFYWGhEEIIvV4vnn76aREUFCQiIyNFUlKS9d6YGfz9/UVwcLBITU0VtbW1Ytq0aSIoKEjMnTtXVFRUiEuXLgkvLy/x5ZdftvalUsNuNlZ35j/UiTBONYxxitqARsdpllGRrKFNY4mJia14RZZx6NAh+Pr6oqCgAOPHj0doaKhZj7fU0n9rmzdvHsLCwgAAjz32GL788kskJCTA0dERjo6OGD16NJKSkvDYY4+18pUSETWMcaphjFPUlrGMimSijW4a27lzJ0JCQhAcHCz3CTeHVIvq7e2NBx54AEePHpVrWQEY1bJ2ZIbL5A4ODrCxsYGXl5fRz8rLy1vj0oiITMI41bExTnVMTDZI1hY3jbW0PletVssDk1qtxs8//4zIyMhGa1mJiKjtYpwian9YRkWyIUOGICMjAxcvXkTPnj2xdetWfPXVV616TUePHkVwcDCCgoIAAFOnTsW2bdsQHh5u0uPz8/PxwAMPAAC0Wi0ee+wxTJgwAUOGDGmw3SIREbVdjFNE7Q+TDZKpVCqsXbsWd955J3Q6HZ588klERES06jW1tD43KCgIycnJ9X7u6enZYC0rERG1XYxTRO0Pkw0yMnHiREycOLG1L0PWVutz25usrCyj/x49enS99oGbN2++dRdERNRMjFMdE+NUx8U9G9SmtcX6XCIiIgnjFFHTmGxQm2ZYn1tbW4utW7fi3nvvbe3LIiIiAsA4RXQzLKOiNq0t1ucSERFJGKeImqZoqNbQQJM3EhHRLcEC8MYxThERtb5G4xTLqIiIiIiIyCqYbBARERERkVUw2SAiIiIiIqtgskFERERERFbBZIOIiIiIiKyCyQYREREREVkFkw0iIiIiIrIKJhtERERERGQVTDaIiIiIiMgqmGwQEREREZFVMNkgIiIiIiKrUN3kdsUtuQoiIqLmYZwiImrDuLJBRERERERWwWSDiIiIiIisgskGERERERFZBZMNIiIiIiKyCiYbRERERERkFUw2iIiIiIjIKphsEBERERGRVTDZICIiIiIiq2CyQUREREREVsFkg4iIiIiIrILJBhERERERWQWTDSIiIiIisgomG0REREREZBVMNoiIiIiIyCqYbBARERERkVUw2SAiIiIiIqtgskFERERERFbBZIOIiIiIiKyCyQYREREREVkFkw0iIiIiIrIKJhtERERERGQVTDaIiIiIiMgqmGwQEREREZFVMNkgIiIiIiKrYLJBRERERERWwWSDiIiIiIisgskGERERERFZBZMNIiIiIiKyCiYbRERERERkFUw2iIiIiIjIKphsEBERERGRVTDZICIiIiIiq2CyQUREREREVsFkg4iIiIiIrILJBhERERERWQWTDSIiIiIisgomG0REREREZBVMNoiIiIiIyCqYbBARERERkVUw2SAiIiIiIqtgskFERERERFbBZIOIiIiIiKyCyQYREREREVkFkw0iIiIiIrIKJhtERERERGQVTDaIiIiIiMgqmGwQEREREZFVMNkgIiIiIiKrYLJBRERERERWwWSDiIiIiIisgskGERERERFZBZMNIiIiIiKyCiYbRERERERkFUw2iIiIiIjIKphsEBERERGRVTDZICIiIiIiq2CyQUREREREVsFkg4iIiIiIrILJBhERERERWQWTDSIiIiIisgomG0REREREZBVMNoiIiIiIyCqYbBARERERkVUw2SAiIiIiIqtgskFERERERFbBZIOIiIiIiKyCyQYREREREVkFkw0iIiIiIrIK1U1uF7fkKoiIqCmK1r6ANoxxioio9TUap7iyQUREREREVsFkg4iIiIiIrILJBhERERERWQWTDSIiIiIisgomG0REREREZBVMNoiIiIiIyCqYbBARERERkVUw2SAiIiIiIqtgskFERERERFbBZIOIiIiIiKyCyQYREREREVkFkw1qt3r16oUTJ040eNtrr72GDz74wKTnGTp0KFJSUix5aURERIxTRGCyQe1UcXExcnJyEBoaWu+2a9eu4bPPPsP8+fMBADU1NZg9ezb8/f3h7OyMAQMG4KeffpLv/9JLL+HNN9+8ZddOREQdnzlxCgAef/xx+Pj4wMXFBf369cOnn34q38Y4Re0Zkw1ql86cOYPAwEA4ODjUu23z5s2YOHEi7O3tAQBarRa9evXC/v37UVpairfffhtTpkxBVlYWAODee+/Fr7/+itzc3Fv5FoiIqAMzJ04BN1Y6srKyUFZWhh9++AGvv/46jh8/DoBxito3JhvULp0+fRp9+vTBwoUL4eXlBV9fX+zevRsA8NNPP2HUqFHyfR0dHbFkyRIEBARAqVTi7rvvRmBgoDyI29nZYdCgQfj5559b5b0QEVHHY06cAoCIiAjY2toCABQKBRQKBS5cuACAcYraNyYb1C6dPn0ax44dw8SJE5Gfn4/58+dj5cqVAG7MJoWEhDT62Pz8fJw7dw4RERHyz8LCwpCcnGz16yYios6hOXHq6aefhoODA0JDQ+Hj44OJEyfKtzFOUXvFZIPapTNnzmDx4sW48847oVQqER4eLt9WUlICZ2fnBh+n0Wgwbdo0zJw506iO1tnZGSUlJVa/biIi6hyaE6fWr1+P8vJyHDx4EA8++KC80gEwTlH7xWSD2h0hBM6ePYt77rlH/tnZs2flgdzd3R3l5eX1HqfX6zF9+nR07doVa9euNbqtvLwcbm5u1r1wIiLqFJobpwDAxsYG8fHxuHLlCj766CP554xT1F4x2aB25+LFiwCA4OBg+WcnT55E//79AQDR0dE4d+6c0WOEEJg9ezby8/Px3XffoUuXLka3p6WlISYmxspXTkREnUFz4lRdWq1W3rMBME5R+8Vkg9qd06dPIyoqCgqFQv7ZyZMn5UF44sSJ2L9/v9FjFixYgLS0NPznP/8x6v4B3GiNe/z4cYwfP976F09ERB2euXGqoKAAW7duRUVFBXQ6HXbt2oUtW7Zg7NixABinqH1TtfYFEJnrzJkzRrM7hYWFyMvLQ2RkJABgxowZ6N+/P6qqqmBvb49Lly5hw4YNsLW1RY8ePeTHbdiwAdOmTcMPP/yA0aNHw9fX95a/FyIi6njMjVMKhQIfffQRnnrqKej1evj7++ODDz7AfffdBwCMU9SuKYQQTd3e5I1EbdX//d//wdvbG88///xN7xsbG4tNmzbJQYCoDVLc/C6dFuMUtUuMU9TBNBqnmGwQEbV9TDYaxzhFRNT6Go1T3LNBRERERERWwWSDiIiIiIisgskGERERERFZBZMNIiIiIiKyCiYbRERERERkFUw2iIiIiIjIKphsEBERERGRVTDZICIiIiIiq2CyQUREREREVsFkg4iIiIiIrILJBhERERERWQWTDSIiIiIisgomG0REREREZBWq1r4Aos5ACAG9Xg+9Xg+dTge9Xg+tVgshBBwcHKBSqaBQKFr7MomIqJMyjFNSrJL+ODg4oEuXLoxT1CxMNogsRAjR6GCt1+uN7icN2DqdDlqtFlqtFiqVCjY2NlAqueBIRETWUXfyq6k4pVAo5Bil0+kYp6hZmGwQmaluUiENwnq9HkIIXLt2DQDg5eUlD9ZKpbLejJA0sCuVSggh5AHdxsZGXungLBIREZmrbpyqm1SUlpaisrISvr6+TcYpSd3EQ6lUyisdjFN0M0w2iBrR1EBtuDoB/D4QS4OxUqmEjY2Nya8lPVYIAZ1Oh8zMTPj5+cHOzg42NjYczImIqJ6GSnSlWFWXFGdsbGzkibLmxim9Xo+LFy/Cw8MDzs7OjFPUJCYb1Kk1VPqk1Wrl/294P1Nmf1pKeo3CwkL4+PhAo9EYlVhxMCci6lzMLdG9VXGqpKQETk5OjFN0U0w2qFNoaqAWQgAA8vPzUVtbi969e7dosJaer6WUSqVcYiUN5oYlVkRE1HHcrEQXAIqLi1FYWIi+fftaPakAfi/3bew16sYpjUYDlUrFOEVGmGxQh2LuxjdpsAZ+HzQtsfGtpYOsYcJiuHTNzeRERO1bc0t0pRIoS8QpcyfFGoppN4tT0uQY4xQx2aB2p+7sT1VVlTxA1x1ApQHS2rM/1lD3epsazLlJj4io7agbp2pra6HRaOT9Eob3uxWlTw0x9bUau1/dxEi6r+H+Q51OB6VSKScdjFOdE5MNarOa6vlt6NKlS3B0dIS3t3eH+dLd1KxT3cE8LS0NISEhrJclIrrFTCnRBYC8vDzodDr06tWrzX7plkqh1Go11Go1KisroVarUVtbCz8/P/Tq1QsqlWlfG+tuJk9PT4efnx8cHBwYpzohJhvU6qRB2nAmxJyNb9J/t5XBq6HZnsaYM2PU0GMVCgWKioqM9nUw6SAisqyWlOgCN+KUXq9vE+OyEAI1NTVQq9W4fPmynFjodDp07doVDg4OcHR0hJeXF3r37g0hBIqLi5GYmIhu3brB398fdnZ2ZsWpkpIS+Pr6cv9hJ8Vkg26Jhja+FRUVwcnJyeg+dQfqjjoQ6XQ6VFRUQKPRwMHBocXvUwpq3ExORNQ8DZ1NUVJSAltb23r7DtpDia5Op0NVVZXRSkVVVZV8u42NDTw9PeHm5gYHB4cGVy2keB0QEIDevXsjLy8PJ0+ehLOzM7RarVnXI8V17j/sfJhskEWZMvsjOXfuHAYNGtRmViUs1UXKkEajkZeipcG+pqYGSqUS9vb2AICsrCz4+/vDx8fHKGlozt8JN5MTETXN1BJdAMjMzETfvn1hZ2fXJuJUQwzjjPS/UpxxcHCAg4MDnJ2d0b17d9jb20OpVCI/Px/V1dXo0aOHya+jVCrh6+sLHx8fFBYW4vTp00hNTUXfvn3h5ubW5N+PYUzj/sPOh8kGmc1SZ1MYDjxtRXNb3dbW1soJRUFBAbRaLS5fvgyVSgVHR0c4ODjA09MTvXv3RteuXaFQKOQgp1KpcOnSJRw5cgQ9e/aEn59fs6/F8H3UHcx54isRdSYtLdEFLBenLNGhsKamBmVlZSgvL0d6ejrUarU8oSTFGQ8PD/j5+cHW1vamX/6bS6FQoFu3bvDw8ICvry8uX76Mc+fOISAgQN472dDrmbqZ3MbGhqXAHQyTDWqUYVIhDQTSjIlh+ROAJgfrjkKv16O6utpo9qiyshJ6vR62trZynauzszOcnZ3h6+tr0vPa2tqiX79+CAoKwpUrV5CYmIja2lrU1NTIqx/NVXeT3rlz59CzZ0/Y29tzMCeidq+hEt3q6mpUVFTAxcVFvk9bKNE15Qu+1GHRMMYYxhlp3O7RowccHBzQpUuXZl+PJRIgJycnxMTEoLKyEpcuXcKFCxfQq1cv+Pr6mnw6uWGcEkLg0qVLcHNzg6urK+NUB8Fkg8wqfSovL0dZWRlcXV1b4Uob1lDL25bQ6XTyAF+3ztXOzs5oBknqrGEoKyurwUFWr9dBCD1sbBoODiqVSq6LPXjwIE6ePAkXFxcEBgbC0dGxRe/JcDN5jx49uJmciNoVc+JUVVUVcnNzb1raYwpLxxeJNHlnGGeqq6sBAPb29vLkVbdu3eTJIQAoKipCYWGhFWOwHoAGgK1J95b+fh0cHBAWFoba/2fv3YPbOM+70d/ifiXuAIkbAZCUKImyJdmylbZHSU4r2/WXccfx1zqO+zkZ23GaxjOZ1OnE09ZuPKdfnHwZf9M/3JykSV07nYmdyyTNqY+O69zcuA5FirLuN4oESPFOAuAFJO7Anj/UXS/ABbALLIAluL8ZjSTyxbsvFovneX/v8zy/J5vFzMwMhoeH0dPTA7/fD6VSybmYHADW19eh1+ul+sMOgkQ2dgnYCt+oP3x6UwiZ998sI84V5fUU8Xgc+XweS0tLJXmu3d3d0Gg0vN57+X3L5TYQi70GYB0Gw0MwGPZVfC2V7nTs2DHEYjFcvnwZSqUSwWAQZrO53rcL4NZzIHV8lSBBghghVIouZavFYM+oFNvV1VUkk0nE43Fks1nI5XLaz5hMJvT09ECr1XJasxDvi833kmQGq6v/imRyFXb7h6DR3M57DpVKhb6+PgQCAczNzeH06dOwWCwoFAqc113up6T6w50PiWx0GCoVvi0sLMDpdJaMZYaVxWCUm4HyegqKXJTnudpsNqjVt05y/H6/4OvIZKYBrAAwIpUahcGwrybRkslkcDgccDgcWFtbQyQSQS6XQzAYhN1ur7u+pFqRnmTMJUiQ0GywpejW8lN8Up9a7c9IkkQ6nd4mBlIoFKBUKqHX60GSJPR6PYLBIF23126UryGdXsTcXBQEoUc2ex6hUG2yUel9yOVy+P1++Hw+LC0tYXZ2FpcvX0YoFILRaOQ8r1RM3hmQyMYOBV/N75mZGfT09DR8XaGjEULNRZLkNjUOtnqKanmumUyGt5QfV6jVXqRSFhSLCWi1h3i/3mw24/Dhw9ja2sLU1BQmJibg9/sF+0ylYnIJEiQIDT6pT0L6KeraQoDp84rF4rYU22QyCZIkS1JsPR7PNinZxcVFZLNZ+lBLjJDJbCCILhQKG1AqqxMNrqDqSyKRCDweD8bHx0GSJILBIKxWq1RMvksgkQ0Rgy31iRlSpr54XArfhPoiCkk26lkTVU/BJBXr6+uQyWQwGo016ylaAbb7o1RaYLd/HsViFnK5geVV3KDX63HgwAFkMhlawSqbzdJRCS5rq3Tfy4vJV1ZWAAA2m00y5hIkSGCFUCm6zN83ikb9VD6fp30MpfxEHcJotVro9Xro9Xo4nU5aSrYToFYb0Nf3ELLZFAyG2im7fCXarVYr7HY7EokEpqamaAUrl8tVsY9JOcqLydfX15FKpdDd3S35KRFDIhsiQLmhpv6em5vbpoFNfdE6/UtVrT9FeT3FysoKNBoNXC5Xu5dNg92RqiCXqwSZn6lg9e6772JkZAQOhwO9vb1VT874dHxNJBIoFovo6uqSisklSNjlqJSiOzc3B5fLVWIXdkKKLjPFlnmARdWwUdFwlUqF3t7eiqfwXCG2+1DJF2i1Omi1Os7z8KnDoMYajUYcPHgQ6XQa09PTCIfD8Hq98Hg8UCgUnIgiNVcqlUI8HofNZpOKyUUMiWy0CGyFb7VSn+bm5uDxeNq46u0QMrJB5bmWG3y2egpmf4pmrmmnQaFQQK1W49ixY1hYWMCZM2dgMpkQCARYFaz4nERJxeQSJOw+8E3RXVhYoE+VxYLy1KdakuU6nQ4ulwt6vX5bim0ikajZs4IrOs1PNfp+NBoN9u7di1AohJmZGYyMjMDpdErF5B0IiWwIDDbNb8pQs4WUW5H6JCTqWVMlY7+1tYUbN27AYDDUrKfYSWiHQ5HJZPB4PHC73YhGoxUVrOohG0D1Ij3JmEuQsLMgZIoutclrN5gpthsbG1hfX8fp06cBcJMsl1Af+OwJKo1VKpUIhUIIBAKYn5/H9PQ0JiYmMDAwAJ2uepSlVjG5VH8oDkhko05UOv2Zn5+vmPrUKZuySo6FrZ6C0g1nM/YXLlzA0NDQjicXbGiXUSMIoqqCFV+ywbVIjyIdkjGXIEE8aEWKbqsjy7lcriS9tjzFVq/Xw2AwYGtrC7fffrsgflcMZEpoCPGehL4vMpkMXq8XS0tLsNlsuHTpElQqFYLBYMW+IrX8VLFYpJ8PqZi8fZDIRhVU0vxmK3wDbj3g8/Pzokt9EhIEQaBQKGB9fZ1TPUW1/hS7Of2pFWAqWEUiEUxMTCCbzaJYLHI61eNaTE7lPq+trcHhcEjGXIKEFqLdKbrNUiikpGSZtXuUlCxFKiql2GYyGaysrAhCNDrZljX63vgWiPOB3W6H1+vF2toawuEw8vk8AoHANtl3Pn4ql8shGo3CarXS0Q4JrYFENlC58K1QKGwbuxMK34RApf4UmUwGhUKBbkhUq55iJ6GZhrMSWnE9vV6PoaEhZDIZvPfeezh16hQ8Hg+8Xm9VBSs+HV8B4Nq1azCbzVKRngQJTYBYU3QbIRvFYhGpVIr2MalUCmNjY7SULEUqenp6oNfrOSnuUWsSG8S4JjGDul9sh2aU7DuVwsfVTxEEgfHxcRw5cgSFQkGqP2whdhXZYEt92traQiqVokN0tTqTtmPNzVwDl+I5Zj1FOp3G7Ows9u7d2/C1231vm4V2kBYuUKvV0Gg0uOuuuzA7O1tTwYrP+2BKWkpFehIk1A82P5VOp7G2tgabzVYyVgwpulzIRj6fZ02xJQiCjobr9Xqo1WocPnyYM6mohmb02WgUYork12PfG5mDD9jmZR6aUbLvbrebdySdJEn6NZKfah06jmxU0/ymCt8oEASBVCqFWCwGq9XaxlWzg1mk1yhIkkQikeBcT1EpzSaTyYiuqZ8YN/ZihkKhQCAQgN/vr6pgVe+zV62YvNMjghIkcAUzml4rRTebzWJ5eXlbd+1GIJRvKU+nLCcVuVyOjoTr9XqYzWa43W5oNJpt15+ZmRF0TRKqQ6h7LTSqfXZM2ffZ2VmEw2H6QFSj0XCam5mdIhWTtwYdRzZeffVVJJNJPProo/TPqhW+iUVJgw31GMxK/SmSySRmZmY411PsNIj1MxQzKilYhUIhmEymhjcjlYrJpSI9Cbsdb7/9Nt59910888wz9M+qpejK5XJBbVwjB1kkSSKVStF+JpFI4OLFiwAAlUpFH1w5HA4EAgGoVNx7C4mRJIjNTonp/jRrLUwlxEqgDs1kMhnW19dx9uxZGI1GBAIBGAyVG+eWP/dSMXlr0HFkA7jV5IWrrN1OJBuV6imq9acYGxvD/v37m7amds8lJuy091SuYBUOh5HL5aBSqSoqgPCdv7xIT2oSKGE3gyAIJJNJXn6KWewtxPWLxWLVDV2hUKDrKZg1FQBK6il0Oh1CoRC6uroEWZdQ0W6xReCFhJhsZqsjG2wwm80YGhpCLBbD1atXIZfLEQwGYbFYOM8h+anmouPIhkqlQjab5TyeMrpiBTMyUaueohMlZHcqWmWYhHaCzGK8c+fO4dq1a8jn8+ju7m44CsYs0tva2sL4+DgOHTokFZNL2HVQqVTIZDKcxwvtp5iHbMxoOFMIpFxd0OVyQavVbrMD8Xhc0Ai5lFrbGnCNbBWLReRyuYpzNAP19IMiCAJ2ux12ux3r6+uIRCIYHx9HIBCA0+nkXZ9CELeUN9977z3cddddUjF5g+g4stMbzKMAACAASURBVKFWq3kZcaFPjOpBpf4UyWQSkUgERqNRNM2IOv3ESMItULKSZrMZGxsbGB4e5qRgxRVMZyIV6UnYbajHTzViK0mS3HZodeHCBVqRh1mz5/V6eXXMFpIIiY20AJ0bgS8HFcna3Nykn5NUKkW//66uLoRCoW0pSq0qEOcz1mQy4dChQ0gmk5iamsLk5CR8Ph/cbndd62AWk0vNbOtDR5KNSiycDc0wJJW+KJXqKSr1p7h06RL27t3LqhTUTghpxDsRrXRMzVS+IkkSKpWKLsabmZmpqWDFZ27qNEoqJpew21CPn+KyoaekZJmkghkNp0iFWq3G4OBgiSBEvRA6FVmMaVRCQCy2jDrc3NjYQD6fx8rKCtLpdElTxK6uLvT09ECj0dC2OZPJ4OrVq1AoFHRdX7PQKNmgoNPpsH//fmSzWdy8eRPDw8PI5XLI5XKcMkHYismlZrb1oePIBt80qmZENtLpdIl2eK16ikoPqtChcyE2ptKXihsalRTkimaTDWpuhUKBYDCI3t5eVgUrvo69WpGeVEwuodPBN42qfENPybYzSQWlLqjVamk/Y7fbodVqt0XDY7GYYCezu6GObydK3zIzJqg/TFKRz+eh1Wrh8/lYlcGYayYIAjabDTabDWtra5icnKQV1JoFIcgGBZVKhf7+fgSDQbz77rsYHR2F1WpFIBCAVqvlPHd5MXk2m5X8FEd0HNloVXi6Un+Kra0tXL9+HXq9vuF6imYYcSHIhhhPsXYihHjvrSIbFJgKVisrK7SCVSAQ4LWOSuuWivQk7Aao1eqah2LU888kE+fOnUM2my2RkjWZTBWlZCtBrARBjAXiYrc5FPFk/qEyJqh9CJvc8M2bN6FSqaputoHtfspsNuPIkSNIJBIYGRnByMgIQqHQts7erQIfHyiXy6FSqfChD30IS0tLOH/+PHQ6HYLBIIxG47bxlUQUKvkpqf6wMnY92agVPahUTwGw96c4f/48hoaGBMlrF6MRFxJCfiHF9t6YoIzR5ubmtmgXSZLwer3w+XyCPDNCopoRJwgCTqcTTqeTVrDa2trCysoKJ6dTSwmHWaRH9YhZXV2l75NkzCXsZDD9FEmSrAdXhUIBSqWS9jEKhQL79++n+wA0ArH6FjH6KbGAaowYj8exvr6O8+fPI5PJlBBPi8XCu+amXlC1pENDQ3Rn72AwCJfL1VL7XM+BG0EQ6O7uhsvlwurqKsbHx0GSJILBIKxWKz1frbnL/VQ6ncb8/DxCoZDkp8ogrt2NAFCpVLxyYak0Kr71FJU2SkKmZYk1iiA5A3ZQpCKVSmFlZQXLy8s0qaD056loF5UrXSwWEY1GMTIyApfLhd7eXl5RsFZHNthgNptx2223YXR0FEtLS5iYmEBvb29VBSs+6yYIArlcDvF4HD09PVIxuYSmYWZmBo899hgWFxchk8nw1FNP4Qtf+ALi8TgefvhhTE1NIRAI4Ic//CGrrOZrr72Gv/u7vwMA/M3f/A0+9alP0b8jSRKXLl3CtWvXcPr0aYTDYRw9ehTPPfccnY6o0+ng8XhocsHEwsICr54V1SBWPyVkZENItNLn5fP5bZEKKppF+Q2NRoOBgYGqaditAtXZO51OIxKJIBwOw+/3w+12t8Q+N+IDCYKA1WqF1WpFIpFAJBLBjRs30NvbC5fLxdtPkSSJaDQKv98v1R+WoePIRrXIRqX+FFRTIj71FJUgRsNLzSXUPGJ8f0KA61qqRSqomiGqqZVer68YsaCMUeC/unnPzc1hdHQUdrud80ZADGSDGqtQKDA0NIRMJoPp6emqCla1Ihts88tkMjrtUer4KqEZUCgUeOmll+g0kTvuuAMnTpzAq6++it///d/Hs88+i6997Wv42te+hq9//eslr43H43jhhRcwNjYGgiBwxx134IEHHqBJCUEQ+Id/+Af4/X4cPHgQw8PD+MlPfsKp67HQELMdF5NPAJqXRlV+wFlOKihFwPK9SCwWw9raWsPCMUL7Do1Gg3379iGbzdL23+v1wuv1NlVBU6j3YTQacdtttyGdTmNqagrhcBgOh6Ou9GDKT0nF5B+gZWTj8ccfx5tvvgmn04lLly4BAB5++GFcv34dALC2tgaz2Yxz585te20gEIDRaKRZ4tjYWMXrUGTj0qVLsNlsNLmo1J9Cq9Xi/PnzOHLkiCDvU6xGXGwbe7GCaQjKySn1p1AosEYqqA31+Pg47HY7L7UOmUwGn88Hj8eDxcVFTE9P48qVKwgGgzVzapuFehVB1Gr1NgUrp9MJv99PO0i+DoJJTsqL9KSOrxKEQk9PD3p6egDc2nzs27cPc3Nz+NnPfoZ33nkHAPCpT30KH/nIR7aRjX//93/HiRMnYLVaAQAnTpzAW2+9hUceeYQe861vfQvArcaz3/zmN9tCNADx+pZO/O5SdTfxeByJRAJra2vI5XK0YAxFKqio9k6/ByqVCgMDAwgGg7QClNvtFkVPDi7QaDQYHBxELpfDxMQEotEoHe2oFVms5KeovQRBELu2/rBlZOPTn/40nn76aTz22GP0z37wgx/Q/37mmWeqbs5+/etfw263s/6uWCzihRdewNWrV3Ht2jXEYjH89V//NZ5//nl4PJ6W9qfYDeHpTiMtTFKRzWaRSCRYSUVPT0/VSAVzvnoNiUwmg9vtRiQSgdVqxblz52A0GhEMBlllKsUS2WCLVFRTsOK7brbxUjG5hGZiamoKZ8+exd13342lpSWahPT09GB5eXnb+Lm5Ofh8Pvr/Xq8Xc3NzrHPzVU0UGp3up4QE1zVRpIL5h0kqSJKETqdDf3+/YOlwjaDZ95mSyO3t7cXs7Cy2trY4b9r5oFk+UKlUwuPxIJfLQavVYmxsDGazGYFAADqdjvU1xWKR1U9Rf+/mYvKWkY3jx49jamqK9XckSeKHP/whfvWrX9U1t0wmw913341HH30UOp0OTzzxBH784x83sNr6IVbDK+RcYlwTF9SKVJDkrb4SXElFM8EsYItGo7h06RI0Gg1CoVCJaoZYyEa1sWwKVsVikVX9oxKqpV2xGXOqbouPQo8ECRQ2Nzfx0EMP4e///u/R1dXF6TVstqzSsyeXy9u6qRarbxEj2SgHmw/J5/N0Mb9er4fD4UAgECjZVEejUWxsbDS80Rby/rTCNsrlcppwUJt2i8WCYDAoSGSPr5/ig2KxCLlcDq/XC4/Hg5WVFVy8eBFqtRrBYHDbATmV7lsJzAMyKhUYuJUN0On1h6Ko2Xj33XfhcrkwMDDA+nuCIHDPPfeAIAh89rOfxVNPPbVtzP333w8AWF1dbeuJkVgNr1gL74RA+Zq4pj+VkwrqFLKZzYr4giAIOBwOOBwOxONxXL9+HTKZDH19ffQ6m2VohSIbFJgKVhMTE1hcXMTp06cRCARqKlhxXQtlzIvFIk6dOoVjx45JxeQSeCGXy+Ghhx7Co48+io9//OMAAJfLhYWFBfT09GBhYQFOp3Pb67xeL51qBQCzs7P4yEc+UvE6nUQ2xBglaQRM2eFEIoHNzU1MT09vIxUulwt6vb4uaftGIEY/XA2U/aY27UtLSzh79mzVqD3fuYUeWz6e6b9WV1cRDoeRz+dL/FctskGBSTqGh4dx7Nixji8mFwXZeP3110vyWsvx3nvvwe12Y3l5GSdOnMDg4CCOHz/OOrbd4WkxEgQh5xJTMWB5+tPGxkZD6U9CodFoQ6V7QqlmxONx3LhxAwDg8Xiatq5mGnFKdcfhcGBqaqqmglW9BeUEQUjF5BI4gyRJPPHEE9i3bx/+4i/+gv75Aw88gNdeew3PPvssXnvtNfzRH/3Rttfee++9+Ku/+iusrq4CAN5++228+OKLgq9PiGdXSIIgZAfxdke7y8U+9Ho9CIKg+1S0mlQ0E3zucz3P3NraGorF4jbVNq5Re67g66f4+hG2uS0WCywWC7a2tmjZX7/fD61Wy+teMYlMpzezbTvZyOfz+MlPfoIzZ85UHON2uwEATqcTDz74IEZHRyuSDb59NgBhT5jEmgsL7FyVj52U/tRsbGxs4Pr1s5DJFAgG92FmZgZra2uc+luIiWxQua0Gg4GWTaSKCdkUrOpZO0U2pGJyCVzx3nvv4V/+5V9w8OBBHDp0CADw1a9+Fc8++yz+5E/+BP/0T/8Ev9+PH/3oRwCAsbExfOtb38J3v/tdWK1WPPfcczh69CgA4Pnnn6eLxdnA99mjfIsQtYdiJQjNIhv1in0AQDgcbkv0ohVolv2Lx5dw8eJbAIro7z+Onp4gay0DW9Q+FArxulaz/VQ1ckLJ/lIKjJOTk1CpVHTtIFfshvrDtu/KfvGLX2BwcBBer5f191tbW3R+99bWFt5++208//zzFedTKBQoFArNWm5NiNXwCvWwNvPkqZPSnxpBNYO4tvY+fL4foliUoVj8LAYHB3H16lUsLS1hcnISwWAQTqeT9fXNJhv1RB4oaDSaEgWrU6dOweVy0QpWfCMb5eN3gzGX0Dh+7/d+r6J9++Uvf7ntZ3feeSe++93v0v9//PHH8fjjj3O6Fl87KubUJ6HmAhqPdmcyGTraffXqVbpBolqtpuXt+R5Mie2gTuzIZq8CmIZMBmSz50GSgao2lorar6+v0w1iY7FYSYO9SuArZNIMH0gpMJrNZkQiEYyMjMDhcKC3t5eXRHEnF5O3jGw88sgjeOeddxCNRuH1evHCCy/giSeewBtvvLEthWp+fh5PPvkkTp48iaWlJTz44IMAbkVBPvnJT+K+++5r1bJ5Q6yRDbHkwgIfkIpMJoPFxUXMzc2JIv1JKDTzPjscccRiBJTKHMzmdRQKoPtbpFIpRCIRTE5OIhAIbEtJaibZEMqIV1Kw0mg0vJ6DSuupVEyuUCh2vDGX0NkQ0reIObLBBUxSwfxDydtT6U+VGiQ2vqYkCGIOJNkNgH/qT6fD6XQhnS6iUCDh9d5ScePy2ZpMJhw+fBjvvvsu5ubmcOPGDYRCoar9LlpVs8EFVMrdwMAAFhcX8f7778NoNCIQCMBgMLDOX22u8mLynVx/2LJd3Ouvv87681dffXXbz9xuN06ePAkACIVCOH/+PK9r8d0wUB+oULmwYj19anXNRq1IRSaTgdFohNPppElFPj+KYvEU5PLjkMvdDa+3XWi0ZqPS67XaY/D5JgCoQZIHsbn5wVitVov9+/cjk8lgamoKkUgEfr8fHo+H3lyIKY2qWlpCuYLVtWvXoFAoYDabOUWwuERCyo350tISTCYT9Hr9jjTmEnYW6kmjEitBaNZcJEkinU7T9RSbm5t0zyyNRkP3zPJ4PNDr9SUpZqurq5yVxPiBRCz2GubmVuFy6eFy/TmA2ilWYjvI4GOz+T+rgwgG/xRAHiS5B/k8v32MXC7HbbfdhmQySR+gVarpa2caFdv8VPNZt9uNnp4exGIxXL16FXK5HMFgsKSGhauwCtNPxWIxqNVqmEymHVV/uLOOjDmi3vC02MiGGJ1LpfScetKfrl27BofDQTuEQmEN6+uvIZVSQae7Dqv1JQCdvemr9MxVfhbdIMkvASAAECDJxLaxarUae/fuLWmq5PF44HK5eK1NDCdGBHFLASSZTCKbzbIqgFSan6uToAz24uIilEol3el1t3d8ldB88NnMiPHwSai5KFKRSqWQyWSwtLRUQiooH+Lz+VrWM6t8fRSKxRwmJhJQq7WIRFKwWJJQqbil8Iolu4Av+K+bAEn2M15fqMuO6nQ6HDhwAOl0GtPT09sO0Ki1tbpAvBLY0nftdjvsdjvW19cRiUQwPj6OQCAAp9NZl5+KRqMwGo3QaDQ7qv6wI8kGX4jN8DZjLqBxQ0eRilwuh5mZGc6kohLK31+hoEA6rYZGk8TWlgMWCwEu3x8xpYgBwtzn6ig1TpWMjEqlQn9/PwKBAGZmZjA2NkafjnBJKxAD2WCONxgM2LNnDzY3N2sqWPFN66JeQ6VSFYvFXd/xVUJzoVKpkMvlOOd0C52iK1RtI99odyqVKlF/okiFVqtFNptFV1cXuru720Iq2LC9sFkJvf4wEokJaLV7oFBIaVS10OhhrkajYT1A83q9ovNTlcabTCYcOnQIyWQSU1NTmJyc5KUmSYHpp3ZS/WFHko16wtNCqnyI8fSJL5uvFqkoFAqQy+WC11SoVAYolX+O1dVrsNsP8/6SiwmNfuH5GM9aoOognE4nzp49i5GRETidzpqdXJt5YlRveBpAiYIVddpVrmDFd37qGtTpEfO7RxlzpVIpis2PhM4ApZzIlWwI7Q+ohmKNgm1d5aSCIhYkSUKr1dIHU3a7HTqdjv6uTk1NQafT1SWB2ioQBIHBwY8imbwbWq12x6Zcis1nckH5AdqpU6dAkiRn4sz3EKoRP1UJOp0O+/fvpyP1GxsbCIfD8Pl8nBTPqPdQqZhcrH6qI8kG3y+RGNOVhJ4L2H5f6k1/On36NC1H3AjYvvROZz+czn6W0c2HUKl0fK7H5+eVxnJds0wmg06nw6FDhzA/P4+xsTFYrVYEAoGKnVzbrfJRbX7qtKuvrw8zMzM0ifL7/bzJD3WN8hA49Xernw0JnQ+1Ws2rJ5QYD7IoOenNzU1EIhE6UgGgJP2pnFRUg9g2wWz3SqFQNKkepLVoVs1GOfgeXNUCdYDm9/sxPDyM8+fP013bq5H3Zkc2+PhBlUqFQCCAra0tKBQKjI6Owmazobe3F1qttuqayr9H5QdkYkRHkg2Af/GT2Iw4NZcQ6yLJWz0G1tfX6cZ3YlF/EtuXQwwbymaFhamxMpmM7uS6uLiIs2fPoqurC8FgEDqdrulrFno8m4IVX/UqoPop1k4qxJOwM8C3Aa3QkQ0+vqVYLG6LVKRSKXou4FbPAafT2dBpv9g3TBLqQ7N8mlwuh0ajwdDQEOLxOM6cOQOz2YxgMMi6Ya/H7/CN2NcTOfH7/fD5fFhaWsL58+eh0+kQDAZZI3w71U91JNmgcmGrpYgwIbSkYLuIS7VIBSWdZrPZdqyk7E5Bq0/B6zXiBEGgp6cH3d3dWFlZwYULF6DX6xEMBlll+vjMXQuN9s1gA1PBanJyErOzszh37hyCwaBgClYSJAgFvg1oW+FbisUiHZ0oJxXM9CcmqYjH44jH43A6nYKsS2zYjQSIqQKWSNwSIQmFQk2QEGa/dj2EgFJ/Wl5exvnz51l9WT1+ik9KEkmSvMYzfQ5BfNBZPR6PY3x8HAAQCARK+o3sVD/VkbtNyohzJRtijEZQc7Gtq570p4mJCbpxjliwG404Fwgdcq41L6X45HA4EIvFcOXKFbo2pxlrrmc8nxMjgiBgMplAkiTsdjtnBaudasQl7Ey0M7JBkiSt+sQkFQRB0HKyBoMBLpeLVr1pxbokn1AbQj4DAOh+JZubmyX9SjQaDQwGAzQaDfL5PEZGRuByudDb28u7m3qzUoOp8cwNu8vlgtPpLPFloVAIXV1dLVGj4nNvKqVE2Ww22Gw2JBIJRCIR3LhxA4FAAC6Xa8f6qY4kG3yNuBhzYSnkcjnE43FeNRWtWJeE5qJZp0DVxjJl+lZXV3HmzBmcOXMGfX19MJvNNdfRTCNebzjbYrHAYrFwVrDaiUZcws5EK2o2CoUCUqkU3Z+CIhXUPFqtFkajEd3d3dBqtXVFFsRa8yg2CE3K+CKXy5WQimg0iuXlZeh0OhgMhor9SvL5PB3ZmJ2dxejoKE06uEJIP8U2N9vrmb7sxo0bAFC1OSAb6ikQF/IQzWg04rbbbkMqlcL09DQmJydBkmTV50iM0UGgQ8kGXyPezlxYoHKkIpPJ0JESIWoqxGjEhViT2L5czZe+bf5Yi8UCnU6H/v5+OjIQCoVKwrnlc7db5aPaeC4KVoD4niUJnQu+aVTV/FShUChJfdra2kI6naZFIfR6Pbq6utDT0wONRoP19XUsLy8jEAg0/D46PbIhxjVVQ6FQ2BapyGazUCgUNKlwuVz0hpxrtgNVW+D1ejE3N4fR0VFkMhlks9maWSR8/IPQilEWiwV33HEHNjY2cO3aNSSTSZqI1LpOKw7FuIzXarUYHBxELpfD8PAwzp49i56eHvj9fs4ZPO1Gx5INseXCAvzTn1ZXV5FIJBAKhQRZl9gg5JrE5gwafW/NOAWqp5bEZDLh8OHD2NzcRDgcxsTEBILB4LYTonaoUdUaz2bEyxWsTp06BZfLBb/fX3NOMX6HJOxc1BPZyOfzSCQSVUmFyWSC2+2GRqOp+MyKNXUYEJ8tFyuo+homqUin05DL5fReglI3YtuQrqys1HVdmUwGn88Ht9uNd999F6dPn6aVoITa+PK1tVzGd3V1IRQKYXFxEUtLS5icnKRTkyq9vtl+ii85USqV0Gq1OHjwIKLRKMbGxmA2mxEIBOoWd2kVOpJsUAXiXCF0nQUlB8hXUrYcUnh6d6JZ+a2NFK4bDAbcdtttSCaTiEQimJycRDAYpA11K9SohFQFYSpYzc/P48yZM0ilUkgmk6I32hI6A9XSffP5/LZIxebmJmQyGcxmM/R6Pcxmc01SUQmdrsBIQSixjnb6TpIk6VS4ra0txONxpFIpRKNRei9hMpng8XigVqublq5UDplMBpVKhWPHjtFS6na7nZV0NNNP8fWXKpUKe/bsQSqVwtTUFMLhMHp7e9HT09Nwc9h6hE/4fgZUUz9KUXJlZQUXL16EWq2mDwLFiI4kG/VENuoxJmyRivX1dWSzWWxsbDQsKdvp4WmgM0+xqqm8ME+hUqkUFAoF+vv7S0K6zZa+bQQ6nQ4HDhxAOp1GJBKhDXUz1KXKxzfD6DNlgH/zm9+UGG0uClYSOg+PP/443nzzTTidTly6dAkA8PDDD+P69esAgLW1NZjNZpw7d27bawOBAIxGI+RyORQKBcbGxipeR61WY21tDZcuXYLNZitJn5XL5XSkwmKxwOv1YmVlBXK5XJAeR2I9yBIyeijUXK2KaDIVoChySfUsoZTADAYDlEolstmsIBkPja6XID6QUne73VhYWMDY2BhsNhuCwSBNOpolekKhHh+o1Wqxb98+ZDIZTE9PY3h4GD6fDx6Ph65ZaUVBeSP9oChxF6fTidXVVUQiEZjNZlGmVklkA7VPU/ikP9lsNqyvr6O/v/HGdGIlG2J0LGIBSd7qZrq6ukorvVDSkVQhHnUKJZPJkMvlMDs7i3A4jFAoBLvd3tS1CXXPNRoN9u3bh2w2i6mpKczNzcFqtcLtdnOS/mtFJISPBCFBEFAqlbj77ruxurqKyclJFAoFWsFKKhzfPfj0pz+Np59+Go899hj9sx/84Af0v5955pmqRPTXv/51xe/xlStX8Morr+DKlSs4e/YszGYzPvrRj+Lpp5+GxWKBz+eDSqVifdblcrlooxFinAtovQw5F5DkrW7P1MET9TelAMVMgWJrhLiyssIrc6PaOoQEJT3e09ODxcVFumlsMBjkva5mfWZs5EGtVmPPnj0IBoO4efMmhoeH4fF44PP5RFeLSL2GbU2UGApfpbBWoSPJBt80KuqUpx5J2XKsrq6K0vCKNbKxU8F8VsolA7PZLHQ6Hcxmc9UmV7lcDjqdDgcPHkQymcTk5CTC4TA8Hg+vdbQyslEOKiQtl8uxsbGBU6dOwe12w+fzVY3kNauQjjm+3vfKpmAVCATQ29sruo2LBOFx/PhxTE1Nsf6OJEn88Ic/xK9+9au65jYYDLjvvvvwzDPP4NVXX0VPTw8eeughTq8Va7qSWP2UGA7YKAWora0txGIxJBIJxOPxkv2Ex+OBTqdrS9+rRuxZJX/C7HlBNVnV6/Wc72E8Hsfc3By8Xq8gvVu4rBm4VQ/R19eHQCCAmZkZjIyM0EX0QszPhnoVEGupSooRHUk2akU2yklFNBpFLpfD1NRUwx21xSqjK8ZQ904hQEyHQRGLfD4PlUpFq3t4vV7odDrI5XJcvHgRPp+PtYNpJTBJx/Xr1xGPx7GyslJTMYOPcZuensbU1BTMZjN6eno4r40LZDIZuru74XA4MDs7i5GREXR3d8Pv97OetLQiF7bRaARTwWplZUW0RlxC6/Duu+/C5XJhYGCA9fcEQeCee+4BQRD47Gc/i6eeeqrk936/nxYj0Gg0bRMyEXMaldh8ApfvPaUAxfQRlAIUlf5ksVigVquxd+/eFqy6Nai16aVIRzgcxtTUFK5evYpgMAiNRsP6mkKhgAsXziCdnse5c6fx0Y/eI+hJPRe/I5fLEQgE4Pf7cfr0aVy9ehWxWAyBQKDiupnzt+pQbKeho8kG10iFyWSCVquF1+tt+NpiNrztPuVpFoT6slJRiYWFhZL8aabDcLlc0Ov1VQ1gIxEEnU6HgYEBXL9+HYuLiyXpVdWUZWohlUphZeV92GxLuH6dRHf3g4IaOcpoKhQK2lBTmuxsSiViKyiv9kxrNBpOalUSOh+vv/46HnnkkYq/f++99+B2u7G8vIwTJ05gcHAQx48fZx3bij4blSBWPyXWuShQtXdMUkGpgVE+wmaz0ZKkTJu1urqKdDot6HoaRaORDa7XsNvtSKVSsFgsOHv2LEwmE0Kh0LbNO0GQ0GguIJtdh0oVh0z2B3Wvr9Kaub5nSuFtcHAQyWQSZ8+eRVdXF4LBYEURkWb7qZ2MlpINtsK7r3zlK/jOd75DV9B/9atfxf3337/ttW+99Ra+8IUvoFAo4Mknn8Szzz5b8vv19XWMjY3h8uXL+PnPf45//dd/xSuvvIJnn322ZqRibm5OsPcoVmPZ6eyZz30qFotIpVIlDiOVSiGfz0OtVkOhUNTMn242lErltvQqNtLB9X0rlWns2/cWCoUE1Op5EMTHq47n+9yVG1mmJjtVNGixWOhTrWbntjZbilfC7kM+n8dPfvITnDlzpuIYqnjb6XTiwQcfxOjoaFWysba2xvn6uyGNChDHYRalAEWlP2UyGczPzwMorb2rVw1MDBDiPvNJ4aWi3y6XC0tLSzTpCAaDdBaATJbHXXctN8cPBgAAIABJREFUYXpahlAoCrm80PAay9fBN6Iul8vR09OD7u5uWvlJq9UiGAzCaDRuG9/qCDwTYn4OW0o22ArvAOCLX/wivvSlL1V8XaFQwOc//3n8/Oc/h9frxdGjR/HAAw9g//799JirV6/i5MmTOHDgAO68807cfvvtePTRRzmtS0jDu1tOn4Sap5mOhSRJWoKYWVcBfKDuweyeOzs7C4VCIXiKUT3rpsBW08EkHVyNp0IBOBxmbG1pYLebwOW2C7FZp4oG3W437WCMRiPdlZYrdkLalYTOxi9+8QsMDg5WjIBTNVtGoxFbW1t4++238fzzz1ecr52Rjd2QRsVlLjYfkUwmUSwWodVqYTAY6HTZQCCwa20Em+3lKznLnIsiHcvLyzh37hzdA+NWfePHYbe/Ca32v4MkhZUhbySiTik/ORwOxONxXLt2DQqFAqFQiBaMqJfM7Aa0lGxUK7yrhtHRUfT399NSb5/4xCfws5/9rIRsHDt2DMeOHQNwS5qQj1GgmiUJATEbXiH1y8UGSgGK6TQKhQLUajVdV1FJ3UNocDU41T7b8tdXIh3cN+EWpFKPIp8/B5KsXZDaaGSjHEwHE41GsbCwgEuXLqGvrw8Gg6Hh+dnGC0k2xHxiJEFYPPLII3jnnXcQjUbh9Xrxwgsv4IknnsAbb7yxLYVqfn4eTz75JE6ePImlpSU8+OCDAG5FQT75yU/ivvvuq3gdITuI84WY5xIK5etiE/Qo9xFWq5WuvaMwPz8vHUawoFFxEoIg4HK54HQ6sbKygvPnz8NgMMDpPIClJTuczoNNWXOjUrYEQcBms8Fms2FtbY1WLgyFQigUCk1Xo9qpEEXNxssvv4zvfe97uPPOO/HSSy/BYrGU/H5ubg4+n4/+v9frxcjISMX56lGj2g3RCDGEp8vBd01UIR5TNjCZTNIysgaDoe6+Jq0GQRC8T4zKScfq6iqsVisnw5/N7kMi4YXL5as6jlpDM4wmQRBwOBzQ6XTwer24evXqttOhaq/lCimyIaFevP7666w/f/XVV7f9zO124+TJkwCAUCiE8+fPc75OJ/kpMa0rn8/T9XaTk5N0iqxSqaRJhdvt5qwAJTbfKWTtZasOUWodrFERg5WVFdy4cQOFQqEpDVb5RhJq+QWz2YwjR44gkUggEolgbW0NsVgMbreb072tJ514p6Ltu7HPfe5zeO6550AQBJ577jk888wzeOWVV0rGsN3gah+kRqPBxsYG5zUIaUzE7BDE9qBW+wwrFeJRza6oQrze3l7E43Fks9mOKeLl4gQo0hGJRLC4uIjR0dGaheTNlMnlm+bEdjpULBbR19e37bChHjQ77UqChEbRKZGN1qdk5UEQi8jnzUgmyZJIBdUQkYqW2mw2UfceqBetjLRW+jz4+ohaYynSoVQqMTk5iYsXL0Kn0yEUCkGv1/NacyU0S5jEaDTitttuw/DwMOLxOG7evIlAIIDu7u6a+5zdUlvYdrLhcrnof3/mM5/Bxz72sW1jvF4vZmZm6P/Pzs5W7aJaTwdxMRIEIY04wIUV50AQUZCkHUB14yzUQ08V4jEdBtU1leqguxML8Vq1TqVSCbfbDYfDUbWQHOD3mbXSCDJPhyYnJ3Hjxg2EQiHYbLa652x2QbkECY2i3WpUYgSb/2QePG1tbUEu/wG2tmagVhuQy/0pdDozq6DHhQsXYDKZBCEaQhVTi+3Ar1E06/BKp9Nh3759iEajuHTpErRaLfr6+homHc1WQZTJZNi3bx/y+TympqYQDofR29sLt9vN6o92UwS+7WRjYWGBLsj96U9/iqGhoW1jjh49ihs3biASicDj8eCNN97A97///YpzqtVqXuFpsZ4YtXauIoB/RD5/FXL5PgCfAyDcQ02S27umxuNxEAQBg8FAh7cdDkfFJniVIDbH2ehnVs+JUa1C8nrnbdaa2WA0GnHo0CFsbW0hHA5jYmICwWCwrsZOrejkKkFCI+BLNsS6WRXK/lJS9clkElNTU9jc3EQqlQLwwcFTV1cXwuEsUqkQFIoU7rrLCaWyu+qcjUJs/kUoNGqz+RaI8yUmVMqt3W5HLBajSUcoFOJU59foOoD6yYBGo8Hg4CCy2Symp6dLupIz07h2U21hS8kGW+HdO++8g3PnzoEgCAQCAXz7298GUFp4p1Ao8PLLL+Pee+9FoVDA448/jgMHDlS8jkqlaqvKh5CnT606ySoWk1hbG8XmphEGwwjM5k9BJmP/QtdSQaJyZpnEgmqCR2mRezweaDQa6HS6kuhWvRCjE24EfAwzE9VIBx+0M7yr1+tx8OBBpFIpRCIRTE5OIpfLNTUys5ONuISdCZVK1bYIfDtBkYryurtisQiFQoFCoQCdTlfx4OnGjf2QycIoFDwoFs0VryNWctZJaIZdLLfzBHGrT4fNZkM8HseVK1egVqs5i4uUz91ogTif8SqVCgMDAwgGg5iZmcGpU6dKmt3W42d36qFYS8kGW+HdE088wTqWWXgHAPfffz9r/w02dEoubCvnKhY1WFw8BLv9EhYWDqGrS4Naz3SxWNzWNZWZM0tFKoLBIGsoe3V1tSOdgRCRDT5j2YwVG+kwmUxQq9UNzVttvNBGUKvVYv/+/Uin0/jtb3+L4eFh+P3+iiHpRtazk8PTEnYm2plG1Sowm+qWqwRS/a98Ph+tALWxsYH5+fmq0cyDB/8Qi4uL/9WRu3pHZ6HQiX6qUTQrsgGwkxhmnV8sFsPVq1ehVCpRKHDvxdHsNCpqneVQKBQIBoPw+/2Ym5ujm93mcrldk+7b9jSqZoBvGpVYazZaOZdCoYDL9RgWFhbQ3d1dotJRXleRSqUwNjZGd9g0GAywWCzwer1Qq9Wcvww79UvDBY2+Nz6RjWrGikk6Lly4gEwmA4PBULWQnJpXLIVrGo0GGo0Gd955Jx2S9nq98Hq9FZVFdlMurISdiU5JowJuff/X19dLSEUul4NSqaSj2VxUArm8R4PBgP7+/pprEup+daqfEiKNqhk1G1w+M4p0RKNRjI2N4be//S2GhobQ1dVV9XXtLsiWy+UlzW6pWuQ9e/bQjQ2rYSf7qY4kG+0MTwv5YLaauFitVhgMBmxubuLmzZsl4W2qwZFer4darcbhw4c7TuFDLGjGhkKn08Hj8SCTyWBxcbFqITlzDVyNbStOXJgh6Zs3b2J4eBhutxt+v3/bBkZSo5IgdrQz3bdeUJKk5QpQqVQKCwsLMBgMcDgcCAQCUKlUvOcX62FdM0jL1tYWLl9+FyqVBgcO/K4o/SlJ3mp6qFQqodE0FkVqBjFRq9VYWlpCIpFALBaD1+tFX19fRdIhVKSiUVDNbldWVmAymXDu3DkYjUYEg8GqRfA72U91JNloZxqVkGim4WUWa1OnUczwtsFgKAlvMzE7OyvIF7BZ9z2ZTOLmzVNQq23o7T3Y8i9nKwvE+Y5Vq9Xo7++vWkgOAKlUCuPj41hcXMTdd98Nq9Uq2Dr4ovx+Un05ent7MTs7i5GRETidTvT29tIbHCmyIUHsELOfKhaLSKVSJZGKVCpVMZo9NjaGwcHBhq8rRl/cLLt28+bPsbl5DoWCDMvLWng8dzflOlzBrLWkPnOq1jKbzcLlciEQCNCkSOhoBXMs13k3NxMgiCiMRjU0mn4EAgGMj49DJpOhv79/G+kQm3QsSZK074pGo7h8+TJUKhX6+vpgNBq3jd/JtYUdSzbalUYlJIR4cKgmeLFYDGtrazh37hyy2SwUCgUdqeju7ubVBE9sDqF8PUtL/wK7/d+Ry6mxtvYVWK17Oc0j5Htq1Zeer8GnDFUt9apodAkWy/uw2TSIRIywWn9PsHXwRaW55XI5ent74fP5MDc3h9OnT8NutyMQCIgqDUyCBDaIwU+RJIl0Ol1CKijpcSqabTQa0d3dDa1W2/TviFgjG0KBuR6zeR1zcwTk8jyMxmTL1kARyVQqhdnZWWSzWbqHFbUncLlc0Ov1UCqVyOVyIAgCsVgMo6Oj6O7uRm9vL69rNusAzWqNIhC4imzWggMH0rBY7sOdd96JtbU13LhxAwRBoK+vj24YKzY7T/lkSnnL4XAgHo9jfHwcwK1Gocy+Uzv5UKwjyQbfNCoxGiW+oAwIU+EjnU5DJpNBr9fTcmx79+6FUqkU1RdOaJhMN5HJqKFUpqHRxHm9Vgz3pVkGkW3eyqRjCkbje1AoFFCpXADERzYoyGQy+Hw+eDweLC4u4syZM0ilUkin05zyYIGdfWIkYWeiHj9VL8oVoKg/p0+fhlarpYu17XY7dDpdWzc0YvPFzdofuN33o6vr+1AodFCr7xJ8fgD0Z079SSaTIEkSWq0W+Xyebo5bq4cV08bOzMxgZGQENpuN8zqaRTZkMjkOHVqA1ZpEsegDxcXNZjPuuOMOrK+vY3JyEiRJor+/X3RqTmzpvlarFVarFRsbG9v6TkkF4iJDJ6t8VDuJorTIjUYjenp6SgzI2toalpeX68qjLYeQhXfNMOIWy6dQKHwbJOmBUnlI8PnFBKF6Z5STDmABfr8eZrMZBGFFra8HHyPO9zPnOrdMJoPb7UZPTw9+85vf4Pz589Dr9Zw60O7kEyMJOxN806i4olaKrF6vh9frRSKRwNGjR0W1eRFrzWMzQJIO6PVfEGQuKoOBediYy+VK5ObL06IvX74Mi8XC6UCG+lxkMhl6e3vh9XoxMTGBaDSKqampbf0jGgV3ifM+zM09ArPZjGLx+Lbfm0wmHDlyhCYdGxsbMJvNdKSj3ajmd7q6unD48GFsbm4iEolgYmKioUa37YZENiBeo5TNZpHP5zEzM0M7jkKhAI1GQxsQridRQoenxQyC2AOF4qW2XX8nq3xQpGNx0YqZmSSWl/Mwm38Hdnv16/A5calHlpYvoVIqlbj77rsRi8Vq5sFS15DIhoRWguopUS/y+XxJsTa1weSaIiuTyUSXVtLpaVSNgirY3traQiQSoRsfUhkMVKSCWb/WDMjlcrjdbqRSKRSLRZw6dQo+nw9er5fVjjbPpwHJ5D4Ui3dUHUeRjjNnzmBubg4LCwvo6+srSVFqB7j4QoPBQPedunLlCjY2NtDV1YXu7u4d5bM6kmzsNJUPqjCLTTYwn8/TX2ydTse5rqIcQhteMal8dCKaRTa4q3zoIJP9ATyewH+lVy3UVK/ifhrVGqUogrjVDMputyMej+P69et0Dq/ZXNoMrFgsilINRkLngs/3han8dP78ebqfERWpqGeDSRCE6Ei2GAlCu9ZERaiYZJKynQqFAg6HAy6Xq+5aGiGIplwuRygUgs/no2XJe3t7t/VCEkMxOXCL4O/duxckSWJiYgITExPo7+9vG+ng4wu1Wi08Hg8MBgMSiQQikQid2iZkVKlZ6EiyIVaVj2KxuO0kiuk02GQDT58+Dbfb3fC1xXhiJKYTtXagUCiwkke+97aZxKRWIXkjc3OFEE6RyoNdX19HOBxGPp9HKBSC1WoV5aZLwu4DUwGK8g9MBSi9Xg+5XI69e/fy6mdUCVRkQygI8T0Vo58SCpXuDXNfQP1hirgYDAa43W76819aWkI6na7a+LAVYN5bpVKJ/v5++P1+TE1N4dSpUwgEAujp6aHfd7sP0JjjDQYDDh8+jEQigcnJSUxMTKCvr6+m6mKtuet5Dd+DN5VKhWAwiFAohJs3b+LUqVNwu93w+Xx1H0a3AuJdWQOoJ41KSJAkiWQyuU02EAB9EmUymeDxeARxGlwgNsPbySi/z+V1NlShHgBaos/hcJQ8B+0+BSofW4t08I1stEuW1mQy0U4mHA5jYmICoVAIhUJBKhCXQOPxxx/Hm2++CafTiUuXLgEAvvKVr+A73/kOHA4HAOCrX/0q7r///m2vfeutt/CFL3wBhUIBTz75JJ599tmS3xeLRdy8eROXLl1CLpfDH//xH+MP//APcfDgQbpY22AwsJ5aLy8vN9zvgEIzNvZiIxtCQYg1kSSJfD6PaDS6rd6S2hdYLBb4fD6oVCrR2xy2z1ulUmHPnj3o7e1FJBLB1NQUQqEQr8yRZpCNYrGItbU1ZLPZEjtvNBpx6NAhbG5ubiMdfO9/IxH4eq6hVCrR19dXIgE/ODgoyOF0M9CRZIOSa2s2yhU+qE0ktSGjnIbT6YRWq+2Yk1MhHUKnRUjy+Tzy+TwWFhZKijM1Gg19SuVwOKDT6ZDP51EoFHDz5k1EIhH09fXBZrO1jEDUM7YS6eBbs9FuWVqj0Yjbb78dyWQS4XAYKysr8Hg8cDqdonqeJLQHn/70p/H000/jscceK/n5F7/4RXzpS1+q+LpCoYDPf/7z+PnPfw6v14ujR4/igQcewP79++kxf/Znf4bl5WUMDQ2BJEl8+ctfxtDQkGAkgiuEjGyIzScIOVc99qC8ZwUVrSBJkvYF7Vb+4npv6rmHarUag4ODSKfTtH11OBycbHkzfNrZs2cRDoeRTCZZu3UbDAbcfvvtNOmYnJxEX19fU1O66gEboVEoFAgEAvD7/U29dqPoSLJRb/5iNeRyuW11FVTDG6oYj1J7OHv2LA4cOCAqciHG8LRYHRTX61FSw9QfSqs8l8uBJEm4XC709fVVDW1qNBoMDQ1ha2sLExMTiEQivE5V2kVMKNIRjUZx7tw5pNNpRKPRijUd9a4DaG7xtk6nw9DQEC5fvozNzU0657inp0dU318JrcXx48cxNTXF+3Wjo6Po7+9HKBQCAHziE5/Az372sxKy8Y//+I/0v0+ePIk777yT8/xC2jkqfVCoucTmE1oRzWdKzlP7AraeFX19fUgmk5ifn0dfX19T18QHXOwwQRB11+lpNBrs378fExMTiMViGBkZQV9fX1U/0QyysbQ0DY0mhkQii2Qyua1mjwJFOra2tjA5OYlkMoloNMpJBaoVqbjFYrHifkImk0lpVDsJhUJhW10FlT/JDG9TDW/YIMaUJTGuaaeAKSVJOZVisVjS9IopNTw2NgaPx8PL8Oj1etx+++1IJBJ0eoXD4djWAZUN7SImJEni/ff/A3L5OFZXdZid9VSs6WCCr1FuRSREJpMhEAhAr9djamoKw8PDVdVVJOxOvPzyy/je976HO++8Ey+99NK2wtK5uTn4fD76/16vFyMjI4JdX6h0JeZcQqDaXARxAzJZGMXinSDJ6r0ZxBpVLBaLtAoUW88Kg8EAk8kEt9tds2dFJ4Hr+1QqlfB4PLBarZiYmEA4HEZ/fz9rrw6+Mupc1nD48Pt4//0k+vrWYbU+UHO8Xq/HgQMHkEgksLCwgMnJyZp+rRWRDbH1CeGDXUs2mMV4VCHe6OgoLSHXSP5k6zb2SRDEJkjSAaD6+sR4YiQ2AkTJCi4tLdEOJZPJVCzUqzYP1+elfJzRaEQgEMDq6irGx8chl8sxMDAAg8HQ8LXYrlcJ3MhGEXv3vgadbgWBgBoez6dRLCqrFpLXs+Z6ajzqzbdVq9XYu3cvQqEQra7i8XgQDAabKiUpQfz43Oc+h+eeew4EQeC5557DM888g1deeaVkDJs9EzLSR0UjhFCfaUVkgyBiiEZfQSwmg893CVrtM3XNI+SaaqG8Z8Xq6ipNNJhZDFSz3J2KRu9zPYdXOp0Ot912GzY3N0tIB5O0NyOy4fVuwOdbwNpaDAoFN7lpkiShUChK0oYp0lFeYwm0LrKxU5+5jicb1AayvK4CAF2MZzQaoVarBWtw1Bop3XUoFP8bwBpI8n4UCvdWHS22jX27wdZZNZPJQK1Ww+FwwGw2w+v1tq1QT6vVYv/+/VhdXcWVK1eg0WjQ398PnU5XMq7VNRtMyGRFeL0ENjbsMJvXoNEUAdRWr2p2pKKe059yI06pqwQCAczMzCCbzUpkY5fD5XLR//7MZz6Dj33sY9vGeL1ezMzM0P+fnZ2tWrCpUCiQz+c5yy4LWWfRipqNZDKJixe1kMlIxOPAsWP1zSPkmihQqbDMaEV5zwq73Q6TyYSNjQ309/cLsi4xQYiCfi4ot+EGgwGHDh1CIpEoIR1Usz2hfVqh8Axksn/D7KwMAwMBTnMzfQKzVjEcDtN+jUk62lWzwYSYI2otJxtsKh9/+Zd/iX/7t3+jG2/98z//M2tOXSAQgNFohFwuh0KhwNjYWMnvY7EYLl68iEuXLmFjYwO/8zu/g0984hP48Ic/TJ9I2Gw21qKsqakpwT4ooTf2bA9xoTCHtbVxJJNqmEy/gV7fOrKxk4gLpVHPJBZUZ9XyU6rZ2VmoVCp0d3e3dc3Me2uxWHD06FHEYjFcuHABRqMRfX19dRWTCk9MlFAonoHd/v/g8uVu2GwfnE5VU69qthpVPac/lV6jUCikqIYEAMDCwgJ6enoAAD/96U8xNDS0bczRo0dx48YNRCIReDwevPHGG/j+979fcU5KOZEP2RBbnUW1uQjCAWAI2ewadLoQp3maAWbPCurgkZkKS9VWsPWsWF1d7TghEyEgxD0xGo04fPgw1tfXMTExAYIgoNVqOdtb7j6tF8Xi01hbO9WQD6Rq/FKpFMLhMB3pcDqdUmSjBlpONthUPk6cOIEXX3wRCoUCX/7yl/Hiiy/i61//Ouvrf/3rX8Nut7P+7uWXX0Y8HqclBN98801euslizoUtX9fGhg3xuAddXTGEw/tx8KAgl+MMsaVRUfKyTGeSTCbp0C2XzqpCF19yWTOX1xPEreZ0NpsNy8vLOHv2LMxmM0KhUFsjG7fGHUehcBzx+G9Zf89GOpgnxEKvG2gsjUqCBAB45JFH8M477yAajcLr9eKFF17AO++8g3PnzoEgCAQCAXz7298GAMzPz+PJJ5/EyZMnoVAo8PLLL+Pee+9FoVDA448/jgMHDlS8jkqlQiaTgV6v57QuIW1mKyIbGo0GR478ATY3N1nz84UGswHi6uoq4vE4JicnS1JhPR5PzVTYZkEoCV2h0Mh+R0h/YjKZcMcdd2B1dRUXL17E2toaurq6KqYP17MGoLJvmJ2dRTQaRX9/P33NanNrtVocOHAAqVQKkUgE4XCYPojgino+x53sp1pONthUPu655x7638eOHcOPf/zjuub+27/9W/rf3/zmN3kRDSEL71px+mQ02jA5+QlMT29g7969dc9T75raCWZO7ebmJlZXV5HL5ZBIJErkZeuRG273ewMqGyGCIOByueB0OrGwsIAzZ86AJEnOzZ2aRUy4gEk6rl69io2NDaysrHBSr2pFZGMnF95JEB6vv/76tp898cQTrGPdbjdOnjxJ///+++9n7b/BBr49oYT2La2IknR1dXESuqDAxU8x06PZelYYDAZotVpYrVZ0d3eLwq4LCSHeTyuzE7j6E4vFAo/HA5Ikq6YP852XifLx6+vr+M///E8UCgUsLi7ivvvuA8DNj1Dpzul0GteuXUMsFsPi4iJcLpegtVrM1+xUPyW6mo1XXnkFDz/8MOvvCILAPffcA4Ig8NnPfhZPPfVUxXnkcjkKhQLn0wuxphlVmkuhUOCOO+7gXCwo1vdXK6eWGa2gcmqZHdedTie6urqQTCZpucmdgnpVLQiCgNvtRnd3N06fPo0rV67A4/Ggt7e3qvRdO8hGNptFPp+nnYVOp0MgEMD8/DwWFxc5qVe1qmaj0zYkEsSPehrQitGONzO1lq1nBdW7iPIDbIdLkUgECoVCkLqEnZI23Eo0U5yEShemUuP1ej36+vq29ccQwk+RZAHATchkWwAG6ppbo9EgEAhAJpNhdXUV4XAYwWCwKtHdbX5KVGTjf/7P/wmFQoFHH32U9ffvvfce3G43lpeXceLECQwODuL48eOsY6nwdCVGXA7qxEgolY/WSAoSbSFTQoH5pcnn89tyaguFQkkRf6Wc2mg02uqlC4ZGDAdVyLhnzx5sbGxgZGQEbrcbfr+/4nPRSrKRSKzjypWvQS5fhdX6FEKhI/TcKpUKe/furVpITqGValQSJLQSlJ/iCjFHNhqdi1KIzOfzmJyc3NazgpKdr9W7qNNRKBQwPz+PXC6HQCDQlnQwvqjnoIuZPryysoJz587BZDIhFArRNYtC+CmLZQ4f/vAZrKyYMTCwCOBP65q7WCxCqVRi3759SKfTiEQiiEQiCAQC6OnpEUS9SioQFwCvvfYa3nzzTfzyl7+seMMoVQ+n04kHH3wQo6OjFcmGWq3mRTaEzoUVyogLlVcrllMskiTpPiZLS0tIJpNYWloqcSg9PT3Q6/VtcSiFQqHtG0++hlkul6O3txderxfT09M4deoUa4+IVkc2kslfYmDg/wNBAOvrAPCtbXNXKySvV+WjGUZcgoRmoJ2RjXYpW5EkSasBMiVmgVtpKcViESaTCR6PB2q1um47JNS9EttB3cLCPKanz2FpSQOARF9ffSpZjdp4vv6ED8prFp1OJxwOB5aWluquWay8Nid8vjx8vmkUi/8nqK1bI6qJGo0G+/btQyaTQSQSwdTUFAKBALq7u2k/s9sOxURBNt566y18/etfx3/8x39UJAeUeoTRaMTW1hbefvttPP/88xXnVKvVyOVynNew01Q+2jUPn7nYHApJftAESafTQafToa+vTxSMfG1tDZHIVRgMZnzoQ/8H56LNdoJp4ORyOUKhEHw+H90jIhAIwO128/78hcgNtVgcSKUUKBZzsNk+KJ5jM5hCqlcJIX1bDjE8nxI6D+2u2Wi2Tyivr9va2qqoBkh9/0ZHRyuKwAixpp0OleoqVKqbIAg5ZDIzgPZJ8jZLnKTStbq7u+FyueiaRa1Wy/lAuTJcyOdfBkEsgCQ/UJgrFovY3NzEhQsX4Pf7K3YdZ44v9yFqtRqDg4PIZDKYmprC1NQUent70dPT0xQ/JWa0nGywqXy8+OKLyGQyOHHiBIBbReLf+ta3SlQ+lpaW8OCDDwK4lXLzyU9+ki7kYQPf8LQYCQI1l1DOpVmGt1gs0tEK6g8l50hFK7xeL3Q6XUnId3l5mVaMEgMymVP43d/9EQoFHdbXndDrj7RlHY2e1lA9IvyjjgokAAAgAElEQVR+PyKRCIaHhxEKhXidpAjhHFSq34Na/XUAUZDkf+M0NxvpMBqNNVVJmKg3r1Usz6GE3QOVStURBeIAkEqlSvoXlfescDgcCAaDnGV+hYCYyIZQ9sXvL0Cnm4darYbP1z6bxffwis/YWgc/VM3itWvXMD8/D5lMhkAg0MCz1QOSLFWTSqfTuHLlClQqFcbHx/HQQw9V/Qyr+TWqWWw2m0UkEsGpU6fqEi6QyAYP1KvyEQqFcP78ec7XodKouELo1CexnT4JpV6RzWaRTqexuLiIubm5bQogfLuui8kZBAKTKBZl0Ok2oVYvtnUtQpACqi4inU4jHA4jFovBZrNBr9cLqpRReSwBkvx91vG1DCaTdJw/fx7RaBRarbYp6lUSJLQLfP2UGNKo2HpWUM3xLBYLXVvBVl/HBUJtymvNk8/nIZfLa45rlSAKV2QyH4bTuQqr1YZ8/g8aWkv5ey8UCkgmk0gkEiUHh9Rmufx0v1kF4lwgk8lgsVigVquhUqkwOjqK7u7umkIpXFEsFqBQLEEuT6JYrNyY84Pxtf0O5ZOz2SzGx8cRi8Xoxp9cfNZO9m2CkI1sNguFQiGqm8A3japVMoDtmouvAWeGv6m/c7kcHfbX6/V0tKLez71VToUrSPIjcDimoNHYkMsdQbt4EN9ToFrvX6PRYP/+/Ugmk4jFYpifn0d/f39V3ftm1nfcMuK/xtbWj6HV/ilkssr65Dqdjm6yyEe9SkhbJEU8OgNi9VNiTaNiNkSl/mZGrPV6Pd2zIhKJwGq18pKbr4RW+M5I5Le4du23MJmsuOuu/wGFonXRlkZBkjqsrf13dHX5G5qnWCxidXUVqVQKm5ubJQeHRqORjkRRh4yTk5OQyWQYGBiAwWAQ3E/VO1Ymk8Hn88HtdmN2dpaTUAoXaLUr+MhH3sPyci/27LkCgvhzTmvhApVKhd7eXhQKBaRSKQwPD8Pv98Pj8VSdY9dK3xYKBfzyl7/E6Ogo1Go17rrrLtx99911dTcWGvWofIiNIAg9FxsoeVlmXm0qlQJBEHS0wm63l4QoJyYm6BOscmSzSWxsXIZOF4RO13jeLZ/30ShSqf2IRv8XnE43gPZ1jG6WpKBMJsPevXtRLBYxMTGBcDiMgYEB1lzUZpKNfP4CDIb/jVyuiFTqfdjt26OdTBSLRej1egSDwaaoV0nobIjdT7Vb+ra8ZwXTB1ANUa1WK3w+HzSaS1Aq/18UCodQKPw3AITg62oFpqd/DZ0ujfX1DSQSl2GxHKo6fie9t3KUpzknEglks1lkMhmsrq7CZDLBZrNVPDjMZrMwGAx0073Lly9Dp9PBZDI1jUDUM5YplHLz5k2cOnUKXq8XPp+vLn9QKJjR3U1iYOA6isXbUSjcesavX7+OpaUlHDx4sIRc803fLRaLUCgUGBgYQCAQwNTUFIaHh1nFXbheQ8wHYw2RjVdffRVf/OIXMTQ0hGw2i2984xt47LHH8JWvfIVXjnUzsPPSqHIgiCWQpBPMja7QIdz19XVWvXKqtsLpdNYMf1db0/Lyc9BqL2F93Q6F4v+GSlW5mZM4HZQO7SQafFHP6ZJer8ftt9+ORCKBGzdugCRJDAwMlDTeaibZSCbTMBhuvSaZTPGan4t61U7WIpcgPCQ/9QGYEuPLy8tIp9OYm5ur2bOCwsbGDzExYYLL9Rt4PHeDJB0AxNk0ttqaensNuH49DZOpCKOxuyXraQWYKW6JRALJZBIkSdKkkUpzVqvVeP/99xEKhWrWOTDfv8ViwV133YWVlRVcu3YNKpWKTpsWCo36HrlcjmAwWCKUQkUN+KBQ0GJ+/v9CX58MJLkfABCPxzE6egokWUAsFsPHP/7xutYNlPoppVJJkw5qzZUUJXfqQVpdZIO6qd/4xjfwox/9CPfeey8A4Nq1a3jwwQcRCATwuc99rq36z/WkUbUvGkFCqfw2ZLJxFIsB5HJfACCve10kSdKhUepPOp1GKpXC4uIiDAYDuru7BZeXJckitNpx5HIWqNVxFApRANw7x+42VPpcWyVRazQaceTIEaytrWF8fBwKhQL9/f10iJxvKJsr1OpDmJj4H9BoZuFyVW7MSYGvetVONsgShMNO8VN8IxtcyAbVs4LpAzKZTInEuNls/v/ZO/PwqMos/39u7ZVU9n3fSAh7SNhBGsXgCqhIu6C0Om3Tm6OO3W79U9seR9yXth3pcWlwdFq7dWwUbGRpEJQtCRAQCUJICEtWsta+3d8f6XutSipJVaoCgeH7PD6Sqrfee+52znvec873yIW1/mLfvjQE4QxVVXFERSmRCPuG58ZR38jJWUZ6eiUqVRqC0L+zMRwhimKvRoc2mw2VSiXf34yMjF6kLD0x2JqaxMREnE4nTU1NlJWVkZSURHZ2dp/ribMR2egJlUpFXl4emZmZctTA4XD4HfkWRRG3OxpR/J7tS622olAcxel0odMleY0fTD+onuMlchdPpyM9PZ309PTzop9KfwhqpdnS0sL48eOBbo+6sLCQdevWsWjRIm666SYSEhJCIuRgcK6bJQWmeG2YzXtpaVERG3sArdYMRPg1l69iPYleVsq9TElJQafTUV5ezsiRI4M7uX5kEgQFavW/oFB8CFyBTpcV9LEuon+EwimIjo5m0qRJtLa2yiFyhULh97yBRhIUCgXp6bf32GmyoFS+gCA04HQ+AHzfDT5Q9qrIyMgQ0CFexIWC4W6nAq3Z8NS9/fWsCAsLIzw8vM+eFY2NjVgsA0cWPaHXT6W19QRabTQaTYT8+XArooaBZNKhVk8NwTxDj54Nb9va2nC73XR1dWEwGELSk2QwEASBqKgoxo8fz8mTJ+W0pczMzF52ZqhSg/2ZV4oaZGVl8dVXX7Fz506y+2i0N9DcUVH1XHXVHs6cSSA7+yiieC8HDhygra2N+Ph4oqKi/JJbmr8v50RylLKysuTeWWlpaf0+h8M9AhdUZCM1NZXjx4+TkpKCWq3G6XSSlZVFU1MTRqPxnCrxc90sKRDHxeVSc+hQIamp3/DddyMpKNCg1XrL5Zl3KRmVnjsZUrHe2fCA+7pWkZHXAdf5Nce5VuLDFcFS3/Y3b3+IjY1lypQptLS08M0332C1WomIiBgwtz1QeX2NF4StOJ1/w+VSoFa/jii+KH/nz46Rp9Oxf/9+mpubCQsL84u9aqDrMtyV+EX4xvlgp3Q6HUaj0a+xLpcLm82G1WqVF55Op1PuWWEwGMjMzPSbtGMwdYoTJhTT3p6LwWDwSr8JNY1uKDDcZPJHD0n3VmKD6tlBPSUlhYiICNxuNxkZGUHJE6ztlX6vUCjIzMyU37MdO3aQlZVFWlqa1zkPRWRDqnvwB2q1Gp1OR0lJCceOHeP48ePk5OSQlJTk83i+7I4oFpKUFE9ycjUu1y84ebKe8vKdgJP6+tg+m0z3JftA5+npdNTV1WEymaitrSUjI+O8i3QMytmQbsA999xDfX09drsdjUaDQqHAarUiCEJAC/2hwHBm+fB1bIfjMvbsmUBkZCSiKNLa2orRaKS1tZXW1laUSqWcdxkdHU16errf9LKhxsXF19DjXBXeCYJAQkICycnJqFQq9uzZQ2xsLLm5uX3m5Q4mV7WnEm9oUKDRmBEEN11dSjztaCDzS+xVoigGxF518Zm+8HC+2KnW1lavzzzTYKWNJYvFItO0qtXqkPSsGMxmj3TsnhgKgpUL8Z2UrpHE9uhJMduzfjI5Odln/aTZbA7pWiWYcZ6fSwvjjIwMub9TXl4eiYmJAdcWBgJ/U5ekZ0qr1TJq1CiZEr6mpoa8vDwSEhK8zsf3M2jA6VwJWIEw1OpKlMoanE4wGNqxWCZTW1tLamrqgHUsgaRdqVQqcnNzqa+vRxRFdu7cSWpqKhkZGSFNhR9KBCXl3Xff7fW3QqGgqamJ++67j6Sk7ny2c6U0znUalT9zSZzWRqMRg8Egy1tVVSUrnMjISBITE0PSUTVUGG4h8/PNKEmUkh0dHSQlJfVSFsOBUhAgLi6OESNGUF9fT3l5OQkJCWT7aJwUisiG0ZjLgQO3o9OZCQubS0ZGEyrVQ4ARtfouFAr/i/tEUUSr1V5kr7oIYHB26q677mLNmjUkJibyzTffAPDrX/+azz77DI1GQ15eHn/60598MrllZ2cTERGBUqlEpVJRXl7ep2xWq5Xa2lpef/11LrvsMkwmE263G71eL9sAz54VjY2NWK1WYmJigr4u5xNr4mAQKpmCmceT6au1tZX29nZ2794tNzuMiIggKSmJvLy882bRKKGva+LZ3+no0aPU1tb61c/Ec95AxgYir6eelyjhLRYL1dXVstMRFxc3gMOroJtIBhITW7jyyn20t0cRFQWff56MKLpJTk7hmmuu8fHbvuXxB4IgkJOTQ2Zmpsy4JdH8ns1mmYNBSJ7ujo4O2tvb6erqQqFQcOutt6L9Zx7QuVoInss0qp5z+UMtKO1U9fSGTSbTebeY9hehPK/hZugkSDU10g6WlE8dHh6OVqtl3759PncphyqNKlCFL6WhJCcnc+rUKZ+NkwJVmr7GZ2dn095+BWazmeLiYhSKTxCEA4iiitjYNSgUs/ye39N58Je96qKzceEjEDt1xx138Mtf/pKlS5fKn5WWlrJ8+XJUKhUPPfQQy5cv59lnn/V5rM2bN/e5QXTq1CnuuusuGhoaEEVRfv/9SYM9l+m+/WEo7Of5Zvc8+5JIOt/pdKLVajEYDHLUYvz48Wf13I4ePcqpU6cYOXKk3LMoEAyWyESn0zF27FhMJhNlZWV8++23jBo1asC6hkDTqAKNbPSEXq+X5ZRsxIgRI/yaWxSnkpqaTVpaDfv334wo1qBWmzlzpoODB7MJCwsjOzu7zzStwT4HEuNWZmYmJ06cYNeuXaSlpYWkJneoEJSzYTQa2bRpE2vXruXAgQO0trai1WpJTk5m1KhRTJkyhcsvv1zePTqb0Ol0ARW/hUrxulwuOe9SCoV7KpyBqAV74kLeMbqQ4Jn6YLPZ2L9/P1arFZVKRUREhMwOEh4eLt93u93OiBEjaGhoYPfu3fIOxXCIbPR0CDwbJ3kqt4yMjKAo/yQolUpKSko8xhTgdAqAna6uPNLS/hulshGX6ydA/1GOQNmrLjobFzYGY6dmz55NbW2t1zzz5s2T/z1t2jQ++uijQcmTmJjIu+++S1JSEp9//jlffvklP/vZz/z67XB2EIabXEMZgbfb7V4pUJ4N8QwGA3FxcWRlZcmbh06nk8bGRgwGAydOnAhKFpfLBUBdXd2AYy0WC2VlZSiVcPr0KWbMmCnr3qioKE6dOjXgHJ4bT55wOp0AVFdXDzhHUlISSqWS06dPc/r0adRqdZ82Q6vVUl9f75dNcTqddHZ2ypt4A52HwWDoV97w8HD0ej2NjY2yDRz4/H4FgEJhIjVVR3NzF0qlwJYtmxEEgQkTikhMTOxlYwK5fhKio6N9jk9KSsLhcHDo0CG/5woGOp2O9PT0gKIpg3Y2KioqePDBBzl58iTz5s3j3nvvJSUlBZfLxYkTJ/jqq6944403WL9+PU899VTQxUyBQqPR0N7e7vf4QJWltLjs2QxPqVSiUChQKBQkJiaek7zaocZwS6M623C5XHR2drJ7925aW1tJSkrCYDDIqQ9KpZKCggK/2EE8F/ES64ROpyMiIqLf30kYSpYPX1AqlWRnZ3s1Tgq0wNYfmQ8fjmL//h+iVDpIS3OQl/ce3bq6BZfr9UHP78vp6FnIeBEXDobKTr3zzjvcdNNNPr8TBIF58+YhCALLli3jJz/xpndWq9WyYzMcmvp5ww2YkNgQh49c52YeiUJYSgmSNpM8i/KzsrIGLMpvaWkhOjqa8PDwoJnyHA4Hoij61dvCajVx5Egldrud2NhIRo0aJX9nMpkICwvzq3Ddl7MhPbf+yGGxWNBqtSgUCpxOJzabDYVCIX/mCbPZjE6n82sDSKJz9icFTVqz+Xv9rVYrTqdTlnOggmyr1cy4cZEIgpN16/ZjNtfjcDiprKwkOjqaG264wcuuB3L9JJhMJsIlvmkfOBubZqIocubMGU6ePElOTo7fvwvY2ZB2AVeuXMmtt97Kv/zLv/gc96Mf/QiAJ598ks7OzkAPEzQGUyAueZo94XA4vJwKk8mEy+XqM6+2paWFzs5On/m8gSKUO1nDDefDAq/nDpbJZPrns2InJmYN+fktnDxZypQpd8m/aWlpCbg7sVKpJDc3l/T0dPbs2cPhw4flYu1A6fn6G+svBkqNkgrWMjIyqKqqoqmpidOnTw9IJwj+hb4tFgudnd3dWePi2hAEBeBEEJpRq2fhdhfjcr2ArwaMgbJXHT58mLa2Npqbm33WdJwPz+lFeGMo7dR//Md/oFKpWLJkic/vv/76a1JTU2lqaqK0tJTCwsI+WWqGV/NZJ2r1f6JQHMHpnIPLtcjvuUK9KXYuNqH6opHXaDS4XC65Id5giFm6F/uxZ52QQKdTc9VVEzlzxkxKSvDrkcHC836qVCpUKhUOhwOz2YxKpTortL2BPlNSMblCoZDf0f6dDgUuVzZKJcyaBRqNgurqelwukebmBlau/BPTp8+gpKREPtdAax2HAwRBIC4ujubm5oB+F7CzIRnx1157DUBm+OgJl8uFUqnkiSeeCPQQIcFgmvr1zLk0mUxyKozBYCA8PJyUlJQBm+H5VrxGBKEVUUxDatjnr1zD5SGTMBxlChYSX73dbqezsxOj0Sg/25JDGR8fL6e/2e0V2O3lOJ0CCQkbgbsGPIY/0Gg0xMbGEh4eTmNjI7W1teTn5/dbEDpUkQ1/xqrVatLT0wHo7OyktraW3NzcPukE/Z179OjRMuuKSqXEaByLWt2MVvsWIKBQbAEexO2+FlG83Ou3gaRFhYWFMWLECI4ePeo3e9VFDH8MlZ1atWoVa9asYdOmTX0+H6mpqUB3utT111/P7t27+3U2gumzEQx66nFBOENNzQlOn85gxIidxMaeG2djqCMbnimv0kZSfzTyUhQ0Li4uaHnOPjRERqYTGWlEFGPPwfH7hlqtRq1Wy5u5arVafkeHYgNtsJCYQKUUeUEQfEZeumVRAErCw7O57DI1qalRbNx4EIfDiUpl5Ysv1nHw4DcsWLDQr6jScMVg5A6qz8Z7771HXV0d06dPJyEhgbCwMCIiIoiMjJQL785VPvRAbFQ9GyG1t7fjcrlkZqhgGuX0VnJGNJrnEIQ2XK6ZOJ03BzHXhYNzdW5Op7MX7aDUy0SiHBxoB0urTcRgSMLtNiMIYwnAr/ULGo2GcePG0dXVxZEjR6ipqaGgoACDweA1bqiuX6AphSqVisLCQqxWK9XV1dTW1pKXl+dz0e6Ps6HRaJgxYwYAW7du5a9/bcbpdHLjjeOJjd2OIDSjUKxHoViHy3U7bvcyIF6eP9BOrmq1mrFjx/qs6biI8xOhtlPr1q3j2Wef5csvv+wzFUNik4qIiMBkMrF+/Xoef/zxPuccTBrVUNVGmM3hHD6cjl7fwr59I7n0Uv+jpqF2NkI1j8vlor293Sti4cn25Y+dP18XhBJEMQ7w7SgNh3OTnA673S7XXgwXggBPGZRKJeHh4TidTiwWS59pYN3QI4ojGDkymrS0OD7+eCenTnWn9Z84cZIVK95g8uQpAfXlGC7XZLAYlBcgKRWTycSf//xnFi9ezBVXXMGSJUv42c9+xgMPPMC3334L+H6Y77rrLhITExk7dqz8WWtrK6WlpeTn51NaWkpbW5vPY69atYr8/Hzy8/NZtWpVnzJKO0aSU9HQ0MDRo0fZt28fu3fv5uDBg5w5cwaVSkV6ejp5eXmkpKQwduxYsrOziY+PR6fTDerm+toxMptP0tjowOHYG9RcwwHDTab+dtCtVistLS3U1NRw4MABdu/eTWVlJQ0NDQiCQEpKChMnTmTy5MkkJiaSkJBAXFzcgE6mKGbgcDyPKP4Gh+O+kJ6Pp1KJiIiguLiYnJwcDh48KDfb8zU21AiE5UMaq9PpGDNmDOPHj5cL33v2EQh0A6Kjo0PeUdq+/Sqczndxu4sBF4JwGqVyBWr1DxCEtYAYMMuHL/aqcePG9Sn/RZwfCMZO3XLLLUyfPp3Dhw+Tnp7O22+/zS9/+Uu6urooLS2lqKiIn/70pwCcPn2aq6++Gujuyj1r1iwmTJjAlClTuOaaa7jyyiv7lHE4pVGp1Tq02mKMxhlEREwIOGJ6LiMbUrSiublZ1vVHjhyhoaGBxsZGWdcXFRUxZcoUxo0bR05OTlB2/nzD008/zfjx45k4cSIlJSUyJfOrr74qF7gD5OXlUVRURElJCSUlJdx33/f27f7772fr1q39Hqeqqori4mJKSkqoqKjgjTfe8Es+jUZDeHg4oihiNpv7zEzZsmUL8+fP73eu5cuXy/+22+3MmTMHp9MZkrQllUpFeHg4arUai8WCxWKR36Pe88djMIzmllt+QFFRujyv0+lix44dvPTSi0yePJmWlhYAZs3yn3XRE3/7299kXTZcEVRTv2XLlrFs2TIAjh07xtdff80f//hHVq9ezbx58xg9erTPxZAvWsFnnnmGuXPn8vDDD/PMM8/wzDPP9KIVbG1t5cknn6S8vBxBECgpKWHBggVyiondbmfTpk0cOHCAjRs3cvjwYdavX8+bb75JZGQksbGxZGZm+gynS0VXoUBPJW40xnDyZDJRUSeprZ1KUZH/cw1FLmywijWUYe5Qwe12e4XFjUYjDodDZgGT+Mx9NUkarEyimI/LlR8K8QeUIyYmhilTptDc3MyePXuIi4sjNzd3SI4NgUVMfC3upUW70Wjk6NGjMp1gdHR0wM9gREQEnZ2dOBwOCgpGUV+vJybmJcLC/hOF4jMEoQVoR62+Cbe7GJXqQRQK/wvXBmKv6quW6yKGN4KxU3/+8597zddX3Udqaiqff/45ALm5uVRWVvot42DSfYdqUa9SqZg8eQomk4nIyMig5gqlXD0hNcTzpJjt2RAvKSlJjl4HqyeH2+baYLBjxw7Wrl1LWVkZWq2WlpYWmUDn97//PUuWLPGK1m3cuFGO6krn3trayq5du3j55Zflcb70+OrVq1mwYAG//e1vqa2t5Y033pBrowaCVIiu0+lwOp3Y7Xa0Wu2Aaes9sXz5ch555BGg24m57LLL+PDDD7nxxhv9ksMfSLUnUqRDFMU+nhM9Gs0orroqktTUvaxdewC3u3ucw+Fk/vz5fPzxxyxbtoyvvvpqULKsXr2aa6+91msDfyA4nc6z2tslZEfKzc0lNzeXSy+9lL/+9a+kpXXTU/p6EHzRCq5evZotW7YA3UV7c+bM6eVsfPHFF5SWlhIb2517WFpayrp167jllluA7kXDli1bmDBhAj/60Y/4xz/+wSuvvOKX/EMZnlYqNZw6dTm1tY6A8z6HQq7htIszGCXuWcjX1dUlL0QdDgcRERG9aAfPN/R1TQRBkKMvUs8Lu92O2+0ekCkjUISCvxzAYDBQVFREZ2cnR44ckVMbAnkGtVotN910E6Io8sUXX9DY2Eh4eDiLFj2PVjsJpfL/IQjNgBuFYjcTJtyIwzEVUVwH6IM617CwsJBf24s4dwjETp0NnOvmsz11jVarlVPLgp0rWLmkOrqeFLNSQzzP/lS+GB89d+vPSzQ3Q20tZGdDgIx/PdHQ0EB8fLx8b6X6w9dee43Tp09z+eWXExcXx6ZNm/qc46OPPuKKK66Q/3766af5/PPPsVqtTJ8+nRUrVvD3v/+dV199FaVSybZt20hMTKS6upoZM2ZQWlrK888/zwsvvMBf//pXbDYb1113neyUXHPNNcyZM4ft27fzySefkJ2djdvtxmazsWbNGh555BESEhKYOHGiLENXVxcPPvgge/bsQRAEHnvsMcrLy7FYLBQXFzN69Gjee+89Fi5cyKOPPurT2YiMjORf//VfWbt2LXq9nk8++YSkpCTq6ur42c9+xpkzZ0hISODtt98mMzOTO++8k8jISCoqKmhoaOCZZ57hxhtvxGQyYbFY5IL3NWvW8PTTT2O324mLi+O///u/KSq6lLAwJe+/vxul8vvld1NTE263m+joaDo7O9myZQsvvvgin332GQD33HMPxcXF3HzzzTzyyCN89tlnqFQqSktLuf766/nss8/YunUr//Ef/8HHH38MwC9+8Quam5sJCwvjzTffpLCwkDvuuIPY2Fj27t1LcXExL774YlDPVSAIytkwm810dXWh1+vR6XRoNBrS09M5cOAALS0tlJSU+L0QamxsJCUlBYCUlBSampp6jTl16pQXNWF6eroXT7ROp5MdlL1797J+/Xq/zyWUhXc959LpdEyePBmj0TgoZ2O4sXyE0gHqD1IalGfEwrNgX+pdYbfbaW1tJT9/aKIMZxsDOYSCIJCenk5KSgpbt25l586dZGVlhZzCNZTF5JGRkZSUlNDe3s6ePXuoqqqisLCwXxo/z/lVKhWiKNLQ0IBWq8VkMmEymVGrb8ftnoVafTOCsB/ofr7V6l1ADHb7r4F/73f+gRyr4eScX0TgCKWdCjXOZfPZ4dSzoyc5y8GDB3G73Wi1WsLDdXR0rMNk6mL06CuIj590VmQ6lxA++ADh7rtBrQaHA/HNNxFv9r/WsydKS0t56qmnGDVqFHPnzmXx4sVMmjSJe+65h1deeYVPP/0Ui8Uip4xefvnl8vtw2223cf/997N9+3YWLfqeNGDZsmX85je/Qa1Ws3TpUtasWcP8+fNZtmwZBoOBBx54gNraWg4ePMj27dvR6/WsX7+eI0eOsHPnTkRRZOHChWzdupXMzEwOHz7M22+/zXPPPSdHWRQKBYIgcO+99/LZZ5+Rl5fHnXfeKcvw7LPPEhUVJUcT29raWLRoEa+//jp79uyRx40dO1ZOGwMoLi6WvzeZTEydOpWnnnqKhx56iLfeeovf/OY3/Nu//Ru33XYbd955J++88w733gVUhLkAACAASURBVHsvn3zyCQD19fVs3bqVqqoqrrvuOm688UYEQUCv18uRt0mTJvH111+jUCh46623ZEfr9de/oDtolAh8vybz51ltbW2VU6YEQaC9vZ3o6Gjmz5/Ptddey+LFiwGYO3cuK1asID8/n127dvHzn/+cf/zjHwB89913bNy48azru0E5GxKDxxtvvMEf//hHSktLSUtLkxcPBw8eHJJOhr5uRt8FvOcuF9bXYjwiIsLv3gk95xpuhXehgue59Rcal65dSkqKz/zavup7LnQolUq0Wi2TJ0/m2LFj7Nixg7y8PBITE4O+14HUPQQSLYuOjiYqKorU1FS++eYbmQ1Krx84AiEIAtOmTWPv3r3Exsaybt06Ro4cycSJE3E4tqNQ/BqV6j+9fqPRPI/dHpyzcRHnJ86VnQoE585OuVCp9hIefhSYwCDLN2UEYqekOkpJz/dsiKfVasnNzZWp41tbyzh8uAW1WuDQoXVccsnkkMvk7zwWi4WWlhbi4uKC7pfRJ5qbEe6+G8FiAakx8d13I86dO+gIh8FgYPfu3Wzbto0tW7Zw66238sQTT8g9YLZt24bVakWr1aJWq32mUdXX13v1VNq6dSsvv/yy7KSMGTOmz1oKaY4NGzawYcMGuYGr0WjkyJEjZGZmkpWVxbRp0zCZTF72pKqqipycHMaPH4/L5WLx4sWsXLkSt9vN5s2bef/99+WxfbE2KpVKNBoNRqMRvV7v5YhoNBquvfZaoNsJ2bhxIwC7du2SG3fefvvtPPzww/JvFi5ciEKhYPTo0TQ2NsrnKAiCXPD+3Xffcfvtt9PU1ITD4SA7O/uf1/prPvroI7Kz7bz22qecOWNk8uRMvxb/UVFR6HQ67r77bq6++mpZbk8YjUa2b98uOx6Al45ZvHjxOdlYGZSzIQl60003kZSURGVlJRs2bGDFihWcPHmSSy65hFtvvdVr7EBISkqivr6elJQU6uvrSUxM7DUmPT1dTrUCOHnyJHPmzPE5n0ajGTa5sMN1Lperk66uxxCE06jVvyIsbOpZkUkyNi0tLbS1tVFWVoYgCH6Fxs832O122tvbiYmJ8ft8Ak11U6vVjBw5Um48VVtbS0FBQb90uf4gEGcjkMW6KIrExsaSnJxMc3Mz+/btIyoqitzc3AH7k4wdO5bCwkJWrlyJSqWioqKCgoICwsPDcbtfwm6fj0Zzld+yDEb+izg/MBR2KtQIlI0qVM6GUrkDUXyPpKQzKBQ5uN3Tg5rPV2aA2+3GbDZ7MUH1bIiXmZlJeHi41/vX1dXllUuu1yehVgs4HCIpKcHptGAgiiJbt67FZGpAp4tj3rzFQ5PzXlvbHdGQHA3o/ru2Nqh0KqVSyZw5c5gzZw7jxo3jT3/6k+xs2GwmNBoRp7PvCJ9er5fJSaxWK/fddx9ff/01ubm5PPnkk17EJX1BFEUeeughuYZKQm1tbb9RbskWSRtsgiB4FWb7A5vN5tO+eHYzVyqVfdboedpDz1RD6bn/3e9+x4YNGwDYs2cPv/rVr7jvvvu48sor2bRpE88++6w8VhAEbLYsfvnLX5KUlEFVlXfnb5VK5XVuVqtVjvDv3LmTTZs28eGHH/Kf//mfsnMkQUrH2rdvn8/z8CebYCgQlIVNT0/ntttu4/nnn2fz5s3U1dXx5ptvsnDhwoDThRYsWCCzS61atYqFCxf2GnPFFVewfv162traaGtrY/369V45hJ44dztGDtTqMsLCDiGldASDoXQ2urq2I4oHsNvNGI19M3v5gj8yiaKIyWSisbGR6upqKisrZSaw1tZW1Go14eHhlJSUMGrUKCIiIkhISAhoYT6c4XK52LhxLbt3f8jmzRuGPKSv0+kYO3YsY8aMoba2lj179tDV1TWouYYqsiHNLYXHExMTmTZtGrGxsXIzw4EWX0qlksjISBwOBzabjS1btng0ZLuUbdvW4naHI4pK7PaVfskz3KJ+FxE6hNJOhRrnalOso6ODzZvj2bEjm46O9qDnk3oQnDhxgkOHDlFeXk5FRQW1tbVYrVZiYmIYNWoUU6ZMYeLEieTn55OSkkJEREQvR7/nu6jXZzJr1p1Mm3Ylo0f/2G+ZQh3ZcLvtWK0H0GpPY7cfwukM/rr5RHY2vbjUHY7uzweJw4cPc+TIEfnvffv2ySnpEREGJkxIIiUllksuycdqtci61Ww2YzabsVqtFBYWcvToUQDZsYiPj8doNMp1Aj0RERHhZYPmzZvHypUrMRqNQHdqfM+U+Z73rLCwkJqaGqqrqwH44IMP5M3JuXPn8oc//EFejEtZDlL/DglnzpwhPj4+oHXFtGnT+PDDDwF4//33mTlzZr/jH3/8cSoqKuSoSUdHB2lpaWg0Gv76178C3SlbM2bMkKMxX3yxhba2dnouxbOysjh06BA2m42Ojg45BcpoNNLR0cHVV1/Nyy+/LDsUntc5MjKSnJwc+ZiiKAZEWjFUCPl2nsTY8dRTTwHdSqgnfNEKPvzww2zYsIH8/Hw2bNggh6zKy8v58Y+7FUxsbCyPPfYYkydPZvLkyTz++ONysXhPBOpshEoxKZUbCQt7j+Tkj1Ao9gc931AWrisUOTidepRKG3b7uIDm6Qmn00lHRwenTp2iqqqK8vJyysrKqKmpwWKxEBUVRWFhIZMnT2bixImMGDFCfvldLheHDr2Gw/ErDhx4O+D7MFwXijabkaKiFcyd+w75+f+Fy+Ufq1GwRfwGg4GJEyeSk5PDoUOHOHDgABbPXbIQyxDoYr3n3IIgkJyczPTp0zEYDJSVlXHkyJF+d5gWLFhAQUEBCoWCkydPsnv3bg95wrDbm3E4TMDAec4X06j+78EfO3U2EOgmV6g2xRobc3E4sjCbU2hoyPL7dxItaVNTE8eOHWP//v3s3r2b6upqzGYzSqWStLQ0mU5copL3h05cgi9bHBaWQ1zcTJTK/iOfQwmlUsmMGY1ERdmYPLkBnS7wQnq/kJDQXaOh1yNGRnb//803g4pqGI1G7rzzTsaNG8fEiRM5dOgQjzzyCC6XizvvvIvbb/81y5e/RE5OGqLYvbE7ffp0Zs2axc9//nNUKhWXXXYZ//jHPxBFkejoaO644w5KSkq44YYbmDTJdx1NXFwcM2bMYOrUqTz44IPMmzePW265hZkzZzJhwgR++MMfDrghptPpWLFiBfPnz2f27NlkZX3/vD744IN0dXUxefJkioqKWL9+PaIocvfdd1NUVMRtt90GwObNm70oqIuLiwe8Zs8//zzvvvsuRUVFvP/++36RDXk+348//jg33XQTP/jBD4iPj5eJDR599FG2bNnCjBkz2LBhA5mZmb1+n5GRweLFi+VzKPonhanRaGTBggUUFRVx6aWXygXeN910Ey+88AITJ06kurqa999/n7fffpsJEyYwZswYVq9ePaDsQw1hgIVdv19WV1dz4MABxo0bh0qlQqPRkJKSwgMPPMDx48f56KOPzjq9loT29nauu+46uZp/INhsNqqqqpgwYUJQx3W7/4LJ9FesVgtxcb9BoQguPH3mzBna29vJy8sLah6AyspKCgsLvUKAnZ0nsNvPEBs7DoVi4FQCURQ5deoUnZ2dhIWFeTGESBSzUrf1ge57V1cXJ0+eJCcnGpvtRtxuHYLgIDJyIwqF/0xS7e3tNDU1UVBQ4PdvfKGmpkZO4woGZWVlTJ48GWhEFK+jrU1LbKwN2AJ8f+3tdjt6vb5X2PrgwYOkp6cTFRU14LG2b98uN7/zBVEUaW5uprq6GovFwqxZs/xi6dqxYwdTp071axF+4sQJRFH0Upr9YefOnUyZMqXPud1uNydPnuTEiRNyWqWvXaXjx4+zfv16jEYjKpWKSy+9lLFjx7J9+3amT5/utwNUW1uLWq2WmYl6Qq1WDwdnZHh61MMD562dApg4ceKAvQs88b1+GTy6urpkNp2rrrrKp67p2fzUZDLhcrnkhnjSfzqdDpPJRF1dHaNHjw5KLujO0U9NTQ2YfrcnQmUX7HY73377LUVFRQjCUZTKbbjdU3G7/T/Xuro6CgoKsNlsftWnAX2yUUk0/YGwLYqiiMvlwu12y/+XmpkqFAqUSgGl0kG3fVL2+q1ESTtr1iw++OADEhMT5fRTf6IFJpPJ747ZUmNlf2A2m+Vu3hJ7mcPhQKPReF2fRYsW8bvf/Y68vLwBU3V9ze0PApFbyvoAZFnPnDnDpEmTqKmp8fkbh8Mhkyb4gnSPzhYOHTrEqFGjeonR1/hBaVdpJ3D//v0sWrSIxMREcnNzKSwsBLrz1STGgHNlpM9VGtWxYyMxm8fS0WEjMzORLP83jXxiqOs/IiMzgAyf4z1zbiWDI4UmVSoVcXFxckfewT7koiii00WhUqXicDSi0eSiUJz/KVTdSESrvZ60tPU4HEtwOv3bCQtlupUnXe6XX35JWVkZKSkpZGVl9ZunHkhkYzBpVP2NVygUZGZmkpaWRl1dHSaTiePHj5ORkeGlTzIzM5k6dSrbtm1Dq9Xy9ddfywuLwTb1u4gLB+eDnYJzE5mNiIjgkksuoby8nMjISCwWi1dthcVikTsmGwwGUlJS+t1AGo51ikMxjyiOwOkcEfScfiEhYVDRDMmR8HQqBEH4p1PRXSitUCjkxfT3GNjuvvjiizIVrN1ul4uh/cFQP+eCIKDVauU6KKPRiFarlVmvRo4cec4imD0h3Q+dTofD4eDIkSNce+21PPDAA+datCFDUE39rr/+etxuN6dPn6a8vJx9+/ZRW1vr1bDvXCnxQAvvQqWY9Pp4jh6dSmtrK6NGDbwzfbbkktDXXE6n04vP3GQyIYoiYWFhGAwGr94VTU1NmM1mkpKSgpLle+UThlr9GlrtYVyusVw4m7gCdvuDwK8J9JxCrZgFQUCj0TB16lROnDjBzp075QV9sO/oYAqs/Tk/pVJJTk4Op0+fxul0smPHDrKyskhNTZVrPgoKCti7dy9NTU2oVCo2bNgQcGH8RWfjwsT5YKcgtJsL/UEQjgDH6OoqpLNTLVOJl5WV9WqIN1Dz095zDz9n40JHdx2Jt2Mh6WLJsQhFVNbpdHLw4EGUSiWTJk2SN6mk45tMpgEb751NeDodNpsNl8vFrbfeOuyeKWmTTqvVMmLECCorK3E6nTgcDp8O3HDrkRYoBv10OJ1OWltb0Wq1JCQksGDBAhYsWBBK2YKCUqk8J7mwaWlp6HQ6Dh06JFPHBYOhUOKeu1hdXV29elekpaURHh7e5873UBgDUUzG5UoO6ZzDB4EpiKFUikqlkuzsbNLS0qipqWHnzp190uUOFRtVoBAEgby8PDIzM2WZs7OzZSrkSy+9lDVr1mA2m6mqqmLcOP/rj+Cis3EhY7jbqaGClFLyvY4/SUrKG4ALUczE5bqHhIQE2tvbmTJlStDHG679oEIl07leqEoLe5fLhdPpxO1243A4ZKdCpVKhVCqHZDFaWVlJRUUF0H1NJf0qbWApFAqsVit2u73PtKOhvH59nbMgdHcilxoDSlTY/uJsLu4lWUVRxGazYbfb0Wg0FwRRjoSAnQ3pBuzYsYOlS5cydepUpk2bxn333YfD4ZAf+OHggZ2tnRlBqEahaMLlGg+EExsbO6gurKGWS9p1kCIW7e3t7N+/X45WREREkJyc7LN3xdlAKMPcFxLOhpJTq9UUFBSQmZlJdXU1tbW15Ofn90m40B+Gks3J8/mQZM7KyuLYsWMcP36c3Nxc0tPTSU5Oprq6GkEQOHbsWMAF7hedjQsLF6qd8oWeDfGkdFdPitmEhDSqq7NpbtaQn68lMzMjJMeWMFz7QZ2P9kWqr/BMg4LujSJP5yKQmo3g5LEDtn/+2+Hxefe1VSgUhIWF4XK5sFgscnrQ2bhu/txfhUKBXq/HZrPhcDgCrsUYSvja4PN0kOx2u1fUaDjoq8EiYGdDOtlLLrmEdevW8cADD1BVVQV8/zIMFwSiaAarLAWhAY3m94ANhaIYh+MnIX0g/JXLs1GSlAYF3ZzKEqWszWYjNzc3aJ7lCzXMfbbPqbGxkf3716LVxnPZZTf4XbgWSuh0OsaMGSM3V6qpqQm4oHIonSNfc2u1WkaNGoXVaqW6upqamhpGjx5NTU2NnA7Y0NBASkrKoI9xEec3LlQ7JRXA1tXVeen5sLAwIiIivNJdPWE0GmlsHIfBYObo0UT85HLwG8Mxjep8eKc9U6BcLpesi6Rn1FcalFQgfrZQVJSBUtmGUqlgzJjUPsdJNT4OhwOTyYRarUaj0Qyb+6BQKNBoNCiVStkp0mq150wX9HcPJQdJcjpsNhsKhWLYpKoNBkFJPnLkSNasWQP8X26MZcNs7sJud6HXt4Z89p6K1zMNSopY2Gw21Gq1V6OksLCwXvejvr7+ggkrDxXOpmLs6Pgjc+d+gsOhob4+mZycy4ChWfwOdL8kutz29naqqqqwWCxYLBa/mFOG2tnoS69IjpLZbObo0aPo9Xo6OztxuVzs2rWL6667zq9jDBTZGC7G8iIGh+FupxQKRa8UD7fb7VPPazQaXC4XarWajIyMXg3x+oJerycyMoeOjg4v6tBQYbg6G8PJ3klRCqvVOmT1FUMBlSqKSZO6i+JFcWCGMKlo3G63YzKZzloEZiBI91ByipxOp0yE0Bct87nW/Z5Oh9lsxuVyIQjCeel0BP1ke7IdDDecDZm6uuKorBxPXV06e/YER5vbEy6XC5PJhMlk4vDhw1RUVFBWVkZ1dTUmk4moqChGjhwp966QGiUZDAafSmu47RgNN2NwtpGTU43LpUSjsZGY2Dikx/LXIYiOjmbSpEmo1Wr27dvHoUOHBiRaGEpnwx/dEhYWxvjx471yz6uqquQGVP4cYzga+YsIHQZjp+666y4SExMZO3as/FlrayulpaXk5+dTWloqNxHriVWrVpGfn09+fr7crLYv6HQ6Pv/8c7788kuvhnhSj6Lo6GivhnharbbPhng9IQjH0Wj+HZ3uLSZPHsvs2bMZOXKk39fAXwxHZyNUCFS3OZ1O2traOHHiBN9++y1lZWVyRAq6mRz1ej3h4eHo9Xo5TWaoddDTTz/N+PHjmThxIiUlJZSXlwPw6quvYjab5XF5eXkUFRVRUlJCSUkJ9933GKKYiyjmcf/9v5FpmkVR5Pjx49TX13sdp6qqiuLiYqZPn05VVRUrVqzA7Xb32TcpUGzZsoX58+f3O2b58uXyv+12O3PmzOl1fJVKJdemmkwmbDZbr41dX8f89NNPefbZZwclu6dcgUCKcknER5LjcT4h6Kdb8syHI86GwlIoFLS2jqW2djZu9+D6M0hFQWfOnKG2tpZvvvmG3bt3s3fvXpqbmxFFkeTkZCZMmMCUKVMYN24cOTk5xMfHB5wbOdx2jP6vQOLVbmho4OjRo+zdu5e6usv+SS05jvDweV5jhyKy4e+c0s7JtGnTiIqKkh3cvozFuYps9MT48eO95Pjoo4/86qB+0dm48DEYO3XHHXewbt06r8+eeeYZ5s6dy5EjR5g7dy7PPPNMr9+1trby5JNPsmvXLnbv3s2TTz7Zyyn53//9X6677jqKioo4evQo//u//4vRaPTZEC8+Pt5r5zUQ/atSraOz04jVegCV6rshS9X0XyaR5uYPOHjwCTo7N/c96jywU5Ldbmlpoba2lgMHDrB7924qKytpbm5GqVSSkZFBSUkJGo1GTtlRqVRB6cvBnNOOHTtYu3YtZWVl7N27ly+++ELuK/T73//ey9kA2LhxIxUVFVRUVPDKK68gimpaW7vYtWsXs2fPBpDnWb16NcePH5d/u3r1ahYsWEBFRQXx8fG89dZbKBQKOb1qoEWyVAzvL3xdS89FvUaj4bLLLpM7aveElBUiCAImkwm73d7vNV6wYAEPPfSQ3/L1JZe/kK6XZAvDwsLk1g7nk9MRdCzGarX2q8Ck3aThGPnoicAKyk+hUq0mIiKNkpKZGI0WEhMTB/ydFB73pJmVmAekou2EhAT0er3MhX3s2DG/GrwNLPPwuwcXotMiiiJdXV3yf0ajUW6GFRERQUxMDJmZmbjdozh16ipOnz5NVpaFtDTRa0ERapkCnVMQBFJTU0lOTqauro5du3aRkZFBenq618JtKBfrgexGazQasrKyqK2tlT/bvHkzWVlZjBgxos9apYvOxoWPwdip2bNnez1L0L2Q2rJlCwA/+tGPmDNnTq9dzi+++ILS0lKZbKG0tJR169Zxyy23yGPGjx/Pyy+/THZ2NldeeSXPPfec381EpUW0P+9FbW0yVVX1KJXhlJREEBHRe0woNgsEQfBrgWiznaSs7BtAzalTmygtnY0geDMEDbeIt3RuUhG+pNMlux0REUFERASJiYlelMFut5szZ84ExIDUEy0tEAJSSxoaGmSnFSA+Ph69Xs9rr73G6dOnufzyy4mLi2PTpk19zvHRRx9xxRVXyH9v2PAFJlO3k/Lyyy/z8ssv8/e//51XX30VpVLJtm3bSExMpLq6mlmzZjFv3jyWL1/Os88+yyeffILdbuf666/nt7/9LbW1tVxzzTXMmTOHr7/+mtWrV3ul+61bt45/+7d/Iz4+nokTJ8qfd3V1cc8991BRUYEgCDz22GOUl5djsVgoLi5m9OjRvPfeeyxcuJBHHnmEW2+9tdd5/fznP5d/c8MNN/Doo49iMpnYsGEDv/nNb3odc+XKlVRUVPDaa69x5513cs0113DjjTcCEBkZSWdnJ/X19dxyyy10dnbidDp5/fXX+fzzz73kWrFiBe+99x5/+MMfsNvtTJkyhddffx2lUklkZCT3338/69ev5/nnn2fWrFleMiuVSrkoX+on56tB8HBCUM5GR0cHTz31FEVFReTm5pKQkIBCoSAiIoLY2NhzXogn0dkOhQwq1ccoFMdRKL4lLq6QmJjeoWmn0+lFMduzd0VMTAwZGRn9MlcFsmPkdH6I270Pleo2FIqxvUYMt1zY4ej8BArPLruSETKbzZw4cUI2QHl5eT5zLO12O1lZ2aSnZ1BdXc3OnTspKCjA7XbT1dUl55KGCoO93gqFguzsbNLT02Xq2dzcXJKSkgJa+AwGgb6/CxYs4Pe//738d2NjIzNmzODAgQMYDAby8vJ61aFcdDYubITSTjU2NsrEAykpKTQ1NfUac+rUKTIyvm+Ump6ezqlTp7zGjBjxfWO4wTagHVjmM7S2xgMTsdkEjMbwXs5GqN5fqYPzwDAgCBqcTgdabTi+kitCmaY7GEjpy5I+l2x3TU2N33YboLy8jOrq79BodIwbF3iK9YkTkJ+v4OhRN+npgzoVGaWlpTz11FOMGjWKuXPnsnjxYiZNmsQ999zDK6+8wsaNG72o+i+//HJ54Xrbbbdx//33s337dhYtWiSPuf32K9i//zv0ejV//vNa1qxZw/z581m2bBkGg4EHHniA2tpaDh48yFdffYXBYGD9+vUcP36c7du3Y7Vaufnmm/nyyy/Jysri8OHDvP322zz77LNenbitVivLli1j48aNjBgxgptvvln+7rnnniMqKorKykoA2traWLRoEa+//jp79uyRx40dO1am7wUoLi6Wv3/qqaeIjY3F5XJRWlrK4cOHyc/P59577+Wzzz5j1KhR3H777QNeY8/n7c9//jPz5s3j0UcfxeVyYTabueSSS7zkqqio4C9/+Qvbtm1DrVbzi1/8gvfff5+lS5diMpkYM2YMTz75pDynr/fU0+kY7uupoJwNQRA4ePAgf/vb32hra8PpdDJz5kySkpJITU0lLS2NpKQkkpKSmDlzZqhk9htSfttQhI7N5iis1pMoFDp0ujAcDmuvpniVlZV+967oC/4qcbv9OyyWN3E4VKjVNURFfdhrzHDbMQoGLpdLbux2tuBwOLwiFmazGYVCIUekpHu8Z88eRo8e7fe8arWawsJCzGYz3333HZ2dH5Ob+xeamlKIifkLBkPw/UdCsaBQqVTk5+f3oss922xU/aEnL7kgCMTHxxMXF0dzczP79u0jOjqa3NxcebFw0dm4sHG27ZQv3djfMxyos+GPHheEGjSa31NQIGI2X4JGk+uz79NQbsj5glYbw7RpS2ltPUJSUlGfRblna1NM0umSU2E2mxEEQWZxTEpKIicnh/3793vV7viD5uYydLpT2O16zGb/u47b7bBkiUBVlYDTKXD11QpGjhT5n/8RGWzbBYPBwO7du9m2bRtbtmzh1ltv5YknnuAnP/mJz/Gezod0Devr672ibxUV3/LSS69hNltpbTUxatQESktLAfqMcm3YsIENGzYwdepUoDsycfDgQVJSUsjKymLatGkYjUav31RVVZGTk0N+fj4AS5Ys4c033wS6ayk+/PD7tU5fDV2lAvyuri5iY2O9HJG//OUvvPXWWzidTurr6/n2229xu91kZWUxbtw4bDYbN954Y7+1Vz2fs0mTJvHjH/8Yh8PBwoULKSoq6vWbL7/8kj179sjXwmL5PjtGqVR6OXYDYah6rIQSQTkbkZGRfP755wBs2rSJW2+9lcjISBITE9m1axcNDQ0AFBQUXDDORnc4tZ29e5NwuWbQ2akiIuII8fFtXh1YLRYLJSUlQR/PX8Vrs2lxONSo1TY6Ogz4yroajhGJwcizf38lx47tJCoqh9mz5w5JypHNZpMNkGfjw4iICAwGA9nZ2T4Zv4JBWFgYRUVFnD59F+AiOvo4HR3rMRiWBj13KJ1DrVbL6NGjMZlMHDlyhPb2dqKiovzq3B2oHIEuhERRJDMzk7q6OgBmzJgBdD+ziYmJJCQk0NDQQEVFBXFxceTk5Azo0Ax3JX4R/SOUdiopKYn6+npSUlKor6/3mTqbnp4up1oBnDx5kjlz5vQ5p0ajkYuH/YE/DWgF4ST19WC3a5k61QZM7GPc2bcJMTHZxMRk9ztX6JvGdut0T8eip07PysryqdNFURyUPFOmHGDnznhSUxuIjg6X5xoIGg3k5cEnn3T/aT6sfwAAIABJREFU/e23cPXVDNrRkKBUKpkzZw5z5sxh3Lhx/OlPf+rT2fAFvV6P1WoFuqMN99zz/9i1axNZWTk8+eTzWK1WPvvsMwRBkO1nT4iiyEMPPcSyZcu8Pv/uu+/Q6/V9kpH09XwFshnlay1YU1PDSy+9xK5du4iJieHOO++Uz1EQBJkNSqPRyIxQnvdQpVLJ76LUSBO6UzC3bNnC2rVr+dGPfsQDDzzA0qXedlwURZYuXcrTTz/dS1adTjesU6IGg5DwZ61bt45f/OIXLFy4kG+++YZ3331X3mE8deoUx44dC8VhAoZWqx2QSacnPB9eh8PhlaNpMpnQapvIzV3NmDFWKivnEBaWweTJk4noEZ8OZSjYn1xYgyGLpqZHMZkOkZJyZZ9zXQhpVCrVK1xxRSXt7XF0do5DqRx83xCJSrirq4vW1lZaWlqorq5Gp9MNqvFhKK6L0TiZyMgvcLnCOHkyHKu1muzs7F7KJ5BjDUX0ITw8nKKiIvbs2UNdXR0tLS3k5+cTFhYWMjkCHe92uykoKGDOnDmo1epeefCCIJCSkkJSUhKnT5+mrKwMh8MRUEHiRZyfCIWdWrBgAatWreLhhx9m1apVLFy4sNeYK664gkcffVQuCl+/fn2/haGDTaPqG24aG0X27UtGFKGrK5d/bgr3wnAk+whWJmlR2NbWhtFoZO/evTgcDrRarexYnI1mtsnJ1/DDH76GKObx3XeBpcPef7/ICy9I9XvdfweDw4cPo1Ao5OjAvn375FS/iIgIurq6fEa+PDFq1CiOHj3KnDlz5AV5XFwWRqObjz/+mEWLFtHRcQboLlg2m83y3BLmzZvHE088wZIlSzAYDJw6dUpmWZKea4m5Sko9LiwspKamhurqavLy8vjggw/k+S677DJef/11Xn75ZaA7jSomJga1Wo3D4ZDf7zNnzhAfH9+Lhrezs5Pw8HCioqJobGxk3bp1/OAHP6CwsJDjx4/Lx/zwww/ltGaHwyF3cc/OzmbPnj0sXryYtWvXypsGx48fJy0tjbvvvhuz2czevXtZunSpl1w/+MEPWLJkCffddx+JiYm0trbS1dXVJzX1+d4PKmhn49NPP+VnP/sZzz77LLfddhvLly/nuuuu4+OPP0aj0ZCWliazHpxt+KvEpQWny+Wiuroas9nca9dD4jRXq79AqdQhinrCwrS4XNO98gt7zhuKwjt/FK8gCOTlzQZmBz3XcEd+/hE6OsKJjW1Dre7EaAz367x6Fvl1dXV5FW7rdDri4uL8bgY3VOjquhu1+heEh6cxeXIMdXV17Ny5k+zsTFJT0wf1TA2lopLSq2w2G5WVlURHR5OXl+eTX30wzkYgkQ23241SqSQ1te/mU9C9YEtPTyc1NZWtW7dSVlZGWloamZmZF9yO0kUMzk7dcsstbNmyhZaWFtLT03nyySd5+OGH+eEPf8jbb79NZmamzHBTXl7OihUreOutt4iNjeWxxx5j8uTJADz++ONysbgvhDqNSqn8OyrVJiAGURyLyxXd51h/oiRnG4HYKZfL5bUhaDQa5bpIURQ5ceIg8fFxTJ9+pV89g/qSZzBwuRZgsVwBaIATAf22rQ2WLBF55BE3y5cLtLVBUtKgxAC6mzree++9dHR0oFKpyMvL46WXXgLgxz/+Mddeey3JyclygbhnzcbYsWNZtWoVV199Nf/1X//Fj3/8Y6Kjo7njjjsoLi4mOzubSZMmATBv3mhWrjyBSuUgIcGAxdI9l1Qg/txzz1FVVSVHEA0GA++++658LJ1Oh9PpxOFwYLPZ0Ol06HQ6VqxYwfz584mPj2fmzJl88803APz617/m4YcfZvz48SiVSh577DFuuOEG7r77boqKipg4cSLvvfcemzdvZt687xkfpZqNCRMmUFRUJDN8SpFwnU7Hq6++2uuYSqXSqzHgkiVLuOWWW5g2bRqzZ8+WSUi2bNnCiy++KDNdrVy5EsBLrj/+8Y/87ne/48orr8TtdqNWq3nttdeGpA/OcIAwwEvd75dut5vk5GRWrlzJ1VdfLS8kFi9ezOjRo+XilnPlkS1atIgnnniCnJwc+bOexV+eTEFdXV3k5eURFRXVZ5MXm62Mrq6XEEUBvf4RDIZxPo9dXl5OcXFx0Gk2LpeLffv2hSQlq7q6mujoaOLi4oKap7Ozk9OnT1NYWBjUPDabjaqqKiZMCKx4TqV6B6XyvxHFYuz2Z+jstPSSx9MI9SzOl9hDDAaDV47/sWPHiIyMHHCHpz+43W4qKirkhUZ/sNvtPsP25eXljBs3zqMAUcRq/RVK5cc0Ns5Er3+VuLh43G43u3btYvr06QMey2KxcOjQIYqLi/06j+3bt8uKdyBUVlaSl5eHwWBAFEUaGho4duwYSUlJZGdnexXHO51OKioq5DzVgdDW1kZ9fb3fNTBGo5Hq6uqAnqnt27czdepU6urqOH36tBfjliAIw6Up1fm7pTX0OK/t1L333stVV13ld6rxt99+S2ZmZp+bXPAH6upO0t4O4eGXk5n5g161TBIOHDjAiBEjBr0Q90RZWZlfem8gnDx5EkEQejl/drvdS6f3rJmT0pilhWtFxf9w9OgBFAo148dPY+TIqwctU7DnVldXR0FBAVartd/Irz+QsjWC1Usmk6lPhj5PSO+FIAjMnj2bTz/9lOjoaCwWi7zwliAItYAJUGK3Z/Huu/+D2WwmMjKSpUuX+vV+GY1GDAYDLpcLq9Xab6dvaexAkNaCo0eP9rshXl9zv/TSS3R2dvLb3/5WdoqUSqW8vvAHoihiNpv9uv4SzGazF9tZT5xt1tdDhw4xatSoXmL0NT6oyIZCoeBvf/sbM2bMkB9IURR57733mDlzplclfV84fPgwN910k/z3sWPH+N3vfsd9990nf7ZlyxYWLlwoOw033HADjz/++IBzC4LAhg0bSExMJD8/32fxlydT0L59+4iJienzJVYq/4EgvIfdruTYsRuIi1PR+1p/f+zhlLIUyrmcTictLZUcP24lM3PCWTfQTuddOJ1L6N4xEnA6u7DZbNTV1cmOhacRSk1N9TJCQw1/r0d/98JzDqfzDPAhXV164uK+5LvvKqmtjWbEiBEBHSsUcg00t2ea0okTJ3rR5QZagzGYmo3BPI9KpZKcnBwyMjKora1lx44dZGVlnbOo7EWEDqGwU0OJUEY2BOEIhw8fp67OjShmUVQ0uk9HY6C5ziVsNhtNTU2yc2Gz2VCr1bJTIVG39qcb4uObqK4GpdJFbOyZsyj9hYvnn3+euro6oqN9R8tEMRMwA1osFgtmcycqlUB7eysul8uvhb6kvwPp9N0f7HY7CxcupKCgIKDf+cKKFStYtWoVH330EfB9t3SbzSb/p9FohmRNNDApxPDejwo6jcpTgUP3CWu1Wt5++215odDfRRg5ciT79u0Dunej09LSuP7663uNu+SSS1izZs2A8mzcuJEXXniB+vp62traEASB6667juzs7AGV00DMTwpFGSpVPFrtMTSazn550S9kZ6O5eRVjxrwP6GhufoHExGnBC+cHpAIsT5pZi8Uif6dWq/ss8vN3/uGAnnK43QaamtKJjz9FS0sqhYXTMZttHDp0CIvFgs1mG5CGMVBnI9i6CoVCIS/Wa2tr2blzJzk5OcTExAx5zUYw0USVSsWIESPIzMykpqaGiooKZsyYMewV+UX0j2Dt1FAilDUbSuVm7HYFTmc4CkUM0H9V8blOo/LVv8JisciN76TNosEsMlNTFzJjxr8TG5tIePhVBKPehxuxyrnCwBFpBdAdDYiIcFNSksmhQ/VMnZrJ119/xcGD3zJhwoQ+o3i+rrNKpUKlUmG32zGZTGg0moCiOhqNhqVLl8p1JsHgpz/9KT/96U97fa5Wq2X6WX9lvBCeh0AQlLMhhZl8XTTPNIavvvqKGTNmDLgI2LRpE3l5eUHlrI0bN4533nmHlJQUfvrTn3LzzTfL+YQDob9ibEE4hdVag8NRic1WzOjR12Aw9J1uEyolHsoHMlTORkzMt7hcSrRaK3AMGJyz0Z88Uh2NZ9hcWlRLaVBJSUly+tvp06dDUmsxXBSApxwajQ63+78pL99KRsYstFol4eH3kphYwcGDN1JeriE5OdlnEbmEoSwm72+8tHjPyOjuJVJTUzOkkY1Q0XhqNBpGjhx53hflXUTo7VSoESgbVd926gxNTQdobHRhs2kYNWrkgCmhoY5siKKJjo7/wm7vJD7+ThSKTPk7qe+UJ+GKKIqEh4djMBhISEggJyeHlpYWnE6nV6+SwSGd5uZ7SE+fGJSj8X8dg9WBgqBm5swxzJxZSGeng3fe2YJaLVBWtpvi4uKAC/Q1Gg1qtVpOpxtMD6qhpmj3JaOvyOJg3rnz3RYF5WwsW7aMm2++meLiYgwGA3q9Xq62b2lp4fjx4+zatYsvv/ySkpKSAfNCP/jgA68uq57YsWMHEyZMIDU1lRdeeIExY8b4HJfkUUUVKBtVfw6CSrWW9nYjbncKp06NQ6US6S9VcDiGp0Mlk15/J6L4GBpNOgbDFQP/YABI7CGeEQun04ler8dgMBAVFUV6enqf4cnhdK1DoRB8nUt2dj7Z2d1MIoKwE6dzO3a7QGbmR4wY8YRHEXk2qampPmXwdxE1FIxREl3umTNnOHDgAGVlZRQUFBDli6N5iGUJBBf7b5z/CLWdCjUGE9nwpSOUygrq6zuxWlPR6yPR6ZIHfBdCrTtbW7exffsZ3G6BnJw/Exl5k9zoVKlU9upJ5GtzZDhmBQwVmpr+P3tnHh5Vlaf/z629KlWVfU8gISELOwEEBNkEFRBFUbGhm25bW5DGwWUcf04rLqP2ONqMS7eK3S6jqLRgu2u7jQhCSMIiOyEBkkAWyJ5UKrXe+/sj1jVLVVKVVCA6eZ+H5wlVp849dzvnfLf3PUdeXh7R0dFMnjz5gm8mPWxQbrdb/hvanjmdThfgfKhAktKBViTJQWiohqamVkJC1Lz00kvodDpuuOEGvyjTPfBEJDUaDXa7vQtzVXcI9Fno7b3oPEaHw4FWq/W7VuTnij6dvYc/WKVSMXHiRJKTk1GpVFgsFioqKigtLUWhUHD//ff3OIE7HA4+/PBDrxSBOTk5lJaWYjQa+fTTT1m8eDFFRUU9ji94ubCNSFIuoaH7qK6OR5KiMZvN3fYV/PC0A4XiQ8CBKF4NBE73GqzJV6EYS0nJsz4Nvu7QvkC/sbGRxsZG9uzZIxduR0VFkZqa2m2e8c8dPW2Yz50LQ60GrdbKiRPpjBnzAWlpuSQl/Yqioia5GLE9EUB/RjYCiSbodDpZVO/48eOoVCoyMjJ8FtZdqMiGBxd68R9E3xHMdao/EKhTzFdkw27/gKqqEBobFZhM4V41QDqjr+tUe+pwu93O8eMN2O1uFAqJ+voQYmP1xMTEdFvY2hk/BSMhWPj22600NNRTVdVGTHG+a8Q8tN9ut1ue95VKJQqFArVaLRu2oih6rZvo+Z6qABNKZRPLll1MdXUju3YV09JSj8Vi56uvvmTEiJGMGDEioLlWEASZucrhcMgb+mDVZfbm+es8fs8YRVGU6zna62f8X1tb+mRsXHbZZRw8eJDt27fz7rvv8vXXX9PQ0EBERATjx49nxYoVzJkzx6++PvvsM3JycjpEJjxov7FfsGABq1evpqampscQsUfUz1/4mniVyj3Y7WU0N8fT3JxCYuKUHoUCgz1hOhz/xG5/DkkCjaYVne7mgPs43x4jbzolnQv0bTab3wxJgcDlclFdXU1YWNgF2UD0JwQhin/84/fodLWo1RrGj/9/gBO9Ppfs7O1YrVYKCwspKSkhMzNTZok6nzUb3bVVKBSYzWYmTpxIbW0tBw4cwGw2k5aW1iU0fr5rNgbx80Mw16n+QG/Wqc7zryCcoKqqhMrKsWi1IlFRw/wSsw1kTRBFUWZw9Mzp7anDlUolY8dehckUj91uITt7FgbDhXOK/RSMltBQFfX1bWxGer3/G+VAN6rtoxUew8ITufCoa3c3b3qrmwjMgaVAo0knKamB7Gw3Z87U43K5KCk5yenTp6murmbq1Km9YtgyGAx+MVf1J7qv9W0TBhRFUa4b+b/oTO1zXEeSJC655BIuueQSn99Dzy/H22+/7TOFqqqqitjYWARBID8/H1EU/aJv7Y3HyNtDo1B8gFJ5hpAQiaKiOaSm9pwrGPzwdAt6vYggCFRXN9HndNYgo7PitscL0lmnpP0kEEiecqDYtu1TJKmA1tYk5s37Ta/yO3uDYNzznjbYUVFRzJlzDeXl5ej1FYiiiCQ5EAQbavXlmEyjGD/+P6mvt3D48GFMJhMxMTEDwtgQRbFD28jISCIiImRVb0/etifk3Bs2qkDbD+Lnj2CtU/0BnU7nVW3ZF7xFNpTKtzh6NBpJcmO3G0lMTPe7L2/vQHeOIqPR2IXJEaC6uhqDwcCoUTP9Phdf+L/yXs6ZM5rS0nDCwkKIiDD1/IMe0N6I8BgXnjnxqaee4p133kGpVKJUKlm/fj0zZ87kmWee4Xe/+50cXU5LS5ONR4Dp06fz7LPPAnDvvfeyePFipkyZwogRI9i5c2ePmkYe2O1qFi36LTU1lfzrv65ApYqmrKwWq9VKfn4eR48e5brrrguIDtYDD3OV0+nEarUyevRo8vPzvYq6Arz//vtkZGTIlOr33HMP8+fP71eng0Kh6GAYee5VIOvVa6+9xp49e3juued48cUXMRgMXZTJe0JDQwNvvfUWq1evDvQU+oQ+GxuemydJUocJwv8wWxt/8JdffsmGDRvkz1588UWgrfp/y5YtvPDCC6hUKvR6PZs2bfKr32CwfAjCaQThWwTBhtMZidM5yi8dhmAbG3r9FRw/fgZJcpCSckOv+gjGmCRJwm63Y7PZOHHiBBaLBZvNhkajkQu3/Q2b9+fCnpn5Z6KizuBwaGltvQytNrXnHwUJ/pxX+1zY3vSRkpJCXFwcu3dLfPjh1URFnWLEiKNERNSiUBQBJiIilnLRRRdRVVXFkSNHZMaMnkLN/R3Z8BZu9tDlnjlzhry8PJKSkkhOTu5VZGMwEjKIzgjGOtVf6E1tYcd5XKK5+V3Ky6/AbtdjMBj9SqGCtvO22WzU1NR0YYPy1Fd4cxT5QjBqpgZiZKO/ng+VKpn0dC2gR5IC09/ozrBQKpWoVCqUSiWCIJCbm8s///lPdu/ejVarpaamhoaGBgCeffZZli9f3iGV9auvvpL3OZ5rWFdXR15enqzWLQgCLpcLq9XqVz2HR8l9794DQCMWyym+/HIfx49X4XA4qKmp4W9/+xtTpkxh1qxZCILg13rVHh4qWmjbVzocDjla0v5Z+OCDD1i4cKFsbKxZs4Zbb721g7HRX/dcqVSi0+mw2+2yU9ajoB4IvLFi+YOGhgaef/75gIwNz7zZJ6bHXv+yEwSh94IiBoOB2tqOPNjtL+SaNWtYs2ZNwP0GI41KpXobQTiLUummocFEaGhar/vqC0JDwxk37g4kSeq1mE+gk6+ncLt9xMLlcqFWq3G5XJjNZhITE3tFS9jfSE5uxWLRYDSCQuEakGwkvq5ZIPfI6XRSWjqM0tJhREU1EhFRgiDUolS+jELxBi7XU8THX4VGo6GoqEimoI2Pj+/2+P0Z2fA1YSkUCoYMGUJCQgKlpaXk5uZiMpkCKiA8X7ocg/hpoi/rVH+h7+vUixw5Ek9TUygKhUhoaJTXFCqPkJjHqPBoEmm1WiIjIzEajcTFxQXMEuSBZ30JhrERDPwU0qhAR5s2RffwGBZutxuXy4UoijgcDtmw8KRB+bp2VVVVREVFyRF+j07Jc889R0VFBXPnziUyMlJWEPeGLVu2cPnlHQlh/vKXv7B161ZEUeS1115jxIgR1NTUcNttt3H6dJtq+vr160lISGDFihVUV1eTkzOBzZs3U1JSyaOP/omMjBFkZ49CEBS4XC62b9/Od999x9atW/nd737HpEmTWLNmDTU1NRgMBjZs2NBFULi2tpZly5ZRU1Mjiy96VOT/+te/8uc//xmAMWPGcNttt/HRRx+xbds2Hn/8cTZv3kxaWhp1dXVUVVURFxfn87l5+OGHKSsr49SpU5SVlbF27Vpuv/12JEniueee44033gDg5ptvZu3atZSUlLBw4UKmTZtGbm4uCQkJvP/++/L90ul0cjRm+fLlVFRUYLPZuP3227n11lsBePXVV3niiSeIiYkhKytLvocPP/wwRqORu+++mzlz5vDUU08xceJEampqmDhxIiUlJRw+fJibbroJh8OBKIq8++67PPDAA5w4cYJx48Yxb948nnzySZ588kneeecd7HY711xzDQ8//DAlJSXMnz+f2bNnk5uby/vvv98nptifdXl839OoJFSqV2hTxFRx9mwGKSn+hQz7Y6Lra55fd9S+nZXVm5ubEUVRLtyOjIwkJSUFtVqNzWbj+PHj3eqM+Iv+WgwE4T+IiHgJt3s6Tqd/KQXBwPn0pEmShNlsZvz48Zw5cwab7Ql27PiICRM2otPZEYQq1OrViOL/oFI9QWhoKGlpaZw4cUIuIo+IiPDa7/mMbHSGSqUiLS2N5ORk9u3bR319PTqdjqioqB5/G6j3ZTCyMYgLjb6uUwbDA+zefRsulwqFQiQ+PhO3292lvqL9fB4eHs6QIUOoqKjAYDD4HQnpCidt2whhQEYkforwGBYul4vm5mbZ+PMUbisUClQqVUBOx3nz5vHoo4+SnZ3NpZdeyvXXX8/EiRO5/fbbefrppztEMgDmzp0rRxR++ctfctddd7Fz506WLFnSoV+z2UxeXh6vv/469957L5s2beJf/uVfuOOOO5g+fTplZWXMnz+fvLw8XnrpJf70pz/x0UcfYbPZmDNnDl9++QWZmVpWrbqfhIQMfhSglpg1axYajYZVq1bx/PPPM3z4cPLy8lizZg1fffUV8OM6+cgjjzB9+nQeeOABPvnkE/76178iCALFxcWsX7+eL774grCwMKxWK7GxsSxatIiFCxdy3XXXyecyfvx4duzYwZIlS3jooYcYPXo0119/fZdrWVhYyNdff01zczPZ2dmsWrWKvXv38sYbb5Cbm4skSUydOpUZM2YQHh5OUVERb775Ji+99BJLly7l3Xff5cYbb5T780RjNmzYgNFoxOVyMWPGDJYsWYLD4eDhhx+moKAApVLJ1Vdfzbhx4/y+7y+++CJr165l+fLlOBwO3G43//mf/8mhQ4dkfbsvvviCoqIi8vPzkSSJq666im3btjFkyBAKCwt59dVXef755/0+pi/8rI0NnU5HXV2d3+27eow+QaGo+OFvF2fPjmbcOO/KmT33deHhmcQ9k1h77xYgh8295eN66ycY4wkGvI3H7Z6F2z0rKP33Zjx9gb/X1rOxnjp1KgAbN26kvj6SioqrWLz4azQaK9CAQvElUVHbsVr/HxrNPWRnZ9PS0sLx48cpKSkhIyMDYzse5954J/ujHkSj0RAdHY1CoaCyslIea3d0ucFOoxpoXvBB/PzQmzQql8sl/3///mxaWtreCVFU0tTUzL59+zAajRiNRuLj4zEajV7TUfqyTjmdH3L27CeEhg7BZLpnwBkbP4V3V5KkDoXbnvlLEAQ2bdpEeXk5JpOJW2+9VY5WORyOgM/NaDSSn5/P9u3b2bp1K8uWLePBBx+Uveed4S2NqrKysouDcenSpQD84he/4O677yYkJIRvvvmGo0ePyufR1NTUpSapsLCQ1NRUMjIykSS47rrreOWVlxk79mJsNiceo+Pw4cOMGDFCPg4gp8a3f0a2b98uq3ovXLhQjoZ/8803LFmyhOTkZFpaWjCZTFitVq/PV0xMDJWVlQA89NBDPkUAFyxYgFarRavVEhMTw9mzZ9m5cyeLFi2S602uueYavvvuOxYtWkRqaqpsIEyYMIHS0lKg6/O5YcMG3n//fSRJ4vTp0xw5coTa2lpmzpxJdHQ0FouFG264gePHj3sdlzdMnTqVxx57jDNnznDttdcyfPjwLm2++OILvvjiC8aPHw+06RIVFRUxZMgQhg4dypQpwRFt/lkbG70RS+roMfptu+9Arx/7k6Pva6+4XV1djd1u59y5c73Kxx3E+YO/kY327QwGA/X19ZSXp1FQcBUTJ/4nWu12wI0gtDB06ANI0ms4nV8TEhLH+PHjqa+vl4vI09PTZZaR/lqoe5PmpNPpSElJoampiaKiIpRKJcOHD/daSCiKYkARwMHIxiAuNPxNo5IkCZvNJjuI6uvrsdls7NhxDeAxGBTMmDHD73egL+tUQcEuqquTUamszJ59PKhrXud+JEmioqIEgISElKDPT6IocurUKQBSU1P7ZU7w5L179CE8hoUnFcqTty8IAhaLhfLycgCam5vZvXu3T3IDf6FUKpk1axazZs1i9OjRvPrqqz6NDW/Q6/VdNuDt74PHuBBFkZ07d8p/63S6Lr/r+pyYsNmU3HnnXB544N0Oc3tkZCR79+7tcXzenonOa5ler8ftduN2u3E6nR2+t9lsskHX3RrYnmxGqVTicrkCat/a2kpZWRnXXnstCoWCW2+9laysLL7++mt27NiBwWBg9uzZ2Gw2uZC8pzVZpVLJToP213rZsmVMnjyZTz75hMsvv5y//e1vDBs2rMs1uu+++1i5cmWHz0tKSnpVrO8LP+tVtu8F4j8uAC0tmoAUTfsy8SoUu1Gp/h2l8mPAfy93a2sr1dXVnDx5kv3795OXl8fhw4flNJSYmBiSk5OZMGECmZmZJCQkYDKZAp5Y+3NRGUTvMX/+fGbMmIHBYCAv7xAvvng1Vuv9tPcpCMIJNJoUlMo2T0t4eDgXXXQR4eHhFBQUcPLkSdxud78ZG32hsjWbzUyYMIHk5GQOHjzI4cOHu7zfg2lUg/ipwds65aGZrayspKioiL1791JQUEBRURF2ux2NRsPw4cOZNGkSWq2OtqW8Lb0mEGPgrPFkAAAgAElEQVS7L3O5zZaEUmlDFENwuaL7NbJx6tS37Nz5Gjt3vkZJyXd9PkZnHD9+iNzcj8nN/Zjjxw91+T7Q87Lb7dTU1HDq1CkOHDiAw+GQN44eetaQkBAMBgM6nQ61Wi0XcwOEhIQQEdEWrVKpFISF9Y1NsbCwsIM22ffffy/vZ0wmE83NzT32kZ2dTXFxcYfPNm/eDMDf//532QM+b948nn/+efR6PTqdTmYQbX8Ns7KyKCkpkfvbuHEjM2fOQakczzvvbKT9EuFyueTjSJLE/v37u4ztkksu4a233gLaZBTq6+sBmDNnDps3b5Zrguvq6lCpVISHh9PS0kJLSwt2ux1Jkjh+/DijRo3q8Tp4w7Rp0/joo4+wWq20tLTw/vvvM336dJ/tk5OTycvLY+/evaxatYrGxkbCwsIwGAwcO3aMvLw81Go1M2bMYNu2bZw+fRqHwyFHbzpj6NCh7NmzB6BDm5MnTzJs2DD+5V/+hauuuooDBw50ud+XX345r7zyihx9Ki8v59y5c726Dt3hZx3Z6GsubEHBFUyY8Clut8Brr93EjTfG97ov/yFhsz1GQ4MNo3Evev3Eri3aFfp50qGcTic6nU6OWCQkJHQp3D579qzX0GBvaB8DOTfP5rXzpu5ChbkrKyvJzf0HarWZOXOuDar1fj4Ljr15bcaOHUtubi7QFtUqKJjFRRdNRqdbjCD8+C4olceAEbjdR2Q2qJiYGMrKyti/fz9arbZfziUY9SAeutyzZ892ocsdZKMaRH+hsLCwQzrHyZMneeSRR7jjjjvkz7Zu3crVV19Namob+921117LunXruu3X7XbT0NDAU089xfz582ltbUWSJK/1cgA1NTU0NTXJ+kHXXnst7733HkqlskMuuD/oSxrVhAnLOHHiAJGRCZjNsQhCVb+lP9lsh5EkEUkCu/0Q0Dcvf2e43fkIQs0Pf+cCY/z6nSfa1J5ExWMMetgZ4+LiqK6uRq/X09ra6pcxKAgCq1Zdy+HDB9DrNaSn91xE3h0sFgtr166lsbFRrolbv349ALfccgtXXnklcXFxcoF4+5qNUaNG8frrr7NgwQJeeuklbrnlFrlfu93O1KlTEUWRN998E4BnnnmGNWvWMG7cOFwuF5dccglPPvkkNptNNjp0Oh0vv/wyS5cuxeVyMXHiRFauXIkkCUiSmlWrZnP48Bnq6y2MHp3EmjXP8Pjjj+N0Olm6dCljx47tcH7r1q1j2bJlvPfee8yYMYMhQ9qu18iRI7nvvvuYPXs2giCQk5PDq6++ytKlS1m5ciUvvPACb775JrGxsRQXFzNxYtt+66GHHmLs2LEdajq6w/jx4/nlL38pG1w333wz48ePp6SkxOdv2j/nV1xxBRs2bGDcuHFkZGTI/SQkJPDggw8yb948YmJiGDNmjFc2vbvvvpsbb7yRN954owOj1t///nc2btyIWq0mLi6OdevWERERwbRp0xg1ahTz58/nySef5OjRo3I6ttFoZOPGjUETSJTH2sPk8JN2O3/++ed89dVXPU72HlRUtGkWJCUlAcgvowd33XWX38cuKSnpVeGdJImcObOcsLBzWK069Pq/ceRIKcnJyXLBn9vtlhcij3HhT7HYuXPnsFqtpKSkyJ/V1++ntXUdomgkIuIpDIaeDSqn08mhQ4fkHL/uUFlZTmHhK0iSgQkTftdBoFEURfbu3Su/4L2FxWKhrKxMprHrCYcOPURGxpu43Sqqqv6b1NQrACguLiY8PNwvDRdfcDgcHDlyxGcRl9PppKmpiaamJmw2G+np6V2MnZ07d3LxxRf3eKzm5mZOnTrFmDEdF8aSkhK2bt1KXV0dSqWSESNGMG5cKrGxU1GpOnqwHI6uxmdNTQ3Hjh2T1b29FZH3ZrzQxopitVq7hHJ94dixY0RHR/u8J6IoUl5eTmlpKUlJSTgcDsLCwvx+7xoaGigvL2fkyJFev/dQSA4ADPwE9AuH875Oud1uEhMTycvL68DQsnXrVp566ik+/vjjbn8viiLLly+nsLAQSZJQqVQsWrSIX/3qV4SHh3e70NfW1tLQ0EBamn/MiN2hsrISl8sVUNTeFw4cOEBGRoZfYoLdobGxkaqqKjIzM+XPnM4DHDz4NwRBYvTolahU/nmgCwoKZHai7iCKb3Hw4OcAjB49D4Xilx2+z8/PZ9KkSbJauudfeyef2WyW1+LOBlNZWRmZmZm0trZ2oJjtHg4E4QwAkpQMtBkpnpqNvhLGtLS0+OVoax8tnjFjBh9++KFcbO0Pxb3HORoSEtJB7dvb+N1uNw6H4wdD2v3DP+97G082h7/Xs7vzfe+99ygoKODf//3f5efX5XL5/SwHek88TnB/i/w9goDto6D33nsvOTk53HTTTReEZe/o0aNkZ2d3/tjnIAbEKtpf0Gg0AaVRdY5GREdHU11dDdCFai3QvvyBy+XCYrFw6tSvsNn2YrXGER9fhtPpxO1291i43Zsx2e0vYzJVoFC4sFg+wmDoOYczkHNrbX2RyZPfQ5IEamsTMJt/FG68UHUt6enHkSQBtdpBXFx5h++C+cK2r5fxCB2qVCrZSNTr9Rw4cICIiIhe31dv401JSWHy5Ml89dVXOBwOjh49SmRkJI2NuWRlTUWhaDM4XC7vei2eMHNKSopcRJ6ZmRmUCFCgkYeeIiEKhYLk5GTi4+MpLS3lzJkzSJJEdHR0r+peBjEIf/D111+TlpbWaypIhULBf/zHf5CamsrJkye57777uPPOO/3+bbDIR4KtRdFfaVRq9Rhycp6gbS/jvzHj73gUiuuYMEEJSLhc13TJHrBarRQUFMhq6R42r97S0PsHDZLU1SnjcrkoLS0lPDycuLi4fjx+Vzz55JOUlZURFhbWq3ut0WhQq9XY7XbZ6PC97il/+OcdgR6/u/Yul4t/+7d/w2AwYLfbcbvdA5JCXalUYjAYuP/++9m1axf33nuvrAI/0PGzNjb6WrOxfPly9u3bh9ls9lrF3x16mngdDkeH0KvVakWpVGI0GklLG4taPZHw8HDUajV79uwhMTGxzw+UtzFptWOBXERRg8HQxUrtM+LjnQgCKBQi0dHuoPffG4SE3I5KtRaIwOFYEBQNDo9hUV9fj8ViIT8/H7Va7VPo0ENtmJycLAvZDR06lMTERL+P2d3zNXz4cE6ePMmRI0dwu91s376dBQsW4HJVA42AC/AeLfBMnCEhIYwfP566ujoOHjyI2WyWi8h7i77UbHQHT2qAzWbDarWya9cu0tPTe6TLHWSjGkRvsGnTJn7xi194/S43N5exY8eSkJDAU0895TNqlp7eRskd6DoVTAMhEMNFEMqx2Z4GFOh0dyFJsf0yLt/96PvctzeIokhLi53m5kt+SEs+1CF7IDIykrq6Oi666KJ+OX6g2LZtG0VFRSgUCpYsWdLvBkf7ezF58uQ+9ycIAjqdTvbWOxwOv0QB+xPtKW71ej0Oh0MW3dNqtf0ytr6sLY8++iiPPvooLpdLTs3ra0Sxv/GzNzYCYaPqPPEqFAomTJjQq2N7+vKwT7Svr7DZbKjVajkFKioqCoPB4PPh689JPDz8t9jt4xAEAxqNf8ZGIOMxGNaiVNqRpFDc7sVdvr8QVIlu91Tc7l20eckCf+EdDgdNTU3yPfXcT5PJhF6vx2AwMH78eL8mE0EQSE5OJi4ujhMnTrBr164OtJbdobuNu0qlYubMmZSUlGCxWBAEgfz8/B+MZt/Usd76jYiIYPLkyVRWVlJQUEB8fDxDhw7tlfHb36J7giCQkpKCTqejqKiIU6dOkZGRQViYd8rqwZqNQQQKh8PBhx9+yB//+Mcu3+Xk5FBaWorRaOTTTz9l8eLFHQpzvaE31LcXIrJx9uzn5OfrAInJk78iOnp5r/sK1pj86as92utJedZjSZIICQnplvZ9IDkdGhtrASeSJNDc3Hjeoxu9gbfrp1AoMBgM8obZk7I6EK61QqFArVajUqnksfUkXhzMiL2/7VUqFSqV6idBtPOzNjb6mkYVKDyhV4vFQk1NDTabjTNnzqDVamUPd3x8vBeF1gZUqr8BRlyuJXTOUexf9icBrTZwg8p/LYh4XK7/9vrdhcgz/BFdN5iiKFJTU4NOp5PzQNsbip0NC7PZ3EVx18NCEuh5qdVqsrKyaGlpITc3l++//56MjIxu81F7mrCMRiOzZs3i888/x+FwcObMGSoqKkhI6F6Y0lu/giCQkJBAbGwsZWVlHZTIA0F/RTba969QKNDr9YwZM4bm5maOHz+OQqFg+PDhHfREetP/IAbx2WefkZOTQ2xsbJfv2tekLViwgNWrV1NTU9NBMK0zAlUQ91CKBgOBrC01NbFI0ikkSUFtbRSdNV2DOa5grHcete3Tp0971ZPqTn+kMwbCBtiDOXMy2brVRkSEkbS0vgvr9jd6upeeDbOHsWugXGtBEOSxOZ1OWlpaUKvVXmtyLvRm/6ewhv2sjY3+9Bi1hV47Km673W45p9NgMBAWFtahGNt3X5toanoXpVJCrY5GoZjbZVwDSeRooEwGwcbZs1vIynqPEydSsdv/FVGkC6tIV0OxK/pyfTx0iElJSXz//fcdmJZ6c6zs7Gx2795NZWUlkiTx2Wef8dvf/rbb33VnECiVSlJTU0lMTKS4uJiysjK/IzGevvszstHZeDCZTEyYMIG6ujoOHz5MSEgI6enpcsh50NgYRKB4++23faZQVVVVERsbK0cSRVHskXCiN+m+wfT8+7vmDR06i+rqtmhwcnJXWs9grlO9qXds7xSyWq0IgoDT6USpVP6s9KQiIxO54QY9gqBAFNvq6FpaWqiqqiIxMXHAp9P4gkdnxG63Y7FY0Ol0PdYx9idFe3t4ohwOh4OWlhafBe6BINCx/9T3XYPGRjv4muTcbneH+goPH7En9BodHc2wYcM6vBhVVVV+H7uiwoLJZMPhEKivb6FzVHSghacvVGF3sCBJktdUqPHjN6LVthIeXk1NzTliYxcG/IIH67pERUUREREh13OkpKSQkJDQYTz+HEuhUDB37lw2btyIJEnU1dVRUlIiU3P6OoeeFmWNRsOIESPkSMy+ffvIyMjosYi8v0X3fBWgR0REcNFFF3Hu3Dn27t1LVFQUqampg8bGIAKC1Wrlyy+/ZMOGDfJnL774IgCrVq1iy5YtvPDCC6hUKvR6PZs2bepxDrmQaVT+GQguBKGOkJAoZs5c4LNVMNeX7tCeeMNiscj1jh6n0NChQzEYDCgUCgoKCnqM5P7U4HJFIoomVCod0OZ1f+utt2htbSU0NJQVK1b8pDemHoHD9kXk3qJP53sPIggCWq22y9h6Q+wSzOL2nwp+1sZGoGlUCoUCl8tFXV1dh4lMEAQ59JqYmEhISEiPoddAJl6HYxGHD7sQRS0ZGV2l4YMVnv6pGwm9Qeeamc486GazWdYkaW5OQ6k8iCDoiYkZfcEnbIVCwZAhQ4iPj6e4uJjTp0+TlZUl1x/46/VPTEzEbDbT2NiI2+1m69atPRob/sJgMGAwGBg6dCgHDx4kNDSUtLQ0n0XkwcpV7a69L+NBEARiY2OJjo6mvLyc/Px8DAYD4eHhPvu70M/AIAYWDAaDLBDmwapVq+S/16xZw5o1awLqU6lUBjS/n18GKTdu92M0NJQRHj4ahWItvurc+joul8slC9t5+ukujdVoNBIdHd1tvWMw0djYyHfffYdOp2P69OkdlKHPNyRJi2f71kaK0YxKBQ0Ntbjdbq8b4Mcff5xNmzahVCpRKBSsX7+emTNn8swzz/C73/1OTtlNS0vDZDLJe5zp06fz7LPPAnDnnXdyzTXXMGPGDEaNGkVBQUG3KYLtYbfbWbRoETU1Ndx7770dNGs6w5MK63a7sdlsKBQKv7IKPBg2bBj5+fk+x/b++++TkZEh0+Xfc889zJ8/v4NGhS+0L3D3GB2ezz2YM2cO//Vf/8XEiRNZuHAhb775ps+6QV/YunUrGo3Gb1r5gY6ftbHRncfI491uP5G1tLTIL6rRaOzgIQkUgUy8qakZmEw3y5SjfekrWGPqqZ+BCF+GRfuaGW9ihx7U1DyGWn0Mg2E8ktQ7Ssv+MObUajXZ2dlYLBaOHTuGWq0mMzMzoGNlZ2eza9cuoE3csTttjEBSnTzGQOci8oSEBIYOHdqln/6u2fCnvYcuNyEhgf3791NSUoJare4SORrEIAYizmdkw+2uYevWVqzWVEJDy7nkEieC4N2R0Jf15fDhdzl8+DAREQaSkhbS0NBAXl5eh7nb3zTW/sK+fTuorj6MJCk4eTKa7Gz/hP/6G0ajhosuSuHIkQomTEhBpVLQ2tqKzWaT9xO5ubl88sknFBQUoNVqqampoaGhAYBnn32W5cuXd6gP/Oqrr+SNuudZq6urIy8vj//+b+81mD1h3759OJ1O9u7dG9DvQkJCOtRMBMPI++CDD1i4cKFsbKxZs4Zbb71VNjb8WafaG0RWqxW73e6VVeuTTz7x+vue+v/2228xGo3yOu3Pc+9yuQaKLlQXDJhRpaSkyNa0SqVi9+7dHb6XJIm1a9fy6aefYjAYeO2118jJyem2T4+x4Xa7aW1tpaWlRU6H8oTAPBGLuLg4uZgsUJpbb+h5QRBRqV5BqSzA5bqR6OjZPlsONGMDgrup9qT3eJic/P2NzWajubmZuro66uvr5YnUszglJiZ6LebyBVE00No6F70+4odjiJw79zAaTS5wN+Hhl/vVT38thkajkYkTJ1JdXc3evXsxGo1+TyxDhgyRjQ1oo0+cMmWK1415IAZB+7bti8hLS0vJzc1l2LBhxMXFdaD87e+aDX/bK5VKIiIiiIyMxGKxkJubS3p6ut8aHYMYxIVAsCMb3a1TTqcJqzUWne4cTU1DEUUlvoL6/o7LI8bW3ilUXLwbrdZFba2N1NQqQkJC/Wb0O1+Ijt5HWZkdQZAIDz+Ivyrj/Q1BUDNt2kimTcsEtNTUNPDWW2/hcrmYNm0akyZNoqqqiqioKHmjHhkZiV6v57nnnqOiooK5c+cSGRkpK4hD230SRVHWnNiyZQuXX95xDXzqqafYunUrABs3biQ9PZ3q6mpuu+02Tp8+LbcZMmQIK1asoLq6mpycHDZv3kxJSQn/9m//JiuIP//88yiVSrKzs/ntb3/Ll19+yerVq5k0aRJr1qyRyVueeeYZRo8e3YFgpra2lmXLllFTU8OkSZM6PIevv/4669evR5Ikxo4dy6pVq/joo4/Ytm0bjz/+OJs3byYtLY26ujqqqqq6ZffKz8/nrrvuorW1Fb1ez8svv0xmZiZOp5NbbrmFo0ePkpWVhdVqlX/jibJYLBauuuoqDhw4gCRJrF+/HqvVyoMPPshzzz3Hhg0bUKlUZGdn88c//pENGzagVCp58803+dOf/sTw4cO544475Ou6fv16pk2bxsMPP0xFRQWlpaVERUXx1ltv9f5h6kcMGGMD4JtvvvEZ9vrss88oKiqiqKiIvLw8brvtNvLy8ry2PXz4MLt372bPnj2UlJQwceJEHnvsMUaMGEFoaChJSUlerWOr1XoeWT7O0NLyD5qa1ISHv4BK1T/GRkvLXlpbP0CjmY0gTBiQaVT79+fS3LwFmy2cSZNu7xJubG9YeP55jEWPYWG32xk7dmxQx9XUtIPo6NcQBBGHYy1wLKj99xYeRe2jR49SVVUlizt1tzCrVCoSEhKoqKiQP/viiy+44oorurQNZMPuzRhQKpUMGzaMpKQkuYg8IyOD8PDw88ZGFUj/er2eoUOH0traSnFxMSUlJQwfPrzb9KpBDOJC4fxR39owGI4yevRMSkubyM5O7zZ92FtfncXxmpubcblcXcTxFAoDx4/biYx0k5AwgubmygFlaACMHWsiNnY7Wq2bsLBZuPtdNkoEqoEYuqdoVyCK6YAN0FNefgiXq80oOn78KJMmTWLevHk8+uijZGdnc+mll3L99dczceJEbr/9dp5++mm+/PJLIiIicDgcSJLEnDlz5Hu9fPlybrvtNnbs2MF1113X4chms5ldu3bx+uuvc+edd/LRRx9xxx13cMcddzB9+nTKysqYP38+u3fv5qWXXuJPf/oTH330ETabjTlz5vDll1+SkZHBr3/9a1588UV+//vfA6DT6di2bRsA8+bN4/nnn2f48OHk5eVxzz338PHHH3cQ3nvkkUeYPn06DzzwAJ988gl//etfgba94B//+Ee2b9+OTqfD4XAQERHBokWLWLhwYYfzGT9+PDt27GDJkiU88sgj5OTkcO2113Y436ysLLZu3YpKpeKrr77iD3/4A1u2bOFvf/sbISEhHDhwgD179jB16tSAZBeeeOIJTpw4gVarpaGhgbCwMFauXInRaOTuu+/G6XSyYsWKLtf18OHDAOzdu5fvvvvuB+X1gYkBZWx0hw8++EAufJoyZQoNDQ1UVlZ6pd7csmULJpOJxYsX8+233/Ldd9/5dYzzmQtrsehoblYTEtJMSUksaWm+N2G9HZcoOnG57kCrtQKf43S+3W/85b2FJElERPyFUaPycbsV1NaOQ6udKy9MTU1NOJ1OdDodJpPJq7FotVqpr68PynjaQ6WKQpIEFAoJuz0Mf6K358uYUygUxMTEoFAoqKuro6ysjKysLEJDvWtoSJLEzJkzefvtt+XPioqKvBob4P/97W5z7ykit1gsshK5Wq0OWg2GNwSqUN7emNHr9YwePZrm5mZZo2PMmDFBUU4fxCCChfO1Tonis5w5c4KoKCUpKY8CET3250knac/Q2F4cLyUlxStBxJgx95CZuR+1ehhudyiSVOGld//Q2NjI9u1folSqMZv9qyfoCZIk4XKtIi5uGJJkxO2eGZR+fUNEobgUyAWmIopf442u/UcogbZ5Ki0tkoICLc3NNiZOHMIXX3xBc3Mzn3/+OYcOHWLr1q0sW7aMdevW8etf/1qONDmdThQKBYIg8PXXXxMdHd3h2aioqMBkMnWYM2+88UYAfvGLX3D33XcD8PXXX3P06FH5dx4ilvYoLCwkNTWVjIwMAFasWMHzzz/P6tWrAbjhhhsAsFgs7Ny5s0N9h91u7yK8t23bNt59910AFi5cKDuKvvnmG5YsWUJUVBQWi4WICN/PcExMDJWVlQCsW7fOa5vGxkZ+85vfUFxcLLOdAXz33XesXbsWgAkTJjBmzBgkSZJ1XLyh/To1evRofvnLX3L11VezeLF3PbJvvvmGwsJC+bP213XRokUD2tCAAWRsCILAZZddhiAIrFy5kltvvbXD9+Xl5SQnJ8v/T0pKory83Kux8eCDD8p/ByI8FuxcWN99iej1DvbsuQm3+ywmU/fFyH1ZXCQJBEEiSKfVrt++L3aeMG1UlAtRBIVCorm5lJqaItmwSE5O7pNidV8QEpJNff0r2O35mM2/AByoVC8iCFaczt8DJq+/83ezGwyDzRN2bm5u5tixY+h0OjIyMrpE7iRJQq1Wo1Qqcf/gkktKSvLaZyDvgD+RCqPRSE5ODrW1tRw4cACn04nZbPbrvvbFeOhte5PJRE5ODvX19QM2/3UQ/3cRTGOju3Vq164mamoSUCqdzJ59DoPhx42aN3E8m82GwWAgOjraK0Nj9+ekQ6drU6cWRWefzu/YsX/S2NhWW6FQJAHTet1XR2hwua4MUl89oRrIRRBcSFLuD//vquviDQZDGDffPBNJgkOH6jl06CCiKOJyubjyyiuZNGkSmZmZbNy4kVtuuQVBEDAYDB0oczvPuQqFgpCQEERRlEXuOrdrnyq7Y8cOefPrdru71M72dH89Dh5RFAkLC/Na59FeeE+SJJxOZ5f1KJBIus1mk6+BLyfXunXrmD17Nv/4xz8oKSnpUFDe+TgajUY+D09aledd82RrePDxxx+zbds2PvroIx577DEOHjzY5didr2t7/BQcYgOG83HHjh3s3buXzz77jL/85S9yCM0Dbw9nsMOs58tjpFa/gF5/N5demktOzjVMmNCVgSoY41Io1AjC0zQ1LcLlegKtNqZP5yeKIhUVFV3YWPyBJ5x+9uxZiouL2bdvHwUFBdjtdurq1qBWX4ZWu4rMzFWMGTOG1NRUoqKizquh4e3ahIfPIS7u/2EwDMXh+Cui+ARu97M4nf9x3sblC+0nUpPJxMSJE4mJiWH37t2cPHlSNirat125ciUJCQkMHz6cRYsW+ezX3w17IMZAZGQkMTExGI1GCgoKOHXqlF+GTX+yV3VnnISHh18wQ3cQg/CFYK57vtYWQTiL3Z6EQqHC7U6grs7ImTNnOHr0KAUFBezbt4/y8nIkSSI+Pp7x48eTlJREUlISycnJhIeH99pQ7+v5xcaWoFBIKJUuYmPL+9TXhUMMMBVJUgFTf/h/95AkCbfbjdOpwW5Pxm6PRa02IggikuSisbGaTZs2cfToUY4cOcKQIUNkyuDOkQdvyM7O5uTJk7JeiSRJcn3A3//+d6ZMadvHzJs3j7/85S/y777//vsufWVlZVFSUkJxcTHQVu8xc2bXaJHZbCY1NZXNmzfL57h///4ObVQqFTNnzuTvf/87LS0tfPzxx3Kmw5w5c9i8ebO8Z6mrqwPwes7Hjx9n1KhR3V6DxsZGmUr5f/7nf+TPp02bJl+LQ4cOceDAAeBH4WKdTkd4eDhnz56luroau90uF46Losjp06eZPXs2TzzxBA0NDVgsli5jvPTSS3u8rgMZA8Zt57mBMTExXHPNNeTn5zNjxgz5+6SkJLkwBuDMmTNB588+P7mwEi0tn1FToyY0dB9ms73HjV1fjKDQ0BxCQ9sK6a1Wa5+MjeLi94mMfIrWVjN2++99tutcANjU1OQ1T1ej0ZCfn09S0kXAJUGPvvQG3S10Z89WM2SIhCBInD1bTXJyJeBGkrxHCPobnTfW7aldS0pK2LVrF+np6cTExMhtzWYzK1asCKjfYLX1ICYmhmHDhlFSUuK1iLyv6M9IyN6hOv8AACAASURBVCAG0V/ozbvUV3Rmo3K5XFgslcADREeDIMTidF5ES0sLRqORxMREjEaj13dmoFC0p6ZeSVTUgyiVKoqLl/d5PP7A5XLJbEsTJkwIAmOS8EPqlO+aDbfbLRdwi6IoO4mUSiVKpQmFQkFWlgqNJocDB4rZs+cMoihSUlJCeXk5999/PwC33HILV155JXFxcXKB+Ny5c1EqlUiSxOjRo3n99ddZsGABL730ErfccotMvGK327nooosA5M32M888w5o1axg3bhwul4vp06d3YbDS6XS8/PLLLF26VC4QX7lypdcr8cYbb7B69Woef/xxnE4nS5culeszPe/LunXrWLZsGR9++CEXX3wxycnJuFwuRo4cyX333cfs2bMRBIGcnBxeffVVli5dysqVK/nzn//MO++8w5AhQzhx4gQTJ04E2mpAJk6cyDXXXNNhLPfccw833XQTTz/9NLNn/1hne/PNN3P77bczbtw4xo4dK18TD5RKJaGhodx///1MnTqVoUOHkpmZKd/HFStW0NjYiCRJ3HHHHYSFhXHllVdyww038OGHH/LUU0+xfv167rzzTvm6XnLJJbzwwgvdPkUDCQPC2GhpaUEURUwmEy0tLXzxxRddcuauuuoq/vznP3PjjTeSl5dHaGio1xSqviBYkyV0Z7hYOXEilaSko5w5M4z4eBNmc8/jGghsVOHhm9HrmwkJqae2dh+wyO8CwJ+Dh1ih+A15eSdRqRzEx1+CwTALkLDZnsXtbqt9ON8bBm/HUigUDBs2jMTERIqKiigrK5OVyf1Bb6hv/YUnEqJUKklLSyMpKYkTJ050KCI/nwi0JmQQg+gPaDQanE6n3/NkMNYDp9NJQ0MDVquVQ4cOyeJ4RqOFEydicTh0REQ4mTv3UgSh53fkQiqIt4ckjcRobPOEt7YW9Hk8/uDo0SPs359HW1G3xMUXByN1S4EndcpjVLjdblwuF8APRkUbe6dHn6QzJCmMYcMySE0dQkXFx7S2OrHZnIwcOYKtW78hKSmpizbMiRMn2v3+x/twySWX8Ic//EEuYD548CBGo5GHHnoIm80m75+ioqLYtGmT/DtPGtWsWbOYNWuW/Pmll17Knj17OozX6XRSWFjY4T1ITU3ls88+83JuP44tMjKSzz//XP7/008//YP+iJVf/epX/PrXv6alpUVON5o2bRqHDh2S27/33nssWbJEjsatW7fOawr+1KlTOXbsR6KYRx55BGir93vrrbe63AO3201zczPmHzZ5a9euZe3atbIwtCcNrHMmD0BGRoYcvbDb25zS7a+rBw8++OCAI1PwhgFhbJw9e1a2IF0uF8uWLeOKK67ooMy6YMECPv30U9LT0zEYDLz66qtBH0ewJkvwNWE2o9X+gbS0kxQXZ1NffyPp6d7z/nvuq3foSz9m86UIwhEkyYDTmcLevXs7GBbdFQD+HJCYmEJo6PM/GMZP4HA0IkkCbvc7KJVq3O7JdM8aElz0dC+1Wi2jRo2isbGR77//npaWFiIiInrc0PRnZKNze61WKxeRFxYWolQqycjI8Nsw6isCrQkZxCD6Ax4B2v5yyngTx/PoSQmCQGpq6g/ieOBwPENRkYhOZ8diGYW/c5o/65TNdpadO1/CZnMxZcrVRER0pa8Pznp3ft9pjeYQgtCWqqPV7sNXnYg/59XesOgcsVCpVAiCINcr9AwFkpQMiKxYMZ1Dh06zfftx3G4XFRWVPPfcc1xzzTUMGzbMr/N88sknKSsr68AYqVAoMBgMuFwurFYrarW6C+V8IOtJMNB+TO1rTHzB5XJx1113BX0co0eP5uabb+5yrzx1Mh79EI1G0+O7/1NfpwaEsTFs2LAueXjQUZlVEIQO+Wr9gf6u2RDFcmprj2GxaEhNrSU7+2K/HqBgjStQClGr1SozHlgsFlyu0URGPopOF4PD4WbSpNF9Nix6Ov/6+nq+++4DBEHFrFmLMRqNXvs4XyxQnuPv2zeSkSMNKJUutNr/Ravdhihm0tLyznkZB/i/0fdEASVJ6lZwL9B+PW2DwRZlNBqZMGECtbW17N+/n7CwMNLS0vzut7cYTKMaxEBAdwK0gUCSvAubajQar+J4Hs+rx+MrSTUUFhbR0pKIwWBlypQ5ARFe9DQPV1V9S3W1HZUKjh37nIsv7l4rq6/wjKmvG7Wefj9ihBW1ejcul4qsrDF+pwT3ZFh4i1j0LvtCQUhINpMnR9LQYGXfvjIUCoHW1lY2bdpETk4Ol112WY9z4eTJk31+5zFe7XY7LS0taLXaC+54VKlUqFQqHA6H/M/bpv7666/v87G8PSNHjhzptr1Go0GtVuNwOLBYLD6v2UCULAgUA8LY6G9ciFzYrhOvhMv1NWr1OUJDQzh0aCaTJgVvEpekVlpankeS7ISErEahCPPRrms/kiR1YBbpTFkYFRVFampqh5egvDz/vEwkFRUbueKKpxFFBaWlSjIyftHvx/QHZvNkXnnlHiIjz/LLX76K2+1EpconMnIm9fWrgJH9PoZAnmtBEAgPDyc9Pb1DPYc3Abv+jmx0t6BFRkYyZcoUKioqKCgowOFw9KtBMGhsDGIgoDfGhqc2zkMz66EJb68/lJCQgFar9fmOdk73dbm+49QpLXFxJ2hszCIszH/qWH/SkMPC0lCrDyCKEBeX7LXN+XQeBQtu9w1kZxcCLTgct/lo45aNQc/fCoUChULRbSpU8GAEDMyf7yYtLYp3392LKLYxQu7evZvCwkJuuukmOeWnt9BqtWg0Gmw2m8/N/fmGWq2W2aosFgs6nS6oTIOBPq/t2wuCIBsZdrsdh8OBTqcLiEn1fKM37+fP3thQqVS4XK7zbmF3nsQFoRK1+nPKy+PRau2YTP5T6PkzidfXv4FW+waCAHV1IlFR9/vsx7M4dTYszGYz0dHRXQyLC4mUlMMoFCJKpYvExOMXejgy0tLSCA29HputlYMHd5Oevh+9XkKprGD48Ptxu6twOO6jjf/8wsMzOXhqJRITEzl+/Lisz9E+YhSIARFoGpI/fQuCQGJiInFxcWzbto3c3FzS0tKIjY0N+mLck7HxUw9dD+KnAU8alS90ro1rbW2loKBATmENCwvzKVbbHTo+3xLV1Z9RXx/PuXORjBiRgkrl/zrgj5EQFjaBK64Iw+lsxmzufwXuYEU2oKe5y4zd/qTczmZrI0hpbGykqqoKs9nMuXPnMJlMKJXKLmlG5w9tAoDp6WZuuknPa6/txOFoYy1sbm7m2WefZdKkSV2UwgOFIAjo9Xrcbjetra1A/zh9A+nP26Zeq9X63NRfCLIGzzXzzAU6nU5O7x8oa5EkSdTW1nagSvYHP3tjQ6vVYrfb/d48B5MRpyPLxw6UykMkJiopKZlGZmaW3335U0tit6tl0Tmbre22iqIop0JZLBaamppoaWmhtLQUk8kUMBf6hYDJdCtqdQGSpEal+sWAYKzyICoqipqaGj7++Ep2757C8uUvYDC0IggiSuV6VKp/0tr6JpI0tMtvg6VT0tsIhE6nY8yYMTQ0NHD48GFMJhPp6eloNJrzWrPRHZRKJVqtlgkTJlBcXExpaSmZmZldFOb7gsHIxiAGAtpHNjzztq9Ic2RkJPX19UyaNCmoGxCF4hAHD2rQ65swmTSkpOQEvJnzZ14zGPo/PbI9fI3J6XTKNRB96dtms/kUoT19+jTFxcWoVCr0ej01NTV99vR7KM376vl2uw1cccU4vvhiPzabS/68oKCAgoICWV3bVzTabrf7Zdx60sQkSZLTw3zBU/zuz54kkLYeHY72114URVnIsPNz4HQ6USqVfq8N/l6LQNq3H58kSahUKp/jOd+GiE6n86nT5QsDd5cZJAQrFzZQdIxsSKhUr9HQYAIU1NRMIyXF/778mcQjIm7k1KkWnE4rCsVlVFXtRpIkeYGKjo5myJAhHDlyhJEj+z/FJ1gQxRzs9l20FfwNvMc1MjKS7OxsTp06xZtvrmThwneIjz+DILhQKA4QEjIem+1hXK7bg37sYBgFYWFhXHTRRVRWVlJQUEBSUlJA0Ypg6lr4glarZeTIkT0WkffGgBs0NgYRCFJSUmTvtEqlYvfu3R2+lySJtWvX8umnn2IwGHjttdfIyfFdl2Cz2Th06BAVFRWsW7eOlJQUlixZ0iHS7M0h1B/ezubmd6mp0dPcbMJsVhMZGdhm4nylP9ntdmpqaoiMjOzRu+rr+hQWFpKbux2j0cyVV17ll5fWs/nzGBXNzc1yyotHhLZzdGn37i9QKs8iiiqghYYGt0yv2luUlZWh0WiIi4vrdR9Op5NDh2rJyUlk5MjfsGHDjdTVdUyZs1gsZGVl4XK5vNZK7ty5k4svvrjHY9XU1FBbW0taWhonTpzg3LlzZGZmelXzLikpQa1Wk5iY2GO/p0+fRpIkhgwZ0mPb1tZWjh07RnZ2dofPJUmiqqqKkydPdqhlPHjwIAkJCX6nlPl7LQJtL0kSlZWVHDlyhMTERDIyMrwamRcuUuY/Bt7uLcjQaDQXxNhoP/EqFLtxuYqJjGyioSGOhISZffIYiaLYpcaizbCYTViYGZPJhNFo7LJAebwLwTq/YMC/8XSNSomiSE1NDSZTz2xe/QlBEJg5cybJycl8/vnnbN78G6644h9kZR0F3IALne4PiOJ/Y7XuQhSjsFgsNDY24nK5SE9P7zB5BHp/gmEUCIJAQkICMTExnDp1ivLycpRKJTEx/glJ9Vdko/O16FxEHh4eTlpamhy17M3mq7sakoE+eQ/iwuCbb74hKsp7LcNnn31GUVERRUVF5OXlcdttt5GXl+e17ZYtW/iv//ovRo8ejSRJXH755Vx33XV+zWkeZ1bwDGUXRUVHaGwcgyBIREdH9yoly9v8VVv7T86dO0BS0lxMpr4VhIuiyOef/w8WyzkMhkgWLbq1Ww+/rzEdOfI5avU5WlrUnD07nKFDx3f4vnOhvdVqZffu3d0aFt4wbdoOvvwyhfDwOoYNK+X77y+MJlNneK6JJKVjtx9l9erJ/PnP82loiJbbKBTfAP5nYHR3LEEQUKlUZGZm0tLSQmFhIWVlZWRmZnZQxO6vqLqvtoIgEB8fT0xMDKWlpXLK7kBhKfSszWfPnkWpVJKbm0tKSgqJiYkDYnyB4GdvbHjSqM432k9yovgaWm09breCioq0gMJPoijKk15jYyMWiwVJkggJCcFkMhEbG0taWprfqVD9TaHb2tpKaWkpERERfm1We4vdu18nKuptTp1KZfjwx/rtOP4iMTERs9lMdbWdLVuuZ/bs7Uyb9pX8vUJRg9GYzrlzkykvf1amnNy1a1ev6xECuZf+TMwqlYrhw4fjcDiora2loaGBrKwsmanGV7+Bspz1VcOjfRF5fn4+iYmJDBkyJOCxwCD17SCCiw8++IAVK1YgCAJTpkyhoaGByspKr5pQ1113nZyqcvvtt5OVleW38yTYUQRB+JRDh4bidCpRqUSSk8f1oo+uY7LZTvPtt9/hdgucPPkuCxaMwx/NDl9wuy1YLGdQq91YrZU4HOfQ6wPX2xoxopBdu6IxGFqJja2S11hPxMJms8mGhclkQq/XM2HChIBTl4YOHcfq1S8jSVqs1tuRpDpEUcRms6HX6y/o3PPjsXVYrftZvXoKr7wylaqqRGJjyxkxYiEuV7dd9AohISHk5ORQXV3Nvn37iImJITU1VRYR7A8mxJ7meaVSKWtTFRcXU1tbS2xs7AV3ZnogSRLJycmkpqZy8uRJmeAlKioKQRB+EmvY/wlj48JGNiyo1R+hVDoRRRXNzdN8agh0Lt72GBaC0CZ371Fv9Ux4ongAt3snKtVcINKvMXWHkyc/wGb7GqNxMUOGzAn0lAE4ePAZ0tPf5dy5VHS6DZjN/SPSNmLEf2M01pGScoyzZ68GLqzHSKPRsHjxYt5++20sFgv/+7+XUF09lMWLX+7QLiYmD4MhGbdbjyiKpKSkcPz4cU6fPk12dvYPXPf9k8IUSK1EWlqaHE720NB6q3sKdLMeSPvuPLfti8g97FpDh3atjfEHP4WJehADA4IgcNlllyEIAitXruTWW2/t8H15eTnJyT+yLCUlJVFeXt6jAG2g65Rv0djeoa7uSWpqrkQUFSiVDhITMwLuwzvduwpJEhAE8YdUIv/78ga1OoTJk89x+LCJMWMa0eu7X1+8jclut5OUdDHLlj2LyxXO4cNzUSqPYjKZMJvNXhm8SktL/R57ezidd+J2L0CSIpGkKESxhi1btlBXV0dmZmYHFeoLDZttF7/+9So0mj/idF6DwzGXNpHCvsHXOhUdHU1kZCRlZWWy062/Unj9betJ2fXUtp47d46MjIyAi6GDDc9aqFaryczMpLW1laKiIkpKSsjMzCQ6OrrnTi4wfvbGRm/SqILJy61SvYVG04AkgSRpCAlpm1x8GRYhISGYzWbi4+Nlw6KiogJRFAkNDZX7dzpLcThWIQittLZ+iNn8pl9j8uUNs1qriI1dh1rtwGbbidu9A6XSd4jY1/UZOfIdlMpWhg3bR2vr90D/TKZ6fQyCUINSqSE8PJHq6uAUXPvbzlMU6PGEOZ1O9Ho92dnZ7NmzB7fbzaFDQ9Fq/8L8+b/v1EMd0JaTqtFoZOG9Q4cOERoaGtA4guXZ6dyvhyp38uTJlJeXk5+fT3JyMsnJyR366e80qp7Or70SeWFhIc3NzbLC7SAGEWzs2LGDhIQEzp07x7x588jKymLGjBny997eXX+e957YqLz1GazIhlp9hm3bMn8wBiSUSnrl0fU2JoMhnosvvpKzZ4+SknKx31EN3+emJC3tMTIyvkcURyNJ3W8CRVGkrq5OLri32Wyo1WrM5imYTLMwmcLJyek5wtD7/YCAKHpSkSRaW600NJxGrbZSWGi/oMaGt3NyOl/E6XwxqMfpbs5XKBSkpKQQHx9PUVERtbW1fjuN+pPIRKVSkZWVRUtLC3v37iUmJoaUlJQLRqbT2fGm1+sZM2YMTU1NlJSUEBkZOeBrD3/2xkagaVQej1GwOI5Vqgf5/+y9eXhkV3nn/7m1q/ZFKm2lrbRVS72vXvCCsR3bQ4wXFkOIA2FzghMm2wADP5IQkpBMnDAQAk9mDL+BkMHgxICN25jF4I2Wulvd6k271Npba6tKJam2e+/8ob63q6Qqqaok0bbR93n6sSXdOnXqVtU5533f7/v9CgIIAiwumllclDh+/DiwzEG32WwpgUU6pFvEFxam0WpjaDQGotGprOay1pfNYNCRSIAoatDr1zcAzLQZFBQcRhBeRBBcGAxNWc0rH+h030Sn+x6StAtoADZHFjfdPVpZYo9Go2qJ3eVyUVlZqapchEIhLl++TF9fH6Io0tY2S0nJMfbuvR5BkEkkfMTjpciyiCAsm2pptVocDgdHjhxheHiY4eFhxsbGKC0t3dSsez4LsyAI+Hw+SkpK6Ovr49ixYzQ0NODxeFZdu9nzyCVAMhqNNDQ0EIlE6Ovr+5U7kW/j1wNlZWUAeL1e7r//flpbW1OCDZ/Px/DwsPrzyMiI+pi1cC0rG07nL/nZz5QDsYDBULDm9ZmQKQAqLT1CaWlmM7jc4UEU37Lqt7FYLKWPcWlpiUgkwtzcHC6XK8XM8FrB7Z7B4xljetrF7t2nrtk8Mu3fymdKkqQV/isJ1RMkH6x3z41GIzt37uTcuXOMjIywuLioqiNmwlYGG8oZUKm+DA8P09LSQnV1NWVlZRtWMMsVmar8drudPXv2vOYDDfg1CTbi8XjW1280YySKolqxMJt/gU63oP5terqQ2traNQOLbOdks+2hq+udaLVnMJnel/d8Feh0hcjyF0kknkOvfweCkJ/PhkbzBbTak0hSDbJcvOF5ZYIslxCPKw7zEWRZ5sKFf0aWz1FU9Che7868xk0kEszMzKjBRSQSUd137XY75eXl6yo/1NTUEAqFuHTpEqIo8uMf/5yysjEMBsMVd1hBVZOJxWJqeVSj0VBeXs7o6Chzc3MqtSqTIsavsplOae5bXFxMae7bSv3vfDYIvV7Pvn37mJ6eTttEngu26VXbSMbCwgKSJGGz2VhYWOD555/nM5/5TMo19957L//8z//MQw89REtLCw6HY10KFeQebGxmZePs2f3AqPpzZeWuvMZJnlM8fpGhoW9iMjkpK/swgpBbs/l6WKkKtbS0hE6nw25fFkjxer0UFBRw5swZqqurc252X4nNut+i6OS97/0p0aiI0VjJ5OQ8P/zhD4nH49x9990ZhQe2CopruSiKKeud0tCtHGITiQSCIKj7VC7I5b4pSaNEIsHx48epqKjA5/Olfc6t6u9YObZGo6GqqoqysjL6+vpoaWmhoaFBVdPK9XORz575RlBNfMMHG7mWp3PJGCUHFgoVShAElQpVWHg85fozZ36Te+5xZBgtM9ItdFqtjqam/7aphz29/hb0+ls2OIoJUbwx66s3a+6ieJo9e/4OrTbB1NRp4Ni6j0kkEin66JcvX2Zubg63260232eTCUtesBUTo5tvvpnvfve7KuXq1KlT3HLL6nurZJCi0agagGo0Gpqampifn6ejowOr1Zo2y7MRZaf1rs00rtlsZt++faoilNFoTKH3bSZyXWCTry8sLMTj8agUMKWJ/PW+YG/j2mFiYoL7778fWF473vOe93DXXXfx1a8u004eeeQR7rnnHp599lnq6uowm818/etfz2rsrdyn1oPNVgKMqT/feuuteY2T7Ad19uy/09MTQ6OZ4Oabf0Jx8X/Je35KYJGsDKXT6dTmbSWw2Ky9ZG5ujpdffhmr1cqNN964aQa3giCQSDiIRL6PVnuWpaWbOH++nZmZUUCmre0Yd96ZvdlvrlD2KUmSEEWRxcVFFhYWKCgoUD0lMlUvBEFI2adyoRPlk+hS1BH7+/vVw71STd/IuLnMeeV90Ov1KrWqu7tb9X3KtVqWb+Dwek9+veGDjVxpVJkyGEpgoWRSFhYWEARBpUIpzdvJH6KnnnozFRVH0ethasqBzZYfP1P5oi9jhETia2g0dWg0775mH0DlPr0WvgDLDfQJYPl9KyhYXclK9/5pNBp1w1Jc04uLi9fk+ycv2CvnoNChqqqqGBgYoLy8nJGREQDa2tq48cYb0+rlKweHZOdQWOZNHzp0SPXAqKysxOfzrcpAZYPNXpgVRahz584xNDSE0WjcdDm+fNzJk79/yRQwpYm8trYWr9f7mvjcbuP1Bb/fT3t7+6rfP/LII+r/C4LAl7/85ZzHvpY0KovFwkMPPURvby979+7N+3CdvE/F4zYEYRFZFkgksu//UBJAsViMc+fOMT8/j16vx+FwYLPZKCwszElEI5+KxLFjP+XSpV5AS2lpIY2N+VXJM0GWq0gklvsSysvbaW9fBMDnO0lvbwCNRkNNTU3G15jN+qzsU8o/BYKwXFU3mUwEAgE6OzspLi5W/SUyYeU+tbS0pCbKsqFc57P36HQ6GhoaUqrpgUBAlcrNZdzNFDKxWCwpCTen0/krCTZe7/i1CDZyoVFpNBri8ThLS0tqJmVlYFFRUYHFYln3A2M21/D5z/85giAhy1r+8A+vz+s1JGeMQqH/D43mzJXDbTUmU/ZGMq8FKA16m9UToyCROEgk8sfASXS6jzM/HyIc/jHhcIK5Ob/6/tnt9ozv38oFI9OCDcvviRJgKD8rKCkpoaioCLPZrAYbiUSCF154gTvuuCNlfKWqosgaazQaiouLVdd7jUajZnmUEm4gEMDpdG669G2u1ypN5BaLhXA4TEtLC42Njbhcm6NAlmvpO9MGodPpqKuro6Kigp6eHgYHB2loaNhuIt/GawYmk4lgMJj19ZtJo1LWnGx6S7Kd0+7d78Nk+hEFBU5KS9+U9vrkyrKyz2q1Wmw2G4IgYLcLnDv3MoIgcPvt78Ljyc/ELtf75HKdYnRUjyDI2O1ngJ2ber+T4fdbeO97v4Yoarh48QF+8YvnkGW46aZb2LUrOzpb8j61cg1MrlYogYaCwsJC3G43Q0NDtLa2Ul9fv6p6oIy/sLBAMBhUk3WwrCYVj8fzolZlQrq9J7mafurUKdXocqsrG9km3IaGhhgZGWFoaCgj5Wvl2NvBxhsQ65WnE4lEChXq8uXLLC0tqZmUbAOLdHC73dx119309vamzWpni+SFLhSScbtlRFFgcTHBtVJky6eycf58CzMz3yAWK+OGG/5oU5p3ZVlmcXGRSCTK4OB/YX7+ZmQ5itv919TVfROA2dm/w+X67TXHSaZCxWKxrBfsTNBqtezZs4djx44RCoUAOH36NMXFxej1etWI0Wq14nA4qKysVCtjK6lVOp1O7ZkIh8N0dnZiNBoxGAxZ38Ot7O/Q6XT4/X4WFhbo7OxEq9WuMmvKB/kEG2tdrzQhzs/P093djVar3VT50G1sI19cSxrVZh2kBUFAEGYZGPgUkiSyc+fvo9Uuuzsn77OhUCglsLDZbFRVVWE2m9Xv79zcHBMTP0EUQ8iywMjID/F4Hlnr6VMwNzfH3NxcXvfohhsMlJe/isUSo7DwzYhizkNkjXj8vTidMrDIqVMmRHESgLGxZUXDlSIXyfvUyiRqcp9FtvuUogZVUlJCV1cXIyMjVFRUEI1GCYVChEIhRFFUE3VlZWXYbDZ17Vy5T21Gb0Wma5XD/fDwMMeOHcNkMmWd2Noq1URBWDYFnJycJBqNcuzYMerr69eUot1sb6fXS5X+DR9sJNOo0mVSkqk0FRUVyLJMVVUVVqt1U56/qamJpqaNqTIlbwY63afo6PgGWm0lzc35VUqWlma4dOnLaDROfL7fQ6vNr2ye6wbldH6WpqZziKKWycn9mM135/x8S0tLanZlfn6eRCKByWRCkqQUg8PJyb9FqxUBmUjkOHA12MhUsbBarfT391NfX6+WRvMJMpPnWVtby6lTV1VHfvSjH/HBD36QhoaGjNWdlSXrWCyGXq9XqzMHRjUbHgAAIABJREFUDhxgcnKS8+fPE4vF8Hq9Wc1zqylXFouFAwcOMD09rWagampq8g6yN0qjygSbzabew/b2drq6uvD7/ZvGzd7GNnLFtWwQ38xgIxw+Rnu7BhCYm/sWBQVvUyu2mQKLTKiudtHXN4lGI1NZub6PlIL5+XmefvrfEMUIVqubhobcPENE8eNUV/8QWfYgimvLGm8cOuLx9wNwww1/ysJCmFhMR1+fjp6en3Phwnne+c53pTy32Wzm4sWLGAwG1dQt331KCSqCwSCJRIKlpSXa29vVBNha62K6fUqr1ar9H8nPs1l7j9KsXVpayvHjx+nu7sZoNGYUUclnDgpyoWhpNBrq6+vx+Xxq9byxsTGthPR2ZeMaYnh4mIcffphLly6h0Wj48Ic/zMc+9rGUa37+85/ztre9jZqaGgAeeOCBVUogyQiFQpw6dYpXXnmFiYkJnnzyST75yU+qVKhMC95rMdu5TNcZR5L+Bq9XR0nJXwH5Kz1dvvxXlJUdRZY1TE87KS5+OK855QqvN4Esg1YLXu/yfc+0gCcvhEo2TPGysNlseDweqqur0ev1RKNROjs7U2gxgvCnzM72kkjosdnegl5/PbJcSCTyOLK8nA1ZWbGorKzE4/HQ2dmJ2Wymrq4uq0UhecEOhUJEo1EKCgqw2+3s27cvJdgA6O7uXlNlSoEyNyWLlawGUlxcTDAYZHFxUaUvKeoYme7nVgUbK++RUp5X5AJramrykvHdLBpVJrhcLux2OxaLhZaWFtVHRHnO10vGaBuvf+RD992sfSqZppsrkkVSZmdnmZszIYoSggCJhDdvZoAgCBQWvod3vOMnCIIWjWa11G0mLC6eQ5Km0GolRHEu15cEGEkkHlg1n1gsxuTkJIWFhZtu8raspPce3vGO9zAyUsi3v70sjz41Nc7jjz/OHXfcgd/vVzPpLpeLrq4uJiYmaGxszEptKxaLqdWKYDBIJBJRBT6cTieVlZUYjUYkSWJwcJDe3t4U1aVMWBl0iKK4iiqdy1qazbUGgwG3243VaqWzsxOLxUJ9fX1GqdytPOAnV9QV/4u5uTkuXLiA1WpdNa/tno1rCJ1Ox2OPPcb+/fuZn5/nwIED3HHHHasqAjfddBPPPPPMuuN1dHTwoQ99iH379uFwOAgEAvzJn/zJmprNCjabm7kZTdTLmYvvs7jYDsiI4hM4HH+Y93gGg3RlXJlckrnxeJxTp76GJA0Ri92S830yGr+IVvt3yHIAQbg95W/JC6HiZaFkLBwOBxUVFVm9f0rVwuE4iCwfx2QSkOW3Ab1AH/H4v2E2/0nGL7vFYmH//v1cunSJEydOqCVm5T2Mx+PqPEOhEIuLixgMBhwOB3a7HZ/Pt2ojUvTDFdTX19PT06MGNOtl1LVabVo1EEW1Q1lwlQa6dBvhVvV3KHKzK5Gcgert7WV4eJjGxsas56CMvZWNd7Iso9VqMzaRb2MbvypcS1O/VAGSzMikvqgk8MrKguj1Z/F4KjAYDtHQcCt6/fprdqY5ybIGrfaunB9bXOxh585Bxsac7NkzsSn3SZZlfvjDZwiFLmM2W3nXu969IYO3dEIjoriLYPAMTuerXH/9lzh3roHLl+1EoyGeffZpDh26juuvX2YzmEwm9uzZw9TUFG1tbZSXl6cYriosDiUBtrCwcMXM0K6aBmdSUVIa1EtKSuju7mZkZCQrF21l7ZUkSa1y6HS6Le0ttFqtHDp0iEuXLnH8+HF8Pl9KwkjBZlOXVs5j5dhOp5PDhw+r8yorK1Ob8LdyLq9lvCaCjdLSUlWL3GazsWPHDkZHR/OmH+3YsYOXX34ZgG984xuMjY1ldVCFreHCbkawMTtbjNstIMsCc3PFbERt1OX6c0IhBxqNG5fr3Vk/bmrqBfbseQytVuTixfPAbTk9ryQ1IklfuyJluLwILi0t0dramrIQlpWVYTQas1bcSCQSxGIxotFoSuO2knHp7S2mvn7ZHdds/gZm81eJxb6IJKWXY0zOHnV2dtLf34/ZbFYP+so8i4uLs5JcvOuuu+jr62NpaQmfz4fb7cblcqkBTWVl5bpGQelK1qIoIssyZrOZ/fv3qxtPSUkJ1dXVW1bKzuVag8FAU1OT2muiGG1lkxncKhpVuvGVJnKfz0dvby+Dg4M0NTWlbZjcxjY2G9dSjSpd4CJJUkqPRTgcBlC5++nUF1tavszAgAeDYZHbby/IO9DINKfs0cDhw+9HoznP+fP3bVKwITE3dxG9fonFRSNLS/PYbNn1CyQHFolEgsXFRZUWu1Judvm/N3H99V+nvv55vvGN+1la0iMIMV599RVGRka477771PNMUVERTqeT7u5uVa5X2QdtNhsOhwO/34/FYsn5HFJQUMCePXuYnp7m9OnTlJaWpj3IJyOdumIikchaECafwETZrxWp3GPHjtHY2Jiydm+lcmamJFfyvAYHB/nlL39JbW0tJpMp56TYGwGviWAjGRcvXuTUqVMcObLacfSXv/wle/bsoaysjH/4h3+gubl53fF+1aZ+WzGWXv8y9fU/JRwOMD5+Lzt23Luh8bRaDy7XX+X8OKczilYrIcsCLlcoq9e20ssiWSPdbrdjNBrx+/2MjY1RVFSUkVq0luSs0WjE6/Vy5syZtIoaBQWf45lniqiouMSBAz8lHhfRav8UQQgiiu8A9BkVN2w2GxaLhenpaQoLC/H7/TkraQmCwKOPPrrqd6WlpRQVFdHX18eJEycIBAJpOZ4KRFFU7+Pc3BzBYJCioiJ1sVPcTpUMfXKj2rUKNhRYrVb279/PSy+9RFtbG8XFxVRXV695Lze7QTyb600mk9pEnkumeRvb2AiudbCh0KCUioWSNVZk3S0WyzrrXoKFBSNa7SKiaGRpyc46LNFVkGWZ/v5jhEKXiMeLNrR3iuItiOItJBIX8h4jGTqdyA03tHDq1C527TqHzTYPrA421lMwrKqq4ty5c/j9foqLM1GhDcRi/4bT+Sof/vDv8m//9pvMzBQCMoODF/nyl7/MbbfdhtFoJBQKqUaTpaWlzMzM4HA4sqqYZ4vCwkJcLheDg4McP36c+vr6NalVSqAaDAYJBoPMzc1RX1+PKIrr7p0b2Xu0Wi319fWUl5enGM+azeYtDTbWG1ur1eL3+ykvL6e3t5e5uTksFsumjf96wWsq2AiHwzz44IN84QtfWHXo3L9/P4ODg1itVp599lnuu+8+enp61h3TYDBcs0VcGWujMq+i+AUikTBW6wxNTXUYjddGgspsvgtJakMQepiaehCbLXUxTS6zK6XblV4WKzXS+/p6mZh4hMbG01y4cBN79jwOsKaUn5IRSm6M8/v9lJaW0tnZyaVLl2hoaFAX24qKKioq/pHBwedJJH6KTiciCGPodH9MOPwUnZ2PEI1a0ipuKKitrWVkZITjx49TV1e3aU6visrU/Pw8XV1dWK1WtcldCX6CwWBK8ONwOKipqVHVnlaqgfj9fsrKyujq6mJ4eJhAIHDNgw0FBoOBI0eOMDg4qFKWiouL046RT/CwWbSrle//NraxlfhV0aiUpEpyxWJxcRFJknC73ZSWlmK1WnP+7M/O/ivB4BKRiI5du5opKsrdhXxy8hTHjh1FlsHp1CLLqxOOuWKzEn6ybGTfvh1cd92/k0jcTjTqQ5Ik4vG42qOQjEwKhhUVFRQXF9PV1cWlS5dUU7h0EMXrEYSv8Nu//VH+4z9uY3i4AhBIJOI8//zz1NfX89a3vjWFzlVbW8v4+HhaCvBGoByYS0tL6erqYmxsTO1FWFxcTOlZVIIfu91OZWUlgUAAIIValWnd3Yy9Z6XxrMfjydl4Lxdku08ZjUaam5sZHh6mr6+PM2fOZEVPW2/810sg8poJNuLxOA8++CC/9Vu/xQMPPLDq78nBxz333MPv//7vq9nmtbBZpn75YHPGmiEWE7HZgiwsFBGLOciiF2wV5udPE49PIMsb8RbQo9H8OQBLS6cIh8MpfiTJ/N1sGwP1+imam1tIJLTs3/88S0shtNrlqF+j0eQk5VdQUMDevXuZmJhQF9vi4mK1H2RiwsmPf/xRDh9+kX37TmAwzONyPc/11x8nGn0KWc5s3qTRaKisrMTr9dLd3c3o6OiaG0UuUORjfT4fY2NjvPjii2ofiMPhSBv8JCOZWqXcL4XTOzs7S3t7O9FoNOsgequCDWXRVDjB5eXl9PT0qL0mKxMM+cgVbmUlZBvb2CrkU9lIJBJrXiNJknoQVNZoWZaxWCxqJtxqtdLd3Y3P51uzqro2ZM6dG0UQJPR6gcJCa17fK41mEkGQkWUNen0oz7lkh3yyxfPznyYa/QyyrAVEZmdnefrpp0kkEtxxxx3U1tZmtU8ZDAZ27drFzMwMp0+fpqysjIqKCuLxuHpgD4VCV+imToqK/pmHHvpjvv/9/XR3B5RXQE9PN//0T//EH/zBH6j7kNLHV1RURE9PD2NjYwQCgZwy6WtBGf/SpUu88sor6HQ67HY7TqcTr9dLXV1dxl6WZKlcnU63SrUKNnfv8Xg8HDlyhJGREfr6+igsLNySKkGuY5pMJkpLS3G73bS1ta2r2vhG2adeE8GGLMt84AMfYMeOHfzxH/9x2msuXbqkZkBbW1uRJCkrPvW1VPnYjGAjHv9zjMZZEgmYmvogDQ01OY8RDL6EXv/76PUiFsvtQG4ZI8XLInnTCofDjI6O4nK5KC0tpb6+ft1smHJflVIzgE5XjCQVYTDMIkkBTCb7FfWR/L5c8XgcnU5HYWEhvb29dHR0YLfbcblc1NTU4PU+TEdHIxbLIs3NZwARQZjCZHoz8fgnSSQ+vub4JpOJ3bt3qxtFNjzWlVCCH2VjUXoYFLnBQCDA0NAQ4XAYt9u97iEgmSebSCRSVKvcbjdHjhzhpZde4sSJE9TV1a3roJ1rAJHv5mAwGGhubmZ+fp7Ozk4KCgqor69XlVW2Wo3qjZIx2sbrHxuVvlUCi2Rpd1EUsVgsan+ZUjFdiY2oUQHEYseYmpKYnHTgcESw2fKrSBQWvpkbbzxBKDSDIBzOek7K9z7d93XlfRJFkaNHf8ilS+McPHiEvXv3ph1P+a8ytk6nY2xs7Ar1c3m9HRu7QCw2jUYj0dX1y5wkdpV12uv1Mjw8TE9PD1arFbfbrfbEpPYtvsw73/kAzz0X5MSJ5Psr86UvfYk/+7M/Sxlfr9fT1NREMBjk/PnzuN1uampqcqpYLfdWXm0yX1xcVEVbSktLqaurY3x8nOnpaZxO57omqdmoVm12oktJEsbjcWZmZmhtbSUQCODYSNPrCuQjTJJMe1ZUG6urq9P2bm4HG5uIV155hW9+85vs2rVL/fL/zd/8DUNDQwA88sgjPPnkk3zlK19Bp9NRUFDAt7/97awl0q6VWdLGx1okkWjFYgkxP2+jqKgqrw9dNNqNyZRAkjRYrRfXvFbxiFDK7IqXhdlsxm63q+6d58+fp6GhIaPknvK6FQOilVQo5d/u3YcZHPzfyPIZysreisWyXI5WsnZrKX6Iopgi5bdSccPn8xGJROju7kaj0eDxeCgqKmJhYYEf/vAhpqc93HLLC1dGi6PXfxat9v8Qjb4ErB3IejwenE4nFy9e5MSJExkdqZU+C4UOlTxHpWqRrsQbCATUQ7jdbs94UEhGshrISmqVwWBg7969qjJUIBDI6CWzUenbta5NN67NZuPgwYNMTk5y4sQJVbkjn+AhF57yG2UR38brH7nQfWVZJhaLEQwG6e7uTgksbDabukZnq5aUrRpVJoyM/ILFRXA657HbjTgc+VFMBcFIVdWnAWhvb88q2Dh/vo3W1lfxeFzcffc7V33/VwYb09PDTE6ewWCI0t4eYu/evSlGecnrjRJkaDQampqaGBwcpL29Xa3C1tV10da2RCKhY/fuVuB30s5RoRcrh/ZwOKwaGjocDnUOnZ2dyLJMYWFhmqDASjT6I+6884NYLD/lF79IlgLOfJ8cDgcHDx5keHh4TQqwJEnqnh8MBtU5rieGUltbq1Kr9Ho9DQ0N64rxZFKtUoLeraiqa7VaysvLsdvtaRNbG0GulY3kfUfp4ykrK6Ovr4+WlpZVcsNvlH3qNRFsvOlNb1p3YXn00UdXNdlmg9czjSoW+y4aTRBRFJmaKqekZH9e4zid9xMM/gStdpqpqXdSWSmrc4tGo+ois5aXxXqvTZIkRkZGmJ2dVRUXlOsUmdZk1Q0FWq2WurpDBIMNnDvXide7iMkUZW7uvwLgdn+R4uKGlKYzJQjKRnGjoKCAQ4cOqc1tjY2NNDU1IQgCR4/GGR+v4F3v+iYazfJmq9EMUlBQSTz+CInEY2veV61WS21treq+ajQaKSsrY2FhQZ2jIAgqfzVXVRDlED4+Ps7x48ez4uBmMlqSZRmTycSuXbuYm5vj3LlzuFyutEHMVtOo0kEQBIqLiyksLFT7ORwOR07UjlwrIb+u5krbeO0h0z6lVJWTRTaURluNRoPP58spsEiHje1TCbq7RwmFStFoZLze3P2flpZmGR19FZerAY+nIac5nTv3HAbDApcvTzE1dZaysrX3SKdzErM5zMKCmcbGLpX1sFLBMN26oPSXKQmg+vr9/N7vvR9JktFoPk5f30VeeOEF3G63KuOf3Gun9DCsVPFScPDgQUZHRzl+/Di1tbVpXKgF4vHHuf76vyYU+iWnTi3L4Or1C2u+ZuVAW1xcrFKAKyoqiEQi6r6vCAIo1fVMc0wHs9nM3r17U2R4fT5f1vtUMrVqq3sLlT1VoVqXl5dTWVm5oX1gM4RJ9Ho9gUCAhYUFuru7VVNAs9m8HWy8XvD6pVFJSNK3sNtnSSQMhELN1Nbm52puMLgpKvq/xGIx4vGTDAwMMD8/r9J3lAO7z+dTI/2pqQFGRr6OzXaYmpqbUmd25f7E43E1AzMzM4xO9zvs2DHOhQsPs3//cm9Htl8Sh8PBoUOHuHjxIpcv/yVNTScB6O7+KwYH/yCl6UzhF2c7ttIjoGwUJpOJurq6K+oQMl/96kf48If/FZ1OVB+j138VjeYosVh6NRNZlolEImrFQhSXObyK6VNFRcWmNBknc3B7e3sZGxujsbFxXYd7ZTFfWlpienqaaDSKKIrodDqcTqfKZU1nunctm8mTlTva2tqYnZ3F5XJlFXTkUwnZpkptIxdshQEtXKVRnT17FofDoSaBlKryyuTPzMwMc3NzuFzZya+uhY3sU5HIc1y86AUkCgrimEzVOY/x859/mZmZBHp9K29960ewWMqznlN9/ShnzxZiNkfweCIpf1P2qUQioZ4BNJodvPvdvSwtjWCxvB1JMlz5fXZ7idVq5cCBA4yMjHDsGPj930CrXWJmpoSf/ewp4vFFwuEJSkqKaWwM5LQHCIKAz+ejqKiIrq4uxsfH0xr2ieKnuP32z3HPPZ9lcbEAi8VJNPqXGcdVgopgMEg8Hlddwl0uFxUVFWv2WWQLhRLm8XgYGBjg+PHjGav9yVD2qUgkwsTEBAsLC1veWygIAiUlJRQVFTEwMMCxY8doaGhQKz65fhfy6S3MdL3FYklpbne5XBkFVF5veMMHG9fSLGkjgYss92G1nkEUBSIRA4uLN+b0+Hg8ztxcF+Hw8ywuNrCw4EWv16vl9vW8LCTp/ezYMUI8/jWCwWewWCpS7ovD4aC7u5vGxkZsNhtGYxt2+zCSpKGx8XtoNJkXv9TXKacshqFQCLvdgywvz8vlqsHv37/hxRCuqlRcunSJkydPctNNN/Hss88yPS3z2GN/wh/90T9hMFwNTLXaQfX/FdqCMs9kl3BFElev1xOPx+nr66Ovry+roCBb6PV6duzYQSgUoqOjQ63mJN+XRCKRQitTOLaKd008HkeSJLVkrSij9Pb2MjIywo4dO9RD/bVWrjIajZSUlCCKYkYn1pXYjAzTNraxFjbbgPZ73/ser7zyCi0tLXR1dfGpT32Kz3zmM/j9/oxVZdg49SkZG9mnurufJRqtADQYjRJ6fY56t8DSUgStVosoyojiJFAOZHfoO3DgA+zc+S/o9U1I0iESiURKEsFut9Pf3099fT1OpxNBMKDR/AcWyxJgIZevf3IlQKkGdHYuYTQaqalxUlU1wMCAG40mjt8/w/T0NIIg5NwfYDQa2b17N9PT07S1teHz+dJUCj5NLPZpdDpIPt4km84Gg0GWlpZSXMKrqqowGAyIosjAwIC6T21WD8MyU6GOhYUFtdq/ct1OphYrqpVKo7nf70ej0aSInWTCRvceZa7JUrmKKe5m9v7lc73H4+G6665jdHSUs2fPYjAY1vTzeD3gDR9sXGv98nwDl3j8SxQURBAEmJ4ux2Aoy3htIpEgHA6r1J2FhQW0Wg1+/x9SWjpLImHBYHgeg8HDyZMnM3BCUzXCXa5JEgkden0MSbqMIFSmSPn5/X48Hg+dnZ14vV6qqm5AEBzAwpqur8mH9pXN0W63m+rqagyGLyBJB5mbm6O//xAlJW1I0vcwmW6itPTuvO6nAkFY9rcoLCykp6eHQCBAa2sr0aiZz3/+v/PpT/+lugFJkoYzZ86ofRaKS/jq5r2rUMqhwWCQCxcu4HK58vLmyAS73c7BgwevZNaO4Xa7kWVZpZUpvSB1dXWrZIaTjZYUNRDFdC8UCnHhwgVsNltexkrZIJ9GOovFQm1trerEulbZe7MX/dfLIr6NXx0224A2FApxxx138Ed/9Efcd999/OAHP8jqcRtt6k5G/vvUIqdOORBFHYIAPt/awhOZcMstN3D+/C8oKyvBbt+77pxSvSx2IQhfQRHmEgRSFAx9Ph8ulyslQQNawLJqzL6+PgDq6upWJW6UQ7vSw1BRUYHRaESWZSYnJ+nv7+fOO/u4dOkEbvcCP/hBOdPTPWg0en7nd96XlxqU4m/R39+f1ocpUy+IsgesZTqbHBR0dnZiNps31ZtDydBPTExw/Phx7HY7Op1OVURLlnBfSS1OplatJZWbCw12rbVeUbFUlBtdLtemVSoyzSWb84Dy+TWZTHR3d6/yznq9YTvYWIHNzBjlv4hLWCzfUQ30JiYOotUuv1XpvCyS+wKqqqqwWCzIcpxIZB5JMqLVRhDFMOBR57SW+dDyNZ9Hr/8SovhmHI6DwOovk0J9GhgY4MSJcZqansFmC6PXL2+86TLtBoNBXbDXOrRrNO/D7YY9eyJEo3twOGaQpK8RCr2A3b4jj3uaCkWtY2ZmhqGhIcbGxgCBz33uL3jve1/F7Z5jZuZr+P1FebmvKvdmZGSE1tZW6urq8l4kkilbyr0URRG73a4acDU3N+ekWrVSDcRut3P48GHGx8cZGRlhZGSE8vLydV93Lgf8fKVslQDR6/WqZe90i+5GGvW2sY1csRkGtA8//DCw/NldT8o2Ga+FpNji4v9gZsYNLAc+VVU3MjERWfdxCkQxxtjYzzCZirn55r9fNSdIr2CoIJOXxUpYLBYOHDigNkk3Njam0M9EUaSl5ZecOnUMWZY5d86Pz1eZ1aFd6TVzu9309n4Kh+Moev1NzMycRJKW38+urlMUFVVRUVGR9b1RoBjVhUIhzp8/j8FgwGQyqev+yn0/1/XMYrGwf/9+Ll26tCneHNFoVKUWh0IhYrGY6mo+Pz9PIBBY0xAQ0vcdrlStgvzcxteC2+3muuuu4+LFi8zPzzM6OppWHWol8lWjyhaCIOB2u6mqqqKnp0ft58hfqvra4A0fbORj6netaVSy/BRG46LyE6HQbcRii5w4cUJt5FKyK5kWGEEwEAp9BvgW8fgdlJdXqJvZ0tKS2sCdecG+/8q/9V+j0sx29ux5jEYjev35K9WVq4ob6TLt2cBkMqHThREEGUGQuHy5H5stkNdiqChtJVdWRFFk586dTE1NqbzeZ565hw996EOUZS4mZQVBEKioqFC9OZR+i/W8OZRSuLJoJ1d/PB4PNTU1KRmoYDBIR0dH1lWUtdRAysrKGBgYIBwO09LSwo4dO9YssW+VcpUyv+SxlWycz+eju7tbdYhVqGrbNKpt/Kqw2Qa0ua5nrwW679NPTwJ+9efy8iYmJtqyfvzp0/9CR8csWq3M7bfH8XiWgzYlAba0tKTSb5QGboDBwUEMBgMVFRVZ3zdBEKisrMTj8XD+/HkULyIlWRcOvwjEEQQoLh7l0KEHs34doFS0b2B2NsDJk93ceeePefnlwxQULPHyy79EEE5w++2/wY4d6yfKFOGW5ORSPB6/kkSUmZ2dpb6+fg0H8tyQXO1X+gKz8ebIRNtVfDcUypaCcDismhnW1dXlrFql0WhUSXfYHCXElRAEgfLycqampgiFQoyMjKwrlZtPkiufpFhBQQG7d+9mbm4uhVq8GRTzXwVeH7PcAHJVo7pWGaNkL4uamt9N+j24XAeZmZlh3759WZXfYrGLLC39KyZTAxbLdwBBlfWrrKyko6OD6upqlQ6QKxQXWmWRURQ37HY7iUSC+fl5duzYgcvlYnp6kjNn/hmNxsnhw49gNptzfr7FxX9BEP6eSOQWdLon0GjeTzB4O3b7t0hXcVEQjUZTekGUPguHw5HSZwHLpfdnn30WQFVr2rkzs8lfLjAajSkmTiUlJSoVSJEcXFkKVyhbpaWl63JIlSrK6Ogora2t+P3+db00MqmBaLVaBEEgEAgQDofp6OigoKAgo6ThVnlyKNen20wUr5O5uTnOnz9/RYayLq9gY9slfBu5YqsMaHPBa6GyMTqa7Pkk5/xdCoWCCAKIIoTDIzidB9X5KAmF8vLylKDi1Kkf0tbWCmh485vvwu/P7OkxPz/PqVOnMJlM2O121d3aarUiyzLBYJCGhgaKioqIxX6KIFxEEGQOHVr7ELwW3G43hw4dYn7+Oj7ykf/N008/yPh4HIjzs58dpaenh7vvvjslWZQpuaT4Q608tEciEbq6upicnFxTfj5XKH2B6bw5lD1fmWM2tN2VsFqt7N+/n4mJCU6ePElFRcWT/DKLAAAgAElEQVS61fN0+5SSHNtK1UStVsuOHTtU+XmTyZTxXue6j2w0KeZ0Ojl8+LBKUdu5cyderzfr8a4VtoONFfhVSN8mN0UrdKirXhZmdLqrj4nHNdTV1TE3N7dmn0XyxhMOfwyzuRvQEIn4sdtvUT+sSslXWax27NiBwWBAlmWmpgYxGq0pOumZqgGKWVQ6d+uFhQXVTA/+iZtv/j4AQ0M2/P4P5Xwf7fa3AW+joCCI0ViDKGpwOH5Ef/8vqK6+GUHQpGRZFHqZQtlSlLbWqigEAgGOHj2qvl9Hjx7F6XRmRSXKFm63m+bmZvr6+hgcHFRLw4rkYL6lcLi6QXu9Xnp6elSH8/WyU+moVco9sFqtqkzg8ePH8fl8qwwMt2rBz+Z6ZdEdHx+ntbVVrc7lMv52ZWMbuUCWt86ANpd957Uk0Q5gt6+9zqzcp0Rxibq6ZiSpDbvdTEXFXej1V5WhjEYjhw8fpre3l7a2NpqamigoKGBx8QSyLCHLEktLrSgGtcl7qvLv3LmXWFgIodXquO22B9i/P1VoJBKJ0NnZeeXQ/kHuuedvAYjHfw9YztxfvnwZl8uVU/ZYq9XidP4NMzOfoLn5w1y+rGNiophoNEpfXwff+U6QffsOIIqiygDIJblkMpnYs2ePKjWbzaE9F9jtdnbu3El/fz8vvfQSBoMBjUajMipyVYNMhqIEVVhYqPaiNDY2rqoOrkQ6atVGDGWzvTaTB1S+e6By/UZpV8mKWtuVjdcIFIWgbLHZGSNJkohEIikmebFYbFXmQsl0yLKEJKE2Kb/wwm286U1X+asrAwvleZSDllarJR4XEAQZSYJEYrWsn16vV6lDJ0+exO/3s7DwH5SWPkY0WkB//78gSWUZqwEajYapqSkcDgcFBQWrXrfCkR0ZGUGrHUCjEREEKCy8vMH7aWZ6ugSXa4pYzEh9/duJRh2cPv2PiGKp2gtSW1ubM2VLkVxVmgQBnnzySW655RYCgUDa17kelGb45MqK2WzG6XRSXFzM2NgYBQUFWZWUs4Xiyq1k/ddzjpVlWW0yVDJWJpMpRQ1EWdT6+/tpaWmhsbFR5d1uZbCRTQZIkQb2er0cO3aMM2fOEAgEssoib0vfbiNXbKUBbS7YzH0q37EOHjzMiRMnMBj0PPzwB9Xfr9UPuPx9lnn55ccYH5cpLNRy+PAn0WhWr39arZbGxkYuX75Me3s75eXl7N+/l2j0GQyGBCUlb6Kvry9FaCSZwnPx4hMsLRWg0cRxOC6i0wVSxlcO7cueC73U1n5CzRDLssyTT36X2dkpnE43Dz30npwP11arDa32Y7znPb/D0aOH6egIEI9rGRsbY3p6mrvuuofm5ua8Ex5FRUW4XC76+vo4efLkmkata2Et2m5jYyMTExMIgkBdXd26FOBsodPpaGhoUKlV6zWoK8yP5P1UEARisZgaDK2FjdB9lb6cwsJCLl68uKpnMJ9KxWbRrpQqz+sBr49ZbgC5fpE3muWJxWJqYDE5Ocn4+DhWqzWtl0X659fw+OO/yx13PE9Hxw4KCz93xR8jzsLCAiaTKSWwWPkaI5EXMZkqmZryIEk3Ul19a8bncjqd+P1+BgYGKC9/AlGUMRrnicd/gcv1EVVxYyVOnvws1dVP0tcXoL7+/2A0rj6IK/0Kkcj/YGnpI8iyA7P5fdnfyCtIppcFg0Gmpr6AJJ1m795vYTINYTDM4vd/iUjkA3i979wQLea+++7jsceuGvnF43Gqqqpob2+ntLR0VVY/GemcwterrJSUlKgl5crKyqya0bKF0+lMaVBX+mpWcoETiQQWi0Wdo9J0tpJapTQqlpeX09HRwfDwMI2NjVuuRpXt2DqdDovFQlVVFUNDQ2o/x1qVnW01qm3kiq00oM3l8/ZaUKO6+eab1fshy7IqrR0KhdQKbbp+wFgsyNiYjMUiMj2tJRKZwGzO3Dxts9moq6tjYGCA/n4nTufd6HRmZmf92O1WysrK0lYD7roryunT45SUzFFc/Luke4lKhlip9l+6dIlAIIAkJZieHkCnizA7O8/S0jwWy9q8/ZUS7vF4/Eo14Mfcdtuf4Xb/kpdfvp5EwkAsFucHP/gBhw8f5uabb857rdHpdDQ2Nqpqgusll/Kh7ZaWlqoU4PX2wVyhUKuUBvWqqipKS0uJx+PqXhoMBonFYiqboqSkhIaGBjWZu55qlfK6c1GuyqTgVVtbu0oqd6uFSd4ovYVv+GADcitP55LlicfjKc6uS0tL6PV6VSGisLAQq9VKSUnJmuOszATdc89fcvbs2/H7a1RliKamJjo7O6moqMh4KI3Hp0gkPorBEMftNmO1/i/1unTa1ooKUXV1NfH4O9Bq/xFJsuP3vxu9PjMHsLn5/0cQRBoajrGwcAKj8aaM15pMu5DlVxgfH+fs2T7q6zUkEjHGxs5TU3MAlytVmSL5MJxcDbDb7Xi9Xmpra9Hr7+TMmR6czhEMhhjFxceBdrq7e3A4PorLtT5lIR0UDmooFFJ/p3BwL168mFLyXclfVRTB1nIzX4nkknJvb++GslPpoPCTFZOoc+fOYbPZcLvdabnAK+9FOtUqs9nMgQMH1BJ+Lgo6W70oy7KsutnOzs5y9uzZjC7p+Yy/jW1sJXKlUf2qezbWUoZSkl/Nzc10dnZSXFxMVVVV2u97ONyOxZJgbs5AU5ONgoLylOdQ9lOFCaA0x1osFkpLSxkeNuD1rk8dslj+jptuehFZrkGWazJeB8sV4V27diVV+z3s3n2Os2cDNDV1YTYvAFeDjVgslhJYpJdwT15bv8b11/8VJtPP+dGPbkeWlys8ra0tdHR08P73v39DvRd2u51Dhw6pilv19fW43e6URN1Kp/BcaLsejwen08nFixdVRa/1DPuyhSRJFBQUUFJSwsDAAB0dHVitVtxuNw6HI2PSE1L3KUW1Kp0y2WZW4JWK2OXLlzlz5gyCIOTUi5qPUMobYZ/6tQg2ckGmhVc5rKd6WSwf1m02G7W1tavk8aLR6KqxspHyczgc3HTTTSkfMI/Hg8PhoKenh8nJSZqamlZ9AROJMBpNFBCIRCSGhoZZWIikNHArwcXqReZR4vF30d09hCguEgjE1qD2BNBoziEIZhyOxnXu6FWqi8fj4ezZNny+R9i/f4y+vt1I0nfUjUWRxlWyLGstMsXFn+OppwLcc88/YrXOo9Uu0tj4P7l8+RU6O/+a2to9eWmGf+hDH+KJJ55gfn6eBx9cViSJx+PYbDai0ShtbW0IgoDL5cLpdKbtWckVOp2OQCCgZqfy8eaQZTltAKS85/v37ycajaqqOC6Xa0OqVUVFRXg8Hl566SVOnjxJY2PjutSlze7ZWInkjJTb7ebIkSOMjo7S0tJCZWXlKmOsN8oivo1fP2w1jSp5n1qZ6V1LclYRq+jv7+fkyZM0NTWlHMRkWeQnP3mWaFSH0SjS0PBWxscvqWvWSrVFq9VKLBbhP//zS0QiS9TUVHPzzb+9qpcjPUxI0p053YuioiKcTic9PT3s2mXgjjv+FVH8TYaGZJ577mssLi5SV1dHYWGhWrXOVFlJhZVE4vPs2/d57PYn+e53334l4ID5+RBf/OIXufnmm9PKKGeLeDxOQUEBTqeTs2fPIssyTqcTl8uF1+vdsFO4ktkvKSmhq6sLk8lEfX19TvvsSqaCEgApibo9e/YgSRLd3d2IoojL5Vp3fOWzKIoi8XgcQRBSVKuU593sCrzL5eK6667j5MmT6nyzlYvPdV/bLP+Ta4lfi2Aj1/K0KIopmZVwOKxmrm02G1VVVZjN5qw+kMnZ4WwX7EzQ6XTs2LGDmZkZ2traqKmpobi4+AqXcRa9/sNYLDHicT09Pf+V0lId5eXlWK3WjAfLcHiCpaUxPJ7d6PVFNDcXpfRypJPXM5m+g0bzKrK8A1nOTgVB6V0pLAxTXDxOLKanrq6dCxfGcDoL8Xq9OfVZFBcXc999f8L3vjfLW97yf/F4ptBqI3g8r+B0vp3W1r+nrOzWnFUaJEniN37jNwgGg4yMjNDd3a06sBYVFVFbW8v09DRDQ0OUlpZuWnYHrmankqlPmea/Us88Ho9jNpvVzS9dAGSxWNTxjx8/rlKr1sJaaiCKfGRzczO9vb0MDw+v2d+y1eXjldcrTfMlJSX09fVx7NixlH6T7WBjG68l6HQ6EolEVgfCzW4Qz7RPCYKQYpKXzfdFo1kWNQkGg5w5c4by8nLKy8uJRqPMzl4gHNYQjwuYzSJDQ5O43RaVGpNunwqF2ohE5tHrJUZHe1b1cvh8vqwbpEVR5OjRZxkdHea6625kz549KX9X9im73c7Fix+jre23MBhMLC4eIxweB2QWFka5667MxrWZIZBIfJKamno+8IH/zuOP/64acIDMiy/+Ap1Ox4EDB7J6HZlou0plJRQK0dfXh16vz9mobi0kG/YlU5/SjZ9cAQoGgypTweFwrBkAHThwgPHx8XXHT4aipJhun9qq3kJBELBarVRUVDA7O0tLSwuBQGDNc8E2jeoNjLUWZUXSLbl0G4lEGB0dVY3nrFZr1oGFLMvqgq3T6ZiYmMDtdqvmdcrhLd/XEY1GSSQSuFwuurq66OjowOl0YrdH8Xh6iMXs6HQx9u69B6Nx7YP2/HwfkvQ2bLZFhocfoKrqH4Cr2Z2uri4mJiYIBAIrqhzWNTNGyc1cyj2VJOkKvayWeHwHJlMXU1M3E4tJOBwODAZDXovh/v0f4T//s4j77/8ixcUTLEswTnH99e9nZOTDtLe/L6O3hSRJqvN6Ojm/TGZO5eXlFBUV0d3dzfj4eFbeGdlC6XVJ9uZQHG3n5uZSlLYcDkdaPfO1oNFoqKyspLi4OEW1ar0G+HRqIHq9HlmWVQfWmZkZTp06hdfrTcsb3urKRqbytMJrXlxcpKurSzVFeqMs4tt4Y0BRTtzqYGOl0IhGo2F2dpbi4mK1J3Aj+xQsJ0NisRgej4eLFy/S09ODw+EgHP4PQECStOzaVcLevbeuO5bHU09V1b8zOurguuuuema5XC4OHTpEb28vp06dori4mF/84ln0egN33/1gWjrq1NQQIyOn0GjitLYepaGhYZVZqtLDVlNTQ3NzMwMDA0SjP6WvD2RZoKamjYGB25AkCb/fn/O+JUlvx+Op5mMf+02+8IX/SrJ8+8sv/3hVsJGuag2o+1Qm2q7JZLpiNrhcBcrGOyNbKBRgj8dDX18fY2NjNDQ0qJLCSgCk0+nUeZaXl2e9TypsiKKiInX8bIzsMu1TW6VcpVyvyAaHw2E6OzsxGAw0NDRkPHdsZrDxeuktfE0FG8899xwf+9jHEEWRD37wg3ziE59I+Xs0GuXhhx/m5MmTeDwennjiCaqrq7MeXzkEJ9OhkmVcS0tLqaqqoqura13znbWUobRaLRrNskGaVqvl7NmzNDQ0ZCV/mIxMvFC73Y7H48Hv9zM3N0dfXx9lZd9Gq43idA7T23s31dXru1VHoyewWBZJJPQ4HC+m/E1RrJqcnFyzygGo7tYdHS2Ew/1YLLtxuz1rZC9+hiTN4vEUcvnyq8zN3YhOl0AU/5XS0utzukeVlZW8+c0P8PWvSzzwwDdpaOhW/+bz/StlZd/g5z//Dj7f8v1Pvp+iKKr81Vzl/AwGAzt37lQb58rKynIymcqE5EBNp9MxPz9PS0sLNpuNsrKyrPtB1oPRaGTnzp0q77SoqIiqqqqsqVWJRILZ2dkUqqDH4+G6665jaGgoRbFDmetWc1XX21DMZjP79u1jZmaG9vZ2RFHE5/NlPf42trGVUAxoszkQ5tJnkU4ZKlnBsLCwEI1Gw/nz59dc5zNBUTNaSYdVDpkVFRUsLi7S03Oa0dE4waATiyWK01m97tiyLBOL2bnttk+j1fYjSQdT/p5c5fjZz77K/PwSsqyhp+co+/a9I+XaZWO4HozGRSIRI9XVPXR1dan76UqzVAWNjY0sLNyE3/8osVgBo6MP8cwz3wNkbrzxFvbvX78SsRKSdBC9/jSf/GQdf/u3n0YJOPbvb2FxcTFFIVCRxV+rap0JCkVX8c5QXudGkyzJkviCIBCPxzlx4gQFBQWUl5dnoGrnjmWzxIDqd6GoTa4XkCtBh5KkW1payvo5NxIMWK3WlJ7GkpISqqurNySV+0ZJir1mgg1RFPnoRz/Kj3/8Y3w+H4cOHeLee++lqalJvebxxx/H5XLR29vLt7/9bT7+8Y/zxBNPpB1PlmW1qXdhYYFbbrmF+++/n9tvvx2bzUZRURF+v3/VhzaRSKTlr663YKdThoJlJQeXy8WFCxeYnp6mrq4u7UKRzidCr9evywv1er04nWYSid/GYIiyuOjAZPovWX2Ync7bWVj4MibTKLHYB9Ne4/V6EcUg/f2foru7kN27P0EiIaZsLMtVm0scOfIxDIYIvb230tj4f9d4Zi2wHAwVFPwffL5BAPr6Pksk8lTOVYLKykpuuulWvvtdeMtbnua661rVv2k0EW677V6ef/5b9PU5VafUZEO/jUBpnBsYGOD48eMEAoF1NcOTkSyPq6huKBtLsurGxYsXGR0d3ZRAIxlKdlBpLFT4yCuRTNtKVrGqqqpSVWiUknX1FcPIrq4ulVplsVhy5qpuVSXE4/Fw5MgRVSq3trY2rejC6yVjtI03BoxGI7FYbP0LMyB5n8qlzwKgsLAQh8NBZ2cnU1NTNDY2pl0fk3sXk9WMlN6wTHRYk8lERUWQtjYTen0cjUaisvLuNV+PLMv85CdfZWhoCp/Pw513/n7G76TL5aK5OcSLL2rRaETc7mEuX76cIoii1+txOkt5z3tOs7g4g8fzNiRpb1b31mJ5C5L0FMHgeS5e7EaSluX0x8ZewmAw5ikLW0wkMs2nP+3m5Zdvorx8mEjkN2hpacHr9eL1enOqWq8Fh8PBwYMHGRoaorW1lYaGBpVOmg3SyeMqkvgKvVir1TIyMsLIyAgFBQXrViFygeJ3MTY2xvHjx6murlbFc5KRSW5e8b1JlnTPhI3uO4Ig4PV6U6Ry6+rq1MTbVqoyvpbxmgk2Wltbqaurw+/3A/DQQw/x/e9/PyXY+P73v89f/MVfAPD2t7+dRx99NOMb8fnPf55jx45x8OBBDAYD3/rWt7LKYioLtRJ0bLTPApYX2n379jE8PMyJEycIBJb1vlcqbigLdq7Z61jsaSyWIIIgIYoyorh+dWBhYYhw+DhO51MYjXaMxtUbi6IMsrT0pxw61IokaWltLcBiuZeSkpIUmlEw+B30+iiSpKGysj2reQPYbIeQpO8BUFBwgLNn/xceTyU1Nffm1NDl9/vp6Ojg+efvIRSyc+edP0m55i1veYypqWfUEqfL5cp6jutBq9VSV1enllCV93BlIJu8UQeDQcLh8KoMYKaGeL/frzbmjY2NUV9fv2neHBqNhqqqKpVaNTIyQmlpqVqxWo+2tbJkrdVqMRqN7N69m8uXL6uqUEajccu5qtl+ZhSFm9raWkZHR1Up3838XGxjG7kgFwNaJSGWSCTSKkPl2mcBy1nkXbt2qVz5hoYGDAaDenBT6DuK2mKuJqQ9PcdYWlpWdCosjDA1Nb1mFSUanWVoaAyzOcbISJxIZIKCgtXKjgoV2u2+i3vv/VskSUN///swmfooLS1Ns58+i9EYRpKWk0LhcJhXX30Vm83GkSNHMr4ejSZAeXkAu/0TJBKXicUM9PeX0d//HGfPlvDud793zdefmbZ7Er+/C7O5GZOpluLiRTo6Orh8+XJOAcF6UBJBxcXFdHZ2Mj4+nnYfUeapHNqTA0qHw7Gm8eBKCnBDQ0NeHlXpIAgC5eXleL1eent7GR0dxefzqTK54XAYnU6Hw+FIu58m71PK9yPde71ZlQeNRoPf76esrIzu7m5VKne7Z+MaY3R0lIqKq1rbPp+PlpaWjNcoH6qZmZm0mdhPfvKT6v8fPXo0LYUpXcVC6bmYmJigpKREbTraSJ/FwsKCml2RJImTJ09iNpspLy9XFTc28mGSpK9gMi2Xj+fm6pmcnCMc7qS+vj5tFSUWu0wi8Vbs9jChUAWFhT9nufFtIaW6ovRZFBWJgIBGA83NVYyO6pmamsLj8ahfSofjLkSxEaOxj0jkz7Keu93+IUSxHlmOIEkvcOjQZwGZ06f/Gw0Nj66SlEvXD6KoWdxwww08/fTTHDv2Ji5ftvGudz2lPi6RuFdVShkcHFSrEA5HZu30XKGUUEdHR2ltbaWyshKtVptWdaOmpibnKoUi66pQ2zbDmyO5HB4MBllaWiIej9PR0aGqYlmt1jWfI7lkvUxV0KhqIC6XiyNHjjA8PExfXx9erzfrxXyrMzqSJGEwGFSubVdXl9rfsVkb5Da2kS0UGtVKZFIw1Ol06oFrM/YpRSUoFAqh0Whob2/HZDJRXl6+YdU9WY5y/rwBUVyuPDQ01DA5Ocnk5CSBQCBtFcVotFFTE2JgwE51dRCTyaWuV+nosHZ7DQ7HE1itVoqLobe3V+2ZTF1HNMDV6vMLL/yAgYFBtFoBl8tMILBn1VySYbf/Ie9614fo74ennioikZC4dGmEr3/969x9992UlJSovhvJWfa1abtXDQcVU9zR0VFVxjZXCvZaUHrskl2xTSZT2nlWVlbmfD4xGo3s2rVLpauWlJRQWVm54QNz8v0Mh8PEYjG6urqwWq3U1tZit9vXfI7kfo5EIpFWtQryc/heTyp39+7dzM3Nce7cOSKRSE5KctvBxiYjHf905RuYzTXpYDQaWVpawmg0rik5q2SCDh8+TGdnJ8FgMONCmOk1KF8IZdGOx+Nqw1lJSQn19fVoNBr6+/uZmJhQ+bL5QpZn8XhOo9HIiKJEJPI29u3bpy5U6ZQRYrExjMYw8bgeq3WQkyePE49f7V0pLi5O6bOQ5a8SjX4OQfDhcr0Xl0uXppfDjlb7AqIooddflUwdGRlRXV0zQau9FQC9/h/Q6eLIskBJSTsjI/8LrfY3sNnc6uaSTDNaOU9Yrnh961vfoqtrD1/5SiEPP/w99PqHkaTlAEij0VBTU4PX66Wzs1NdqDbqwrlSdQOgv78fnU6H3+/fsOygAkFYdjNVGvOUSlm2JetEIpFCh0ouhyf310iSxNDQEOfPn896s1Mel04NpLKykng8zszMTNZz3upFdi2urdfrpamp6Q1Rvt7G6wNGo1E9iCQLjShI7gcUBIEDBw7Q29vLuXPnaG5uzsmnYaUBnbKuKv5QSmV2eHiY8fFx3G73huS9x8e/wcLCcsO2LGvw++/G6axUFY3q6+tTkoaJRJxXXvk6oVAxt97qRqN5K6dOnScajarrlTLPvr4uxsYGKSu7LiV5pPRytLW1YbVaCQQCae+RydSOIFgAGaPxBLB2sCHLZcRiP6Si4lX27v2fdHTUsbBgZnZ2gu9+99+pq9uB0+mkoKAg5X7mQttVlPQKCwvVKoRSadoIlPVfee8BhoeHEQSBmpqaTaMXwzJd1eVy5eXNoSiCKvuUQtd2OBwp/TWyLDM6OkpHR4eqzrnemp0s6Z7OEDAfadps9imn08mRI0d48cUXOXHiBNXV1VlL5W4HG5sIn8/H8PCw+vPIyAhlZWVpr/H5fOqXJpsy48jICCdPnuTWW29dtWBnKn/t3LlTdbUMBAJp6RXRaDRlwVYWwmT5uUyLQ11dHXNzc7S3t1NZWZmVtFs6hMN/gdcrAsuLuNv9oLpQud1uLly4gM1mw+PxqFJ5S0sXKCnZhcs1wOzs+9m9e++ai5gg+DCZvpryO6/XqypipSpWXb2fZ878Aw0NX2FmpgT4D5zO1PdzJbTaTxMKfQBJ0lBa+hylpc8xMvIcnZ2fpLq6mp07d667oSoKFlNTU0xNlfPYYx/l0UcfZWWi2mKxsH///pTs0f9j78zj46rLNf6dNZlkkkz2NE2apUmzUdrSBVoKgshWVkGR4qXFAgKKwEUQEK+CuFAURWVfxIuAFBWUTdkqFOhCWlpaskz2pM2+zGSd7Sz3j/A7PZNMkpk28VrI8/nw+ZDOds6ZOb/3977v8z7PVD4RAqFcWCdS3ejp6aGuro5AIDDO5+FwICrwYnBOqJLoExpVVYPa4YODg2G3w0XLPSMjg5qaGlpbWydU1xj7ulBqIOKey8rKIjY2lsrKSuLj4ykoKJgwuP07Ohtj73/hH9LT0zObaMzi34q6ujq2bt1KUVFRUJyaTBlqwYIFmhT6RFLZgmYiCmCi8CaKQPPmzZtwXZ03b54WR8QMwaHcFx98sBvIBcBolHA45gGjEuYOh4Oqqio6OjpIT09neHiY9vY3qK1twWhU2bt3mOOPv4A5c+LHrT/d3U28995fURSZrq4KLr74e0GPOxwODhyooaenk/Lyd7j44svHqVSdcoqJ9PSdxMZ6yM//PgMDg7S1tZGdnR3SrO3g+p/JokUx5Odv5q9/XYMkWfB6A1RWfsIxxyzjuOOOO+w1RBjIieLeRLMKoSDoZaHWf1FUFDRot9uN0+nE5/ONG2g+HAgqUUZGBtXV1URFRY2jbunZCuJ3KqwGEhISKCgomFAWX+x1BLVKqFZNJbIQKk6ZTCZMJtOMqiaKbsry5cupr69nx44dU9J3PytqVIYpFC2mR8g7DEiSxIIFC3j77beZO3cuy5cv59lnn6WsrEx7zgMPPMC+fft4+OGHee6553jhhRd4/vnnp3zvd955h1tuuYULL7yQb33rWxHdSB6PR9uwJyUlaZz7sYob8fHxmrxtpOddU1NDIBCgpKQk4sqF1RqLKDh5PNHIcpe2wRQ8WzF/Mm/ePGJiqoiLuw6jUcHlupI5c24L+b6SFMDpvBuDoZ6UlJtISztq3HMCgQButxtJkmhqahqnZDI0VEpsbC+qCj09PyctbYP2mNgI6/mrYoGxWP5OYeEvMJlkVNWIoixhz57riYs7KqyFsKmpiT//+c/a3waDgZtuumnC5/t8PqqrqzEajRQVFY1bCAXNSFuztfYAACAASURBVByroJcJbuhUvGVZlmloaMDtdkfUhQgXorrT3NysbTZEtVK0wxMSEiJS2xqL3t5eamtrI26Jy7KsVYr279+P3W7XaAZtbW00NTWRk5MTssKzdetWVq1aFfYxRvr8bdu2TcjRNhgM0zYTM004MiLK/w/+bXFqJrFr1y6++93vsnz5cn7wgx9EVGH2+/1UVVVhNpvJyMjQYsDIyIhWCBGxamoDuvFQFIX6+noGBgamMNILhVZ+8YunET/hqKgRrr32h+PWf0mSkCSJuXPnYrFU8vbb5RgMKsXFBlat+knId3a53uEvf3kbWTaRmjrIhRfeO+a6eHj88V9gsfiQJAsLFx5Lbu7CMetNAJPpFVQ1Da93OU8++SiBgJfY2HjWr78iaP3X02HF9YyL243L9R2efvoCvN6D1yU5OYW1a9dOGyUzEAhQW1uLz+cL6Wc0lrYlSZJmkBjO+q8oCs3NzXR1dbFgwYJpn19TVZWuri7q6+tJTk7GbDaP894Qe6lD7aL19/fjdDojNsYVSYfBYKCrqwtZlskNU+l0586dLFy4MOzOoj5ODQ8PU11drRUPQxXzdu/eTVFR0YQu5YfjPD8DmHBh+Y9JNgBee+01brjhBmRZZsOGDdx+++388Ic/ZNmyZZx77rl4vV4uvfRSdu/eTVJSEs8995w2UD4VfD4ft912G1VVVTz44IOTDqaFUtwQijs5OTmkpqZGZEAXDrq7u6mrq9NUC8KBz/cEDsd12t+trXOorX0maCEUG+GhoSEqKytxOF4hJ+cPKIqZ/v7FZGS8EPK929tfICXleoxGme7uYjIyNgc9LkkSlZWXM2/ehzQ2nkFZ2UZqampQFEXrcng8VxAd/Spgxut9lYGBdC24CDUjsbjo+cD9/R0MDZ3L3LmNWCzeT6X1sujo+DHNzfOmVHxSVZV77703iC53xhlnsHDhwkmvZ1dXF3V1daSmpmI0GjW5YdG2F9f0UOlQogvhcDgidggfC313xe12a5rmkiRhNBqnfR5FfGZzczOdnZ1hq5mIpNLtdtPW1qa1ukXQCwQC1NXVMTAwMO6YZzrZ2Lp1KytXrpywYjabbBwx+EwkGzAaezZu3Mirr77Ko48+Sl5e3oTP1Q/y6gtLkiSRnZ1Nenr6tKvXuVwunE4nOTk5YVfYq6oe4pVXBrW/U1MHKS4+SyuEiPXfaDTi8XjYu/c9amrexes1kJencPLJ/43JNFFMHKat7So6O70UFa0mJuaGMY/7+PDD9ezZU0hJSSOrVt1Lba2LkZERSkpKxm3Ye3qaeO65P6KqCqpqYtmyE4mNTQgq2IRa/w2G3fT3r+fZZ89lcDBe9+9GvvKVr4S9cQ0HfX192oY6KioqSBVSv2E/1PVrZGSE6urqQ3IIH4tQw+aio6AoCkVFRdM6jwKjMUeoYuXn55OWljbl71TM1rrdbjo7OzUp+HCKauXl5SxatCjs6x0qTnV3d1NbW0t6ejq5ublBe4Ndu3ZRVlYWMhE5kuLUf1Sy8e/AP/7xD2655RbuuusuTj31VCRJCqpcjFXc0G/YR70kqg6L9jQZ/H4/lZWVREVFjXNTFYZ++gq73f5Dlix5V3vO5s13sXLljRO+v8/Xjcv1VdLSKhkaSkGWHyEx8YSQzx0cfI3o6KswGmUGB8twON4c87iTmJiTUJTRgT9VrcVstmsb9rS0NEwmFUX5gIGBBBRlHn7/n3E49mAwXEVx8ZcnvRY+n4/6+js56qiHiYryoapgMJgZHLyL3btXTrlh/+STT/jHP/6h/Z2Wlsb69euDnjPRQijLo7Q0Me8ynd+zWAhbW1snlJkN9Rp91Up0V8TvU/xGxXH29fVRU1NDenp62AtmJPB4PNTU1GAwGMZRqwRlQ+8Yq08qxXFaLJYgGqNIxGw2m8ZN/nckGxM9/0haxGfx2YtTO3bs4Oqrr+a6667ja1/7GrIsa4PR441S44M27MPDw1RUVBwW7WkySJKE0+lEluUQhq/j6cV9fW3s3l2pPX7FFVdOWjVvanqRt97ahdksYbfDV75yd8jnud1uWlv3k52dTXy8CQjdMTYaP8Bs/iOSdD6KMur87XK5qK6uJjk5GavVqgm4xMYO4vffT0VFEcuXf0xHx00cONDBCSecMGniB2AwVBIIXMITT5zFwIC+0DPqZfKNb3xj0tdPhIlM/cRcz4IFC0hJSZn2ONXZ2UljY2NE1K2JuisiTumHzQcGBnA6nSQkJGjSudMJv9+vdYLGUquE1YAo1o0tKorjFNSqyWLojh07WLp0adhFyInijijmCSNfkSRN1jk5kuLU5yrZUFWVuro63nrrLTZu3IjFYsFqtfL444+TmJg4rsIeCvqFtqSkZNqGqfTHKKQ4586dq82m6A39xA3R2tpAbu4KbDaZ1tZUEhLqJv3Bd3Y+iMNxD7JspKcnD3hyEiM6lZGRR1CUKmy272IyzRtznEPI8lIMhkEkKZ2urr/R3z/I0NAQBoMBSZI0Q6H4+Hh6e7eTnHweZrPE0FA8NlvTlNeiu7uLXbt+xDnnPIvZLGn/Lssrqa9/iNbW3pAD8AK/+MUvtP//6le/Snp6+oTqIGMXQlHBmzNnDvPmzZv2gO31eqmpGTUgLCoqClpI9AuhUIiKjo7G4XBo3/1UC9uhdCEiRXd3NzU1NdjtdkwmkyaRKK6noGyMhZ5apVcDEQGuvr6e7Oxs9u/fz/HHHx/WsaiqyrZt26Yt2RCKWv9BmE02JsZnKk4BNDc38+6777Jx40bNOPORRx4hMzNTSy4mWwP0tKeJqqKHC1FYErOVgrYlZLLFcUZHR+N0VvPRRztZuXL1pFX+UW+Nn1BRYcBoVFm+PEBq6jry8/ODNnyBgJ9nnvklPt/oJvHrX78Zk2ni66Gn7Y5K+25HVVUWLBilhBUVFWmqVSbT/2I2b6Kp6b/4619bkGWZ6GgbV101cSHvIHowmb7II4+cicsVXLG32WK49tprp3yHsZ5GepEZ0V0Re5TBwUGqqqpISkoiLy9v2jfsgrrl9XopLi4OovIIBojb7R43xC3+m2oN1XchJpo5OlyIxDI6Ohqr1artUcReyuFwhKQWCmoVoM0dhsL27dtZsWJF2EW9qYpiPp+PmpoavF4vJSUlVFZWsmTJkpDXcjbZ+A+FJElccsklHHPMMSxdupTy8nJefvllHnroIRYsWBDRe3V2dtLQ0KAtUoeDUP4LRqMRn8835YBUfX091dXVnHDCCVNQi2R6e88hM/N9fL5oDhz4Bl7vFeM4uKMJ2U+IinoPVb2SnJyv6t4jeH7B620kJqYSWT6W2Nh54zbsgp+Zn59PQkIX0dGnYDJJeL1JyPJePJ5+HI7JKyZ+v59XXrmJr3zlj0RF6WUhrbjdr/DJJ9HExsaOU3sSSVplZaU2DBYdHR3UZp5qIZRlmcbGRvr6+igpKZn2WQsYvUa1tbUkJCRo1C3huTLZQhguPB4P1dXVWCyWw1YzCWVAGBsbq1VdRYUtHIg2uqIo49RAJEmioaGB5uZmli1bFhZ3WFEUduzYwcqV4TvQzyYbnxl8puIUwOWXX05OTo42SPrEE0/w29/+lqVLI3OrFpSb6djIhaJtCaO0mJgYFixYMKVM9lTw+Vw8+eR9REVJKIqBr33tcrq7RwsbpaWl2nC3x9PC008/9ulnqaxffwMWy+jmfnQDu58333wJk8lIQcEiTCaTtmGvq/sXFRUtAJSWZnPMMefhdDrJysoKmuUYHPwXTz21BUUxEhc3jMFQwvz58zn++OOnOEc/FsuJPPLICfT2CvrX6HFef/0NQWuwXnVJdFdEsib+m2rNVlWVlpYW2tvbZ8wrSPyOYmNjsVgsDA4OoqpqUHf9cKjlPp+P2tpaJEk6bOlxYUAokiAxEyISzoKCgrBUq2DyOCUwGR03FMLtwPf391NdXc3w8DCrVq2apVEd6di9ezeXX345V155JevWrYvoZvF6vVRUVBAfH8/8+fPDymwnaomKgWNBNRG8xqamJnp7eyktLZ1SYWEyDA3txWY7HUmyYDTKQDUWSxxut5vq6mqys7PJzMykv38PMTHnYTCoBAJRjIxsD6LE2Gw2zGYzSUlJpKamBm3IenoO0Nh4J6oaS2npndjtCQQCAaqrq1FVlcLCSuBdJOl8zOarsNsHqKu7mry8n0167K2trbzxxoNs2PAQ0dHBpleSVEpDwwu0tLSQkpKCJElBJoliIRSqG4eC6Zy1mMgxPBAIIEkSpaWlETmQhwNVVenu7tY6BuHK7Y1VsrJYLEFBUN+N8Xg8OJ1OTCYTCxYsCHtoTV89Gmu09P7772Oz2bREabLqrCzL7Ny5k2OPPTasz4XZZOMzhM98nKqrq2P9+vWsWbOG66+/PiJqZCAQoLKyEovFQlFRUVjrl953Q2yE9bQtfWFJdOMPHDhw2OtXVdWTvPnmAQIBM1lZbi666CcYDBatgi+oYbLsp67ueurqjBx1lEJc3F0MDAxpneDW1s10dY3KqB97bBHLll2sfUZj4138858Sqmrg1FM9FBZuRJZlamtrg2Y5DIZ2+vvPoqsrkVdf/SKqCkajmVNPPZvCwqIpu8tW68ls3HgykjS6blkswyxffhpJSUmoqqrF/unasHs8HqqqqrDZbJOq/IWDiRzDFUXB4/FQXFw87bMWEDkFWD9rMVZxSzAB9HFDJDWBQGDSoeuxEHFKVVXMZnMQtWom6b6qqvLuu+9iNpvJyckZp2g5m2wcYRgeHua6665jYGCA3/zmN2FrQcPoj0EoOJSVlQUlBIJrr79pZfmgn8XYluhEGBgYoLKykrlz5x6yfGpn5zVkZT2LwaDQ0VFEYuKHGAyjN4ssyzidToaGhkhKCpCV9XVMJj/Dw8l0d7+gHWtUVBQ1Nc+Qnv5TBgeTiYt7DofjoCt7c/N6cnJeQ1UNNDd/h9zc/9Ee03c5VHUTGRl3YTTKDA8nYbPtBuxMtp+qqKhg8+a/8O1v309s7EjQY8PDc9i79y8a3ai0tHTaFRpUVWX//v2aK2o43Sz9ELfoWE22YR8YGKC6unrGWuKiYyD8Y/SdmrGtez3X1uFwhO0ULJKazMxMsrKywt4U6dVABLVKLMqi+5OZmTlhAAoEAuzevZsVK1aEfT1mk43PDD4Xccrv9/M///M/fPTRRzz88MPMmTMn7NcK9bf9+/eHTAjGzgPq/YzCoW3BaBytrKwkOTn5kOVTn3/+Nlpa4jGZZI4/3syKFbdqjwlqWGtrEw0NHxIIKBQUOIiPX0Z8fJIWp2w2G5WV3+O992wYDHDeeelkZHxbex+DYS8DA99EVRUcjvvp7Mzjk08+IScnR5NzF10Oo3E/BkMVTz75Fv398fj9ViwWIykp6axd+1+TnuPoNSymsjKVvj4Hy5fXsm/fC3i9XkwmE2VlZWFvdsOFqqp0dHTQ1NQUdjdrMolcsWHXK22KAfLpSGomOp6mpia6u7vHqWKJmUCRXIjuujjOcBUXXS4XNTU1pKSkjBvInurYxlKr/h2zhStWrKC+vp6+vr4gK4bZZOMIxaZNm/j5z3/Or371K4477riIXjswMEBFRQVJSUmYzWZNyUhQd8SCfag/DFF58Xg8EW+mVdWPouQQEzOALJtobLyVpKTrgxaXUdfMABbLwzgcvdjtK3A4NmA25wS9V1/fcTgcTaiqge7u28jIOKiG5Xavx+F4FTDgdt+Aw3F70GsPdjn2U1Z2JWazD48nEbu9m56eo0lI+BcGQ/BNL+YXBgYG2LJlC11dbdxww6+IixsGwGAAVQWPZzhooY1E1SsSCFqS0AsXC+1EjrF6idxwKAb6pGa6nWMFBL3MYrFgsViCeNbhtu4ngyzLNDU10dPTE5GE4tiWdXl5uTazId6zs7MzpIKJz+dj3759LFu2LOzjnE02PjP4XMWpt956ixtvvJEf/ehHnHnmmRG9VgyPx8bGEhMTE6RkNFbG/VCgKIpGPY10M+3zdfHgg48hSSbMZpmLLz6OhISVQbKzo54IW6mqcmEyKaSmGjnvvLvGvZfBUIHLdSsWi43Y2N8BY9UnJUBFVc089thv8HhGMJst/Nd/XYndHjeuyyHLp7Nvn4V33lmJohiQZTNxcXF87WsXk5iYqFGhxfov6FCjG/YRHA4/Fsty7dN7enqora0lJydnxsRmnE6npg6p/z4nKiyFK5ELwUlNuIpPkUIkr6qqEh0dzcjICCaTSUssxhbrIoUwHW5ra2P+/Plh7xcmi1Ph4HCSk+HhYZxOpybTL2ht/0GYTTbCRXNzM+vWreMLX/gCN99884QZb6jFRS89Kvil030D9vb2UlNTM87TYjL09PyJuXOvAECSLGzZ8iRxcfODgovJZOLAgZ+QkfEgAI2NJ5KT89S4Tefg4A3YbM9jMJiQpL8QFXWQtqKqPUjS7ahqHFbrj+npcdPRsYX09ONJTc3WntfV1UVDwz5ycmSysy9Clo2YzQr9/etR1atxu+ODvDdEoma323nyySfx+/3cdtudWCyjP09ZXo7f/472/n6/n+rqagwGwzjfjOmAqBI2NjaSkJCAoiiMjIxEPBMyGbxebxAt6XDOYaySlUiCFEWhv78/Ig5rJBgZGcHpdGKxWCgsLJwyMAhOrcvlwu124/P5WLp0aVDLWiR7QJDWvNfrpbKykmOOOSbs45tNNj4z+NzFqe7ubjZs2EB2djY/+clPJqQYiu7q2HlAoWRUUlIy7Yp7gKbcOHYOYjLs2vULNm9WAANWq4/ly08kJiYhqFhnsViorf0L77zzMaoKy5YNUlp6t3b+gUCArVv/RSDg4/jjvxTE/W9paaG5uZmysjKtM62qKo8//kNGRqIwm2UuvfQc4uKWYDAYNErP6DkkYDL9hVdf3UJFRTGj2yYDRqOBBQuKSUtLG+e9NNU5S5KkFRBDyfBOB7q6uqipqSEuLg6DwXBIMyGTQSg++f3+kN4fkb6Xvmsh5PGNRiMulyskjWg6EOm8iJ665Xa7GRgY4LjjjptStUq8djqETIRUbmZmJkVFRWG/178Bs8lGJJAkibvuuot3332XRx99lLS0NHp6elAUJUgeV2+UpF9cBGVoOobHQyEQCFBVVYXJZKKoqGjcYLSoAgmFiLKyy0hPPwCA2+3Aaj0Q8oZta7udjIzfoyhG2tvP4cCB9UEdArfbjcGg4HDsQ1UzgIl/5JLkp79/KUlJHfT3pxATswur9WCVy+/3U1HxCXPnXk5mZhMmUwBFMeDxJNLV9TQxMUeHpJjV1dXx4osvApCRsZ/s7ExOOulWQkF8D8Lb4VAXKb3Dqdvt1pKg2NhYRkZGtORypoJFfX098+bNIzMzc8pzCJUET6YQog8WkXBYw4WYF2loaBhHAxw7vxIIBILUwcRQn8Fg0HiyAj09PRq3Nzc3F5/Ph9PpZMmSJWEf12SLvhgG/A/CbLIxMT6XcUpVVe6//36efvppHnroIQoLC+ns7AQYJ487VtITDg79RlK4igSyLFNTU4PP56OkpCSo2BBKFGXv3pcYGBjtgqaluVi37mfj1ruOjo955ZUXkGWF+fPhmGO+RU1NsyZHv2fPy2zbthtVhaOOyuYLX9jw6fVw88wzD3+6gY1hw4aDylJu91ns2eMgL6+dffuupq5udPbktNPOQJZlqqurGRwcxOFIID39blpbW3j99dNQFLEeGSgtLWXNmjWHFGNEUnM4NGkIFnAR3SBVVbHb7Xi9XmRZDhqyn06IcwjX9FXMBIrEYmhoaNIkSJIkTV1tJoxxxTnU1tZqHhsi3gixGRH/9XLugmIs9tGTqVaJ854uIRNxDWeCwXEYmE02IkF7eztbtmzhxRdf5M033yQhIYF169Zx8cUXhyWPCwcrrXFxcWEPj0cCVVVpb2+nsbGR1NRUbTBa3wkY3bDZsNniEOtXQ8Mq5sx5c9z79fS8QVzclVitI/T0HEty8iYkyaK50sbENJCefj2KYmJw8PfMmTPqz6EoCnv2/AKjcS9JSdcxb95op8Pv7yY6uphAwIzZLDM8/CE+X+I41Q2DQcHrfYnVq+/Hag1gMKjIcjSBwG9R1a+HPPdf//rXSNJBKdwLL7xwQnPHQCCgObQXFxeHJQHp9/uD5mz0i0uoORvhrn24wWIiiIV2aGiI4uJibS4olP+GcLcVC2G4A4eCw5qamnrIfOupzqGmpoa+vj5iY2Px+XyYzWbtmjocjpAVtsnUQIS8b3t7O1lZWbhcLhYtWhTW8ciyTHl5+YR0ydlk44jC5zJO9fb2smXLFl555RX+9re/ERcXx7nnnsvVV189qQGdHpMVrqYLQiI7OTlZG4zWr1Px8fHYbPDrXz+kvaasbIA1a8a7htfU/JG33mrAZFLIzPRyzjkbtbXF7/djtb7Ju+8OoapGli3zsmLFzwEYGNjFM8+8jCSZsNl8XHHFXRy8pboxmd6gv7+I3//+DYzGAJJk5cQTT8XvH5Vwt1qt9Pf3k5WVxfz55VRX/5aXXjobVT24TlqtUVxxxRWHJOQiy7K2mS4pKQnrPfSbYP0Qt767rv8+hbqRGLKf7jVeUOh6e3spKioKMmgV1C2RXBwKxRgOirUkJCSQn58/7b9X8T10dnYSGxtLIBDQxGZCDZzrz30q1SoY/c527dr1uRQymU02QuDll19m7969rFixgoKCAm6++WYSEhLYuHFjRJXfyYbHI8VEw+bR0dEMDw+TkJBAUVHRuB+e17uQxMQG7e/KyrvIyxuvF97WdhNz5jyFJFlxuZaQlvay9rkdHR2MjHyTvLwPMRigo+MC5swZDQwdHW+RlvZfmEwSPT1zSU7+WGszBgJXkZLyOq2tx9HQUEh6+m5GRi4jL+8rQZ2gkZER9u27ipUrX8Fi8WM0gqoakeVLCAQeAoJv2h07drBly5agf7v55psnvX4iIRCqW+KzQxn76TfBQid+KhxKsIgUfX192ryIyWTSBuLFZj2cIc7JMJ3eHHpzP7fbjd/vx263Y7PZ6Ovr06Qyw23h6wfzxhotCVW4wcFBli9fHta1n2qgfDbZOKLwuYxT77//Pm+++SYrVqygrKyMu+++m46ODu6///6I7l1RuGppaaGkpCRok3goCDVsbrPZ8Hg8REVFUVZWNo5S+fTT/017+8GB93POyaK4+JKg58hygE2bfkpvrwGTSWHt2qNITDwozd7V1cUnn7xGcvLLREcHKCy8BaNRcOndNDdfQn19KkuWeElM3KRVqQWddGSknsrKt3G5HMTGjtDfn0pycjJf+9paoqKighSrFi7sorv7Vp55Zi3Bt6aB1atXR1S51kMkBOnp6UEdgslUl/SeRuGoDIo9SXFx8WF/16EwODioSc5brdZD8t+YDHovMjFrcagFPsEEEdfV6/USExNDbGwsbrcbs9kckRTvVEmH3+/n448/Zvny5ZO8SzBmk43PEUZ5nY/z8MMP88ADD3D00UdH9PrBwUEqKioi4q/qpecGBga0jaV+cdEPJ7e0tNDZ2TmuTWq1xqJvwrjd3URFBSdMXm8nqrqK+PgeZNmE2/0X4uO/GHT+XV1PkJz8Q8CAx/MwcXHnADAy8g7R0RdjNEoMDaXjdD6lKUSMHms8slxLaupZn3psxGA2t4U853ffvZ1TTrkfi0XSfXYaXu92xg746Q37YOpkA0YXFtEST0hIYHh4OMjYLxLVpYkggsXhdghCtcQB7Ha7RkEINSg9HRAytmazOexZi1CBUFzTsYN8o7+nLhoaGsjKyoqoGzSR0VJ/fz91dXX4/X6Sk5OnrHpNNVA+m2wcUZiNU5/ihRde4M477+See+7hhBNOiOi1IyMjVFRUaAo94dyToWi7ejqMfthcn9SMNWP91a/uQpYPrhHf/e5N49bO/v56nnrqWYxGBYtF5rLLrsVqPai29NZb/0t9fRNg4phjTmTx4pVYLBZ8Ph+dnZ0kJUnAXrq7C6ipacHp3AcYOPbYEygsLCI+Pg6r9UoGBrazadNFDA1ZMRigtHQhS5eeqAldCMpQfv4wVuu3eOyxDYy9PW+++XsRXXs9FEWhrq6Onp4eHA4HHo8npLHf4cSp4eFhqqqqiIuLo6Cg4JCVD1VVxefzaWu/XhgFRqnXYiZwuuH3+4NYC+HMWng8nqBjNRgM40xo9b/7SKV4BRRFQZIkTV1RXF+fz8cnn3wSkV/ObLLxOURVVRWXXXYZF110EVdffXVE2bSev1paWhpU0Q01xHcoVQuhRS6qIgaDgYGBeaSn9wLgcsUQHd097nWdnX9g7twbkGUTPl8sVmtL0OM1NXeQlfUUfX15qOqPaGkxkZycjN/vZ3h4iPT0l3E4qlGU/yY6+hj2738HqzWJ/PxRoxuvtwab7XhMJgmfL4auroUEAquZN+8HQZ/j8/l4/fX7OP/8u8cY+IHffxuyfPD5lZWVvPrqqwCceuqpLF68OOQ113eCRIUlKioKl8vF3Llzww6qkeBQqkd613C32x3UEhddC31AEIPSVquVwsLCGZG/E/MiYxOCUKZJhyI/KMsyDQ0NuFyucW33yRCqetTf3097ezslJSXs379fq3pNNKsjNOknGiifTTaOKMzGKR0OHDjAunXrOPbYY7ntttsi+h0rikJDQwNut5uysrKgDZxeIlVv6hdM252atunxeKioqNB8i4xGI++88wDl5aMKg8nJw2zYcOe4133wwX2Ulw8iy2ZKS7s488xfob8tnn76+wwNmQEDJ55YSF9fLklJSWzb9k+83hHM5ihOOOEskpKS+Pjj/8Xp9KOqBpYuTWD16u/oPinAe+99kz178vH5rJjNCiZTFJdcsl4r7oguhyQ1MX/+ldx/v3j9qHnfd7/7XYzG8OVUx0qkW61WoqOjcbvdpKamUlBQMCNUbNEhKCwsDMuQNZRr+GTCKCIhkCQpbBpzpBAU4LH0MFmWg7rromuhP9ZwkixFUWhpaaGjoyMihchQccrn800ad8biDFQt0gAAIABJREFUszRbOJtsRAiv18stt9xCXV0dDz74YMTDOZ2dndTV1ZGWlqZthicySzoUCC3ygYEBysrKCAT81NVdSiBg5qij/hiSBuZ2LyU1tQaDAVpaziEj41ng4GC0xVIMKBiNCh999GOio4/9tCrwJqmp21HVSykq+i8AnM7vUlT0FIpiorX1MbKzRQfkJSTpdWy2F4mJGUaSzLhcT+FwrEH/+xwaGuL55+/n8svvGWfgJ8uZ+P21Ic9bP8St7wRMZJgkNrr9/f0zRnsaHh6murp6nMP5RAPnkbqG6zsEMyWhKEkSTqcTl8ulDRqKroU+ET4ciOskdNsjpVYJHnhvby8lJSXAwSAnlF7GDkUKpayJBsrHDqT/B2A22ZgYs3FqDGRZ5mc/+xlvvPEGjz76KDk5OVO/SIe+vj6qqqpISUnBYDAEeUTpefaHUxEXBRnRjXe7XciyRHLy+Jiqqgq///0duFwxGI0qp59uo6zsBu29enq62br1Xjo6rMyb10ZGxhpMpkKGhnr46KP3MJtlZNnE+vUbsNuz6em5nRdeMGI0KqxZ46C6eikOh4MlS0aVqIzGR3G77+OJJ76OohhRFBNms4GTT/4SixcfXDP6+vqoq/uYxYsv4De/uRUwEBPTz7e//WNg/LUJNWenKIqWsInuup7mK1gLM0V7mkj5UByrvhNwqK7hokMwZ84crRA6ndDPWsTFxeHz+Q4ppk4GcZ0MBsOUJrN66JMOn89HY2Nj2EImUw2UzyYbnwO8+uqr3Hbbbfz0pz/llFNOmfB5QnFHVK19Ph9RUVF4PB7sdjslJSUzUpV2uVw4nU5tEzoRRkbqiY8/BrNZQlWNOJ2PIssrtGM1mfooKbmR2Fg3kmTHaNyK2ZxOf38tcXEnYDRKBAJWjMZGTKYo3O5VJCY6Aejs/A5paXcEfZ7Xm09cXC9Go4KiGOjpWU5Cwlvof6M9PT388Y+P8N//fS8222jCoffTgOCZAHGsh1K1mOmhOeGb0dzcjMPhIBAIBB2rw+EIS3BgMgQCAerr6xkeHg4aID8UTKS8ER0dTV9fHw6HIyhxmi6oqkpnZyeNjY1hu5yLbpDL5aKnp4eUlBTmz58fdC3F95uQkBB03ENDQ9TX1084UD6bbBxRmI1TE2Dbtm1cc8013HjjjXzlK1+Z8HmhaLtWq/XToWvrjKntCX7/nDlzyM7OnvCe7+ur4ve//zuqCmazxEUXrcbvz9Ecw5ub36S3V8VkUjjzzGXk5IwWuVTVz65dX2fPngLKypo47rhHMBjigV7M5v8BLPz5z2U0N/dgMhk488yzKShY+OmnjlBXdzavv34iIyM2wIDBAEVFxZx11tlBFfTaWieLFq3AaFSRpBMJBP4BBHesxbFGR0dr3eqEhISw1lJBexKD0TOxNrW3t1NfX098fLzmFD7ZwHmkkGVZ82ApLi4+LKd5UagViZA41tjYWFwuFzabbUZk7+Hg/Ge4ylv6Y+3t7SUmJobS0tKwvsOpBspnk43PCdrb27nssssoKyvjhz/8IX6/n5GREY1rPzw8rDlGi4VFZMOTzVlMFwKBgGbsU1JSEtTeFK1bl+tblJT8DQCvN4rq6vdxOJK1Y+3rW4rDsR8wMDDwNA7HqZ++dzsm0zGYTAH8/mhaWt4mL6+QQOAfREdfiSQl0Nl5Jx7P43i9R1FcfA8mk5nh4b2MjPyMzMxRp3GLRWJgoBSz+WUMhgzt+MrLy3nnnX9x++13Bs2clJfv0Ghm01Vd16tolJSUHJas3kTzC7GxsVp7fCyNbrrgdrtxOp1hz4tM1GHRz1ror6vwF2lpaTlsOeGJoHc5Lyoq0gKSvsomtM31fFthmBlqME/QBZqbm8nNzSUzM5PBwUGam5tZuHBhyOOYTTaOKMzGqUnQ39/PVVddhdVq5Ze//CVmsxm3260VbKai7ba3t9Pc3DxuzmK6IMsydXV1DA8PU1paGrTmCOrW++//kk8+iWG0czDMKad8jYSERO1Yn3vuB/T0xGC1BjjvvKOYM+er+P1+XnvtOdzuHk45RWF4OAefbwGffFKOz+dnzZovk5Exh7///WaamhIwmRQKC5OIjV3M8uXLiYqKwmj8kJ6e7/PUUycTCByMn3Z7LOvXfyOIKdDb26sVNoxG47iOtXA3PxxpW2H4WlRUFLZZ6kTvNZYJYDAYsNvtjIyMoKrqjDicw2ihp7q6WlPqnGqzPFmHRcQp/XUVcuv19fVhF64ihZ4qXVhYGCTIIDpX4njHHqvFYvnURHl03mIqF/rJBspnk40Zxq9//Wsef/xxDAYDCxcu5Mknn6S9vZ2LL76Yvr4+jjnmGP74xz9itVrx+XysW7eOXbt2kZyczKZNm8jNzZ2W46iurmbr1q089thjNDU1ER8fz1133cWSJUvCNvYRlZ3MzMwZkU0F6OjooL6+nrS0NE15Q1C3Fi5crc1HdHTkk5CwT3udx9OHxVKG1erD74/G43meuLjV+P1eamruxG6vIikpnZiYb9LUlEBz8z9JTU2mrOyrmExmBgZKSEzsQFHMtLc/QkbGBQDIsp+BgaXMmdOI+IkFAnZk+QXgeE3N5JlnnkGSJK644kGSktx89NGPUdXjxwWk6cLQ0BBVVVUkJiZqXOKpIAK2WFjEcPxE8wtiDmKmaE96funYgDSR8kakHRbhzeHz+SguLp6RgDQwMKApmlgslilnWAT0LWuRMIjrHwgEqKurY2BggKysLI2XHgqzycYRhdk4NQmampp4//33+d///V/27t1LXFwcN998MyeddFLYtF2Px8Mnn3xCcnIyeXl5MxKnJqJu2e123n77Bfz+0TU/J6eHiy66R3tddfUbbN68lUDAxIIFnZxxxk8wGBw4nW/w9tvbUFVISTFx0UW3s2XLH9izpxWDQSU3N5Zzz70Rv/8WysubGBqKobq6EFU1UFKSz+mnfw0Ag6EFr/fLPPLIefh8+rgzqjyVmpqqrf0xMTH4/X4URWHhwoUzsjaKebOYmJiwu8yhZHInYwIIZsRM0Z708yJ6Ly+YeC5E7xwebldAFK5myptDdJwkSSI6OlpTXNMfa6jvJ1ypXKG0ONFA+WyyMYNobW1l9erVVFZWYrPZuOiii1izZg2vvfYaF1xwARdffDFXX301ixYt4pprruHBBx9k7969PPzwwzz33HO8+OKLbNq0aVqO5c477yQuLk6Tz7zuuuu45ppruOSSSyIeHq+trcXr9U5L1VvfEhcLS1RUFCMjI8TFxVFcXExUVBSDg3tISztee11NzQays3+n/b1//1VkZ/8ViyVAe/sqUlJeAwzs338vWVkbMRhUDhz4IllZm6ivv5f8/I0A7Nt3NQsX3snIyBLi45s/nd9Yis+XQnr6b4iJScHv99DSso7S0n9iMinAKE2qoeEyOjouJyEhAbfbzebNm4PObf369dTX1zN//nzS0tKYbkzGkRVVNr0RkV4md6zq0kQQm96ZdI4dGRmhsrISAJvNxtDQkFZl03ctDieIiE5KSkoKeXl5h0VB0yua6JNhQHOPjaRCNZFqFYwm+Pv27UNRFFasWBHyfptNNo4ozMapSXDffffh9Xo59thjiY+P59prr+X888/n29/+dkT3rOgAu1yuccPjhwI9xWhgYEBTsvL5fFoHOCYmBp/PxW9/+5j2upUrPaxe/SPt7zff/Bn79hkwmyUWLfLxhS+M+nN0dDzHiy9WA3DUUd2ccMIDtLf/jhdeGBVMWb68naysH2GxGElP38fevVvZvNmEJBkBI3Z7PBdddAkOhwNF6UeWT+WRR87E4wmmqcbExHLFFVcErf1iRmGs1Pp0QXSZxXC3fmhZdNf1XQuj0Tip6lIoCNqTy+Wasc261+uluroan8+H3W5neHj4kOdCJsLg4CBOpzPsTspkCOVyLop0vb29zJ07NyxqlcBkcQo+W7OFR2Sycdxxx/Hxxx8THx/P+eefz3e+8x2+/vWv09HRgdlsZtu2bdxxxx28/vrrnH766dxxxx2sXLkSSZLIyMigu7t7RiozQ0NDXHvttXi9Xn79619HPMzV3d1NXV1d2MoQENonYqKWuKqqHDhwgLa2NkpKSkhKmoNeNa2j40MSEkYrvZLkJRAoITGxC0my0Np6A+npdwDQ2noHc+c+gMEAPT1fIDn5L3R1rSMj4xUAmptPpqvr+xQXx6Io9+P3V5KWthuApqZzMJl+xsDAED093dhs97Jq1VsYjarunLLx+fYC1pASt36/H6dzdC5kpniZw8PDVFRUYLVaiYmJYWBgAEmSghyuD2eQH4KrR9nZ2Yf1XqGUN2w2GyaTif7+fvLy8mYk6B2KUof+N+t2uzV6mcPh0LoWesrfobrHTlY96u7upqWlBa/XS3Z29ji++JG0iM9iNk5FAr/fz/e//3327dvHQw89REZGxtQv0sHtdlNdXT3lPKAeqqpq97yQyhVmaaKyrt9UdnR00NjYSFFREX/6032MjBykGV966WIyMk779FxGeOyxX+L3mzAYVNavX0Ri4jkMDvazadMjeL1eiovb+NKXLsfrXUhV1Q6iox8nPb2Vioq17N7dg8FgZM2ac8nPb2TnzkfZvbuUwUE7qmrEZrNQWLjw0wHuaBYsuJqHHz4Dj0d0LEbVp6666upxMwiSJFFXV8fIyMiMdeOFebAwSRwaGhpnQhsfH39YsUUY6SUmJpKXl3dY66KiKEGqi6JrYbFYcLvdZGVlkZOTM2PJWUtLC/n5+aSlpYUlwDKWEi2Ki6Jgp997yLJMc3Mz3d3dLFiwICKamyzLKIqiSeWK7+uzNFv4H9V/CQdz587lpptuYt68edhsNk477TSWLh1VkhAZa1ZWFq2trcDoop+dnQ2g/VB6e3vD3sxHArvdzh/+8Af+9Kc/ceaZZ3LfffdNaBoWCqmpqcTHx1NZWUlvb+84/euxShZC01psgOfNmzfpBthgMJCdnU1SUhKVlZWMlWEXiQZAb+8rZGX1oijGT9vQo0aAXV1bSUl5HJMpQG9vGUlJD+D1jjA8vBqvt/xT2stFJCXdSENDAvAj7PYHSEvbjdGoMH/+iwwPv43R+BIFBSvxep/mjTeu4YwzXtA+22jcj82WiMfzexYuXMi+ffuCjtNqtbJw4UK6urrYtWvXtHQ5xAZY37WwWCwoiqLxMqdbKzwxMZHly5fT2NjIzp07w54XGevB0d/fH8QNFhKDeqna2tparVszna19o9FIbm4u6enpOJ1O2traWLBgQVCVLxAIBHUtAoEAdrsdh8NBbm7ulO6xwlhJVKjsdjvz58+fUl/caDRiNBqDlEDE4iyqZ4sWLaK+vp4dO3YE0c5mYpM3i88P/pPjlJjdeP311zn//PO58847Of3008N+vcPhYOnSpTidTs0teuy9KKiwY+/5hIQEMjMzKSoqmnSTlJGRgcPhoLKy8tPhbP1jp2n/39q6DY/HiqoaiInxYrePDtJ2dr5FIDCCyaTS0ZEKLOef/3yUlpYuTKYSLrjgTkZGnkFRjICC0/ln3O6zSEo6n6VLX+W99xYRCJgZGQmwb9/HnHbaGZSWLgS2ccMNX2Tjxi+hKAfPeWTkI+LjTwo6TrPZTHFxMX19fezZs2dauhz6pE0/E2g2m+ns7CQ/P5+5c+ce8vuHQlxcHMuWLaOlpYXy8vKIDF/18wt65U0hNBJKIXLnzp3T3kkxGAzMnTuX1NRUamtrtbkXfSycSBzF4XCQlZU1paS7yWQiPz+fOXPm4HQ6aW1tDcunSrzWYDBocUp8p4qiTLtozf8Xjrhkw+Vy8fe//53GxkYcDgdf/epX+cc//jHuefphoYkemymsXbuW4447jnXr1vGlL32JG2+8MezsMyoqisWLF7N//37Ky8vJzs7WWnd6x+iUlBTy8/MPydAlNjaWpUuX0tBQwPz5dQDs3ftFCgsPPsdg+C1m82h7z+2eQ0zMaNUmEHiZ6GgPgYAVRTFgMKTT2bmGvLxyPJ5YWlr+TGzsdRQUVKMoRj766Bm6utZjs8WRmvo8UVHD2O0DmEzn4fE8it2+hkWLfsezz8ZwySVPBx2nzbaBM84YJjMzk9raWk477bSgx9PS0nA4HDidTjo7OyPqcoyl7QgjooSEhHEbYMGR7evro7CwcFo5kiaTiYKCAm1eRGjP638vEylvJCQkkJ6eTmFh4aS/L4vFQmlpKW63m3379s2I8pbNZmPx4sV0dXWxc+dOrZomgqGoBM2bN++QO1FxcXEsXbqU9vZ2du7cGfbciz7pkCQJWZaRZRmDwaAlMoJ7GxUVxYIFC/7TjJJmcYThSIhTp59+OkuWLOEb3/gGmzdv5sc//nFYGyMYXVOOOuoo7V7MysrSuqt6Yz+Hw0FOTs4h3fPR0dEsWbKE/fv/RE3NKKc/K6s36DkfffQ+qjraMcjIGMRiGS06NTd3MjISjdGocPLJowmdx9MA2IEAvb1vUlTUS3u7FVCpq3NQW/sGhYUFnHXWDcydeyPPPXc2gUAUiqLyz3/+g7o6J+eddwGS9C9uu62Un/98LYoSRUJCDxkZuROeR1JSEsuWLaOuro7du3dH1OUQsV+ftImuxdgNsOj49/b2UlxcPK0df4PBQE5ODmlpaVRVVWmd7FCiMyJODQ8Pa87h4exXTCYThYWFWiclISFhnLrg4cJqtVJWVkZfXx8ff/wxMTExWCyWceIomZmZh9yJErGwu7ub3bt3a/O4U8VbfZySZRm/368ZA34WcMQlG2+99RZ5eXnaQNEFF1zA1q1bcbvdSJKE2WzmwIEDZGZmAqPVo/3795OVlaVlruFm5YeDvLw8Nm/ezB133MF5553Ho48+qh1TKOgNk0TFAqCuro7k5GQKCwsPm7uoh9FoJDPzY3bu3EJ7u5uSkqWoqorBYECWvWRlfaQ9t7//VGJiwOt1kZDwZ6KifMiyCa93LbW1teTl7UaWDURHjwCVny74TsDAnDk78fk+prb2KoaHJYqLn8ZolImJcWGzfY3BwTtxOG6krOy7PPZYFFde+QRwUOoW/Bx99NETuraH0+WYbBFMSkoiLy9v0kXQZrOxZMkS2tvbKS8vj4jmFi7sdrtWPdqxYwdpaWkap1mvZpGenn7IiiYOh4Ply5fT3NxMeXk5RUVFh60uIzYY+qFzu92Oz+fD7/dTWlo6rQo2BoOBzMxMUlNTqa+v1ypU4VTBDAYDPp8Pl8tFV1cXqampWuVIJOAiWVq8ePG/ZZ2YxWcTR0qcSktL45VXXuE3v/kNZ555Jg899BBFRUUTPl9QS0TxQ2zSGhsbiY+PZ/78+VN2KiOBwWDgvPN+QVvbTpqamsnMvEiLU4oi09RkQTA38vNHacter5vKyl5stgCKYiIu7jJ27/4IRTERGztMSUktbncR27fnU1g4zJw5A7z+uoKiqNTXO3nhBTNnnPE31q69kGefPQtJGo0NdXUN/O53v+Xyy6/Abq/kllvOxWx+F0m6HEnKnfQ8wulyhKJE62cCs7OzJ00GRSzs7u5m165d5ObmkpGRMa2bVRELOzo6+PDDD7XftyjYie56fn5+WAI5oSA6KQcOHKC8vJyCgoLDjrcTDZ2Lfy8uLp72mJ6amkpSUhJNTU0RxVuDwUAgEMDlctHd3Y3NZvtMdDiOuJmNHTt2sGHDBsrLy7HZbFx22WUsW7aMLVu2cOGFF2qDd0cffTTf+ta3eOCBB9i3b582ePfCCy/w/PPP/1uP+Z133uH666/n9ttv5+yzzwZGq+X64ThJkoJ4lqJiIYbHPR4PpaWlYVeeIoEsyzidTm1z2NX1QwoLDw6Kd3fvRJLm0Nb2F44++iZk2YCqGqmt3YrP9z75+XeRkOBiYKCUtrarUJRNn2pyq6SmvonRqNLWdhQdHfcSCLzLihU/w2oNaO/v9Z6Eqv6NZ555npGRcq67bnQYUJKsBAKusM/D7/dTVVWFqqqkpKRoyZswTRLX9lAXQRjtiFRXV2MymULSByJFqEXQYrHg8/mw2WwzOkCuN9IL5zzGShDq6Vti3kJfDRISh+HSng4FogomNjr6rpO+I6R3kBVzIcKPZOw8hyzLWK3WI0blYxazcWo6sGfPHi6//HKuuOIK1q1bpyXnerERobann7UwmUwoikJTUxO9vb0zJpk61t28vf09/vrXPdrjV1yxDJNpGVVVb/L++zWoqpHk5D5OOOEiystfp7tbwWSSOeOMRbz2WiWqKmM0woUXFrJly3ba21OQZSNGIzgcsZxzzhrM5nU8+eTZWsIxCgPLly/npJNOOqTzELMcQ0NDZGRkaJRY/UygMPc71A2moM76fD5KSkoOe15E73Ludrs1Wf9AIIDJZJox+X6fzxc0nxnu/kcwF8bKz4prqy/YeTyeIFPDmdhjiUFvq9VKYWFhUNdJXwwVcyx6fxNxXUOpVh1JMxtHXLIB8KMf/YhNmzZhNptZsmQJjz/+OK2trZqkYEFBAW1tbVpLStBTsrOzsdlsdHV1kZuby/PPP09iYiKqqnL99dfz2muvERMTwx/+8Iew7eTDwdDQEJs3b+a2227DaDQyMjLCvffey/z584M8AiZDT08PtbW142TiphPd3d3U1tZy4omnYbGMfvWKAlu2bCYuLo7ExCvIzd37afKwgsTEf+Hx5BETM4CiGOno+B0ZGddhsfgJBKx0dd3EnDkbMZkkjEYFvz+KhoZH2b+/gi9+8ZdYLJL22aoazeDgu9x3XzDV4Oabb570mMWGUgTDkZERLUhmZWUxb968GdnkdnZ20tDQQH5+fkSzHGM36xMpb+hN7sIdaIsUqqrS0dFBU1NTSN+MiYb5IpEgVFVV0+mfKW8O8RmNjY0kJydrspn6jtBEDrITqYFMpX/+/4DZZGNizMapaYDX6+WDDz7g9ttvZ2BgAJ/Px/e//32WLVsWttqeGB6fN2/ejEh76z+jouJl+vqECIvKCSecgN0ez759mzhwIBaTSWHFin5Wrfo5f/rT7XR1xWG1Brjwwnzefns3LpcDo1HF4UgmLs7DnDn7+OCDBfj9VozGUVGJU089ifnzr+PBB89GloPjyBe+cFLYM5n6zbroWog4lZ6eTl5e3oxscoUBXVZWVkRqfmLmRsQqPc14rMu52JvMlKcFoPlmzJ07d5xFQKh5S8FcELEqnMKRkKXPysqaERsCvf+HOKaxHSGHwxGSwaKPUyLhCMen4/8Bn61kIxLIsszcuXPZsWMHDzzwAElJSdx6663cfffduFwuNm7cyGuvvcbvfvc7XnvtNXbs2MH111/Pjh07puXzVVXlrLPOYv78+axYsYLGxkZeeuklHnrooQk1/ieC3++noqICm802JU8/XOjlB0eVgT7itNOu0x73+cwoSj8eTxdxcYVYLBKKAm1t96Eoy0hNPfVTHw4bLtcmkpK+itEooygmamq+QGxsgMTEKuLjOzGZZPz+aNrbb2HvXhNnnvnjoIQDwOnMZdOmy7S/r7vuOm0RnszcZ+xm3e/3U11djcFgmDHFKr/fT01NDbIsa3LCeky0WddLEE61CIrPkCRJG/yebogqmMfjIS0tTau0iUVQLNiHQ+MTn+H1eikqKjosl3M4OCgpqkFioF+SRn9PkRiQ6VWrxAI+auh1ZCzis5iNU9OBSy+9lJiYGI499ljcbjdPPfUU9957LytXrozofSRJorq6GlVVKS4unpZiz9ii0tDQEFu3fqB/BjfffBuBgJf77/8VkjSqTrV2bRou11z+9a8dSJKJoqI2bLYT+fjjBuz2AeLjA7S2JmE0KixdasdsfocPPjj208HxUcfwsrIyTj/9Ln71q/NR1YNrgtXq5/rrfxDyePVCLpNt1v8dilXCNHFoaIiSkpJxXadQm3X9zI0wopsMQjFwcHCQkpKSw17fJzqP+vp6XC4XmZmZWvdCdIREUelwmAtiSN3tdgeZyh4qhHmiiFNihlFVVQKBAEVFRRHRt8aqVh1Jceozn2y88cYb3HnnnXzwwQcUFRXxzjvvMGfOHNrb2znppJNwOp1cddVVnHTSSaxduxYg6HkzgYqKCr7xjW+wdu1avvnNb0Z0Y+jla0tLSyNSbAhHf7un5xcsWLBRe01V1ank5v6NlpZbKSoapVbJshGPp53OzkuYN+9dDAZobT0JRelk7lwnfr+VkZEkEhPbkWUzLS1fIS/vOSyWAIoCRiN0d69l8+YVfPnLN2O1BiccL720hj17RitGGzZs0IKMfjBadISm2qyLDsRM+XLAQcnizMxMoqKigroW+gBzOJt1UaEKVdk5FOg362JGSFTaUlJSKCgomJFKm96bIzc3N+yEWfDYxfEKXXYRYPQ88YGBAZxOp8YdDpcOJcsyVVVVbN++nYKCAtasWXPI5zkDmE02JsZsnJoBtLS0sG7dOlavXs33vve9iGmFomsaqdv1WHfrwcHBcUWlQKCVxx77m/aauLh+rr76p1RVvcIrr1QAo74b3/72pWze/EcqKowYjQqLFg1QWWlHVUcFIvLzXdTXJxEImDEYDNhsZo455n3efXcl+lsuJSWJyy77Jb/85Te0fz/xxK0ce+zfNCqsvqikr6yPlfIOhZn25YCDHaG0tDTsdrt2ffWbdUHbOdTP7+/vp7q6mtTUVHJzcw97I6zfs4jNOowW4RISEliwYMGM0Iz1FOBwjRNhcuquMM4V12R4eBin00l0dDQFBQVhF0RFQrRt2zYSEhK09eA/BJ/fZGPDhg0cc8wxXHvttTgcDtxut/ZYYmIiLpeLs88+m1tvvZXVq1cDcMopp7Bx40aWLVs2Y8fl9Xq56aabaG5u5oEHHoh4OGloaIiKigrNoyHU4jBWyWIqd2sAWfZhsaQQFaXg8xn58MN/kZiYSG7uSuz2YQDc7hRU9SOs1hJiYoZRFCNtbd8nI2MjkmTGaFTp708nMbEVcVjt7YWkpjYQHe3TPsvjyeGJJy7lmmt+ismkakPhg4MFvPbaXaROY7/5AAAgAElEQVSmppKYmKgFmEMdjJ6JLsfYofOhoSEkSdLk71JSUqadviUqO8JvIhKO7Finc70UpX6zLrjXPT09FBUVRewVEw4URWH//v20t7eH9OaYaDZEX2mbqgKod6idiL7l8XjYuXMn27dvZ/v27Rw4cIAFCxawatUqzj777EkHZf8fMJtsTIzZODVDkCSJn/70p2zevJlHH31Uk+cNFx6Ph4qKCk1lL9TmU5jQ6gsJk7lbAyiKn3vvvRcY/feTTsrEbl9KeflzdHaOVtUdjn7Wr/8fHnrol8iyEYNB5dxz5/HPf9bj9UYTFzeE12vHZPIiy0Zk2YQsm7HZDCxbto0tW45Df9sZDAa+851f43QWMmdOOyMjP6GzM1crKh1uB3gmuhxjjWgHBweRJAlVVbV1cbq7/oqi0NzcTFdX1zhj3KkgmBZ6wZFQexZVVdm/fz9tbW1h+ztFinAowBPNhkxG3R37GV1dXTQ0NExIQ/P7/ezZs4dt27axY8cO6uvryc3NZdWqVZx++unTSqWcBnw+kw2/309mZiYVFRWkp6dPuIifddZZ3HbbbUGL+D333DOhRfx04qWXXuIHP/gBd999d8QDZ4qiUFtby/DwMCUlJUHJhWiF6qtBh1KpVlWV2tq9LFq0Svu3hoazkOUTKSi4BQBJMuN0XkRJySZMJhm3O5WYmNHrbDZLSJIJs1mhqyuXhIQ2bDav7v3h2WfXsnbtnxBxqL39TeLijp32wafD6XJMJJUrFkHRuhUVqkg5spFgYGCA6upqkpOTQzp36yuDQt/cZDIFbdan+i0MDw9TXV1NbGxsRJWdSOD1erXhv8zMTDwejzZ8GOlsyETw+/3U1dXx8MMP8+Uvf5n+/n62b99OeXk5gUCApUuXsmrVKlavXn3YLugzjNlkY2LMxqkZxnvvvce1117L9773Pb785S9H9FpVVbUCRmlpKYqijPOJGOtuHQ4Cgf0cOPAEc+dejMVSRGtrK3/60zOIW6WkpIOysgv4618/QFUhOtpPTo6J+noVg0ElLs7H4ODoZyUm9tPTk4SijL7WaITFiz/mo48Wj/vcBQuKND+LcLrrkeJwuhyhpHJDDZ2LGJKamjrtMugCIoYIgZCx12myopI43ql+CyKGGI3GGaVL19XV4fF4mDdvnkaRGxwcDDKjDXc2JBQkSaKxsZHHHnuMk046CYPBwPbt29mxYweDg4MsWrSI448/ntWrV1NUVHRExqnPdLLx97//nQceeIA33ngD4D+mPT0WbW1trF+/nsWLF/ODH/xgyor4WHO/3t5ePB6PJo16uK3QsQgE0omPH9L+bm39CybTf5ORsR+Arq48oqLc2O1uJMmC3x9NdLQHRTEiSWaioz2YTAqKAh6PHZCIjfUGfUZfXzwPPvgdFMVMWlo6l1566YzcUOF0OUIpb4hFRSyEk31HU3FkpwPCubuzs5OCggJtKHqq1m0k0Fd2pnNIXW/yJ443EAiQnJx8WJKJegiFtW3btrF9+3YaGxupr68nPz+fm2++mZNPPhmHw3EkaZgfMQf6/4DZOPVvgMvl4pvf/CZ2u5177rknLF6+3tyvt7eX4eFh4uLiNNO+yUxoI0V19Tu8/PKH2t9f/KKPpiYDDQ2jXhpZWd0MDMQxMGDDbJZwOIbp+7/2zjwsynL9498ZNgVZZpB9kX1VVAQVXEpzz9NmKWlpbllWrln2szx4TidxLVMzNZcW08wWT6VmbmUqayAKSCAgyKYMIPsyM8/vD3zfMwMzMAMzsnh/rovLy5l5531meHnu93nu7/29S5tkyAYGTcW3crkAMtn/bhhdXXOQm+umdB6BQI7x4yfzPXr0sREjlUqVXChV3XQrNvjj6tYUrXKtrKxavflWzGJr2lBWWxQzzJ6enjA2Nm5hP8vFKVUZLE3hpMy6LFJvLt2tqamBVCqFhYUFPDw8YGlp2eHzcA5rXNbixo0buHXrFvr27YuVK1di0qRJsLGx6RFxqkcvNiIiIjBx4kTMnTsXQJOzkbW1NV94V1paio0bN+KXX37Bjh07+MK7JUuWIDY2to131y1yuRwbN27ETz/9hN27d8PDw4N/TrGIu3lzPy7NzBhDamoq35RMl1mBXr3MoHit//77fzF69BP8Y1lZE+DqehaGhrL7qWgBjI2lYAz309cCAHIYGDRdTowJUFtrDFPTeqXzXLvmjx9+mAE7Ozv4+vrC39+/wwVa6lDMclhaWip1Dm3NeUMbuPoEe3t7uLq66mTCUFxoNrfKc3V1hVgsbjN1qy2KReq+vr5aaWTVZVm4hRsXEGUyGbKzs1FaWtou+VZNTQ0SEhKUJFG+vr581mLw4MEQCoXYu3cvcnJyEBUVpe3X0Nl0m2jTCVCcekAwxrBv3z7s2rULO3bswMCBA/nnFGsXmvcz4jLsQqEQ6enpkMlk8Pf316nUdNOmD6DYOmzMGFf8/nsW5PKmxwYOLMH161ZgrClzYWAgg0wmvN+cVgCZzOC+uQnAybMAwNi4Dg0N/7vZNzGpwhtvrOM3Ynx9ffXWE0Uxy2FjY9NCFt281qI9CzeuoaxIJFIrdWsPio5WZWVlvK27q6srrK2tddo3DFAuUtdWZqypdJfb5OOaGmor36qvr0dSUhIfp27evAl3d3eEh4djxIgRGDp0KExMTHD06FGcOXMGe/fu1fZr6GwevsVGTU0NXFxckJWVxd+4SCQSTJ8+Hbm5uXB1dcW3334LoVCI+fPn49y5c6iuroaLiwv27NmDqKgo5OTkPFDrQQCIjo7G3LlzMXLkSEgkEjz11FNwdXVVkkOpq13gdhHy8/O1Lh5vDaHQHCYmcgBARUUf5OU9isDAn/nnc3N94eraJIeprTWBiUkD5HIBhEI5AAGEQgaZrOlfxWFLpQIYGv7vEquuNsFHH72LJUuWoL6+HqmpqbC2ttZJoRmHYuOk0tJSlJaWQigUwt7eHmKxGFZWVjoNgNzORVlZGfz9/bX2IlfVkLC5xEgoFKKgoAC5ubl6tUbmAh9XJ6Tqd6LYN6S8vBy1tbW8/prbvWrtd1lVVYX09HSYmpqq7f/B6Vy5rEVcXBykUqmSJEqX10wXgRYb6qE49YDj1I0bNxAREYGgoCA0NDRg1KhRfMxR7GOj7maS2+zR5Y36pk3/AcDNFwz/+MdQ/PRTHP+8p2cVbt5smn9NTatRU2MGgYABYGBMgKbLSIj/XU7Nxy6DsXEdliz5CwLBTwCaZDypqam83FRXm3xcYTR3oy6RSMAYg729Paytrdsti27tfNxNdHuavSrWhnBZFlVqAM5etl+/fnqzRuYkYlzDXlW/E1XqBW2ku5x8i1NIqPpdMMZQVlbGLyxiYmJQVVXVnSRR7eHhW2xoypw5czBq1CgsWLAADQ0NqKmpwQcffNAp1oPPP/880tLS4Onpibt378LOzg6bNm3Sur6gurqa1//qYke9oaEclZUTIZXaQiT6DubmIr6+gjFALhfCwKBpMaK4gOAWGMD/OoK3NpTExHXw83uT/79imre9TYMaGhqUditUOW9wBVr6dKyqrKxEWloa78KkboJRHC+XZWnLg5tDsQGSn5+fXvSrMpmMb97F2f0qpprlcjl/w9G8eZKmcP0/MjMzkZ2djeefb+pUzy0uUlJSIBaLERYWhpEjRyIsLEwnKe0uTo/+cB2E4tQDjFPLli3D+fPn4ebmxvc02Lhxo1I2XhPq6uqQkpLCN+Ts6E1XTs5mfPttk7PhP/5Rgfh4QxQWcjGDwcioEY2N3JzY1NCPMcH9juTC+wuO1nnlFTHMzRcoPcY5RObn58Pf379dphqcgQc3l6oycykvL9e7Y1VNTQ3S0tJ4FyZ1N9yaGo6oO5argdBX01pFExIfHx/+++PGLJVKdaJe4ORbKSkpmD17Nm7dusVLoq5evQpTU1MMHz4cI0eOxIgRI9C3b9+HNk491IuNiooKDBw4EFlZWUoXQGdpZu/du6c0UX311VfYsmULtm3bprXjiFwu5+sG2utsoZgF4IrOGYvH2LFv869pbBTwDQA7wr/+9R6MjHph2bJlLZ6rrKxEampqm3Kk5s4bXO8FxeJDdTfgirUcuvKGVzW+W7du4e7du/Dz84O5uXkLzS03Xk39zVWhr90jxd22kpISlJaWwtDQkM8KtXe8zampqUF8fDwuXbqE06dPIzMzEyEhIZg4cSJGjRqFQYMG6eX308Xp0RGqg1CcesBxysLCgh/LyZMn8fbbb+P999/HuHHjtHovxhjvXBQYGNiu/gyK85Ji0fmFC+cVXiVHU9ZC3f8BQAZF+ZQqRowQIzx8gcrnampqkJqa2qrzluJ4Fe1cFYvkrays1GYtuFqOuro6nXQGVze+/Px83p1PJBLxvSI4y3xVUlhtKSsrQ3p6equZ8vaOn5PuSiQSlJSUQCgUws7Ojlcv6GIjrr6+HomJibh8+TJOnz6NlJQUBAYGYsqUKRgxYgRCQ0P18vvp4qiNU7qvbOpGZGVlwcbGBnPnzsXVq1cxZMgQbNu2DcXFxfzE7ODggDt37gAA8vPzlaz/nJ2dkZ+fr7NJvPmOyAsvvICwsDDMmTMHEydOxPLlyzX+gxQKhfDx8YFEIkFiYqJGu/aKxXyKWQBLS0v069cPZmZmKCw8pXSMVMrA3fcpZi7aymI0x8amGHZ2E1Q+Z25ujtDQUNy8eRMJCQkICAiAqalpq84b3Hg1/b6MjY0RFBSE4uJixMfH6yXLwWUp6urqkJCQAIFAALFYDJFIpPV4W8PW1hZisRgZGRkoLCxsd5G6TCZTqg2pr6/nd9s8PDwwYMAAFBUVITc3t93yM65TenNJVEhICMLDwzF37lwUFxdjxYoVCAoKQmhoqNbnIIjuTFePU5MnT0ZwcDDmzJmDc+fOITIyUuObOYFAADc3N4jFYly7dk2jXXtFg4nmlu7Ozs73N3Fu4cIFxfPIlBrytVxoAG0tNACG0FD1n8vU1BRDhgzBrVu3EBcXx8vKVPUIMjMzg6WlJT9eTed9Q0ND+Pv7o7S0FElJSXrJcsjlcpiamkIsFiM5ORmMMYhEIohEIjg5OcHPz08ncUokEiE0NBTZ2dmIj49vd5F6a9JdFxcXBAQEQCKR4ObNmxr1PFEFYwylpaWIiYnhMxfV1dUYNGgQRowYgT179kAmk2HJkiVwcXHBqFGjtD5HT+ehXmxIpVL89ddf2L59O4YNG4alS5e2WjiqKguk75SYp6cnzp8/j7Vr1+Kpp57C7t27tQoa1tbWGDJkCNLS0vjeCQYGBmodl7idin79+qkMGI6O76Ku7kP06iVFdbUxzMwa+OcUvwptvhbGgP79IxASMkztawQCAezt7cEYQ0xMDAwMDJS82F1dXXWyW2FnZweRSIQbN26guLi43VmO5oXRlZWVfANFTkt6584dFBQUwNXVVedOIFxQKi8vR3JyMp8Vai1IcAVyXCE3AN4z3NHRUeUujZOTE2xsbJCeno7CwkL4+vq2upsjk8lw48aNFpKo8PBwTJkyBf/+979bSKI8PDxw8eJFyGSyDnwjBNE96Q5xys7ODidOnMCHH36IyZMn49NPP4W3t7fGx1tYWCA0NBTp6em8bNbIyEhltlrRccnFxUVlFqBXL+X5VHmhofQMmjZjuX/V88ILP8PI6GKbn8XGxgZyuRwJCQkQCoW8mYuVlRWcnJx0ststFosREhKCjIwM3Llzp91ZjuaGI83nfRcXF5SVleHWrVtwcnLSed8lAwMDeHl5obKyEjdu3OA3slqrl1AsPG8u3eWa/DW/3rkNuMzMTBQWFsLPz6/VLJpcLsfNmzeVJFFmZmYYPnw4Ro8ejdWrV6uURJ05cwZ1dXVq3vXh5qGWURUVFWH48OHIyckB0OQlHhUVhczMzC5pPXj27FksX74ca9eu1bq7cW1tLbKzs3Hnzh1+UuqoZrGszB+OjrlaHaOKjz5ajMcfXwpnZ2f+seaaUM55g9OvFhUVtWoLqAu06cuh2DmUcwzTpDCa08iamZnB29tb571FgKaJMzs7m6+zsLCw4CVy3OKCc45RtCHU1tKR63JuYmKCAQMGwNDQENXV1UhISOAXFwUFBfDz8+MLuR9SSVR7IBmVeihOdaE49ddff2HBggVYtGgRXnjhBa37RNy6dQv5+fn8vM7N+9o6LhUXb8Sff96El5cJTp92bvuANhg1SoDhw1cpPcZlLbgfRdtxCwsLSCQSlJWVISAgoF0SMU3Qpi+HouEI52SoSWE0Vw/IKSb0UQ+o2KjPx8cHYrFYSSKnK6nxvXv3kJ6ezm/I9e7dm5dEccXcWVlZ8PDw4ONUaGioTgvyezBUs6GOUaNG4bPPPoOvry8iIyNRXd3UJburWg+WlJRg3rx5cHJywn/+8x+VN9qKFoTchMJZEPbq1Qu3b9+GnZ0d+vXr16Edr+LiyXBz+6MjHwcAsG3bG5gx4x1+zJyGVXECVPWHLpFI8Pfff8PNzQ329vZ62b3jajm4pkHcxNbcJo8x1qIZkabjUdTI6qsbamNjI4qKipCdnQ2BQABDQ0OlQm5d9Lbg+nJs2bIFv/32G3r16oXevXtjyJAhvPuGm5tbTy+Q0xf0pamH4lQXi1NVVVV44403UF1djW3btqncEVfMris2ouUcFwsLC2Fpadnh4vFff12L5GTtzUWUkWHlyiGorR2hlK3WpAldRUUF0tLS+NoEfcx/6mo5mmcBtDEcUQW3Aefh4QE7Ozudfw6ZTMYbtshkMhgaGvKGLrrqy8IYg0Qiwa5du3D48GGYm5vD0NCQl0SNHDkSPj4+Pc0l6kFBiw11JCUl8Q4fHh4eOHDgAORyeQvrQW6V/frrr+PUqVMwNTXFgQMHtC7c1gWMMezcuRNffPEFdu7cCSMjI1RWVqJPnz78ja9id+vmEwqXIqyoqEBgYGAHMgMMVVX9YWmZB2Pj9klcqqoM8euvh+Hs7KzkvKHpH3pjYyPfA8Lf318vOy5cYXdubi5MTU0hlUqVUuO66iJbV1eHGzduwNjYGN7e3u3e8WeM8d24m3cQt7S0RE1NDYqLizu8sGkuibp+/Tr69u2L8PBwODk54auvvsKIESOwYcMGWmB0HPoC1UNxqgvGKQA4cuQIoqKi8OGHH8LOzg5FRUUQi8X8jS+XXVfViFYXxeMAcO/eGezZ81eHPoehYT2GDx+nlK02NzfXOBMtk8n4HhABAQF6cWDiNq2ysrLQq1cvyGQyJWm0rgw8Ghsb+V4pnCNhe1En3bW0tERDQwMKCgo63FCWM8vhshacJCosLAze3t44duwYnJ2dsXv3bsqydxxabHQUNzc3fnIxNDREfHw8SktLMWPGjAfuc56YmIjffvsNv/76KxISEuDh4YFXXnkFU6ZM0erGt7S0FOnp6R3epaisvApb2/B2HXvjhhdsbK50uMs2Z0Gni8JurvCc2w3ibPL69OkDiUQCIyMjvTlWcbavOTk5GvfMkMvlShIursmfYgfx5kGxrq4OaWlpMDY2ho+Pj0Zd67nGec0lUdxu0MCBA5XeRy6XIzo6GuHh7bs2CCVosaEeilP36UpxKj09HSdPnsSZM2fw559/wsXFBS+++CIiIiK0uvGtqKhAamoqnJ2d290duqHhe5SWfocvvxzY9otVMGJEDMLCjnV404RzYNJFYXdzu1zOIIWzeZXJZHqVGZeUlCAzMxOurq4auR62R7qr2FDWz89Po89SV1enJInKzs6Gp6cn3zivuSSKMYZLly5h5MiR7fsiCEVosdFR3NzcEB8fj759+/KPvfXWW53ic/7999+jpqYGYWFhcHBwwMqVK1FQUIAdO3ZovVPd2NiI1NRUGBoawtfXt9079BUVg2Fn97fWx23aFIXXX3+jXedsjjrJU2uo0oQaGhoq2fo1fx9tajk6+lm4pkGKGRt1vTjUZbLUwTlBZWdnw93dHXZ2dvxxii5RV65cQVxcHORyOUmiOg/6otVDceo+XSlOnTlzBrm5uQgLC4Onpyfef/99XLx4Ebt371aqz9MEmUyGv//+G/X19QgICGhnBpth06aNaM+f0qpVLwPQrtGdOhQlTwEBARplBlozHOHm/ebvo00tR0c/i6qeGep6cbRHustJpp2dneHs7KwUpyQSCb+wiI2NRU1NDQYPHszXW3h7e5Mk6sFBi42OomoS7yyfc1X88MMPiIyMxIYNGzB69GitjmWMoaCgAHl5eVo1JWo+AUqln2LYsH0an1cmA65fT4SPj49W420LrjbBx8enxeKruQ0hZ5uo2IxIk4mpPQub9lBcXIzMzEyIRCJ+Z6itxZC2NDY24uTJk9i2bRsmT56MtLQ0pKSk8JIornGeorc+8cChL149FKfu09Xj1O+//44lS5bgnXfewRNPPKH18VwPIVVzuzo4x6UmW9RDuHLlJgoK3LU4qxyrVq3Weqxtwd1Au7u7w97eXuk5RcOR8vJypcJzS0tLtYYjzXkQfTm4z5Kens6PS5PFkLbIZDLExsZi1apVePLJJ5GVlYXk5GT06dOHb/AaHh4Oa2trilOdBy02Ooq7uztEIhEEAgEWLVqEl19+GVZWVigvL+dfIxKJUFZWhqlTp2L16tV8Wu6xxx7Dhg0b9K6bvX37NubMmYOQkBD83//9n9Y3oTU1NfxNpqpd69acN7hai7KyN+Hiskej8yUn+8HbO0GrMWpKfX09UlJSYGRkBJFIhMrKSlRUVPAFfdyYOzr5clkOTSVPbcH1tuAWRHV1dbxbhlAoRGBgYIclZ9wiMT4+nt8RKigogJubGxITEzFr1iz861//IveNrgVFT/VQnLpPd4hTpaWlWLBgAcRiMaKiorSez7i5nety3fymm7tR5+bQ2tpaJcclU9MKbN9+GG331WjCwqIEixZt1GqMmsLVPzQ2NsLGxgZVVVUtahe0NRxRBecUqKnkqS1USXd79eoFqVQKmUyGwMBAndi5c5IoTrqbk5MDT09PJCUlYcKECdi6davObeOJDkFN/TrKpUuX4OjoiDt37mD8+PHw8/NT+9rO8DkHmpo3nT59GlFRUXj88cexZ88euLm5aXw815QoKysLCQkJ8PT0RG1tLd81VNF5w8HBQeUE2Lfvh8jNlcDV9bs2z3fq1HQYGGTBzc1NJ2nO5kGmvr4eDQ0NKCsrg7u7O3x8fHRuLdu8L4e2WQ5Fj/PmrlacRpX7jiUSCZKTk7VOi3N1IJwkKj4+HnK5HCEhIRgxYgTmzp3LO5PV1NTgP//5DyQSCRwdHdv1nRAE0Tl0hzglFovx3XffYc+ePZg0aRJ27tyJAQMGaHy8iYkJBg8ejNzcXMTFxcHLy4tv9FdRUQHGGH+jrrrvghirVkVg69YvIJO1Xag9dGhTnYWXl5dO4oeqHlcCgQA3b96Ei4sLgoODdWI4ooi1tTUsLS2RkZGB4uJirbMcrUl3vby8lKS79+7dQ2pqqtaOl6okUbW1tbwkauvWrfzisrGxEVu3bkVOTo5W1w7ReVBmox1ERkaiT58+2Lt3b5dJTzcnOjoar7zyCpYtW4bp06e3+XqpVKp0o15dXY2GhgbY2NjA2dkZFhYWWk20xcXL4Oa2t9XXFBbmQyKRoLS0tF079qpu1BVrF7ggU1tbi9TUVJibm8PT01MvvSyAtrMcjDGlArmqqirekpgbc1tBRiqVIjMzEzU1NS00shwymQypqan8blBqaipsbGx4SdTw4cNJEtX9oF+WeihOqaA7xKnU1FS89NJLiIiIwKJFi9qckxS7RXN2uQ0NDbCysoKrq6tGc+j/3isRH398HFJp63HnzTdfw+3bpSgoKOC7gmuDqht1Rdtx7ka9oaEBaWlpHa6fbIu2shxcHaNifYi20l2ut1NpaSn8/f3Rp09L62G5XI6MjAx+caEoiRo1ahTCw8MhFospTnUvSEbVEaqrq/kuldXV1Rg/fjzWrl2Ls2fPdlmfc6Bph+HVV1+FoaEhNm/ezP/Bc9aoil1DBQJBi7StVCpFWloaDAwM2jX5FRW9CXf3XSqfk8uB+vpqfpxpaWmtuo205mTBZQJaGx/XMKiwsBD+/v6wsLDQ6rNoimIth6enJ2pqavgxc40JFT3D2zuRlpWVITExEYmJiViwYIGS+0ZRUVELlyh9Ba7uhEwmQ0hICJycnPDzzz8jOzsbERERKC0tRXBwML788ksYGxujvr4es2fPRkJCAqytrfHNN99olSHUExRx1UNxCt03TtXV1eGtt95CVlYWdu7cqbRRo6qfUXNbd+6mlSu41qZ4nLFb+OijA60uOFategtA0/ebkpICGxsbteYY3IYSF1sVm9BpcqOu6Ebo6+sLsVis8WfRBsVaDh8fH6V+HHV1dTAzM2uXFX1zKisrcf36dZw/fx6LFy9GamoqH6c4x0XOJSokJISku+i5cYoWGxqQlZWFp59+GkDTH+nMmTOxZs0aSCSSFj7nlpaWGDJkCMrLy3n7QUNDQzQ0NHTKhcIYw969e7F582aMHj0aeXl5WLVqVYvu1up2+7lGbbm5uVoVj3MU5zwBN/+zLR7Py7NA376F/P+bu40AUKpd4OxnO9LxHGgKGKmpqRCLxXB3d9eZS4ViEWJ5eTkkEgnq6+thbW0NBwcHWFlZ6WQiVZRE/fnnn0hMTERGRgYmTpyIqVOnYuTIkXB1daXdIBVs3boV8fHxqKiowM8//4zp06fjmWeeQUREBF555RUMHDgQr776Kj755BMkJyfj008/xZEjR/DDDz/gm2++6ezh0y9UPRSn0L3jFNBkcrJq1So8+uijyMnJwbJly/iNGU02lDj7c22Kx5sox+bN28CYKkmVcnG4XC5HVlYWysvLERgYCCMjI5WGI9yY29uErq6uDqmpqTA1NYW3t7dOs/GKigCJRILa2lpYWVnB0dFRJ/UhQFOcKikpQXR0NK5cuYKEhARcv34do0ePxpNPPomRI0eqrLchem6cosWGjulKFwMfkGUAACAASURBVEpWVhZmzpwJxhi8vb2RnZ2N0aNH45133tF6p5srHre2toa7u7vmk1FtLdJywhAcnKH08Jgx3+PEiYkAlO1ni4uLUV5ejt69e8PGxoaftHXZrI9r0nf37l0EBASoTPFq8h5cOp/r0s4VIXILOK7pXUccqzSRRKWnp+P111/HZ599hqCgIK3P8TDAmSesWbMGW7duxU8//QQbGxsUFRXB0NAQV65cQWRkJH799VdMnDgRkZGRCAsLg1Qqhb29Pe7evdvZCzhabKiH4pSWdKU4de/ePUydOhW1tbXw9fXFrVu3MGDAALz//vtaN7+rr6/nb9K1q7Gov2+Lq7whZGeditkRnwCmpkrNUu/cuQOJRAITExM+TulqQ4mDa9J3+/Zt+Pn5wcpKe+tdTaS7ADrsWCWXy/H3338rSaIsLCyUXKLu3r2LV155BWvWrMH48eO1PsfDQE+OU7TY0CFd7UJpbGzkd1qAJonPu+++i6SkJOzatUtrbW7zXR1NA4GxmRmEMkDxo90RWuLutT/5Qm7F3SATExOkp6fDyMgIPj4+epMAVVZWalzIxhUgNm/011x3qwpNHau4RZeiS5SmkqiGhgYYGRl19kTTZXn22WfxzjvvoLKyEps3b8bBgwcxfPhwZGZmAgDy8vIwefJkXL9+Hf3798epU6f4XgCenp6IiYlRshPtBOgXqx6KU1rQ1eIUYwz37t3jb6blcjk2b96MH374AXv27IGnp6fW78dJZgMDAzXeTOplZoZ/R74H4H+77WvfX4e0pOsov3cPtbW1SoqA3r174+bNm2hsbIS/v79ON8QUqampQWpqKiwtLeHp6dlqNkDR2l1b6a42jlV1dXVISEjg49StW7fg7e3Nb4IFBwerXHjJ5XLIZDLq1K2GnhynSMitQ5YtW4aNGzeisrISQNMfr5WVFX9z6OzsjPz8fABAfn4+XFxcAACGhoawtLSERCLR6YViZGSk9EdtbGyMjRs34vTp03j66acRGRmJSZMmafx+QqEQXl5eKCsrQ1JSkkp/8OYwxtDo7g7rPldQWWkPgQDYtm02luAL1JiZwdHRUeVOSlBQEAoLCxEfH9/uXZ22MDc3R2hoKO++FRAQANP7O1hcrQXnxGVgYMBP2P369dMqsCg6VhUWFsLBwQE2Nja8RE3RJQoA7xI1f/58jSVR+gp0nUldXR1Gjx6N+vp6SKVSPPvss1i3bh1eeukl/P777/yu3MGDBzFo0CC1HZF//vln2NraYsiQIbhw4QKA1p14OsulhyAeBF0tTgkEAqX5XSgU4q233sLYsWMxe/ZsvPbaa5g5c6ZW7+fq6gqRSISUlBQ4ODjAxcWlzb/h8h9+wNqnn8aG9YtRXy/G9JEHIZMCJr16wdfeXqW8KCAgAHfv3kVCQoLO7M+bw7lE3rp1C3FxcXyRenPpbnNrd2dnZ60yLc0dqxwcHGBvbw/GGO7evavkElVXV4chQ4YgPDwcH330UZuLIA6hUNjjpFMUpzSDFhs6ojtdKBMmTMDZs2cxd+5cnDt3Dv/+97+1mpREIhFCQkJw48YNlJSUwM/Pjw9Uzd1Campq0Purr7Bm0g4cN5yESTiDJIShvvxj2LWyuyEQCODo6MgHDCsrK3h4eOh8ohIKhXB3d0dBQQHi4uL4m3auf4iTkxP8/Pw6fF5jY2MEBATgp59+wowZM+Dh4QGJRAJbW1uEh4fjqaeeQlRUFMzNzbv0hPEgMTExwblz59CnTx80NjZi5MiRmDx5MgBg06ZNePbZZ5Vef/LkSWRkZCAjIwMxMTF49dVXERMTg0uXLuG///0vTpw4gbq6OlRUVGDZsmV8LZChoSFu377NW/06OzsjLy8Pzs7O/E6hvgo1CeJB0p3iVEhICP744w+8/vrrOHv2LD788EOtjD3Mzc0REhKCjIwMJCUlITAwkJ/fVdnPmvTti+Mjvsf6d56BEEA+7NFQXY228v82NjawtLREamoqSkpK9GKxzi2gTExMkJSUBAMDAwgEAl66a29vr5Pzck5Yf/75J1544QW4u7vzi9GwsDCMGzcO7733HrlEKUBxSjNosaEjutuFYmNjg59++gkff/wxJk2ahF27drXqyd4cIyMjDBgwALm5ubhy5QosLS1RX18PuVzOu1p5e3vz9rODigbxx6r2p1JN7969+V2d+Pj4dtdYKFJfX69kRchZ5nJZm/r6evj6+nao4R8niYqLi+N3hDh/80WLFuHcuXMICgrCzp07tS66f1gQCAT877qxsRGNjY2tBrjjx49j9uzZEAgEGD58OMrLy1FYWIj169dj/fr1AIALFy5g8+bNOHToEJ577jkcO3YMERER+Pzzz/Hkk08CAJ544gl8/vnnCAsLw7FjxzB27FgKrESPoLvFKXNzc3z++ec4dOgQJk2ahG3btiE0NFTj4w0MDODn54eioiLExMTA0tISDQ0NSoYjHh4evOFI8GmgAU0uidqUmBsbG2PgwIH8plV7zFSao0666+7ujsrKSlRWVsLHx4eXSbeX2tpa/PXXXy0kUfPnz0dCQgJ69+6Nffv2talieFihOKUZVLOhB7gL5eeff8Zzzz2HadOm8YV3QUFBWLx4MXbu3Ilr167xhXfff/89jh492injvXr1KubPn4+5c+fipZdeUnvBcvazirZ+JiYmMDMzg0QigY2Njcbp1PbA1VhomhYHWloRVlZWwtjYWMnhpLl+tLS0FOnp6XBzc4O9vb3G5ykoKFCSRAmFQl4SNXLkyBZj/vHHHzFhwoQOdwTvychkMgwZMgSZmZl47bXXsGHDBrz00ku4cuUKTExM8NhjjyEqKgomJiYadURW/NvMysriLQUHDx6Mr776CiYmJqirq8OLL76IxMREiMViHDlyBB4eHp31FXB03SjS+VCcagfdLU5lZWVh9uzZmDBhApYvX96qgyJnOHLv3j2+T4S5uTnKy8vRp08f+Pn56a3fEmemoo3jYVvSXVUmKRUVFUhLS9M6HjaXRNXX1yM4OJivt2gew8+cOYOgoCDY2tpq/2U8JFCc4qEC8QdJd7xQampqsGzZMpSUlGD79u0QiUSoq6tTSjVLpVKlYjNF+1muiU9ZWZlWxePaIpPJkJmZierqagQEBLTIPnDNCbkdofr6evTp04fXsWpqRSiVSpGeng6pVKqy+E8qlSIlJYV3iUpLS4O9vT0/YQ8bNowkUTqkvLwcTz/9NLZv3w5ra2vY29ujoaEBL7/8Mjw9PbF27Vo8/vjjeOedd5Qm8Y0bN2LIkCGdPHqdQBeSeihOtYPuGKcaGxsRGRmJ6Oho7N69G46Ojqivr1faUGptzmeM4fbt2+1u0KcpjDHk5OSgpKQEAQEBLbIPMpmMj1P3FIrPudiqaW8LmUyGrKwsVFRUICAgoEXclcvlSE9P5xcX165dg6WlpZJLlEgkojilIyhO0WKjS6GuoKgzm7dwk9L27dtx/Phx9OrVC4888giWLl3KT9qaFCGXl5fjxo0b6Nevn1470XLZB2dnZxgZGfGTNteckJu0OyKFApq823fv3g0HBwd4eHjwi4vi4mIEBATwWYugoCBqnHcffV3f69atg5mZGd58803+McUbpq7QEVmP0N2AeihO6YGuGKcYY8jKysJnn32GAwcOwNzcHIGBgfjnP//JZwA0mfOrqqq0Kh5vLxUVFbzjoampaQvpLjdmTm7cXsrLy/HVV1+htrYWw4YNQ2xsLKKjo5GbmwsfHx8ll6ieaCbSHihO6QW1F3HPsgXoJnAFRVevXkVSUhJOnTqF6OhovP3221i+fDkyMjIgEomwb98+AMC+ffsgEomQmZmJ5cuX4+2339b5mLZt24bIyEj4+Phg165dvP2du7s7bG1tNZ6grKysEBISAolEgmvXrqGxsVFnY5TL5aioqEBubi5u377NB57s7GyIxWKEhIRg6NCh8PPzg/1995D2wPmbf/vtt4iKisL58+fx0UcfYcWKFejXrx/279+P5ORkfPPNN1iyZAmCg4NpoaGArq7vu3fvory8HECTrvjMmTPw8/NDYWFTM0jGGH788Uf0798fQJOG9YsvvgBjDNHR0bC0tOwpEzhBPHC6Ypz6+uuvsXLlSohEInz22WcIDAyEnZ0dXF1dYWdnp/Gc36dPH4SGhqK2thaJiYmor6/X2RgZY6isrMTt27eRm5vLW/FmZGTA3NwcgwcPxrBhw+Dv7w8HB4dWbdPbOs+dO3dw/PhxbNiwAcePH8fRo0exaNEiWFhY4OOPP0ZycjK+//57vPnmmxg+fDgtNBSgOPWAYYy19kM0o1+/fuzLL7/U2ftVV1ezwYMHs+joaGZtbc0aGxsZY4xdvnyZTZgwgTHG2IQJE9jly5cZY4w1NjYya2trJpfLdTYGVUilUrZu3To2atQolpaWxqqrq7X+yczMZGfOnGH5+fntOr68vJzdunWLXb16lf3xxx/s7NmzLCYmhqWnp7Pi4mJWVVXFqqur2c2bN9mZM2dYXl5eu85z7949dvnyZbZlyxb23HPPsf79+7Nx48axtWvXstOnT7N79+4xuVzOjhw5wmbMmKHX772n0ZHr++rVq2zQoEFswIABLDAwkK1bt44xxtiYMWNY//79WWBgIJs1axarrKxkjDEml8vZ4sWLmYeHB+vfvz+Li4vrhE+sN9qaqx/mH6IZD0ucksvlbOfOnSwkJITFxsa2a/7Pzc1lZ86cYTk5Oe2OH3l5eezatWvs4sWL7MyZMyw6OpqlpaWxwsJCVllZqXSerKysdp2nsrKSxcXFse3bt7NZs2axoKAgNnr0aLZ69Wr2888/M4lEwuRyOTt9+jSbMGECk8lkev3uexIUp3SG2nmatmM7ieYFRZ6enp3qdd4cAwMDrF27Fo899hgiIiKwcuVKTJs2Tav3cHBwgJWVFVJSUiASiVotlmPsf91ZOc9wAwMDXsLl6uqqdlfG3t4eVlZWvPWgp6dnq8WDVVVViIuLw5UrVxATE4M7d+4gMDAQ4eHheOeddzBgwACVmYoZM2Zg+vTpWn0HDyu6uL6DgoKQmJjY4r3PnTun8pwCgQA7d+7U0yciiIePrh6nBAIBFi9ejNGjR2Pu3LmYNWsWFi5cqFWmwNraGkOGDNHYurZ5bwsAvCRKXd8o7jwhISFIS0tDSUkJfH19W82I19bWKjV4zcvL4yVRr7/+ulpJ1Pjx4zFu3Diqw9AAilMPDlpsdBIGBgZISkriC4rS0tJavKYreJ2PGDECFy5cwKJFi3D27Fls2rRJK6s9zro2OzsbCQkJCAwMhKmpKS+JUurH0bs3P2H7+vpq5RbSq1cvDB48GHl5eYiPj4e1tTW8vLx4SdTly5cRHR2NhIQECIVChIaGYsSIEXjllVfg7Oys8ffZUyfwvLw8zJ49G0VFRRAKhXj55ZexdOlSREZGYu/evXyzqg8++ABTpkwBAKxfvx779u2DgYEBPv74Y0ycOJF/v+5yfRMEoZ7u8nfcv39//PHHH3jzzTcRERGBTz75BNbWmpvXcta1+fn5vMW6ubk578DILS6qq6thYmICKysr2NrawtvbW6s4xVnGFxUVIT4+HlZWVvDz8+MlUVxNYGxsLBobG/nGeVzPC02dHnvq/ElxqvtCi40OUFNTg+effx5SqRRHjx5tl9+1lZUVHn30UURHR3dZr3MrKyscOXIEBw4cwKRJk7B9+3YMGjSo7QPvIxAI4OzsDKFQiNjYWBgZGcHAwIDfDVLsx9ERuEaAeXl5ePbZZyEWi1FVVQVHR0eEh4fj2WefxaZNm9CnTx+aJJphaGiILVu2IDg4GJWVlRgyZAjGjx8PAFi+fLlSsRsApKam4siRI0hJSUFBQQHGjRuHv//+u0Xg7Q7XN0H0ZB6WONW7d2/s3LkTx48fx9SpUxEVFYVHHnlE4+MFAgHs7OzAGENiYiKEQiEMDAxgbm7ON5VVdGBsLwKBALa2tiguLsaCBQtgaGiI2tpaWFtbIywsDBMnTkRkZCS5RKmA4lT3hQrE20lRUREeeeQRODo64r///a9WE7iqgiJ/f3+MGTMGx44dAwCVzVsAdFrzFoFAgHnz5uHw4cNYuXIlduzYAblcrvK1nFTp9u3bSElJQXR0NJKTk3kbWQsLC5iamsLb2xuOjo4dKpCrqKjA2bNn8f777+Mf//gHRowYgQMHDmDx4sUIDAyEra0t9u/fj8jISIwbN47saNXg4OCA4OBgAE2NtPz9/fn0sSqOHz+OiIgImJiYwN3dHV5eXoiNjQXQPa9vguiJPGxxCgCefPJJnDhxAlu2bEFkZKRakxJ2v7dFQUEBUlNTER0djaSkJNTW1sLX1xfW1tYwMTGBl5cXnJ2dO7RJVVNTg4sXL2LTpk2YNm0awsLCsGXLFjz//PMICwuDqakpPv30U6xfvx6PP/44dehWA8Wp7gtlNtpBamoq1q5di0WLFrXLcaOwsBBz5syBTCaDXC7H9OnTMXXqVAQEBCAiIgLvvvsuBg8ejAkTJmDMmDEoKChAYWEhvvvuO7i7u+PTTz/F+PHjkZOTAzc3Nxw9ehQikQiMMSxduhQnTpyAqakpDh48yP9h6gofHx9cuHAB//d//4fnnnsOn3zyCUxNTVFcXAwDAwOUl5ejrq4OZmZmsLKygrOzcwvPcDs7Oz6N7OfnB5FIpNG52X2P9MuXLyMmJqaFJGrx4sVwcnJSmgAuXbrUomkf0To5OTlITEzEsGHDcOnSJezYsQNffPEFQkJCsGXLFohEIuTn52P48OH8MYraVk2v7/nz5wMA5s+fjxdffBFeXl68lz9BEB3jYY5TTk5O+PXXX7Fx40Y8/vjj2L17N+zt7XH79m0YGRlpJN21s7ODRCJBYmIiPD09NW5q11wSFRcX16Yk6vr163prhttToTjVvaA+G1ri5uaGuro69O3bF9HR0Xyben1QWFiIwsJCpZThjz/+iIMHD0IsFmP16tWIiopCWVkZNmzYgBMnTmD79u04ceIEYmJisHTpUsTExOh8XAUFBbh06RIOHTqEixcvwtraGgsXLsQzzzzD97bQZMVfV1eHlJQUWFpawsPDo8VkK5VKcf36db7e4saNG7wkimucR5Io3VJVVYVHHnkEa9aswTPPPIPi4mL07dsXAoEA7733HgoLC7F//3689tprCAsLwwsvvACgaSKeMmWK1iYChMbQRa4eilPNoDgFlJSU4NKlS/juu+/wyy+/wMrKCjNmzMC8efNgaWmpcUa9oaEBaWlpMDIyUllLKJPJkJ6ezi8url+/DrFYzDfOCwsLg5WVFcUpHUJxqstCfTZ0SVRUFAYMGIBx48ahrKxMb+dRlzI8fvw45syZAwCYM2cOfvzxRwBNKcPZs2dDIBBg+PDhKC8v572edcnBgweRlpaGN954A7GxsfD09MTt27chFou1qr3o1asXgoODYWBggJUrVyI2NpaXRE2dOhUjR47Ejh070KtXL7z77ru4evUqTp8+TZKo++Tl5WHMmDHw9/dHYGAgtm3bBqCp4eH48ePh7e2N8ePH89coYwxLliyBl5cXgoKC8Ndffym9X2NjI6ZNm4ZZs2bhmWeeAdC0u2dgYAChUIiFCxfyKWhOv8qhqG0lCKLzedjj1LFjxxAXF4eIiAj89ddfCAsLQ25uLszNzbWqvTA2NkZQUBAsLCywdu1anDt3Dn/88Qc2btyIZ555BmFhYYiKikJjYyOWLl2K+Ph4nD9/ni9SfthrLyhOEQCoz4a2cP7lMpmMLViwgAUFBbGioiK9nzc7O5u5uLiwe/fuMUtLS6XnrKysGGOMPf744+zixYv842PHjn0gHs4ymYxt2bKFDRs2jCUmJmrkGV5VVcVu3LjB9u/fzxYtWsRGjRrFxGIxGzt2LPv666/Z7du39e7R3t0pKChgCQkJjDHGKioqmLe3N0tJSWGrVq1i69evZ4wxtn79evbWW28xxhj75Zdf2KRJk5hcLmdXrlxhQ4cO5d9LLpezF198kS1durTFOTi2bt3K9xm5fv06CwoKYnV1dSwrK4u5u7szqVSq18/7kNPZvSy68g/RDIpTqvn888/ZoEGD2B9//KFxnLp58yb76quv2Ouvv85GjRrFbG1t2bBhw9j+/ftZZmYm9bNoA4pTDxXUZ0PXCIVC7N27FytWrMDo0aPx22+/wdXVVS/nqqqqwrRp0/DRRx/BwsJC7etYJ1mzCYVCrFixAmPGjMG8efOwcOFCfueKo7GxEdevX+dTzenp6bwk6rnnnsOwYcPAGMOKFSsgkUjg5OSk93F3dxwcHPjOo813FC9cuACgaUfx0Ucf5TvMqtpRdHBwwKVLl/Dll19iwIABvNPYBx98gMOHDyMpKQkCgQBubm7YvXs3ACAwMBDTp09HQEAADA0NsXPnTq0sIAmC0D8Up5SZPXs2wsPDMWfOHEyZMgVLly5Vku/KZDLcuHGjhSQqPDwckydPxr/+9S+YmpoiMjISGRkZmDt37gMZd3eG4hQBgDIbXZ2GhgY2YcIEtmXLFv4xHx8ffiVfUFDAfHx8GGOMvfzyy+zrr79W+boHRVVVFZs3bx576qmn2KFDh9jq1avZmDFjWFBQEJs5cybbsWMHS0pKanV3geveSWhOV99RJDpMZ2cPuvIP0cl0tzhVX1/P3n77bTZ27Fh26NAhtnbtWjZhwgQWGBjIpk2bxrZs2cJiYmJYQ0OD2vegOKU9FKd6PGrnaarZ6MIwxjB//nz4+/tjxYoV/OOKFmzNrdm++OILMMYQHR0NS0tLfkfhQWFmZoZ9+/YhODgYX375JQYOHIgvv/wSSUlJOHToEF577TUMHDiw1d2F1rqqdnfmzZsHW1tb9O/fn38sMjISTk5OGDRoEAYNGoQTJ07wz61fvx5eXl7w9fXFr7/+qvI9u8OOIkEQPZPuGKeMjY0RFRWFF154AZ9++ik8PT2xa9cuJCcn49ixY1ixYgWGDh3aqpMhxSmKU4QWtLYSecArIqIZFy9eZADYgAED2MCBA9nAgQPZL7/8wkpKStjYsWOZl5cXGzt2LJNIJIyxJj3j4sWLmYeHB+vfvz/tBnRBfv/9d5aQkMACAwP5x/75z3+yTZs2tXhtSkqKkt7Uw8OjRUaou+0oEu2ms7MHXfmH6EQoTvU8KE4R7YQyG92RkSNHgjGG5ORkJCUlISkpCVOmTIG1tTXOnj2LjIwMnD17FmKxGPPmzYOdnR1+//133Lx5E9euXYOHh0e73B4I/TF69GiNu4621pAI6J47igRB9CwoTvU8KE4RuoYWGz2El156CadOnVJ6LCoqCo899hgyMjLw2GOPISoqCgBw8uRJZGRkICMjA3v27MGrr77aGUMmFNixYweCgoIwb948Ptjm5+fDxcWFf41iQyIAfLHcuXPnlFLbq1evxm+//QZvb2/89ttvWL16NQBgypQp8PDwgJeXFxYuXIhPPvnkwX5IgiAeaihOdW8oThHtpeeKDh8yRo8ejZycHKXH2uP2QDx4Xn31Vbz33nt8Q6KVK1di//79bWpXuR1FVZw9e1blsTt37tTdwAmCILSA4lT3heIU0REos9GDKS4u5idmBwcH3LlzB0DbOxGEMqqK5drbkEgV1JCIIIiHFYpTuoHiFNGVocXGQ0hbOxGEMvpO/St2z/3hhx/4YPHEE0/gyJEjqK+vR3Z2NjIyMjB06FAdfjKCIIiuCcUp7aA4RXRlSEbVg7Gzs+PTzoWFhbC1tQVAOxHaosvU//PPP48LFy6gpKQEzs7OWLduHS5cuEANiQiCeCihOKUbKE4RXRlabPRgOLeH1atXt3B72LFjByIiIhATE0NuD+1A29Q/99rDhw+3eK/58+erPc+aNWuwZs0aXQ6dIAiiy0BxSn9QnCK6CrTY6CGo2olYvXo1pk+fjn379sHV1RXffvstgCa3hxMnTsDLywumpqY4cOBAJ49ev7i5ucHc3BwGBgYwNDREfHw8SktLMWPGDOTk5MDNzQ1Hjx6FSCTq8Lko9U8QBKEailPqoThF9GRosdFDULUTAWjv9nDq1CksXboUMpkMCxYs4O3oujvnz59H3759+f9zWtbVq1cjKioKUVFR2LBhg8bvR6l/giAI7aA41ToUp4ieChWIEzwymQyvvfYaTp48idTUVBw+fBipqamdPSy9cPz4ccyZMwdAk5b1xx9/1Or47taQyM3NDe+//z7GjBmDPn36YMCAAUhOTsbhw4fh5eUFS0tLLFiwAFKptLOHShAEoRaKU5pDcYroMrTWXlzvjc2JLsXly5fZhAkT+P9/8MEH7IMPPujEEekGNzc3NnjwYBYcHMx2797NGGPM0tJS6TVWVlZqj4+IiGD29vbM0NCQOTk5sc8++4yVlJSwsWPHMi8vLzZ27FgmkUgYY4zJ5XK2ePFi5uHhwfr378/i4uL098G0oF+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" ] }, "metadata": { @@ -708,36 +777,46 @@ } ], "source": [ - "## 3D scatter plots of all grids and grids after dct\n", + "## 3D scatter plots of consumption function \n", + "## at all grids and grids after dct for both adjusters and non-adjusters\n", "\n", "## full grids \n", "mmgrid,kkgrid = np.meshgrid(mgrid,kgrid)\n", "\n", - "## rdc grids \n", + "## reduced grids \n", "\n", - "fig = plt.figure(figsize=(10,10))\n", - "fig.suptitle('Marginal utility at grid points of m and k(for different h)',fontsize=(13))\n", + "fig = plt.figure(figsize=(14,14))\n", + "fig.suptitle('Consumption at grid points of m and k(for different h)',\n", + " fontsize=(13))\n", "for hgrid_id in range(EX3SS['mpar']['nh']):\n", " ## prepare the grids \n", " hgrid_fix=hgrid_id\n", " fix_bool = mut_rdc_idx[2]==hgrid_fix # for a fixed h grid value \n", - " rdc_id = (mut_rdc_idx[0][fix_bool], mut_rdc_idx[1][fix_bool],mut_rdc_idx[2][fix_bool])\n", + " rdc_id = (mut_rdc_idx[0][fix_bool], \n", + " mut_rdc_idx[1][fix_bool],\n", + " mut_rdc_idx[2][fix_bool])\n", " mmgrid_rdc = mmgrid[rdc_id[0]].T[0]\n", " kkgrid_rdc = kkgrid[rdc_id[1]].T[0]\n", - " mut_rdc= mut_StE[rdc_id]\n", + " mut_n_rdc= mut_n_StE[rdc_id]\n", + " c_n_rdc = cn_StE[rdc_id]\n", + " c_a_rdc = ca_StE[rdc_id]\n", " \n", " ## plots \n", " ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d')\n", - " ax.scatter(mmgrid,kkgrid,mut_StE[:,:,hgrid_fix],label='StE(before dct)')\n", - " ax.scatter(mmgrid_rdc,kkgrid_rdc,mut_rdc,c='red',label='StE(after dct)')\n", - " ax.set_xlabel('m')\n", - " ax.set_ylabel('k')\n", - " ax.set_zlabel(r'$u^\\prime_c$')\n", + " ax.scatter(mmgrid_rdc,kkgrid_rdc,c_n_rdc,c='red',marker='o',\n", + " label='StE(after dct):non-adjuster')\n", + " ax.scatter(mmgrid_rdc,kkgrid_rdc,c_a_rdc,c='blue',marker='*',\n", + " label='StE(after dct):adjuster')\n", + " ax.scatter(mmgrid,kkgrid,cn_StE[:,:,hgrid_fix],c='gray',marker='.',\n", + " label='StE(before dct): non-adjuster')\n", + " ax.scatter(mmgrid,kkgrid,ca_StE[:,:,hgrid_fix],c='yellow',marker='.',\n", + " label='StE(before dct): adjuster')\n", + " ax.set_xlabel('m',fontsize=13)\n", + " ax.set_ylabel('k',fontsize=13)\n", + " ax.set_zlabel(r'$c(m,k)$',fontsize=13)\n", " ax.set_title(r'$h({})$'.format(hgrid_fix))\n", - " #ax.set_xlim(0, 200)\n", - " #ax.set_ylim(0, 400)\n", - " ax.view_init(40, 160)\n", - " ax.legend(loc=10)" + " ax.view_init(20, 240)\n", + "ax.legend(loc=7)" ] }, { diff --git a/HARK/BayerLuetticke/DCT-Copula-Illustration.py b/HARK/BayerLuetticke/DCT-Copula-Illustration.py index c07623923..bf4a3fa18 100644 --- a/HARK/BayerLuetticke/DCT-Copula-Illustration.py +++ b/HARK/BayerLuetticke/DCT-Copula-Illustration.py @@ -455,75 +455,136 @@ def do_dct(self, obj, mpar, level): hgrid_fix = EX3SS['mpar']['nh']//2 -mut_StE = EX3SS['mutil_c'] +xi = EX3SS['par']['xi'] + +invmutil = lambda x : (1./x)**(1./xi) + +### convert marginal utilities back to consumption function +mut_StE = EX3SS['mutil_c'] +mut_n_StE = EX3SS['mutil_c_n'] # marginal utility of non-adjusters +mut_a_StE = EX3SS['mutil_c_a'] # marginal utility of adjusters + +c_StE = invmutil(mut_StE) +cn_StE = invmutil(mut_n_StE) +ca_StE = invmutil(mut_a_StE) + + +### grid values dim_StE = mut_StE.shape mgrid = EX3SS['grid']['m'] kgrid = EX3SS['grid']['k'] hgrid = EX3SS['grid']['h'] +## indexMUdct is one dimension, needs to be unraveled to 3 dimensions + mut_rdc_idx = np.unravel_index(SR['indexMUdct'],dim_StE,order='F') +## these are filtered indices for the fixed grids of other two states + mgrid_rdc = mut_rdc_idx[0][(mut_rdc_idx[1]==kgrid_fix) & (mut_rdc_idx[2]==hgrid_fix)] kgrid_rdc = mut_rdc_idx[1][(mut_rdc_idx[0]==mgrid_fix) & (mut_rdc_idx[2]==hgrid_fix)] hgrid_rdc = mut_rdc_idx[2][(mut_rdc_idx[0]==mgrid_fix) & (mut_rdc_idx[1]==kgrid_fix)] -## compare marginal utility before and after dct -plt.figure(figsize=(15,5)) -plt.title('Marginal utility of consumption at grid points of states') +# %% {"code_folding": [0]} +## 2D graph: compare consumption function before and after dct +fig=plt.figure(figsize=(15,8)) +fig.suptitle('Consumption at grid points of states') -plt.subplot(1,3,1) -plt.plot(mgrid,mut_StE[:,kgrid_fix,hgrid_fix],'x',label='StE(before dct)') -plt.plot(mgrid[mgrid_rdc],mut_StE[mgrid_rdc,kgrid_fix,hgrid_fix],'r*',label='StE(after dct)') +## for non-adjusters +#c_n(m) +plt.subplot(2,3,1) +plt.plot(mgrid,cn_StE[:,kgrid_fix,hgrid_fix],'x',label='StE(before dct)') +plt.plot(mgrid[mgrid_rdc],cn_StE[mgrid_rdc,kgrid_fix,hgrid_fix],'r*',label='StE(after dct)') plt.xlabel('m',size=15) -plt.ylabel(r'$u_c^\prime$',size=15) +plt.ylabel(r'$c_n(m)$',size=15) plt.legend() -plt.subplot(1,3,2) -plt.plot(kgrid,mut_StE[mgrid_fix,:,hgrid_fix],'x',label='StE(before dct)') -plt.plot(kgrid[kgrid_rdc],mut_StE[mgrid_fix,kgrid_rdc,hgrid_fix],'r*',label='StE(after dct)') +## c_n(k) +plt.subplot(2,3,2) +plt.plot(kgrid,cn_StE[mgrid_fix,:,hgrid_fix],'x',label='StE(before dct)') +plt.plot(kgrid[kgrid_rdc],cn_StE[mgrid_fix,kgrid_rdc,hgrid_fix],'r*',label='StE(after dct)') plt.xlabel('k',size=15) -plt.ylabel(r'$u_c^\prime$',size=15) +plt.ylabel(r'$c_n(k)$',size=15) plt.legend() -plt.subplot(1,3,3) -plt.plot(hgrid,mut_StE[mgrid_fix,kgrid_fix,:],'x',label='StE(before dct)') -plt.plot(hgrid[hgrid_rdc],mut_StE[mgrid_fix,kgrid_fix,hgrid_rdc],'r*',label='StE(after dct)') +## c_n(h) + +plt.subplot(2,3,3) +plt.plot(hgrid,cn_StE[mgrid_fix,kgrid_fix,:],'x',label='StE(before dct)') +plt.plot(hgrid[hgrid_rdc],cn_StE[mgrid_fix,kgrid_fix,hgrid_rdc],'r*',label='StE(after dct)') plt.xlabel('h',size=15) -plt.ylabel(r'$u_c^\prime$',size=15) +plt.ylabel(r'$c_n(h)$',size=15) plt.legend() -# %% {"code_folding": [0]} -## 3D scatter plots of all grids and grids after dct + +### for adjusters +## c_a(m) +plt.subplot(2,3,4) +plt.plot(mgrid,ca_StE[:,kgrid_fix,hgrid_fix],'x',label='StE(before dct)') +plt.plot(mgrid[mgrid_rdc],ca_StE[mgrid_rdc,kgrid_fix,hgrid_fix],'r*',label='StE(after dct)') +plt.xlabel('m',size=15) +plt.ylabel(r'$c_a(m)$',size=15) +plt.legend() + +## c_a(k) +plt.subplot(2,3,5) +plt.plot(kgrid,ca_StE[mgrid_fix,:,hgrid_fix],'x',label='StE(before dct)') +plt.plot(kgrid[kgrid_rdc],ca_StE[mgrid_fix,kgrid_rdc,hgrid_fix],'r*',label='StE(after dct)') +plt.xlabel('k',size=15) +plt.ylabel(r'$c_a(k)$',size=15) +plt.legend() + + +## c_a(h) +plt.subplot(2,3,6) +plt.plot(hgrid,ca_StE[mgrid_fix,kgrid_fix,:],'x',label='StE(before dct)') +plt.plot(hgrid[hgrid_rdc],ca_StE[mgrid_fix,kgrid_fix,hgrid_rdc],'r*',label='StE(after dct)') +plt.xlabel('h',size=15) +plt.ylabel(r'$c_a(h)$',size=15) +plt.legend() + +# %% {"code_folding": []} +## 3D scatter plots of consumption function +## at all grids and grids after dct for both adjusters and non-adjusters ## full grids mmgrid,kkgrid = np.meshgrid(mgrid,kgrid) -## rdc grids +## reduced grids -fig = plt.figure(figsize=(10,10)) -fig.suptitle('Marginal utility at grid points of m and k(for different h)',fontsize=(13)) +fig = plt.figure(figsize=(14,14)) +fig.suptitle('Consumption at grid points of m and k(for different h)', + fontsize=(13)) for hgrid_id in range(EX3SS['mpar']['nh']): ## prepare the grids hgrid_fix=hgrid_id fix_bool = mut_rdc_idx[2]==hgrid_fix # for a fixed h grid value - rdc_id = (mut_rdc_idx[0][fix_bool], mut_rdc_idx[1][fix_bool],mut_rdc_idx[2][fix_bool]) + rdc_id = (mut_rdc_idx[0][fix_bool], + mut_rdc_idx[1][fix_bool], + mut_rdc_idx[2][fix_bool]) mmgrid_rdc = mmgrid[rdc_id[0]].T[0] kkgrid_rdc = kkgrid[rdc_id[1]].T[0] - mut_rdc= mut_StE[rdc_id] + mut_n_rdc= mut_n_StE[rdc_id] + c_n_rdc = cn_StE[rdc_id] + c_a_rdc = ca_StE[rdc_id] ## plots ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d') - ax.scatter(mmgrid,kkgrid,mut_StE[:,:,hgrid_fix],label='StE(before dct)') - ax.scatter(mmgrid_rdc,kkgrid_rdc,mut_rdc,c='red',label='StE(after dct)') - ax.set_xlabel('m') - ax.set_ylabel('k') - ax.set_zlabel(r'$u^\prime_c$') + ax.scatter(mmgrid_rdc,kkgrid_rdc,c_n_rdc,c='red',marker='o', + label='StE(after dct):non-adjuster') + ax.scatter(mmgrid_rdc,kkgrid_rdc,c_a_rdc,c='blue',marker='*', + label='StE(after dct):adjuster') + ax.scatter(mmgrid,kkgrid,cn_StE[:,:,hgrid_fix],c='gray',marker='.', + label='StE(before dct): non-adjuster') + ax.scatter(mmgrid,kkgrid,ca_StE[:,:,hgrid_fix],c='yellow',marker='.', + label='StE(before dct): adjuster') + ax.set_xlabel('m',fontsize=13) + ax.set_ylabel('k',fontsize=13) + ax.set_zlabel(r'$c(m,k)$',fontsize=13) ax.set_title(r'$h({})$'.format(hgrid_fix)) - #ax.set_xlim(0, 200) - #ax.set_ylim(0, 400) - ax.view_init(40, 160) - ax.legend(loc=10) + ax.view_init(20, 240) +ax.legend(loc=7) # %% [markdown] # #### Observation From 643f4857517f612e84257a6bd60acafcda5fa3e6 Mon Sep 17 00:00:00 2001 From: Tao Wang Date: Thu, 6 Jun 2019 23:29:38 -0400 Subject: [PATCH 72/77] include graphs of consumption for adjusters and non-adjusters before/after dct --- .../DCT-Copula-Illustration.ipynb | 187 +++++++++++++++--- .../BayerLuetticke/DCT-Copula-Illustration.py | 111 ++++++++--- 2 files changed, 244 insertions(+), 54 deletions(-) diff --git a/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb b/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb index 81c142d8a..39a38f339 100644 --- a/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb +++ b/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb @@ -589,17 +589,13 @@ "\n", "#### Policy/value functions\n", "\n", - "- Taking marginal utility as an example, one can plot its values at different grid points in both 2-dimensional and 3-dimensional spaces before and after dimension reduction. \n", - " - 2-dimensional graph: marginal utility at different grid points of a state variable fixing the values of other two state variables. \n", - " - For example, how the reduction works for liquid assets for given level of illiquid assets holding and productivity. \n", + "- Taking consumption function as an example, let us plot consumptions by adjusters and non-adjusters at different grid points before and after dimension reduction. \n", + " - 2-dimensional graph: consumption at different grid points of a state variable fixing the values of other two state variables. \n", + " - For example, consumption at each grid of liquid assets given fixed level of illiquid assets holding and productivity. \n", "\n", - " - 3-dimensional graph: marginal utility at different grids points at grid points of liquid and illiquid assets with only value of productivity fixed. \n", - " - There is limitations at 1-dimensional graph, as we do not know ex ante at what grid points the dimension is reduced. So the 3-dimensional graph gives us a more complete picture. \n", - " - In this context, as we only have 4 grid points for productivity, we can fix an arbitrary one of the 4 grids and focus on how the number of grids is reduced for liquid and illiquid assets.\n", - " \n", - "#### Marginal distributions\n", - "\n", - "- We can also graphically show marginal distributions versus joint distribution. " + " - 3-dimensional graph: consumption at different grids points of liquid and illiquid assets with only value of productivity fixed. \n", + " - There is limitations at 1-dimensional graph, as we do not know ex ante at what grid points the dimension is reduced the most. So the 3-dimensional graph gives us a more straightforward picture. \n", + " - In this context, as we only have 4 grid points for productivity, we can fix grid of productivity and focus on how the number of grids is reduced for liquid and illiquid assets." ] }, { @@ -618,9 +614,9 @@ "### In 2D, we can look at how the number of grid points of \n", "### one state is redcued at given grid values of other states. \n", "\n", - "mgrid_fix = EX3SS['mpar']['nm']//11 # \"//\" is for floor division unambiguously \n", - "kgrid_fix = EX3SS['mpar']['nk']//11\n", - "hgrid_fix = EX3SS['mpar']['nh']//2\n", + "mgrid_fix = 0 ## these are or arbitrary grid points.\n", + "kgrid_fix = 0\n", + "hgrid_fix = 2\n", "\n", "\n", "xi = EX3SS['par']['xi']\n", @@ -666,7 +662,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -675,7 +671,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -688,6 +684,8 @@ ], "source": [ "## 2D graph: compare consumption function before and after dct \n", + "\n", + "\n", "fig=plt.figure(figsize=(15,8))\n", "fig.suptitle('Consumption at grid points of states')\n", "\n", @@ -736,7 +734,6 @@ "plt.ylabel(r'$c_a(k)$',size=15)\n", "plt.legend()\n", "\n", - "\n", "## c_a(h)\n", "plt.subplot(2,3,6)\n", "plt.plot(hgrid,ca_StE[mgrid_fix,kgrid_fix,:],'x',label='StE(before dct)')\n", @@ -750,13 +747,15 @@ "cell_type": "code", "execution_count": 13, "metadata": { - "code_folding": [] + "code_folding": [ + 0 + ] }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -765,7 +764,7 @@ }, { "data": { - "image/png": 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iMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMoW9yP1yU0pBRESFiK0ugIWxnSIi2np52ymObBARERERkSkYbBARERERkSkYbBARERERkSkYbBARERERkSkYbBARERERkSkYbBARERERkSkYbBARERERkSkYbBARERERkSkYbBARERERkSkYbBARERERkSkYbBARERERkSkYbNC21dvbixdffDHnfX/8x3+Mv/iLv9B1njvvvBMvvfSSkUUjIiJiO0UEBhu0TS0tLeHy5cu48cYbN9w3NzeHL3/5y3j44YfV2xYXF3HffffB4/Ggv78fX/va19T7PvKRj+Cxxx7blHITEdHOUGo79fnPfx633347nE4nHnjggYzj2U7RdsZgg7al06dPw+/3w+12b7jvySefxD333IO6ujr1tve///1wOByYnZ3FV7/6Vbzvfe9Te4nuvfde/PjHP8aVK1c2rfxERFTdSm2nfD4fHnnkETz44IMbjmc7RdsZgw3alk6dOoXBwUF88IMfRHt7O3w+H77//e8DAL73ve/hF37hF9Rjw+Ewvv3tb+MTn/gEvF4vXve61+Hee+/F3/3d3wEAXC4XDh48iGeffXZLXgsREVWfUtopAHjb296Gt771rWhtbd1wLrZTtJ0x2KBt6dSpU3jhhRdwzz33YHZ2Fg8//DA+85nPAEj1Jt1www3qsRcuXEBNTQ12796t3nbrrbdmzH/ds2cPTp48uXkvgIiIqlop7ZQebKdou2KwQdvS6dOn8bGPfQx33303bDYb9u7dq963vLyM+vp69fdQKITGxsaMxzc2NiIYDKq/19fXY3l52fyCExHRjlBKO6UH2ynarhhs0LYjpcSZM2fw5je/Wb3tzJkzakXe3NycEUh4vV6srKxknGNlZSWjog8Gg2hqajK55EREtBOU2k7pwXaKtisGG7TtjI2NAQCuu+469bahoSHs378fAHDLLbfgwoUL6n27d+9GPB7H8PCwetvJkydx0003qb+fPXsWt956q9lFJyKiHaDUdkoPtlO0XTHYoG3n1KlTuPnmmyGEUG8bGhpSK+F77rkHP/nJT9T7PB4P3va2t+Gxxx5DOBzGz3/+czz11FP4rd/6LQDA+vo6jh8/jje+8Y2b+0KIiKgqldpOAUA8HkckEkEikUAikUAkEkE8HgfAdoq2NwYbtO2cPn06o3dnYWEBMzMz2LdvHwDgXe96F5555hmsra2px/zlX/4l1tbW0NHRgfvvvx9/9Vd/pY5sfPe738XrX/96+Hy+zX0hRERUlcpppz75yU+irq4On/70p/GVr3wFdXV1+OQnPwmA7RRtb0JKWej+gncSWdWf/MmfoKOjA7//+79f9NhXvepV+NKXvqQ2AkQWJIofsmOxnaJtie0UVZm87RSDDSIi62OwkR/bKSKirZe3neI0KiIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDSIiIiIiMgWDDaItIqVEMpnc6mIQERHlxXaKKmXf6gIQVTsppRpYJJNJxONxJBIJJBIJSCnh9XpRU1MDm42xPxERbb7sdkppoxKJBJLJJNxuN2pra9lOUVkYbBAZpFBFnU0IASEEEokE4vE44vE4bDYbamtr1fuIiIiMlN1OadurbNp2SjnWZrPBbrfDZrOxnSLdGGwQlSC790cZqVD+DwBXrlyBEAJdXV0QQuStlJXjbTabes719XXYbDbU1NSgpqaGlTkREZVE205JKXN2fi0uLiIUCqGvr69gO6VQAo9kMoloNAohBOx2O9sp0oXBBlEO2QGFtqKWUmYcq1TC2spaCIGamhrdz6ecQ2kkzp49i/7+frjdblbmRES0Qb4putp2Skqpti+5gopy2ykAGBkZQXt7OxoaGmC329lOUV4MNmhH01bU0WgU8Xhc7b3RHpNdWZtFqaxXVlYgpUQsFkM8HkdNTQ0rcyKiHUjbTsViMUSjUdhstoJTdM2cjqucNxwOo7m5WZ0KrIx0MK+DsjHYoKpXKPFNO0oxOzuLWCyG3t7eiuajZo98lMtms6lTrFiZExFVLz1TdAFgaWkJCwsLuP766zclb0J57nzPk6ud0naOsYOMAAYbVEXyTX0qlPimrQyVituIC/lKK1htwKIdumYyORHR9lXpFF2ls6nSdqqUTrF87Uy+dkp5Tcw/JAWDDdp2skcpQqEQAMBut2cck2+O6naRXWZtZc5kciIi68pup1ZXVxGPx+FwODKO2awpurkY0SlWqJ3STgVmMvnOxmCDLKmUxLfLly/D4/Ggvb3dEhWZEdOoCp0juzIfGhrCvn37UFtby8qciGiT6J2iCwBzc3OIx+Po7e3dEXW0drEUKSVeeuklDAwMwO12M/9wB2KwQVuqnDW/s4d0tZWaVegpSyKRQCwWy3lfrh6jfM+hjOzEYjHEYjHY7XZW5kREBql0im727VZgVKeYntcjhEA4HGb+4Q7GYINMly/xbXZ2Fi0tLWplVQ1Tn3KJRqMIh8NYXV1V/1VWEwEAl8sFv9+P5ubmsp9DORcrcyKi8uQapZibm0N9fb26RGw1tVO5yp5IJDLaq3A4jGg0it7eXuzataui9kR5z5hMvvMw2CDDaIOK7E2EciW+TUxMoK2tzTIXw5X09EgpEYlEsLq6iuXlZcTjcUxPTyORSKC2thYejwdutxvt7e1wu91wOBxqIl08HsfY2BguXLgAv9+P9vZ23T1G2ZhMTkSUX6l7U0xPT+P666/PyLWoBvF4HGtra5iens7oBKupqYHb7YbH40FjYyO6uroAAAsLCzh06BC6u7vR29uL2traktop7bFMJt95GGxQyfLNUS205neugMKKFUqxMiWTSaytrak9Pqurq1hdXYWUEi6XC263G0IINDc3o7e3NyNpPZvSsDU2NmL//v1YXV3F+Pg4RkZGEIvFKgp+mExORDuZEVN0lfuMsBV1rrYTTDtaoeSU1NTUwOv1ZnSCZZdTCciuu+46+P1+TE9P4+jRo2htbUUikSgr2FAwmXznYLBBOeVLfAsGg1hfX0dTU1PG8dUwpKwVj8c3VNCRSARCCLjdbrXnR6mktcHUxMQEXC5XwUAjF7fbjb179yIajeLnP/85Dh8+jF27dqGnpwe1tbVlvY7syvzll19Gf38/PB4PK3Mi2tbyTdGNRCK4du0aWltb1eOsMPXJqD2Ysmk7wZT2am1tDclkUu0E83g88Pl8aoL2zMwMotEoenp6dD9PTU0N+vr60NPTg9nZWUxPT+P8+fO47rrrUF9fX/Tx+d737GTykZERtLW1obGxkfmHVYLBxg5XauJbdiVuBUoFVSqlJ0WpoK9evYr19XVcuXJlw1Cyz+eDy+XSVenlK0s8HkMiEYPT6S74eIfDAZfLhdtvvx2XL1/G0aNH0dbWhv7+frhcrpJfJ5CZTK68biaTE9F2UOoU3Wg0ivn5eXR0dFT83OW2L2aIx+PqqPrFixczOsHq6urg8Xjg8XjQ1ta2oRNMr0Qihnh8HQ6HJ2+7YLPZ0N3djStXrqCzsxPnz5+HEELNPSy2J0chQgh1mWDmH1YPBhs7hJ6pT3p6f4y8KN2sSlwZSs5O0tbmU3g8HtTX16OpqQkDAwMVv87sx6+vL2Fx8W8BBFFX91Y0Nd1ctMx2ux39/f3o7e3F7OwshoaG4PV64ff74fV6yyqXlLLgjq+szIloq1TzFN1S5FtURJkGm0wmS+4Ey5ar7Y3Fwpif/1sIMYfa2jegtfV1Rc/T3NwMn8+HlZUVjI2NYXh4GAMDA+jo6MgoV6n5HbnaKeYfbl8MNqpIoTW/L126hN7eXvXYSoaUrdLLky2ZTKo5FLnyKZQk7V27dqlDyVpXrlxBPB43pRKLxaYgxAKABsRiQwBuLvo+KuVQepG6urqwsLCAs2fPoqampqwVrPQk6SlBBytzIjJaoXZqcnJyw7SectopK41G5JOdT5GrE0xZVMTj8agX2YuLi1hcXERbW1vFZch+T+PxqxBiBkK0Ih5/EUDhYEPbnjQ0NODWW2/F2toaxsfHcfHiRfT29sLn86GmpsaQZHLmH25fDDa2oXIS365evYqBgYGKn9voP+xyGgRtPkU4HMb8/DySySQuXbqUkU/R0dGBuro6S/TWu1y9WF9vRzK5gvr6g2WdQwiBtrY2tLW1qb1IFy5cUJPJy/1ssvM6otEohBBM0iOispWzN8Xs7Cz6+/u3oLT5VRq4KPkUKysrWF1dRTAYLKkTbDM5nd1wOHoQj8/C47m7rHPU1dVhz549iEajuHTpEg4dOgSfz4dkMslk8h2MwYZF5Up8K3fqE2DsihpG9RgVKpNy0ZudpB2LxVBTU6NW0ErPvtfrVZfo22q5Kkq7vQmtrb8HIA6gvLwLLaUXaXV1FYcPH8ahQ4fQ19cHn89XMLgqFJRk33716lVIKdHR0cG8DiLKyagpuoA12ym9ii0qkkwm4XA40NfXV3Y+hdlsNhdaWh4CEAPgLHp8ofbE4XBkrGAVCoVw/vx5+P1+XbmHepPJFxcXEQ6H0dPTw3bKwhhsbLFCa35PTEygr68vo6Le6tU0jCal3LCKhjKU7HA4MlZ9GhgYyLnWeTgc3ibvhx1G/8m53W41mfzSpUt4/vnn4fP51HXQs5WyM/nq6qr6nWSSHtHOVWjq08TEBHp7ezdMe6m2dkr5V7uoiDK6rnSCFVpUZHZ2Fuvr62Xn2xktf1tgg55AQ1HsM1ZWsJqcnERTUxNOnDgBt9sNv9+fdwWrUpLJo9EogsEg2ymLY7CxScpJfJubm4Pf79+C0uZXbo+Rkk+hraSDwSCGhoZQV1enVtJNTU1lDSVbfX6u2bS9SFNTU+o66AMDAxm9SEYk6XHHV6LqVM4U3cXFRfT19ak7bFtBpSMb2nyK1dVVjI2NIRqNblhUROkEU/Ip9Jy3mpTyeoQQ6OrqQldXFxYXFwuuYMVk8urDYMNA+db8Vv6vPW4zh5SNVKwSV5bm0wYWkUgENpstY2m+jo4ORKNR3HzzzWXvIWFVW9mg1NTUZKxgdeLECXg8HnUFq3IqcYA7vhJVCzOm6FrtIlpvmfQsKmKz2dDe3o62trYtzaewqlIT9wFZIom1AAAgAElEQVSgtbUVra2tGbmHAwMD6OzsVD87JpNXF/7llKFQRa1MfVJU65Dy+vr6hqBC2bdBGaVobm5GT08PnE5nztdeLe9HLlv92rQrWC0uLuLcuXMQQiAWi+k+B5P0iLavzZqia8VgI1u+fAqlE6zQJq1nz56Fx+OpukDDiM+s0nNkr2A1OjqK3t5eJpNXoer66zFYqYlvQgjMz89bbupTuZR8Cm0lvbKygvX1dUQiETVJu7OzE263O2c+RSFWa6SsVBajCCEyepGOHj2KY8eOYSDHOujZ9CSTK59hLBbD1NQUdu3axSQ9ok201VN0ja7Hy11ZT5tPce3aNYRCIQwNDW3Ip2hqaqpof4pqUunrr2QVRC3tClaTk5NYXFzEpUuXEAgEis58KLWdmp6eRldXFxwOx47//DfTjg82CiW+5dqZtNpGKQAgkUioSdpKULG2tgYpZUavT0tLC+LxOGZmZrBnz56tLrYpjPhcS53HulkaGhrgdrtx8803Y2JiYsM66Nn0NiTK38XY2Bg6OzuZpEdkMCtP0d3sYEPJpyi0qIjT6YTD4cBNN92kO5/CbFYog9U5HA4MDg5icXERtbW1au5hf38/6urqcj6m1HZqYmICra2tSCaT3Mx2E+2YYCNf4lswGMTa2hpaW1vVY7UjFVtdQRjVcwAAsVhsw1CyMqdR2Z+ivr4enZ2defenCAaDhpQFqO7K18qvze12b1gHvbu7G319fRm9SOV895hMTlS+fFN0I5EIFhcX0dnZqR5rlc4vI4MN7euoZJPWaDSKpaWlkkfb85XJqNdnpdHzUur3fMcZeX2SraenB36/H7Ozszh58mTeFaxKLYMSZAghuJntJqq6YCMajSIej6O2tlbX1KdYLIZr166ho6NjC0udW6mJUsAr+1NkL80XDodx+vTpjP0pCuVTFCuTUYw4FyuH8mSvg57di1RuQ8JkcqLC4vE4otGo7nYqkUhgcXERPp/PsDIYdaFYaZugzadYX1/H6dOn1U4w7ch6KZu0Wm2KrlUZ9fmbQfnud3V1obOzM+8KVuVcI2k7k5UAn5vZmqvqgo0nnngCa2treOCBB3Qlvik9sVZUqMJMJpM5h5KTySScTqdaQXd2dsLj8eDEiRO47bbbNvkVbB6rfobbgbIOurKCldKLZLPZ0NDQUPZ5maRHlNu//uu/4rnnnsPHPvYxAMVHKYxup8q5SCt0rlz5IVrafIpCm7Ta7XYMDg7C4/FYpn6wSjkUVmrrzCpL9nczO/dwfHxcXcGqlGRy7fm0/9deayntlHZUnipXdcGG0+lEMBjUvXKEzWYrWlFuFSGE2uujDSjW1tYAIGMouaWlBW632/S1zo0eMrdSxWkUM4eWzZTdi3TmzBn1byl7HfRSz6v8q1x0RKNRAKnEwO34XhFVwuFwIJFI6K6vjW6nlADBiLnq2npcTz5FoU1aFxYWSh5tL1YmI1itnbJSnWlGWQq93w0NDbjlllvUFaxmZ2fR3NyMXbt2VXT9k91OxeNxxGIxSCnhdrst9Z5vR1UZbCgXMnpY5YJXm0+hVNDBYBCnTp1SA4r6+np0dXXB5XIxoYkAmNMIKr1IXV1dqKurw9TUFIaHh9Hf36+ug17JuYUQCIfDuHDhAm699VYmk9OO43Q6sb6+rvt4o4ONSkdKsvMpRkZG1GW1XS5X2Zu0GtUe88KwOCM6xTZrZCMXZQUrp9OJ5eXlvLmH5dBOXzx27Bhe9apXMf+wQjs+2NjMkQ1lf4rsJG1laokSVLS2tqKvrw9nz57F3r17DUlyM0q19xhtR2ZVfFJKeDwe9PX1bVgHPd8KVqWcW5kywh1faacpNdgwut7Vez49m7TW1taiu7sb7e3tFXcYWDEZ2yodklZl1siG3vPabDZ0dnaiq6tLzT1saWnBwMBA3hWsSimH0k4xmbwyVRdsOByOLR/ZSCQS6qZ32lU0svMpurq64Ha780bhVswnMWvlkWqymZ+Zmc+lrfCz10GvtBcpX5Ied3ylncDhcJS0waYZIxvK+ZRFRbI7wbSbtCqLiuzatWvD/hTr6+uWG223YoDAuky/cnYQz5d7mGsFq1LLkZ1/yGTy0lVdsLGZIxuJRGJDBR0Oh3H8+HG1gvZ4PGhtbS0rn0JP4l0ptmsuwXak531OJpMVN4hmfqa5zq2sgz4wMJDRi9Tf31/RuZlMTjvJVoxsaDdpXVtbw/DwMKLRqJpPoYysd3R0lLRJ607I47PaaIsRrFSWXEoNNrSP0+YeXrhwAQDUFaxKkevcyr/adorJ5MVVZbBh9FxYZRUNbWCh3Z/C4/Go+RTr6+s4cOCAIYnaZlTiVkq82ynzc5X5zdpliNfW1tRgsqOjAwMDA3A6nSWfe7ODDUV2L9KpU6ewtraGYDCoqxcpX3Jqrspc6WFlZU7VwszcQm19o/ybvUlrTU0NOjs70dLSojufwoiybda5OAKvT77Xpu1IDQaDSCQS6iphVpOvndKuYBUMBjE2NoYLFy6oSd96OwTzjdhpO8iUqcDMP8yv6oKNcqdRKfkU2UGFNp9CGaXo6+vLu9W9kVOfrFqJW5EVemm0SZNXrlzB1NSUGlRkB6Uul0v93i0vL+P48eNoamqC3++veJ6pUfRUyEovUltbGw4fPqz2Ig0MDKClpSXv4/WeW/neBoNBzM3NIRAIsDKnba/UdioXPfkUXq835/4UL730UkmJ24Wwndq+kskkgsFgRkeY8h1S2qzGxkYkk0mcOXMGLpcLgUCg7GlJZtDTltTX16srWB06dAjPP/88ent7i65gVWo7FYlEMD4+jhtuuIHJ5FmqLtgo1mOUTCbVoWTlj2tlZQXHjh2D0+lUh5K7u7sL5lPkY+TcWitW4oA1Luy3UqGRCrfbjVgshsbGRrWRz1fZxONxCCHg8/nQ3d2Nq1ev4uTJk/B4PEgkErrKslUjG7nU1tbi4MGDai/S8PAwBgYGcq5gVerutfF4HMFgkMnkVBX0jsBrN2mNRqM4f/48VldXM/IpPB5PyZu0VnunmNGsWKZSaEcqtNc9NTU1aGxshNfrRWNjI3w+34acHGWzyb6+PnVjPZvNhsHBwS18Ra8opS2pq6uDy+XC7bffjsnJSRw+fBhdXV15cw+VBHE9lO/uysoKN7PNYdOCjQcffBBPP/00Ojo6cObMGQDA29/+dpw/fx4AsLy8jKamJpw4cWLDYwcGBlBfX6/Oi3vhhRfyPo9Sia+srKhzVLW9PgAydiVtaWnB6uoq7rjjDkNep5UrXisOT1upEs8uS7GgQjtSoQ0qLly4oC75qJcQAp2dnejo6MD8/DxmZmZw4sQJDA4OFuxFskqwoa2Utb1I4+PjuHjx4oZepFLX+E8mk+qIBpPJabvL7hTT5lNoFxXR5lModUQp+RT5WLUet1qbAGyvUZJcQYV2pMLr9aKpqQk+nw+zs7Ooq6tDZ2en7vO3tLSgpaUF165dw8WLFxEOh7G4uFjRPkyVKqcNzM49PHbsGJqbmzGQtYJVqRsGKu2a0k4x//AVmxZsPPDAA/jABz6Ad73rXept3/zmN9X/f/jDH0ZjY2Pex//4xz9GW1tbzvvi8TieeOIJnD17FsePH8fIyAjuvvtufO5zn0N3dzcaGxvR3d2ds5fZ6IrNqhXvTv2C66EEFSsrK4hGo1haWtIVVJhBCIH29nZ1yVmlFykQCKCpqcm0582lnBVBtJQVrGKxGC5duqSuYNXb21tyA6E9nsnkZBazO8UikQiGh4dx8uRJrKys4L777sM73/lO9PX1ZexPkWuTVuW5jWDVdgrY/qMIm0GZ/hQKhdTgQgkqlCnfTU1N2LVrV97RrkrqycbGRtx222342c9+hsnJSQwPDyMQCKCtrW3T699KOtyKrWBVaTul/Mtk8k0MNu666y6Mj4/nvE9Kib//+7/Hj370o7LOXVNTg2QyiTe/+c14xzvegccffxxf//rXdT3W6A+82qdRWbHnSa9iIxWJRAIulwu9vb0VBRVGjTZoe5FGR0cRj8cRCAQyciGsMrJRqAeotrZW7UW6fPkyXnjhBTidzpJyU3INZzOZnIxmZqcYkApcGhoacOONN0JKiT/90z/dkr2UrNxOWc1W5pEoIxXaoCIUCiGZTCIej+sKKvIx4jXV1NTg1ltvRTgcxtjYGEZGRuD3+yve/LUURi18o13BSulc6OjoKGtkI9f5d3oyuSVyNn72s5+hs7MT119/fc77hRD4lV/5FQgh8PDDD+M973nPhvvf+973AgBmZ2crTryrhBUDBCPPZcUyZUskElhbW0MoFFKnJOgZqZiengaAkqY/bYbGxkYcOHAAoVAIo6OjaoXe3t5e8uhDKSod2chWU1OD3t5e9PT04Pz585iZmUEsFoPf70dDQ0PBxxYbzs5Vma+vr6OhoWHHVOZUOTM7xQDgqaeeUv//rW99C/v37y/7XJUwsu61auBiRflemzaoyE72zx6pCAaDWFlZMSRnwqiAwOPxYN++fVhbW8PY2BhGR0fR39+P7u5u0+tfI9tAITJXsDp//jxWVlYwMzOjK4AqVpZC7VS15x9aItj4+te/jvvvvz/v/T//+c/h8/lw9epVvPGNb8SNN96Iu+66K+expW6WZDSrXoxXYyWuVNDBYBDRaBTz8/MZ81PdbnfBKXTbjdfrxS233ILV1VWMjo7i4sWL8Pl8pj2f0cGGQgiBxsZG1NbWorm5GcPDw5BSwu/3513BSu/5lQo7mUxiaGgIr371q7njKxmi0k4xK1H+Row6F9spfZLJJFZWVjJG15XcMyWoKJbsHwqFLFuP1dXVYe/evVhfX8f4+DgOHTqk5uuZxax2qr6+HoODg5icnMTS0hJGR0fR09NTcAUrvbmI2qDjxRdfxGte85qqzz/c8mAjHo/jH/7hH3D8+PG8xygXVB0dHbjvvvtw9OjRvMFGqftsGM2qvTxWHNkA9PW2F0t6i8fjcLlc6rznrf5DrXRYt9B7okwXUpZfHh4extLSEqampuDz+QpWdJXMPzXyWOV4m82mThXTrmDV39+Pzs7OjNdSakK5cn7looo7vlKljOwUA8rrVTZq2iRXo9KvnDIlEomM9kr5UervclYQM1opr6nU8jmdTvT19aGrqwtzc3M4dOhQxvQhI5W7kIkeyWQStbW1eXMPs6c/lvr3qQQd2Xkd1dhObXmw8YMf/AA33ngjenp6ct4fDoeRTCZRX1+PcDiMZ599Fo899lje8211sGF0xWv0DuJWkv2HVCyoUNb8zl6eT5n+ZJX9Kcw0OzuDiYmfArDj+ut/EYODg4hGo1hdXc3oRcrV82J2sFFqJa49XlnBSlmnfHR0NOO1VBJsaP8md3qSHpXH6E4xoPT6WOnIsuqGsVY712bIFVTkG6kIBoMIBoMIBAJbXWyVWfXftWvXcPbszwDEMTj4WvT19eH555/H4cOH0d3dnXep2XJsRqcYkDv3MHsFq1LbKcVOSCbftGDj/vvvx3PPPYf5+Xn09PTg8ccfx7vf/W584xvf2NBbdPnyZTz00EN45plnMDs7i/vuuw9AqsJ/xzvegV/91V/N+zzKhclWsWrFa9SX1YgyKUHF2toapqamEIvFdAUVO0WhCjGZPI6enn+BlEAs1gWncz9qamqwe/du+P1+XLp0CYcPH4bP50Nvb29GL5LVRjZyHe9yuXDjjTciFothcnIShw4dQldXl7qnhl7ZlX52Za7tZauWypzMY3SnWDms2pFl1SlZRkokEoZMf9op1tfPw+f7Dmw2IBJxoqXlV+ByuXDHHXdgenoaR48eRXt7OwYGBipeHKGUjq5Sl7LN1U5pcw+zV7AyKlm9GpPJNy3YyLc61JNPPrnhNp/Ph2eeeQYAEAgEcPLkyZKeaysvHIycRmXVoW695yk2UpFIJODxeNDe3p4VVMRhgUG3spm5QlRHRw1WVjyw2ZKor7djbe2Vz0Lpeenv78fU1BSOHDmCzs5O9Pf3o7a21tRgo5xKvFDFWVtbi0AggP7+fly+fBkXL16E2+1GR0eHrgT+QuXJVZlre5AYeOxcm9UpBpTeTu2EtgXY2hH4XCMVwWAQdrsdjY2Nlpj+tB20tCRw7ZoDiYRAU1NCbUuUpWZ7enoyRgf8fj9cLldZz1XK96WSkY1sQryygtXS0hIuXLiA9fV1eDwe3c9TqOy52qntvJnt9r2iM5hRF4hWHtkwa/m+cqc/nTt3Dk1NTZrpT0mEw19DIvECamt/GXV1v1ZxebejQt9Fu/11aG0NA3BAylsBxDcca7fbMTAwgL6+PrUXqa2tDbt27bLMyIbe6SBKL1IymUQoFMLp06fhcrmKrmClZzhbW5knEglMT0+jubkZ9fX1TCbfoTazU6xUVg0QrDgCX0y+6U81NTUbdmW/fPky6uvr0d7erj4+Gv0RVld/ALv938Hp1NdOWa0+KXVRj1LY7Tejvf0uAFEkk69FPJ55v81mU5OtZ2ZmMDQ0hPr6egQCgZJXgzS7nSp2vBBCzT2cmJjA5OQkjhw5goGBgaIrWOkpj7adSiaTuHz5MlwuF5qamrZVXgeDDbxSWTLYKEwJKmKxmLp7qJHTn2KxBYTDP8X6eieczqfhcr0JQlQ+P9nKCvW+51YPKf+D+puUwbzH2mw2Ne9hZmYGJ0+eRCwWw9ramq78lq2YRlXo+MbGRuzduxfLy8sYHh5GMpncsO+I9ni9Q85KZb6wsACPx8NkctoUyvSjUr6n1T71yehRklKCikKb32nLlExGce3atxCJNMDlehrNza+F3d6sqzxWnCKmR+nl9iKZfGUkUMpY3ve2u7tbTSQ/ffo06urqkEgkSiqbWbmFpbZTTqcT3d3d8Pl8mJiYwMWLF4vmUZbaTi0tLamjbNspmZzBBkqv9AuphtWocm0kpA0qlAu/SnMqssskRAMikT44HJewtrYPgP4/QitV4pWWpdTHF3v/bTYbfD4fWlpa8OKLL+LEiRPwer0IBALweDwFy2Fmj1G5Cd/Nzc04ePAgQqEQxsbGcOHCBbUXSTlnqdO6lMco82KrNUmPrMPhcCAajeqePmL0NKp4dndzmawQbMTj8Q2j6+FwGCdOnNAdVBQqU+bvtVhb64XTOY5IpAuAtfZlsqJi7YMQAh0dHWhvb8fi4iJmZ2cxNDSEQCBQcBNNPecu91igvHZKCIG6ujo197DQClblXHdKKTe0U9thM1sGG9g5w9PZ5yq0kZDb7YbX60VTU9OGoOLYsWMFd8ktl93uRHv7hxAOz6KlpfQpP1ZiRJKYHqW+bqfTidtuuw3z8/M4c+YMnE4nBgcHUV9fn/PcVukxylUpe71e3HzzzYhEIpiYmFDXQe/p6SmrEtc+Jtd8WYfDYchKQETAKysn6g02tlPbYta5cgUV+UYqTp06hYMHDxpSruwytrV9ANeuXURzcz/sdqfhz7EZrNZmAq9squfxeOD3+zEyMgIpJQYHB9HcnHv0aLMTxIudX1uWYitYldspth3bKQYbqI7RiEISiQSi0SgWFhYwNzdX1koaWmbOz62r86CubmuWBjQzsTvf85Vye75jS+3VEUKgvb1d7UU6f/48bDYbAoEAmpqaMh6j99xmV+KFjne5XLjhhhsQCAQwOTmJw4cPo7GxseTvaa5GS1uZW7XHiLYnp9NZ0ga0RrdTVmzzAKgXTnqDikLTn4yQ6/W53fVwu7dm93cjmZWzka2cdqqpqQkHDx7EysoKRkdHMTw8jEAggNbW1oxzmZ0gbsTx+VawKrY3Vr7nKNROWVXVBhulJj8Z9SFtZYNQaM1vJeJta2uz1EoaVvvjsMp7YsawcK5jlcS2a9euYXR0FPF4XM2DMKvMQOnBiZ6RCmUFq4GBAQwPD2N6ehovv/wyBgYGDFvBisgoDoejpD2hjByBt8pofnZQMT8/j0QigampqYygQpl+wr/B7anUdkqroaEB+/fvV6fNjoyMwO/3o6OjQ/3u6b1gN2shE+3xhcoiROYKViMjIwiFQlhYWMiZe5jvObZjO1WVwYYyF9bp1De8ud1GNkrZSEgJKoaHh9HW1pZ3KLLUclF+m90LbkRg0tjYiAMHDiAYDGJ0dBQjIyOIxWIlLeFXag+QWdOubDYbWltbIaVES0sLTp8+DafTCb/fX3D+r1F5W0R6lLoBrVVHI/Scq9hIhdfrRXNzM2w2G1wul7pBohVYvce4XJv9miptp5Rps2traxgbG8PFixcxMDBQUsfVZoxs6GlDhEitYLV3716cO3cO09PTGB4eVnMPCz3ndm2nqjLYcDqdJQUbRle8RjUIUkqsra3hypUrZW8kpC2X1SpMK5bJCirpBar0vPX19bj11lvVHckPHz4Mv99vyBJ+lRxfTqJeTU0NOjo60NHRgaWlJVy8eBGJRAJ+v3/DUHw5z0FUCYfDsaXTqMwINvQGFflGKlZXV6u2M8uo12Vkm2lGR1K+c5RybKHnq6urw969exGJRDA+Po5r167h8uXL6O3tLVp/b0ZuYSk7o0sp4XQ61SBKzwpW27WdqspgQxnZ0GurK/F8FXQikYDdbofL5ap4IyFe2G8vmzWNKh+32426ujocOHAAY2NjGB0dRX9/P7q7u3NWdOVU4puZUN7c3Izm5maEQiGMj49jeHgY/f396OrqyljBajtW4rQ9lTOyYZXpvkqbFQqFsLS0hOXlZSwtLWUEFS0tLSVPf6r2dsrMfEcrM6OdcrlcuPHGG7G0tIT19XUcOnQIu3btQm9vb96pT5u1GlU558+1glVXVxf6+vpKWsHKqt+Nqgw2Sq3EjZ4Lm68SLzXpbX5+HqFQCH19fRWXy6qVuBFlstof12YufWv2lC2Xy4U9e/YgGo1ifHwchw4dytnrUk4OhpmVfr7ze71e7Nu3T13BamxsTN1cCrDed4mqVznt1GZ3immDCqXtUtosZXRd2Vxz7969Ff/9WLGdsmKZthszl6cFgOuuuw6BQEC9SPf5fOjr64PdnnmJu9mrUek5Pvv8elaw2o6dYlUbbJQysmH0XNhEIoGVlZWKVtJQzmX0Kh9WYuSF3XZ7bclkEuFwGE6nM+fSl2Zc9FYSmDgcDuzevRt+vx+XLl3C4cOH4fP50NvbC7vdvmWrfBQ6vlCFrF3BampqCocPH0Y0GkU0Gs3oRdJiIEJGKnU1KjPzLLRBhbIMenZQkW+kIhgMIhQKGfL3YWRbTIVV2lFVaqdYKcppG+x2OwKBAPr7+zE1NYUjR46go6MD/f39ap1utXaqUOCgXcHq6tWrOHXqFFwuF6LRKIMNq9isaVS5en3C4TCklFhfXy86P7UYoxsX2nxSSkQiEYTD4YyGHEhd8EYiETQ3NyMQCKhBh9lL31ZC6XXRVuidnZ2w2+0l9+hY4fja2lr4/X709/fjpz/9KV544QU0NTXpXsGKqFzlrEZV6YV4PB5HOBzGwsIClpeXceLECUSj0YygorW1VZ26oae+sHKnmFEjv1brzDJbPB5X26tgMAibzYbrrrtO954wuWxWO1VTU4P+/n709vbi8uXLOHbsGFpbWzEwMLApIxVGTycWQqCzs1PNPVQ25lVWjdwu13ZVGWwYPRdWqaCVn0K9PsFgENeuXcPg4GDFr2OzVwzZbFYsU7mkTO3kqVTQFy9exPr6OpLJJFwuFzweD7xeL9ra2uB2u2Gz2RCLxWCz2bCysoKhoSE0NjYiEAhs6tK35bLb7RgYGEBfXx+mp6dx8eJFeL1edHZ26lqYYTN6jEo53mazweFw4DWveQ3m5uZ0r2BFVC4zczay26xwOJwRVNjtdjgcDuzZs6fiJWWt2immlMuIqV3VKplMqiNT2bMwvF4vPB4P2tvbEY1G8eKLL6K5uRl+vx8ul8vUdqpUuc5ts9nQ09MDn8+HmZkZvPjii3A4HPB4PLrPa6V2SlnByuPxYPfu3ZiYmFBzDzs7Oy0/2lG1wUY5IxvFKmg9vT7KyIYRqj3Y2K6U74lSQYdCIcTjcdTW1sLr9cJms6GrqwstLS1F1+jW9lpcvXoVQ0NDcLvdSCQSusuzFcGGwmazobe3F4lEAqFQCMePH0dTUxP8fj/q6uoMK0s5PUylrAqiEELkXcHK5/NV9YUHbS4jNvUrt81SLir1rthYCNsp60smk1hbW8tot5aXl7GwsICGhoaCszDi8TiEEOjr68Ps7KwadAwMDJRUhq1qp2w2G3w+H7q7u3HhwgVcuXIFsVgMgUAAXq/X0LKYPRKilKmxsRG33HKLuiLX6OiomktpVVUZbBSbRpVdQc/Pz6tLkJU7lKywasXLSrx0iURCnR6nVNC5en38fn/Ghe3p06fh9Xp1bQakfLe0QcelS5cwMjKCl19+WddFu16xWAyRSMSUBDNlT4ubbroJs7OzOHHiBOrr6+H3+3P2JJm9ykepq13lol3BamZmxlJr/9P2V8o0qng8jkgkgtXVVfWisZLpT0bnKVqxnTLyXNul7VSmcGtHKpQOUCVftL6+Hl1dXZicnERnZyeampp0nVuIVzakm52dxdDQECKRCCKRSNHpVaW8f8lkEolEwvCgQwiBhoYG2O12NDY24uWXX4bD4UAgEFAXOchVFiu3U8qKXLFYDJOTkzhy5AjuuuuukjYi3CybGmw8+OCDePrpp9HR0YEzZ84AAD7+8Y/ji1/8Itrb2wEAn/rUp3DPPfdseOy//Mu/4IMf/CASiQQeeughfPSjH837PMrwdCQSwfr6etFen5qaGtTV1aG7u7vi17jVy+huxrmsWKZKSCmxurqKYDCIWCyGhYUFrK2twWazZawNvxk7rwsh0NbWhsXFRbS2tuLEiRNoaGhAIBDIGXTordwSiQROnjyJhYUFCCFw4MABQ8utlEPbIM3Pz+PMmTNwuVwIBAKor68vudwKo1ajKofX68X111/PUQ0yVK5pVIVGKmw2m7phZTkdYVpW2UHczHMZxai/e6NfWzQaVb8jSnCRSOIkTpQAACAASURBVCTgdDrVzrDW1lZ12m6u8pRDqePb2tpw6NChDdOrcimlvn/55Zdx4cIFOBwO3HLLLYYvJGOz2dDW1oa2tjYsLS1heHgYABAIBDZserxd2qna2loEAgH4/X5LBhrAJgcbDzzwAD7wgQ/gXe96V8btH/rQh/CRj3wk7+MSiQTe//734/vf/z56enpwxx134N5778XevXvVY6ampvDss8/ipZdewlNPPYVvf/vbOHjwIP7oj/6oaK/PTpj6ZMVK3CilDM8qwae250fp9YnH46irq0Nvby/q6urKrgSMWOXDZrNlTK/KF3Tofa719QgaGn6C/v4xLC4uQMr9hlfi2vMJIdDe3o729nYsLi7i/PnzsNlsGBwcRGNj45au8pHv/ETA5nSKKRuRzc3NYWRkBG95y1uKjlTMzc0hFAqhpaWl4tdo1bbFqufaStkrhS0tLSEajWJ5eVnNBezu7lZzcUpRaRvgcDhw5513ZkyvyhV06K2/U6/1CPbtexmh0Aqi0RsNmeqXrxzNzc04ePAgrl27htHRUYyMjGQkXpc68mB2O1WMlTvENjXYuOuuuzA+Pl7y444ePaquowwAv/Ebv4GnnnoqI9hQ9qS45557IITAbbfdhje/+c26zr9Z+2yUyqqrfFjxC5392rTJ2vl6fZRkK+WPfXJyEna73RKrEOWaXjU3N1d0pCOfuroQrrtuBPPzErfccqroZ1jqd6VQJdvS0oKWlha1Qo/H41hfXzc9eDCzkaDqZWan2IsvvogHH3wQDQ0NsNls6O7uxp133qkrUdvI9sCq7ZQVz2XklLNCkslkRlARDocRiUQ2BKBerxeRSAR+v7+i5zPqvck1vUrJ29MGHXrqV7s9gX37DmN+PoS+vjAcjgcqLqNWvnahsbERBw4cQCgUUoMOv9+PRCJhqXaqGCu3YZbI2fj85z+PL3/5y7j99tvx2c9+dsNQ1vT0NHp7e9Xfe3p6cOTIkYxj9u/fj/379wMAjh07VnKCeDwer+AVvMKKlaVyLqNY6fXF43F1DvPw8DBCoRBisRhqa2vVCrrcXp+tkus9URKX29vbM4IOr9er67MVohEeTz+czsvweg+gWNtpxsiDUqEHg0EcPXoUx48fx+DgINra2nQFP2auRrVdd2Ul45nZKbZ//34MDQ1BCIGvfvWrmJiYwJve9CZd57dygGDkxXg1jEYUIqVU2yylM2xtbQ0A1LyKxsZG+Hw+uFyuDXXP3Nzcppc5V/2X/TkVCjr0f6Y1aGrqR03NRbS2+pFIlL7IRyHF2hGv14tbbrkFq6urGBsbw8LCApqamuDxeHS1AeW0U9vluqRSW/4q3/e+9+HRRx+FEAKPPvooPvzhD+OJJ57IOCbfxVc+5WzqZ6ULaLPOtZ03SyrU6yNEakOf1tbWjA18toreCqfQZ5vv8dlBx7lz51BbW4vu7u4iIx0uBIPvxerqGLze1xUtW6Ey5FLKxX19fT3cbjf27duH8fFxtReps7Mz7zmsuCoI7SxGdIppv2PV0k4Znf9hlK2eRiWlVPMqQqEQFhcXEQqFsLS0hLq6OnUKVHt7O+rq6rZd/ZOvTs4VdLhcLp0X1Xasrr4Hi4tH0NT0yzD6EjWZTOrKaXC73bjpppuwtraGYDCIQ4cOoa+vDz6fr+DntJW5hVa35cFGZ2en+v/f/d3fxa/92q9tOKanpweTk5Pq71NTUwVXhtmsTf1ysWqDsF1GSbS9PkpgUazXZ2ZmBtFo1JC5zJtJGYLOpudzUoKOWCyGxcVFXdOrkkk3YrE+AMWDsVIrwXKmLXk8Huzbtw+RSARjY2MYHR1Ff38/uru7K05oLLU8DDaoEDM6xba6ndoJbZ5R5ylWplgstiFZOx6Pq/s6aIOKG264oaLyGLnQi9mLnShBx8jICCYnJ3H27NmCieQAkEw2YXV1LwDj2/NSX7MQAtdffz1sNhvGx8dx6NAh9PT0oKenJ2fQYvZqVNt51G/Lg40rV66oq0B95zvfwb59+zYcc8cdd2B4eBhjY2PYtWsXvvGNb+BrX/ta3nO6XC4sLy/rLgPnwpbGiPNIKZFIJBAMBtUdtsPhMJLJZEavT0dHx7bs9alEqRVWfX099u3bh7m5OZw8eRJerxeDg4Mbgo5Szmt2AjfwysWAy+XCnj17sL6+jomJCRw6dEhdM7zclTWMnkZFO5sZnWLl7gdlBKuOIFg1T1GhLIeevQme3W5Xp+12dnbC4/Fs2OdnaWkJq6urhpRjM3vD872Heut8IVKb0cXjcTQ3N2dsYJsr6DAzCCq3XXM4HNi9ezf8fj8uXbqEQ4cOYdeuXejt7c0YsTF7ZGM75xZuarBx//3347nnnsP8/Dx6enrw+OOP47nnnsOJEycghMDAwAC+8IUvAAAuX76Mhx56CM888wzsdjs+//nP4+6770YikcCDDz6Im266Ke/zbHUlbsXhaaB4xSvERdhsQ0gmb4OUgaLn0vulV3p9tBV0PB5HIpFQ96ro6emB2+227LJtpdjMCkG75Kx2elWuoMNqwUY2p9OZUaEfPnwYPp8vY2qKXpxGRUYyo1Nsq6dRWZG+17gMm20YUvZAyvzL1VfyGrWb4M3NzWFtbQ1XrlzZsBx6rk3wCpXHSj3TRiWI630uZbNb7fSqXEGH2cFGJQuH1NbWYnBwEP39/ZiamsLhw4fR1dWlrhq31dN9rfp3DWxysPH1r399w23vfve7cx7r8/nwzDPPqL/fc889OZcazKXU4Wmr9swYOdRdPHBZRzj8FwgGQ6iv/wnc7j8HkHvJuXxf6EQikbE+fCgUQjQaVXt9vF5vRq/PxMQEXC5XRq9hOaz8B1aOSoKCfEFHIBCwfLChyK7Qjxw5gmg0qib+6y3PVq4KQtvXZnWKlbKpH2Bsp5iRNnuUJB7/K6yuXoDT2Qyn888A5N8Futi5lOXQtSsXKqMPygi7y+WC2+3G4OBg1bU1lbyeUq5ztO1DrpwObdBhpZGNfBf3drsdAwMD6Ovrw/T0NI4dO4a2tjbE43FTO7m2c6fYlk+jMsNWjmxYdZQEKFw5xGIJLC9H4HYnsbwcQW1tEoWu65QVNLR5FTabTR1KbmlpKanXp1JW6jEyQqXvmTbomJ+fx6lTp2Cz2XTvFruVwYZCW6H/9Kc/xdGjR9HW1oaBgYGia6+XM42q0PHVdpFB+W1Wp9hWjmxYlZ7XOD8/DptNIBK5ioaGIFyu3MFG9rm0ydpKh5ieTfCUnEDWARuVmv+Q/XuuoKO5udm099roaUs2m02d8jszM4OJiQmcO3cOfr9f19L0W70vx2ZisIGdsZtqsS+03V6H5eXfxNWrL6O2di+6u1M9DEo+hVJBr66uIhwOY2JiAl6vF/X19ejq6ip7E7xqbDwrfU3l9hjlIkRqc722tjacO3cOV65cQTQaRSAQKLifiJUqQZvNBofDgVe/+tW4cuUKjh8/XnTH2p3UY0Tb01Z2illZsfpvYeE+2O2HEYkMorm5bcP9yi7skUgE4+PjiEQiGcuhl7MJXjW2U5UqtZ3KJzvoGB4eRk1NDSKRSMFE8lLLoBxvRgK3zWaDz+fD2NgYWlpacOLECXVGgcfjyfu4ndROVWWwUc40KiuORmzmuWKxGDo69mFpaRei0SiOHz+OZDIJl8ulVtBtbW1wu90YGhrCnj17Ks6vqOaeIiNGJvTQWxkKIVBfX69ODTh16pRaGeYKOqwwspHNZrNh165d8Pl8ak9YfX09/H7/hgqdq1GR1W3ldF+r0lOH7N79BiwvH4Tb7cb6+joWFhZyboInpURTUxNaW1srWg69WtupSutso6flKkFHTU0NJicniyaSl1qGco8vhTZwmp+fx5kzZ+B0OjE4OIj6+voNx5ezCeB2/T5WZbDhdDq3bC6s1Vf5UHp9tHNU4/E4amtr1aHkzs5OuN3ugr0+Vm70pJQIBoNwOBxFe0asyKgeo1zH2mw2daRDmV6VK+iQUmJ1dRUzMzPo6OgoWiFuZiWYq0J3uVwIBAJqhc7VqMjqtrKdsqpcbV6x5dC9Xm/OTfBeeuklNDQ0GLLvklnJ1MoO1dVe95TaPni9Xhw4cKBgIrly3ng8juXlZTQ2NhZ9jkpWTSz1Me3t7Whvb8fi4iLOnz8Pm82GQCCQMZXZSjMIzFa1wUY19BhVUq5kMpmRqL28vIy1tTUsLy9nrPs9MDBQcmVs1EWlWe/7pUsvIhL5FmKxVvj9D8Pj2dijYCYjplFVMhdWz3m106vm5+dx+vRpNQnS7XZjYWEBIyMXMDc3gz179hVdG97spL5c8lXog4ODO2p4mranctqpag42pJSIxWKIRCK4dOmSZZZDN6teW1q6ivHxb0GIOuze/f/C7c4/3caKzKrztSssKp1KV69ezRl0hEIhjI6OYnFxEbt378aePXuKnnuz6/mWlha0tLTg2rVruHjxIpLJJAKBAJqbm7ka1Xa3lZslGUnPF0fp9dEmva2urqpL9Hk8HjQ3N8Pr9WJlZQW7d+82pFxWCs6yy1Nb+4/weoch5cuIxf5/9t48PpK7vPN/V/V9qtWSuqXWrZY00njuGY8Njr0cHpvTjs2PeG1Yx9iwrAMhhCTEyQaCeXljvOyy+9vsBvPbkGCWXQNOAg4GbIjX5jBzamY096H7vq9Wt6Q+qn5/iCpaUnerj2qpNdbn9ZqXLanqW09XVz3P97k+z1uA29JaR8vPtNHUt+keu1pRJXM69PppbrnlBRwOmdnZjwGb62yst/Zqha5M6nW73WnJte1sbGOjkY2dKiS9mwuSDcHT6XRqf0WudOiFZqdgpY0JBr9Hbe0rSBIEg1VYrUc2TZaNuFYuDIterxePx7PG6Zifn6K5+f9SXh5hfPz9wPrOxmZtyIuKijhw4ACBQICuri6uX7/+psrA35DORqbp6UJUSquhUPStZtOQZRmLxbKiBCpRs/b09HRBe71aoqSknoWFi+j1dqzW8ozOLYR7tBGZjURrxDsdo6OvU1ISxGAoprm5T3OZM0EmaysK/Re/+AUDAwN0dHTQ0NBAaWlpyjW2csRoG1sTN6KdWo3VQ/DWo0MPhUJ0dnaqM00KAfm67263lYUFAZ0OrNbU7Hr5QiH1bKx3bCKno6Skj9raHkTRzu7dF9ZdO9PNfT7gcDjYu3cvwWCQEydOcObMGRoaGvB6vevKtu1sFBhMJhORSCTt4wstsxGJRFTlvLi4SFtbm0rRpyhot9udUdSnEBvX86XETaYPYbHsQZZdyLJf8/ULCVoyV8FvnA6T6Z3EYr9AEBbo7a2goiKUkr0qEyWY6XeejYLV6XSqQu/u7qajo4P6+vqkCn0rN95tY2si0zIq0N7p1eq5l2V5TaZCoUPPZAheofY85gNW6/2/1qlW4K2bLU5KbMT3pSAdhkXF6RgZMSCKDux2HQZD6qxGOmtvJJQxAa2trQwMDNDV1UVtbS0VFRVJ7V0hOEvZ4oZ0NrbKsCRlCF581CcSiaDX69VMhcFgYO/evWlT9CVDITob+YMJSTq0aVcvNJaPbCBJPoaHP0trayPRaFQtr0pG5ZeJHBvJwGGz2di1axeLi4t0d3cnVehbOWK0ja0JnU5HLBbbtOsrPSCZlCklG4IXCoXo7u7GZrPlRIf+5rJTNuADGZ9VKJ9J60BXpscKgkBx8S6uXv0UVVU2zp2DoqJLKWdcFJKzAcvyWK1WWltbWVpaoqenh6NHj1JdXU1VVdUam7QZPSda4YZ0NgqtQVySJHU+RXzGQqfTqVGfkpISamtr1zRrDw0N5exoQOEq3kKUKZ9QnoX5+XkCgQBOp5Oqqqo1CnAj09PJjgUrguCitBRKSkqYnJzkwoULCZ2OfDpIWjgCZrNZVei9vb2qQq+srESn0yFJUtrTybexDS2w2Zue9XpAkg3BU+jQ44fgtbW1sWvXrpxlKkQ7Vagy5QotNt750vmZrBuLlVJScoBbb5UZGxvj7NmzFBUVJXQ6Cs3ZiLdtJpOJHTt20NDQoNqoyspKqqqq1D3gVg6KbTsbaKdMZHl5CF40GqWnp0edsg2ozdqJKPo2AoUYMSqkl15rJIoABoNB4Dd0jQ6Hg9nZWQYGBvD7/ZSVlWV1bzfCMREEgdLS0hVOh8Viwe/3q5z2uTSqayX3ejCZTDQ3N1NfX09fXx/Hjh3D5/OpZYrb2MabBavp0OP1VDZD8LTavBaandIKW8HeybJMOBxmfn5e/RcMBlW9WVRUtOLYTJBZoCt9eeMZFuN7OhI5HfnMDGTzrCV6ZwwGA42NjdTV1dHf38+xY8eoqKigpqZmS/cW3pDORqZlVJl+QcoLuTrqo1D0xWIxLBYLZWVlG0rRlwpaK95CUuKbjfgmSOVfW1sbJpNJLYerrq7GZrOteBYikQjl5eXEYjE6Ozvp7u7G7/dnkYFID1o16SVyOqLRaEZlVJvNwGEwGPD7/dTW1tLf309fXx8lJSWUlpYmzHAUshLfxjbSQXxWVbFdp0+fVpu1bTZbTnTohehsaIUb0d7JsqzS4it2KxqNqnZLGeSr1+tZWlqio6MDURRpbGzE4XAUrJ1K5HQ4nU4aGhrymtnQ2k7p9Xrq6+upqalhcHCQEydOYDKZKC4u1uwaG4kb0tkwGAxEo1FN1kpG0Wc0GtWoT2VlJTabTa19PXnyJB6Pp+A2KDeqEt+oCJaSuVKeBSVzJQiC+iyUlJQwPT3NoUOH0lY8FouFXbt2EQwG6ezsZG5ubkUEaT2ZNqPkarXTMTo6yoULF9RMh1ZyZHN8JlAU+tLSEpIkceLECUpLS6mrq9vOdGxjS0KhQ19vCN7c3BwtLS1J69szQaGShhRaBn4znBbFbsUHwxYWFlhcXEQQBFwuF2VlZdTX1ycMtITDYRwOBwcPHmR6eprLly9jMpnwer0ZybDRdiqR07G0tMTS0lJaz3ym31W+7JROp6OmpoaqqiouXLhAf38/4XCY+vr6LTW0+IZ0NrL5wmV5eep0fKZiaWlpRdQnnqJvvesXWm1gIbJ8FFqaOx7xk9aVf/H1yspQxESZq2yjGzabjT179tDd3c3g4CBtbW00NjamdDw2y9lQ0Nvby5UrV1hYWGDfvn1ryqsSoVAH7nm9XlpaWhgeHqatrY3i4uItp9C38eZBfMlLfLN2ukPwhoeHNZNFS11+o5ZRbQSi0eiaEiil0kLJVij0+EpwaL2NtyAIqi0oLi7m5ptvZnJykqtXryJJEgsLC+uukUn5ktZ2ShAEDAYDkiQxNzfHxYsXcblcNDQ0pJR7M3oLU0EURZxOJ263G71ez5kzZ3A4HDQ0NKRkiiwU3JDORiooL8fqqE8oFKK/vz9tir5UKEQlV4gyFQKUKOD8/DyTk5NEo1H6+vrQ6XQrZpf4/f60G/Vzvc8Wi4WKigpKS0vXpK5zgdZKXJIk+vv/nl272rh2rRqb7S4OHz7M1NRUSqcj35mNbBx95RxRFKmsrMTn8zE6Oqoq9NbW1i2h0LdxY0LJsIfDYa5evboiwx5fqnkj0KEXIgrNdipUw/FOxeLi4gomy3T6bLKBktXW6XR0dnZy9uxZdfOeLBucaRmV1o7JuXM/oKTkDWZmXDQ2/hHAivKqRE5HoTkb8Jt7o0xWHx8f5/z581gsFhoaGnC5XHm9fi7YcGfj0Ucf5aWXXsLj8XDhwvIQlj/5kz/hBz/4AUajEb/fz9///d8nvGl1dXU4HA50Oh16vZ5Tp04lvY4sy1y/fh1RFNHr9WrUB1CjPvEUfadOnWLnzp2afMaN2djH0Ol+hCj2Eo3eiyxXb5hMWzWzocwviVfQShTQbrdjMpkoKSmhurp6w1g6Eh2nKLmioiI1dX3lyhWMRiONjY1Zs0BlIld6EaMou3b9jPl5Pa2tpzAa5xGEZYYat9ud1OnItxLPRumvPkcQBFWhT0xMFOwmaBtbH/Hvw3pD8ARheQ5OQ0NDzuxpWlK+p9LlgtCJKPYQi+0DSrJeR0uZMl1nM7G6YXt2dhZJkpifn1dL4iorKzGZTBkHZXKFQts6MjJCW1sbJSUl1NfXJ+z52cwM/M6dLyLLI7jdEazWj+BwtOLxeBgfH0/qdBRSua+CeDslCAIej4eysjKmpqa4fPkyTU1NGZW3bSQ23Nl45JFH+OQnP8nDDz+s/u7IkSM8/fTT6PV6/vRP/5Snn36aZ555JuH5r732GqWlpQn/9u1vf5vXXnuNixcv0tfXx6c+9Skef/xxDh8+rFL0bUQ5xkbM7RCELkKhf2JhQcDpDCKKf7rO8W+ezEZ8I6RitJWSOCWVvLrPBqCvry/rbJbWiJehuLiYQ4cOqZt3m82mpr83s0lPEAy43YdwOi8zNVWFXu+K+5uwwum4ePEiZrM5qya9bJyTTL/DZA6KsrnbpsV9cyHfQbFoNEpHRweyLPPHf/zHtLa2smfPHkRRVMt2E2XYT58+jdPpLDg69ORrTSHL/43FxSBG40lk+YkCkKkwIUnSmrlb4XAYg8Gg2q3q6mqKi4sJh8PU1tbmfE0tgmqCIFBRUYHX62VoaIiTJ0/i9Xqpq6tTn9PNDIoBeDz1RCLzzM9HsVqL1Gsom/VETkc286Dyvb9MJJNia0tKSjTPYmmJDZfsjjvuoKenZ8Xv7rrrLvX/b731Vv7hH/4hq7W9Xi+PPfYYO3fu5I477uDHP/5x2udqqZTy0eS2+oWamYHFxSX0+gh9fTJ1dRsnU6EocaVmORAIMDs7y8WLF9XsVSZTawsNie5t/OZdUYxFRUUZbay1jxgJyPIX0es76OoapaxsbQp9tdNx6dIlRFHMaJDYRij9rUwpuA3tkc+g2Be/+EVefPFFmpqaWFhYYPfu3bztbW+jtrZ23edso7IRWq0VDi8yOzuKLEvAAB7P+usUGrTOkCSil1XsltJn43a7qampSZghUI7dbKy+J6IoUlVVhc/nY2BggOPHj+Pz+aipqQHyT32bGn+GyXSMS5cC7N5dvuIvyZyORPOvUmEjpntvZTtVcG7Q3/3d3/HAAw8k/JsgCNx1110IgsDHP/5x/u2//bcr/v72t789p2trlQbLh0FYLZfBUMnFix9EFKdxOteflr3VG8STTVs3Go1qqVxtbW1O2SutHc5crpeKYUNRjKOjo1y8eJFYLIbNZluXrjI/zeRWZHkPsdivUh4V73T09fXR1dVFe3s7fr8fu92umdygTRnVNt7cyGdQ7HOf+xyf//znAbjzzjt5//vfT0lJ6vIiBVo6CBvhuMRixXR334XDMcL8/J51nQ24sRrE4+3W1NQUMzMzzM3NraBFz7TqYqPtVCo5Ep0viiI1NTVUVlaqs4wsFkva5T35sVOlSNL7CAZ/lZZtHR8f5+LFi4TD4bQa4DOVWzk+U2xlO1VQzsZ/+A//Ab1ez4c+9KGEf3/jjTfw+XyMjY1x5MgRWlpauOOOOzS5tjJNVYtN+UZEjKxWK3v33sXCwkLavMv5Lu3SAqtp+oLBIKFQSC0vULi/46etT0xMMDc3t+6mNR0UQmQgnWdH6SmYmJjAZDKpdMv19fVJU6n5Yq7KBIIg4HQ68Xq9eL1eLl26hMlkSul0ZNOzoVUZ1Ta2kQi5BMXin02TyUQkEkn7uoWc2Ugkl8Vioa7ufUxOTtLSUqXJtQoRyehlRVFUS6DcbjeCIHDTTTflfD0tdHO+HTGdTkd9fT3V1dWcPn2a69evI8syPp8vpa7Nt51avzx42emw2WycP39+3UZyBdlk4N9MdqpgnI3nnnuOl156iVdffTXpF+Dz+QDweDzcd999nDhxIqmzodPpiEajadewFWqZUaq1lNredNfRClp9vlgsxsLCAoODg2voZVfT9BWCE6A1kn2mTJWQx+PB7/erqWtl2ujqZ38znI2pqQGWlmbweneqSlJRmEqd6eTkZEqnYzuzsY1CgpZBsWwG0Bais6EE6xLB4/HgSSelESeXFshXBj4VvawSEEtkt5Ry3xsF6eplvV6P2+2msrKSYDDIsWPHqK+vp7y8POH5m2GnlO/S4/GssANms5m9e/embCRXsBlEJlsJBeFsvPzyyzzzzDP87Gc/S0ovqTAHORwOgsEgP/nJT9RUdCKYTCa1KTgdKBGjTGrJk6EQHRetZcoEsiwTCoVWUPUtLi4iSZLatF1eXp4Xmr5Ch1aGNT513d/fz/Hjx6msrKSmpkZVThutxKemzhMO/zsMhgW6uz+G3/94wrXXczo2omdjI5r7trH1oXVQzGQyEQ6H075+qk19pijULEmh2E6FJn96eprZ2VnOnTvH4uKiSotut9vzRi97I8JgMNDc3ExtbS1dXV309PTQ0NCwZgDyRtup+flJBgc/gcMxwOXLj3DTTR9dsfZ6jeTZypIPO1XIQdkNf0MefPBBXn/9dSYmJqiqquLJJ5/k6aefZmlpiSNHjgDL9bDPPvssQ0NDfPSjH+VHP/oRo6Oj3HfffcByZOGhhx7iXe96V9LrKEo83ch/oSpxreTKR9N6IoTD4TX835IkqQ3bTqeTiooKzGYzY2NjLCwsUFlZqZlcuSAcDtPX18fU1BTNzc2aOJ7ZIBdlq9PpqKuro6qqit7eXo4ePao6IRutxGOxCxgMQaJRMxbLL4FlZyOZ85DM6dgoNqpCVtTb2HzkKyiWqbNRiA5CIfRH5ILVwxGDwSCyLGOxWNDr9epGOVN62XxAlmWmp6cxmUxrNuqZrpOPno1kxyowmUy0traysLBAZ2cn3d3dNDY2UlJSkrRHVQsZkiEcPkNp6TWiUQtu9z8Cy85GIjr0eKejvb0du92+ghlyIzIbm/38ZYsNdzaef/75Nb977LHHEh7r8/n40Y9+BEBDQwPt7e1pX8doNGakxAs1PV2omY1YLLYilbyaps9msyWkl129TiFhePgyTufLLCwUMTz8caqq6jZbpHWRTNnq9Xr8fj81NTX09PRw9OhRdDpd2s+AFpH+4uJ3EAh8G7N5Er3+N+/4egZCy3GewwAAIABJREFUcToU7vBoNJqU2ScR8pGeLrRndRv5xUYFxbIpoyo025KPtbRaZ7VMq2nRE9HLVlVVrRiOODs7y8jICGazWRO5csXIyDDj4z9lZsbE0tJ91NTkToGbLXJxCiwWC7t27SIUCtHR0UFXVxdNTU0b7my4XLuZny9Bp5tFr/8NyVCytZM5HS6Xa0OCYls1A3/D5v6UMqp0oXU2QkvHRau1sjEGiWj6pqammJqawul0bll62USoqTmKw3EenU5EEH4LqNsUObRszjYYDDQ1NVFbW8vx48c5d+4cjY2NeL3elNfINmoVD73eS3Hx94EY8aom3bXdbjdut5vLly8zMjJCMBiksbFRc/YqBVv52d2GttiooNhmZjYKNUuiBWRZJhaLMTc3pzZuZ0Ivmw9opV+s1hMcOPASOp2OUKgM2BxnI1OK2mSwWq3s2bOH+fl5rl+/ztzcXFrsT8q6ud5XUfTidH4XQZhAlv0r1l4vABXvdFy9ehWdTkd1dXXa7FXbPRs3ADJ1Ngo5yqMlx3cqxNP0Kf+i0Sgmk0lV0CUlJRgMBsrKytJmwUqFQjJQRUWV2Gx2TCYLUMFmiqZ1ZMdoNOJwOKipqWFkZERNXZeWlua5SU9gtZpZ7o2KAKOA59fHJIfFYlHrYy9fvozBYEjpdGxlhbyNNxe27dRaZLpOMrslSRIWiwWHw5HTUN9C60fxenXo9SYMBgMuV/aOkhYb9UzOX+9Yu93O/v37aW9vZ3BwkMnJSRobG3E4HEnP0a7XrhhZXrmfSTfzoDgdsViMsbExNdPR0NCQtNwyk/VXn7NVbZsmzkY4HEav1xfUTTAajTcMpaDWzkYqmj7FqSgrK6O+vj7h5ORUMimZkHSyHFqmy7VAIPAudLpqzOYqJGm/Jmtmg0wjRpk4BWazmZ07dxIKhejs7KSrq0utl81l3cyMziwezx8jy7MIwmPAR9JaX8l0KOVVBoMBv9+/xhhprZC3Mx43BgrVTm33bKSHTO1Wf38/er2e8vLy9RffQlhaejt6/QQ2WwnRaPISPa2QytZnska6etRoNFJdXY0gCFy5cgWj0UhjY2PC/tt8PnOyHMPt/p9Eo5cRxX+HKN67zvEyTqeTPXv2MD4+zrlz51I6HdtsVBkgFovx6quvcuLECUwmE4cPH+aWW24piNrG7YjRMuJp+hYXF2lra8uZXjbVcf3938RofJHFxf3U1Pw5orgxTdZa3G9J0rOwcCtOZ3rDh/KFzDfvmTsFVquV3bt3Mz8/v6Je1uVyZSxDpvJGIhdZXOxhbs6GxfIdiopSOxurFWy803HlypU1Tsc2s9Q24lHodirT3kItbUuhOi7xdkvJWmhJiz47e42pqW+j1++gqup31l2jkDLwsmxnaupD2O01myxHfmyEcmxxcTE333wzk5OTXLhwAZvNpjZjZ7NupohErmEw/ISJCStW61/hdK7vbCRir0rmdOSjjKqQA2M5ORvf+MY3+MM//EN27dpFOBzmy1/+Mg8//DBf+MIXNBmwlgu2Wi2sTvdjdLrXiMVuJxZ7v/r7dJW4QtMXr6BX0/QZDAb27t2bM01fMplkWcJq/S6y7MLh+BWLiz1Yrf4EK2T22TYSG/myavHZc40u2e129u3bRyAQUIcuKU16mUy0zURpTk1VEIl4cLsnuXbtMDffnPr4ZAo2mdOxlRk7tqE9Ct1ObWZvYTQazegcQRhCFH+JLLcgSXvifp+dLk9Eix4MBtVSlFxo0VPJFAr9J4qKBpDlXxEItOJ07kl4nLLOjYh0N+obXYGQiBrd7XartLNFRUX4/X5MJlNeA0uhkAODwYbDscDISBNOZ+rj12OvWu10ZGunturzmNWuU3kYvvzlL/PCCy9w9913A3DlyhXuu+8+6urqePzxxzeNOhSyY6PavGzEArHYi0xMmHG5XkIQ3g7Yk66ViqbPbrdTVFREZWXlGpq+oaGhvPKBC4KITrcfSTqFIPgwmyvydq0bGfktYUp8rMPh4MCBA8zMzHDt2jUWFhbSbpzMVIaiIh9XrvwJsdgShw69JU62q8AksnwL8Bvdsd76itMxPT3NlStXiEajuN3utOXZxo2JG9FObXYZlU73/xGJjKPX/wL4AlCS9lrr2S2FFv3cuXMcPHgwuw8Vh1QyGQx2IIogGDCZ0qPH38ZKaEEiku668Rv3kZER2tra1P7RfO1prFYPFy8+gV4/TENDfLnaEhAAVjIkpsNeNTExoTodbrf7TZWBz+lbmpiYYM+e5YhAJBKhpaWFl19+mQ984AM88MADlJWVaSJkNtjMiFGmSjwWM9Dfb8ZuH6C3t4LKSgMGw7KnHI1GmZiYYGxsLC2avo1Aqs9XXPyXxGI9iKIPQUjeHLUNbaC1Y+JyuTh06BDt7e309fUxMzNDY2Ojpo1ugiCwZ8/eFfNVBOEiev0ngCix2INI0idWyJ2OUlbS7levXlVnuCTq6djGmwuFbqc2KyiWjc0bHZ1BkgYRBCtFRRJKS1+8XNnQy8ZjIyK3bvdfEAz+FIOhAZMpefZdkaeQMvCFJMtGB8UEQaCiogKv18vw8DDXrl2jqKgIn8+XF6ejrm4Pfv99cb+ZQa//KIIwSiz2GJL0iPoXSZIS9rjGy15WVkZpaSkTExNcuXIFURQJhUIp7Wu6KPSMR06ZDZ/PR29vLxUVFRgMBqLRKLW1terGeKsp8c0roxLo7X0f4fAAS0tFBALXWFxcBJbrjRX2p42k6VsPyRWeAZ2uKa01Ck2JbzQikQgmk2nN7/NVh5rJvTabzeogwHPnzuFwOPD7/Qnr3DOVN3FZ1BCx2AKSpEOn61xzfCbrWywW6urqsNvtXLlyBb1en5LVZL37UuhKfBuJsRXslNlsZm5uLu3jNzuzcfXqnTidXczOutmzx4wkLTE/P8/s7CyTk5P09vYCv6GXzYYWXcuynWT3ShTdOBwPaHKdTOQppLVytb35JDJZ71hRFKmsrCQcDhMIBDh27Bg+n4/a2tqkgddMP28iOyUIV4nFBgmHLZjNLwGPZCT38hrLTockSQwPD6/bSH6jICtnQ/kCfv/3f5/h4WGVfUgURRYXFxEEIaONfj6QTXp6I8qoktH0Wa1FWCxOPB4PpaWlKk3f9evXKS0t1YRmVitsb74yg9JPEwgEVkT6RFFEFEWam5vXlP3ks/EuXZkFQaC0tJTS0lLGxsY4c+YMLpeLhoaGFU5Sps5GouPHxloYG6vGYpkmFruLpiYZQfi/CMICUJ2xM2MwGNRMh1Jelczp2MoMH9tIjhvRTm1Wua9it1yuBrq6luW+dOkSZrNZ7adwOp3U1tZq8i7ls/F3M1FowbX17vHS0hKzs7O4XK6Egc58BcUyWbe0tJSbbrqJvr4+jh49SnV1NdXV1WueQy3s1NSUj/l5Aw7HMJ2d76ClBWAcQZhBkoSMnn1ZlnE4HOzdu3dFedWN6nTklHf62Mc+tuJnURQZGxvj05/+NF7vMqPPZimNzS6jisViKxq242n6lFRyKnrZ+LUKUUEVEu94IRmlaDRKNBplYGCAQCBAMBhEkiSsVuuaSF8kEkGSJLq6uuju7qapqQmn07mpEaN4KIpTEAS8Xi8ej4fh4WHa2tooLS1Vn91slPhqpTw1FeLEifei0+morDTQ3Pwv6PV/AUgUF78HUfxM1uvHOx3K4KV4p+NG3dhsYxmFbqc2s2dj9Vrr0cs6HA5uu+02lXBEwcDAgBo80QJazYDQyr4Umg3WGskCYkajEYvFQm9vLx6Ph/r6erVcKZ92KhMIgoBOp6O+vp7q6mp6e3s5evQotbW1+Hw+9ZnMtJk80fHBoMCrr34EvV7C5fLS0tKFwfAYsERR0T3EYqmZFeOhBLlWl1fdqE6HJkVus7OzzMzMEAgEEEWRhx56SI18bpYR38ha2EgksiJbMT09TSwWY2ZmJmeaPi3LuwoNWj4bG20MZFlmaWlphXJeWFhAp9MRjUYRBIHKykpsNlvKfhqr1cq+ffuYnZ3l6tWrmEymjCex5ysLkug6Pp+P8vJyhoaGOHHiBOXl5ZSUlGSkxBOVRdXW1tLV1UUoFGLv3r0Iws+AKCAiinPodOMIwgCyvJP1hgAmK7sqLi7m0KFDa5wOk8m0ndl4E+BGsFNaOhuKYzEwMJAzvazWGZdt5A8KrfDS0hJXr14lFAohy/KagJjybij9NmNjYxw/fpzKykpqamryTn2bDlbrer1ej9/vp6amhu7ubo4dO0ZdXR0VFRVZlfuuPt7n87Fz501MT09z6NAhRPEUEESWLVgsZwiFHgVixBOcpPs5b3SnIydnY35+nldffZUf/vCHnD9/nqmpKUwmE+Xl5bS2tnL48GHuvPNONXq0kTCZTIRCobSPT0eJr6aXVV5YvV6P3W7HZrNRUVGB0+kkEolQW1ub68fQtLxLK7wZIj3xkCRJNcaKcxGNRgmFQkiSRGNjo8r/LQgCJ0+eXNH8nAqKsikqKuLmm29mYmKC8+fPEwwGKS4uXrdHJ1/RpVSlRaIoUlVVhc/no7+/n3PnzqHX64nFYmkRFSSSw2w28+53vzvu+u8FeoF5ZmZaKC//XXS6CLHY765oHs9UdljrdCjnbOPGRCHbqY0oo0pEL6v0BMqyTHFxcdb0svFyFdrMjkLLwG80FGdycnKSiYkJBEFgaWlJpcOH5c2zw+FYV2+Lokh1dbXa/3T06FGcTmdeyDe0cEwMBgPNzc1qEKunpyfj/ViizIYoihw+fFj9WZLeQiy2E+hncvL9+Hx/iMFwjVjsj5Ckf51y/WR2KpnTUV9fn3K9QnfSs3Y22tra+OxnP8vAwAB33XUXf/AHf0BFRQWxWIz+/n5++ctf8tWvfpWf/OQnPPXUU1RXV2sp97owmUxMT0+nffxqZanQ9Cn/4r1/m82WlF4WlusctaoFLsSN/Y2sxKPRKFNTUys434E1E2rn5+c5efJ/Y7FM0tMTpqZGm0mupaWlVFZWIkkSp06dwuPxUFdXl3QTsBk0uQpEUaS2thaHw8HVq1c5duwY1dXVVFVVpdzsp9PwPTUV4pVXlqNR+/YN/bp3w4Iovg6YkeVWZPmtCc/NhL3q0KFDjIyMcOnSJc6cOYPf78e5HqH6NrYMtoKd0jKzEQ6H18ysiC/jVOhlzWYzgUCA4eHhtAMjqbBZvSQbtU6hI1lATK/X09FxBZ1uDperkXe8453q5zl16hROp3NdXRn/+XU6HQ0NDVRVVdHe3s7k5CQ2m43S0tKU92mzsiAmk4nW1lYWFxe5du0ac3NzjI+PrysvrM8uBTA+vsSPf/wOJEmitXWW2torgAtR/DtkuQVZLgMSv1/r2anVTsf58+dZWFjQjL1qo5Gxs6F4Y9/4xjd46KGHeOyxxxIe97u/+7sAPPnkkxmxbWiFdJW48pLOzc0RCoUYHx8nEomsoOmrrq7GZrOlXWqR2CDIQATIjE2qEJ0NrbCZSlyWZTVLpSjnQCCAwWDA7Xav+73rdEMcPvw8Ol2YublBQBtnA5bvi9vtprm5mf7+fo4fP05VVZUmTW/pIhMGKFEUcTqd7Nixg56enoT1svFIxxno6OhgYWEBQRC4fr2MnTsPIgh9wDw63X8FzEQi/xE4DKzkyc+Uvcput1NSUkJtbS3Xrl1Dp9OtcDq2wmZjGyuxlexUJr2Fij1Yj17WZrOtW8ZZiNkIrde6EW2nklXv6+tLGRAzGAzMzY3j8XwBl2uC/v49CMKdWV1ztQ40Go2Ul5cTDocZGRmhu7ub5uZmXC5XwvM3u+TKbDbT3NzM4uIiw8PDdHd309jYmHIeUzprT05OEolE0Ol0DA1ZkeVSYBqoQK9/DDASiXwLWEuvLElSWplExelwuVwcP36cc+fOqdPUt5LTkbGzoWwS/vqv/xpAbSJaDaWk4i//8i9zFDE7KA24CpQa+/iITygUQhAE9Qszm800NjbmTC+7VlkuYTB8FVHsJhq9n1jsX+Ww1uajEGVKhVgstsIoK7XJq4cgDg8PY7PZ8Hg8667pcoEs2wiHbbhcemIx7eRVlJySOaisrKSnp2dF/enqWs90kc9eEIXtKb5etr6+nvLy8hVrpaPEq6qquHz5MgB2ezlLS/8dMKHXf4BltTWCwfB7gI9I5KtAs3pupuxSyvHKjBFlsKHidBQSE9w20sNWslOpgmKyLK/Isk9PTxMIBJiZmUlIOpHp+71aj4viGXS6s0SjtyPLjTmtlQsKLSOxGfIkCogtLS0hSRJGoxGn07luINTpnMdqnWdmxkVra9+a9XOBLMsYjUb8fj+BQIDr168jCAJNTU1qmVY8CqEP0WAwsGfPHubn5+no6KCrq4umpiaKioqyWru2tpaOjg5CoRBebxWzs89RVBRBr3+CZTs1hcHwAWR5P9HofwN+U3aWaVBMlmVMJhOHDh1SMx1Wq3XLOB05zdn41re+RV9fH295y1soKyvDarXicDhwOp1qc1Eiw//oo4/y0ksv4fF4uHDhAgBTU1M88MAD9PT0UFdXx3e/+92ERv65557jqaeeAuAv/uIv1MhUPObn5+nu7ubSpUt86lOf4pFHHiEajWIymVYwQVksFlW28fFx5ufnNZljsVrxCsIQ4fAlQiE7DscrwNZ2NkA75au1gVJ4t+PL3+IZwFLVJmfWPLYTvf5RbLarRCIf1uwzJEL8Jr6zs5Pe3l4aGxspLS3N27ORiSJcrZSNRiM7duxgaWlJrZdtaGjA4/Go3/l6a/t8Pj74wQ8CcPHiRSYmJpBlmaqqZ9Dpvoko/hPgQBDOYDC8F0m6j1js3wNFGbOOrNZR8U7H9evXKSkpYceOHWmvt43CQK52aiMQn4FPRotuNBpX6C+lNCRXrM1szKDXf4NoVIfBcJlw+EtAevekEBvEC7GMKhUlfjoBMZPJxMjICNFolIqKijSuWI3VejsOxxkikQeIRlf+VQvGLwCHw8GBAweYnp7m4sWL2Gw2Ghsb1blMhcCwGH+s3W5n3759zM3NrXCS4ntQ0tEJFouF973vfQC0t7fT3n6NUCjEb/3WY7hc/xFBmAb0iOJP0evfhiQ9gST9P4CQtZ1KVF5ltVqTOk2FgpycjWAwyPPPP89XvvIVTCYTNTU1VFZWUl5ezu/93u+xc+fOhA/CI488wic/+Ukefvhh9Xdf+tKXeOc738kTTzzBl770Jb70pS/xzDPPrDhvamqKJ598klOnTiEIAgcPHuSee+5RnZLx8XHuuusurFYrxcXFiKLIe9/7Xnbt2rWuE6Ely8fqtYJBJ5OTYDb3MjT0r2hKb+YdUJjORiGUlcSXEUxMTDA3N6c2fsY7lFarNScGpuQQiEZTN4Bli2QK1Gg00traSigUoqOjg56eHpX5aqNkSIRkSlnZFC0sLNDZ2ammrmOxWFpKVonWTE9Pc+7cOQAOHDjA/v1fRJZb0OmeBPQIwgw63dcQxX8kGn0GSWrOmeIQlp2OgwcPpr3ONgoLudqpfAXFZFmmt7eXc+fO8eqrr3L27FkOHDjAf/kv/wWPx6Pqrrq6ujV2a35+nqmpKU3uz2ryEUnSMzo6Ryw2jShW43an/w4VahlVoSG+wkIJiq0OiHm9Xvx+v0YTsfWEw18AwsDaAbK5INF3VFxczOHDhxkfH+fMmTO43W4aGhryRn2bC3MVgNPp5ODBg+osJqPRSGNjIzabLeMS5ampKTo6OhBFkdde8/D+97+MKP5XdLr/CQQQBB16/aNI0rNEo3+DJGVGFb3azq52OsbGxm48Z0P5wB//+Mf5+Mc/DkBXVxdvvPEGX/va13jxxRe566672LlzZ8Iv7I477qCnp2fF71588UVef/11YLmO9m1ve9saZ+OVV17hyJEjap3dkSNHePnll3nwwQeB5ebatrY2RFHk5z//Od/5zne4++670/pM+VWWFq5evR+dLozF4tlUZ6PQ+MvTQSQSWRHxCQaDK6j6HA4Her2eluUJOxsiU76RSg6r1cqePXsIBAIcP36c9vZ2mpqasNlsSc/RWoZ4rPdMWSwWdu3apTpJk5OTlJaWps1ksri4qBoKhfRBkh5Ckt6DXv9hRPFngIQgDGEw/BtuuqmShYX/DdyS1vrrZXHSYdjaRuEhVzuVr6CYLMt89rOfpaWlhebmZjo6OvjWt76Vdv12vuxUOKzn3Ll3UVY2z9iYm9tvT49dLt9ybdV1VvfVzM7OEgwGuXz5smq3VldY5AcCyRyNXOxdMr0vCAIej4eysjIGBwc5ceIEkiSlHczdjP4OZRbT5OQkFy5cUIOUmdwfvV6vOvBKGZkk/QGyfCt6/WcQhOWAhSgexWg8REXFERYX/9+0109mpxSno9DtlCZzNgAaGhpoaGjg7W9/Oy+88ILKcJHulzU6OqqmBSsqKhgbG1tzzODg4Aq2kKqqKgYHB9Wf4x+OzR7qF6+crFYre/ceZHZ2Fp/Pl/FaWstVKBtqWBnFUKj64qM+i4uLKrWw3W5P2PQ4PT296ZOAtUS6hs3hcGC1WqmpqeHChQvY7Xb8fr+aus4FuZRRJYPiJJ06dYrh4WGGh4fTSv26XC4sFgtLS0vs3buXkZERiouLMZlcRKP/jCh+Fb3+3wPLdJ5m8wBm879CksxEoyNA6vuxPUH8zYNM7FS+gmKiKPLd734XWLZpP/zhD9OOYufTTplMJrzevQwODlJXV5fR5qUQnY2NhDK7QrFbqwNixcXFlJaWMjAwwK5duzZb3A2BIAhUVVVRUVHBr371K06fPk19fX1S4hAFm9lMXlJSgtvtZnx8nAsXLhAOh7Hb7WnZVJvNxp133kk4HMZisfDDH/4Qn8/Hvn23EIm8iE73p+h0LwASIOF2/wj4EdHo/UjS/0lL/q1sp3JyNkKhEIFAAIvFgtlsxmg0UlVVxfnz55mYmODgwYNIkqSZx5VIASV7eDZyqN9qJDIIZWVllJWVZbxWISpxLRwgpT45GAxy7do1AoHAioFSDodDpWgsJOdoI5CpQ+h2uzl8+DBjY2OcPn16xXTvjZAhU3lFUaS1tZVIJJK0XjYeer2eO+64A1mWeemllxgbG8PhcHD//fej1+uRpE8QifwWev37EYSxuOssYjC4iEQWU8qznrPxZnv+bjRoaae0CIrFIxvq23zZKUEQaGlpySpDXKh2SusMfHxAbPUwV8VuJWMBy2TuVyoUiiOWrt7X6XRYLBZaWloYGhri2LFj+P1+tYcv23XzdaySmfF6vZhMJk6fPk1JSQn19fUpy/FlWcbr9WI2m3n++edZWFhgZGSEyspKPB4PsdjXkaR3YjB8ElhQz9Pr/wlZdhCJBFLKtdWDYlk5GwqDx1e/+lW+9rWvceTIESorK2lpacFms3Hx4sWMGyq9Xi/Dw8NUVFQwPDyckBGoqqpKjSoBDAwM8La3vS3hepkOS8pnxKhQ1oLVmYQI09N/Awxhsz2OyVSX1TrrIVHTtiAIGI1GJElaQdW3jewgCAJer5eysjJ1urfP56OmpiYrZz+fzoZyfKp62USQJInR0VFMJhNzc3MsLCyoDoos7yUS6UCvfw+i+Ev1nHTE2upKfBuJkQ87lQ4yCYplM9SvUO2UloMxV8sVDncQifRhsdyKKKbHvJPr51No8aenp5mfn+f06dNEo9GsJqxrja0WAFGYq5qbm6mpqVF7DpuamtbQz+bT2chUz5eWluL3+xkaGuLkyZPq3KtEe5V4O+JwOJifn0ev1xMOh9VxCrL8IcLh2zEYbkMQxtVzBSHC8niE5J9lq9uprJwNZfPywAMP4PV6aW9v56c//SnPPvssAwMD3H777Tz00EMrjl0P99xzD8899xxPPPEEzz33HPfee++aY+6++27+/M//XK3b/slPfsLTTz+dcL1sMhvaKEsZvb4Dg2EMuCnn1bR2guIxN/c6gvB9ZFlkdva/4fF8Jat1FMiyrNaoKs6Fwv3ucDjUeQZWqxVRFJmbm2NoaIji4mKi0SgzMzNpTTPdKpBlmdOnT9PV1UVra2va6fNcSt2U6d4VFRX09vZy7NgxlT43E2jRIJ5q7fjjE9XLNjY2YrFYVpyn0+m4+eabaW9vp6amhvPnz9PQ0EB5efmvjzASjf4LExP34fP9+Neyrb+Z3OpKfBuJkQ87pXVQrJDKfXOBlhmX1XItLfURCv0+sMjCwi2Uln5Jk+vEI55eOBAIqFkIm82GxWLBaDSye/funAJihewgjI+Pc/36daqrq9cMtkz2vWZqp5RjzWYzu3btYn5+nuvXr6szOuIz2/lwNrKhmlXYnyorK6moqGBgYIATJ05QUVFBTU3NivLHeFnuvPNO+vr66Ojo4OWXX8Zut3P//ff/OjNSQyTSTzTahNXaH3fF9YcMbmU7lVMZVVVVFR/+8If58Id/Q/359a9/ndnZWUpKSpKe9+CDD/L6668zMTFBVVUVTz75JE888QS/8zu/w9e//nVqamp44YUXgOUpl88++yx/+7d/i9vt5nOf+xw333wzAJ///OeTDmXJRolroSx1utex2b5NZeU8glCHLGfQDZ4A+cySyLILWdYhCFGi0eTfV6J1YrEYMzMzK9LJ8ZNq0+V+l2WZWCzGqVP/B4PhArL8Fg4e/O2CVszpYmEhhMHwN7zrXRe5evUQ0eh/1YhhZH0ok16rq6vp6uri6NGjRCKRtJVzPjMbyZR+fL3s2bNnKSoqoqGhYcUxe/fu5aabbuJb3/oWkUiEq1ev8qEPfWhFeruz8wmKi19Ap5OB9TcHW12JbyM1srVTiaB1UGz1PKj1oF1p0BgWy99QXz+AIHwRWc6slzBfcilrxWNxcRRZXkIQLMRifUnOSk8mWU48uyJ+iG9tba0aEINlR2R2dhaDwUBX1zUGBo7j8exhx449GdupQimBiockSZw9+218vjNcvdpEaemn1wR6kiEXhim73c7+/fuZmZnh8uXLmM1mmpqaNr2MSsFqOyWKospk19/fz7Fjx9Rhuzpd1vBXAAAgAElEQVSdboUdUYYIvvHGGxiNRpUgIL6U/uLFf6C5uRub7RUkaX0HeqvbKc13Po899hhf+cpXeOqpp/irv/orNZUdj+effz7hua+++uqa3x06dIi//du/VX9+9NFHefTRR9eVY7PS07FYP/PzAZaWAlit4whC4TobRUWHmJz8ItHoOGVlRxKek4iqLxBYri1cWlrCbrdTUVGB3W7POCOhvMhLSxM0N/8dRmOUhYUzyPL7EYT0H81CdUzM5gDNzWcJBs3s2XMMnS5KOq+clk38BoOBHTt2sLi4yBtvvMGJEydobm5ed1DdRpRRJUI8k8no6CinT59mcXFxzVA25TmOxWJMTk6u4JxfNhIi6c4I2OqNd9vIHOnYqY0IimWq37XKbIji6V+TJwQQxVPEYvfktF4+7ZTDsZ/BwfcCV7BaE0+CT4RYLMbS0hKDg4Przq5IR3/FYlHgixw40MP0dBmLi9/EYkmPUa+QIQgSt9zyPKIYora2HVH8CLC+s6EVRa3L5eLmm29mYmKCs2fPJtT32ayby7GpjtfpdNTV1VFVVaVWD9TU1CQMou3fv58TJ04Qi8X4wQ9+wDvf+U5qa2uBZTsly3chSb+dtjxbubcwJ2ejs7OT8+fPs3v3bvR6PUajkYqKCgYHB+nt7QU2z5PfrMa7zs4dSNIpZmeLWVz0kGH1yhrkU4kLgkBp6VvVn5Ua1fh0cvwwRIfDgdfrZW5ujqWlJfWlyRUWiwm93kwoFMTlsiKKmb80hRgxEsUSnM49OBxdwAEikfR5zrVWHGazGYvFwk033aSmrlM1Zec7YrTe5l4QBMrLy/F6vfzsZz9bUy/7nve8hzNnztDT08NLL73Ebbfdxs6dO7OWJ5WjXOhKfBupka2d2oigGORnsvJ6mJrysLg4ydJSiFismCy4S9bIlS87JYp6qqv/KOnxspx4mKuyhsvlSjnMNR0sb/RilJf3MzfnoLh4AqNxnviJ0FsVgiBQUlJGJDKGXm8nGk0vq6Gcmw7W08kKfWtpaSk///nPOXnyJOXl5dTW1qb8zjbTTun1evx+PzU1NfT09DA/P8/w8DA+n0+9zr59+7DZbLz22msAtLW1rXA2Mp2zsZVtUVZvnnKTzp07xwc+8AE8Hg8NDQ0qi8Xp06f5yEc+ArBpEcPNqoXV68u5dOndTE9PU1ub+4CVfCnxSCSyQjkHg0FguUZV6a1I1gg1Pz+vmUzLCsCF2fw0VusxYrE7keUbo2cDDITD/wNR7EOS6lmvJlNBPh2n1alri8WSsD9iszIbq6EQCdx6660qZ7tSL1teXk5vby+SJNHZ2UlLS4uqb7R2frax9bAV7BRsTqBkZqaEjo6HmJ6eYvduT0E7G/FYPbsiEAgQiUQwGo1qX6AyzHVubo6RkRGqqqpylmf5vyZsto9it7+ALH8QWS5f58ytAh3R6FfQ619Hkg4D6e1bMv2+02WCMhgM3HrrrfT19XH8+HGqq6upqqpK+I4Wgp0yGAw0NTUxOjpKIBDg6NGjNDQ04PV6VSfKYDCoZee/+tWveOtb35pxRn2r26mchvrdd999SJLE0NAQp06d4uzZs/T09KwYhLRZN2ezamFramowmUxcu3YtK6pbreWKr1ENhUJcvnyZSCSCXq9XlXN1dTU2m23Dv6v4F1mS9iNJ+zf0+hsDK5KUHZVkPrE6dV1cXExDQ8OK1HUmjXeZRgwz/XyiKFJdXY3P56O/v5/jx49TUlJCcXExAwMDDA4O8vrrr/OOd7wjo3Vh6yvxbSTGVrBTm4WKigqmpuqYnZWpqanJeb18OBvRaHRNQGz17Iqampq0ym20giz/G2T5w6QbONoqkGU/0ag/w3PyMxUclt9HpVSpu7ubY8eOrdjAZyNDNkQmmdgpURRpaWlhcXGRzs5Oenp68Pv9lJaW8u53v5vvfe97mM1mzpw5w/79+7PKbGxlPZV1GVU0GmVqagqTyURZWRn33HMP99yTW92nlsi0LCqXzIYgjCAIk0hSM4JgwOPxMDAwkNVaa9dOX4nHYrEVDdura1T1ej11dXW4XK6cNrNa0/FuYyU26t7Gp66Hh4dXpK4zwUb2PMTXy/b19eFwONSBS9evX+ctb3lLxmtudSW+jeQodDsFm1OmZzQa2b9/P9FoNO1m4FTIxSbEz64IBAJMTEwwOTm5IluRbHZFvmRKJGPcypqs+WZCtn2Ier2epqYmampq1A18U1OTSuyw2eW+iWA2m7npppsIhUJ0dnbS1dWlOh29vb3odDpefvllKioq3lQZ+IydDeULO3r0KA8//DC33HILt956K5/+9KeJRCLodDoEIbMx7/lCJoomW8UkCOMYjV8GFonF3kI0+mF1rVgMZmchSW9g1nIpNarxTduhUAhRFFfwf/v9/hUR52AwmHYz3EZAO2aVwvg8mSISidDT06MyoCifQ8sG8XQgCAI+n4/y8nI1dR2JRNJWbvmUN9nzodfraWhowOfzMTk5ydDQEAaDgZ/+9KeUlpZmdI2trsS3sRY3qp3KHhJ6/f9CpztDNPrbxGJv03T1dHV5LBZThywqAbHVsyuKi4vxeDwZM4VlK1M662iBQnjWssX09DQGgwGXy6X+biPtlMlkYufOnQSDwRV0uYVQRpUMVquV3bt3Mz8/T0dHB2VlZYyNjRGNRunu7sbhcGSc2UhFvVzoz1fGzobygW6//XZefvll/uiP/ogrV64Ay1HHrWq0s/+iZllYmGZxUcRq7Vux1sCAwKlTOu6/P0q2yyuOxcjIyLo1qut9Bi2V742Y2djoTX5n5/PU1f0PQiEXQ0PPUlm5e8OunQjxqeuf//znSVPXq5FvZyPV2mazmTvvvJPvf//7BAIBrl27htFozGt6fRuFjxvVTmULQRhDFE+wtFSCyfTPG+JspJpd4XA4kg5znZubK/iNU7bYinazt/ciweBfE4sZqa7+C6qqGtY/KQG0+E5tNhv79u1jdnaWa9euEQgEWFxc1Jy5Kpvjk8Fut6sy9/b2Mjo6il6vp6urizvvvDPtdba6ncqJjWrHjh289NJLQGHSR26EwpqbK6Onpx67fZRAYDd79sDcHPz856WUlOiYmxP4znf0NDVJHDyYukwrUY1qNBpFkiScTmfONar5SytvIx3EZ6MCgQAlJf+EKEZwuYZZXDwGLDsb+di8Z/J96fV6zGYzBw8eTJi6TrR2vt61dBSs1+uloqKCYDCIKIoqB7rf76esrGxd2bY6peA2UqPQ7ZROp0tIvaslolEno6MCgnCeaPQW4piic4bSF6jUqqczuyIVCi0odqMG19KFxfItamvfQJZhamoH8KfAxgfn4lFUVMShQ4f45S9/ycWLFykqKsLv92MyJWd83KgyqmQoKiri7rvv5vnnn1dnt/T19aXdM1WIuisT5DxnQ+lz2Mo3IRcIgo6RkVuRJEndjDkcUF0dYnJSwG6XMRqhufk3jsbqGtX5+XkWFxfR6/WqclZqVBcWFujr69OEZrYQ08o3ovOjfL+KUxH//TocDhwOB6WlpYjifej1/51o1IQg7NrUDEEiJEtdO53OnNdOF+msLQgCBw4coKOjg0gkwtzcHH6/n7GxMbq7u2lsbExZkrHVKQW3sT4K2U4pzIlWqzVv11hagvb2d1NcDNPTesrLs3tno9GoSo+u6DZJkjCbzcRisYxnVyTCm31zv5mQZVktc1O+X5dLAAT0epGKipUb483Wm3q9nkOHDjE+Pk5bW5tKj56IsCTfDeLpoLy8nLKyMoaGhojFYrz66qscOnQIv9+/7vu/1e1Uzs5GISrvjYAgjKHTvUxRURUHDuxnbi6gDhYTBLBaY8zMgNksEQotMjc3wfDw2hpVh8NBRUUFZrM54YOkteLdjvRoi3jlHF8ucPXqVdWxSPb9hsMPY7W+i3DYTH//NN3dJ9mxY0feMhvZrrk6da1Q/SnKMZ9KMF0DUV1dTVFREZOTk0QiEdrb2zly5AjBYJCOjg66urpobGxMOMxwq6ent7E+Cvn7VWZCZeJsZEYf3YfVasHnq2N4eBi/vyGtbF+i2RXxfYEVFRXq7IpYLMbZs2cz7pdKLO92RiIVtAzQzc3NrXAcY7EYVqsVh8NBSUkJtbW1yPLniEabGBqaRac7QF3dchauEO6tEu2vqKjA6/UyMDDA8ePH1cne8e99NpmBfNi1Xbt2MTg4CMDo6CjBYJD29naKiopoaGhQCU9WY6vbqZydjcXFxaQ3B36zEdkKHllmpSbfRhQ7geOUltZQUtJIOBxmamqKQCCAKM5TW3uSoqIYgUAxkmRIWqOaCpkozGj0X4hGz2E03o8o1q35eyE+qIWgsNKFMvQwPuqjKOd4Ksb29nb27t2bxooCslyPySSya1clgUCAq1evqmUINptNM9m1cGCU1PXk5CTnzp3D6XTi9/vzmt7NRO7W1lZ+8YtfAMvDk3bs2EFNTQ179+4lEAhw/fp1urq6aGpqWpGd2epKfBvro5DtlNFozGgArSAI6w6iVKDT/Ry9/ruAgV27/pCbbrppzTGxWIyFhYUVjkUkElGHuabTF5jZxj6IIAwhy7VA4pLgQsp4F6LTkulzGo1GV5TwBoNBQqEQg4ODSQllFITD4HD8Hk6noE7Mrq+vL4hIe7x9EEWRmpoafD4fvb29HD16lPr6epX1aTOITBJhx44dvPLKK+rPb7zxBp/5zGcYHR3l9OnTuN3uNTT0sPXtVE7OxuzsLE899RT79u2joaGBsrIyRFHE4XDgdrs3vRFPUcpayyDLMjMzMktLPciykZGRLoLBKQwGg9q07XaLHD68J+drp6vowuEOFha+RCwmsbh4Fpfrm0llzxWFVo6VD8QrZ8UAw2+aG1Mp52zhcDg4dOgQbW3fIhh8ikCgiYqK/4zRqE15hVb3u6SkBLfbzejoKG1tbQiCkDNzTDJk8v7GOxsAr7zyCh/72MeA5Xt74MABNTuj0+loamrCbrdveSW+jdQodDuVzQDadPWvIHSyuCih080hCKOEwxUrqNFDoRBtbW0rhrnW1tZm3BeYrk2Q5SVCoU8Ri/Wg0+3HZvsyq6lkb1T2p42SR8lIxTNV6nQ6tZJCmavV1tZGa2tr2uuKokh9fT2VlZV0dnYyMjKAKP4LktREaem78/iJUmP1fVUme1dXV9PZ2Ulvby+NjY15dY4ycWQsFgu7du3i/PnzwDIr5cTEBOXl5Xi9XpWGXikJU4LT69mpQnveVyOnnZIgCFy8eJHvf//7TE9PE41Gue222/B6vfh8PiorK/F6vXi9Xm677TatZE4bSno6VURrPSg1qvGbTkmKMDVVid1+B+Gwk5aWXezcuZIzub+/XxMDlu78j8VFiVgMDAaJuTmIY6hTcaP0SHR2dnLuXDtVVdUcOnQo55dMUc6zs7NMT0/T1dW1QjlXVlZit9s3bENSXv5ViooGgOu0tzfg8fzOmpRwptBa0QqCQHl5OR6Ph5MnT3L58mWCwSA1NTWa3qdMlLjb7aa6upr+/n4AJicn1yhoJTszNTXFpUuXMJvNRCKRbWfjBsZWsVPpQgmipYLSN9bbW41O9xoLC2ZmZyexWs+tmF0RCoXYu3evpkGTVFhaGiMc7iIadaPXn8ZiiSCKKx2bdD5fOthse7e4uEh3dzcul0stsdYSq3s/A4GA2pifKVNlJjAajbS2tiLL/4mqqp8gyyIDA89QVfWvNbuGFlDkDIVCdHR0MDMzQ2VlZV6ulWnW5MiRI6qzAfCrX/2Ke++9dwUN/eDgICdOnFBnX231oFhOGsbpdPKjH/0IgFdffZWHHnoIp9OJx+Ph+PHjjIyMANDc3LwpStxoNLK0tJSWsyHLMpIkMT4+rjoVCwsL6qZTqVF1OGYxm59lZmaGtrbbkWUPTqczb15lugrT4Wiiq+vTLCxcwuO5N6e10pFJK2Qjz/T0/+Duu3/G0FADs7PPIorplaWtp5x1Oh1lZWVUVlZuapRgft6Fy9WPJOmorj7I/HxEZVfyeDwFFcFQargbGxuZmZlZk7pejUy/70wV7Fvf+la+853vqD/39PTQ0LCWqtHtdnPzzTczOTnJmTNnuHr1Kk1NTQl1RSHd721kjq1gpzJxNlZnNmKxmNq0rei2WCxCWdk1JGmczs57AAt79+6iqqoq5VrZIt13xGCooK/vNoqKTjM29j5crrW6u9Det2zlaWt7Cbv9x3R2VmA2fwar1ZbT4EOlN3BsbIylpSUGBwdX9H76fL4NnaNlMo3+uvRQYmzsHHNze9ZlhNoMWK1W9uzZw6VLlxgdHSUQCNDU1KRpmXKmdmp15lDRQQpEUaS6uhqfz0d/fz/Hjx8vuPciU2gSznj55Zf5xCc+wb333suFCxf45je/qaZ+BgcH6erq0uIyGSNZxEipvY9XztFoVKUjczqdeL1eLBbLmi9Yp/sFEMTl0nPokIgk3arpQ7sa6ToIgiDg978PeF/Oa2klUzrrZIPdu48SCFiorOzEYJggFFobNUrEqhGJRFI25nd1dSVt1E8XWtyXqanPEI2ewGrdQV3d2ykvF6iurub69ev09PSwY8cOXC5XRtfKd71qfOq6q6tLTV2XlpauuG6+uc7Ly8tX/Hz9+vWEzgYsP3+lpaVqBPDMmTO4XC4aGhoKzmBuI3cUsp1Kt4wqHA4TjUbp7+9naWmJUCiEIAhqGZTSF2gyXcZgOEo0KuFwwOzs3Xi93jXraeVspAudTk99/V8SCATw+xMH6QrNTmWLpqb/g93eiSwLRCLvBg6ldZ4kSSv2Jqsbt202G263WxOGylwQDH6c6en/jiB42LXrs0xNLXLq1CnKy8upq6vLK5VzNjAYDDQ2NqLX67lw4QJ2ux2/359T5YuCbOyaQnkNJL1XOp2Oul/Pvjp27Bjt7e3U1dVRWVm55bIcOTsb//zP/8zjjz/OM888w4c//GGefvppfvu3f5t//Md/xGg0UllZmTJ1dfXqVR544AH1566uLr74xS/y6U9/Wv3d66+/zr333kt9fT0A999/P5///OfXlc1kMjEyMsLk5CRWq1WdXQGsqFFV6uLa2tqor69P+ZLMzxcTDI4gSSIWSysOR3JHQ4sNnpYKc7OVr1aw2d6H3f5TYBeRSA2StKRGehQlHa+cFcWc7XySjYbR6MLv/8MVStBieZF9+/4XodD7OX9er/YcpPt8bdQsDKPRSEtLCwsLC3R0dKh0ucrk2Xxzna9+vhcXF9M6RymjGRkZoa2tjZKSEurr67fMM7ON1MjVTuUTiYJiSrAkvr9CGV4WDocxGAyUl5cnnV0hyxAKhYjFYlRVVVNdvSvhtbUqWcoEBoMBt9ud9O+F5mxkqze9Xh+xWD86nRkoIZEqStS4DajVFIl6AxUmo82GTteAw/FdioqKACgvB48Hpqdf4Nw5N17v25NmuDcDiu1xu90cPnyYsbExTp8+TWlpacbEPauRjZ2qqKhgYGAAQRDW7Z3R6/VYrVaampoYGRnh6NGj1NXV4fP5Cub+roecnA1JkvjoRz/KN77xDd7znvcgyzJ/9md/xgc/+EGefvppnnzySSD1BmPHjh2cPXsWWE4HV1ZWct9996057vbbb1cHM6XC6dOn+d73vkd7eztvvPEGjz/+OA8++CD333+/2hiV7KFQ+iOSe5n/gk73vwmHjXR23ofHY2LHjsRyaMV+UIjUt7FYjLGxLmw2PZWV69MoJkM2ny0ajTI9/XGWlt7J9LSR+fkLxGIxZFmmuLg4L43bmUCL+7v2uZlHlj/P/LyMxfKfOXjwKBMTEufOnWNhYYFIJLKuotzoWRgWi4Xdu3erLFCCIKhlSvnkOpckaUWkON3GdeUaCoXi0NAQJ0+epLy8nNbW1i2j0LexFlrYqXwGxQRB4MyZMwwNDdHS0qLOrlBY7oqKiqiqqsJoNCIIApcuXaKkpAS73Z5kxRlCoWMMDVmZm6vFZPKzc2fyaxdaAEpLmWKxGNFodJPswRcwGl9Clv3EYi1EIvOEw2F6enpSNm5vlYh1ou9IEB7B5bpCcbGdzs5ajh/vo6mpKac1tUL8+y0IAl6vl7KyMrU3wufzUVNTk1VGJlNGRkmSaGhoYMeOHQiCwL59+9I6x2g00tTURG1tLV1dXRw9epSGhoaEWctCQ05v4P/P3nuHR3aXd9+fU6Zqika99+bd9XbtrnshtrFD3ChxN3kIxvELgYS84Q3xw0vCk/CGEgiBGJz4iuHBTxzHJmDAgI0b9hbVXW3f1TZJK61WXRqNNOXMOe8f2hmPpOlFZa3vdfnySjrnd34zc+a+z12+31sURX76059y9dVXBz9ITdP48Y9/zDXXXBM04vHi9ddfp7a2NqXyoKZpNDc388lPfpIvfOEL/Pmf/zlNTU1xnRvLyElSG5KUj9F4GoNhOuoHvNKyM4G10oELF15g/fpnUNUshoa+RWHhlrSsuxDRVTUqKCubKylPT08zMDBAeXl5RvaRKBKpNsSzhtcrMjUlYjY7GR3NIitLJi/PRnZ2Nnv27KG1tZXS0tKo5OxEHtoTvd+irR1QgRofH+fIkSMYjcaEMqnJZIyampo4c+YMer2eK6+8Mu5zAxBFkbKyMkpKShgdHV0LNFY50uGn0p0Ua29v52tf+xrd3d2MjIywbt067rzzznmzKyIhlk+Q5Z9jMrVhtw8xMrIJg8EUda2lrmzEg/S0o/YA3+HkSTMlJU+SnV2S+sbiwHvcQA2n8/pL3MBWJEkKtvJmgri9HFi4/+np04iiiiBMUlxspbS0nhMnTgSrdJED5DksdVIslBsRkPUNVAwSQaICLKqqotPpaG5uTuichR0EbrebM2fOcO7cOa699toV17oWipTD/VADDnM3n8Fg4Jlnngm+OfF+CM8//zz3339/2L/t3buXTZs2UVJSwje+8Y2weuEA27ZtY9u2bcBcdjVR4l0kwysIPczMnERRjjI728yGDR/CbA4j+bRgrVQ//JXYRpWX14rfL2E0uvD5jgHJBRuB+yJgnEMH4y2FqsZCrJQM38J9iKKeV199HIejm4mJWj7yEQOS9A9I0l7y8z9EQ8Mfce7cOfbt2xfMcix8nxINNtLNq3A4HOzYsYOBgQGOHTvGiRMnqKmpSXtFRlVVysvLueGGG5BlOaU2KFEUKSgoSPr8NawcpNNPpSMpVllZyVe+8hXq6ur4m7/5GzZs2MDtt8cnHxpdoVBDVcdRlCGMRjPl5Y3k50fOLC81ZyMepMtPKcp/UFvbhSRJOJ0vkJ39udgnJYhwQ10XcgMDxG2fz8exY8cW8cpWK8J9Rrt3P0h9/W/p729ky5YazOYLbNvWzZ49Bo4cOYLVao1KIs90sBEpcSVJEjU1NZSVlXH27Fn27duHz+eLez/J+MxEK1jhEm9Go5F169bh9XpXdKABKQYbgUg13JscOtTs3Xff5eqrr4765nq9Xl5++WW++tWvLvrb1q1b6enpwWKx8Morr3D33XfT3d0dc38BNap4Ec3IyfLPGR/3ommlnD+/Ab3eT7SBr8vdLxpprXTsyWz+GILwd+j1JVgsNyV0rqqqwSzHxMQEk5OTtLW1LauqRihWSqYpdB+yLHPbbQ/S29tLc3M5ev0RJOlZVFWgtva7SNIfU1tbS1lZGadOnaKnpydIIg9guYONwGvKycnB4XCQlZVFa2srxcXFVFZWRjSUiVY2AscnMo15DZc30umnID1Jsfz8fPLz84Hk1KgiBRuS9C4u1z68Xic9PdvJz78iakB/ObdRZWdXomlz62Vnp06mjkXcDgx1fb/wvMLZ/Z07P8uJEx+kuroUq1VFp/soguBi0yYrRmMrFy8ORSWRL3VlYyH0ej2NjY3MzMywd+9e2traqK+vx+FwRD0vGT+V6OuMdo3VcM+lFGx86lOf4r777mPr1q1YLBZMJhM6nS44pKSnp4eWlhbefvtttm3bhskUuZz7q1/9iq1bt4ZtTQqd9nvHHXfwxBNPMDIyQl5eXtT9JapfHtmIj6Jpe7Hb9zM8XIwklQRJUZGQfiPuRRBeAXxo2h8AiSsopC8Aupbe3m+zbt0mot1CAUnG0KxPoBc5QNyemZlh69atKe5n5TjMdBjLcK8lNzc3yD/QtFm8XlDVGVyucgyGgwhCFwbDB1m/fj3T09OcOHECSZJoaGgIPnhnMtiI19AGji0rK6O4uJje3l727dtHZWUlJSUli9ZZiozRGi5vpNNPZSIpluhQv2j2ThTbkeVBpqf1eDzhyePzj89EZWMaQXCjadH9cySky56bTPdz9qyX+vom4NqEzg1H3A4MP0xlqOtK8VOZgsPhYNeuXZd+Oo+iTOH1qsjyKILgp6hIpqBgF729ffNalkJtfCaFTOJd22g0YjabWbduHSdPngwKnURqA1sqPxXpGislSRoNKQUbjz76KJ///OeRZZnt27dTXl6OLMvBHvqenh5EUeTJJ5+MasAB/uM//iNitmhwcDDYGtLa2oqqqnERP5MZlhTOGEhSC15vH9PTxTid9ZSWNseMJEVVRThwAHFyEhwO1I0bIQU5Tbf7FbzebwEaOp0Lk+njCa+RXh6JkdDbR1GUeUGFy+UKSjJarVaKiooWGWefz5cxZQ1N05iensZkMi0bWTxZxDJcw8NGXn/9PrKzLzA1VcqDD96PIMyiqs+jKL/EYrGwbds2RkdH6erqwuFwJNQOlEzrUjKBjCRJVFdXzytdL5wlshQZo2hYDUZ8DdGRTj+VqaSYz+eL+/VESooJQi9ebyuK4kRVc7BYbg5WTyIh/ZyNAXy+vwCmkaRPI4q3JrxC+pJHEk7ndiB6MivADQz4rnDE7enp6YT66zOJTGb/07kPp9NOZ+d1VFcfpqurmXvueRhRbMPvv42qqu9RUlLC6dOn6e3tpaGhgdzc3GWvbIQeG5ghFeAcHj16FLPZTF1d3SK53GQr8O8npPQUduutt3Lo0CHeeecdXnrpJV5//XUmJrunT9IAACAASURBVCbIyclhy5YtPPLII9x8880x15mZmeG1117jBz/4QfB33//+9wF4/PHHefHFF3nqqaeQZRmTycTzzz8f102TaBtVJCMuiv+NIAxiMmmMjNxGQ0PsqoK9qwvd9DRCfj5CXx/ixYsot98OSfbVjY9PYTarCAKMjIyTDBc6XUbc5/Phdrs5d+4cTqczOPwwwK9YCaoae/e+w9jYbgShiltv/UhKsnYrDaqqMjFRyOhoHgUFw4CCpkkIwnkk6fNoWhOq+sfk5uaya9cuBgYGOHz4MDqdLi4jl8lZGOGur9PpaGhooKKigtOnT3Pu3Dnq6+vJycnJuFTuGi5/pMtPQeaSYvFINAcQOSm2F0E4hChqTE9vR6+3x/zupLsqPDnZjqb14/UaEMWfk5e3fMHGwnXeI26/1wbldrvncQPz8vKWlLg9PDxMe3s7eXl5bNu2LW7btdTBRjLXEwSB48d3cfjwdiyWcUTxVTTNjiT9ClXdh15fGJzwffLkyYgDWNOFRP1U6LEOh4Pm5maGh4fZv38/OTk586TRM5mgu1yQcspX0zSuu+46rrvuuoh/h+g3q9lsZnR0dN7vHn/88eC/P/3pT/PpT3864b2lo41KEE4iCHsRxVm83gJgU8z+PWZnMfX14V+/HtlkQrPZEPr7YWIC4pTiXAiz+UOcOjUA+Kis/MOYx0dCIkY8lLgdapxlWcbn82EymSgoKAg7/DAT+0kExcXfYNu208zMZOF0Xk1OztIMQIr39UQ7LpbhKiws5IYbbqC/vx+9fgOKUoQgvI0oXkAUX0QQdCiKhKrejSDkUFpaislk4vjx4+zdu5fa2tqwJPJ4r5/K8dGONRqNrF+/HpfLRXd3N2fPnsVutycUKCZanr7c2xrWMId0+KlMJcUMBgOTk5Nxv5bwSTEFQXgOv9+Doujp76+nuTm2ok66Kxs+3zrc7jyMxinGxq4nRlEnIlL9Xmqahsvlwuv1curUqSBx22AwYLVaww51XQ6cOPFjmptfYnS0mMHBb1BSsryD+hJBLLtvsVj4/d//ffr6+nC5pvD5DiGKLUAlOt1DgIzP9xxm83Y2b97M+Pg4x48fx+OZm5uV7sGqqfopQRAoKCggPz8/KI0e4Bwmo5r4fkuKpRxshCoKhRqIUNWP5UI62qh0uh8hikNIksbIiIOcnDgib0GYM+KXpkMCXGKqxb2XhbDbc9i06S/QNC3pLH20zyJA3A4tJyuKgtFoxGq1YrfbKS0tDWbhTp06lbK2cybvjaqqizideqxWN5I0k7HrhEO86hVARIMTa40rrriC6upqurq6+PGPrUxMXMvDD798ybmPI8v/L5r2bXy+54F1iKKI3W6nrq6O06dP09PTQ0NDQ9jAebmCjQCysrLYvHkzk5OTHDx4EFmWg2pksbBWzl5DOKTDT2UyKZZIG1W4AEEUOxDFo8jyNIKQiyRtjmsycro5Gzk5NQwMfJvx8RmqqqqTWiPRyoaqqkFuYChx22QyoarqiiZub978SwRhgqqqMfz+bmD1BBsQ+3tTVlZGWVkZ77zzDs8++0H8/mbuuONnlJdrCIITWX4ITduJojyFw+Fg8+bN7N+/P65J5MlItMdr66P5BUEQKC0tpaioKMg5zMnJSUgN6v3od9LWzC5cesBeSUi9jUpBkp4D3MiyxMWLTVxxRRyydUYjs/X1OAYGEPLywOVCq66G7MhSufEgVe5BwIj7/f5FqhqapgWJ26FT1SNhpWeEdbovUFj4L/j91+D1Niz3dsIiWmUhHgR4KZOTk+h0Ol5++YM8+ujtiOJ3Lq1/Br3+Zvz+R9C0zxGQ+1y3bh3T09PB0nVDQwNZWVnz1s0U2S0RI2u32ykvL2d2dpauri6ys7Opra2N+tCQjN75+83ov5+xUv1UohX4xUmxpxHFMWQZhoeLKS6O70E/nW1Ugfe1rCy1eUfRPp9IE7fDEbc1TaO9vT3uwZ6ZRrj3Ojv7GuCXCIIZRWlkhbvVeUjET3m9XlyuWWTZxjvvXMMDD7yNpvUAM4jiK8jyzfj93wS2YjQa2bx5c/BBPhyJPNn9pjMpFso5PHz4MBMTE9jt9nmcw0h4P3ILVxdzNkGkWtkQhO8hinOZLFX1MzKyBavVGtdaMxs3MtvQgG52FrKzURsaYBkeanw+X9A4Dw0N4fF4uHDhQtA4FxcXY7FYEorK03ljZypoUZR7UZR7M7J2NKTbcceCzWbDarUyNTVFTc1Opqe3YDLlotd/EZAAJ5L0L+Tl/TcTE18D5kYJB4hvo6OjHDp0CLvdHnyQTybYSOTYRO8fm81GU1MTFy5coK2tjcLCQqqqqsIG38m0Ua0FG2tYTiSjRjU/KeZHln/GXCsV9PfXsWlTfFXndAcb6eRaxCJul5WVYbFYVvX3V1GeRBTvQNNK0LSVMZR2IaJ9pvHacpPJRFNTE6dPn6aq6sMcPvxHVFR8F7v9p4ALUTyBKN6O0fgAovhxRFEMBhkLSeSh+1oKIZNY0Ol0FBcXYzabGRkZmcc5jLaX1XzfJoPLOtgwGo3BzEc8WFjZMJm+PO/vDse6+NeSZXxVVaix+B1pQqhxDhjo2dlZZFkOGufc3FwkSaKioiKla6XTqaQDK0n6FlJ/XYlkjGRZ5oEHHsDj8XDw4EGeeeYZrFYr9923F4vlQwjCCUBDkvpobLwfVb0bRfkxga9+bm4uOTk5wQf5kpIS8vLyEn4NmZLVDR24VlJSQlFREX19fbS0tFBWVkZ5efk8o51u9arVkDFaw+pGMmpUiqIEf9bpHgTmkmqCAJOTm+KeMxN9QGBiSNYOLyRuj42NMTs7y9TUVErE7dXx3dWhqjuXexNRkY4KvCAI3Hzzzdx888288cYbtLS0IEmN3H//k+Tn/z2CMASAwfBDtmz5CfA8mnYzer1+EYm8sbERi8WSVGIpWYJ4PMcHZnS4XK55crnhEtTvR27hZR1spD4s6b0PeGLCktDE2FQegEXxLeAlNO16NO0ji/6uaRqzs7PzyskBQlXAOBcWFi4ibg8ODib0fqxh+RAv70MQBCRJwmw2c+TIESRJwuVyMTjop7KyHUn6FJL0fwjcy6L4U/R6C17v/w18JXitkpISCgsL6enp4cCBA+h0uozIECZDpAvdgyiKVFZWUlpaGpyaXl1dTVFRUTDju9Y7u4bVhFTbfXW63wb/rWlgs20Kd1pYpJao8SCKh9G0UjStKK61NC38xO1Q4nZWVhaTk5M0NKyc9tfVEbgsLeL1DwuPGx8fv9TOLTA4eAMOx05k+X4E4SwAOt0UcAeqWoOitAFZmM3mIIn8yJEjWCwWqqsT4wQlI30bL0KDk6ysLLZs2cLExATHjx/HYDBQX18/T1Y70WBmpcgdp4LLOthIdVjSq69+iJtu+jkej56nn36cJ56InxCdvBH3MzPzdSYmBCyWExiN1+L3+7lw4ULQQCuKgslkwmKxYLfbKSsrQ6/XJ30zKoqCKIoJye4l8toURQk+FKeyTrowNTVFS8sbmExWdu68fslkcQMTaJ1OJ1NTU7jd7pjl1mhY+N5deeWVtLa2YjabOXfuHACVlc+gadchy08A7z2g6PVfR1HaUNVfB38nSRI1NTVkZ2dz5MgRWltbI5LIk0W6pGxlWaauro7y8nLOnDkTLF0n4yTWgo01LCdS9VNTU1dgt3cBcPJkHaWlpUmvlQhU9Vt4PG8jy3Yk6buL1opE3DabzVgslojE7fHx8aT2c7lB0zT6+voAKC8vX7UPmwtt/o033sibb76J3+/n9ddfp6srj3vueRez+cFLidY5iOIZ9PpcvN5vAHPCCw6Hgx07dnDx4kU6Ozvx+/34/f64EkyJ+IZ08Bazs7PZvn07IyMjHDhwYB7n8P0oZHLZBxupDEtqbd1Ka+t7A4ESyZgmo/Lh9/txOqcYGckiJ+ciIyMmBgaOoShzUrN5eXlUV1enpEa1sGQ+PNyKonwZRbHicPwjFktsR5WIg+rv7+P48R8CWTQ3f2Le4KvlwrlzT3PDDT/A6zVw/vw/U119U9rWDhip0OnpU1NTuFwuNE0LtrQFMvE9PT309PTQ2NgYd+tDKEIN4o4dO9i0aRPPPfccXV1dHD58mIceegi7/eMMDlZRWHgHgvDe5y/LbxGu0CXLMg6Hg6qqqogk8mSR7tK3wWAIltlPnTrFxMQE5QkMobkcjPgaVjdS9VM/+MFHufLKAmZmzBw9uoXPfCa25G2ktRLB+HgHqqoiyxcQxbMoikB/f3+wJQrCE7djIZx/8fsVBgZ+DmiUlNyJJKX30UVRFI4dO4amaaxbt25FDII9caINv/8bqKqAx/NX1NdvXpLrBpQpp6amgiT8qqqqRXLpyWbbc3Nz+chHPsKzzz6LIAgMDw9z/vwkNTW/RlG+jNn8/807Xq//C7ze91TeBEGgqKiI7Oxs2tra4iaRZ7KyEel4QRDIz88nLy8v2KpcVFSEXq9fCzYuJyTaRrXwYbyqqiqYIb7hhhsSunYs/XKfz7do4nZgYuVvfvMliov3UVNTw1VX3UR7e3taMhvhzvf7n8ViGUEULzAz8ysslj9O6RoL4XY/zdVX/xRNg9HRUmy2j6V1/WRQXd2CpgkYjTPk5h4H5oKNZDN8AXUvp9PJ5OQkk5OTdHZ2BgOL0tJSsrKy5gWrqqqiKApbtmwJTvrOycmhtrY27uuGM54GgyF43/n9fnp7e1m/fj2Kso6jR9tZt25rUIFZVa+MuC68RyIfGxtbRCJPFplSizKbzWzcuJHDhw9z4cIFJicnqa+vjxkgXQ5GfA2rG4m2US18GNfpjLS3XwPMJcQSSUYlU9kIcAOPHv0AhYWvMjpah6q6EQQJURTD2rtEEM4+XLz4Y7Kz/xmAwcERSksfS2rtSDh37l3y87+MIGicPv1FGhtvS+v6ycBuf4GCgjYAhoZeBNIfbISrPqmqitlsxmazUVBQgCiKXLhwgb6+PpqamuZxEJJpowqgqqqKw4cP4/V6eeONNxAEgdzcP6e3t4qmpsfDrLQYZrOZK6+8kjNnztDS0kJ9fX1E9bFEfE8yfira/b6Qc3jq1CkcDscizmG09aMdtxqqXpd1sJFoeXoh8e7ee+9leHgYs9mccFY3YMQXEredTmdwMF7gYbSyshKz2czAgMT4uMDQkExJyUaMRnC71eBa6Qg2FjoWk2k7gtCKphnIylqf9DqRUFLiATRkWaOgYH7gt1xtVDbbJ5Hlv0TTstDpbp8nNxjrPQ4NLAIVC0EQ5lUsvF4vW7ZsiXs/gUnf58+fp6WlBZ/PF/fnHe6YO++8k3fffZfe3l7efvttxsfHaWxsRBD0+Hxu4BkEQULTPh52zYVZmpycHHbu3DmPRF5RUZHUw0QmZXXhvWnkgiBw+PBhLBYLtbW1EWcOrAUba1hupDp89iMf+QgvvfQSgiDw0Y9+NKFrR7PBmqbh8XgWDXXV6XRYLBYqK+9gaupaamsLKCgo4NChQ8Ehr6kg3J50utFLVVkBvX4kpfXDweF4F7N5CE0TkKS3gfQHG4n6u8LCekRRhyAIFBbWpXz9hYGFy+Wio6MjZvXJ6/WyYcOGSwHmUSwWS7BlNR5Esvk33HAD2dnZvPPOO7jdbt58803uvfdeZma24/WeQ6drBFR8vt8uXvQSBEFAr9fT1NQUlkQezz4S2XO04+PxIwHOod/vZ3x8fBHnMBIuBz912QcbqQ71y8/Pj/v8AHE7oKjh9Xo5d+7cPPJbOOJ2AP39Ai+9pEOWNd55R0d/v0pZmZq2h/Jw69jtf4SibEcQspCk+LPq8cJs/lMkaRawoSh3pX39ZKBpt+LzXQXogMiDr8JpuocGFuXl5WRlZc0zAh6PJymjIAgC5eXlFBUV8c4779DS0kJDQ0NUPkeke6KgoICGhgb6+/tRFIXTp09TUVERcs99Iqqee7h1Q0nkAf3z6upqiouLE3mZSWWMkiHSBVS2Ar29kVoQL4eM0RpWN5IJNkK/ozk5OXzyk59M6tqBwCUe4nasidvpUrYK56dycv4Ip3MAQfCTk5Pca42G3NwbEYRXmJs39YFFf1+OpJgo/g9kOR+Q0LTfT+jcUH5g6CytQGBRVFTE5OQkzc3NMdcKvHa73c6OHTsYHBykra0Nv9+f0vsiCAJVVVXs2bOHmZkZZmdneeutt6irqwOK8PkmY77G0HsxHIm8rq5u3iTyRCob6VQ1XAhRFCkuLiY3Nzc4ZDdWVWYt2FjBSF2NKjJCswSBL3VgaqnVasVkMpGbmxuXzKwgDCLLz3DttdmcPv04ra1mZBnuukshOzv9+uULfossb0zDOuGhaWUoyj9H+XumXlcszJejUxQFt9vN0NAQFy5cYGZmBkEQgo42XGCRCeh0OoxGI1deeSUnTpwI6ouH43NEy77U19dz4sQJzpw5w+TkJG+++WbcrYDRDGdgkFFpaWlQ/zy0GhgLmZQrhPlGOdDbW1BQQH9/P62trYuqMpeDyscaVjeSaaNK5aE+1HcNDQ3hcrkYGhqKSdyeu3YfoKJpi5UZM+mnJCmH7OxvJrxW/Pu5FngeQVDRtKqEr5MZ6MLOilrMZ4nOD4w0SysZuycIAsXFxRQUFPDuu+/S3t5OQ0ND1KRsNBubnZ3NXXfdxQsvvIBOp+P06dNxJ7AirRtKIg+dRJ4I0iVkEu14QXhvyO7MzAzd3d1BudyF3NbLwU9d1sFGqiofAYS2zgQy3IEsgcViIT8/f1HWtL+/P+7rer0/ZmLiDWRZZXr6Vh56qJFTp0TGxgSqq7W0thutpHWW68ujKMq8tjaXy4UkSXg8e6io+C9cro1s3vyP6HTJcxNSRVZWVnDoXldXF7m5udTU1MwrcUczQAaDgeuvv54LFy7g8XgYGRlhZGSEmpqauK4fy3AG9M9dLhd79+6ls7OTxsbGmO2GS2HEFx4viiLl5eWUlJTQ09Mzj1B4OWSM1rC6kWobVTSEVmenp6cXEbcdDkcwAxwLgtAB/P2lhMEXFs2HyGxSLPPQtNTmTy0F5tpvRpmc/A2nTxdjMDTNq7aXlJQkPKQ3GUiShNFoZP369Zw5c4aenh6ampoWtS5BbJtfXFxMUVERvb29CIJAW1sbO3bsiOknoq0bmmgKVOITaU9Opo0qFb9mNpvZtGkTU1NTnDx5ElmW5yUZLwc/ddkHG4mqfPh8PsbGxoLGOZS4HYnsGw6JZJ8GBiSys/14vfAHf3CW4uJ6tm9/79xklK0i7SkdWE0RdigRf2pqKjiFNlCxCPBlRFHE6fw4ZrMT6GNi4i5yc29J+HrpzkDk5uayc+fOIJ9joepGtGvl5eXR1NREZ2cnoijS0tLCjh07Yl4zkaxpVtacBnp1dTWHDh3CZrNRV1cXkUQ+p14Tv9lJpo0qklEOSPuWlZVx9uxZ9u3bR05OzopQnlnD+xfpaPeFud760KTYwonb4XzX2NgYY2NjcV13YqIdTRtG00DTWsjJyUywAelLiq0mX7UQC5Oc09PTlwKLr3PVVS1omsTw8L9SUnLzsu3RaDSyceNGJiYmOHLkCFarNaz9j/Y5iKLI7bffzrPPPhtUMjt+/DhXXHFF1GvH42tDJ5G/++677Nu3b9Ek8nDItDRtJD9ls9nYvn07o6OjHDx4EJvNRm1t7VqwsdIRrTwdSn4LzfwEboI5Atx7D6KJYiHZPBok6cN0dcmoqonNmxc/DK60jNFyZZ5iIRBYhDpbWZaDgw6rq6ujTqH1+XIRhElUVU9WVvzyqZmGKIpUVFRQXFzM6dOnaWlpobGxMeZ5giCwfft2jhw5wuzsLKqq0tXVxaZN0Yd+JdrqBHOl6507dwb7eYuLi6msrFwUlKdLUjAS4glOApNe3W43XV1deDwecnJy0jpPZA1riBfJtFH5fD6Gh4fDEretVmvcE7cTseVjY1sQhLcQBA2PZwsL6WQr0b+sRD8VDoHAIlRuNrRiUVpaisViQRRFJiYGABBFP1lZA0ldLx3vS+jDfnZ2Njt27AiKiJSVlQWVluIJCrKzs2lqaqKrqwtN03jjjTdobGyMavsTSezp9XqMRiObNm2KSiJPZu1kjo/lp0I5hx0dHVgslqhJsdUQVK+YYKOqqgqr1YokSciyTHt7+7y/a5rGZz/7WV555RXMZjPPPvssW7dujbDaHAJtVH6/n9nZ2XlKDF6vF6PROE9FyOPxcPHiRerr61N+PfEZTA1wU1ZWQU7OJxBFMaxqTqo9uontaXXA5/MFDfPExARTU1McOnQoWLGI19mGYmLiG6jqbuz2qzEamzK4++Sg0+loamrC5XJx/PjxIEcoGrKzs9mwYQPt7e2oqsqePXvYsGFDXJW5eBB6PwX6eQOTyENJ5IH1Mq1GlUgGyGg0UlJSgtvt5uzZs5w9e5b6+vqEpR3XsIZUIElSVEWohcRtj8cTTKzEQ9yOhkR8QlnZFo4c+QKBWRSprBVrT5czIgmPBJJisfiBs7Ofx2z+KlCHzXbP0m5+AUI/q4CISEFBQbByHIlvGA67du3i0KFDQQ7K0aNH2bBhQ8Tjk+kiiIdEnszamfBToa1gx48fZ3BwELPZTEVFxaqscqyYYAPgzTffJC8vL+zffvWrX9Hd3U13dzctLS38yZ/8CS0tLWGP7erqoqOjg3379nHs2DG2b9/ON77xDWpra4PaxgtvLpgzAul4qId4+mq9yPJXEYQjqOrHMJs/EvHIlZYxSmfQElhrZGQkOBV9IRZKB8/OzqLT6YKBhcViYXBwMKphigeqmo0oPoxOF5qy+xHwDnMTTKNXBGBpiFxZWVls27YtODm7u7ub6urqiJmPHTt20NHRgaZpTE9Pc/r0aRoaGiKun0jrUrjXK4riIhJ5QGkjE5WKhftJdH2TyUR9fT3j4+McO3YMk8lEXV1dyhKea7j8kImkWAButxuXyzWv4h5u4jbA0aNH4+ZfRUM8/A9BOIcoHkIQtrB5c+RZDyvVvyznOqGBxeTkJE6nkwMHDqQkPKIo2xgd/ekCIvU0gnASTVtHNIXFdCLSZyTLMvX19ZSVlXHixAm8Xm9cry87O5vCwkIGBgZQVZVXX32VK664ImJiLBVfG4lEHrhWptWoEvFToiiSm5uLLMv4/X727t27KIm3GrCigo1o+NnPfsYjjzyCIAjs2rWLiYkJLly4EFa54D//8z8pKCjgwx/+MAcPHuS1116L6xqZeIiOBFXtZWLiXaamzDgcz2EyZSbYGB//NV7vfyJJt6PXf3BFVjYOHHiXmZkXcLsdbNz4GQRBmDeTJDSwCOi4h37JApmhdMPn60KWv4QgKPj9bUjSgbRfIxXY7XaKi4vR6/W0tLRENEBWq5WcnBxGRkbw+/387Gc/47Of/WzU4XypBBsBhJLIT548SU9PT1BKM16kuzwd7viAsIPD4aC5uZmRkREOHDiAw+GgsbEx4oyONbw/ka6kWEdHB3v27GH//v2cP3+e6667jr/6q7+iubk56sRtn8+XtqRYbN/iRhC+hNs9gl5fCDzNnGR4MmtFhqb5mZ09gV5fhiAYVqSfmpqaYvfudzAYjFxzzbWLEpaRhEdCpdLdbjfbt29P6740zY3PdxOyfBGPpx6T6bdA8uTqUATus2hE7EgwmUxs3ryZgYEBjh07xrFjx6irq4s6dHLDhg0MDMy1hnm9Xl588UX+8A//MKXXEAnhSOQBfsdSqVElcrwsy9TU1FBeXh6Uy62rq4toi1YaVkywIQgCt956K4Ig8KlPfYrHHps/IbS/v5/y8vf66MvKyujv7w8bbPz93/89MPfl/+u//uu495AunXCIbXinp+1MTOTgcIxw6tRWrgw/zDmutSLB759Fkr6M0Sggikfw+bavqIyRx+NBURTy8r7Nhg378fslDh8uxuG4NdjaFk97QKbawyYnfeTkgCRpTEwIZGen/RIpIZAdqaysDPI5+vr6aGxsJHvBZuvq6hgZmRuG5ff7+cUvfsG99y6WVYTUKxsLkZWVxZYtWxgfH6erqwu3243dbg9bXQy3l0wT9Ra2AuTn55OXl8fAwAA+n28t2FhD3EgkKdbR0YHRaOSJJ56gs7OTd955J65rpEswBGLbTkXx4HQOXrIJA9jtCpKU/mBjaOgLmM1vMTWVi8HwoxUZbJw58wI33PAtvF4D5879AwUF2xcFFuGERwLw+/0ZSYrNzJzDbL6IougwmU6gKDPIcmJDiKNBEISw+473M7LZbOTm5mK1WmltbaW8vJzy8vKwa5aUlMx7Djt79ix+vz9sdSNdXQQBEnmgEr9v3z6ys7Mj8jnCIdPBSej6gSTe7Ows3d3dnDt3jg0bNizy+SsNKybY2L17NyUlJQwNDXHLLbfQ1NTE9ddfH/x7pEFj0SDLckLBw9JVNmaw2fo4ceLjHD48Tm1t9DJ7svsSBBlFsaDXj6EoFkTRiKa5E14nHBLZj6bNTVEPcCwCfcd6vR6/309RkQFBkNDrdWzdWo8opn+4YDLIzt7Mvn2fRpIOYrU+THa2gk73Q2AGn+8TQPjZF0uFcAZoenqaEydOIMvyvKx8RUUFra2twe/DyZMn41o3FhIxmg6Hg6KiIiRJClu6TnV9SF/vrCAIlJaWRs3CreH9h3QmxULPTeSeTWdSLPZaWRw+fBe5uccZHb2Sq66K/H1IxX8aDC0oihWdbhSP5xSaFl+f/1LA5/OhKAqVlW8gCD7M5lkE4S2GhyuDgUVWVtaytbQYjXWcO9dMeXkn3d13UFdnBmYQhBk0LbNZ73hlZEVRpKysjKKiIs6cORNVFWrLli10dHQEf25tbeWqq64Ku2463/MAJ3JmZob9+/czNjYWlIaOhUwnxcIdbzKZ2LhxI06nc1UoKq6YHZaUlABz04/vueceWltb5wUbZWVl9PX1BX8+f/588Jx0YWmMuIYs/x1+/zF27SrA6/0mkhQ9C5GsERdFHUbj07hcb2M270LTbMBEwusE4PP5OHv25WKfkwAAIABJREFUFEajOSqhMdBzHBpYhE6iLSkpwWAwIAhzmtpm89fQ6Z5C0yrw+W5Ken/phizLXH31XwSNmiz/CJ3u7y79PInX+2TY8+I1gOkwlAvXsFgsbNu2jeHhYTo7OykoKKC6uhpRFNm0aRP79+8PHjs8PBx2GFMiRjwZIl1ubi61tbWLSteRsmeZbqNajWS7NSwPMpEUSxTpXC+abxHFY5jN/8GOHZWcOvUYGzaUxVTESTbY8Hgewmx+BpdrI9nZG9G07qTWSRWRFA0VRcFg+AMsluOIooGmpgdR1ci8t6WEJMmUl/8nk5OT1NRkI4o9GI33IAjTeL1fRlEeXHTOUleOAvdsYH7EzMwMJ06cCM7nCCWR19TUzAs2Dh48uCTBRgBms5mSkhIURYlKIk9lL+mshFit1lWRFFsRwYbL5UJVVaxWKy6Xi1dffZUvfelL84658847+e53v8t9991HS0tLsF89nViaNiqViYlOXC4Bk+kUZvMU8F6wIQwMIF4iHapbt6KVlaVkxM3mGszmOSJhYBhhsjh16oeUl38Ht9uKx/NnaNoO3G73POPs9XrnBRalpaXo9fqoXyxNK8Pr/buk95UuRDIAgd+Njw9ht89VhiYnB7HZRgA/mla4lNsMItpnmZ+fT25ubvCB3mKx0NTUNC/Y6Ozs5Lbbbgu7biaDDUEQ5umfx8p0LZcRT/Taa7j8sRKSYulENN+iqt9hbKwfo7GTDRuuR9OiS0On4qcKCz+Fpn0SqzV+yfhIGBsborv7O4CM13tjxONCFQ0DwiMBqfSFioZtbW3Y7fejqh/A79cBK0smW5bloO10u18HhlBVAb//35HlxcEGLJ1tC3dPmM1mtmzZwtjYGF1dXeTk5FBbWxu2Mh1tfEGmXoOmacGZIZFI5AuPz3RlYzUEFNGwIoKNixcvcs89cxJuiqLwwAMP8MEPfpDvf//7ADz++OPccccdvPLKK9TV1WE2m/n3f//3tO9jadqo+ujrqyMn5wKnTm2lsdFKIFkk9Peje/ppuHQzS+3t+B57bEWofGiaRn7+b9A0MJvHsFgO0tZWgdFoxGq1YrfbKSsri6sPf7Xi+PFdCMJ16HQeoIKrr74G0HC7v4ffn/gAwFQRy9iGPtB3dnYyNTU17++RhnllMthYaGT1en2wdB2P/nk8WKtsrCETyHRSbCmU7BYiGv+jt1fCbp9iYsKALMfmrKWarBME8dL/U/N3TudTbN78EpoGR4/6gRvDKhqGBhbhhEfCQdMKkt7XUqG3t5bycjNG4yxdXVu46qonEcVBPJ4vsRxT0qPd1zk5OezatSs4tNZut2M2mykuLubChQsAEb8/ifqpRBCokEcjkYdeO9MV9cvBT62IYKOmpoaurq5Fv3/88ceD/xYEge9973sZ3Uem26gEYQhJepK6ulEuXKgkK+thzOb3qhpieztIElpR0dzxQ0OILS0IGzcuabChaRqzs7PzjLPP5yM//2pstm7AgShuiWsadTzXWi1obNzIL395Nz6fjwceOAq4AQFZ/gmgx+/fjqYtbcYonmvp9Xry8/MXGatoA43iNWzpKh9H0j/PNC4HI76GpUEmk2J6vT5YFV5KCEK4GU4asvwi2dnD9PVtYHBwJ7t2xQ6YVkJSDCA7W2TudA2j0Utra2tMRcNYWE3zQ4qLt/Nf//VFvF4Xd9+toNN9B/BjMPhxu9OfpI2FWD5CEATKy8spKiqiq6uLkZERrr/+el5//XUEQeCWW8In8paiAh9AOBJ5aCV+ubiFqwkrIthYKch0ZcPjGcTj6cXj0WOzuaioCDMFOvSGEgS4dNNnyogvDCympqZQFAWTyYTVag1qu8/JpO5AEB5E04z4fCdS3k88GB8fZ9++5xFFA9dc87GUMt6pwmazcf/99wPgcrXgdL6AKCqYTK9jNL6Gql6By/X8su0vGjRNw2w2s337djo7Oy9VqvLDKn0kShBPZ5tTqP55R0dHcChnrCGEyeJyMOJrWBpkMikWGEC7HMHGQp8gCMN4vc/h90vU1fVQXv6/yMqKrW60HMHGQn6g2+3GaLyJsrIp9PosXK6baG5uzviDvqZp9Pb2oihKkCMX6bh0XCsaLBYLDz30cVRVRVV/idvtZa7dtwezeRc+3wMoyhNLWkWL51o6nY6ioiJmZ2cZHx9n+/btUYcCZkrIJNraoSTy0Er8GrcwNtaCjRBklnjnQZb/N6o6iarmcOTI73H11fPPUbduRWpvRxgenvuF243a3IwQx7BBRbmI0/nXl9QyvoJeH75cqigKg4ODQeMcObAIj0AZeakqEoOD/8YttzyFpgmcOydRX//oomMyJX0bDfv3+zh48M/IzR3ioYeeRa/XI0n7yM39AOPjjwHrM76HZDI7N998M3V1dRiNRtxuN/v27aOmpoaioqLgWomum8nJqW+//XZMEnkqWI7WlTWsYSEMBgM+n2/JrxuuAu92X8DrvYhOJzI4WEN5eXxzceK1w37/OVR1HJ1uM5HmQYRbJ1xgodPpsNlsYaTSr710Vltce08VZ88exuP5EjqdhyNH/oIrr7w5o9eLRw5ekiSOHKlkYOBe7PZxrr/+twiCGb3+6yiKBVlOfShkPEhUudJgMFBfX8/o6ChdXV3k5uZSU1OzSJxgKdt9F2JhJd7lcuHz+eJOiqUr+AlgNfiw90WwsVy9sKFGXNPO4/e3MjxciMmkUVd3+6JztPJyfJ/8JGJrK4Km4d+xA62yEvHs2Zhf2PHx5zCZOgCBiYl/JT//b5mZmVnUCuXz+fB4POTm5lJVVbXiSUfl5acRBA1R9FNc3Bf7hCVCSUkJhw8fZny8mImJD5Kf/zYgI4p91Nd/Eb//Al7v/wNkJiMPid/XocTsAEpKSuju7qa3t5empibsdvuSlqejQRRFDAYDzc3NYUvX6cDlkDFaw+qHXq+PSITNJMIlxczmbzM7K11K8HyMior4vh/hW7LmY2ZmP37/EwiCj+npR3E4PhP2OFVVGR4eXiSVHmiFincG01LBbP4ZVVVzCkrDw88BmQ024kVBQSH79m0AfFx99QFk2YkkTWM2/w2bNmUDP884jyNZf5Kbm8vOnTuDfI6FCaeV4KcClfjf/e53dHZ2UlhYGJFEvhBrlY3LDDqdDp/PFzVbH4pMtVGp6kuI4gWqqnx0dd3D+vWLJUcBtIoK/BXzv/yxMkaqqjI7W4heLwAa588bOXeuDbPZjNVqDQYWmqZx9OhRKisr0/IalwI2258iSYcAA5L0MCuF4lFTU8PHPvYxFEXh5ZcdqGoFH//4v2IwTKNpKpL0j0jSr3G7n1sWUt5CRLp/9Ho969evx+l0cvz4cYxGI3q9PmLpOty6mVSLgvCl64aGhoQmkUfCmhrVGlYCAm1U8SKdfmr+ugN4vR0YDAIuVwHNzdfG/R2Ip7Lhch3BZHKjaTo8nnYiSaUHfrdQKj2Z15eOZGOsdQoL1yFJc5PPCwoyX9WOF0VFRdx3333Mzs7y4osyJSXvct11v8VgcJOVdQ64Go/nSXy+TxFr6niySPReXciVqKiooLi4mFOnTgWH1jocjhURbAT2q9Pp2LVrV1xy7slgLdhYBTAYDHi93riDjXRhvuGdQZZfwu32MznpQNPiN+AL11JVdV7F4vRpLw6HG4tlE9PTX8Ro1LFhwwfR6xf3/vp8vlVFyAZQ1XWo6u7l3kZY5OTkMDw8zMTEBKJYwI9+9AkeffQtdLouwIckdZGVtRm3+2/x+f4vPB5PUGpxcnISVVVpbGxMOlOfTmNrtVrZvn07w8PDHDlyBLfbTX5+fswMTTJGPFmjGShdT0xMcPToUbKysqivr0+pz/1yMOJrWP3Q6/UJtVGl6yF6IUTxGVwuCb3eQ1/fTtavj38SdaxgQ9M0JOl6RkZeRqcbY2DgFnp728LOYGpra6OmJj1tPkvh8wThbgTBgii60bTFXQvLCZvNhizLXLzoZ3j4akwmF9dc04qq+hDFKQyGLyLLLzI7+wKQk5E9pOqndDrdoqG1Vqs17raldLdRhUMsEnkquBz81GUfbATK00tNLA5to5Kk11CUIUwmLy5XMUVFV8c4ew6qquJyuXA6nYyNjXH+/HlUVcVsNmOz2XA48nnjjQIeftjPhg0qsCHqesvBbYiFZJ2lpmlMT09jMpnSvKPE4HA4cDgcjI6OAg389Kf13HjjcxQU7AH8gILR+EUE4R84cuRZsrJqsNvtwaFBvb29wRamRF9LujM7giBQUFDAxMQEHo+Hffv2UVtbS2FhYcRzM02MC4fs7Gx27NjB0NAQHR0dFBQUxF26DrefSEZ8raqxhqVCopWNzAQbo6jqb7Dbh3A6bVgsVyWUpAv1L5qmRZzBZLF8FZPJwsaN2RknxKfz/Yn+fov4/bem7Vrphslk4oorruD48eMcOHAnfX2NfPjD/45erwAKktSKxVLH7Oy38PkeZnp6OpgU83g8NDQ04HAkN1sknX4qdGhtQLWwrKwspu1PN0ciGiKRyFN5Bl0LNlYBApWNpUao4VWUH2A0TqEoMoODDTQ05C06PhBYTE1NBb/omqaRlZWFpmnYbDYqKyuDJKnnnpPZv1+it1fg6adFCgs1nnjCS7QgOp7Mk6IoKfE4xsfHOXr0MIWFxRmVLj1y5N8oKPg3ensbKC39p4xdJxZkWeaee+7h6NGj7N69m6EhP729H+KjH62lsvLZ4HEGwyS7dt2D1/t7eL0/we/3I8syW7ZsYWRkhP3791NYWJhwi1umyshFRUU0NDTM43PYbLZFxyWjRpWIrG4kCIJAYWEh+fn59PX10dLSQkVF4u1ql4MRX8PqR6LBRiCZlc57VxB+gyT14/eDy5WD1botrvMCgcXU1BRTU1OMjY3h8/lWxAymaD7P4/Egy3La++tDMT4+ytGjz2M0VrB58x1JrZEOCILATTfdxPr163nppZfo6yvnqac+x2c+8wKieOrSUQom02fQ6f6c48dfxWYrpKSkBE3TOH36NAaDgYaGhoQ/x0zMw8jPz6e6uvqSWuU+qqurKS4uTltSLJUKfAChlfh4J5FHwuXgp9aCjQzhPSN3FqOxHUHwIwgibvctiKI4b3Lp9PR0MLCw2WwUFRVRV1cXNILnz59HFMV5agw33qjQ1taNzeZkYqKJe+81kROjAhrN8KqqyrFjX8Ni+R2zsx+mqekTSb3unp6/ZceOnzM0VMH4+HM4HOmd8h5ATc23MZmmycsb5OLFd4HqjFxnIRRFCUoEO51OZmZmkGV53hAgVdW4cOEx8vM/iNl837zz9frf4vXOnxqfl5dHTk4OPT09tLS0oChKXMYxUZWPRAMTg8HAhg0bmJqa4sSJE5hMpkVtS8moUaVT6UoURSorKykpKeH06dO4XC5GRkbIy1sc0Ee6xloFYw3LjUTVqDJRpRaEp5EkL7KsMTi4jg0bChcdE2kGk9FoRBRFjEYj9fX1CyoiPgRh9JKSYeoPTE7nOMPDnTgcG3A4Fu8xHpw40cnIyLN4vYXs3PlncXPUEsXo6F9w1VVv4PdLDAxkoWmp88yShaZp6HQ6RFG8xIsx8E//9AkeffQlcnLag8fJso8dO25ienoYVdWhKArbtm1jaGiI9vZ2SktLE07sZCoplpeXx7p16+bxObLDTJ9cjgp8AIFKfOgk8kS/u5cDt/CyDzYCw5LiRbo+tPfK3F9Hlt0AzM7KjI3l0tHRgcViwWq1UlxcjMViiZpdCedY7PYDaNo5jEYRt3uWoqKriGfrkW7y2dleamr+N6BDVb+Nqj6MKEYuoUd6n5qa3sDjMZCf34fP1w1kJtgQxSrgIIJgwG6vZ3g4/bKRfr9/3vwRl8uFJEnB/uLq6mrMZnPwvcjLy+O1117D5XLxu9/9Drv9DygvP4zFsrC9bYzQYGPu9YhUV1dTVFTEnj176OzspKmpKaq+faYIcguPtdlsbN++PehsiouLqaqqCk4fzhTxLhGDr9PpqK2tZWJigv7+/mDpOh4S+Wow1Gu4vJGoGlU6B9ACCEI3ev1BRFFBUXR4vdcBzOMHRp/BBCMjI0xNTc0LNDTNy8zMJxDFE/j9N2CxfD2lfaqqn8nJj1Nc3M3UVDFe78vo9ZFtZKSgzGz+O3btOoDfLzI6ugWzOXrVIdngrqTkFJoGsqxgt/czONiU8BrJILSNLZAY8/l8mEwmbrrpJvbu3cv4+Dhut0pb21e48caXMRiemreGLD+F1/unwHuV5Ly8PM6cOcO+fftQFCXuvSSy70T9lF6vZ926dUxPT3P8+HH0ej0NDQ0Yjcak1k3m+FgIlXPv7e3F5XLR398fN4k8HZWW5cZlH2wkWp6G5G80v98fbIFyOp24XC5Mph8Hg4DJSRvXXXdDwmW0cJKCPp+B229/jebmdrq6ric7e1dc60SCyeTA680CnIhiMaIYvZUqkgExGu9CFP8bqECv3xRzT8nCaPw/CMKrwDpEsRo4mdJ6qqri8XgYHh5mcHCQ6elpBEEIBhaVlZWYzeaoX/iqqiosFgszMzN4vV52797NXXfdBYyRlZWHIKj4/WVAFXNDlha/hwaDAbPZTE1NDQcPHiQnJ4fa2tpFGuOQuUnf4Y4NbVs6d+4ce/fuDbbJZTLYSLRqIssymzZtYnJykmPHjmE2m4NzRdawhpWKZNuo0gVJ+p9IkoIggKKIjI8X09bWltAMpnAP5G53H5p2FJcrB6PxjUuk5OTbdDXNQ35+NzMzVuz2QTRtiFhV7XB2tqBAQNNAkgTy8jL3GGQyfRlZ/jx+fxVW652k6qciIVR8ZGpqCo/HE/Wzm5iYYO/evfh8Pvbv30929iNs3FiOyfTF4DGKcvei60iSRH19PaWlpezZs4cDBw7Q2NgYlW+4VEmxUD7HQhna5WijCocAiby/vx+n0xk3iTydlZblwlqwsQABIx6rjzMQWAS+4C6XC0EQghWL0tJSsrO/PW8g+NBQLQ0NiffrhTPi2dlNNDV9iOHhGjZvvpecnNjZg2g3qyjaMRiex+9vR5avJ3kZvC+jaf8DTctnYfY+HJJvBbCjaR+99G83oDE+/i0EYT9G419iNEYmy4cS7wMcGU3T8Pv95ObmUlpaisViScrY3Hjjjfz85z9nZmaGixcv0trayg033IDTOYamaZceEHxB4xcYBBS4VuD3DoeDXbt2BfkICwfvBY5NBOkw+KIoUlNTQ2lpKd3d3UxMTCRMHMykXGHgfbTb7TQ3NzM0NERnZ2eQRB4uaFvDGpYbibb7ptJGpWnavIrFzMwEBsMrwaTY7KyRa665NmHBinB70unKuHhxPXl5RxgcvJHa2tS+f5Jkxut9CJvtBbze2zAaq2LuKRyMxq8jSd9G02rx+z+Q0p6iX/8D+P0HLv009974fG6mp3uw2+sQxcRFLQLPHmfPnsXpdDI7OxtU9LLZbJSWlqLX66Pazo0bN3LmzBkuXryIz+djz5491NR8Ap/vQxgMf4vb/aeoaingRxAEvF4vsiwH7avZbMZsNlNaWhrkG0aanL5UwQa8J3KSl5cXlKGtqanBZDItWxvVQgT2nQiJ/HIQMrnsPW+ykoKhUBRlUcUiNOtdXl5OVlbWopthbMwd/LemwdGj99HQkPhrCLcnQRCoqrodSJ/MniDUxD1VNJoqh6ZVpW1P8UIUO3A4voko+nE6jwJzPagBxxrKkfH7/UF+TGgb28mTJ8nPzw9LhI4XWVlZQek7RVHo6uoKBgpz+xQRBCGYbfH7/fj9fnQ6XTDADby3giBQUVFBUVER3d3d9PX1ccUVV8xrDVoqI74QAT7HmTNn6Ovr4/DhwynL0C5EMpWN0OPDkcgrKyspLS2N671YLUZ8Dasfibb7xlvZ0DQtmFgJtX+hM5h0uufmteD291dRU5O4yl+4CrwsGygqepqJiWEqK4vS8p2ay77/FSZTfN/hcEGZplWjKPELi6SDIzO3xiyzs1eTmztIf/81FBf/V9RzwnEEA59fUVFR0sMNBUGgsrKSsbEx3G43LpeL119/ndtuu42ZmR8giiKSJAT9ld/vx+v1zvNTMEfUzs3Npaenh71791JfX09BQUFS70/o3uJBrKRY1aVZF4GkWLSW5ETWDndsIghNisVLIl8jiK8CJCMpOD4+HiTBzczMIIpisGIRKbAIh927P4DNto/8/GF+97vr2LbtpqReQ6hjUZQ30bR/QFUb0Ov/AUFYHunXlSah6/fPfR6CoOH1igwMdON0OlEUJehY8/PzqampSWt2W1VVNE0LVi00TSM3N5fR0VHGxsZQFIW9e/fysY99LGy1LOCgvV5vxGpaYPDe5OQkR48exWq1Ul9fnzGDmMi6RqOR8vJyzGYz7e3tlJSUUFlZmRbDmC5SXyiJPNBvXFdXR35++MGaa1jDUiNZ6dtQLJzBFBpY2Gy2oILPQrXBY8fqWLcORBFUFd5991GSGXER4HBd2g2i2IWm2TAaaykqKk98wahYnYkASTqDzTaEz6entHQ3qqoReC3ROII2m428vDzMZjO9vb0YDAYKC+Mnx4f6qUCSC+YqHO3t7aiqyqlTp2hubqakpCTiGgE/FepDA3zD4uJiTpw4QV9f3zy+4XL6qYDvPH/+PKdOneLQoUNxKWol8nCfDj8VSiLv6OgIO4l8tSe/3tfBRiBrEFqxcLvdjIyMkJ2dHVeffjSUl1fx7LOfAuZ0rq+5JjmDG+pYpqe/hao60et34/Xuxmr9vaTWTBXvEeATa3MZGRkhKysrZfUPTXtv8uz4+DiTkzW0t38Cq/Us8HHy8mw0NHQiSVkoyh2kQwVlocEOhSAIyLKM3W5n3bp1QVUkgAsXLtDb20t19eLeYlEUg8Fk4H4MBC4L7zu73c6OHTsYGBigpaWFrKysuB1OJkvZoihSVFQU5HOEPsynYiCT0UaPdrxOp6OxsZHZ2Vm6u7vp6ekJOt01rGE5kUwbVWi1fXp6OlixTTSx4nIV8IMfPMa6dUdoadnF7bcn11YU6qdmZ59GVZ9BFGUk6bvo9fHJ6KYb6VTtSsc6qlrH9HQ5NlsPg4O34PX2Bz+/RDiC0exqIDEZ8FehCPgbnU7Hxo0bOXnyJHq9HrfbjaqqvPHGGzz00EOL1gz1U36/n4mJCVRVnWejjUYjmzZtYmxsjIMHD5Kbm0tNTc2StlFFgslkorCwkJycHDo6OigqKqKysjJigi/T3MJwxy8kkYdOIr8csCKCjb6+Ph555BEGBwcRRZHHHnuMz372s/OOeeutt7jrrruCD2z33nsvX/rSl2KuHWijmpmZmTdkaGZmZp6yUGVlJVlZWRw+fJiqqqq0DIszmUx87nOfw+fzpdReEmowx8aayM9/A6/XBJSmvMelRGfny5hMz3LqVDHr138Vm80e97lerzeo4+50OnG73UEdd6vVyszMDJs3/03IGV9Flr/LXNboq8DDCe01HoMdKDEvNBwOh4NbbrmFs2fPMjU1hd/v5+23354XbASCpcnJyeDr8nq9wT7YcCVrmLsXSktLKSwspKOjg1OnTmE2m8PK/YViKQy+JEnU1tYG+Rw9PT00NTXFpQiV6j4g/j5bk8nExo0bmZycpLW1lUOHDlFfX79GIl/DssFgMDAxMRH2b6Ecs8CD6czMDLOzs+Tm5lJQUBBRRCIe5Obm4nZfz+7dFTQ1NYVNisSDUD81OdnBXGLbzfT0UfLzV0+w4XQ6aWt7C6PRyo4d1yLLcspJk0Arr9utcfz4PyEI05jNxWRn99PU9KdIkhu//0cIQmPCawPBakXoPgPtupH8lM1mY9u2bczOzrJv3z5gLjE2Ojo6j7AcIJ8HfJXH48FoNEb0Uzk5OezcuTPYuupwOOK2rZlOigXaant6eqIOrc3UPkL3EgnhJpHHq/y1krEigg1ZlvnmN7/J1q1bcTqdbNu2jVtuuYV169bNO+66667jF7/4Rcz1XC5XUD70Jz/5CU6nky1btvDkk09itVqD5chILRfpVPkQRTHlPvb3qghDFBd/hp6e6zGZyqmoaAKvFyRp7r84MTrayezs/0RRsiko+DZmc+LtJMlUNiorv0Vu7hlUVWR09BZstnvCHufz+eb1qc7OzqLT6bDZbFitVkpKSjAYDMFrezwehoaG5q0xNHSQkpK5L+jg4EGKilTmAo/w+w0EFT6fbx7HJ5bBjgRBmBui9PLLL6NpGsPDw+zevZuioqJLTmcuWLLZbMEqWkAtJJAxCi1ZL7yuLMvk5eUhSRLd3d0Yjcao5eGlzC4ZjUauvPLKYNuXxWIJo70fG+nKGEWCzWbDbDZTWFhIZ2dnsM1kjUS+hnDIZFIsUNlQFGXRHAtN04IVi8LCQmprazl37hw5OTnkxBquFAcEQeCOO+5I+b4PfbD3eh9CUQbxeOzk5t6c1HpzlfCDAOTlbVyyNpKzZ7/H9df/AJ/PQF/fv1BdHf/+Y3EE5wa5bgs+nI+P/yU63VlAYHz8f2G3R+dwxOOnAv/FA0EQuOqqq+jo6Aiu98Mf/pC77747mJTV6/XY7XZsNhtlZWXzAgdFUfB6vcFqSeC6gdbV4uJi9u/fz8jICPn5+TETT0vhpwJtXwE+R2A+RyhXM9OVjXjWDp1EvmfPHjo6OsKSyFdLe9WK8KrFxcUUF8/NY7BarVxxxRX09/cvCjbiRW9vL7/+9a/Ztm0bt99+O9XV1Tz44INxnZvOsmu6IAgCOl0LHs9TSJJMff23kXyN6P71XxHb2hAkCd+HP4z/934P4rjxfL5vkZd3FkFQmZz8CWbzp+LeS6iRS/R9cjgKEIQzSJJMXl5R8Pfj4+PB4CIwJC/Qp1pQUJCwkgRAf/8juN0D+Hz/P3tnHt/YXZ3979UuS5ZsSZYtr5J3z745M5MGEggpgZawJM0bCKEB2r4kTVlKtwCloaUFWkJZQoC8bWkKgYQEaICEQEIgCUlmsWf1zNjjbTzed0nWLl3d9w/PvSNZki157GQmzPMyJPhMAAAgAElEQVT55JOPx1dXv3st/c495zznebQYjfWYTE1IUinh8I8RxaoUZajU9ZUyMDCAx+PB6XTm5SybDYlEAr/fn8GNfumll7juuutoamrCZDItO9wmJ72yJK/sdJu6qUmShNFoTPPAkA2XcrVp88Fabfgy7WtiYoKDBw9SVVW1brMjsLpNX61WK+olIyMjihN5VVXVJT+Qdxlri7UuiskCEp2dnTz66KMMDg7yk5/8hC9/+ctKYpFq7pqKbMPYq8VaxbzF8yQQhMeprQ3i9/8HWq1l1SyB8fFHKSn5R0BgYuIeXK4bV7mmwq7N43kJEDAYQthsx4HFZGPpeSTpvHN6qg/JcjOCExMTaX/PublGbLbF309NNWG3/wBQk0i8g2RSSItT8n5otVo5ffo0KpVK8WlYzV4lz4j4/X7KysoYGxtT/v3gwYNcf/31WCyWZfdg2dBWjlNLi2M6nY6qqioCgQAnT57EYrHQ2NiYERtT7+krFadSTWu7u7spKipKEzlZr85GoXFKVv5qaGi4YCfyVxMXRbKRijNnznD48GF2796d8buXX36ZrVu3UllZyRe/+EU2btyY9RxtbW3ce++9AMzNzRXEy17rzsZaYFEN4rdEImFUKpFQ6Fkqf92Net8+knV1SPE42u9/H8nlIrkpt+SrDJ2uBehEkjQYjQ15ryMQ8NPdfTcm0yQ+39uB7QVex1cRxQfx+ysYH7cSCBwgFAoxPT2d1STvQrB165s5fdpzrsLytyQSYVSqEPC/JJO3AKVpylCwmPTa7XZOnz7NxMQEra2tKwbKZDKpbNhy0FGpVFgsFiwWCxs3bqSrq0s5fmxsjHA4nNeA8lKe7FLVKlj8bMiqS6mGSy0tLWmt8PXaxGVvi1wQBAGXy4XT6eTMmTMEg0GmpqbymudYzczGajd9lUpFbW1t2hB5W1uboiJ2GZex1kUxr9fLfffdx86dO3nTm97E3Nwcn/rUp/J6bfow9oVhrWKeIAjo9c8TCv0XkiShVk9hNH501edSqztQqcRzazwI5J9sDA8PMTc3TDxuKPg+FRf/GVrt3yBJRqzWP1T+PZX2urCwQCwWU6i8+fiQZENNze3s31+EKIa44go/Gs3HEQQQxUlE8U8BMuKUw+GgpKSE/v5+Dh8+TGtra07JVBkyFS91/YvXuljUu/766/mv//ov5fizZ89y8uRJpTux3L66NE5lo1YVFRXR0tLC6OgoBw4cUGYRsp13veJUrlhisVhob29XHL4rKysL+sys9WxhLuQaIr9UimIXVbIRCAS48cYb+fKXv5whP7pjxw6GhoYwm808+eSTvOMd76C3t3fFcxoMBvx+f95rWOvORqEPQNkgCEmCwUas1iMkEgbi8StRnXySZFnZYidDp0PSahEGBiCPZMNmu5tI5ArU6lIMhva81xEMPsXWrb9ApUpiNnuRpHfnPDaZTCoDjLKXhSAImM1vx2KxUFNTjMlkorOzk+bV6AHneM9UZaiGhsVE6vjxBjZufBlJEtBqv4zJ9K/EYvcgin+WcQ6dTsemTZuYm5vj6NGjlJeXK+pKsoykzF+VKQ5ms/ncNdVk+HO4XK60ZOP06dPccccd9Pb2MjIyQktLy4rD8vL5llKrln5OUw2Xuru7lfaw0Whcdy7sSpDnOcbHx5mYmODs2bMrBslXorORjZ7W3NxMTU0NkUgkxysv43cda1EUczgcfPvb3wbgqaee4plnnsn7/S/WzkYoFFBoInNz0xSgNppxLovlfSST+5AkgZKSP877tV7vKEbjLWzYMEVX19XA1wp89z8kFNrDwkIUny/GwsIx/H4/fX19aXSi1VaWU+OUIAhs3/5/APD57sRgWBSzCYefwWqdQRTfTzYpeY1GQ0tLC36/n1OnTlFSUkJ9fb0iqS5TueT/RFFU4lRlZSXFxcUZHTOLxZL2rLRjxw4GBwcZGxujpaVlRQqUnHSIokg8HieRSKDVapXPliAIVFdXU15eTl9fHyMjI7S2tmK1np/dLMTfYi1jmjycLYucFFIUW6/ZwuXWmTpE3tDQQF1d3arO90riokk24vE4N954I7feeivvete7Mn6fmny89a1v5c4772RmZgaHw7HseV9NZ9bVzDVkg1b773g8vwDKiEa/RGVlC5KzA9WJE0gmE0gSQiwGefN31RgMv1/wOmw217mHMxG9Pn2DWMpTlR/CZYNDk8mUk5Y0Pj7K6GgXbvcOHI785kdSN2xRFIlEIkQiEYU3KrdyBUHgzJk3cfCghfr6Pq699llAQKv9AoIwRiLxMSBzUL20tJTNmzfT39/PCy+8oJgkFRUVYbVaqaiooLm5eUWqlfyaUCgEoIgFbNq0ifn5eY4fP47D4cC9ROZuKbJRqxKJRNaHhKKiInbs2MH09LRiuPRqbeLZrmPLli2KrnhxcTGNjY1ZK4LrPbOx3PFGo3HFauFl/G5iPYpihapRrWVnY62SDZVqjpISL1NTG/D5yqis/NMLWpNO14Za/XzBr1WrezCZZojFDDQ3H1jx2nLNCMpV/4qKCnp6emhra1NiSyEVdfn/8qxFKBRSZg5T5wF7em7A5+tFp4tQX78PrfZlNJpfEIk8C2QvSFksFjZt2sTg4CAvvPCCkgDJcscOh4P6+vqctKVU7Nq1i2effVb52e/309raysLCAt3d3VgslrzOpVarlWRYjlOpsU2r1dLW1qac12g00tzcrMSAVzNOpRbFJicn8xI5We84le3zmzpELnepLnZcFMmGJEl88IMfpK2tjb/8y7/MeszExISiGnDgwAGSyeSKFu+wOlO/i61iFI8/Ryymw2icwmz2IwgCiT/6I3SDgwgjIwiShLh5M+IVV6zBqnNDq70KleqrxGJ9nD3bwtxcH8FgMG0AbjmecfZri6BS3UR7+1kGBzdhs/0844u4kjKUwWDA7XYrSmJLnbavueYa9u0zYDRuJxp9EfCiVofRar+OSrWfePyrRCK1GYobRqNR2bDHx8fR6/Vpm2K+qKmpoaenJ+PfS0tLaW9vZ3h4mIMHD9LQ0JCTWiXzg+X1+Xw+4vE45eXliKKY9X6nGi55vV5mZmbyktFbzy6IDLklPD4+rsxzLJ01WWuVjwtZ+2VcBqxfUWy9TP1eyXPF4/9CSclv0Wj0uFwfXtHdeyWsNnYWF19BMlmP2XyW8fE/ILVmIBv0yomF7GUhi4/kmhEUBIFDh76HXv8U4fBe9u798LJxamlxRy4WtbS0cPLkSaqrq6murk47Ztu2N/H00xKlpUN4PJ8nkYiiVp/CaKwnFvsSovge4vF4moKh7B5usVhobm5menoaSZJobm4ueFamtbU1LdmQ11ZcXMyuXbsYGxujo6Mja4xNhTzH4vP58Pl8xGIxmpqaFLUs+b7J55Vn+mpqai6qoliqyMlyRbFXM05ptdoV95aLBRdFsvHiiy/yne98h82bN7Nt2zYA/uVf/oWzZ88C8KEPfYjHHnuMb3zjG2g0GoxGIw8//HBef+BXs2Ikb+KrHTRexEvodCHM5nmGhnZTUbFIOZKcTqL33INqaAhJp0PyeCALdz4SgW9/W+Ctb/08avUkyWT+HY3sA3A2ioquIR5fwGaz0djYeEEqJnr9NOXlw0SjBurru4jFQkBRmpSf/GVbTnK2srKSsrIyent7GR8fp7W1VaEnmUwmrr32Wnp6evjGNz7Ctm2/5fWv/w06XRiV6iW02j0MDt5NPP52rFZrhuIGLNKhpqen6ezspKamJm8XaoCmpqa0ZCM1OZBVO8rLy+nt7WV0dJSWlhbUanVa8hOJRJTkx2634/F4UKvVK6pWycobk5OTTE1NKfdmOTfVV2pITxAEKisrKS8vZ3BwkH379tHU1KQkXOul8pF6/KXCd72MVx/rWRRbC1O/1WJtziURCo1hMokkkyLBYJDVKElLUhJRTFzgmsyoVD9DFL1MTo4RCEwQi8UIBoMIgqB0LArx0RLFGDt3fhaDIUw8vo9g8J2YTDU5JWdTO+up53c6ndjtdgYGBujo6EirmpvNZt75zncyMjLC008fZsuWDsrLx9Bqo2i1dzA39zC9vZ/AYrFhsVhwuVwZ7uGVlZVZKcD5wGQy0dDQQH9/P9XV1WmfW0FYlFx3Op309fUp1Cq9Xq8kPj6fT0l+rFYrJSUl1NbWotfrFWrVotjNedUqeaavrKyMgYEBpqensdvtecmlF/LQvtrCUjaRk6VFsVc7Tl0qBbOLItm46qqrVtxY7rrrLu66666Cz30x0KguBMHg54lEdMRiViyW92MypfgpmM0kc/CBJQm6u1UMDKj4yU9mqK09Q23tMDbbOHB9luPPm+QtHYCzWCwZA3BHjhyhpKTkguUSRdFFMrmdoqKjRCJvRpL0yuYtt6vzlfLTarVs2LABr9fL8ePHcTqdVFdXK3MWs7OzxGLQ0bGHoiI1e/b8ApVKRK1O0NZ2D6L4MrHY94HM6oUgCDidTmw2W9ZAsRxkOdVEIpHVfV4URcLhMBaLhenpaV566SV0Oh3l5eVYrVaqqqrSpH5TsZRalSvpEASBTZs2sbCwkGa4lO3vt57D5Nn+jmq1msbGRqqrq+np6eHs2bO0tLS8KjMbqbhUNvHLeGVwsRXF1kp7fy3ilCh+h+LiESBKb+9baWraW/A5YrFZ/P73YDKNoVa/G9ic92vlAWg5dgUCASRJIpFIYDabqampybr35guVSkCng2RSQKMR0OvVSpwqVHJWnq2TaURWqxWPx0M4HMbn8zE5OcmxY9s5c6aW22//f+h0C0iSioqKZykvP0U0+kMkKfeco81m44orruDMmTMcPHiQ5uZmSktL87rOd73rXSQSiaxxQRRFQqEQJpOJSCTCgQMH0Gg0lJWVUVJSQnl5eU7lyKXUqqVxSp6VW1hYYGRkhNnZWZqbm5f151jPIe5UpIqcZCuKvdpx6lLBRZFsrCdWQ6Nay87GhZ2rH72+B6vVx8xMJYJQnfcrEwn4n//RcvCgCoMB/v3fP8yePfu57bZfA+dN8uTkItUkT67uLzcAt5r7JNOgUpM5g8HEwMC9uN1W1OoyVKr0LlChX8pgMEggEMBsNjM8PMzAwAB2ux2Hw8HmzZtpamri2LFjPPeclqkpE2972/+iVscACbX6KYxGF+HwU0D2wfnUTVHmsa5kqGUwGLj99tsZHR3F7XYTCATSVEHkapvValUUsIaHh5mYmMBms61oiJSPGoi8IZaWlrJnzx7FcKm+vj6jJb5eGuMrnVd2oPV6vXR1daFWqwvyESi0Pf1a2cQv45XBehbFCqVRrXWcutACWzD4Y7TaMLGYkcrK7RiNy4teZMPCwvOYTCOIop7KyieQpL/Kelw2L4tkMql4kbhcLsxmM2q1mu7u7rz8HVKROg8o32OjsZizZz9HTc2vgXeg19cAhcWn1PXLJo1ms5mpqSmGh4cpKSnB6XTS1NSEx+Ph+PHjfPObFm688UHq6oYACUEYw2D4PeLxvyGR+Puc76FSqZS9vaenh7Gxsby9jmTxkVxiKFarlcbGRoqKihgfH2d4eJjS0tIVJeqzxSk54ZDvo1qtprW1lWAwSGdnJ5WVlTm7M+shZLIcchXF1luN6rUSp17zycar3dm4kHMFg/8PvT6O31/M6OgG2tpa836tVgt33x3lAx8wEAqVU1wc5r3vHWJq6hZ8vgNpJnkVFRUZ7Vgo/EGys/NpQqFB2treid3uyNiwAaVTIVeENm/ezMjICIcPn6W1ddEJe2bmAGr1+8+t4UHs9l1Z3285xY2qqipaW1uJRqN0d3ezsLBAeXm5IlMoCAInT24hEqnn5pvvQ6UKnDtzBKPxGkSxjljsZM7rlfmmo6OjHDx4kPr6epxOZ8ZDezgcTmsxd3V1pa1RDopLIfNiT58+rVCr8k06shktpW7MgiBQW1tLRUWFYmrU1tamBORXUus8G0pKSti9ezcnTpzg7NmzaDQaampqVvwsFkpZfK1s4pdx6WM1nY2LpQMvSYfQ6QYwmWaYmqpEFPNXOEyFybSTeNyMRhNgfv5qLBZJ2UNTO+6yl4XFYsnqZbHStSUSCYLBIBaLJS1G5ZqzkGctJiasHDzoPidbvnj/u7sfRhRnaWh4PwZDZoKVSkWW40Dq+svLy5V5hp6eHubn53E6nej1emprazl27BgPP3wbb33rz9iy5dC5sybRaj+PRvNNIpEjQG5RlaKiIrZt28bU1FROCrDMalg6DyjPYS4nhlJdXa1Qq+Q4tZKwRmrSkUgk0qhVcoyQ6WZnzpxh3759NDc3Z8wmrOfMxnJYWhTT6XR5O6TD7y7d93KysQQXDxc2hF7/BEVFfvR6DX7/rrw+cIlEQqn2nDgRQxDKefOb59i/v4JI5E50ugF27Vr5XN3dj6BWf5dQaA+bN9+dlZYjX1symWRs7CCbNt2FVhtjYOC3lJQsanYvN2ch/97tduN0OhVlCovlK1RVTQESY2NfAx7M2LBjsVheihtFRUVs376diYkJOjo68Hg8bN68mWAwyMGDB+nrK+JrX/sof/EXD6BSnXchV6uH0OsdRKMzOe+RLOXndDoVKdvy8nJl45aHzK1Wa0GqIDIMBgNbtmxhdnaWI0eO5M3BzWa0lG2z1el0bNy4MW0Irqmp6RWnUWWDIAhKMhyLxXIGnNWefzXHX8ZlrBde7aLYhcS8UOj7lJRMEI0amZ5upKUlf++mVOj1NYjiT5ifP8PsrMjCQheiKGI0GikuLsZut+N2u9P20Hg8zvBwH1arA5tt+UFZeT/s7PxH7PYT9PTcyPbt7wGWn7OQUVVVhcPhoKenh/HxcYqLO2lquhtBgKGhLjyebxCLxdIGuGXGgExFrqury9pd0Gg0bNmyhenpaQ4dOkRNTQ1ut5trr72Wp59+mp/+9B3MzJTxxjf+QnmNIHgxGt2Ew38O/GvO6xaERR8mu91Of38/Bw4coLKykng8rgyZy2ssKSnJucZc0Ol0bNiwQYkjpaWlCnV4OaRKumeLU7IyVGVlJT09PYqUuzyL+WolGzLkolh3dzfj4+OYTKa8i2K/i3HqNZ9sFEqjulg28Xj8l5jNY4BEMGhGo9mZcYwoihnKGiqViuLiYoqLi9my5Vm++tUfo9H8Ph/60J+j05XR2Tm04ntLkkRNzT+h18dIJrtZWHg7Vuuif0dqi1keTAawWIbR6WKIooqamm50Ol1BXxA5KRgfH2d62oPLpUaSwOttZGjoZUVxw2q1UlNTU5DGucy5tNvtygD59u3bOXLkyLngoOGhh77Ae97zz6jVfcrrVKpwznPKSZ0cWOThw/7+fkpLS9MG1C8Udrud0tJShoaGOHDgAE1NTSsOncr3fmFhAa/XSzQaRRTFrAFAHoIbGxvjwIEDBX1f1nPDlyRJ4TfLreuhoaGc1bPf1fb0ZVz6eLXpvquPeQmKiv4XUQSVKs70dDutrfl9x2Uqr9/vIxDoJRwuwmCwUlxchlY7R2NjY5oHQzb093+GhoaH8fnK8PkexWpdpBqnum7HYjHl3gYCx9m792HUapFgcACd7vaC9gC9Xq8kBZOTnajVIpIEBkM3L7/8MjqdTjF0XW7WLhfKysqw2Wz09/fT2dlJW1sbDoeDqakpfvvbvczMVHDzzQ+mvcZo/DrhcPZkI9Ul3OfzEQwGARgcHFQUlsxm85o8iFutVtrb2xkZGcnZ7V8K+d4Hg0G8Xi8LCwuIopi2NxuNRrZt26YU3ZxOJx6PZ90GxAv5XgmCgM1mQ6PREIvF2L9/P01NTa9oUexSmS18zScbr2Zn40I2cUH4N9TqOJIkMDPjRq02pc1YnDfJW/SyWDoAJ4phYrF7EUU1avX9SNINCMIiz3Sl61ukOFkRhDEEwYjBUJwRCOVOhDx8ZjD8AZL0EHp9L4nEp/L+MomimNaxCIVCqFQ3cPjwYmu8ru4GmppOkEx+FrgevT7TiC9fyJX8ubk5urq6uPLKK3nhhRcAGB4e5cCBB7niihtQq2cBSCZLz/0/qSR1Mn9VpTrvEt7Q0KA4nyeTSYaGhjh27BjNzc0FzRwsB1lVKpValTpAl0orkxMgSZIU5ZVt27aRSCQUF/JsnaqqqirKy8t5/vnnOXDgAK2trZSUlGRbjoL15M0mk0mliikHnPn5ebq6uhTecGqVs9D29FrweC/jMtYCl2pnQxR/RFHRLIIgEQgUAVuyHrfUyyIUCikP5nr9v9PU9CzRqBOT6cdotaUEg8G8usDV1b8gHtdhscywsHCAWCz94dbhcNDf36/IipeVlQNqJCmB0Vic9/c/mUymPbQHAgE0mhuYnT2KVruARvMP7NmzB5hGEKzA6gz/YLGiL88Fnjp1iu3bt/Piiy+ysLBAT089P/jBP3LzzZ/OeJ08Z5HaWYHzLuFut1t5RpAkidHRUbq6upR7sxYPrYIgUFNTk6GuKKsfpkq4y+uU6c9Wq5XNmzej0WiIx+MZccput7Nnzx7FzK6Qz+16xym1Wk19fX3GPEc21cf1lsq9WPE7kWxcalxYURynuLiLxc+jxMjITqLRRZUGuWKynEkegEqlIxotxWCYIRYzYzRalTXlgty1WKyAf4dE4meoVFegVldlUKFkabxTp04xPT19TgL3JySTkOt7sfShXU6Y5OHo+vp6TCbTuTVewfT0NKdOnaK9/d0YjUGSyd8SiVyBwbCt4HuaClmtY3Bw8NyMyAyJRILnnnsOj+c4en030WgXExNX4fcfTNsMq6urKS7OHaRSk4LUwbzVus0uhdFoZOvWrYyPj9PR0aFoqcu0MqvVitPpzCpJnI9qlUajwWAwsGnTJk6dOoXBYKC5uTnn+gvZCFeTDCw9vrS0lN27dytdmJqaGqqrq5Xv7VpKEF4qFaPLuPRxqdJ9BeFf0WgSJJMCU1O16PWlaVRev99PKBRCo9EoD71LvSwCgZdJJMzo9dOEQl1Yra9bdk2pcUoQ3ole/yDJZBkWy5WoVOnKUAaDgdLSUrq7u5mamjon1/rfCMI+JOlmsr2FJElKnJKTo9TCTW1tLWaz+dxe8/t4vV4GBnoIh++iru4HRKPFwPPodPkLumSDPBc4PDyMy+XC5/MBcPq0wG9+82OuvvqdCAJMTLyb3t4OZRbEarXicrmWNZ1NpQDLxStZnGQtIBf2ZmZmOHLkCDqdDrVaneFflY1avFyckmnXLpeLl156ia6uLtra2lacEykkNlxInEotih0/fpySkhIaGhoyimKFzha+FmLRaz7ZeDXNkvLZxOWKdGrVp6Hhb7FapXO/B5fr/czOhtiwYUMB761Cp/suPt+vMZuvRKu1pK0pmzKU/PtFKT8PWu1Hlv2CGo1Gtm/frpj9pNJ75Ovy+XzMzs6mJRYWi2XFh3ZYbCmXlpYSiWjQ6SQkSSAYjGE0diNJHi6keqRSqWhoaMBqtfI///M/ijrGo48+Snt7OxbLGykrsxY8ZyFD3nSmpqY4dOhQVhOnfLG0+xMMBtHpdDidTqLRKIFAgJaWlhW7KLlUq7JxlM1mM7t27WJycpKOjo6s+uJQ2Ea4VhKBqV2YgYEB9u/fT3Nz8+8sF/YyLn3I0tj54mIoisEMZvNpBAFUKonR0SsIhUIcPXpUofJ6PB6l65sLfv+N2Gzfxe9voLR0u7ImyK5gKP9erVaj1f4davWfIElWVKrstFWdTseWLVuUIWmPp4Xy8tcr150q4iFX2uXh6JUe2mGRu9/e3k4k8m6SSdBqA0xP/xKn8wOF3tAMyGIepaWlTE1NMTc3RzKZ5IUXjgHP4nA4sFqtbNliKdhsFhbvzaZNm1btzZEKufsj38dAIIBaraasrIx4PI7X66WxsTEvatXSOKVWqxWJYVhMzs1ms6LaZbPZllWFXE+6b7Y4IhfFRkdHlaJYTU2Nwn4o5JnitRKnXvPJRqGdjfWkUcktRDmxWDTJS6Rl+h6Pm5KSA8prkkmorGxgZuZY3u87P/8djMYvEovV47A/iBAWEONx5JXIknupHhaplYNCIAiLxmxms5nu7m5Onz6d5ikRjY6h138Nnc6Mx/NVbLbygs6v0WiYmvpPTp9+AK+3lde//m/Qak8jSfXE478G8v/SJhKJDMqWPNx2/PhxAObn55VBwLXAUhOnlpaWNJfhpUhthcuUrdzdn0WEw2F6enoUatVKXRT5753LaEmGIAhUVFQohkvyQ33qvMh6DYjnc25ZhjgUCnH69Gl8Pt+KtK8LWc9lXMZ6QRCEgh5wLlTpMBX5Ji6pXhZ+vx+P592KO7cggMVyC5IksnNn5nxhjjMSDv8Gh+MGJOnD2Gx6BOG8f8jCwoLyAJ2qYCj/fB4u8rl1TqeToqIienp66O/vR6/XE4/HlQH0ublpDAYDO3a0F2zEq1KpmJq6AY/nuwQCZgIBNRUVfw5cSzKZ6TS/HOQ5i9SHdq1WS3t7O7/85S+Vmcnp6Wl2795d0LlzQe72y3OBK1GAV6Lt1tXVZfiaRKNRent7FUPAlWYalyYdMgVY/tskk0lKSkrSpNw9Hg8ulyvju/RqxCm5e1RRUcHAwIAicvK7Olv4O5FsvFpcWFEU8Xq9zM3NpZnkyfKrqSZ5MpLJQNrG6fVa0OnUeSdAix/MB4jF1BjnTqL6m9swzKiRzGZiH/sYDQ0N9Pb2KhWM863sOYaG/gO12kVz863LfrhzKW44HA6SyaRCq3I6nYyM/ANVVR1IksDY2H9js/1twfexvv4NwBtIJOYxGL5CNKpGre5Gkp5GpdqMJNVkvQ9L/SzkOYtUnXBBEEgkEvT29hKJRAD44Q9/yPve9741aynLQ86BQIDu7m6Ki4uVKozMX02VRTSZTFitViorKykuLl4x8MldlOnpaQ4fPozL5cpLFSOX0VKu9VdVVdHd3c3w8DCtra0YDIZXvGKUDbK8Y2dnJ/39/fh8vozWda7zF/pQcRmXsV4opMgl8+7XAtkKbKleEKlUIrniX1lZid0+kHI8NIx6XOEAACAASURBVDZu49ChQ0tPnxXJZJK5uc9gNj+KKKqJxe6nqGgPkpRUqDI9PT2EQiEaGhrS9gG527HSXiKrLcn/hUKhNHfryclJ6urqzqkdPcr27X+NIEicPv152treU8AdXERd3b8zO/tRjEY7dXVbEYQ5kslHSSbbUKnasr4mlbIlF5cApbjk8XjSikvj4+McO7ZYeDx16hS7du2ioqKi4LVmQyoFuLu7m7GxMZqbmxV2SOqcRTQaXZG2uxR6vZ5NmzYpFCOHw4Hb7V5xD05VrZK7HPJ7yUl6qpT7yMhImpQ7rH9nY7lrSC2K9fT0EAgEqKurK+j8l5ONSwCvlFlSLBZL25jD4TCiKCpfxpVM8mRIkhGfrwirNYQowk9/+mluuin7mrKZDwEsLLRjL30KwxdCaHwCQm0tQiCA8YtfRH3//bS3tyuV9g0bNmAymZid/TiNjb8mmVQxOWnB5Xo7kN4NkFWX4vEo0WgvDscmWlu3ZXh01NXV0dPTw8TEBB6P3MkQsNsvbFNMJk0cP76NzZsPMz9fis32ftRqHdHoTwgEmtOqLMlkUqmy1NTUpHBsM6HRaNizZw+/+c1vAPD7/fz85z9n586deT205wuDwYDH42FkZITnn38erVarJBbLySLmC1nJpBDn2KXVo2g0qnBml153UVERO3bsUOQZy8vL15VGVejxWq2WpqYm/H4/Bw4coLa2dlnqWqHt7Mu4jIsFa9nZEASBSCTCxMSEEsNkKlFxcTHl5eUrGpdOT9soKsr+wCWvU45XMkTxBJKkQqWKE42epqTk9cqeo9Pp2LVrF2fPnuXgwYO0trZitVqZmTlOMPgxkkkdpaX3U1LiPneuTNUltVqtiHhkc7euq6ujr6/vXIHmF+h0i8Umh+MXQOHJxqIy0eJ6wmERnU4AkiwsfBybrY1E4jNEIuq04pJ8n/MtLr3+9a9Xkg2Ahx56iLe//e14PJ41K5zodDrcbjdjY2O8+OKLqNVqZY1yPL2QGcTS0tI01Sp5QH05LI1TkUgEURTTPk/ynIjf70+Tctdqtes6IJ7v8bLi5tGjRxkaGiIcDq/4vYLLalSXDNZD+napskY4HEar1SoPt7JJ3pkzZzCbzSt+kVKhVqt5/PFvcfbsSdRqC3feuai+JPNWl27Y8poX28wSfv+dOJ0vEhi5Cuv0JKo6z+JBxcWwsIAwOorKZqOxsRGfz0dXVxcVFRWYTHMsOpQmiUTO0NXVpXAu5euSVZf6+/+QurqjLCyUEo//EqPRlbYenU7H5s2bzw1430xraxXFxbUYje/N+z5kg06nw+//HF//eifvec9/IknziGIMUbyFcPh6otGP5l1lWYpt27YpyQZAf38/W7ZsoaOjg9bW1mWpT9mQrbMiBz+n04nb7WZ4eJhYLIbL5VozmVxZm9zlcinUquUG1LNVrKxWa1Y1EBllZWWK4dLc3Bzz8/N5rX+9zYzkTV9uXff39yut62xSwa+VitFlvDZQyHdjtZ2NpVReWRlKq9UqlE/3Ei+LXPj5z6/jTW96hkCgmCef/Bw33ZRfnFpcuxed7mqCwWkikUqczndlpXHW1dXhcDgU/waD4cu4XL2AxNDQ1xgb+6BCNZVFPGTVpcHBA8zOfpa5uSZ27vy7jPur0WhobW1lfn6e0dFdVFQ8g0olYLPdVvB9XYrOzk9jtf4Ah2OW2tp9SNI+JiammZ7+M8zmSux2Ox6Pp+Bih9FoxGazMTc3ByzuYaOjo8zMzKxK/XA52m5paSnV1dVMTEzg9/sVU9y1gEqlora2VlGtGhkZWXZAXe5SyeuMRCIUFRUpNODURMtisaRJudfW1q7rgHihxxsMBqqqqgiHw+zfv5+6uroMo8Wl538txKnXfLJRaKdiacUokUhkeFnID43FxcUZyhoX8t4y3va2twFvUzTCBUFAFEV8Ph8WiyVDGUpGKNSNXv9bQqFiispeQF20HSkUgqIiSCQWB0DOcdoXH8zUDA01kUyeJpm8kYaGKMlkJVrtH1JbW5azG1BX10U8rsNs9pFInAVcGcfA4kNpScnr6O2tIBqN0tYWx2BQMzT0a/z+Zygru4WKis3L3gu5s5LasWhv38P+/SO84Q3/hdEYwmCYoKrquyQSUUTxc6zmY63VamltbaW7u1v5t2g0yoYNGzKoT0uRa8hQDn65OitWqxWv16u0lD0ez5ptKjK1SO5CVFVVUVlZmaEGJstPpvqX5KNapVKpqK+vZ2ZmhqmpKSYmJmhtbc0q9Zd6n9ZyZmMpUjdljUZDS0uL0rqWpQhTk6KV1nOpVIwu47WBQmlU+XQ2otFoWmKRajJXUlJCbW0ts7OzJBIJamoy6ajLoaXlfh544Oe4XC5uuOENSlFvdnaW0tJSZaB3aZySpCQ+3y0YjcMkEjbKyv4753C3fE+qqqoYHR1Fry/GbhcAAa22AaezCrPZnLWqX1T013g8fSQSzzE7247L9Zas71FaWorF8mcMDu5kYcFPY+NeTKbzFKelswfZsFTEQ6Vqw+v9FEbj/YjiAGq1iNv9ODU1zxIKPYNOt7xP0nK46aabeOCBB5SfOzs7ueOOOxT1Q5n6lA2roe1arVYWFhbo7u5Wio6FFvNyQaZWzc3NcezYMRwOB7W1tYTDYSWxkIuecpxyuVwKhXcptUr+O6WKiPT19TE5OanQ51bCenfg5ThVU1OjzHPI85DZksXL0revQYiiSCgUIhAIcPLkScWobenQU74frHyTjXQpv/PnFoTzrqZbt26lu7sbm82W86FUFHvRaOKYTDNMTe3E/JGPob33XpifR0wk8L71rYyFQvgPHiQaTTI/b+Mb32jkC1/YRH19M2fOtFBRUUFtbe2y1yhJ/xeT6QFisZ0UFW1f9tq0Wi0bNmxQDHnKyrTU1NxGbW2cQOBRJKkbQTjPyVyqZZ7aCpcH/ARB4KmnprjvvnJuu+0buFwTqFQRtNrvIQi/IZH4EZC/cpeMP/iDP0hLNl544QU2btzIzp07GR0dVVq+JSUlaYlFJBJZtVO4rGRy9uzZvA378oGcAImiSElJCWfOnKGvr4/S0lLKysoyuMCpyKYGIn8Os1UfN27cSCAQ4NixY9jtdurr63MmZevZ2chF/dq+fTuzs7McPXo0TbXktSIpeBm/e8gWW1JnFOTEQqfTKfGrsrIyq8lcoXFKTnJsNhu33nqrwptPjVNer5empqasSYAoRtBohkkkilCr5wiHJzGZFk3aotFo2jxgqqR3Q0MDavU/MDjYhMlko7b2ZlSq3NQhi0VNMqlCEKC01LDstS36JFyhdPvLysrwer9Jbe2T9PdfzZYtX0ubHUj1s1hOxOO558KMjyfZs+d5iorCaDQ+zOa9TE//PRbLx1a1/yw1OpRd1rdu3aoobtXV1VFWVqYMmvt8PsLhsDKzUihtV5bhHRsby9uwLx/InbZYLEZJSQljY2OcOXOGkpISnE7nEpnhdMifudTimEajSVOtkrtXoVCIkZER5ufnVxRReSXjlFarpaWlhWAwqBTFmpub04pir5U49TuRbGT7Q6Uqa8ia4IDSxltqkrcaZKs+rSw5m10Zqri4mJ07dzI0NKTMWqRqSycS8wjC3SQSWgRBQq3+F3odKsJ/9mcIo6OonU4MbW04LBbq6uq54w4TZ84I+HyzfPjDC9TX2/n2t9sZHh5QnEtzVal1uk8gSX9dUAvYbrdTUlJCX9/TaDRxJEnCbPYxNjbCwkIobc7CarUuu8kAXHvttbhcLp54QmTPnh+yYUMXGk0CrXYYjWY3odBnUak+kvf6IFOJS3YzXVhYIJlMYjQaOXHiBIIgUF5ejs1mW5VLbLb3dbvdlJeXp1WnCuHFxuNxJaj4fD5lgE/mKzc1NRGNRunp6cHn8+UVKFKTjkQikVW1St6YbTYbu3fvZmRkhP3791NfX09FRUXae6x3Z2O588uGUPL66urqEEXxNVExuozXBuSH/nw+83JFd2hoSKFCaTQapeMuU3nzOddycWrpPKD8gJdLGUqj0bB9+3alOJPNGDQc/hZ6fZhkMszAwE3o9Un8/sMZXZfUh+Hz/k8aLJY7GRoa4uDBDtra2nJSXE2m/yKR+HfU6q1otdeseB/gvAt2X99Rtm//byRJwG5/jLNnbycSsaR1rWXPq1ydFYA9e65m3z49+/dbeMMbfgAkUavjVFT8PbOzT5BMPoLZXLjyYXl5OZOTk8rPMm03FothNps5ffo0PT09CuU128xKoZC7BWVlZQWpSqUilakgJ0AGgyGtUCeKIqdPn2Z2dhaHw7HiHr2SahUsfi4bGxsJhULLSrnD6pKHC41TJpOJHTt2KEWx1KLda2W28KJKNp566ik+8pGPIIoif/Inf8Lf/d3fpf0+Go3yvve9j87OTux2O4888ghut3vF80qSRGdnJwaDAY1Gk6Gs4XK5lCpMOBymr69vzbiJoiiSSCQK2rBzQVaLkDmsTqeTqqoq/H4/Xu+vqK+fQpIE5ufLCQZjlJQ4qX7d6xSX6VTcc0+c228PYLUOolJJfOQjn0Kv/8+MWY7cXY7CPvyRSAS/349K1cDg4E04HL9ldPQmioqSOJ1qPJ5K9Pr8TZC0Wi1bt26lo6ODn/3sHcTjWnbs6EQQkghCEpPpE0Sj/4EoHkAQ8lOVWkyAzAQCAeXfOjo6lASooaEBs9nM3Nwcvb29mEymC040UpGqKrWcN0c2PXP5QcNqtVJVVZX1by4/CMjVr3y9P1IrekupVakPRzIPV3Y5l3m48ndpvWc28jHpS21dj4+PU1RUtCadpMu4jAuFLBm+9MFCFMU0k7lgMIhKpSIej6PT6fLyslgJcpxa+h1KnbNYSofKBVny02azcfLkSaxWK3V1dQSDQbzeeZzO+/H5rOj1EVSqN2E2m6msrMyZHCUSUYaH/5jS0uN4vX+C2/0x3G43ZWVlyixHfX19lrXVo9F8raD7IM+wqVRGotFidLoF4nEDomgsuGsNizShq6++mu9/f4wHHijjAx/4FgbD4iC63b6PRGIDg4P/TWXl9XnvdZIkceWVV/LjH/8YWJxlPHDggELbraurY+PGjeccx3swGo2Ul5evWZySB7FlVamysjLcbnfG+lM7QPI8yEpD+7AY27ds2VKw90cu1So5TqlUqgwp92z+VOvt8L1cnLLb7WlFO7fbjSiKa2YK/GpCWKF9ujbaenlAFEWam5t5+umnqa6upr29ne9///tpRnb3338/x44d45vf/CYPP/wwP/7xj3nkkUeynu+ZZ57hySefpKOjgxMnTrBz504+9KEP0d7ejtlszsk5jEajdHd3s3Xr1oLWn1oJkj9M8/PznD17lra2NoxGY0EbdjYsVdyQDX6cTicVFXdiNg8gCCKTkx+jpuavlz3X6KjA7bcv0NLyJL29DXzhC19l27YH065nYGAAr9dLa2sr4XAYo9GY06kzFovR2flPaDR9WK1/jt3epqwztX0rPxCr1WoGBgYIBJ5i27bPIggSc3NfxmYrTAVkenqaX/7yl0xOTtLe/iLXXfdUhua6z3c3Ot2nMl6b2rL3+XzEYjHGx8fp7+8HFo3t7rjjjqzvK4pi2v1Zq+R06fnn5ubweDwkk8m0eRA5AbJaravqwMnnn5+fp6WlJaM1nwty9QgWqQcdHR3s3r076/v7fD6F59vY2MjMzAzhcJj6+vq83uvw4cMFVc7279/Prl278lZlOXLkCNFoFJ1Ol/V9dDrdxdS+vmgWchHiFYtTsH5Fsauuuorvfve7igS3XEiQKTryfzJFp6Ojg/b29oLWnq1jEQgE6O3tpaWlRTFavZA4tZQOOzc3RyKRwOFwYLcfxeX6OFptnKmpBkymZzAYlufRz809T1HR7YiiFkkSMJvPU10lSWJoaIjJyclluxyp8Pv9zMzM4HK50mYYZLNUOUZZLAG02t8yPl7PyIgau/1XaDQzVFR8AqPRWdA9mZ+f5+c//zmBQDfvf/8DWCwLab+fmHgjkvSdrDMFsVgsK203FosRjUbZunVrzutOJpOcPXuWiYkJWlpaVlQnLBSp53e73QiCkHUexGq1rmjiu9L5C6EYy9SqZDKJRqOhq6srY48PhUJ0d3ejUqkUKXdY/FuNj4/nbaJ86tQpKioq8r63R44coampadn5RlhkK8jzJrW1tTnj5qUSpy6aZOPll1/mnnvu4Re/+AUAn/vc5wC4++67lWPe/OY3c88997B3714SiQQVFRVMT09nvdG/+tWvANixYwfXXnstzz33XF7riMVinDx5km3btuU8JleLGc53LeQN2+/3093dTW1tbVazmeXeIxsvVK5eWCwWTCYTCwsL9PY+zfbtd2EwhIlETMzNPUZZ2ZXLnj8Ugv5+qKu7nxMn/LS13aLICKbC5/Nx+PADbNnybYJBGybTdxV5PzhfdRsZ+V9aWv4OtVpkfNzNwsJ3lE17ufbtyMj7qKv76TkfjtdRUfF4XvcnFX19fTzxxBPEYjEaGk5z663fy3LMl9Hr36koRMmGfvJGaLFYFJOnp556Cq/Xy/XXX7+iklggEODUqVMpnOILkx9cqroRCoWIx+MUFRXhdrspLS1d05ZqMBiku7sbo9FIY2Nj3hzeSCTC/Pw8vb29XHXVVTnXJEmSwsO1WCyK62s+6OzsZOPGjVm7NNnw8ssvs2fPnry/Y8eOHcPj8RCLxTh9+nTGvMmlsolfxqVbFDtz5gzPPvssHR0dPPbYY5SWlvLBD36Qt73tbcr3JdcD2sGDB5dNNnJJzgJpCYUgCIRCIU6ePEl5efmKM3upkNWMUuXR5Q6xvP+bzWbl/PX1f0VJSS+iqGFq6g5qaj6x4nvE45NEo9eh0QQIBrdjt/8w45hgMMjx48eJx09is1XQ2vrmtL1YphlNTY2TSNxFTU0vhw+/GYfjY0qlfbl5zLNn76O29jOoVElGR6/E5fppXvcnFVNTU3z/+98nmQzwwQ/+B+XlU2m/lyQ4fHg/TqdLif3BYDCta221WlfVTQ+Hw3R3d6PT6Whubr7gGCIL1qTOV6Z22ux2+wXJuC9FJBKhp6cHgJaWlrxjQjQaZX5+noGBAbZv3561WDo9PU1vby8VFRW43W7m5+eZnp6mtbU1r/c4ceIEVVVVeRvKHj58eFnlraXo6upiYWGBoqIimpubM153qcSpi4ZGNTo6mqaGUV1dzf79+3Meo9FosFqtCq9vKa699tpVrWMpf3WlDXulFnNJSQm7du2ip6eH6elp2traMr6EqW6c8n+pvNDl9LcXjX+ex2AIIUkCPl8ZDsfeZa9x0X30C9jtzxII/Al7996Z81ir1crGjT/FbPZjNvsYHf0Bkch7lbao7BpqtcofeIGSEi2VlS3LrkGGXn8bsdjTCEKSsbHdOBwfRqt1Ikl/A+S3WTU0NLBjxw4OHDhAf38z999/F3feeV/aMQ7H13j55Saqq6vTDP2WQqvVnlMDyw9ms5ldu3alDZDnK3WcKo+7nOoGoHRcPB7PmrbEZa7o5OQknZ2d1NTUZMjwpdK2UlWsiouLaW1tVWiC2VSrZJ6v0+nkyJEjzM3NYbPZ8uqkvBLqVYIgKPMcsgvtSlKEl/G7iwMHDtDY2KhUGW+55RYef/zxtGTj8ccf55577gEWlYPuuuuunJ/NU6dO4ff7ufXWW+nv7+e+++5blUmbHKeyCY3kE6fkfay/v59Dhw5lTfLlYd7UKnsikUibDcslO242m9myxYxG04vRGCQe16HX//6K1yVJSaJRAb3+54hiHzZbdsdsk8mEzfZr6uruB6C7+++x2d6WEU8NhtM0NZ0gmVSxe/cTaLXfWnENAAZDQl4RkjTP3NzsOfpN/nuE0+nkTW96E0899RTf+tYd3Hrr/9DQMKj8XhCguflGnn/+m7hcLtxuN2azeU32IZmiOzk5SUdHB263O2OmLhdyyeNmM8idmpqiv7+fRCKRF0U3XxgMBrZu3aoIzWSjVi1H25JFdVLFTmSkSrnv27ev4Pi6ljSqbJBNAZPJJIcPH8bpdK6pr8orhYsm2cjWYVn6B8nnmGxQq9WIopjXHydVTm2pMlQuKb+VoNFo2LhxI1NTU8oXXaPRKBu2rLhhsVgoKysrUFpOoqTkMVSqJJIkMDy8lWh0YtmNJBA4SXn5A0iSimTyk0jSTYoiVOp9kGlGorgNGESSNExNlaLXT1FbW0tzc3PKPW1DFOcRxaMUFf1N3vemrOw6otFeJEmiqelD6PVPIkkqAoEiTKa/XPnqz63T7XZz+PBhotEoMzMO7r33Hj7+8XuU4/T6r7N79y5OnTpFPB6nsbFxzToEMke5rKyMnp4exsfHaWlpSeNZLg3UsqGT3KlaaSC+srIyYzBvpTZsIeuvqKjA4XDQ39/PgQMHKCsrU7osqbStbAEw1RBwqRqIDK1WS0VFBZFIhNOnT2M0GpeVaJTPu54P/KlBQhAEpfso34Orrrrq8gD5ZaRhrYtib3nLW3jLWxYlWYuKivI2oJWLX9nilEajSeuw5wuVSkVTUxPz8/McOXJEmf2S96xoNKoM89psNtxud0HV63D4AcrLA0gSzM/biUScyxYIJCnJ6Oi7KSvbx9TUDiorH0MQMmN4NBrF7/djNB5EpRIRBAnYz+joDtxud9qchSS5kCQLavUCicSOvNdeVnYnMzOnUanGMRpvw2LZhiSJhMMPU1T0+rzOEY1GcTgc2Gw2ZmZmeOihP+amm77Hhg2nlWOKitRceeWV9PT0MDg4mBFHLgTyPm+32+nr62N8fJzW1tYM+qgc9+XnE7mzno/xoOzVIpsGt7S0FOxRtRzsdjulpaUMDQ2xb98+KioqlC5LIpFYVm5+OUl3Wcq9srKSo0ePEo1Gqampuaj8o+x2O3a7neHhYfbt24fH48Hlym47cDHiokk2qqurGR4eVn4eGRmhsrIy6zHV1dUkEgl8Pl9eJjY6nU5R50lFNmUoefMbGRnB7XZnfXAqBDLfUt6wAeVhy+12U1tbe0GbSTj8BKWlPgQBRFGgrOz9zM3NMTU1lbWLAqDX24nHdWg0YaL+MoSidPqO3+9XVCIWW8yfRK2+BY3GRXt7Hf39/QwMDCxRrBJQq9M7JIGAj4GBRykqaqGx8XU5r0GvX2xtTk+HsFoFJAmCwf/FZDqMINyDJDUox6bKO6aqWVgsFq655hqefvrpcxUO+PWvf8XevUkkaQNQgtEI27dvZ2Jigo6ODhoaGnA6C+PeLge9Xs+WLVuYmZnh0KFDlJaWnjMi9KepbqzW0EmWEfZ6vXR1deFwOJTP6GqRTR1Eq9UyNjaGyWRi06ZNK7Z781EDARRFr8bGRiYnJzl48CDV1dU5XdoL7VQUimybvuy3Eo/HLycal5GB9SyKyXFqKXIpGBqNRgYHB9O6Cav9zGbzMxoYGECj0eDxeNZAdU/Can0ElSpJMing811JPB7l0KFDbNiwIeseE4vNUla2n2jUgsNxmHB4BJ2uKm3/T6fD/gUwSDJppLX1UwwPC/T399PW1qbstYJgRxCeR6XqRaXamffqBUGHw7HYNZmcvB29fgFJgunpz2Iy/TcOR/qziuy7kUqHldd57bXX8thjjyGKIo899h7e+94fUF9/EklSE42+rMQRWSiktraWysrKNdsLtVotbW1teL1ejh07hsViwWg0Kj5i8jqXKoLlC7VaTVNTE4FAgO7ubsxm86qMdlMhz6vK9zMYDKLVahkfH0en0+U1N5lN0n1pnDIYDLjdbqampjh69KgiCrBcjF0LNarlkHp+lUpFXV0dLpeLvr4+hoeH2bt37yWhVnXRJBvt7e309vYyODhIVVUVDz/8MN/7Xjr3/oYbbuDBBx9k7969PPbYY7zxjW/M64+s1+uJRCKKYVkqUitA8gdg165dDAwMcPToUTZu3Jg3t26psU+qHOFSWszo6CiDg4O0tbVdULKhVv+lMhCdSKgoL38dlZUapqen6ezszHigjsV8TE19Ev1sOZYvBCgehFDRdQx98IOod+1S1LkMBgOBQIBgMIDD4UQQypEkUKmgqakpL8Wq6enb2bTpZURRzeTkDykv37PstSQSn+HAgb/HYvGzYUMXgnCMYLCbqan/wOtVZ9CMssk7dnd3MzQ0BMBLL73E9u1/nnZ/BUHA5XJht9uVLkRra+sF/Q1k86eldCiv16twvB0Ox5oFC9mbY3h4mIMHD+Y9OJfKr/Z6vRnt8FR1EEmSmJiY4MiRI3kHu+XUQOT3lzX581EFWW/n1OXOv5Z848t47WA9i2JynBJFMes84FIFw61btzI8PMyRI0cypNCXgzxnl6pml8vPaHJykoGBAfR6fd48+WyIRp/CavUjCLAozf5u3O4mvF4vR48epbq6OoO6qFZbmJ3dhNXaxexsI0NDI6jVk1npO4toBA4rr3e7yaFY5SSZPB8TR0eP4vV+kljMTlPTVzCbl+feh0K/RyLxBIIgUV09gNG4kbNnbyMa/TjBYDBtX5XFMZbSdq+55hplrvS7372ZG264gZaWdOpxWVkZpaWl9PX1cejQIdra2gqSmV0Kma69lA61sLDA7OwsjY2NbN68ec3ilNlsZufOnYyPj3Pw4MG8KcCyR1Rqd+U8XTvdxwRgZmaGEydOKM8ihapWqVSqNEl3SZIoKipi48aNShehoaEh59rXm0aVLU7pdDo2bNigPGNeCrhoBsQBnnzyST760Y8iiiIf+MAH+OQnP8mnP/1pdu3axQ033EAkEuG2227j8OHD2Gw2Hn744byUbTweD5/4xCe48cYbC5KcnZ+fp7u7G4/Hk8GjTeXbywPcKpVK2WBWGjiDRTWEEydOYLfbcWeRjlsZ8xiN5+Vi5+ZKMBpHlZ9jsRinTp0imUxit9sJBoPEYt+mpfl7mO4IEj9TjMb1e6gjEVSxGNEHH4Rzrf75+UESiRuxWmcYHPwILS0fz3j3M2e6GBz8BSrVZnbt+r0MWk8oFSqCdQAAIABJREFUtAGDYQYQ8Hr/DZvtfTmvRN5gZmdnmZ//EVu3/iMaTRyVSkKSDExO3ovV+t4V75Hf7+db3zrPw21sbOSd73xnzuNnZmbo7e3NOquQC8u5sC5V3ZCdV0tKSlaskKwG8uCcIAg0NzenPRDIMo7yWqPRaMY6V1pPIpGgv78fv99fUEt8qRqIWq1maGgIvV6f8XCWSxXkpZdeYu/evXlvzC+99BJXXrm8MEIq9u/fz86dO7Nu1oIgXGwJx+UBktx4xeJUIpGgubmZX/3qV1RVVdHe3s73vvc9Nm7cqBzz9a9/nePHjysD4j/60Y/4wQ9+sOK529vbue666/irv/qrjAHu5fa9hYUFTp48SWVlZQZPPtVPSi6EyA9u8kP7Smp20WiUEydOYDKZaGxsXNUelkjUUVw8Ayx24EMhr/K9E0WRnp4eQqEQTqfz3APxIB7PJzGb5xgbuw2H46NYLKVZ1zk09BLB4FEqK99FSUl5xu9XUqwaG7ueqqoOkkkVo6N/S3V1ZqxLPdfinvsMavUBtmz5BoIgIggq+vv/GEH4INXVrSveI0mSuO+++xTlMY1Gw4c//OGcr/N6vfT09CgD/Pk8K6Tu/7Laoiz3L4uiyO8n78EGg4GmpqY1r5THYjH6+vqIRCIZ1K2loiipJrnyOld6oBZFkaGhIaanp2lqasoruYf0OCUXxyYnJ4lEIoqQSTQapbe3l3A4TFtbW0ZSL3fn8k3GC41rR48eVWT3s+Eik8W9+NWo1hNDQ0N86EMfoqmpic985jMFm6WdOnUKURRxOBzKxp2quCEbKa2mCptMJjlz5gyzs7Ns3LixoMqF1/sWXK7nlZ+PHLmW2tpH0trh8Xhc8Q9xu91otd/GqX8A4/8JErG5MRoXVbeEyUni//zPJHfu5NAhFb/5zUv8xV/8EaKoIhJxUlJyMO29g8FZEonfw2TyMTNTyZkz9+FyVaZ1OcLhRzAY7iYabUCv/zFgUnTk5Y0wlQ8sz60Eg0FGR7/Cjh0vUlU1zOJtVTE19UcYjd9YcSN86KGHGBsbU36++eabqaury3m8/EAdCARobW1NS5qytcNlGV95I1zpoVSSJEZGRhgdHV0zh/ClmJqaore3V+Gpyiom8jplFZPVQtZsN5vNNDQ05B2MUqVyR0ZGMJvNOQdgZVWQ8vJyPB4P+/btKyh5KDTZePnll3PK9l5ONi4pvCaKYlNTU3z0ox9FEAS+9KUvFSSnLRuhBYNBKioqlDgliqJSYJDj1GqSBXkPGxsby1ti9jxmMBrP77+hkI5YbCJDdlyj0RAKhc4Z5T1HSclnEUUtkYgTu/23Wc/s9fag070VjSbG9HQrVVVPZxxz/PhDJJOPEo1eA1yJzWZL8+Xw+W7Ban0GSVITCHyB4uLbldcuR9uNxyfxeG7GZAqgUiURBB0LCxvp67uHhoYrV9xvDx06pHQ3YNGo733vy12QSyaTDA4OMjs7S2tra9rfIFUW3+v1KjSjQvZ/uZt95syZNRcikTE/P8+pU6cwGo1otdoMtoLVas3bjDIbwuEwPT09qNXqgoxxU+PU9PQ0oijiXiJX7fV6lcJhKi2so6ODzZs35/1ehcaplSTgLycbFxmSySRf+cpXeOSRR/jmN79Jc3Nz1uNyKW6oVCol23W5XGveuvL5fJw6dSprOzkXjhy5h717/+3cuuFnP/sRdrtTCSypD8LRaJSTJx+kpeVzaImie7sKTcluhCIziCLC5CRTX/gWnd5GHn9czdGjMe6++09xu3uprLyW4uJPp713JNKNTvcG4nE1Ol2cRGKAM2fG8Pl8Ge7joigyPz+FKL6D8vJ+jh17O7HYHWm+G6lVAUmSOHXqFGNjT3DddV/AYAgjSSoEIUkg4GF+/lEcjrac92VsbIyHHnpI+Vmn0/GRj6zsJu71ejl58iQmkwmNRqNo3Mv30mq1XpB5ltyFUKlUBTuEZztXatVKfqiIx+NEo1Ha2tryluLLF5IkMT4+ztDQkMIbXelexONxvF4vXq+X6elpmpubKSsry5mYy8n3xMQE8Xicq6++Ou/1FbqJL1dhupxsXFJ4zcQpgO985zt86Utf4qtf/So7d+aeK5DjlPyfPPgaDoepra2lurp6zSvUwWCQEydOKEZu+eyFk5Ofx+3+J+XnI0c2Ewzen7avynthPB6nu/sUWu23aGr6EaKoY3b2/+JyZZfI9fufxWC4nWRSQJIMGI09ab+Px8Mkky3odBESCS3h8IvMzbGkyzFPPP5loJxQ6N2MjBwhFHqOcHg7RmNlzgdhSZL49a+fwGL5Dldc8Uv0+hiSpEKSzBw69I+Ulv7BsntkMpnk3nvvTfu3j33sYys+W8idLK1Wq8xapNKM5G7VauNUPB6nt7eXSOT/s/fe8W2V5/v/+2ha8pL3jh0nXhkEshgJBcIuLVBaIEBLIKFAGSUto4yW0Q9QKJQdAvlSyihlFgqlgULZ2Xt6JY73tixZli1rnfP7w3lOJFty5NVfA75eL15E1jo60nnu577v676uPtUjbKQYOGwuuiui4ybiwVijvb2dqqoqtdt3uEKwmFkSsrfhZgkVRaGxsZHa2lry8vLIzMxky5YtHH300RFfa8ONU0NJwB9Jceo7k2wI7Nixg2XLlnHVVVdx+eWXqz4dYsHu6+tTB3kHbthdLhd79uxRB3zHOuv3+/1qu27atGlBm9HAASnBs9VoNNjtz1BUtJsdO5Zx3nk3DHlMTU23kZ7+Mh6PAdeHk0j/fwfbcrKM78c/pvTka/nlTUYaG2X0+j50OiO3397Cj3+cxODfkIzbfQ1RUZ/S13cFRuO9wKENe0xMDHq9HofDAYDZvJsZM/oDhqJIaDSthz0fGzduZNOmL1i2bAVJSZ2H3lnWU1n5MBkZV4a90J555hlcLpd6O9QiHk51w+/309vby7Rp08bcBAn6F8L9+/dHTN0aqrtisViIi4sLWuicTicVFRWYzeYxVd0S8Hq9aidIGIHBoZkQu90eJD0YSNsyGAxB1KpwQaCvr481a9aQkJAQMVd5JMlGuMcfSYv4BL59caqqqoolS5Zw1llnsXz5cqxWq8qvDzRKDaTEiHjh8XjYu3cvJpOJgoKCMaduigq7zWZj2rRpQdfmQEM/p9NJb28bZ511CVot+P1QU1NJZmZW2Nfv7PwIk+k6wE9PTxbJyeuHOBo/PT3Xoddvxuf7DWbzxUH3KooHWS5Eo3Ehy3pgK1ptGk6nkz179hAVFaUqbsmyjNksUVh4IUZjHz09cZhMpWi1Q68Du3dvBW5n7tz1SJJycIZSR2Pj1dTWXkpJSUlYis1XX33Fpk2b1NvTp0/n+9//ftBjAk39BAtAJABOp5PCwsIxFToRsNlsVFZWRkzdCpRIF92VQC+r+Pj4oDU1kAI8lqpbAn6/n5qaGjo6OigsLFRj+cDZFYfDETS7KPZ8iqIMmjsUEAlZd3c3Pp+PY489NuIC9HDj1ObNm5k1a1bIeHQkxanvVLLh9/v5+uuvWbt2LX/+85/xeDxkZ2fz3HPPkZCQoC7YQ23+Ahfa4QyPDwft7e1UVFSQlJSEoih0d/e7jY6mcuHxtOH1LiQ2tgNZ1rJly+Mk9eQwGZBSUlBKSkCS+Oc/u7njjh4kSeHUUzfxxBPnDfm6AzfsNpuN6Oho9Ho9Pp+PadOmERcXh8fTCszHYHBhtc4mPv52/P4KDIbLkKTQXESv18tXX31FaelGFi/+K7m5dUH3d3Yeg8Pxd1JTUwediz179vDRRx+pt0877TQmT54cpGYx1ELY09NDWVkZsbGxI+YpDwVB3eru7qa4uFjlY4ZbCAO/+0i6K4Et8bxhaKoPB52dnZSXl6PVatFqtXi9XqKjo7FYLEM6xga2rEOpVgmsW7eO4uJi9VoINNwbCEVRWL9+/ZglG2Jo8H8IE8lGeHyr4hT0U/zWrVvHiy++SGdnJykpKaxYsYKsrCxVPWio6zmQ9jRt2rRhUbIihd1uZ+/evVgsFjQaDd3d3ciyHDQPImida9d+xYED31BcfAbz5s0f8nVbWn5HVtYKvF49jY0zcDieCmlE19S0lq6uf5GQcC7p6eHFR2R5I17vS/T2nkZn59FBG3ZZllUufmJiIi7XAczm+ciyhFar4PFUodMN7Qnk8XhYvXo1iYmvcsYZ/z4ovdsPr3cK69Y9SWZmQdjC0iOPPKL+Oy4ujsWLFwcVFQea+gUmLn19fZSXl6teDGO98RSdZmFyJ/yRBAMksLs+0u6KKL5lZWWRk5Mz5nHK4XCos6sGg0G1GgikQoeKQQPjVKjZqe7ubjZs2EBGRkbE53+4ycamTZuYPXv2ET9b+J1LNm677TZmz57NvHnz2L59Ow8++CCPPfYYxx8/tBHeQAj+nqCTjBSBF62oXvj9fkwmE729vURFRQ3qcowEHR2vkJFxI4oi0d2diMFQTmNji6rGJBaRJ5+sYd++DRQU7Gfbttm8/PKhKovP56O5uYG6utdxu5OIipqh8lfj4+Pp7PyMnJw78HiMuFyvYDYXUV5erqpE+P2deDwVyHIPsbGXAjJdXfOJi/sozFH3v+fzzz9PX18PP/7xm5SUlA96zIYNH1NcPF89R0Id6rnnnlMfk5yczHHHHRfkvh7Jhr2xsZH6+noKCgpC6uSPFp2dnZSVlWEwGFTToUgWwkjh9XrZv38/vb29g+ZRhgPR9hZdi8AgKMsyVquV/Pz8iJOaUIN5AxdysSjLskx9fT0NDQ1hVUFkWWbjxo3Duo4nko1vDb5VcQrgnnvuIS8vj3nz5tHU1MQtt9zC3XffPajqfTg4nU727t1LRkbGqDdyA+nFYq3q6+tDkiRmzJgxKrUkAL+/F7+/BEnyotH46e7+AJdrEjU1NRQWFqrzbm53Fz7fXIzGXvr6ojEat6PXRx98jdAyqYFrf1XVRyiKh2nTLsTr9QYpVnV334rZ/D69vUtwu+swmzfi8dxFYuLFQx06q1atIjFxB5dd9ioaTbDqZXX13TQ2nhbUpRWiKCtWrFAfl5KSwrx584I27IfrKCiKQltbGwcOHBi3WQuHw0FpaSkajQadTqd6rojCUiRD3EPB7/dz4MABbDYbRUVFEZm+hsJAA0KHw6HOhEiSNCRFaqhjEwpSgapVAmvXrmXy5MlUV1eTk5Nz2OtsuMnGhg0bmDdvXsh9wESycQShrq6Oyy+/nIULF3LbbbcN64Lx+XyUl/dvfouKiiLanAS2RAVty2QyBbXwDhkQ9XPk6+rqKC4uHhUHv6trDklJ+wCJ2tqfkJX1Z6C/gl9aWkpiYiKTJ0/Gaj2AXr+MuLgq2touw+e7UaXE9KttPczUqetRFA2trS+QnX2W+h5tbWeRmroFSYLGxiVkZv5JXUS6urrUlntT00qysn6HVuvD4UglLu7X+P2LUJTQczQ2m42XXnoJn8/HGWes5rjjNgXdL8sa/vOf1cTGxqr62WazmU8++UR9zAknnMCCBQtGdO5Eu1cMnY304h7KMVxIZBYVFY3LAHlXVxcVFRXq93y4BMbj8aiJxUDDpMCKpUBgUlNUVBSxDOdQScfARdnj8VBZWYnL5Rqkq+7z+di6dSvHHhvaYTgUJpKNbw2+9XGqo6ODpUuXkpWVxQMPPDAsGVq/369em5EWrkINRoejbcGh6vTUqVNHxcG3Wl8jLe06NBoFhyMJjWYbBkMCfX19lJaWYjabKSgowO/vRJLmIMv9ztttbZ/S3S2rtF1JchAV9SyyHEN+/uPExR0qFFVVrWTy5PsAhaqq6ykouDukYpXN9m8SEn6KRuPD4zGj0zWGOep+dHV18eqrrxITU83VV69Cqw1OOFyu41m79j61KyViv9/vZ/369cTGxrJ48eIRFxa9Xi+VlZV4PB6Ki4tHzLoItWEXSpuKotDZ2TmkFOxoICjA0dHREQmReL3eoO7KQMXFuLi4oDjl9/uprq6ms7OTwsLCiPdUkcQpwVbo7OykuLg4LAX7uzpb+J1PNqB/o3L//ffzxRdfsGrVqiCH2EgghmYHJgQDfTcGVljEYNzhLliXy6W2qwNVNCKFy1VJbOw8dDofsqyhqektkpPPVu+XZZn9+/fT1vYpc+Y8giT52bnzGqKjz1ePU1BiHI7jiI7u97Ho6vo9iYk/V1/H6XyG6Oj7AQ19fX/BZDpTva+rq4vy8nIyMjJITo6nsfESLJY60tI60Gg8gBmfbx8QeoHctGkTX331FQALF37BokVfqfcpCuzcuY2enh5kWVbpbWvWrGH9+n6TpGXLlo3acVtozkdKSwolkTvUhv2/0RIX1IqpU6eqnZpArq1IgoaimA0Fh8NBRUWFqoceafIeiloVblEWvyWhYa/X6/F6vezYsYN58+ZFeDYmko1vEb4TcUpRFFasWMErr7zCypUrKSkJL5IRCkLme2BCEHj9h/LdiI+PPyxtCw5Jrev1egoLC0dU6e7qmk1qaiWyrKGm5hyysl5X71MUhZqaGurr6zGZdhMf/yUWSys22/lERf1YPV6tVkt9/WKys79AUSQaG68nJ+d36uu0t99AauobKApYrYtISjokSyyKbwkJCaSkuIiLOxWNRsblisNgiMPnOwOD4WHCXY779u3jH//4B9HRDpYvfxytNvinuWPHNlwuF263e9zobZ2dnVRWVpKZmRlRNyucRG44iXSPx8O+fftGndSEQ6AQSWCsHehnNXAmcCDFbCj09PRQUVGB0WikoKAg4vgWilo1UDVRULANBkPIWZTv6mzhRLIRgG+++YYbbriB2267bUhvhlDo7e1l165dGI1GDAaDqmQkOIyRUnfCQVRe2trahmXgBNDWdhm5uf8AwOUy0ddXQ0/PoVkLMRRvNj/HlNz3kTZCT+s04hf+FWWAJ4LX+zWStByfLxej8RUkKXixlKS9gAlFyaem5iU0mtfw+xczefKyoC5HSUkJWm0P0dHFSJKMVutHkhLxev8Pv/9y9TMHmvt88sknqsPuaad9yAknbAGgr+9FFKW/xS0W2qysLLKzs3G73UPOBQwX4ZQ6ROvebrfjcDjo6ekZtkSuQFtbG1VVVRErPg0Xosvh9XrR6XSDuLYxMTGjes9A+tlw2voDq0ebN28O240KVAXJzc0lOTmZPXv2MHfu3IiPcyLZ+NbgOxWndu/ezZVXXsnll1/OsmXLhnWtut1udu/ejSRJmEwm1XcjsAgSCXUnHBRFoampifr6+mF34z2eRmJiig+6jGuorV2F0XhmkEmu0WhEq91McfH9SJJMa+sicnJeHfRaNtt1WCx/R1EknM67iYu7Tr1PlquQ5QuRJC+K8lc0mpl0dnYGDQaLLkdGhhVJ2kFu7mNIkg9F0eLxPIxOdylwiA4VOL+wfv162tvbAT+/+93/Efj1uFw9QD/Xv6ysjOTk5BF6bA0NEWvtdnuQN0SowtJwJXIFRKwVFL2x/gw9PT2Ul5er37ssy2H9rEaCQPpZdnb2II+aoRBIrdqyZcugOKUoiirlnpmZSW5ubtiO/eHwbYlTE8nGANhsNq6++mpiY2N5+OGHQ1bDxSY4kA4lpEd9Ph99fX3MnDlzWAlBpBCyd5FycPuHohLQ630A1NZOobb2z4MGzvr6rHS2XsrkxzfAZuhzZ2CMScf35JMoRx/Nzp29yPIjaLVGioqWYzQeqiD0b7JtZGUdkphzuzsxGIrRan34/Trc7j2sXJlJXp7CokX9g8WpqanU1DzI3Lmfk5DQgUbT71De0XEllZWX4Xb7VO+N+Ph4XC5XkKv8UUcdxZlnnslA+P1+1YhuoAzvWEAMX+/fvx+j0ag6/Qae09FI5EJ/t23//v309PSMetYiUMkkMAkSDsGiAjbWwUIYOblcroioVaLCJqRy9Xo9M2fODDnPISDoWzabDa1WO0Gj+m7iOxenXC4Xv/71r2lubuaZZ54JaWKmKIoq4CHWACHiIGawpk+fPuYS2eL4htONl2WZ9vZLyM39EEkCj8fA2rUfYLGkqMWaQ67mz5KY+ACg0NY2ldjYD4M8J/rXYycezzNIUjwGwzVA+GJTefkvKCx8l8bGQtLS/o3B0J9EBHY5UlPPxmKxYjB48PlM+Hy5VFe/TldXj+q9ERhPn3/+ebUC/otfPEtSkh63ez1wiCIry7JqRDfQN2MsoCgKVquV8vJytQovBvjHqrAU6P0xmlmLUBRj4RMljPaE3PJ4iLUI0Z9IPoOgGNpsNnXGVpjDDvydC9pWW1sbRUVFJCYmfmeFTCaSjRBQFIU///nPrFy5khUrVpCUlITVaiU6OjpIySJQHjfwCxeeGZMmTRqXyrTYTDudziDnSqFkFJgEGY3/4rjjHlWfW1Z2JXl5zwx6zcbGB8jc8wRR9/bRl5CIVjcLV2sruvR03K//g9NOa+bRR3/Bscduor7+JvLybgegu7sFt/tMkpObKS29nGnTHj14jE40mqnodG4qKkrY+cU7LL8ri9RYF7+9V2HeKXrs9nJaW1txuao555w7MJlcgIIkafH55uH1vgsEX/ivv/46DQ0N6u3LLrtskCO1gKDbDMd1NRRCOZyazWZiY2Pp6elR1UzGoyUuuhBCbnmozzAShRAhDzjaYBHJZ0hISGDy5MlqN8XpdA4aOBdyviLIRKJaBf2c8d27d5OamhoRBe1w6lWCl/s/hIlkIzy+k3EK4L333uPee+/lj3/8I8XFxTQ2NhIXF4fD4VDnLAJpu4HXhdPppLS0VF0fxzpOCdpTe3s706dPVwsm4URRFi48Hb2+f86htTWDuLj9g16zp6cOt/sHJCc30NU1Ca/3OaqqtCQlJZGXl4fD0UhHx8/R6VyYTE+Slna0+tzq6k3Y7WvJyvoRqal5Aa+agixLSBLY7W+QkLBIvUfMu1RXbyEp6UOOP/4jJElGp/Pj9yficLyL0Th30LkLpP0CzJo1izPOOCPkeRJJjUjMRrqZ9vv9QWt/oAGhSDqHmiUYDUQXQpi+Hm7tDFSxHEgxtlgsg7prQiSkubk5iAI8lhDzIiaTialTp6pdrt7eXjVOBQ6cizhlMBjUOCUSjoFx2uVyqfO9LpdrItkIge/kIl5eXs6HH37IZ599xvr168nOzuaqq67i/PP7Zxgi4QWK4XFFUSguLh6XH0RrayuVlZXExcXh9/uDXLhFgDGZ4gm87puatpCQEMz1lWU/7e1nkrNuA9IfwZ2Ug9FQguL1cvPuy/k66zxaW31kZVWTmdnEPfdsobj4DgA6Ol4nMfFXKAp4vWaiog6or+tyfYPb/TovPbSEu54+BT1uQMKPlgduW8+pP+mnnNXX13PgwBouuOCvZGY2qm1nRTHhdn+Aohy60Do7O/nzn/+s3tZqtfz6178Oe45E5aWzszOonRwO4XihgYvLwDmb7u5uysvL1c30eOja19XV0dLSQmFhoVrFDOW/EVhhExv2SCCChdDmH+vfq8fj4cCBA7S0tKht+piYGPWcDpxfCYTP50OWZXVhDfU4p9PJ/v37SU9Pp6qq6rCKI36/n82bN3PccaElMyeSjSMK38k4VVtby/vvv8+XX37J559/Tnp6OhdffDFLliyJ2IlZzOs5nU6mT58+Lm7EQnFPqDAFelmJOOX1PkdS0u3qcyorTyMn5/1Br9XSspKkpPvx+3X09qaTmLhRlWe1Wq2kpLzNpEkvI0nQ3LyQzMy/A9Dd3YhefwJ6vZvOzgySk7ep58bpPJ3o6J34/THYbH/Bbv8Sj+cEXK4EfD4f0dHRmEwmGhsbyMj4LTNn7gRk+pcWHV7vA/h8Nww6rwMN+66//vqwal2KolBfX09TU1NE9LNAiXS73U53d3eQV0SoOZve3l51jR8P/yVBoaurq2PKlCmq98dw/TeGQqA3R2Fh4bCEEiKB1+ultraWxsZGtRs01MB5IMQ8RzjVKujfs+3atYv8/PyIuzQTyca3GN988w3V1dXMnz+f3Nxc7rzzTioqKnj22WeHbZ4jvA6KiopGVVEIJednMBiIiYmhu7sbnU7H9OnTB/3w9PpoNdmQZXC7ewa9dmfnV8THX4S23I/xajdK3AIkYxS0tbF1xs+4cvvVuFw2ZFni17/+gKVLf4RGYzl4XI3AiWg0vfT0nE9MzHODOgEly5fyqy138hqXAXCv5j5+c2UT3qeeVj/b9u3b2br1P5x33qvk5dUE8Vy93qX4fE+rt1etWkVXV5d6+5JLLiE7O3vI8yc4sgM7BKEqLCPhhSqKQl1dHc3NzUEJwVhBURRVbllRFDQazaDgMlrqVqA3x2jmRcKZ+4mKVUdHBz6fj6KioojpYYeTynU4HNTW1jJz5ky1Ld7R0aG2rgfC6/Wyfft25s8Prfk/kWwcUfhOxqkdO3awZcsW5s+fT1FREY8++igfffQRq1atIi8vb1ivZbVaqaysDNokjgThhs1jY2Pp7e3F7/czY8aMQUPFGk0sRuMh9aaqqo/IzPzegNf2YbWeRFbWLvx+HS0tN5GScq96v8PhoK7uCUpKVgLQ3X0FFssfAOjt3UZU1NkoSr9/hqI0I8vKweO04vNtobMzmmOO+QUGgxu324TPtwez+VCnV1EU1qxZg8ezirPP/keQ2pTfvwCPZzVwaM344osv2LJli3o7NjaWa6+9dsjz19vbS1lZGdHR0UydOlVdg0IZ+w01xB0OgcPXo/2uw0EU38S8ZGB3XcSA0XbROjo62L9/f8QO4aEQLmET51MkRkVFRRFT3A4XpzweDzt27CAlJYWmpiYKCgoO+x1MJBvfMaxevZrbb7+d+++/n9NOO21Yzx0uf3Wg9Jww9QtUBxl4wQqlpIGbq507/8DcufcjSbB+/R+ZM+f6Qe/X0HAZU6Z8AEDXW5nEPzMJenuRjz+enjvu4aQftKHR+OjujuXWW2u5+OI5GAwGuro6aWsrxWJJQ5abaG9PxGbbS0bGc3g8ycTEPExKSibx31vI/NLXyKGeFtKYRD2vf/8KPG/XAId66JX5AAAgAElEQVQ2m19//TVbtqzn/PPfZvr0sgHnBPr6OoB+/5FAbfKEhASuuuqqw34Pgt/f3t5OdHQ0Ho9nxBWWcHC5XJSVlREVFTWqDoHP5wsKLi6XS6XuybJMa2sreXl540LTEw7hYqE9XDcoUH7QbrervPChEja73U5lZWXEUrwC4QwBu7q6aGhoYPr06epjRSVPq9VSVFQUVAUTg7LhBsonko0jChNx6iA2bNjAtddey/Lly7nooouG9VyPx0NpaSlGo5HCwsLDXpOhaLsDZwIG0mFEUpOfn09aWpr69+7uSaSmWoHwRTGHYxfx8afg9fZXnH2+HRiNh16joeE/+HwPoih6PJ7vMWnSLzGZYg8eq5+enmswmb6ksXExzc0/pK+vj9jYWDIyMg5WrDuJjj6afiov2O33YjTOxWAI3uh98803dHa+zEUXvYFGE/jT0+JyfQHMUf8SaNgHcOuttw55TuHQcHdzczPR0dGqkEdgdX20FX2Px0NFRQWyLFNcXDzijlagMErgEH98fDwajYaWlpZxM+sLlLGNhAIsYqo4VkGHFklQqIRNUKvMZvOwukGBcUqY3mo0Gvr6+ti7dy9z5syhr6+PyspKvF7vkHOZE8nGdxAtLS1cccUVlJSUcM899wxrYyr4qx0dHUyfPj2onTqQvxpqsxbJZkxokYuqiHiO+I5DXew+Xy9abS4mUy8+n47GxrtJS/11/4qv1dLXJ/PGG7fzox+9y9q1C4iOvgqNBkwmHbm5V5OS0sKBAzPQ6V7GYrHgcp1HRsZOZFlDY+NtZGXdhv6ee3A98zKxfe0oSPSYzET9qQ/XZT9Cr78ZRTlKPZ5//etflJaWctZZHzJ//pZBxyvUPA63iA9UsnI4HGqFJSoqivb2dhISEpgyZcqY054COwSRVI/CuYaLxNJisQyiRIyVWd9QCOXNEZgIi2pQoPygxWKJOHAJl2Nh1peSkjIiNRC9Xk9XVxfNzc1MmzZt0GOFKkh6erqq/CKSwtmzZ4d8/Ylk44jCRJwKQFdXF9deey16vZ5HH310WEIlQuWtoaGBadOmBVV0AyVSHQ5HSNpuJNeM1+ulrKwMjUaj+lNZrU3I8kmYzX3U1f2NkpITBz2vsfGn5OV9gFYr09paQFzcViTpUCJjsx1DfHwTiqKlpuZBmpqmEB0dTXf3PrTab4ATyMlZQHx8PHb7V2Rm3oAsa7BaXyQr61QA2truJibmbUAiJqb1oAnuP4iJWRh0jj799FPa2lazdOkLg+RtPZ6T8fv/BcBTTz2lKihC6GRj4Jyd3+8nNjYWs9mM1WolKioqYh+v4UL4o0yaNInMzMzDutKLYxWqiyKmCjrswO56YEIwHkPwcIgCHJgQiPgfOGshYqo41khohuJzi3geyXkKRCC1SqfT4fF4KC8v55hjjlEfY7PZKC8vJykpaZBc/LdptnAi2RgmZFnmscce4+9//zvPP/88U6dOHdbzOzs7KS0tJTY2Vr0ghhriGy4E91NsvA43tNzc/DSTJ99+8LNp6empQa/v74z4fD727VtOScnrKIqGAwfOxeG4npiYWByOLcyefRM+nx6DwY3f34RGY8Bq/QkpKV8gyxpstkcxGk/BIMVjWn4bujffRJF8KDeB9AcFBQ2KYsbr/RBF6fdHcLvdvPjiizidTk466TNOOukboN+4SVEOJRtPPPEEXq8X6M/ub7rppqAFWxgmBVaDBl7EgiM7WopbOAgTOr/fH1RZFwZ+AwfOh6qwhIPdbqeiokJV6hhrRSmR1LS1tanKW9HR0eqCPVr5QQjWbS8qKorYhTiwZe1wONS5nHCPrampoaWlhYKCAsxmM/v27ePoo48O+XidTjfmSegoMZFshMdEnBoARVF4+eWXeeqpp3j66aeDNjeRwOFwsGfPHkwmE1qtVqXtBiYWo62uCzpPJLRTWfYiSVmYTD3IskRt7a1kZNwD9G9ordZ29PqzsVga8Pv17N37B8zmE+jq6qSkZDFmcy8ejxFZ3obJlExLy4/IzPwKSVJoaDiXjIyXg96vp6eQ+PhWQMLvj8HnuwSt9o+Iy9Dr9fLqq6/i9e7nxhufDmHg5wQk6urqePPNNwEoKCjghz/84aA5u0CJ9Pj4+KCkInCjO1rDxHAQZnROpzOocCVmAsWGXQycj8Q13Ol0Ul5eTmxsbEQD5CP5DDU1NTQ2NqpxymQyBR3raNdzcZ4cDscgU9mhEBin3G43NTU1g+KO2I/U19eTn5+veot8m2YLJ5KNEWLr1q38/Oc/55prruGnP/1pyEw3UM7N4XCoLtyxsbEql3XGjBnjYsrS09PD3r171U1ouEy8p2cKycktANhsSXR0rFOpWxqNhqNn/Qjdex70a330xZ2P/sY/YI9K56uvrJx88jlYLFXYbKdisbwBgKI46O5+Eq02i66uDWRnv4PLFYeirCPKmEFXl519+z/k2GNvRKfzHWxDm/B6b8PnuxWQaGpq4rXXXgPgnHPeZc6cXQdf20xfX7vKnd2wYQMAycnJTJ8+fZC5TyTVB1HhFlWRsb5wRfW+urpadYsVA+eBnYDRyg8KD5bCwsIRJ04DlTcOucb3e8S0t7er1cixHsyD/gpPZWWlqj0/VHAI7AYJCcKsrCxyc3ORJClsAiSMEz0eDzqdLmxnYyLZOKIwEafCYN++fSxZsoQf/vCH3HjjjSGvi3C03djYWPr6+vD5fMycOXPMzdsAlVYSGxvL1KlTw163bW2vMGnSLwDw+7VUVn5MX1+C6hiekPAI+flr0Gr9dHb+lqSkmw4+1olGk48sS2g04PFswmjMpbv7FWJjbwEkbLbHsNub0OkmkZ19EZIkUVv7JvHxvyMtrQlZ1iBJ4PMtRpYfA/o7RXa7nRdeeAGtto877vgDkhRYFOtCUbT09vbS1NRER0cHkiSNeM5OVMQlSaKoqGjM9wzCc6KyslLdrB9u4Hwk7yH8l0aTOIViLUA/zTwmJgabzYbb7R4X2Xvon0mpqKhQlbeG6jiJbpCQc7fb7SQnJ6uMioG/d1F4E4wFk8nEtm3bvhWzhRPJxijgdDq58cYb6e3t5bHHHqO1tRW9Xq9yA/1+vyrnFhcXN6gKHG7OYqwgy7Jq7CNctQX6+vqw2drIzz9UCS4tPRmtdqVasW5oeIUpq29E94KMX6dD8k7GnZjN06c9xGvvRfHvf59JS8v1uFyn4Pc3k5b2eyRJIirqRZKSSvB6czAYelEUDe3tD5CScikQg8fj4fPPb+S00/5BdLTzoKqHBr//e3g8fweieOutt6it7XcqlyQ/s2fnEB9fDKA6nDocDvR6PbNnzx5VizlQRaOwsJCkpKTDPykMAucXAgf5YmNj6e7uVp1jx2MRFDMKkc6LhOuwBFaDBi6GVqtVpSSNRk44HAJdzgW1CoKrbHa7PWQ3SDx/KDUQgfr6eiorK5k0aVJIucmJZOOIwkScGgIej4ff/va37Ny5k5UrV9Lb24vP50OSpIhou52dnVRUVIzbQLEQ12hpaRnUjRfULZPpOFJS+oti3d2xNDRsUNcoSZJwuYoxm234/XpstgdISbkSRVGoqvoIo/ENUlNLaW09HZttMampCSiKj8xMBUnS0dBwE9nZa1AUiZaWZ0lPv/Dgccn09EwhIaETnc6HLEvI8lR8vn+iKDlA//xGf9HLz113/R9aLdhssygtfUalmQV210e7pgjD1+GYpYZCuPmFuLg4ent71S7HeEihu91uKisrkWU5osJVoOqi3W4Pml8U/w08r6EowGOJwEH7QDGVQF8ru92uKkSKmCr8rQ4n6S5k+6Ojo+np6QnrHzWRbIwzHn/8cV544QUkSWLmzJn85S9/obm5mcWLF9PZ2cns2bN59dVXMRgMuN1uLr/8crZu3UpSUhJvvvnmsJU6wuHLL7/km2++4b333lPl3u6++26OOuqoQd4b4SAqO3FxcUyZMmXMN2/Qv0EsKysjLi4OWZZV6taUKReSnt6iPq6m5hXS0n6s3u5on0/2uXtRosEjZ1DlP43Fu39LV0w8mug+tFo/J5+8maef/gG1tYvJz/8UgPr6H5CV9QpNTdeTnf06Ho8Zg6EPkHA6X8NsPoOOjg7WrXuWc8996qC/Rj8UJZqOjn/S1JTA++8HSx+efvrpeDweZsyYETHVZjgQVW+9Xk9hYeFhv79wMrkDOyyBEIvIeNGeFEWhtbWV6upq8vLy1HbsUMobgXMhkUB4cwi1p/EwBevq6qKyspK+vj5VglDwbUPNsAgcTg1EwG6309DQcDCp7p8ZCQzeE8nGEYWJODUENm3axOeff86HH35IaWkpeXl53HLLLSxYsCBi2q7H46GsrAy9Xk9RUdG4XBvhqVuxHHXUIRpYXd0cUlK+Vm83NDxHVtbviIrqw2bLIipqExpNHM3Nn5CaugRJUqivX0hu7jtUVv6LvLyrMRg87Nv3WwoKbqKrax6JifuQZS2trT9CpzuOhIQlSJKe0tI1mEy3UVS0O4AqZaCv7yUcjkV0dnbyzjvvIMuHaFQnnXQSsiwzffr0cfFe8nq9QYPFh1u3D7f2h2IC9PT0UFZWpnacxuP7FopSWVlZqnt3uLmQwFmLSDss4QpXY4menh4qKipwOp3qdRTIWgjXuYokTgkn+3379lFSUkJWVtag15pINsYRjY2NLFy4kNLSUkwmExdddBHf//73Wb16NRdccAGLFy/m2muvZdasWfziF7/g2WefZdeuXTz33HO88cYbvPfeeyqHcrR4/vnnSUxMZP78+ciyzJIlS1i0aBE333zzsC5O8aNqa2sLMj8aCUK1xCVJIiYmht7eXiRJYsaMGRiNRqKiooMkZnt7HUhS/3Hb7XtIsHyPqBPcKDHgYxZabTxv7JvD7e6bkaL9FBZWsmJFPTk5F9PZ+QCJiU+gKFBdvZSYmPOIikrDaIzGbr+GrKwvkSTo7p5MdPSd+P0XUlFRyRdfvM5VVz1HbGx30Oew28/jzTfPorW1Vf3bggULmD59OmVlZarr9XgYUYnN+sBKXuCA5MCKoMViGdIrIhCBtKfxqh4JE6Genh6ioqLwer0jngsJB7HQRkVFqSZII0EoHXaj0YjFYkGr1dLc3Dxs99hANZBQRkudnZ20trZSUlKiztYIc8aYmJiJZOPIwkScGgKCkjp//nzi4+NZunQpeXl5/N///d+wVIhEB7i+vn7Q8PhwEUrJSlEUYmJicLvdeL1eZs6cidlsxuksICWlSX3uvn13k539G/V2W9tJ5ORsxefT09R0Bmlpbx78+3MkJ9+DJCk4ndnExm6jpeUOMjOfP9jFKCQx8Ws8ni1A/7B4XFwVINHcfD7p6f1+Tnv3biMq6gamTdsVpD5ltZ6M1focFRUVbN++Xf27Xq/nqquuoqysTK2sj1cRUXRnA4eWhfS86FqMRiZXiAUUFBSMquMfDkIVy263Yzab8Xg8I54LCQeh+qQoyqgowAONaLu7u1U1S4PBQHNzMxaLZVgzKSJOKYqixpzA34qQQjabzeqsSOB+4UiKU0dksnHcccexc+dO4uLiOP/887nxxhu57LLLaGlpQafTsX79eu69917+/e9/c+aZZ3Lvvfdy/PHH4/P5SE9Pp729fcw3qdDfmrzvvvtYs2YNq1atIisra1jPdzgclJaWkpOTE7HiQaBPhMPhCNpUhlpYRBt26tSpZGTkIYr3vb0GJMmmPq6u7moKC1+Dx0H5fxqImonk8fBq3yk8IN1E3pQa2tsz+eabfGprX8dofAxZTiUh4Wc0Nn5DUdGbyLKGzs636eqqZsqUWzAY3CiKhCzraGj4KTU1F9LSYqWmZg/XXvssCQldQZ9NlqO4//5DRk+SJHHLLbeo0oBdXV1MmzZt3LocpaWleDweTCYTLpcrSH7QYrGMmjc7XNfVcAjksIpqkOgE6PV62traSEtLG/dOSqTeHB6PR12w7Xa7qr4STod9NO6x4YyWOjo6VNd0AdF1iouLi6hi+F/GRLIRHhNxahhQFIWnnnqKv/3tb6xcuZLi4uJhPV/MA6ampqozUodDKJ8IEaeEpHvg+mez2aioqCA3N5e8vKkELltOZydarfHgv2uIizsGnc4LaLBa/0ls7Ek4nW04HD8kO3sfbnccsvwiBsMptLV9Rmrq5Wg0Purqbqa5uZ7o6ASKin5HW9vd5OQ8j0bjx+cz0N1dTG3tA3R16WlsbKCgYBXHH79+0Gdzuap55JHgAfNbb71VNWNta2ujpKRk3LocZWVlOJ1OtaB4uO76cCE6/jqdjsLCwlHFPTG/IH4HgQqRbW1tJCYmjotCJByiAGdkZAxp+CowUNLd4/GoRrShXM4D6diBrIJIEE7S3el0UlVVxaxZs1T/EpPJpH4PE8nGOOPJJ5/krrvuwmQyccYZZ/Dkk09y3HHHsX//fqCfj3322WezZ88eZsyYwccff6yavk2ZMoWNGzeOi929wFdffcUvf/lL7rjjDs4999xhPdfv91NRUYHX62XatGlBVJ5wxn7D9YkQeuo+XwPFxdfjdkcTH78dne5QlUuWUzGbe0AG6zMlWHYei5yczPp5VaQftZesrEY+/vgmvv/9u+jtnUxMjANF0dLR8Tp6/S0kJNQAEmVlP0FRfkRDg4fqqg2YzU6uuvpF/H4DijIXt/st3nzzYxoa6rj++qdISrIDhwbtHnjgbvz+Qxd0oHSgMLkbiy5HKHO/mJgYtFotVquV/Pz8cfG0CByaKygoiOh36ff7g45VcFjDKW8IJab29vZxoz0Jbw7B9RVym4FUM7vdjtPpRKfTBXFYh+Meu2/fPmRZprCwMOKB1VAt646ODrq6uigoKAh6rPg+LBbLuFTyRoGJZCM8JuLUCLBz506WLl3K0qVLueKKK4a1tsmyrCrzTJ8+PWhDG8rYTxRqBMUkkg2wz+c7qLT3Y3Jz6wGw2WKIijrU7a6r+yWFhf0diL4+M7Jch0Zjorl5FWlpdwMKPT3JxMbupaLiQQoKnqKrKxG9/m90dq4kN/edg+fiEjSaS0hIuJnMzCoMBjeS1K9Kpde/jCyX8NZbb5GV9QZnnPGp+v4iTj333NO0tVnVvy9fvlyN3U6nc8y6HAO762ID3C8fbCU7O5tJkyaNS5IqZkwj3UiHm18I3K8MVIhsaGigsbFx2EWlSBGOAjyQauZwONSkTcSqSLuAgbL0kfhUCYSKU06nUzWrFcfZ2tpKVVUV2dnZTJ48+Yjx2fifIntFApvNxvvvv091dTUWi4ULL7yQjz76aNDjxIUQKpkajwsxECeddBJffPEFV111FZ999hl/+MMfIq6+a7Vapk2bRmtrK5s2bSI1NRWfz0d3d7fKXYyPjyc/P3/ETpwGg4FZs2bR2JjMnj1vUlJSEpRotLZ+SF7eQVMlLbh/cjveq3+CzbaFecYzMfzTAx0S5xw9n1de7WPLlgdZseI6fD4dVquHvr5FWCwv4/FEUVz8d1qa1rDv1+ez4qvr8aMh9e1W5rywnazcdeh0szj//H+xcmUzK1Ys59ZbH8Bk8iK+toyMHBoaGkN+DovFwrx58zhw4ABbt26NuMsRirYjkjaLxUJubm7QBtjr9bJv3z6VdjOWFW9JksjOziYlJYXy8nKam5uD1EYC1SwGKm/Ex8eTnp5+WOUtjUajyukFKm+N5SKl1+spLi6ms7OTnTt3YjAY0Gg0ajCMj49n0qRJEVPNQiEqKoqZM2eq75GWlkZubu5hX09QqET1yOPx4PV6Q54z8X38jy3gEzjCcCTEqVmzZvHNN9+wfPlyfvazn/H0009HrGSn0WgoKCjAarWqcyaSJKnCKKJTmZubO6gCHCl0Oh3Tp0+npeVLPvvsPszmGI4+OthfKTv7TZUKbLUWk5RkQlEUfL53MJud+Hw6HI45Bx/7ChqNH4ulg337PsJgaEaj6Z+1SEiopLPzr2i1L1NT80emTn0Xg8FDTEw5inIiPt9bnH/++Tz/fCtOZwwXXPCeegySBBdemMiKFYeSjcBNdExMDHPmzKG2tpYtW7ZE3OWQZZmenh517Q9M2uLj48nJyQnaAIuO/9atW8dFiSktLY3ExET27dtHS0uLqpYkMHDWItDgcerUqYdV3pIkiZycHFJSUqisrFRl6UdqOBgKWq1Wnd8oK+s3Dtbr9UGdtszMTIqLi0ccp/R6PSUlJTgcDsrKytT92uGYC4FxSsjkejyeoHMmSRLp6ekkJydTXV1NS0sLOTk5IzrO/zaOuGTjP//5D5MnT1aHfS644ALWrVuH3W7H5/Oh0+loaGggMzMTgOzsbOrr68nOzlbVd8ZD+WkgEhMT+fvf/86qVas466yzePbZZ5kxY0bYx4eqWJjNZtra2oiLi+OYY44Z0w2Q2FQlJiayd+/eoKqL2fwr9XGKAgkJPwKgq+05Mu/tg71Q6prG33pq+Dg+mw5O5d577yEnZxLnnPM70tKqaW/Pw+0+mUmT/sya3y7gqq8eRoOMhMKFn73L87/6OVe8+wpgJTHxOC666Je8/noijzxyF5de+hdyc5uR5ac54YQFvPXWWwAhL36tVktBQQF2u51du3aF7HIMXAQDg2EkSZter2fatGlYrVZ27NhBdnZ2yGGt0cBoNDJr1ixaWlrYtGlT0DC/UN5ITU2loKBgxG1Ts9nM7NmzaWlpYcuWLeTn55OamjrizxFonBQ4eJicnIzP58Nut1NYWDjmg3liTqquro5NmzZFzCcWlTabzUZHRwdZWVnIsjwufOoJfLdxpMQps9nMqlWreOeddzjnnHN49NFHwxqIwaF5gIF+RjabDaPRyMyZM8ecfpienk5CwhOUlpZSXl6uroEdHd+Qk+NUH+f3Xwb0U6vy8jbT12dEo1Gw2a5l//5/k5urJzbWj9cbhU5XhMfTidXahSRJTJq0lby8LTQ1baOn5/+xbVsUc+b8Fb3eB7jRas9Do/kFP/jBtbz7rgefT+aii95Xi2Jm8xlcdtl8KioqmDVr1qA1VaPRqL+HcF2OQHppYHc90kKNiIVdXV3s2bNHpbmN5fomYmFHRwfbtm1TE0kxZxcfH09ycjL5+fkj3q9ERUVx1FFH0d7ezrZt28jJyRl1vA2Unw0cOpckCavVSl5e3rDM+iJBXFwcc+fOpbGxkc2bN0esIKYoijpz09HRQUJCgiqZL6DT6SgoKPhfGw4fEkccjWrjxo0sXbqUzZs3YzKZuOKKK5g7dy5ff/01P/7xj9XBu6OOOorrrruOFStWsHv3bnXw7t1331U3r/8tlJaWcuWVV3LxxRdzzTXX4HK56OvrU+klQskgUNNaZPNjOTweDoJmY7VaKSjIIyMjT73P65VwOjvo7OwgY8/RmH/jAguUO47mqtoXOODJQJvux2Ry8eCDezj33Evx+7VotX6am1eQkHALsRfaues/D/An5WYArudZ7p99F+YtrqDj6OqK5cknfwX0L47Lli0jMTGRLVu2UF1dzcKFC8nIyAj7Ofx+P/v378dms5GUlERvb2+QaZLFYolYJSwcfD4f+/fvp6enh5KSklHNi4RT3oiJicHlcqEoyiC3+bFCoJHewApVOISTIBQc1oHzQULiUJgajpdOf6jhv4H65mKOZeDvIJwayOGkc/9/wASNKjwm4tQYoL6+nssvv5wTTjiB3/zmN/j9fpxOp0ovEd5QgXFKdFWFFGhdXR0lJSXjInox0N1cozlGlcMF6OxsorvbTUfHrRxzzLtIkozdnkxr6xcYjZeTmVmKLGtobf09JtPTxMW1I8s6mpt/TXb2w+h0XhRFg9OZyu7dv8Xp/Iozz3wryLBPlrNZufJ6rFY3en0vs2dvJSrqeubPPzPizxFoMJqSkqLuBUZCiR7qPaqrq7FarWMyL+J2u4MSISHr7/V6VTn38XAH9/v9QUZ6kVCSBHNBHK8QSQlc+wM36YIC3NPTMyza03Dg8XjYv38/LpcryDgRgouhYo4lUC7ZYDCgKErIODUxszHOuOeee3jzzTfR6XQcc8wxvPDCCzQ2NqqSglOnTqWpqUltSZWVlWGxWMjJycFkMtHW1kZeXh5vvfUWCQkJKIrCTTfdxOrVqzGbzbz00kthzb6GC0VR2L9/P2vWrOGJJ57AZrNhNpt54oknmDJlCvHx8RHRocTw+HhU1QPfo6bmlxx77Nvq31pa0jlw4B30+rXMbf4N0u2ABfy+o3i15QRua/o9UpLCOed8yi231DFp0h+IinLR3Z1GdHQbbrcZ+SojP/37C/QqZvT4UJB4Zs5NdD4dx7HHbhp0HL///V2AnujoaK677rqwxxtus240Gunu7iYjI4P8/Pxx2TSOZF5EzNyIRWWgBne4IcmMjIxx4+F2dnZSWVkZ0jcj1CIYKOsXqQTheHtzQP/Ad0VFBWazGY1GE3RuRYAJtSgHDuZptVpVDWQi2TiiMBGnxgB1dXWsW7eOp556irq6OkwmE/fddx9z5swhPj4+Igpkb28ve/fuVY05x2PN6unpYffunZxyyunq32QZ1q37mri4WPLyvkdcXDeyDHV1PyMu7mEkaRaxsZ14vUZstsdJSPgVkiSjKBINDT/HYNhKQkIZZnMXkqTgciVQUXEzFRX7uPjil9Bqg39iH3xwFjt2HHJ1DpwlDAWxlgZu1qOionA6nSQlJVFYWDgum0an00lpaSmJiYkRx8JwNOPAObvAgl13d3dQt2a8ZJErKipISEgY9B7hBEfE8UZKNxfeHAkJCSG9l8YCdrudsrIyDAYDer0+qCMkjjdUtyKcVO6RFKeOyGRjOPD7/WRlZbFx40ZWrFhBYmIit99+Ow899BA2m42HH36Y1atX8/TTT7N69Wo2btzITTfdxMaNG8fsGJYtW0ZRURHHHnssLS0tPPjggzz44IOccsopw/4slZWVaiVhtGpIgUNRYh5AlstYtOh69TFWaxVmczpW61Fka6vg+yD3SWCcyYqGE/EV65l8ywHeeusmpmY06X0AACAASURBVE5dw513PgBocLliiI52oCgSttJiDGfXYemzI6HgMMURu66Xv629mEmTavne99YAh4btDhyYxF//uhQIXsQDjd3EYHS4zbqoiHR3d4+6AxEOgiNrt9tVydRAhFPeEMcbiXus3++nurqazs7OcVM0EeaPbW1tJCUl4Xa7I14Eh/MeYkh9NC7nAl6vV+1aBFIORDW2uLh4WAPeA9VAoqKijphFfAITcWoscPPNN5OQkMCxxx6Lz+fjjjvu4JZbbuGCCy4Y1usMZSY7EoSaW/P7K1m06Br1MQ5HDHp9Ky0tHzF58k8OHoeE1fopVusHTJ78PDqdj46OKbS2nkRh4SuAhMORRVxcIyBRW/sD8vI+wGh0H6RHaaiqup7PPvPw85//P3W+Q+Cf/zyL7dv7E46f//zn6rBx4GC0qKyLtXTgZl3IoLe3t4/r+l5XV0dra2tIqfVAcZRI1AFDQRgzipnD0a7vQ71HQ0MDSUlJ6iyrXq9XY5TFYhm1wW9DQwMNDQ2jcjkXEJRIEavEDKOgSk2dOnVY5oyyLKuGnEdanPrWJxuffPIJ9913H2vXrqWoqIgvv/ySjIwMmpubOfnkk6moqOCaa67h5JNP5pJLLgEIetx4oLm5mSVLlnDUUUfxu9/9btgXh5CvHa7bdTh3a2GYI2gwzc1vIUlPUVW1jIKCc0hJScZkiu0fxNsDrtsSkDrS0c8tR7Io7K6fyb90d7PikxN4/vmrycurJS3NRmJiCxqNjCxL+K06Ov6aRGZGM5wJHKQjb9t2NF1dMZxyyhr1ONevn8+nn34fgEsvvRSHwxEk5yoW7Egq62OpWBUOouskjkl0LYRXRCjljeFCyN5ZLJYxqbqIGaHAzXpUVBQ9PT2q9Ot4DEkLl3Oj0UhBQUFECbNIigNnQ7Rardq1GEg5cLlcVFZWIkkShYWFEXHIFUWhq6uLjRs3smHDBmbNmsWll146qs86xphINsJjIk6NA+x2O9dccw0mk4lHHnlk2BRem81GeXk5kydPJj09PeLnCXdrsT6JuZBAaolGoxAVZUGr7S9Q7d79BVOnzqOzcw7Z2RUAeDwGvN52urtnkJ5ejyxraGg4n5SUj+n/yUh0dWWTnHwAAJcrFqu1gMzMbRgMPvV4rNajeOWVk1m+/Ckk6VBRrLfXwKOP3gnAokWLiImJoaurC1mWg+JUJJv1sVSsCoeenh5KS0uJiooiLi5OVQoLpG+NdrPucrkoKysjKiqKgoKCUceQQM8QsVkXUvRRUVFMmzZtTAfIBYbrcg6Hp+5aLJagYxVUZrfbTVFRUUTXl/BR27x5Mxs2bCAtLY0bbrhhVJ91jPHdTTaWLl3K7NmzueGGG7BYLNjtdvW+hIQEbDYbP/jBD7j99ttZuHAhAKeeeioPP/wwc+fOHbfjkmWZRx55hPfff59Vq1aRn58/rOe73W727t1LTEwMU6dOHbQ4ybIc5G49EvlBoeGt093LnDn/Vv9eW/sQ/vr3yL9xIzarhVNbP6PePwl9nAefTs+8eZv5xz/OOzi74UOjUdBqZSr2FFJVOZnvX/DvAe+j4fPPT2DRonVYrUWsWnWxet95551HSkpKWBpMJBiPLkeooXPoD5RiKHo8ZHLr6+tpamqisLAw4gFSsUAFmhGJ38LAzbrgXtfW1g4yNRzLz9HW1saBAwcGGVJB6A6W2WwOmg2JJBgLh9pQ9C1R7Vu/fj0bN25k27Zt6PV65s+fz4IFCzj55JPH5bOPAhPJRnhMxKlxgqIovPjiizz77LM8/fTTHH300cN6vtfrpby8HEmSKC4uHlRwGWhC63A40Gg0Q7pbC/j9Hmy2zcTFzaaqqga3281xxy1Q1alaW6fg9f6FzMyTAAVZ1rJ//x0UFPwBjUbG5TLj82kwmfqQJJn+n5GWpqZppKWVYTR61PfyeAw899wyfvnLlerf1q1bwH/+czoajYbzzjuPhISEURWVxqPLEaqoJEkSbreb/Pz8caFkK4pCS0sLNTU1w4ohoZzONRpNkHt44J5FFF1DxZCxwlAU4MDZkEBp38NRdwfCbrdTWVkZkoYmpG7Xr1/P+vXr2bJlC7IsM2fOHBYsWMBJJ530v6ZG9d1MNjweD5mZmezdu5e0tLSwi/g555zDHXfcEbSI//GPf2TOnDnjfoybN2/m6quv5vrrrx92JVW0FVtbW5kyZYrasgsc4BIL9kglRxVFwWSKCXIadziaiXk1E80jCiRDWfdRnHrgYxQNJE618dZbP6GkpBxF0QAKih90t8r87NlXqJKn8OUPT8bwuhcGdNefeOI6HI5UdDodPp+P2NhYrrnmmjFbREba5QjncB1q6FxUqATvczwqVMIdXHQHBlaPAn8Hdrtd7WCJBTuSzbpw1fb5fONmcOfz+aiqqqKrq4u0tDS1nS8qg+J4I50NCQW/309tbS133303CxcuxOfzsXHjRiorK8nOzuaEE05g4cKFzJ8/f1zEF8YQE8lGeEzEqXFGRUUFS5Ys4YILLuC6664b1romNp+igAEM8okINKEd6ZpZVXUhM2asVm8fOHA5Xm8bRUUfA9DbG4PNlkFaWr9LuMORSGxs/3n2eAwYjX3odH78fg3d3YmYTF0Yjd6g99i5swS7PZGGhhxOP/0JdDrdYaXHh4uRdjlCSeXq9fqQQ+eiA2EymcZN1UjEECEQMjCGhCsqibgaidO5iCGikDgea7jw5mhvbyc9PV1liIg9ikguIqFFh4Ogbz3wwANql2Pjxo2UlpaSnJysxqnjjjtOVdH6H8V3M9l4//33WbFiBZ988gnA/0x7eiC6u7u5/vrr8Xq9PP7444dVdQhUBRKLitvtJiEhgZycnEEDXKNFVFS0mmzIMlRWPkjxJ3fCY0AyfNJ5Nr9qeoTzUt7nL76lTJpUy9q1J6DVyni9et689iJueGkFfnRIKEjI/Ob4h7hr7R+C3sfpjOKxx27nxBNPJCMjg8zMzDGn8kTS5QilvDGcgbNA/mpxcfG4mOiJAF5dXU12djY6nU6tDAa2bkfrICsqO2NFQxto8ieqV263m/j4+Ihb1od7D4fDwcaNG1m/fj2bNm2it7eXjo4OkpKSeOihhzjppJP+11Q8Dof/2ejyP4CJOPVfgNvt5s4776S0tJRnn32WtLS0IR8fWKQRHVW3201MTAy5ubnDMkqLBEZjdJDTeEdHBfHxM9Dr+xOGhobZpKbuxmDwoigSvb0moqN7kWUJn0+HViujKAo6Xf9shs+nRZalIEoVQGlpIe+8cynJyclccskl41KIiaTLMVAu3+v1DqvAGNjFjtRQdiTo6OhQuwOCZhwoODIWRSUx3J2UlEReXt6o1/ZQ1F1JklRLgoGKUqN5jy1btrBhwwY2bNhAR0cHvb29aDQaHnzwQc4555wjSt6Wb5Op33Dw+uuvqwszwLnnnsvLL7/M7bffzssvv8x5552n/v2ZZ55h8eLFbNy4kfj4+P/aAg4QGxvLK6+8wmuvvcbZZ5/NE088wbx584DBQ9wDzf2EWY4sy+zbt4+GhoYxl6A7cOAX5OevRFFg166XyM39JZwFrAS6YKH0OS/qr+SB7t8So+tm374CzjlnNWec8W9uvvlxLnK+zX80Z/CufAE+tJyoWc+NrmcGvU9LS/rB/7egKArR0dFjvgBqtVoKCwtVX46MjAwsFktQ4iaUN0RlabgJjyRJ5Obmqprqguo2FpvbgYpWADU1Neh0OqZMmTLmqiZJSUlYLBaqq6vZvHkzxcXFw/p9Cf61WLTFZmOgdryQtdy+ffuw6VuBlKgNGzawbds2jEajSolavny56nj74YcfsmHDBhYtWjSS0zGBCYw5jpQ4ZTQa+dOf/sTHH3/M+eefz+9//3tOP71fESpQGfDQEPehQePJkycTHR2NoihUV1dTV1fH9OnTx/T4PB4dUVH9iYEsw86dr3LqqYc6E253NAZD/22fT4vB4Mbn06LRyGi1frRaGVnun8eQJNDp+qmxfj8ELqlFRZVA/6ze9u3bI3bUHg4CfTmEmlRKSoq69g80+Js0adKwBWMkSSIzM5OkpCTKy8tpaWmhqKhoTAp8A9kAonIvSRKTJ09mypQpY7qJjo+PZ+7cudTX17N58+Zh0YxhaOpudna22nETtKZdu3YNm74lCoQDKVFz585lwYIFXHnlleTm5iJJEmvWrOGDDz5Qr/1vA761nY3e3l5ycnI4cOCAqr5gtVq56KKLqKurY9KkSbz99ttoNBqWLVvG559/Tk9PDzk5OaxatYqHHnqImpqa/6r0IMCOHTu47LLLyM3NxWazsWzZMqZPnx40HDfUZrK9vZ39+/cPe3g8UrhcNhITsw8eLMgPg+Yz6PGZ+ZXvcT7oPQ+NwY8+xcurr17GiSeuRboTrvnTc7zhu4QoqY+jpF18eu7p8G7/y8gyVFdP4rXX+hWoJk+ezA9/+ENVIq6wsHDMFqaBMnm9vb0AqnNpTEzMmAaNQG344S6AMFh+NpBiFCg/a7VaVWpQdnb2uLRZnU4n5eXlxMbGhg0WgQNyXV1d6lC/ON7DVQED6VvhvDm8Xi+7du1S5y0qKyvJyclRW83z5s37X6dEjQQTnY3wmIhT/+U4deDAARYvXkxcXBxut5tzzz2XE044IYiyM9SaLSitubm5Y5Yw9fRUYzQei0aj4PFsR6OZQ3y8M+B+E9HR/d5ObrdepUj5/RJaraImGaEQeF9ZWQFvv30Zc+bM4cQTT1TXq5KSklErRAoIqk4gJUpRFLKyskhLSxsxLTocAufo8vPzD9u1GohQsyGhFK26urooLy8fF8NBAZfLRUVFBTqdjsLCwpDfyUD2wnCpu4H0rXD+H36/n9LSUrUIVlpaSkpKypFEiRoJvps0qkiwZMkS/j/27js8yiptA/g9k4H0TgIJgRQC6Qk9EUIXRbCLiCJFaaKrqGvBDwusi4AiKyugoOyyNtDVXXEFQQQpCySEFiCJIRACgTRIz6RNOd8f7Ps6k8ZMMkPa/bsurl0z7R2YnGeec57znBEjRmDOnDmora1FZWUl3nnnnVZpPThp0iSUl5djyJAhuHLlCoqLi7F+/Xr5lFlT1dTUIDU1FQ4ODhabUQekGeSnEBb25e+vdUQJ28f1gAPwXdmDmJ+zESqhRYmNK77+5hHcd98PEEXAvqjRyCgIRpY+ALc5JeCeUz8Cgb8/9+rVL6Ci4kawvfvuuxEWFmZ0UFRzvqgbbpKXZoMaOjjpVnSsqq6uRlpaWqP7LAyv1zDAmHMgoeEBSNaqX5WSp+zsbPTp0wd2dnZyclH38CRXV9dmf/aKiopw+vRpHDx4EHPnzpWTi6NHj6K4uBhRUVEYPnw44uPjERYW1t5KopqjQ0UkC2OcusWt3NPS0jB48GCUlJQgKysLH3zwAUJDQ816Ho1Gg/T0G52jLDWjDkid5Qrh4+Mv/0yvBxr6XttUgtGQL76YAq12AsLCwhAdHS3/XJrka05TjYYaeNjY2BiNo7a2tvKZGZ6enlbrWGW4zyI0NLTBMjdzGo40RCoRKygoaLAVryXUbULi4uJiVLrb1Lkh5pA6Ue7cuRPz589Henq6XBKVl5eH0NBQOU7FxMS0t5Ko5mCy0ZCysjLExMQgMzPT6Atma9XM6vV6owFk7969eOGFF/D6669j0qRJZj2X1LkoNzcXERERzToV03AVQJqtsLe/jLi4J35/nROA4mEADsDPZbdjcc4yXNIFoKKLExxd1HB3L8HWd6bgwpPBeF/9R1wWvbHGfiGiVp5B6B/S5ed5992X0bWrD+Li4hAdHW3071FdXY3U1FQ4Ojo2mTw1NLsiley4ubk1uWpxK87lMKyRDQ4OljeCStcsXa80CDZ3lUWqX5UO1rJUUDKcbSsuLpaX8v38/ODl5WXy4UlNkQKRtNSclpaG1NRUjB8/Ho888gji4+PN6kvegXS6N2wGxqlWjFMnT57E7NmzMXfuXMyYMcPs301pTGzu/jbDlu5Se1Rb2ywMHz5Hvk9trQJduzb8MZESDlMSj19+GYOEhDH44x//WO+22tpa/Pbbb1AqlU0mT421oDelgcetOJcD+D15ksqBDUuMDK9XiqvNiTFqtRppaWlNrpQ3h2HpblFRESoqKqBQKNCzZ094e3tbZFVIiuVHjhzB4cOHkZKSgtOnT+O2227D448/jvj4eKsdxNvGdc49GzeTmZkJLy8vPPHEE0hOTsagQYOwZs0a5OfnywOzj48PCgoKAABXr141ajPm5+eHq1evWmwQr/sLMHbsWOzduxezZ8/Gnj17sGzZMpMPSFIoFOjduzc8PDyQkpICHx+fJmft627mM1wFcHNzg7+///9mK+KQn+8CrXYtystVCI3aDYQCOA3cUfAL+ugzcbfiRyi0AiUl7njooX+ibKsr5lV8glp0hRJ6zKr6B1at/qOcbBw92h/V1Y5QqfSIiYmpd212dnYYMGAArly5gqSkJISFhcHFxaXeKoDh7Mrv12uauns5evbsadFyJGk2SNqLcvbsWSgUCvTo0QMeHh7NqrltjFS/eunSJSQlJSEkJMTsIC6EQFVVlVHPcMPZNul6pQ3kKpWqWSspGo0GycnJcklURkaGXBI1ffp0DBkyBAUFBXjuuefg6OhoVr9+oo6grcepAQMG4ODBg3juueewd+9erFmzxqzxRto3l5KSIp8Q3diXQcMGEw2tAvTs2RN2dnYQIhZa7RyoVDeSiMYSDeD3BMOUoT49PaTRa+vatSuio6ORl5eHY8eOyavxdVv7Stfr6uoqX6+p6u7lsPQqhzTuazQauLi44Ny5c0hPT0f37t3h6elp9vU2xdHREYMGDcLVq1eRlJTU7E3qTZXu+vr6ws7OTi7fUiqVzZp4lUqiDh8+jISEBKSlpcHb2xvDhg3DAw88gJUrV6KyshIvv/wyqqur4e/vf/Mn7WQ69crGsWPHEBcXh0OHDiE2NhYLFy6Ei4sLPvzwwzbVelAIgfXr12Pz5s346KOPEB4ebtbjdTodMjIyUFVVhYiICHTt2tVoM59hxyXDThY3+6KtVgegW7drQAWAOQB+BKoc7eBXeAVaoUKFcIaHZxEGOyRhwNUTWK1/CXooMV35GTb1nwMcu/E8H388DwUFvggMDMTkyZMbfC1p1eL69evIy8uDUqmEh4eH0SqApQZcS6xyNLQxuu7s1bVr1+QaWW9vb6vMgqjVavz222/yqlBjs0fSqbfS9VZVVcHe3t6oZ3hjf7+GJ6mHhoY2OttmeHCeVBJVUlJiUkmUEAJCiLZ2Wuqt1OmmyMzAOIXWj1MA8PXXX2P58uVYvXo14uLizHqsXq9HVlYWCgsLERkZCXt7+wY7Ljk6OhqtVjc+JgjodLnIzv4rwsI+bPF7+/nnkUhIGItp06Y1WtostR0vLCxETk4OAMDNzQ3u7u5mnRFkCkuschh2tqw77kt7REtLS+X9gNY4lwO4kTCkp6fLk36NTbwZlhqbW7orNRKRNsI3dsq5NDFo2CUqLy8PYWFhcpyKjo5uMpYyTtXXqVc2pM20sbGxAIDJkydjxYoV6N69O3Jzc+XlaakG08/PD9nZ2fLjr1y5YvZ+iuZQKBR45plnMGrUKDzxxBN4/PHHMWfOHJN/6aUZ9JycHBw6dEiehXZ1dW12xyUA0Go3AbgXcAIwEcAhoGvXWrxatBKb9E8iH3oUFbrjmqMXEvWxcIAaNbDFf/XxONezL/ohA0IABQU+6NKli1Fnk7p7LaRVC09PTwQFBSE3NxcFBQUICAiw+FKyuaschieHSkGxodmVurp37w4PDw+kp6fL9Z2WPg3V0dERAwcORE5ODpKSkhAcHAwvLy+5RE66Zp1OJ19vv379zGpDaGNjg759+8qnnJeWlmLo0KFwcnJCVlaW0cF5dnZ2iI2NRXx8PF588UWTS6IUCkVnXJImajdxCgAeeeQRxMXFYcaMGRg1ahRefvlls/ZTdevWDRqNBgkJCVCpVPLJ4YarqaZTwMbGF1275pn/RhoghC3i4+Plv8ubHULXq1cvFBUVITs7W+4eaUnNWeWo23DEsLNlY+O+h4cHhgwZgvPnz+PEiRNWKTO2s7NDTEwM8vPzcfz4cbnDl5S8Sdcrtfd1c3NDUFCQWaW7SqUSAQEB6N69O3777TecPHkSkZGR8PLykkuipC5RADB48GDEx8dj9uzZZpVEdeJEo0mdemUDAEaMGIFPP/0UISEhWLJkCdRqNYAbLT+ljXdFRUV49913sX37dqxdu1beePfcc8/h6NGjt/R6q6qq8NJLLyE7Oxvr1q2r13GqbgtCaUCRVi0cHBxw8eJFODg4oG/fvhboR12DgoJvEWAzDxgH4DIALeCrv4p8dIeAEgrc6FuuggZKAAro8I7PYjx/dQ3y8z2xYcOzuOeee+SzIgwPempq9kraMOfl5YWAgACrfBltaJXDcBWgtLS02SeHGpJ6kUvdWSzdEUutVuP69evIzs6W94ZIs22WOpeltrYWp06dwpdffokdO3agS5cuiIiIwLBhwzB8+HAMHTrUKnthOglmWo1jnGpjcUqr1eLtt9/G/v37sXHjRvj5+dW7j3SIp/RlUjqIVhrzpYQpNDS0ReOTEFooFN6ws6sxazO4Ib0e+POf38Ltt4+Hq6srSktLUV1dbdIhdFVVVUhNTZX3JlijmUVDqxw3azjSnNPOpWYqPj4+Ft+TIJVwFRYW4vLly3I1gBSn3NzcLFJqrNVqkZKSgm+//RZff/01lEol+vbtK69axMXFwdnZmZNbzcMN4o05deqU3OEjKCgIf//736HX6+u1HvTw8IAQAn/4wx+wc+dOODg44O9//zsGDx7cKte9bds2vPnmm1i6dCmEEFAoFOjevbv8xbepFoRSz+ucnByEh4e3eGVAoymFi4svcAbAUAAKYEH1OvwLD6EI7tChC9xRiGJ4AFBgDPZiY4956JOTiXXrnkJhYQ9MmDAB3bt3N/ugJ71ej4sXL6KoqAjh4eFW6cBUU1ODq1ev4vLly1CpVFCpVEZt8lpycqghrVYrl7uFhYWZvD+nLp1OZxTEq6urjUq4qqurkZmZ2aLExrAk6vDhwzh69ChKS0sRHR2N4cOHo1+/fvjoo49gb2+PzZs3c+BuOf4FNo5xqo3GqQMHDuDZZ5/FSy+9BE9PT1RUVKB3795Qq9VGewJdXFwa/CKZl5eHrKysJsteTCWEDg4OzTuDqrzcHp9++meMGTNGjqvmnBxu2LBF2nNoabW1tcjPz8fFixehUCigUqmMTri2RAMP4EbMzczMRHFxMcLCwpq1B0J6nrqTdoalu1L5d0sSG2miLSkpSS6Jys/Pl0uioqOj8dVXXyE7Oxs//PCDxQ8R7oSYbLSUVK5jY2MDlUqFY8eOoaioCI888sgt73O+Z88efP/99zh8+DAuX76M8PBwLFiwAOPGjTPri29FRYVJm8dNUV19AcAouI8tBs4BNdVd4CGKUQV7GH/+BBQQeMfxNbxa/h7+9KclGDVqFIYOHdrs1wYgbwBr6XvR6/VyWz/DdrnSzFVRURHUajXCw8ObnQzcTFFREc6dO2fyJvWGlsalgCidxVGXRqPB+fPnUVVVhdDQ0JuuOEj11HVLouLi4hAfH4/hw4c3uO9E2pNCLcZko3GMU//TluLU0aNH8fXXX+PQoUO4cOECgoOD8cQTT+D+++8364tvVVUVUlJS5NKZlpSpaLXecHZWm/241asXIjZ2cov/ftRqtXxIX0s2djfVftbFxQXl5eUoKiqyaseq8vJypKWlmdz1sKnSXcMzowzpdDp5MtGU9yKEQE5OjryR+9ixY1AqlfLBefHx8Q1+P2CcshgmGy0VEBCAY8eOGXVLeOWVV1qlz/m+ffugUCgwePBg2NnZYeXKldi+fTs2btyIwMDAmz+BAenkcbVajYiIiBbtGVCr0+GZORCKEYDQAgvEOmzEU6j7+bNDJRapVuKZ3NVYt+5lzJs3zyL1rIYlT6YmA3UPT5JqQptqlystJVu6Y1VD76XumRnSfhbDZKglS+PFxcVytxE/Pz95Zqe2trZelyh/f3+jg/NYEnVLMdloHOPU/7SlOJWUlITi4mIMHToUrq6u+PTTT/Hxxx9j3bp1RmdUmEIIgaysLFy/fh0REREtGntyc/+KoKDXzHrMn/60BC+99JLFVgakkqfw8HCTVgakvQtSrDKlXa5UZmzpFuiNvZfQ0FB5xaaxZEhKLMwt3ZX2A7q7uxvt3ZFKoqSD89LS0tCjRw85TsXGxrIk6tZistFSDQ3irdXnvCGJiYmYP38+Fi5ciEceecTsx0snUEsbiE3R8ABoj9i3hwM7gQR9LEbiILT1+hAIvID30Wt1FoBgzJ8/3+zrbYr0BbpXr17w9fWVBxppQ580ANZt52pOCVdzEpvmKCoqQlpamrzBvLa21uhkVkuceK7T6bB9+3YsXboUcXFxyMzMNCqJkrpEceNbq2K0bBzj1P+09TiVlpaGWbNmYcqUKXjqqafMHrtKS0uRlpaG3r17m1wCKnVcksb9G2WqM+Djk2PSa1ZUdMF3323ElClTzLrWm5FWBqTTtA3jlGE717KyMigUCqPValPbz96qczlKS0uRkpKCLl26wMbGxqyzQ0yl1+tx9OhRzJ8/HyNGjMDly5dRUFAg7wuMj49HVFRUZzg4ry1jstFSgYGBcHd3h0KhwPz58zFv3jz5UDaJ1Hrw7rvvxqJFi4xaD65cudLqdbNlZWVYsGABFAoF3n//fbMHltraWqSmpsLW1hb9+vUz2sjWUOcNaQCUBhRpAKw6txMeQx4CtEB35OIautd7rbvwI2KXnMDDDz+MgICAFr3vhmi1WqSnp0OtVsPd3R0VFRXyhj5pdsUSA6AlVzmaCjI1NTVy6+Lm1shKpH0u0qrFyZMnYWdnh4iICBw6dEj+vHLlok1hstE4xqn/aQ9xqrq6Gq+++irOnz+PD2ugAAAAIABJREFU9evXmzy5JZHGdp1Oh7CwMKMZ8rqdAcvKyuSOS1Kcksp1ysvD4O19+aav98UXU3D//Z9abWP3+fPnUVRUBE9PT1RWVsp7Fwz3s7T0tS29ytFYVyspsQsPD2/WAY2GhBC4evWqXBJ1/PhxKJVKxMTEICkpCeHh4Vi7dm2L9/KQRbH1bUsdOnQIvr6+KCgowPjx4xEaGtrofRtK4G7FMp6Liwu+/PJLfPbZZ5gwYQI+/PBDs2pMu3btipiYGPmQHT8/P7nPuWHnDV9f30Y7bwCAfb8JqLxnNBz+vQ8vYSVexep698mGH2JxApWVldDpdBboilU/yEjvKTc3FwEBAS3el9IQNzc3DBkyBBcuXMCJEyfMWuWQDlKUkgvDrlY9evSol/CVl5c36yAnqUuUlFycP38eAQEBGDZsGGbMmIG1a9fK16zT6bBx40ZUVVUx2SBqZ9pDnLKzs8OaNWuwfft23HPPPVi2bBnGjRtn8uNVKhUiIiKQn58vxym9Xl+vM6C3t3eTHRedndOQmxsDH5/zTb5eZmY4zp49i7CwMIt0Q6qpqTFqk67T6WBnZ4e8vDz4+voiKirK4qvITk5O8kGvx44dM2uVo6nS3W7dutU7/buyshJpaWk3PduprqZKoiZPnoz33ntPXskXQuCrr75CUVERk412gisbzbBkyRI4OTnhk08+aTPL03WdP38eM2fOxKRJk/Dcc881OXgZnhYtfVHX6/Wora2Fh4cH+vTpY9a5CwCAoiLYBfpDp1WgKzT1blahAp999SMGDx6M7OxshIWFmbVvo6kv6tJysxRkNBoN0tPTodfr682EWdLNVjmkxE26ZukgRWmlxZTN/dKyeEFBAUJDQ+v9nQkhUFxcjMTERCQkJCAxMRHl5eVGJVGhoaEsiWp/uLLROMapBrSHOJWbm4tZs2YhIiICb775ZpNf5htq667T6aDRaODs7Iy+ffs2q+OSfDhtg68J7Nq1A2FhYbhw4YLZp1xL7WcNz4ySGo5IqxZSPNLpdDh//rzcgMRSJ3XXdbNVDsOzLUpKSkzax1iXtCpx5coV9O3bt8EW/RUVFUhKSpInwVgS1SGwjKol1Go19Ho9nJ2doVarMX78eLz55pvYs2dPm+1zDtz4cvvGG2/g5MmT+Oijj+Qg0lBrVGnVQupmYWNjIy/xVlRUNGvw67JwIVSffgoldKj/GRS4fr0Y9vb2qKysRGpqKtzd3RudsZd6stftZCENgKZ8Uc/Pz0dmZqZZ+1LMJQWM8vJyBAYGyqstht1CpISoJbNkarUaR44cwfbt2/HEE08gOTkZiYmJOHXqFOzt7Y26RHl5eXGDHG782wwePBg9e/bEjz/+iIsXL2Lq1KkoKirCwIED8fnnn6Nr166oqanBjBkzcPz4cXh6euLrr7+2SqmfmfgP2DjGKbTfOKXX6/H+++/jX//6FzZu3Ig+ffoAuPH7Kk0oGa5aGJYXqVQqefP4tWvXEBER0bz258IZ9g76ej/W6YCamgooFArU1NQgNTUVdnZ29VadJRqNxmjVojlf1KVuhObsSzGX4aRVUFAQtFptvdJdKSFqSdOY6upqnDp1Cp9//jkWLFiA1NRUo5KoIUOGYPjw4RgxYoTVTidvbzpqnGKyYYLMzEw88MADAG5k/Y899hgWL16MwsLCen3OXV1dMWjQIJSUlMjtB1UqFWpra1vlgyKEwBdffIHFixcjMjIS+fn5WLVqVb3NZk39kkubx/v06SOfUmuS2lrYu7tDCS2A+gmEWl1ldJ1St5GwsDAIIYw6WRj2ZG/JIXS1tbVIS0uDSqVCSEiIxWZODDchlpSUQK1WQ6PRwMPDA/7+/nBxcbHIaoJhSdSRI0dw7do1pKen4+GHH8a0adMwdOhQq82ItXerV6/GsWPHUFZWhh9//BFTpkzBgw8+iKlTp+Kpp55CTEwMFixYgPXr1+P06dP4+OOPsXXrVvz73//G119/3dqXzyjcOMYptO84BQA7duzAM888g9DQUFy5ckU+Jd3U84zKysqQmpparzGIKRRfb4XtzNn1Dv07caIPwkKTId1gOGMvHTYojfnl5eWwsbGRr7clh9BJ+1K0Wq3FyreA+hUBFRUVqK2thYuLCwICAuDm5maRvSlarRZnz56V41ROTg7S0tIwYcIEzJ49G7GxsRZpbtIRddQ4xWTDwtrSB+XcuXOYPHkyAgMDER0djaSkJPj7+2P58uVmfyGVvqR36dIFISEhJg9IqilTMHP7fdiKOXVuUUOtvvHlW2o/W1paiuvXr6OiogJOTk7w8fGBu7s7HB0dLVr2I4RAbm4uLl26hJCQEHh4eJj9HFLdbd0NctJskJ2dXb2VIXM7VjVUElVWVoaYmBh5NigkJASXL1/G/Pnz8d5775ndUrKzuHLlCmbOnInFixdj9erV+M9//gMvLy/k5eVBpVLhyJEjWLJkCXbt2oU777wTS5YswW233QatVosePXrg2rVrrR0YGZUbxzhlprYUp0pLSzFy5Ej4+vpi0KBBOHv2LLp27Yo1a9aY3RJdp9MhPT0dGo0G4eHhpk9KVVfjHr+d+EU9zejHzz67GO++Oh/w9IRWq5UnlIqKilBaWgp7e3v4+vrC3d3dIg1H6rp27RrOnz+PoKAgdO9ev9HKzRiW7paWlkKr1dYr3RVCtKhjlRAC5eXlcklUQkICrl+/jsjISKOSqMLCQjz77LOYNWsW7rrrLrPfS2fQkeMUkw0LamsfFOnf1rCl3tq1a/H555/j448/bnLzYGPPJ83qhIeHm3wKqr2jI5QwXqLWQIVTSYkNtp9VqVQ4f/48KisrER4e3qJl3KZUV1cjNTVV3sjWWAJluEHOcKXFsGd4Uyskpnaskk5mlWpYzSmJkk6Rp4ZNnjwZr732GsrLy7Fq1Sps3rwZcXFxOH/+xubQ7Oxs3HXXXTh79iwiIyOxc+dO+Pn5AQD69OmDxMREs2q1rYD/uI1jnDJDW4tTwI2xz/CL+pYtW7By5Up88MEHzTrwtaCgABcuXDBrMsnO0RE1lYDhPFy+0gU5R39BacWNUirDrlZdu3bFpUuXUFhYiPDw8OaVb5mgtrYWv/32G5RKJUJCQhpNoAxbuzendNfUjlVCCFy5cgWHDx9GYmIijh8/DhsbG6OSqKZWlhirGteR4xR331jQ888/j3fffRfl5eUAbpQfubm5yV9E/fz8cPXqVQDA1atX0atXLwCQB4TCwkKLflDq/kIrFAo8++yzGDVqFJ588knMmjULTzzxhMm/+AqFAn5+fnB3d0dKSkq9/uAN0el0KB83Dtijw41SKjUAJwjo4evr2+hm5ZCQEBQWFuLEiRPNntW5GTs7OwwYMABXrlxBUlKSvEndcINcaWkpampq4OTkBDc3N/j7+5u9/Ct1rDp//jyOHj0Kd3d39OvXDzU1NTh16hQSEhKQkJCACxcuIDAwEMOGDcOsWbPMKonqiIN3dXU1Ro4ciZqaGmi1WkyePBlLly7FrFmzsH//fnnWc/Pmzejfv3+jJyL/+OOP8Pb2xqBBg7Bv3z4ATXfiaa0uPUS3QluLUwDqxYBHH30UcXFxmDFjBm6//Xa8+OKLZpX3eHt7w8XFBSkpKSgsLESfPn2aXHXQ6/W4tmIFeji8hNxcD3h6lmHRotfxGD5Hdx8f9DVoOGIoKCgInp6eOHPmDPz8/Kyy76Br166Ijo5GXl4ejh07Jm9Sr1u6a9ja3c/Pz+yVFqljVVZWFpKSkuDg4ICoqChotVqcOXNGXrVIT0+Hr68vhg0bhocfftioS5QpOtpYyjhlGiYbFtKePijR0dE4cOAAXnjhBTz++OP48MMPzSolcnR0xODBg+V2rxEREfKX4rrnRAgh4Pruu0j7y8vw+eJTOKECFwPHQHNWjZt14fb09MTgwYORlpaGa9euNTmr0xLdunWDXq9HcnIyAMDW1lZetejZs6dF9kAolUp4eXkhISEB7777Luzs7GBra4v+/ftj+PDhWLFiBUJCQtglyoCtrS327t0LJycnaDQaxMfHy8vv7733HiZPnmx0/59++gkZGRnIyMhAYmIiFixYgMTERBw6dAg//PADduzYgerqapSVleH5559HSUkJtFotVCoVrly5Al9fXwA3vmxlZ2fDz89PTjybU2pH1Na0pzgVGBiIvXv3YsmSJbjvvvuwceNG+XfUFHZ2dhg4cKDc7tVw83iDDUfGjUPi8x+g2scJNijDFPyEsLIzwE2SHFdXVwwZMgQZGRk4deqU1Vbj3d3dodVqkZaWBr1eL7efdXNzQ2ho6E33XppCoVCgW7duSE5OxpIlS+RD+qKiojBs2DC88cYbiIyMtMqZI+0V45RpmGxYSHv7oDg4OGDDhg347rvvMGnSJLz33nvy4U6mUCqV6NOnD65evYrExETY29vL/cJdXV3h7e1t3GN7wzJgwzLUADA9XABdunRBdHQ0cnNzcfz4cYSEhLSor7a0QU4KNGq1Wm6ZGxkZiZKSEly7dk0+S6Qlr3PhwgWjkihHR0fExcVhxYoV+PXXX3HhwgWsWrWqtZc92yyFQiEfYKjRaKDRaJoMptu2bcOMGTOgUCgQFxeHkpIS5ObmYvny5Vi+fDkAYN++fVi1ahW+/PJLPPzww/j2228xdepU/OMf/8B9990HALj33nvxj3/8A7fddhu+/fZbjB07tk3PGBGZqr3FqS5dumDZsmXYt28fHnroISxevBh33323yY9XKBTw9/eHra0tjh8/DltbW+j1ernhiIeHBwIDA3+fxFoWDixbgGoAUWZcp42NDUJDQ3H9+nWcOHHC/GYqdTRVuhsWFobKykrk5OSgR48eLYqHTZVEvfXWWzhz5gwOHDiAZcuWITAwsNmv05ExTpmGezasQPqg/Pjjj3j44Yfx0EMPyRvvoqOj8fTTT2PdunU4c+aMvPHuX//6F7755ptWud4rV65gxowZiI2NxWuvvdbo/oPa2lp58DM8J8LZ2RnXrl2DnZ0dQkNDrTbrUV1djZSUFDg7OyM4ONikVQDDVoRSQHV2dpZnhBrqyy7Vrnp5ecHf39+k16mpqcHJkyflkqjMzEwEBQVh2LBhGD58OIYMGVJvhSQpKQn9+/e32rkfHYFOp8OgQYNw/vx5PPPMM1i5ciVmzZqFI0eOwNbWFuPGjcOKFStga2tr0onIhr+bmZmZckvBAQMG4IsvvoCtrS2qq6sxffp0nDx5Eh4eHti6dSuCgoJa669A0najSOtjnGqG9hanioqKMHv2bHh5eWH58uWNNtyQGo5I435tba0cp0pKSiCEQHh4uMU6PNXVnI6HTZXuNtYyt6qqCqmpqXB2dkafPn1MirsajUbuElW3JCo+Ph6xsbH1YuKZM2cQEBDQosm3jo5xSsYN4rdSe/yg6HQ6vPPOO/j555+xceNG9OzZEzk5OVAqlXKLPJVKZdTWz/BLshACOTk5zTqgzxxCCFy+fBn5+fn1OmdIG+SkQVvafN6cVoR6vR4XL15EUVFRvc1/QggUFRXJiUViYiLUarVcEhUfH49+/fqxJMqCSkpK8MADD+DDDz+Ep6cnevTogdraWsybNw99+vTBm2++iUmTJuG1114zGsTfffddDBo0qJWv3iKYbDSOcaoZ2mOcEkJgw4YN2LRpE9avX4+wsDBcvXoVSqVSPpC2bsORuiVN0ubxfv361TtszpLXmZubi8uXLyM0NBRubm5Gt0nlxlIXRsOzLVxdXU0u3RVCIDs7G7m5ufUOeZW6RB09elReYS8sLDTqEsWSKMtinOIG8Vtq9OjRGD16NIAbG8jqHpRkuKHI1tYWd911F4KCglr18JaKigrExsYiKysLI0aMgLOzM+6//3489dRT6N27N5ycnJr88qxQKNCzZ0+4ubkZdbSw9LKetCzu6emJlJQUOZiUlpaiqqpKPpywqc3nppDKxLy8vLBixQpotVqEhITg6NGjSE5OlkuiRowYgUWLFqFbt25tegnzVmpsw1xLPt9ubm4YPXo0du7ciZdeegnAjVrZJ554AqtWrQLwe8mHxLAchIiMtcc4VVlZidDQUAwfPhx33XUXHB0dMXr0aCxatMjkMd/b2xuurq7y5nFTV8nNoVAo5Ja4KSkpsLOzg7OzM0pLS41Kd729vdG3b99mf+FXKBTo3bs3PD098cknnyAjIwO33XYbjh8/jhMnTsDGxgZDhw7F8OHD8Yc//MHs80c6MsapW4tTr61A2lCUnJyMU6dOYefOnUhISMCrr76KF154ARkZGXB3d8emTZsAAJs2bYK7uzvOnz+PF154Aa+++qrFr+mf//wndu3ahYkTJ+LgwYOIjY1FYWEh3N3dzTqQztHREYMGDYJOp8OJEydQXV1tsWusra1FQUEBzp07h7S0NPkcitzcXPTu3RtxcXGIiYmRDydqbgCpqalBQkICPvjgA8ybNw+7d+/GgQMH8Je//AWTJk3CkSNH8N///herVq3C/fffzxO667DU5/vatWsoKSkBcKNk4JdffkFoaChyc3MB3Ji5+/777xEZGQngRg3rZ599BiEEEhIS4OrqCh8fn1b4GyBq/9pinNq1axe+++47xMbG4tChQ7j77rtRVFQER0dHs8Z8W1tbDBgwALa2tjh27BgqKiosdo0ajUY+HyMlJQUajQYVFRXIzs5Gjx49EBcXhwEDBiAwMBAeHh7NTjQ0Gg1OnDiBdevW4amnnsKWLVuQmpqKt99+G0OHDsWvv/6KhIQE/PWvf8UjjzzCE7rrYJy6xYQQTf2hOvz9/cXnn39usedTq9ViwIABIiEhQXh6egqNRiOEEOLw4cPijjvuEEIIcccdd4jDhw8LIYTQaDTC09NT6PV6i11DQ/R6vfjb3/4mBgwYIP773/8KtVpt9p8rV66IX375RWRmZpr92IqKCpGfny/S09NFYmKi2LNnjzhw4IBITk4Wly5dEiUlJUavs2fPHnH+/HlRUVFh9utcvnxZfPPNN+LFF18Uw4cPF/379xezZs0Sn3zyiUhLSxM6nU4IIcTBgwfF3Llzrfr33tG05POdnJws+vfvL6KiokRERIRYunSpEEKIMWPGiMjISBERESGmTZsmysvLhRA3PrNPP/20CAoKEpGRkSIpKakV3rHV3Gys7sx/qI7OEqeEEGLbtm0iKipKbN++vVlxKi8vT+zdu1f89ttvzYofBQUFIiMjQyQlJYk9e/aIffv2iVOnTomLFy+K4uLieq+TlpbWrNfJyckR33//vXj11VfF6NGjRXR0tJg2bZpYt26dSE5OFlqtVgghRHJyspg6daoct+jmGKcsptFxmmVUraTuhqI+ffq0eq9zQwqFAk888QSGDx+OmTNn4v7778czzzxj1mqBu7u73Lr2+vXrTW6W0+l08l6LkpIS1NTUyLNVNyvjkl4nPT1dPgW1sU3X0qne0n4LqSTqtttuw8iRI5ssiYqPjzerY1dnZonPd3R0NE6ePFnvuffu3dvgayoUCqxbt85K74io82nrcQq4MVM8ePBgzJw5E7/++itef/11s5puODs7Y/DgwcjIyEBycnKTm8f1er3R2RbmlO5Kr3PhwgWcPHkS4eHhje7N0Ov1uHz5srzX4sSJE1CpVHJJ1HPPPQcfH58G41R0dDS2bNli8vvvzBinbh0mG63ExsYGp06dkjcUpaWl1btPW+h13q9fP+zfvx//93//h8mTJ+Ojjz4y64C9Ll26ICoqCrm5uTh27Ji8ebzueRwA5NNZfX19zT7bQqVSISIiAvn5+UhKSoJOp8OIESNQXV1t1CXq4sWL6NOnD4YNG4Y5c+ZgyJAhVjuhvD3Jzs7GjBkzkJeXB6VSiXnz5mHhwoVYsmQJPvnkE3h5eQEA3nnnHUycOBEAsHz5cmzatAk2Njb461//ijvvvFN+vvby+SaixrWX32NfX1/s2rUL7777LiZOnIgNGzaYtZFdal177do1HD9+XN48LnVgNDqP439xql+/frC3tzfrPdrY2KBfv34oKirCyZMnUV1djTFjxkCj0eD06dNycpGeng4/Pz8MGzYMU6dOxerVq+X2qp0Z41T7xWSjBSorK/Hoo49Cq9Xim2++MepYZCppQ1FCQkKb7XXetWtXrFq1Crt27cL999+PpUuX4o477jD58UIIODk5wcvLCydPnoRCoYCzs7O8Qc7oPI4WEELAxsYGeXl5eO+996BWq+Hk5ISBAwdi+PDhWLVqFfr27csuUQ1QqVR4//33MXDgQJSXl2PQoEEYP348AOCFF16QN7tJUlNTsXXrVqSkpCAnJwe33347zp07V6/+uD18vok6ss4Sp5RKJRYtWoSxY8di+vTpePbZZzF16lSTHy+EgL29PXx8fJCSkgK9Xi+3n613HkcLSHGqrKwM7733HhYuXCjvIRk2bBjeeustREREsEtUAxin2i9+62qmvLw8jBo1Cr6+vvjhhx/MGsAb2lAUFhaGMWPG4NtvvwWABg9vAdCqh7fceeed2L17Nz755BMsWrQINTU1Dd5Po9Hg+vXruHDhAo4fP46jR4/i8uXLsLW1xcCBA+Hr6wu9Xg8fHx94eHg0O9HQ6/VIT0/HP/7xDyxYsADDhw/HtGnTcPr0aSxduhTz58+HSqXCa6+9htmzZ/OE7ib4+Phg4MCBAG4s90stJRuzbds2TJ06Fba2tggMDERwcLDczaa9fr6JOprOGKeGDh2KAwcO4Ndff8XcuXNRXl7e4P10Oh2KioqQmZmJEydOyOcjKZVKxMTEIDAwEDqdDj169EC3bt2anWjo9XpkZWVhy5YteP755zFq1Cg8+OCD2LdvH55//nm8/PLL6NKlCxYuXIinn34a0dHRTDQawTjVfnFloxlSU1Px5ptvYv78+c3quJGbm4uZM2dCp9NBr9djypQpuPvuuxEeHo6pU6fi9ddfx4ABA3DHHXdgzJgxyMnJQW5uLr777jsEBgbi448/xvjx45GVlYWAgAB88803cHd3hxACCxcuxI4dO+Dg4IDNmzfLv5iW4u3tjR9//BFr1qzBXXfdhfXr10MIgbKyMri4uKCsrAxKpVLuGe7n51evTMnFxQXFxcVITk5GQEAAevToYdJrV1dXy0EhISEBWVlZcknU3LlzGyyJuu+++8wq+yIgKysLJ0+elDu+rF27Fp999hkGDx6M999/H+7u7rh69Sri4uLkxxjWtpr6+Z49ezYAYPbs2Zg+fTqCg4PlXv5E1DKdOU45Ozvj888/xxdffIEJEyZgzZo18PDwQF5eHjw8PEwq3ZVOGE9JSYGvry/8/PxM+nJZtyTq3LlzcknUo48+ir/85S/1kr67776bX1zNxDjVvvBQPzMFBASguroa3bp1Q0JCglXrKHNzc5Gbm2u0ZPj9999j8+bN8PDwwKJFi7BixQoUFxdj5cqV2LFjBz788EPs2LEDiYmJWLhwIRITEy1+XadOncLevXuxY8cOHD9+HH369MG8efMwadIkuLi4mDwro9Fo8Ntvv0GhUCA0NNRohUMIgevXr8uJxdGjR1FZWYmBAwfKBxKxJMryKioqMGrUKCxevBgPPvgg8vPz5Q3zb7zxBnJzc/G3v/0NzzzzDG677TY8/vjjAG4MxBMnTsRDDz3Uyu+gw+I3kcYxTtXBOAWkp6dj9+7d+Pnnn3HgwAH4+/tj+vTpmDp1KlxcXExeUdfpdMjIyEBVVRUiIiKMNo8LIVBaWmp0cF5xcTGio6PlOBUeHs6VCgtjnGqzGo1T/KbWDCtWrEBUVBRuv/12FBcXW+11Glsy3LZtG2bOnAkAmDlzJr7//nsAN5YMZ8yYAYVCgbi4OJSUlMi9ni3p2LFjcHd3x/r165GdnY3+/ftj165dUCgUZg2q0uZxT09PzJs3D1999ZVRSdTjjz+O48ePY8yYMdi2bRtOnDiBTZs2sSTqf7KzszFmzBiEhYUhIiICa9asAQAUFRVh/Pjx6Nu3L8aPHy9/RoUQeO655xAcHIzo6GicOHHC6Pk0Gg0eeughTJs2DQ8++CAAoHv37rCxsYFSqcTcuXPlJejOejARUXvR2ePU6dOn0aVLFyxbtgz5+fmYOHEidu7ciZqaGrNKd6XN47169cJLL72EjRs34quvvsLChQsxcuRIPPTQQ9i3bx8GDx6MLVu24NSpU/j888+xYMECREVFdfpEg3GKAPCcDXNJ/ct1Op2YM2eOiI6OFnl5eVZ/3YsXL4pevXqJ0tJS4erqanSbm5ubEEKISZMmiYMHD8o/Hzt27C3r4fzNN9+I6Oho8csvv5jUN7ywsFDs3r1bvP3222LSpEli8ODBwsfHR4wfP17s27dPVFdX35Lrbs9ycnLE8ePHhRBClJWVib59+4qUlBTx8ssvi+XLlwshhFi+fLl45ZVXhBBCbN++XUyYMEHo9Xpx5MgRMXToUPm59Hq9mD59uli4cGG915CsXr1aPPLII0IIIc6ePSuio6NFdXW1yMzMFIGBgXKfd7KK1j7Loi3/oToYpxr2yy+/iKioKPHPf/7TpDhVUlIi9u/fL1asWCEeeOABMWDAANG7d28RGxsrfvrpJ1FRUXFLrrs9Y5zqVHjOhqUplUp88sknePHFFzFy5Ejs3r0bvXv3tsprVVRU4KGHHsIHH3wAFxeXRu8nWrE128MPP4zY2FjMmDED8fHxeOWVV+TZI9FASVRVVZVcErV69WoEBwdDr9fjnXfewenTpzFq1Khbct3tmY+Pj3zyaN0ZxX379gG4MaM4evRorFy5stEZRR8fHxw6dAiff/45oqKi0L9/fwA32gdKM3UKhQIBAQHYsGEDACAiIgJTpkxBeHg4VCoV1q1b1+ln8IjaGsYpY+PGjcPevXvx5JNPYs+ePVi2bJm8V0P8ryQqMTERR44cwdGjR41KopYuXYrw8HAolUp89NFH+O9//4sJEybckutuzxinCABXNtq62tpacccdd4iPalAiAAAgAElEQVT3339f/lm/fv3kTD4nJ0f069dPCCHEvHnzxFdffdXg/W4VjUYjlixZIuLi4sTbb78tpk+fLmJiYsSIESPEK6+8In744Qdx/fr1W3KybGfS1mcUqcVae/WgLf+hVtbe4pRerxdr164VAwYMEH/605/E7NmzxYABA8Rtt90mFi5cKL755huRk5PDOGVhjFMdXqPjdOcuem/jhBCYPXs2wsLC8OKLL8o/N2zBVrc122effQYhBBISEuDq6irPKNwqKpUKb731FubNm4esrCw89dRTSExMxIEDB7By5Urcc8898PT07LSdN5588kl4e3sjMjJS/tmSJUvQs2dP9O/fH/3798eOHTvk25YvX47g4GCEhIRg165dDT5ne5hRJKKOqT3GKYVCgWeeeQYrVqxAamoqHn/8cRw8eBCHDx/GBx98gIcffrjRE7o7A8YpsrimMpFbnBFRHQcPHhQARFRUlIiJiRExMTFi+/bt4vr162Ls2LEiODhYjB07VhQWFgohbszWPP300yIoKEhERkZyNqAN2r9/vzh+/LiIiIiQf/bWW2+J9957r959U1JSjOpNg4KC6tWbtrcZRWq21l49aMt/qBUxTnU8jFPUTFzZaI/i4+MhhMDp06dx6tQpnDp1ChMnToSnpyf27NmDjIwM7NmzBx4eHnjyySfRvXt37N+/HxcuXMCZM2cQFBTUrG4PZD0jR440+dTRpg4kAtrnjCIRdSyMUx0P4xRZGpONDmLWrFnYuXOn0c9WrFiBcePGISMjA+PGjcOKFSsAAD/99BMyMjKQkZGBjRs3YsGCBa1xyWRg7dq1iI6OxpNPPikH26tXr6JXr17yfQwPJAIgb5bbu3ev0dL2okWLsHv3bvTt2xe7d+/GokWLAAATJ05EUFAQgoODMXfuXKxfv/7Wvkki6tQYp9o3xilqLnaj6iBGjhyJrKwso581p9sD3XoLFizAG2+8IR9I9Mc//hF/+9vfblq7Ks0oNmTPnj0NPnbdunWWu3AiIjMwTrVfjFPUElzZ6MDy8/PlgdnHxwcFBQUAbj4TQcYa2izX3AOJGsIDiYios2KcsgzGKWrLmGx0QjebiSBj1l76Nzw999///rccLO69915s3boVNTU1uHjxIjIyMjB06FALvjMioraJcco8jFPUlrGMqgPr3r27vOycm5sLb29vAJyJMJcll/4fffRR7Nu3D9evX4efnx+WLl2Kffv28UAiIuqUGKcsg3GK2jImGx2Y1O1h0aJF9bo9rF27FlOnTkViYiK7PTSDuUv/0n23bNlS77lmz57d6OssXrwYixcvtuSlExG1GYxT1sM4RW0Fk40OoqGZiEWLFmHKlCnYtGkTevfujX/+858AbnR72LFjB4KDg+Hg4IC///3vrXz11hUQEABnZ2fY2NhApVLh2LFjKCoqwiOPPIKsrCwEBATgm2++gbu7e4tfi0v/REQNY5xqHOMUdWRMNjqIhmYiAPO7PezcuRMLFy6ETqfDnDlz5HZ07d2vv/6Kbt26yf8t1bIuWrQIK1aswIoVK7By5UqTn49L/0RE5mGcahrjFHVU3CBOMp1Oh2eeeQY//fQTUlNTsWXLFqSmprb2ZVnFtm3bMHPmTAA3alm///57sx7f3g4kCggIwJ///GeMGTMGTk5OiIqKwunTp7FlyxYEBwfD1dUVc+bMgVarbe1LJSJqFOOU6RinqM1o6nhxqx9sTm3K4cOHxR133CH/9zvvvCPeeeedVrwiywgICBADBgwQAwcOFBs2bBBCCOHq6mp0Hzc3t0YfP3XqVNGjRw+hUqlEz549xaeffiquX78uxo4dK4KDg8XYsWNFYWGhEEIIvV4vnn76aREUFCQiIyNFUlKS9d6YGfz9/UVwcLBITU0VtbW1Ytq0aSIoKEjMnTtXVFRUiEuXLgkvLy/x5ZdftvalUsNuNlZ35j/UiTBONYxxitqARsdpllGRrKFNY4mJia14RZZx6NAh+Pr6oqCgAOPHj0doaKhZj7fU0n9rmzdvHsLCwgAAjz32GL788kskJCTA0dERjo6OGD16NJKSkvDYY4+18pUSETWMcaphjFPUlrGMimSijW4a27lzJ0JCQhAcHCz3CTeHVIvq7e2NBx54AEePHpVrWQEY1bJ2ZIbL5A4ODrCxsYGXl5fRz8rLy1vj0oiITMI41bExTnVMTDZI1hY3jbW0PletVssDk1qtxs8//4zIyMhGa1mJiKjtYpwian9YRkWyIUOGICMjAxcvXkTPnj2xdetWfPXVV616TUePHkVwcDCCgoIAAFOnTsW2bdsQHh5u0uPz8/PxwAMPAAC0Wi0ee+wxTJgwAUOGDGmw3SIREbVdjFNE7Q+TDZKpVCqsXbsWd955J3Q6HZ588klERES06jW1tD43KCgIycnJ9X7u6enZYC0rERG1XYxTRO0Pkw0yMnHiREycOLG1L0PWVutz25usrCyj/x49enS99oGbN2++dRdERNRMjFMdE+NUx8U9G9SmtcX6XCIiIgnjFFHTmGxQm2ZYn1tbW4utW7fi3nvvbe3LIiIiAsA4RXQzLKOiNq0t1ucSERFJGKeImqZoqNbQQJM3EhHRLcEC8MYxThERtb5G4xTLqIiIiIiIyCqYbBARERERkVUw2SAiIiIiIqtgskFERERERFbBZIOIiIiIiKyCyQYREREREVkFkw0iIiIiIrIKJhtERERERGQVTDaIiIiIiMgqmGwQEREREZFVMNkgIiIiIiKrUN3kdsUtuQoiIqLmYZwiImrDuLJBRERERERWwWSDiIiIiIisgskGERERERFZBZMNIiIiIiKyCiYbRERERERkFUw2iIiIiIjIKphsEBERERGRVTDZICIiIiIiq2CyQUREREREVsFkg4iIiIiIrILJBhERERERWQWTDSIiIiIisgomG0REREREZBVMNoiIiIiIyCqYbBARERERkVUw2SAiIiIiIqtgskFERERERFbBZIOIiIiIiKyCyQYREREREVkFkw0iIiIiIrIKJhtERERERGQVTDaIiIiIiMgqmGwQEREREZFVMNkgIiIiIiKrYLJBRERERERWwWSDiIiIiIisgskGERERERFZBZMNIiIiIiKyCiYbRERERERkFUw2iIiIiIjIKphsEBERERGRVTDZICIiIiIiq2CyQUREREREVsFkg4iIiIiIrILJBhERERERWQWTDSIiIiIisgomG0REREREZBVMNoiIiIiIyCqYbBARERERkVUw2SAiIiIiIqtgskFERERERFbBZIOIiIiIiKyCyQYREREREVkFkw0iIiIiIrIKJhtERERERGQVTDaIiIiIiMgqmGwQEREREZFVMNkgIiIiIiKrYLJBRERERERWwWSDiIiIiIisgskGERERERFZBZMNIiIiIiKyCiYbRERERERkFUw2iIiIiIjIKphsEBERERGRVTDZICIiIiIiq2CyQUREREREVsFkg4iIiIiIrILJBhERERERWQWTDSIiIiIisgomG0REREREZBVMNoiIiIiIyCqYbBARERERkVUw2SAiIiIiIqtgskFERERERFbBZIOIiIiIiKyCyQYREREREVkFkw0iIiIiIrIK1U1uF7fkKoiIqCmK1r6ANoxxioio9TUap7iyQUREREREVsFkg4iIiIiIrILJBhERERERWQWTDSIiIiIisgomG0REREREZBVMNoiIiIiIyCqYbBARERERkVUw2SAiIiIiIqtgskFERERERFbBZIOIiIiIiKyCyQYREREREVkFkw1qt3r16oUTJ040eNtrr72GDz74wKTnGTp0KFJSUix5aURERIxTRGCyQe1UcXExcnJyEBoaWu+2a9eu4bPPPsP8+fMBADU1NZg9ezb8/f3h7OyMAQMG4KeffpLv/9JLL+HNN9+8ZddOREQdnzlxCgAef/xx+Pj4wMXFBf369cOnn34q38Y4Re0Zkw1ql86cOYPAwEA4ODjUu23z5s2YOHEi7O3tAQBarRa9evXC/v37UVpairfffhtTpkxBVlYWAODee+/Fr7/+itzc3Fv5FoiIqAMzJ04BN1Y6srKyUFZWhh9++AGvv/46jh8/DoBxito3JhvULp0+fRp9+vTBwoUL4eXlBV9fX+zevRsA8NNPP2HUqFHyfR0dHbFkyRIEBARAqVTi7rvvRmBgoDyI29nZYdCgQfj5559b5b0QEVHHY06cAoCIiAjY2toCABQKBRQKBS5cuACAcYraNyYb1C6dPn0ax44dw8SJE5Gfn4/58+dj5cqVAG7MJoWEhDT62Pz8fJw7dw4RERHyz8LCwpCcnGz16yYios6hOXHq6aefhoODA0JDQ+Hj44OJEyfKtzFOUXvFZIPapTNnzmDx4sW48847oVQqER4eLt9WUlICZ2fnBh+n0Wgwbdo0zJw506iO1tnZGSUlJVa/biIi6hyaE6fWr1+P8vJyHDx4EA8++KC80gEwTlH7xWSD2h0hBM6ePYt77rlH/tnZs2flgdzd3R3l5eX1HqfX6zF9+nR07doVa9euNbqtvLwcbm5u1r1wIiLqFJobpwDAxsYG8fHxuHLlCj766CP554xT1F4x2aB25+LFiwCA4OBg+WcnT55E//79AQDR0dE4d+6c0WOEEJg9ezby8/Px3XffoUuXLka3p6WlISYmxspXTkREnUFz4lRdWq1W3rMBME5R+8Vkg9qd06dPIyoqCgqFQv7ZyZMn5UF44sSJ2L9/v9FjFixYgLS0NPznP/8x6v4B3GiNe/z4cYwfP976F09ERB2euXGqoKAAW7duRUVFBXQ6HXbt2oUtW7Zg7NixABinqH1TtfYFEJnrzJkzRrM7hYWFyMvLQ2RkJABgxowZ6N+/P6qqqmBvb49Lly5hw4YNsLW1RY8ePeTHbdiwAdOmTcMPP/yA0aNHw9fX95a/FyIi6njMjVMKhQIfffQRnnrqKej1evj7++ODDz7AfffdBwCMU9SuKYQQTd3e5I1EbdX//d//wdvbG88///xN7xsbG4tNmzbJQYCoDVLc/C6dFuMUtUuMU9TBNBqnmGwQEbV9TDYaxzhFRNT6Go1T3LNBRERERERWwWSDiIiIiIisgskGERERERFZBZMNIiIiIiKyCiYbRERERERkFUw2iIiIiIjIKphsEBERERGRVTDZICIiIiIiq2CyQUREREREVsFkg4iIiIiIrILJBhERERERWQWTDSIiIiIisgomG0REREREZBWq1r4Aos5ACAG9Xg+9Xg+dTge9Xg+tVgshBBwcHKBSqaBQKFr7MomIqJMyjFNSrJL+ODg4oEuXLoxT1CxMNogsRAjR6GCt1+uN7icN2DqdDlqtFlqtFiqVCjY2NlAqueBIRETWUXfyq6k4pVAo5Bil0+kYp6hZmGwQmaluUiENwnq9HkIIXLt2DQDg5eUlD9ZKpbLejJA0sCuVSggh5AHdxsZGXungLBIREZmrbpyqm1SUlpaisrISvr6+TcYpSd3EQ6lUyisdjFN0M0w2iBrR1EBtuDoB/D4QS4OxUqmEjY2Nya8lPVYIAZ1Oh8zMTPj5+cHOzg42NjYczImIqJ6GSnSlWFWXFGdsbGzkibLmxim9Xo+LFy/Cw8MDzs7OjFPUJCYb1Kk1VPqk1Wrl/294P1Nmf1pKeo3CwkL4+PhAo9EYlVhxMCci6lzMLdG9VXGqpKQETk5OjFN0U0w2qFNoaqAWQgAA8vPzUVtbi969e7dosJaer6WUSqVcYiUN5oYlVkRE1HHcrEQXAIqLi1FYWIi+fftaPakAfi/3bew16sYpjUYDlUrFOEVGmGxQh2LuxjdpsAZ+HzQtsfGtpYOsYcJiuHTNzeRERO1bc0t0pRIoS8QpcyfFGoppN4tT0uQY4xQx2aB2p+7sT1VVlTxA1x1ApQHS2rM/1lD3epsazLlJj4io7agbp2pra6HRaOT9Eob3uxWlTw0x9bUau1/dxEi6r+H+Q51OB6VSKScdjFOdE5MNarOa6vlt6NKlS3B0dIS3t3eH+dLd1KxT3cE8LS0NISEhrJclIrrFTCnRBYC8vDzodDr06tWrzX7plkqh1Go11Go1KisroVarUVtbCz8/P/Tq1QsqlWlfG+tuJk9PT4efnx8cHBwYpzohJhvU6qRB2nAmxJyNb9J/t5XBq6HZnsaYM2PU0GMVCgWKioqM9nUw6SAisqyWlOgCN+KUXq9vE+OyEAI1NTVQq9W4fPmynFjodDp07doVDg4OcHR0hJeXF3r37g0hBIqLi5GYmIhu3brB398fdnZ2ZsWpkpIS+Pr6cv9hJ8Vkg26Jhja+FRUVwcnJyeg+dQfqjjoQ6XQ6VFRUQKPRwMHBocXvUwpq3ExORNQ8DZ1NUVJSAltb23r7DtpDia5Op0NVVZXRSkVVVZV8u42NDTw9PeHm5gYHB4cGVy2keB0QEIDevXsjLy8PJ0+ehLOzM7RarVnXI8V17j/sfJhskEWZMvsjOXfuHAYNGtRmViUs1UXKkEajkZeipcG+pqYGSqUS9vb2AICsrCz4+/vDx8fHKGlozt8JN5MTETXN1BJdAMjMzETfvn1hZ2fXJuJUQwzjjPS/UpxxcHCAg4MDnJ2d0b17d9jb20OpVCI/Px/V1dXo0aOHya+jVCrh6+sLHx8fFBYW4vTp00hNTUXfvn3h5ubW5N+PYUzj/sPOh8kGmc1SZ1MYDjxtRXNb3dbW1soJRUFBAbRaLS5fvgyVSgVHR0c4ODjA09MTvXv3RteuXaFQKOQgp1KpcOnSJRw5cgQ9e/aEn59fs6/F8H3UHcx54isRdSYtLdEFLBenLNGhsKamBmVlZSgvL0d6ejrUarU8oSTFGQ8PD/j5+cHW1vamX/6bS6FQoFu3bvDw8ICvry8uX76Mc+fOISAgQN472dDrmbqZ3MbGhqXAHQyTDWqUYVIhDQTSjIlh+ROAJgfrjkKv16O6utpo9qiyshJ6vR62trZynauzszOcnZ3h6+tr0vPa2tqiX79+CAoKwpUrV5CYmIja2lrU1NTIqx/NVXeT3rlz59CzZ0/Y29tzMCeidq+hEt3q6mpUVFTAxcVFvk9bKNE15Qu+1GHRMMYYxhlp3O7RowccHBzQpUuXZl+PJRIgJycnxMTEoLKyEpcuXcKFCxfQq1cv+Pr6mnw6uWGcEkLg0qVLcHNzg6urK+NUB8Fkg8wqfSovL0dZWRlcXV1b4Uob1lDL25bQ6XTyAF+3ztXOzs5oBknqrGEoKyurwUFWr9dBCD1sbBoODiqVSq6LPXjwIE6ePAkXFxcEBgbC0dGxRe/JcDN5jx49uJmciNoVc+JUVVUVcnNzb1raYwpLxxeJNHlnGGeqq6sBAPb29vLkVbdu3eTJIQAoKipCYWGhFWOwHoAGgK1J95b+fh0cHBAWFoba/2fv3YPbOM+70d/ifiXuAIkbAZCUKImyJdmylbZHSU4r2/WXccfx1zqO+zkZ23GaxjOZ1OnE09ZuPKdfnHwZf9M/3JykSV07nYmdyyTNqY+O69zcuA5FirLuN4oESPFOAuAFJO7Anj/UXS/ABbALLIAluL8ZjSTyxbsvFovneX/v8zy/J5vFzMwMhoeH0dPTA7/fD6VSybmYHADW19eh1+ul+sMOgkQ2dgnYCt+oP3x6UwiZ998sI84V5fUU8Xgc+XweS0tLJXmu3d3d0Gg0vN57+X3L5TYQi70GYB0Gw0MwGPZVfC2V7nTs2DHEYjFcvnwZSqUSwWAQZrO53rcL4NZzIHV8lSBBghghVIouZavFYM+oFNvV1VUkk0nE43Fks1nI5XLaz5hMJvT09ECr1XJasxDvi833kmQGq6v/imRyFXb7h6DR3M57DpVKhb6+PgQCAczNzeH06dOwWCwoFAqc113up6T6w50PiWx0GCoVvi0sLMDpdJaMZYaVxWCUm4HyegqKXJTnudpsNqjVt05y/H6/4OvIZKYBrAAwIpUahcGwrybRkslkcDgccDgcWFtbQyQSQS6XQzAYhN1ur7u+pFqRnmTMJUiQ0GywpejW8lN8Up9a7c9IkkQ6nd4mBlIoFKBUKqHX60GSJPR6PYLBIF23126UryGdXsTcXBQEoUc2ex6hUG2yUel9yOVy+P1++Hw+LC0tYXZ2FpcvX0YoFILRaOQ8r1RM3hmQyMYOBV/N75mZGfT09DR8XaGjEULNRZLkNjUOtnqKanmumUyGt5QfV6jVXqRSFhSLCWi1h3i/3mw24/Dhw9ja2sLU1BQmJibg9/sF+0ylYnIJEiQIDT6pT0L6KeraQoDp84rF4rYU22QyCZIkS1JsPR7PNinZxcVFZLNZ+lBLjJDJbCCILhQKG1AqqxMNrqDqSyKRCDweD8bHx0GSJILBIKxWq1RMvksgkQ0Rgy31iRlSpr54XArfhPoiCkk26lkTVU/BJBXr6+uQyWQwGo016ylaAbb7o1RaYLd/HsViFnK5geVV3KDX63HgwAFkMhlawSqbzdJRCS5rq3Tfy4vJV1ZWAAA2m00y5hIkSGCFUCm6zN83ikb9VD6fp30MpfxEHcJotVro9Xro9Xo4nU5aSrYToFYb0Nf3ELLZFAyG2im7fCXarVYr7HY7EokEpqamaAUrl8tVsY9JOcqLydfX15FKpdDd3S35KRFDIhsiQLmhpv6em5vbpoFNfdE6/UtVrT9FeT3FysoKNBoNXC5Xu5dNg92RqiCXqwSZn6lg9e6772JkZAQOhwO9vb1VT874dHxNJBIoFovo6uqSisklSNjlqJSiOzc3B5fLVWIXdkKKLjPFlnmARdWwUdFwlUqF3t7eiqfwXCG2+1DJF2i1Omi1Os7z8KnDoMYajUYcPHgQ6XQa09PTCIfD8Hq98Hg8UCgUnIgiNVcqlUI8HofNZpOKyUUMiWy0CGyFb7VSn+bm5uDxeNq46u0QMrJB5bmWG3y2egpmf4pmrmmnQaFQQK1W49ixY1hYWMCZM2dgMpkQCARYFaz4nERJxeQSJOw+8E3RXVhYoE+VxYLy1KdakuU6nQ4ulwt6vX5bim0ikajZs4IrOs1PNfp+NBoN9u7di1AohJmZGYyMjMDpdErF5B0IiWwIDDbNb8pQs4WUW5H6JCTqWVMlY7+1tYUbN27AYDDUrKfYSWiHQ5HJZPB4PHC73YhGoxUVrOohG0D1Ij3JmEuQsLMgZIoutclrN5gpthsbG1hfX8fp06cBcJMsl1Af+OwJKo1VKpUIhUIIBAKYn5/H9PQ0JiYmMDAwAJ2uepSlVjG5VH8oDkhko05UOv2Zn5+vmPrUKZuySo6FrZ6C0g1nM/YXLlzA0NDQjicXbGiXUSMIoqqCFV+ywbVIjyIdkjGXIEE8aEWKbqsjy7lcriS9tjzFVq/Xw2AwYGtrC7fffrsgflcMZEpoCPGehL4vMpkMXq8XS0tLsNlsuHTpElQqFYLBYMW+IrX8VLFYpJ8PqZi8fZDIRhVU0vxmK3wDbj3g8/Pzokt9EhIEQaBQKGB9fZ1TPUW1/hS7Of2pFWAqWEUiEUxMTCCbzaJYLHI61eNaTE7lPq+trcHhcEjGXIKEFqLdKbrNUiikpGSZtXuUlCxFKiql2GYyGaysrAhCNDrZljX63vgWiPOB3W6H1+vF2toawuEw8vk8AoHANtl3Pn4ql8shGo3CarXS0Q4JrYFENlC58K1QKGwbuxMK34RApf4UmUwGhUKBbkhUq55iJ6GZhrMSWnE9vV6PoaEhZDIZvPfeezh16hQ8Hg+8Xm9VBSs+HV8B4Nq1azCbzVKRngQJTYBYU3QbIRvFYhGpVIr2MalUCmNjY7SULEUqenp6oNfrOSnuUWsSG8S4JjGDul9sh2aU7DuVwsfVTxEEgfHxcRw5cgSFQkGqP2whdhXZYEt92traQiqVokN0tTqTtmPNzVwDl+I5Zj1FOp3G7Ows9u7d2/C1231vm4V2kBYuUKvV0Gg0uOuuuzA7O1tTwYrP+2BKWkpFehIk1A82P5VOp7G2tgabzVYyVgwpulzIRj6fZ02xJQiCjobr9Xqo1WocPnyYM6mohmb02WgUYork12PfG5mDD9jmZR6aUbLvbrebdySdJEn6NZKfah06jmxU0/ymCt8oEASBVCqFWCwGq9XaxlWzg1mk1yhIkkQikeBcT1EpzSaTyYiuqZ8YN/ZihkKhQCAQgN/vr6pgVe+zV62YvNMjghIkcAUzml4rRTebzWJ5eXlbd+1GIJRvKU+nLCcVuVyOjoTr9XqYzWa43W5oNJpt15+ZmRF0TRKqQ6h7LTSqfXZM2ffZ2VmEw2H6QFSj0XCam5mdIhWTtwYdRzZeffVVJJNJPProo/TPqhW+iUVJgw31GMxK/SmSySRmZmY411PsNIj1MxQzKilYhUIhmEymhjcjlYrJpSI9Cbsdb7/9Nt59910888wz9M+qpejK5XJBbVwjB1kkSSKVStF+JpFI4OLFiwAAlUpFH1w5HA4EAgGoVNx7C4mRJIjNTonp/jRrLUwlxEqgDs1kMhnW19dx9uxZGI1GBAIBGAyVG+eWP/dSMXlr0HFkA7jV5IWrrN1OJBuV6imq9acYGxvD/v37m7amds8lJuy091SuYBUOh5HL5aBSqSoqgPCdv7xIT2oSKGE3gyAIJJNJXn6KWewtxPWLxWLVDV2hUKDrKZg1FQBK6il0Oh1CoRC6uroEWZdQ0W6xReCFhJhsZqsjG2wwm80YGhpCLBbD1atXIZfLEQwGYbFYOM8h+anmouPIhkqlQjab5TyeMrpiBTMyUaueohMlZHcqWmWYhHaCzGK8c+fO4dq1a8jn8+ju7m44CsYs0tva2sL4+DgOHTokFZNL2HVQqVTIZDKcxwvtp5iHbMxoOFMIpFxd0OVyQavVbrMD8Xhc0Ai5lFrbGnCNbBWLReRyuYpzNAP19IMiCAJ2ux12ux3r6+uIRCIYHx9HIBCA0+nkXZ9CELeUN9977z3cddddUjF5g+g4stMbzKMAACAASURBVKFWq3kZcaFPjOpBpf4UyWQSkUgERqNRNM2IOv3ESMItULKSZrMZGxsbGB4e5qRgxRVMZyIV6UnYbajHTzViK0mS3HZodeHCBVqRh1mz5/V6eXXMFpIIiY20AJ0bgS8HFcna3Nykn5NUKkW//66uLoRCoW0pSq0qEOcz1mQy4dChQ0gmk5iamsLk5CR8Ph/cbndd62AWk0vNbOtDR5KNSiycDc0wJJW+KJXqKSr1p7h06RL27t3LqhTUTghpxDsRrXRMzVS+IkkSKpWKLsabmZmpqWDFZ27qNEoqJpew21CPn+KyoaekZJmkghkNp0iFWq3G4OBgiSBEvRA6FVmMaVRCQCy2jDrc3NjYQD6fx8rKCtLpdElTxK6uLvT09ECj0dC2OZPJ4OrVq1AoFHRdX7PQKNmgoNPpsH//fmSzWdy8eRPDw8PI5XLI5XKcMkHYismlZrb1oePIBt80qmZENtLpdIl2eK16ikoPqtChcyE2ptKXihsalRTkimaTDWpuhUKBYDCI3t5eVgUrvo69WpGeVEwuodPBN42qfENPybYzSQWlLqjVamk/Y7fbodVqt0XDY7GYYCezu6GObydK3zIzJqg/TFKRz+eh1Wrh8/lYlcGYayYIAjabDTabDWtra5icnKQV1JoFIcgGBZVKhf7+fgSDQbz77rsYHR2F1WpFIBCAVqvlPHd5MXk2m5X8FEd0HNloVXi6Un+Kra0tXL9+HXq9vuF6imYYcSHIhhhPsXYihHjvrSIbFJgKVisrK7SCVSAQ4LWOSuuWivQk7Aao1eqah2LU888kE+fOnUM2my2RkjWZTBWlZCtBrARBjAXiYrc5FPFk/qEyJqh9CJvc8M2bN6FSqaputoHtfspsNuPIkSNIJBIYGRnByMgIQqHQts7erQIfHyiXy6FSqfChD30IS0tLOH/+PHQ6HYLBIIxG47bxlUQUKvkpqf6wMnY92agVPahUTwGw96c4f/48hoaGBMlrF6MRFxJCfiHF9t6YoIzR5ubmtmgXSZLwer3w+XyCPDNCopoRJwgCTqcTTqeTVrDa2trCysoKJ6dTSwmHWaRH9YhZXV2l75NkzCXsZDD9FEmSrAdXhUIBSqWS9jEKhQL79++n+wA0ArH6FjH6KbGAaowYj8exvr6O8+fPI5PJlBBPi8XCu+amXlC1pENDQ3Rn72AwCJfL1VL7XM+BG0EQ6O7uhsvlwurqKsbHx0GSJILBIKxWKz1frbnL/VQ6ncb8/DxCoZDkp8ogrt2NAFCpVLxyYak0Kr71FJU2SkKmZYk1iiA5A3ZQpCKVSmFlZQXLy8s0qaD056loF5UrXSwWEY1GMTIyApfLhd7eXl5RsFZHNthgNptx2223YXR0FEtLS5iYmEBvb29VBSs+6yYIArlcDvF4HD09PVIxuYSmYWZmBo899hgWFxchk8nw1FNP4Qtf+ALi8TgefvhhTE1NIRAI4Ic//CGrrOZrr72Gv/u7vwMA/M3f/A0+9alP0b8jSRKXLl3CtWvXcPr0aYTDYRw9ehTPPfccnY6o0+ng8XhocsHEwsICr54V1SBWPyVkZENItNLn5fP5bZEKKppF+Q2NRoOBgYGqaditAtXZO51OIxKJIBwOw+/3w+12t8Q+N+IDCYKA1WqF1WpFIpFAJBLBjRs30NvbC5fLxdtPkSSJaDQKv98v1R+WoePIRrXIRqX+FFRTIj71FJUgRsNLzSXUPGJ8f0KA61qqRSqomiGqqZVer68YsaCMUeC/unnPzc1hdHQUdrud80ZADGSDGqtQKDA0NIRMJoPp6emqCla1Ihts88tkMjrtUer4KqEZUCgUeOmll+g0kTvuuAMnTpzAq6++it///d/Hs88+i6997Wv42te+hq9//eslr43H43jhhRcwNjYGgiBwxx134IEHHqBJCUEQ+Id/+Af4/X4cPHgQw8PD+MlPfsKp67HQELMdF5NPAJqXRlV+wFlOKihFwPK9SCwWw9raWsPCMUL7Do1Gg3379iGbzdL23+v1wuv1NlVBU6j3YTQacdtttyGdTmNqagrhcBgOh6Ou9GDKT0nF5B+gZWTj8ccfx5tvvgmn04lLly4BAB5++GFcv34dALC2tgaz2Yxz585te20gEIDRaKRZ4tjYWMXrUGTj0qVLsNlsNLmo1J9Cq9Xi/PnzOHLkiCDvU6xGXGwbe7GCaQjKySn1p1AosEYqqA31+Pg47HY7L7UOmUwGn88Hj8eDxcVFTE9P48qVKwgGgzVzapuFehVB1Gr1NgUrp9MJv99PO0i+DoJJTsqL9KSOrxKEQk9PD3p6egDc2nzs27cPc3Nz+NnPfoZ33nkHAPCpT30KH/nIR7aRjX//93/HiRMnYLVaAQAnTpzAW2+9hUceeYQe861vfQvArcaz3/zmN9tCNADx+pZO/O5SdTfxeByJRAJra2vI5XK0YAxFKqio9k6/ByqVCgMDAwgGg7QClNvtFkVPDi7QaDQYHBxELpfDxMQEotEoHe2oFVms5KeovQRBELu2/rBlZOPTn/40nn76aTz22GP0z37wgx/Q/37mmWeqbs5+/etfw263s/6uWCzihRdewNWrV3Ht2jXEYjH89V//NZ5//nl4PJ6W9qfYDeHpTiMtTFKRzWaRSCRYSUVPT0/VSAVzvnoNiUwmg9vtRiQSgdVqxblz52A0GhEMBlllKsUS2WCLVFRTsOK7brbxUjG5hGZiamoKZ8+exd13342lpSWahPT09GB5eXnb+Lm5Ofh8Pvr/Xq8Xc3NzrHPzVU0UGp3up4QE1zVRpIL5h0kqSJKETqdDf3+/YOlwjaDZ95mSyO3t7cXs7Cy2trY4b9r5oFk+UKlUwuPxIJfLQavVYmxsDGazGYFAADqdjvU1xWKR1U9Rf+/mYvKWkY3jx49jamqK9XckSeKHP/whfvWrX9U1t0wmw913341HH30UOp0OTzzxBH784x83sNr6IVbDK+RcYlwTF9SKVJDkrb4SXElFM8EsYItGo7h06RI0Gg1CoVCJaoZYyEa1sWwKVsVikVX9oxKqpV2xGXOqbouPQo8ECRQ2Nzfx0EMP4e///u/R1dXF6TVstqzSsyeXy9u6qRarbxEj2SgHmw/J5/N0Mb9er4fD4UAgECjZVEejUWxsbDS80Rby/rTCNsrlcppwUJt2i8WCYDAoSGSPr5/ig2KxCLlcDq/XC4/Hg5WVFVy8eBFqtRrBYHDbATmV7lsJzAMyKhUYuJUN0On1h6Ko2Xj33XfhcrkwMDDA+nuCIHDPPfeAIAh89rOfxVNPPbVtzP333w8AWF1dbeuJkVgNr1gL74RA+Zq4pj+VkwrqFLKZzYr4giAIOBwOOBwOxONxXL9+HTKZDH19ffQ6m2VohSIbFJgKVhMTE1hcXMTp06cRCARqKlhxXQtlzIvFIk6dOoVjx45JxeQSeCGXy+Ghhx7Co48+io9//OMAAJfLhYWFBfT09GBhYQFOp3Pb67xeL51qBQCzs7P4yEc+UvE6nUQ2xBglaQRM2eFEIoHNzU1MT09vIxUulwt6vb4uaftGIEY/XA2U/aY27UtLSzh79mzVqD3fuYUeWz6e6b9WV1cRDoeRz+dL/FctskGBSTqGh4dx7Nixji8mFwXZeP3110vyWsvx3nvvwe12Y3l5GSdOnMDg4CCOHz/OOrbd4WkxEgQh5xJTMWB5+tPGxkZD6U9CodFoQ6V7QqlmxONx3LhxAwDg8Xiatq5mGnFKdcfhcGBqaqqmglW9BeUEQUjF5BI4gyRJPPHEE9i3bx/+4i/+gv75Aw88gNdeew3PPvssXnvtNfzRH/3Rttfee++9+Ku/+iusrq4CAN5++228+OKLgq9PiGdXSIIgZAfxdke7y8U+9Ho9CIKg+1S0mlQ0E3zucz3P3NraGorF4jbVNq5Re67g66f4+hG2uS0WCywWC7a2tmjZX7/fD61Wy+teMYlMpzezbTvZyOfz+MlPfoIzZ85UHON2uwEATqcTDz74IEZHRyuSDb59NgBhT5jEmgsL7FyVj52U/tRsbGxs4Pr1s5DJFAgG92FmZgZra2uc+luIiWxQua0Gg4GWTaSKCdkUrOpZO0U2pGJyCVzx3nvv4V/+5V9w8OBBHDp0CADw1a9+Fc8++yz+5E/+BP/0T/8Ev9+PH/3oRwCAsbExfOtb38J3v/tdWK1WPPfcczh69CgA4Pnnn6eLxdnA99mjfIsQtYdiJQjNIhv1in0AQDgcbkv0ohVolv2Lx5dw8eJbAIro7z+Onp4gay0DW9Q+FArxulaz/VQ1ckLJ/lIKjJOTk1CpVHTtIFfshvrDtu/KfvGLX2BwcBBer5f191tbW3R+99bWFt5++208//zzFedTKBQoFArNWm5NiNXwCvWwNvPkqZPSnxpBNYO4tvY+fL4foliUoVj8LAYHB3H16lUsLS1hcnISwWAQTqeT9fXNJhv1RB4oaDSaEgWrU6dOweVy0QpWfCMb5eN3gzGX0Dh+7/d+r6J9++Uvf7ntZ3feeSe++93v0v9//PHH8fjjj3O6Fl87KubUJ6HmAhqPdmcyGTraffXqVbpBolqtpuXt+R5Mie2gTuzIZq8CmIZMBmSz50GSgao2lorar6+v0w1iY7FYSYO9SuArZNIMH0gpMJrNZkQiEYyMjMDhcKC3t5eXRHEnF5O3jGw88sgjeOeddxCNRuH1evHCCy/giSeewBtvvLEthWp+fh5PPvkkTp48iaWlJTz44IMAbkVBPvnJT+K+++5r1bJ5Q6yRDbHkwgIfkIpMJoPFxUXMzc2JIv1JKDTzPjscccRiBJTKHMzmdRQKoPtbpFIpRCIRTE5OIhAIbEtJaibZEMqIV1Kw0mg0vJ6DSuupVEyuUCh2vDGX0NkQ0reIObLBBUxSwfxDydtT6U+VGiQ2vqYkCGIOJNkNgH/qT6fD6XQhnS6iUCDh9d5ScePy2ZpMJhw+fBjvvvsu5ubmcOPGDYRCoar9LlpVs8EFVMrdwMAAFhcX8f7778NoNCIQCMBgMLDOX22u8mLynVx/2LJd3Ouvv87681dffXXbz9xuN06ePAkACIVCOH/+PK9r8d0wUB+oULmwYj19anXNRq1IRSaTgdFohNPppElFPj+KYvEU5PLjkMvdDa+3XWi0ZqPS67XaY/D5JgCoQZIHsbn5wVitVov9+/cjk8lgamoKkUgEfr8fHo+H3lyIKY2qWlpCuYLVtWvXoFAoYDabOUWwuERCyo350tISTCYT9Hr9jjTmEnYW6kmjEitBaNZcJEkinU7T9RSbm5t0zyyNRkP3zPJ4PNDr9SUpZqurq5yVxPiBRCz2GubmVuFy6eFy/TmA2ilWYjvI4GOz+T+rgwgG/xRAHiS5B/k8v32MXC7HbbfdhmQySR+gVarpa2caFdv8VPNZt9uNnp4exGIxXL16FXK5HMFgsKSGhauwCtNPxWIxqNVqmEymHVV/uLOOjDmi3vC02MiGGJ1LpfScetKfrl27BofDQTuEQmEN6+uvIZVSQae7Dqv1JQCdvemr9MxVfhbdIMkvASAAECDJxLaxarUae/fuLWmq5PF44HK5eK1NDCdGBHFLASSZTCKbzbIqgFSan6uToAz24uIilEol3el1t3d8ldB88NnMiPHwSai5KFKRSqWQyWSwtLRUQiooH+Lz+VrWM6t8fRSKxRwmJhJQq7WIRFKwWJJQqbil8Iolu4Av+K+bAEn2M15fqMuO6nQ6HDhwAOl0GtPT09sO0Ki1tbpAvBLY0nftdjvsdjvW19cRiUQwPj6OQCAAp9NZl5+KRqMwGo3QaDQ7qv6wI8kGX4jN8DZjLqBxQ0eRilwuh5mZGc6kohLK31+hoEA6rYZGk8TWlgMWCwEu3x8xpYgBwtzn6ig1TpWMjEqlQn9/PwKBAGZmZjA2NkafjnBJKxAD2WCONxgM2LNnDzY3N2sqWPFN66JeQ6VSFYvFXd/xVUJzoVKpkMvlOOd0C52iK1RtI99odyqVKlF/okiFVqtFNptFV1cXuru720Iq2LC9sFkJvf4wEokJaLV7oFBIaVS10OhhrkajYT1A83q9ovNTlcabTCYcOnQIyWQSU1NTmJyc5KUmSYHpp3ZS/WFHko16wtNCqnyI8fSJL5uvFqkoFAqQy+WC11SoVAYolX+O1dVrsNsP8/6SiwmNfuH5GM9aoOognE4nzp49i5GRETidzpqdXJt5YlRveBpAiYIVddpVrmDFd37qGtTpEfO7RxlzpVIpis2PhM4ApZzIlWwI7Q+ohmKNgm1d5aSCIhYkSUKr1dIHU3a7HTqdjv6uTk1NQafT1SWB2ioQBIHBwY8imbwbWq12x6Zcis1nckH5AdqpU6dAkiRn4sz3EKoRP1UJOp0O+/fvpyP1GxsbCIfD8Pl8nBTPqPdQqZhcrH6qI8kG3y+RGNOVhJ4L2H5f6k1/On36NC1H3AjYvvROZz+czn6W0c2HUKl0fK7H5+eVxnJds0wmg06nw6FDhzA/P4+xsTFYrVYEAoGKnVzbrfJRbX7qtKuvrw8zMzM0ifL7/bzJD3WN8hA49Xernw0JnQ+1Ws2rJ5QYD7IoOenNzU1EIhE6UgGgJP2pnFRUg9g2wWz3SqFQNKkepLVoVs1GOfgeXNUCdYDm9/sxPDyM8+fP013bq5H3Zkc2+PhBlUqFQCCAra0tKBQKjI6Owmazobe3F1qttuqayr9H5QdkYkRHkg2Af/GT2Iw4NZcQ6yLJWz0G1tfX6cZ3YlF/EtuXQwwbymaFhamxMpmM7uS6uLiIs2fPoqurC8FgEDqdrulrFno8m4IVX/UqoPop1k4qxJOwM8C3Aa3QkQ0+vqVYLG6LVKRSKXou4FbPAafT2dBpv9g3TBLqQ7N8mlwuh0ajwdDQEOLxOM6cOQOz2YxgMMi6Ya/H7/CN2NcTOfH7/fD5fFhaWsL58+eh0+kQDAZZI3w71U91JNmgcmGrpYgwIbSkYLuIS7VIBSWdZrPZdqyk7E5Bq0/B6zXiBEGgp6cH3d3dWFlZwYULF6DX6xEMBlll+vjMXQuN9s1gA1PBanJyErOzszh37hyCwaBgClYSJAgFvg1oW+FbisUiHZ0oJxXM9CcmqYjH44jH43A6nYKsS2zYjQSIqQKWSNwSIQmFQk2QEGa/dj2EgFJ/Wl5exvnz51l9WT1+ik9KEkmSvMYzfQ5BfNBZPR6PY3x8HAAQCARK+o3sVD/VkbtNyohzJRtijEZQc7Gtq570p4mJCbpxjliwG404Fwgdcq41L6X45HA4EIvFcOXKFbo2pxlrrmc8nxMjgiBgMplAkiTsdjtnBaudasQl7Ey0M7JBkiSt+sQkFQRB0HKyBoMBLpeLVr1pxbokn1AbQj4DAOh+JZubmyX9SjQaDQwGAzQaDfL5PEZGRuByudDb28u7m3qzUoOp8cwNu8vlgtPpLPFloVAIXV1dLVGj4nNvKqVE2Ww22Gw2JBIJRCIR3LhxA4FAAC6Xa8f6qY4kG3yNuBhzYSnkcjnE43FeNRWtWJeE5qJZp0DVxjJl+lZXV3HmzBmcOXMGfX19MJvNNdfRTCNebzjbYrHAYrFwVrDaiUZcws5EK2o2CoUCUqkU3Z+CIhXUPFqtFkajEd3d3dBqtXVFFsRa8yg2CE3K+CKXy5WQimg0iuXlZeh0OhgMhor9SvL5PB3ZmJ2dxejoKE06uEJIP8U2N9vrmb7sxo0bAFC1OSAb6ikQF/IQzWg04rbbbkMqlcL09DQmJydBkmTV50iM0UGgQ8kGXyPezlxYoHKkIpPJ0JESIWoqxGjEhViT2L5czZe+bf5Yi8UCnU6H/v5+OjIQCoVKwrnlc7db5aPaeC4KVoD4niUJnQu+aVTV/FShUChJfdra2kI6naZFIfR6Pbq6utDT0wONRoP19XUsLy8jEAg0/D46PbIhxjVVQ6FQ2BapyGazUCgUNKlwuVz0hpxrtgNVW+D1ejE3N4fR0VFkMhlks9maWSR8/IPQilEWiwV33HEHNjY2cO3aNSSTSZqI1LpOKw7FuIzXarUYHBxELpfD8PAwzp49i56eHvj9fs4ZPO1Gx5INseXCAvzTn1ZXV5FIJBAKhQRZl9gg5JrE5gwafW/NOAWqp5bEZDLh8OHD2NzcRDgcxsTEBILB4LYTonaoUdUaz2bEyxWsTp06BZfLBb/fX3NOMX6HJOxc1BPZyOfzSCQSVUmFyWSC2+2GRqOp+MyKNXUYEJ8tFyuo+homqUin05DL5fReglI3YtuQrqys1HVdmUwGn88Ht9uNd999F6dPn6aVoITa+PK1tVzGd3V1IRQKYXFxEUtLS5icnKRTkyq9vtl+ii85USqV0Gq1OHjwIKLRKMbGxmA2mxEIBOoWd2kVOpJsUAXiXCF0nQUlB8hXUrYcUnh6d6JZ+a2NFK4bDAbcdtttSCaTiEQimJycRDAYpA11K9SohFQFYSpYzc/P48yZM0ilUkgmk6I32hI6A9XSffP5/LZIxebmJmQyGcxmM/R6Pcxmc01SUQmdrsBIQSixjnb6TpIk6VS4ra0txONxpFIpRKNRei9hMpng8XigVqublq5UDplMBpVKhWPHjtFS6na7nZV0NNNP8fWXKpUKe/bsQSqVwtTUFMLhMHp7e9HT09Nwc9h6hE/4fgZUUz9KUXJlZQUXL16EWq2mDwLFiI4kG/VENuoxJmyRivX1dWSzWWxsbDQsKdvp4WmgM0+xqqm8ME+hUqkUFAoF+vv7S0K6zZa+bQQ6nQ4HDhxAOp1GJBKhDXUz1KXKxzfD6DNlgH/zm9+UGG0uClYSOg+PP/443nzzTTidTly6dAkA8PDDD+P69esAgLW1NZjNZpw7d27bawOBAIxGI+RyORQKBcbGxipeR61WY21tDZcuXYLNZitJn5XL5XSkwmKxwOv1YmVlBXK5XJAeR2I9yBIyeijUXK2KaDIVoChySfUsoZTADAYDlEolstmsIBkPja6XID6QUne73VhYWMDY2BhsNhuCwSBNOpolekKhHh+o1Wqxb98+ZDIZTE9PY3h4GD6fDx6Ph65ZaUVBeSP9oChxF6fTidXVVUQiEZjNZlGmVklkA7VPU/ikP9lsNqyvr6O/v/HGdGIlG2J0LGIBSd7qZrq6ukorvVDSkVQhHnUKJZPJkMvlMDs7i3A4jFAoBLvd3tS1CXXPNRoN9u3bh2w2i6mpKczNzcFqtcLtdnOS/mtFJISPBCFBEFAqlbj77ruxurqKyclJFAoFWsFKKhzfPfj0pz+Np59+Go899hj9sx/84Af0v5955pmqRPTXv/51xe/xlStX8Morr+DKlSs4e/YszGYzPvrRj+Lpp5+GxWKBz+eDSqVifdblcrlooxFinAtovQw5F5DkrW7P1MET9TelAMVMgWJrhLiyssIrc6PaOoQEJT3e09ODxcVFumlsMBjkva5mfWZs5EGtVmPPnj0IBoO4efMmhoeH4fF44PP5RFeLSL2GbU2UGApfpbBWoSPJBt80KuqUpx5J2XKsrq6K0vCKNbKxU8F8VsolA7PZLHQ6Hcxmc9UmV7lcDjqdDgcPHkQymcTk5CTC4TA8Hg+vdbQyslEOKiQtl8uxsbGBU6dOwe12w+fzVY3kNauQjjm+3vfKpmAVCATQ29sruo2LBOFx/PhxTE1Nsf6OJEn88Ic/xK9+9au65jYYDLjvvvvwzDPP4NVXX0VPTw8eeughTq8Va7qSWP2UGA7YKAWora0txGIxJBIJxOPxkv2Ex+OBTqdrS9+rRuxZJX/C7HlBNVnV6/Wc72E8Hsfc3By8Xq8gvVu4rBm4VQ/R19eHQCCAmZkZjIyM0EX0QszPhnoVEGupSooRHUk2akU2yklFNBpFLpfD1NRUwx21xSqjK8ZQ904hQEyHQRGLfD4PlUpFq3t4vV7odDrI5XJcvHgRPp+PtYNpJTBJx/Xr1xGPx7GyslJTMYOPcZuensbU1BTMZjN6eno4r40LZDIZuru74XA4MDs7i5GREXR3d8Pv97OetLQiF7bRaARTwWplZUW0RlxC6/Duu+/C5XJhYGCA9fcEQeCee+4BQRD47Gc/i6eeeqrk936/nxYj0Gg0bRMyEXMaldh8ApfvPaUAxfQRlAIUlf5ksVigVquxd+/eFqy6Nai16aVIRzgcxtTUFK5evYpgMAiNRsP6mkKhgAsXziCdnse5c6fx0Y/eI+hJPRe/I5fLEQgE4Pf7cfr0aVy9ehWxWAyBQKDiupnzt+pQbKeho8kG10iFyWSCVquF1+tt+NpiNrztPuVpFoT6slJRiYWFhZL8aabDcLlc0Ov1VQ1gIxEEnU6HgYEBXL9+HYuLiyXpVdWUZWohlUphZeV92GxLuH6dRHf3g4IaOcpoKhQK2lBTmuxsSiViKyiv9kxrNBpOalUSOh+vv/46HnnkkYq/f++99+B2u7G8vIwTJ05gcHAQx48fZx3bij4blSBWPyXWuShQtXdMUkGpgVE+wmaz0ZKkTJu1urqKdDot6HoaRaORDa7XsNvtSKVSsFgsOHv2LEwmE0Kh0LbNO0GQ0GguIJtdh0oVh0z2B3Wvr9Kaub5nSuFtcHAQyWQSZ8+eRVdXF4LBYEURkWb7qZ2MlpINtsK7r3zlK/jOd75DV9B/9atfxf3337/ttW+99Ra+8IUvoFAo4Mknn8Szzz5b8vv19XWMjY3h8uXL+PnPf45//dd/xSuvvIJnn322ZqRibm5OsPcoVmPZ6eyZz30qFotIpVIlDiOVSiGfz0OtVkOhUNTMn242lErltvQqNtLB9X0rlWns2/cWCoUE1Op5EMTHq47n+9yVG1mmJjtVNGixWOhTrWbntjZbilfC7kM+n8dPfvITnDlzpuIYqnjb6XTiwQcfxOjoaFWysba2xvn6uyGNChDHYRalAEWlP2UyGczPzwMorb2rVw1MDBDiPvNJ4aWi3y6XC0tLSzTpCAaDdBaATJbHXXctN8cPBgAAIABJREFUYXpahlAoCrm80PAay9fBN6Iul8vR09OD7u5uWvlJq9UiGAzCaDRuG9/qCDwTYn4OW0o22ArvAOCLX/wivvSlL1V8XaFQwOc//3n8/Oc/h9frxdGjR/HAAw9g//799JirV6/i5MmTOHDgAO68807cfvvtePTRRzmtS0jDu1tOn4Sap5mOhSRJWoKYWVcBfKDuweyeOzs7C4VCIXiKUT3rpsBW08EkHVyNp0IBOBxmbG1pYLebwOW2C7FZp4oG3W437WCMRiPdlZYrdkLalYTOxi9+8QsMDg5WjIBTNVtGoxFbW1t4++238fzzz1ecr52Rjd2QRsVlLjYfkUwmUSwWodVqYTAY6HTZQCCwa20Em+3lKznLnIsiHcvLyzh37hzdA+NWfePHYbe/Ca32v4MkhZUhbySiTik/ORwOxONxXLt2DQqFAqFQiBaMqJfM7Aa0lGxUK7yrhtHRUfT399NSb5/4xCfws5/9rIRsHDt2DMeOHQNwS5qQj1GgmiUJATEbXiH1y8UGSgGK6TQKhQLUajVdV1FJ3UNocDU41T7b8tdXIh3cN+EWpFKPIp8/B5KsXZDaaGSjHEwHE41GsbCwgEuXLqGvrw8Gg6Hh+dnGC0k2xHxiJEFYPPLII3jnnXcQjUbh9Xrxwgsv4IknnsAbb7yxLYVqfn4eTz75JE6ePImlpSU8+OCDAG5FQT75yU/ivvvuq3gdITuI84WY5xIK5etiE/Qo9xFWq5WuvaMwPz8vHUawoFFxEoIg4HK54HQ6sbKygvPnz8NgMMDpPIClJTuczoNNWXOjUrYEQcBms8Fms2FtbY1WLgyFQigUCk1Xo9qpEEXNxssvv4zvfe97uPPOO/HSSy/BYrGU/H5ubg4+n4/+v9frxcjISMX56lGj2g3RCDGEp8vBd01UIR5TNjCZTNIysgaDoe6+Jq0GQRC8T4zKScfq6iqsVisnw5/N7kMi4YXL5as6jlpDM4wmQRBwOBzQ6XTwer24evXqttOhaq/lCimyIaFevP7666w/f/XVV7f9zO124+TJkwCAUCiE8+fPc75OJ/kpMa0rn8/T9XaTk5N0iqxSqaRJhdvt5qwAJTbfKWTtZasOUWodrFERg5WVFdy4cQOFQqEpDVb5RhJq+QWz2YwjR44gkUggEolgbW0NsVgMbreb072tJ514p6Ltu7HPfe5zeO6550AQBJ577jk888wzeOWVV0rGsN3gah+kRqPBxsYG5zUIaUzE7BDE9qBW+wwrFeJRza6oQrze3l7E43Fks9mOKeLl4gQo0hGJRLC4uIjR0dGaheTNlMnlm+bEdjpULBbR19e37bChHjQ77UqChEbRKZGN1qdk5UEQi8jnzUgmyZJIBdUQkYqW2mw2UfceqBetjLRW+jz4+ohaYynSoVQqMTk5iYsXL0Kn0yEUCkGv1/NacyU0S5jEaDTitttuw/DwMOLxOG7evIlAIIDu7u6a+5zdUlvYdrLhcrnof3/mM5/Bxz72sW1jvF4vZmZm6P/Pzs5W7aJaTwdxMRIEIY04wIUV50AQUZCkHUB14yzUQ08V4jEdBtU1leqguxML8Vq1TqVSCbfbDYfDUbWQHOD3mbXSCDJPhyYnJ3Hjxg2EQiHYbLa652x2QbkECY2i3WpUYgSb/2QePG1tbUEu/wG2tmagVhuQy/0pdDozq6DHhQsXYDKZBCEaQhVTi+3Ar1E06/BKp9Nh3759iEajuHTpErRaLfr6+homHc1WQZTJZNi3bx/y+TympqYQDofR29sLt9vN6o92UwS+7WRjYWGBLsj96U9/iqGhoW1jjh49ihs3biASicDj8eCNN97A97///YpzqtVqXuFpsZ4YtXauIoB/RD5/FXL5PgCfAyDcQ02S27umxuNxEAQBg8FAh7cdDkfFJniVIDbH2ehnVs+JUa1C8nrnbdaa2WA0GnHo0CFsbW0hHA5jYmICwWCwrsZOrejkKkFCI+BLNsS6WRXK/lJS9clkElNTU9jc3EQqlQLwwcFTV1cXwuEsUqkQFIoU7rrLCaWyu+qcjUJs/kUoNGqz+RaI8yUmVMqt3W5HLBajSUcoFOJU59foOoD6yYBGo8Hg4CCy2Symp6dLupIz07h2U21hS8kGW+HdO++8g3PnzoEgCAQCAXz7298GUFp4p1Ao8PLLL+Pee+9FoVDA448/jgMHDlS8jkqlaqvKh5CnT606ySoWk1hbG8XmphEGwwjM5k9BJmP/QtdSQaJyZpnEgmqCR2mRezweaDQa6HS6kuhWvRCjE24EfAwzE9VIBx+0M7yr1+tx8OBBpFIpRCIRTE5OIpfLNTUys5ONuISdCZVK1bYIfDtBkYryurtisQiFQoFCoQCdTlfx4OnGjf2QycIoFDwoFs0VryNWctZJaIZdLLfzBHGrT4fNZkM8HseVK1egVqs5i4uUz91ogTif8SqVCgMDAwgGg5iZmcGpU6dKmt3W42d36qFYS8kGW+HdE088wTqWWXgHAPfffz9r/w02dEoubCvnKhY1WFw8BLv9EhYWDqGrS4Naz3SxWNzWNZWZM0tFKoLBIGsoe3V1tSOdgRCRDT5j2YwVG+kwmUxQq9UNzVttvNBGUKvVYv/+/Uin0/jtb3+L4eFh+P3+iiHpRtazk8PTEnYm2plG1Sowm+qWqwRS/a98Ph+tALWxsYH5+fmq0cyDB/8Qi4uL/9WRu3pHZ6HQiX6qUTQrsgGwkxhmnV8sFsPVq1ehVCpRKHDvxdHsNCpqneVQKBQIBoPw+/2Ym5ujm93mcrldk+7b9jSqZoBvGpVYazZaOZdCoYDL9RgWFhbQ3d1dotJRXleRSqUwNjZGd9g0GAywWCzwer1Qq9Wcvww79UvDBY2+Nz6RjWrGikk6Lly4gEwmA4PBULWQnJpXLIVrGo0GGo0Gd955Jx2S9nq98Hq9FZVFdlMurISdiU5JowJuff/X19dLSEUul4NSqaSj2VxUArm8R4PBgP7+/pprEup+daqfEiKNqhk1G1w+M4p0RKNRjI2N4be//S2GhobQ1dVV9XXtLsiWy+UlzW6pWuQ9e/bQjQ2rYSf7qY4kG+0MTwv5YLaauFitVhgMBmxubuLmzZsl4W2qwZFer4darcbhw4c7TuFDLGjGhkKn08Hj8SCTyWBxcbFqITlzDVyNbStOXJgh6Zs3b2J4eBhutxt+v3/bBkZSo5IgdrQz3bdeUJKk5QpQqVQKCwsLMBgMcDgcCAQCUKlUvOcX62FdM0jL1tYWLl9+FyqVBgcO/K4o/SlJ3mp6qFQqodE0FkVqBjFRq9VYWlpCIpFALBaD1+tFX19fRdIhVKSiUVDNbldWVmAymXDu3DkYjUYEg8GqRfA72U91JNloZxqVkGim4WUWa1OnUczwtsFgKAlvMzE7OyvIF7BZ9z2ZTOLmzVNQq23o7T3Y8i9nKwvE+Y5Vq9Xo7++vWkgOAKlUCuPj41hcXMTdd98Nq9Uq2Dr4ovx+Un05ent7MTs7i5GRETidTvT29tIbHCmyIUHsELOfKhaLSKVSJZGKVCpVMZo9NjaGwcHBhq8rRl/cLLt28+bPsbl5DoWCDMvLWng8dzflOlzBrLWkPnOq1jKbzcLlciEQCNCkSOhoBXMs13k3NxMgiCiMRjU0mn4EAgGMj49DJpOhv79/G+kQm3QsSZK074pGo7h8+TJUKhX6+vpgNBq3jd/JtYUdSzbalUYlJIR4cKgmeLFYDGtrazh37hyy2SwUCgUdqeju7ubVBE9sDqF8PUtL/wK7/d+Ry6mxtvYVWK17Oc0j5Htq1Zeer8GnDFUt9apodAkWy/uw2TSIRIywWn9PsHXwRaW55XI5ent74fP5MDc3h9OnT8NutyMQCIgqDUyCBDaIwU+RJIl0Ol1CKijpcSqabTQa0d3dDa1W2/TviFgjG0KBuR6zeR1zcwTk8jyMxmTL1kARyVQqhdnZWWSzWbqHFbUncLlc0Ov1UCqVyOVyIAgCsVgMo6Oj6O7uRm9vL69rNusAzWqNIhC4imzWggMH0rBY7sOdd96JtbU13LhxAwRBoK+vj24YKzY7T/lkSnnL4XAgHo9jfHwcwK1Gocy+Uzv5UKwjyQbfNCoxGiW+oAwIU+EjnU5DJpNBr9fTcmx79+6FUqkU1RdOaJhMN5HJqKFUpqHRxHm9Vgz3pVkGkW3eyqRjCkbje1AoFFCpXADERzYoyGQy+Hw+eDweLC4u4syZM0ilUkin05zyYIGdfWIkYWeiHj9VL8oVoKg/p0+fhlarpYu17XY7dDpdWzc0YvPFzdofuN33o6vr+1AodFCr7xJ8fgD0Z079SSaTIEkSWq0W+Xyebo5bq4cV08bOzMxgZGQENpuN8zqaRTZkMjkOHVqA1ZpEsegDxcXNZjPuuOMOrK+vY3JyEiRJor+/X3RqTmzpvlarFVarFRsbG9v6TkkF4iJDJ6t8VDuJorTIjUYjenp6SgzI2toalpeX68qjLYeQhXfNMOIWy6dQKHwbJOmBUnlI8PnFBKF6Z5STDmABfr8eZrMZBGFFra8HHyPO9zPnOrdMJoPb7UZPTw9+85vf4Pz589Dr9Zw60O7kEyMJOxN806i4olaKrF6vh9frRSKRwNGjR0W1eRFrzWMzQJIO6PVfEGQuKoOBediYy+VK5ObL06IvX74Mi8XC6UCG+lxkMhl6e3vh9XoxMTGBaDSKqampbf0jGgV3ifM+zM09ArPZjGLx+Lbfm0wmHDlyhCYdGxsbMJvNdKSj3ajmd7q6unD48GFsbm4iEolgYmKioUa37YZENiBeo5TNZpHP5zEzM0M7jkKhAI1GQxsQridRQoenxQyC2AOF4qW2XX8nq3xQpGNx0YqZmSSWl/Mwm38Hdnv16/A5calHlpYvoVIqlbj77rsRi8Vq5sFS15DIhoRWguopUS/y+XxJsTa1weSaIiuTyUSXVtLpaVSNgirY3traQiQSoRsfUhkMVKSCWb/WDMjlcrjdbqRSKRSLRZw6dQo+nw9er5fVjjbPpwHJ5D4Ui3dUHUeRjjNnzmBubg4LCwvo6+srSVFqB7j4QoPBQPedunLlCjY2NtDV1YXu7u4d5bM6kmzsNJUPqjCLTTYwn8/TX2ydTse5rqIcQhteMal8dCKaRTa4q3zoIJP9ATyewH+lVy3UVK/ifhrVGqUogrjVDMputyMej+P69et0Dq/ZXNoMrFgsilINRkLngs/3han8dP78ebqfERWpqGeDSRCE6Ei2GAlCu9ZERaiYZJKynQqFAg6HAy6Xq+5aGiGIplwuRygUgs/no2XJe3t7t/VCEkMxOXCL4O/duxckSWJiYgITExPo7+9vG+ng4wu1Wi08Hg8MBgMSiQQikQid2iZkVKlZ6EiyIVaVj2KxuO0kiuk02GQDT58+Dbfb3fC1xXhiJKYTtXagUCiwkke+97aZxKRWIXkjc3OFEE6RyoNdX19HOBxGPp9HKBSC1WoV5aZLwu4DUwGK8g9MBSi9Xg+5XI69e/fy6mdUCVRkQygI8T0Vo58SCpXuDXNfQP1hirgYDAa43W76819aWkI6na7a+LAVYN5bpVKJ/v5++P1+TE1N4dSpUwgEAujp6aHfd7sP0JjjDQYDDh8+jEQigcnJSUxMTKCvr6+m6mKtuet5Dd+DN5VKhWAwiFAohJs3b+LUqVNwu93w+Xx1H0a3AuJdWQOoJ41KSJAkiWQyuU02EAB9EmUymeDxeARxGlwgNsPbySi/z+V1NlShHgBaos/hcJQ8B+0+BSofW4t08I1stEuW1mQy0U4mHA5jYmICoVAIhUJBKhCXQOPxxx/Hm2++CafTiUuXLgEAvvKVr+A73/kOHA4HAOCrX/0q7r///m2vfeutt/CFL3wBhUIBTz75JJ599tmS3xeLRdy8eROXLl1CLpfDH//xH+MP//APcfDgQbpY22AwsJ5aLy8vN9zvgEIzNvZiIxtCQYg1kSSJfD6PaDS6rd6S2hdYLBb4fD6oVCrR2xy2z1ulUmHPnj3o7e1FJBLB1NQUQqEQr8yRZpCNYrGItbU1ZLPZEjtvNBpx6NAhbG5ubiMdfO9/IxH4eq6hVCrR19dXIgE/ODgoyOF0M9CRZIOSa2s2yhU+qE0ktSGjnIbT6YRWq+2Yk1MhHUKnRUjy+Tzy+TwWFhZKijM1Gg19SuVwOKDT6ZDP51EoFHDz5k1EIhH09fXBZrO1jEDUM7YS6eBbs9FuWVqj0Yjbb78dyWQS4XAYKysr8Hg8cDqdonqeJLQHn/70p/H000/jscceK/n5F7/4RXzpS1+q+LpCoYDPf/7z+PnPfw6v14ujR4/igQcewP79++kxf/Znf4bl5WUMDQ2BJEl8+ctfxtDQkGAkgiuEjGyIzScIOVc99qC8ZwUVrSBJkvYF7Vb+4npv6rmHarUag4ODSKfTtH11OBycbHkzfNrZs2cRDoeRTCZZu3UbDAbcfvvtNOmYnJxEX19fU1O66gEboVEoFAgEAvD7/U29dqPoSLJRb/5iNeRyuW11FVTDG6oYj1J7OHv2LA4cOCAqciHG8LRYHRTX61FSw9QfSqs8l8uBJEm4XC709fVVDW1qNBoMDQ1ha2sLExMTiEQivE5V2kVMKNIRjUZx7tw5pNNpRKPRijUd9a4DaG7xtk6nw9DQEC5fvozNzU0657inp0dU318JrcXx48cxNTXF+3Wjo6Po7+9HKBQCAHziE5/Az372sxKy8Y//+I/0v0+ePIk777yT8/xC2jkqfVCoucTmE1oRzWdKzlP7AraeFX19fUgmk5ifn0dfX19T18QHXOwwQRB11+lpNBrs378fExMTiMViGBkZQV9fX1U/0QyysbQ0DY0mhkQii2Qyua1mjwJFOra2tjA5OYlkMoloNMpJBaoVqbjFYrHifkImk0lpVDsJhUJhW10FlT/JDG9TDW/YIMaUJTGuaaeAKSVJOZVisVjS9IopNTw2NgaPx8PL8Oj1etx+++1IJBJ0eoXD4djWAZUN7SImJEni/ff/A3L5OFZXdZid9VSs6WCCr1FuRSREJpMhEAhAr9djamoKw8PDVdVVJOxOvPzyy/je976HO++8Ey+99NK2wtK5uTn4fD76/16vFyMjI4JdX6h0JeZcQqDaXARxAzJZGMXinSDJ6r0ZxBpVLBaLtAoUW88Kg8EAk8kEt9tds2dFJ4Hr+1QqlfB4PLBarZiYmEA4HEZ/fz9rrw6+Mupc1nD48Pt4//0k+vrWYbU+UHO8Xq/HgQMHkEgksLCwgMnJyZp+rRWRDbH1CeGDXUs2mMV4VCHe6OgoLSHXSP5k6zb2SRDEJkjSAaD6+sR4YiQ2AkTJCi4tLdEOJZPJVCzUqzYP1+elfJzRaEQgEMDq6irGx8chl8sxMDAAg8HQ8LXYrlcJ3MhGEXv3vgadbgWBgBoez6dRLCqrFpLXs+Z6ajzqzbdVq9XYu3cvQqEQra7i8XgQDAabKiUpQfz43Oc+h+eeew4EQeC5557DM888g1deeaVkDJs9EzLSR0UjhFCfaUVkgyBiiEZfQSwmg893CVrtM3XNI+SaaqG8Z8Xq6ipNNJhZDFSz3J2KRu9zPYdXOp0Ot912GzY3N0tIB5O0NyOy4fVuwOdbwNpaDAoFN7lpkiShUChK0oYp0lFeYwm0LrKxU5+5jicb1AayvK4CAF2MZzQaoVarBWtw1Bop3XUoFP8bwBpI8n4UCvdWHS22jX27wdZZNZPJQK1Ww+FwwGw2w+v1tq1QT6vVYv/+/VhdXcWVK1eg0WjQ398PnU5XMq7VNRtMyGRFeL0ENjbsMJvXoNEUAdRWr2p2pKKe059yI06pqwQCAczMzCCbzUpkY5fD5XLR//7MZz6Dj33sY9vGeL1ezMzM0P+fnZ2tWrCpUCiQz+c5yy4LWWfRipqNZDKJixe1kMlIxOPAsWP1zSPkmihQqbDMaEV5zwq73Q6TyYSNjQ309/cLsi4xQYiCfi4ot+EGgwGHDh1CIpEoIR1Usz2hfVqh8Axksn/D7KwMAwMBTnMzfQKzVjEcDtN+jUk62lWzwYSYI2otJxtsKh9/+Zd/iX/7t3+jG2/98z//M2tOXSAQgNFohFwuh0KhwNjYWMnvY7EYLl68iEuXLmFjYwO/8zu/g0984hP48Ic/TJ9I2Gw21qKsqakpwT4ooTf2bA9xoTCHtbVxJJNqmEy/gV7fOrKxk4gLpVHPJBZUZ9XyU6rZ2VmoVCp0d3e3dc3Me2uxWHD06FHEYjFcuHABRqMRfX19dRWTCk9MlFAonoHd/v/g8uVu2GwfnE5VU69qthpVPac/lV6jUCikqIYEAMDCwgJ6enoAAD/96U8xNDS0bczRo0dx48YNRCIReDwevPHGG/j+979fcU5KOZEP2RBbnUW1uQjCAWAI2ewadLoQp3maAWbPCurgkZkKS9VWsPWsWF1d7TghEyEgxD0xGo04fPgw1tfXMTExAYIgoNVqOdtb7j6tF8Xi01hbO9WQD6Rq/FKpFMLhMB3pcDqdUmSjBlpONthUPk6cOIEXX3wRCoUCX/7yl/Hiiy/i61//Ouvrf/3rX8Nut7P+7uWXX0Y8HqclBN98801euslizoUtX9fGhg3xuAddXTGEw/tx8KAgl+MMsaVRUfKyTGeSTCbp0C2XzqpCF19yWTOX1xPEreZ0NpsNy8vLOHv2LMxmM0KhUFsjG7fGHUehcBzx+G9Zf89GOpgnxEKvG2gsjUqCBAB45JFH8M477yAajcLr9eKFF17AO++8g3PnzoEgCAQCAXz7298GAMzPz+PJJ5/EyZMnoVAo8PLLL+Pee+9FoVDA448/jgMHDlS8jkqlQiaTgV6v57QuIW1mKyIbGo0GR478ATY3N1nz84UGswHi6uoq4vE4JicnS1JhPR5PzVTYZkEoCV2h0Mh+R0h/YjKZcMcdd2B1dRUXL17E2toaurq6KqYP17MGoLJvmJ2dRTQaRX9/P33NanNrtVocOHAAqVQKkUgE4XCYPojgino+x53sp1pONthUPu655x7638eOHcOPf/zjuub+27/9W/rf3/zmN3kRDSEL71px+mQ02jA5+QlMT29g7969dc9T75raCWZO7ebmJlZXV5HL5ZBIJErkZeuRG273ewMqGyGCIOByueB0OrGwsIAzZ86AJEnOzZ2aRUy4gEk6rl69io2NDaysrHBSr2pFZGMnF95JEB6vv/76tp898cQTrGPdbjdOnjxJ///+++9n7b/BBr49oYT2La2IknR1dXESuqDAxU8x06PZelYYDAZotVpYrVZ0d3eLwq4LCSHeTyuzE7j6E4vFAo/HA5Ikq6YP852XifLx6+vr+M///E8UCgUsLi7ivvvuA8DNj1Dpzul0GteuXUMsFsPi4iJcLpegtVrM1+xUPyW6mo1XXnkFDz/8MOvvCILAPffcA4Ig8NnPfhZPPfVUxXnkcjkKhQLn0wuxphlVmkuhUOCOO+7gXCwo1vdXK6eWGa2gcmqZHdedTie6urqQTCZpucmdgnpVLQiCgNvtRnd3N06fPo0rV67A4/Ggt7e3qvRdO8hGNptFPp+nnYVOp0MgEMD8/DwWFxc5qVe1qmaj0zYkEsSPehrQitGONzO1lq1nBdW7iPIDbIdLkUgECoVCkLqEnZI23Eo0U5yEShemUuP1ej36+vq29ccQwk+RZAHATchkWwAG6ppbo9EgEAhAJpNhdXUV4XAYwWCwKtHdbX5KVGTjf/7P/wmFQoFHH32U9ffvvfce3G43lpeXceLECQwODuL48eOsY6nwdCVGXA7qxEgolY/WSAoSbSFTQoH5pcnn89tyaguFQkkRf6Wc2mg02uqlC4ZGDAdVyLhnzx5sbGxgZGQEbrcbfr+/4nPRSrKRSKzjypWvQS5fhdX6FEKhI/TcKpUKe/furVpITqGValQSJLQSlJ/iCjFHNhqdi1KIzOfzmJyc3NazgpKdr9W7qNNRKBQwPz+PXC6HQCDQlnQwvqjnoIuZPryysoJz587BZDIhFArRNYtC+CmLZQ4f/vAZrKyYMTCwCOBP65q7WCxCqVRi3759SKfTiEQiiEQiCAQC6OnpEUS9SioQFwCvvfYa3nzzTfzyl7+seMMoVQ+n04kHH3wQo6OjFcmGWq3mRTaEzoUVyogLlVcrllMskiTpPiZLS0tIJpNYWloqcSg9PT3Q6/VtcSiFQqHtG0++hlkul6O3txderxfT09M4deoUa4+IVkc2kslfYmDg/wNBAOvrAPCtbXNXKySvV+WjGUZcgoRmoJ2RjXYpW5EkSasBMiVmgVtpKcViESaTCR6PB2q1um47JNS9EttB3cLCPKanz2FpSQOARF9ffSpZjdp4vv6ED8prFp1OJxwOB5aWluquWay8Nid8vjx8vmkUi/8nqK1bI6qJGo0G+/btQyaTQSQSwdTUFAKBALq7u2k/s9sOxURBNt566y18/etfx3/8x39UJAeUeoTRaMTW1hbefvttPP/88xXnVKvVyOVynNew01Q+2jUPn7nYHApJftAESafTQafToa+vTxSMfG1tDZHIVRgMZnzoQ/8H56LNdoJp4ORyOUKhEHw+H90jIhAIwO128/78hcgNtVgcSKUUKBZzsNk+KJ5jM5hCqlcJIX1bDjE8nxI6D+2u2Wi2Tyivr9va2qqoBkh9/0ZHRyuKwAixpp0OleoqVKqbIAg5ZDIzgPZJ8jZLnKTStbq7u+FyueiaRa1Wy/lAuTJcyOdfBkEsgCQ/UJgrFovY3NzEhQsX4Pf7K3YdZ44v9yFqtRqDg4PIZDKYmprC1NQUent70dPT0xQ/JWa0nGywqXy8+OKLyGQyOHHiBIBbReLf+ta3SlQ+lpaW8OCDDwK4lXLzyU9+ki7kYQPf8LQYCQI1l1DOpVmGt1gs0tEK6g8l50hFK7xeL3Q6XUnId3l5mVaMEgMymVP43d/9EQoFHdbXndDrj7RlHY2e1lA9IvyjjgokAAAgAElEQVR+PyKRCIaHhxEKhXidpAjhHFSq34Na/XUAUZDkf+M0NxvpMBqNNVVJmKg3r1Usz6GE3QOVStURBeIAkEqlSvoXlfescDgcCAaDnGV+hYCYyIZQ9sXvL0Cnm4darYbP1z6bxffwis/YWgc/VM3itWvXMD8/D5lMhkAg0MCz1QOSLFWTSqfTuHLlClQqFcbHx/HQQw9V/Qyr+TWqWWw2m0UkEsGpU6fqEi6QyAYP1KvyEQqFcP78ec7XodKouELo1CexnT4JpV6RzWaRTqexuLiIubm5bQogfLuui8kZBAKTKBZl0Ok2oVYvtnUtQpACqi4inU4jHA4jFovBZrNBr9cLqpRReSwBkvx91vG1DCaTdJw/fx7RaBRarbYp6lUSJLQLfP2UGNKo2HpWUM3xLBYLXVvBVl/HBUJtymvNk8/nIZfLa45rlSAKV2QyH4bTuQqr1YZ8/g8aWkv5ey8UCkgmk0gkEiUHh9Rmufx0v1kF4lwgk8lgsVigVquhUqkwOjqK7u7umkIpXFEsFqBQLEEuT6JYrNyY84Pxtf0O5ZOz2SzGx8cRi8Xoxp9cfNZO9m2CkI1sNguFQiGqm8A3japVMoDtmouvAWeGv6m/c7kcHfbX6/V0tKLez71VToUrSPIjcDimoNHYkMsdQbt4EN9ToFrvX6PRYP/+/Ugmk4jFYpifn0d/f39V3ftm1nfcMuK/xtbWj6HV/ilkssr65Dqdjm6yyEe9SkhbJEU8OgNi9VNiTaNiNkSl/mZGrPV6Pd2zIhKJwGq18pKbr4RW+M5I5Le4du23MJmsuOuu/wGFonXRlkZBkjqsrf13dHX5G5qnWCxidXUVqVQKm5ubJQeHRqORjkRRh4yTk5OQyWQYGBiAwWAQ3E/VO1Ymk8Hn88HtdmN2dpaTUAoXaLUr+MhH3sPyci/27LkCgvhzTmvhApVKhd7eXhQKBaRSKQwPD8Pv98Pj8VSdY9dK3xYKBfzyl7/E6Ogo1Go17rrrLtx99911dTcWGvWofIiNIAg9FxsoeVlmXm0qlQJBEHS0wm63l4QoJyYm6BOscmSzSWxsXIZOF4RO13jeLZ/30ShSqf2IRv8XnE43gPZ1jG6WpKBMJsPevXtRLBYxMTGBcDiMgYEB1lzUZpKNfP4CDIb/jVyuiFTqfdjt26OdTBSLRej1egSDwaaoV0nobIjdT7Vb+ra8ZwXTB1ANUa1WK3w+HzSaS1Aq/18UCodQKPw3AITg62oFpqd/DZ0ujfX1DSQSl2GxHKo6fie9t3KUpzknEglks1lkMhmsrq7CZDLBZrNVPDjMZrMwGAx0073Lly9Dp9PBZDI1jUDUM5YplHLz5k2cOnUKXq8XPp+vLn9QKJjR3U1iYOA6isXbUSjcesavX7+OpaUlHDx4sIRc803fLRaLUCgUGBgYQCAQwNTUFIaHh1nFXbheQ8wHYw2RjVdffRVf/OIXMTQ0hGw2i2984xt47LHH8JWvfIVXjnUzsPPSqHIgiCWQpBPMja7QIdz19XVWvXKqtsLpdNYMf1db0/Lyc9BqL2F93Q6F4v+GSlW5mZM4HZQO7SQafFHP6ZJer8ftt9+ORCKBGzdugCRJDAwMlDTeaibZSCbTMBhuvSaZTPGan4t61U7WIpcgPCQ/9QGYEuPLy8tIp9OYm5ur2bOCwsbGDzExYYLL9Rt4PHeDJB0AxNk0ttqaensNuH49DZOpCKOxuyXraQWYKW6JRALJZBIkSdKkkUpzVqvVeP/99xEKhWrWOTDfv8ViwV133YWVlRVcu3YNKpWKTpsWCo36HrlcjmAwWCKUQkUN+KBQ0GJ+/v9CX58MJLkfABCPxzE6egokWUAsFsPHP/7xutYNlPoppVJJkw5qzZUUJXfqQVpdZIO6qd/4xjfwox/9CPfeey8A4Nq1a3jwwQcRCATwuc99rq36z/WkUbUvGkFCqfw2ZLJxFIsB5HJfACCve10kSdKhUepPOp1GKpXC4uIiDAYDuru7BZeXJckitNpx5HIWqNVxFApRANw7x+42VPpcWyVRazQaceTIEaytrWF8fBwKhQL9/f10iJxvKJsr1OpDmJj4H9BoZuFyVW7MSYGvetVONsgShMNO8VN8IxtcyAbVs4LpAzKZTInEuNls/v/ZO/PwqMos/39u7ZVU9n3fSAh7SNhBGsXgCqhIu6C0Om3Tm6OO3W79U9seR9yXth3pcWlwdFq7dWwUbGRpEJQtCRAQCUJICEtWsta+3d8f6XutSipJVaoCgeH7PD6Sqrfee+52znvec873yIW1/mLfvjQE4QxVVXFERSmRCPuG58ZR38jJWUZ6eiUqVRqC0L+zMRwhimKvRoc2mw2VSiXf34yMjF6kLD0x2JqaxMREnE4nTU1NlJWVkZSURHZ2dp/ribMR2egJlUpFXl4emZmZctTA4XD4HfkWRRG3OxpR/J7tS622olAcxel0odMleY0fTD+onuMlchdPpyM9PZ309PTzop9KfwhqpdnS0sL48eOBbo+6sLCQdevWsWjRIm666SYSEhJCIuRgcK6bJQWmeG2YzXtpaVERG3sArdYMRPg1l69iPYleVsq9TElJQafTUV5ezsiRI4M7uX5kEgQFavW/oFB8CFyBTpcV9LEuon+EwimIjo5m0qRJtLa2yiFyhULh97yBRhIUCgXp6bf32GmyoFS+gCA04HQ+AHzfDT5Q9qrIyMgQ0CFexIWC4W6nAq3Z8NS9/fWsCAsLIzw8vM+eFY2NjVgsA0cWPaHXT6W19QRabTQaTYT8+XArooaBZNKhVk8NwTxDj54Nb9va2nC73XR1dWEwGELSk2QwEASBqKgoxo8fz8mTJ+W0pczMzF52ZqhSg/2ZV4oaZGVl8dVXX7Fz506y+2i0N9DcUVH1XHXVHs6cSSA7+yiieC8HDhygra2N+Ph4oqKi/JJbmr8v50RylLKysuTeWWlpaf0+h8M9AhdUZCM1NZXjx4+TkpKCWq3G6XSSlZVFU1MTRqPxnCrxc90sKRDHxeVSc+hQIamp3/DddyMpKNCg1XrL5Zl3KRmVnjsZUrHe2fCA+7pWkZHXAdf5Nce5VuLDFcFS3/Y3b3+IjY1lypQptLS08M0332C1WomIiBgwtz1QeX2NF4StOJ1/w+VSoFa/jii+KH/nz46Rp9Oxf/9+mpubCQsL84u9aqDrMtyV+EX4xvlgp3Q6HUaj0a+xLpcLm82G1WqVF55Op1PuWWEwGMjMzPSbtGMwdYoTJhTT3p6LwWDwSr8JNY1uKDDcZPJHD0n3VmKD6tlBPSUlhYiICNxuNxkZGUHJE6ztlX6vUCjIzMyU37MdO3aQlZVFWlqa1zkPRWRDqnvwB2q1Gp1OR0lJCceOHeP48ePk5OSQlJTk83i+7I4oFpKUFE9ycjUu1y84ebKe8vKdgJP6+tg+m0z3JftA5+npdNTV1WEymaitrSUjI+O8i3QMytmQbsA999xDfX09drsdjUaDQqHAarUiCEJAC/2hwHBm+fB1bIfjMvbsmUBkZCSiKNLa2orRaKS1tZXW1laUSqWcdxkdHU16errf9LKhxsXF19DjXBXeCYJAQkICycnJqFQq9uzZQ2xsLLm5uX3m5Q4mV7WnEm9oUKDRmBEEN11dSjztaCDzS+xVoigGxF518Zm+8HC+2KnW1lavzzzTYKWNJYvFItO0qtXqkPSsGMxmj3TsnhgKgpUL8Z2UrpHE9uhJMduzfjI5Odln/aTZbA7pWiWYcZ6fSwvjjIwMub9TXl4eiYmJAdcWBgJ/U5ekZ0qr1TJq1CiZEr6mpoa8vDwSEhK8zsf3M2jA6VwJWIEw1OpKlMoanE4wGNqxWCZTW1tLamrqgHUsgaRdqVQqcnNzqa+vRxRFdu7cSWpqKhkZGSFNhR9KBCXl3Xff7fW3QqGgqamJ++67j6Sk7ny2c6U0znUalT9zSZzWRqMRg8Egy1tVVSUrnMjISBITE0PSUTVUGG4h8/PNKEmUkh0dHSQlJfVSFsOBUhAgLi6OESNGUF9fT3l5OQkJCWT7aJwUisiG0ZjLgQO3o9OZCQubS0ZGEyrVQ4ARtfouFAr/i/tEUUSr1V5kr7oIYHB26q677mLNmjUkJibyzTffAPDrX/+azz77DI1GQ15eHn/60598MrllZ2cTERGBUqlEpVJRXl7ep2xWq5Xa2lpef/11LrvsMkwmE263G71eL9sAz54VjY2NWK1WYmJigr4u5xNr4mAQKpmCmceT6au1tZX29nZ2794tNzuMiIggKSmJvLy882bRKKGva+LZ3+no0aPU1tb61c/Ec95AxgYir6eelyjhLRYL1dXVstMRFxc3gMOroJtIBhITW7jyyn20t0cRFQWff56MKLpJTk7hmmuu8fHbvuXxB4IgkJOTQ2Zmpsy4JdH8ns1mmYNBSJ7ujo4O2tvb6erqQqFQcOutt6L9Zx7QuVoInss0qp5z+UMtKO1U9fSGTSbTebeY9hehPK/hZugkSDU10g6WlE8dHh6OVqtl3759PncphyqNKlCFL6WhJCcnc+rUKZ+NkwJVmr7GZ2dn095+BWazmeLiYhSKTxCEA4iiitjYNSgUs/ye39N58Je96qKzceEjEDt1xx138Mtf/pKlS5fKn5WWlrJ8+XJUKhUPPfQQy5cv59lnn/V5rM2bN/e5QXTq1CnuuusuGhoaEEVRfv/9SYM9l+m+/WEo7Of5Zvc8+5JIOt/pdKLVajEYDHLUYvz48Wf13I4ePcqpU6cYOXKk3LMoEAyWyESn0zF27FhMJhNlZWV8++23jBo1asC6hkDTqAKNbPSEXq+X5ZRsxIgRI/yaWxSnkpqaTVpaDfv334wo1qBWmzlzpoODB7MJCwsjOzu7zzStwT4HEuNWZmYmJ06cYNeuXaSlpYWkJneoEJSzYTQa2bRpE2vXruXAgQO0trai1WpJTk5m1KhRTJkyhcsvv1zePTqb0Ol0ARW/hUrxulwuOe9SCoV7KpyBqAV74kLeMbqQ4Jn6YLPZ2L9/P1arFZVKRUREhMwOEh4eLt93u93OiBEjaGhoYPfu3fIOxXCIbPR0CDwbJ3kqt4yMjKAo/yQolUpKSko8xhTgdAqAna6uPNLS/hulshGX6ydA/1GOQNmrLjobFzYGY6dmz55NbW2t1zzz5s2T/z1t2jQ++uijQcmTmJjIu+++S1JSEp9//jlffvklP/vZz/z67XB2EIabXEMZgbfb7V4pUJ4N8QwGA3FxcWRlZcmbh06nk8bGRgwGAydOnAhKFpfLBUBdXd2AYy0WC2VlZSiVcPr0KWbMmCnr3qioKE6dOjXgHJ4bT55wOp0AVFdXDzhHUlISSqWS06dPc/r0adRqdZ82Q6vVUl9f75dNcTqddHZ2ypt4A52HwWDoV97w8HD0ej2NjY2yDRz4/H4FgEJhIjVVR3NzF0qlwJYtmxEEgQkTikhMTOxlYwK5fhKio6N9jk9KSsLhcHDo0CG/5woGOp2O9PT0gKIpg3Y2KioqePDBBzl58iTz5s3j3nvvJSUlBZfLxYkTJ/jqq6944403WL9+PU899VTQxUyBQqPR0N7e7vf4QJWltLjs2QxPqVSiUChQKBQkJiaek7zaocZwS6M623C5XHR2drJ7925aW1tJSkrCYDDIqQ9KpZKCggK/2EE8F/ES64ROpyMiIqLf30kYSpYPX1AqlWRnZ3s1Tgq0wNYfmQ8fjmL//h+iVDpIS3OQl/ce3bq6BZfr9UHP78vp6FnIeBEXDobKTr3zzjvcdNNNPr8TBIF58+YhCALLli3jJz/xpndWq9WyYzMcmvp5ww2YkNgQh49c52YeiUJYSgmSNpM8i/KzsrIGLMpvaWkhOjqa8PDwoJnyHA4Hoij61dvCajVx5Egldrud2NhIRo0aJX9nMpkICwvzq3Ddl7MhPbf+yGGxWNBqtSgUCpxOJzabDYVCIX/mCbPZjE6n82sDSKJz9icFTVqz+Xv9rVYrTqdTlnOggmyr1cy4cZEIgpN16/ZjNtfjcDiprKwkOjqaG264wcuuB3L9JJhMJsIlvmkfOBubZqIocubMGU6ePElOTo7fvwvY2ZB2AVeuXMmtt97Kv/zLv/gc96Mf/QiAJ598ks7OzkAPEzQGUyAueZo94XA4vJwKk8mEy+XqM6+2paWFzs5On/m8gSKUO1nDDefDAq/nDpbJZPrns2InJmYN+fktnDxZypQpd8m/aWlpCbg7sVKpJDc3l/T0dPbs2cPhw4flYu1A6fn6G+svBkqNkgrWMjIyqKqqoqmpidOnTw9IJwj+hb4tFgudnd3dWePi2hAEBeBEEJpRq2fhdhfjcr2ArwaMgbJXHT58mLa2Npqbm33WdJwPz+lFeGMo7dR//Md/oFKpWLJkic/vv/76a1JTU2lqaqK0tJTCwsI+WWqGV/NZJ2r1f6JQHMHpnIPLtcjvuUK9KXYuNqH6opHXaDS4XC65Id5giFm6F/uxZ52QQKdTc9VVEzlzxkxKSvDrkcHC836qVCpUKhUOhwOz2YxKpTortL2BPlNSMblCoZDf0f6dDgUuVzZKJcyaBRqNgurqelwukebmBlau/BPTp8+gpKREPtdAax2HAwRBIC4ujubm5oB+F7CzIRnx1157DUBm+OgJl8uFUqnkiSeeCPQQIcFgmvr1zLk0mUxyKozBYCA8PJyUlJQBm+H5VrxGBKEVUUxDatjnr1zD5SGTMBxlChYSX73dbqezsxOj0Sg/25JDGR8fL6e/2e0V2O3lOJ0CCQkbgbsGPIY/0Gg0xMbGEh4eTmNjI7W1teTn5/dbEDpUkQ1/xqrVatLT0wHo7OyktraW3NzcPukE/Z179OjRMuuKSqXEaByLWt2MVvsWIKBQbAEexO2+FlG83Ou3gaRFhYWFMWLECI4ePeo3e9VFDH8MlZ1atWoVa9asYdOmTX0+H6mpqUB3utT111/P7t27+3U2gumzEQx66nFBOENNzQlOn85gxIidxMaeG2djqCMbnimv0kZSfzTyUhQ0Li4uaHnOPjRERqYTGWlEFGPPwfH7hlqtRq1Wy5u5arVafkeHYgNtsJCYQKUUeUEQfEZeumVRAErCw7O57DI1qalRbNx4EIfDiUpl5Ysv1nHw4DcsWLDQr6jScMVg5A6qz8Z7771HXV0d06dPJyEhgbCwMCIiIoiMjJQL785VPvRAbFQ9GyG1t7fjcrlkZqhgGuX0VnJGNJrnEIQ2XK6ZOJ03BzHXhYNzdW5Op7MX7aDUy0SiHBxoB0urTcRgSMLtNiMIYwnAr/ULGo2GcePG0dXVxZEjR6ipqaGgoACDweA1bqiuX6AphSqVisLCQqxWK9XV1dTW1pKXl+dz0e6Ps6HRaJgxYwYAW7du5a9/bcbpdHLjjeOJjd2OIDSjUKxHoViHy3U7bvcyIF6eP9BOrmq1mrFjx/qs6biI8xOhtlPr1q3j2Wef5csvv+wzFUNik4qIiMBkMrF+/Xoef/zxPuccTBrVUNVGmM3hHD6cjl7fwr59I7n0Uv+jpqF2NkI1j8vlor293Sti4cn25Y+dP18XhBJEMQ7w7SgNh3OTnA673S7XXgwXggBPGZRKJeHh4TidTiwWS59pYN3QI4ojGDkymrS0OD7+eCenTnWn9Z84cZIVK95g8uQpAfXlGC7XZLAYlBcgKRWTycSf//xnFi9ezBVXXMGSJUv42c9+xgMPPMC3334L+H6Y77rrLhITExk7dqz8WWtrK6WlpeTn51NaWkpbW5vPY69atYr8/Hzy8/NZtWpVnzJKO0aSU9HQ0MDRo0fZt28fu3fv5uDBg5w5cwaVSkV6ejp5eXmkpKQwduxYsrOziY+PR6fTDerm+toxMptP0tjowOHYG9RcwwHDTab+dtCtVistLS3U1NRw4MABdu/eTWVlJQ0NDQiCQEpKChMnTmTy5MkkJiaSkJBAXFzcgE6mKGbgcDyPKP4Gh+O+kJ6Pp1KJiIiguLiYnJwcDh48KDfb8zU21AiE5UMaq9PpGDNmDOPHj5cL33v2EQh0A6Kjo0PeUdq+/Sqczndxu4sBF4JwGqVyBWr1DxCEtYAYMMuHL/aqcePG9Sn/RZwfCMZO3XLLLUyfPp3Dhw+Tnp7O22+/zS9/+Uu6urooLS2lqKiIn/70pwCcPn2aq6++Gujuyj1r1iwmTJjAlClTuOaaa7jyyiv7lHE4pVGp1Tq02mKMxhlEREwIOGJ6LiMbUrSiublZ1vVHjhyhoaGBxsZGWdcXFRUxZcoUxo0bR05OTlB2/nzD008/zfjx45k4cSIlJSUyJfOrr74qF7gD5OXlUVRURElJCSUlJdx33/f27f7772fr1q39Hqeqqori4mJKSkqoqKjgjTfe8Es+jUZDeHg4oihiNpv7zEzZsmUL8+fP73eu5cuXy/+22+3MmTMHp9MZkrQllUpFeHg4arUai8WCxWKR36Pe88djMIzmllt+QFFRujyv0+lix44dvPTSi0yePJmWlhYAZs3yn3XRE3/7299kXTZcEVRTv2XLlrFs2TIAjh07xtdff80f//hHVq9ezbx58xg9erTPxZAvWsFnnnmGuXPn8vDDD/PMM8/wzDPP9KIVbG1t5cknn6S8vBxBECgpKWHBggVyiondbmfTpk0cOHCAjRs3cvjwYdavX8+bb75JZGQksbGxZGZm+gynS0VXoUBPJW40xnDyZDJRUSeprZ1KUZH/cw1FLmywijWUYe5Qwe12e4XFjUYjDodDZgGT+Mx9NUkarEyimI/LlR8K8QeUIyYmhilTptDc3MyePXuIi4sjNzd3SI4NgUVMfC3upUW70Wjk6NGjMp1gdHR0wM9gREQEnZ2dOBwOCgpGUV+vJybmJcLC/hOF4jMEoQVoR62+Cbe7GJXqQRQK/wvXBmKv6quW6yKGN4KxU3/+8597zddX3Udqaiqff/45ALm5uVRWVvot42DSfYdqUa9SqZg8eQomk4nIyMig5gqlXD0hNcTzpJjt2RAvKSlJjl4HqyeH2+baYLBjxw7Wrl1LWVkZWq2WlpYWmUDn97//PUuWLPGK1m3cuFGO6krn3trayq5du3j55Zflcb70+OrVq1mwYAG//e1vqa2t5Y033pBrowaCVIiu0+lwOp3Y7Xa0Wu2Aaes9sXz5ch555BGg24m57LLL+PDDD7nxxhv9ksMfSLUnUqRDFMU+nhM9Gs0orroqktTUvaxdewC3u3ucw+Fk/vz5fPzxxyxbtoyvvvpqULKsXr2aa6+91msDfyA4nc6z2tslZEfKzc0lNzeXSy+9lL/+9a+kpXXTU/p6EHzRCq5evZotW7YA3UV7c+bM6eVsfPHFF5SWlhIb2517WFpayrp167jllluA7kXDli1bmDBhAj/60Y/4xz/+wSuvvOKX/EMZnlYqNZw6dTm1tY6A8z6HQq7htIszGCXuWcjX1dUlL0QdDgcRERG9aAfPN/R1TQRBkKMvUs8Lu92O2+0ekCkjUISCvxzAYDBQVFREZ2cnR44ckVMbAnkGtVotN910E6Io8sUXX9DY2Eh4eDiLFj2PVjsJpfL/IQjNgBuFYjcTJtyIwzEVUVwH6IM617CwsJBf24s4dwjETp0NnOvmsz11jVarlVPLgp0rWLmkOrqeFLNSQzzP/lS+GB89d+vPSzQ3Q20tZGdDgIx/PdHQ0EB8fLx8b6X6w9dee43Tp09z+eWXExcXx6ZNm/qc46OPPuKKK66Q/3766af5/PPPsVqtTJ8+nRUrVvD3v/+dV199FaVSybZt20hMTKS6upoZM2ZQWlrK888/zwsvvMBf//pXbDYb1113neyUXHPNNcyZM4ft27fzySefkJ2djdvtxmazsWbNGh555BESEhKYOHGiLENXVxcPPvgge/bsQRAEHnvsMcrLy7FYLBQXFzN69Gjee+89Fi5cyKOPPurT2YiMjORf//VfWbt2LXq9nk8++YSkpCTq6ur42c9+xpkzZ0hISODtt98mMzOTO++8k8jISCoqKmhoaOCZZ57hxhtvxGQyYbFY5IL3NWvW8PTTT2O324mLi+O///u/KSq6lLAwJe+/vxul8vvld1NTE263m+joaDo7O9myZQsvvvgin332GQD33HMPxcXF3HzzzTzyyCN89tlnqFQqSktLuf766/nss8/YunUr//Ef/8HHH38MwC9+8Quam5sJCwvjzTffpLCwkDvuuIPY2Fj27t1LcXExL774YlDPVSAIytkwm810dXWh1+vR6XRoNBrS09M5cOAALS0tlJSU+L0QamxsJCUlBYCUlBSampp6jTl16pQXNWF6eroXT7ROp5MdlL1797J+/Xq/zyWUhXc959LpdEyePBmj0TgoZ2O4sXyE0gHqD1IalGfEwrNgX+pdYbfbaW1tJT9/aKIMZxsDOYSCIJCenk5KSgpbt25l586dZGVlhZzCNZTF5JGRkZSUlNDe3s6ePXuoqqqisLCwXxo/z/lVKhWiKNLQ0IBWq8VkMmEymVGrb8ftnoVafTOCsB/ofr7V6l1ADHb7r4F/73f+gRyr4eScX0TgCKWdCjXOZfPZ4dSzoyc5y8GDB3G73Wi1WsLDdXR0rMNk6mL06CuIj590VmQ6lxA++ADh7rtBrQaHA/HNNxFv9r/WsydKS0t56qmnGDVqFHPnzmXx4sVMmjSJe+65h1deeYVPP/0Ui8Uip4xefvnl8vtw2223cf/997N9+3YWLfqeNGDZsmX85je/Qa1Ws3TpUtasWcP8+fNZtmwZBoOBBx54gNraWg4ePMj27dvR6/WsX7+eI0eOsHPnTkRRZOHChWzdupXMzEwOHz7M22+/zXPPPSdHWRQKBYIgcO+99/LZZ5+Rl5fHnXfeKcvw7LPPEhUVJUcT29raWLRoEa+//jp79uyRx40dO1ZOGwMoLi6WvzeZTEydOpWnnnqKhx56iLfeeovf/OY3/Nu//Ru33XYbd955J++88w733gVUhLkAACAASURBVHsvn3zyCQD19fVs3bqVqqoqrrvuOm688UYEQUCv18uRt0mTJvH111+jUCh46623ZEfr9de/oDtolAh8vybz51ltbW2VU6YEQaC9vZ3o6Gjmz5/Ptddey+LFiwGYO3cuK1asID8/n127dvHzn/+cf/zjHwB89913bNy48azru0E5GxKDxxtvvMEf//hHSktLSUtLkxcPBw8eHJJOhr5uRt8FvOcuF9bXYjwiIsLv3gk95xpuhXehgue59Rcal65dSkqKz/zavup7LnQolUq0Wi2TJ0/m2LFj7Nixg7y8PBITE4O+14HUPQQSLYuOjiYqKorU1FS++eYbmQ1Krx84AiEIAtOmTWPv3r3Exsaybt06Ro4cycSJE3E4tqNQ/BqV6j+9fqPRPI/dHpyzcRHnJ86VnQoE585OuVCp9hIefhSYwCDLN2UEYqekOkpJz/dsiKfVasnNzZWp41tbyzh8uAW1WuDQoXVccsnkkMvk7zwWi4WWlhbi4uKC7pfRJ5qbEe6+G8FiAakx8d13I86dO+gIh8FgYPfu3Wzbto0tW7Zw66238sQTT8g9YLZt24bVakWr1aJWq32mUdXX13v1VNq6dSsvv/yy7KSMGTOmz1oKaY4NGzawYcMGuYGr0WjkyJEjZGZmkpWVxbRp0zCZTF72pKqqipycHMaPH4/L5WLx4sWsXLkSt9vN5s2bef/99+WxfbE2KpVKNBoNRqMRvV7v5YhoNBquvfZaoNsJ2bhxIwC7du2SG3fefvvtPPzww/JvFi5ciEKhYPTo0TQ2NsrnKAiCXPD+3Xffcfvtt9PU1ITD4SA7O/uf1/prPvroI7Kz7bz22qecOWNk8uRMvxb/UVFR6HQ67r77bq6++mpZbk8YjUa2b98uOx6Al45ZvHjxOdlYGZSzIQl60003kZSURGVlJRs2bGDFihWcPHmSSy65hFtvvdVr7EBISkqivr6elJQU6uvrSUxM7DUmPT1dTrUCOHnyJHPmzPE5n0ajGTa5sMN1Lperk66uxxCE06jVvyIsbOpZkUkyNi0tLbS1tVFWVoYgCH6Fxs832O122tvbiYmJ8ft8Ak11U6vVjBw5Um48VVtbS0FBQb90uf4gEGcjkMW6KIrExsaSnJxMc3Mz+/btIyoqitzc3AH7k4wdO5bCwkJWrlyJSqWioqKCgoICwsPDcbtfwm6fj0Zzld+yDEb+izg/MBR2KtQIlI0qVM6GUrkDUXyPpKQzKBQ5uN3Tg5rPV2aA2+3GbDZ7MUH1bIiXmZlJeHi41/vX1dXllUuu1yehVgs4HCIpKcHptGAgiiJbt67FZGpAp4tj3rzFQ5PzXlvbHdGQHA3o/ru2Nqh0KqVSyZw5c5gzZw7jxo3jT3/6k+xs2GwmNBoRp7PvCJ9er5fJSaxWK/fddx9ff/01ubm5PPnkk17EJX1BFEUeeughuYZKQm1tbb9RbskWSRtsgiB4FWb7A5vN5tO+eHYzVyqVfdboedpDz1RD6bn/3e9+x4YNGwDYs2cPv/rVr7jvvvu48sor2bRpE88++6w8VhAEbLYsfvnLX5KUlEFVlXfnb5VK5XVuVqtVjvDv3LmTTZs28eGHH/Kf//mfsnMkQUrH2rdvn8/z8CebYCgQlIVNT0/ntttu4/nnn2fz5s3U1dXx5ptvsnDhwoDThRYsWCCzS61atYqFCxf2GnPFFVewfv162traaGtrY/369V45hJ44dztGDtTqMsLCDiGldASDoXQ2urq2I4oHsNvNGI19M3v5gj8yiaKIyWSisbGR6upqKisrZSaw1tZW1Go14eHhlJSUMGrUKCIiIkhISAhoYT6c4XK52LhxLbt3f8jmzRuGPKSv0+kYO3YsY8aMoba2lj179tDV1TWouYYqsiHNLYXHExMTmTZtGrGxsXIzw4EWX0qlksjISBwOBzabjS1btng0ZLuUbdvW4naHI4pK7PaVfskz3KJ+FxE6hNJOhRrnalOso6ODzZvj2bEjm46O9qDnk3oQnDhxgkOHDlFeXk5FRQW1tbVYrVZiYmIYNWoUU6ZMYeLEieTn55OSkkJEREQvR7/nu6jXZzJr1p1Mm3Ylo0f/2G+ZQh3ZcLvtWK0H0GpPY7cfwukM/rr5RHY2vbjUHY7uzweJw4cPc+TIEfnvffv2ySnpEREGJkxIIiUllksuycdqtci61Ww2YzabsVqtFBYWcvToUQDZsYiPj8doNMp1Aj0RERHhZYPmzZvHypUrMRqNQHdqfM+U+Z73rLCwkJqaGqqrqwH44IMP5M3JuXPn8oc//EFejEtZDlL/DglnzpwhPj4+oHXFtGnT+PDDDwF4//33mTlzZr/jH3/8cSoqKuSoSUdHB2lpaWg0Gv76178C3SlbM2bMkKMxX3yxhba2dnouxbOysjh06BA2m42Ojg45BcpoNNLR0cHVV1/Nyy+/LDsUntc5MjKSnJwc+ZiiKAZEWjFUCPl2nsTY8dRTTwHdSqgnfNEKPvzww2zYsIH8/Hw2bNggh6zKy8v58Y+7FUxsbCyPPfYYkydPZvLkyTz++ONysXhPBOpshEoxKZUbCQt7j+Tkj1Ao9gc931AWrisUOTidepRKG3b7uIDm6Qmn00lHRwenTp2iqqqK8vJyysrKqKmpwWKxEBUVRWFhIZMnT2bixImMGDFCfvldLheHDr2Gw/ErDhx4O+D7MFwXijabkaKiFcyd+w75+f+Fy+Ufq1GwRfwGg4GJEyeSk5PDoUOHOHDgABbPXbIQyxDoYr3n3IIgkJyczPTp0zEYDJSVlXHkyJF+d5gWLFhAQUEBCoWCkydPsnv3bg95wrDbm3E4TMDAec4X06j+78EfO3U2EOgmV6g2xRobc3E4sjCbU2hoyPL7dxItaVNTE8eOHWP//v3s3r2b6upqzGYzSqWStLQ0mU5copL3h05cgi9bHBaWQ1zcTJTK/iOfQwmlUsmMGY1ERdmYPLkBnS7wQnq/kJDQXaOh1yNGRnb//803g4pqGI1G7rzzTsaNG8fEiRM5dOgQjzzyCC6XizvvvIvbb/81y5e/RE5OGqLYvbE7ffp0Zs2axc9//nNUKhWXXXYZ//jHPxBFkejoaO644w5KSkq44YYbmDTJdx1NXFwcM2bMYOrUqTz44IPMmzePW265hZkzZzJhwgR++MMfDrghptPpWLFiBfPnz2f27NlkZX3/vD744IN0dXUxefJkioqKWL9+PaIocvfdd1NUVMRtt90GwObNm70oqIuLiwe8Zs8//zzvvvsuRUVFvP/++36RDXk+348//jg33XQTP/jBD4iPj5eJDR599FG2bNnCjBkz2LBhA5mZmb1+n5GRweLFi+VzKPonhanRaGTBggUUFRVx6aWXygXeN910Ey+88AITJ06kurqa999/n7fffpsJEyYwZswYVq9ePaDsQw1hgIVdv19WV1dz4MABxo0bh0qlQqPRkJKSwgMPPMDx48f56KOPzjq9loT29nauu+46uZp/INhsNqqqqpgwYUJQx3W7/4LJ9FesVgtxcb9BoQguPH3mzBna29vJy8sLah6AyspKCgsLvUKAnZ0nsNvPEBs7DoVi4FQCURQ5deoUnZ2dhIWFeTGESBSzUrf1ge57V1cXJ0+eJCcnGpvtRtxuHYLgIDJyIwqF/0xS7e3tNDU1UVBQ4PdvfKGmpkZO4woGZWVlTJ48GWhEFK+jrU1LbKwN2AJ8f+3tdjt6vb5X2PrgwYOkp6cTFRU14LG2b98uN7/zBVEUaW5uprq6GovFwqxZs/xi6dqxYwdTp071axF+4sQJRFH0Upr9YefOnUyZMqXPud1uNydPnuTEiRNyWqWvXaXjx4+zfv16jEYjKpWKSy+9lLFjx7J9+3amT5/utwNUW1uLWq2WmYl6Qq1WDwdnZHh61MMD562dApg4ceKAvQs88b1+GTy6urpkNp2rrrrKp67p2fzUZDLhcrnkhnjSfzqdDpPJRF1dHaNHjw5KLujO0U9NTQ2YfrcnQmUX7HY73377LUVFRQjCUZTKbbjdU3G7/T/Xuro6CgoKsNlsftWnAX2yUUk0/YGwLYqiiMvlwu12y/+XmpkqFAqUSgGl0kG3fVL2+q1ESTtr1iw++OADEhMT5fRTf6IFJpPJ747ZUmNlf2A2m+Vu3hJ7mcPhQKPReF2fRYsW8bvf/Y68vLwBU3V9ze0PApFbyvoAZFnPnDnDpEmTqKmp8fkbh8Mhkyb4gnSPzhYOHTrEqFGjeonR1/hBaVdpJ3D//v0sWrSIxMREcnNzKSwsBLrz1STGgHNlpM9VGtWxYyMxm8fS0WEjMzORLP83jXxiqOs/IiMzgAyf4z1zbiWDI4UmVSoVcXFxckfewT7koiii00WhUqXicDSi0eSiUJz/KVTdSESrvZ60tPU4HEtwOv3bCQtlupUnXe6XX35JWVkZKSkpZGVl9ZunHkhkYzBpVP2NVygUZGZmkpaWRl1dHSaTiePHj5ORkeGlTzIzM5k6dSrbtm1Dq9Xy9ddfywuLwTb1u4gLB+eDnYJzE5mNiIjgkksuoby8nMjISCwWi1dthcVikTsmGwwGUlJS+t1AGo51ikMxjyiOwOkcEfScfiEhYVDRDMmR8HQqBEH4p1PRXSitUCjkxfT3GNjuvvjiizIVrN1ul4uh/cFQP+eCIKDVauU6KKPRiFarlVmvRo4cec4imD0h3Q+dTofD4eDIkSNce+21PPDAA+datCFDUE39rr/+etxuN6dPn6a8vJx9+/ZRW1vr1bDvXCnxQAvvQqWY9Pp4jh6dSmtrK6NGDbwzfbbkktDXXE6n04vP3GQyIYoiYWFhGAwGr94VTU1NmM1mkpKSgpLle+UThlr9GlrtYVyusVw4m7gCdvuDwK8J9JxCrZgFQUCj0TB16lROnDjBzp075QV9sO/oYAqs/Tk/pVJJTk4Op0+fxul0smPHDrKyskhNTZVrPgoKCti7dy9NTU2oVCo2bNgQcGH8RWfjwsT5YKcgtJsL/UEQjgDH6OoqpLNTLVOJl5WV9WqIN1Dz095zDz9n40JHdx2Jt2Mh6WLJsQhFVNbpdHLw4EGUSiWTJk2SN6mk45tMpgEb751NeDodNpsNl8vFrbfeOuyeKWmTTqvVMmLECCorK3E6nTgcDp8O3HDrkRYoBv10OJ1OWltb0Wq1JCQksGDBAhYsWBBK2YKCUqk8J7mwaWlp6HQ6Dh06JFPHBYOhUOKeu1hdXV29elekpaURHh7e5873UBgDUUzG5UoO6ZzDB4EpiKFUikqlkuzsbNLS0qipqWHnzp190uUOFRtVoBAEgby8PDIzM2WZs7OzZSrkSy+9lDVr1mA2m6mqqmLcOP/rj+Cis3EhY7jbqaGClFLyvY4/SUrKG4ALUczE5bqHhIQE2tvbmTJlStDHG679oEIl07leqEoLe5fLhdPpxO1243A4ZKdCpVKhVCqHZDFaWVlJRUUF0H1NJf0qbWApFAqsVit2u73PtKOhvH59nbMgdHcilxoDSlTY/uJsLu4lWUVRxGazYbfb0Wg0FwRRjoSAnQ3pBuzYsYOlS5cydepUpk2bxn333YfD4ZAf+OHggZ2tnRlBqEahaMLlGg+EExsbO6gurKGWS9p1kCIW7e3t7N+/X45WREREkJyc7LN3xdlAKMPcFxLOhpJTq9UUFBSQmZlJdXU1tbW15Ofn90m40B+Gks3J8/mQZM7KyuLYsWMcP36c3Nxc0tPTSU5Oprq6GkEQOHbsWMAF7hedjQsLF6qd8oWeDfGkdFdPitmEhDSqq7NpbtaQn68lMzMjJMeWMFz7QZ2P9kWqr/BMg4LujSJP5yKQmo3g5LEDtn/+2+Hxefe1VSgUhIWF4XK5sFgscnrQ2bhu/txfhUKBXq/HZrPhcDgCrsUYSvja4PN0kOx2u1fUaDjoq8EiYGdDOtlLLrmEdevW8cADD1BVVQV8/zIMFwSiaAarLAWhAY3m94ANhaIYh+MnIX0g/JXLs1GSlAYF3ZzKEqWszWYjNzc3aJ7lCzXMfbbPqbGxkf3716LVxnPZZTf4XbgWSuh0OsaMGSM3V6qpqQm4oHIonSNfc2u1WkaNGoXVaqW6upqamhpGjx5NTU2NnA7Y0NBASkrKoI9xEec3LlQ7JRXA1tXVeen5sLAwIiIivNJdPWE0GmlsHIfBYObo0UT85HLwG8Mxjep8eKc9U6BcLpesi6Rn1FcalFQgfrZQVJSBUtmGUqlgzJjUPsdJNT4OhwOTyYRarUaj0Qyb+6BQKNBoNCiVStkp0mq150wX9HcPJQdJcjpsNhsKhWLYpKoNBkFJPnLkSNasWQP8X26MZcNs7sJud6HXt4Z89p6K1zMNSopY2Gw21Gq1V6OksLCwXvejvr7+ggkrDxXOpmLs6Pgjc+d+gsOhob4+mZycy4ChWfwOdL8kutz29naqqqqwWCxYLBa/mFOG2tnoS69IjpLZbObo0aPo9Xo6OztxuVzs2rWL6667zq9jDBTZGC7G8iIGh+FupxQKRa8UD7fb7VPPazQaXC4XarWajIyMXg3x+oJerycyMoeOjg4v6tBQYbg6G8PJ3klRCqvVOmT1FUMBlSqKSZO6i+JFcWCGMKlo3G63YzKZzloEZiBI91ByipxOp0yE0Bct87nW/Z5Oh9lsxuVyIQjCeel0BP1ke7IdDDecDZm6uuKorBxPXV06e/YER5vbEy6XC5PJhMlk4vDhw1RUVFBWVkZ1dTUmk4moqChGjhwp966QGiUZDAafSmu47RgNN2NwtpGTU43LpUSjsZGY2Dikx/LXIYiOjmbSpEmo1Wr27dvHoUOHBiRaGEpnwx/dEhYWxvjx471yz6uqquQGVP4cYzga+YsIHQZjp+666y4SExMZO3as/FlrayulpaXk5+dTWloqNxHriVWrVpGfn09+fr7crLYv6HQ6Pv/8c7788kuvhnhSj6Lo6GivhnharbbPhng9IQjH0Wj+HZ3uLSZPHsvs2bMZOXKk39fAXwxHZyNUCFS3OZ1O2traOHHiBN9++y1lZWVyRAq6mRz1ej3h4eHo9Xo5TWaoddDTTz/N+PHjmThxIiUlJZSXlwPw6quvYjab5XF5eXkUFRVRUlJCSUkJ9933GKKYiyjmcf/9v5FpmkVR5Pjx49TX13sdp6qqiuLiYqZPn05VVRUrVqzA7Xb32TcpUGzZsoX58+f3O2b58uXyv+12O3PmzOl1fJVKJdemmkwmbDZbr41dX8f89NNPefbZZwclu6dcgUCKcknER5LjcT4h6Kdb8syHI86GwlIoFLS2jqW2djZu9+D6M0hFQWfOnKG2tpZvvvmG3bt3s3fvXpqbmxFFkeTkZCZMmMCUKVMYN24cOTk5xMfHB5wbOdx2jP6vQOLVbmho4OjRo+zdu5e6usv+SS05jvDweV5jhyKy4e+c0s7JtGnTiIqKkh3cvozFuYps9MT48eO95Pjoo4/86qB+0dm48DEYO3XHHXewbt06r8+eeeYZ5s6dy5EjR5g7dy7PPPNMr9+1trby5JNPsmvXLnbv3s2TTz7Zyyn53//9X6677jqKioo4evQo//u//4vRaPTZEC8+Pt5r5zUQ/atSraOz04jVegCV6rshS9X0XyaR5uYPOHjwCTo7N/c96jywU5Ldbmlpoba2lgMHDrB7924qKytpbm5GqVSSkZFBSUkJGo1GTtlRqVRB6cvBnNOOHTtYu3YtZWVl7N27ly+++ELuK/T73//ey9kA2LhxIxUVFVRUVPDKK68gimpaW7vYtWsXs2fPBpDnWb16NcePH5d/u3r1ahYsWEBFRQXx8fG89dZbKBQKOb1qoEWyVAzvL3xdS89FvUaj4bLLLpM7aveElBUiCAImkwm73d7vNV6wYAEPPfSQ3/L1JZe/kK6XZAvDwsLk1g7nk9MRdCzGarX2q8Ck3aThGPnoicAKyk+hUq0mIiKNkpKZGI0WEhMTB/ydFB73pJmVmAekou2EhAT0er3MhX3s2DG/GrwNLPPwuwcXotMiiiJdXV3yf0ajUW6GFRERQUxMDJmZmbjdozh16ipOnz5NVpaFtDTRa0ERapkCnVMQBFJTU0lOTqauro5du3aRkZFBenq618JtKBfrgexGazQasrKyqK2tlT/bvHkzWVlZjBgxos9apYvOxoWPwdip2bNnez1L0L2Q2rJlCwA/+tGPmDNnTq9dzi+++ILS0lKZbKG0tJR169Zxyy23yGPGjx/Pyy+/THZ2NldeeSXPPfec381EpUW0P+9FbW0yVVX1KJXhlJREEBHRe0woNgsEQfBrgWiznaSs7BtAzalTmygtnY0geDMEDbeIt3RuUhG+pNMlux0REUFERASJiYlelMFut5szZ84ExIDUEy0tEAJSSxoaGmSnFSA+Ph69Xs9rr73G6dOnufzyy4mLi2PTpk19zvHRRx9xxRVXyH9v2PAFJlO3k/Lyyy/z8ssv8/e//51XX30VpVLJtm3bSExMpLq6mlmzZjFv3jyWL1/Os88+yyeffILdbuf666/nt7/9LbW1tVxzzTXMmTOHr7/+mtWrV3ul+61bt45/+7d/Iz4+nokTJ8qfd3V1cc8991BRUYEgCDz22GOUl5djsVgoLi5m9OjRvPfeeyxcuJBHHnmEW2+9tdd5/fznP5d/c8MNN/Doo49iMpnYsGEDv/nNb3odc+XKlVRUVPDaa69x5513cs0113DjjTcCEBkZSWdnJ/X19dxyyy10dnbidDp5/fXX+fzzz73kWrFiBe+99x5/+MMfsNvtTJkyhddffx2lUklkZCT3338/69ev5/nnn2fWrFleMiuVSrkoX+on56tB8HBCUM5GR0cHTz31FEVFReTm5pKQkIBCoSAiIoLY2NhzXogn0dkOhQwq1ccoFMdRKL4lLq6QmJjeoWmn0+lFMduzd0VMTAwZGRn9MlcFsmPkdH6I270Pleo2FIqxvUYMt1zY4ej8BArPLruSETKbzZw4cUI2QHl5eT5zLO12O1lZ2aSnZ1BdXc3OnTspKCjA7XbT1dUl55KGCoO93gqFguzsbNLT02Xq2dzcXJKSkgJa+AwGgb6/CxYs4Pe//738d2NjIzNmzODAgQMYDAby8vJ61aFcdDYubITSTjU2NsrEAykpKTQ1NfUac+rUKTIyvm+Ump6ezqlTp7zGjBjxfWO4wTagHVjmM7S2xgMTsdkEjMbwXs5GqN5fqYPzwDAgCBqcTgdabTi+kitCmaY7GEjpy5I+l2x3TU2N33YboLy8jOrq79BodIwbF3iK9YkTkJ+v4OhRN+npgzoVGaWlpTz11FOMGjWKuXPnsnjxYiZNmsQ999zDK6+8wsaNG72o+i+//HJ54Xrbbbdx//33s337dhYtWiSPuf32K9i//zv0ejV//vNa1qxZw/z581m2bBkGg4EHHniA2tpaDh48yFdffYXBYGD9+vUcP36c7du3Y7Vaufnmm/nyyy/Jysri8OHDvP322zz77LNenbitVivLli1j48aNjBgxgptvvln+7rnnniMqKorKykoA2traWLRoEa+//jp79uyRx40dO1am7wUoLi6Wv3/qqaeIjY3F5XJRWlrK4cOHyc/P59577+Wzzz5j1KhR3H777QNeY8/n7c9//jPz5s3j0UcfxeVyYTabueSSS7zkqqio4C9/+Qvbtm1DrVbzi1/8gvfff5+lS5diMpkYM2YMTz75pDynr/fU0+kY7uupoJwNQRA4ePAgf/vb32hra8PpdDJz5kySkpJITU0lLS2NpKQkkpKSmDlzZqhk9htSfttQhI7N5iis1pMoFDp0ujAcDmuvpniVlZV+967oC/4qcbv9OyyWN3E4VKjVNURFfdhrzHDbMQoGLpdLbux2tuBwOLwiFmazGYVCIUekpHu8Z88eRo8e7fe8arWawsJCzGYz3333HZ2dH5Ob+xeamlKIifkLBkPw/UdCsaBQqVTk5+f3oss922xU/aEnL7kgCMTHxxMXF0dzczP79u0jOjqa3NxcebFw0dm4sHG27ZQv3djfMxyos+GPHheEGjSa31NQIGI2X4JGk+uz79NQbsj5glYbw7RpS2ltPUJSUlGfRblna1NM0umSU2E2mxEEQWZxTEpKIicnh/3793vV7viD5uYydLpT2O16zGb/u47b7bBkiUBVlYDTKXD11QpGjhT5n/8RGWzbBYPBwO7du9m2bRtbtmzh1ltv5YknnuAnP/mJz/Gezod0Devr672ibxUV3/LSS69hNltpbTUxatQESktLAfqMcm3YsIENGzYwdepUoDsycfDgQVJSUsjKymLatGkYjUav31RVVZGTk0N+fj4AS5Ys4c033wS6ayk+/PD7tU5fDV2lAvyuri5iY2O9HJG//OUvvPXWWzidTurr6/n2229xu91kZWUxbtw4bDYbN954Y7+1Vz2fs0mTJvHjH/8Yh8PBwoULKSoq6vWbL7/8kj179sjXwmL5PjtGqVR6OXYDYah6rIQSQTkbkZGRfP755wBs2rSJW2+9lcjISBITE9m1axcNDQ0AFBQUXDDORnc4tZ29e5NwuWbQ2akiIuII8fFtXh1YLRYLJSUlQR/PX8Vrs2lxONSo1TY6Ogz4yroajhGJwcizf38lx47tJCoqh9mz5w5JypHNZpMNkGfjw4iICAwGA9nZ2T4Zv4JBWFgYRUVFnD59F+AiOvo4HR3rMRiWBj13KJ1DrVbL6NGjMZlMHDlyhPb2dqKiovzq3B2oHIEuhERRJDMzk7q6OgBmzJgBdD+ziYmJJCQk0NDQQEVFBXFxceTk5Azo0Ax3JX4R/SOUdiopKYn6+npSUlKor6/3mTqbnp4up1oBnDx5kjlz5vQ5p0ajkYuH/YE/DWgF4ST19WC3a5k61QZM7GPc2bcJMTHZxMRk9ztX6JvGdut0T8eip07PysryqdNFURyUPFOmHGDnznhSUxuIjg6X5xoIGg3k5cEnn3T/aT6sfwAAIABJREFU/e23cPXVDNrRkKBUKpkzZw5z5sxh3Lhx/OlPf+rT2fAFvV6P1WoFuqMN99zz/9i1axNZWTk8+eTzWK1WPvvsMwRBkO1nT4iiyEMPPcSyZcu8Pv/uu+/Q6/V9kpH09XwFshnlay1YU1PDSy+9xK5du4iJieHOO++Uz1EQBJkNSqPRyIxQnvdQpVLJ76LUSBO6UzC3bNnC2rVr+dGPfsQDDzzA0qXedlwURZYuXcrTTz/dS1adTjesU6IGg5DwZ61bt45f/OIXLFy4kG+++YZ3331X3mE8deoUx44dC8VhAoZWqx2QSacnPB9eh8PhlaNpMpnQapvIzV3NmDFWKivnEBaWweTJk4noEZ8OZSjYn1xYgyGLpqZHMZkOkZJyZZ9zXQhpVCrVK1xxRSXt7XF0do5DqRx83xCJSrirq4vW1lZaWlqorq5Gp9MNqvFhKK6L0TiZyMgvcLnCOHkyHKu1muzs7F7KJ5BjDUX0ITw8nKKiIvbs2UNdXR0tLS3k5+cTFhYWMjkCHe92uykoKGDOnDmo1epeefCCIJCSkkJSUhKnT5+mrKwMh8MRUEHiRZyfCIWdWrBgAatWreLhhx9m1apVLFy4sNeYK664gkcffVQuCl+/fn2/haGDTaPqG24aG0X27UtGFKGrK5d/bgr3wnAk+whWJmlR2NbWhtFoZO/evTgcDrRarexYnI1mtsnJ1/DDH76GKObx3XeBpcPef7/ICy9I9XvdfweDw4cPo1Ao5OjAvn375FS/iIgIurq6fEa+PDFq1CiOHj3KnDlz5AV5XFwWRqObjz/+mEWLFtHRcQboLlg2m83y3BLmzZvHE088wZIlSzAYDJw6dUpmWZKea4m5Sko9LiwspKamhurqavLy8vjggw/k+S677DJef/11Xn75ZaA7jSomJga1Wo3D4ZDf7zNnzhAfH9+Lhrezs5Pw8HCioqJobGxk3bp1/OAHP6CwsJDjx4/Lx/zwww/ltGaHwyF3cc/OzmbPnj0sXryYtWvXypsGx48fJy0tjbvvvhuz2czevXtZunSpl1w/+MEPWLJkCffddx+JiYm0trbS1dXVJzX1+d4PKmhn49NPP+VnP/sZzz77LLfddhvLly/nuuuu4+OPP0aj0ZCWliazHpxt+KvEpQWny+Wiuroas9nca9dD4jRXq79AqdQhinrCwrS4XNO98gt7zhuKwjt/FK8gCOTlzQZmBz3XcEd+/hE6OsKJjW1Dre7EaAz367x6Fvl1dXV5FW7rdDri4uL8bgY3VOjquhu1+heEh6cxeXIMdXV17Ny5k+zsTFJT0wf1TA2lopLSq2w2G5WVlURHR5OXl+eTX30wzkYgkQ23241SqSQ1te/mU9C9YEtPTyc1NZWtW7dSVlZGWloamZmZF9yO0kUMzk7dcsstbNmyhZaWFtLT03nyySd5+OGH+eEPf8jbb79NZmamzHBTXl7OihUreOutt4iNjeWxxx5j8uTJADz++ONysbgvhDqNSqn8OyrVJiAGURyLyxXd51h/oiRnG4HYKZfL5bUhaDQa5bpIURQ5ceIg8fFxTJ9+pV89g/qSZzBwuRZgsVwBaIATAf22rQ2WLBF55BE3y5cLtLVBUtKgxAC6mzree++9dHR0oFKpyMvL46WXXgLgxz/+Mddeey3JyclygbhnzcbYsWNZtWoVV199Nf/1X//Fj3/8Y6Kjo7njjjsoLi4mOzubSZMmATBv3mhWrjyBSuUgIcGAxdI9l1Qg/txzz1FVVSVHEA0GA++++658LJ1Oh9PpxOFwYLPZ0Ol06HQ6VqxYwfz584mPj2fmzJl88803APz617/m4YcfZvz48SiVSh577DFuuOEG7r77boqKipg4cSLvvfcemzdvZt687xkfpZqNCRMmUFRUJDN8SpFwnU7Hq6++2uuYSqXSqzHgkiVLuOWWW5g2bRqzZ8+WSUi2bNnCiy++KDNdrVy5EsBLrj/+8Y/87ne/48orr8TtdqNWq3nttdeGpA/OcIAwwEvd75dut5vk5GRWrlzJ1VdfLS8kFi9ezOjRo+XilnPlkS1atIgnnniCnJwc+bOexV+eTEFdXV3k5eURFRXVZ5MXm62Mrq6XEEUBvf4RDIZxPo9dXl5OcXFx0Gk2LpeLffv2hSQlq7q6mujoaOLi4oKap7Ozk9OnT1NYWBjUPDabjaqqKiZMCKx4TqV6B6XyvxHFYuz2Z+jstPSSx9MI9SzOl9hDDAaDV47/sWPHiIyMHHCHpz+43W4qKirkhUZ/sNvtPsP25eXljBs3zqMAUcRq/RVK5cc0Ns5Er3+VuLh43G43u3btYvr06QMey2KxcOjQIYqLi/06j+3bt8uKdyBUVlaSl5eHwWBAFEUaGho4duwYSUlJZGdnexXHO51OKioq5DzVgdDW1kZ9fb3fNTBGo5Hq6uqAnqnt27czdepU6urqOH36tBfjliAIw6Up1fm7pTX0OK/t1L333stVV13ld6rxt99+S2ZmZp+bXPAH6upO0t4O4eGXk5n5g161TBIOHDjAiBEjBr0Q90RZWZlfem8gnDx5EkEQejl/drvdS6f3rJmT0pilhWtFxf9w9OgBFAo148dPY+TIqwctU7DnVldXR0FBAVartd/Irz+QsjWC1Usmk6lPhj5PSO+FIAjMnj2bTz/9lOjoaCwWi7zwliAItYAJUGK3Z/Huu/+D2WwmMjKSpUuX+vV+GY1GDAYDLpcLq9Xab6dvaexAkNaCo0eP9rshXl9zv/TSS3R2dvLb3/5WdoqUSqW8vvAHoihiNpv9uv4SzGazF9tZT5xt1tdDhw4xatSoXmL0NT6oyIZCoeBvf/sbM2bMkB9IURR57733mDlzplclfV84fPgwN910k/z3sWPH+N3vfsd9990nf7ZlyxYWLlwoOw033HADjz/++IBzC4LAhg0bSExMJD8/32fxlydT0L59+4iJienzJVYq/4EgvIfdruTYsRuIi1PR+1p/f+zhlLIUyrmcTictLZUcP24lM3PCWTfQTuddOJ1L6N4xEnA6u7DZbNTV1cmOhacRSk1N9TJCQw1/r0d/98JzDqfzDPAhXV164uK+5LvvKqmtjWbEiBEBHSsUcg00t2ea0okTJ3rR5QZagzGYmo3BPI9KpZKcnBwyMjKora1lx44dZGVlnbOo7EWEDqGwU0OJUEY2BOEIhw8fp67OjShmUVQ0uk9HY6C5ziVsNhtNTU2yc2Gz2VCr1bJTIVG39qcb4uObqK4GpdJFbOyZsyj9hYvnn3+euro6oqN9R8tEMRMwA1osFgtmcycqlUB7eysul8uvhb6kvwPp9N0f7HY7CxcupKCgIKDf+cKKFStYtWoVH330EfB9t3SbzSb/p9FohmRNNDApxPDejwo6jcpTgUP3CWu1Wt5++215odDfRRg5ciT79u0Dunej09LSuP7663uNu+SSS1izZs2A8mzcuJEXXniB+vp62traEASB6667juzs7AGV00DMTwpFGSpVPFrtMTSazn550S9kZ6O5eRVjxrwP6GhufoHExGnBC+cHpAIsT5pZi8Uif6dWq/ss8vN3/uGAnnK43QaamtKJjz9FS0sqhYXTMZttHDp0CIvFgs1mG5CGMVBnI9i6CoVCIS/Wa2tr2blzJzk5OcTExAx5zUYw0USVSsWIESPIzMykpqaGiooKZsyYMewV+UX0j2Dt1FAilDUbSuVm7HYFTmc4CkUM0H9V8blOo/LVv8JisciN76TNosEsMlNTFzJjxr8TG5tIePhVBKPehxuxyrnCwBFpBdAdDYiIcFNSksmhQ/VMnZrJ119/xcGD3zJhwoQ+o3i+rrNKpUKlUmG32zGZTGg0moCiOhqNhqVLl8p1JsHgpz/9KT/96U97fa5Wq2X6WX9lvBCeh0AQlLMhhZl8XTTPNIavvvqKGTNmDLgI2LRpE3l5eUHlrI0bN4533nmHlJQUfvrTn3LzzTfL+YQDob9ibEE4hdVag8NRic1WzOjR12Aw9J1uEyolHsoHMlTORkzMt7hcSrRaK3AMGJyz0Z88Uh2NZ9hcWlRLaVBJSUly+tvp06dDUmsxXBSApxwajQ63+78pL99KRsYstFol4eH3kphYwcGDN1JeriE5OdlnEbmEoSwm72+8tHjPyOjuJVJTUzOkkY1Q0XhqNBpGjhx53hflXUTo7VSoESgbVd926gxNTQdobHRhs2kYNWrkgCmhoY5siKKJjo7/wm7vJD7+ThSKTPk7qe+UJ+GKKIqEh4djMBhISEggJyeHlpYWnE6nV6+SwSGd5uZ7SE+fGJSj8X8dg9WBgqBm5swxzJxZSGeng3fe2YJaLVBWtpvi4uKAC/Q1Gg1qtVpOpxtMD6qhpmj3JaOvyOJg3rnz3RYF5WwsW7aMm2++meLiYgwGA3q9Xq62b2lp4fjx4+zatYsvv/ySkpKSAfNCP/jgA68uq57YsWMHEyZMIDU1lRdeeIExY8b4HJfkUUUVKBtVfw6CSrWW9nYjbncKp06NQ6US6S9VcDiGp0Mlk15/J6L4GBpNOgbDFQP/YABI7CGeEQun04ler8dgMBAVFUV6enqf4cnhdK1DoRB8nUt2dj7Z2d1MIoKwE6dzO3a7QGbmR4wY8YRHEXk2qampPmXwdxE1FIxREl3umTNnOHDgAGVlZRQUFBDli6N5iGUJBBf7b5z/CLWdCjUGE9nwpSOUygrq6zuxWlPR6yPR6ZIHfBdCrTtbW7exffsZ3G6BnJw/Exl5k9zoVKlU9upJ5GtzZDhmBQwVmpr+P3tnHh5Vlaf/z629KlWVfU8gISELOwEEBNkEFRBFUbGhm25bW5DGwWUcf04rLqP2ONqMS7eK3S6jqLRgu2u7jQhCSMIiOyEBkkAWyJ5UKrXe+/sj1jVLVVKVVCA6eZ+H5wlVp849dzvnfLf3PUdeXh7R0dFMnjz5gm8mPWxQbrdb/hvanjmdThfgfKhAktKBViTJQWiohqamVkJC1Lz00kvodDpuuOEGvyjTPfBEJDUaDXa7vQtzVXcI9Fno7b3oPEaHw4FWq/W7VuTnij6dvYc/WKVSMXHiRJKTk1GpVFgsFioqKigtLUWhUHD//ff3OIE7HA4+/PBDrxSBOTk5lJaWYjQa+fTTT1m8eDFFRUU9ji94ubCNSFIuoaH7qK6OR5KiMZvN3fYV/PC0A4XiQ8CBKF4NBE73GqzJV6EYS0nJsz4Nvu7QvkC/sbGRxsZG9uzZIxduR0VFkZqa2m2e8c8dPW2Yz50LQ60GrdbKiRPpjBnzAWlpuSQl/Yqioia5GLE9EUB/RjYCiSbodDpZVO/48eOoVCoyMjJ8FtZdqMiGBxd68R9E3xHMdao/EKhTzFdkw27/gKqqEBobFZhM4V41QDqjr+tUe+pwu93O8eMN2O1uFAqJ+voQYmP1xMTEdFvY2hk/BSMhWPj22600NNRTVdVGTHG+a8Q8tN9ut1ue95VKJQqFArVaLRu2oih6rZvo+Z6qABNKZRPLll1MdXUju3YV09JSj8Vi56uvvmTEiJGMGDEioLlWEASZucrhcMgb+mDVZfbm+es8fs8YRVGU6zna62f8X1tb+mRsXHbZZRw8eJDt27fz7rvv8vXXX9PQ0EBERATjx49nxYoVzJkzx6++PvvsM3JycjpEJjxov7FfsGABq1evpqampscQsUfUz1/4mniVyj3Y7WU0N8fT3JxCYuKUHoUCgz1hOhz/xG5/DkkCjaYVne7mgPs43x4jbzolnQv0bTab3wxJgcDlclFdXU1YWNgF2UD0JwQhin/84/fodLWo1RrGj/9/gBO9Ppfs7O1YrVYKCwspKSkhMzNTZok6nzUb3bVVKBSYzWYmTpxIbW0tBw4cwGw2k5aW1iU0fr5rNgbx80Mw16n+QG/Wqc7zryCcoKqqhMrKsWi1IlFRw/wSsw1kTRBFUWZw9Mzp7anDlUolY8dehckUj91uITt7FgbDhXOK/RSMltBQFfX1bWxGer3/G+VAN6rtoxUew8ITufCoa3c3b3qrmwjMgaVAo0knKamB7Gw3Z87U43K5KCk5yenTp6murmbq1Km9YtgyGAx+MVf1J7qv9W0TBhRFUa4b+b/oTO1zXEeSJC655BIuueQSn99Dzy/H22+/7TOFqqqqitjYWARBID8/H1EU/aJv7Y3HyNtDo1B8gFJ5hpAQiaKiOaSm9pwrGPzwdAt6vYggCFRXN9HndNYgo7PitscL0lmnpP0kEEiecqDYtu1TJKmA1tYk5s37Ta/yO3uDYNzznjbYUVFRzJlzDeXl5ej1FYiiiCQ5EAQbavXlmEyjGD/+P6mvt3D48GFMJhMxMTEDwtgQRbFD28jISCIiImRVb0/etifk3Bs2qkDbD+Lnj2CtU/0BnU7nVW3ZF7xFNpTKtzh6NBpJcmO3G0lMTPe7L2/vQHeOIqPR2IXJEaC6uhqDwcCoUTP9Phdf+L/yXs6ZM5rS0nDCwkKIiDD1/IMe0N6I8BgXnjnxqaee4p133kGpVKJUKlm/fj0zZ87kmWee4Xe/+50cXU5LS5ONR4Dp06fz7LPPAnDvvfeyePFipkyZwogRI9i5c2ePmkYe2O1qFi36LTU1lfzrv65ApYqmrKwWq9VKfn4eR48e5brrrguIDtYDD3OV0+nEarUyevRo8vPzvYq6Arz//vtkZGTIlOr33HMP8+fP71eng0Kh6GAYee5VIOvVa6+9xp49e3juued48cUXMRgMXZTJe0JDQwNvvfUWq1evDvQU+oQ+GxuemydJUocJwv8wWxt/8JdffsmGDRvkz1588UWgrfp/y5YtvPDCC6hUKvR6PZs2bfKr32CwfAjCaQThWwTBhtMZidM5yi8dhmAbG3r9FRw/fgZJcpCSckOv+gjGmCRJwm63Y7PZOHHiBBaLBZvNhkajkQu3/Q2b9+fCnpn5Z6KizuBwaGltvQytNrXnHwUJ/pxX+1zY3vSRkpJCXFwcu3dLfPjh1URFnWLEiKNERNSiUBQBJiIilnLRRRdRVVXFkSNHZMaMnkLN/R3Z8BZu9tDlnjlzhry8PJKSkkhOTu5VZGMwEjKIzgjGOtVf6E1tYcd5XKK5+V3Ky6/AbtdjMBj9SqGCtvO22WzU1NR0YYPy1Fd4cxT5QjBqpgZiZKO/ng+VKpn0dC2gR5IC09/ozrBQKpWoVCqUSiWCIJCbm8s///lPdu/ejVarpaamhoaGBgCeffZZli9f3iGV9auvvpL3OZ5rWFdXR15enqzWLQgCLpcLq9XqVz2HR8l9794DQCMWyym+/HIfx49X4XA4qKmp4W9/+xtTpkxh1qxZCILg13rVHh4qWmjbVzocDjla0v5Z+OCDD1i4cKFsbKxZs4Zbb721g7HRX/dcqVSi0+mw2+2yU9ajoB4IvLFi+YOGhgaef/75gIwNz7zZJ6bHXv+yEwSh94IiBoOB2tqOPNjtL+SaNWtYs2ZNwP0GI41KpXobQTiLUummocFEaGhar/vqC0JDwxk37g4kSeq1mE+gk6+ncLt9xMLlcqFWq3G5XJjNZhITE3tFS9jfSE5uxWLRYDSCQuEakGwkvq5ZIPfI6XRSWjqM0tJhREU1EhFRgiDUolS+jELxBi7XU8THX4VGo6GoqEimoI2Pj+/2+P0Z2fA1YSkUCoYMGUJCQgKlpaXk5uZiMpkCKiA8X7ocg/hpoi/rVH+h7+vUixw5Ek9TUygKhUhoaJTXFCqPkJjHqPBoEmm1WiIjIzEajcTFxQXMEuSBZ30JhrERDPwU0qhAR5s2RffwGBZutxuXy4UoijgcDtmw8KRB+bp2VVVVREVFyRF+j07Jc889R0VFBXPnziUyMlJWEPeGLVu2cPnlHQlh/vKXv7B161ZEUeS1115jxIgR1NTUcNttt3H6dJtq+vr160lISGDFihVUV1eTkzOBzZs3U1JSyaOP/omMjBFkZ49CEBS4XC62b9/Od999x9atW/nd737HpEmTWLNmDTU1NRgMBjZs2NBFULi2tpZly5ZRU1Mjiy96VOT/+te/8uc//xmAMWPGcNttt/HRRx+xbds2Hn/8cTZv3kxaWhp1dXVUVVURFxfn87l5+OGHKSsr49SpU5SVlbF27Vpuv/12JEniueee44033gDg5ptvZu3atZSUlLBw4UKmTZtGbm4uCQkJvP/++/L90ul0cjRm+fLlVFRUYLPZuP3227n11lsBePXVV3niiSeIiYkhKytLvocPP/wwRqORu+++mzlz5vDUU08xceJEampqmDhxIiUlJRw+fJibbroJh8OBKIq8++67PPDAA5w4cYJx48Yxb948nnzySZ588kneeecd7HY711xzDQ8//DAlJSXMnz+f2bNnk5uby/vvv98nptifdXl839OoJFSqV2hTxFRx9mwGKSn+hQz7Y6Lra55fd9S+nZXVm5ubEUVRLtyOjIwkJSUFtVqNzWbj+PHj3eqM+Iv+WgwE4T+IiHgJt3s6Tqd/KQXBwPn0pEmShNlsZvz48Zw5cwab7Ql27PiICRM2otPZEYQq1OrViOL/oFI9QWhoKGlpaZw4cUIuIo+IiPDa7/mMbHSGSqUiLS2N5ORk9u3bR319PTqdjqioqB5/G6j3ZTCyMYgLjb6uUwbDA+zefRsulwqFQiQ+PhO3292lvqL9fB4eHs6QIUOoqKjAYDD4HQnpCidt2whhQEYkforwGBYul4vm5mbZ+PMUbisUClQqVUBOx3nz5vHoo4+SnZ3NpZdeyvXXX8/EiRO5/fbbefrppztEMgDmzp0rRxR++ctfctddd7Fz506WLFnSoV+z2UxeXh6vv/469957L5s2beJf/uVfuOOOO5g+fTplZWXMnz+fvLw8XnrpJf70pz/x0UcfYbPZmDNnDl9++QWZmVpWrbqfhIQMfhSglpg1axYajYZVq1bx/PPPM3z4cPLy8lizZg1fffUV8OM6+cgjjzB9+nQeeOABPvnkE/76178iCALFxcWsX7+eL774grCwMKxWK7GxsSxatIiFCxdy3XXXyecyfvx4duzYwZIlS3jooYcYPXo0119/fZdrWVhYyNdff01zczPZ2dmsWrWKvXv38sYbb5Cbm4skSUydOpUZM2YQHh5OUVERb775Ji+99BJLly7l3Xff5cYbb5T780RjNmzYgNFoxOVyMWPGDJYsWYLD4eDhhx+moKAApVLJ1Vdfzbhx4/y+7y+++CJr165l+fLlOBwO3G43//mf/8mhQ4dkfbsvvviCoqIi8vPzkSSJq666im3btjFkyBAKCwt59dVXef755/0+pi/8rI0NnU5HXV2d3+27eow+QaGo+OFvF2fPjmbcOO/KmT33deHhmcQ9k1h77xYgh8295eN66ycY4wkGvI3H7Z6F2z0rKP33Zjx9gb/X1rOxnjp1KgAbN26kvj6SioqrWLz4azQaK9CAQvElUVHbsVr/HxrNPWRnZ9PS0sLx48cpKSkhIyMDYzse5954J/ujHkSj0RAdHY1CoaCyslIea3d0ucFOoxpoXvBB/PzQmzQql8sl/3///mxaWtreCVFU0tTUzL59+zAajRiNRuLj4zEajV7TUfqyTjmdH3L27CeEhg7BZLpnwBkbP4V3V5KkDoXbnvlLEAQ2bdpEeXk5JpOJW2+9VY5WORyOgM/NaDSSn5/P9u3b2bp1K8uWLePBBx+Uveed4S2NqrKysouDcenSpQD84he/4O677yYkJIRvvvmGo0ePyufR1NTUpSapsLCQ1NRUMjIykSS47rrreOWVlxk79mJsNiceo+Pw4cOMGDFCPg4gp8a3f0a2b98uq3ovXLhQjoZ/8803LFmyhOTkZFpaWjCZTFitVq/PV0xMDJWVlQA89NBDPkUAFyxYgFarRavVEhMTw9mzZ9m5cyeLFi2S602uueYavvvuOxYtWkRqaqpsIEyYMIHS0lKg6/O5YcMG3n//fSRJ4vTp0xw5coTa2lpmzpxJdHQ0FouFG264gePHj3sdlzdMnTqVxx57jDNnznDttdcyfPjwLm2++OILvvjiC8aPHw+06RIVFRUxZMgQhg4dypQpwRFt/lkbG70RS+roMfptu+9Arx/7k6Pva6+4XV1djd1u59y5c73Kxx3E+YO/kY327QwGA/X19ZSXp1FQcBUTJ/4nWu12wI0gtDB06ANI0ms4nV8TEhLH+PHjqa+vl4vI09PTZZaR/lqoe5PmpNPpSElJoampiaKiIpRKJcOHD/daSCiKYkARwMHIxiAuNPxNo5IkCZvNJjuI6uvrsdls7NhxDeAxGBTMmDHD73egL+tUQcEuqquTUamszJ59PKhrXud+JEmioqIEgISElKDPT6IocurUKQBSU1P7ZU7w5L179CE8hoUnFcqTty8IAhaLhfLycgCam5vZvXu3T3IDf6FUKpk1axazZs1i9OjRvPrqqz6NDW/Q6/VdNuDt74PHuBBFkZ07d8p/63S6Lr/r+pyYsNmU3HnnXB544N0Oc3tkZCR79+7tcXzenonOa5ler8ftduN2u3E6nR2+t9lsskHX3RrYnmxGqVTicrkCat/a2kpZWRnXXnstCoWCW2+9laysLL7++mt27NiBwWBg9uzZ2Gw2uZC8pzVZpVLJToP213rZsmVMnjyZTz75hMsvv5y//e1vDBs2rMs1uu+++1i5cmWHz0tKSnpVrO8LP+tVtu8F4j8uAC0tmoAUTfsy8SoUu1Gp/h2l8mPAfy93a2sr1dXVnDx5kv3795OXl8fhw4flNJSYmBiSk5OZMGECmZmZJCQkYDKZAp5Y+3NRGUTvMX/+fGbMmIHBYCAv7xAvvng1Vuv9tPcpCMIJNJoUlMo2T0t4eDgXXXQR4eHhFBQUcPLkSdxud78ZG32hsjWbzUyYMIHk5GQOHjzI4cOHu7zfg2lUg/ipwds65aGZrayspKioiL1791JQUEBRURF2ux2NRsPw4cOZNGkSWq2OtqW8Lb0mEGPgrPFkAAAgAElEQVS7L3O5zZaEUmlDFENwuaL7NbJx6tS37Nz5Gjt3vkZJyXd9PkZnHD9+iNzcj8nN/Zjjxw91+T7Q87Lb7dTU1HDq1CkOHDiAw+GQN44eetaQkBAMBgM6nQ61Wi0XcwOEhIQQEdEWrVKpFISF9Y1NsbCwsIM22ffffy/vZ0wmE83NzT32kZ2dTXFxcYfPNm/eDMDf//532QM+b948nn/+efR6PTqdTmYQbX8Ns7KyKCkpkfvbuHEjM2fOQakczzvvbKT9EuFyueTjSJLE/v37u4ztkksu4a233gLaZBTq6+sBmDNnDps3b5Zrguvq6lCpVISHh9PS0kJLSwt2ux1Jkjh+/DijRo3q8Tp4w7Rp0/joo4+wWq20tLTw/vvvM336dJ/tk5OTycvLY+/evaxatYrGxkbCwsIwGAwcO3aMvLw81Go1M2bMYNu2bZw+fRqHwyFHbzpj6NCh7NmzB6BDm5MnTzJs2DD+5V/+hauuuooDBw50ud+XX345r7zyihx9Ki8v59y5c726Dt3hZx3Z6GsubEHBFUyY8Clut8Brr93EjTfG97ov/yFhsz1GQ4MNo3Evev3Eri3aFfp50qGcTic6nU6OWCQkJHQp3D579qzX0GBvaB8DOTfP5rXzpu5ChbkrKyvJzf0HarWZOXOuDar1fj4Ljr15bcaOHUtubi7QFtUqKJjFRRdNRqdbjCD8+C4olceAEbjdR2Q2qJiYGMrKyti/fz9arbZfziUY9SAeutyzZ892ocsdZKMaRH+hsLCwQzrHyZMneeSRR7jjjjvkz7Zu3crVV19Namob+921117LunXruu3X7XbT0NDAU089xfz582ltbUWSJK/1cgA1NTU0NTXJ+kHXXnst7733HkqlskMuuD/oSxrVhAnLOHHiAJGRCZjNsQhCVb+lP9lsh5EkEUkCu/0Q0Dcvf2e43fkIQs0Pf+cCY/z6nSfa1J5ExWMMetgZ4+LiqK6uRq/X09ra6pcxKAgCq1Zdy+HDB9DrNaSn91xE3h0sFgtr166lsbFRrolbv349ALfccgtXXnklcXFxcoF4+5qNUaNG8frrr7NgwQJeeuklbrnlFrlfu93O1KlTEUWRN998E4BnnnmGNWvWMG7cOFwuF5dccglPPvkkNptNNjp0Oh0vv/wyS5cuxeVyMXHiRFauXIkkCUiSmlWrZnP48Bnq6y2MHp3EmjXP8Pjjj+N0Olm6dCljx47tcH7r1q1j2bJlvPfee8yYMYMhQ9qu18iRI7nvvvuYPXs2giCQk5PDq6++ytKlS1m5ciUvvPACb775JrGxsRQXFzNxYtt+66GHHmLs2LEdajq6w/jx4/nlL38pG1w333wz48ePp6SkxOdv2j/nV1xxBRs2bGDcuHFkZGTI/SQkJPDggw8yb948YmJiGDNmjFc2vbvvvpsbb7yRN954owOj1t///nc2btyIWq0mLi6OdevWERERwbRp0xg1ahTz58/nySef5OjRo3I6ttFoZOPGjUETSJTH2sPk8JN2O3/++ed89dVXPU72HlRUtGkWJCUlAcgvowd33XWX38cuKSnpVeGdJImcObOcsLBzWK069Pq/ceRIKcnJyXLBn9vtlhcij3HhT7HYuXPnsFqtpKSkyJ/V1++ntXUdomgkIuIpDIaeDSqn08mhQ4fkHL/uUFlZTmHhK0iSgQkTftdBoFEURfbu3Su/4L2FxWKhrKxMprHrCYcOPURGxpu43Sqqqv6b1NQrACguLiY8PNwvDRdfcDgcHDlyxGcRl9PppKmpiaamJmw2G+np6V2MnZ07d3LxxRf3eKzm5mZOnTrFmDEdF8aSkhK2bt1KXV0dSqWSESNGMG5cKrGxU1GpOnqwHI6uxmdNTQ3Hjh2T1b29FZH3ZrzQxopitVq7hHJ94dixY0RHR/u8J6IoUl5eTmlpKUlJSTgcDsLCwvx+7xoaGigvL2fkyJFev/dQSA4ADPwE9AuH875Oud1uEhMTycvL68DQsnXrVp566ik+/vjjbn8viiLLly+nsLAQSZJQqVQsWrSIX/3qV4SHh3e70NfW1tLQ0EBamn/MiN2hsrISl8sVUNTeFw4cOEBGRoZfYoLdobGxkaqqKjIzM+XPnM4DHDz4NwRBYvTolahU/nmgCwoKZHai7iCKb3Hw4OcAjB49D4Xilx2+z8/PZ9KkSbJauudfeyef2WyW1+LOBlNZWRmZmZm0trZ2oJjtHg4E4QwAkpQMtBkpnpqNvhLGtLS0+OVoax8tnjFjBh9++KFcbO0Pxb3HORoSEtJB7dvb+N1uNw6H4wdD2v3DP+97G082h7/Xs7vzfe+99ygoKODf//3f5efX5XL5/SwHek88TnB/i/w9goDto6D33nsvOTk53HTTTReEZe/o0aNkZ2d3/tjnIAbEKtpf0Gg0AaVRdY5GREdHU11dDdCFai3QvvyBy+XCYrFw6tSvsNn2YrXGER9fhtPpxO1291i43Zsx2e0vYzJVoFC4sFg+wmDoOYczkHNrbX2RyZPfQ5IEamsTMJt/FG68UHUt6enHkSQBtdpBXFx5h++C+cK2r5fxCB2qVCrZSNTr9Rw4cICIiIhe31dv401JSWHy5Ml89dVXOBwOjh49SmRkJI2NuWRlTUWhaDM4XC7vei2eMHNKSopcRJ6ZmRmUCFCgkYeeIiEKhYLk5GTi4+MpLS3lzJkzSJJEdHR0r+peBjEIf/D111+TlpbWaypIhULBf/zHf5CamsrJkye57777uPPOO/3+bbDIR4KtRdFfaVRq9Rhycp6gbS/jvzHj73gUiuuYMEEJSLhc13TJHrBarRQUFMhq6R42r97S0PsHDZLU1SnjcrkoLS0lPDycuLi4fjx+Vzz55JOUlZURFhbWq3ut0WhQq9XY7XbZ6PC97il/+OcdgR6/u/Yul4t/+7d/w2AwYLfbcbvdA5JCXalUYjAYuP/++9m1axf33nuvrAI/0PGzNjb6WrOxfPly9u3bh9ls9lrF3x16mngdDkeH0KvVakWpVGI0GklLG4taPZHw8HDUajV79uwhMTGxzw+UtzFptWOBXERRg8HQxUrtM+LjnQgCKBQi0dHuoPffG4SE3I5KtRaIwOFYEBQNDo9hUV9fj8ViIT8/H7Va7VPo0ENtmJycLAvZDR06lMTERL+P2d3zNXz4cE6ePMmRI0dwu91s376dBQsW4HJVA42AC/AeLfBMnCEhIYwfP566ujoOHjyI2WyWi8h7i77UbHQHT2qAzWbDarWya9cu0tPTe6TLHWSjGkRvsGnTJn7xi194/S43N5exY8eSkJDAU0895TNqlp7eRskd6DoVTAMhEMNFEMqx2Z4GFOh0dyFJsf0yLt/96PvctzeIokhLi53m5kt+SEs+1CF7IDIykrq6Oi666KJ+OX6g2LZtG0VFRSgUCpYsWdLvBkf7ezF58uQ+9ycIAjqdTvbWOxwOv0QB+xPtKW71ej0Oh0MW3dNqtf0ytr6sLY8++iiPPvooLpdLTs3ra0Sxv/GzNzYCYaPqPPEqFAomTJjQq2N7+vKwT7Svr7DZbKjVajkFKioqCoPB4PPh689JPDz8t9jt4xAEAxqNf8ZGIOMxGNaiVNqRpFDc7sVdvr8QVIlu91Tc7l20eckCf+EdDgdNTU3yPfXcT5PJhF6vx2AwMH78eL8mE0EQSE5OJi4ujhMnTrBr164OtJbdobuNu0qlYubMmZSUlGCxWBAEgfz8/B+MZt/Usd76jYiIYPLkyVRWVlJQUEB8fDxDhw7tlfHb36J7giCQkpKCTqejqKiIU6dOkZGRQViYd8rqwZqNQQQKh8PBhx9+yB//+Mcu3+Xk5FBaWorRaOTTTz9l8eLFHQpzvaE31LcXIrJx9uzn5OfrAInJk78iOnp5r/sK1pj86as92utJedZjSZIICQnplvZ9IDkdGhtrASeSJNDc3Hjeoxu9gbfrp1AoMBgM8obZk7I6EK61QqFArVajUqnksfUkXhzMiL2/7VUqFSqV6idBtPOzNjb6mkYVKDyhV4vFQk1NDTabjTNnzqDVamUPd3x8vBeF1gZUqr8BRlyuJXTOUexf9icBrTZwg8p/LYh4XK7/9vrdhcgz/BFdN5iiKFJTU4NOp5PzQNsbip0NC7PZ3EVx18NCEuh5qdVqsrKyaGlpITc3l++//56MjIxu81F7mrCMRiOzZs3i888/x+FwcObMGSoqKkhI6F6Y0lu/giCQkJBAbGwsZWVlHZTIA0F/RTba969QKNDr9YwZM4bm5maOHz+OQqFg+PDhHfREetP/IAbx2WefkZOTQ2xsbJfv2tekLViwgNWrV1NTU9NBMK0zAlUQ91CKBgOBrC01NbFI0ikkSUFtbRSdNV2DOa5grHcete3Tp0971ZPqTn+kMwbCBtiDOXMy2brVRkSEkbS0vgvr9jd6upeeDbOHsWugXGtBEOSxOZ1OWlpaUKvVXmtyLvRm/6ewhv2sjY3+9Bi1hV47Km673W45p9NgMBAWFtahGNt3X5toanoXpVJCrY5GoZjbZVwDSeRooEwGwcbZs1vIynqPEydSsdv/FVGkC6tIV0OxK/pyfTx0iElJSXz//fcdmJZ6c6zs7Gx2795NZWUlkiTx2Wef8dvf/rbb33VnECiVSlJTU0lMTKS4uJiysjK/IzGevvszstHZeDCZTEyYMIG6ujoOHz5MSEgI6enpcsh50NgYRKB4++23faZQVVVVERsbK0cSRVHskXCiN+m+wfT8+7vmDR06i+rqtmhwcnJXWs9grlO9qXds7xSyWq0IgoDT6USpVP6s9KQiIxO54QY9gqBAFNvq6FpaWqiqqiIxMXHAp9P4gkdnxG63Y7FY0Ol0PdYx9idFe3t4ohwOh4OWlhafBe6BINCx/9T3XYPGRjv4muTcbneH+goPH7En9BodHc2wYcM6vBhVVVV+H7uiwoLJZMPhEKivb6FzVHSghacvVGF3sCBJktdUqPHjN6LVthIeXk1NzTliYxcG/IIH67pERUUREREh13OkpKSQkJDQYTz+HEuhUDB37lw2btyIJEnU1dVRUlIiU3P6OoeeFmWNRsOIESPkSMy+ffvIyMjosYi8v0X3fBWgR0REcNFFF3Hu3Dn27t1LVFQUqampg8bGIAKC1Wrlyy+/ZMOGDfJnL774IgCrVq1iy5YtvPDCC6hUKvR6PZs2bepxDrmQaVT+GQguBKGOkJAoZs5c4LNVMNeX7tCeeMNiscj1jh6n0NChQzEYDCgUCgoKCnqM5P7U4HJFIoomVCod0OZ1f+utt2htbSU0NJQVK1b8pDemHoHD9kXk3qJP53sPIggCWq22y9h6Q+wSzOL2nwp+1sZGoGlUCoUCl8tFXV1dh4lMEAQ59JqYmEhISEiPoddAJl6HYxGHD7sQRS0ZGV2l4YMVnv6pGwm9Qeeamc486GazWdYkaW5OQ6k8iCDoiYkZfcEnbIVCwZAhQ4iPj6e4uJjTp0+TlZUl1x/46/VPTEzEbDbT2NiI2+1m69atPRob/sJgMGAwGBg6dCgHDx4kNDSUtLQ0n0XkwcpV7a69L+NBEARiY2OJjo6mvLyc/Px8DAYD4eHhPvu70M/AIAYWDAaDLBDmwapVq+S/16xZw5o1awLqU6lUBjS/n18GKTdu92M0NJQRHj4ahWItvurc+joul8slC9t5+ukujdVoNBIdHd1tvWMw0djYyHfffYdOp2P69OkdlKHPNyRJi2f71kaK0YxKBQ0Ntbjdbq8b4Mcff5xNmzahVCpRKBSsX7+emTNn8swzz/C73/1OTtlNS0vDZDLJe5zp06fz7LPPAnDnnXdyzTXXMGPGDEaNGkVBQUG3KYLtYbfbWbRoETU1Ndx7770dNGs6w5MK63a7sdlsKBQKv7IKPBg2bBj5+fk+x/b++++TkZEh0+Xfc889zJ8/v4NGhS+0L3D3GB2ezz2YM2cO//Vf/8XEiRNZuHAhb775ps+6QV/YunUrGo3Gb1r5gY6ftbHRncfI491uP5G1tLTIL6rRaOzgIQkUgUy8qakZmEw3y5SjfekrWGPqqZ+BCF+GRfuaGW9ihx7U1DyGWn0Mg2E8ktQ7Ssv+MObUajXZ2dlYLBaOHTuGWq0mMzMzoGNlZ2eza9cuoE3csTttjEBSnTzGQOci8oSEBIYOHdqln/6u2fCnvYcuNyEhgf3791NSUoJare4SORrEIAYizmdkw+2uYevWVqzWVEJDy7nkEieC4N2R0Jf15fDhdzl8+DAREQaSkhbS0NBAXl5eh7nb3zTW/sK+fTuorj6MJCk4eTKa7Gz/hP/6G0ajhosuSuHIkQomTEhBpVLQ2tqKzWaT9xO5ubl88sknFBQUoNVqqampoaGhAYBnn32W5cuXd6gP/Oqrr+SNuudZq6urIy8vj//+b+81mD1h3759OJ1O9u7dG9DvQkJCOtRMBMPI++CDD1i4cKFsbKxZs4Zbb71VNjb8WafaG0RWqxW73e6VVeuTTz7x+vue+v/2228xGo3yOu3Pc+9yuQaKLlQXDJhRpaSkyNa0SqVi9+7dHb6XJIm1a9fy6aefYjAYeO2118jJyem2T4+x4Xa7aW1tpaWlRU6H8oTAPBGLuLg4uZgsUJpbb+h5QRBRqV5BqSzA5bqR6OjZPlsONGMDgrup9qT3eJic/P2NzWajubmZuro66uvr5YnUszglJiZ6LebyBVE00No6F70+4odjiJw79zAaTS5wN+Hhl/vVT38thkajkYkTJ1JdXc3evXsxGo1+TyxDhgyRjQ1oo0+cMmWK1415IAZB+7bti8hLS0vJzc1l2LBhxMXFdaD87e+aDX/bK5VKIiIiiIyMxGKxkJubS3p6ut8aHYMYxIVAsCMb3a1TTqcJqzUWne4cTU1DEUUlvoL6/o7LI8bW3ilUXLwbrdZFba2N1NQqQkJC/Wb0O1+Ijt5HWZkdQZAIDz+Ivyrj/Q1BUDNt2kimTcsEtNTUNPDWW2/hcrmYNm0akyZNoqqqiqioKHmjHhkZiV6v57nnnqOiooK5c+cSGRkpK4hD230SRVHWnNiyZQuXX95xDXzqqafYunUrABs3biQ9PZ3q6mpuu+02Tp8+LbcZMmQIK1asoLq6mpycHDZv3kxJSQn/9m//JiuIP//88yiVSrKzs/ntb3/Ll19+yerVq5k0aRJr1qyRyVueeeYZRo8e3YFgpra2lmXLllFTU8OkSZM6PIevv/4669evR5Ikxo4dy6pVq/joo4/Ytm0bjz/+OJs3byYtLY26ujqqqqq6ZffKz8/nrrvuorW1Fb1ez8svv0xmZiZOp5NbbrmFo0ePkpWVhdVqlX/jibJYLBauuuoqDhw4gCRJrF+/HqvVyoMPPshzzz3Hhg0bUKlUZGdn88c//pENGzagVCp58803+dOf/sTw4cO544475Ou6fv16pk2bxsMPP0xFRQWlpaVERUXx1ltv9f5h6kcMGGMD4JtvvvEZ9vrss88oKiqiqKiIvLw8brvtNvLy8ry2PXz4MLt372bPnj2UlJQwceJEHnvsMUaMGEFoaChJSUlerWOr1XoeWT7O0NLyD5qa1ISHv4BK1T/GRkvLXlpbP0CjmY0gTBiQaVT79+fS3LwFmy2cSZNu7xJubG9YeP55jEWPYWG32xk7dmxQx9XUtIPo6NcQBBGHYy1wLKj99xYeRe2jR49SVVUlizt1tzCrVCoSEhKoqKiQP/viiy+44oorurQNZMPuzRhQKpUMGzaMpKQkuYg8IyOD8PDw88ZGFUj/er2eoUOH0traSnFxMSUlJQwfPrzb9KpBDOJC4fxR39owGI4yevRMSkubyM5O7zZ92FtfncXxmpubcblcXcTxFAoDx4/biYx0k5AwgubmygFlaACMHWsiNnY7Wq2bsLBZuPtdNkoEqoEYuqdoVyCK6YAN0FNefgiXq80oOn78KJMmTWLevHk8+uijZGdnc+mll3L99dczceJEbr/9dp5++mm+/PJLIiIicDgcSJLEnDlz5Hu9fPlybrvtNnbs2MF1113X4chms5ldu3bx+uuvc+edd/LRRx9xxx13cMcddzB9+nTKysqYP38+u3fv5qWXXuJPf/oTH330ETabjTlz5vDll1+SkZHBr3/9a1588UV+//vfA6DT6di2bRsA8+bN4/nnn2f48OHk5eVxzz338PHHH3cQ3nvkkUeYPn06DzzwAJ988gl//etfgba94B//+Ee2b9+OTqfD4XAQERHBokWLWLhwYYfzGT9+PDt27GDJkiU88sgj5OTkcO2113Y436ysLLZu3YpKpeKrr77iD3/4A1u2bOFvf/sbISEhHDhwgD179jB16tSAZBeeeOIJTpw4gVarpaGhgbCwMFauXInRaOTuu+/G6XSyYsWKLtf18OHDAOzdu5fvvvvuB+X1gYkBZWx0hw8++EAufJoyZQoNDQ1UVlZ6pd7csmULJpOJxYsX8+233/Ldd9/5dYzzmQtrsehoblYTEtJMSUksaWm+N2G9HZcoOnG57kCrtQKf43S+3W/85b2FJElERPyFUaPycbsV1NaOQ6udKy9MTU1NOJ1OdDodJpPJq7FotVqpr68PynjaQ6WKQpIEFAoJuz0Mf6K358uYUygUxMTEoFAoqKuro6ysjKysLEJDvWtoSJLEzJkzefvtt+XPioqKvBob4P/97W5z7ykit1gsshK5Wq0OWg2GNwSqUN7emNHr9YwePZrm5mZZo2PMmDFBUU4fxCCChfO1Tonis5w5c4KoKCUpKY8CET3250knac/Q2F4cLyUlxStBxJgx95CZuR+1ehhudyiSVOGld//Q2NjI9u1folSqMZv9qyfoCZIk4XKtIi5uGJJkxO2eGZR+fUNEobgUyAWmIopf442u/UcogbZ5Ki0tkoICLc3NNiZOHMIXX3xBc3Mzn3/+OYcOHWLr1q0sW7aMdevW8etf/1qONDmdThQKBYIg8PXXXxMdHd3h2aioqMBkMnWYM2+88UYAfvGLX3D33XcD8PXXX3P06FH5dx4ilvYoLCwkNTWVjIwMAFasWMHzzz/P6tWrAbjhhhsAsFgs7Ny5s0N9h91u7yK8t23bNt59910AFi5cKDuKvvnmG5YsWUJUVBQWi4WICN/PcExMDJWVlQCsW7fOa5vGxkZ+85vfUFxcLLOdAXz33XesXbsWgAkTJjBmzBgkSZJ1XLyh/To1evRofvnLX3L11VezeLF3PbJvvvmGwsJC+bP213XRokUD2tCAAWRsCILAZZddhiAIrFy5kltvvbXD9+Xl5SQnJ8v/T0pKory83Kux8eCDD8p/ByI8FuxcWN99iej1DvbsuQm3+ywmU/fFyH1ZXCQJBEEiSKfVrt++L3aeMG1UlAtRBIVCorm5lJqaItmwSE5O7pNidV8QEpJNff0r2O35mM2/AByoVC8iCFaczt8DJq+/83ezGwyDzRN2bm5u5tixY+h0OjIyMrpE7iRJQq1Wo1Qqcf/gkktKSvLaZyDvgD+RCqPRSE5ODrW1tRw4cACn04nZbPbrvvbFeOhte5PJRE5ODvX19QM2/3UQ/3cRTGOju3Vq164mamoSUCqdzJ59DoPhx42aN3E8m82GwWAgOjraK0Nj9+ekQ6drU6cWRWefzu/YsX/S2NhWW6FQJAHTet1XR2hwua4MUl89oRrIRRBcSFLuD//vquviDQZDGDffPBNJgkOH6jl06CCiKOJyubjyyiuZNGkSmZmZbNy4kVtuuQVBEDAYDB0oczvPuQqFgpCQEERRlEXuOrdrnyq7Y8cOefPrdru71M72dH89Dh5RFAkLC/Na59FeeE+SJJxOZ5f1KJBIus1mk6+BLyfXunXrmD17Nv/4xz8oKSnpUFDe+TgajUY+D09aledd82RrePDxxx+zbds2PvroIx577DEOHjzY5didr2t7/BQcYgOG83HHjh3s3buXzz77jL/85S9yCM0Dbw9nsMOs58tjpFa/gF5/N5demktOzjVMmNCVgSoY41Io1AjC0zQ1LcLlegKtNqZP5yeKIhUVFV3YWPyBJ5x+9uxZiouL2bdvHwUFBdjtdurq1qBWX4ZWu4rMzFWMGTOG1NRUoqKizquh4e3ahIfPIS7u/2EwDMXh+Cui+ARu97M4nf9x3sblC+0nUpPJxMSJE4mJiWH37t2cPHlSNirat125ciUJCQkMHz6cRYsW+ezX3w17IMZAZGQkMTExGI1GCgoKOHXqlF+GTX+yV3VnnISHh18wQ3cQg/CFYK57vtYWQTiL3Z6EQqHC7U6grs7ImTNnOHr0KAUFBezbt4/y8nIkSSI+Pp7x48eTlJREUlISycnJhIeH99pQ7+v5xcaWoFBIKJUuYmPL+9TXhUMMMBVJUgFTf/h/95AkCbfbjdOpwW5Pxm6PRa02IggikuSisbGaTZs2cfToUY4cOcKQIUNkyuDOkQdvyM7O5uTJk7JeiSRJcn3A3//+d6ZMadvHzJs3j7/85S/y777//vsufWVlZVFSUkJxcTHQVu8xc2bXaJHZbCY1NZXNmzfL57h///4ObVQqFTNnzuTvf/87LS0tfPzxx3Kmw5w5c9i8ebO8Z6mrqwPwes7Hjx9n1KhR3V6DxsZGmUr5f/7nf+TPp02bJl+LQ4cOceDAAeBH4WKdTkd4eDhnz56luroau90uF46Losjp06eZPXs2TzzxBA0NDVgsli5jvPTSS3u8rgMZA8Zt57mBMTExXHPNNeTn5zNjxgz5+6SkJLkwBuDMmTNB588+P7mwEi0tn1FToyY0dB9ms73HjV1fjKDQ0BxCQ9sK6a1Wa5+MjeLi94mMfIrWVjN2++99tutcANjU1OQ1T1ej0ZCfn09S0kXAJUGPvvQG3S10Z89WM2SIhCBInD1bTXJyJeBGkrxHCPobnTfW7aldS0pK2LVrF+np6cTExMhtzWYzK1asCKjfYLX1ICYmhmHDhlFSUuK1iLyv6M9IyN6hOv8AACAASURBVCAG0V/ozbvUV3Rmo3K5XFgslcADREeDIMTidF5ES0sLRqORxMREjEaj13dmoFC0p6ZeSVTUgyiVKoqLl/d5PP7A5XLJbEsTJkwIAmOS8EPqlO+aDbfbLRdwi6IoO4mUSiVKpQmFQkFWlgqNJocDB4rZs+cMoihSUlJCeXk5999/PwC33HILV155JXFxcXKB+Ny5c1EqlUiSxOjRo3n99ddZsGABL730ErfccotMvGK327nooosA5M32M888w5o1axg3bhwul4vp06d3YbDS6XS8/PLLLF26VC4QX7lypdcr8cYbb7B69Woef/xxnE4nS5culeszPe/LunXrWLZsGR9++CEXX3wxycnJuFwuRo4cyX333cfs2bMRBIGcnBxeffVVli5dysqVK/nzn//MO++8w5AhQzhx4gQTJ04E2mpAJk6cyDXXXNNhLPfccw833XQTTz/9NLNn/1hne/PNN3P77bczbtw4xo4dK18TD5RKJaGhodx///1MnTqVoUOHkpmZKd/HFStW0NjYiCRJ3HHHHYSFhXHllVdyww038OGHH/LUU0+xfv167rzzTvm6XnLJJbzwwgvdPkUDCQPC2GhpaUEURUwmEy0tLXzxxRddcuauuuoq/vznP3PjjTeSl5dHaGio1xSqviBYkyV0Z7hYOXEilaSko5w5M4z4eBNmc8/jGghsVOHhm9HrmwkJqae2dh+wyO8CwJ+Dh1ih+A15eSdRqRzEx1+CwTALkLDZnsXtbqt9ON8bBm/HUigUDBs2jMTERIqKiigrK5OVyf1Bb6hv/YUnEqJUKklLSyMpKYkTJ050KCI/nwi0JmQQg+gPaDQanE6n3/NkMNYDp9NJQ0MDVquVQ4cOyeJ4RqOFEydicTh0REQ4mTv3UgSh53fkQiqIt4ckjcRobPOEt7YW9Hk8/uDo0SPs359HW1G3xMUXByN1S4EndcpjVLjdblwuF8APRkUbe6dHn6QzJCmMYcMySE0dQkXFx7S2OrHZnIwcOYKtW78hKSmpizbMiRMn2v3+x/twySWX8Ic//EEuYD548CBGo5GHHnoIm80m75+ioqLYtGmT/DtPGtWsWbOYNWuW/Pmll17Knj17OozX6XRSWFjY4T1ITU3ls88+83JuP44tMjKSzz//XP7/008//YP+iJVf/epX/PrXv6alpUVON5o2bRqHDh2S27/33nssWbJEjsatW7fOawr+1KlTOXbsR6KYRx55BGir93vrrbe63AO3201zczPmHzZ5a9euZe3atbIwtCcNrHMmD0BGRoYcvbDb25zS7a+rBw8++OCAI1PwhgFhbJw9e1a2IF0uF8uWLeOKK67ooMy6YMECPv30U9LT0zEYDLz66qtBH0ewJkvwNWE2o9X+gbS0kxQXZ1NffyPp6d7z/nvuq3foSz9m86UIwhEkyYDTmcLevXs7GBbdFQD+HJCYmEJo6PM/GMZP4HA0IkkCbvc7KJVq3O7JdM8aElz0dC+1Wi2jRo2isbGR77//npaWFiIiInrc0PRnZKNze61WKxeRFxYWolQqycjI8Nsw6isCrQkZxCD6Ax4B2v5yyngTx/PoSQmCQGpq6g/ieOBwPENRkYhOZ8diGYW/c5o/65TNdpadO1/CZnMxZcrVRER0pa8Pznp3ft9pjeYQgtCWqqPV7sNXnYg/59XesOgcsVCpVAiCINcr9AwFkpQMiKxYMZ1Dh06zfftx3G4XFRWVPPfcc1xzzTUMGzbMr/N88sknKSsr68AYqVAoMBgMuFwurFYrarW6C+V8IOtJMNB+TO1rTHzB5XJx1113BX0co0eP5uabb+5yrzx1Mh79EI1G0+O7/1NfpwaEsTFs2LAueXjQUZlVEIQO+Wr9gf6u2RDFcmprj2GxaEhNrSU7+2K/HqBgjStQClGr1SozHlgsFlyu0URGPopOF4PD4WbSpNF9Nix6Ov/6+nq+++4DBEHFrFmLMRqNXvs4XyxQnuPv2zeSkSMNKJUutNr/Ravdhihm0tLyznkZB/i/0fdEASVJ6lZwL9B+PW2DwRZlNBqZMGECtbW17N+/n7CwMNLS0vzut7cYTKMaxEBAdwK0gUCSvAubajQar+J4Hs+rx+MrSTUUFhbR0pKIwWBlypQ5ARFe9DQPV1V9S3W1HZUKjh37nIsv7l4rq6/wjKmvG7Wefj9ihBW1ejcul4qsrDF+pwT3ZFh4i1j0LvtCQUhINpMnR9LQYGXfvjIUCoHW1lY2bdpETk4Ol112WY9z4eTJk31+5zFe7XY7LS0taLXaC+54VKlUqFQqHA6H/M/bpv7666/v87G8PSNHjhzptr1Go0GtVuNwOLBYLD6v2UCULAgUA8LY6G9ciFzYrhOvhMv1NWr1OUJDQzh0aCaTJgVvEpekVlpankeS7ISErEahCPPRrms/kiR1YBbpTFkYFRVFampqh5egvDz/vEwkFRUbueKKpxFFBaWlSjIyftHvx/QHZvNkXnnlHiIjz/LLX76K2+1EpconMnIm9fWrgJH9PoZAnmtBEAgPDyc9Pb1DPYc3Abv+jmx0t6BFRkYyZcoUKioqKCgowOFw9KtBMGhsDGIgoDfGhqc2zkMz66EJb68/lJCQgFar9fmOdk73dbm+49QpLXFxJ2hszCIszH/qWH/SkMPC0lCrDyCKEBeX7LXN+XQeBQtu9w1kZxcCLTgct/lo45aNQc/fCoUChULRbSpU8GAEDMyf7yYtLYp3392LKLYxQu7evZvCwkJuuukmOeWnt9BqtWg0Gmw2m8/N/fmGWq2W2aosFgs6nS6oTIOBPq/t2wuCIBsZdrsdh8OBTqcLiEn1fKM37+fP3thQqVS4XK7zbmF3nsQFoRK1+nPKy+PRau2YTP5T6PkzidfXv4FW+waCAHV1IlFR9/vsx7M4dTYszGYz0dHRXQyLC4mUlMMoFCJKpYvExOMXejgy0tLSCA29HputlYMHd5Oevh+9XkKprGD48Ptxu6twOO6jjf/8wsMzOXhqJRITEzl+/Lisz9E+YhSIARFoGpI/fQuCQGJiInFxcWzbto3c3FzS0tKIjY0N+mLck7HxUw9dD+KnAU8alS90ro1rbW2loKBATmENCwvzKVbbHTo+3xLV1Z9RXx/PuXORjBiRgkrl/zrgj5EQFjaBK64Iw+lsxmzufwXuYEU2oKe5y4zd/qTczmZrI0hpbGykqqoKs9nMuXPnMJlMKJXKLmlG5w9tAoDp6WZuuknPa6/txOFoYy1sbm7m2WefZdKkSV2UwgOFIAjo9Xrcbjetra1A/zh9A+nP26Zeq9X63NRfCLIGzzXzzAU6nU5O7x8oa5EkSdTW1nagSvYHP3tjQ6vVYrfb/d48B5MRpyPLxw6UykMkJiopKZlGZmaW3335U0tit6tl0Tmbre22iqIop0JZLBaamppoaWmhtLQUk8kUMBf6hYDJdCtqdQGSpEal+sWAYKzyICoqipqaGj7++Ep2757C8uUvYDC0IggiSuV6VKp/0tr6JpI0tMtvg6VT0tsIhE6nY8yYMTQ0NHD48GFMJhPp6eloNJrzWrPRHZRKJVqtlgkTJlBcXExpaSmZmZldFOb7gsHIxiAGAtpHNjzztq9Ic2RkJPX19UyaNCmoGxCF4hAHD2rQ65swmTSkpOQEvJnzZ14zGPo/PbI9fI3J6XTKNRB96dtms/kUoT19+jTFxcWoVCr0ej01NTV99vR7KM376vl2uw1cccU4vvhiPzabS/68oKCAgoICWV3bVzTabrf7Zdx60sQkSZLTw3zBU/zuz54kkLYeHY72114URVnIsPNz4HQ6USqVfq8N/l6LQNq3H58kSahUKp/jOd+GiE6n86nT5QsDd5cZJAQrFzZQdIxsSKhUr9HQYAIU1NRMIyXF/778mcQjIm7k1KkWnE4rCsVlVFXtRpIkeYGKjo5myJAhHDlyhJEj+z/FJ1gQxRzs9l20FfwNvMc1MjKS7OxsTp06xZtvrmThwneIjz+DILhQKA4QEjIem+1hXK7bg37sYBgFYWFhXHTRRVRWVlJQUEBSUlJA0Ypg6lr4glarZeTIkT0WkffGgBs0NgYRCFJSUmTvtEqlYvfu3R2+lySJtWvX8umnn2IwGHjttdfIyfFdl2Cz2Th06BAVFRWsW7eOlJQUlixZ0iHS7M0h1B/ezubmd6mp0dPcbMJsVhMZGdhm4nylP9ntdmpqaoiMjOzRu+rr+hQWFpKbux2j0cyVV17ll5fWs/nzGBXNzc1yyotHhLZzdGn37i9QKs8iiiqghYYGt0yv2luUlZWh0WiIi4vrdR9Op5NDh2rJyUlk5MjfsGHDjdTVdUyZs1gsZGVl4XK5vNZK7ty5k4svvrjHY9XU1FBbW0taWhonTpzg3LlzZGZmelXzLikpQa1Wk5iY2GO/p0+fRpIkhgwZ0mPb1tZWjh07RnZ2dofPJUmiqqqKkydPdqhlPHjwIAkJCX6nlPl7LQJtL0kSlZWVHDlyhMTERDIyMrwamRcuUuY/Bt7uLcjQaDQXxNhoP/EqFLtxuYqJjGyioSGOhISZffIYiaLYpcaizbCYTViYGZPJhNFo7LJAebwLwTq/YMC/8XSNSomiSE1NDSZTz2xe/QlBEJg5cybJycl8/vnnbN78G6644h9kZR0F3IALne4PiOJ/Y7XuQhSjsFgsNDY24nK5SE9P7zB5BHp/gmEUCIJAQkICMTExnDp1ivLycpRKJTEx/glJ9Vdko/O16FxEHh4eTlpamhy17M3mq7sakoE+eQ/iwuCbb74hKsp7LcNnn31GUVERRUVF5OXlcdttt5GXl+e17ZYtW/iv//ovRo8ejSRJXH755Vx33XV+zWkeZ1bwDGUXRUVHaGwcgyBIREdH9yoly9v8VVv7T86dO0BS0lxMpr4VhIuiyOef/w8WyzkMhkgWLbq1Ww+/rzEdOfI5avU5WlrUnD07nKFDx3f4vnOhvdVqZffu3d0aFt4wbdoOvvwyhfDwOoYNK+X77y+MJlNneK6JJKVjtx9l9erJ/PnP82loiJbbKBTfAP5nYHR3LEEQUKlUZGZm0tLSQmFhIWVlZWRmZnZQxO6vqLqvtoIgEB8fT0xMDKWlpXLK7kBhKfSszWfPnkWpVJKbm0tKSgqJiYkDYnyB4GdvbHjSqM432k9yovgaWm09breCioq0gMJPoijKk15jYyMWiwVJkggJCcFkMhEbG0taWprfqVD9TaHb2tpKaWkpERERfm1We4vdu18nKuptTp1KZfjwx/rtOP4iMTERs9lMdbWdLVuuZ/bs7Uyb9pX8vUJRg9GYzrlzkykvf1amnNy1a1ev6xECuZf+TMwqlYrhw4fjcDiora2loaGBrKwsmanGV7+Bspz1VcOjfRF5fn4+iYmJDBkyJOCxwCD17SCCiw8++IAVK1YgCAJTpkyhoaGByspKr5pQ1113nZyqcvvtt5OVleW38yTYUQRB+JRDh4bidCpRqUSSk8f1oo+uY7LZTvPtt9/hdgucPPkuCxaMwx/NDl9wuy1YLGdQq91YrZU4HOfQ6wPX2xoxopBdu6IxGFqJja2S11hPxMJms8mGhclkQq/XM2HChIBTl4YOHcfq1S8jSVqs1tuRpDpEUcRms6HX6y/o3PPjsXVYrftZvXoKr7wylaqqRGJjyxkxYiEuV7dd9AohISHk5ORQXV3Nvn37iImJITU1VRYR7A8mxJ7meaVSKWtTFRcXU1tbS2xs7AV3ZnogSRLJycmkpqZy8uRJmeAlKioKQRB+EmvY/wlj48JGNiyo1R+hVDoRRRXNzdN8agh0Lt72GBaC0CZ371Fv9Ux4ongAt3snKtVcINKvMXWHkyc/wGb7GqNxMUOGzAn0lAE4ePAZ0tPf5dy5VHS6DZjN/SPSNmLEf2M01pGScoyzZ68GLqzHSKPRsHjxYt5++20sFgv/+7+XUF09lMWLX+7QLiYmD4MhGbdbjyiKpKSkcPz4cU6fPk12dvYPXPf9k8IUSK1EWlqaHE720NB6q3sKdLMeSPvuPLfti8g97FpDh3atjfEHP4WJehADA4IgcNlllyEIAitXruTWW2/t8H15eTnJyT+yLCUlJVFeXt6jAG2g65Rv0djeoa7uSWpqrkQUFSiVDhITMwLuwzvduwpJEhAE8YdUIv/78ga1OoTJk89x+LCJMWMa0eu7X1+8jclut5OUdDHLlj2LyxXO4cNzUSqPYjKZMJvNXhm8SktL/R57ezidd+J2L0CSIpGkKESxhi1btlBXV0dmZmYHFeoLDZttF7/+9So0mj/idF6DwzGXNpHCvsHXOhUdHU1kZCRlZWWy062/Unj9betJ2fXUtp47d46MjIyAi6GDDc9aqFaryczMpLW1laKiIkpKSsjMzCQ6OrrnTi4wfvbGRm/SqILJy61SvYVG04AkgSRpCAlpm1x8GRYhISGYzWbi4+Nlw6KiogJRFAkNDZX7dzpLcThWIQittLZ+iNn8pl9j8uUNs1qriI1dh1rtwGbbidu9A6XSd4jY1/UZOfIdlMpWhg3bR2vr90D/TKZ6fQyCUINSqSE8PJHq6uAUXPvbzlMU6PGEOZ1O9Ho92dnZ7NmzB7fbzaFDQ9Fq/8L8+b/v1EMd0JaTqtFoZOG9Q4cOERoaGtA4guXZ6dyvhyp38uTJlJeXk5+fT3JyMsnJyR366e80qp7Or70SeWFhIc3NzbLC7SAGEWzs2LGDhIQEzp07x7x588jKymLGjBny997eXX+e957YqLz1GazIhlp9hm3bMn8wBiSUSnrl0fU2JoMhnosvvpKzZ4+SknKx31EN3+emJC3tMTIyvkcURyNJ3W8CRVGkrq5OLri32Wyo1WrM5imYTLMwmcLJyek5wtD7/YCAKHpSkSRaW600NJxGrbZSWGi/oMaGt3NyOl/E6XwxqMfpbs5XKBSkpKQQHx9PUVERtbW1fjuN+pPIRKVSkZWVRUtLC3v37iUmJoaUlJQLRqbT2fGm1+sZM2YMTU1NlJSUEBkZOeBrD3/2xkagaVQej1GwOI5Vqgf5/+y9eXhkV3nn/7m1q/ZFKm2lrbRVS72vXvCCsR3bQ4wXFkOIA2FzghMm2wADP5IQkpBMnDAQAk9mDL+BkMHgxICN25jF4I2Wulvd6k271Npba6tKJam2e+/8ob63q6Qqqaok0bbR93n6sSXdOnXqVtU5533f7/v9CgIIAiwumllclDh+/DiwzEG32WwpgUU6pFvEFxam0WpjaDQGotGprOay1pfNYNCRSIAoatDr1zcAzLQZFBQcRhBeRBBcGAxNWc0rH+h030Sn+x6StAtoADZHFjfdPVpZYo9Go2qJ3eVyUVlZqapchEIhLl++TF9fH6Io0tY2S0nJMfbuvR5BkEkkfMTjpciyiCAsm2pptVocDgdHjhxheHiY4eFhxsbGKC0t3dSsez4LsyAI+Hw+SkpK6Ovr49ixYzQ0NODxeFZdu9nzyCVAMhqNNDQ0EIlE6Ovr+5U7kW/j1wNlZWUAeL1e7r//flpbW1OCDZ/Px/DwsPrzyMiI+pi1cC0rG07nL/nZz5QDsYDBULDm9ZmQKQAqLT1CaWlmM7jc4UEU37Lqt7FYLKWPcWlpiUgkwtzcHC6XK8XM8FrB7Z7B4xljetrF7t2nrtk8Mu3fymdKkqQV/isJ1RMkH6x3z41GIzt37uTcuXOMjIywuLioqiNmwlYGG8oZUKm+DA8P09LSQnV1NWVlZRtWMMsVmar8drudPXv2vOYDDfg1CTbi8XjW1280YySKolqxMJt/gU63oP5terqQ2traNQOLbOdks+2hq+udaLVnMJnel/d8Feh0hcjyF0kknkOvfweCkJ/PhkbzBbTak0hSDbJcvOF5ZYIslxCPKw7zEWRZ5sKFf0aWz1FU9Che7868xk0kEszMzKjBRSQSUd137XY75eXl6yo/1NTUEAqFuHTpEqIo8uMf/5yysjEMBsMVd1hBVZOJxWJqeVSj0VBeXs7o6Chzc3MqtSqTIsavsplOae5bXFxMae7bSv3vfDYIvV7Pvn37mJ6eTttEngu26VXbSMbCwgKSJGGz2VhYWOD555/nM5/5TMo19957L//8z//MQw89REtLCw6HY10KFeQebGxmZePs2f3AqPpzZeWuvMZJnlM8fpGhoW9iMjkpK/swgpBbs/l6WKkKtbS0hE6nw25fFkjxer0UFBRw5swZqqurc252X4nNut+i6OS97/0p0aiI0VjJ5OQ8P/zhD4nH49x9990ZhQe2CopruSiKKeud0tCtHGITiQSCIKj7VC7I5b4pSaNEIsHx48epqKjA5/Olfc6t6u9YObZGo6GqqoqysjL6+vpoaWmhoaFBVdPK9XORz575RlBNfMMHG7mWp3PJGCUHFgoVShAElQpVWHg85fozZ36Te+5xZBgtM9ItdFqtjqam/7aphz29/hb0+ls2OIoJUbwx66s3a+6ieJo9e/4OrTbB1NRp4Ni6j0kkEin66JcvX2Zubg63260232eTCUtesBUTo5tvvpnvfve7KuXq1KlT3HLL6nurZJCi0agagGo0Gpqampifn6ejowOr1Zo2y7MRZaf1rs00rtlsZt++faoilNFoTKH3bSZyXWCTry8sLMTj8agUMKWJ/PW+YG/j2mFiYoL7778fWF473vOe93DXXXfx1a8u004eeeQR7rnnHp599lnq6uowm818/etfz2rsrdyn1oPNVgKMqT/feuuteY2T7Ad19uy/09MTQ6OZ4Oabf0Jx8X/Je35KYJGsDKXT6dTmbSWw2Ky9ZG5ujpdffhmr1cqNN964aQa3giCQSDiIRL6PVnuWpaWbOH++nZmZUUCmre0Yd96ZvdlvrlD2KUmSEEWRxcVFFhYWKCgoUD0lMlUvBEFI2adyoRPlk+hS1BH7+/vVw71STd/IuLnMeeV90Ov1KrWqu7tb9X3KtVqWb+Dwek9+veGDjVxpVJkyGEpgoWRSFhYWEARBpUIpzdvJH6KnnnozFRVH0ethasqBzZYfP1P5oi9jhETia2g0dWg0775mH0DlPr0WvgDLDfQJYPl9KyhYXclK9/5pNBp1w1Jc04uLi9fk+ycv2CvnoNChqqqqGBgYoLy8nJGREQDa2tq48cYb0+rlKweHZOdQWOZNHzp0SPXAqKysxOfzrcpAZYPNXpgVRahz584xNDSE0WjcdDm+fNzJk79/yRQwpYm8trYWr9f7mvjcbuP1Bb/fT3t7+6rfP/LII+r/C4LAl7/85ZzHvpY0KovFwkMPPURvby979+7N+3CdvE/F4zYEYRFZFkgksu//UBJAsViMc+fOMT8/j16vx+FwYLPZKCwszElEI5+KxLFjP+XSpV5AS2lpIY2N+VXJM0GWq0gklvsSysvbaW9fBMDnO0lvbwCNRkNNTU3G15jN+qzsU8o/BYKwXFU3mUwEAgE6OzspLi5W/SUyYeU+tbS0pCbKsqFc57P36HQ6GhoaUqrpgUBAlcrNZdzNFDKxWCwpCTen0/krCTZe7/i1CDZyoVFpNBri8ThLS0tqJmVlYFFRUYHFYln3A2M21/D5z/85giAhy1r+8A+vz+s1JGeMQqH/D43mzJXDbTUmU/ZGMq8FKA16m9UToyCROEgk8sfASXS6jzM/HyIc/jHhcIK5Ob/6/tnt9ozv38oFI9OCDcvviRJgKD8rKCkpoaioCLPZrAYbiUSCF154gTvuuCNlfKWqosgaazQaiouLVdd7jUajZnmUEm4gEMDpdG669G2u1ypN5BaLhXA4TEtLC42Njbhcm6NAlmvpO9MGodPpqKuro6Kigp6eHgYHB2loaNhuIt/GawYmk4lgMJj19ZtJo1LWnGx6S7Kd0+7d78Nk+hEFBU5KS9+U9vrkyrKyz2q1Wmw2G4IgYLcLnDv3MoIgcPvt78Ljyc/ELtf75HKdYnRUjyDI2O1ngJ2ber+T4fdbeO97v4Yoarh48QF+8YvnkGW46aZb2LUrOzpb8j61cg1MrlYogYaCwsJC3G43Q0NDtLa2Ul9fv6p6oIy/sLBAMBhUk3WwrCYVj8fzolZlQrq9J7mafurUKdXocqsrG9km3IaGhhgZGWFoaCgj5Wvl2NvBxhsQ65WnE4lEChXq8uXLLC0tqZmUbAOLdHC73dx119309vamzWpni+SFLhSScbtlRFFgcTHBtVJky6eycf58CzMz3yAWK+OGG/5oU5p3ZVlmcXGRSCTK4OB/YX7+ZmQ5itv919TVfROA2dm/w+X67TXHSaZCxWKxrBfsTNBqtezZs4djx44RCoUAOH36NMXFxej1etWI0Wq14nA4qKysVCtjK6lVOp1O7ZkIh8N0dnZiNBoxGAxZ38Ot7O/Q6XT4/X4WFhbo7OxEq9WuMmvKB/kEG2tdrzQhzs/P093djVar3VT50G1sI19cSxrVZh2kBUFAEGYZGPgUkiSyc+fvo9Uuuzsn77OhUCglsLDZbFRVVWE2m9Xv79zcHBMTP0EUQ8iywMjID/F4Hlnr6VMwNzfH3NxcXvfohhsMlJe/isUSo7DwzYhizkNkjXj8vTidMrDIqVMmRHESgLGxZUXDlSIXyfvUyiRqcp9FtvuUogZVUlJCV1cXIyMjVFRUEI1GCYVChEIhRFFUE3VlZWXYbDZ17Vy5T21Gb0Wma5XD/fDwMMeOHcNkMmWd2Noq1URBWDYFnJycJBqNcuzYMerr69eUot1sb6fXS5X+DR9sJNOo0mVSkqk0FRUVyLJMVVUVVqt1U56/qamJpqaNqTIlbwY63afo6PgGWm0lzc35VUqWlma4dOnLaDROfL7fQ6vNr2ye6wbldH6WpqZziKKWycn9mM135/x8S0tLanZlfn6eRCKByWRCkqQUg8PJyb9FqxUBmUjkOHA12MhUsbBarfT391NfX6+WRvMJMpPnWVtby6lTV1VHfvSjH/HBD36QhoaGjNWdlSXrWCyGXq9XqzMHRjUbHgAAIABJREFUDhxgcnKS8+fPE4vF8Hq9Wc1zqylXFouFAwcOMD09rWagampq8g6yN0qjygSbzabew/b2drq6uvD7/ZvGzd7GNnLFtWwQ38xgIxw+Rnu7BhCYm/sWBQVvUyu2mQKLTKiudtHXN4lGI1NZub6PlIL5+XmefvrfEMUIVqubhobcPENE8eNUV/8QWfYgimvLGm8cOuLx9wNwww1/ysJCmFhMR1+fjp6en3Phwnne+c53pTy32Wzm4sWLGAwG1dQt331KCSqCwSCJRIKlpSXa29vVBNha62K6fUqr1ar9H8nPs1l7j9KsXVpayvHjx+nu7sZoNGYUUclnDgpyoWhpNBrq6+vx+Xxq9byxsTGthPR2ZeMaYnh4mIcffphLly6h0Wj48Ic/zMc+9rGUa37+85/ztre9jZqaGgAeeOCBVUogyQiFQpw6dYpXXnmFiYkJnnzyST75yU+qVKhMC95rMdu5TNcZR5L+Bq9XR0nJXwH5Kz1dvvxXlJUdRZY1TE87KS5+OK855QqvN4Esg1YLXu/yfc+0gCcvhEo2TPGysNlseDweqqur0ev1RKNROjs7U2gxgvCnzM72kkjosdnegl5/PbJcSCTyOLK8nA1ZWbGorKzE4/HQ2dmJ2Wymrq4uq0UhecEOhUJEo1EKCgqw2+3s27cvJdgA6O7uXlNlSoEyNyWLlawGUlxcTDAYZHFxUaUvKeoYme7nVgUbK++RUp5X5AJramrykvHdLBpVJrhcLux2OxaLhZaWFtVHRHnO10vGaBuvf+RD992sfSqZppsrkkVSZmdnmZszIYoSggCJhDdvZoAgCBQWvod3vOMnCIIWjWa11G0mLC6eQ5Km0GolRHEu15cEGEkkHlg1n1gsxuTkJIWFhZtu8raspPce3vGO9zAyUsi3v70sjz41Nc7jjz/OHXfcgd/vVzPpLpeLrq4uJiYmaGxszEptKxaLqdWKYDBIJBJRBT6cTieVlZUYjUYkSWJwcJDe3t4U1aVMWBl0iKK4iiqdy1qazbUGgwG3243VaqWzsxOLxUJ9fX1GqdytPOAnV9QV/4u5uTkuXLiA1WpdNa/tno1rCJ1Ox2OPPcb+/fuZn5/nwIED3HHHHasqAjfddBPPPPPMuuN1dHTwoQ99iH379uFwOAgEAvzJn/zJmprNCjabm7kZTdTLmYvvs7jYDsiI4hM4HH+Y93gGg3RlXJlckrnxeJxTp76GJA0Ri92S830yGr+IVvt3yHIAQbg95W/JC6HiZaFkLBwOBxUVFVm9f0rVwuE4iCwfx2QSkOW3Ab1AH/H4v2E2/0nGL7vFYmH//v1cunSJEydOqCVm5T2Mx+PqPEOhEIuLixgMBhwOB3a7HZ/Pt2ojUvTDFdTX19PT06MGNOtl1LVabVo1EEW1Q1lwlQa6dBvhVvV3KHKzK5Gcgert7WV4eJjGxsas56CMvZWNd7Iso9VqMzaRb2MbvypcS1O/VAGSzMikvqgk8MrKguj1Z/F4KjAYDtHQcCt6/fprdqY5ybIGrfaunB9bXOxh585Bxsac7NkzsSn3SZZlfvjDZwiFLmM2W3nXu969IYO3dEIjoriLYPAMTuerXH/9lzh3roHLl+1EoyGeffZpDh26juuvX2YzmEwm9uzZw9TUFG1tbZSXl6cYriosDiUBtrCwcMXM0K6aBmdSUVIa1EtKSuju7mZkZCQrF21l7ZUkSa1y6HS6Le0ttFqtHDp0iEuXLnH8+HF8Pl9KwkjBZlOXVs5j5dhOp5PDhw+r8yorK1Ob8LdyLq9lvCaCjdLSUlWL3GazsWPHDkZHR/OmH+3YsYOXX34ZgG984xuMjY1ldVCFreHCbkawMTtbjNstIMsCc3PFbERt1OX6c0IhBxqNG5fr3Vk/bmrqBfbseQytVuTixfPAbTk9ryQ1IklfuyJluLwILi0t0dramrIQlpWVYTQas1bcSCQSxGIxotFoSuO2knHp7S2mvn7ZHdds/gZm81eJxb6IJKWXY0zOHnV2dtLf34/ZbFYP+so8i4uLs5JcvOuuu+jr62NpaQmfz4fb7cblcqkBTWVl5bpGQelK1qIoIssyZrOZ/fv3qxtPSUkJ1dXVW1bKzuVag8FAU1OT2muiGG1lkxncKhpVuvGVJnKfz0dvby+Dg4M0NTWlbZjcxjY2G9dSjSpd4CJJUkqPRTgcBlC5++nUF1tavszAgAeDYZHbby/IO9DINKfs0cDhw+9HoznP+fP3bVKwITE3dxG9fonFRSNLS/PYbNn1CyQHFolEgsXFRZUWu1Judvm/N3H99V+nvv55vvGN+1la0iMIMV599RVGRka477771PNMUVERTqeT7u5uVa5X2QdtNhsOhwO/34/FYsn5HFJQUMCePXuYnp7m9OnTlJaWpj3IJyOdumIikchaECafwETZrxWp3GPHjtHY2Jiydm+lcmamJFfyvAYHB/nlL39JbW0tJpMp56TYGwGviWAjGRcvXuTUqVMcObLacfSXv/wle/bsoaysjH/4h3+gubl53fF+1aZ+WzGWXv8y9fU/JRwOMD5+Lzt23Luh8bRaDy7XX+X8OKczilYrIcsCLlcoq9e20ssiWSPdbrdjNBrx+/2MjY1RVFSUkVq0luSs0WjE6/Vy5syZtIoaBQWf45lniqiouMSBAz8lHhfRav8UQQgiiu8A9BkVN2w2GxaLhenpaQoLC/H7/TkraQmCwKOPPrrqd6WlpRQVFdHX18eJEycIBAJpOZ4KRFFU7+Pc3BzBYJCioiJ1sVPcTpUMfXKj2rUKNhRYrVb279/PSy+9RFtbG8XFxVRXV695Lze7QTyb600mk9pEnkumeRvb2AiudbCh0KCUioWSNVZk3S0WyzrrXoKFBSNa7SKiaGRpyc46LNFVkGWZ/v5jhEKXiMeLNrR3iuItiOItJBIX8h4jGTqdyA03tHDq1C527TqHzTYPrA421lMwrKqq4ty5c/j9foqLM1GhDcRi/4bT+Sof/vDv8m//9pvMzBQCMoODF/nyl7/MbbfdhtFoJBQKqUaTpaWlzMzM4HA4sqqYZ4vCwkJcLheDg4McP36c+vr6NalVSqAaDAYJBoPMzc1RX1+PKIrr7p0b2Xu0Wi319fWUl5enGM+azeYtDTbWG1ur1eL3+ykvL6e3t5e5uTksFsumjf96wWsq2AiHwzz44IN84QtfWHXo3L9/P4ODg1itVp599lnuu+8+enp61h3TYDBcs0VcGWujMq+i+AUikTBW6wxNTXUYjddGgspsvgtJakMQepiaehCbLXUxTS6zK6XblV4WKzXS+/p6mZh4hMbG01y4cBN79jwOsKaUn5IRSm6M8/v9lJaW0tnZyaVLl2hoaFAX24qKKioq/pHBwedJJH6KTiciCGPodH9MOPwUnZ2PEI1a0ipuKKitrWVkZITjx49TV1e3aU6visrU/Pw8XV1dWK1WtcldCX6CwWBK8ONwOKipqVHVnlaqgfj9fsrKyujq6mJ4eJhAIHDNgw0FBoOBI0eOMDg4qFKWiouL046RT/CwWbSrle//NraxlfhV0aiUpEpyxWJxcRFJknC73ZSWlmK1WnP+7M/O/ivB4BKRiI5du5opKsrdhXxy8hTHjh1FlsHp1CLLqxOOuWKzEn6ybGTfvh1cd92/k0jcTjTqQ5Ik4vG42qOQjEwKhhUVFRQXF9PV1cWlS5dUU7h0EMXrEYSv8Nu//VH+4z9uY3i4AhBIJOI8//zz1NfX89a3vjWFzlVbW8v4+HhaCvBGoByYS0tL6erqYmxsTO1FWFxcTOlZVIIfu91OZWUlgUAAIIValWnd3Yy9Z6XxrMfjydl4Lxdku08ZjUaam5sZHh6mr6+PM2fOZEVPW2/810sg8poJNuLxOA8++CC/9Vu/xQMPPLDq78nBxz333MPv//7vq9nmtbBZpn75YHPGmiEWE7HZgiwsFBGLOciiF2wV5udPE49PIMsb8RbQo9H8OQBLS6cIh8MpfiTJ/N1sGwP1+imam1tIJLTs3/88S0shtNrlqF+j0eQk5VdQUMDevXuZmJhQF9vi4mK1H2RiwsmPf/xRDh9+kX37TmAwzONyPc/11x8nGn0KWc5s3qTRaKisrMTr9dLd3c3o6OiaG0UuUORjfT4fY2NjvPjii2ofiMPhSBv8JCOZWqXcL4XTOzs7S3t7O9FoNOsgequCDWXRVDjB5eXl9PT0qL0mKxMM+cgVbmUlZBvb2CrkU9lIJBJrXiNJknoQVNZoWZaxWCxqJtxqtdLd3Y3P51uzqro2ZM6dG0UQJPR6gcJCa17fK41mEkGQkWUNen0oz7lkh3yyxfPznyYa/QyyrAVEZmdnefrpp0kkEtxxxx3U1tZmtU8ZDAZ27drFzMwMp0+fpqysjIqKCuLxuHpgD4VCV+imToqK/pmHHvpjvv/9/XR3B5RXQE9PN//0T//EH/zBH6j7kNLHV1RURE9PD2NjYwQCgZwy6WtBGf/SpUu88sor6HQ67HY7TqcTr9dLXV1dxl6WZKlcnU63SrUKNnfv8Xg8HDlyhJGREfr6+igsLNySKkGuY5pMJkpLS3G73bS1ta2r2vhG2adeE8GGLMt84AMfYMeOHfzxH/9x2msuXbqkZkBbW1uRJCkrPvW1VPnYjGAjHv9zjMZZEgmYmvogDQ01OY8RDL6EXv/76PUiFsvtQG4ZI8XLInnTCofDjI6O4nK5KC0tpb6+ft1smHJflVIzgE5XjCQVYTDMIkkBTCb7FfWR/L5c8XgcnU5HYWEhvb29dHR0YLfbcblc1NTU4PU+TEdHIxbLIs3NZwARQZjCZHoz8fgnSSQ+vub4JpOJ3bt3qxtFNjzWlVCCH2VjUXoYFLnBQCDA0NAQ4XAYt9u97iEgmSebSCRSVKvcbjdHjhzhpZde4sSJE9TV1a3roJ1rAJHv5mAwGGhubmZ+fp7Ozk4KCgqor69XlVW2Wo3qjZIx2sbrHxuVvlUCi2Rpd1EUsVgsan+ZUjFdiY2oUQHEYseYmpKYnHTgcESw2fKrSBQWvpkbbzxBKDSDIBzOek7K9z7d93XlfRJFkaNHf8ilS+McPHiEvXv3ph1P+a8ytk6nY2xs7Ar1c3m9HRu7QCw2jUYj0dX1y5wkdpV12uv1Mjw8TE9PD1arFbfbrfbEpPYtvsw73/kAzz0X5MSJ5Psr86UvfYk/+7M/Sxlfr9fT1NREMBjk/PnzuN1uampqcqpYLfdWXm0yX1xcVEVbSktLqaurY3x8nOnpaZxO57omqdmoVm12oktJEsbjcWZmZmhtbSUQCODYSNPrCuQjTJJMe1ZUG6urq9P2bm4HG5uIV155hW9+85vs2rVL/fL/zd/8DUNDQwA88sgjPPnkk3zlK19Bp9NRUFDAt7/97awl0q6VWdLGx1okkWjFYgkxP2+jqKgqrw9dNNqNyZRAkjRYrRfXvFbxiFDK7IqXhdlsxm63q+6d58+fp6GhIaPknvK6FQOilVQo5d/u3YcZHPzfyPIZysreisWyXI5WsnZrKX6Iopgi5bdSccPn8xGJROju7kaj0eDxeCgqKmJhYYEf/vAhpqc93HLLC1dGi6PXfxat9v8Qjb4ErB3IejwenE4nFy9e5MSJExkdqZU+C4UOlTxHpWqRrsQbCATUQ7jdbs94UEhGshrISmqVwWBg7969qjJUIBDI6CWzUenbta5NN67NZuPgwYNMTk5y4sQJVbkjn+AhF57yG2UR38brH7nQfWVZJhaLEQwG6e7uTgksbDabukZnq5aUrRpVJoyM/ILFRXA657HbjTgc+VFMBcFIVdWnAWhvb88q2Dh/vo3W1lfxeFzcffc7V33/VwYb09PDTE6ewWCI0t4eYu/evSlGecnrjRJkaDQampqaGBwcpL29Xa3C1tV10da2RCKhY/fuVuB30s5RoRcrh/ZwOKwaGjocDnUOnZ2dyLJMYWFhmqDASjT6I+6884NYLD/lF79IlgLOfJ8cDgcHDx5keHh4TQqwJEnqnh8MBtU5rieGUltbq1Kr9Ho9DQ0N64rxZFKtUoLeraiqa7VaysvLsdvtaRNbG0GulY3kfUfp4ykrK6Ovr4+WlpZVcsNvlH3qNRFsvOlNb1p3YXn00UdXNdlmg9czjSoW+y4aTRBRFJmaKqekZH9e4zid9xMM/gStdpqpqXdSWSmrc4tGo+ois5aXxXqvTZIkRkZGmJ2dVRUXlOsUmdZk1Q0FWq2WurpDBIMNnDvXide7iMkUZW7uvwLgdn+R4uKGlKYzJQjKRnGjoKCAQ4cOqc1tjY2NNDU1IQgCR4/GGR+v4F3v+iYazfJmq9EMUlBQSTz+CInEY2veV61WS21treq+ajQaKSsrY2FhQZ2jIAgqfzVXVRDlED4+Ps7x48ez4uBmMlqSZRmTycSuXbuYm5vj3LlzuFyutEHMVtOo0kEQBIqLiyksLFT7ORwOR07UjlwrIb+u5krbeO0h0z6lVJWTRTaURluNRoPP58spsEiHje1TCbq7RwmFStFoZLze3P2flpZmGR19FZerAY+nIac5nTv3HAbDApcvTzE1dZaysrX3SKdzErM5zMKCmcbGLpX1sFLBMN26oPSXKQmg+vr9/N7vvR9JktFoPk5f30VeeOEF3G63KuOf3Gun9DCsVPFScPDgQUZHRzl+/Di1tbVpXKgF4vHHuf76vyYU+iWnTi3L4Or1C2u+ZuVAW1xcrFKAKyoqiEQi6r6vCAIo1fVMc0wHs9nM3r17U2R4fT5f1vtUMrVqq3sLlT1VoVqXl5dTWVm5oX1gM4RJ9Ho9gUCAhYUFuru7VVNAs9m8HWy8XvD6pVFJSNK3sNtnSSQMhELN1Nbm52puMLgpKvq/xGIx4vGTDAwMMD8/r9J3lAO7z+dTI/2pqQFGRr6OzXaYmpqbUmd25f7E43E1AzMzM4xO9zvs2DHOhQsPs3//cm9Htl8Sh8PBoUOHuHjxIpcv/yVNTScB6O7+KwYH/yCl6UzhF2c7ttIjoGwUJpOJurq6K+oQMl/96kf48If/FZ1OVB+j138VjeYosVh6NRNZlolEImrFQhSXObyK6VNFRcWmNBknc3B7e3sZGxujsbFxXYd7ZTFfWlpienqaaDSKKIrodDqcTqfKZU1nunctm8mTlTva2tqYnZ3F5XJlFXTkUwnZpkptIxdshQEtXKVRnT17FofDoSaBlKryyuTPzMwMc3NzuFzZya+uhY3sU5HIc1y86AUkCgrimEzVOY/x859/mZmZBHp9K29960ewWMqznlN9/ShnzxZiNkfweCIpf1P2qUQioZ4BNJodvPvdvSwtjWCxvB1JMlz5fXZ7idVq5cCBA4yMjHDsGPj930CrXWJmpoSf/ewp4vFFwuEJSkqKaWwM5LQHCIKAz+ejqKiIrq4uxsfH0xr2ieKnuP32z3HPPZ9lcbEAi8VJNPqXGcdVgopgMEg8Hlddwl0uFxUVFWv2WWQLhRLm8XgYGBjg+PHjGav9yVD2qUgkwsTEBAsLC1veWygIAiUlJRQVFTEwMMCxY8doaGhQKz65fhfy6S3MdL3FYklpbne5XBkFVF5veMMHG9fSLGkjgYss92G1nkEUBSIRA4uLN+b0+Hg8ztxcF+Hw8ywuNrCw4EWv16vl9vW8LCTp/ezYMUI8/jWCwWewWCpS7ovD4aC7u5vGxkZsNhtGYxt2+zCSpKGx8XtoNJkXv9TXKacshqFQCLvdgywvz8vlqsHv37/hxRCuqlRcunSJkydPctNNN/Hss88yPS3z2GN/wh/90T9hMFwNTLXaQfX/FdqCMs9kl3BFElev1xOPx+nr66Ovry+roCBb6PV6duzYQSgUoqOjQ63mJN+XRCKRQitTOLaKd008HkeSJLVkrSij9Pb2MjIywo4dO9RD/bVWrjIajZSUlCCKYkYn1pXYjAzTNraxFjbbgPZ73/ser7zyCi0tLXR1dfGpT32Kz3zmM/j9/oxVZdg49SkZG9mnurufJRqtADQYjRJ6fY56t8DSUgStVosoyojiJFAOZHfoO3DgA+zc+S/o9U1I0iESiURKEsFut9Pf3099fT1OpxNBMKDR/AcWyxJgIZevf3IlQKkGdHYuYTQaqalxUlU1wMCAG40mjt8/w/T0NIIg5NwfYDQa2b17N9PT07S1teHz+dJUCj5NLPZpdDpIPt4km84Gg0GWlpZSXMKrqqowGAyIosjAwIC6T21WD8MyU6GOhYUFtdq/ct1OphYrqpVKo7nf70ej0aSInWTCRvceZa7JUrmKKe5m9v7lc73H4+G6665jdHSUs2fPYjAY1vTzeD3gDR9sXGv98nwDl3j8SxQURBAEmJ4ux2Aoy3htIpEgHA6r1J2FhQW0Wg1+/x9SWjpLImHBYHgeg8HDyZMnM3BCUzXCXa5JEgkden0MSbqMIFSmSPn5/X48Hg+dnZ14vV6qqm5AEBzAwpqur8mH9pXN0W63m+rqagyGLyBJB5mbm6O//xAlJW1I0vcwmW6itPTuvO6nAkFY9rcoLCykp6eHQCBAa2sr0aiZz3/+v/PpT/+lugFJkoYzZ86ofRaKS/jq5r2rUMqhwWCQCxcu4HK58vLmyAS73c7BgwevZNaO4Xa7kWVZpZUpvSB1dXWrZIaTjZYUNRDFdC8UCnHhwgVsNltexkrZIJ9GOovFQm1trerEulbZe7MX/dfLIr6NXx0224A2FApxxx138Ed/9Efcd999/OAHP8jqcRtt6k5G/vvUIqdOORBFHYIAPt/awhOZcMstN3D+/C8oKyvBbt+77pxSvSx2IQhfQRHmEgRSFAx9Ph8ulyslQQNawLJqzL6+PgDq6upWJW6UQ7vSw1BRUYHRaESWZSYnJ+nv7+fOO/u4dOkEbvcCP/hBOdPTPWg0en7nd96XlxqU4m/R39+f1ocpUy+IsgesZTqbHBR0dnZiNps31ZtDydBPTExw/Phx7HY7Op1OVURLlnBfSS1OplatJZWbCw12rbVeUbFUlBtdLtemVSoyzSWb84Dy+TWZTHR3d6/yznq9YTvYWIHNzBjlv4hLWCzfUQ30JiYOotUuv1XpvCyS+wKqqqqwWCzIcpxIZB5JMqLVRhDFMOBR57SW+dDyNZ9Hr/8SovhmHI6DwOovk0J9GhgY4MSJcZqansFmC6PXL2+86TLtBoNBXbDXOrRrNO/D7YY9eyJEo3twOGaQpK8RCr2A3b4jj3uaCkWtY2ZmhqGhIcbGxgCBz33uL3jve1/F7Z5jZuZr+P1FebmvKvdmZGSE1tZW6urq8l4kkilbyr0URRG73a4acDU3N+ekWrVSDcRut3P48GHGx8cZGRlhZGSE8vLydV93Lgf8fKVslQDR6/WqZe90i+5GGvW2sY1csRkGtA8//DCw/NldT8o2Ga+FpNji4v9gZsYNLAc+VVU3MjERWfdxCkQxxtjYzzCZirn55r9fNSdIr2CoIJOXxUpYLBYOHDigNkk3Njam0M9EUaSl5ZecOnUMWZY5d86Pz1eZ1aFd6TVzu9309n4Kh+Moev1NzMycRJKW38+urlMUFVVRUVGR9b1RoBjVhUIhzp8/j8FgwGQyqev+yn0/1/XMYrGwf/9+Ll26tCneHNFoVKUWh0IhYrGY6mo+Pz9PIBBY0xAQ0vcdrlStgvzcxteC2+3muuuu4+LFi8zPzzM6OppWHWol8lWjyhaCIOB2u6mqqqKnp0ft58hfqvra4A0fbORj6netaVSy/BRG46LyE6HQbcRii5w4cUJt5FKyK5kWGEEwEAp9BvgW8fgdlJdXqJvZ0tKS2sCdecG+/8q/9V+j0sx29ux5jEYjev35K9WVq4ob6TLt2cBkMqHThREEGUGQuHy5H5stkNdiqChtJVdWRFFk586dTE1NqbzeZ565hw996EOUZS4mZQVBEKioqFC9OZR+i/W8OZRSuLJoJ1d/PB4PNTU1KRmoYDBIR0dH1lWUtdRAysrKGBgYIBwO09LSwo4dO9YssW+VcpUyv+SxlWycz+eju7tbdYhVqGrbNKpt/Kqw2Qa0ua5nrwW679NPTwJ+9efy8iYmJtqyfvzp0/9CR8csWq3M7bfH8XiWgzYlAba0tKTSb5QGboDBwUEMBgMVFRVZ3zdBEKisrMTj8XD+/HkULyIlWRcOvwjEEQQoLh7l0KEHs34doFS0b2B2NsDJk93ceeePefnlwxQULPHyy79EEE5w++2/wY4d6yfKFOGW5ORSPB6/kkSUmZ2dpb6+fg0H8tyQXO1X+gKz8ebIRNtVfDcUypaCcDismhnW1dXlrFql0WhUSXfYHCXElRAEgfLycqampgiFQoyMjKwrlZtPkiufpFhBQQG7d+9mbm4uhVq8GRTzXwVeH7PcAHJVo7pWGaNkL4uamt9N+j24XAeZmZlh3759WZXfYrGLLC39KyZTAxbLdwBBlfWrrKyko6OD6upqlQ6QKxQXWmWRURQ37HY7iUSC+fl5duzYgcvlYnp6kjNn/hmNxsnhw49gNptzfr7FxX9BEP6eSOQWdLon0GjeTzB4O3b7t0hXcVEQjUZTekGUPguHw5HSZwHLpfdnn30WQFVr2rkzs8lfLjAajSkmTiUlJSoVSJEcXFkKVyhbpaWl63JIlSrK6Ogora2t+P3+db00MqmBaLVaBEEgEAgQDofp6OigoKAgo6ThVnlyKNen20wUr5O5uTnOnz9/RYayLq9gY9slfBu5YqsMaHPBa6GyMTqa7Pkk5/xdCoWCCAKIIoTDIzidB9X5KAmF8vLylKDi1Kkf0tbWCmh485vvwu/P7OkxPz/PqVOnMJlM2O121d3aarUiyzLBYJCGhgaKioqIxX6KIFxEEGQOHVr7ELwW3G43hw4dYn7+Oj7ykf/N008/yPh4HIjzs58dpaenh7vvvjslWZQpuaT4Q608tEciEbq6upicnFxTfj5XKH2B6bw5lD1fmWM2tN2VsFqt7N+/n4mJCU6ePElFRcWT/DKLAAAgAElEQVS61fN0+5SSHNtK1UStVsuOHTtU+XmTyZTxXue6j2w0KeZ0Ojl8+LBKUdu5cyderzfr8a4VtoONFfhVSN8mN0UrdKirXhZmdLqrj4nHNdTV1TE3N7dmn0XyxhMOfwyzuRvQEIn4sdtvUT+sSslXWax27NiBwWBAlmWmpgYxGq0pOumZqgGKWVQ6d+uFhQXVTA/+iZtv/j4AQ0M2/P4P5Xwf7fa3AW+joCCI0ViDKGpwOH5Ef/8vqK6+GUHQpGRZFHqZQtlSlLbWqigEAgGOHj2qvl9Hjx7F6XRmRSXKFm63m+bmZvr6+hgcHFRLw4rkYL6lcLi6QXu9Xnp6elSH8/WyU+moVco9sFqtqkzg8ePH8fl8qwwMt2rBz+Z6ZdEdHx+ntbVVrc7lMv52ZWMbuUCWt86ANpd957Uk0Q5gt6+9zqzcp0Rxibq6ZiSpDbvdTEXFXej1V5WhjEYjhw8fpre3l7a2NpqamigoKGBx8QSyLCHLEktLrSgGtcl7qvLv3LmXWFgIodXquO22B9i/P1VoJBKJ0NnZeeXQ/kHuuedvAYjHfw9YztxfvnwZl8uVU/ZYq9XidP4NMzOfoLn5w1y+rGNiophoNEpfXwff+U6QffsOIIqiygDIJblkMpnYs2ePKjWbzaE9F9jtdnbu3El/fz8vvfQSBoMBjUajMipyVYNMhqIEVVhYqPaiNDY2rqoOrkQ6atVGDGWzvTaTB1S+e6By/UZpV8mKWtuVjdcIFIWgbLHZGSNJkohEIikmebFYbFXmQsl0yLKEJKE2Kb/wwm286U1X+asrAwvleZSDllarJR4XEAQZSYJEYrWsn16vV6lDJ0+exO/3s7DwH5SWPkY0WkB//78gSWUZqwEajYapqSkcDgcFBQWrXrfCkR0ZGUGrHUCjEREEKCy8vMH7aWZ6ugSXa4pYzEh9/duJRh2cPv2PiGKp2gtSW1ubM2VLkVxVmgQBnnzySW655RYCgUDa17kelGb45MqK2WzG6XRSXFzM2NgYBQUFWZWUs4Xiyq1k/ddzjpVlWW0yVDJWJpMpRQ1EWdT6+/tpaWmhsbFR5d1uZbCRTQZIkQb2er0cO3aMM2fOEAgEssoib0vfbiNXbKUBbS7YzH0q37EOHjzMiRMnMBj0PPzwB9Xfr9UPuPx9lnn55ccYH5cpLNRy+PAn0WhWr39arZbGxkYuX75Me3s75eXl7N+/l2j0GQyGBCUlb6Kvry9FaCSZwnPx4hMsLRWg0cRxOC6i0wVSxlcO7cueC73U1n5CzRDLssyTT36X2dkpnE43Dz30npwP11arDa32Y7znPb/D0aOH6egIEI9rGRsbY3p6mrvuuofm5ua8Ex5FRUW4XC76+vo4efLkmkata2Et2m5jYyMTExMIgkBdXd26FOBsodPpaGhoUKlV6zWoK8yP5P1UEARisZgaDK2FjdB9lb6cwsJCLl68uKpnMJ9KxWbRrpQqz+sBr49ZbgC5fpE3muWJxWJqYDE5Ocn4+DhWqzWtl0X659fw+OO/yx13PE9Hxw4KCz93xR8jzsLCAiaTKSWwWPkaI5EXMZkqmZryIEk3Ul19a8bncjqd+P1+BgYGKC9/AlGUMRrnicd/gcv1EVVxYyVOnvws1dVP0tcXoL7+/2A0rj6IK/0Kkcj/YGnpI8iyA7P5fdnfyCtIppcFg0Gmpr6AJJ1m795vYTINYTDM4vd/iUjkA3i979wQLea+++7jsceuGvnF43Gqqqpob2+ntLR0VVY/GemcwterrJSUlKgl5crKyqya0bKF0+lMaVBX+mpWcoETiQQWi0Wdo9J0tpJapTQqlpeX09HRwfDwMI2NjVuuRpXt2DqdDovFQlVVFUNDQ2o/x1qVnW01qm3kiq00oM3l8/ZaUKO6+eab1fshy7IqrR0KhdQKbbp+wFgsyNiYjMUiMj2tJRKZwGzO3Dxts9moq6tjYGCA/n4nTufd6HRmZmf92O1WysrK0lYD7roryunT45SUzFFc/Luke4lKhlip9l+6dIlAIIAkJZieHkCnizA7O8/S0jwWy9q8/ZUS7vF4/Eo14Mfcdtuf4Xb/kpdfvp5EwkAsFucHP/gBhw8f5uabb857rdHpdDQ2Nqpqgusll/Kh7ZaWlqoU4PX2wVyhUKuUBvWqqipKS0uJx+PqXhoMBonFYiqboqSkhIaGBjWZu55qlfK6c1GuyqTgVVtbu0oqd6uFSd4ovYVv+GADcitP55LlicfjKc6uS0tL6PV6VSGisLAQq9VKSUnJmuOszATdc89fcvbs2/H7a1RliKamJjo7O6moqMh4KI3Hp0gkPorBEMftNmO1/i/1unTa1ooKUXV1NfH4O9Bq/xFJsuP3vxu9PjMHsLn5/0cQRBoajrGwcAKj8aaM15pMu5DlVxgfH+fs2T7q6zUkEjHGxs5TU3MAlytVmSL5MJxcDbDb7Xi9Xmpra9Hr7+TMmR6czhEMhhjFxceBdrq7e3A4PorLtT5lIR0UDmooFFJ/p3BwL168mFLyXclfVRTB1nIzX4nkknJvb++GslPpoPCTFZOoc+fOYbPZcLvdabnAK+9FOtUqs9nMgQMH1BJ+Lgo6W70oy7KsutnOzs5y9uzZjC7p+Yy/jW1sJXKlUf2qezbWUoZSkl/Nzc10dnZSXFxMVVVV2u97ONyOxZJgbs5AU5ONgoLylOdQ9lOFCaA0x1osFkpLSxkeNuD1rk8dslj+jptuehFZrkGWazJeB8sV4V27diVV+z3s3n2Os2cDNDV1YTYvAFeDjVgslhJYpJdwT15bv8b11/8VJtPP+dGPbkeWlys8ra0tdHR08P73v39DvRd2u51Dhw6pilv19fW43e6URN1Kp/BcaLsejwen08nFixdVRa/1DPuyhSRJFBQUUFJSwsDAAB0dHVitVtxuNw6HI2PSE1L3KUW1Kp0y2WZW4JWK2OXLlzlz5gyCIOTUi5qPUMobYZ/6tQg2ckGmhVc5rKd6WSwf1m02G7W1tavk8aLR6KqxspHyczgc3HTTTSkfMI/Hg8PhoKenh8nJSZqamlZ9AROJMBpNFBCIRCSGhoZZWIikNHArwcXqReZR4vF30d09hCguEgjE1qD2BNBoziEIZhyOxnXu6FWqi8fj4ezZNny+R9i/f4y+vt1I0nfUjUWRxlWyLGstMsXFn+OppwLcc88/YrXOo9Uu0tj4P7l8+RU6O/+a2to9eWmGf+hDH+KJJ55gfn6eBx9cViSJx+PYbDai0ShtbW0IgoDL5cLpdKbtWckVOp2OQCCgZqfy8eaQZTltAKS85/v37ycajaqqOC6Xa0OqVUVFRXg8Hl566SVOnjxJY2PjutSlze7ZWInkjJTb7ebIkSOMjo7S0tJCZWXlKmOsN8oivo1fP2w1jSp5n1qZ6V1LclYRq+jv7+fkyZM0NTWlHMRkWeQnP3mWaFSH0SjS0PBWxscvqWvWSrVFq9VKLBbhP//zS0QiS9TUVHPzzb+9qpcjPUxI0p053YuioiKcTic9PT3s2mXgjjv+FVH8TYaGZJ577mssLi5SV1dHYWGhWrXOVFlJhZVE4vPs2/d57PYn+e53334l4ID5+RBf/OIXufnmm9PKKGeLeDxOQUEBTqeTs2fPIssyTqcTl8uF1+vdsFO4ktkvKSmhq6sLk8lEfX19TvvsSqaCEgApibo9e/YgSRLd3d2IoojL5Vp3fOWzKIoi8XgcQRBSVKuU593sCrzL5eK6667j5MmT6nyzlYvPdV/bLP+Ta4lfi2Aj1/K0KIopmZVwOKxmrm02G1VVVZjN5qw+kMnZ4WwX7EzQ6XTs2LGDmZkZ2traqKmpobi4+AqXcRa9/sNYLDHicT09Pf+V0lId5eXlWK3WjAfLcHiCpaUxPJ7d6PVFNDcXpfRypJPXM5m+g0bzKrK8A1nOTgVB6V0pLAxTXDxOLKanrq6dCxfGcDoL8Xq9OfVZFBcXc999f8L3vjfLW97yf/F4ptBqI3g8r+B0vp3W1r+nrOzWnFUaJEniN37jNwgGg4yMjNDd3a06sBYVFVFbW8v09DRDQ0OUlpZuWnYHrmankqlPmea/Us88Ho9jNpvVzS9dAGSxWNTxjx8/rlKr1sJaaiCKfGRzczO9vb0MDw+v2d+y1eXjldcrTfMlJSX09fVx7NixlH6T7WBjG68l6HQ6EolEVgfCzW4Qz7RPCYKQYpKXzfdFo1kWNQkGg5w5c4by8nLKy8uJRqPMzl4gHNYQjwuYzSJDQ5O43RaVGpNunwqF2ohE5tHrJUZHe1b1cvh8vqwbpEVR5OjRZxkdHea6625kz549KX9X9im73c7Fix+jre23MBhMLC4eIxweB2QWFka5667MxrWZIZBIfJKamno+8IH/zuOP/64acIDMiy/+Ap1Ox4EDB7J6HZlou0plJRQK0dfXh16vz9mobi0kG/YlU5/SjZ9cAQoGgypTweFwrBkAHThwgPHx8XXHT4aipJhun9qq3kJBELBarVRUVDA7O0tLSwuBQGDNc8E2jeoNjLUWZUXSLbl0G4lEGB0dVY3nrFZr1oGFLMvqgq3T6ZiYmMDtdqvmdcrhLd/XEY1GSSQSuFwuurq66OjowOl0YrdH8Xh6iMXs6HQx9u69B6Nx7YP2/HwfkvQ2bLZFhocfoKrqH4Cr2Z2uri4mJiYIBAIrqhzWNTNGyc1cyj2VJOkKvayWeHwHJlMXU1M3E4tJOBwODAZDXovh/v0f4T//s4j77/8ixcUTLEswTnH99e9nZOTDtLe/L6O3hSRJqvN6Ojm/TGZO5eXlFBUV0d3dzfj4eFbeGdlC6XVJ9uZQHG3n5uZSlLYcDkdaPfO1oNFoqKyspLi4OEW1ar0G+HRqIHq9HlmWVQfWmZkZTp06hdfrTcsb3urKRqbytMJrXlxcpKurSzVFeqMs4tt4Y0BRTtzqYGOl0IhGo2F2dpbi4mK1J3Aj+xQsJ0NisRgej4eLFy/S09ODw+EgHP4PQECStOzaVcLevbeuO5bHU09V1b8zOurguuuuema5XC4OHTpEb28vp06dori4mF/84ln0egN33/1gWjrq1NQQIyOn0GjitLYepaGhYZVZqtLDVlNTQ3NzMwMDA0SjP6WvD2RZoKamjYGB25AkCb/fn/O+JUlvx+Op5mMf+02+8IX/SrJ8+8sv/3hVsJGuag2o+1Qm2q7JZLpiNrhcBcrGOyNbKBRgj8dDX18fY2NjNDQ0qJLCSgCk0+nUeZaXl2e9TypsiKKiInX8bIzsMu1TW6VcpVyvyAaHw2E6OzsxGAw0NDRkPHdsZrDxeuktfE0FG8899xwf+9jHEEWRD37wg3ziE59I+Xs0GuXhhx/m5MmTeDwennjiCaqrq7MeXzkEJ9OhkmVcS0tLqaqqoqura13znbWUobRaLRrNskGaVqvl7NmzNDQ0ZCV/mIxMvFC73Y7H48Hv9zM3N0dfXx9lZd9Gq43idA7T23s31dXru1VHoyewWBZJJPQ4HC+m/E1RrJqcnFyzygGo7tYdHS2Ew/1YLLtxuz1rZC9+hiTN4vEUcvnyq8zN3YhOl0AU/5XS0utzukeVlZW8+c0P8PWvSzzwwDdpaOhW/+bz/StlZd/g5z//Dj7f8v1Pvp+iKKr81Vzl/AwGAzt37lQb58rKynIymcqE5EBNp9MxPz9PS0sLNpuNsrKyrPtB1oPRaGTnzp0q77SoqIiqqqqsqVWJRILZ2dkUqqDH4+G6665jaGgoRbFDmetWc1XX21DMZjP79u1jZmaG9vZ2RFHE5/NlPf42trGVUAxoszkQ5tJnkU4ZKlnBsLCwEI1Gw/nz59dc5zNBUTNaSYdVDpkVFRUsLi7S03Oa0dE4waATiyWK01m97tiyLBOL2bnttk+j1fYjSQdT/p5c5fjZz77K/PwSsqyhp+co+/a9I+XaZWO4HozGRSIRI9XVPXR1dan76UqzVAWNjY0sLNyE3/8osVgBo6MP8cwz3wNkbrzxFvbvX78SsRKSdBC9/jSf/GQdf/u3n0YJOPbvb2FxcTFFIVCRxV+rap0JCkVX8c5QXudGkyzJkviCIBCPxzlx4gQFBQWUl5dnoGrnjmWzxIDqd6GoTa4XkCtBh5KkW1payvo5NxIMWK3WlJ7GkpISqqurNySV+0ZJir1mgg1RFPnoRz/Kj3/8Y3w+H4cOHeLee++lqalJvebxxx/H5XLR29vLt7/9bT7+8Y/zxBNPpB1PlmW1qXdhYYFbbrmF+++/n9tvvx2bzUZRURF+v3/VhzaRSKTlr663YKdThoJlJQeXy8WFCxeYnp6mrq4u7UKRzidCr9evywv1er04nWYSid/GYIiyuOjAZPovWX2Ync7bWVj4MibTKLHYB9Ne4/V6EcUg/f2foru7kN27P0EiIaZsLMtVm0scOfIxDIYIvb230tj4f9d4Zi2wHAwVFPwffL5BAPr6Pksk8lTOVYLKykpuuulWvvtdeMtbnua661rVv2k0EW677V6ef/5b9PU5VafUZEO/jUBpnBsYGOD48eMEAoF1NcOTkSyPq6huKBtLsurGxYsXGR0d3ZRAIxlKdlBpLFT4yCuRTNtKVrGqqqpSVWiUknX1FcPIrq4ulVplsVhy5qpuVSXE4/Fw5MgRVSq3trY2rejC6yVjtI03BoxGI7FYbP0LMyB5n8qlzwKgsLAQh8NBZ2cnU1NTNDY2pl0fk3sXk9WMlN6wTHRYk8lERUWQtjYTen0cjUaisvLuNV+PLMv85CdfZWhoCp/Pw513/n7G76TL5aK5OcSLL2rRaETc7mEuX76cIoii1+txOkt5z3tOs7g4g8fzNiRpb1b31mJ5C5L0FMHgeS5e7EaSluX0x8ZewmAw5ikLW0wkMs2nP+3m5Zdvorx8mEjkN2hpacHr9eL1enOqWq8Fh8PBwYMHGRoaorW1lYaGBpVOmg3SyeMqkvgKvVir1TIyMsLIyAgFBQXrViFygeJ3MTY2xvHjx6murlbFc5KRSW5e8b1JlnTPhI3uO4Ig4PV6U6Ry6+rq1MTbVqoyvpbxmgk2Wltbqaurw+/3A/DQQw/x/e9/PyXY+P73v89f/MVfAPD2t7+dRx99NOMb8fnPf55jx45x8OBBDAYD3/rWt7LKYioLtRJ0bLTPApYX2n379jE8PMyJEycIBJb1vlcqbigLdq7Z61jsaSyWIIIgIYoyorh+dWBhYYhw+DhO51MYjXaMxtUbi6IMsrT0pxw61IokaWltLcBiuZeSkpIUmlEw+B30+iiSpKGysj2reQPYbIeQpO8BUFBwgLNn/xceTyU1Nffm1NDl9/vp6Ojg+efvIRSyc+edP0m55i1veYypqWfUEqfL5cp6jutBq9VSV1enllCV93BlIJu8UQeDQcLh8KoMYKaGeL/frzbmjY2NUV9fv2neHBqNhqqqKpVaNTIyQmlpqVqxWo+2tbJkrdVqMRqN7N69m8uXL6uqUEajccu5qtl+ZhSFm9raWkZHR1Up3838XGxjG7kgFwNaJSGWSCTSKkPl2mcBy1nkXbt2qVz5hoYGDAaDenBT6DuK2mKuJqQ9PcdYWlpWdCosjDA1Nb1mFSUanWVoaAyzOcbISJxIZIKCgtXKjgoV2u2+i3vv/VskSUN///swmfooLS1Ns58+i9EYRpKWk0LhcJhXX30Vm83GkSNHMr4ejSZAeXkAu/0TJBKXicUM9PeX0d//HGfPlvDud793zdefmbZ7Er+/C7O5GZOpluLiRTo6Orh8+XJOAcF6UBJBxcXFdHZ2Mj4+nnYfUeapHNqTA0qHw7Gm8eBKCnBDQ0NeHlXpIAgC5eXleL1eent7GR0dxefzqTK54XAYnU6Hw+FIu58m71PK9yPde71ZlQeNRoPf76esrIzu7m5VKne7Z+MaY3R0lIqKq1rbPp+PlpaWjNcoH6qZmZm0mdhPfvKT6v8fPXo0LYUpXcVC6bmYmJigpKREbTraSJ/FwsKCml2RJImTJ09iNpspLy9XFTc28mGSpK9gMi2Xj+fm6pmcnCMc7qS+vj5tFSUWu0wi8Vbs9jChUAWFhT9nufFtIaW6ovRZFBWJgIBGA83NVYyO6pmamsLj8ahfSofjLkSxEaOxj0jkz7Keu93+IUSxHlmOIEkvcOjQZwGZ06f/Gw0Nj66SlEvXD6KoWdxwww08/fTTHDv2Ji5ftvGudz2lPi6RuFdVShkcHFSrEA5HZu30XKGUUEdHR2ltbaWyshKtVptWdaOmpibnKoUi66pQ2zbDmyO5HB4MBllaWiIej9PR0aGqYlmt1jWfI7lkvUxV0KhqIC6XiyNHjjA8PExfXx9erzfrxXyrMzqSJGEwGFSubVdXl9rfsVkb5Da2kS0UGtVKZFIw1Ol06oFrM/YpRSUoFAqh0Whob2/HZDJRXl6+YdU9WY5y/rwBUVyuPDQ01DA5Ocnk5CSBQCBtFcVotFFTE2JgwE51dRCTyaWuV+nosHZ7DQ7HE1itVoqLobe3V+2ZTF1HNMDV6vMLL/yAgYFBtFoBl8tMILBn1VySYbf/Ie9614fo74ennioikZC4dGmEr3/969x9992UlJSovhvJWfa1abtXDQcVU9zR0VFVxjZXCvZaUHrskl2xTSZT2nlWVlbmfD4xGo3s2rVLpauWlJRQWVm54QNz8v0Mh8PEYjG6urqwWq3U1tZit9vXfI7kfo5EIpFWtQryc/heTyp39+7dzM3Nce7cOSKRSE5KctvBxiYjHf905RuYzTXpYDQaWVpawmg0rik5q2SCDh8+TGdnJ8FgMONCmOk1KF8IZdGOx+Nqw1lJSQn19fVoNBr6+/uZmJhQ+bL5QpZn8XhOo9HIiKJEJPI29u3bpy5U6ZQRYrExjMYw8bgeq3WQkyePE49f7V0pLi5O6bOQ5a8SjX4OQfDhcr0Xl0uXppfDjlb7AqIooddflUwdGRlRXV0zQau9FQC9/h/Q6eLIskBJSTsjI/8LrfY3sNnc6uaSTDNaOU9Yrnh961vfoqtrD1/5SiEPP/w99PqHkaTlAEij0VBTU4PX66Wzs1NdqDbqwrlSdQOgv78fnU6H3+/fsOygAkFYdjNVGvOUSlm2JetEIpFCh0ouhyf310iSxNDQEOfPn896s1Mel04NpLKykng8zszMTNZz3upFdi2urdfrpamp6Q1Rvt7G6wNGo1E9iCQLjShI7gcUBIEDBw7Q29vLuXPnaG5uzsmnYaUBnbKuKv5QSmV2eHiY8fFx3G73huS9x8e/wcLCcsO2LGvw++/G6axUFY3q6+tTkoaJRJxXXvk6oVAxt97qRqN5K6dOnScajarrlTLPvr4uxsYGKSu7LiV5pPRytLW1YbVaCQQCae+RydSOIFgAGaPxBLB2sCHLZcRiP6Si4lX27v2fdHTUsbBgZnZ2gu9+99+pq9uB0+mkoKAg5X7mQttVlPQKCwvVKoRSadoIlPVfee8BhoeHEQSBmpqaTaMXwzJd1eVy5eXNoSiCKvuUQtd2OBwp/TWyLDM6OkpHR4eqzrnemp0s6Z7OEDAfadps9imn08mRI0d48cUXOXHiBNXV1VlL5W4HG5sIn8/H8PCw+vPIyAhlZWVpr/H5fOqXJpsy48jICCdPnuTWW29dtWBnKn/t3LlTdbUMBAJp6RXRaDRlwVYWwmT5uUyLQ11dHXNzc7S3t1NZWZmVtFs6hMN/gdcrAsuLuNv9oLpQud1uLly4gM1mw+PxqFJ5S0sXKCnZhcs1wOzs+9m9e++ai5gg+DCZvpryO6/XqypipSpWXb2fZ878Aw0NX2FmpgT4D5zO1PdzJbTaTxMKfQBJ0lBa+hylpc8xMvIcnZ2fpLq6mp07d667oSoKFlNTU0xNlfPYYx/l0UcfZWWi2mKxsH///pTs0f9j78zj46rLNf6dNZlkkkz2NE2apUmzUdrSBVoKgshWVkGR4qXFAgKKwEUQEK+CuFAURWVfxIuAFBWUTdkqFOhCWlpaskz2pM2+zGSd7Sz3j/A7PZNMkpk28VrI8/nw+ZDOds6ZOb/3977v8z7PVD4RAqFcWCdS3ejp6aGuro5AIDDO5+FwICrwYnBOqJLoExpVVYPa4YODg2G3w0XLPSMjg5qaGlpbWydU1xj7ulBqIOKey8rKIjY2lsrKSuLj4ykoKJgwuP07Ohtj73/hH9LT0zObaMzi34q6ujq2bt1KUVFRUJyaTBlqwYIFmhT6RFLZgmYiCmCi8CaKQPPmzZtwXZ03b54WR8QMwaHcFx98sBvIBcBolHA45gGjEuYOh4Oqqio6OjpIT09neHiY9vY3qK1twWhU2bt3mOOPv4A5c+LHrT/d3U28995fURSZrq4KLr74e0GPOxwODhyooaenk/Lyd7j44svHqVSdcoqJ9PSdxMZ6yM//PgMDg7S1tZGdnR3SrO3g+p/JokUx5Odv5q9/XYMkWfB6A1RWfsIxxyzjuOOOO+w1RBjIieLeRLMKoSDoZaHWf1FUFDRot9uN0+nE5/ONG2g+HAgqUUZGBtXV1URFRY2jbunZCuJ3KqwGEhISKCgomFAWX+x1BLVKqFZNJbIQKk6ZTCZMJtOMqiaKbsry5cupr69nx44dU9J3PytqVIYpFC2mR8g7DEiSxIIFC3j77beZO3cuy5cv59lnn6WsrEx7zgMPPMC+fft4+OGHee6553jhhRd4/vnnp3zvd955h1tuuYULL7yQb33rWxHdSB6PR9uwJyUlaZz7sYob8fHxmrxtpOddU1NDIBCgpKQk4sqF1RqLKDh5PNHIcpe2wRQ8WzF/Mm/ePGJiqoiLuw6jUcHlupI5c24L+b6SFMDpvBuDoZ6UlJtISztq3HMCgQButxtJkmhqahqnZDI0VEpsbC+qCj09PyctbYP2mNgI6/mrYoGxWP5OYeEvMJlkVNWIoixhz57riYs7KqyFsKmpiT//+c/a3waDgZtuumnC5/t8PqqrqzEajRQVFY1bCAXNSFuztfYAACAASURBVByroJcJbuhUvGVZlmloaMDtdkfUhQgXorrT3NysbTZEtVK0wxMSEiJS2xqL3t5eamtrI26Jy7KsVYr279+P3W7XaAZtbW00NTWRk5MTssKzdetWVq1aFfYxRvr8bdu2TcjRNhgM0zYTM004MiLK/w/+bXFqJrFr1y6++93vsnz5cn7wgx9EVGH2+/1UVVVhNpvJyMjQYsDIyIhWCBGxamoDuvFQFIX6+noGBgamMNILhVZ+8YunET/hqKgRrr32h+PWf0mSkCSJuXPnYrFU8vbb5RgMKsXFBlat+knId3a53uEvf3kbWTaRmjrIhRfeO+a6eHj88V9gsfiQJAsLFx5Lbu7CMetNAJPpFVQ1Da93OU8++SiBgJfY2HjWr78iaP3X02HF9YyL243L9R2efvoCvN6D1yU5OYW1a9dOGyUzEAhQW1uLz+cL6Wc0lrYlSZJmkBjO+q8oCs3NzXR1dbFgwYJpn19TVZWuri7q6+tJTk7GbDaP894Qe6lD7aL19/fjdDojNsYVSYfBYKCrqwtZlskNU+l0586dLFy4MOzOoj5ODQ8PU11drRUPQxXzdu/eTVFR0YQu5YfjPD8DmHBh+Y9JNgBee+01brjhBmRZZsOGDdx+++388Ic/ZNmyZZx77rl4vV4uvfRSdu/eTVJSEs8995w2UD4VfD4ft912G1VVVTz44IOTDqaFUtwQijs5OTmkpqZGZEAXDrq7u6mrq9NUC8KBz/cEDsd12t+trXOorX0maCEUG+GhoSEqKytxOF4hJ+cPKIqZ/v7FZGS8EPK929tfICXleoxGme7uYjIyNgc9LkkSlZWXM2/ehzQ2nkFZ2UZqampQFEXrcng8VxAd/Spgxut9lYGBdC24CDUjsbjo+cD9/R0MDZ3L3LmNWCzeT6X1sujo+DHNzfOmVHxSVZV77703iC53xhlnsHDhwkmvZ1dXF3V1daSmpmI0GjW5YdG2F9f0UOlQogvhcDgidggfC313xe12a5rmkiRhNBqnfR5FfGZzczOdnZ1hq5mIpNLtdtPW1qa1ukXQCwQC1NXVMTAwMO6YZzrZ2Lp1KytXrpywYjabbBwx+EwkGzAaezZu3Mirr77Ko48+Sl5e3oTP1Q/y6gtLkiSRnZ1Nenr6tKvXuVwunE4nOTk5YVfYq6oe4pVXBrW/U1MHKS4+SyuEiPXfaDTi8XjYu/c9amrexes1kJencPLJ/43JNFFMHKat7So6O70UFa0mJuaGMY/7+PDD9ezZU0hJSSOrVt1Lba2LkZERSkpKxm3Ye3qaeO65P6KqCqpqYtmyE4mNTQgq2IRa/w2G3fT3r+fZZ89lcDBe9+9GvvKVr4S9cQ0HfX192oY6KioqSBVSv2E/1PVrZGSE6urqQ3IIH4tQw+aio6AoCkVFRdM6jwKjMUeoYuXn55OWljbl71TM1rrdbjo7OzUp+HCKauXl5SxatCjs6x0qTnV3d1NbW0t6ejq5ublBe4Ndu3ZRVlYWMhE5kuLUf1Sy8e/AP/7xD2655RbuuusuTj31VCRJCqpcjFXc0G/YR70kqg6L9jQZ/H4/lZWVREVFjXNTFYZ++gq73f5Dlix5V3vO5s13sXLljRO+v8/Xjcv1VdLSKhkaSkGWHyEx8YSQzx0cfI3o6KswGmUGB8twON4c87iTmJiTUJTRgT9VrcVstmsb9rS0NEwmFUX5gIGBBBRlHn7/n3E49mAwXEVx8ZcnvRY+n4/6+js56qiHiYryoapgMJgZHLyL3btXTrlh/+STT/jHP/6h/Z2Wlsb69euDnjPRQijLo7Q0Me8ynd+zWAhbW1snlJkN9Rp91Up0V8TvU/xGxXH29fVRU1NDenp62AtmJPB4PNTU1GAwGMZRqwRlQ+8Yq08qxXFaLJYgGqNIxGw2m8ZN/nckGxM9/0haxGfx2YtTO3bs4Oqrr+a6667ja1/7GrIsa4PR441S44M27MPDw1RUVBwW7WkySJKE0+lEluUQhq/j6cV9fW3s3l2pPX7FFVdOWjVvanqRt97ahdksYbfDV75yd8jnud1uWlv3k52dTXy8CQjdMTYaP8Bs/iOSdD6KMur87XK5qK6uJjk5GavVqgm4xMYO4vffT0VFEcuXf0xHx00cONDBCSecMGniB2AwVBIIXMITT5zFwIC+0DPqZfKNb3xj0tdPhIlM/cRcz4IFC0hJSZn2ONXZ2UljY2NE1K2JuisiTumHzQcGBnA6nSQkJGjSudMJv9+vdYLGUquE1YAo1o0tKorjFNSqyWLojh07WLp0adhFyInijijmCSNfkSRN1jk5kuLU5yrZUFWVuro63nrrLTZu3IjFYsFqtfL444+TmJg4rsIeCvqFtqSkZNqGqfTHKKQ4586dq82m6A39xA3R2tpAbu4KbDaZ1tZUEhLqJv3Bd3Y+iMNxD7JspKcnD3hyEiM6lZGRR1CUKmy272IyzRtznEPI8lIMhkEkKZ2urr/R3z/I0NAQBoMBSZI0Q6H4+Hh6e7eTnHweZrPE0FA8NlvTlNeiu7uLXbt+xDnnPIvZLGn/Lssrqa9/iNbW3pAD8AK/+MUvtP//6le/Snp6+oTqIGMXQlHBmzNnDvPmzZv2gO31eqmpGTUgLCoqClpI9AuhUIiKjo7G4XBo3/1UC9uhdCEiRXd3NzU1NdjtdkwmkyaRKK6noGyMhZ5apVcDEQGuvr6e7Oxs9u/fz/HHHx/WsaiqyrZt26Yt2RCKWv9BmE02JsZnKk4BNDc38+6777Jx40bNOPORRx4hMzNTSy4mWwP0tKeJqqKHC1FYErOVgrYlZLLFcUZHR+N0VvPRRztZuXL1pFX+UW+Nn1BRYcBoVFm+PEBq6jry8/ODNnyBgJ9nnvklPt/oJvHrX78Zk2ni66Gn7Y5K+25HVVUWLBilhBUVFWmqVSbT/2I2b6Kp6b/4619bkGWZ6GgbV101cSHvIHowmb7II4+cicsVXLG32WK49tprp3yHsZ5GepEZ0V0Re5TBwUGqqqpISkoiLy9v2jfsgrrl9XopLi4OovIIBojb7R43xC3+m2oN1XchJpo5OlyIxDI6Ohqr1artUcReyuFwhKQWCmoVoM0dhsL27dtZsWJF2EW9qYpiPp+PmpoavF4vJSUlVFZWsmTJkpDXcjbZ+A+FJElccsklHHPMMSxdupTy8nJefvllHnroIRYsWBDRe3V2dtLQ0KAtUoeDUP4LRqMRn8835YBUfX091dXVnHDCCVNQi2R6e88hM/N9fL5oDhz4Bl7vFeM4uKMJ2U+IinoPVb2SnJyv6t4jeH7B620kJqYSWT6W2Nh54zbsgp+Zn59PQkIX0dGnYDJJeL1JyPJePJ5+HI7JKyZ+v59XXrmJr3zlj0RF6WUhrbjdr/DJJ9HExsaOU3sSSVplZaU2DBYdHR3UZp5qIZRlmcbGRvr6+igpKZn2WQsYvUa1tbUkJCRo1C3huTLZQhguPB4P1dXVWCyWw1YzCWVAGBsbq1VdRYUtHIg2uqIo49RAJEmioaGB5uZmli1bFhZ3WFEUduzYwcqV4TvQzyYbnxl8puIUwOWXX05OTo42SPrEE0/w29/+lqVLI3OrFpSb6djIhaJtCaO0mJgYFixYMKVM9lTw+Vw8+eR9REVJKIqBr33tcrq7RwsbpaWl2nC3x9PC008/9ulnqaxffwMWy+jmfnQDu58333wJk8lIQcEiTCaTtmGvq/sXFRUtAJSWZnPMMefhdDrJysoKmuUYHPwXTz21BUUxEhc3jMFQwvz58zn++OOnOEc/FsuJPPLICfT2CvrX6HFef/0NQWuwXnVJdFdEsib+m2rNVlWVlpYW2tvbZ8wrSPyOYmNjsVgsDA4OoqpqUHf9cKjlPp+P2tpaJEk6bOlxYUAokiAxEyISzoKCgrBUq2DyOCUwGR03FMLtwPf391NdXc3w8DCrVq2apVEd6di9ezeXX345V155JevWrYvoZvF6vVRUVBAfH8/8+fPDymwnaomKgWNBNRG8xqamJnp7eyktLZ1SYWEyDA3txWY7HUmyYDTKQDUWSxxut5vq6mqys7PJzMykv38PMTHnYTCoBAJRjIxsD6LE2Gw2zGYzSUlJpKamBm3IenoO0Nh4J6oaS2npndjtCQQCAaqrq1FVlcLCSuBdJOl8zOarsNsHqKu7mry8n0167K2trbzxxoNs2PAQ0dHBpleSVEpDwwu0tLSQkpKCJElBJoliIRSqG4eC6Zy1mMgxPBAIIEkSpaWlETmQhwNVVenu7tY6BuHK7Y1VsrJYLEFBUN+N8Xg8OJ1OTCYTCxYsCHtoTV89Gmu09P7772Oz2bREabLqrCzL7Ny5k2OPPTasz4XZZOMzhM98nKqrq2P9+vWsWbOG66+/PiJqZCAQoLKyEovFQlFRUVjrl953Q2yE9bQtfWFJdOMPHDhw2OtXVdWTvPnmAQIBM1lZbi666CcYDBatgi+oYbLsp67ueurqjBx1lEJc3F0MDAxpneDW1s10dY3KqB97bBHLll2sfUZj4138858Sqmrg1FM9FBZuRJZlamtrg2Y5DIZ2+vvPoqsrkVdf/SKqCkajmVNPPZvCwqIpu8tW68ls3HgykjS6blkswyxffhpJSUmoqqrF/unasHs8HqqqqrDZbJOq/IWDiRzDFUXB4/FQXFw87bMWEDkFWD9rMVZxSzAB9HFDJDWBQGDSoeuxEHFKVVXMZnMQtWom6b6qqvLuu+9iNpvJyckZp2g5m2wcYRgeHua6665jYGCA3/zmN2FrQcPoj0EoOJSVlQUlBIJrr79pZfmgn8XYluhEGBgYoLKykrlz5x6yfGpn5zVkZT2LwaDQ0VFEYuKHGAyjN4ssyzidToaGhkhKCpCV9XVMJj/Dw8l0d7+gHWtUVBQ1Nc+Qnv5TBgeTiYt7DofjoCt7c/N6cnJeQ1UNNDd/h9zc/9Ee03c5VHUTGRl3YTTKDA8nYbPtBuxMtp+qqKhg8+a/8O1v309s7EjQY8PDc9i79y8a3ai0tHTaFRpUVWX//v2aK2o43Sz9ELfoWE22YR8YGKC6unrGWuKiYyD8Y/SdmrGtez3X1uFwhO0ULJKazMxMsrKywt4U6dVABLVKLMqi+5OZmTlhAAoEAuzevZsVK1aEfT1mk43PDD4Xccrv9/M///M/fPTRRzz88MPMmTMn7NcK9bf9+/eHTAjGzgPq/YzCoW3BaBytrKwkOTn5kOVTn3/+Nlpa4jGZZI4/3syKFbdqjwlqWGtrEw0NHxIIKBQUOIiPX0Z8fJIWp2w2G5WV3+O992wYDHDeeelkZHxbex+DYS8DA99EVRUcjvvp7Mzjk08+IScnR5NzF10Oo3E/BkMVTz75Fv398fj9ViwWIykp6axd+1+TnuPoNSymsjKVvj4Hy5fXsm/fC3i9XkwmE2VlZWFvdsOFqqp0dHTQ1NQUdjdrMolcsWHXK22KAfLpSGomOp6mpia6u7vHqWKJmUCRXIjuujjOcBUXXS4XNTU1pKSkjBvInurYxlKr/h2zhStWrKC+vp6+vr4gK4bZZOMIxaZNm/j5z3/Or371K4477riIXjswMEBFRQVJSUmYzWZNyUhQd8SCfag/DFF58Xg8EW+mVdWPouQQEzOALJtobLyVpKTrgxaXUdfMABbLwzgcvdjtK3A4NmA25wS9V1/fcTgcTaiqge7u28jIOKiG5Xavx+F4FTDgdt+Aw3F70GsPdjn2U1Z2JWazD48nEbu9m56eo0lI+BcGQ/BNL+YXBgYG2LJlC11dbdxww6+IixsGwGAAVQWPZzhooY1E1SsSCFqS0AsXC+1EjrF6idxwKAb6pGa6nWMFBL3MYrFgsViCeNbhtu4ngyzLNDU10dPTE5GE4tiWdXl5uTazId6zs7MzpIKJz+dj3759LFu2LOzjnE02PjP4XMWpt956ixtvvJEf/ehHnHnmmRG9VgyPx8bGEhMTE6RkNFbG/VCgKIpGPY10M+3zdfHgg48hSSbMZpmLLz6OhISVQbKzo54IW6mqcmEyKaSmGjnvvLvGvZfBUIHLdSsWi43Y2N8BY9UnJUBFVc089thv8HhGMJst/Nd/XYndHjeuyyHLp7Nvn4V33lmJohiQZTNxcXF87WsXk5iYqFGhxfov6FCjG/YRHA4/Fsty7dN7enqora0lJydnxsRmnE6npg6p/z4nKiyFK5ELwUlNuIpPkUIkr6qqEh0dzcjICCaTSUssxhbrIoUwHW5ra2P+/Plh7xcmi1Ph4HCSk+HhYZxOpybTL2ht/0GYTTbCRXNzM+vWreMLX/gCN99884QZb6jFRS89Kvil030D9vb2UlNTM87TYjL09PyJuXOvAECSLGzZ8iRxcfODgovJZOLAgZ+QkfEgAI2NJ5KT89S4Tefg4A3YbM9jMJiQpL8QFXWQtqKqPUjS7ahqHFbrj+npcdPRsYX09ONJTc3WntfV1UVDwz5ycmSysy9Clo2YzQr9/etR1atxu+ODvDdEoma323nyySfx+/3cdtudWCyjP09ZXo7f/472/n6/n+rqagwGwzjfjOmAqBI2NjaSkJCAoiiMjIxEPBMyGbxebxAt6XDOYaySlUiCFEWhv78/Ig5rJBgZGcHpdGKxWCgsLJwyMAhOrcvlwu124/P5WLp0aVDLWiR7QJDWvNfrpbKykmOOOSbs45tNNj4z+NzFqe7ubjZs2EB2djY/+clPJqQYiu7q2HlAoWRUUlIy7Yp7gKbcOHYOYjLs2vULNm9WAANWq4/ly08kJiYhqFhnsViorf0L77zzMaoKy5YNUlp6t3b+gUCArVv/RSDg4/jjvxTE/W9paaG5uZmysjKtM62qKo8//kNGRqIwm2UuvfQc4uKWYDAYNErP6DkkYDL9hVdf3UJFRTGj2yYDRqOBBQuKSUtLG+e9NNU5S5KkFRBDyfBOB7q6uqipqSEuLg6DwXBIMyGTQSg++f3+kN4fkb6Xvmsh5PGNRiMulyskjWg6EOm8iJ665Xa7GRgY4LjjjptStUq8djqETIRUbmZmJkVFRWG/178Bs8lGJJAkibvuuot3332XRx99lLS0NHp6elAUJUgeV2+UpF9cBGVoOobHQyEQCFBVVYXJZKKoqGjcYLSoAgmFiLKyy0hPPwCA2+3Aaj0Q8oZta7udjIzfoyhG2tvP4cCB9UEdArfbjcGg4HDsQ1UzgIl/5JLkp79/KUlJHfT3pxATswur9WCVy+/3U1HxCXPnXk5mZhMmUwBFMeDxJNLV9TQxMUeHpJjV1dXx4osvApCRsZ/s7ExOOulWQkF8D8Lb4VAXKb3Dqdvt1pKg2NhYRkZGtORypoJFfX098+bNIzMzc8pzCJUET6YQog8WkXBYw4WYF2loaBhHAxw7vxIIBILUwcRQn8Fg0HiyAj09PRq3Nzc3F5/Ph9PpZMmSJWEf12SLvhgG/A/CbLIxMT6XcUpVVe6//36efvppHnroIQoLC+ns7AQYJ487VtITDg79RlK4igSyLFNTU4PP56OkpCSo2BBKFGXv3pcYGBjtgqaluVi37mfj1ruOjo955ZUXkGWF+fPhmGO+RU1NsyZHv2fPy2zbthtVhaOOyuYLX9jw6fVw88wzD3+6gY1hw4aDylJu91ns2eMgL6+dffuupq5udPbktNPOQJZlqqurGRwcxOFIID39blpbW3j99dNQFLEeGSgtLWXNmjWHFGNEUnM4NGkIFnAR3SBVVbHb7Xi9XmRZDhqyn06IcwjX9FXMBIrEYmhoaNIkSJIkTV1tJoxxxTnU1tZqHhsi3gixGRH/9XLugmIs9tGTqVaJ854uIRNxDWeCwXEYmE02IkF7eztbtmzhxRdf5M033yQhIYF169Zx8cUXhyWPCwcrrXFxcWEPj0cCVVVpb2+nsbGR1NRUbTBa3wkY3bDZsNniEOtXQ8Mq5sx5c9z79fS8QVzclVitI/T0HEty8iYkyaK50sbENJCefj2KYmJw8PfMmTPqz6EoCnv2/AKjcS9JSdcxb95op8Pv7yY6uphAwIzZLDM8/CE+X+I41Q2DQcHrfYnVq+/Hag1gMKjIcjSBwG9R1a+HPPdf//rXSNJBKdwLL7xwQnPHQCCgObQXFxeHJQHp9/uD5mz0i0uoORvhrn24wWIiiIV2aGiI4uJibS4olP+GcLcVC2G4A4eCw5qamnrIfOupzqGmpoa+vj5iY2Px+XyYzWbtmjocjpAVtsnUQIS8b3t7O1lZWbhcLhYtWhTW8ciyTHl5+YR0ydlk44jC5zJO9fb2smXLFl555RX+9re/ERcXx7nnnsvVV189qQGdHpMVrqYLQiI7OTlZG4zWr1Px8fHYbPDrXz+kvaasbIA1a8a7htfU/JG33mrAZFLIzPRyzjkbtbXF7/djtb7Ju+8OoapGli3zsmLFzwEYGNjFM8+8jCSZsNl8XHHFXRy8pboxmd6gv7+I3//+DYzGAJJk5cQTT8XvH5Vwt1qt9Pf3k5WVxfz55VRX/5aXXjobVT24TlqtUVxxxRWHJOQiy7K2mS4pKQnrPfSbYP0Qt767rv8+hbqRGLKf7jVeUOh6e3spKioKMmgV1C2RXBwKxRgOirUkJCSQn58/7b9X8T10dnYSGxtLIBDQxGZCDZzrz30q1SoY/c527dr1uRQymU02QuDll19m7969rFixgoKCAm6++WYSEhLYuHFjRJXfyYbHI8VEw+bR0dEMDw+TkJBAUVHRuB+e17uQxMQG7e/KyrvIyxuvF97WdhNz5jyFJFlxuZaQlvay9rkdHR2MjHyTvLwPMRigo+MC5swZDQwdHW+RlvZfmEwSPT1zSU7+WGszBgJXkZLyOq2tx9HQUEh6+m5GRi4jL+8rQZ2gkZER9u27ipUrX8Fi8WM0gqoakeVLCAQeAoJv2h07drBly5agf7v55psnvX4iIRCqW+KzQxn76TfBQid+KhxKsIgUfX192ryIyWTSBuLFZj2cIc7JMJ3eHHpzP7fbjd/vx263Y7PZ6Ovr06Qyw23h6wfzxhotCVW4wcFBli9fHta1n2qgfDbZOKLwuYxT77//Pm+++SYrVqygrKyMu+++m46ODu6///6I7l1RuGppaaGkpCRok3goCDVsbrPZ8Hg8REVFUVZWNo5S+fTT/017+8GB93POyaK4+JKg58hygE2bfkpvrwGTSWHt2qNITDwozd7V1cUnn7xGcvLLREcHKCy8BaNRcOndNDdfQn19KkuWeElM3KRVqQWddGSknsrKt3G5HMTGjtDfn0pycjJf+9paoqKighSrFi7sorv7Vp55Zi3Bt6aB1atXR1S51kMkBOnp6UEdgslUl/SeRuGoDIo9SXFx8WF/16EwODioSc5brdZD8t+YDHovMjFrcagFPsEEEdfV6/USExNDbGwsbrcbs9kckRTvVEmH3+/n448/Zvny5ZO8SzBmk43PEUZ5nY/z8MMP88ADD3D00UdH9PrBwUEqKioi4q/qpecGBga0jaV+cdEPJ7e0tNDZ2TmuTWq1xqJvwrjd3URFBSdMXm8nqrqK+PgeZNmE2/0X4uO/GHT+XV1PkJz8Q8CAx/MwcXHnADAy8g7R0RdjNEoMDaXjdD6lKUSMHms8slxLaupZn3psxGA2t4U853ffvZ1TTrkfi0XSfXYaXu92xg746Q37YOpkA0YXFtEST0hIYHh4OMjYLxLVpYkggsXhdghCtcQB7Ha7RkEINSg9HRAytmazOexZi1CBUFzTsYN8o7+nLhoaGsjKyoqoGzSR0VJ/fz91dXX4/X6Sk5OnrHpNNVA+m2wcUZiNU5/ihRde4M477+See+7hhBNOiOi1IyMjVFRUaAo94dyToWi7ejqMfthcn9SMNWP91a/uQpYPrhHf/e5N49bO/v56nnrqWYxGBYtF5rLLrsVqPai29NZb/0t9fRNg4phjTmTx4pVYLBZ8Ph+dnZ0kJUnAXrq7C6ipacHp3AcYOPbYEygsLCI+Pg6r9UoGBrazadNFDA1ZMRigtHQhS5eeqAldCMpQfv4wVuu3eOyxDYy9PW+++XsRXXs9FEWhrq6Onp4eHA4HHo8npLHf4cSp4eFhqqqqiIuLo6Cg4JCVD1VVxefzaWu/XhgFRqnXYiZwuuH3+4NYC+HMWng8nqBjNRgM40xo9b/7SKV4BRRFQZIkTV1RXF+fz8cnn3wSkV/ObLLxOURVVRWXXXYZF110EVdffXVE2bSev1paWhpU0Q01xHcoVQuhRS6qIgaDgYGBeaSn9wLgcsUQHd097nWdnX9g7twbkGUTPl8sVmtL0OM1NXeQlfUUfX15qOqPaGkxkZycjN/vZ3h4iPT0l3E4qlGU/yY6+hj2738HqzWJ/PxRoxuvtwab7XhMJgmfL4auroUEAquZN+8HQZ/j8/l4/fX7OP/8u8cY+IHffxuyfPD5lZWVvPrqqwCceuqpLF68OOQ113eCRIUlKioKl8vF3Llzww6qkeBQqkd613C32x3UEhddC31AEIPSVquVwsLCGZG/E/MiYxOCUKZJhyI/KMsyDQ0NuFyucW33yRCqetTf3097ezslJSXs379fq3pNNKsjNOknGiifTTaOKMzGKR0OHDjAunXrOPbYY7ntttsi+h0rikJDQwNut5uysrKgDZxeIlVv6hdM252atunxeKioqNB8i4xGI++88wDl5aMKg8nJw2zYcOe4133wwX2Ulw8iy2ZKS7s488xfob8tnn76+wwNmQEDJ55YSF9fLklJSWzb9k+83hHM5ihOOOEskpKS+Pjj/8Xp9KOqBpYuTWD16u/oPinAe+99kz178vH5rJjNCiZTFJdcsl4r7oguhyQ1MX/+ldx/v3j9qHnfd7/7XYzG8OVUx0qkW61WoqOjcbvdpKamUlBQMCNUbNEhKCwsDMuQNZRr+GTCKCIhkCQpbBpzpBAU4LH0MFmWg7rromuhP9ZwkixFUWhpaaGjoyMihchQccrn800ad8biDFQt0gAAIABJREFUszRbOJtsRAiv18stt9xCXV0dDz74YMTDOZ2dndTV1ZGWlqZthicySzoUCC3ygYEBysrKCAT81NVdSiBg5qij/hiSBuZ2LyU1tQaDAVpaziEj41ng4GC0xVIMKBiNCh999GOio4/9tCrwJqmp21HVSykq+i8AnM7vUlT0FIpiorX1MbKzRQfkJSTpdWy2F4mJGUaSzLhcT+FwrEH/+xwaGuL55+/n8svvGWfgJ8uZ+P21Ic9bP8St7wRMZJgkNrr9/f0zRnsaHh6murp6nMP5RAPnkbqG6zsEMyWhKEkSTqcTl8ulDRqKroU+ET4ciOskdNsjpVYJHnhvby8lJSXAwSAnlF7GDkUKpayJBsrHDqT/B2A22ZgYs3FqDGRZ5mc/+xlvvPEGjz76KDk5OVO/SIe+vj6qqqpISUnBYDAEeUTpefaHUxEXBRnRjXe7XciyRHLy+Jiqqgq///0duFwxGI0qp59uo6zsBu29enq62br1Xjo6rMyb10ZGxhpMpkKGhnr46KP3MJtlZNnE+vUbsNuz6em5nRdeMGI0KqxZ46C6eikOh4MlS0aVqIzGR3G77+OJJ76OohhRFBNms4GTT/4SixcfXDP6+vqoq/uYxYsv4De/uRUwEBPTz7e//WNg/LUJNWenKIqWsInuup7mK1gLM0V7mkj5UByrvhNwqK7hokMwZ84crRA6ndDPWsTFxeHz+Q4ppk4GcZ0MBsOUJrN66JMOn89HY2Nj2EImUw2UzyYbnwO8+uqr3Hbbbfz0pz/llFNOmfB5QnFHVK19Ph9RUVF4PB7sdjslJSUzUpV2uVw4nU5tEzoRRkbqiY8/BrNZQlWNOJ2PIssrtGM1mfooKbmR2Fg3kmTHaNyK2ZxOf38tcXEnYDRKBAJWjMZGTKYo3O5VJCY6Aejs/A5paXcEfZ7Xm09cXC9Go4KiGOjpWU5Cwlvof6M9PT388Y+P8N//fS8222jCoffTgOCZAHGsh1K1mOmhOeGb0dzcjMPhIBAIBB2rw+EIS3BgMgQCAerr6xkeHg4aID8UTKS8ER0dTV9fHw6HIyhxmi6oqkpnZyeNjY1hu5yLbpDL5aKnp4eUlBTmz58fdC3F95uQkBB03ENDQ9TX1084UD6bbBxRmI1TE2Dbtm1cc8013HjjjXzlK1+Z8HmhaLtWq/XToWvrjKntCX7/nDlzyM7OnvCe7+ur4ve//zuqCmazxEUXrcbvz9Ecw5ub36S3V8VkUjjzzGXk5IwWuVTVz65dX2fPngLKypo47rhHMBjigV7M5v8BLPz5z2U0N/dgMhk488yzKShY+OmnjlBXdzavv34iIyM2wIDBAEVFxZx11tlBFfTaWieLFq3AaFSRpBMJBP4BBHesxbFGR0dr3eqEhISw1lJBexKD0TOxNrW3t1NfX098fLzmFD7ZwHmkkGVZ82ApLi4+LKd5UagViZA41tjYWFwuFzabbUZk7+Hg/Ge4ylv6Y+3t7SUmJobS0tKwvsOpBspnk43PCdrb27nssssoKyvjhz/8IX6/n5GREY1rPzw8rDlGi4VFZMOTzVlMFwKBgGbsU1JSEtTeFK1bl+tblJT8DQCvN4rq6vdxOJK1Y+3rW4rDsR8wMDDwNA7HqZ++dzsm0zGYTAH8/mhaWt4mL6+QQOAfREdfiSQl0Nl5Jx7P43i9R1FcfA8mk5nh4b2MjPyMzMxRp3GLRWJgoBSz+WUMhgzt+MrLy3nnnX9x++13Bs2clJfv0Ghm01Vd16tolJSUHJas3kTzC7GxsVp7fCyNbrrgdrtxOp1hz4tM1GHRz1ror6vwF2lpaTlsOeGJoHc5Lyoq0gKSvsomtM31fFthmBlqME/QBZqbm8nNzSUzM5PBwUGam5tZuHBhyOOYTTaOKMzGqUnQ39/PVVddhdVq5Ze//CVmsxm3260VbKai7ba3t9Pc3DxuzmK6IMsydXV1DA8PU1paGrTmCOrW++//kk8+iWG0czDMKad8jYSERO1Yn3vuB/T0xGC1BjjvvKOYM+er+P1+XnvtOdzuHk45RWF4OAefbwGffFKOz+dnzZovk5Exh7///WaamhIwmRQKC5OIjV3M8uXLiYqKwmj8kJ6e7/PUUycTCByMn3Z7LOvXfyOIKdDb26sVNoxG47iOtXA3PxxpW2H4WlRUFLZZ6kTvNZYJYDAYsNvtjIyMoKrqjDicw2ihp7q6WlPqnGqzPFmHRcQp/XUVcuv19fVhF64ihZ4qXVhYGCTIIDpX4njHHqvFYvnURHl03mIqF/rJBspnk40Zxq9//Wsef/xxDAYDCxcu5Mknn6S9vZ2LL76Yvr4+jjnmGP74xz9itVrx+XysW7eOXbt2kZyczKZNm8jNzZ2W46iurmbr1q089thjNDU1ER8fz1133cWSJUvCNvYRlZ3MzMwZkU0F6OjooL6+nrS0NE15Q1C3Fi5crc1HdHTkk5CwT3udx9OHxVKG1erD74/G43meuLjV+P1eamruxG6vIikpnZiYb9LUlEBz8z9JTU2mrOyrmExmBgZKSEzsQFHMtLc/QkbGBQDIsp+BgaXMmdOI+IkFAnZk+QXgeE3N5JlnnkGSJK644kGSktx89NGPUdXjxwWk6cLQ0BBVVVUkJiZqXOKpIAK2WFjEcPxE8wtiDmKmaE96funYgDSR8kakHRbhzeHz+SguLp6RgDQwMKApmlgslilnWAT0LWuRMIjrHwgEqKurY2BggKysLI2XHgqzycYRhdk4NQmampp4//33+d///V/27t1LXFwcN998MyeddFLYtF2Px8Mnn3xCcnIyeXl5MxKnJqJu2e123n77Bfz+0TU/J6eHiy66R3tddfUbbN68lUDAxIIFnZxxxk8wGBw4nW/w9tvbUFVISTFx0UW3s2XLH9izpxWDQSU3N5Zzz70Rv/8WysubGBqKobq6EFU1UFKSz+mnfw0Ag6EFr/fLPPLIefh8+rgzqjyVmpqqrf0xMTH4/X4URWHhwoUzsjaKebOYmJiwu8yhZHInYwIIZsRM0Z708yJ6Ly+YeC5E7xwebldAFK5myptDdJwkSSI6OlpTXNMfa6jvJ1ypXKG0ONFA+WyyMYNobW1l9erVVFZWYrPZuOiii1izZg2vvfYaF1xwARdffDFXX301ixYt4pprruHBBx9k7969PPzwwzz33HO8+OKLbNq0aVqO5c477yQuLk6Tz7zuuuu45ppruOSSSyIeHq+trcXr9U5L1VvfEhcLS1RUFCMjI8TFxVFcXExUVBSDg3tISztee11NzQays3+n/b1//1VkZ/8ViyVAe/sqUlJeAwzs338vWVkbMRhUDhz4IllZm6ivv5f8/I0A7Nt3NQsX3snIyBLi45s/nd9Yis+XQnr6b4iJScHv99DSso7S0n9iMinAKE2qoeEyOjouJyEhAbfbzebNm4PObf369dTX1zN//nzS0tKYbkzGkRVVNr0RkV4md6zq0kQQm96ZdI4dGRmhsrISAJvNxtDQkFZl03ctDieIiE5KSkoKeXl5h0VB0yua6JNhQHOPjaRCNZFqFYwm+Pv27UNRFFasWBHyfptNNo4ozMapSXDffffh9Xo59thjiY+P59prr+X888/n29/+dkT3rOgAu1yuccPjhwI9xWhgYEBTsvL5fFoHOCYmBp/PxW9/+5j2upUrPaxe/SPt7zff/Bn79hkwmyUWLfLxhS+M+nN0dDzHiy9WA3DUUd2ccMIDtLf/jhdeGBVMWb68naysH2GxGElP38fevVvZvNmEJBkBI3Z7PBdddAkOhwNF6UeWT+WRR87E4wmmqcbExHLFFVcErf1iRmGs1Pp0QXSZxXC3fmhZdNf1XQuj0Tip6lIoCNqTy+Wasc261+uluroan8+H3W5neHj4kOdCJsLg4CBOpzPsTspkCOVyLop0vb29zJ07NyxqlcBkcQo+W7OFR2Sycdxxx/Hxxx8THx/P+eefz3e+8x2+/vWv09HRgdlsZtu2bdxxxx28/vrrnH766dxxxx2sXLkSSZLIyMigu7t7RiozQ0NDXHvttXi9Xn79619HPMzV3d1NXV1d2MoQENonYqKWuKqqHDhwgLa2NkpKSkhKmoNeNa2j40MSEkYrvZLkJRAoITGxC0my0Np6A+npdwDQ2noHc+c+gMEAPT1fIDn5L3R1rSMj4xUAmptPpqvr+xQXx6Io9+P3V5KWthuApqZzMJl+xsDAED093dhs97Jq1VsYjarunLLx+fYC1pASt36/H6dzdC5kpniZw8PDVFRUYLVaiYmJYWBgAEmSghyuD2eQH4KrR9nZ2Yf1XqGUN2w2GyaTif7+fvLy8mYk6B2KUof+N+t2uzV6mcPh0LoWesrfobrHTlY96u7upqWlBa/XS3Z29ji++JG0iM9iNk5FAr/fz/e//3327dvHQw89REZGxtQv0sHtdlNdXT3lPKAeqqpq97yQyhVmaaKyrt9UdnR00NjYSFFREX/6032MjBykGV966WIyMk779FxGeOyxX+L3mzAYVNavX0Ri4jkMDvazadMjeL1eiovb+NKXLsfrXUhV1Q6iox8nPb2Vioq17N7dg8FgZM2ac8nPb2TnzkfZvbuUwUE7qmrEZrNQWLjw0wHuaBYsuJqHHz4Dj0d0LEbVp6666upxMwiSJFFXV8fIyMiMdeOFebAwSRwaGhpnQhsfH39YsUUY6SUmJpKXl3dY66KiKEGqi6JrYbFYcLvdZGVlkZOTM2PJWUtLC/n5+aSlpYUlwDKWEi2Ki6Jgp997yLJMc3Mz3d3dLFiwICKamyzLKIqiSeWK7+uzNFv4H9V/CQdz587lpptuYt68edhsNk477TSWLh1VkhAZa1ZWFq2trcDoop+dnQ2g/VB6e3vD3sxHArvdzh/+8Af+9Kc/ceaZZ3LfffdNaBoWCqmpqcTHx1NZWUlvb+84/euxShZC01psgOfNmzfpBthgMJCdnU1SUhKVlZWMlWEXiQZAb+8rZGX1oijGT9vQo0aAXV1bSUl5HJMpQG9vGUlJD+D1jjA8vBqvt/xT2stFJCXdSENDAvAj7PYHSEvbjdGoMH/+iwwPv43R+BIFBSvxep/mjTeu4YwzXtA+22jcj82WiMfzexYuXMi+ffuCjtNqtbJw4UK6urrYtWvXtHQ5xAZY37WwWCwoiqLxMqdbKzwxMZHly5fT2NjIzp07w54XGevB0d/fH8QNFhKDeqna2tparVszna19o9FIbm4u6enpOJ1O2traWLBgQVCVLxAIBHUtAoEAdrsdh8NBbm7ulO6xwlhJVKjsdjvz58+fUl/caDRiNBqDlEDE4iyqZ4sWLaK+vp4dO3YE0c5mYpM3i88P/pPjlJjdeP311zn//PO58847Of3008N+vcPhYOnSpTidTs0teuy9KKiwY+/5hIQEMjMzKSoqmnSTlJGRgcPhoLKy8tPhbP1jp2n/39q6DY/HiqoaiInxYrePDtJ2dr5FIDCCyaTS0ZEKLOef/3yUlpYuTKYSLrjgTkZGnkFRjICC0/ln3O6zSEo6n6VLX+W99xYRCJgZGQmwb9/HnHbaGZSWLgS2ccMNX2Tjxi+hKAfPeWTkI+LjTwo6TrPZTHFxMX19fezZs2dauhz6pE0/E2g2m+ns7CQ/P5+5c+ce8vuHQlxcHMuWLaOlpYXy8vKIDF/18wt65U0hNBJKIXLnzp3T3kkxGAzMnTuX1NRUamtrtbkXfSycSBzF4XCQlZU1paS7yWQiPz+fOXPm4HQ6aW1tDcunSrzWYDBocUp8p4qiTLtozf8Xjrhkw+Vy8fe//53GxkYcDgdf/epX+cc//jHuefphoYkemymsXbuW4447jnXr1vGlL32JG2+8MezsMyoqisWLF7N//37Ky8vJzs7WWnd6x+iUlBTy8/MPydAlNjaWpUuX0tBQwPz5dQDs3ftFCgsPPsdg+C1m82h7z+2eQ0zMaNUmEHiZ6GgPgYAVRTFgMKTT2bmGvLxyPJ5YWlr+TGzsdRQUVKMoRj766Bm6utZjs8WRmvo8UVHD2O0DmEzn4fE8it2+hkWLfsezz8ZwySVPBx2nzbaBM84YJjMzk9raWk477bSgx9PS0nA4HDidTjo7OyPqcoyl7QgjooSEhHEbYMGR7evro7CwcFo5kiaTiYKCAm1eRGjP638vEylvJCQkkJ6eTmFh4aS/L4vFQmlpKW63m3379s2I8pbNZmPx4sV0dXWxc+dOrZomgqGoBM2bN++QO1FxcXEsXbqU9vZ2du7cGfbciz7pkCQJWZaRZRmDwaAlMoJ7GxUVxYIFC/7TjJJmcYThSIhTp59+OkuWLOEb3/gGmzdv5sc//nFYGyMYXVOOOuoo7V7MysrSuqt6Yz+Hw0FOTs4h3fPR0dEsWbKE/fv/RE3NKKc/K6s36DkfffQ+qjraMcjIGMRiGS06NTd3MjISjdGocPLJowmdx9MA2IEAvb1vUlTUS3u7FVCpq3NQW/sGhYUFnHXWDcydeyPPPXc2gUAUiqLyz3/+g7o6J+eddwGS9C9uu62Un/98LYoSRUJCDxkZuROeR1JSEsuWLaOuro7du3dH1OUQsV+ftImuxdgNsOj49/b2UlxcPK0df4PBQE5ODmlpaVRVVWmd7FCiMyJODQ8Pa87h4exXTCYThYWFWiclISFhnLrg4cJqtVJWVkZfXx8ff/wxMTExWCyWceIomZmZh9yJErGwu7ub3bt3a/O4U8VbfZySZRm/368ZA34WcMQlG2+99RZ5eXnaQNEFF1zA1q1bcbvdSJKE2WzmwIEDZGZmAqPVo/3795OVlaVlruFm5YeDvLw8Nm/ezB133MF5553Ho48+qh1TKOgNk0TFAqCuro7k5GQKCwsPm7uoh9FoJDPzY3bu3EJ7u5uSkqWoqorBYECWvWRlfaQ9t7//VGJiwOt1kZDwZ6KifMiyCa93LbW1teTl7UaWDURHjwCVny74TsDAnDk78fk+prb2KoaHJYqLn8ZolImJcWGzfY3BwTtxOG6krOy7PPZYFFde+QRwUOoW/Bx99NETuraH0+WYbBFMSkoiLy9v0kXQZrOxZMkS2tvbKS8vj4jmFi7sdrtWPdqxYwdpaWkap1mvZpGenn7IiiYOh4Ply5fT3NxMeXk5RUVFh60uIzYY+qFzu92Oz+fD7/dTWlo6rQo2BoOBzMxMUlNTqa+v1ypU4VTBDAYDPp8Pl8tFV1cXqampWuVIJOAiWVq8ePG/ZZ2YxWcTR0qcSktL45VXXuE3v/kNZ555Jg899BBFRUUTPl9QS0TxQ2zSGhsbiY+PZ/78+VN2KiOBwWDgvPN+QVvbTpqamsnMvEiLU4oi09RkQTA38vNHacter5vKyl5stgCKYiIu7jJ27/4IRTERGztMSUktbncR27fnU1g4zJw5A7z+uoKiqNTXO3nhBTNnnPE31q69kGefPQtJGo0NdXUN/O53v+Xyy6/Abq/kllvOxWx+F0m6HEnKnfQ8wulyhKJE62cCs7OzJ00GRSzs7u5m165d5ObmkpGRMa2bVRELOzo6+PDDD7XftyjYie56fn5+WAI5oSA6KQcOHKC8vJyCgoLDjrcTDZ2Lfy8uLp72mJ6amkpSUhJNTU0RxVuDwUAgEMDlctHd3Y3NZvtMdDiOuJmNHTt2sGHDBsrLy7HZbFx22WUsW7aMLVu2cOGFF2qDd0cffTTf+ta3eOCBB9i3b582ePfCCy/w/PPP/1uP+Z133uH666/n9ttv5+yzzwZGq+X64ThJkoJ4lqJiIYbHPR4PpaWlYVeeIoEsyzidTm1z2NX1QwoLDw6Kd3fvRJLm0Nb2F44++iZk2YCqGqmt3YrP9z75+XeRkOBiYKCUtrarUJRNn2pyq6SmvonRqNLWdhQdHfcSCLzLihU/w2oNaO/v9Z6Eqv6NZ555npGRcq67bnQYUJKsBAKusM/D7/dTVVWFqqqkpKRoyZswTRLX9lAXQRjtiFRXV2MymULSByJFqEXQYrHg8/mw2WwzOkCuN9IL5zzGShDq6Vti3kJfDRISh+HSng4FogomNjr6rpO+I6R3kBVzIcKPZOw8hyzLWK3WI0blYxazcWo6sGfPHi6//HKuuOIK1q1bpyXnerERobann7UwmUwoikJTUxO9vb0zJpk61t28vf09/vrXPdrjV1yxDJNpGVVVb/L++zWoqpHk5D5OOOEiystfp7tbwWSSOeOMRbz2WiWqKmM0woUXFrJly3ba21OQZSNGIzgcsZxzzhrM5nU8+eTZWsIxCgPLly/npJNOOqTzELMcQ0NDZGRkaJRY/UygMPc71A2moM76fD5KSkoOe15E73Ludrs1Wf9AIIDJZJox+X6fzxc0nxnu/kcwF8bKz4prqy/YeTyeIFPDmdhjiUFvq9VKYWFhUNdJXwwVcyx6fxNxXUOpVh1JMxtHXLIB8KMf/YhNmzZhNptZsmQJjz/+OK2trZqkYEFBAW1tbVpLStBTsrOzsdlsdHV1kZuby/PPP09iYiKqqnL99dfz2muvERMTwx/+8Iew7eTDwdDQEJs3b+a2227DaDQyMjLCvffey/z584M8AiZDT08PtbW142TiphPd3d3U1tZy4omnYbGMfvWKAlu2bCYuLo7ExCvIzd37afKwgsTEf+Hx5BETM4CiGOno+B0ZGddhsfgJBKx0dd3EnDkbMZkkjEYFvz+KhoZH2b+/gi9+8ZdYLJL22aoazeDgu9x3XzDV4Oabb570mMWGUgTDkZERLUhmZWUxb968GdnkdnZ20tDQQH5+fkSzHGM36xMpb+hN7sIdaIsUqqrS0dFBU1NTSN+MiYb5IpEgVFVV0+mfKW8O8RmNjY0kJydrspn6jtBEDrITqYFMpX/+/4DZZGNizMapaYDX6+WDDz7g9ttvZ2BgAJ/Px/e//32WLVsWttqeGB6fN2/ejEh76z+jouJl+vqECIvKCSecgN0ez759mzhwIBaTSWHFin5Wrfo5f/rT7XR1xWG1Brjwwnzefns3LpcDo1HF4UgmLs7DnDn7+OCDBfj9VozGUVGJU089ifnzr+PBB89GloPjyBe+cFLYM5n6zbroWog4lZ6eTl5e3oxscoUBXVZWVkRqfmLmRsQqPc14rMu52JvMlKcFoPlmzJ07d5xFQKh5S8FcELEqnMKRkKXPysqaERsCvf+HOKaxHSGHwxGSwaKPUyLhCMen4/8Bn61kIxLIsszcuXPZsWMHDzzwAElJSdx6663cfffduFwuNm7cyGuvvcbvfvc7XnvtNXbs2MH111/Pjh07puXzVVXlrLPOYv78+axYsYLGxkZeeuklHnrooQk1/ieC3++noqICm802JU8/XOjlB0eVgT7itNOu0x73+cwoSj8eTxdxcYVYLBKKAm1t96Eoy0hNPfVTHw4bLtcmkpK+itEooygmamq+QGxsgMTEKuLjOzGZZPz+aNrbb2HvXhNnnvnjoIQDwOnMZdOmy7S/r7vuOm0RnszcZ+xm3e/3U11djcFgmDHFKr/fT01NDbIsa3LCeky0WddLEE61CIrPkCRJG/yebogqmMfjIS0tTau0iUVQLNiHQ+MTn+H1eikqKjosl3M4OCgpqkFioF+SRn9PkRiQ6VWrxAI+auh1ZCzis5iNU9OBSy+9lJiYGI499ljcbjdPPfUU9957LytXrozofSRJorq6GlVVKS4unpZiz9ii0tDQEFu3fqB/BjfffBuBgJf77/8VkjSqTrV2bRou11z+9a8dSJKJoqI2bLYT+fjjBuz2AeLjA7S2JmE0KixdasdsfocPPjj208HxUcfwsrIyTj/9Ln71q/NR1YNrgtXq5/rrfxDyePVCLpNt1v8dilXCNHFoaIiSkpJxXadQm3X9zI0wopsMQjFwcHCQkpKSw17fJzqP+vp6XC4XmZmZWvdCdIREUelwmAtiSN3tdgeZyh4qhHmiiFNihlFVVQKBAEVFRRHRt8aqVh1Jceozn2y88cYb3HnnnXzwwQcUFRXxzjvvMGfOHNrb2znppJNwOp1cddVVnHTSSaxduxYg6HkzgYqKCr7xjW+wdu1avvnNb0Z0Y+jla0tLSyNSbAhHf7un5xcsWLBRe01V1ank5v6NlpZbKSoapVbJshGPp53OzkuYN+9dDAZobT0JRelk7lwnfr+VkZEkEhPbkWUzLS1fIS/vOSyWAIoCRiN0d69l8+YVfPnLN2O1BiccL720hj17RitGGzZs0IKMfjBadISm2qyLDsRM+XLAQcnizMxMoqKigroW+gBzOJt1UaEKVdk5FOg362JGSFTaUlJSKCgomJFKm96bIzc3N+yEWfDYxfEKXXYRYPQ88YGBAZxOp8YdDpcOJcsyVVVVbN++nYKCAtasWXPI5zkDmE02JsZsnJoBtLS0sG7dOlavXs33vve9iGmFomsaqdv1WHfrwcHBcUWlQKCVxx77m/aauLh+rr76p1RVvcIrr1QAo74b3/72pWze/EcqKowYjQqLFg1QWWlHVUcFIvLzXdTXJxEImDEYDNhsZo455n3efXcl+lsuJSWJyy77Jb/85Te0fz/xxK0ce+zfNCqsvqikr6yPlfIOhZn25YCDHaG0tDTsdrt2ffWbdUHbOdTP7+/vp7q6mtTUVHJzcw97I6zfs4jNOowW4RISEliwYMGM0Iz1FOBwjRNhcuquMM4V12R4eBin00l0dDQFBQVhF0RFQrRt2zYSEhK09eA/BJ/fZGPDhg0cc8wxXHvttTgcDtxut/ZYYmIiLpeLs88+m1tvvZXVq1cDcMopp7Bx40aWLVs2Y8fl9Xq56aabaG5u5oEHHoh4OGloaIiKigrNoyHU4jBWyWIqd2sAWfZhsaQQFaXg8xn58MN/kZiYSG7uSuz2YQDc7hRU9SOs1hJiYoZRFCNtbd8nI2MjkmTGaFTp708nMbEVcVjt7YWkpjYQHe3TPsvjyeGJJy7lmmt+ismkakPhg4MFvPbaXaROY7/5AAAgAElEQVSmppKYmKgFmEMdjJ6JLsfYofOhoSEkSdLk71JSUqadviUqO8JvIhKO7Finc70UpX6zLrjXPT09FBUVRewVEw4URWH//v20t7eH9OaYaDZEX2mbqgKod6idiL7l8XjYuXMn27dvZ/v27Rw4cIAFCxawatUqzj777EkHZf8fMJtsTIzZODVDkCSJn/70p2zevJlHH31Uk+cNFx6Ph4qKCk1lL9TmU5jQ6gsJk7lbAyiKn3vvvRcY/feTTsrEbl9KeflzdHaOVtUdjn7Wr/8fHnrol8iyEYNB5dxz5/HPf9bj9UYTFzeE12vHZPIiy0Zk2YQsm7HZDCxbto0tW45Df9sZDAa+851f43QWMmdOOyMjP6GzM1crKh1uB3gmuhxjjWgHBweRJAlVVbV1cbq7/oqi0NzcTFdX1zhj3KkgmBZ6wZFQexZVVdm/fz9tbW1h+ztFinAowBPNhkxG3R37GV1dXTQ0NExIQ/P7/ezZs4dt27axY8cO6uvryc3NZdWqVZx++unTSqWcBnw+kw2/309mZiYVFRWkp6dPuIifddZZ3HbbbUGL+D333DOhRfx04qWXXuIHP/gBd999d8QDZ4qiUFtby/DwMCUlJUHJhWiF6qtBh1KpVlWV2tq9LFq0Svu3hoazkOUTKSi4BQBJMuN0XkRJySZMJhm3O5WYmNHrbDZLSJIJs1mhqyuXhIQ2bDav7v3h2WfXsnbtnxBxqL39TeLijp32wafD6XJMJJUrFkHRuhUVqkg5spFgYGCA6upqkpOTQzp36yuDQt/cZDIFbdan+i0MDw9TXV1NbGxsRJWdSOD1erXhv8zMTDwejzZ8GOlsyETw+/3U1dXx8MMP8+Uvf5n+/n62b99OeXk5gUCApUuXsmrVKlavXn3YLugzjNlkY2LMxqkZxnvvvce1117L9773Pb785S9H9FpVVbUCRmlpKYqijPOJGOtuHQ4Cgf0cOPAEc+dejMVSRGtrK3/60zOIW6WkpIOysgv4618/QFUhOtpPTo6J+noVg0ElLs7H4ODoZyUm9tPTk4SijL7WaITFiz/mo48Wj/vcBQuKND+LcLrrkeJwuhyhpHJDDZ2LGJKamjrtMugCIoYIgZCx12myopI43ql+CyKGGI3GGaVL19XV4fF4mDdvnkaRGxwcDDKjDXc2JBQkSaKxsZHHHnuMk046CYPBwPbt29mxYweDg4MsWrSI448/ntWrV1NUVHRExqnPdLLx97//nQceeIA33ngD4D+mPT0WbW1trF+/nsWLF/ODH/xgyor4WHO/3t5ePB6PJo16uK3QsQgE0omPH9L+bm39CybTf5ORsR+Arq48oqLc2O1uJMmC3x9NdLQHRTEiSWaioz2YTAqKAh6PHZCIjfUGfUZfXzwPPvgdFMVMWlo6l1566YzcUOF0OUIpb4hFRSyEk31HU3FkpwPCubuzs5OCggJtKHqq1m0k0Fd2pnNIXW/yJ443EAiQnJx8WJKJegiFtW3btrF9+3YaGxupr68nPz+fm2++mZNPPhmHw3EkaZgfMQf6/4DZOPVvgMvl4pvf/CZ2u5177rknLF6+3tyvt7eX4eFh4uLiNNO+yUxoI0V19Tu8/PKH2t9f/KKPpiYDDQ2jXhpZWd0MDMQxMGDDbJZwOIbp+7/2zjwsynL9498ZNgVZZpB9kX1VVAQVXEpzz9NmKWlpbllWrln2szx4TidxLVMzNZcW08wWT6VmbmUqayAKSCAgyKYMIPsyM8/vD3zfMwMzMAMzsnh/rovLy5l5531meHnu93nu7/29S5tkyAYGTcW3crkAMtn/bhhdXXOQm+umdB6BQI7x4yfzPXr0sREjlUqVXChV3XQrNvjj6tYUrXKtrKxavflWzGJr2lBWWxQzzJ6enjA2Nm5hP8vFKVUZLE3hpMy6LFJvLt2tqamBVCqFhYUFPDw8YGlp2eHzcA5rXNbixo0buHXrFvr27YuVK1di0qRJsLGx6RFxqkcvNiIiIjBx4kTMnTsXQJOzkbW1NV94V1paio0bN+KXX37Bjh07+MK7JUuWIDY2to131y1yuRwbN27ETz/9hN27d8PDw4N/TrGIu3lzPy7NzBhDamoq35RMl1mBXr3MoHit//77fzF69BP8Y1lZE+DqehaGhrL7qWgBjI2lYAz309cCAHIYGDRdTowJUFtrDFPTeqXzXLvmjx9+mAE7Ozv4+vrC39+/wwVa6lDMclhaWip1Dm3NeUMbuPoEe3t7uLq66mTCUFxoNrfKc3V1hVgsbjN1qy2KReq+vr5aaWTVZVm4hRsXEGUyGbKzs1FaWtou+VZNTQ0SEhKUJFG+vr581mLw4MEQCoXYu3cvcnJyEBUVpe3X0Nl0m2jTCVCcekAwxrBv3z7s2rULO3bswMCBA/nnFGsXmvcz4jLsQqEQ6enpkMlk8Pf316nUdNOmD6DYOmzMGFf8/nsW5PKmxwYOLMH161ZgrClzYWAgg0wmvN+cVgCZzOC+uQnAybMAwNi4Dg0N/7vZNzGpwhtvrOM3Ynx9ffXWE0Uxy2FjY9NCFt281qI9CzeuoaxIJFIrdWsPio5WZWVlvK27q6srrK2tddo3DFAuUtdWZqypdJfb5OOaGmor36qvr0dSUhIfp27evAl3d3eEh4djxIgRGDp0KExMTHD06FGcOXMGe/fu1fZr6GwevsVGTU0NXFxckJWVxd+4SCQSTJ8+Hbm5uXB1dcW3334LoVCI+fPn49y5c6iuroaLiwv27NmDqKgo5OTkPFDrQQCIjo7G3LlzMXLkSEgkEjz11FNwdXVVkkOpq13gdhHy8/O1Lh5vDaHQHCYmcgBARUUf5OU9isDAn/nnc3N94eraJIeprTWBiUkD5HIBhEI5AAGEQgaZrOlfxWFLpQIYGv7vEquuNsFHH72LJUuWoL6+HqmpqbC2ttZJoRmHYuOk0tJSlJaWQigUwt7eHmKxGFZWVjoNgNzORVlZGfz9/bX2IlfVkLC5xEgoFKKgoAC5ubl6tUbmAh9XJ6Tqd6LYN6S8vBy1tbW8/prbvWrtd1lVVYX09HSYmpqq7f/B6Vy5rEVcXBykUqmSJEqX10wXgRYb6qE49YDj1I0bNxAREYGgoCA0NDRg1KhRfMxR7GOj7maS2+zR5Y36pk3/AcDNFwz/+MdQ/PRTHP+8p2cVbt5smn9NTatRU2MGgYABYGBMgKbLSIj/XU7Nxy6DsXEdliz5CwLBTwCaZDypqam83FRXm3xcYTR3oy6RSMAYg729Paytrdsti27tfNxNdHuavSrWhnBZFlVqAM5etl+/fnqzRuYkYlzDXlW/E1XqBW2ku5x8i1NIqPpdMMZQVlbGLyxiYmJQVVXVnSRR7eHhW2xoypw5czBq1CgsWLAADQ0NqKmpwQcffNAp1oPPP/880tLS4Onpibt378LOzg6bNm3Sur6gurqa1//qYke9oaEclZUTIZXaQiT6DubmIr6+gjFALhfCwKBpMaK4gOAWGMD/OoK3NpTExHXw83uT/79imre9TYMaGhqUditUOW9wBVr6dKyqrKxEWloa78KkboJRHC+XZWnLg5tDsQGSn5+fXvSrMpmMb97F2f0qpprlcjl/w9G8eZKmcP0/MjMzkZ2djeefb+pUzy0uUlJSIBaLERYWhpEjRyIsLEwnKe0uTo/+cB2E4tQDjFPLli3D+fPn4ebmxvc02Lhxo1I2XhPq6uqQkpLCN+Ts6E1XTs5mfPttk7PhP/5Rgfh4QxQWcjGDwcioEY2N3JzY1NCPMcH9juTC+wuO1nnlFTHMzRcoPcY5RObn58Pf379dphqcgQc3l6oycykvL9e7Y1VNTQ3S0tJ4FyZ1N9yaGo6oO5argdBX01pFExIfHx/+++PGLJVKdaJe4ORbKSkpmD17Nm7dusVLoq5evQpTU1MMHz4cI0eOxIgRI9C3b9+HNk491IuNiooKDBw4EFlZWUoXQGdpZu/du6c0UX311VfYsmULtm3bprXjiFwu5+sG2utsoZgF4IrOGYvH2LFv869pbBTwDQA7wr/+9R6MjHph2bJlLZ6rrKxEampqm3Kk5s4bXO8FxeJDdTfgirUcuvKGVzW+W7du4e7du/Dz84O5uXkLzS03Xk39zVWhr90jxd22kpISlJaWwtDQkM8KtXe8zampqUF8fDwuXbqE06dPIzMzEyEhIZg4cSJGjRqFQYMG6eX308Xp0RGqg1CcesBxysLCgh/LyZMn8fbbb+P999/HuHHjtHovxhjvXBQYGNiu/gyK85Ji0fmFC+cVXiVHU9ZC3f8BQAZF+ZQqRowQIzx8gcrnampqkJqa2qrzluJ4Fe1cFYvkrays1GYtuFqOuro6nXQGVze+/Px83p1PJBLxvSI4y3xVUlhtKSsrQ3p6equZ8vaOn5PuSiQSlJSUQCgUws7Ojlcv6GIjrr6+HomJibh8+TJOnz6NlJQUBAYGYsqUKRgxYgRCQ0P18vvp4qiNU7qvbOpGZGVlwcbGBnPnzsXVq1cxZMgQbNu2DcXFxfzE7ODggDt37gAA8vPzlaz/nJ2dkZ+fr7NJvPmOyAsvvICwsDDMmTMHEydOxPLlyzX+gxQKhfDx8YFEIkFiYqJGu/aKxXyKWQBLS0v069cPZmZmKCw8pXSMVMrA3fcpZi7aymI0x8amGHZ2E1Q+Z25ujtDQUNy8eRMJCQkICAiAqalpq84b3Hg1/b6MjY0RFBSE4uJixMfH6yXLwWUp6urqkJCQAIFAALFYDJFIpPV4W8PW1hZisRgZGRkoLCxsd5G6TCZTqg2pr6/nd9s8PDwwYMAAFBUVITc3t93yM65TenNJVEhICMLDwzF37lwUFxdjxYoVCAoKQmhoqNbnIIjuTFePU5MnT0ZwcDDmzJmDc+fOITIyUuObOYFAADc3N4jFYly7dk2jXXtFg4nmlu7Ozs73N3Fu4cIFxfPIlBrytVxoAG0tNACG0FD1n8vU1BRDhgzBrVu3EBcXx8vKVPUIMjMzg6WlJT9eTed9Q0ND+Pv7o7S0FElJSXrJcsjlcpiamkIsFiM5ORmMMYhEIohEIjg5OcHPz08ncUokEiE0NBTZ2dmIj49vd5F6a9JdFxcXBAQEQCKR4ObNmxr1PFEFYwylpaWIiYnhMxfV1dUYNGgQRowYgT179kAmk2HJkiVwcXHBqFGjtD5HT+ehXmxIpVL89ddf2L59O4YNG4alS5e2WjiqKguk75SYp6cnzp8/j7Vr1+Kpp57C7t27tQoa1tbWGDJkCNLS0vjeCQYGBmodl7idin79+qkMGI6O76Ku7kP06iVFdbUxzMwa+OcUvwptvhbGgP79IxASMkztawQCAezt7cEYQ0xMDAwMDJS82F1dXXWyW2FnZweRSIQbN26guLi43VmO5oXRlZWVfANFTkt6584dFBQUwNXVVedOIFxQKi8vR3JyMp8Vai1IcAVyXCE3AN4z3NHRUeUujZOTE2xsbJCeno7CwkL4+vq2upsjk8lw48aNFpKo8PBwTJkyBf/+979bSKI8PDxw8eJFyGSyDnwjBNE96Q5xys7ODidOnMCHH36IyZMn49NPP4W3t7fGx1tYWCA0NBTp6em8bNbIyEhltlrRccnFxUVlFqBXL+X5VHmhofQMmjZjuX/V88ILP8PI6GKbn8XGxgZyuRwJCQkQCoW8mYuVlRWcnJx0ststFosREhKCjIwM3Llzp91ZjuaGI83nfRcXF5SVleHWrVtwcnLSed8lAwMDeHl5obKyEjdu3OA3slqrl1AsPG8u3eWa/DW/3rkNuMzMTBQWFsLPz6/VLJpcLsfNmzeVJFFmZmYYPnw4Ro8ejdWrV6uURJ05cwZ1dXVq3vXh5qGWURUVFWH48OHIyckB0OQlHhUVhczMzC5pPXj27FksX74ca9eu1bq7cW1tLbKzs3Hnzh1+UuqoZrGszB+OjrlaHaOKjz5ajMcfXwpnZ2f+seaaUM55g9OvFhUVtWoLqAu06cuh2DmUcwzTpDCa08iamZnB29tb571FgKaJMzs7m6+zsLCw4CVy3OKCc45RtCHU1tKR63JuYmKCAQMGwNDQENXV1UhISOAXFwUFBfDz8+MLuR9SSVR7IBmVeihOdaE49ddff2HBggVYtGgRXnjhBa37RNy6dQv5+fn8vM7N+9o6LhUXb8Sff96El5cJTp92bvuANhg1SoDhw1cpPcZlLbgfRdtxCwsLSCQSlJWVISAgoF0SMU3Qpi+HouEI52SoSWE0Vw/IKSb0UQ+o2KjPx8cHYrFYSSKnK6nxvXv3kJ6ezm/I9e7dm5dEccXcWVlZ8PDw4ONUaGioTgvyezBUs6GOUaNG4bPPPoOvry8iIyNRXd3UJburWg+WlJRg3rx5cHJywn/+8x+VN9qKFoTchMJZEPbq1Qu3b9+GnZ0d+vXr16Edr+LiyXBz+6MjHwcAsG3bG5gx4x1+zJyGVXECVPWHLpFI8Pfff8PNzQ329vZ62b3jajm4pkHcxNbcJo8x1qIZkabjUdTI6qsbamNjI4qKipCdnQ2BQABDQ0OlQm5d9Lbg+nJs2bIFv/32G3r16oXevXtjyJAhvPuGm5tbTy+Q0xf0pamH4lQXi1NVVVV44403UF1djW3btqncEVfMris2ouUcFwsLC2Fpadnh4vFff12L5GTtzUWUkWHlyiGorR2hlK3WpAldRUUF0tLS+NoEfcx/6mo5mmcBtDEcUQW3Aefh4QE7Ozudfw6ZTMYbtshkMhgaGvKGLrrqy8IYg0Qiwa5du3D48GGYm5vD0NCQl0SNHDkSPj4+Pc0l6kFBiw11JCUl8Q4fHh4eOHDgAORyeQvrQW6V/frrr+PUqVMwNTXFgQMHtC7c1gWMMezcuRNffPEFdu7cCSMjI1RWVqJPnz78ja9id+vmEwqXIqyoqEBgYGAHMgMMVVX9YWmZB2Pj9klcqqoM8euvh+Hs7KzkvKHpH3pjYyPfA8Lf318vOy5cYXdubi5MTU0hlUqVUuO66iJbV1eHGzduwNjYGN7e3u3e8WeM8d24m3cQt7S0RE1NDYqLizu8sGkuibp+/Tr69u2L8PBwODk54auvvsKIESOwYcMGWmB0HPoC1UNxqgvGKQA4cuQIoqKi8OGHH8LOzg5FRUUQi8X8jS+XXVfViFYXxeMAcO/eGezZ81eHPoehYT2GDx+nlK02NzfXOBMtk8n4HhABAQF6cWDiNq2ysrLQq1cvyGQyJWm0rgw8Ghsb+V4pnCNhe1En3bW0tERDQwMKCgo63FCWM8vhshacJCosLAze3t44duwYnJ2dsXv3bsqydxxabHQUNzc3fnIxNDREfHw8SktLMWPGjAfuc56YmIjffvsNv/76KxISEuDh4YFXXnkFU6ZM0erGt7S0FOnp6R3epaisvApb2/B2HXvjhhdsbK50uMs2Z0Gni8JurvCc2w3ibPL69OkDiUQCIyMjvTlWcbavOTk5GvfMkMvlShIursmfYgfx5kGxrq4OaWlpMDY2ho+Pj0Zd67nGec0lUdxu0MCBA5XeRy6XIzo6GuHh7bs2CCVosaEeilP36UpxKj09HSdPnsSZM2fw559/wsXFBS+++CIiIiK0uvGtqKhAamoqnJ2d290duqHhe5SWfocvvxzY9otVMGJEDMLCjnV404RzYNJFYXdzu1zOIIWzeZXJZHqVGZeUlCAzMxOurq4auR62R7qr2FDWz89Po89SV1enJInKzs6Gp6cn3zivuSSKMYZLly5h5MiR7fsiCEVosdFR3NzcEB8fj759+/KPvfXWW53ic/7999+jpqYGYWFhcHBwwMqVK1FQUIAdO3ZovVPd2NiI1NRUGBoawtfXt9079BUVg2Fn97fWx23aFIXXX3+jXedsjjrJU2uo0oQaGhoq2fo1fx9tajk6+lm4pkGKGRt1vTjUZbLUwTlBZWdnw93dHXZ2dvxxii5RV65cQVxcHORyOUmiOg/6otVDceo+XSlOnTlzBrm5uQgLC4Onpyfef/99XLx4Ebt371aqz9MEmUyGv//+G/X19QgICGhnBpth06aNaM+f0qpVLwPQrtGdOhQlTwEBARplBlozHOHm/ebvo00tR0c/i6qeGep6cbRHustJpp2dneHs7KwUpyQSCb+wiI2NRU1NDQYPHszXW3h7e5Mk6sFBi42OomoS7yyfc1X88MMPiIyMxIYNGzB69GitjmWMoaCgAHl5eVo1JWo+AUqln2LYsH0an1cmA65fT4SPj49W420LrjbBx8enxeKruQ0hZ5uo2IxIk4mpPQub9lBcXIzMzEyIRCJ+Z6itxZC2NDY24uTJk9i2bRsmT56MtLQ0pKSk8JIornGeorc+8cChL149FKfu09Xj1O+//44lS5bgnXfewRNPPKH18VwPIVVzuzo4x6UmW9RDuHLlJgoK3LU4qxyrVq3Weqxtwd1Au7u7w97eXuk5RcOR8vJypcJzS0tLtYYjzXkQfTm4z5Kens6PS5PFkLbIZDLExsZi1apVePLJJ5GVlYXk5GT06dOHb/AaHh4Oa2trilOdBy02Ooq7uztEIhEEAgEWLVqEl19+GVZWVigvL+dfIxKJUFZWhqlTp2L16tV8Wu6xxx7Dhg0b9K6bvX37NubMmYOQkBD83//9n9Y3oTU1NfxNpqpd69acN7hai7KyN+Hiskej8yUn+8HbO0GrMWpKfX09UlJSYGRkBJFIhMrKSlRUVPAFfdyYOzr5clkOTSVPbcH1tuAWRHV1dbxbhlAoRGBgYIclZ9wiMT4+nt8RKigogJubGxITEzFr1iz861//IveNrgVFT/VQnLpPd4hTpaWlWLBgAcRiMaKiorSez7i5nety3fymm7tR5+bQ2tpaJcclU9MKbN9+GG331WjCwqIEixZt1GqMmsLVPzQ2NsLGxgZVVVUtahe0NRxRBecUqKnkqS1USXd79eoFqVQKmUyGwMBAndi5c5IoTrqbk5MDT09PJCUlYcKECdi6davObeOJDkFN/TrKpUuX4OjoiDt37mD8+PHw8/NT+9rO8DkHmpo3nT59GlFRUXj88cexZ88euLm5aXw815QoKysLCQkJ8PT0RG1tLd81VNF5w8HBQeUE2Lfvh8jNlcDV9bs2z3fq1HQYGGTBzc1NJ2nO5kGmvr4eDQ0NKCsrg7u7O3x8fHRuLdu8L4e2WQ5Fj/PmrlacRpX7jiUSCZKTk7VOi3N1IJwkKj4+HnK5HCEhIRgxYgTmzp3LO5PV1NTgP//5DyQSCRwdHdv1nRAE0Tl0hzglFovx3XffYc+ePZg0aRJ27tyJAQMGaHy8iYkJBg8ejNzcXMTFxcHLy4tv9FdRUQHGGH+jrrrvghirVkVg69YvIJO1Xag9dGhTnYWXl5dO4oeqHlcCgQA3b96Ei4sLgoODdWI4ooi1tTUsLS2RkZGB4uJirbMcrUl3vby8lKS79+7dQ2pqqtaOl6okUbW1tbwkauvWrfzisrGxEVu3bkVOTo5W1w7ReVBmox1ERkaiT58+2Lt3b5dJTzcnOjoar7zyCpYtW4bp06e3+XqpVKp0o15dXY2GhgbY2NjA2dkZFhYWWk20xcXL4Oa2t9XXFBbmQyKRoLS0tF079qpu1BVrF7ggU1tbi9TUVJibm8PT01MvvSyAtrMcjDGlArmqqirekpgbc1tBRiqVIjMzEzU1NS00shwymQypqan8blBqaipsbGx4SdTw4cNJEtX9oF+WeihOqaA7xKnU1FS89NJLiIiIwKJFi9qckxS7RXN2uQ0NDbCysoKrq6tGc+j/3isRH398HFJp63HnzTdfw+3bpSgoKOC7gmuDqht1Rdtx7ka9oaEBaWlpHa6fbIu2shxcHaNifYi20l2ut1NpaSn8/f3Rp09L62G5XI6MjAx+caEoiRo1ahTCw8MhFospTnUvSEbVEaqrq/kuldXV1Rg/fjzWrl2Ls2fPdlmfc6Bph+HVV1+FoaEhNm/ezP/Bc9aoil1DBQJBi7StVCpFWloaDAwM2jX5FRW9CXf3XSqfk8uB+vpqfpxpaWmtuo205mTBZQJaGx/XMKiwsBD+/v6wsLDQ6rNoimIth6enJ2pqavgxc40JFT3D2zuRlpWVITExEYmJiViwYIGS+0ZRUVELlyh9Ba7uhEwmQ0hICJycnPDzzz8jOzsbERERKC0tRXBwML788ksYGxujvr4es2fPRkJCAqytrfHNN99olSHUExRx1UNxCt03TtXV1eGtt95CVlYWdu7cqbRRo6qfUXNbd+6mlSu41qZ4nLFb+OijA60uOFategtA0/ebkpICGxsbteYY3IYSF1sVm9BpcqOu6Ebo6+sLsVis8WfRBsVaDh8fH6V+HHV1dTAzM2uXFX1zKisrcf36dZw/fx6LFy9GamoqH6c4x0XOJSokJISku+i5cYoWGxqQlZWFp59+GkDTH+nMmTOxZs0aSCSSFj7nlpaWGDJkCMrLy3n7QUNDQzQ0NHTKhcIYw969e7F582aMHj0aeXl5WLVqVYvu1up2+7lGbbm5uVoVj3MU5zwBN/+zLR7Py7NA376F/P+bu40AUKpd4OxnO9LxHGgKGKmpqRCLxXB3d9eZS4ViEWJ5eTkkEgnq6+thbW0NBwcHWFlZ6WQiVZRE/fnnn0hMTERGRgYmTpyIqVOnYuTIkXB1daXdIBVs3boV8fHxqKiowM8//4zp06fjmWeeQUREBF555RUMHDgQr776Kj755BMkJyfj008/xZEjR/DDDz/gm2++6ezh0y9UPRSn0L3jFNBkcrJq1So8+uijyMnJwbJly/iNGU02lDj7c22Kx5sox+bN28CYKkmVcnG4XC5HVlYWysvLERgYCCMjI5WGI9yY29uErq6uDqmpqTA1NYW3t7dOs/GKigCJRILa2lpYWVnB0dFRJ/UhQFOcKikpQXR0NK5cuYKEhARcv34do0ePxpNPPomRI0eqrLchem6cosWGjulKFwMfkGUAACAASURBVEpWVhZmzpwJxhi8vb2RnZ2N0aNH45133tF6p5srHre2toa7u7vmk1FtLdJywhAcnKH08Jgx3+PEiYkAlO1ni4uLUV5ejt69e8PGxoaftHXZrI9r0nf37l0EBASoTPFq8h5cOp/r0s4VIXILOK7pXUccqzSRRKWnp+P111/HZ599hqCgIK3P8TDAmSesWbMGW7duxU8//QQbGxsUFRXB0NAQV65cQWRkJH799VdMnDgRkZGRCAsLg1Qqhb29Pe7evdvZCzhabKiH4pSWdKU4de/ePUydOhW1tbXw9fXFrVu3MGDAALz//vtaN7+rr6/nb9K1q7Gov2+Lq7whZGeditkRnwCmpkrNUu/cuQOJRAITExM+TulqQ4mDa9J3+/Zt+Pn5wcpKe+tdTaS7ADrsWCWXy/H3338rSaIsLCyUXKLu3r2LV155BWvWrMH48eO1PsfDQE+OU7TY0CFd7UJpbGzkd1qAJonPu+++i6SkJOzatUtrbW7zXR1NA4GxmRmEMkDxo90RWuLutT/5Qm7F3SATExOkp6fDyMgIPj4+epMAVVZWalzIxhUgNm/011x3qwpNHau4RZeiS5SmkqiGhgYYGRl19kTTZXn22WfxzjvvoLKyEps3b8bBgwcxfPhwZGZmAgDy8vIwefJkXL9+Hf3798epU6f4XgCenp6IiYlRshPtBOgXqx6KU1rQ1eIUYwz37t3jb6blcjk2b96MH374AXv27IGnp6fW78dJZgMDAzXeTOplZoZ/R74H4H+77WvfX4e0pOsov3cPtbW1SoqA3r174+bNm2hsbIS/v79ON8QUqampQWpqKiwtLeHp6dlqNkDR2l1b6a42jlV1dXVISEjg49StW7fg7e3Nb4IFBwerXHjJ5XLIZDLq1K2GnhynSMitQ5YtW4aNGzeisrISQNMfr5WVFX9z6OzsjPz8fABAfn4+XFxcAACGhoawtLSERCLR6YViZGSk9EdtbGyMjRs34vTp03j66acRGRmJSZMmafx+QqEQXl5eKCsrQ1JSkkp/8OYwxtDo7g7rPldQWWkPgQDYtm02luAL1JiZwdHRUeVOSlBQEAoLCxEfH9/uXZ22MDc3R2hoKO++FRAQANP7O1hcrQXnxGVgYMBP2P369dMqsCg6VhUWFsLBwQE2Nja8RE3RJQoA7xI1f/58jSVR+gp0nUldXR1Gjx6N+vp6SKVSPPvss1i3bh1eeukl/P777/yu3MGDBzFo0CC1HZF//vln2NraYsiQIbhw4QKA1p14OsulhyAeBF0tTgkEAqX5XSgU4q233sLYsWMxe/ZsvPbaa5g5c6ZW7+fq6gqRSISUlBQ4ODjAxcWlzb/h8h9+wNqnn8aG9YtRXy/G9JEHIZMCJr16wdfeXqW8KCAgAHfv3kVCQoLO7M+bw7lE3rp1C3FxcXyRenPpbnNrd2dnZ60yLc0dqxwcHGBvbw/GGO7evavkElVXV4chQ4YgPDwcH330UZuLIA6hUNjjpFMUpzSDFhs6ojtdKBMmTMDZs2cxd+5cnDt3Dv/+97+1mpREIhFCQkJw48YNlJSUwM/Pjw9Uzd1Campq0Purr7Bm0g4cN5yESTiDJIShvvxj2LWyuyEQCODo6MgHDCsrK3h4eOh8ohIKhXB3d0dBQQHi4uL4m3auf4iTkxP8/Pw6fF5jY2MEBATgp59+wowZM+Dh4QGJRAJbW1uEh4fjqaeeQlRUFMzNzbv0hPEgMTExwblz59CnTx80NjZi5MiRmDx5MgBg06ZNePbZZ5Vef/LkSWRkZCAjIwMxMTF49dVXERMTg0uXLuG///0vTpw4gbq6OlRUVGDZsmV8LZChoSFu377NW/06OzsjLy8Pzs7O/E6hvgo1CeJB0p3iVEhICP744w+8/vrrOHv2LD788EOtjD3Mzc0REhKCjIwMJCUlITAwkJ/fVdnPmvTti+Mjvsf6d56BEEA+7NFQXY228v82NjawtLREamoqSkpK9GKxzi2gTExMkJSUBAMDAwgEAl66a29vr5Pzck5Yf/75J1544QW4u7vzi9GwsDCMGzcO7733HrlEKUBxSjNosaEjutuFYmNjg59++gkff/wxJk2ahF27drXqyd4cIyMjDBgwALm5ubhy5QosLS1RX18PuVzOu1p5e3vz9rODigbxx6r2p1JN7969+V2d+Pj4dtdYKFJfX69kRchZ5nJZm/r6evj6+nao4R8niYqLi+N3hDh/80WLFuHcuXMICgrCzp07tS66f1gQCAT877qxsRGNjY2tBrjjx49j9uzZEAgEGD58OMrLy1FYWIj169dj/fr1AIALFy5g8+bNOHToEJ577jkcO3YMERER+Pzzz/Hkk08CAJ544gl8/vnnCAsLw7FjxzB27FgKrESPoLvFKXNzc3z++ec4dOgQJk2ahG3btiE0NFTj4w0MDODn54eioiLExMTA0tISDQ0NSoYjHh4evOFI8GmgAU0uidqUmBsbG2PgwIH8plV7zFSao0666+7ujsrKSlRWVsLHx4eXSbeX2tpa/PXXXy0kUfPnz0dCQgJ69+6Nffv2talieFihOKUZVLOhB7gL5eeff8Zzzz2HadOm8YV3QUFBWLx4MXbu3Ilr167xhXfff/89jh492injvXr1KubPn4+5c+fipZdeUnvBcvazirZ+JiYmMDMzg0QigY2Njcbp1PbA1VhomhYHWloRVlZWwtjYWMnhpLl+tLS0FOnp6XBzc4O9vb3G5ykoKFCSRAmFQl4SNXLkyBZj/vHHHzFhwoQOdwTvychkMgwZMgSZmZl47bXXsGHDBrz00ku4cuUKTExM8NhjjyEqKgomJiYadURW/NvMysriLQUHDx6Mr776CiYmJqirq8OLL76IxMREiMViHDlyBB4eHp31FXB03SjS+VCcagfdLU5lZWVh9uzZmDBhApYvX96qgyJnOHLv3j2+T4S5uTnKy8vRp08f+Pn56a3fEmemoo3jYVvSXVUmKRUVFUhLS9M6HjaXRNXX1yM4OJivt2gew8+cOYOgoCDY2tpq/2U8JFCc4qEC8QdJd7xQampqsGzZMpSUlGD79u0QiUSoq6tTSjVLpVKlYjNF+1muiU9ZWZlWxePaIpPJkJmZierqagQEBLTIPnDNCbkdofr6evTp04fXsWpqRSiVSpGeng6pVKqy+E8qlSIlJYV3iUpLS4O9vT0/YQ8bNowkUTqkvLwcTz/9NLZv3w5ra2vY29ujoaEBL7/8Mjw9PbF27Vo8/vjjeOedd5Qm8Y0bN2LIkCGdPHqdQBeSeihOtYPuGKcaGxsRGRmJ6Oho7N69G46Ojqivr1faUGptzmeM4fbt2+1u0KcpjDHk5OSgpKQEAQEBLbIPMpmMj1P3FIrPudiqaW8LmUyGrKwsVFRUICAgoEXclcvlSE9P5xcX165dg6WlpZJLlEgkojilIyhO0WKjS6GuoKgzm7dwk9L27dtx/Phx9OrVC4888giWLl3KT9qaFCGXl5fjxo0b6Nevn1470XLZB2dnZxgZGfGTNteckJu0OyKFApq823fv3g0HBwd4eHjwi4vi4mIEBATwWYugoCBqnHcffV3f69atg5mZGd58803+McUbpq7QEVmP0N2AeihO6YGuGKcYY8jKysJnn32GAwcOwNzcHIGBgfjnP//JZwA0mfOrqqq0Kh5vLxUVFbzjoampaQvpLjdmTm7cXsrLy/HVV1+htrYWw4YNQ2xsLKKjo5GbmwsfHx8ll6ieaCbSHihO6QW1F3HPsgXoJnAFRVevXkVSUhJOnTqF6OhovP3221i+fDkyMjIgEomwb98+AMC+ffsgEomQmZmJ5cuX4+2339b5mLZt24bIyEj4+Phg165dvP2du7s7bG1tNZ6grKysEBISAolEgmvXrqGxsVFnY5TL5aioqEBubi5u377NB57s7GyIxWKEhIRg6NCh8PPzg/1995D2wPmbf/vtt4iKisL58+fx0UcfYcWKFejXrx/279+P5ORkfPPNN1iyZAmCg4NpoaGArq7vu3fvory8HECTrvjMmTPw8/NDYWFTM0jGGH788Uf0798fQJOG9YsvvgBjDNHR0bC0tOwpEzhBPHC6Ypz6+uuvsXLlSohEInz22WcIDAyEnZ0dXF1dYWdnp/Gc36dPH4SGhqK2thaJiYmor6/X2RgZY6isrMTt27eRm5vLW/FmZGTA3NwcgwcPxrBhw+Dv7w8HB4dWbdPbOs+dO3dw/PhxbNiwAcePH8fRo0exaNEiWFhY4OOPP0ZycjK+//57vPnmmxg+fDgtNBSgOPWAYYy19kM0o1+/fuzLL7/U2ftVV1ezwYMHs+joaGZtbc0aGxsZY4xdvnyZTZgwgTHG2IQJE9jly5cZY4w1NjYya2trJpfLdTYGVUilUrZu3To2atQolpaWxqqrq7X+yczMZGfOnGH5+fntOr68vJzdunWLXb16lf3xxx/s7NmzLCYmhqWnp7Pi4mJWVVXFqqur2c2bN9mZM2dYXl5eu85z7949dvnyZbZlyxb23HPPsf79+7Nx48axtWvXstOnT7N79+4xuVzOjhw5wmbMmKHX772n0ZHr++rVq2zQoEFswIABLDAwkK1bt44xxtiYMWNY//79WWBgIJs1axarrKxkjDEml8vZ4sWLmYeHB+vfvz+Li4vrhE+sN9qaqx/mH6IZD0ucksvlbOfOnSwkJITFxsa2a/7Pzc1lZ86cYTk5Oe2OH3l5eezatWvs4sWL7MyZMyw6OpqlpaWxwsJCVllZqXSerKysdp2nsrKSxcXFse3bt7NZs2axoKAgNnr0aLZ69Wr2888/M4lEwuRyOTt9+jSbMGECk8lkev3uexIUp3SG2nmatmM7ieYFRZ6enp3qdd4cAwMDrF27Fo899hgiIiKwcuVKTJs2Tav3cHBwgJWVFVJSUiASiVotlmPsf91ZOc9wAwMDXsLl6uqqdlfG3t4eVlZWvPWgp6dnq8WDVVVViIuLw5UrVxATE4M7d+4gMDAQ4eHheOeddzBgwACVmYoZM2Zg+vTpWn0HDyu6uL6DgoKQmJjY4r3PnTun8pwCgQA7d+7U0yciiIePrh6nBAIBFi9ejNGjR2Pu3LmYNWsWFi5cqFWmwNraGkOGDNHYurZ5bwsAvCRKXd8o7jwhISFIS0tDSUkJfH19W82I19bWKjV4zcvL4yVRr7/+ulpJ1Pjx4zFu3Diqw9AAilMPDlpsdBIGBgZISkriC4rS0tJavKYreJ2PGDECFy5cwKJFi3D27Fls2rRJK6s9zro2OzsbCQkJCAwMhKmpKS+JUurH0bs3P2H7+vpq5RbSq1cvDB48GHl5eYiPj4e1tTW8vLx4SdTly5cRHR2NhIQECIVChIaGYsSIEXjllVfg7Oys8ffZUyfwvLw8zJ49G0VFRRAKhXj55ZexdOlSREZGYu/evXyzqg8++ABTpkwBAKxfvx779u2DgYEBPv74Y0ycOJF/v+5yfRMEoZ7u8nfcv39//PHHH3jzzTcRERGBTz75BNbWmpvXcta1+fn5vMW6ubk578DILS6qq6thYmICKysr2NrawtvbW6s4xVnGFxUVIT4+HlZWVvDz8+MlUVxNYGxsLBobG/nGeVzPC02dHnvq/ElxqvtCi40OUFNTg+effx5SqRRHjx5tl9+1lZUVHn30UURHR3dZr3MrKyscOXIEBw4cwKRJk7B9+3YMGjSo7QPvIxAI4OzsDKFQiNjYWBgZGcHAwIDfDVLsx9ERuEaAeXl5ePbZZyEWi1FVVQVHR0eEh4fj2WefxaZNm9CnTx+aJJphaGiILVu2IDg4GJWVlRgyZAjGjx8PAFi+fLlSsRsApKam4siRI0hJSUFBQQHGjRuHv//+u0Xg7Q7XN0H0ZB6WONW7d2/s3LkTx48fx9SpUxEVFYVHHnlE4+MFAgHs7OzAGENiYiKEQiEMDAxgbm7ON5VVdGBsLwKBALa2tiguLsaCBQtgaGiI2tpaWFtbIywsDBMnTkRkZCS5RKmA4lT3hQrE20lRUREeeeQRODo64r///a9WE7iqgiJ/f3+MGTMGx44dAwCVzVsAdFrzFoFAgHnz5uHw4cNYuXIlduzYAblcrvK1nFTp9u3bSElJQXR0NJKTk3kbWQsLC5iamsLb2xuOjo4dKpCrqKjA2bNn8f777+Mf//gHRowYgQMHDmDx4sUIDAyEra0t9u/fj8jISIwbN47saNXg4OCA4OBgAE2NtPz9/fn0sSqOHz+OiIgImJiYwN3dHV5eXoiNjQXQPa9vguiJPGxxCgCefPJJnDhxAlu2bEFkZKRakxJ2v7dFQUEBUlNTER0djaSkJNTW1sLX1xfW1tYwMTGBl5cXnJ2dO7RJVVNTg4sXL2LTpk2YNm0awsLCsGXLFjz//PMICwuDqakpPv30U6xfvx6PP/44dehWA8Wp7gtlNtpBamoq1q5di0WLFrXLcaOwsBBz5syBTCaDXC7H9OnTMXXqVAQEBCAiIgLvvvsuBg8ejAkTJmDMmDEoKChAYWEhvvvuO7i7u+PTTz/F+PHjkZOTAzc3Nxw9ehQikQiMMSxduhQnTpyAqakpDh48yP9h6gofHx9cuHAB//d//4fnnnsOn3zyCUxNTVFcXAwDAwOUl5ejrq4OZmZmsLKygrOzcwvPcDs7Oz6N7OfnB5FIpNG52X2P9MuXLyMmJqaFJGrx4sVwcnJSmgAuXbrUomkf0To5OTlITEzEsGHDcOnSJezYsQNffPEFQkJCsGXLFohEIuTn52P48OH8MYraVk2v7/nz5wMA5s+fjxdffBFeXl68lz9BEB3jYY5TTk5O+PXXX7Fx40Y8/vjj2L17N+zt7XH79m0YGRlpJN21s7ODRCJBYmIiPD09NW5q11wSFRcX16Yk6vr163prhttToTjVvaA+G1ri5uaGuro69O3bF9HR0Xyben1QWFiIwsJCpZThjz/+iIMHD0IsFmP16tWIiopCWVkZNmzYgBMnTmD79u04ceIEYmJisHTpUsTExOh8XAUFBbh06RIOHTqEixcvwtraGgsXLsQzzzzD97bQZMVfV1eHlJQUWFpawsPDo8VkK5VKcf36db7e4saNG7wkimucR5Io3VJVVYVHHnkEa9aswTPPPIPi4mL07dsXAoEA7733HgoLC7F//3689tprCAsLwwsvvACgaSKeMmWK1iYChMbQRa4eilPNoDgFlJSU4NKlS/juu+/wyy+/wMrKCjNmzMC8efNgaWmpcUa9oaEBaWlpMDIyUllLKJPJkJ6ezi8url+/DrFYzDfOCwsLg5WVFcUpHUJxqstCfTZ0SVRUFAYMGIBx48ahrKxMb+dRlzI8fvw45syZAwCYM2cOfvzxRwBNKcPZs2dDIBBg+PDhKC8v572edcnBgweRlpaGN954A7GxsfD09MTt27chFou1qr3o1asXgoODYWBggJUrVyI2NpaXRE2dOhUjR47Ejh070KtXL7z77ru4evUqTp8+TZKo++Tl5WHMmDHw9/dHYGAgtm3bBqCp4eH48ePh7e2N8ePH89coYwxLliyBl5cXgoKC8Ndffym9X2NjI6ZNm4ZZs2bhmWeeAdC0u2dgYAChUIiFCxfyKWhOv8qhqG0lCKLzedjj1LFjxxAXF4eIiAj89ddfCAsLQ25uLszNzbWqvTA2NkZQUBAsLCywdu1anDt3Dn/88Qc2btyIZ555BmFhYYiKikJjYyOWLl2K+Ph4nD9/ni9SfthrLyhOEQCoz4a2cP7lMpmMLViwgAUFBbGioiK9nzc7O5u5uLiwe/fuMUtLS6XnrKysGGOMPf744+zixYv842PHjn0gHs4ymYxt2bKFDRs2jCUmJmrkGV5VVcVu3LjB9u/fzxYtWsRGjRrFxGIxGzt2LPv666/Z7du39e7R3t0pKChgCQkJjDHGKioqmLe3N0tJSWGrVq1i69evZ4wxtn79evbWW28xxhj75Zdf2KRJk5hcLmdXrlxhQ4cO5d9LLpezF198kS1durTFOTi2bt3K9xm5fv06CwoKYnV1dSwrK4u5u7szqVSq18/7kNPZvSy68g/RDIpTqvn888/ZoEGD2B9//KFxnLp58yb76quv2Ouvv85GjRrFbG1t2bBhw9j+/ftZZmYm9bNoA4pTDxXUZ0PXCIVC7N27FytWrMDo0aPx22+/wdXVVS/nqqqqwrRp0/DRRx/BwsJC7etYJ1mzCYVCrFixAmPGjMG8efOwcOFCfueKo7GxEdevX+dTzenp6bwk6rnnnsOwYcPAGMOKFSsgkUjg5OSk93F3dxwcHPjOo813FC9cuACgaUfx0Ucf5TvMqtpRdHBwwKVLl/Dll19iwIABvNPYBx98gMOHDyMpKQkCgQBubm7YvXs3ACAwMBDTp09HQEAADA0NsXPnTq0sIAmC0D8Up5SZPXs2wsPDMWfOHEyZMgVLly5Vku/KZDLcuHGjhSQqPDwckydPxr/+9S+YmpoiMjISGRkZmDt37gMZd3eG4hQBgDIbXZ2GhgY2YcIEtmXLFv4xHx8ffiVfUFDAfHx8GGOMvfzyy+zrr79W+boHRVVVFZs3bx576qmn2KFDh9jq1avZmDFjWFBQEJs5cybbsWMHS0pKanV3geveSWhOV99RJDpMZ2cPuvIP0cl0tzhVX1/P3n77bTZ27Fh26NAhtnbtWjZhwgQWGBjIpk2bxrZs2cJiYmJYQ0OD2vegOKU9FKd6PGrnaarZ6MIwxjB//nz4+/tjxYoV/OOKFmzNrdm++OILMMYQHR0NS0tLfkfhQWFmZoZ9+/YhODgYX375JQYOHIgvv/wSSUlJOHToEF577TUMHDiw1d2F1rqqdnfmzZsHW1tb9O/fn38sMjISTk5OGDRoEAYNGoQTJ07wz61fvx5eXl7w9fXFr7/+qvI9u8OOIkEQPZPuGKeMjY0RFRWFF154AZ9++ik8PT2xa9cuJCcn49ixY1ixYgWGDh3aqpMhxSmKU4QWtLYSecArIqIZFy9eZADYgAED2MCBA9nAgQPZL7/8wkpKStjYsWOZl5cXGzt2LJNIJIyxJj3j4sWLmYeHB+vfvz/tBnRBfv/9d5aQkMACAwP5x/75z3+yTZs2tXhtSkqKkt7Uw8OjRUaou+0oEu2ms7MHXfmH6EQoTvU8KE4R7YQyG92RkSNHgjGG5ORkJCUlISkpCVOmTIG1tTXOnj2LjIwMnD17FmKxGPPmzYOdnR1+//133Lx5E9euXYOHh0e73B4I/TF69GiNu4621pAI6J47igRB9CwoTvU8KE4RuoYWGz2El156CadOnVJ6LCoqCo899hgyMjLw2GOPISoqCgBw8uRJZGRkICMjA3v27MGrr77aGUMmFNixYweCgoIwb948Ptjm5+fDxcWFf41iQyIAfLHcuXPnlFLbq1evxm+//QZvb2/89ttvWL16NQBgypQp8PDwgJeXFxYuXIhPPvnkwX5IgiAeaihOdW8oThHtpeeKDh8yRo8ejZycHKXH2uP2QDx4Xn31Vbz33nt8Q6KVK1di//79bWpXuR1FVZw9e1blsTt37tTdwAmCILSA4lT3heIU0REos9GDKS4u5idmBwcH3LlzB0DbOxGEMqqK5drbkEgV1JCIIIiHFYpTuoHiFNGVocXGQ0hbOxGEMvpO/St2z/3hhx/4YPHEE0/gyJEjqK+vR3Z2NjIyMjB06FAdfjKCIIiuCcUp7aA4RXRlSEbVg7Gzs+PTzoWFhbC1tQVAOxHaosvU//PPP48LFy6gpKQEzs7OWLduHS5cuEANiQiCeCihOKUbKE4RXRlabPRgOLeH1atXt3B72LFjByIiIhATE0NuD+1A29Q/99rDhw+3eK/58+erPc+aNWuwZs0aXQ6dIAiiy0BxSn9QnCK6CrTY6CGo2olYvXo1pk+fjn379sHV1RXffvstgCa3hxMnTsDLywumpqY4cOBAJ49ev7i5ucHc3BwGBgYwNDREfHw8SktLMWPGDOTk5MDNzQ1Hjx6FSCTq8Lko9U8QBKEailPqoThF9GRosdFDULUTAWjv9nDq1CksXboUMpkMCxYs4O3oujvnz59H3759+f9zWtbVq1cjKioKUVFR2LBhg8bvR6l/giAI7aA41ToUp4ieChWIEzwymQyvvfYaTp48idTUVBw+fBipqamdPSy9cPz4ccyZMwdAk5b1xx9/1Or47taQyM3NDe+//z7GjBmDPn36YMCAAUhOTsbhw4fh5eUFS0tLLFiwAFKptLOHShAEoRaKU5pDcYroMrTWXlzvjc2JLsXly5fZhAkT+P9/8MEH7IMPPujEEekGNzc3NnjwYBYcHMx2797NGGPM0tJS6TVWVlZqj4+IiGD29vbM0NCQOTk5sc8++4yVlJSwsWPHMi8vLzZ27FgmkUgYY4zJ5XK2ePFi5uHhwfr378/i4uL098G0oF+/fszLy4ulpqayhoYGNmvWLObh4cEWLlzIqqqq2K1bt5iNjQ07dOhQZw+VUE1bc/XD/EM8RFCcUg3FKaILoHaeJhkVwaOqaCwmJqYTR6QbLl26BEdHR9y5cwfjx4+Hn5+fVsfrKvXf2bz88svw9/cHAMycOROHDh1CdHQ0zMzMYGZmhkcffRRxcXGYOXNmJ4+UIAhCNRSnVENxiujKkIyK4GFdtGjs1KlT8PX1hZeXF+8Trg2cFtXW1hZPP/00YmNjeS0rACUta09GMU1uamoKAwMD2NjYKD1WWVnZGUMjCILQCIpTPRuKUz0TWmwQPF2xaKyj+tzq6mp+Yqqursbp06fRv39/tVpWgiAIoutCcYoguh8koyJ4QkNDkZGRgezsbDg5OeHIkSP4+uuvO3VMsbGx8PLygoeHBwAgIiICx48fR0BAgEbHFxcX4+mnnwYASKVSzJw5E5MmTUJoaKhKu0WCIAii60JxiiC6H7TYIHgMDQ2xY8cOTJw4ETKZDPPmzUNgYGCnjqmjndryLwAAAZxJREFU+lwPDw9cvXq1xePW1tYqtawEQRBE14XiFEF0P2ixQSgxZcoUTJkypbOHwdNV9bndjZycHKX/P/rooy3sAw8ePPjgBkQQBNFOKE71TChO9VyoZoPo0nRFfS5BEARBcFCcIojWocUG0aVR1Oc2NDTgyJEjeOKJJzp7WARBEAQBgOIUQbQFyaiILk1X1OcSBEEQBAfFKYJoHYEqraECrT5JEARBPBBIAK4eilMEQRCdj9o4RTIqgiAIgiAIgiD0Ai02CIIgCIIgCILQC7TYIAiCIAiCIAhCL9BigyAIgiAIgiAIvUCLDYIgCIIgCIIg9AItNgiCIAiCIAiC0Au02CAIgiAIgiAIQi/QYoMgCIIgCIIgCL1Aiw2CIAiCIAiCIPQCLTYIgiAIgiAIgtALtNggCIIgCIIgCEIvGLbxvOCBjIIgCIIg2gfFKYIgiC4MZTYIgiAIgiAIgtALtNggCIIgCIIgCEIv0GKDIAiCIAiCIAi9QIsNgiAIgiAIgiD0Ai02CIIgCIIgCILQC7TYIAiCIAiCIAhCL/w/2p8GFD23UXsAAAAASUVORK5CYII=\n", + "image/png": 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jK3luGP3uZKozCvVaKEEoUVlgsEElI6V8EsCroPSanRZChKAkDbcC+LfkZn8IJSg4BmUVkQ4Ad6ZN58nlVwAcF8rUnAEoeQJfTz72eSiBywUoOQ//Uux7SvoKlAu6q1CS098ipVSnZ/wEwJMARpND869Of7KU8hsAHoIyrWceSlB2WEo5VkhhpJRXAfw6lFWQZqCsyvIolBVrTGHRa/w1lADxMpRjtIrUKRY/gzKv+u+hJKu/Hkoya7YyXoCyGs7f6rbX3yuhDcrFziKUaQ29SC43mRyJ+xKAo8nj9o7kFJlfhXL+vgjleP8rtp6z/VYALybPye8B+NPke0kvbxjKak1/kdz2f0A5f/U+D+VCakkI8XyW13s0+dwfQDk2XVBWvMk0Zz4jKeUSlLnqb4Xy+fx/UBKPt5L1u2D2eY7M361+KCsULUM5d9agXMBuIqV8GsDvQrmnxCKUi/S3SymPFFievBk85lv5MyjfvyNQprpdhMGpSMnP/pcA3CmE+JIQQkgl/2oAyrHP9rwYlLyeruTrHYWSXP3BPMv+KID/DuWYzUBpCzZ9N/IhpZwG8MsA3gzlHFiEkosT3OKpfwol0HosOZ1tCErCv6HrpEx1RiHlF0LUAzgM4H8W8nwiOxK5R9KJKBchxFNQhuw/vd1l2e2SvdkxKKsJFZM4XlaEsvDCRQB/krzgt+p1ngK/C2VBCLEfSmL7jTJ5Yz8qjeQoU1xKacnNO4m2g+1vOEVEZNAtUJYqLiZpvCwIId4KpZfWAeU+Bl4oIx1EW5JSngFw/XaXYzeSUn5ku8tAZDZOoyKiHU8oy3g+DuBjssC7HJeZ/wJlWsoUlKmFh6WyVC8REVFJcRoVERERERFZgiMbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkCQYbRERERERkicotHpclKQUREeUitrsANsZ2ioho+2VtpziyQURERERElmCwQURERERElmCwQURERERElmCwQURERERElmCwQURERERElmCwQURERERElmCwQURERERElmCwQURERERElmCwQURERERElmCwQURERERElmCwQURERERElmCwQTtWd3c3nnvuuYyPfeQjH8EXvvAFQ/u5/fbb8fzzz5tZNCIiIrZTRGCwQTvU4uIiLl++jOuvv37TY7Ozs3j00UfxwAMPaH9bWFjAXXfdBa/Xi97eXnzjG9/QHvvgBz+IBx98sCTlJiKi3SHfduqLX/wibr31VrhcLtx7770p27Odop2MwQbtSKdPn0YgEIDH49n02COPPILDhw+jurpa+9t73/teOJ1OzMzM4Otf/zre8573aL1Ed955J376059iamqqZOUnIqLylm875ff78fGPfxz33Xffpu3ZTtFOxmCDdqRTp06hv78f73//+9HS0gK/348nn3wSAPCDH/wAr371q7VtV1ZW8J3vfAef+tSn4PP58KpXvQp33nknvvrVrwIA3G43Dh06hCeeeGJb3gsREZWffNopAHjLW96CN7/5zWhqatq0L7ZTtJMx2KAd6dSpU3j22Wdx+PBhzMzM4IEHHsBnP/tZAEpv0nXXXadte/78eVRUVODaa6/V/nbzzTenzH/du3cvTp48Wbo3QEREZS2fdsoItlO0UzHYoB3p9OnT+NjHPobXve51cDgc2Ldvn/bY0tISampqtN/D4TDq6upSnl9XV4dQKKT9XlNTg6WlJesLTkREu0I+7ZQRbKdop2KwQTuOlBJnzpzBG9/4Ru1vZ86c0SryhoaGlEDC5/NheXk5ZR/Ly8spFX0oFEJ9fb3FJSciot0g33bKCLZTtFMx2KAdZ3R0FACwZ88e7W+Dg4M4cOAAAOCmm27C+fPntceuvfZaxGIxDA0NaX87efIkbrjhBu33s2fP4uabb7a66EREtAvk204ZwXaKdioGG7TjnDp1CjfeeCOEENrfBgcHtUr48OHD+Ld/+zftMa/Xi7e85S148MEHsbKygl/84hd47LHH8I53vAMAsLGxgePHj+O1r31tad8IERGVpXzbKQCIxWJYX19HPB5HPB7H+vo6YrEYALZTtLMx2KAd5/Tp0ym9O/Pz85iensb+/fsBAPfccw8ef/xxrK2tadt86UtfwtraGlpbW3H33Xfjb/7mb7SRje9973t4zWteA7/fX9o3QkREZamQdurTn/40qqur8ed//uf42te+hurqanz6058GwHaKdjYhpcz1eM4Hiezqox/9KFpbW/EHf/AHW277spe9DF/5yle0RoDIhsTWm+xabKdoR2I7RWUmazvFYIOIyP4YbGTHdoqIaPtlbac4jYqIiIiIiCzBYIOIiIiIiCzBYIOIiIiIiCzBYIOIiIiIiCzBYIOIiIiIiCzBYIOIiIiIiCzBYIOIiIiIiCzBYIOIiIiIiCzBYIOIiIiIiCzBYIOIiIiIiCzBYIOIiIiIiCzBYIOIiIiIiCzBYINom0gpkUgktrsYREREWbGdomJVbncBiMqdlFILLBKJBGKxGOLxOOLxOKSU8Pl8qKiogMPB2J+IiEovvZ1S26h4PI5EIgGPx4Oqqiq2U1QQBhtEJslVUacTQkAIgXg8jlgshlgsBofDgaqqKu0xIiIiM6W3U/r2Kp2+nVK3dTgcqKyshMPhYDtFhjHYIMpDeu+POlKh/h8ApqamIIRAe3s7hBBZK2V1e4fDoe1zY2MDDocDFRUVqKioYGVORER50bdTUsqMnV8LCwsIh8Po6enJ2U6p1MAjkUggEolACIHKykq2U2QIgw2iDNIDCn1FLaVM2VathPWVtRACFRUVhl9P3YfaSJw9exa9vb3weDyszImIaJNsU3T17ZSUUmtfMgUVhbZTADA8PIyWlhbU1taisrKS7RRlxWCDdjV9RR2JRBCLxbTeG/026ZW1VdTKenl5GVJKRKNRxGIxVFRUsDInItqF9O1UNBpFJBKBw+HIOUXXyum46n5XVlbQ0NCgTQVWRzqY10HpGGxQ2cuV+KYfpZiZmUE0GkV3d3dR81HTRz4K5XA4tClWrMyJiMqXkSm6ALC4uIj5+Xlcc801JcmbUF872+tkaqf0nWPsICOAwQaVkWxTn3IlvukrQ7XiNuNCvtgKVh+w6IeumUxORLRzFTtFV+1sKradyqdTLFs7k62dUt8T8w9JxWCDdpz0UYpwOAwAqKysTNkm2xzVnSK9zPrKnMnkRET2ld5Ora6uIhaLwel0pmxTqim6mZjRKZarndJPBWYy+e7GYINsKZ/Et8uXL8Pr9aKlpcUWFZkZ06hy7SO9Mh8cHMT+/ftRVVXFypyIqESMTtEFgNnZWcRiMXR3d++KOlq/WIqUEs8//zz6+vrg8XiYf7gLMdigbVXImt/pQ7r6Ss0ujJQlHo8jGo1mfCxTj1G211BHdqLRKKLRKCorK1mZExGZpNgpuul/twOzOsWMvB8hBFZWVph/uIsx2CDLZUt8m5mZQWNjo1ZZlcPUp0wikQhWVlawurqq/auuJgIAbrcbgUAADQ0NBb+Gui9W5kREhck0SjE7O4uamhptidhyaqcylT0ej6e0VysrK4hEIuju7kZnZ2dR7Yn6mTGZfPdhsEGm0QcV6TcRypT4Nj4+jubmZttcDBfT0yOlxPr6OlZXV7G0tIRYLIbJyUnE43FUVVXB6/XC4/GgpaUFHo8HTqdTS6SLxWIYHR3F+fPnEQgE0NLSYrjHKB2TyYmIssv33hSTk5O45pprUnItykEsFsPa2homJydTOsEqKirg8Xjg9XpRV1eH9vZ2AMD8/DwGBgbQ0dGB7u5uVFVV5dVO6bdlMvnuw2CD8pZtjmquNb8zBRR2rFC2KlMikcDa2prW47O6uorV1VVIKeF2u+HxeCCEQENDA7q7u1OS1tOpDVtdXR0OHDiA1dVVjI2NYXh4GNFotKjgh8nkRLSbmTFFV33MDNtR5+o7wfSjFWpOSUVFBXw+X0onWHo51YBsz549CAQCmJycxNGjR9HU1IR4PF5QsKFiMvnuwWCDMsqW+BYKhbCxsYH6+vqU7cthSFkvFottqqDX19chhIDH49F6ftRKWh9MjY+Pw+125ww0MvF4PNi3bx8ikQh+8Ytf4MiRI+js7ERXVxeqqqoKeh/plfkLL7yA3t5eeL1eVuZEtKNlm6K7vr6Oq1evoqmpSdvODlOfzLoHUzp9J5jaXq2trSGRSGidYF6vF36/X0vQnp6eRiQSQVdXl+HXqaioQE9PD7q6ujAzM4PJyUmcO3cOe/bsQU1NzZbPz/a5pyeTDw8Po7m5GXV1dcw/LBMMNna5fBPf0itxO1ArqHypPSlqBX3lyhVsbGxgampq01Cy3++H2+02VOllK8tGLIFoPAGfK/fXzul0wu1249Zbb8Xly5dx9OhRNDc3o7e3F263O+/3CaQmk6vvm8nkRLQT5DtFNxKJYG5uDq2trUW/dqHtixVisZg2qn7hwoWUTrDq6mp4vV54vV40Nzdv6gQzKhpPYD2agM+VvTPK4XCgo6MDU1NTaGtrw7lz5yCE0HIPt7onRy5CCG2ZYOYflg8GG7uEkalPRnp/zLwoLVUlrg4lpydp6/MpvF4vampqUF9fj76+vqLfZ/rzwxsxPPniHDaicdzW14D+Zs+WZa6srERvby+6u7sxMzODwcFB+Hw+BAIB+Hy+gsolpcx5x1dW5kS0Xcp5im4+si0qok6DTSQSeXeCpcvU9q5H4/jxuTmE1+O40V+DG/xbj1Y0NDTA7/djeXkZo6OjGBoaQl9fH1pbW1PKlW9+R6Z2ivmHOxeDjTKSa83vixcvoru7W9u2mCFlu/TypEskEloORaZ8CjVJu7OzUxtK1puamkIsFrOkEru6FsNaJA6vswJTV9fR3+zZ8nNUy6H2IrW3t2N+fh5nz55FRUVFQStYGUnSU4MOVuZEZLZc7dTExMSmaT2FtFN2Go3IJj2fIlMnmLqoiNfr1S6yFxYWsLCwgObm5qLLsLlTLI7wehw17gpcXFzbMtjQtye1tbW4+eabsba2hrGxMVy4cAHd3d3w+/2oqKgwJZmc+Yc7F4ONHaiQxLcrV66gr6+v6Nc2+4tdSIOgz6dYWVnB3NwcEokELl68mJJP0draiurqalv01rf4nPDXubC8HsP1bYWNSgjeF7umAAAgAElEQVQh0NzcjObmZq0X6fz581oyeaHHJj2vIxKJQAjBJD0iKlgh96aYmZlBb2/vNpQ2u2IDFzWfYnl5GaurqwiFQnl1gpVSvacK3Y1uzCxv4GV99Vs/IYPq6mrs3bsXkUgEFy9exMDAAPx+PxKJBJPJdzEGGzaVKfGt0KlPgLkrapjVY5SrTOpFb3qSdjQaRUVFhVZBqz37Pp9PW6Jvu2WqKJ2VDrzm2uJ7olRqL9Lq6iqOHDmCgYEB9PT0wO/35wyucgUl6X+/cuUKpJRobW1lXgcRZWTWFF3Anu2UUVstKpJIJOB0OtHT01NwPoXVKh0Crww2Gt4+V3vidDpTVrAKh8M4d+4cAoGAodxDo8nkCwsLWFlZQVdXF9spG2Owsc1yrfk9Pj6Onp6elIp6u1fTMJuUctMqGupQstPpTFn1qa+vL+Na5ysrK2XzeeTL4/FoyeQXL17E008/Db/fr62Dni6fO5Ovrq5q5yST9Ih2r1xTn8bHx9Hd3b1p2ku5tVPqv/pFRdTRdbUTLNeiIjMzM9jY2Cg4385sxYyG6221D3UFq4mJCdTX1+PEiRPweDwIBAJZV7DKJ5k8EokgFAqxnbI5BhslUkji2+zsLAKBwDaUNrtCe4zUfAp9JR0KhTA4OIjq6mqtkq6vry9oKNnu83Otpu9FunTpkrYOel9fX0ovkhlJerzjK1F5KmSK7sLCAnp6erQ7bNtBsSMb+nyK1dVVjI6OIhKJbFpURO0EU/MpjOy3nOTzfoQQaG9vR3t7OxYWFnKuYMVk8vLDYMNE2db8Vv+v366UQ8pm2qoSV5fm0wcW6+vrcDgcKUvztba2IhKJ4MYbbyz4HhJ2tZ0NSkVFRcoKVidOnIDX69VWsCqkEgd4x1eicmHFFF27XUQbLZORRUUcDgdaWlrQ3Ny8rfkUdpVv4j4ANDU1oampKSX3sK+vD21tbdqxYzJ5eeE3pwC5Kmp16pOqXIeUNzY2NgUV6n0b1FGKhoYGdHV1weVyWb6Mrt1s93vTr2C1sLCAF198EUIIRKNRw/tgkh7RzlWqKbp2DDbSZcunUDvBct2k9ezZs/B6vWUXaJhxzIrdR/oKViMjI+ju7mYyeRkqr2+PyfJNfBNCYG5uznZTnwql5lPoK+nl5WVsbGxgfX1dS9Jua2uDx+PJmE+Ri90aKTuVxSxCiJRepKNHj+LYsWPoy7AOejojyeTqMYxGo7h06RI6OzuZpEdUQts9RdfserzQXAJ9PsXVq1cRDocxODi4KZ+ivr6+qPtTlJNi379ZeR/6FawmJiawsLCAixcvIhgMbjnzId92anJyEu3t7XA6nbv++JfSrg82ciW+ZbozabmNUgBAPB7XkrTVoGJtbQ1SypRen8bGRsRiMUxPT2Pv3r3bXWxLmHFc853HWiq1tbXweDy48cYbMT4+vmkd9HRGGxL1ezE6Ooq2tjYm6RGZzM5TdEsdbKj5FLkWFXG5XHA6nbjhhhsM51NYzQ5lsDun04n+/n4sLCygqqpKyz3s7e1FdXV1xufk206Nj4+jqakJiUSCN7MtoV0TbGRLfAuFQlhbW0NTU5O2rX6kYrsrCLN6DgAgGo1uGkpW5zSq96eoqalBW1tb1vtThEIhU8oClHfla+f35vF4Nq2D3tHRgZ6enpRepELOPSaTExUu2xTd9fV1LCwsoK2tTdvWLp1fZgYb+vdRzE1aI5EIFhcX8x5tz1Yms96fnUbP86nfs21n5vVJuq6uLgQCAczMzODkyZNZV7DKtwxqkCGE4M1sS6jsgo1IJIJYLIaqqipDU5+i0SiuXr2K1tbWbSx1ZvkmSgEv3Z8ifWm+lZUVnD59OuX+FLnyKbYqk1nM2Bcrh8Kkr4Oe3otUaEPCZHKi3GKxGCKRiOF2Kh6PY2FhAX6/37QymLn0aTH1uD6fYmNjA6dPn9Y6wfQj6/ncpNVuU3TtyqzjbwX13G9vb0dbW1vWFawKuUbSdyarAT5vZmutsgs2Hn74YaytreHee+81lPim9sTaUa4KM5FIZBxKTiQScLlcWgXd1tYGr9eLEydO4JZbbinxOygdux7DnUBdB11dwUrtRXI4HKitrS14v0zSI8rsRz/6EZ566il87GMfA7D1KIXZ7VQhF2m59pUpP0RPn0+R6yatlZWV6O/vh9frtU39YJdyqOzU1llVlvRzMz33cGxsTFvBKp9kcv3+9P/XX2up7ZR+VJ6KV3bBhsvlQigUMrxyhMPh2LKi3C5CCK3XRx9QrK2tAUDKUHJjYyM8Ho/la52bPWRup4rTLFYOLVspvRfpzJkz2ncpfR30fPer/qtedEQiEQBKYuBO/KyIiuF0OhGPxw3X12a3U2qAYMZcdX09biSfItdNWufn5/Mebd+qTGawWztlpzrTirLk+rxra2tx0003aStYzczMoKGhAZ2dnUVd/6S3U7FYDNFoFFJKeDweW33mO1FZBhvqhYwRdrng1edTqBV0KBTCqVOntICipqYG7e3tcLvdTGgiANY0gmovUnt7O6qrq3Hp0iUMDQ2ht7dXWwe9mH0LIbCysoLz58/j5ptvZjI57ToulwsbGxuGtzc72Ch2pCQ9n2J4eFhbVtvtdhd8k1az2mNeGG7NjE6xUo1sZKKuYOVyubC0tJQ197AQ+umLx44dw8te9jLmHxZp1wcbpRzZUO9PkZ6krU4tUYOKpqYm9PT04OzZs9i3b58pSW5mKfceo53IqopPSgmv14uenp5N66BnW8Eqn32rU0Z4x1fabfINNsyud43uz8hNWquqqtDR0YGWlpaiOwzsmIxtlw5Ju7JqZMPofh0OB9ra2tDe3q7lHjY2NqKvry/rClb5lENtp5hMXpyyCzacTue2j2zE43Htpnf6VTTS8yna29vh8XiyRuF2zCexauWRclLKY2bla+kr/PR10IvtRcqWpMc7vtJu4HQ687rBphUjG+r+1EVF0jvB9DdpVRcV6ezs3HR/io2NDduNttsxQGBdZlwhdxDPlnuYaQWrfMuRnn/IZPL8lV2wUcqRjXg8vqmCXllZwfHjx7UK2uv1oqmpqaB8CiOJd/nYqbkEO5GRzzmRSBTdIFp5TDPtW10Hva+vL6UXqbe3t6h9M5mcdpPtGNnQ36R1bW0NQ0NDiEQiWj6FOrLe2tqa101ad0Men91GW8xgp7Jkkm+woX+ePvfw/PnzAKCtYJWPTPtW/9W3U0wm31pZBhtmz4VVV9HQBxb6+1N4vV4tn2JjYwMHDx40JVHbikrcTol3u2V+rjq/Wb8M8dramhZMtra2oq+vDy6XK+99lzrYUKX3Ip06dQpra2sIhUKGepGyJadmqszVHlZW5lQurMwt1Nc36r/pN2mtqKhAW1sbGhsbDedTmFG2Uu2LI/DGZHtv+o7UUCiEeDyurRJmN9naKf0KVqFQCKOjozh//ryW9G20QzDbiJ2+g0ydCsz8w+zKLtgodBqVmk+RHlTo8ynUUYqenp6st7o3c+qTXStxO7JDL40+aXJqagqXLl3Sgor0oNTtdmvn3dLSEo4fP476+noEAoGi55maxUiFrPYiNTc348iRI1ovUl9fHxobG7M+3+i+1fM2FAphdnYWwWCQlTntePm2U5kYyafw+XwZ70/x/PPP55W4nQvbqZ0rkUggFAqldISp55DaZtXV1SGRSODMmTNwu90IBoMFT0uygpG2pKamRlvBamBgAE8//TS6u7u3XMEq33ZqfX0dY2NjuO6665hMnqbsgo2teowSiYQ2lKx+uZaXl3Hs2DG4XC5tKLmjoyNnPkU2Zs6ttWMlDtjjwn475Rqp8Hg8iEajqKur0xr5bJVNLBaDEAJ+vx8dHR24cuUKTp48Ca/Xi3g8bqgs2zWykUlVVRUOHTqk9SINDQ2hr68v4wpW+d69NhaLIRQKMZmcyoLREXj9TVojkQjOnTuH1dXVlHwKr9eb901ay71TzGx2LFM+9CMV+uueiooK1NXVwefzoa6uDn6/f1NOjnqzyZ6eHu3Geg6HA/39/dv4jl6ST1tSXV0Nt9uNW2+9FRMTEzhy5Aja29uz5h6qCeJGqOfu8vIyb2abQcmCjfvuuw/f//730draijNnzgAA3vrWt+LcuXMAgKWlJdTX1+PEiRObntvX14eamhptXtyzzz6b9XXUSnx5eVmbo6rv9QGQclfSxsZGrK6u4rbbbjPlfdq54rXj8LSdKvH0smwVVOhHKvRBxfnz57UlH40SQqCtrQ2tra2Ym5vD9PQ0Tpw4gf7+/py9SHYJNvSVsr4XaWxsDBcuXNjUi5TvGv+JREIb0WAyOe106Z1i+nwK/aIi+nwKtY7IJ58iG7vW43ZrE4CdNUqSKajQj1T4fD7U19fD7/djZmYG1dXVaGtrM7z/xsZGNDY24urVq7hw4QJWVlawsLBQ1H2YilVIG5iee3js2DE0NDSgL20Fq3xvGKi2a2o7xfzDl5Qs2Lj33nvxvve9D/fcc4/2t3/4h3/Q/v+BD3wAdXV1WZ//05/+FM3NzRkfi8ViePjhh3H27FkcP34cw8PDeN3rXofPf/7z6OjoQF1dHTo6OjL2Mptdsdm14t2tJ7gRalCxvLyMSCSCxcVFQ0GFFYQQaGlp0ZacVXuRgsEg6uvrLXvdTApZEURPXcEqGo3i4sWL2gpW3d3deTcQ+u2ZTE5WsbpTbH19HUNDQzh58iSWl5dx11134e1vfzt6enpS7k+R6Sat6mubwa7tFLDzRxFKQZ3+FA6HteBCDSrUKd/19fXo7OzMOtpVTD1ZV1eHW265BT//+c8xMTGBoaEhBINBNDc3l7z+LabDbasVrIptp9R/mUxewmDjjjvuwNjYWMbHpJT4x3/8R/zkJz8paN8VFRVIJBJ44xvfiLe97W146KGH8M1vftPQc80+4OU+jcqOPU9GbTVSEY/H4Xa70d3dXVRQYdZog74XaWRkBLFYDMFgMCUXwi4jG7l6gKqqqrRepMuXL+PZZ5+Fy+XKKzcl03A2k8nJbFZ2igFK4FJbW4vrr78eUkr86Z/+6bbcS8nO7ZTdbGceiTpSoQ8qwuEwEokEYrGYoaAiGzPeU0VFBW6++WasrKxgdHQUw8PDCAQCRd/8NR9mLXyjX8FK7VxobW0taGQj0/53ezK5LXI2fv7zn6OtrQ3XXHNNxseFEPj1X/91CCHwwAMP4F3vetemx9/97ncDAGZmZopOvCuGHQMEM/dlxzKli8fjWFtbQzgc1qYkGBmpmJycBIC8pj+VQl1dHQ4ePIhwOIyRkRGtQm9pacl79CEfxY5spKuoqEB3dze6urpw7tw5TE9PIxqNIhAIoLa2NudztxrOzlSZb2xsoLa2dtdU5lQ8KzvFAOCxxx7T/v/tb38bBw4cKHhfxTCz7rVr4GJH2d6bPqhIT/ZPH6kIhUJYXl42JWfCrIDA6/Vi//79WFtbw+joKEZGRtDb24uOjg7L618z20AhUlewOnfuHJaXlzE9PW0ogNqqLLnaqXLPP7RFsPHNb34Td999d9bHf/GLX8Dv9+PKlSt47Wtfi+uvvx533HFHxm3zvVmS2ex6MV6OlbhaQYdCIUQiEczNzaXMT/V4PDmn0O00Pp8PN910E1ZXVzEyMoILFy7A7/db9npmBxsqIQTq6upQVVWFhoYGDA0NQUqJQCCQdQUro/tXK+xEIoHBwUG8/OUv5x1fyRTFdorZifodMWtfbKeMSSQSWF5eThldV3PP1KBiq2T/cDhs23qsuroa+/btw8bGBsbGxjAwMKDl61nFqnaqpqYG/f39mJiYwOLiIkZGRtDV1ZVzBSujuYj6oOO5557DK17xirLPP9z2YCMWi+G73/0ujh8/nnUb9YKqtbUVd911F44ePZo12Mj3Phtms2svjx1HNgBjve1bJb3FYjG43W5t3vN2f1GLHdbN9ZlIKbEYEWju3oM9LmBoaAiLi4u4dOkS/H5/zoqumPmnZm6rbu9wOLSpYvoVrHp7e9HW1pbyXvJNKFf3r15U8Y6vVCwzO8WAwnqVzZo2ydWojCukTPF4PKW9Un/U6Z6FrCBmtnzeU77lc7lcaOsOoq6tC+G5KQwMDKRMHzJToQuZGJFIJFBVVZU19zB9+mO+30816EjP6yjHdmrbg40f//jHuP7669HV1ZXx8ZWVFSQSCdTU1GBlZQVPPPEEHnzwwaz72+5gw+yK1+w7iNtJ+hdpq6BCXfM7fXk+dfqTXe5PYaWz0yH88PkrcAjgzQf86O/vRyQSwerqakovUqaeF6uDjXwrcf326gpW6jrlIyMjKe+lmGBD/53c7Ul6VBizO8WA/OtjtSPLrjeMtdu+SiFTUJFtpCIUCiEUCiEYDG53sTVW1X+Xl9bxncFJxBMSh/e34faeHjz99NM4cuQIOjo6si41W4hSdIoBmXMP01ewyredUu2GZPKSBRt33303nnrqKczNzaGrqwsPPfQQ3vnOd+Jb3/rWpt6iy5cv4/7778fjjz+OmZkZ3HXXXQCUCv9tb3sbXv/612d9HfXCZLvYteI162Q1o0xqULG2toZLly4hGo0aCip2i1wV4uJKFA4BxKXE1bUoWtxKD8i1116LQCCAixcv4siRI/D7/eju7k7pRbLbyEam7d1uN66//npEo1FMTExgYGAA7e3t2j01jEqv9NMrc30vW7lU5mQdszvFCmHXjiy7TskyUzweN2X6024xF95ANC5R6RCYurqO/mYP3G43brvtNkxOTuLo0aNoaWlBX19f0Ysj5NPRle9StpnaKX3uYfoKVmYlq5djMnnJgo1sq0M98sgjm/7m9/vx+OOPAwCCwSBOnjyZ12tt54WDmdOo7DrUbXQ/W41UxONxeL1etLS0lFVQYeUKUTd31WFpLYpKh8B1bT7ENta0x9Sel97eXly6dAnPPPMM2tra0Nvbi6qqKkuDjUIq8VwVZ1VVFYLBIHp7e3H58mVcuHABHo8Hra2thhL4c5UnU2Wu70Eql/OQ8leqTjEg/3ZqN7QtwPaOwGcaqQiFQqisrERdXZ0tpj/tBP0tXlyYXUEklsBNXXVaW6IuNdvV1ZUyOhAIBOB2uwt6rXzOl2JGNtIJ8dIKVouLizh//jw2Njbg9XoNv06usmdqp3byzWy3fRqVXZh1gWjnkQ2rlu8rdPrTiy++iPr6+pTpT8OzKzh/ZQU3d9ais76wymeny3Uu+tyV+I0b27Xfwxubj0llZSX6+vrQ09Oj9SI1Nzejs7PTNiMbRqeDqL1IiUQC4XAYp0+fhtvt3nIFKyPD2frKPB6PY3JyEg0NDaipqWEy+S5Vyk6xfNk1QLDjCPxWsk1/qqio2HRX9suXL6OmpgYtLS3a8+fCETx3eRGBJg866oy1U3arT/Jd1CMfXlcl7jr40uIlsVgs5XGHw6ElW09PT2NwcBA1NTUIBoN5rwZpdTu11fZCCC33cHx8HBMTE3jmmWfQ19e35QpWRsqjb6cSiQQuX74Mt9uN+vr6HZXXwWADL1WWDDZyU4OKaDSq3T3UzOlPq5E4fvTCLFxVDkwsruH3X9kDxw75IhUqV++7EbnOW4fDoeU9TE9P4+TJk4hGo1hbWzOU37Id06hybV9XV4d9+/ZhaWkJQ0NDSCQSm+47ot/e6JCzWpnPz8/D6/UymZxKQp1+lM95Wu5Tn8weJcknqMh18zt9meIJiX8+NY1oLIHTkyHc+4puuCqNHUM7ThEzothyZ6vvhRDo6OhAe3s7Zmdncfr0aVRXVyMejxe972zb5pv7l0/973K50NHRAb/fj/HxcVy4cGHLPMp826nFxUVtlG0nJZMz2ED+lX4u5bAaVaYbCemDCvXCr9icivQyVToEqqsqEN6IodnnhNG92m2erxkVcz62+vwdDgf8fj8aGxvx3HPP4cSJE/D5fAgGg/B6vTnLYWWPUaEJ3w0NDTh06BDC4TBGR0dx/vx5rRdJ3We+07rU56jzYss1SY/sw+l0IhKJGJ4+YvY0qvTe5kLZIdiIxWKbRtdXVlZw4sQJw0FFrjKl/g5UCIF1KVHhEIbbqd1sq/ZBCIHW1la0tLRgYWEBMzMzGBwcRDAYzHkTTSP7LnRboLB2SgiB6upqLfcw1wpWhVx3Sik3tVM74Wa2DDawe4an0/eV60ZCHo8HPp8P9fX1m4KKY8eO5bxLbqGclQ781sF2zIQi6Kwr/d1QzWRGkpgR+b5vl8uFW265BXNzczhz5gxcLhf6+/tRU1OTcd926THKVCn7fD7ceOONWF9fx/j4uLYOeldXV0GVuP45mebLOp1OU1YCIgJeWjnRaLCxk9oWq/aVKajINlJx6tQpHDp0yJRy6TmEwJtuasPo/Cq6G6rhNDiqYTd2azOBl26q5/V6EQgEMDw8DCkl+vv70dDQkPE5pU4Q32r/+rJstYJVoZ1iO7GdYrCB8hiNyCUejyMSiWB+fh6zs7MFraShZ+X83LrqKtRVm7MkXr6sTOzO9nr5/D3btvn26ggh0NLSovUinTt3Dg6HA8FgEPX19SnPMbpvqyvxXNu73W5cd911CAaDmJiYwJEjR1BXV5f3eZqp0dJX5nbtMaKdyeVy5XUDWrPbKTu2eQC0CyejQUWu6U9myPT+Gr1ONHqLW0XJDqzK2UhXSDtVX1+PQ4cOYXl5GSMjIxgaGkIwGERTU1PKvqxOEDdj+2wrWG11b6xsr5GrnbKrsg028k1+MusgbWeDkGvNbzXibW5uttVKGnb7ctjlM7FiWDjTtmpi29WrVzEyMoJYLKblQVhVZiD/4MTISIW6glVfXx+GhoYwOTmJF154AX19faatYEVkFqfTmdc9ocwcgbfLaH56UDE3N4d4PI5Lly6lBBXq9BN+B3emfNspvdraWhw4cECbNjs8PIxAIIDW1lbt3DN6wW7VQib67XOVRYjUFayGh4cRDocxPz+fMfcw22vsxHaqLIMNdS6sy+UytP1OG9nI50ZCalAxNDSE5ubmrEOR+ZaLsit1L7gZgUldXR0OHjyIUCiEkZERDA8PIxqN5rWEX749QFZNu3I4HGhqaoKUEo2NjTh9+jRcLhcCgUDO+b9m5W0RGZHvDWjtOhphZF9bjVT4fD40NDTA4XDA7XZrN0i0A7v3GBeq1O+p2HZKnTa7traG0dFRXLhwAX19fXl1XJViZMNIGyKEsoLVvn378OKLL2JychJDQ0Na7mGu19yp7VRZBhsulyuvYMPsitesBkFKibW1NUxNTRV8IyF9uexWYdqxTHZQTC9QsfutqanBzTffrN2R/MiRIwgEAqYs4VfM9oUk6lVUVKC1tRWtra1YXFzEhQsXEI/HEQgENg3FF/IaRMVwOp3bOo3KimDDaFCRbaRidXW1bDuzzHpfZraZVnQkZdtHPtvmer3q6mrs27cP6+vrGBsbw9WrV3H58mV0d3dvWX+XIrcwnzujSynhcrm0IMrIClY7tZ0qy2BDHdkwarsr8WwVdDweR2VlJdxud9E3EuKF/c5SqmlU2Xg8HlRXV+PgwYMYHR3FyMgIent70dHRkbGiK6QSL2VCeUNDAxoaGhAOhzE2NoahoSH09vaivb09ZQWrnViJ085UyMiGXab7qm1WOBzG4uIilpaWsLi4mBJUNDY25j39qdzbKSvzHe3MinbK7Xbj+uuvx+LiIjY2NjAwMIDOzk50d3dnnfpUqtWoCtl/phWs2tvb0dPTk9cKVnY9N8oy2Mi3Ejd7Lmy2SjzfpLe5uTmEw2H09PQUXS67VuJmlMluX65SLn1r9ZQtt9uNvXv3IhKJYGxsDAMDAxl7XQrJwbCy0s+2f5/Ph/3792srWI2Ojmo3lwLsdy5R+SqknSp1p5g+qFDbLrXNUkfX1Ztr7tu3r+jvjx3bKTuWaaexcnlaANizZw+CwaB2ke73+9HT04PKytRL3FKvRmVk+/T9G1nBaid2ipVtsJHPyIbZc2Hj8TiWl5eLWklD3ZfZq3zYiZkXdjvtvSUSCaysrMDlcmVc+tKKi95iAhOn04lrr70WgUAAFy9exJEjR+D3+9Hd3Y3KysptW+Uj1/a5KmT9ClaXLl3CkSNHEIlEEIlEUnqR9BiIkJnyXY3KyjwLfVChLoOeHlRkG6kIhUIIh8OmfD/MbIspt2I7qvLtFMtHIW1DZWUlgsEgent7cenSJTzzzDNobW1Fb2+vVqfbrZ3KFTjoV7C6cuUKTp06BbfbjUgkwmDDLko1jSpTr8/KygqklNjY2NhyfupWzG5cqPSklFhfX8fKykpKQw4oF7zr6+toaGhAMBjUgg6rl74thtrroq/Q29raUFlZmXePjh22r6qqQiAQQG9vL372s5/h2WefRX19veEVrIgKVchqVMVeiMdiMaysrGB+fh5LS0s4ceIEIpFISlDR1NSkTd0wUl/YuVPMrJFfu3VmWS0Wi2ntVSgUgsPhwJ49ewzfEyaTUrVTFRUV6O3tRXd3Ny5fvoxjx46hqakJfX19JRmpMHs6sRACbW1tWu6hemNeddXInXJtV5bBhtlzYdUKWv3J1esTCoVw9epV9Pf3F/0+Sr1iSKnZsUyFklK5k6daQV+4cAEbGxtIJBJwu93wer3w+Xxobm6Gx+OBw+FANBqFw+HA8vIyBgcHUVdXh2AwWNKlbwtVWVmJvr4+9PT0YHJyEhcuXIDP50NbW5uhhRlK0WOUz/YOhwNOpxOveMUrMDs7a3gFK6JCWZmzkd5mrayspAQVlZWVcDqd2Lt3b9FLytq1U0wtlxlTu8pVIpHQRqbSZ2H4fD54vV60tLQgEongueeeQ0NDAwKBANxut6XtVL4y7dvhcKCrqwt+vx/T09N47rnn4HQ64fV6De/XTu2UuoKV1+vFtddei/HxcS33sK2tzfajHWUbbBQysrFVBW2k10cd2TBDuQcbO5V6nqgVdDgcRiwWQ1VVFXw+HxwOB9rb29HY2LjlGt36XosrV65gcHAQHo8H8XjccHm2I9hQORwOdHd3I9PSUZQAACAASURBVB6PIxwO4/jx46ivr0cgEEB1dbVpZSmkhymfVUFUQoisK1j5/f6yvvCg0jLjpn6FtlnqRaXRFRtzYTtlf4lEAmtraynt1tLSEubn51FbW5tzFkYsFoMQAj09PZiZmdGCjr6+vrzKsF3tlMPhgN/vR0dHB86fP4+pqSlEo1EEg0H4fD5Ty2L1SIhaprq6Otx0003ailwjIyNaLqVdlWWwsdU0qvQKem5uTluCrNChZJVdK15W4vmLx+Pa9Di1gs7U6xMIBFIubE+fPg2fz2foZkDquaUPOi5evIjh4WG88MILhi7ajVqPxjG3GkcsnkBlhbm9IOo9LW644QbMzMzgxIkTqKmpQSAQyNiTZPUqH/mudpWJfgWr6elpW639TztfPtOoYrEY1tfXsbq6ql00FjP9yew8RTu2U2bua6e0neoUbv1IhdoBquaL1tTUoL29HRMTE2hra0N9fb2hfQvx0g3pZmZmMDg4iPX1dayvr285vSqfzy+RSGAjbn7QIYRAbW0tKisrUVdXhxdeeAFOpxPBYFBb5CBTWezcTqkrckWjUUxMTOCZZ57BHXfckdeNCEulpMHGfffdh+9///tobW3FmTNnAACf/OQn8Xd/93doaWkBAHzmM5/B4cOHNz33hz/8Id7//vcjHo/j/vvvx4c//OGsr6MOT6+vr2NjY2PLXp+KigpUV1ejo6Oj6Pe43cvolmJfdixTMaSUWF1dRSgUQjQaxfz8PNbW1uBwOFLWhi/FndeFEGhubsbCwgKamppw4sQJ1NbWIhgMZgw6jFZukVgCXz8+g4uzYVyWl/Hbt3WZWm61HPoGaW5uDmfOnIHb7UYwGERNTU3e5VaZtRpVIXw+H6655hqOapCpMk2jyjVS4XA4tBtWFtIRpmeXO4hbuS+zmPW9N/u9RSIR7RxRg4t4PA6Xy6V1hjU1NWnTdjOVpxBqHd/c3IyBgYFN06syyae+/97pK/j3syuYcEziP93SafpCMg6HA83NzWhubsbi4iKGhoYAAMFgcNNNj3dKO1VVVYVgMIhAIGDLQAMocbBx77334n3vex/uueeelL//4R/+IT74wQ9mfV48Hsd73/tePPnkk+jq6sJtt92GO++8E/v27dO2uXTpEp544gk8//zzeOyxx/Cd73wHhw4dwoc+9KEte312w9QnO1biZslneFYNPvU9P2qvTywWQ3V1Nbq7u1FdXV1wJWDGKh8OhyNlelW2oMPoa4U3YpgNR9BQ7cD5K2HTe43S9yeEQEtLC1paWrCwsIBz587B4XCgv78fdXV127rKR7b9EwGl6RRTb0Q2OzuL4eFhvOlNb9pypGJ2dhbhcBiNjY1Fv0e7ti123dd2Sl8pbHFxEZFIBEtLS1ouYEdHh5aLk49i2wCn04nbb789ZXpVpqDDaP29EY3j9OUQWrwVOHM5hMP74/C5zLtMTS9HQ0MDDh06hKtXr2JkZATDw8Mpidf5jjxY3U5txc4dYiUNNu644w6MjY3l/byjR49q6ygDwG//9m/jscceSwk21HtSHD58GEII3HLLLXjjG99oaP+lus9Gvuy6yocdT+j096ZP1s7W66MmW6lf9omJCVRWVtpiFaJM06tmZ2e3HOnIpsFThUNdPhwfW8Bv3ZD7buBA/udKrkq2sbERjY2NWoUei8WwsbFhefBgZSNB5cvKTrHnnnsO9913H2pra+FwONDR0YHbb7/dUKK2me2BXdspO+7LzClnuSQSiZSgYmVlBevr65sCUJ/Ph/X1dQQCgaJez6zPJtP0KjVvTx90GKlfXVUVONBVg5+9sIxf3lMHr9PcXvps7UJdXR0OHjyIcDisBR2BQADxeNxW7dRW7NyG2SJn44tf/CIeffRR3Hrrrfjc5z63aShrcnIS3d3d2u9dXV145plnUrY5cOAADhw4AAA4duxY3gnisVisiHfwEjtWluq+zGKn9xeLxbQ5zENDQwiHw4hGo6iqqtIq6EJ7fbZLps9ETVxuaWlJCTp8Pp+hYyuEwC/vqceBJolr+xq23N6KkQe1Qg+FQjh69CiOHz+O/v5+NDc3Gwp+rFyNaqfelZXMZ2Wn2IEDBzA4OAghBL7+9a9jfHwcb3jDGwzt384BgpkX4+UwGpGLlFJrs9TOsLW1NQDQ8irq6urg9/vhdrs31T2zs7MlL3Om+i/9OOUKOvI5pm/Y24ygM4RbD5ifI7dVO+Lz+XDTTTdhdXUVo6OjmJ+fR319Pbxer6E2oJB2aqdclxRr29/le97zHnziE5+AEAKf+MQn8IEPfAAPP/xwyjbZLr6yKeSmfna6gLZqXzv5Zkm5en2EUG7o09TUlHIDn+1itMLJdWyzPT896HjxxRdRVVWFjo6OLUc68q0Irbq4r6mpgcfjwf79+zE2Nqb1IrW1ZR9xseOqILS7mNEppj/HyqWdMjv/wyzbPY1KSqnlVYTDYSwsLCAcDmNxcRHV1dXaFKiWlhZUV1fvuPonW52cKehwu92GL6qllHBXWvNZJBIJQzkNHo8HN9xwA9bW1hAKhTAwMICenh74/f6cx2k7cwvtbtuDjba2Nu3/v//7v4/f/M3f3LRNV1cXJiYmtN8vXbqUc2WYUt3ULxO7Ngg7ZZRE3+ujBhZb9fpMT08jEomYMpe5lNQh6HRGjpMadESjUSwsLBiaXpXPBXu+lWAh05a8Xi/279+P9fV1jI6OYmRkBL29vejo6Cg6oTHf8jDYoFys6BTb7nZqN7R5Zu1nqzJFo9FNydqxWEy7r4M+qLjuuuuKKo+ZC71YvdiJGnQMDw9jYmICZ8+ezZlIrpbLKoV0uF1zzTVwOBwYGxvDwMAAurq60NXVlTFosXo1qp086rftwcbU1JS2CtQ//dM/Yf/+/Zu2ue222zA0NITR0VF0dnbiW9/6Fr7xjW9k3afb7cbS0pLhMnAubH7M2I+UEvF4HKFQSLvD9srKChKJREqvT2tr647s9SlGvhVWTU0N9u/fj9nZWZw8eRI+nw/9/f2bgo589mt1Ajfw0sWA2+3G3r17sbGxgfHxcQwMDGhrhhe6sobZ06hod7OiU6zQ+0GZwa4jCHbNU1Spy6Gn3wSvsrJSm7bb1tYGr9e76T4/i4uLWF1dNaUcpewNz/YZGq3zhVBuRheLxdDQ0JByA9tMQYeVQVCh7ZrT6cS1116LQCCAixcvYmBgAJ2dneju7k4ZsbF6ZGMn5xaWNNi4++678dRTT2Fubg5dXV146KGH8NRTT+HEiRMQQqCvrw9f/vKXAQCXL1/G/fffj8cffxyVlZX44he/iNe97nWIx+O47777cMMNN2R9ne2uxO04PA2YW/Hmc9KrvT76CjoWiyEej2v3qujq6oLH47Htsm35KGWFoF9yVj+9KlPQYbdgI53L5Uqp0I8cOQK/358yNcUoTqMiM1nRKbbd06jsyC7vUX8TvNnZWaytrWFqamrTcuiZboKXqzx26pk2K0Hc6GupN7vVT6/KFHRYHWwUs3BIVVUV+vv70dvbi0uXLuHIkSNob2/XVo3b7um+dv1eAyUONr75zW9u+ts73/nOjNv6/X48/vjj2u+HDx/OuNRgJvkOT9u1Z8bMoW4jgcu/Dc/j2bGruLWvDq/e05SzXJnE4/GU9eHD4TAikYjW6+Pz+VJ6fcbHx+F2u1N6DQth5y9YIYoJCrIFHcFg0PbBhiq9Qn/mmWcQiUS0xH+j5dnOVUFo5ypVp1g+N/UDzO0UM1OpR0kuLqxhYHQR17Z6cbC7Lue2W+1LytSb4IXDYW30QR1hd7vd8Hg86O/vL7u2ppj3k891jr59yJTToQ867DSyke3ivrKyEn19fejp6cHk5CSOHTuG5uZmxGIxSzu5dnKn2LZPo7LCdo5s2HWUBMhdOaxH4xgYWURbjQtHRhZxe289qquyjzKoK2jo8yocDoc2lNzY2JhXr0+x7NRjZIZiPzN90DE3N4dTp07B4XAYvlvsdgYbKn2F/rOf/QxHjx5Fc3Mz+vr64HK5cj63kGlUubYvt4sMyq5UnWLbObJhV0be4z+dnIZDCIzNr6KvyYMGT+YOiPR96ZO11Q4xIzfBU3MCWQdsVsyCI9mCjoaGBss+a7OnLTkcDm3K7/T0NMbHx/Hiiy8iEAgYWpre6pEQO2Gwgd1xN9WtTmhXpQPBJg9G5lcRaPLAVal8Jmo+hVpBr66uYmVlBePj4/D5fKipqUF7e3vBN8Erx8az2PdUaI9RJkIoN9drbm7Giy++iKmpKUQiEQSDwZz3E7FTJehwOOB0OvHyl78cU1NTOH78+JZ3rN1NPUa0M21np5idbVX/NXudmFhcg9dVAXfV5u+sehf29fV1jI2NYX19PWU59EJugleO7VSx8m2nskkPOoaGhlBRUYH19fWcieT5lkHd3ooEbofDAb/fj9HRUTQ2NuLEiRPajAKv15v1ebupnSrLYKOQaVR2HI0o5b6i0She0+vC9TUROKJLeO74ZSQSCbjdbq2Cbm5uhsfjweDgIPbu3Vt0fkU59xSZMTJhhNHKUAiBmpoabWrAqVOntMowU9Bhh5GNdA6HA52dnfD7/VpPWE1NDQKBwKYKnatRkd1t53RfuzJSh7zlYDsmFtfR5KlEfGMN0/OZb4InpUR9fT2ampqKWg69XNupYutss6flqkFHRUUFJiYmtkwkz7cMhW6fD33gNDc3hzNnzsDlcqG/vx81NTWbti/kJoA79Xwsy2DD5XJt21xYu6/yofb66OeoxmIxVFVVKQGFzwufrxEejydnr4+dGz0pJaaXN+BxVqCu2tgcfzsxq8co07YOh0Mb6VCnV2UKOqSUmAon4JpaxnVtNahwmHvTvWJkqtDdbjeCwaBWoXM1KrK77Wyn7CpTm5dtOfSrgJasnekmeM8//zxqa2tNue+SVcnU0XgCDiG2rF93unzbB5/Ph4MHD+ZMJFf3uxrD/8/em4dHUp/nom/1vqs3qdW71N3SSDOaVZphMWDADODhAAYfm2BuDAe8PI4dkjiLuRxDgmMfsBPbNzk3ju+Nl2Cc2MEOXsAYiDEDGEaaGc2MhtFoNJJa+772vnedP+Qqulu9VHVXt0pC7/PMA5Kqf/V1Ld/3+7b3w9RqBFbtxgGIlcoBlLenoyoK6uvrsbKygsHBQQgEArhcrqxSZj5VEFQb29bZ2A4Ro0rkSqfTWY3aa2triEQiWFtby+L9bmpqYq2MudpUVuu6vzmyglcHlyATCfHJ9zlgVNV2yB8XZVSV1MIyWTezvGppaQnvvPMO3QSpUCgwshjCTy6FoJ4Zw20dJtyyp3gTf7Wb+vKhkEJ3u93vqfT0DrYmyrFT29nZIEkSiUQC0WgUExMTvKFDr5ZeG1+J4N9OTUMmEuD+K20wKDd3GC1bVEvnZzIsUkGlhYWFvE7HUjCG/7gUg3JuGDe3N+CDHaXtVK31vF6vh16vh8/nw8jICNLpNFwuF3Q63Q4b1VbHZg5L4hJMHhwq6pPZ9BYOh2mKPqVSCZ1OB5VKBb/fj9bWVk7k4pNzlivP6FIYcrEQ4VgKy6E4Y2eDy+9Ua+pbpsfmKqpCTsdCWoUUCYgEBJZCpd+lzWYQyVXo1KRevV7PSK4dZ2MHtUY5dopPercSFBqCJxQK6f6KSunQ+WangGwbc2bSB5AkVsMJjC6Ha+5s1PLaVMqwaDKZ0NDQsMHpWPDHEEkCDRIhBuYCjJyNzdqQ19XV4dChQwgEAvB6vRgaGnpPZeC3pbPBNj3NR6WUC4qiL5dNgyRJyOVymk3DZDLlbdZeXV3ltdfLJW7cZcTPzs2hyaBAk6FwE3Q+8OEa1SKzkW+NTKdj+dIg2tQp6I0y3Lq7NDXxZjsbFCiF/uabb2JqagrDw8NwuVwwGo1F19jKEaMdbE1sRzuVi9wheKXo0MPhMEZGRuiZJnxAta77XrMKF2YCUMtEcOpKMxdVA3zq2Sh1bD6nQypXorlOAIFQgNtKOBoA+/LaakCtVmP//v0IhUI4efIkzp49C5fLBZPJVFK2HWeDZ5BKpUgkEoyP51tmI5FI0Mo5Go2it7eXpuijFLRer2cV9eFj43q1lLhdJ8fDNzRzvi4fwSVzFfCu03FALIZKPoxUagUzo1HIf19eVQhslCDbe16OghUKhbRCHx0dxfDwMJqbmwsq9K3ceLeDrQm2ZVQA904vV889SZIbMhUUHTqbIXh87XmsBjwNKvzFTS4ICQISEb83kLW4XxSYMCxSTsf09DSua1iAxZKGU1u6P5NPep4aE9De3o6pqSl4vV44nU6YzeaC9o4PzlK52JbOxlYZlkQNwcuM+iQSCYhEIjpTIRaLsX//fsYUfYXAR2dju4JvLB/lQi6XY/fu3VheXqbLqwpR+bGRo5YMHEqlEh0dHYhGoxgdHS2o0LdyxGgHWxNCoRCpVGrTzk/1gLApUyo0BC8cDmN0dBRKpbIiOvT3mp0qNsuqEPjynbgOdLE9liAIGI1GmnGMKq8qNuOCT84GsC6PQqFAe3s7YrEYxsbGcOLECdjtdthstg02aTN6TrjCtnQ2+NYgnk6n6fkUmRkLoVBIR30MBgOcTueGZu2ZmZmKHQ2Av4qXjzJVE9SzEAwGEQgEoNFoYLPZNijAWqanix1LKXSDwYDl5WVcuHAhr9NRTQeJC0dAJpPRCn18fJxW6FarFUKhEOl0mvF08h3sgAts9qanVA9IoSF4FB165hC83t5edHR0VCwTH+0UX2WqFFxsvKul89n2IWaWV507d66g08E3ZyPTtkmlUuzatQsul4u2UVarFTabjd4DbuWg2I6zAe6UCUmuD8FLJpMYGxujp2wDoJu181H01QJ8jBjx6aXnGvkigKFQCMC7dI1qtRo+nw9TU1Nwu92or68v69rWwjHJ53TI5XK43W6a076SRnWu5C4FqVSK1tZWNDc3Y2JiAt3d3bBYLHSZ4g528F4BpWsoOvRMPVXOEDyuNq98s1NcYSvYO5IkEY/HEQwG6X+hUIjWm3V1dVnHsgEb+8BG3kyGxVJORzUzA+U8a/neGbFYDI/Hg6amJkxOTqK7uxtmsxkOh2NL9xZuS2eDbRkV2xtEvZC5UR+Koi+VSkEul6O+vr6mFH3FwLXi5ZMS32xkNkFS/3p7eyGVSulyOLvdDqVSmfUsJBIJNDY2IpVKYWRkBKOjo3C73aw37kzBVZNePqcjmUyyKqPabAYOsVgMt9sNp9OJyclJTExMwGAwwGg05s1w8FmJ72AHTJCZVaVs15kzZ+hmbaVSWREdOh+dDa6wHe0dSZI0LT5lt5LJJG23qEG+IpEIsVgMw8PDEAgE8Hg8UKvVvLVT+ZwOjUYDl8tV1cwG13ZKJBKhubkZDocD09PTOHnyJKRSKXQ6HWfnqCW2pbMhFouRTCY5WasQRZ9EIqGjPlarFUqlkq59PXXqFBoaGni3QdmuSrxWESwqc0U9C1TmiiAI+lkwGAxYXV1FV1cXY8Ujl8vR0dGBUCiEkZER+P3+rAhSKZk2o+Qq1+mYn5/HhQsX6EwHV3KUczwbUAo9FoshnU7j5MmTMBqNaGpq2sl07GBLgqJDz23WBrKH4Pn9frS1tRWsb2cDvpKG8C0DvxlOC2W3MoNhkUgE0WgUBEFAq9Wivr4ezc3NeQMt8XgcarUanZ2dWF1dxcDAAKRSKUym0uxPmTLU2k7lczpisRhisRijZ57tvaqWnRIKhXA4HLDZbLhw4QImJycRj8fR3NxccLI6H7EtnY1ybjhJkggEAlmZilgslhX1yaToK3V+vtUG8pHlg29p7kxkTlqn/mXWK1NDEfNlrsqNbiiVSuzbtw+jo6OYnp5Gb28vPB5PUcdjs5wNCie8K3ipfxHahAQHDpg3lFflA18H7plMJrS1tWF2dha9vb3Q6XRbTqHv4L2DzJKXzGZtpkPwZmdnOZOFS12+XcuoaoFkMrmhBIqqtKCyFRQ9PhUcKrXxpnr3AECn0+Hw4cNYXl7G4OAg0uk0IpFIyTXYlC9xbacIgkBEpMa5uAnp1UmI+/uh1WrhcrmKyr0ZvYXFIBAIoNFooNfrIRKJcPbsWajVarhcrqJMkXzBtnQ2ioF6OXKjPuFwGJOTk4wp+oqBj0qOjzLxAVQUMBgMYnl5GclkEhMTExAKhVmzS9xuN+NG/Uqvs1wuh9lshtFo3JC6rgRcK/FUmsQzPVNQy4Tonk3hAUUdjhw5gpWVlaJOR7UzG+U4+tRnBAIBrFYrLBYL5ufnaYXe3t6+JRT6DrYnqAx7PB7H4OBgVoY9s1RzO9Ch8xF8s50U1XCmUxGNRrOYLJn02ZQDKqstFAoxMjKCc+fO0Zv3QtlgtmVUXDsm33lrDGvhBJaWSdx61R6oiFhWeVU+p4Nvzgbw7rWhJqsvLi7inXfegVwuh8vlglarrer5K0HNnY0HH3wQL7zwAhoaGnDhwgUAwF/+5V/i+eefh0Qigdvtxve///28F62pqQlqtRpCoRAikQinT58ueB6SJDE0NASBQACRSERHfQDQUZ9Mir7Tp09j9+7dnHxHPm7s+WpYanmdqPklmQqaigKqVCpIpVIYDAbY7faasXTkO45ScnV1dXTq+tKlS5BIJPB4PGWzQLGRi8m6AgJo0svhXQpBIyGglAhBEAQMBgP0en1Bp6PaSrwcpZ/7GYIgaIW+tLTE203QDrY+Mt+HUkPwCGJ9Do7L5aqYPY1Lync+2oTt4gDlNmz7fD6k02kEg0G6JM5qtUIqlbIOylQKirZ1bm4Ovb29MBgMaG5uztvzs5kZeL1CgjlfDEIBoJRJYNJq0dDQgMXFxYJOB5/KfSlk2imCINDQ0ID6+nqsrKxgYGAALS0trMrbaomaOxsPPPAAPve5z+HjH/84/bujR4/iySefhEgkwhe+8AU8+eST+OpXv5r386+99hqMRmPev/34xz/Ga6+9hv7+fkxMTODhhx/GZz7zGRw5coSm6KtFOUYt5nakSRLHLy9j2hfFTbuMMNcVL/XgowNULWQ2QlJGmyqJo1LJuX02ADAxMVF2NotrZMqg0+nQ1dVFb96VSiWd/t7MJj2CIPDwjW6MLocwc/kdSDM443Odjv7+fshksrKa9MpxTtjew0IOCrW526HFfW+h2kGxZDKJ4eFhkCSJv/iLv0B7ezv27dsHgUBAl+3my7CfOXMGGo2Gd3Toxdaa8UUx44uipV6JOjmzEuRqy8RHpNPpDXO34vE4xGIxbbfsdjt0Oh3i8TicTmfF5+QiqEYQBMxmM0wmE2ZmZnDq1CmYTCY0NTXRz+lmBsUA4FPXNOGdGT8WxwZRr5bS56A26/mcjnLmQVV7f5lPJsrWGgwGzrNYXKLmkl133XUYGxvL+t3NN99M//+VV16Jn/70p2WtbTKZ8NBDD2H37t247rrr8Otf/5rxZ7lUStVocst9oaZWo/jdyCpkYgFe7F/AQ1c7aiYTX5Q4VbMcCATg8/nQ399PZ6/YTK3lG/Jd28zNO6UY6+rqWG2sqxExkkuE2G3WYG20cJNeptNx8eJFCAQCVoPEaqH0tzKl4A64RzWDYl/60pfwi1/8Ai0tLYhEIti7dy+uv/56OJ3O0tlEHmYjiq0ViCbxg54pxJJpWOpk+OT7StspvoHrDEk+elnKblF9Nnq9Hg6HI2+GgDp2s5F7TQQCAWw2GywWC6amptDT0wOLxQKHY/2ebyZzlUomwlUuPU7Mb5SjkNORb/5VMdRiuvdWtlO8c4O+973v4Z577sn7N4IgcPPNN4MgCHz605/Gpz71qay/33DDDRWdm6s0WDUMQq5cKqkQUpEA0UQKDWoVo3W4wmakzAtNW5dIJHSpnNPprCh7xbXDWcn5ijFsUIpxfn4e/f39SKVSUCqVJekqq9VMzgSZTsfExAS8Xi/6+vrgdruhUhV/fmtRO7uVhyXtgHtUMyj22GOP4fHHHwcA3HTTTbj99tthMBgYfZZLB6EWjksqTSKVJmlbxQTbKSiWabdWVlawtrYGv9+fRYvOtuqi1naqmBz5Pi8QCOBwOGC1WulZRnK5nHF5TzXtVCn2qkyno7+/H/F4nFEDfLmysMVWtlO8cja+8pWvQCQS4b777sv797feegsWiwULCws4evQo2tracN1113FybmqaKhcbrFpEjPRKCR662o61SAJOPTPqwmqXdnGBXJq+UCiEcDhMlxdQ3N+Z09aXlpbg9/tLblqZgA+RASbPDtVTsLS0BKlUStMtNzc3F0ylbqazQYEgCGg0GphMJphMJly8eBFSqbSo01FOzwZXZVQ72EE+VBIUy3w2pVIpEokE4/PyObORTy6tQowPHzRjZDGETgd/m1crRSF6WYFAQJdA6fV6EASBPXv2VHw+LnRztR0xoVCI5uZm2O12nDlzBkNDQyBJEhaLpaiurbadYlIe3NDQAKVSiXfeeadkIzmFcjLw7yU7xRtn4+mnn8YLL7yAV199teANsFgsAICGhgbcddddOHnyZEFnQygUIplMMq5h42uZUbG1jCoJjCpmw5f4mNlIpVKIRCKYnp7eQC+bS9PHByeAaxT6TmyVUENDA9xuN526pqaN5j77m+FseJdC8EUS2GvRQCRcV5KUwqTqTJeXl4s6HTuZjR3wCVwGxcoZQMtHZ4MK1uVDm0mFNhPzQBBXur5aGfhi9LJUQCyf3aLKfbcLmOplkUgEvV4Pq9WKUCiE7u5uNDc3o7GxMe/nN8NOLQZiWAzG0Nqgou0UAMhkMuzfv79oIzmFzSAy2UrghbPx0ksv4atf/Spef/31gvSSFHOQWq1GKBTCK6+8Qqei80EqldJNwUxARYzY1JIXAh8dF65lYgOSJBEOh7Oo+qLRKNLpNN203djYWBWaPr6DK8OambqenJxET08PrFYrHA4HrZxqrcSHF4L4Xy9dRiJF4rYOE/7gsC3v2qWcjlr0bNSiuW8HWx9cB8WkUini8Tjj8xfb1LMFX7MkfLGdFE3+6uoqfD4fzp8/pCzqBQAAIABJREFUj2g0StOiq1SqqtHLbkeIxWK0trbC6XTC6/VibGwMLpdrwwDkWtupxUAMjz0/gEg8hWs9BnzimqastUs1kpcrSzXsFJ+DsjV/Q+69914cP34cS0tLsNlseOKJJ/Dkk08iFovh6NGjANbrYb/97W9jZmYGn/jEJ/Diiy9ifn4ed911F4D1yMLHPvYx3HrrrQXPQynxUtOMKfBViXMlVzWa1vMhHo9v4P9Op9N0w7ZGo4HZbIZMJsPCwgIikQisVitnclWCYCyFV7yrsCwSuLndCLFwczaflShboVCIpqYm2Gw2jI+P48SJE7QTUmslvhyKI5FKQyQgMO2L0L8v5DwUcjpqxUbFZ0W9g81HtYJibJ0NPjoIfOiPqAS5wxFDoRBIkoRcLodIJKI3ymzpZasBkiQxvBxDUBxAe6OqbHkq1fHlMiFKpVK0t7cjEolgZGQEo6Oj8Hg8MBgMBXtUuZChEBaDMUTiKcjFAgwvhujf56NDz3Q6+vr6oFKpspgha5HZ2Oznr1xw6mxQQ2WKefk/+tGPNvzuoYceynusxWLBiy++CABwuVzo6+tjLItEImGlxPmanuZrZiOVSmWlknNp+pRKZV562dx1+ITXx0Lom49DvJBEo0aKTkfhyd18QSFlKxKJ4Ha74XA4MDY2hhMnTkAoFDJ+BriI9B+0a3HjrnrM+2P4gy5bSZkpUE4HxR2eTCYLMvvkQzXS03x7VndQPpjYqVoFxcopo+KbbanGWlytkytTLi16PnpZm82WNRzR5/Nhbm4OMllxevla4exMGD+7sAb5cBz3HbbgiibdpslSiVMgl8vR0dGBcDiM4eFheL1etLS01NzZaG1Q4VqPAcOLIfzhFfaSaxdyOrRabU2CYls1A8+JsxGPx/H888/j9OnTkEgk6Orqwg033MBJw265oMqomILrbASXjgtXa5VjDPLR9K2srGBlZQUajWbL0svmg0IsQJoEBAQBmXjzXmgum7PFYjFaWlrgdDrR09OD8+fPw+PxwGQyFT1HuVGrTEhEAvyPqzdywTNdW6/XQ6/XY2BgAHNzcwiFQvB4PJyzV1HYys/uDkqDjZ2qVVBsMzMbfM2ScAGSJJFKpeD3++nGbTb0stUAV/olEEuBxPqsrbVIkpM1ywFbitpCUCgU2LdvH4LBIIaGhuD3+xmxP1HrVnpdRUIBXTqVu3apAFSm0zE4OAihUAi73c6YvWqnZ4Mlvvvd7+LRRx9FZ2cnEokEvvvd7+KOO+7AU089BY1Gw8UpWIOts8HnKA+XHN/FkEnTR/1LJpOQSqW0gjYYDBCLxaivr4dOV3lEhU8G6hqnEhadAlaTEbsbN89RBrgfaiSRSKBWq+FwODA3N0enro1GY82b9NgqTLlcTtfHDgwMQCwWF3U6trJC3kH1sGOntsZabNcpZLfS6TTkcjnUanVFQ3351o9y2KbCYjAOg06Ha1zl22AuNupsPl/qWJVKhYMHD6Kvrw/T09NYXl6Gx+OBWq0u+Jlq9toxzTxQTkcqlcLCwgKd6XC5XAXLLdmsn/uZrWrbKnI2qIf1G9/4Bp577jl6zoXX68WHP/xhfOc738Gf/MmfcNJ0zRYSiWTbUApy7WwUo+mjnIr6+no0NzfnnZxcTCaSJBGMpaCUCiFgQDHHBTjLAggJ7DfLYTIVVm61ANuIERunQCaTYffu3QiHwxgZGYHX66XrZStZl21j3FI4hcC0D+2N6pK9MdT6VKaDKq8Si8Vwu90bjBHXCnkn47G1wXc7tdOzwQxs7dbk5CREIhEaGxurKletoZAIcEebBg5HbXodi9l6Nmsw1aMSiQR2ux0EQeDSpUuQSCTweDx5+2+r+cyRJIm1aBq9E2vY3aiGXFJcP5AkCY1Gg3379mFxcRHnz58v6nTssFGVgdXVVbS1tQFYT1W7XC68+OKLOHbsGO677z7Gw1y4xE7EaB2ZNH3RaBS9vb0V08sWO+4X5+fxlncVbqMCn3ifAyJBbTZqXFxvvmRZ2G7ey3EKFAoF9u7di2AwmFUvq9VqWcvAVt6FQBxf/908COEqrm814tPXNRc9PlfBZjodly5d2uB07DBL7SAf+Gqn2PYWcmlb+Oq4ZNotKmvBJS362HIYv+pfgMugwC2760sGxvhiG/iEatkI6lidTofDhw9jeXkZFy5cgFKppJuxy1mXLXyRBP53zypIURh7LBp88dguRnLnllcVcjqqUUbF58AYJ5kNu90Or9cLs9kMiUSCZDIJs9mMpaUlhEKh0gtVAe+1WliKpi9TQefS9InFYuzfv79imr5CMpEkie7RNTSqJRhZCmE1FEe9Wlrxd6slavmy1tpByqeYVSoVDhw4gEAgQA9dopr02Ey0ZaM0F0NxxJMk1NJs9o9CKKRgCzkdW5mxYwfcg+92ajN7C5NJbmr+y9XlJLmRFj0UCtGlKJXQoheT6adn5+CPJjC2HMFus7roYNztqkuYbtRrXYGQK5fBYIBer6dpZ+vq6uB2uyGVSqsaWFoLJxBJktArhBhdYm+nSjkd5dqprfo8VrTrpC7s5z//eaysrCAej0MikUAkEiEcDkMsFnOmzNiiHDaqzcxGjK9EcHbShw6zCp6Gd2vR861VjKZPpVKhrq4OVqt1A03fzMxMVfnACYLAdR49jg8to82kgk5Zm8a77YZqZhUKHatWq3Ho0CGsra3h8uXLiEQijBsn2crg1ktxxKHCUlyEB65yVLw+5XSsrq7i0qVLSCaT0Ov1jOXZwfbGdrJTm11GFU+mMbUWhUEpRp383RJbJmuVslsULfr58+fR2dlZ1nfKRDGZLHVSzPmjkIuF0Mh2ZmSUAy5IRJium7lxn5ubQ29vL90/Wq09jUUjxg0uJSYjEtzTWbpkjQl71dLSEu106PX691QGvuK75PP58Id/+Icbfj85OYk//uM/ZkVZySU2M2LEVoknUmn8+6lpgAD6ZwP4/AdckIuFSKfTSCaTWFpawsLCAiOavlqg2Pc71tGAG3cZIBUJtqwHvpXAtWOi1WrR1dWFvr4+TExMYG1tDR6Ph9NGNyEB/OGhesbzVZhGr6i0++DgID3DJV9Pxw7ee+CzndqsoFg5Nu+5vjkMzgWhkorwmeucUPy+jj1TrnLoZTNRC7vxkUNmHLTXwagUQ6fY2JeYKw+fMvB8kqXWQTGCIGA2m2EymTA7O4vLly+jrq4OFoulKk7HbbvWsyhMkE6n8/a4UiAIAvX19TAajVhaWsKlS5cgEAgQDoeL2lem4Pt+q6K781//9V947rnn8LGPfQzve9/7EAwG6bkKu3btwq5dxWvcqolylPhmlVERACQCEgv+CCRIYXBgAInY+hC0VCpFsz/VkqavFIopPJmYmePDNyVeayQSCUilG8vMqlWHyuZay2QyehDg+fPnoVar4Xa783LNc8VGNeeLIhRPwWVUZK3H1pmRy+VoamqCSqXCpUuXIBKJirKalLoufFfiOygOPtspmUwGv9/P+PjNzmxMr0ahkgkRjCURiCYgJNd7K3w+H5aXlzE+Pg7gXXrZcmjRuSzbKXStxEIB2mvMOMilHuFirUptbzWJTEodKxAIYLVaEY/HEQgE0N3dDYvFAqfTWTDwyvb7FrJTI4shzPmj6HJoIc3Y67ApS6uvr0c6ncbs7GzJRvLtgrKcjVQqBaFQiK9//ev44Ac/iGeeeQYmkwmvvPIKbr31Vng8HpAkuamNmuWkp2tRRlWIpu9InRhraik8JgPs9XU0Td/Q0BCMRiMnNLNcYWfzxQ5UP00gEMiK9AkEAggEArS2tm4o+6lm4x1TmQmCgNFohNFoxMLCAs6ePQutVguXy5XlJJXDRpV7/OX5IP76+QEk0yQ+fqUdt+8zl70+FWGiMh1UeVUhp2MrM3zsoDC2o53arHJfym5dZSZwfGgNNlkSYwOrkMlkdD+FRqOB0+nk5FpWs/F3M8G34FqpaxyLxeDz+aDVavMGOqsVFGOzrtFoxJ49ezAxMYETJ07AbrfDbrdveA65sFNjy2H8z19cRDyZxvtbjfiTG9/NerC1IyRJQq1WY//+/VnlVdvV6SjL2aBuQHt7Oz760Y/i8ccfh1qtxsWLF7F37154PB4A2FQDvtllVKlUKqthO5Omj0olF6OXzVyLjwqKT7zjfDJKyWQSyWQSU1NTCAQCCIVCSKfTUCgUGyJ9iUQC6XQaXq8Xo6OjaGlpgUaj2dSIUSao95cgCJhMJjQ0NGB2dha9vb0wGo30s1uOEs/VDeMrYUSTaUiEBPpnAlnORjlKPPP4TKeDGryU6XRs143Nex1bxU5tZs9G7lql6GXtGhU+8/5mmnCEwtTUFB084QJcvJNc2Sk+2mCuUSggJpFIIJfLMT4+joaGBjQ3N9PlStW0U2xAEASEQiGam5tht9sxPj6OEydOwOl0wmKx0M8k26BCvuNXQnEkUiTEQgIza9ENx5eT4c8tr9quTkdFZVQ6nQ4//OEP4fV60dfXh5WVFd68lLWshU0kElnZitXVVaRSKaytrVVM08dleRffwOUGr9bPHUmSiMViWco5EolAKBQimUyCIAhYrVa6XKMQFAoFDhw4AJ/Ph8HBQUilUtaT2KuVBcl3HovFgsbGRszMzODkyZNobGyEwWBgpcTzlUVd0aTD60NLWA0l8JGcZjy2zkahsiudToeurq4NTodUKt3JbGxjbCc7xaWzQTkWU1NTFdPLcp1x2UH1QNEKx2IxDA4OIhwOgyTJDQExKntN9dssLCygp6cHVqsVDoej6tS3TJCr60UiEdxuNxwOB0ZHR9Hd3Y2mpiaYzeaynIHc4/fb6nD7vkaMLoVwfw65STlBscz1t7vTUVFm44YbbsDLL7+MhoYGfOUrX8GVV15J85hTimezFIdUKkU4HGZ8PBMlnksvS72wIpEIKpUKSqUSZrMZGo0GiUQCTqez0q/BaXkXV3gvRHoykU6naWNMORfJZBLeoAC+lATXtxrhdr9rlE+dOsW4+Zl6P+rq6nD48GEsLS3hnXfeQSgUgk6nK9mjU63oUjHFKRAIYLPZYLFYMDk5ifPnz0MkEtFlK+XIoZGL8eU7dm84jq3cpWQHNjod1Gd2sL2wFexULcqo8tHLRqNR+m86na5setlMufg2s4NvGfhag3ImZ5d9GFvwQSuIIRl/lw4fACwWC9RqdUm9LRAIYLfbYbFY6MyBRqOpCvkGF46JWCxGa2srnE4nvF4vxsbGWO/H8mU2hAJiA4PivH+915Btb2EhO1XI6WhuLj6Piu9OekXOxrXXXotrr7027zGV1stVCqlUitXVVcbH5ypLiqaP+pfp/SuVyoL0ssB6nSMbA1JKLr4puu2sxJPJJFZWVrI43wFsmFA7E0jiX349hGQ6jVUygkecFk7ObzQaYbVakU6ncfr0aTQ0NKCpqangJmAzaHIpCAQCOJ1OqNVqDA4Ooru7G3a7HTabrehmn4lSHlsO46+fHwAAfNRFoqXC9Hc+UE7H3NwcLl68iLNnz8LtdkOj0TA+1w74i61ip7jMbMTj8Q0zKzLLOCl6WZlMhkAggNnZWcaBkWLYrF6SWq3DdxQKiEEkxff74wglCewxq/FXt+ylv8/p06eh0WhK6srM7y8UCuFyuWCz2dDX14fl5WUolUoYjcai12mzsiBSqRTt7e2IRqO4fPky/H4/FhcXS8oLlGaXAtZ7DR/75UUkUiRucYrwkYYGRnJTspca0JfpdLzzzjuIRCKcsVfVGpxyhS0tLWFxcRE+nw/JZBJyuRw2mw0mk6nmLyxTJU69pH6/H+FwGIuLi0gkElk0fXa7HUqlknGKbLMZQ7YKNlOJkyRJZ6ko5RwIBCAWi6HX6xnc93VefgIEAG7vD0EQ0Ov1aG1txeTkJHp6emCz2ThpemMKNlEagUAAjUaDXbt2YWxsLG+9bCaYOAOvDS7CF0mCIIDTs0ncWGH6uxhUKhUMBgOcTicuX74MoVCY5XRshc3GDpiDb3aKTW8hZQ9K0csqlcqSZZx8zEZwvdZ2tJ3JZBLhcBgTExNFA2JisRgzvigSA5dhUAsxthYv+/nO/ZxEIkFjYyPi8Tjm5uYwOjqK1tZWaLXavJ/f7JIrmUyG1tbW9UzP7CxGR0fh8XiKzmNisvbocgiRRBoSoQCXV9hd33Q6zSiTSDkdWq0WPT09OH/+PD1NfSs5HZw4Gz6fD8ePH8dLL72E/v5+rKysQCgUor6+Hna7HR0dHTh27Bja29u5OB0jUA24FKga+8yITzgcBkEQ9A2TyWTweDwV08vyVfFyBT7KVAypVCrLKFO1yblDEGdnZ6FUKtHAIDrh1MvxqWscGF+J4PoWA6fyUkqOyhxYrVaMjY1l1Z/m1noyRTV7QSi2p8x62ebmZjQ2NmatxUSJdzq0ePHCPACgVS9k3bNRzvHUjBFqsCHldPCJCW4H5YOvdqpYUIwkyaws++rqKgKBANbW1vKSTrB9v/lqp/iWkdgMefIFxGKxGNLpNCQSCTQaTclAqFkjxdE2I3onfPjIIcuG9SsBSZKQSCRwu90IBAIYGhoCQRBoaWmhy7QywYc+RLFYjH379iEYDGJ4eBherxctLS2oq6sra+0rmvT4beMSVsJxfKBJzElvYTH5pVIpurq66EyHQqHYMk5Hxc7GxYsX8cUvfhEDAwO44YYb8NnPfhZ2ux0EQWBubg6nT5/Gb3/7W7z55pv4whe+gKuuugoPPvggXnjhBTQ0NODChQsAgJWVFdxzzz0YGxtDU1MTnn322bxG/umnn8aXv/xlAMAXv/hF3H///RuOCQaDGB0dxcWLF/Hwww/jgQceQDKZhFQqzWKCksvl9MOxuLiIYDDIyRyLfIrXuxTG5GoE+60aaEsMESq1Fh/ApzQ3BcowZzZth8PhLAawYrXJbI3BYacWh535IzlcInMTPzIygvHxcXg8HhiNxqo9G2wUYa5Slkgk2LVrF2KxGF0v63K50NDQQN/zUmvvs9Xh/7vvAABg5GIfLi+EECcjOGirg0BQ/LNsWUdynZNMp2NoaAgGg2FTZzHsoHKUY6dqgcwMfCFadIlEkqW/qNKQSpEvs5FIpeGLJKFTiCEs8Z7lrsW3BnE+llEVo8RnEhCTSqWYm5tDMpmE2WzOu1au7Pd0WnBPZ/4y30q/G/V5tVqNQ4cOYXV1Ff39/VAqlfB4PPRcJj4wLGYeq1KpcODAAfj9/iwnKbMHhUnQSqsQ46t37wEAnOs7j3/vncNcaAYPXu2E01DcASjXTuUrr1IoFAWdJr6gbGeD+uK/+c1vcPjwYTz33HN5j7vrrrsAAN/73vfoprQHHngAn/vc5/Dxj3+cPu6pp57CBz7wATzyyCN46qmn8NRTT+GrX/1q1lorKyt44okncPr0aRAEgc7OTtxxxx20U7K4uIibb74ZCoUCOp0OAoEAt912Gzo6Oko6EVyWPuWutRKK44cnp5Ei0xicD+JT1zBvVOKjs8GHspLMMoKlpSX4/X6srKxscCgVCgUrZcQHFFKgEokE7e3tCIfDGB4extjYGM18VSsZ8qGQUqY2RZFIBCMjI3TqOpVKMVKyeuX6O/vicgLPnlpv5L7viB33dNnKkqcQCil9rVaLzs5OxuvsgH+oxE5VKyhGkiTGx8dx/vx5vPrqqzh37hwOHTqEb37zm2hoaKB1V1NT0wa7FQwGsbKyUvmFwUbykVSaxDM9U5hYjWKXSYl7u5j3cvA5S8InZFZYUEGx3ICYyWSC2+2uykRsLpHvHul0Ohw5cgSLi4s4e/Ys9Ho9XC5X1ahvK2GuAgCNRoPOzk56FpNEIoHH44FSqWRdojywFMOvBgIQCAT4f4+n8Hcf7igpTyVBsVynY2FhYXs6G9SXfvjhhwEAoVAISqVyw3HUDX7wwQfp31133XUYGxvLOu4Xv/gFjh8/DgC4//77cf31129wNl5++WUcPXqUrrM7evQoXnrpJdx7770A1ptre3t7IRAI8MYbb+A//uM/cMsttzD6PtVUluTv/wlAsK7ur0b0n4toRi0jRolEIiviEwqFsqj61Go1RCIRzTBTC5mqjWJyKBQK7Nu3D4FAAD09Pejr60NLS0ve969aMmSi1DMll8vR0dFBO0nLy8swGo2MmUyWIykkU0IIBQKML5dmmCuHFaTY8UwYtnbAT1Rip6oVFCNJEn/1V3+FtrY2tLa2Ynh4GD/84Q8Z129Xy06F4ilMrEZhUkswOB9CIpWGWMhsM8RHZ2Oz18ntq/H5fAiFQhgYGKDtVm6FRa1Rib0rpPcJgkBDQwPq6+sxPT2NkydPIp1OMw7mbkZ/BzWLaXl5GRcuXKCDlGyuj1JMQEAIkCaBBrW05PFc2SnK6eC7narIdaZu3ksvvYRXXnkFV111FUwmE7RaLbRaLfR6PZRKJZ2uLXbz5ufn6bSg2WzGwsLChmOmp6dht9vpn202G6anp+mfM9ff7KF+mcrJoJTg3k4zptaiOGBjx3RTjSY+vmyogewoBkXVlxn1iUajNLWwSqXK2/S4urrKGfsXH8DUsKnVaigUCjgcDly4cAEqlQput5tOXVeCSsqoCoFykk6fPo3Z2VnMzs4ySv12GAQISQ3wRZP4yCErLs764dAroJIWZuiqZo/HDrYWyrVT1QqKCQQCPPvsswDWbdqvfvUrxlHsatoptVSIK5q06J3w4YZWA2NHI99aXMq1FUDNrqDsVm5ATKfTwWg0YmpqCh0dxSPe2wUEQcBms8FsNuPtt9/GmTNn0NzcXJA4hMJmNpMbDAbo9XosLi7iwoULiMfjUKlUjGyqTSXAE8c8WI0DOoUYj/68H3utdfiDLmve83KVgd8q4MTZEAqF6Onpwc9+9jOkUil60JdOp8P9999PZxcq3eTmU0CF1qzlUL9c5DMIrSYVWk0bm6ZqKReXkZ5KDR5VnxwKhXD58mUEAoGsgVJqtZqmaOSTc1QLsHUI9Xo9jhw5goWFBZw5cyZrunctZGArr0AgQHt7OxKJRMF62UzIRQQePbZrPSL8XD8uzQVh0kjwrXsPQCLaqHwrTU/n4r32/G03cGmnuAiKZaIc6ttq2SmCIPDBPQ344B7m9J2Zn+WjneI6A58ZEMsd5krZrUIsYGzmfhUDXxwxpnpfKBRCLpejra0NMzMz6O7uhtvtpnv4yl23WsdSmRmTyQSpVIozZ87AYDCgubm5aDk+SZLYbVZDJpPhgafPYDUcxzvTARxyaLErz97vvRYUq8jZoL740aNHcfToUQDrtIInT57Ev/7rv+LnP/85rrjiCtxyyy0lXxCTyYTZ2VmYzWbMzs7mZQSy2Wx0VAkApqamcP311+ddj+2wJL7S1VajjIpCmiTx1sgKlkMJ3NBqQJ2c+eaUjUz5mrYJgoBEIkE6nc6i6ttBeSAIAiaTCfX19fR0b4vFAofDUVZ6tZrOBnV8sXrZfEimSVycDUAjF2POH8NqOA6TZmPEiavhSjvYHuDSTjEBm6BYOUP9+GqnuByMmSuXP5rEajgBm1bGuHG90u9H0eKvrq4iGAzizJkzSCaTZU1Y5xpbLQBCMVe1trbC4XDQPYctLS0b6Ger6Wyw1fNGoxFutxszMzM4deoUPfcq314l0440qCVYCEQhFQkRiiYQTaQgEws3HP9eslOcdyAZjUYcO3YMN954I5555hmYTCYAG4cn5eKOO+7A008/jUceeQRPP/007rzzzg3H3HLLLXj00UfpYX2vvPIKnnzyybzrlZPZ4KMS59oJysTgfAg/PTsHEIAvksT9VxZvvC20DgWSJOkaVcq5oLjf1Wo1Pc9AoVBAIBDA7/djZmYGOp0OsWQak6sRNGqkrNL3fAZJknjm5DReH1rBbR31uPtAafYQ6nPlGhNqurfZbMb4+Di6u7tp+lw24KJBvNjamcfnq5f1eDyQy+VZnxMLBXjgKgd+emYGR5p0+PnZGVzTYsQei6bo+lzLv4Otj3LtFNdBMT6V+1YCLjMuuXL5Igl8/VUvAtEkrnbpNlC4coFMeuFAIEBnIZRKJeRyOSQSCfbu3VtRQIzPDsLgfBCvDi7hiFOLrhyGxUL3la2doo6VyWTo6OhAMBjE0NAQPaMjM7NdDWejHKpZiv3JarXCbDZjamoKJ0+ehNlshsPhyCp/zJTlsWNt6BldwWuXF/HY85dg0kjxv+/ZB2VG6S/XGXi+gxNnIxQKYWZmBnV1dVAqlVAqlZDJZIhEIvjnf/5n3HHHHUilUvSNuffee3H8+HEsLS3BZrPhiSeewCOPPIKPfvSj+O53vwuHw4Gf/OQnANanXH7729/Gd77zHej1ejz22GM4fPgwAODxxx8vOJSlHCXOx2xENdcSCdZH0qXSJKR5ylGKrZNKpbC2tpaVTs6cVMuU+50kSSRSaXztv0YwtRbFHrMKf/z+Jl4rZqZYCSfw8sVF1MnF+MnZOdzWYWJ1nSsBNenVbrfD6/XixIkTSCQSjJVzNTMbhZR+Zr3suXPnUFdXB5fLlXXMRzqtuHO/Gfd+9xQiiRReGljED/9H53taie+AGdjaqXzgOiiWOw+qFPgcyKoW9e1yKAF/NAm1VITLC6GKZCLJ/LMrMof4Op1OOiAGrDsiPp8PYrEYbw4v4+WBJVzt0uGDu+tZ2ym+lEBlIpUm8Xe/8SKeTKN7dA3/8JE90DGk52djI3KhUqlw8OBBrK2tYWBgADKZDC0tLZteRkUh104JBAI4HA5YrVZMTk6iu7ubHrYrFAqz7IhaJsJN7Q34p9e9UElFWPDHML0WzSqlf68FxSpyNqgv/8Ybb+DTn/40brzxRphMJjQ3N8NoNOLll1+GxbIehci8aT/60Y/yrvfqq69u+F1XVxe+853v0D8/+OCDWYwhhbBZ6elgLImXLi5jcT6K1vZ0xZvLajobnnoFHrrajrVIEl3O/E26+aj6AoEAACAWi0GlUsFsNkOlUrEu16GeCV8kiam1KAwKMfpng0hGh9HYAAAgAElEQVSRgIiFDuerY6KRiWDVyjDti6K1QQmJsDpKsRjEYjF27dqFaDSKt956CydPnkRra2vJQXW1KKPKh0wmk/n5eZw5cwbRaBTxeDyrXnb9KSYQT6bhXQxhr+3d57fcCNYOtifKtVO1CIqx1e9cOgjzgTgWw/x0NjLXcujlOOzUYngxhDv3mRivk0qlEIvFMD09XXJ2BRN9kUil8W+nZqCWCvHL8/O4ulnHamYWXyEgAKlIgGAsCYVEyLhMjSuKWq1Wi8OHD2NpaQnnzp3Lq+/LWbeSY4sdLxQK0dTUBJvNRlcPOByOvHbnD7ps+P7bE4gn0/jCcxfwyK2tuKJ5XRdwbaf4ug+iwEnPxvve9z780z/9E/r6+nDu3Dm8+OKL6O/vh8fjwTe+8Q0AtaeP3KzGu7e9qzg75cfqagLvzPjR5ahs6Fs1lThBENhrfbcEhapRzUwnZw5DVKvVMJlM8Pv9iMVicDqZzwspBoNSjOs8enSPruLOfSaIWAyTosDHiJFYKMAT/60V02tROHTs6nq5VhwymQxyuRx79uyhU9fFmrKrHTEqtbknCAKNjY0wmUx4/fXXs+plJWIx/tedu/HvJydxwruCR35+EZ+9vhnHOhrLlqeYfuK7Et9BcZRrp2oRFAOqM1m5FLxLYfygZwoLiwlYXD4csFfGz1/tDPx9hwuXgZJk/mGu1BparbboMFcmIEkSIgEBh06O8dUw6pVSKCT8phplCoIg8MVbPTg94cPuRhU0MubXiCsbQRDvzox44403cOrUKTQ2NsLpdBa9Z5tpp0QiEdxuNxwOB8bGxhAMBjE7OwuLxUKf554uG+pVEnztv4aRIoEfdE9mORvVnDjON3BSRqXRaHD77bfj9ttvp3/3m9/8Br/73e+g0bCjeuUKm1ULu/6irj8Q6gLUnGxQLSWeSCSylHMotJ6eViqVdG9FoUaoYDDImUyUArjvsLWoQdmqkIuF8NSzm39RTccpN3Utl8vz9kdsVmYjFxSRwJVXXklztpvNZngcDnRY6tA9uopEKo3jl5dwc3sDRL/v93kvNd7tgBn4aKeAzQmULARiSKZJECAwF2BuJwuhVqXDubMrAoEAEokEJBIJ3RdIDXP1+/2Ym5uDzcasF7GYPNR//+wDzRhbjsCuk+VlwtuqMNfJcPtedpTpbO83UyYosViMK6+8EhMTE+jp6YHdbofNZsuro/lgp8RiMVpaWjA/P49AIIATJ07A5XLBZDKBIAi0mNRQiIWYXI1gMRDDt1734o/e79opo+IKN910ExKJBD73uc/hP//zP5FKpWqa3disWtgjTVpopEIMDwXz0p2xRaVyZdaohsNhDAwMIJFIQCQS0crZbrdDqVTW/EHeyl56tVHta5ObutbpdHC5XFmpazaNd2wjhmy/n0AggN1uh8ViweTkJHp6etCiN6HJoEDvxBrOTPjw9d8M4wu3tLJaF9j6SnwH5WOz7dRmYa9FjdHlMIYjy7iiqXhJJRNUw9lIJpMbAmIkmT27wuFwMCq34QpysRDtjZXb9e0Atht9NhAIBHSp0ujoKLq7u7M28OXIUA6RCRs7JRAI0NbWhmg0ipGREYyNjcHtdsNmNOLLd7bj4f84D41MhB+fnsZ9R+zvud5CTpyNxcVF/OIXv8CBAwcgk8mgVCrR0NCAyclJDAwMcHEK1mBbFsVVZkNAEGhrVCE8w81mkY0ST6VSWQ3buTWqIpEITU1N0Gq1FW1muTQsO9iIWl3bzNT17OxsVuqaDWrZ85BZLzsxMYFrdEGMLgohIIBXLy3i09c2s15zqyvxHTADH+0UsDlBF6VUhHu7rDhFzjBuBi6GSmxC5uyKQCCApaUlLC8vZ2UrCs2uqJZM+WTcQfkotw9RJBKhpaUFDoeD3sC3tLTAYDCwXrca5b75IJPJsGfPHoTDYYyMjMDr9aLZ5YG7XoUT3hVIRQQe++UAPubcob5lDOrmLS0t4fOf/zwdJW9uboZMJsOFCxfwoQ99CEBpSsFqgI2C2CoMUsC7NaqZTdvhcBgCgSCL/9vtdmdFnEOhEONmuFqAq+vEl+/DFpFECq+NzMOoluHKZh39PbhsEGcCgiBgsVjQ2NhIp64TiQRj5VZNeQs9HyKRCC6XC40WK3630Iu3J8JQSQT48ouDuJslM+ZWV+I7KI7tZKf4Cqa6PJVKIRwOZ2UscmdX6HQ6NDQ00BvKasvEZB0usFXtFACMLUcgFQth1yvo39XSTkmlUuzevRuhUCiLLpcPZVSFoFAosHfvXgSDQQwPD+M2SxSX50WIJtL43cgyrtOJcQXLzEYx6mW+P18VORvUl2tvb4ff70coFMLp06fR19eHkZERPPDAA3STHN8vRCXy9c8GML0WRZejDnplcapXtqAci7m5uZI1qqXOy6Xy3Q4GMhe13uQ/3TOD14bXIBQQePy2tg385rVGZur6jTfeKJi6zkW1nY1iaytkUjx8Swe8z57Hgj+K316awxGlqKrp9R1sLWwnO1UJEqk0fJEk9EoxBBx/z3w2odjsCrVaXXCYq9/v37b3YSvazdeHV/H/vz0NgYDAY8d24VCZpDdc3FOlUokDBw7A5/Ph8uXLCAQCiEajnDNXlXN8IahUKhw4cADNPh+eH+nDiekk5GIBnr0cx3+/eSezwRipVArj4+NQqVRQKBR4//vfj/e///1cyFYxaqGw5vwx/OTMLABgYjWCT1ztKHutfDWqyWQS6XQaGo2m4hrVnbTy5iIzGxUIBDA27UMyIQAhkSCSSNHHVWPzzuZ+iUQiyGQydHZ25k1d51u7Wu8aEwW7x6zGfqsGr4XiEAkEeMGbhL27G263G/X1pbnwtzql4A5Kg892SigUVr1XJJFK41+7pzC9FkWHRY3/fpDZgFEmoPoCqVp1JrMrioFvQbHtGlxjikvzQaRJEqkUicvzQdrZqHVwLhN1dXXo6urC7373O/T396Ourg5utxtSqbTgZ2pVRlUIdXV1+NNjB9D3dC+CsRSGV4HzYwvY17RxMGg+bHWK9oqdjcXFRdxyyy3Yu3cvbDYb/vEf/xHJZJK+KFv54jCBkFjnnkr+nhqPCXJrVIPBIKLRKEQiEa2cqRrVSCSCiYkJTmhm+ZhW3o7OD3V/Kaci8/6q1Wqo1WoYjUb8mSmJH5wYgzgZhkvJfOBeuTKxXbtQ6jqXuWez5SYIAvceseO3l5cQiqcwuAY0utqxsDCF0dFReDyeoiUZW51ScAelwWc7RTEnKhSK0geXiUB0fZaRSS1B/2wAHz7QWNYzn0wmaXp0Srel02nIZDKkUinWsyvy4b2+ud9MkCRJl7lR99eRjqBOJIS+To0b2+qzjt9svSkSidDV1YXFxUX09vbS9Oj5CEuq3SDOBHssGnga1Dg/5UM4SeKbL/fj81fPw+12l3z/t7qdqtjZaGxsxKlTp/Doo49ienoawHqkZitfFDaoV0vxh1fYMOOLYp91I31iKpXKml2RW6OqVqthNpshk8nyXjOuFe9OpIdbZCrnzHKBwcFB2rEodH/F4jj++u5OxONxDA8PY3pyArt27apaZqPcNXNT1xTVH6Ucq6kEmRqILqcONq0c3sUQfHHg33vn8D+PdSAUCmF4eBherxcejyfvMMOtnp7eQWnw2U5RM6HYOBts32edQoyDNg3Oz/hxY6uBUbYv3+yKzL5As9lMz65IpVI4d+4cjEYjY5kKYScjURxcBuj8fn+W45hKpaBQKKBWq2EwGOB0OrGbJHG134+xsTEEFqZgUDRBKBTy4tpS0X6z2QyTyYSpqSn09PTQk70z9Xo5mYFq6Ie7DjTi7OQaSABvz6Rws1+CYF8f6urq4HK5IJPlpyDe6naKEzYqrVaLb33rW/TPfFDg5aDcl8dlVMBlVCAej2NlZQWBQACRSASnTp0CQRD07IpCNarFwEZhroYTWAzG0WyQQyzc+FDy8UHlg8JiCmroYWbUh1LOmVSMfX192L9/P+N1ZTIZOjo6EAgEMDg4SJchKJXs5nMUAxcODJW6Xl5exvnz56HRaOB2u6ua3mUj97E9Jvw/r3kBAD/omcLNe0w47NRh//79CAQCGBoagtfrRUtLS1Z2Zqsr8R0wA1/tlEQiYTWAliCIkoMo833mQ/sb8aH9jRv+lkqlEIlEshyLRCJBD3Nl0hfIx6AYV+vw0Wlh++wmk8msEt5QKIRwOIzp6emChDIU4vE4GhsbYTab6YnZzc3NvIi0Z9oHgUAAh8MBi8WC8fFxnDhxAs3NzTCbzfQ9rDWRST7c1N6Av37+Ev3z3x2fRu+jN2B+fh5nzpyBXq/fQEMPbH07xdmcjXQ6DZIkecVRTillrm8QSZIYmVvF8+fnoBYmcdCQRioRh1gsppu2JRIJOjs7Kz43U0Xnjybx978ZQTCWwn6bBg9eZS8oe6XgWzlWNZCpnCkDDLzb3FhMOZcLtVqNrq4uvPL6CfzjS+fhbqzDPdfs4YxHnqvrbTAYoNfrMT8/j97eXhAEUTFzTCGweX9v29uIf3jNC+rJ/JtfDuBXf3w1gPVre+jQITo7IxQK0dLSApVKteWV+A6Yg492qpwBtEz1L0mSGF2OQCIiYNPKkUgksrLs4XAYvb29WcNcnU4na53D1CaQJIkX+xfRM7aKo231uNajz7sWF+CbfamVPFRGKpOpUigU0pUU1Fyt3t5etLe3M15XIBCgubkZVqsVIyMjmJmdw8VVEi1OIfbbKptAXwlyrys12dtut2NkZATj4+PweDxVdY7YODI6hQR37W/ET8/NAQBC8TSGF4NoaWyEyWSiaeipkjAqOF3KTvHtec8FZzul3HQVsPlfnkpPF0pLMQFVo5q56Uyn03hthsBqQohZgQhXtTmx25o9u2JycpKTDQzT+R++SALBWApKqRBjy+G8x2yXHonjl5fwk7Oz6HRo8cCVtoqfM0o5+3w+rK6uwuv1Zilnq9UKlUpVsw3pT4eT8PpFeGs+ACLag2v3ODakhNmCa0VLEAQaGxvR0NCAU6dOYWBgAKFQCA6Hg9PrxEaJOwwKXNFch+5RHwBgeCmMVCoNYUaWj8rOrKys4OLFi5DJZEgkEjvOxnsEfLZTTEEF0YqB6ht749I8Xrq0hFQygRutBOx14qzZFeFwGPv37+c0aFIMa5EkXhlYhFYhxnPnZnGVS7eh15HJ92OCzbZ3vkgCb42swqaT5S2x5kKmzN7PQCBAN+azZapkA4lEgvb2dvz0wipeHZ2H4O15PHF7G65tY8k5XmVQcobDYQwPD2NtbQ1Wq7Uq52KbNfmft7XRzgYAfPv1UXz9I/uyaOinp6dx8uRJevbVVg+KcaZhgsEgVKr1yZr5LjqlPGp5sSQSCWKxGCNngyRJpNNpLC4u0k5FJBKhN51UjapKpYJQKITv0iLeHF6BVCRAQx23L3MmmCpMq1aG97cYcGk+iDv3mSpai4lMXKEceZ7umYZUJMBvB5dwtM2IOoZBylLKWSgUor6+HlardVM3IOn0uuISi0Ro370LicQqun/PrtTQ0LDpm6NMUDXcHo8Ha2trG1LXuWB7v9kq2M9d14Tu0T7657e9y7i2pX7DcXq9HocPH8by8jLOnj2LwcFBtLS05NUVfLreO6gMfLVTbJyN3MxGbl9gIBBAKpWCTCbD9CoBqUQMQi6Hw2PdQFvKdvhtITB9R1RSIRrUEiwE4nAZFRDm+Rjf3rdy5fnWG+M4N+WHRCTA3/63VphV5fc5ZPYGLiwsIBaLYXp6Oqv302Kx1HSO1koUEEvEIEng7CUvDOm1koxQmwGFQoF9+/bh4sWLmJ+fRyAQQEtLC6dlymztlEKSvfW+OBfI+lkgEMBut8NisWBychI9PT28ey/YgjNn42/+5m/oQUl2ux1isRh1dXWor6+HTCbbFI+sUMSIqr3PVM7JZBLxeBw+nw8ajQYmkwlyuTzvDU6k0rjOo4enXgm1VASjipsyl3xg6iAICAJ3H9hYj1vOWlzJxGSdctDeqML5aT90CjH0CjFSscSGY/KxaiQSiaKN+V6vt2CjPlNwcV3+YLcc/ZE6uE0aHHLqQRAG2O12DA0NYWxsDLt27YJWq2V1rmrXq2amrr1eL526NhqNWeetNtd5h1mV9fNrg4t5nQ1g/fkzGo10BPDs2bPQarVwuVy8M5g74AZ8tVNMy6ji8TiSySQmJycRi8UQDoeL9gU6wgn8un8BcokQu83qDetx5WwwhVgowJ/d6MKcPwabtrqkKJvdaxGOpyASrssQS6YBMIuKpdPprL1JbuO2UqmEXq/nhKGyEty5SwXhWAqNOhU+cV0z/KtLOH36NBobG9HU1MSrUkUAEIvF8Hg8EIlEuHDhAlQqFdxud0WVLxTKsWsqIRD8PeO9pACTqVAoRNPvZ191d3ejr68PTU1NsFqtWy7LwZmz4fP58Nvf/hZra2uYnZ3F1VdfTU8BbWhogMVigdFopCe1UhgcHMQ999xD/+z1evGlL30Jf/qnf0r/7vjx47jzzjvR3NwMALj77rvx+OOPl5RJKpVibm4Oy8vLUCgU9OwKAFk1qlRdXG9vL5qbm4u+JPP+GH7QM4VkmsT/dcRa1NHgYoPHpcLcbOXLFf7sxmZ4l8Iw18mglIqwFknTkR5KSWcqZ0oxc9X7UG3oZAJ8utORpQSlUindRJ7Zc8D0+arVLAyJRIK2tjZEIhEMDw/TdLlabXnc7OXQFWZiLZIqcGT2Z0wmE0wmE+bm5tDb2wuDwYDm5uYt88zsgBnKtVPVRL6gGBUsyeyvoIaXxePr/YGNjY0FZ1eQJIlzU374Igncub8RCkl+m8ZVyRIbKCRCuIyFmbf45myUqzf/6Donnn9nHi6jAq0NyryBz3yN2wDoaop8vYEUm9pmo14pxGO3OFFXt96vofh9We3ExATdRF4ow70ZoGyPXq/HkSNHsLCwgDNnzsBoNLIm7slFOXbqWocQL4+mQBDArR3FA8UikQgKhQItLS2Ym5vDiRMn0NTUBIvFwpvrWwqcORv/8i//AgB45513cMcdd9Cbo/7+fpw7d45O6x47dizLgO/atQvnzp0DsJ4OtlqtuOuuuzasf+211+KFF14oKceZM2fws5/9DH19fXjrrbfwmc98Bvfeey/uvvtuujGq0ENB9UcUczaGFkMIxVMQCQj0zwZg18nzHscV+wEfWT7iKRLdUxH4pavocmrL/o7lfLdkMolgIABFPIgp77pyTqVSIEkSOp2uKo3bbMDF9c333PSMruCl/gXcsrsBV3Z2YmlpCefPn0ckEkEikSipKGs9C0Mul2Pv3r00CxRBEHSZUjW5ztPpNIxyAkuR9fvgrmeWKqfOQVEozszM4NSpU2hsbER7e/uWUeg7KI5y7VQ1g2IEQeDs2bOYmZlBW1sb3RdIsdzV1dXBZrNBIpGAIAhcvHgRBoOBLgfLh5GlMJ77fU24P5rEHfvyb2b4GIDiUqZ4aj2rIBXVPgpsrpPhU9e8m31IJBKIx+MYGxsr2ri9VSLW+e6RdymMXw4ncdDmhs+3iomJCbS0tFS0JlfItCUEQcBkMqG+vp7ujbBYLHA4HGVlZNgyMqbTadzdKseBVguEAuCjnaV7SdLpNCQSCVpaWuB0OuH1enHixAm4XC6YTPlL5/kETndkb775Jj75yU/izjvvxJtvvol77rkHHo8HAOD3+3Hx4sWim8BXX30Vbre7ovQgSZI4fPgwPvnJT+ILX/gCPv/5z6OtrY3RZ5koOWudFDKxAGlyfXJxJWtxJRObtbjAzy8s45XLYRyfmcCfioXYX4XmN4A5q0YwGMTMzAzs9vwMXLUGm2wDkzXC8RT+5oVLSJMkukdX8JNPHYHRaIRWq8Xbb7+NkydPwmq1Fm3OZrNpZ/u8FVubYoFaXV1Ff38/ZDIZq0hqORGjeztU+MmlOFQyIT50gP2kZIFAAJvNBovFguXl5R1HY5uhHDvFdVDs9OnT+NrXvoahoSEsLS1h9+7duOOOO7JmVxQCE5tAACAIgCSL66PNyGwwARc2b3gpgn84HULd0EX85U0uNBmqNzQxE4V6A4VCIV3KW43G7c1Arvz/988vYjUcxysXBfj+/YfgdKYxODhIZ+mKOchA7YNimb0RFK0vlTFgA7YELOl0GnKxEA8cdrD6TG4FQTQahdfrxdjYGK655hrela5lgjNn47XXXsNDDz2Ez372s/jzP/9zPPvss7j//vvxzDPPwOVyQaPR4Morryy6xo9//GPce++9ef924sQJ7N+/HxaLBX//93+PPXv25D2us7MTnZ2dANajq2wb74op3t6JNfzqwgJUUhHuv8IGQ5ESKiZZEibgYxlVLJmm14onyzdU1MtJKefMwXi1YNXIBV8ifLlyiAQEpCIB/NEk1FJhFnuLVCpFV1cXxsbG0N3dTUc5cq8TW2eD674KnU6HI0eOYGZmBgMDAxgcHITL5eI8I5NOp3F9kxIfu+EgZGJhwfIRJhAIBGhoaCj78zvgH7iwU1wExZxOJ/72b/8WHo8HTzzxBDo6OvDBD36Q0WdL2alIIoWx5TDaTCo4dDJ0OrUFj611zwYTcGWnTo37kEyTiCRS6Jv2V8XZyDfUNbc3kGrcTiQSGBgYQGNj8ZKZrYJ890goIH7v4AICYr05++DBg3jjjTfQ398PtVpdtIm82s5GocCVUCiEy+WCzWbD6Ogouru7kUgkGMtTjs1km8HKF3iTyWTYvXs34vE4rx0NgANng7oAn/3sZ/HFL34RDz74IFKpFD760Y+ip6cHX/nKV/DNb34TGo2maJQyHo/jl7/8JZ588skNfzt06BDGx8ehUqnw4osv4kMf+hCGhoZKykaxUTFFKSV3dtIPlUQIfzSJpVC8qLOx2fWihdbiQqa79tUjFvShzWXBITs7fu10Ok1HOdbW1uDz+XDq1KlNZdXIBF8iTZlySEQCfOMje9HjXcGRZj1k4mylIhQK4Xa7YbPZMDw8jPHxcbqJnMJmOxsA6HpZnU4HpVKJkydPwmw2w+l0FlSUbDMb1PF65U6vxQ7eBVd2CuAmKFZfX4/6+nXignLYqIo5G28MLeN33lWQJLDfqoFcXHgTsp3LqK5o0uI3F6agkAhxkIM5EKUat6mhru+VPq98ev9rd+/BS/0LOOTQwqR5t+dQJBLhyJEjmJ+fL9pEXuvMRi4kEgl27dqFcDiMEydO4NSpU2hpaYFOpyv6uXLsFNvvWewcW+GZq9jZoL78v/3bv+HgwYMA1jc/qVQKX//613HTTTdhbW0NGo2mqAL59a9/jUOHDuWtPcuc9nvs2DH80R/9EZaWlmA0GovKxpa/vJgSXwnFsRZJ4NJcEAftdQV7NShUQ4knUmmQ5PrmsxxwJVOdTIRb3XLs2VM86ktRMmZGfahaZKpxOxwO49ChQxXJwyeDyYWyzPddXEYlXMZ3+w/eGFrCxRkfmoh3j5VKpdizZw+CwSD+D3tvHh7XXZ79f87s+2hmtI32XfJueSdkKRCyAYEkQEMoS6GkgdLSl7YvpU1pX2hp+xZaKEsI/Ci0pQV+obRQCC4BsjixLcuyLa/abO37OjMazT7n/UM+4xlpds1IsqP7unRdtnTme75z5szznGe576e7uxu5XE5TUxM63XJGL5/BRrqGVjq2oqICu90eIRNWV1dTVla2ap31yBht4dZHrvxUPpJimQ71S2Xv3P4QHl8IrUqGPIHKjYRbubLRWKzno60a9rVuz9hnxiNuS8MP1zLUdbNd61yjyqrj8TtqIv8/1jvD8Wtz1BDmNuHGbCbJ7scjOedTyCTdtTUaDTqdju3bt9PT0xMROknUBrZefirROTZLkjQZctZG1draGnPBpYj1K1/5SiSDk6zM893vfjdhtmhiYiLSGnLq1CnC4XBaE4uzGZaUyBi80DvHkj9EiUnNnQ3WlO0ZsnAY4dw5ZA4HWCyEd++GNchpji54+dIL/QTDIh++ozpt4ms08lltCQaDMUGF2+2OSDIajUZKS0tXGedAIJA3ZQ1RFJl0+bDqVFkHZxuFVIarZ3KRT/+0G38wRKkmzH13xv7dYDCwf/9+Zmdn6ezsxGKxZNQOlE3rUjaBjFwup7a2NqZ0vXKWyHpkjJLhZjDiW0gfa/VT+UqKBQKr5bsTIVlSrHtykfMjLnzBEK9vttFcktxP5JqzERZFjl+bZ34pwK812jBqMn/EyGXySCUXUtp/iRso+a543MDFxUUOHjyYkz2tFfnM/udyH5NOL5/+aTfBsIgs5OOd9ywfL5PJIkHG1atXGRoaoqmpCZvNtuGVjehjpRlSEufw8uXL6HQ6GhoaVsnlZluBfzUhJ8HG7OwsZrM5bpTf3Nwc+fcLL7zA3r17Y9o7AJaWlnjuued4+umnI7/72te+BsATTzzBD37wA5566ikUCgVarZbvfe97ad00mbZRJTLi3kCIzhEHF8Zc1BfqMGtTS6SZOztRLi4iFBUhDA8jm5wkeP/9kGVf3eVxF4v+EEqZwOkhx4YGG4FAAK/Xy8DAAC6XKzL8UOJXbAZVjS+9OMCLvbPYzRr+7qFtSVsJbjaEwtcnHwPBJJ+nzWbjyJEjjI2NcfHiRZRKZVpGLp+zMOKdX6lU0tTURFVVFVevXmVgYIDGxkasVmvepXK38OrBWv0U5C8p5vV6034fyez4pfFF5j0BzDolRo0y5Xcn11XhKxOLfK9jDICFpQDvOVyR8Rr5EldZSdyW5ISjuYGFhYXrStzunlzkn0+O0FCs532HK1JWoiSsd7CRzflkgoAgLAegyjgvj57w3dPTw8DAAHV1dTnYbXxk6qeij7VYLBw8eJDp6WnOnj2L1WqNkUbPZ4LuVsGagg3JsX/605+mrq6ON77xjVitVkwmE2q1mlAoxNTUFNeuXaOtrY1jx47xxS9+cZUR1+l0zM7OxvzuiSeeiPz7ox/9KB/96Ecz3l+u2qiuTCwys+inyqql0qqlypq8hQqPB7l0r2sAACAASURBVO3wMKEdO1BotYgmE8LoKCwsQBrOJx622438snuGQFhkf1X2/aeZDoKTiNvRxlmhUBAIBNBqtRQXFyccfpjr/WSCV67OY9IoGHf4GFvwZhWcZYN030+y41IZrm12I39wdwPnh+fZpXMQCot4/CEMcbKIgiBQXl6OVqulq6uLEydOUF9fH5dEnu7513J8smM1Gg07duzA7XbT29tLf38/ZrM5I/3zTMvTt3pbwxZy56fylRRTq9U4HI60308iP7WwFKBz1Mmkyweo2F2eWC1RQq4rGzJBQGD5AVOW5oNzPKz1eymKIm63G7/fT19fX4S4rVarMRqNcYe6bgSeOjbIqMNH95SbfZVm9lbkR9kxH0hl94uMav7moR20D8xT5BvF5Q0yMLtEc4kBdVTyT6fTsXfvXubn5+nq6sLn8+Hz+XI+WHWtfkoQBIqLiykqKopIo0ucw2xUE19tSbE1BRvSxfrgBz/IH/7hH/KNb3yDXbt2UV5ejlKpxOPxMDc3x+TkJIIg8Du/8ztUVaUv9bVW5KqN6sXeWfpnPSgVMt6UgqdwfaFlIx6KGigmSTRkifICDZ9+czNhUVxFEE4Xyb5oEnE7upwcDAbRaDQYjUbMZjPl5eWRLFxfX9+atZ3zaeQf3lvK9zvG2FVuTB0c5hjpqlcACQ1OqjXu31nC6xrMdHRe5H3/3MHwnIf3Hq7kg7fXxD1eJpNhNptpaGjg6tWrDA4O0tTUFJf4tlHBhgS9Xs/evXtxOBycP38ehUIRUSNLha1y9hZWIld+Kp9JsUzaqBIFCN2Ti1wYdWJQK6gv1KdVgc81Z6O5WMf7jlSwsBTgtjprVmtkWtkIh8MRbmA0cVur1RIOhzc1cbvMrGFozoNKIcOmz36o3EYhlS3fV1XAvqoCnj82xgf+9Sxzbj/NJQaeemzvqmMtFgt79+7l7NmzaU0iz0aiPV1bn8wvSAm80tLSCPfEarVmpAb1avQ7OWmj2r17Nz//+c/p7u7mmWee4fTp00xPT2MymdixYwePPfYY9913Xy5OlRFy0UblWPLz8tU5QmIYm1rB7nRmSmg0eBobsYyNIRQWgtuNWFsLccrymWCt3APJiIdCoVWqGqIoRojb0VPVE2GzZ4R/fX8Zj7TaY2RiNxuSVRbSgSiKDDlDjC0E0Chk/LBzPGGwIT3kq9Vqtm/fzuLiYqR03dTUhF6vX3VsusiVEV8Js9lMZWUlHo+Hzs5OCgoKqK+vT/rQkI3e+avN6L9asZn9VKYV+Hg24n8uTxMWRZb8IfZVppchz7W0OsCBqrX5uWTf30QTt+MRt0VR5PTp02m1sq0H4l3r3/u1Gm6rs1Bm1qQUndlsyMRPOXwis4t+1EoZl8ddhMJiwpYxjUbD3r17k5LIs91vLpNi0ZzDixcvsrCwgNlsjuEcJsKrkVuYM4J4OBymubmZJ598MuEx601sykVl45fds8tlaVHAVKSkxJReaW9p9248TU0oPR4oKCDc1AQb8FATCAQixnlqagqfz8f4+HjEONvtdgwGQ0ZReS4/w3wGLRsRaOTacadClVlBiVHGmMPLo3vLmF/yY1ArUMqTqzpJxLfZ2VkuXLiA2WyOPMhnE2xkcmym94/JZKKlpYXx8XHa29spKSmhpqYmbu99Nm1UW8HGqweb1U9lqka1Mik27vByetiB0xPEolOyPcnA2ZVrbbY5TtI6qYjbFRUVGAyGm/r7q1HKub0+uwrQeiHZZ5ru96RIJ+O+HYW80DPDB26r5lfd0xQb1exZIUksffeSkchXHpsushUySQWlUondbken0zEzMxPDOUy2l5v5vs0GOQs20rlw6x19aTSaSOYjHcSrbHy3fQSXN4RcJrC7LP1+SplCQaCmhnAKfeZcIdo4Swba4/GgUCgixtlmsyGXy9fcypZPVats19lMlZZ8SN8mOk6nlPEvv9mKyxvkP86O8davtlFsUvPN97TGtFEkWtNms2G1WiMP8mVlZRQWFmb8HvIlqysZZUEQKCsro7S0lOHhYdra2qioqKCysjLG9uRavepmyBhtIX1sRj+VjRpVMBiM+d0vuqaZdPpAENlmMqQ9ZybVzI5MkK0dXkncnpubw+Px4HQ610Tc3vru5ga5qMDLBIFP3NvEJ+5t4m+O9vDNVwaRyeAL79jN3qhZXSv9QzwSeXNzMwaDIavEUrYE8XSOl2Z0uN3uGLlco3F14P9q5BbmLNjYjFjrsKThOTdd0x4AxJDIoZr0y8NreQAOiyJz7gBm7eoMNSzfeB6PJ6acLBGqJONcUlKyirg9MTGR0fXYwsYhXd6HIAgo5ctD7P77/AQqhYyZRR9XJlwcqY3NrCTrQS0rK6OkpITBwUHOnTuHUqnMS4Y3GyJd9B5kMhnV1dWUl5dHpqbX1tZSWloayfhu9c5u4WZCLtp9v3NqlKVAGLkAe8rjzwKIh/WubIhi/Inb0cRtvV6Pw+GgqakpJ/vKBbYCl9VI1z+sPG543kNYFCEsMOH0AomDDQnRJPJLly5hMBiora3Ny36lY7NNWun1elpbW1lYWKCrqwu1Wk1jYyNarTbu8bne+2bFLR1srHVY0vv/5Vzk32FgR9n6GPHvnBqlfdBBtVXD795VTSgUYnx8PGKgg8EgWq0Wg8GA2WymoqIClUqV9c3oD4aRyYS0244yfW++YBiZwKrAaaMqEmMOL199aRCrTsmH76xeN1lcaQKty+XC6XTi9XpTlluTYeW1e3hvGd88PohNr+TEtTkE4PD1gCOd6yyXy6mrq6OgoIBLly5x6tSphCTybJErKVuFQkFDQwOVlZVcu3YtUrrOxklsBRtb2Eis1U+93DvNmHM5iRQSWdWakslamWDS6eOF3lmqbVoOVxesWisRcVun02EwGBISt+fn57Paz62GsCjSPriAgMDBavNN+7C50uZ//O4GPvdcL4GQyF8f7eEHZ8b44jt3oVcvP44me58WiyUyifzMmTOEQiFCoVBaCaZMfEMueIsFBQUcOHCAmZkZzp07F8M5fDUKmdzywcZahiXNuG+oSckBVQYZ02xUPkKhEE6Xi5e7JzAownRem+dF9Swa0U8gEKCwsJDa2tqMpECjEa/X9+KYky8+P4BeLeeP72mgNA1OSiYO6tywg8/98hpapZz/8+Ymysya1C/KM/7p+DBnhx2ERdhuN3Df9vQH3qWCZKSip6c7nU7cbjeiKEZa2qRM/ODgIIODgzQ3N6eltrQS0Qbx/bdV8fZ9ZfzGt07z/58e5b/OjfPvHzxAeYE2I+OpUCiwWCzU1NQkJJFni1yXvtVqdaTM3tfXx8LCApWVlWmvfysY8S3c3Firn/qTH3fF/L01TXJ4vLUywT+3jTC64OWVa3OUGpQEg0FGR0cjLVEQn7idCvH8SyAU5lfdM4jAG5oL41b81wJ/MMx/X5xEFOEtu0pQb4JBsD+5MMnXXxkG4HfvquHe7UXrcl5JmdLpdEZI+DU1Navk0rPNttcX6Xnqsb08/HQbAstDas8MO7ijwZbWc4UgLE8iLygooL29PW0SeT4rG4mOFwSBoqIiCgsLI63KpaWlqFSqrWDjVkKmbVQrH8bvqC/gxasLAPzxPZmV7FLplwcCgVUTt6WJlXfU6Dk+4uOuHSXcfVsVHadPU1lZuebMRrzXP98zCwLMuv1cGnelFWxkgl/0zCIC80sBOkecmyLYKDWrQRBQCFBouJFVyzbDJ6l7uVwuHA4HDoeDM2fORAKL8vJy9Hp9TPYlHA4TDAZpbW2NTPq2Wq3U19enfd54xtOgURAILf8+EBI51T/Pg3s0GRtauEEin5ubW0Uizxb5UovS6XTs3r2bixcvMj4+jsPhoLGxMWWAdCsY8S3c3Mi0jWrlw7hWJWfeu8zh0CtBo0zfrWdT2ZC4gb4lF3MOD0I4RE/XFQyyADKZLK69ywTx7MOvumf4TvsoogjBkMibd61Ndn0l/ufKNP/ePsrylBB4e6s9p+tng0mXPzLAdXl2Su4Rr/oUDofR6XSYTCaKi4uRyWSMj48zPDxMS0tLDAchmzYqCa+ts/GjznEW/UH+5mgPsvua2FWcehBlNHQ6Hbt27YrMyGlsbEyoPpaJ78nGTyW731dyDvv6+rBYLKs4h8nWT3bczVD1uqWDjUzL0yuJd19+dDd9k4tY9EpshswewiUjvpK47XK5IoPxpIfR6upqdDpd5GZqaYHHoqThpLVyEWysdCy31VnoHHVh1ihpKUmvTSwTB3VXg5UzQw5MWgU7y2KJUhvVRvW+wxU0FRswaxXsWSFlnOoaRwcWUsVCEISYioXf76e1tTXt/UiTvkdGRmhrayMQCKT9ecc75vNv38lXXrhGW/88f//LPobnPfz6DkPWWR2r1crhw4djSORVVVVZPUzkU1YXbkwjFwSBixcvYjAYqK+vR6OJH+RuBRtb2Gisdfjs19+1hye+dx4Q+PZ7dmd07mQ2WBRFfD7fqqGuSqUSg8HAo3uL6HNAXYmZ5hIDFy5ciAx5XQvi7UkUl38A8uExlk2SgMjyYMJ8IFN/98jeUsYdXuQygbfuXntwtTKwcLvddHR0pKw++f1+du7cicvl4vLlyxgMhkjLajpIZPP/1xvqqbBo+Mfnr+L0BPi753r59rtaMhYBUKlUtLS0xCWRp7OPTPac7Ph0/IjEOQyFQszPz6/iHCbCreCnbvlgY63Stw1pPoDDDeK2pKjh9/sZGBiIIb/FI27HQ7QGda4lBaOxv6qAL7zdgEImZD0sMBkOVhfw9cd25W39bKCUy7izITVPIp6me3RgUVlZiV6vjzECPp8vK6MgCAKVlZWUlpZy7Ngx2traaGpqSsrnSHRPtJQaecO2YjqGFvAFw7zQM8M9tWqUGVY2Vu5PIpFL+ue1tbXY7ZllALPJGGVDpJNUtqTe3kQtiLdCxmgLNzeyCTaiv6PVhXp+9tHXZHVuKXBJh7gdb+J2Y5y11op4fuoNLYUEwyIicM+23LcT3Xt9TVG88e9obERSzKpX8akHsiPJR/MDo2dpSYFFaWkpDoeDgwcPplxLeu9ms5lDhw4xMTFBe3s7oVBoTddFJhO4rc7K114aYNbtZ8ET4LPPDfDebek9lq70DfFI5A0NDTGTyDOpbORS1XAlZDIZdrsdm80WGbKbqiqzFWxsYqxVjSoZorME0pdamlpqNBrRarXYbLa0ZWaTRdL5DDYADOrMboNM95Ns/Xy+r0wQDAbxer1MTU0xPj7O0tISgiBEHG28wCIfUCqVaDQadu3aRXd3d0RfPB6fI9k9c3dLEc9dnuKl3hlGFjz82f8M8bf3lKa1h2SGUxpkVF5eHtE/XynDmQz5lCuEWKMs9fYWFxczOjrKqVOnVlVlbgWVjy3c3MimjWotD/XRvmtqagq3283U1FRK4nY6+8qXPVfKZVm1TqW7H6Vcxpt35rY1K19Y+Z5S8QMTzdLKxu4JgoDdbqe4uJiXX36Z06dP09TURFFR4gAwmY2ttOr4+3fs4vHvnEWjFHiue443VNjYmcZeEq0bTSKPnkSeCXIlZJLseEG4MWR3aWmJ3t7eiFyuyRTbcXEr+KlbOthYq8qHhOjWGSnDLWUJDAYDRUVFq7Kmo6OjaZ93YHaJr788hEEt58N31mDRxWZfc9lutJnW2agvTzAYjGlrc7vdyOVyToz6+OmgyO5yM59+SyvqDHqfcw29Xh8ZutfZ2YnNZqOuri6mxJ3MABnUCj72+nrOjzpY9AXpnvZwecpLXV16509lOCX9c7fbzYkTJzhz5gzNzc0pORLrYcRXHi+TyaisrKSsrIzBwcEYQuGtkDHaws2NtbZRJUN0dXZxcXEVcdtisUQywKkgiiKXxxcJiSI7y4yr2o3ynRTbwvKzyOicmx93OSk1THC4KIgsqtpeVlaW8ZDebCCXy9FoNOzYsYNr164xODhIS0vLqtYlSG3zd5eb2G43cmpgHpkg8H9fmeOeI6n9RKoErZRokirxmbQnZ9NGtRa/ptPp2LNnD06nk56eHhQKRUyS8VbwU7d8sJGpykcgEGBubi5inKOJ24nIvvGQSfbpxd45vMEQC54AF8ec3NEQW0rLRtkq0Z5ygZspwo4m4judzsgUWqliEc2X+YuTxxFkcs6PL9E1tbSKz5EOcp2BsNlsHD58OMLnWKm6kexcjcV67t9RwnfbR1DI4HMvT/HmI9tTnjOTrKler0en01FbW8uFCxcwmUw0NDQkzIqGw+G0FGmij89V76wk7VtRUUF/fz8nT57EarVmtJ8tbCHXyEW7Lyz31kcnxVZO3I7nu+bm5pibm0vrvKeHHHz75AgAv77fzp0r/NRmTIrdTL5qJVYmORcXFxEEgW90+uhbEFErwxxoaeRwffzWm/WARqNh9+7dLCwscOnSJYxGY1z7n+xzkMsEPvvW7Tz0dBtOT5BRV4ijFye5f1fySnw6vjZ6EvnLL7/MyZMnV00ij4d8S9Mm8lMmk4kDBw4wOzvL+fPnMZlM1NfXbwUbmx3JytPR5LfozI90ExgMhlXE7UwQb8prIuwoM3B2xIFOJafGtrpdZrNljDZr5kkKLKKdrUKhiAw6rK2tTTqFdluhinOTAUxaOZUFG6+aJUEmk1FVVYXdbufq1au0tbXR3Nyc8nWCIPCeI1X89/kJ5pcC+ENBnukY5R37y5O+LtNWJ1guXR8+fDjSz2u326murl4VlOdKUjAR0glOpEmvXq+Xzs5OfD4fVqs1p/NEtrCFdJFNG1UgEGB6ejoucdtoNKY9cTsTW+7wBK4fK+DwrPZtm9G/bEY/FQ9SYBEtNxvNDywvL8dgMCCTyTD3nUHm9CCTC2hU2T3C5eK6RD/sFxQUcOjQoYiISEVFRURpKZ2goNyi5d7txfzw7BjhMPztz3u5Z0dJDHc12flTQaVSodFo2LNnT1ISeTZrZ3N8Kj8VzTns6OjAYDAkTYrdDEH1pgk2ampqMBqNyOVyFAoFp0+fjvm7KIp87GMf49lnn0Wn0/Htb3+bffv2JV1TaqMKhUJ4PJ4YJQa/349Go4lREfL5fExOTtLY2Jh03XSQicE8UFVArU2HUi7DpFn9kay1RzebPW12BAKBiGFeWFjA6XRy4cKFSMUiXWcbjQ+2mpkXdbRUFlOgy26WST6hVCppaWnB7XbT1dUV4QglQ6VFy4N77Pxb2xCBMHztpX7etteeUqc+U5lc6TV2uz0yiTyaRC6tl281qkwyQBqNhrKyMrxeL/39/fT399PY2JixtOMWtrAWyOXypIpQK4nbPp8vklhJRNxOF5n4hNvqrMy4A4TCIq9rWp0ZzmWwcSsjkfCIlBRLxQ/8rQNWXhhYYltVMa0VmVffc4noz0oSESkuLo5UjhPxDePh8Ttq+a9z4wQRmVn085MLE7x1T2IBkmy6CNIhkWezdj78VHQrWFdXFxMTE+h0Oqqqqm7KKsemCTYAnn/+eQoLC+P+7Wc/+xm9vb309vbS1tbGhz/8Ydra2uIe29nZSUdHBydPnuTKlSscOHCAz33uc9TX10e0jVfeXLBsBHLxUA/p9dUGQmEWfSEKtAps+sRkvM2WMcpl0CIIAmFRpHfKjUWnpNi4+nNZKR3s8XhQKpWRwMJgMDAxMcHOnenQyhJDLhNosmpjAg23L8iky0+1VZs0yyJhPYhcer2e/fv3RyZn9/b2UltbmzDz8YHbqvi3tiEEYMrl48WeGe7elniQYSatS/Her0wmW0Uil5Q28lGpWLmfTNfXarU0NjYyPz/PlStX0Gq1NDQ0rFnCcwu3HvKRFJPg9Xpxu90xFfd4E7cBLl++TF26BKwkyIT/oVPJeXR/WcK/b1b/spHrRAcWDocDl8vFuXPn1iQ8YlTLeWiHBbs99llpvUnEiT4jhUJBY2MjFRUVdHd34/f703p/lRYte8r0tA8vEgiLfOanXTywoxilIn7L+lrebyISuVSJz7caVSZ+SiaTYbPZUCgUhEIhTpw4sSqJdzNgUwUbyfCjH/2I9773vQiCwJEjR1hYWGB8fDyu9Ob3v/99iouLeeSRRzh//jzPPfdcWufItZFLtpY/GOaLz/czNO/hjgYr79yXHyN+ftTJL7pneG2dhW3WxBm0jcS/to3wn52TqBQy/vrNDZjlN4ILqT1AMs6Sjnv0l0zKDOUaTm+QD3ynE4cnyF2NVp68b+0Vr1zCbDZjt9tRqVS0tbUlNEAlJg1NRRouTXrxh0Q+/swFTn7iLnRJVMJyoUceTSLv6elhcHAwIqWZLnJdno53vCTsYLFYOHjwIDMzM5w7dw6LxUJzc3PCGR1beHUiV0mxjo4Ojh8/ztmzZxkZGeGOO+7gk5/8JAcPHkw6cTsQCOQsKZaObxmYXeLCmIs95SaqrIkD8LX4KVEUmXT5seiUm7YCP+7w8qUXBzCqFfzur9WsUllMJDwSLZXu9Xo5cOBATvcVFkU+87NeXuyd4+G9pXz0rpqUr0nXrkr3WTIidiJotVr27t3L2NgYV65c4cqVKzQ0NKySH4/Go3uLaB9eFjFw+UI88d1Ovvme+MH6WoOreCRyid+xXmpUmRyvUCioq6ujsrIyIpfb0NCQ0BZtNmyaYEMQBO655x4EQeC3f/u3efzxx2P+Pjo6SmVlZeT/FRUVjI6Oxg02PvvZzwLLX/4//dM/TXsPudIJh9SGd8btZ2jeS5FBRdvAQl6CjUAozD++MIBMgItjLv7yvqpNlTHy+XwEg0GO904Q8PtY8oQ5dq6bOxqskda2dNoD8uWchuY8ODxBlHKBE9fmc77+WiFlR6qrqyN8juHhYZqbmykoKIg59sEWM5cmvQD4QiKf+OElvvSuPXHXXWtlYyX0ej2tra3Mz8/T2dmJ1+vFbDbHrS7G20u+iXorWwGKioooLCxkbGyMQCCwFWxsIW1kkhTr6OhAo9HwkY98hDNnznDs2LG0zpErwRBIbTt9wTBffWkQXzDEy1fn+Mu3NCdswVyLHf6XthGe75ml2KjmT95YsymDjX9vH+XCqIuwKLKtRMdd1dpVgUU84REJoVAoL0mxSaePY31z6FRy/uPcOB96bRVqRe7abARBiLvvdD8jk8mEzWbDaDRy6tQpKisrqaysjLvmgQo9Khn4rz+Gvdw3RyAYilvdyFUlRyKRS5X4kydPUlBQkJDPEQ/5Dk6i15eSeB6Ph97eXgYGBti5c+cqn7/ZsGmCjVdeeYWysjKmpqZ44xvfSEtLC3feeWfk74kGjSWDQqHIKHhYz8pGkUHF9lIDVyZcPLAzcUvLWvYlEwRMGgXTLh8mrRJlDp1UJuuI4vIUdYljIfUdq1QqQqEQj7ba+aeOOaqtOt75ukZ0qs0x/K+pWM9Ou5HzY04+dFtl6hewvoTEeAZocXGR7u5uFApFTFb+zho9nz92w4g/1zWd1rqpkInRtFgslJaWIpfL45au17o+5K53VhAEysvLk2bhtvDqQy6TYtGvzeSezWVSLJ21JIsmRk3xjoe1+M+TAwuYtQqmXD4mF/2bKtgIBAIEg0G04SX8fh+IIotTI0zrCiKBhV6v37CWlkKDiooCDaMOH3vKTajk67ePdGVkZTIZFRUVlJaWcu3ataSqUB85bOULJ24opH37xBAfuqM27rq5vOYSJ3JpaYmzZ88yNzcXkYZOhXwnxeIdr9Vq2b17Ny6X66ZQVNw0OywrW87sFxcX89BDD3Hq1KmYYKOiooLh4eHI/0dGRiKvyRXWy4h7AiH6Z5Z41wE7enVlWmTdbIyvXCbwyXvquTS+SHOJHq08/eFr8eANhHipb44CrTIpoVHqOY4OLKIn0ZaVlaFWqxEEgfb2dm7fVcPrdtWsaW/5gEoh4x/evj3GqA3Pe/AHw9QXJZ4nka4BzIWhXLmGwWBg//79TE9Pc+bMGYqLi6mtrUUmk/H+/YV8vX0GWH6A6Jlw0VS6uqUpEyOeDZHOZrNRX1+/qnSdKHuW7zaqm5Fst4WNQT6SYpkil+ul8i1qhYzfubOazhEneyvNqJJkzNcSbLxtTynfOz3GrnIjlQUausY2JthIpGgYDAZ5265CdlYVYTXp2Fm2scTsaCjlMp5+bDcj8x6qrMstxou+IEv+UFwOJKy/Spd0z0rzI5aWluju7o7M54gmkd/TaI4JNn5wZnRdgg0JOp2OsrIygsFgUhL5WvaSy0qI0Wi8KZJimyLYcLvdhMNhjEYjbrebn//853zqU5+KOebBBx/ky1/+Mo8++ihtbW2RfvVcYr3aqJ4+NsTVGTcFWiV/cl8DyqjErjA2huw66TC8bx9iRcWajHiRUc2vXTc40jDCbPHPbSMcvTyNQibwUJXIIVHE6/XGGGe/3x8TWJSXl6NSqW4KIlMiAyD9rm1gnj/5cTeiCB9/Q+2GT5xNWjkrKsJms0Ue6A0GAw/tskWCDYDvnR7lU29uibtuPoMNQRBi9M9TZbo2yohneu4t3PrYDEmxXCKZbxmc8/CTC5PUFep4y+6SVUP8MlkrFe7dVsQ9LYUIgpC2ZHwi9M8u8dmjfSjlAm8pS+zPoxUNJeERSSp9paJhe3s7ZXY7Zbl95MgZ1ApZJAE2NOfht797AV8wzEfurObtrfE3vV62Ld49odPpaG1tZW5ujs7OTqxWK/X19ZEqiMCNiprbF/9+yCchXhTFyMyQRCTylcfnu7JxMwQUybApgo3JyUkeeughYJln8dhjj3Hffffxta99DYAnnniCBx54gGeffZaGhgZ0Oh3f+ta3cr6P9WijEkWRoXkPJo0ChzeI2xdCez3aEEZHUX7963D9ZpafPk3g8cc3hcqHKIrMLXoJh0J4AyEcSwHa29vRaDQYjUbMZjMVFRVp9eHfrLg45sIfDCMI0DHk2BTBRjJjG/1Af+bMGZzOgZi/988sZrVutsfCaiOrUqkipet09M/TwVZlYwv5QL6TYuutJgTJ+R//3j7KrDtA96Sb5hIDdYXJJUzXmqyT3vta/d3Pi09JlAAAIABJREFUr0yz4AkSFkXOT4e4n/iKhtGBRTzhkZsV50aceAIhZMLytUgUbKwXkt3XVquVI0eORIbWms1mdDodu8pNnB91AiSsImXqpzKBVCFPRiKPPne+K+q3gp/aFMFGXV0dnZ2dq37/xBNPRP4tCAJf+cpX8rqP9WijmnUHuL3OwuXJRe5ssGLT34hWZadPg1yOWLo8OVOYmkLW1oawe/e6BhuiKOLxeGKMcyAQ4HabiqBXhd2iZ5tylkOHDq15T5upNzcV7t9RzK96ZvEGwrzrQPzBeOv5wJDuuVQqFUVFRchkMlTCVfzXL3mRMT7xOZMsTa7Kx4n0z/ONW8GIb2F9kM+kmEqlilSF1xOCkHiGU4lRzajDi0YRf/5TvLU2OikGsLNEy3OXg4ihEHZ1gFOnTqVUNEyFm2l+yJHaAmx6FfNLAR49sPFVtVQ+QhAEKisrKS0tpbOzk5mZGZ58QxN/cnQQhSDwp2/altW62R4b7/h4JPLoSvxGcQtvJmyKYGOzIN+VDYcnwOd+eQ23L8TeCiP3bo9DDI++oQQBrt/0+TLiKwMLp9O5TIbTajEajRFtd5VqeQ7IHQeXX9fe3r7m/aSDwTkPn/lZL2qFjD9/oDFhD+p6oMys4d/e3wosczfe8f91EBbh7x7aljLrt9EQRRGdTsdjhyv591PDCMBr7csKKfGmfGdCEM9lm1O0/nlHR0dkKGcyEvlacCsY8S2sD/KZFJMG0G5EsBHPt5y4NseCN8DrGm28ps5CoSHxHKhUa+VqT/Gwkh/o9XrRKZX80REzJqMB59QIBw8ezPuDviiKnBxYwBcIc0eDNeFMplxcn1RrFBvVPPNb+wiFRRyeIO/59jnmlvz81YPN7K0wR9ZYzypOOudSKpWUlpYuP484Jvir12qvDwWML7ecLyGTZGtHk8ijK/Fb3MLU2Ao2opBv4p3DE2TJF0SvUjA87131mvC+fchPn0aYvq4U5PUSPngQIY1hg55AiP84O0EwHObtrfZVGuASgsEgExMTEeOcLLBIhvWqSDxzZoyheQ+iKPI/l6d5z+GKVcfkS/o2Gf7z3ARjC14Q4PsdY3zy3vxn4Vcim8zO/763ibuaCinQKtD55zl58iR1dXWUlpZG1sp03XxOTn3xxRdTksjXgo1oXdnCFlZCrVYTCATW/bzxKvAOT4Dvd4yjVcoZXfDy1j2laa2Vrh32BkL4Q2LSakm8deIFFkqlEpPJlFAqvX1mLK29rxXP98zyd7+4hiiKjCyU8xuHVvupXCKVzZIJAjK5wK96ZuifW0IA/un4CP/4TnNe9xUPmSpXqtVqGhsbmZ2dpbOzE5vNRl1d3SrFpfVs912JlZV4t9tNIBBIOymWq+BHws3gw14VwcZG9cJGG/FQWOSX3dNMuHwUG+A3X1Oz6jViZSWBD30I2alTCKJI6NAhxOpqZP39Kb+wz/fM8rNLUyAsT3p9R6udpaWlVa1QgUAAn8+HzWajpqZm05OOtpUa+FXPLAICTSWJFaDWG3srTPxn5wQisKfCxJx7eSDVRknfpgNBEJDLBG6rl0jYZsrKyujt7WVoaIiWlhbMZvO6lqeTQSaToVarOXjwYNzSdS5wK2SMtnDzQ6VS4fP51v288QIEXyCELxTGGwxRZdWRrpJqspYsCZNOH395tBe3P8QHb6vktXXWuMeFw2Gmp6dXSaVLrVDpzmBaL0wv+glef+/Ti/4N3s0N7Cg1olbICIVFWiuNXBpzUVekQ7mOly1bf2Kz2Th8+HCEz7Ey4bQZ/JRUiX/ppZc4c+YMJSUlCUnkK7FV2bjFoFQqCQQCaWXrIXcZ+5VGfMrl49Sgg+2lRhyeAE3F8R+cxaoqQlVVSddaiXA4jCzkJxAMEAqGmB4doj04gk6nw2g0RgILURS5fPky1dXVOXmP64E37SyhoUiPUi7bVK1Kdzba+Kf37MEXCPOXR/v47NE+7ttRxO/fvnqC92ZAovtHpVKxY8cOXC4XXV1daDQaVCpVjBRhqnXzqRYF8UvXTU1NGU0iT4QtNaotbAZIbVTpIpd+KhqhsMjTrwyDCAqZjCdur8pIyjvVvnqm3Dg8QbRKGa9cnee2WktcqXTpdyul0rN5f7lINqZa5007ixmY9bAUCPGePFc1MsGOMiPfed9e5j1BPvNsD98+OUqFRcM337VjQ9WokmElV6Kqqgq73U5fX19kaK3FYtkUwYa0X6VSyZEjR9KSc88GW8HGTQC1Wo3f70872MgVVhrejqEFBmc99M8s8c59md2E0WuFw+FVFYtwOEy5Vst7dptRabTctc2ORrW6YhEIBG4qQraE5pLslYnyiVqbju7JRYbnPSjlAkcvTfP7t8dX/pDmj0hSiw6Hg3A4THNzc9aZ+lwaW6PRyIEDB5ienubSpUt4vV6KiopSZmiyMeLZGk2pdL2wsMDly5fR6/U0Njauqc/9VjDiW7j5oVKpMmqjytVD9Eq4fEF6p9zo1XIUcgGNMn2uVKpgQxRF6iwKTEqR+aUlauR+2tvb485gam9vp66uLhdvaV18nkGt4BP31Of9PNnAbtagVvgZnveikAsMzHpYTCAnmy+s1U8plcpVQ2uNRmPabUu5bqOKh1Qk8rXgVvBTt3ywIZWn1yKlmQ2i26jCosiPL0xh1StRKWTc3mBJa41wOIzb7cblcjE3N8fIyAjhcBidTofJZKKoqCiml3FXivU2gtuQCtk6S1EUmXT5sOjWN4hciRqbjmqrlqszS9QV6vjUz65xbyXUX8/MScGF1+tFo9FgMpkwm82RoUFDQ0ORFiatNj4RLhFyndkRBIHi4mIWFhbw+XycPHmS+vp6SkpKEr4238S4eCgoKODQoUNMTU3R0dFBcXFx2qXrePtJZMS3qhpbWC9kWtnIV7Dx7MUpguEwQ3N+PnhbFTpVdsGGKCaewfSxQyYMRhPWAlPeCfG5vD43M7/LolPyll0lPHtpiiK9kl//1nnuqxJobV19bDgcZnFxMZIU8/l8NDU1YbGk99yyErn0U9FDayXVwoqKipS2P9cciWRIRCJfyzPoVrBxE0CqbKw3og1v54iTCaeX+aUg9YU6qq2rW1SkwMLpdEa+6KIootfrEUURk8lEdXX1msbSp5N58gXDGWWzVmJwzsOPz0+y027gdc2FWa+TCl96cYAfn5+i0KDkiw815u08qaBWyPj6o9v5+cVR/vb5UbqCYbrHoFh9GZPJhMlkitsGEAqFUCgUtLa2MjMzw9mzZykpKcm4xS1fZeTS0lKamppi+Bwm02q982zUqDKR1U0EQRAoKSmhqKiI4eFh2traqFrRfpgObgUjvoWbH5kGG1IyK5f3ri8Y5hddM4w7fNjNapoTtPquhBRYOJ1OnE4nc3NzBAKBTTGDKZnPc3mDaJQylPLU1zDbB8+B2SW++tIg1VYtj9+euX3KFQRB4BP31PPW3SU8/t0LhEWRH/YG+YPrnRLRww2l5w7Jd4miyNWrV1Gr1TQ1NWX8OeZjHkZRURG1tbXMzy+LnNTW1mK3J25hXs8KvIToSny6k8gT4VbwU1vBRp4QbeR+fGGSaVcAuQz2VZpQyoj5ci8uLsZ8wUtLS2loaIhE6yMjI8hkslWBxrWZJUYWvLRWmDCuUQc9FBb52+f66Bhyct/2Ij702uwM42d+1su4w8svuqapLdRRY8sPz+KX3bOoFQJz7gD9sx7WizoeDAZjKhZLS0soFArUoWWyoiATWApBv2DnobrEFYFoFBYWYrVaGRwcpK2tjWAwmJZxzFTlI9PARK1Ws3PnTpxOJ93d3Wi12lVtS9moUeVS6Uomk1FdXU1ZWRlXr17F7XYzMzNDYWF6ge7NnK3cwq2DTNWo8lGlPj/iZMzhRSkXUCvkbLOvzsSulEqXhEc0Gg0ymQyNRkNjY2Ne25YnnF7ODDvZaTdSZc2sGizhpxcnefrlIax6FX//8Das+vzs9+9+cY3L4y7ODDvYWWZEt4GdBaIoYlGFUctFnN4Q/iA8+OVX+OPXFlBVZKakpIT6+vqY54xwOEwwGGT//v1MTU1x+vRpysvLM07s5CspVlhYyPbt22P4HAUFBWteNxcVeAlSJT56Enmm391bgVt4ywcb0rCkdJGrD01yBk6Pn19emcYbCKGSgy0wRUfHLAaDAaPRiN1ux2AwJC0DxnMsUy4fn/2fPryBEDvsxrSlVxPd5NOLPs4OO7HplRy9PM1vvqYSRQKtcGlP8aCUC4TFG8pH+cI799n55vFhGop0NJfoGe7PvREPhUIx80fcbjdyuTzSX1xbW4tOp4tcC4Ntjs/8rIcZl59/+NU1Ki0aDlavNnzxIJPJqK2tpbS0lOPHj3PmzBlaWlrQ6xOHUfkiyK081mQyceDAgYizsdvt1NTURKYP54t4l4nBVyqV1NfXs7CwwOjoaKR0nQ6J/GYw1Fu4tZGpGlUuB9BK+NH5CWbdy0mx+8oMCBDDD0w1g2lmZgan0xkTaIiiyH91TnK8f4437Szh1xrX1r8eCov82X/3MOv2Y1Ar+Nq7diVt9UoUlD17aRq1Qsac28+l8UXuaIivipVqnVQoMaromhCQCQIWnRLfQsZLZIXoNjYpMRYIBNBqtXz+3lL+8qUZrs14mfYKnHUZuX1/8uBBqiQXFhZy7do1Tp48STCYHu8j30kxlUrF9u3bWVxcpKurC5VKRVNTExqNZtWx+dhHOoiWcx8aGsLtdjM6Opo2iTwXlZaNxi0fbGRanobsb7RQKBRpgXK5XLjdbp7+WTvz3hAAOrmMh+7ah06dWRYlnqSg2x8iGAqjUsiYc6eXEUv2ngoNahqK9PRNu7mtzpI00IDEBuTPH2ji51em2VZqoNKSXdYpHbz7YDnvaLWjlAs5kYwMh8P4fD6mp6eZmJhgcXERQRAigUV1dTU6nS7pF/619VZKDCpmFv0s+kJ86YUB/vEdOyjQxZcXjncN1Wo1Op2Ouro6zp8/j9VqXZVtin59PiZ9xzs2um1pYGCAEydORCZ85zPYyLRqolAo2LNnDw6HgytXrqDT6WhoaIhxPFvYwmZDtm1UuYLb6+dX3TP4g2FUcigMzdDePpvRDKZ4D+Sz7gA/Oj+JQSPnn0+OcHu9NaVvSYawKLLkD6FSyPAFw/iD4ZS8knh29oEdRZHKxo44FZxc4Q/vrmdflRm7ScOuMiOn8zT2I1p8xOl04vP5kn52983I+drLg3gCYf6tfZRKi4a3pTFPRS6X09jYSHl5OcePH+fcuXM0Nzcn5RuuV1Isms+xUoZ2I9qo4kEikY+OjuJyudImkeey0rJR2Ao2VkAy4qkIR1JgIX3B3W43giBEKhbl5eW4XC7+6/SNc1t0qowDDYhvxGusWn59fxlXJhbTMhLSOomgkAl85i3NLCwFsOmzn71RalLz3jiD9xJhLa0AKkWsMXj56hw9k4s8uLs06cTbaOK9xJERRZFQKITNZqO8vByDwZCVsfmze6r42H90M74Y4sqEi28eH+IP7q4nHA4jimLkAUEyftIgIOlc0u8tFgtHjhyJ8BFWDt6Tjs0EuTD4MpmMuro6ysvL6e3tZWFhIWPiYD7lCqXraDabOXjwIFNTU5w5cyZCIl8L52kLW8gXMm33XUsblSiKMRWLpaUl/uFHp3D5w5G1H75jLwZtZr3l8fZk1Ciw6pXMuf3U2NKf2ZEISrmMP3pjHUcvT3NHvTVhIid6T/Hwpp0l3NlgS5uzkS10Kjlv3lkC3LDXnkCIcYePaqs2q+q/9OzR39+Py+XC4/FEFL1MJhPl5eWoVKqktvOx/SV09o/xykgQbyDEV18a5K5GG2bN8oO59APL19Dv96NQKCL2VafTodPpKC8vj/ANa2tr4/rM9Qo2pL0WFxdTWFgYkaGtq6tDq9VuWBvVSkj7zoREfisImdzynjdbScFoBIPBVRWL6Kx3ZWUler1+1c1wecaH03djrSpLdgS5eHsSBIH7dxRz/47irNaMB4VMSPqQvvL8mwkDjiBfeL4HfyjMyYEFvv7YbuCGY43myIRCoQg/JrqNraenh6KiorhE6HRRbFBxZ5WG/+j24AmE+f6ZMV7XaGFX2XI7j0wmW24xu55tCYVChEIhlEplJMCVrq0gCFRVVVFaWkpvby/Dw8Ns27YtpjVovYz4Skh8jmvXrjE8PMzFixfXLEO7EtlUNqKPj0cir66upry8PK1rsdnu8S3cusi03TfdyoYoipHESrT9i57BND8/zw/6vJHX6JRCxoEGxK/AqxUy/vyBRobnvdQX6XLyndpbYWZvRXqTsJMFZenwHNNZJ5M1fEGR9/1zJ9NuP4erzfzN27YlfU08jqD0+ZWWlmY93FAQBN5Up6ZjUsTpDTK96OOvj/byV29ZFluRyWSRH0EQCIVC+P3+GD8Fy0Rtm83G4OAgJ06coLGxkeLitT2T5CopVnN91oWUFEvWkpzJ2vGOzQTRSbF0SeRbBPGbANlICs7Pz0dIcEtLS8hkskjFIlFgEQ8dU7E34Qdek92wn2jHsrAU4Fc9s5QXqDlUXbBhD0SbTUI3IPk4UcS55KW3txeXy0UwGIw41pVSwbmAVLGQqhaiKHJnSYDTU2r6Zjx4AiJffmmYf3rPnrjVMslB+/3+hNU0afCew+Hg8uXLGI1GGhsb82YQM1lXo9FQWVmJTqfj9OnTlJWVUV1dnRPDmCtSXzSJXOo3bmhooKioaM173MIWcoFspW+jEW8Gk/RgKkml19bWolTGVgOuzQcIRf2/xJRdy6HE4VoJs1aJWZt9tfxWwrRHZNrtQymTcXIglsCRjCNoMpkoLCxEp9MxNDSEWq2mpKQk7fNG+ykpyWUUfLxvfxFfPTFBMCzyy55Z3j9bxa7y+IFctJ+K9qES39But9Pd3c3w8HAM33Aj/ZTkO0dGRujr6+PChQtpKWpl8nCfCz8VTSLv6OiIO4n8Zk9+vaqDDSlrEF2x8Hq9zMzMUFBQkFaffjK8uUbBS6NBwkChXsaBmuzIcdGO5RvHhzg/4kQuE7Ddr6KhaL10mOLvKaMvmSjSO+Wm0KDCtkb1D1EUI1Nm5+fnKRIWub9SwYhbzjv3WLBardTU1KxyrGvBSoMdDUEQUCgUmM1mdmzfzttcXfzt1PLfOkednOhf4PaG1Z+/lD2SVD8k6cF4xs5sNnPo0CHGxsZoa2tDr9en7XDyWcqWyWSUlpZG+BzRD/NrMZDZaKMnO16pVNLc3IzH46G3t5fBwUFCoVDC47ewhfVCNm1U0dX2xcXFSMU208RKYMXz3R++oSbD3d/Yk2QXe6bcfOmFAaw6Jf/r9bUp253yhVyqduViHbteoLXCzOkhB+/YY2NkZCTy+WXCEUxmVyPzva77q2hI/kapVLJ7927UPT2Y1QKzHhF/SOSvj/bx7x/cv2rNaD8VCoVYWFggHA7H2GiNRsOePXuYm5vj/Pnz2Gw26urq1rWNKhG0Wi0lJSVYrVY6OjooLS2luro6YYIv39zCeMevJJFHTyK/FbApgo3h4WHe+973MjExgUwm4/HHH+djH/tYzDEvvPACb33rW6mtrQXg4Ycf5lOf+lTKtaU2qqWlpZghQ0tLSzHKQtXV1ej1ei5evEhNTU3GA9biwapXcuaTR/AEwhjUuZmPEQ6LcP07EN5cxYWUeOqlQf774hQahYyvPrqTMnP6GTS/3x/RcY8ekid9fktLS/zRnXsjx88s+nnyp31olHL+6O66jK9/OgZbKjGvNBwWi4X33HuE73a/wqAjgD8k8g+/vBoTbEjBksPhiLwvv98f6YONV7KG5XuhvLyckpISOjo66OvrQ6fTxZX7i8Z6GHy5XE59fX2EzzE4OEhLS0tailBr3Qek32er1WrZvXs3DoeDU6dOceHCBRobG7dI5FvYMKjVahYW4ksVRXPMpAfTpaUlPB4PNpuN4uLihCIS6WB7kYZ7m1Ucu7rA/TuLOFib3XykaD/17MUpPIEg12YCdI46uWuNKlTZIptgY8Lp4+mXB7HpVTz+2ipUCtmakyZSK2/A7+OxahmPVsoxGkOERZGuJR0oC3jzrpKM+SPRHMBQKBSzT6ldN5GfMplMHNi/n990dfH5YxOIwLlRJ/0zbmoLbyQxJfK55Kt8Ph8ajSahn7JarRw+fDjSumqxWNK2rflOiklttYODg0mH1uZrH9F7SYR4k8jTVf7azNgUwYZCoeDzn/88+/btw+VysX//ft74xjeyffv2mOPuuOMOfvKTn6Rcz+12R+RDf/jDH+JyuWhtbeXJJ5/EaDRGypGJWi5yqfIhl8kwqNfWUhJtMH/rtVU8d2WaCouWxiId+P0gly//pInL4y7+9rmrFGiVfOqBxqyqDNlUNk4POZAL4A2E6J9ZShhsBAKBmD5Vj8eDUqnEZDJhNBpXDcnz+XxMTU3FrPH1l4c41jeHIAhUWzR84Lbk0n5SUBEIBGI4PqkMdiIIgsAf3NvM7z9zkbAIVybdfOely7ymXI3T6YyZKC5V0SS1ECljFF2yXnlehUJBYWEhcrmc3t5eNBpN0vLwemaXNBoNu3btirR9GQyGrLT3c5UxSgSTyYROp6OkpIQzZ85E2ky2SORbiId8JsWkykYwGFw1x0IUxUjFQpqFMDAwgNVqxWpNLtmaDgRB4G/etm3N9320n9pTYaRz1IlWJct61pIoilwaXwRgh92wbm0kTx8b5NjVOQQBGop03Lc9fQ5CKo6gNMhVejj/8fkJ/uHFfsKITLj8fPiO5ENd0/FT0k86EASB37ijma+enGLpei/yI19v5wePVkWSsiqVCrPZjMlkoqKiIiZwCAaD+P3+SLVEOq/Uumq32zl79iwzMzMUFRWlTDyth5+S2r4kPoc0nyOaq5nvykY6a0dPIj9+/DgdHR1xSeQ3S3vVpvCqdrsdu90OgNFoZNu2bYyOjq4KNtLF0NAQR48eZf/+/dx///3U1tby7ne/O63X5rLsmisIgoDLF+JrxwZRyWU8eqAMXTiA8hvfQNbejiCXE3jkEUJ33w1p3Hj/dX4SbyDMsNfD6SEH925Lv3fdEwix5F9uPcn0On3gtko+/4trNJXoaK280Rc6Pz8fCS6kIXlSn2pxcXHGShIAxUYVcpmAIAgUGleTraKVoSRYLBauXbtGbW0txcXFKRXJEiEYDOJ0OqlXLyIXblSgPvvCJP/2oIXGxkb0en1ScpsU9EqSvAqFIka1Cpavv1arjZmBIQ1cSlSmTQe5MvhS29fExATt7e2Ul5fnjTsC2Rl9uVweUS8ZGRmJTCIvLy+/6Ql5W8gtcp0UCwaDdHZ20tHRwTPPPEN/fz8//vGP+cIXvhAJLKKHu0YjHhk7W+TK50nr+IJhDtdYaCkxoFHKsWTZQvWr7hmeOjYEwO/cVcPrmjKvjmTz3ix6JQIgEwRMmht7X7mOKN6YnB49hyQZR3BiYiLm81zwBAldb8udX4oVskmkYGg2m+np6UEmk0XmNGRjqySOiNPp5ECpgpeGl9v4vEGRp14Z48kHd2EymZLaYIVCEeOnVibHVCoV5eXlLC4ucvnyZUwmEw0NDQnbm9czKRY9tLarqwudThcjcpKvykamfkpS/qqvr1/zJPKNxKYINqIxMDDA2bNnOXz48Kq/nThxgj179lBWVsbnPvc5duzYEXeNbdu28fnPfx6Aubm5jPqy8zEsaa0QBIEXB5Y4Pb2EKIqUmtU8ePF55CdPEq6uRgwEUH73u4h2O+GdO1Oud6DKzLkRJ1qVnMYMOB9TLh+//4PLuLxB3lAWorU1s/dxW42Z3Y824nQ6GejrjrQDTE9Pxx2Stxa8/zWVlBdoUMoF9lcYefpYPxatkrfuLkYmCBEDLWWEYDnotdls9PT0MDExQUtLS8p2unA4HDHYktORyWSYTCZMJhNv21XMM503qi5HB8N4PRfSIiiv7JNdqVoFy/eGpLoUPXCpubk5Rrs7X0Zcmm2RCIIgYLfbKS4uZmBgALfbzdTUVFp8jmw4G9kafZlMRlVVVQyJfNu2bZSWpicrvYVbH7lOii0sLPDlL3+Z/fv3c/fddzM3N8eTTz6Z1msTkbGzQa58niAITCwG+coPLuEPifzer9Wwuzw7ZT9BEBh1+Ahdf4+jC56MXt8+uED3pJsqIUxthtfpt2+vpqFIT4FWwWtqb7SnRre9ulwu/H5/pJU3nTkk8fD2VjujC16W/EF+6zXl/LJrCkS4o/6G+MtKP1VYWEhBQQFXr17l7NmztLS0JJRMlSC14kXvH4gk9f7Pgzt43VfORo5/tj/Aw5cvR6oTyezqSj8Vr7VKp9PR3NzM6Ogop06dinAR4q2bLz+VyJeYTCYOHjwYmfBdVlaW0Xcr19zCREhEIr9ZkmKbKthYXFzkkUce4Qtf+MIq+dF9+/YxODiIwWDg2Wef5W1vexu9vb0p19RoNDidzrT3kOvKRqYPQPEgCAImtQCIy5NItQpkly8TLiparmSoVIhKJcK1a5BGsHHPtiJ2lhnRZph1ujKxiNMbRKWQ0THpT3qdwuFwhMAozbIQhOU5JCaTKaLq1dHRQVNTU9p7SIaVylB3Ny3Pf/jM0as81z2LXBAw6dTcv6M44RdUpVKxc+dO5ubm6OzspKSkJKKuJMlISv2rUotD9HtaOZ/jLx60xwQbP7zs4uP3HaG3t5eRkRGam5vR6ZK3GUjrrWytWnn9owcudXV1RcrDWq02772wqSDxOcbHx5mYmGBoaCilk1yPyka89rSmpiYqKyvxer0JXrmFVztykRQrLCzkW9/6FgBHjx7lF7/4Rdrn36yVjavzAVw+BSq5wPFr82sKNu7fXsjA7BKCIPBABhLvQ3Me/vJoH/5gmGJ1iP0ZxoJqhYy7GwtwuVwMDAxEfFhfX19MO1G2meVoP6UURP7w9cutU98/M87XXhkB4PdfV8s79pUltGcKhYLm5macTidXrlyhoKCAurq6iKS61Mol/YRCoYifKisrw2hwt+p5AAAgAElEQVQ0rqqYVRaoGV5YFtMRWX7m6u/vZ2xsjObm5pQtUFLQEQqFCAQCBINBlEpl5N4SBIGKigpKSkro6+tjZGSElpYWzOYbXQ6ZzLfIpU+TyNmSyEkmSbF8cQuT7TOaRF5fX091dfL2u82ATRNsBAIBHnnkEd797nfz8MMPr/p7dPDxwAMP8JGPfISZmRkKC5OT2TZyMms2vIZ4GJz3cXXOz221RRyptbC3woRYXIzs0iVEvR5EEcHvhwz6dzMhZ0vYXW6i2Khi2uXnzuobt040AS66zzh6wKFer0/YlnR+1Mnxa/Pc3VKYtrpWtMEOhUJ4vV68Xm+kb1Qq5QqCQFAUEEUQBQiGSeuB1GKxsGvXLq5evcqxY8ciQ5J0Oh1ms5nS0lKamppStloJgkCRXsG0e5ng5Q6EI+Xb+fl5Lly4QGFhITUrZO5WIl5rVTAYjPuQoNPp2LdvH9PT05GBSxtlxOO9j927d0d0xY1GIw0NDXEzgvnmbCQ7XqvVpswWbuHViXwkxTJVo8plZSOXwUalQY7JocAfDHNnQ/Z8EkEQsOqUfOqBzBNRgdCyb5AJEAyvVg5cdXwCjqCU9S8tLaW7u5tt27YREhRolekTxlcKjUhiNRLnMJoPOLEYIhha3uuYw5eWLTOZTOzcuZP+/n6OHTsWCYAkuePCwkLq6urSUmV876Fy/urn1yL/H1rw09LSgsvloqurC5PJlNZacrk8EgxLfiratymVSrZt2xZZV6vV0tTUFPEBG+mnopNik5OTaYmc5NtPxbt/o0nkUpVqs2NTBBuiKPLBD36Qbdu28fGPfzzuMRMTExHVgFOnThEOh1OOeIfshvpttozRN09NMO0IMuJ18Kady9cg+I53oOrvRxgZQRBFQrt2ETp0KAe7TgyLTsk3HtuFw+Wm58ol+vr6cLvdMQS4ZH3G8bAYEHnyP6/gCYT5ycUpfvTbB1ZNVU2lDKXRaKipqYkoia2ctP2/72nApFFg1ip4rmuKbx4f5FMPNHOgOrZEvlJxQ6vVRgz2+Pg4arU6xiimiwM1Fn52aXrV7y0WCwcPHmR4eJj29nbq6+sTtlZJ/cHS/hwOB4FAgJKSEkKhUNzrHT1waWFhgZmZmbRk9PJZBZEglYTHx8cjfI6VXJNcq3ysZe9b2ALkLymWr6F+67nW8f4Fvn1xicpiC3/+QCPFxrX1lGfrO+uL9Pze62q5NOZil34x5m/SgF4psJBmWUjiI4k4goIg8He/HOC5njkOVJr5vw9tS+qnViZ3pGRRc3Mzly9fpqKigoqKiphjPvCaSobmlpALAtMuH2996hQfe30tr2++4RMCgUCMgqE0PdxkMtHU1MT09DSiKNLU1JSxoua9u0pjgg1pb0ajkQMHDjA2Nsbp06fj+thoSDwWh8OBw+HA7/fT2NgYUcuSbLS0rsTpq6ys3FRJsWiRk2RJsY30U0qlMqVt2SzYFMHGK6+8wr/+67+ya9cu9u5dli/97Gc/y9DQMjnsiSee4Ac/+AFPPfUUCoUCrVbL9773vbQ+4I3MGElGPFuiMVzP0gBLARGDXkCnWl5LLC7G9xd/gWxwEFGlQqythQS984FQmGfOjDO3FGCbPDOCbiICnCiKWK1WGhoa1qRiEg6LhMLXs1ChMKFwGAEhRspP+rIlk5wtKyujqKiI3t5exsfHaWlpibQn2fQq/vT+Jo5enuI7p0YJhsL81bNd/P19JRFSukqlwmQyYTabVyluwHK/9vT0NB0dHVRWVqY9hRrg7uaimGAjEAqhvH5PSKodJSUl9Pb2Mjo6SnNzM3K5PCb48Xq9keDHZrNRW1uLXC5PqVolKW9MTk4yNTUVuTbJpqmuF0lPEATKysooKSmhv7+fkydP0tjYGAm48qXyEX38zdLvuoWNRz6TYrkY6pctcrXW873zKGUw4w4wNO/JKtgQRZFASFzznu5uLuR1DRYuXrzIxMQEfr8ft9uNIAiRikUmc7T8IZHnumcxaBScHXEy6fJRalQllJyNrqxHr19cXIzNZuPatWucPn06JmteZFTzlUd3c2ZogY9+/wLBsMinf9pNvWYJh8MREU+R+IB2u33V9PCysrK4LcDpoFCv5vVNNl7onWV/pYm6qC4DQViWXC/+f+ydeXhjZ3n2f1ot2bJly5a87/sy+0wmk4VAkkISmvCxBAI0YQktKaQlFAq0ZS/0+spXAgRCUtKwE7KWBkIoISSQZHZ7xjPjTd73fZEtyVrPOd8fznsi2ZItzXiSmcnc1zUXMZaPjo7OeZ/3eZ77uW+Hg97eXpValZKSoiY+i4uLavJjtVrJzMykpKSElJQUlVql0WiiVKvETJ/dbqe/v5+ZmRmys7MTkktPZtN+uoWlWCInq4tir3WcOl8KZudEsnHFFVdsuLDceeed3HnnnUkf+1ygUZ0JfnRwhFnvCl3mjitLyLFEZNYWC3IcPnAkDvYv8PjxCQCG08Jcc8Xa1wjfh8h2shiAy8jIWDMA19raSmZm5hnLJaYbNXzp+gqe757nL5vsaBQZSXplwY5MMjaCwWCgoaEBl8vFqVOncDgcFBUVqXMW0sI8shResSmRwzzR6eGDl5WTbU3f8IHVaDQ4HA5sNlvMQLEeLqu0YTZoCIQUctIN6Fd9FkmS8Pl8ZGRkMDMzw4EDBzAajeTm5mK1WiksLIyS+o3EampVvKRDo9HQ1NSE2+2OMlyK9f2dzWHyWN+jTqejqqqKoqIinE4nw8PD1NbWviYzG5E4Xxbxi3h1cK4VxTZLe38z4tS8N4ikwJRXZovNQFVO8mazbn+YL/zGyfiSn2vzJbZsSfxvxQC0iF0ejwdFUQiHw1gsFnVG8HSLCwYtXFKSQfOIm2p7KlkmrZpkJCs5K2brBI3IarVSXl6Oz+dbmQWcWkCWwigKaBX4zsFZPvaGMrbk2xK6l2w2G5dccgmDg4McPXqUmpoasrKyEvqc37tlK8GwjFG/9nNIksTy8jJpaWn4/X6OHDmCXq/HbreTmZlJbm5uXOXI1dSq1XFKzMq53W5GR0eZm5ujpqZmXX+OsznEHYlIkZNYRbHXOk6dLzgnko2zidOhUW1mZ+NMjqUoCs3Di0iyQopeg9lweh0Ss1E86AqpL3/jwiRPJBeRJnmiur/eANzpXKdYkrMmkwmbZpF/enNF1OIjXrF6Y77R8b1eLx6PB4vFwsjICP39/WRnZ5OTk8Mbtlbyiyp49NgE/3NigkdOLhDUmvjn6xIfYoxcFAWPdSNDLavZwBN/cwknRpe4rCITj8cTpQoiqm1Wq1VVwBoZGWFychKbzbahIVIiaiBiQczKyuLSSy9VDZcqKirWtMTPlsb4RscVDrQul4u2tjZ0Ol1SPgLJtqcvlEX8Il4dnM2iWLI0qs2OU2daYPtl8zgjLj+pBrh5Z95pOYZ3TXkYXwxgNurYPxbgg3E+XywvC1mWVS+S/Px8LBYLOp2Orq6uhPwdIhE5Dyiusdls5oP1En93VSN5mWmkGFbW+9NZP4TQiNvtxmKxMD09zcjICJmZmTgcDrbXlPLL8gqeaJ3isZYx/jTgwS2N8sD7E5f+1Wq16trudDoZHx9P2OvIqF9fDMVqtVJVVUVqaioTExOMjIyQlZW1oUR9rDglYr64jjqdjrq6OrxeLy0tLRQUFMTtzpwNIZP1EK8odrbVqC6UOHXBJxuvdWfjTI61v3+BOU+QaU+ApmwtNbmnN7C6LT+VO/bamVxwky8vc+TIkSiTvLy8vDXtWABJVtbwUuNBVhQeeGmY7mkvH72ylKoc85oFG1A7FaIitGXLFkZHR2ltbaWuro7MzEzax9383aOnAPjee7bQkL82UGykuFFYWEhdXR2BQICuri7cbje5ublUpxsoy14ENIRlhUMD8xwaWODS8sQqPwKCbzo2NsbRo0epqKjA4XCs2bT7fL6VGZDFRQqCS/S1D0adowiKqyF4sd3d3Sq1KtGkI5bRUuTCrNFoKCkpIS8vTzU1qq+vVwPyq6l1HguZmZns3buX9vZ2hoeH0ev1FBcXb7jgJktZvFAW8Ys4/3E6nY1zpQMfDMt0TLoZW/Bj0UG66fR8NarsaWSlGZj3hrgyz6DGDmFyKJILQeXNyMiI6WWx0WcLhGVmPUHyrSkQEaPizVmIWYvJyUmG+7uwVFVhttsJyzK/ODyCyxfitr3FWM1rP3ckFVls3CPPPzc3V51ncDqdLCws4HA4KLemsKc0wOPHxpFlmVNjS3zrj33cdXVFUlX01NRUtm/fzvT0dFwKsGA1rJ4HFHOY64mhFBUVqdQqEac2EtaITDrC4XAUtUrECEE3Gxwc5NChQ9TU1KyZTTibMxvrYXVRzGg0JuyQDq9fuu/FZGMVziUu7O87ZhhfDJBh0lGdpUOfwMY/HA6r1Z5Ik7yy9HS2OLIZGvKwe/fuDW/enxwa4dFjE+wstvKlt9asee/IzybLMkcGF3iidRJZUfjGM73c954VvcH15izE78vKynA4HKoyxZODWtU48KlTUzTkp69ZsIPBYEKKG6mpqezYsYPJyUmam5spLy/nXTvymfEE+PHBEYbmlvnEo2387s692JJ0UhdSfg6HQ5Wyzc3NVRduMWRutVqTUgURMJlMbN26lbm5OVpbWxPm4MYyWoq12BqNRhobG6OG4Kqrq191GlUsaDQaNRkOBoNxA87pHv90Xn8RF3G28FoXxc4kTh0ZcjG5FCAtRUeuUaIx//SKYplmPd+4sZKpORdz40O0tbUhSRJms5n09HSys7MpKyuLWkP9IYkDg4sUZ5ko38CpXJZlvIEwH3uknSl3kKtrsvjU1eXA+nMWAoWFheTk5OB0OpmYmKAvbOP+F4eQFYUFb4gvvrWWYDAYNcAtGAOCilxaWhqzu6DX69m6dSszMzMcO3aM4uJirqws4F+uq+Yrv3USCEn8snmMvWVZXFaZnMqXRrPiw5SdnU1fXx9HjhyhoKCAUCikDpmLc8zMzIx7jvFgNBppaGhQ40hWVhbl5eUbUqwjJd1jxSmhDFVQUIDT6VSl3MUs5muVbAiIolhXVxcTExOkpaUlXBR7PcapCz7ZSJZGda4s4rOeAIcGF9DrVirw2+xrvypJktYoa2i1WtLT00lPT2dWl82zk3reWJPNzuoVfuHo6OiG760oCo8fnyTDpKd1dImRBZ+6kEe2mMVgMoDVpEenBVmCPKsJo9GY1AMikoKJiQnypH50yKCBIu0CBw8eVBU3rFYrxcXFSWmcC85ldna2OkD+4UtqeKR5HG9QwhMI8y9PdvGddzfF5KrGgkjqRGARw4d9fX1kZWVFDaifKbKzs8nKymJoaIgjR45QXV294dCpuPZutxuXy0UgEECSpJgBQAzBjY+Pc+TIkaSel7O54CuKovKbRet6aGgobvXs9dqevojzH6813fdMYt7v2qcZdwXQ6zTszUlcFlZQeZeWlpicX4KQn7TUlcTCYDBQVVUV5cEQC9/4Qx9HhhYx6DTcc3MjxVkrCkyRrtvBYFC9tkNzy0x7gqQatLzY5+KfrksuTqWkpKhJwbMvdCFJMooCc/NzHDx4UBUaEZ3reLN28WC327HZbPT19dHS0sKb6ut5yGGhc9KDJyDxD4+38fMP70pYIj7SJXxxcRGv1wvAwMCAqrBksVg2ZSNutVrZs2cPo6Ojcbv9qyGuvdfrxeVy4Xa7kSQpam02m81s375dLbo5HA7Ky8vP2oB4Ms+VRqPBZrOh1+sJBoMcPnyY6urqV7Uodr7MFl7wycZr2dk4k0X82a5Z5r0rC2R+jpnMFCVqxiLSJC89PX3NAFxIkrnr6VZ0Wg3Ol4bZXZKp8mg3+nwajYbLK7LY3z9PvtWEPVW3JhCKToQYPmsqNvHNdzYxsuDjqprshB8mSZKiOhbLy8s0OYx8IS2I0WBgZ1M1OlMahwZc2LMt2LOSk/OLhKjkz8/P09F2ii++yc4X/jhNKCxzeGiB/26d4JbdhWv+ThgURvJXtdpXXMIrKytV53NZlhkaGuLkyZPU1NQkNXOwHoSqVCS1KnKALpJWJhIgRVFU5ZXt27cTDodVF/JYA+SFhYXk5ubywgsvcOTIEZXWth7OJm9WlmW1iikCzsLCAm1tbSpvOLLKmWx7ejN4vBdxEZuB87WzsegL8VLfAgBpKTq22mMfZ7WXRaQC4DNDIZ7tDVOancHXb6rFbNDh9XoT6gKPLPhVJcPpJT+5abqoNSAnJ4e+vj5VVry+0EhDfjpt425u3VuU8PMvy3LUpt3j8XBpgYHlcAhPSOGjb6qkrMCBAmjPcPOn0+nUucDOzk7+cW8Gn3suyOSSH09Q4iu/dfLDW7dj0EWfu5iziOyswCsu4WVlZeoeQVEUxsbGaGtrU6/NZmxaNRoNxcXFa9QVhfphpIS7OE9Bf7ZarWzZsgW9Xk8oFFoTp7Kzs7n00ktVM7tk7tuzHad0Oh0VFRVr5jliqT6ebanccxWvi2TjfOPCSpLEjw+NEJQUtECpyY/HLTE6OqpWTNYzyQPQaTVkpRqY8QRJN+nUiv16N7noWkiSxCffVMJ7d+WSnWbEZHhFbUO0mIuKisjOzqazs5OZmRmqqqrYUpjBlnUcY1dv2kXCJIajKyoqSEtLU89xZmaGzs5Ovn1Sw6g7jEGn4fG/3hOtyHUaEGodAwMDNNqgZUohFFL45rO9XFllI8uoRC3YkYthUVER6enpcR/+yKQgcjDvdN1mV8NsNrNt2zYmJiZobm5WtdQFrcxqteJwOGJKEieiWqXX6zGZTDQ1NdHZ2YnJZKKmpibu+SezEJ5OMrD69VlZWezdu1ftwhQXF1NUVKQ+t5spQXi+VIwu4vzH+Ur3/Z8Tk7j9IRQ05Bt05KbKcam8YtO72svi0P5WrKkGRhf8DM/7qM21rHtOkXHqziuLefDQKLW5aTTlW9DpXqHtarUrHkxZWVl0dXUxPT1NbW0t37m5ibCsrNmsCyiKosYpkRxFFm5KSkqwWCxotVou2wculwun08mzndM8eMxFeXYqD/zVNiwpZ6jU+PJc4MjICDeWSNx/csXZ++ToIj89NML7djqiiktiFsRqtZKfn7+u6WwkBVgUr4Q4yWZAFPZmZ2dpbW3FaDSi0+nW+FfFohavF6cE7To/P58DBw7Q1tZGfX39hnMiycSGM4lTkUWxU6dOkZmZSWVl5ZqiWLKzhRdCLLrgk43X0iwpkUVcVKQjqz6DC37GFldmFhTg5stq0btGaGhoSPi9tRoNX7uxlpNjSzTkW1R/DnFOsZShxO+FlF+ZPWXdB9RsNrNjxw7V7CeS3iM+1+LiIpNzLpY9bvSaVxbsjTbtsNJSzsrKYuTF/S+7wuqZ9QTPONmAle+5srKSb1gzueE/T+KTwBuU+dRDh/mny7Oihg+TmbMQEIvO9PQ0x44di2nilChWd3+8Xi9GoxGHw0EgEMDj8VBbW7thFyWealUsjrLFYmH37t1MTU3R3NwcU18cklsIN0siMLIL09/fz+HDh6mpqXndcmEv4vyHXq9PSsr2XCiKATx0dIyQDBoUKtIlfMs+Tpw4oVJ5y8vL1a5vPLyl3s5/n5ikzGam1GZWzwliKxiK3+t0OrYWZ/Ld0pV1L96zbDQa2bp1qzokXV5eTm5uLhAt4hFZaRfD0Rtt2mGFu79nzx6+eM9+ZCnM8PwyzUMu3lhz5mZrQszjg1lZHBhv4cSsQkiG7zzXT41+loKcTGw2G2VlZUmbzcLKtWlqajptb45IiO6PuI4ejwedTofdbicUCuFyuaiqqkqIWrU6Tul0OnVfAivJucVioby8nFOnTmGz2dZVhTybdN9YcUQUxcbGxtSiWHFxscp+SGZPcaHEqQs+2Ui2s3E2aVSihSgSC1GNiMz0y8vL+eIPjgGS+nfbi7M4tTCc8PseG3bxwIERqu1p/N1Vpeh8y0ghnSonKyT3Ij0sIisHyUCjWTFms1gsdHV10d3drQbOtLQ0Opd0fP/wAiajjvvfu43ynOTmGPR6PV/6y3ru/VMf1ZYwIdckL7oz2VJoTVpeMRwOr6FsGY1G/mqblf86trhSNZpVsBRUUm4/vSHH1Vht4lRbWxvlMrwaka1wQdlar/sD4PP5cDqdKrVqoy6K+L7jGS0JaDQa8vLyVMMlsamPnBc5WwPiiRxbyBAvLy/T3d3N4uLihrSvMzmfi7iIswWNRpPUBudMlQ4jkWjiEullsbS0RM/kAuNLKwmSAly3pYjU5TF27dqV8HuPufzc0Ojgpq25pOg0K5Sol5Mut9utbqAjFQzFz8nC4XCQmpqK0+mkr6+PlJQUQqEQZrOZtPQMDs9oMZnyePuewrhdj3jQarW8uSmfJ45PoFUkTL5ZlgNWzEZ90gUmMWcRuWk3GAx88goHtz85haRAWIGnxkz8+97KpI4dD6LbL+YCN6IAb0TbLS0tXeNrEggE6OnpUQ0BN5ppXJ10CAqwuAdkWSYzMzNKyr28vJz8/Pw11/y1iFOie5SXl0d/f78qcvJ6nS18XSQbrxUXVpIkXC4X8/PzUSZ56enpa0zyBGRZZtj1yvlqAb0ucb8OWZZ58OAIS74QzuNO/L/4d3JcMygWC8FPfpLKykp6enrUCoZKWXIH+PGhEXIzUnjfnsJ1/S3iKW7k5OQgy7JKq3I4HPz48TbQaPAEJA4NLCSdbAC8pcHBWxochMISb/v+QWa9I2SmpvCbj+8lRR+74iQoW5F+FmLOIlInXKPR0BSWeKzzAC7fSpD75MPHeOQjezatpSyGnD0eD11dXaSnp6tVGMFfjZRFTEtLw2q1UlBQQHp6+oYtV9FFmZmZ4fjx4+Tn5yekihHPaCne+RcWFtLV1cXIyAh1dXWYTKZXvWIUC0LesaWlhb6+PhYXF9e0ruMdP5l29kVcxNlEMkUuwbvfDMQqsEV6QURSiUTFv6CggDuenov6mzfW53P82HhC7ynLMr9tm+anR8Yw6jR89a1VlGSZkZVXqDJOp5Pl5WUqKyuj1gFJVpAVZcPZCKG2JP4tLy9HuVtPTU1RWlpKQUEBDx0d478OD6IAvrDCBy4tSeziReAfrqnk7dvzyU4z8N1nOvjbp16ixpHKTz+0O26ciqRsieISoBaXysvLo4pL7xjS8tjLBr1PtU3zN/vyqMxP3H9jPURSgLu6uhgfH6empkZlh0TOWQQCgQ1pu6uRkpJCU1OTSjHKycmhrKxswzU4UrVKdDnEe4kkPVLKfXR0NErKHc5+Z2O9zxBZFHM6nXg8HkpLS5M6/sVk4zzAq2WWFAwGoxZmn8+HJEnqw7iRSZ6AtOqttxXG56/GMh8CqHOk8WLPHB/8w0/JsCpoSkrQeDyY/+M/0H3/++zZs0ettDc0NJCWlsZ3/zTA4cEFtBoNpbZUrqpeWcAiuwFCdWk2qOPojJY9ZZlc21i3xqOjtLQUp9PJ5OQkNzQ4ODaySLpJn7SXxWqEZJhZltFqdcx7Axxr6+aSLTVotdo1VRZZltUqS3FxscqxjYUUvY6PXlHGv/+hF4DeBYlv/+Yo79tbmtCmPVGYTCbKy8sZHR3lhRdewGAwqInFerKIiUIomSTjHLu6ehQIBFTO7OrPnZqays6dO1V5xtzc3LNKo0r29QaDgerqapaWljhy5AglJSXrUteSbWdfxEWcK9jMzoZGo8Hv9zM5OanGMEElSk9PJzc3NyZFZd4f/f76ON0AcZ4iXgkcHXKh14IvJDO0EKQq16quOUajkd27dzM8PMzRo0epq6vDarXSMeHm87/uwqDT8I13NFBqWylexVJd0ul0qohHLHfr0tJSent7OX78OIveDGQF0KzQaU/3Ola87Jz+dLeHFL2OgVkfTx84xV/ua4xZXBLXOdHi0j9cW6kmGwrw7h+e5JH3FFNeXr5phROj0UhZWRnj4+Ps378fnU6nnqOIp2cyg5iVlRWlWiUG1NfD6jjl9/uRJCnqfhJzIktLS1FS7gaD4awOiCf6eqG4eeLECYaGhvD5fBsaAsNFNarzBmdD+na1sobP58NgMKibW2GSNzi4YuC20YMUCYNOyzU1WfyxewFbqpaffHAngMpbXb1gi3MWbebHj0/inFnmHZUWrkgLYSgsXnlRejq43WjGxtDabFRVVbG4uEhbWxt5eXmkp+hRFFAUGY9rjra2CZVzKT6XUF161wPNzHsDHBqfYWdVEYXmtf4NW7ZsYWZmBm9vL//1jlIK8/MwG8/sdks16vjolaX88ugYuwrT+e7BGSxHJnhvnQGbNSOpKstqvHt3Ad/4Qy/iyv6sS+LmnSGam5upq6tbl/oUC7E6KyL4ORwOysrKGBkZIRgMkp+fv2kyuUKbPD8/X6VWrTegHqtiZbVaY6qBCNjtdtVwaX5+noWFhYTO/2ybGYlFX7Su+/r61NZ1LKngC6VidBEXBpJ5Nk63s7GayiuUoQwGg0r5LFvlZZEIrijPUI+/UZzSarXIClzb4GBo/zBFWSnsKbPFpHGWlpaSk5Oj+jc81a/gD0m4/TJPHu3j2mKdSjUVIh5CdelZ5yyff6aPanuAb7xj7ZyAXq+nrq6OhYUFFtu7uLYijfSMdG7bW5T0dV2Na+pyeKZjmqw0A0fHfBz55Yv8RamerIyVjkV2djbl5eVJX2er2UBFjpn+WR8APgn+PORjdvboaakfrkfbzcrKoqioiMnJSZaWlsjNzU3KiX09aLVaSkpKVNWq0dHRdQfURZdKnKff7yc1NVWlAUcmWhkZGVFS7iUlJWd1QDzZ15tMJgoLC/H5fBw+fJjS0tI1Rourj38hxKkLPtlItlOxumIUDofXeFmITWN6evoaZY0zeW+Bu9/ZCLyiEa7RaJAkicXFRTIyMtYoQwlMuwM8fnwCk0HLr/t9fDDVBMvLkJoK4TDIMrzMaRceBoWFhYyOjtKkWYYCAwVZaWzNM2O15sXtBmg1GnVTvt4zZrfbyczMpKenh672Gerr6zGZTDzbNc3ubs0AACAASURBVMPzzllu3lnA9uL1ddRFZ0Vshhu1y/y/N5j41jE/nfMyep2OBpeRa206CgoKTrszYDbouK4hh991zKr/X8eyhasb8tZQn1Yj3pChCH7xOitWqxWXy6W2lMvLyzdtURHUItGFKCwspKCgYI0amJCfjPQvSUS1SqvVUlFRwezsLNPT00xOTlJXVxdT6i/yOm3mzMZqRC7Ker2e2tpatXUtpAgjk6KNzud8qRhdxIWBZGlUiXQ2AoFAVGIRaTKXmZlJSUkJc3NzhMNhiouLkzrfRz+8nX/5TRdN+el8/i2ValFvbm6OrKwsdaB3dZxSFIV//30PLcOLVDvS+NpNdXFpu+KaFBYWMjY2hsXtJuDXoNfraHSYKCx0YLFYYlb1f3RgBK0GnNNeWkcXuawi9kY8KyuLN+y7hKL+fhYXXWjCAUjRIysKM+4VQRLdBoa6q0U8rs/2cNk+Ey9MwO/7fWg0OjIsJq7LgIKCgqQcp1fjB+/bxrX3HFJ/vvfwLPs/eamqfiioT7FwOrRdq9WK2+2mq6tLLTomW8yLB0Gtmp+f5+TJk+Tk5FBSUoLP51MTC1H0FHEqPz9fpfCuplaJ+yxSRKS3t5epqSmVPrcRznYHXsSp4uJidZ5DzEPGShYvSt9egJAkieXlZTweDx0dHapR2+qhp0RvrESTjUgpv8hjazSvuJpu27aNrq4ubDZb3E2pJxAmLCu4AxKVOemEP/EJDN/8JiwsIIXDuG64gfHlZZaOHo1S3Kirq0OWZWw9PeTlZW+omvSNtzfw27YpdpVYKbCuv2gaDAYaGhpUQx5TVh5f+e0oIUnhQP8Cf7xrn8q9jaVlHtkKFwN+Go2G8rEunHPTAIwELfR4U3C3tFBWVkZeXt5pbRS/8Y5GftfxZ/Xnbz/Xx01bL2fXrl2MjY2pLd/MzMyoxMLv95+2U7hQMhkeHk7YsC8RiARIkiQyMzMZHBykt7eXrKws7Hb7Gi5wJGKpgYj7MFb1sbGxEY/Hw8mTJ8nOzqaioiJuUnY2OxvxqF87duxgbm6OEydORKmWXCiSghfx+kOs2BI5oyASC6PRqMavgoKCmCZzycYpkeRU5Zh55EM7VN58ZJxyuVxUV1fHTAKCkszRIRc5aUa6p7wseEPY01NQFIVAIBA1Dxgp6V1ZWUlNjY7aU13YsjLZVl+57vqwrzKLX5+YItWoo3oDEzwxlya6/Xa7nW83e2keWqQh38L979uqJkRiUD6yax1PxOPkM71oNGOAhpGAicGwhUBrK4WFhaetTliQaUYDasHPH35FEl0obpWWlmK329VB88XFRXw+nzqzkixtV8jwjo+PJ2zYlwhEpy0YDJKZmcn4+DiDg4NkZmbicDiiZIZXQ9xzkcUxvV4fpVolulfLy8uMjo6ysLCwoYjKqxmnDAYDtbW1eL1etShWU1MTVRS7UOLU6yLZiPVFRSprCE1wQG3jrTbJOx3Eqj4lIjkbSxkqPT2dXbt2MTQ0pM5aRGpLB8ISX326G0mBFB18fK+NnkAKvr/5GzRjY+gcDkz19eRkZKibYVlR+NnhEfpPTfKhfSXqLEdLSwv19fVxq9TlOanc+cbypK5FdnY2mZmZNLc5kcJhZEWLQQvj4+N4Xk4wxJyF1Wpdd5EB+OfrqtlSmMEDLw3xnHOWl/o0PPj+LSwsTKhV9mQHvHVabdQiPrkUVLnAsixjNptpb29Ho9GQm5uLzWY7LZfY1RADkbm5uVHVqWR4saFQSA0qi4uL6gCf4CtXV1cTCARwOp0sLi4mFCgik45wOBxTtUoszDabjb179zI6Osrhw4epqKhYk/Sd7c7GescXhlDi/EpLS5Ek6YKoGF3EhQGx6U/knhcV3aGhIZUKpdfr1Y67oPImcqz14tTqeUCxwYunDKXX69mxY4danIllDNo54cag1dI/u8xVFeksTY8y2ru0pusSuRlWFIWgJJOi13H15SuqSUePHqW+vj4uxfXOq8p5a2MuORYjVnNixR/hgt3e3ceB3lksJj3tE246B8YwhpejutbC8ypeZwXg41eVodPA892zHByYp2XYxd3vbMDvX6C5uTkhj4hYaMxPo23Cq/4saLvBYBCLxUJ3dzdOp1OlvMaaWUkWoltgt9uTUpWKRCRTQSRAJpMpqlAnSRLd3d3Mzc2Rk5Oz4Rq9kWoVrNyXVVVVLC8vryvlDqeXPJxpnEpLS2Pnzp1qUSyyaHehzBaeU8nG//7v//KJT3wCSZL4yEc+wuc+97mo3wcCAW677TZaWlrIzs7mkUceoaysbMPjKopCS0sLJpMJvV6/RlkjPz9frcL4fD56e3s3jZsoSRLhcDipBTsehFqE4LA6HA4KCwtXZAjH5uifWsSgUZANWvxeN3ZbJkVXXhm3Zdsy5OKR5nEURWFhOcQ9796yZpajpKRkU7Jqv9/P0tIStlQDH9tm5MSkj8uK9YRDIbJy7BSXVZBmSpwCZTLouHlnAT8+OIyCQiCscM+fh/jSW+swy8ucOHGCgoICVds6ESiKQm66gUn3KzM+zc3NagJUWVmJxWJhfn6enp4e0tLSzjjRiESkqtR63hyx9MzFRsNqtVJYWBjzOxcbAVH9StT7I1INZDW1KnJzJHi4wuVc8HDFs3S2ZzYSMemLbF1PTEyQmpq6KZ2ki7iIM4WQDF+9sZAkKcpkzuv1otVqCYVCGI3GhLwsNoKIU6ufocg5i9V0qHgQkp82m42Ojg6sViulpaV4vV5cLhdf/u0oYUnCqMC1JXosFotKLYr1GQJhic/9qpPOSQ/vv6SQW/cWU1ZWht1uV2c5Kioq1lI9NRoqN+horIaYYTPrNexw6Dk+HaI2S4uZIFlJdq0BLCl6Pv0XVZwcczPm8uMPy/y/Z/v51xvrqM3NpaOjQ53fSHStUxSFT76xhNt/2QlAthGOHDmi0nZLS0tpbGzE7XbjdDoxm83k5uZuWpwSg9hCVcput1NWVrbm/CM7QGIeZKOhfVip9m/dujVp7494qlUiTmm12jVS7rH8qc62w/d6cSo7OzuqaFdWVoYkSZtmCvxaQrNB+3RztPUSgCRJ1NTU8Ic//IGioiL27NnDL3/5yygju+9///ucPHmS+++/n4cffphf/epXPPLIIzGP9+yzz/L000/T3NxMe3s7u3bt4o477mDPnj1YLJa4nMNAIEBXVxfbtm1L6vwjK0HiZlpYWGB4eJj6+nrMZnNSC3YsrFbcmJ+fR5ZlHA4HD3UGOTDsxR9W+MTV5dyye+MhN+eUh0890U4oLLOv0saX31ob9Xn6+/txuVzU1dUxsbwynOZIj33Te4NhPvlYO/2zy3zq6jIuKTCq5xnZvhUbYp1OR39/P61Dc9xzIoyiwLdvbmJ3aeI+CQDdUx6+/FsnHRMrnaldJZk8eOt2JElSzz9e9SiyZb+4uEgwGOTpYXjC6QcgL93Ic5+8POb7Rh4/ckO9WRDHn5+fp7y8HFmWo+ZBRAJktVpPqwMnjr+wsEBtbS1W6/qzMwKiegQr1IPm5mb27t0b8/0XFxdVnm9VVRWzs7P4fD4qKioSeq/jx48nVTk7fPgwu3fvTliVpbW1lUAggNFojPk+RqPxXGpfnzMncg7iVYtTcPaKYldccQU///nP8ftX1h9RSBAUHfFPUHSam5vZs2dPUuceq2Ph8Xjo6emhtrZWNVo9kzi1mg47Pz9POBwmJyeHoDGdf3x6jICkUJqdyv3v3bph16Fz0s2nn+ggzaglKCn8zx2XqL9TFIWhoSGmpqbW7XJEYmLRT8+0l+2FFqTAsrquCrNUEaPS09MJKlrmp8aZnJrilN/GYkjL7ZeVYEtLbjZwcG6Zz/+6k1NjSygK2DNS+N2dl6LXwPDwMFNTU9TW1sacKQgGgzFpuyP+FMZ9Wt6xsxi7NfYaKcsyw8PDTE5OUltbu6E6YbKIPH5ZWRkajSbmPIi4nsneU5HHT4ZiLKhVsiyj1+tpa2tbs8YvLy/T1dWFVqtVpdwBFhYWmJiYSNhEubOzk7y8vISvbWtrK9XV1evON8IKW0HMm5SUlMSNm+dLnDpnOhtHjhyhqqpKvaC33HILTz75ZNQX/uSTT/LlL38ZgHe9613ceeed6xqqvPWtb+ULX/gC11xzDU888URC55EIfzVei1n8vcimRebe0dFBSUlJTLOZ9d4jFi80UnFDVC+Onerg4HAYl1/CbkmhPi+xjW9troWv31THmMuvSt0KaLVatctx91OtPD+uYDYa+O57tkRVi0TV7Q/t47SPuVAUme/+sZtvXZ+3bvUCoLq6msd7Qnj8U2g1Gn7VOpF0slGTa+Ejl5fy2V91sBxc8fL4zvP9fOJNFVRXV+N2u+no6CAzc8VpVShECUM/sRCK4egduyV8v+lidN7HV2+si/u+kd4ZnZ2daufjTOUHI1U3PB4P4XCYjo4OUlNTKSsrO21H83jn7/V66erqwmw2U1VVtSGHV1Q5/X4/s7OzqgRhrCBitVqjVEEyMjKSogycbUdwrVZLQ0MDwWBwTev6Ii4iFiRJ4uMf/3hUUeymm26KilMPPvggWVlZ9Pb28vDDD/PZz342blFscHCQ5557jubmZrq7u3nLW97C7bffzo033qhSdM5k0y/+dz1lKJvNxpYtW+jo6CA3NzepbrZQM4qUR1cURaUZlZSU0NDQwPLyMh0dHTzWHsCg16HRyry1KTchelOJzUxeRgrji36ub3RE/U6j0ahdjuMn2zk4o6cwz87Nu6KN+QTNaHhqnr//9TCBsERxho4vv8lBxsvU4lgzbGbAWl7OiQUdDzzfh6xomFry8x/vbEro+giUZafy+etrue0nx/AGJKaWAnzop8f5+Qd3UlZWhsPhUNdhh8Ohxn6v17umay266dsTeN9Iim6kd8aZxhAhWBMp4dvV1aV22s5Uxn31+efl5eF0OhkdHaW2tnbDAXtxbwcCAaanp/F4PGuogqul3PPy8igrK3tNaFSxYDAYqK+vR5IkpqamcLvd1NTUbJr/16uNcyaqjo2NRalhFBUVcfjw4biv0ev1WK1Wlde3Gtdcc81pncdq/mqiC3a8SlBmZia7d+/G6XQyM7OiyLT6IYx04xT/Inmh6+lvW61WFtNKWfQ70SiQZtSytXD96o6iKPz08Agv9s7zvj2F3NCUG/e1VquVSSUdRVlkadlPc+8E5lCq2hYVrqEVNjNpKQaCksJfbMmjtjYxZ9PrtxTy2455wpJEpcHF2MwCmdZ00pKQyX1TTTbvv6SQHx0cQZIUHtw/zLWV6Vi1ATX4TU1NMT4+TlFRUZSh32qYDDr+4x2NCb+3xWJh9+7dUQPkiUodR8rjrqe6ATAxMUFfXx/l5eWb2hIXXNGpqSlaWlooLi5eI8MXSduKVLFKT0+nrq5OpQnGUq0SPF+Hw0Frayvz8/PYbLaEOimvhnqVRqNR5zmEC+1GUoQX8frFZhfFOjs7WVpa4v3vfz99fX1873vfIy8vL+nzEnEqltBIInFKrGN9fX0cO3aMxsbGNRs6McwbWWUPh8NRs2HxZMctFgs1Tds59tIhZpcl0lJ0bCnYuCgmKwrLAYnvvWcL7kAYuyX2BjYtLY3WgJ1fd48gdy4SDizzF1UZa+LpnGRC0mgxGnXMhVYq2olAULBDYYn5+Tl1HUsGdXkWvnB9DZ//dRdhWeH4yBI/eqmP6ypT1Ws5NzfH5OQk+fn5lJWVYbFYNmUdEhTdqakpmpubkxJSiSePG8sgd3p6mr6+PsLh8GkPwMeCyWRi27ZtqtBMLGrVerQtQVOLFDsRiJRyP3ToUNLxdTNpVLEgTAFlWeb48eM4HI5N9VV5tXDOJBuxugmrv5BEXhMLOp0OSZIS+nIi5dRWK0PFk/LbCHq9nsbGRqanp9UHXa/Xqwu2UNzIyMjAbrcnLS33yLExAtKKDG2RccWcab2FZMzl59GWcYw6Ld/6Yz9vqsmJea0FzejNJTr6JhVyTWBxjzA9veJ+XlNTE3VNHy4uZMYTpNqROE92e7GV3915KbKi8JJzkrc90IpBp+XBW3fQULBxS1yc541VZh5t1uCSFGRZ4WOPdvDTm8uiBvh8Ph+dnZ2EQiGqqqo2behKcJTtdjtOp5OJiQlqa2ujeJarA7WoBolO1UYD8QUFBWsG8zZqwyZz/nl5eeTk5NDX18eRI0ew2+1qlyWSthUrAEYaAq5WAxEwGAzk5eXh9/vp7u7GbDavK9Eojns2N/yRQUKj0ajdR3ENrrjiiosD5BcRhc0uil1//fVcf/31wEqlNVEDWlH8ihWn9Hq9GqOS7fRVV1ezsLBA68uKSSaTSV2zAoGAOsxrs9koKytLqnp9sH+eOZ+MVqPBrJHIkhdRlIy4z7iiKHzxN06OjbhoKsjg//6f+tgzHYGVotL8/DyyJKMAo2PjjJnda7rBsqJw04yGQwML/PUVibuEv7nezpwnyIwnwA11Nj7zq3YUjZZ/e8d2cjMTo3kGAgH25Gqotxs5ORUA4O4/jbDFVkxVQa46NyqEPAYGBtbEkTOBWOezs7Pp7e1lYmKCurq6NfRREffF/iQUCqmKYBsZDwqvFmEaXFtbm7RH1XrIzs4mKyuLoaEhDh06RF5entplCYfD68rNryfpLqTcCwoKOHHiBIFAgOLi4nPKPyo7O5vs7GxGRkY4dOgQ5eXl5OfnJ3yc1xrnTLJRVFTEyMiI+vPo6CgFBQUxX1NUVEQ4HGZxcTGh6oLRaFTVeSIRSxlKVKBGR0cpKyuLuXFKBoJvKRZsQN1slZWVUVJSckaLycSij7ZxD7ICOuCv3tjE/Pwk09PTMbsosDJ7kZaixxOQqMnQoSGavrO0tKSqRGRkZHB5TS437FnZGMqyTF9fH/39/WsUq2xpxigu67Q7wKMtY1TZ07iuMX73JN20chv+vtuFotHiC8s89KcTfPqGLWs4rJHyjpFqFhkZGXzpzSV85qlBQjLMLss8M6zw16WvVNDNZjM7duxgcnKS5uZmKisrcTii2/JngpSUFLZu3crs7CzHjh0jKysLo9EYdT3PxNBJyAi7XC7a2trIyclR79HTRSx1EIPBwPj4OGlpaTQ1NW3Ytk1EDQRQFb2qqqqYmpri6NGjFBUVxXVpT7ZTkSxiLfoGg4G6ujpCodDFROMi1uBsFsVEnFqNeAqGZrOZgYGBqG7C6d6zq/2MxMyeXq+nvLx8U1T3fnJohLC8cm0ur8kjEAhw7NgxGhoaYq4x3qBE85ALW6qBU2NLLCyHsKZoo9b/SDrsbXsLsNuWyUhN4V0785kYHaGvr4/6+np1rdVqNHzq2sS67pHQajS8/5KVOcjv/qmf1mkJWQnzr79q5ss3NqxJJIXvhlhXI8/zq9eVccsvuvGHFWQFvvbCPE/+bZX6tyKOCHpPSUkJBQUFm7YWCnqOy+Xi5MmTZGRkYDabVR8xcZ6rFcESRSTFuKurC4vFclpGu5EQ86rienq9XgwGAxMTExiNxoTmJmNJuq+OUyaTibKyMqanpzlx4oSqkLVejD1bNKpYx9dqtZSWlpKfn09vby8jIyPs27fvvFCrOmeSjT179tDT08PAwACFhYU8/PDDPPTQQ1Gvuemmm/jJT37Cvn37ePzxx7n66qsT+pJTUlLw+/2qYVkkIitA4gbYvXs3/f39nDhxgsbGxoQ5cquNfSLlCFfTYsbGxhgYGKC+vv6Mko3v/3kA6eXYptPCztIsDLpsZmZmaGlpWbOh9gbD3PfnAepCC3zk9w+QNz3M8o/TGLr9dnS7d6vqXCaTiSl3gFlPkPKcdNULQ1S/ElGs+ucnOzk1voRRpyUvw7Shgd+7duRzZNBFmlGH2Wrjs/99ipvrLVQX2HC73WtoRqvlHSuBR0+5ODjgQgHufWGQW/YUqckMrHzf+fn5ZGdnq12Iurq6M/oOFEWJSYdyuVyq8EFOztru0elCeHOMjIxw9OjRhAfnIvnVLpdrTTs8cr5GURQmJydpbW1NONitpwYi3l9o8ieiCnK2nVPXO/5m8I0v4sLD2SyKiTglSVLMecDVCobbtm1jZGSE1tbWNVLo60HM2UWq2cXzM5qamqK/v5+UlJQzMqKb9QZpn1gpimk1cE2dg+rqbFwuFydOnKCoqGgNdTFFB3tLLezvc7HFrsN5sgV9xPofiw77t4WvvKeY5VhPsQrg8MACn3uyk+xUA/e9dyv2OCIoArnpKeh1WhSgINfO40f62WofptCRjdfrjVpXhTjG6vP81LUyX//fHgB6Zrw80znDm+uj6bd2u12d/Tl27Bj19fVJycyuhqBrr6ZDud1u5ubmqKqqYsuWLZsWpywWC7t27WJiYoKjR48mTAEWHlGR3RVB117tYwIwOztLe3u7uhdJVrVKq9VGSborikJqaiqNjY1qF6GysjLuuZ9tGlWsOGU0GtVZqPNlxvCcOUu9Xs/3vvc93vKWtyBJEh/+8IdpbGzki1/8Irt37+amm27i9ttv59Zbb6WqqgqbzcbDDz+c0LEHBgZ45plneOc735mw5GxVVZXaTi4vL1/Do43k24sBbq1Wqy4w8QbOBIQ0YHt7O9nZ2ZTFkI5LBE+0Tqn/bTHq1KE4u92O1Wqls7OTsbExsrNXFsL/7Zzjt71Bvvqr76APuTBVl6Pz+9n+i18QePOb4eUKzeDcMh/75UlCssLbtuVx51XRvhpWq5VQdiXfP9jP7t5Jbri0aQ2tJyQpoKx8flHRigdFUdhdmMpDt1TwUs8M9xyeIiTDpGeJT213U1FRQUNDw4bX6Gs31XPNdw4CEJQUPvc/Hdx7y9Y1rzMajWzZskXtQsSaVYiH9VxYi4qKolQ3hPOqy+XasEKSDESFQ3hzjI2NUVNTE7UhEDKO4lwDgcCGbrECIimz2+309fUl3BKPrB6tNlpa3akQFbDCwkK6uroYHh6OUgV5LTobF3ER6+FsFsU6Ozt56qmnqKurS1hytqSkhKysLNrb2ykoKFjDk4/0kxKFELFxizSqjXf83NxcMjMzaW9vZ3Z2lqqqqtNaw777XJ9aFEOBSytWOtaicOJ0OpmcnMThcLC8vMzY7CL3HvezFILbdtq5aXsBGRkZMc/zzz2ztI27edvWPIqyoguDaWlp7N69e11fjh8dHCYQkhhblHi+e45374pOHiOhKAp/2WBDDngZm3PzcOsEMlCSruWurW5KSkqora3d8Bq9b08h3/vzAIu+MACf/VU7V9e8Ab0u+vMJUzqXy8WpU6fUAf5E1q3I9V+oLQq5/7y8vCgatFBmWlhYoLq6elMpxgUFBeTk5NDb28v4+Pga6lYkq2JxcTHKJNfhcGzYFcnJyVGpVaL4tlFyHytOieKYSB5EjM3Ly6Onp4eRkZGYypank2xs1uvPVPL61cQ5I317NjE0NMQdd9xBdXU1X/nKV5I2S+vs7ESSJHJyctSFO1JxQxgpnc7GRZZlBgcHmZubo7GxManKxQMv9vOt54fUn3cVW/nBLfVR7fBQKKTOK5SVlXF4RsfPn+3kmz//AqmlRZTnrCQImqkpQl//OvKuXQA855zl67/rRqeFElsq//VX0doXc94g7/pBMyFJxqTX8K+X6ijIz4/qcowu+PjxoRFqHGncvHNlAfeHZcwGnboQRvKBxdxKr1vL5383RFBSyLemcGOjnR1pi2SkmRJaCN/3YDOtY2715x/fuo1LyuMvPuFwmL6+PjweD3V1dVFJU6x2uJDxFVK+G1XBFUVhdHSUsbGxTXMIX43p6Wl6enpUnqpQMRHnabVaz6h7IzTbLRYLlZWVCQejSKnc0dFRLBZL3AHYmZkZenp6yM3Npby8nEOHDnHZZZclfI4HDhxI6vUHDx6MK9ur0WjOte7G+RFRXhu8qnHq6aef5q677lKLYv/yL/8SVRTz+/3ceuutHD9+XC2KJSL3PD09zV133YVGo+Huu+9OSk5bGKF5vV7y8vLUOCVJklpgEHHqdJIFsYaNj48nLDEbiYavPq/+d4oODn36sjWy43q9nuXlZQoLCxkKpXP3cwMYdVryrSa+/961BSOAgdllPvrQCYKSTKnNzE8+sHPNa+57YZDHjo1zVWUm1+d6sNlsUV2Oh46Oct8Lg+i0Gu5779YoNcf1aLvDy3o+9/QQIUkhxaDlC2+ppIA5NIqcULf8F0dG1e4GQFO+hUf/Or6UsSzLDAwMMDc3R11dXdR3ECmL73K5VJpRMuu/6GYPDg5uuhCJwMLCAp2dnZjNZgwGwxq2gtVqTdiMMhZ8Ph9OpxOdTpeUMW5knJqZmUGSJMpWyVW7XC66urrIzMyMSoCam5vZsmVLwu+VbJzaSAL+HPPgiPvFvS6SDVi5mb7zne/wyCOPcP/991NTUxPzdfEUN4TcpxjK2ezW1eLiIp2dnTHbyfHwtac6eejY5Cs/79VQ7nhlExy5EQ4EAvzmwEn+8/gyyPCjx75EeYkdTWoqSBKaqSmC992HUrXCHfUGw3z+110Mz/v47JuruKQsWkN63hvkXQ80EwjLWFJ0/OaOPQwNDrC4uLhmlkOSJGYXFvnbx7oYWghyTbGWd9enRvluRFbkFUXhqVNTPHlykuMjK3Mu79lVwK1bLAwODlJVVbWu4tPJsUVuefCY+nO6Ucvhz1214fV0uVx0dHSQlpaGXq9XNe7FtbRarWdUSfD7/TidTrRabdIO4bGOFVm1EpuKUChEIBCgvr4+pmb7mUBRFCYmJhgaGlJ5oxtdi1AohMvlwuVyMTMzQ01NDXa7PW5iLpLvyclJQqEQV1218fcmkOwifuDAAfbt2xdXOvtisnHe4IKJUwA/+9nPuPvuu7nnnnvY9XLxJxZEnBL/RHXW5/NRUlJCUVHRpnO5vV4v7e3tqpFbImvhHzsm+LvHu9Sf7Snwrb+wRq2rYi0MhUJ0dXXxTN8yvxkIk6LXcseVpbx9e+xuQ/+sl48+dJKwJFOcZeanH4xONvwhiWvvObhS4ArLPPThnYRcZP9gUwAAIABJREFUU2t8OQZml0k1aDARpKV/mv398zRYJUqshrgbYVlR+NrT3bzQO8ecN4hBp+WtTbl87BIbvb29G66Rkiyz9Wt/jrp5j//zlaRssLcQcu4Gg0GdtYikGQnvpdONU6FQiJ6eHvx+v+oRdrpYPWwuuiui4ybiwWZjZmaGvr4+tdu3USFYzCwtLCwwMzMTd5ZQURTGxsYYGhqirKyMgoICmpub2b59e8LPWrJxqqWlJaY6HJxfcep1k2wItLa2cvvtt/ORj3yE2267jZmZGdWIRhjmiEHe1Rt2n89HW1ubOuC72Vm/JEn09PTg8/loaGiI2oxGDkipZk9aLZ/44zLuMFxWls4Dt+5a95x+dniE+/88gF4jcauvn4/vf3TlF7JM+J3vRProR0GjYczlp3faw+7STNJS4i98zUMu9vfNc32jg5rcldai2LBbLBYMBgNLS0sAjAdT+PcDiysUNq2Wlz59xYbX44H9Q9z/wiAhSSHDpOeuqyu4sXFl1kKj0VBbWxv3Qbvymy8x533FCfzEP78Bgz66ohdPdUOSJJaXl2loaNh0EyRYWQh7e3sTpm6t113JzMwkIyMjaqHzeDw4nU5SU1M3VXVLIBQKqZ0gYQQGr8yEuFyuKOnBSFMnITIQT7VKwO/389JLL5GVlZUwV/l0ko14rz+fFvGLuPDiVF9fHx/4wAe47rrruOuuu5ibm1P59ZFGqWIjnJGRocaLYDBIe3s7ZrNZVTjaTIgK+8LCAg0NDVHP5mpDP4/Hw/CSzJcPvTL4/tiHt9NYFH9dPTW2xD8+cQqfP0hNnoUHblvfuPA55wynxt28fVs+JbbojbGiKPzNQyfpnfbiSDfysw/uxKjX4vF4aGtrw2QyqYpbsiyjNaXxyWcW8L8cc575+31Rfh2x8Pixcf7vM70EwjJpRh1//6Zy3r0jj+7ubrXwE2/e5dt/7OMH+4fVn9++LZevvy3aTC7S1E+wAEQC4PF4qKmp2VShE4GFhQW6u7sTpm5FSqSL7kqkl5XVao1aU0XxTcTyza7QS5LE4OAgs7Oz1NTUqLF89ezK0tJS1Oyi2PMpirJm7lBAJGRut5twOMzevXsTLkAnG6eOHj3Ktm3bYsaj8ylOva6SDUmSeOGFF9i/fz8PPvggwWCQoqIi7r//frKystQFe73NX+RCm8zweDKYmZnB6XSSnZ2Noii43SuUoDOpXLj9YW77yTHGFvzodBr+aY+RLRo/5YDGbkeprweNhoXlILf++Dj+kEyNIy1u+1pg9YZ9YN5PRpoZm1EmHA7T0NBARkYGnkCY9/6whRl3kOsbHdx1dQULyyFKbbEN/wB8IYm7n+3j4eYxNIBOp+XJO/ZQYktVBxcrKipwOBxrjvHrE+N87kmn+vMXb6jkuqqMKDWL9RZCr9dLZ2cn6enpp81TXg+CuuV2u6mrq1N5oPEWwsjvPpHuSmRLvCwJTfVkMD8/T1dXFzqdDp1ORygUIi0tjczMzHUdYyNb1rFUqwQOHDhAXV2d+iysZ7inKAoHDx7ctGRDDA2eQ7iYbMTHBRWnYIXid+DAAX74wx8yPz+P3W7n3nvvpbCwUFUPWu95jqQ9NTQ0JEXJShQul4v29nYyMzPRarW43W5kWVbnQaxWq0rr/N7zfTzeOs6H9pXwgUtL1z3uE8fH+MYzfaTotdRmarjr0syYRnSHBuZ5pmOGG5py1zWDDYQlusYXydQHCfu8URt2WZbx+XzU19djs9mY9wb5y+8fJiwrGHRanr/rMoz69TfZy0GJz/1PB885Z9EABr2W79+yhUvLbczPz9Pd3b0uYyGSYlaYYeTxDzVFFRUjRWZWswD8fj9dXV2qF8NmbzxFp3lmZoa6ujrVH0kwQCK766fbXRHFt8LCQoqLizc9Ti0tLdHZ2YksyxiNRtVqIJIKHSsGrY5TsWan3G43hw4dIj8/P+Hrn2yyceTIEXbu3Bkz9l1MNs5RSJLEZz7zGXbu3MmePXs4fvw4//Zv/8bdd9/Nvn37kjqW4O+JVunpIvKhjXTjNJvNLC8vYzKZ1nQ5TgeH+uf52MOn0Gs12NONPHnHJYyPjapqTGIR6Zn28rFfnkSrBZ1Ww9Mfv1Q9Rjgcpm98jh8fGiXLEObyXAnzy/xVq9XKc4M+vvfCMBo03P2uRqozNXR1dakqESFJYdYbRJJk3v/j4wTDMn91SRF3vrE83mkTDMtc/e0DuHwhZAVsqXp+/bd7saUZCYVCOJ1OJEmK4sgKdai932pWj3NpnoZ/vDJXXVwSWQhFy3RkZITq6uqYOvlnivn5eTo7OzEajarpUCILYaIIhUL09vayvLy8Zh4lGYi2t+haRAZBWZaZm5ujoqIi4aRGDObJshy3eiQWZVmWGRkZYXR0NK4qiCzLHD58OKnn+GKyccHggopTAF/60pcoKytjz549jI+P8+lPf5ovfvGL3HDDDUkdx+Px0N7eTn5+/hlv5FbTi8Va5ff70Wg0NDU1nZFaEkBQknnHfx7F4w8hKfCT27ZjDrsZHBykpqZGnXfzBMK88wdHV5ICrZZf3bEHs2FlnYwnkyrWU1NaOg+3ThOSFD60rxhCgSjFqj86Z/lt2zTv3plP56SHw4Mu7riylJ0l69NS33zPQcYX/cgKpBq1PHz7bqrsaUiSRG9vLx6PJ6pLK1SXLv2Pw4RfPsa+PA3/cLk9asO+UUdBURSmp6fp7+8/a7MWS0tLdHR0oNVq0ev1queKKCxlZGScsbRtf38/CwsL1NbWJmT6GgurDQiXlpbUmRCNRrMuRWq9cxMKUpGqVQL79++nvLycgYEBiouLN3zOkk02Dh06xJ49e2LuAy4mG+cRhoeHue2227jiiiv4zGc+k9QDEw6H6epa4aLW1tYmtDmJbIkK2pbZbI5q4YnjCI68UOk5Ew7+HQ+d4PDAAgB/tbeIT1378myG10tHRwc2m43y8nIG53z84ugoPdNe3rfDTlO2RqXEaLVa7jsVpmsujEGn5Ws31fGG6lc24J/573Ze6J1HURQ+emUZH76sRF1EFhcX1Zb7021TfPXpbsKyQqHVxJN/e8m65z487+P/3H+EQHhFtviq6mzuveUVib6JiQl6e3tJT09X9bNTU1N575Pz6jE+flUZH78qflKzHkS7Vwydne7DvZ5juJDIrK2tPSsD5IuLizidTvV73iiBCQaDamKx2jApsmIpEJnU1NbWJizDuV7SsXpRDgaDdHd34/P51uiqh8NhWlpa2Lt3b8LX5GKyccHggo9Ts7OzfPjDH6awsJCvf/3rScnQis2uoIYmUriKNRgdj7YFr1SnN5qn2wgtQy7++hcnVmJDponH/2Y3aUY9fr+fjo4OUlNTqa6uJiDBzQ804w2ESTNqufdtxfg8bpW2OxM2cW+Lh3Szge+8ewv51lcYCD85NML9Lw6CArfsLuQTV1egKApDQ0NRsxzt424+8vNWgpJMVqqBZz+x/gZxzOXjPf/VwsJyaEUW15rCk3dcolKRZ2dncTqdaldKxP7JUApf//MMeVYzP7x1R5RUezIIhUJ0d3cTDAapq6s7bdZFrA27UNpUFIX5+fl1pWDPBIICnJaWlpAQSSgUiuquRCouivs0Mk5JksTAwADz8/PU1NQkvKdKJE4JtsL8/Dx1dXVxKdiv19nCc0b69rVCSUkJzz77LF/72te48cYb+cEPfhDlELse9Ho9TU1NTExM0NLSsiYhWO27sbrCspFRkpCNE9KGmZmZcbXC18OcJ0jz0AIhWUGn0XB17SsJQlpaGrt27aK3t5cfPvUSP+9e0Xd/R5WeIt0ispwRJef62Gg72oUFtBoNxlUzEB+4tIS2cTdpKXqub1zhkAp508XFRU6dOkV+fj77yvPITU9hfNFPQ76Ft99/hHfsyOfWvbGve4nNzJ1vLOM/nu0H4M89c/zo+Q722GX1mjocDrxeL4qisH37dsxmM3fM9/OfLw6RYdLxnl2FMY+dCEwmE9u2bWNqaoqWlpaEaUmxJHLXcwwXLfGJiYlNb4lbrVZ2797N6OgoR48epaqqSu3URHJtRRKUrLGTMIoSLWuhh75R8p6I0ZKA0WikqWmFYtDR0aFq2BsMhqTlBy/iIs4n5OTk8OSTT3Lvvfdy3XXXcd9991FfX5/Q3+p0Ompra1WZ79UJQeTzH8t3I9J/Jx4ipdaFEMTpVLp/eGCIkCRj0Gm4vDKLNOPKMUwmEzt27GBwcJD9+/fTtZxKfUaIFK3M5aVmNLIUJef6z0924gkpLAUD/KFrltsiYotOu7IjUgCxZGg0mjW+HJasfNCs/C49Rc9jx8bZVpihzieuRmGmma/8ZS1//2gbAOOLAT732HE+ut2sXtOcnBx8Ph+BQICmpia1YHLdnsS+y/VgMBhobGxkfn6eEydOUFBQkFA3K55ErpBIXy3lGwwG6enpUVkRm0klt1gs7Ny5k4mJCZqbm6Ni7Wo/q9UzgcLxfj3odDqqqqrwer04nU5SUlKorq7eML7Fi1OR11av11NbW6tSsI1G46bNopwv8rbr4XXf2YjEiy++yJ133slnPvMZ3v72tyf1t8vLy5w8eZKUlBSMRqOqZCQ4jIlSd+JBVF6mp6eTMnACuO+FAb77p0EA0lN0PP+Jvfi8rzy0Yij+90NhftuxyPaJbq4qSOXmD7wZZZVhlWs5xOPHxynKNPGWhrWzEpF4/Ng4jx0b55078nn3rsKoLkd9fT1hrZGrv30ARQGtVsMzf3dplAP5anOfO34zwbRv5XdGLfzxzl3YrOlR5yA4soWFhRQVFeEOhDEbdBsO+SWKeEodonXvcrlYWlrC6/UmLZErMD09TV9fX8KKT8lCdDlCoZCqKx7JtbVYLGf0npH0s2Ta+qurR0ePHuXyyy9f9z2EMlZOTg5tbW3s3r074fO82Nm4YPC6ilOnTp3iQx/6ELfddhu33357Us9qIBDg1KlTaDQazGaz6rsR2bVMhLoTD4qiMD4+zsjISNLdeE8gzJXffIlAWEGv1XDPuxrY6tD/f/bOO7Ct8mrjv6thWx6yvEe84j2y4wySQCCFUpqWAgUCLTtA2jIL4St0AaVl7wBpA2V0USi0DaVhJk0IJHacTbz33luyrXm/P5z3WrIlR47tloCf/2xrvJLvfc97znnO87iY5Pr6+lLWp+b5fV3IqDg7O4r7vpU55rX+eaSZp3dUoZIknvhuDgudDGUtdgd/KWjAanPw/aXx+GpUVHcOEBvsh7+P2qXLYQuOp7rPwesFDbT1W9CoJLb+cCmRTsZ/o+cXHtrVyr4Wu/L31y7PZHFKlMt32t/fT3FxMeHh4SftsTUeRKzt6elx8YZwV1iaqESugIi1gqI31Z/BZDJRUlKi/N8dDodL1+Jk7QYEnOlncXFxYzxqxoMztWr//v1j4pQsy4qUe2xsLImJiR479ifClyVOzSQbo9Dd3c2NN95IUFAQjzzyiFuOuzgEO9OhhPSozWZjaGiIuXPnTigh8BZC9s5bDq7D4WD5Y7sxmocpSMl6uHdl4JiBs95BG794+3O+8eJDzGspI9xfjT44CNszzyAvWECXycKT2yvx06q5fU0ygU4qVS19QzR0D7EgXo9GmNkN2Tj72b1IyIDE+7csx6Abvil6e3spKSkhPDKa67c2YjLbsDtkEkL9uG5xBNkh8hjvjeDgYBpMEt979TDCH/CSRTHc7ybQ2O12Kisr6evrGyPDOxUQw9cVFRX4+voqTr/O3+lkzXZsNhsVFRWYTKZJz1o4K5k4J0HCIVhUwKY6WFgsFioqKhgcHPSKWiUqbEIqV6vVMnfuXLfzHAKCvtXd3Y1arZ6hUX018ZWLU4ODg9xxxx00Nzfz3HPPuTUxk2VZEfAQe4AQcRAzWDk5OVMukS3WN5FuvMPh4Pkd5Wze0wSAVoLnzvYnImSkWCP21I9L2vjNe+XYbHayQuHxSxa4eE7IsozMsJytn1bNLMP41e473y5kb1UXoQE+vLF+sUJ7EhTjkJAQrvx7M2abA5VK4ttzIlibEUyYxuzivSH2frWfP2c+k4/FNnxZxuh92H772KKJw+GgtrZWGb6eqHfJiSDLMp2dnZSUlChVeDHAP1WFJWfvj8nMWrijGAufKI1GQ2trqyK3PB1iLUL0x5vPICiG3d3dyozt4sWL3cYpQdtqa2sjIyOD0NDQr6yQyUyy4QayLPP73/+ezZs38/zzzxMWFkZnZycBAQEuShbO8rjO/3DhmZGQkDAtlWlxmDYajWRnZ7s4Lg8MDLgkQS29Ju7eO/LcDSvjue1rqWNe86/7Gzn08lvc9OHLyFFRzA7zY7C1FU10NKp//pPHPqpk69EWJAmuX5nANcsTAGjqHeKKVw5is8ucnhbKb84fbgdb7A7WPp+P0WwjwEfNexcm4Pfxh6BWY//2t7EGBVFaWkp1Szd5HRq2VQ5hc0CAj4q/X5VBaIjBbUv06lcPUlDXq/z8xvqFzJ3lPliKpGYirqvu4M7h1N/fn6CgIEwmk6JmMh2KL6ILIeSWx/sMJ6MQIuQBJxssvPkMISEhzJ49W+mmGI3GMQPngrolgow3qlUwzBn//PPPiYyM9IqCdiL1KsHL/QJhJtnwjK9knAL4xz/+wX333cejjz5KZmYmjY2N6PV6+vr6lDkLZz8j5/vCaDRSVFSk7I9THadkWVaUjHJycpSCiSdRlFt3DGI8Pik9O8yPf980Vuyhrd/MD/5yhOY+Mwvigrnna/G01lYQFhZGUlISdd1D3PLG5wzZHDx+UTbz40b2s51lHXxa2cV3F8a4GPctf3Q3KklCkuClK+a7/E3Mu3xc1MJ7dRJ1fXYcMgT6qnjrijRCQwxuKWYvf1bL49urlJ8vXxzLL9ZmuP2eRFIjErOTPUzb7XaXvd/ZgFAknePNEkwGogshTF9PtHc6q1iOphgbDIYx3TUhEtLc3OxCAZ5KiHkRnU5HamqqIn87MDCgxCnngXMRp3x8fJQ4JRKO0XF6cHBQme8dHBycSTbc4Cu5iZeUlPDuu++yfft29u7dS1xcHNdffz0XXHDBsJqFF8N5YnhclmUyMzOn5YJobW2lrKwMvV6P3W4f0wnQ6/XkPrwbi2PkOf++aSmzw1yr5HaHzO1/O0bA1n/ww/y3CEyKIzbYD9lqZaizk8PPPUdBfzB/PtCCBGw8O4Vvzxt2gd5b1cX//aPouFKUln/9aKSy3NI3xL6aHpaYmoj51jeQbFbUKgkC/Nn3u9/hm5SEj48PJXVt/GqfDatDxmqXiQjy4c3rc11a1QLVHSbWvrBP+dlXLXHoZ2d6/I5E5aWrq8ulnewJnnihzpvL6Dmb/v5+SkpKlMP0dOja19XV0dLSQnp6ulLFdOe/4VxhEwd2byCChdDmn+rr1WKxUFVVRUtLi9KmDwwMVL7T0fMrzrDZbMMa+Mc3VnePMxqNVFRUEB0dTWVl5QkVR+x2OwUFBSxfvtzt32eSjVMKX8k4VVtby9atW9m5cyc7duwgOjqadevWcfXVV3vtxOxwOBSlpJycnGlxIxaKe0KFydnLSsSphp4h1m4eUQ/89pwIHrlozpjXer+ojYc/KMdPoyYuxI+XrligyLN2dnay3xTCn/a3oJLgjLQwpfjV1DvEupf2Y7HLBPtp+OCW5cp3s/mTal7ZW8/iBAO3nB7Hp2WtZIWAzm7CZrMREBCATqejoqGNn+6xYHMMx8zUSH9e/P4Ct3HK5nCw4Ne7cAq9fHbnCkIC3H+/sixTX19PU1OTV/QzZ4n0np4e+vv7XbwigoODxyRBAwMDyh4/Hf5LgkJXV1dHSkqK4v0xUf+N8eDszZGenj4hoQRvYLVaqa2tpbGxUekGjTdw7gwxz+FJtQqGz2xHjx4lOTnZ6y7NlyXZ+EJF0y8K2tvbiYyM5KmnniIxMZGf/vSnfPjhh1xwwQVeX9xieLylpYUDBw6QkZExqYqCOzk/Hx8fIiMj6e/vR6PRkJubO+bCc040gDGJBkBpq5HDDb2kxidDgYoYHxkcDqTOTnzXrMEaGMWRwxWsTgpmdVYsazJHhgsXJxhYmhRCUXM/t69JBkY6AQO9vcTbe/G983b8hgZQHT8TyA47yz74AOuzzwKQnJyMX1AJ9+5op98B3SYrD2wr5YnvzhmjcT47PIB4gy/1PcMmUWa7zKG6HhZ6kCVUqVTKxldUVDSmQ+CuwiI2F+fB+PEQFBREbm4udXV1FBQUuCQEUwGVSkViYiLBwcFKAqtSqVyCS2pq6qSoWwEBASxatIiWlhb2798/qXkRT+Z+BsOwXn5HRwc2m43k5GSv6GEajUaZ5RAuyaNb1mLOIzo6mvDwcKqrq8nPz1da16MhkpcZzOBURXd3N/7+/tx33328/vrrPP7447z33nusW7eO6Ohor15DpVKRnp5OZ2cnBw8edDkkngw8DZuHhYUxMDCA3W5XBDycse73e1x+vnjRWMdwu0Pmbwea6B6wolbZuPnMJOUzJCcnEx4eTsXeY2glGUml4kwnpUSVBBISMEyFgpFOwHkJEsv1enqNRq7/81HMDgjwUbPth0sI9B+J9ykpKbRRyPN72zE5oKp9gJ9uLWLTunmK7K6ARqXimtPieHlvw8hnfGk/H97mfgZNkiQSEhIIDw+nuLiYgIAAUlNTlYKHO2M/5yHuoKCgEx5c/f39WbhwoTJ8Pdn/tbvPIHxgSkpKKCsrU8Q7RHdd7PknG6eEWEtHRweHDx/22iHcHTwlbMHBwaSlpSmJUVJSklcUN7GG8eJUSEgIwcHBqFQq8vLySEtLmxZDxi8iZjobXmLbtm3cfffd/PrXv+bss8+e0HMnyl8dLT0nTP0EZcsdHUaY3I0+XD3xUTm/P77h3bAijh+fnTbm/R56v4zX9zchAzeaStm4/y0YGMBx2mlY772Xb/2xiJ4BK5Js5xcrAjlzyVx8fHxo7BnkaEMfcyK0YBle77GmPl4ptBAe4MP95yYyKzIU/RlnoC0qcnnP2hVriPjoXy6/e35HGZs/a8Qhg1YFly+J4+5zx663y2Rm1RMjwSklzI9/uWm5j4bg97e3txMQEIDFYjnpCosnDA4OUlxcjJ+f36Q6BDabzSW4DA4OKtQ9h8NBa2srSUlJ00LTEw7hJpPJq1kLZ/nBnp4ehRc+3iBfT08PZWVlXkvxCngyBOzt7aWhoYGcnBzlsaKSJ9R4nAsFYlDW00D5TGfjlMJMnDqOvLw8fvCDH3D77bdz6aWXTui5FouFoqIifH19FVWn8eCOtjt6JmA0Haazs5OysjKSk5OJiopSfn/awzvptYz8G4t+edaY96vrGuDiF/fjo5bQqFW888Ol6P1G9tfdFZ089lEFYT4OLk5VsWbpPCWpkWWZ/xQ3s7usjeVRKgyqQdoHZfRBgaTFhqHX67Gg5dzn8nDIMipJ4sXvzSc+VOciWgLwwn/K+e1nDdgcw8pWZ6WH88wlc05o2Ofpc42GGO5ubm4mICBAEfJwrq5PtqJvsVgoLS3F4XC4eFRNFM7CKM5D/OJA3dLSMm1mfc4ytt5QgEVMFWsVdGhB3XKXsAlqlb+//4S6Qc5xSpjeqlQqhoaGKCwsZPHixQwNDVFWVobVah13LvPL0tmYSTYmgJaWFq655hqysrK49957J3QwFfzVjo4OcnJyXMyPRvNX3R3WvDmMCS1yURURz3HIMhLu5dPMNjtnPrWHvkEbPmqJBy/I4rzsSHA4hucrHDLn/3Yf3SYLflo1T5wXi7GtHpVvAPfs6sNslwnXadh8YSIGg4GN71RxqKEPlSSx8ZwULlkUi/bee1E//zyqwWEpqQGtL49+fQPnP/szMqNdD7L3/LOQd462IQNqCZ5bN5fV6WP5mc6buEaCo79w3cRHK1n19fUpFRY/Pz/a29sJCQkhJSVlymlPzu7d3lSPPLmGi8TSYDCMoURMlVnfeHDnzeGcCItqkLP8oMFg8DpwCZdjYdYXERFxUmogWq2W3t5empubyc7OHvNYoQoSHR2tKL+IpHDRokVuX38m2TilMBOnnNDb28sPfvADtFotjz/++ISESoTKW0NDA9nZ2S4VXWeJ1L6+Pre0XW/uGavVSnFxMSqVSvGnqm43cuHvCrA64JHvZPCt+WM7Gw9/MFwUsztkliUZeOmKBS77xaUv7qfdZAYZ7lodQ5CpkYCAAD5vs/CfOjOnJ/hzdlYkwcHB7G0Y4oH3KpAkePTCbFamDBfo/nmkmb8fasbfV82hul40ahV/Xb+Y+JCRTowsy/xqWxl/O9CEA1ABt69J5vpVY53Rlz+yiz7zCL3AXbIxes7ObrcTFBSEv78/nZ2d+Pn5ee3jNVEIf5SEhARiY2NP6Eov1ipUF0VMFXTY0d1154RgOobgYYQC7JwQiPjvPGshYqpYqzc0Q/G5RTz35ntyhjO1SqPRYLFYKCkpYeHChcpjuru7KSkpISwsbIxc/JdptnAm2ZggHA4HTz75JG+//Ta/+93vSE0dO2w9Hrq6uigqKiIoKEi5IcYb4psoBPdTHLxONLT8flErd75VhAz4qCX2/t8qdNrhi9dms/GLrYXsquhBlmWuztawLD6AwMBAKpq7uX/PwPCwL7DrjhVoVCqe/U8Vf93fhCTBpkvnkBjmT5BKJuD2W9G88QZWGV5ecgFPn3UNqZGBXLsinvNyRipcRrON8zfvo6VvmCal00jsumMlgX6uG+2yh3fRf5wj5quGgp+c7rJhC8Mk52rQ6JtYcGQnS3HzBGFCZ7fbXSrrwsBv9MD5eBUWT+jp6aG0tFRR6phqapBIatra2hTlrYCAAGXDnqz8IIzotlssFjIyMrx2IXaWyu3r61Pmcjw9tqamhpaWFtLS0vD396e8vJwFCxa4fbxGo5nyJHSSmEk2PGMmTo2CLMu89tprPPvss2zatMnlcOMN+vr6OHbsGDqdDrVardB2nROLyVbXm5ubqa2t9Yp2anfInPHkZ/QMWFFJ8Mvr83n/AAAgAElEQVS1GVxynGplt9tp6ejm/7aWU9llxlcN9ywPICXaQFdvPz/e3oesUqFRqfjXD5dh8Ndy77sl/PtYGwCX58YqJrcC33ohn6beITQqiQvmR3PVsnjinBKOQaudS1/cT2XHADCccLx9Yy4Z0a7xtqC6m6v/eBiAczLDePK7OWPm7Jwl0oODg12SCueD7mQNEz1BmNEZjUaXwpWYCRQHdjFwfjKu4UajkZKSEoKCgrwaID+Zz1BTU0NjY6MSp3Q6nctaJ7ufi++pr69vjKnseHCOU2azmZqamjFxR5xH6uvrSU5OVrxFvkyzhTPJxkniwIED3HDDDWzYsIErrrjCbabrLOfW19enuHAHBQUpXNY5c+ZMiwOkyWSisLBQOYR6ysS/szmf8vbhDTPO4MvLFyW4OIbfsXOAc4s+ZXHt5yxbmk7Yj2+C6GjKWo28vLuczxt6uXJZHJedNrxZO2SZ/OpuwgJ8eL+ojT/mNxAW6MNfr1uMQaehsWeQvx5o4o/5DcPdC5XE9ttOI8R/5Ds40tDL5S8fVH5eHqPhue8vVg6hsizz250VbNo9TA87LVpiwwJ/lw3b26qFqHCLqshU37iiel9dXY1Op8NutysD586dgMnKDwoPlvT09JNOnEYrb4hrQHjEtLe3K9XIqR7Mg+EKT1lZmaI9P15wcO4GCQnCWbNmkZiYiCRJHhMgYZxosVjQaDQeOxszycYphZk45QHl5eVcffXVfPvb3+aWW25xe194ou0GBQUxNDSEzWZj7ty5U2reJiBoJUFBQaSmpnq8bz+t7OTGPx8Fhim2f1qXjMY2oDiGv1ri4EirBUmSeOiCbM44Pq9htTv4xrN76B20EuCr4d2blhPkp6W4pZ/b3jyGWiXxyIVZHKjrJVrvxzeyh7ur737ewv3byrDahg0GtWo17928jGDdSCLQ0D3Iec/lYT9+9YX5wbYf5iqHULFHlTd2UN/eS4R6YMwQt7dzdqIiLkkSGRkZU35mEJ4TZWVlymH9RAPnJ/Mewn9pMomTO9YCDNPMAwMD6e7uxmw2T4vsPQwLwpSWlirKW+N1nEQ3SMi59/T0EB4erjAqRl/vovAmGAs6nY6DBw+ydOlSt68/k2x8RWA0GrnlllsYGBjgySefpLW1Fa1Wq3AD7Xa7Iuem1+vHVIE9zVlMFRwOh2Lsk5OT4xIshoaG6Oru5uwXS5TfnTPblztXxykV63ePtVF932N8d/+/Uev8SAxUI4WH89mjv+OOjxuRkVl/WhwL/HtoGpB4ssCESpJ4/rK5pEUG8rVnhulZGrXEIxdkc0ZaGAAmi42zn97LgMWO1SET5KvmtasXuVCqrv/TYfZUdSs//3SxiqzjfxcOp0e7NcgaXy5dmoiv9uRvOGcVjfT0dMLCwk76tZznF5wH+YKCgujv78dsNpOdnT0tm6CYUfB2XsRTh8W5GjR6M+zs7FQoSZORE/YEh8NBQ0MDTU1NCrUKXKtsPT09brtB4vnjqYEI1NfXU1ZWRkJCglu5yZlk45TCTJwaBxaLhZ///OccOXKEzZs3MzAwgM1mQ5Ikr2i7XV1dlJaWTvlAsYAsy4ra3uhuvKBuXfN6CdU9w7q4Bl/4y7rZyh6FpOKi3xXQ3DuEv4+aX5+fyarUMGRZ5l+ft3K4oZdgXzUpfkbiAyW04YmYHRIL4vRIksSdbxeyq7wDjUrFoxeOxCmHLJP70Cc4ZBmbQyYtIoBn1811oVQ9s6OS331ap/x8caqa89P9UalUCs3Mubs+2T1FGL5OxCzVHTzNL+j1egYGBpQux3RIoZvNZsrKynA4HF4VrpxVF3t6elzmFxVvk1HfqzsK8FRClmWlM+cspuLsa9XT06MoRIqYKvytTiTpLmT7AwICMJlMHv2jZpKNacZTTz3FSy+9hCRJzJ07l1deeYXm5mYuu+wyurq6WLRoEX/84x/x8fHBbDZz1VVXceDAAcLCwnjjjTdISkqaknXs3LmT3bt3849//EORe/vlL3/JvHnzxnhveIKo7Oj1elJSUqZFIaezs5Pi4mL0ej0Oh0Ohbj2Yb6Ko3aY87g9XzSc3aSTpueqVA/zqgWsY1PoRFRZITLAfdHTwwfdv5Sfm4Qry6amhPHphNj99+zDvl/agVqm4eFEsd5+bxuZPqvn9nnrCArQE+WnQqlQ8/t0cZhn8KG8zccdbx6jpHMAhQ3pkAL+/cgEGnYb+/n5qWjq5/I1aZS0q4E/nh2CxWJgzZ47XVJuJQFS9tVot6enpJ/z/eZLJHd1hcYbYRKaL9iTLMq2trVRXV5OUlKS0Y8dT3nCeC/EGwpujo6ODjIyMaTEF6+3tpaysjKGhIUWCUPBt3c2wCIx2IfdkCNjT00NDQwNBQUHKzIhz8J5JNk4pzMSpcbBv3z527NjBu+++S1FREUlJSWzcuJGVK1d6Tdu1WCwUFxej1WrJyMiYlntjPOrWd14fUXU6KyOU59fNV37eeqSZRz6sYMBiZ0F8ML+/YgFqlcSeqi5+8o8irHaZpUkGnr10Lh8erubn79WgVqu5fmUi61cmsuEvR9hf24NWrWL9igTmxAaxfHYIkiTx90NNPPpRJUazDWTIjA7k91cuIMh3eH2dXd2c/8dKbE6qj7/9hgEfh5mcnJxp8V6yWq0ug8Un2rdPtPe7YwKYTCaKi4uVjtN0/L87OjqoqKhg1qxZinu3p7kQ51kLbzssngpXUwmTyURpaSlGo1G5j5xZC546V97EKeFkX15eTlZWFrNmzRrzWqdSsqG+7777xnviuH/8X6CxsZENGzZw5MgRbrvtNt58803MZjObN2/muuuuY8uWLWzfvp2mpiaWLFnCli1b6Ovr48MPPyQoKIhNmzZxySWXTMlaPvjgA+Lj47nzzju54YYb+Ne//oWvry9r1qzx+gLQaDTExMTQ399PeXk5BoNh0jMbJpOJ9vZ2GhoaqKqqoru7m+DgYIaGhpBlmfnz5xMfH89971W5PPeB87OUi7m6w8TmT2q45MA2LGoNieEBaNQqHEYT74Rnc1gXRUSgL/d8I43wQF/Uag2fVHQhOxx8PUGNrPFjQbyBG09PpLFniD2VXXSYLFhtDs5ICyMswIfwAB8+LunALkOXycru4kZm04rZbCYkKIDDLWbajNbhzwXExERzVk48hYWFOBwO9Hr9lCpcaDQaoqKicDgciiqLcwfCYrHQ1dVFc3Mz1dXV1NXVKZzbqKgoZs+eTVxcHGFhYQQGBrq9Bvz8/IiNjaW3t5fy8nJlYH2qIEkSgYGBhIeHU1NTQ2VlJW1tbdTV1dHf349WqyUiIoLZs2cTHx9PeHi4x7V6gkqlIjQ0lJCQEMW92111yVuIalBbWxu1tbVUVVXR19envIfRaCQiIoKUlBQliff0fxcUKlFlEhUk8TeBgYEBBgYGSE1NJTo6mvr6eurq6pTDlztjpv8x7v9fL+ALjPv+1wsYjS9SnNq5cyfBwcHcdNNNbNy4kR07dtDf3895553ntZiDWq0mKipKGe7W6/WT8uQQB+DOzk4aGxupqqqivb0dvV6PzWbDbDYzb948EhMTuf2dapr7LMpzrzstnuyYkUHjxz6qoKpjAI1KxZqMcFalDncmKtqNbC/tRJYhSu/L2jlR7K0zkV/Ti9VuZ2jAxLfmxZCbFEKH0UJCqI6/H2rmg+J2rHYHS5JCyIoJIiLQh90VXdgcMh1GCx8fayCZFoYGB9Hp/FBrfTjcaFTWs6vOwl3fWkBJSQlDQ0MYDIYpjVNqtZrIyEi0Wi2FhYUKNVu8h9Vqpaenh+bmZmpqaqitrcVoNHrc+93tpz4+PsTExCgFOJ1ON+UFPn9/fyIjI2lsbKSsrIyOjg5qa2vp6elBo9EoRo0JCQlEREQQFBQ07t4/GiKhCg8Pp7a2lpaWFgwGw0kfzkVxsa2tjfr6eiorK5XYFxYWhtFoJCQkhLS0NOUc522cstlsyu/FcyRJwsfHB5PJhCzLVFVVjTkvuKNi/Y/hMU6dcp2NxsZGli9fzpEjR9Dr9VxwwQXccsstfP/736elpQWNRsPevXu57777+OCDDzj33HO57777OO2007DZbERHR9Pe3j7lMmww3Jq8//77+fTTT9myZQuzZs2a0PP7+vooKioiPj7ea8UDZ5+Ivr4+rFarS+t2dEtctGFTU1NZvfmY8nsfFRz++YhSxiMflPOH/AauLfgnV37+IQmxIUgWC63aQC457//o8xuu/jx9yRz+fqiJ53fVkBUdyJ1np/DG3gre/rwLjUbNS1cspKilj8c/qsLqcCA7ZGL1Gu5e5o/GbuY/jfB68RB2ebh7cfVpcdx1zrDc7YDFRu7Du5U1CdUpIQ3Y29tLdnb2tHU5ioqKsFgs6HQ6BgcHXeQHJ5sUwsRdVz3BmcMqqkGiE6DVamlrayMqKmraOyneenNYLBalfd/T06Oorzi7xzq/xmTcYz0ZLXV0dCiu6QKi66TX672qGP6XMdPZ8IyZODUByLLMs88+y1/+8hc2b95MZmbmhJ4v5gEjIyOVGakTwZ1PhIhTQtLdef/r7u6mtLSUxMREvuZE9QU48rPVaNXD93Fbv5lzN+3F7pDRqCTevnEJs8MDaO83c9VrB2nrtzAnNojHLsohItCHA3W9PLW9kn6zjZuWh/PB0UYiw0P58blZ/HV/E8/sqMIhy8TqfTg/I5DlUTIDJhP/qpH5d9WwoZ8EfCMnkie+OyKz7U7iVpixtrW1kZWVNW1djuLiYoxGI4GBgQwMDJywuz5RiIRDo9GQnp4+qbgn5hfEdeCsENnW1kZoaOi0KETCCAU4JiZmXMNXgdGS7haLRTGidedy7kzHdmYVeANPku5Go5HKykrmz5+vmAfrdDrl/3AqdeC/UP0XbzBr1iw2btxIQkICOp2Or3/96yxevNglY42Li6OxsREY3vTj4+MBlMNiZ2fntNjdazQaHnjgAXbt2sXFF1/MPffcw/nnn+/18/V6PUuWLKG0tJSOjg6ys7NdqDyejP3EIS0xMfGEG0FkZCQGg4GioiKeOjuE+z/tI9BHzbs3u3IC/364GRl4Jfd80nOSiOuvwBEewUPRp9PUoULnkMmOGd48n/lPNWabg0MNffQN2SjqsuOQJMxWO+/lF/K15AB+NE/NH45Y6LLLNPXDR82+3HPePJYuU1Pyh0Psr+vFAby6t4GvZ0UyPy4Yfx8NPqoRY0Lb8SOFWq1WTHeOHj1KbGzspHW83Zn7BQYGotPp6OzsJDk5eco9LYSRXmNjIwUFBaSlpXl1XQozqtEeHAaDgejo6DEa+bNnz6ampoaCgoIppz1JkkR0dDRhYWFUVlYqDrhCbtOZatbT04PRaESj0SgbtjfXrDA1jIqKory8nMbGRtLT070aWPVktOTO1C84OJilS5fS2NiIyWT6oiUbMziF8EWOU5Ikcdttt3HmmWdy3XXXcd1113HNNdd4vbcFBASQm5tLZWUlBw8eJCcnx+VecWfsJz6TXq9n1qxZJ7y3QkJCyM3NpbS0lGgdtAwef28tSqIB8MaBRiw2GRkw+GtJCB0uPB2s76VvyIa/Vo3JYicyyJdndlTx5sEmfNQq/nTtQv60r4FPmmQcDe1YTH2cmx5MUhDUG2Uae828uN+CbnUCV62Yx9LlMs1/OsK+mh5k4P3CNlalhHLhghgA5kQFcKzVpKxr0GpHp1WTlJSkGPWJ+YHJFHyc5Yd7e3uVA3BoaCidnZ3ExcWRkJAwpXHKz8+PBQsW0NrayoEDB7w+SHuaXxDdhtEFttmzZ9PQ0EBBQcGEi0reICwsDIPB4DYWjqaa9fX1KUmbwWAgLi7uhJ08YWoYGRlJRUWFonTpjfS0c5yyWCxu45QwD25tbaWgoIC4uLhpmUeZLpxyyUZ3dzdbt26luroag8HAJZdcwnvvvTfmceJGcNe5mY5qkTNWr17Nf/7zH66//nq2b9/OQw895HX1Xa1Wk52dTWtrK/v27SMyMhKbzUZ/f7/CXZysE6ePjw/z58+nsbGRJ88YIisrCx+nm76gppt+83CWLUsqEm++DmtcMKWt/ex5MZ9LS3ZhGOjlxtMvpsMYQ4i/lubeIbQqiYHOZuYGmalUyeh1at4uG+Svx4xsLf0r17/7Ng6HzD+yz+TXjlvpGLDzwuXzeOLiHM55Zi8W+3DQuPmNz/nkjpVIksSi+GDyanvdfg6DwcCSJUuoqqriwIEDXnc5nIOhcAkdL2mzWq2Ul5fT2tpKVlbWlNOe4uLiiIiIoKSkhObmZhe1EWc1i9HKG8HBwURHR59QeUs47EZHR7sob02lbrtWqyUzM5Ouri6OHDmiUJFEMAwODiYhIYHAwMCTDrZ+fn7MnTtXeY+oqCgSExNP+HqCEiWqRxaLBavV6vY7E/+PL5hR0gxOMZwKcWr+/Pns3r2b22+/nSuvvJJNmzZ5rWSnUqlIS0ujs7NTmTORJEkRRhGdysTExDEVYG+h0WjIycnhL2Fh/PrfxYSEhPLARfNdHvN6QaPS1lqSEIxaJeGQZf5S0EDvoA2VJHHT6tkA7CrvxOFwMORw8J/DlXS29x0XlID2QZkPSjp48rtZbCno5L3CNobsDh7dXkv7gION56TyzCVzOOfZvRjNdmTg3ndL+WZOJL5aNU9dOodzNuUr6/LVjHzewMBAFi9eTG1tLfv37/e6y+FwODCZTMre75y0BQcHEx8f73IAFh3/AwcOTIsSU1RUFKGhoZSXl9PS0qKoJQmMnrVwNnhMTU09ofKWJEnEx8cTERFBWVmZclifDF1vNNRqtTK/UVxcDAzHLudOW2xsLJmZmScdp7RaLVlZWfT19VFcXKyc107EXHCOU6I4ZrFYXL4zUdwLDw+nurqalpYWpUjxRccpl2x8/PHHzJ49Wxn2ueiii9izZw89PT3YbDY0Gg0NDQ3Exg5rcMfFxVFfX09cXJyivjMdyk+jERoayttvv82WLVv4xje+wQsvvMCcOXM8Pt5dxcLf35+2tjb0ej0LFy6c0gOQOFSFhoZSWFjoUnV5YFupy2PnzRrmx/49r4YH33qI9PYafNQq1Dd8wF+WX0pd8mokFdy+KoRH9nTT0m8jPjSQb+ZEsWlnNVcX/JPYT/+JSgJJJfGt0k9pDI5kk+NyHvmwnJ98PY1bzpzNE9uHZ0g6TVZ++s8SHrowixvPSCLvj0eAYcnD0fCmyzF6E3QOht4kbVqtluzsbDo7Ozl8+DBxcXFuh7UmA19fX+bPn09LSwv79u1zGeYXyhuRkZGkpaWddCXD39+fRYsW0dLSwv79+0lOTiYyMvKkP4ezcZLz4GF4eDg2m42enh7S09OnfDAvNDSUpUuXUldXx759+0hLS/NKQUxU2rq7u+no6GDWrFluOxwzmMFkcarEKX9/f7Zs2cJbb73F2rVrefzxxz0aiMFw4cWZDiX8jLq7u/H19WXu3LlT3hGMjo7miStCKCoqoqSkRNkDi5r76BkcETg5f96wX1NL3xCfN/aj06pQSxI5Bivv7TmC0WRkwAJJBi3xYQE0DWo4L1DG30fN1qMt2B1Q3F3Cnasi6TYZ2FPVhV2GV/bWE2vw43tL4njsomx++PrnANgcMute2s8/f7iMWSH+vH7dIj4sbuPiRbGoRu2pKpVKuR48dTmc6aXO3XVvCzUiFvb29nLs2DGF5jaV+5uIhR0dHRw8eFBJJE0mk+IXEh4eTnJy8kmfV/z8/Jg3bx7t7e0cPHiQ+Pj4ScdbZ/lZ56FzSZLo7OwkKSlpQmZ93kCv15Obm6swF7xVEJNlWVEK6+joICQkRJHMF9BoNKSlpX3RhsPHxamz0uNISEggLy+PgYEBdDod27dvJzc3l7POOou33nqLyy67jNdee43vfOc7AJx//vm89tprnHbaabz11lusWbNm2itGApIksWHDBk4//XSuvfZa1q1bx4YNGxgcHGRoaEihlwglA1Gtdq5YCEWCQ4cOkZOTM+XVCn9/fxYvXkxNTQ0HDhwgLS2Nio5B5e9a1TDFqKOrh6Z3Pyals54unZ6oQA0OLVy8401eTl6Nv1ZLZHQsDbsLUaugpnOQjKgA/LQqVlUdRGu1YlWp0KjA127l9JpDbFp5Oa/lNbAoPphrVyTw/Cc1DFmHOVNbP2/hB6sTWT47lLu/nsqnlV3ccuZsj59DdDkqKirIz88nLCxMGQKeqk0wLCyM4OBgKioqOHjwIFlZWZOaF/GkvBESEsLg4KAyzD+VMymSJBETE0NYWBjl5eUK7ckbSpInCUKDwUBsbOyY+SAhcdjY2EhGRsaU6vSrVCqlnV9WVkZDQ4OLjOJofXMxxyK6V7NmzVIqWuOpVs1gBieDUylOAVx88cUsW7aMq666ihUrVvCTn/wEu92O0WhU6CXCG0rEqZiYGKWrKqRADx8+TFZW1pRLpvr6+rJgwQLl4Jadnc1P/lHk8pjc+CDa2tp4Zns1VpuDISDFoEbvI/HHIjPdFjX+PhK3nJ3Bgx9V0N5vwVerYuPXUniHYePAGqOKlw718c04O6WtPrT0Dw+mP/h+OQadlm/OiSIhxI+67iEAytoHePHTGm5YlcT8uGDmx43/uUWXo6amhry8PCIiIpSzwEQp0Z4QHBzMkiVLqK6unlAnZTyYzWaXRMhutxMcHIzVamVgYICcnJwpdwePiIggNDSUyspKDhw44ELPHQ+CuSDWKyixomA32k/LarUqlEBvaU/eQhR1BbWqsbHRxTgRXIuhYo5FdK9ycnLw8fFxoVadqnHqlBsQB7j33nt544030Gg0LFy4kJdeeonGxkZFUjA1NZWmpialJVVcXIzBYCA+Ph6dTkdbWxtJSUm8+eabhISEIMsyt912G9u2bcPf359XX33Vo9nXRCHLMhUVFXz66ac8/fTTdHd34+/vz9NPP01KSgrBwcFe0aHE8Ph0VNWd3+Pxdw/yVsXIvz0+AB5eY6C4V8Pel//NPf95hT6/AObE6mnvG8Ta0cXXb/gtZ2RGclqSgWd31TBodTAvNohjTX1EBPpy37vPsDLvfZAkJECNzEeZK/jh2ruAYWO/925aRn51N794d6SrEhmoYecdp3tcr6fDuq+vL/39/cTExJCcnDwtN2dPTw8lJSUTmhcRMzdiUxmtwe1pSDImJmbKebgCXV1dlJWVufXNcLcJOsv6eStBON3eHDA88F1aWoq//7DGvfN3azAYPGrcOw/mqdVqRd3jRD4d/wPMDIh7xkycmgLU1dWxZ88enn32Werq6tDpdNx///0sXryY4OBgryiQAwMDFBYWKsac07FnmUwmPv/8c677cNDl939cqydIr+fKvzfRb3YgARvPSeHCBTFc9Lt9tButBPqo2Xz5XG55s5ABiw2tWsUPT0/kcGMfxc39tPSZkSSJpQl6Lkow8/PPhjBZhy8vCfj7jbnYZJlLXjzg8t5FvzyL8SD2UufDup+fH0ajkbCwsDFzdlMFo9FIUVERoaGhXsdCTzRjZ68I54Jdf3+/S7dmumSRS0tLCQkJGfMengRHxHq9pZsLb46QkBC33ktTgZ6eHoqLi/Hx8UGr1bp0hMR63XUrPEnlnkpx6pRMNiYCu93OrFmzyM/P5/nnnyc0NJS7776bhx9+mO7ubh555BG2bdvGpk2b2LZtG/n5+dx2223k5+ef+MW9xPr168nIyGDZsmW0tLTw4IMP8uCDD3LWWeNvUO4+S1lZmWIMN1k1JOehKDEP0NBn5Rd7rcpj8jauQO/vy4W/3UdXVT2vvXkvfthJiDHQ3NjBjoSF/Oa8H/GjM5J48bNhcyNJgrAAH9r6zWhUEt+NkdjwkysIMA87lQ9p/djzwkvcWeiDECaNCfblo1tPY+4DO10uOudN3NnYTQxGezqs2+12Kisr6e/vn3QHwhMER7anp4esrKwxFRFPyhtivd64x9rtdqqrq+nq6po2RRNh/tjW1kZYWBhms9nrTXAi71FTU0N7e/ukXM4FhLyj+H4F5UBUYzMzMydkzjhaDcTPz++U2cRnMBOnpgJ33nknISEhLFu2DJvNxj333MPGjRu56KKLJvQ645nJngzcza219Fm5e8+IHG5EgIZdd57OvpourvnDMO1WJcEHtyznX0db2fJpDQ4ZliaFEB/ixz+PtKBVq5gbG8TRxj6QJM7LjuTfx1oZsNjRqCXiQ3TctDiQuz5sw3H8CvPXqti9cRVnPL5bSUIAPrh5GfHHh9OdB6NFZV3spaMP6w6Hg9raWtrb26d1f6+rq6O1tdWtUZ+zOIo36oDuIIwZxczhZPf38d6joaGBsLAwZZZVq9UqMcpgMEyKbi7LMg0NDTQ0NEzK5VxAUCJFrBIzjIIqlZqaOiFzRiGTK9QVT6U49aVPNj788EPuv/9+PvvsMzIyMti5cycxMTE0Nzdz5plnUlpayoYNGzjzzDO5/PLLAVweNx1obm7m6quvZt68efziF7+Y8M0h5Gsn6nbtyd1aGOYIGkxeZScvf1bFuTGDnD4/k/CICOY8sBOA9PYaHj36FkkOE+8QSZ9PAH52Czk3rOPKav2wKohOQ1iAlprOQRwy+Ggkou1DLCjKw2aX+WT2IqSwUM7PNvDa/nZlfVcsieVwYx/HmkY0yz9an05fX5+LnKvYsL2prJ9MB2KiEF0nsSbRtfD19XWpBk3msC5k7wwGw5RUXcSMkPNh3c/PD5PJpEi/TseQtHA59/X1JS0tzauEWSTFzrMharVa6VqMNiYbHBykrKwMSZJIT0/3ikMuyzK9vb3k5+eTl5fH/Pnz+d73vjepzzrFmEk2PGMmTk0Denp62LBhAzqdjscee2zCFN7u7m5KSkqYPXs20dHRXj9PuFuL/UnMhTg7cUsqFXN/vUt5zhvrEpiTnsy6l/ZzrHk4fuj91Hy28XTWPp9HffcQkgQ3nzmbV/bUgQRqSSIl3J9jzUYkIC7Ej4QQHZ+Ud2KXhy+q7Jgg1qQG89zuEVPBlckhfHcDodYAACAASURBVD83mh+9Waz87uFvzCIjeHgPER5QYr3eHNaNRuOUKVZ5gslkoqioCD8/P/R6vaIUJuhbU3FYHxwcpLi4GD8/P9LS0iYdQ8SMkPNhXUjR+/n5kZ2dPaUD5AITdTmHE1N3DQaDy1otFgvl5eWYzWYyMjK8ur+Ej1pBQQF5eXlERUVx8803T+qzTjG+usnGddddx6JFi7j55psxGAz09PQofwsJCaG7u5tvfetb3H333axatQqAr33tazzyyCPk5uZO27ocDgePPfYYW7duZcuWLSQnJ0/o+WazmcLCQgIDA0lNTR2zOTkcDhd369Hyg97obwsN7y0He9lRNzKI9/xlc9i+/SjXP3obQUMmVBo1kiTxwJnXsjNrJRGBPrT2m9GoVAxa7WjVEnYHZIZqqWjuZ0g7fMOlRfrT0mtWlK8ArpgbyJ8+H0k2/rYugdjIUI80GG8wHV0Od0PnMBwoxVD0VCc2sixTX19PU1MT6enpXg+Qig1KJBb9/f3KtTD6sC6417W1taSkpBAZGTmln0G8R1tbG1VVVSQkJIwZzHPXwfL391c27KCgIK+CsXCodUffEtW+vXv3kp+fz8GDB9FqtSxdupSVK1dy5plnTstnnwRmkg3PmIlT0wRZlnn55Zd54YUX2LRpEwsWLJjQ861WKyUlJUiSRGZm5piCi9ibnLvrKpVqXHdrAZvdQVmbkeQwHdWVFZjNZr73Trfy92VJBu4+N5WLfrcfmWGFqLvOTuGRjyqw2mVi9L70DVmx2mVUEtjsMr5aNVnRgeyvG1FAXBivp7PfTF2PWfndD5eE8PqRbnosoFPDX9clEBoyuQ7wdHQ53BWVJEnCbDaTnJw8LZRsWZZpaWmhpqZmQjHEndO5SqVycQ93PrOIoqu7GDJVGI8C7Dwb4izteyLq7mj09PRQVlbmloYmfKz27t3L3r172b9/Pw6Hg8WLF7Ny5UpWr179RVOj+momGxaLhdjYWAoLC4mKivK4ia9du5Z77rnHZRN/9NFHWbx48bSvsaCggBtvvJGbbrppwpVU0VZsbW0lJSVFadkJTqhQsvCWb+vpPXKOdzUEDtxzOk9dejc/yHuLbp0eg78Wc5+Rdn8DV135CEE+anqGbGjUKlSA1WbjZx+/yGVHP0QCdiTncsfaOzBrfVkYpeFQ60gio5GGOyEDVplovQ/bb1sxZZvIyXY53HFYnSlGwjwPRipUgvc5HRWqwcFBl+7A6OqR83XQ09OjdLDEhu3NYd1isVBWVobNZps2gzubzUZlZSW9vb1ERUUp7XxRGRTr9XY2xB3sdju1tbX88pe/ZNWqVdhsNvLz8ykrKyMuLo4VK1awatUqli5dOuXiC1OMmWTDM2bi1DSjtLSUq6++mosuuogf/ehHE9rXxOFTFDCAMT4Rzia0J7tnbnzjINtKR5KEu85O4fOmPt4vGu6exwb7EqLTUtJqRK2C2GA/mvvMgIROq8JotmNzyOg0KqKDfanuHJkJWRLrQ0GTxeX98n+ci0Pji97Pe1drb3CyXQ53UrlardYlcRNFJdGB0Ol006ZqJGKI3W532x3wVFQScXW04Ig7iBgiConTsYfb7XaFAhwdHa0wRGw2m0KLNhgMXtGiPUHQt37zm98oXY78/HyKiooIDw9X4tTy5csVFa0vKL48pn4TwXvvvceiRYuIihqWxIuKiqK5uVlpT4uMW8gOCjhLEk43lixZwieffMJNN93E9u3beeqpp06o6uCsCtTb24vVauXIkSOEhIQQHx/P7Nmzp4wC4+6i/tfRFjT2kW6ELMvYkdA47PhqVBgtdiTAYnPgo4LLjnzIxce2Yz9+HZ5RfZA7d/+JB9es51CrDT81DB1/OZsMT64OIjgmiblxhim9qYRilVCe8NTlcKe8ITaVE0nlBgYGkpubq8iyZmZmTqmJHoBOp2PBggW0tLQo5j4ajUapDDq3bmNjY08qUfDx8WHOnDmK3O9U0dBGm/yJ6lVtbS3BwcFTIp0p+LD5+fns3buXffv2MTAwwPPPP09YWBgPP/wwq1evPmXMkGbw5capEKcyMjLYtWsXP/3pT7nkkkt44YUXlPV6gnORRhQRjh07RmBgIImJiWN8IiYL50QD4IJ5ETy1o1L5ed4sPTvLjlOkHNButGC2yaglmSEJZGQ0EgzaHFR3DrrEpYImCxEBGtpNI4Wx214/yAtXLZvyg5+3vhyj5fKtVqvXUrk6nY6FCxfS3Nw8IUPZiUDEkI6ODg4dOkR0dLRCM3YWHDEYDERFRZ1UUUmj0ZCRkUFvby+FhYWEhYWRlJQ06b3dHXVXkiTq6+vx9/efksRGvMf+/fvJy8sjLy+Pjo4OCgoKUKlUPPjgg6xdu/aUkrcdD1+OT+EBr7/+usJvhRF5wbvvvnuM7OBzzz3HZZddRn5+viLr999CUFAQf/jDH/jzn//Meeedx9NPP82SJUuAsUPco839hFmOw+GgvLychoaGKZeg+/l5Kfz6veFN+3fnx3DHv8uJTF7MFYe2EWQ2YbeoiBnowarWcMOuv/DC4gtwHKdKWRywrOYoWpsVm3r4clM77Cyv+1x5feEQLvBxI3xHKsMYlIbfFG+AarWa9PR0xZcjJiYGg8HgQjcTyhuisjTRxE2SJBITExVNdUF1m4rD7WhFK4Camho0Gg0pKSlTrmoiXFerq6spKCggMzNzQteX4F+LTdtsNrsNiLIs09jYyKFDhyZM33KmROXl5XHw4EF8fX0VStTtt9+uON6+++675OXlsWbNmpP5OmYwgynHqRKnfH19eeKJJ3j//fe54IIL+NWvfsU555wDuCoDiqKH86Dx7NmzCQgIQJZlqqurqaurIycnZ0rXp9OAk+0Gf/64ANuo2DJ0/Be+GgmLfTi5sMtgtTmUIXDlsXbXnzucEg2Aoi6ZQ4cOee2oPRE4+3IINamIiAhl7x9t8JeQkDBhwRhJkoiNjSUsLIySkhJaWlrIyMiYkkLlaDaAqNxLksTs2bPHuIdPFsHBweTm5lJfX09BQcGEaMYwPnU3Li5O6bgJWtPRo0cnTN8SHb7RlKjc3FxWrlzJtddeS2JiIpIk8emnn/LOO+8o9/6XAV9aGtXAwADx8fFUVVUp6gudnZ1ceuml1NXVkZCQwN/+9jdUKhXr169nx44dmEwm4uPj2bJlCw8//DA1NTX/VelBgMOHD/P973+fxMREuru7Wb9+PTk5OS7DceMdJtvb26moqJjw8Li36Bu0sPyxzwDIbq1iQ/7brKw5hNrhoM/XH63soCAuh9u+vVGRur1r56tcffBdHMdvSrXDwSezF/GDi37m9j3OSAnlmYszFYm49PT0KduYRsvkDQwMK2QJ59LAwMApDRriEN3Q0DDhDRDGys86U4yc5Wc7OzsValBcXNy0tFmNRiMlJSUEBQV5DBbOA3K9vb3KUL9Y74m6Fs70LU/eHFarlaNHjyrzFmVlZcTHxyut5iVLlnzRKVEngy9s3/wLgJk49V+OU1VVVVx22WXo9XrMZjPnn38+K1ascKHsjLdnC0prYmLilCVMXSYzl2wpwCHD1h8t5evP7KXPPJJt6DQSg7bhS0UrgRCTUgEOhm+wiVxI1y6fxe1nzVb2q6ysrEkrRAoIqo4zJUqWZWbNmkVUVNRJ06I9wXmOLjk5+YRdq9FwNxviTtGqt7eXkpKSaTEcFBgcHKS0tBSNRkN6errb/8lo9sJEqbvO9C1P/h92u52ioiKlCFZUVERERMSpRIk6GXw1Zza8wdVXX83pp5/O9ddfj8ViYWBggAcffPB/Ij24du1a+vv7WbJkCQ0NDXR3d/PCCy9MuFVuNpspKirC399/yirqMFytePqjUl7Kb1F+N6+lgk3/fJhBjc+w5q0sE2gd4sIrH6c9cPhgrR8y8q/Xbie6vwNkMPr6852rnqTB4F6h5PELs/jm3GhlWLmuru6kDurOQ/KiGuSsvCE4rP8NxaqhoSGKi4s9zlk4r9c5wHiaDXEHMQjf19c3bfxVkTzV19eTkpKCn5+fklw4myeJTftkr72uri6OHj3K7t27ueGGG5TkYt++fXR3dzN37lxWrlzJqlWryMrK+ipQor5UEWmKMROn/stS7sXFxeTm5tLT00NNTQ1PP/00mZmZE3odq9VKaemwr9JUVdRheI/q7u1j1bMHnX/LVN1Cp80O5rycKC5eNEv5nSjynYyohjsBD7Va7bKP+vr6Kp4ZYWFh06ZY5TxnkZmZ6ZbmNhHBEXcQg/BtbW1upXinAqNFSPR6vQt1dzzfkIlAKFG+//77bNiwgdLSUoUS1dLSQmZmphKn5s+f/6WhRI2DmWTDHfr6+pg/fz5VVVUuB8z/lfSgw+Fw2UB27NjBj3/8Y37+85+zdu3aCb2WUC5qbm4mJyfnpFwxnbsAoloxgC83fzgyvJjTWsnmfzzIgMYXNQ4MA/342S38/Os/YmvOsEdGSkc9b/75/wi0DA/cDWl82Lj2x3yUfprL+yWE+HLtikQuXeTamhwaGqKoqIiAgIBxkyd31RVB2TEYDON2Lf4bvhzOSk+pqanKIKhYs1iv2ARPtssizImEsdZUBSXnalt3d7fSyo+LiyMiIsJr86TxIAKRaDUXFxdTVFTEOeecw7p161i1atWEdMm/RPjKfeAJYCZO/Q/j1KFDh1i/fj033HADV1111YTvTbEnnux8m7Oku5BH7bH7cOeOPuUxGsDm+SUmBK0Kjvx8rEeWxWKhpKQElUo1bvLkSYLeGwGP/4YvB4wkT4IO7Ewxcl6viKsnE2NMJhPFxcXjdspPBs7U3a6uLoxGI5IkMWvWLCIjI6ekKyRi+d69e9mzZw+FhYUcPXqU0047jSuuuIJVq1ZNmxHvFxxfzQHxE6GqqoqIiAiuvfZajhw5wuLFi3nmmWdobW1VNuaYmBja2toAaGxsdJEZi4uLo7Gxcco28dE3wJo1a9ixYwfr169n+/bt/OY3v/HaIEmSJBISEggNDaWwsJCYmJhxq/ajh/mcuwAGg4HExESlWvFuaj/vHm2lrW+Qdw7ZqAiLJ6utmrCBXlSyHYtKy092voq/dYjXF5zH9w+/h85mUSRvNQ4bP8x7a0yyYbXLrFs8a8za/Pz8WLhwIQ0NDRQUFJCVlYVerx/TBXCurjiv1xuMnuWYNWvWlNKRRDVIlmUCAgI4duwYkiQRHR1NaGjoSXFuPUHwV2traykoKCAjI2PCQVyWZQYHB100w52rbWK9QhpQo9GcVCdFiBsISlR5eblCibryyitZsmQJbW1t3HrrrQQEBExIr38GM/gy4IsepxYuXMju3bu59dZb2bFjB88888yE9hsxN1dYWKg4RHs6DDoLTLjrAsyaNQs/Pz9kWeYnO3YqCcZUJRoAPmr3McHHx4d58+bR0tLC/v37lW78aGlfsd7g4GBlvd5i9CzHVHc5xL5vtVrR6/WUlZVRWlpKVFQUYWFhE17veAgICGDx4sU0NjZOakh9POquEEgR9C2VSnVShVdBidqzZw95eXkUFxcTGRnJihUruPDCC3nkkUcYGBjgrrvuYmhoiMTExAm/x5cdX+nOxv79+1m+fDmfffYZy5Yt47bbbkOv17Np06YvlPSgLMu88MILvPrqq2zevJns7OwJPd9ut1NeXs7g4CA5OTn4+Pi4DPM5Ky45S+We6KB9/gt5VHQM4m8Z5JFtz/C1yn2YfHT0+gbiZ7NgVWs4a8OL/OrDF1h35ENUyEiyjF2lpiw8gW9f84zL652RGspvvzff7XuJrkVHRwctLS2oVCpCQ0NdugBTteFORZfD3WD06OpVe3u7wpGNjIycliqIyWSipKRE6Qp5qh4J11ux3sHBQXQ6nYtmuKfv19lJPTMz02O1zdk4T1Cienp6vKJEybKMLMtfNLfU/ya+ciWyCWAmTvG/j1MAb7zxBg899BBPPvkky5cvn9BzHQ4HNTU1dHZ2MmfOHHQ6nVvFpYCAAJdu9Xh7gtXu4A97a3liR80kP9kIXr9uEfPj3FN/hOx4Z2cnTU1NwLAKYkhIyIQ8grzBVHQ5nJUtR+/7Yka0t7dXmQecDl8OGE4YSktLlaKfp8KbM9V4otRdISQiBuE9uZyLwqCzSlRLSwtZWVlKnJo3b964sXQmTo3FV7qzIYZply1bBsDFF1/Mww8//IWTHpQkiZtuuonVq1dz7bXXcsUVV3D99dd7fdOLCnpTUxOfffaZUoUODg4+acUlgKcumcO3Nxcw4KPjP6lLyG0sxqLREt/XhsoxLHR72eH3ORyTzhWHtilXocruoDzM1YgmyEfiF99MB8bKozp3LcLCwkhOTqa5uZm2tjaSkpKmvJU80S6Hs3OoCIruqiujERUVRWhoKKWlpQq/c6rdUAMCAli0aBFNTU0UFBSQmppKRESEQpETa7bb7cp609PTJyRDqFarSUtLU1zOe3t7Wbp0KYGBgdTU1LgY5/n5+bFs2TJWrVrFHXfc4TUlSpKkr2JLegYzOGXiFMC6detYvnw5V111FatXr+auu+6a0DxVeHg4VquVvLw8NBqN4hzu3E2dCLRqlaJANRW4/azZSqJxIhO6+Ph4urq6qK+vV9QjpxIn0+UYLTjirGzpad8PDQ1lyZIlVFRUjCsZPxn4+fkxf/58WltbOXDggKLwJZI3sV4h7+uNDP1oqFQqkpKSiIqKoqSkhEOHDjFnzhwiIiIUSpRQiQLIzc1l1apVrF+/fkKUqK9wojEuvtKdDYDTTz+dl156iYyMDO677z5MJhMwLPkpBu+6urp49NFH+fe//81zzz2nDN7deuut7Nu377+63sHBQTZu3Eh9fb3iG+CM0RKEYkMRXQt/f3+qq6vx9/cnLS1tSvSoD9X1cNemj/jLX39GbG8baocdJIkBrQ+DWh17EuextuRTNI7jLtuSiorwBL553XPK6/z+4hQifCxjjJ7Gq16JgbmIiAiSkpKm5TDqrsvh3AXo7e09aedQZ3R0dFBeXq6os0y1IpbJZKKjo4P6+nplNkRU2yYzIOcMi8XC4cOH+fOf/8y2bdvQarXk5OSwYsUKVq5cydKlS6dlFuYrgplMyzNm4tQXLE7ZbDYeeOABdu3axZYtW4iLixvzGGHiKQ6TwohW7PkiYcrMzJzU/iTLMmue+pRW4+TJVI+uTSQ7ZHgubmhoyCsTusHBQYqKipTZhOkQs3DX5TiR4MjJuJ0LMZWYmJgpn0kQFK7Ozk7q6uoUNoCIUwaDYUqoxjabjcLCQt566y3eeOMNVCoVaWlpStdi+fLlBAUFzRS3Tg4zA+KecPjwYUXhIzk5mVdeeQWHwzFGejA0NBRZlrn55pt5//338ff355VXXiE3N/d/su6tW7fyy1/+kvvvvx9ZlpEkiaioKOXgO54EodC8bmpqIjs7e9KdgSGrjUUP7Sa9rZp3/nAHMqBx2JFkGQloCzAQZepWrkIZaAkKY8WPXlNe49ULokiKDleUN7yFw+Ggurqarq4usrOzp0WByWw209jYSF1dHRqNBo1G4yKTNxnnUGfYbDaF7paVleX1fM5o2O12lyA+NDTkQuEaGhqiqqpqUomNMyVqz5497Nu3j97eXubNm8fKlStJT09n8+bN6HQ6Xn311ZmNe/KY+QI9YyZOfUHj1CeffMItt9zCxo0bCQsLw2g0kpCQgMn0/+zdeVxU9foH8M8ACi5sAqaIsojKOoAbuIPmitmmabnvW2Z5s+jnNbGuqWleK7XULNNMTSv1qrmLmgoCAiqgAoIbiCyC7AzM9/cHd85l2GQbWfy8Xy9exSznnBnG88xzvs/3+WapzQk0MDAo84vko0ePEBcXV2HZS2UphYDT537Vfn4nA+DL4eZSXNXT06vSGguqhi2qOYe1LT8/H4mJiYiNjYVMJoOOjo7aCte10cADKIq5d+7cwZMnT2Bvb1+tORCq7ZS8aFe8dFdV/l2TxEZ1oS0wMFAqiUpMTJRKouRyOX799Vfcv38fhw4dqrWOaC8wJhs1pSrX0dbWho6ODoKCgpCamoqxY8c+9z7np0+fxoEDB3Dp0iXcu3cPDg4OmDt3LgYNGlSlL76ZmZmVmjxeGek5+ZjwUzC+XTMTVqkP0bSwABACWhBQQgbtEh+lpOaG6LlgFwBg8eCOmNqrQ7X3DUCaAFbT16JUKqW2fsXb5aquXKWmpiIrKwsODg7VTgaeJTU1Fbdv3670JPWyhsZVAVG1FkdJCoUC0dHRyMnJgZ2d3TNHHFT11CVLojw8PNC3b1/06dOnzHknqjkpVGNMNsrHOPVf9SlOXblyBXv37sXFixcRExMDW1tbTJ06Fa+99lqVvvjm5OQgPDxcKp2pSZnKsG8u4V5aXrWeu3S4Ld7u0f7ZD6xAVlaWtEhfTSZ2V9R+1sDAABkZGUhNTdVox6qMjAxERkZWuuthRaW7xdeMKq6wsFC6mFiZ1yKEQHx8vDSROygoCFpaWtLCeX379i3z+wHjVK1hslFTVlZWCAoKUuuW8NFHH9VJn3M/Pz/IZDJ0794denp6WL16NY4cOYItW7bA2tq6SttSrTyelZUFR0fHGs0ZSM7MxbuLf8b+Xz6CtrIQWhDldjfP0WkKh3/8AQA4vdADbQ1r/sW9eMlTZZOBkosnqWpCK2qXqxpKru2OVWW9lpJrZqjmsxRPhmoyNP7kyROp24iFhYV0ZSc/P79UlyhLS0u1hfNYEvVcMdkoH+PUf9WnOBUYGIgnT56gZ8+eMDQ0xA8//IDvv/8eGzduhFwur9K2hBCIi4tDcnIyHB0da3TuOR2ZiAX7Iqr8vPClnrU2MqAqeXJwcKjUyIBq7oIqVlWmXa6qzLi2W6CX91rs7OykEZvykiFVYlHV0l3VfEBjY2O1uTuqkijVwnmRkZFo06aNFKfc3d1ZEvV8MdmoqbJO4nXV57wsAQEBmD17NhYuXIixY8dW+fmqFahVE4gro7wTYOaEBfCMCYIMRauzlvlcmRY6fXQIFkZNceK9PlU+3oqovkC3b98e5ub/W7NDNaFPdQIs2c61KiVc1UlsqiM1NRWRkZHSBPP8/Hy1lVlrY8XzwsJCHDlyBMuXL4eHhwfu3LmjVhKl6hLFiW91itGyfIxT/1Xf41RkZCSmTJmCt956C3PmzKnyuSs9PR2RkZHo0KFDpUtAVR2XVOf9nJwcrL+aj+splZ843tfGCFsmuFXpWJ9FNTKgWk27eJwq3s716dOnkMlkaqPVlW0/+7zW5UhPT0d4eDiaNGkCbW3tKq0dUllKpRJXrlzB7Nmz0a9fP9y7dw+PHz+W5gX27dsXzs7OL8LCefUZk42asra2hrGxMWQyGWbPno1Zs2ZJi7KpqFoPjhw5Ej4+PmqtB1evXq3xutmnT59i7ty5kMlk+Oqrr6p8YsnPz0dERAR0dXXRuXNntYlsZXXeUJ0AVScU1QkwKTIa5j26QkcUlrsvAcDm48P4YbwcvTualPu46iooKMCtW7eQlZUFY2NjZGZmShP6VFdXauMEWJujHBUFmby8PKl1cXVrZFVU81xUoxYhISHQ09ODo6MjLl68KH1eOXJRrzDZKB/j1H81hDiVm5uLjz/+GNHR0di0aVOlL26pqM7thYWFsLe3V7tCXrIz4NOnT6WOS6o4pSrXmbbjKvzj0iu1z2tLBkBHWzMjA9HR0UhNTYWJiQmys7OluQvF57PUdFJ5bY9ylNfVSpXYOTg4VGuBxuKEEHj48KFUEhUcHAwtLS24uLggMDAQDg4O2LBhQ43n8lCtYuvbmrp48SLMzc3x+PFjDB48GHZ2duU+tqwE7nkM4xkYGGDXrl3YsWMHhg0bhm+//bZKNbhNmzaFi4uLtMiOhYWF1Oe8eOcNc3PzcjtvAICZvS0yXnkVRof+KPeTp7rdKC8RhYVGtdIVq2SQUb2mhIQEWFlZ1XheSlmMjIzQo0cPxMTE4OrVq1Ua5VAtpKhKLop3tWrTpk2phC8jI6NaCzmpukSpkovo6GhYWVmhd+/emDRpEjZs2CAdc2FhIbZs2YKcnBwmG0QNTEOIU3p6evj6669x5MgRvPLKK1ixYgUGDRpU6efr6OjA0dERiYmJUpxSKpWlOgO2bt26wo6LP07qijm7QnA+RpWICfW0tdh7EX7jOuzt7WulG1JeXp5am/TCwkLo6enh0aNHMDc3h7Ozc62PIrds2VJa6DUoKKhKoxwVle6ampqWWv07OzsbkZGRz1zbqaSKSqJGjx6NNWvWSCP5Qgj8+uuvSE1NZbLRQHBkoxp8fX3RsmVLbN26td4MT5cUHR2NyZMnw9vbG++9916FJ6/iq0WrvqgrlUrk5+ejVatW6NixY5XWXQAApKZC18oaWoXltxuct/0SFvU2xf3792Fvb1+lPuQVfVFXDTergoxCocCtW7egVCpLXQmrTc8a5VAlbqpjVi2kqBppqczkftWw+OPHj2FnZ1fqPRNC4MmTJwgICIC/vz8CAgKQkZGhVhJlZ2fHkqiGhyMb5WOcKkNDiFMJCQmYMmUKHB0d8emnn1b4Zb6stu6FhYVQKBTQ19dHp06dqtVxaczWKwhPyCz7UySTYU4fC4xzNkRMTEyVV7lWtZ8tvmaUquGIatRCFY8KCwsRHR0tNSCprZW6S3rWKEfxtS3S0tIqNY+xJNWoxIMHD9CpU6cyW/RnZmYiMDBQugjGkqhGgWVUNZGVlQWlUgl9fX1kZWVh8ODB+PTTT3H69Ol62+ccKPpyu3TpUoSEhOC7776TgkhZrVFVoxaqbhba2trSEG9mZma1Tn5NFi6Ezg8/lHt/YvITGDRriuzsbERERMDY2LjcK/aqnuwlO1moToCV+aKemJiIO3fuVGleSlWpAkZGRgasra2l0Zbi3UJUCVFNrpJlZWXh8uXLOHLkCKZOnYqwsDAEBAQgNDQUzZo1U+sSZWZmxglyKPrbdO/eHe3atcPhw4cRGxuLcePGITU1FV27dsXOnTvRtGlTwascxQAAIABJREFU5OXlYdKkSQgODoaJiQn27t0LKyuruj58/gHLxziFhhunlEolvvrqK/zxxx/YsmULOnbsCKDo36vqglLxUYvi5UU6OjrS5PGkpCQ4OjpWq/15j8+OI0uUfREq/FMvyGQy5OXlISIiAnp6eqVGnVUUCoXaqEV1vqiruhFWZV5KVRW/aGVjY4OCgoJSpbuqhKgmTWNyc3MRGhqKnTt3Yu7cuYiIiFArierRowf69OmDfv36aWx18oamscYpJhuVcOfOHbz++usAirL+d955B0uWLEFKSkqpPueGhobo1q0b0tLSpPaDOjo6yM/Pr5MPihACv/zyC5YsWQInJyckJiZi7dq1pSabVfSPXDV5vGPHjtIqtZWSn49mFQxx5vx3YSrVcaq6jdjb20MIodbJonhP9posQpefn4/IyEjo6OigS5cutXblpPgkxLS0NGRlZUGhUKBVq1awtLSEgYFBrYwmFC+Junz5MpKSknDr1i2MGTMG48ePR8+ePTV2RayhW7duHYKCgvD06VMcPnwYb731Ft544w2MGzcOc+bMgYuLC+bOnYtNmzbh2rVr+P7777Fnzx78+eef2Lt3b10fPqNw+Rin0LDjFAAcPXoU8+fPh52dHR48eCCtkl7Z9YyePn2KiIiIUo1BKkO29zfYR5qolU4BAIRAxLKB0u3Fr9irFhtUnfMzMjKgra0tHW9NFqFTzUspKCiotfItoHRFQGZmJvLz82FgYAArKysYGdW8pBkoOv4bN25IcSo+Ph6RkZEYNmwYpk+fDnd391ppbtIYNdY4xWSjltWnD8rt27cxevRoWFtbQy6XIzAwEJaWlli5cmWVv5CqvqQ3adIEXbp0qfQJSeett9DkyJEy71MlG6r2s+np6UhOTkZmZiZatmyJtm3bwtjYGC1atKjVsh8hBBISEnD37l106dIFrVq1qvI2VHW3JSfIqa4G6enplRoZqmrHqrJKop4+fQoXFxfpalCXLl1w7949zJ49G2vWrKlyS8kXxYMHDzB58mQsWbIE69atw3/+8x+YmZnh0aNH0NHRweXLl+Hr64vjx49j6NCh8PX1Ra9evVBQUIA2bdogKSmprgMjo3L5GKeqqD7FqfT0dPTv3x/m5ubo1q0bbty4gaZNm+Lrr7+uUmktUHTR59atW1AoFHBwcKj8RancXBSatYHzh3+o3Wz6+B7Of/YKYGKCgoIC6YJSamoq0tPT0axZM5ibm8PY2LhWGo6UlJSUhOjoaNjY2OCll16q8vOLl+6mp6ejoKCgVOmuEKJGHauEEMjIyJBKovz9/ZGcnAwnJye1kqiUlBQsWLAAU6ZMwfDhw6v8Wl4EjTlOMdmoRfXtg6L62xZvqbdhwwbs3LkT33//fYWTB8vbnuqqjoODQ6VXQW1WzrB2cGBgme1ndXR0EB0djezsbDg4ONRoGLciubm5iIiIkCaylZdAFZ8gV3ykpXjP8IpGSCrbsUq1MquqhrUqJVGqVeSpbKNHj8Ynn3yCjIwMrF27Ftu3b4eHhweio6MBAPfv38fw4cNx48YNODk54dixY7CwsAAAdOzYEQEBAVWq1dYA/nHLxzhVBfUtTgFF577iX9R3796N1atXY/369ejZs2eVt/f48WPExMRU6WKSXosWsPngd6DJ/0YRYr98BSFXriA9MxMymUytq1XTpk1x9+5dpKSkwMHBoVrlW5WRn5+PmzdvQktLC126dCk3gSre2r06pbuV7VglhMCDBw9w6dIlBAQEIDg4GNra2molURWNLDFWla8xxynOvqlF77//Pr788ktkZGQAKCo/MjIykr6IWlhY4OHDhwCAhw8fon37otVIVSeElJSUWv2glPwHLZPJsGDBAgwYMADTpk3DlClTMHXq1Er/w5fJZLCwsICxsTHCw8NL9QcvS2FhITIGDYL+6dNqtxcAMDc3L3eycpcuXZCSkoKrV69W+6rOs+jp6cHNzQ0PHjxAYGCgNEm9+AS59PR05OXloWXLljAyMoKlpWWVh39VHauio6Nx5coVGBsbo3PnzsjLy0NoaCj8/f3h7++PmJgYWFtbo3fv3pgyZUqVSqIa48k7NzcX/fv3R15eHgoKCjB69GgsX74cU6ZMwblz56Srntu3b4erq2u5KyIfPnwYrVu3Rrdu3eDn5weg4k48ddWlh+h5qG9xCkCpGPD222/Dw8MDkyZNwssvv4xFixZVqbyndevWMDAwQHh4OFJSUtCxY8cKRx2USiWSVq1CrM+bcHh3B3L0DPGP0z9CCaB127boVKzhSHE2NjYwMTHB9evXYWFhoZF5B02bNoVcLsejR48QFBQkTVIvWbpbvLW7hYVFlUdaVB2r4uLiEBgYiObNm8PZ2RkFBQW4fv26NGpx69YtmJubo3fv3hgzZoxal6jKaGznUsapymGyUUsa0gdFLpfj/Pnz+OCDDzBhwgR8++23VSolatGiBbp37y61e3V0dJS+FJdcJ0IIAcMvv0T7776DyX8niys7d4YiJATP6sJtYmKC7t27IzIyEklJSRVe1akJU1NTKJVKhIWFAQB0dXWlUYt27drVyhwILS0tmJmZwd/fH19++SX09PSgq6sLV1dX9OnTB6tWrUKXLl3YJaoYXV1dnDlzBi1btoRCoUDfvn2l4fc1a9Zg9OjRao//66+/EBUVhaioKAQEBGDu3LkICAjAxYsXcejQIRw9ehS5ubl4+vQp3n//faSlpaGgoAA6Ojp48OABzM3NARR92bp//z4sLCykxLM6pXZE9U1DilPW1tY4c+YMfH198eqrr2LLli3Sv9HK0NPTQ9euXaV2r8Unj5fZcGTQICh8fRHuOwkAILS1kff0KVo9I8kxNDREjx49EBUVhdDQUI2NxhsbG6OgoACRkZFQKpVS+1kjIyPY2dk9c+5lZchkMpiamiIsLAy+vr7SIn3Ozs7o3bs3li5dCicnp1qZ19FYME5VDpONWtLQPijNmzfH5s2b8fvvv8Pb2xtr1qyRFneqDC0tLXTs2BEPHz5EQEAAmjVrJvULNzQ0ROvWrdV7bH/9NXK//rrKx9mkSRPI5XIkJCQgODgYXbp0qVFfbdUEOVWgycrKklrmOjk5IS0tDUlJSdJaIjXZT0xMjFpJVIsWLeDh4YFVq1bh7NmziImJwdq1a+t62LPekslk0gKGCoUCCoWiwmB68OBBTJo0CTKZDB4eHkhLS0NCQgJWrlyJlStXAgD8/Pywdu1a7Nq1C2PGjMH+/fsxbtw4/Pzzz3j11VcBAKNGjcLPP/+MXr16Yf/+/Rg4cGC9vmJEVFkNLU41adIEK1asgJ+fH958800sWbIEI0eOrPTzZTIZLC0toauri+DgYOjq6kKpVEoNR1q1agVra+v/XcRycEDu4sVVPk5tbW3Y2dkhOTkZV69erXozlRIqKt21t7dHdnY24uPj0aZNmxrFw4pKopYtW4br16/j/PnzWLFiBaytrau9n8aMcapyOGdDA1QflMOHD2PMmDF48803pYl3crkc8+bNw8aNG3H9+nVp4t0ff/yB3377rU6O98GDB5g0aRLc3d3xySeflDv/ID8/Xzr5FV8nQl9fH0lJSdDT04OdnZ3Grnrk5uYiPDwc+vr6sLW1rdQoQPFWhKqAqq+vL10RKqsvu6p21czMDJaWlpXaT15eHkJCQqSSqDt37sDGxga9e/dGnz590KNHj1IjJIGBgXB1ddXYuh+NQWFhIbp164bo6GjMnz8fq1evxpQpU3D58mXo6upi0KBBWLVqFXR1dSu1InLxf5t37tyRWgq6ubnhl19+ga6uLnJzczFx4kSEhISgVatW2LNnD2xsbOrqLVCpv1Gk7jFOVUNDi1OpqamYPn06zMzMsHLlynIbbqgajqjO+/n5+VKcSktLgxACDg4OtdbhqaTqdDysqHS3vJa5OTk5iIiIgL6+Pjp27FipuKtQKKQuUSVLovr27Qt3d/dSMfH69euwsrKq0cW3xo5xSsIJ4s9TQ/ygFBYW4osvvsCJEyewZcsWtGvXDvHx8dDS0pJa5Ono6Ki19Sv+JVkIgfj4+Got0FcVQgjcu3cPiYmJpTpnqCbIqU7aqsnn1WlFqFQqERsbi9TU1FKT/4QQSE1NlRKLgIAAZGVlSSVRffv2RefOnVkSVYvS0tLw+uuv49tvv4WJiQnatGmD/Px8zJo1Cx07dsSnn34Kb29vfPLJJ2on8S+//BLdunWr46OvFUw2ysc4VQ0NMU4JIbB582Zs27YNmzZtgr29PR4+fAgtLS1pQdqSDUdKljSpJo937ty51GJztXmcCQkJuHfvHuzs7GBkZKR2n6rcWNWFsfjaFoaGhpUu3RVC4P79+0hISCi1yKuqS9SVK1ekEfaUlBS1LlEsiapdjFOcIP5ceXp6wtPTE0DRBLKSCyUVn1Ckq6uL4cOHw8bGpk4Xb8nMzIS7uzvi4uLQr18/6Ovr47XXXsOcOXPQoUMHtGzZssIvzzKZDO3atYORkZFaR4vaHtZTDYubmJggPDxcCibp6enIycmRFiesaPJ5ZajKxMzMzLBq1SoUFBSgS5cuuHLlCsLCwqSSqH79+sHHxwempqb1egjzeSpvwlxNPt9GRkbw9PTEsWPH8OGHHwIoqpWdOnUq1q5dC+B/JR8qxctBiEhdQ4xT2dnZsLOzQ58+fTB8+HC0aNECnp6e8PHxqfQ5v3Xr1jA0NJQmj1d2lLwqZDKZ1BI3PDwcenp60NfXR3p6ulrpbuvWrdGpU6dqf+GXyWTo0KEDTExMsHXrVkRFRaFXr14IDg7G1atXoa2tjZ49e6JPnz549913q7z+SGPGOPV88dJrHVBNKAoLC0NoaCiOHTsGf39/fPzxx/jggw8QFRUFY2NjbNu2DQCwbds2GBsbIzo6Gh988AE+/vjjWj+mffv24fjx4xgxYgQuXLgAd3d3pKSkwNjYuEoL0rVo0QLdunVDYWEhrl69itzc3Fo7xvz8fDx+/Bi3b99GZGSktA5FQkICOnToAA8PD7i4uEiLE1U3gOTl5cHf3x/r16/HrFmzcPLkSZw/fx7//ve/4e3tjcuXL+Pvv//G2rVr8dprr3GF7hJq6/OdlJSEtLQ0AEUlA6dOnYKdnR0SEhIAFF25O3DgAJycnAAU1bDu2LEDQgj4+/vD0NAQbdu2rYN3gKjhq49x6vjx4/j999/h7u6OixcvYuTIkUhNTUWLFi2qdM7X1dWFm5sbdHV1ERQUhMzMzFo7RoVCIa2PER4eDoVCgczMTNy/fx9t2rSBh4cH3NzcYG1tjVatWlU70VAoFLh69So2btyIOXPmYPfu3YiIiMDnn3+Onj174uzZs/D398c333yDsWPHcoXuEhinnjMhREU/VIKlpaXYuXNnrW0vKytLuLm5CX9/f2FiYiIUCoUQQohLly6JIUOGCCGEGDJkiLh06ZIQQgiFQiFMTEyEUqmstWMoi1KpFD/++KNwc3MTf//9t8jKyqryz4MHD8SpU6fEnTt3qvzczMxMkZiYKG7duiUCAgLE6dOnxfnz50VYWJi4e/euSEtLU9vP6dOnRXR0tMjMzKzyfu7duyd+++03sWjRItGnTx/h6uoqpkyZIrZu3SoiIyNFYWGhEEKICxcuiJkzZ2r0fW9savL5DgsLE66ursLZ2Vk4OjqK5cuXCyGE8PLyEk5OTsLR0VGMHz9eZGRkCCGKPrPz5s0TNjY2wsnJSQQGBtbBK9aYZ52rX+QfKuFFiVNCCHHw4EHh7Owsjhw5Uq049ejRI3HmzBlx8+bNasWPx48fi6ioKBEYGChOnz4t/Pz8RGhoqIiNjRVPnjwptZ/IyMhq7Sc+Pl4cOHBAfPzxx8LT01PI5XIxfvx4sXHjRhEWFiYKCgqEEEKEhYWJcePGSXGLno1xqtaUe55mGVUdKTmhqGPHjnXe67w4mUyGqVOnok+fPpg8eTJee+01zJ8/v0qjBcbGxlLr2uTk5AonyxUWFkpzLdLS0pCXlyddrXpWGZdqP7du3ZJWQS1v0rVqVW/VfAtVSVSvXr3Qv3//Ckui+vbtW6WOXS+y2vh8y+VyhISElNr2mTNnytynTCbDxo0bNfSKiF489T1OAUVXirt3747Jkyfj7Nmz+Oc//1mlphv6+vro3r07oqKiEBYWVuHkcaVSqba2RVVKd1X7iYmJQUhICBwcHMqdm6FUKnHv3j1prsXVq1eho6MjlUS99957aNu2bZlxSi6XY/fu3ZV+/S8yxqnnh8lGHdHW1kZoaKg0oSgyMrLUY+pDr/POnTvj3Llz+L//+z+MHj0a3333XZUW2GvSpAmcnZ2RkJCAoKAgafJ4yfU4AEirs5qbm1d5bQsdHR04OjoiMTERgYGBKCwsRL9+/ZCbm6vWJSo2NhYdO3ZE7969MWPGDPTo0UNjK5Q3JPfv38ekSZPw6NEjaGlpYdasWVi4cCF8fX2xdetWmJmZAQC++OILjBgxAgCwcuVKbNu2Ddra2vjmm28wdOhQaXsN5fNNROVrKP+Ozc3Ncfz4cXz55ZcYMWIENm/eXKWJ7KrWtUlJSQgODpYmj6s6MKqtx/HfONW5c2c0a9asSq9RW1sbnTt3RmpqKkJCQpCbmwsvLy8oFApcu3ZNSi5u3boFCwsL9O7dG+PGjcO6deuk9qovMsaphovJRg1kZ2fj7bffRkFBAX777Te1jkWVpZpQ5O/vX297nTdt2hRr167F8ePH8dprr2H58uUYMmRIpZ8vhEDLli1hZmaGkJAQyGQy6OvrSxPk1NbjqAEhBLS1tfHo0SOsWbMGWVlZaNmyJbp27Yo+ffpg7dq16NSpE7tElUFHRwdfffUVunbtioyMDHTr1g2DBw8GAHzwwQfSZDeViIgI7NmzB+Hh4YiPj8fLL7+M27dvl6o/bgifb6LG7EWJU1paWvDx8cHAgQMxceJELFiwAOPGjav084UQaNasGdq2bYvw8HAolUqp/Wyp9ThqQBWnnj59ijVr1mDhwoXSHJLevXtj2bJlcHR0ZJeoMjBONVz81lVNjx49woABA2Bubo5Dhw5V6QRe1oQie3t7eHl5Yf/+/QBQ5uItAOp08ZahQ4fi5MmT2Lp1K3x8fJCXl1fm4xQKBZKTkxETE4Pg4GBcuXIF9+7dg66uLrp27Qpzc3MolUq0bdsWrVq1qnaioVQqcevWLfz888+YO3cu+vTpg/Hjx+PatWtYvnw5Zs+eDR0dHXzyySeYPn06V+iuQNu2bdG1a1cARcP9qpaS5Tl48CDGjRsHXV1dWFtbw9bWVupm01A/30SNzYsYp3r27Inz58/j7NmzmDlzJjIyMsp8XGFhIVJTU3Hnzh1cvXpVWh9JS0sLLi4usLa2RmFhIdq0aQNTU9NqJxpKpRJxcXHYvXs33n//fQwYMABvvPEG/Pz88P7772Px4sVo0qQJFi5ciHnz5kEulzPRKAfjVMPFkY1qiIiIwKefforZs2dXq+NGQkICJk+ejMLCQiiVSrz11lsYOXIkHBwcMG7cOPzzn/+Em5sbhgwZAi8vL8THxyMhIQG///47rK2t8f3332Pw4MGIi4uDlZUVfvvtNxgbG0MIgYULF+Lo0aNo3rw5tm/fLv3DrC2tW7fG4cOH8fXXX2P48OHYtGkThBB4+vQpDAwM8PTpU2hpaUk9wy0sLEqVKRkYGODJkycICwuDlZUV2rRpU6l95+bmSkHB398fcXFxUknUzJkzyyyJevXVV6tU9kVAXFwcQkJCpI4vGzZswI4dO9C9e3d89dVXMDY2xsOHD+Hh4SE9p3hta2U/39OnTwcATJ8+HRMnToStra3Uy5+IauZFjlP6+vrYuXMnfvnlFwwbNgxff/01WrVqhUePHqFVq1aVKt1VrTAeHh4Oc3NzWFhYVOrLZcmSqNu3b0slUW+//Tb+/e9/l0r6Ro4cyS+uVcQ41bBwUb8qsrKyQm5uLkxNTeHv76/ROsqEhAQkJCSoDRkeOHAA27dvR6tWreDj44NVq1bhyZMnWL16NY4ePYpvv/0WR48eRUBAABYuXIiAgIBaP67Q0FCcOXMGR48eRXBwMDp27IhZs2bB29sbBgYGlb4qo1AocPPmTchkMtjZ2amNcAghkJycLCUWV65cQXZ2Nrp27SotSMSSqNqXmZmJAQMGYMmSJXjjjTeQmJgoTZhfunQpEhIS8OOPP2L+/Pno1asXJkyYAKDoRDxixAi8+eabdfwKGi1+Eykf41QJjFPArVu3cPLkSZw4cQLnz5+HpaUlJk6ciHHjxsHAwKDSI+qFhYWIiopCTk4OHB0d1SaPCyGQnp6utnDekydPIJfLpTjl4ODAkYpaxjhVb5Ubp/hNrRpWrVoFZ2dnvPzyy3jy5InG9lPekOHBgwcxefJkAMDkyZNx4MABAEVDhpMmTYJMJoOHhwfS0tKkXs+1KSgoCMbGxti0aRPu378PV1dXHD9+HDKZrEonVdXkcRMTE8yaNQu//vqrWknUhAkTEBwcDC8vLxw8eBBXr17Ftm3bWBL1X/fv34eXlxfs7e3h6OiIr7/+GgCQmpqKwYMHo1OnThg8eLD0GRVC4L333oOtrS3kcjmuXr2qtj2FQoE333wT48ePxxtvvAEAeOmll6CtrQ0tLS3MnDlTGoJ+URcmImooXvQ4de3aNTRp0gQrVqxAYmIiRowYgWPHjiEvL69KpbuqyePt27fHhx9+iC1btuDXX3/FwoUL0b9/f7z55pvw8/ND9+7dsXv3boSGhmLnzp2YO3cunJ2dX/hEg3GKAHCdjapS9S8vLCwUM2bMEHK5XDx69Ejj+42NjRXt27cX6enpwtDQUO0+IyMjIYQQ3t7e4sKFC9LtAwcOfG49nH/77Tchl8vFqVOnKtU3PCUlRZw8eVJ8/vnnwtvbW3Tv3l20bdtWDB48WPj5+Ync3NznctwNWXx8vAgODhZCCPH06VPRqVMnER4eLhYvXixWrlwphBBi5cqV4qOPPhJCCHHkyBExbNgwoVQqxeXLl0XPnj2lbSmVSjFx4kSxcOHCUvtQWbdunRg7dqwQQogbN24IuVwucnNzxZ07d4S1tbXU5500oq7XsqjPP1QC41TZTp06JZydncW+ffsqFafS0tLEuXPnxKpVq8Trr78u3NzcRIcOHYS7u7v466+/RGZm5nM57oaMceqFwnU2apuWlha2bt2KRYsWoX///jh58iQ6dOigkX1lZmbizTffxPr162FgYFDu40QdtmYbM2YM3N3dMWnSJPTt2xcfffSRdPVIlFESlZOTI5VErVu3Dra2tlAqlfjiiy9w7do1DBgw4Lkcd0PWtm1baeXRklcU/fz8ABRdUfT09MTq1avLvaLYtm1bXLx4ETt37oSzszNcXV0BFLUPVF2pk8lksLKywubNmwEAjo6OeOutt+Dg4AAdHR1s3Ljxhb+CR1TfME6pGzRoEM6cOYNp06bh9OnTWLFihTRXQ/y3JCogIACXL1/GlStX1Eqili9fDgcHB2hpaeG7777D33//jWHDhj2X427IGKcIAEc26rv8/HwxZMgQ8dVXX0m3de7cWcrk4+PjRefOnYUQQsyaNUv8+uuvZT7ueVEoFMLX11d4eHiIzz//XEycOFG4uLiIfv36iY8++kgcOnRIJCcnP5eVZV8k9f2KItVYXY8e1OcfqmMNLU4plUqxYcMG4ebmJj777DMxffp04ebmJnr16iUWLlwofvvtNxEfH884VcsYpxq9cs/TL3bRez0nhMD06dNhb2+PRYsWSbcXb8FWsjXbjh07IISAv78/DA0NpSsKz4uOjg6WLVuGWbNmIS4uDnPmzEFAQADOnz+P1atX45VXXoGJickL23lj2rRpaN26NZycnKTbfH190a5dO7i6usLV1RVHjx6V7lu5ciVsbW3RpUsXHD9+vMxtNoQrikTUODXEOCWTyTB//nysWrUKERERmDBhAi5cuIBLly5h/fr1GDNmTLkrdL8IGKeo1lWUiTznjIhKuHDhggAgnJ2dhYuLi3BxcRFHjhwRycnJYuDAgcLW1lYMHDhQpKSkCCGKrtbMmzdP2NjYCCcnJ14NqIfOnTsngoODhaOjo3TbsmXLxJo1a0o9Njw8XK3e1MbGplS9aUO7okjVVtejB/X5h+oQ41TjwzhF1cSRjYaob9++EELg2rVrCA0NRWhoKEaMGAETExOcPn0aUVFROH36NFq1aoVp06bhpZdewrlz5xATE4Pr16/DxsamWt0eSHP69+9f6VVHK1qQCGiYVxSJqHFhnGp8GKeotjHZaCSmTJmCY8eOqd22atUqDBo0CFFRURg0aBBWrVoFAPjrr78QFRWFqKgobNmyBXPnzq2LQ6ZiNmzYALlcjmnTpknB9uHDh2jfvr30mOILEgGQJsudOXNGbWjbx8cHJ0+eRKdOnXDy5En4+PgAAEaMGAEbGxvY2tpi5syZ2LRp0/N9kUT0QmOcatgYp6i62I2qkejfvz/i4uLUbqtOtwd6/ubOnYulS5dKCxL94x//wI8//vjM2lXVFcWynD59usznbty4sfYOnIioChinGi7GKaoJjmw0YomJidKJuW3btnj8+DGAZ1+JIHVlTZar7oJEZeGCRET0omKcqh2MU1SfMdl4AT3rSgSp0/TQf/HVc//8808pWIwaNQp79uxBXl4eYmNjERUVhZ49e9biKyMiqp8Yp6qGcYrqM5ZRNWIvvfSSNOyckJCA1q1bA+CViKqqzaH/t99+G35+fkhOToaFhQWWL18OPz8/LkhERC8kxqnawThF9RmTjUZM1e3Bx8enVLeHDRs2YNy4cQgICGC3h2qo6tC/6rG7d+8uta3p06eXu58lS5ZgyZIltXnoRET1BuOU5jBOUX3BZKORKOtKhI+PD9566y1s27YNHTp0wL59+wAUdXs4evQobG1t0bx5c/z00091fPSaZWVlBX19fWhra0NHRwdBQUFITU3F2LFjERcXBys9paRlAAAgAElEQVQrK/z2228wNjau8b449E9EVDbGqfIxTlFjxmSjkSjrSgRQ9W4Px44dw8KFC1FYWIgZM2ZI7egaurNnz8LU1FT6XVXL6uPjg1WrVmHVqlVYvXp1pbfHoX8ioqphnKoY4xQ1VpwgTpLCwkLMnz8ff/31FyIiIrB7925ERETU9WFpxMGDBzF58mQARbWsBw4cqNLzG9qCRFZWVvjXv/4FLy8vtGzZEs7Ozrh27Rp2794NW1tbGBoaYsaMGSgoKKjrQyUiKhfjVOUxTlG9UdHy4hpf2JzqlUuXLokhQ4ZIv3/xxRfiiy++qMMjqh1WVlbCzc1NdO3aVWzevFkIIYShoaHaY4yMjMp9/rhx40SbNm2Ejo6OaNeunfjhhx9EcnKyGDhwoLC1tRUDBw4UKSkpQgghlEqlmDdvnrCxsRFOTk4iMDBQcy+sCiwtLYWtra2IiIgQ+fn5Yvz48cLGxkbMnDlTZGZmirt37wozMzOxa9euuj5UKtuzztUv8g+9QBinysY4RfVAuedpllGRpKxJYwEBAXV4RLXj4sWLMDc3x+PHjzF48GDY2dlV6fm1NfRf12bNmgV7e3sAwDvvvINdu3bB398fLVq0QIsWLeDp6YnAwEC88847dXykRERlY5wqG+MU1WcsoyKJqKeTxo4dO4YuXbrA1tZW6hNeFapa1NatW+P111/HlStXpFpWAGq1rI1Z8WHy5s2bQ1tbG2ZmZmq3ZWRk1MWhERFVCuNU48Y41Tgx2SBJfZw0VtP63KysLOnElJWVhRMnTsDJyancWlYiIqq/GKeIGh6WUZGkR48eiIqKQmxsLNq1a4c9e/bg119/rdNjunLlCmxtbWFjYwMAGDduHA4ePAgHB4dKPT8xMRGvv/46AKCgoADvvPMOhg0bhh49epTZbpGIiOovximihofJBkl0dHSwYcMGDB06FIWFhZg2bRocHR3r9JhqWp9rY2ODsLCwUrebmJiUWctKRET1F+MUUcPDZIPUjBgxAiNGjKjrw5DU1/rchiYuLk7td09Pz1LtA7dv3/78DoiIqJoYpxonxqnGi3M2qF6rj/W5REREKoxTRBVjskH1WvH63Pz8fOzZswejRo2q68MiIiICwDhF9Cwso6J6rT7W5xIREakwThFVTFZWrWExFd5JRETPBQvAy8c4RURU98qNUyyjIiIiIiIijWCyQUREREREGsFkg4iIiIiINILJBhERERERaQSTDSIiIiIi0ggmG0REREREpBFMNoiIiIiISCOYbBARERERkUYw2SAiIiIiIo1gskFERERERBrBZIOIiIiIiDRC5xn3y57LURAREVUP4xQRUT3GkQ0iIiIiItIIJhtERERERKQRTDaIiIiIiEgjmGwQEREREZFGMNkgIiIiIiKNYLJBREREREQawWSDiIiIiIg0gskGERERERFpBJMNIiIiIiLSCCYbRERERESkEUw2iIiIiIhII5hsEBERERGRRjDZICIiIiIijWCyQUREREREGsFkg4iIiIiINILJBhERERERaQSTDSIiIiIi0ggmG0REREREpBFMNoiIiIiISCOYbBARERERkUYw2SAiIiIiIo1gskFERERERBrBZIOIiIiIiDSCyQYREREREWkEkw0iIiIiItIIJhtERERERKQRTDaIiIiIiEgjmGwQEREREZFGMNkgIiIiIiKNYLJBREREREQawWSDiIiIiIg0gskGERERERFpBJMNIiIiIiLSCCYbRERERESkEUw2iIiIiIhII5hsEBERERGRRjDZICIiIiIijWCyQUREREREGsFkg4iIiIiINILJBhERERERaQSTDSIiIiIi0ggmG0REREREpBFMNoiIiIiISCOYbBARERERkUYw2SAiIiIiIo1gskFERERERBrBZIOIiIiIiDSCyQYREREREWkEkw0iIiIiItIIJhtERERERKQRTDaIiIiIiEgjmGwQEREREZFGMNkgIiIiIiKNYLJBREREREQawWSDiIiIiIg0gskGERERERFpBJMNIiIiIiLSCCYbRERERESkEUw2iIiIiIhII5hsEBERERGRRjDZICIiIiIijWCyQUREREREGsFkg4iIiIiINILJBhERERERaQSTDSIiIiIi0ggmG0REREREpBFMNoiIiIiISCOYbBARERERkUboPON+8VyOgoiIKiKr6wOoxxiniIjqXrlxiiMbRERERESkEUw2iIiIiIhII5hsEBERERGRRjDZICIiIiIijXjWBHEiIiJ6ASkUCjx48AC5ubl1fShENaanpwcLCws0adKkrg/lhSMTosJGHuzyQURU99iNqnyMUxoSGxsLfX19mJiYQCbjR5AaLiEEUlJSkJGRAWtr67o+nMaK3aiIiIio8nJzc5loUKMgk8lgYmLCUbo6wmSDiIiIysREgxoLfpbrDpMNIiIiqpdWrFgBR0dHyOVyuLq6IiAgAACwfv16ZGdnS4+zsrKCs7MzXF1d4erqivfee0+67/3338f58+elxyUnJ1d6/3l5eXj55Zfh6uqKvXv31tKrKtuzju3AgQOIiIiQfv/www9x5swZjR5Tbdi+fTveffddAMD333+PHTt2VHkbaWlp2LRpU20fGj0nnCBORERE9c7ly5dx+PBhXL16Fbq6ukhOTkZ+fj6AomRjwoQJaN68ufT4s2fPwtTUVG0bqamp8Pf3x/r166t1DCEhIVAoFAgNDa30cwoLC6GtrV2t/VXkwIEDGDlyJBwcHAAACxYswMyZMzFw4MBa35emzJkzp1rPUyUb8+bNq/RzhBAQQkBLi9fV6xr/AkRERFTvJCQkwNTUFLq6ugAAU1NTmJub45tvvkF8fDy8vLzg5eVV4Tb279+PYcOGqd22Zs0a9OzZEz179kR0dDQAICkpCW+++SZ69OiBHj164OLFi3j8+DEmTJiA0NBQuLq6IiYmBqdPn4abmxucnZ0xbdo05OXlASgalfjss8/Qt29f7Nu3DzExMRg2bBi6deuGfv364ebNm6WOLSUlBUOGDIGbmxtmz56N4g17duzYAblcDhcXF0ycOBGXLl3CoUOHsHjxYulYLC0tkZKSgkePHlX4Hvj6+mLatGnw9PSEjY0NvvnmG+m+devWwcnJCU5OTlJCFhcXB3t7e8ycOROOjo4YMmQIcnJyytz2a6+9hm7dusHR0RFbtmyRbv/pp5/QuXNnDBgwABcvXlQ7lrVr1wIAPD09ERQUBABITk6GlZUVACA8PBw9e/aEq6sr5HI5oqKi4OPjg5iYGLi6umLx4sXS37FHjx6Qy+VYtmyZ2rHPmzcPXbt2xf379yt8b+g5UWV+5fwQ1VsWFhYiODi4zPt8fHzEv//970ptp0ePHuLGjRu1eWhEte1Z5+oX+Yc0JCIiosrPKSxUisdPc4VSqazx/jMyMoSLi4vo1KmTmDt3rvDz85Pus7S0FElJSWq/Ozk5CRcXF+Hi4iLWrVsnhBBi0qRJ4tChQ2qP+9e//iWEEOLnn38W3t7eQggh3n77bXHhwgUhhBB3794VdnZ2Qgghzp49Kz0mJydHWFhYiFu3bgkhhJg4caIUZywtLcXq1aul/QwcOFDcvn1bhIWFibNnzwovL69Sr2/q1Kli8eLFQgghDh8+LACIpKQkcePGDdG5c2fp9aWkpIiIiAgxYcIEsW/fPrVtzJgxQ+zfv18IIcTSpUvFwYMHS+1n2bJlolevXiI3N1ckJSWJVq1aifz8fBEUFCScnJxEZmamyMjIEA4ODuLq1asiNjZWaGtri5CQECGEEGPGjBE7d+4s82+UkpIihBAiOztbODo6iuTkZBEfHy/at28vHj9+LPLy8kTv3r3F/PnzpWNZs2aNEEKIAQMGiMDAQCGEEElJScLS0lIIIcS7774rfvnlFyGEEHl5eSI7O1vExsYKR0dHab/Hjx8XM2fOFEqlUhQWFgpvb29x7tw5ERsbK2Qymbh8+XKZx1udzzRVWrnnaZZRUYP05MkTxMfHw87OrtR9SUlJ2LFjh3TFKi8vD/PmzcOpU6eQmpoKW1tbfPHFFxg+fDiAorrXTz/9FL///vtzfQ1ERI2JUinw9lZ/BN99gm6Wxtg90wNaWtWflNuyZUsEBwfjwoULOHv2LMaOHYtVq1ZhypQpZT6+rDKqhIQEmJmZqd329ttvS//94IMPAACnTp1Smw/x9OlTZGRkqD3v1q1bsLa2RufOnQEAkydPxsaNG/H+++8DAMaOHQsAyMzMxKVLlzB69Gjk5OSgefPm0giIikKhwMWLF3HkyBEAgLe3NwwNDXHjxg0cPXoU/fv3l0Y6WrVqBQDIysoq9Zpbt26N+Ph4AMBnn31W5vui2r6uri50dXXRunVrJCYm4u+//8brr7+OFi1aAADeeOMNXLhwAaNGjYK1tTVcXV0BAN26dUNcXFyZ2/3mm2/w559/AgDu37+PqKgoPHr0CJ6entL7PnbsWNy+fbvcYyupV69eWLFiBR48eIA33ngDnTp1KvWYEydO4MSJE3BzcwNQ9J5HRUWhQ4cOsLS0hIeHR6X3R5rHZIMapOvXr8Pa2lqtXldl+/btGDFiBJo1awYAKCgoQPv27XHu3Dl06NABR48exVtvvYXr16/DysoKo0aNwpw5c5CQkIC2bds+75dCRNQopGTlI/juExQoBYLvPkFKVj7M9HVrtE1tbW14enrC09MTzs7O+Pnnn8tNNsrSrFmzUu1Oi3clUv2/UqnE5cuXpbhRFtWX//KovrQrlUoYGRnh77//RlxcHJydnUs9NiUlBdra2mpzO7S0tODg4IBr165BJpMhPj4ezZs3R4sWLWBkZISCggIUFBSobSc3N7fCY1ZRlaIBRe9pQUFBha+n5ONzcnJw//59vPLKKwCK5l7Y2dnh1KlTuHz5Mpo3bw5PT0/pva5M5ycdHR0olUrpdai88847cHd3x5EjRzB06FD88MMPsLGxUXuuEAKffPIJZs+erXZ7XFyc9Heg+oNzNqhBunbtGjp27IiFCxfCzMwM5ubmOHnyJADgr7/+woABA6THtmjRAr6+vrCysoKWlhZGjhwJa2trBAcHAyhaVbRbt244ceJEnbwWIqLGwLRlU3SzNIaOlgzdLI1h2rJpjbZ369YtREVFSb+HhobC0tISAKCvr19q5KEs9vb20ii3iqqr1N69e9GrVy8AwJAhQ7Bhwwa1fZVkZ2eHuLg4aXs7d+5UizUqBgYGsLa2xt69e6Grq4u7d+9i7969CAsLw9OnTwEA6enp6Nu3L3bt2gWgKG49efIEWlpaGDRoEP744w+kpaUhLy8Pqamp0NLSgqGhIR4/fqy2r9u3b8PJyemZ70NZ+vfvjwMHDiA7OxtZWVn4888/0a9fv3If3759e4SGhiI0NBRz5sxBeno6jI2N0bx5c9y8eRP+/v4AAHd3d/j5+SElJQUKhQL79u0rc3tWVlZSHN6/f790+507d2BjY4P33nsPo0aNwrVr10r9vYcOHYoff/wRmZmZAICHDx+Wem+o/mCyQQ3StWvXEBQUhBEjRiAxMRGzZ8/G6tWrARSNenTp0qXc5yYmJuL27dtwdHSUbrO3t0dYWJjGj5uIqLGSyWTYPdMDlz8ZhD2zPGq8rkFmZiYmT54MBwcHyOVyREREwNfXFwAwa9YsDB8+XG2CuJeXl9T6dtKkSQCKyof8/PzUtpuXlwd3d3d8/fXX+Pe//w2gqBwoKCgIcrkcDg4O+P7770sdj56eHn766SeMGTMGzs7O0NLSKre70q5du7Bz506MHDkSQ4cOxc2bN2FmZoaEhAQAQE5ODv75z3/i/Pnz6Nq1K06cOIEOHTrg/v37yMvLw/jx4zFjxgx4eXlh0aJFAIomY3/77bdwc3NDTEwMFAoFoqOj0b17dwDAp59+ikOHDlX6/e3atSumTJmCnj17wt3dHTNmzJDKkipj2LBhKCgogFwux9KlS6XSpbZt28LX1xe9evXCyy+/jK5du6o9T/W5+PDDD/Hdd9+hd+/eai1/9+7dCycnJ7i6uuLmzZuYNGkSTExM0KdPHzg5OWHx4sUYMmQI3nnnHfTq1QvOzs4YPXp0pZJPqhuyZwwLVjxmSFRHevXqhTFjxkgn4X379mHz5s04deoUmjRpguvXr5c5n0OhUGD48OHo2LEjNm/eLN2+ZMkSJCQk4Mcff3xur4GoCrgaVfkYpzQkMjIS9vb2dX0YNda3b18cPnwYRkZGz3W/kZGRMDY2Rps2bQAUteFNSkpCly5dEBwcDAcHhzJLoIQQyMzMREZGBtq0aSO1bn348CEUCoXUtenPP//E1atX8fnnnz+311RTCxYsQNeuXTF16tQ62X9j+UzXU+XGKY5sUIMjhMCNGzek2lEAuHHjhtR73NjYuMwrHEqlEhMnTkTTpk3VhssBICMj47kHIiIi0ryvvvoK9+7de677FEIgJydHLa7k5ORIyYW2trY0X6EkmUwGfX19KBQKJCUlSbeXXL+joKAA//jHPzT0Cmrf0qVLERAQgFGjRtX1odBzxmSDGpzY2FgAgK2trXRbSEiI1DlDLpeX6nwhhMD06dORmJiI33//HU2aNFG7PzIyEi4uLho+ciIiet7c3d0hl8uf6z5Viw8Wn2idnZ0tNTUpa+J6SUIItS5WJSeDjxkzpkFdJPv8889x5coVmJiY1PWh0HPGZIManGvXrsHZ2VmtHjgkJERKFkaMGIFz586pPWfu3LmIjIzEf/7zn1LD1nl5eQgODsbgwYM1f/BERNToZWdno1mzZmpxqvjIhqGhodoIvEKhQGpqKgoLCyGEQHp6OlJTU2FgYACgaGQ+KytL+p2oIWHrW2pwrl+/rjYKoVpBVdWRY9KkSXB1dZVO7Hfv3sXmzZuhq6sr1c4CwObNmzF+/HgcOnQInp6eMDc3f+6vhYiIGh/V+hoqBQUFUCgUUrJhYmKCiIgIKJVKaU7G48ePcffuXQghoKuri/bt20sjF2lpadDX10fTpjXr8EVUFzhBnBql//u//0Pr1q2lxZYq4u7ujm3btlW7fSDRc8AJ4uVjnNIQTqbVrAcPHqBJkyZ46aWXnvnYyMhIWFlZVWpNDSofP9MaVW6cYrJBRFT/MdkoH+OUhvCLGTU2/ExrFLtRERERUcOyYsUKODo6Qi6Xw9XVFQEBAQCA9evXIzs7W3qclZUVnJ2dpXU23nvvPem+999/H+fPn69wPzdv3oSrqyvc3NwQHByMTZs21err8PPzw8iRIyt8zBdffCH9f35+Pvr3719qxfD6wsrKSlobo3fv3tXaxoEDBxAREVGbh0X1FJMNIiIiqncuX76Mw4cP4+rVq7h27RpOnTqF9u3bAyidbADA2bNnpRWuv/nmGwBFa1v4+/ujf//+Fe7rwIEDePXVVxESEgITE5MqJxtCiHJb2VZW8WSjadOmGDRokLTaeX126dKlaj2vOslGfU2+qGJMNoiIiKh2JCUBgYFF/62hhIQEmJqaSu1jTU1NYW5ujm+++Qbx8fHw8vJSW0G8LPv378ewYcOk3z/77DP06NEDTk5OmDVrFoQQOHr0KNavX48ffvgBXl5e8PHxQUxMDFxdXbF48WIAwJo1a9CjRw/I5XIsW7YMABAXFwd7e3vMmzcPXbt2xf3799X2fezYMdjZ2aFv3774448/pNszMzMxdepUODs7Qy6X4/fff4ePjw9ycnLg6uqK8ePHAyhaMXzXrl1lvq6WLVtiyZIlcHFxgYeHBxITEwEAd+/exaBBgyCXyzFo0CBpfZEpU6bgvffeQ+/evWFjY4P9+/eXud3//Oc/cHd3h5ubG15++WVpuykpKRgyZAjc3Nwwe/ZsFC/Bb9myJYDSozfvvvsutm/fDgDw8fGRVoL/8MMPcenSJRw6dAiLFy+Gq6srYmJiEBMTg2HDhqFbt27o168fbt68KR37okWL4OXlhY8//rjcvzXVY0KIin6IiKjuPetc/SL/kIZERERU7Qm//ipEs2ZCGBoW/ffXX2u0/4yMDOHi4iI6deok5s6dK/z8/KT7LC0tRVJSktrvTk5OwsXFRbi4uIh169YJIYSYNGmSOHTokPS4lJQU6f8nTJgg3bds2TKxZs0aIYQQsbGxwtHRUXrc8ePHxcyZM4VSqRSFhYXC29tbnDt3TsTGxgqZTCYuX75c6thzcnKEhYWFuH37tlAqlWLMmDHC29tbCCHERx99JBYuXCg9NjU1VQghRIsWLdS2UVBQIExNTaXfXVxcpP8HIB374sWLxeeffy6EEGLkyJFi+/btQgghtm3bJl599VUhhBCTJ08Wo0ePFoWFhSI8PFx07NixzPc8NTVVKJVKIYQQW7duFYsWLRJCCLFgwQKxfPlyIYQQhw8fFgCk91913GfPnpVeoxBCzJ8/X/z0008iJSVFdO7cWdrukydPpGPat2+f9PiBAweK27dvCyGE8Pf3F15eXtLjvL29RUFBQZnHXBVV/kxTVZR7nmbrWyIiIqqZpCRg+nQgJ6foByj6/eWXATOzam2yZcuWCA4OxoULF3D27FmMHTsWq1atwpQpU8p8/NmzZ2Fqaqp2W0JCAsyK7f/s2bP48ssvkZ2djdTUVDg6OuKVV16p8DhOnDiBEydOwM3NDUDRyERUVBQ6dOgAS0tLeHh4lHrOzZs3YW1tjU6dOgEAJkyYgC1btgAATp06hT179kiPNTY2LnO/2traaNq0KTIyMqCvr4/Q0FDpvqZNm0qjCN26dcPJkycBFJWeqUZRJk6ciI8++kh6zmuvvQYtLS04ODhIIxYlPXjwAGPHjkVCQgLy8/NhbW0NADh//ry0XW9v73KPuSwGBgbQ09PDjBkz4O3tXebclczMTFy6dAljxoyRbiu+oOGYMWPUVk+nhoXJBhEREdVMXBzQtOn/Eg0AaNKk6PZqJhtA0RduT09PeHp6wtnZGT///HO5yUZZiq/UnZubi3nz5iEoKAjt27eHr6/vM1fxBooqQD755BPMnj1b7fa4uDi0aNGi3OcVX9Cv5PbKu6+kvLw86Onplbq9SZMm0ja0tbXLnctQfD/FVzMX/y2DWrJkCY4cOQIACA0NxYIFC7Bo0SKMGjUKfn5+8PX1febrUdHR0VGbt6J6b3V0dHDlyhWcPn0ae/bswYYNG3DmzBm15yqVShgZGaklVMVV9D5T/cc5G0RERFQzVlZAfr76bQpF0e3VdOvWLURFRUm/h4aGwtLSEgCgr6+vtgJ3eezt7REdHQ3gf19+TU1NkZmZWe68hZLbHjp0KH788UdkZmYCAB4+fIjHjx9XuF87OzvExsYiJiYGALB7927pviFDhmDDhg3S70+ePAFQlEAoFArp9pSUFJiZmaFJkybPfJ0qvXv3lkZNdu3ahb59+1b4+BUrVkiT6gEgPT0d7dq1AwD8/PPP0uP69+8vzR/566+/pGMuztLSEhEREcjLy0N6ejpOnz4NoGjUIj09HSNGjMD69eulfRV/nw0MDGBtbY19+/YBKEqGwsLCKv26qX5jskFEREQ1Y2YGbNsGNGsGGBgU/XfbthqNamRmZmLy5MnSxOKIiAjpSvusWbMwfPhwtQniXl5eUuvbSZMmASgq+fHz8wPw/+y9eXBjZ5X3/73aV1uyLMlaLcl2uxf3mm7STELIvPPrwJuhMgQYkpCqwJuEMBRUMUyASTEkkF8xBGoqNcxUan7DACGBGhIYCiYvefvNhCVNQtJtt5vuTne62+62Zbf3TV60Wev9/dHzXCRZy73SlXRtP5+qVBL76tEjWTrnnuec8z2AyWTCJz/5Sezduxcf/OAHceTIkaLPa7FYcMstt6Cvrw9f/OIXcccdd+BjH/sY3v3ud2Pv3r34yEc+UjHQ0Wg0+Ld/+zf8+Z//OW699VYuSAKAr3zlK1heXkZfXx/279+P1157jXtN+/bt4xrEX3vtNdx5553c4w4cOFDxPfvnf/5n/OAHP8C+ffvwox/9CP/0T/9U8TG5fO1rX8Nf/uVf4j3veU9eSdpXv/pVvP766zh06BBeffVVeL1e7nck4+HxePDRj36Uew2k7CwcDuMDH/gA9u3bh/e+9734x3/8RwDAvffei3/4h3/AwYMHMTIygn//93/H97//fezfvx979uzBSy+9JGjvFOlCh/pRKBSK9KFD/UpD/VSdqGoA2sLCjdIpn6+mQENMbr31Vrz88sswmUzN3oogPvShD+Gpp55Cb29vs7dSkqWlJRw6dAjj4+PN3gov6FC/ulLST9GeDQqFQqFQKOJgtUomyCA8/fTTuH79+qYKNpLJJD74wQ9KOtCYnp7G7bffji984QvN3gpF4tDMBoVCoUgfmtkoDfVTdYKeAlO2GvQzXVdK+inas0GhUCgUCoVCoVDqAg02KBQKhUKhFKVC9QOFsmmgn+XmQXs2KJQGwLIsstksstksMpkMstks0uk0WJaFTqeDQqHgrbtOoVAojUCj0WBpaQkWi4Xap20AuRkv9m+ZTAaGYTbt54BlWSwtLRWdWUKpP7Rng0IRCZZl84IKEliQ4CL3OmKwM5kMdDodgBuDj+RyOWQymnCkbGBzevjGQP1UnUilUpicnOQ1+I6yuci996t04k98Vm6gsRmDDo1GA7fbLWhuCUUQJT8UNNigUARSGFSk02kuoGBZFgsLCwAAq9WaZ6ALjTN5rMFg4NYEbkyDJZmOzWjQKXWBfhBKQ/0UhVJAoZ8iB1/EV62uriIWi8HpdJb1U8CNKeZ6vR4Mw3B+SiaTcVPMqZ+i/DdU+pZCEUo5Q52bnQDAGVyGYZBOpyGTySCXy3k/F3ksy7LIZDIYHR2F2+2GRqOBXC6nxpxCoVAoGyhWokt8VSHEz8jlcu6wq1o/lc1mEQwG0dbWBqPRSP0UpSw02KBsa4qVPqXTae6/c68jhpbUrtYD8hxLS0twOBxIpVJIp9NciRU15hQKhbK9EFqi2yg/tbKyAoPBQP0UpdXCRhcAACAASURBVCI02KBsC8oZapIWnpubQzKZhNfrrclYi6V4IZPJIJPJwLIsZ8xzS6woFAqFsnWoVKILAMvLy1haWkJPT0/dgwoAXDBT6jkK/VQqlYJCoaB+ipIHDTYoW4pypU+51+SWPZGGbGI0xWjQrtXI5gYsuanrdDqdd4JEm8kpFAplc1FtiS4pgRLDTwk9FCvm0yr5KXI4Rv0UhQYblE1H4elPPB7nDHShASUGst6nP/WgcL/ljDlt0qNQKBTpUOinkskkUqkU1y+Re10jSp+KUem5WJZFMplEPB6HwWDYcH1hYETWzO0/zGQykMlkXNBB/dT2hAYbFMlSqvSpsPFtfHwcer0eNptty9x0lzt1KjTmly9fRm9vL62XpVAolAbDp0QXAGZnZ5HJZODxeCR3082yLNbX1xGNRhGLxbh/ZzIZKJVKyGQyjI6OorOzEx0dHbwyFYXN5ENDQ3C73dDpdNRPbUNosEFpOsRI556ECGl8k9qwoWKnPaUodR2fNchrDoVCeX0dNOigUCgUcamlRBe44aey2WxT7XImk0E8Hkc0GsX8/DxisRgn1a7RaKDT6aDT6eByubhhs6R3RKFQYHx8HMFgEC6XC263W5CfWllZgdPppP2H2xQabFAaQrHGt1AoBIPBkHdNoaHeqoYok8kgEokglUpBp9PV/DqJU6PN5BQKhVIdxWZTrKysQK1WbzjNl3KJbiqVystQRKNRJBIJyGQyLqDQaDTQaDTo6urilanQaDTo7e1FV1cXJiYm0N/fz5VZ8YX4ddp/uP2gwQZFVPic/hCGh4dx0003SSYrIZaKVC65Rp8YfmL0tVotAGBsbAydnZ1wOBx5QUM17wltJqdQKJTy8C3RBYDR0VH09PRAo9FIwk8RWJZFIpHYEFQQm6/X66HT6dDW1ga32w21Wp23/7m5Oayvrwv2CwqFAn6/H52dnejv78elS5dgNpvh8/mg1+vL7jd3cCDtP9xe0GCDIhixZlOUm1jaLKqVuk0mk1xAMT8/j3Q6jevXr+cZfYvFAq/XC5VKBYZhOCdH0tMnT57k0tPV7iX3dRQaczrxlUKhbCdqLdEFxPNT1T6eiKCQYGJlZQWRSATLy8tQq9XQ6XTQ6/Xo6OiATqeDUqnktW6th2vkwKynpwexWAzvvPMOlEol/H4/TCZT0efj20wul8tpKfAWgwYblJLkBhXEEJB0bG75E4CyxnqrkM1mizbRZbPZPKNvNBphNBrhdDp5ratWq7Fjxw4EAgFMTk6iv78fyWQSiUSCy35US2GT3vDwMFwuF7RaLTXmFApl01OsRHd9fR2RSAQtLS3cNVIo0S13g0/8a65vWV9fBwBotVrOv6hUKmi1WvT29ta8HzEk2hmGgdVqhdVqxerqKoLBIJLJJHw+H6xWK6/nyPVTLMtifHwcJpMJra2t1E9tEWiwQRFU+hQOh7G2tobW1tYm7LQ4xSRvayGTySAWi+UZ/Xg8DuBG3Wpuepooa+QyNja24WcAkMmyyLIslPLiaWuFQgGfzwev14s33ngDZ8+eRUtLC/x+f9n0NB9ym8k7OjpoMzmFQtlUCPFT8XgcMzMzMJlMomQkxPIvuVnwXP+STCYhl8u5gKK1tRUOhwNarXbD/kOhEKLRqCj7KUaWZZHOsFAp+JVX5e6vtbUVBw4cQCwWw9jYGK5duwav1wun08m7mRwAVldXodfraf/hFoIGG9uEYo1v5B8hsynErPsXO0gQSmE/RSgUQjqdxtzcHNdEZzQa0dHRAY1GI+i1F75vsWQGrw0vIpbM4t0BE9ym0hkLUu509OhRLC0tVUxPC4FlWTrxlUKhSBKxSnSJrW6WPSsmJbuysoJMJoNQKMQdWFmtVuj1eq68lS9ivK5ivjeVyeK14SUsx1K4yduKbmv5Q65S/lun02H37t1IJpMYHx/HW2+9xd1v8CnzKvRTtP9w80ODjS1Gqca3mZkZ2Gy2vGtz08pb9SazsJ+iVBOdxWKBWq0GAHi9XtH3EYolEV5PQ6eSY3QhBrdJWzHQkslkXHp6ZWUFwWAQqVQKfr8f7e3tVfeXlGvSo8acQqHUm2IlupX8lJDSp0b5s2w2m5cFj0aj3JDZ3Cy4y+VCS0sLMplMXfxLtRS+T6vxNJaiSZi0Slydj/IKNsq91yqVCj09PQgEAvj973+PwcFBtLe3w+fzQaPR8FqXNpNvDWiwsUkRqvk9MTEBh8NR8/OKnY0QMz1dqMpRrJ+iXBNdIpFAOp0WZT+FtOlUaNEoEE1m0G3VCX68yWTCwYMHEY1G89LTYv1NaTM5hUIRGyGlT2L6KfLcYsAwDFKpFFZXVzm/QvopSJO0Xq/nBstqtdqihzVkSJ6UadUqYDWosBRN4bBXvFJpuVwOtVqNm266CYuLizh37hx0Oh38fj+MRuOG62kz+daDBhsSpljpU25KmXzx+DS+ifVFFDPYqGZPpJ8iN6hYXV2FTCaD0Wis2E/RCIq9PzqVHO/fY0MmW7pngw96vR579uxBIpHgFKySySSXleCzt1Lve2EzORn2ZLFYqDGnUChFEatEN/f3tVKNnyrWTxGNRhGJRBCLxTj/Yjabi0rJbgWUchn+R287bz8lRKKdZVnI5XI4HA50dHQgFAphaGgIDMPA7/fDbDbnrcXHT7Esi9XVVcTjcXR0dFA/JWFosCEBCg01+ffU1BQ6OjryriVftK3+pSo3n6Kwn2JhYQEajQZ2u73Z2+Yo9reRMQxkcnH+ZrkKVm+88Qb6+/thtVrR2dnJlYMVQ8jE13A4jGw2i5aWFtpMTqFsc0qV6E5NTcFut2+4UZRqiW6uqmBupqIwC26326HX6zE8PIzOzs4NCoxCkdr7UMoXCPVTQoKN3NIoi8UCi8WCcDiMYDCI4eFh+Hw+2O12XoEiWSsejyMUCsFisdBmcglDg40GUazxrVLp09TUFFwuVxN3vRGxlTnW19c3nCYV66fInU9Rzz1tNhQKBdRqNY4ePYqZmRmcOXMGra2t8JUYsCT0JIo2k1Mo2wuhJbozMzPcqbJUYJgbc4zC4XCebykmJWuxWMpmwcW0c1vNT4nxeoxGI/bt24d4PI6xsTGMjIxwfz++e6DN5NKHBhsiU0zzmxjqYinlRpQ+iUk1eyo1nyIajeLq1aswGAxVDSWSKs1wKDKZDC6XC06nE4uLiyUVrKoJNoDyTXrUmFMomwsxS3TJTV6zSCaTG/xKIpFAIpHA5OQkdDodWlpa0NHRUVRKllIbYqloabVa7Nq1C6lUCv39/Th//jwcDgd30FiKSs3ktP9QGtBgo0pKnf5MT0+XLH3aKjdlpRxLsX4KcpJUbD7F22+/jb6+vk0fXBSjWUaNYZiyClZCgw2+TXok6KDGnEKRDo0o0W1EZplkwQv9C5FSJb6lvb0dXq8X2WwWV69exa5du0R7/q2GGK+pHu+LUqmETqdDb28vQqEQBgcHYTKZ4PP5oNNtFFep5Key2SxXgk2byZsHDTbKUErzu1jjG3DjAz49PS250icxYRgGmUyGU+ao1E9Rbj7Fdi5/agS5ClbBYBDXrl1DMplENpvlVfLAt5mcNFaurKzAarVSY06hNJBml+iKXVobDocRj8fz+ikKpWSdTid0Ol1JUYxEItFUIZPNQq2vTcjhldB15XI5PB4P3G435ufnceHCBajVavj9/ryhwkL8VCqVwuLiItra2gTPNqHUBg02ULrxrZhMnZQb38Sk1HyKRCKBTCbDTTut1E+xmaiX4SxHI55Pr9ejr68PiUQCb775Jk6dOgWXywW3211WwUrIxFcAuHLlCkwmE23So1DqgFRLdKsJNtLp9IayWpK5GB8f5yUlW2lPUkOKe5IyuaVRdrsddrsdy8vLGBkZQSaTgc/n452tz11reHgYhw4dQiaTof2HDWRbBRvFSp/IEB4SKVeaTNqMPddzD6X6KUrNp1hfX8fk5CR6e3trfu5mv7f1ohlBCx/UajU0Gg3e9a53YXJysqKClZDXQa6jTXoUSm0U81Pr6+tYWVmBxWLJu1YKJbqlgo1iUrKxWIwTmSC+xWw2w+VyQaPR4MyZM9i9e7cor0fMzIaYmRupUI19r2UNIZRa12w2w2w2IxKJcPOmiCSxkLVJ9p36qcax5YKNcprfpPGNwDAM4vE4lpaW0NbW1sRdFye3Sa9WSHqabz9FqTIbMdPTZF+1IsUbeymjUCjg8/ng9XrLKlhV+9kr10y+1TOCFApfcrPplUp0k8kk5ufnN0zXrgUxbxRjsRjC4XDJAyudTsdJyZbr0RPrxp6W6PJDjL9/vYKNchgMBvT19WF9fR0XL17EwsICGIapmK0na+dWp9Bm8saw5YKN5557DrFYDPfffz/3s3KNb81W0ihHNQaz1HyKWCyGiYkJ3v0Umw2p/g2lTCkFq0AggNbW1ppvRko1k9MmPcp259VXX8Ubb7yBRx99lPtZuRJduVwuqo2r5iCrlABIPB7n5vHUOlBVikGC1OyUlN6feu0lVwmxHGS+Fsn48Zk3Vfi5p83kjWHLBRvAjSEvfA3dZgw2SvVTlJtPMTg4iN27d9dtT81eS0psttdUqGA1OjqKVCoFlUqV14hXy/qFTXp0SCBlO8MwDGKxmCA/xXfuAN/nz2azRW/oiknJJpNJTgBEr9fnHVhdvnx5Q1a0ln1JMbMhNZsuJZvZjMxG4bUKhQIejwderxezs7P4wx/+AKPRCJ/Px3sYI/VT9WXLBRsqlQrJZJL39cToSpXczESlfoqtKCG7WWmUYRLbCeYqWJ07dw5XrlxBOp1GR0dHzVmw3Ca9aDSK4eFhHDhwgDaTU7YdKpUKiUSC9/Vi+ylSQpzbUxGLxZBOpznpUTLwrpIAiBRv7KktqUylzBbLskgkEgiHw0in00Ub9euZ2eD7N8zNgshkMjidTjgcDiwuLuLy5cuQy+Xw+/0wm8281sv1U5lMBm+++Sbe9a530WbyGtlywYZarRZkxMU+MaqGUunpWCyGYDAIo9FYc3paLKToWCjiQ240TCYT1tbWcPLkSV4KVnzJdSa0SY+y3ajGT1VjK7PZLBdI5AYVsVgMIyMj3EDVSlKy5RAzEBLzu08z8PwgM0zI5yMSiXCHmhqNBlqtFizL4uTJk3C73XC73Xn3II1sEOd7bW62fnV1FcFgEMPDw/D5fIKzJiTjQYfZ1saWDDZSqRTv6+thSEp9UUr1U5SaT3Hx4kX09vaWrD1sFlS/vDyNdEz1VL5iWRYqlQo7duxAIBDAxMQEr5pYvmsTtTfaTE7ZblTjp8rd0KfT6Q2+ZX19HTKZDFqtlstUWK1W6HQ6XLhwATt37hTFt4hdiizFMioxaLYtyw0qotEo5ufnkUqlMDExwYnE6PV6eDyevENNIrSjVqtx/fp1nDp1Cg6HAx6Pp657rSXYyKW1tRUHDhxALBbD2NgYotEoJicn4XQ6KwYMxZrJ6TDb6thywYbQMqp6ZDZI0xzffopy6Wkx9ybGjSn9UvGjVklBvtQ72CBrKxQK+P1+dHZ2FlWwEurYyzXp0WZyylZHaBkV8VOJRCIvQxGNRpFKpSCXyznfkislW+p7I6bf2w59fJtJ+pZlWe7+g/xDmvhzA0+TyQSdTsd7uKNCoUAgEEBnZyempqYwMDCARCKBZDIJlUol+usQK9gg6HQ67N69G6FQCOvr63jrrbfgcDjg9XpLlqBXaiYnvUzUT1VmywUbjUxPF5tPEY1GMTQ0xJ0S1NJPUQ8jLkawIcVTrM2IGK+9UcEGIVfBamFhgVOw8vl8gvZRat+0SY+yHVCr1SUPxcjNYmGmIhaL4fLly1xQYbPZoNPpqrrRk2qAIMUGcananGJBRSwWA4C8TEV7ezt0Ot2GU/xkMsmrJLvwfZTL5fB6vXC73Xj99dcxODgIs9kMv98PjUYj3gvkiVAfKJPJ0N3dDb/fzwVNFosFnZ2d0Gq1edeWElEo5ado/2Fptn2wUSl7UKqfAig+n+L8+fPo6+sTpa5dikZcTMT8QkrtteVCjFEkEtmQ7WJZFm63Gx6PR5TPjJiUM+IMw8Bms8Fms3EKVtFoFAsLC2hvb6/4ty1lxHPXJ/8mM2KWl5e594kac0o9mJiYwAMPPIDZ2VnIZDI88sgj+NznPodQKIR77rkHY2Nj8Pl8+OlPf1q04fT555/H17/+dQDAV77yFXz84x8v+jzET5GSFOJn4vE4gOK+5Q9/+AMOHDggyuuUqm+Rop9qNtlsNi+oWF5eRiwWQygU4jIVBoOhZFBRL2QyGVQqFd797ndjbm4OZ8+ehdFohN/vF0WZjC/VHriRoMnj8WBubg7nz5+HTqeD3++H0WjktXahn1pfX8f09DQCgQD1UwVI6+5GBFQqlaBaWJJOFtpPUeoLLdX0NLB1a2GlAgkq4vE4FhYWMD8/zwUVKpUqL9tFjHE2m8Xi4iL6+/tht9vR2dkpKAvW6MxGMUwmE/bt24eBgQHMzc3h2rVr6OzsLKtgJWTfDMMglUohFArB4XDQZnJK3VAoFHj66adx6NAhhMNh3HTTTTh27Biee+45/Nmf/Rkee+wxfPOb38Q3v/lNfOtb38p7bCgUwpNPPonBwUEwDIObbroJd911FxeUsCyLL33pS7h06RKuX7+O5eVlfPvb38YXv/hFeL3ehs4+kqqfEjOzISaN8HmFQQUpfwIArVbL+Q+VSoV4PI7u7u6674kPDMOgo6MDdrudm9ekUqnQ1dXF3bTXEzHmQZH9h0IhDA8Pg2VZLlMjxE+xLIvFxUV4vV7af1jAlgs2ymU2Ss2nCIfDuHDhgqB+ilJI0fCStcRaR4qvTwz47qVcpoL0DOl0OlitVuj1+pIZC2KMfP89zZukdNvb23nfCEgh2CDXKhQK9PX1IZFIYHx8vKyCVaXMRrH1ZTIZV/ZIJ75S6oHD4YDD4QAAGI1G7Nq1C1NTU3jppZdw4sQJAMDHP/5x3H777RuCjf/6r//CsWPH0NbWBgA4duwYXnnlFdx3330Abti7u+66C3/9138NnU6Hu+++Gy+++GLjXlwOUrbjUvIJgPiBC1EIKwwqGIbhDjb1ej1sNltRudmlpSWuuqIWxPYduQpQ5KYdALq6umAymUR7nkLEeh0Mw8BiscBisSAcDiMYDCIcDkMul/P2V2QvxE/RZvI/0rBg48EHH8TLL78Mm82GixcvAgDuueceDA0NAQBWVlZgMplw7ty5DY/1+XwwGo1clDg4OFjyeUiwcfHiRVgsljwd8WLzKbRaLc6fP49Dhw6J8jqlasSldmMvVXINQWFwSv7JZDJFMxXkhnp4eBjt7e2CBuLJZDJ4PB64XC7Mzs5ifHwcly5dgt/v31BH2iiqVQRRq9UbFKxsNhu8Xi+nfiPUQeQa+8ImPTrxlVIPxsbGcPbsWdx8882Ym5vjghCHw4H5+fkN109NTeUp9LjdbkxNTeVd8573vAfAjcGzQsp9xUaqvmUrfXcLgwrSmLy4uFh0OOJWytK2tbWhra0Na2tr3JBYUjZcj6yT2GsajUbs27ePm9VRSva3kFJ+itxLMAyzbfsPGxZsfOITn8BnP/tZPPDAA9zPfvKTn3D//eijj5a9OXvttdfQ3t5e9HfZbBZPPvkkLl++jCtXrmBpaQl/93d/hyeeeAIul6uh8ym2Q3p6qwUtuUFFMplEOBwuGlQ4HI6ymYrc9ao1JGQoUTAYRFtbG86dO1e2DlYqmY1iJz/lFKyE7ruUljptJqfUg0gkgg9/+MP49re/jZaWFl6PKWYXS30Ghaomis1W91NiUmlPuX2d5GAzHo9vKMEmB6GBQKCBuy9NI97nlpYWHDhwAJFIBP39/ejv74ff74fNZhPNPtfTB6pUKphMJuzcuRMTExM4efIkV+5cTJghm80W9VPk39u5mbxhwcZtt92GsbGxor9jWRY//elP8dvf/raqtWUyGW6++Wbcf//90Ol0eOihh/Czn/2sht1Wj1QNr5hrSXFPfKiUqWDZG3Ml+AYV9aSwDvbixYvQaDQIBAJ5dbBSCTbKXVtMwSqbzQqq5y2Xxi5mzEnflpCaWwoFuDEP6cMf/jDuv/9+fOhDHwIA2O12zMzMwOFwYGZmBjabbcPj3G43V2oFAJOTk7j99tuLPodcLm/qTbVUfYsUgw1CYVBBxGJIUKHX69Ha2gqn01nU7iwuLoqSzRLz/WmUbTQYDNBqtdi/fz+CwSBGRkbg8/nK9vXxRaifEgLxO0qlEoFAAD6fD9PT0xgcHITJZILP54NOp8tbv5LwCfmMk1Jg4EY1wFbKbBVDEj0bb7zxBux2O3p6eor+nmEY3HHHHWAYBp/61KfwyCOPbLjmzjvvBAAsLy839cRIqoZXqo13YlC4J77lT4VBBSl5EFL+VG8K62CHhoYgk8nQ1dXF7bNehlasYIOQq2B17do1zM7O4vTp0/D5fBUVrPjuhRjzbDaLU6dO4ejRo7SZnMIblmXx0EMPYdeuXfibv/kb7ud33XUXnn/+eTz22GN4/vnn8Rd/8RcbHvu+970PX/7yl7G8vAwAePXVV/HUU0+Vfa5mIbZvkWKWpFoymUye31hYWMDc3Fye7ygXVDQCKfrhcpC/qVarxe7du5FIJDA2NoZgMMiVD1dbeSK2nyp3vUwmg9vthsvlwsLCAi5cuAC1Wg2/34/W1taKwQYhN+g4efIkjh49uuWbySURbLzwwgtcE10x3nzzTTidTszPz+PYsWPYuXMnbrvttqLXNjs9LcUAQcy1pNQMWFj+tLa2VlP5k1jUmm0o9Z6QOtil0DKGr14DA5b3QKZq9lVPI06GSVmtVoyNjVVUsKq2oZxhGNpMTuHNm2++iR/96EfYu3cvJzP7jW98A4899hg++tGP4vvf/z68Xi/+4z/+AwAwODiIf/3Xf8X3vvc9tLW14fHHH8eRI0cAAE888QTXLC4WYjbDihUgiDlBvJHBRuHU9Wg0ikQiAblczmUqzGYzstkszGYzrFZrQ/bVKIS8z0I/c8l0FgNjy0imszgaaINWmW+71Wo1ent7EQgEuKnkTqezKgl4oX5KqB8ptnbuwdny8jLXlyL0M5Kbld/qw2ybHmyk02n8/Oc/x5kzZ0pe43Q6AQA2mw133303BgYGSgYbQudsAOKeMEm1FhbYvCofm6n8qd7Mrq7jFxdWoJBb8L6eFkxPBrGyssJrvoWUgg1S22owGNDX14f19XVcv369pIJVNXsnwQZtJqfw5dZbby1pJ3/zm99s+Nnhw4fxve99j/v/Bx98EA8++CCv5xL62SO+RYzeQ6kGCPUINtLp9IbBd7lBhcFggNlshsfjKapAubq6umXtRL1e17X5CE4FQ5AxDFQKGf4kYC76XEqlEl1dXejs7MTk5CQnAS/kM1BvP1UpODGbzTCbzdxA55WVFUxNTcHhcPAObLZD/2HT78p+/etfY+fOnXC73UV/H41GufruaDSKV199FU888UTJ9RQKBTKZTL22WxGpGl6xPqz1PHnaSuVPtVDOIA7PRZDJskikM1hKyrBz505cvnwZc3NzGBkZKdt8V+9go5rMA0Gj0eQpWJ06dQp2u51TsBKa2Si8fjsYc8rmQqgdlXLpk1hrAdUfihWblTUwMAC5XM75jmpl7aV2UCd1tCo5ZAwDFiz0anlFX6JQKODLkYAnN+4+n49TMCyFUCGTevlAvV6Pzs5OqNVqRKNRnDx5UnC2Zis3kzcs2Ljvvvtw4sQJLC4uwu1248knn8RDDz2EF198cUMJ1fT0NB5++GEcP34cc3NzuPvuuwHcOKH42Mc+hve///2N2rZgpJrZkEItLIEEFYlEArOzs5xxaXb5k1jU833uthlwcWYNOoUcHrMWQJqbbxGPx8s239Uz2BDLiJdSsNJoNII+B6X2U6qZXKFQbHpjTtnaiOlbpJzZqEQqldpwIEW+w7lBRSgUwpEjR2r+Tpc6uIklM9xNNSUff7seHznkRCrDwm/RgWX5+QciAX/9+nUYjUb84Q9/4BQMcxuxc2lkz0YlstksFAoFd3BGsjXt7e3o7OyERqPZsH4pijWTb+b+w4bdxb3wwgtFf/7cc89t+JnT6cTx48cBAIFAAOfPnxf0XEKNC/mDilULK9XTp0b3bFTKVCQSCRiNRthsNi6oiCTSWIwkYW5RQ6usv1Rxvai1Z6PU450mDR6+xQeGAZRyGSKRFHdtseY7r9cLl8vF3VxIqYyq3KT0QgWrK1euQKFQwGQy8cpg8cmEFBrzubk5tLa2Qq/Xb0pjTtlcVFNGJdUAoR5r8QkqrFYrfD5fURnSeg5Q+/XQIi7PRtBl0eF/9tl4BRxSO8gQYrOr2bu37Y/BQTotPIvndDrhcDi4RmytVotAIACDwZB3bbPLqArXJ9fnZmtmZ2dx9uxZGI1G+Hw+7jXwFVbJ9VNLS0tQq9VobW3dVP2Hm+vImCfVpqelFmxI0bmUOuWppvzpypUrsFqtnI59KpPFz87OYm09BUeLGn95yFnzfqVOqc9cuc+iSlE+W0Ga7/x+f14fhN1uF7Q3KZwYkUa8WCyGZDKJ0dFRpNNp+CooWAkp6yIGe3Z2Fkqlkpv0ut0nvlLqj5CbGSkePomxVq7vCIVCWFxcxMjICJRKJa+gohHkvr5UJosrsxE4WtQYWYohnsxAr+Z3KyWV6gKh1Lrvau+viP0naoyXL1+GQqFAIBDgDp2a0SBeimLfZzI7y+FwYGlpCZcvX4ZcLoff74fRaBTspxYXF2E0GrlBkJul/3BLBhtCkZLhrddagDgGI5lMIpVKYWJigndQUYrC15dMZxFNpGFUy7EUTSHLsrxPjKRkxMV4n4VQysioVCp0d3fD5/NhYmICg4OD3OkIn5IkKQQbudcbDAbs2LEDkUikooKV0LIu8hhSSpXNZrf9xFdKfVGpVEilUhVr0glil+iK1dvIx/6SksXCA6l0Op0XVBgMBphMJm5auxQo/N4r2E/RLwAAIABJREFU5TIc8rTiDxNr2OMwQqfavBn4RlHrYS7DMLBYLLBYLFhZWcHIyAiy2SwCgYAgW98IP1XuALG9vR3t7e1YW1tDMBhEPB4XPFk9109tpv7DLRlsVJOeFlPlQ4qnT0Kj+XKZikwmA7lcLnpPhV6twO07LBiai+Cgp1VQLayUgg2g9pS5EONZCdIHYbPZcPbsWfT398Nms5Wcgpq7dr1OjGpJT+cqWI2PjyMYDG5QsBK6PnkOcnpUWM5BborEsBEUCvBH5US+wYbY/oAMFKuV3H3xzXJ3dHRAp9NtKKVMpVKb4jt2S1cb3uUzQSnfvOWWUvOZfDGZTDh06BDC4TBGR0exvLyMUCgErVZb0V8JPYSqxU+Vo6WlBfv378fKygrOnTuHkydPwuPxwOl0Vvz8k9dQqplcqn5qSwYbQr9EUixXEnstYOP7Um350+nTpzk54loo9qXf4zBij4P/ZGkxEauUTsjzCfl5qWv57plMuj1w4AA3BbWtrQ0+n29D4xqh0Q3iQtbXaDTo7e1FV1cXJiYmuCDK6/UKDn7IcxQqWJF/N/qzQdn6qNVqQTOhpHSQles7lpaWsLa2hlAoJIrIh9Rugku9V5s50CDUs2cjF6EHV3wwGo3Yv38/BgYGsLS0hImJCfj9ftjt9rLltY32U+XQaDRoaWlBX18fJiYmcPLkSTgcDni93pI9jcV8W+EBmRTZksEGILz5SSpGvHAtMfbFsjdmDKyurnKD76Si/iS1L4cUbijrVb5Ers2dgkoa11paWuD3+0sqfoi5D7GvL6ZgJVS9Cih/irWZGvEomwOhA2jFzmzw8S0syyKRSGyYU5HJZKBWq6HX6yGXy7kSx1p9h9RvmCjVUc+SXJlMhl27diGbzWJsbAyjo6Pwer1wOp0b7Hk1fkdoxr6a4ESlUqGrqws+nw9TU1M4ffo0zGYzfD4ftFrthsdsRj+1JYMNUgvLt5lMbEnBZgUu5TIVRDrNYrFsWknZzUKjT8GrNeIMw8DhcKCjowMLCwt4++23odfr4ff7Nyh+CF27ErXOzShGroLVyMgIJicnce7cOfj9ftEUrCgUsRA6gLaevoVlWayvr2+YqJ3NZrmgQq/Xw+VyQafT5fmOUCiEUCgkij+R4o3SdguAWJZFPB5HNBpFJBJBJBIBwzDo6emB0Vh91UE9+yqAG1mCnTt3IplMYnx8HCdPnoTb7Ybb7ebKiqrxU0JKkliWFXR9oc+Ry+Xwer3weDyYm5vD+fPnodVq4ff7OSGdzeqntuTdJjHifIMNKWYjyFrF9lVN+dO1a9fQ1taGtrY2UfYmBtvNiPOlHinncuvmKn4sLS3h0qVLXG9OPfZczfVCTowYhkFraytYlkV7eztvBavNasQpm5NmZDZIUBEOh7GysoJLly4hFoshm81Co9FAr9dDp9PB7XZDp9PxunGScunwVkSs9yebzSKVSmFpaQmRSITLWrEsC41GA4PBwM0sSaVSGBoaglwuR1dXF3fjW48915p9UKlU6Onp4dQYT506BYfDAY/H0xA1qnKS7pX2TmAYBh0dHbDb7VheXsbVq1fBsiz8fj8ymcym9FNbMtgQasSlVAtbSCqVQigUEtRT0Yh9UepLvU6Byl2bq5axvLyMM2fO4MyZM+jq6oLJZKq4j3oa8WrT2WazGWazmbeC1WY04pTNST17NsjpdOFEbRJUkMFgHo+Hd1BRbl802KiM2EGZEMjhJAkqyOdhdXUVZrMZer0ebW1tRT8L6XSay4IvLy9jeHgYMpkM3d3dvJ9fTD9VbO1iEInczs5OTE5OYmBgQJDULFBdg7iYh2gMw3CHxOFwGGNjY1haWsLi4iK0Wm3JQEWKbMlgQ6gRb0YtbC6lMhWJRILLlIjRUyFFIy7GnqT25Wqk9G29rjWbzdDpdOju7uYyA4FAAG1tbUXf73qXUVUTzORez0fBCpDeZ4mydRFaRlXMT+WWvOTeRLIsC61Wy2UqLBYLtFotdyO5srKC+fn5mspiyu1LCmuJhRT3VIp0Or0hqEilUlAqlVymwul0QqfTYWxsTHC1g9lsxuHDh7GysoKrV69yAUulMlWh0q5i+hK5XI7Ozk54PB5cvXoVU1NTuHz5Mvx+f0lhlGr2Ta6vVzBjNBqxd+9exGIxRCIRrkzM5XJtipJ46e+wCqRUC5uL0PKn5eVlhMNhBAIBUfYlNcTck9ScQaOkb4VcW00vSWtrKw4ePIhIJILR0VFcu3YNfr8fVqs1b61mqFFVur6YES9UsDp16hTsdju8Xm/FNaX4HaJsXoQcimWzWaTTaaysrOQFFQC48ie9Xo/29nbodLqKNzBSLR0GpGfLpUg2m0U4HM4LKhKJBNesTwYh+v3+sqpG1ULkZ3//+99jZGQELMvyyoDzRait5XO9TCaDxWIBy7IwmUzcRG+/3w+9Xl/0MfX2U9WoJspkMvT09IBhGExOTuapMPKV0W4GWzLYIA3ifBG7zyKbzW5Q8Kim/Immp7cn9apvraVx3WAwYN++fYjFYggGgxgZGcmTGWyEGpWYqiC5ClbT09M4c+YMV3ZSrSIXhSKEYuW+2Wx2Q6YiHo8DAFdL39rayjuoKMVWV2AkiCXW0Szfmc1mNzTth8Nh7uCSlD95PB6oVKq63KSXQy6X49ChQ1hdXeUG7XV3d28IOurpp4T6S5lMxvVDLC4u4p133uHUoAozffWey1Ht8FmZTFbUh7W2tqKvr493v3Ij2ZLBRjWZjWqMSbFMxerqKpLJJNbW1mqWlN3q6Wlga55iFXtNxGnknkTF43EoFAp0d3fnNS7XW/q2FnQ6Hfbs2YP19XUEg0GMjo6is7OzLupShdfXw+jnygC//vrruHDhAtRqNW8FK8rW48EHH8TLL78Mm82GixcvAgDuueceDA0NAbhRgmQymXDu3LkNj/X5fDAajZDL5VAoFBgcHCz6HMlkEuFwGL/73e8wNDSEW265hQsqSPmTXq+HzWbjarMnJychk8lgs9lqfo1SPcgSM3so1lqNyGgWlsNFIhHu86DT6aDX69HS0gKHw4FwOIxoNAq/31/3fVXaM3lvWltbcejQIaytrWFkZASZTAZdXV0wm80bruWzrlCq8YEMw8BqtcJqtSIUCmF4eBgA8jI0jWgor3UeVK4PW1xclGxJlTR3VSPV1MKWO00RUv5ksViwuroqqHmq3L6k6BCk6FikAsuyyGQyWF5extzcXN7JpE6ng8FgQGtrK1wuF2QyGVKpFCYnJzE6OopAIID29va67k2s91yj0WDXrl1IJpMYGxvD1NQU2traeE1ArWYv1VwvpOmVYRgolUrcfPPNWF5e5hwmUbCijePbh0984hP47Gc/iwceeID72U9+8hPuvx999NGygehrr71W8nv8m9/8Bl/60pcgk8mQSCSwZ88e7N+/H36/v2TDJ0GqQiZSXQtovAx5JYgaWG5QkasApdfrYTAYYLVaS34eIpGIaHsRm5aWFhw8eBDhcBjXrl3DtWvX0NXVJVihqV5/s1LBA+ldIcFSOp2G3++XzMTxwseUUpUsLG+WElsy2BBaRkVOeaqRlC1keXlZkoZXqpmNzUruZyU3W5HNZpFMJqHT6WAymfJOJgtJpVLQ6XRc09fIyAhGR0fhcrkE7aORmY1CVCoVduzYAblcjrW1NZw6dQpOpxMej6fsCUs9G+nI9dW+1mIKVj6fD52dnZI15BTxuO222zA2Nlb0dyzL4qc//Sl++9vfVrX2rbfeioGBAcjlcnzzm99ER0cHPvjBD/J6rFTLlaTqp5p9wJZ7LxGJRLiqh1gsxgUVYqiB1UIt9qycPzEajVzQMTIygng8zjvgeHM0hOMXY1jWLeDPdlqr3l8xKvlAEixFIhEEg0EsLi5Co9HAbDbzeq8akdkAKqtKSpEtGWxUymwUBhWLi4tIpVIYGxureaL2djl9EmudzRAApVKpPKcRjUaRTqehUqm4ZrxcXfoLFy7A4/FsmPxZjtygY2hoCKFQCAsLC2XnQgDCjNtbwRUMjq7hwy1r2OsSppNeCVIHa7Vauaa1jo4OeL3eok6mEbWwtWYjchWsFhYWJGvEKY3jjTfegN1uR09PT9HfMwyDO+64AwzD4FOf+hQeeeSRvN/nNnA2U8hEymVUUvMJlb73RAEq1z8UKkA5HA5OUrzUZ2czUum9MRqNOHDgAGZmZjA8PIyBgQF0dXWVVDVMprM4MRyCVsng9auLuNlvhkEt3m0qX79jMBiwd+9enDt3Dmtrazh58iR8Pl9RyfTC9Rt1KLbZ2NLBBt9MRWtrK7RaLdxud83PLWXDK7UyKrEQ68tKshIzMzN5Ch8KhYI7ibLb7dDr9WVPaWrJIOh0OvT09GBoaAizs7N55VWl1uTzXMuxJH4fXIUiC7x0fhp9TqOoRo4YTYVCAZ/PB6/Xy2mbW61W+Hy+vKY1qTWUl/tMazQaXmpVlK3PCy+8gPvuu6/k79988004nU7Mz8/j2LFj2LlzJ2677bai19ZzzkYlpOqnpLoWAGQymQ19d8UUoAptHWF5eVm0vYhFrZkNvuh0OrS3t6OzsxOjo6MYGRlBIBCAxWLJ24NSzqCzTYPzwVXs92ihVYqb8RHqR4hsrlqtxtjYGILBIDweD1wuV9FsVL391GamocFGsca7r33ta/jud78Lq/VGuuwb3/gG7rzzzg2PfeWVV/C5z30OmUwGDz/8MB577LG836+urmJwcBDvvPMOfvWrX+E///M/8eyzz+Kxxx6rmKmYmpoS7TVK1Vhu9ehZyPuUq/hCHEc8Hkc6nYZarYZCoYDZbK5a4UMMlErlhvKqYkEH39etU8rRolZgbmUdPWZdxdck9HNXaGRlMhm8Xi/cbjdmZmYwODgIs9nMaZvXu7a13lK8lO1HOp3Gz3/+c5w5c6bkNU6nEwBgs9lw9913Y2BgoGywsbKywvv5t0MZFdD8w6xC/0AqH2ZmZri+u2b7BzEQ430WWsJLVA2j0ShGRka4oIP4NYZh8JEDdnSrI7jlsBdymbjvbbVlTmq1Gr29vQgEAtxU8mLlws3IwOci5c9iQ4ONYo13APD5z38eX/jCF0o+LpPJ4DOf+Qx+9atfwe1248iRI7jrrruwe/du7prLly/j+PHj2LNnDw4fPoz9+/fj/vvv57UvMQ3vdjl9EmudejoWlmU5CeLc0yjgj4ovRqMRHR0d0Gq1mJychEKhgMPhqNue+O6bUKynI9c48zWeaqUc9x+yYnRGhlsO8MvgiXGzLpPJ4HK54HQ6MTc3x2mbk6m0fNkMZVeUrc2vf/1r7Ny5s2QGnPRsGY1GRKNRvPrqq3jiiSdKrtfMzMZ2KKOqtBZp1i6cqg38UQGKTJyWy+XweDyi7GszUsz2CpWczUWv13NBx+joaJ5fU8gYWHRyqBTi2+NaM+pKpRJdXV3cVPL+/n5uTpNKpaoqmGlWv06jaWiwUa7xrhwDAwPo7u7mhtvde++9eOmll/KCjaNHj+Lo0aMAbkgTCrlxkMlkSKfTgvdVDCkbXjH1y6UGUYDKDSwymQzUajWX4rZYLDVp0/OFr8Ep97ctfHypoEPITbhWKYO7VcXLiNea2SiEYZg8bfOZmRlcvHgRXV1dMBgMNa9f7Hoxgw0pnxhRxOW+++7DiRMnsLi4CLfbjSeffBIPPfQQXnzxxQ0lVNPT03j44Ydx/PhxzM3N4e677wZwIwvysY99DO9///tLPo8YE8SrRcpriQXZFymnzg0qSGCYKzNcSgEqlUptad9ZLWKIk+j1es6vkfKqjo4Osbeatw8xpGxzy4WnpqZw+vRptLe3I51O112NarMiiZ6NZ555Bj/84Q9x+PBhPP3005w2M2FqairvVMHtdqO/v7/ketWoUW2HbESz09PFELqnTCaTF1AQ6UAiI2swGKqea9JoSNq4kHLvSWHQsby8jLa2Nl6GX6hzqIfRJPJ8Op0Obrcbly9fhkKhQCAQqDjXgmY2KI3ghRdeKPrz5557bsPPnE4njh8/DgAIBAI4f/487+fZSn5KKvtKpVJ5QcXbb7+NbDYLtVrNBRW5Yh6N2JPYiNl72ahDlEp71ul06OvrQywWw5UrV7CysoL5+XnRpVyFZhIq+QWZTMb1cMzOzmJiYgJXr15FV1cXr+Gw1ZQTb1aafjf26U9/Go8//jgYhsHjjz+ORx99FM8++2zeNcXe4HIfQI1Gg7W1Nd57ENOYSNkhSO2DWu5vmDs5lTiP9fV1yOVyrm7WYrGgs7MToVAIyWRyyzTx8nECJOgIBoOYnZ3FwMBAxUbyesrkCi1zYhgGFosFFosFKysr3PTZ3EFQtVDvsisKpVa2SmajGSVZuQpQxEcQBSgSVKjVauzatQt6vV6UvUmJRmZaS/09hPoIPtfqdDoEAgGMj49jYWGB6+mw2WyivOZ6CZPIZDI4nU5MTEzAYrHgwoUL0Gq1CAQCZTP326m3sOnBht1u5/77k5/8JD7wgQ9suMbtdmNiYoL7/8nJSa4RrxjNlBSUqhEHxI2KxfrQs+yNyanl6mZbW1vhdDqh0Wg2zRetUftUKpVwOp2wWq1lG8kBYX+zRhpBk8mEQ4cOcZrsV69eLapUIoR6N5RTKLXSbDUqKVLoP4kCVO6h0/XlOIaWgR02HQ51tnEqR4UKUEtLS6Jlt8VqppbagV+t1OPwimVZKJVK7N69G/F4HMFgEKOjo/D7/bDb7TUraNXbr9lsNjgcDoRCoYqZ++2UgW96sDEzM8M15P7iF79AX1/fhmuOHDmCq1evIhgMwuVy4cUXX8SPf/zjkmuq1WpB6Wmpnhg1eq3VeAqTK+twmzRo1fKf+MkHlmXzUtyRSAShUIhTqMiVDqw0SbcQqTnOWv9m1ZwYVWokr3bdeu25GESTnTQNXrt2DX6/HzabTfBajZjkSqHUgtBgQ6o3q2LYX6IAtbKygtXVVVy4cAHxeBwymSxPAcrtduPEyRmoTMDlaBbvtdjL+qpGKi5tNmq12UIbxKsJTLRaLXbv3o319XUEg0EEg0H4/nveRTV7r1aNSuj1pTL3gUAgb0DgduotbGiwUazx7sSJEzh37hwYhoHP58N3vvMdAPmNdwqFAs888wze9773IZPJ4MEHH8SePXtKPo9KpWqqyoeYp0+NOsnKZFm8MDiN5VgKJq0Sn7zVC0UJ2blKKkgkxZ0bWJAheGRehcvlgkajgU6ny8tuVYsUnXAtCDHMuZQLOoTQzPQuaRokp1ojIyNIpVJ1zcxsZiNO2ZyoVKqmZeCbBVGAyvUPJJOt1WqhUCg4xR+tVlv0e9emU2JyZR0GdXnFIqkGZ1uJetjFYnZeo9Fg165deUGH3+8X3EwuVoO4kOtzM/ejo6Nc5r69vb0qP7tZD8UaGmwUa7x76KGHil6b23gHAHfeeWfR+RvF2Cq1sI1cK5NlEU1moFfJEU1mkMmyJYMNQjab3TA5tdiQI7/fX3QI3vLy8pZ0BmJkNoRcW8xYFQs6Wltb8yYYV7NuuevFNoK5p1pvvfUWTp48Ca/XC6fTWfG5xD4xolDEppllVPWGKEAVyo5ns1loNBrOP7S3t+cpBK6trWF6erpsc+2HDzowsRyHzagWfehbuddDyademQ2gdBBDgo5EIsEFHUIOohpRRlXqeqPRiP3793PKW9euXYNcLq9rb6SUaHoZVT0QWkYl1Z6NRq6lUshw9z47zk+Fsc9lhDrnxKiwryIej2NwcLBoilutVvP+MmzWLw0fan1tYqlG5QYdb7/9NhKJBAwGQ9lGcrKuVBrXNBoNNBoNDh8+jPHxcZw8eRJutxtut7ukssh2qoWlbE62ShlVKpVCOp3G5OQkF1SQAamkWdvlckGv11dUAuLzGnUqOXrtleWyxXq/6umnUpksZAwj+vA6PohRRlWvno1KqNVq7Ny5E/F4HL/4zVv4wf/5Pd671we/x1XWjkuhIZsob62vr+P06dO4cOEC/H4/r0O0zeyntmSw0cz0tJgfzEYHLv52HVwtCkQiEVy/vsTJyhI9cnIapVarcfDgwaLZCkrt1OOGQqfTweVyIZFIYHZ2tmwjee4e+BrbRpy4qFQq9PT0wO/34/r16zh58iScTie8Xu+GRlCqRkWROs0s962GdDqNWCyWVx6bSqWgUCi4IZ0dHR3Q6XRV+wapHtbVI2gZW4rhx4PT0Chk+PhRNyx6VYlHNo90Oo21tTXuwKcW6hGYLMazeHVSDkOrEUvn5nDL1AQ6OzvhcDiK2nMxMxW1otFo0NLSAq/Xi8XFxbocokmJLRlsNLOMSkzqaXgL9cgjkQg3BI/0VXg8nqJ65JOTk6J8Aev1vi9Fk/jN0CKsBhVu67Y0/NSokQ3iQq9Vq9Xo7u4u20gO3BAL+PE7Mfz8+mU8eEsn/O3l5SPrmdkofD+JukfuFFebzZanSEMzGxSpI1U/lVseS/4hsuMkU0Fkx8n37fTp03C5XDU/txR9cb3s2tmJVQDAciyF4FKsqcEGy7J5ql+RSIT7m2u1WsTjcRgMhrz5EWJnK3Kv5btuPJlGFiw0Sjm0LUYcPuzB2NgYTp48ic7Ozg3ZAqlJx5IZMIWHaA6HA16vd0PQvpl7C7dssNGsMioxEeODQ4bgLS0tYWVlBefOnUMymYRCoeAyFR0dHYKG4EnNIRTu55VLC7i2EMWFDAu3SYseGz+ddTFfU6O+9EINPjFUldSrhuaiWIhl4DCw+N3VpaYHG8XWlsvl6OzshMfjyZvi6vP5JFUGRqEUo9l+imXZDbMqchWg9Ho9TCYTXC6XoPLYWpBqZkMscvez12nExZkIjBoFOs3ahu2BTFOPRCIYHR1FMplENpvl/uZGoxEOh4OTmk+lUpDJZIhEInj77bdhNBrR1dUl6DnrdYDma9PiVpcKepsJx3bboFKpsGPHDvh8Pq7k1uv1wuVycaMEpGTnc31y4SHawMAArFYrfD5f1YdoUmJLBhtCy6ikaJSEQqQDc1Pc6+vrkMlk0Ov1kMlk0Gg06O3thVKplNQXTmzMOiVS6SyUchl0KmENhFJ4X+plEIutWyrocLUqoVXKkEpnsc/V0rQ981m7cIrrmTNnEI/Hsb6+Dq2WnxPfzCdGlM1JNX6qGkopQMViMQSDQS6TbbfbSypANRKp+eJ63R902wz4wv8TgJxhyqpqVQs5aMzNVpDBhwaDATKZDB0dHbBYLLx6aaxWK9rb2zE/P4+zZ89Cr9fzDn7rFWwAwMEOFY4c8eT9jJTc+nw+LtPh8Xgkd7NerNw39xBtZmYGg4ODMJvN8Pv9tEFcamx1lY9S0oGlTiYAYGVlBfPz8xsGH1WDmI139TDix3a2o7NNixaNAi5TbXWmUkcslY/CoGNtbQ0f26XGnr29aOOR3hei/iT0b853bTLF1eFw4PXXX8f58+eh1+sRCAQqThGWmhOibH2EllFVIlcBKtc/EAUoElQQBagzZ85gz549krp5kWrPY70QQ02L3BOQgILcE5CDxmJlbwDwzjvv8GraB/74d2EYBna7HTabDWNjYxgdHcXQ0BD8fr8o9xaFz1eJSv5PqVRyQcf4+Djm5uagUqlgNBolYe/L+R2ZTAaXywWn04m5uTmcPXsWSqWy5t6ZZkGDDUjXKCWTSaTTaUxMTHAOJJPJFHUclb44YqenpYxSLsMeh7Fpz7+ZVT5I0DE7O4urV6/i6sWzZRvJCUJOXKqRpRUaUCmVStx8881YWlrCO++8A5VKha6uLhiNxT8XNNigNBqFQoFMJlPVY1Op1AZZ2WKzjMrdTBKfICV7vtXLqGqF9FqGw2FcuXIl756ADMe12WzQ6XR1/bsyDIP29nasra3BYDBgcHCQK/kpJg5QT5/G51qlUonu7m5u5hefZuxGwMcXEuEFu92Oa9euYWpqCufOnSvrz6TIlgw2NqPKR+FpFEl3ptNpyOVyOJ1O6HQ63n0VhYhteKWk8rEVaaZhBm4E7BaLBT6fr2wjeTVrN0opijjE9vZ2hEIhDA0NgWEYdHV1wWQybXgOqq5GaSR8vgOkFCb3xHpgYCCv585ut0Ov1wv+/DIMI7kgW4oBQjP2lM1mOeWv3BlWCoUCMpkMcrkcDodDUK9lIWIEmuT03eFwYHp6GgMDA+jo6EBnZ2fevqTQTA7c+Ft6vV7s2LED4+PjOHXqFFwuFzweT1OCDiG+kGEYGI1GeDwemM1mzp+RqeRSZ0sGG1JW+SiUDiRD8MhpVGFD0OnTp+F0Omt+bimeGEnpRK0ZZDKZoo5C6Htbz8CkUiN5LWvzRQyn2NbWhra2NqyurmJ0dBTpdBqBQABtbW2SvOmibC8KfQNRgCKlMAaDARaLBaFQCEeOHBHFdpKGWbEQ43sqRT8lFqUOaRKJRN49QW5ZNJlh5fF4oFKpwDAM5ubmsL6+jtbW1ka/hA17J8hkMrjdbjidTk4hkCgqER/X7AO03OsVCgW6urrQ2dmJ69evixJ0VPNZE7p/4qcK/dm1a9fg9/tht9sF76FRbNlgQ2gZlZgUysiRQXgAuBrK1tbWTavyQSlP4ftc2GeT61BkMhm6u7thtVrzPgfNPgUqvLZS0CE0s9EsWdrW1lYcPHgQ4XCYM9KBQACZTIY2iFM4HnzwQbz88suw2Wy4ePEiAOBrX/savvvd78JqtQIAvvGNb+DOO+/c8NhXXnkFn/vc55DJZPDwww/jsccey/v9yMgIzp8/j4sXLyIUCuHQoUP4q7/6K9x6662cb3A6nXk9dwSZTCbaZ7EeN/ZSCzbEQow9ZTIZJBIJTE1NcfcFZPghyVJZLBZeZdFSoNjfWyaTcepPExMT6O/vh8vlAsMwTQ02Iok0LkytIRROozfnvSUKUF6vFxMTEzh16hScTic8Ho/gjFEtGfhqn4P4s0gkgmAwiHQ6jc7OTsF7aARbMthQKpWCJAWrhTTkFZ5KkBsyciJls9mg1Wo3hQHhg5gOYatlSNLpNNLpNGZmZvIMMGt2AAAgAElEQVTml+TW1FqtVuh0OqTTaWQyGVy/fh3BYBBdXV2wWCwNCyCqubZU0CG0Z6PRmY1CjEYj9u/fj1gshtHRUSwsLMDlcsFms0nq80RpDp/4xCfw2c9+Fg888EDezz//+c/jC1/4QsnHZTIZfOYzn8GvfvUruN1uHDlyBHfddRd2797NXfOTn/wE6XQafX19aGlpwYkTJ5pSwidmZkNqPkHMtYTaA6IMWTizgvyura2t6tI3MeH73lTzHsrlcvh8Prjdbly/fh3j4+Noa2uD2+2ueB9UD5/27FvjeHtyDfFIFH07k+gsUClUKBTw+/1c0EEyM/Us6aqGUgGNwWDA3r17qy6pawTS3VkNVPMHr/ShIg15udkK0pBHTiXIELyzZ89iz549kgoupJielqqD4vt8RGq4cAhSKpUCy7Kw2+3o6uoqawA0Gg36+voQjUZx7do1BINBrrSH7z6E7FksI06CjomFFRw/exXqRAyLi4sVG8mF7gOob/O2TqdDX18f3nnnHUQiEW4YVKkJtJTtwW233YaxsTHBjxsYGEB3dzcCgQAA4N5778VLL72UF2x8+ctf5v7761//uqAbTrFnAYnVqyhFn9CIbH7uzApy2MiyLLRaLQwGQ54yZDgcxvT0NNxud133JAQ+drhUVoKPHSeZA+CGImapYXtC1xV67UosBbWCQSTLYj1V+jNPgiSPx8MJ84yOjuaVg5WiEaW42WxW0gFFOTbnrutIJpPZ0FdBhuDl6pGXO5WQYsmSFPe0Wcidtk6cSjabLepQGIbB4OAgN0SIL3q9Hvv370c4HMbFixeRSqVgtVrR0lJ5xkWzAhOWZfH/vTmNhbAC8bAcAfd0xUZyQLhRbkQmRCaTwefzQa/X5+my8zmJo2wfnnnmGfzwhz/E4cOH8fTTT29ozJyamoLH80fNf7fbjf7+ftGeX0wFKSne2Es1q5jJZLC2tpbnB3JnVhgMBu6wsZnqRo2G799LLpfDZrNxkrmnTp2Cz+eDw+HYsIZQGXU+e/hff9KJ/3txFhl9FD6Ljtd+yZwLuVyO/v7+oo3v1eylFoQqOUqJbRtsFKY64/E4BgYG8rSpCxuz+CLFG3upOhYpvU+kWW9ubo4LKogCCHEoTqezoja5EKNTeJ3RaITP58Py8jKGh4chl8vR09MDg8FQ83MVe75S8Fk3ywLRZBoapQxhFujduRtKNlVRvaremY1qBh+R51Cr1ejt7UUgEOAm0LpcLtF15Cmbj09/+tN4/PHHwTAMHn/8cTz66KN49tln864pZs/EzPSRbIQYN7SNyGywLIvXR0K4Nh/F7T0WdFnLz7tptp8i/XW52QpSCpvJZLhG/cKZFZuRWt/nag6vcid8j46OYmxsDIFAAHa7nfsO1COz4TFr8ch7/BgYWBAUyMhkMnR2dsLtdmNqagr9/f2w2+3o7OzccNjcqMwGDTYkSjm1B61Wyw3BU6vVoqp8NEJKN5XJIpHOwqCu/GeU2o19symW/k4kElCr1bBarTCZTHC73YIDTbHQarXYvXs3lpeXcenSJWg0GnR3d0Onyz+VaaZ+uVzG4LO3B3BieBEq4xpatUoAyorqVfXOVFRz+lNoxIkuu8/nw8TEBJLJ5Ka/uaDURq7Syyc/+Ul84AMf2HCN2+3GxMQE9/+Tk5Nl1QQVCgXS6TTvUiox+ywa0bOxGEnijashGNRy/O8Lc/j8/whUtY6YeyIUZqzJzApyX0CqGOLxOJaXl9Hd3S3KvqSEGA39fCi04SqVCjt37sT6+jpGR0e5nkUivlDP/j++viHXJ8jlcni9XrjdbkxOTmJgYAA2mw2+nLkizezZIEg1Mwg0IdgopvLxxS9+Eb/85S+5wVs/+MEPNujgA4DP54PRaIRcLodCocDg4GDe75eWlnDhwgVcvHgRa2tr+JM/+RPce++9eO9731tR7WFsbEySKh9A8Q9xLJnBD/snsRhN4lhvO272l9dZbvaJUbPIZrN55U8k/V3Ya6PX6zE5OQmVSoWOjo6m7jn3vTWbzThy5AiWlpbw9ttvw2g0oqurq6opovUITHpsBvTYDHjrrem8n5dTr6q3GlU1pz+lHkMaB2mgQZmZmYHD4QAA/OIXv0BfX9+Ga44cOYKrV68iGAzC5XLhxRdfxI9//OOSaxLlRCHBhtT6LMqtpVcroFfLsZbIYKetfFaDrCM25WZWkIx1uZkViURiS5eIVYsY74lGo8Hu3bsRj8c5P0HkfvnuoV4HV+XUtkimgwQdnZ2dNLNRgYYHG8VUPo4dO4annnoKCoUCf/u3f4unnnoK3/rWt4o+/rXXXkN7e3vR3z3zzDMIhULYu3cvtFotXn75ZbS1tfHem5RrYQv3NR9OYCGSgFmrxNnJtYrBhthIrYyqMP1NBmAxDMPplVdKf4vdfMlnz3wezzA3htNZLBbMz8/j7NmzMJlMCAQCTc1s8KFY0CFUC7yasqtqy6goFAC47777cOLECSwuLsLtduPJJ5/EiRMncO7cOTAMA5/Ph+985zsAgOnpaTz88MM4fvw4FAoFnnnmGbzvfe9DJpPBgw8+iD179pR8HpVKhUQiAb2+8o04IK7NbERmQ6eS48F3e7AUTcFjFn5AIoTcKoZIJILFxUXMz89z/Za1lEaLuUcprEGo5T0Q059otVr09fUhFovh3LlzWF5ehkajgcViEW0PQHHfwLIs/vP8DC7NhPGRQy702g0V15bJZPB4PHC5XJiensbp06fR2toquLRMKJvZTzU82Cim8nHHHXdw/3306FH87Gc/q2rtr371q9x//8u//IugQEPMxrtGnD45WjXwmLWYXk3gzj2VX6fYAVAzyZ2qG4lEsLy8jFQqhXA4nCcvW43ccLNfG1DaCDEMA7vdDpvNhpmZGZw5cwYsy8Jms/Fet9HBBiE36Lh8+TLW1tawsLDAS72qEZmNzdx4RxGfF154YcPPHnrooaLXOp1OHD9+nPv/O++8s+j8jWIInQkltm9pRJakVav87xJLfvDxU+l0ekPGOpPJcDMrDAYDWltbYbFYuNKcrYQYtrmR1Ql8/YlOp4PdbodcLsfk5CRGR0fR3d1dMtNRjZ8qvD64FMNL52agUsjwb28E8fRH9gLg50dyhxmOjIxgbm4OQ0NDvLLh1ex9M/spyfVsPPvss7jnnnuK/o5hGNxxxx1gGAaf+tSn8Mgjj5RcRy6XI5PJ8G6kk2qZUam11AoZPn6zG+ksC6W88odPqq+v3DrFmvXi8XjexHWbzYaWlhbEYjFOYm+zUMrQVDJCDMPA6XSio6MDp0+fxqVLl+ByucoqZfBZt9pryxFNpJFIZ9Gmv2F4dTodfD4fpqenMTs7y0u9qlE9G1IINCnbi2oG0ErRjtertJYIueRmrInEOCmDtdvtCAQCG0rRSG9mrWymsuFGUk9xEq1WC5/Ph3A4jGvXrmFkZAQ9PT0bpqaL4adaNUqolTLEklnssP0x+yZkbZlMBqvVimQyCYPBgMHBQVgsFvh8PqjV6qKP2W5+SlLBxt///d9DoVDg/vvvL/r7N998E06nE/Pz8zh27Bh27tyJ2267rei1JD1d2FBbCnJiJJbKRyOMOMMwUMr5q5hIzWDmfmnS6XReUFHYrGc0GmG326HVajd82RYXFxu9ddGoxXAQ5bQdO3ZgbW0N/f39cDqd8Hq9JT/HjQw25tbW8f/+nyHEkml8/KgXt/daubVVKhV6e3vLNpITGqlGRaE0EuKn+CLlzEYta7HsjQG50WgUqVQKly5dQjQaBQBOYrzcZPXtRCyZxf+9soa2uSnctc8OPQ+BmGZT7UGX0WjEwYMHsba2hqtXr4JhGPT09MBoNApetxQWgwpP/PlOTK2so8/5R6n5alUTXS4XnE4nV33Q1tYGv9+/IegQs7eQIOXvhWQ+pc8//zxefvll/OY3vyn5hhFVD5vNhrvvvhsDAwMlgw21Wi0o2BC7FlYsIy5WXa1UTrFYluWa9ebm5hCLxTA3N8edVFVq1qs3qQyLTJaFXNa8L61QwyyXyzl5vvHxcZw6darojIhGZzZGFqIIr6ehU8lxKricF2yQtcs1klerXlUPI06h1INmZjaapWyVWwabO7OCiHYwDCPKzAqx3iupHdSdnozg3EwcmlUGbQYV3rerujKxWm28UH8ihMJ1W1pacNNNN2FlZQVDQ0NQKBTo7u4WLQPvMmnhMuVPFa+l+ZxUHzgcDi7oMJvN8Pv9nLjLdjsUE/Vubn19HQqFQvBN4iuvvIJvfetb+N3vflcyOIhGo8hmszAajYhGo3j11VfxxBNPlFxTrVYjlUrx3sNmU/lo1jpC1iLysrmOhaRIDQYDdDoddDodurq6JBGRj4YS+MWVECyta/jMbT60G6SvQJRr4ORyOQKBADweDzcjwufzwel0Cv77i1EbutvRAqdJg6VoEv9zzx/7SooZTDHVq8SQvi1ECp9PijhU66fqQbN7NurpE1iWzZtlRcpgSUbWYDCgvb19g2hHKBTiTq7F3tNWoEVzIwBjAJgF9MLUg3qJk5TCZDLh8OHDCIVCuHTpEliWFeWzUoxsNouzc0m8/Mow/nyPDfs9GxVSC68v9CG5Qcfs7Cwn7uL3+wFgWx2KiWJtk8kkfvnLX2JwcBAqlQqHDx/Gn/7pnxYdRFZM5eOpp576/9l78/C2yjNt/D7ardWWZO2yZcuyHeKszsJSIKGkBFoCtB0odIbQ0IUptDPT6Vyl/ChTOmUIc3WZ9vvaMh9Lm2lnaKGlpaUQwpYQSuzYSZzFSRzvdrzLki1Z+3J+f7jnIFnbOdKRLSe+r8tXbOXVe96zPc/7bPeDUCiEHTt2AJgvEn/66aeTWD4mJiZwxx13AJhPubnnnnuwc+fOjGtiG54uRQOBmosr5VIswbuQWpDqup7YXdVisaR4qiYnJ2nGqFLAibEgeAQw7QujZ8q3ZMZGod4aqkdEVVUV+vv7ceTIEdTW1rLypHChHMqlQjx5+xUgSYCXECnKNnc6o0OhUGRsapgO+ea1lspzuILigI2eWiyIRKJLokA8Ho/D4/EkddmOx+O0Y4mqrUiXBltMlJKxwdV5rzdKISbiMBgMWGUo/rOb6RoWi3mJibNIrVZj8+bN6OnpwcjICGKxGOx2O+NMFiZw+cL4/XkfypUEusa9eP7ejVkzHrLpNYIgYDQaYTAYMDExgRMnTkAmk7F+Pi97Y+O5557DI488gubmZkQiETz33HPYtWsX9u7dC6VSmTQ2X5aP2tpanDx5kvGaqDQqpuA69anUvE9csVeEw2EEg0GMj49jZGSELsKjPFVsqQVLSRk06cXom/GhXCZEjYY7oZUPuDAKqLoIqnHS9PQ0NBoNZDJZzvm5SrkiCAIL/4uJMkk0Ok6ePAmn04mysrKisFet4PIAGz21WGCrp5Y6jYrqW5QYrQiHw4hEIohGo9BoNDCbzZDJZHmnQHG1Kc82D0mSmJoLQykRQCLMvs7FIkRhCoIgUKcRo8pYmEc/ndyOxWIp3dOpppMNDQ0prFDFKhBnOp9cLofVaoVSqcTJkyehVCrz7kO1EEI+AYmAh0A4DoNKjFyZ1Uz0DkEQMBgM0Ov1GBwcRG9vLzo7O1FbW4uysrKs32V6jFJFQcYG9bD+4Ac/wMsvv4zt27cDAPr6+vCpT30Kzz77LP7hH/6Bk6JrtmCbRrVYNIBLNRdbAZ4pr5YK+8tkMjpake/DvxhKhQ0atBJ8Xa+E0aCHWLB0LzRbL1Cu86caJ/n9fkxPT2N0dBR1dXVZOcyLWd8Rj8fhDsQQGvfCoZNn9RZJpVK6ySIb9iouBfJKxGN5o9T1VCmmUZFkcs8Kqm8RkN6x1NvbC7VazYpuPhMWQ3f++cwk3utxoUIqxFe32ZZFkTWXIEkS8Xgc09PTNNsXleJGZSPo9XrY7XbE43FEIhEMDAygr68P9fX1UCgUnOupfMdSTFBarTalD1UmJigmKBMQ+PKWCsTkeqyzqBg56JjqHYIgoNFo4PF4oNVq0dHRAYVCgdra2qzRmcue+tbtdqOxsRHAfKi6trYWr732Gm655RZ89rOfZd3Eiwvkw/JRagYC13OlA0UvuzCvliCIpLxam81GUwv29PSgoqIibfqBPxxDn9MPo0oMjWzx0pC4uEYkSaJMyFtSQ4NaRzE8RjweDw0NDYjH4+jp6UFfXx8cDgfKy1NzUYtpbEzNhfG9wxMg+W7cuEqH+6+pzjo+Ho9DJpOhpqamKOxVK7g8UKp6aqmpbzP1rJBIJLQOyNW3aLnVR5we9aK8TAC3P4KpuXBOY2M5ndtCxGIx+P1+eL3epGhFKBSC1+uFUqmETqeDVCpNK8cp5+KGDRswMzOD8+fPQywWQ6XKvQGnUExjI7Eom+pDNT4+jmPHjkGj0TDqeZFpbr1ciIaGDwvwY3ESz7w/gFMjHuy+0oqtNR8a12zTdyn2U2rNU1NTOHXqFORyeUajI9cxStkxxklkw2q1oq+vD0ajESKRCNFoFEajEU6nk6avW2yspFGlgiRJzM7OplUqlDdDp9PlzKvNtqbnjwxhYDoIhYSPf7nRDqkos7ewFBVUKb+s6ZCPd0kmk2HdunXwer3o7u4GSZJwOBxJqSTFNDYmvGEEoyRUEj7OjXtYzc+EvWo5c5GvgHus6Kl5LOxZMT09jXA4jPHx8RRvdqbi+ZMjHrzb5cQVBjl2rKpM2uhxaQRxNU+mNX1sVSVe7hjDKoMc5vLsKTfLSZYkRqPm5ubomkipVAqFQoHKykrU1NRAKBTi+PHjSU7ETEg8f6pAe3p6GmfPnoVQKITFYikogrAQheoeqj5Cr9djbGwMbW1t0Ol0sNlsrNaRzmnVM+XD2+enIBHy8Mz7g0nGRj4R/sT3R6fTobKyEk6nE6dOnYJMJkupQ7lsIxvUSX/ta1+Dy+VCOByGSCSCQCCA3++HUChENBrlZKFskU8aVSlGI/KZi2IBSRQ6wWAQgUCAViwGg4FzelmSJDHhDUMh5sMXjsEfjmU1Ni53ZEthKNZGPxEKhQIbN27EzMwMLly4QNMJyuXyvELZTGFXi7HRLMNUiI97t1blHM+WvWo5C+QVcI9S11NsIxu5jA2qtm4hEyCQ3LNCJBIhHo+jujp7ZDERr5+ZRJmIhyMDM9hYVU6TaJSi4ygbmqtU2GhVLitDIhEkSSbpd6/Xm0QfLJfLodFocqY550ukodVqUVdXh6mpKRw7dgxarZY2YjKtd7GjIFTPC6PRiJGRERw9ehThcBjRaJTRvifd3JVyEWRiPuZCMawxJ9d55dMPKh17FZUS5nQ6cfr0aZSVlcFut0MmkzGeuxRR8E5zdnYWf/d3f5fy+fDwML7yla9Aq9UWeoi8sNTNktgK3tYBNz7odWNDlQrbHB/m0ueaKxKJpDTDo+hlqWZ4RqMREokE7e3taGhoyPu8cq2JIAj87WYz3r0wjSaTAhrZ0tLyXQ7gwihIpBPs7OykFRTTedlGEgQ84HObdTCbzYzXzYa9SqlUcspKsoLlj1LWU2xrNhJlb2JtXWKKTOKm02q1QiaTpbz7ExMTCAQCrNZbq5Xi3MQc1FIRFJIPtw+lVkQN5F4TU5m11IZUJBJJSoGamZmhjQ2qdqaqqiqvVKFCQBAElEol1qxZQ2/mMzWVLVZqMJN5eTwerFYrzGYz3nvvPUbNbzPNrZaJ8L1PNWF0JohGgwLhaBz/591e9Dn9uN0hZsUOls0plmh0TE9P48yZMygrK0MsFss4X6kbzgUZG2+++SZefvll3HPPPbjmmmswNzdHs1A0NDRwsrHNF0vdLImN4RKJxfHGuSlUSIU4dGEazVYVLcipdSXSy1LKJRQKQSAQ0EqlUBYQNsh0rRr0cjTomb1wSy3ESxWFUt9mmzcb1Go1tmzZAqfTiTNnziAYDEKhUORk9mC73nTjSZJE++AMPMEoPmJXQ5zAEMPEY5RodJw6dQpTU1OQSqWM2KtyXZdSF+IryI5S1lMSiQRzc3M5x1HRap/Ph2AwSBf2JvasSEyRYYJ86hQ/ucGIcU8QaqkoqbaNS4IVrlBqa2IihxZSyCfqeIVCAavVCpVKhXg8DqvVWtB6CtW91PepzbzJZMLQ0BBaWlpQVVUFs9mcJLeLEdmIx+OMszMIgoBIJMLWrVsxNDSEI0eOwGKxwGq1pt0zZdI7WrkYWvl82lj7oBuHe6Yh5PPwm1N+fMfEnNWOiZOOiiJpNBq4XC5MTEzg5MmTsNvtS0rZnQ/yMjZisRj4fD6+//3v4+abb8Yvf/lL6PV6HDhwADt37qQ7Oy5lOkOpsnykg4BHwKaWos/ph0EpBh8xuFzzfOUulwsulwt8Ph9SqRRyuRzl5eWwWCyM6WW5xsrmq/hYqsI7yqNiMBggEAhw/PhxqNVq1NbWZvSc5ZOrulAuHBuaxVNvXEAsDgxO+7EnoWiczfwUexVJkqzYq1ae6UsPy0VPuVyupM/C4XBStCKxZwVJkhCJRKiuri64Z0U+zh4Bj4ClPJWisxgEK5fiO0ldo2g0mkIxG4/HaR2vUqlgNpshFotTrgM1lguwie7k+pzP56OmpgYWiwUDAwNoaWlBbW0t9Ho969pCNmD67lLPFLVOq9VKG0dWqxUWiyVpLibPoEEpgVjARzgah61ShC5nEO+PXsT19VroldmddGzSrij2KooF9Ny5cxAKhbDb7UVrasg18jI2qBuwatUq3HnnnXjsscegUChw9uxZrFmzBnV1dQDYd0fkEkudRsVkLoolYm5uDptUfhiiXsh4blw476ajFRRTxFKF+dOh1ELmy00pUVz1s7Oz0Ov1KZ6ZUqAUBACNRoO6ujqMjY2hvb0dlZWVaQsKuYhs+MJRxEiAIIDZYHKtVT4dxMVi8Qp71WWOQvTUnj178Oqrr0Kn0+HMmTMAgH/5l3/Bn/70J4hEItjtdvz85z9Py+Rms9mgUCjA5/MhEAjQ3t6edn2hUAijo6Po7OzEo48+ik9/+tNJzVBlMllKtHpiYgLBYJCTNMGlri0sNrhaUyHzUGyPlONwZmYGR48eBZ/Pp3W80WiEXC5fEurlQpDpmgiFQjgcDlRVVaG3txcDAwOszo2tTmMzb+K7LhAIUFtbC6vVisHBQRw5cgTV1dUwmUy08ZxrHZaKMvzwb9Zgai4En3MUTx4aRYzg450uJ57+7HpW62EKjUZDRzrOnz9PGx1L1SuIKQpKo6qoqMCvfvUr9PX14eTJk3C5XCUjcJYyjWrhXCRJprBEUPSylCfDoNOirtaW4j32+XzLbjPNFFyeV6k8dwtB1dRQObdUoaZMJoNYLEZHR0fa9IdipVGxNUwIgoDJZILBYKDzcg0GA6qrq2kjia3QTDf+6lo1hl0BuP1h3LPZmnN8NiQaD0zZq1aMjUsX+eip++67Dw899BDuvfde+rMdO3bgySefhEAgwDe+8Q08+eSTeOqpp9J+/913383oIBoaGsIdd9wBgUAAmUwGpVKJm2++GatWrcoZrV7KdN9sKIb+XG56L11DvES2R0rXr127dtHOjSRJ/ObYGI4Nz+K2tXp8xM6+D0q+RCZisZju79TW1oazZ89i1apVaQ10NvMmgo3szjSvUChEXV0dqqqq6IiMzWZDLBZjNLepXAJTuQR/mRoFifno32wwgh+81QONTITPbrFAwE+dp1DWRKqvjdvtRldXFyQSCZqbm/Oer9goKLKxfft2vPHGG9DpdHjiiSdw5ZVX0jzm1AO6VAJDIpGwKn7jSvDGYjHak0ExQkWjUYjFYtqTkYuzPBGXssfoUkIiA1goFMKpU6cQDAYhEAigUCjSFmqGw2HU1dVhfHw8qbiuFCIbCzf4iXm5w8PDaG1thdlshtVqLYjyj4KQz8PfbrUmjCHxlz4X4iQJRTRWcJpWNqNjxdi4NFGInrruuuswMDCQ9NnHPvYx+vcrr7wSv/3tb/Nal8ViQWtrKwQCAV577TUcOnQIt956K6PvlrKBUGrrKlYEPp3z0O/30/UzCoUiLYWwz+eDx+NZ1D3RuCeEt7ucUEgE+J+jI7imtoI+/mIZdFKpFBUVFdDpdOjt7QWPx4PD4chYc1CsYvJcm3uRSIT6+npUV1ejv78f4+PjqKysZLwerZSHL15pRM8sibHZIN48Nzk/r4CHGxsrUalIpgfOJ2KfDhUVFdi0aRMr9tWlQEHGxrXXXotrr7027ZiFF3GxPRUikQgzMzOMx7MVlokFe4nRCj6fDx6PBx6PB51Ox6pgL9O6Sm1jX2ppVIuNWCyGGY8Xz30wjF6nH9vNQK2SoGkl+Xw+6uvr0+bbLkTiJn5wcBAtLS2QSCSM8zCLyfKRDnw+HzabDRaLhc53raysTDs229y51vHmuUn89FA/SAA7LEBDfWFpWhTSGR1ms3nZeVFXkBvF1FPPP/887rrrrozH/djHPgaCIPClL30JX/ziF1OOSR23FJr6XapzcTEPRcwSCARw4cKFjM7DTA3xlhqqMiEqpEK4/RGsNsrzWiMXtW4kSUIul6O5uRlutxtnz55FWVkZ6urqUFaWWgPElQGRz3rFYjEaGxvB5/MxMzODlpYW2O12VFZWZv1+PB7HNbXl2KXR4Mfv9OL0iAe+cBTPHB7Ab9ov4l8/0Yj1CZ3I88kIyBVJKmVw12QBgNPpxNTUFGZnZxGNRlFWVgaLxQK9Xr/oL2I+BeKZuNYjkUhKwV4sFqM3l1QzJKpgz+l0wuPx5AwXMl1XKTFqcIlSFM4LEQ6Hk2gHfT4feDweJiMinB4PQi4R4+ScGJ+5cRX9HafTmZPBaSH4fD5qa2thsVhw/PhxdHV10cXa2a7TYuW3LkRivuv58+cxOTmJ0dFRGI3GnOth4tHxBKOI/7WGwxsunL98IRKNjq6uLrjdbkxNTaWt6VgOz+kKmKNQPfXEE09AIBDgs5/9bNr//8tf/gKTyYTJyUns2LEDjY2NuO6669KOXWk+mx5L4TplwjsAACAASURBVIRaKOv9fj+A+XtEkmTOXhKlCKmIj2/d7MCYJwSbOnVTv1hI1FMVFRXYvHkznE4nOjo6UF5eDrvdTqeQLyVNbiL4fD4sFgvKy8tpx5Tdbs9INpKoM7/wERtqtVL89sQoprwhDLkC+MqvT+H6ei0e2VkPmVhAdxBniuUegefE2JidncXBgwexf/9+dHZ20uxJlZWVsFqtaGpqwi233IJVq1blnowj5NPUjyrcTdxYUqkwVMGe0WjM2QyvFL08XKIU11QoSHK+EVY4HIbHM88ERjX/ogxKrVZLp7+5fGG8MdINfyiG1Sbu2CBEIhHUajVkMhkmJiYwMDAAh8OBioqKjN9ZSsFMdZAFAI/Hg4GBAZqBpBCP2C1NejjnwoiTJNZKXAhF44gGo0nc/pnARihLpVLU1dWhp6eHMXvVCpYnuNBT+/btw6uvvoq333474/NhMpkAADqdDnfccQeOHj2a1dgopM9GIUhXW/jmeSdOjniw3aHBpmrmzrJS1HmZ5kmkkad+qMJ8KuU1sSEeFQVVq9nXOyxcz1JAIRGklZtsrnGmzXW+uodypGm1WrrLt16vh81mY12zkW9qMJPxBDGfsdDU1AS/34+enh709fWhrq4OGo0maXyi3ikT8fGJtUZUqaX49qvn5+cDiT90jOFovwuP71oFA7h3opUyCjY2zp49i0cffRTnzp3D9u3b8eCDD8JqtYIgCIyPj6O9vR3vvPMODh8+jG984xu46qqruFh3TuRio1rYYXVmZoYu8MpFPZcLpSh4SxFLdW7RaBQ+ny/Ji0WS871MqO7qVqs1a7GmWibCdz5ej2lfBDYN9x4jkUiENWvWwOv1oru7G/39/aivr0/Jcy3W9WObUigQCNDY2IhgMEgzkGTyAjFRJjKxAH9/fQ0A4M+HpvD3v+lEMELi6zvqcE2dJut38ykoFwqFtEJZWNOxguUPLvTU/v378dRTT+HQoUMZ2aAomlqFQgGfz4cDBw7gsccey7iufNKoihWNmAlE8UGfGxqZEK+fnURzlYqx7uNa53E1TzQahdvtpkk6KIIOqli7oqICVqs1awrKpUxkUui5FRpVTyQguXjxIlpbWxGPxxlfp2KyMS7UI1KpFGvXrsXc3FyS0UE5AtPNv9aiwotf2IyHf38Wb5ydAEEAwzNBfP6/T2CjUYLvfryW8XouW2ODOvG33noLmzdvxssvv5x23B133AFgPsc1GAwCSE8r6HK5cNddd2FgYAA2mw0vvvhiWm/uvn378N3vfhcA8Oijj2L37t1pj0t5jKifdF4MymNtsVig0Wjg9Xpht9vzvSQ00gneY0Mz6HX6cU2tGuZy5ik2pWhslNqasnnQqUI+yrCg6mrS0Q729/dDJpOleCwyQVUmhKqM+5B6otBSKBTYuHEj3G43Ojs7IZPJUFdXR6dpFbMWig3LBzVWIpFg9erVSZt2h8OR5BVkKzR73FF4ggQkAj7eOj+V09hgy/KRi72qsbEROp2O8XwrKB3kq6fuvvtuHDx4EE6nExaLBY8//jiefPJJhEIh7NixA8B8kfjTTz+N0dFRfP7zn8drr72GiYkJeq5oNIp77rkHO3fuzLi+Ukqjkov50CnEmPSG0KCXsfZaL6WDjSRTG+JRhgXlRLJYLIvW9PZyAJdEJjweD1VVVTCZTPjggw/Q3t4Om82Ws56uWFGQbOPlcjnWr18Pr9ebZHRkcnIJ+Dz8xydXY3N1Ob77ehdIEogBaBsN4qZnzuLBbX58dXsdo/VclsYGddJf/epXAcx7dGQyWco46obt2bOH/iwdreDevXvx0Y9+FA8//DD27t2LvXv3ptAKulwuPP7442hvbwdBEGhubsauXbtooyQcDuPtt9/G6dOn8dZbb6GrqwsHDhzAM888A6VSCbVajaqqqrTNySKRSNEoBZ1zYbx6ehIiAQ+jsyH84/YaxnMVIxeWC28GV2FurhCPx5OMirm5OUQiEbqQj2IIydUIq1RSZxauo6KiAlu2bMHU1BSOHz8OjUaD2lrmXhG2YHN/0wllatO+0AtUXl7O+hmsUxFoc4ngD8dwY2Mlzo55YK2QZkypYiuUc7FXZarlWkHpI189tX379pQx999/f9pjmEwmvPbaawCA2tpanDx5kvH68kn3LdamXsjnYc9VFrj9kRTmnKVc10JkaohH1VBSWQlerxder7dgOVlqzjUuUYi+K0ZUQSAQQCKRoKmpCcPDwzhy5Ajsdjt0Ol3BqVz5RDayjVcoFNiwYQNmZ2fR09MDj8cDg8GQlmWLxyNw12YLmswKfG7fCcwGP9QpPzk4gF+1DOHoN28oaD2lsnfJhILSqKiT379/Pw4cOICrrroKer0e5eXlKC8vp3PPqdAvQczz9qejFXzllVdw8OBBAMDu3buxbdu2FGPjjTfewI4dO2gv6Y4dO7B//37cfffdAOYVxsGDB7Fu3Trs3r0b77zzDv7zP/+T0bkUMzwtEvAgEvDgj8RgVLEX4lyvq5QeynyEONW7gjIuPB4PIpEIIpEIFAoFNBoNqqurM3a8LnVkCznrdDpUVlbSPS/C4TDrQjMm4IK/HPjQC+TxeNDd3Q2CIBCLsaOyVUsIPH/vBsRJ4NuvnkfnqBdauQg/vXsdykSp551PGlWm8VKpdMUTusyRr55aDCx189mFskYi5MOoYv+8F8PYIMkPG+ItZHxMjExnqqGcm5vjZD0rWFyQJAmRSISGhoaktNx0dRLFNDaYRkJUKhWam5vR2tqK/v5+jI6Ooq6uLq3RsdqkwhtfvQq3/+woxr0fvvezwTjC0RhEgszv3mUb2QCS27+3trbi97//PWKxGAwGAzQaDSoqKrB7927cdNNNALJbXhMTEzAajQAAo9GIycnJlDEjIyOwWj/k4rdYLBgZGaH/lkgktIFy4sQJHDhwgPG5cFl4t3AupUSAz11lxaQ3hLrKVK9aNpQiyweXBlA2JCobKmKRWLBP9a4Ih8NwuVxwOBxFX9NigIkHw2KxwGg04r333kNLSwuqq6s5p3DlUogrlUo0NzdjZmYGx48fx/nz59HY2JjWy5xufpGAj3icxJlRDxRiAZxzIUz7wrCIUutlCkmjSodSMs5XwB5c6imusZTNZ0uJjSoWiyXV0Z05cwYkSUIikUAileHNgTDGfHzcvbkRq03MOiVfqhGJYpzTkCuAw70urLcosdrIPe16vmOptFyfz0fXLjocDqhUqqKugRrPZnMvEAjoFOKzZ89CIpHAbren6LgKmRgH//kj2Prvb2M24dXn51jbZW1sUCe+Y8cOOo/V6XTi6NGj+MUvfoE//OEP2Lp1K2666SZOXpB0c2R6eJYyFzbdZtygFMOgZM+DXIqFd1wh8dwoZZMYsaC6ryoUCigUChiNRkgkkpTzcLvdS7H8JQefz4dYLMbmzZvR19eXM+TMBsXgLweA8vJyqFQqmEwmnDlzhmaDSse1vhA8HoHPX1ONX7ePoFYrxb/+6RxuXKXD3ZstKWu/nFg+VpAdi62n2GCp9dRi11kk1tGla4gnl8shFotht9vpDWX3pA89M4OQi/n405nJRTc2EueZ8ITQOeZFvV4GS/nSUclyXbT+5IEeeAJRHDg3hR99ejXKpdzWIhZqFMhkMjpCfuHCBQgEAjgcDtbzctnXYiEoPaJWq7F582ZMT0/jzJkzkMlksNvtSTqOIAg8f5sBvzgbQuvgHL79iVXgp+kynm7+5QpO+2wAgFarxS233IIbbrgBv/zlL6HX6wHkLjbV6/UYGxuD0WjE2NhY2oJMi8VCp1oBwMWLF7Ft27a084lEopLJhS3VuSKxOP54agKT3jBuX6eHUcWscL3QNVEF+06nE263G21tbSAIglY2lZWVy47PPBN8oSiG3EHUaMogETJLT2Ar5IRCIR1y7unpwcDAAOrr67PS5TJBMYW4Wq2GwWDA1NQUOjo6oFKpUFtbm7M/ye3rTbilyYA7nm6FRMjHr1qHsWNVJbTyDw35fNaznIX4CtgjXz3FNdiyUXFpbAQjcQQi3BWbL9QJC6nkvV5v2oZ4FJ04BY/Hk5S6WCkXQS7hYy4Uw0aripP15oNYnMQP3unDtC8MhViAf7+tEWUMZTpQes6+RJAkgAzLy6Tri5mSnWlepVKJTZs2weVy4cyZMwiFQnRGTC5wXbORbTxBENBqtdBoNBl1XDwex7/eXM+4ge+KsYH5orvR0VGoVCrIZDLIZDJIJBIEAgH87Gc/w65duxCLxbL2pti1axf27duHhx9+GPv27cNtt92WMuamm27CI488QnuyDxw4gCeffDLtfJebxyifubomfHivxwUhn4c/np7Alz5SzXgupl6shQwhoVCI5jMXCoWQyWRoamqCyx/FgMsPc6UcSga9FJYDIrE4Hnv1Aia8Idg0UvzbJ+qLqnCowrq5ubmkkDNTYZaIYkU2qLl5PF5SDcrExARd+F5TU5O13kbIJ2Aul+CiOwBfOIYfvNWLr26vhUH1oRAvRm7uCpY3uNBTXGOpnGLD7gD+u2UYE5MhWBwBVBfY8C0ajSIQCGBoaAher5duiEc5kNjU0S18F8ulQvzLjXbMBiJLwuRIzRMnSfjDcUgEfISiJGJx9nOXYloXQRD45k11eK9nGutMCpTx43A6nUl0wRqNBg6HI+X+FSuNKhfUajW2bNmCo0eP4vz587SDMtvzxXV6LZPx6XRcRUUFamtri1ZDUqooSKpSF/e9997Dl770Jdxwww3Q6/WoqamBVqvFG2+8QTc4SrxI6WgFH374Ydx555147rnnUFVVhZdeegkA0N7ejqeffhrPPvss1Go1vvWtb2Hz5s0AgMceeyxjox22xkYp58IWywhSlQkg5PMQicWhZ8E+ku6Bp3pXJNZXxONxms9cpVLBYrEk9a7wer0IBoMIx0j8x5u9mAlEYFRJ8O1bHKxeqlJ9Ab3BKMY9IajKBOif9iMcIyEW5F5roR4juVyODRs2wO1249y5cygrK0NdXW5qvXzXwFYILpybIAgYDAbo9XqMjo6ira0NOp0ONTU1aTd+BEHg+59eg+f+MoA/nRrHsaEZ/PyDIXzz5np6PStpVCugkK+eWgyw1RVc6ZY+57w8ipPAhck5xsZGpoZ4PB4PsVgMQqEQ1dXVdEO8fJBOFyslgiV3Qgn5PDx0fTXe73Vhc3U55OLl7RQjSZLW2RGvF2tEcwiOjuKcU0Q3N9TpdODz+ZidnUVbWxuMRiOqq6vB5/M5pb5dCCZjCYKAWCzG6tWr6fUZDAZUV1en1RuLGdlIt1ZKx42NjeHYsWOIRqOIxWKs5l/OtYWc1Gxcc801+MlPfoKTJ0+io6MDr732Gjo7O1FXV4cf/OAHAJAUFn3hhRfSzvf222+nfLZp0yY8++yz9N979uxJotHNBLYeI66E+GwggldOjmN6IgjHqhikadhy2KCYHWOtFWX4pxtqMBuIoF6fypyQDiRJ0tSDAwMDSTm3FMUsky7riQhG4vAEo5CJ+HDOhREjAQZ78pR1lRoqpELsWqvHoe5p3N1sgljAXPlyITgqKiqwefNmOowbDAbprui5sBSFdwRBwGw2w2g00g2ejEZj2nurkAhwZa0ab5ydxJQ3jD+fGcdqkwK71hnz8hhlS9krdSG+guzIV0+VIrh6FlcZ5GgfmsWcAFiToQYikfWP6luR6ECiGuKJRCL4fD4MDQ0xTmnJhmLUWnCFBr0cDQx1ZSlhYQE+pbf7+/tzNjcMh8MwGAwwGo0YGhpCS0sLbCw7fQPFkaOULqH0xvDwMFpbW2GxWGC1WpP0TLELxJnMTxAfNjE8cuQITp06BYPBAJvNljNtfLk7xTgxzZVKJW699Vbceuut9GdvvfUW3n//fSiVzIq5uMZSpVG1DsygezIA10wMp0Y8uLKm8Lz5YqZkmcslGUPTiV4sSkhRBpxAIIBGo0FlZSWkUmnegoQkSZRLhbhnswkt/TPY0aiFgHdpbO4IgsBdzSbc1Wxi9T0uFWRiGPfQoUMp3qlsa1gqSkGqwZPZbMbQ0BB8Ph8GBwdTlMdWWwW+eK0N//l2LxQSPv7vwT7cuGq+1quY4fIVLE+Uop4ClsaY1SnE+KcbatDW5oReIUqR86FQKIn1z2w2Z22IV4qpw6U2z2KBMhKpe+nz+eiaSKrflN1ux4kTJ9DU1MR4Xj6fj5qaGpjNZvT19WFsbAwWi4Wud8qGYtV3JM7L4/FoVsaBgQEcOXIENTU1MBqN9D0sdtoS0/E8Hg9isRhNTU2YmprC0aNHs0ZlqPUsZz1VtDjgjTfeiEgkgoceegi/+93vEIvFFtVrxLbwjiuBopWLQBDztVYaWeF9HrgWdJnmikajSV4Pn88HkiRpL1Zizu3k5CT8fj8jIZMNiS/mdXUaXJejM/TlBK4FM0EQEIlE2Lp1K4aHh9HS0kJv6AsVYPkUWDM5P0q5jY6OIhqN4siRI6iurobJZKJrPm5s1OF/j17E+fE5SIQ8/Nufz+NWlo/lchfiK8gfS62ngMWNylLprpSsD/j9aGtroxviKZVKmEymtKx/2VCKxsalDorZi7qXVFoyZSQqFApYrVbIZLKC5NtcKIpftA1AJODjb7dWQSoSobGxEfF4HC6XC+3t7aivr89qsBfrfqYzIAQCAerq6lBVVYW+vj4MDg7CbrezlvPFLIAHQPfHslqtMJlMuHjxIlpaWmA2m1FVVZUih5a7nuLE2JiamsIrr7yC9evXQyKRQCaTQafTYXh4GOfOnePiEKzB5/OXJBd2o1WFijIBus554NCx66mRDsUQ4oFAIIkhZGHvisX0Yq0gFcW8tnw+HzabDWazGf39/WhpaclIl1ssNiq2IAgCdrsdVVVV9JptNhuMRiMUEgG+cZMDX//tGcz4w9h/dhLryoS4msX8y12Ir4AZSlFPFQuJPYoS02biBB9qlYJuiOfxeLBly5aCj7dYTjE24HJNS63vErMMJicnEQgEMDo6CrFYTNdXGAwG1kYiE7xyegqvd06BIAhUSEX4m2YzgHkWxJqaGojFYnR1dUEsFsPhcKSlMV+MyMZCiP5qFAUCAfT29sLtdmes8U2HYuuFRL3J5/NRXV0Ni8WCoaEhHDlyhE4Fo/Zhy11PcdLUz+l04mtf+xrdZK2mpgYSiQRnzpzB7bffDmDxKQUBdt5hLgWTTSOFU8LNi1XIuijqQcrzMTMzg1OnTtHRCoVCUTQBxQRchrkvJRTbowLMK4r6+npUVVXRHVodDgcrYUyhmCwZic8Htebq6mraY1VbW4sNlkqsMStx6IITfJLEb7rCuOsmdgXuy1mIryA7LiU9lQ4LexTNzc0hGo1CIpHQDiRNpQ5/6HSjd8qPbeVqbDdrOTk2Ba6dYlyhlPQL03lisVhSGhRFtkIxe8lkMpSXl8Nms3GyrlxQiPkgQIDAfK0cBeq9UqlU2LRpE5xOJzo6OqBWq1FbW7so1PVMdGVZWRmamprQ19eH0dFRHDt2DA6HI2fq5GJENhbKGyqab7VaMTg4iJaWFlitVlgslmVfW1iQsUGd3KpVq+DxeODz+dDe3o6TJ0+it7cX9913H13MvRQXgo2gKVVhyXRdVO+KxDQoAHSeZmVlJUKhEGpraxl1beZiTcsNi31OnWNz+PF756FXivGvn2iEqmzx+4pQHVoT6XLr6+tZzVFMoZxubrFYjFWrViEYDKK3txf9/f24e50J7/e6EIrGMOABzox4sMbCjJN/MYy7FSwdLhU9RZIkwuEwIpFICjlHrh5Fk94Qep1+6OQifNDnxvZ6LafnUIppVKX4Ti88L0pvJ9ZXUGQrVPRJLpcnZRmMjo5yxlDJBLesrkSlSgaRgI9rM6Q6EwSByspKaLVajIyM4OjRo3Q6ELWhXuzIxkKIxWJYLBZUVFSgq6sLIpEIDocDUqm04LnzQbb5BQIBHc2n6k9kMhkqKyuLtp5io+A0qlgshsHBQcjlckilUlx//fW4/vrruVjbssHpEQ+G3UFssZVDKy+8TiMRCwVvYhpUYjGfUCikBVRVVVVa6sGxsbFLJqxcLCymgvrdyQkEIzF0T86hbdCNGxvni5uLIeRy3S+KLndmZgbnz59HIBBAIBBg1Nm72MZGJm8zZSj5/X709PTALCPR6waCceDZDwbwozvXMTpGrshGKW5aVsAOpaynKNrYxA3lwoZ4FDmHSCRCLBaDRCJJ2xAvEyqkQlSryzDkCuAae2GkJelQqsZGqeg7Kq0tFAqhr68PXq83SW8rFApoNJqCKIOLBQGPwA0N2pS06nRynyAIWCwWGI1GepNM9ZQoBvIhMlGpVNi8eTOcTidOnToFpVKZtqFsKUS8hUIhHA4HqqurceLECfT09IAgCLrofTmhYGNjamoKN910E9asWQOLxYIf//jHiEaj9E1aypu1GDdj0hvC7zrGwSOA4ZkAq8Z4uUCFx30+H7q6ujA3N4dYLEYX86lUKpjNZojFYsa81KXkMSolZbAU2GBW4sJUAGUiPuzawut7soGpUC4vL8emTZtw+PBhdHR0oLy8HHa7PStdbjGNDSYpWlKpFGvXrsW9gX489lovSAD7O6dwqHsK1ztye4JKQamsoLjIR0/t2bMHr776KnQ6Hc6cOQMAcLlcuOuuuzAwMACbzYYXX3wRFRWpm/d9+/bhu9/9LgDg0Ucfxe7duzOuTSKR4JVXXkF5eTkMBkNSVDpdQzyKuYYNhHwe7rvSgkA4BlkR+kOUorHBFdjKtkRDkXIIRqNR2lBUKBQwmUyM9XapIhIj8W7PDAwzBD5iVyedC5/Ph91uh9VqRU9PD/x+P1wuV15putlQCGsi1eGbaran1WqTooKlFPEWiURQq9WQSqXweDwYGBhAbW0t9Hp9yawxFwqWOgaDAW1tbXjkkUcwMjICYP5BK4ULsBgCi88jwOfNv3hs+igkggqPJwonKjwuFotBkiQMBgOr3hXZjlUoSk0ZLAeQ5Hw39UTmEFM4iH/aWolVdTXQKcuSxhYjssF0ToIgIBAIcOWVV2JsbIzzZklswKb4/BPrrfj2672I/fXR/PKvTqLl61tzdlBfMTYufeSjp+677z489NBDuPfee+nP9u7di49+9KN4+OGHsXfvXuzduxdPPfVU0vdcLhcef/xxtLe3gyAINDc3Y9euXUlGyUsvvYSf//znGB0dxfj4OPbv349PfepTjNiD2NJ4jswEIRHyoJGJimJoJK6JCTouzuLUiBfX1qlRo8mcwrKYa8oXVM+pRN0NIG1aWzAYxIULFwpOhSkV3fvHLg/eGQhBKJjEN3fW4zpHamqeSCTCFVdcgenpaQwODqK/vx8NDQ2Qy7npVcLW2EjX4dtgMECn02F0dBRHjx6lqeGp/y8VxONxiMVimM1mBINB9PX1ob+/H3a7fVmkV3EiecrLy/HTn/6U/ruUbhAb5LNujUyEe7daMeEJYpUh+6YGmH9gAoFAUvEX1WiNCqcmhsf9fj/6+vqgUjHLP8+GUrwvpSI4uQRJkvB6vfRPYkRKoVCgoqICVVVViMfjmJ6eRvfp4wj/lR+cukdLaWxQIIgPGxANDQ2htbWVLlZLFNrF3KyzKT6XigW4qrYC7/e6AQBRAL94qwPX1SpRV1eXsVZpxdi4PMBWT1133XUYGBhI+uyVV17BwYMHAQC7d+/Gtm3bUoyNN954Azt27KC9uDt27MD+/ftx991302M2bdqEa665BkajETt37sQTTzzBeMPAxthoHXDj9c4pCHgE9lxthUmV2lOJC2cBQRCM6ghm/BH8d+sIeDwC58a9+PddjWlTcUop4k2tJxQKpTgE+Xx+RhbHSCyOzrE5GPgxGDguli4FXe4Nzd/vOAnMhbJ3wubz+XSabmdnJ2QyGerq6lJSl9iCzbNLUc2mA4/Ho9O/qMaFkUiEsW5YjH1M4lokEgmuuOIKmmmrr68Pzc3NnBlxxQBnbo54PA6SJEuqAytFZ1vsjUS1ugzV6tTc9oVej4W9K7J17aTARvA658KY8IRQp5OljbKUWi5sKQjMQpHIW5+ohIaHh6FQKKDT6WC329NGBMLhME1319vbi5aWFtTX1yMej2PME4KeL4KcQ09kvtebx+PBZrPBYrHQ1LOJIdxip1GxeX9/+OnV2PzU+/TfB0Z4uPtaM06fPg25XA673Z5Sh7JibFw+KFRPTUxM0F2yjUYjJicnU8aMjIzAarXSf1ssFjqaQqGmpob+Pd8GtEye2WF3EAIegVAsDudcOMXY4Or95fF4jHSCkE9AyCfgD8egUqTXe4vN/rQQVF1kYhR6bm4O586dS3II5mpm+//eH8KRfjckQj6euLUB5eLlr+8SsatBjjJpHPpyBT7awIxwoLy8HFu2bMHk5CSOHz9OR34KydjIN40qHSg2KIvFgsOHD6OlpQU1NTUwGAxZv7sYKVfp3nmKacvv9zOqr1xKcLaTWdgWHlj6zSTV2K9Q6zkdxj0hvN45CY1MiJ1XVCIejaQ0xTt58iTj3hWZwFSIzwYi+P7bffCFolhtVOBL16bWjpSax6gQRGJxCHjEoj5jkUgkKWJBpbpRCoi6x8ePH8cVV1zBeF6hUIjGxkb4/X5cuHABf+h0ofU9H8plEvyfz6yFLoNSZgMuhKFAIIDD4Uihy11sNqpsKBMuLGL8MDd3amqKrkOpra2ljfwVY+PywWLoqXSyMdsx2BobbOT4tXY1XL4wlBJh2r5Pi+WQoyATC/CVbTb0O/1YbVSkvS6L6RSLx+MpaVCJUWiVSgWTyYQzZ85g/fr1rI7fP+2HSMBDMBKD0xdGuZhb8pilhkrMw1euNSelB055Qzh4wYlGgwJrzOmpZQmCgF6vR2VlJS5evJgxYs412OgSoVAIiUSC5uZm9PX1YWBgAHV1ddBqtWnnWCpjg0IpEgssBGfGxtzcHB3CSXfRqRDrYl6QYhgbVPHX749eRN+0D75gGJGpATgqpbRhodfr+xFZwwAAIABJREFUEQgE0NzcXPDxmApebygKfzgGqViAkdlgQXMxWRNXyGc9vz0xht+fHEeTUYGv31hblM1CYsg8sfEh1UDJZrNx/oJLpVKsX78eT7z/LshoGE5PDGdH3NA1sisGTQcujUOxWIwrrrgCPp8P3d3dmJmZgUqlSlsoW+g68un6ekutEK/3RUAA+PI2O4D5Z1an06GyshLj4+M4duwYNBoNampqciqKpXaarIA7FKqn9Ho9xsbGYDQaMTY2Bp1OlzLGYrHQqVYAcPHiRWzbti3jmkQiESKRCONzYNKAliRJdI554QvHsftKa8Z6wqXQCZbyMljKM3thi1VrEYlEUjINgA/p4fV6fdoodL5r+dxVVvzP0RHUVcrQqJcjGgkvuZOu2PjmH86id8oHiZCH5+/dCGOatD0KPB4PVVVVMJlMSQ1mS4G5igJFs04xHlLU8OXl5UnjFsNgL6WC9XzAmbHx7W9/m26UZLVaIRQKoVKpUFlZCYlEsiRWl1gsRjgcZvWdxBtKCadEDmxgXjipJQQGhWIYFApcs9kGgzLZ+8xlKJhJLqxZJcHOKypxbnwOH29KVYDUXJdCGtWfTk9ALRWic8yLsdkQygt4ihND5i6XC06nE729vXRDLLaND7m4LjtsEvyhPw6LXIDYZC96hT7YbLa01INMUQxBJZPJsH79ehw/fhxDQ0NwOp1ZecvzWQfb8fF4HJ9dLcPnPuqARMhHvT45h5WiDdTr9RgdHUVbWxudm7uCSx+F6qldu3Zh3759ePjhh7Fv3z7cdtttKWNuuukmPPLII3C752uHDhw4gCeffDLjnPmmUWXDhUkffnN8DCQJeIIR7GhMXw9SimQfha6Jcha53W74fD6cPn0agUAAfD6fdhYxKcQvFKuNCvz7bY3039GiHWlpkE42e4NRCPkE4iQQjGSv46BARcwTmatmZmZSNvRcrDff+00xHnq9XnR3dwMAHA4HTT6Sj15ji+UegefM2JidncU777yDmZkZjI2N4eqrr4ZOp4NGo4FOp4PJZIJWq6U7tS4GmApxasMZi8XQ29sLv9+f4slOFE5xkoQjTmLLTBByER+VGdJcuCq8Y/JgEgSBm1frcPPq9IYGm7lKHdfa1TjYPQ1rRRn0SjFC/gij81pISej1epNC5hKJBBqNhs7JXio0G0X4249egTKJBCRJ0gVrNpsNJpMpr2eqmF4RSlmEQiGcPHkyK11uPkKZjYCligDX5mjoRxUEmkwmvPfee2hra6ObUJVS3dkKuAUbPXX33Xfj4MGDcDqdsFgsePzxx/Hwww/jzjvvxHPPPYeqqiq89NJLAID29nY8/fTTePbZZ6FWq/Gtb30LmzdvBgA89thjWSk/i5FGFYuTAAnwCCAazzyWieGy2GCjp9Kx/EUiEYjFYnjjIvzqXAw6DR9fvWEdtPL80lGXsze52Fh4bR6/tREvtF3Epupy1GhlGJ0J4vToLOKh3PdTIpGgqakJbrcbvb294PF4qK+vL7gJMQU2ZCOZoFAosHHjRrjdbpw7dw4SiQQOhwMEQbCOwOdDqbxibAB45plnAACnT5/Grl27IBaL0dTUhM7OTnR0dNCNiG655ZasnP1cgkqjSgTVuyKxvoLacMbjcSgUClgslowc2NO+MP679SLC0Tg+u8Wc0dDgqvCuFA2EpY6Q3H+1FbevM0BVJoCQz0M6NR2LxZKMisTifKrAb2Gn3b6+vpTOu2zBxXUhSRJ8Hg8EMV+TYrPZECtT4+X2bljODeC6DY3QaDQFcYxzCWpuqjZifHwcbW1t0Ov1sNlsSWkJ+UQq2I5nI5B5PB5EIhG2bt1KG3WJ+cMrG41LC2z01AsvvJB2jrfffjvls02bNuHZZ5+l/96zZw/dlTwXuE6jCkRicM6Fscogh0EpwlW1mQ2dUtQvQHo5mqi7Kf0dj8dpmU6x/FH7i5//ZQDuEBCcDaN1YAYfb9Iv9mlc0kh3jxoNCjx+6yoAgD8cwwP/24G5UBQSMoybrifB42WXpxR5Q3NzM1wuF06fPg2lcp5NsNB9I5c6sKKigm4M2NHRAblczuo9ysdwWDE2EnD48GF84QtfwG233YbDhw/jrrvuQl1dHQDA4/Hg7NmzKfmQXV1duOuuu+i/+/r68J3vfAf/+I//SH928OBB3HbbbTSDxyc/+Uk89thjOddDEATefPNN6HQ6OBwO+P1+EASRMUezo6MDFRUVWR/q7kkfZvwRiAQ8dAx7MuaeLvWGvJhzBSJxvDcYwJTAhWvsFeAt8oaMIIikTu3RaBShUAhDQ0O0YZFYuG0ymSCXyxfNY83GAGAyRzgax9d/fw4z/gjkYh7MFUN0wVoxjA22z0ji3IlpSsPDwynFf2wFZj41G/koFIqFxGq10p1vq/9KR7yCSwv56KliguvIxjtd02gZmE/h2lRtSSFNYDPXUoAgCESjUbhcLtphtFB3G43GnH2nHDoZ3gAg4vMy9vNYQWHIJmv94Sh8oSgEfAIeP4lonIQAyGpwJMpvtVqNrVu30s4rg8GQNp2YKbh2uBEEgcrKSmi1WgwMDGBiYgLd3d2M2LXySenKpQtL3THGmUR99913cf/99+PBBx/EP//zP+PFF1/E7t278ctf/hK1tbVQKpW48sorU77X0NCAjo4OAPOeC7PZjDvuuCNl3LXXXotXX3015zreeustfO9738PY2BjcbjcIgsDtt98Om81G967IBCbMT+ZyCSRCPuIkiVWGzJzGl7Kx8coZJ97sD+DI5DBkYj42WgvvAcIEJDnf/DDRsxUIBOj/EwqFqK6uzrtwu1SU7sJ1ROMkgpEYxAICkRhwRdMaRPxenDt3DoFAAKFQKCt9MjVnsaIg6cbzeDx6sz4wMEBTCFZUVBS9ZqMQ749AIEBdXR2qqqrQ39+PY8eO4eqrry55Qb4CZshXTxUTXNdszIUiCEfjEDFoMrvUaVQkSSIYDCbVRnq9XvB4PGi1Wsjl8rxl+pZqFe5fW4Y1TQ0pNZX5rLNQXGoyJNc10crF+PL1Ndh/dhJrLVE8uf8C3jo/hZtX6/DNnfWMWJ0SnVdU5Ll6QU8qNustxj2gjI7Z2VmIxWK0trbCbDbDarVmNIzySem67CMb1AV48MEH8eijj2LPnj2IxWK488470draiieeeAI//OEPoVQqc16st99+G3a7ne7emA/WrFmD559/HkajEQ888AA+85nPYNOmTYy+m6sY+9y4Fy93jEMlEeCuZlPGFCqAOyHOtSXOhdCMY35NJDn/U4z1UHU0ialQ1KZaoVDQkamysjJ4vV6Mjo5yUmtRKgohcR1SER+P3dKAP5+ZwM7V+vneG+IKNDc3o6WlBe3t7Tm9PsUsJs82ntq8W61W9Pb2or+/v6iRDa4EskgkQkNDw7JnAFnBPLjUU1yDbRpVNj11dtyLzrE5zIWi2FWvh6Myu0ef68gGSZK4MOmDLxzDGpMCQn5y88+F9RXRaDSJjMNoNMLtdiMWiyX1KskHBEFAW0YUbGisID2YyMZPbTTjUxvN2P/uX/Dk8SkoxXzs75zEfVdWoUImQpkolfQk3ZxUryez2Yy+vj60tLSgrq6uaHotH9ZEPp+PqqoqmM1mDA4O0oaRyWRKkScraVR5gDr5//mf/8GGDRsAzKckxGIxfP/738eNN96ImZkZKJXKnDfw17/+dVKX1UQcOXIE69atg8lkwve+9z2sXr067Ti9/sO8TLZsVLkMhJaBGUiEPDh9EUz7IlmNjVINT3OxpjuatIjMzeCKOjPWW9JzabNBohKiFFE0GkVZWRnkcjlUKhUsFgtEIlFRedm5ABeb03TncpVdg6vsGvrvV0+N4fTILJokQnz8+i2MisiZCqpiMEZRdLnT09M4ffo02traUF9fD5Uqe1Ss2OxVubCchfsKPgSXeopr5BPZyLTG0xc98IfjUEtFUJUJc74LXMvOC5M+/OS9QURiMWyrUeAaszBtzZxGo0F1dXXalOXZ2VlEo4VzN5WSXqCwcD2dY148/8EwHDoZPn9NFQQ5ahqKjcSC+0Qnn16vR11dXd7phXIRUK+Xo3tyDgaVGJ95rg0SIR8/u2c97JXJBeDZnlmhUIiGhgYEAgF0d3fD7/djdnY2px4B2G3WC9E7fD4ftbW1sFqtaZvgUuPz0S2Z1rQcHGKcpVFt2LAh5YIDwE9+8hNUVlYmfZYO4XAYf/zjH9NSBG7cuBGDg4OQy+V47bXXcPvtt9P0Y9nAZS7sXCgKfyiGnik/HDoZTOXZe3cUIzwdi5MggbwFElfCVy4R4AabBKsZdg1NRGKR3+zsLGZnZ3Hs2DFaCWm12pTC7csNuQTduTEvfvhOH6KxOI4Ko7h1O482Mnp7ezE0NIT6+npoNJqkObk6/kKwEeISiYRuqnfhwgUIBALU19dnpMtdqsgGheUgxFfAHIXqqWKArVMsU2Rj2hfGmbE5DLr8sJZLsqb5UuBCT1GpraFQCKfPd2N62gceD7g4GQVh0bNuaFuKRkKx8F/vD2HSE8KgK4Arayo4cd4xBUmSSQ4+ip0x0SC02WyIx+NwuVwp9Xds9ASPIPCTz6zD2GwQP3qnB+OzQcwGovi3P5/HjY2VuHuLFXwec1KdsrIyrF27FocPH0Z3dzeEQiGntOtc6EChUIj6+vqUJrgajYYTZqzlBk6MjenpaahUqrRWb0NDA/37wYMHsX79+rT8ya+//jo2btyYFJmgoFR++ALecsst+PKXvwyn0wmtNvtmNx0bVTZkE7yHul2YmgtDJRHgow4NlJLsl45rgTnhCeH/HhpAKBbHAx+pRq2WfcHbYteRpOtTsrBAPxgMYuPGjQWvaSFC0Ti6JuZgrShDhfTSMlyEfAIEyPk6lQTDUyQS0Q2Iurq6MDAwgIaGBpopYzFrNrKN5fF4UCqV2LRpE6anp3Hq1CkolUrY7faU2pPFrtlYwaULLvRUMZCPnkonf8+PeXFufA7KMgHq9XKoynLLPbY0s4mprXNzcwgGgxCJRFAoFODxeNix1gFRxRxmAlHcukafl+wtxXrHYsGmLsPITBAiAQ+VcmZsS/l4xdMxefn9fgwNDdHsjLW1tWnfjXA4DKvVSqcwtba2wuFwsDo+AIgEPFRrpPjkBhOOD88iEouhc8yL8xNz6J/24++vr4FSxG4DzufzsWnTJjidTpw8eRIVFRWora0tmHad7TXONrdEIsHq1avh8/nQ09ODvr4+mM3my05PFWRsUIr9O9/5Dmpra7Fjxw6o1WoolUqIxWLEYjFMTk7SD+jhw4fxox/9KK0Qf+GFFzKmUI2Pj9MhqKNHjyIejyd5bTMhH49ROuEUjZM4O+bBuQkvqsrLIMthaGSbK1+cHfPCHYhAzOehdWAmL2OjmFjYcZtJEyU2ecpssfdAD06PeKGQCPDjv1kNBYN7xgW4or7NJhTrdHJ859ZVOHNxBjX8aXRPzqF70odr6zRQSASQSqXYsGED3G43Ojs7oVAooNPpSsLYWOjR0Wg0UKvVdFdvipKYUnr5sFGxHb+CSxtc6qliQCKRYG5ujvH4TJGNN85NIRYn4QlEGHvIM+kpqifRQq93Ymqr2WxOooh3u91QKeXYtTZ3RCUXLpf38ivbbLi2Tg2DUgxzjmwJplhI+049W4l1MXK5HMePH8+Yjp4OVBQ6EAigq6sLbrebZnpkg4/UafHaQ1fjX/90Du91OzEbiOD3HaN4p2sKT93emBe7JUW7TjVqNZlMKT2TCtFTTMbn0jsymQzr1q2jGe+oPRPb67dcUdAOjLq4999/P77+9a/jmWeewZo1a2A2myEUChEIBOByuTAxMQGCIPDggw+iqqoqZR6/348333wT//Vf/0V/9vTTTwMAHnjgAfz2t7/Fz372MwgEApSVleHXv/41oweBK5aPnsk5jMyGoJOLoVeKUVeZu8kM18aGQy+DTMRHJEZig0WR1xxcrIkk57uzBoNB9Pb2pni3qI1tWVkZo3zhYqFrwgeJiA9vMAqnL7xoxgbA7Lyo5yyTgMo1x9V2DTaaZTjU5sKXXziJSDSOP59W4id3r6PHVFRUYMuWLRgfH8fZs2chFAoRi8VypjMUO7KxcGwi48jFixfR2toKi8UCq9Vacn05VrD8wJWeKhbyqS1cKMeH3QF0XPTAG4pBIxMySqGi5opEInC73fQG1efzAQAdgU7XkygTuKiZKsXIRrF0lZDPw+bq/I3aaDSaVF9BUQRThgXbFDYmKCsrw/r163H06FF0d3djamqKdR8MqYiP/+/mBqhlIvzu+AjmQjH4wyF87pensM0qxIaNJPgMenIkgiAImM1mGAwGukC7pqYGRqORfhZKweGmVCrR0NCA/v5+dHZ2QiaToa6uDhIJN8ZmqYKTHdjatWtx4MABdHV14aWXXkJ7ezumpqagVCqxevVq3HPPPdi5c2fG70ulUkxPTyd99sADD9C/P/TQQ3jooYdYr4urNKoj/W70TPkh4BG4kWGdAtc1G5byMnz74/WIx0nIxPndNrbClyrcTvSSRKNRCIVCRKNRKJXKFO9WqeAL11Thf9tGcK29AtXq9L1QlhqZrhmbe+QJk4jF5wXd2Gww7TGMRiNEIhG6u7tTBHCm4xczspFpc8/j8VBVVQWTyYTBwUEcOXKEbtbFFIvVl2MFyw+F6qligQs9tf/sFMY9IfB4BMyq9ClU6ajDZ2dnIRAIoFar6Qi0VCrNa3PKdkOXbR4uUGppVIWeVyQSgdfrhcvlQigUwsWLF+nsAYVCURDtez4QCoVYvXo1Zmdn0dbWBrPZjKqqqpTjZ7oH5VIhvrmzHtZyCf7jzW6EovO9ON4aDOPqpw7hV3ua4dCzd6xSBdoWi4WuYXQ4HJzpKa7GS6VSNDQ0YGpqCsePH4darc6YApYLy0GHcebujcfjaGhowKOPPppxzGIrdi7SqAKRGN46Pw2SJCEXC7DeWlh4uhBka87EBNkoExd2Vvd6vUndWaliMaFQiGAwiAsXLtAFlYWgWMpge70G2+tzp9pxjcX0pJEkiZpyPu5qNuPE8CxuXq3H1397BqtNCtx3VVXSHDweDyqVCna7PamIXK1O7S682JGNhRAIBLDb7bBarThx4gTcbjckEgm0Wm3O77JNo1qJbFxeuFT11IvHRhGMkuDzSKy3qmhWoURHUTgcpqnD5XI59Ho9JiYmIJPJoNPpCj6PUoxIlBqYnlcoFErpJyUQCKBQKOh/bTbbojyn2dbM4/FgMpmg1+vpfkp2u51V2u7fXlmFVUYlvvCrE/CFYwCAmWAUn/hpK5qMMvzugasyrivbMRJrGC9cuAC32w2/35+1iJzp3IWOp/QOQRDQ6XSorKzE2NgY3bywurp6URuLLgY4OxsmCnuxrS+JRAKXy8V4fDqPUUufC2OeIKIxEkalGNYKZl7ypW6WlA6UEKdCrwvD5lTodWFn9UzzcLEeLlBqyqkY1LeZxvF5PHzpuhoAwN3PtWPY7Uf7kBvrLCpsrCpPGgt8KIB9Ph8uXLiAgYEB1NfXJ+WN5rPZKkZ4WiQSobKyEjweD2NjY/Ras9Eccp1GtRw8RitgjlLUU/mkUSVSw759dhTj3vnvR+OA1DeGtrZxSKVSyOVyVFRUwGq1pm38yefz89ZT3mAUp0e9MJdLUK0uKzljYzm8u1RacmIqFJWWTIqk+F1XCGKRCF+9oQHl0nmP98WLF+mN6mKASQSez+fDbrfDYrGgu7sbg4ODaGxspKmkc621ubocf37oKnzqv1ow7fvw2T4z5sOVew+i5eFtaY/P5BpIpVKsX78eR44cQU9PD8bGxuBwOLKmLRWbmGTh/ARBwGQywWAwYHh4OCmVmEmz6eWAS8t0WoB8miUtvKm/Pj6KGX8EPB6B1QY54+KlUtkAJ4bNp6amEAqFMDk5SRsW6Qq3V7D0YBrZSBynlgox5JpvtHh8eAaqMiHNYb5wrEwmSykip/Jui+nZzUcoSyQS2Gw2eDwedHd3g8/nw+FwQCZLrZ2Kx+OsaJNXIhsrWGqwSaOKRCJ0xMLj8cDv9+ObbwaSxtx67QZIxcxSMQrRU88dGUL3hA9iIR+P3FTHqc5bOE+cJNFx0QMCwHqLknP5FI2TOHTBCQC4vl5blH4XJEnSZATpok0KhQImk4lOS372L0M4MTYLEkHYTk/i3q0WztdUCNLpCbFYjKamJng8HnR1dUEikcButzO6X0aVBK///Sbc9rOjGPN9aAC7A+l7rrDVUwKBAGvXrsXs7CyOHz8OrVabkYGr2Om4mebn8Xh0h3Qqldhms8FgMCwL4zkbLmljo9ACcX8ogta+WcQA8EkSG1kUchUqeMPR+DzFKQuPcTAYTAqbJxZuy+VyOlReSId2gFtDqhQMsksFT9x2Bd48N4n/bbuIfR8M4X+PXsRvPr8ZlQpxRmGYWETe1tYGo9EIjUZTNMFWiFBWKpVobm6mGwNSBlKix3YljWoFyw3p9NRCeU4RcQgEAvD5fHpTIpVKIXrvLwgE5+WolA/GhgZQmCz3BmMQCXmIxkmEY/GiRjYOdbvwi5ZhAMD9V1lxnYPbFNn9Zyfxi5aLAIBANI5PNCVT8LM9LyqNLTFiEYlEEA6H4fP56Ea16aJNFHQKEXg8AsRff19OoKjNp6amcOLECUQiEUbkJHwC+PFOLb7wmhMzgfm9mCIDHW4+zIM8Hg86nQ5arRYjIyMpEYTEscWMbOQan5hK3NfXh8HBQcRisWVdY3jJGxuF5MJ+8YX/n703j2/rrtLGn3u174tlyVq8y7Kc1XHWli4MJKUTStu0hZZlCu8P6MKUtwwwM8DM2w58hmWgzAzvFFoGZtp3mKGlDcwUSgqF0tImJHbsxE7ixPIS77u8aF/vvb8/3HsjyVquZMl2gp7Pp5/G9tVXX11J53zPOc95znlQb/+bYoDWPFSg1mJ4f3F+Fq9emsdWswqffMdqVZRUQ+b3+xGLxSCVSrmKRWKGhMXs7CzC4dVNxOw+8+Uo8kXsbUeUmi3aqC/NuUkv/v5Xg9DJRfjq+5ph4KlvzgfraQxSn0srF+H9u6341+MjAEEgGKXws+4pfPRATdZ9sU3kRqMRY2Nj6OnpgUSSOUAp5p4LuZ6Vy52dnV0ll1tWoyqjVHC5XLj33nu5ny9fvoyvfOUr+MxnPsP97o033sAdd9yB+voVauNdd92Fxx57LOu6AoEAPp8PP/jBD3DzzTcjEAggFosl9VeYzWZIpVIQBAG32w2v18tRH79z9zY8erQXAhL4z4/uyus1rYXu+7EDNrzmcsNeqYBZLcF8CelPS8EoaIYBASJjpnstiMRpgEn4dx5ghVQSA4vU4Xi1tbUgCAIXLlzgPhu5cNt2EypVEpAE1qRYVSrksuVsP4JWq8XJkyd5i5MAQPsX3oU3B+Yx4g7hzw5UZ722kP2SJInq6mqYzWau1yRxynepezb4Xi8Wi+F0OuH1etHV1YWOjg40NTWl7bXc7Ljmg418aFSphvfcZLL2uUbG/1BaqBFnGAa/7XPDqBKjd9qHOW8YFEVhcnISfr8ffr8/yZDp9XrU1tbyUjBIFwANzgfwT78bhlIiwF8ebOR18M7nS3Vu0otv/fYy5GISjx92wKLZeHm3F85MIxyjMLYYx6mRJS6LtR5VllgsBq/XC6/Xi3A4DLvdnpYOxAeZDNZXbnPiid8OYtgdwLN/GMW8L4oH9uhyvm8CgQD19fVQqVTo6+tDe3t7xibyQlGsygNBEKiqqoLRaMTk5CROnToFm80GiqLKlY0ySoLm5mZ0d3cDWBHUsFqtOHLkyKrrbrzxRrz88stZ16JpGg8//DDOnTsHr9cLoVAIi8UCtVqNurq6rPY81Y7vrtPh+OdvKOg1rSUpVq2T4WMJB8FSVjYOOSuxEIiBJAgcbC6+8MfhrUaE325OPrw1c7M8TdNJ1SafzweGYTh/nG04Xr5zpUiCwHX1/JX4NgJ8zgIEQXByuaw4SXNzc1qVwUSfdlNTJW7KMjuwGL5EKBTCbrejuroag4ODGB0dhcPhAMCvvytx7VLSd8ViMSeZOzAwgOHhYTQ1NXEDr6+Gasc1HWyIxeK8aFSpRs5plOHS3Aof9rZt/CRvM63FB/F4HH6/H04tg47xRRgkFEZc5xF/uwSZq3G7kD395pIb0TiNqXAc3RMeHHTmVpjK57X9ts8Nmmaw4I/h7LgnKdjYqL6WA/VanJv0QiYSwGlK1qMv5pc2sV+GHXTIKokolUrIZDKcO3cOer2+4Pc13X6va6zAx0MxfPWVfgQjcfzywgxajQI4tfxem1AohE6nQ11dHddE3tzcXHBQlIh8Kw+5MkCJGarR0VFMTEyAYRhUVlYW1PdSRhl88Nprr6GxsbFgSipJknjggQfQ0tKCyclJfPGLX8Tf/d3f8X5sscRHimmDSxlsqKTCtFX+XOC7H5lIgA/vS+6JSByOFw6Hcfr0aQBXhFSqqqrystvFsjOROI3OqSCcjBc7rfzUMUuBfIRMCILgxEn8fj9cLhcEAgEcDkeSOlS+yob57jfT2hKJBFu3boXf70d/fz9isRhkMv6S+YVUNgoJlJRKJXbt2gWPxwOXywWxWAy73c4FHZsZ13Swsdaejec+vhvPnZ5ElUqCg1vykwbMZXij0WhSf0UwGIRAIIBSqcTtW3S4bYcFJr0aYpEQXV1dsFqtax7Mk25PbTVqdI4tQykW8BpWmC9utOvRNe6BSirEdsvm+ELcts2EnVY15GIBKhTFoVCxgQU7HKujowMikSjjoEOaphGPx1FdXc0NsmMbw/gi2+froNOItwYX8MtzM6AoBl99bQLPHOHXYMgaTraJfHFxEefPn4darc57eFOmtfmCbwaI5biGw2EEg0GcOnUKdrs9p1xuWY2qjELw/PPP44Mf/GDav508eRI7d+6ExWLBE088kXFK8+7duwHk76eKGSDkG7i4/Su05HQV8M2mRpUP0ik0EsSV4Xiin4yFAAAgAElEQVQikQhtbW1r9sHFeF3PdC3i5EQQku5lfOtIC7aaCxvyyxfZ9lyIfVQqldi9ezfcbjd6enqSkm35+IdCqtK51lYqlWhra8PIyAg3dC+1L7AYe6FpOq/kYmqSTqPRYO/evVz/otVqhd1u573eRuCaDzbWQqMSkCQ+sj89X5DvWqnSdmyjn0gk4gyZwWCAXC7PyqkvlRHfX6eDvVIBIUmkHQS11v3srdXi6fu2Qygg0s4J2SjnxFfCOB2i0Si8Xm9SIz4bWMhkMsjlcuzatYt3ibm6uhpVVVUYGhrCqVOnkmQtsyGbYRYLSXz23XacGFrEvC8CkgD+8cQ8/tWR2yCtUrnS67F//35OB9xsNqO2trYgx1tqlQ+CIFBXVwepVMqVmx0OB7Ta9JznMo2qjHwRjUbx85//HF//+tdX/a2trQ2jo6NQKpU4duwY7rzzTgwMDGRdrxDp242obPRO+/DDE2MAgAduqEFLVfIhdzMGG+lsBzscL12iL5NC4+TkZFGncK8F88E4wAA0zWAxkB81q5jIt7KRCoPBgIqKCi7ZVlNTA4VCkbe9LwUUCgUsFgs0Gg26urpgNBpRV1eXMUAopFJRjEoI2794NQjtXNPBxlppVPmCbdz2+/1wu90Ih8OYmJhIkrZLbPRbr32l7jEVhWT389mPSpp5XsdmyhxTNINLc0G0SJTQv31P0mmgs4GFWq1GVVVV0vsZiUTgdrvzfl0ikQhOpxOBQAAnT55Ed3f3qhJzKnIdxCtVEvzVoSY8/otLCEYpHB8N4NyEBztsmedUZFqX1QE3mUwYGxtLavbLB+uhX06SJGQyGXbs2AGfz4f+/n6QJImmpqakeSKFrF9GGa+88gra2tpgMplW/S2RznD48GF86lOfgtvthsGQmYab7wRxgsg8nDVf5ONbhuaDoJiV79iQO5g22CjWvorl7yiKwsLCQloqK59EH4vN5Kc+tkuH58574LDocaB+4xrH+dryXOIkLA328uXLGBkZ4U3XLeUcM9aPsH2B7OyLmpoaWK3WVT6jWMFDJmTzUwRBXBUDADf/DteAUmaMaJpeNXGboijIZDKoVCrI5XJotVrU1dXlXMvtj+KFM1OQigS4b7cFcnFyBqVYQ12KZTA3k+EtJv61axnn5uYgFY7jr/bJIGGinHQwy9HlEyiu5f4oFArI5XLYbDZ0d3cnKS0V8ly3bjXi2ZNjuDDlBcMAf/vzS3jp4f1ZH5fNObBN5FarFYODgxgbG+NdiWHXXk/9cpVKhd27d2NxcRG9vb1QKBSw2+3cQKdysFFGvnjuuecyUqhmZmY4RZuOjg7QNI2KiuwNzYXQfYuZ+efr8/bXaXFh2gcCwP406kjF9FOF8PHTMQgikQiWlpbSUlmvVtg0YnzxneakRM+8L4LeaT9abWpo5fwbldcLue65UCiEw+GASqXCwMAAurq60NzcvCo5lIpS2e5EP8XKTFssFgwPD3MU3cS+wI2Wyr0aUA42EpDJyCU2irGGDFg5GGZSoJiZmeH93K+53Lg0GwBF02iqlONGe7Jz2mzl6Y1q7C4WGIZJS4W6NBcBARIRCpDordjbkJ3vn2ntYsBgMECv13Ml5rq6OlgslqT98HkuoYDE3xx24MP/1gmKAYbdAZwYWsQN9swHID4BgVgsxpYtW7hKzNmzZ+FwOHJmpUqt2pEpw6TX67Fv3z7Mzc1xA53q6+uvCSNexvohGAziN7/5Db7//e9zv3v66acBAA899BCOHj2Kp556CkKhEDKZDM8//3xOG7KRNKp8AgSTWoIvvSczDbOY/iUb2BkkiRXn1OF4LIOgs7Nz03PZ14pQjMKDz52HL0KhUinGjz7aCkGOoYTFeJ/yqWzwhVgshtFohMlkWjVsttDnLwTp/IhIJILD4UBNTQ0GBgY44RSNRlM0P5Xt+qu9t/CaDjbypVGRJIl4PI7FxUUuqAgGg0mNYlarFQqFIid/Mx/Da9ZIAIaBiCRhUK5uRCpWefpqDxIKQWrGy+fzIRKJcBULtVrNzST5YPACjrqC2FWtxa660g224wuSJFFTUwOz2YzBwUGMj4/D6XRy/Qd8je2uai2qNWKMLEcRpRj8428HcwYbfCGXyyGXy1FbW4vz589Do9GgsbExYxP5euiXZys3m0wmVFZWYnJyEh0dHZDL5WklGBMfU0YZLORyORYWFpJ+99BDD3H/fuSRR/DII4/ktaZAIMjLvq+3gpQ/Esfkchg1elnavrti7YuiGQhIImkdlpqcyCBgZ0qpVCpew/GKhYnlEP7v6yNQS4X4zLvqoZRsjuNTIELBG45DSBKY80UQf/s+rgeKrfjHXssOm2X7BK1WK2pqapJse74H9nyQbc9SqRTbt2/nKLrskM1yZSM7Nse3pUTIljFis9uJh9BAIACKoiAUCqFUKrkJrYW8yfkY3hsb9ahSSyERkqjVr25c3mwVic16AMsUWCRmvNINO2RxXbUM791pXdNciVIEcyKRiJMM7Ovrg0gkQnNzc17P9YEdenzzzRkAK42e33/zMh68qSHttflQnVijnNpEbrFYUFtbu2qdzVBuZuVyLRYLenp6MDIyApFItKpyVEYZmxHrWdmIUTT+6XfDcPujqNZJ8bl3N5REyOTlC7M4enYaToMUH2iRwefzoauri5sppVQqodPpUFNTsyY1vLXgvzqm4Jr1g2KANwYWVk0Zz4VS2RaDUoyPHbDh1UtuvL/NDImQxGIgCk84jjp96Whja20Qz3VtYp8gO3ivsbERRqMx6ZpSgE8gw1J0FxYW0NPTA4ZhoFAoeH0+S00n3ozYNMFGXV0dVCoVBAIBhEIhOjs7k/7OMAweffRRHDt2DHK5HM8++yza2tqyrskGGxRFIRQKIRAIcNkRtuyaqJkdj8cxPj6OpqYsk2R4Ih+HQBAEHMbM9JPNFmwAxT1UMwyD4YUQNDIh72b1xFL64uIilpaWcPr06aTAwmq1QiwWF/wlZRgGL56dRteYB/fvs2GrhZ/EYKmMglKpxJ49ezA/P48zZ85AqVTybgy7uV6Jb7555ed/fO0yPnlDXVqDVwznMDo6ipMnT6KhoQFVVVXcNaVWo8on2yUQCKDX61FRUQG/34+TJ0+u4uKWUcZmQ7ErG9n8VDBKwe2PQiMVYmIpjDjNQCRYe7CR2PPo8/nwn8fnIRUwODMaxA2WCkjFYuzYsSMvymWpUW+Q4eQwAREJWAscTlsqZsFH9tnwkbfnhAwvBPHwcxcQo2h87Dob/mzfaslzvnY1Ho8jEolk7J0otp1Mty+BQIDGxsakPsHm5uaSVzb4+qmKigoYDAYoFAreao3FplFdDdg0wQYAvP766xlVO1555RUMDAxgYGAA7e3tePjhh9He3p722t7eXnR2dqKrqwsjIyPYs2cPvvrVr2LLli1Zy67BYHDdVD5ohsELZ6bRPeHFbduMuKExczZ9Lc5lYjmE06MebLeoYJJuThrVjzun8OKZaUiEJJ64q2WVLC0fjm4kEsHOnTuLuq/B+SB+eGIcFMNgcD6In35yd1HXLxSVlZWoqKjApUuXMDMzA51Ol3SgTwexkMSBajlOjQe53/39L/vw2Pu2rLo2H0OYyTk0NDTAZrNxzsHhcECn062bGlU+68tkMtTW1iIUCmFwcBAjIyNoamrKSq8qo4yNwnpK36qlQrxvuxEnh5fx/jYzRILsvHE+PY+BQIDLAqtUKphMJrx7mwR/GPbArpFgh70Wlwf6NlWgAQDv32WGvVIBhViAZlP2xuWNxMVpPyJxCgQBvDW4mDbYSAc2AGR7Gf1+P2evZTIZnE5n0qC7UlQ2gMwBjFQqxbZt2+D1euFyuUAQRMnoc4XQd00mE+rq6ji1xnR9lizKNKpNjJdeegn3338/CILAgQMHsLy8jOnp6bTSm0ePHoVKpcKdd96J3//+9zh+/Div51hPLuycL4o/XF6CXi7Cz7qn8Y4GXdHL0xTN4Ju/uQxPKIZf9c7jy7dUl1S/vBAwDIOucQ+EJIFQjMJldxAGKTjH5PV6c3J0g8EglpaWirKfRCgkApAkEIsx0PFU+FivYI4kSRiNRpAkicXFRYyNjcHpdEKjSS9ryzAMvnzIivf8+xXN/19fmsdj70u/fj7BRiYjyDaRs1NZWbpSqSUCCw1mZDIZx8VlZ3Ts2LGjKJPTyyijWFgvPxWMUuid9sFZpcRBZyWvteLxOJaXl5MCCz49j594hwa374iu2Fk6vqbXN+0J4zuvj0AiInGztniVfIIg0FadXTZ8M+C6ei1sOhlmvBF8cLcFf//KAGZ9EXz+YCNH06ZpGhRFYWpqinuvEgNAs9kMpVLJvafhcBhnz57lxHDY969UPRvZoFarsWfPHgwNDWFsbAxDQ0Ooq6sr6hyUfIfusX4qUa3x8uXLOHXqFJqamlYl0YuddLsaKvGbJtggCAK33HILCILAgw8+iAceeCDp75OTk6iuvjJgz2azYXJyMm2w8fjjj3P/zucDWGwubLa1dHIRjEox5v1RbLeosn5Y1uJc4hQNAUmAYhgwRfw8FkvNgqZpHG6U4ak5D4wSBpgfxEBIxgUW1dXVG8bRtWikeOLIFrhm/bi5qYLbM5D9y833i18MAyEQCNDS0gKfz4e+vj5IpVI4HI5VGR+GYSASiSAVAGFq5Xd7ajIPuuMLPkaTncq6sLCAc+fOIRaLQa1W83pfS11uTne9SqVCW1sblpaWrgr98jL+uFDMYCObn3rm5DguzfghFZH44nvsqyiu7HA8tmrBfl90Ol3G4XjZXpNJLXl73bW9vpfOzWLIHQBFM9BE43hXwStdndArVhSpAOAX52fxap8bFEXj68cu4i/2KuH3+0FRFEcxN5lM3ATvVLCfDVYdcXx8HKdOnUJDQ0NJEmt8D+EEQUCr1SIej0MgECTNfSqGX11rkkssFsPpdCIYDHLKVQ6Hg5vDU65sbCBOnDgBi8WCubk5HDp0CE6nEzfddBP393Qf7GJHc+uVMWIYBtE4jb94dz0WAzGYc/A/C92XgCTw+YONOD64iN01aqilAiyuUS3k/JQXamn+5W2GYRAKhZKoULFYDJFIBI0m4F8/sKKxvVGBBbvHVGyzqLDt7V6NscUQHj3ai0icxrfubOHdw1EqJBpmlUqFPXv2YG5uDp2dnat4o+y1v3r0BnzmhR5UKqX45t3bMq7L17DlEwxUVFTAaDRCLBZnbSJPRSnVq7IZcZ1Ot6GfxzLKSIdi+r1svmUxGINUSCJKMfAEwkD4iu1OHI6XKKaiUChgNBrXvKe1oMEgx+/6CYiEBEzyq/uAli9Y9S6WCrU8vQwqHgNAwBeO4wuve3DHzir82T4ruru7kxK4ucDOmzCbzRgYGOCeJ3GQZaY9FbOykXgtSZIcXSmxn2OtFNhiqSbK5XLs3LkTHo8HLpcLEokETU1NBQUbm41WmC82TbBhsVgAAEajEUeOHEFHR0dSsGGz2TA+Ps79PDExwT2mWFgvLuzPumfwxuAiGivk+PObayHMIVO3liCowSBHg2FlCnUwGFxTMPXCmSn8d/csSBK4p5bBvgzXpQYWXq8X8XicG3iYqCrS0dGB+vr6gvdUbGQzMK/2zcPtj4IkgKNnpzdVsAEkS7uy6h12ux1Go5G71qyR4ief3J/XusW6loXRaERDQwNGRkbSNpGvFX9sjXdlXBvYCMWZVDUqVqVxfG4J9bII+n0BOBQ0licGQavVWYfjLSwsbAqJ9kNOA2r1MogEJBaGe9e8Hz6Ixmn8uHMS4RiNj+yzrossbqIsMBtcsOpdarUalZWV+GBDA5zNfkwuh/Gt3w5BQBL4wR8mUKkUQxMv7B6LxWJs3boVi4uL6Ovrg0KhQFNTU1HkzvN53xPXTaTsulwuCAQCOBwOyOXyvNdlry9m5UGj0WDPnj1wu93o7u7mqkrFWv9qwKYINgKBAGiahkqlQiAQwKuvvorHHnss6Zrbb78dTz75JO677z60t7dDo9GkpVCtBbmUOfJBtsDlxOUlmJRiXF4Iwu2PlqyyUex1RhdDAAHEKQaLoZXXlmjw2P8yBRZXO3ZXa/B85xQYANc3ps+crPeBId1zkSSJhoYGWK1WDAwMYGxsjJtMzgeFSN/yBVsJYRVGbDYbx71lm8jXE/k6lTLKKAXEYjFisRhvO1ksGmskEkE4HMbQ0BD8fj8ikQgYgRg/dsURoUnUV+rwiUN2Xt+RjZwgnvp4toHbfXl9euhevjCHH5+eAgAwDPDwTbVp91Uo2ASe1+vF/Pz8SkA4Pg65XA6VSsUNKU2X/d5bq8WuagbPnJrAUjCGQDSOb/xmCDoxg50741BJCzsGCgQC7N27FzMzMzh9+jRsNhuqq6vTflZKVdlIvVapVGL37t1wu93o6emBXq9HY2NjQXMwip20IggClZWVMBgMOH78OM6ePQubzbZqfkg6XAt+alMEG7Ozszhy5AiAFam1D33oQ7j11luTJrMePnwYx44dg91uh1wuxzPPPFP0fRTLWAKZDSbNMLi+QYc3BxfhMMphUOZ2LptFsvaDuy1Y9IWhFNFoUAZw5syZpMCioqICdXV1V325LxN2VWvw4/+1C3GaAc0w+NAzZ0HRDL5xpxP1FfwO8sVErvdSIpFg27Zt8Hg86O7uRiAQgF6vz3mgKWVlI/V6iUSSNSNVapRSPrGMMviCHUBbqqRMJhqrRCIBRVGc8IZYLMZiMAZcHoBeIoA7EM+rBy2XTfJH4nj6+BiWgzE88I4a1JRwrtR6QixMsGnCzPeLz+vKxQyQy+UwGo2w2fipTAGAkCTwzEd24JWL8/jemyOIxhkMh2gc/l4HvnZ7M27MMuQ1GwiCgNlshtFo5Bqim5ubUVFxZb1S0qgyXWswGFBRUYGJiQm0t7fDZrOVlI6bz/UEQUAkEqGtrQ3j4+O8qvvXgp/aFMFGQ0MDenp6Vv0+cTIrQRD47ne/W9J9lLpnIxqn8eSbIxhdCOHdjgrcvsPEuxmqGPvKV0I0kfvp9/sRj8dxf7McKpUGk5N+bN++fc2BRa7XP7oYwt+/MgCxkMTfHW5CpSr9hPX1ck7Gt5///74xvFLpAYOfdE3hC7fY1+X5E8HXwLFVQIZhePVKFMKb5YtMRpPNSLEDkrRaLRobG3mvWyiuhfJ0GVc/sg2gzRc0Ta+aup2p2kxRFLq7uzm1HIZhcHxwAcuhGCiawQM31BQ12Dgz5kH3uAciAYGXzs3g0+8sLYWW3dNaD2q5Hn/rFiNoGojEKbxvO/9hf6ysO+tn2SAwGzNgbGysIOEKvUKMD+2xYGo5jBfPTkMAYDkUx/9+sRdHdlbhS7c25aR0Z4JAIEBTUxOsVitcLhfXOyGXywumRq31WoIgUF1dDbPZjMHBQfh8Prjd7ozjFRJRasl1VrAlsbo/OjoKh8ORdqhwWY3qKsFGcGHTGd5ZXwSjCyFUKEQ4NbKMO3ZWFbxWKlgJ2RjFYG+tNqPRSLcOwzBJA5YSuZ+ZSrSzs7PrUsE4enYaY0sh0AyDX12a560ZXmrssKjw390zAAjU6mX4/cAC9tRo1jUjl282RafTwW63J/VzpBtgV+rKRjajWVFRgQMHDmBqagqnT59GNBotaUBQDjbK2AwoNNhIHY7HKg2xEqa5qs2pdF9/hMJr/QtoNMixEIihKcuw2VTwoSFbtFJIhCTiNIPGyvRrX42VDSFJ4PYd2YMMVn1xbm4uraz7elCOCYLAXx5qxN4aFT73sz5QNBCngRfPzuCNgQX86KOtsGpXV5v4Qi6XY9euXVzSyGAwQKvVrntlIxFCoRANDQ1YXl7GxMQERkdH0dzcnHFQYb77WOv1bHU/EAigv78fw8PDq/Z3Lfipaz7YEAqFiMfj607tSdezMbEcgjccRyAax92t/PtN+Bjxk8NLeOqtMYAB7tsTw3u3rVYEYddJHLCUrqksE/dzI7ClSonfudwAsk9ZX2+802GATSeDJxTDF3/uQiRGo75Chifvsm/KLAPrvBOnsfb393PzORINWz6GM9/yLp+1CYKA1WpFVVUV3nzzTZw8eRKNjY0wmfhVAvPBtZAxKuPqB0ujygaKorjAIhwOo7OzM2k2QjYJ00xI/XwPzgcw7A5hYC6Ag85KSIX8Dzh8ggR7pQJfua0ZgSiFpsrSUyWLVdkA8j8IRyIReL1eXJxYBBkLQoIV9cVAIJB1uPB64B0NOnz5HTJ8rSMKX2SlUXneH8Ot3z2ND+wy4f8cbl7T+hUVFdi/fz/Gx8dx8eJFKBSKvGRt+SDf90MoFKK1tRVLS0u4cOEC1Go17HZ72uBuIxLUCoUCu3btwtLSEnp7e6FQKGC32yGVSsvBxtUAiUSCSCTC+/BcTEWcRMO7FIzhha5pGBQi0Axw65bcQ5JY8Okl8YYp0AwDAoAnHANwpZzu9Xrh9/vh9XoRCAQwOjoKlUrFDejZzLME/nSrEQ0GOUQCklPV2iywVyowOB9AJEaDAYPL7iDGl8O8eblrxVqyQFKpFDt27MDy8jJ6e3uhUqk4w7uePRvZIBAIIJFIsHv3bgwODnIZKa02/XyQQnAtGPEyrn6kVjbi8fiqqdsEQXCBhVgsRmtra9Ft9//0zMCoEoMBg3c26fP6bvOtSFi12QVRio1Me6JoBiSxdp/PBhaJCbxwOAypVIpfjzN4dSgIqUiI7923HXOXL24a9UWrSohjn9qJ9//wDGZ8Vz57L5ydxU/PzqL7b27K8ujcYKVypVIpBgcH0dHRgZaWlqxSuetBudLpdNi/fz+mp6dx+vRpWK3WVU3aG+kXdDod9u3bh7m5OZw5cwaVlZWIx+NXvZ/avKfMIqGYXNh8kFrZmFwKYcgdhEhAYld1dl3qVPAx4jc2ajGx4IUvEIZT4kVn5zQYhuGoUJWVlaipqcHFixexdevWgl7TRoFVFtmMaDTIcccOE94YWMByMIpP/sSFm6tF+Oo63OJiBAVarRb79u3jDK/NZsurWlHMuRaZIJFIsHXr1pxN5IUEcOVgo4x8UFdXB5VKBYFAAKFQiM7OzqS/MwyDRx99FMeOHYNcLsezzz6Ltra2rGuyE7d/8IMfwGq14vrrr4dAIOCmbqcbjjczM1P0z+20J4xLs37MeCMwqSRoMORXSV4v+lMoRmFkIYRavQxycfahvZls0+9cbnznjRHYtFJ84w4nL0Um1taxgQXbZ8EGFiqVipuSLhaLQRAEvnO+BwKBAJE4jf65AIqXJikOtHIxjv35Ptzzgy5cXghxv6cA/LxnCrdt50f1zgaBQACDwQCLxYK+vj7IZDI4HI41VxTW0ltIEAQsFgtMJhNHK25sbITRaCxqNaxQJMrYs9QvuVwOpVJ51fqraz7YEIvFGxJspBreF85Mo0IhRoyicXircU0Zo1Sers/nA8MwuE4vh7pOzQ1aSs16URRV1Ab4YqDQ/VA0g4G5AMwaCTZSVJcgCHz23Q3YU6vF3/7CBYahcXw8iuGFYFqFKpbG5vF4EI/HYbfbk6bc53s/imGYWcNrNBoxPDyMyclJCAQCXsO5SlnZSL0XqU3kOp0OjY2NXNWyEAeRrYekTKEqIx1ef/31jE2mr7zyCgYGBjAwMID29nY8/PDDaG9vT3vtyy+/jL/927+FRqPB8vIyWlpacPDgQWzZsiXngYJNZhXz4PH7wQVMeyMQEIDDqIBOnh+VNlOwMbYYwuB8ADusal7qi9lAMwwef7kfI4shWNQSfOuuFogE2WmQ6fb04tlpiAUEJpbDOD/lw/UN6SW3o9EovF4votEozp8/j3A4DIlEApVKBbVaDYvFAolEktFW/K8DNnz91SHU6WXYW6vFQO9oYS+8yEi8JyIBiZce2ov3PdWOkcUrVD6hIHsgl89zEQTBDZ6dnZ3NWFFYb+WqRFoxKxPf3Ny84cEGC5IkUVNTA7fbjVgstioouppwzQcbLI1qvZFo5KY9YZyf9mExEINVK0VtGrm/TKBpmsukeDwe+P3+NfF0Sy2h6wnFcOLyEhoq5HBWla4i8Y1XB/H7gUUoxAJ89x5HyZ6HL9qq1bBqJOibDUBAMPjof/Tg3z68HWY5kpRGGIaBUqmEUqkEQRCc8SikH6HYJWehUIimpiZEo1EsLCxgeXkZTqcTCkXmDGchKhxrneGR2ETe0dHBOa1CtMivBUnBMjYPXnrpJdx///0gCAIHDhzA8vIypqen086EOnToEA4fPgySJPHpT38ahw4dwrZt23g9T7GrCAzD4KWeGURiFIQEiQP1+c+7SbcnbziOr/16EIEoBYtGgm/c4VzT9y0cozG8EIRaKsKUJwJ/hIKugCnhN9n1eL5rCiqJkOsdiUajqyoWIpEIarUaJElysvv57P9GewUnK8swDBiGAUUzCEQpqAucb1EspL6OXzy8Hx/6ty6cnwlga5UCh7eZijZ3LPE5q6qqUFlZieHhYZw6dQoOhyNJDa0USoi57LxUKsX27dvh9XrhcrkQDAbzmq6+HqivrwdBEBgcHMTIyEgSpfhq8GF/FMHGRlc2ftvnhtsfhZAEmirlqFCkz+6kNm+zgQVBEJBKpbBarVAqlVwmPBKnsRSMQcMzA5HrA/nf3dN4Y2ARt+8w4d3NueXh0uErxwZwadYPqZDEk/dugyXHwMJCcXrUAwFJIBClML4cwUZ3cyjEAvzw3mZ88NkeTPvi8IZi+OsXz+Kvr9fCYtDCZDIlVTEoigJN06irq0N/fz/Gx8fR0tKSlzPLN/uST68EOwjp/PnznAxtur6nQoYf5dN8nq3qwDaRs2Xw2trVg7T44Gow1GVsDhAEgVtuuQUEQeDBBx/EAw88kPT3ycnJpEOKzWbD5ORk2mAjsTk4Xz+VbWhsIRhdCKB/LoA4DQhEDFpt+VF9gcxy71GKhlREwhuO57VWOsjFAty724JjvXO4fYcRWln2I0ymoOyu7Qa0VgqAaBAzI/0YDoUgEom4ikVVVRWkUim3jwg8f7gAACAASURBVKWlpaSfC0WMZvDgc+cxMBfA7dtN+NzBhjWtV2z8+OO7i75mOj8lEAhgt9uTpHKdTueGzORIhFqtxp49e3Dq1Cn09vbCbDajrq4uiX2wEWB9ITs7i6UUkyQJh8OxYUID+eCaDzYKoVEVU5ebZhi8dG4GkTgFiiSxv24lY5QpsFAoFFCr1TCbzVxgMTU1BZqmodFouPWDUQqP/7Ifc74I3tVswEf355aEzZYNc/ujeK5rGlIhie8fH8NNdn3O8nQ6LAZjEJEEKJqB/22Vi1LgE++oxvfeHEWrTY0WkwIjRZgUyzdTyGqjJ2bBWG30j+5Q4VsnlxClgVEfg2NTYnx5V+YMiVgs5gbvXbhwARoNf/ncYmZ2UtdlpXL379+PyclJdHR0oLq6GtXV1UnrlJpGlev1JU4id7lc8Pl8WF5eLmoTeRllsDhx4gQsFgvm5uZw6NAhOJ1O3HTTlUbadN9dPp93PmpUqWsWs7Lx/eNjiL5troUCErYCmrjT7cmgFOMT19ega2wZt7SsltnOhGyv7Z5dZtyzi7+aYywWS7LVoVAIQqEQarUaKrUKKmsVZDJZ1r0VKyExE1gREpGKSLx8YXZDg431SrJks/kymQytra1YXFxET08PSJKETMaP+VGqwIQgCEgkEjQ3N2N+fh6nTp1CfX09zGbzhiWmUhNviZRil8uF/fv3b/pejms+2MiXRsVmjIoVyZ6f9GLIHQRNA6SIhjoyi9OnV3ibbANgYmCRDumM+JQnjHlfFFqZEO0jy7yDjUxQSgTQyIRYDq5QvXIN98nkDL5wSyN+3DmFHVZVSaUNb9tmwm3bVnTNw+Fw0dZNd49SGwIjkUhGbfRarxeuhSh+5goiEmfwy/NzuMlegT9pWhnUQ9M0V04nCAIURUEgEECj0XBSgePj45iamiq6cSvEMBMEAZvNhqqqKgwNDXFlb3ZCbCGBeT6VDb7XSiQSOBwOhMNhDA0Nrfsk8jL+OGCxWAAARqMRR44cQUdHR1KwYbPZMD4+zv08MTHBPSYbNrKywTAMXu51cz+rJIKC7E6mAOj6Bl3Gnohigw0sEqdvX7x4ERqNBiqVCkajMWdgUUqY5ASqdRKMLobwnhb+ipTFRib/zX6maJpO+nyxakiFHmhz3W+9Xo8DBw6gq6sLAwMDAJDT/5VSNZGmaQiFQtTV1cFisWBwcBDj4+NwOBzQ6db2WS6mkElFRQUMBsOmDzSAP5JgIxaL8b5+rRkjiqK4ikU4HMYTL/Ug/vZ3VioAtttrsgYWfPdUq5dhq1mJ3hk/PrwntzPLBalIgH+4w4khdxBOk7JgY9xsUuLL713/HgqaYfAvb4zgwpQXf35zHXZY86cBACtGdWFhgXNU4XAYYrGYK68nKo1kwu1NMlxYItE77UOEYvDlX7rQ9kAblBIhZ7BZOeNoNAqSJCESiUCSJKxWKyYnJ7G8vMxRqzJJBa5nyVkoFKK5uRnBYDBpQmwpG+kKcRAikQi7du2C2+1O20SeD8r0qjISEQgEQNM0VCoVAoEAXn31VTz22GNJ19x+++148skncd9996G9vR0ajSYthSoV+QYbxaxsnJxK9o9bqgoL0BP3FKNodI55oJEKscWsWvMe0yEejycFFcFgEAKBYKVi8fYw2ng8jpaWljXTTIp1v8UCAv/+kZ1YDsWgl4sw54vgCy/1IRyj8dXbm9OKipQSbPKLoqgke0cQBIRCIXeIjcfjIAiC81P5gO99IwgCGo0GVqsVS0tLGB8fh9PpTGJ0pK5biv6O1LXFYjG2bNmSUQ0x389FIT7zWlBNvOaDjXzL0/lkjBIDC5YKRRAER4WiaQYXl69cb9LKMn5xsiGdoRMJSPzloUbQDAOySIcivUIMfYZ+klKhWAc61yKF/z43A5ph8NVfDeInH88uNwmsdlZLS0tYXl6GXq/nmu/58HQTDTZFUQiHgvibg/X48H/2AgywFKLwo84Z/MW77Wkfy4oAsAEoSZLYsmULfD4fLl26BKVSmXb40FqUnXJdm5EznTIhViKRFPSZ5oN8DWzi9QaDARUVFRwFLJ3ySRll5IPZ2VkcOXIEwIrt+NCHPoRbb70VTz/9NADgoYcewuHDh3Hs2DGumfiZZ57htXYp/VQumFP0Hx4/XFiyKHEe1Atd0/jVpXkISQJ/ebBhzQFHoq1m544IBAJObra+vj5tv1sh/mXGG8ZTb42hUinGgzfUZKUT5wN2LwKS4Po2Xzo3i74ZPwDg2VMTJU3UsX6KpmlQFIVgMIhAIACZTAaBQJCUCEu390Q/lc98l3z9lFAo5PxfX18fpFJp2r6EUs+DSr0PLHWJTWTp9XqutzHfpFghfuhqT35d88FGvjSqTBkMNrBgqTTskCWWCsU2byd+iP7l9csArqz1jrrCym/sFx1Y+RKMLIagk4mglYuKFmgUsqfNIg9HEAS0EgIkAdAM0jalp3v/SJJMclYikQgmkykr3z/RYKfugaVD1dbWYnh4CNdVK3B8NAAA+M+OSfz5zQ0Qp0zkZY076wASP6sqlQp79+7lZmDU1NTAZrOtykDxQbENM6sIdeHCBYyNjUEikcBqtRb181DIdPJULXWWApZOS72MMvJBQ0MDenp6Vv3+oYce4v5NEAS++93v5r32RtKo6rVifOP2avzi/CweuqEWKllhVYBEP7UYjIEkViTKfRH+jeHASmBBURTGx8fh8XgQCAQgFAo5W11bWwu5XM7rwFZIReJ7b47iD5eXQBIEmoyKktKdmk1KzidsMytxfGgRJAFcV6/LaKP42OdEum7i6ycIAiRJQiqVwul0oq+vDyaTCbW1tVnvZ6qfCoVCXKIs1/tQqO9JlMrt7OyExWJJ2mc+6xZTyIRNZE1MTKC9vX2VT+az9h9j0uuPItjIh0ZFkiRisRhCodCq6a3Zhiylg1NP4pejV5qkP3VzYWo5iRmjF89O45XeechEJP7uvQ4YVZtfhSAR3nAcMhFZtGwRC7NSgCc/sA2XF4K4sUELn8+H4ZlFhAJ+kLEg9/6p1eqM71+qwchksAFw2YzEagQLVtqPkfVzwUYoRuMfXh3A/zncnLQ+W1VhZY1JkoTJZOKm3pMkyc3AGBoaQnt7O5xOJ7RabdGlb/O9lm0iVygU8Pv9aG9vR3Nz85o5rYn7yLeykW7fQqEQdrsd1dXVGBgYwOjoKBwOR7mJvIxNA6lUCo/Hw/v6YtKoSJLErVsq8d41DnBL3NO9u80gAFQoRGirzlz5zJTEi8VimPRReKorCpFQiscPO1Ct4y8Zn4h871OFQvz2oRyc2lWxG/JZ3GTX4+n7tiNC0bg07cOXXuoDA+Bz727AnTv5vR+JfirVBiZWK9hAg4XBYIBer8fY2Bg6OjrQ1NTE9eKlrh8IBODxeLj3CQAqKysRi8UKolZlQqrvIYgrUrlswqipqWnFv5a4spFLMKC6uhpmsxkDAwPw+Xxwu90Z5++krl0ONq5B5CpPx+PxJCrU0tISQqEQ11DGN7BIh62VYvzD7Ta85lrAI++sy6v0mIhEQ3dhygepiEQwSmFyObxhwUYhlY2fnp3GD06MQa8Q48kPbC0KZYthGASDwZVhS55JGEM+9F0YxUWPED/sCUBAkvj2XS3YXZv9AJxIhYpGo7wNdiYIBAJc39qC6jeXML688vl7rnMKN1pImCTxpJkbGo0GNTU1XGUslVolFAq5ngm/34++vj5IJBKIxWLeDdCl7O8QCoVoaGhAIBBAX18fBAIBmpubeauKZFu7UBpVOrCygT6fD/39/RAIBEXXkS+jjEKwkTSqYh2k2crGmXEPYhSNh2+qhSBBaCSRduz1epOSeKlJoI6ODpxdJBCMMaAjcbw1uIgP7bXy3su8L4IpbwR0AS/roRtr0fT2UMN9taVPSLDzqH51cR5RamXDnWPLoGgGB50GaGRX+s0S/VRqEjWxz4KvnyJJEnV1daiqqoLL5cLExASqq6sRiUTg9Xrh9XpBURT3HlksFqhUKs52pvqpdM9ZDN/Dqg5aLBb09/dz1fSNCjZYCIVCNDY2wuv1cpO+m5uboVRmnjFW7NlOV0uV/poPNhJpVOl4n4lUmurqajAMg9ra2qwflnxweLsZh7fzl+lLh0Rn8P5dZvzwD+OwWxVoKXBo3lIwhuc6J6GRCvGB3ZaCqwz5OqiXL8xBJCCxFIzhwrQfN9n1eT9fKBRKGpIXj8chlUpB03TSgMOfv+wCjRAoisHpMW9SsJGpYqFUKnH58mU0NTVBq9XyNtjZ9nlXswzfab9yiPjzn0/g+Kdb4XA4MooEpJaso9EoRCIR55h3796Nubk59Pb2IhqNwmg08qYU8N1/IdcqFAqO03r27FlUVlaivr6+4CB7rTSqTFCpVNw97OnpgcvlQkNDQ0FN5GWUUQxsZIN4MYONc7NRvNI1DJpmcOdWHQ6YhUn9jCw7wGazraIdp2J3tRqv9y9AKAB25iH44fZH8cgLvQjFKDRraHy5Kb/XJhGSOLzVuOr3pahsJOJj+20YXQghStF4o38Rr7kW8NK5Wfz7h7clPbdcLsfIyAjEYjEMBsOa/BQbVHg8HsTjcYRCIfT09HAJsGx2MZ2fEggEXP9H4vMUy/fIZDLs3LkTi4uL6O7uRjweh1arzeljSq2aKBQK0draiqWlJVy4cAFqtTptnyW7l3JlY4MwPj6O+++/HzMzMyBJEg888AAeffTRpGveeOMN3HHHHaivrwcA3HXXXauUQBLh9Xpx9uxZnDhxArOzszh69Ci++MUvcsYuE+9zM2Y72YxR75QPApLAP9+zZU3R7H+dnsTvXAsAGJg1UryrgAF+hTz/7dtNePr4KAwKMbZbVhoGMxnwREPIZsLYWRYqlQoVFRWoq6uDSCRCJBJBX19fEi3m7lYz/nB5CSIBiYPNFRiY9UIhEqBSdeXLn1qxqKmpQUVFBfr6+iCXy2G323kZhUSD7fV6EYlEIJPJoFarcc+eGnyn/YpKAAOgv78/q8pU6v7YLFaiGojJZILH40EwGOToS3p95uCtlJWN1HvElufHx8fR3t5esEZ5sWhUmaDT6aBWq6FQKNDe3s7NEWGf82rJGJVx9aMQum+x/FQiTbcQsDOjFhYWMO8JwOddWWtslsZ1lipYrVYoFIq8FRh312jx5Ae2gSSQVxV8yhNGKEZBLCAwvFycWU8EQSAapzG85EeNXgaZqLhD3miaRqVShH95fwu6x5fx6E9doGgGvdM+vPfpLnz5vU04UK8HQRAwm83Q6XRwuVyYnZ1Fc3MzL7WtaDTKVSs8Hs8KG+BtgQ+tVouamhpIJBLQNI3R0VEMDg7C4XBk9SvA6qCDoiiIRKKk9zsfW8rnWr1ej6qqKpAkifb2dtS9LVGbrc+lVAf8xIo6O5uK7bNMJ0xS7tnYQAiFQnz7299GW1sbfD4fdu/ejUOHDmHLli1J19144414+eWXc6536dIlfPKTn8SuXbug0WjgdDrxuc99Lm2UmYpiczOL0URNEAROT4bx6vgQCILAgzfU4Lr6wnnxSrEADFZUrGRi/kYzHKPwzd8MYXQxhFstNHbmeZ+OtFbhoNMAaZqejURDyM6ykEgkUKvV0Gg0qK6u5vX+sVWLLUYpfvlgGwgCOHp2Fk+fGAdJEPjevduwzarO+GVXKBRoa2vDzMwMOjs7uRIz+x7GYjFun6zcolgshkajgVqths1mg1Sa3KB+T6sJR7tnuZ/rm5wYGBjgAppcGXWBQJBWDYQgCFgsFiiVSvT19XFTWFOfHyieGlUqWLnZVJAkidraWpjNZk6jvLm5Oc0K2dcuZeMdwzAQCAQZm8jLKGO9sJFD/RIbu3OB5e+zdpodRqtUKiESS1ClFuOgvhISoQAf2G2BWro26rBBmT/VtqVKiRsb9bgw7cN7GgVFuU8Mw+Dz/9OPQXcQFo0U//aRHWvqO8wmNLLTpsF9e8x49ZIbM94IFvxRfPanl/DwTXX46IGVAbFSqRQ7d+7E/Pw8zpw5A6vVmjRwlWVxsAmwQCAAkUgEtVrNDQ3OpLRIkiTq6+tRVVWF/v5+TExMwOFwpPUrqY9jXxtb5RAKhSXzPQA4NsPg4CAmJibQ3Nycthev2NSlRKTrM7FYLDCZTGmFSUq5l82MTRFsmM1mTotcpVKhpaUFk5OTq4INvmhpacHx48cBAP/xH/+BqakpXgdVoDRc2GIEGwtBCjRDADSNeV9+E9FT8cG9Vpg1EqikIhyo489J7RzzoGPUAyFJ4OWhKI7clPsxqVBJhYjFYlj0LMPr9SIUCqGjoyPJEFosFl58TNZgx+NxRKNRRCKRpMZt0dsZl5OjHlA0gzgYnJ/2Y0d19tecmD3q6+vD5cuXIZfLuYM+u0+TycRrQNSX39eCN/oX4A7GsbdGDaNeg0rdlYCmpqYma1YGSF+ypigKDMNALpejra2NczxVVVWoq6srWSk7n2sTNcr7+voQCoUQDodzOi6gdDSqdOuzTeQ2mw2Dg4MYHR3Fli1b0jZMllFGsbGRalSZAhc2sEikHjMMA4VCkXYY7TN/GMEr/TFUaP14/L2OggONbHviA1YWHgB6e3uLEmzEGQJ9swHIJQJMesJYCMRQpebXL5kYWMTjcQSDQY4Wmyo3y/7/0XfZcajFhE/8Zzc8oThiFIVvv3YZnWMePHHXFkjfrqxUVlZCq9Wiv78fx48fh1Kp5PygSqWCRqNBQ0MDFApF3ucQlrLkdrvR3d0Ns9mcVPlNh3TqivF4nHdVq5DARCQSoaWlJamfMVUqt5TKmZmSXGyfidVqxcDAQNJsqnyTYtcCNkWwkYiRkRGcPXsW+/fvX/W3kydPYufOnbBYLHjiiSewdevWnOut91C/UqwVoxkYFSRaBHKopCK807G2A5BESOI9W/LP3FbrZBALScQoGrUafhmjdIOXWBlDtVoNiUSCivqtODvhxfUGHSrTyNYC2TNBEokERqMR586dS6uo8f9dV4OL0xehlYlwftKLj/2/s/irW+xJ2u+ZFDdUKhUUCgWnNNHQ0JD3dHmCIPDm529c9Tuz2YzKykoMDQ2hs7MTTqcTKlVmPXqKorj7uLy8DI/Hg8rKSs7YVVZWoqKiYpVqB7BxwQYLpVKJtrY2vPXWWzhz5gxMJhPq6uqy3stiN4jzuV4qlXJN5PlkmssoYy3YyGCDpWomqkL5fD7QNM3Rjk0mE+x2e9bv65A7BJEACMVpzPoivA/jieiZ9GLWG4E8vloBsBAU64ApFhC4p9WIly64cbDZAJMqffIyl4JhbW0tLly4gIaGBphMpqzPucWswvMf34MHftyD8cUQaJrB71xu3PyPJ/DEn9pQKV5hA7CDJs1mMxYWFqDRaHhVzPnCYDBAp9NhdHQUp0+fRlNTU1ZqFUur83g88Hg8WF5eRlNTEyiKyuk71+J7EvsZU6VySxls5FpbKpVi+/bt8Hq9cLlcIEmSd/Kbz/pXCzZVsOH3+3H33Xfjn//5n1fx2dva2jA6OgqlUoljx47hzjvv5MbaZ4NYLN5QI07TdN6H01Q8e3oWHZeDMGhJ/P37ateUMVoLavUy/Mv7t2IhEEVsdnCVMU2nNpI6yyJ18FLf0Ag+/WIvghEK/3V6Ekc/sRsErhjrdFJ+bEYosTGuoaEBZrMZfX19mJmZgcPh4Izt3jodfv/Zd+A3l+bx2C/6EKNofPnlPvzzbbacihssGhsbMTExgdOnT8Nut/OSuOMDVmXK5/PB5XJBqVRyTe5s8OPxeJKCH41Gg/r6ek7tKVUNpKGhARaLBS6Xi5vCutHBBguxWIz9+/djdHSUKy+bTKa0axQSPBSLdpX6/pdRRimxnjQqhmGSKhaLi4vw+XycnU4U2uCLieUQwjEKngiDWx0abClAvKR/LoCv/WoQMYpBoyKG/W3FCTaKlTz85PU2PPInjdzPNE2DomnQNAMyxexkUjCsrq6GyWSCy+XCzMwMmpubM1Z5GYZBpQz49mEbvnRsBAOLK0lTX4TCg/8zint3GPA3t7UlvU+NjY2Ynp5OSwFeCwQCAedjXS4Xpqam0NTUBLFYjGAwmNSzyAY/arUaNTU1cDqdAJBErcpkd9fqewiCgMlkgsFg4JJudrt9QyobqVCr1dizZw+GhoYwNjaGoaGhnAk3PutfLYHIpgk2YrEY7r77bnz4wx/GXXfdtervicHH4cOH8alPfYqXrnGxhvoVgmKtNbYUgUQIBGMUFoOxgiRjRxdD8IZioNe4H6tWCqtWirNzBPx+f9I8kkS1Eb6SwVEKiMZpCEgCvnAckWiU48KSJJmXlJ9MJkNrays3BKiurg4mk4nrB4l65jnq0awngMd/PYbPv6sOrTyUiEiSRE1NDYxGI/r7+zE5OZnVUeQDVj7WZrNhamoKb775JtcHotFo0gY/iUikVrH3i+X0Li4uoqenB5FIhHcQXapggzWaLCc4sbzsdDpXJRgKkSssZSWkjDJKhUIqG/F47mF5rDQ4mwDy+XygKApyuRxqtZqblVBTU5O1qpoLP+mawkIgDokA2FenLaifIRilQDMMBCQQiG8+6ghFUYjH45xPH18K438fvYRgjMbXb3dg/9sN3Llsilgsxvbt27GwsIDu7m5YLBZUV1cjFotxB3av18vRTTUaDb53jwOPvTqBkyNXZrH85JwbPzn3Frq/8A4uU872C1RWVmJgYABTU1NwOp1QKBSZtpMX2PVnZmZw4sQJCIVCqNVqaLVaGI1G2O32jEFqolSuUChcpVoFFM/3JFKY+vv74fV6c1aSCkU+eyYIAhqNBlarFQKBAKdOncopoHKt+KlNEWwwDIOPf/zjaGlpwWc/+9m018zMzHAZ0I6ODtA0zYtPvZEqH8UINs6Me7AQiGExQOHebQbUV/Cbq5AI16wfj/+yHxTNYI8ujv378ns867ASS+x+vx+Tk5PQ6XQwm81oamriFaGz/2fvS4VChI/ulKJrjsY9u8yQSyUFS/kBK0GrUCiEwWDA4OAgLl26BLVaDZ1Oh32NRvxLpREv9szhdZcbXdNhPHtmAV+7k78Rkkql2LFjB+co+PBYU8EGP6xjSXQqbCZobGwMfr8fer0+5yEgkScbj8eTVKv0ej3279+Pt956C52dnbDb7TknaOcbQBTqHMRiMbZu3Qqfz4e+vj7IZDI0NTVxXNtSq1FdKxmjMq5+FEP6NjGwSJQGl8vlUKlUnBR1amJlbm5uTX4qFKNw2R3E6FIIGuGKTS8EO6wq3LvbgrHFEHbIlotGo0pcJ04z+IdXB3Fm3ItPXF+NP00jcZvop1ibIhQKMTU1xWWiSZLEH0bmsBSKQ0AS+Gn3HK638580ztppo9GI8fFxDAwMQKlUQq/XQ61Ww2q1rupb/OGfVeJL/3MRL52fS1qr9RsncPGxP0n6nUgkwpYtW+DxeNDb2wu9Xo/6+vq8qrWxWCypyTwYDHKiLWazGXa7HdPT03C73dBqtTmHpPJRrSp2oov115cuXcLU1BQoisoaEBWCQoRJ2PkmFouFE1BxOBxpB+KWg40i4sSJE/jRj36E7du3o7W1FQDwta99DWNjYwCAhx56CEePHsVTTz0FoVAImUyG559/nteHciOHJRVjrRfPTEMhESAWJbG7Wp00JIkvJpfDXPVgKpDdgLMzIhIzYazDYjNhDQ0N6O3tXdWElQj2dbMDiFKpUOx/O3fuhHp4GNs1fjhtV+QRI/EVyUKJMLNxpCgqScovVXHDZrMhHA6jv78fJEmioqIClZUkZgMU3hpcRDhG4RfnZxGO03ji7q0g8zhcVlRUQKvVYmRkBJ2dnRknUrN9FiwdKnGPbNUinSqI0+nkDuFqtZoXrSFRDSSVWiUWi9Ha2soZNqfTmXGWzFqlb7Ndm25dlUqFPXv2rOLaFhI85MNTvlaMeBlXP/Kl+xIEgUgkgtnZ2SQ7zUpuJ0qD81lrLX7q9KgH874odHIRbGIajZWFZdFJgsA9u1aEYnp6engFG28OLODp42NoqVLiC7c0rqqopAYb/bN+vDW0CBFJ4PvHx/CnW41Jg/JSVYVY+7llyxaMjo6ip6eHq8LurdPh/7VPIE4xONiSmWHB0ovZQ7vf74dAIOAosa2traBpGn19fSsKXAZD2qCAIAh8/chW6JUSPHNyPOe9AQCNRoM9e/ZgfHw8KwWYpmnO53s8Hm6PucRQGhsbOWqVSCSCw+HI2Y+QSbUq396KfK6VyWRobFyhwLW3t6O2thZWq7UoCaV8K/CJfidRQMXlcqUdiHut+KlNEWzccMMNOQ3LI488gkceeSTvta9mGtWUJ4yBOT+mPRFYlQTMGZqnc+FAvQ4do8tw+2O4SR/nvhzsLAvWyGSbZZHrtdE0ja6xZYwsBPEuRwXXV8Ia7ETljUSwXy6Px4MLFy7AaDTCK9ThMz+9CAD4zvu3YYdVndR0xjpXPoobMpkMe/fu5Zrbmpub8b7tJpAE8KWX+iAREvidy42fnZ3GPW2WvO4rW6plp69KJBJYLJYkaUiCIDj+ar6qIOwhnNXs5sPBzTRoiWEYrlFteXkZFy5cgE6nSxvElJpGlQ6JXFu2n0Oj0eRF7ShE5eNaMOJlXP3I5qcYhkE4HE6qLIdCIZAkCZFIBL1ezzuwSIe1+qnXXW5MLIchEhDYX5v/4S0Uo9A77UONTgajSpLXnv7t5DiicRqnR5fRO+1Hqy377KIqlRgKsQCBCIXtFgXHemAVDFPVoRLB9pexCaDmxkb87MG9iMZpGFUSdI978I+vDaGpQoqPtqrh9yULjbA9DJmGGe7ZsweTk5M4ffo0GhsbOXGPVPzlITumloP49aUFAIA0R7GCbUw3mUwcBbi6upr7THm9Xk6+mK2u5xq4mAi5XI7W1tYkGV6bzcbbTyVSq0o9D4qVpB0aGkJ7ezucTmfOikwuFEOYhG1uZ5W/Kioq0NDQAKFQWA42rhZczTSq3/a5EY7RMKnFaNHRBWmOPYI33wAAIABJREFUA4BcLMAXbrEjGo2iq6sLw8PD8Pl8HH2HPbDbbDauUnFpxo//6pjGvloKh1qSjR57f2KxGJeB6Z8L4P+8PIAYxeD0mA/fumtFtpjvl0Sj0WDv3r0YGRnBM787j2B45Tn+680+hBuR1HRms9mgUql4r832CLCOQiqV4pZmO16oVuPsmAc0A3z91wOw6qS4rp7fVHP2AMBWLCiKwuLiIubm5mAwGFBdXV2UJuNEDu7g4CCmpqbQ3Nycc8I9a8xDoRDcbjcikQgoioJQKIRWq8X+/fsxMTGRdujeRjaTs42IVqsVZ86cweLiInQ6Ha+go5BKSJkqVUY+KMUAWuCKn6JpGuFwOEkZKhaLQSqVcnTQmpoa+Hw+LC8vo66ubs2vaS1+KhCJ462hRQhIAiqpEFsr8ved3/zNEC5M+aCQCPGde7ZAIxPx3tMOixq/H1yAXCyATZucjGP9VDwe584AKgmJp+/diklPBC1VSojFKwEaX1/CHgonJibQ0dHBDWxzTXnx18dmsBCi0T/jxfZKEu90Zu+1SwVBELDZbKisrITL5cL09HTGgX3/9P4dCETjmFwKwWHKbhvZoMLj8SAWi3FTwnU6Haqrq4tCK2IpYRUVFRgeHsbp06czVvsTwfqpcDiM2dlZBAKBkvcWikQiOJ1OTipXLBYnzRHJ97tQSG9hpusNBgMqKio431xTUwONRnNN+KlrPtjYyGFJawlc4jSDVy/Nwx2IQkiScDbmF2jEYjFMu5dxdnQBFcIIxHQEIpEIFEVBoVDknGXxlWP9CEQptI8so9koh0UjSbovGo0G/f39aG5uhkqlQpQOAQQBggQ84Xhe1JpEY+j1etFqANqnVw6C79lmQluLtSgcS7lcjl27dmFmZgZdXV346sE6PPCzCMaWwgjFaPzFixfwy0/tR4VytXGPRqPc/jweT9KUcFYSVyQSIRaLYWhoCENDQ7yCAr5gtcS9Xi8uXbrEVXMS70s8Hk+ilbEcW3Z2DXuYYUvWrDIKOxCppaWFO9RvtHKVRCJBVVUVKIrCxYsXoVQqOfWTTChGhqmMMrKh2ANox8bG0NnZiZMnT2JoaAhtbW341re+Bbvdzk11TveZXyv1KRFr8VOvudzwhGKgGKBKJYFBlvsxqRh2ByEVCRCMUlgIxKCRrQQAfPzwo+/6/9k78/DG6rL9f7I3TZukTfd9X2dhls6wiCCguAGKioA48hMX3LcXARU3UOF9FV+UTRAXFARFEBGQF1D2WTqdztZ93/cladM06/n9Ub5nTtIkTTodhbH3dc11Tdvk5OTk5Pt8n+e5n/su5l216aQn6UgxavD5fEFFBLPZTHd3N+Xl5VitVlQqFVkJCWSlxL8uKzsBohvQ2dmJwWCguLiY8uxF5gYcaNQqqopz6bT7KdH5STXFV3AyGAxs2rSJyclJDhw4QF5eXthOgUmvXZZoKE1n7XY7LpcryCW8sLAQvV6P3++np6dHjlMWiyXu6xEOGo2GsrIynE6n3O0PXbeV1GKhWikGzUtKSlCr1UFiJ5FwvLFHJI4TExM0NDSQnZ0tJ+9rOfsX7+NVKhX5+flkZ2fT1dVFX19fVCGaN0sictInG29Es6RY0DzsoHNigYAkkWLUUmiOfHP6fL6gSpjT6USj0XD3ER8TLokUk4E7Lt1Kol5LQ0NDRE6oUiM8Ua9mZsGLUadGpyaIBqVSqSgpKcFms9Ha2kpGRganFBSwa2ce7WPz/L/TCyKeq3LTHjocLegA2/V6Lj7Hy8jIMDPjIxzp1fJSv5u6Qiunl8bWeYgElWrJ3yItLY2Ojg6u3qTjhpcX8QXAsejnmseauffyTUHJj5izEC7h4Yb3BETVxG6309zcTEpKyqq8OSJByOcNDg6yZ88eUlNTkSRJppWJWZCysrJlMsNKoyWhBiI4ow6Hg+bmZpKTk1dlrBQLVjNIZzKZKC0tZXR0lPr6enJzc+Vq4vEef31AfB3xYq0NaH/6059isVg4/fTT+cc//sELL7wQ0/MEv30tcDxx6qH9w3h8EioVbM4zo1bNxn2Mz7y1kAfrh9mab6HYZlzxnEK9LMrTEuTfK+csRKcgJSUlqEATDpIkcXDQAcApeeZlhRuxaRczDPn5+RgMSwW48fFxuru7+doZhTTNZlKSZuJ/n+/myPAcCTo1f/5UHVZj/BQ34W/R3d0d1ocp0iyIiAHRTGeVSUFrayuJiYlr6s1hMpnYsmULY2Nj1NfXYzab0Wq1sjGkUsI9lFqspFZFk8qNhwYbaa0XHRkhlbt7925KSkrWrFMR6Vxi2Q8IWXyz2UxHRwcNDQ1rWsD8V2M92QjBWlaMjmcRv3/vIG6fHxVQkmYkQbPUBg7nZaGcCygsLMRkMuGXwHGwEXOiCqdXYsETIFF/7JyimQ+pVCpuuqCS3T2z1GabyUsLz4MV1Keenh4ONDTwvppqknfmy38PV2nX6/Xygh1t02426jCXFLKYk8kFd+5lelHijweGePiq7eSlrKJ8FgKh1pE5NcW5vUd4pm/pGuzpmeUPz+1lW0HKcbmvimsj2u1lZWURObgrQUnZEtfS7/djNpuZn59HkiRqa2vjUq0KVQMxm83s2LGDkZERBgcHGRwcjGmALp4N/mqlbEWCmJGRQU9PzzLDwtUef72zsY7jwVoY0P70pz8Flu7dG2+8MebXfiMUxRwuL00j8wQAlQSnl6SAI/ZkQ5IkuiYXKLYlctuHgq+P+B6HUzAUiORlEQqTycS2bdvkIenKysog1R+/388j9X3c8coggYDEB8vVnJGXENOmXcyapaam0t7eTq7PQ7G1iiPDc/gDEi5PgBfaJ6nKTKZqFd4jGo2G8vJyHA4HTU1N6PV6EhIS5HU/NO7Hu56ZTCa2bt3K6OjomnhzuN1umVrscDjweDyyq/nc3BxVVVVRDQEh/NxhqGoVrM5tPNprCn+q1tZWnE4n8/PzMW3sV6tGFSv0ej0ZGRlkZmZy9OhRLBYLpaWlcRkDvhFw0icbqzH1+3fTqNw+Py90TOMLgBrYmKZhYcHO/v375UEuUV2JtMBoVXD1mYU8fniUM0tTSE3UyrrsLpdLbstFWrDzDAY+lLryF02tVsvDbEePHsVgMKDT6eTuiqhghKu0x4KEhATUOj0qtwef18fw6Di51oJVLYZCaUvZWfH7/Xx0cyqvDU8x9/pozwMdaq44vzru44dCtEOFN4eYt1jJm0O0wsWirez+2Gy2ZfKVdrudlpaWmLso0dRAcnJy6OnpYX5+nr1791JdXR21xX6ilKvE+SmPLapxeXl5tLe309/fH1TpWadRreNfhbU2oI13PXsj0H1//Fwn4lkScGqxlaZDsT//kcZR/tQ4gk6j4gcXVFKQcowzL9ZqpX+ERqNBpVLROuYkQa+lLD12wRSVSkVBQQE2m42mpibZi0gU6472gy8AKpUatSWHurrSlQ+qgE6no7a2VvY1+sgmC3886iDTbOBHf+9YUpK6qJq3Va5sBiuEW5TFJa/Xi8lkQpIkpqenKS8vXzPfCGW3X8wFxuLNEYm2K3w3BGVLQCgujY6OUlZWFrdqlRBEEL9fCyXEUCQkJFBTU0NDQ4O8sV+p47OaItdqimIpKSns3LlTFowRXf43C076ZCNeNap/V8VI6WXxQP0Qbv/S8wLAzlwjzhktW7Zsian9NufysKdnloxkHf9zUQWALOtXUFBAS0sLRUVFMh0gXgQCAVlxSelubTYvtZ/n5uaorq4mJSWFjnEn//W3VizGCX5wURUpifFn47d9aCN/2D9IXb6Z1qFRfvpiPx+qK+LibflRn+d2u4PoUGLOwmKxBM1ZAHxLPcq1f2kBlgwQnzw0xHs258Z9ruFgMBiCTJyysrJkKpCQHAxthQvKVnZ2dlhpXCVEF2VoaIh9+/ZRUlKyopdGJDUQEdDFAF1LSwtGozGipOGJ8uQQjw8XTIR2+uzsLE1NTZjNZsrKylaVbKy7hK8jXpwoA9p48EbobDx2eCLo50R9fNuJI8MO1CpY9Pjpnpgn16yTz0cUFHJzc8nPz5fXjb8eHuUXL/ehUsF33l1BXdFyXwIBh8vLs03DpOr8ZBs8srt1UlISkiRht9upqKggPT2dwio3U0+2AXBpXV5c70OJ1NRU6urqsHV3U5eyyB/79HSMO5EI8MNn2nmla4rrzi8PkuiNVFwSggChm/bFxUXa2toYHx+PKj8fL8RcYDhvDhHzxTnGQtsNRVJSElu3bmVsbIyGhgby8/NX7J6Hi1OiOHYiVRP1ej1bt26VY2pBQUFEha1448jxFMWEYExmZqbskL5p06aYPOf+3VhPNkLwr5C+DZUydDgcQV4Wvz2yEPT4DRVF7N8/GXXOQhl4fv5iL/v7Heg1an54USWl6cdk7ETLVyxW1dXV6PV6JEmiZXQei1FLrvUYTSlSN8BkMmE2m8O6WzudTtlM774mL/0zLqRpF880T3Dp9vg38FVZSXzvvVW4fX7OurULn9/PD5/ppCzJy8aKYlQqVVCVRdDLBGVLKG1F6yi8szaDbzzewus5Htc83s5Gm4r83MjOnvEiNTWV2tpaeehLtIaF5OBqW+FwLEBnZGTQ0dEhO5yvVJ0KR60S92xSUhLbt2+Xebd5eXnLDAxP1IIfy+OtVqtM/dq3b5/cnYvn+OudjXXEA0k6cQa08cSdf0WcigfJK+x3Q+OUJEm8f2M6d73qJtusZ0dRiryhVqvVGAwGduzYQWdnJwcOHKCmpgaj0Ujb2Dz+gETgdQqWSDaUMVX8+/n+ebrsEnqdhp9cWMrWrcGqS4uLi7S2tsqb9jsv2xR8zpLEtNNLqkkXlweToD7Nzc1xuqOJIyY1EwsBxh1uHjs0ytjsAldttaD1OmUGQDzFpYSEBDZv3ixLzcayaY8HZrOZDRs20N3dzcsvv4xer0etVsuMinjVIJVQqVRkZWWRlpYmz6KIuYRoCEetOh5D2VgeK2JqVlaWLJUbSsGL99ji8cdLu1I6pL9Z6FQnfbIhFIJixVpXjISUodIkz+PxLKtciAp7QJKAXvkYCepg/mpoYiFeR2y0NBoNs4t+tBo1AWDBu/xG1el0bNiwQVZhKCkp4bk+Lw/WD6FWSXzzbTmk6TwRuwGSWkP72DyWFGPYwTfBkR0cHCTJM4YaFRqtmtK0+N3PlVCrVCTqNThcAQw6DS+1j9PY3k+5TS+rWQgDvHgpWzqNmrPKbfyjfUr+3Yd/18ad7xyjqqoqyGQnVohheGVnJTExEavVSmZmJsPDwxiNxphayrFCuHKLqv9KzrGSJMlDhqJilZCQEKQGkpWVRXp6Ot3d3fKCK3i3JzLZiKUCJCo9GRkZ7Nmzh8OHD1NVVRVTFXld+nYd8eJEGtDGg7WMU6s91hlFFl7ttaMCHvnEdvn30eYBRZy697VBnm2d4IySVL52XmnEIebKykpmZmY4dOgQubm5XLI1h57JBQxaFdsyNHR1dQUJjSgpPNLRoxgWF5eSKb1pmaKh2LSPjY2xf/9+SktLychYchSXJImvPdLEvr5ZNueauf3SjXElHLBUrDm3roaStB7+59Vpjk7DojfAC52zNA7NcdvFleyoTV91wSM9PZ2UlBS6urpoaGiIatQaDdFou5WVlYyNjaFSqSgrK1uRAhwrtFotFRUVMrVqpQF1wfxQxlOVSoXH45GToWg4HrqvGNQWw/TiZ3EtVtOpWCvaVUJCwpoN9Z9onPTJRrxf5OOt8ng8HjmxGB8fZ2RkhKSkpLBeFmHPV6VCxRIHFuAHFy75Y3i9XpxO59IMgyKxgOD3ODHn5i2lNpINdqqzktmUG7liYLVaKSkpoaenh2cPunC5QEJF28gsJRuyZMWNUFz3l2Ya+u2YE7T8ZtcWkhOW30ZiXuFrF9gof+0oKUkJbMmL3aBNQEkvs9vtfOUUDY1jAYYW1Nzf5AZUfG6zijPKo2+qY8HPLtnAhptelH+2e6GwsJBDhw6RnZ29rKqvRDin8JU6K1lZWXJLuaCggJycnDXblFit1qABdTFXE8oF9vl8mEwm+RzFkHkotUpU63Jzc2lpaWFgYIDKysoTrkYV67G1Wi0mk4nCwkL6+/vleY5onZ11Nap1xIsTaUAbz/32RlCjuvsjm4MSCyGt7XA45A5tuHlAl9fPc62TpCcZ2N0zw5TTG9VDKjk5mbKyMnp6elhY6OaKQt1SccY9R9Lr3fVw3YDrz6/g3lf7qMlOYnMEsz9RaRfd/tHRUaqqqnD5VeztnSFBp+HQkIOpeQ/pUdo34STcvV4vSUlJWK1Wbrwgndtf6OH5Pg+eAMy4/Fz5YDNff0cpu3aunncvNr5CTXCl4tJqaLvZ2dkyBXilOBgvBLVKDKgXFhaSnZ2N1+uVY6ndbsfj8chsiqysLCoqKuRi7kqqVeJ9x6NcFe67KIqooqOUmZlJUVHRCRcmOVlmC0/6ZAPia0/HU+Xxer2yo6vD4cDlcqHT6WSFiLS0NJKSksjKyop6nNBK0D8+v42HDozytrJUqnLMqFQqampqaG1tJT8/P+Km1OX1c+1jLdgXvWSZDXxn27HHhdO2FipERUVF/D+jk/99aYB8WzIXn14TNoEQODToQKdR41j0MWxfpDIhcjUlMTGRD59bJw81lZeXM+rRs693lnOr0ihMDe52KDfDym6A2WwmIyOD0tJSLtDpuOp3B/EG7GjUKlrdFvIcASbDKI3EA7VaTa5Fz5B9SVBAxTEObm9vb1DLN5S/KhTB4lGwUraUOzs7j6s6FQ6CnyxMoo4ePUpycjKpqalhucCh1yKcalViYmLQgitEB2LBiV6UJUmS3Wynp6c5cuRIRJf01Rx/Hes4kYiXRvWvntmIpgwlil+1tbW0traSmZlJYWFh2O/7/KKP9GQ9w7OLbM6zkJJ4rDIrNsNi/RezAaakZCzpWeTqNfT19ZGVlbUidagqK4mffCC8Clgo9Ho9GzdulLv9hYWFnFaSyu7uabYVWLGFJEMejycosQgn4R66tv7PR7K4+ckjPNA4jQQEJLj5mS4ePzTGQx/fik67+kKZ2Wymrq5OVtwqLy8nNTU1qFAX6hQeD23XZrNhtVrp7e2VFb2O13lbIBAIYDQaycrKoqenh5aWFpKSkkhNTcVisUQsekJwnBKqVeGUydayA5+eno7NZqOvr489e/ZgMpniitmrEUo5GeKUaoVFZm1KJ/9mbNmyhZdeeimmxzqdTnp7e5dJFYrNeqiXhdlslpOLUHm8gYEBtFpt0CD2SlJ+SkpU6A3m8/no6OhgcXGRmpqaZV/AaaeHTz1wCINWjcvj4yfvysHlnAsa4Bb/wi0yXq+XtrY2AoEAVVVVETeijx0c4Vev9VNXaOWb76pAo47tS+x2u9l3qJnrXnDgk1SkJOr4/eUVzL0eWIQ0rqiyWCyWiItMy+gc336ijaFZFx5fAI1GzW8/UsvCWC8JCQmUl5evqr3oDwS46ncHGXEscvdlmylOM8kVq8nJSbmlnJKSgtVqxWKxrIlTOIDD4aC1tXVV3hySJIVNgMTnbbFYcLvddHR0kJaWRlFRUUzHF4N5YghOWT0KBAK8/PLLcnVtJerS+Pg4drud8vLymN7T4cOHKS4ujsk9HGD//v1s3LhRvmckSWJoaIi+vr6wA36tra1y4AgHvV7/RupuvGFO5A2I/7g45ff7OXjwINu2bTvu1x0fH2dhYYEihRu5Mk6FVnpXilOBQIDu7m5mZ2epqakhMfFYQUmSJD7/8BGGZl3otWpuvbAMtc8lr1lKtUWLxbIkm+pfojT1Ti/woa057NqRS2dnJ/Pz8/Isx1rC6/XKcTa3uBxbspGxKTvfebqTyXkPH6lQU5xqkM/RbDavOGehxB/r+/ne013Lbtq7LqvlrPKMVZ+3oO1OTU0xOjqKJElYrVZSUlLkOLAW5rjCsG81cTaUqSASIFGos1gsBAIB2tvbSUpKorS0NObj+/1++V5VqlZBfLHEbrczMDDAhg0bVnys2+2mvr4etVrNpk2bYko6jh49Sn5+fsxGir29veh0OnJzw8+7hr7XfzMifgn+Izob8ban/X5/UGVlfn5erlwnJydTWFhIYmJiTB+wsjocumDHohGuhFarpbq6mqmpKQ4cOEBxcTGZmZkyl/H+fcNMzM6ToFVxxaYkNCqJ3NxckpKSIm4sx+fcjM+5qc5KDjvLEU5e7/2nZPP+U+JTshKzK8kWK4GAHX/Aj8PpZ2xsnNQUKxkZGXHNWVRnJfOnT27nvNtew+Xx4/H7uebxDv7n4hos0vwyDm6skAIBbr2gaGkhHOpkd8eC7MCanp5OaWkpk5OT9Pf3k52dvWbVHThWnVJSnyKdf6ieudfrJTExEYvFEnZoH5bawOL49fX1MrUqGqKpgQj5yNraWjo7OxkYGIg633Ki28ehjw8d8NuzZ0/QvMnJUjFax8kBrXZJnjyWDeFaD4hHilMqVbBJXizfF7VaTVlZGXa7ncOHD5Obm0tubi5ut5vJ6Rk6R+1IAT8eNXT19lGUmSJTY8LFqZ6xOfpnXCTptTx5dIz/d1pB0CxHXl5ezAPSAUnitn92s69nlk+fWcjZFcEFEhGnzGYzbrebowf2odfrOWA3cnh0EQl4YdrCh96xKfwLxIBL6grITU3iUw8cCko4PvOHJm69WOKdG1aWtI1G2xWdFYfDQVdXFzqdjpSUlDUrnCgN+5TUp3DHV3aA7Ha7zFSwWCxkZGRQVlYW9n7ftm0bIyMjKx5fCaGkGC5OnajZQoPBQFpaGkajMUgZMVqCtE6jOokRbVEWkm7K1u3i4iJDQ0Oy8VxSUlLMiYUkSfKCrdVqGRsbIzU1VTavE5u31b4Pt9uNz+cjJSWFtrY2WlpasFqt6ExmXh3yUpJlwe7yc9FbNmNOiF4RGJpd5AsPH8HtC3B+TTpffNuSu2p6ejpWq5W2tjbGxsaidjkinadIgMQ1DQQCJCcnYzObuea8Il7udnBahg+f10OyxYp2lVXkH1xYzQ1PtDLqWKR3aoGv/rmJh67axjabjfb2dkZGRiJ6WwQCAdl5PZycXyQzp9zcXNLT01c8/mogZl2U3hxlZWX4fD5mZ2eDlLYsFktYPfNoUKvVFBQUkJmZGaRatVJ1MJLRkiRJGI1GTjnlFKampmhsbCQjIyMsb3it1ajCPT6s58zrnZeFhQXa2tro6+ujsrLypFnE13FyQCgnnuhkI1RoRK1WMz09TWZmpjwTeDxxCpaKIR6PB5vNRm9vLx0dHVgsFl4d14BKjdMPV+4o4KwdhSseq8iWSL7VSN/MAh9QFLlSUlKoq6ujs7OTxsZGUvNKuPPVIUx6LV89tzQsFbh1dJ6/HhoDlcTNz3SwM9+0zCxVzLAVFxdTW1tLT08PNtfsklytSkVFZhIto3NIEtRkxz+HCHBGaSpPXF3He++uD/r9d59qXZZshOtaA3KcikTbTUhIIDU1VVb0isU7I1YICrDNZqOrq4vh4WEqKipkSWGRAAnhFovFQm5ubsxxUgh/pKeny8evrKyM27hWxKkTpVwlHp+cnExBQQHDw8MxSeWuZbLxBuq+R8UbKtn4+9//zpe+9CX8fj+f+MQnuO6664L+7na72bVrFw0NDdhsNh5++OGg1u9KEJtgJR1KKeOanZ1NYWEhbW1tVFdHN3WLpgyl0WhkgzSNRsORI0eoqKiIWws5Ei/UbDZjs9koKSlhdnZ2SUrVZ2ZywcfYvJczSlNJMqz80Q7OuHD7AmjVKo4MOYL+Jroc4+PjUbscgOxu/UjjCB3j85yTq6IwLSli9SI/Hy5+Xbzk1eZ+3v6zPajUGm67ZGNU3fRw2Fmcwo0XVPG5h4+w4PEzMO3io79u5C9X17FhwwYmJydpbGwkLy8Pm80WdD39fr/MX41Xzk+v17NhwwZ5cC4nJydID361UCZqWq2Wubk59u7dS3JyMjk5Oat2NA+FwWBgw4YNzMzMcPjw4SWt+cLCmA0BfT4f09PTuN1uedNjs9k49dRT6e/vD3L5Fud6ormqKwWUxMREtmzZwtTUFIcOHcLv95OXt3pN/XWsYy0hDGhj2RDGM2cRThlKSYNKS0tDrVbT1NQUdZ2PBKFm5Aihw4pNZn5+vpzoNwyrsbv9WI06imNUJ0zUa/j5pRtwuv1YQtQPlYpV3338MI2ToNZoqMpK4sPbgmknHo8HtWcOleTH7fWTmaSira1NjqehZqkClZWVZGbOopGa0SenYDQn8vHfHQTgG+eXc8Gm6DOZkVCSkcRr/3UGp//4Vfl3p+YksLCwEKQQKGTxo3WtI0Gr1VJVVSV7Z4j3ebxFFqUkvkqlwuv1sn//foxGI7m5uRQVFa1axl0JnU5HVVUVc3NztLa2ymqTKyXkIukQRTqXyxXza642GVCpVOTm5pKZmSl30quqqo5bKvdkKYq9YZINv9/P5z73OZ599lny8vKoq6vjwgsvpKamRn7MfffdR0pKCp2dnTz00ENce+21PPzww2GPJ0mSPNTrdDo566yzeP/73895551HcnIy6enplJSULLtpfT7fsgQilgU7nDIULCk5pKSk0NzczOTkJGVlZWEXinA+ETqdTl6wIyluZGRkYLVaueK+vRjVfgwJBi7alBWTTN8p+Wa2F1ronFjgE28JX2HKyMhgwqvni48exWbs5Mb3bQK/JyiwGAwGBt0GHm6awx8AhyqJX527ecXXB6gf9eKV1Ei+AL/6ZzMbL9sWd5dgZ3EKXzu3hFv+rxOPX6Jjwsn1f2niv87Mwm63YzAY6OrqoqurS3ZKVRr6HQ/E4FxPTw/19fVUVVWtqBmuhFIeV6huiMCiVN3o7e1laGhoTRINJUR1UAwWlpWVhZ29UNK2lCpWhYWFsgqNaFkXvW4Y2dbWJlOrTCZT3JJ/J6oTYrPZ2LlzpyyVW1paGlZ04c1SMVrHyQGDwYDH41n185VxKtycRTTablpaGhaLhdbWViblzEQRAAAgAElEQVQmJqisrAy7PipnF5VqRmImIBIdNiEhAVtxLb2vNOL3BfD6NZxRmrrie7rv1T6ePDrOO2vT+dRbiiI+LiUlhS2VhRyc7CPg82LRSczMzAQJouh0OiwWC7deUMS4W8vp5WkkGWKLAVarlUvO20l3dzd37e7E7V1iL7zaPY0tSc/2Ait6bfwbwiS9muc/XcO1T3RRaZZ4S+Yie/fuJSMjg4yMjLi61tFgsVjYvn07/f397Nu3j4qKCplOGgvCyeMKSXxBL9ZoNAwODjI4OIjRaIx51i4WJCcns337doaHh6mvr6eoqIisrKxl91kkuXnhe6OUdI+E4407SqlcZSddsAdOpCrjGxlvmGRj3759lJWVUVKyROW59NJLefzxx4OSjccff5zvfve7AHzwgx/k85//fMQP4uabb2bPnj1s374dvV7PAw88EFMVUyzUIuk43jkLWFpot2zZwsDAAPv376eqqgpgmeKGWLDjrV73z3oYd6mZcfkw+RbJNfpXfM7EnJv2cSf/dV5ZROUpoQzys+c7GJ73MeSQ+NUz+zivIpWsrKwgmpF+2IFmzyx+SUKvif2L8baKNP7cOEJAggtOyeXPLxygMDeTMzaUxCWp+q7yJJ49amTPwJIh4l+PTFBj8XF+bbacqIkBbL1ev2rFqnDQaDSUlZUxPz8vV1/CJbLKQG2325mfn19WAYw0EF9SUkJWVhZtbW0MDw9TXl6+Zt4carWawsJCmVo1ODhIdna23LFaibYV2rLWaDQYDAY2bdrEzMyMrAplMBhOOFc11ntGrVZjNBopLS1laGhIlvJdy/tiHeuIB/EY0IqCmM/nC6sMFe+cBSxVkTdu3Chz5SsqKtDr9fLGTdB3hCBKvCak/+yYxu6RkFBh0XhxTE9ijNJFmVv08eeDIyQbtPzl0CiXbMsN6+skqNDn5KnRbknG45pHO95Gl8dCdnb2ivF03u3jTweGyUjS8+4NmREfJ+S/P5lgpfmRJiSVhpc6pnihfYrthVZu//DGqO8/Gm33lvcUY7FYMBqNLCws0NLSwszMTFwJwUoQhaDMzExaW1sZGRkJG0fEeYpNuzKhtFgsUY0HQynAFRUVazbALzoHGRkZdHZ2MjQ0RF5eniyTOz8/j1arlQfNQ+OpMk6J70e4e3etOg8mk4mtW7cyMTFBY2OjLJW7PrPxb8bQ0BD5+fnyz3l5eezduzfiY8RNNTU1FbYSe/3118v/f/rpp8NSmMJ1LMTMxdjYGFlZWfLQ0fHMWTidTrm6EggEaGhoIDExkdzcXPLz82OeCYmEPx8cYcblRaWCmmwLcxNDtDpnKC8vD9tFcbp9fOHhI8y5/RSmGrnj0qVFUpxn6JxFcYqezmkPWrWad5xWhcYxxMTEBDabTf5Sbsgxc8O7K+iaWOB9p8TeVt6cZ+Gpz52KX5L42+Ex7jjix39wgM8OTXLJWzcGKZmI6xk6DyI4k9eelcWVj/RhX1xKtv53j50PvmUjRv3SNbBYLNTV1dHX1yd3IWJVhIgFSUlJbNu2jaGhIZm3qdFowqpuFBcXx92lELKugtq2Ft4cyna43W7H5XLh9XppaWmRVbGSkpKivoayZe3xeFCr1bJCRkpKCjt37mRgYICuri4yMjJiXsxPdEUnEAig1+uprq6WzaVEVWqtFW7WsY6VIGhUoYikYKjVauUN11rEKaES5HA4UKvVHDp0iISEBHJzc+Om74TDY4dG8fklNGoV7z6lkPHxccbHx6mqqgrbRUnUayixJdI77aIoNZEkg0Zer8LRYc1mMxdsL5EVgTo7O+WZyWjryH//XyfPtk6gVaswGbTLhsZDUZ2fzlNfeitP7G3lphfG8KPmte5pPv3AIa5/ZzlFtkTZd0NZZY+Vtiv8HIaGhmQZ23gp2NEgZuzGx8fZv3+/XIwLd54FBQVx708MBgMbN26U6apZWVkUFBQc94ZZeT3n5+fxeDy0tbXJqlVmsznqayjnOXw+X1jVKlidw3csUrmCXrxOo/o3Ixz/NPQDieUx4WAwGHC5XBgMhoiSs8qOxY4dO2htbcVut0dcCCO9B/GFEIu21+uVB86ysrIoLy9HrVbT3d3N2NiYzJddLQKSxBOHR/H7JVRqOLc6gy1bcuWFqqqqapli0qzLi2PRi0YFnWN29jc04Hv9PM1mM5mZmUFzFlXVEm/rmyXVpKc8wwSkhJ3lOKcynXMqj72OLxDgQL+dHEsCeSmRN2+is9I4aMfjCwAqHFoLjYcOk5WRTnJyshxclDSj0PMEuOtyK5f/6gAAi74At7/QzdffcUxqVa1WU1xcTEZGBq2trfJCdbySgKGqGwDd3d1otVpKSkoiqm7EC5VKRWZmpjyYJzplsbasfT5fEB1K2Q5XztcEAgH6+/tpamqKOdiJ54VTAykoKMDr9TI1NRXzOZ/oRVZ5fJEoCv+QjIwMampqTor29TreHDAYDCwuLi4TGhFQzgOqVCq2bdtGZ2cnR48epba2NqpZbChCDejEuir8oURndmBggJGREVJTU48r0eiddDIw7UIC1JLEmeVpVGYVy4pG5eXlQUXDgCTx+KFRClMSeE9FEgUmP4cPHsTtdsvrlTjPlnEXbeNO3mazYTEdq9KLWY6DBw9iTs+hqiQfTZj1ZMHjR5KW9JNd3ti8S9RqNe/eUcU/+jy81jODxwd7ema46rf7+cQmI0UmH0ajMeh6xkPbFUp6aWlpchdCdJqOB2L9F589LEn0q1QqiouL14xeDEt01ZSUlFV5cwhFUBGnBF3bYrEEzdcIefOWlhZZnXOlNVsp3R7OEHA1Dt8rxSklvfjVV1+lsbGR6urqmOL2erKxxsjLy2NgYED+eXBwkJycnLCPycvLk780sbQZBwcHaWho4Oyzz162YIf7ENVqNRs2bJBdLcMN+cASj125YIuFUCk/F2lxKCsrY3Z2lkOHDlFQUBCTtFs4vNwxxfTCkrGaBjizLFVeqFJTU2lubl5SgbLZZKm8/iknG1Nh0Knm4u05bN6UH3UR06hV7CwOfv8ZGRmyIlYkxaofPt0hV4x+vWsLRbboA4FXnZZH87AdrUri0YOj/Not8eGyAU7PXvqibtiwYcWAekqehapME61jTgB+s2eQz5xZSLIx+NxEi1NZPVrJJ0IgnAtrJNWNyclJOjs78Xq9EdUpVgNRgReDc0KVRJnQSJIU1A6fm5uLuR0uFsesrCza29sZGhqioqJixXmaSGog4juXl5eHyWSiubl5RZnAf0VnI/T7L6pQk5OT64nGOv6l6Ozs5LXXXqOysjIoTkVThqqoqJCl0CNJZQuaiSiAicKb2WzGarVSUFAQcV0tKCiQ44iYIVjN9+LeV3tlmVeViteLVpCZmYnVaqWlpYXR0VEyMzNxOp3s7pri3sY5JKA/08gN5xdjLsxftv4MzLj4+qNNuH0Bnmud4K7LgiVprVYrj49aeOnlXkos/dx9xTaSTMFx6Np3lGF9uY8Ms4G3V6fh9PjonligItOEIYzRnnL9/0SthppEHfcc8eLyS4zM+7l5r5Nr3l7KFafkL3tuvEhISGDz5s1ycS/SrEI4CHpZuPVfFBUFDXp2dpa2tjbcbjdFRUVrtrlVq9UyBbi1tRWDwbCMuqVkK4j7VFgNWCwWysrKIsrii72OoFYJ1aqVRBbCxSmNRoNGozmhqokGg4GEhAQqKirkvVlZWVnU/dfJokb1hjH18/l8VFRU8Pzzz5Obm0tdXR0PPvhgkLneHXfcwZEjR7j77rt56KGHePTRR/njH/+44rFfeOEFrr32Wj7wgQ/w2c9+Nq4vksvlkm+K1NRUmXMfqrhhNptledt433d7ezter5fq6uq4KxfvvWM33VOLACTq1Oz9+lvkDabg2Yr5k4KCAmZI4rt/7yYgwSfPKOSizeEpTx5fgB8/10n35AJfOaeU2pzlGbjLu6T+ZArM09/bs0zJ5IP31jM8u4hGreLGC6qC2tNiI6zkr4oF5sCkip+9No4vIJFpNvDzi8uZGuiSzehW+vz2dM/w8d8flH/WquDwDW+L+Hi3201raytqtZrKysplC6GgGYlzFfQywQ1dibfs9/tlk6t4uhCxQmleJzYbolop2uHCfHC1QWRqaoqOjo64W+JKo6WBgQGSkpLIyspCkiSGh4fp7e2lsLAwrE7+a6+9xumnnx7zOcb7+N27d7Nz586w70WlUq3ZTMwa4c0RUf49OClM/RoaGvja175GXV0d3/rWt+KqMHs8HlpaWtBqtWRlZckxYGFhQS6EiFgVjwGdQCAQoKurC4fDsSojvZrv/1P+f5JezZ6vn7ls/ff5fPh8PnJzc5mQkvnBs734AhJnlKby7XdXhj1uy+gcX/rjUfySREaygT98PNjkcMHj5x0/302SQYNz0cf127VsKsuP6Mvh8QW45Jf7mZj3UJaeyK8/ekrQ+q+kw4rraTKZeKlzmq88chS3b+lWVAGnl6Zw56WbliRz1wDCbNDtdof1Mwqlbfl8viCDxJXW/0AgQF9fH+Pj41RUVKz5/JokSYyPj9PV1YXNZkOr1S7z3hB7qdV20ex2O21tbXEb44qkQ6VSMT4+jt/vj1npNNRMdiWIOCVJEiMjI/T09JCfnx9RzbKxsZHKyspllHKBeDqa/wJEXFjeMMkGwFNPPcWXv/xl/H4/H//4x/nmN7/Jt7/9bbZv386FF17I4uIiH/3oR5c0tVNTeeihh+SB8pXgdru5/vrraWlp4c4774wq7xdOcUMo7hQWFpKenh6XAV0smJiYoLOzk7KyshWN1gSm5xd5y6275Z9TDfCTsxOXLYRqtZr5+Xmam5s5Mmfkj83zaNVqdhRZ+c57wi/iL3VM8f2n2gAVRTYjv7zilKC/e3wBPnZ/I6P2RaqzkvnfD1TR3t4e5D7+atcUNz/TSWm6ie++sxj3wrHgItSMxOKi5AMPzS5y2X0NOD0+fAEJvUbN9y+ooCrRxcTExIqKT5IksemmF/Ar7t4fvLeC928N78ApMD4+TmdnJ+np6ajValluWLTtxTVdLR1KdCGsVmvcDuGhUFbXZmdnZU1zn8+HWq1e83kU8Zp9fX2MjY3FrGYiksrZ2VmGh4flVrcIel6vl87OThwOx7JzPtHJxmuvvcZpp50WsWK2nmy8aXBSJBuwFHtuueUWnnzySe655x6Ki4sjPlY5yKssLPl8PvLz88nMzFxz9bqZmRna2tooLCyMucK+u3Ocqx5skn/OMcJNZxrlQohY/9VqNS6Xi4ZDTdxzxM3APJxVbuMr55Yuk7wVkCSJPx0Y5uCgg10786nKSlr292890crLHdNszjPzk4ur6e7qZGFhgerq6mUb9p5xO5f9+hAqAnj8Ej99qwFLsimoYBNp/d/fN8vnHjrMnPuYQItJr+HRT28nPyU2md9YMD09LW+oDQZDkCqkcsO+2vVrYWGB1tbWVTmEhyLcsLnoKAQCASorK9d0HgWWPnOhilVSUkJGRsaK96mYrZ2dnWVsbEyWgo+lqFZfX8/mzZtjvt6hccrn89Hd3c3U1FSQ6axAQ0MDtbW1YVkFb6Y49YZKNv4VePrpp7n22mu58cYbefvb347P5wuqXIQqbig37Ha7nZaWluOiPUWDx+OhubkZg8GwzE1VGPopK+z3Hpzj5eFjz79saxY3vDeyP8j8opdvPNLI/iEnOdZEfvC+Wiozk8I+tnPCyecfOoIvEOCcyjS+8c6KoL8PzS5y+a8aMGjVLHj8PP/l0zBoNfKGPSMjQx6Odrlc6PR67m/xc3TCw2fOLORD26O3mOfdPn7yXBePNo4gARnJer5/QRWbMw00NzevuGF/4uAw1/61Tf65NtvEnz65I+gxkRZCv38pWIh5l7X8nMVCODQ0FFFmNtxzlFUr0V0R96e4R8V5Tk9P097eTmZmZswLZjxwuVy0t7ejUqmWUasEZUPpGKtMKsV56nS6IBqjSMSMRqPMTf5XJBuRHv9mWsTXcfLFqb1793L11VfzxS9+kQ9/+MP4/X55MDpUwEN0LcSG3el00tTUdFy0p2jw+Xy0tbXh9/vD0mdD6cVHR+a5ueHYBvz3H93A1uLIBbXd3dN874lmVAEfmSlJ/ObK7WEfN+/20TnupCIziUR95MKNJEnYXT7MRq0sCT8zM0Nrays2mw29Xh8kj/v7tgB7hxb50NZssq2J7O+b5RNnFFCdtXJHundqgQ//cn9QwgHwgVMyuPHC2gjPio5Ipn5irqeiooK0tLQ1j1NjY2P09PTERd2K1F0RcUo5bO5wOGhra8NiscjSuWsJj8cjd4JCqVXCakAU60KLiuI8BbUqWgzdu3cv27Zti7kIGSnuiCRPMCxEIhytc/JmilP/UcmGJEl0dnby3HPPccstt6DT6dDr9fzyl78kJSVlWYU9HJQLbXV19ZoNUynPUUhx5ubmyrMpSkM/8YVoGJjjqgcOy889/I0zo97wz7ZMcOvzXUhSgAKjj2+9oyiqEV3nhJPh2UVOLU5ZpiEekCS+/2Qb/2yf4sJaG5fUmOQNu0qlwufzyYZCZrOZppE5Pv3gYfyvdype+toZK16LjvF5rrz/IDMLS8PsWo2ah6/aRnmGiYGBAYaHh8MOwAts+P4/EeN+912xiVOyEyOqg4QuhKKCl52dTUFBwZoH7MXFRdrb24GlQUblQqJcCEWylpCQgNVqlT/7lRa21XQh4sXExATt7e0kJSWh0WhkiURxPQVlIxRKapVSDUQEuK6uLvLz8xkYGOCMM1a+T8Rzd+/evWbJhlDUegNhPdmIjJMqTgH09fXx4osvcsstt8jGmb/4xS/IycmRk4toa4CS9hSpKnq8EIUlMVspaFtCJlucZ0JCAjc83swzrRN8cEtOkGBHONz7Sh+/eLkPg1bFuwpVXLo1i5KSkqANn8cf4KrfHWR8zk2e1ci9V2yO6i2lpO0Ojs/wi/3T+CW4rEKLWY9cUVau803Dc3zigYN4fAEykg08/flTY7ouU043771zH3aXL+j3pWlGnvjsyscI9TRSisyI7orYo8zNzdHS0kJqairFxcVrvmEX1K3FxUWqqqqCqDyCATI7O7tsiFv8W2kNVXYhIs0cHS9EYpmQkIBer5f3KGIvZbVaw1ILBbUKkOcOw2HPnj3s2LEj5qLeSkWxyclJ2tvbycjIoLi4mIaGBrZs2RL2Wq4nG29Q+Hw+Lr/8crZu3cq2bduor6/niSee4K677qKiomLlAygwNjZGd3d32LZXvAjnv6BWq3G73SsOSL3UNsFTzeN85dwSMs2RebSSJPGDp9t59OAoSQkaPlqXx9lZ3mUc3IAk8bN/9rC7e5orT8vnXbWZQccI5a/6/f6gLpBywy74mSUlJeiTU/jAPftx+wJszEnmlvfXMLPgpTDVGHUj7/T4OPvWV1nwBJBYmkv506e2U2wzyXrkJpNpmdqTz+dj1m7nmaPD2DQeLBoPCQkJQW3mlRZCv99PT08P09PTMStHxIvx8XE6OjqwWCwydUtor0dbCGOFy+WitbUVnU533Gom4QwITSaTXHUVFbZYINrogUBgmRqIaCv39fWxffv2mLjDgUCAvXv3ctppp8X8ftaTjZMGJ1WcArjqqqsoLCykrq6Orq4u7rvvPn72s5+xbdu2lZ+sgKDcrMVGLhxtSxilJSYmUlFRsaJM9kpY9Pq56O59JGjUuP0B7v/YFpxTI0xMTFBTUyPL2k45PVz6ywYSdGpcXj+Pf2YHJv3S+i9JEn0TDn78bCd6lZ/3l6jQ4Zc37I+2LvDwwQkk4OJTsvlknY22tjby8vKCZjm6Jpxc8ZsDeHwBMpMNVGYlcXa5jYs2Z6/4Prz+AG/98cvY3cHqVgevOyNoDVaqLonuikjWxL+V1mxJkujv72dkZOSEeQWJ+8hkMqHT6Zibm0OSpKDu+vFQy91uNx0dHfh8vuOWHhcGhCIJEjMhIuEsKyuLSbUKoscpgWh03HCIpQMv1CCHhobw+/2cfvrpYYsL68nGmwiNjY1cddVVfPKTn2TXrl1xfVkWFxdpamrCbDZTWloaU2YbqSUqBo4F1UTwGnt7e5mamqKmpmZFhYVoGLEvcsWvD6BRqfBJAZ763Kkk6DTMzs7S2tpKfn4+OTk5dE8ucPUfDqPTqPD7JR64vDyIEmM0GlnUJJKVaiE3IzVoQ9Yx7uT6x5sx6bX8z8U1ZCQb8Hq9tLa2IkkStrwSBuxeMpP1XHn/Qdy+AB/ZkcsXzo4+d3NwwM6V9zfieX0Iw2LU8swXTsWcoJMrI/39/aSlpeHz+YLMksRCKFQ3VoO1nLWI5Bju9Xrx+XzU1NTE5UAeCyRJYmJiQu4YRBqOVCKUYjY3Nyc78Ip/ym6My+Wira0NjUZDRUVFzENryupRqNHSK6+8gtFolBOlaNVZv9/P/v372blzZ0yvC+vJxkmEkz5OdXZ28rGPfYx3v/vdfOlLX4qLGun1emlubkan01FZWRnT+qX03RAbYSVtS1lYEt34wcHB416/Xuue4kt/bMLjD1CebuLBq7ah16jlCr6ghgHc80ofTzeNc9GmTC6sTJLP1eVy8fs2PwfG/ahVKj731kIu31kov8ZfDo3wk+e6APj0W4q4Ymcefr+fjo6OZbMcr3VNc3jYwW93D7DoC6DXqPjlFZvZkGOOKZ4oB+PVwC/O1ZOamookSXLsX6sNu8vloqWlBaPRGFXlLxZEcgwPBAK4XC6qqqrWfNYC4qcAK2ctQhW3BBNAGTdEUuP1eqMOXYdCxClJktBqtUHUqhNJ9/V4PLz88sskJyeHLXiuJxtvMjidTr74xS/icDi47bbbYtaChterKK8rONTW1gYlBIJrr/zS+v1+2c8itCUaCQ6Hg+bmZnJzc1ctn3r/nj5++o8eAhJsyjFz/5Vb5OP4/X7a2tqYn58nwZzKtc8M43QHKLSo+fY52fK5GgwGHqgf5L5XBzDo1Nxz+WYKUo9VIL75eAvPtU4AKj75lgI+ccaxBV7Z5TgwqeL7T7bhC0hkWxL462d2hJ7uMvz18AjX/aVV/rnMZuDHb0+TeaEJCQky3aimpmbNFRokSZKpW7HSkpRD3KJjFW3DLhzOT1RLXHQMhH+McuEKbd0rubZWqzVmp2CR1OTk5JCXlxfzpkipBiKoVWJRFt2fnJyciAHI6/XS2NjIjh0r30sC68nGSYP/iDjl8Xi44YYbOHDgAHfffTfZ2StX2AWE+tvAwEDYhCB0HlDpZxQLbQuW4mhzczM2m23V8qlf/dNRnm+bQK9b6r5/8ZxjhShBDesbmeD3HSomnF4uq9BQadMHqW0ZjUbueaWPP+wfQgV85z2Vy5QQX+qcxh+QOLvCxrTTy0udU2zMMZOm84Ttcpx3226mnG58fgmtWsU5VWn85AMbor4XUVi64ckOxhxuvrxFhyU5icXFRTQaDbW1tTFvdmOFJEmMjo7S29sbczcrmkSu2LArlTbFbMFaJDWRzqe3t5eJiYllqlhiJlAkF6K7Ls4zVsXFmZkZ2tvbZYXLeFWr4Bi16l8xW7hhwwZaW1sxmUxB0sHrycabFA8//DA/+tGPuPXWWzn11Nj4mQIOh4OmpiZSU1PRarWykpGg7ojFcLU3hqi8uFyuuDfTAUniPXfsYXBmEa1axXXnl/He6pSgxUWSJLxoeeCIg4AukYu3F/HWijSMuuAv4VW/O0j3lJNAAL7+jjLes+EYzerhhiFuf6EHgJvfV8MZpcEbctHlcLj93FLvZsThpjY7mcNDDs6vTueHF1UvS6TE/ILD4eDG5wbZPeyV//aVM7P52Bkl8jVVLrTxqHrFA0FLEnrhYqGN5BirlMiNhWKgTGrW2jlWwG63y5VOnU4XxLOOtXUfDX6/n97eXiYnJ+OSUAxtWdfX18szG+KYY2NjYRVM3G43R44cYfv28MOk4bCebJw0+I+KU8899xxf/epX+c53vsO73vWuuJ4rhsdNJhOJiYlBSkahMu6rQSAQkKmn8W6mPX4/Z/74VRY8fjRqFXddtonNWcecrcWaemBSxYNNC+i0Gjblp3DbJRuXHcsXCPBC+xSJeg2nFadEXXc/cM+SRLteq+bPn6ojxahZ1uXonVrggX2D/LlxBAkJt09iW4GFn1+yEWuiTqZCi/Vf0KGUXQvlNZ2cnKSjo4PCwsITJjbT1tYmq0MqXztSYSlWiVwIjrWxKj7FC5G8SpJEQkICCwsLaDQaObEIvabxIhAIMDg4yPDwMKWlpTHvF6LFqViw2uREXPPu7m7y8vLIz89Hq9W+aeLUerIRgr6+Pnbt2sVZZ53FNddcEzHjDbe4KKVHBb90rb+AU1NTtLe3L/O0iIaDfZN89LdHCABaNdx0up7CtOSg4KLRaPhD/RC/eLkXn8/H6Xl6bvrgtmWbzn+2T3LjU+2kJ+m567JNpJqCPSmODM+RoFVTkZlEx7iTFzsmObPMFqR6JQYL03IKufQPnWhUEJDgjg9vpCZdx1yI94ZI1IymZN55d6Os9KFVQ+M3zkajDr7GHo+H1tZWVCrVMt+MtYCoEvb09GCxWAgEAiwsLMQ9ExINi4uLQbSk43kPoUpWIgkKBALY7fa4OKzxYGFhgba2NnQ6HeXl5SsGBsGpnZmZYXZ2FrfbzbZt24Ja1iLZA4K05hcXF2lubmbr1q0xn996snHS4D8uTk1MTPDxj3+c/Px8brrppogUQ9FdDZ0HFEpG1dXVa664B8jKjaEdgmj42+Fhrv1LGxKQoIFbzzJgTQruruh0Ovb2TPPNv7bi9/l4e6GOr753i/z+/QGJZ5rHWfT6ec/GzCBTvr7pBZpH5jmtJAWrQkr3rT95FY8/gEat4ndXbqEkbYmdICg94j34AhK7ftPIkWEHAWnpC5lsUPHlLQaKLepl3ksrvWefzycXEMPJ8K4FxsfHaW9vJzk5GZVKtaqZkGgQik8ejyes90e8x1J2LYQ8vlqtZmZmhsLCwjU1xgln0DMAACAASURBVBWId15ESd2anZ3F4XBw6qmnrqhaJZ57vEImPp+Pnp4eJicnqaysjKvD+S/AerIRD3w+HzfeeCMvvvgi99xzDxkZGUxOThIIBILkcZWtW+XiIihDazE8Hg5er5eWlhY0Gg2VlZXLBqNFFUgoRPz8oIeDE0uDamkmHS9+9YywX9gH9w3yi1f60GlUXFybwhbTbFCHYGDGhVqlIstsQK2K7lzp8QV4x893M7/ow2TQ8tTnd8oDfLC0sBxtauKGlxyMOSXcfgmtCrZl67nhvLyIFLMX2if57ENH5J/PKk/lrss2hz0H8TkIb4fVLlJKh9PZ2Vk5CTKZlobURXJ5ooJFV1cXBQUF5OTkrPgewiXB0RRClMEiHg5rrBDzIt3d3ctogKHzK16vN0gdTAz1qVQqmScrIBQ7MjMzKSoqwu1209bWxpYtW2I+r2iLvhgGfANhPdmIjP/IOCVJErfffju///3vueuuuygvL2dsbAxgmTxuqKQnHBv6jadwFQ/8fj/t7e243W6qq6uDig3hRFFu2rdIj33p74UpCTz1+VOXrXe9Uwt8/uEjuLx+zqtM41N1Njo6OmQ5+r8dGePHz3UhSXBZXQ6feeuST8nMgocP3rsfjy9AQYqRBxTmfy93TnHfa/2cXW7D7Qvw+OFRLt2Wy65T8/H7/bS2tjI3N4fVamXe6eT+w/P8czAg+zipgOvOL+OjO1fnGC6SmuOhSUOwgIvoBkmSRFLSEnXL7/cHDdmvJcR7iNX0VcwEisRifn4+ahLk8/lkdbUTYYwr3kNHR4fssSHijVAEFfFfKecuKMZiHx1NtUq877USMllYWGB6eprS0tI43+kJxXqyEQ9GRkZ46aWXeOyxx3j22WexWCzs2rWLSy+9NCZ5XDhWaU1OTo55eDweKN0n09PT5cFoZSdAbNg23PiC/EGeW2Hj55duWna8xgE71z7WzKLXz3nV6XzznRX4fV7ZlXZQSuXm53pQAT+6qJqdxUvUGF8gwPefbOfIsIOvnFPCW8uXuLELHj/n3bYbXyCAVq3ib5+pQ+U9pmQlKiwBlYbnO+w80i3hCyx1OHbtzOPL55agjXDNtv3wBVy+Y7fmvZdv5Iyy8CpIXq9XdmivqqqKSQLS4/EEzdkoF5dwSZBw1z7eYBEJYqGdn5+nqqpKngsK578h3G3FQhjrwKHgsKanp6+ab73Se2hvb2d6ehqTyYTb7Uar1crX1Gq1hq2wRVMDEfK+IyMj5OXlMTMzw+bN4RPPUPj9furr6yPSJdeTjTcV/iPj1NTUFC+99BJ/+9vf+Mtf/kJycjIXXnghV1999YoGdALRCldrBSGRbbPZ5MFo5TolkqDaG1+Qn3N+dRo//dByetRzrRP88O8d6DUqimyJ3H35Znlt8Xg8HHGlcM9rA0jARRuzuOYdZQB0Tzq58rcHkZDQqFS88NXltJfJeQ/vuWMPEktry53npxDwuNBqtej1eux2O3l5eRQUFnL7i73c+0ofAcWdV5GeyJ8/tX1Vs3Z+v1/eTFdXV8ckBqPcBCuHuJXddeXnabfbaW1tlYfs13qNFxQ6YU6nNGgV1C2RXKyGYgzHxFosFgslJSVrfr+Kz2FsbAyTyYTX65XFZsINnCvf+0qqVbD0mTU0NPxHCpmsJxth8MQTT3D48GF27NhBWVkZ11xzDRaLhVtuuSWuym+04fF4EWnYPCEhAafTicViobKyctmN99/PtPKbvSPyz99+dzmXbs9bdvxfvdbHL1/tx6BV85YyGzdeUCW/7ujoKD/4eweNkypUKhUf2ZErV4z29c7wtT83IUlgTdTxt8/ulNuMzx4d5ommSbanB+iYkWiakdi1NYP3bc0L6gTNLSyy61f1tE8v6ZLrtWo+tDWH684vC6ud/qtX+/jx891Bv2v+9tuiXj+REAjVLfHa4Yz9lJtgoRO/ElYTLOLF9PS0PC+i0WjkgXixWY9liDMa1tKbQ2nuNzs7i8fjISkpCaPRyPT0tCyVGWsLXzmYF2q0JFTh5ubmqKuri+narzRQvp5svKnwHxmnXnnlFZ599ll27NhBbW0tN998M6Ojo9x+++1xfXdF4aq/v5/q6uqgTeJqEG7Y3Gg04nK5MBgM1NbWLqNUfuevzfzp4Jj883Xnl7ErpFMQkCS+/KejvNI5jV6r5rYP1bKz+Nj7nJycZPfhNg7OJaNNMPKZM4uwJh6bqbvnlT5e6pzmqtPzOacyHa/fz6Jrkfm5pfOcnLHzzVdcLPohQafBnKClOtvMTRdWo9eqlylW/fnwFD98pmPZ+3/441vYmBe7yIwSIiHIzMwM6hBEU11SehrFojIo9iRVVVXH/VmHw9zcHM3NzajVavR6/ar8N6JB6UUmZi1WW+ATTBBxXRcXF0lMTMRkMjE7O4tWq41LinelpMPj8XDo0CHq6upiPsf1ZOM/CJIk8ctf/pK7776bO+64g02blncGomFubo6mpqa4+KtK6TmHwyFvLJWLi3I4ub+/n7GxsWVtUqX0HkD9tW/BZAi+OWddXj766wMMzS5i0Km5/2NbKc8IVtV6tWOc7/2tFb1Wze2XbaE4fek1hmYXueLXDXh8ATZk6PjUBp2sECHOdcSl5qrfH8YXCKBRq9nz9TPDvuerfrOPvQNOAtLSPMYl23L51rvC+5+Evq+Vkg1YWlhES9xiseB0OoOM/eJRXYoEESyOt0MQriUOkJSUJFMQwg1KrwWEjK1Wq4151iJcIBTXNHSQT5IkxsfH5UG3eLpBkYyW7HY7nZ2deDwebDbbilWvlQbK15ONNxXW49TrePTRR/ne977Hf//3f3PmmeHX2UhYWFigqalJVuiJ5TsZjrarpMMoh82VSU2oGespN/0Tj8KSouH6MzHqgr9/E/NuLv5FPYl6DT6/xGNX12FOOBbLfr27n9/uGcCo9vOtMyzs2FyDTqcjIElMOT0k61Q455copvt7pvhp/TwatYobz8tmc1E6ZrOZ2cUABwft/O/z3Yw4FtFq1Hzi9AIu3Z5LcsLS+ShnOQ7PaPmvx1qWXZdY4lEkBAIBOjs7mZycxGq14nK5whr7HU+ccjqdtLS0kJycTFlZ2aqVDyVJwu12y2u/UhgFYHZ2Vp4JXGt4PJ4g1kIssxYulyvoXFUq1TITWuV9H68Ur0AgEMDn88nqiuL6ut1ujh49Gpdfznqy8R+IlpYWrrzySi655BKuvvrquLJpJX+1pqYmqKIbbohvNVULoUUuqiIqlYrtP/onC68LOKmApjCL4Isdk3zlT01oNSpSEnU884VgPuGtz3Xxl0OjFNmM3PDWVMZHhrDZbHg8HpxOJzNeDXMYOb0sneRkM0+3zpBq0vGO6qWKw7TTw/t+UY/XFyA9WU9+ipHTilO5Ymdwh2Vu0cfH72+kaXRePt8vvq2YT59ZtOycnzwyzDWPtQHw3feUc8m25d0apWGS0uHUYDAwMzNDbm5uzEE1HqymeqR0DZ+dnQ1qiYuuhTIgiEFpvV4fJIW3lhDzIqEJQTjTpNXID/r9frq7u5mZmVnWdo+GcNUju93OyMgI1dXVDAwMyFWvSLM6QpM+0kD5erLxpsJ6nFJgcHCQXbt2sXPnTq6//vq47uNAIEB3dzezs7PU1tYGbeCUEqlKU79Q2u5K66nL5aKpqUn2LVKr1Vz9QCMvdc0CYFBD47eWx6kH6wf4yXPdeHwBTi1O4Z6PbA56rQ/eU4990Yc/IPGVMzJIcY+SmprKT3bPcGTcQ4lVw7fPziA1xcrPdk/yXPs0kgQf2ZHLl88J5r1/9ZGjvNY9g9Pjw6DVkGLU8fhn6kh8fe5Q2eWYScjiC4+0BT3/yLeWC5dEu+ahEul6vZ6EhARmZ2dJT0+nrKzshFCxRYegvLw8JkPWcK7h0YRRRELg8/lipjHHC0EBDqWH+f3+oO666FoozzWWJEsY7I2OjsalEBkuTrnd7qhxJxQn02zherIRJxYXF7n22mvp7OzkzjvvjFtedWxsjM7OTjIyMuTNcCSzpNVAaJE7HA5qa2uxe+Hy+xqQJHj009uxJi6vUv+/+xtp7LcjqeCKHXlc8/YlnqsYjH733QdQE8Djk/jiVgPVmSYcDgdPDGg5OClxxY48eTjue0+28dTRsaWK0YVVnFu5dH0GZ1w0j87xo793MOvyotdo+PWuU6jJDh70mph38/6765l+PUNSAfdesZnTS6LTApRD3MpOQCTDJLHRtdvtJ4z25HQ6ZW1spcN5pIHzeF3DlR2CEyWh6PP5aGtrY2ZmRh40FF0LZSJ8PBDXSei2x0utEjzwqakpqqurgWNBTii9hA5FCqWsSAPloQPpbwCsJxuRsR6nQuD3+/nhD3/I//3f/3HPPffIRnixYnp6mpaWFtLS0lCpVEEeUUqe/fFUxEVBRhjW3vlSL9Nzbr7xrvKwx73k3v00jcxh0Kr48jkl7Dq1QD7W7NwCP3y6jZd750hLgK9s1ZNuMTFpn+e6VzwkGXV4fBJ//lQdmWYDL7ZP8o2/tqJSwY8vrmF8zkOW2cCOoqVZRI8vwD/aJ/j6o82o1Sq8/5+98w6Pssze/2cmk14nIb2Q3pCasNJkWRuifu262FBWf6vusvau6+Kqu4gVlUUQ17qruK59rYgVSUhCAphGeiA9k0x6pr6/P8LzMpNkkplkIqBzX5eXpMy8T2beec5zzrnPfRslfD3d2HzZLObGHunIiMp3tc6Xh79pB8AN2G+jszHanJ3ZbJYTNtFdt6T5CtbCVNGebCkfirVadgIm6houXqfIyEi5EOpMWM5a+Pv7o9PpJhRTx4J4nRQKxbgms5awTDp0Oh01NTV2C5mMN1DuSjZ+Afjf//7HPffcwyOPPMIpp5xi8/eE4o6oWut0Ojw9PRkYGMDPz4+MjIwpqUp3dnZSXl4uH0Jtob1Xx+nP5KAzDkn/PXNuArE+RnmtnSZPni8aoLnXSHqEP1sun42XuxsHO/q5ZGsemM2gdOPb25bg7qbklreHOLVuSgV3nZ7M+XOsr33Opt0c7BzALEl4KBVcnBXNnYeH+ASq2no5Z1OefPOpFPDDbQvx8zny4bacCRBrnUjVYqqH5oRvRl1dHUFBQRgMBqu1BgUF2SU4MBYMBgNVVVX09fVZDZBPBLaUN7y8vOjo6CAoKMgqcXIWJEmipaWFmpoau13ORTeos7OT9vZ2pk2bRlJSktVrKd7fwMBAq3X39vZSVVVlc6DclWwcV3DFKRvYtWsXN9xwA7feeisXXXSRzd8bjbbr4eGBXq/Hw8NjytT2BL8/MjKS2NhYm595Ta+OUzbswmiScFcpePHiFNQqvewY/lq5mX1tQ/4cz15yArNih5IGs9nMzW8WklPXzdzYADZdPk+eA2zv1aNUwBNfVvFlWTtKBWy4+ATmxx/xBXru6xpe2FmH0SQhAUoF3HJKItcsOpK8iS5HX18f05PSmBZ0pLBh2bEWa/Xy8pK71YGBgXbtpYL2JAajp2JvampqoqqqioCAANkpfKyBc0dhMplkD5b09PRJOc2LQq1IhMRafX196ezsxNvbe0pk7+HI/Ke9yluWa9VoNPj4+JCZmWnXezjeQLkr2fiFoKmpiauvvpoZM2bwwAMPoNfr6e/vl7n2fX19smO02FhENjzWnIWzYDAYZGOfjIwMq/amaN0+93U1/9o71L72coN/XRhFsPqI6sIlL+TT1D2Im1LB5stmy34Z/XoTl2zNp6tfT7CHmSfPiiEhIYHWXj1PfVlNmL8n82IDePLLamZE+vPXc9LxcFPS1DXIm/kNvJJzEIUCTGa4a3kyl2ZHWw2Dv7yrnvVfVMlfz5kG9y8Llwe6nVldt1TRyMjImJSsnq35BV9fX7k9PpxG5yxotVrKy8vtnhex1WGxnLWwfF2Fv0h9ff2k5YRtwdLlPC0tTQ5IllU2oW1uybcVhpmjDeYJukBdXR3x8fFERUXR09NDXV0dM2eOVLwBV7JxnMEVp8ZAV1cX1113HR4eHjz++OOoVCq0Wq1csBmPttvU1ERdXd2IOQtnwWQyUVlZSV9fH5mZmVZ7jqBubfm2mhcLOgDwUcGr50eiDgqUHcMvf2kPdR0DeLgpefDsNJamhGAyS7yws47a9j5+Ozccs6YOdy8f3qtV0DVo4vZTk4gM9GL1q4Xsa+jG3U3JpdnRZEb6c3LaNJkG9X2lhjVv/YjeeGSo5MR4NVuvmIWbxR6r0WjkwoZSqRzRsRZrnYy0rTB8TUtLs9ss1dZzDWcCKBQK/Pz86O/vR5KkKXE4h6FCT1lZmazUOd5heawOi4hTlq+rkFuvqqqyu3DlKCyp0ikpKVaCDKJzJdY7fK3u7u5IkiTPW4wVp8cbKHclG1OMp556iq1bt6JQKJg5cyYvvfQSTU1NrFy5ko6ODubNm8drr72Gh4cHOp2OVatWUVBQQEhICNu2bSM+Pt4p6ygrK+OHH37ghRdeoLa2loCAAB566CHmzp1rt7GPqOxERUVNiWwqQHNzM1VVVYSFhclGboK6dfWHbfTph97m2TEBvGGhP64dMHDupt30DBoJ9HbnpVVziA/xoU9n5G+fVdDWo+OcWREsSwmh4WA9/y1qJjwigisXJeDhpuSM53Jo79XhrlTy2AWZLEke4jrqTWbO3bSbxq5BjCYJDzcFK7OHOhwKhUJWMznrxRL6DEduwcdOcicy0GdEQHIWent7KS0tRa1Wy1zi8SACtthYxHC8rfkFMQcxVbQnS37p8IBkS3nD0Q6L8ObQ6XSkp6dPSUDq7u6WFU3c3d3HnWERsGxZi4RBvP4Gg4HKykq6u7uJiYmReemjwZVsHFdwxakxUFtby/fff88rr7zCvn378Pf354477mDZsmV203YHBgb48ccfCQkJISEhYUrilC3qlp+fH3/4VEN7/9Bhf3a0H29cc+TwlV+n5bb//kj3gJGFiWqe+e1MVEol3xxo588flmMwm5kVHcDzl85i8/Zitua1oVAq+U1qKOsvyKSitY91n1WgVMK+Q92gUHBZdjQ3nZwoXyO3ppPr/r0XvenIrebhpuClSxLxV+jlvd/Hxwe9Xo/ZbGbmzJlTsjeKeTMfHx+7u8yjyeSOxQQQzIipoj1ZzotYenmB7bkQS+dwe7sConA1Vd4couNkNBrx8vKSFdcs1zra+2OvVK5QWrQ1UH48JRvH1CrtQUNDA8888wwlJSV4e3tzySWX8Oabb/Lxxx9zyy23sHLlSq6//npefPFFbrjhBl588UXUajWVlZW8+eab3HXXXWzbts0pa9m2bRv+/v489thjANx44410dXXZZb4m4O/vT3Z2NhUVFezdu9cpVW/Llril/rbgM86ePRtPT0/aegbo07fKj/t1svXg09bv69AZzCgU8JvUEOJDhjbOD/e38FV5OxIQ4uvB2TMj+L7NnferTZgq6uno6uH2s2aTGuZLV78BFPCv3Yd4b28z961IQe3jwdv/L5tb3y7mh+oOdCaJ13cf4mBzG5enKmSZvHtOjeX+T+rl9dzxnYGvr4+hsLCQpKQkwsLCJvU6DYefnx9ZWVnU19eTl5c3giMrqmyWRkSWMrmxsbHjKjeFhYXJ92Nzc7PTnWOVSiXx8fGEhYVRUlICgLe3N729vXKVLSgoiMjIyAlzWD08PJgxYwZarZb9+/czbdo0EhISJkVBs1Q0seQxA7J7rD0VKqVSiVKplOc5TCaTrAbi7u5ORkYGPT097N+/H7PZLNNEXHDBmTiW4tR7773H4OAg9957LwEBAaxZswatVkt0dLTdn1lvb2+ysrKoqamhoKBgxPD4RGBJMeru7pYPlRqNRt5jfHx8MJvNtL/zjfy4GcPm/L460E6f3oyPpxsRAV6yP5OvpwoUQ6efQG93FAoFSTHheBR1YjAacTf10z2gJzrIixevnMOrOQfZU9+FySyxraCB8pZe/n5eBoHe7syfHsgLv03lmjfKERZPepPE5W9UcVqyP+svnGm193d0dLBv374RUuvOgLe3N3PnzqWxsZH8/PwRQ8uiu27ZtVAqlXKcsmfvV6vVzJ8/n5qaGvLz851+WFcoFMTExDBt2jTKysqorq7Gz8+Pvr4+q7mQ5ORku+dChkOlUpGamkpPTw/l5eV2d1LGwmgu56JIp9FoiI6OtotaNTxO6fX6UQ0BzWbzmM81FUn/VOG462w0NDSwYMEC9u7dS0BAAOeddx5/+tOfuPzyy2lubkalUrFr1y7Wrl3LZ599xvLly1m7di0LFy7EaDQSERFBW1vblLxJvb29rFmzhsHBQZ566imHh7na2tqorKy0WxkCRveJsNUSlySJQ4cO0djYSEZGBr9+pgCdhdzgp2tOJC54KKHQG82c/Y9cGroG8XRT8sCZqZx3eP7izfxDbPiqBjelgpVZ0axZlsDGb2p4NWfITOmsFB/Onq4gKTWdPY0D7Chv4+Mfh5KaC2aH8bu5gXR3d3OwrYvH8gY41Dt0mymAK+ZHc4+F3O1oErd6vZ7y8iH1j6niZfb19VFcXIyHhwc+Pj50d3djNBqtHK4nM8gP1tWj2NjYST3XaMob3t7euLm50dXVRUJCgtODHkxMqcPyntVqtTK9LCgoSO5aWFL+JuoeO1b1qK2tjfr6egYHB4mNjR3BF3d1No4ruOKUA9Dr9dx7773s37+fTZs2ERER4dDjtVotZWVl484DWkKSJPkzL6RyhVmaqKxbHiqbm5upqakhLS2NP71XTeGhHvm5Nv72BH5zWHhEbzRz1sYcmnt0KBUKXrt6HrOiA+jTGbnpPz9S3tzLyenTuP3UZHw83Nh7qItqTT9KBRh7tTz+XQvuKhWbL59NXLA3971fxp56Lb06I6AgNUTFqgx3Qr2GTAj1bj6s+k8tpmF33K7bFxPoYx2HjEYjlZWV9Pf3T1k3XpgHC5PE3t7eESa0AQEBk4otwkhPrVaTkJAwqX3RbDZbqS6KBNPd3R2tVktMTAzTp0+fkk6KoAAnJiYSFhZmlwDLcEq0KC6KzoXl2cNkMlFXV0dbWxupqakO0dxMJhNms1mWyhXv189ptvC462xER0dz++23ExcXh7e3N6effjpZWVkEBQXJGWtMTAwNDQ3A0KYfGzuklCRuFI1GY/dh3hH4+fnx8ssv88Ybb7BixQqefvppm6ZhoyE0dEjru6SkBI1GM0L/eriShdC0FgfguLi4MQ/ACoWC2NhYgoODKSkpsUo0ADnRAMiv19LcPYiSoaG4s2YO6WQXHeziH9/UIkmwKFHN9Uun0zVgINTXg1PSQ1H7uLMoUc3dH5XhszOP+38dRpBChwIzRjNs29PMl+XtbL44ld8kJzNnrklWn5KA1/IaiFZ7yUojF84J479FrVbr9PDwYObMmbS2tlJQUOCULoc4AFt2Ldzd3TGbzTIv09la4cOrR/bOiwz34Ojq6rLiBguJQUup2oqKClnRxJmtfdFJCQ8Pp7y8nMbGRlJTU62qfAaDwaprYTAY8PPzIygoiPj4+HHdY4WxkqhQ+fn5kZSUNK6+uGX1SCiBiM1ZVM9mz55NVVUVubm5VrSz46li5MKxh2M5TonZjc8++4zzzjuPBx98kOXLl9v9+KCgILKysigvL5fdood/FgUVdvhnPjAwkKioKNLS0sY8JEVERBAUFERJSYlVogGwNOVIQaOitZfmHj2SBH5eKhIOd99zajupaO1DoYCy5l78vVQ88skBPiluxU2p4JWr5vL8t1rMKOnTG3nju1KunDeNa9PN/Fsv8UU9DJokStsMPNRlZsvls5kRO1Q8/OGOaE5cv9NqTZVtfWRNt042VCoV6enpdHR0UFRU5JQuh2XSZjkTqFKpaGlpITExkejo6Ak//2gQ7AvR8XfE8NVyfsFSeVMIjYymEDlVnZTo6GhCQ0OpqKiQ514sY6EtcZSgoCBiYmLGlXR3c3MjMTGRyMhIysvLaWhosMunSjxWoVDIcUq8p+N1No4nHHfJRmdnJ++//z41NTUEBQVx8cUX88knn4z4PcthIVs/mypceumlLFiwgFWrVnHqqady66232p19enp6MmfOHA4ePEheXh6xsbFy687SMXratGkkJiZOyNDF19eXrKwsor/7lobDXYUlidZdmFdzDyLm4ZJCfXF3G7rh9xzsYsBgRqVUMKA3o1Iq+dO2vVS09eKmgA1nx7D+81Laew0oge3lGn4dqyLm5OlszW2mrUdH54CJtV8c4m/n+hMf4sMb12Rx1sYc+XrrPq9iQUIwqeF+PHTODObEqtlR3safz0yzWmNYWBhBQUGUl5fT0tLiUJdjOG1HGBEFBgaOOAALjmxHRwcpKSlO5Ui6ubmRnJwsz4sI7XnL+8WW8kZgYCDh4eGkpIwuFSng7u5OZmamTHuaCuUtb29v5syZQ2trK/n5+XI1TQRDUQmKi4ubcCfK39+frKwsmpqayM/Pt3vuxTLpMBqNMr1KoVDIiYzg3np6epKamnqsGSW5cJzheIhTy5cvZ+7cuaxevZodO3bw17/+1a6DEQztKSeccIL8WYyJiZG7q5bGfkFBQUyfPn1Cn3kvLy/mzp3L9G+/pa57KDj4e2A1lP1WQSMms4QCiA/2ko33mrSDdA8Y8FApWZg4VEAoPNiFSZKQTBK7yw8yR61nB2Y8lfBNXT8fV9azZkkUj1w2kxn5DTz9ZTWDRjO9ehOXv7yH205J5HeLpuPv5UHR3YuZs24o4VApYF6c7cH54OBgsrOzqayspLCw0KEuh4j9lkmb6FoMPwCLjr9GoyE9Pd2pHX+FQsH06dMJCwujtLRU7mSPJjoj4lRfX59MibbnvOLm5kZKSorcSQkMDByhLjhZCHpeR0cHe/fuxcfHB3d39xHiKFFRURPuRIlY2NbWRmFhoTyPOxFqlTAG/DnguEs2tm/fTkJCgjxQdMEFF/DDDz+g1WoxGo2oVCoOHTpEVFQUMFQ9OnjwIDExMXLmam9WPhkkJCSwY8cO1q5d+tyV2wAAIABJREFUy7nnnsuWLVvkNY0GS8MkUbEAqKysJCQkhJSUlAlzF0eDUqnki1uX8UlhLdrWQyw9IRxJklAoFOhNZr6v7JB/99T0oepa96CBf+cdQm8yY5YUXHJCABUVFdS2dWEwSJiVCjr6DZyYNI363jYASvp9Kdir57dJTZyZGsjrhRoGjWb2NXRz1SuF/Gv1PGLV3jx54Qnc+J8f5Wte8fIedt+1FIAL50Zx4dzRXzt7uhxjbYLBwcEkJCSMuQkKjmxTUxN5eXkO0dzshZ+fn1w9ys3NJSwsTOY0W6pZhIeHT1jRJCgoiPnz51NXV0deXh5paWmTVpcRBwzLoXM/Pz90Oh16vZ7MzEynKtgoFAqioqIIDQ2lqqpKrlDZUwUT4gOdnZ20trYSGhoqV45EAi6SpTlz5vwk+4QLP08cL3EqLCyMjz76iA0bNrBixQo2bdpEWlqazd8X1BJR/BCHtJqaGgICAkhKShq3U+kIFAoFH9+0lL/9r5TW9nZu/nWsHKckSeLTkqGutwSyR0afzsjm7+vw9XDDJMH/pfhSVHKAUNUgh0xG4tXu+LiZ+bpNwd1npOKmVLLus0qUSjP/zGlEaTZyxdIh9cSHPzmAWQJJgse3V/NlaRuvrZ6Hh4eHQy7h9nQ5RqNEOzITKGJhW1sbBQUFxMfHExERMSXzIs3NzezevVu+v0XBTnTXExMT7RLIGQ2ik3Lo0CHy8vJITk6edLy1NXQuvp+enu70mB4aGkpwcDC1tbUOxVuFQoHBYKCzs5O2tja8vb1/Fh2O425mIzc3l9/97nfk5eXh7e3N1VdfTXZ2Nt9++y0XXnihPHg3a9Ys/vCHP7Bx40b279/P888/z5tvvsk777zDW2+99ZOu+euvv+amm27ivvvu4+yzzwaGquWWw3FGo9GKZykqFkLDe2BggMzMTLsrT47AZDJRXl4uHw7fLGhmnYXs7I6bFuAl6fiypJEHv2xBoRiSyX3tohh2NUlsymlGoRia34gI8GBbQSML4tX4eKp4OecgJpPEnBh/bpjlTmHTIFuK+hg4PGXn4+HGe9fNJ0btzWUv5lPUcKRlfv2SWG48OXnEem1Br9dTWlqKJElMmzZNTt7EsLF4bSe6CcJQR6SsrAw3N7dR6QOOYrRN0N3dHZ1Oh7e3t9MHyAX6+/utjPTs+TuGSxBa0rfEvIVlNUhIHNpLe5oIRBVMHHQsu06WHSFLB1kxFyL8SIbPc5hMJjw8PI4blQ8XXHHKGSgqKuKaa67h2muvZdWqVXJybik2ItT2LGct3NzcMJvN1NbWotFopkwydbi7eX2XkfO35Ms///fquSQGKsmpaOa2jxswSxDspWDT/0XxXH43exr7cXdT8tRFM7jr3VJ6Bg24q5Q899uZ3P7fErQDBiRJwk0Bi6JUPHzhXHIP9XHr28WYh91h71+XRUr4xHwixCxHb28vERERMiXWciZQmPtN9IApqLM6nY6MjIxJz4tYupxrtVpZ1t9gMODm5jZl8v06nc5qPtPe849gLgyXnxWvrWXBbmBgwMrUcCrOWMJE1sPDg5SUFKuuk2UxVMyxWPqbiNd1NNWq42lm47hLNgD+8pe/sG3bNlQqFXPnzmXr1q00NDTIkoLJyck0NjbKLSlBT4mNjcXb25vW1lbi4+N56623UKvVSJLETTfdxMcff4yPjw8vv/yy3Xby9qC3t5cdO3Zwzz33oFQq6e/v54knniApKcnKI2AstLe3U1FRMUImzploa2ujoqKC67/UyYobAC8t98Hf35+/ft9FccsgEnDBnEj++n/pnLphF/16I0qFgofPSefOd0uQpKE5jwfPTucvH5VjNJsxmMx4u6t49MxYvvmxnv9UmBAjI14qBR//cQH+3irmr/vOak3jVY/EgVIEw/7+fjlIxsTEEBcXNyWH3JaWFqqrq0lMTHRolmP4Yd2WI6ulyZ29A22OQpIkmpubqa2tHdU3w9YwnyMShJIkyTr9U+XNIa5RU1NDSEiILJtp2RGy5SArWtaAlRrIePrnRwGuZMM2XHHKCRgcHGTnzp3cd999dHd3o9PpuPfee8nOzpY/7+MdxMTweFxc3JRIe1teY3OxRG7DoPz9rad5EeDvz7N7+smt7wOFgovmRXL/ijQu+2cB5S29eLm78cg56fz9swrae/V4qJScNSOcyEBPWnoGeXtP82GDWwj3UfDceYloJF/+37/2jbjJXrtqNlnT7es+WR7WRddCxKnw8HASEhKm5JArDOhiYmIc8psQMzciVlnSjIe7nIuzyVR5WgCyb0Z0dPQIi4DR5i0Fc0HEKnsKR0KWPiYmZkpsCCz9P8SahneEgoKCRmWwWMYpkXDY49NxFPDzSjYcgclkIjo6mtzcXDZu3EhwcDB3330369ato7Ozk0cffZSPP/6YZ599lo8//pjc3FxuuukmcnNznXJ9SZI466yzSEpK4le/+hU1NTV88MEHbNq0yabGvy3o9XqKi4vx9vYel6dvLyzlB7VaLdVtXfw558jb7qVSsOfeZXT26/nNUz+gN0koFbDlstkEeruz6pU96IxmQnw92HL5LK59fS96o4Snu5L0MF8Cfdw52DlAaVMPJgmCfdy5/4wkvtxbw4eVOovrKPnm1sUsfuw7q0Qn766ThiQMGdvcZ/hhXa/XU1ZWhkKhmDLFKr1ez4EDBzCZTKSnp48IFrYO65ZmhONtguIaRqNRHvx2NkQVbGBggLCwMLnSJjZBsWFPhsYnrjE4OEhaWtqkXM7hyKCkqAaJgX6j0QjgkAGZpWqV2MA9PT2Pm03cBVeccgauvPJKfHx8OPHEE9Fqtbz66qs88cQTLFy40KHnMRqNlJWVIUkS6enpTin2DC8q9fX1ce12ndXvlDzwG4xmM4sf+54enQmlAp69ZCbubgrueLeEAb2JxUnBnJ4ZynNf1RDo7Y7aR0XhwR7clHDLKUl8X6nh6woNkjR0U/m6w30L/UlMjGfly/utrhfgqSTnrl+Pul5LIZexDus/hWKVME3s7e0lIyNjRNdptMO65cyNMKIbC0IxsKenh4yMjEnv77b+jqqqKjo7O4mKipK7F6IjJIpKk2EuiCF1rVZrZSo7UQjzRBGnxAyjJEkYDAbS0tIcom8NV606nuLUzz7Z+Pzzz3nwwQfZuXMnaWlpfP3110RGRtLU1MSyZcsoLy/nuuuuY9myZVx66aUAVr83FSguLmb16tVceuml/P73v3fog2EpX5uZmemQYsN4+tuBgYF8UNzOgx9Xyo85LzOYv100m83f17JhRw0wZGaUd/dS7nynhK8OtKNUKLhobiQ5tZ209uhxUyoI8/OgtmMAD5WSlVlRvL77EAMGM24KQKHgqQszeaegjq+re+VrqX1UvHTlbM7bXCB/77s/zpKDjOVgtOgIjXdYFx2IqfDlEBCSxVFRUXh6elp1LSwDzGQO66JCNVplZyKwPKyLGSFRaZs2bRrJyclTUmkTLufTpk0jPj7e7oRZ8NjFenU6nVWAseSJd3d3U15eLnOH7aVDmUwmSktLycnJITk5mTPPPHPCf+cUwJVs2IYrTk0B6uvrWbVqFUuWLOHOO+90mFYouqaOul0Pd7fu6ekZUVTqM7ux7Kld8mO8VVBw728oqO9k1ctFSAzFqa9uWcTm7+p4M78BlZuCU9ND2VnVgcFkRqlQMjvan9112sM0KYnZ0YGofdz5rLRNfm4FcGW6G8tnx3H5thr5+6emhvDMylkyFdayqGRZWR8u5T0aOjo6OHDgwJT4cgiIjlBYWBh+fn7y62t5WBe0nYlev6uri7KyMkJDQ4mPj5/0QdjyzCIO6zBUhAsMDCQ1NXVKaMaWFGB7jRNhbOquMM4Vr0lfXx/l5eV4eXmRnJxsd0FUJES7du0iMDBQ3g+OEfxyk43f/e53zJs3jzVr1hAUFIRWq5V/plar6ezs5Oyzz+buu+9myZIlAJxyyik8+uijZGdnT9m6BgcHuf3226mrq2Pjxo0ODyf19vZSXFwsezSMtjkMV7IYz90awGgyM/eRbzAxpLLxzxX+qNVqLn+7gW7dUBsvOdSH166ex+kbdtGrN6FQwNMXzeDOd0txUypQKhTEh3hT2daH0SwhAWlhvtS099F/2BFcqYCHzk7j6R3VtPUZ5Ov/v/khLIpU8EN1F9mRKkLVRyg7Ex2Mnooux/Ch897eXoxGoyx/N23aNKfTt0RlR/hNOMKRHe50bilFaXlYF9zr9vZ20tLSHPaKsQdms5mDBw/S1NQ0qjeHrdkQy0rbeBVAS4daW/StgYEB8vPzycnJIScnh0OHDpGamsqiRYs4++yzxxyUPQpwJRu24YpTUwSj0cgjjzzCjh072LJliyzPay8GBgYoLi6WVfZGO3wKE1rLQsJY7tYAgwYj8/5+hHJ7W7YnixLUPL1Hx3dVnQCE+rnzxU2LOPXpXXT061EoFDy/ciZ//qgcbb8BHw83ooI86R4w0tarR5IkzBKcEOVPcqgvb+1psrpmfICS2+Z78Wi+gRmhnlyfFWBVVJpsB3gquhzDjWh7enowGo1IkiTvi87u+pvNZurq6mhtbR1hjDseBNPCUnBktDOLJEkcPHiQxsZGu/2dHIU9FGBbsyFjUXeHX6O1tZXq6mqbNDS9Xk9RURG7du0iNzeXqqoq4uPjWbRoEcuXL3cqldIJ+GUmG3q9nqioKIqLiwkPD7e5iZ911lncc889Vpv4+vXrbVrEOxMffPAB999/P+vWrWPZsmUOPdZsNlNRUUFfXx8ZGRlWyYVohVpWgyZSqZYkiZLySi5+65D8vdULYkgJ9+O+98uQAG93JWfNDOejfc3oTRJJ03xo7tZhNEvojGY83ZQYzBKZkf6UNfegt3BEWjXLn1f3Weuof79mtl3zAI5iMl0OW1K5YhMUrVtRoXKUI+sIuru7KSsrIyQkZFTnbsvKoNA3d3Nzszqsj3cv9PX1UVZWhq+vr0OVHUcwODgoD/9FRUUxMDAgDx86OhtiC3q9nsrKSp5//nnOP/98urq6yMnJIS8vD4PBQFZWFosWLWLJkiWTdkGfYriSDdtwxakpxnfffceaNWu48847Of/88x16rCRJcgEjMzMTs9k8wifCsrtu7yE7r66Dp7+s4bLsKM6cGUFDQwNn/LNCngVckqTm5pOTuGRrPmYJ/DzdOCVtGl+UtiFJEKP24qB2EKUCogK9ONg5iM5oRgGofdw5NS2EtwqbR1z3ifkSMTGhREdH29VddxST6XKMJpU72tC5iCGhoaFOl0EXEDFECIQMf53GKiqJ9Y53L4gYolQqp5QuXVlZycDAAHFxcTJFrqenx8qM1t7ZkNFgNBqpqanhhRdeYNmyZSgUCnJycsjNzaWnp4fZs2ezePFilixZQlpa2nEZp37Wycb777/Pxo0b+fzzzwGOmfb0cDQ2NnLVVVcxZ84c7r///nEr4sPN/TQaDQMDA7I06mRbocOx5LFv6Bg44gD432vncds7pdR2DACwID6QyrZ+NP0GfNzd8PV0QztgRKVU4OGmpGfQgEkaugtDvJVoBsxWN9Y0HxXt/Ub563kRHrx67cIp+UDZ0+UYTXlDbCpiIxzrPRqPI+sMCOfulpYWkpOT5aHo8Vq3jsCysuPMIXVLkz+xXoPBQEhIyKQkEy0hFNZ27dpFTk4ONTU1VFVVkZiYyB133MFvfvMbgoKCjicN8+NmoUcBrjj1E6Czs5Pf//73+Pn5sX79ert4+ZbmfhqNhr6+Pvz9/WXTvrFMaB1Fc9cAJ2/Ikb++fkkc7X0G3i4c6lBkRvhikuBAax9eKiVxwd7UagYwS0NziO5uSvQGE/rDoU4BzA5VUtRm7X7r7Q7/PDNY9uiZikKM0Wi0UqEc7dBtafAn5tYspXKDgoLGPHxbdrHtNZR1FJYd5qSkJDw8PEbIz4o4NVoHy14IKrMzh9SHU3f7+/sxGo0EBASQmJhIYGDgpK8jFNZE16KsrIy6ujqmTZvGbbfdxhlnnEFoaOjPIk79rJONlStXsnz5clavXg3AHXfcQUhIiDx419HRwfr16/nf//7Hc889Jw/e3XjjjezevfsnXavZbGb9+vV8+OGHbN68mcTERPlnlkPcw839RJtZkiRKSkpkUzJndgUy//qV1debTvPnhi+OdCMumRfJu3ubMZokvNyVmCUz+sO5g+WdJ7ZsXw83+vQmm9fLivJkzUw3MjIyJj2gZQuWXY7AwEAr59CxlDccgZhPiIiIIC4uzikbhmWiOVwqLy4ujuDg4HFbt47Cckg9LS3NIY6srS6LSNxEQDSZTNTU1NDR0TEh+lZ/fz8FBQVWlKi0tDS5azF37lyUSiUvvPACtbW1rFu3ztGX4WjjuIk2RwGuOPUTQZIkXnzxRTZt2sRzzz3H7Nmz5Z9Zzi4M9zMSHXalUkl5eTkmk4mMjAynUk3n//1r+gxHboV1S7x5aLeOvsPZw//NDOPLsnZZ5MTDTYHeKA0lG0owmsBdCfrDvhoCcWov6juPqF55KKHwvmVyISYtLW3KPFEsuxyhoaEjaNHDZy0mkrgJQ1m1Wm2T6jYRWCpadXZ2yrLucXFxhISEONU3DKyH1B2lGdtL3RVFPmFq6Ch9S6fTUVRUJMepqqoqEhISWLRoEYsXL+ZXv/oVnp6evPXWW2zfvp0XXnjB0ZfhaOOXl2z09/cTGxtLdXW1fHDRaDRccskl1NfXExcXx3/+8x+USiXXXHMNO3bsoK+vj9jYWLZs2cK6deuora39SaUHAXJycli9ejVLlixBo9Fw3nnnERcXZ0WHsjW7IKoIDQ0NDg+Pj4V5j3zF4OHcINxXxYlx/nxQ2in/fEa4N8UtQ10OXxX0G4dmMiQJpMP/d1OABXsKpQIr7XKVAowS+KgU/HDnSRh0g5SUlBASEuKUQTMBS+Okjo4OOjo6UCqVREREEBwcTFBQkFMDoKhcdHZ2kpGR4bAW+WiGhMMpRkqlksbGRurr66dUGlkEPjEnNNp7YukbotVqGRgYkPnXono11nvZ29tLeXk5Pj4+Nv0/BM9VdC3y8vIwGo1WlChn3jPHCFzJhm244tRPHKfKyspYuXIls2bNQq/Xc9JJJ8kxx9LHxtZhUhR7nHlQH14Ue+PKDC59rVT+ekVGMJ+WdgypTKlgwAhieaLzLjEyNg3H29fOIzNq6L0aHByKU4Ju6qwinxiMFgd1jUaDJElEREQQEhIyYVr0WNcTh+iJmL1azoaILstobAAhLzt9+vQpk0YWFDFh2DvaezIae8ER6q6gbwmGxGjvhSRJdHZ2yolFbm4uvb29xxMlaiL45SUb9uKqq67ipJNO4tprr0Wv19Pf38/f/va3oyI9eOmll1JaWkpSUhJtbW2Eh4fz2GOPOTxf0NfXJ/N/nVFRH9AbueOdYsIDvbjrlATmrNtp9XOxSQMoD/9bGvZve3DTsniuW5ogf23Z5p2oaZBer7eqVoymvCEGtKZSsaqnp4fS0lJZhcnWBmO5XtFlGU+DW8DSACk9PX1K+Ksmk0k27xJyv5atZrPZLB84hpsn2Qvh/1FZWUlNTQ2XXnopFRUVcnJRXFxMcHAwCxcuZMmSJSxcuNApLe1jHD/rP26ScMWpnzBO3XzzzXz11VfEx8fLngbr16+36sbbg8HBQYqLi2VDzskeuh774gAv7WoA4LT0YJRmE58d6Dr8U4kgDwVa/dBX7sqhhMIsHSmMmUd/WisU3XsSHqPMHhw6dIiGhgYyMjImJKohBDzEXjqamItWq51yxar+/n5KS0tlFSZbB257BUdsPVbMQEyVaa2lCElqaqr8+ok1G41Gp7AXBH2ruLiYVatWUVdXJ1Oi9u7di4+PDwsWLGDJkiUsXryYadOm/WLj1C862eju7mb27NlUV1db3QBHizPb1dVltVG9/vrrPPHEE2zYsMFhxRGz2SzPDUxU2cKyCyCGzqu0Zh7ZfUTfXAUYbT+FQ/BxV5B/z7IR3+/p6aGkpGRcOtJw5Q3hvWA5fGjrAG45y+EsbfjR1ldXV0dbWxvp6en4+/uP4NyK9dqrbz4apqp6ZFlta29vp6OjA5VKJXeFJrre4ejv7yc/P5+dO3fy+eefU1lZSXZ2NsuXL+ekk05izpw5U/L+HOP4WUeoScIVp37iOBUQECCv5ZNPPuGuu+7i4Ycf5tRTT3XouSRJkpWLZsyYMSF/Bst9qVXTSW9vD94e7lz1Se/4D7aAZdHMFtb8ejp/+PXoSVV/fz8lJSVjKm9ZrtdSztVySD4oKMhm10LMcgwODjrFGdzW+hoaGmR1PrVaLXtFCMn80aiwjqKzs5Py8vIxO+UTXb+g7mo0Gtrb21EqlYSHh8vsBWcU4nQ6HYWFhfzwww98/vnnFBcXM2PGDM4880wWL17M/Pnzp+T9OcZhM045f7LpOEJ1dTWhoaGsXr2avXv3kpWVxYYNG2hpaZE35sjISFpbWwFoaGiwkv6LiYmhoaHBaZv48IrIFVdcwcKFC7nqqqtYvnw5t9xyi90fSKVSSWpqKhqNhsLCQruq9pbDfJZdgMDAQKZPn46vry8NRY1AhfwYZyUaACsyR1+fv78/8+fPp6qqioKCAjIzM/Hx8RlTeUOs197Xy8PDg1mzZtHS0kJ+fv6UdDlEl2JwcJCCggIUCgXBwcGo1WqH1zsWwsLCCA4OpqKigqampgkPqZtMJqvZEJ1OJ1fbEhMTmTlzJs3NzdTX10+Yfiac0odTorKzs1m0aBGrV6+mpaWFW2+9lVmzZjF//nyHr+GCC8czjvU4tWLFCubNm8dVV13Fjh07WLt2rd2HOYVCQXx8PMHBwezfv9+uqr2lwMRwSfek+Dj8/f3p0RnhkyMd+LESCfEzezLWMzLDbf7Mx8eHrKws6urqyMvLk2llo3kE+fr6EhgYSExMjEMCHiqVioyMDDo6OigqKpqSLofZbMbHx4fg4GD27duHJEmo1WrUajXR0dGkp6c7JU6p1Wrmz59PTU0N+fn5Ex5SH4u6GxsbS2ZmJhqNhqqqKrs8T0aDJEl0dHSQm5srdy76+vqYM2cOixcvZsuWLZhMJm688UZiY2M56aSTHL7Gzx2/6GTDaDSyZ88enn32WU488URuuummMQdHR+sCTXVLLCkpia+++ooHHniA8847j82bNzsUNEJCQsjKyqK0tFT2TnBzc7OpuCQqFdOnTx81YFwwN5onvqhCqzMT4uuGps/2oLcjuOf0RK5cMN3mzxUKBREREUiSRG5uLm5ublZa7HFxcU6pVoSHh6NWqykrK6OlpWXCXY7hg9E9PT2ygaLgkra2ttLY2EhcXJzTlUBEUNJqtezbt0/uCo0VJMSAnBjkBmTN8KioqFGrNNHR0YSGhlJeXk5TUxNpaWljVnNMJhNlZWUjKFGLFi3izDPP5KGHHhpBiUpMTOS7777DZHLOveaCC8cTjoc4FR4ezscff8xTTz3FihUreP7550lJSbH78QEBAcyfP5/y8nKZNuvu7j5qt9pScSk2NnZ0vvywr92wXRizty323EUZJIaO33kJDQ3FbDZTUFCAUqmUxVyCgoKIjo52SrU7ODiY7OxsKioqaG1tnXCXY7jgyPB9PzY2ls7OTurq6oiOjna675KbmxvJycn09PRQVlYmF7LGmpewHDwfTt0VJn/D73dRgKusrKSpqYn09PQxu2hms5mqqiorSpSvry8LFixg6dKl3H333aNSorZv387g4KCNZ/1l4xdNo2pubmbBggXU1tYCQ1ri69ato7Ky8piUHvzyyy+55ZZbeOCBBxx2Nx4YGKCmpobW1lZ5U5osZ/GCTbmUtfU79Bhb+PfqucyJPTKUNpwTKpQ3BH+1ubl5TFlAZ8ARXw5L51ChGGbPYLTgyPr6+pKSkuJ0bxEY2jhramrkOYuAgACZIieSC6EcYylD6Kiko3A59/T0ZObMmahUKvr6+igoKJCTi8bGRtLT0+VB7l8oJWoicNGobMMVp46hOLVnzx6uvfZarrvuOq644gqHfSLq6upoaGiQ93Wx7zuquPTYZwd4Pb+BBdMD+a66a/wHjINrF8Vy66nJVt8TXQvxn6XseEBAABqNhs7OTjIzMydEEbMHjvhyWAqOCCVDewajxTygYExMxTygpVFfamoqwcHBVhQ5Z1GNu7q6KC8vlwty3t7eMiVKDHNXV1eTmJgox6n58+c7dSD/ZwzXzIYtnHTSSWzdupW0tDTWrl1LX18fwDErPdje3s7vfvc7oqOjeeSRR0Y9aFtKEIoNRUgQenl5cejQIcLDw5k+ffqkKl5rPyzjrcKm8X/RDuy8KYu+nm4royfLDXC0D7pGo+HAgQPEx8cTERExJdU7McshTIPExjZcJk+SpBFmRPaux5IjO1VuqAaDgebmZmpqalAoFKhUKqtBbmd4WwhfjieeeIIvvvgCLy8vvL29ycrKktU34uPjf+4DclMF14tmG644dYzFqd7eXv70pz/R19fHhg0bRq2IW3bXLY1oheJiU1MTgYGBkx4ef+ijUt7YM9Kcz1F8d+tCvBQmq261PSZ03d3dlJaWyrMJU7H/2ZrlGN4FcERwZDSIAlxiYiLh4bYpZROFyWSSBVtMJhMqlUoWdHGWL4skSWg0GjZt2sQbb7yBv78/KpVKpkQtWbKE1NTUn5tK1E8FV7JhC0VFRbLCR2JiIi+99BJms3mE9KDIstesWcOnn36Kj48PL730ksOD286AJEls3LiRV199lY0bN+Lu7k5PTw9+fn7ywdfS3Xr4hiJahN3d3cyYMWNSnYEb3ixiT10XPTp7tDxGx53zlMyLU1spb9j7QTcYDLIHREZGxpRUXMRgd319PT4+PhiNRqvWuLNcZAcHBykrK8PDw4OUlJQJV/wlSZLduIc7iAcGBtLf309LS8ukE5vhlKgff/yRadOmsWjRIqKjo3n99ddZvHgxjz76qCvBmDxcL6BtuOLUMRinAN58803lZst0AAAgAElEQVTWrVvHU089RXh4OM3NzQQHB8sHX9FdH82I1hnD4wC7azq4+rW9k/o7PBSw6VQvq261v7+/3Z1ok8kke0BkZmZOiQKTKFpVV1fj5eWFyWSyokY7S8DDYDDIXilCkXCisEXdDQwMRK/X09jYOGlDWSGWI7oWghK1cOFCUlJSePvtt4mJiWHz5s2uLvvk4Uo2Jov4+Hh5c1GpVOTn59PR0cFvf/vbn1znvLCwkC+++ILPPvuMgoICEhMTuf766znzzDMdOvh2dHRQXl4+6SpFg7af056ZuLTiztsXo/aZXJIgJOicMdgtBs9FNUjI5Pn5+aHRaHB3d58yxSoh+1pbW2u3Z4bZbLaicAmTP0sH8eFBcXBwkNLSUjw8PEhNTbXLtV4Y5w2nRIlq0OzZs62ex2w2k5OTw6JFiyb2YrhgCVeyYRuuOHUYx1KcKi8v55NPPmH79u18//33xMbGcuWVV7Jy5UqHDr7d3d2UlJQQExMzYXfo94qaeKewkfyD3Q4/FuDEuAD+edW8SRdNhAKTMwa7h8vlCoEUIfNqMpmmlGbc3t5OZWUlcXFxdqkeToS6a2kom56ebtffMjg4aEWJqqmpISkpSTbOG06JkiSJnTt3smTJkom9EC5YwpVsTBbx8fHk5+czbdo0+Xt33nnnUdE5f+edd+jv72fhwoVERkZy22230djYyHPPPedwpdpgMFBSUoJKpSItLW3CFfpb/7OPT0s1Dj8uxNuN7+5YOqFrDoctytNYGI0TqlKprGT9hj+PI7Mck/1bhGmQZcfGlheHrU6WLQglqJqaGhISEggPD5cfZ6kStWvXLvLy8jCbzS5K1NGD64W2DVecOoxjKU5t376d+vp6Fi5cSFJSEg8//DDfffcdmzdvJiYmxqHnMplMHDhwAJ1OR2Zm5oQ62JIkMeOhrx1+HEDJA7+Z0ONGgyXlKTMz067OwFiCI2LfH/48jsxyTPZvGc0zw5YXx0Sou4IyHRMTQ0xMjFWc0mg0cmKxe/du+vv7mTt3rjxvkZKS4qJE/XRwJRuTxWib+NHSOR8N7777LmvXruXRRx9l6VLHDu+SJNHY2MjBgwcdMiUavgFuP9DO8/scUwzacFEmp40hJzgRiNmE1NTUEcnXcBlCIZtoaUZkz8Y0kcRmImhpaaGyshK1Wi1XhsZLhhyFwWDgk08+YcOGDaxYsYLS0lKKi4tlSpQwzrPU1nfhJ4frhbcNV5w6jGM9Tn3zzTfceOON3HPPPZxzzjkOP154CI22t9uCUFzSarV8UNTAP/J6MDh4XWcmGwLiAJ2QkEBERITVzywFR7RardXgeWBgoE3BkeH4KXw5xN9SXl4ur8ueZMhRmEwmdu/ezR133MG5555LdXU1+/btw8/PTzZ4XbRoESEhIa44dfTgSjYmi4SEBNRqNQqFguuuu47f//73BAUFodVq5d9Rq9V0dnZy9tlnc/fdd8ttuVNOOYVHH310ynmzhw4d4qqrriI7O5t7773X4UNof3+/fMgcrWo9lvKGmLX4X3Erd79XZvc1p2ITh6HBuOLiYtzd3VGr1fT09NDd3S0P9Ik1T3bzFV0OeylP40F4W4iEaHBwUFbLUCqVzJgxY0KeGZYQSWJ+fr5cEWpsbCQ+Pp7CwkIuv/xy/vrXv7rUN44tuKKnbbji1GEcD3Gqo6ODa6+9luDgYNatW+fwfib2duFyPfzQLQ7qYg8dGBiwUlwaVHhy6rP2D8z7uEH+fVMTp8T8g8FgIDQ0lN7e3hGzC44KjowGoRRoL+VpPIxG3fXy8sJoNGIymZgxY4ZT5NwFJUpQd2tra0lKSqKoqIjTTz+dJ5980umy8S5MCi5Tv8li586dREVF0draymmnnUZ6errN3z0aOucwZN70+eefs27dOs466yy2bNlCfHy83Y8XpkTV1dUUFBSQlJTEwMCA7BpqqbwRGRk56gZ4zqxIOnr1rN9ePe71pnkMGVbFx8c7pc05PMjodDr0ej2dnZ0kJCSQmprqdGnZ4b4cjnY5LDXOh6taCY6qeI01Gg379u1zuC0u5kAEJSo/Px+z2Ux2djaLFy9m9erVsjJZf38/jzzyCBqNhqioqAm9Ji644MLRwfEQp4KDg/nvf//Lli1bOOOMM9i4cSMzZ860+/Genp7MnTuX+vp68vLySE5Olo3+uru7kSRJPqjb8l349A+/4ox/2JdwnJfsRnl5OcnJyU6JH6N5XCkUCqqqqoiNjWXevHlOERyxREhICIGBgVRUVNDS0uJwl2Ms6m5ycrIVdberq4uSkhKHFS9Ho0QNDAzIlKgnn3xSTi4NBgNPPvkktbW1Dt07Lhw9uDobE8DatWvx8/PjhRdeOGba08ORk5PD9ddfz80338wll1wy7u8bjUarg3pfXx96vZ7Q0FBiYmIICAhwaKP9z54G/vLRgcNfSSPvJIWC7X+az0BHCx0dHROq2I92ULecXRBBZmBggJKSEvz9/UlKSpoSLwsYv8shSZLVgFxvb68sSSzWPF6QMRqNVFZW0t/fP4IjK2AymSgpKZGrQSUlJYSGhsqUqAULFrgoUccfXG+Wbbji1Cg4HuJUSUkJV199NStXruS6664bd0+ydIsWcrl6vZ6goCDi4uLs2kMFypp7uGBL/sgfiDPR4bXsv28pjY2NNDY2yq7gjmC0g7ql7Lg4qOv1ekpLSyc9PzkexutyiDlGy/kQR6m7wtupo6ODjIwM/Pz8Rv2diooKObmwpESddNJJLFq0iODgYFecOr7golFNBn19fbJLZV9fH6eddhoPPPAAX3755TGrcw5DFYYbbrgBlUrF448/Ln/ghTSqpWuoQqEY0bY1Go2Ulpbi5uY2oc3v3cIG7vvwwJHNexhK/nKyvM7S0tIx1UbGUrIQnYCx1icMg5qamsjIyCAgIMChv8VeWM5yJCUl0d/fL69ZGBNaaoZPdCPt7OyksLCQwsJCrr32Wiv1jebm5hEqUVMVuI4nmEwmsrOziY6O5qOPPqKmpoaVK1fS0dHBvHnzeO211/Dw8ECn07Fq1SoKCgoICQlh27ZtDnUIpwiuiGsbrjjF8RunBgcHufPOO6murmbjxo1WhZrR/IyGy7qLQ6sYuHZkeLxRO8Cpz+Qc+cbwWKWAkgeG4lRfXx/FxcWEhobaFMcQBSURWy1N6Ow5qFuqEaalpREcHGz33+IILGc5UlNTrfw4BgcH8fX1nZAU/XD09PTw448/8tVXX/GHP/yBkpISOU4JxUWhEpWdne2i7vLzjVOuZMMOVFdXc/755wNDH9LLLruM++67D41GM0LnPDAwkKysLLRarSw/qFKp0Ov1R+VGkSSJF154gccff5ylS5dy8OBB7rjjjhHu1raq/cKorb6+3qHhcYHn38nlmf19I39gNlPy4Knyl8PVRgCr2QUhPzsZx3MYChglJSUEBweTkJDgNJUKyyFErVaLRqNBp9MREhJCZGQkQUFBTtlILSlR33//PYWFhVRUVLB8+XLOPvtslixZQlxcnKsaNAqefPJJ8vPz6e7u5qOPPuKSSy7hggsuYOXKlVx//fXMnj2bG264gX/84x/s27eP559/njfffJN3332Xbdu2He3lu95Q23DFKY7vOAVDIid33HEHy5Yto7a2lptvvlkuzNhTUBLy544MjwN0DehZ+NjO0YtikkTJ2lPkL81mM9XV1Wi1WmbMmIG7u/uogiNizRM1oRscHKSkpAQfHx9SUlKc2o23ZARoNBoGBgYICgoiKirKKfMhMBSn2tvbycnJYdeuXRQUFPDjjz+ydOlSzj33XJYsWTLqvI0LP9845Uo2nIxj6Uaprq7msssuQ5IkUlJSqKmpYenSpdxzzz0OV7rF8HhISAgJCQn2b0YDA9x65UN8OvNUq29f8/W/uO2rFwFr+dmWlha0Wi3e3t6EhobKm7YzzfqESV9bWxuZmZmjtnjteQ7Rzhcu7WIIUSRwwvRuMopV9lCiysvLWbNmDVu3bmXWrFkOX+OXACGecN999/Hkk0/y4YcfEhoaSnNzMyqVil27drF27Vo+++wzli9fztq1a1m4cCFGo5GIiAja2tqOdgLnSjZswxWnHMSxFKe6uro4++yzGRgYIC0tjbq6OmbOnMnDDz/ssPmdTqeTD+mOzFgMGgzM+9u3DP+YeWtbKXj0AvDwsDJLbW1tRaPR4OnpKccpZxWUBIRJ36FDh0hPTycoKGhCzzEedReYtGKV2WzmwIEDVpSogIAAK5WotrY2rr/+eu677z5OO+00h6/xS8DPOU65kg0n4li7UQwGg1xpgSGKz/33309RURGbNm1ymJs7vKpjbyDw8vUl8Y4PZA4sQM36/6N4/355kNuyGuTp6Ul5eTnu7u6kpqZOGQWop6fH7kE2MYA43OhvOO92NNirWCWSLkuVKHspUXq9Hnd396O90RyzuOiii7jnnnvo6enh8ccf5+WXX2bBggVUVlYCcPDgQVasWMGPP/7ICSecwKeffip7ASQlJZGbm2slJ3oU4HpjbcMVpxzAsRanJEmiq6tLPkybzWYef/xx3n33XbZs2UJSUpLDzycoszNmzLC7mKTy9SP1jvet4lTp+nOpKchDOzjIwMCAFSPA29ubqqoqDAYDGRkZTi2IWaK/v5+SkhICAwNJSkoasxtgKe3uKHXXEcWqwcFBCgoK5DhVV1dHSkqKXASbN2/eqImX2WzGZDK5nLpt4Occp1xEbifi5ptvZv369fT09ABDH96goCD5cBgTE0NDQwMADQ0NxMbGAqBSqQgMDESj0Tj1RnF3d7f6UHt4eLB+/Xo+//xzzj//fNauXcsZZ5xh9/MplUqSk5Pp7OykqKhoVH3w4ZAkCV1CAvueuJBZt70NCiVX7dyGGfD19SUqKmrUSsqsWbNoamoiPz9/wlWd8eDv78/8+fNl9a3MzEx8fHxkaViRWHR3d+Pm5iZv2NOnT3cosFgqVjU1NREZGUloaKhMUbNUiQJklahrrrnGbkrUVAW6o4nBwUGWLl2KTqfDaDRy0UUX8eCDD3L11VfzzTffyFW5l19+mTlz5th0RP7oo48ICwsjKyuLr7/+GhhbiedoqfS44MJPgWMtTikUCqv9XalUcuedd3LyySezatUq/vjHP3LZZZc59HxxcXGo1WqKi4uJjIwkNjZ23M+w4eTfUPbYOaQfLoyp2w7ihRnPwEDSpk8flV6UmZlJW1sbBQUFTpM/Hw6hEllXV0deXp48pD6cujtc2j0mJsahTstwxarIyEgiIiKQJIm2tjYrlajBwUGysrJYtGgRTz/99LhJkIBSqfzZUadccco+uJINJ+F4ulFOP/10vvzyS1avXs2OHTt46KGHHNqU1Go12dnZlJWV0d7eTnp6uhyohquF9Pf34/3668xauZKax84FwAzotFrCx6huKBQKoqKi5IARFBREYmKi0zcqpVJJQkICjY2N5OXlyYd24R8SHR1Nenr6pK/r4eFBZmYmH374Ib/97W9JTExEo9EQFhbGokWLOO+881i3bh3+/v7H9IbxU8LT05MdO3bg5+eHwWBgyZIlrFixAoDHHnuMiy66yOr3P/nkEyoqKqioqCA3N5cbbriB3Nxcdu7cyQcffMDHH3/M4OAg3d3d3HzzzfIskEql4tChQ7LUb0xMDAcPHiQmJkauFE7VoKYLLvyUOJ7iVHZ2Nt9++y1r1qzhyy+/5KmnnnJI2MPf35/s7GwqKiooKipixowZ8v4+mvys54MPMquhgZrHhswGJWCgomJcBkBoaCiBgYGUlJTQ3t4+JRLrIoHy9PSkqKgINzc3FAqFTN2NiIhwynWFEtb333/PFVdcQUJCgpyMLly4kFNPPZU///nPLpUoC7jilH1wJRtOwvF2o4SGhvLhhx/yzDPPcMYZZ7Bp06YxNdmHw93dnZkzZ1JfX8+uXbsIDAxEp9NhNptlVauUlJQjGudlZQxMYJ3e3t5yVSc/P3/CMxaW0Ol0VlKEQjJXdG10Oh1paWmTMvwTlKi8vDy5IiT0za+77jp27NjBrFmz2Lhxo8ND978UKBQK+b02GAwYDIYxA9z777/PqlWrUCgULFiwAK1WS1NTE3//+9/5+9//DsDXX3/N448/zr/+9S8uvvhi3n77bVauXMkrr7zCuecOJcPnnHMOr7zyCgsXLuTtt9/m5JNPdgVWF34WON7ilL+/P6+88gr/+te/OOOMM9iwYQPz58+3+/Fubm6kp6fT3NxMbm4ugYGB6PV6K8GRxMTEI4Ije/ZMKE55eHgwe/ZsuWg1ETGV4bBF3U1ISKCnp4eenh5SU1NlmvREMTAwwJ49e0ZQoq655hoKCgrw9vbmxRdfHJfF8EuFK07ZB9fMxhRA3CgfffQRF198MRdeeKE8eDdr1iz+8Ic/sHHjRvbv3y8P3r3zzju89dZbR2W9e/fu5ZprrmH16tVcffXVNm9YIT9rKevn6emJr68vGo2G0NBQu9upE4GYsbC3LQ4jpQh7enrw8PCwUjgZzh/t6OigvLyc+Ph4IiIi7L5OY2OjFSVKqVTKlKglS5aMWPN7773H6aefPmlH8J8zTCYTWVlZVFZW8sc//pFHH32Uq6++ml27duHp6ckpp5zCunXr8PT0tMsR2fKzWV1dLUsKzp07l9dffx1PT08GBwe58sorKSwsJDg4mDfffJPExMSj9RIIHLtR5OjDFacmgOMtTlVXV7Nq1SpOP/10brnlljEVFIXgSFdXl+wT4e/vj1arxc/Pj/T09CnzWxJiKo4oHo5H3R1NJKW7u5vS0lKH4+FwSpROp2PevHnyvMXwGL59+3ZmzZpFWFiY4y/GLwSuOCXDNSD+U+J4vFH6+/u5+eabaW9v59lnn0WtVjM4OGjVajYajVbDZpbys8LEp7Oz06HhcUdhMpmorKykr6+PzMzMEd0HYU4oKkI6nQ4/Pz+Zx2qvFKHRaKS8vByj0Tjq8J/RaKS4uFhWiSotLSUiIkLesE888UQXJcqJ0Gq1nH/++Tz77LOEhIQQERGBXq/n97//PUlJSTzwwAOcddZZ3HPPPVab+Pr168nKyjrKq3cKXDeSbbji1ARwPMYpg8HA2rVrycnJYfPmzURFRaHT6awKSmPt+ZIkcejQoQkb9NkLSZKora2lvb2dzMzMEd0Hk8kkx6muri55+FzEVnu9LUwmE9XV1XR3d5OZmTki7prNZsrLy+XkYv/+/QQGBlqpRKnValecchJcccqVbBxTsDVQdDTNW8Sm9Oyzz/L+++/j5eXFr3/9a2666SZ507ZnCFmr1VJWVsb06dOn1IlWdB9iYmJwd3eXN21hTig27clQoWBIu33z5s1ERkaSmJgoJxctLS1kZmbKXYtZs2a5jPMOY6ru7wcffBBfX19uv/12+XuWB6ZjwRF5CuE6DdiGK05NAY7FOCVJEtXV1WzdupWXXnoJf39/ZsyYwV/+8he5A2DPnt/b2+vQ8PhE0d3dLSse+vj4jKDuijXLdOMJQqvV8vrrrzMwMMCJJ57I7t27ycnJob6+ntTUVCuVqJ+jmMhE4IpTUwKbN/HPSxbgOIEYKNq7dy9FRUV8+umn5OTkcNddd3HLLbdQUVGBWq3mxReHfChefPFF1Go1lZWV3HLLLdx1111OX9OGDRtYu3YtqampbNq0SZa/S0hIICwszO4NKigoiOzsbDQaDfv378dgMDhtjWazme7uburr6zl06JAceGpqaggODiY7O5tf/epXpKenExERMeFEQ+ib/+c//2HdunV89dVXPP3009x6661Mnz6df/7zn+zbt49t27Zx4403Mm/ePFeiYQFn3d9tbW1otVpgiFe8fft20tPTaWpqAobep/fee48TTjgBGOKwvvrqq0iSRE5ODoGBgT+XDdwFF35yHItx6t///je33XYbarWarVu3MmPGDMLDw4mLiyM8PNzuPd/Pz4/58+czMDBAYWEhOp3OaWuUJImenh4OHTpEfX29LMVbUVGBv78/c+fO5cQTTyQjI4PIyMgxZdPHu05rayvvv/8+jz76KO+//z5vvfUW1113HQEBATzzzDPs27ePd955h9tvv50FCxa4Eg0LuOLUTwxJksb6z4VhmD59uvTaa6857fn6+vqkuXPnSjk5OVJISIhkMBgkSZKkH374/+3de1CU5/UH8C8sRotRFjAgQiwui4gLiMRL0IiCgBZt2khUlEQaFG0wLVPshdaEYicjJA6dtoLVpqKNRRhrptKpxKgYIgPhogENFy0xkqAgDjQaIBoue35/WN6fyEVAVhb4fmb2D5Z9932XWd4zz/Oc55w8CQwMFBGRwMBAycvLExGR1tZWsba2Fr1eP2jX0J22tjbZsWOHLFq0SCoqKqS5ubnfj88++0xOnz4t169fH9Dxt27dki+++EIuXLggZ8+elaysLCkoKJDLly9LXV2dNDU1SXNzs1y5ckVOnz4t1dXVAzrP7du3JS8vTxITE2X16tXi5uYm/v7+EhsbKydPnpTbt2+LXq+X9PR0Wbt2rUH/7iPNo3y/L1y4IJ6enuLu7i46nU527NghIiK+vr7i5uYmOp1OQkNDpbGxUURE9Hq9REZGikajETc3NykqKhqCT2wwD7tXj+YHPWC0xCm9Xi/JyckyZ84cKSwsHND9/8svv5TTp09LVVXVgONHdXW1fPrpp5KTkyOnT5+W/Px8qaiokNraWmlsbOx0ns8//3xA52lsbJSioiLZvXu3hIaGioeHh/j4+EhMTIz8+9//loaGBtHr9XLy5EkJDAyU9vZ2g/7tRxLGqUHT432a07FD5MENRU5OTkNa6/xBKpUKsbGxWLp0KUJCQrBt2zYEBwf36z3s7OygVqtRVlYGS0vLXjfLifx/d9aOmuEqlUpJ4Zo6dWqPszKTJ0+GWq1WSg86OTn1unmwqakJRUVF+Pjjj1FQUICbN29Cp9NhwYIF+PWvfw13d/duVyrWrl2LNWvW9OtvMFoNxvfbw8MDxcXFXd77zJkz3Z7TxMQEycnJBvpERKOPsccpExMTREZGwsfHB6+88gpCQ0MRERHRr5UCa2trPPPMM30uXftgbwsASkpUT32jOs4zZ84cVFRUoL6+Hi4uLr2uiN+5c6dTg9fq6molJeq1117rMSUqICAA/v7+3IfRB4xTjw8HG0NEpVKhpKRE2VBUUVHR5TXGUOt84cKFyM7OxpYtW5CVlYVdu3b1q9ReR+naq1ev4vz589DpdDA3N1dSojr14/jOd5QbtouLS7+qhYwbNw6zZ89GdXU1zp07B2tra2i1WiUlKi8vD/n5+Th//jxMTU0xd+5cLFy4ED/+8Y/h4ODQ57/nSL2BV1dXY8OGDbhx4wZMTU2xefNmREVFIS4uDu+8847SrGrnzp0ICgoCAMTHx2P//v1QqVT405/+hGXLlinvN1y+30TUs+Hyf+zm5oazZ8/i5z//OUJCQrBnzx5YW1v3+fiO0rXXr19XSqxPmDBBqcDYMbhobm7G2LFjoVarYWNjA2dn537FqY6S8Tdu3MC5c+egVqsxY8YMJSWqY09gYWEhWltblcZ5HT0v+lrpcaTePxmnhi8ONh7BN998g3Xr1qGtrQ1HjhwZUL1rtVqNJUuWID8/32hrnavVaqSnp+PAgQNYvnw5du/eDU9Pzz4fb2JiAgcHB5iamqKwsBBjxoyBSqVSZoM69eN4BB2NAKurq/Hiiy/CysoKTU1NmDJlChYsWIAXX3wRu3btwpNPPsmbxAPMzMyQmJgILy8vNDY24plnnkFAQAAA4Gc/+1mnzW4AUF5ejvT0dJSVlaGmpgb+/v74z3/+0yXwDofvN9FINlri1He+8x0kJycjIyMDK1euREJCAhYvXtzn401MTGBrawsRQXFxMUxNTaFSqTBhwgSlqez9FRgHysTEBDY2Nqirq8OmTZtgZmaGO3fuwNraGt7e3li2bBni4uJYJaobjFPDFzeID9CNGzewePFiTJkyBf/617/6dQPvbkORq6srfH19cfToUQDotnkLgCFr3mJiYoLw8HCkpaVh27ZtSEpKgl6v7/a1HalK165dQ1lZGfLz83Hx4kWljOzEiRNhbm4OZ2dnTJky5ZE2yH399dfIysrCm2++ie9///tYuHAhDhw4gMjISOh0OtjY2CAlJQVxcXHw9/dnOdoe2NnZwcvLC8C9Rlqurq7K8nF3MjIyEBISgrFjx2LatGnQarUoLCwEMDy/30Qj0WiLUwDwgx/8AJmZmUhMTERcXFyPRUrkf70tampqUF5ejvz8fJSUlODOnTtwcXGBtbU1xo4dC61WCwcHh0eapPrmm2+Qk5ODXbt2ITg4GN7e3khMTMS6devg7e0Nc3Nz7N27F/Hx8VixYgU7dPeAcWr44srGAJSXlyM2NhZbtmwZUMWN2tpahIWFob29HXq9HmvWrMHKlSsxc+ZMhISE4PXXX8fs2bMRGBgIX19f1NTUoLa2Fu+99x6mTZuGvXv3IiAgAFVVVXB0dMSRI0dgaWkJEUFUVBQyMzNhbm6OgwcPKv+Yg2X69OnIzs7Gb37zG6xevRp79uyBubk56urqoFKpcOvWLdy9exfjx4+HWq2Gg4NDl5rhtra2yjLyjBkzYGlp2adzy/9qpOfl5aGgoKBLSlRkZCTs7e073QByc3O7NO2j3lVVVaG4uBjz589Hbm4ukpKS8O6772LOnDlITEyEpaUlrl+/jmeffVY55v7c1r5+vzdu3AgA2LhxI15++WVotVqllj8RPZrRHKfs7e3xwQcf4O2338aKFSuwb98+TJ48GdeuXcOYMWP6lLpra2uLhoYGFBcXw8nJqc9N7R5MiSoqKnpoSlRpaanBmuGOVIxTwwv7bPSTo6Mj7t69i0mTJiE/P19pU28ItbW1qK2t7bRkeOzYMRw8eBBWVlaIiYlBQkICvvrqK7z11lvIzMzE7t27kZmZiYKCAkRFRaGgoGDQr6umpga5ublITUvnxugAAAyNSURBVE1FTk4OrK2tERERgVWrVim9Lfoy4r979y7KyspgYWEBjUbT5Wbb1taG0tJSZb/FpUuXlJSojsZ5TIkaXE1NTVi8eDG2b9+OVatWoa6uDpMmTYKJiQneeOMN1NbWIiUlBVu3boW3tzdeeuklAPduxEFBQf0uIkB9xi95zxinHsA4BdTX1yM3Nxfvvfcejh8/DrVajbVr1yI8PBwWFhZ9XlFvaWlBRUUFxowZ0+1ewvb2dly+fFkZXJSWlsLKykppnOft7Q21Ws04NYgYp4wW+2wMpoSEBLi7u8Pf3x9fffWVwc7T05JhRkYGwsLCAABhYWE4duwYgHtLhhs2bICJiQmeffZZ3Lp1S6n1PJgOHjyIiooK/OQnP0FhYSGcnJxw7do1WFlZ9Wvvxbhx4+Dl5QWVSoVt27ahsLBQSYlauXIlnnvuOSQlJWHcuHF4/fXXceHCBZw8eZIpUf9TXV0NX19fuLq6QqfT4Y9//COAew0PAwIC4OzsjICAAOU7KiL46U9/Cq1WCw8PD3zyySed3q+1tRXBwcEIDQ3FqlWrANyb3VOpVDA1NUVERISyBN2Rv9rh/txWIhp6oz1OHT16FEVFRQgJCcEnn3wCb29vfPnll5gwYUK/9l488cQT8PDwwMSJExEbG4szZ87g7NmzePvtt7Fq1Sp4e3sjISEBra2tiIqKwrlz5/Dhhx8qm5RH+94LxikCwD4b/dVRv7y9vV02bdokHh4ecuPGDYOf9+rVq/L000/L7du3xcLCotPv1Gq1iIisWLFCcnJylOf9/PweSw3n9vZ2SUxMlPnz50txcXGfaoY3NTXJpUuXJCUlRbZs2SKLFi0SKysr8fPzk8OHD8u1a9cMXqN9uKupqZHz58+LiMjXX38tzs7OUlZWJr/4xS8kPj5eRETi4+Pll7/8pYiIHD9+XJYvXy56vV4+/vhjmTdvnvJeer1eXn75ZYmKiupyjg6///3vlT4jpaWl4uHhIXfv3pXPP/9cpk2bJm1tbQb9vKPcUPeyMOYHPYBxqnt/+9vfxNPTU86ePdvnOHXlyhX5+9//Lq+99posWrRIbGxsZP78+ZKSkiKfffYZ+1k8BOPUqMI+G4PN1NQU77zzDqKjo+Hj44NTp05h6tSpBjlXU1MTgoOD8Yc//AETJ07s8XUyRKXZTE1NER0dDV9fX4SHhyMiIkKZuerQ2tqK0tJSZan58uXLSkrU6tWrMX/+fIgIoqOj0dDQAHt7e4Nf93BnZ2endB59cEYxOzsbwL0ZxSVLligdZrubUbSzs0Nubi4OHToEd3d3pdLYzp07kZaWhpKSEpiYmMDR0RH79u0DAOh0OqxZswYzZ86EmZkZkpOT+1UCkogMj3Gqsw0bNmDBggUICwtDUFAQoqKiOqXvtre349KlS11SohYsWIDvfe97+N3vfgdzc3PExcWhsrISr7zyymO57uGMcYoAcGXD2LW0tEhgYKAkJiYqz02fPl0ZydfU1Mj06dNFRGTz5s1y+PDhbl/3uDQ1NUl4eLj88Ic/lNTUVImJiRFfX1/x8PCQ9evXS1JSkpSUlPQ6u9DRvZP6zthnFOmRDfXqgTE/aIgNtzj17bffyq9+9Svx8/OT1NRUiY2NlcDAQNHpdBIcHCyJiYlSUFAgLS0tPb4H41T/MU6NeD3ep7lnw4iJCDZu3AhXV1dER0crz99fgu3B0mzvvvsuRAT5+fmwsLBQZhQel/Hjx2P//v3w8vLCoUOHMGvWLBw6dAglJSVITU3F1q1bMWvWrF5nF3rrqjrchYeHw8bGBm5ubspzcXFxsLe3h6enJzw9PZGZman8Lj4+HlqtFi4uLvjggw+6fc/hMKNIRCPTcIxTTzzxBBISEvDSSy9h7969cHJywp///GdcvHgRR48eRXR0NObNm9drJUPGKcYp6ofeRiKPeURED8jJyREA4u7uLrNmzZJZs2bJ8ePHpb6+Xvz8/ESr1Yqfn580NDSIyL18xsjISNFoNOLm5sbZACP00Ucfyfnz50Wn0ynP/fa3v5Vdu3Z1eW1ZWVmnfFONRtNlRWi4zSjSgA316oExP2gIMU6NPIxTNEBc2RiOnnvuOYgILl68iJKSEpSUlCAoKAjW1tbIyspCZWUlsrKyYGVlhfDwcNja2uKjjz7ClStX8Omnn0Kj0Qyo2gMZjo+PT5+7jvbWkAgYnjOKRDSyME6NPIxTNNg42BghfvSjH+HEiROdnktISMDSpUtRWVmJpUuXIiEhAQDw/vvvo7KyEpWVlfjLX/6CV199dSgume6TlJQEDw8PhIeHK8H2+vXrePrpp5XX3N+QCICyWe7MmTOdlrZjYmJw6tQpODs749SpU4iJiQEABAUFQaPRQKvVIiIiAnv27Hm8H5KIRjXGqeGNcYoGauQmHY4yPj4+qKqq6vTcQKo90OP36quv4o033lAaEm3btg0pKSkPzV3tmFHsTlZWVrfHJicnD96FExH1A+PU8MU4RY+CKxsjWF1dnXJjtrOzw82bNwE8fCaCOutus9xAGxJ1hw2JiGi0YpwaHIxTZMw42BiFHjYTQZ0Zeun//u65//znP5Vg8fzzzyM9PR3ffvstrl69isrKSsybN28QPxkRkXFinOofxikyZkyjGsFsbW2VZefa2lrY2NgA4ExEfw3m0v+6deuQnZ2N+vp6ODg4YMeOHcjOzmZDIiIalRinBgfjFBkzDjZGsI5qDzExMV2qPSQlJSEkJAQFBQWs9jAA/V3673htWlpal/fauHFjj+fZvn07tm/fPpiXTkRkNBinDIdxiowFBxsjRHczETExMVizZg3279+PqVOn4h//+AeAe9UeMjMzodVqYW5ujgMHDgzx1RuWo6MjJkyYAJVKBTMzM5w7dw7//e9/sXbtWlRVVcHR0RFHjhyBpaXlI5+LS/9ERN1jnOoZ4xSNZBxsjBDdzUQA/a/2cOLECURFRaG9vR2bNm1SytENdx9++CEmTZqk/NyRyxoTE4OEhAQkJCTgrbfe6vP7cemfiKh/GKd6xzhFIxU3iJOivb0dW7duxfvvv4/y8nKkpaWhvLx8qC/LIDIyMhAWFgbgXi7rsWPH+nX8cGtI5OjoiDfffBO+vr548skn4e7ujosXLyItLQ1arRYWFhbYtGkT2trahvpSiYh6xDjVd4xTZDR6ay9u8MbmZFTy8vIkMDBQ+Xnnzp2yc+fOIbyiweHo6CizZ88WLy8v2bdvn4iIWFhYdHqNWq3u8fiQkBCZPHmymJmZib29vfz1r3+V+vp68fPzE61WK35+ftLQ0CAiInq9XiIjI0Wj0Yibm5sUFRUZ7oP1w3e/+13RarVSXl4uLS0tEhoaKhqNRiIiIqSpqUm++OILeeqppyQ1NXWoL5W697B79Wh+0CjCONU9xikyAj3ep5lGRYruNo0VFBQM4RUNjtzcXEyZMgU3b95EQEAAZsyY0a/jB2vpf6ht3rwZrq6uAID169cjNTUV+fn5GD9+PMaPH48lS5agqKgI69evH+IrJSLqHuNU9xinyJgxjYoUYqSbxk6cOAEXFxdotVqlTnh/dOSi2tjY4IUXXkBhYaGSywqgUy7rSHb/Mrm5uTlUKhWeeuqpTs81NjYOxaUREfUJ49TIxjg1MnGwQQpj3DT2qPm5zc3Nyo2pubkZJ0+ehJubW4+5rEREZLwYp4iGH6ZRkWLu3LmorKzE1atXYW9vj/T0dBw+fHhIr6mwsBBarRYajQYAEBISgoyMDMycObNPx9fV1eGFF14AALS1tWH9+vVYvnw55s6d2225RSIiMl6MU0TDDwcbpDAzM0NSUhKWLVuG9vZ2hIeHQ6fTDek1PWp+rkajwYULF7o8b21t3W0uKxERGS/GKaLhh4MN6iQoKAhBQUFDfRkKY83PHW6qqqo6/bxkyZIu5QMPHjz4+C6IiGiAGKdGJsapkYt7NsioGWN+LhERUQfGKaLecbBBRu3+/NyWlhakp6fj+eefH+rLIiIiAsA4RfQwTKMio2aM+blEREQdGKeIemfSXa7hfXr9JRERPRZMAO8Z4xQR0dDrMU4xjYqIiIiIiAyCgw0iIiIiIjIIDjaIiIiIiMggONggIiIiIiKD4GCDiIiIiIgMgoMNIiIiIiIyCA42iIiIiIjIIDjYICIiIiIig+Bgg4iIiIiIDIKDDSIiIiIiMggONoiIiIiIyCDMHvJ7k8dyFURERAPDOEVEZMS4skFERERERAbBwQYRERERERkEBxtERERERGQQHGwQEREREZFBcLBBREREREQGwcEGEREREREZxP8BbQ+Cr15uOPgAAAAASUVORK5CYII=\n", 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" ] @@ -783,13 +782,13 @@ "## full grids \n", "mmgrid,kkgrid = np.meshgrid(mgrid,kgrid)\n", "\n", - "## reduced grids \n", "\n", + "### for adjusters \n", "fig = plt.figure(figsize=(14,14))\n", - "fig.suptitle('Consumption at grid points of m and k(for different h)',\n", + "fig.suptitle('Consumption of non-adjusters at grid points of m and k(for different h)',\n", " fontsize=(13))\n", "for hgrid_id in range(EX3SS['mpar']['nh']):\n", - " ## prepare the grids \n", + " ## prepare the reduced grids \n", " hgrid_fix=hgrid_id\n", " fix_bool = mut_rdc_idx[2]==hgrid_fix # for a fixed h grid value \n", " rdc_id = (mut_rdc_idx[0][fix_bool], \n", @@ -803,20 +802,150 @@ " \n", " ## plots \n", " ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d')\n", + " ax.scatter(mmgrid,kkgrid,cn_StE[:,:,hgrid_fix],marker='.',\n", + " label='StE(before dct): non-adjuster')\n", " ax.scatter(mmgrid_rdc,kkgrid_rdc,c_n_rdc,c='red',marker='o',\n", " label='StE(after dct):non-adjuster')\n", - " ax.scatter(mmgrid_rdc,kkgrid_rdc,c_a_rdc,c='blue',marker='*',\n", - " label='StE(after dct):adjuster')\n", - " ax.scatter(mmgrid,kkgrid,cn_StE[:,:,hgrid_fix],c='gray',marker='.',\n", - " label='StE(before dct): non-adjuster')\n", + " ax.set_xlabel('m',fontsize=13)\n", + " ax.set_ylabel('k',fontsize=13)\n", + " ax.set_zlabel(r'$c_a(m,k)$',fontsize=13)\n", + " ax.set_title(r'$h({})$'.format(hgrid_fix))\n", + " ax.view_init(20, 240)\n", + "ax.legend(loc=9)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "### for adjusters \n", + "fig = plt.figure(figsize=(14,14))\n", + "fig.suptitle('Consumption of adjusters at grid points of m and k(for different h)',\n", + " fontsize=(13))\n", + "for hgrid_id in range(EX3SS['mpar']['nh']):\n", + " ## prepare the reduced grids \n", + " hgrid_fix=hgrid_id\n", + " fix_bool = mut_rdc_idx[2]==hgrid_fix # for a fixed h grid value \n", + " rdc_id = (mut_rdc_idx[0][fix_bool], \n", + " mut_rdc_idx[1][fix_bool],\n", + " mut_rdc_idx[2][fix_bool])\n", + " mmgrid_rdc = mmgrid[rdc_id[0]].T[0]\n", + " kkgrid_rdc = kkgrid[rdc_id[1]].T[0]\n", + " mut_n_rdc= mut_n_StE[rdc_id]\n", + " c_n_rdc = cn_StE[rdc_id]\n", + " c_a_rdc = ca_StE[rdc_id]\n", + " \n", + " ## plots \n", + " ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d')\n", " ax.scatter(mmgrid,kkgrid,ca_StE[:,:,hgrid_fix],c='yellow',marker='.',\n", " label='StE(before dct): adjuster')\n", + " ax.scatter(mmgrid_rdc,kkgrid_rdc,c_a_rdc,c='blue',marker='*',\n", + " label='StE(after dct):adjuster')\n", + " ax.set_xlabel('m',fontsize=13)\n", + " ax.set_ylabel('k',fontsize=13)\n", + " ax.set_zlabel(r'$c_n(m,k)$',fontsize=13)\n", + " ax.set_title(r'$h({})$'.format(hgrid_fix))\n", + " ax.view_init(20, 240)\n", + "ax.legend(loc=9)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "### compare adjusters and non-adjusters after DCT\n", + "\n", + "fig = plt.figure(figsize=(14,14))\n", + "fig.suptitle('Consumption of adjusters/non-adjusters at grid points of m and k(for different h)',\n", + " fontsize=(13))\n", + "for hgrid_id in range(EX3SS['mpar']['nh']):\n", + " ## prepare the reduced grids \n", + " hgrid_fix=hgrid_id\n", + " fix_bool = mut_rdc_idx[2]==hgrid_fix # for a fixed h grid value \n", + " rdc_id = (mut_rdc_idx[0][fix_bool], \n", + " mut_rdc_idx[1][fix_bool],\n", + " mut_rdc_idx[2][fix_bool])\n", + " mmgrid_rdc = mmgrid[rdc_id[0]].T[0]\n", + " kkgrid_rdc = kkgrid[rdc_id[1]].T[0]\n", + " mut_n_rdc= mut_n_StE[rdc_id]\n", + " c_n_rdc = cn_StE[rdc_id]\n", + " c_a_rdc = ca_StE[rdc_id]\n", + " \n", + " ## plots \n", + " ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d')\n", + " #ax.scatter(mmgrid,kkgrid,cn_StE[:,:,hgrid_fix],marker='.',\n", + " # label='StE(before dct): non-adjuster')\n", + " #ax.scatter(mmgrid,kkgrid,ca_StE[:,:,hgrid_fix],c='yellow',marker='.',\n", + " # label='StE(before dct): adjuster')\n", + " ax.scatter(mmgrid_rdc,kkgrid_rdc,c_n_rdc,c='red',marker='o',\n", + " label='StE(after dct):non-adjuster')\n", + " ax.scatter(mmgrid_rdc,kkgrid_rdc,c_a_rdc,c='blue',marker='*',\n", + " label='StE(after dct):adjuster')\n", " ax.set_xlabel('m',fontsize=13)\n", " ax.set_ylabel('k',fontsize=13)\n", - " ax.set_zlabel(r'$c(m,k)$',fontsize=13)\n", + " ax.set_zlabel(r'$c_a(m,k)$',fontsize=13)\n", " ax.set_title(r'$h({})$'.format(hgrid_fix))\n", + " ax.set_xlim(0,400)\n", " ax.view_init(20, 240)\n", - "ax.legend(loc=7)" + "ax.legend(loc=9)" ] }, { @@ -825,7 +954,7 @@ "source": [ "#### Observation\n", "\n", - "- For a given grid value of productivity, the remaining grid points after DCT to represent the whole m-k surface concentrate on low values of k and m. The reason, to put it simply, is that the slopes of the surface of marginal utility are very steep around this area. \n", + "- For a given grid value of productivity, the remaining grid points after DCT to represent the whole m-k surface are concentrated in low values of k and m. The reason, to put it simply, is that the slopes of the surface of marginal utility are very steep around this area. \n", "- For different grid values of productivity(4 sub plots), the numbers of grid points operation differ. From the lowest to highest values of productivity, there are 78, 33, 25 and 18 grid points, respectively. They add up to the total number of grids 154 after DCT operation, as we print out above for marginal utility function. " ] }, diff --git a/HARK/BayerLuetticke/DCT-Copula-Illustration.py b/HARK/BayerLuetticke/DCT-Copula-Illustration.py index bf4a3fa18..1aac59115 100644 --- a/HARK/BayerLuetticke/DCT-Copula-Illustration.py +++ b/HARK/BayerLuetticke/DCT-Copula-Illustration.py @@ -432,17 +432,13 @@ def do_dct(self, obj, mpar, level): # # #### Policy/value functions # -# - Taking marginal utility as an example, one can plot its values at different grid points in both 2-dimensional and 3-dimensional spaces before and after dimension reduction. -# - 2-dimensional graph: marginal utility at different grid points of a state variable fixing the values of other two state variables. -# - For example, how the reduction works for liquid assets for given level of illiquid assets holding and productivity. +# - Taking consumption function as an example, let us plot consumptions by adjusters and non-adjusters at different grid points before and after dimension reduction. +# - 2-dimensional graph: consumption at different grid points of a state variable fixing the values of other two state variables. +# - For example, consumption at each grid of liquid assets given fixed level of illiquid assets holding and productivity. # -# - 3-dimensional graph: marginal utility at different grids points at grid points of liquid and illiquid assets with only value of productivity fixed. -# - There is limitations at 1-dimensional graph, as we do not know ex ante at what grid points the dimension is reduced. So the 3-dimensional graph gives us a more complete picture. -# - In this context, as we only have 4 grid points for productivity, we can fix an arbitrary one of the 4 grids and focus on how the number of grids is reduced for liquid and illiquid assets. -# -# #### Marginal distributions -# -# - We can also graphically show marginal distributions versus joint distribution. +# - 3-dimensional graph: consumption at different grids points of liquid and illiquid assets with only value of productivity fixed. +# - There is limitations at 1-dimensional graph, as we do not know ex ante at what grid points the dimension is reduced the most. So the 3-dimensional graph gives us a more straightforward picture. +# - In this context, as we only have 4 grid points for productivity, we can fix grid of productivity and focus on how the number of grids is reduced for liquid and illiquid assets. # %% {"code_folding": [0]} ## Graphical illustration @@ -450,9 +446,9 @@ def do_dct(self, obj, mpar, level): ### In 2D, we can look at how the number of grid points of ### one state is redcued at given grid values of other states. -mgrid_fix = EX3SS['mpar']['nm']//11 # "//" is for floor division unambiguously -kgrid_fix = EX3SS['mpar']['nk']//11 -hgrid_fix = EX3SS['mpar']['nh']//2 +mgrid_fix = 0 ## these are or arbitrary grid points. +kgrid_fix = 0 +hgrid_fix = 2 xi = EX3SS['par']['xi'] @@ -487,6 +483,8 @@ def do_dct(self, obj, mpar, level): # %% {"code_folding": [0]} ## 2D graph: compare consumption function before and after dct + + fig=plt.figure(figsize=(15,8)) fig.suptitle('Consumption at grid points of states') @@ -535,7 +533,6 @@ def do_dct(self, obj, mpar, level): plt.ylabel(r'$c_a(k)$',size=15) plt.legend() - ## c_a(h) plt.subplot(2,3,6) plt.plot(hgrid,ca_StE[mgrid_fix,kgrid_fix,:],'x',label='StE(before dct)') @@ -544,20 +541,20 @@ def do_dct(self, obj, mpar, level): plt.ylabel(r'$c_a(h)$',size=15) plt.legend() -# %% {"code_folding": []} +# %% {"code_folding": [0]} ## 3D scatter plots of consumption function ## at all grids and grids after dct for both adjusters and non-adjusters ## full grids mmgrid,kkgrid = np.meshgrid(mgrid,kgrid) -## reduced grids +### for adjusters fig = plt.figure(figsize=(14,14)) -fig.suptitle('Consumption at grid points of m and k(for different h)', +fig.suptitle('Consumption of non-adjusters at grid points of m and k(for different h)', fontsize=(13)) for hgrid_id in range(EX3SS['mpar']['nh']): - ## prepare the grids + ## prepare the reduced grids hgrid_fix=hgrid_id fix_bool = mut_rdc_idx[2]==hgrid_fix # for a fixed h grid value rdc_id = (mut_rdc_idx[0][fix_bool], @@ -571,25 +568,89 @@ def do_dct(self, obj, mpar, level): ## plots ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d') + ax.scatter(mmgrid,kkgrid,cn_StE[:,:,hgrid_fix],marker='.', + label='StE(before dct): non-adjuster') ax.scatter(mmgrid_rdc,kkgrid_rdc,c_n_rdc,c='red',marker='o', label='StE(after dct):non-adjuster') - ax.scatter(mmgrid_rdc,kkgrid_rdc,c_a_rdc,c='blue',marker='*', - label='StE(after dct):adjuster') - ax.scatter(mmgrid,kkgrid,cn_StE[:,:,hgrid_fix],c='gray',marker='.', - label='StE(before dct): non-adjuster') + ax.set_xlabel('m',fontsize=13) + ax.set_ylabel('k',fontsize=13) + ax.set_zlabel(r'$c_a(m,k)$',fontsize=13) + ax.set_title(r'$h({})$'.format(hgrid_fix)) + ax.view_init(20, 240) +ax.legend(loc=9) + +# %% {"code_folding": [0]} +### for adjusters +fig = plt.figure(figsize=(14,14)) +fig.suptitle('Consumption of adjusters at grid points of m and k(for different h)', + fontsize=(13)) +for hgrid_id in range(EX3SS['mpar']['nh']): + ## prepare the reduced grids + hgrid_fix=hgrid_id + fix_bool = mut_rdc_idx[2]==hgrid_fix # for a fixed h grid value + rdc_id = (mut_rdc_idx[0][fix_bool], + mut_rdc_idx[1][fix_bool], + mut_rdc_idx[2][fix_bool]) + mmgrid_rdc = mmgrid[rdc_id[0]].T[0] + kkgrid_rdc = kkgrid[rdc_id[1]].T[0] + mut_n_rdc= mut_n_StE[rdc_id] + c_n_rdc = cn_StE[rdc_id] + c_a_rdc = ca_StE[rdc_id] + + ## plots + ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d') ax.scatter(mmgrid,kkgrid,ca_StE[:,:,hgrid_fix],c='yellow',marker='.', label='StE(before dct): adjuster') + ax.scatter(mmgrid_rdc,kkgrid_rdc,c_a_rdc,c='blue',marker='*', + label='StE(after dct):adjuster') + ax.set_xlabel('m',fontsize=13) + ax.set_ylabel('k',fontsize=13) + ax.set_zlabel(r'$c_n(m,k)$',fontsize=13) + ax.set_title(r'$h({})$'.format(hgrid_fix)) + ax.view_init(20, 240) +ax.legend(loc=9) + +# %% {"code_folding": [0]} +### compare adjusters and non-adjusters after DCT + +fig = plt.figure(figsize=(14,14)) +fig.suptitle('Consumption of adjusters/non-adjusters at grid points of m and k(for different h)', + fontsize=(13)) +for hgrid_id in range(EX3SS['mpar']['nh']): + ## prepare the reduced grids + hgrid_fix=hgrid_id + fix_bool = mut_rdc_idx[2]==hgrid_fix # for a fixed h grid value + rdc_id = (mut_rdc_idx[0][fix_bool], + mut_rdc_idx[1][fix_bool], + mut_rdc_idx[2][fix_bool]) + mmgrid_rdc = mmgrid[rdc_id[0]].T[0] + kkgrid_rdc = kkgrid[rdc_id[1]].T[0] + mut_n_rdc= mut_n_StE[rdc_id] + c_n_rdc = cn_StE[rdc_id] + c_a_rdc = ca_StE[rdc_id] + + ## plots + ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d') + #ax.scatter(mmgrid,kkgrid,cn_StE[:,:,hgrid_fix],marker='.', + # label='StE(before dct): non-adjuster') + #ax.scatter(mmgrid,kkgrid,ca_StE[:,:,hgrid_fix],c='yellow',marker='.', + # label='StE(before dct): adjuster') + ax.scatter(mmgrid_rdc,kkgrid_rdc,c_n_rdc,c='red',marker='o', + label='StE(after dct):non-adjuster') + ax.scatter(mmgrid_rdc,kkgrid_rdc,c_a_rdc,c='blue',marker='*', + label='StE(after dct):adjuster') ax.set_xlabel('m',fontsize=13) ax.set_ylabel('k',fontsize=13) - ax.set_zlabel(r'$c(m,k)$',fontsize=13) + ax.set_zlabel(r'$c_a(m,k)$',fontsize=13) ax.set_title(r'$h({})$'.format(hgrid_fix)) + ax.set_xlim(0,400) ax.view_init(20, 240) -ax.legend(loc=7) +ax.legend(loc=9) # %% [markdown] # #### Observation # -# - For a given grid value of productivity, the remaining grid points after DCT to represent the whole m-k surface concentrate on low values of k and m. The reason, to put it simply, is that the slopes of the surface of marginal utility are very steep around this area. +# - For a given grid value of productivity, the remaining grid points after DCT to represent the whole m-k surface are concentrated in low values of k and m. The reason, to put it simply, is that the slopes of the surface of marginal utility are very steep around this area. # - For different grid values of productivity(4 sub plots), the numbers of grid points operation differ. From the lowest to highest values of productivity, there are 78, 33, 25 and 18 grid points, respectively. They add up to the total number of grids 154 after DCT operation, as we print out above for marginal utility function. # %% [markdown] From 7e4d9558c83f4aeeb3ddadf53130f9dc41be0c6c Mon Sep 17 00:00:00 2001 From: llorracc Date: Fri, 7 Jun 2019 19:09:08 -0400 Subject: [PATCH 73/77] Edit BL TwoAsset and DCT-Copula --- .../DCT-Copula-Illustration.ipynb | 255 +++++++++++------- .../BayerLuetticke/DCT-Copula-Illustration.py | 151 ++++++----- HARK/BayerLuetticke/TwoAsset.ipynb | 101 ++++--- HARK/BayerLuetticke/TwoAsset.py | 57 ++-- 4 files changed, 337 insertions(+), 227 deletions(-) diff --git a/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb b/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb index 39a38f339..e35c443b3 100644 --- a/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb +++ b/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb @@ -9,16 +9,36 @@ "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/econ-ark/HARK/BayerLuetticke?filepath=notebooks%2FHARK%2FBayerLuetticke%2FTwoAsset.ipynb)\n", "\n", "\n", - "This is an accompany to the [main notebook](TwoAsset.ipynb) illustrating dimension reduction in Bayer/Luetticke algorithm.\n", + "This companion to the [main notebook](TwoAsset.ipynb) explains in more detail how they reduce the dimensionality of the problem\n", "\n", "- Based on original slides by Christian Bayer and Ralph Luetticke \n", "- Original Jupyter notebook by Seungcheol Lee \n", "- Further edits by Chris Carroll, Tao Wang \n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Preliminaries\n", + "\n", + "In StE in the model, in any given period, a consumer in state $s$ (which comprises liquid assets $m$, illiquid assets $k$, and human capital $\\newcommand{hLev}{p}\\hLev$) has two key choices:\n", + "1. To adjust ('a') or not adjust ('n') their holdings of illiquid assets $k$\n", + "1. Contingent on that choice, decide the level of consumption, yielding consumption functions:\n", + " * $c_n(s)$ - nonadjusters\n", + " * $c_a(s)$ - adjusters\n", + "\n", + "The usual envelope theorem applies here, so marginal value wrt the liquid asset equals marginal utility with respect to consumption:\n", + "\\[\n", + "\\frac{d v}{d m} = \\frac{d u}{d c}\n", + "\\]\n", + "\n", + "In practice, the authors solve the problem using the marginal value of money $\\texttt{Vm} = dv/dm$, but because the marginal utility function is invertible it is trivial to recover $\\texttt{c}$ from $(u^{\\prime})^{-1}(\\texttt{Vm} )$. The consumption function is therefore computed from the $\\texttt{Vm}$ function" + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "code_folding": [ 0, @@ -69,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "code_folding": [ 0 @@ -77,7 +97,7 @@ }, "outputs": [], "source": [ - "# Change working folder and load Stationary equilibrium (StE)\n", + "# Load precalculated Stationary Equilibrium (StE) object EX3SS\n", "\n", "import pickle\n", "os.chdir(code_dir) # Go to the directory with pickled code\n", @@ -93,40 +113,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Dimension Reduction via discrete cosine transformation and a fixed copula\n", - "\n", - "#### What is it whose dimension needs to be reduced?\n", - "\n", - "1. Policy and value functions\n", - "1. The distribution of agents across states\n", + "### Dimensions\n", "\n", - "Grids are constructed for values of the state variables:\n", - " * liquid ($nm$ points), illiquid assets ($nk$), and idiosyncratic pty ($nh$)\n", + "The imported StE solution to the problem represents the functions at a set of gridpoints of\n", + " * liquid assets ($n_m$ points), illiquid assets ($n_k$), and human capital ($n_h$)\n", + " * (In the code these are $\\{\\texttt{nm ,nk ,nh}\\}$)\n", "\n", - "So there are $nm \\times nk \\times nh$ potential combinations\n", + "So even if the grids are fairly sparse for each state variable, the total number of combinations of the idiosyncratic state variables is large: $n = n_m \\times n_k \\times n_h$. So, e.g., $\\bar{c}$ is a set of size $n$ containing the level of consumption at each possible combination of gridpoints.\n", "\n", - "In principle, functions are represented by specifying their values at each specified combination of gridpoints and interpolating for intermediate values\n", - " * In practice, for technical reasons, interpolation is not necessary here\n", - "\n", - "There are two kinds of functions:\n", - "1. Policy functions and marginal value functions\n", - " * At each of the gridpoints, there is a number\n", - " * This is value for the value function\n", - " * This is consumption for the consumption function\n", - " * $c_n$ is the consumption function for the nonadjuster\n", - " * $c_a$ is the consumption function for the adjuster\n", - "1. The distribution (=\"histograms\") of agents across states\n", - " * In principle, distributions need not be computed at the same gridpoints used to represent the value and policy functions\n", - " * In practice, the same grids are used" + "In the \"real\" micro problem, it would almost never happen that a continuous variable like $m$ would end up being exactly equal to one of the prespecified gridpoints. But the functions need to be evaluated at such points. This is addressed by linear interpolation. That is, if, say, the grid had $m_{8} = 40$ and $m_{9} = 50$ then and a consumer ended up with $m = 45$ then the approximation is that $\\tilde{c}(45) = 0.5 \\bar{c}_{8} + 0.5 \\bar{c}_{9}$.\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 76, "metadata": { "code_folding": [ 0 - ] + ], + "scrolled": false }, "outputs": [ { @@ -142,13 +147,13 @@ "30 gridpoints for illiquid assets;\n", "4 gridpoints for individual productivity.\n", "\n", - "Therefore, the joint distribution across different is of size: \n", + "Therefore, the joint distribution is of size: \n", "30 * 30 * 4 = 3600\n" ] } ], "source": [ - "# Recover dimensions of the marginal value and consumption functions\n", + "# Show dimensions of the consumer's problem (state space)\n", "\n", "print('c_n is of dimension: ' + str(EX3SS['mutil_c_n'].shape))\n", "print('c_a is of dimension: ' + str(EX3SS['mutil_c_a'].shape))\n", @@ -161,56 +166,84 @@ "print(str(len(EX3SS['grid']['k']))+' gridpoints for illiquid assets;')\n", "print(str(len(EX3SS['grid']['h']))+' gridpoints for individual productivity.')\n", "print('')\n", - "print('Therefore, the joint distribution across different is of size: ')\n", + "print('Therefore, the joint distribution is of size: ')\n", "print(str(EX3SS['mpar']['nm'])+\n", " ' * '+str(EX3SS['mpar']['nk'])+\n", " ' * '+str(EX3SS['mpar']['nh'])+\n", " ' = '+ str(EX3SS['mpar']['nm']*EX3SS['mpar']['nk']*EX3SS['mpar']['nh']))\n", - " \n" + " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Intuitively, how does the reduction work?\n", + "### Dimension Reduction\n", "\n", - "##### Reducing the dimension of policy/value functions\n", - "- The first step is to find an efficient \"compressed\" representation of the function (e.g., the consumption function). The analogy to image compression is that nearby pixels are likely to have identical or very similar colors, so we need only to find an efficient way to represent the way in which the colors change from one pixel to another. Similarly, consumption at a given point is likely to be close to consumption at a nearby point, so a function that captures that similarity efficiently can preserve most of the information without keeping all of the points.\n", + "The authors use different reduction methods for the consumer's problem and the distribution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The consumer's problem: Discrete Cosine Transformation\n", "\n", - "- We will be using the discrete cosine transformation (DCT), which is commonly used in image compression. See [here](https://en.wikipedia.org/wiki/Discrete_cosine_transform) for the Wikipedia page on DCT. \n", + "The idea is to find an efficient \"compressed\" representation of our functions (e.g., the consumption function). The analogy to image compression is that nearby pixels are likely to have identical or very similar colors, so we need only to find an efficient way to represent the way in which the colors change from one pixel to another. The analogy is that consumption at a given point $s_{i}$ is likely to be close to consumption point another point $s_{j}$ that is \"close\" in the state space (similar wealth, income, etc), so a function that captures that similarity efficiently can preserve most of the information without keeping all of the points.\n", "\n", - "##### Reducing the dimension of joint distribution\n", + "Like linear interpolation, the [DCT transformation](https://en.wikipedia.org/wiki/Discrete_cosine_transform) is a method of representing a continuous function using a finite set of numbers. It uses a set of independent basis functions to do this.\n", "\n", - "- The other tool we use is the \"copula,\" which allows us to represent the distribution of people across idiosyncratic states efficiently\n", - " * In general, a multivariate joint distribution is not uniquely determined by marginal distributions only. A copula, to put it simply, characterizes the correlation across variables and it combined with marginal distributions determine the unique joint distribution. \n", - " * The crucial assumption of fixed copula is that what aggregate shocks do is to squeeze or distort the steady state distribution, but leave the rank structure of the distribution the same. Think of representing a balloon by a set of points on its surface; the copula assumption is effectively that when something happens to the balloon (more air is put in it, or it is squeezed on one side, say), we can represent what happens by thinking about how the relationship between points is distorted, rather than having to reconstruct the shape of the balloon with a completely independent set of new points. Which points are close to which other points does not change, but the distances between them can change. If the distances between them change in a particularly simple way, you can represent what has happened with a small amount of information. For example, if the balloon is perfectly spherical, then adding a given amount of air might increase the distances between adjacent points by 5 percent. (See the video illustration here)\n", + "But it turns out that some of those basis functions are much more important than others in representing the steady-state functions. Dimension reduction is accomplished by basically ignoring all basis functions that make small contributions to the steady state distribution. \n", "\n", - "- In the context of this model, the assumption is that the rank order correlation (e.g. the correlation of where you are in the distribution of liquid assets and illiquid assets) remains the same after the aggregate shocks are introduced to StE\n", + "##### When might this go wrong?\n", "\n", - "- In this case we just need to represent how the marginal distributions of each state change, instead of the full joint distributions. \n", + "Suppose the consumption function changes in a recession in ways that change behavior radically at some states. Like, suppose unemployment almost never happens in steady state, but it can happen in temporary recessions. Suppose further that, even for employed people, _worries_ about unemployment cause many of them to prudently withdraw some of their illiquid assets -- behavior opposite of what people in the same state would be doing during expansions. In that case, the DCT functions that represented the steady state function would have had no incentive to be able to represent well the part of the space that is never seen in steady state, so any functions that might help do so might well have been dropped in the dimension reduction stage.\n", "\n", - "- This reduces 3600 to 30+30+4=64. See [here](https://en.wikipedia.org/wiki/Copula_(probability_theory)) for the Wikipedia page on copula. The copula is computed from the joint distribution of states in StE and will be used to transform the marginals back to joint distributions." + "On the whole, it seems unlikely that this kind of thing is a major problem, because the vast majority of the variation that people experience is idiosyncratic. There is always unemployment, for example; it just moves up and down a bit with aggregate shocks, but since the experience is in fact well represented in the steady state the method should have no trouble capturing it.\n", + "\n", + "Where it might have more trouble is in representing economies in which there are multiple equilibria in which behavior is quite different." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### For the distribution of agents across states: Copula\n", + "\n", + "The other tool the authors use is the [\"copula,\"](https://en.wikipedia.org/wiki/Copula_(probability_theory)) which allows us to represent the distribution of people across idiosyncratic states efficiently\n", + "\n", + "The copula is computed from the joint distribution of states in StE and will be used to transform the marginal distributions back to joint distributions.\n", + "\n", + " * In general, a multivariate joint distribution is not uniquely determined by marginal distributions only. A copula, to put it simply, is a compressed representation of the joint distribution of the rank order of points; together with the marginal distributions this expands to a complete representation of the joint distribution\n", + " * The crucial assumption of a fixed copula is that what aggregate shocks do is to squeeze or distort the steady state distribution, but leave the rank structure of the distribution the same. Think of representing a balloon by a set of points on its surface; the copula assumption is effectively that when something happens to the balloon (more air is put in it, or it is squeezed on one side, say), we can represent what happens to the points by thinking about how the relationship between points is distorted, rather than having to reconstruct the shape of the balloon with a completely independent set of new points. Which points are close to which other points does not change, but the distances between them can change. If the distances between them change in a particularly simple way, you can represent what has happened with a small amount of information. For example, if the balloon is perfectly spherical, then adding a given amount of air might increase the distances between adjacent points by 5 percent. (See the video illustration here)\n", + " \n", + "- In the context of this model, the assumption that allows us to use a copula is that the rank order correlation (e.g. the correlation of where you rank in the distribution of liquid assets and illiquid assets) remains the same after the aggregate shocks are introduced to StE. That is, the fact that you are richer than me, and Bill Gates is richer than you, does not change in a recession. _How much_ richer you are than me, and Gates than you, can change, but the rank order does not.\n", + "\n", + "- In this case we just need to represent how the marginal distributions of each state change, instead of the full joint distributions\n", + "\n", + "- This reduces the number of points for which we need to track transitions from $3600 = 30 \\times 30 \\times 4$ to $64 = 30+30+4$. Or the total number of points we need to contemplate goes from $3600^2 \\approx 13 million$ to $64^2=4096. " ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 60, "metadata": { - "code_folding": [ - 0 - ], - "lines_to_next_cell": 2 + "code_folding": [], + "lines_to_next_cell": 2, + "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The copula consists of two parts: gridpoints and values at those gridpoints:,\n", - " gridpoints with dimension of (3600, 3), where the first element is total number of gridpoints, and the second element is number of states,\n", - " and values with dimension of (3600,), \n", - " each entry of which is the probability of the three state variables below the grids.\n" + "The copula consists of two parts: gridpoints and values at those gridpoints:\n", + " gridpoints have dimensionality of (3600, 3)\n", + " where the first element is total number of gridpoints\n", + " and the second element is number of state variables,\n", + " whose values also are of dimension of 3600\n", + " each entry of which is the probability that all three of the\n", + " state variables are below the corresponding point.\n" ] } ], @@ -218,16 +251,17 @@ "# Get some specs about the copula, which is precomputed in the EX3SS object\n", "\n", "print('The copula consists of two parts: gridpoints and values at those gridpoints:'+ \\\n", - " ',\\n gridpoints with dimension of '+str(EX3SS['Copula']['grid'].shape) + \\\n", - " ', where the first element is total number of gridpoints' + \\\n", - " ', and the second element is number of states' + \\\n", - " ',\\n and values with dimension of '+str(EX3SS['Copula']['value'].shape) + \\\n", - " ', \\n each entry of which is the probability of the three state variables below the grids.')" + " '\\n gridpoints have dimensionality of '+str(EX3SS['Copula']['grid'].shape) + \\\n", + " '\\n where the first element is total number of gridpoints' + \\\n", + " '\\n and the second element is number of state variables' + \\\n", + " ',\\n whose values also are of dimension of '+str(EX3SS['Copula']['value'].shape[0]) + \\\n", + " '\\n each entry of which is the probability that all three of the'\n", + " '\\n state variables are below the corresponding point.')" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 61, "metadata": { "code_folding": [ 0 @@ -266,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 62, "metadata": { "code_folding": [ 0 @@ -290,7 +324,7 @@ " self.Vm = Vm # Marginal value from liquid cash-on-hand\n", " self.Vk = Vk # Marginal value of capital\n", " self.joint_distr = joint_distr # Multidimensional histogram\n", - " self.Copula = Copula # Encodes rank correlation structure of distribution\n", + " self.Copula = Copula # Encodes rank marginal correlation of joint distribution\n", " self.mutil_c = mutil_c # Marginal utility of consumption\n", " self.P_H = P_H # Transition matrix for macro states (not including distribution)\n", " \n", @@ -467,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 19, "metadata": { "code_folding": [ 0 @@ -492,23 +526,23 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 20, "metadata": { - "code_folding": [ - 0 - ] + "code_folding": [] }, "outputs": [], "source": [ "## Choose an accuracy of approximation with DCT\n", "### Determines number of basis functions chosen -- enough to match this accuracy\n", "### EX3SS is precomputed steady-state pulled in above\n", - "EX3SS['par']['accuracy'] = 0.99999 " + "EX3SS['par']['accuracy'] = 0.99999 \n", + "\n", + "## 20190607: CDC to TW: Please try to figure out what this is" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "metadata": { "code_folding": [ 0 @@ -524,15 +558,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 39, "metadata": { "code_folding": [ - 5, - 7, - 9, - 12, - 14, - 18 + 10, + 12 ], "lines_to_next_cell": 2 }, @@ -544,17 +574,19 @@ "What are the results from the state reduction?\n", "\n", "\n", - "The dimension of policy function is reduced to 154 from (30, 30, 4)\n", - "The dimension of value function is reduced to 94 from (30, 30, 4)\n", + "If we want to achieve an accuracy of 0.99999\n", + "\n", + "The dimension of policy function is reduced to 154 from 3600\n", + "The dimension of the marginal value function is reduced to 94 from (30, 30, 4)\n", "The total number of control variables is 259=154+94+ # of other macro controls\n", "\n", "\n", - "After marginalizing the joint distribution, \n", - " the dimension of states including exogenous state, is 66\n", - "Dimension of gamma_state is (64, 60). It simply stacks all grids of different \n", + "The copula represents the joint distribution with a vector of size (64, 60)\n", + "The dimension of states including exogenous state, is 66\n", + "It simply stacks all grids of different \n", " state variables regardless of their joint distributions. \n", - " This is due to the assumption of the rank order remains the same.\n", - "The total number of state variables is 62=60+ # of other states\n" + " This is due to the assumption that the rank order remains the same.\n", + "The total number of state variables is 62=60+ the number of macro states (like the interest rate)\n" ] } ], @@ -564,21 +596,24 @@ "\n", "print('\\n')\n", "\n", - "print('The dimension of policy function is reduced to '+str(SR['indexMUdct'].shape[0]) \\\n", - " +' from '+str(EX3SS['mutil_c'].shape))\n", - "print('The dimension of value function is reduced to '+str(SR['indexVKdct'].shape[0]) \\\n", + "print('To achieve an accuracy of '+str(EX3SS['par']['accuracy'])+'\\n') \n", + "\n", + "print('The dimension of the policy functions is reduced to '+str(SR['indexMUdct'].shape[0]) \\\n", + " +' from '+str(EX3SS['mpar']['nm']*EX3SS['mpar']['nk']*EX3SS['mpar']['nh'])\n", + " )\n", + "print('The dimension of the marginal value functions is reduced to '+str(SR['indexVKdct'].shape[0]) \\\n", " + ' from ' + str(EX3SS['Vk'].shape))\n", "print('The total number of control variables is '+str(SR['Contr'].shape[0])+'='+str(SR['indexMUdct'].shape[0]) + \\\n", " '+'+str(SR['indexVKdct'].shape[0])+'+ # of other macro controls')\n", "print('\\n')\n", - "print('After marginalizing the joint distribution, \\\n", - " \\n the dimension of states including exogenous state, is '+str(SR['Xss'].shape[0]))\n", - "print('Dimension of gamma_state is '+str(SR['Gamma_state'].shape)+\\\n", - " '. It simply stacks all grids of different\\\n", + "print('The copula represents the joint distribution with a vector of size '+str(SR['Gamma_state'].shape) )\n", + "print('The dimension of states including exogenous state, is ' +str(SR['Xss'].shape[0]))\n", + "\n", + "print('It simply stacks all grids of different\\\n", " \\n state variables regardless of their joint distributions.\\\n", - " \\n This is due to the assumption of the rank order remains the same.')\n", + " \\n This is due to the assumption that the rank order remains the same.')\n", "print('The total number of state variables is '+str(SR['State'].shape[0]) + '='+\\\n", - " str(SR['Gamma_state'].shape[1])+'+ # of other states')" + " str(SR['Gamma_state'].shape[1])+'+ the number of macro states (like the interest rate)')" ] }, { @@ -589,13 +624,9 @@ "\n", "#### Policy/value functions\n", "\n", - "- Taking consumption function as an example, let us plot consumptions by adjusters and non-adjusters at different grid points before and after dimension reduction. \n", - " - 2-dimensional graph: consumption at different grid points of a state variable fixing the values of other two state variables. \n", - " - For example, consumption at each grid of liquid assets given fixed level of illiquid assets holding and productivity. \n", + "Taking the consumption function as an example, we plot consumption by adjusters and non-adjusters over a range of $k$ and $m$ that encompasses x percent of the mass of the distribution function. \n", "\n", - " - 3-dimensional graph: consumption at different grids points of liquid and illiquid assets with only value of productivity fixed. \n", - " - There is limitations at 1-dimensional graph, as we do not know ex ante at what grid points the dimension is reduced the most. So the 3-dimensional graph gives us a more straightforward picture. \n", - " - In this context, as we only have 4 grid points for productivity, we can fix grid of productivity and focus on how the number of grids is reduced for liquid and illiquid assets." + "We plot the functions for the top and bottom values of the wage $h$ distribution\n" ] }, { @@ -747,9 +778,7 @@ "cell_type": "code", "execution_count": 13, "metadata": { - "code_folding": [ - 0 - ] + "code_folding": [] }, "outputs": [ { @@ -954,8 +983,8 @@ "source": [ "#### Observation\n", "\n", - "- For a given grid value of productivity, the remaining grid points after DCT to represent the whole m-k surface are concentrated in low values of k and m. The reason, to put it simply, is that the slopes of the surface of marginal utility are very steep around this area. \n", - "- For different grid values of productivity(4 sub plots), the numbers of grid points operation differ. From the lowest to highest values of productivity, there are 78, 33, 25 and 18 grid points, respectively. They add up to the total number of grids 154 after DCT operation, as we print out above for marginal utility function. " + "- For a given grid value of productivity, the remaining grid points after DCT to represent the whole consumption function are concentrated in low values of $k$ and $m$. This is because the slopes of the surfaces of marginal utility are changing the most in these regions. For larger values of $k$ and $m$ the functions become smooth and only slightly concave, so they can be represented by many fewer points\n", + "- For different grid values of productivity (2 sub plots), the numbers of grid points in the DCT operation differ. From the lowest to highest values of productivity, there are 78, 33, 25 and 18 grid points, respectively. They add up to the total number of gridpoints of 154 after DCT operation, as we noted above for marginal utility function. " ] }, { @@ -964,8 +993,8 @@ "source": [ "### Summary: what do we achieve after the transformation?\n", "\n", - "- Via DCT, the dimension of policy function and value functions are reduced both from 3600 to 154 and 94, respectively.\n", - "- Via marginalizing the joint distribution with the fixed copula assumption, the marginal distribution is of dimension 64 compared to its joint distribution of a dimension of 3600.\n", + "- Using the DCT, the dimension of policy function and value functions are reduced both from 3600 to 154 and 94, respectively.\n", + "- By marginalizing the joint distribution with the fixed copula assumption, the marginal distribution is of dimension 64 compared to its joint distribution of a dimension of 3600.\n", "\n", "\n" ] @@ -1101,7 +1130,25 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.7" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false }, "varInspector": { "cols": { diff --git a/HARK/BayerLuetticke/DCT-Copula-Illustration.py b/HARK/BayerLuetticke/DCT-Copula-Illustration.py index 1aac59115..4517c4a33 100644 --- a/HARK/BayerLuetticke/DCT-Copula-Illustration.py +++ b/HARK/BayerLuetticke/DCT-Copula-Illustration.py @@ -19,13 +19,29 @@ # [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/econ-ark/HARK/BayerLuetticke?filepath=notebooks%2FHARK%2FBayerLuetticke%2FTwoAsset.ipynb) # # -# This is an accompany to the [main notebook](TwoAsset.ipynb) illustrating dimension reduction in Bayer/Luetticke algorithm. +# This companion to the [main notebook](TwoAsset.ipynb) explains in more detail how they reduce the dimensionality of the problem # # - Based on original slides by Christian Bayer and Ralph Luetticke # - Original Jupyter notebook by Seungcheol Lee # - Further edits by Chris Carroll, Tao Wang # +# %% [markdown] +# ### Preliminaries +# +# In StE in the model, in any given period, a consumer in state $s$ (which comprises liquid assets $m$, illiquid assets $k$, and human capital $\newcommand{hLev}{p}\hLev$) has two key choices: +# 1. To adjust ('a') or not adjust ('n') their holdings of illiquid assets $k$ +# 1. Contingent on that choice, decide the level of consumption, yielding consumption functions: +# * $c_n(s)$ - nonadjusters +# * $c_a(s)$ - adjusters +# +# The usual envelope theorem applies here, so marginal value wrt the liquid asset equals marginal utility with respect to consumption: +# \[ +# \frac{d v}{d m} = \frac{d u}{d c} +# \] +# +# In practice, the authors solve the problem using the marginal value of money $\texttt{Vm} = dv/dm$, but because the marginal utility function is invertible it is trivial to recover $\texttt{c}$ from $(u^{\prime})^{-1}(\texttt{Vm} )$. The consumption function is therefore computed from the $\texttt{Vm}$ function + # %% {"code_folding": [0, 6, 17, 21]} # Setup stuff @@ -65,7 +81,7 @@ def in_ipynb(): sys.path.insert(0, my_file_path) # %% {"code_folding": [0]} -# Change working folder and load Stationary equilibrium (StE) +# Load precalculated Stationary Equilibrium (StE) object EX3SS import pickle os.chdir(code_dir) # Go to the directory with pickled code @@ -77,34 +93,19 @@ def in_ipynb(): EX3SS=pickle.load(open("EX3SS_20.p", "rb")) # %% [markdown] -# ### Dimension Reduction via discrete cosine transformation and a fixed copula -# -# #### What is it whose dimension needs to be reduced? +# ### Dimensions # -# 1. Policy and value functions -# 1. The distribution of agents across states +# The imported StE solution to the problem represents the functions at a set of gridpoints of +# * liquid assets ($n_m$ points), illiquid assets ($n_k$), and human capital ($n_h$) +# * (In the code these are $\{\texttt{nm ,nk ,nh}\}$) # -# Grids are constructed for values of the state variables: -# * liquid ($nm$ points), illiquid assets ($nk$), and idiosyncratic pty ($nh$) +# So even if the grids are fairly sparse for each state variable, the total number of combinations of the idiosyncratic state variables is large: $n = n_m \times n_k \times n_h$. So, e.g., $\bar{c}$ is a set of size $n$ containing the level of consumption at each possible combination of gridpoints. # -# So there are $nm \times nk \times nh$ potential combinations +# In the "real" micro problem, it would almost never happen that a continuous variable like $m$ would end up being exactly equal to one of the prespecified gridpoints. But the functions need to be evaluated at such points. This is addressed by linear interpolation. That is, if, say, the grid had $m_{8} = 40$ and $m_{9} = 50$ then and a consumer ended up with $m = 45$ then the approximation is that $\tilde{c}(45) = 0.5 \bar{c}_{8} + 0.5 \bar{c}_{9}$. # -# In principle, functions are represented by specifying their values at each specified combination of gridpoints and interpolating for intermediate values -# * In practice, for technical reasons, interpolation is not necessary here -# -# There are two kinds of functions: -# 1. Policy functions and marginal value functions -# * At each of the gridpoints, there is a number -# * This is value for the value function -# * This is consumption for the consumption function -# * $c_n$ is the consumption function for the nonadjuster -# * $c_a$ is the consumption function for the adjuster -# 1. The distribution (="histograms") of agents across states -# * In principle, distributions need not be computed at the same gridpoints used to represent the value and policy functions -# * In practice, the same grids are used # %% {"code_folding": [0]} -# Recover dimensions of the marginal value and consumption functions +# Show dimensions of the consumer's problem (state space) print('c_n is of dimension: ' + str(EX3SS['mutil_c_n'].shape)) print('c_a is of dimension: ' + str(EX3SS['mutil_c_a'].shape)) @@ -117,43 +118,61 @@ def in_ipynb(): print(str(len(EX3SS['grid']['k']))+' gridpoints for illiquid assets;') print(str(len(EX3SS['grid']['h']))+' gridpoints for individual productivity.') print('') -print('Therefore, the joint distribution across different is of size: ') +print('Therefore, the joint distribution is of size: ') print(str(EX3SS['mpar']['nm'])+ ' * '+str(EX3SS['mpar']['nk'])+ ' * '+str(EX3SS['mpar']['nh'])+ ' = '+ str(EX3SS['mpar']['nm']*EX3SS['mpar']['nk']*EX3SS['mpar']['nh'])) +# %% [markdown] +# ### Dimension Reduction +# +# The authors use different reduction methods for the consumer's problem and the distribution # %% [markdown] -# #### Intuitively, how does the reduction work? +# #### The consumer's problem: Discrete Cosine Transformation # -# ##### Reducing the dimension of policy/value functions -# - The first step is to find an efficient "compressed" representation of the function (e.g., the consumption function). The analogy to image compression is that nearby pixels are likely to have identical or very similar colors, so we need only to find an efficient way to represent the way in which the colors change from one pixel to another. Similarly, consumption at a given point is likely to be close to consumption at a nearby point, so a function that captures that similarity efficiently can preserve most of the information without keeping all of the points. +# The idea is to find an efficient "compressed" representation of our functions (e.g., the consumption function). The analogy to image compression is that nearby pixels are likely to have identical or very similar colors, so we need only to find an efficient way to represent the way in which the colors change from one pixel to another. The analogy is that consumption at a given point $s_{i}$ is likely to be close to consumption point another point $s_{j}$ that is "close" in the state space (similar wealth, income, etc), so a function that captures that similarity efficiently can preserve most of the information without keeping all of the points. # -# - We will be using the discrete cosine transformation (DCT), which is commonly used in image compression. See [here](https://en.wikipedia.org/wiki/Discrete_cosine_transform) for the Wikipedia page on DCT. +# Like linear interpolation, the [DCT transformation](https://en.wikipedia.org/wiki/Discrete_cosine_transform) is a method of representing a continuous function using a finite set of numbers. It uses a set of independent basis functions to do this. # -# ##### Reducing the dimension of joint distribution +# But it turns out that some of those basis functions are much more important than others in representing the steady-state functions. Dimension reduction is accomplished by basically ignoring all basis functions that make small contributions to the steady state distribution. # -# - The other tool we use is the "copula," which allows us to represent the distribution of people across idiosyncratic states efficiently -# * In general, a multivariate joint distribution is not uniquely determined by marginal distributions only. A copula, to put it simply, characterizes the correlation across variables and it combined with marginal distributions determine the unique joint distribution. -# * The crucial assumption of fixed copula is that what aggregate shocks do is to squeeze or distort the steady state distribution, but leave the rank structure of the distribution the same. Think of representing a balloon by a set of points on its surface; the copula assumption is effectively that when something happens to the balloon (more air is put in it, or it is squeezed on one side, say), we can represent what happens by thinking about how the relationship between points is distorted, rather than having to reconstruct the shape of the balloon with a completely independent set of new points. Which points are close to which other points does not change, but the distances between them can change. If the distances between them change in a particularly simple way, you can represent what has happened with a small amount of information. For example, if the balloon is perfectly spherical, then adding a given amount of air might increase the distances between adjacent points by 5 percent. (See the video illustration here) +# ##### When might this go wrong? # -# - In the context of this model, the assumption is that the rank order correlation (e.g. the correlation of where you are in the distribution of liquid assets and illiquid assets) remains the same after the aggregate shocks are introduced to StE +# Suppose the consumption function changes in a recession in ways that change behavior radically at some states. Like, suppose unemployment almost never happens in steady state, but it can happen in temporary recessions. Suppose further that, even for employed people, _worries_ about unemployment cause many of them to prudently withdraw some of their illiquid assets -- behavior opposite of what people in the same state would be doing during expansions. In that case, the DCT functions that represented the steady state function would have had no incentive to be able to represent well the part of the space that is never seen in steady state, so any functions that might help do so might well have been dropped in the dimension reduction stage. # -# - In this case we just need to represent how the marginal distributions of each state change, instead of the full joint distributions. +# On the whole, it seems unlikely that this kind of thing is a major problem, because the vast majority of the variation that people experience is idiosyncratic. There is always unemployment, for example; it just moves up and down a bit with aggregate shocks, but since the experience is in fact well represented in the steady state the method should have no trouble capturing it. # -# - This reduces 3600 to 30+30+4=64. See [here](https://en.wikipedia.org/wiki/Copula_(probability_theory)) for the Wikipedia page on copula. The copula is computed from the joint distribution of states in StE and will be used to transform the marginals back to joint distributions. +# Where it might have more trouble is in representing economies in which there are multiple equilibria in which behavior is quite different. -# %% {"code_folding": [0]} +# %% [markdown] +# #### For the distribution of agents across states: Copula +# +# The other tool the authors use is the ["copula,"](https://en.wikipedia.org/wiki/Copula_(probability_theory)) which allows us to represent the distribution of people across idiosyncratic states efficiently +# +# The copula is computed from the joint distribution of states in StE and will be used to transform the marginal distributions back to joint distributions. +# +# * In general, a multivariate joint distribution is not uniquely determined by marginal distributions only. A copula, to put it simply, is a compressed representation of the joint distribution of the rank order of points; together with the marginal distributions this expands to a complete representation of the joint distribution +# * The crucial assumption of a fixed copula is that what aggregate shocks do is to squeeze or distort the steady state distribution, but leave the rank structure of the distribution the same. Think of representing a balloon by a set of points on its surface; the copula assumption is effectively that when something happens to the balloon (more air is put in it, or it is squeezed on one side, say), we can represent what happens to the points by thinking about how the relationship between points is distorted, rather than having to reconstruct the shape of the balloon with a completely independent set of new points. Which points are close to which other points does not change, but the distances between them can change. If the distances between them change in a particularly simple way, you can represent what has happened with a small amount of information. For example, if the balloon is perfectly spherical, then adding a given amount of air might increase the distances between adjacent points by 5 percent. (See the video illustration here) +# +# - In the context of this model, the assumption that allows us to use a copula is that the rank order correlation (e.g. the correlation of where you rank in the distribution of liquid assets and illiquid assets) remains the same after the aggregate shocks are introduced to StE. That is, the fact that you are richer than me, and Bill Gates is richer than you, does not change in a recession. _How much_ richer you are than me, and Gates than you, can change, but the rank order does not. +# +# - In this case we just need to represent how the marginal distributions of each state change, instead of the full joint distributions +# +# - This reduces the number of points for which we need to track transitions from $3600 = 30 \times 30 \times 4$ to $64 = 30+30+4$. Or the total number of points we need to contemplate goes from $3600^2 \approx 13 million$ to $64^2=4096. + +# %% {"code_folding": []} # Get some specs about the copula, which is precomputed in the EX3SS object print('The copula consists of two parts: gridpoints and values at those gridpoints:'+ \ - ',\n gridpoints with dimension of '+str(EX3SS['Copula']['grid'].shape) + \ - ', where the first element is total number of gridpoints' + \ - ', and the second element is number of states' + \ - ',\n and values with dimension of '+str(EX3SS['Copula']['value'].shape) + \ - ', \n each entry of which is the probability of the three state variables below the grids.') + '\n gridpoints have dimensionality of '+str(EX3SS['Copula']['grid'].shape) + \ + '\n where the first element is total number of gridpoints' + \ + '\n and the second element is number of state variables' + \ + ',\n whose values also are of dimension of '+str(EX3SS['Copula']['value'].shape[0]) + \ + '\n each entry of which is the probability that all three of the' + '\n state variables are below the corresponding point.') # %% {"code_folding": [0]} @@ -203,7 +222,7 @@ def __init__(self, par, mpar, grid, Output, targets, Vm, Vk, self.Vm = Vm # Marginal value from liquid cash-on-hand self.Vk = Vk # Marginal value of capital self.joint_distr = joint_distr # Multidimensional histogram - self.Copula = Copula # Encodes rank correlation structure of distribution + self.Copula = Copula # Encodes rank marginal correlation of joint distribution self.mutil_c = mutil_c # Marginal utility of consumption self.P_H = P_H # Transition matrix for macro states (not including distribution) @@ -392,39 +411,44 @@ def do_dct(self, obj, mpar, level): #EX3SS['par']['rhoS'] = 0.84 # Persistence of variance #EX3SS['par']['sigmaS'] = 0.54 # STD of variance shocks -# %% {"code_folding": [0]} +# %% {"code_folding": []} ## Choose an accuracy of approximation with DCT ### Determines number of basis functions chosen -- enough to match this accuracy ### EX3SS is precomputed steady-state pulled in above EX3SS['par']['accuracy'] = 0.99999 +## 20190607: CDC to TW: Please try to figure out what this is + # %% {"code_folding": [0]} ## Implement state reduction and DCT ### Do state reduction on steady state EX3SR=StateReduc_Dct(**EX3SS) # Takes StE result as input and get ready to invoke state reduction operation SR=EX3SR.StateReduc() # StateReduc is operated -# %% {"code_folding": [5, 7, 9, 12, 14, 18]} +# %% {"code_folding": [10, 12]} print('What are the results from the state reduction?') #print('Newly added attributes after the operation include \n'+str(set(SR.keys())-set(EX3SS.keys()))) print('\n') -print('The dimension of policy function is reduced to '+str(SR['indexMUdct'].shape[0]) \ - +' from '+str(EX3SS['mutil_c'].shape)) -print('The dimension of value function is reduced to '+str(SR['indexVKdct'].shape[0]) \ +print('To achieve an accuracy of '+str(EX3SS['par']['accuracy'])+'\n') + +print('The dimension of the policy functions is reduced to '+str(SR['indexMUdct'].shape[0]) \ + +' from '+str(EX3SS['mpar']['nm']*EX3SS['mpar']['nk']*EX3SS['mpar']['nh']) + ) +print('The dimension of the marginal value functions is reduced to '+str(SR['indexVKdct'].shape[0]) \ + ' from ' + str(EX3SS['Vk'].shape)) print('The total number of control variables is '+str(SR['Contr'].shape[0])+'='+str(SR['indexMUdct'].shape[0]) + \ '+'+str(SR['indexVKdct'].shape[0])+'+ # of other macro controls') print('\n') -print('After marginalizing the joint distribution, \ - \n the dimension of states including exogenous state, is '+str(SR['Xss'].shape[0])) -print('Dimension of gamma_state is '+str(SR['Gamma_state'].shape)+\ - '. It simply stacks all grids of different\ +print('The copula represents the joint distribution with a vector of size '+str(SR['Gamma_state'].shape) ) +print('The dimension of states including exogenous state, is ' +str(SR['Xss'].shape[0])) + +print('It simply stacks all grids of different\ \n state variables regardless of their joint distributions.\ - \n This is due to the assumption of the rank order remains the same.') + \n This is due to the assumption that the rank order remains the same.') print('The total number of state variables is '+str(SR['State'].shape[0]) + '='+\ - str(SR['Gamma_state'].shape[1])+'+ # of other states') + str(SR['Gamma_state'].shape[1])+'+ the number of macro states (like the interest rate)') # %% [markdown] @@ -432,13 +456,10 @@ def do_dct(self, obj, mpar, level): # # #### Policy/value functions # -# - Taking consumption function as an example, let us plot consumptions by adjusters and non-adjusters at different grid points before and after dimension reduction. -# - 2-dimensional graph: consumption at different grid points of a state variable fixing the values of other two state variables. -# - For example, consumption at each grid of liquid assets given fixed level of illiquid assets holding and productivity. +# Taking the consumption function as an example, we plot consumption by adjusters and non-adjusters over a range of $k$ and $m$ that encompasses x percent of the mass of the distribution function. +# +# We plot the functions for the top and bottom values of the wage $h$ distribution # -# - 3-dimensional graph: consumption at different grids points of liquid and illiquid assets with only value of productivity fixed. -# - There is limitations at 1-dimensional graph, as we do not know ex ante at what grid points the dimension is reduced the most. So the 3-dimensional graph gives us a more straightforward picture. -# - In this context, as we only have 4 grid points for productivity, we can fix grid of productivity and focus on how the number of grids is reduced for liquid and illiquid assets. # %% {"code_folding": [0]} ## Graphical illustration @@ -541,7 +562,7 @@ def do_dct(self, obj, mpar, level): plt.ylabel(r'$c_a(h)$',size=15) plt.legend() -# %% {"code_folding": [0]} +# %% {"code_folding": []} ## 3D scatter plots of consumption function ## at all grids and grids after dct for both adjusters and non-adjusters @@ -650,14 +671,14 @@ def do_dct(self, obj, mpar, level): # %% [markdown] # #### Observation # -# - For a given grid value of productivity, the remaining grid points after DCT to represent the whole m-k surface are concentrated in low values of k and m. The reason, to put it simply, is that the slopes of the surface of marginal utility are very steep around this area. -# - For different grid values of productivity(4 sub plots), the numbers of grid points operation differ. From the lowest to highest values of productivity, there are 78, 33, 25 and 18 grid points, respectively. They add up to the total number of grids 154 after DCT operation, as we print out above for marginal utility function. +# - For a given grid value of productivity, the remaining grid points after DCT to represent the whole consumption function are concentrated in low values of $k$ and $m$. This is because the slopes of the surfaces of marginal utility are changing the most in these regions. For larger values of $k$ and $m$ the functions become smooth and only slightly concave, so they can be represented by many fewer points +# - For different grid values of productivity (2 sub plots), the numbers of grid points in the DCT operation differ. From the lowest to highest values of productivity, there are 78, 33, 25 and 18 grid points, respectively. They add up to the total number of gridpoints of 154 after DCT operation, as we noted above for marginal utility function. # %% [markdown] # ### Summary: what do we achieve after the transformation? # -# - Via DCT, the dimension of policy function and value functions are reduced both from 3600 to 154 and 94, respectively. -# - Via marginalizing the joint distribution with the fixed copula assumption, the marginal distribution is of dimension 64 compared to its joint distribution of a dimension of 3600. +# - Using the DCT, the dimension of policy function and value functions are reduced both from 3600 to 154 and 94, respectively. +# - By marginalizing the joint distribution with the fixed copula assumption, the marginal distribution is of dimension 64 compared to its joint distribution of a dimension of 3600. # # # diff --git a/HARK/BayerLuetticke/TwoAsset.ipynb b/HARK/BayerLuetticke/TwoAsset.ipynb index 22395cac9..2515daae8 100644 --- a/HARK/BayerLuetticke/TwoAsset.ipynb +++ b/HARK/BayerLuetticke/TwoAsset.ipynb @@ -24,12 +24,14 @@ "The Bayer-Luetticke method has the following broad features:\n", " * The model is formulated and solved in discrete time (in contrast with some other recent approaches )\n", " * Solution begins by calculation of the steady-state equilibrium (StE) with no aggregate shocks\n", - " * Dimensionality reduction is performed immediately after calculation of the StE\n", - " * This involves finding a representation of the individual policy function using a particular class of basis functions\n", - " * The method captures the business-cycle-induced _deviations_ of the individual policy functions from those that characterize the riskless StE\n", - " * This is done using the same basis functions originally optimized to match the StE individual policy function (akin to image compression)\n", - " * The method of capturing dynamic deviations from a reference frame is akin to video compression\n", - " * Similar methods are used for capturing dynamics of distributions" + " * \"Dimensionality reduction\" of the consumer's decision problem is performed before any further analysis is done\n", + " * \"Dimensionality reduction\" is just a particularly efficient method of approximating a function\n", + " * It involves finding a representation of the function using some class of basis functions\n", + " * Dimensionality reduction of the joint distribution is accomplished using a \"copula\"\n", + " * See the companion notebook for description of the copula\n", + " * The method approximates the business-cycle-induced _deviations_ of the individual policy functions from those that characterize the riskless StE\n", + " * This is done using the same basis functions originally optimized to match the StE individual policy function\n", + " * The method of capturing dynamic deviations from a reference frame is akin to video compression" ] }, { @@ -88,7 +90,7 @@ " \\bar{v} = \\bar{u} + \\beta \\Pi_{\\bar{h}}\\bar{v}\n", " \\end{equation}\n", " holds for the optimal policy\n", - " * A linear interpolant is used for the value function\n", + " * A linear interpolator is used to represent the value function\n", " * For the distribution, which (by the definition of steady state) is constant: \n", "\n", "\\begin{eqnarray}\n", @@ -108,8 +110,10 @@ "\n", "This can be solved by (jointly):\n", " 1. Finding $d\\bar{\\mu}$ as the unit-eigenvalue of $\\Pi_{\\bar{h}}$\n", - " 2. Using fast solution techniques for the decision problem, e.g. EGM\n", - " 3. Using a root-finder to solve for $P$" + " 2. Using standard solution techniques for the micro decision problem given $P$\n", + " * Like wage and interest rate\n", + " 3. Using a root-finder to solve for $P$\n", + " * This basically iterates the other two steps until it finds values where they are consistent" ] }, { @@ -137,7 +141,7 @@ " v_t = \\bar{u}_{P_t} + \\beta \\Pi_{h_t} v_{t+1}\n", " \\end{equation}\n", " holds for policy $h_t$ which optimizes with respect to $v_{t+1}$ and $P_t$\n", - " * and a sequence of histograms, such that\n", + " * and a sequence of \"histograms\" (discretized distributions), such that\n", " \\begin{equation}\n", " d\\mu_{t+1} = d\\mu_t \\Pi_{h_t}\n", " \\end{equation}\n", @@ -197,15 +201,25 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { - "code_folding": [ - 0 - ] + "code_folding": [] }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'os' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode_dir\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Go to the directory with pickled code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m## EX3SS_20.p is the information in the stationary equilibrium (20: the number of illiquid and liquid weath grids )\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'os' is not defined" + ] + } + ], "source": [ - "## Change working folder and load Stationary equilibrium (StE)\n", + "## Load Stationary equilibrium (StE) object EX3SS_20\n", "\n", "import pickle\n", "os.chdir(code_dir) # Go to the directory with pickled code\n", @@ -252,10 +266,10 @@ "metadata": {}, "source": [ "#### So, is all solved?\n", - "The dimensionality of the system F is still an issue\n", + "The dimensionality of the system F is a big problem \n", " * With high dimensional idiosyncratic states, discretized value functions and distributions become large objects\n", " * For example:\n", - " * 4 income states $\\times$ 100 illiquid capital states $\\times$ 100 liquid capital states $\\rightarrow$ $\\geq$ 40,000 variables in $F$\n", + " * 4 income states $\\times$ 100 illiquid capital states $\\times$ 100 liquid capital states $\\rightarrow$ $\\geq$ 40,000 values in $F$\n", " * Same number of state variables " ] }, @@ -270,7 +284,9 @@ " * Use Chebychev polynomials on roots grid\n", " * Define a reference \"frame\": the steady-state equilibrium (StE)\n", " * Represent fluctuations as differences from this reference frame\n", - " * Assume all coefficients of the DCT from the StE that are close to zero do not change when there is an aggregate shock (small things stay small and unchanged)\n", + " * Assume all coefficients of the DCT from the StE that are close to zero do not change when there is an aggregate shock (small things stay small)\n", + " * When would this be problematic?\n", + " * In video, \n", " \n", "2. Assume no changes in the rank correlation structure of $\\mu$ \n", " * Calculate the Copula, $\\bar{C}$ of $\\mu$ in the StE\n", @@ -342,7 +358,9 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "code_folding": [], + "code_folding": [ + 0 + ], "lines_to_end_of_cell_marker": 0, "lines_to_next_cell": 1 }, @@ -496,32 +514,31 @@ "metadata": {}, "source": [ "2) Decoding\n", - " * Now we reconstruct $\\tilde{v}_t=\\tilde{v}(\\theta_t)=dct^{-1}(\\tilde{\\Theta}(\\theta_i))$\n", - " * idct is the inverse dct that goes from the $\\theta$ vector to the corresponding values\n", + " * Now we reconstruct $\\tilde{v}_t=\\tilde{v}(\\theta_t)=dct^{-1}(\\tilde{\\Theta}(\\theta_{t}))$\n", + " * idct=$dct^{-1}$ is the inverse dct that goes from the $\\theta$ vector to the corresponding values\n", " * This means that in the StE the reduction step adds no addtional approximation error:\n", " * Remember that $\\tilde{v}(0)=\\bar{v}$ by construction\n", - " * Yet, it allows to reduce the number of derivatives that need to be calculated from the outset.\n", + " * But it allows us to reduce the number of derivatives that need to be calculated from the outset.\n", + " * We only calculate derivatives for those basis functions that make an important contribution to the representation of the policy or value functions\n", " \n", "3) The histogram is recovered the same way\n", " * $\\mu_t$ is approximated as $\\bar{C}(\\bar{\\mu_t}^1,...,\\bar{\\mu_t}^n)$, where $n$ is the dimensionality of the idiosyncratic states\n", " * The StE distribution is obtained when $\\mu = \\bar{C}(\\bar{\\mu}^1,...,\\bar{\\mu}^n)$\n", " * Typically prices are only influenced through the marginal distributions\n", - " * The approach ensures that changes in the mass of one, say wealth, state are distributed in a sensible way across the other dimension\n", + " * The approach ensures that changes in the mass of one state (say, wealth) are distributed in a sensible way across the other dimensions\n", " * The implied distributions look \"similar\" to the StE one (different in (Reiter, 2009))\n", "\n", - "4) Too many equations\n", - " * The system\n", + "4) The large system above is now transformed into a much smaller system:\n", " \\begin{align}\n", " F(\\{d\\mu_t^1,...,d\\mu_t^n\\}, S_t, \\{d\\mu_{t+1}^1,...,d\\mu_{t+1}^n\\}, S_{t+1}, \\theta_t, P_t, \\theta_{t+1}, P_{t+1})\n", " &= \\begin{bmatrix}\n", " d\\bar{C}(\\bar{\\mu}_t^1,...,\\bar{\\mu}_t^n) - d\\bar{C}(\\bar{\\mu}_t^1,...,\\bar{\\mu}_t^n)\\Pi_{h_t} \\\\\n", - " dct[idct(\\tilde{\\Theta(\\theta_t)}) - (\\bar{u}_{h_t} + \\beta \\Pi_{h_t}idct(\\tilde{\\Theta(\\theta_{t+1})}] \\\\\n", + " dct\\left[idct(\\tilde{\\Theta}(\\theta_t) - (\\bar{u}_{h_t} + \\beta \\Pi_{h_t}idct(\\tilde{\\Theta}(\\theta_{t+1})))\\right] \\\\\n", " S_{t+1} - H(S_t,d\\mu_t) \\\\\n", " \\Phi(h_t,d\\mu_t,P_t,S_t) \\\\\n", " \\end{bmatrix}\n", " \\end{align}\n", - " has too many equations\n", - " * Uses only difference in marginals and the differences on $\\mathop{I}$ " + " " ] }, { @@ -578,14 +595,16 @@ "\n", "- Individual state variables: $b$, $k$ and $h$, the joint distribution of individual states $\\Theta$\n", "- Individual control variables: $c$, $n$, $b'$, $k'$ \n", - "- Optimal policy for adjust and non-adjust cases are $c^*_a$, $n^*_a$ $k^*_a$ and $b^*_a$ and $c^*_n$, $n^*_n$ and $b^*_n$, respetively \n" + "- Optimal policy for adjusters and nonadjusters are $c^*_a$, $n^*_a$ $k^*_a$ and $b^*_a$ and $c^*_n$, $n^*_n$ and $b^*_n$, respectively \n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { - "code_folding": [] + "code_folding": [ + 0 + ] }, "outputs": [], "source": [ @@ -2268,7 +2287,25 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.7" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false }, "varInspector": { "cols": { diff --git a/HARK/BayerLuetticke/TwoAsset.py b/HARK/BayerLuetticke/TwoAsset.py index 8e6022d6e..f3ac12da2 100644 --- a/HARK/BayerLuetticke/TwoAsset.py +++ b/HARK/BayerLuetticke/TwoAsset.py @@ -28,12 +28,14 @@ # The Bayer-Luetticke method has the following broad features: # * The model is formulated and solved in discrete time (in contrast with some other recent approaches ) # * Solution begins by calculation of the steady-state equilibrium (StE) with no aggregate shocks -# * Dimensionality reduction is performed immediately after calculation of the StE -# * This involves finding a representation of the individual policy function using a particular class of basis functions -# * The method captures the business-cycle-induced _deviations_ of the individual policy functions from those that characterize the riskless StE -# * This is done using the same basis functions originally optimized to match the StE individual policy function (akin to image compression) -# * The method of capturing dynamic deviations from a reference frame is akin to video compression -# * Similar methods are used for capturing dynamics of distributions +# * "Dimensionality reduction" of the consumer's decision problem is performed before any further analysis is done +# * "Dimensionality reduction" is just a particularly efficient method of approximating a function +# * It involves finding a representation of the function using some class of basis functions +# * Dimensionality reduction of the joint distribution is accomplished using a "copula" +# * See the companion notebook for description of the copula +# * The method approximates the business-cycle-induced _deviations_ of the individual policy functions from those that characterize the riskless StE +# * This is done using the same basis functions originally optimized to match the StE individual policy function +# * The method of capturing dynamic deviations from a reference frame is akin to video compression # ### Setup # @@ -82,7 +84,7 @@ # \bar{v} = \bar{u} + \beta \Pi_{\bar{h}}\bar{v} # \end{equation} # holds for the optimal policy -# * A linear interpolant is used for the value function +# * A linear interpolator is used to represent the value function # * For the distribution, which (by the definition of steady state) is constant: # # \begin{eqnarray} @@ -102,8 +104,10 @@ # # This can be solved by (jointly): # 1. Finding $d\bar{\mu}$ as the unit-eigenvalue of $\Pi_{\bar{h}}$ -# 2. Using fast solution techniques for the decision problem, e.g. EGM +# 2. Using standard solution techniques for the micro decision problem given $P$ +# * Like wage and interest rate # 3. Using a root-finder to solve for $P$ +# * This basically iterates the other two steps until it finds values where they are consistent # #### Introducing aggregate risk # @@ -121,7 +125,7 @@ # v_t = \bar{u}_{P_t} + \beta \Pi_{h_t} v_{t+1} # \end{equation} # holds for policy $h_t$ which optimizes with respect to $v_{t+1}$ and $P_t$ -# * and a sequence of histograms, such that +# * and a sequence of "histograms" (discretized distributions), such that # \begin{equation} # d\mu_{t+1} = d\mu_t \Pi_{h_t} # \end{equation} @@ -166,8 +170,8 @@ def in_ipynb(): sys.path.insert(0, code_dir) sys.path.insert(0, my_file_path) -# + {"code_folding": [0]} -## Change working folder and load Stationary equilibrium (StE) +# + {"code_folding": []} +## Load Stationary equilibrium (StE) object EX3SS_20 import pickle os.chdir(code_dir) # Go to the directory with pickled code @@ -205,10 +209,10 @@ def in_ipynb(): # * Standard techniques can solve the discretized version # #### So, is all solved? -# The dimensionality of the system F is still an issue +# The dimensionality of the system F is a big problem # * With high dimensional idiosyncratic states, discretized value functions and distributions become large objects # * For example: -# * 4 income states $\times$ 100 illiquid capital states $\times$ 100 liquid capital states $\rightarrow$ $\geq$ 40,000 variables in $F$ +# * 4 income states $\times$ 100 illiquid capital states $\times$ 100 liquid capital states $\rightarrow$ $\geq$ 40,000 values in $F$ # * Same number of state variables # ### Bayer-Luetticke method @@ -218,7 +222,9 @@ def in_ipynb(): # * Use Chebychev polynomials on roots grid # * Define a reference "frame": the steady-state equilibrium (StE) # * Represent fluctuations as differences from this reference frame -# * Assume all coefficients of the DCT from the StE that are close to zero do not change when there is an aggregate shock (small things stay small and unchanged) +# * Assume all coefficients of the DCT from the StE that are close to zero do not change when there is an aggregate shock (small things stay small) +# * When would this be problematic? +# * In video, # # 2. Assume no changes in the rank correlation structure of $\mu$ # * Calculate the Copula, $\bar{C}$ of $\mu$ in the StE @@ -269,7 +275,7 @@ def in_ipynb(): # \end{array}\right. # \end{equation} -# + {"code_folding": []} +# + {"code_folding": [0]} ## State reduction and Discrete cosine transformation class StateReduc_Dct: @@ -414,32 +420,31 @@ def do_dct(self, obj, mpar, level): # - # 2) Decoding -# * Now we reconstruct $\tilde{v}_t=\tilde{v}(\theta_t)=dct^{-1}(\tilde{\Theta}(\theta_i))$ -# * idct is the inverse dct that goes from the $\theta$ vector to the corresponding values +# * Now we reconstruct $\tilde{v}_t=\tilde{v}(\theta_t)=dct^{-1}(\tilde{\Theta}(\theta_{t}))$ +# * idct=$dct^{-1}$ is the inverse dct that goes from the $\theta$ vector to the corresponding values # * This means that in the StE the reduction step adds no addtional approximation error: # * Remember that $\tilde{v}(0)=\bar{v}$ by construction -# * Yet, it allows to reduce the number of derivatives that need to be calculated from the outset. +# * But it allows us to reduce the number of derivatives that need to be calculated from the outset. +# * We only calculate derivatives for those basis functions that make an important contribution to the representation of the policy or value functions # # 3) The histogram is recovered the same way # * $\mu_t$ is approximated as $\bar{C}(\bar{\mu_t}^1,...,\bar{\mu_t}^n)$, where $n$ is the dimensionality of the idiosyncratic states # * The StE distribution is obtained when $\mu = \bar{C}(\bar{\mu}^1,...,\bar{\mu}^n)$ # * Typically prices are only influenced through the marginal distributions -# * The approach ensures that changes in the mass of one, say wealth, state are distributed in a sensible way across the other dimension +# * The approach ensures that changes in the mass of one state (say, wealth) are distributed in a sensible way across the other dimensions # * The implied distributions look "similar" to the StE one (different in (Reiter, 2009)) # -# 4) Too many equations -# * The system +# 4) The large system above is now transformed into a much smaller system: # \begin{align} # F(\{d\mu_t^1,...,d\mu_t^n\}, S_t, \{d\mu_{t+1}^1,...,d\mu_{t+1}^n\}, S_{t+1}, \theta_t, P_t, \theta_{t+1}, P_{t+1}) # &= \begin{bmatrix} # d\bar{C}(\bar{\mu}_t^1,...,\bar{\mu}_t^n) - d\bar{C}(\bar{\mu}_t^1,...,\bar{\mu}_t^n)\Pi_{h_t} \\ -# dct[idct(\tilde{\Theta(\theta_t)}) - (\bar{u}_{h_t} + \beta \Pi_{h_t}idct(\tilde{\Theta(\theta_{t+1})}] \\ +# dct\left[idct(\tilde{\Theta}(\theta_t) - (\bar{u}_{h_t} + \beta \Pi_{h_t}idct(\tilde{\Theta}(\theta_{t+1})))\right] \\ # S_{t+1} - H(S_t,d\mu_t) \\ # \Phi(h_t,d\mu_t,P_t,S_t) \\ # \end{bmatrix} # \end{align} -# has too many equations -# * Uses only difference in marginals and the differences on $\mathop{I}$ +# # ### The two-asset HANK model # @@ -491,10 +496,10 @@ def do_dct(self, obj, mpar, level): # # - Individual state variables: $b$, $k$ and $h$, the joint distribution of individual states $\Theta$ # - Individual control variables: $c$, $n$, $b'$, $k'$ -# - Optimal policy for adjust and non-adjust cases are $c^*_a$, $n^*_a$ $k^*_a$ and $b^*_a$ and $c^*_n$, $n^*_n$ and $b^*_n$, respetively +# - Optimal policy for adjusters and nonadjusters are $c^*_a$, $n^*_a$ $k^*_a$ and $b^*_a$ and $c^*_n$, $n^*_n$ and $b^*_n$, respectively # -# + {"code_folding": []} +# + {"code_folding": [0]} ## Construct the system of equations (including decoding): The F system def Fsys(State, Stateminus, Control_sparse, Controlminus_sparse, StateSS, ControlSS, Gamma_state, indexMUdct, indexVKdct, par, mpar, grid, targets, Copula, P, aggrshock): From b8e878480b524ec1a067c31d57fbaf5029812e2a Mon Sep 17 00:00:00 2001 From: llorracc Date: Mon, 10 Jun 2019 10:39:05 +0200 Subject: [PATCH 74/77] Update setup.py --- setup.py | 1 - 1 file changed, 1 deletion(-) diff --git a/setup.py b/setup.py index 75c39a6ee..cbc72e075 100644 --- a/setup.py +++ b/setup.py @@ -155,7 +155,6 @@ 'future', # Optional 'funcsigs', 'jupyter'], ->>>>>>> e29e7d4e2305bf926df16e2424b60a5e3f2f55b1 python_requires='>=2.7', From b9312f3a110fd6c270a53cf4d00f236fba142769 Mon Sep 17 00:00:00 2001 From: Tao Wang Date: Fri, 14 Jun 2019 01:12:07 -0400 Subject: [PATCH 75/77] plot distributions;small edits to dct graphs --- .../DCT-Copula-Illustration.ipynb | 346 +++++++++++------- .../BayerLuetticke/DCT-Copula-Illustration.py | 163 +++++---- 2 files changed, 313 insertions(+), 196 deletions(-) diff --git a/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb b/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb index e35c443b3..124661fa4 100644 --- a/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb +++ b/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb @@ -29,10 +29,7 @@ " * $c_a(s)$ - adjusters\n", "\n", "The usual envelope theorem applies here, so marginal value wrt the liquid asset equals marginal utility with respect to consumption:\n", - "\\[\n", - "\\frac{d v}{d m} = \\frac{d u}{d c}\n", - "\\]\n", - "\n", + "$[\\frac{d v}{d m} = \\frac{d u}{d c}]$.\n", "In practice, the authors solve the problem using the marginal value of money $\\texttt{Vm} = dv/dm$, but because the marginal utility function is invertible it is trivial to recover $\\texttt{c}$ from $(u^{\\prime})^{-1}(\\texttt{Vm} )$. The consumption function is therefore computed from the $\\texttt{Vm}$ function" ] }, @@ -126,11 +123,12 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 4, "metadata": { "code_folding": [ 0 ], + "lines_to_next_cell": 2, "scrolled": false }, "outputs": [ @@ -170,8 +168,7 @@ "print(str(EX3SS['mpar']['nm'])+\n", " ' * '+str(EX3SS['mpar']['nk'])+\n", " ' * '+str(EX3SS['mpar']['nh'])+\n", - " ' = '+ str(EX3SS['mpar']['nm']*EX3SS['mpar']['nk']*EX3SS['mpar']['nh']))\n", - " " + " ' = '+ str(EX3SS['mpar']['nm']*EX3SS['mpar']['nk']*EX3SS['mpar']['nh']))" ] }, { @@ -226,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 5, "metadata": { "code_folding": [], "lines_to_next_cell": 2, @@ -261,11 +258,9 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 6, "metadata": { - "code_folding": [ - 0 - ] + "code_folding": [] }, "outputs": [], "source": [ @@ -295,12 +290,14 @@ "import scipy.fftpack as sf # scipy discrete fourier transforms\n", "\n", "from mpl_toolkits.mplot3d import Axes3D\n", - "from matplotlib.ticker import LinearLocator, FormatStrFormatter" + "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n", + "\n", + "import seaborn as sns" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 7, "metadata": { "code_folding": [ 0 @@ -501,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "metadata": { "code_folding": [ 0 @@ -526,9 +523,11 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 9, "metadata": { - "code_folding": [] + "code_folding": [ + 0 + ] }, "outputs": [], "source": [ @@ -542,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 10, "metadata": { "code_folding": [ 0 @@ -558,9 +557,10 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 11, "metadata": { "code_folding": [ + 7, 10, 12 ], @@ -574,10 +574,10 @@ "What are the results from the state reduction?\n", "\n", "\n", - "If we want to achieve an accuracy of 0.99999\n", + "To achieve an accuracy of 0.99999\n", "\n", - "The dimension of policy function is reduced to 154 from 3600\n", - "The dimension of the marginal value function is reduced to 94 from (30, 30, 4)\n", + "The dimension of the policy functions is reduced to 154 from 3600\n", + "The dimension of the marginal value functions is reduced to 94 from (30, 30, 4)\n", "The total number of control variables is 259=154+94+ # of other macro controls\n", "\n", "\n", @@ -631,7 +631,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "code_folding": [ 0 @@ -681,99 +681,6 @@ "hgrid_rdc = mut_rdc_idx[2][(mut_rdc_idx[0]==mgrid_fix) & (mut_rdc_idx[1]==kgrid_fix)]" ] }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "code_folding": [ - 0 - ] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "## 2D graph: compare consumption function before and after dct \n", - "\n", - "\n", - "fig=plt.figure(figsize=(15,8))\n", - "fig.suptitle('Consumption at grid points of states')\n", - "\n", - "## for non-adjusters \n", - "\n", - "#c_n(m)\n", - "plt.subplot(2,3,1)\n", - "plt.plot(mgrid,cn_StE[:,kgrid_fix,hgrid_fix],'x',label='StE(before dct)')\n", - "plt.plot(mgrid[mgrid_rdc],cn_StE[mgrid_rdc,kgrid_fix,hgrid_fix],'r*',label='StE(after dct)')\n", - "plt.xlabel('m',size=15)\n", - "plt.ylabel(r'$c_n(m)$',size=15)\n", - "plt.legend()\n", - "\n", - "## c_n(k)\n", - "plt.subplot(2,3,2)\n", - "plt.plot(kgrid,cn_StE[mgrid_fix,:,hgrid_fix],'x',label='StE(before dct)')\n", - "plt.plot(kgrid[kgrid_rdc],cn_StE[mgrid_fix,kgrid_rdc,hgrid_fix],'r*',label='StE(after dct)')\n", - "plt.xlabel('k',size=15)\n", - "plt.ylabel(r'$c_n(k)$',size=15)\n", - "plt.legend()\n", - "\n", - "## c_n(h)\n", - "\n", - "plt.subplot(2,3,3)\n", - "plt.plot(hgrid,cn_StE[mgrid_fix,kgrid_fix,:],'x',label='StE(before dct)')\n", - "plt.plot(hgrid[hgrid_rdc],cn_StE[mgrid_fix,kgrid_fix,hgrid_rdc],'r*',label='StE(after dct)')\n", - "plt.xlabel('h',size=15)\n", - "plt.ylabel(r'$c_n(h)$',size=15)\n", - "plt.legend()\n", - "\n", - "\n", - "### for adjusters \n", - "## c_a(m)\n", - "plt.subplot(2,3,4)\n", - "plt.plot(mgrid,ca_StE[:,kgrid_fix,hgrid_fix],'x',label='StE(before dct)')\n", - "plt.plot(mgrid[mgrid_rdc],ca_StE[mgrid_rdc,kgrid_fix,hgrid_fix],'r*',label='StE(after dct)')\n", - "plt.xlabel('m',size=15)\n", - "plt.ylabel(r'$c_a(m)$',size=15)\n", - "plt.legend()\n", - "\n", - "## c_a(k)\n", - "plt.subplot(2,3,5)\n", - "plt.plot(kgrid,ca_StE[mgrid_fix,:,hgrid_fix],'x',label='StE(before dct)')\n", - "plt.plot(kgrid[kgrid_rdc],ca_StE[mgrid_fix,kgrid_rdc,hgrid_fix],'r*',label='StE(after dct)')\n", - "plt.xlabel('k',size=15)\n", - "plt.ylabel(r'$c_a(k)$',size=15)\n", - "plt.legend()\n", - "\n", - "## c_a(h)\n", - "plt.subplot(2,3,6)\n", - "plt.plot(hgrid,ca_StE[mgrid_fix,kgrid_fix,:],'x',label='StE(before dct)')\n", - "plt.plot(hgrid[hgrid_rdc],ca_StE[mgrid_fix,kgrid_fix,hgrid_rdc],'r*',label='StE(after dct)')\n", - "plt.xlabel('h',size=15)\n", - "plt.ylabel(r'$c_a(h)$',size=15)\n", - "plt.legend()" - ] - }, { "cell_type": "code", "execution_count": 13, @@ -784,7 +691,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -793,7 +700,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -828,6 +735,8 @@ " mut_n_rdc= mut_n_StE[rdc_id]\n", " c_n_rdc = cn_StE[rdc_id]\n", " c_a_rdc = ca_StE[rdc_id]\n", + " mmax = mmgrid_rdc.max()\n", + " kmax = kkgrid_rdc.max()\n", " \n", " ## plots \n", " ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d')\n", @@ -838,6 +747,9 @@ " ax.set_xlabel('m',fontsize=13)\n", " ax.set_ylabel('k',fontsize=13)\n", " ax.set_zlabel(r'$c_a(m,k)$',fontsize=13)\n", + " \n", + " ax.set_xlim([0,mmax*1.1])\n", + " ax.set_ylim([0,kmax*1.2])\n", " ax.set_title(r'$h({})$'.format(hgrid_fix))\n", " ax.view_init(20, 240)\n", "ax.legend(loc=9)" @@ -855,7 +767,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 14, @@ -864,7 +776,7 @@ }, { "data": { - "image/png": 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6NX4GTwP4ayhZmcbUbf+/DGU6BCVw/D6UaQvNUD57Ta+6rzkoUzmWoQQwQIr3UEo5CuAXALxT3X4aSqahngzH5IAyLWZYPff+O4DfTvU+q/Pe/xjAt6EEJR/H+gvfzwH4LaHcXO3f0rzmn6hl71PLeR3KouVM06aSy3IByr0VHlPL8lEoayay+Z9QFqRPQ8no9CvquQEoo0bPQsm+Mwble/M2dd56If4BSk/6qPoZhaEssP4ZbmZpexlKhqF1zP4OFCLHzzybD0OZOnUewGko53ROdZeU8mUo5/SnhBDauf8DAGlvCqg+z4zvO6Ccqz+AkmFrCEr9W8zUSEgpz0BZJ/QRKOf+OJTAMtt0xN+FkpnskPpdPQ3gf0Pua0iy1ru5UAO37VDaEaItT6wdhSSiUhDKXZx/LKX8XLnLYifq1KRBKNmHip33vWmoPbPTAN4spXzBwtcZhpJo4RmrXoNKQx2t+6SU8u5yl2WrEUI8C+DfpZS5BPdEm14+C2CJiKx2O5SRnuTF0FuKEKIOSgadf4cyjemvoPQWb4k7YFPxpJTfhzIiSCUmpeTN/IgMOI2KiGxBCPGXUO7L8b9LDrk6oUy3moIyNaUDwDukkkaZiIhow+A0KiIiIiIisgRHNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBKuLI/LkpSCiIgyEeUugI2xnSIiKr+07RRHNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNoiIiIiIyBIMNmjD6uzsxMsvv5zysU984hP44he/mNN+Xv/61+Ps2bNmFo2IiIjtFBEYbNAGNT09jWvXrmHXrl3rHpuYmMDTTz+Nhx9+WP/b1NQU3vWud8Hv96O7uxtf//rX9cceffRRPPbYYyUpNxERbQ35tlNPPvkk7rjjDng8Hjz44INrtmc7RRsZgw3akE6fPo1wOAyfz7fusaeeegr33nsvvF6v/rcPfvCDcLvdGBsbw9e+9jV84AMf0HuJ7rvvPvz0pz/F9evXS1Z+IiLa3PJtp9ra2vDpT38aBw8eXLc92ynayBhs0Ib0yiuvoLe3Fx/+8IfR1NSEtrY2/OhHPwIA/Nu//Rve/OY369suLi7iH//xH/HZz34WgUAAb3rTm3Dffffhq1/9KgCgsrISBw4cwA9/+MOyHAsREW0++bRTAPDud78b73znO9HQ0LBuX2ynaCNjsEEb0iuvvIJjx47h3nvvxdjYGB5++GF84QtfAKD0Ju3cuVPftr+/H06nEzt27ND/tm/fvjXzX3fv3o1Tp06V7gCIiGhTy6edygXbKdqoGGzQhnT69Gl86lOfwtvf/nY4HA7s2bNHf2xmZgZVVVX67wsLC6ipqVnz/JqaGszPz+u/V1VVYWZmxvqCExHRlpBPO5ULtlO0UTHYoA1HSokzZ87gHe94h/63M2fO6BV5XV3dmkAiEAhgbm5uzT7m5ubWVPTz8/Oora21uORERLQV5NtO5YLtFG1UDDZowxkaGgIAbNu2Tf/biRMnsH//fgDAbbfdhv7+fv2xHTt2IBaLYWBgQP/bqVOnsHfvXv33c+fOYd++fVYXnYiItoB826lcsJ2ijYrBBm04r7zyCm699VYIIfS/nThxQq+E7733XvzsZz/TH/P7/Xj3u9+Nxx57DIuLi3j++efx7W9/G+973/sAAJFIBMePH8db3/rW0h4IERFtSvm2UwAQi8WwsrKCeDyOeDyOlZUVxGIxAGynaGNjsEEbzunTp9f07ty4cQOjo6O45ZZbAAAPPPAAnnvuOSwvL+vb/M3f/A2Wl5cRDAZx//3342//9m/1kY3vfOc7uOeee9DW1lbaAyEiok2pkHbqc5/7HLxeL/70T/8UzzzzDLxeLz73uc8BYDtFG5uQUmZ6POODRHb1yU9+EsFgEB/5yEeybnvXXXfhy1/+st4IENmQyL7JlsV2ijYktlO0yaRtpxhsEBHZH4ON9NhOERGVX9p2itOoiIiIiIjIEgw2iIiIiIjIEgw2iIiIiIjIEgw2iIiIiIjIEgw2iIiIiIjIEgw2iIiIiIjIEgw2iIiIiIjIEgw2iIiIiIjIEgw2iIiIiIjIEgw2iIiIiIjIEgw2iIiIiIjIEgw2iIiIiIjIEgw2iMpESolEIlHuYhAREaXFdoqK5Sp3AYg2OymlHlgkEgnEYjHE43HE43FIKREIBOB0OuFwMPYnIqLSS26ntDYqHo8jkUjA5/OhoqKC7RQVhMEGkUkyVdTJhBAQQiAejyMWiyEWi8HhcKCiokJ/jIiIyEzJ7ZSxvUpmbKe0bR0OB1wuFxwOB9spyhmDDaI8JPf+aCMV2v8B4Pr16xBCoKWlBUKItJWytr3D4dD3GYlE4HA44HQ64XQ6WZkTEVFejO2UlDJl59fU1BQWFhbQ1dWVsZ3SaIFHIpFANBqFEAIul4vtFOWEwQZRCskBhbGillKu2VarhI2VtRACTqcz59fT9qE1EufOnUN3dzd8Ph8rcyIiWifdFF1jOyWl1NuXVEFFoe0UAAwODqKpqQnV1dVwuVxspygtBhu0pRkr6mg0ilgspvfeGLdJrqytolXWc3NzkFJidXUVsVgMTqeTlTkR0RZkbKdWV1cRjUbhcDgyTtG1cjqutt/FxUXU1dXpU4G1kQ6u66BkDDZo08u08M04SjE2NobV1VV0dnYWNR81eeSjUA6HQ59ixcqciGjzymWKLgBMT0/jxo0b2L59e0nWTWivne51UrVTxs4xdpARwGCDNpF0U58yLXwzVoZaxW3GhXyxFawxYDEOXXMxORHRxlXsFF2ts6nYdiqfTrF07Uy6dko7Jq4/JA2DDdpwkkcpFhYWAAAul2vNNunmqG4UyWU2VuZcTE5EZF/J7dTS0hJisRjcbveabUo1RTcVMzrFMrVTxqnAXEy+tTHYIFvKZ+HbtWvX4Pf70dTUZIuKzIxpVJn2kVyZnzhxArfccgsqKipYmRMRlUiuU3QBYGJiArFYDJ2dnVuijjYmS5FS4uzZswiFQvD5fFx/uAUx2KCyKiTnd/KQrrFSs4tsZYnFYlhcXEQ0GoXf71+3faoeo3SvoY3srK6uYnV1FS6Xi5U5EZFJip2im/x3OzCrUyyX4xFCYHFxkesPtzAGG2S5dAvfxsbGUF9fr1dWm2HqU7JoNIrFxUUsLS3p/0ajUTidTni9XgDAyMgIurq60NbWVnDFqz2PlTkRUWFSjVJMTEygqqpKTxG7mdqp5LInEgksLS3p7dXi4iKWl5cRj8fR3NyMUCgEj8dT1OtxMfnWxGCDTGMMKpJvIpRq4dvIyAgaGxttczFcaE+PlBIrKyt6BT0+Po7V1VVcunQJFRUV8Pv98Pl8aGpqgt/v1xd2a+9TRUUFLl26hBdeeAFtbW3o7OzMuccoGReTExGll++9Ka5evYrt27evWWux0cViMSwtLWFhYQGDg4NYWlrCysoKHA4HvF4v/H4//H4/gsEgPB4PYrEYFhYW8PLLL6OqqgqhUAiBQCCvdsq4LReTbz0MNihv6eaoZsr5nSqgsGOFkqlMiUQCy8vLeo+P1gMkpURlZSV8Ph/8fj+qqqoQCATQ3t6e02u63W5s27YN4XAYV65cQV9fH6LRKCKRCHw+X8HHwcXkRLRVmTFFV3vMDKWuc6WUiEaja0bVFxcX9am2gJLZqq6uDu3t7aisrExZxkQiAYfDgba2NrS2tmJychLnzp2Dy+XC6upqQcGGhovJtw4GG5RSuoVv8/PziEQiqK2tXbP9ZhhS1mi9PsYKemVlBUII+Hw+PahoamqCz+dbF0hFo9E1mbFy5XQ60d3djc7OThw+fBinTp1CIBBAOBxGIBAo6FiSK/NXX30V3d3d8Pv9rMyJaENLN0V3ZWUFs7OzaGho0Lezw9Qns+7BlLzP5eXlNW3W0tIS4vE43G63PrIeDAbh8/n0EZrR0VFEo1H9PcqFEAJNTU1oamrC7OwsXn75ZZw6dQq9vb05JWhJ93jyYvLBwUE0NjaipqaG6w83CQYbW1y+C9+SK3E70CqofGi9KMZRiunpaayurmJ8fFwPKGpra9HW1pa21yfdvlNbARAFUJ3x+Q6HA263G3fddRemp6dx/vx5CCEQDodRV1dX8PQqQFlMrh07F5MT0UaQ7xTdaDSKyclJBIPBol+7kPbFCtp6iqmpKczOzuLMmTNYXl4GgDUj6/X19fB6vQV1eCmiAJahtFPp24WamhrU1taiu7sb169fx+DgYMa1h7m+h0IIPU0w1x9uHgw2tohcpj7l0vtj5kVpKSpxbT1F8iJtrdfHOErh9XohpURXV1fRr7v+fZqDy/UdAMuIx++GlDuzllsIgYaGBjQ0NGBubg5DQ0MYGBhAKBRCMBgs6LOQUma84ysrcyIql808RTdXWqZC4yiFcT2F1hkVDof13wuRuu1dhsv1XQCzSCQOIJG4Pes+vF4v9u7di0gkgkuXLuHIkSP62sNU977KtWyp2imuP9y4GGxsIplyfl+6dAmdnZ36tsUMKduhlydZIpHAwsLCuiwa2noKbSi5vb1dz/OdbGVlBbFYzJLyCTENIRYhZQBCXIKUO7O+j8bPpbq6Gvv27cPS0hJGRkZw4cIFdHZ2oq2tLa9y5LJITws6WJkTkdkytVOXL19GR0fHmu0LaafsMhqRjraeIrkTTBtt1jrB6urq0NHRAY/Hox//1NQUpqam4Pf7iy7H+jUUcxBiFolELRyOoZyCDW0fHo8H27dv19ceHj16FI2Njeju7kZlZaUpi8m5/nDjYrCxARWy8G18fByhUKjo1zb7i51vg6D1+hgr6Lm5OTidTlRXV6/JolFMr4/ZpGxBItEJIWYg5W0F78fn82H37t2IRqN6L5I2JaqioqKgfSav64hGoxBCcJEeERWskHtTjI2Nobu7uwylTa+YwMW4nmJychLLy8sYHx9HIpFYs56iublZz1RYTlI2IJHogRDXEI/fXdA+XC4XQqEQurq6MDo6ihMnTqCqqgrxeJyLybcwBhs2lWrhW6FTnwBzM2qY1WOUrkzZsmjA1M0yAAAgAElEQVRoi7S1LBpjY2Pwer1obm42pVzFSt2D40E8fq9pr2HMYHX48GH09fWhoaEB3d3d+v07ci+bIvnv4+PjkFIiGAxyXQcRpWTWFF3Anu1ULuLx+LpMhcb1FFog4fF40NPTo9+zw35ciMd/MeetM7UnyRmsRkdHcfLkSX3tYTa5LiafmprC4uIiOjo62E7ZGIONMsuU81u72Zuxoi53Ng0zab0+kUgkrywayewyelEOTqcTbrcbb3zjGzE2NoZTp07B5/MhHA6jqqpq3fb53Jl8aWlJPye5SI9o68o09WlkZASdnZ3rpr1slnYKuDkCv7q6uq4TTJvWo3WCVVVVobm5ed3I+tjYGCKRiG0CjULv5ZQsl/ZEy9zY09OD4eFh9Pf3IxwOp81glc9i8mg0ivn5ebZTNsdgo0QKWfg2MTGBcDhchtKmV0iPUbpen6WlJVy8eBGBQEDPouHz+QqqjO08P7cUhBBoaWlBc3Mzpqam8Nprr6XMYGXGIj3e8ZVocypkiu7U1BS6urpscxENFD/1ybieYn5+HufOnYOUEi6XS+8Eq6+vX7eeIpd9byb5Hk9NTQ3279+PpaUlDA8Pp81gxcXkmw+DDROly/mt/d+4XSmHlM2UqRJPzqKRrdfn1KlT2LNnT9nnqZqtnA2KMYPV/Pw8hoaG0N/fj1AohObm5oIqcW2/qRaTc5Ee0cZixRRdu11E51ImbWQ9eQ1gIpGAx+PRF2lXVlYiFAqtu7cUKfJduA8oaw/37NmDaDSKkZERvPDCC2hvb0dHRwcqKiq4mHwTYrBRgEwVtTb1SbPZhpS1i82pqak1mZ+04ctMWTS2Ejscc1VVFW677TYsLy9jeHgYFy9eRDQa1e8Imw0X6RFtXKWaomvHYMMoHo/rU3TTrafQRipSjazPzs7aatTGLGZ8ZsXuw+126xmsrl69ir6+PjQ2NiIWi3Ex+SbDYCODfBe+CSEwOTlpu6lPhUjX67O6uopEIoGKigr4/f6ismjYrZGyU1nM5PV69QxWzz//PF588UW0trais7Mz7ToYILfF5NpnuLq6iitXrqC9vZ2L9IhKqNxTdM2uxwtdS2BcTzE3N4fZ2Vn09fWtG1lvaWlBZWUl5/Sj+E4xs9Z9uFwudHd3o7OzE6Ojo7h8+TJee+01bNu2DYFAoOAypGqnrl69ipaWFrjdbrZTJbTlg41MC99S3Zl0M41SAPn3+iwuLuLq1avYvn17mUtuDTM+13wa3lKeR263Gx6PB3fddReuXr2KY8eOoa6uDqFQKGUGq1wbEu17MTQ0hObmZi7SIzKZnafoljLYMK6nMHaEaXWO1l7V1dVhaWkJr3vd62zTVtulHHamZbC6evUqmpubce7cOTidzowZrPJtp0ZGRtDQ0IBEIsGb2ZbQlgk20i18m5+fx/LyMhoaGvRtjSMV5a4gzOo5kFJidnZ2TSWdaj1Ftl4fsxupzcrOx+Z0OtHV1YXOzs6MGawKOfe4mJyocOmm6K6srGBqampNem+7dH6ZGWxox5FIJLCysrLuTtrJ6ylaWlrg8/nWjaxHo1Fcv37dtGxLZh2fnUbP86nfMwV/Vp17DQ0NaG1txezsrL72MFUGq3zLoAUZQgjezLaENl2wEY1GEYvFUFFRkdPUp9XVVczOziIYDJax1KlplVw+cxcjkci61HyxWAzLy8sYHR2Fz+dDQ0MDurq6ChpGtKIXq1isHApjzGA1PT2N/v5+SCkRDodRX19fcEPCxeREmcViMUSj0ZzbKW2dXFtbm2llMDP1aaH1uDaybgwojh07BiEEvF6vHlQ0NDTklanQblN07cqsz98K2n6zZbDK9zxOnvquBfi8ma21Nl2w8ZWvfAXLy8t48MEHc1r4pp2sdpSuwszW66MNJRt7fV566SXs3LmzDEdRGnb9DDcCIQTq6+tRX1+vZ7AaGBiAx+NJea+OfPbLRXpE6/3gBz/AoUOH8KlPfQpA9lEKs9upfDuysu0r1foQo9XV1XXr/4wj636/H1VVVfB6vdi/f7+tMhTarZ6yU1tnVVlSnZupMlhpAUe+06CM+za2UwD0dso4Kk/F23TBhsfjwfz8PFyu3A7N4XBkrSjLaX5+HisrK3olvbKyAgBF9foUw+whcztVnGaxcmjZasYMVidPnsT58+cRiUTQ3t5e8PmVapFeNBoFoJzHG/W9IiqU2+1GPB7P+TtldjulBQhmzFU3diikG1nX1lNo7VW6kfUrV64UXR5jmcxit3bKTnWmFWXJ9H4nZ7DS7tUVDAZRWVlZ8Gsmt1OxWAyrq6uQUsLn89nqPd+INmWwoV3I5MIOF7xar49xlCISiWB5eRlXr15FIBBAdXU1WlpaeHFGa1h17nq9XtTX16OmpgaLi4s4cuQIWlpa9IuEQmm9SIuLi+jv78e+ffu4mJy2HI/Hg0gkkvP2ZgcbxYyUJBIJ/aasWuans2fPQgiRdmQ9V2a1x2wjszOjU6yUIxvJtAxWUkosLCzgxIkTCAQCCIfDWTNYZWOcvvjSSy/hrrvu4vrDIm35YKNUIxtar0/yULK2vsQ4SqFd0L3yyivYvn17URd3ZtvsPUYbkVUVn5QSFRUV6O3tRSgUwrVr1/QMVt3d3fD5fEXtW5sywju+0laTb7Bhdr2by/6S11OkG1kPBALo7Ow05aZ3dlyMbYcOSTuzamQjn/3W1dVh7969uHHjRk4ZrPIph9ZOcTF5cTZdsOF2u8s6sqFF2do9KhYXF7G8vLxuPUVra2vWXh87VnJWZB7ZbEr5mVn5WsYK3+l0orOzEx0dHRgfH8fp06dRWVmJcDiM6urqgvedvEiPd3ylrcDtdmN1dTXn7a0Y2dD2l25k3bieItPI+szMjO2+p3ZtOyk3hdxBXAiBxsZGNDY2Ym5uDhcvXkR/fz9CoRCCwWBB73+qdkpLv8zF5PnZdMFGqUY24vH4ulEKbW3F0NAQAoEAfD4fGhsb4fV6C5rvnsvCu3xs5LUEG02291m7aSKAokcIrBzZSN63EALNzc0IBoOYnp7GwMAApJQIhUJ5Ne7J++ZictpKSj2ykTyyvrCwgNOnT+ujl6lG1nP9zm2FdXx2G20xg53Kkkq+wYZRdXX1mgxWFy5c0DNY5SNVO6X9a2ynuJg8u00ZbJg5FzYaja4bSo5Go3A6nXoFXVNTg9bWVni9Xrz88svYs2ePKYu1rajEi/0y2LFhsfMXXAsqtJ5DbaRLSgmPx4NYLAaPx4Oenh7U1NQUtP9SBhuaVBmslpaWcP36dTQ3N2ddf5FucWqqynx1dRUul4uVOW0aVq0tTF5PkW5k3ev1ore3t6BRyULLVsp9cQQ+N6mOLbnNWlhYQDweR29vL+rr68tQyswytVPGDFaXLl3CCy+8oLcpuawlypREwdhBpk0F5vrD9DZdsFHINKrkClr7Nx6Po6KiQq+gGxsbs/b62LHiNXNfdq14y91LY6yg5+bmsLCwgMHBQQA378Tu9/vR2NgIn88Hh8OBeDwOAIhEIhgcHISUEr29vUXPMzVLroFMVVUV9u7di4WFBczOzuLixYvo6OhAR0dH2qA7l30bK/P5+XlMTEygp6eHlTltePm2U8mM6ymMI+uAsp7C2GalGlmfnp427TvEdmpj0qYDTUxMrDmPgLVtVl1dHRKJBC5fvoyBgQH09PSgsbHRNu9xLm2J2+3Gtm3bEA6HcfjwYfT19aGxsRHd3d0ZM1jl206trKxgeHgYO3fu5GLyJJsu2MjUY6QFFcnzUxcXFzEwMKCPVLS1tcHn8+WcPtfIzLm1dqzEgfJf2JdTqpGK5Ara6XSiubk5aw+/VpHV1tbiwIED+jzTgYEBxGKxnCq6co1spNrW6XRi165dWF1dxeXLlzNmsMpn39oi8vn5eS4mp00h1xF448h6JBLByZMn14ys+3y+NSPruX4XzLxvh13bKTPZsUy5StdmRSIReDweNDQ0rOsIM9JuNrlv3z4sLi7i4sWLuHDhAsLhsC3el3zaEqfTCbfbjTe84Q0YGxvLmsFKWyCeC+3cnZub481sUyhZsHHw4EF873vfQzAYxJkzZwAA73nPe/Daa68BUBaZ1dbW4uTJk+ueGwqFUFVVpc+LO3bsWNrX8Xg8WFlZweHDhxEKhRCNRvUsGkIIvYL2+/1oampCZWUlTp48idtuu82U47RzxWvH4Wk7VFYaY1lyCSrSVdD9/f3wer159xxq80wXFhZw9OhRHD16FOFwOOPiNjsFG9rxVlRUoKenZ00Gq9raWoRCIX19Sr45/hOJhD6iwcXktNEZO8WyZSrURilcLhd2796d13qKdOxaj9utTQA2zihJvm3W5cuXUVlZiebm5pxfw+/349Zbb8Xy8jKGhoawuLiIa9euoaWlpWyjzYW0gQ6HA62trWhpacHU1BTOnz8PIYSewUrbXyKRyGvfWrumtVNcf3hTyYKNBx98EB/60IfwwAMP6H/75je/qf//Yx/7WMY56z/96U/R2NiY8rHV1VU8+uijOHfuHC5duoSVlRUsLy/jE5/4BJqbm9HW1obKysq08xNLnVKwXPui9YwV9I0bNxCJRHD9+nUA2YMKqwQCAXi9Xuzbtw9DQ0O4cOECQqFQySv0QjKCGDkcDnR0dKC9vV3PYOXxePQesXzOSeP2XExOVrG6U+y1117D2bNn8corr2B0dBSvf/3r8eijj2LXrl16J1hrayv8fv+6kfXr16/D4/GYcpx2baeAjT2KUApanZdt+pOVbZbX68WePXswNTWlr9nr6upCe3t7yYOOYjrchBBoaGhAQ0MD5ubmMDQ0hIGBAYTUDFbFtlPav1xMXsJg4+6778bw8HDKx6SU+Na3voWf/OQnBe27oqICv/7rv46dO3diZWUFH/rQh/DMM8/k9FyzP/DNPo3Kjj1Pucil16eiogKBQAChUKioCtOs0QatQo9EIhgeHk5ZodtlZCNTD5CWwaq5uRnT09O4cOEClpeXEQgEcn6NVMPZXExOZrOyUwwAnnzySTQ2NmLXrl3w+/147rnn4Pf7zSl8HuzcTtlNudaRpGuzYrEYhBBwu91FBRVmHJPD4cDOnTvR09ODkZERHDlyBO3t7ejs7DQlSU4uzGoDq6ursW/fvjUZrPJdm5Ip8clWX0xuizUbhw8fRnNzM7Zv357ycSEE3va2t0EIgYcffhi/93u/t26bu+++GwAwNjaWVzYqs9kxQDBzX3Ysk1EikcDKykrKoEK7EVUgEEhZQV+9ehUAbPfl93g82LlzJ8Lh8LoKPd/Rh3wUO7KRSl1dHerq6jA8PIxr167hxRdfRHd3d9ZRm2zD2akq80gkgurqatt9nmRfVnaKAcBf//Vf6///whe+UJZAAzC37rVr4GJHqY4t3+lPU1NTmJ2dRU9PT9HlMSu4q6iowLZt2xAKhXD58mW8+OKL+nq9fO4gXwiz20BjBqv+/n6Mj4/j4sWL6OzszHos2cqSqZ3a7OsPbRFsPPvss7j//vvTPv7888+jra0N4+PjeOtb34pdu3bpwUWyfG+WZDa7XoxvtkrcmEFsamoKkUhEDxa0bCzaupxC1k/Ykdvtxvbt2xEOh3Hp0iUcOXIETU1Nln2uVgQbGo/Hg5aWFrS1tWFkZARDQ0P6lKtUiRly3b9WYScSCZw4cQJveMMbeMdXMoUZnWJ2oX1HzNoX26nszJz+ZNd6zOVyIRwOo6urC1evXkVfXx+amprQ3d1t2Wta1U653W60t7cDUI6rr68PDQ0NCIVCaTNY5boW0Rh0vPzyy3jjG9+46dcflj3YiMVi+Kd/+iccP3487TbajViCwSDe9a53oa+vL22wke99Nsxm114eO45sANl7GpIziGl544GbQYXb7TZl+pNZih3WzfaeuFwu9PT0oLu7G0NDQ5iZmdHvlJqc9amYclkZbGjbV1ZW6sPwV65cwdGjRxEMBtHd3b3mWPJdUK5Nu9IuqnjHVyqWmZ1iQGEXjGZNGWE2qtzlW6Zs05+06bqlXAeYrpy5yvecczqd6OrqQkdHB65du4bjx49jZWUFKysrGVPNFqLQRCa50BKTaMeiZbDy+/0Ih8OoqqoquCzAzaAjeV3HZmynyh5s/PjHP8auXbvQ0dGR8vHFxUUkEglUVVVhcXERP/zhD/HYY4+l3V+5gw2zK16z7yBuJ8YvUi5Bhd/vRzAYXDdSYdfpT9aIQ4hXAbjgdO5EW1sb5ubm4PP5cOzYMdTV1SEcDqes0K0ONvKtxI3bV1RUIBwOo7u7O2UGq2KCDeN3cqsv0qPCmN0pBuRfH2sdWXa9Yazd9mW1TNOfUo2uT01NYW5uzpTpT2axtv4bhsMRR0dHGK2trfiP//gPnDhxAtXV1QiHw3pmwmJZ3SmmtTvJGaxee+21dRms8m2nNFthMXnJgo37778fhw4dwuTkJDo6OvDEE0/g/e9/P77xjW+s6y26du0aHnroITz33HMYGxvDu971LgBKhf8bv/Eb+KVf+qW0r+N0Ok29QM+XXStes07WYstkDCrm5+dx4cIFfdpbtqBiq8hUIQrRB4fjOQBAIvFeSBlak/VpdHQ0bYVux5GNZMZjmZiY0DNYaQv4c5Vc6SdX5sZFepulMifrmN0pVgi7dmTZdUqWWaRUbtY2MTGBhYUF/f5cwOadslssIQYgxNcgRAKJxK8B2A+Px4O77rpLr9e9Xi96enpS3t8iH/l0dOWbyjZVOyVE+gxWZoxGbNbF5CULNp599tmUf3/qqafW/a2trQ3PPadcUPX09ODUqVN5vVY5LxzMnEZl16HuXPaTy0hFRUUFWltb0dTUZPgSxQHMAfAC2JhfLCszRAFRAAJAAsDqms9CCKH3vKSq0K0MNgqpxLPNSQ4GgwgGg5iensarr76KRCKhz2nO9lrZsmMlV+bGHiQGHltXqTrFgPzbqa3QtgDlG4E3rgNcWFjQ26xIJAKv14v6+vqkjrBVAOakIt585iBEHIATwKzelmj1elNTE6ampnDu3Dm4XC709vaiurq6oFfKd4qbme2UlsFqeXkZw8PDGB8fh9/vRzwez2kEMlPZU7VTG/lmtmWfRmUXZl0g2nlkw4r0fcVMfzp//nzS3+Jwub4Gh2ME8fitiMffWXR5N6JM56KUd0G53nBBylsArKTseUlVoXd1ddlmZCOf6SB1dXXo7OxENBrF2NgYBgcHs2awymU421iZx+NxXL16FXV1daiqquJi8i2qlJ1i+bJrgGDHEfhM8m2zRkZGEAgE0NTUpO/D6fwBnM4+tZ36NSgdQJnZrT7Jp87Ot+xS3oJEYhxAFFK+PuX+tNGBmZkZDAwMAFC+R3V1dXm+lrXtVC7be71e7N69GzU1Nbh8+TKOHDmC1tbWrNm4cimPsZ1KJBK4du0aKisrUVtbu6HWdTDYwM3KksFGesYKOhKJ4MyZM+sq6EAgUOT0p3k4HCOQsg1O5xnE4/dho45u5CpT73tqlZDyzfpvmadcra3QBwcHMTc3h+np6Zwq9HJMo0onkUigsrIS27Ztw8rKip6Nq729HR0dHesyWOUztK5V5jdu3IDf7+diciqJfOd4b4WpT2aPwBsDinw6wlLt66ZVOJ19kLIDTudpxOO/CKAq3VMz7GfjyL/cHkj5y4bnr6atR2tra3HgwAHMzc3h4sWLGBgYQCwWy7mNsHJtYb7tlMPhQGNjI0KhkJ6Nq6GhAd3d3fB6vUWVR2unpqenUVNTA7/fv6EWkzPYQP6VfiYbPRtVIpHQ56Rq81O1Ctrn8+nZM3p6elBZWVnUe7a+TDWIx2+H03kSsdg9yDXQsNs832LLku/zc6lkamtrceutt+LkyZMYGRnBwMAAenp60NDQkGEUxdoeo0IWfANKmsgdO3agp6cHly9f1jNYdXV16XdYzndal/YcbV7sZl2kR/bhdrsRjUZzzs5j9jSqWCxmyr7KHWykG6lYWlrC0NBQ0esA13/vKxCPvw5O5wnE47sAlOdeKRtJLu1DdXU19u/fj4WFBRw9ehRHjx5FOBxGMBjM+Fy7tVNCCD2DVWdnJ8bGxnDq1Cn4fL51GawKue6UUq5rpzbCzWwZbGDrDE8b96UFFcYKemVlBYASVPj9flRVVaGlpWVdUDE5OWlaJomkUiIe/xXE4/cil2FpIzsFG0DxQ+b5VJ65klLC5XLpFfrQ0BAGBwfTVuh26jFKVSlrOd27u7tx/fp1HD9+HDU1NQiFQgVV4sbnpJov63a7S3ZXXNr8tMyJuQYbG6VtsWpf+U5/On78OG655RZTypVMaad+AYAP+bZVdmG3NlMTCATg9Xqxb98+DA0N4eLFi+ju7kZra2vKNqPUC8Sz7T85MUlLSwuam5tTZrAqtFNsI7ZTDDaw8UcjMjGOVFy7dg2XLl3KOahIx/r5ueWpvK1d2J369fL5e7ptC+nVCQQCuPXWW7G8vIyhoSFcuHABoVBo3ToIKxeIm7W9w+FAe3s72traMDExgbNnzwJA3neuTdVoGStzu/YY0cbk8XjyugGt2e2UHds8QKlLzJr+ZIbUxyewGUY0rFqzkayQdsrr9WLPnj1YWVnB8PAwhoeH0dXVhba2tjWfudULxM3Y3jileW5uDsPDw+jv70d7e3sB62Eyt1N2tWmDjXwXP5n1IZWrQcg2UhGPx+H1evUK2i4XTnb7ctjhfbFqWDjVtlqFHolEMDw8jKGhIXR1del3TrWizED+wUmuC761DFaXLl3C8PAw+vr6EA6HTctgRWQWt9ud1z2hzByBt8NofqqRitnZWQghUF1dzTTom0i+7ZRRZWUldu3ahWg0ipGRERw5cgQdHR3o6OiA0+nMa2TDykQm2vbZylJdXY3bbrsNy8vLGBwcxPT0NC5fvoy2tracXmujtlObMtjQ5sJq87ez2UgjG6mCiuXlZQgh1o1UGIOKgYEB1NTUmDL9ya4ns12UuhfcjMDE4/Fg586dCIfDeoUejUbzSuGXbw+QldOufD4fmpub0dHRgeHhYQwODqKrqwutra1FZbAiMku+N6C162hEtn3lM/1pcnISbrcbra2tppTNDHbvMS5UqY+p2HbK7XZj+/btCIfDeoKQtrY2xONx2yQyyadd83q9CIfDiMViiEajOWew2qjt1KYMNjweT17BhtkVrxkNQiKRQCQSweLiIi5evJhzUJGpXHarMO1YJjsopheo2P0aK/TDhw+bmsKvmO0LXVDu9/uxd+9eRCIRPYhKl8Fqo1bitDG53e6yTqMyO9goJg26ZmpqypQy2ZFZHVBmtpm5lMmMzjMz2ymXy4Wenh50d3fjypUreharcDgMt9uddd9Wry3MZ/quto6yt7cXIUMGq/r6eoRCoZQZrDZqO7Upgw1tZCNX5azEM41UOBwOOJ1OBIPBnIMKs8pF5VWqaVTpuFwu/Y6vV65cQV9fH5qamhAKhVJW6IVU4qVcUO7xePQMVleuXMHRo0fR1NSE7u7uNRmsNmIlThtTISMbdpjumxxUzMzMYGFhATMzM0WnQd/s7ZT16x3tyYp2yul06slBvF4vjh07pl+kp0u6UKpsVIXs36wMVnY9NzZlsJFvJW72XNhUlXg8Hsfy8rKeTlYLKhwOh55Strq6ek1QMTExgYWFhTU3EyqUXStxM8pkty9XKVPfWj1lS6vQOzs7ce3aNRw7dgx1dXUIh8NrKvRC1mBYWemn27/L5UIoFEJXVxeuX7+Ol19+GVVVVQiHwwDsdy7R5lVIO1XKTrFMIxVamxUIBOD3+3Hjxg3s3r27JOUqNTuWaaOxMj2tlFJfwzE6OooTJ06gpqYG4XB43chAqbNR5bJ98v6TM1j19/cDAEKhEOrr6zdsp9imDTbyGdkwcy6slBIrKysYHR3F4uKifp8KLajw+/2orq5Ga2srKisrM57IZldydqswzbyw20jHJqVENBrF7OwsKisrU/bCWHHRW0xg4nA40NHRgfb2dr1Cr66uRjgchs/nK1uWj0zbZ6qQjRmsJicncfbsWSwtLWFmZga1tbUpn8NAhMyUbzYqq9ZZGIOKVPdW0kYqmpubU2YsnJ+fN6VMWrnMaosps2I7qvLtFMtHIW2DEAJtbW1obW3F+Pg4Tp06Bb/fj3A4jEAgsGbbfPdt1faZAgdjBqv5+XkMDQ3pNzzciG3Rpgw2SjGNKh6Pp8z+pEWqlZWVqKmpQVtbW9agIh2zGxcqvdXVVb0R186TWCwGj8cDr9eLpaUlXLp0Cb29vaipqQFQmtS3hRJCoLW1FS0tLZiYmMDp06fh9Xrh8/myzpc1KqQHyIrthRBoampCU1MTDh8+jOHhYUSjUYRCITQ1NfF7Q5YpJBtVMRfixjToU1NTmJqawksvvQSg8DTogL07xcwa+bVbZ5aVpJT6LIzFxUXMz8/D6XRi+/bt+kV7IUrVTgkh0NzcjGAwiBs3buDVV1+F2+1GT09PSUYqrJhOXFVVpWewevHFF/HSSy+hs7MT7e3ttrynRiqbMtgwcy5suqDCOFJhDCqmpqYwMzOD7u7uoo+jlBlDysGOZSpUPB7Xz4/5+XmcO3cO8XgcLpdLn2rQ3NyMQCCgL0xeXV2F0+lEJBLB4OAghBDo7e1FRUVF2ddsZKOlmW1qasLU1BReffVVOJ1O1NTU6EGTmWWxOrUuoEwZ279/PxYXF/UMVtrNpDbisDXZm1VrNnLJWOj3+xGNRnHLLbcUfW7btVNMK5cZHSybkTbCfuPGjTWjWlLKNQv6GxsbEY1Gce7cOVRUVGDbtm0IBAKWpmjPV6p9CyHQ2NiIxsZGTE9PY2BgAJFIZM36h1zKYqd2yuv1orKyErfffjuuXLmiJ3Dp7OzMq7OvHDZtsJHvyEYsFsP8/HzOQUW6E8SuAcJmurAvJ23KgXGkQpsmp003cLvd2LZtG6qrq3OqSGpqanDgwAHMzs5icHAQ8Xg8rzKVI9gwvnZDQwO6urr0oElKid7eXtTV1ZlWljOlymcAACAASURBVEJ6mPK9qZ/GmMHq0qVLeOGFF9DR0YEdO3Zs2gsPKr1ib+qXTxr05JGKxcVFzM3NmRJEs52yv9XV1TVtlhZYzM3Noba2FoFAAJ2dnfD5fOt6ymOxGHw+H1pbWzE1NaUHHT09PXmVoZztVF1dHQ4cOIChoSFcvXoVL730Enp6elBfX591yrOdRkK0Mnk8njUZrF566SV9cbxdg45NGWxkmkaVaqRibm5O75XNNahIx64VLyvx/Ghrb4xToJaWlgCsnXKQau3NzMwM3G53TueOcRst6BgdHcW5c+dw/PhxbNu2LeNIgV0WkycSCQQCAezYsUNPRTgwMICenh40NDSse12rs3zkm+0qFY/Ho6cBnpycZKBBpsp1GpUWVMzPz2N5eRnT09NFpUEHNu49O8q1r43Sdmoj7MbAIhqNrhlhb2lpgd/vx8WLFxEMBtOuUUulvr4e9fX1mJqawvnz5/XzMttogV3aqcrKSrS1tSEYDOLixYsYHBxEOBxOO2XW7u1UcgarM2fO4I1vfKMtp1aVNNg4ePAgvve97yEYDOLMmTMAgMcffxx/93d/p2dc+vznP49777133XO///3v48Mf/jDi8TgeeughfPzjH0/7Oh6PB7Ozs3jttddQU1OTdaRifHwcHo8HLS0tRR+j3XOhm8GOZSpGNBrFwsICpqenEY1Gce3aNSQSCVRWVuoVdGNjI3w+X0mm01RVVekZnwYHBwEgbdCR/1C2qUVNWY7q6mrs378fCwsLGBoa0iv0YDCob1OOLB+FcrlctrrJGG0OydOoso1USCnhdrsRUvPvF3N+2+EO4lbvyyxmTj01sw1OHqlYWVmB0+nUs4Q1NDSgu7vbkp7u+vp6HDhwAEeOHMH58+dRUVGB3t7etEFHuab7ptt3IBDAbbfdhqWlJQwNDeHChQsIhUJoaWlZ89obpZ3SMli1tLTYMtAAShxsPPjgg/jQhz6EBx54YM3fP/rRj+LRRx9N+7x4PI4PfvCD+NGPfoSOjg7ceeeduO+++7Bnzx59m7Nnz+KrX/0qXn31VfT19eG73/0u3vKWt+CRRx7JOlKxFQIEO1biZsn1yxqLxdb0+iwsLCAWi8HtdsPv98PhcKC2thahUKioL6wZWT6EEGumV124cAFSynVBR+6vlYDX+020tx+FEO+DlG8puHyZymwUCARw6623Ynl5eV2FXs4sH+n2TwRY3ykWjUbR39+PM2fOYHJyEocPH8bBgwezjlRoqdB9Pl/Rx2jXtsWu+yqXVCPsc3Nzeir9fLJbplJsO1VRUYE777xTH+lIF3TkU387nS+ire27EOK/QMo3FVy+dGU2lsPn82Hv3r1YWVnB0NAQhoaG0NXVhba2Nj0gL+X9oDazkgYbd999N4aHh/N+Xl9fH7Zt26bPEXzve9+Lb3/722uCjcrKSrz5zW/GI488gq985SvYuXMnfvVXfzWn/ZfiPhuFsGuWDztOJzEem3GqnFZBRyIROJ1OfaSiqakJ4XB4zbz+y5cvw+Vy2aJnIHl61e23354x6MhuEhUVJxGJNMHh+FfE45mDjXzPlUyVrNfrxZ49exCJRDA8PIyhoaG816VYleXDuL0dz2sqPSs7xY4dO4ZHHnkEO3bswPLyMpqbm3Hw4EHs3r076/lnZntg13bKjvsyc8pZOlLKNesqtH+TR9ibmpr00Qzt3kDFvGaxtHM2eXpVqqAjt/p1GW73P8HhSMDh+AfE4wcArL+LdqHStQuVlZXYvXs3otEohoeHceTIEXR0dCAej1veyWVmsGHnNswWazaefPJJPP3007jjjjvwF3/xF+sWll69ehWdnZ367x0dHTh69OiabXp7e9Hb2wtAOXHyTSkYi8WKOIKb7FhZavsyi12OT0vRNzc3h6WlJczOzmJlZUXvIQwEAqirq9MzNdj5i2iU7j1JFXTU1tbmeFx1iMU6UFExCCl/OacymD3y4PF4sHPnToTDYTz//PPo6+tDR0cHOjs7swZ4Vmf52Kh3ZSXzWdkpdscdd6Cvrw8A8LWvfQ0jIyNrHs/EzgGCmRfjG300IptsI+yBQADt7e3w+/0p60Vt7WAppar/Un1O6YKO3D9TNxKJZlRUnIeUtwIwdwpYtnbE7XZjx44dCIfDuHTpEiYnJ+H1etHb26tnkSxm/8kSiURO+90Myn6UH/jAB/CZz3wGQgh85jOfwcc+9jF85StfWbNNqhM10wdayE397HIBbeW+NurNkqSUiEQi6xa+AUqvuZlzmc0qby5lyPTZZnq+Meg4e/Ys4vE46uvrsyz0q8D8/PuxsHAVgcCdWcuWrQzJ8rm4d7vdqKysxIEDB9ak7+vq6kqbQcqOWUFoazGjU8xos7RTZq//MEu5p1Elj7DPzMzonWPaSEUwGFw3wr5RZKqTk4MOLYNSdk4sLf0uxsePo6bmFwCYO8sgkUjkNHNBC5JmZ2fhcDhw9OhRNDc3o6urK+MamHKuLbS7sgcbzc3N+v9/93d/N+XUp46ODly+fFn//cqVK2hra0u7z1Lc1C8duzYIG2WUJNVQcjweh8fj0SvohoaGNYu1R0dHEY1GTZnLXEpCiJx7jFKpqalBKBTC7OwsLl68qKecTRd0SOlCIlEPIPvnl28lWMi0JS19Ynd3N65cuYK+vj40NTUhlCZ9n5XlYbBBmVjRKVbudmortHlm7SdTmbQRdmO7paVDN46wV1VVYXZ2Fjt27CiqPGYuMrf6QlcLOoaHhzE8PIyTJ09mXEiulMuHSCQEwG96eQoZse/s7ERvb6+eYraxsRGhUChl8GR1NqqNPOpX9mDj+vXreqaXf/7nf8Ytt9yybps777wTAwMDGBoaQnt7O77xjW/g61//etp9VlZWYmZmJucycC5sfszYTywWQyQSQTQaxezsLBYWFrC6uoqKigp9KLm1tRV+v3/LDDNq8s3c4fV6sXv3bszNza25z0Vy0GFlRpBCGi5te6fTie7ubnR2duLatWs4duyYno2rsrIyr31qzJ5GRVubFZ1ihdwPysy2xSx2bacA8+9GnmmEXWu3mpubU46wT09PmxoAlUq69zCfOl9LeNDc3Jw1e1UpslHlu73D4dDv2D06Oorjx4+jtrYW4XAYXu/NNSVWj2xs5LWFJb2Ku//++3Ho0CFMTk6io6MDTzzxBA4dOoSTJ09CCIFQKIQvfelLAIBr167hoYcewnPPPQeXy4Unn3wSb3/72xGPx3Hw4EHs3bs37euUuxK34/A0kL3idThOwuk8gnj8jUgk9mfdVy4nvZbO0Vg5ayn6tHszBINBy1L0lUMpK4TklLO333475ubmcOHCBcTjcWzbtk0POuwWbCRzOBzo6OjQK/QTJ06gurq6oIWQnEZFZrKiU6zc06jsKJdjFGIcDsdJJBK9kLI3474KZRxhHx8fx8rKCq5fv75mhL2+vl7PYpiLck/rSmbmAvFcXksIgbq6Otx5552Ynp5OG3RYHWwUkzjE4XCgra0Nra2tGBsbw8mTJxEIBNDT0wO/31/26b52/V4DJQ42nn322XV/e//7359y27a2Njz33HP67/fee2/KVIOp5Ds8bdeeGTOHurMHLsuoqPgWEokAXK5vIRrdDSD1HMt0U3+Wl5fX9PosLy8DgD6UXFNTg/b2dng8HgghMDIygsrKyox3ms6Fnb9ghSg2KKiursbrXve6NUGHtkjPzsGGRgiB1tZWtLS0YGJiAqdPn9anKQQCgZzLU86sILRxlapTLNeb+mnM7BQzU6lHSVyu/xdCzMDl+hkikT8CUJ1222z7Sr4JXvIIu9/vR21tLRwOB0KhUAFHZG/Fpr7NZ1vja2UKOuw0spHu4l4IoY/UTE5O4syZM/B4PFhdXbW0k2sjd4ptyvkp5RzZsOsoCZCtcnAjkaiDEJOQMoh0p4aUElJK3LhxQx+xWFpaQiKRWDOUHAwG4fV6S/bFsFOPkRnMqGyTg46lpaWcA7tyBhsaIQSCwSCamppw+PBhnDt3Di6XCz09PVnT/hYyjSrT9pstoKX0StUpVs6RDbvK7RgFAIlsa8+M+zLeMFELLHK9CZ62JpDWy6deTLVtctDhcrnQ2NhoWX1r9rQlIQSamprQ1NSEqakpHD9+HCdOnMi4djKf/SdjsGEzhQQbdh3ZKN2wuROrq/8VDscVJBId6u+r6+anxmIxrKysYHp6GoFAAJ2dnfD5fAXfm2IzNp7FHlMxPUapaEHH4OAgRkdHcezYMfT29mYMPOxUCQoh4HK5cOedd2JmZmbNupR0x7CVeoxoYypnp5idZav/YrGDcDheQSIRRvKohpTKTfC0TrD+/n79PdZumJjtJr+FlGkryredysQYdJw7dw6xWAzt7e0ZF5LnWwZte6sWcGvT6rZt24aLFy8iFouhp6cH9fX1aV9zK7VTmzLYKGQalR1HI0q1L20oWQksXFhcvIDo/8/em4dHdld33p+71KJatG8lqbRUSerF6s3dzZKAAUNj4oCNwwMeA6/NmC0ZQ4BhMvAwBDAJYzPJkAzzBpgXQ/AQIJiExIPHA54YOxgs9b53u1trr+puqbVVaanl3vv+Id/rKqmqVMst1ZXQ93n6Aatu/e6pu5zzO9v3RKPIsmzUpzY0NOB2u7HZbBw+fJhAIFDw8Lv1HC0u9LflYgSzVT5OpxO/3091dTUDAwMMDg6m3bBbIbORCpWVlezevZuZmRmGhobo7+8nEAhQU1OTdP4NNqoNWB2lLPe1KrKjDK8lHn8T0WiU2dmJpGBYYoZdkiRaWlqoqqoq6N1er3aqUJ1djLLcqqoquru7uXLlCufOnUOSJDo7O9M6HcW2U/m8bxUVFezatYtQKMTQ0BADAwMEAoGUGZt8hgCu1edxXTobDoejZLWwVmb5UFV1WaZCp+jT61NramoMLulMv2W9G71SwsyI0dJjBUHA6/UaynBgYICBgQE6OzuTnA6rOhs6ysvL2blzJ+FwmOHhYQYGBujo6KC+vt541q2SmdnABlKhlHbKqkhl8+LxeJLNynYI3vz8PE6n05T3ejWbqdcj8mFY3LFjB5OTkxmdjtVkTcwVXq+XHTt2MDc3ZzgdHR0dNDQ0GGtaqYKg2Fi3zsZ6iBjlK5eeSk50LGZmZlAUhUgkgtvtxuv14vP5ckolJ8plBop13QXhCrL8EzStkXj8XcDqDkwyo4yq0FrYbNbN5HRomoYkRYApwPza01yQ6Xp6PB62bdvG/Pw8w8PDDA4O0t7ejqIoG5mNDVga+dip9exs6EPw5ubmGBgYYHZ2lkgkgiRJJR2CVzwnYR5J+legDEV5HWYPsCs2iqXzE9etqqpiz549aZ2OYs+1MAMul4uenh4WFhYYHh5maGiItrY2fD7fBhvVWkcphyWZiWwenGg0umwInqqqOJ1OI+pTW1tr9Fl0dXWZIpeVnLOl8sjyk4jiZaAfVe1BVZfTVKaCmb+pFNS32RybSlElOh2Dg4MMDAzg9wu0tv4VsuxCUf4dmrbHNDlyRTZrl5WVsXXrViKRCCMjI4TDYS5fvkxLS0tWynzD2djAaiMfO2UlvZsvVFVNyVwoiiIOhwNVVamqqsLv96+YYc8Eq9kpSLYxkvR/keVfAAqa5kFVd5dMltU4VyElV+mcDqdTw+//OyTp+6jqH6JpmW19KcuQnE4nW7ZsIRKJcOHCBXp7ew2ZssVatlPr0tnINT1tRaW0FPF4fBlFX2Iq2e12p0wl64jFYiWQujRQ1RZE8QzgRNOqc/quFSIDq5XZWAqv18vOnTsJhUKMjv4Qm+0mkYgTm+2A5Z0NHQ6Hg02bNjE+Pk4kEqG3t5fm5mb8fn/GHqO1HDHawNrEerRTidC0xSF4iSVQS4fg6cPe9CF4c3NzDA4OUlNTU2LpX0HxrrsEqCyyapUmq2G1no2Vjl3qdHg852lsHAACiOI/oyiZnY1cy2uLAYfDQXd3Nx0dHfT29nLkyBFaWlrw+/0rDjDecDYsBp3vOFtYKbOhp5J1h2Jubo4DBw4kpZLr6upyTiVbsXG9WEpcUd6Oqm4BytG0hhWPX8swm7kKeJnrfB/xeC/h8CyXLjXQ1DRBdXV6xy0XJZjrPc9HwQqCQFdXFx0dHVy6dIne3l58Ph+tra0p35u13Hi3gbWJXMuowHyn16znXtM0JicnkwJiiqIkDcGrqanB5XKtmlNvdedMUd4CeNE0J6q6vdTiZES6+1LsMqp00J2O8XE3Cws/YXLyIjbbG0gY5p332qsFm82G0+lk27ZtXLt2jb6+vow2CqzhLOWLdelsrIVhSfoQvMRMhZ5K1nm/q6qquHnzJnv37jWF3chqzkbxIKJphZeL5QsrsnzkClWtZnT0P3DLLVsQxcVoo85elcrpyEWO1WTgkGWZjo4OWltbuXz5MgcOHKCuro729vYkPv21HDHawNqEJEkoilKy8+s9ILmwCsbjcWO+UuIQvPn5ecbGxvB4PPh8Ptxu94pR2nQy/fbYKQeKclvO37LKbypGoCvXYz2eDl566VN0dNRy+vQskrQ446K8PPWgRys5G7Aoj91uN2zUlStXOHDgALW1tbS3t+NwOJYdv1bt1Lp0NqzUIK6nkpeyQAFJQ/AaGhqMVHIihoaGTHk5rKp4rShTMbFI17j4LMzMzFBRUUF7e/syBbLa6enUx4qAlFRelc7pKKaDZIYjIEkSbW1t+P1+rl69yqFDh6iqqqKjowOn04mqqqvadLqBDZR605OpB0Qfgpdos/QheInMhfoQvIMHD9Ld3V2wTFa0U1aVqVCYsfEuls7PZV1V9VBRsZU9e2Bqaorz588jSVJKp8NqzkaibZMkidbWVlpaWhgdHeXw4cNJNmrp8WsNG84G5imTWCxGOBwmGo3y0ksv5Z1KLgasGDGy0ktvNvQmyHA4bPyLRCLY7XbjWWhsbCQUCtHb20tbWxtNTU15NYGulmOSyekwo1HdDLlXgiiKtLS00NzczLVr1zh69Cjl5eXIsrwsirSBDaxn6JkNvWw3McMOuQ/BM2vzajU7ZRbWgr1LdDIT7ZYsy3R1dSXNMyqmncoWS5+5yspK9uzZk9bpKGZmIF/m0KXXRRRFmpubaWpq4vr16xw9ehSv10sgEFjTvYXr0tnItYwq1xuULpVss9lwu90ABaWSiwGzFa+VlHgpoWmawQim/wuFQhw7diypHC4Vs0osFqOmpoaOjg6Gh4fp6+ujvb0dSZJWRTHne6zudITDYWM4YCwWy6mMqtTc4oIg4PP5aGxsZGxsjDNnzjA1NYXX68Xj8aQ8fgMbWKvQ9dRSOvSjR48aToXH46Guro6ysrK8eqSs6GyYhfVm7zRNQ1EUJicnGRsbM/pDAeNZ0O2WIAjEYjEuXrzI8PBwEj26Ve1UOqejmJkNs+2UIAg0NjbS0NDA+Pg4J0+eRFVVS5En5AJr7IRNhs1mIx6PF7xONqnk2tpaI5WsY3p6mvLycsttUNarEl+tCJY+aT3RsYjH4zgcDkNB19TUMDc3x+7du7NWPDabje7ubtrb2xkaGuLGjRuUl5dnpRhLwQiiw+PxGE5HX18fhw4dIhgMrqgM8+FGL9a7JAgC9fX1TE5OIssyZ8+eRZZlAoEAFRUVRTnnBjZQTMRisaRMxezsrMFcqGdVm5ubiUajbNq0ySjRKARWJQ2xWga+FE5LOrsVjUZxOBxUVlZmrLiIRqO4XC527NhhzGQaGhqisbExaxlKZacSnY7+/n6mp6cJh8OUrdRJTu73qlh2ShAE6urqqK2t5aWXXuL69euEw2GCweCaslHr0tnI9YYv1v2phoev834DRnQ621Syfn6r1QZakeXDamluHZq2OBQxUTknTlrXI4DpGMHyvdZ2u53Nmzfjcrm4cuUK+/fvJxgMUltbm3bNUjobOjweDy6Xi61btyaVV6VzOsweZGQGNE2jsrKSYDDI1NQUAwMDaJpGMBhMmqy+gQ1YBTpzYWIwTC950YNhDQ0NuN3ulHrKzLkdZupyK8q0FpCr3Tp58iRtbW0rbrwFQTBsgT6TaXp6mrNnzxKJRAiFQssme6eSLRe2QrPtVGVlJbt376a3t5fh4WEuXbpEZ2dn2kbyXOWA4tspQRAoKysjEAjgdrsZHBxEVVUCgUBGpkirYF06G+mQWPKSyPutqqrx0ng8Hurr6/NKJeuwopKzokxWQDweNxTzjRs3jHSx0+nE4/FkbN5Ph0Kvs91up76+Hp/Px+DgIENDQ3R1dRWsUIqhxEXxx4jiP9DU1IHH81p27NhBOBzO6HQUO7ORj6Of+B3dMM3MzDA0NER/fz87d+5c0aBuYAPFQOIQvEgkwsmTJ1MyF+Y6BM+K/RFWCtAlwmq2U1EUpqamkkq5FUUpyG7lgoqKCjZv3szQ0BBnz57F6XTS2dmJy+VKeXyuZVRmOyaCcBZR/An19W78/o8xNzdPf38/giCkdTqs5mzAKzJVVlZy6623Gv2T/f39BAKBnLJNq41VdzYefPBBnnrqKerr6zl16hQAf/Inf8LPfvYz7HY7wWCQv/3bv6WysnLZd9vb2/F6vUiShCzLHDp0KO15JEnimWeeoba2FrfbbaSS9ZIXt9tNS0sLLpcLSZI4ePAggUDAlN+4Ohv7KLL894jiCLHYv0HTMjOBWNWwrJYSX9qwnVgSpytnvV6/ubm54PPlYvCXQlcoLpeLbdu2JfVGdHV1Jb0bxWT5WPnYGKL4Y8BHTc1BYByow+PxpHQ6qqur88r6rUYmJNV3ysvL2blzJ7OzsxvN4xsoGvT3QWcuTNxA6nX0OnOhTuXsdrsL3kSaSfmeSZeL4kkE4Tyq+jtomi/vdcyUKdd1SgVN05IatvXnAhbL5TweD42NjXn1hxZ6bTRNM6Zi37x5kxMnTuDxeAgGgymzJaXMwEvSXwFhGhquIUnvpLIyyO7du43yqlROh5XKfXUstVN6/+Ts7CxDQ0PYbDYaGqw5W2zVnY0PfOADfOxjH+P+++83/rZv3z4eeeQRZFnmM5/5DI888ghf/epXU37/ueeeo7a2NuVnX//61/k//+f/MDo6yrVr1/jxj3/M/fffb6SdVovacjXmdgjCBSTpCJpWgSw/RSz271c4/rcns7G0YXt2dhZN05JK4pqbm3E4HEnK4eLFizlxzhcTiXLpvREzMzMMDAwAGIqxtE16Mpq2G0E4zNxcM15vcoAgndNhs9mKqsTzGXyUyUGxEtHDBlYHxQ6KjY2NcerUKWKxGPfffz+7du3ida97HU6nM4ladmkd/eTkZFalvNlgNTb2gnATm+17aJqIKJ4jFvtcyWWyKhKrLvT/VVV1WSn39PQ0kUiEtra2gs9pRkO/IAjU1tZSU1PD2NgYx44do7KykkAgYARpShsUA03zIQjHUFU78EqGWs9ip3I68pkHVezMRjqZ3G4327ZtszR9+6pb0Ntuu42RkZGkv731rW81/v9rXvMa/uEf/iGvtW+//Xbe/e5309jYyO7du/nOd76T9XfNVErFaHJb+kJpWi2a5kUQZlCUV62qTFZR4qqqMjs7y+TkJKFQiOnpaaLRKDabzchW+P1+I3u1VpDu2paXl3PrrbcyNTXFuXPnsNlsiKJYwoiRgKJ8FrjC0NAwDQ2pFd1Sp2N2dha73Z61PKuh9NcypeAGzEcxg2IPP/wwL7zwAtu2bQPg/e9/P695zWuyavZcrWyEWWtpmgjICEIETVu5Ed2K75nZGZJU9LJL7VZzczNutzul3ZqZmSlYFjOw9JroZBt1dXVcu3aNw4cPG2yL+uf5rLvSsdmsqyh/giAcZWDgJjt3Ln8vUzkdra2tRQ9y5YqNORsm4rvf/S733ntvys8EQeCtb30rgiDw0Y9+lI985CNJn/f09BR0brPSYMUwCMvlqiIa/RMEYQZNW7nsZy03iCeWF+gRn0SaPlgsM+js7ExiBcsVZjuchZxvJYaNvXv3MjExwfHjx4lGo2zdujVtvWzi+cx3TCSgFU27vOKRutMxOjpKf38/Bw8eTCqvMkNuMK+MagO/vShmUOyLX/yi8f/f8pa3sGfPnqxZZcx0EFbHcakiGv0jRPEiirItq7XWS1AssUc0HA4bQbFQKLSMXjaXXpvVtlOZ5Ej1fUFYpBVvaGjg6tWrHDx4ELvdjs+XuYRupXULO9aDpr2ehYUXV7StutNx/vx5Y/hupkbyfOTWj88Va9lOWcrZ+MpXvoIsy7zvfe9L+flvfvMbmpqauHHjBvv27WPz5s3cdtttppxbZ+ZYK+npRZSjaSu/BDqKXdplBhRFWVafqvfa6FGf2trapPKC8fFxZmZmCnI0dFghspbts1NdXU1d3WKPxIkTJ/B6vQSDwbRUlsVio8oVTqeTuro6/H4/Q0NDDAwM0NnZmdbpyKdnw8wyqg1sYCkKCYolwuFwEIvFsj6vlTMb6eTStHYUpd2U81gV2dCi+3w+7HY7W7duLfh8ZujmYjti+gDVpqYmjh49ysDAALFYjNbW1oyVBsW2U9myV/X09HDq1KkVG8l15JOB/22yU5ZxNh5//HGeeuopnn322bQ3oKmpCYD6+nruueceDhw4kNbZkCSJeDyeda21VcuMrNjkZoZMetQnFAoxPDycRNOnOxWZ6GXXA9Ldk1yVUFVVFW1tbdy4cYMjR45QXV1NIBBY5nyVxtm4jiCE0LQg8EoZgSAIeDwetm/fzuzsLIODgwwMDBjsVYnn3shsbMBKMDMols8AWis6G2bT6Jq1jtkZ+EJo0UOhEBMTEwXLYxVkz1goGv0mCwsL9PX10dLSgt/vT6lzS2OnNEAhcUusaRp2u52dO3cyPT1tOB3p5luUishkrcASzsbPf/5zvvrVr/Kv//qvaUtB9GYpr9fL7OwszzzzDF/4whfSrulwOAzO8WygR4zMqO23qrNRKmMQi8WWNb4pioIgCMiyTH19fVFp+qwMMw1rQ0MD9fX1jI6OcujQIWpra5OM3uorPbiCSgAAIABJREFU8WFk+d8jCAsoyr9FVd+Tcm23253kdCRS5uqbq2L3bKxGc98G1j7MDoo5HA6i0WjW5zdzU2/VLIlVbKdOiz42Nsbk5CSHDx9eVXrZ9QZJkujo6MDv9zMyMkJvby9tbW00NTUl6d7Vt1OzyPLnEIRB4vGPo2l3LFu7oqKC3bt3Mz09zcDAQEqnI58yKrPtlJWfw1V3Nu677z6ef/55xsfHaWlp4eGHH+aRRx4hEomwb98+YLEe9lvf+hZXr17lQx/6EE8//TTXr1/nnnvuARaVwHvf+17e9ra3pT2PrsT1mv6VYFUlbpZcxWhaX4ql9LLhcNhw+PTJtT6fz2D3uX79OvPz89TV1Zkm13pAIcpWEASamppobGzk6tWrHDhwgMbGRtra2lZdiQvCCDCLppUhCMeARWcjE6NGKqcj17KofMuorKyoN1B6FCsolquzYUUHodT9EYVAp5dNLINKpEWXJImysjK2bt26rljpCtXx+TIhyrJMZ2cnra2tjIyM0NfXR0dHB42NjRl6VAuXIR0E4RxwHk2rRJJ+TDy+6GykslOZnI5cnYd8Mxtr1U6Z+uYsLCwgy3LGF/JHP/rRsr998IMfTHlsU1MTTz/9NACBQIDjx49nLYvdbs9JiVs1PW3VzEY0GmViYiKpvwIWJ6673e609LJL17ESJGmcqqr/hSw3E4/fD6zMnlJqpFO2ifWyly5dYv/+/QiCgKIoWa9baKRf0/aySI17FVV9/4oy61jqdExNTVFbW1s09qpsvmO1Z3UD+SMbO7VaQbF8yqisZluKsZZZ6yyVaSV62fLycnw+XxK98PT0NNeuXbOQo6Hgch1Eki6iKK9lkaSjNCjEKbDb7XR3d9PW1sbQ0BAjIyMEAoGcNtRmOBuaFgDqgTFU9e1Zrb3U6QCWlf+uhN+23kJT3p5oNMrPfvYzDh06hN1uZ8+ePbzpTW/C4/GYsXxe0MuosoXZ2QgzHRez1srHGCiKsoymLxwOMz09TWVlpUEv63a71+xLoKOi4hfY7WeQ5ZfQtC0oyutLIoeZzdmiKNLW1kZLSwt9fX2cOHGC9vZ2WlpaMt6vfKNWyfCgKI/kvbbudPT393Pz5k0OHDiQsqejENkTseFQrG/kYqdWKyhWysyGVbMkZkBVVaLRKPPz84RCoWX0sm63OyO9bDFgln5xuX6D1/t9bDYXsICivMWUdXNFrhS16eBwONiyZQvz8/MMDg4yNjaG3W7PavNujq2sJB7/H8AM8EqVRTYBt0Sn4/Tp08TjcRoaGrJilysGRbuVYYqz8Z3vfIfPfe5z7N69m1gsxne+8x3uuusuHn300awow4qBXJ0NK0d5VqNBfCm9rD69VhRFI+pTU1NDW1sbFy5coLa2lqqqqoLlspKBisXqEQQFcKBpNSWVxezIjiRJuN1uOjo6GBsbS1svm+u6uR4LiwpTlqcRhLMsTr7PbOxtNht+v5/KysqUPR2p1l+rCnkDxcOGnVoba+W6iV06xFWnRYdFSnSd+S4XetlEWK0fRRAW0Mk2YKEgWQovQTJvUF9ZWRk9PT2cPHmSmzdvMj4+TldXV8Z9hnm9dk6WVjK8knlQWMlGVVRUEAgEuHnzZtLg3UxOx0ZmIwfoD+vXvvY1fvrTn/KmN70JgKGhId71rnfx2GOP8YlPfKIkA9Xsdvu6oRQ029mIx+PLaPoURUmil62rq6OsrCztg51ZJgUQeUUhrixTITBrnVDozQhCgOrqVjSt05Q180GuxjYXp8Bmsxn1ssPDw8vqZfNdNzejM0ZT02eR5TiKcheq+sms1k8srxoaGmJwcJBAIEBtbW3S+c1WyBsZj7UNq9upjZ6N7JANvWzi1PVLly4hyzI1NaUNHJmNubnbUNUwNlvtqmQ10t3XYtkpWZYJBoPY7Xb6+/sZHBykq6sr5ca9mM+cpkVpavprbLZ+FOXBpFLgVNDL8W655Zak8qp0TscGG1UemJycZPPmzcBiqjoQCPD0009z55138r73vY+GhgYzTpMT1mbEKAI4TJNL07Skhu35+XkOHDhgRLl1No1AIJATvWwmpSFJ/wub7UkUZTex2EdZrXpSM+6dpglEo7egaav/vCbLkevmPXenwG63s2nTJiKRiFEvGwwGqaurK3qTnixfRRRn0bRqRPEwK+2dlipYt9vNtm3bDKdjaGgoyenYYJbaQCpY1U7l2lu4GrMx8lnLzI1fot3Sh7hmQy+bvbyXkOWnUNXOlzfsK2f+rQJNczI9/XYqKlpLLEdxbIR+rMfjYdeuXcbGXRRFOjs78Xq9ea2bKwThIi7XCTTNjyT93YrORib2KljudBSjjMrKgTFTMht+v5/BwUFjaE08Hsfn8zE+Pm40Dq821lotrCz/PZL0IoryGuLx9xp/z1aJ6/SyiQpaVVXKysrweDx4vV4cDgd79+41JXWaWiYNm+1nqKoPSTpEPP4HaFpjHuuUDqv5sprjIBUWXVpaLzs0NERnZ2dOEZRclebCQhcLC3txOC6jKH+44vGZ2Ku2bdvG3NycIXsgEDBolTewAbC+nSplb2E8Hs/pO4IwjCw/i6L0oKqvSfh7frpcp5dNbNqem5ujv7+/YHrZTDLZbN9FEK4iigdR1eDLTcLp11mPyHajvtoVCEvl0jfuk5OTvPTSS9jtdjo7O3G73UUNLClKI7FYM2Vl46jqHSsenw17FbzidOTLLLVWn8eCnA39wn76059mYmKCSCSCw+FAlmXm5uaw2Ww5KzOzkA8bVekyG3NI0otoWguS1Ec8fhfgSbmWqqpJDduzs7NJ9LIejydt49vIyEiRH1SBePx3kOUXUNVAyfse1iqKW8KU+li9XnZubo6BgQEmJiaQJIn6+nrTZVBVG5OT/wG3uyVxFQShF0G4iaq+GXiFWnSl9V0uV5LTMTk5SXV1dVGjXhtYO1hPdqrUZVSLm/QooniSaDSIptVltdZK9LIej4fGxkbcbjdHjhxh+/btBf22lWTStCpEcQhNcwLZ0eNvIBnmkIhkv25VVRV79+7l5s2bnDp1CrfbTVlZWdGG/qqqg9HRL+NylQPNCZ9MIQhjL5daZ1d6nOh0DA4OomkadXV1v1UZ+IKcjdnZWTRN4/3vX55eunTpEh//+Mepra0t5BR5o5QRo9yVeBmqeiuieARV3QG4jca3SCTC6Ogoly9fTqKX9Xg8VFVVFdT4li8y/b54/AEU5R1oWiUWmRm5rmG2Y+Jyudi+fTunTp3ixo0bTExMpK2X1ZFrhCaVHIJwGFn+DBBDEE6jKJ9NOj4bpaw7HUNDQ9y4cYP9+/cTDAaX9XRs4LcLVrdTpQqK5WPzNK0aQRhE09xo2islv4lyRaPRJKciG3rZ1UYs9kFUdS+q2rhi2azVMvBWkmW1g2KwSDFbXV3N2NgYZ8+epaysjIaGBhwOR9rv5IPF6+wAEoNiY8jyv0UQZlCUd6GqnzA+UVV1RcenoqKCW2+9lenpac6cOUMsFsPn82XFXrUSrG7j8toN6g/OX//1XxOPx+np6aG5uRmn08mWLVtwOBxs2rSJTZs2mS1v1shHiZeqjEpRVGZm7mFu7rWEQgLh8DFisZgR9dKzFXrjmxWQXuGJaFp2httqSnw1offT2O32lJ8VQ3Hkcq1tNhtdXV3Ispy2XjZx3VzZqJY/xyFeYf2YWnZ8bj0hMs3NzdTU1BiN5JmcjpWui9WV+AZSYy3YKafTyczMTNbHlzqzEYt9CFE8h6a1oKoe5uYWHYqJiQmuX7/OyMhIwfSyZpbtpL9W7pdnVKwezNQjZqxVqO0tJpHJSscKgkB9fT2zs7PMz89z+PBhampq6OjoSGlTc5UXUtspQRhGEKbRNA+i+GKSs5HLb6yoqCAYDHLjxg0j07ESe9VaR17Ohn5B3/ve93LgwAEOHjzIn/3Zn3Ht2jV++ctfsnXrVuMlL9XmOJ/0dLHLqDRNY2FhISnqs7TxrabGTVtbu/HC9Pf3U11dXdKZJUuxsfnKDTr7l873Hg6HjShIPB6ns7OT+vr6pOtazMa7bKC/v5nqZfNZN93xmvZ6FOXfIgijKMqHgTii+AQQRhBuzdmZsdlsuFwuozQsk9Oxlhk+NpAe69FOlaLcdzm9bBlzc5eAS0a2wuVy4XK58Pv9pm2E16OdsVpwLdM11gNi09PTVFVVpdzEFysolsu6lZWVbN68mdHRUQ4ePEh9fT0dHR3LhjCaY6e2o6p7EIQzKMpHXv7rIIIwjqpWIYplWa+v99T29PQklVetV6ejoDqXjo4OOjo6uPfee3n00Uf5xje+YdzgUhvvUpdRxWIxpqenl9HLOp3OJHpZl8uV8QWwYvTfTJnMpvUtJTRtcVZJPB5nZGSEUCjE/Py8wf7l9Xrx+Xy43W5kWSYajSIIAsPDw4yMjNDV1WX0GuRyzmKlshPf4VT1ssFgkLKysryU+HL9IKOqH04498+QpK8BUFNzO6L4n/JeP5XTEQgE8mLe2sDag9XtVCl7NpaulSu9rI7Lly8jiqJp75EZ76RZdsqKNthsKIqybHCvoiiUlZVRVlbGlStXqKqqMihpobiZjVwgCAKiKNLc3IzP5+Py5cvs378fn89Ha2ur8a7n2kye+ngnivJXCed+CVn+MBCjqmofsdjHsl4/MciVWF6lOx3BYJDKysqs17M6CnI2+vr6EEWRiooKGhoaePrpp9m8eTPd3d0lN+CrVQubqvEtFAohCAILCwu43W4aGhoIBoPLPO1s5TLLuFgNZj4fq20M9Eb9xGxFLBbD4XAYSrq+vn5FFhWHw0FPTw/hcJj+/n6Gh4cNBrFsUawsSCok1sseO3aMiooKGhsbc1Li2ZVFCS//01BVsNkGEYQZNO13gcx1senWX+p06OxVFRUVJd90bqB4WE92yixnQ9M0YrEYc3NzDA8PF0wva3bGZQPFQWKWamFhgTNnzjA/P48oissa9fX9SjQapbOzk5s3b3Lw4EEaGhpob28vWqAr1wx84rGiKNLa2kpzczOXLl2ir68Pv9+P3+/Pq9x35eMvARE0zYbDMYCiqMAEUL3i+qnkWc9OR17Ohu6RDQwM8MQTTzA3N4fTuTh9sa6uLumYUikOh8ORNEl0JWSjxDM1viVGrqenp4lEIrS1tRX6M0wt7zILvw2RnkQk0gqHQiGjUT8x0tfW1mZEfHSFnA3090PnFJ+amuL48eOEQiG8Xi8ulyvj94sVXcpUWqTXy9bV1XH9+nVOnz6NIAhEo9G09bK5yqGqbwPmgBDT0258vj9EklRU9R4UJXOWY6WyqKVOh05JWOqN5wbMxVqwU6tRRpVuiKskSaiqSmNjY970solyWW1mh9Uy8KuNpcyVoVDI6AX1er2Iokh7ezsejyerHgmfz0dDQ4Oxifd6vUUp9zHDMZEkifb2dlpaWrhw4QK9vb00NzenWCHz2isFoTTtdajqmxGEi9y8+Qe0tHwEm20URfkgqvpHGb+byU7pTsfMzAwDAwNomkYgkJ6eGazvpOflbOgX6P3vf39Khg9gWUPYahtyh8PB5ORk1scnKsulL2k4HCYajWbd+BYKhVa9rnY1sV6VuF4GdePGjSR6xkRaYb/fj9vtRhRFRPEIovgS8fibgJU32dmgsrISn8+HIAgcP36cysrKpNR1KplLwQiif97Y2IjT6eTcuXNGvWx7e3vGaGh2ESMZVX0PAJL0/yEIccCGIBxFkr6Ipu1EVe9J+c1c2Kt6enqYmJjg5MmT9PX1JQ023MDaxlqxU2ZlNnKhl5VlmZmZGUZHR7Oit14JpeglWc11rI54PJ6UZdcZ2FwuF16vRF3dPK2tm7DbXwleTU5O4na7s9LzOkRRpK2tjebmZo4fP87IyAh2u92wWelQqiyIPo28tbWVgYEBpqenuXr16oryQnbsUlCGojzy8vH/gCRdBaqQpH9A0zYBPjRtS1rZV7JT5eXlSU7H3NwcU1NTazLTUTA3aeLNvnnzJtevX2dmZoZ4PE5ZWRktLS00NDSs+gubjRJPTClOTEwwNzfH6OgoUBi9bGqDEAPCQFVOv8OKzoZZKKUS12uTExX0/Pw8TqeTmpoaI0uVjp5REIZxOL4MRBHFo0Sjf2aabIIgUFFRQWdnp9H0pqeuC216yxa5MkB5PB62bNnClStXOHDgwLJ62UTkWjs7M7OdWOxO7PZLwDVE8X8BTwELqOrtQHIWKVfZ7XY7lZWVdHV1JTWS607HWthsbCAzrGyncukt1O1BqiGumqYZJZjZ0MtaMRth9lrrzXbqexa9/C0UChkOpdfrTTFnax6H41OI4lUU5XVEo/8xr/MufYZkWaa+vp6qqiqmp6e5cOECXV1daSmkS11yZbPZ6OjoIBwOMzMzw8jICMFgcBkxS75yACwsdKCqbcBFNK0VWf4PgEw8/m00bRuQbPNUVc26tL68vJzt27ezf/9+hoaG1mR5VcHOhiAITE9P89xzz/GLX/yC06dPGwPB6urq8Pv99PT0cOedd7JlS2oPrxiw2+3EYjHjv1dqfLPb7QbTTqH128uV5Rw2239DFEeJx38PRfm9AtYqPawoUyZEIpGkVHJibbLX6zV6ai5fvozL5coq0icIUUBF02RgwVR5dSUnCAJNTU00NjZy6dIl9u/fj9/vp6WlJekZNTNbkc+xuryiKOL3+2lqajLkbW5uxu/3J0WQc6+dtRGL/Smi6ECW/x9gFLiBJP05kvTfiMe/iqa9Hl2Z58oupR+fjr2qsbEx67U2YE1Y2U5lCootzbJPTU0Z/10IvSyk1uOi+Bsk6RCK8hZU9ZaC1ioEVstIlEIeRVGW9QXqwyftdjs1NTVGZjm9Q3kDURxFVWsRxf1Jn5lBfWuz2QgEAsbU9+HhYbq7u1OWVxXT9mR7rCzLbN68mYWFBQYHBxkeHk5Li56rnYrHHczMPEZFhR1Z/jggIAgTyPJ70LRu4vHHSezlyDUopqoqdrt9WXnVWnE6CnY2zpw5w+c//3nOnj3Lm970Jh566CGD/u7atWscOnSIX/7yl7zwwgt85jOf4bWvfS0PPvggTz31FPX19Zw6dQqAiYkJ7r33XkZGRmhvb+eJJ56gqmp5FuDxxx/nz//8zwH4/Oc/zwMPPJD0uaqqXLhwgaNHj3L48GHe+9738slPfnLFxrexsTHC4bApjaJLFa8gXEMUr6JpdUhS75p3NsA85Wvmb0tX/ma3240ygtraWsrKygq+z6raTTT6cURxgHj8nSb9gtRITF0PDw/T29tLIBCgsbGxaM9GLopwqVJOrJe9ePEifX19RtOe3oOUb6NePP6XiOJTSNI3WZw6/hKy/EE07dXE418FWnLOnCx1TnSnY35+nqGhIcLh8KpuQDdgPqxmp3ToGXhVVZdlK/Sew8Qse2VlJWNjY6Y8j8szGxPYbD9G07zYbN8lEvkLlkZjM61ltQZxK5ZRpZMnkVpYD4gBRkAscc8yOjpKPB7PKiimaX4U5Q2IYh+x2AeWfV7ob9O/73K52LFjBzMzM5w/fx5Zlunq6jIo0q3AsJh4rNPp5JZbbmFubo6BgQGGh4fp7OykujrZGciVvUoQZMCDovxHJOkR4AhQhigexGZ7NYry71DVhwB7QXYqsbxqcHAQRVHo7u42etGsiLydDf2H/8u//At79+7lpz/9acrj7rlnsa76u9/9LgsLixHgD3zgA3zsYx/j/vvvN4579NFHefOb38xnP/tZHn30UR599FG++tWvJq01MTHBww8/zKFDhxAEgd27d3PXXXcZyv7GjRvccccdRtOT1+vlE5/4BLfccsuKN9VMSsGlay0OQOpGFAeJxd6d01pWdDasUFYSj8cN5Xzz5k1CoRCTk5PLyt9ymSqa23UWUJR9KMq+3IXPQo5U11hX4K2trQwODjIyMkIsFivK/TCjmVyWZQKBAH6/n5GREXp7e2lvb0dRlAKcgQZU9YMvp6n/E5pWhiBMIAg/w25/lnj8U6jqW02gOISysrKsdMcGrAsr2ilN0zhy5AgnTpzg+eefp7e3l927d/ONb3yDurq6tPSyAOFw2DR7sJx8pAxNK0cQplDVdrJ1NMC6ZVRWg95Xk5itiEQiRj+ox+Ohra3NxAG+ItHovzdhneVIdY/Ky8vZs2cPN2/e5OTJk3i9XoLBoClMiOmOzZe5ChadpO3btxMOhxkYGGBoaIjOzk4qKyvzCorp90zTthCP/09E8XtI0n8BIgiCDVn+Apr2d8Tj33x5Lkf+QTFYvN67du1iZmaG2dnZ9els6D/6j//4jwGYnZ1NGvSlQ7/BDz74oPG32267jZGRkaTjnnzySZ5//nkAHnjgAd74xjcuU+K/+MUv2Ldvn+F97tu3j5///Ofcd999ANTX13P06FEAfvWrX/HjH/+Ybdu2ZfV7iqss7cRiHwfi5HrJi5GeNiOasVoRI01bHISYGPHTZ1foDmVNTQ1Op9OUScBWcKQgsxwOh4OtW7cyNzfHiy++yKFDh9KmroslQyJWeqb0aeRtbW0MDw8zPj5ORUVFViwo6dbXtH3EYq9Hkj6GJP2Exenj08jyF9mx4y+Yn/9rIHVT8FKslMWxyjOxgdxhRTsF8Nhjj7F582be+ta3MjExwQ9/+MOsfk9x7VQZ0einEMUrqGqwwLXMlGttrpM4uyIUCjEzM8PCwgJDQ0N4PB4qKipobm7G4XCUTMcUct5Mel+nSL9+/TpHjhwhHo+jKErB6xbrWI/Hw86dO5mZmaG/vx9BELDZbDldn1Trq+oHUNXXIct/iCgeAEAQzmKzvZG2th2EQt8DsnMQMtmp8vLylBlWK6GgMir94v785z/nmWee4bWvfS0NDQ1UVlZSWVlJdXW1wXagX6h0F+v69ev4fD4AfD4fN27cWHbMlStX8Pv9xn+3tLRw5cqVlOuVeqhfauVU2jkbulxW2jwlXidVVZc1bcfj8aRBiKlqVCcnJ5mfny+F+EVBtoZNn9q7adOmlKnrQlBIGVU62O12Nm3aZDTpZcv+lD6d7URRHkPT7kKWPwTMACDLYbzeD6FpHyIWOwt0ZJRrY4L4+obV7JQgCHzzm980jv3BD36Q9W8pvp2qQVVrTFrLTLmsC71pO9Fu6X2But3y+Xw0Nzdz4cIFenp6Si3yqkAQFtkK6+vrefHFFzl69Ch+v5/W1taM+raUzeTl5eXs3r2bqakpjh07ZpDGZDP3Kr0d6SQefxJR/M/I8teBxffX4zmKx7MDRelGUU5kJf9atlOmOBuSJLF//37+6Z/+CUVRaGxspKamhqqqKh544AHuuOMOoPAIYSoFlG7N1Rrqlwqr47iUbi0zHKBoNMrMzAyhUIjTp08zNzeHIAgvU/V5cxootd6Qq0OYLnWtzxQotgz5UOp2dXUBGE16nZ2d1NSk3+RkdkbuIho9jCz/PqJ4PuE7YLNtIRbL3MC/krNhJed8A7ljPdkpM+cu/TbYKbMz8JmGueqORV1dHS6Xa9k9z2XuVyZYxRHLVu+LoojT6WTr1q2Mjo4a5bRNTU0pv19q5ipYpKCvr6/H5XJx+vRpXC4XwWAw49yrzOu7UdWvEIvdgc32bmDa+ESSziOKzoLtlNVRkLOh//B9+/axb99i7fr4+DgHDhzge9/7Hv/8z//Mq1/9au64444VX5CGhgZGR0fx+Xxpub9bWlqMFDbA5cuXeeMb35hyvVyHJa13xasjea0FbLbvIgjXiMUeRNPa81wn83FLm7b1GlW73W40P5tXo/rbi6Wp69ra2rwdtmI6G/rxTqeTnp4eZmdnk+pl80sH+4nHjyNJH0GSvm/8NRux1roS30BmrCc7VYxMt1lrmSUXLLcvgtD/Mn3rrYA3a5kK+X16w/7U1BShUIiDBw8CGAGxpcNcVxNrLQCiM0EFg0H8fj9DQ0NpM9vFdDZy7eWrrKykra2N8fFxjh8/TkVFBYFAIGUgLxs7omm3EY2eQ5ZvWxYYAw3IJ8O/NlAwG9VS1NbWcuedd3L77bfz/e9/35ikvNJFuuuuu3j88cf57Gc/y+OPP87dd9+97Jg77riDz33uc8awvmeeeYZHHnkk5Xr5ZDbMUZYKNttJnM5xIHvqwHQw2wlKXvsEkvRrwIksP0Eslh0Hd7qXW59Umxj10Ses6zWqLS0txsySmZkZrl69mlWKcu1CY7FXJ/sNfyGlbomp6ytXrhj0s62trTmtY0aDeKa1E493u93s2LGDUChkOB1dXV2Ul5fnJPNi0/63GRioYdOmvwZAUd5suvwbWPuwkp0qXbmvwqJuKhxmZlyWOgmCMIrD8RVgAUm6Ne9ZEemQ2Beo267EYa5OpxOn08nOnTtzphdOxFpzEHSku6/5ZLRh0cHevHkz8/PzDAwMMDIyQnd3dxJ9azGcjVypZnU7JQgCdXV11NbWGoG86upqAoFAkqOZvSyVxOMnmJ29jaqqAy9/VySTo6HLv5btlCnOxuzsLFevXqWiogK3243b7cbpdDI/P883v/lN7rrrLhRFMQaY3HfffTz//POMj4/T0tLCww8/zGc/+1ne85738J3vfIfW1lZ+8pOfAHDo0CG+9a1v8dhjj1FdXc2f/umfsnfvXgC+8IUvJFGVJSIfJW6GspSk/4vL9Y/4fGFEMYCqbi1ovWJmSTStAXACETQtkNNasViM8fHxpBpVSZIMqj6fz2dMqs0EXR5RPIoo9qGqb0ZVu3P9aRaFht3+50jSc8TjdxOLfWLVzpw48+LChQv09fURi8WyVojFzGykU/per5ddu3YxPT3N+fPnkSTJKLfKBTdu3ENr61ey3hisdSW+gexgRTu1dB7USjCvNOgKZWX/Lx0dowhCI5qWWzCiWHLpayVjjkUCCCeCMFOQTEvnbYVCIRRFMeryUw1zjUajxkwWWEAUh1HVViD33jirlEAthSgeRZb/CUW5HUV5Y9bfK4RhqqysjG3bthEKhejv7wegu7vbEmVUsNxO6YG8hoawpRp7AAAgAElEQVQGrl69ysGDB6mvr6e9vR2bzZazHRkY+O90djpxuw8C92Ylz1q2UwU5G/qP/9WvfsVHP/pRbr/9dhoaGujo6KC2tpZf/OIXNDU1AckP5Y9+9KOU6z377LPL/rZnzx4ee+wx478ffPDBJMaQdChVenpRGUosKsdZE9YrprPRRiTy5wjCTNoNfqoaVd2x0DQNj8dDfX09ZWVlOUduXjl+GofjUTRNBV5kYeF7LF7DXNexFgRhDEl6Hk3zIctPEot9lEXnLjPMbOKXJIlAIEBLSwu//vWv6e3tpbOzc8Wm7NUoo0qHiooK9uzZw8TEBGfOnGF+fp65ubmM9bKJyDeCtYH1CSvbqVz1u1mZDVE8iSCEEcUoongERbGWs5FspwLEYvcjiv3E4+/Iep1YLMbCwgIXLlww7JYgCBnnbWWGht3+ZSTpLKraQiTyX4HVL6EyHwoOx+cBFUnqY2FhG5q2MkmAWRS1Xq+XW2+9lcnJSU6fPs38/DwLCwtZ9RyWwk4JgkBzczM+n48rV65w4MABGhsb8xrSJwh+IDsWzZXslFX3QTpM6dn43d/9Xf7mb/6G48ePc+zYMZ5++mlOnz5NZ2cnX/va1wAKSj/mg1I13sXjdwDz3Lw5gcu1o+D1it3/oWlNaNqioU0cKhUKhZidXXSWltaoTk5OsrCwQFtbmylygYimSUCExXrc3F8aK0aMNK0GVd2GKJ5EUX4XyH7mh9mKw26343Q62bVrlzHEqLu7O21/RLEjRtls7qurq9m7dy8vvPACJ06cwOPxEAwGKSsrM12eTPrJ6kp8A5lhZTsFxZmsvBJUdSua9iyaJqOq2wter7h2KvNMo3R9gfo6dXV1aeeWZItFeeKI4ksvT+O+jCCEstqUWx8ii/NVRgEvmpa9A2WmjaiqquJVr3oVv/rVrzhy5Ag1NTUEAoGMzmAp7VRi9cClS5cIh8NcvHgRv9+flR7JNVORqzNjNZhSRlVeXs473vEO3vGOV6IO//Iv/8Kvf/3rPOquzUHpamEriMffx9jYCfz+wqMexVDimqYxPz+fpJwTa1Q9Hg9+vx+3253yZTBTpsV1vEQiDyNJJ1CUV5PLMClrQyIS+UsEYRxNqydbJ6qYjpOeug6Hw5w/f95wOpb2zZQys5EIQRCQZZlXv/rVjI2NcezYMSorKwkEAhkHNhbD+dnA2oYV7RSUJlCiaa1Eo3/G4OAhdu/OrYQ2FVaLFCWXvsCZmRmuXbtGY2NjwfIswkYs9iA220+Jxd6FpqUujVt7EIhEvoYk7UdRtpFtA36u9zsbnSwIi/MtXvva1xpZA5/PR1tbW8oNvBXslCRJtLe3c+XKFRRFoa+vj9bWVpqbm1ek+C10qN9agukN4jre8pa3EIvF+NjHPsY//uM/oijKqkaNSlULa7W19BpVXTmfPHkSTdMoKytLW6O6WkhmoOgiHs+9Pt/6kNG03I1dse+Fx+MxUtdnzpyhrKyMzs7OpKxBLo13K/XmLEU+JXf19fXU1dVx7do1Dh8+TE1NDR0dHQWzwax1Jb6B/FFqO1VaOFHV7LOtmVAMZqvEYa6hUChpmKs+u8Lj8azq/VKUt6Mob1+1860WNM1HPP7OHL9TnKngsPgMtLS04PP5uHjxIn19fbS1tdHU1JSkq4tNZJJr1jEYDNLa2srIyMiKFL/5ZDbWsp0yxdkYGxvjySefZOfOnTidTtxuN/X19Vy6dImzZ8+acYqckWtZVCGZDUEYQRDGUNUeoKxkzkYkElmmnPUaVa/Xi9PppLOzs+Aontl0vBtIxmpe26qqKvbu3WtkDaqqqggEcot0rmbPgyAI+Hy+pCa9hoYG2tvbc3Z4dKx1Jb6B7GBFOwWrXaYXpRh9BoXYhKV9gWNjY4yPjxu9Ffn2BZqfgd9Avsi3D1GSJDo6OmhpaUmiy62vrzfub6nLfZfCZrPR1dVFW1sbQ0ND9Pb2EggEaGhoSDp/Pj0ea9lOmTLU7+bNm3z605/G4/HQ0tJCR0cHTqeTU6dO8c53LnrLpbhIuSiIfBWTIFzDbv8rIIKqvopY7EFjrVgMJicF6uvzV1Sp5FJVlfn5+aRUcjQaxW63G8q5traWsrKypOs+NTWV94asGDCPWWXt1jHCAou9HK/cFzMbxLNBYtZA38BHo9Gso7zFlDfd8yGKIi0tLTQ1NXH58mX2799PU1NTzhS/sPaV+AYyYz3ZqfwRx2Z7DFE8TTz++yjK20xdPVtdnk1foKIo1NfXp2XwMlumbNYxA2vZTqWSfTXtlM1mY9OmTSwsLBh0uV1dXZYoo0oHneJ3YWGBoaEhY3htbW2t8WzmmtnI1L9i9eeroJ2n/uM2b97M9PQ009PTHDt2jOPHjzM4OMgHPvABg5HD6hcif/nmWOQqdwKTSWtduiRw8KDEu98dJ18bpigKCwsLXL582VDOmqYZNapVVVX4/f6M9es6zFS+6zHSs9qbfFn+V5zOr6BpNcTj3wYKqy0uFDrLRmNjI7/61a/o7e2lra0tq9rTYjobmdYWRdGoj9XT7bFYLCcHYsPZWN9YT3YqXwjCGKJ4Ck1rQpafKbqzoc+uSAyIpeoLdLlcywIaExMTpspmJaxFuykIU8jyf0UUy1CUh8iH8ndxncLfLX0QbDgcpr+/n5mZGWZnZ7MqpV1tZ0OHPj19bm6OwcFBY3jtamVarAJTw9wVFRW84Q1v4A1veIOZy+aN1TAcmtZBPP4uRPHSy0xUMD0Nzz7bQHW1TDQKjz8us3Wryqtfnb5MK3GwkP5vfn4eeCXd1tzcjNvtzrtGdSOtXDpomkYkEiEUChkGuK3t29hscRyOa4jiQVT1HcaxZj+7udwvSZJwOBzs3buXkZER+vr6UqaBE9cu1ruWrYLV0+1+v58XXnjBcJSW1vimwlqnFNxAbrCanZIkqei9IppWi6oGEcVB4vE3mbq2oiiGvTp37hzhcNiYXeH1enPuC7RaUGy9BteyhcPxP5GkJ1++Ds2o6vuA1Q/OJcLj8bBr1y5+85vfcP78ecrKyujq6srIVFjqzb3L5TLIWQYGBpidnWVqaippmGEmrHWK9oKdDVVVGRkZoaKiApvNRnl5eVLvw1q+ONlBeHkQzit/qaiATZtmGB3VcDiguhq2b3/lmqiquoxRIx6PG4OFPB4PDQ0NlJWVMTs7y8WLF2lubi5cUgumldej86NpWtL9DYVCxGIxHA5HkvF1ON6HIDzCzIyNUMhHfX3pMgSpoNee+v1+BgcHjdR1TU0y3WMpMxtLIcuy4SgNDw/T19dHe3s7Pp8v7TprnVJwAyvDynZKZ07Mdo5MfrARi/0xEAYq8l4lGo0um7kkCAJlZWVomkZ9ff2KdKUr4bd9c19KJO5N9PtcVzdDR0cEh8MJJG+MS603JUli9+7dTE5OGkyFwWAwZaaj2A3i2cLj8bBz505eeOEFhoaG0DSNrq6uFXtp17qdKtjZmJiY4G1vexs9PT34fD7+5m/+BkEQ1vRFyQaCcBlZfhJVbUVRfp+ldK2yvOhoVFbGmJyMcOPGDcLhEHNzcwBG0/ZKg4XMVrwbkR5zoapqUg2yboCHh4fxer1UVVXR2tqaUvlFo7+H3X47siwzMXGV4eH9OU9QzRaFrOl0OrnllluYnZ2lv7/foMvVlWMxlWA+BgIWHaXu7m7a2toYHh7mwoULBAIBo7GwkHNsYO3BynZKnwmVi7OR/fusIQgDgJvFeUrZORqJsyt0vRaNRrHZbHi9XjweT9LsCkVRDIKJQrGRkcgMMwN0U1NThmOh98/oe5OGhgaCwSCKsplweBvnz99Elrvo6Igjy7Ilrq0e7a+rq6O2tpbR0VEOHjxIY2MjbW1tST2q+WQGiqkfJEni1ltvZWpqivPnzyPLMp2dncto6HWsdTtVsLNRW1vLoUOH+PznP8+FCxeA0nu7+SKXl0eWn0AUryCKZ1DVzahqZ1LTtizP0N5+gIoKgYWFKux2B21tbTkPFspeYWpI0tOI4hni8XvQtM5lR1jxQbWCwsoW8XjcML6JyjkxGxUMBjl69Cg9PT1ZrlqF3S6yZUsFs7OznD9/nqmpKebn53G786uNTQUzHBi3283OnTuZnp7m3Llz2O12o0mvWM9WoXW2DofDaNIbHBxkeHiYYDBoNOnB2lfiG1gZVrZTdrs9pwG0OjVsNmVXkvQssvxPgEw0+kk0rWPZMbFYjNnZ2aQS3sTZFXpfoN1uT3vNctvYTyOKF1HVIJDawbJSxtuKTkuuz25iRioUWgx6zs/Pc/36dbxeLy0tLXg8npR6UFVVPJ5/w6ZNgkHG0draaolIe6K+FwSBpqYmGhsbjf691tZWWlpaDHbS1SYyyQaVlZXs2bOHiYkJTp8+bdDQLw0+rHU7ZdpQv69//etmLGUqdKVs9g2Kx+NEo+VI0gkiEYHBwRHm5yeTBgtVV4/zqlftKvjhzlbRCcIINtsPADuieJVI5K9SHmcl5VtqRZUJunJOzFboHO9erzfj0MN84Xa72bVrF6dPP47D8UWmpjopK/srHI78yx4SYdb1rqioYO/evYyPj3P8+HFUVTUlopkK+WQ2Uh2vZ2cSm/S6urqorq5e80p8A9nBqnYqnwG02epfUbzCopmPIgjjzM/7krIVs7OzHD9+3MhW5Du7InubEMHh+MLLVPFBotEvs3TY6Xplf1qdHtJXej/1e7ywsJCUkaqtrcXlcnHo0CE2bdqU9do6GUdTUxNDQ0PcuHGRurrnEIRNaNqbyXZordlYel1FUaS9vZ2WlhajlDYQCBTVOTLDkamuruZVr3oV4+PjnDhxAq/XSzAYxOl0AivbQqs970thaoO4fsF1pVPqH6+np/WblSs0TUtZo2qzKbjdu6iu7sDhaGbLls5llLLDw8Om/P7s5394WaRQnUPTUiuQ9dUjEWYxKiYWdJ0TlfPk5CQ3b95kaGgoSTnX1dXhcrlW7Xlub/8B5eVhVPUAp0//TxyOO2lvby+ogbQYira2tpaamhoOHTrEuXPnmJmZoaOjw1R65Xya+jIdv7RJb3BwkFgstuFs/BbBqnYqW+hBtEzQa+/n53fhcAwQDtsZHY1gs50zAiYNDQ1Eo1F27NixapToghBGEMbRtCpEcRhQWLoNyeb3ZXeu9WTvlkMvdVupN9Dsgb2yLNPd3U1V1Teorv7fxOMi0eh/pazMWsMOZVmmq6uL1tZWBgcHuXHjBk1NTUU5l1lZE0EQjJKwGzducOTIEaqrqw1naS3bKdM0TDgcNmrNUl10XXms5sWy2+1EIpGsnA1VVVEUhdHRUcOx0F/cxMFCLtd17PZvAhCLPYSmtRX1N2SrMDWtlkjkS4jiZRRlR0FrZSOTWchHHpvtfyDLT6Cq24hE/ktO59KVs/4vsTHf4XBQXV2dsZl4NTA7G6Si4jqy7GLLlrcxMiIajc7pppGWCoIg4HK5CAaDhMNh+vr6aGlpobW1NeW7nuv9LtaUVb1Jb2ZmhoMHD3LixImkPpREWOl6b6AwWNVO5eJsLM1s6LMrErMVoODzncHjUVHVj1BX10Bz8/K+sVyH36ZD9rMOqonF3o0sv0As9m9ItQWx2vuWrzyCcAGb7Ueoahfx+B8A+TstiY3b169fJxKJcOXKlaRSt3S9gcWCzRbGZrMhCAIXLpxjdraNrq6uvIO7xYLD4WDr1q0IgsDExASHDh1Kq+vzhdmOgCAINDQ0UF9fb/ShaJpmihNeKpjmbHzpS1/C7/cb9JM2m42Kigrq6upwOp0l8cjSRYzSDRZaLI+KUlNTQ1tbW8oXV5JOIwiLa4riaRTFGs4GgKa1ZZTHao13+SpxWf4nNK0eUTyNIFwC6pYdoyjKMkYovQ5ZHx619B4PDQ1lrEvOBmZcl8uXP0BV1b3YbG0IQgcdHdDc3Mzg4CAXL16ku7ubmpqanM5V7HpVSZKMFPuFCxfo7e2lo6NjmeNWbK7zXPtHysvLcbvdBAIBzp07h81my9ikt4G1DavaqWzKqDRNY35+nlgsxsjICJFIJOXsCrfbjSwfx2Y7CIgoSjnx+HtSrmmWs5E9BBTlnSjKO9MfYTE7lS/s9kcRxX4k6VlUtRvozup7ib2B+j4FXmnc9nq91NbW5jXA1Excv34/Hk85dnsLHR0PMTY2zZEjRwzSGysNEIbFTEcgEMDhcHD+/HmDbdEMFrh87FQ2SOxD6evr4+TJkzQ1NS1rfl8LME3a6elpfvnLXzI1NcXo6Ci/8zu/Q319PTU1NdTX19PU1ERtba0xqVXHuXPnuPfee43/Hhoa4stf/jKf/OQnjb89//zz3H333XR0LDa3/cEf/AFf+MIXVpTJbrdz9uxZRkZGqKmpSTtYSK+9P3z4MC0tLRnKVTQ0zYemyYANVd2e8fxmbPDMVJilVr5mIR7/fWy2J1GUTWhaC4oyx8LCApcuXTKcR0EQlrFqrJWXU9NkFOU12GyvRIiczm+zffsPWFi4m5MnyxgZGaG7uzuHiOLqzMKQZZlgMGjQ5V64cGHZ1NRicp3nUy6m95zs3bvXaNLTszXFpSPdwGojXztVTKQKii0NluizK8rKylAUBbfbTWtra4YSGT2IoqBp6SPNZpUsmQmrORv56k1NK0cQYmiaE01LrUdSNW4n9gY2Nzcva9y+cuVKXvKYjXi8htnZLyLLFQgC1NfXU1c3z8zM33H2bAOVlXesOBB2NaHbnoqKCvbs2WP0HFZUVBAMBrMajJwOxabVFUURp9PJpk2bGBsbMyoI/H5/UefzmAnTdl/f/va3ATh58iR33XUXDoeDnp4eTp8+zbFjx4whP3feeWdSNHnTpk0cO3YMWFSwzc3N3HPPPcvWf/3rX89TTz21ohwvvvgiP/zhDzl+/Dhnz55lcnKS97znPbz97W9fsX5R749Id/Mk6Slk+f+iaV6i0U8BtWnlyGdjlWkds2CW8pWkMcAH5J+KzOe3RaNRbt68j9nZNzA9rTI/fxx4JbpejMbtXGDG9V3+3ISRpG8AHpzO77Nz5weZnJQ4ffo08/PzRCKRFRXlas/CsNvtbNmyhbm5OQYGBowZHekYT3JZOxPyTWfr51japFdeXs7OnTstV9qxgfyQr50qZlBMURR+85vfMDg4yLZt25ibm0MUxbTBkjNnzlBZWZl2gJkg3ESS+lCUTajqHlT1VWnPbcUAlBVlygfR6OdQ1edQ1TZUNUAkEiIajTI0NJTUuK07Fnrj9lrRNanukd3+YWprL1BbW8a5c5vp67tkMBbmu6ZZWGpL9J7Da9eucfjw4YIyMrlm1POxU6qqIsuy0fx+8eJFent7DcYtq8PUUO8LL7zAhz/8Ye6++25eeOEF7r33Xjo7FylYZ2ZmOHPmTMYb+eyzzxIMBmlry780qaKignvvvZevfOUr/NEf/RGf+tSn2Lx5c1bfXUnJSdIpNK0CQZhEECbRtJWdjUJhdmbDDDidzxEI/Heczhoikb9A08xP5+qN24mMUJFIxGjc9nobCAQ8uFwuQqEQV69eLVrzV67IJduQ3RouNK0TQRgCWoAKqqps7NmzhxdffJFDhw7R2NiYsYk8l017rs9bprVdLhfbt28nFApx/vx5gJwiqWaxUeWCxCa9mZmZNWP8N5Ad8rFTZgfFent7+dKXvmT0CO7YsYO7776b9vb2FenRV7ZT/xtRPA7EUdU38kqWI/VaVstsgDmbTlGcpqXlb7HbW4hGP0IhgbFc8Epv4DzhcM/LjdsHsdlsxOPxojVulwLL5Z9jcVupEgi04PM10N/fz9zcHDMzMyv2Sax2UEwQBHw+Hw0NDVy+fJm+vj78fj9+vz+ntXPNqOfrbCRWEAQCAfx+PyMjI/T19XHbbbdZOsthmrPx3HPP8cEPfpCHHnqIT3/60zzxxBM88MADfP/73ycQCFBeXs5rXvOajGv8/d//Pffdd1/Kz3p7e9mxYwdNTU385V/+JbfcckvK4xL/7nQ6c268S6d4BWEAQRhBFC8Qj79jxcbwlbIk2cKKZVR2ex/xuIwghBDF8yhKfs5G4pyDpawaeuO21+ulvLyc5uZmHA5HUZWzVaJpy+UQicd/gCCcQdO2AAKS9GlkeT+Nje+is/MTBq94uibyXJ0Ns/sqvF4vu3fv5vr165w8eZJTp07R2dm5YjOh2WxUuUAQBCorK1c+cANrBmbYKTOCYps3b+Z73/sejY2NfPGLX6Snp4ff+73fy+q7mRkKFQRh5mXGpxo0zbviWlbRezrMslM225NUVh5Gks4hy23E4+82QbpkpJq4rShKysbtaDTK2bNnqatb3mO4FpHqHsXj30IUH0fT3gC043KdYOfOPvbvr+LcuXM4nc6MTeTFdjbSbfATaX31nsN4PJ61PMXuLYTUDoredxIIBCztaIAJzoZ+AR566CE+//+z9+bhcZ3l3f/nOcts0ox2jXZZu7fYibwkDQEKJSxpgQAtpaGEvkDTQNNSli5pU1qgAV5IXpYXCBBauEppeEt+ULZAwxayWd7t2PEm2/IiL7Jsy9pGM3O23x+jczIjzb5IsqPvdeWKJZ15zjNnztz3uZfv977vPt71rndhGAZvfetb2bp1K/fffz+f/exnCQQCaaO5aDTKD3/4Qz75yU/O+1t/fz8nT56kvLycxx57jNtvv53BwcGMe7PVqLJFOiOnKD8GfJhmF4ZxC+myRZnWygXF/OIVa0/h8BtQlD2YZjuGcUNOr43vRR4fH2d8fJydO3c6xrmmpoYVK1aknKheaiyVTNP8fZRjWZtn/7YdSXoMy1JYseLfkKQPsGLFCodEPjAw4JDIbSx2sGEjEAhQVVVFXV0du3btoqamhs7OzpSfd6nUqJbx4kKx/BQUJykWP5cmHzWqVMGGLP8GSdoHWOj667CsrrRrLcWWpeLtKQhIgIRlFf6An4m4fbVxAwtFMrtvWWsxjM/M/nQZRXkHMM26dWW4XLsZHb2YlkS+0JWNubA5hy0tLTzzzDMMDAwkcA5TYaG4hanOsdQDDShCsGG/+W9/+9vccEPswVOWZQzD4MEHH+RVr3oVV65cIRAIpDUgP/3pT+nv7ycYDM77W3zp7bbbbuN973sfFy9epLY2dRsT5K5fnsqICzGKJO1Clg9gGOuwrMztOsU34mEU5f8DNHT994Hc1XKKtSddv45jxz4z60hTf2GSSTLGE7fr6+uJRCLOfZMvlpLDLIaxzPReYm1rPoQIMT29Grf7SSRpO/D7rFy5klAoxJEjRzhx4gR9fX1ppT6L8R5yydLYxwaDQerq6jh79izbtm2jqamJtra2eUZzITJGy7j2USw/VYqkWK5D/dLZO0naOavQFwQyV+VKU9m4jBAhLKuZfAa9Fc9PvY6hoQn6+lZjmrn5mGg06vguW3Rk7969CcTtsrKynB/yloqfWhhEAA1QkeUZhIhQXz9Jbe1GhofPMzAwQHt7+zwSeSmFTLJdW1VVvF4v69evdziHvb29VFQkH7C7UH4q1TmWSpI0HYoWgt9www0JF9z+En7pS19yyobpvpiPPPJIymzR+fPnCQaDCCHYtm0bpmkmZG1TIZ9hScmMgSRtQZLOYRhdmGZ/VlmS7IfxZQdZ/jWK8v3Z6+tF1/8w42vmorjVFvu/GCKRSEIb1MzMDLIsO1J9yYjbmqZx+vTpgvdzrSGz4QqiaT/CMAYZHr5IMHgXlhVGkn6Cpv0Kn8/H9ddfz5UrV3j++ecpLy+noaGhiOdPRC5GPH5tSZJoaWmhsbHRaQNrb2+nqanJuU8WImOUDleDEV9G9ijUT5UqKaZpWtbvIXVSbAhZ3k9MNVHFMDZkXKvYnA0hTuN2/xMwg6b9CYbxmjzWKFbySDA5uRrT7E95hGVZju+aO3E7nrgdCoXYsCHz9VwIlDL7X9x9NGIY/xshfsrhw+tZv/4PEOIwlnUjbW3fciaRDwwM0NPTQ21t7aJXNuKPlSRpHudQkiR6e3spKytLOH65Ap8ZRQk2Ll26REVFRdLyYV/fC9Osn3jiCa6//vp5fdChUIif//znfPWrX3V+95WvfAWAu+++m0cffZSHHnoIRVHwer185zvfyeqmybWNKpURV5TvIcRFZPkKmvaBrNYqfra9jFhJ2MzYh1vKPdnGeWZmhmPHjjnEbZfL5Uzcrq+vx+v1LrJBnEGW92CaXVhW/SLuo1RowjBqgR/NfqYScAVFeTuWtRrD+DsqKyvZvHkzIyMjHDhwAEmSMAwjYzaulG1UyYysLMt0dHTQ0tLiOJ+uri7q6+sXTI1qGdc+CvVTULqkWDgczvp9pLLjsvwUkrQfUNG01wGZpTyL7ack6SQwBZQhy7sXNdiYu84LxO2FnbhdCiz0/vI9n2m+Hng94fB/I0mHsCw/kvQMQvwUVW2it/d6WltbGRwc5MSJE3R2dhZ343HI1U/FH2tzDi9fvsz+/fspLy+nq6vL4Z4sJrfwakFBwYbt2D/2sY/R2dnJrbfeSnV1NYFAALfbjWEYXLhwgePHj7N161aeeuopPv/5z88z4j6fj0uXLiX87u6773b+fc8993DPPffkvL9itFEJcQBJ2g3MYFkdmObqrNYqthE3jFuwLDdCGBjGjXmvk8ueUhG3VVXFMAwqKipoaWkpaBBeqcrKbvdfI8u7sSw/MzOPAFUZX1MMFEPiLxdSWiTSPps9ehohtiHEM0jSAJblxjT/ACHaaWhowO12c+jQoYTSdapzlDLYSHesqqr09fURDoed0nVlZWVOE2lzLU+/uNoaXpwolp8qVVLM7XYzPj6e9ftJnhTTUJT/wrJ0oBzDeFlWaxW7smEY65Dl1Qgxgq7PV+vKFoV+L03TZHJykkgkwuHDh9MStxcTsvwULtenMM1VRCL/Aiyt6dvpkIvdj0RqMYxbkeX/wTS7UNW/BASa9k283pewbt06xsfHOXToEKFQbG5WsSeRF8NP2dLoFy5cYNeuXdTW1tLR0bEoqolXGwoKNmfdVswAACAASURBVOyL9e53v5sPf/jDPPzww1x33XU0NzejqiozMzNcvnyZkZERhBD8+Z//+YJOvSxGG5WqfgNJGiE2aK2bbOXzit1GBRKmmX+QAemzE4ZhJExVn5qawrKshInbNnF7ZmaGo0ePZmwPKGQ/hUKSDmJZXoSYnFVmWZhgA7J7X/Z9lsrg5KKAYZq3A7ejKO+a7dceQ5Y/jyw/jK5/Dct6GUIIAoEAvb2980rXqdbNFsUmn3s8HtauXcvU1BTPPfccpmlSXV2N35+5ordczl7GXBTLT5UyKZZLG1WyAEGStiFJRxAiimlWYZqrslqr2JyN2Ayqfy5ojVwTdamI2z6fD8uyljRxW1W/SKwKv2W2EvRbi72lnJC9n5AwjIcwDANFeReWdRQhIijKHVjWenT9W1RUVLBu3Tp2796d1STyfCTas7X16fyCEMLhHJ45c4Zt27ZRUVGRk6jNi9HvFOXbt27dOh5//HEOHz7Md7/7XXbs2MHo6CiBQIA1a9Zwxx138NrXvrYYp8oJhbdRxbJFMIMQatbZIlhapGUb9p7iidv21FIhhDNVvbGxkfLy8rTtNkvtvc1FNHovLtdX0LTfw7K6F3s7SZGuspAN5j646/rnkaQfI8sfJXbPDqOqb8M0Xw38M0IIp3oQCoUYHBzk5MmT9Pb2JjzIl5LslouRLS8vp7m5mUgkwsGDBx3ZxFTDzOz1l9uulpEMS9lP5VqBn58U+ypCXAJkDGMN2bRQQWmk1QtNIqV7/VzidvzEbdte2MRty7LYsWPHkpGvTnatDWMDqvpDLKsM01yxOBvLE/lV8WUM415keRQh9gMCSXoGVb0ZXX8QeDkej4frr7+e4eFhtm7dSltbW1EmkRc7KSZJEq2trTQ1NfH8889z5swZysvLk0rPz8WLkVtYtFDfNE36+vq477770h6zkI690MqGJH0CIcZmf9IwjFfkvdZiwLIsotGoY5hHR0fRNI2RkRGH/Nbe3p5xiNRcFPPGLtU1MoxXMTPzqpKsnQ7FdNy5H+fHNP8IqESWPwRMAONI0qPU1j7O5OTHgFgboM/nY/369Vy5coWDBw/i8/mcuRf5BBu5HJvr2mVlZfT09HDx4kX27NlDZWUlXV1dSVsg8mmjWg42XjxYqn4qVzWq+UmxnxBT/tExzZfmtFaxg41irGOa5ryhrlf7xO1k0LQPYxi3YVnBrIRnFgPpPtN8/JRlrUTXH0OWP4EsPwSEEeIcqvoWfL7XoCgfSJh7MbcSb6+1UEImmSDLMvX19Xg8HqamptiyZQvd3d3U1dWlXOPFmOQqWrBhXzjLsubdnEIIhBALfnE9Hg/T09NZHz+3suHx/J+4v1pA9kTj4rdRpYdlWczMzCRkfaLRqEPc9vv9SJKEEKLgVrbiqloVjqUQ2MWj1NK3mY4zzddhmjejqrcjxFbAQJLG6Or6C0zzm+j6T7HbASsrK9m0aZPTg1pfX099fX3O76FUsrq2URbihane586dY/v27TQ0NNDe3p5QZi+2etXV/CCzjPlYin4qHzUqXdedn12u3ycWaEBsvsZLclqrWH4qXzs8l7h9+fJlIpEI09PTBRG3r47vroRprl3sTaRFMSrwyWAY92Kam1GUuxDiHACq+hj9/b/ENB/GNN+Koij09vYmkMj7+vociepcE0v5EsSzOV5VVTo6Opw28xMnTtDT05MwUyd+Ly82bmHRmxhtg70UUPiwJBmIGXXDWJHTuQt5AJbln6Gq30fXb0HX/3je3+OJ27aB1nUdr9dLeXl5SuL2+fPnc7oey1g85MLZSI4KNO1XyPI/IcsPAgYQ0+F3uerR9d/BNH/inMvuQT19+jR79uxBVdWSyBDmQ6SL34MQgqamJhoaGjh9+jQDAwO0tbXR0tLifH9z0b5/MWaYlrH0/FQh7b6K8uycI1qzXquwRE0IWd6LaTZjWW1ZrZXNxO1AIMD4+Dg9PT157qv4WCr3ylJCLkImyY8TWNar0LRfoKpvnW2rAkmKIEl3YlnvR9N2AM14vV6HRG5PIu/o6CjJfu1j801aeb1errvuOqampjhy5AhDQ0P09vY6867mHl/svS9VLD3GVBFR6LCkcPjLeDwfxLIqCIcHcjp3/kbcQFX/HcuqQVEeIxp9FYZhMDw87AwXiidu2ySqhZy4XSppwoWEECNYlptsBl8VCzYJf3JykomJCcLhsDOdNB9kvnYCw/jYbPbobQjxQjZUUX6JYWzAMHY6v5Mkifb2dgKBAM8//3xaEnm+KJaUrb3X5uZmhoaG2LJlC52dncsE8WVcdSjUTxnGTSjKbwCIRnNTgCrEBrtcX0SSdgA+IpHPzFvLJm7Hk7ch88TtsbGxayKTu4wYMtv8TjTtaWT5T5Hl7zq/FWIMl6uLaPQvgU8DUFFRwcaNGxkdHWXv3r3ouo6u61mR/3MJIIrBWywvL6e/v5+xsTGef/55p1XZ6/W+KP3UNR9sFDIsyTTfRij0trzOnY/Kh6ZpTE5O4PW2oijPMz1dxbFjQ2iagRAi76mlNpKrmBzB5foXLKuCaPSfsazMuvC5OSgdWf4NllWOaW4mn4myxYai/Dcu1/2Ah5mZf8OyipdBs43U3MDCnp5u9xvbJLLh4WFOnTpFX1/fvEFB2SC7zNLvce7cAA0NtyBJL+j5y/LzGMb842VZdngR8ZPIs1GDyryX4pa+FUWhp6eHtrY2jh07xoULF2hubs56/WvBiC/j6kahfioS+SGa9giWVY1l5UZwL6SNSojzgBeIoGmXnCGtMzMzaYnbmddN5l9MZHkLYM0qNuXnA9PDPufi+6gYLuN2fxqQiUT+Bkg+vbrYsKtPExMTDgm/vT0moZ7IvShmtt2DYXyLUOhG/P4PJ/zF5foC0einnZ+FENTX1xMIBNi+fXvWJPJSVjbSHV9VVcXmzZsZHR1l9+7d1NTU4PF4loONawm5tlEVU3M83VpziduTk5OEw2EURcHv9/PMM3/LzTeP0t3dzIYN5Wzfvj0rhYNs9jQXivIoQowhxDlkeQBd/92CzjF//X/H5foGIBGJ3I9h3FLU9fOBLD9ObAjeJLK8C12PBRv5ZtPipRfHx8cZHx9n9+7dTmCRbHq6aZrous7111/P5cuXee6556iqqqKrqyvr8+bWg1rPwYPbWbVqI5IUy6Iaxu+kXBdi5eD169czPj4+j0SeL0qlFuV2u1m9OkZ+v3z5Mtu3b6e3t5eKivQO+low4su4upFrG9X8h3GBad6R17lzrWzEZvtEZmdYvB6f7weMj7cyNjaNruu4XC4aGhoKIm4ne50s/xyXK8ahjEbvwTBen9faqc95Arf7wwhhEg5/ekkoGKrqN5HlnwEWqtqMpr2v6OcwTTOB52lL3tvVp4aGBiRJ4ty5cwwPDzt8CRuFt/smIhK5k5Mnm1m7NvngzLmwhU5SkcjnvtdScjbSBdJ2gFRXV8fZs2c5cuSI0+6eTQCeyU9dDS1W13SwkWt5ei7xrhDYRtwmbsd/maPRKG6323kYbWhowOPxMDQkcemS4MgRFbe7nslJk74+s6iSgsmk92T5acCHaWZnYHNxUJJ0AcsSCKHHKXvlvk4xoet/PDvwrxHDSFRuyXSNdV13PseJiQlHNtgmMjY1NaFpGjfccEPW+6muruamm25yNLs1Tcv6886VmK3r48B+Ylyk61MeG2/YKioq2LRpE6Ojo1npn2ezj1yOzyUYkGXZUao6cuSIU/lIVTVaDjaWsdgoxvDZfJHOBtvE7fiH0cSJ22vxem+iqspDR4dg37591NbWppWmzndPMd9hAWKeHykGFOXnCDEKCBTlx2jaXxX9HLkHdg3EkmJgWcGCz28YhsOXsavtO3fupKysjEAgkFLyPhqNsmbNGqampjh06BA+n4+enp6cCOK5+KlotIdo9AqK0oUQBpr2dMrjhRBpSeSF7KMUfsruUIlGo4yPjzucw0xVmWvBT13zwUahQ/1yQTz57eLFi0SjUU6ePInX68Xv91NVVUVraytud3IN9IkJwXe/q6KqFs8+K3PpkqC31yzaQ3nyYOM1hMOrAG9JpPc07V3Epq/70fWFl6JNBsO4mVDoaWKGPLVBsQML+7/p6WmnNSCVbHAkEsnLKAghaGlpoaGhgSeffJKBgQF6e3upqUnd1pa/5Gx69ZNk69qZmdra2gT985aWlqz3APlljPIh0gUCATZu3MilS5fYt28ffr+f7u7ued+9ayFjtIyrG/kEG8VK0tiBS7zvsoOLXCduFysISuandP33ZoMBE12/veBzzIVhbEBR/h9CWEmH5y5OUuxtsz5Zykl2HxL5gfF8mfg23snJSTZt2pRxLfu9BwIBNm3axMjICDt27MAwjKJflxf8lAddP5P22Lm+IRmJvKenJ6ESn0tlo5iqhnMRX+kYGhpiYGCAzs5OgsFgyqrMcrCxhFG4GlVqzP0y28Rtu/zo9/vxer20tmZWBhFiGJfry2zeXMn+/R9i504figK/+7s6fn/pCdmWlZsUbi77sax6otGPpvn7YhHNE7M3mqYRDoe5cOECZ8+edXqO7c8yn3kk+UBRFDweD+vXr+fw4cMOn8Pn8807NtfsSzFKyLb+eWNjo2MkdV3PSZmkVHKF9t7j16+pqaG6upqRkRF27txJbW1tgqDCtaDysYyrG/m0URXyUB9P3B4dHWV6eprR0VHHd9XV1dHZ2ZmkcmkhxFHAmm0zSvzelNZPlaNpf5HzWtnuxzT7CYcfIfbespe4Ly0kDOPWeb+dnzBMzg+0Kxap+DL52D0hBA0NDdTV1fH000+zY8cOenp60s6UKFVFIdWx8STy+Ep8LiiWkEm644WIDdnt7e11OIcnT56kp6eH6urqgvazFHFNBxuFqnzYiBG3X8gQxLfOpCK/nTmTPiqPh6L8N0KcRIhBhBjiPe/p5ehRwfi4IGbgi9dutJTWWawvT/znaRPgYnyZXXR3/xdC3Ixl/SNClIKEmB18Ph833HADly5dYu/evVRXV89TbillWTiT4bSN5MzMDM8++yw7d+6kt7d3Xum60H0UQ7XDdpD19fVOq1pzczNtbW3XRMZoGVc3StlGlWzitiRJju+qqalxqn6Zz7sNl+tzAGjaX2AYNyf8/WpXKVyqQ/XiYRgG4fAEXu8WhoaCXLzYkCA80tLSQnl5ecltmizLeDwe1q5d6zwkr1y5MqmISKmSYunWTVaJ1zQta3ufTxtVIX7N4/E4rWqDg4OOXK59Pa8FP3XNBxu5qHwIIYhGo4yOjjoGOp647ff7qampySrDnUv2yTS7kOVnADfveIeBaZqsWfPC34tVNi/Ww/3VFGFrmuaoakxOTjIzM5PwecZPoZWk96GqIRTle8zMvAHT7M/5fMXOQNTU1HDTTTfNa12yz5GLYc4WuWRNvV4vXq+Xnp6elKXruWvnwvXIp40q1XdTkiRaW1tpamrixIkTbNmyhcrKyoII78tYRqEoRrtvPHE7fuL2XFvn9XoTvh+XL1/m8uXLWZ1XkoaJzesRCHEKKE2wYb+fYuBq8lVzES88YndPCCHo7PwqweBTyLKbcPhbwLpF26Pb7XZalw4cOOAErnPb7Urhp7LxtfGTyO325HQkchullqZN5afKy8u54YYbuHLlCocOHcLtdtPd3b0cbCx1pCtPJyO/zczMOK8rLy93iNv5GKxcyOaG8dpZcrYXy5rfA7/UMkaLlXnKhLkKX3ZgEQgEKC8vp76+Hq/Xm/LzjETacbn2Y1neohDyigUhBK2trTQ2NnLs2DEGBgbo6+srWeYl11YnSF26nhtYFFNSMBmyCU5sEnlrayvPPfcco6OjTvvI1fxwsoyrE/m0UUWjUUZGRlIQt7OfuJ2LLdf1VyBJR4hNKZ/f3rMU/ctS9FPJkIwfaFeg5isafhxJkpBlE1k+g2HkHmwU47rE+5SKigo2b97MuXPn2L59u1M5thOlC9lGlQx2e3J/f3+CnHuqSvxCVDbSHV9ZWcnGjRu5ePEie/fuxePxpOVLXQ1+a8kEGytWrMDv9yPLMoqisGPHjoS/W5bF+9//fh577DF8Ph/f/OY36e9Pn3m226hmZmaIRqNMT0+nJb/NzMxw/vx5Ojs7C34/2RlMA5gEKkg366HQHt3c9nR1IBqNOhWLK1euMDk5STQadYxzpsAiGU6fvo/GxsP4fP1YVvazGhYKiqLQ19dHKBTi8OHDRKPRrJVfSlnKjn9NMhJ5vNJGqdWocskAuVwugsEgVVVVjIyMcOLECXp6eqiqqkp4T8tYRikhy3JKu5yMuK3rOpqmMTMzkxVxOx1y8wnVRKP3Fmmt9Hu6lpFJeKStrW2eVHo8Lly4h2DwC8BqDOOVC7v5OYj/rIQQNDU1EQwGHT5fOiXAZChlazDE2pUykcjzWbsUfkoIQV1dHbW1tRw5coQzZ87gdrtZsWJFXkqQi40lteNf//rXKacV//SnP2VwcJDBwUG2bt3Ke9/7XrZu3Zr02C1btrBjxw4GBgbYv38/L33pS3nggQfo6upKQ36LKQkVS1Iwc19tBJfro0jScXT99ej621MeudQyRsUMWrL5Qse3B9itbaqqEggE8Pv9BAIBzp07x9q16VWWMsE0vczMvBKv1yZnWajql5Hlp4lG/yqpSslcLASRy+ZzDA0NMTQ0xJEjR1Le0/nsK5fWpWTrxpeu5+qfl6JSMXc/ua7vdrvp6upicnIyoV+2vLw863WW8eJAKZJiNsbHxwmHww6nbK7oiO27TNPkwIEDrFixouD3kw3/Q4hBZHkXhrEZy0pNtL1a/Usp14kPLMbHx5mcnGTv3r0FCY9EIis5e/ZrNDY2xv328uyQ1huAhbFbqT4jWZbp7u6mpaWFw4cPEw6Hsx5EXOpgw0amSnyp1ahy8VNCCCorK5EkCVVVHbnclpaWq6q1akkFG+nwgx/8gDvvvBMhBDfddBNXrlzh3Llzc75wMfzyl78kGAxy9913c/z4cR5//PGszlFsI5duLSHOIklDWFYQRfllyYINWX4SRfk+un4rQtyyRCsbY6jqo5hmC6HQK5iYeCGwiEQiuFwuxzgna22ze1mLDUnaj8v1ZSzLwuN5P6HQQNHPUQhsbXSPx8PWrVtpb2+nubk56bVYDJKerX/e1tbmlK5tGehsUezydLLjbWUqv99Pf38/Y2NjPP/88/h8PlavXp1UCWwZL14UMyn29NNPs3v3bk6fPs1rX/ta7r33XjZs2JB24rZNdC0GMvuWGdzuTwARFOUXhMNfBtQ810oHA0k6hmk2IIR7ifopgDAxJcP51yCZ8MhcRcNIJMKGDRuKvKcZfL7bEeISprmCmZkfk2kKerZ21b7P0hGxU8FWVTx79iwHDx7k4MGDdHd3O/a2kH3lemwyJKvEt7a20tLSsmBqVLkcL8uy4+OHhobYsmULnZ2dNDQ0ZL3OYmLJBBtCCF796lcjhODP/uzPuOuuuxL+fubMmQQZ2ZaWFs6cOZM02LjvvvsAnHJztlioYUkAltWMaa5Gkg6g639Q0FqpEcXl+gyW5cLl+iKStHbJZIxsQmNMcvZvkOVnMU2JM2f+FrjZ0QJ3u91F7TvObY/VgIIQEUyzqejrFwo7O2JL0R47doytW7fS19eX0AoUf2w2KLSyMRd26XpiYoLdu3czMzNDIBDIipi9EES9ufuvqqpi8+bNjI6OLuGHnmUsReSSFDty5AiNjY3cdtttHDx4kCeffDKrcxRzzkZm22lhD9OL/b+QtVLD5XoAWX4Cy6pB0z6/JL93srwFt/vDWJaXqamHGB+vSaJoOF94xIZhGCVJigkxhhCjWJYbSToGRIHks7zyW1+kTGBlg0AgQE1NDYFAgG3bttHS0kJra2tSO72QwYaNZJX4ioqKkifF8vVT9qDaeLncdevWzfP5Sw1LJth45plnaGpq4sKFC9x6662sXLmSl73sZc7fk93YmT5cRVFyCh4WrrIxgSQdRNPuxrL8QPqHrvz3pWBZdQhxFsuqniU+T+WxznzkqhoRDocTsj72FPVYG4vA5XIjhEJPzwoMo3DOTDFgWc3MzPwnkrR/lhAZRlW/gBDTRKMfBCqSvGbhnGS8AVJVlZUrVzI9Pc3hw4c5efIkfX19DqejVEY8F6MZCAQIBoOoqsquXbuora3N2P5VLJWPXNe3s17psnDLePGhmEmxd77znQnrZotiJsUyr+UjGv17JGk7hnETqaoaUJj/lKSdWFYFQlxCks4vqWBD0zR0XScU+ncsKwSMcf78fxEKvTVlYLGQsKxGotE/QVV/SDT6F8QCjUsIMYllrSjpubN5z7ZNbm5uTuBz9Pb2zqsQLkawYSO+Er9r1y7GxsaorKzMKOcOpU+KJTve7XazevVqQqFQykHRSwlLJthoaopljuvr63nTm97Etm3bEoKNlpYWTp8+7fw8PDzsvKZYWBgjbuFy3Y8kncSyKolE/k/GtfI34hLh8API8j5McxVCZEcmTo8Q6RzO3MBiYmICTdPweDz4/X4qKipoaWlxvhzbt29Hku5H17+JZbVjGC8pwh6LB9Ncg2nGdIhV9eu4XF/FzvZFox9L+ppsDWAxDOXcNcrKyujv7+fixYvs2bPHGWKXy/lKafAty6KqqoqOjg7OnDmTULouNNMF+ZWnr6a+12UsLkqRFMsVxVwvnW+RpOdQ1f/AMFaj63cCmQmt+QYJmvYeVPXrGMbLMc1OLOtgXusUilSKhrquEw6/hoqK3QhRSUvLW9OKuiwsBJr2t2ja38Z+EoP4fG8BIkSjH0LT7pr3ioUO5uZm5W0+hz20Np5EvljBhg2Px0NjYyOmaWYl557PXopZCfH5fFdFUmxJBBvT09OYponf72d6eprHH3+cj3zkIwnHvOENb+CLX/wib3vb29i6dSsVFRVJs0WFYGHaqCwk6RyW5UeICYSYwbLS94QXVnGpxjBePrvOdEFGRpafwO3+KJZVidt9F5ZlMTMzk2Cc0wUWqWBZdWjaX+e9r2IhkwGwLA8v9MJ6EGII0BfN6aT7LGtra6murnZ6UcvKyqiry25oVamDDSGEM/MifhJ5d3d3UvnZxTLiuZ57Gdc+lkJSrJhI51tU9V8RYhpF+RmG8ZKMdq4QP2UYr8YwXj27TnaS8akRRVF+AKgIkbqfPV7RMF54JJmi4fbt26mouJ1w+BXEkm1Ll8clyzuBGUBCUX6aNNiAhbNtye4Jr9fL9ddfz9jYGPv27aOyspKurq5FrWzMXbusrIzOzs6Mcu728aWubFwNAUU6LIlgY2RkhDe96U1AjGdxxx138NrXvpavfOUrANx9993cdtttPPbYY3R3d+Pz+fjGN75R9H0sRBuVEMcxjFsQ4ji6/lYsq2bB9lXIOrHXPUokYgLDuN172b69xiH8FirBeDVA1/8IMBFiBtNsw+d7DWARiTyArr9xwfeTydjG8zl27tzJsWPH8Pl8GXs7S2nw5xpZO9PV2trK4OAgJ0+epLe3l4qK+S1q2WK5srGMUqDUSbGFULKbi3T8D9PsRpafBfyz/LXMay0FiXZF+U9crq8BUFt7O3BTSkVDW80wGAxmKZWev11aKBjGK7GsryDECNHou3C770GSzhMOf3JREmPp7uuqqipuvPFGzpw5w7Zt2wgEAllL5ebqp3KBXSFPRyKP9xulrqhfC35qSQQbnZ2d7N27d97v7777buffQgi+9KUvlXQfpW6jEuI8bvfHgDCmeQOGMX8wUjIsdLBhWbGBh/HGWdd1GhrW09GxCyFqCIfXsHnz5oL3tJR6czNDnm0nAJfrfmLKJAJZfgwAXX8ZluVa0IxRNudSVZW6ujpkWeb48eNOb2qqGR25ZGmKVT72eDxcd911TExMcPjwYdxuNz09pXeM14IRX8bCoJRJMZfL5fDYFhJCJJvhZKEo30aSnkPXX4lhvAm4OpJiALoeQpJ0TNNA00Js3bo1YeBhPsN6r6b5IZZVTyj0a2Kf4yMoymOAidv9sdmJ4wuLTD5CCEFLSwsNDQ3s3buXS5cuUVFRQU1N+ntuISrwNtLJudvfocXgFl5NWBLBxlJB6SsbV4ApoAwhxhZ8X8nWsQMLu5w8NTWFruv4fD78fj81NTWsWLFitoS3GcP4Q8CFpu0reD/ZQpIOYFnqEuqRBU1726wRD6Mov0JRfoGqrmB6+oeLvbWksCwLr9fLhg0bGB0dZffu3WmnfOdCEC9mm1MgEHAmp+7Zs4dwOIyu6yUbYnQtGPFlLAxKmRSzB9AuRrAx1ycIcQFF+QmWVYuiPIGuvzvvtYq1p2SwFQ3nViy83htob7+M213OlSsb2Lx585JphyzG9cluDQEILKuFmFSvhBAjlJWtQdP+iFDo7xb0mmRzLkVRCAaDBAIBTp065fA5UkmPl0rIJN3ayeTc+/r6lrmFWWA52IhDaYl3YVT1GwhxBdP0Eo2+L6e1Mg9euoDL9UlAIxq9l1QTsDVN49y5c45xtiep26oaHR0dGXoDY8oMC1WRUJTv4XJ9HCEE4fDnMIyXzTumVNK36WBZXYRCz8yS8V4LmMjyXmprNzExcTewZgH2kF9mp66ujpqaGk6fPs3WrVvp6OigsbHR+Xuu65ZqcmpNTQ1PPvlkRhJ5IViM1pVlLGMu3G53TjLtxULyav4lYApJmp5VoMp++Fg2dliI4wgxjmmuT7l2sqTY3MDCDs7sikWiVPpvzb5ye1Z7LxwTuN0fBaaJRv8Jyyoun3QusrVZhvFywuF/R4gh3O57ARcu11fQ9XJcrtL7KMhdudKual+6dIm9e/dSXV1NV1dXQUmxYnP54uXcDx8+zPT0NNFoNGW3QK7rz0U21aGljhdFsLFYvbDxRlyIYWR5P6bZixCRnCTpstFVl+WfI0nPEyOF/YhI5K6kFQtN09A0Le0k9aUEWd41Sxg0kaT9SYONxYRldaNp70FRvgtEEGKcrq6PYxgniUQ+RTrlrsLPU+CfDQAAIABJREFUnf+gPkmSaG9vp7GxkaNHj3L69Gn6+vqorKxc0PJ0OkiShMvlYvPmzRlJ5PniWsgYLePqh8vlIhKJLPh5kyXF3O4HAR+WZc1KqWa/VqakmCQdwu3+MKCjaW9H19+R9DjTNLlw4cI8qfTkgcXiQ1UfRVF+BoBlfZlo9OOLvKMXYBi/BWzC5XoIIc4AIbzeB7j++nJM8/uY5qqSnj9ff1JTU8NNN93kcCXmDq1dCn7KrsQ/+eST7N69Oys5dxvLlY1rDKqqomla1sTlUrVRKcoPEGIERTlOOPyRNK/MvNZcmKbJ1FQTPp+JYUQ5dgzGxnY6FYv6+nq6urowTZMDBw7Q1taW9/taaESjf4Ik7cGyvOj6mxZ7O0kgiEbvRdPegc/3KiTpApZloqr/hiz/nHD4PzDNdYu9yZT3j8vlYvXq1UxNTXHo0CFcLhdutzvrqdmllvyDRBL50aNHndJ1ISRyG8tqVMtYCrDbqLJFMf1U4s9nkaQDgIppdgPZC35kU9mIPexGARVJGiSZVHo0GiUSiTA1NZW1omGmPRX6Pc60TmzoqwJYWFZr0mMWFwqh0GPI8mN4PH8DzODxnANeRTT6l0SjHybWblV85Hqvxl9jIQStra00NDRw7NgxBgYG6Ovro7q6ekkEG/YeVVVNCIyKXYlfDjauArjdbqLR6IKrJCUa3mlU9QeAgWF0Y5q5Eavj1zJNk+npaadicfiwTn19mIqKINXVH6e83MOKFdfR3T3/o9U07SojZMcqBzMzP17sbWSEZbUQCv0aRflTXK5nAR1JOoHP9zKi0fcSidxPJBJ1HOrExASGYdDX15eRCJf6nMUztuXl5WzcuJHR0VH279/PzMyMQyov1h7s4/M1mh6Ph7Vr187e94dRVTUt0T0bXAtGfBlXP1wuV05tVMV6iJ4LVf0GllUOzKBpt5PLJOpMwYZlWUxOrkdRNmFZZzl+fDOTk9uTSqVv27aNzs7iDHddCJ9nGK8hHC4HwhjGK0t+vvzgxzD+kGj0Mi7XZzBNHUmaxOX6FIryPWZmHi1ZoFSon5o7tPbUqVMEAoGM/inTuqmQj1+IJ5Hblfh4EnkhuBb81DUfbNjl6fLy8gU9b3wblSw/DlwkltEpx7I6slojVrGYYmJigkuXLnH69Gls/edAIEBtbSNf+1otd95psHp1ZhWtxeA2ZMK1lDm2rEbGxv4NSfog9fU/AQzAxOX6EpL0rwwOfgVFuZ6KigqamprQdZ1Tp045LUy5PjSXIrNTV1dHc3Mz0WiUgYEBOjs7aWhoSPnaUhPjksHv9zuB0Z49e6iurs67JTCdEb+W7s1lLG3kWtkoRbAhxCVk+Qkk6SyW1YBprs3x9S/4l/QzmN5DIBCgq8tf8iRgMa9P+ustMIxbinauUkLT3kskshmP5y24XGOAgSQdpKxsLZHIPxKJfNBJaE5MTBCJROjt7aWysjKv8xXTT8UPrd2/fz/l5eU0NzdntP3F5kikQ3wlPp5Ens0k8lRYDjauAtiVjYXGC4bXQlW/iRCXADeGcTPJSHGGYSRULKampgCcIKmyspL29nYnkv/mNxV27pQZHRU89JBMXZ3JBz6gUVOTOphYuGAjBHjIlliYD2T517hcn8IwbmRm5m9Ldp5soGmaU7G4fPkyU1N30dvbQXf3551jFCVMf/+foGlriES2YBgGiqJwww03OOpQwWCQFStW5HTuUpWRg8Egvb29HD16lFOnTrFy5cqkbUv5qFHlIqubDnV1ddTW1joa7c3NyUUR0uFaMOLLuPqRa7BhJ7OKee9K0k+RpDNYloRhtGFZ2VUW7MBifHycyclJLl26hK7rS2IGUzEVsvKDiSxvwzSDCLGi4H0UC7p+HTt2/Ds33vj3yPJzs7+1cLs/hqp+nCNHvk95ea8zSXtwcBCPx0Nvb2/OLW2lmIdhi9lcuXKFrVu3smLFCpqamoqWFCukAm9jLonc7XbT29ubdhJ5KlwLfmo52CgRXjByx5DlbYAGqOj6bRiGwdTUlJPxmZqaQgjhVCyam5spKytzAovh4WEkSUooGf7u7+ocPjxINDpDONzN617npbo6U79sesOrKI+iKP+Dpt2BYbwir/etql9DVb+KafYRDn+dUk1adbvvA8ZR1e+hKK9moYYt6bqe0F8cCoVQFMUZDtXa2sro6CgNDR9nevqNlJW9Cnih6qSqzxOJXE7Yr628dPLkSQYGBtD17Cbo5qrykWtgYvM57LYll8s1z1jmo0ZVTKWreI3248ePMz09zcjICPX19VmdZ1mNahlLAbmqUZUiceRyfRMIIYTAMH476TGpZjB5vV4kScLj8dDT0zMnsIggxLk4GdZCEUWShjDNNiDfFspxFOVHWFYrhvHyIuwpOVyu+1HV7xDjTPy/Re0ssIPCiYkJrly5wpUr8JvffIb16/83NTW/co6TJIuNG29nauospulD13U2btzIhQsX2LFjB83NzTnzPkuVFKupqWH16tUJIifJhtYuRgXeRryc+65du6itrc35PrgWuIXXfLBhD0vKFsX60GxnoKqfBqYB0DSVbdsuIcQY5eXl+P1+mpubKS8vz3gjzb05a2sPY5onCAZNzp+PEAhsIJutp7rJY9K5X8Cy3LjdHyUUeinpbo9U10lRHsGyAkjSIJJ0CNPsz7ypPGCafcjyVizLjWU1Y1mTRT+HHRTGV5tkWXYUUTo6OvD5fAnXYnJyMk4tYyNTUycoK2ub89mEmBscSZJER0cHwWCQLVu2sHPnTlauXJl2mmqpCHJzj/X7/c58jl27djkVGFmWS0q8y8XgK4pCR0cHY2NjjI6OOpPIsyn9Xw2GehnXNnJVoyrmAFoAIQ4jy3sAHfBiGC9PGVgkn8EEFy9eZGJiYk6gEcXt/iCSdBzDuIlo9J8K3KmF2/0BZPk5THMF4fC/ko7Eniooc7s/iiz/ClAIh7+W0U/lG9zJ8i4sC4TQkKSjQDDnNfJBPPHe9l+apjnVpurqakKhEP39/cB/E4k8MDtw+AUoyteIRv8KiL3/YDBIbW0tx48fZ+vWrUsmKaaqKqtWrWJqaorDhw8nHVq7EEIm6RAv537mzBmmp6c5depU1iTyYlRaFhvXfLCRa3ka8r/RdF1PqFhMT0/jcn3HedC0rFY2bNiY802TTFJQiEre/vZf09//HMeOvRqvN/NDfbr3ZFllWJafmP555gxUKgOi67+Hqj6CZbVgmqUbwhcOfwlZfhrT7MY0G4HCgg3TNIlEIly8eJHz58871aby8nKnYlFWVpbxs5t/71QyPT2Bz1eHJEXQ9XVAC2AkvYYejwefz0dnZyfPPfccNTU1KfkIubYkFRKYCCGor6+ntrbWqcB0dnYiSVJJg41cqyayLDsk8iNHjjj9s9mqay1jGYuBfNuoigVV/UdinELQNMGOHVMYxvaUgUUyJHsgF+ISknQMy6pHlgeI8dgKqW5EkeU9WFYNkjSEEJexrIa0r0hmZ4WYIjb4DoQIFbCf9IhE/g6P514MowNd/23gYInOE0kILMLhMB6Ph0AgQGVl5bw2tmg0yrlz55yfNe3DaFoX5eXvdH6n62+bdx5Zlunp6aG5uZlnn32WPXv2sHLlyrStQQuVFCsvL08YWltfX09HR0feSbFSPNxLkkRrayunTp0iEolkLedezErLYmE52JgD24hnUjmIb6exAwtZlp2KRWtrKzU1DyZktC3rJXndwMmzKkHWr/8zLOsk7e0bgWyGKaW7WcsIhx+enWWxAdsQ5wpN+yC6/g4sq5JsZBPzLyt7MYxbZ/8dJsaN+TKStBtN+wCmuTrlK03TTJhBMjk5iWVZGIaB2+2e18ZWDExNjWBZ1uwDguYYP03TkGXZuS/s31dVVXHTTTc5g/eSEbULkRRMh3SG2a7ANDc3Mzg4yNjYWM7EwVLKFdrX0a7GXLx40RkM1dnZmWFg5TKWsTjItd23kDYq2/7Zti8Uuoyq/sz5u2VVsW7d+py/K8n2ZFlBDOOVyPITaNofUXgblZto9H/hcj2Cpr0By0pfKUhlPyKRj6CqX8E0u2Z5lKWBad5IKGS3KNnXJowQ57GsNvLhNdoV9xMnTjAxMcHMzIwzgyQQCGQ1gyT5vfMmJiZeiaJ8lWj0bVhWHWAghCAajaIoimNffT4fPp+P5uZmdu3aRUNDAytWrEj6fLNQwYYNu4Jw6tQpBgYGknYgZEIpH+7tfceTyE+ePJmWRH4tCJlc88FGvpKC8UgVWNjtNO3t7fh8vnk3w/R0fG876Po/5vUe5u/JAgSW1YFhZKdslQ0sqwnDaMp6Tyn+gmXVF21P2cLv34/L9XlAQ5ZPEAr9HEjsMbaDC8MwHH5MMBiku7sbWZY5cuQIdXV1BalG2Oc0jFjlYu69ZFcC7GyLYRgYhoGqqk5wY19bIQRtbW00NDQwODjI8PAwK1euxO/3O+stpBGPh8vlYs2aNQwNDXHq1Cn27duXF3kwHfKpbMw9vra21ild2yTytra2rNa9Woz4Mq5+5Nrum21lw5ZKj+cH2vbP7/dTV1eH2/2fc5Jir8grKE9WgQeJaPRvgL8m3wTWXOj6e9D192S9p2QP1pbVQjT6L1mfsxgcGSEEkhTG670NSRpG128lEvlS2tfYzx627wqFQhiGgc/no6GhgWAwiMfjyctWxfupFx6ufRjGB5AkCUkSjr8yDINoNJrgp+CFB/sTJ044Uq91dXU57yUexUqK2aTxo0ePMjQ0lFN1uxSk9vjjbf+TLYl8mSB+FSCfNqrLly87sn2hUGheYFFWVpbVjXj69J20t59AlvcRDv8D+ZKlE2V0f4PL9SCm2UMk8vG81ywUS01C1zDKiAU6FjMzPo4ePcrExERCj3EppqabpukEFfa/JyYmmJqacrg4tsFOZixsBx2NRlNWUuwH+/HxcQ4cOEAgEKC7u7tkBjGXdd1ut9NitnPnThoaGhJU0wpBsUh9Nom8sbHRcYqdnZ0EgwvTP72MZWRCvtK38ZgbWNgVW5/PRyAQcIa7zrV/p05tmOUVxJJimvbZvN6DJElxezKQpJ1ABabZR7ECjasdXu9ZJOkM4EZR/odIJJY4hESO4MTExLykpp2hP3XqFG63Oyf7Fe+n7CDDVhALBAJO1SKVn7LXsP1U/D0kSRKdnZ00NTU5MzBWrVrlPNwvpp+yRU6Gh4c5evQozz33XFaKULk83BfDTyUjkc99Vrnak18v6mAjXrJ0cnKSmZkZwuEwly5doqqqitra2pzLb4mQiER+kv/mZxHvWBTlP7AsD5J0AEl6HtPcVPD6hexpsb4AlmU5fapjY2OMjNRgmh+isvIskcgbqKry0dPzBLLsQ9f/kGKooMw12PEQQqAoChUVFaxatYrBwUGqqqro7OxM++BtByOmaTpZLDtwmWvsKioq2Lx5M2fPnmXbtm2UlZVRX59dFamUpWxJkggGg9TV1Tl8jq6uLoLBYEH3Rz7a6OmOl2WZrq4uWlpaOHr0KCdPnsya5LiMZZQS+bRRzRWvsCzLqVgEg8GkgUUyRKMNTE1tR1UfQ9ffCeTXapjop76Nqn4bkIlEPoVprstrzUJRTNWuYqwTCrVhGDcgy1uZmnonZ8+edZJT8RzBtra2tBzBTP399v+TVdYlSUJVVdatW8fg4CA+n8+p7qdCvJ8yDIMrV65gmmaCjfZ4PKxfv57Lly+zd+9e54F5oduoksHr9RIMBqmpqZknclLo2sWowMN8Enn8JPJrAUsi2Dh9+jR33nkn58+fR5Ik7rrrLt7//vcnHPPEE0/wxje+kY6OWNvQm9/8Zj7ykY9kXNtuo5qYmHCCCzuwsCVL7ay3z+dj//79rFixoqCpxMVGvME0jJeiqt/BsqrIdjjgUoEQx3G5voRp9qFp7yGXftVoNOo41omJCYcAZ39+oVCI3t4/dY53uT6Bqn6NWNZIR9fvzGmv2RjsVJmgqqoqNm3axPDwMNu2baOrq2seAcwOlsbHx51MVjQadfpgk5WsIXYvNDc3EwwG2blzJ8eOHaOsrCzpDIx4LITBt/kcTU1NDA4OOvM58m1LK5VcodvtZs2aNUxNTTEwMMCePXvo7e1dJpEvY9Hgdru5cuVK0r/Zw13jW6HsxFhNTQ2NjY2Ul5fnXU2MPUR2o+sfKOQtJPgpSTpFLMGjIcSFgtYt1p5yw2Vi09PLnHXyRTxHcGZG5+mnPwTolJVVUl19khtu+AskSSMc/iamuTLnteGFlqj4fdrtuqn8VCAQoL+/n/Pnz7Njxw5n+vXc9xqJRJiYmHB8VSQSwePxpPRT1dXV3HjjjZw+fZqBgQGqqqqyni1R6qTYXJGTVEmxUu0jfi+pYJPIGxsbnUnkmqblfJ6lhiURbCiKwoMPPkh/fz+Tk5Ns2LCBW2+9ldWrE0m+L33pS/nxj3+ccb3JyUmefvppdu7cyXe/+12mp6fZtGkTf//3f4/f76e+vh6v15v0gyu2ykcx8EIV4QyG8RoM4xVYVhWQ31R0IY7jdn8Cy6ohErkP8Gd8Teo9ZX/zu933Isv7sayfY5prMIyXJD0uWcVJVVUCgQB+v5+GhoaEPtVIJMKFC3Md2iQ2IS+mPDJD7HZPnrWzgwpN0xI4PpkMdioIIWhtbSUYDDI4OMjp06dpaGggHA4nBEu2Wkh7e7ujFmJnjOJL1nPPqygKtbW1DtfE6/XS29ubcnDWQmaX3G43a9eudfpQvV4vPT09OfM5ipUxSoWysjJ8Ph+tra3s3buXqqoqurq6lknky0iKUibF7MpGNBpNkCydnp7GsixHeMQOLIaGhqiurqa6urrg91Ws7H/8Opr2TmI2uDalnc9uzVMAs2TqhYEs/xiP5x+wLC8zM/+BZXVn/dpMHMHYINd+5+Hc5XoAWR4FBKr6MJHIQ2nXz8ZP2f9lAyEEjY2N1NXVcezYMbZv305jYyO6rjtzpFwuFxUVFQQCAVpaWhICB13XiUajTrXEPq8kSbS3t9PY2Mju3bu5ePEi9fX1zpDidNdvoZJitshJsqRYqSsb2awdP4n8mWeeYceOHUlJ5FdLALIkgo3GxkYaGxuBmIrMqlWrOHPmzLxgI1ucOXOG3/zmN2zYsIE3vvGNtLe3c8cdd2T12mKWXYsFIQQ+3zY8nv8g1pp1P5bVmvd6qvowknQI0FGUl6Drb8hrndyJURXEBtwpWNYL8yPGxsaSDsnLFBimQzT6YYTQsCwvphmkrGw1UEYo9EMMY4XTqhSPqqoqjh8/TkdHB/X19XlnCm1DHd97K4Tg6NGjVFZW0tPTk5b3E1+ytiV5FUVJUK2C2PX3er1s3LiRkZERtm/fnpYAvdClbLsPdWRkhB07dtDU1FQy7gjkH5zU1NRw0003Oe1puZDIl/HiQbGTYpqmsWvXLnbu3Mmjjz7KiRMn+NGPfsTnPvc5/H4/TU1NKSsWycnY+aGYwQZoKMr3gOnZmRr5Vwsl6Uk8npioSiRyP4ZxS157yvW9qer3sSyBEBPI8lZ0PRZszFfaSpxlYXMEvV4vgUAgad/9+fPnEz5Pw7gZVf0qYGEYr0RVvwFIaNodmKac4Kdse1hRUcGRI0eQJMmpRORjqwzDSNj71NQUAENDQ5SVldHd3U0gEEhrgxVFSfBTc5NjLpeL5uZmpqam2L9/P5WVlXR3d6ds71vIpJjNhZycnOTQoUPzJqSXqrKRq5/yeDx4vV76+voKnkS+mFgSwUY8Tpw4we7du7nxxhvn/W3Lli2sX7+epqYmHnjgAdasWZN0jZUrV/KpT30KgJGRkZz6spdqZcPnO0AsUx9Gkg4W1AMbI+w9DXgwzVyCFhNV/RqSdByPJ/epqzMzn8Qw/ouJiVpGRlxMT28nFAoxOjqackhe/qghEnkA0zTx+X6f2LCqKwjxA0zzTUBLgjIUxILempoajhw5wvnz51m5cmXGdjrTNBMM9uTkJJIkEQgECAQCdHZ2OoGFaZoMDw+zb98+R1s7Heb2yc5VrYLYvSGEoKGhgbq6OmfgUl9fX0LWs1RG3DTNtH3h8Xs7efJkThO+8+Fs5Gv07fa0hoYGh0Te29tLc3Nz1ust49pGsZNiExMTfP3rX2fDhg3cdtttXLx4kX/4h3/I6rWJZOzCUCyfJ4QgEBhAVb9LrH01krVqVLK1JGkPoM3ucW+OwYaGEOPAfH5dxldqd+B278SyajCMlzr7sdtebXsfiUScIXlVVVXzZllkA8O4mVDoCUwzgqp+D5frC7O/n0DT7gaY56dqa2uprKzk2LFj7N69m5UrV2asGNjiAXYr1ORkbC6VLZdrC9/Y99W5c+c4cOCAU51IZ1fn+qlkrVU+n4++vj6Hi2ArRaUS9MgGxao++P3+hAnpjY2NOd0zxeYWpkIqEnmu99xiYUkFG1NTU7zlLW/hc5/73LxSUX9/PydPnqS8vJzHHnuM22+/ncHBwYxrejweJiYmst5DsSsbxeizE8IgFOrCso4Cfgwj9wf9eOj6OzHNNcSUQnqzfp0sP4WqPgyYdHYexbJek/LY+D7jRALcK2bnkAQcBaPe3uz3kA5zlaFeaLP6A3y+XYCC1/slfL7PomkfQtc/OG8Nl8vF2rVrHZJbMBikvb3dMcLT09NO/6pN5o4f/JduGrwkSbS1tTmtVcPDw/T19WXkCtjrzW2tmnufxg9cOnTokFMe9ng8Je+FzQRZluns7OTs2bNcuHDB2Vu8jG8h+4DitF3Fk8jD4XDWay3jxYViJMVqamp4+OGHAfjZz37GL37xi6zPv1QrG5YleEF5Kn9RDiEEmvZ6FGULALr++hxePYnX+w6EOE1T06uB+3I6t2H8Dleu/IbJyTATEyEmJ59jfHycwcFBKisr8fv9NDc35y3zPV9oJCbyoSgTxCr/ApdrO6p6EsN4L5Y13z8qikJfXx8TExMcPHiQyspKR4zEbuWKr64bhuH4qaamJvx+f8rKvRCCpqYmp7Xq7Nmz9PX1pbXV8ELQYRgGmqah6zqqqjr3lq0KGAwGOXr0KMPDw6xatSrhWS+X+RbF9GlCCEfk5MSJEzklxUrFLUy1z7kk8o6ODqdtcyljyQQbmqbxlre8hbe//e28+c1vnvf3+Bvytttu433vex8XL16ktrY27bqLOZm1WIpNgcD/paLiCaCFcPgzQHoycBY7y0vFyrL8gEAIE03zYwfUqYbk2X2qmYfkacSGHDWRrYOKN9iGYRAOhwmHw07faKKU3x8TDr8aWf4pqvoPgIWifAEhTqBpHwHm30NVVVVcd911HDt2jKeeegqXyzVbYfJRUVFBQ0MDvb29ebVa2ZyGsbEx9u3bR21tbVplDEjeWqXretKHBJ/PR39/P6Ojo47yxmIZ8WTv47rrrmN8fJyDBw9SVlZGT09P0uxMqTkb6Y53u93LhPFlJEUpkmK5qlEVs7JRrGBDkkbxeE5gGC/BNFfnGCDM35NpthEOP5LHPo4gxBksy09Nza+YnExfLbLbXu3EmC0eY1enGxoaOHz4MKtWrcqLd2b/3+ZahEIhZ+hePB8Q/gbDCANjKMrjQBRFeZJw+H9I5qMg9ly0du1ahoaGeOqpp5z92XLHdvY7Hx6aqqqsXLnSaTOyK/WZ1pJl2QmGbT8V79tUVWXVqlVMTk4m9QGL6adsGd+zZ88yOjqaVVKs1H4q2XcznkRut78tdSyJYMOyLN797nezatUqPvjB+dlmiPU62qoB27ZtwzRNampqMq6dz1C/pZYxcrn2EQ6XI8QYkjSCaRYabOQH0+wnHP4smnacI0da8fuPMj09nTAkKhfJRQAhdLze30eSDmEYtxAOf525muyZlKE8Hg8rVqxwlMTmTtqOoR7DeB2q+jngFEKEUZT/RJL2Eo1+gXB45TzFjfje23Pnzjm9ksUqW9qqVadPn2b79u2OalUy2P3B9v7Gx8fRNI1gMIhhGEkDlfiBS1euXOHixYs0NWUe2ljKKoiNiooKNm3a5HBNmpqanApSPmvbx5ey7WoZyyhVUqxUQ/0Wci2f70uo6jPIsh9dvx0oTNExX99pmisxzU4kaZDR0TcS39pu8xTiCfj2LAv7YTpZK28sMNiPy/VjDOPWpFPH4/3U3OSOnSzq6+vjwIEDtLS00NLSMuc8FWjagwjxPIryc8BEiKN4vdcRjX4cw3gPmqYlKBja08MDgQC9vb2Mjo5iWRa9vb1FU9S024zOnj3Ljh070vjYGGwRlPHxccbHx4lGo/T09DhqWbaN9vv9bNq0ifPnz7N9+3ZaW1uXVFJs7dq1TlLM5/Ol9P2L6acURSmKSMRCYEkEG8888wzf+ta3uO6667j++usB+MQnPsGpUzElirvvvptHH32Uhx56CEVR8Hq9fOc738nqA17MjJFtxAsZcCbLTyNJl/F4RjGMd2CaXTm9fmYGHn5Y5i//8mFUdRRV3ZD1a5MT4Nx4vf3o+hTV1dUFq/e4XBeRpENYVhmy/BtMcwbwJEj52V+2dJKzdtl3cHCQc+fOsXLlyiSZ6XrC4Z3A1/B4/hkhphBiFy7Xb3Pq1F8RDt9BRUXFPMUNiPVrj46OsnPnTlpbW2lubi7Kg6qt2mG3Vp05c4a+vj5kWU4IfsLhsBP81NTU0NHRgSzLGVWr7EzNhQsXuHDhAufOnUsYuJQMC0XSi+dz2DwJm8tiB/2lUPmIP36ZBL6MbFHKpFgxhvrli+KsZc3a0wgxdcN8EzIWsUp3IXsqIxx+BNOc5syZ45SXj3D69GmnlTd+QK/P58vSBhgEAu9GiClU9b+Ynn4S06xK8FM2hBAph+TV19dTU1PD8ePH2bFjR9KsuWWtIRr9v0jSvyHLA0AYVf0gExPf4dChj+H31xIIBGhsbJw3PbypqSlpC3ChsDlt9fX1HD161GmtcrvdzrPB+Pi4E/xUVFRQWVlJW1sbbrfbaa0SQiSoVs1VwxodHaWmpiZjy1bsOmX/0J5vYimbpNhi+6mrJWFgFMzhAAAgAElEQVS2JIKNW265JaNhueeee7jnnntyXnsptFEVAlX9OrpeiaZZyPLryL7NCJ57TmJoSOLnP59g9epjtLWdoaXlOPCqpK+x9bTtknI0GnVmWcwlwO3Zs4fKysqCZULD4Vo07WZU9UkikbdgGCpgOAY7PsjIBFVVWb16NVeuXGHfvn3U19fT0tLi8Cxs7ogsX09Hxz20tDyIEFGEiNLb+2kM40mi0R8A81UehBDU19dTXV2d1lHkA3uaayAQYHR0lGeffRaXy0UwGKSiosLpD05mVOa2VqUKOoQQjhzt3r17qampSTlNvZRk8mSfo82TaG5u5siRI5w8eZKVK1cuCmcjHleLEV/GwmCpJcWKNZCyGH5KUR5BUY5gGFGi0f+FaeZDmp/A4/lzJOkktbVvAa7L+pXx09NtO29ZFrquU15eTnNzc1o+XTawLBMhmF1Xw7KMhFaobP2Uza2z25MqKiro6OhwJnrH9t+Ix/On9Pc/j8t1HpCprd3CS17yViKRR5PyOGxUV1ezefNmTpw4wfbt2+nt7aWqqirv923DMAxCoRBlZWWEw2G2bduGoijU1dVRWVlJMBhMqRw5t7Vqrp+yOShTU1MMDw9z6dIlJ5hJhVKSuOORKSm22H7qasGSCDZKiXzaqIpZ2ShkLSEGEeJ5XK4RotFOLCv7SZKmCY8+qjAwIFNVBZ/5zLt45Su38M537gBiQ/LmzrKwy7G2nna6L3o+18lug4oP5jye/5+9M4+P6yzv/fcss2kb7ftq7ZKXxLGzsfXSNAS4TctyIZQSaAgQLhRSCikNlEAbaCk0XLgEUmigpFxIUkqaQllKQyAh8b7GlrXL2m3JkkbL7Ge5f4ze4zOjGWlGlkls6/f5+JNImjnznjPnvM/7Ps/v+f2y6eq6l7q6r6CqOTid53W6M4UIOEtLS+Tk5DA6Osrg4CBFRUUUFxfHKW7A9UQiu3G5hB48KMpePJ4qQqHHUza/q6pKS0tLHI81E9pYMlUQkW3zer2WAtbo6ChnzpyhsLBwTYm7dNRAxIQoDJfGxsbYt29fUlOji6UxvtZx3W4327dvZ35+nq6uLmu86SLT8vTlMolv4reDi5kUy5RGtdFx6kITbIryNJIUQtOycTpXV9lLfYwXkOXTmGY2ZWU/xzTfn/R19gZoQYmye1nYTQ67u7spKSnJyFh0ZQN3LE51d/81W7bswzBei8MRUyVbz/whhEYWFxfJyclhamqK0dFR8vPzrQRZbm7ucrP1a9D1t6EovwBAkuZwu29C0+4iGv0CiZRjAVHRFv0mExMTKXvjVhtjMjEUr9dLU1MTWVlZTE5OMjo6SkFBwZoS9cnilNhwiOuoKAqtra0EAgFLLj1VdeZiCJmshsSk2MjICK2trRddjepyiVOX/Wbjxa5sXMixHI5/RZJCGEY5Pt9uCgvXLscLqCp8/OMR3vlON35/AV5vmHe+M0B//+sZGdmPw+GweKqJJnnrhSSdRZImMYztGAYrJuzYa6Q4Kb9t27YxNjbG0aPdtLW1kZ+fjyT1LG8CIBx+JGkWZy3FjaqqKtra2giHw3R3d7O4uEhZWVlC+fM1hEK/weV6NZI0s/xbDbf7jRhGLuHwmZTnKnis4+PjHDhwgC1btqxQrjBNk2AwGEeHShxjKh19wYvt7e21qFXpbjqSGS0lmhrV1tZaxx8dHY2r0vw2tc6Twev1cu2119LV1cXo6Ciqqqble5EpZfFymcQ3celjPZWNl0oFXpaPIMu9yPIYul6PrmcuPgKxXgvTLEaSzjE7ewuFhaZF5bU3cGuaRlZWFrm5uZSUlKSs0KZzbqkUDGPndb5a0draypkzXvbsqVzOaouk2D7Ah2HcRDLWgX38Ig6I8efl5VFWVmb1M/T09DA3N0dpaaltXnISiTyKw/GXqOpDxGhmJqr6dRTlO4RCB4D6lOeXlZXFVVddxdTUVEoKsGmalqyvvR9QbN5WE0Oprq62qFUiTq0lw2vfdGiaFketEgtxQTezy5An9j1dzJ6N1SCSYj6fj66uLquKmS6uVLrv5mYjAS8dLmwARfkRkjSDojiWNxtrv0vTNJaWllhYWKCrK4rDUcLv/u4Me/ZUcubMG3A4Btm1a9eaN68sH8LheARdfxWatrIR0n5usaA3Qnb2/0KSQkQibyQY/OTycVL3WYi/19fXU1paSnd3Nx6Ph61bvwOMA6CqjxCN3r9iwo5EImkpbmRlZXH11Vdz5swZDh48SENDQ1wm3zSbCIVO4HJdjywP28a1iNudTyjkS3mNhJRfaWmpJWVbVlZmTdyiydzr9a5LFURMajMzMxw9ejRtDm4yo6VUpkaiCa6rq4u8vDyampp+6zSqZBDVnpycHKLRKHv37qW5uXlVb5IrtTy9iUsfL3ZS7EJinqr+CFkewDTzmJ9vp6Rk9cVmKhhGIXNz3yIQOMPp0z4mJ0+iaRput5u8vDwKCgqoq6tLMocuEWtGXz3RIDYWqvokivIskcg70fWYLPFqfRYCVVVVFBcX09PTw+TkJJ2d47jdsSqWpr0HTfs4kUgkroE7FAqtGH+y6oKqqmzfvt1SEIzfFDiIRr+IYbTjdH7Ieo8kBfB4OgmHb8MwHk553kLWtaioiIGBAfbv309lZSXRaNRqMhdjzM/PTznGVHA6nXR0dFhxpKCggIaGhjWr/XZJ92RxSlQSKisr6e7utpJiYmH/Ym02BPLz87n22mutylFWVlbaSbErMU5d9puNTGlUL5VJXFF+iSwPEXMWrWJpaaWOsq7rcV4WwqlaVCxe/er9/P7v/wjTfA3ve98fIcslHDo0vPLDksDt/jMghKI8j67vwjRrgfhMkGhMBlDVXiQpCKg4HPvQdWdGD4jYFExOTnLqVDGdnbHj9vdXMDGxx6J4eb1eampqMpIgFE1oRUVFcQ3k57MReYTDJ3E6X4miHLa9L/V9o2kai4uLVmAR135gYICCgoIUDerrQ1FREQUFBQwPD7N//36am5vXbDoV135xcRGfz0c4HEbX9aQBQFQShIN2Js/LxZzwTdNEURTq6uqorq6O6+dIlj27UsvTm7j08WLTfdcf84RjuAJozMxcxxpepRYikchyxWKBcLiXxcUsnE4vubm5OBwOGhsb8XpXV15U1UdwOr+MadYQDH4HIQtvd92ORCLWtZXlQTye+5AkHafzIMHgbzKaA1wul7UpGBt7lKamEJJkMj+/j+PH9+B0Oi0q8mq9dqlQUlJCYWEhAwMDHDx4kPb2dmuu0/V3Ew634nLdQqzCIcb0KMFg8s2G3SV8fn4ev98PxFzCc3NzaWpqIicnZ0MW4qKZemxsLGW1PxHi2vv9fnw+n0WJs8/NHo+Hq6++mpmZGY4cOUJZWRn19fUXrUE8k+dKkiQKCgpQFAVN016UpNil0lt42W82XszKxoVM4k7nl5GkEKYpo+tXo+tqXI+F3QE0ZpJXY+tHAIiQlfUVTNODJH0dXb8Z04yVIdM5P9MsQJJOA9lomhPDiA+EohIhms9k+VWY5rXIcjfR6MfSfph0XY+rWAQCAWT5lezfX4uqqtTUvJr6+uM4HF/AMG5G0+5K9xKugNPppLOz01LrKC8vt2UiJCKRZ3E4OlDV2IbMMCqX/2tY1SLBX5Xl8y7hjY2NllyiYRgMDw9z/PhxWlpaNkyWTpZlGhoa4qhVLS0tFrXKTisTGyDTNK2N51VXXYWmaZYLebIGcqE28uyzz3LgwAHa2trWDPYXkzdrGIaVxbSXrk+ePGkFSnsGLtPy9EbweDexiY3ApVrZiFXfJ5EkHdPMx+9PrpYYjUZX9AgKKm9t7XfIy/slUEEo9G0gF7/fn1YV2OH4PqaZjSSNA8eIRK6PmwOKi4sZGBiwZMUVJQ9JUog5k2en/fwbhhG3aF9aWkJVf5eCghdwOOaJRu/l+uuvQZbHMc1CIDut4yaDoihWX2CiYZ9hvJxgsBuPp3XF+0Sfhb2yAuddwuvr6+NcwsfHxzlx4oR1bTZi0SpJEjU1NSvUFbOzs60xCgn3RGqx1+tl27ZtqKpKNBpdEaeKioq4/vrrGR4eZu/evRndtxc7TgnT2sR+jlRJsSsxTl0Rm41LjQtrmhPI8gFibqLQ09POwsICo6OjVgNcc3PzGvx0B4ZRjSyPYpqFmGa2NaZUEFULXdfRtK/icPwaXd+OaRajKPFUqOrqaoqKijh16hTT09M0NTUB31n1vBIX7XYpQq/Xy5YtW8jOzl4e47VMT09z6tQpXvnKO5DlIIpyCF1/FabZntkFTYBQ6xgaGuLgwYO0trZai+pI5CTz8yP4/UPMzlawsHAgbjK0N+8lg31TYG/MW6/bbCI8Hg87duxgcnKSgwcPWtUZQSvzer2UlpbS1NS0ooqRjmqVw+HA7XbT2dnJqVOn8Hg8q3qLZDIRrmczkPh6UbqenJzkwIEDVFdXU1NTYz23GylBeKlkjDZx6eNSpfs6nf8HSQoBErq+E8NwxVF5FxcX8fv9qKpqJcZKS0vjmok9ngNAAZI0hSyPYBidq47JHqck6Q9wu7+JYZSh6x0rlKHcbjcFBQV0d3czNTVFa2srkvRNZPkQun5r0uObpmmNX5yDPXFTW1trU7b6XXw+Hz09PXR2fpzi4keBPEKh/wbK13VNBURfoPBhOl/RriYQmMbtrkCWNSYn76K//6DVC+L1eqmoqFjVdNZOARbJq/hq/4VBJPbOnTvH0aNHcTqdKIqywr8qGbV4tTgl4mtFRQV79uzhxIkTdHR0WJuZVMgkNlxInBLxeW5ujhMnTljN9PZzXE9v4eUQiy77zcaLaZaUziRubyIWmZP29nvIzTWWj2FSVXUHPt8AHR2ZyAnqhEIPoijHl2UIPXFjSqYMJf4em7CrgXfgcKR+QEV5U5j92Ok9ItNuz7IYhmFN2Gst2iFWUi4oKCAS8eJ0LiLLTsCJLB/CMFqB9XGDIfY9NzY2UlhYSFdXF7IsoygK0Wh0uRekjpIS77rdVz0ej9WYd/jw4RQmTukhsfrj9/txOp2UlpYSDodZWlqitbV1zSpKKtWqZBzlnJwcdu3aZemL2xf1dlwsEyZx7GT3hyRJVFZWUlpaytDQkFW6vlK5sJu49KGqakZSti+FpBicQ1GOLv+/yenTV+P3+zl69Kg1zwsFwNWe+2j0XTidD6Lr12AYzdaYILmCofh7LNP/PkKhtwC5OJ3JBTScTifbt2+3mqRjfXvXWeedTMTDrmy12qIdYgmQ3bt3o6p3omk6irKILB/BMF6b/qVMAUmSqK2tpaCggFOnTtHb22utadzuX1nU4u3b89ZlNiv69jbCm0NUf87L9y6hKAolJSVEo1F8Ph9NTU1pUasS45SiKNZGEmKV7pycHOrr6zl+/Ljl+ZWqT+Ri0n2TxZGCggKuu+46i5pcU1NDdXW1dV6ZrCkulzh12W82Mq1sXEwaVaJJ3uLiItFoFI/HQ25uLkVFRdTX15Gff8D2HglVzcloTKr67zidD2AYzYRCX8EwPJimbo1FSO7ZPSzsmYNMIBZ+OTk5dHd309vbawXO7OxsCgoiNDd/C4ejHMO4h2QeFqufiwr8GL//cfr68unouB2PZ5RYyf1ZMjGP0jRtBWXL6XRSXFyMruvMzMykNRlmgkQTp9bW1lVlGO2lcEHZSl39iSEYDNLT02NRq9aqoojvO5XRkoBdX3xwcJB9+/at2NRcrAbxdI6tqirNzc1WP8fc3NyatK8LGc8mNnGxIElSRnPOhSod2pHuxkXIdovY1dz8tri/5+S8haysEXbt2pXmJ2soyn4MYweBwDMYhlAvjG26FhcXrQW0XcFQ/Hwe6TWJlJaWkpWVRU9PDwMDA7hcLiv+xkQ8ctmypQqHI/MkVmx8H0JV78PvL+DsWR91dXdgGL+Lrr89o2OJPgv7ot3hcJCfnw/A9PQ0dXV1G2YsC+er/aIvcC0K8Fq03XiZ+RjC4TB9fX2WIeBaPY2Jmw5BARb3gGEYFBQUcP311zM6Osq+ffssud/E6/JixClBTS4rK2NoaIh9+/bR3Nx8xfYWXhGbjReLC6vrOj6fj7m5ORYWFqwSYjKTvPMIYL9vdb0+ow1QrET3XQwjC+jBNI+i69da59bY2EhfX5+VwTj/kIRQlJ9jmiUYxo2rfkYqxY3i4mIMw7BoVaWlpTgcH0ZVf4JpykSjW9D1P07v4sWhAqfzw7S3L+B2fwxNU1DVUWT5J5hmR1JpXEHZsvtZiD4Lu064fZKIRCL09vYyOTlJa2vrhpWUhYnT0tIS3d3d5ObmWlkYwV+1yyJmZ2fj9XqprKwkNzd3zZKrqKJMT09z5MgRKioqklYhko1LLFrsJetU46+qqrJUQYQU7287Y5QMonR96NAhhoaGmJ+fX1G6TnX8TMrZm9jExUQmCSXBu98IJIsvybwsDMOIy/jn5Q3YXg+5ueVI0mhanxnL7j6E0/l9QMHv/z/o+jbr3Orr6+np6SEQCNDY2LiuxZZQWxL/AoFAnLv12bNnqauro7KyElk+ist1O2AQiTyCYVyT8efp+nvQ9duRZQe1te1I0gyq+l8YxjWYZlvS99gpWyK5BFjJpYaGhhXJpS1btjAwMMChQ4dSimWsB3YKcHd3NxMTExaFVsR8EavC4fCatN1EuFwutm7dytzcHC+88ALFxcXU19evOQfbVatElUN8ltikCyn3vr4+RkdHaW9vjzPcvdhxarVzsCfFenp6WFxcpLa2NqPjb242LgH8tsySEk3yQqEQmqZZWtpCmWJtZKHrFSjKJKYJodCzKceUzHwITCKR38HtfmxZs7wdp/O8MlRhYSG7d++2Mu2C76iqf4eqPgEoRCJfxzCuA+KrAUJ1yelUKCnxkZvbTGVl2wqPjrq6Onp6ejhz5gxXXZWLokiADKRvrJQMspyHYbwXVf1n/P4iPJ67kGWVcPiHLC11xmVZ7JStmpqatNxjE0vK6S7a04Xb7aahoYGxsTGeeeYZHA6HtbFYTRYxXQglk0ycYxOzR+Fw2OLMJp53VlYWO3futOQZy8vLLyqNKtPXOxwOy5k3sXSdDJmWszexiZcKNrKyIUkSoVCIs2fPWvHL7gWxlpcFgK6vbFgWEOMU8UrA5RoAJCRJw+GYQlHOxymn08muXbsYGRmJE6uQpHEcjr/CNN1Eo/cDhcufv1J1SVEUS8Qjmbt1XV0d/f39HDlyhJ07f0hMQhcU5d/WtdlYPiskCVQ1F0maRdM0gsG7ycmpIxr9e0IhZ1xySVC2MkkuCbftmLx9F4WFhTQ0NGxY4sTpdFJfX8/ExATPPfcciqJYYxTx9EJ6EAsKCuJUq0SD+mpIjFOhUAhd1+PuJ9Enkijl7nA4LmqDeLqvF0nBY8eOMTIyQjAYpLGxMa2k2KYa1SWAiyF9K5Q1xOQWDAZRVdWa2IRJ3unTp8nJyVnzQUpEMNgDRBAUIUnC4q0mTthizLF/Ji7XvSjKb9D116FpH0VRVpYqZVmmqamJ+fl5Tpw4QXl5OU1NZwEwjCjnzvUwMZFtcS7Fol2oLjmdH0JRfolplhEO/weJLqZOp5Nt27YxPT3Nnj030dlZQl5eLYbxPzO6DskQjd7P0tJf4Xa/DhjDMCKY5m1EIq8mHL437SzLahAbMtFA3tbWlpEDLSSvrIjgV1paSn19PaOjo0QiESoqKjZMJldok1dUVFjUqtUa1JNlrLxeb1I1EIGSkhLLcGl2dpa5ubm0xn+xzYzEpG8vXacyhFrP8TexiYuJTJ6N9VY2hIGbvUcwEAjgcDgsymd9fX1am/BQ6AFcrnsxzQpCoT3W8deOUzKS5EOSbsA05zCMLcCrk9I46+rqKC4utvwbWlu/iywfWO4P+CdGR/+nRTUVIh7nVZemUNXvYRhtGMbrVoxfVVXa2tqYm5ujq6uB7dtVVFVF11+f8XVNRDj8r8C/YBiH8Hp/g2EcYHp6gbNn30t2dh1FRUU0NDRcULIjLy8vroF8PeqHq9F2CwoKqK6u5syZMywsLFBWVhZXLbgQyHLMWFaoVo2Nja3aoC6qVGKcoVCIrKwsiwZs32glSrnX1dVd1AbxTF/vdrupqqoiFAqxf/9+amtrV+3nvFzi1GW/2ci0UpGYMbJnTISyhlg05ubmsmXLlhV0nPV+tkDs81VMU19W3JDQdZ35+Xny8vJsE3Z8Y68kjaIozwNlKMpP0bR7U35GbGGm0NfXgq73MDHxKlpbZ4kpXbyC2tqilNUARXkacBNzDD+NaW5N+hklJSXk57+Mvr5YI3N7e2S5CtKDLO/FMG7GNCtWvRaisiIWw6IUXl5+F3V196Gqk7hcc5SX/5Di4hC6/iU24rZWFIWmpqak1KdErOYULnxBkl1Lr9eLz+ezSsoNDQ0bNqkI51hRhaiqqqKysnKFGpjQhbf7l6SjWiXLMlu2bOHcuXNMTU1x9uzZNb1F1pMxWu/mJLF0LaQI7aola43nUskYbeLyQKY0qnQqG8LLQsSvcDi8wrNoZmYGTdOoqanJaLy6fidLS3fYlKFiSb2ZmRnLeyBZnAIDp/MDy5SrIsLhvyRVL5+4JlVVVYyPj9PVFaWlJYosy0QilVRVVZGTk5M0q+90/hmyfBBwEg5XYJpXJ/2MgoIC8vLeRU/P1SwszNPSchVriBsluRYrRTwcjtfR0jKP270XVY1SU/Mzqqqewe//GU7nlsw+IAVkWaauro7S0lJL/XA19cD10Ha9Xi+Li4t0d3dbSccLSebZIahVs7OzHD9+nOLiYmprawkGg9bGQiQ9xT1bUVFhUXgTqVXiPrP3S/T393P27Fny8/OtvpfVcLEr8CJOVVdXU15ezsDAgJUUS+aftSl9exlC13UCgQBLS0t0dXXFmeTl5uZSV1dHVlZW2l98upuNeCm/8zetJJ13Nd2xYwfd3d1WyTS5Sk8fEEKShtG0/4n4epMthgMBk9nZQv7pnxq5//6tNDS00NfXYHlPrK4e8gEcjq9gGC/DNFOXzyFGbeno6LBcsGtrc9iy5U3EPEQeJBTah6iMJNMyt5fCRYNfbGw7iUb/F7J8M7J8GAjicDyJLD+Ppv0Q07xqzeueDnJycrjmmmsYHx+3Sr75+flx1zIUCq3bKVwomYyMjKRt2JcOxHeu6zr5+fmcPn2a/v5+CgoKKCkpScoFFkimBiLuw2TZx87OTpaWljh27Jh1/smC/8Xq2Vjt9aJ0LXjC+fn5Vun6cpEU3MSVh2SxJZWXhZg/Kysrk5rMZRqnUisYno9TPp9vFXn2KJI0RsyLYhZYBNxW1cXeD2iX9G5sbERRPkZv7xZycoqoqLh1jflB0KdNJEljtVOMVYR3WtX+0tJSGhufweF4GF2/DU3733HXwe5nsbqIxw40bTsOx98iy9PI8gJ5eS9nevqj5OR8csPmH9G3JhS36urqKCkpsRrN5+fnCQaDVs9KprRdIcM7MTGRtmFfOhCiOZFIhPz8fCYmJjh9+jT5+fmUlpYmyAzHQ2xi7ckxVVXjVKtE9SoQCDAyMsLc3NyqmzExpt9WnBKUuEAgEJcUsyftLpc4Ja0xyWxMB9qLjJ07d/LrX/867neJyhpLS0uYponH4yEYDNLW1rZCTSFTjI6OoqoqFRXns/erTdh2dShYqQwlDOOmp6fp6OhIaAxbwOWK0ZRMM8r4+GPMz0fiGtMF5zI7O4/3vz+bwUGJaHQBpzNAXZ2Xb3wDRkcH8fl8tLe3r6FdbZJIn1oLuq4zMvIMzc3vWA5CMkNDz7Cw4I/rsxDjTKfPAnw4HPehqt8FQsu/kwgEPo4kfTKj8aUaswjggjIkSRJlZWUUFhbi9XozdolNBaEqJUydMuHFRqNRK6jMz89bDXwiG5SXl0c4HKanpweXy0Vzc3PagUZsOpKpVu3fv5+rr77aWryPjo4yNjZGY2MjZWVlcddlamqKhYWFZU+WtXH8+HEaGhrSLt0fPHiQ7du3r+oJIoJZTU0Nfr/foo4kg9PpfClN8i+ZgbwEcdnEqV/96ldp3XPhcJijR49SUVFhVXyFl4Wouif2KKTC1NQUgUCA+vp663fJ+wHPx6nUylCx52x8fNyixiRmkxXlG6jqlwGFubkPcObM71nUGLfbbW2OvF5v0mfZNE2Gh4c5e/Ys7e3tKSmukjSConwT0+xE199Kuo+QYRicPn2SlpZbUBQHkmQyOvqfzM464qrWYoypKivx5/xvOJ3vJbYBMgETn+8qNO2HZGeXpTWutcYsaLs+n4+ZmRlM07Qor16vN+37YS1EIhH6+vqIRCJpqUrZYWcqiA2Q2+3G6/VacUrXdXp7ezEMIyOhFhGngDjVKojFkvr6egKBAAMDAyml3AHm5+cZHR1l69bkjI1E9PT0UFhYmDZd/sSJE9TW1ia9b2dmZujt7Y2T8j19+jQOh4Oqqqqkx0tFd36RkPIGe0lVNn72s5/x4Q9/GF3XufPOO/n4xz8e9/dwOMztt9/OoUOHKCoq4rHHHoubIFPBNE0OHz6My+XC4XBYMm3Z2dnk5uZSUVFhTRjBYJD+/v4N4ybGDPK0FRP26lJ+ySHUIgSHtbS0lKqqquUM0HPU1AxgmhAMNjA/7ycvr4jq6mrLZdqO+++PcscdYRTlNKqq8fnPfx6X6+9W9HKkrnJkNmmFQiEWFhbQtGoGB99FYeGvmJj4XzidOhUV0NxchqLUZXTMGPKJRr+EaebgcPxfQAdMsrL+lmj0n4lGDyFJ6cmhJvMGEXJ+IrOWk5PD7OwsfX19ZGdnb9hGA+JVpVbz5kimZy56hrxeL1VVVUm/c1VVufrqq63sV7reH3Y1kERqlT0LJEr6FRUV9Pb2WqogYlN8sXs20jHpE6X1wcFBJicn8Xg8G1JJ2sQmLhRCMjyxKqrr+gqTPEmSrL6qhoaGlFTedCHiVOIzFN9nsdKPJxkkKWYYJzyMvF4vdXV1y9UAH3V1D+L3F+Bw+JmaaiY7O9uixs7sWf4AACAASURBVCQ/Bw2H45PLVesPo+tvor6+npKSEquXY8uWLUk2PbVo2t9kdB1ED5tpOolEClDVOXQ9m6UlOeOqtR26/iZCoetwu28EZgDIzz+Krrdz+vS3KC9fq0pzHmvRduvq6ujs7GRxcZGenh48Hs+KxM+FQDRii2pxSUkJ9fX1SZOjif0gazXtQ2zxvH379oy9P1KpVok4JcvymlLucPEdvleLU8IlfWxsjH379lFXV4eu6xtmCvxi4iVT2dB1nZaWFn7xi19QXV3N7t27+f73vx9nZPe1r32N48eP89BDD/Hoo4/yxBNP8NhjjyU93lNPPcVPfvITDh48yIkTJ9i5cyfvf//72bVrF7m5uSk5h+FwmO7ubnbs2JHR+O2ZIHEzzc3NMTIyQnt7Ox6PJ6MJOxkSFTdmZ2cxDIPS0lLa2/8St7sfWTaIRu9H19+86rFGRyXuuivEjh3/zvHjbfzjP36PqqrPx53P4GC6VY4YFOUJJKmbUOjtzM9nW+O0l2/FglhRFAYHBzHNp9m+/bNIEkQiD6Lr628iV5R/xun8IIm37eLiu1DVB1e83l6yn5+fJxKJWNKOYqypMla6rlvXp62tbcM2p4nHn52dpaGhAcMw4gKL2AB5vd51VeDE8efm5uIc1NeCPXukKAoHDx7kuuuuS5khEjzfpqYmzp07RzAYZMuW9PjKR44cyShztm/fPnbt2pW2KsvRo0eJRqMoirKinwM2KxuXEH6rlY2LlRR7+ctfzne/+13C4TCmaVqJBNH8LCoWgqJz8OBBdu/endHYk1UslpaW6Ovro7W11TJavZA4lUiHnZ2NqTIVFxdTU9NNefk9yHIAXb+RSORR1vJekqQTuFy3Y5oeJMkkFHre+lu6VY5k0DQtLmkjzFLF/O/1hnG7DzA6WsX4uMnVV79AVtYCmnYnkL6fTzymcbtfhSQNx/323LndRKP/nrSnQPTdJKPtioV7qg2QYRiMjIxw5swZWltb11QnzBT249fXx2T6k/WDeL3eNU181zp+JhRjQa0yDANVVTlx4sSKWBIIBOju7rbmf5Ggm5ubY3JyMm0T5VOnTlFeXp72tT169CjNzc1rrqmi0SgDAwNMTk5SW1tLY2Nj0tddKnHqJVPZ2L9/P01NTdZC5LbbbuPJJ5+M+8KffPJJPv3pTwPw5je/mQ9+8IMpd6GmafLa176WT3ziE9x000388Ic/TGsc6fBXU5WYxfvFblrs3Lu6uqitraWioiLtmyIVL9SuuCGyF0NDv8TlOo6iLGKahRjGzjWPX1xs8sADLjo6VHp7D1BQ8Gdxf7crVo2MPEZHx0M4HFuIRB4Czk+IIusWDj9LRcVHgDDR6H9z7tzXVs1eAMuuzw9jGAEkSUaWf3pBmw1dfxfhcCMu1+uA8zS13Nx/ZnCwBYfj7VapWRj6iYkwUzk/u3fGqVOnbJziC5MftKtuLC0toWkaXV1dZGVlUV9fv+7MWqrx+/1+uru78Xg8NDU1rUmtElnOUCjEuXPnLAnCZEFEqIKMj4+zf/9+a2OULi62I7gsy7S1tRGNRtNyod3EJnRd5wMf+EBcUuzWW2+Ni1MPP/wwBQUF9Pf38+ijj/IXf/EXKZNiw8PDPP300xw4cIDe3l5uvvlm3v3ud3PrrbdSVVVFdnb2uueUVJKzEF+xKCwsZNu2bXR1dVFWVrZmz54dQs3ILo9umqa1OaqtraWjo4NAIEBXVxc5OY8gSQqmWbJMbVrb5NU0qzBNL5I0j66/Iu5vkiTFVTnKy2eprt6CJDWvuBax6tAc+fl/SU7OASYm/ohA4G3k5eUlNUuNfXYN1dVQVvY4Hs99yLKOqvaiaf+Y1vVZiRJCoSO4XK9Bls+b9xYXH8A0qzhyZA+lpVVW7Pf7/Suq1plU04VnSVlZWZx3xoXGECFYY5fw7e7uxul00tDQcMEy7onjLy8vp6enh7GxsbiNwWrvk2WZcDjM1NQUS0tLK6jryaTc6+vrN7yinoh0KyEOh4O2tjZ0XWdqaorFxcUN9f/6beMlE1XHx8fj1DCqq6vZt29fyteoqorX62VmZiaprOVNN920rnEkqnykO2GnygTl5+eza9cuenp6mJ6epr29fcVDaDdQEv/svNDV9Le9Xi/XXHMMRfGh6xKGsQXTXDtznJPzXbZt+y807U4aG9+U8nVer5eSkp+j6z40bT9+/xPMz7/GKosKmlFpqY6qKkiSA683m9bW1RvHBRTldmT5SXQ9RFdXJ83N78PhKCEa/SSZuo0DGMYrCASOk5UVz7csLPwWe/bsoLq6Oqmh33qRk5PDrl274hrI0+Vu2nm2q6luAExOTjIwMEBDQ8OGlsSzs7PZuXMnZ8+e5dChQ9TU1KxwprXTtuwqVrm5ubS1tVk0wWSqVYJSUVZWxtGjR5mZmbF6XdbCb0O9SpIkCgsLuf766xkfH2ffvn1rShFu4srFRifFTp48yezsLLfddht9fX08+OCDlJeXZzwuEaeSCY2kE6fEPDYwMMDhw4fp7OxcsaATzbz2LLvdj6OsrCyl7HhOTg67dxficJzENH1ANobxyjTOzARkwuEfIEkjKZUPs7Ozue66YWT5E2iawfz8PxAI7F4RT0tLJykq2o8kqbS0PEYo9Jk0xgBut4TDoWAYOnNzY0Sj08v0m/VsBF2Ew0/jcNyGqv7Y+q0kQWvrG/j1r/+JiooK6uvrycnJ2ZB5SFB0z549y8GDB60FfDrHTiWPm8wgd2pqioGBATRN29A51O12s2PHDktoJhm1ajXalhDVsYudCNil3Pfu3ZtxfN1IGlUyiB5O0zQ5cuSIJfByqSXFXjKjTVZNWJllWPs1yaAoCrqup5UhssupJSpDpZbyWx2qqtLZ2cnU1JT1oKuqak3YQnFDGChlll01cDi+iyTFssuDg9tQlMlVJxJJGsHheADTVHE6P0YotJfE6pddGcTlaqGg4BCGodLVJeFwTFFbW0tLS4vtmrajaUvI8kk07a60r41pdhAKdQEmLS3vw+F4AklSkOVqDGPt49jHaQ+A2dnPsnv3KxCXwOX6Ntdd186pU6eIRqNpOUynC7GgLikpoaenx3Igt1dKEgO1yAaJStVqqhsAlZWVlJSU0NfXx8TERFLaz4WMv7y8nOLiYgYGBti/fz8lJSVWlcVO20oWAO2GgIlqIAIOh4Py8nKCwSC9vb14PJ41VUEutgqHPUiI71BIEe7bt49XvOIVL6XGu028BLDRSbHXve51vO51MQ+I7OzstA1oRfIrWZxSVXWF2Eg6kGWZ5uZm5ubmOHr0qNX7JeascDhsNfMWFhZSX1+fUfba4fg+DsfcMkWsnDNndGpqVksQmDgcH0NRfoGuv5Jo9MvEzGHjIXxDsrL+i9zcCLJsMDf3FOfOVSapBlcjSfnAAoZxfdpj1/U3EI0OI0njuN2vIT//GmTZIBL5AZKU3nFW0nbvZts2nZKSn1qvcbuzuPHGG+np6WFoaGhFHLkQiHm+qKiI/v5+Jicnk8qVJ8bTaDRqKYKtZTwoBDeEaXBra2vGHlWroaioiIKCAoaHh9m7dy/l5eVWlUXTtFXl5leTdBdS7pWVlRw7doxIJEJNTc1Lyj+qoKCAwsJCq5+jvr6eysrKtI/zYuMls9morq5mdHTU+nlsbGzFhRSvqa6uRtM05ufn0zKxcTqdljqPHcmUoUQGamxsjPr6+qQLp0xg1zmfn58HsBZb9fX11NbWXtBkIkm/RJIml/9fpqLiPXR3zzI1NZW0igJgmjnL/NclDKMekOLoO8KoUCiDyPKfoml/gKqWc801lQwMDDA4OJjQyyGh629nmc6/DA1Z3odp1mKaq2m4S4CEqhagKCqGAYuLPyAn5xfA5+LkdcU47f0gYpyJATAU8gNjQCngxOOBq6++mjNnznDw4EEaGxspLS1d55VfCZfLxfbt2zl37hyHDx+moKAAp9MZdz29Xu+6DZ2EjLDP5+PEiRMUFxdb9+h6kUwdxOFwMDExQXZ2Nlu3bl2zbJsolavr+go1EIg9b1lZWTQ3N3P27FkOHDiwqipIppWKTJFs0hdShJqmbW40NrECFzMpJuJUIlIpGHo8HoaGhuKqCeu9ZxP9jETPnqqqNDQ0ZEzfWQkTVf0WMdlbBbf7jYTDYQ4fPkxHR0eKOWYRRfkvTLMERXmWaHQGTSuIm//tdFhVfReK0ocsZ1Nf/zFAY2BggPb2dttcm08o9HNkeRDDyKQvU0XTPgpAVta9qGoQ0zTx+f4ew/gSRUXx4ibCd0PMq/Zxxrtw/4Bw+E5cru9jmi4ikX1WHBH0ntraWiorKzdsLnQ4HLS3t+Pz+Th+/Dh5eXl4PB5LfECMMz8/f110KDvFuLu7m5ycnAs22hX9quJ6xnxMHExOTuJ0OtPqm0wm6Z4Yp9xuN/X19Zw9e3ZNKXeBi0WjSnZ8WZYtSn5/fz+jo6PceOONG5Y4vZh4yWw2du/eTV9fH0NDQ1RVVfHoo4/yve99L+41t956K9/5zne44YYb+MEPfsCrX/3qtL5kl8tFKBSyDMvssGeAxA2wa9cuBgcHOXbsGJ2dnWlz5BKNfYQcYTJazPj4OENDQ7S3t1/QZsPh+Jj1/6bpQlFa6OyUmJ6e5tChQ0kW1H4U5UuEww3Mz1/NxMRu5uf3oiiKNRGeVwaBWO+DApRimiDLsV6LdBSrYlmpJwEH4fBP16R3RaOfxjRrkKTTFBV9D9MMEQi8nTNnvsbcXNYKmpFwal/9HqiO+0mSJCoqKigqKrKqEG1tbRf0HZimmZQO5fP5LOGD4uLiDQsWwptDOMem2zhn51f7fL4V5XB7f41pmpw5c2bZGyW9YLeaGoj4fCGbKSopQ0NDKVVBLrZz6mrHvxQm70389nExk2IiTum6nrQfMFHBcMeOHYyOjnL06NEkUuipIfrs7Gp2qfyMzp49y+DgIC6Xa02e/GqQ5V8iSVPLP5mY5ptobm7G5/Nx7Ngxqqurk9A3PQSD16Oqz7Gw0MkLL/Qhy2pS+k4MTUSjT1vvr68nhWJVEYZhny/P4nB8EdMsQ9PuZq1lka6/BlX9FyTJpLy8G9jJ+Phb8fv/Ar/fHzevCnGM1Wi7hvFPBIP/tOL3JSUlVu/P4cOHaW9vz0hmNhF2tUU7HWpxcZGZmRmamprYtm3bhsUp4VE1OTnJgQMH0qYAC7Ute3XFrgqZ2F9z7tw5Tp48aa1FMlWtkmU5Tj5WKJVu3bqVkZER9u7dS1NTU0pfkYtNo0oWp8SGMRgMXjJ0qpeMGhXAT37yE+6++250XeeOO+7gE5/4BJ/61KfYtWsXt956K6FQiHe84x0cOXKEwsJCHn300bSUbRoaGrj33nt505velJHk7NzcHN3d3TQ0NKzg0dr59qKBW5Zla4KJ+VkkN00TCAQCnDx5kqKiIuqTSMetjSk8ngbbmOoJh09aP0ciEU6dOoVhGBQVFeH3+/F4fkB9/SPIskwg8Bqi0c+mUDOaweX6IyRpjGj079D131/x6YYxx+TkLzl7tpzW1qtW0HpcrpcjSWNIkkw4/DUM45aUZ2KfYHT9VzQ03I0sR5AkHV13MT39t+Tl3bHhi89z587R19eXtFchFVZzYU1U3RDOq/n5+WtmSNaDUChET08PkiTR0tIStyAQMo5irOFweMU41xqPpsWygwsLCxmVxBPVQBRFYXh4GJfLtWJxlkoV5Pnnn+eGG25Ie2J+/vnnufHGG9N6LcTUq6655pqkk7UkSRvS4LiB2GwgSY3fWpzSNI2Wlhaeeuopqqqq2L17N9/73vfo7Oy0XvPggw/ywgsvWKqJP/zhD3n88cfXPPbu3bv5vd/7PT760Y/G0XXXou0uLi7S1dVFZWXlCp683U9KJELEwk0s2tdSswuHw5w8eZLs7GyamprWNYe5XK3I8tjyTyrB4Lz1N13X6enpIRAIUFpaSiAQIBgcpb39M7jdc/h870GS3klurjfpOGMxpm+ZFrUyMbiWYpXT+R4U5aeAQiTyBXT9LSnPQ9B2FxcH0PVfU1Pz14COJEF//1swzfdRXd254fO8z+ejp6fHauBPJw7a5/+11BbFHOx2u2lubt7wZEskEqG/v59QKLSCumVnVQi/FaG2Jca51oJa13XLg6y5uTmtzT3ExymRHDt79iyhUIiGhtjaKhwO09vbSzgcpq2tbcWmXlTn0t2MZxrXjh07ZsnuJ8NLTBY35Um9pDYbFwvDw8PcddddNDc385nPfCZjs7RTp06h6zrFxcXWxG1X3BCShOtZCMcMhE4zMzNDZ2dnRpkLVf1jHI4nrJ8jkbtZWvpkXDlcSHsGg0Hq6+tpaPgZTucDgAtNey+a9sEVxz1wQObw4S4+/OE3AiqmWUs4/OOEVwVxuW5CkqaJRJrYs+dTlJdXxFU5ZPkXOJ33YBgdRCLfBLKIVUpkayK084HtJnSFhXtwub6GLD8H6JgmzMz8Pk7nP+NwbOwiUCyol5aWLDNHgWTlcCHjKybCtRalpmkyNjbG+Pj4hjmEJ2Jqaoq+vj6LpypUTMQ4hfngeiE023NyciwH7nRgl8odGxsjJycnZQPs9PQ0fX19lirI3r17M9o8ZLrZ2LNnT0rZ3s3NxiWFyyIpNjU1xd13340kSTzwwAMZyWkLIzS/3095ebkVp3RdtxIMIk6tZyEs5rCJiYmMJWYTk2Km6WV+fmiF7LiqqgQCAaqqqmhs7Mbj+RSm6cI0a4hEUm3WzuB2vx4IYRjXEYl8a8UrJOkoqvoD/P7f4dgx7wpfDofjT1HVf8c0FaLRr6Drt1rvXY22m58vUVn5h8jyBKABKouLrfT1fYYtW16x4YtAwzAYGhpiZmaGtra2uO/ALovv8/ksmlEm87+oZp8+fXrDhUgE5ubmOHXqFB6PB4fDsYKt4PV602ArpMZ6jXHtcWp6ehpd16lPkKv2+XxW4tBOCzt48CDbtm1L+7MyjVNrScBvbjZeYjAMgy9/+cs89thjPPTQQ7S0tCR9XSrFDSH32dDQQEVFxYaXrubn5zl16lTScnIq6Pq/k5Pz9uVxw9NP/xy3+3xgsS+Ew+EwIyOP09LyeZzOKIbxaqLR/4u9ZDwzE9to/OhHCl1dGp/85J/T2NhDY+Pr0fX4Zm1JGsHlejXgQpKCLC2dZHBwjPn5+RW+HLGJ8Bx5eW/H7T7O6dNvY2LiHXG+G8myApJ0Arf7dcRMkBRAJxCoYm7u3ygs3JbpJV4TPp+Prq4usrOzUVXV0ri3O9peiIqVqELIspyxQ3iyY9mzVmJREY1GCYfDtLe3J9VsvxCYpsnk5CTDw8OWed9a1yIajeLz+fD5fExPT9PS0kJJSUnKjbnYfJ85cwZN03jlK9NRrIkh00l8tQzT5mbjksJlE6cA/uVf/oUHHniAr3zlK1xzzTUpXyfilPgnGl+DwaClqLbRGWq/38/JkyctI7f01Iy+RVbWn1o/j43dxPDwfXHzqpgLo9EoPT3dlJZ+j9rax4EsotGPo+tvT3psSTqCy/XHxOJD1rLYiR0abvdVwBLgIhh8juHhpYQqxwKq+m0Mo4S5udfi9w9imns4c6YZwyhcYyEcRlEexun86+XPkDFND0eO3IfX+4cZyd2nC1HJcjgcVq+FnWYkqlXr/dxoNEpfXx+hUMjyCFsvEpvNRXVFVNxEPNhoTE9PMzAwYFX71koEi56lubk5pqenU/YSik33yMiItRY8ePAgV111VdrPWqZx6tChQ0nV4eDSilNXzGZD4OjRo7z73e/mzjvv5Pbbb2d6ehphRCMMc0Qjb+KCPRgMcuLECavBd6MnEV3X6evrIxgM0tHREbcYtTdICZ5tzMJ+DyUlzxCN/i1ud+OqY1KUL6Mo/0Ak4sQwXo0sfyPu7/39Eh/8oJOJCXC7o5imwl/91RS33prMrMbE4fhLFOU/0bT3oGkfAs4v2HNyciy3doDy8mGam/98mbOvEAoNpXlVorjdNyJJXec/2VTo7/9rSkv/9wU9aKlUN3RdJxAI0NHRseEmSBCbCPv7+9Ombq1WXcnPz19h6rS0tERPTw9ZWVkbqrolIMyGlpaWLCMwON8T4vP54qQH7bQtp9MZR61KFQRCoRC/+c1vKCwsTKqYkgzr2Wykev2lNIlv4vKLUwMDA7zzne/klltu4e6772ZmZsbi19uNUu2UGBEvIpEIJ0+exOPx0NzcvOGUHpFhn5ubo6OjI+7ZTDT0W1paQlUjvOxlr0WWwTAkQqHFVec8Wd6PLN9JNBoCWpGkH602GlT1PhRlD9HoPRjGzQl/j99sxAwBi1haWuLEiRO43W5LccswDPLyHGzf/nYUJQCUEg7vY2152yiq+jkcjn8gVrk3AYXJyXcwNHQn7e3tF9TvYjf1EywAsQFYWlqipaVlQ4VOBObm5ujt7U2bumWXSBfVFbuXldfrjZtT7RTgjVTdEtB1ndOnT3Pu3DlaWlqsWJ7Yu7KwsBDXuyjWfKZprug7FBAbsqWlJaLRKNddd13aCehM49SBAwfYsWNH0nh0KcWpK2qzoes6zzzzDM899xwPP/wwkUiE6upqHnroIQoKCqwJe7WJ0D7RZtI8ngmmp6fp6emhqKgI0zRZXFwEuMDMxRwu1y3I8giG4eDw4a/gcHSsMKL7xS8ifOxjC5imzhvf2M19971q1aMmy1xkZWURjUbRNI2Ojo7l7NH8snPqLLp+C5p2G7I8iKa9BVirJB/A6bwNRXkq7rfz863Mzf2E0tK1y73J1CxWmwj9fj+nTp0iNzd33Tzl1SCoW4uLi3E80FQTof27T6e6Yi+J12egqZ4JZmdnrV4LRVGIRqNkZ2eTn5+/qmOsvWSdTLVK4Pnnn6etrY2enp41VUFM02TPnj0bttkQTYMvIWxuNlLjsopTEKP4Pf/883zrW99idnaWkpISHnzwQaqqqiz1oNWeZzvtqaOjIyNKVrrw+XycPHmS/Px8ZFlmcXERwzDi+kHOy4/qwBRQzlq3sqJ8DafzM5imh3PnrmV4+L6kRnSSNIAsP4dhvALTbEhxtBiNSpIex+e7kenp9rgFu2EYBINB2tvbl3n+k3g812KaIEkGwWAvkJ7EeKzC8RFilKoYNK2G55//KhUVbWklluwLdpFUtIvMJLIAQqEQ3d3dqKq6ppT4eiAqzdPT07S1tVn+SIIBYq+ur7e6IpJvVVVV1NTUbHicWlhYsHpXnU6ntUaxU6GTxZXEOJWsd2phYYF9+/ZRWVlJc3NzWtc/083G/v372blz5yXfW3jFbTbuuecedu7cye7duzly5Aif+9zneOCBB7jhhhsyOpbg7wk6yXphf2jtbpwej4dAIIDb7V5R5VgPZPlnuFx/RKzMW08wuI/R0XFLjUlMIt/85hSDg7+ko+M0zz57Nd/4xmusY8RKjXPo+lPMznqYnS2Ny64VFPSTk/M+TDOHSOT7+Hz5dHd32xSr/EjSOJI0h8v1ZkBD1/8Hkcj30zgDA4fj3ajqSu7u3r1P0Nb2KusaJVPdME0zLnORzkRomibj4+OMjo7S3NycVCf/QjE7O8upU6dwOp2W6VA6E2G6iEaj9Pf3EwgEVvSjZAJR9hZVC3sQNAyDmZkZtmzZkvamJlljXuJELiZlwzAYHR1lbGyMxsbGpFxiwzDYt29fRs/x5mbjssFlFacA7rvvPurr69m9ezcTExN89KMf5VOf+pTlyZEulpaWOHnyJBUVFRe8kEukF4u5KhQKIUkSW7duvSC1pOVPwe3ejWlGkCSNYPBnnDmTw+nTp2lpabH1uwVxu18FzGOaBYTDzwCxRVcqmVQ7bdfjGQSimOYO/H5/nGKV0/kAivI4mvYeZLkfWf4N0ehfrSpwIiDLz+FyvR6Ixv1+dPQjDA//YZyilF0URVxXsVmzL9jXqiiYpsnU1BSDg4MXrddiYWGBrq4uZFlGVVXLc0UkltJp4l4Nuq4zODjI3Nwcra2taZm+JkOiAeHCwoLVEyJJ0qoUqdXGJhSk7KpVAs899xwNDQ0MDQ2lZQib6WZj79697N69O+k6YHOzcQlhZGSE22+/nZe//OXcc889GT0wmqbR3d0NQGtra1qLE3tJVNC2PB5P3EJYHEdw5EdGRmhra7sgDr7LdTOyfJCYp8bdaNpfAVgTbWFh4bLL5gxO558iy0fw++9kcvJt1oJdlmXa2r5NUdF/Icsqkci/Yprbrc9wOu9Eln+OJBlEox9D0+62JpH5+Xmr5K4oP8bpvAsIYxiN6Ppt6PrNmGZH8sHboKr34XB8Me53pinxi1/8lNzcXEs/eyMX7KLcK5rO1vtwr+YYLiQyW1tbL0oD+fz8PD09Pdb3vNb1iEQi1sYi0TApPmMZg31T09ramrYM52qbjsRJORKJ0Nvba2Ui7Z+haRqHDh3iuuuuS/uabG42Lhtc9nHq3Llz3HHHHVRVVfHZz342I1qOruvWs5lu4ipZY3Qq2hacz043NTVdEAc/lhT7Y0DDNBsIhZ4j1osRoqury/LpUZQl3O6XYZpCbepJfL6oRdvNzzeoq3scp7MYSfpzJMlt+4yf43S+f/k8P4uuvy2pYpUsH8DleuPyWHIIhfrSOgdJ6sbtvgGIN2gMha7mN7/5vFWVWo/q0mqIRqP09vYSiURoa2tbN+si2YJdKG2apsns7GzKpM+FQlCAs7Oz0xIiiUajcdWVRMXF2Pd4Pk7pus7Q0BCzs7O0tLSkvaZKJ04JtsLc3Nyq67Urtbfwit9sQGyhcv/99/P000/zjW98I84hNh2IptnEGyzRdyNZhiUdo6RgMGiVq+0qGuljGI9nJxAGVEKhX2KaO62/GoZBf38/weDzXHXV3wMa3d0fIhr9HWucghLjct1q9U9Eo/+Arv+BdRxF+T4OxyeIqU09gmGcf6Dm5+fp7u5ezrBV4HR+Gkk6gaIcAkJANsHgKWKKVakR08b+AgUFf239zjTh2LHD+P1+DMO4aPQ2oTmf3t7CbwAAIABJREFULi0pmUTuagv230ZJXFArmpqarEqNvXQvNkGrUcxWw8LCAj09PZYeerrBMxm1KtWkLO4loWHvcDiIRqMcPXqU3bt3p3k1NjcblxGuiDhlmiYPPvggjzzyCF//+tdpb2/P6P1C5jtxQ5CMumP33YhVAlanbcF5qXWHw0FLS8u6Fs5O580oyl5AJRp9H5r2t9bfTNPk9OnTjI2NUVMzRk7O0zid48zN3QrcYo1XURQcjo+jqo9imjLR6KfR9dut48T6Kx7ENCV0/U3LzuQx2KscjY1OsrJeiWlqQBkQ89iIRj/LWo+jYZwlK6sVSYpVOCQpFqeOHj1MMBgkHA5fNHrb7Owsvb29VFZWplXNSiWRm0oiPRKJ0NfXd8GbmlSwC5HYY22in1ViT2AqoZlk8Pv99PT04HK50qY/QXJqVaJqojAzdLlcSYVgrtTews3Nhg3PPvssH/zgB7nnnnt4wxvekNF7A4EAx48fx+Vy4XQ6LSUjURJNl7qTCiLzMjU1lZGBE4Cq/jkOx0PLxylgcbGPhYXzNCPRFF9b+68UF/8/FMWJJP0B0egXVxxLlg/hcNyDaW4hEvkyiZsDSeoFPJhmDbL836jqd9G0P8Iwbk5S5dDxeJqJcVwjQA6RyH3o+vusc05l7lNb+x1KSr4KQCj0OKb5euD8RFtVVbVmOXM9SKXUIUr3Pp+PhYUF/H5/xhK5AlNTUwwMDKSt+JQpRJUjGo2iquoKrm1OTs4FfaadfpZJWT8xe3TgwAFe9rKXrfoZIiAVFRVx4sQJdu3alfY4Nzcblw2uqDj1wgsv8Cd/8ifcfvvtvPvd787oWQ2Hw7zwwgtIkoTH47F8N+xJkHSoO6lgmiYTExOMjo6uoxo/hcfTQiweOPD7n8Dn2x5nkutyuSgp6aem5l5i69/b0PX7VxxJVT+Nqn4HSZKJRD6Hrr/V+pskjeB0vgsIE4l8C9NsXXEOosqxdatKXt4QTueHEPK2kcj9y8fLtl5v718QksM5OVns2nUD4usxTQiF/EBMUerUqVMUFxev02NrdYhY6/P54qrAyRJLmUrkCohYKyh6G30Ofr+f7u5u63s3DCOln9V6YKefVVdXZ7ResFOrDh48uCJOiWOLXhR7g/1GbjYupTi1udlIwNzcHO9973vJzc3l85//fFKOu1gE2+lQQnpU0zRCoRDbtm3LaEOQLoTsXbocXMMwyMoqQpJiJd3Z2as4ceL/JJHzC+Bw/BmK8p9Eo7l0dd1LTc2bLX5pV5dBe/sTqKoTXX89YH/ITWINgPZM1hIeTyexTYSDYPAF/u7vimhoMLnlllmrylFffxiH40vI8kliSh465869jZ6eOwiH9TjvjXTLzLquW0Z0iTK8GwHRfN3f34/L5bKcfu3X9EIkciFWbevv78fv919wr4VdycS+CRIOwSIDttHBQhg5BYPBtKhVIsMmpHIdDgfbtm1L2s8hIOhbPp8PWZY3aVRXJq64OBUMBvnIRz7C5OQkX/3qV5OamAkDOnt1VYg4iB6szs7ODZfIFuPLpBofW7h9iKysbwOgaR6ef/4/yM0ttOZ+MacqypM4HH+BpmnMzrYD/y+J70cAVX0E08xB19/GaopSqvolHI4H0PWXE4k8AsSee3uVo7PzDmS5H1gEsolGq+nr+3/Mzwct7w17Ysk+dzgcr0KS5olEnsEuhmIYhmVEl+ibsREwTZOZmRm6u7utLHxiT8iFJpbs3h8X0muRjGIsfKKE0Z6QW74YYi1C9CedcxAUw7m5OavHVpjDJt7ngrYlZN8LCwuvWCGTzc1GEpimycMPP8zXv/51HnzwQYqKipiZmSE7OztOycIuj2v/woVnRm1t7UXJTIvF9NLSUpxzpWiMtm+CvN5fcNVVn7PeGw5/FsO4e8UxFeURHI77ARNd/33OnbuPU6dOUVVVRWFhNa9/vY8vfOEDvOIV+4lG70fXb1t+5xIu1xuQ5Z7lqsS7l38fwu3eiiQtcPLkTvbseZJ77smjuFjinnui3HBDmECgG5/PR0FBlO3b34HDMY8kmYCMpm0nGv0PID0n0GQQdJtMXFeTIZnDaVZWFrm5ufj9fquH4GKUxEUVQsgtr3YO61EIEfKAFxos0jmHgoICGhoarGrK0tLSioZzIecrgkw6qlUQ44y/8MILlJaWpkVBW0u9SvByX0LY3GykxhUZpwCeeOIJPv3pT/P3f//3tLW1MT4+Tl5eHgsLC1afhZ22a38ulpaW6OrqsubHjY5TgvY0PT1NZ2enlTBJJYryqlfdgizHaEe6vp1IZE+So87gcr0VSerCNLfi8/0DJ06EKCoqWq4QLOFwfAJJWiQa/RymWWl77xlk+eSy0/j55I3HU4NpykgShEJPYppXWX8T/S6zs4PU1z9PXd2DSJKBLEcxjHwWFh7H6bzxgq6d2NSIjdl6F9O6rsfN/XYDQrHpbGtruyhy7qIKIUxf15o77SqWiRTj/Pz8FdU1IRIyOTkZRwHeSIh+EY/HQ1NTkyV/GwgErDhlbzgXccrpdFpxSmw4EuN0IBCgp6cHiG3ENzcbK3FFTuLd3d38+Mc/5qmnnmLPnj1UV1dz55138od/+Ifk5eWlxQsUzeOmadLW1nZRboizZ8/S29tLXl4euq6vcOHOy8sjJyd/eQEfQzB4Gkhs4DNwOP4EVf0RppmLpn0YTfsIuq5z111Bjh934fPplJWNUF5+loce6qew8E8AkOWncDrfu3wcL6HQYeuoknQcRfkJDz30R3z4w62oqgZImKbM3/zNcW66KYrT6WR6epraWg9btnwURdlPrMIB4CIc/jcM43+s+xqJzMvs7OyKpuJkSMULtU8uiX02i4uLdHd3W4vpi6FrPzIywpkzZ6zsCCT337Bn2MSCPR2IYCG0+Tf6fo1EIgwODnLmzBmrTJ+Tk2Nd08T+FTs0TcMwDGtiTfa6paUl+vv7KSsrY3BwkJqamlUrf7quc+DAAa6//vqkf9/cbFxSuCLj1PDwME8++SS/+tWv+OUvf0l5eTlvfetbeec735m2E7Po11taWqKzs/OiuBELxT1RJbd7WZ2vBHwbj+fPrPdEo+9F07604lixqsY9gBvTrCccftKSZ52ZmWHnzj1kZ3+JWNLsjUSjDyy/cx63+xXAEqbZRjj8E+uYTufbUZTfYJoFLCx8i2h0D9PT2zl3zoOmaWRnZ+PxeJienmLHjn+kqOhJYpV8g1hvyafQtD+/oGtkmiajo6NMTEykRT+zKy76fD4WFxfjvCKS9dkEAgFrjr8Y/kuCQjcyMkJjY6Pl/ZGp/8ZqsHtztLS0XJB/STJEo1GGh4cZHx+3qkGrNZzbIfo5UqlWQWzNdvz4cbZs2bJm8lBgc7NxGePZZ59laGiIa6+9lrq6Ou699156enr42te+lrF5jvA6aG1tvaCMQiqfiJycHBYXF1FVlc7OzhU3ntudiyTFFu92zqgdknQCl+tWhBlRKHQKUUp+4QWJ973PJBDwoesmn/rUf/HmN78OSRJZ/Cnc7puBeTTtHWjaX6+oBDgcx/jqV71873sx3uwnP+njnnsc1mcIfunS0gS7dn0Bp/NX2G+9aPQtaNq3133t4DxHNrFCkCzDsh5eqGmajIyMMDk5Gbch2CiYpmnJLZumiSzLK4LLhVK37N4cF9IvksrcT2Sszp07h6ZptLa2pk0PW0sqd2FhgeHhYbZt24amaQwODjIzM5MykxeNRjly5AjXXntt0s/b3GxcUrgi49TRo0c5ePAg1157La2trXzxi1/kpz/9Kd/4xjeor6/P6FgzMzP09vbGLRLXg1TN5rm5uQQCAXRdZ+vWrSuail2uImQ5ZP0cDB4HGhOPjtP5FhTlvwHncu/Ee62/LiwsMD39TdraHkRRZDTtA2habAMjST38f/bOPD7K8mr/32f2yb6QsCQQEgLZIKDsi9Vqfa3FWneptYCiIrWiVq1al2KtikrdRaX+3uprtWK1Vl+ltn2luLCEAAKShCysCSRknSyT2ef5/TG5H55JZsJMMlHQuT6ffiqTmXnu2e5zn3Ouc11G4/mA1COpexCPx9uzzhZcrp20t8cyc+Yv0GodQDxdXTswGI4Xp8Qshyy/QWHhE6jlbb3e6Tgc/0bEtIGiu7ubiooKYmNjyc3NVfagQMZ+/Q1xB4N6+Hqwn3UwiOKbw+FAr9f7dddFDBhsF625uZmampqQHcIDIVjCJt5PkRjl5eWFTHE7UZxyOp3s3LmTtLQ0pUtzos8gmmx8x7Bu3Truvvtufv/73/ODH/wgrMeGy1/tLT0nTP3U6iC9f7BCKSkvL8/voCtJ/+6R7wO7fT3QV61Hp7sHvX41AB7PXJzO41UfhwN+8IPD6HTdtLUlcM89DVx88cSeSoQbWW6gq0uD1XqA5uYEXK795Ob+N5CJ1XoXiYlpmEwyZ5zRTF7eV9TXZ5GWdoS1a3+D3f4pcHwz99Ge9nD66c+TkPBBgPexARg4VUnw+5uamoiNjcXpdA64whIMNpuNiooKTCbToDoEPk+TDr+WuKDueb1ejh07xtixY4eEpiccwsVGe6JukFp+0GKxKLzw/hI2i8VCVVVVyFK8AsEMAdvb26mrq6OoqEi5r+jW6HQ68vLy/KpgYlA22EB5NNk4pRCNUz3YsmULN954I7feeitXXHFFWI91Op2Ul5crKjon+k0Gou2eyCdCJDU5OTkMHz5cud1gOB2ttrLneQMXxeAAZvNcZNkEaLDbtwHHOwCSVIFOt4r2dhfHjk1hxIgbMJuFWaoX+DU63b84evQqDh78PpIk5uySemZCbMTEFCO+Tk7nQ8jyJLxe/zmwrq4u6utfY9Kke5Akj+ovGmy2j4HAohahQhTf6uvriY2NVYQ81NX1wVb0nU4nlZWVeL1e8vPzB9zRUgujqIf4ExMT0Wg0NDQ0DJlZn1rGNhQKsIipYq2CDi2SoEAJm6BWxcTEhNUNUscpYXqr0Wiw2+2UlZUxdepU7HY7VVVVuFyufucyo8nGdxANDQ0sXryYgoICfvvb34Z1MBX81ebmZoqKivzMj3rzVwMd1kI5jAktclEVCe0A58BkGo8kteGrFr2gmscAlws++OBeLr74f/n88xnExCzFbrcTH2+ksPAWYmIO0d4+h9bWp0hMTCQ1dRk63Sf4VDsex+O5yncVhwejsR2TqRiXS4PB4MTjOR+XazmyfJpyPbHRJiWtZMyYt/qs1mYLFIT6oj8lK5PJRFNTU4+84biI057UHYJQqkfBXMNFYpmUlNSHEhEps77+EMibQ50Ii2qQWn4wKSkp5MAlXI6FWV9aWtqA1ED0ej3t7e3U19dTWNjXq6WpqYnq6mpGjhxJVlYWGo1GSQpPP/30AM8eTTZOMUTjlArt7e3ceOON6PV6Vq1aFZZQiVB5q6uro7Cw0K+iq5ZI7ejoCEjbDeU343K5qKioQKPRqPypHBgMFwAtOJ3/Bvp6Den1v0Gnewlw9Qxzr0P9szAaf4gkVQJ6GhufZNcuX7Kj0zURH1+K0zkNk6mgZ6/ah9n8M0CHw/EOspwP+FzAdbrXATcazX5Awm7/0C9Gifepvv5fZGdfgUbj9vubyzUDt/s/obzdAH3m7DweD/Hx8cTExNDS0oLJZArZxytcCH+UMWPGMGrUqBO60ou1CtVFEVMFHbZ3d12dEAzFEDwcLyqpEwIR/9WzFiKmirWGQjMUr1vE81DeJzXU1CqdTofT6WTv3r2cdtrx71NbWxt79+4lNTW1j1z8t2m2MJpshAmv18uTTz7Ju+++y8svv0xubm5Yj29tbaW8vJz4+HjlB9HfEF+4ENxPcfA60dCyRvMuRuMifB+1EZvtMKLb4OPKP0lc3JN4vVoOHbqcxsaFxMXF43TuYfLkm9HpYpAkKzbbIUCHXn8zOt27+Dbxl5HlKchyMuCrxPgCxh/xeWto8c1l/A2v178a5Bsavq+nS3JcpzxYsiFM8dRD3P0ZJqk5soOluAWDMKHzeDx+lfVAaz1RhSUYLBYLlZWVilJHpBWlRFLT2NioKG/FxsYqG/Zg5QfBX7c9Ly8vZBdidcu6o6NDmcsJdt+DBw/S0NDA+PHjiYmJobq6milTpgS8v06ni3gSOkhEk43giMapXpBlmddee41nn32W5557zu9wEwo6OjrYs2cPZrMZrVar0HbVicVgq+uCzhMa7dSDyZSDJLUAWpzOp/F4fHODvtm1NhITL8NorMLjMVBe/hha7el0dVmYMmURZnM3EIvdvhWIRa9fjlb7ds/jr8Pl8pfPNRrnoNFU4ZsxjMXjWYDL9Si9f4ZWayUpKTP6JBw2W1ef+x5fq/+cnVoiPTEx0S+pUB90B2uYGAzCjK6rq8uvcCXWKg7sYuB8IK7hwnsiPj4+pAHygbyGgwcPcuTIESVOmc1mv7UOdj8X71NHRwf5+fkhC8Ko45TD4eDgwYN94o44j9TW1voZJn6bZgujycYAsX37dq6//nqWLl3K1VdfHTDTVcu5dXR0KC7c8fHxCpd14sSJg6buBILVaqWsrEw5hAbLxI3GqWg0Phd0tzuHAwf+oVC3NBoNs2f/DEnSoNV6cTrfQpan0toKO3e6OPvsC9HpvsThuAStdrW4Mjrdm8jycCRpB3r9GmQ5Fbt9A8erVfsxm2cDNnyDdmZcrltwu32GgAIej4eurqWMGPGXnvczAYejPuBMgEaj6WPuE0r1QVS4RVUk0j9cUb0/cOAAZrMZj8ejDJyrOwGDlR8UHiwTJkwYcOLUW3lDvK/CI6apqUmpRkZ6MA98FZ6qqipFe76/4KDuBgkJwoyMDLKyspAkKWgCJIwTnU4nOp0uaGcjmmycUojGqSCorq5m0aJF/PjHP+bmm28O+LsIRtuNj4/HbrfjdruZNGnSkBilClpJfHw8ubm5QX+3kvRPTKZLetZr4ODB9bS1mRTH8Pz81xg27GO0WhdO5+N4vT/teaQNozEPt9uLVqvB4diKJI1Aq/1fDIZfIssSTufLSFI7sjwKr3cecNxPSqMpxSfpLuN2X4bL9QfU8rW+9Vgxm9ORJHVRrB1Z1vbpWMPAJNJFRVySJPLy8iJ+ZhC+EFVVVcph/UQD5wO5hvBfGkziFIi1APQI4sTR1taGw+EYEtl78M2kVFZWKspb/XWcRDdIyLlbLBaGDRumMCp6f99FgdJmsymGiTt27PhWzBZGk41BoKuri5tvvpnu7m6efPJJjh07hl6vV7iBPmOfOCWz7l0FDjZnESl4vV7F2Ke3q7avHdrM2LHHDY0aGi6jvf0hpWKt023AaFwA2JHlXOz2DXR3J7B2bTNr1nTxn/9cBPyWvXuziY3toLDw2R4TpWeBEZhMhUhSO6DF4Xgar/dsBMdWo/kYg2E5klSPT9VDg9c7C4fjfXobBTY3N1FVtROj0fceOZ1OPxOqSJj7CBWNCRMmkJrat4UfKtTzC+pBvvj4eDo7OxXn2KHYBIXaSKjzIsE6LOpqUO/3taWlherqakaMGDEoOeFgULucC2oV+FfZLBZLwG6QeHx/aiACtbW1VFVVMWbMmIByk9Fk45RCNE71A6fTyX333ceuXbt48cUX6e7uxu12I0lSSLTd1tZWKisrh2ygWIhrNDQ09OnGC+pWevrZmEz7e24bxr59n/pVrI3GWT2xxIDL9Qwez48AkKS9aLV/QaPZSG3tmTQ0XEhBwTgMBglJaga06HQr0eneAzQ4HG/i9Z4hVobJNAtJqgYcgBZZzsHh+DuyPLbP6zAaE9BoPLS0zGDv3j8oNDN1d32we4owfA3HLDUQgs0vJCQk0N3drXQ5hkIK3eFwUFVVhdfrDalwpe4GWSwWv/lF8b/e72sgCnAkoR60V4upqH2tLBaLohApYqrwtzqRpLuQ7Y+Li6Orqyuof1Q02RhiPPXUU7zyyitIksSkSZP405/+RH19PQsWLKC1tZXTTz+d119/HYPBgMPhYOHChWzfvp3U1FTWrl0btlJHMGzYsIHPP/+c9957T5F7e+CBByguLu7jvREMorKTkJDAuHHjIn54A98BsaKigoSEBLxer0LdmjJlMbGx+5T72Wz/B8xW/m0wXIRWuxFZ1uF238qePXfz858bsVo7gXa0Wi/z55fx6KNn4XTeQlzcX9BqtUpbWqd7Er3+cWQ5pWcmRIPD8ZaymUtSGUbjfCSpSbmmLJtpaXmPlpYsxX/BYDAQHx+PzWbD4XAwceLEkKk24UBUvfV6PRMmTDjh5xdMJrd3h0UNsYkMFe1JlmWOHTvGgQMHGDt2LCNGjECSpH6VN9RzIaFAeHM0NzeTl5c3JKZg7e3tVFVVYbfbFQlCwbcNNMMicCI1EAGLxUJdXR3x8fHKzIg6eEeTjVMK0TjVD7Zu3cr69ev58MMPKS8vZ+zYsdxxxx3MnTs3ZNqu0+mkoqICvV5PXl7ekPw2glO34ikuPk4Dc7svwOVaq/xbq/0IvX4ZktSO11vUowoV26OyeBE+GdyLcblW0dq6hdTUn2IwuHC51uD1/gijcX5PB0OPy7UEWZ6Cx3Mhvo5GO3r9Heh07+AzqAXQY7f/kY6OH/r5BImOtdPpVAwTh8J7yeVy+Q0Wn2jfPtHeH4gJYLVaqaioUDpOQ/F5C0WpjIwMxb072FyIetYi1A5LsMJVJGG1WqmsrFTOKYAfayFY5yqUOCXmfGtqaigsLAw4KxJNNoYQR44cYd68eZSXl2M2m7niiiv40Y9+xLp167jkkktYsGABN954I5MnT2bZsmWsXr2a3bt389JLL/HWW2/x3nvvsXbt2hNfKAS8/PLLpKSkMGPGDLxeL4sWLeLss8/m9ttvD+vHKST1Ghsb/cyPBoJALXFJkoiLi6O7uxtJkpg4cSJGoxGz2f86NlsngsYkSbUYjTORpA5Ah92+EVku4m9/k7n/fhsej43Cwipee62D+Pjz0Gr/H3r9g7jdburrlzNs2IVIUgoQh15/FzrdW4CM11uE230DHs/VyDI4HHXEx5+NTnfUby2dnT/Abn+jj+qWMEwUrtdDYUQlDuu9K3nqAcneFcGkpKR+vSLUUNOehqp6ZLPZ2Lt3L1arFZPJhMvlGvBcSDCIjdZkMikmSANBIB12o9FIUlISWq2W+vr6sN1j1WoggYyWWltbOXbsGAUFBTgcDqqrqxVzxri4uGiycWohGqf6wRtvvAHAjBkzSExM5Nprr2Xs2LE89NBDYakQiQ5wbW1tn+HxcBFIyUqWZeLi4nA4HLhcLiZNmkRMTAx6/TR0ugrlsQ7HKyqaFBgMV6HVfoQsm/B4ru6hOglPjlsBb4+3xj/R6V5Ar38Itxu6uqag0fwvBkMVBsMdgAaN5ksA3O4luFwP9VzBiUZzEwbDW4qMPEBb2xyamv47oE9QV1cXFRUVSmV9qIqIojurPogK6XmRCA1GJleIBYwfP35QHf9gEKpYFouFmJgYnE7ngOdCgkGoPsmyPCgKcG8j2s7OTkXN0mAwUF9fT1JSUlgzKSJOybKsxBz1d0VIIcfExCizIurzwqkUp07JZGPWrFns2rWLhIQELrroIm6++WZ+9rOf0dDQgE6nY/PmzaxYsYJ//vOfnHfeeaxYsYLZs2fjdrsZMWIETU1NET+kgq81+eCDD/LFF1+wZs0aMjIywnp8R0cH5eXljB49OmTFA7VPREdHh9+hMtDGItqwubm5jB6diyT5Btu83kQcjuMHfq329xgMKwEZWR6J3V4NSLz77hYefjiTceMO0NyczSefZKLTbUKnW4Usj8Lj+TEdHZtJTn4JjcaA0/kRktSF0XgF0IksS8iyjiNHfkpNzWUYjfEkJRkYP/4K9PqDfq/N6zXgcLT1ec1Csaq9vZ3CwsIh63KUl5fjdDoxm83YbDY/+cGkpKRB82bDdV0NBjWHVVSDRCdAr9fT2NjI8OHDh7yTEqo3h9PpVDZsi8WiqK8E02EfjHtsMKOl5uZmxTVdQPiYJCUlDdlcyiAQTTaCIxqnwoAsyzz77LO8+eabvPjii+Tn54f1eDEPmJ6ersxInQiBfCJEnBKS7ur9r62tjcrKSrKyssjOzkV9CZutHV/XAaAZs7kQH81Jg92+AVmeDHRjMCzsMesbjtO5Bq93Zk83/SdIkpPGxvtoaalj+PAxmM3XoNO9jF7/IODC6zVitxdQUfFb2tsNGI0Gxo9/jfT0/9fntdls5UBWn9uFGWtjYyMFBQVD1uWoqKigq6tLKSieqLseLkTHX6fTMWHChEHFPTG/IL4HaoXIxsZGUlJShkQhEo5TgEeOHMno0aNPGAt7S7oL+rborveWdVbTsdWsglAQTNK9q6uLffv2MXnyZMW/xGw2K59DNNkYYjzzzDPce++9mM1m/uu//otnnnmGWbNmUVNTA/j42Oeffz579uxh4sSJfPzxx2RmZgIwbtw4SkpKhsTuXuDTTz9l+fLl3HPPPVx44YVhPdbj8VBZWYnL5aKwsNCPyhPM2C9cnwihpx4T08nEibciy6m4XB+jHs42mbJ6OK3gdv8cl+slAOrr7wc+ZMyYRjZt+g3Tpt2EVjsFg6ER0ONwfIDBcBeS9CUej0xd3UI6O8+hrS2OPXu+QKdr4oYbXkaWTXi9xTid7+Gb4/BiNE5XhtWPD9pZCGaUJA6HkehyBDL3i4uLQ6vV0tLSQk5OzpB4WqiH5saPHx/S99Lj8fitVXBYgylvCCWmpqamIaM9CW8OwfUVcptqqpnFYqGrqwudTufHYQ3HPba6uhqv18uECRNCHlgN1LJubm6mvb2d8ePH+91XfB5JSUlDUskbBKLJRnBE49QAsGvXLq699lquvfZaFi9eHNbe5vV6FWWeoqIivwNtIGM/UagRFJNQDsBut5vKykrGj7+WxMQ9AHg8GTidVcp9fFTdBwCQ5RTs9hrA0EOtuhlfsWw0DsdnaLXvYDDcjtebhcPxKjrdWnS6Z/F4ZA4evIGurh+TkbGCpKSbmPfQAAAgAElEQVSd6HQ+NSmPpxC3+yVkuRgArXY1BsOdyvXVA+HHEyB/RLLL0bu7Lg7Aer2elpYWMjMzGTNmzJAkqWLGNNSDdLD5BfV5pbdCZF1dHUeOHAm7qBQqglGAe1PNOjo6lKRNxKpQu4BqWfpQfKoEAsWprq4uxaxWrLOhoYH9+/czevRosrOzozSqoUJbWxuXXnopa9euJSkpicsvv5xLL72UBx980G8T/9GPfsRXX31FUVER//znP/028a1btw75QaK1tZXrrruO1NRUHn300bCr78eOHaOmpob09HTcbjednZ0Kd1Fs2oNx4lS3SAsKCvxac5K0HZPpe8q/7fZ/IMvfQ5L2YTSehRiWs9ur+Mtf4igtfZ/Vq29Elo1UVb2KRrOJ7OwX8Hji0Onaqa0dxRdfrOR3v5uPx9PCE0/cyrRpW8jKqgUSsdv/gyxPAMBozESjOd7NCCYhKDCQLkcg2k5/SZvL5aK6ulpRuBiKirfD4WDv3r2K2pO4vlrNIpCiSX/zC72hbsmGY1AUDlpbW6moqMBgMKDRaPyG+cOhmp3oGlVVVQwfPlzxzQgF6upRS0sLNpstqHT1iQbMvwFEk43giMapAaK7u5tbb72V5uZmnnvuubCV7FpaWhSPAEmSFGGU/oz9wkVjYyMdHWsYOXIksbFL/P5mMmUjSY0AuN3zcbl8crZ6/fU91F0jbve1uFyPYzTORZIOI8sSR47ci063kfT0jwANLS1n0dqaTXz8jaSnv4Ne/yQgaMVGHI438HrPA4Rc/EIApeNis/0dWT436GsQ1NmmpqaQuxxer9dPdVGdtIn/qQ/A6lg4VEpM6lgo1JIEes9aqA0e+5tf6A1BewLIy8sbsOFgf+jo6KCiwkfN0+v1fp22pKSkgOIoA7lGZWUliYmJffwz+oM66bBarTQ0NDBx4kS/+7jd7h4/siRGjx49qHVGGEE/4JMqJQoF//d//0d2drYy7HPJJZewadMmLBYLbrcbnU5HXV0do0aNAiAzM5Pa2loyMzMV9Z2hUH7qjZSUFN59913WrFnDD3/4Q1avXt3nC6NGoIpFTEwMjY2NJCQkcNppp0X0cChJEpmZmaSkpFBWVuZXddHrf+F3X1n2yQFqte8iSRYAdu/+L154Qcf27YdpaZnLXXetID8/l6uuehSjcU+PCdJ56HQPUVo6jeuv/1GPNOBwrrrqz7zyyrX8/Od/BiyYTKfhdD6Mx3MrDkcdOt0V6HSf4XA8z4nOWFqtlvHjx2OxWNi9e3fALkfvTVAdDHNyck6YtOn1egoLC2lpaWHnzp1kZmaSkZER0eqR0Whk8uTJNDQ0sHXrVr9hfqG8kZ6ezvjx4wfcNo2JieH000+noaGBbdu2kZOTQ3p6+qASVmGcpB48HDZsGG63G4vFwoQJEyI+mCfmpA4fPszWrVtD5hOLSltbWxvNzc1kZGTg9XpPtqQiim8BTpU4FRMTw5o1a3jnnXeYP38+q1atCmogBsfnAXr7GbW1tWE0Gpk0aVLEizHp6ekkJt7ZM/+yV7UHliuJBoDHc3XPfzWj032ALCcgy14OH15AR8dGsrIgIcGO1xuPRjOBhAQZMfSdnv4ZaWmf0ta2g4qK1UyYkIbJdCc+eXYbRuMlPXMcz+D1XorNJmM2L0LUakWMDAaNRqN8H4J1OdT0UnV3PTExkTFjxpywUCNiYXt7O3v27FFobpHc30QsbG5uZseOHUoiKebsEhMTGTZsGDk5OQM+r5hMJoqLi2lqamLHjh2MHj160PFWLT+rHjqXJImWlhbGjh0blllfKEhISGDatGkcOXKE0tLSkBXEZFlWZm6am5tJTk5WJPMFBKXtJOtq9ItTrrNRUlLCtddeS2lpKWazmcWLFzNt2jQ+++wzLr30UmXwrri4mF/84he88MILfPXVV8rg3d/+9jfefvvtr3XN5eXlXHPNNVx55ZUsXboUm82G3W5X6CVCyUCtaS2y+UgOjweDoNm0tLRQWDieYcOOz5rIsh6LpYH29lYyMmaj1zfj9eooLb2P5cvvpLa2A53OTVxcN6tXH+Gss34CGAAndvsbGI23IElHue++3/LEE7cDEtdf38nTTy9Dp3u71zrScDj24TP7Cx8ej4eamhra2tpITU2lu7vbzzRJVCwGk7S53W5qamqwWq0UFBQMal4kmPJGXFwcNpsNWZb7uM1HCmojvd4VqmAIJkEoOKy954OExKEwNRwqnf5Aw3+99c3FHEvv70EwNZBoZ+OUQjRORQC1tbUsXLiQOXPmcNddd/X4HHUp9BK14lJvFSMhBXr48OE+nfJIobe7+bBh56DVlit/98UpGwbDHxgx4kUkyYvdPppDh9aRlXUnJtNGfIqIL6LXP4EkHQD0OJ2PYjTeji+x0OB0jmTnznuZMMFJUtLNgFu1hpHY7TvweW10odG8jdd7BcIINxSoDUbT0tKUs8BAKNH9XePAgQO0tLREZF7E4XD4JUJC1t/lcily7kPhDu7xePyM9EKhJAnmglivEElR7/3qQ7qgAFut1rBoT+HA6XRSU1Oj+Geoz3HqM4CYY1HLJRsMBmRZDhinojMbQ4zf/va3rF27Fp1Ox2mnncYrr7zCkSNHFEnB3Nxcjh49ikajwev1UlFRobSbzGYzjY2NjB07lrfffpvk5GRkWeaWW25h3bp1xMTE8OqrrwY1+woXsixTU1PDF198wdNPP01bWxsxMTE8/fTTjBs3TmkznyjbFcPjQ1FVV1/DYnmAvLw/Kre1t4+jrOw1Ro6sJDv7OnxfCQ02WwVvv13NnXfORJK8XHLJRp5+2oHBcBuS1IbXm4tGcwhZTsTjOYuLL74Kt1tCp/Nit8ewZo2bzMwP0euf7bMOm60NX8LSP4Id1o1GI52dnYwcOZKcnJwhOTQOZF5EzNyITaW3BnewIcmRI0cOGQ9XUJIC+WYE2gTV9K1QJQiH2psDfAPflZWVxMTEoNFo/N5bEWACbcpqapVWq1XUQKLJximFaJyKAA4fPsymTZt49tlnOXz4MGazmQcffJCpU6cGVFwKhO7ubsrKyhRjzqHYs3wD6rs588wfKLfJMmzc+BmJiQkUFp6HVnsM0OBy3YXb/WtMpuk9FKp4nM41GI03AC5Ah8t1D5K0A612C5J0BPDi8QynvHw5cXGpZGffiDrhALDbf48s3xbymsVeqj6sm0wmurq6SE1NZcKECUNyaOzq6qK8vJyUlJSQY2EwmrF6zk5dsOvs7PTr1gyVLHJlZSXJycl9rhFMcESsN1S6ufDmSE5ODui9FAlYLBaFZqzX6/06QmK9gboVwaRyT6U4dUomG+HA4/GQkZFBSUkJL7zwAikpKdx9992sXLmStrY2HnvsMdatW8dzzz3HunXrKCkp4ZZbbqGkpCRia1iyZAl5eXnMnDmThoYGHnnkER555BG+//3vh/1aqqqqlErCYNWQ1ENRYh7AaDzE7NnXKPfp7j6KJCViMPwXWu3GnselYrcf4tVXn8LtbiQ39xDvv383w4d/xooVv0eStMhyGpLUgG/Ibh5Wq5n4+A8BJ1ZrLLGxTsrLf0d2toWYmMeA48N2bvccXK5/B3z9akk/m80W9LAuKiKdnZ2D7kAEg+DIWiwWRTJVjWDKG2K9oXBYPR4PBw4coLW1dcgUTYT5Y2NjI6mpqTgcjpA3wXCuIYbUB+NyLuByuZSuhZpyIKqx+fn5YfHde6uBmEymU2YTjyIapyKB22+/neTkZGbOnInb7eaee+7hjjvu4JJLLgnrefozkx0IAs2tGQy1zJ69ULmPxzMCp3MfkrQTk2luz61a7PbNaLWbeobIbXi90/B4LkanewhJ0uLx5KHV7gUkXK4b0etXAx34fm4aGhp+RW1tHtOnB0o4ViLLNwd8/WqqmXov7X1YH8gsR7gQqljHjh0LKLWuFkcJRR0wEIQxY319PXl5eYPe3/u7Rl1dHampqcosq16vV2JUUlLSoJgLYki9rq5uUC7nAoISKWKVmGEUVKnc3NywzBm9Xq9iyHmqxalvfbLxr3/9iwcffJCNGzeSl5fHhg0bGDlyJPX19Zx11llUVlaydOlSzjrrLH76U592t/p+Q4H6+noWLVpEcXEx999/f9g/DiFfG67bdTB3a2GYI2gwkrQRj+dZdu68lMzMs0hPT8NsPn6QdrsvxO2+G5Pp+8iyi507T2fDhldYtSqNl166iby8o+TmdqPRVAIefLQoIx7PWWi1H6H+WtXXn41ON5m0tKeU25zO5bjdjyjzACLACDlXsWGHUlmPpGJVMIiuk1iT6FoIr4hAyhvhQsjeJSUlRaTqImaE1Id1k8mE1WolISGB/Pz8IRkgFy7nRqOR8ePHh5Qwi6RYPRui1WqVrkVvyoHNZqOqqgpJkpgwYUJIHHJZlmlvb6ekpIQtW7YwefJkrrrqqkG91ggjmmwERzRODQEsFgtLly7FbDbzxBNPhE3hbWtrY+/evWRnZzNixIiQHyfcrcX+JOZC1NQSnU6DyZSAJPk++urqEjIyijAaf4RW+1nPM8Vhs9VjNJ6JRrMD0OF2L0OnexNfXNLg8RSi1W7Hp1qVisczE53uY6BbWY/TOZNt225l9uyrkCRZKYp5vYnY7Uf6dC28Xq9fnArlsP51+HJYrVbKy8sxmUwkJCQoSmFq+tZgD+s2m42KigpMJhPjx48fdAxRe4aIw7qQojeZTBQWFg7JAHm4LudwYupuUlKS31oFldnhcJCXlxfS70v4qJWWlrJlyxaGDx/OL3/5y0G91gjju5tsXHvttZx++un88pe/JCkpCYvFovwtOTmZtrY2LrjgAu6++27mzfMNeZ1zzjk89thjTJs2bcjW5fV6eeKJJ3j//fdZs2YNOTk5YT3e4XBQVlZGXFwcubm5fTYnr9fr5249EPlBoeGdmfkCGRl/Vl37VbTa9eh0/0NbWxLnnvsphw6NwWi04nQamTNnB3//+0WI2Q1fu9pNTU0Rra0zmDHjVdRfLa9XS3X1EsaPf42urmns2fMYdrvdzy8kGA0mFAxFlyPQ0Dn4AqUYih4Kmdza2lqOHj3KhAkTQh4gFRuU2oxIfBd6H9YF9/rQoUN9TA0j+ToaGxvZv39/H0MqCNzBiomJ8ZsNCSUYC4faQPQtUe3bvHkzJSUl7NixA71ez4wZM5g7dy5nnXXWkLz2QSCabARHNE4NEWRZ5r//+79ZvXo1zz33HFOmTAnr8S6Xi7179yJJEvn5+X0KLr1NaDs6OtBoNP26Wx+HB9iPx5NFVdU+HA4Hs2fPVf7q9c7C4XgFs3ky4MUnzf4iRuMyfF2KVGRZhyS19zwX+BKQs9Bqv8CnRiXWaaay8lXy869UbqutvZrq6sX9yrmGg6HocgQqKkmShMPhICcnZ0go2UKe9eDBg2HFkEBO5xqNxs89XH1mEUXXQDEkUuiPAqyeDVFL+56IutsbFouFqqqqgDQ04WO1efNmNm/ezLZt2/B6vUydOpW5c+dy5plnnjJqVN/qZMPpdDJq1CjKysoYPnx40E18/vz53HPPPX6b+OOPP87UqVOHfI2lpaXccMMN3HTTTWFXUkVb8dixY4wbN05p2akHuMQGOFDJUVmWMZvjepkqNWM2ZwJ2APbuvZWzz74LWYaUlFbef38hubk7Oe7boQFcXH31a+zbl8vGjVeh0Rzoc60NG54jKWkuY8aMCVnONRwMtMsRzOE60NC5qFAJ3udQVKiEO7joDvSuHqm/BxaLRelgiQ07lMO60+mkqqoKt9tNfn7+kMj9ut1u9u3bR3t7O8OHD1fa+aIyKNYb6mxIIHg8Hg4dOsQDDzzAvHnzcLvdlJSUUFVVRWZmJnPmzGHevHnMmDFjSMQXIohoshEc0Tg1xKisrGTRokVccskl/OIXvwhrXxOHT1HAAPr4RKhNaAe6Zzocy0lKOm6653Tei0bTgk73Us860vB689FqSwAJrzcLjaYOX1cjvifpcAB6ZDkTaESSrH7XaGiYTVvbOGAmaWlXhiznGg4G2uUIJJWr1+sDDp2LDoTZbGb8+PFDomokYogQCOkdQ4IVlURcDcXpXMQQUUgcij1ceHM0NTUxYsQIhSHidrsVWnQ40r6BIOhbDz/8sNLlKCkpoby8nGHDhilxatasWYqK1kmK72ay8f777/PCCy/wr3/9C+CkaU/3RmdnJzfddBMul4unnnrqhKoOalUgsak4HA6Sk5MZPXp0nwGuwcJsPv4DlmWw2/+M2Xy1ctu///0Iv/rVj7noovd45ZUbGDPmMFu2zEGSPEAMf/7zJSxbthpZltBovHi9Gu6663Huv/93ftfxepPZtu0fAENG4wmlyxFIeSOcgTM1fzU/P39ITPREAD9w4ACZmZnodDo/upm6azGYREFUdiJFQ+tt8ieqVw6Hg8TExIg4dws+bElJCZs3b2br1q10d3fT3NxMamoqK1eu5MwzzzzZVDxOhJM2upwEiMaprwEOh4Pf/OY3lJeXs3r1aoYPH97v/dVFGtFRdTgcxMXFkZWVFZZRWigwmWL9imKdnQeIiytEkmwAeDzz0Wr/g48ipUWWk5CkVnzFMBPgQpa9SJK7Z/16ZFmLVmv3u47T+UPKyh6mu7ubwsLCISnEhNLl6C2X73K5wiowqrvYoRrKDgTNzc1Kd0DQjNWCI5EoKonh7tTUVMaOHTvovT0QdVeSJMWSoLei1GCusW3bNrZs2cKWLVtobm6mu7sbjUbDI488wvz5808peVu+q8nGggULOO+887jmGt/A85133klqaqoyeNfa2srjjz/ORx99xPPPP68M3i1fvpytW7d+7et94403WLVqFU8//TTTp08H+g5x9zb3E4PGXq+X6upq7HZ7RIbH1dBonsFo/A2yDPv2vUtGxlLM5mbl73b7CHbtGs1DDz3A3r0FtLUlcdppX3LeeR9z551/wOnUc+ONL/HXv16B263jzDM38M47lxEX5181crvPweX6QHEqHcoNUHQ5Ro4cSVJSkl/i1p/yRjgQJnqC6haJw21vRSur1aro9o8bN45hw4ZF/BCtHlLPz88PS+JQ8K/Fpi0OG71N/tRO6uHSt9SUqC1btrBjxw6MRqNCiZo3b57iePvhhx+ye/dufvOb3wzkrfgmEU02giMap75GfPzxx9x111387ne/49xzfUZ2amVAUfQINGgsyzIHDhygra0tIsPjaphMaUhSd896YMeOVUydeofyd5frZ+j1b/T8y4wse5FlGUlyI8saNBrf/0uS1+95ZVnbUzgT/9Zgt3fS2tpKZWVlyI7aA4FaTSotLU3Z+3sb/CUlJQ045gtDWa1WS15eXkSKfL3ZAF1dXXg8HiRJUvwmIn2I9nq91NbWUl9fHxbNGEKn7gpa04EDB8Kmb4kCYW9K1LRp05Q4lZWVhSRJfPHFF3zwwQc8/vjjA307vil895KN7u5uRo8ezf79+xX1hZaWFq644goOHz7MmDFj+Otf/4pGo2HJkiWsX78eq9XK6NGjWbNmDStXruTgwYNfq/QgwM6dO/nZz35GVlYWbW1tLFmyhKKiopBnF5qamqipqQl7eDx02DGbjz+vj/PqprvbzO23P8l7712ERuPFYHDx5ps/Zc6czQAsXfoSa9cuwGSyMXFiGf/3f/6yhR7PD3C53lduczgcikRcJM1resvkdXf7gtPo0aNJS0sjLi4uokFDrQ0f7gYIfeVn1RQjtfxsS0uLQg3KzMwcssC3d+9e4uPjGTduXMDPRD0g197ergz1i/WeqAqopm8F8+ZwuVzs3r1bmbeoqqpi9OjRSqt5+vTpJzslaiCIJhvBEY1TX3Oc2r9/PwsWLCAhIQGHw8GFF17InDlzQp5dEMWerKysCHZmGjEaZwNuHI4d6PXT0emOKX/1eFLQalsBcLtN6HS+joUvmRAJxom/Si7XBbjda3v+26XsVwUFBREr8gmqjpoSJcsyGRkZDB8+fMC06GBQz9Hl5OScsGvVG4FmQwIpWrW3t7N3794hMRwUsNlsVFZWKsZ3gT6T3uyFcKm7avpWMP8Pj8dDeXm5UgQrLy8nLS3tVKJEDQTfvWQjVCxatIgzzjiD6667DqfTSXd3N4888sg3Ij04f/58Ojs7mT59OnV1dbS1tbF69WrFZTZUOBwOysvLiYmJiVhFHYTW80PExx/Pth0OA0ajz4X13XcvYenSNej1LtraknjrrQVcdJEvgfj883lUVuaxb18OZ5zxBT/60T/8nttmqwIy/G5TG0UN5KCuHpIX1ZVAxklfh2KV3W6noqIi6JyFer3qABOOIaHaAGmo+Ku9OxAmk0lJLnqbJyUmJg74u9fa2sru3bv5/PPPuf7665XkYuvWrbS1tTFp0iSlGlRQUHCqUaIGgm9VRIowonHqa5Zyr6ioYNq0aVgsFg4ePMjTTz9Nfn5+WM/jcrmorKwEiFhFHcTQeTtpaWpzWojEtm63v48s/6DP7aLINxBRjUACHlqt1m8fNRqNSpcjNTV1yBSr1HMW+fn5AWlu4QiOBIKgiDU2NgaU4o0EeouQJCQk+FF3I8VeEEqUH3/8MUuXLqWyslKhRDU0NJCfn6/EqcmTJ59qlKiBIJpsBEJHRweTJ09m//79fgfMb4oz6/V6/TaQ9evXc9ttt3Hfffcxf/78sJ5LKBfV19dTVFQ0IFdMdRdAVCtSUpqYPHlBwPv/+9/ncO+9D3PwYA5dXbHExFhJTOzg7bcvp7p6Ak89dRu1tWN48snbKC7eTWFhhfJYm+0wELgTY7fbKS8vJzY2tt/kKVB1pTdlJ1gi8XX4cqg5srm5ucogqFizWK/YBAfaZRH8VWGsFamgpK62tbW1Ka38zMxM0tLSQjZP6g8iEIlWc0VFBeXl5Zx77rlceeWVzJs3Lyxd8m8RvnMvOAxE49Q3GKe+/PJLlixZwvXXX8/ChQvD/m2KPXGg821qSXchj5qS0sSUKT9V7uPx6NFqXWE/d284nQ/g8dwV5G9O9u7di0aj6Td5CiZBH4qAx9fhywHHk6esrCzS0tL8KEbq9aqpsOHCarVSUVHRb6d8IFBTd1tbW+nq6kKSJDIyMkhPT49IV0jE8s2bN7Np0ybKysrYvXs3s2fP5uqrr2bevHlDZsR7kiPoC/7Wp1n9Yf/+/aSlpXHNNdewa9cupk6dyjPPPMOxY8eUjXnkyJE0NjYCcOTIET+ZsczMTI4cORKxTbz3D+Dss89m/fr1LFmyhE8++YSHH344ZI6rJEmMGTOGlJQUysrKGDlyZL9V+97DfOouQFJSEllZWUq1wm7/D1rtGjyeWEymV5TnOPfcT8jN3cf8+euQJBmLJYnLLnuHrq54li17EafTiEbjZcmS/+app25Vkg2X6zqCJRoAJpOJ0047jbq6OkpLSykoKCAhIaFPF0BdXVGvNxRotVomTJiAxWJh9+7dZGRkRJSOJKpBsiwTGxvLnj17kCSJESNGkJKSwpgxYyLWgk9MTGTatGkcOnSI0tJS8vLywg7isiwrPidC3lddbRPrFQPkOp1uQJ0Ul8vFrl27FEpUdXW1Qon6+c9/zvTp02lsbGT58uXExsaGpdcfRRTfBpzsceq0007j888/Z/ny5axfv55nnnkmrP1GzM2VlZUpDtHBDoNqgYlAXYCMjIweqqaMLEuKD0ckEg0Aj+eCoH8zGAwUFxfT0NDAtm3blG58b2lfsd7ExETVekODRqMhOzubtLS0IelyiH3f5XKRkJBAVVUVlZWVDB8+nNTU1LDX2x9iY2OZOnUqR44cobS0dMAzmv1Rd0eNGoXJZFLoWxqNZkCFV0GJ2rRpE1u2bKGiooL09HTmzJnDxRdfzGOPPUZ3dzd33nkndrudrKyssK/xbcd3urOxbds2Zs2axcaNG5k5cya33HILCQkJPPfccyeV9KAsy6xevZpXX32VF198kcLCwrAe7/F4qK6uxmazUVRUhMFg6GNEpB7mC7WqbjBM7XFfPQ6bzURmZh1ut46urniSk1uZNm0bU6duZ9WqO5BlDT//+f/w//7fdarHbAIm93st0bVobm6moaEBjUZDSkqKXxcgUhtuJLocgQaje1evmpqaFI5senr6kFRBrFYre/fuVbpCwapHwvVWrNdms2E2m/00w4O9v2on9fz8/KDVNrVxnqBEWSyWkChRsiwjy/LJ5pb6deI7VyILA9E4xTcfpwDWrl3Lo48+ypNPPsmsWbPCeqzX6+XgwYO0tLQwceJEzGZzQMWl2NjYPgITgSEDzUjSu5hMtw/6tdnt9yPLd/d7HyE73tLSwtGjRwFISkoiOTk5LI+gUBCJLoda2bL3vi9mRNvb25V5wKHw5QBfwlBZWakU/YIV3tRU43Cpu0JIpKGhoV+Xc1EYVKtENTQ0UFBQoMSp4uLifmNpNE4F+MN3OdloaGhg1qxZHDx4EIDPP/+clStXUlNTc1JKD+7Zs4drrrmGq6++muuuuy4sn4iOjg6OHj3KsWPHlCq0enZhYJzFbZjNZ/rd4vFoeOqp23jllSU0NIygszOB007bQWJiO19+eToOh5HRo+v4+99/Qn6+j69rs3Wh/o72lkftrbyRmJhIfX09jY2NQ9pKFrMcJ+pyqJ1DRVAMdTBa8Jb748gOFrIsc/ToUQ4fPkxubi5paWkKRU6s2ePxBBw8DwfC5by9vZ0ZM2YQFxfHwYMH/YzzTCYTM2fOZN68ecydO/e7SokaCKJvUnBE49RJFKcOHTrEwoULOfPMM7nzzjtDnqcSB8n6+nqOHj2KTqdTnMNDmQUIBq32fgyGJ8N+XG84HI/g9d6i/PtEJnQJCQm0trZSW1vb7+F2sAhnlqO34Iha2bK/fd/j8VBTU0NXV9eQ0YwBRYlSKHyJ5E2sV8j7ijg1EOqu8Klqa2tj4sSJpKWlKZQooRIFMG3aNCVOfUcpUQNBNNkIhjPOOINXXnmFvLw8VqxYgdXqk2M9WaUHbTYbdw91xSEAACAASURBVNxxB7W1tbzwwgt9FKd6SxCKDUV0LWJiYjhw4AAxMTGMHz8+AoO1LuBzzOYf+906atRRjh0b3tPK9il96HQuJAk0Gg8rV97D8uXP4fHkY7Vu8dtQ1EZP/VWvxCablpbG2LFjh2QzCNTlUHcB2tvbB+wcqobQIhfqLJFWxLJarTQ3N1NbW6vMhohqW6R8WZxOJzt37uSNN95g3bp16PV6ioqKmDNnDnPnzmXGjBlDFqS+A4hGuuCIxqmTLE653W4eeughPv30U9asWUNmZmaf+wgTT7H3CyNasefX1tYCkfBc8mAyje4x7RsYZBm6uiy0tx/3t7Lb7SGZ0NlsNsrLy5XZhKEQswjU5TiR4MhA3M7VkvGRPoALCldLSwuHDx9W2AAiTg1G3lcNt9tNWVkZ77zzDmvXrkWj0TB+/HilazFr1izi4+OjycXAEE02gmHnzp2KwkdOTg5/+tOf8Hq9faQHU1JSkGWZX/7yl3z88cfExMTwpz/9iWnTpn0j637//fd54IEHePDBB3u0wiWGDx+uHHz7kyAUbpVHjx6lsLAwAp2BbszmNL9bfvnL5/jrXy+jtTUFj0dPcnILbW0pgMT3v7+el19eSm7uPjZvfgWXK7eP8kao8Hq9igdEYWHhkCgwORwOjhw5wuHDh9HpdOh0Oj+ZvEi5yLrdboXuVlBQMGANeo/H4xfE7Xa7H4XLbrezf//+QSU2akrUpk2b2Lp1K+3t7RQXFzN37lwmTJjAiy++iNls5tVXX41u3INH9A0MjmicOknj1GeffcbNN9/MHXfcQWpqKl1dXYwZMwar1eo3E5iQkBDwINnQ0MDBgwcj1BnwYjYPLNY5HAls2fK+X1w1mUxheSwIwRYxcxhpOJ1OxQNCkiR0Op2fw3UkBDzAF3P3799PW1sbBQUFA5qBEM/Tu2inpu4K+vdgEhtRaCstLVUoUceOHVMoUcXFxbz55pvU1tbywQcfDImR8HcM0WRjsBg7dqxSudDpdGzbto3W1lauvPLKr13n/JNPPuHvf/87mzZt4vDhwxQWFrJs2TLOOeecsA6+XV1dIQ2Ph4ajGAznotUeBMDh0JOS0obNZsb/+ycjSTIrV97NnXeuwmrtjAi/UQyADfa1eL1eRdZPLZcrKletra1YrVYKCwsjakilRmtrK1VVVSEPqQdqjavNnoJ5VdTU1GCz2cjPzz9hx0HwqXtTombNmqW0mgPNnYiZlCgGjWiyERzRONWDkylObd26lbVr17Jx40b27dtHbm4u11xzDRdddFFYB1+bzUZZWRlJSUnk5OQMKl4YjdloNI1hP66rqxytdvBDv1arVTHpG8xgd3/yswkJCXR2+kwHh5Jm3NnZSUVFRciqhwOh7qoNZUN5LYIyLAa5t23bhkaj8TPOC3Q+iMapiCGabAwWY8eOZdu2bX5qCb/+9a+/EZ3zDRs2IEkS06ZNw2Qy8dhjj/HRRx+xZs0asrOzw3ou4TxutVopKioa1MyALNcSE5Pf899w003P89JLy+j9/TMau7n77se5774/4XTuDfBMA4Oa8hRqMtDbPElwQvuTyw11liMSr6W3Z4aYZ1EnQ4Npjbe1tSlqI5mZmUplx+l09lGJysrK8jPOi1KivlZEk43giMapHpxMcaq0tJS2tjZmzJhBYmIir7zyCi+99BIvvPACxcXFYT2XLMscPHiQ5uZmioqKBrX3SNI7mEyLwnqMzWYd8PV6Q015KiwsDKkzIGYXRKwKRS5X0IwjLYEe7LXk5+crHZtgyZBILMKl7op5wOTkZD/lRkGJEsZ5FRUVjBgxQolTM2fOjFKivl5Ek43BItAm/k3pnAdCSUkJS5cu5ZZbbuHKK68M+/HCgVoMEIeCwBtgDDNmzEGSoKRkBmec8QVud+/Dr8xtt/2B3//+XGBC2Gs9EcQBevTo0YwaNUrZaMRAn9gAe8u5hkPhGkhiMxC0trZSUVGhDJg7nU4/Z9ZIOJ57PB4++ugjHnzwQWbNmsX+/fv9KFFCJeo7rLBxMiAaLYMjGqd6cLLHqYqKChYvXswVV1zBjTfeGPbe1d7eTkVFBWPGjAmZAioUl8S+b7PZmD59GfHx1SFd0+tNxOE4GtY6Q4HoDAg3bXWcUsu5dnR0IEmSX7c6VPnZr8uXo729nbKyMvR6PVqtNizvkFDh9XrZunUrS5cu5YwzzuDw4cM0NjYqc4Hz5s1j0qRJ3wXjvJMZ0WRjsMjOziY5ORlJkli6dCk33HCDYsomIKQHL7jgAu6++24/6cHHHntsyHmzHR0dLFu2DEmS+MMf/hD2xuJ0OikvL8doNDJhwgS/QbZAyhtiAxQbyvENsBKz2deOHz68nqam4X2udf75H/Lii8UhJzbhwu12U1lZidVqJTk5ma6uLmWgT1RXIrEBRrLL0V+QcTgcinTxQDmyAmLORXQtvvzyS0wmE0VFRWzcuFH5vkY7FycVoslGcETjVA9OhThlt9u56667qKmpYfXq1WHHALG3ezweCgoK/CrkvZUBOzo6FMUlEacEXcdgmIdW++UJr1dX9xIpKVcPSXXc6/VSU1NDa2srqampdHd3K7ML6nmWwQ6VR7rLEUzVSiR2hYWFAzJoVEOWZY4cOaJQorZv345Go2Hy5MmUlpZSWFjI888/P2QqX1EMCFFTv8Fi48aNjBo1isbGRs4991zy8/OD3jdQAvd1tPESEhJ44403+J//+R9++MMf8txzz4XFwTUYDEyePFkx2cnMzFR0ztXKG6NGjQqqvOFDHnb7JkymOdx556P8+tdP97nH4cM+o6mmpiby8vIGvZkGCjLiNdXX1zN27NgIzKX0RVJSEtOnT2ffvn3s2LEjrC6HMFIUyYVa1WrEiBF9Er7Ozs4BGTkJlSiRXNTU1DB27FjmzJnDwoULef7555U1ezwe1qxZg81miyYbUURxiuFUiFMmk4lnnnmGjz76iB//+Mc8/PDDnHPOOSE/XqfTUVRUxLFjx5Q45fV6+ygDpqen96u46HR+gcFwDlrtln6v19BwOnV1uykoKIiIGpLD4fCTSfd4PJhMJhoaGhg1ahSTJk2KeBc5Li5OMXrdtm1bWF2O/qi7w4YN6+P+3d3dTUVFxQm9nXqjP0rUZZddxhNPPKF08mVZ5s0336S1tTWabJwiiHY2BoAVK1YQFxfHH//4x5OmPd0bNTU1LFq0iPnz57N8+fJ+Ny+1W7Q4qHu9XpxOJykpKYwbN25AvguS9G90uksxGPq6t+p0XVgsGo4ePUptbS0FBQUkJiaG/Nz9HdRFu1kEGeFl4fV6+1TCIokTdTlE4ibWLIwURacllOF+0RZvbGwkPz+/z3smyzJtbW2UlJSwZcsWSkpK6Ozs9KNE5efnRylRpx6inY3giMapADgV4lR9fT2LFy+mqKiIBx54oN/DfCBZd4/Hg8vlIj4+nvHjxw9IcclgmIJWG5hSJctgt1tpbGxk3759YbtcC/lZtWeUEBwRXQsRj4SXhRAgiZRTd2+cqMuh9rawWCwhzTH2huhK1NXVMX78+IAS/V1dXZSWlipFsCgl6luBKI1qMLBarXi9XuLj47FarZx77rk88MADfPLJJyetzjn4Drf3338/X375JS+++KISRAJJo4quhVCz0Gq1Sou3q6trwJufVvsrTKbV9P0OylitdsBXCSkvLyc5OTloxV5osvdWshAbYCgHdWEYFM5cSrgQAaOzs5Ps7Gyl26JWCxmMQZWA1Wpl8+bNfPTRR1xzzTXs2rWLkpISdu7cidls9lOJSktLiw7I4ftspk2bRkZGBh9++CEHDhxgwYIFtLa2cvrpp/P6669jMBhwOBwsXLiQ7du3k5qaytq1axk7duw3vfzoBxgc0TjFqRunvF4vf/jDH/jb3/7GmjVrGDduHOD7vYqCkrproaYX6XQ6ZXi8qamJoqKiAcmfm0zDkCRbn9vdbnC5fMPhDoeD8vJyTCZTn66zgMvl8utaDOSgLtQIw5lLCRfqolVOTg5ut7sPdVckRIMRjbHb7ezcuZPXX3+dZcuWUV5e7keJmj59OnPnzuWMM84YMnfyUw3f1jgVTTZCwP79+7n44osBX9Z/1VVXce+999LS0tJH5zwxMZGpU6disVgU+UGdTofT6fxGviiyLPPnP/+Ze++9l4kTJ3Ls2DFWrVrVZ9isvx+5GB4fN24c6enpYa8hNtYI9E0grNbjm7tabaSgoABZlv2ULNSa7IMxoXM6nVRUVKDT6cjLy4tY5UQ9hGixWLBarbhcLlJSUsjKyiIhISEi3QQ1JWrz5s00NTVRWVnJ5Zdfzs9+9jNmzJgxZBWxUx1PPvkk27Zto6Ojgw8//JArrriCSy65hAULFnDjjTcyefJkli1bxurVq9m9ezcvvfQSb731Fu+99x5r1679ppcfjcLBEY1TnNpxCmDdunXcdNNN5OfnU1dXx8qVKxk+fHjIfkYdHR2Ul5f3EQYJDTJGYxy9t+jXXvsJV1zx5vF7qSr2wmxQ7PmdnZ1otVplvYMxoRNzKW63O2L0LejLCOjq6sLpdJKQkMDYsWNJSkqKiOmg2+1mz549Spw6evQoFRUV/PCHP2TJkiXMnDkzIuIm30Z8W+NUNNmIME6mL0pVVRWXXXYZ2dnZFBcXU1paSlZWFo8++mjYB1JxSNfr9WHPWCxerOGvf+1dHfFitTqA4/Kz7e3tNDc309XVRVxcHCNHjiQ5OZnY2NiI0n5kWaa+vp5Dhw6Rl5dHSkpK2M8heLe9B+RENchkMvXpDIWrWBWIEtXR0cHkyZOValBeXh6HDx9m6dKlPPHEE2FLSn5XUFdXx6JFi7j33nt58skn+d///V/S0tJoaGhAp9OxefNmVqxYwT//+U/OO+88VqxYwezZs3G73YwYMYKmpqZvOjBGo3JwRONUmDiZ4lR7ezvf+973GDVqFFOnTmXPnj0YDAaeeeaZsKi14Cv6VFZW4nK5KCwsDKsodcEFDj75xD8WGAydtLf7Yo/b7VYKSq2trbS3t2M2mxk1ahTJyckRERzpjaamJmpqasjJyWH48L5CKyeCmrrb3t6O2+3uQ92VZXlQilWyLNPZ2alQorZs2UJzczMTJ070o0S1tLRw8803s3jxYs4///ywX8t3Ad/mOBVNNiKIk+2LIj5btaTe888/z+uvv85LL73U7/BgsOcTVZ3CwsKwXFBjY/0P2nv37qOz0xJQflan01FTU0N3dzeFhYWDauP2B7vdTnl5uTLIFiyBUg/IqTstas3w/jokoSpWCWdWwWENhxIlXOSjCIzLLruMe+65h87OTlatWsWrr77KrFmzqKmpAaC2tpbzzz+fPXv2MHHiRD7++GMyMzMBGDduHCUlJWFxtYcA0Q83OKJxKgycbHEKfHuf+qD+l7/8hccee4ynn36aGTNmhP18YsYi3GKSxxOLWuxvy5YajEZfIUySJD9VK4PBwKFDh2hpaaGwsHBA9K1Q4HQ62bt3LxqNhry8vKAJlFrafSDU3VAVq2RZpq6ujk2bNlFSUsL27dvRarV+lKj+OkvRWBUc3+Y4FZ2+iSBuvfVWHn/8cTo7OwEf/SgpKUk5iGZm+hSYAI4cOcLo0aMBlA2hpaUlol+U3j9oSZK4+eabOfPMM7n22mtZvHgx11xzTcg/fEmSyMzMJDk5mbKysj764IEg6EVgxPc9tAExeDxORo0aFXRYOS8vj5aWFnbs2DHgqs6JYDKZOO2006irq6O0tFQZUlcPyLW3t+NwOIiLiyMpKYmsrKyw279CsaqmpoatW7eSnJzMhAkTcDgc7Ny5ky1btrBlyxb27dtHdnY2c+bMYfHixWFRor6Nm7fdbud73/seDocDt9vNZZddxoMPPsjixYv59NNPlarnq6++ypQpU4I6In/44Yekp6czdepUNmzYAPSvxPNNqfREEcXXgZMtTgF9YsBPf/pTZs2axcKFC/nBD37Ar371q7C66enp6SQkJFBWVkZLSwvjxo3rt+vg9Xrp6OggI8NDRUUO2dmHuPLKt7jySjtnnBFc1SonJ4fU1FS++uorMjMzh2TuwGAwUFxcTENDA9u2bVOG1HtTd9XS7pmZmWF3WoRi1cGDByktLSUmJoZJkybhdrv56quvlK5FZWUlo0aNYs6cOVx++eV+KlGh4Nu2l0bjVGiIJhsRwqn0RSkuLuazzz7jtttu4+qrr+a5554Lq/oTGxvLtGnTFLnXoqIi5VDc2ydClmUSExPZutVJWloCw4aZqK21k5U19oTXSU1NZdq0aVRUVCgSuUOhJDVs2DC8Xi+7du0CwGg0Kl2LjIyMiMxAaDQa0tLS2LJlC48//jgmkwmj0ciUKVOYO3cuK1euJC8vL6oSpYLRaGT9+vXExcXhcrmYN2+e0n5/4oknuOyyy/zu/49//IPq6mqqq6spKSlh2bJllJSUsHHjRj744APWrVuH3W6no6ODW2+9FYvFgtvtRqfTUVdXx6hRowDfYau2tpbMzEwl8RwI1S6KKE42nEpxKjs7m/Xr17NixQp+8pOfsGbNGuU3GgpMJhOnn366IveqHh4PJjjy2Wd2bLZtuN1mbr9dw9SpJ26aJSYmMn36dKqrq9m5c+eQdeOTk5Nxu91UVFTg9XoV+dmkpCTy8/NPOHsZCiRJYtiwYezatYsVK1YoJn2TJk1izpw53H///UycODEicx3fFkTjVGiIJhsRwqn2RYmJieHll1/m3XffZf78+TzxxBOKuVMo0Gg0jBs3jiNHjlBSUoLZbFb0whMTE0lPTw+qsZ2VFTrrQa/XU1xcTH19Pdu3bycvL29QutpiQE4EGqvVqkjmTpw4EYvFQlNTk+IlMpjr7Nu3z48SFRsby6xZs1i5ciX/+c9/2LdvH6tWrfqm254nLSRJUgwMXS4XLper32D6/vvvs3DhQiRJYtasWVgsFurr63n00Ud59NFHAdiwYQOrVq3ijTfe4PLLL+edd95hwYIFvPbaa/zkJz8B4MILL+S1115j9uzZvPPOO5x99tkndcUoiihCxakWp/R6PQ8//DAbNmzg0ksv5d577+WCCy4I+fGSJJGVlYXRaGT79u0YjUa8Xq8iOJKSkkJ2dnbAIlYoiYaAVqslPz+f5uZmduzYMWAxFYH+qLsFBQV0d3dz9OhRRowYMah42B8l6re//S1fffUVn332GQ8//DDZ2dkDvs63GdE4FRqiMxtDAPFF+fDDD7n88su59NJLlcG74uJifvGLX/DCCy/w1VdfKYN3f/vb33j77be/kfXW1dWxcOFCZs6cyT333BN0/sDpdCqbn9onIj4+nqamJkwmE/n5+UNW9bDb7ZSVlREfH09ubm5IXQC1FKEIqPHx8UpFKJAuu+CupqWlkZWVFdJ1HA4HX375pUKJ2r9/Pzk5OcyZM4e5c+cyffr0Ph2S0tJSpkyZMmS+H98GeDwepk6dSk1NDTfddBOPPfYYixcvZvPmzf+fvfsOj7JK/wb+nfRKeiVlEkJLDwiIIBJYkKaLugpKUxAU3RV1VxfXAnYQsa26oouLuooLugIvolIkgAoBAgklQAIkIZBCSCFtkkw57x/ZeX6ZZGYyk8wkk+T7ua5ckCnPc2YYzj33c859DpydnTFx4kSsWrUKzs7OJu2I3PL/5sWLF6UlBVNSUvDvf/8bzs7OaGhowLx583D8+HH4+vri66+/RnR0dHe9BVq2G0W6H+NUB/S0OFVRUYFFixYhICAAr7/+usEFN7QLjmj7/aamJilOVVVVQQiB2NhYi63w1FpHVjw0NnXX0JK5CoUC2dnZ8PT0xIABA0yKu0qlUlolqvWUqLFjx2LUqFFtYuLJkychl8s7dfGtt2OckrBAvCv1xA+KWq3Ga6+9hp07d+Ljjz9G//79UVRUBDs7O2mJPAcHB51l/Vp+SRZCdHiDPnMIIXDp0iWUlpa2WTlDWyCn7bS1xecdWYpQo9EgLy8PFRUVbYr/hBCoqKiQEov09HTU1dVJU6LGjh2LQYMGcUqUBVVVVeGOO+7A3//+d/j5+SE4OBhNTU1YsmQJBgwYgBdeeAHTp0/HM888o9OJv/HGGxg+fHg3t94imGwYxjjVAT0xTgkhsG7dOqxfvx4ffvghhg4diitXrsDOzk7akLb1giOtpzRpi8cHDRrUZrM5S7azuLgYly5dwpAhQ+Dt7a1zn3a6sXYVxpZ7W3h5eZk8dVcIgcLCQhQXF7fZ5FW7StThw4elEfby8nKdVaI4JcqyGKdYIN6lxo8fj/HjxwNoLiBrvVFSy4IiZ2dnTJ06FdHR0d26eUttbS1GjRqF/Px83HzzzfD09MTMmTPx8MMPIyIiAh4eHka/PMtkMvTv3x/e3t46K1pYelhPOyzu5+eH06dPS8Hk+vXrUCgU0uaExorPTaGdJhYQEIBVq1ZBpVJh8ODBOHz4MLKysqQpUTfffDOWL18Of39/mx7C7EqGCuY68/n29vbG+PHj8eOPP+Ivf/kLgOa5sg888ADefPNNAP835UOr5XQQItLVE+NUfX09hgwZgjFjxmDq1Klwd3fH+PHjsXz5cpP7/MDAQHh5eUnF46aOkptDJpNJS+KePn0aLi4u8PT0xPXr13Wm7gYGGi4+N/U8ERER8PPzwyeffILc3FyMHj0aGRkZOHbsGOzt7TFy5EiMGTMGf/zjHzuw/0jvxTjVtXjptRtoC4qysrKQmZmJH3/8EYcOHcJf//pXPPHEE8jNzYWPjw/Wr18PAFi/fj18fHxw/vx5PPHEE/jrX/9q8TZt3rwZP/30E6ZNm4YDBw5g1KhRKC8vh4+Pj1kb0rm7u2P48OFQq9U4duwYGhoaLNbGpqYmXL16FTk5OThz5oy0D0VxcTEiIiJw4403IikpSdqcqKMBpLGxEYcOHcI777yDJUuWYNeuXdi/fz/efvttTJ8+HQcPHsQvv/yCN998EzNnzuQO3a1Y6vNdVlaGqqoqAM1TBnbv3o0hQ4aguLgYQPOVuy1btiA+Ph5A8xzWzz//HEIIHDp0CF5eXggJCemGd4Co57PFOPXTTz/h22+/xahRo/Drr79ixowZqKiogLu7u1l9vrOzM1JSUuDs7IyjR4+itrbWYm1UKpXS/hinT5+GUqlEbW0tCgsLERwcjBtvvBEpKSmIioqCr69vhxMNpVKJY8eO4YMPPsDDDz+MjRs3Ijs7Gy+//DJGjhyJvXv34tChQ3jvvfcwa9Ys7tDdCuNUFxNCGPuhViIjI8UXX3xhsePV1dWJlJQUcejQIeHn5yeUSqUQQojffvtNTJ48WQghxOTJk8Vvv/0mhBBCqVQKPz8/odFoLNYGfTQajfj0009FSkqK+OWXX0RdXZ3ZP5cvXxa7d+8WFy9eNPu5tbW1orS0VJw7d06kp6eLPXv2iP3794usrCxRUFAgqqqqdM6zZ88ecf78eVFbW2v2eS5duiQ2bdoknnzySTFmzBiRnJws7r//fvHJJ5+IM2fOCLVaLYQQ4sCBA2Lx4sVWfd97m858vrOyskRycrJISEgQcXFx4sUXXxRCCJGamiri4+NFXFycmDNnjqipqRFCNH9mH3nkEREdHS3i4+PFkSNHuuEVW017fXVf/qFW+kqcEkKIrVu3ioSEBPH99993KE6VlJSIn3/+WZw9e7ZD8ePq1asiNzdXHDlyROzZs0ekpaWJzMxMkZeXJyorK9uc58yZMx06T1FRkdiyZYv461//KsaPHy8SExPFnDlzxAcffCCysrKESqUSQgiRlZUlZs+eLcUtah/jlMUY7Kc5jaqbtC4oGjBgQLevdd6STCbDAw88gDFjxmDBggWYOXMmHn30UbNGC3x8fKSla69du2a0WE6tVku1FlVVVWhsbJSuVrU3jUt7nnPnzkm7oBoqutbu6q2tt9BOiRo9ejTGjRtndErU2LFjzVqxqy+zxOc7MTERx48fb3Psn3/+We85ZTIZPvjgAyu9IqK+x9bjFNB8pfiGG27AggULsHfvXjz33HNmLbrh6emJG264Abm5ucjKyjJaPK7dj0Mbp8yZuqs9z4ULF3D8+HHExsYarM3QaDS4dOmSVGtx7NgxODg4SFOiHnvsMYSEhOiNU4mJidi4caPJr78vY5zqOkw2uom9vT0yMzOlgqIzZ860eYwtrHU+aNAg7Nu3D3/729/whz/8Af/4xz/M2mDP0dERCQkJKC4uxtGjR6Xi8db7cQCQdmcNDQ01e28LBwcHxMXFobS0FEeOHIFarcbNN9+MhoYGnVWi8vLyMGDAANx000148MEHMWLECKvtUN6TFBYWYv78+SgpKYGdnR2WLFmCZcuWYeXKlfjkk08QEBAAAHjttdcwbdo0AMDrr7+O9evXw97eHu+99x5uvfVW6Xg95fNNRIb1lP/HoaGh+Omnn/DGG29g2rRpWLdunVmF7Nqla8vKypCRkSEVj2tXYGy9H4e3tzcGDRoEV1dXs16jvb09Bg0ahIqKChw/fhwNDQ1ITU2FUqnEiRMnpOTi3LlzCAsLw0033YTZs2fjrbfekpZX7csYp3ouJhudUF9fj3vvvRcqlQqbNm3SWbHIVNqCokOHDtnsWudOTk5488038dNPP2HmzJl48cUXMXnyZJOfL4SAh4cHAgICcPz4cchkMnh6ekoFcob24zCXEAL29vYoKSnBmjVrUFdXBw8PDwwbNgxjxozBm2++iYEDB3KVKD0cHBywdu1aDBs2DDU1NRg+fDgmTZoEAHjiiSekYjet7OxsfP311zh9+jSKiorwu9/9Djk5OW3mH/eEzzdRb9ZX4pSdnR2WL1+OCRMmYN68efjTn/6E2bNnm/x8IQRcXV0REhKC06dPQ6PRSMvPGtuPw1zaOFVdXY01a9Zg2bJlUg3JTTfdhBUrViAuLo6rROnBONVz8VtXB5WUlOCWW25BaGgotm3bZlYHrq+gaOjQoUhNTcU333wDAHo3bwHQrZu33Hrrrdi1axc+9iqSdwAAIABJREFU+eQTLF++HI2NjXofp1Qqce3aNVy4cAEZGRk4fPgwLl26BGdnZwwbNgyhoaHQaDQICQmBr69vhxMNjUaDc+fO4bPPPsPSpUsxZswYzJkzBydOnMCLL76Ihx56CA4ODnjmmWewaNEi7tBtREhICIYNGwagebhfu6SkIVu3bsXs2bPh7OyMqKgoxMTESKvZ9NTPN1Fv0xfj1MiRI7F//37s3bsXixcvRk1Njd7HqdVqVFRU4OLFizh27Ji0P5KdnR2SkpIQFRUFtVqN4OBg+Pv7dzjR0Gg0yM/Px8aNG/H444/jlltuwZ133om0tDQ8/vjjeOqpp+Do6Ihly5bhkUceQWJiIhMNAxinei6ObHRAdnY2XnjhBTz00EMdWnGjuLgYCxYsgFqthkajwT333IMZM2YgNjYWs2fPxnPPPYeUlBRMnjwZqampKCoqQnFxMb799ltERUXho48+wqRJk5Cfnw+5XI5NmzbBx8cHQggsW7YMO3bsgJubGzZs2CD9x7SUwMBAbN++He+++y6mTp2KDz/8EEIIVFdXo1+/fqiuroadnZ20ZnhYWFibaUr9+vVDZWUlsrKyIJfLERwcbNK5GxoapKBw6NAh5OfnS1OiFi9erHdK1O9//3uzpn0RkJ+fj+PHj0srvrz//vv4/PPPccMNN2Dt2rXw8fHBlStXcOONN0rPaTm31dTP96JFiwAAixYtwrx58xATEyOt5U9EndOX45Snpye++OIL/Pvf/8aUKVPw7rvvwtfXFyUlJfD19TVp6q52h/HTp08jNDQUYWFhJn25bD0lKicnR5oSde+99+Ltt99uk/TNmDGDX1zNxDjVs3BTPzPJ5XI0NDTA398fhw4dsuo8yuLiYhQXF+sMGW7ZsgUbNmyAr68vli9fjlWrVqGyshKrV6/Gjh078Pe//x07duxAeno6li1bhvT0dIu3KzMzEz///DN27NiBjIwMDBgwAEuWLMH06dPRr18/k6/KKJVKnD17FjKZDEOGDNEZ4RBC4Nq1a1JicfjwYdTX12PYsGHShkScEmV5tbW1uOWWW/Dss8/izjvvRGlpqVQw//zzz6O4uBiffvopHn30UYwePRpz584F0NwRT5s2DXfddVc3v4Jei99EDGOcaoVxCjh37hx27dqFnTt3Yv/+/YiMjMS8efMwe/Zs9OvXz+QRdbVajdzcXCgUCsTFxekUjwshcP36dZ2N8yorK5GYmCjFqdjYWI5UWBjjlM0yGKf4Ta0DVq1ahYSEBPzud79DZWWl1c5jaMhw69atWLBgAQBgwYIF2LJlC4DmIcP58+dDJpPhxhtvRFVVlbTWsyUdPXoUPj4++PDDD1FYWIjk5GT89NNPkMlkZnWq2uJxPz8/LFmyBF999ZXOlKi5c+ciIyMDqamp2Lp1K44dO4b169dzStT/FBYWIjU1FUOHDkVcXBzeffddAEBFRQUmTZqEgQMHYtKkSdJnVAiBxx57DDExMUhMTMSxY8d0jqdUKnHXXXdhzpw5uPPOOwEAQUFBsLe3h52dHRYvXiwNQffVjYmIeoq+HqdOnDgBR0dHvPrqqygtLcW0adPw448/orGx0aypu9ri8fDwcPzlL3/Bxx9/jK+++grLli3DuHHjcNdddyEtLQ033HADNm7ciMzMTHzxxRdYunQpEhIS+nyiwThFALjPhrm065er1Wrx4IMPisTERFFSUmL18+bl5Ynw8HBx/fp14eXlpXOft7e3EEKI6dOniwMHDki3T5gwocvWcN60aZNITEwUu3fvNmnd8PLycrFr1y7x8ssvi+nTp4sbbrhBhISEiEmTJom0tDTR0NDQJe3uyYqKikRGRoYQQojq6moxcOBAcfr0afHUU0+J119/XQghxOuvvy6efvppIYQQ33//vZgyZYrQaDTi4MGDYuTIkdKxNBqNmDdvnli2bFmbc2i99dZbYtasWUIIIU6dOiUSExNFQ0ODuHjxooiKipLWeSer6O69LGz5h1phnNJv9+7dIiEhQWzevNmkOFVVVSX27dsnVq1aJe644w6RkpIiIiIixKhRo8QPP/wgamtru6TdPRnjVJ/CfTYszc7ODp988gmefPJJjBs3Drt27UJERIRVzlVbW4u77roL77zzDvr162fwcaIbl2a7++67MWrUKMyfPx9jx47F008/LV09EnqmRCkUCmlK1FtvvYWYmBhoNBq89tprOHHiBG655ZYuaXdPFhISIu082vqKYlpaGoDmK4rjx4/H6tWrDV5RDAkJwa+//oovvvgCCQkJSE5OBtC8fKD2Sp1MJoNcLse6desAAHFxcbjnnnsQGxsLBwcHfPDBB33+Ch6RrWGc0jVx4kT8/PPPWLhwIfbs2YNXX31VqtUQ/5sSlZ6ejoMHD+Lw4cM6U6JefPFFxMbGws7ODv/4xz/wyy+/YMqUKV3S7p6McYoAcGTD1jU1NYnJkyeLtWvXSrcNGjRIyuSLiorEoEGDhBBCLFmyRHz11Vd6H9dVlEqlWLlypbjxxhvFyy+/LObNmyeSkpLEzTffLJ5++mmxbds2ce3atS7ZWbYvsfUritRp3T16YMs/1M16WpzSaDTi/fffFykpKeKll14SixYtEikpKWL06NFi2bJlYtOmTaKoqIhxysIYp3o9g/103570buOEEFi0aBGGDh2KJ598Urq95RJsrZdm+/zzzyGEwKFDh+Dl5SVdUegqDg4OWLFiBZYsWYL8/Hw8/PDDSE9Px/79+7F69Wrcdttt8PPz67MrbyxcuBCBgYGIj4+Xblu5ciX69++P5ORkJCcnY8eOHdJ9r7/+OmJiYjB48GD89NNPeo/ZE64oElHv1BPjlEwmw6OPPopVq1YhOzsbc+fOxYEDB/Dbb7/hnXfewd13321wh+6+gHGKLM5YJtLFGRG1cuDAAQFAJCQkiKSkJJGUlCS+//57ce3aNTFhwgQRExMjJkyYIMrLy4UQzVdrHnnkEREdHS3i4+N5NcAG7du3T2RkZIi4uDjpthUrVog1a9a0eezp06d15ptGR0e3mW/a064oUod19+iBLf9QN2Kc6n0Yp6iDOLLRE40dOxZCCJw4cQKZmZnIzMzEtGnT4Ofnhz179iA3Nxd79uyBr68vFi5ciKCgIOzbtw8XLlzAyZMnER0d3aHVHsh6xo0bZ/Kuo8Y2JAJ65hVFIupdGKd6H8YpsjQmG73E/fffjx9//FHntlWrVmHixInIzc3FxIkTsWrVKgDADz/8gNzcXOTm5uLjjz/G0qVLu6PJ1ML777+PxMRELFy4UAq2V65cQXh4uPSYlhsSAZCK5X7++Wedoe3ly5dj165dGDhwIHbt2oXly5cDAKZNm4bo6GjExMRg8eLF+PDDD7v2RRJRn8Y41bMxTlFHcTWqXmLcuHHIz8/Xua0jqz1Q11u6dCmef/55aUOiP//5z/j000/bnbuqvaKoz549e/Q+94MPPrBcw4mIzMA41XMxTlFncGSjFystLZU65pCQEFy9ehVA+1ciSJe+YrmObkikDzckIqK+inHKMhinyJYx2eiD2rsSQbqsPfTfcvfc7777TgoWt99+O77++ms0NjYiLy8Pubm5GDlypAVfGRGRbWKcMg/jFNkyTqPqxYKCgqRh5+LiYgQGBgLglQhzWXLo/95770VaWhquXbuGsLAwvPjii0hLS+OGRETUJzFOWQbjFNkyJhu9mHa1h+XLl7dZ7eH999/H7NmzkZ6eztUeOsDcoX/tYzdu3NjmWIsWLTJ4nmeffRbPPvusJZtORGQzGKesh3GKbAWTjV5C35WI5cuX45577sH69esRERGBzZs3A2he7WHHjh2IiYmBm5sb/vWvf3Vz661LLpfD09MT9vb2cHBwwNGjR1FRUYFZs2YhPz8fcrkcmzZtgo+PT6fPxaF/IiL9GKcMY5yi3ozJRi+h70oEYP5qDz/++COWLVsGtVqNBx98UFqOrqfbu3cv/P39pd+1c1mXL1+OVatWYdWqVVi9erXJx+PQPxGReRinjGOcot6KBeIkUavVePTRR/HDDz8gOzsbGzduRHZ2dnc3yyq2bt2KBQsWAGiey7plyxaznt/TNiSSy+V45ZVXkJqaCg8PDyQkJODEiRPYuHEjYmJi4OXlhQcffBAqlaq7m0pEZBDjlOkYp8hmGNte3Oobm5NN+e2338TkyZOl31977TXx2muvdWOLLEMul4uUlBQxbNgwsW7dOiGEEF5eXjqP8fb2Nvj82bNni+DgYOHg4CD69+8v/vnPf4pr166JCRMmiJiYGDFhwgRRXl4uhBBCo9GIRx55RERHR4v4+Hhx5MgR670wM0RGRoqYmBiRnZ0tmpqaxJw5c0R0dLRYvHixqK2tFQUFBSIgIEB8+eWX3d1U0q+9vrov/1AfwjilH+MU2QCD/TSnUZFEX9FYenp6N7bIMn799VeEhobi6tWrmDRpEoYMGWLW8y019N/dlixZgqFDhwIA7rvvPnz55Zc4dOgQ3N3d4e7ujvHjx+PIkSO47777urmlRET6MU7pxzhFtozTqEgibLRo7Mcff8TgwYMRExMjrRNuDu1c1MDAQNxxxx04fPiwNJcVgM5c1t6s5TC5m5sb7O3tERAQoHNbTU1NdzSNiMgkjFO9G+NU78RkgyS2WDTW2fm5dXV1UsdUV1eHnTt3Ij4+3uBcViIisl2MU0Q9D6dRkWTEiBHIzc1FXl4e+vfvj6+//hpfffVVt7bp8OHDiImJQXR0NABg9uzZ2Lp1K2JjY016fmlpKe644w4AgEqlwn333YcpU6ZgxIgRepdbJCIi28U4RdTzMNkgiYODA95//33ceuutUKvVWLhwIeLi4rq1TZ2dnxsdHY2srKw2t/v5+emdy0pERLaLcYqo52GyQTqmTZuGadOmdXczJLY6P7enyc/P1/l9/PjxbZYP3LBhQ9c1iIiogxineifGqd6LNRtk02xxfi4REZEW4xSRcUw2yKa1nJ/b1NSEr7/+Grfffnt3N4uIiAgA4xRReziNimyaLc7PJSIi0mKcIjJOpm+uYQtG7yQioi7BCeCGMU4REXU/g3GK06iIiIiIiMgqmGwQEREREZFVMNkgIiIiIiKrYLJBRERERERWwWSDiIiIiIisgskGERERERFZBZMNIiIiIiKyCiYbRERERERkFUw2iIiIiIjIKphsEBERERGRVTDZICIiIiIiq3Bo535Zl7SCiIioYxiniIhsGEc2iIiIiIjIKphsEBERERGRVTDZICIiIiIiq2CyQUREREREVsFkg4iIiIiIrILJBhERERERWQWTDSIiIiIisgomG0REREREZBVMNoiIiIiIyCqYbBARERERkVUw2SAiIiIiIqtgskFERERERFbBZIOIiIiIiKyCyQYREREREVkFkw0iIiIiIrIKJhtERERERGQVTDaIiIiIiMgqmGwQEREREZFVMNkgIiIiIiKrYLJBRERERERWwWSDiIiIiIisgskGERERERFZBZMNIiIiIiKyCiYbRERERERkFUw2iIiIiIjIKphsEBERERGRVTDZICIiIiIiq2CyQUREREREVsFkg4iIiIiIrILJBhERERERWQWTDSIiIiIisgomG0REREREZBVMNoiIiIiIyCqYbBARERERkVUw2SAiIiIiIqtgskFERERERFbBZIOIiIiIiKyCyQYREREREVkFkw0iIiIiIrIKJhtERERERGQVTDaIiIiIiMgqmGwQEREREZFVMNkgIiIiIiKrYLJBRERERERWwWSDiIiIiIisgskGERERERFZBZMNIiIiIiKyCiYbRERERERkFUw2iIiIiIjIKphsEBERERGRVTDZICIiIiIiq2CyQUREREREVsFkg4iIiIiIrILJBhERERERWQWTDSIiIiIisgomG0REREREZBVMNoiIiIiIyCqYbBARERERkVUw2SAiIiIiIqtgskFERERERFbBZIOIiIiIiKyCyQYREREREVkFkw0iIiIiIrIKJhtERERERGQVTDaIiIiIiMgqmGwQEREREZFVMNkgIiIiIiKrYLJBRERERERW4dDO/aJLWkFERMbIursBNoxxioio+xmMUxzZICIiIiIiq2CyQUREREREVsFkg4iIiIiIrILJBhERERERWUV7BeJERERERimVSly+fBkNDQ3d3RTqQ1xcXBAWFgZHR8fubgoZIRPC6EIeXOWDiKj7cTUqwxinbEBeXh48PT3h5+cHmYwfV7I+IQTKy8tRU1ODqKio7m4OcTUqIiIispaGhgYmGtSlZDIZ/Pz8OJrWAzDZICIiok5jokFdjZ+5noHJBhEREfV4r776KuLi4pCYmIjk5GSkp6cDAN555x3U19dLj5PL5UhISEBycjKSk5Px2GOPSfc9/vjj2L9/v/S4a9eumXz+xsZG/O53v0NycjL+85//WOhV6dde27Zs2YLs7Gzp97/85S/4+eefLXb+8ePH4+jRowCAadOmoaqqyuxjpKWl4bfffrNYm8h2sUCciIiIerSDBw9i+/btOHbsGJydnXHt2jU0NTUBaE425s6dCzc3N+nxe/fuhb+/v84xKioqcOjQIbzzzjsdasPx48ehVCqRmZlp8nPUajXs7e07dD5jtmzZghkzZiA2NhYA8Kc//QmLFy/GhAkTLH6uHTt2dOh5aWlp8PDwwE033WTyc1QqFRwc+NW1p+HIBhEREfVoxcXF8Pf3h7OzMwDA398foaGheO+991BUVITU1FSkpqYaPcY333yDKVOm6Ny2Zs0ajBw5EiNHjsT58+cBAGVlZbjrrrswYsQIjBgxAr/++iuuXr2KuXPnIjMzE8nJybhw4QL27NmDlJQUJCQkYOHChWhsbATQPCrx0ksvYezYsdi8eTMuXLiAKVOmYPjw4bj55ptx9uzZNm0rLy/H5MmTkZKSgoceeggtF/f5/PPPkZiYiKSkJMybNw+//fYbtm3bhqeeekpqS2RkJMrLy1FSUmL0PTh8+DBuuukmpKSk4KabbsK5c+cAAAqFArNnz0ZiYiJmzZoFhUIhPUc7ypKfn4/4+Hjp9jfffBMrV64EALz33nuIjY1FYmIiZs+ejfz8fHz00Ud4++23kZycjAMHDuh9XwFg5cqVWLJkCSZPnoz58+cbbT/ZKCGEsR8imxUWFiYyMjL03rd8+XLx9ttvm3ScESNGiFOnTlmyaUSW1l5f3Zd/yAZkZ2d34FlqIUSJEELT6fPX1NSIpKQkMXDgQLF06VKRlpYm3RcZGSnKysp0fo+PjxdJSUkiKSlJvPXWW0IIIebPny+2bdum87hXXnlFCCHEZ599JqZPny6EEOLee+8VBw4cEEIIUVBQIIYMGSKEEGLv3r3SYxQKhQgLCxNbt24VtbW1Yt68eVJMioyMFKtXrxaFhYWipKRETJgwQeTk5AghhDh06JBITU1t8/rmzJkjnnvuOSGEENu3bxcARFlZmTh16pQYNGiQ9PrKy8uFEEIsWLBAbN68WecYDz74oPjmm2+EEEI8//zzYuvWrW3Oc/36daFUKoUQQuzatUvceeedQggh1q5dKx544AEhhBBZWVnC3t5eHDlyROf9zcvLE3FxcdKx1qxZI1asWCGEECIkJEQ0NDQIIYSorKwUQgixYsUKsWbNGunxht7XFStWiGHDhon6+vo27RWio589sgKD/TTHoqhHqqysRFFREYYMGdLmvrKyMnz++efSVajGxkY88sgj2L17NyoqKhATE4PXXnsNU6dOBdA8l/WFF17At99+26WvgYio79IASAXwG4CbAOxFZyZbeHh4ICMjAwcOHMDevXsxa9YsrFq1Cvfff7/ex+ubRlVcXIyAgACd2+69917pzyeeeAIAsHv3bp16iOrqatTU1Og879y5c5DL5QgNDYWLiwsWLFiADz74AI8//jgA4M4770R5eTmioqLw22+/4e6770ZTUxPUajWamppw8uRJBAcHS+3JysrCwoULAQDTp0+Hj48PAODnn3/GH/7wB+m1+Pr6GnyPAgMDUVRUBAB46aWX9D7m+vXrWLBgAXJzcyGTyaBUKgEA+/fvl2pbEhMTkZiYaPA8+iQmJmLOnDmYOXMmZs6cqfcxxt7X22+/Ha6urmadk2wHkw3qkU6ePImoqCidObhaGzZswLRp06SOSaVSITw8HPv27UNERAR27NiBe+65BydPnoRcLsftt9+Ohx9+GMXFxQgJCenql0JE1AeVoTnRUP3vzzIAQZ06or29PcaPH4/x48cjISEBn332mcFkQx9XV9c2y6i2XO1I+3eNRoODBw8a/fIrhIBGo4Gzs7PemozGxkZ4eXkBALy9vZGZmQmFQgFnZ2fY2dlBoVAgJycHbm5ucHd3h729Perr69HU1AQnJyed85i6IlNDQ0O7X9iff/55pKam4rvvvkN+fj7Gjx+v973Qx8HBARqNRud8Wt9//z3279+Pbdu24eWXX8bp06fbPN/Y++ru7m703GTbWLNBPdKJEycwYMAALFu2DAEBAQgNDcWuXbsAAD/88ANuueUW6bHu7u5YuXIl5HI57OzsMGPGDERFRSEjIwNA8w6kw4cPx86dO7vltRAR9T2BaB7RcPjfn4GdOtq5c+eQm5sr/Z6ZmYnIyEgAgKenZ5uRB32GDh0qjYhraVeV+s9//oPRo0cDACZPnoz3339f51ytDRkyBAUFBSgpKcGlS5fw7rvvIiYmBtXV1QCAmpoaeHp6ol+/foiKisLmzZvh6uoKmUyGrKws6Yu9ts5j3Lhx2L17N6qrq/HDDz+gsrISADBx4kRs2rQJ5eXlAJqL3A295pycHJ2aCn2uX7+O/v37A2i+cKc1btw4fPnllwCAU6dO4cSJE22eGxQUhKtXr6K8vByNjY3Yvn07gOYkorCwEKmpqXjjjTdQVVWF2traNm005X2lnonJBvVIJ06cwNGjRzFt2jSUlpbioYcewurVqwE0j3oMHjzY4HNLS0uRk5ODuLg46bahQ4ciKyvL6u0mIiKgebPhvQAuA0iDkc2HTVJbW4sFCxZIRcjZ2dlScfKSJUswdepUnQLx1NRUaelbbdHx9OnTkZaWpnPcxsZGjBo1Cu+++y7efvttAM3FzkePHkViYiJiY2Px0UcftWmPi4sLVq9ejT/96U+YOnUq/Pz8sHTpUhQXFwNoLrh2cXEBAHz55ZdYv349kpKSMGjQIPzjH//AqVOn4OjoKI1+rFixAseOHUNqaip27tyJiIgIAEBcXByeffZZ3HLLLUhKSsKTTz4JAJg9ezbWrFmDlJQUXLhwAUqlEufPn8cNN9wAAHjhhRewbdu2Nu1++umn8cwzz2DMmDFQq9XS7UuXLkVtbS0SExPxxhtvYOTIkTrPk8lkcHR0xAsvvIBRo0ZhxowZ0jRntVqNuXPnIiEhASkpKXjiiSfg7e2N2267Dd99951UIG7K+0o9k0y0WNFAD6N3EnWX0aNH4+6775Y61s2bN2PdunXYvXs3HB0dcfLkSb31HEqlElOnTsWAAQOwbt066fZnn30WxcXF+PTTT7vsNRCZgTtXGcY4ZQPOnDmDoUOHdnczOm3s2LHYvn07vL29O32sM2fOwMfHB8HBwQCaRx3KysowePBgZGRkIDY2Vu+UISEEamtrUVNTg+DgYNjZNV8XvnLlCpRKJeRyudlt+e6773Ds2DG8/PLLnXpNranVagQGBqKkpASOjo4WPbapestnrxcwGKc4skE9jhACp06dwm233SbddurUKWk9cR8fH71D5hqNBvPmzYOTk5POUC3QPKRtieBCREQ919q1a3Hp0qVOH0cIAYVCoRNXFAqFlFzY29vr1De0JJPJ4OnpCaVSibKyMun2zuzJoVKp8Oc//7lDzzUmLi4ODz74YLclGtQzsECcepy8vDwAQExMjHTb8ePHpRUuEhMTkZOTgxEjRkj3CyGwaNEilJaWYseOHW06xjNnzmDu3Lld0HoiIrJVo0aNsshxtBsKavf9AID6+nppFSltMbqxwmchhFSzATQXXBtbbcqYu+++u0PPa4++PUGIWuPIBvU4J06cQEJCgs7KGMePH0dSUhIAYNq0adi3b5/Oc5YuXYozZ87g//2//9dm2LqxsREZGRmYNGmS9RtPRES9Xn19vVTwrdVyZMPLy0tnBF6pVKKiogJqtRpCCFy/fh0VFRXo168fgOaR+bq6Oul3op6EIxvU45w8eVJKLABIu6JqV9mYP38+kpOTpY69oKAA69atg7OzszR3FgDWrVuHOXPmYNu2bRg/fjxCQ0O7/LUQEVHvo1AodJZmV6lUUCqVUrLh5+eH7OxsaDQaqSbj6tWrKCgogBACzs7OCA8Pl6ZhVVVVwdPTU2fZW6KeggXi1Cv97W9/Q2BgoLSBkjGjRo3C+vXr210SkKgbsUDcMMYpG8AiXfNdvnwZjo6OCApqf3+RM2fOQC6Xc2M7PfjZsxkG4xSTDSIi28dkwzDGKRvAL3zUXfjZsxlcjYqIiIh6r1dffRVxcXFITExEcnIy0tPTAQDvvPMO6uvrpcfJ5XIkJCRI+2w89thj0n2PP/449u/fb/Q8Z8+eRXJyMlJSUpCRkYEPP/zQoq8jLS0NM2bMMPqY1157Tfp7U1MTxo0bB5VKZZFzbtu2DatWrerQcVq2i0iLyQYRERH1aAcPHsT27dtx7NgxnDhxArt370Z4eDiAtskGAOzduxeZmZnIzMzEe++9B6B5H4xDhw5h3LhxRs+1ZcsW/P73v8fx48fh5+dndrIhhDC47K2pWn6pd3JywsSJE6Xdzjvr9ttvx/LlyzvdLlO13DyQeicmG0RERNQtrl2zzHGKi4vh7+8vLTXr7++P0NBQvPfeeygqKkJqaqrODuL6fPPNN5gyZYr0+0svvYQRI0YgPj4eS5YsgRACO3bswDvvvIN//vOfSE1NxfLly3HhwgUkJyfjqaeeAgCsWbMGI0aMQGJiIlasWAEBIZaHAAAgAElEQVQAyM/Px9ChQ/HII49g2LBhKCws1Dn3jz/+iCFDhmDs2LH473//K91eW1uLBx54AAkJCUhMTMS3336L5cuXQ6FQIDk5GXPmzAEAzJw5E19++aXe17V06VLccMMNiIuLk9pj7JwbNmzAH//4RwDA/fffj2+++Ua6z8PDQ3q/x40bh+TkZMTHx+PAgQN62/Xvf/8bI0eORHJyMh566CEpsfDw8JB2Gz948KDRfxfqBYQQxn6IiKj7tddX9+UfsgHZ2dlmP+fSJSEcHIQoLOz8+WtqakRSUpIYOHCgWLp0qUhLS5Pui4yMFGVlZTq/x8fHi6SkJJGUlCTeeustIYQQ8+fPF9u2bZMeV15eLv197ty50n0rVqwQa9asEUIIkZeXJ+Li4qTH/fTTT2Lx4sVCo9EItVotpk+fLvbt2yfy8vKETCYTBw8ebNN2hUIhwsLCRE5OjtBoNOLuu+8W06dPF0II8fTTT4tly5ZJj62oqBBCCOHu7q5zDJVKJfz9/aXfk5KS2rwOlUolbrnlFpGVlWX0nP/617/Eo48+KoQQYsGCBWLz5s3SsbTnffPNN8Urr7wiHbe6urpNu7Kzs8WMGTNEU1OTEEKIpUuXis8++0wIIQQA8Z///KfNe9ERHfnskVUY7Ke59C0RERF1maYm4N57gbNnAZUKmDIFGDwY+PproKMbUXt4eCAjIwMHDhzA3r17MWvWLKxatQr333+/3sfv3bsX/v7+OrcVFxcjICBA5zFvvPEG6uvrUVFRgbi4ONx2221G27Fz507s3LkTKSkpAJpHJnJzcxEREYHIyEjceOONbZ5z9uxZREVFYeDAgQCAuXPn4uOPPwYA7N69G19//bX0WO2mgK3Z29vDyckJNTU18PT0RGZmpnTfpk2b8PHHH0OlUqG4uFhactfQOU0xYsQILFy4EEqlEjNnzkRycnKbx+zZswcZGRnSBrsKhQKBgYFSe++66y6Tz0c9G5MNIiIi6jJOTkBMDKCduXP6NDB9escTDS17e3uMHz8e48ePR0JCAj777DODyYY+2l29gebduh955BEcPXoU4eHhWLlypXSfMUIIPPPMM3jooYd0bs/Pzze6W3jLzf9aH8/Qfa01NjbCxcVF57a8vDy8+eabOHLkCHx8fHD//fdLr8OU4zo4OEj1JUIIaWf0cePGYf/+/fj+++8xb948PPXUU5g/f36bti9YsACvv/56m+O6uLjA3t7epNdFPR9rNoiIiKhLPfnk//1dJtP9vSPOnTuH3Nxc6ffMzExERkYCADw9PXV26zZk6NChOH/+PABIX8j9/f1RW1urU7fQUutj33rrrfj0009RW1sLALhy5QquXr1q9LxDhgxBXl4eLly4AADYuHGjdN/kyZPx/vvvS79XVlYCABwdHaFUKqXby8vLERAQAMdWGVt1dTXc3d3h5eWF0tJS/PDDD+2esyW5XI6MjAwAwNatW6VzFhQUIDAwEIsXL8aiRYtw7NixNu2aOHEivvnmG+n1V1RUoKCgwOh7Qb0Tkw0iIiLqUpWVwNy5wJkzwJw5zb93Rm1tLRYsWIDY2FgkJiYiOzsbK1euBAAsWbIEU6dO1SkQT01NlZa+1V6Rnz59OtLS0gAA3t7eWLx4MRISEjBz5kxpKlBrfn5+GDNmDOLj4/HUU09h8uTJuO+++zB69GgkJCTgD3/4Q7uJjouLCz7++GNMnz4dY8eOlZIkAHjuuedQWVmJ+Ph4JCUlYe/evdJrSkxMlAqx9+7di2nTpknP005rSkpKQkpKCuLi4rBw4UKMGTOm3XMC/zfqsXjxYuzbtw8jR45Eenq6NDqTlpYmLf/77bffYtmyZW3aFRsbi1deeQWTJ09GYmIiJk2ahOLiYqPvBfVO3NSPiMj2cVM/wxinbEBv2Vht7Nix2L59O7y9vbu7KWa588478frrr2Pw4MGdPtbatWtRXV2NF1980QIts77e8tnrBbipHxEREZExa9euxaVLl7q7GWZpamrCzJkzLZJofPTRR9iwYQPmzp1rgZYRNePIBhGR7ePIhmGMUzaAV5epu/CzZzM4skFERERERF2LyQYRERF1WjszJYgsjp+5noH7bBB1ASEENBoNNBoN1Go1NBoNVCoVhBBwc3ODg4ODyWupExHZGhcXF5SXl8PPz499WQ+l/eKu7087OzvIZDKb+rcVQqC8vLzN3iJke1izQWQhQgidpEKbWGiTi5aP03bYarUabm5uAJo3T7K3t4edHQccqQ3bifC2h3HKBiiVSly+fNmkje+o+7X87tfe6IA2ZrVMNGwl6XBxcUFYWFib/UWoWxj8UDDZIDJT66RCpVJJCYUQAmVlZQCAgIAAnQ66deesfa6Hh4d0TKB5F1ztSIetdOjU7fhBMIxxiqiV1nFKe+FLG6uuX7+O+vp6hIaGGo1TQPPO5O7u7pDJZFKcsrOzg6OjI+MUtWTwg8BpVEQGGOuoW45OAJA6XJlMBpVKBTs7O9jb25t8Lu1zhRBQq9W4ePEiwsLC4OLiAnt7e3bmRETUhr4putpY1Zo2ztjb20sXuzoapzQaDfLy8uDr6wtPT0/GKTKKyQb1afqmPqlUKunvLR+n7Wi1c1etQXuO8vJyhISEQKlUQqVSSVOs2JkTEfUt5k7R7ao4VVVVBQ8PD8YpaheTDeoTjHXU2mHh0tJSNDU1ISIiolOdtaVWx7Czs4OdnR2EEFJn3nKKFRER9R7tTdEFgMrKSpSXl2PgwIFWTyoASMmMoXO0jlNKpRIODg6MU6SDyQb1KsamPrV8TMtpT9qCbG2naYkC7c52si0TlpZD1yqVSucKEovJiYh6lo5O0dVOgbJEnDL3opi+mNZenNJeHGOcIiYb1OO0vvqjUCikDrp1B6rtIK199ccaWrfXWGfOIj0iItvROk41NTVBqVRK9RItH9cVU5/0MfVchh7XOjHSPrZl/aFarYadnZ2UdDBO9U1MNshmGZr61LrwraCgAO7u7ggMDOw1X7qNXXVq3ZmfOXMGgwcP5nxZIqIuZsoUXQAoKSmBWq1GeHi4zX7p1k6FqqurQ11dHerr61FXV4empiaEhYUhPDwcDg6mfW1sXUx+7tw5hIWFwc3NjXGqD2KyQd1O20m3vBJiTuGbrW02pO9qjyHmXDHS91yZTIaKigqdug4mHUREltWZKbpAc5zSaDQ20S8LIdDY2Ii6ujpcunRJSizUajWcnJzg5uYGd3d3BAQEICIiAkIIVFZWIj09Hf7+/oiMjISLi4tZcaqqqgqhoaGsP+yjmGxQl9BX+FZRUQEPDw+dx7TuqHtjR6RNDGpra6FUKuHq6trpOa3a57OYnIioY/TtTVFVVQVnZ+c2fXRPmKKrVquhUCh0RioUCoV0v729Pfz8/ODt7Q03Nze9oxbaeC2XyxEREYGSkhIcP34cnp6eUKlUZrVHG9dZf9j3MNkgizLl6o9WTk4Ohg8fbjOjEpZaRarl8RoaGqSh6JZXjxwdHeHm5gYhBC5duoSwsDD0799f6uzNGR1picXkRETGmTpFFwAuXryIgQMHwsXFxSbilD5KpVKKM9o/GxsbYWdnBzc3N7i5ucHT0xNBQUHSxa3S0lI0NDQgODjY5PPY2dkhNDQUISEhKC8vx4kTJ5CdnY2BAwfC29vb6PvTMqax/rDvYbJBZrPU3hTGdiztLh1pi7ZIXZtQlJWVQaVS4dKlS3BxcYG7uzvc3NzQv39/natH2iDn5OSEy5cvIz09HYGBgYiMjOxwW1q+jtadOXd8JaK+pLNTdAHLxSlLrFDY2NiI6upq1NTU4Ny5c6irq5MuKGnjjK+vL8LCwuDs7Nzul/+Okslk8Pf3h6+vL0JDQ3Hp0iXk5ORALpdLtZP6zmdqMbm9vT2nAvcyTDbIoJZJhbYj0F4xaTn9CYDRzrq3UKlUba4eNTQ0QCaTSVeP3N3doVKp4OHhgZCQEJOO6+joiKioKERGRqKoqAhHjx6VRkRav8/mal2kl5OTg/79+8PV1ZWdORH1ePqm6DY0NKC2thb9+vWTHmMLU3RN+YKvvXjVMtbU19dDo9HA2dlZ6reDg4Ph5uYGR0fHDrfHEgmQh4cHkpKSUF9fj4KCAly4cAHh4eEIDQ01eXfylnFKCIGCggJ4e3vDy8uLcaqXYLJBZk19qqmpQXV1Nby8vLqhpfrpW/K2o4QQaGpq0kko6uvr0dTUBHt7eymh8PLyQmhoqN6h9fr6egNTltT/+3HSe247OztpOtWBAwdw+vRpODs7IyoqqtPvd8ti8uDgYBaTE1GPYk6cUigUKC4ubndqjyksGV9a0l68axlnGhoaAACurq5SrPH395cuDgFARUUFysvLrRiDNQCUAJxNerT2/XVzc8PQoUPR1NSEwsJCHDx4ECEhIYiIiICjo6PJxeQAcP36dbi7u7P+sBdhstFH6Ct80/6YszeFJef9W6sTN4W+eoqqqiqo1WqUl5dLQ9IBAQFwd3eXph+Zqu1j6+Dg8D1kslqoVBMghNzocx0cHDBy5EhUVVXhwoULUKvViIqKgp+fX6c6XCEEd3wlIptkqSm62jhlC/2Z9uJVZWUl6uvrUVFRoXPxys3NDV5eXggJCYGrq6tJbbbE69Ife5vg4LADMlk5VKqbIMRQs4/h5OSEAQMGQC6X48qVKzhy5Ah8fHygVqtNbnfrOMX6w56PyUYvY6jwrbi4GIGBgTqPbTmsbAudsjW0rqfQDkkLIdrUU3h6ekIIgfDwcIu3QyYrA1AFjcYTdnbnoFbL2020ZDIZfHx84OPjg9raWuTl5SE3NxeRkZEIDg7uUIfbXpEeO3MisjZ9U3Tbi1PmTH3q6njW3mIg7u7uEELA3d0dUVFRcHJysomY27aGohIyWRk0Gl/Y22dDpWo/2TD0Ouzt7REREYHw8HCUlpbi8uXLOH36NKKjo+Hp6WnycVlM3jsw2eihzF3zu7Cw0OQaAmMsPRphqWOp1WpUV1e3W08REBAANzc3vV+mtcV21iBEAABfyGQ1UKuNd+D6eHh4ICEhAQ0NDSgoKEBeXp405aqzWExORNZgztQnS8Yp7bktoWXM02g00gWr9i5etV5KtqSkBE1NTXB2Nm16UncQwgdCBMHOrgwq1RiLHFNbX5KXl4f+/fsjJycHQghERUXB19eXxeR9BJMNG6Zv6lPLIWXtfzxTCt8s9R/RksmGuW1qubtpy86+trYWDg4O8PLyareeoivof3/coVLdieaajY4X9Lm4uGDw4MGIjo7G5cuXcejQITQ1NaGpqQlOTvprQVq3zdB70rqYvKysDADg5+fHzpyI9LLUFN2W93dWZ+OUSqWS4ot25SftRRhXV1e4u7vD3d0dgYGBFtknyXY4QaWaAVPjlLlLtPv6+sLf3x81NTXIz8+XVrAKCgoyuI9Ja62Lya9fvw6FQoHg4GDGKRvGZMMGtO6otX9euXKlzRrY2v9ovfk/lSlD0tpRCnd3dxQXF8PFxQVBQUHd3XSJ/n8bu//9dF7LFaz279+Po0ePwtvbG3K5HG5ubgafZ86OrzU1NdBoNOjXrx+LyYn6OENTdK9cuYKgoCCdfqEnTNE1tBiItoZNOxru5OSEyMhIg1fhTWVr74PhWGBenDKnDkP7WE9PT52R+osXL+rsNWVKoqg9lkKhQEVFBfz8/FhMbsOYbHQRfYVv7U19unLlikWmyViSpVd+qq2t1enwTR2Stlabeho7Ozs4OTlh9OjRKCsrw8mTJ42uYGXOlSgWkxP1PeZO0S0uLpauKtuK1lOfGhoa2iQV2qVktdNsg4KCpMVAWqqpqWl3zwpT9bY41dnX03KkvrCwUNprisXkvQ+TDQvTt+a3tqPWN6TcFVOfLKkjbTK0P4VCoUBhYSE8PDzarafoSbojoMhkMgQGBiIwMBCVlZUGV7DqSLKhPb6hIr2e/u9F1NdYcoqu9kted1Or1VJ8qa6uxvXr13HkyBEA0Ll45evrCzc3N5tKjnqyzq3S2MzR0RHR0dGQy+UoKipCQUEBzp8/j4EDBxodqQfaLyZn/aFtYLLRQYau/hQVFRmc+tRbvpTpCyz66inq6uqgVCoN7k+RmZmJIUOGdGpTIlvVnZ2asRWszE02TC3S0yYd7MyJbEdXTNHt6pFlbZxpORre2NgIOzs7Kc54eHigrq4OSUlJFom7tpBMWZolXpOl3xftXlOlpaXw8/PDqVOn4OTkZHSvqfbilEajkT4fLCbvPkw2jDC05re+wjeg+QNeVFRkc1OfLE2pVKK8vLzdegq5XG50f4q+PP2pK+hbwUq7SZIpCZ6pxeTauc9VVVUICAhgZ07Uhbp7iq61VijU1u21vHiljTPapMLPzw8RERFtlpJtbGxEWVmZRRKN3tyXdfa1mVsgbg5/f3+EhYWhqqoKFy9ehEqlglwuh7+/v845zYlTSqUS165dg6+vr9l7Z1HnMNmA4cI3tVrd5rE9ofDNEgztT6ENYNodTturp+hJrNlxGtIV52s5L/aXX35Beno6goKCEBkZaXQFK3N2fAWAs2fPwtvbm0V6RFZgq1N0O5NstIwz9fX1UCgUOHr0qFS3p00qQkJC4O7ubnKcscV+xxbbZMu075e3tzdSUlJQV1eHvLw8nD9/HhEREQgJCZGm8Jkap2QyGXJycjBs2DCo1WrWH3ahnv8N0Qz6pj7V1dVBoVBIQ3Tt7UzaHW22ZhsM1VPIZPr3p6irq8Ply5cxYMCATp+7u99ba+mOpMUUjo6OcHZ2xo033oiioqJ2V7Ay53W0XNKSRXpEHacvTjU0NKCqqgp+fn46j7WFKbqmJBumxhlnZ2ekpKRY5OKVNfbZ6CxbGsnvSP/emWOYQ99x3d3dER8fj8bGRhQUFODgwYMIDQ01eyRdCCE9h3Gq6/S6ZMPYmt/awjctmUwGhUKB8vJy+Pr6dmOr9WtZpNcZ2vejsrLS5HqKrpr6ZIlj2eIXe1umnRfbv39/oytYdfSzZ6yYvLePCBKZquVoentTdJuamnD16tU2u2t3hqW+KLaeTtk6qWgdZ7y9vQ3GmcLCQou2iYyz1Httacb+7ZydnTFo0CBpr6mLFy/C3d0dwcHBcHFxMenYLWensJi8a/S6ZGPDhg2or6/HnDlzpNuMFb7Zykoa+pjbYRrbn6KhoQFlZWUm11P0NLb6b2jL2lvBqrNfRgwVk7NIj/q6nTt34sCBA/jzn/8s3WZsiq69vb1F+7jOXMgSQkChUEhxpqamBidPngQAODk5Sas+aeOMKZuNtm6XLbG1fsqW3h9rtaXlSoiGODg4QC6Xw87ODtevX8fx48fh6ekJuVwODw8Po8dufdGZxeTW1+uSDaB5kxdTl7XricmGoXoKY/tTHDlyBIMGDbJam7r7WLakJ74mfStYOTs7G1wBxBz6ivS4SSD1ZTKZDPX19WbFqZbF3pY4v0ajMfqFTq1Wt4kzCoUCAHTqKdzc3BAdHY1+/fpZpF2WGu22tRF4S7KlPrOrRzb08fb2Rnx8PMrLy3HmzBnY29sjKioKPj4+Jh+Dccq6el2y4eTkhKamJpMfr+10bVV1dTUaGxtNqqfgfEPb0VUdk6WDYMsVrI4fP44zZ85AoVBIO7t2Rssivbq6OuTk5CA5OZnF5NTnODk5obGx0eTHWzpOtbzIplQq20x9armUrJubGzw9PREUFARXV9c2caaiosKisYdTa7uGqSNbGo0GSqXS4DGsoSP7QclkMvj7+8Pf3x/Xr19HXl4ecnJyIJfLERgYaHZ9ikwmg1qtxq+//oqRI0eymLyTel2y4ezsbFYnbukrRuYytj9FQ0MDiouL4enpaVI9RVfo7VeMqJmLi4s02lFXV4dDhw6ZtIKVqVoGExbpUV/TkTjVmb5SCKFz0aq+vh4nTpyQVuRpueFdWFiYWTtmWzIRsrWkBei9I/CtaUeyamtrpc+JQqGQXn+/fv0QHR3dZopSVxWIm/NYLy8vJCcno76+Hvn5+bhw4QLCw8MRGhraoXa0LCbnZrYd0yuTDUNZuD7W6Ej0ffiN1VMY2p/i5MmTiImJgbOzs0Xb11mW7MR7o64MTNZc+UoI0WZn1/ZWsDLn2NqrUSwmp76mI3HKlC/02im2LZOK+vp6aDQaODs7S0mFs7MzhgwZAnd39868DACWn4psi9OoLMFW+jLtTuvV1dVQqVQoKytDQ0ODzqaI/fr1Q0hICFxcXKS+ubGxEWfOnIGDgwOio6MtMsXWkM4mG1pubm6IjY1FU1MTLl26hIMHD0KpVEKpVJq115Sh+kNuZmu6XpdsmDuNyhojG7W1tTprh7dXT2GIpYfOLfHFlP+pTNPZJQVNZe1ko+WSttoVrK5evSqtYKWdq21uYDdWpMdicurtzJ1G1foLvXbZ9pZJRUNDAwDA1dVVijP+/v5wdXVtUxtSXl5usSuzfaGOrycufatNKrQXN7XTsLVJhUqlgqurK8LDw43OmND21X5+fvDz80NVVRUuXLggraBmLZZINrScnJwQExODqKgoHDhwAIcPH4avry/kcjlcXV1NPnbrYvKmpibGKRP1umSjq4anDa0brlAoUFBQoDNS0dF6Cmt04pZINmzxKlZPpC1E6+wxuiLZ0JLJZAgKCkJQUBAqKytx/vx5qNVqyOXyTh9be3wW6VFv5+zs3O5FsdZTbBsaGpCZmYmmpiazlyxvzVYTBFssELf1PkebeLb80dbcaL+H6Ftu+NKlS3BycjL6ZRtoG6O9vb0xbNgw1NTUID09Henp6YiOjm6zs3dXMScG2tvbw8nJCaNHj0ZpaSmysrLg5uaGqKgoeHp6tnm8oUUUDMUp1h8a1ueTDWOjB8bqKQx19sePH8eQIUMssimRLXbilmTJ/5C29tpa0i4V2TIYKBQKaeff4OBgi9VCWFJ7nXjLFawuXryIuro6FBcXIygoqN3kur2VcFoW6QkhUFNTg8rKSoSHh7Mzpx6vZZzSTrFtPVLRcoqtdhQ8NjbWIkuW22psscU4ZSu0FzgrKipw/fp1ZGVlobGxUee7iI+Pj9k1Nx3l6ekJNzc3xMfHSzt7R0VFISgoqEv7545ccJPJZAgODpYumuXk5EAIgaioKPj6+krHa+/YreNUQ0MDioqKEB0dzTjVSq9LNpycnMyaC2tnZycVRplTT2HoQ2TJaVm2OorAYKBfy/XnS0tLUVRUhPr6egCQptC5u7vD398fbm5uUrJRUVGBI0eOwM/PD1FRUWbV6HT1yIY+Hh4eiI2NRV1dHaqrq3Hx4kWEhYUhLCzM4NKe5rRbJpNBqVSioqICISEhLCYnqyksLMT8+fNRUlICOzs7LFmyBMuWLUNFRQVmzZqF/Px8yOVybNq0Se+ymp999hleeeUVAMBzzz2HBQsWSPcJIXDq1CmcPXsWR44cwcWLFzFixAg8//zzkMvl7U6xLS4uttgFCVuNU5Yc2bCkrox5KpWqzUiFdjRLW2Pj4uKCgQMHwsnJqdu/0Gp39m5oaEBeXh4uXryIiIgIhIaGdkn/3JkYKJPJ4OvrC19fX9TU1EjLvkdGRiIoKMjsOCWEwLVr1xAREcH6w1Z6XbJhbGTD0P4UdXV1yM3NNauewhBb7Hi1x7LUcWzx9VmCqW3RN1LRMqloamqCp6cnAgMDjU6h017dDw8PR//+/VFSUoKMjAx4e3ub/EXAFpIN7WPt7e0xePBgREdHo7CwUFrBKiIios2XpPZGNvQd387OTpr2yB1fyRocHBywdu1aaZrI8OHDMWnSJGzYsAETJ07E8uXLsWrVKqxatQqrV6/WeW5FRQVefPFFHD16FDKZDMOHD8ftt98uJSUymQwffPABIiIikJCQgIMHD+K///2vSbseW5ot9+O2FBMA602jarnksL6kwt3dHX5+flL/qW1HeXk5qqqqOr1wjKVjh4uLC4YOHYqmpiYUFBTg4MGD7V50sgRLvQ5PT08kJiaioaEB+fn5uHjxIgICAsw6trYt2jjFYvL/02XJxsKFC7F9+3YEBgbi1KlTAIBZs2bh3LlzAICqqip4e3sjMzOzzXPlcjk8PT2lLPHo0aMGz6NNNvbv34/IyEjpKoGh/SlcXV1x7NgxJCYmWuR12monbmtf7G1Vy46gvaSi9UiF9stzTk4OfH19je5i2pqdnR1CQ0MREhKCq1ev4sqVKzh58iT+P3tvHuVGfeeLfqq0by2pF+3q1tJtuvESjO2EmZvrG2aeCcPkMcfDJATIIVxDYHLCTF7i5MHNBE44MwnhzktezrsMQyZAIJMbCJNLJjfEh5Cw5BjGdreNbfDei9S0u9vdrW61e9FeVe+PpoqSVJKqpJJU3dbnHB/bUumnn0ql77c+3+XzDYVCsijGVINqFUHEKFhJdRB8clLYpNea+NqCXHC73XC73QDWbj4GBgYwOTmJX/3qV3jzzTcBAJ///OfxiU98oohs/Pa3v8WePXvQ3t4OANizZw9eeeUV3HbbbdwxTz75JIC1wbNPPPFEU4gGoFzfshF/u2wp9sLCApaXl7G4uIhsNstJDrOkoqenR5YSuWZDq9Wir68PwWCQU4DyeDyKmMkhBnq9Hv39/chmsxgZGUEsFuOyHZUyi6X8FMMwyGQyIAjiiu0/bBjZuOuuu3D//ffjzjvv5B77+c9/zv17//79ZaXU3njjDXR2dgo+R1EUvvrVr+Ls2bO4ePEiVldXkcvl8MADD8Dtdjd0PsWVkJ7eaKSFJRUrKytIp9NYXFwURSrKrVdLWtfpdMJoNMLtduP06dPQarUIh8OCDWxKyWwIZSrKKVhJ3XepZvVWM3kL9UI0GsXx48fxsY99DDMzMxwJYQMChZicnITf7+f+7/P5MDk5Kbi2VNVEubHR/ZScELsnllTw//BJBcMwMBqN6O3tVUR/Xr3PMyuR29PTw92Xib1pl4J6+UCNRgOv14tsNguDwSBK9p2maUE/xf59JTeTN4xs7N69G9FoVErEV+wAACAASURBVPA5hmHw4osv4vXXX69qbZVKhU9/+tPo6+sDTdO466678NOf/rSG3VYPpRpeOddS4p7EoFKmgqZpaLVa+P3+pk9kJ4gPp6HG43GcP38eJEkiFArBZrNxxymFbJQ7VkjBKplMcpK5Yt6jXNmVkDFn+7aaPQSzhfWJlZUV3HLLLfjBD36AtrY2Ua8RsmWlrj2VStXUm2ql+hYlko1CZDKZIlKRy+UE+zv5N9WxWAxLS0s132jLeX4aYRtVKhVHONibdrvdjmAwKEtmT6qfkgKapqFSqbig2dzcHBc0CwaDRQFytty3FPgBMrYUGFiryNno/YeK6Nk4ePAgnE4n+vr6BJ8nCAI33HADCILAfffdh3vvvbfomI9//OMAgHg83tSIkVINr1Ib7+RA4Z5KkQqGYTgNeqFMBRuFlFL+1AjY7Xbs3LkTly9fxtjYGHK5HEKhEFeuUS9DKxfZ4INVsIpEIpiensbhw4cRCAQqKliJXZ815jRN4/Dhw7juuutazeQtSEI2m8Utt9yCO+64A3/5l38JAHA6nZienobb7cb09DQcDkfR63w+H1dqBQAXL17EJz7xiZLvs5HIhhKzJLWAr0S5vLyMlZUVjI+PF5EKp9MJk8kkakCcnFCiHy4H1n6zN+0zMzM4fvw4LBYLgsFgTaXC9fBTQscTBAGHwwGHw4F4PM754kAgwMn+ViIbLPik49ChQ7juuus2fDO5IsjG888/n1fXWoi3334bHo8Hs7Oz2LNnD/r7+7F7927BY5udnlYiQZBzLSU1A7KkYnl5GZlMBvF4XBSpqDdqzTaUOidWqxXbt2/H8vIyxsbGMDIyAq/XK6mxvdbSJbnW1ul0XH38+Ph4RQWrahvKCYJoNZO3IBoMw+Duu+/GwMAAvvrVr3KP33zzzXjuuefw4IMP4rnnnsNf/MVfFL32k5/8JL7xjW8gHo8DAF599VU8+uijsu9PjmtXToIg5wTxZmS7+ZkKtmE7l8tBq9XCZDKBIAhuTkWjSUU9IeU8y2kvCeJD2dlYLIZTp05Br9cjFAoJlgpXglQ/JdWPCK3NBs1WV1c52d/u7m4YDAZJ54pPZDb6MNumk41cLoeXXnoJx44dK3mMx+MBADgcDuzduxeDg4MlyYbUORuAvBEmpdbCAutX5aNSpkJJ5U+NgMVC4yMfCSORIHHhwgUsLi7i0qVLFfXNlUQ22NpWvV6fp2B16NAhuFyuIgWravbOko1WM3kLYvH222/jX//1X7F161Zcc801AIDvfOc7ePDBB/GZz3wGTz/9NLq7u/Fv//ZvAICjR4/iySefxFNPPYX29nY89NBD2LVrFwDg4Ycf5rKPQpB67bG+RQ5lH6UShHqRjUJSwf6hKIojFSaTCS6XCyaTKU+JcmxsrCnZi0agfvYvC4J4C0AGDPOfwTDF5awEQaCrqwtdXV1YWFjIKxWWgnr7qXL3E6zsbzqdxvj4OEZHR6HVarneQbG4EvoPm042fv/736O/vx8+n0/w+dXVVdA0DYvFgtXVVbz66qt4+OGHS66nVqtBUVS9tlsRSjW8cl2s9Yw8Vav+pNTyp2pR3iCOQKV6AYAKRuOd6OvrA0VRXFq3p6cHbrdb0EDWm2xUk3lgUUnBSmpmo/D4K8GYt1A7Pv7xj5e0b6+99lrRYzt37sRTTz3F/X/fvn3Yt2+fqPeSakeVXPok11pA7dnudDrNyciePXuWm5ml0+k4eXu3211EKuq1pysRBHEKJPk6AAI0rQHD/JeyNpaddcGWCq+urmJ+fj5vwF4pSBUyqYcP1Ol02LRpE2w2GyKRCI4cOYKuri709PRIkijeyM3kDSMbt912G958803EYjH4fD488sgjuPvuu/HCCy8UlVBNTU3hnnvuwYEDBzAzM4O9e/cCWMuC3H777bjxxhsbtW3JUGpmQym1sMCHpIKdtslORgWqU39SGup5ngliFAxDgCDSIIgpADaoVCoMDAxw0ZVDhw5xszv4UdB6kg25jHgpBSuTySSpsbLUfko1k6vV6nVvzFvY2JDTtyg5syEGfFLB/0PTNGcvCIKoaWZW+T0lQBCTYBgXAOmlPxsfRgAEAAbAWhBQzHfLlgofPHgQk5OTGB4eRigUKjvvolE9G2LAltz19fXh0qVLeOedd2CxWBAIBASDoeV+N0LN5Ou5/7BhZOP5558XfPzZZ58teszj8eDAgQMAgFAohJMnT0p6L6k3DOwXKlctrFKjT43u2aiUqchms9DpdJxDWBvadgQkeRg0/Z9A086a99ss1NqzUdqwXgOCOAeG0YFh+vKOZaMrwWCQIx1erxd+vx9qtVpxZVTlyhIIIl/B6vTp02AYhtOkr/ReYjIhhcZ8ZmYGVqsVJpNpXRrzFtYXqimjUipBqNdaDMMglUpxvmNlZQWJRAI0TUOv13Mzs7xeL0wmU15wJR6Pi1YSkwYGGs3/BEFMg2Hakc3eB6ByiZXSAhlSbLbUvTPMJtD0nQByYJhNYBhp9zEqlQrbtm1DIpFAJBLB6Ogoenp64HK5imxzM8uohNZnh8+yc7Pm5+dx9uxZqFQqBINBbsin2P0U+qn5+XnodDpYrdZ11X/Y9DKqeqDa9LTSyIYSnYvQOaq2/OncuXMFw++WoFb/Dgxjg1r9v5HJ9APY2Dd9pa650teiEzT9Ze5/DLNcdKxGo0Fvby8CgQA3ydvtdsPlcknam1IiRna7HT6fD7lcDtPT0xgeHkaggoKVlLIu1mBfunQJGo2Gm/R6pU98baH+kHIzo8Tgk1xrsaQimUwinU5jZmYmj1SwPoTty6vnROpS+/sQFAgiBoaxgSAWAWQhhmwUr7N+IH3fBBiml/d6qio7ajQasXnzZqRSKYyPjyMSiaC7uxter5f73TSjQbwUhMp3WQn7y5cvIxKJ4MKFCwgEAnA4HFX5qVgsBovFAr1ev676Dzck2ZAKpRneeqwF1G7oWFKRzWYRjUZFk4pSKP58BjCMHQSxAJruhliioaQSMUCe8ywFpYyMWq1GMBhEd3c3Jicncfz4cVAUhUwmI6okSSlkgz2eHYjFOp5yClZSy7rY17ClVDRNX/ETX1uoL7RaLZfdFQO5S3Tl6m2sJdudSCQ4UmEwGJDJZNDW1gaXy9UUUiGE4t+9GrncXpDkICjqE1grGWqhHGoN5rJCIvyp5F6vFz6fT3F+qtTxVqsV11xzDRKJBKLRKEZHR+H1ekWvzYLvp9ZT/+GGJBvVpKflVPlQYvRJKpsvl6mgKAoGg6EOPRUaZLN3gSBmwTAeSa9UEtkAak+ZSzGelaBSqdDd3Y3Ozk4cP35c9FClekaMqk1PAxClYCV1ffY92OgR/7fHGnONRqOIm58WNgZY5USxZENuf8AOFKsVQvuqpCBYKjAVjUZhNBqrkkBtJGi6HzTd3+xt1ASl+Uwx0Gq1RVl7hmFEE2epQaha/FQpGI1GXH311chkMhgbG8PS0hLGxsbg9/tFKZ6xn6FUM7lS/dSGJBtSf0RKLFeSey2g+LxUW/40NDQEp7P2fgrhH70JDBOsee1qIFcpnZT3k/J4qWOl1N0aDAZs3749b6hSKBSC0SgcnWu2yke59cspWEklP+x7FKbA2b8bfW20sPGh0+kkzYRSYiCLlZNeWVlBJBLhMhVA9WIfSrsJVlrmXE7Uq2ejEFIDV5XAz9ofOnQIJ0+e5Ka2lyPv9c5sSPGDWq0WgUAAq6urUKvVGBwcREdHB3p6emAwGMruqfB3VBggUyI2JNkApN+EKc2Is2vJsS+2sSgejyMej9dc/iQnlPbjUMINZb3SwvzIPTtUaW5uDu+99x4MBgNCoVDV8sHNTE8LKVgRBCFJchAoH8VaT414LawPSB1AK3dmQ4pvoWm6KDCVTCa5tYC1mQMOhwMGg6FqH6L0G6YWqkO9fJpKpYJer8eWLVuwsLCAY8eOwWazIRgMCt6wV+N3pGbsq8mcdHd3w+/3Y2ZmBidPnoTRaEQwGBTM8K1XP7UhyQZbCytWKlNuScFmEZdymYpcLgeCINDR0bFuJWXXCxodBa/WiBMEAYfDwQ1VOnv2LNRqNUKhEKxWq6Q91FvlQ6y6FKtgFYlEMDExgaGhIQSDQdkUrFpoQS5IHUDbCN9C0zSXnSgkFfzyJz6pWFhYwMLCAhwOhyz7UhquRALEVwFbXl4TIQmFQnWQEBZ+72oIAav+NDs7i5MnT8JkMiEYDOYF0KrxU1JKkhiGkXQ83+fwg4ALCwu4cOECACAQCOTNG1mvfmpDkg3WiIslG0rMRrBrCe2rmvKnkZERbnCOUnAlGnExkDvlXGldloR2dHRgcXERIyMjACCppltJ6WlgbcCj2+2G2+1GJBIRpWC1Xo14C+sTzcxsMAzDqT7xSQVBEJycrNlshtPp5FRvGrGvlk+oDDmvAQDcvJKVlZW8eSV6vR5msxl6vR65XA5HjhyB0+lET0+P5Gnq9SoNZo/n37A7nU44HA7Mz8/jzJkz0Gq1CIVCaGtra4galZRzU6okivXHy8vLRf5rvfqpDUk2pBpxJdbCskin05ibm5PUU9GIfbVQX9QrClTpWJvNhh07dmBpaQmDg4MYHBxEKBSqmBmotxGvNp1tNpuxdetWpFIpRKPRigpW69GIt7A+0YieDYqikEwmufkULKlg1zEYDLBYLHC5XDAYDFVlFpTa86g0yE3KpCKbzeaRilgshtnZWRiNRpjN5pLzStiqiFAohIsXL2JwcJAjHWIht58qXFvo9azkbDwex/DwMACUHQ4ohGoaxOUMolksFmzbtg3JZBLj4+MYHR0FwzBlryMlZgeBDUo2pBrxZtbCAqUzFdlsltNQlqOnQolGXI49Ke3H1Ujp23od29bWxqlmRCIRjIyMIBgMwuFwCJ7vepdRVUNm+Mfr9Xr09/cjm82WVLAClHcttbBxIbWMqpyfoigqr/RpdXUVqVQKJElymYq2tja43W7o9XpcvnwZs7OzCAQCNX+OjZ7ZUOKeyoGiqKJMRSaTgVqt5kiF0+nkbsjFVjuwvQU+nw+Tk5MYHBxEOp0WJaUuxT/IrRhlt9u5ANq5c+eQSCQ4IlLpfRoRFBNzvMFg4PzXoUOHcPz4cbjd7iL/pWRsWLKhtFpYQHr50/z8PJaXlxEKhWTZl9Ig556U5gxq/Wz1iAJV00vCZgZYbfCxsTEEAgG4XK68tZqhRlXpeCEjXk7BqhKU+BtqYf2imsxGLpfD8vJyWVJhtVrh8Xig1+tLXrNKLR0GlGfLlQq2v4ZPKlKpFBecNJlMnLqR0A3p3NxcVe9LkiT8fj88Hg8OHjyIoaEhTglKrhtfqbZWzPFtbW0IhUK4dOkSZmZmMDo6ypUmlXp9vf2UVHKi0WhgMBiwdetWxGKxPP9VSlFSKdiQZINtEBcLufssWCNQjlSYzeaKmYpWevrKRL3qW2tpXGezHGw5EjvJ1ePxcNdpvdWo5FQFEVKwSiQSWFpaQltbm+j3aaGFalGu3DeXyxX5kJWVFZAkCZvNBpPJBJvNVpFUlMJGV2BkIZdYRzN9JxukZEnFwsICkskkYrEYRyqsViu8Xi90Ol3dypUKQZIktFotrrvuOi5w09nZKUg66umnpPpLrVaLTZs2IZlMcgG0np4euN3uIh9T77kctQyfZf0Xqyip0+kQDAbR1dUlab1GYUOSjWoyG9UYE6FMxdLSEpfGFEsqSmGjp6eBjRnFEvpMuVwu76aBTW2rVCqEw+G88qR6S9/WArYcKZPJYHx8HIcOHeKmuFarwiH2+HoYfX5D4cGDBzE8PAyaphEKhfIUQFq4crBv3z68/PLLcDgcOHXqFADg1ltvxfnz5wEAi4uLsNlsOHHiRNFrA4EALBYLVCoV1Go1jh49WvJ9dDodFhcXcerUKXR0dHD2IZ1OQ6VScZkKu90On8+Hubk5qFQqeDzSBp4KQamBLDl/b3Kt1SgbwFeAYv0EG6RklcDMZjM0Gg0ymYwsFQ+17pcgCC5w4/F4MD09jaNHj6KjowPBYJAjHfUSPWFRjQ80GAwYGBhAOp3mfJnf74fX6+V8WSMaymuZB8UqSjocDsTjcUQiEdhsNkWWVrXIBipHU8SUP7GkIpVKYXFxEb29vTV/DqWSDSU6FqWApmnkcjnMz89jenq6KLVtNpu5lDNBEMhms5iamsLY2BhCoZAs8pGlIKckr1arRV9fH4LBIN5//32Mj4/DZrPB7XaLkkdsRCZECvkhCAJqtRo7duzgBpRduHCBS7MrcSJrC/XBXXfdhfvvvx933nkn99jPf/5z7t/79+8vKw39xhtvoLOzU/C5M2fO4JlnnsGZM2dw/Phx2Gw2XH/99bj//vtht9vh9/uh1WoFr3WVSqXYbIQS1wIaL0MuBgyzNu2ZDTqxf7MKUPwSKKEg5dzcnKTKjXL7kBMkScLr9cLtduPSpUs4evQo2tvbEQwGJe+rXt+ZEHnQ6XTYtGkT58sOHToEr9cLv9+vuF5E9jVCe7Lb7bDb7ZKVwhqFDUk2pJZRsVEeKaSiVKYim80q0vAqNbOxXsG/VlhnwerRZzIZpNNp2Gy2sqntbDYLg8HAlSeNjY1hbGxMUuSy0ZmNQrBzOQBgZWUFR44cgcPhKFknzN9LPRrp+MdX+1mFFKz8fj96e3sVd+PSgvzYvXs3otGo4HMMw+DFF1/E66+/XtXaZrMZN954I/bv349nn30Wbrcbt9xyi6jXKrVcSal+SgkBNlYBanV1levBXFhYgFar5UiF1+uF0WisaYZFtajFnpXyJ/yZF9PT0zh27BhMJpPoc1hvslFqbY1Gg3A4jEAggImJCRw5coRropdjfSFUq4BY7j2U6qM2JNmolNkoJBWzs7PI5XIYHx+HwWDgpOC6urokT0NVqoyuElPd64EAMQyDTCYjqEPOT23zh1y999578Hq9ghNMS0Gv13Ok4/z581hYWMDMzExJ9Sf+/uqZnhYLgiDQ1dWFLVu2YHJyEkNDQ1z9rtAk70bUwtYqY8tXsIrFYoo14i00DgcPHoTT6URfX5/g8wRB4IYbbgBBELjvvvtw77335j3f3d2N7u5uAGvXV7OETJRcRqU0nyDmd8+WTheWyarVas5H2O126HQ6XHXVVQ3YdWNQ6aaXJR1jY2OIRqM4e/YsgsEg9Hp9ydcxzAqMxiiAPgDmksdVAzF+R6VSIRAIoLu7G0NDQzh79izm5+cRCATK7ptdv1FBsfWGDU02aJrOq4HkZyr4pKK9vR1Go1GWWlglG95mR3nqBbl+rBRFIZVKYXJyknMYuVwOWq2Wkwz0+XwwGo1ly2pqiczo9XqEw2FQFIX5+fm88qpyyjJioFKNw2I5AaATgLxNZKzRZJVKvF4vLl26hGPHjsFmsyEYDOaRL6U1lJe7pjUajSy2oYX1j+effx633XZbyefffvtteDwezM7OYs+ePejv78fu3bsFj23EnI1SUKqfUupaLPjiL6yPYNXAWFLR0dHBSZLybVY8HkcqlZJ1P7Wi1syG2Pfo7OxEMpmE3W7H8ePHYbVaEQqFBG7eaeh0/wyn8zxI8ixo+v+GnLepUvwIq/DW39+PRCKB48ePo62tDcFgsKTyU7391HpGQ8mGUOPdt771LfzoRz/iOui/853v4Kabbip67SuvvIIvf/nLoCgK99xzDx588MG852OxGN566y2cPn0av/nNb/Diiy+iv78ff/d3f1cxU3EllD5tdPYs5TyxmvT8TEU6nQZFUdDr9TAYDHA6nTCZTE2rf1Sr1UXlVUKkQ/znXoLR+BRUqmWoVJOgqIfKHi31uis0svxU+uzsLE6ePAmTyYRQKASTyVT32tZ6S/G2cOUhl8vhpZdewrFjx0oew5JSh8OBvXv3YnBwsCzZWFxcFP3+V0IZFaCMYBa/+mF+fh7pdBpTU1MAwN1PiJEYVjLkOM9SsuokScLlcsHpdGJmZoYjHfmBqAxIchYUZQVBzALIoFlkA1jzIyqVCm63Gy6Xi1N+MhgMCAaDsFgsRcc3OgPPh5Kvw4aSDaHGOwD4yle+gq997WslX0dRFL70pS/hd7/7HXw+H3bt2oWbb74ZV199NXdMNBrFiRMnsHnzZlx//fXYunUrPvOZz4jal5yG90qJPsm1Tj0dC03Tgn0VbMSCTW2zTZkXL16EWq2G2+2u257EgH9O+OVVQqRDvPGkP/ijAkCJ2occN+t8tadYLIbTp09Dq9VyU2nFYj2UXbWwsfH73/8e/f398Pl8gs+z5ZUWiwWrq6t49dVX8fDDD5dcr5mZjSuhjErMWgzDIJ1O5/mIRCLBlcmazWYusx0IBK5YGyFke6VKzvLXYknH7OwsTpw4wc3AMBgMSKf/ChT1Mmj6rwDIOzuilow6q/zU1dWFhYUFnDt3jutZZAUjqiUzVwIaSjbKNd6Vw+DgIHp7e7lG1M9+9rP41a9+lUc2du7ciZ07dwIAJiYmQFHibqiAD4clyQElG1459cuVBFbdY35+Pi9bAXwoGWixWOByuWAwGOrO/sUanHLfbeHrS5EO8TfhNqys3IlM5l1YLH9R095KHV+pfrerq4sz1O+88w5OnDiBcDgMm81W8/pCx8tJNpQcMWpBXtx222148803EYvF4PP58Mgjj+Duu+/GCy+8UFRCNTU1hXvuuQcHDhzAzMwM9u7dC2AtC3L77bfjxhtvLPk+ck4QlwolryUXCvcl1HtHURR0Oh1XJsuWVPNvAKemplrBCAHUKk7CD0TNzc3h5MmTH/Q/bsbMTCccjq112XOtUrYEQaCjowMdHR1YXFzE6OgoKIpCKBQCRVF1V6Nar1BEz8bjjz+On/zkJ9i5cye+973vwW635z0/OTkJv9/P/d/n8+HIkSMl16tGjepKyEYoIT1dCKl7EpIMTKfT3A+2nGSg0kAQhOSIUSHpmJubg91uF2X4s9kwVlYcACpnbqoxymKPZx16OBxGJBJBLpdDOByG3W6XTWWjldlooVo8//zzgo8/++yzRY95PB4cOHAAABAKhXDy5EnR77OR/JSS9sXONEqn0xgdHUUymUQul4NGo+FIhcfjEa0ApTTfKWfvZaOCKJUCa2zGYG5uDsPDw1yps9xTsaVmEir5BZvNhmuvvRbLy8uIRCJYXFzE/Pw8PB6PqHNbTTnxekXTycYXv/hFPPTQQyAIAg899BD279+PZ555Ju8YoRNc7ovU6/VYWloSvQc5jYmSHYLSLtRy3yGr7sEnFtlsFhqNhmvEc7vdMJlMiMViyGQynMrLeocYJ8CSjmg0ikuXLuHw4cMVG8nrKZNbjaqGzWbD9u3bsby8jLGxMQwPDyMUCqGzs7NmJ1jvsqsWWqgVGyWz0fiSrBwI4hJyORsSCaao906lUsFsXlMx6ujoUPTsgWrRyExrqe9Dqo+odCxLOjQaDUZHR/Hee+/BaDRyfX5yoF7CJBaLBdu2bcOhQ4ewsLCA999/H4FAAC6Xq+zrr6TewqaTDafTyf37C1/4Aj71qU8VHePz+TAxMcH9/+LFi2XVYaqZIK5EgiCnEQfEsOI0CGIGDOMEUCxXWriWHBc9wzBFmQp2CB7bV9HR0VFxboPS0CiDwPaYOJ1OrrwqGAzC6XQW7UHKd9ZII2ixWPCRj3wEq6uriEQiGBkZKfkZpOynJUHYgpLRbDUqJULIf/IVoFZXV2E0/hw63TlkMp2Ixz8Po9EmOBDx3XffhdVqlYVoyNVMrbSAX62oV/DKaDRiYGAAsVgMp06dgsFgQDgcrpl01FsFkSRJDAwMIJfLcTOaenp64PF4BP3RlZSBbzrZmJ6e5hpyf/nLX2LLli1Fx+zatQvDw8OIRCLwer144YUX8LOf/azkmjqdTlJ6WqkRo8auRUOjeQok+T5ouhvZ7BcByHdRMwyDVCqVF4WKx+MgSRJWqxUmk6lqdQ+lOc5av7NqIkaF5VWRSKTohl3uSFQtexaCyWTCli1bkEwm8wx1NQ37jZjk2kILtUAq2VDqzapc9pdh1mYaJRIJRKNRrKyscINSjUYjTCYT2tra4HAsQqXaCoKYg8vlBcN0lF2zVijNv8iFWm221AZxqcSE7fPr7OzE/Pw8RzpCoRCXuapmz40ox2VnNGUyGYyPj+dNJeeXcV1JvYUNJRtCjXdvvvkmTpw4AYIgEAgE8MMf/hBAfuOdWq3G448/jk9+8pOgKAr79u3D5s2bS76PVqttqsqHnNGnxkWyUiDJCdB0F0jyIoA0AOGhdOVUkNhm7cJsBU3T0Ov1XM1sV1cXTCYTJzNbK5TohGuBFMPMRznSIQXNTO8aDAYMDAwgnU4jGo3i0KFDyGazkgy/3GVUSjbiLaxPaLXapmXgmwmWVPB9BKsApVarQVEUjEZjmaG6t4AgfgeK+s9gmPaS76NUcraRUA+7WOhLCGJtTkdHRwcWFhZw5swZ6HQ6hMNhyaRDjgZxKcdrtVr09fUhGAxiYmIChw8fhsvlQnd3NzQaTVV+dr0GxRpKNoQa7+6++27BY/mNdwBw0003Cc7fEMJGqYVt7FpG5HI3QaV6C7ncn6EU0eCDbcTjOw12CB7bV+H1emEymQSbsmKx2IZ0BnJkNqQcK2SshEiH1WoVPdW83upPYsBO2w2FQnjrrbe46JDP56vY2Cl3xKiFFuRGM8uoGgVWAYrvJ1gFKJPJBJPJBL/fzylALS0tYWpqCg6Ho+SaNP1RZDIfbeCn2HjBLDlQr8wGIExi+CpQ8/PzOHv2LDQajSTl0XqXUbH7LIRarUYwGER3dzcmJycxODiIrq4uZLPZK6bct+llVPWA1DIqpfZsNHotitoNiioeQEXTdJ7DSCQSGBoaglqtzstUBINBSfWx6/VHIwa1fjYpmY1yxopPOt59913Mzs5y2aRy79EIoywWGo0GOp0OH/3oR7nokNvt5qJDQriSamFbWJ/YKGVUwNrv//Lly3mkopSgR7lAgRL950b1U3KUUdWjZ0PMd8aSY6jR4AAAIABJREFUjlgshhMnTnAy6oVD9grR7IZslUqF7u5u+Hw+TE9Pc73ImzZtEhUIXM9+akOSjWamp+W8MBtteBlmbWoqP1MhNAQvHo/jmmuu2XAKH0pBPW4o9Ho93G43MpkMFhYWBHs6CvdQbzUqqSgVHQoEAkXiAS01qhaUjmaW+1YLVpK0UAEqmUxienoaZrO55G9SDJRINoD69X4QRASADgxTWvCmmWCYtaGHGo0Ger2+prXqQUzsdjvMZjN6enpw/vx5qFQqhMNhtLW11bw2i3r4NZIk4fV6MTc3B6vVihMnTsBisSAYDJZtgl/PfmpDko1mllHJiXoZXtaAFKa3GYbhpqaaTCY4nU7BIXjj4+Oy/ADrd94ZEMQEGMYMoHRNb73QyAZxqcdqtVqEw+GyjeRS163meCkoPJ/86NDU1BSOHj0Ku92OYDDIOcRWZqMFpUPJfoqmaSSTyTz/IBR48vl80Ol0OHr0KPr7+2t+XyX64nrZNZJ8GxrN8wBUyGT+BgzTW5f3EQt+WTT7nbNl0ZlMBk6nE4FAgAsyyp2t4B8rfl0aVusJdHSMw27fg3icwIULF0CSJHp7e4tIh9KkYxmGgcPhQE9PD2KxGE6fPs35aKEszXruLdywZKNZZVRyQo4Lh23WjsViiMfjuHz5cl7NrNlszquZFbsvJTmEwv2oVL+FWv0bAAZkMvs/kPKtDDk/U6N+9FINPmuoKqlXMUwWNtt/gCSHQdN/BqC85GC9yYbQ2iRJwufzwev14tKlSzh+/Dja2toQDAYVRZZaaEEISvBTrEogn1QkEgkA4AJPFosFLpdLMPAkN5Sa2ZAL/P2Q5AQAFYAMSDIGimoM2WCJZDKZxMWLF5HJZDi5eX6g0WQyQaPRIJvNgiAIzM/PY3BwEC6XCz09PZLes14BNOA8PJ7/DZJsBzAFu/0r2LlzJxYXFzE8PAyCIBAOh2G1WqtYu/5gfTKrvNXV1YWFhQVcuHABwNqgUP6Q6/UcFNuQZENqGZUSjZJUsEPw+E4jk8lwfRUEQcBkMqG/v1/U1NT1DJIcBmAEsAqCmBVNNgBlRAbqZRCF1i1FOiyWU+jsfBkkaQOQBk3f0ZQ9i1mbIAi43W64XC7Mzc3hvffe46KyYtVK1nPEqIX1iWr8VLUoVIBi/wwNDcFgMHDN2p2dnTAajU29oVGaL67X/QFF/SkIYhaACRS1Vfb1AXDfOfsnkUhwFQy5XI6bY1VJbp4kSfj9fni9XkxMTODIkSPo6CgtOVyI+mXriQ+OpbFG3NZgs9mwY8cOXL58GaOjo2AYBr29vYpTcxIq921vb0d7ezuWlpYwOjrKDbvt6OhoNYgrDRtZ5YONSvD7KlKpFEiS5DIV7BA8jUbDXZiLi4uYnZ2VhWjI2XhXDyOey30KGs3/BMNcBZreJPv6SoJcszMKScfMTBTBIA2GoQFUrr2WYsSlfudi1yaItQm0XV1dOHjwIM6ePQu1Wl22hpfFeo4YtbA+IbWMSiz40uOsjyhUgPL5fFheXsauXbsUdfOi1J7HeoBhupDN/q0sa7HBRv59QTabzVOGLKxgOH36NOx2u6jGZPZ7IUkSPT098Pl8GBkZQSwWQzQaLZofUSvEXgc0Hcbk5G2w2Wyg6WJxG6vVimuvvZYjHUtLS7DZbFymo9ko53fa2tqwfft2rKyscMNuOzo6FPV7lYIW2YAyjRKb3mYnUfLT2+yAI4vFArfbLWoIntzpaSWDYYLIZL7ZxPdfvyofLOmYmbEjGqUwOZmB3f5ROJ3l30dKxKUaWVqphEqtVmPXrl2Ix+MYHh4GUJySLnyPFtlooZFgZ0pUi1wul9eszd5g8lUCXS5XSQUokiQVV1ay0cuoagW/3zISieSJuBQGG6tp0BcLlUoFj8eDZDIJmqZx+PBh+P1++Hw+QTtaP58GJBIDoOkdZY9jScexY8cwOTmJ6elphMPhkv6gURDjC81mM7Zu3YpkMokzZ85gaWkJbW1tcLlc68pnbUiysd5UPgoHHPGH4FEUBYPBUHN6W27DqySVj42IepENscdqtXqo1f8ZwWDog/Kqw7KpVzVSKcput2PHjh1YWlrC2NhYXkqavweaplvqai00FFJ+L3zlp5MnTyKdTkOlUnGZimpuMAmCUBzJViJBaNae2AwV/96AtZ1qtRpdXV0lRVzEQA6iqVKpEAqF4Pf7uUnZPT098Hg8edeVEprJgTWCf9VVV4FhGIyMjGBkZAS9vb1NIx1SfKHBYIDX64XZbMby8jIikQhX2iZnVqle2JBkQ6kqH6zag5AWORuJ8nq9MBqNXCRqaGhIlgnbSowYKSmi1gywUchCSD239SQmlRrJa1lbLORwim1tbbjmmmuwsrKCsbExjIyMIBQKoaurS5E3XS1ceeArQAlJj7PDUa+66irodLqafxNsZkMuyPE7VaKfkgulzg1N01yGiv3D77c0m83weDzc9z8zM4NUKlV28GEjwD+3Go0Gvb296O7uRjQaxeHDhxEIBOB2u7nP3ewAGv94s9mM7du3Y3l5GaOjoxgZGUE4HEZ7e/XqldVca9VIzGu1WgSDQYRCIbz//vs4fPgwPB4P/H6/ovtxlbuzGlBNGZWcYBimKCLBj0TVqkVeDZRmeDcyCs8zwzBIJBJ5soKpVIq77sLhcNHNe7OjQIXHVlavkpbZaJYsrdlsxrZt25BIJBCJRDA6OopAIACKoloN4i1w2LdvH15++WU4HA6cOnUKAPCtb30LP/rRj9DV1QUA+M53voObbrqp6LWvvPIKvvzlL4OiKNxzzz148MEH856naRrvv/8+Tp06hWw2i09/+tP4sz/7M2zdupVr1jabzYJR69nZ2ZrnHbCox4290siGXJBjTwzDIJfLIRaLFSl/sRkqu90Ov98PrVareJsj9H1rtVps2rQJPT09iEQiiEajCIVCkipH6k02+HbeYrFwQahC0iH1/Ffrp6rN8ms0GoTDYfT09ODixYs4cuQI+vv74fEoc2bLhiQbrFxbvcEwjKAWeSKRwPj4OMxmM6xWK7xeryyRKKVAToew0TIk2WwWuVwOk5OTXISSP7+kra2N67PJ5XKgaRoTExOIRCIIh8Po6upqGIGo5thSpENqz0azZWmNRiM2b96MVCqFaDSKqakpuN1udHV1tTIcLeCuu+7C/fffjzvvvDPv8a985Sv42te+VvJ1FEXhS1/6En73u9/B5/Nh165duPnmm3H11Vdzx/z1X/81ZmdnsWXLFjAMgwceeABbtmyRjUSIhZyZDaX5BDnXqsb2FM6sYLMVDMNAr9fDbDY3XflL7Lmp5hzqdDr09/dzfmJubo7zbZXOZzOy5GazGR/5yEc40jE6OopwOFzXkq5qIERo1Go1AoEAuru76/retWJDko1q6xfLPVfYV5FIJEDTdF4kyuFwwGAw4J133sHAwICiblqUmJ5WqoMSg1Kpb41Gg1wuB5VKBa/Xy6W+S0Gn0+Hqq69GMpnE6OgoIpEI2tvb66LsJKcRLyQdq6urmJmZKdnTUe0+gPo2b+v1evT394OmaWQyGRw6dAg+nw8+n29d1MG2UB/s3r0b0WhU8usGBwfR29uLUCgEAPjsZz+LX/3qV3lk41/+5V+4fx84cAA7d+4Uvb6cdo4tH5RrLaX5hEZk8/nqkOz9gdDMinA4jEQigampKYTD4bruSQrE2GGCIASPE2PHWT8xMjKC+fl5HDlyBOFwGJ2dnSVfWx+ysQCSPAKdbgUEsbnkUSzpWF1dxejoKBKJBGKxmCgVqEaU4tI0XbJUiiTJVhnVekI2my3qq2CnaLLGo9IQPCWWLClxT+sBQkRzdXUVAPIm6fJT30ePHoXb7ZZkeAwGA7Zs2YLV1VWcOnUKqVQK7e3tohrXmllypdcnsXnzApaX01hYWCjZ08GHVKPciEwIQRDw+/2wWCzrqg62hcbi8ccfx09+8hPs3LkT3/ve94p+n5OTk/D7/dz/fT4fjhw5Itv7y1WuxF9LDpRbiyCGQZJjoOmdYJjysxmUlKXmg6ZpTgVKaGYFW8Xg8XhEqUNuFIj9nBqNBl6vF+3t7RgZGcHY2Bh6e3sFZ3VIlVEXsweV6h9BEKfR3Z0CSX4MQPksgMlkwubNm7G8vIzp6WmMjo4iFArJRpKqhdLmhEjBFetFKYrKkw1MJpMYHByEWq3Oq5llp2hKQeNu7JdBEEtgGA+AxkrfKi2KJQfYKNX09DTnUFitcrZRr7u7u2LqW4rRKTzOZDKhu7sbi4uLiEajGBsbQ19fX8k5EVINnLwRIxpq9cMALqKvj0J7+4tIpVC2kbyaPVfT41FtvS1bBxsIBLjhVU6nE729vdDpdJLWbGFj4Ytf/CIeeughEASBhx56CPv378czzzyTd4yQPZMz08dmI+TIujUis0EQ89BqfwiGoUCSp5DN7q9qHTn3VAmFMyvi8ThHNPgBR5PJtG5v/IDas2TVBK+MRiO2bduGlZWVPNLBJ+31yWysAtCBIFZAEOJK7BmGgVqtxtatW5FIJLjyKr6wCB+Nymys12tuw5MNvsIHn1iwutRsU1YsFpNtwFFjpHTj0Gr/XxDEMnK5/wMU9edlj1bajX0zwTBrWuX8EqhkMolMJgODwYCuri50dnbWXau8HPR6PQYGBnD58mVcuHABarUavb29RROxG92zkQ8KwCwAC9TqiwBS0OttFdWr6p2pqCb6U2jEVSoVVwc7OTlZ0zyEFjYG+KqAX/jCF/CpT32q6Bifz4eJiQnu/xcvXizbsKlWq5HL5UQHtOTss2hMzwb7mHgy1SiyUdhzKTSzorOzE1arFUtLS+jt7ZVlX0qCHA39YlBow81mM6655hosLy/nkQ522J7cPo2i9oMkf42LF0n09QVErc33CUajkSMdY2NjGBsbKyIdzerZ4EPJGbWGkw0hlY+vf/3r+PWvfw2tVotwOIwf//jHsNlsRa8NBAKwWCxQqVRQq9U4evRo3vOTk5M4ceIETp06hXg8jl27duHOO+/E9ddfzw3Bc7lcgrrUY2Njsn1Rct/YC13EBBH7IKthBUmebSjZWE/EpbBRjy2LY5v0WGUwo9GIiYkJaLVauFyupu6Zf26tVit27tyJhYUFnDlzBnq9Hr29vTAajVWtK68R14CivgKSfBmTkzvR1vbhb7acelW91aiqif6Ueg1JklyJXAtXNqanp+F2uwEAv/zlL7Fly5aiY3bt2oXh4WFEIhF4vV688MIL+NnPflZyTVY5UQrZUFqfRbm1GKYT2ew9IIgoaLpyb0q9bpb4Myv4s6zYEii2t0Lo3iAej284IRM5IMc5sVgs2L59Oy5fvoyRkREQBAGDwSDa3or3aT2g6fuxuHi4Jh9oNBqxZcsWJJNJjI2NcZkOh8PRymxUQMPJhpDKx549e/Doo49CrVbjgQcewKOPPorHHntM8PVvvPEGOjs7BZ/76U9/isuXL2Pr1q2wWCx47bXXJCl8KLkWtrgUpQc0vR0EEQVF/Z+yvJcUKK2MipWX5TuTZDJZ1KhnNptL1uDL3XwpZs9iX9/e3o5du3YhFovh3XffhcVi4dQympfZABjmOlDUdYjH/0PweSHSIXVuTDVlV42SLWxhY+K2227Dm2++iVgsBp/Ph0ceeQRvvvkmTpw4AYIgEAgE8MMf/hAAMDU1hXvuuQcHDhyAWq3G448/jk9+8pOgKAr79u3D5s2lG1K1Wi3S6TRMJpOofclpMxulRkXT/QD6ZXmfSuAPQIzH41hYWMDo6GjezAoxwh31glwSunKhlvsdOf2J1WrFjh07EI/H8d5772FxcRFtbW1Fmfxa9gBI8w3l1jYYDNi8eTOSySQikQjGxsa4QIRYVPM9rmc/1XCyIaTyccMNN3D/vu666/CLX/yiqrUfeOAB7t//+I//KIloyNl415jokxbZ7OdlWKe6PTUTbJSKzVgsLi5yGQyz2QyLxcLJy1bTKNxslG60JLgSr9nZWRw/fhwURXG6/2LWrQcxEQM+6Th79izi8TguXbokSr2qEZmN9dx414L8eP7554seu/vuuwWP9Xg8OHDgAPf/m266SXD+hhCkzoSS27coMUsiZp3CUtjCmRVmsxkGgwHt7e1wuVyKsOtyQo7P08jqBLH+xG63w+v1gmEYUZn8asVAxECMHzEYDJxfO3fuHObn50X7tWr2vp79lOJ6Np555hnceuutgs8RBIEbbrgBBEHgvvvuw7333ltyHZVKBYqiREcvlFpmpMRm7EY5lkJZQXY4YuFk1fb2dqysrHByk+sF1apaEAQBp9MJh8OBoaEhnDt3DouLiwgEAmXLMZpJNljo9Xp0d3dDo9GIVq9qVM/GRrshaUH5qGYArRLteD1La4VmVlAUBb1enzck12Aw5P3uI5EI1Gq1LH0J66VsuJGopzgJm7mfn5/He++9B5PJhHA4DIPBUNMehEGBJJ8ASZ4BRd0LhtkueW29Xo9AIACSJBGPxzE2NoZgMFiW6F5pfkpRZOPb3/421Go17rjjDsHn3377bXg8HszOzmLPnj3o7+/H7t27BY9l09Nia9vZiJFcKh9KM+JKNJj8Hw0rL1soK2g0GmEymcoOR5TirJWGWgwHq+7R19eHlZUVDA4OwuVyoaenp2SpWGPJBgWS/F8A5kDTnwXQwa2t0Whw1VVXlW0kZ9FINaoWWmgkWD8lFkrObNS6FhtcyuVyGB0dLZpZwSpEhsPhlhz1OkM1gS6CINDZ2YmOjg7Mzc3hxIkTsFqtCIVCXNWKHH6KIM5ApToAhjFApfon5HJPVbU2TdPQaDQYGBhAKpVCJBJBJBJBIBCA2+2WRb2q1SAuA5577jm8/PLLeO2110qeMFbVw+FwYO/evRgcHCxJNnQ6nSSyIXctrFxGXK66WqUQILamdmVlBbOzs0gkEpiZmYFGo+EcykaQFawFUg2zSqWC3++H1+vF+++/jyNHjsDr9cLv9+eR50ZnNgjiEEjyKawtswSa/m9Fa5drJK9W5aMeRryFFuqBZmY2mqVsxTClZxcZDAbQNF02uCQWGzdQR8NgOAmSnAFN7wBQnd2q1cZL9SdSwF+XIAg4HA50dXVhZmYGx48fh81mQygUksVPMYwDDGMEsAqavi5vz1Iz6ny/NjAwgHQ6jUgkgmg0ikAgAJfLxfmZKy0oJivZSKVSUKvVkqMOr7zyCh577DH84Q9/KEkOWPUIi8WC1dVVvPrqq3j44YdLrqnT6ZDNitNTBtafykez1hG7lpiaWovFApPJpChJQYJYBZACIL7fp5ngGziSJBEIBODz+bjBdN3d3fB6vZJvLOSpDdWDIEisSeR+2AArZDDlVK+SQ/q2EEqOGLUgDdX6qXqg2T0b9fYJ/JkVrB/gzy4SmlkxODhYUgRGjj2tdxgM/4G2tn+FVmtANvvXoKhPNG0v9RInKfVeLpcLTqcT09PTOHbsGAwGQ1XKjPlwIpd7HAQxDYb5UGFOjl5BnU6H/v5+pNNpRKNRRKNR9PT0wO1218VPKRmyWNtMJoNf//rXOHr0KLRaLXbu3Inrr79eUElASOXj0UcfRTqdxp49ewCsNYk/+eSTeSofMzMz2Lt3L4C1Gs7bb78dN954Y8k9SU1PK5EgsGvJ5VzqZXgLHcrKygpyuRx0Ol2evGxhTS2b2VAK9PpTcDh+Ap2uHZnMQ1gblth41BqtUavVCIVC8Pv9iEajOHToEAKBgGxKHELHCj++A7ncwyCIedD0n4paW4h0WCyWkkMNhVBtXWuLUGxsSPFTjYJWq90QDeIAuFlFpWZWdHV1IRgMSh6SWwuURDbksi8kuYK1+SUMgCVZ1qwGUoNXUo6tFPjxeDxwuVw4d+4cpqamuEBb9deWGwyTryZVS2ajEDqdDldddRUymQwikQgOHz5clXDBFU82nn76aXzjG9/Ajh07kM1m8fTTT+Pmm2/Gd7/73aKbhGpVPkKhEE6ePCl6T2wZlVjIXfqktOiTXOoVqVQKyWQSU1NTmJiY4BwKn1RIcShKcgYm0yAYRgOCWARJXgBFNYdsAPJEjDQaDfr6+tDT04OxsTHEYjHY7XaYTCZZlTJKH0uAYf4YhV+xmIgOn3ScOHEC8/Pz0Gq1dVGvauHKgBQ/1ShI9VNKKKMSmlnBDsez2+1cb4XQzAoxkOumXM51Gqm0VQmJxG4wzAI0mg5Q1J9WfkGZvRSeI4qikEgksLy8zH2/mUyGu1kunH9WrwZxMSBJEna7HTqdDlqtVlTPohQwDI2urhehVl8ARd0Fhvlk2ePF+B2tVsuRjgsXLmB+fp4b/CnGZ61n31bTN8JerN///vfx0ksv4frrrwewNiDvlltuwVNPPYUvf/nLTdGzllpGpVQZwGaRjVwuV+RQKIqCwWBANpstOyCxXnuq9zorK/8JZvMwGKYLNH21LGtWA6lRoEqfX6vVor+/HysrK7h8+TKOHDmCcDiMzs5OWRSgpPdUUDCb3wBJ/jto+jMAvCWP1ev1cDqdIElSknqVnAa5lfFY31C6n1JqGRW/v479mx1AyJZAsTMrIpEI2tvb0d7eXvO+GuE7CWIUavX/Ak1fBYq6GWInnCsBDGPE4uJfoa2tu6Z1aJpGPB7nFB/5Zc4Wi4ULHLI9NqOjoyBJEn19fTCbzbL7qWqPZQevejweXLx4EUeOHIHH40F3d3dNv2mCmIDd/ioAJ9Tq7yObvQHlrhMpfker1aKnpwcURSGZTOLQoUN5Jc9yvIfSIEtmIx6Po79/bWhPJpNBKBTCgQMHcNNNN+GOO+6QPMRLDlSj8qE0giD3WkIolJcVUgBxu90wmUxctGBkZAQ2m61ErWQGBDEOhnEBsNRt34WQ4xylUpswM/MYnE4PgMbfeLCol6Qg6ygYhsHo6CjGxsbQ29uLjo6OmvYgdb8azXm0tz8NlUoDkhxDLvc/yh7PTvrt6empi3pVC1cGlOqnmi19W9hfx5ZAsWp3ZrMZ7e3t8Pv90OtPQaP5DSjqGlDUn4O9+Vpv/REazTMgiDhI8hxoegsYJlz2+PX02QpB03TewNvl5WVkMhmk02nE43FYrVZ0dHTAaDQK2s1MJgOz2cwN3Tt9+jSMRiOsVmvdCEQ1x6pUKvT09OT1LPp8Pvj9/qr8AUXZQFE2EMQSaPojqERIpZbv0jQNtVqNvr4+BAIBruTZ7/fD5/MJ7rnSeyg5MCZLZsPv92N0dBRutxtarRa5XA5utxuxWIxTmWg01l8ZVfqDBiUX+M3JchpxmqaxsLCQF61iGAYGgwFms1m0Aki5PWk0T0ClehcM04V0+lsADILHVVqnWSAIHZpJNKSimuiSwWDA1q1bsbKygpGREYyNjaGvry8vRV5PskFRKqwZbhoMo5O0vhj1qvWsRd6C/Gj5qQ/Bz1jPzs4ilUphcnKy4swKFlrtz8AwbVCrfwea/hgYZm2gqNwkSK51SveTuUGSFz9QIbI2ZD+NAL/EbXl5OU9C3mw2w263w+/3Q6fT4Z133kEoFKpY8sz//Ha7HR/96EcxNzeHc+fOQavVwu/3Q6vVyvYZavU9KpUKwWAQfr8f4+PjeVkDKaAoA6am/h7hMAmG4Vc6ZLHWK5MfpKtGNZE9ni15DgQC3J6FSMcVm9lgP/T+/fuxsLCAdDoNnU4HtVqNRCIBjUaDXC4ny0alopoyquZlIxhoNP8MkhwDTfuRzX4V7A1vNfsqjGSw6e9UKoX5+XmYzWb4fD4YjUaZSwcYqFTnQNMdIMkYCOIyGKY02bjSUdoRNkai1mw245prrsHy8jKGh4cBAH19fbBYLFWlssUikwliYeHrcDoToOnydbCAdPWq9WyQW5AfSvdTUjMbYshGqYGo/Iy1zWbjGmvFgqY3gSRPg2G6wDAfZq6VGDgqh2z2HlDUH4Fh3GCY2pWvGg2GYYpEWQoH3vr9/oo+vtqeGofDgVwuh9nZWQwNDcHpdCIQCJTslWhEZqMQarUa4XAY3d3dXNYgm82KznwzDAOatoFh+GqZS1CrvwCCmARF7QNN7+OekUO9SqPRoLe3N490+Hw++Hy+ppR5yomayAYbGf/c5z5X9NzExAT+5m/+RhYJu2rQ7GFJ0gxvGiQZBcN0gSQvAkgCMFdci69Xzh+GB3woL8tGMrRaLY4ePYq+vr7aPlzZPRHIZO6ERvPvyOVuBMM0vizhSoMcpMBiseDaa6/F4uIizp07B41GI2nyrtRMwtp1uxM0zY80JaBSfRsEMQ2K+m9gmL6846WoV7W1tTVVYagFZUHpfkpqzwbf9vJ9QOHMikoDUWdmZpBMJiXtN5v9ryCIiQ9se30y8I0J+uk/mFFR6zr1R2H/ZDweB03TWF5ellSRIDcIgoDVasW2bdtw8eJFrmypu7u7yM/UqzRYzLp8oZS33noLhw8fRqDEoL1KaxPEBRDEFBjGCpL8dRHZkNKYXi4oxhKlnp4ejI+P4/Dhw/B6vWWvQ6Vn4KoiG+yX8IMf/AC5XA5btmyB1+vlBpmwygVXXXWV3PsVjWYPS5JGXPTI5f4CKtUbyOU+BZZo8PdVTl6WJRbl6i7lRqlzRdN/jHT6j0Wt0WwjrlTUKn1bbt1ysNls2LVrF+bn5/Hee+8hmUzCZDJV1DGXQyKQJA+CJN8Aw2hAkv8Mivo+95yYiBGfdJw8eRKxWAw6nU6UelWl86J0I96CMNaDn9Lr9VhZWRF1LEVRSKfTSKVSeT6AnVlhNpvR3d0t2gdU16eoFexvkFtGVw4obU9i7BD73bJqUEL9kxaLBTRNw+/317SfWn0v+3qSJNHd3Q2Px8NF43t6euD1evM+cz0yG1Ju8DUaDfR6PXbs2IGxsTGMj4+XFRsR8jsM0w+GCYIgxkBRX/rg0TkQxMIHvktaZqPS5+STjvfffx+rq6uIRqNFA3vXA6oiG+wJuv322zE4OIihoSH8/d//PS5duoTXX38dV199Nfcjb1Y5g5JVPoRAUdeDoq7/wODHMhlLAAAgAElEQVR8mP6en5/H/Pw8NBpNU/XK+WjdfNUfzWy86+jogNPphFarxcmTJ2G1WhEKhaDXCw86rKZWtdiI+wDoQBA50PRA1euz6lUAJKlXta7pjYf14qcWFhbyHmMYhiuBYgNMyWQSKpUKBEFAo9HI4gPkDrDJnZHYiL9J9hyxwUO+xCxFUdDr9RyxKKX2mEgkZL1XqeU4/uPsjbHf70ckEsGhQ4cQDofhcDgk9xZKgdjfLntN6XQ6DAwM5GXDw+Ewurq68j6P8DVoRi73LNYG/xoBjEOj+a8AkrBaP4VMRniMgxCklF2xs7Omp6fBMAwOHz4Mj8cDv9+viOGkYlDTLoPBIILBIG699VZ897vfxRNPPMF98GbXTDe7jErMWrlcrihbUWhwbDYbHA6HoGJQs6C0lPl6c0qso7l8+TKcTmdRyY8SJAWBtWbAUCiEmZkZvPPOO2hvb0coFCpqBpQjs8Ewm5HNPgWCiINhrgUwDbX6qwBWoNHcB5IU39zHMAx0Oh0CgUBLvaqFqvzUvn378PLLL8PhcODUqVMAgK9//ev49a9/Da1Wi3A4jB//+MdFcwcAIBAIwGKxQKVSQa1W4+jRoyX3lkqlEI1G8U//9E/4kz/5E6yurnLqa6wP4M+smJmZQSqVgt1ur/m8rCfVxGog155qWYev9LWwsIDFxUUMDg5yww4tFgucTifC4fC6uWlkUeqcsLMkUqkURkZGEI1GOaIsdl0px0rZL//3zmbDk8kkRkdHOdLR0dFRgfCSWCMaAEGMAFgFwxhgMLyDbPZuABkAlRvmq+ktJAgCwWAQ3d3dnOIWK/PbrOCzWNR0dR8+fBgkScJqtcLpdOLAgQPo7+/Hpk2bmh6ZaGYZVeFa/EgV+4efHjWZTCUNzvLysix7UiLkvD6U5uhYZDKZvOhVIpEAQRAwmUzQ6XR49913BW/i61VGJdXgEwQBl8sFp9OJ6elpDA0NweFwIBD4cFqrVKNZ+vgw2K+RJH8PghgGw2jR3v4bkOR/Eb0+nzyIVa9qkY2Ni2r81F133YX7778fd955J/fYnj178Oijj0KtVuOBBx7Ao48+iscee0zwPd94442SfSCTk5PYt28fLl26BIZhuCwFO7OiUkNv88p9S6Me/nO9BZH4c0nYUii21NlsNnNBxG3btq2rz1atkIler8eWLVuwurqKoaEhnDlzBgMDA7Bay6t/SS2jkprZKITBYOD2yZeEF7M2w3wMNH0tCCKChYW/hNO5HxrNeVDU/aDpuyruvdrrgFXc6u7uxsTEBI4cOQKv19vUktBKqIpssF/CyMgIXnzxRSQSCa7EoqurK++YZv2o9Hq9pOY3uQxvNptFMpnkbjALI1UWiwVutxt6vV7UudnIEaONBIZhOAWwdDqNEydOIJPJ5NVTd3Z25klKZjIZ9Pb2YmZmBkNDQ3C73ejp6VFEZqOQEBAEAY/HA5fLhcnJSQwODsLtdqO7u7uqMqrK9ctbARhAEBSWl/vh9f4LVKpLoKj7AfRUXF+KelWLbGxM1OKndu/ejWg0mvfYDTfcwP37uuuuwy9+8Yuq9uVwOPCTn/yEIz5/+MMf8MUvflHUa5VMEJS2r3pm4IWCSADy+id7enryAkhs2VTj74kWQJLvg6avAl+KvlGEzmQyob29HV1dXRgeHuZmS5hMJsHj69VMXsnvmEwmbNu2jZOEX15eFjF7xwyK+mcAQC73K2g0ZwG0Q6V6FgyzBQzTASBYcj9Sg3SFUKlUCAQC8Pv9WFpaEr1WM1AV2WBP0Oc+9zlBhQ8ARdGZRkcqtFotFhcXRR8v1VgWysuurq5y0nNsylBMpErMvpR2Y6+0MqpGgy1/4zsbhlmbV8KWT7ANqJXAXiculwsTExM4fPgw9Ho9LBZxAxHrqfIhBP60VjaiIrXET8yeGeYaZLM/B5ABTb8Gvf6fQJIECGIFudwTqDTJVYp6lcfjWVeRxhbEoZ5+6plnnsGtt94q+BxBELjhhhtAEATuu+8+3HvvvXnPazQa7iZGCUP98kEDWIXUgaxKLMmSYx1WQpgtCWKDSfwgUk9PT8OEWaRjBXr9V0EQC6Corchkvi15BTl63RiGgcViwc6dOzE/P49Tp07BZDKht7dXsBdQLgJRzX5ZSfhz584hFotheXkZvb29FTMyqZQXNO0GMAeGcUOtvg+AGrncs2CYgaLjq6kIKLV/lUqlqFJ7IdRcJMg/AfPz85iZmcHS0hJyuRwMBgN8Pp8oRRi5UU2DeCmt9UJ5Wb60YKG8LEEQiMViWFpaQltbW82fQ85IltKg9Bs8fr0tSyzYRk2z2Qiv91X4fBNgmLtBEB8ak9nZWVFEgw82QuH1enHs2DGcO3cODMNU/O00qr611H59Ph/Onz+P2dlZTExMwOv1VjSg4iM6HgBANmvEWp0sDSAGjeajoOlrQVH/H4Di8yxVver8+fOIx+O4dOmS4PlW+nXaQmXI6ae+/e1vQ61W44477hB8/u2334bH48Hs7Cz27NmD/v5+7N69W/BYZQ2fzUGjeQIkOYxc7hOgqFtEryV3UKwZQSj+QDz+0FutVguKoor8/HoAQcRBEHHQtBUkeaFp++D//jo6OtDe3o7Z2Vm888476OjoQDAY5LJAzZTJ5UOr1SIYDMJoNGJkZAQkSaK3t7dkIJCijFhcfBp2OwW1+iEAkwDiUKtvAcNsRS73JIAPe61ompYUiF7vGfiayQZBELh8+TLeeOMN/Pa3v8Xp06exsLAAlUqFrq4u+P1+bNmyBTfddBMGBorZXb1QzVA/iqLyotUrKyvIZrNcFMNkMsHv98NkMpX90oUN7xIIIgaG6YaU077RMxtKAU3TSKfTSKfTuHz5Mvfds/W2bCMf26hJkqeg070GhtGCYZ5AOv0/ZNmHRqNBe3s7zGYz5ufnMT4+jr6+PrS3t5d8TTMNs1qthsfjASvbKEbDXKrRX1nZhFTq+9DpFqBS/QCAAST5HwD+FgxzM2j6z/OOl2KU9Xo9QqEQGIYRrV7VwvqDXH7queeew8svv4zXXnut5PXh8ayRZIfDgb1792JwcLAs2ahlzkYtKLTjBDEPkrwAhvFCrT7YNLJR78wGv4eS9feFA/H4VQmJRAKjo6M1R46bYU8Yxods9i+hUh1CNvuFgufEn2OhvddCCgiCgNPphMPhwNTUFIaGhuByubgy4nplNqrJJFitVuzYsQPxeJybQ9Xb21sk7LLmdwwArKCo/wsq1cMgiAtY81cHodF8HBT1t6DpewCoZBkCuJ5QM9k4c+YMvvnNb+Ls2bO4/vrr8aUvfQl+vx8EQeDSpUs4evQoXn/9dRw8eBAPPPAA/uiP/kiOfVdEOTUqfsSa/bO0tMQZoVI1l2JRbOSWodX+dxDEIijqOuRywil9cWttHDTrs/EjWMvLy1ymimEYTlq40nfPMHYAWhBEGjRdm965EDQaDTZv3ozV1VVcuHABkUgEmzZtKoqq1Ov8SYmeMgwDlUrFDU4aGxtDNBrlZA+Llaek93gwzHWgaS0I4hRI8pcAYiDJt0AQryOXOwSavh+Ai1tfqhFXq9UlezpaWP+Qw0+98soreOyxx/CHP/yh5OwZtkfPYrFgdXUVr776Kh5++OGS+6qmjKpevREM0wma3gKSPI1c7oYyr6y8Vq37kmsdiqKwuLiY5+v5PZRiBuKtbyETArnc55HLfV742Ro/W61ZdbaM2O12c+pKDMOIPk/1VGMs9CN2u52bQ3X69GkYDAb09vZytoC/PsNsQy737yDJpz4IkK0CUEOt/hoY5hnkck+AYWwtsiEG7Af//e9/j127duGll14SPG7v3r0A1mpcU6kUAGFZwYWFBdx6662IRqMIBAJ48cUXBeX9nnvuOfzDP/wDAOCb3/wmPv954R8RGzFaXl4GTdN5muV8hQj25jKRSGBpaQnhcPHAIqkojhjFQRCXwTB2kORwTWspAUrbU7kIutCQJDaCZbFY8jJVkUiEa2arBIbxIpX6f0CS06Co7bJ+Hr7RMplM2L59OzfdW6fToa+vDwaDoehYuSFF5YM9VqvVor+/H6lUKk9OsLOzk9tnNY1x7PEU9XegqNugVu8HQZwGMAOV6jmoVC+Coh4BTX9WsspHOfWqsbExDAwMwOVyiV6vBeWgWj9122234c0330QsFoPP58MjjzyCRx99FOl0Gnv27AGw1iT+5JNPYmpqCvfccw8OHDiAmZkZbq1cLofbb78dN954Y8n9KauMSoVs9j4AafCng4tBszMbfFvPDyQxDINcLscNxDOZTOtOYlapkFPIhCRJriz37bffxtGjRxEKhaqa8l0K1fgFoePZMrD5+Xm8++67MJvNCIfDgkEu+v9n783j46rvu9/3WWbRaEa7Rvu+Gdt4xxASErK4UG4DIbeBkPSGPKRJaELb5Kb3luYhPCEbpDdpuoRCEngaQlu4kCaBJzXglISwWfKGbLzL1mJJtmVLlixZGs1yzu/5Qz7HM9LMaJYz0sjo83rxwpr5zff8zpwz3+/5bp+v/hl0/Rpsti8iSZ0ASNLb2Gzvo7p6E+PjPwYSC2i9Y50N46T/4i/+ApiJ6ERjFzAu2F13XRrrHo1W8KGHHuKDH/wg9957Lw899BAPPfTQHFrBc+fO8cADD7Br1y4kSWLjxo3cfPPNplPi9/vZunUr+/bt47e//S09PT28/PLL/PjHPyYvLw+v1xtzEJLf77dMWc5W4kJUo2nXIcuHCYVuTUpWJmphrYhmWJXmtgq6rjM+Pj5nwnqyLGDJRT7q0bR6C3Y//z4KCgrYtGkTw8PDdHZ2UlBQYIljHAvJXN9oStnpdLJq1SqmpqY4duwYPT09NDc3U1RUlCZ7lQQ0EQo9jKL8EFl+FkkaAsZQ1T9FiO9jt9+LLEdnAIklPx57VaxermVkP1K1U+9///vnrPnMZ6IP7KqsrGTr1q0ANDY2snfv3oT3l0q5b2Yf6iWSdTQWZl+XYMwpCncqZs+nKisrM0k8GhsbM7qfpYx0bHAmsgqqqpKTk8OqVas4ceIEfX19UQfuZXIPiayXJImSkhKKi4s5e/YsnZ2dBIPBKL9lCVhNMPg8svwAqvoYM72HOh5POx7PGjRtLZrWkfb+s73sNy0X3zj5F198kW3btvGud72LsrIyCgoKKCgooKioiNzcXDP1K0kzvP3RaAWfe+45XnnlFQDuvPNOrr/++jnOxksvvcSWLVvMyPOWLVt48cUXueOOO8w1e/fuZe3ataxatYoXX3yRH/zgBwmdSybT0yATCt2Wsiyr95VNN2UqStxo2DeyFePj42iahqZpuN1uvF4vjY2NSzaCFS/lXFpaSklJiTnzIhAIoGma5edqBX85zJAohNMJdnd3p53OnkEVmvYdhNiAovz1RYdDQ5L2c+WVf0IotApdfxmYO3RtNuKdq9PpTItNbhmLj1Tt1EJgsYfPZlufRbgsIURUilljIJ5RlRArgGjQ0S5jaUEIYU759vl8ZrCqtbV1TrVLJp2NRDIhkiTh9XopLS2lvb2dgwcPUlxcHGX4bT66/ncEgzdis30SuDQ/TVH2IstOgsHpeffzjsxswKWLpygKHR0d/PKXv0TTNMrLyykuLqawsJA777yTG264AYjveQ0NDVFRUQFARUUFZ86cmbNmcHCQmppL9fHV1dUMDg6afzscDr7+9a8D8NZbbyUVMbKy8S6TTXzpwqqMxEIwZAkhIuiFjUY+m82Gx+Mxe2sCgQAjIyO0tLRkfE8LgUQiGMbMi9///vd0dHRQU1NDdXW1pcrISiVu0AmOj4+za9cuDh48yIoVKxKi+I0tX0LXP3YxTf1JJGknMHN/q+oBoJxA4LNA/Ob9+ZR4Njnny0geVtopq7GYw2ezyXGZPRDvwIED6Lp+sdw5h4qK39DQMIAQdwIrFmRP2YpsOadMPeiHr83JyeHKK6/kwoULdHV10d3dHdG7uJA9G/EgSRJ2u52VK1cyOjrKrl27KCkpob6+PsLpEOIPCAQOo6rvRZaPh30eZjIesY/3jnY2jBPfsmWLWcc6PDzMjh07+OlPf8qvfvUrrr76am644QZLfiDRZMS6eRazFjazWZL0ZEWHIN7cgkwh/NyM2RXhTduappn0wvn5+VRXV0elHRwdHV3wvWcDZFnG4XCwefNmenp6aG9vp7Gx0RImpUzwlwPk5eWZ1/Lw4cPY7Xaam5tjDnhKDDUEg69cTFNHZkPt9p8QCKTnbCxjaWOh7VQyWDw7paGqb5GbewxYS7yHnESQjJ0Kp5KfmJiYMxDP4XDQ2NhIQcFMVlKWD2O3vwm4EOKn+P0PWb6nhZBjJZZaACRdp8Dtdkf0LtrtdlpaWpKWa9Vci2gwqGyNQKDBslVWVkZdXV1Y9q2YUOgAk5PvobBw18VjwXy/waVupyyvMykpKeGmm27iAx/4AE8++aTJ5jLfl1RWVsapU6eoqKjg1KlTeL3eOWuqq6vNUiuAgYEBrr/++qjy7HZ7ltXCZqOsCez27yNJJwkG/xxdvzLjewpPjY+MjHDu3Dl27NhxcXaF22zkc7vd79jylWSVnDGRtba2luPHj9Pb20tLS8uCUTWmosQLCgooKyuLGPDU1NRkNr4nDxldf4BA4MPY7e9J6pPJ7n8ZSx+p2imrkSwblVXOhqJsR1GepLJyBFluQNfTY4mMls2fPfg22kC82traOVTyExMTEWWhQhQxM/l6Cl3flNY+04Uk9aAob6Drm9D1xDIslz6bvQ6Cqv4Km+3fCIU+eJEk4NJeY9n6TJZkx5Jr9C4azdmBQMCkmp4PVvZszLdelmWqq6uprKxkYGCAHTt2UFFRQW1trXlvHz/+zzQ06OTlvYwQX5xX/rKzwUzT3cmTJ8nPzyc3N5fc3FycTic+n49HHnmEm2++ed668ptvvpknnniCe++9lyeeeIJbbrllzpobbriBr371q2Yke9u2bTz44INR5S1exCiA3f4mublngJUsZMQoWVmyfAhZPgZ4UNX/RSCQmLMBiaVyw42NUXcbPrvC4XDg8XhYvXp1Vivi9OBDkgYQog5InkY5GTgcDlauXMnk5CRdXV1mnWsqwyUzldkwZBtK02D2MJrs8vPzaWpqSnoo4iVs4rXXtvHud38CSbpAMPjvCe3n8r3/lmHACjtlNRYvKOYDQAgJSfKlLU3TNKanp+nv748YiGcQdCQzEG/2+0J48fu/hSSdQdcTn9VlfWYjhMPxdWbq7f+T6ekfk+yk9WzLkMxAw2Z7GCHyUdWfMz7+B5w/7zLttt/vJz8/n7a2tjnTvjNVRhUP4c3ZHR0dHDx4kLKyspi9OwbSYSlMdb0sy9TW1lJVVUV/fz8dHR1UVlZSW1t78TtpRoh1CctfynYqLa1qfLmvvvoqn//85/nABz5gXvSSkhJeeukl0+sM/5Ki0Qree++93HbbbTz++OPU1tby7LPPArBr1y4effRRHnvsMYqKivja177GVVddBcD9998fk6Y0WWfDKsWkKL9BVX+F1zuOLF+BrqdHi5rJkiwh6hEiD0m6gKYlHjGKdsOHQqGIRj7D2LhcLjweT9S5JRMTE/h8vov11C+hqr8jGLwl6Shb9v4Agzidf44k9aLra/D7v08i5WrpRoxyc3NZt24d58+f58iRI2bKORlkklJwtuzwJruhoSF27949Z6psMtB1G4HAiYQNxVKPGC0jPlK1UwuBZINcVgXFNO09wBTDw8fxeN6d8OeEuDQQz/hvenoaSZqZaVFSUhIxEC8VRLPFQpQjRDbQTwtmAojZ6DQkByEE4+PjTEyMU1RUid1+hECgkO7uUXJzZQoLC82H4snJSfbs2UNJSYn5UG8l9e1sJLJWkiScTierV69mdHSUHTt2mA/y0e69hcxszIaiKNTX11NTU2POE0kW82Xgs/c5aAaW9Gy8+93v5uGHH2bv3r10dnaydetWDhw4QHNzM3/3d38HEHHxn3rqqajyXn755Tmvbdq0iccee8z8+6677oqg0Y2FZCNGVtbCzlx0AWhpS8tks/lMxOi7SJIPIeaWrUWDEMIcitfT08PExETE7Aq32011dTUulysJYzOC3f5jhMjFbv87pqefItlbMxsjRjPzVXoRohhZ3gsEgMQi9lYojvz8fK666iqGh4fZu3cv09PTBAKBhB7gF6PxTpIkysvL8Xq9JttWWVlZ0tc2lUxLvIhYtivxZcRHqnYqG2HdvZiDpt3C8PBOGhqi6ySjj84IIhl9dNHoxCcnJzlx4kTCJS3xkL29Fip+//0oymvo+tUkm9VYTASDQfM6Gn0yU1NTDA4O4vF4CAS+i9N5kpycZlatisyEBwIBSkpK8Hq9DA4OsmPHDqqrqzPiQCQLw5bU1NRQWVlpPsgb2YRwO5PJBvFE5SuKQkNDAzU1NWzfvp233nqL+vr6OXuNhqUeFLMkX5yXl8eHP/xhPvzhD5uv/dd//Revv/56SiUcVmCxyqg0bQuhkMSZM6fJzU1/2Fvm+z88CBFdaRoMIeEZi1AoZDK7lJaWUl5ePu/siniY2Y8LIQqAEYSoBbLb4CcKIUoJhf4IVd1GMPgpEnU0rHacjJTzq6++aj7A19fXxy0XWUxKQVmWzamyAwMDTE5O0tPTEzNiFQ2ZTJcvY2kiG+0ULL4zK8TcgXg+nw9FUcym7fkG4mVjn2Im5AjRTCjUnLbMTCH8Ws4eZGswONbV1eFyudi9ezdXXBFelhY/c2Q81FdUVNDX10d/fz8VFRV4vd557+FM9XeEyzUe5Kurq+nt7Z1DmJIJ6tvZSHS9MU/kiiuu4OTJk2zfvp36+noqKytjyljqdipjxakf+tCHCAaD3HPPPfzHf/wHmqYtaNQo2cY765RlDrp+E+fOddLQkP75LhT1bTSGEEmSovKZnzlzhqmpKUpLS9Pay6UfVQ5+/0PIcheatpLFYMbKDCSCwS8RDH4p+U9arJglScJms3HNNdeYtaPV1dXU1NSkrcBSabBO5PyMetf+/n4A2tvbLdtzOJa6El9G6lhsOwULl5WVpC6gm4mJFYyP28y5FTt37pwzEC8nJyfpqHW2ORuXO4yeyPCMRSgUMq9looNsk4WqqjQ1NZnPDDt27KClpSVmSTtk7h6P5kDYbLYIwpSenh5aWlqS1vOZbICHmetnlDjX1dXR09PD9u3baWhooLy8fM6xl7qdssTZOHv2LM899xzr1q3D6XSSm5uL1+ulv7+fQ4cOWXGIpKEoyqLUwkL2Kl5D1uymbWN2hWFsjMhHrBs7E8ZAiFI0LT3n5XJBJg2tLMvU1dVRVVVFb28v27dvp7GxMapyyxQbVbKQJMlMPff19bF9+3bq6uqorKy05LhLXYkvIzFko53KFAzWP0PPT08PUFHxCLIcQtPq0LQ/p7S0lLGxMTZv3pz28RYqKJYMrNzTYjs/4SVtQ0ND+P1+Tp06FdETWV9fH7cc1GqoqkptbS0ul4uuri56e3tpbW3F7XbPWbsQmY3ZMAhTpqamOHbsGOPj43OGAsZDpu1CuN202+20tbXh9/vp7u6mt7eXxsbGiKzRUrdTlgz1GxkZ4Stf+YpZr9/Q0IDT6WT//v185CMfARaeUhCSiw6no5gkqQtZPoWmrQc8WeNsaJoWkRofGxtj37595Obm4vF4yM/Pp6qqCofDsSipfCvT3JcTMh1RgRlD0dzcTE1NTQRdbklJSdKyMsmSEX5/GBG12tpac65IrChQMljqSnwZ8XE52alomD0Qz2D9C6eYLSurIT+/GEkCXS+goKDGkmMbsNrmWYVssi+JyjGo4Y1goDE13SiDKigowGazUVtba8m+5oMsd+F0/g3gIBT6J2DmuMbvypiBMTo6yoEDB3C73TQ3N6fBKJg4ErGVLpeLNWvWcPz4cU6ePMmePXtiOkXJyk4H0eyOMTl9enrazMo0NTVRUlKy5HsL03I2jJNbsWIF58+f5/z583R2drJ3716OHz/Opz/9abOZezG+iGQUTarKUpJOYbf/E5IURJYPEAz+maXnmsi+ZkexjJpbQxG43W7Ky8sJBAI0NjamOUDt8k1zL/w5CWS5F8gHkn/ItwLh0Z9wutxkkEmlHCtN3traSl1dXcwoULrHWMblg8vJTgUCAYLBICdOnDCbtoG4rH8GQqHPIknH0PXE2acSRbYE2GbLyTZEErRcYvaaXWVgOBalpaW4XK6Iczl58qRlVRiJwOH4FXAOSdKQ5RfR9c9FXVdYWMjmzZtNRkGv1xvRG7jQmY3ZcDqd1NTUkJ+fz8GDB8nJyaG5uTnmbKdM24V48p1OJ6tWrWJqaorjx4/T3d1Nbm5uUpmZbIOlPRv5+fm8733v433ve5+VYrMcQUBHCBWYtlz6bMU7e1DSxMSEGcWKp6BgprTsckkrZwoLaaDs9v8fu/0xwEkw+DjQBGRGyc13vVwuF2vXrmV8fJyjR4+abCUulysh2Zl0NmJFm40okM/nM6NAzc3NSd+b82U2svGhZRmpI9vslCzLc3pFdF2P+iBqt9vRNA2bzUZNTc2cgXjxMDO0NfFZSskgW52NbLF3uq6bzdpHjx5lYmIigtlrsasM4iEY3IzDsQ2wIcQG8/Voej+cUdDoDTToczOBVIhMCgsLTZbGzs5OCgoKaGpqmuOgZ0PG2+VyceWVVzI5OUlnZydjY2Pk5OTE7Y/JVqTtbOi6Tm9vL/n5+dhsNvLy8iK87sW8WAvxoxWilmDwT5CkfnT9ektlh0Ih06k4dOhQxOwKY1BSbW1twrMIsi1ilE3GYDFgs7UjhIokTSLLh9D1powdK1GlnJeXx6ZNm3j11VfZt28feXl58w7Zy6SzkUiJVk5ODqtXrzZrc6emphgdHU04CpQNRmUZmUUqduquu+7i17/+NV6vl/379wNw7tw5br/9dnp7e6mvr+eZZ56Jep898cQTfOtb3wLgvvvu484774y5N6fTydatW8nLy8Pr9XxtwNgAACAASURBVM4ZiFdQUBAxEM+YRpwIJKkPm+1nCOG9yIgXPYqbLrLR2bAKyeq28JlTExMTZvbJ6XQihIggW1kKCIXeRyBwJbJsZzZjVazvJrw3sKenh8nJSc6cOUNpaanlZXKpsCZKkkRpaSklJSURNOvhmZhsyngb/WV2u50TJ07Q3d1Nc3MzBQUFi721hJG2s3Hu3DluvPFGVq9eTUVFBQ8//DCSJGXFRVoohaXr1wDXpPx5IQR+vz+CYtagHjQmdqY7KCn8WOki24zBUsDsaeoTExM4HFexevVBHI4VEaUNmcpsJCNTVVWuvvpqMyVuGMho1JeLldmYDaM297XXXqO3t5fjx4/T0tJCfn5+3M8tOxuXP1KxU5/+9Ke55557+NSnPmW+9tBDD/HBD36Qe++9l4ceeoiHHnqI7373u3OO9cADD7Br1y4kSWLjxo3cfPPNEU7JL37xC372s5/R29vLqVOn+MUvfsEf//Efs379+nn1fDI0nqr6IpJ0Hkk6iSxvTnvI7Hx7mh8CRXkOVX2TYPDWmANcl4KdMsqXwx0Lw24bVQbh2Scjq5FuKcxi2F4hqphNSZ/IPlRVpaWlhaGhIc6cOWM2kVv1kJysszFbz0uSRGVlJeXl5QwMDEQwHhrvZwt0XSc3N5e6ujrGx8c5duwYQghaWloWlbo7UaTtbJSUlLBr1y7uu+8++vr6gOy6QMkguYbyAVT1F+h6NZp2C4nOhpjdzDebrm429eDU1BTd3d2W3EzZeF0uR6dFCGMy6yXnUdM0k0bYYA7RtJUMDt7A4OAgtbUXqK4uiIi8WL2nVDjDjZT4wMBATLrcTD6spzKdXFVV1q9fz/nz5zl27BiyLNPc3IzHE3uezLKzcXkjFTv13ve+l97e3ojXnnvuOV555RUA7rzzTq6//vo5zsZLL73Eli1bzFKHLVu28OKLL3LHHXeYa9asWcMPfvAD6uvrufHGG/nbv/3bhKnEk3E2dL0FWX4byCXWBG4rggWSJCXURyBJZ7HZnkGIfOz2R5ievpqZidyRsrIp422cm2G3Db1uDEj1eDx4PB68Xm/SlMHp7GmxYbMNUlDwTRSlEk37H0DshmtZllm9ejUTExMcPXoURVFobW1NqEw3HpK5d3Vdj+nEGzTrlZWV9PX10d7eTjAYTFj+QjzHhNupvLw8NmzYwPnz583vc82aNWl/n5mEZUP9/vEf/9EKUZbCoLPNxIOEqv4cWR5Alo8ixEp0fcWcNeFTO8Ob+Qw2qJKSknnp6pKJGM3s6W1CodvQ9dVpyIqPbCvHWkxES5mHT2YtLy+POQxLCGFOOu3u7qa9vT2s58B65ZXq9z1bERtc4BUVFUk9+KSCZH+/4evz8/PZuHEjo6OjHDp0CKfTSVNT0xyChGVn450BK+zU0NCQWcJUUVHBmTNn5qwZHBw0I6MA1dXVDA4ORqxpbr40GC7VAbTz37Mj6PoqAoEWhMgF5tZ5W/X7lWU5IZsghBshipCkEXS9ldmOhrEnK5CqHE3TIobZGnq9p6fHLF+uqalJgG1JIEkjzAzNzb5ejHRRUvKv2GydyPJehLgKXb9t3s94PB42btzIyMgI+/btIz8/P2q/RDJIpYwqFgzGw5qaGl5//XW2b99OU1PTvOQjC1FyFe03n5+fz6ZNmxgdHc36sjxLG8SNL9xQOov94zIG+xmlSFZC16uR5WOAA13Pn9O0PTk5ydtvvx01nZoMElXiktSLqv4ScGGz/Qi//5+irMmuiNFSQzRKQkVRzAFKxjWeO5k1Pmw2G21tbfh8Prq6ulCUF8nN/W8oShuh0E+A9LNaVijDcEV8/Phx+vr6aG5uzjj1bTKyo+3FaAgcGRnh7bffxu1209TUZLKQLDsb7yxk2k5F043xjpGss5GIHpekHuz2fwRCBIN3IsSmqOsyGZCLDhd+/zeR5RPoekvUFQsZFAsPCM4eZuvxeCgrK6OhoYF9+/axevXcAF48qOqT2GxPIkQZ09M/JFP9MosFv78G2AnYL5ZZJY7i4mKKiorMfomKigrq6uoyOlAzGVtit9txOp1s2LAhYjBgcXFx2rJTRbzfaWFhYdSAZjbBst1duHDB5C2O9qUbKdaFNOqZcDZmoh7nuHBhLcGgi/FxFZ+vn5ycYdxuN3l5eVRWVrJv3z42bNgwv8B5kKjiFSIfyAUmEKItLVmJ7MkqpLYfgSSdQohiMhExEkIwPT0d0V+RCCVhusjJyWHNmjVcuPDn+P0aqroPTXsTm+3GtGVb6Rza7XauuOIKsyF7ZGSE/Pz8hGqRrWaKiiY/2npJkigpKaG4uJizZ8+aLCSNjY3zGorFDposwzqka6fKyso4deoUFRUVnDp1Cq/XO2dNdXW1WWoFMDAwwPXXXx9zT3a7nWAwmPA5JDKAVpYHkSQ/QtiR5S50PbqzsTg2oRBdj60rMtFrMbsvcmbQ4TSqqpo6PdYwWyFESvtR1W0IkYcsD10MTF55WQXphoc/Rm7uVbjdVREsVYkivF/CKF2qq6vLCuYqAwYF7eTkpEkNH60PcCEc9mxqWE8FljkbX//616mpqTEn/dpsNvLz8yktLcXpdC5K5NDhcBAIBJL6TPgF9fv9EdkKn8+H03mGxsZfUVJiw++/m6qqlVE9SitTwYlxahfh938LSRpA11fFlHU5lFHZ7d9DUV5CiHqmp+dmcJKBEDMT1ScmJjh37hzDw8N0d3ebPTQej4fKysqEKQmt+F7On7+GurqXCIXc7NkToKDgWARLRirHyoSiMhqy9+zZw8DAAMPDw7S0tMSd45LsPqzIbIRDkiS8Xi+lpaWcPn2a3bt34/f7TTrRZVzeSNdO3XzzzTzxxBPce++9PPHEE9xyyy1z1txwww189atfZXR0FIBt27bx4IMPxpSZahlVbOjougtdLwcUNC02xW82kn2kuyeDjGN0dJQLFy7w1ltvEQwGcTgcpmNRXl6O0+nM6MNbMHgbdvs/oetN6Hrime6lAiFkgsHrEOJSs7csP4ei/D26fh2a9nWilcnNhizLNDQ0UF1dTXd3N5OTk5w9ezbhHqbE95s42chs5Obmsm7dOpMa3mh8N2xdKnYtWSz1DLxlzsb58+f57W9/y9jYGKdOneLaa6/F6/VSXFyM1+ulsrKSkpISc1LrQiBRJW48cIZCIY4dO8bk5KTZ/GU0bZeUlJCTk4PN9hKq6gJkcnJ60bQ1ceVa0XiX6I0pRBlClFkiK5uhKL9FiBIkqQ9JGgS8CZ2X0eQXXgqlaZo5ECsnJ4fi4mLKy6M3Ui4UTp/+GCUl/w2Ho5ING9wmX3ldXQVVVQ0p3VOZjIoYijcQCJilg01NTVEziqko5VR7NuJBkiQqKiooKyvj1VdfZffu3XOoD5dx+SEZO3XHHXfwyiuvMDw8THV1NQ888AD33nsvt912G48//ji1tbU8++yzAOzatYtHH32Uxx57jKKiIr72ta9x1VVXAXD//ffH5cW3uoxKUV5AVV8A7AQCX0GIyphrE8mSLDSSsVOapkU0bV+4cAGDHt7pdJKXd4a2tpWoavSSrUT3kwo07WZ8vhsAOyABiV/jpYLZ342ifBMAWf4lun47QkQPfEaDUU589uxZTp06RW9vL21tbZYxLVlR7mtQw4+MjLB//35yc3PN/qtkM/DJ7mXZ2biIn/zkJwC8/fbb3HzzzTgcDlavXs2BAwfo7OxE0zScTic33XRTWs1AycAoowpH+OyKcOWUk5ODEIL8/Hxqa2tjNn8JUY0QdkBG19fFPLZVjXfZ6CAsdoYkGPwkNttP0bSrEKIO8M1ZY1zncCMERNTiNjU1RTxYdnd3p/2gacX3MiOiAXAgy1BXV0t9/Q8R4hecPXs1kvQPlJSUJk37l+m+CqMOd2hoiD179kSly7U6UxFtfTIKWZZl7HY711xzjenUVVZWUltbi6IoSzptvYy5SMZOPfXUU1FlvPzyy3Ne27RpE4899pj591133WVOJZ8PVpdRyXIvYAP8SNK5uPX02WhfILoeDQQCEaWtU1NTyLJsZqArKipwu91m3b+ub0OIH+B25+H33x+zlCyzmK+JfOki2jUSYg2StBPwIERis2Bmy5RlmTVr1jA+Ps6RI0dwOBy0tLTEnPSdjGyr9Llh686cOcOePXvIy8tL6neUiuOw7GyE4bXXXuOzn/0st9xyC6+99hq333676fWNj49z8ODBOQ9zR44c4fbbbzf/7u7u5hvf+AZf+tKXzNdeeeUVbrnlFhoaGgD46Ec/yv333x93L0ad5datW/F6vaxYscLkwDayFZWVlRGc5kYddyxnSFH+C1V9HiE8BAJfAmKn+Rb7gTyTsiQJbLaTSFIJQpRYsLPkEAr9CaHQHRh0w8HgONPT0/T19ZlGKLxx26oZJYkiGQcgMRnnsdl+jRAVVFS8xd69B+ntzaWlpSUjzkay98jsYUkGXe7g4KDJW15bW5tSM2oqPRupGBRjCFV1dTUnTpygvb2dmpqaCFahZVweSMVOZRJWZjYkqQtJehtJOkEo9BF0PXr/XiKyFhN+v58zZ86YzsXsnjmj0iCeblDVIwQCAggiy8cXydm4vDFb14ZCDyNJexCiGShEUb6LLP8vysvfD1w7r7xw/W1kEYxJ30VFRTQ2NqZc7mp1wE2SJMrKyvB6vfT09HD69GmOHz9OXV3dvPojlZKu+WxhtgfGLNOov/vd7/jMZz7DF7/4Rb7yla/wzDPPcOedd/Lkk0/S2NhIXl4e11wzd/BdW1sbnZ2dwExKtKqqiltvvXXOuuuuu45f//rX8+7jpZde4rvf/S4jIyOcP38eh8PBrbfearLPxLsg8zE/yfJuhChAkkaR5RF0/Z3pbDgc22hoeASns5Dp6e8iROYmX4cjvMnPyFhMT0+bfS0Oh4OSkpKUG7ezxejO3Uceun71xYjRRlatuo7z5yc4fPggU1M+pqen5yVByGQWJNp6WZapqamhsrKS3t5etm/fTn19PcXFxRnv2Ugn+qMoilnP39vby+7du7n22muzXpEvIzGkaqcyCSt7NhTld0iSjBD1CHElMyU8qclaCESbX+Hz+VBVFUmSku6ZC0cg8GGmp9txuSrRtA+mtc9sI1bJBkT/TnIQwhhQ248s/wvgpL7+Z8DXmO9+nK3vJenSpO/BwUF27NhBVVWVGbxKdr+ZuAZGH+D4+DiqqsacRxWOVEq63vGZDeML+OIXv8h9993HXXfdhaZp3HbbbXR0dPDtb3+bH/zgB+Tl5c37Zb388ss0NTVRV1eX8n42bNjAM888Q0lJCZ///Of5+Mc/zqZNiUU04jVjS1I/knQKRTlKKPQhdL0+riyrlLjVnrgVStNu70TTFGASWe5G01JzNuLtRwiBz+czHYuJiYk5TX4VFRU4nU4mJiY4efKkJb0W2WIQIvchEwo9DgwClYCf4uLP8Z73dHLo0B+zZ48Tr9cbt98gk83k8dYrimLS5XZ3d9Pb22s69YkcI9OZkFhQVdWMdmfLPbGM1GGlnbIayZZRxbZTIyjKLhTlLTStBV1vTEiWtUGWKWy2HyNJ5wgGP4cQ1eY7s0tbJycnEUKYw06Nssvh4WFCoVDaWUUhqujq+ivWr8/M1PR3CjRNi/r6/Dq8+OJ/55iaqsHj2Ycsv4yu/1HMXo5YMiVJorq6moqKCjN41dDQkDG7lgproqIo1NXVUVVVFbFHYx7V7PXLZVRJwjj5f/u3fzN/1IqioGka3//+9/nQhz7E2NhYQjVtTz/9dMSU1XBs376dtWvXUllZyfe+9z1WrYp+s4YzGCTLRhXPQVDVF5AkBV1vRNPeD8SPJGdjetqqPU1PfxRJ2n/xu0g/NR3euG0YI03TyMnJwePxUFhYSG1tbczytmz6rq2InkQ/FwWoBUCSdiJJbyNEDrW1z9PY+M2wJvIZZRdtD4kqqkwwRtntdlasWMG5c+fYt28fO3fupLW1lYKCgrifyzR71XxYdjQuD1hpp6xGKpmNaHtUlN3I8hE0rRZdXxuXLMSA1bpzxtl5A11XCQSeZHDwjqgzieKVtmZjVYBVyLb9hEMIETFLyqAHNhzCFStWJDmh2kUw+DyStJ9Dh85z7bV3AlPI8r8TDM7M54iGeDo3PHh1/PhxpqamGBkZiTn/IhzJPKynY3eMQFVtbS3d3d309fXR1NREaWmpuSZVZqxYe1oKdsqyMqr169dHfOGGEnn44YdNByBezXwgEOD555+PShG4YcMG+vr6cLvdbN26lY985CN0dXXNuyframHHkOUOFGUfmrY6ISVufXraj6r+CggQCt0KuJOWYJXy1bQ2enu/HdPhi//ZS+wh58+f5/z58+zevdts3C4tLaWxsfEdzQg0n6KbmaNSjCSdZXT0OioqnqGx8XWqq/+UY8cmaW9vp6WlhZKSkgiZVh1/NpJR4g6Hg4KCApqamjh69CiyLNPS0mLOPkhHdirr58NSUOLLSBzp2qlMINmgWKzMhqr+AphEUaYIBr+QkKx07VR4Btrv93Pw4Cg1NZMoimB8vJicnBy8Xu+8JczhyEYnIXO4gKK8hhC1MSnrMwEhBLquc+bMGTPI5/f7cTgcpkNo0AMHg0F8Ph979+6lsLCQpqYmbDZbgnaiGCHehxDbAJ0ZKtxpVPXTCFGOpn2Tmflgl/aVyH1izHoaGRmhv7+f3t5eWltb8Xg8cc85U6XE0eyOEWDz+XwcO3aMnp4eWltbKSwszOgg3GyFJU90xlCvaA+IbW2XGtReeeUV1q1bFzWa+cILL7BhwwbKyuY+yIdTn91000184QtfYHh4OOJhKhqisVHFQyzFqyi7kaShi6npVcSjEjRgfcTo96jqsxdvUPVig3RyWOiIUazprOHsIX6/35Lhh+8sFBEMvkAwOMDZs3uprv4aoOF0vkVb2xtMTU1x9OhR+vr6TAW80D0b8dbKsozH42Hjxo2cO3eOAwcOmBSCs3tPFrpnYxmXL6ywU5lAKnZqtv6VpOPIcgeyfAFdr4zLlBj5ucRtgq7rEXOnDOpwIwOtKAr19TficKxDlqdwOFYwQ/maHC7nzMZsOBwPoCivAQ58vv+ZUP9jslHxWCXJfr+fyclJ8vPzqa6uxm63x9S1xcXFEX0TNTU1SX23oZCbUOhHyPLzQDey/AYzp2BD1+9EiJXmXpOBLMusW7eOsbExDh06hMvlimpHDNnJ2qlEEU92Tk4OV155JRcuXKCrq4vu7m4qKyvfcXYqLWfDMOzf+MY3aGxsZMuWLRQVFZGXl4fD4UDTNM6cOUN3dzcdHR289tpr/MM//ENUJf7UU0/FLKE6ffo0ZWVlSJLEjh070HU9obRZKhGj6OnpXyHLg8BpQqE/TUtW6shBkgyWrWRSmZlHrPTrfNNZk6lTTh7jKMpOdL0VIRaOUcga6ttElKIbXW9AiKMXG0IDQAib7X14PCtZt+7vGRvzcfDgQXJzc83fj3XHT2397IhOUVERmzdvNikES0pKaGhoMBlHrJogHm/9Mi5vWGmnMgGn02lScyeCaJkNRfl3ZPkUM5TstczUyicmK9pvIBgMzumvkCTJ7K+IRh1+9uzZi5TxdaSb1H+n/C4l6RQzj2EhZmiK0yNbMQYahgf5QqFQ1JLknTt3mgyfie11pm+ivLycnp4ehoeHKS4ujpmVng0h3oumvRdF+RbQyYyNfhJFeZZQ6Fvo+m0pl8EWFBRw1VVXmXYkXdr1TFCuu91u1q9fbzpGwWCQqampJEvTli7ScjaML/czn/kMf/VXf8VPfvITrrzySqqqqrDZbPh8Ps6dO8fQ0BCSJPHFL36R2traOXKmpqb4zW9+w49+9CPztUcffRSAu+++m5///Oc88sgjqKpKTk4OTz/9dEI3ghUsH5LUh6K8CUwhRC2atjYhWVY7G5r2bgIBGxBA096Vkgwr9iSEYHp6Gp/Px/Hjx5mYmCAQCERNv853jTKZRnQ6v4IsH0CIXHy+p0nU+FqBRBufIXYfRSIyZpy8KoLB/4kkvYWiPAUMIMv9wAMUFt7O5s2bGRoa4tChQ6iqiqZp85aJZDqzMXutQSFYWlrKyZMn2bFjhznrItvmcixj6cEqO5UppNJbGKnHBXb7vwBTgA1Nm59i1IAkSUxPTzM8PDyHDcrQ5zU1NeTm5ib0O7GiZyobMxuZslWBwNew2R5G169A1zcm9dlYQ2qNkuTi4mLq6+tTpoqNBWOI6/j4OGfPnmVoaIgVK1Yk7HRo2l8jxGpk+XvMDOYdQlW/jK4/jt//aMpN3OF2ZGBggI6ODmpqaqiurk6KlMSQnSkbWFBQwIoVK+jp6WHfvn3k5eXR2Ng4L6PkUoclZVRr1qxh27ZtHDlyhGeffZZdu3Zx9uxZ8vLyWLVqFZ/4xCe48cYbY37e5XIxMjIS8drdd99t/vuee+7hnnvuSXpfVpRRqepTyPJphJDRtMQjRtb3bMhoWnqUjMkq3/AoiaHMQqEQdrsdTdPM9GusAYiLCUnqRQgnkjSFJI0ixMI5G4kilnJK5hrNXNNrEeJaJOkosvyfSNI5FOUJFOUpgsEHKS//GDabjWPHjtHe3h63idw4fiYzG7EeWmRZNhlH+vr6aG9vx+12x52+nIz8aMgUHeIysg/p2qlMIX079SiSNHzx30FCofdF/ZwQYk7Ue3JyEofDYUaoEw0URYNhX6xwNqxAtpVRRTsvXW/D7//HeT+raZrpWAwNDeH3+xkcHIw7pDbTkGWZtrY2AoEABw4cwOPx0NzcPIfIZe41sKHrH0GIOlT1buACM8yWb+J2X01FxV3ABlIpwTP2VVtbS2VlJT09PbS3t9PY2JhUICrTvYK6ruNyuVi/fr05BHd2Vj8ZLAUbZtmdqes6bW1t3HfffXHXLGQUMf0yKoHN9lNgEkmyJ5VRyDZFB/GpfcMbt8Mnq7tcLrNx2/ghTE9Pc/To0Xl7ZhJBpr6jQODr2Gw/IRh8T9qp6WSwkJG02cfStO+h6zejql9Gks4BQ9hsX0LXf4aq/n/k5eXR3NzM8ePHaW9vp7W1NWo54kJnNmZDURQaGxuprq6ms7OT7u5uHA5HBJtHPPmL2VC+jOzG5WinXK6vzVqxwtTn4TrdeMAJL6c5efIkLpcLr9eb4u6DzDxGSFmZkcg2JHJe4RTBhkNolLB5PB7y8vLIycmhurp6XllWIN6eJUmioKCAzZs3c/r0aXbu3EllZSV1dXXz/oaEWE8w+Aaq+qfI8jOAQJKmqK//IUL8nGDwJSD2UMr5bImRgTGYq0ZHR5mYmEiobCnTLIiGjpGkS0Nwjax+RUUFdXV1C05UkWlY5mwYN9ZMT8Hc9JYkSQtu1J1OJ+fOnUt4/dyI0XNI0smL//ZfpLxNVdbiw1Di4fW4sRq33W53zJvdSqNiBaLtR9Pejaa9O8YnMovMUN9GXxd5LBtCfJBQ6Meo6v+NJPmA88jyK5SUvIfp6S9js32NFStWmE3kvb29tLW1RaTAU4lOZiI9bbfbKSkpQVEUhoaG6O3tpaWlhcLCwpifsbqMailEjJaROLLRTqVSRhUKhcy/ZzK4M5/3+wvZuXO3qc+NeUSx9Hk6dkpR/hOb7V/R9VUEAv9v1jkbS+G3O9sWT05ORlAERythGxgYWNB7NJEMvCRJVFRU4PV66evrY/v27TQ1NVFWVjaPzlcJhX6KLN+Iqn6WGecVJOk0dvtadL2UUKg/5vETucZOp5NVq1YxMTFBf38/J0+epLW1ldzc3JifyTQxyWz54Vn9EydO0N7eTm1tLVVVVfMOm14qsDznZijsbEAqw5IiI0afC3tXAIlHyLMlOhM+cXt4eBi/38/Zs2dNZRatcXsZi49EMxvR1glxDcHgy6jqncjyfwIakuSjpuY7CPEkweBvcLnqTRaPAwcO4Ha7aW5uxuFwZLS0KBWl7HA4TDaPo0ePmhSC0WqEdV1PKg29nNl4ZyLb7FQizobRL2c8lI6OjjI9PU1BwXdYufJ/AG7Gxl5i48byhO/pdOyUzfYrhChBlvcjSScstXnR5Uxf/P/SrG0PBAIEg0F6e3vN2SPhvTHRbLEsH2Cm6f+Kxdt4DESzE0ZWuqqqiq6uLk6cOEFLS8u8vzVd/zjB4EpU9YNI0oT5uiyfxWYrJBgcTej48aAoCmvWrOHChQu8/fbb5OXl0dTUFLUMPNPluLHkK4pCQ0MD1dXV9Pb20t7eTkNDA16vN2v0Vaq4rIcZpN8gfslR0fXY0dRoSEfxynI7NtsvCYXehabdktBnwg1ROG+23W43GaGMKEM6E9rBWkcqGxyyyxM5hELPIMsPo6r3cili1I/dvgJNq0bTjpkp8KGhIXbt2kV5eTnFxcUZU2zpKGW3282GDRsYHR2NSZe7XEa1jKWGaHYqFquQ0U9ht9vNplJJ2kwo9CkAEuzRNZGOLg+F3ouq/idC1CBEJZJ0NGOZDVk+jMPxVUDC7/8Ouh67vCY1TKOqM6U8odDtzHZokj2v8CCfwc6oKArBYDDh2SOK8mscjq9flPcgmrYlyXNaPDgcDlavXs3ExASHDx9mamqK6enpuE3QQqxhdPQwLtdmnM5B8/WZDH209anR0xYVFXH11Vdz+vRp0+bV1dWlzFwF1vd42Gw2WlpaqK2t5fjx4/T29qJp2pLuMbzsnY10amEDgXtxOB5ECDs+34tJHTt1JS6w2/8ZIVzYbM+g63OZReIZIo/HE5M3e2hoiOnp6TnyUsFST3NL0gnAhRDp952EYyGVQSLH0vUvEgxuRFVvRJIuXXtFGUCS2giFjkTUjZ44cYJ9+/ZlLMNhRS1sYWEhmzdv5uzZs+zZs4fi4mIaGxux2WzLbFTLyBiOHDnC7bffbv7d3d3NN77xDb70pS+Zr73yyivccsstJqXoRz/6Ue6///64cjVNY2xsjO9973v84R/+IT6fL6Jfbjar0PDwMOPj4+Tk5KR9TumUUYVCf4Km3YQQHsCe0fInWbhpNgAAIABJREFUWX4NI7Mhy69b7myo6i+w2w02TMl03uaDEeQLL4UKD/KFszOGQiH2798fdZZYNMjyfiAECGT5QNY5G4noco/Hw7p16+jo6GDPnj14vV7q6+tjNrILYWf//l+ybt0dqOpxAHR9Rczjp7pfo+yrrKyMEydO0NHREUGckumejUTXOxwOVq5cyfnz59mzZw+7du2ipaVlwWi5rcRl72wkU0Y1W/GGQn9NKPTXKR07HSWu6/XI8lGEKCIUykHTNAYGBqI2bidDbxfNGEhSH3b73yKEh0Dgr4H5MzjJPYBqKMrrCOFC1zeRKsOElVDV/8BufwiwMz39uKnMFiLLYswjGR8fZ3p6mubm5ri1o/GQqMIS4hpOnuzE630XNtuldLQs90Wsk2WZ+vp6PB4Phw4dittEniqsyjxIkoTX66W0tNQcNFVRUUEoFFrObCwjI2hra6OzsxOYcRCqqqq49dZb56y77rrr+PWvfx1Xlq7rfPKTn+TIkSMIIVBVldWrV1NWVkZhYWHc5lCrKV1TlyVFsPxlsmdD198DvHDx35noxQvXAdG/eyGiD8dzOp243W7y8vKoqqqKOxwvGYRCn0RROgGFUOi2tOVlAsn0323atIn+/n46Ojqor6+nsrJyzucNm6brBwgEJoDzQPRGeCtsiWHzqqqq6O7uZvv27TQ3N6OqatKyM1m+63A4yMvLo7W1la6uLgBaWlrMielLIdtxWTsbdrs9qTKqxVbioVDoYj3unxAK7Wd0NA9NO0IwGEQIMW/jdip7stmeQZZ7gSCq+gah0B+lJCcWVPVfsdsfZyb9/W007T0pybESivJbZpyeSWR5X0TkxMofbbhjMTExgc/nw2azkZeXR25uLjk5Oezbt4+ioqKUaQsTp/LLoavrNdrarkdRZmgyQ6E7o65VFIXCwkIaGhpiNpGnimQzD/M5VJIkRTTWDQ4Oous6JSUlCzYXYBnvPLz88ss0NTWlXJIqyzLf/OY3aWhooLu7m7/5m7/hy1/+csKftYp8ZLFtXqJydP0KfL5/Y0ZvJ96zkeh+QqGPXpQtCIVuJZwm2CBR2blzZ9TheMmcVzIQoobp6aeT+sxCIlkiE1mWqauro7KykmPHjtHf309ra2sEtXmkPvZc/C+940eXHQmbzUZbWxs+n49jx44lzFqViOxY61NxlDwej1lKfOjQIZxOJy0tLZbY5kzjsnY2rBjqlyrmU7yzJ277fD4URTH7K4qLt1BbO8NCsXv3biorK9OmQovO2rQCRXkFcKLr6fVyRIMsn0IIkKQQknTWcvmpIBi8E0XZhxAVaFp0TvpkEQgEGB8fZ2xsjImJCXbs2IHNZjNT6WVlZRE1urquEwqFqKmpYXBwkI6ODmpra5OiM0xG2RrKUNMG0DQ/M4QH0Y22sdblcrFu3TqzRyK8iTxVZKoW1mism5qawu/3097eTlNT07yNdctsVMtIBU8//TR33HFH1Pe2b9/O2rVrqays5Hvf+x6rVq2Kuq65uRlI3k5Z6SAkY/MkaRC7/fuAQiDwVwhRNuv9TLNRpV82Fg0zw/H8TExcd7E0eT+apkVUD5w7d47Nmzenfayl2KM4H/VtsrDZbFxxxRVMTk5y9OhR+vr6aGtrw+VyJWUfUslKzyc7JyeHK6+8kv7+frq7u9m7dy8tLS3zOh6p9GwkE1ycHaQrLCzkqquuYnh4mM7OTmpqamhqWjiK/1Rw2Tsb6ZRRpQNDVnjjtlHXOT09HVHTOV+zWCaVuKb9EX5/K0LkIERiU3OT2U8weBcwDuQRCt0w5/3FoErU9c1MTb1KqiVds5v/fD6feT1zcnLIzc1l/fr1CSlNIzJfXl5uzr8Ip7WMh2Qf3C+tje8szJZr9EiEN5HX19en5PxmmuVDkiTq6urIycnh2LFj9Pb20traGpMud7mMahnJIhAI8Pzzz/Pggw/OeW/Dhg309fXhdrvZunUrH/nIR8yyh1hIhfp2MTIbivI7JOkUoKMorxEK/XHKsqzaUyKywhE+HM+wyUKIeYfjZVPQweV6nfLyh1CUFny+x4D8RdlH6hTtMzDs5MjICHv37qWwsDBpcpJMXRen00lFRQVFRUXs3buXgoICmpqaYmayUslUpJsJkSSJ0tJSSkpKsm7MQjRc1s7GQpdRhadez549i9/vZ2BgwKzp9Hg8VFZW4nA4Zt1oo9hsP0OI3Iu1mZEPg5llf5JSarZLXNF4CQS+E/W9xaWfTOy4hmNhlEIZjmJeXl5E859xHn6/n+Hh4aTPS1VV2tramJqa4s033+Stt94yoz2xkMyDeLprZzeRt7e3m3W3yWAh+MtlWcbpdLJ69WouXLhAV1cX3d3dtLa2mjWuqcpfxjJeeOEFNmzYELXRNy8vz/z3TTfdxBe+8AWGh4fjDkBNdoL4TE37wjsbM/SrLwAKut6a0X1ZYe80TSMUCtHf32/SBQMJz5MKRzY5G0VFPwN0ZPkwqvo6odD/sSj7SLxnMP664uJirrnmGgYHB83SoET0ciYfsI09l5SUUFxczMmTJ82BhbW1tXPuGSuch3iI930Y7HTZjsva2chkxEjX9TkTt8NTr263m6KiooRqelX1FyjK7wANIarnDA+0aqiLVQozmxSvVRBCkJPTTlHRT5maWkNX1//F9HQAh8NhZqAqKioiHItYSOf7cblcuFwuamtr2bt3r8m0FCvlmowDkSjiGQejoa6yspLjx4/T39+fcCbGkL2Q/OVut5v169czNjbGoUOHyMnJobm52WTyWXY2lpEsnnrqqZglVKdPn6asrAxJktixYwe6rs9LsJBKua+Vkf/Ebd5G/P6/Y6YxfO7EcSvtVKr9jrMH1QaDQRRFiTocb6licvJdOBxPI0QumrZysbeTEBKxmdXV1TgcDo4dO0Z7ezstLS2UlpbG/Vymrme4nZIkiaqqKsrLy81AW0NDAxUVFeZ5LTZV7lLAsrMRhlhKLhQKzZnyCZgTWqOlXk+fPp3EsQsAHVAQIm/Ou9mWnl6sxm6rIISIyoN+1VV/j6pOUVR0mlWrPoaiXJNCU58130txcTFXX301AwMDMdk7rHIgoq2dT7HZ7Xaz7nb79u3s2bMn5qC9cGSatSNWhKmgoCCixrWwsJDGxsbLQokvY+EwNTXFb37zG370ox+Zrz366KMA3H333fz85z/nkUceQVVVcnJyePrpp+f93S1mGVViDkIISTqHECVz+jTCkUnq23CE9zsaw/GMfsfZw/GMaPTlhHPn7sTnez+lpVdEsIElAyuuUzKZjUQhyzKFhYXU1dVF9HPMzkgnc/xUEM2OGH2BVVVVHD9+nL6+PlpaWswypkyWUV0OvYWXtbORbBmVLMsEg0FGRkbmKDIj9ZpohCQZxRsK3YIQVQjhRNfXRpVlhXFZ6k5CKgh3LIxSKL/fb84kCS9tCwabcbneRpZzsNnqEGJxf8CyLFNbW0tFRYXJ3tHW1mb2H2SyjCpRGJmYhoYGDhw4gMfjobm5OW5tq5VsVNHWx0s3GzWuRlo8Jycngg0l2meWsQwDLpeLkZGRiNfuvvtu89/33HMP99xzT1IyFUVJSr8vLIOUht3+LWT5MJr2LoLBvyBWCWomglnRgkIG8Ybb7aa0tBSXy/UO+51KBINNYY6GwG7/Bqr6vwgG7yQY/POF24kFZVTR1ubk5LB27VrGxsY4ePAgbreblpaWCLuS7AN7Moi3ZyPQNjU1RVdXF729vaiqupzZmAeXtbMRL2I0O7p94cIFJicn0TQNu92etiJLTvGqaNo1Fsmyak/x5WQjwpvxwwcsGY6FwYM+t2dmBv39/52Kii5yc9cjRGrRsEw4cwZ7x4ULFzhy5AiqqtLa2rqomY3Zco0m8tOnT7Nz504qKiqoq6ubU9uaDenm8LT4vn376OnpQVEUqqqqlrxCX8blj4XMbEjSKLJ8CCGqUJQ3CQa/AETPTKZjX2T59yjK41y4sIb+/g8zNjZGR0dHRBnr7P64hYYkncVm+3uEyCMY/EuSoeCd+Xxm9i1JJ7HZnkYIB3b7PxEMfjbpvaWCdBvEE1lbUFAQYVcqKyupq6uLKHHKBBJxZFwuF2vXrjUH7mmaxqpVq+JOSTeQ6XLibETWOBv1F4eJKYqCqqrs2rUr4n0hBH/5l3/J1q1bcblc/PSnP2XDhg1xZRrORigUwufzMTk5aZZDBYPBOfX4RjOZQUmYDuY3CDqq+mNUdRfB4MfRtA/FXJltzgYsPoVfuGNx7tw5RkdH2blzp3lNjSnqydC0CpHD9PT1uFxGpFtgsz2MorxBMPiXcR3CcGRKKbjdbjZu3MjZs2d56623cLvdCdPnLUQzuSRdmsra19cXtbY102xUyUS7jHkixcXF+Hw+2tvbaWxsNGvul7GMbITVmY14dkqIIjTtOhTldUKhDxPL0UhmX0LMHY63cuU3kCQbDsd/Ulj4Hi5cSJzRb6Fgsz2Mqr4ICIRoJhT6P5OWkQm7KUQxQpQjSWcuNvHPb/MS0auappll47GISqy+PrHISSoqKvB6vfT19bF9+3aTGSqTmY1E7VR+fj7FxcXk5+ezZ88es88yXrmw1WVUSwFZ42wA/O53v4vJ2vHCCy/Q1dVFV1cXHR0d/Nmf/RkdHR1R1+7bt4+dO3eye/duenp62Lx5M9/5zndYsWJF3GE8U1NTC8byIUmDqOrvEaIYm+1fM+ZsyPIRFGUbmnYtkpRcNHzhMImqvsTM3It3zXk33LEwSqECgYA5YMnj8eD3+1m7dm4JWjqQ5f3Y7Y8ghMDh+DJTU9stlZ8qSktLKS4u5tChQ5w+fZqCgoKIB/pYSFS5JaMIoxkHWZYjaltPnDhhDm9aKDaqZOQ7HA5qa2upra2NqMWNV161jGUsFhaO+nYaWT5AKPQxgsE/I9Zk7Xiywhkajf9CodCc4Xhu97tQlN0IUU5BQTODgwNZ5WgAYWVLCkIULOpeIuFkaupXyPJhdP1KwIfD8f8gy334/Q+h66vnlRBOeDM+Ps6FCxeAGXpaRVHo6OigubmZ0tLSiKboRJA6RXskFEWhsbGRqqoqurq6OH/+PLm5uQnLTQaplO8WFxdTU1PDyZMn2bFjB1VVVdTW1ka1R8tlVFmM5557jk996lNIksQ111zD2NgYp06doqKiYs7a559/nry8PD7+8Y/z5ptv8tprryV0jIWshZ2JRpQhSUNRH7Ct2ZeGw/HfAR+qug1JesTS87MCQgjs9gdR1RcAFZ/vn5mcvCLCOAWDQbMUqqCggJqamoiMxdTUFKOjo5bsJ3JvRYCKJPnR9cTKqhbKmZNlGa/XiyzLjI2N0d/fz4oVK8jPj865nuy+rCi5Cm8iNyaR22y2jFMEpurMOJ1OVq1aZe63p6eHtWvXZsygLWMZqWCh7JTd/o/I8h7AzfT03wLzO9/GUE3jwXX2cLz6+vqoEd9A4GvI8kF0vRYhPGmfnySdRYj0huCGQwhBMPh5hGhACLdlw2CtQz66fjUAqvpzVHUboONwPIDP92zESl3XzYDd4cOHzVkjRl9qVVWV6WQYw2dVVeXo0aOcOHGCFStWmEQgmerZiAeHw8Hq1avp7+/n+PHj7Nu3j9bW1oTKlxJFskP3DDsly7I5N8vIwjQ2NlJeXj6H3GW5QXyRIEkSf/AHf4AkSXz+85/nc5/7XMT7g4OD1NTUmH9XV1czODgY1dm47777zH8n86CSiaF+0aEjSaP4/fcjSeeZb5heOsZFCBuSNAHkIEnWKl8rZOi6jt8/iK4H0fUAR4/uwOezRUS9FotDWogqfL6nkOX9hEIfAqax2R5GkiYJBP6SWMOUEv3hW6EgFEWhtbWViYkJDh8+jNPppKWlZY7iTUa5JfMbSESuMbxpdHSUzs5OgsEgeXl5CV3XTKebo6039js2NpYUc9YylrEQsNLZiGenJKkXcAOTSNL4xeDLDKINx5uensblclFaWkppaWlcyu65cKDr6y8eN5jW+SnK6zgc9yGEQm7uZ4CrUpYVCTuh0B9ZJCtz0PV6ZrJQEprWit+/jUCgn1OnrmZ8fBohhFkWleisEafTyZo1axgbG2P//v3k5+dnqCQscTuVk5NDeXk5RUVF7NmzB6/XS319fVJOQrx9pBPkUlWVpqYmqqurzWy5kd031i9nNhYJb7zxBpWVlZw5c4YtW7awYsUK3vve95rvR7uxrfbmFipiNNMH8Ca6Xkcg8A1SSU8nBgW//0EU5Q10fSOQhxDnUpCTPow6XaMMykin+/1+Tp78LBUV+ShKEy0tn2O+7yOTe5wNXV+Jrs9wmdtsj2O3/zMgAI1A4IGF3eAshCtmj8fDpk2bOHv2LHv27KGsrIz6sCnfmWoQT8YZKCwsxOv14nA44jaRz0Ym2aviKfGCgoJlZ2MZWQcr7V4s2yJJQ4RC16EonYRCWxgbK2BiYsB0LCRJMqduGw+s/f39uFwuvN65MziS3VM6UJTXmAnoBcjLO5yWrKUEXdeZmppifLySUOhvCYVOoOsyq1Z9DkkCt/uP0fXvoigKmqbR2dkZMxMeCwUFBVx99dUMDg7S399Pf38/1dXVca9ZJvsFjQx/SUkJ/f39MWnik4VVrIkOh4OVK1eaw2V7e3tpbW1NydlY6rYoa5wNgwvb6/Vy6623smPHjghno7q6mv7+fvPvgYEBy/mzF6YWVqAo7QhRhiz3IknD8zIfpZfZqCcUqr8oZyotZ0qW9+Nw3I8QXmy2j8U5Zvw6XWPYod1uv1jbuBm4lmxoJ4mvNG1con10IEknmBnE2LAQW4uyn0gFJ0mSqXiNBu2mpibKysoWpEE8UZSWltLQ0BBzQFK6eKc13i3j8sBiMM7MZqOamSl1Co/nXnR9lPHxKnp6mvB4hnC73VRVVeF2u6P+ZuZrNk8U6Qb9QqFbUJRXAQejo1dRXZ32lrIOQoiI2V9GyVpubi55eXnk5b0Xt9uN0/ksqioBOi7XYTTt24RCN6Fp61M+tiTNDODr6+tjamqKjo6OCEr2aHtNxp4kinC5sixTV1dHZWWlSRMfnklI9n5KJbMRb70xXHZ0dJQDBw7g8/nM8nAr5C8FZIWzMTk5ia7reDweJicn2bZtG/fff3/Emptvvpkf/vCHfPzjH6ejo4P8/PyoJVTpwCplCfEclwuEQtejqr9H065DiPKE9pUNbFQ220+RpFEk6TT5+W3AB+d1LOLV6S41hEKfQJJ0YBJdb8bl+hAgmJ7+AZo2k15f6AeGaMcyGrQrKyvp6urixIkTuN3umIwis5EK9W2iMDIhswckhTeRLySSNSrLWEYmYLfbCQaDCZeMWmEPgsEgY2NjTE1NsX//fnOmVGHhNIWFk8hyGW63RnHxRmLN1gjHYk4QD4eur8Tn2wqAz7cz7f0kBoGivA74LpK9RHfGUpJ80cYaVQHDw8MIISgoKMDj8cQtWZtxvNqR5UFk+S1kuROb7UlCoReYydCnDkmSaGtrY3JyksOHD6OqKm1tbVEfoBcq0GXQxBt9d8ZQwGQpkzNVvmtQxL/xxhvs27cPr9dLQ0PDvKVfl4OdygpnY2hoiFtvvRWYiax84hOf4MYbb4yYzHrTTTexdetWmpubcblc/Mu//Ivl+7BKWUIshTmOw/FVJGmEUOj9hEKfi/rZxGSlhvQiRpuADjRNZWzMy+7duxNuALw8oBIMfhoAu/2bgB+QUNVfAwqa9m4WsgRsvmtpNNKNj4/z1ltvMTk5SWFh4bx0wJnMbMxeP7uJ3KhtXaim7EwOhlrGMhKFMYA2U/1p0YbjqaqK2+1GkiQaGhouzpQCm+2fUNXzCCETCNxPIo4GJGqnxnA4vo0kncPvvxchWlKUM+9u0vx8clCUrTidXwUEgcA9BIPRbft85zU7eDc+Pm7a2Ly8PEpLS7HZbDidzgSDrS78/n8AJsnNXQtISNIIHs/1tLZeBzyV5JnORW5urknJbkUJrxVrjb67kZER9u7dS0FBQUbLcZNZL0kSqqqyceNGTp783+y9d3ycaXnv/X3KNGmaei9Wd1t77XUhhBayCYf3BBaWJASSTSEhG0LgEBJ6lpRzgCSQl5M3BMhhDxwCnCUQQiAsBELJFta2XNZdxbLVJcuWrD7lae8fo/vxzGi6Rm7o9/nsZy3NM/fc82jmuu6r/X6THDlyhObmZhobG9MGFPeCn7ojgo22tjZOnz697vfxyqySJPGJT3xiU/ex2TMbknQVSZrFssrWemFv7b7ypRCNHwCMlWm3UV7+HjyeGsLhKAcO3LfhwCKXL1CMWUQGKtI8fuuV0TXt9ajqN4EoqvoDVPU/MM12Vla+dsv2kKuB8/v91NXVYVkWx48fXyeMVOi64tpisEUJ5zA3N8fZs2fx+/1F0bvJZT93e8ZoC3c/MgnQ5gPLstYFFiKISSWOJ7QUbgb3s6jqjzDNPUjS9JpuQ27IxQ4rynPI8vOAE4fjS0SjHyz8zeaxp40e1LI9X5anAX3t2omc1kw3x5gtebe0tFTA+yklHP4cDsenUJQfARJ1dU9imgcJhf4vltWe53rrISjZRQtvR0cH1dXVBbdGFePaiooKDh8+zNjYGOPj44yOjmY81AtsNuW6ZVmoqkpLSwsNDQ1cuXLFbnuurq5e97622KjuEtyOXtj1htdEUf4TSZpCklaIRN6zgbVSIYSqfh5JiqJpvw74U16VegjaXNcKlZxN2bZtW4LRm5g4dksqGIryQ1yudwIy4fCn1wbdbz8sq5PV1WPIcj8ezyuJldFPUFn5U8zP/x6w8xbsIb9sSllZGR0dHQwPD9vOIJ43vZB1C8kAZTKa5eXlHDp0iKmpKXp7e4lGoxiGkXWIvFBsBRtbuBNQSLAhDqvx+gjJYrX19fW4XK6039Hkdl9F+TGSNIqq9hGN/hqQe6UllzZky2ojpm6tY5r3pV3nViePNgpN+0VkuQ9YWdMkSYSoWGiaxqVLl1hcXLzl7caG8dMYxgE8nlejKMewLBlZ7qO09BCRyLvQtD9moxWh+BZeQZWbz7D2ZvgeSZKora1lenqaSCTCkSNH6OzspKqqqij72Oj1qqrS2dlJU1MTly5dsqv7weBNLZd7wU/d88GGqqroun7LW3uSjbgkTaOq38U0dyFJS5hmZm2NeORixFX1OzgcXwEkLMuNrv9O2nWWl5ftbMry8rI9WJat//N2QFX/A0nSASOOVevOgWl2oWm/hqr+MxBGkq7R2flBdH1iLWt3Z9xH4bwVRaG9vZ2GhgbbGXR3d+Pz+RKuzYcmt9hGWZIk6uvrqamp4amnnrKVvZO5youBeyFjtIW7H6KNKh2S22tCoRC9vb32YTUYDNLY2Ji1RTIZiZ9vC4fj/2JZXkyzFsN4kHwOn7kECabZTTj8aWAFy9r8ymWxKhuQzXb5iUT+2r4uHA4liNCKYWDTNG8zpbuLUOhbSNLf4fH8dyTJAKK4XP8dh+NLrK5+B8g+R5r1VVwudu/ezcLCAmfPnkWSJDRNy+kctpnMVZ2dnTQ2NibMc8T7vkLWLhbcbje7du1iaWmJgYEBFEWhs7OT0tLSrWDjboDL5SISieQcbBSTESfe8MryUWT5ItCPrr+efIx4LrMkluUjNpRmIaoapmkmtEItLi6yurrKyMgIPp+P6upq2tvb75jAIhU07RdRlO8T4zj/L7d7OykgEY3+KZr2m5SU/CyyPItlmTidn0BV/51Q6AtYVte6ZxVLp6RQwyx40xcWFrhw4QI+n4+Ojg6cTuctndnIBEVRcLlcPPDAA3bGJxPrSSG4F4z4Fu5+xFc2MlWaRRb8xo0bHDhwoKgHIlk+hyRdRpavYprb8mqhgtwrEtnYF4uNzaySxAKLcIKPjRehjQ8sTNPk5MmTVFSkbge+dVCIRt/CyAjs2PE/gDAAsnwZr7eLaPQNRKOfKsorBQIBenp6GB4e5tixYzQ1NdHU1JT2c3srWq48Hg979uxhfn6eCxcu4PV66ezsTAj+bqdf8Pl87N+/n9nZWc6ePUsgEEDTtLveT925p8wioVi9sPkisbJh4XD8I5ZVh2Up6Pov5LVWLkZc017K6qpOKLTAzMwuFhePY1mW3QpVU1NDS0sLFy5cYOfOzW/xKRZMcy+rqz9e++nO/bJZVgurq/+Bw/E7OBzHAT2uRP2+tRJ1sV9z40FBIBDg4MGDdttSQ0NDXtWKYupapIPT6bS5yoUSeaoh8kIOFVvBxhbyQWtrKz6fD0VRUFWV48ePJzxuWRZvf/vbefLJJykpKeFzn/sc+/btS7teOBzm3LlzTE5O8thjj9Ha2srDDz+c0MKaqtIsElDFDDZU9avI8jigYJr7gPxIGu7E9qdM90eSprAsP7m+T9M00TQtoWIRjUbtwCIQCKyrLinK93C734VptrKy8vhG305RMTv7YlZW/h9KSl6OJN2wf+90fgmH40usrCwW7bV8Ph979+5laGiII0eO0N3dnZJ5cLM1OeIRDAY5ePAg09PT9Pb2Jswy3o7KRjIqKiooLy9namqKCxcu4HK5bGX3uxH3fLDhdDpvS7ARb3hluRdZHkGSZjHNbkyzreC14GbFIr4VyrIsSkub1yoWPtravOsclGEYRR2ALwZy28/dcRi0rBbm5v4PDsfbqKj4D8AADFyuv8Dp/P9YWXkW02xgZWWF+fl5DMOgvb09wXjk+/cphmGOb1u6fPky4+PjtljSRtbd6PXJ98Lr9bJv3z57iDwQCNDe3m5npApxEJlmSG63s9nCnYkf/vCHVFZWpnzs29/+NoODgwwODnL06FF+7/d+j6NHj6a89qtf/Sp/9Vd/xe7du7Esi5//+Z/nda97Xcq2jmSIZFbxAmUdVf0KsSy3C017ed4rpAs2FOUpFOXH6Pqr866WpIKifBuH45/Q9V9A119b0J5U9XGczk9hWQHC4S+QTEGfPGi/urrK8ePHMwYWqeB0/i2WpSHL/TgczwC3tqqTDuKeWFYHKyv9lJR1cdprAAAgAElEQVQcQpav2I9LEijK45jmbxbltQQDk6DK7e/vt1t4PR7PumvzWXcj10qSRF1dHdXV1YyMjPDcc8/R3t5+x7A/Cd989epVFEXhueeeo7W1lYaGhjtif/ngng82RBvVrUa8kVPVLyBJVwEVXX8BkJveAcQCC2H0FhYW4gKLUptZRGTacsGdQKFbDMjyORyOv8cwDgC/fFv3Eg/L8nL58p/h9b4Al+tP7d9L0jxe706uX9/D2Nin8Xq9WJaVILxXyEE5n2uzrS96RKPRKHNzc5w4cYLu7m68Xm/GdTeLtSPdnpOHyEVGqhAu8jvFqWzh3sC//uu/8sgjjyBJEocPH2Z+fp6pqamUNKWve93reN3rXgfAH/zBH9DT05NToAHFryLI8reR5UnAxLJcec0UZtqTJM3gdP4lIKEoJwiF/omNDSGHcLk+jGW5cTo/jq6/FMhfm8fh+A6W5UKS5pHls6yuliVULMLhsB1Y+Hw+PB4P+/fvzzurrOsvw+m8jGW5MYydWNZc3nvdLNy0e25WV0/jdh9GVS/Yj1tWy6a8bmlpKfv27ePatWucOnXK1ppQFGXTmBCz2XlFUWhra6OhoYFLly4xOztLTU1Nzt/HzYZlWTQ1NbFt2zYuX75sE7xUVlYiSdJd4cN+IoKN21vZWEZVvwWEAM+a4E9qiOFtkU0RgYUkSbjdblu9VRg8WT6LLB9fEwdMnWlL3lMmyPIZFOUZdP1BUvGf5wJF+QFO599gGA8QjX6AzfqIud1vR5ImUNX/JBzeRYzh5PZBMMPMzs6yuLjEkSMvwef7K/bte1fCdZWVp3G7mzCMEkzTpLW1lcHBQcbHx+np6Vnjut+cFqZ8ZiXa29uRZZlz584RCATo6OhIOfdUiPhRPsPnmaoOohojMlItLYU5x7vBUG/hzoAkSfzcz/0ckiTxu7/7u7z5zYl6ChMTEzQ1Ndk/NzY2MjExkVUTIV8/lV40tjC4XO8FYutZVikQzHh9KqQKNizLQcwHrObko+LXSg0nplmDLE9hWeVkS9yl2lMkEmFl5SGCwb9mdbWaM2ckFOUiPp8Pv9+fksFrZGQk573HQ9PegWG8EsuqWHv/d06wkYxw+AgOx6M4nV9B016Daf4s4jOxEaTzU4Iqd3R01E66bVYLb67Xulwudu7cycrKCiMjI8zMzNDV1ZWz0vdmQfhCh8NBd3c3oVCIwcFBhoeH6e7uzsisdafgng82CmmjKiYvt6p+EVm+DliYph/TfACItTSJ4e3FxUVWVlawLAuv14vP56Ours4OLCYnJzFNk0AgELf+JC7X+4AwivKjNeGe7HtKnw1bwOV6B5IURlW/SSj0TTJ9PNLdH6fzI8AqqvrkWtn8/qz7KgSWFUSSRgEHllWKZeUoWpJxzdwyhaLELrJgYijQ4/HgdDpxu93s2LEDh2M/y8uvxuvtTlphHuEkhfDe/Py8fbDPZx/Fyuwkryuocg8dOsTk5GTa4b7NbqPK9v5ERqqxsZH+/n6Wlpa4ceNGUYfIt7AFgWeffZb6+npmZmZ48MEH6enp4cUvfrH9eKrvbi6f92xsVKnWLFZlw+EYR1FG7Z9Ns7BWn9R7KiMc/iiKch7DOEyuVY30700hHP4UivI8prmbbEkm0zSZm5uzB+7D4TAOhwO//zArK9/C5ytj3z5PTgx5hUHCNHvW/m0Rm9/8S1T1e0Sjb8cw8pvfLCZSvSdN+xSaVpzhcIFMNl+WZVpbW6mrq2NwcJDZ2dmck0abSWSiqio9PT2srKxw8uRJqquraW1tvW1kOsmJN4/Hw3333cfi4iLDw8NUVFTc8bOH93ywkW8blcgYFWsIx+n8U2JGBqJRLxcvXmF5eRnADizq6+sTKhbJSG3EI8RmAhxI0kpOe8n8ZbOIZTFkcslmpHMGprkLRXkGKMGyGnLaVyEIhz+Fqn4D09yJYbQBA0VZN9U9Em1sIriIRCK4XC78fj+BQICmpiZ7bmBxcZHJycm4KkAdy8s3KC2tRJIMdL0NTavFsgwkSbI1JILBIIcOHWJsbIyxsTEmJyepq6srata9EMMsSRINDQ32PMeRI0fo6uqyGVUKCcw3g1bX6XTS2dlJKBTiypUrdsanpCT3lsUtbCEb6utjB/Hq6mpe85rXcOzYsYRgo7GxkbGxMfvn8fFx+zmZcDsrG9XVzyX8HI0W1qefLgCyrE50vbBKeWpUYBjrZ0qi0WgCg1coFCIcDjM/P09ZWVmCmOHtQknJGE7n/wbA7X4XKyu3J9hI779N+//xny9d15FlueADbbZ7LpJu586dY3x8nNXVVZsdMR02M9gQZ0BRfRkbG+Po0aO0trbmpRuSbi/5Il2V3+/3s2fPnjs+0ICfkGBD07Scr99oxsgwDLsVqqTkh8jyzUAgGo3pG+TLKJC6PL2NaPQdKMppNC3zkFxuCBKJ/BWK8p8Yxisp9KMRiXwEWT6FZbVgWdkHjAuFZVWhaW9a+ylG3aco30eWz6Prv4xl1RS0rq7rzM3N2YFFKBRay4T505bY1+/NwjRNLMvCMGJBxfz8DBA7JCiKZDNeRKNRuzwqyzINDQ1MTEwwPz9vt1b5/ekFGm9VyVlVVbq6ulhdXaW/v5+RkRF6eno2lbWjEAfhcDi4//77mZ2d5cyZM+uGyPPBVnvVFuKxsrKCaZr4fD5WVlb47ne/y2OPPZZwzate9Sr+7u/+jte//vUcPXqUQCCQtYUK8g82ilnZmJ7+eTo7P4Mk6ZimD9P89YLWid+TJE3gcPwDllW7ZqeLqymRzAoVCoVQVRW/32/Tuns8Hs6cOUNra2ve+iPJKNb9jkYDxKoxobyJYjYDyX5KQAx0i0OsrutIkmT7qXyQz31zuVx0dXWh6zq9vb00NTWlVfzerPmO5LVlWaalpYX6+nqGhoY4evQoXV1dNptWvp+LQnzmvcCaeM8HG/mWp/PJGMUHFqIVSpIku2JRU/N80tqPpT04ZkI6Q2cYD64JLxUHpnkA0zywwVWcmOahnK8u1oGupOQSbvd7gSiq+kNCoX/J+hxd1xP40W/cuGFnwfx+v+2wsu0x3mCL9rhQKITL5UJVVSRJSpsVEhmkSCQSN4sjs2PHDhYXF+nr67N5wJNnJjbC7JTt2nTrlpSUcP/99zM3N8fp06dxOp0J7X3FRL4GNv76eNrAZFrDLWyhEFy9epXXvOY1QMx2vOENb+AVr3gFn/pUrO3k0Ucf5ZWvfCVPPvkkHR0dlJSU8NnPfjantTfTT2WDYfhZWbkG3AByn6tItSdhZxyO/4WiHAMMTHMHhvGSgtcVgUU8M5Sqqvbwdq52+nZDkiR0PcDq6jdQlLPo+ouAME7n3wArRKN/TCGzMrlC+CnTNDEMg9XVVVZWVvB4PCiKYvuoVDZSkqQEP5VPO1Ehia76+nqqq6u5fPmyfbhP1ifZbD2o5PvgcDjs1qp4UcB8q2WFBg53+uc7G+75YCPfNqr0B3sjweAlBxaNjY14vd6ED9HJk79Bc/N3keUout4FpOdbz7Yn4VgkaRyH44uYZhu6/jo2xuxROMR9uhO+ALHDvPgbS8D6trJUgaEsy7bD2rZtGw6Hg5qaGoLB9AY/3mAn70FRFAKBAC0tLZw/f57GxkYaGxsz3iNh3IUDiP+s+v1+Dhw4wNTUFMeOHaO5uXnderdrmK68vJzDhw9z7tw5xsbGbAKDYn4eClEnj//+xQ+RDw8P27SGhTB/bWELbW1tnD59et3vH330UfvfkiTxiU98Iu+1b/eAuGXJSFLhgQYk+inTrENRDEBdG+TODSIBFI1GOXfu3LrAorKyMi8SjUIqEpI0htP5ESyrmmj0PcDGqiLJsKwWdD02l+BwfAqH49OIeY5o9EM5PD+7fRZ+SvwnIBJfbrebnp4e+vr6bA2uTAfgZD8VCoXsRFm2g3Ohvie5mj46OkpPT49NlZvPusUkMiktLbWr56dPnyYYDN6SYONux09EsJFPG5Usy2iaRigUWhdYCIPX1NREaWlp1g+MYZSxunqNmCEp/MMVnzFyOj+OLJ9HUX6IaXZs2gD23Ybl5R1EIu9FUU4RibyFpaVFdP37LC8bTE+3JASG6f5+yQYjncGG2N9EBBjiZ4Ha2lqqqqq4fPkyvb29dHd3p8z+m6ZpBz+C1liWZWpqamzVe1mW7SyPKOH29PQQDAaLTn2b77ViiLy0tJTl5eV15eWNIt/SdzoHIdi1GhsbbSXyrq6urSHyLdwxcLvdLCws5Hx9MduoijWnGL8nXf8tLGsHllWGae5KeX18ZVn4WUVR8Pl8SJJEW1s5gcC/Aiqa9vtAegruTMj3PjmdH0FVfwDImOZudP21Rb3fiXBzc07SgyRNAhKWlb31TiDeTyXbwPhqhQg0BCorKykvL2d0dJRjx47R2dmZUt1c6HotLCzYbWsQY5PSNK2g1qp0SOV7RDV9dnaWU6dO2UKXm13ZyHZ9RUUFhw8fZnR0lPHxcUZHR9O2fCWvvRVs3IPIVp7WdT0h433jxg1CoRCBQCCvwCI9JDZafYg3dJYVBHTAQb4Kr8VEYZWNRVT1SSyrdY2ZZOOwLIvV1VXC4Qjnz7+ApaVdWNYi27b9E42Nn0WWobn5f2JZmQfx4luhotFozgY7HYRmxcrKCn19fbjdburr61ldXbUNtmAfCwQCNDc325Wx5NYqVVVtQaTl5WV7PYfDkfMA9GbOd6iqSltbW0axpkJQSLCR6XpBa7i8vEx/f3/Rs8Nb2EKhuJ1tVMU6SMeSLzdwud4JaESj77ZJQpL9bHxg4fP5aGlpoaSkxP7+zs/PEwh8HYfjmwBYVg26/hsF7SlfxOb9JEDGstYfvosJTftVYsnIVUyziZKSFwIS4fDjGMbL1l0f76eSk6jxcxa5+inBBlVbW0t/fz/j4+M0NTXZbIuLi4sYhoHX67VnFoWuVyo/VYzZinTXisP92NgYR44cwe1255ww2izWREmKiQLOzMwQiUQ4cuQInZ2dGaloi63tdLdU6e/5YCO+jSpdJkVkvJubmwFoaWnJKGR2qxHvDKLR/4ai7MeyajHNrgJXXEFVvwYE0PX/SqFVl3wdlMv1vrWMkZNQ6POY5n15v56oOIkDu67ruN1uTNOkurqa9vZ2VFXF5foiiqIjSeBw9BON3gw20lUsvF4vly9fprOz0y6NFhJkin0KYy3oF2dmZigvL6e5uZmurq60mcTkknU0GsXhcNjVmf379zMzM8P58+fRNI3q6uqc9rnZLVdCrOn69et2Bmrbtm0F0wVutI0qHcQ9vHbtGs8//zwXL14seIh8C1soBm7ngHgxgw2//zlk+Qy6brG8/FlGRl5rV2zTBRbpYJpl3EzW5VctVZRvoiincbsPYFnteT03Gn03prkLy6rAMDLTGm8cKpoWY/9yuf4QiALWGtOiimEcwDTVhNcuKSlheHgYp9Npi7oV6qdEULGwsICu64RCIU6fPm0nwNra2lLqK0FqP6Uoij3/Ef86xfI9Yli7rq6O3t5eBgYGbGbIjaybCvm0aMmyTGdnJ42NjQwODtrzHKlEAbcqG7cRY2NjPPLII0xPTyPLMm9+85t5+9vfnnDNj370I1796lezbds2AF772teuYwKJx8LCAqdOneKZZ55hZmaGr3zlK7zvfe/LavBExH4nIZaxmMTl+hjgIBr9wIaYnhyOT+Nw/BOxzI2noCHzQqJpSZoh5jgMJOkGkN6AZ9Ky8Pl8VFRU0NraisPhIBKJ0NfXl5DliEbfhiwPYFluNG0vHs9hLKuS5eXP2Nmq5IpFc3MzFRUV9PX1UVJSQkdHR05GId5gLy4uEolE8Hg8+P1+Kioq7HkQXde5fPkyly5doru7O6uBFHsTWax4NpCamhoWFhZYXV3l6NGjdHd3Z2xf2szKRvI9EuV5QRe4bdu2gmh8i9VGlQ7BYNBmGevt7aWhoYHm5mb7Ne+WjNEW7n4U0u5bLD8V36abL+Jn4ebm5gAflZURFEVG0zoK7gyIDVP/IlABKCmz/Okgyxdxuf4c0Nm27Wmi0Rfl9drgQtcTGR432xbEKgS/ulb5t1DVr6OqX0PTXsTq6ufsA7zIpJeVldHf38/Vq1fp7u7OiW0rGo3aCbCFhQXC4TAul4tAIEAwGKS5uRmXy4VpmoyMjHDp0qWc2mKTgw7DMHA4HAnJtHzuXy7XOp1OysvL8Xq99PX1UVpaSmdnZ9qE0WYe8OMr6kL/Yn5+ngsXLtjkLvH72prZuI1QVZWPfexj7Nu3j6WlJfbv38+DDz7Ijh07Eq570YtexL/9279lXe/ixYu8+c1v5v777ycYDLJ9+3be+c535pS5LHZvZjGGqCVJIhD4IbJ8BTBRlO+h62/cwIoaN1u7jLyeKcsnkeUJFKUi7/sUifwPnM6/wTS7MYxEByAMoQgshJaFz+dbp2WRCTfLzI1Eo/8KgNf7q8jyODCBx/NNdP3Nab/sIjs/PT3N8ePH7RKz+BtqmmYb7MXFRVZXV21GJr/fT2NjY1q1UTHwJtp4RECTLnMkoChKSjYQMQAtDO7Y2JjNjpGMzZrvEHSzyYjPQA0NDdl7yweFDPXlS2+oKAoNDQ3U1tauGyLfwhZuFW6nqF/8YHcmxAcWS0tLLC8vJ8zCNTcv4XD8JyUlL0PTfplgcDuFthDH3p+MYfxc3s+1LHGssbAstYj+3ECSLmNZjWyUyjcV0Yhh7GZh4Qyq+hylpb+OJBk4nd9DVf8r0ejHsKybnQBut5s9e/Zw7do1Tp48SUNDQ4LgqujiEAmwlZWVBAr3urq6tCxKsiyzbds2amtrGRgYYHx8PCcVbWF7TdO0qxyqmt/9zzfR5fV6OXDgANPT0/T29tLY2EhTU9M6P1Ds1qXkfSSvHQwGOXjwoL2veDbEzdzLnYw7Itioq6uzuch9Ph/bt29nYmJiXbCRK7Zv387TTz8NwOc//3kmJydzbpHYjF7YYgQbq6ttwFFAxTTzO7QlQ9MeBXxYVhDD+NmcnyfL53C7HwU0tm27HziY1+taVieRyCfXqAznWVxcJBQKcezYMdsQCvX0bFoWcNNg67pONBolEokkDG7fpPE7hCT1ElNv/Qwu14eJRv8Ww3h1ynXjs0d9fX1cvnyZkpIS+6AvDHZNTU1BlIterzchoGlubs4qFJSqZG0YBpZlUVJSwr59+2zHU1tbS2tr66aVsvO51ul0sn37dnvWRAhtZXNcsHltVKnWTzVEvmPHDiorN8bQs4Ut5ILbyUaVKnAxTTNhxiJeiNbv99PQ0LCOfVFV/4JweApFmUXXfwHLKtzvbSSYivmZjyLL57lyZQf19cVoEQOv97dxOE5gmp2EQl8jNjeZHfGBha7rrK6u2m2xyXSzsf//NKb5EhTlR4CGLJ/E5XoITfswhvHLCWtXVVURDAYZGBjgmWeewev12n5QJOra2tooLS3N2095PB727NnD9evXef7556mrq0t5kI9HKnZFXddzJh8oJDAR/lpQ5R45coTu7u6EYffNZM5Ml+SK39fIyIidyHK73Xknxe4F3BHBRjyGh4c5deoUhw6t12p47rnn2LNnD/X19Xz0ox9l586dWde71aJ+m7GWy/U0paVfxjB2o2mPYlnbNrhiAE17S97PkqTrxCohMi7X9ZzemxgMFFWLlZWVBCpDl8vFgQMHcg4sUlHOulwuqqurOXPmTEpGDV3/I0zzhcjyMVT1o4CBw/FeYB7D+BVi2iCpGTd8Ph+lpaVcv36dyspK2traiqIuLwxRVVUVQ0NDHD9+nJ6enpQ9ngKCfnlxcZH5+XkWFhaoqqqyjZ1QOx0eHl43qHa7gg0BMSfx1FNPcfLkSaqrq9m2bVvGe1nsAfFcro8fIs/n8LeFLWwEtzvYEG1QomIhssY+ny9HIVodcKGqy0AdhYqqStIIkjS9Yd9pGC/BMF6Crl8oeI3EfUVR1aNYlh9ZHkCSprGspnXXZWMwbGlp4dy5c7S1tWWonjqJRr+ALP8Yl+sXgUUk6RpO5++i6//BjRt/yeJiOGEmUCTpZmdnCQQCOVXMc0VlZSVlZWWMjIzQ29tLZ2dnxtYqEaguLCywsLDA/Pw8nZ2dGIaR1XduxPcIUpaGhoYEopKSkpJNDTayra0oCm1tbTQ0NHDp0iXm5+cpLc2d3OdOkRjYKO6oYGN5eZmHH36Yj3/84+v62fft28fIyAher5cnn3yShx56iMHBwaxrOp3O22bEi0Up6PV+nlDIWhMCCnO7Al3D+Gk07Q3I8hDDw6+grc1Kejy1yKHP58Pv99PS0rIuwzI6OoLL9aeo6jfRtN9A096WlcpPZITiB+Pa2tqoq6ujr6+P6elpurq64oythGn+FJZVjar+T2JDeNOo6rtYWfk6Fy++hUjEl5JxQ6C9vZ3x8XF6e3vp6OgoWsZbsEwtLS3R39+P1+u1h9xF8LOwsJAQ/AQCAbZt22azPSWzgbS1tVFfX2+3VuWr9L2Z1zqdTg4dOsTo6ChHjhyhra0toU0tHoUED8Vqu/J6vUUJKrewhVxwq9qoRFIlvmKxurqKaZqUl5dTV1dX0GdfVR9HUS6gaRbR6DtSHsSzQZKu4Ha/CUmK0ty8H8vaOK17sRJ+luUiEvkl3O5/Qtd/FstqyBpYpGIwbGpqoqamhv7+fqanp9O2vQIYxgtYXPwCpaW/haLMACaq+iWCwW+xtPRZamoO0tHRkUDA0d7eztTUVMoW4I1AHJjr6uro7+9ncnLSnkUQDIsiUSeCH7/fT3NzMz09PQAJrVXp7G4xfE88Ve7p06epqKjIW3gvH+Tqp0Qia2xsjKGhIc6cOZNTe1q29e+WQOSOCTY0TePhhx/mjW98I6997WvXPR4ffLzyla/kLW95i51tzoRiifoVgmKsJUmzWJaMyzWNZfVgmrUFrjOIJM0T4/MuFCqa9k4AQqFTLC8v2+xQ8dkwv9+f82Cgy3Udh+MJLMuF0/m3rK7+JjHu8ZjBzofKz+PxsHfvXq5evWob25qaGnseZGHBIhT6B2prv0Bj47dQlBWCwR/yghecIBL5GpaVmg9e7KW5uZnq6moGBgaYmJjI6CjygaCPbWxsZHJykqeeesqeAwkEAimDn3jEt1aJ++V2u9m7d69tcCORSM5B9GYFG8JoCrrF+vp6BgcHbbGmZC2SQugKN7MSsoUtbBYKqWzoup7xGtM0E6i2Bd12aWmpnQn3er0MDAzQ2NiYsaqaGRaK0oskrWJZTgo9VsjyBJIUARyUlg4XuJfNw9LSB4hEHsOyFOJnHfOlRnc6nezevZvZ2Vmef/556uvraWpqQtM0+8C+uLi41m4apKrqcVpb/xsOxxAADscCbW2vRdN+HV3/+4S1xRxfVVUVg4ODTE5O0tPTk1cmPRPE+tPT0zz77LOoqorf7ycYDFJdXb0u+IlHPFWuqqrrWKuguL6noqKCQ4cOMT4+ztDQEJWVlZtSJch3TbfbTV1dHeXl5Zw8eTIra+O94qfuiGDDsize9KY3sX37dv7wD/8w5TXT09O28u+xY8cwTTOlAE0ybifLRzGCDYfjr4FrRKMq0egfAvkLkcnyWdzuPwB0GhpeCqxvUcsEoWUR77SWl5eZmJggGAzmnA27qS5r2vfFNMuJRCpwOm9gGNtxOLxIklzwl0vTNFRVpbKykkuXLnHx4kX8fj9lZWVrjBsvxuV6AZZVAnwRSTKAWdzul6Npf4iufzDj+m63m/vuu892FLn0sSbjZvCzEOdU3DbdYE9PD6OjoywvL1NeXp71EBDfJ6vregJrlTC4Tz/9NMePH6ejo4Pq6uqMxjHfAKJQ5+B0Otm5cydLS0u2dkhXV5fNrLLZbFT3SsZoC3c/Nkp9KwKLeGp3wzAoLS2158tExTQZG2Gjij3/BLI8hCTNEIl0Icv5zfIJGMZBdP3nkOVBxsdfRUtLceh417+3KJJ0Y43RMXVFVfxf2BRVVZmcnKS1tRVFEcHFFG73G5GkBaLRz2CaD+S8L2Gnq6urGRsbY3BwEK/XS3l5uT0TEz+3qOs/RlFeiyw/a6/hcPwfHI7/Qyg0CNQnrO9wONixYwcLCwucP3+e8vLyrG2ryYjNVt4cMl9dXbVpZuvq6ujo6GBqaorr168TDAYJBoMZ18uFtarYiS6RJNQ0jdnZWY4dO5YysbURFEJMEt/2LFgbRQIu+T1tBRtFxLPPPss//uM/snv3bvbu3QvAhz70IUZHRwF49NFH+epXv8onP/lJVFXF4/HwxBNP5EyRdrvEkja+1iqKcgqYxrLKKFTET5LGiLUOqZSWjmS8Np2WRUlJCX6/31bvPH/+fMLBMBnifQsBouRWKPHfffcdZGDgE1jWeRoaXklpae4fScMwEqj8khk3GhsbCYfDDAwM2Afv2JfWhaZ9EsvqwOH4s7XVdByOv0JVv0A4/GMgvSgPxLImwWCQ4eFhjh8/TldXV0pjK+YsRDtU/B5F1SJVibenp8c+hPv9/rQHhXjEs4Ekt1Y5nU727t3LpUuX7NaqdFoyG6W+zXRtqnV9Ph8PPPAAMzMzHD9+nLq6OlpbWwsKHvLpU75XjPgW7n7k0+5rWRbRaJSFhQUGBgYSAgufz2fb6Fz1bXJlo0oHRfkusjyEZQVZXm7H4ymUqclJNBpL9qyuns4xAFpGUZ7CstowzZ51j64PNlbweH4RWR5B036JaPSDCUJ58fZGBBmyLLNjxw5GRkY4ffo0PT09+P1+VPXf1t63jKr+LdHo51PuULQXi0P78vKyLWgYCATYu3cvpmnS19eHZVlUVlamCAq8RCL/jsPx26jqEwmPeDydhEIrKV87EAjwwAMPMDY2lrEF2DRN2+cvLCzYe8xGhtLe3m63VjkcDrq6urKS8aRjrRJB72ZU1QXroN/vp6+vD4/HQ2dnZ06UwcXcByT6HTHHU19fz9DQEEePHl1HN3yv+Kk7Itj46Z/+6ayG5a1vfStvfetb8177boHiYY0AACAASURBVG6jUtV/Q5IWsCyJ5eUOfL7CWKgM48UYxouRpKuMjf0Cfr9l7y0SidhGRmhZuN1u/H4/5eXltK5pWWR7b9kMdjzzRjxiQ10PsLDQyblzfVRXL9PS4sfl+u8AaNoHgWDC0JkIgnJh3PB4PBw4cMAebuvu7raDAl1/J6a5H5frIWJ0wCBJk3g8rWjar6Lrn854XwWDkVBfdblc1NfXs7KyYu8xfmYlX1YQcQifmpqit7c3px7cdEJLlmXhdrvZvXs38/PznDt3jrKyspRBzGa3UaWCJEnU1NRQVVVlU9AKOuFckW8l5CdVXGkLdx7S+SlRVRbVCqHoLFpQGhsb8wosUmFjfkrH4fhnYsxMUa5fP0QG8eQ0CKEopzHNNoR+VK57crneg6L8mJhQ7JexrJYsz+hHksYwzVJU9eusrLzPfr14BsNUdkFQYosEUGfnHlTVDZgYxkuBmzMx4tAeP2snZhiSWbwEHnjgASYmJujt7aW9vT2FCrWEpj2OabbhdH4o670REAfampoauwW4qamJcPjmkLlogRbV9XR7TIWSkhL27t2bQMPb2NiYs5+Kb63a7NlC4VNFq3WytlIhKAYxicPhoKenh5WVFQYGBmxRwJKSkq1g427B3dtGZaKqX0GWxzBNN4uLO/H5Cm3r8BKJfIRoNMrS0gmuXLnC0tKS3b4jDuyNjY1xkf4iinIU09xOjFc8bmdr90fTtLgMzApe71tQlD4ikT/DNF8FkPOXJBAIcODAAYaHh5mZ+ROam2MaGfPzEgMDr08YOhP9xbmuLXjDhaNwu910dnbicDgwzZcSDh/B7X4hELaf43B8AVn+IdHoQMo1LcsiHA7bFQvDMGyV8MrKSpqamjLOWeSK+B7cS5cuMTk5SXd3d1aFe2HMQ6EQ169fJxKJYBgGqqoSDAbtXtajR4+uG9K+ncPkYsC9oaGBkydPcuPGDcrKynLqJy+kErLVKrWFfLAZArRws43q7NmzBAIBOwkkqsrJQqazs7PMz88nCJkWio34KUX5PpI0hSTpWFYjq6vNea/hcr0XRTmBZfkIhb4AlOe8J1keARRAR5JmEoIN4ad0XY87A3RiGJ0oSh/R6G/ZWfhcfYlg1RsfH+fIEWhr+xKKEmJ2thbDeILm5s8Rje7AMN6WddYuGZIk0djYSFVVFf39/UxNTaUU7DOM9xMKWXg8H157n/WplrMhgoqFhQU0TbNVwsvKymhqaso4Z5ErREtYRUUFV65cobe3N221Px7CT4XDYa5evcrKysqmzxZKkkRtbS1VVVVcuXKFI0eO0NXVZVd88v0uFDJbmO760tLShOH2srIye3zgbsc9H2zcTrGkjQQuknQZRTmKZclYlo8bNw7S0JD78zVNY3V1CNN8hmvXGpmfL8fhcNjl9kyiPgBu91uR5fNYVikrK1/DNIMJ9yUQCDAwMEB3d/eaQX0WVT0HqLhcf0sk8lBO+xSH9vgZBkXxYhixL6TbXc++ffs2bAzhJkuF0LcQAQj0EApdxu3uRpKW7OsVZcL+t2hbEPuMVwkXlLgOhwNN0xgaGmJoaCinoCBXOBwOtm/fzuLiIhcvXrSrOfH3Rdf1hLYy0WMrtGs0TcM0TbtkLZhRRGvV9u3b7UP97Waucrlc1NbWYhgGFy9ezKoQC8XJMG1hC5lQbAHar3/96zz77LMcPXqU/v5+3v/+9/PYY4/R1taWtqoMG299isdG/JTD8SkkaQXLUjCMPZhm/oQZstyHZXmQpGVk+TqmGWshycUPRyJ/jtP5cXR9D5q2F8vSE5IIfr+fy5cv09nZSTAYRJKc6Pq/oOshoJR8vv7xlQBRDejrC+Fyudi2LUhT098hy0NAH9HoqzDNl+Z9LyBm++677z6uX7/OyZMnaWxsTFEp+ACh0AfWPTdedHZhYYFQKJSgEt7S0oLT6cQwDK5cuWL7qWLNMCiKQkdHBysrK3a1P9lux7cWC9ZKMWje1taGLMsJZCfpsFHfI/YaT5Xb09OTN3PVZvidiooKDh8+zMTEBGfPnsXpdGbU87gbcM8HG7ebv7zQwMXh+AywgiSBae5G03LTXxBaFooic//9H8DlmqO2NkA4/M9Ikp8TJ06k6QlN5AiXpEtYlnONYeQaklSWwLjR1tZGRUUFfX19VFdX09raQ2ymZAXDeFnavcYf2pOHo0XbltN5P5a1j7m5OS5e3ENXVz/V1c9img9gmvsKup8CQt+isrKSwcFBpqam6OnpweMJEA5P43b714bGwTQVzpw5Y89ZiLae5OG9eIhy6MLCAhcuXKCsrKxo2hwQc54PPPDAWmbtCOXl5ViWZbeViVmQjo4OSkpKEvYYL7Qk2ECcTic7duxgcXGRCxcu4Pf78/rMFquNKt3apaWltLe320qsmcrexTb6d4sR38KtQ7EFaBcXF3nwwQd5xzvewUMPPcQ3vvGNnJ630aHueBTup1ZRlKcAC0ky0fX81b4BotF343R+Ck07jGl2Zt1TIuXsLiKRz9iPSRIJDIaNjY2UlZUlJGhilZDM84/pDu1ihqGpqQmXy4VlWczMzHD58mVqavx4PKI67EeSzq5VWnJvBY2H0Le4fPlySh2mdLMgwgdkEp2NDwr6+vooKSkpqjaHyNBfvXqV3t7etRkX1WZEi6dwT24tjm+tykSVm08bbCZbL1gs5+bm7GpCsSoV6faSy3lAfH7dbjcDAwPrtLPuNmwFG0koZsaocCNu4nB8EUnSARlNexCxjJhdEIYwXsvC5/PR0tJCSUkJsmxSUhLFsrxIUgRZjmJZN/eUiSNckiTC4T/H5fo0uv4SnM6dpGLtEK1PsbLpBDt2fBWfbwXLis2WpMq0O51O22BnOrSb5usJBGDv3jBO508hy5OoqodI5Ol1bV2FQLB1zM7OcvLkSbxe75ox/C5tbZ/B651mfv7vaWsrL0h9Vdyb8fFxjh07RkdHR8FGIr5lS9xLwzDw+/025fDOnTvzYq1KZgPx+/0cPHiQyclJxsbGGB8fp6GhIev7zueAXyiVrQgQq6urbcFCcT/j19vIoN4WtpAviiFA+8gjjwCxz242Ktt43AlJMVX9ayRJtCeZGMbPAZfzWMFAUXoxzUZCoS+v2xOkZjAUyJVytrS0lP3799tD0t3d3QntZ7Fk3Tym+RVCoRCjowdQVVdOh3Yxa1ZeXk5f3wcIBL5NZeWLKC39nyjKD7CsIOHw00DmdqJ0EEJ1i4uLnD9/HqfTidvttu1+soZVvvastLSUffv22dX+XOYCMyESiditxYuLi0SjUVvVfGlpiZ6enoyCgJB67jCZtQoKUxvPhPLycg4fPszw8DBLS0tMTEykZIdKRqFsVLlCkiTKy8tpaWlhcHDQnuconKr69uCeDzYKEfW73W1Usvx1JGlh7SeDqamDrK6ucvz4cXuQS8wupB/kkgmHP4TD8WU07WcxjHJMM+bMQqGQrQ+R3mD/PJr28zm9RzHMdvbseVwuFw7H+bXqyk3GjVSZ9lzgdrtxu1exLAldj3Dt2hAVFdkPwakgmLbiKyvi0K7rOgsLC2zfvp2yslimrKQk75dIgCRJNDU12docYt4imzaHyKoJox1f/amoqGDbtm0JGaiFhQUuXryYcxUlExtIQ0MDw8PDLC8v50QTuFnMVWJ/8WuLgfyGhgYGBgbssrdoVdtqo9rCrUKxBWjztWd3Qruvy/WxpN9UkE+w4XB8EofjCUAlHP4Eur4dwE6AhUIhu/0mfoA7U2CRDpIk0dzcTEVFBefPn0eWY1pEIlm3bdu/U1//v5Fliaam92Cab85r/VhF+6eYm+vhxIkBXvzi76+97gKy/O9Y1l47CZcNgrglPrmkaRqlpaVYlsXc3BydnZ0ZFMjzQ3y1X8wF5qLNka5tV+huiJYtgeXlZVvMsKOjI2/WKlmWbUp3KA4TYjIkSaKhoYFr166xuLjI+Ph4UX0gFE7R7vF4uO+++5ifn+fChQt4vV46OzuL0mJ+K3B37HIDyJeN6nZljOK1LNra3hT3e4hEmlDVSe6///6cym+WNYbD8QSa1k4k8pfEqhKGbXAvXrxIa2ur3Q6QL9IxbohD+9LS0tqhvQwwkeX/BPyY5v6CXi8afRxV/RsM42dwOL6Ey/VL6PqrMM1/IFXFRSASiSTMgog5i0AgkDBnISBKyqJ1p1glZZfLlSDiVFtba7cCCcrB5FK4aNnKNlsDN6soExMTHDt2jLa2tqxaGunYQBRFQZIkenp6WF5e5uLFi3g8nrSUhpulySGuT+VMhNbJ/Pw858+fx+fz0dHRUVCwsaUSvoV8sVkCtPngTqhsxH+VTTN/WnZJGsSyJCCCaY5iGF32fhobGxkYGKChoYGmpqa44d5zOJ1/AJQQjX46Y5Vb+NT45JJpmni9XizLYmFhga6uLqqqqlDV76OqFmAhy7MUemvLy8s5cOAAMzOPUF39GSyrGZfrHYBMJPI4prk+gZcuuST0oZIP7eFwmP7+fmZmZjLSz+cLMReYSptD+Hyxx1zadpPh9XrZt28fV69e5cSJEzQ1NWWtnqfyUyI5tpmsiYqisH379rQaUKmuzxUbTYoFg0EOHjxot6jt2rWL6urqnNe7XdgKNpJwK6hv44eixazFTdYRD4pixF3rpqmpiatXr2acs4h3PCUlf4GqnsXhUAiH24D77Q+rKPkKY7V9+/Y4Q7ZCjMLwpmFLVw0QYlGpGDdWVlZsMb2enm/hdP49IBGNfhLTfHne99E0X0g0+kJghdraNkzTgaJ8g/Hx36Su7jCSJCdkWUR7mWjZEkxb2SoKoqQ8NTXF8ePHczq054Py8nJ27tzJ0NAQIyMjdmlYUA4WWgqHmw66urqawcFBW+E8W3YqVWuV+Mx6vV6bJrC3t5fGxsZ1AoabZfBzuV4YXUENLLKe+ay/VdnYQj6wrM0ToM3H79wKP5UNut6Jqg5iWbC6+p2M1yb7KcuKYFmvwOW6gWU1Y1kvSWCGcrlcHDx4kEuXLnHy5El27NiBx+NBVb+AJN0ArqEoT6Lrb15bz1o3wO3xnKKj4wsEg7txu/9kHetSOBymr69v7dD+20jStbX39Xt534t4KIpCMPghZmffg2X9OlVVl5FlCafzHWjaK5idfS+LiyFbdynf5JLb7WbPnj021Wwuh/Z84Pf72bVrF5cvX+bpp5/G6XQiy3JCR0U+bJDxEExQlZWV9ixKd3d3VorzVK1VGxGUzfXaZA2o+vp6WlpaCvaB4vqNtl3FM2ptVTbuEAiGoFyxGRkjkWEXwUU0GrUpZ8vKymhubo478FtrsxWxn1ZX35PQv5ocWIjXEQetWKnZs/Z8GUVxY1nrOZ137drFtWvXOHHiBG1tbdTVncTp/ACG4WN6+uPcuOHNqRqQCqJHdnx8nLm5Z6ipia7t8UrBGaMYSjDN3UhSP5IUpLX11USjfk6c+BiGUW/PgrS3txfUsgU3qWbFALkoKXs8nrzXEsPw8ZWVkpISgsEgNTU1TE5O4vF4ciop5wqhyi2y/tmUYy3LsocMRcbK7XYnsIEI53DlyhWOHj1KT0+P3fO8mcFGLhkg8feqrq7myJEjnDlzhp6enpyyyFvUt1vIF5spQJsPiumnCl0rHD4BzAIB4o8SmeYBY34KSkrejSyfxbI6iUb/IuWBSVEUuru7uXHjBqdPn6ahoYGWlhehKN/FslzMzbVx7dpQAtFIfAuPz/futQDiB0Sjv4RpvihhfXFoj2kuXKK9/T1JGWINSRrCsrYB+VcPvF4fodC70LTzqOo1JGkCVf0cbvcA4fCf0draVXByCaCqqoqysjKGhoY4ceJERqHWTMjUttvd3c3Vq1eRJImOjo6sCbtcoaoqXV1ddmtVtgH1+CqV8KeSJBGNRu1gKBM20u4r5nIqKyvtmcH4Qe1CKhXFarsSVZ67AXfHLjeAQno7N5IximlZxKoVMzMzTE1N2VUAkRXIXPaUWF39AU7n+4lGH0LXfwfTjKJpGisrK7jd7oTAIvk9yvJRoALDeDWm+UIsK/VgIsQyw21tbVy5cgWX6xP4fGEUZQXTfBa//xdtxo1kyPK3cDj+XwzjJej6+4HUdGxNTU1EIn/O4uJbMc0gDseryfd7Ed9eFlM2/TM8niG6uz9JaekcLtciPT2fZGnp16iq+pWitcWIQ7tgqKirq1uX1Y9HKqXwbJWV2tpau6Tc3Nyc0zBarggGgwkD6mKuJrkXWNd1SktL7T2KobPk1ipVVens7KS+vp6+vj5bLXaz2ahyXVtVVUpLS2lpaWF0dJTR0dGslZ0tNqot5IvNFKDN5/N2Z7BRgWmWrQUVup0IW1xctA/RqecBV5HlM0AFkjQA3ADSk2eINskrV65w+bIXn+8DqGoJHk8Lfr+X+vr6lNUA0+xBUSYBR9p2K5EhFtX+6elpenp6cDpVXK6H1xilOohEvk2m41IqCndN09aqAd+jqemP8Hi+jyxHKCt7imDwtUSjX8E0D+Zzu9dBVVW6u7ttNsFsyaVC2nbr6ursFuBsfjBfiNYqMaDe0tJCXV0dmqbZvnRhYYFoNGqfo2pra+nq6rLJfLKxVon3nQ9zVToGLzEzGE+Vu9nEJPfKbOE9H2xAfuXpfLI8onVHBBehUMjmixb6C16vl9ra2ozrrM8E7SEa/be1/cQUuHfs2EFfXx9NTU0ZDqU3cDjeBRiAB11/t/1IKm5rwULU2tqKZf0SkvRhZLmC6upXIZRcU8Hp/BNAR1WfwDBeg2XtSnuty7UTy/oBU1NTjIxcorNTorKyDLgOVJM8cxF/GI6vBvj9fqqrq9dmKV6IqoaQ5T8DVqis7KW8/BQDA4P4/W+jrCx7y0KuED24w8PDCSXf5P5VwQiWSc08GfEl5UuXLm0oO5UKoj9ZiESdO3cOn89HeXl5yl7geKRjrRJVq5mZGU6ePJkXg85mG2XLsmw12xs3bnD27FmCwWDa+Zt7xYhv4d5Avm1Ut3pmIxMzlEh+7dy5k76+PmpqamhpaUn5fZfli1iWF1keQ9ffANysQorDcHwngJgNaFgTmhoZcVJdnb11SNM+imE8hWVtW6tOpIfT6WT37t1x1f4KWltPYVklyPIAcA24OeMYjUYTAovUFO7xtvWzGMZfIEmfQZLCSNJ1XK6fQdP+CF3/IJlmD3OB3+/nwIEDNuNWZ2cn5eXlCYm6ZKXwfNp2KyoqCAaDDA8P24xe2QT7coVpmng8Hmpra7ly5QoXL17E6/VSXl5OIBBIm/SERD8lWKtSEQgUswIvKmI3btzgzJkzSJJESR5sMoUQpdwLfuonItjIB+kMrzisC0MYz7YkxGiSW3cikci6tQql8quoqCAQCDA4OMjMzAw7duxI8QWMAhHAQtdLGB+fYHFxJWGAWwQX643MI2jaK7l4cQTDuEFPT03ag6hp9iDLzwMlWFbmQAputrpUVFTQ33+OQOBhfL4r6PormJn5iG0IBTWuyLJkMjK6/rsYxn/B5fovSNIksrxCd/cnuHHjWfr6Pkx7+96iDXhrmobP5yMSiXDy5EkkSaKsrIxgMJi3SmwqqKpKT0+PnZ0qRJvDsqyUAZD4m+/bt49IJGKz4pSVlW2ItUqoxT7zzDOcOHGC7u7urK1LxZ7ZSEZ8RqqsrIxDhw7ZQ/PNzc3rhLHuFSO+hZ88bHYbVbyfSs70ZvJTgqzi8uXLnDhxgh07diQdxEw7IWZZPhYXH2Z+fsq2WfFsi01NTWtsi6Cq/wNFeQ5Newc1NT+zbpYjNdyYZn7aH1VVVQSDQQYHB3E6X05d3X+gab/A/LybhYURuxrgcDjsqnW6ykoivOj6R4AgDseHiSUELRyOv0ZVv0g4fBTITAmbDZqm4fF4CAaDnD17FsuyCAaDlJWVUV1dvWGlcJHZr62tpb+/H7fbTWdnZ15+NrlTQQRAIlG3Z88eTNNkYGAAwzAoKyvLur74LBqGgaZpSJKUwFolXrfYFfiysjIOHz7MiRMn7P3mShefr18r1lnmduInItjItzxtGEZCZmV5ednOXCdqWWT/QMZnh3M12Omgqirbt2+3tSGEAnbsy3uDsrI/wDQXMQwXw8Nvw+WK0bh5vd4MB8tlJOkGltWIw1HJzp2VCbMcqej1otH/hSw/h2VtJz4rle0+hMNhqqtX8XiGCIcVHI5/ZWbmbQQCFVRXV+c9Z2FZzUQiX8Dp/D1kuQ9JClFefoRA4GF6e/+aurqX5c3SIKpV8QGQUGCtqqqivb2d69evMzo6Sl1dXdGyO3AzOxXf+pRu/8l85pqmUVJSYju/VAFQaWmpvX5vb6/dWpUJmdhAFEXB7Xazc+dOW4U803zLZpePk68XQ/O1tbUMDQ1x5MgRurq67GHdrWBjC3cSVFVF1/WcDoTFHhBP56ckSUoQycvl+yLLMh0dHSwsLHDmzBkaGhpoaGggEomwvDxITc0YkhQiFKrjypVpvN5GuzUmlZ+SpDOo6r9gWU4cjr/ANB9MmOVobGzMc0BaIzZrUkNyRUH4Kb/fz/Dw2zl58o04nW5qai7S1fUBnM45NO0fsKwHcnythHeCrr8X0+zE5fotYgEHSNIkHk8T0egjGMYnc1opU9uuqKwsLi4yNDSEw+HIW6guE+IF++Jbn1KtH18BWlhYsDsVAoFAxgBo//79NlFLpvXjIZgUU7VWbdZsoSRJeL1empqamJubs2caM50Lttqo7mFkMsqmadqRtvjyhsNhJiYm8Pl89mE918DCsizbYKuqytWrVykvL7fF68ThrdD3EYlE0HWdsrIy+vv7uXjxIsFgkIoKE59vEEmqQZIidHa+mOzKpVdxuX4FSZpH138LXY/1GYvsTn9/P1evXl3rYY2vcpRimj+bcZ+pKAdjVaBGLGsPbvcZrl9/kEjEsO9PYffkfiKR7+ByvXKtD9hCUWY5fPi3mJz8TU6f/p202hbxAomp6PzSiTk1NDRQVVXFwMAAU1NTOWln5Aox6xKvzdHR0YGu68zPzycwbQUCgZR85pkgyzLNzc3U1NQksFZlG4BPxQbicDiwLMtWYJ2dneXUqVPU1NTQ2tqaUoRpMysb6crToq95dXXV7rXt7u6+Z4z4Fu4NCObEzQ42kolGZFlmbm6OmpoaeyZwI34KYsmQaDRKRUUFw8PDDA4OEggE6On5LLLsRFEkSkrezM6dL8y6lmXVYVklSNIKpnm//fuysjIOHDjApUuXOHXqFNu3b6ekZAhwY1kdaVZbweV6FZI0jK7/EktLj60TSxUzbNu2bWPnzp1cuXKF0tIv4nb3EatGfIRo9GsF3xvTfB2RSCsu188gAg4Ap/PzRCK1mOYHk97/+qo1YPupdG27breb8vJyuwqUi3ZGrhAtwBUVFQwNDTE5OWnP8MUHQKKtPBAI0NDQkLOfFN0QVVVV9vq5CNml81ObxVwlrhe0wcvLy/T19eF0Ounq6kp77ihmsHG3zBbeUcHGd77zHd7+9rdjGAa//du/zXve856ExyORCI888ggnTpygoqKCL3/5y7S2tua8vjgEi1aopaWlBBrXmpoampub6e/vZ/v27RnXysQMJcSH6uvrURSFs2fPJmRUc0W6vlC/309FRQVtbW3Mz88zNDRETc13UJQIcAFdfwvZAw2Q5QtI0jyW5UZRvmMHG3CTsWpmZiZjlQOwB+OWlycIh0dYWKimpKQ0bfbCML6JYczj9ZbR2HgB03wQSZKxrM8CmXtrUyNIJPIMTucbUZRv2L+tr/8sdXVf5Ec/+icaGnqoqKhIuJ+GYdj9q/nS+TmdTnbt2mUPztXX1yfwwReK+EAtpmi+xNGjR/H5fNTX1+c8D5INLpeLXbt22X2nVVVVtLS05Nxapes6c3NzCa2CFRUVHD58mNHR0QTGDrHXze5VzeZQSkpKuP/++5mdneX06dN22XsLW7gTIARoczkQ5jNnkYoZKp7BsLKyElmWOX/+fEY7nw6CzUj8J9phxSGzqamJ1dVVLl06jc/3YxyOKSyrDNPM7GNvoopI5CvI8hCmmVhRiGesmp7+GJ2d/4iqOtZo1hPZp6LRKKHQUSoqrmAYCvAE/f2/bPvTZLFUge7ublZWXoKuP44sKxjGYWT5KcDANF9KIfMWpvkAodAgHk8HcPMMoSifYnn5jxMYAgUtfqaqdTqIFl2hnSHe50aTLPGU+JIkoWkax48fx+Px0NDQkKZVO3/ExBJ7bL0LwTaZLSAXQYdI0oVCoZxfcyPBgNfrZf/+/TYtcW1tLa2trQW3dBWynzsVd0ywYRgGv//7v8/3vvc9GhsbOXDgAK961avYsWOHfc3jjz9OWVkZly5d4oknnuDd7343X/7yl1OuZ1kWIyMjHD9+nNXVVV760pfy0EMP8fKXv9we3m5ra1v3odV1PWX/ajaDnYoZCmJMDmVlZVy4cIHr16/T0dGR0lCk0onIpS+0urqaYLAEh+N3MM0FJKkW03xJDnccTHM/ptmOLA+haW9KeU11dTVlZSpzc59gdLScysrfIBQKJzgWl8tFeXmYrq5HUdUQuv5r6PpjGV5ZRvSn1tT8Cw7HAIZhcu3aB3C5Hi+wSiATjf5fVPX9OBwft38rSVFe9rKH+N73Ps/QUIWtlJoLhW8uEINzV65cobe3l56enqyc4fGIp8cVrBvCscSzbgwPDzMxMVGUQCMeIjsoBgs7OjpSzl7Et23Fs1i1tLSgaRqmadol69Y1wcj+/n67taq0tDTvXtXNqoSIoOi5557j7NmztLW1pWzDuFsyRlu4N+ByuYhGowU/P95P5TNnAVBZWUkgEKCvr49r167R3d2d0j6K9h1h/wWbkZgNS9cO63a7OXAgiqJMoOtOJMmPaf5U1vekKF9BUZ7AMF6HYfxK2uvKysqoqrqOZeloWgRNO8Hi4q4EQhSHw0EwWEUw2IbLNYSuv8mmL86G0tKXE41+n6mp8+j6Rbq730CMevUxDOP/Z+/N4+Oq6/3/51lmy96muZM8xQAAIABJREFUTdIkzdJm7QJ0oQUErKJyQS7uCt6vFdkE4YIXRURcigtS7/UKKouI142rwu8K4gKiyAUESmlr6ZJmaZYmaZsmaZLJOjNnzvL7Y/I5OTOZSWayeC3k9XjweNBk5syZk3M+78/7/X69X69rkjrGZOQzPHySjIzFiMvV2Hg57e07ycvLIy8vL6Wu9VTIzs5m48aNdHR08Nprr1FVVcXixcnPiMSTxxWS+IJerCgKR48e5ejRo/h8vmm7EKlA+F0cP36cXbt2UVZWRkFBwaT7LJHcvPC9cUq6J8Js444kSeTl5UVJ5VZUVNiFt/lUZfxHxj9MsvHaa69RUVHBihUrALjssst48skno5KNJ598km3btgHwwQ9+kBtvvDHhH+Luu+/m1VdfZePGjbhcLn7+85+zfPnyac9DLNQi6ZjtnAVEFtp169bR2dnJ7t27qampAYiruCGGzVPZVHq9L+B2D47L74UYGKhh+nXkJLLciKb9GPAB8Q0Dh4eH8Xi+QGHhMxiGxL59IUzzbRQUFETRjGT5z6hqEFBQlGemSTYmYFmrAA+KAl7vRlpbH2HJklKWLHlHSgNdE/zVy8jJ0aiquj/qNW9/+z309v7ebnEKn4i5gKIoVFRU2C1U8TeMTWSdgToi4zsyqQKYiE62YsUKezDv+PHjVFZWzpk3hyzLlJaW2tSqo0ePsmzZMoLBYBQfOBFtK7ZlrSgKHo+H0047ze6c5Obm2lTCZDGTik4qXFufz8fKlSs5fvw4O3fupLq6ek7viwUsIBWkYkArCmK6rsdVhkp1zgIiVeS1a9faXPmqqircbre9cRP0HSGKkqoJqdv9y/EYYXH8eAma1jNNF2UEl+ubWJYHl+tbGMZFwGQuvHC3DgTeR27ua4RCLv72t0K83haWLVs2KZ5a1p8IBkdIpvvvhCzXUFRUg2nehmkGkSSQ5ZeAbAzjn6Y9XmLa7h6WLm0kLW015eUryc8fo76+noGBgZQSgunPP1IIys/Pp6Ghga6urrhxRJyn2LQ7E8rs7OwpjQdjKcBVVVUz8qiKB0mKzKDm5eXR3NzMsWPHKC4utmVyR0ZGUFWV7OzsuPHUGafE8xHv3p2rzoMsy6xYsYLCwkKamppsqdyFmY3/Yxw7diwqGSguLmbnzp0JXyNuqr6+vriV2Ntvv93+/6effjrua+J1LMTMRXd3NwUFBfbQ0WzmLEZHR+3qimma7Nmzh7S0NIqKihyKGzO/mRTlYSRpEElSUNVzOHLkOD09fiorKxO0W0fxeD6CJA1gmtVo2i+jzjN2zqKqahRVVXC5VE47bQV1dS56e3vJzc21H0rTPAvLqkKSGtD1m5I+d8N4P6HQMkAjPX0f69ZtwzRNGhq+SEHBFZMk5eLNgwg1iwmlre0EAufg8/0/+326/l5bKaW9vd3uQmRnZ8/kkseFaKE6FZAURYmrulFeXp5yl0LIugpq21x4czjb4YODgwQCAcLhMPX19bYqVkZGxpSf4WxZa5qGLMu2GohQ7Ojs7KSlpYW8vLykF/P5ruiYponb7aampsY2lxK0jFSkDBewgLmAoFHFIpGCoaqq9oZrLuKUmF0cGhpClmX27duH1+ulqKhoDlT3NBTlKUAHVHJyPsLBgz309PRQU1OToMvswbLykKQeLGspEfXDyHoVjw6blVXOyMjTZGRkcPbZ0NzcbM9MRq8jMs7EQJIO43LdhmUVEg5/C5j62VeUm4GDhEJ9qOofcbn+hKqeMe7FMXE9RbEmOdpujf1eIS9+7NgxW8Y2VQr2VBAzdk5XbK/XG/c8S0pKUt6feDwe1q5da9NVCwoKKCkpmfWG2Xk9R0ZG0DSNxsZGMjIyWLlyJVlZWVN+hnOeQ9f1uKpVMDOH7+mkck877TT8fj8HDx4kGAympCS3kGzMMeLxT2P/gMm8Jh48Hg+BQACPx5OU5OymTZtoaGhgcHBwioUw/ncQD4RYtMPhsD1wVlBQQGVlJbIs09raSnd3t82XnTn6UNXniQyaSVjWh1i3bp29UMVXRjiJZfWh624saz979uxC0/So2ZXoOYtvYZr3YFlFSNIlrFmjxpnlyCQU+j1gkSqH1TTPBkBV/wtJ0lAUKCtrorX1IWT5n8jIWGwHFyfNaPJ5OvE+AoHX8HguJxy+DNP8DBD5W5eXl5OXl0dDQ4O9UM3WhTNWdQOgtbUVVVVZsWLFrGUHBSQp4mYqBvNEpyzZlrWu61F0KGc73DlfY5omHR0d1NXVJR3sxPviqYGUlJSgaRr9/f1Jn/N8L7KJuLZ79+4lLy+PVatWvSHa1ws4NeDxeOyNiFNoRMA5DyhJEhs2bKC5uZmDBw+yevXqlEQ2Yg3oxLoaSzHu7Oykq6uLxYsXz0reW5Z/hSSNOr7LRaxdm2MrGlVWVsYUBC0U5Qk07UxGRkrp7T2N/v6DhEIhe72aoMMC+IkoI05cr2QVq1yuLyPLrwEKpnk2hvEvU34XyypE0/6ALO9AVT+IZQWQpFdR1fV0dGyju3u5va46r2cqtF2hpLdkyRK7CyE6TbOBWP/F3x6gs7MTSZIoLy+fM3oxROiqixYtmpE3h1AEFXHKqQrpnK+xLItjx45RX19vq3NOt2Y7Jd3jGQLORJo2mTiVk5PD5s2befHFF9m9ezdlZWVJS+UuJBtziOLiYjo7O+1/Hz16lMLCwrivKS4uth+aZNqMR48eZc+ePWzZsmXSgp2o/bVmzRrb1bKmpiYuvSIUCkUt2GIhdMrPJVocKioq8Pv97Nu3j5KSkqSk3eJBVe8h4q8BIGOaW+yFavHixRw6dIjMzExyc3NtqpFltVBaegY5Oa0EAtexdu3p0yxiywiHt0f9JDLLsSiOYpWTdvYsbvet492THwFTDz6GQp/F5arDMCy83l9QWyvR3f08Bw/eQllZGWvWrElRtWo1odD+uL9JT09n/fr1UdWj6XwiBOK5sCZS3Th58iTNzc2Ew+FJPg+zgVBYEoNzQpXEmdBYlhXVDh8eHk66HS5a7gUFBTQ1NXHs2LGE6hqx74unBqIoCoqiUFxcTHp6OocOHSIrK4uKioqEwe3v0dmIff6XLl1Kbm4uJ0+eXEg0FvB3RXNzM6+88grV1dVRcWoqZaiqqipbCj2RVLagmYgCmCi8ZWVlkZOTQ0lJScJ1taSkxI4jYoZgJs+Fy3WH/f+WpSLoUPn5+eTk5FBfX8+JEyfIz89ndHQUy3qBsrJvIssmsryZtLRLKSjIirP+DI87fXei61dEGdmC8NvJZmDgAZqa1lJS8p5JtB7LKkeSdmBZMpY1vWDExPq/jJycd5GT87+4XH4UpYXS0hvJz78bWb58TtYPYSAninuJZhUSnWesipVzvsZJg/b7/TQ2NhIKhSYNNM8GgkpUUFBAQ0MDHo9nEnXLyVYQ96mwGsjOzqaioiKhLL7Y6whqlVCtmk5kIV6cEjFqPlUTRTflzDPPpKWlJSn67htFjUqaRtFiboS8k4Cu61RVVfGXv/yFoqIizjzzTH7xi1+wevVq+zX33XcfBw4c4MEHH+RXv/oVjz/+OI899ti0x37++ee57bbb+MAHPsCnPvWplB6kQCBgb9gXL15sc+5jFTeysrJS5qSL793U1EQ4HKa2tjblyoXXm4EkCQrYIkZH2+0NpuDZivmTkpISioq6yMy8GUkyCYc/jWF8LMGRLRTlN0hSM7q+lYgmeXz09PTQ0tIyScnE4zkPSeokMrx9H6Z50cTRxzfCTv6qWGAKC18hP/9LSJIGKITDa/jb324iI2PtnC6EAqFQiIaGBmRZprq6etJCKGhG4lwFvUxwQ6fjLRuGQWtrK36/P6UuRLIQ1Z329nZ7syGqlaIdnp2dnZLaViz6+vo4fPhwyi1xwzDsSlFnZycZGRkUFBRgWRbHjx/nyJEjlJWVxaWDvfLKK5xzzvRDpDN9/Y4dO9i8eXPc7yJJ0pzNxMwRTo2I8n+Dv1ucmk/s2bOHz3zmM5x55pl88YtfTKnCrGka9fX1qKpKQUGBHQPGxsbsQoiIVdMb0E2GaZq0tLQwNDQ0jZFePBzD56tyHCuPQKBl0vqv6zq6rlNUVERJSQfZ2Z8BDAzjbYTD3417ZFl+Dbf7E4AXUAgGX4t5hY7XuxEYwDR9vPzyfSxbVh1TUQ6jKL/HsvIwzbcgSU3I8suY5jswzeIpabuRdXUvPt9lwBCRW1FB067BMP6dCGVrbhAOhzl8+DChUCiun1EsbUvXddsgMZn13zRN2tvb6enpoaqqas7n1yzLsvcKubm5qKo6yXtD7KVm2kUbHByksbExZWNckXRIkkRPTw+GYVCWpNLp7t27Wbt2bdKFUGecGh0dpaGhwS4exivm7d27d0pq70xtA+YJCReWf5hkA+Cpp57i05/+NIZhcOWVV3LHHXfw5S9/mY0bN3LppZcSDAb52Mc+xt69e1m8eDG/+tWv7IHy6RAKhbj99tupr6/n/vvvn3IwLZ7ihlDcKS0tZenSpSkb0E2H3t5empubbdWCZGBZPyUt7VP2vwcHK9m79wdRC6HYCI+MjHDo0CEqKl5l2bIfAi5M83zC4XviHluWX8XtvhIIY5rnoGk/nfQaVb0HRXkcXb+GYPAyGhsbMU3T7nK4XJ9FUR4HXAwNPcHAwIT0rFAzEotLNB84gNt9NbL8EpI0BIBpLqej4w7a28tTVnxKFj09PTQ3N7N06VJkWbblhkXbXlzTmdKhRBciJycnZYfwWDi7K36/39Y013UdWZbnfB5FfGZ7ezvd3d1Jq5mIpNLv93P8+HG71S2CXjgcprm5maGhoUnnPN/JxiuvvMLZZ5+dsGK2kGycMnhDJBsQiT3bt2/nD3/4Aw899BDl5YmlwJ2DvM7Ckq7rLF++nPz8/DlXrxsYGKCxsZHS0tKkK+yS9FO83ok41d7+IVpbr7MLIWL9l2WZQCBAY+NOVq/+OhkZ7ZjmFsLhu4FENE4xf9iMrl+Prt8c8/sQXu/pRIz8FMbGXqKpqZ+xsTFqa2snbdhDoV4yM88CxgiHM9ix48f4fJlRBZt4678k7cXj+WckacD+ma6XEA4/z1SFupmgv7/f3lB7PJ5J5rPims50/RobG6OhoWFGDuGxiDdsLjoKpmlSXV09p/MoEIk5QhVrxYoV5OXlTXufiplVv99Pd3e3LQWfTFFt165dnH76dOyQCcSLU729vRw+fDiuR9WePXtYvXp13ETkVIpT/1DJxt8DTz/9NLfddhtf+9rXeOc734mu61GV61jFDeeGfXBwkPr6+lnRnqaCpmkcOnQIj8czyU1VGPo5K+wlJfdQWvqE/ZqRkWdRlLMTHt80B9D1K/H5XkOWV6DrPxhXg5oMWX4Ft/tqIsnGZjTtkajfS9JxPJ7zARcQJhg8CHjtDXteXh6KAqb5CoODWUAJxcW7yc19Hcv6JKp6+rTXQ1Eexe3+NJGKkQQojIxs429/O3fONuzxFkLDiBgtiXmXufw7i4Xw2LFjCWVm473HWbUS3RVxf4p7VJxnf38/TU1N5OfnJ71gpoJAIEBTUxOSJE2iVgnKhtMx1plUivN0uVxRNEaRiKWlpdlt9r9HspHo9afSIr6AN16c2rlzJ9dddx033XQTH/nIRzAMwx6MnmyUmhW1YR8dHaWurm5WtKepoOs6jY2NGIYRx/B1Mr1Y0wY5//x/RlEsTFNlZOTklBtYSXoOWb6ZUEjG4ynEMJ5O+NrIwLkMjALxO8ay/DKq+nN0/b2Y5j8BkaSpoaGB3Nxc3G63LeCSnj7Mxo1XIEkWkqQQDDYT6ZpMD0k6hMfzPiTpqPOnjI39EElKLNs7HRKZ+om5nqqqKpYsWTLncaq7u5u2traUqFuJuisiTjmHzYeGhmhsbCQ7O9uWzp1LaJpmd4JiqVXCakAU62KLiuI8BbVqqhi6c+dONmzYkHQRMlHcEcU8YeQrkqSpOienUpx6UyUblmXR3NzMs88+y/bt23G5XLjdbh5++GEWLVoUp8I+Gc6Ftra2ds6GqZzneOzYMTo7OykqKrJnU5yGfhOVCwuPpwRZHkHXTyMc3jHlsRXlN7hcX8YwZE6eXMHg4D1TGNFZKMqjSFIThnENlrUs5vdjeL3nA0PoegFtbT9ncHCYkZERJElC13XbUCiiEtGC13sBlhUCCggGX0/maiDLT+HxfByYMOUxjE20tDzIsWMDCQbg4xxpGnWQ2IVQVPCWLVtGSUnJnAfsYDBIU1MTEBlkdC4kzoVQKER5vV5ycnLsv/10C9tMuhCpore3l6amJjIyMlAUxZZIFNdTUDZi4aRWOdVALMvixIkTtLa2snz5cjo7O3nLW6Z3GBbv3bFjx5wlG0JR6x8IC8lGYryh4hRAe3s7L7zwAtu3b7eNM3/wgx9QWFhoJxdTrQFO2lOiquhsIQpLYrZS0LaETLY4z8hnW8AIiRICJyJeSQ9iWV6amz9OMHglK1asiNnwWbhct6AoT2EY7yccvoupHpFY2m4gcAyw0LRMLMuiurraVq1SlJ+iqo+i61ejKM8gy68QDm/DMD6QxFU5ide7BUlqi/qprmcSDp9I4v2TPY2cIjOiuyL2KMPDw9TX17N48WLKy8vnfMMuqFvBYJCampooKo9ggPj9/klD3OK/6dZQZxci0czRbCESS6/Xi9vttvcoYi+Vk5MTl1ooqFWAPXcYD6+++iqbNm1Kuqg3XVEsFArR1NREMBiktraWQ4cOsW7durjXciHZ+AeFrut89KMfZf369WzYsIFdu3bxu9/9jgceeICqqqrpD+BAd3c3ra2t9iI1G8TzX5BlmVAoNO2AVPIwcbmuQVUfx7Ky0bTP0dj4zrgcXEV5DEX5C7p+NaZ5pv3z2PmFQKCN9PQGDGMz6enFkzbszlmOgoIRvN63jycbywgGdwMhppMZhEhlyuP5IJEOh4CLwcEnOXAgg/T09ElqT4k27M4283QLoWEYtLW10d/fT21t7ZzPWkDkGh0+fJjs7GybuiU8V6ZaCJNFIBCgoaEBl8s1azWTeAaE6enpdtVVVNiSgWijm6Y5SQ1E13VaW1tpb29n48aNSXGHTdNk586dnH124s5eLBaSjTcM3lBxCuCqq66itLTUHiT90Y9+xHe/+102bNiQ0nEE5WYuNnLxaFvCKC0tLY2qqqppZbKnxyhe73oiYiJhgsE/09YWoLe3l1WrVpGRkTH+um683rcC2cAgweDO8f8XLIAggcBLDA9b9PQsjqLt5uY2kpt7PWCiaT+kr289jY2NkxSrZHkHHs+HAR3LyiAYPJzkd9Bwu89HUQ5E/dQw3IyMdEetwU7VJdFdEcma+G+6NduyLDo6Oujq6po3ryBxH6Wnp+NyuRgeHsayrKju+mz2KKFQiMOHD6PrOtXV1bPy5hAGhCIJEjMhIuGsqKhISrUKpo5TAlPRceMh2Q784OAgDQ0NjI6Ocs455yzQqE517N27l6uuuoprrrmGrVu3pvSwBINB6urqyMrKYuXKlUlltolaomLgWFBNBK/xyJEj9PX1sWrVqmkVFqaCJLXg8VxIpCWsEwzuAzz4/X4aGhpYvnw5hYWFyHI7Hs8lgIllpXHs2B+jKDHOVuPkDXsAVX0Ey0rHMD5CZLg7TENDA5ZlsWZNOx7PK+j6pXg81yFJPWja1zGM+O7l0effhtd7LhGJwwkYRiUtLb+jo6ODJUuWoOt6lEmiWAiF6sZMMJezFokcw8PhMLqus2rVqjmfR7Esi97eXlpaWli+fHnScnuxSlYulysqCDq7MRGudcSnoqqqKumhNWf1KNZo6a9//StpaWm4XK5J3Z9YGIbB7t272bx5c1KfCwvJxhsIb/g41dzczMc//nEuvvhibr755pSokeFwmEOHDtnPUTLrl9N3Q2yEnbQtZ2FJdOOPHj066/VLUX6N230DEMI0TyMU+gug2hX8CWqYidt9ObJ8EF1fR3f3PQwNjdiFpZKSpyktfQRZVggEHkJR3mZ/hqreiao+BMgYxvsIh7+LYRgcPnw4apZDkrrweM5BknRMcxmSNIJhvItw+D9JZvDb7X4bihIZWJcksCx49tk/sXjxYizLsmP/XG3YA4EA9fX1+Hy+KVX+kkEix3DTNAkEAtTU1Mz5rAWkTgF2zlrEKm4JJoBzky6SmnA4nJKfkohTlmWhqmoUtWo+6b6WZfHCCy+gqiqlpaWTFC0Xko1TDKOjo9x0000MDQ1x7733Jq0FDZGbQSg4rF69OiohENQd50NrGIbtZxHbEk2EoaEhDh06RFFR0YzlU1X1q7hc9wAmhnEemvZbxH1hGAaNjY2MjIyQnw9lZR9HkgIEgwW0t//cPlePx4Oi7Mbl+gyWVYqm3Y+zLR5ZxH80Xu26K0qv3NnlKCx8CZfr00iSgWUVEQz+mUhVarrvNYDXux5J6on66dhYPvv2PW53L1atWjXnCg2WZdHZ2Wm7oibTzYp2No90rKbasA8NDdHQ0DBvLXHRMRD+Mc5OTWzr3sm1zcnJSdopWCQ1hYWFFBcXJ70pcqqBCGqVqBiJ4bmISk18JaxwOMzevXvZtGlT0tdjIdl4w+BNEac0TeNLX/oSf/vb33jwwQdZtiyW2poYQv2ts7MzbkIQOw/o9DNKhrYFkTh66NAhcnNzZ6wa6Ha/G0X5K5blRtc/g65PmPMKalhfXxdr1vwaSeqko+NdjIycTlbWYjtO+Xw+PJ7rkeVnkCSLcPjz6Pp19nEkaT8ez0cAHU37b0xzYs0Q9FnR5ZDlTiSpHo9nKxNKU3diGJczHSUscg1rcLm6x69PPvv3P04wGERRFFavXj3n5qGCinrkyJGku1lTSeSKDbtTaVMMkM9FUpPofI4cOUJvb+8kVSwxEyiSC9FdF+eZrOLiwMAATU1NLFmyZNJA9nTnFkut+nvMFm7atImWlhb6+/ujrBgWko1TFI8++ijf/OY3+c///E/OOuuslN47NDREXV0dixcvRlVVW8lIUHfEgj3TG0NUXgKBwAw202G83lokqRtwEQp9n+Hh90QtLpZl4XZb5Ob+N3l5w6Snb8Y034NlRXuduN3vQ5Yj7eFweHsUj9XluhVV/SWWJaHrX0TXPxl9FuNdDperm9NPvwFZHsE0lyHL7RjGuWjar4mtGAk6lAiCY2MjvOUtH8XjOQlMVIwCgdGohTYVVa9UIGhJQi9cLLSJZkKcErnJUAycSc1cO8cKDA4O2pVOl8sVxbNOtnU/FQzD4MiRI5w8eTIlCcXYlvWuXbvsmQ1Baevp6YmrYBIKhThw4AAbN25M+jwXko03DN5UcerZZ5/llltu4Stf+QoXXXTR9G9wQAyPp6enk5aWFqVkFCvjPhOYpmlTT1PfTPvx+VYS8Y3yEAg8xdjYmijZWcMwWLbsVUpL70VVZeB8dP3Hk44kSXW43TdgWVlo2g+ZrAilE7ltXMjyH3G5vo1h/BO6fguGYU7qcng8b0eWG4BhIB3LKiYYfAkxPC6o0GL9F3SoyIZ9jJwcDZdrgpJ88uRJDh8+TGlp6byJzTjVIZ1/z0SFpWQlciE6qUlW8SlViOTVsiy8Xi9jY2MoimInFrHFulRhmiZHjx7l+PHjrFy5Mun9wlRxKhnMJjkZHR2lsbHRlukXtLZ/ICwkG8mivb2drVu38ta3vpVbb701YcYbb3FxSo8KfulcP4B9fX00NTVN8rSYCrr+BzIzL8OyLEzTzcsv/zceT1FUcImoLvwMVf06uh6mt/ciMjLumbTpVNWvoqqPADKh0GNY1mnOs8PlugvLykDXPw/ISFIjllUJTHAwe3p6aGuro7LSRVHR+4goWmmEw9cyNPQxBgayorw3nBrxos3s8SxCliNmhoZxFpr2F/v4mqbR0NCAJEmTfDPmAqJK2NbWRnZ2NqZpMjY2lvJMyFQIBoNRtKTZfIdYJSuRBJmmyeDgYEoc1lQwNjZGY2MjLpeLysrKaQOD4NQODAzg9/sJhUJs2LAhqmUtkj3xtxXc3mAwyKFDh1i/fn3S57eQbLxh8KaLU729vVx55ZUsX76cr3/96wkHwEV3NXYeUCgZ1dbWzrniHmArN07l3D0Z/47Ptw0AXU/jr399HK83M6pYF+l6/gW3+1OEwzo9PW8nPf17Md/fT0TqNrnNo9dbDmhIkkow+AyWVQNMUHoi3yEbVf01bvetRBIVDdPMpqnpu3R3l9jeG/HUARNB13W7gBhPhncu0NPTQ1NTE5mZmUiSNKOZkKkgFJ80TYvr/ZHqsZxdCzFnI8syAwMDcWlEc4FU50Wc1C2/38/Q0BBnnXXWtKpV4r1zIWQiuv2FhYVUV1cnfay/AxaSjVSg6zpf+9rXeOGFF3jooYfIy8vj5MmTmKYZJY/r3AQ7FxdBGZqL4fF4CIfD1NfXoygK1dXVcQejJzoBY5x55vVkZkaG2wyjBE2rj3tcVf0BqvotQGFw8CP87W8Xx+kQGMjyq1hWPpZVMcVZmng8/4QsN2KapePc24nNW0Tmt46amn8jO/sgkhTCsmR0PZfOzh+TlnZ6EhSzA4ABnBH3t+LvILwdZrpIOR1O/X6/nQSlp6czNjZmJ5fzFSxaWlooKSmJa3wXi3hJ8FQKIc5gkQqHNVmIeZHW1tZJNMDY+ZVwOBylDiaG+iRJsnmyAidPnrS5veXl5XZytm7duqTPa6pFXwwD/gNhIdlIjDdlnLIsi+9///s88sgjPPDAA1RWVtLdHaHsxMrjxkp6wsTQbyqFq1RgGAZNTU2EQiFqa2ujig3xRFHOOecjeL19AOj6GjTt1UnrnSQdxe3+AJJ0EsO4mO7uL9DU1G7L0cvy63g8/wIYaNo9DiNZE5fr8yjKs+NmtlvtY3o85yHLbViWl2DweSRpDMsqB1wYhkFDQwPDw8Pk5GSTn383BQVPIkk6kmRhWQqa9iVM89YZXyeR1MyGJg3RAi6iG2RZFhkZGQSDQQzDiBmynzuI75Cs6auYCRSJxcjIyJQ28X+VAAAgAElEQVRJkK7rtrrafBjjiu9w+PBh22NDxBuhCCriv1POXVCMxT56KtUq8b3nSshEXMP5YHDMAgvJRiro6urixRdf5IknnuDPf/4z2dnZbN26lcsuuywpeVyYqLRmZmYmPTyeCizLoquri7a2NpYuXWoPRk/uBPhIS5t4MHX9MsLhH006XsSU6OPAIIZxEeHwd9A0xXalrakJkZ5+NZblRtMeiUo0ZPnPyPJ+DOOjDoncQXy+WizLgySFGR19jcFB3yTVDVWVcbleYN26byLLY0RuOR+a9h0M4//N+jqFw2Hbob2mpiYpCUhN06LmbJyLS7w5G+GuPdtgkQhioR0ZGaGmpsaeC4rnv+GssOXk5CQ9cCg4rEuXLp0Xl3Zd12lqaqK/v5/09HRCoRCqqtrXNCcnJ26FbSo1ECHv29XVRXFxMQMDA5x++vT+LRDZ7OzatSshXXIh2Til8KaMU319fbz44ov8/ve/5ze/+Q2ZmZlceumlXHfddVMa0DkxVeFqriAksnNzc+3BaOc6FUmCFNLSJjZN4fDH0PUHJx1LUZ7E5boN8GBZZYRCT9pri6ZpnHHGX/B6vwuomOaF43OFjM9dvJvIY2QSDLYy8Uj1oih/wjTPwO2+EUlqJBCo5uDBexgdDaCqKm63m8HBQYqLi1m5chcez7VEuicRmOZmQqFncBbUUoFhGPZmura2NikxGOcm2DnE7eyuO/+eQt1IDNnP9RovKHR9fX1UV1dHGbQK6pZILmZCMYYJsZbs7GxWrFgx5/er+Dt0d3eTnp5OOBy2xWbiDZw7v/t0qlUQ+Zvt2bPnTSlkspBsxMHvfvc79u/fz6ZNm6ioqODWW28lOzub7du3p1T5nWp4PFUkGjb3er2Mjo6SnZ1NdXX1pBtPVc/B5dpn/zsU+gWm+Z5Jx1eUe3G5vg2kYZpb0LQH7c89ceIELtetFBa+hCyDrn/SHtyTpIN4PJciSSFMcy2h0B/tNqPLdSuZmU/Q3X0+AwNnUFDwOoHAlXg850Z1gjRNY2DgLpYvvx9FCQAmoKDrHyEcfhCY/aC0SAiE6pb47HjGfs5N8IRO/NSYSbBIFf39/fa8iKIoUXK+OTk5SQ1xToW59OZwmvv5/X40TSMjIwOfz0d/f78tlZlsC985mBdrtCRU4YaHh9m0aVNSz+h0A+ULycYphTdlnHrppZf485//zKZNm1i9ejV33303J06c4Pvf/35Kz64oXHV0dFBbWxu1SZwJ4g2b+3w+AoEAHo+H1atXT6JUut2rUJR2+9/B4P9gWbHzKCZu91Uoym8AD5r2EIZxqf3bkydPcuzYX9iw4euoqoGm/QjTFBVk/7gv1AiWtYpQ6PeTOgHBYCubNl2BZakoik5//+/xemuR5UixzqlYtXZtD1lZkSHzCSgEAs8CyYtUxEIkBPn5+VEdgqlUl5yeRsmoDIo9SU1Nzaz/1vEwPDzMoUOHkGUZt9s9I/+NqeD0IhOzFjMt8AkmiLiuwWCQtLQ00tPT8fv9qKqakhTvdEmHpmns27ePM888c4qjRGMh2XgTwbIsHn74YR588EHuu+8+TjvttOnf5MDw8DB1dXUp8Ved0nMRT4tAlKmf84EVOtvd3d2T2qRebzrOjwsEhpi8effj8bwDWW4D3IRCf8A0o7nvhvEbXK5/Q5JkDONHwBYAZHk3bveHsKwQoVAxe/Y8YCtETJxrL+npbwc0LCs7oV750NCvWbLkGhQlZP/MspYQDL4KJK+8kgi6rtst8ezsbEZHR6OM/VJRXUoEESxm2yGI1xIHyMjIsCkI8Qal5wJCxlZV1aRnLeIFQnFNYwf5LMuip6eH1tZWiouLU+oGJTJaGhwcpLm5GU3TWLJkybQSxdMNlC8kG6cUFuLUOB5//HHuvPNOvvWtb3Heeeel9N6xsTHq6upshZ5knsl4tF0nHcY5bO5MamLNWD2efGR5xP53IDAa59O68Xo3AxlEPDh2AhPHkOUXUdUv0ddXREfHp6mqijZC0/WjBIO76e2tYHAwQE7OH1BVH+HwB8jOXkxWViZpaVejKE9hmpXIciuWlUko9EKUUIqgDK1YMUpp6QdwdjgANO2tGMZT0167RDBNk+bmZk6ePElOTg6BQCCusd9s4tTo6Cj19fVkZmZSUVExY+XDiKdJyF77ncIoAH6/354JnGtomhbFWkhm1iIQCESdqyRJk0xonfd9qlK8AqZpouu6ra4orm8oFOLgwYMp+eUsJBtvQtTX13PFFVfw4Q9/mOuuuy6lbNrJX121alVURTfeEN9MqhZCi1xURSKyaBtQlIbxcyhA01omvU+W/4jH8/8AN5a1mGDwYNTvFeVXuFz/jmGcQXv7VbS1WeTm5qJpGqOjIyxf/idyc5sJBj9FWtoGvN5OLCsToQAiSZ14vecAQSxrEaZ5PoZxfhRvVkDX60hLexuKEh1sNO0WDONrSVzpCTgNk5wOpx6Ph4GBAYqKipIOqqlgJtUjpwmh3++PaomLroUzIIhBabfbTWVl5bzI34l5kdiEIJ5p0kzkBw3DoLW1lYGBgUlt96kQr3o0ODhIV1cXNTU1dHZ22o60iWZ1hCZ9ooHyhWTjlMJCnHLg6NGjbN26lc2bN3P77bendB+bpklrayt+v5/Vq1dHbeCcEqlOU794Ah5TIRAIUFdXZ/sWRbx1fonHczUA4fD70PVHJr1PUR7E7f4SEQ+OswmF/ojzsfB4tiBJxwGJ7u67OHAgn9zcXNLTX2bRoifo7b2AcPjdZGdnk5//Y9LS7gNA07ZhGFc7PimM17vJPpZhnIuu34BpTnh1iC6Hrh9h48bLkKTohCMQGCHZRzaeRLrb7cbr9eL3+1m6dCkVFRXzQsUWHYLKysqkDFnjuYZPJYwiEgJd15OmMacKQQGOpYcZhhHVXRddC+e5JpNkmaZJR0cHJ06cSEkhMl6cCoVCU8adWLyRZgsXko0UEQwGue2222hubub+++9PeTinu7ub5uZm8vLy7M1wIrOkmUBokQ8NDbF69Wq8Xg+K8k0sy41pfjbuezyei5GkHUiSha5/knB4OzAxGJ2Tsx7TNLAsnf3778I01zA8PExJST3l5fsxza2YZqRCrCg/xO3+GqASDD6OZUWGt2X5+fHK0yNIUh/gIRj8/7Csc5h8f/bi8ZyGLA9F/dQwlqFpzXG/g3OI29kJSGSYJDa6g4OD80Z7Gh0dpaGhYZLDeaKB81Rdw50dgvmSUNR1ncbGRgYGBuxBQ9G1cCbCs4G4TkK3PVVqleCB9/X1UVtbC0wEOaH0EjsUKZSyEg2Uxw6k/wNgIdlIjIU4FQPDMLjrrrv405/+xEMPPURpaWlK7+/v76e+vp4lS5YgSVKUR5STZz+birgoyEx0463x/+LFPguP5xxk+SDgJhz+Krp+g32sYDCAx/Nx3O5dGIbK669/C0mqIBAY4JxzPkZk/6sSDO4CcnG5Po+q/hSQCIf/DV2PHvCOxLE7gOD4+bgJhR7GNP8Z56PY399Pc/M+3vrWSwCnFHs8BkH8OTvTNO2ETXTXnTRfwVqYL9pTIuVDca7OTsBMXcNFh2DZsmV2IXQu4Zy1yMzMJBQKzSimTgVxnSRJoqqqKum450w6QqEQbW1tSQuZTDdQvpBsvAnwhz/8gdtvv51vfOMbXHDBBQlfJxR3RNU6FArh8XgIBAJkZGRQW1s7L1VpYU4kNqGJ0YXPtwYIAQrd3Y+Nt5kj55qVFWTNms/hdvcAuYRCzwKLsaxu3O5NWFYIRckkFKoH1HEfjteQJAiHv4KuXxv1aV7vBiTpCBGuq4JhvANNe5TJ92gAr7cCSYo4hjv9NCB6JkCc60yqFvM9NCd8M9rb28nJySEcDkeda05OTlKCA1MhHA7T0tLC6Oho1AD5TJBIecPr9dLf309OTk5U4jRXsCyL7u5u2traknY5F92ggYEBTp48yZIlS1i5cmXUtRQSnLHnPTIyQktLS8KB8oVk45TCQpxKgB07dnD99ddzyy238MEPfjDh6+LRdt1uN5qm4Xa7501tT/D7ly1bxvLly6d45o/h860mEjfcnDjxNP39ebZjeEXFoyxb9ltkWSIQ+C8U5a0AWJaGy3U6ktSPJGWjaXuImPH14XJ9iYji1Htxu2/DNEvRtP9iwqxvDJ+vEhgZ/1wfuv4vhMPfBibWv0iXo5HTT9+ELFvo+vmEw08D0R1rca5er9fuVmdnZye1lgrakxiMno+1qauri5aWFrKysmyn8KkGzlOF8EoSxnSzcZoXhVqRCIlzTU9PZ2BgAJ/PNy+y9zAx/5ms8pbzXPv6+khLS2PVqlVJ/Q2nGyhfSDbeJOjq6uKKK65g9erVfPnLX0bTNMbGxmyufWRI2mU/qM4q8FRzFnOFcDhsG/vU1tZGtTdF61ZVt7FkyUNARNu8ru6vZGcvss/V47kESToISIRC/4NlCa7hyXH+bJBQyEtLyx8pK1uJqr6I230lkcTkHlT1l5jmegzjE4CEJLWhqvehqv9FpGIUxDQ3EAo9ChTEfAMLrzcTSZq4DXft2mnTzOaquu5U0aitrZ2VrF6i+YX09HS7PR5Lo5sr+P1+Ghsbk54XSdRhcc5aOK+r8Bfp6OiYtZxwIjhdzqurq+2A5KyyCW1zJ99WGGbGG8wTdIH29nbKy8tZtmwZw8PDtLe3s3bt2rjnsZBsnFJYiFNTYHBwkE9+8pO43W7+4z/+A1VV8fv9dsFmOtpuV1cX7e3tk+Ys5gqGYdDc3Mzo6CirVq2KWnMEdUtRvkRu7g8BCIczqKt7nqysHNsx3Ou9AEk6BliEw9/BMN4NRMz9ZPk1dL2fxsYSJOk0KitBUcbGu+4SHs+7kOV9gIqmbY9SQZTl13C5rkeWm4gIl8iY5ppxCld0l6Gvr89WSpJleVLHWpzrbKRtheFrdXV10mapiY4VywSQJImMjAzGxsawLGteHM4hUuhpaGiwlTqn2yxP1WERccp5XYXcektLS9KFq1ThpEpXVlZGCTKIzpU439hzdblcWJZlz1tMFaenGyhfSDbmGd/5znd4+OGHkSSJtWvX8uMf/5iuri4uu+wy+vv7Wb9+PT//+c9xu92EQiG2bt3Knj17yM3N5dFHH6WsrGxOzqOhoYFXXnmFH/7whxw5coSsrCy+9rWvsW7duqSNfURlp7CwcF5kUwFOnDhBS0sLeXl5tpGboG6tX79lXHIWDGMzmvac450BvN7TiFR2MseTjdMAE0V5DFnejWV50PWP0NqaQSCwi5UrV+D1biSyiJ837rrqJRR6zKEMYuDxXIws7yCygFtYVva4SeC5kxxO1669loyMTvbt+ybB4PpJAWmuMDIyQn19PYsWLbK5xNNBBGyxsIjh+ETzC2IOYr5oT05+aWxASqS8kWqHRXhzhEIhampq5iUgDQ0N2YomLpdr2hkWAWfLWiQM4vqHw2Gam5sZGhqiuLjY5qXHw0KycUphIU5NgSNHjvDSSy/x05/+lP3795OZmcmtt97Kli1bkqbtBgIBDh48SG5uLuXl5fMSpxJRtzIyMli37l2o6iAAhnE2mvas/T5JOjCuiNg3ngg8DWQjSfvxeN4PmBjG+9C0/6Cv70kKCm5AURQM4zZ0/QZcrs+hqr8ELAyjBkky0bQHsKzq8eN34PFciCR1MnGreRgZeYL+/lV2nNI0jbS0NDRNwzRN1q5dOy9ro5g3S0tLS7rLHE8mdyomgGBGzBftyTkvEuvllWguxOkcnmxXQBSu5subQ3ScdF3H6/XaimvOc43390lWKlcoLSYaKF9INuYRx44d49xzz+XQoUP4fD4+/OEPc/HFF/PUU0/x/ve/n8suu4zrrruO008/neuvv57777+f/fv38+CDD/KrX/2KJ554gkcffXROzuXOO+8kMzPTls+86aabuP766/noRz+a8vD44cOHCQaDc1L1drbExcLi8XgYGxsjMzOTmpqacYWQJny+Ce6gpn0Vw/iM/W9V/Saqeh+SFEDX3zPuzyGhKP+Dy/VZJMkYbyvfjaI8iar+K4ah09PzFRYtuhav9x3I8gHAjWFcjGUtIxz+HJAOmKjqnbhc3wW08U+UaG39GF1d10yqWAuIzfrKlSvJy8ub1XWKh6k4sqLK5jQicsrkxqouJYLY9M6nc+zY2BiHDh0CwOfzMTIyYlfZnF2L2QQR0UlZsmQJ5eXls6KgORVNnMkwYLvHplKhSqRaBZEEf//+/ViWxaZNm+I+bwvJximFhTg1Be655x6CwSCbN28mKyuLG2+8kfe+973ccMMNKT2zogM8MDAwaXh8JnBSjIaGhmwlq1AoZHeAI5v1MXw+pwfHTej6N+1/u1zbUJQHAS+GcQnh8PcB4cnxacDEsmoJhf6Iqt6Pqn4VwzAZG9uMojyJLFvI8nMoyguo6kOAiWmePZ5wFI9/90E8ni2oalPUdxgcPB+//6eT1n4xoxArtT5XEF1mMdztHFoW3XVn10KW5SlVl+JB0J4GBgbmbbMeDAZpaGggFAqRkZHB6OjojOdCEmF4eJjGxsakOylTIZ7LuSjS9fX1UVRUlBS1SmCqOAVvrNnCUzLZOOuss9i3bx9ZWVm8973v5V//9V/5l3/5F06cOIGqquzYsYNt27bxzDPPcOGFF7Jt2zbOPvtsdF2noKCA3t7eeanMjIyMcOONNxIMBvnOd76T8jBXb28vzc3NSStDQHyfiEQtccuyOHr0KMePH6e2tpalS4uQZdM+ViDQBBSN/0vH4zkTWT4M+AiHv2HPXyjKf+FybRsfKH8P4fD3cbluR1F+DEBf3z/T0HAda9Zkk5HxCHAYVY1UosbGPsGJEzcyODjC0NAgJSWPUFb2UyTJcHynYkKhfUD87oWmaTQ2NgLMGy9zdHSUuro63G43aWlpDA0Noet6lMP1bAb5Ibp6tHz58lkdK57yhs/nQ1EUBgcHKS8vn5egNxOlDuc96/f7bXpZTk6O3bWIlqycmXvsVNWj3t5eOjo6CAaDlJSUTOoqnkqL+AIW4lQq0DSNL3zhCxw4cIAHHniAgoJY+urU8Pv9NDQ0JDEPOAHLsuxnXkjlCrM0UVRybipPnDhBW1sb1dXVFBRsRFG67WMFg3/BsoQZZ9gxBygRCv0a03wHkWLWv6OqP8OysgiHvzcuYnICj+ejSFI/HR23oyhPs2hREbL8JWT5eTyeq4AQlmVgWR5aWv6Trq5KDMMgK8vL6tXX4vXWxXw3mWBweNJ31nWd5uZmxsbG5q0bL8yDhUniyMjIJBParKysWcUWYaS3aNEiysvLZ7UumqYZpboouhYulwu/309xcTGlpaXzlpx1dHSwYsUK8vLykhJgiaVEi+KiKNg59x6GYdDe3k5vby9VVVUp0dwMw8A0TVsqV/y93kizhadcsgFw7733cscdd+Dz+XjXu97Fvffey1lnnUVzc0SpqLOzk4suuoiDBw+yZs0a/vjHP1JcHKlQrFy5kp07dya9mZ8JfvnLX7J9+3buueeehKZhiRAKhTh06JDdInXeSLFKFkLTOtUN8OjoKIcOHeK887bEeHBMyM3K8kt4PJcCBuAmEDgCpCNJzeM/78U0z0PTfgDkIsv/i8v1BSRJQtP+HdN8iJ4eFwMD/8ayZT9nyZIfI0k6kmSi60vp63uS9PTq8YflZ7jd1086z0DgYeDyhN9jLrscYgPs7FqIh35sbIzKysp50Qp3Ds0lOy8S68ExODg4pfJGOBy2O2fzRXsS3hxC0cRZ5QuHw1Fdi3A4TEZGhr1gp+Ie29jYSEZGBitXrkxaXzweterkyZN2EtbS0mJX7wQnfTou7f8BFpKNxFiIUzPAM888w6233sqdd97JhRdemNJ7xTwgENdMNpYKK555sflNhrIpNtJnn30ezkfRGackaT9e73lEuhc5BIP7gMXI8l9wu68BwlhWhFoly8/idt+CaVahaT/F5foqivITDMPixImrGBq6Fst6keLi75KZ2YIs61iWSih0B5Yl1Kos3O63oyivjX++EC75KxBfznQuuxzOpM05E6iqKiMjI6xYsYKioqLpDzSDz+3o6KCrqyslw1fn/IJTeVOs/fEUIv1+/7x1UgQFWNM0qquro2JhInGUVCXdU/WpEohXHBsZGXnDzBb+Q5G9ksHAwABPPvkkbW1t5OTk8KEPfYinn3560uucw0KJfjdfuPzyyznrrLPYunUr73jHO7jllluSviE8Hg9nnHEGnZ2d7Nq1i+XLl9utO6djtDAum4mhS3p6Ohs2bGBo6Gyys3cAEApFb/YV5T4iClVgmrVEqE+gKM8iSX4sK53IfbUUl+tfUdUnMYxFtLX9gsWLP0dW1k6KihRGR8toaLiEdesyycz8CZLkx+XqJT//vWjaw5jmWzGMrQSDy/B63xt1Dj7f1QQCl5Ho/s3LyyMnJ4fGxka6u7tT6nLE0naEEVF2djZlZWVRG2DBke3v76eysnJOOZKKolBRUWHPiwjteef9kkh5I6IXn09lZeWU95fL5WLVqlX4/X4OHDgwL8pbPp+PM844g56eHnbv3m1X00QwFAt2SUnJjDtRmZmZbNiwga6uLnbv3p303EtEx1+2jZYMw8AwDCRJsh1ixdCix+OhqqrqH80oaQGnGE6FOHXhhReybt06PvGJT/Dcc8/x1a9+NemNkcvlYs2aNfazWFxcbHdXncZ+OTk5lJaWzuiZ93q9rFu3jsHB97Fo0RMAhMPRRoWq+kMiSlESllUGiE1wD5I0AiiY5uLxc94G9CPLrzE4+BgwwpIlJiChqodIS/saLtdn8Hi+iyx/GBhEkgy83m0Yxk407ReAG03730mO5xOfOxmLFy9m48aNNDc3s3fv3pS6HCL2O5M20bUoLi6O2gCLjn9fXx81NTVz2vGXJInS0lLy8vKor6+3O9nxRGdEnBodHbWdw5PZryiKQmVlpd1Jyc7OnqQuOFu43W5Wr15Nf38/+/btIy0tDZfLNUkcpbCwcMadKBELe3t72bt3rz2PO128dcYpwzDQNM02Bnwj4JRLNp599lnKy8vtgaL3v//9vPLKK/j9fnRdR1VVjh49SmFhxPGzuLiYzs5OiouL7cw12ax8NigvL+e5555j27ZtvOc97+Ghhx6yzykenIZJomIB0NzcTG5uLpWVlbPmLjohyzJu97OcONFKQ0MHxcWVFBZa48cPo6oTDqiGIZKAwDhVahhJctHf/x66ug5TU/NbNM1AlvtwuZrxevNRFBVQqapqpLS0lb17P0Bt7VXk5n4bMJCkLjyeS9G0uzCMG7CsdxIIvIjPdz4wUTGKuLMmXjTdbjdr166lp6eHPXv2xO1yTLUILl68mPLy8ikXQZ/Px7p16+jq6mLXrl0p0dySRUZGBhs3bqSjo4OdO3eSl5dnc5qdahb5+fkzVjTJycnhzDPPpL29nV27dlFdXT1rdRmxwXAOnWdkZBAKhdA0jVWrVs2pgo0kSRQWFrJ06VJaWlpsZZZkqmCSJBEKhRgYGKCnp4elS5dimiayLJORkcGGDRvsZOmMM874u6wTC3hj4lSJU3l5efz+97/n3nvv5aKLLuKBBx6guro64esFtUQUP8Qmra2tjaysLFauXJl0pzIZSJKE1/sIJ0/W09TUzKJFayktFXHKQlH+R5yZw3QviNv9VSxLAmROnLiNgYFm8vOXkJPTCiiMjhaRnV2AYRSgKP0UFDyKZWn4/U20t/+C5csP4PNtRJIi9C1FeRqvt5pg8BVgGZp2CFW9FFV9AV2/Ciib8nuoqkpNTQ39/f28/vrrcbsc8SjRzpnA5cuXT5kMiljY29vLnj17KCsro6CgYE43qyIWnjhxgtdee82+v0XBTnTXV6xYkZRATjxkZmayceNGjh49yq5du6ioqJh1vE00dC5+XlNTM+cxfenSpSxevJgjR46kFG8lSSIcDjMwMEBvby8+n8+OU6cyTjka1c6dO7nyyivZtWsXPp+PK664go0bN/Liiy/ygQ98wB68O+200/jUpz7Ffffdx4EDB+zBu8cff5zHHnvs73rOzz//PDfffDN33HEHl1wSMf8JBAJRw3G6rkfxLEXFQgyPBwIBVq1alXTlKRUYhkFjY6O9OfT5foTbPWEAODZWz+BgFqHQc5SUXINpyoBCff2LLF3aRl7ebchyJ6a5iXD4DhTl10TyWA1VfRTQCYffzv79t+Hz7WPVqtuAMcfnX4Cm/RpwAQfx+SKa0paVRjDYm/T30DSN+vp6LMtiyZIldvImTJPEtZ3pIgiRjkhDQwOKosSlD6SKeIugy+UiFArh8/nmdYDcaaSXzPeIlSB00rfEvIWzGiS6BanSnlKBqIKJjY6z6+TsCDkdZMVciPAjiZ3nMAwDt9t9yqh8LGAhTs0FXn/9da666iquvvpqtm7daifnTrERobbnnLVQFAXTNDly5Ah9fX3zJpka626eltaA13uu/ftA4C8MDa1ibGwPZWXvAyx0PYvGxicpKvo1OTk/B0DTHkFV70FRdmNZWWjaXXg81xMx8LMIh7Opq/sGpaVvJSfngnEVqgmEQvdimlczU4hZjpGREQoKCmxKrHMmUJj7zXSDKaizoVCI2traWc+LOF3O/X6/LesfDodRFGXe5PtDoVAUXS/Z/Y9gLsTKz4pr6yzYTUUBniuIQW+3201lZWVU18lZDBVzLE5/E3Fd46lWnUo0qlMu2QD4yle+wqOPPoqqqqxbt46HH36YY8eO2ZKCFRUVHD9+3G5JCXrK8uXL8fl89PT0UFZWxmOPPcaiRYuwLIubb76Zp556irS0NH7yk58kbSefDEZGRnjuuee4/fbb7RmAb3/726xcuTKu4lI8nDx5ksOHD0+SiZtL9Pb2cvjwYd72touQZR2IdBdeeOE5MjMzqa6+nYyM54hIBF6Cpv0Kr3c9cAJJUgmFHsHj2QqMAmlo2l243bcSoWPpQCZdXT/j+PHDrF9/O5IUsj/bsmPDfdcAACAASURBVLwEgy8BtSmds9hQimA4NjZmB8ni4mJKSkrmZZPb3d1Na2srK1asSGmWI3aznkh5w2lyl+xAW6qwLIsTJ05w5MiRuL4ZiYb5UpEgtCzL1umfL28O8RltbW3k5ubaspnOjlAiB9lEaiALMxunFBbi1BwgGAzy8ssvc8cddzA0NEQoFOILX/gCGzduTFptTwyPl5SUzIu0t/Mzzjzzc2Rk7LF//r//+2cyMrKoqtpOVtYfxucDP0Q4/EM8nrciSS2ATDj8bdzum4k8UjKh0EPI8kEU5fFxqXYTw1jMoUM3kpb2z5SXfwhZbo46B9PMIRQ6lvQ5Ozfromsh4lR+fj7l5eXzsskVBnTFxcUpqfmJmRsRq5w041iXc7E3mS9PC8D2zSgqKpok5hFv3lIwF0SsSqZwJGZAi4uL58WGwOn/Ic4ptiOUk5MTl8HijFMi4UjGp+P/AG+sZCMVGIZBUVERO3fu5L777mPx4sV8/vOf5+6772ZgYIDt27fz1FNP8b3vfY+nnnqKnTt3cvPNN7Nz5845+XzLsnj3u9/NypUr2bRpE21tbfz2t7/lgQceSKjxnwiaplFXV4fP55uWp58snPKDfr8f02zk3HOvsn9vmj5CoZPAED5fOZHqj4ymPYJpbsTjOQtJGh73yfgtXu+7iUjZugmHP44kDSPLO8YX60jCMTZ2F4cPZ7F27TXIcjDqfMLh89D1P8Y916nMfWI365qm0dDQgCRJ86ZYpWkaTU1NGIbhkBOeQKLNulOCcLpFUHyGruvU1NTMi6KJqIIFAgHy8vLsSptYBOMN8830M4LBINXV1bNyOYeJQUlRDRID/boeSZJTMSBzDuaJBdzj8Zwyi/gCFuLUXOBjH/sYaWlpbN68Gb/fz89+9jO+/e1vc/bZZ0//Zgd0XaehoQHLsqipqZmTYk9sUWl0dJQLLnin/XvLkggGRwADr7cMSRoAJDTtQUxz3bhPxgCWVU0o9CNcrq+gKLswzYpxxUUXmvYF3O5twDARw1mJo0dvoLPzg2za9EkUpT7me76TcPg3cc/XKeQy1Wb976FYJUwTR0ZGqK2tndR1irdZd87cCCO6qSAUA4eHh6mtrZ31+p7oewgxj8LCQrt7ITpCoqg0G+aCc0jdaSo7UwjzRBGnxAyjZVmEw2Gqq6tTom/FqladSnHqDZ9s/OlPf+LOO+/k5Zdfprq6mueff55ly5bR1dXFli1baGxs5JOf/CRbtmzh8ssjykfO180H6urq+MQnPsHll1/Otddem9KD4ZSvXbVqVUqKDcnob6elPYrXe4P9nuHhK1DV+1CUB3G7hQeHi0CgK8oMSdc/jCSdRFFewLIysKyi8SqRm3D4Jlyu/yBCnZLHX38tR49ewfLlW1CUUNR5BoPfwbKujZJzjR2MFh2h6TbrogMxX74cMCFZXFhYiMfjiepaOAPMbDbrokIVr7IzEzg362JGSFTalixZQkVFxbxU2pzeHGVlZUknzILHLs5X6LKLAOPkiQ8NDdHY2Ghzh5OlQxmGQX19Pa+++ioVFRVcfPHFM/6e84CFZCMxFuLUPKCjo4OtW7dy7rnn8rnPfS5lWqHomqbqdh3rbj08PBynqOQnLa3Kfo9h5KBpx5Ck3Xi9bx3/qZtAoB5VfQiX67tYlhvTfBeyfABJ6iDiy1GLorwOGJjmWej6xbjdXweG7GMHg5vZsWMbb3nLtbjdE5Qq08wlFOqwqbDOopKzsh4r5R0P8+3LARMdoby8PDIyMuzr69ysp6IOGA+Dg4M0NDSwdOlSysrKZr0Rdu5ZxGYdIkW47Oxsqqqq5oVm7KQAJ2ucCFNTd4Vxrrgmo6OjNDY24vV6qaioSLogKhKiHTt2kJ2dba8H/yB48yYbV155JevXr+fGG28kJycHv99v/27RokUMDAxwySWX8PnPf55zz43wPy+44AK2b9/Oxo0b5+28gsEgn/3sZ2lvb+e+++5LeThpZGSEuro626Mh3uIQq2Qxnbt1BAZebx6SFMQ0vbz88jMsWrSI1avfgST1AGCaKwmFduD1rkGSegEVTfsObvdtgIfIwr0SWa4ncgtZGMZbUJS/ARPX3zQrGBz8HdnZa5Blwx4KHx2tYteuB6PkXLOzs2c8GD0fXY7YofORkRF0XUdRFFasWMGSJUvmnL4lKjvCbyIVjmys07lTitK5WRfc65MnT1JdXZ2yV0wyME2Tzs5Ourq64npzJJoNcVbapqsAOh1qE9G3AoEAu3fv5tVXX+XVV1/l6NGjVFVVcc4553DJJZdMOSj7f4CFZCMxFuLUPEHXdb7xjW/w3HPP8dBDD7F8+fKU3h8IBKirq7NV9uJtPoUJrbOQMJW79fiZ4fNNrE27d/8EWV7Daad9EVWNdMYtawnBYCte7xkOH46f4XZ/elypykc4/G5crseJdOMlwEM4/GlU9XtI0sTfwLLS2b//EVat+iguVwCAhoYf091dZheVZtsBno8uR6wR7fDwMLquY1mWvS7OddffNE3a29vp6emZZIw7HQTTwik4Em/PYlkWnZ2dHD9+PGl/p1SRDAU40WzIVNTd2M/o6emhtbU1IQ1N0zRef/11duzYwc6dO2lpaaGsrIxzzjmHCy+8cE6plHOAN2eyoWkahYWF1NXVkZ+fn3ARf/e7383tt98etYh/61vfSmgRP5f47W9/yxe/+EXuvvtutmzZktJ7TdPk8OHDjI6OUltbG5VciFaoc7M+k0q1ZVkcOVLPqlVn2j8Lh6/HNM/H4/kokVvESzj8OVyuu4HweKJxfPzVISJqUvr/3955xzdZrv//k9E90kEXLaV7QllFaCkICAWBn4ooVFCQJYocWaJ4VE49X48UEBQB2cOBIOJRjgqIgCgCnbaMpq0tbSl0QdOdNmnG/fujPI9JmrRJmtDB/X69+lKynjvJk/t6rvW5oFAMBZebp7aJAxwIhR8gPPwtduZHRcU52NsPN3njU2eyHLqkcplNkEndMhEqQ2tkDaG+vh65ublwdXXVOrlbNTLI6JvzeDy1i/WOzgWxWIzc3FzY2dkZFNkxBIlEwjb/9e3bF83NzWzzoaG9IbpoaWlBQUEBdu3ahenTp6Ourg7JyclIS0uDTCbDsGHDEBsbi7i4uE5PQTcz1NnQDbVTZubixYtYtmwZ3njjDUyfPt2g57bakNYARkREBJRKZZs5EZrTrfWjHDzeTigU80BIAEpLSxEYGM4OqlUoHoVMthXW1sMAKAHYQyZbAj5/z/1gWgS43EK0yubagMMRoVX90BZy+UTweL+Cw6lXO2Jx8VRkZ78MNzc3eHt765VdN5TOZDm0SeVqazpnbIibm5vJZdAZGBvCCIRofk7tBZVU50W1B2NDuFyuWculCwoK0NzcDF9fX7ZErqGhQW0Yrb69IdqQy+UoKirC3r17MXbsWHA4HCQnJyMlJQUNDQ0YNGgQRo0ahbi4OISGhvZIO9WrnY0TJ05gx44dOHPmDAB0m/S0JmVlZZg3bx4GDx6Md955p8OIuOZwP5FIhObmZlYatbOpUE0sLYPA45Wz/25qOg9r6zXgclub85TKaHA4d8HhlAGwBSH24HDqAHBBiPV950KG1hpaZwByWFqqb+IKRT/U1aVAKMyHo2OrvrY5flD6ZDm0KW8wmwqzEbb3HXVUI2sKmMndlZWVCAoKYpuiO0rdGoJqZMeUTeqqQ/6Y9cpkMri6unZKMlEVRmHtypUrSE5ORlFREW7evImAgACsWbMG48aNg5OTU0/SMO8xC+0CqJ16ANTU1OCll16Cvb09Nm7cqFddvupwP5FIBLFYDAcHB3h6erIZVdPt8zdhYxPF/qulZQ24XAn4/G0AAKUyBK0D/9IBWEKp9AWXWwzG2Wj9idWCw1EA4EAs7g8rq7vg85vUjiKRPIusrJXsjB5zBGLkcrmaCqW2i27VAX9M35qqVK6Tk1O7F9+qWWx9B8oaimqGOTAwEJaWlm3kZxk7pT2DpR9MKbMpm9Q1S3ebmpogl8vh6OiIgIAACASCTh+HUVhjsha5ubm4desW+vTpg9WrV2Py5Mlwc3PrFXaqVzsbCQkJmDRpEubPnw8AWLNmDVxdXdnGu+rqamzcuBE//fQTtm/fzjbevfbaa0hNTX2ga1Uqldi4cSN++OEH7N69GwEBAex9qk3cmsP9mDQzIQRCoZAdSmbKrICNjbpRuXLlFGJipoA5PWSyRbCw+AJMFoMQHjicZgAcKJWt6+BwyP1NHGh1Qhw1MhyATPYEZLKvUFJSgoqKCoSHh3e6QUsXqlkOgUCgNjm0PeUNQ2D6Ezw9PeHr62uSDUPV0dSUyvP19YWLi0uHqVtDUW1SDw0NNahGVleWhXHcGIOoOkndmPKtpqYmZGRkqJVEhYaGslmLIUOGgMvlYu/evSguLkZSUpKhH0NX02OsTRdA7dQDghCC/fv3Y+fOndi+fTsGDRrE3qfau6A5z4jJsHO5XOTl5UGhUCA8PNykpaZWVq5qgiOpqZ8jOnoFuNxqAIBc/jT4/DNo7R20ACFWYCTYCWltCCeEBx5P1bmwASEKcDgtKp8BF83N9WwgJjQ01GwzUVSzHG5ubm3KojV7LYxx3JiBss7OzjpL3YxBVdGqpqaGlXX39fWFq6urSeeGAepN6oaWGetbussE+ZihhoaWb0mlUmRlZbF26ubNm/D390dsbCxGjRqFRx55BFZWVjh27BjOnj2LvXv3GvoxdDUPn7PR1NSEfv36obCwkL1wEYlEmDlzJkpKSuDr64tvvvkGXC4XCxcuxPnz5yEWi9GvXz/s2bMHSUlJKC4ufqDSgwCQnJyM+fPnIy4uDiKRCE899RR8fX316l1gogilpaUGN4+3h7V1n/vOA6BQeEIsfgqOjrvY+6XSOFhZ/QEAkMns70eCeOBw5GhtCFegde6GAqqnVKtTomD/rVQKIJW2ll+JxWIIhUK4urqapNHs72P8PTipuroa1dXV4HK58PT0hIuLC5ycnExqAJnIRU1NDcLDww3WItc2kFCzxIjL5aKsrAwlJSVmlUZmDB/TJ6TtO1GdG1JbW4vm5ma2/pqJXrX3XTY2NiIvLw+2trY6538wda5M1iItLQ1yuVytJMqU50w3gTobuqF26gHbqdzcXCQkJCAqKgotLS0YPXo0a3NU59jouphkgj2mvFC3trYHh/P3qXD37hW4u/+tpNXc/AJsbL5Eq0CJHbjcFnZ9HI4SrWqJFmj9qbWovjQIAVvmK5ePg0z2I4DWMh6hUMiWm5oqyMc0RjMX6iKRCIQQeHp6wtXV1eiy6PaOx1xEGzPsVbU3hMmyaKsGYORl+/fvbzZpZKZEjBnYq+070Va9YEjpLlO+xVRIaPsuCCGoqalhHYuUlBQ0Njb2pJIoY3j4nA19mTdvHkaPHo1FixahpaUFTU1N+OCDD7pEevC5555DTk4OAgMDce/ePXh4eGDTpk0G9xeIxWK2/tc0EfUmWFg8B0I80dS0DY6OzuzG23r68O87FkDrZi27//+aDgYH7Z1SEskxEDKV/bdqmtfYoUEtLS1q0QptyhtMg5Y5FasaGhqQk5PDqjDp2mBU18tkWTrS4GZQHYAUFhZmlvpVhULBDu9i5H5VU81KpZK94NAcnqQvzPyPgoICFBUV4bnnnkN+fj7rXGRnZ8PFxQUxMTGIi4tDTEyMSVLa3Zxe/eY6CbVTD9BOrVixAr/++iv8/PzYmQYbN25Uy8brg0QiQXZ2NjuQs7MXXVzuVlhZ/RMA0Nz8KpTKStjZHWfvl0j6wtq6NZjVWuorRuupY3H/v8r7f4A2W6VU8iCTbYNSOU/tdkYhsrS0FOHh4UaJajACHsxeqk3Mpba21uyKVU1NTcjJyWFVmHRdcOsrOKLruUwPhLmG1qqKkISEhLCfH7NmuVxukuoFpnwrOzsbc+fOxa1bt9iSqKtXr8LW1hYjR45EXFwcRo0ahT59+jy0duqhdjbq6+sxaNAgFBYWqp0AXVUzW1dXp7ZRffnll9i8eTO2bt1qsOKIUqlk+waMVbZQzQIwTeeOjn9h2LBXVB5jAS5X1s6r6Edzcz0A7RtbQ0MDhEJhh+VImsobzOwF1eZDXRfgqr0cptKG17a+W7du4d69ewgLC4ODg0Obmltmvfrqm2vDXNEj1WhbVVUVqqurwefz2ayQsevVpKmpCenp6bh06RLOnDmDgoICREdHY9KkSRg9ejQGDx5slu+nm9OrLVQnoXbqAdspR0dHdi2nTp3Cm2++iffffx8TJkww6LUIIaxyUWRkpFHzGVT3JdWm89Gjx0L31sc4E6pOBRd/OxvakUi+AiFPar2vqakJQqGwXeUt1fWqyrmqNsk7OTnpzFowvRwSicQkk8F1ra+0tJRV53N2dmZnRTCS+dpKYQ2lpqYGeXl57WbKjV0/U7orEolQVVUFLpcLDw8PtnrBFIE4qVSKzMxMXL58GWfOnEF2djYiIyMxZcoUjBo1CsOHDzfL99PN0fmLM31nUw+isLAQbm5umD9/Pq5evYphw4Zh69atqKysZDdmLy8v3L3bKvlaWlqqJv3n4+OD0tJSk23imhGR559/HjExMZg3bx4mTZqElStX6v2D5HK5CAkJgUgkQmZmpl5Re9VmPtUsgEAgQP/+/WFnZwc+X2j0+2ufGwAGab3HwcEBw4cPx82bN5GRkYGIiAjY2tq2q7zBrFffz8vS0hJRUVGorKxEenq6WbIcTJZCIpEgIyMDHA4HLi4ucHZ2Nni97eHu7g4XFxfk5+ejvLzc6CZ1Zs4JkxqXSqVstC0gIAADBw5ERUUFSkpKjC4/Yyala5ZERUdHIzY2FvPnz0dlZSVWrVqFqKgoDB8+vOMXpVB6Ed3dTj3++OMYOnQo5s2bh/PnzyMxMVHvizkOhwM/Pz+4uLjg+vXrekXtVQUmNCXdfXx87gtiiNQcDUK490ul2FtU/ss4Ge07Gq3rbYSu+KytrS2GDRuGW7duIS0tjS0r0zYjyM7ODgKBQGW9+u37fD4f4eHhqK6uRlZWllmyHEqlEra2tnBxccG1a9dACIGzszOcnZ3h7e2NsLAwk9gpZ2dnDB8+HEVFRUhPTze6Sb290t1+/fohIiICIpEIN2/e1GvmiTYIIaiurkZKSgqbuRCLxRg8eDBGjRqFPXv2QKFQ4LXXXkO/fv0wevRog4/R23moMxvp6ekYOXIkLl26hBEjRmD58uVwdHTEtm3bupX0oEwmw7p165CWlobdu3cbbDRaWlqQk5MDCwsLhIaGgsfj6VRc6jgLoIC1tSc4nCYolY7gcuu1PMZw2stsAH8rb5SXl6O0tBQ8Hk9Ni91U0QrANFkOzcbohoaGNgMU7969i7KyMoOmXRsKM8iJyQq1ZySYBjmmkRtAG81wbbS0tCAvLw9KpRKhoaHtRnMUCgVyc3PblEQxvRa6SqLkcjkUCoVZBg32EGhmQzfUTqHr7ZRSqcRHH32E48ePY9euXQgODjbo+YyCnEwmQ0REBCwsLLRmq1UVl3T3LtTDxuZvO0mIpVqTt3Hvrz+k0vaDbcy+X1lZiZKSEnC5XFbMRd8ZQfpiiiyHpuCI5r4vEAhQU1ODW7dumbUfsKGhAbm5uWwgq71+CdXGc0NKd5k5JmKxGGFhYe1m0ZRKJW7evKlWEmVnZ8eWRMXGxmotiWI+T3OUhvUQaBmVNioqKjBy5EgUFxcDaNUST0pKQkFBQbeUHjx37hxWrlyJdevWGTzduLm5GUVFRbh79y67KXW2ZtHCYjT4/D8Neo72tf0JQH2AmmZNKKO8wdSvVlRUtCsLaAoMmcuhOjmUUQzTpzGaqZG1s7NDcHCwyWeLAK0bZ1FREdtn4ejoyJbIMc4FoxyjKkNoqKQjM+XcysoKAwcOBJ/Ph1gsRkZGButcMM4V41w8pCVRxkCdDd1QO9WN7NSff/6JRYsWYcmSJXj++ecNnhNx69YtlJaWsvs6s+8bqrjE422EhcVmyOWPw8LiG6Pei/ra3oNC8brabUzWgvlTlR13dHSESCRCTU0NIiIijCoR0wdD5nKoCo4wSob6NEYz/YBMxYQ5+gFVB/WFhITAxcVFrUTOVKXGdXV1yMvLY7NENjY2bEkU08xdWFiIgIAA1k4NHz78YQ50GQJ1NnQxevRo7Nu3D6GhoUhMTIRYLAaAbis9WFVVhQULFsDb2xv/+c9/tF5oq0oQMhsKI0FobW2NO3fuwMPDA/379+9U+pXPXwwLi68683YAAE1NNyAWu7WpuVXdALX90EUiEf766y/4+fnB09PTLI1XTJaDGRrEbGyaMnmEkDbDiPRdj2qNrLmmocpkMlRUVKCoqAgcDgd8Pl8tGmSK2RbMXI7Nmzfjl19+gbW1NWxsbDBs2DBWfcPPz6+3N8iZC/qh6YbaqW5mpxobG/GPf/wDYrEYW7du1do0rZpdVx1EyygulpeXQyDo/MwlPn85LCz2debtAACam09ALI5Ry1brM4Suvr4eOTk5bG+COfY/XVkOzSyAIYIj2mACcAEBAfDw8DD5+1AoFKxgi0KhAJ/PZwVdTDWXhRACkUiEnTt34siRI3BwcACfz2dLouLi4hASEtLbVKIeFNTZ0EVWVhar8BEQEICDBw9CqVS2kR5kvOxly5bh9OnTsLW1xcGDBw1u3DYFhBDs2LEDn3/+OXbs2AELCws0NDTA3t6evfBVnW6tuaEwKcL6+npERkZ2IjNAYGk5BlxuNjgcqVGvIJXa4NKlH9oob+j7Q5fJZOwMiPDwcLNEXJjG7pKSEtja2kIul6ulxk01RVYikSA3NxeWlpYIDg42OuJPCGGncWtOEBcIBGyav7OOjWZJ1I0bN9CnTx/ExsbC29sbX375JUaNGoUNGzZQB6Pz0A9QN9ROdUM7BQBHjx5FUlISPvroI3h4eKCiogIuLi7shS+TXdc2iNYUzeOtXIKNTXyn3odcboHffz+llq12cHDQOxOtUCjYGRARERFmKbNhglaFhYWwtraGQqFgnbfOCI5oIpPJ2FkpjCKhsegq3RUIBGhpaUFZWVmnB8oyYjlM1oIpiYqJiUFwcDCOHz8OHx8f7N69m2bZOw91NjqLn58fu7nw+Xykp6ejuroas2bNeuA655mZmfjll1/w888/IyMjAwEBAXj55ZcxZcoUgy58q6urkZeXZ4IoRSFsbAYa9cyWlglQKE504titMBJ0pmjsZhrPmWgQI5Nnb28PkUgECwsLsylWMbKvxcXFetfIKpVKtRIuZsif6gRxTaMokUiQk5MDS0tLhISE6DW1nhmcp1kSxUSDBg0apPY6SqUSycnJiI2NNe7DoKhCnQ3dUDt1n+5kp/Ly8nDq1CmcPXsWf/zxB/r164cXXngBCQkJBl341tfXQygUwsfHx+jp0BzO/8Dn7weff9bg5wKAQuEBqfRmp4MmjAKTKRq7NeVyGYEURuZVoVCYtcy4qqoKBQUF8PX11Uv10JjSXdWBsmFhYXq9F4lEolYSVVRUhMDAQHZwnmZJFCEEly5dYnucKJ2COhudxc/PD+np6ejTpw972xtvvNElOuf//e9/0dTUhJiYGHh5eWH16tUoKyvD9u3bDY5Uy2QyCIVC8Pl8hIaGGh2ht7ScCB7vssHPa25uhKmuo3SVPLWHtppQPp+vJuun+TqG9HJ09r0wQ4NUMza6ZnHoymTpglGCKioqgr+/Pzw8PNjnqapEXblyBWlpaVAqlbQkquugH7RuqJ26T3eyU2fPnkVJSQliYmIQGBiI999/HxcvXsTu3bvh4+Nj0GspFAr89ddfkEqliIiIMDKDTWBjY/isJgBobi4FYBoRD9WSp4iICL0yA+0JjjD7vubrGNLL0dn3om1mhq5ZHMaU7jIl0z4+PvDx8VGzUyKRiHUsUlNT0dTUhCFDhrD9FsHBwbQk6sFBnY3Oom0T7yqdc2189913SExMxIYNGzBmzBiDnksIQVlZGW7fvm3QUCLNDdDW9ijCwzcbcFxAIhEbtFZ9YHoTQkJC2jhfmjKEjGyi6jAifTYmYxwbY6isrERBQQGcnZ3ZyFBHzpChyGQynDp1Clu3bsXjjz+OnJwcZGdnsyVRjEqUqrY+5YFDP3jdUDt1n+5up3777Te89tpreOutt/DEE08Y/HxmhpC2vV0XjEJQq3LXUfTv/3/g8Qwr+21uNr2dYi6g/f394enpqXafquBIbW2tWuO5QCDQKTiiyYOYy8G8l7y8PHZd+jhDhqJQKJCamoo1a9bgySefRGFhIa5duwZ7e3t2wGtsbCxcXV2pneo6qLPRWfz9/eHs7AwOh4MlS5bgpZdegpOTk1bpwWnTpmHt2rVq0oMbNmwwe93snTt3MG/ePERHR+Of//ynwRehTU1N7EWmtqh1e8obTK+FpeVmWFq+p9fxxOJp4HK/NmiN+iKVSpGdnQ0LCws4OzujoaEB9fX1bEOfqWQImSyHqWQBmdkWjEPEyOhJpVJwuVxERkYaNTNDFcZJTE9PZyNCZWVl8PPzQ2ZmJubMmYN///vfVH2je0Gtp26onbpPT7BT1dXVWLRoEVxcXJCUlGTwfsbs7cyUa82LbuZCndlDm5ubNRSXmmBvH6rj1duiVPIhldYZtEZ9YfofZDIZ3Nzc0NjY2KZ3wVDBEW0wSoH6ljx1hLbSXWtra1aiPDIy0qiZGZowJVFM6W5xcTECAwORlZWF+Ph4bNmyxSTHoZgMOtSvs1y6dAl9+/bF3bt3MXHiRISFhel8rDYH7kF42j4+Pjhz5gySkpIwdepU7NmzB35+fno/nxlKVFhYiIyMDAQGBqK5uZmdGqqqvOHl5aV1A1Qo3kBLSzUsLbd1eLw//1wGF5dC+Pn5mSTNqWlkpFIpWlpaUFNTA39/f4SEhJhcWtbDwwPOzs7Izc1FZWWlwVkOVY1zTVUrpkaV+YxFIhGuXbtmcFqc6QNhSqLS09OhVCoRHR2NUaNGYf78+awyX0erqwAAIABJREFUWVNTE/7zn/9AJBKhb9++Rn0mFAqla+gJdsrFxQXffvst9uzZg8mTJ2PHjh0YOFD/nj8rKysMGTIEJSUlSEtLQ1BQEDvor76+HoQQ9kI9JCREy9wFFzQ334CNzQC9jldaOhUiUR6CgoJMYj+0zbjicDi4efMm+vXrh6FDh5pEcEQVV1dXCAQC5Ofno7Ky0uAsR3ulu0FBQWqlu3V1dRAKhQYrXmoriWpubmZLorZs2cI6lzKZDFu2bEFxcbFB5w6l66CZDSNITEyEvb099u7d223S05okJyfj5ZdfxooVKzBz5swOHy+Xy9Uu1MViMVpaWuDm5gYfHx84OjoatNHyeBs7zHCIxQ0oKipCdXW1URF7bRfqqr0LjJFpbm6GUCiEg4MDAgMDzTLLAug4y8EMJlTtD2EkiZk1d2RkmMFETU1NbWpkGRQKBYRCIRsNEgqFcHNzY0uiRo4cSUuieh70y9INtVNa6Al2SigU4sUXX0RCQgKWLFnS4Z6kOi2akcttaWmBk5MTfH199dpD/+YabGxiOnxUU9Nd3LlTjbKyMnYquCFou1BXlR1nLtSZ4bud7Z/siI6yHEwfo2p/iKGlu8xsp+rqaoSHh8Pevm2vjFKpRH5+PutcqJZEjR49GrGxsXBxcaF2qmdBy6g6g1gsZqdUisViTJw4EevWrcO5c+e6rc450BpheOWVV8Dn8/Hhhx+yP3hGGlV1aiiHw2mTtpXL5cjJyQGPxzNq8+Nyt8DK6l2t9ymVgFQqZteZk5PTrtpIe0oWTCagvfUxA4PKy8sRHh4OR0dHg96Lvqj2cgQGBqKpqYldMzOYUFUz3NiNtKamBpmZmcjMzMSiRYvU1DcqKiraqESZy3D1JBQKBaKjo+Ht7Y0ff/wRRUVFSEhIQHV1NYYOHYovvvgClpaWkEqlmDt3LjIyMuDq6oqvv/7aoAyhmaAWVzfUTqHn2imJRII33ngDhYWF2LFjh1qgRts8I01Zd+ailWm4Nqx5vAQ2NuHtPoLp1xCLxcjOzoabm5tOcQwmoMTYVtUhdPpcqKuqEYaGhsLFxcWA96I/qr0cISEhavM4JBKJ0VL0mjQ0NODGjRv49ddfsXTpUgiFQtZOMYqLjEpUdHQ0Ld1F77VT1NnQg8LCQkyfPh1A64909uzZePvttyESidronAsEAgwbNgy1tbWs/CCfz0dLS0uXnCiEEOzduxcffvghxowZg9u3b2PNmjVtplvrivYzg9pKSkoMah5n4PFWwtJyT5vb09ODEBl5lf23ptoIALXeBUZ+tjMTz4FWgyEUCuHi4gJ/f3+TqVSoNiHW1tZCJBJBKpXC1dUVXl5ecHJyMslGqloS9ccffyAzMxP5+fmYNGkSpk2bhri4OPj6+tJokBa2bNmC9PR01NfX48cff8TMmTPx9NNPIyEhAS+//DIGDRqEV155BZ9++imuXbuGXbt24ejRo/juu+/w9dfm6S0yAPqF6obaKfRsOwW0ipysWbMGY8eORXFxMVasWMEGZvQJKDHy54Y0j7dSCxsbb633aIqYKJVKFBYWora2FpGRkbCwsNAqOMKs2dghdBKJBEKhELa2tggODjZpNl61IkAkEqG5uRlOTk7o27evSfpDgFY7VVVVheTkZFy5cgUZGRm4ceMGxowZgyeffBJxcXFa+20ovddOUWfDxHSnE6WwsBCzZ88GIQTBwcEoKirCmDFj8NZbbxkc6Waax11dXeHv72/QZnTx4mTEx19Uu43Pl6ChQQFAXX62srIStbW1sLGxgZubG7tpm3JYHzOk7969e4iIiNCa4tXnNZh0PjOlnWlCZBw4ZuhdZxSr9CmJysvLw7Jly7Bv3z5ERUUZfIyHAUY84e2338aWLVvwww8/wM3NDRUVFeDz+bhy5QoSExPx888/Y9KkSUhMTERMTAzkcjk8PT1x7969rnbgqLOhG2qnDKQ72am6ujpMmzYNzc3NCA0Nxa1btzBw4EC8//77Bg+/k0ql7EW6YT0WUlhbu0DzJ15VxYWdXQMA9WGpd+/ehUgkgpWVFWunTBVQYmCG9N25cwdhYWFwcjJcelef0l0AnVasUiqV+Ouvv9RKohwdHdVUou7du4eXX34Zb7/9NiZOnGjwMR4GerOdos6GCeluJ4pMJmMjLUBric8777yDrKws7Ny50+DaXM2ojr6GQKkEbG3t1DbyP/8sgZXV343cqtEgKysr5OXlwcLCAiEhIWYrAWpoaNC7kY1pQNQc9KdZd6sNfRWrGKdLVSVK35KolpYWWFhYdPVG02155pln8NZbb6GhoQEffvghDh06hJEjR6KgoAAAcPv2bTz++OO4ceMGBgwYgNOnT7OzAAIDA5GSkqImJ9oF0C9WN9ROGUB3s1OEENTV1bEX00qlEh9++CG+++477NmzB4GBgQa/HlMyGxkZaUAwSQFra0c1O7Vv302MGiVhVa1UKwJsbGxw8+ZNyGQyhIeHmzQgpkpTUxOEQiEEAgECAwPbzQaoSrsbWrpriGKVRCJBRkYGa6du3bqF4OBgNgg2dOhQrY6XUqmEQqGgk7p10JvtFC3kNiErVqzAxo0b0dDQGgkRiURwcnJiLw59fHxQWloKACgtLUW/fv0AAHw+HwKBACKRyKQnioWFhdqP2tLSEhs3bsSZM2cwffp0JCYmYvLkyXq/HpfLRVBQEGpqapCVlaVVH1yT1vKiZri61kEsFoDLBZYu/RBLllTBzq41dastkhIVFYXy8nKkp6cbHdXpCAcHBwwfPpxV34qIiICtrS0rDcs4FvX19eDxeOyG3b9/f4MMi6piVXl5Oby8vODm5saWqKmqRAFgVaIWLlyod0mUuQxdVyKRSDBmzBhIpVLI5XI888wzeO+99/Diiy/it99+Y6Nyhw4dwuDBg3VORP7xxx/h7u6OYcOG4cKFCwDaV+LpKpUeCuVB0N3sFIfDUdvfuVwu3njjDYwfPx5z587Fq6++itmzZxv0er6+vnB2dkZ2dja8vLzQr1+/Dn/DEokMdnZNUChsweEAhYUCODg0wsrKDqGhoVrLiyIiInDv3j1kZGSYTP5cE0Yl8tatW0hLS2Ob1DVLdzWl3X18fAzKtGgqVnl5ecHT0xOEENy7d09NJUoikWDYsGGIjY3Fxx9/3KETxMDlcntd6RS1U/pBnQ0T0ZNOlPj4eJw7dw7z58/H+fPn8X//938GbUrOzs6Ijo5Gbm4uqqqqEBYWxhoqTbWQpqYm2NjY4NVX++PgwQo8+aQtxGJLhIYq2j0Gh8NB3759WYPh5OSEgIAAk29UXC4X/v7+KCsrQ1paGnvRzswP8fb2RlhYWKePa2lpiYiICPzwww+YNWsWAgICIBKJ4O7ujtjYWDz11FNISkqCg4NDt94wHiRWVlY4f/487O3tIZPJEBcXh8cffxwAsGnTJjzzzDNqjz916hTy8/ORn5+PlJQUvPLKK0hJScGlS5fwv//9DydPnoREIkF9fT1WrFjB9gLx+XzcuXOHlfr18fHB7du34ePjw0YKzdWoSaE8SHqSnYqOjsbvv/+OZcuW4dy5c/joo48MEvZwcHBAdHQ08vPzkZWVhcjISHZ/1yY/a2VlhWeeCcasWbXYuZOD+HgbXL8u6/A4bm5uEAgEEAqFqKqqMovEOuNAWVlZISsrCzweDxwOhy3d9fT0NMlxGSWsP/74A88//zz8/f1ZZzQmJgYTJkzAu+++S1WiVKB2Sj+os2EietqJ4ubmhh9++AGffPIJJk+ejJ07d7arya6JhYUFBg4ciJKSEly5cgUCgQBSqRRKpZJVtQoODmblZwcP/vu5u3e372ioYmNjw0Z10tPTje6xUEUqlapJETKSuUzWRiqVspEsY2FKotLS0tiIEKNvvmTJEpw/fx5RUVHYsWOHwU33DwscDof9rmUyGWQyWbsG7sSJE5g7dy44HA5GjhyJ2tpalJeXY/369Vi/fj0A4MKFC/jwww9x+PBhPPvsszh+/DgSEhLw2Wef4cknnwQAPPHEE/jss88QExOD48ePY/z48dSwUnoFPc1OOTg44LPPPsPhw4cxefJkbN26FcOHD9f7+TweD2FhYaioqEBKSgoEAgFaWlrUBEcCAgJYwZHPPvv7ufo4GgyWlpYYNGgQG7QyRkxFE12lu/7+/mhoaEBDQwNCQkLYMmljaW5uxp9//tmmJGrhwoXIyMiAjY0N9u/f32EVw8MKtVP6QXs2zABzovz444949tlnMWPGDLbxLioqCkuXLsWOHTtw/fp1tvHuv//9L44dO9Yl67169SoWLlyI+fPn48UXX9R5wjLys6qyflZWVrCzs4NIJIKbm5ve6VRjYHos9E2LA22lCBsaGmBpaammcKJZP1pdXY28vDz4+fnB09NT7+OUlZWplURxuVy2JCouLq7Nmr///nvEx8d3eiJ4b0ahUGDYsGEoKCjAq6++ig0bNuDFF1/ElStXYGVlhcceewxJSUmwsrLSayKy6m+zsLCQlRQcMmQIvvzyS1hZWUEikeCFF15AZmYmXFxccPToUQQEBHTVR8DQfa1I10PtlBH0NDtVWFiIuXPnIj4+HitXrmxXQZERHKmrq2PnRDg4OKC2thb29vYICwsz27wlRkzFEMXDjkp3tYmk1NfXIycnx2B7qFkSJZVKMXToULbfQtOGnz17FlFRUXB3dzf8w3hIoHaKhTaIP0h64onS1NSEFStWoKqqCtu2bYOzszMkEolaqlkul6s1m6nKzzJDfGpqagxqHjcUhUKBgoICiMViREREtMk+MMMJmYiQVCqFvb09W8eqrxShXC5HXl4e5HK51uY/uVyO7OxsViUqJycHnp6e7IY9YsQIWhJlQmprazF9+nRs27YNrq6u8PT0REtLC1566SUEBgZi3bp1mDp1Kt566y21TXzjxo0YNmxYF6/eJNATSTfUThlBT7RTMpkMiYmJSE5Oxu7du9G3b19IpVK1gFJ7ez4hBHfu3DF6QJ++EEJQXFyMqqoqREREtMk+KBQK1k7V1dWxzeeMbdV3toVCoUBhYSHq6+sRERHRxu4qlUrk5eWxzsX169chEAjUVKKcnZ2pnTIR1E5RZ6NboauhqCuHtzCb0rZt23DixAlYW1vj0UcfxfLly9lNW58m5NraWuTm5qJ///5mnUTLZB98fHxgYWHBbtrMcEJm0+5MKRTQqt2+e/dueHl5ISAggHUuKisrERERwWYtoqKi6OC8+5jr/H7vvfdgZ2eH119/nb1N9YKpO0xENiP0akA31E6Zge5opwghKCwsxL59+3Dw4EE4ODggMjIS//rXv9gMgD57fmNjo0HN48ZSX1/PKh7a2tq2Kd1l1syUGxtLbW0tvvzySzQ3N2PEiBFITU1FcnIySkpKEBISoqYS1RvFRIyB2imzoPMk7l2yAD0EpqHo6tWryMrKwunTp5GcnIw333wTK1euRH5+PpydnbF//34AwP79++Hs7IyCggKsXLkSb775psnXtHXrViQmJiIkJAQ7d+5k5e/8/f3h7u6u9wbl5OSE6OhoiEQiXL9+HTKZ/nWvHaFUKlFfX4+SkhLcuXOHNTxFRUVwcXFBdHQ0HnnkEYSFhcHT09NoR4PRN//mm2+QlJSEX3/9FR9//DFWrVqF/v3748CBA7h27Rq+/vprvPbaaxg6dCh1NFQw1fl979491NbWAmitKz579izCwsJQXl4OoPV7+v777zFgwAAArTWsn3/+OQghSE5OhkAg6C0bOIXywOmOduqrr77C6tWr4ezsjH379iEyMhIeHh7w9fWFh4eH3nu+vb09hg8fjubmZmRmZkIqlZpsjYQQNDQ04M6dOygpKWGlePPz8+Hg4IAhQ4ZgxIgRCA8Ph5eXV7uy6R0d5+7duzhx4gQ2bNiAEydO4NixY1iyZAkcHR3xySef4Nq1a/jvf/+L119/HSNHjqSOhgrUTj1gCCHt/VE06N+/P/niiy9M9npisZgMGTKEJCcnE1dXVyKTyQghhFy+fJnEx8cTQgiJj48nly9fJoQQIpPJiKurK1EqlSZbgzbkcjl57733yOjRo0lOTg4Ri8UG/xUUFJCzZ8+S0tJSo55fW1tLbt26Ra5evUp+//13cu7cOZKSkkLy8vJIZWUlaWxsJGKxmNy8eZOcPXuW3L5926jj1NXVkcuXL5PNmzeTZ599lgwYMIBMmDCBrFu3jpw5c4bU1dURpVJJjh49SmbNmmXWz7230Znz++rVq2Tw4MFk4MCBJDIykrz33nuEEELGjRtHBgwYQCIjI8mcOXNIQ0MDIYQQpVJJli5dSgICAsiAAQNIWlpaF7xjs9HRXv0w/1E0eFjslFKpJDt27CDR0dEkNTXVqP2/pKSEnD17lhQXFxttP27fvk2uX79OLl68SM6ePUuSk5NJTk4OKS8vJw0NDWrHKSwsNOo4DQ0NJC0tjWzbto3MmTOHREVFkTFjxpC1a9eSH3/8kYhEIqJUKsmZM2dIfHw8USgUZv3sexPUTpkMnfs0Dcd2EZoNRYGBgV2qda4Jj8fDunXr8NhjjyEhIQGrV6/GjBkzDHoNLy8vODk5ITs7G87Ozu02yxHy93RWRjOcx+OxJVy+vr46ozKenp5wcnJipQcDAwPbbR5sbGxEWloarly5gpSUFNy9exeRkZGIjY3FW2+9hYEDB2rNVMyaNQszZ8406DN4WDHF+R0VFYXMzMw2r33+/Hmtx+RwONixY4eZ3hGF8vDR3e0Uh8PB0qVLMWbMGMyfPx9z5szB4sWLDcoUuLq6YtiwYXpL12rOtgDAlkTpmhvFHCc6Oho5OTmoqqpCaGhouxnx5uZmtQGvt2/fZkuili1bprMkauLEiZgwYQLtw9ADaqceHNTZ6CJ4PB6ysrLYhqKcnJw2j+kOWuejRo3ChQsXsGTJEpw7dw6bNm0ySGqPka4tKipCRkYGIiMjYWtry5ZEac7jYDbs0NBQg9RCrK2tMWTIENy+fRvp6elwdXVFUFAQWxJ1+fJlJCcnIyMjA1wuF8OHD8eoUaPw8ssvw8fHR+/Ps7du4Ldv38bcuXNRUVEBLpeLl156CcuXL0diYiL27t3LDqv64IMPMGXKFADA+vXrsX//fvB4PHzyySeYNGkS+3o95fymUCi66Sm/4wEDBuD333/H66+/joSEBHz66adwdXXV+/mMdG1paSkrse7g4MAqMDLOBTOPw8nJCe7u7ggODjbITjGS8RUVFUhPT4eTkxPCwsLYkiimJzA1NRUymYwdnMfMvNBX6bG37p/UTvVcqLPRCZqamvDcc89BLpfj2LFjRuldOzk5YezYsUhOTu62WudOTk44evQoDh48iMmTJ2Pbtm0YrDo4owM4HA58fHzA5XKRmpoKCwsL8Hg8NhqkOo+jMzCDAG/fvo1nnnkGLi4uaGxsRN++fREbG4tnnnkGmzZtgr29Pd0kNODz+di8eTOGDh2KhoYGDBs2DBMnTgQArFy5Uq3ZDQCEQiGOHj2K7OxslJWVYcKECfjrr7/aGN6ecH5TKL2Zh8VO2djYYMeOHThx4gSmTZuGpKQkPProo3o/n8PhwMPDA4QQZGZmgsvlgsfjwcHBgR0qq6rAaCwcDgfu7u6orKzEokWLwOfz0dzcDFdXV8TExGDSpElITEykKlFaoHaq50IbxI2koqICjz76KPr27Yv//e9/Bm3g2hqKwsPDMW7cOBw/fhwAtA5vAdBlw1s4HA4WLFiAI0eOYPXq1di+fTuUSqXWxzKlSnfu3EF2djaSk5Nx7do1VkbW0dERtra2CA4ORt++fTvVIFdfX49z587h/fffx//7f/8Po0aNwsGDB7F06VJERkbC3d0dBw4cQGJiIiZMmEDlaHXg5eWFoUOHAmgdpBUeHs6mj7Vx4sQJJCQkwMrKCv7+/ggKCkJqaiqAnnl+Uyi9kYfNTgHAk08+iZMnT2Lz5s1ITEzUKVJC7s+2KCsrg1AoRHJyMrKystDc3IzQ0FC4urrCysoKQUFB8PHx6VSQqqmpCRcvXsSmTZswY8YMxMTEYPPmzXjuuecQExMDW1tb7Nq1C+vXr8fUqVPphG4dUDvVc6GZDSMQCoVYt24dlixZYpTiRnl5OebNmweFQgGlUomZM2di2rRpiIiIQEJCAt555x0MGTIE8fHxGDduHMrKylBeXo5vv/0W/v7+2LVrFyZOnIji4mL4+fnh2LFjcHZ2BiEEy5cvx8mTJ2Fra4tDhw6xP0xTERISggsXLuCf//wnnn32WXz66aewtbVFZWUleDweamtrIZFIYGdnBycnJ/j4+LTRDPfw8GDTyGFhYXB2dtbr2OS+Rvrly5eRkpLSpiRq6dKl8Pb2VtsALl261GZoH6V9iouLkZmZiREjRuDSpUvYvn07Pv/8c0RHR2Pz5s1wdnZGaWkpRo4cyT5HtbZV3/N74cKFAICFCxfihRdeQFBQEKvlT6FQOsfDbKe8vb3x888/Y+PGjZg6dSp2794NT09P3LlzBxYWFnqV7np4eEAkEiEzMxOBgYF6D7XTLIlKS0vrsCTqxo0bZhuG21uhdqpnQedsGIifnx8kEgn69OmD5ORkdky9OSgvL0d5eblayvD777/HoUOH4OLigrVr1yIpKQk1NTXYsGEDTp48iW3btuHkyZNISUnB8uXLkZKSYvJ1lZWV4dKlSzh8+DAuXrwIV1dXLF68GE8//TQ720Ifj18ikSA7OxsCgQABAQFtNlu5XI4bN26w/Ra5ublsSRQzOI+WRJmWxsZGPProo3j77bfx9NNPo7KyEn369AGHw8G7776L8vJyHDhwAK+++ipiYmLw/PPPA2jdiKdMmWKwiABFb+hJrhtqpzSgdgqoqqrCpUuX8O233+Knn36Ck5MTZs2ahQULFkAgEOidUW9paUFOTg4sLCy09hIqFArk5eWxzsWNGzfg4uLCDs6LiYmBk5MTtVMmhNqpbguds2FKkpKSMHDgQEyYMAE1NTVmO46ulOGJEycwb948AMC8efPw/fffA2hNGc6dOxccDgcjR45EbW0tq/VsSg4dOoScnBz84x//QGpqKgIDA3Hnzh24uLgY1HthbW2NoUOHgsfjYfXq1UhNTWVLoqZNm4a4uDhs374d1tbWeOedd3D16lWcOXOGlkTd5/bt2xg3bhzCw8MRGRmJrVu3AmgdeDhx4kQEBwdj4sSJ7DlKCMFrr72GoKAgREVF4c8//1R7PZlMhhkzZmDOnDl4+umnAbRG93g8HrhcLhYvXsymoJn6VQbV2lYKhdL1POx26vjx40hLS0NCQgL+/PNPxMTEoKSkBA4ODgb1XlhaWiIqKgqOjo5Yt24dzp8/j99//x0bN27E008/jZiYGCQlJUEmk2H58uVIT0/Hr7/+yjYpP+y9F9ROUQDQORuGwuiXKxQKsmjRIhIVFUUqKirMftyioiLSr18/UldXRwQCgdp9Tk5OhBBCpk6dSi5evMjePn78+Aei4axQKMjmzZvJiBEjSGZmpl6a4Y2NjSQ3N5ccOHCALFmyhIwePZq4uLiQ8ePHk6+++orcuXPH7BrtPZ2ysjKSkZFBCCGkvr6eBAcHk+zsbLJmzRqyfv16Qggh69evJ2+88QYhhJCffvqJTJ48mSiVSnLlyhXyyCOPsK+lVCrJCy+8QJYvX97mGAxbtmxh54zcuHGDREVFEYlEQgoLC4m/vz+Ry+Vmfb8POV09y6I7/1E0oHZKO5999hkZPHgw+f333/W2Uzdv3iRffvklWbZsGRk9ejRxd3cnI0aMIAcOHCAFBQV0nkUHUDv1UEHnbJgaLpeLvXv3YtWqVRgzZgx++eUX+Pr6muVYjY2NmDFjBj7++GM4OjrqfBzpImk2LpeLVatWYdy4cViwYAEWL17MRq4YZDIZbty4waaa8/Ly2JKoZ599FiNGjAAhBKtWrYJIJIK3t7fZ193T8fLyYiePakYUL1y4AKA1ojh27Fh2wqy2iKKXlxcuXbqEL774AgMHDmSVxj744AMcOXIEWVlZ4HA48PPzw+7duwEAkZGRmDlzJiIiIsDn87Fjxw6DJCApFIr5oXZKnblz5yI2Nhbz5s3DlClTsHz5crXyXYVCgdzc3DYlUbGxsXj88cfx73//G7a2tkhMTER+fj7mz5//QNbdk6F2igKAZja6Oy0tLSQ+Pp5s3ryZvS0kJIT15MvKykhISAghhJCXXnqJfPXVV1of96BobGwkCxYsIE899RQ5fPgwWbt2LRk3bhyJioois2fPJtu3bydZWVntRheY6Z0U/enuEUVKp+nq7EF3/qN0MT3NTkmlUvLmm2+S8ePHk8OHD5N169aR+Ph4EhkZSWbMmEE2b95MUlJSSEtLi87XoHbKcKid6vXo3Kdpz0Y3hhCChQsXIjw8HKtWrWJvV5Vg05Rm+/zzz0EIQXJyMgQCARtReFDY2dlh//79GDp0KL744gsMGjQIX3zxBbKysnD48GG8+uqrGDRoULvRhfamqvZ0FixYAHd3dwwYMIC9LTExEd7e3hg8eDAGDx6MkydPsvetX78eQUFBCA0Nxc8//6z1NXtCRJFCofROeqKdsrS0RFJSEp5//nns2rULgYGB2LlzJ65du4bjx49j1apVeOSRR9pVMqR2itopigG054k8YI+IosHFixcJADJw4EAyaNAgMmjQIPLTTz+RqqoqMn78eBIUFETGjx9PRCIRIaS1nnHp0qUkICCADBgwgEYDuiG//fYbycjIIJGRkext//rXv8imTZvaPDY7O1ut3jQgIKBNRqinRRQpRtPV2YPu/EfpQqid6n1QO0UxEprZ6InExcWBEIJr164hKysLWVlZmDJlClxdXXHu3Dnk5+fj3LlzcHFxwYIFC+Dh4YHffvsNN2/exPXr1xEQEGCU2gPFfIwZM0bvqaPtDSQCemZEkUKh9C6onep9UDtFMTXU2eglvPjiizh9+rTabUlJSXjssceQn5+Pxx60SU6TAAAGk0lEQVR7DElJSQCAU6dOIT8/H/n5+dizZw9eeeWVrlgyRYXt27cjKioKCxYsYI1taWkp+vXrxz5GdSARALZZ7vz582qp7bVr1+KXX35BcHAwfvnlF6xduxYAMGXKFAQEBCAoKAiLFy/Gp59++mDfJIVCeaihdqpnQ+0UxVh6b9HhQ8aYMWNQXFysdpsxag+UB88rr7yCd999lx1ItHr1ahw4cKDD2lUmoqiNc+fOaX3ujh07TLdwCoVCMQBqp3ou1E5ROgPNbPRiKisr2Y3Zy8sLd+/eBdBxJIKijrZmOWMHEmmDDiSiUCgPK9ROmQZqpyjdGepsPIR0FImgqGPu1L/q9NzvvvuONRZPPPEEjh49CqlUiqKiIuTn5+ORRx4x4TujUCiU7gm1U4ZB7RSlO0PLqHoxHh4ebNq5vLwc7u7uAGgkwlBMmfp/7rnncOHCBVRVVcHHxwfvvfceLly4QAcSUSiUhxJqp0wDtVOU7gx1NnoxjNrD2rVr26g9bN++HQkJCUhJSaFqD0ZgaOqfeeyRI0favNbChQt1Huftt9/G22+/bcqlUygUSreB2inzQe0UpbtAnY1egrZIxNq1azFz5kzs378fvr6++OabbwC0qj2cPHkSQUFBsLW1xcGDB7t49ebFz88PDg4O4PF44PP5SE9PR3V1NWbNmoXi4mL4+fnh2LFjcHZ27vSxaOqfQqFQtEPtlG6onaL0Zqiz0UvQFokADFd7OH36NJYvXw6FQoFFixaxcnQ9nV9//RV9+vRh/83Usq5duxZJSUlISkrChg0b9H49mvqnUCgUw6B2qn2onaL0VmiDOIVFoVDg1VdfxalTpyAUCnHkyBEIhcKuXpZZOHHiBObNmwegtZb1+++/N+j5PW0gkZ+fH95//32MGzcO9vb2GDhwIK5du4YjR44gKCgIAoEAixYtglwu7+qlUigUik6ondIfaqco3Yb2xoubfbA5pVtx+fJlEh8fz/77gw8+IB988EEXrsg0+Pn5kSFDhpChQ4eS3bt3E0IIEQgEao9xcnLS+fyEhATi6elJ+Hw+8fb2Jvv27SNVVVVk/PjxJCgoiIwfP56IRCJCCCFKpZIsXbqUBAQEkAEDBpC0tDTzvTED6N+/PwkKCiJCoZC0tLSQOXPmkICAALJ48WLS2NhIbt26Rdzc3Mjhw4e7eqkU7XS0Vz/Mf5SHCGqntEPtFKUboHOfpmVUFBZtTWMpKSlduCLTcOnSJfTt2xd3797FxIkTERYWZtDzTZX672peeuklhIeHAwBmz56Nw4cPIzk5GXZ2drCzs8PYsWORlpaG2bNnd/FKKRQKRTvUTmmH2ilKd4aWUVFYSDdtGjt9+jRCQ0MRFBTE6oQbAlOL6u7ujunTpyM1NZWtZQWgVsvam1FNk9va2oLH48HNzU3ttoaGhq5YGoVCoegFtVO9G2qneifU2aCwdMemsc7W54rFYnZjEovFOHPmDAYMGKCzlpVCoVAo3RdqpyiUngcto6KwDB8+HPn5+SgqKoK3tzeOHj2Kr776qkvXlJqaiqCgIAQEBAAAEhIScOLECUREROj1/MrKSkyfPh0AIJfLMXv2bEyePBnDhw/XKrdIoVAolO4LtVMUSs+DOhsUFj6fj+3bt2PSpElQKBRYsGABIiMju3RNna3PDQgIwNWrV9vc7urqqrWWlUKhUCjdF2qnKJSeB3U2KGpMmTIFU6ZM6eplsHTX+tyeRnFxsdq/x44d20Y+8NChQw9uQRQKhWIk1E71Tqid6r3Qng1Kt6Y71udSKBQKhcJA7RSF0j7U2aB0a1Trc1taWnD06FE88cQTXb0sCoVCoVAAUDtFoXQELaOidGu6Y30uhUKhUCgM1E5RKO3D0VZrqEK7d1IoFArlgUALwHVD7RSFQqF0PTrtFC2jolAoFAqFQqFQKGaBOhsUCoVCoVAoFArFLFBng0KhUCgUCoVCoZgF6mxQKBQKhUKhUCgUs0CdDQqFQqFQKBQKhWIWqLNBoVAoFAqFQqFQzAJ1NigUCoVCoVAoFIpZoM4GhUKhUCgUCoVCMQvU2aBQKBQKhUKhUChmgTobFAqFQqFQKBQKxSxQZ4NCoVAoFAqFQqGYBX4H93MeyCooFAqFQjEOaqcoFAqlG0MzGxQKhUKhUCgUCsUsUGeDQqFQKBQKhUKhmAXqbFAoFAqFQqFQKBSzQJ0NCoVCoVAoFAqFYhaos0GhUCgUCoVCoVDMAnU2KBQKhUKhUCgUiln4/8V8MsVu2/hzAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -892,16 +804,20 @@ " mut_n_rdc= mut_n_StE[rdc_id]\n", " c_n_rdc = cn_StE[rdc_id]\n", " c_a_rdc = ca_StE[rdc_id]\n", + " mmax = mmgrid_rdc.max()\n", + " kmax = kkgrid_rdc.max()\n", " \n", " ## plots \n", " ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d')\n", - " ax.scatter(mmgrid,kkgrid,ca_StE[:,:,hgrid_fix],c='yellow',marker='.',\n", + " ax.scatter(mmgrid,kkgrid,ca_StE[:,:,hgrid_fix],marker='.',\n", " label='StE(before dct): adjuster')\n", - " ax.scatter(mmgrid_rdc,kkgrid_rdc,c_a_rdc,c='blue',marker='*',\n", + " ax.scatter(mmgrid_rdc,kkgrid_rdc,c_a_rdc,c='red',marker='*',\n", " label='StE(after dct):adjuster')\n", " ax.set_xlabel('m',fontsize=13)\n", " ax.set_ylabel('k',fontsize=13)\n", " ax.set_zlabel(r'$c_n(m,k)$',fontsize=13)\n", + " ax.set_xlim([0,mmax*1.1])\n", + " ax.set_ylim([0,kmax*1.2])\n", " ax.set_title(r'$h({})$'.format(hgrid_fix))\n", " ax.view_init(20, 240)\n", "ax.legend(loc=9)" @@ -919,7 +835,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, @@ -981,12 +897,192 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Observation\n", + "##### Observation\n", "\n", "- For a given grid value of productivity, the remaining grid points after DCT to represent the whole consumption function are concentrated in low values of $k$ and $m$. This is because the slopes of the surfaces of marginal utility are changing the most in these regions. For larger values of $k$ and $m$ the functions become smooth and only slightly concave, so they can be represented by many fewer points\n", "- For different grid values of productivity (2 sub plots), the numbers of grid points in the DCT operation differ. From the lowest to highest values of productivity, there are 78, 33, 25 and 18 grid points, respectively. They add up to the total number of gridpoints of 154 after DCT operation, as we noted above for marginal utility function. " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Distribution of states \n", + "\n", + "- We first plot the distribution of $k$ fixing $m$ and $h$. Next, we plot the joint distribution of $m$ and $k$ only fixing $h$ in 3-dimenstional space. \n", + "- The joint-distribution can be represented by marginal distributions of $m$, $k$ and $h$ and a copula that describes the correlation between the three states. The former is straightfoward. We plot the copula only. Copula is essentially a multivariate cummulative distribution function where each marginal is uniform. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "### Marginalize along h grids\n", + "\n", + "joint_distr = EX3SS['joint_distr']\n", + "joint_distr_km = EX3SS['joint_distr'].sum(axis=2)\n", + "\n", + "### Plot distributions in 2 dimensional graph \n", + "\n", + "fig = plt.figure(figsize=(10,10))\n", + "plt.suptitle('Marginal distribution of k at different m')\n", + "\n", + "for hgrid_id in range(EX3SS['mpar']['nh']):\n", + " ax = plt.subplot(2,2,hgrid_id+1)\n", + " ax.set_title(r'$h({})$'.format(hgrid_fix))\n", + " ax.set_xlabel('k',size=12)\n", + " for id in range(EX3SS['mpar']['nm']): \n", + " ax.plot(kgrid,joint_distr[id,:,hgrid_id])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "code_folding": [ + 0 + ] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "## Plot joint distribution of k and m in 3d graph\n", + "\n", + "fig = plt.figure(figsize=(14,14))\n", + "fig.suptitle('Joint distribution of m and k(for different h)',\n", + " fontsize=(13))\n", + "for hgrid_id in range(EX3SS['mpar']['nh']):\n", + " ## plots \n", + " ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d')\n", + " ax.plot_surface(mmgrid,kkgrid,joint_distr[:,:,hgrid_fix], rstride=1, cstride=1,\n", + " cmap='viridis', edgecolor='none')\n", + " ax.set_xlabel('m',fontsize=13)\n", + " ax.set_ylabel('k',fontsize=13)\n", + " #ax.set_zlabel(r'$p(m,k)$',fontsize=10)\n", + " ax.set_title(r'$h({})$'.format(hgrid_fix))\n", + " ax.set_xlim(0,400)\n", + " ax.view_init(20, 40)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Commulative probability distribution function in StE')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "copula_value = EX3SS['Copula']['value']\n", + "fig=plt.plot(copula_value)\n", + "plt.title(\"Commulative probability distribution function in StE\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice the cdfs in StE copula have 4 modes, corresponding to the number of $h$ grids. Each of the four parts of the cdf is a joint-distribution of $m$ and $k$. It can be presented in 3-dimensional graph as below. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "code_folding": [] + }, + "outputs": [ + { + "data": { + "image/png": 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e3rPjFxGRO5vilEiFkg25LT333HMcP36cd7/73QwODnLo0CE+/elPA/Anf/InfM3XfM262z/wwAPE43EAzAwz4/z58wAkEgne/OY386d/+qd7+yJEROSOpTglUqFkQ25Lzz33HGfOnOGxxx5jdnaWd77znbzvfe8DKmeT7r333uvu8653vYtUKsV9993HyMgIjz32WP26U6dO8eyzz+7Z8YuIyJ1NcUqkQsmG3Jaef/55fvzHf5yv//qvx/M87r///vp1i4uLdHV1XXefD33oQ6ysrPDZz36Wb/3Wb62fQQLo6upicXFxT45dRETufIpTIhVKNuS245zj7NmzfNM3fVP9srNnz9YH8r6+PlZWVpre1/d9vuqrvopMJsOv/uqv1i9fWVmht7e3vQcuIiJ3BcUpkWuUbMht57XXXgPgxIkT9cueeeYZ3vCGNwDw0EMPce7cuU3vH4Yh5XK53gsL8KUvfYmHH364TUcsIiJ3E8UpkWuUbMht57nnnuP06dOYWf2yZ555pj4IP/bYY/zFX/xF/bq5uTk+9rGPsbi4yPLyMn/0R3/Exz72Md761rfinKNQKPDUU0/xtre9bc9fi4iI3Hl2GqeWlpZYWVnh8ccfV5ySO0bkVh+AyHY9//zz687uLCwsMDMzw4MPPgjAd33Xd/GGN7yBXC5HIpGgWCzygQ98gB/4gR/AOcf4+Di/8Au/wNvf/nYKhQKPP/44b33rWzl06NCtekkiInIH2W6cKpVKfPCDH1wXp97//vfX49THP/5xxSm5bZlzbqvrt7xSZL96z3vew8DAAO94xzsIw5BXX32V4eFhent78X0f3/eBSl/tW97yFn71V3+Vhx9+GN/38TwV/GTfsRvf5K6lOCW3pfe+97309/fzjne8A+ccExMTpFIpDh48iOd5RCKV88GNceqhhx4iEonUl8YV2Uc2/UIq2ZA7hnOOcrlMoVCgXC7jnMP3fcyMl19+meHhYTzP4/LlywwNDdHb21sfrJ1z1P4WaoO853kazGW/0Bdxc4pTcttwzhEEAYVCgVKpBFCPNa+//jqpVIrOzk5mZ2cZGBhgYGBg0zhVO3GmOCX7hJINuXOFYUipVKJQKBCGIcC6RCEMQ5599llKpRKJRILh4WEWFhbIZrOMj48zMjKyrtKxtrbGa6+9xoMPPojv+/WzSCK3kL6Am1Ockn3POUexWKRQKBAEAWa2Lk4553jxxRdZW1sjGo1y6NAhlpaWWF5eZnR0lNHR0XWVjnK5zLPPPsub3/zmetKhqrzcYpvGKc3ZkNuSc44wDCkUChSLRaCy42otaQAoFApMTU0xPz+PmXHkyBGGhobwfZ+xsTHy+TyZTIYnnniCwcFBxsfHSaVSAPWkpVwuUy6X1yUdSjxERKQVQRDUkwznXL0iUYsjpVKJ6elpZmZm8DyP4eFh0uk0nucxPj5OqVQik8nw5JNP0tfXRzqdprOzEzMjCALgWpxSVV72K1U25LbinKtXMWoD7cazQ0tLS2QyGfL5PKOjowwNDdXnbPT09KybswGVxGJ2dpaJiQmi0SiDg4MsLCzUlyhU6Vr2AX3ZNqc4JftKLU4Vi0VKpdJ1VQyo7JmRyWRYXV1lZGSE4eFhMpkMqVSKoaGhdXM2ao85Pz/PxMQEzjlGR0eZmJjgK77iK+rX1+KUmRGJRBSnZK+psiG3tzAM150d2jh4B0HA7Owsly5dIplMMj4+Tnd3d0sDred5jIyMMDIywvLyMufPn2dhYYGLFy/WS9dmVh/MS6USpVKpPpirdC0iIs3iVOMP/jAMmZ+fZ2pqikgkwtjYGPfdd19LccrMGBoaYmhoiLW1NS5evMjKygoXLlxgbGyMWCy27qSb4pTsJ0o2ZN+qTaRbW1tjZmamvkJH46CZy+XIZDJcuXKFgwcP8tBDDxGLxXb8nN3d3Zw4cQIzIwzD60rXQD3xUOlaROTu1tjS+/rrr3Po0KEtW3oPHDjAqVOnSCaTO37Ojo4OTp48ycrKCpFIhDNnztDZ2cnhw4fp6empt/sqTsl+oWRD9p2NE+lKpRJzc3P19cWdc1y5coVMJkMYhoyOjnL8+PFdPXPjeR5Hjx7lyJEjzM/P89JLL+GcI51OMzg4WB+wa4GmWCyqdC0icpdo1tI7PT3N+Ph4/fqNLb2PPPLIuiTkZpkZ6XSa8fFxrl69yoULFygUCqTT6frqi41V+Vqc0sInsteUbMi+sdlEusbJ2tPT00xPT9Pd3c3x48fr1YZ22Vi6npiY4NVXX2VkZKRp6TqXy3Hp0iWOHTtWTzo0oIuI3Bm2aumtTdreaUvvTpkZ/f399Pf3k8/nmZyc5HOf+xxDQ0OMj4/XqyiN1Y5XXnmFEydOaOET2RNKNuSWamUi3draGmtrazz99NOMjIzwxje+kWg0uufH2tHRwalTpyiXy1y6dKlp6Rpgfn6edDqt0rWIyB1gs70xNrb05vN5nnrqKQYHB2+6pXenEokEJ0+e5Pjx48zMzPDcc88Ri8VIp9P09/fXE4vZ2VmOHj1KEARa+ETaTsmG3BKtTKS7fPkyU1NTmBnRaJRHH310XwyEkUhk09J1X19fPWFS6VpE5PbV2NLbbA+njS29vu/zyCOP7Nlk7K1iied5HDp0qL5fx8TEBC+//DJjY2P1luTGOKUJ5dJOSjZkz7SyN0axWOTSpUvMzs7S39/PvffeSzQa5ezZs/vuR3qz0vX58+dxzpHP50kkEvXbNU7U054dIiL7V62KUSwW17X01mzW0vuFL3xhz36k32DbgnV6eno4ffo0xWKxvmdHoVBgbW2Njo4OQAufSHsp2ZC2a2VvjOXlZTKZDLlcjkOHDq2bSFcqlbY1sO7G8W53cK2VrkdHR3nmmWd49tlnm5aua+X42g6ymlAuInLrtdLSu7q6ytTUFEtLS7e0pXenYrEYx44d4+jRo/zlX/4lL774Yn2S+eDg4Lo4pYVPZDcp2ZC2qQ1WFy9e5ODBg033xpibm2NqaopEIsHY2Ni6uQ81t9MA53keyWSSN73pTU1L1417dgAqXYuI3EK1ODU5OUl/f/91P6wbW3o9z2NsbIx77rmnbXFpr06s1VqTV1dXmZiY4JVXXuHQoUOMjo7ecM8OVeVlu5RsyK5qNpEuk8nUe0SB+opNly9fZnBwkNOnTxOPx9t6XHs1MDYGimal6/7+ftLp9Kal63w+T1dXl84iiYi0SS1OFYvFekvv9PQ0AwMD9RM+zVp6U6lUW4/rVoz5nZ2d3H///ZTLZaampjhz5gzd3d2k0+n6Klob41SxWCSVSmkOorRMyYbsiq0m0tUGqqtXr5LJZCiXy4yOjnL06NGWz+TvZRvVzdo4+DaWrufm5rYsXX/xi1/ky7/8y+v9sko6RER2x41aeoH63hjNWnrvJBvjSiQS4fDhw6TTaa5cucKrr75KqVQinU7XN9StxakXX3yxnnxp4RNphZINuSnNJtI1Dt61syC1ZWKPHj1KV1fXtp7jVgxiO33OrZIiM+PgwYMcPHhw09I1gO/7Kl2LiOySrfbGgGt7PH3xi18kmUxu2tK737QrTg0MDDAwMEAul2NiYoLz588zPDzM2NgYiUQC51w9AdPCJ9IKJRuyba3ujTE1NcXi4iIAb3jDG26biXQ3W0VpZaDdrHRdO9vWrHSt1UFERFpTa5XK5/OUy+X6D+SNe2PUWnrDMOTUqVN0d3ffwqNu3V7EqWQyyb333suJEyeYnp7mmWeeIZlMrltNUgufSCuUbEjLGs8OhWFYXw6wcSJZbSIdwNjYGCdPnuTMmTNEIjv/qjVOqL4Ze9GKtd3naCxdLywsMDMzw5NPPtm0dK09O0REttbK3hjNWnrPnj3b9rmD+8V2V1z0fZ+xsTFGR0dZWlri6aef5umnnyadTjMyMlL/HaCFT2QzSjZkS80m0tXOsNeUSqX6RLre3l5OnjxZnwB9t9nJsrlQSagOHDhAKpXi9OnT9T07GkvXtdtpzw4RkfVa2RtjZmaG6enpTVt6b6e5gTdjp6/TzOjt7aWrq4tTp04xOzvLE088wYEDB0in0/UJ9KrKy0ZKNqSpWqvU7OxsfUDeOFCsrKyQyWRYXV1lZGSEN73pTU0rGLWBR4NMa1KpVNPS9eHDh+nt7VXpWkSEa3Fqbm6OVCp13ZxBWN/SOzw8vGlL725V0O8Gzjni8TjHjx+vL3xy9uxZfN8nnU5z4MAB7dkh6yjZkHU2TqQ7d+4cjz766Lo1x2t7Y8RiMcbGxuo/gDdzs4P47RYEdmsQ3Vi6vnjxIi+99BLj4+NNS9cLCwvkcjlGR0dVuhaRO9bGlt7z589z+vTpeiVjs5bedsapvXazJ/Bu5r6Nz+15HsPDwwwPD7OyssLExATnzp1jdHSU0dFRotHodRv4LiwscOTIES18chdRsiFbTqSrDQSFQoGpqSnm5+c5cOAADzzwQL21507UjlU+dqpWuu7t7aVQKJDJZJqWrguFAqurqypdi8gdZ6uW3tr4djMtvXfTGHmzicpm9+/q6uKBBx6gVCoxNTXF3/zN39Db20s6naarqwszo1wus7y8rDh1l1GycRdrZSJduVzm7NmzFAqFHa85vhuVjb20F6t87NRWpevapH3P8zShXETuCLVxrFgsUi6Xm65+GIYhL7/8MrlcbsuW3lae625ws6/zRslKNBrlyJEjHD58mMuXL3Pu3DmCICCdThOPx+ufoeLU3UPJxl3oRhPpgiBgZmaGS5cuUSqVGB8fp6enZ8fPd7uVp2/GXr3OZqXr+fl5urq6KJVK9Z7kjRP1tGeHiNwOgiCob8BX+3Hb2Ovf2NKby+UYGxtjaGhox+Pa3RSnblarlREzY3BwkMHBQbLZLBMTE8zOzhKLxSgUCvXVv7TwyZ1PycZdonHn1M3ODmWzWaamprh69SpDQ0M8/PDDPPvss7fNuuP7xV4PjrXS9eTkJPPz801L181WB/F9XxP1RGTfqI1RtTjVbG+MZi29r7zyCt3d3Tc9h+FuSjb2etxPpVLcd9999Pf3c/HiRZ5++mk6OjpIp9Na+OQuoGTjDtfK3hhXrlwhk8kQhiFjY2McP368PrjvxkpSd9MgfitfZ23n13Q6fV3pemho6Lo9O0ql0nVnkURE9loYhvWTYZu19C4uLjI1NdW0pfduijG7YTdWh9zp/T3Po7u7m3vuuYfFxUVef/118vk84+PjDA8Pa8+OO5SSjTvQxrND0HxvjOnpaWZmZujp6eH48eN0dnZe91i7MYjfboHgVq7ycTNqx92sdN24Z8dmpWszIxqNaqKeiOyJcrlMLpcjCAKAeqtUTWNLb+0seLNKu+LU9u97qzTGqb6+Pvr6+sjn80xOTvLEE08wNDTE+Pg4yWQSuD5OQWVOiKodtxclG3eQxol0q6urXLhwgdOnT6/7g1xdXSWTybC8vNzSRLq7cRC/GbdyP5Fmz10rXQdBwKVLl7YsXb/wwguMjo7S29ur0rWItMXGlt5nnnmGRx555IYtvbFYbNPHvJPi1F4dw36KU4lEgpMnT3L8+HFmZmZ47rnniMVipNNp+vv718Wp8+fP09HRUa+CqCp/e1CycQdoNpEuEonU/x2GIZcvXyaTyRCJRBgbG+Pee+9teYLXfhiAbxe3+ozRZiVm3/cZHx9nbGxs09J1rT8aVLoWkd3VrKV3Y7vuVi29W9mtZONW26tjuNVxarPX6Xkehw4d4tChQywtLTExMcHLL7/M2NgYhw4dqv+uqcUpLXxy+1CycZu60UQ6z/MIgoDXXnuNubk5BgYGOHXqVL002ara8nQ3QwnL3milqrJV6bpUKtUH62YTyrUWuohsx1Z7YzTeZmJi4oYtvVvZrRhzN8Wp/VTZaKanp4fTp09TLBaZmpriySefpK+vjzAMt4xTWvhkf1KycZtpZSLd8vIyExMTrKysMDw8vKO9MWrupPL0XtlskKtMzM4RhGWikd1f4Wu7LVwbS9eTk5O89NJLHDt27LrStdZCF5FWtbI3Rq2lN5vNYmY73hsD7t441Y45G5XxvkAQFohGdr7k/WZqVa1WxWIxjh49ypEjR5ifn+fFF19keXkZ5xyDg4PXxSktfLI/Kdm4TbSyN8bs7CyXLl0imUwyOjpaX7XQV0fGAAAgAElEQVTjZtTasG7WfhjE230M5XCNq8XHKaT+Ny/OZfDIgsvjKGCERK2MjwOi+BYFEnheH743QsQ/QixyL5GYRxh+OZ63/eRwu4N4Ta10PTs7y6FDh7h06dJ1pWvQWugisrXGKsZme2M0tvSOjo6yvLzM+Pj4TT3v3Zhs7PRYgzDHlfz/IOz5Y740l8G5ZTyK1ThVJkJIxEIMH88SQBTP68f3DhLxDxP17yUS9wjDR/G86I6OeyfxwswYGhpiYWGB7u5uFhYWeOWVVxgZGWFsbKw+p0dxan9SsrGPNU6kq63WsfHsUC6XY2pqioWFBYaGhjh9+jTxeHxXEgS4M3thwzDcstKzncFwrfA6mdXfJFd6Dt/m8VyRSKyMcyElSiSsE9+t4pkBMcAH83BEMEq4cIpSeIGw9FdgxvHDsLDwM3jeAJHIm4nH/xHx+Fe3dDw3Ozk9DEN6eno4ePDgdaXrdDpNZ2dn07XQVboWuXu1ujfGpUuXmrb0Xrhw4aaP4XZLFFqxWzEcIFucZWr1w6yWPk+EBTwKxJMlQucIKRKxTny3gm9G5WehjzO/GqcCXDhNKXyNoPQEzozjaVhY+Fk87wB+5CES8W8hHv+GPYtTHR0djI6OUi6XuXTpEmfOnKGrq4t0Ok1PT4/27NiHlGzsQ5tNpGtslbp69SqZTIZyuczo6CjHjh1bN7jv1uB7p5wxcs6xurrK1NQUa2trpFIpjhw5Um8X2o4gCJjJfozLuT8iDC/hUQRW8KyDwK1SGa4dURxRK+FcbYAt4uHwqm+Fw/Ax4gaYEVqCUtnwIw5z84Sl/062+AnWVmOYd5Ro7GtJxr+NSPTwpq/xZgbRxvtvLF2/9NJLOOc4fPjwpqXry5cv09PTQyKR0GAucodrtaU3k8mQy+Wu2xtjN90pcQogn8/zpS99iaWlJRKJBIcPH97RzujOOWbXPsFc9g8IwtfwKGGsYF6SIFwFgxghUSBKAUctBpQwivgNccqrxikzI7Q4pbKHHwHcVcLip8gWP8XaagTz0kSjX0088U+JRe/b9Lh2K05FIhHS6TTj4+NcuXKFCxcuUCwW6wufbPw+lkolFhYW6OjoIJVKaeGTPaRkY59onEh34cIFBgcHSSaT63pYy+Uy09PTTE9P093dzdGjR+nq6mr6eLv1Y2+/TLzb6f3DMGRubo65uTk6Ojo4fvw4fX19ZLNZLl68yMsvv8z4+DiHDh26YRBcK55jcuU3yZWfw2OJOIt4XqnyPA6cK5DyyiSs8YzUMjSMZ2VnODxi5gCPwIUUHUQMfPKVAbx27PhgcRxFXPAypdzLlHK/juf14ntpIrFvIZb4dnw/UX+PbvaM0cbBt1a6HhoaYnV1lcnJyU1L16+//jonT54EKj86otGoStcidxDnHGEYUigUmJycpLOzk87Ozi1besfGxm56d+8bud2TDeccly9fZmpqCt/3OXnyJPfffz+lUomLFy/y6quvMjo6ytjY2A3ntWRLU2RWf5210hksXCBuyyS8yuT80DlCBykLSHpBw70qyUdN0BCnjBhlV6TkjIg5fArr4lSlApLAUcIFFygFFyjl/wue14Vvo/ixbyCe/Of4flf9tbYjTg0MDDAwMEAul2NycpLPfe5zHDx4kPHx8foJMDPj0qVLHDx4sF5908Ine0PJxi3WbCLd6uoqBw4cqH/519bWyGQyLC0tMTw8zBvf+Eai0e33Su7E7dpGVSwW15XtBwYGGB4erm8I1dXVxYMPPkixWGRycpLPf/7zDA4Okk6n1z1OGAZcWvsI89lPgJvDcPgWUAzXCC2BhRC3EN8Mz3yc6yEbBvi+AQEeZXyKhC5JhCIdVsbqyUhQH+Cdg5KrDPIAvhkRAsyyhA7MUkTNx1yR0C1BcBbyL1DIvw/nHSES/0ac+0rM+nf8nt0oCHR2dnLq1KlNS9dhGNZ7Y2s/SFS6Frn9NWvpzWazxOPx+g+/zVp698J+qUpsV+MJxN7eXgYHB+ns7KSvrw8zI5VK1cfcTCbDk08+SX9/P+l0et146pxjJvsHzK39AUE4hQf4VqLo1giJ4IWOmIVEzIhYDFyStXKZSKQWpwJ8CoQk8F2ZlJUa4lSBaMPQXXa1k2bgmyNKiGfZSgXfkkQsiucKhG4V3DnIn6NQ+BWcN4ofezvO/T08b3DH71ltNarNJJNJ7rnnnvrCJ1/84hdJJBKk02n6+vrqbX6174wWPtkbSjZuka0m0tWWrZ2fnyeTyeB5HqOjo9xzzz17/oewX84Ytfq6a2ffV1dXGR0d5c1vfjO+7/PKK680vX0sFuP48eMcPXq0PjD5vo+XmORLV36DXOk5PFZIslyvYpQdYDE6LU/Szzc8WmHd2aF8GKFIlH7fp0yRvPMouQ6iOOLkMLv2nphRKWfXL3OUXQRzIYbhU6z8sRr41VJJSOWMEsErBNkPMD7w/xAwRmHt7UST37vtAb3VCeabla5LpVL1tVyrZtR+pGjPDpHbT2NLby1O1c4Ce55HGIb1vTE2a+ndC/slTrUql8uRyWS4evXqus11JyYmmt4+Eolw5MgRDh8+zNzcHC+++CIAZbvEy1d+j7XSU3isEGcJv1rFCBw4YnRYmY5mcar6ERVDnyIxev0YzhXJ4bPiUsSAGDk8W/+eRAwiDZcFxHCujIcRoUSU0oY45VUqH8FFLPcbDHb8JmV3kPzq1xGNfz9+dHuLA2y1n1Qj3/cZHR1ldHSUxcXF+p4dtc+5FqM0oXxvKNnYQ61MpCsWi6ytrfHCCy9w4MAB7r33XlKp1C075tthEHfOsbCwwOTkJJ7nMT4+zn333betgcLzPIaHDxJ2fZLp5d8ldAsUC3k6GwbpfJAg4kXptmV8L9f0cUJnZF2cuMFApAgUgMoAnSAAKj/IAwdFusiHBXzziRMQsSKBM0KMmBmdnqMxeym5SqJjQNzAswBYq044NwIXAhcp536DIP9hsFG86NcQS/4LvMhYS+/jdt6zjaXrJ598kjNnzjQtXWvPDpHbQ62lt1Ao1E8gNG7AB5Uz8isrK8zMzNDf379lS2+rz3kzY8HtEqcWFxeZnJwkCALGxsY4ceLEtsfcoaEhXOdfMrX82/jBNLlCng4vR+1hCmEcXIRef41IpHmccs7IuhgRM/r8ImaF6hNAnJBanAodFOkkFxbx8YlbSMQKhM4IMKIYHV4ADa+hXK3SGxAz8CwEstU4VWnlMpsmyP8OYeG/gR3Ei34l0eT34Ufubel93G4y29vbS29vL4VCgTNnzvD8888zNDREOp0mlUpp4ZM9oGRjD9xoIh1Qn0i3trZW79k8cODArTrkuv1cni6Xy8zMzHDp0iV6e3t3nJhlS68zsfwBsqWnMLdG3IvgnBEQYbXcg4URYv4qvjk8olwNunCU8HAkvSiOHBASEqffD+iy4g2f0zdIskrSByixFkQouURlcrlFiZAH1q9GEjXq5ezQQcFV2q9iVilnR8wAqw7oIc5NEBZ+h6D4+5il8aKPEk29G9/fvOKx00E1mUwSj8d55JFHmJubu650rT07RPa32t9lrVWq2d4YjS298Xicw4cPMzo6elPPu/FM8808xn6w8bUEQcDc3BxTU1OkUqkdJ2aF8iwXlz/AavEJcEvELE7gOUJ8skEPBBFikSxmAREvzkroU3YFPCDpR3EuixFQdgn6fceQX7jhc3oGCdZIVONUNvQphgl8HGZRIlYEgnX3qVQ+Kv8OHeTrccrDr7Yc106iVeaQTBMW/pCg+DjmjWP+G4kl/xX+Jguh3KiNaivxeJxUKsU999zDysoKL7zwAp7nkU6n663r2rOjPZRstEnjRLrazqm1H1c1tcnLU1NTxONxRkdH6e3t5fz58/um1WQ/njFqLEHvdA5LGIbMZ/8/5rK/T+im8d0Vui2H1UvLcRyddPqLRK3UcM9svfzsHBRIAhH6vAJZF5ALO1hxOeIW0OGV2WpsKjufbBghZSH9fgiUa9dUB+kuimGRpAdRWx8YKkEAsMpxFF1lYPdrCYlF8EnikwXKBOGruMJ5SsU/oOSdwE98O5HEt+/q4Omcq6+dv7F0PTY2xsjIiPbsENlnGlt6a62UG/fGWFhYuK6lN5PJ7Mrf6n6JMbsdpwqFAlNTU8zPzzM0NMRDDz1UX1BjO+bXPsVs7r8RBK9j9ThVOc4SMUK66fCXiEUbY8T6ikbe9eKI0O8H5FyZvOtipUw9TnlbfIyB88iGEeIGfV7AtTgV4BzkXCfFsEjCM2JbxCkI63HKM4jiwHx8UvhkMRzl8CIuuEip9AmK3mEi8X+Mn/jedft57MZqVr7vMzw8zPDwMCsrK0xMTHDu3Ll67Kr9nmhWldfCJzujZGOXtbI3Rj6fZ2pqisuXLzM4OMgDDzxAIpGoX1/rhd0P9ssg3rjcb6lUYmxsjOPHj287KSsGV5lZ/RVWS/8bXJaklYhYrvqj3VF0/eAK9HirmDUvQRfDCGsuQZ9fZMBbrF/eaSXg2n+XnJENonjWSehWSHplYuZYCyKEROjzAjr8cpNnqAzGKVZJVXPTXOhTIELcfGJk1yUxZpUdPBzVVisMR4hna/WzSL75lRIxAS68SJD99wTZX8Cif4tI6odbKl+3ovF73li6zmQyfP7zn+fAgQOblq5nZ2dJpVL09PSodC3SRhtbeoF6e2NN4yIbfX1911WOPc+7o5ZX361kY3l5mampKbLZLKOjozz66KPbjlNBmCWz9iGWi58Et0TCQpK2Vu+qLYY9hIT0est4Ntf0McrOZyVM0O0FDPjL9cs7rAxcvfZczlgKIph1EbpVElYi4TlyoU/ZRen1QlJ+0OQZKrEnxVo9TuWdR95FiVmEuGWrdfZrYtYQp8wD5/BZw/cqcSqGgUHZheDmCHL/kSD3y1jkzUSSP4gfe3THm9fWbKyMdHV18cADD1AqlZiamuILX/gC3d3dHD58mK6urnVxKgxDZmdnMTMOHDigOLUNSjZ2SRiGrK6uUiwW62doN67xvLi4SCaToVgsMjo6ytGjR5v+0dRW89kPbvXSt7U/7mw2y/T0NIcPH66vKLUdpcizTAeP4xbP41uBTstjXuU9DlyUkusiaSv0egubPsZSkMQRYTiSo8+yN3zOqDl6/CJwhcAZq2GMAHB4RAgxWv+Mk15IkkqFrOQ8cmGUiBcl5tZwFiNwReIGHRtOURWdo1xttYqYEcGAIs5BwBpB8dO40p9R8g4xPPBGwvCNeF6y5eNqRTwer0/Cn5ub27R0ffXq1foKLLVqhyaUi+yeMAzJZrPkcrn6GdqtWnoPHTpUX2RjI8/z6onKzdiNk2u3OtlwzjE/P19fUn18fJze3t5t/xAteedY8T/KwuJLmMvT4RXwqkvUBs6jTB9RVuj1r276GCtBnDIxhiMFevzmJ8wa+ebo8UvAFUIHq2GMMDBCPHxCjOaJRjMJcySsCBQpO1hzMXyLE7M1Kku9l4mZbRqnKq3CRsQ8IIcDAvKEpb+iVP4cJRtieOA0cA/Q1/JxNdosWYlGo/VJ+AsLC5w7d44gCEin0wwNDdX/TlZWVvA8j56eHi18sg1KNm7Cxol0k5OTJBIJhoeH67cpl8v1Ncc7Ojpa+rF8J1Y2tqu2g/X8/DwHDhwgmUxy//33b+sxwrDETO7DLBY/geu6QtwrEm+Y2F1yXYTOp9u7iu9dbvoYgfO4EnTS48Oh6Cq1Cd+tyoU+jj46bJGhDVWMsvNYCioDVIdXJtbi2xQ1h0+R1TAEokQsiW9xPFavu23MrHI2yTnyocMBcTO8WuJhlb7ZIMwwPDBF8eqnsMhpIokfwI9/7bZe641UJuFvXrqu7exeO2OqCeUiN29jS+/c3ByFQoEjR47Ub7NZS+9Wf2+7dVLsVicKN6O2DPjMzAx9fX0kk0kefPDBbf3wdM4xl/soVwq/R5iYI+oVSK6LUykCF6fbWyRi800fI3QeV8odJH0Yjq5Rm+DdqnzoE9BDh60wuKGKEThjMfAJgZQXkLDW3ueIQRclVp2j6CJELI5nHj6rcF3Fo1mc4vo45WYZ6p2jvPS3KPun8BPfTST+LduKCzeqjNSqFgcOHCCbzTI5Ocmrr77K8PAw4+PjhGFINBpVnNomJRs7sNlEukgkUh/wstksmUyGxcVFDh48yMMPP9xyv+Zuladrx3qrJ95t5zFWVlaYnJwkm82uO6u2sLB5xWGjfPkiU2u/TC54Cp8cUfMpW5mSCwjDFEYHUVsiaWs4S1CglyCMkg1zRCyB5wznQvDiDPqXORK7/kf8VkIHy0EfUfPp865i1vwsVMRC+vzw2n1Cn7Iz4hbS4TUP4vnQo4hPl+cY8ByVQbtyfCXnsRYmiVpIbEMLmJmRqH4NAufIOw8jrCceXnVAD1yRoPQUrvxOSmu9eNGvJdLxI3je0LbegxtpVroOgqCeiG8sXdcmlGvPDpHWbNbSG4lEyOcrq+zdqKV3K7sVp3arsrGX1ZHG+N64dO1TTz3V8mMUg1mm1n6ZtfKTeG6ZqBchsDKBC8iFSaCTCCukvDyhQZFuctU4FbUU5sCFAc6P0udd5XB8bduveancg28x+ryreLbU9Da+OXqrJ8qcg9XQp+A84hbS6TWvehSdkXcROszR79fiVCUmBc5YDRLEPCN6XUvwtTgVOkfBRXGu2CROBYTl5wnWfowg+9NY5CuJpn4EL3Lkhq95O21YqVSKe++9lxMnTjA9Pc3TTz9NGIYMDw/Xf1tp4ZPWKNnYhhtNpDMzlpeXmZ2dxTnH2NgYJ0+e3PYXbrcqG7XHudHO2FvZizaq2u6pmUwG3/d3XIK+nP0fXC7+LuXgPFEr0G1ZantZlCxGmSQ93hpxrzF5uPbvXh+WgxgOOBhdxDPIhVFmS50UnUfKC+n280SteetALvRZLh+g21/mYGS56W024xl027WBOxd6FEnhuSwpLyTrIvhmdHshZs2/G5USdGVAz4cRAksSIXfd8fpm+FSW1S1Vy9eRavnaNw+/eoYpcFcIih8nLD4O3gm8+LcRTf6zXR1AG0vXZ86cIZPJMDU1dV3pGrRnh0grttobAypjejab5fnnn79hS+9WditO7ZfKxo0eozZvcHJykjAMGR8f31F8v5r/S+byH6EYvkKUbHXCd7Wl1/Mphp10ezkS3mzDvdbHqZXAp0SEkfgivjkKoc98uZN86NHhGx1elrg1r24UQp+r5T46vRxDke2dSDODTgvorLZW5UOPvIvhUaLDC8g5H8Oj29v8hJlv0F1dCavgfEouRZQC0Q2rOHpmxCmBGWXnKIWV+8bMKjGsNt/PLRMUP4kr/SnYOBb7R0SS34/vb75ozHY/M9/3GRsbY3R0lLNnz3L58mXm5uYYHx9nZGSk/htLC59sTsnGDbQyka5UKjE9PV1vo7rvvvvo6OjY8XOaWf1M1M3YT4N4Mxt3T91q6drNjiEIs0znPsRS4U/ALZLwHB3+tfkUpbCLgIB+fw3f8k0fo+x8rpR76PFLDEcW112X9EokvWuDdqVq0cPVsgGVMz4l5yi6GKPRNXpjzduxtivphfguRw6fAA/w8Jyrr5Z1IwkvBCpnutbCOGZxoqzgbyiBR83qy+kWnCN0rrpEoWFEwJXxgSA8B7mfpZT/RYi8hUjHe/D8kV15rVD5jkSj0Xrw3li6jsfj684iqXQtck2re2PMzs4yOTkJwP3337+j+W81d1qysZna4hVTU1N0dnZy/PhxOjs7t/UYlZbe32Kx+HHCcIGE5+j1rlUiSq6DwBl9/gqRTeZjhM5jodxDl891J7PiXsDBhserxKnOSvuTgy6vhGcB2SDFoegah2Obz/nYjoQX4rsCWRehXN0p0HC0OhLHzRGvxqlsGMNZghir+BtOqFV2Pq/8u+gcgXNEzSNihkcEo4yHI3QXofBBwuKvEUS+nEjqx/AiJ3bltcK1OHXixAk6OjrIZDI88cQTDA4OMj4+rj07tqBkYxO1vTHy+XzTs0NQafnJZDKsrq4yMjLCsWPHKBQKN5VowO5NvNvr0nKrj7FZCXo7sqVXuZT9IPngDJ4r0uEV8b3aRkRG0fUQtTX6I81LwwC5MMVa0MFgZIkjsc1v18gz6PWX6fKM6VI3nsXp83LMlz3mSh0MR1fX7a66E9mwg4CAXq9UPzvUUT2TlAuNrPPosIBEi4lHh1cCSgTOyLkUHo6Y5a5bKSRulamAtc0DPQJino8BPrVqxyqu/BcES5+i7N2Dn/x+/Pg339TrralVC5PJ5HWl61QqRTqdrle8VLoWWd/Su9keThtbek+ePMnly5dvKtGA3ZuzsRtJSztasWqr6C0sLDA4ONhSK/TGWFcIppla+wC58l+Dy9HhlfD9yhl856BIDx45+v2VTR+zEMZZDroY8Fc5HGutYl6JU2t0ezBT6sKI0evlKYYwX04xFFkjvknloVU5l6TkoMcrkqo/VrXi4Yxs4FVbgluLhymvDKwSukps9syINV3RqrK0SiXpMIyAuPmYVeIUQNnloPw3BMvfSNk7jBf/DvzEd+xKbKjFqUQiwYkTJzh27Bizs7OcPXuWSCRCOp1mYGDgujjVuGfH3ViVV7LRoNW9Mebn55mamiISiTA2Nlbfrfry5cvkcjde/eFGdrONaj+cMaoN4rUSdG331O2UoGu3W8j/T+Zy/xncND5Fum21vuZ42cUJ6KDTFunxr2z6WMtBP+Uw4FB0lYEWNjZqlA0iLIeD9PmLnIhfK0F3+ZVEp+R8LpUTlENjIJKjw2staSw7Y67cScoLGIw0r8AAJD1Hsrq++XLoETijywvqZ3224ht0VPtmyy5KgQQeWWJWpugcOI+EecQbVgqpVG3CerWjsppVvrIZU/AlgrUfI8z+LETfhue9taXXupmNvbSNpevanh0vvfTSpqXrtbU1pqenOX78uErXckerVTGKxWJ9R+WNLYe1vTE2tvSurKzsm/gC+6eyUXuM5eVlJicnyeVyjI2N7ajFbLHwV8zmfo0gfA2fIl0NcSp0EYquiw5vhW5vq1WluimEHoeiq/RtsfpUM/nQZ6HcT6+/xrH4tUr/0WpSU3YeM+UOis6jz8vX49eNBA7myh3EPBjeIk4lzJGoTjRfDT2Kzuj0gpYWQfEMOqpdCGUXIU8CnwJRCpSpnExMmE9nQ5wqVyeWR6qV+spqVnlCB2FwgTD3H3C5X4LoVxPx39bSa93MxjjleR4jIyOMjIywvLx83cInG/eWKhaLXLhwgXvuueeuqsor2aC1vTEKhUJ9zfEDBw5w6tQpksn1S4Tu9lyLm7UfBvEgCFheXq63Su1k99QgzGP9v88LV94Dbpmk54g2zLsohZ0EBAz4WbxN9sYoO5/Lpa5qq1RrVYxG86UOHN0MRy7TH2m+rjlA1ALGo5WycOhgPkhRch0kWKYvcn1isxpEWQoTDEUKHI7deDndGjPottrSvbAYeHhAlxfSyrgVsTLOrbIWejjrwMwjbmt4G+5cabPyK5P1wspEv5jnVSfrVffucIu4wh/yppN/SGnpNF7yh/BjX9nya6nZbOKemdHX10dfXx/5fH7T0nXtu6bStdyJanGqWCxSKpWaVttrLb0zMzP09vZy8uTJ6yrtilPXC8OQ5eVlFhYWWFpaYmxsjJ6enm2uchTgej7By8vvxbkrJD0j5V2rRJRcB2UH/f4qPdZ8wZPAeSyUu0h5AQcj25/wfaWUpGT9DPnzHI9vfsItYiGj0WvxZr6cJBsmSFiOAT9/XQzJhRGuBEkG/CLp2OZJRjOdXuMiKFFCF9DthVtuJHjtOANwa/U45XlR4qzi28bbGZHqdyBfrSTEzcMz8Kr1jrJbJSz+CW848UmKi7+On3wnfvzvb+u1wNYTzLu7u3nwwQcplUpkMhmefPJJ+vr6SKfTdHZ21r9PV69evesWPrmrk43ah53JZEilUnR0dFx3dmhpaYlMJkM+n2d0dJRHHnlk0wnXuzn47pdgsNNBvHH31FgsRjqdZnR0dFuPkSudZyr3QfLlL5DsKdBhpQ2tUt1ELUv/FpOxc2GSlXKSg9E1jsU3L1U3EzpjsthLtx9lPLYAbG+Q9QwORnLUVuFYCuJcDWJEa+uWm89oJEtfpPUkoxnfoLe6qlXBGWuBETe3afl6NTSgkw5bra4UUnldlTarDjzyxG39nCHPjGT176LkHGXrJOJWG6odldVDyuUvwur3E9gAXuyb8ZLvxvPiLb2OIAhuuJjBVqXrSCRSP1PUWLrWhHK5ndXi1MzMDGZGb2/vdT9MNrb0btWaut+SjVsZp0qlEpcuXWJ2dpZ4PM7Bgwc5fvz4th6jEMwylf1FcqW/Jt6TJWVlIl6tVcpRpLeFVqkYy0E3A/7qtlc/BJgs9NDhJxmOzuPZzLbvX6mmV+LAShDjSjmObwEeIYGLMRbN0u3fXJyqLIJS6wCApcAjao7OTeJUNjTKpOi0LH2+o7LsfIEQyLkOjCJxiuv+DsyMZH1jQEeJDjy3RqRe7aguoRt8iWDtRwmzP41F/x5e6sfwvNZOgrYSp6LRKEePHuXIkSPMz8/z0ksv4ZwjnU7T3d29LhbdLXHqrks2mk2kW1n5/9l70xjJsvy673fvW2PPiNyzsva11+rpnhmSkkDxCyGDgOlVxtCWKYGWZUm2NDZs2pZtCOAHGyBAkZTIISVRM0NLsgmZI8iDGTQlmgIpihyyZ7pn2PtS3dXdlfuesb393esPEZEVmRkZFZEZWZ3VUwfoD10ReeNlZMT9v/89/3NOHdM0907cO4Kw5eVlMpkM58+fp1gsPrDrPEvjTzDaEahB0QmE6k5P7bhMDYqt4LfYCL5Ckn6MLRRF0USYnVEpm0g7lGSDUh9qeSspgJacs5qMO9GRz+uFQJlsxdOMW3VuusO5SvVDVibUUoe8FDgyZSOxqKYZyubJR+86cITGMVrvVYu+bp0sGUBdGdhCU5T37XK7YQjItMV6obZRwsTWnRTy+yVRdr0AACAASURBVLCEwKKJBoIO2yFabIeNgdaKRK+TBl9Fh/+Y1HwBI/PTSOvpvtc+jE1zL+p6e3sbx3FIkuQQdf1YUP4YjxI6darjfgjg+/4+0Xe/kd5+OEuMxKjWGXaNZrPJ4uIi1WqVubk5nn/+eba2tvYsgQdBNfxDVv2/T5K+hyFSCl11SmmTQGfJyyYTfUalqkmOSFvMW82hR6UiZbAZTZE3fa67dXrt6cdBViZUU4eMkBRlyloiqKY5KkM6V/WDJdizffeUINCSrGiNWdWUxBS069ThBkfuq1MWCger3VB0oxVi66G5b37i7GPlFaneQYW/iY7+OanxdJuV/1N9r30Y61whBFNTU0xNTdFsNvdGrIQQRFGEbdvfN8Yn3zfNxlHZGB1NRpqm+L7P0tIS29vbTE5O8uyzzw6cjQFn66QHHt4mrpTas661LIvz58/vo6AHWSNVAcvNX6EW/2uUXsMRJrZQKEIi7RAnNoYRUzE8Up2SkmUr0cQaFAJX5ElJCFVEXuq9UaZhUEuKVNMsF+0dxtzhT4eOQkM5bCcOM2bA5a5Rqbzd0nNsJi4NZTBhBmSP8C0/DvJS4SuBr0ykAFNocgOK1512CmyKxCeH1B7OAYcQsY/tUMRKYwkDQ4BFy60r1REq/iNI/jypmEM4/wnS/ctI2bv5PM7G2qGuV1dXuXfvXk/q+nFmx2M8Cug30msYxp4z4oNGevvBMIwzVacelrZQa8329vaejuX8+fPcuHFjqDqlVMqq92V2oxdJ9RKOMLC66lSa2miRMG56uLpBist24hJrTYrAkXmUTojSGEdqLtgNhmXMm2me3bTEnLXDlczRI73DwlcGG0mOSSPkkn3/AOyS3aqlO6lNNbUZMwKKxskNbDrISo2pFb42SXQr1yN/hL37QTgiAZIutiPAITnEdrhdbEeoFaYwWmzHHiufkiTfQzf+C1IxgbT/PWTmb/Rk5YdpNrqRy+V44oknqFarvPnmm7z88svk83kuXry4d7/0aTY++dQ3G72EdN03GFprgiBgY2MDy7I4d+4cV65cOdaH6aw1G6dNT3fPB5fL5aGLHoAX3WXF/yW89E+Q1MmJcG9USmsIdRFHaia6LGlN0XJXyrb/RKFy2Ul9ZkyfrJWgNWwnWQJdIFIBJSM6kj3QGhajMgaCS06VqWM0KUdhPckSK5N5y2PMPnrdCTNkgtYY00qcRdEavzqJq1VDZYGIMakRXev4yqCZQk5q7AE2dENoMjRAQKgdlJDY2uvBdkisrgRYhMARtHI7oM123EMEfw8d/Cqp+UMY2f9poBCmQSGlpFwuc+3atUPU9eTk5AMzOx4Lyh/jk8KDsjG669Ta2toDR3r74SzVFzh918Q0TVldXWV5eZlCobBnWzoMonSVxeYv4SV/DLpKTsaYsqXB01oTUsJGULB27usdhAIae3Uq1hZbicGEEVK0WmxVNc3SUDkiFVGUMRVzf8hdN5aiIlrbXHZ2GR8hG76VOHjK4ZzlcaVPnSobMWUjRmlYS7JESjBtedgnqFOeypASMyb3j/2GSlJVgozQuAPUqf1sh43CarMd+9/MlrajpTUMtUZrsKU4wMqvkQZfQYe/Tmp+FiP700jzqb01jttsdCCEoFgs8tRTT7Gzs8Pdu3cJw5ALFy4wMzOzt/anLbPjU9lsDCKk6854MAyDiYkJrly5cqLXHdWJ0ag0G6NgNnoVFM/zWFhY2EdB97Ou7XUd2+HvsO7/Gkn6IZZQlERz76a4RUFnKMgmRWO315IA7CY54j0K+v48rBBQMT26KdhGalFPxwg05AxFTjRYiQqcsxOuj3BUKtWC5ThPVirmzIDWnOlgMITmnNUqIr4yWI5dcjJhvI/rRzciLdlIMhSNlMoRLlsZqcnIllivqgwEmsKAFoiOaK2ZIvF1DoPmoUJziO3QGlsaSAFWK7GDVMek8e+ia99ByPNI9ycx3P9goGvoh84sbS/q+v3332d2dpb5+fnvK+r6Mc4uDo70aq0PzWt3j/RKKSkUCgONSvXDWdIEdq7nNJiNjpnE9vY2U1NTPPfcc1hW/6C3g2vUom+z4v0KcfoWplAUhIfcEzxLAp0jK3wm+9SpeprBVw7nLJ+SvV+3UTI8Sl1aCE+Z1OIxPA05CQWjzmqUZ8oSXHV26GQnnRSthqGIRLeF4oMzFVLAbLvZCZVkMXFxhWTcqA9kTtJyXsySlYpJs/eIsyPBodUMVJWBRlMQg5mfOO3DSIXAJ4ehQ+wDwba92A5LmBhCtVl5RapTkvhb6Np/jpDnkPZ/hMz8xRM3Gx3zEiEElUqFSqVCEAQsLCzwrW99i6mpKS5cuIDruvvq1KNufPKpaja6T4d6JXwDNBoNlpaWqFarzM7O8pnPfIbNzc2R5FqcJVp5VNfTTesdTE/tpqAHWUOpmBX/19gN/wVab+NKyBn3N89EZ4iUYNz0KInejlFKCzaTPK6AOctjUAo6b8TkjQ12YpftJM+YbZCViq3ExRYJOeNkJ0WeMliLC0wdGJU6LjIy3aOvt1OHujIpGwH5HmNWtdTC13kmjAaXBnQKkYK26A48JfG1ICfSvYC/fthjO2ixHRqwCXo4WbXYDtU5RRISU+QwabZZkAZJ+ibK+9to/2e5OPUMSj0zsFDvIHoVgQ51nSQJy8vLD6SuP/74Y/L5POVy+VNBXT/G2cMg2Ri9RnobjQbb29sn/kyO6jM9Ks3GqBn4arXKwsICYRgyPz8/8KRC9z6w7v9TtoLfQOl1HAkl475eIdEOoTapGEfXKYDNuIAhBHNmE3HETfVBZGVC1t6kntqsxUXKhkVWKmppFkvk913HcRAqg+U4z7gRH2vU+CAcqbggW/WumtrsKouijCj1sNJtKANPlxmTNS4OWKeEgLF2nQqUpKkEGaFwBmBT9tgOAZG2UUgs/EOs/H22QxFqTaollsxiCJ8MGq09kvRddPBzpMEvc3nmSeA6MDHQ73AQSqlDjKTruly/fp2rV6+yurrKq6++umesU6lUDtWplZUVhBBMT08/MoLyR77Z6CWkO5jw3dEULC0tIaVkfn5+342ylHIkid1nkZ4+aTHo+I53btKOk56askXD/DK7u68gScgKf4+CBgh0AaEjxo0m4oipgFAZ7CQFxs2QS8e4mV+OSiTa5KpTZbadnlpsn/4rDavxOIHKUTSqfYMAD2Ijdgl1jjmrxvUh3a4GRcWIqBhR6zqTLKmGKdNnK3GRQjJnBghxfHYmKyFLa5OtKYFE79kVPgh7bIeWNFMHU/o4XZt52s7pMNuNR6TrhBoc0c12JCTaY6L0B+jdHyIxbiOzP420nhvq9+jnEtJxrDp//nxf6rper++JzD8N1PVjnB0cNdLbQbemoJNF1H2j3NEWnhWctTrleR6vvPIKjuPs6QaHQUodz/1V3tp5CUmMKwIsef8QKlR5FAkThnekbWuiJZtxkTEj5sIx6tRalCdQGa44u0xmOnXKB1r7+0ZcpqHy5GWDSWtwQflO4uDrEpPGLted0Qm9u1EyYkpGjNawnmQItWDS8KkphxSTOdOnLI6fXO5KcNEoLagpCbS0HYNsy7Zo3RsqLWimLqYMcLp+rnMgZojW68S6SagVrjQPsPI+lcJ30Lt/lsR4Apn5m0j7h4f6PTrsRC9IKZmbm2Nubo5qtcq9e/d49913mZ+fZ25ubu++1vM8LMt6pFj5R7bZGCQbI4qiPSFduVzm5s2bZLPZQ2udtfGns7CJd9JT19bWcF13oPTUg6hF32bV/xKh8xaWEGREAyl6UdBH39zXUxdPucyZHuUhb+ZTLfg4LFMwUq702WClgBlrC2h5n2/FBbbjPLbR4LxdP1RYtIbVZBoDmLM2gaMp9FFCCpgwAtaSLA3lojDJiXigzXYQGF1sR1MZrZGzIdiOnBEAggiXREegE1whyHSJwW0hQYDSCl8rTGFhCYVJjGmA0glp8jKi/p+RiFmE8xPIzE8NtIEqpfqOSQAPpK7TNN1nn9uhrh8Lyh/jOBh2pLdYLB55oDOqujAqnAWGJI5jlpaWWFlZQUrJ7du3cV13qDWa8TuseL9IIL6LdCAnPGR77EZrCHQRW/hM9LFY95RNLc0yY/rHsFiHhaiMLeCyU6Njld4Lk9YOk7Ru2HeSLOtRHksGXHDqPTV+6/EkqTY5Z20gj8j2GDWEgAkjZCXJ0NQZEm2QkcmhbIzjQnaxHb6SeErsOVkN8rOtOgWRdkhQaB2QEZJMVwNwP19K4WuN0TasMYTCEC1BuUpeRzX+OkpUEPa/j8j8DaTsX3+gN7PRC6VSiWeeeWYvnuGll16iUqns1SnXdffq1KNgfPLINRvdo1JvvPEGTz/99KHNu9t+dW5ujhdeeKHvH/esjT99kvR0Jz21kyty48YNdnd3B2409ijo8DdQah1HCsbM+5tvh4IuS49SnyajmpZJtWLObCDNwdJNO/BSk81kggmzxpOZ4QP8xq0641brmuupzW46gdYpZbPKXa/ErO1z3tkcet2ToKEMdlKXabPbKaR1WrOaOGhcJo3qQEnigyAnNTlajEVVCYwB2I5EQ0MbuCKmICHVNiEWUjexxP6TnFZuhwEoQq3QWuII2kI9gdYpib4HwS+ggl9BWz+MzP0tpJw68vU7G/Cg6EVd+75PqVQ6NGIFfF94oT/GaHCwTj311FMDjfT2a5ZHxcCfNRynTjUaDRYXF6nX68zNzfHUU0+xsLAw1Pd/M/gGG8FXSdN72FJQlI093WCqLQLlUDK8vrrBejpGqOCc1WCiT45GLwTKYDOepmjUjmWxXu7KaPKVxUo8jtKCirnDR36Rihlx0T062O804CnJZpph0owOjUptJDYpeSrG9kCNwSDISMigUe06JQZgO1INDS2xREJBalJtE+GCbmAf+MH7+VKtOqW0wBGibZ8r0Doh0Wvo8MvI8B+TmD+AzP6tvsYn/ZiNXrBtmytXrnD58mXW19d566239qIFOmYSj4LxySPVbCRJwo/8yI/wjW98A8MwCIJg74+Wpinr6+ssLy/jOM5QCaDf7/R0x699cXFxj4Lu5Irs7OwMtEaiqix7f5d69Dugm2SkwurSQQQqhybtS0GnWrKZFBgzNLN9TpGOwmacoZbmuOLUuOFuDP3zvVAwIlJV46MwT8WwMYWgmT68r812msVXoiUuPEJXMmO2wo6aymApdigb0cisCQ1BO/iv1fCEGvIi3dfU+EoQIilJzXiXo4ghFFlCNCYhGZT2sFGHvpNOm+2IlUGiI2xptu1zW9R1ohvo6F+h4/+PxLjRCgq0/+yhax0kbKkXuqnrb3/722xsbHDv3r1D1PVBQXlHwPcYj9ENrTV/5s/8GV588UWklHt5Th3mu99Ibz+MioE/axi0Tmmt2draYnFxESEE8/Pz3Lx5EyEEnucNtEaqAlb8X6EafROtq2SFxuoSaIdphlTAhOExdoTJRks3WCAnBdPm8KOzu4nDVlLgkl3nmrs29M/3QkbGjMldPvDGKBsOpmgduinNQEndJ0VNZamlgjkr4PIRdaolBt8mUJJ7sUtexlSGPEg8Coc0iD3YjkC38jwKUlORAK3ntzSIPgiDQLsoHWCTHlmnEi0IVIItLMx9gnIfFf8+ovYHJPIyMvNfIZ1/99C1HrdOdTQa09PTvPbaa9RqNb71rW8xNzfHuXPnjjQ+cRznTNSpR6rZMAyDRqOxT48RBAFLS0tsbm4yOTnJ008/jeMMlljcwVmjpx+W9qM7PbVSqfDUU08dOhl6UCFoxm+z4v1dwvR7SFLyIkDK+xS0r/M4ImSyz6bsK4tammPK9Ll8zPRUgcEVp8q8PVyAXz+shhWqicPN7CafLbQ20LF8S1S3FuVYirKct+tMDih2GxRKw0qSxRaaGWvwtXMyJWd7KA3LsYsGZs1gZMUmLzV5WnPJuwokWaTwKRmaPEd/RoQAFx+EINYukW61H4fsc2WKxWHqupX8qtsjVm9C479GiXKbuv6be9T1SV1CoPWdefLJJxFCHKKuc7nc3maulBoqhPAxvn/QufntzsfwfZ/Nzc0HjvT2w6iZjbPy+X1QjUmShNXV1b0xs+vXrx+yrn3QGn58j2X/FwiSP0YQkRMxRlfKd6iLGCJkoo9oOlIG22mBCSPcM+8YBitRgVjbXHGqzAyht3gQtuIxtqI81zLrfLbYOmTrBPDtJFk+CnJMWx5zzuhs3TtYjTMgBHNmQHnA+2dXqj09y1riECvJjOWPjJU/qEGELAKP4gF73Z7XJoJ2Q+EQIjG038M+V7cF5SmB1ggMHKG7bN5TkvQ9VPN/QXn/B1j/DjL7PyBl6zOrlBp6JP0gpJRcvXqVXC7H0tISL7/8MsVicS+h/CArfxbwSDUbwN582s7ODkEQ8OabbzI/P8/ly5ePfaNx1ujp056F7aSn1mo1Zmdn+46ZHbXGZvANNoOvkrQp6ILYT0FHOktBNigaRzMUu0mGRNvMWU3Gh6SgYy1ZCMuUjYjr7ugEb0oLPgomyErBvLPF/BF967TdZNpukmrBh8EEjVRzI7ODM6CwuhdCZbKZjFEy61w4QQMjBcy1m5R6arKd2owbIXnj5J/xWENdCXJC40qPuhLspIKS1AM1NZaI2w5VJuEA1HWkFakWuHte6C3qOtXrqPCr+6jr454YdaOzhmmah6hrIcReZgeM7nv6GJ8+dFxjarUazWaTt956i/n5+QeO9PbDKJmNzr5+Fj7DRx2KdTtyTU9P97WuPapO7YS/x7r/D0jS9zEFB+qUQahz5GXQXzeYuPjaZc5qMjZknUq1YDGskDFSLjt1eiViHxcf+eOYmFxwN5m2el9X2fQo51uvuRiNsxULrrk75E5QC2ItWY/L5E2Pc/bg1u69MG22ft5rs/JjRkRpBKx8qqGqBK6ArGzSVIKqEhSlHkg7YooEE1AYhGTQurHP+AS67HPRxLr1nysPGJ+obXT4/6Cjr5EYn0Fm/0fSNHPiQ7HuOnXx4kUuXLjA1tYW77//PnEcc+HCBaanp0/0GqPGI9Vs1Ot1hBB8/vOf52d/9mcpFAo8++yzDxSFPgjfD/R0x+lkYWEBYGDr2u419ijo8BtADVfst66NdZZYa8YND1McRUHDVlLA2bOuHY6JqKcW63GJi7bPk5nRCbN9ZbIczjFh1biRGXzO1RCay25Lv9FILd5pVCibEReGmMHdjDKsJDmuuTUuOKOdsS0YCQUjIdWwGGcw0MyYwdCi8p3EIhFZKrJOxbj/XSm0T4pCDc1UkJUadyCh3n3qOtQuiQ5welDXHUF5x9XKkjamSNvJr63MDhX/W0TtD7k0PgP6vwS+MNwv14WDDUs3dd1oNLh37x537tzh8uXLXLp06div8xifXgRBgGEY/Ok//af54he/yM2bN7l27RqFwvEsnTsYJQPfWess6I4O1qmOdW0cxwNb13avoVTKWvBldsKvodQWrhRk91nXuoRaUjE8xsTRNWQzzmMI2bKuFcON+/jKYDkqc84KuDnCOhUryb3wHGXT51pmOHZk3t5i3m5pRV5rjlOQmovu9sDM925isxAVueQ0uOCMjpkByMqUi7aH1rASuyiOx8rXUgNf56kYDSpdDVVHgxhrqKatsMDMAB/9ln1uq05F2iHWMTZxD5t3gSVEl/GJjSXSNiuvUDolTV5C1L/AXGGCSPx5tP7rx272e9WpiYkJJiYm9nLQPvjgA86fP8+1a9eO9RqjxkibjZ/6qZ/im9/8JlNTU7zxxhuHHtda88UvfpEXX3yRbDbLr//6r/P8888/cN179+7xcz/3c/z+7/8+tVqN3/7t32Z6eppXX311ZONGZ4nZGBWklCRJwtLS0l56ai8Kuh+EEKRihbu1f4DfTUGL+xR0oIsYImK8jwd4h4IeN0IuHsMScD3O4aUZrjrVPUvAUWAnzrESlbjm7nAzs3SitfJGzO18q/FYCnNsJBku2jXKVu+G6sOgiJQ5LtrrTDsnOyF6EAwB8+3AwN3UoqpMpoyQzAOYmJXYQSOYNQNEH095R4BjaJSG3bRln1sc8ADXES0bwlhZRNhtQfn+TdhoC8q1TgjQCDruIPepa9taAPW/k+z8Ilh/rmWfK4ezaYajGYt8Ps+TTz5JkiT4/ugSfB/j4eM0atXm5iY///M/zze/+U183+crX/kKzz33HO+8887INHijwlk6YOvU39XVVRYXF8lkMly8eJFisTjwGkIIlNjlo/r/SiP+PYT2ycoUs0t3EaoCmojxB1jXbiUFSjLhgj38d3w7zrDb1g0+NcI6VUscPgzGuOo2uZlZPtFarkx5NtdyplqPMixHOebsBlNHsOmLYZ5ElDhvrfFc7nQF50LA7AFWfsKIyfXI7ejGWmITa4NZ06fYp05ZbQ2ibjcdAEWpB7TPDbEFJNokJIvU1T7GJ2nL+ISDxicJprGMya+idr6Ktn4EmfufkXK4zI5+LH42m9074Gg2m2eCvQQY6bHGX/pLf4l/+S//5ZGP/9Zv/RZ37tzhzp07/MN/+A/5a3/trw20ru/7/OiP/iivvPIKY2NjjI+PA6MTdo964z0Lc3JBEOwJXZMk4bnnnuPWrVtDNRq74b9hMf7LqPGfJk7+gLyoUpBNDBGhtKSZFpDCZMqsMn6EKKyROqzHY2SEwRW7TskYnMlQGj4Oy6xEZS7YAbcyO1gnGFPqxsd+ibv+OcpmxLO5NbJDXNcgOOc0eS63ScGMedsr864/Tqxb4rK3vHEWwhLXMg2uOGsYAwQUjRJjRsxFy8cSmoU4w3qyf3400nAvyrKdWsxaIXPW4ExIx5awaEBTCbZTQTrgr2dJRVYEOMIgJkeoDv+gEAJXSByhSLTCVwpFaz7VEgaWSEDtoMOvtTI7qn8BFb822AUMCMMwhvoePcbZw2nUqiRJePbZZ3n55Ze5efMm5XIZOHsGJDBaluQk9S6KItbW1lhZWaHZbPLMM8/w1FNPDdVo1KPv8mHzr2DO/vcE8b+iIHYpttl1pQWeKqC0xYRZZdL0ezYavrJYi8cwsbhsN6iYw42xLkYlFsMKM1bEk5kd3B7hq8fBcpjnrn+BnNS8kN9kzBztIceU7fNcfpMJK+COP8Z7/jSBkigN7/llPgjKXHQ9rjor2COqvYOiYCRctD1cmbAUu6zELt0ftVTDQpRhI7GZNiPmLX9ge10hoGRoSoYm0LCdCuIBP8am0GRFE0eYxGQJVe/vgCMkbpuV91RKiuyqUyla19HRi+jdHyGp/seo6FuDXQCDicwNwxg6E+00MVJm44d/+If56KOPjnz861//Oj/5kz+JEIIf/MEfZHd3l5WVFWZnZ/uue/PmTW7evAm0ujbP8ygWiyPbxE+Dnj7p7Phx0JkR7qSn5vN5xsbGuHjx4sBr7FHQwddI9RY2Jg6KVMcEZEE5xIRYIiUjEyQ2kU6xxf73byvJITDb1rXDzbmGymAxKjNpBseyBDwKqRbc8cZxpeJapgqcTrhRN0yheSK7QzM1ecObRiKYs+rMOJ/8ybgpNOfbbMdWYlNLDRCCGTM4ViDVQRyXut4TlBsGkbZJdIxF0iehvC0ox8SW6gB1/R1E/T8lETNI9y8g3L84kpOes2In+BjHw2nUqpmZGb7whdYIXy6Xw/Na36GzxCJ0MGoTkmHrXaPRYGFhgUajQblcZmpqiqtXrw7881prNoJ/xqb/f6H0CpYwcURKqlN8XIRyiQkxRYorYixhEuoER+y/X9hJsqTa4twxdIOJFtwLy5SMhGsjDsp73ytjCJur7iZSnIxxHwRSwPXMLqEyeMefQCCYtDzmT0FUPiwMoTnXZjt2U4udxEILmDJDzh+DfTqIjn1u0m46HKHJDVynAjAksbZa4bVEfRLKO4JyE0ceND55HRp/BSXGEfZ/iMj89b6ZHWdlBHIYPFTNxtLSEufPn9/7//n5eZaWlh7YbHQjm83SbDZH2myM8qbhk2g2lFKsr6+ztLSE67p76anr6+t7Be9BiNItlpu/RCP5Q4TeJi8TLHn/dCdQeVJixs1az5OhVBv4ymEndXGExhIGBZkwzDvbSHOsxRku2fWRUtCBMnnPG2fW9ng6N9pZ0wdhM8qwGhe4manx+XyLulYa7vhlUg03MrvIh8xsHLrGxCbQJpfsgFhL1pIMBSNi7AHU9aA4GXUdYYuW6UD/zI77XuhaGzhSAxJNjKd8Uj7A8P9v7OR9rOx/izSGo60f4/sLJ61VnUMx+HQzG8M43nSsaxcWFjAMg/n5eW7dukWtVmN1dXWgNVLVZKn5JWrx74LaIG+k2LJd4ySEKkdMyri52ZMxVlria5vdJN+aqcegaChEHze9g/CVw1JU4LzVPFaO01GIleRdb5yKGXMrO7pDtkGwm9jcC8e4kWny2fz9UakPgjE8JbmZ2cUUn2zDvJtY1LW9Z56yHDuEMmHcHM1Ugtll815TAqVbdWp44xMbdLOlN+xCi5XvNj6RuBKEkKATAuWTcA8j/BfY6T2s3H+HNOaPfM0H3beehQmbbjzUZqPXLz/sjX73idFZ1Fo8zFOsTkL62toaExMTh6xrBykEjehPWPb/HnHyJqZIGBM+sk0Baw1NXcAWIZU+7ESgTHbTPLNmzJS5f/P1lUE1KRGmJqb0mLYahyzuVqICkba55tSYGJK+7oetyGUpGuNmpsrz+YcbwvdxUMRXDk9kd5k9IPqWAm62i9ROkufj0OGCU2VsRJ7jg0BrWE9yCKHaOR0tBxBDKC7aPlrDcpyBYwrKe6FDXQP4qpXPUZB6wITyVmYHwmwJylUTRxzeP2wEHgnLSUoAFKxnsInQ6dsk6g5JeAcjeZ1i+euHXuOsbc6P8cnhpLUql8vRbLZOhT/NzcYg63QnpI+NjR2y/R2kTjXjO6z6fxc/fhlTxJREiNHeL7XW+LqIFNGe5WsvRNpgOykwbaZMHhA4h8qgmhQJUgspfSbN5iFnwY04RyPNcM2ta8AfrAAAIABJREFU8/RI9Rg2HwZlrjgNnss/3BC+5SjHTpLjicwuL/R47avtyYJ6muGtIMOc3WBiCDv2UWAzKRDrlDkzoCLuMxmd0MDVJEOsYcYcfIzqQSi2jU8CBZ4S5KUeMKFck9mrUw6JDrBRh1l5BAmK1TTB04qc+TSulOj0dRL1MUn0MSL+I0qV30eIk92mnxUG/qE2G/Pz83tuSACLi4vMzc0NtUY2m6XRaG0on2Z6Go72Qe+moOfm5vjsZz/bk0np5AH0Wncj+OdsBf8EpRZbKd+yvndDmWgLTzmUDJ+pPta11cQl1C7nrCaVIyjojEzJyG2wOmsbrMYldhMQWlA0R28JuBCUqCcOT2R3mLZHE+43CFpzrhXyhtF2Cnnw71Q2m5TNJpGSvOWNk5Exl0c4OnYQsRasJhkKMmXWOpqCFgLOWS1x5U7qUE1Npkwfd0Rzuwepa1dosgOywo4IcAyjTV2nGLRuOmo6paZSum/r6m29hiMvkTMnIHmDNH2NNHkHw7y1b91hqOmzsoE/xungpLUql8vt1alRHop19vRR5Mmc9giy7/ssLi6ys7PDzMzMkQnp/dbYDn6b9eDXUOmHWALK+1K+JZ7KkTN8JvrUqUbq4KkMs6bHFbt3nXJkypTc2atTqZZsxGW2EokEMlJz1akiR5iptB4VWY8y3Mru8MJDPgz7wB/DEi6X3U0uDmBOUjB8nsv5pFrwjlfBEClX3eqpBQamGlaSDBmhmTL7j3LNtO1z66nFVmozYfpkR1SnXAkumlTDTiqwhCY/cJ0KcYQg0TahFkhCJFDXKVWVct/gV9BM3qQJWHKGgnkB0jfReoUk/kOsHgG2g+Is1amH2mz8+I//OL/8y7/MF77wBV566SVKpdJQI1TQcoT5fqKnu2PoOxS0lJLz589z69atvh8mKeW+E6NUeSz7X6Ie/hZa75KTYHdZ10a6JXYaNz2KfQTTG3EBSwhmj2EJGGtoppLzts+4GeAri4+jWaI05IKze+ycCqXhPW8cR7ZmTx+GHqODSBm8648zbQU8kzteo2BLxdPZ1mnZvbDATmJzLbPzQLeoQeEpg83EZdKMuDjkyVTZiCkbMaGSLEQZ8jKiPCIW5iB1LciSE82Bqeu6ktS4ghKQqHeOfG6oPiaMPsYQeQrms4Th75A90GyMIqfjMT4dOGmtOlinomg0ox6jsqwdZZ3qXkdrze7uLgsLCyRJsme92a9OHWQ2lIpZDX6N3fDraLVJVoLT5XQYawdfmVRMj0KfJmM7ycExdYOphnoqmbEiZiyPUBksxdP4acJ5p0pGHj8L4o5XRmuDW9kd5o4RDnhcpFryrlehaCbcyjY4To00hObJbMvKdzXKsRpnuOzuUhhBNgZAqCVriUtFJlywhnNoLBgpBcMn0YLFOIsrEiZGNGJldCWUN5RAkycr6gNmdqQ0tWCHCyiRIUlfP/K5sVplO1pF4FCwPkcY/ZsTNRtnCSNtNn7iJ36C3/u932Nzc5P5+Xl+5md+hjhu3ZT81b/6V/mxH/sxXnzxRa5du0Y2m+WrX/3q0K9xmvT0KEKORn1ipJRidXWV5eVlSqUSN27cGNgJp7OJ+/FH7fTUl7AwyIsaprz/JfRVAYipGE3EEfdaiZZsJgXGZHIs69rdxGU7yXPZ2a/HyMiYi/YK0PL/ftcvo7TgglMdKHjITw3e98eZsXyezo3Oz3wQVBObj4IyN7NNnh8h/X3BaXLBadJITe4EE+TlLrPHFGyvRhk2kyw33SqXTiimc+T9EauVOIPEZsIc3elWi7puEmjw0qOp61TDSmpwLzFoaAmsAWDHVzlfGEclr6LpXahS3WA3/g4BkM39N/sfe9xsfN/gtGvVaQnEOzXPNE9Wukc5RtUJ2l1bW2NpaYlsNsvly5cHzhXp1Kk43WDR+3m8+N9gIsiLBqZxUDeYMm54lI74miot2EwK5KTas/oeBp0cp/N2c1+OkyNTztutfSZSkjv+GLGWzNt1igMcvMRK8p43zpgZ88RD1mM0U5MP/HGuZAJu50dXI2dsnxnbJ1AGd8M5TF3jwjEDdjdjh5W4wHWnxqUTjmmZQnOhvcZa4qJ0hglzZ2QjVnmpgTpRW4OYFRq3R++vNKylknupSVVJoKXZtJKLzOdmQb2B1r3fL01ILX4ZL10jn//b+x97RMd9R9ps/MZv/Ebfx4UQfOlLXzrRaxzcxEd1YjSqRNVRbeJaaz744AOq1WpfCrofPP1HeMVf425tAUtASTSRbZGX0oKmzpMVAePm0ZufpyxqaY4Z0+eKPfxGshIViLXNVafKjNV/ztWVKTfbjUiiDd7zxgm05KJTpXTghGIrdlkKS1xzq3ymLbx+WFgOczRVmSvuBp8tnN5r542Em0ZLONkqboJrmV3MAQTld4MiiTa44VaZO2HK60EIAXNWCIRUU5tdZTJpBCMbsXIFuMZh6tpXsJCaLCYGvawHImubD4JtbDHJjH0RS79PqtZ6vkaSrhz6t2GajbNETz/G8DjtWpXL5ahWW7qsUR6KPUytxSDQWrOwsEC1WmVycpJnnnkGx3GGWsNXrxJXfoH3qh9ioikJDynu6wY9XcQSQV/dYKgMdtICk0bApWOwBa0cJ5erTu2BOU62VG32vOVweNcvU09N5u064wfGrGqJxUd+hYtug9sPWY+xGWXYVZOct9d54RTrlCtTrjmtOvVhUKSRmlzP7A5klXsvzNNMbW5mqkxZo39/ps0IiGgoi63YomIG5EZUp+w2K38/WwqKhibUsJgYLCQmUY86FZs1PgxrmCLPrP0stl4kVfd6vkaiVtFatUTkbTyqdeqRShCH02M2zgI93Z2e2mw2mZmZ4fr160NdU8u69ivshL+J1pvkTch0jUol2sZTFmXDZ7oPBb2bZEi0w5zVYGJIS8BUCxbCMXKG4rLT4Dh6DFOk3Mhu7a33vj9GM7XIihgpslxxN5h+yHOuH3rjhEiezO5giMHcU0aF65nW32ordlmIcsw7NSoHGrBEC97zxygYKdeOecI0LEpGQslIiNrUdU7EIxux6lDXO6ngu6HBpjJgAH+zSDe5F74FCKbtz1MQTZL0zX3PSdQaWqeILipv0E18FIcSj/HpRj6fZ2Wl1dCOsk6dFbv3er3OwsICOzs7fXWDR0FrzWbwm2wG/wSllsk7reyC+7pBE0+5lAyfSeNox6d64uJrlzmrSXnIOgVwLyxhCskVpwYMf6JuCM2VdnOiNHwUFNlNHPIyJiHLVXeT5wsPt07dC0rUU4ensjvMypMFAA6Ly+26U00s3vXzTFvNQ2GBSsOdYAxLaK65o9VrHoW8TMnbKYkWLMVZbJEwOaIRq062VE0JXg1N1pWBHqBOJTpkIXwb0ExaL1AyUpLkVdjnjBaTqi1MY3LvXx5VBv6RbjZGKbz7JOnpbgq6k54KUKlUBm40YrXDcvMXqMf/Gql9clJhdQXttSwBFeOGT9HofdKtNGwmRVyhmbM8YLgvo68MlqMyM5bPrczoqGKBRmmDkqG5mvH4yHf5MJjgSmbz1O34Ui1435vGNSJu5EZnc3hcjFsh41ZI3BbqWSJhxm6yEk9TMXZ4OvvJXKPdRV2vJhkS3XKxOu6Ildawmko+Tkxq+rgHAJq16A5rQNF8kgmzgE5eRRPQ2sQ3MY3pvWc/qpv4Y5w9nPah2Cexjtaazc1NFhYWME2T8+fPYxgGlUpl4O9NSzf4S9TCF0HXe+gGM4RKMG76FPs0D1txHikkc8fQDUZKshiVqZgRN0Z4KCMFKG2Ql4Jb2SYfBxYf+eNcyWzhjCjk7yi0NIsTmAKeyO0Cn2w+RsmMuW3ukLbDATWai06N5XiGrKjyxAjvD4ZBK1uqVac2EpdAC6bNYKBpgV7QGjaV5OPEYFsdt3YINuK7bMSQM64xZU1A+vreiFWilo/dbJylQ7FHrtnI5/MsLbVCbkY5C/tJbOJRFLG0tMT6+vohCnrQdRrR66z4f48ofRWDlJIIMNriNa01XjqGaQSU+1gCxtpgKylQMaJjUdC1JMtmkj2kxzgpQmVwx5tg2g73Ca+vtRmPrdjhA6/EjN3g3IhP8r3U5ANvmouZJk8/ZAZlEFhCM2N5LER5NuPWeX9+xCnox0XHHaSW2uwokwkjGFjoHmmTPwkKvBJUyJszXHQFInkNPWTjexC1ZIVasoIlxpm1L2LpuyRq5XGz8RingtOyaB8ls5Ekg4l6kyRheXmZ1dVVyuUyTzzxBJlMBoCtra2BZsj9+G5bN/gdDGKKIsLo0g16SREhIyqGd6RuMNWCzaRIUSbHCnPzUpeVOH9Ij3FSJFpwx5ukZCY8kb3fIF1pv0YtsXizNknF8rg04pvsSBnc9WeZsgOeGaEeY1QwBMxZTT4Mi6xHeUBSHBGjcFJ0mI2mMllOLCpGSH7ApjDRkjfCIt/xy0hjiqsZF0O/TqpPxtI0000+TDcxRI5Z+1kcvUSSLoN1e+85wzDwZwmPZLPh+62N5lGlp+v1OouLizQaDc6dO9eTgn7QOpvB/8tG8OuotG1dK+pdloAGTZ2lJCMm7aM3oGZq01DZvpaA/dCtx5ga0jmiH3YTm4WgwtVMnRcKR89xjlsh46V1AN5pVmimJk/kTnaKVI3zrITjXMps8Hyx97z/J42lKMt24nDLrXI713l/GtRSi3ebBebtJuMj/HscF0UjoWgkxO0Rq6xIDo1+dVBNTV4JKmzwA3wU3AGgFm2xHEFOXuFWbhxDv0usTtb4xdrjXvg2AIW0htslg3rcbDzGqJDP5880szGI3tHzPBYXF9nd3WV2dpbnn3/+EPP/oOvZCX+Xdf8fkKTvYx+hG8zJhAn7aEbWVya1NM+U6XP5GLrBPT2GW6Niju4wrJlafOiPc8Fp8lz+6HWLZszn2nXqrjfGVuxwI7tN/gTjps3EZTGcZt7Z4pn8wx3pHRTrkcNqnON6Zn+d8pXBa80Kk5bP7AgSwE+KnEzJyZRUw3KcxRIpk2bv+tlQBt8Lyqzqz3M3uNv6R1VlM67iiHM8mZvC4S6hOqwJHAapjlhsj1jZqUe++7HHzMbDwaNKT3co6MXFxX3pqUd9GHoFHSkVsux/iWr4DaBKVoDTRUHH2sXXknHDpyD6WQJm0dpizmoyObQl4Mn1GEdhJcyzk+R5MrvD9JCCtlttJ6pmavPd2iRl0+PyEK4fK/44vipyNbvEhL001Gs/LHwQFIi15IZb5VwPd6qiEXM7t02iBW95Y7gy5Yo7fBM5algH3EFiLZgxfaSA5djlO0GFd6MCGsG8e9gEoakCXqkvIShwK3eTMblOkN458XUlev/N1qO6iT/G2cNpuVE9jDq1s7PD4uIiaZoyPz/P9evX+9apg+u0dINfZif8GlptkpGCQpd17aC6wWriErX1GOOfgB7jKGxGGVajErey1aGF11eyNa4AQWryan2ajIy4kRu8AdoIx6gmFa5kV3g6fzbr1L0wRz21uZnZZaqHOUlGpjyb20ZpeNcvIdDc+ITGqrphCJhv16nNxMVTghmrNWK1kdh8J6jwVlgkRXLOPWyCEOqY7zWWAJvr2R9i0qzhJ28eet5wECR6/3v4qB6KPZLNxmnkbJwWs/Gg9NRB1gmTJZa8v4OffAupYwoyxhT3P4C+yqNIGDd8SkfcAykt2EgK5KRm3vLgCGvQo3BaegyAu34ZV+a46KxxyT3ZiXzOiPadIgWqwLyzTu6Ik4oPGrOYUnIttwZ88pqMg1Aa3vHHyMiEqwM2DmaXF/rHYZ56anHD3cWWnzytOm1GaA2vhWN8Lyyykjz4u9CBRvN2s1Vg553Pct7RhMmraI7n8a71/pPFx83GY4wK3TkbZ3WMqrtOpWm6pxvM5/NcuXKFfD7fZ4XD68Rqh6Xm36ER/25P3WCgciQP0A1qDVtpHgvJrNUEzoYeA+DjoIQURS45a5xzTubu5BoJzxdbgbMLQZFqMsa8s0nB7H14d8+bItUu13IrzD0k849h8Z5fBGg3Dg8ex5YCbmZaNXc5yrIZO9zIVEfmangSdPI53gnzfDcY4+NksLiBFgR3vBXuAFPWc1zJ2MTpayh9vIb3JHXqLOGRbjZGuYmP8sQoSZJD6am9KOgHrdNIXmJt9x+TpO9hCd2ioGWHggZPF3BEyPiDLAGTAhNmyOVj6DGOysc4KRIteM+bpLw35zr6DfRKtgbUCFOT7+3OkJExt4pbREryYXOenNHgVuHhJYwPg1AJ3vXLzDnxXuNwHFx0Wu/rTmLztp/ngtOg/AnNzCZa8EZY4CW/wrayez5n0O/zYrjJYggF4wY3s2OI9C0ShnufFIc38WFtOx/jMXqhu06NsjEdNbMRhiFLS0tsbGwwNTXF7du3se3e382j1gn0W9yp/m9E6WuYvXSDuogpIip9dYOS7aRIyYi5MMJ8jJNCabjjTeAacCNT5TSck867Dc7TIFGS16rTSBRPFjfRwPuNOUwRcqvPOPEniUTD236ZKUtz4wTv+5ztMWd71FOTd5tjzNrNkY5mDwOl4Z2owB/7ZdZSt+dz0mSwOrUe77Iegysv8mR2ApM7xGp9uOvh08HAP5LNRvcY1Sjp6ZM2LlprfN9nbW2Nzc3NgdJTe62x5v+feKV/Sii2cbWk0EUhp9qioRzGDJ+pPhR0PXXwVIY506PsnEyP8aB8jGHQmXO96Db5TJ8511HCMRI+W16nFlu8XrtBMw54fmyT3BkRqnWjlprcDUpccuo8mxtdgSmbEWVzm0hJ3vDKFI2IC87DcSzxleR7YYmX/TJN3X/L8TwPhoiTqaceL9c9JBUuiStM56sE6QcD/aw6cGKklHokT4we4+whn8/TaIz+AMUwjIGF3f3g+z6bm5tUq1XOnTvH5z73uaFt3zeCf8Gu+2WkWMFRgjHROKAbzJCXAZN96pSXWtRVK8fp8jF0g8PkYwyDQBm8708wawU8m384jLcpFZ8Z28BLDN6qXaUWx9wubTN2BnQNB+EryXv+GHO2xzPZ0b3vBSPhdm6bVAve9sawZTowo39SRFrweljkJb9CVfUvQkEQDHX3HKiQ7zaWgCzz+jYXShF+8vZAP3uwTj1uNh4SCoXCPuvbUSnuT9K4pGnK+vo6S0tLGIZBqVTiiSeeGGqNRFVZ8n6RRvQ7oJsUjBRH3qfdWpaAMG76FPq4Du0kRUAwazaQZ0iPsRFlWYuL3MoMP+d6UmyGee42CzxT3OL5UkvUFaQW396eZs4NmP+E7GK7sRVnWIwy3MhUeS53eu+PLRVPt4vDvXCMRgo3M7sjS1ftRi01+U4wxp8EY0QMdiNTKBSoHoNtViju6m3u1uG88znOOylB8iqaow8Q9AHNRpIkA99wnaVN/DHOHlzXJQzvn8yO6vNyEmZDKbWnGxRCkMlkuH379lDXlqqAZf9L1MJvIHSNvKFx5P06EWsHXxlUTJ+COLr+7CY5lHaYtWpMDFmn4PT0GLuxw2JY5lqmzgsPOYSvGmd5pzbGrcIOnxn7CIBYGby8M8Wkk3Ix+3DrZi/UEoe7YY4rbr1L9D16GELzRJvRX4lLbMXy1EaBPWXwSlDilaCMrwe7ic/lc+we82O3KKos1mDGfp5LrkGcvHqIvejGSZqNs4RHrtnonoUdJY7DbBykoJ999lmazSabm4M75jTjd1j2fpEo/R4GCUURYsj7Hy5PFRDEA1kCFmTKOWv40+rT1GN8HIwRKIcnszvMn3DOdVh80ChRjTM8V9pkqrL/M+MaMZ+vtNym7jSmAYOrueVj50IcF4temdXQ4nZpg9u5h3uCdcFpbeabscNSlOOKW6NgnPzUdCMp8Vo0y8t+ihog3GjUWAg3WAihaNziZrYI6m0SfZjiPzhG9ZjZeIxRQUp5Kg3pcTQbcRyzvLzM2trannWt1pq7d+8OfI1hssCi93cIkj9G6qilG+yyrg1UnrSjGzziK3Q4x2m4u7XT1GMshQVqSa5lTmI/3Dp1r1lgPczz3NgWPzS+31nKkimfLbfGbu55E3ipy7XcMuZD1jWsBkXueRmeLW3y3Ck2Gb0wa1WZtVoulW/7ec7bDSrWyacSdtI8b0Tz/LGXkAx4GDZKrEbbrHa5LZr6PSJ1eKxb96hTljUE/X9G8Mg1G92zsKPEMPR0Jz3V87xDFLTv+wOdPG0HL7Ie/CPS9B62YB8FrbSkobLkZMiEefTNf6BMdtM8U2ZwLEvA09JjpFpwxx8nJ/XAYrFR4vXqBIaAp4vbwINPza7nW03HVlhiPSgzn12hcMrzoq9XxwF4prTF/OAa6VPBhBUyYYUEyuB1r8y4GTLXw+3qQXjdq/Cb21f4TrMVQPRscYwruSY78btoHr44vZY2+U69iUmFW7mnKMglgvSjvcc/LSdGj3F20Umb77g2DTuqdBDDMBvNZpPFxUWq1Spzc3P7dINBEAy0zm74B6z5v0KS3umpG2yqPI6MqPRhJyJtsJ0UGD9mjtNp6TEA3vcqSGFwM7OLdEfHkAyCd2oVImXybGmTS7kHvy8Xsq1DzGqcY6k+wZy7wZhzuunb79bH8BKLZ8c2mXE/WceoMTNizNwm1oI3vDIFGXHRHf7zdCco8s+3r/Bv67MoBDdzBW4VY6rx26g+TPhp4b7bYp6b2etUzC385N29x1UPBj6XG0ywfpYY+Eeu2TBNc2Q6jW48iNnQWrOxscHi4iKWZTE/P8/Y2NihP2a/YtCyrv371MKvo9ntYV3r4CuTsuEzYx29edcSl0C7nLOaVI5hCXhaegxfGXzgTTDnBNx+yGnbkZK8Vp3kfCbhdul4Jy/jTpVxp0qQWry+PU/ZbTI/wnnURMGr1SlKVsgzpU+eEj8IV6Z787d3/CIKwXW32pftSTX8UWOar21f5d1gbN9jr9WqvFaDOedpXihb+OodInW6xbEXEhRvNJcAzSX3B5i1Q4Lk9U+Ny8djnD0crAsdRuKkzcaDmA2tNdvb2ywuLqK1Zn5+nhs3bgxVp1q6wa+yHf6zLuva+3Um0RaecigZPtN96lQjtWmeIMfptPQYsZLc8SaoWDFP5R6uNXiqBa9XJxm34cni8TKDSlaTUqlJrCRv7cyRt2Mu5EZndKI0vFadwJGKJ4pnT5huCb03CvxhUMBTrWbRfMB99SvNCb62fYU/8Sb2/fu7zTrvNmHcvsUPlF1SfQdf9W+sTuMWXqN5x1sGYM5+gYuuIExeRR1wWxz00OIsNRrwCDYbcDgZsXN6dBIcpdnol57aC7028TBdY6n5c/jJHyB0SF4m+6xrQ5UjblsCloyjT1c24zymkMyaTYQYzhLwNPUYO7HDUljheqbaN4TvNFCLLd6uT3A1V+Pz5dFsuK4R80xlEYAPGzPshinPlDcwj3mf4CUG7zdnmbB3eKE8nBPFJ4Xr7XG61SjDepzhqlslZ9y/yQmV5Lv+E/yjtQmW4/6nLMuhz/KqT0ae50+NF3GNe9STTyIwUfBRsMZHAZTNJ5nW+5ujR1V49xhnE1rrvdrUaRJOOv5wVJOQpimrq6ssLy9TKBS4evVqX+vaXuvEapdl7xeoR/8aqb1D1rWhzhC1dYPFPrrBTo7TOavB1BnSY9QTi4+CcS67DT5TeDjmJB34qeSN6hTnMh7Pj50smLQDSyqeLLduTheak6z6ktuVDexjjliFqeCD5jmyRpXnRnSNp43LbfH4VuywGOW47NQomvdvzhMteM2/xpfXp7kbFvuutRWFvLgWYolpfmj8FiVzhWqycKrXfxSW24G2eXmdkju977HHCeIPCZ0i39nEO5vmSU8kD26+g6SnPmidWvRtVrxfJlFvY6IoCR/ZTrfWWtPURSwRUe5jCZhoyWZSYEwmXDiGK8Vp6jGWgiKhLnLV3WDGfrib02qQ5Z5X4uniNj9UOb0b18v5VcjDip9jsVnienGTMXuwedGt8P9n7z1jLLvPM8/fCTfnWPFW7NzV3Wx2N5sUJa9kaxxkWxh5MJKwY8GyRh6s17swBoax3sXC8AfHXXs8smVYwbS1DoqwLXkkixRFSSRFkU2yY1V15Zxvzvmesx9u5a5wU1VX0fUAgojqe0+duuF9/294nkfHWMLJWWuYy7b5A7vHg0SzNkOzNkOqJPMg5cQq5Xk92cTXI934TOdYLExWfK2MUuKlQAQwc83uw2eIEy6MHtzN74FIMUFkm3zhyWTjBI2EXq8nm81iNBobJtO+/TrZbJaFhQVCoVBV0rWb81Sq8JDF9KfIl+4hUdwiXQtlHyco7skb3PBxUmjXZKjWx+kg+Rj+vImU4qJT6+f6IYuThHM6xlNOzpqj3HQeXKPJZwrgM0E4Z2A0bqfXEsWjr+y8EC/IPIy76THFuGA9nnnKpcnh0uTIKSL9aQdmscD9tJt/inTj0V9gMjdV8bUKqsorwTCg5ZLlSXrNWSKFYVQO3/8jqaRZKdQufXuUcOyKje1Y6xjV++KvcTbC4TBzc3MoioLP59vTPXUnCIJA0fBtHkZ+C1VZRi8KmIUEa5dYkwS0iNk9pWsziky8ZMYrZ+g5QnwMgLG0E0mQVtUiDpfUPJm0UVCtnDIt0qo/vO54iyFFiyFFpiTxVrCZVmOeNuPOU5yVrIXplJHL9iBPu5cO7R4PEhlF4o1UE9+KdJBdla/11Xw1gdvRKLej0GG4zBM2kXh+EEWsblpXL4RtpMBK4shR6xad4OhiTabdaDQ2TKZ97TqxWIy5uTlyuRxtbW10d3dXtaIliiKq8XWGo79PqTSL7hHeoEBKNWGsxMepZMEt1ebjdJB8jKmMnaKq47wxjCQcbhyeT5tJllz0GBd5+gCbYdvh1GV42pOhoIjcDntx66DTtHORE84ZGU1auWgN8fQ2YvpxRU6VuJd2841wF4lVLyfP7oso+0CgPxGnPwFNugvccGhJ5R9SEg+X2yNsW9o6rhP4Y1lsrBUYsiw3xFG1VCoRCoUIBAKoqrrvCHonFJUEi+lPEc+/gMaexAhopc2SgHodRPquAAAgAElEQVQyqohL2k8S0EBB1dGmSeI6QnyMvCIylnHjkQv0HfKeK6ySvhHos4WohPR9UDBIJW64y4H5YcxJriRz2eFHEmA0bidZ0nDFHqTpkLTBDxrzeRNfC/Xw3Vj7Dood9R+8ZzNpZjNgoIv3eG1U6xhcD7YXG9UQeI9SED/B0cRaseHxeBqSpxRFIRQKEY1GEUURn8+H1Wqt6rNY5g3+JbHcNzB4o+hUYQtvsKjqSK/yBpv2yD+Jko6MYqBVTuE4QnyMkiowlnZjlhTOGQ9fnGQk7iSraLhsCyIJj2cFB8orVtdWJynjCTuRvI4rjvKK1XTKQiBr5AlH8B1TZAQKev4p3M2/RjeaYetoQH9oJZflm8tZtGo7z3oce0qqNxqiUH1T7CjiWBYba0HcZrPVFcRzuRzz8/MEg0EcDkdN/hjpwhiL6T8lV7qNSAGbkEeSNw5MGcWMShGnlMG2S05QVQiWLGgRaNWkYA/N5Z1Qlr5tRiTdcD7G2p5rjz7Jk4dkwreGvCLyIOrFo8vWTPo+SFxYvaf+WCeBjMI15wpnKlyxOuoYy1r5SugUP0w0ox6CfG0Gle/4o/RZurHIN/DqQ0SqWNGqBTsd0io5uJ0UGieoBJuVE+vJU4VCgYWFBfx+Pw6HA4PBQF9fX1XXyJWWVnmDP0JUc1jEIrK8M2/QugdvMFQ0IyDRWoOPE4C/4KWoFhrOx0iXZCazLtq1Ga6YGzsh2Q9rpG+jVOK89XBzZCU4ZSm/HiOJdhZScMkeosv1ODhzjcdczsTXwj28tGMzbBUNDNd5Ab4fjHDR0oVOvE6HMUG4MLL/E+tAPZONo4RjWWwYjca6io14PM7c3ByZTIb29na6u7vJ5/OMjla+Px7JvsBK9jlypVlEQYsWPRmlRBYzpaKFnKSiqFmcUhaLpKegKmjJsfmcUlBFwkULdqlAp6b6AmEzH6NT29h90OW8iVDBwnlj9ND3XBMFmYcJT5n0fYB7rvWgqAjci3hx6TJcss2ADdJFmVuhZrpNKbzHdLJxP+XkK+FT3F6Vr90LB7FQpAKvRyKAyBnTVS5Yi0QLQ48ocjQC2ycbJzhBI2E0GusqNlKpFHNzcyQSCVpbW7l27RqiKPL2229XfI1Y/nWW039JThkDVUQn6sgqRTIlDWrRS0ESySkZnHIWq6ShoCoIZLco0G32cfJpql+bLagiszkHLjlPR4M9LEJ5PUt5O2eM8UM34VsjfbfqG0f6bjQUFe5HPBjkIues85y1QL4k8la4iRZ9gfZdVoGPOkYzNr4S7uW1Q2qGPQqBO7Eod2Krq8B2kWRxiKLaeMn8d8q677EsNjZ3jCol3q25p87NzaHVavH5fNhstvUuZSXJQFGKBHLfJJR7gWhxHMRu0qqTUH4Go9iERmxhIT9LUc2hKRlxyN08zARJlsoysC65BZMoECtMYRKztGrSnNOlcEjVcTKiRT2hA+JjTGUcFFUN540ROg/ZhG8lY2QmbeOi7WBJ3/UgVZQYiHo4ZUlxfVt3yCgXuelapqQK3I140EvFI9np2g5FhTeSTXwl1Mtw1vG4b2cdo6kEoylwac9y06GnqI6SVRpXxCXiCRR7/d4HJzjBTlibwEPlxcaadO3c3ByCINDe3s7Zs2ermqapqkoo9x2CuW8TKTxEEdvIK9fwFybQiQ4MUidLuQVyahqppMWlOcV4Jkq0VD542mU3VslArDCFQUjTJGW4oE/ilKpbSdrMx7jYYD7GXNZKqmTggjFC6yHnqUhex1jSyZkDJn3Xg7wicD/cRIc5x1XnVpVGraRwYzW/DsRcKCr02UKHbmhbC+6mXHw11Mud9P7NsDVsnww0GmurwFa5h2ecJkRhklSpcUVcMpmi6CyuCxSpqlqx9O1RmsIf22IjmSwf0Pcj3m12T3U6nVy4cGFH6dq9dcdLBHKvspj9V0KFYQxSD2GliVRhHpvcg1G6yGJ+GhjDLLkpppqIaaNEihMICDRpT5EqFZnJLQECEi5adN3cSif5ZiyEScxzRpfkoiFHkzSPcxd1qjU+Rk+D+RglVWA07cYqlzhnPPyO/GTSRqJo5KI1QIvhaBYZ4ZyesbiDs7YQN91777lKgsrVVRne0YSdREHLFXsAWTxa3YaiKvCDeCtfDfUyk7c87tsB2NH8b0OSsJlnXeexyPPEiot1/65iociPfvQjmpub8fkqp7sfpQB+gqOLzXlqv6ZYqVRiaWmJpaUlrFYrp0+frti4aw2qqhLO32Ih+y0C+XsYpG5iShvxwgoWyYdZusxifgqlMIZBtKPPeEjq4kxmyuuKXk03OVVkPjcPJBGw0qK9woNcnhcSK+iFAqe0cS4ZizTJC3jknb2U/AUTqZKBXl2soXwMRYXxjAutIHDWGOOwxUnm02YiBQdnzMuHSvquBsmihoGIi15LfJ1buBf6Vv2eplMW/FkjVxxBdOLhG9vtBWXVy+kroVOPeDk9LuyUAeLFAi/4o4g4ecbZi1sXIFKYrv+XqXDr1i1cLhednZ31X+8x4dgWG/uNp/dyT90JOxUbilLCn3+VmfTXSCt+NEI70ZJEqDiJXXOWjGJjJlf+QjvlLkroWMjNgCaJFj0t2vOs5ENMZMpyckbRglPTzlx2meHUDABWyYZdbuLt1DI/TOSALrwahWuGAk5pkm79CsmiAeMB+GOkSjJTGRc+XYYnDnnPFWAg5kZAWDW4O5prR4Gclam4kcvOIDc91SuanFndlw3mzYwnzFywhrBqDld1aTsyJYmvLZxlIP8ewnKJufzRUczaa/pbUFV+EAwDBq7artNpTK7uy9ZWxHncXp7seYalpSVu375NJpMhHo9jte6tx36CE1QCs9m8JU/l84/yubLZLPPz84TDYZqamnjiiSeq9uJQVZVg/i1m018lVpxAJ3URLxkIFidxyGfQiFbm8vNACLvcjoCF+dw0qmYFSdHQoj1HqBBnMluOAzrBiEfbwWIuyGi6THI2iiY8mjYGsgFupdJAB3apxFNmFSeTdBtWyCkaZEFoOB8jr4iMpT14tTkumQ7fxXok7iCraLlsC9JhPHxT0koQzRkZjtnoc4R42lM96bvLlKDLlCBeMHA3YuO0OYJL1/iVoGqQV0T+ZamHB7n3sSKKTGePjizvXhlHAV4LRwCZ8+arnLUUVleBayvibFY7z3Q/g9/v58GDB6TTaSKRyI6m0kcZx7bY2Gk8Xal76k7Y/piCkuRB/PfJlPwUVQP+QgqtsIhFPsdyfomp7BQiEh7NOeLFNLOrRYdZcqKkTUQ1McLFsrazU/aiFe1MZebw59e6SC1IgonJ9DxLuXLhYZdtOGQPE5ll/jGbB64A0KWN8ePWCFZxFnsNpLztCOYNLOftnDVGD92EL6+I9Mc8OLU5Lh9B0vca5pMOkiUTp60LeDz1Jzi3NonblSRZ1HAr3IzPEKfVcLiJK1bQ8ndz5/nCzEXCBQMOnYZIrsh51ymc2ihhbYSiWk1AfFyTGoG7sSh3Y+DTX+KqXSReGESp0uhSoGy21t7eTlNTE7du3WJ8fJxisUhnZyder7dmEvkJTrBbU0xVVWKxGPPz8+RyOdrb2+np6alpna+k5niY+BSRwgDgIFQsIpZmscinSReCTOVmERDwaM6SUorM5xaAIAbRipSxEdMmGctMA2CVnJglD9OZBUKF1dylcWMQHUymFwjky3nKJJlo0rYwnQnw9XAG6AP6aNUkeJ81glWaxy3XP9GIFrXMZ530GBI8ech5qkz6dmOUlCO9CructhIp2Om1LPC0t/6mkVWT4WlXhmxJ4q1wEx5dmq5DVp9Ml2S+Mn+G52YusZg102OxMZmI0GProcWQJKaPkFMeb8Ou0gwwlEwwlASv9hxPOfWkC4MUheoKcYGyn1xzczPNzc28+uqrzM7OMjIyQkdHB83NzcdiFfjYFhubORtrah2Vuqfuh0RxmpHkFwjm/cRKi5ikFqzyZRZy0/iLY+hEM15NH8v5Zaay0wC4NR0oqp7Z3CzIaVChWdtFriQwm50HoggI+HTdJIslpjLLQDmAtmibkQQjY+l5FnOzW+6lU+8jXWrnz5ZWgNOc0Yd5t2WBd1kWcMrVfWhns1bSioGLxghtusMltCWLMoMxDz2mODca5PR9EJhKtFBSFU5ZV4DGJxmzXOCmc5miUuZ1OHXQaTzY12Mla+SvZy/yxblzpEobhl9rJLKhUBKQ8RjbuNZqwK/MkSjuXwgdDAetuovOZdPMLYNB6OLdbhuyOEWqVNkOt7BJUlBRFAwGA08++STpdJqZmRnGx8dpa2ujvb19y77sCU5QCUwmE5FIOYas+TgtLy8zPz+PwWBY5w3WAlVVyZT8jCSfI1ScJVQIYBDBouljMTdLsDiORtDj1V4kkA8xlS3nFYfciizYmMnOosgBUMCraUdRdcxk51Apr3216jrIKyIzmUXWps4ejRujZGMsvUAgvzVPtetaUNVOPru8CJyiSxflWcsCz5rnadZW11RZypmJFM1cMEZoOmRxkmxJpH+d9H24v7saLCS9pEoyvdYlmo2Nn/bopRI3nCsoKvTHXBhlDb2mg5XJjRa0/O3sBf6/2YtECvr1n69F3MlYmsmYiE3bxFPtZmLCIpHC4U+6Nt9TpfDnc3xzOYcWH884rVi0C8SLlb2em/OUqqpIksSVK1fIZrPMzs5uWQXW6XSbnne0mmLHtthIpVKk02mCwSDxeJz29vaK3VP3gj/Xz+vR3yerxLDLp9GL51jIzwAxrFIzGsHFfG6GUGEcEYlm7WnixRzT2TJRTBY0mHJuMlqF8XR5r1wr6GnRdbCUjTCcKv9MQKRT30m8WGAiE2Ct8ADQCDI+XTtziQiDhbXd0PIHZzTrYjTr4m8Cl3jakuHdlmX6DKNYpJ33VxUVxjJu9CKcMRz+nqs/a2Q6beOCJcwzR1Rur6TAdMKHQc7RbTmclSJZ3OB1jCTspIoaLtsCyA1sUEynLXxu6jL/tHiavPqoekVx2/phIJ3n+fE8WtHOM74O0IZYzB1uYbgTZ6MSZFSVFwNRBBw87eipSDp3s8pHqVRa7w4ZjUbOnz+/3sS4desWTqeTjo4ODAbDkQviJziaMJvNLCwskM1m8fv9+P1+RFHk0qVLWw4F1UIQBGKFGX4Y/R1SJT9WuQej1MdifgoKY5gkFzbxFAu5eSLFCUCgSdtLpqQyl1sAIoiI2Ist5DUCk5m13CXTpusikEsyltrIOz69j4IiMJlZAjYOdxIinQYfi/EYw4XQ+uMBpnN2pnN2/iF4kSumDD9uDXBRP4xTszvJfCLjAFXDOWOELv3hrvBE8lpGE27OWiJHlvQNMBVrRRJUOqyHk6dEgdU1Z5hKWQnm9Fy2B9A1kH+4nDXy3EwfX5o/R7r06AphLr/1sxDLF3lxMoooGHmmrRW9McZs9uisAu+FPCovh2OAiWv26/gMccKFvVVQN+cpVVXX849er+fMmTP09vayuLjI7du3sVgsdHV11dVsPygcy2LD7/fz9a9/nZdffpnf/M3fxGQy0dXVVfd1c5plXo9+BaPUQUaJMZ1bAMClOUVeEVnMzwJRdIIRr+Y0S/kA45nyTqtJtGKXW5jNLhEVQ1Dc4GNMZZZ4mCx3gnSijjZdOwvZKA9TW0muFslEk7aZ8ZSfB4ndCbDtei+iYuQ7gRWeD7g4Y3ovFuEB77PP8G7rHBa5QFaRGM+4adbkuGzamch3kJhK2QjnDVyxBWk+RKfvapArSgwGm2i3Jum1PT4DprOrvI7FjIm5TJnXYZFrl3odjDv5zNQVvr3ShbKHvOtu6SKvKLw8EwVErnhP0+woMJ2ZQTmEtal6f8Nm6dyzpquct+6+L7u5Y7STnKBGo6Grq4vOzk78fj+Dg4NIklTTXv0J/u0hGAzy0ksvcevWLf7oj/4Il8tFb29v3dctaWK8Gvk0OslNVjGvr/A65E5UDMznpoEJNIKOFu05AvkoE5lyLtOLJtwaHwvZAPOEoAAmyYxb08pMxs/DZDkOyoJMh74Dfz7JSGprw8sg6mjTtTGTifAgvvshr1nrwijaeD28wishJy26p3BLY7zXPsOP2eZwarIUV8VJHHKJC49BnGQhY2YxY+GSLcQzR9TgrqAI9Pu9eE0Fum31C2PUim5TnG5TnGBOz92knTOWCE5t7UXhVMrKZ6cv88+Lpyjs0AxbgyDsnMMUFV6bL+fOc85TdLsVpnMzVa4CPy4I3I5GuR2FTsMVrtggWRreUTpX3JantnOPJUnC5/PR3t5OOBxmdHSUUqnE5cuX62pqNBoHVmw8//zz/Pqv/zqlUolPfvKT/NZv/daWf5+dneWXfumXiEajlEol/vAP/5APfOADe17za1/7Gp/61KcoFArcuHGDP/iDPyASiRAM1r8SFC+usGL5PvGiwHJhAknQ4tVeIJyPMZMtB2qr5EUvupnNzRNa5WO4NM3IWJjKzLKSL//MWnJg1DuZzGzwMWyyDecqH2MgufVg610NysPJRZayOx96BQR6DO3E8gqD0TCbCdWCIHM72cLtZAv/XbjBk6Zlfso5x/vtc0iH3IQdjLtQVZHLthC9j8FpvBLEslpm4k10WIM82fz4gvd2tBpStBpSJAoa3gg102WK0ayvfBJ1K9zMX05d4ZVQe2VP2LdDL3Dfn+C+H9rMnVxu0bJYnCFdWguIjS8+GrmmNJJKMJICt/YcNx16CtukczdLIu6lXS4IAk1NTTQ1NRGLxfYUmjjB8cJB5KnvfOc7/PEf/zGJRAKfz8fnP/95stksk5P1m1RmSjGWLC8SLwlkCtMIa7zBUoa5XLkoMEl2LFIrc7mFdT6GTXZhFN1MZ+YIruYpi2rDrHExnVvCn3uUjzGQ3ErIdWrs2GUnI8llArndybpdhlZyRZnhuJ/NgiayKNOf9tKf9vIXi9e4YvLzPvsCP+ecRivurih5EBhNOEiXtFyxBfEdMneuUqQLEpORVjymME82H52GnVuXxa1bJlOSeDPcRJMuRaepcvn+/riLz0xd4fmVroo8MiR5fwO74XCS4TC4DG1cbzMQVOaJFw/XPb5WzGRSzKxK577LaQZhnHRpY4V7e57ajZ8hCAIulwuXy0UikThShQYcULFRKpX4tV/7NV588UXa29u5ceMGH/zgB7lw4cL6Y373d3+XD3/4w/zqr/4qDx8+5AMf+ADT09N7XjeXy/GlL32JN954g7t37wL1ObOuQVFLvBT+FEHdLAbBhkfuZSm/sC4J6NF0UVBl5nNzlA/5Ai3abjIlZXWnNbzOx0gUiywU/ZApB7BmbTPyKh9j6RE+Rju5ksB4apnd1Jh0ooYufTvTqSR3IjsXVdKm7nVRlXgz2cZE6gz/GhDpM77FL3jHcGkbpw6yHQVF4EHMi0OT59IRJtOtpAwsJJycdfm57H18k4z9YNEUeNq1TEERGE60IQpJzph3nk4pKnwv0ME3F5/hX/zVjU6rWVlaSGZZGMtilF083W4lJ/sPyNSv8VcN5nN8a1U6992u85jkeeLFxYqLjc2wWCwna1TvEBxUnkomk3z6058mHA7zmc98BlEUG5KnVFXllchnWdGOo1VNeLV9rORX1nmDTk07AiZms7OsUM5dTdoOiorMTHYtd0HbKh9jOrMIpXIjza1xY9qFj9Gma0ZQdYyklpjdRQ1REkR6DD5W0lnuR9ZywNbviUbY+H4piNxNNTOZ6uKl0Ps5b3ibD3lHaNMf3AGxzD/wohePptP3GiIZHTNxD122AH1HOE8ZpBJPrfI6xlMtZIs5+vYQfXk93MK3l57l7xeq4ylV038KZfK8MJ5HI1p5pt2HoAtzIE2xA7hmvFjgeX8ECTfPOE/h1PmJFmaghjxlMpmOnMv4gRQbb775JqdOnaKnpweAj370o3zjG9/YEsQFQSAeL+9/rsnT7odf/MVfBGBgYKBqs6S98CD5TXJKCm2ug5AcJFAYR0KmWXuWaCHFVLY8XtUIOpq0XazkYoyly0FaJxpo1nawlA1v8DFUAZ/OR0opMbmNjyELMp16H/5cisHE2m7oo4cXm2zGkDUwr+Z4M733eHfz4adNtKNgZCgRZiKp8BJX+Yu5y/w71ywfaRrhuq1x+6iposxg3EOXMXGkSd8zMSvBtJHLXj9NpoXHfTsVQyOqnLOU73c44SBTkrlsCyAJ5QLvm8s9fHbqMqNJJ6eMVjbvU1cCpYYpQrqo8L3pKKDhp7ucdOkVpneZxtWCg1zUKqgq398knVtSN4qzSoM4cCyUP06wPw4qT/3CL/wCAP39/Q3NUxOZ1wgVpjEWugmJAcLF8XUfp2SpyGx2GQgiItGq6yGcTzGxmjtkQUubrpNALsnoprWoVk0riiA/wscQEeky+Ijm84wkdydKmyQ91ryFZUq8Hd47t4ib8lSzYEUjmRmIhZlJZXmZPj47f5Efc8zz0eYRnrUvNsxkbo303aJPc/WIOn0DLCXMzCct9LkDPNF0dGRe94MowClTeZ1uImkjktdx2R5AK6ooKnzX38FfTl3hfszLRYuTzeehSlDL+m5BUXllNgoI/GSngx69ylR25kCKhEajhMoPwxFAwwXzk5RU58a/VZGnjhoOpNhYWFjYYpLV3t7OrVu3tjzmd37nd/jJn/xJ/vzP/5xUKsV3v/vdiq9fi4P4bgjnlxhIvs5KPgiygF4wY9d0sJhbYTxTHi2bJRs2qZmZ7BLDqWkAbLIdm+RlMrPEw1VJwDU+xkwywHBma4Fgkoy0aFuYSAe5H9/9wNuic6HHSn98hZJa2WgymUjSLXsIFQQGEwkgx+YCpqhKfDvYzbeD3ZwyRvhI0yg/75nELNcmH+fPGphO2+mzRo6suRHAcNBJSdVw3r1Cp+3xqFY0Cucs5U7cZMrK88tdfGn+HAvZDSO+WkJofStLAssJuDddoMvSSa+zxIqwTEGtnWsCB9MxehRl6dz3ujcUT45zED9BbTjoPLXdZ6OePJUqxnk79h2W8jEQ42jQ49We3uLjtMbHmM+uMJJaW4sy49a0MZNZ2cLH8Ok7WEpFGc+uHb5XSaeijnZdG3OZ6J58DI/WjlV0Mhj3s6hUlqeyqQwdkou0omE4GaOs9reRp1QEXo74eDnio12X4MPNo3zIO45DUxsvIJrXMpJ0H2mnb4CJiJ1YTsdlr58WS+XrSEcRvasT+KWskW8udfO1hbOMpxx1XbPePBVOC7w9k6XV1MEZR5GIJkBWedTz5ijiYTLOtdyG6NFxzlMHUmzs9OHYvnrwpS99iY9//OP8xm/8Bq+//jof+9jHGBgYqKhrWImpX6V4LfY1VvLz2OVmChkdQTFIsFgeQbs1LYiYmc7Mskx5z7VJ24qAgcn0PIvsz8fwaJyYJTvDySVW9ugAdxvaSBcEhmNBKjXu0yPTJnsQJRNvhSvr2I+nHfze1E3+28yT/Lxnko80j3LWVNlIeTplJZQ3HmnSt6JCv9+LXi5yzn10fTyqRaKg4R/mzvPXM30E84ZHH1BDQK6X8L32PZ9O5JhOgNPQylNtBkJq7fuyhyktK+1DED/BOxuHmafqXb17K/4vLOQnMUsuxLyNAEEimXJOssseDKKDqcwcwfxa7vKiE6xMpBfw56bL97OJjzG4jY/hkK04NR5Gksvc24OP0aFvRlF0DMZXgMoUgDSI+GQPsmjhzUhleWo+Z+G/zVzjz2ef4Kfd03ykeYQnLJVNJRYzJhYy1jLp23k0Sd8AgwE3KtDnObrTlmqRLUl8deEMn5u6xOKmZtgaaonvdWeE1QsspnIspsCs8fIun5mEuEi4Runcw8xTco156qit+x5IsdHe3s7c3MbBen5+/pHx83PPPcfzzz8PwDPPPEM2myUYDOL1eve9/nZTv+3O35ViOTdBtBDEJvUyn5sHUUBAoFXbQ7JUZDqzBIQQEfHpuokXC0ymNzokZT6GgbH0wiN8jDZNM4qgZSy1BOzcrdAIEt0GHwvpDPciawfj/T8gNvS4NC4G4zGWlShX7aaq//aMouGrK2d5kPoxejWLvMv2Bj/TPLUjUW8w5kKlTPruqYIIdpjIlwQe+JtotWS50nR0u1jVIlow8lfT5/m72QskirvLOtcS+mpZo9qMfHHrZCycWZPOtfGsz4eiC7Kcqy6RHobi1RpkcSP8nRQb//Zw0HnKbDav56l6ECsEmMuO4pTPlH2cWOMDdpJXxFUfp3JHuU3XSbYEM5ml9Z/txcfwSi50soWR5CJzmZ2bYSICPUYfoWyR/ujOfIydYEZLk8bNaDLJnVScS7bq81RBlfgfgV4epm9yShvlhvk1fr5lAtMOan2jCTvpko7L1iDthsOVeK8UJQUeBLy4DQoX30FFRrqk429nzu7eDKsDdeepwtYpRrJQ5Dur0rnvamtFZ4wyn6uuKD3MZSxZqC1P/ZsoNm7cuMHY2BhTU1O0tbXx5S9/mS9+8YtbHtPR0cFLL73Exz/+cYaGhshms3g8noqub7FY1oO4KIo1FxuvRl5gMrvhhWEueokKaUbT5c7OGh9jIRtmOFXuyAgIdOg7SRYLq3yMDUiCRJfex2Iyzkhh9+LBLBlo1bYwmgjzVqbyCUGTYEW7tuda5d7jZsiCyAVLC8vxHPeWotzDyD/y4/zeSIb/2DbCR9uHaNanNkjftqNLpkvmZR4GPXTZYlxvObpdrGqxmDHx+elLDKTfw+1gBe91DdGv7iCe33kNL68ofH8mCkhcbzmDy5qpnNdxiB2jzYTVUqlUsUfPUQviJ6gNB52ndDoduVz9fhE/ir24nqckZBxqCzE1x3i6PFnQCFpadV34c3FGU2sxUMCnbyevSExlFtnMxxAQ6DL4CKUzTGZj7CZOohe1dOrbmEwmuB2unJPnEkxYZDsDsQjz6mZVneogABcsLcTTCoPLUQaR+AY/xh+OPs2HWkf5T74hekxR+mMe9OLRdvrOFkX6A15azUmuvoOaYaGcnr+ZuUh/6id41d2aBygAACAASURBVH8wJoj1ThEKhZ1XexUVfrgqndvnPk2Hq8hUdrqihtdhcj9kcWueOq5NsQMpNmRZ5tOf/jQ/9VM/RalU4hOf+AQXL17kt3/7t7l+/Tof/OAH+ZM/+RN+5Vd+hT/90z9FEAS+8IUvVJzEN+/C1pr4Y8UQQ+m7WCQ7VqmJ6ewiYXUFVLDJDmySh8n04gYfQ9DSpu9gIRtlaJs/hnG1eJhKh7kf311K1at1YBbtDMT8LKQqG0ELgG91z3Vkhz3XamCStJw2NTEajPNm/NGuSjhv4LNTT/C5qSu82z3P/9r9gFO7qCA9bgTTBiaidi64QjzVejwMfSrBZMTOc/eu8sOVy7T6HKCrbEWwluBXb7g0GAyQ3WsMLfD2UhyWoNvWw4VmkZnc9J68jkOdbAjvjCB+gtpw0HlKFMW6C9O8kuVe4kcYRTNOTTtz2WWmS2sStxbccgsz2ZV1H6c1PkYwn2IktVWARCdq8enbmU/H6I/v3phxaCy4ZQ+DseC+4iSb0SbaUTHyMBFmJxKwUGHe0oky58zNzEbT3F589DrJopa/m+3j72b7uG5f4n/p7ue9nqOp2hTLaRgOeTjlCHPjHdQMW0qY+Zv7V3hh5gqtHR4kkwjsX2zUtkZVX04wGo2Q3rsQHQgmGAhCq7mTJ1q1LBZmyCiHayy5G94peUrY580/ktT9QqHAzZs3efnll4GyqshTTz1V1TV+GPkO/ck7TGVm1/9IJx5E0chsfnn9ZzbZhlPjYSK9/AipyK1xYJEcjCSXyO9hJNOpb6FQ0jKUWKHSQkGDiLdkJYTEcnZvDsdVext3o7vvwrpkI216Fw/8IbJV8lsuWQN8smuAn26aQtNA19BaMR83409buOj2o5OPg3lPZRgMuPn83Sd5cbJni/b4xVYPeW0J0Q4D8RVKu3xfu/RmpnOHu+J20eFhMFJdl86h03DTZySsLhArPnq/PoOXuczhdP5+7/x/4ZKtbLI2PDyMx+PB5XLt+RxFUdDpdI9DkepknLI7Hn9g2gGqqnL16lVeeeUVBEHgrbfe4vr161UVIPcSr3Mr9ipTmTlKqznGLjiRVTPzxQAlylP9zXyMZGnrCpFNtuDWeBlLrpDegxjbpvMgYWIwtkKluwIiAi2KjTga5jJ7x58rtlbux3ZvxlklLd3GJh4GIiQK1YmX9Jqi/Oeufj7UMoFOevx5YSVlYCnp5JTDj1lbmxDLUcRaM+x/jJ2mqGwceq/6momqWQxumf7EMoVdtk3Om+wMpaNV/U6XzkgoV7sXypPuFu4Eq/PTMmkk3uWzkBKXCRUevd8zJh+jqcMpcP9r70d4n+dJAKanp9FoNLS1te35nKOYp46lO5UsyzWvTkE5CbwafZVwYZWPoe8iVsgxlwuyNm5u1jat+mM8ysfw6VspKTKjqSVg551cEZFeow9/Js+D6NqHdf8kY0JDs+xhLJVksFhZZb3bVbuMTsSMyHAkzgK1HeD64x5+/cH7aNXf4Jc7B/hI+2jNKlb1YCzsIFXQcMkToN16NHkjtWA41MWfvH6R1+Y7dn6ACmMzYZgBr9VKR6eFiXyYYB3BF0CEig8Uu6GWNaxIrsDz4zE0ooV3+doRdCGWchsrGop6eOZem8fTiqIc213YExxNbP+crCknVmMK+aPo60ytGvOV+RgCs9lF1vKOW+PCJNl35GO0aL1oBBPDyUUWduFjCECPoZ14Hh7GguzGL9yOsjiJm6lMjsF8lrIC4t7Y7WvTqrdhLup5GI3hj9aWpyZSdv6vwffwJ2PX+ZhviI91DOE4QG+p3TATtRLIGLlyzGTW98NUrIVP3br0SDNsAwJTC1FYALvBQE+Pg1klxlJm64peTaqJDRIyqQapQokXJ6MI6Him/RxGU4y57MYGxWFO4DXb1qj0ev0ejz66OJbFBpQ/QKqqrgf0zf+9HxZyc6RLKbr0Z5jPhhhKbvAxWqQW8qLAZMZPeWWpDAmRLkMHoXyWocRWucDNMIp6PIKTmWx6X93xzXAJRiySg4F4hAW1vt3T85ZmMlkYXgmv3mP9hyORZr61cJq/uDvPf+wZ5uOn+2kxHrxDZ7/fjSSqXHAfzD7o48IPpjv5/N2riNpr3J7fveuyOaRF4hki/RlEAa72tJI3FRmMr37GqgyooiDUzdmoZ5e2oKi8PBMFRC7YO2l1FZgvLtVdAFWDzcS7YrF4bMfTJzjaWMtNa8qJlRYb8WKMhewcnfrTj/AxvIIXQWNY5WNsHOjKfIx2kgWVsdQKsHMXWStqaJO8zKfT3MmG1p+9H9bESR7GYywr1a7Ybr3+aZMHihr6A0GgMYWBiJNXAj/H3/Rf52d9w3zizAN6rAe/CjwccpIvyfR5/HTaj7fM+ma8vdjC5+48SVq4yZ25ytaVE5kC9wf9qKhc7moGW3kqX2u+qTdP1QMVgR+t8jpO2zrpdBZZVJc4zGFqrQTxo4ZjWWzs1DGqpth4mBwhUZAJbeNjzKWDjOe3FhIGUU+brpXpzN58DJfGhkN2MRgLsKRUXiyUTfgMDCUiVGt2swYV0Agi5y0tLMay3F2sXDFkP5yzuikkCkyuJFkkC+h4buQKXxi9xMfOLPLvfbfoczZWVaOgCDzwe3Hqs1zyvnMUO4qKwAsTvfzV3auMhNwAXNtloLGGnd5BRYWhifLr0uVy4G0zEchVN5ou70/XWWzU9eyNO3kYzfEwCp3WLuytekJijLxy8NOzzbuwiqKcmPWdoOHQ6/VkMpl1R99qJvIPk0PkSvpH+BgrmTjTxSgUy6ZlAFpBQ4fex0ImRn98w7RvO6yyiWZNE8OJEPeKleebJsGCTrLWLU4iInDB0kI4VaR/ea0IqD9P9VqcaLIwGogRwg+IfHnyAl+ePM/PdSzwP/fc4SlvY7l9W2TWXe8cmXWAl2c6+NydJ7m73ALAk/vkqZ0gIDA6XX5dmm1WfB0WEqXqtxLqzTONKgvGYjnGYtBk8nG23YJRCpEuHfz0bDtn47jmqWNZbGzHWseo0jehPzFEXi1gk604NV4m0ssMJLeOoJ0aO3bJyUhqmcAeuuM+fRMoegbjK0xWqDsuItAhuYiUxB1N+KqBRdZhEAzIOfOOpO9aIAkCfbYmYvE8o3PlQ+x2cl9JFfne0kW+MNLO054FPnH2Ae9tma3L9TVdkBgMePFZ41xrPpo+HrUgXxJ5Ze4Kf/zaeWbjtqqeu1+gXA6lWA6l6LIaud7SRkCTYia1f+EhCkLdUbjRHaeZeIZCUSZdauKmz0xEnd+R19EobB9P79cxOkxt9RO8M7Am075WbFTjCTWYHCWtZPf0x7DKZryaJsZSfu7Fd89TzVoXBtHKQGyFObUacRInGUXLSDJKrc0wAL0oYxL0WEp23l5s3JS6z+Yln1YYm4+s5qjtCUjgleUevjnbTp8jwH8+e5+fbp9Cs4PEe6XIlwT6/U24jel3lMx6SRF4c+ki/89r59ebYZVj79gYjmUI92c47bTwhL2ZlLnAWKKyz0G9cbfRcXsllWNoxcps0smzPhspaWlHXkejsF2NqtLJ6FFb9z22xYYsyxSLRTQazXoQ12g0+z4vr+TJKjl8uh5G0/OP8DG8ggutxsxIaonZXcz1BAR6De1E8goD0TBlnsf+b6wOiXbZy0wmy7069debdGaaNA4e+EPExBKRBkgsGiSZC1Yv84EE/TP7Fy5rf/EbgTbeCLTRa4nwy2ce8O87x9BXQd6OZnWMhJ2cdYa58Q5SlkoXZL42eIEv3L+CWdfKbPzgxuslRWVgJICKSl9HE5JT4EFsedcUIDYgEB3E3mpJVYnmirwwHkUjWnjW1w7beB2NQq0qH0ctiJ/g6GK7J1SlxYaqqoQKYTr1vTvyMRzYsOqcDCcXWdyFjwFls9hMUWIk7qdSs1gZkQ7Zw1KuxINUCshQazPMoTHQoXczGIgSlUuspOvjmgFoRJE+axPBaIaHs+UCaG+lq/K/DUQ8/Nc33s//a0zw8dP9fLhnGLOm8gnqZpn1a+8gZal8SeRfRs7y3J2rGLQdjIRqWTur8POhwvBk+T071+LB2KThQXyZ4h5cvbrNZ+t69s5QVIV0UeHFqQgCOt7Vfg69KcZ8tvHnl3fKBP7YFhtrQdxut68T7yrBXGaZsXSAzV8OEZEug49gJsNkPgr5nXXHdaKGTn0706kEtyOVTxGs6HBrXAwlEtwu1VcBdxud6BQD9wMBZmokfW+HQ6un1+hkZCnC3Sp4JtvDy0TCwf99+3/ivw08xcdODfCfeh/i1O8+ZlxKmJhLWLnsDXDzHVRkJPMG/vb+Bf7+wWViuTKZy6o/2APqWvNGQGB8NgKz0G6309pp5mHGT2KbsVGlMpR7/84DKDY2rZkUFJUfrPI6rjWfwWPLMFWpX0cFqKXYOCk0TlANNruIV1NsRApxHiYX2R5lu/Q+kvkS07kAFHY2rpNXzWKXqjSLNaKhVeNhNJniTqq+xkibwYZDsHLPH2BJbUyeMstazpndTPpj3I9U3nzYPm1fTFv4/fvv4s8eXuMjFfAPg2k9E1EnF1zBd5TMerao4SsD5/nC/Sv4U2YALjQdLB9gc8qYXorBErjMJrp67Izlg4Rzj36m655sHFBTbOP6Aq9t8uvwuQpMZ2ca1ox7p0jfHttiw2g0rhcb1ezC+gsh1gKvXtTRrm9nNh3mQXz3IGKXzXg0Xh7Gg7xVhe64V7CgX91znaV+0nc6A0MrYcpKJPUfetqMFpokMwOLQe4EakgIuxy8YnkjLy//HC+Mvpdr7rf5+KX7dNs3uiWTERvRnJ5L3gAtloMnmR8W/Ckjf3vvCsOxH+f16a0rB5Uaxj2CCuPVTgE1EE0TiKbRaSSeOtXOspRgNlV+H+pZd1vDQZC5s4WdpDkFbi/HYRl67T2ca9rfr6MSbHYQV1X12HaMTnB0YTQa14uNappigU15SiPIdOp9LGWTDCR252OYJQOtuhZG4xHersIs1okRm2xnIB5jsU5xktMmD2pRw4C/caRvj95Ih87O0GKIO6FaCpedg12yoONW4P38aOa9nDW9xS9fvr/F1XshYWYpaabPE+Bma3XSqUcZsayOLz7o437w/bw6vbWo1Gl1tV204oLg0cfFkjnuP1hBEgWun2ojosswkdzIn/ULmdT19B2R2mVCt+bX0Wbu5EqrhoXCzCOWCdVCI1ZHED+q677HttiotWPkz4VwyDacGjcjyWXu5XbvlLbq3Ggx0x9bYaZCPoYAtAkOEgWR0VyKevZcZUTOmZpYTuYbSvo+bXGiK2oYXAqyxM7dsUqw/U70ksxFh4fFlQSDE+XO00TgIl8dusB7O2b40JlhWi1J+jwB4GiaBdaClZSDz7x5ia8PnaOgSFzt3CFg71KYJZM7T9E2nlfZPewVX3KFEg+GyuoglzqbERwwvY/JUSU4EJlaUdyzipmIppmIgsvQylNtBvzKHMlSbasZmztGJzjBQaDWNaqVXAiLZKJJ28R4KsC9+O4yqh6tHavoZCBeuVksQItgI5+XmcinoI5mmIjAGYObeE7Ym/RdZerqNNlwCEYGFgOE6piObA+9sihyyeklFEozMlnmDYxzhm9NnOGplgU+fH6QVnOSy14/bZZ3jsx6JGPmr+/08ZWBi6QLWp7wGdnsLA+797YS8f3yVGVv7l55qqSoDIyWzw3nWj0YveUVq/oJ4o0/fGt1OijuXkwvJLMsjGYxa7y8y2cmLi4QKezzGu6CWifwR20Kf6yLjWSyHAiqCeKxQo6FTIa5PfZcewxtpAoiQ7EAu/lobEd5z9XNcl5hILP2nNpJ370GL4MrId5O7F+sVPJlEoA+WxOZVImJ+Z1J37XCrtNx2uxkfD7Cg9BOHTWBH8x28YPZLp7wLvPJJ+7yvs7phnTXHydGg07+6vZVlrLv5u5cbclQrzewWbpyOxrZpBAQ1j072t0WzrV5GEivkC7WpvykHEADpVRhARPK5Pn2eB6dZOeaxwvGCKFSdQWspoZi46gF8BMcbWzPU5VO4CP5DIFciaXs7qTvTn0LRUXLw/gKVClOEi2JDCUTQJ5a85RelDlramY8GONeonF8tPNWN0JOZGgxxFyF+XcvrP11Jo2G8zY3s0sxBsZ3jtdvLrXx5lIbpx0hPvnEXX6md6IuMvlRwFzMwt/cucpw9N08WNhcVD4awHcLb0aTEWJ7FV6NTQbTizFYBI/FTEe3jeFsgFihtknZQUjnVroilSwU+c5kFEkwcd3rQWOMEKhCrRRO1qgeO2rtGC3lojs2TjWCTKeulZl4nLvZ6vZcW2Q34+k0d1K1Va5r2Ez6fqMKZ+a9zj8aQeS0zkYsrTA421ivCqMsc83RzNBskHsrlY3t7/mb+d++8zP02sN88om7/Oyp8WMXzO8tNfH9mZ/gubesgMDVjkrWb3YOTtUYfFV/9d2RSBWYe5DAoNNwut3OvJggVGUwP4hx7V5EwZ2QKyn8aDkL6LnZ2ozNkmQmW5mZlnQy2TjBAcNsNm+ZwBeLla3+BQrxHb8LIgI9Bh/zsQQPspWbxZbFSTzMZHN1i5NskL4j3Io1RrhBEgR6tVYKBYmRXRQQa4VJo6XX6GB0PsT9QGV5aizi4v/4/vv51FtP8Ykr9/mFs8MY5PrWNg8bY0EnL0y9l8/d8qCoIhdatueaR1/f3UL6/qs7ld1TtTkjksgSG8wjSfBERxNLYpyVXbhKu/7Oqh5dGUpV/h0lFW6tpAEtT3hP0+LIM5WdrahRvD1PHdeG17EuNmpZo0pt00W2SEZadM2MxMPczlQeOB0YsMtOBhNRFpV6Sd8udIq+oaRvi0bLWZOb8aUow9H6iqDt6LHZMStacpEiD6oglG/GRNTJ//mDn+DP3nqK//3GED/VfR+j5mgH81uj7XzrzrO88tDDpQst3PCWyAqlitZxak2cB7V/uaZGlckVGZ6IIQhw7VQb0W37snvhINSoilX4EGyFwK3F8mTjrLOXXo/KdG56z0mJJJSLxGpe4+Ma6E/weLA9T+UqVA1MFrceqAyijg59GxPJGLerIEdb0OHVuBlKxLld5eRvO+omfe/wNVtTQJxdiTMebSx/r81soUk2sRxMci9Zm5T6YtLK7772Hv7i9nU++cQg/+HsfWy6+nbwDxoDM15e7H8vX3/DzYXzLVx3q2QlBVnevylWa3Q7iFWl9WurKvmCytBEBBWVJ7pbyFuKPIxX9j04aCGT6iBwz5/gnh+6rN1cbJaYLUzv6Su1WaL9OOMdUWxUQ7xLlcpBvEnrxCTaGIj5ma9mz1W0IQomBuNh6uFjAFywNJNaJ30naQQfo0lvol1r4+FikDvBxmqAX3C6UdMwNhNCQKDHaa/7msGMjX8Z+xB//8ZN/l3vLT765AB2Q/0yvo2CosIrA918b/B9vHhbS4vHSi4bJ58oMjRe/tz09TZxBit2r4nFZGTHwqLWcJdKVbYzXG2w336HqgoDY2Xp3AvtTWhce0vnQuUrT9WgEWlhJJxiJAxeo49rbXqWSzOPmC/JgrReOFQqJ3hUiXcnOLowm80Eg2XScTVNsbXPq1NjxSm7GYwFWE5Xnqc8ghmjZKM/Fmauzjx1xuxBKTSW9O3Q6uk1uBhdDlelgFgJTtud6AsSQ/NB/KRwGQ11XzOWM/LSzM/yrw+e5Zm2W3zs+gO8R0zc5M3RNl4Zfj//+KqB051u8vkg2XiBoeny63va56a3YMbZasGfiSHt0DipNcLty+mo8foqW+OugMDQVHlD41STG1uzlvv7SecegQn8TpiOp5mOg13XxNM+M+FdfKVq4RYexabYsS42Nq9R5fOVdRtskpVWjY6huB8qJEeLCPgkF7GSxFAiTj17rlpR4py5mcVYljsNJH13m+1YVQMDSwECDZIahPJ4+5zFQSyaY2xyq6Z5pa/5TjBpNZxzupiZixBYTjAXUhlffoovvHmV/3DlIR+7fp9m6+ML5sWSyNvjl/jnN27yar/IqQ43ENxxZU0QBBbnYyzOlzuHnaLCNZuXol5gNBwiUyjW/A6bTGZINd6lVNqFMCMgMDEfgXlodzho6TDRn1wmU3p06tToIC4LYkOC+Br86RzfHsthlJ28y2clIy8TzJenkJoTj40THAJMJhOzs2WPjGqaYjpRR4e2i8H4ClMV8jEAfKKDPHpGEhHqaYZJgsB5c9np+8FS45y+24wWvJKFwcUAd5TGGreetznJxHJMT29dw6rGSHE7tJJEn8fN8nKC2eko8UyOscUn+OLtS/x83wgff+oeXc7HJ3aiqHBv8hz/8PI1Xn9ooe9UM7C8/rdvfscEAVZWEqyslAuDllNmrpo8YJEYi4RJ5vKV8rwfgdVqhUQFQh1V5gyB3QuUuZU4cyvgtpjp7LIylA0QLzzaqDxo6dt6Ec0VeX6Tr5SqDbKc31BFe6cImbxjio1KA8r9aIh4sTKTIu3qnutcNs/9Ovdc10jfI6EYb8Ya4/QNcMHmwVDQMr3gR6BxyhlrylILK3FGZ3deE6tFKtRpNNBttjE+E6TfX5YRtuv16/+eKWj4+7ev8OU7ffzshTE+fvMuPa6Dc+fcjkJR5pWBy3z7zjMk816SmRwQXv+4rB02t4SabXFHLcHISDmR6mWRc6c8mDQ69LJMtsKd7XU0QI1q58vuf2F/JIU/ksKo03H5VAuTpTCB7Mb3oNHFhkYUKZYaPy1JFxW+OxVFQMcz7ecwmmJEixsHBEVRji3p7gRHG7XmqdF4nMl0ZU0jCYEOycNKQaU/laQ8faid9H3O3MJkONFQp+/TFidmRc/ikr8uBcTtWFeWCqYZn2lcnrDotJyxO5mZizAwXI7lVsOGymBRkfjnBxf4+oPz/MSZSX755l36WhpvPLobSorIG8MX+efXrxPN+vBHkkB6U54q//+Waey2cC0gMLb6t4miwJVeNzZBi1mrJVlHI3EvVJsxRAH2+8ZEElki/dl1ifcFIc5CekOw4CCETGpf990dG75SEtdbzuC2ln2l1iTaj/u677EtNsxmM+FwuXNTTRCvpHNqQYtHdjOcTHKnzj3XJp0Fr8ZOf5Wk770gCyJ9Ni+RaI7h2QhPNukaRqaz6XSc2VNZatN9VEFuduu0tJtsjMwE6V/clmx2uPWiIvGNgXN8Y+AsP356ik/cvMul1saO2zcjk9fx+vgzvHL3GWYC5S+rTr/xBV/vFK0F833ufw2FosLYsJ/z3V7MywqXTjeT1BQZDgQPcMt1f1QTjNK5AvcGVxC38ToOYrJxkFAR+NGqEttTzT3rPy8WiyceGyc4EGznbFSqRlXJiqIBDa2ym4l0mrs1ymquYTPp+41YY+LsmgJidlUB0dZkbFieMsoaLtj3VpZaQzWNBLtOQ6fJxuRchP6VrZ5aO4VMFYHvjvby3dFebnbO84mbd3i6qzKBilqQL8q8NXmDH9x9lvElLdl8AZORTaf4rRMNddPrre7x0iuKysRYgCvIaGbyXDvjJWdQGQqG6uAnPIrqiw2h4ilCrlDi/qrE+xM9LeTMRYbigQPhFh7ECvEGBN5eisNS2VdKppybKp3Aq6p6Umw0ErUSxEvq7o/zCGaEvJaJQoa5Os2NmgUjdp2joaRvo6ThvMXDbCDBg5nGTUcAmowmfDorQ7OBipWlKpm59jjtmBQNw9MB4uzc+dn7KgLfG+vhe2M93OhY4L88c4+nOmcru78KEE8b+f79p7k1/R7yih4hqyKQQFXV8v/W7mK9yFidbGxuGFUSy1TIZUsM95dXItrtBlp7HIj7HLAPSuWjFtlhZRuvQ28XuZtfbFgolw7xwL8Q3+jcKYpSceF8FIP4CY4utqtRNaIp5sCAvmBgophjqU5xEregp9ngaajTt1aU6LN6WYlkGq6A6NTr6TE5GJ0LcT/YuDWsdpsVt6RnaMrPkLpzbt2vULo1086tmXYuNPv5xM27/MTpKUSxMdExndPxw4EbvDbxY6QLVsgoCCRALcd+Zb0pVsb6b93sdF1hjigWVUYfll9br1mH75Rri7FcPVCrLFxEQahh9UpgaNU/5XSTG4/TwJwQbejq00FMNnbCdCyDtEoQP86yt3CMiw2z2bw+nq5mF3anIN4uOiis77nWvt8JG6TvkUgYCNKIPVenzkC33snoUqjhZLpuqx2bquPhfICw2rjx9nmP+/9n782CI9vv+77PWfr0vu8AGsBgGWwzc9e5pETRlG3dkKZomqJlLbTjWM6DH+QXP6fK5VJVSoq2B5ceklIlqVTJkmxX2ZZScilaIiayi+Llvbx3BjPYGnsDaPS+offl5KG7gQbQAHqb0czNfPnAO+jTp/99+pzf7//bvl8ahQa7u0kEhr8KiixSKD3gf/rDZfTiNv/d3/iUv7WyizSgMY8kTfy/z7/MXz5/l2pNg8GgoNEI1FGbtu28Dt38v/b6z/eat3yhrk7pyiY1nS6S/mGRR3N+liU7WpfCs1iU6pWab6/Xre/h5SE2ze25DmNUw5TZgdZSY0/IU2kM9+zcNEfyIqCRLs9s9FrZeBNsvEE/GLSNqltSzC9YEEVTi5xkOFt9PvQdj3OUHSUDopudSIrPujBmDfPktJml1g5jPBnhrMec046uKrF5ECfK8FohkihQr83zr//zPX5bPOYffulT/s7bWyjyYJvT9Jme/2f1R/izT9+jrprQ6WQUBeqozUCjNZFw1f4LXUvwV3H3L3J2Vmb9sxPemvNzv27BMmbkWTxCacB2137dlDikvT2MZGmUwatasNhVQnKR/IC6Up0Y5WzhbVDEz4fGBrzGwcYg5WlVVc/LX+0+11hN5VluuD5XRZRYNPo4yY526NunNeJUdWzFs3x6i4Ed5JOW7S4owdZhgqMRlbYlQWDF4yabKLITjPe8ttsyRha9ljmnnd2dGNvJJFajjlDexf/wbz9kwpHhH335M37y7S20mt6c+EHExr/97rv80fdmWJkPoAICKjQa0BBb61HPtVjOW4WEdnm6WYL+KgAAIABJREFUh28kdLOo3a2sIAgc7idhH0yKwPySj6haYj+VvuVd3c/TD7oxkfSLekMlnMhDAqwGAzOztqHEl2ReniHVdHz/192Iv8Gri87KRj9JsWorcG+SkzjI1WXWz7I0lb4He3bPh77PqjwNtzfWw9sBl6LHo+rYSxf44S3VhkHSQrNWO8aGhrVQjOgIBP7aWPG4qOTqHOw0/XVPfuqWg/SKzKLHydFhkv3dpu0WBSv/43/6cf6XP3/Md770lJ96vIZR29tGN5I08Xv/9yP+/LOHTPj8VOsNtDLQUFEbKqoqtIIMoJ0g6/giFzMbHSe90kfV7y9/cpLh5CSDVhZ4sOglq6mxFW9WEHr2U30mlIYNNqDZIpbIlkhkQK9V+MKcj91agli5h4H2bmuCrlptLwKdtLev+2zh5ybYuMuIFwoFDkKH6JEZk93sFIpD97m+qKHv+2YnSk3m+WmcKHfTwPb6oIuCwCOHh2ymTHDvMrPUMNDKEisuN+Fwlo3NAbJkXZbgMhmYtJgJ7kR5HrlgY+m0PUdJK7/yB1/ht//8MT//o0/59gdrmHTdB9s2Qy7+6Ps/wn/8Sz86rUK9xWstoDY/v3FRrm3Z70uGWrj6H7elaLo0x/ZynasVlbUnYVRgMWDD6DVQoDfn9Ncx/9HZt5otVPhsNYqiEflgLsARaU6K/T1fL7OyUSuVWV1dZXp6+k2w8QYvDFcp2u+qQJbLZY6OjmhUa8zIXo5KNZ7kB9sUtfGihr6njTas6HkejpFUh88Wd+KBw00132DnIInQ+t+wkESBh24PyXie4NYg/vr6Gqx6LbNOO7u7cdbil2c82j91PGfkX//xj/C/f/dd/sEXn/EzX1zFYeqekDmI2PgPf/ke/+m/3KPekNBpZRA6PrmhQqPpB8/JStTO2cKb13qVlWlQlqZ6TWXjWfO7znotOAKmZqXlBeCuNuNe0DlbWCzX+Ox5FEkUeH9ugrgmz36+T0VvUaTyktqoxEaDzz77jOnpaQRBeK0r8K9tsNHZRnVbsJHJZDg8PKRareIZ85M80g7d5/oihr7bw3TFfJ3to/4UVO86ShFEHjo9hKNnPN8ZHWOGJAi85/Wxd5jk2RVD2w861z9ht+BSdGzsRHl+cj2T1e2aJM4M/NaffJH/4y/f5duPn/HzP/oMh6npoLeOJvmf//ABH20EmPBaUNXslWxJ84yCAKLaaqJTLxZ1vjm4UpW+lDC6ZmiHM7wCcBRKQyjN4j0PHzj9HFfPOL6Fy3yQwbthUe9C81GpNniyHgVU3pkdJ28os5XrzbGPotrSKxwWK36/n83NTcrlMlar9ZUdrHuD1xedfuq2e+vs7IzDw0MKhQITExMU6ib28sO19TgUAwGtc6RD3wBLFheURTZOEv0xIN5hpETgLZeXZKLIZqvnfhRBhiyKvOfzcXScZm1z8Baszp/PYzEybjJdS4Z14urXzZW0/G/ffY9/81/e4md/dJuf/uATfLamTd+PePlf/+gh330yg8Wko95oJhnVhtryT8J5EkxEoNGR6Omc2bgocKjnr920olFYukgkSySS5dGcn8c2H3GhxF5qdKxgozDH3Ya56w2V1a3m/OHDKT+qtcGzbG/3hixIVF5SbcOo0zM1NcXe3h6FQgGdTteTn3oV/djnMthQVZVYLEYoFEKr1TI1NYXFYqHSqFFeHfwmeRFK3+fDdMnC4MN0N9xYFkXLvMlO8DDO0xEFRQAuo4FJo5lytsbq6eBBRhsqMOu2o2uIBPfixLiZAey2Z8hscPLpzt/hP/7pIz58d4v9Uzt18S3WdlpG5Ap97aXzNmiWqNVmpaNtuq+zUXWlJOl9kX2iVq2z/VkUFXg056JhFXkejQ2dRxqFE7+djUpgbacZZCyOe9G6RJ5mwreuexRZrF6hSBIulwuXy0UwGCQej/NXf/VXTE1N4fP5bswgvYpG/A1eXSiKcqMekaqqpFIpDg8PEQSBQCCA3W5HEATKG4P7qXG9FZtg5kk0zsmIhr4lQeCB1Us2W2Ez1F8y7Bw3HK6XZZZtbvZCCZ5tjy4ZZtVpmbPaScbyrB4N76dQIeCw4JB1bO5EyaiDUc2bDRZWD/4m/+nPFvjygyDZvJbY2TvshtoB1gWaQ83CRfAggKCqqMJFkkxVheuVjW6CfVcp2kfUPg3NVqX11aYI7PK0A8WlsBqLXWOy6ntmYwTV7tv8lIDA1kGzy2PW48Lm1/IkG751JkMWxWFHe3uGIkrY7XbsdjtHR0ccHh7yve99j4mJCcbHx1+rivxrHWy0y9PtDUC9XiccDnNycoLNZmN5eRm9/kI9dNAN1pLZR6Gosh5JMSql77uG6frClYfJazAyobWwEYrxNDq6IGPCasYt61nfi/KsUWDaPbyC+JLfhUXQ8NnGCdDDle1iRKe9NozI7O8niFezgMT/+b0lAFbmOt565RQqzcFzbStYVRvieU+p2h7Au6EXthPXTNkLqCgLwO52c/M+5TLhnrawnk5wVq4M9JHl8nADpv30re4dp+EYAg4H3oCB1bNTSl1EAl9mZUPpYFfRaDRMTk7idDrPjfnY2BgTExNoNJqXtqY3+PxBFMVrAWqj0SASiXB0dITJZGJ+fh6j0XjpmEGehPsmD42qPFKlb70ks2zxEIrlWB2SAfGqjbJrdcyZ7GwdJXk6QmYpj8lIQG9iYy/G6ukpDuPwCuJzHgcWUebZVoToLcmw2zDmNOPS6tncinKQqlIqwZ9+PN88/+TFcZ1V50areixLAkathlqtgVprXCmxd8xsXKW+vUSbONzMxm3o5E1pzyD6rHrG5+wEcylSxcHux3q/ulRd0OhRaOMomuMomsNtMTM5beF5IcJZ7XqiQH6JrImdA+KyLOP1egkEAoRCIb73ve/h9XqZnJxEq9XecpZXA69tsGEwGCgWmxumcrlMuVzm448/xufz8c4773TdJPQTbDSVvv0cZwp8OsKhb4/OSECxshZO3DpM1xdaxmnaYsWGjrXQaJmlZmxWxEKNg4McUS5aeQa9GqIgsDLmJpcssrsZY9pn7+O9F/99f9wFxTp7wSbjlaLpEuV3HK+2shWNRh2/Q4+mWqNRFCgLNfQmBbGdQWrZpobaLRNz0Sfb5SNu+AOMMgKJx8+Ix89QFInHiz5OGwUKpf6MuVarheLd80A3QRLFnjUD2ogk80SSecx6HW/N2tmsxEhXLtYtvczKxhWWD51Oh06n4/79+8zMzHB8fMxHH32Ew+Fgenr6UtLiDd6gX6iqSq1Wo1wu84Mf/AC3282jR49u3CT0WkGTBIEls59krjpSpW+7omPW4GAznBodA2LLBI4ZTfg1JtZCfdCs94CA1Yy21CB0nCetdrTgDnE5lv0uamc1DrYTOCyGnk8lCsJ5Rv2e144eiWAwRrzFeHUtcX7p977wFU6Lglyr0ShDsVBCb1KQBJFqK7WuCs3M/c2zQBfnVa8Ql4xSWbvbmTKZIplPioiiwPtLXlJype8kl0bWQG24MkK/lLfJbInk0xI6ReYLc172GkmiHWK20kjDtNuhSNfZqBRFYXZ2lnv37nFycsInn3yCxWJhenoak8kEvJoV+Nc22JAkCa1Wyz/+x/+YX/iFX0Cr1fL48eNbB2h6uf4GQWZW72E7fcZHmdGVdKeNNmzoWQ3HiI+otN2GQZRZNrjYOkxwzHBD751Ydruo5+vs7Xdn7OjXVGkkkRW/m2g4y9b6YE5GAFYmPeSTJfbXY5fW1XUOoWOR9Xqd+YATRRXZ2jjFLMg06jV0Oj06nQZBIzTPJgjoJBFFI6GR2pmidhtVl4/o6UIM9vDfdu5Kpc7602Zr0tSkCb/HyrNob/esPGT5VRIFqgN2euSKVT57FkUji7x7z8+RmiZaK45kjqRXdDPibciyzNTUFJOTk0QiEZ4+fYpWq2V6ehqPx/PS1vgGrz8EQUBRFP7ZP/tnfP3rX8fj8fDOO++gKMrt77vjvFpBYk7nJpQr8fHx6Ia+xw1mvJKZZycxfhgbrZ/SiRJvmT2shWLEGG7ovRP3nQ6UqsDmQaLrdRuEbvXBWItVcfPCnvZjnQQBFsZcNAp19oPX6d9vIwpoqCr3xmzIDZHd7RgujZZypYpWq6DVSgiShIqAIIBBkVG0GnSKTKF8vQpwubJxdWZjMCKT7rjl+zRUNp4329juBcy4vF5Wo9GegoBRkIYMKkpYqtT5bC2KIMA7M2NEhBQnteJL1YO6jfpWFMXzdqp4PM76+jqiKDI1NYXf739pa+wVr12woaoqf/EXf8Gv//qvc3BwwD//5/+cr3zlK3zyySd3TuqLtzxI7aHvJ6cxfpAdnfHuHKY77GeY7g6IgsBDh5uzTIVKpk7wJDkaxg5B4IHHTSZRYjs4GoYtg1bDpFFPPFZkfe1672wve0xZEph12xArAsFn3QOVbrR6Z2dniKLAtNeMQZDYXI/isBua6zIYQFUpl0ukkknMZgOyyYCSrlCvC6AHNVXCW5WxI/PutA9JJxPRK7w0/qcero0AZBIV0qE89zwmnAEzzxIxSreUoIe9V5ql5OEyTtVag+fBZEvxdQyjrGGT0YpV3gRtD/zlgiDg8/nwer2k0+nzGbE3eINe8NFHH/Ebv/Eb7O7u8gu/8At84xvfYHV1taes402Bd3voezWS4JPscMKznZgzO9DXNTwPxwkPqeFxFcsON2qhQTVTZy08Gu0pgBWPm2quyv5Om1lxOCiyxIzNRCpeZLNLMqwXPyUIMOO2oBYF9q4kwzpxNdgotlrC7/ktKHXY3Uo0SUsAvd6Aoq1TLpdJpzLUrQZkgw4lVyefK1NTGtTDBdxaGXNd5N0pH1qtBptJd6l6cX1mo8u6BvZrvV394lmN8FEEv12Pf8Z2qRW4G0bNRjUIVBXWdhKoqDwI+DDpNJwWR7eXuw1a6WKLXq/Xu1ZCBUHA7XbjdrvJZrOk06Mb0B8lXmiI9sd//McsLCwwNzfHr/zKr3Q95t/9u3/H8vIyKysrfOc737nznH/2Z3/G7/zO7/Crv/qreDwevvnNb54HGXfRCnYz8vcMThZ14xzGy/wgHKUyApVJSRCYVyxMYmczlGYzOppAAJqR7rsuH/6GkbWdOIfx4YWIoElf+67Ph6ehZ30zykkv573jUtkMOt4d96FkG+ztZ8jlbzYqN0GvyLw97cNSldnbSpLN3twudPUKa2QRr8OGS1YIBVOUis3N9/nQmaoiiiIGgwGHzUatWid9GqVWqaM2VEBFUAWyqSLFdImNj0IUj86oHeQxlUTenfJzf8J1fWPQ5boMnLTv83aMRs9Y/ySMJSrygduPy2joetywRYReKfh6QVvxtXBUZ7Hi4W2r/9bEwChwVdTvtkE7QRCw2+2vZLboDYbHi/BTq6ur/Nqv/Rr/4l/8C+bn5/nWt76FLMs9C/td9RcTehsrhgmiyQbfP4lS6FGv4y7MaS3MinZ2j7I8DycYVSAgCQJvO71Mi1aCuwm2T1MjIc6QRIG3fV4mJRPBrRj74V42VrcbUaNWw7sTPixlib3dNOls/+2lGknk0ZQXr6gjtJ3hNHK7/+zcAIuigNdpZdxo4CiYItf6/AsbqyIKInqdHrvVilprkI7GqRYrtMU2VFXlLFemmquw8YMQlUiR0m4OS1HgnYCPpYAbWb7bZg/+C/XnqFKpImufhFGO6jx2+vCbTd3X84JYEweBgEAwlCK+V2Im7+Q96zjyC2797VfUz2KxMDEx8ULXNCheWGWjXq/zi7/4i/zpn/4pExMTPH78mG9+85ssLy+fHxMMBvnlX/5l/ut//a/Y7XaiPQwzf/jhh3z44YfX/t424rLc21d6EUPfeklm2ewhFM+xkx5dmRjApFFYtDjZP0mzun35OrVnVwaBWatw3+ZgP5Tk2UafjB03XDKvxciY0cTWdoTnp91pAS+f5vqJzAYts247+ztx1p526mzc/Du1g02dVmJ+3MXRboJKtkoq2SYSaB7XDg4uZr8FUAUsFjP6go5oPkm5VALxYris82MFoFKosLHVzH7dv+/hoccFeomtk1jX6zJoDNtrpunqUflCheefhhFFeG+x2S+7m7zIhA4b/L6IlidBENg7ycAJjNvt+CYNPM2FKQ+pTN4N/RrxvhXa3+C1wIvyUw8fPuTf//t/D1yoiJtMpr5UxOHq0PdoKg4aUeSBxUssXWA3OtpqnU6SWbG7OY5kr9Gsl4bwU3pZZsnl4vgkzdrGaOY87EYd92xWdnbjPI8N5qf0isx9v4vjgxQbqxf+8y77WK830MgiCwEX0eMM5XSFaCR36XNEUaB5qwggNLmjRFHEYjKhlbWkShkqlTK1RqPrELQAlPNV9lvXKzBh44HTiWTWEAzHuvqWQa1cr+bx6nHlcp21J00Wq7fvuykZVDbiF9XtUUgvDdpGdRMkUSQUy3IcO8NtMRNoDZPnuwyTD4vb2n1vw/+vZjY++ugj5ubmmJmZAeDnfu7n+IM/+INLRvy3f/u3+cVf/EXs9uaAcL/90BqNhmq1iqIoPQcbb1smCI146Nuu6JjVO9k6TY5umK4Fm6xhzuJiMxTnSay7ke01wOqEy6BnymRlay/GamQwWsCrG7BJhwW7rGVzJ0a6H1rAjp/AaTEQsJnZ3oqy1iVQue3XMuhk5sac7AejbDw9ufaGc2fRehCNeg1SA6qlGpIiI6gqkgoaWUZFQhCgXLngO+9462WoAtstkSNJFDBNSrw17SMYTlAoV29+Xw8Y9u5sNGBjLXJOSSg7ZJ7FugdE/eBFMEd1OpZoKk80lcdiMHBvxsZmeXBl8m7oNOL9KLO+ikb8DQbHy/BT7WADehOgBZg3epAr+pEOfZtkhUWTi91ohifDMiBegVGSWLZ52D5K8TRxU5tr/3NiFq2W+zY7OweJgbWcrm5wfVYTfoOBre0oz8N9BEAdP4HFoGXGbWdvO8766t2BSif0Wpm5cQdHu0k2W+/1OC4y+8JlN4VWkdAIIuWzMrJOABUkVUDWyChapanz1/JPbdIO4UpCrfkPgZ1We5gKmH0S70z72Yklyebb1ZzB7rNh7aIABLea9+T9cStGn57VWHQk1bB+B8TvQqfvaw+T67UaPphtDpPHSqML4PtNir3KeGHBxvHxMYFA4PzfExMTfP/73790zNbWFgBf+tKXqNfr/Kt/9a/42te+1vNnGAwG8vk8iqIgimJPRvzjk8StHMr9oDlMZ+LZSZwfNkbHqgEQMFtwCjrWQzGe3MFa1Q8957jFjEdjYH0vwmpjNJmyOY8DpSawvR9noKuggt9hxq3Xs7UVZe3o5oe1m1FzWHSMO8ychjJsHJ9cO/fFm5v/53GZMMkyxWQJWRKasx61BiripWqHomjRaKpAlbOzZvB0EWB1N4JqQ6WarRBcO0bWSLy14KamG7zUOmjG6CoE4KBFSTjhNOJXDIQ0WQrVAZV/X0Cmv9tvmy1UePKsqUz+eC7A8QDK5N3QSX1bq9V6MuJvAo3PH16Gn+pUEe812NiI5TgujKZF1qU1MK21NRkQE6NNhnkNRgI6M8/3o3x2Q5DRhqL07qc8RgMBg4XN/ShPB0yGXcWk04pNVNjcjZJSB7MhLouBCauF4FakazKsjW6VDbNBYdprYz8YI/j8lHq9O6Vh284ExuzIdZVqrkxNpbnxrtRQFblJldh6WyfZR9tPXdxjHTMbHWsRgGquwuZmGEEUeLDgQTTJg4+Hj9AfHB9n4DiDx6JjxmPjrFwhWx6MOVFg9NOV3bQ/iuUaT9aiiAK8Pz9OTJPnID/87MSbYKMHdLv5rjrrWq1GMBjku9/9LkdHR3z5y1/m2bNn2Gy96Te0M0Z2ux1Jknqi4pRFkVp9uGBj3uxAV1N4Fo6NfJjuvs2BUpXYCMU57bG9q5eHadZhx1CX2DiIExsRY5XLaEApCxxuXxcj6hVTHhsevZ5PNhMkuNu5dt5XXocBp9HA9kaUjZM8FrPu2vGdWSKTXiHgMJEJ5wiHMywu+s5XLUKL9lZoUQk2P6v9eRaLGY7PzjcN9U6diBsMba1aJ9iqeDx4OMG7Ez7ilRKH0b/eAa5EIo9Tr8eUhAcLftYzMXLV/vjM1REF7J24rfWgUm3wtK1MPjfOmb5EMDc4kUOnEW80GiOdQXmD1wcvw08ZDIZzu9FrUkweQf/IpNGKQzA0maVGzIA4CM16L34qYLXglHRs7EdZbYymFXnMZmJMa2B7LzFYMoxmMmzCaubp6smtybBzdPx8zWSYheBGhI1oM0C5umHtbNGymBT0go3YQYpCocKDB+OtNqomLbvYUFHFi3c0uHBBFosFyHX4qU47feUXaI8uNlR2WxWPtx5O8PaYl5xaZSecvPt7vkBksiWi+xnkbI0Plvxs5xMkbxkm7wZRgPqIo43b/FRDhdWtOCoqj6b91C11nmcHf/auzhbe5ade5XbfFxZsTExMEAqFzv99dHTE2NjYtWO++MUvotFouHfvHgsLCwSDQR4/ftzTZwySMRpmoOeB1UM532DnaEAF1dvO7XRTydXY2x/tuZfdLs7iZxzupq7R7w0CqaWRkU0UyUbyHEYHEziaH3MillV2txPoA71/20q1woTHjFHSsBOMkezU/eimDC4IrNz3EQklKGfKhI8zjI01NwlqS1JDFAQaDRVNo9UbK4g0JcU7Ht7WuU0mE0QKFPIXDraXx7teqZ/T/80FbJh8JoKno2M969fEiIJAoVBlrTXX8XjFx6laIJTpLZuq02qhNPoe1bshsLbdNObLE15Ua531fP8MVtoBe2Hf4POFl+WnOtuoekqKDeGnFi0uxLLE+kmcI0Y7k7FodyKWhcFo1m8xUnNOO/VMiaODLJEekk69YNnvopKrEg/lyORLAyfDTIKGrWAUydE4b6e9C41G41oyrBPdzrM45yETy1FMlgiHcxiNncxDrbYolfOqBm2P3uV2spjNQI5yR0XgGhtVl6/SaKhsPWl2B0z7zTgmrOzERsd61refEgUqlTprT5pB2ruLHjLaKjvJ3tYkSxL1IXU6rq2phwq3gMDmfjNYm/e6MXgkVnOnPQvhtqEVL7NR9dIyLwjCK1mFf2HBxuPHjwkGg+zt7TE+Ps7v//7v87u/+7uXjvnWt77F7/3e7/FP/sk/IR6Ps7W1dd472wuMRuN52bDXYKNf3ub2MF00VWTtcLSRviyKPHS4iceLbO6MbuMpCQIrnqZoXpu+dthbTxYFHox5iJ5kz2kBAx5r3+dZmnBTzlY42Oh/XQGPEZfRxOrqSdfAqfP50ioy8/dcaOoqT58cA+CwGa8cpyIKAlqlKdOj1hugXgQ+ajdRv9aLTfGcpgPJF/rLwJ2E0hBKI2skLJMys37HjVmkno1zv1a841o1GrC+2jGkZ7w8pNcNL4Itqp+hcwGhGfQfwbjVgm/azNNcmGqPw4DKlf7xXozzq2jA32A4vLp+qr9gQ0Tgoc1LNlthKzTahJUoCDxwuMlnKuzstTWX+j93t4HkFY+Laq7G/k53Lad+IQrwYMxDJl4418iwGPtXV+5MhvWbpPM69LhNJjbWTi8lwzrRvhKyJLIw60ErCjz99AgA/5il+V069iqCADpFbr6x0fxD0xyp59e1W3uPXqenrSZf6lPFOxrOEQ3nEEQBk8/NwriLzeOXQ01+gcvfaWsjigo8uOcAu9ScP7wFsiBSHpKi/Sr6rYIfRrIQAZfBxPScjdX8KcV6b50EV2cLX+cK/AsLNmRZ5rd+67f46le/Sr1e55/+03/KysoK//Jf/kvef/99vvnNb/LVr36VP/mTP2F5eRlJkvi1X/s1nE5nz5/RmTHqtTzdq0rxixymM8galmwuDk/SPNse3bkVSeKBy8VpJMfG5mjK5gathkW3k92d6HWNjB43uKIoMOMyU8xU2Vnrtq6bTbkAzAUcFFNFTnezCL6b3ZwgCBgNCjMBJ4fbUTY+O2ZlZazj9YvjrFY9FqOOYq5ItVpHZ9AiaiREVLSqilhXMWkkjH4bNb8NjSy11tMevLv48jqtFi5pqHTjvr3+p1q1TiVVJrweY8pvRuuQCWWKFMv9z1H0HWt0qwIB260hvcWADa1Hy2os2pWn/FWyefFMmfiTMg6zialpK+ulCLnq7VUX5U0l4w14OX7KZDL1XYHv1U9pRYkVi5eTxBmrB6PdCCqixAOHm0jsjPWd0Z1bEgUeuN2kE0WCW6M5ryJLrPhchPbj1zUy+jCOs14r1Vy1ezLsjojj3pgNtVTn6DBN2VC59fBmMsxN+CDJ5pPjVktv62PafkoUMBgUjHoNlbMylXIZnUFBlAUaLT9VV1UUvQb7nBdBltAqcjPoaJ2j0fHlNVqFduAB9Hxd2nOIofUIAbcJnVPmtFghV+iomvR2qr7RLTcsAPt7SdiDWa8ZW8DI03iUapf2+G7aW8Ni0DNmClWePI1h1Ol4NGcnWI2TvENRvTMpVqvVBiIDelXwQlf+9a9/na9//euX/vZLv/RL5/8tCAK/+Zu/yW/+5m8OdP4X0UblVHR40bOfKox8mM6h0zFjtLMVSvD0jqHvfiAA73t97IdSPNsczXltBh0zdiu7PdICdoNGllgadxE7znK4fVu7VbfNrMD9gJNsrHCuFA43k1NYLTqmx+1sPT9h/bOjGz/J6TTidZmIHSTIWc+QFanZSqWqNBp1hHqDWqlOtdYgny4Ri2TJ5kosPxzHiohdr7Cy4Efs4Cy/moXMZvufiYmFcxAGnU5maclHtFTkKN5Pi1p/5v4ug3kUSkMIAk4j7nuWayKBoxBburamISsH6VyZ9GoUnSLzwZyX/UaS6A3MIFcrGy9jfW/wauJl+ilRFKn2QMog33GvWTRaxtFznK3wWWq0fsqkUVi0Otk/vk6zPgxUVeU9n4/jkwzrI0qGGbUaFtxODg+SrHURjO0FoiiwPOEmEysSCvY/TzcfcFDJVTnauphdvMlWGPQKs5MODrfjbHT4qatcs6etAAAgAElEQVTU6haLjnuTDrZXjymkighiqz2mAY16DRGRWqlGpVSneJbnNJxB0kgsrIxhqKoYJYmHi35qHe1aVyvHuVz/fioRO4MYyBqJt5c8ZNUau6fJnjfg/Y4U3GVzI5EckUgOj0XH+LydtcxlkcAXofY9LO17vlTls2dRZEng8dwEYSnL0Q1kEJ1JMVVVX2sf9PqGSVwPNnrphb2JsnPKaMXeGqYblKniJowZTfg0piaz1OnoggynQc+0yYqch0/3R8PY4bEYGTca2dqJ8fz09nPeZDf0Wg0LPiehK9zjN+PiN5ElgfuTLuLHWXaed1FxvfJvp8OI32lme/2Uk1qScrHW9Q0Ohx6jTubgJENClprCfa02KYFmr6pGkGjU6jTF/ABV7dDZEMjnypwlC2xtnDJ338u004LFbULQXDZoJpMRBhzCL5dqbHx6jAoszDixGPUcisKdwkR9z4X1aLMSiTyJRB6rUeHRgp/gWYpUsfRCdDZGlR8rVernzCDvzU2QUPLs5y/3+CpSf6bvVR68e4NXGyaT6VybY9jKhl9vwq+x8Pw4xmZjtCrGLr2Bab31Vpr1QWDWKizYHFBSeXowGj/V1MiwsbMbuzMZdtOTq5EllifcRI8ybD27+/t2WjxRgPuTTnLx4qVkWNeDAYtZx5Tfzu7WKVur4SuD2xdvsFp12MwK8YM0x9UGpWL10utqo4EkaKjV6ud/uxjhEBBUKBWqlHMVNlaPGJuwMWE14vBZaIhwfHqRwDKajFyuyPeOWrXOVkv/ajZgw6bVopElqiOej+jVy2SyJTKfhNFqJZaX/OyXs0TP8kgvwE2NyvXV6ipPN5t6J2/NjFEyVtjMXa70fZ7afT9XwUatdncf3NVItzlMJ7J+kiA04mG6gMGIXTSyfhQjxuhE/sYsJryKkY29KKsnRR5Neoc+Z8BhwaHRsbkdJdOPRkYH2tzju9sx1vrgHi8WCmgViflxByf7KYK3BCjtB8nnMeMw6Qmun5I5zrZeu368Ikvcn3GxsxbGZdSiNtQOUT8Vs1nHzJyHo4ME9Xr90qZSBZpRB9c8liAIHO8nON5PMHvfw4TFgMNv5TCc6NpXWan0N0wtAKHdBJMVFW9FwTfnYCuS4KzY/Tx9D971WQzO5yus/TCMLAs8XvLR6L8N+qWjocKzYNOYP5zy07BeMIO0jXg/QcSrasTf4NXGQEmxKzZk1mTH2NDyLBwjwuj0ZgC8Wj1jOgtroRhPGqM7t8toYMpoYXOvSV+7OOYa+pxeixG/0URwO8Lz8GAV985k2PrT3s9RrlSaybCAi/hJlp3nN1dn2j7GYTcw5rKwvX7KerRZyeim5C3LIkvzHoLPTzjTStSq9Q6tDBW9TmF23sPpcZpKuYpwKfnUmtnoap4EIkdpIkdpfGNWfAYt3oCDo2iyqz0rlfqnmA2H0li1Wuw5gcCCn91UmlRuNCyd/bZBlct11j4LIwjw3pKXil4lWRjt8zLqeUUBgY3dZlVsYcyDzi3xNBNGZbAK/KuK1z7Y6By8K/fAxSwL4vkwXeYFDNMBLDtcVLJV9k+ynFJkFIJMAPfsNsyqhvX9GPHOjMQQSddZtx1dXSS4HyfKYMxSTouBQA/c491g1MlMuO3ko6ds9lAFMRm1LE65CW6cEudyybvTeE5NOdGoKtWzMjvPr69petbNP/iHP8IXf2weSRb58//8hO//ZRBRFJDqddS6ikmrw3zPRbmmIl5JkXTuUVUVIicZIicZVEA/4ebRgp/9kxTZXNPQ9bLBuAnpZIH0RwU0Gol3H/g4LRU4SQzJ2DLgLVmrqayvnjIz5uAdi4ekWOAgN9oM66ghILB10BzAn/O6sfg050b8DRPVG7xoDCLq126jWrF6qBVVto6TLR81Oj81b3OgKcNWOEWSwXQMumHCasYl65vJsA762mGqgwGHBccggrHNDwYGT4YBaDUS075m+1Obzvw2WC16Am4LW+unbJxcrnJ3+qmxMRtmnQa1UGHr2WWNKEEU8I3Z+NrffZsf+/FFFK3MJ9/b5v/6w09BEBAbdeRqHaNBj2vZjyqK1wIZ9UpMkoiekYg2r9+ky86jhTGOoxkSqfzFQYNAgLNcmfWPjxAlkbdXfKTVCvuR4ZisBr3bVRU21yK4bAYeOl2UDDWC6RFRzr/ApNP+SQZOIOCw4wkY0fVZgYdXNyn2WgcbJpOJeIs1p1cjvmByUcrURz5MJwkCDx0e0qkSwd3mxmZUAcyi24lQUNneS3Z1N91YPu48p89Fo1DjYKe91v5h0Mo8GveytRXpjXu8AxajwqTHxu5mlGyiSLF4ex/zxJgFqdagmChwfJzuvl4BZmfcNEoVDjeaDmH50cTFywLML/r4mf/2R/jCl+YvPZTzi2P81V9sQK0ZaDTqDXLJApGTNOVSjeV3Jgl4rdgseiYmHRcVDy5fO4GmIvn66jGCKLC44EXQyQiD1nM7vEW1Wj9vsXqw6KFmENk4irUOe7ltPpIgEGwxgyxMmNF4dHcyg7wKCLWYQXRLTYGxN8HGG7xoDDJbOKmzcUKe9cPR+hJo06zXR06zPmO3Ymxo2DiIE+3SRjqIiZr12NHV2smwwWDWa5l1OQZKhhl0TbbAg9048eMsudztQZnfa0YrqGRjZ4Qz3TPqgiAwOelAEQR2N06JAEsPxzteh8C0k7//nS/w4397BakjgFh8GOCP/8PHoApQV1EbKslIhmQ8j96gMH3fi99pwqjXMH3PRaV6ca9d3SfUy3XW149Qgfn7HhSTFkkZzBZ2/raNeoOtpyfnrcAah5a1UJNopP9u3+HuTUkQ2WuRG8y4DVimzDyNdic96X1NLx6RZIFIssDPTjzs632v8lzHax1sXB2868WIH8ZyRM9GJ8Snk2RW7G6OI1me74xusyUKAg88buLhNLvBwUXzrp5zZczNWbLI3tbga530WDGLGvLxMvvR/oI2h0XHmN1McDPKZkvg6Fb+9XsuGsUahy1q4IkJe9fj7s97kFWVzdUrCuKtk88tuPjGTz/ix77yEFmWrz2Q3jEbEs3ZjeZLAmpDpVa7qEgc78cxmbScbERYeDjGg2U/ucLN7VFqQ2VnvSXq91aAt+b9RDN5wtGLqkShePu92O3SCMDuRtP1TvvMmHwG9lJZ6IPib9h7qX39BOD4KAdHOeZ8FiwTBp7EotQHqOQUCqMVyLwNbZ2NfoKNV9WIv8GrjUHYqGKZIseZ0VUMmzTrHhLxwkhp1qGp5ZRP5AntZW6tvTT62Gou+V3Uz2ocbA+eDPM7zLj1Bo5DKdb2+6sEt5Nhe1tRNhJNP2U2XheNbWM64EBBYGcrigBYrfqux83MuJDqDXY2Il2v1eQ9Bx9+Y4GvfeMLyLJ0KdAAMJq0KIpMuVAFoekf6i0fJckigiAQOUrhcps4fB5mYsbF8pKfUqVG8QZdJAHY32r6k4dvT/Bw1kumVOHw+KIqUeyT3r193tBuAnbB7zTgmrJyetbfPT0sD0mn1EE0ViAaK5yTnqwmopQHmDEpDHAtBoW2VdloNBqvvf95rYMNk8nUd3laMyJ2AqtWy7zJwc5RiqeJ0Q3TKZLIittN9PSsZ/rau0y4LAos+Vykonm2rtIC9oFZvwNNFXa2E5wCYx5Lz+912XTYdVr2d5Nshq9UQa48Q4IAC7Me8qki+2uRa6+1IYoC9+c9ZCI5dp4e4/Vf1/0ITDn5R//9j6E1VDAYDDc+sFqdBlkUqDdpP4AG9bp6bsg7lcgBquU6m8+bgc39lTEeLI+RSBcIn3Qv1darddafNLM9s3NudFYdW/uxodkyoqc5oqc5LDYtC/M+dpJp0mc99KgOabi6XcfT0yynp1n8Nj1jczaeJeMUemDeaUN6if2pivwm2HiDl4NB2qhG5afaNOuhcIZnI2SWEgWBhx432T60nO5KJgsCLHkcFLJVdjeHSIa5rZglDcFgjISaxaDX9Pxem1nBbdQR2s9cJMPa6+tyfDMZVudgK3YpeLhqK+bnPJSyJQ6eh9HpNdfO5fFa+OnvfAHvuEK1Wu2qmdGGXhEp59ULBfHW/IYkidcq3JVSlY0fHgAwPu3kwVIzQXZw0D3gVOuwsdqcLZmatGPxmNk+jCMNSbmaShRIJQp4PAbeC/gIneWIpu/uhhi2stGNyKRNemIzKkwteNjMJcn0Maui0fR+Pw2LQfzUq4rXOtgYZPBOHtKIew1GJrRmNg7jPImMLsgwKhoWHU4OQimeb/R33puMuF6RWfI42d+Ls7UxuKNZnHCRi2YJbV6usPRSiRx3mzDLCtvbMTJ3DOCLosDinId0JMf2TT21goAsi9yf8xA7ThHsQnMrCPDFL8/z7Z//gJk5DwD7+/t3rlWURajUURQRjUbDWa5yfr6Lc7cZQC6+fKVcY3+tuV6XR49Rp8Fi0ZPNXs/UC8BhS1tFb1Jwmkx4XRUi8eEY0EqFGhsfHyHLIu+u+IlUihzfMtcxfGXj5tfS6SLpj4uYDBoeLvrZajFY3QWN8hKNeEfG6HU34m/waqOzstFrBX5YP2XX6pg1jZ5mXZEkHrhdnJ7m+qavvanVUyOJrPjdnIRS7AQHr7rM+h1UMwXCO2kuTVX04Kc8dgMuk4HgZoS8eneFdWHWQzFTOk+GXTOHQtOfzc95yMXP2F09vnipw3i+/f4Uf/87H7D8sNnue3oHAyQ09zpiyw8aTQrFfK31985go+WnOgbJy8Uq658cAmCza7EYFZxOE4lE92rDyWGKk8MUilbGcd/AhN/GUXi4uYdatcHGD48QBIG3V3xkhBp7Q8513IbbEkRt0hNFkfhg2c9BOUvk7O4ASHmZwcYAFfhXFZ+rYKO3jNFgP9i0xYoNLWuhOMkejFGvsOt1zJhtbB/EWY0OSgt42Zpa9FrmnPamRkZ8QO5xQWAp4OYsUWR37aYs081WfNpvQa6L7O8liHL35vbBgo9IKM3Wk6ttUBfQaETcDiNn0Rybn4aur1kU+PEPl/ipn/ugOVdxdbV3REeKJFKqN6idlSkX61QKVSbHbVjcpouNcOuLNOodrFUd501FixTHihSiWRaWxmjIIjs73Z1y8axCMVMisZtkZtZJXQOH4ey1ob5e0O6GrdUabD5pznU8XPJS1sLWSTcHPvrKxlUUClWetxisPlj2E6rlCN8yTP4yp060rYxRrVZ7rVVZ3+DVxyBJsUH91CWa9REmw0yKwoLDwcFhimd9JsPauGp/DYqGRY+T0GHqumBsH1iYaLZcHW52bze+za6MuU1YNArbwRipu+jKBVia95KJnrHz7ObZD0kSCIzbiNQabD/pngz7wpfm+PbPP2ZuwdflDLdDp2gQGg3q+QrFbIli/IxxnwX3uKMpTCsKtL91o4NiV+0IPHKpMrl4gdRhkrlFH7KxmRDshkq5RilT5nQzxr1pB6pOIHSau0zH3usMRJvcUVUJPmsyLi3NuhBtGp6Hrn/+sBTrt1WI2qhULhis3l/2EhNLHKRvIct5iQVu7eeoAv9aBxsDtVFJ/W0sFu1OpLLA5mGC4xHeZT6zkTGdiY3dKKvh4bjH28+5y2wgYDYT3InyPDIYLaAsiSxPuImHcwTv4B7vZl9mJ+zUC3WOt5vZituumFYrMz/lRqjWWf/s+Mbj9HoNk+NWjnbipMIZsqnLPZOyRuJvfXWFb/3M+11bqaBXfmoVtdZope0FGg2VUDCCNpRkdmUcu0GD2aAwPee+ZGgbVzUwVFDrKtvPmt/JajdgMWhxuUzE41c222qzHB5q9VF73SY8Uw62Q3EKxSrFUo+0fVepeYHdVsvc3KQNg8fI86Po+bqFIbf2/dizWk1l7WkYBHhv0UNaqbKTvJ7NGoToYFBoWoa70Wi81qqsb/Dqo9NP9boR6LeNatZqx9TQsBYaLc16W8tpe3+YZFgT7afbqtcy57CzsxfjeXwwP9WZDNu7MRnW+twujmrKZ0FpSOztxYlxu5+SZZGFGQ+1QpXg09uSYRKz95yE92KEd+OkEpez5KIo8KUfX+DbP/eYyXuD0wALIqj1BghNxioBlZPdOLVSDbvbjFESMGo1zC34SCYv/M1VbQ+15Xv2WmQqBpMWo1aDz2fl9PTqZrt57NF+c4bGZtMzcd/NXrjJuHiW740g5uovIQCHrQHuaZ8Fe6CpIVNpz1IMueXqJ1hRVdh4HkEF3ppzUTbDRrzbTOrL28xrPkezha+1lx1k8K4XIy4AD50e8pkKO3vtTfNofsApmxWboLC+HyOpjkbXQxFF3vZ62NiJsnY82Dm1GpnFcRcn++kehfgu6FxFoamimk+VOdy8e2DcaFCYmXBwEIyx8dkRM7PurseZTFqmJ+zsb5x2bavS6mQ+/MlHfPOn38PpMvW05tsgy1LTgAOgnlcvJFkEATLxMwrpAodPj5ha9LHyYJxMrnQt2Li6ac6mCpwl86RCKeYX/aBIbG9HmsHaFcOQiJ2RiJ2hUWQeLvsoNWrEk3dX0m5LLIUP03CYxu804p1xsH4aH35mY5DnQYXN9SaD1cMZJw2byPMOBqtrQdsLRGfGqJfKhiAIr6wRf4NXG1qttifV8E5oetxYLNtdqEWV4MFoqXHHzCZ8WiPrLS2nUUArSbwz5mVrO8qzoZNhZ3cmw9rotI2z4zbqxQbHOz0kwxSZ+WkXJwdJNj87xufrPqOo02mYnXFxtB0l+KSZYHI4L1ptZFnkKz+xzE/93GP847ae1nwbJFk4/06dm2lRElGBfKZIMVNg99MDnH4Ly8tjFMpVUsnLe4OrQVjhrEwxUyS6G+fevAetWcf2duQSSUob2XSRtY8PESWRlWUfok7i+HS4/UzsNEvsNIvNrCWw6CcY664F0g96qWxchQDsbDf3MYsBGzqvlieRyIVXf4nMj9pWIuxNG9VfM65mjIad2dBKEg/sbg6Pk6zvjJYad8HlRC6rbO0nGczMXsc9lw0jMo1Mlc2jwXpdTXqFOY+Dg91EXwJH0GxBWZp2kTo9Y7ebiurVzzIqTI852NuK3FrJsFr1BPxWttfCrF9tlxLAYFD42t97m298+x2sNkNfa74NstQsP2sUEUHQcpZuVhVESTxPybRNV7VYYX2r6exmV/ysPBzn+ChFOlXoWrcXWpXt3fXmNXa6TPjvuW7c9FcrNTY+O8I/buO+30FdI7BzePNv3EtVIJXIk0rk0etlrAEZp9lAIjdYFnQYHyAAey0Ro4BdwTXr4Gk08lLbqPrthX2jIP4Gw6Bz09TLvSTfQsPTplmPnmYI7o2WGret5bRxECM+oLjrVYzbzbgUPYV0ibU+2Qvb0GpkFsdchA97T4a1oaoN7gccFDJlDrfu9pNarcT8lIfQdoyNW/yU0agwPenkcPOUjdYsxDkEAUWR+ImvP+Tv/YP3cXnMfa35NmhECYEGsiygM2iQNTKC0Bwqv3pvlfMVNj7eB8A7aSfwcJxINEcskr3xPhSAw2Cz9dds1RNY9iHJ3W1ko95ga/WE+fs+7jktaK1atvZiDJM3yufKbPwghKyRcPnc+OwmTlOD3YvDPhdHoTSEwG9R8M/beZaIv2Q/1bQDb4KNv2bo9XpKrTaT3svT138wk0Zh0eJk7yTN08ToGDsEYMXjppSpsLc9GvpagPteJ2K5we5uU3fj/kT/JVmbSce0w8ZOMMpan1kmRRaZDzipnFXZfn53dslhN+AyaznYTrIRu9l4O51GvC4T28/DrJ9eH242GDV85SeW+PDrDzGabqYhHBSiKCKqKtV8mVK+BrUKCyt+JEXD1Ri1s40qn20JB4oC/ikrBoOCLItdM0JtpOJnpOJnrLwzyfK8j/RZgZPw9e8sILDXoiUc81mw+UxsHcSvnbufvXCpWKOSKlPZP+OtJTfHhTPiZ/2Jeo1qc5NKVUh9fMq4w4BT0qGTZUq12kjOfRs+Tywfb/B6oB8O/G7tvi+KZh1g0eVEKF1oOY0CM247hobE1l6MOFnGXb2zF7Zh1Cnc9zrY302w3qcQnyQKzAccFFJl9jbuvl5mk5Zxt4nD7QQbXYhH2rBYdATGbeytnbLxw8NrrytaiR//cJmvf+tt7A5jX2vuCWKT87ZWKFMp1NCKKv4lH3qLnkql2d3RdgedrVO5ZIHIYQoV8ATM2Cx6FK1Mpdxhb6/8+LlMkbWPD1h+K8DijIdivcZBSyD1KtotVi6nEc+kjWAoTrl8udukn6RNrVqnEC+S20nzaMlLpJInku1PDXxUxehstkL2kwgOsxabT8GkVTgr30x7Pyoo8gWRSa+zha9qBf61DjYkSbp08/ZykTvbqFx6A9N6K5uhOE9ioxumk0WRB24XJ0cptobQs+iEACyPNQOXgytUg/08wG6bkTGzieBmhLWT/oy3TisxN+YgtJdk69kpHvft2RqPy4TbaiC4FiF7fPPwnaKRWJr3EHx2QvroOtuFzW7gw59c5uF7blYeLPa15n4ga0Qa7V5RVaV0VmHzk33sbjPeSQf3l3zoDRpkjYTasddX2/9oqET20jhsZgyCwGRHFunGe1NV2WwNEY5NWLB6rARvKF23y8wms5bAnJe9cJKzfLV1mv7zLY16g+1nzYrCo2UfebnBzml3R3IVo7ZnyWQBV6iANSXyaGGMzVyiLzrCfiAKwnmFs16vo9Vqe3rfq2rE3+DVxtX7RhCEOwOPTj/1omjW21pOiXCG3REmwxZ8Tig22Nu5LELbT5tkZzLsebQ/P6WRBeYnnESPc+ysRVHuEKqzWfVMeKzsrJ+yHb250qvValhZ8LH9/ISNyHV/ZjRp+fAnV1h8ZOHxB+/0teZ+oFVk1FZCRhAF6tU6B+thvBN2ZEVk7r4Ho1FBq9NcIjJpd34IQDyUw2JMI5drzK2MkcoUCR+lbslaqQRbCudurxFvwMnWdpRKpbmOSyQprQq6Vidzf9HLUSxDqhUkDFQgVmFnremnVubd1E0SG8e9VchGbbNzuTKlaBntcZ3lJT+7pQzx/IvT3RhkQPxVxWsdbEDzJu8vYyQRMFtwiQbWQlGeNPqLlG+DXiOz7HBxdJJmrU9awJsgCQKLXge5RIntG+hre7HhPpsRt8HA1maMTJ/lcaNeg8ekIX5aZPNZRwn7Bssx5rNi1SkE109JHt7M6jA2ZsOi01A/K7MbvP7dXG4z3/rZ9/lbX3tAPp8lmextIzwoFEVGFECjiGhsBtLxZoteu41q64f7rHzhHopQx+O3oDNoONyJodYvXwdVhbNMkbXv76ECM8t+DHoNoihcd7gd923kKEvkKIvFpmdixc/hUbJrEJHPldn49BhJFllZ8nKazpG4Qa32JnRWJgRgp8UGs9hiBlnrwgxyedmj33jXG2qLjvCkSUe45Ge/kiXaAx1hP1Ak6Xz9nwcj/gavPtptvpIknc8X3kZMoBElvAYjAZ2Z9YPR0qxrRJEHHjexSO9aTndBAO67bdTyDfa3uutu9KLa7DDpCNitAyXDtIrEuENP8rRIsLPifsPHup0mPHYj22unbIRv9olutxm3zUA2ccbGxvUWLotVz9/96Xf52t99C5VaTzTrw0DRSgiARpYQjRKKTkOlUkMQBVRVYPvJIUuaadRymZkHE5RrKnubp10HxEuFChs/2EcFJuc8GPQKGo1EtXp1/vXi10xG8iQjeQxGhYXlMU4i2a6XuFyqNdvQBFha8pEuFcjeIoLbDZ1+RgD2g02/NBewYfBeJj3phkFmNu5CQ1Upl5sMVqIo8HjFR1gtcJTpTziyF7yhvn1F0G3Dc1fgYUdHOJTjlNGps1p1WuasdnYOEqzGhmPsaEOnkVnyuggdxNneuiuKv4WC1mtDLtU5OMyS7PM7W00KAZeVna0YR/Hrm9mrnzo5bkcnSuxsRrjNhQUCdnSSyM76KRFgZt5z6XXfmI1v//xj/sbfXkKjaT5ghcL1ftRRQ5YlGtU6tWKFaklFbNRYeDSOrNVcGDRVoJArcxI8JRnN4gk48AfslErVc5aszmUKwP5aGL1OwaLITNz3cHiQJJtpDV12+U7ZdJG1HxwgSiJzH7gQRZHj4+vsTfVag63VJn3g9D0bDUXksEsrVjfcdCVDrVmlmTEL5nEzz0KxgdTAB8El7ZJKnbUnYUSxSUcYFUoc3kZH2AeUjv7jz4MRf4NXH3q9nkKhgNls7inYMKOQChdHSrNu0GhYcjo5PEr3reV0E2RRZGXMTfQ4zf7O7XoJt9nvcacFkyCyt5divc+BdKNe5p7PwcFOjNDOdRtx9VN9HgsOo47g+imp0M02xeezYjNq2V47IXmYvMZ06HAa+Xs/8z4/8XceomsJBxYK9RfupzSKjFprUCmWqVUbSGqDqVkPikEh1/IraqMp6Lf5yR6qCnavBf+Ml8hJmkS0WZW56qeOtqNYrXq0qsrCw3HCpxkSsbPWsde/UyFfYe3jQxBgZtJJY9rF3n6XvYoK261k1tSsg4ZDZLdLB0M33LSVC4fSEEoz5jbimrazdhKjfC1AejFJsc5L0WiorK+eogLvLHjI6apsd2FaHBSfp3bf1zrYuIrO7NFNqFYbI+s395iMBPQmNvZirPYgxtMLTDqF+y4HB/sJ1tZ6y+x0MwTzY06EUoO94OVSdi9wWnX4rGa2t6JsRm7+Xu2PvTfpRKyp7Adjt37W+LgFnSCxtxXtelxg2snf//kP+NGv3Efqk6J4FJBFAUEASZaoy42mSvjHe/inXVhcZpbfnUKrb/VQtrJE0VCSWrlGLplndmWcQrl8404+k8yT+as9JFlk8dEEhfLtswmNeoPIYYrwcZrZBS+iTibYRQVYAI73msZ7csKGxiqze5QeijQjcpIlcpLF5zDgnXWyHo5TrPTHqNMvuhE8NBqw0Wr1evu+m4KxwVZ8cOEvuMgWQW9G/M1w+BsMi7aKeGewcRvUhjoy0hu7XseMxdakrx1pMsxJ+CjDRo8aGd0qG9NeGwZkgsEY/TYcW4wKkx4bu5tRNm+h0G0/vxNjNkwaDfd9on8AACAASURBVMH1U25L33k8BmxGA9tr4a5r8vgs/NTPPuZv/jfLaJSXv4XSyCKqWm+2/Taa3y+8F8XhtVCt1ll8O4DBpAAXPjoVyZKO5lCBew/GKdUqXR21CuSzJf4/9t40SNLELu/8vW/e931UVmXdV1d19/TdGkkgCYRAGjFCGgldCzaHDZiItfESLI5d/MHhiP0i8LFsBMsGtj/YhBcL1sKAxSUDazGamZ7pme6uKzMr7/usrKy8r/2QldV1ZNbd0sxsPxETE1311pvXm///+/yP51l5LQACzF0bo3PSDXsXtrM7hDdSjE9ZUJvUeLypgWNz+XSZUqnOiEOHzq7CHy3Qah9zoZ/w2PlMmXymjEGnwL3gxJMpUKo+Hb99FoOvg65jAfBu9JQWr06ZwSThSebiI/T7OxtyufxUf/NuHfd9z5MNuVxOo9FAoVDsGSYdd/MgvYS2mtugxyJRshZIs3VJ8rUmjYopgx6fP3PmGdX93+krbhv1rQah9cGt7OPgMKsxq1X4PGm24ye/LodFi04iIbI73jXssWZnbNS2KySGzAWbrVo+/5X73P3gzDNpe54WMqWEbqtFs9aAFozPOUAQKG9X6bQ6bLzpY+neNA6nHvuEhXa7Q2mrQqfdodvusPmop5xluq1m+YabcDBLaatXadr/qtqtDhu7i4U3PjTLlWUXnvUE7QFBt6ejDsGNXiVyZNSAecSAx5fea3ULwtOkkohuQRRsFg0ml4ZAfItG8/ydia18ha18BY1axtKSE19ui2K5dmmEfT+Om+kWAN/u/tPShAmJVcbj9PlGQES6eyTj/aBf/hzvfuw39juNi/hFHcQBHFoNo0ot64GLezn1cbAYdrZz7v96z7ksiPUufm/uzMUws16Jy6TDu5Fm4xS50u0yIu2A33N8MWxiwozYbhPyZslRPHKc0azhC//Dfb7vBxZ7MukD0N/HeZaQyiSIHWhWm3SaHSwOPaJEpFZpUK82WX/Dz/zNcSxmNc5pG4lIgXxffUoQCOz6P01dGWH5hpt4rEBht4NxoFDWBd+j3j7h1fuTLF0dxedJ7i2hH3rhAMQCOQjkMNu0jExa8Poz1GpPi1T9tyabKpFNldDplTgmDISSW1TrJ1sXDEO5VGf9QRS5XMrtqw5CpR2yxfIz6mwcn6eCgTwEYG5Ej3ZUzaN0ivY5rgmJINBpt5HsxovnnY3vMdRqNeVyeY9stNttZAPs5FutFvF4nOwFZl/nLWbkTYGNUI4UlzOf5zRocarUeDbTrCTOt2jU6XS5OuGgmC6zuXL2G7BRmxatVM6mL0P+BBdVQYD5aTuVQpVsZItcbjgpmZ+zU9mqEngy2AhpYWmEz3/1PjfvTp4YFL4rQVwuRRQFZFIJEoWU+GaW8naV0Rk7Wr2C8QUndLukQllK+R3qtSbzL4wjSES29mmYN5stvG8GkcgkLFwfp9ZoDx1batYabLwZRmNQMDJvJZHcobRPcePwTX06ViQdK+5KEroIRvKUy7UjVdA9mVuNjOlpC5F0kXL1aSflrDG4Wmmy/iCKVCbh1lUnUunld55apxzXCocKEIIZhw7B0CZQr51p1Esuirz66qs4nU6azeapgvhzovEcF0E/T8HxLuLtdptkMkk8Ehn4+9Og7+W0HsxQuKRimFmjYvKcxbA+up1urxhWPF8xzG5SY9Wq8W2k2Uic/LpmJq10ay1i3hztdmfoY01PWek2WoTWUwPJyPiUlc9/5R4f+L6570nH/TAUGjkIHWRyCTK5hO1siVq5jslpxGjVIlfK6HYhl9gil9hCoZIzu+gEqZSQN0lzlyw06y1WXvUiiAJz1910jpFbbtfbrL8dRKWRMz5rIVeoHzAtPPyeFTI7FDI7KNUyri25iKWK5PPlIzl8Z7vGzuMacoWUxTkL8XyJ7Z2nex1nfbcbjRbrb8UQRIEby85TdwPOgtMKHSQS25DYZtSsRmkVCDVr1FunJ1Ryicjrr7+OxWKh1Wo9Jxvfa/Tb02azeWB7utFoEIlEyOVyjIyMMOEeg+jZbsiX7TYapRbBzb6m+cUxbtZjlCjY8KcpdI+/wR8GmVTC0piNbrl1Zu1xgIkRPdKWSCiYI83xr0sUYH7axnamzOauBKHFclTWTxQF5uccFNPbbD4aLHN77YabV756n6svjL2rbuJUCjmteotOq0Wz0kSrleOastJsddhKbxN6EuX6h+dZvjtFIpSjUqrheRBAo1ditWmwjpkJbiT3bvzbzTYbbwYAuPH9C1y5PoZvPbEX7IG9SlK5WMf3MIZULmHpupvCdpVEbGsoKSgVe3sdUrmEyRkj25U22ezRBFwtN9l8nEIiFVmYt5Le3qGw3aB9hqC3H61mm42HMa5fcXHDYSNW3iZzRtncYTirqV8qVYIUuEwqnNNGnhQyVJsny+ZqVEpefPFF4vE4+Xwev9/P7OwsKpXqvE/9OZ7jWOzvbAzKU81mk1gsRiqVwm63Mz05CZvDZcIHYcFqQVIHbzB3aV5Ol1EME0WBpTEbzVLrXMUwl02LXibH581QOKEYBjAzYaZZbhFaTe09/iDMzdqpl2qEdseVDx81M+/g81+9z50PTH9PO+6HIVNI6bQ6tJtNWrU2CpkE85SVTheS4SypcI6FO5Ms3Zsi5s/QqDXxvdPrpNvcFmxuC0FfYu/Gv9vp4n279/trL86xdMONfyNJrXp0bLZabuB/J4EgEVi87qbSaBEODh9rrVWarD4IIUgEpuesNDodyuWjS+KNeovNJykEUWB+3kqhViWTr9E6hVHzIHQ7XbyPE1yZd3LNYiHfqhArXs7+01nzVD5fgTyYtArGFxysFbOUTiGbq5DJePHFF0mlUqytrVGv11EoFGi1gw2M3+3jvu8LsjEoiFcqFcLhMKVSibGxMaamphBFEXnqdMs7kl1ZwK1cFe8lydcCzNrNyFsCvmCW8/ZYVHIpCy4rkWCBtUcJRh2Gk/9oH/ZcVH0nu6hKpSIL03biwSy+Q4Rm/+yiRCoyP2snF9vC+/bgqtziVQc/8bMfZXHZdabnC5fX2dhzPRfFI0RHqpBCt0Or0UQulaN1GEhHC8jVMlRaJVfuTNFutVn5ziYypYyFG+NUqw1SkTzZ2BbZ2BaiTESnVTA2bSPqf3rdNGtN1t8IojGomL/hJhbJszWgK9RqtFl/0FMHmbkygtqsIRoersLVarSJePK0u10WrrqotTqEQkeDf7vVwb/amymdnbPSqF9Qrk8Q8D6O92ZUF+zUVQLe+MV2Kc67iF4oVCm8WUWvlnN90cX6CbK5cokUiUSC2+0mHo9jNpt55513UKvVTE1NodNdngHXczwHPC2KwcE8Va/XiUQi5PN5XC4Xd+7cQSKRsLG+earz7nk5bTcv1ctp3GLAKMovXgwbtZKObeN5kkKrPluVedypR9GREAhkyXBSMUxgYcZOLl4gdMhg9qA8PszPOSjlyvgfDyZz49NGfvJnP8aNOxNnLoZdZge+0+kMFLxRqxXQ7dJptAABnVnDdqGMVCrpjbvem6bV6rDybQ+CKLB0b5ZarYl/JUZpq0ImVgBRYGZuBJlCSnCfwla70WLtrRAKlZyl624y6Z7c+uGX1G138TzsEZTxWRs6rXKw2uK+40PrGVRaGbPjFpBJ8G0eJZ7dTpfAeqa3WzJlptu6WCFLFAT8a73XtzBlRmJSsBq92P3caVTVBmFnp87qm3EUCgn3r4yweYJsrlwiQRRFRkZGSKfTmEwm1tbWkEgkTE9PYzQedaMXBOFdVcDdj/cF2dgfxEulEuFwmEajwfj4OAsLCwfefPkJbVClVMqMVksmW2XtkmQBAa6MWGnvtAhdICHo1ApmbSb8viyr+9y+TxPcBAFmx8xUt07noiqXS5ifshEP5tkY5qLaBZlMwtysnVQoh+ew2/cu7n9olo+/tIDBLGFm5uxE4zLQ7XbpdDq02+09g5w+4ehfH3KVDIEutNtIlCLNepNauYpj3EynA+/8zToLd6ZYvjdNJrHFxoNe18I1a8NgM7K5EqNRa1LMloiuxxmbd6AxavA9ie8Nq5aLVVZf9SFIRBZecKNQHh35g12X7bUE7hkrTosa84gRz3pysFFgt9vbaXjSu/mfmrUhUyvweI/SWQEIebNcvTbKpLlLU9omdoy2/EkQgMDu9+SiwbzVvpjqVbnSYOWtODKZyL2l4bK5in3z1p1Oh5GREVwuF/l8no2NDQRBYGpqCpPJtHdtvFsD+HO8N3A4T1UqFdbW1iiXy7jdbqanpw+Ydp2Up6SiyLzRwFauemleTrBbDGuCL5C7cDEsGiywtq9AddqbtOlRI91qh+jmKYphEpH5aTu5RBHvo8Hjut1ur7uxMO+gkNzG985gw77rt8Z56bPXUekaLC9Pnuq5Pgt0Oh0ajcaBHLW/OCZXKei22nQ6bSQSKZ1Wi3q5is5lRiIVWXvNh3txhCt3JtnOV4j5UhRzO1hHTYzM2NlciVMp1UjH8qTCeZyTFkxOI5urcbq7rfZ6tcHaa5sgCMxdG0OjGU4Uo74MKpUMs0aGc9KG15OiPkT8RBQFAhu9a8I9bkZr0bLhSR4hKQIQDeRZvjrKqK6JRCMQSZXPLJqwP2xHd3cpZkYNaEa0J8rmDsNF1Rnr9TYru7K5d5YcpKgSGSCbezhPWa1WxsfHKRaL+P1+ms0mU1NTWK3W90R+el+Qje3tbWKxGJlMBrlcztzcHAbD4Gq/dICDOIBOIWfeaCYQzuPNnk6W7SQIwLLLxk6hhn/j/AnBoJYzZTXj86RYSR5tkB/X1pOIAlMjerYyFYKncFFVqWTMui2EfVnWHw5v4ysUUsbHTIQ2UnvLzvshigIf/Mg8r3z5HuNTVnK5HFtbl/O+ngX96pBEIiGZTKLT6fYq1+12+0Agl8lE6HYQJAJ02ghI0BlUCAKU8iVMNi1ao4r11wPsFCvM3JhEIpfifxIl7sug0iqYWLSh2q3gRT29dK0zaVBrFehN6qfyuO0OnrdCLN+ZZMJtAoVA2F+gO+CzTEcLpKMFDCYNo1dG8PszVPa1ovf/hQCEfb3PecRlwOw0sOEdbBIYC/WSucOmRmVWEE6UTuXZAoMtVvrBfNqlR+vSnTmYnyfwD0Kz2Xkqm3vFQVo8KJu7X42qXzkUBAGLxYLFYmF7exu/34/X62VychKbzfaeCObP8e6FVqulXC6TTCZJJpN0Oh3m5uYwm80Dry3ZkPlslVTKktVKNLaFz395EpsLdhOdapfwBYphaoWUeaeV4ObBYlgfnWNUhwQBpl0GdvJVIqcphsl6xbBkqIDnneF5SrrbcU9HC0OLYbc/MMXnv3yf+aURdnZ2CIVCJz7+8Ndxvjixv4uRyWQwGo2YTKYjvxNFEYVaDkIXmVRCp9VB6HRQqeSIIhTTRVRaBSa7nsDjGFuZbRbuz+KctOJ7FKFRa9LcqTM2Z0at0ZAK50kGcySDOVRaBVqtAotDTy61e/Pb7eJ7FGHhBTcjdh1qs5KgL0f7UD4RBIF8ukQ+XUKtVTB3bZRIJE9x69Do0r73Jx7OQziP2abFOWHB60sPJCnpeO+5mI0KjE4NoWTpeAWrgw945CfJWBFixT3Z3JVYhsYZxorPOkZ13Hn6Sos3F2zsqNp4c0+nGIapJhoMBm7evEm5XCYQCODz+ZiYmMDhcJzaZfx7gfc02Wi328TjcX75l3+ZL33pS7z88ssYjcahRAMOauwDWDVqJjR6NgJpHh8j83oWyCQiyyM2YuE83iFGfKeB3ajFopAR2CywOoBk9DGoYiSTisyNmUnHSoQ8J5vhaTUKpkZNBD1p1oZ1MgC1Ws70pIXQRpKoN72n692HRCLykY9f4bNfuotrzLT384u2l8/69/0A3ScUo6OjaLVafD4fMpmM6elpNJrezkn/mMmr43z65z/B3/7payTXc4gIyBVS6HRoVmtYR000a02qOxUW705TLtWIPApjcuhxzToJrsYJPYozvjjCzNIIXQT8q3FKhTKVrQrlfInFFyYoFqskdkedukDY07vujHYtrhknm/vmZfcnr2KhTPF1PwqVjOWrYySS2+Szw71T0vEi6XixR1IWbPj8aaoD5nDzmQpkKljMavR2JaHUzuAOygEM/yz6srkjFg32aRMrQzTQD+OinY3D6HRgfeWpbG5V3WEjlzsSAw7fIOj1em7cuEGlUiEYDLK5ucmNGzdO7TT+HM+xH91ul3A4zL//9/+ehw8f8nM/93NIpVIsFsvQv5Ed6mz0vZz8oRyPs5eTpySCwJLLRj5RJOg7v2GqSadiVKchsJllNXO2PCURBebcZoqZCuFT5CmlQsbshJWYP9szjBsCuVzK3IyNeCDLxgCSIQjwgQ/P8bmv3GN61r7v5xcvKpwnT/XHe202G2q1mmAwSDgcZnp6GoPBsHdMt9vF6DDwoz//Qzz8748JvBlBEHt5l06X2k4F55iFVq1JMVNk/tYkdDqsv+FHZ9YydWWEwFqC6EoKs1PP5KwdmVqB73GU6k6dUmGHfCzH3AvjNJodQrsFsy6QDGUhBBqDkokbbkL+DDvbR/23Kjt1Vl/3I5FJWLrmJrdVIZXoFXsGvbv9ZXK1VsH8sotgOE+p1Dvv/o9je6vO9lYdnU6BZUxLJL1DfZAy1j4c93H2ZXPNeiWjCyOsp3KUayfvUlxWUayPnmxur0B4ddIM5p5srnyfD88gNSqNRsPVq1ep1WqEQiH8fj/Ly8s4nc5LfX6Xhe8K2fjmN7/JP/yH/5B2u83P/uzP8qu/+qsDj/v617/OF77wBd544w3u3Lkz9Hzdbpff/u3f5rd+67dQq9X8wi/8Aj/zMz9DJBI5UVJQtsv8xgw6rFJVTxawczl282q5jEW7hUikwNoZZQH3Y8yqxyhT4PVk2DpF4NrPtJVyCTOjZuLBwkEX1SEwGlS47QZ8a0nWMseRDCnjoyYi3gxrb/Y6GTLj04tfKpPwgz+yzI/9+B3szqNk77uhJgW94N3/TxRFZDLZXjvaarVitVopFApsbGwgk8mYmpraW7hSaZVc/fACMhtYTFae/MUGf/317yB0u0glUiSCQLvRYmTCRqfVJhvJMDHvQKFV4XngB2D8ig2tXsXqa71/OyYsmF1mBKG3ML6+e9zkkgup8uDN61Z6h620D6VawfILYyRiWwOjc73aZO2NAILYW9LLFXfIpIersxQLZYoPyihVPWWQSHxwh6kvc6vVKbCOGohmd6hdQI6wr4hl0ClwL46wkcqxc0wwv2yy0ccB2dxxE26N/lR/p1arWVpaolqtolQqn8lze453Dy47TwH83u/9Hr/+67+OSqXipZde4td+7ddIp9N7I1XD0O9s2DVq3Gp9L09dkpdTvxiWjm/jWTu/OqPdqGVEq8GzkcITOzmH7new7hfDMvESm6snF+Q0ajnTYxaCnjTrbw8egwJQKCRMjJtIBQusD+m4f/hjC3zuy/dwTxwle9/NPLWfZPSd5UVRxGg0cuPGjb0Oa6fT2ZvR73a7yGQSrn54EZ1bxcd/8sOE30zw119/jVajjUwhQyIVaNWb2EZNSESRQrrIyKQFo81AzJeilNtmdM6KRqvD87DXxTGPGBmZsiNIhN5y9e7Px2YdaEzaA6WlcrHG6qs+ZAopSy+MD1WkbDfbrL/V2z+cuzpKud6gtDM8/ld26qw+CCGTS7m67CKVG1xI2ynV2Vmro1BKGZ8yEM+VDygtnhWl7Rrrb0RQKKUsLjvxF4oUdoYvkz9Lk9tgMA/BnmzuvOFpsbbdbg/tWiiVShYWFvb2kt+teOZko91u84u/+Iv8+Z//OWNjY9y9e5eXX36ZpaWlA8eVSiX+9b/+19y/f//EcwqCgFar5Vvf+ha/8zu/s1dxPI1Zkloq5ZrBynooS/oUyhangV6lYNZiwu/PsHKMudBJmHKYUHVEvJvZYx24D6Neb+y6qJoIbWbxPDk5KVnNGhxmLb7VJGuJ4dVxo1HFqNOA70kMb+7gTGy3C3KFlE98+jqf+fxtzNbBKgmXheOSQJ9g9NvNcrl8r+18GCaTidu3b1MoFPB6vXsLV2q1mnA4TD6fZ3x8nI999UN87Ksf4sE33+Gb/+a/sZ0vI5EpkCskNKt1DGYtGr2K0FoMo0WD2WUkuBrHOtJmasmFKBHZfBwlFcpx9UOzLN+dJOrPUsztEFztvZfXPzjH0q1xPI9jtHar/7VKndXvbCKIAhMz87RbHWIDlr67nS6et8N0gYl5O1K1gk3v8CunVm2y+mYIiVREO2vD4dCTSh2dFS2X6pTXe8F8btJAolBmp3L+YF4u1XvBXCHl9lUnga0i+dLRYH5e5ZGzIBwusOS0nelv+rLaz/H+xbPIU9DLSd/4xjf467/+ax4/frz3s2HSt30oRAk3zHbWg8+gGBa+WDFs1KrHtFcMO30O7XS6KOQSZs9QDDPolYw7jfjXU6wdQzI0WgVTbjP+tTibA0a4ZDKR7//BJT77pbuMjB5drO3jMjrwx+E4knEY/Q5rqVTC7/fj9/uZmprCYDAQjUZJJBIsLS3xgZdv8+Jn7vD4b9b5+q//EaVCGaVWiVIhpVlroFTJUOvVxLxxjHY9tluTBNdjFKVlxmZsqPVqfO+EySe2WLg9wdLtiZ7TeKJI1JcCUizcmmD59gS+tTj1Sq873qy3WHvdTxcYG19gcs5OcED+EYDNXW+PiUU7Iy4j3n2qjYfRbLRYe7jrTO42MTZmIho9OjJYr7UIruWQSEXmZkykS1WKpYNk5ix+UPVai/U3o0ilIrevjRCtlEkVjt4bPUuy0Ucisc3IIaGSk4iETCZ7V4/7PnOy8frrrzM7O8v09DQAX/rSl/jGN75xJIj/2q/9Gr/yK7/C1772tVOd96tf/SpwdPGu2Tze5VjSEdgIZS9FscOqU+PW6fBupllJnZ9kzI9aoNolcA6DI71GzrTThGctxcYp2utOuw6zVoV3NclWdLhXiMWiwWHV4ltJsJ44epxKLeeHXrrGSz92E4NJfeLjPqsgfphk9L9wp2H4JpMJk8lELpfj0aNH1Ot1RkdHuX//PqIo0ul0EASBOz/yAnc/eYO173j51n/4NhtvbCJXK1BrFXQaLdqNFo4xM8lQhlatgcVlJOHPkIkVsI9bsI6aoQsrr3qRyCQs3piguFUlEczSajRZfRBEpZUzfd1FIrxFaWt3r6PTZStZJOZJ9ciLXMrmxtHPWAAinl6QH5u0orVq8WwcXbrro93qUCnWyAZzTM5aqLW7JJNHbxrqtRbB9V4wn502kd2pstUP5uf4KOv1XjCXSERuXRshslMis/30Ruq7pdy3vz39HM8Bzy5PvfLKK8DJ0reHIRNEVv2XI1BiUCmYMZvwBy5WDJt0mFB3JXh9Z3f7ViulTDtNBAOZUxXDzCY1Lou+13FPDu+46/VK3KNGAqtJ1gZ0MmQyCT/4yat85gt3sDtP19G8KAblucNjvVKpdK/jfhJ0Oh0vvPAC29vbrK6uUqlUsNvt3LlzB6lUupenrn7fAtc/coXAozD/9Xf+G0/++zpKrQKFQka33aFermO06khH81SKFa7cn2Urs8PGG5uYnAZc0z0T29Xv+HqL4TcnqNdahL2pnoz7W17kShkzy04KmQr5dC9nCEB5q0roURT3nAOVQYV3NT4wnucTJUrFNPZRI5ZREx5Paq/IdvSNhNpOg7gnzcSMhbYI0djRe5F2q0NwIwcCzM6a2arVyW7tqlgJZ08qrVaH9YcxBEHgxlUn2VaNaO5pfhxkvvssoBhiHHkc/n9NNmKxGG63e+/fY2NjvPbaaweOefjwIZFIhE9/+tOnDuJ9aLVa0rtOwqdxZh22eHcWuEw67AoV6740q7HzmSYJAlwZs1Et1Amund3gaL+LaqSa33OTHgabRYVFp8W3liTbHb6obbfrsBpUeJ7E2YoePU6jVfDpz93kk5+5iU5/+tGSy25P95Wl+uc+C8noo9VqEYlESCaTjI2NodPpCIVCPH78eK+C1K9CCYLA4v1Zll6cJ7Qa4w/+5R8T2UgAXVRqeS/gFso4Jky0600ykRxzNydpNFqsvupl+cVZlu9NE/amWH+jN0o1ddWNStPrylV3GngeBJHIJVy5MUk+u0MqWgBh10F8txPimrSit+vwrMYHLlzGg1kIZrE49dgmrMMdXwG6EPHmenK4Cw66Mgmb/qO3Ee1Wh5CnF8xnps0Um3WqtaOzuqdFu93pKZwJsDhjoqqUEM9t03xGY1SH0d/Z6H+uz/EczzpPDZO+HQb5JeQpm07NmE6Hx3exYticy4JQ6xDw5s9cDNOpZUzYjQS8GTZyyRPNRI16BaM2A97VFOux4V0Tk0mNy67HtxJnPXX0OIVSyideus5nvnAbk+X0Hfdnkaf2k4z9Y71nOUcsFiMajWK327FYLEQiER49erSnmtc/rtPpMHF1jH/wr/4OqVCW/+df/VdCq1GEbgelSoYowE5+B9uYgXajRWIzxdQ1N6JEZOVvPczdnGDp7hTJcB7vW0EA3Asj6M1qRFGgUWuy+TDS88O4MUGl0iTqz+x9rtFdBUSby4R13Ix3LX7AV6p/XCa2RSa2hcGsYfTqKJv+DNXK4BErAYhu9jr7k9NW5HolHs+ArlgXQt48XWByykSNFpXK+b01ut2eV0cXmJ3QI5hUeOO570pnA3rS0WfBIJnkdxOeOdkY9MXd/4Z0Oh1+6Zd+iX/37/7duc5/uGJ0Unv68HLoWTBlNaJBisefIXdOB3FRFFh22ymkdvCdw+DIblJh1WoOuKgep44wMWZG2u4Q3MxRYPi41MiIAb1Kjm81Tm7A6TRaOT/y8jV+7Mfvo9acfVH2ol+CfhLok4xut4soikil0jOPuOwnGS6Xi3v37u2dw2w2UywWCQQCdLvdgQt67sURfum3/z6ZaJ4//D/+lHQ4A90OcrkEqVSkul3FNWVFFAUiazHGZhyotAre+tYaolTCmCltgAAAIABJREFUlduT5NLbBJ5EUKqkOEcNKHRyIt4c7Uabtdd7Gvsz192odAcJXSKYJRHMYrbrcM7Y8a0nqdeOdvNyyW1yyW20BhXzV0fxB7JUyoM1y3vytb3gPTFlRWlQsjEkmIc3e8F89LoOp1VFMnsBo6QuRHwFusDigp3aJXh1nAZyWe+zHrR0Nwzv5iD+HBfHs85TWq12L0+dqih2gTw1atJhlavY2Dx/MQxgyW2junU+t2+TXoHLpGdzI81G5mknQyIRB4pPjDj06JVyNtdTbCSHj41ZrVrsZg3eJ3HW48Ujv1copXz0E/N88Se+D4Px5I77YVyWkMlxu4OnxWGScefOHWSynlS60WikXC4fGK8ym817f9fpdLC5zfz8b/wE29kSf/R//gXJUBq6IJX0hE/K+TI2lwGVSob37RAOtwm9WcM7/91Du9Vh/vZUTwRlI4FMJsFk1mBwaol4szTrLTy7ZGTiimtPgbGPTLxAJl5AZ1Iz/4KboD9DuXS0QFXMlym+5kepkXN1eZRIbIvi1vDPP+LvXYuuUSNGh571IbK5scCu0uI1DWN2NdELyrsnQtsQ2mZhyozcouJx+Py7TqdFP0+9X/DMycbY2BiRyFM1iGg0isv11GuhVCrx5MkTPvrRjwKQTCZ5+eWX+cM//MMTl+/gYBA/VcXoHEF83mFBrHfw+89e2dn/uFdGraSi22ycw+3bZdWglyt2XVQPkoZBZGN6woLQ7BD0ZI59zu4xEyqpBN9aYuCeiNmi4TM/fofpRTVOp/1cRKOPiwTxfgBttVp7wfsiJGN0dPQAydgPg8Fw7IJen3RYXEZ+5n/7Ml/8lZf55r/9K6Ibcej2nF01OiU7hRJ6swa9WUN1u4rJqsUxacP3dohGrcnszQlUGiVrb/T8OvQWLe75EfzrCao7dTYfRZhaHsU9bkZlUON9Ett7D/sygxqdEseMkUKmRmmAMshOscrqa7sKVtfGiCeKFPLDb0CigV4wH3EZMO3K5rYPdRwEgJZILlDCYVch08kuHMxT0SI7lToLk2YkZgWrkcvzDjiM/o3cc7LxHH086zx1uLNx4s7GOfLUlM2IptsrhmUvWAzbSpfPVQyzGVXY9Rq862k8AxzHDztxu0dNqCUSfOupY0ezHA49Zp0S70qcQuTo/L5Wp+TTn7vJ9Ts29Ab1uYjGZaCfG/bnqbOSjL7KZjQaxeFwHCAZ+6HRaLh27dqeBGqfdPRVzvo5U2vW8JX/5bN87h99im/97reJemJ0O21a9SY6o5bKdhWFUoZ1xEhlq4xaJce96CKwGqNSqjG1PIrerMH/JEouUUCtUzB/d4qwL0WpUCG0FkelluNw6DGNGPA+ie1J45YKFVa/s4lcJeuN7NY6bB+WwwVq5Qarrwd6ClYvuMnmy6QH7BP2kYptkYptYbZqcU5a8PhSAzv4EqSkNrexmBRorEqiqfKp5d0HIRrII4tJmLZp0V7Aq+M06N+rvtudwU+LZ0427t69i9frJRAIMDo6yn/8j/+R3/3d3937vcFgIJvN7v37ox/9KF/72tdOFcDh7O3p045RCcCSy0at2CDkPXtlpw+lXMqiy0okkD9gcHRauO1aqLdIhLaHuqjuJxtzUzaaOw3Ca+ljn/PkpAVJu0vAkxpIRmwOPZ/94h0+9sPLyOVSNjc3v6vStX30Oxl9I6xIJMLk5OSZiEar1SIcDpNKpY4lGYcxbEHPZDIdIB1qg4rP/+OX+MhX7vFf/q8/48lf+qDTptNsYTCpqZdryJRSnOMWIhtxZBKY+8AM4Y0kUonI2JQNQQ5RT4aVV70oNXKW7kwSD+UQBIh4e9eNfcyEZcyM90l8b861XKpRflJDuqsMkkoWyWUG7F9Um6y9HkCUiFx5wY30hJuZvmyuaS+Yp2k0ni6JPyU9VUhXcdq1yA0SoqnKuRxWpbvJOLKrxjHlMqB7RsH8PJ2N53h/41nnqbMWxc4y7rvgtCBUO/g3z18Mk0pElsZspM9ZDHNaNMg7beLRHbZiwzvofbIxNW5B0uoS8B5fDBt1GdEqpXhXEmQH/N5gVPPy52/xwz/6Aiq1nGg0+j3LU30C2el0CAQCTE5Onkkuu91uE4vFiMViOBwO7t69i/QU+2V9CdRKpUIgECAQCAwkHXKVjE/9vR/gI1/+AL/3m/+ZB7//GLoduq0OepOaZq1Jo9FibNZOMpCiXWuwdG+aZChHt9vF7jKiNimJbKRZ+VsPUrmUK7cme54cgkAqnCUVzmK06XDNOthcS1DflVpvVJuEHicx2bRcWXZR2KqSjB0ljT3FxiAIsHDdjUJ+/OsvZHcoZHfQ6pUsLI+yGchQ2TeO1f8stwt1tgt1zGY1WqucaLp8Bq+Og2i1OiTjRYgXcdm0WCeMPIllaJ7Bq+M02D/u+25WmTotnjnZkEql/OZv/iY//MM/TLvd5qd/+qdZXl7mn/7Tf8qdO3d4+eWXL3T+sy7eyaXHf2gSUWDJaSUT38J3AY8MvVrBqE5NLFwcaHB0EqZdRqh3iAROdlHtdrsszjrYyVUIrByfKGambXRqDcJryYHndbqMvPLle3z/xxcP3JB+L3wy9u9kaDQa7t+/TzKZ5K233sJisTAxMYFcPtzZtNlsEolESKVSjI2NnZpkHEZ/Qa9UKu1VkKanpw+Qjk6ng1Qh5QOv3OQn/8mX+M433uQ//fp/QaBLp9lCopbRaraolarM3JgkE8pQLZbR6JTkEltkPAXMLgPOSTu+dyKsvupFlIpMXxnBOW4hGc49NfezaBm7NoZ/I0l1dzSqtasMIkhEFm+4KW7XSAxQ8Oi0O2y8FWLp5jgLszYKO1XSyeE3B/1grtErWVgaxR/KUC43jiz/5dI7kAazRY3WcvZgLpEcvBJT8SKpeJERmwbbhOlSg7niHJ2N53h/492Wp2Qn5Km+YWw+WSToOX8xTKWQMW3RE48UWT9PMcyhQyXI8G8eTxr6mHKbqRUbRNaPL4aNu83IRQH/enLgec1WLT/243f4+CevolA+rfp/r/OUQqHgzp075HI5Hj9+jMFgYHJy8ljp7P0kw+l0nppkHIZarWZ5eXnPH6hfHLNarXvPtdPpIIjwwicW+alf/QpvfPNtfv83/oTWVpluq4VU7N1PlHIlpq66KaaLFJIF3HN2GrUmgbejvQ78ogv/Soy113rL5O5ZO+5ZOxFfmq1Mia1MCbVOyfKtccL+7AHRk/UHu3K4L7iptzqEB+wJ0gXvOxGWbo4zM26h1m0Rixwdm+tjZ7vG6hvBnrz7sotwrECxeLSDUsxXKOYr6HQKTCNqYrnKiV4dByAc9IvJZXbIZXYwG5SMzo2wnsxSqR8vUnRayGW9a+D90oH/rsiyfOpTn+JTn/rUgZ/9s3/2zwYe+1d/9VdnOnffmRUuVjFSyqRccViJR7fwXIBkWHRq3CYdPk+azeTZlmgFYGbMRGO7ScR78uy6KAosztgpZsr4HsWPPXZsVIvYFAiuDD7OPWnhlS/f44Mfme8ZBB1+bt9F/fH+ZyiK4p40YB8ulwun07lHOqxWK+Pj4wdIR7PZJBwOk06nGRsb21OXuih0Oh3Xr19nZ2fnSKejVCqxubmJ2Wzuabm/co8Pv3KPt/7iMV//9T+GToduq41Gp0Sgy1a6yNTyKJ1mm0w4i3vJQaspsPq3XnQmDfM3xgmsxdnOlkhuJpl9YZxGq0PYk6KY26H4qhelWs7yrXGCvhSVUp1ut+dMvvFmqLfUdn2MZhdCvsHXs/dRrLdMN29HVMrxDzkOoLxdY/VBEMWuV8cwIdxirkIxd/ZgLh1wzcE+4yWDktE5J+vJ3IWD+XnGqJ7j/Y/vVp4SRfHEWDpsQVwqij2Skdq5kGGsTq1gxmYi4MvizQzqGRyPqREDQhMi/p4R30m3OAszdqpbNRKbeXZ2Bu+OATgcajQyBcEhHXe7U89nv3iXj31iCdmAqrcgCCeOqB2H0+a5wyTj8O6g3W7HZrORTqd55513BpKOdrtNNBolHo9fiGQcxn5/oH6nY3JyEpvNRqVSYXNzE5VKRafT4e6P3ODeJ2+y8rcefv9f/DHFzDbdVguVSoZEKpJPFJi8MgLdLnFPgrFFJ4gSVr7tQaVVsHxvmuhmmup2lchajMmlUSQKGZtPeiNYK6/6eh2Qm+PEI7m9DXEB8L0T6eWfRSdSlQLf+tGirCAIBHZ/7ho3obXq8KwPJ8W1apPVByEkMglXr44OvJeBXa+OUh2lSsqoW0eyUKVSO1neXSoRBwqZlIo11h9EUKpkXFly4s0W2K4Mv85Pg/fbuO97XgNyf8XoNIt3h3c2tAo581YzoVCO1dXzK3Y4TFocajUez9mX8kQR5sYsbOcqhDZODvxSqcjCtJ10ZAvPO/Ejc7B9CALMzznYzmyT8B2tcgNMzdr5/Ffuce9Ds0PP0zvXs60YnUQy9kMUxYGkw+VykUgkLp1kHIZWq+X69euUy2U2NjZ49OgRKpWKhYUFDAbDgcXAWx+/xu0fus76Gz6+/rU/Ihcv0Gm1UCplyOVSapUqE4sjNJtNEp4MU9fcCBIJT77tQaGSYbBoMNp0+N7pyTpOLLr2HF9rlQYrr/oQJAILtyfJpXfI7HNq3XzU06WfmHci0yrwrQ0I5kB4VzZ3fMaGUq/CM0Bet4/6rlfH/LKLxVkbyUyJreKAXZEzBnNxSFLooxfMo7vBfARvNn/uYH7WipEgCO/qIP4c737I5XJardN71UglIoLwVA66VwyzEI8WWb+AR4ZZp2LcaMDnTbGaPFu+6xfDmjtNoqdwHBcFgYUZO8XMDpuPe49lMKgGHjs7Y6NZrhMN5MhylGS4xkx87st3+b4fWDx2BPRZF8XOIlAiCAIOhwO73X6AdLjdbrLZLPF4nJGRkUsjGYehUqn2SIfP52N1dRW5XM78/Dxms/lAnlp6cY7lD/4SwZUIv/8bf0zCn4J2G6kEVGoFlWIF17QNUSIQWY3hnh9BpVez8re9Drz6hXFsoyaCqz2Z4pFpO3qLDu+jCK1Gi7XX/CAKLN2bQq1REN/1jRKA0C55cE1a0Nn0eFfjA/dQk+EChAs43abePuFaYri8e7PN2sMwy9fHWJyxkS6UyOeP5qlatUXEU0AqkzAzpSe9XaNUHl7MGkY2np6vydqbUWQyCbevjhDa3ia7fb59RsX7bNz3PU82dDrdmRbv+mTDpFExZTTg20yzkjk/yRizGjDK5Hg8aQpnMDiCnirE3JiFXLKEf+3kKpVCLmVu0ko8mO9Jh+7i8BdOFAUW5h3kE0V87ww2Qpq/MsLnv3qfW/cmT3Uj9azIxllIxmH0SYfFYmF1dZVXX30Vk8nErVu3zjQrex709zgAFhcXyWazeL1epqenj6iCiKLIwp0Z/tf/+x8RfBLh9//FH5PcTNFtd+i026i1CiLeHI5xE3KFFO9bQVzTDnQWLcX0NsXUFou3p8hndgit9zpTzkkrRocBzztROu0O3rdCtNtd5m+Os1NuEA8+7YyFPb1gPjJhweDQ41mNM6gWGd3stbOdblPv3OvDg3m33cG7kgQBlq6NUSjXSCSOtrn3B/PpKT2ZIcF8WAXq6PmarL0ZQSaTsDitJ91skh+gcnIc+jsbnU7nVNfa+2VB7zneOxAEAZlEglwq6RXDgjlWL0AyHCYtTk3P7Xv1FG7f+yEKMOc2U8rXTlcMk4jMT9vJJYp4D3XcD6ea+Tk7lUKFwJPBHffxSQuvfOU+L37/3KlixLPOU32ScRaBkj7psFgsrK+v853vfGevS67RaM79XE+D/h5HrVZjYWGBra0tNjc3abfb2O124GCemlga43/6nZ8nGUjzB//yT4isx6HToVVtoDepSUayGO1atEY13ochbE4DljELlWKFTDDD3O1JKuUGsc00CX8ay4gRx6QN76MozUaLZCBLPrXN9LUx2giE9qkeJoI5EsEclhED9gkr3rX4wJZZOlIgHSlgduhwTFjxbCSHSv93Ae/jOF1gYWmEaqdDOHyUKLeabcKeAqJEZHrKQL5aZ2v7qBSvRCqBxslFg2azzdrDaG9HcsZIvtMiVTzb907+PuvAv+fJhkqlorar+X+am2aZKHJzxM6GL83KALWM02LaaULRFvGd0e0bQC4TmRu1kIxs4Vs9WUJNIReZm3QQ8aVZf3uwwZEg9G7Y5uccpMM5PA8jA49bfmGML3z1PldvuM9UrX3WPhnnkbBtNpuEQiEymQxut5tr166RSqV46623sNlsTExMDFTxuAj6I1StVmtPoQrA6XTuBfZhqiCiKDKxfDCYBx5HESRdOo0WerOGQnILo12H0arF+zDI4v1ZFm9N4nkYpN3sMHdrkmq1QdSXJhnMYnYa0FiVZKNlquU6nrdCAExfG6ODQHBfME+GciRDOcx2HRq1DJlcckADvY/9wdw+YcU7KJh3n/5/41G0F8yXXVRbbcKRwcE8si+YF6p1CvuC+XFdtUFoNtuENwqIosDNqyOkGlXiudOR/X4Qb7Va74sg/hzvDfRj6Gnj7u1RJysbCVbS38NimNtCPrGDf+1kdTipRGBxxkEyVMDzzuA8JUpERFFgfs5BMb3N5qPBx83M2Xnlq/e5++LMmWJD34z1vDic5wb5OZ01ZrTbbSKRCIlEgpGRERYXF8nlcjx58gSj0XjmRfLToFqt4vf7qVQqe7lIEARGRkao1WoEg0GCwSATExM4HI6919rPU45JG//gX/1dvvRPfoxv/Oaf8va3niAArVoD25iF7cw2ao0c25gJ/+MQc7emWLo3zeaTKLVynenrbhAlBFZj5BJb6EwaXDM2qqXejbr/ca8I6l4YQaFR4Ns33p1LFMkliujMatRKGSqNnGr56I1/PlUinyqhM6lxXxtj05emWj103O5HKQC+1Z5nxsy8A+SDPaU67Q4RXwEEmJoyUmo1yRaedtClZ5yU6LQ7hDw9ufjrS062xRbB1HCfs/14Pkb1LsNZx2QUMimrG+fXSF4YtdKptAl5Tjeruh8qhZQZl4lwIIfnhEVuAK1GwdSoCd9qgvW3B3coAORyKXMzVuKBLBsDXFQBpudN/PQv/BBXro6e4Rk/xWXqj7darb3RlL404FlwmGTsH5caHR1lZGSERCLBgwcPLo109ElGs9ncWw4/jNMu6O0P5vlkgW/85p/hfxxEoEujUusF8+w2CpUMuULK6ls+1Go57gXX02B+zQ2SXjDPJ4uYHHqmr0wSWE9Q2anvBfPxeSdyrfJAMM+nSzgLFVSigGvWRCq2Q616tNtwXDA/fCUIgG+lV0GanXfQPUUwd7u1VLtdslt1xHMGyU6ny8aj+JmC+X6Vj/dDEH+Odz8GXT8nEY9AMHe25dV9mHKaUHZEfL5zFMOkInNuM4lwEd/KyblSqZAxO2Eh6EkOLYYBSKQi7lEj0kYb79uDi2Gj4zr+7s/9IDfvnq7jfhiXVRS7SMe9j/0k47Cf0/7xqrfffvvSSEd/T6NcLh8gGfuhVCpZXFykXq8fIB1Op3PvtffzlMlh4Kf++RcpFV7iT377LwmthhFFgXq5hsWpp1Ks0G23kStlbLwZQKTD8gdmCK0n2Nmq4J4fQW1Q43knTKlQxjVtY/nOJJHNDNuF8q4xbq9Tb7Dp8T6J7XXTS/kK1WIVodpgas5COrlDuXR0dLZUqLD6mh+VWs7Vq6OEwrmnMvCHLiEB9gpw45MWVEbVUE+pqH+LLjA2pqUpgVS+dkTI5LTokZ0kXWB5zk5TLeA5wVPqPAvi72a858kG9IL2aStGEomIKAhnkucUBLjitlPJ1wicosJzGFq1jCm7Eb8vw8aTk0mG0aBizG5gcy3JWmZ48FaqZMxMWol4U/hX4jTqRxPT3Q/O8MqX71LciZ2baMDFg3jfRbXdbh/QHz8LGo0G4XCYTCbD+Pj40J0MURT3SEc8HufBgwfY7XbGx8fPTDp2dnYIBAI0Go2hJOMwjlvQg8PB3MhP/fMvsvCJCXx/FeHP/u3fIEoEaqUqVqeBVr1Jt93CPecmvBZD7PaCeXgjQalQYXTOSVfaIR8v78nm7g/m/REq58Tu2NWTGJ32Uw30UqGCSiPn6gtugoEMOwO8Og4E82ujhEI5jshR7UIAAp6nBoEKw3C312R4hy4wMWFAysWWvg8GcxtNtTg0mD9fEH+O7wUEQaDdbiOVSvcq8Mddf+fyhBq10q20CXrOLoOrVEiYdZmJBPJ4npxMMjRqOdNuC8GN1LEkQyaTMD9rJxnKkQ7myaWPdliu3XDzylfvU20kuHVv6gzP+iAuO0+dh2QcZxp7+Ln2SUcqlboQ6ejnmp2dHaamprhy5cqJ90MKhYKFhQXq9TqhUIhQKMT4+DhOp3Nv0b6fp7RGDV/8nz/DwscnWf2LTf7sd/4GqUSkXKphcRrotlrUShXmbk2R8KWol6ss3ZsmEcwS8SSwj1tRGmTUyi1WXvUilUtZujVJKr5FLlkkGcySDGZ3R6hs+FZie133arlB8FECmULK8o1x4rEChdzRvdhqpefVIVNIWb4+RiK5PTRPAcR2R41PMghMRXtqjWOjeqRqgdxwUawTIQABb4/+z0+YkVmUrEQGlwP6yqnvlzz1nicb56l+yKQi9SEzfvshEQWW3HbyyR18pwi+h2HQyrFq5MTCJdYzJ5MMq1mD06zFu5pkPTFcjlSjVTDlNhNcT7K+28lQqWRAv90LH/zIPJ/78j0mp210u10ePBjeGTkNLqI/vn9cZX19nZmZmTMF00ajQSgUIpfLHelkHAdRFBkbG8Plcp2ZdPTdWRuNxgF31rNg/4Le/grSoFlZlU7B53/5Jb7wj3+Ub/6b/8Z//t+/iVSqo9NsYXEaaFRq1HaeBvPaTnVPAz2fKjI6P4LeqsX7dnhwMA9lSYZ6wdwxYTtwF1ItN1jZNV5avu4mFi2wNcD4r1ppsPpaL5ibFpwYjCqKAwya+ugbBI6OmdDbdWwMCebxUJHpaRtuvYqOQiCWuZhBYMDbKwgMC+b7F++e9W7PczxHH2q1mkqlgl6v31NOPO4m4iT52z4EAa6M2akWagR3i2FnyYoalRSHXkEmXjlVMcygV+J2GAlspFh7ODyvKBRSZqdtxP2ZvTzlGNEfOObWvSle+co9Fpd7BopvvHH+vRS4uJ8T9ARAnjx5wuzs7N6Y7GlwWtPYQc/Z6XTicDjOTDpqtRqBQIBSqXRqknEYCoWC+fn5vTz7+uuvDyUdcpWMH/l7H+UL/+hH+dbvfpuv/8YfoTao6bTamKw6Os0229kis7emyMXyFNNFFu9MUUiXCD9OMHXdje3uFL7HUVZf8/Wk2m9OslUokwzn90ao9GYN81fHDozQNestVl/zI5FKuHLTTTZbJpM8euffrLdYeyOIKBGZmbZhtWnJZobfT/UNAi12HXa3mQ1vcqDLfSq2jcOhx6VSItGJRDOV47jMiYiG8hCCqRE9ulEdK5GDnlLPOxvvUuzvbJxkgiKXSY8lGwqZhFGdkp2t9rkMjqxGFQ69Bu9GmnBq+M1YHw6bDotOhXc1yVZ0uGumXq/EPWoksJpk7dC4VH8W9vt/8Aqf+/JdRt1Pb44vYwTkrJKC+4O3RCJBKpVy+/Zttra28Hq9SCQSZmZm0Gq1Q8+xn2SMj48zMzNzLnWps5COvhtrrVY7sOx9EahUKq5cuXJgVnZycnKPdORyOUqlEoIgoNQo+Oz/+Ele+vs/yF/+h2/z2p88RK6UUS/XMdn0tButXjC/OUkulmcrtcXovBU6Hda+48PkMOCatuN9FDk2mF9/cZblW+N4VmI0dztijWpzL5gv3RwnnSmRHeDi2qy3KKS2KadLTC/YKJXbZNLDr9tktEAyWsBq12EbN7PhORrMJRKRVLRX8XQ5tUi0zy6Y7w/iJ11Pz5fDn+Oy0Deg3U82joP8BIUiiSgwYdZQ3+6catzpMAxaOW6rgU1Phkj2ZGU3s0mNy6LHt5ZkPTm8k6Ha7biHPKk9ktFH//t2/0OzvPKVe735+X246PftIj4Z/U5GX958c3MTv9/PzMwMBoNh6DnOSzIGPff9pOPhw4eYTKaBpGM/yZicnGRxcfHCeV4ulzM3N8fExMQe6XC73YyMjCAIAsVikUKhgMvlQiqX8sM/9VE+/hPfx//7B6/z2h89RKGU0azW0RvVCN0uuViOmWsTlHI7pIIZXPNWJBKBlVe9aI0a5u5N41+Nsf6gJ7Qyd3OCWq1FxJdmO9/r1F/9wAzLt8bxbySo7gqLtFtt1t8IIogCCzfcbO/USQxwle+0O1S2KuTDecbnrbS6EuIDvKf6yKdL5NMl9CY1Y/NOvP40tUMKihKJSCq+DSmwW1QozXKimcqFjGdTiW1SiW2cVg32SRMrsQyNVvvAgvhp73vezeO+7wuyoVAoaDQaKJXKPUWq4z4c2RBVC7VCxvyIhYg/TzQ3nAkPg9OiwaRS4vOkKR7jotrHqNOATinHt5Yk1x0+Z67VyhgftbC5Emc9dbQFLZWKfOSHlnjpszdwjpy+EnMWnEd/vC8NuP+zMBqN3Lp1i0KhwMbGBnK5nOnp6QOqHJdFMg7jONLRbDbx+/3UarW9TsZlf3H7s7J90uHz+RBFEY1Gw/Xr15FKpbRard6YmULGJ3/mY3zi73w/f/2fXuM/fe2PUCplNHdVQQTYDebj5DJb1HeazF93Uy7XWXl116vjhfGDwfzGBLV6L5i3mq0934ylmxOEfBnKu6pO7VabtTcCCKLA4o1xiqUBBoFd6LQ6BFdSveOuj1HcqZOID7+Oc+kSuXQJg1nD6IKVdU+SZrNHOva/1dldk0GbVY3KJCOSqQxVxToNDgfz/vf//bJ49xzvDZxVpn1YZ0MulXBl1EYyukXUf7albwCLQYnT0POC2kidXEyzW7XY/j/23jy69fs87/xg3wESC0kQO7iTd19d2Y4iRbYiL7IsR17SNs1knMStO00zTXN8ZjqJZtrkNHZUyCFQAAAgAElEQVSTTDs5x50ziXPmxOlISjwZy07jNOlJnDjSvdLV3bmBxEISIEAS+75j/gCBC5LgeklZur3PX7oiiB8A4ve+3+ddnkerYmE2wlxo9+vJ5SKGnCYCczuLYdC8x89fcvLLv3oau8t46Nd9EBwmT+21O6hWq9tGrl6vl0ajwdDQEFrtg85MtVpleXmZtbW1hyIZ3d5Di3REIhFu3bqFXq/H6XRSr9cJBAKkUilcLtexkIztaJEOp9PJ0tISb7zxBmKxGKlUyvj4OCqVqp2nhCIhP/rZv8eP/MRV3vrPt3n1a99BppBSLVVQqqRIJEI2ljdwTw6SzefJbGRwTwxSB+6/4UGuanp1LC+ssXCrKW7imrKAWIx/ZpVGrc70NS9imYixM3YiqylSmyNUjXoDz83lpkHgGSulWmNXg8DgfLT5uFMWSvU6y4Hd9yXSiTwzN5ZQqmWMTA5uIR2dimiJWAFiBXp75KgHZISiBSpdOiIHRSKaIxHN0auVYxkdaHfg6/X6sQvd/DDwSJANpVJJLpdrk43WXOxuaElftqBVynCbevEvbDBzBBlca58GpVCMzxslxu4V3hYc1l5kAiHe+XX2qkcZjWr69Co890LMb+xcqJNKRTzzsdO88NlLGEyaQ7/uw+CwPhnbScZ29Pb2cvHiReLxOLOzs8jlcqxWK+vr68Tj8WMlGdvRSTr8fj8/+MEPEIvFjI6O0tfXd+IHy3K5TKFQQKFQIJFIyOVyZLPZdpens20tFAl5+gtP8ORnP8C177zDq1/9DnKljEqxjEotQyIRkQqnGb7gpJQvE5pbxX3WQa1e5/4bLeMlF8ueNRZuPwjmMkXTBLFUqDDz5iIyhZTJ83aCgRjpxAO31/mbS5tur1aKlTorm6NRnd+FRr2B53bToGnktIVitc7K0u7BPBXPkYrnkCvFjE8O4luKdf07J6N5klHQbwbzYLTQtb19ULSCufwnD7ez8ZhoPMZxoJWn4HAy7e3fbxXD/HFm7x0+T/XrleiVChY966RX9/eCMvdr6VHKWZyNEK/vPqiu0ymwmnUs3A8x10UFUSgU8OQzE7z4hSsMWvfeeXvYe+0geapFMg6yO6jRaDh37hzpdBqv14tAIMDhcJBIJFhbW8NqtR4byej2XsxmMwMDA6ysrPDGG28gEAgYHh5mbGzsXclT+XweuVyOQqEgnU6TyWTQarU7xquEQiEf+MQFrn78PLf+631e+9p3kcql1CtVpBIRCpWU4GKY4fMuyoUKvjvL2CcGkSpl3H/Dg0QmZvKyi/ByDP90s2tmHelHoWrmqWqpxvzbfkQSERMXnKxH0sQ2u+4CYHFTEdE5YUYok+Db9Irq/C4IAO/9ppGte3wApOI9jWzz2RIzN5aQSEVMTpoJraURdlkQTyeLpJNFVGopvYNqVuOFIws7AGTSTU8p2bOXgGae2suB/v2CR4JstNrTBoPhYC7im0HcoFVi3azwHNbgCMBp1iGqwrL/YMpUbocBYbmO3xPdc3mvv1+LXitn4f4qiS7tQblcwrPPn+GTn7lIr/5kdbpb2E1S8LAkYzv0ej1KpZL5+Xlu3rzZ1h9XKpXH9tq7oVMa8NSpU+1/53I57Hb7iZgs7VYl61zQa0kRdgvmH3zhMk986hI3/vwur361WUGqlStIJE1FsuXZVexjAwgadZbureCYsiCWSbj/xsKDYL7UDOaTKhlWl4EqNSL+JKVCmZlNt9fJ804i4STxzUXOpttrM5i7JwdBIuo63iQAvPeawXxowkxdLMTv3V1QoZivMnNjGblCglYlQ6dTkErtHDtsBXO1Rkqv+eGD+aM2C/sY7w90djYOk6daxTDf4gYzdw+fpywmNWqxFO/iBnH274TYBntQisUszq2xlxyKXq9iwKRmcTrMXGRnkU0sEfH0Ryd54XOX6TfvPobUiZMaozosydgOrVbLqVOnWFhY4NatWygUCqamprZ0Ok4CLcWoZDLJ+Pg49Xqd5eVlcrkcTqcTqVR67NfM5/PtLn/nKHGlUmF5eZnr16+3i3Wtc0FnnrrwzGkuPHOa+z+Y57V/9x0kUhH1ag3qNaRSEcH5MGaXEYVShudmgEF3H2qDmplrTYPa8Qsu4usZggtraPUq+ge1SFQSQt5406zvurfZTb/gJJEssLbZdRcAS5vGtbahPhQ93c8QAiCwaSRoHzIh0ypY2MPItlKuMX87iEgiYthhJGcsEY3unFzJZ8vkPWVkcjEWu5pIqki+cHAjz+2QSg+ep94P476PDNk4THva3KuhV9JUyZkNHs7tG5ouqrVcjdABXFQBRtwmKpkyy5vGfbuRjMHBHjRyMQvTYbpZJymVUj726fN84sXzaLTdnVhPCnvpjx+FZMCDQJpIJHA4HJw+fZp4PM79+/dRq9W43e5jZ/R7SQNarVZCoRBvv/02/f39x0Y6WvO/tVptizdHC9sX9PZSBREKhVz+8bNcevYMd/9mlte++h1EYhGNah1Bo4Fap2DFE8EyZEImk+C5GcAyMoCqV8X0m0231/GLbsQSEcGW2Z/LhMaoYeFOkGq5+mDX44KTeCzH+uZolADwzzQldE8/MYTdrWfZt/MeEAD+zaDvGO5rupd3U6TaRLFQIZfIU4zlOHXKQngjTayL2kg+UyafKW+6kqtZTRQodlFg2w+SR8yZ9THeH2gVxeBgZMOgUXLO3s/C/NGKYY4BLZK6kIA/xjr7F8OcNj2SOvgWNvYshplMGkw9Chamw6RCO8cmpVIRH/n4GT710sVDd9wP60Wy2++30G138ChS68vLy6yvr2O1WhkfHyeVSrGwsNB1DPg40JkbnU4no6Oj7c/EbDYTiUS4efMmBoMBh8NxLKSjswDXIhmdfweJRMLQ0BAOh4Pl5WXeeustLBYLFoulK+k49aExTn1oDM87Pl772ncRCoU0ag2qxTI9hj4iS1GMZh1avQrPO36Mll4MFj3z7/hp1BuMnHcik0tY2+ySG8w99DmMeO4Em3sbN/wAjF5wkitUCPkfnJqC3uZZ6+wHh3GPmvAvRLseyFtGtha7Ho1Jw/xceNc9wVqlRjqWJRlKMnnKSjxTINJlQb1UrBL0JDeNbDWspYrkjkA6Dqua2BoJfK/ikSEbh2lPF6JF5v2Hk7AVCmDYpiefKLF8ABdVgQBG3Sbi4ST+fRQ+bLZe5EIh3rkIa+wM8nKFmBc+e5mPvXAOlfqH007r9Mk4bpLRGUiNRiMGg4GNjQ3u3r2LVqvF6XQ+NOk4iDSgUCjEZrNhsViOhXR0KlodRDZ3vwW97cH87JOTnH1yktd+909Y/KsQYrEQag1q5SraXjXhwAZ91l40OiWed/z0W/XoB3uZv+Fj/JKL0bM21iNxwv4Nwv4N+qx69BY9C3dXqFXrzL3tA4GA0fOOpit5x2hUOpplxbNGn7WH3oEePDOrXYP0ymab2uowoDKq8ewSzAUCqJZrzN5cRiASMHXaSjSVZ63Lgnqx0AzmIrGQIZeW9Wypqyv5rp/zIcnGezmAP8b7B5156iBFsWq6wswB1KG2w23poV6oE/I+qPjuhWGnkWw8TXB+byWrgQEdPSoZizOrxLu4MEukIp79xGk+/fkr9PQe7fDdinNHLQIcZ57qJBnbVRC7jQG73e6H7siXy2UCgQDxeHwHyeh8j2azub1I/rCkY7uildFo3DPmicVi3G43drudlZUV3nrrLQYHB7FarV1Jx+hFN//qlX/G//eH32Xl2gaLN/0IgFKuSN/4ILHVODq9CpO5h4Wbfnr0agbcfSzcXmbkvIOhU4OkUzk2VpLEwkl6TBoGh20s3AtSKVXx3AwA4D5ta7qSLzwobBWzJfx3QvSYNp9zZrXrKG54OU54OY5psAejpZf5uTC12s7HCQQCGrUG83eaY8PjpyxkyxWCXRbPq5UaQU8SgVDAkFtHrFju6kq+G6TSR6so9kiSjX1VPiQHf9sioYARm57kWu5AHhtCoYDxoT6Saxm8+yhZuZxGhNUafs9610pST6+S51+6SJ+lzpWrF3+oX7hGo0G1Wm0rIzwsydgtkELzhu7r68NkMrG+vs6dO3feVf3xFuloLZK//fbbDAwMYLPZDkQ6dmtDHxTbF/SuX7/efj3dSId1sp/PfvHTLN4ONCtIApoGgbkiAw4j0VCMHqMKw4AOzzs+eg0aVBo5t/5mjmq5xsh5J8VNV/L1YBz9gI4BZ18zmJcfBPOhMzYqdVheXG+7+m0Ek2wEk5isPRgG9czPhKjXdrKJ1aUYLMXoG+xBb+lhfja8Zem78y/SqDWYu70CApg4ZSFdrBAK7QzmtWqdlYXkpiu5lnihTDKzfzB/3Nl4jB8G1Gr1ieUpgQCGrXoKqRIrnr3NwloYdfdRTBUJzOytZGWx9KCSiVmcDncdq1KqZHzshXMMTciZOjX2UFV+oVD40D4ZD5un9jKN3Q69Xk9vby/xeJzp6WmUSiVutxuF4nCTB50kw+FwMDIycqA81SIdR+l0lEol/H7/kZfNxWIxLpcLm81GMBjcl3T0ufS88Pc/waf/hx/nj37zTwncW0YkFFBI5TEP9ZOIJJErJQw4jHhvBZDLpag0cnz3VijmyzgnLQjFYvwzIZIbmaYIymkn3plVivkyvnvNfSHHxCBiuRTv7INuYHIjS3IjS49Jg3moj4XZMJXyzm5DdDVJdDVJj1GD2W1kwROh3DGy2/nxCICFzR2QkYkBKkCgy65io95gZTHZdCV395CuVIgl91d/e9TGff8bJRv7/+EkYgFucw+xSA7vzP4erGKxkDF3HxvBJJ47q3s+dmjIRC1fZnnzZth+extMal747GV+7LlTyGRibt269VDVnodBa9ZVqVSyvLyM1+vF7XYfqoLSUl9KpVI7Ohl7YbvpUacqx37X3y4NeBT9cZFI1D7ktzode5GOzhGtbm3ow0IikTA8PNxuW3cjHblcjnq9TqPRYPick//pD/8pSzPBzWC+glAAhXQe66iZ2GoClUpGv8NAIZVHpZCgcfeyNBuiXKwwdMZOrd5gaS5MPJJCa1AzcsaKd3qVUqGM924zmDsnBlHrtibTFunQ92nod5vwzISpdpGX3lhNsrHafJxCLyUSzlOp1OhaT22AZ3MHZHTSTKneYGl5ZzCv1+oEF5MgEOB26UhVKsRSuwfzw5KNx52NxzgOHLYDf5A8JRQKcA1oySfLBOYOUAwTCBgb6iO9kcN3f+/RLIddj1QgwDsX6VoM02jlfPIzF/nxT51FpZIxMzPzrkvXttDqZEilUqrVKvPz8wwNDR0qTx2GZGx/zQaDAb1eTywW4969ewceA+5UXjwoydgOoVDI4OAgAwMDByIdncTG5XI99LK5WCzG6XRis9nanQ6z2YzVam1/zwuFAtVqlUajgXXUzC/+n18kEtjgW7/9n/HfW0YiFpFL5OizG8hE04iEYB8doJQtIqKBbcLE+kqMfKaIbdSMXC1n8e4K028uoNDImLrsJDAfIZcusjTbPH9ZhvpQqra+/+RGZpOoKBk9a8PriVAs7OyKJ6MZktEMmh4F/XYN0Y0ihUKle3EU8M02C8vu4T6ECgmL3i5nxgaEvE1XcqdTR54a6/GdJrotPGod+EeCbKjV6sMt3u0RxOVSEUMWPSu+KL65/celpFIRo04Tq0tx5vdwUQUYHemjkCoQuN+djPQNaHnxC1f40WcmkEgf/Gl2W84+SXQu1InFYmQyGZcvXyYajXLv3r0DjTd1kgyn03nkoLZdCvDmzZsYjUYcDscOSbjOax6XNKBIJMJut28Zr+okHcdhrLQXWrOydru9TTr6+/spFotks1lGRkYeSBEKhTgmrfzS7/08oYUIf/xbf4r/3jIikZB8Mke/00RqPY1+QId1ZICF20tIZBJGrg7hux+kkCvhnBhEJJPgux9i5s0FVFoFw5ed+GfD5LMlArOr2Ef7MfUpUes1+OceVEZbWuU6vQrL5CCL8xFKxZ3BPL6egXWamuanLQh2kaOGzcXzmXBz8Xy0D6RivF0lDhsEfc1g7nLqyHUJ5mKREFHHAWK/v9P7YfHuMd4fUKvVrK42Y79IJKJS2Xv0by+yIRELGbHqiawkWV7Yf3dQLBIy6u4jtppm4e7exTC3ywjlGoH5tV077p966RIf+cRpFIoHh7njyFOHfY7t41JSqZRLly6RSCSYm5s70HhTuVxmeXmZjY0N7Hb7gUnGdggEggOPAZ+EvPt+pOM4iM1eEIlEbdIRDAZ5++23MZlM1Go1EokEQ0NDW/LUgNPEl//9P+Jzv/xJ/uQ/fA/fvSWkUjGx1QR9FgO5VB6FRo5zwoJveoV6rcHUVTfLnggZT5hBdx8ag6ZpZPvGQlPG/ZKDoC9KOp4j5F1HpZFjMMjR9ekIzK+3u+mZRJ6Za16UGhmnztrw+x5Iv3cikyyQSRZQqqScOmWhIdz781raHBu2O40oeuTMd9lVFACrgRQNwG7TUpE0CEd3CqM8ah34R4JstJxZ4eidDZVCjGuglyVvFM8B5mQVCgnDdiPLCxvM7UEyBAIYHeknE83ivdv9cYO2Xj7zhSt86KkxxOKdr+24yMZBFu9qtRq1Wq1NMkQi0ZYgaDKZMBqNrK+vc/v27a6dhtbhO51OPxTJ2I7OWdVIJMI777yDyWTCbrdTq9WOhdjshe2k4/r160gkEur1Om63+0Q0zzshkUiw2WxUKhWCwSACgQC73U5vb2/X8SrLyAC/8PX/ns9/5VN863//z/juLiGRitlYidLvMBANJWnUqjgnHCzdW0FQrzN1dYiluVWyqQKWkX5UWiULd1aYfmMBuUrK1CUny5tVm43lBBvLic0Fc22zpbx5OE/Fc6SueVFp5Qyfs+FbWKOQ2znilE7kmbnuZ+qCg1OnLfh8UfK57l0JARDwtIK5gZq4Tmh1pyqIAAjtEsz3KjTshvd6xegx3h9QqVQUCs3v4VGLYjKpiGGLntVAgoUDGPlJJSJGXSYiywk8d/Yuhg0PmajkSizNHKzjvh3HkaeOy8+pc6dienoalUqFy+XaMt7Uefg+TCfjIO9htzFgoVDI0tIS0Wj0xOTdt5OOd955p+3h5HA4TkxSvgWRSITFYqFSqbTJtcViae+CbM9ThsFefvY3fpKf+Bcf59u/818ITK8gsxmIR1LoDGqyqTylXInxy0OE5iMUM3mmrrgJ+TdY9a3TZzdgMPfiub3cVlScuOBkbXP0NhZKEQulMAz20Gc34rkfora5t5HPlJi+5kWmkDJ1xsrKcpx0Mr/jPeVzZWbe8jN6apBTUxZWQglSXR7XQijQLFQPWnoQKmA1stMrSgBEVpo7iRazGoFKSHD9wXMetrPxXscjQTZUKhWZTFPSTygU7lsxknQc6LUqKXaTDp9ng/no/iRDrZLhsvQS8Kwzeyu46+NEIiGjI31EAuss3un+OLvTwE/8/at84MMjW8xituOoreVuz7HbwalWq7UTRTeSsf25WuNNnZ2G/v5+gsEg6XT6xAyHYGswXVpa4u/+7u8QCoXvmv54rVajWCwiEolQKpWk02ny+fy+/i4Pg1aLPxqN4nA4GBsbo1artdvWe6mC9DuM/JPf/qlmBenffw//vSWEQDaeQWdSk0/lKRdLjJx3sTITolIoM3XFzcriGqGFNQacJnpMWhbuLDP95gISqQTjaQvx9RTZZPHBgrmtuWDuuRds723k0kVm3vQiV0qZOmtjyb9BNt2lddxoMHPdj0zZrCAtLcfJpHdWe1oIbZoyWay9aExqPAtrewfzQTUCpZBUrvrIBO/HeH/h0OO+HXlKKRfjNutZPmAxTC6TMOI0EPTG9iyGQbPjHg/H8e/Sce8363jx85d58iOTe5L11kHyYbDfc2yXsN1rJ6NzvCkajXL37l10Oh0Wi4VIJHLsprHdrt/Kk6urq1y7do1Go4HL5eLKlSsneuCH5mdVLDZjrUKhIJPJtPPUSV27lZPC4TBWq5UPfehDNBqN9u5jf39/eyKglacEAgEikYgek5af/t9e4jP//Dle//pf8N3/+JeIRQJy8Sw6o5JysUwmnmb0opuIb51MNMvkJRdroQSz1xcxmHvod5jw3Flm9voiQrEQ97iZnj41yfUssdUksdUkvX1azEP9LEyHqGzuY5QKZWau+5rS7+fshMNJEl3kbQUImNn0+5g6ayO8nibe5XEtrG2qtRn7NJjseuY9ka4L6hvh5nMM9KmQ6kQENwoIBM18tp9Jdfu1vceLYqKXX355r5/v+cP3Cubm5lhbW+OJJ56gUChQKpW6Kv/UajVCoRB/d2ORYrmOe0DPxkqa6FpmX4dinVbBqN1IPJRiLZSiWukeEMViIeNjA9RzJUK+KKXizoQyNNLHz/3Cj/Hf/eMfxeEyItynNReNRtFqtQ8lbxeJROjr69vxpW11MhqNBiKRCIlEglgsPvBOhUajQa/Xs7y8jM/nQ6FQcPr06bbxz0mhVCrh8/mIxWJtz4qVlRWq1SoajeZEgmmlUsHv9+P1eunr62N8fJz+/n4GBwfJZDLMz89Tq9WO9frVapVAIMDCwgJGo5Hx8fH2ZysUCunt7cVsNpNKpZifn6der6PVatuLlq3ELRAIUGoUXHjmNB/5hx8mE8ty9/uzqHvlFNNldMamRGU0FGPkvJOwd51cMs/4ZTfJaIbgQgTjQA+OCTPRcBKxSEgsnGTknB0EAgrZErl0gWgwjt6owjUxSDyaobF5X1UrNTaCCajVGTtro1KpbZmV7TPr2IikqFVqbISSUKkxftpCtVbvOlPbQjZdJB5JY9KrcLhNxBP5rsQ8nymTi5cYNKrR9+apVqtkMhnsdvuen3+j0UAoFL4XyMn/+sN+Ae9hvPzDfgEHwerqKjdv3uTZZ5+lUqmQTqcxGnc6adfrdcLhMG/e9BBNlRi1GEiGs6yH0/uaWqqUUsZdJjLrOcIrScql7pKbQqGAsdF+JPUGwcUNivmdjxu09fLTX3qSL/3iMwyPDexZEANIJpPI5fKHUmSKRqP09PTsyHX1ep1KpXLkPKVSqTCZTEQiERYXFxGJRJw5c6bdFT4pVCoVAoEAq6urOJ1OjEYjoVCIYrGIRqM5kbhSq9VYWlrC4/HQ29vLxMQE/f39WCwWisUis7OzlEqlY71+vV5nZWWFubk5tFotExMT9PT0tPNUi+Tlcjnm5uYol8totVpEIlE7T7WKoVKFlDM/MsHHvvg0xXyJm39xD4VWRq1UR6GSIVVIWfWuMXLeQTycILaaYPyii1KxTGAmhLZXydBpG4mNDEq1jNDiGu5Tg8gUMrKpAsVciY1gHLVKyvBpK+lkvt3pqNfqREMJyvkS42esCEQC8tkHnXZjv47YeppGvUF0NUk5W2L8VHMMOLdLRx6gkCsTXU2hU8kZGu0nmSp0Vbsq5Cpk4yX0KjlWS7U9Ku1wOPb8/Fv3xXs5Tz0SZMPn8+Hz+XjyyScpFosUCoUtCkDVapXl5WUWFhZQqVSkMwLu3AgS28ju2zEw9KoYsupZX0qwHk5T66K0A00DlvHRfsrpAqv+GMX8zgPS2KSZf/yLH+Ef/uyHsdoPvjwci8VQq9WHVmLqRItstL6MD0MyWigUCiwsLBAMBnE6nUxOTlIul9uH3pM49JfLZbxeL36/n4GBAcbGxtBoNOh0OgYHB8lms8zNzR3rob+VMLod+KHZaenp6WmTjuO4fq1WY3l5mfn5+XbC0Ol0Xf8+nddvkY7O628nHQqVnMm/N8qzP/0kCzNeVu6uoh/oIRvPodIpEUtEhAMbjF5wsrEcI7meYvyyi1ymuXynM6gxu0ysB+NEgwmKuSLj5x0IRSJymSKFbDOY63QK3FMWkokc9c3AWq82g3mlUMY61INEKqWQK9M3qGMj/ECzvF6rE11NUs2XGT9loSEQkM/vrjSVz5aIhVP0ahS4R/tJpvJd71W1Us4//iefIJvNEg6H29/T3YJ06/446SrkAfCYbOyOl3/YL+AgiEaj/O3f/i2f+MQnqFQqJBIJ+vr62j+v1Wqsrq4yPz+PVCqlVpPzzvVlNtYyu+adFrQaOSMOI8lImkgw1VWcAUAkFjIx2g/FKkFvlFxm5wHJ4TbyxS8/xRf/6VPNhdd9imEtpFIpJBLJQ6lRRaNRdDpdm2x0kgyxWHykPNWZMywWC1NTUwDMz88f+6G7hc6codfr2/Fbo9EwODhIuVxmbm7uWK/f6irMzc2h0+m2HPihGfu1Wm2bdMzNzT006anX64RCIWZmZlCpVExMTKDX67vGS4FA0CYdhUKh/f53Ix0SmZixS0N8/Gd/jIAvwNLtED0mDaVcGalUjFKjYMUTZvS8g3Q0w1ogyugFJ/UG+O6toFTLGHT3EQsniIaSZBNZRs7aUajlZBJ5SoUKGytx5DIRo2dtZNPF9n3TIhOFdAGzU4dKqyKbLmIya4l2yLE3Gg2i4RT5VJ7RqUGkcgnZLrsfLZQKFaKrKZRSMaPjZjLZEtXqznu1UW/wS7/0GUqlEqurq5TLZTQaza6TE++HotgjQTZWVla4f/8+zzzzDOVymWw2i8FgaKsu+Hw+dDodY2Nj9Pb2cvPOCrN7OEYCGPVKzHol68spopEHFdrtkCskjI30k49lCS/FKRV3VohOnbPy5V/6KJ//R09gthy+ktIiGw/jNbG+vt6uoh0nybBYLIyMjKBWq9sVjM5DL4BGo3no6lErYfh8vnZXYfvzdl4/nU4zPz9Po9Fov7bDorOr0Jkwdnsvx0E66vU6wWCQ2dlZNBoNk5OTWxLGXth+/fn5earV6q6dDolcgshQ5+de/inyqQL3/maW3n4dhXQRuUqGXCkltBhh9LyTRCRFNBhj/KKLSrlKuVCmVCwxfMZGOpZjbTlGLpVn7Ly9GXBTBYr5MhvBOCqlhJEzNjKpwpZgno7mKWZLjJ22IFPIiHSRt23Um8G8mCkyPmVBLBWRze5eQSoWmhUktUzCyPgAmagaRFEAACAASURBVGxxSzVYb1DxyU+eR6PREI1GMRgMzM7Oks1muwbz1iHnMdl4T+PlH/YLOAhSqRR//ud/zqc//WlqtRqxWIz+/n6q1SorKyt4PB6USiVjY2MYDAY8i+vcuLW853P26ORYTEpSkRzrq6l2hXY7JBIRE2MDVLPNjnu3/SnXsIkv/cIz/PSXnsR+gI57t/cnFotRq9WH+r1ORKPR9n3YUi86Kslodb+35wyhUNg+9LcOvZ1x8mHQmTN2KxK1JgI6r1+pVI5cnOrsKqjVaiYnJ+nt7d1zvEyr1TI4ONjudLQOswc9rDYaDSKRCDMzM0ilUiYnJzEYDAce9+lGenYjHWKJiIa2wpf+9U/TqDe4/Vf30ejVVMpVhA3Q9KpZng8xfMZOIVNkdSHC8Dk7YklzVCsVTTF01kYhW2ZtKUomlmHolBVNr4pUPEelVGVjJY5ECGPn7BQK5QcdwQZk4wWy8RwjU4OoNIqueQogHkk3Hzc+gEqrIJ3afQy4UqqysZpCIoDxyUEKxQrlDilehULCZz93Fa1WSyQSYXBwkNnZWVKpFCqVakfn7zHZeJewtrbGtWvXeO6556hUKsTjcVKpFIFAAKPRyOjoKDqdrn0j3J8NMz3XXfZvcECHxahl1RcnFSu0/QS2Q6WWNSUEI2kiy/H27F8nLlxx8dHnnXzxy88yMHj0dm0ikUChUBxau7sTa2tr7Zv5OEmGSqXqao7X09OD2WwmkUgwPz+PUChErVYf+jMol8tdE8Zez9N5/WQyeWjSc5iuwl7XPwzpqNfrrK6uMjMzg0KhYHJyctcK0WGu3+r0VCqVHaSjNa7hGnJx6oNjfOyLT1MpVrjz19PojM0KkkQiRqltVpBGzjnIxHNEfOu4z9jIZQuszEVQKGWMnnOQSeRYW4qRjWcZOWtDqZGTTuQpF5sVJKlYyOhZO7ls6YHGeaNBLJzCYFChN6iRKaTddzUaEIukyCbyjE2YkcjFZLtUZVsobwZzmVDA2OQg+UKZcrlGf5+WH3/uDNVqlY2NDSYmJrBarVSrVebm5kilUqjV6rbK2ePOxvsCL/+wX8BBUCwW+da3vsVnP/vZ9mGtUCjg9XrRarWMjY1tued9S1HevrnU9blMBjWuwV7WAgmS0UJXbxsAmazZcS8l84T8sa4jieNTgzz34gg/80+exjU0cOQ8lU6n2wf5oyIWiyGTyZDJZA9FMlqdjL1yRuehuxUnj9qRr1arO0aX9isSdR66W+NFhxkDPkxXYb/rt0jPfqSj0Wiwvr7O9PQ0QqGQiYkJTCbTkQ65nZ9/q9OzG+kIBoM4Xc5mp+PnfgyRWMTtv5pGqVXQqDeoVWr09OkIzARxnbJSLVVZmVvFMTFIpVJlaTqMSCRg7KKTQq5JOlIbaVwTZnpMWpLR7OaobxxqNcbP2ymXa5Q27xcBEF9L09OjQNejRKtXkUrsXBAXAIn1DOlolqHhPjS9SlLJ3UlHtVJnYzUF1Trjk4NUanWKxQpqtZwXP3MJgGAw2M5TQqGQ+fl5YrEYKpWqPe3yfiAbgn3GiN4Xuo937tzh13/91/n1X/911tfXicfjjI2N0dfX1/Vmf/VP3uEPXn1ry/+zW3pRiEQszq3t6biq1cqxWXrwz3SX9AS4+sFhPvOTVxga7ef27dtMTk4+1L6F1+ulp6cHg8Fw6N9tjUuFw2HW19dxu92YTKZDBe98Pt/2jziIw+h2bF9uHhjYP6FtlwYcGBg48oGvNUa3trbW9qno9ly1Wo1gMMjq6mrbnOg429vhcHjH87YOHUtLSxgMBpxO5w453+O4/urqKsFgsL2gl06n8fl86PV63G53+/UIBAJKhTL/5f/+G/74t/6UHpOWhkBAJpal39WH55Yf1yk7YqmYubd9DJ1zUC5XWVlYQ6mV4z5lxz+7Sn6zlTx0xk6lVme5QwJQppTiOm1l2btBPtussE5dcjJ9I9A0SDpjo1itseLfXXq6AViH9Ajlcpb2eFwLUrmE4alBlFo5v/ryCxQKBWZnZ7lw4cKD52w02NjYwO/3o1AocLlc7SrSeyCIv7e3/364eF/kqUQiwfPPP88f/MEfEIvFWF1dZXR0dNfY9l+/P8dvf/2vtvy/gT4tvSoZi7Nru3bbAZRKKW6HgSVPpH2Pbcfp8zZe+vsfYPKMhbm5OaxW60MRhWAw2BbvOCxaAiXRaJSVlRUcDgdms/nQJKPTNLa/v/9Qv98qMkUikT3zRCdaXalIJILVam2LdBwFrZ3SUCiE2WzGZrN1jTutItHy8jJ9fX3Y7fZjyRmtgtfKysqO5200GkSjUfx+/4Fk7496/UgkwvLyMnq9HofDQbFYxOv1IpfLmZiYQCgUIhAIEAgEVCtV/vrVa/zRv/suQokQpVrJ2koU27iFhdsBLEMD6Exa7vzNHLYJM2KZFP90CIlMzOgFFyHfBqlYczHbPm5GopDhvf9ATEEkETF6wcnK0gbZTRO+iQsOZm8uNb0yJswIpWJ8XeRtO9Fn06E2aFnc53HQHHMcOW2hLhTym7/1eer1OteuXeOJJ57Y8rh4PI7P50MoFOJ2u9FqtW1i/kPGrjfcI0E2/uzP/oyvfOUrqFQqfv/3f5+NjQ3Onj276+P/3+/e5hvffBMAt92AoNogsLixZzbv7VUy2KdlcXq16zysQABPPDnGT/zkFeyuB0t/d+/eZXR09KFuTJ/Ph0ajwWQyHejxrWpAS8K21c1odQlyuRxDQ0P7Olu3SEY+n8flcmEwGB5qHKpcLuP3+0kmk7hcrq6kp5OYPCzJ2I5KpdLWU7fZbJjN5rZ6UygUIhgM7hnkHxadpMNsNiOXy1laWqKnpweXy/VQhPQgqNfrLC4uEgwGkcvlTE1NodVq23tLre9K678rpQp/+Yc/4I9/809RaBRIZBJi4SSWUTPpRJZKtYxMpmB5Loz7jJ1ao2kIKFNKGT7nYMWzRmZTHtA5ZUEgEuOffaB4I5KKGLvgYjWYwOI0Mn0j0P5ZAxiaslATCggs7m6q2QBcYwMgE+Pb43EtnL1g59/89hfIZrN4vd6ucaLRaLSDuVwu58KFC++FzsZjsrE73hd5yuPx8OKLL9JoNHjttdeIRCJcuXJl18f/zRuLfPU//AUA1sEe1BIJC7ORPb8IarUMp02Pfza8q7DCxasuPvOTVxibfEAKPB4P/f396HS6I703aC7A1+t1rFbrgX+nUwWxladao0iJRGLXPNGJ7aaxhyUZ29GZJ+x2e1fS0xnLW0qAx5UzOotenc/dWZjazWfqOLCddGg0GpaWlo7sjn5YNBoNAoEAgUAAsVjc7vK3ftZaOm+Rjlqtzt9+6zqvfe271Kp1tAYNocUIjlM2qpUa8WgSjU6N/34I+/ggUpWMxbsriKVN0hFZiZHY3MOwDPej1KlY2DSwBRCIBIxddBHbyGIy9zCzrdtoG+lHqpGzOLu3SabFZUTRq8Qzt7+anMXWy3/85s9RrVZ55513uHr1atfHpVIpfD4ftVqNq1evPiYbAN/73vf4hV/4BWq1Gl/84hf5yle+suXnv/Vbv8Xv/u7vIhaLMZlMfOMb39h3A//evXv86q/+KvF4nFKpxPe+9z1qtRr379/n/Pnzu/7ed//8Hn/51/PU8hVW/DsdiTthNKrp06tYuL/aXnLthFAo4MlnJvj05y9jse08vN+/f39fU6H9EAgEUCqVW5YJu2E3krH9oJTL5fB6vVSrVYaGhnYkmHw+j8/no1AoHAvJ2I5OEzy3243BYGi3oaPR6BYicBLodIrVarWk02n6+/ux2+0nfrO2quetfQqbzYbL5TrxynnrcN3yBMlkMqysrGAymXA4HIjF4l1JR7VS5a9feZPXfvO7iCQilGolCKFYKbLmS2AZGkAgFOKfDuKcsiKQiHavII2ZkSplLG6rIJ3/0Qn8njViHct3LdjHBxDLpXj32bOyDpmQaeQs7FFBunjVzctffYlUKsXKygqnTp3a8zlbC5SPycZ7GsdKNo47V/n9fl5++WW8Xi/RaJRr164hEol4++23uXz58q6/9+bbfv7wtbeQNMDn2bsYptMpsJp1eGfCVHZRoLr6oWF+4iev4B7p3/GzxcVF9Hr9vgWovRAOh6lUKvsqvEF3krH9HisWi3sWx7abxu42yXBUdHbXW52S1n7ESZCM7Wh1WsLhMDqdjkwm09XX6qSQSCTaylUDAwMMDw+fCLnpRGussFgsMjQ0RKlUYmlpCZ1Oh9Pp3DI2tJ101Ot13vzOTV79jdfJZ4rozb0IRUJyhTzR5SS9/TqUWiULt5awjPSj7FGzcHsJoVjI2EU30XCS6GpTqnbAaURn0uG5s0zriCwQCrjw1ASh5QSRlZ1GmmaXEY1BjWdmlb2O1f22XnT9Ojxz4V1VUJ1uE//H7/8MpVKJe/fucenSpX0/t5NSNzskdr0B3xUaVKvV+PKXv8xf/MVfYLVauXz5Ms8//zyTk5Ptx5w/f54bN26gVCr5+te/zi//8i/z6quv7vm8+Xyer3zlK0xNTfH000+3g9V++uVyoYjA9N4Hl74+NQoxhPxJEis7F4LEEhFPf3SSFz53mX7z7tWgd8NVtUUyDuqToVKpOHPmDJlMhsXFRYRCIUNDQwgEAvx+P4VCAbfbjV5/cMWsw6DVEi0UCiwuLjI7O4tAIHjX9Mdb/hhAW/deqVSe+I2aSCTaLeGLFy8ilUoJBoNbfDKO+zW0iGOpVGJoaIienh6A9iJ9OBzmnXfeaTvNSiQSGo1GW8teJBIhEot45h9+mKe+8AR/+623eO1r30Vn0pDNVpFKxCi1cnx3l7GN9iMSCfDeXmpXkKbfXEAkETNxyUlkJc7yfLP6M+A0IFFJWfFEqVVqFFJ54qE44xccxKM51jeDPsDyZiXINtSHvEfBwkz3ClLQ23QVH3QY0BjVeObCO4K+WPwgRhzks343kvpjvHdwErmqUCjwUz/1Uzz11FOcP3/+wPe4VCQkON/8Tu8WhfV6JWq5gMhyhrnITqIuFAr44I+O8eIXLmN37pTZffC448lT+6k7dprG7kYyWpDL5UxOTpLP59s7GMPDw8hkshM3cIXmvT8yMoLdbsfn8+HxeBAIBNhsNq5cuXLi+UIoFKJUKhEKheRyOWq1Gkql8sQLYul0Gq/Xi0Ag4PTp06hUKkKhEDdu3DixolypVGobAbcKkK2/aX9/f9sccTvpaH2XWqTjg5+6xBPPX+TtP7vDK199HYVaTr1So1GtoTOoWZ4PM+AwoNIqWLjhaxrSmjTMveUFAYxfdBOPZogEokQCUYyWHhQ9ckLeBPVanUK6QGQxwsh5B/l8pW3eBxD2Rwn7o/Tbeukx9+CZXu1KJtZWEqytJDAO6DDaevHM7/TeEB0yTz2MUum7hXeFbLz11lsMDw/jdrsB+PznP8+3v/3tLQH8qaeeav/3Bz7wAb75zW/u+7yt1lKtVqNUas7UHSTgdXPpbmFwsAeNXMzCdLhrgJdKRXzk42f41EsXMZj2n28ViUQnRja2k4z9gvd2aDQazp8/33YZBdozxCdtENNyF81ms1gsFjKZDJFIBJVK1T4QHze270dcunQJiUTSnvVdWlo6kQpZKpXC6/UiFouZmJjYIg3pdDqxWq07zPkeNpG1lFgymUy7Kthtkd9isWA2m9vmjJ2kA9hioCUUCfnRz/09fuSlq7z5nZt843/5f1Bp5FSLFYQCAZoeFf7pIJahPmQKCQvv+NsVpJlrTZOl8YsuouEUkU1Tvn6Hgd7+5t+7Uasz97YfBIKmnGG6uKWCFNx0Lh90GJDqpCwtxrpWkCJLMSJLMfoGe9BbepiffVBBat37j439HqMbTiJXTU5OMjk52a7G7mWu2gnpHgZ6JpMGU4+ChekwqS4dd5FI2O64D1p3ek5tx0kXxTpJxn7FsO1QKpWcPn26bcxXrVZxu93vmoFrJBIhmUy21Zs2Njba/lIncf3t+xHnzp1DLpe3x7veeuutE+n+t7rftVptx8SDzWbDYrEQCoXa5nzHQTpaEsGxWAyXy9X1b9ppjtgiHdv3RlrfrVauuvKxc1x+7iy3/2qa/+t//kNkcgmNap1qsUJvn5bQ4homSw86vQrP2z6MVj2GwV7mb/pp1BuMXHCSTRUIB6IQAr1Zx4CjD8Hm571wqzlKNXTGRqVaZ3mz0AUPyITBrKPHrMXv2ejqqRGNpIhGUvQYNZjHjSx4IpQ3hYaOkqfe66Z+7wrZCIVC2Gy29r+tVivXr1/f9fG/93u/x3PPPXfg5z/sDSeV7vzj2Wy9yIVCvHMR1thZSZLLJfz482f55E9coKf34DriQqFw307LftjuqvqwJKOFXC7XrnqfOnWKRqOBz+cjmUzidrtPhC1vX9a+evVq+3VnMhm8Xi+NRoPh4eGHWlbsREtBIxAI0Nvby4ULF7ZUrGUyGWNjY+22fCAQONCs8H5ovR+AkZGRXd+PWCzG5XJhs9kemnS0Wv/xePzALu6djuwt0tFa0Gt9Tt0qSBhKiJIKXvmN11GqpNTKVajV0RlULM+FMTuNqLQKPG97Mbv7NitIPhCA+4yVZKypXrW2FOPsj4wxccGB5+4KtWodz80AAMPn7BSKVUIdS+DhpSZR0Zu19NmMzM+EuqrxbKwm2VhNou/X0Ocw4pmPHLpiBO/9IP4Yx4eTzFWH/R5JJTvT88CAjh6VjMWZVeLLO0c5xBIRTz87xac/d4m+gYPvX5wU2XgYktFC59jt2NgYEomkXUh52BHl3bB9Wbuzk9HqFvv9foaGhroaCB8FjUaDWCyG3+9HpVJx+vTpLfsREomEoaEh7HY7S0tLvPXWW7vulBwGrffTGl3a7f0IhcJjIx2d5wCHw3EgF/dO0rGxscG9e/dQq9W4XC4UCsUOyVyhUMj5p0/xU7/9KWQFNX/0tT9FKhNDvU45X8Iy3E/Yv0FvvxbDgI6FGz56+7T0OUx4bi9Rq9Zxn7GSzRTYWEkRD6c488ERJi86WbgfpFKq4t3c7XBNWWgIhQQ6Rnhj4RSxcAqtXoll1MLC7GpX1dJkNEMymkHTo2D0tIXFxfVDd+DfD3hXyEa3TsNuN8c3v/lNbty4wfe///1DX+OgFSNJB9lwOY0IqzX8nnUE7CQZSpWMj71wjk+8eB6N9vCLUccVxKvV6o6djJb+/2GDdzabxe/3UyqVcLvdW1xUDQYD6+vr3Lp161jVkTqDi9Vq3UIyWtBoNJw7d45UKsXCwgJisRi3231k3fZOdSGdTse5c+f2JFByuZzx8fF2cmuRjsOqb+23E7MbHoZ0tD7f9fV17HY7w8PDh05ALdLR6nTcunWL3t7eLTPCW0iHUMAHPnGBqx8/zzv/5S6v/NvXkauk1Ct1qqUKvSYNKwsR+m16tHoVC2/7MNn06Af1eG4FaNQbjF5wkUkXqRQrzL7lQz+ga6pe3QlSq9ZYvN30GRg6Y6VSbWypIMXDaeLhNPoBLf0OI56Z1a4Oy/G1DPG1DDq9ij6juv0+HpUg/hjHh5POVa3iU0vOtV6v7xq/O/OUxdKDSiZmcTrMRpfHSmViPvrx0zz/0iUMxsPHy+PIU62uTbc8dRSSUSgUCAQCZDIZnE7nlsLJhQsXiMfj3L9/H41Gg8vlOhZ1pE6SMTAwwOXLl3ccpJVKJadOnSKbzW4hHQ+zXB+Px/F6vSiVSqampvYkUBKJhOHhYex2O4FAgOvXrx9JfatzJ+YwY9PdSMfAwAA2m21f0tHykQqFQlgslq7ngP0gEAjo6+vDZDIRjUbbpMPpdKJUKneQjkajwdknJzn/1Cmm/87DK199HYlEiKDeoJgp4DplY20pirZXRZ/NwMI7PjS9agaH+/HcWqJaqeE+Y6NWa1Cr1pi57kOjVzF62sni9CqlQhn/dHMH0TFuRqyQ4u0Y9U3H86SvedHqVVjPDrI4313JNJMsMHPdj1ItwzLYJHyPUp56V8hGa0ykhWAw2FUe7y//8i/5tV/7Nb7//e8fqqp+2EOVRCxiaMhELV9meVNBYPszaLRyzl/t54tf/jgq1dEr/McVxKvVanukRSKRHJlk+Hw+KpUKLper6zJgZ/UgHA4/9JzmdmnAgwQXnU7HhQsXSCQSzM3NIZfLD1XB6mxDazQazp49e6hE1LlT4vP5CAQCBwrGrcd37rwcBd1Ih9VqZXBwcEfg6VQusVqtx7LzIhAIMJvNDAwMsLa2xu3bt3fMykaj0bZXh0Ag4NKzZ7n40TPc/qsZXvm33262ret1KoUy+pEBwr519AM69P06PG950RrV9Fp6WLzTrCCZHXqsw30EF9eJR1L0mLQMDltZuB/arCAFgc0KkkhEoGNZPB5JE4+k0RnVWKb68cyGH/h4dCAVz5GJ59uf26MSxB/j+HDSuUqpVJLL5dq+T7Vabdf7VSoV47DrkQoEeOciXYthcoWEc5f7+dl/+rFDddy347jyVKuTAfvvDu6GQqGA3+8nm83uIBmd1zIYDOj1ejY2Nrhz586Owshh0KlIuBvJ2A61Wr1l91EgEDA0NHSojnwikcDn87WN8Q7jvi6VShkdHT30GHBrPyKVSuF2u5mYmDhSZ6RFOgYHB1ldXd2TdHTK9Q4MDBzLzotAIMBkMmE0GolGo0xPT6NUKnG5XG3SkUwmKZfL7Tw19cFR/vW3f4m5t7y8+huvIxIKEAigkMphn7QSXYmhUssZdBlZuOFHoZJiHO8j6IlQLlY4//QkrslB/DOrTL/hQaVTMHzJiW82TCFXYmnTv8063I9Cp2ShQwglHc8xc82LSitn+JwN38JaV3PNfLZEPNzcWXyU8tS7QjYuX77MwsICfr8fi8XCK6+8wn/6T/9py2Nu3brFz//8z/O9731vX9Wl3dDZ2diryyEVCQncX+36s55eJZ966RIf+cRp7t+/81BEAx4uiLcOc2KxuK2cZDQaT4RkbIdAIGiP1rSqF4cZ7ekkGRaL5UjBpbe3l4sXLxKPx5menkalUuF2u3clDp2Spd3a0IeFQqFgampqS9u8G4nopq51HKM3u5EOi8UC0JYn3N7mPy4IBAIGBga2LOgpFArK5TJSqZQzZ87smJU9//QU556a5P4P5nnl3367OXdeb1DMFnG5TKwFNugxqOgxa1m6G0Rj0DA43E8hU2RlbrVdQVqej5DcSKPRqxg57cA7Hd5SQepz9qLUqAh4HsjdpqJZUtEsml4lo2dteD2RHfKfYsmDWdj9Don77X49xqOHk85VKpWqTTb22+cTi4SszK91JRkqdbPj/vFPX2Bu/t5DEQ140D0/ClpVZLFYTCKRIB6P09/ff+h41EkyXC7XgQ7BnVXucDjMzZs3tyjsHeS1H5ZkbEdr97GzIz80NLQncUgmk/h8PsRiMWNjYw/lun7QMeDt6lrHtfMiEonapGN7p0MkErG2ttY2WG7tSR4nOklHLBZjenoaiUTSvrdOnz69pdMmFAoZvzLEr37rF1m8FeCV33gdQb2BSCggG89iGx8kFkogU4gZcBlZmg4hVcgYuTpEtVTBd3e5KYAil+K9H2T6DQ8KtYypS04Cngi5dJHgYnOcymjVoTFoCMxvtPNJLl1k5k0vCpWUU2dtBPwbZNPFLe+pM089KuO+7wrZEIvF/M7v/A7PPvsstVqNn/mZn2Fqaopf+ZVf4dKlSzz//PP8y3/5L8lms7z00ksA2O12Xn/99QNfQy6XUyqVUCgU7QP+bn8kqWzn2zaY1Lzw2cv82HOnkHX5+VFxFLLRIhktwmQ0GlGr1fh8PoLB4IFbtp0kozUudZTX3wokreW01pzobsZ4ndKAD3sI7qxgRaPRtiKFy+Xacljs9EXYrw19WLTa5q0dlxbpUKlUW3xDDrIfcRR0ko6lpSV+8IMfAGA2m08keG+HQCBArVajUCjI5/PU63VUKlW7u9ZtVvb0h8c5/eFxZt5c4NWvvo5YJEDQgHwqj3PKSmQlilItZdBpZOGGj/ErQ0xdcbNwd4VysdKU0BUJCcysMvPGQruC5J1dpZgrsx5IAAksw31NXfSOClImkWfmmhelRsbUWRt+7zr5bGnzs2x+Z/eKD9vf+3s9iD/G8eGkc5VKpSKfb3bXRCLRnvt8Mpl4B8nQ6hR88jMXePb5s1sKYQcdId4NR81TnbuDKpWKCxcuEAgECIVCB/Jyggcd4ZZp7FEq7d2KY3sZs3Z6SfT39x+JZGxHqyMfj8eZnZ1FLpczNDS0peDVqfS01x7fUbDbGHBPT097xPag+xFHgUgkwm63Y7FYCAaDvPnmmzQaDUwmE+fPnz9xxaRWnlKpVCSTyfa/W+Pm3fLU8Hkn/+qVf4b/3jKvfvU7NOo1xGIhmVgGy/AAqfUUQiE4xsz4bgUYOudg6qob/0zTuNY6MoBCo2Dx7grTb3iQKaRMXnSw4t0gk8wTDaaIBlP02fTozb147gfb+4WFXJnpa16kCglTZ22sLMdJb7qSH2W38L2OR8LUD5oKId/4xjcwGo37unYv+aP8jz/3BwD0m3W8+PnLPPmRSSTb1D/200E/CEKh5iGoVY3eC9tJRmuutzMwdMrVDg8Pd62eZDIZfD5fW7XjuBbYYKvpXuec6F4u2ceJRqPRrpQYDIZ2IJVKpW0CcNJIJpPMzMxQLBZxOBy43e4TP5BuHw0TiUTEYrGHdq3dD9207lu7MIFAYMeCXuu727lMDuC54eOV33id2WuLOE/bWLyzhGV0gFyqSCFXZOzSEPd+4EGqkGCftLJ4b4VSvoxjYhCxTIJvk0woNDLcp+2bpONB12LAaURr0rJwL7SjIyFXSnGfsbIciPHM8+f46X/2DB6Ph97e3j2NMlvPc9xOuUfEY8azO943eeof/IN/wJe+9CXOnj3L7Ozsnq7d2UyRf/Ti1wHo0Tc77h/9+Bnkiq3FhRs3bjy08WQ0Gm3Lju6Hg+wOtuRqK5UKw8PDaLXaHc/TaRrrdDoPvRu3FzrzUacb+F4u6draswAAIABJREFU2ceJ1rJ3y5C3r6+PlZUVGo0GQ0NDXT+P40Y2m+X+/fvkcjksFgsjIyPvysG1U+JdoVCwvr5+4J2Oo6KlatUSRmnF9UQigd/vRyKRtPc/98pTy3MhXvvad3n7e7dxn3HinwlisPbQqAuIh5NMfXCMmeteGvU67nMOlubCZFMFBt0mNHoNns0dQ4lMwsgFJ8veNbLJB10Lg7mHPocBz70QtW37hWKpmJFzNiLhFKcuOvgX/+ZFlpaWEIlE+xpl1ut15HL5e6Ew9sP12Xg30GpPG43GfStGEomIQVsvn/nCFT789Dgi0cn5OhykPV2v19uvt7WTsZ1ktNBq2SYSCWZmZraMFrVIRq1Ww+12n4iEbGs5zWaz4ff7WVpaQq1Wk06nGRwcPHH98dZoj0wmY25urt3+fjcMh7YraGg0Gnw+H7dv38btdj/UguBe6Fwe7BwNq1QqrKyscP369WMnHZ2ShNvneg+7oCcUChm95OZX/uifs3g7wGtf/Q6CRh2RSEA2lsEyaqaUKyEWC7CNmvHeCiCWiBi+OoRvOkghW8I2OoBc3aogLSCRiZk47yDoj5FJ5tu66CZrLwaLHs+9BxWkYr7MzDUfErkEjfqBPvt7wG31Mf4bw2E6GxKpGKNJwwufu8SPPXcKqbT79/X/Z++949sq7/3xtyRvO94jXrKWR5xNVhPaS4AOypfSW6C0kNt8X00L6WBdCsEkxZA4kISSAoVSaOFXvqUXKLRlXBpSRtmQOAlJSGLH0bS8hyxZ1rKs8fsjfQ6PjjXOkc5RbKP3f7eXSMe29Hyez+fzHuR1Evnuc9lssJuMaNpBclaR4ZhMJmOoRXSTIUZoLHD2d6JQKFBdXY3u7m4cPHiQCXAtLy8XfSNMGAmZmZno7OzEwMAASktL0dTUJHp2Dy3CrqqqQnFxMbq7u3H06NGEtISxYLfbodPpplm8KxQKZttUWVnJ0KuEAAk+HBwcRF1d3TRjFBJUOTY2hq6uLqSnp0OpVGLevHlMnSImDVKpFPKmatz21Gb0nhnAiw+8Bu2nBmRkpmHEbEWlogz+KR/8k15olivR/W9HqoWrVTBrh9BvGEFFXSmKKgqgPWZGxydaSNOlqF8ix+jQBKzDE7AM2GAZsKGwfB6q1BVnNYn/dqjyeX3obDdCmiZFUVEz8/PF+rzQYbwzGXOm2tKHeCy72YrKAjz0h42cmgwx19OkyQgGg5BKpYygjguKioqwcuVKjI6O4tNPP0UgEEB2dnZIcJuYSEtLQ3Z2NiwWCxwOB9LS0pCfny/65IReQy9atAi5ubno7+/H4cOHMX/+fMjlcsGfgZ6SsUXuy5YtC3kmvgLBaKAP73DiQTKtqa2thdlsxsGDB0OmePEg1uFNg4tAb9raepkCW5+9Ee/+40O8/8wh9Hb2Iz1dhqlJLyoVZbAN2SBBEIqmKhiPdwPBIBauUcHUOQCnfRBVqnLkFedBe8yMzoN6pGemo3mFAn0mC8YtDoz0WjHSa/33BKkUZ070MhOkKc8UvP/WcCR6OUshhXhAhmJA7GYjI0OGR//fD6dt3NkQQtwdTT9CLmTkUsbHoIQMx8bGxnDixAmmyVer1aI0GWzIZDJmOONwOCCRSJCfny/6oIFQbr1eL+rr61FYWIihoSEmy0gol0ca9NaGrT8JRwMWivFAuy+G22Kx6VXt7e0JNx201oaLqxVpOqxWK7RaLWQyGVQqVcSmo6ahEv/9++vwHxtX4Y0nP0Bv5wAys9Ix5fGiUlkOp82JSZcH9eep0HO6Hx6nB82rVegzjOB0ux5l1cUorS3GmU+7oT1igjRNigXnKTEyZMfowDhswxOwDU+goCQPNYuqoesYYByqAr4AXONuANxpVDO90QDmULNBXD6A2EF6XDcZsbQffF6DBrvJSE9Pj+s9JiYm0NfXh6ysLOTn52NkZARWq1XU2Hq2//iXvvQlyGQy5sAhKa9Cr4nJ1iYQCEzbItTU1KCyshJ9fX2CBuPRB1o0EXZ+fn6IQJBM8eIV/ZFwpUAgwInXS/uvx9t00D8r3w1VOIFednY2lEolcnNzwzYd89Wl+PlvN8I1MokXHvhfjPZZkZGRhsERO6qUZRgftSPg80G9VIHuE73wTfmwcI0K5jOD6DcMo3D+PFTUlEF7vAcdn2ghy5Ch+TwlBnqtUSdIfP3LZ8MhnsLsAd1sxBqKnd0exP6MxmpauCDcsyTSZNBwOp3o7++HTCZDWVkZU6cKCgpE2y4EAgEMDg7CbDaHCJMJLdRkMnHWlPAB2dqEcyScP38+ysvLMTg4iMOHD6O8vJyzkD0a6LDaaCJsYppCW/YmwoCg3Re55I3IZDLU1dWhpqYm7qaD/lnLysp4a22KiopQVFQEm80GrVYLqVQKlUqF/Pz8sE1HSU0h/u99V+JHO67BXx/chz7dILKy0zFktqCyrgxuuwuucScaVqjQf2YQDpsLzSuVGOyxoPOADvklOahUzYf+ZB862/WQSCVoWqHEmMWJ4V4rxi0OjH+iQ25hNjTnyaE/PQiPyxuiLZwrQ7E502zk5eVxnhhxhdDNhlBNht1uh8FgQDAYDLl4q1Qq5kssNK0mlv94bm4ulixZwkz5pVJpQhduAnIwEv1JpIORnp7QKavxTPlpmz4+4kEiELTZbMzKNpYrCQ232w29Xh8zXCkS4mk6aA1MPIc3DUIdKCkpYUSSmZmZjJaGbjp8Pt9Z84GmKvziyc3o0w7gxb37oDtm/Pfa2oJKRRkmxhzweiahWa6AuaMPHtckmlepYOrqQ9dhA8pris/Spo6ZP08pP0+JUdYEKb84Fw2LqpGRdbYIzyXhXQqzB2LWKaFeg60dzMjIiEjrjQan0wmj0QiPx8O4IEokEqhUKsZWXehtNN1klJSUTAtwzcrKQnNzMzMcI01HohRY4qRFRO6RtjZ0gGqi1KJYYbWRQFv20k0H198Bsc4lGh++G6p4mg5aA0PqbCJ0tMLCQqZWE1ZCuKbD5/MhMzMT5fJS/OzBjRjpteCl3+yH4bNuVNQWY6TPhvl1pZh0TsJusaNxpRqDhmHYLBNYsFKFvu4RdB0yoLAsH9WaCmiP9+D0IQMgkaDhPAUm7B4MdFvgtLlx6hPdWVer8+TIyv0812qu1Kk5IxBvaWnB8uXLcemll8JgMDA2sYngs88+Q2NjY0IuClarFSMjI1AqlQAQEnLEF+QiDyDq4eDz+dDd3Y2RkZG4wn5osK0BuYq8iEgsOzsbKpWKtwUt11TTSJiamoLZbMbIyAjnlFX2hKiuri6hyRvtkBUtJyTRwzsSyO9geHh4WtNBH975+flQqVSCc4mDwSAj0MvIyGCCt0wmE0ZHR7Fs2TJkZWWFCPQGjcN4ce8/8P5fD0K9THF2i1GeD4lMhl7dABrOU6FPP4yJcSeaVqkx0G2BddiOkqpCVMjLcOa4Gf4pPyRSCRqpCRLBD+/8P/j2pv/AoUOHsHTp0qg/MxkKiM2x5ojUiiUyZk2devTRRzE1NYUf//jHvMxDoqGrqwvz589P6MLsdruh0+nQ1NQE4PM6FW+TYTAYwobG0qAzghKlf9Jnd0lJCerq6jh9b2nDlXiGY7TteTwBsPTvoLq6GjU1NTF/B7RZSH5+/jRnRr7gSgOOZA6TKOjfQThzGZvNBp1OF/ddggvGx8dhMBgAgBGSE+r0okWLMG/evJA6NTZow0sP78f+p9+FcrEcYwPjSM9OR05+DgwnetCwUoXRPitGB21oWKGAbdSJ4d4x5JfkobaxCvoTPZj8N6W3frkCbvcUeg2fx3V+64dfxo+2fgsnTpxAXV1dVKYIGQqI7fbFERE/EHOm2Whra0NNTQ2uuuoqdHd3IysrCxUVFQm95smTJ3mFybERCARgs9lgNBqxaNEiZGZmxtVksL8IXIuK1+sNCe/hc4mlm4x4Q/3YF1ouhyJZzbpcLl6pppHA9hYPd0DSSeOFhYVQKpWCXTDp7I+cnJyQw5IWYSuVyphhTPGCbrxqa2uRk5MDg8GAzMzMadaMYmF0dBRdXV3wer2Qy+VQKpVMuisAZm1Nfv5h8yj++uA+vPPcx1Atq8NwtwW5RblIz0pHd2cfKhvK4LRMwm51onGlCkO9Y7AMjqOwPB/V6gqcOWaGb8o/bYJ0Xeu38X9+sA4HDhzAqlWron4fScaN2MYDHJFqNiJj1tSpp59+Gr29vbj55psxODjIfB8SgVarRWlpadwc/EAgALfbjRMnTmDJkiXIycmJq045HA4YjcaYTQYbiQzH6CajuLg47lA/cqENZ1cbDiRIj9ieszMt+ILOpYrWeNFmIUJfvMfHx6HX66fRgGkdXzTb+0TBdrQsLCyE0WgEAGg0moRZElwwPj6Ozs5OuFwuVFZWoqGhgbHNBabXKduIHS//Zj/2PfUO6pprMD7qgEQmxbySPOiPm1HZWAqvI4CRPisaV6kwPubEYPco8gpyULewGoaTffC4zgb7qZbI4fMHYdYO4Yrr12Pj7d/EsWPHUF9fH5UdkWo2koxf//rXyMzMxMaNG9Hb28usKxNBR0cH5HI57w85211qeHiY4cLX1tZy/qKSJoNe8cUDcoEnW4JoHE22/3htbW3Cly2y7jUajRHFcYRL63A4BJ3uE9CpqbQ1Hm1PSCbvYoBuvHJycpCRkQGLxYK6ujrMnz8/KbxMm82GU6dOYXJyEnV1dVAqlaK/L30ZqKiowLx582A2m6cJ9CId5qN9Y/jbQ6/jrWc+gGpJHUb6rMjOy8QUfBjptqFxpQrDPWMYG7ajcZUKlsFxjPRZUVCah5r6SuhO9MLr+XyC9M2N5+OiK1bi448/xtq1a6N+xlLNxqzBrKlTL774Io4dO4aWlhaMjIwwAXaJIN5NPpvWOzY2xnDh+WgJ4gmNDQc+wzGa/plIk8F+TXKZnzdvHlQq1bQLnNfrZSxWhZzuE9DbA1IbJBIJQ/dJhsU7ea+0tDTk5uZiZGSE89ZFCBDLXpfLhaqqqqRY9pJtkcFgQHFxMUpLS2E2m0OcPaPVqfHRCbz62Bt47Ym3UNtYjQmbC8FAELJcKQa0FjSsUGLc4sCgaRQNK5RwTHjQbxxBTn4WVIvkMHaeze0AAOXCGvzHd1bgO9etx5EjR7Bw4cKo95LZsoGfM83GE088gfHxcfz0pz9Ff38/AoFATG/iWOC7nqabDKlUCplMxnxJ2C4/0Wg9QjUZbDgcDuh0OgDTJwXJ8B+ntRCEkuXz+WAymWC326FQKBKeEMUCaWrGx8cBnHVLSdZ0PxAIoKenB2bzWS/ukpKSqInoQoEW8pFsFjJJFGtSxebYsrdFJEGXLdCLdJjTa2vFIjkGzcPIy89DTmEODJ/1oGGFEpYBG0YHbGhYqYLN4sCQ2YK8wlzUNVdBf6IPk24vbrj/+/jq99bg448/xrp166L+DKlmY9Zg1tSpffv24Y033kBbWxssFgusVis0Gk1Cr2kymZCTk8M5zTyaCyLb5SfaBVOoJoMNol2bnJyERqMJqb90k1FUVASFQiH4RDfccAxA2CZALJCmZmRkBDKZjNm4CBkCGAlE90J0oQUFBby0h/GC3WwWFhait7eXCQgWwvQlHGiallqtDqnHExMTMBqNIcHI0eqUw+rEq797E6/+7k1Ua+bDMmKFTJqOkqoinPnUBM2yOjjtHvQbhqE5TwGPewq9uiFk5WZCs0R+NoF83I1rbr0E37v5G2hvb8fy5cuj1iCiK57pzcacEoj39/cDOCtAmpqaivEvYoOr8I69yQinyZDJZFAqlaipqYHRaER7e/s0+z9yAZPJZNBoNIIfLHl5eVi2bBlsNhtOnz6N7OxsKBQK2Gw29PT0oKysTFT/calUiurqalRWVsJkMuHDDz+ETCZDfX09Ghsbk+L8Mzk5CY/HwwTgeDweeDweUZsNdpO1bt06SKVSjIyM4Pjx4ygsLBSlaJLD22azTfusaTQaRkhOxPRCNR3j4+PQ6XTIzMwMyQWhQQR6ZHUfSaBHDvPi+YX40a7v48pbL8XLv9kPw3Ej5teWYXx0AuXVRQj4/BjtG0PDChVsgzYM9YyhcaUSdpsLp/6dQN68WoX0CFkFKaSQDLCtbxMVdpPX4SI0D5fnxK5TUqmUOQvI2cCe4NNNhtChsQCQnZ2NRYsWMcMxcja4XC6YTCYUFhaKmkgtkUhQUVGB8vJy9Pb24qOPPmKeYfXq1UmZ7nu9XrjdbmRmZiIjI4OpU3l5eaLVSXaTtWbNGqSlpWFsbAwdHR0htuZCgk2ja2hoYH5GhUIRIiQXsulwOp3Q6XQIBoNoamoKy2CZN28elixZwnzmDQYDlEol85ln16m8olxcu/U/8e2ffx2v/f5t/PXB11BbXw2n1YnisnzI0qQYNA5Ds7QWngkPerWDUC2tg88fwMlPtMjISkfzKiWycvgJxGeDa+Kcqbx5eXmcw5K4ItbrELcCkkRJrAGjIT09HQ0NDcz0htBLhoeHmYu32NMLcljrdDocOHAAeXl5WLx4cVLSt2kNRX19PUNv8vv9onFBgbMTCiKup3/HtA2gEK4kNOgpXDhLQhKMNzQ0hKNHjwpGB6AP77q6upDDm0ZGRsa0pkMul8dN6yIOL36/Hw0NDZw+xwUFBYxtMNv8IFzTUViWj/+747uoWVeKgUPWz9fWFgfKqgqBYADD5lE0nKeAfXQCA8YR1J+nhMs1iVMfn8ElG9by+plmwyGewuyBGHUq1lAs2sY9EtLS0qBSqVBTUwODwQCz2YyqqipYLBb4/f6QC5dYyMvLw9KlS2E0GnHo0CFkZWVh4cKFooWn0qA1FCRVvbe3l2FMiEXrIcYotO4F+NztymQyMRQzocDWVrIbuZKSEhQXF8NiseDkyZPIy8uDUqlMeEBHC8NramoiNnJpaWlM09HT05Nw00HYDU6nk8lAiQXi4EV0SaTpINs8dp3KLcjB927/FsqXz4PlMyde+s1+lNWWwmV1oaAoB+npaTDoe6BaXAOfxwtz1wCUi2sRlEpx6hMtVl58NtSPbB/nAuZMs8EnmZUrIh3idJNB+4/zQXZ2Nqqrq6HVaqHX65GXl8dQXMQE23/8y1/+MhO4xJerywc0F1Uul0OtVjO/s9raWphMJrS3twsulKYDh8I1E7QNIJmiJbqujnV40yCJ6BUVFRgcHGRCn7i6qdCgk2OjHd5s0E1Hd3c376ZjcnKS0dvE611Pmg5i60zyVAhXlj7Mg8Eg8opy8IPWi/CdGy/Bq797E6889gZqNPPhsDhQUlEAqUSCQeMw6pfVwTXuRJ9+GJpldcjI5Le1SzUbKQgJseqUz+eb9r/H02SwkZGRgerqarhcLuj1emRlZWHBggWC5yixQZt2FBQUYO3atXA4HDh9+rTgJh40aJEyOyyOtlUXmn5KW+eGM0bJzs5mLHvpnJBEA3xpYXykLTQQams+OjqKEydOxK1zDAaDGBgYQHd3N+bPn8850yktLQ1KpRK1tbVxNR3EkGVsbAxKpRILFizgfb6TwSyxdTYYDCF5KuymIzMnA1fduh6Xbf4q9v9/7+KvD/4DxZVFcE+4MS8/G1nZ6ejq6IWyuRpBfwCmEz2oa65GTq641OpzgTmj2fjkk0/wxBNP4JFHHoHdbkd/fz9j4xcvenp6kJaWhsrKSgDxbTLCwWq1wmAwID09HUqlEvPmzWOEaXl5eWGFaYmC7T/Ovszy4eryAZmyDw8PxzyghbSApa1z2eFK0UDEcXwzMggStfxlN4Nc7HfZh3eivvX09ila00H0NqOjo4I3icQDPpxAz+FwQKvVYtWqVcz7OcddeO33b+Pl3+xHpaoCk54puCY8KFeU4swRE5RLauF1T2HDnd/Gmm8uwcGDB7F2bfQtRyAQQEZGxkzxOU91PZExa+pUb28vNm3ahL/97W+YnJzE6dOnsXTp0oRec3h4GC6Xi9EWCNFkAOG/g3a7HTqdDunp6dBoNILTT0mTYTKZwl5macMJoi0UYjgWy36VhpAWsLRxiUql4mydSwe/qtVq3s1fopa/7GaQi9Mk+TcGg0GQFHV6+xSt6aAbSKGbRKfTCZPJBJfLxeSrAGd/1snJSRw7dgznn38+8zeddHvxz6ffw98e3If84jzIMtIx2j+G2qZqaI+aUKmugCxNhouvWYtvXXfRnNIWzplm47PPPsPOnTvx1FNPweFwoLu7GwsXLkzoNYkPemVlZcjhnZaWlnCTQbycadCcSaG2DHz9x4mQfWhoiHM+RTjQBwHfgEG+yaQ0aN/zRBoWYlfLtWkgRTgtLU0QMR2t84gk2KcPfELBEvLAidR00BuURP3xY4EW6MnlcoyNjcFmszHrb7ZAzzXhxr4//At/f/h1lMvL4PcHYB9zolJdjq4jRtz5/36K8y5uxpEjR7BmzZqo751qNmYNZk2dstlsuOyyy/D666/D5/Phs88+w3nnnZfQa46OjjIGG0I0GXRorFKpDDs9t1gs0Ov1Yc0f4gGdHcFlYi7UcIx+Hb7hesT+ljgc8snY4GLJzgV8Q3Rjbfr5gqsrGBmm5ubmCm6Kwr5rVFVVQSaThQzh4g1O5AqSHO90OiGXy+F0OjEyMsI0kOw65fVM4a0/f4AX9/4DOfOykZmTiSGzBfKF1dAd68aPdnwXl/zwAs7NRnp6uiiMlDgw95sNg8GAG2+8Ec8//zwTULR48eKEXrO/vx9er5e5KMfbZIyNjcFoNEZsMtgQ4iBN1H+cuGFYrVZeExf2GjoRMRef6Q0thhbC9xwIpUNFKoC0TiGeCVMsRApVFPPwZoMUxtHRURQWFsJmswme/BsNfr8fOp0O/f39yMzMRGNjI7OpiuQK4nFO4vWn3sHfHtqH4soiSKRSjA3acOsTP0bz+RqcPHkSK1asiPq+gUAAmZmZM4Uzm2o2ImPW1KmpqSmsXr0a77//PgKBAI4cOYJVq1Yl9JoWiwUjIyNQq9UJbdzpJoNLnhOpMSaTCRUVFairq4srBZs0GfFoAXw+HxNcysclim3xnsiGhM9wjB02K5TteSxrXDq/Kl6qazREGmoKPYSLBtJ0DAwMoKioCHa7XZQhXCQEAgGYTCZ0d3cjLS0NDQ0NjENcpDo15fXhX//zEV7Y+xoyss6GAg7oh3Hdnmuw/ntfwieffMKp2ZgNQ7E502wMDQ3h+9//Pl599VV4vV50dHRg2bJlcb0WWUM7nU50dXWhqqoKcrmc16FA0pNJeJpSqeS9qqQPUq7TD6GtAYmYihxSkQ5Sv9+Pvr4+9PX1CT5FiHZg0cF4YlkShgv98/v9cW9f4gH5/ZrNZkgkkqRpfIDP/ee1Wi0kEgkjEBXb/jHcZIpwmycnJ0MEepEOc3ptPa84D9fvuRaaVXXo6urC8uXLo75/qtmYNZg1dSoYDGLZsmX48MMPAQCHDh2Ku9kgtN6pqSmcOnUKZWVlUCgUvC/NZDoO8AuNpZ+D76Yz0SaDDa7DMZqmWlZWJqjFO7010Gg0IYMn2vpezG0wO/RPJpOF5EvxTTjnC/L7NZlMAIDMzEzU19eLrvEhsNls0Gq18Pv98Pv9kMvlvFgV8YCmh5WWlkKhUDD3komJCcbSn/y3wPQ65Zvy4d2/HMALD/wvJBIpNmz7T3z5ilVziu47Z5oNl8uF9evX4+2334bf78exY8diTi7ZYHNd09LSEAwGGVoRl8ssu8kQIoDH6/XCYDDAbrczFqbh3ndoaAjd3d2iWKkSm7hAIBBiyxtp8i4G6INULpdjZGQkhO6VjIC63t5eZm3d0NAgeKhTJLhcLuh0OkxNTSEvLw9jY2MxucVCgLaxJXkkdLCVGA0e2Sjp9XoUFRVBqVROuxAQgZ7b7Q7R5EQ6zL2eKbz5zAdoXluP0rqzybRLliyJ+hypZmPWYNbUKSGajXDaQQDMOcyVtkrynID4mgw2uGi4yHfbaDSKkoLt8Xig1+unDYH40okTAU1tUiqVsNls6OvrS8p5DXz+s2q1WsZoo7a2Nil1ig7nLSwshMViES23iwZtY0syxKamptDT04OhoSHeVG6uIAJ78llmMww8Hg9DtVMoFDE3HX6fH+//9SBKq4vRuEaF48ePxzwfUs1GkuH3+7FixQp88MEHCAaDOHz4MOdDPFyTwf5Q0oEzGo1m2hqSTH+NRiOysrKgVCoFnzqTC6fP52PsW4nOw2QycRZqJQJy+UxPT0deXh6GhoYSXkPzgc/nQ1dXFwYHB1FQUICFCxcmJZCPnexK6GJi/+xEg8J2emLT1YROdyWfNb/fHzHzRYymw263Q6vVhjQ30RBNoAdMP8wBMMU/lqYr1WzMGsyaOgUAy5Ytw/vvvw+JRMKr2eBSp2jba5VKFZZOKlZoLAHtTqfRaJggNKKDEyuvgQah4Pr9fhQXF2NoaIjZ9Ccj/CwQCECn06G3txc5OTlYtGgRb2ZDPGBvUNLS0tDd3S36z04zDOjPndjDSNrGlnzWwj0baTqE2irRw9f6+vqYf1u66airq0NFRQWA6HXK4/Hg1KlTc4buO2eajWAwiOXLl+ODDz4AwG1ixOXwZoO+hNXX1yM3NzdETCxGk8GG3W7HmTNnEAwG4fP5mINE7CRqIDRdNBAIoLS0FGq1WtQGh7wvLeSrqanB6Ogok2HBxbUpHtCHN3uDkoi4MBbowzua0xOh2gl1kMZjYzs5OYnu7u6Emg6is5qamppGQeACItAjrjylpaUAwh/mhOMey60uEAgw4Y8zADPiIWYoZk2dAvg3G/HUqcnJSej1erhcLtTX16OgoIAJjZVKpaI0GWw4nU5otVpMTk6etav+t9OimE0GAaG2kOFcQUEBGhoaRB9MEeoncROUy+WMFiY/P1+0YSBdi9gbFHqrI3StpOtjXV1dREMZoWnW9PAvUlMd7t8QWnq8tTJRm3diKmC1WplaCYSvUyTUMpYcINVsJBlkPf3BBx/EPMTZORnxCL9tNhs6Ozvh9XpRVFQkuviJgLaoUWAgAAAgAElEQVQGJMmihGoi5sQm3Bo6PT2dcc8S68JPuzKF2yKw07mFEi3TPORYmwPaNjHRLUO8Nn30QRoPrYyeiMZrY0sfpFw1RlNTUzAajbBardBoNAmHVRFNh8PhiMiVJe499fX1UV8r1WzMGsyaOgUA5513Ht566y2kp6fj0KFDWLlyZUR9QaIuiA6HAx0dHXC73cjLy+MctpkoaDqxVCpl6J9qtVrUoVg4V6vMzEyGgivWhZ92kgxnyMJO5xZKtEzr2mJt2WPVUj6I1txEAx+L4Vj/Pl76dDxNh9/vZyz8hbB5Zw/oSK2k65TD4YDZbI5pdJRqNs4Bli5dGrXZEKLJoDmn2dnZKCgoQF9fn6iBeOR9w/mP000AOUCE5O7RNK1IYUq0s4dQE36+0xh6cpIIP5Nsbrgc3mzQFnx8Jyf07zARbi87PDGWdTE7CFAIXiuXpoNuqqJNxOKF2+0OK9Dz+XzQarXIzc2FUqmM+btJNRuzArOqTn3lK1/Bs88+i8LCQhw5cgTLli0L+a4L0WQAYDYZMpkMJSUl6O/vR35+PlQqlaiDKbLppzWLpAkwGAyiOATRNC3y3WZvMWjzFCGt5Ulzw6WRYQ/H4qUVsW3P+WhQEtnIC/U75EsDFjpLCuDWdNB1WQy6Mk1FJu5kxITFZDIhEAigqalpTtSpOddsvP/++5BKpSHNBt1kEO/xRJoMNueU/vIKLUTi6tohdPYB3dzk5+dzomkJEZ4TzvmJT2GkaUV8LrFCTp7Y+o5otCL68BZyO0T7uId7BnY4Vjy2lbFAmg6bzRbCUyVuJWJ7nwOfc2VtNhtDJamqqmK+o+G4sgCYAMHZcIinMLvq1CWXXIKHHnoI1dXVOHbsGJqbm5GRkSFYk2G1WmE0GiGTyaBSqZhNBn3WiDGYIpuMSPar5BmEzj4gTUZWVhYnmpZQgx228xMfihafzTkN0lTp9fq4E7wJ6Hod695C28AL2bDGogHTTWoks5BEEa7pkEgkGB0dhV6vF5WmTcC2lx8fH0dZWRnTjEaqU0Cq2Tgn+NKXvoRXXnkFOTk5OHz4MJYuXYpgMJhwk0G0AZEmJgRcxHl835evNWA8XEb2+5JDhXBs+R5mXPUGkd430UOUzzOwD1EhV+y0HSNxoiDPQBcNMbm89IWfPAN5X6ECubg+w+joKACgtLQ0Ke9LYLFYoNVqmf+bbFuAyAI98r8nQwfFETOiksxQzKo6deWVV2Lbtm2or6/HiRMnQi5uYobGEgQCAfT09KC/v1+QrSLX96UhxGYzVrYEl2fgojcQ+n0jPUOsAR15X7IxEkr7Ql/4wz0D/b5cTDviQTgaMEk5J02k2HobcncaHByERCJBQUEBNBpN0mrA+Pg4urq64Pf7EQgEUFdXxzQ+7DoFgPmsppqNc4CLLroITz75JIqLi3H8+HHI5XIUFxcn1GTE4wNOi/M0Gk3YBNZo7yuENSB5hmguDez3pd1ChPhycxVT8U3rjucZJiYmmGcgX0piW5eVlSXaIUqewWg0wm63M5MZsQ/vcM/Q1dWF0dFR5Ofno7m5OSlCTeBsCrhWq4VUKkV6ejqjp0iU9xoLDocDWq0WMpkM9fX1yM7O5izQI0g1G7MCs6pObdy4Eddddx2WLFmCjo4OFBcXo6KiImmhsQREL2Wz2SJaqkcDoWmlpaXxel/2MxAaCdfw2PHxcej1eshkMk6p2XyeIVogLN+07nieIdxwjFy6xXhf9jPQG/m8vDwmf4XYyYqNqakpaLVaDA0NITs7G83NzUnL6KDt5bOzs2G321FbWyu6rb7b7YZWqw1xGY20bSF1igzQAcyaDfycaja+9rWvYc+ePVAoFIzfdmZmJq/ulKYPJRo2RC47UqkU9fX1US930WhaiYCLRZvVaoVer0+KZS/tNESKRjLSRV0uFwwGAyYnJ1FZWYmhoSHRD282LBYLOjo6mEOlpqYmKe9L//5JPsnExIToIU9utxt6vR6Tk5MhwU7hvMeFfIZwTjzh/ptoAj3gbFCoWq0W7LkSxIyoJDMUs6pObdy4Ed/97ndx/vnnM5bqgUAADQ0NvM7AeJsMNsj31Ov1MpedaKC1IDRNKxHQ+RiRBnTksi+RSKBWqwUXutM2qvRwjNjoBgIBqNVqUS+/9GCqqqoKY2NjjENfojkoXGG323Hy5El4PB4olUooFIqkXGTpAWldXR3Gx8dFDesloHPM6FgDuvkSo+ngYo5CNx2E6jZb69ScajYuv/xy9PX1YdeuXTj//PNDeHfFxcVQKpURxUxsAbZCoRBs4jw2NgadTheW60hvFMhkX4yJM5niZ2dnM44gZD2aaLHiCpIETvjwpMlIhjsKACYRfmJiAtnZ2ViwYEFS3tvtdjNp4xqNBunp6TAYDPB6vVCr1bw2X3wQ6RAlz2Q0GuF0OplQPKEOc3pSSCam4V5b6KaDiOr40BhpbYvVasVFF12EkydP4q677sLq1atx//33x/08AiPVbETGrKpTmzdvxnvvvYcdO3bg0ksvhVQqZZKPiWNTJIohOzRWqVQKdm7TGTfhBnT0RkGoJoMNekCn0WiQm5uLiYkJxmpdiPDBWCBJ4JOTk5DJZAgGg6Ke02x4PB6cOXMGVqsVGRkZaGpqislMEAKk8SWbrtzcXMZkQ6VSRTzHE0U06je9iRbCBYoGTWFTKpUR3RO9Xi/MZjNjujJ//vyEmg5aryOXy5mtRTTQWSHj4+P4yle+gt7eXrS2tqKgoAB//vOf434egfHFaDYAoLOzEy0tLQCAHTt2oL6+PkQQxhZCidlk0GCL82prazE+Pi5qCGC4ZxgdHWWSRXNzcyMGtokBcoi73W4EAgEUFBTEpQnhC3LZd7lczMQqGVsV+vAORxEQa2LGx8aW5FOwk7jjAc0F53qIAok3HcFgEP39/TCbzXE7hni9XvziF7/AG2+8gXnz5uGPf/wjVq9ePVNW00Cq2YiGWVenenp6sHXrVvT19WHnzp1YunQpAEQUcLObjES1ApFANuxkQKdQKJitsFgbhXCwWq3o6uqCz+dDVlYWbzpyIiCDGLvdDolEgqysrJjMBCEQrl6QminmVoWuF+G2CPSwLNEaQYOuF7FMbcjGZ3x8PCrVjQvoelFVVYXa2lpO9SLRpoM2oYnXlMXn82Hnzp147rnnkJaWhscffxxf//rXZ0WdmnPNBnD2j/rOO+9g69atWLFiBVpaWlBSUgKfz8cIVVUqFYLBILq7uwURJHNFIBBAV1cXBgYGkJeXh+bm5qTReCYmJqDX6xEMBpGfn4/h4eGkOAKRYuXxeKBWq5lE2ZGRERgMBkF9x2nQqe+RKENELJ2bmytY4xPr8GZDKC5wIja2TqeT2baoVCpekzTibGUymRKyJaSbDq4FxWKxQKfTJWSnOTExgb179+Ltt9/GLbfcgtOnT2Pfvn3YvXs3vvGNb/B+PZEwY6rJDMSsrVNHjhzBli1bUFVVhbvvvhvV1dUIBAIwm82MeDojIwMmkylpQynybHq9HmazmeHOJ4vGQ9eL4uJiDA8Po7S0FAqFQjRreWC6to7UC1IjiGGK0EYehCoTrV4QVoCQwzG+9YI0PlNTUwltehKxsfV4PDAajXHRgIWyX6a34VybDrK5zM3NjTsE2ePx4PHHH8fzzz+PG264AWNjY3juuefQ0tKCa665hvfriYQvVrNB4Pf78cwzz+DXv/41rr32WmzevBlpaWk4deoUxsbGIJPJsGDBAiZtWGwQjm1GRgZqa2sxPDwMq9UKtVot6jOQCbrf74darWaKhthZB4QDSwvEw1mMiuGfTdaykXIe2M9ADqF47HYJEs2siNflhN6alZeXJxTW5HA4YDAYOBcUQhEU0tmKLigkI4P99yOi8/T0dGg0mri2kT6fD3/605/wxBNPYPPmzdi8eTNTfKxWK2w2G5RKZcI/j0BINRuRMavrVCAQwCuvvIK2tjZ84xvfwH//938jJycHnZ2dsFgsCAQCaGxsRGVlZVKehyReB4NBKBQKWK1WDA8PczpLEwG9gaapncnIOiCXx0ibYDFC+ej6y9Wu3mq1QqfTJWTikmjNpYeWfLYt9NasqKgIClbwIR/QNGClUhmT4jU+Pg6dTieoKQuXpoPoJf1+f0TNbCwEAgH8/e9/x969e/Hd734Xt956K7Nlczqd6O3tRWNjY8I/j0D4YjYbBC6XC/fffz+eeeYZSKVSXHXVVbj99tvh9Xqh1WoZNyKxNhvRrAH5ivP4gOskghYqcXUEiQbabpXrhJpOBo03J4TmYMbzGnyDBOl/J2TDxMe/nRzeQtvYxuJJE4cUmUwGjUYjCsUgXNNBRIRutzui+DsWgsEg3nrrLbS1teHCCy/Etm3bkkbRSACpZiMy5kSd8nq9eOyxx/Dwww8jKysL559/Pnbv3o1gMAidTgcAjI5BDNCXSPZ3nui/JiYmOLkb8gE9lIqmDaBTnIUQDdMbBS4BqEBoKF+8rIBEQvUA/kGC9L+jgwATbZj4uIKJcdkHPqcBE4p0UVFRyN+QNkcR+n5FEK7p8Pv9MBgMGB8fn6aX5IpgMIiDBw+itbUVCxcuxPbt2xn3xBmML3az8dJLL2HXrl1YunQp3G43zGYz2trasHLlSgBgRORCp4DzEWDT4rxEv4z0GpoPx5I4gng8nrjcL2j7vninYDQFietr0Id3IiFN9OtxSUSnD+9EJzXhXpsuKApWqCJZq6enp0OtVovGJyZTTgCMuUGil32+IBNPi8UCqVSKhoaGuDm7p06dwrZt21BcXIz77rsPKpVKhCcWBalmIzLmRJ1655130NraCrlcjpycHBw9ehR33303LrroIkgkElitVmi1WsFTwMlggb35DgfibhgMBlFfX59Q40PTliJtMMOBpsfGY9mb6FCKvAbf4Rg9zBLirsEnEFYMujAB7WbJNrih3TDF1IcSGvDk5CRDM0vkMxIPSK4WyelQq9Vxs0VMJhPuuusuuN1u7NmzB4sXLxbhiUXBF7vZeOWVV7B69WpmDX38+HFs2bIFBQUF2L59O+rq6kIurLW1tYzFWDygrQH5cPDDifP4TB4iraH5IpwjSDSQUCASyJOoWwMQ6qQUi4JlNpsTpg+FA11Q2LQoss4W4/CmQa/viR+/2Wxm7BCT5UFusVjQ2dnJbMlqa2uTIkqjG7+Kigq43W5mdc5nAzc4OIidO3dCr9dj165dWLt27UwS1XHBrHrYJGNO1Kk33ngDGo2GaYD1ej3uvPNO2O127Ny5E83NzSH6qMrKSsjl8rjPWrrJUKlUvLZ7tHsWXx0DLYROxIXO7XZDp9MxrIBYZ6Hf70dfXx+T2l1dXS0oZTcaBUusoRQQum1hb9bF0HqEA+2qmZubi+rqavT398PhcAi+CYuG8fFxdHR0wO12o66uDiqVKinnPGn8jEYjysrK4PP5YLPZeG/gbDYbfvWrX+HDDz9kaJVzpU59IZqNcAgGg3j99dfR2tqKCy64ALfddhsKCgpCbDsjeR9HglDWgHzddRIRTUUDuVBHKig05zQejQIX0FkNZNvCXgcTEaVYoLctZWVlGB8fZ+hDyRBrAmengCdPnoTNZkN5eTmampoEF9SHA61Dqa2tRW5uLu+04HhA62gIR5o0koSv63A4Yn7eXS4XfvOb3+DVV1/F1q1bcdVVV4ka0CQiZlXFSTLmdJ36+OOP0dLSgsbGRmzbtg0VFRXMdH5oaIj3Fpnosnw+H+8mg/1sZBDCZVJP002EzE6IteWlBxZC6QLZoLc09CTdYrEwF3CxnRfpZqqsrAwul2tatpXYmJqaQmdnJ0ZGRlBUVITm5uakGO/QVObKykoUFRXBZDJx2tYliki21WTTQbKcon3evV4vnnrqKTz99NO48cYbsWnTJlHNEEREqtmIBJ/PhyeffBK//e1vsWnTJmzatAnp6enM1GRqagoNDQ0x+YhiWAPGKihC2sFFAl1QiCOIVCplLqBC0Ja4gAiC/X4//H4/o1FIVsKzy+WCVquF3W5nGg0xft9ssGllZWVlGBgYYCb9Qm9zCNiic7ZNH5liiWHDOTExgTNnzsQM5IzWdPj9fjz33HN49NFHsXHjRtx4442Cu8gkGalmIzLmfJ0KBAJ44YUXsGvXLnznO9/BDTfcgJycHHi9Xuj1ejgcDtTX10dtHGjzB76Oc7GejWYFsClF9PRfqM13OBBWAKGZpaenMxP/srIyyOVy0Qc0LpeLCRQFgJycHFFprmwQKvTY2BgkEglUKpXgxi/hwB5KVVZWMjTgkpIS0QaCbNG5UqkM+RvTZgdCWweTO0EsSiE7QJZuOgKBAON8eOmll+KOO+5IWhSBSEg1G7Fgt9uxe/du7N+/H1u3bsUll1wCqVSK8fFxaLVa5tCgLyx0k6FSqUSbHrDFeSRsZ2xsTPCgm0ggBcVoNAIAqqurBdW3xALRv0gkEkxNTTFTBLGbjcnJSeZ3TyZWZJNEBI1CBuIR0AU83NaIPtyFbviihVDSIN7/RMyeaOq9x+OBTqebljgeC6Tp+Ne//oWysjJUVFRg+/btWLNmDVpbW5PmNicyUs1GZHxh6pTH48HDDz+M//mf/8HNN9+Mq6++GjKZjKG+hjNsEKvJYIO2lieGJDS9VugE5nAgNDOiK6moqBDUOCMWiMje5/MhGAwiIyMjKRtwWi9J7gTk7xHNZStRxLI9p6nOQjd8fHSuQgZSRgvLjQbSdHz00UeYmprC8uXLsX37dtTU1ODee+9FbW1t3M80g5BqNrjCbDZj27Zt6O/vx86dO7FkyRIAYDIhysvLmRWd2E0GGzabDadOnYLX64VSqURdXV3S+IhE2EYu1kKvwiOBuB7RGRQ0xUYMDizAjYtLhPhElCaEoxG9UeBCTRCSysZXq0M/s8VigdFoRG5uLpRKJS+DA/YlJV4a4Jtvvol7770XBoMB9913H6677rrZxneNhjnzg4iAL1ydGhkZwT333INPP/0U27dvx/nnn89kQuh0OhQWFjL6rsnJSUHD2GLB6XTixIkTcLlcqK2thVqtTgp1kTbVmDdvHjIzMzE0NCQaxZcG2Wh4vd6QWiC2to8Wu0dq6GiKl1BJ4PRGgYtdvJBUNpp1wpcixse0hw261ioUirjvPkeOHMG2bdtw8uRJ3HbbbdiyZctspUyFQ6rZ4INgMIjDhw9jy5YtqK2tRWtrK6qqqqDT6TAyMpJ08RHboi87O5s5wOINiOGCaMK2RB1BYoG4SxBBcjjOJd0ECSUQpw8UrtM4h8PBTNIScdxg0wD4NFBEpD80NMR7ikhW7263O6GkXnaxj0Vzi0W/4IrR0VHs2rULx48fx3333Qe5XI5du3bBZrPhxRdfjOtnmYFINRuR8YWsUwDQ2dmJlpYWAMCOHTtQX18Po9GIkZERTExMoKqqCg0NDaLTXIHQs7O6uhoFBQWMUYpY9tgEtDaCHnbQNFQxpvvEttfpdDIXeTbEGI7Rl3eum22ifyRuk/Ge84nY2NK6Er4bedqin6+elg0+NGC2KUO8IcgTExP49a9/jTfffBN33303Vq1ahb179+Lw4cN45513ZquWkI1UsxEPAoEAXn75ZWzduhVZWVkoKCjAc889x9iq2Wy2uD2UuYC+QLIvY3zFeXxAphYGgyHmpTGRKUM40Ic3caGKBZpSFG/oE1e722ggq1q+drRC2tjSG5lYmyd6o6BSqQTToNBNKtHW0A0xKb56vT6hZGA6UfW2227Dhg0bQv5mbrdbMD/3GYBUsxEZX+g6FQwG8c477+DWW29FWloafD4f/va3v6GsrIyhMQn5/WaDvkCGOzvFygICQoNQo52dhA5LB8wmAiL+tVqtnPWStIi5oqJimg6OC/jY3UYCCfnla0dLcrv8fn/CNrb0RibW5kms8GGaBpydnT3NtheAIHbTPp8PzzzzDB5//HFcd911+MlPfhLyOl+UOpVqNqLg9OnT2LZtGxwOB5qbm/HWW2/hZz/7GTZs2IC0tDRB0iHDgc8XUajpMAHp+MmXj+uXINELM22HGG+wIG3By5XixRbAx3N4s8HV05ys3sWwsaV/n+xiyBbzJfqZiQT6d0vEex6PJ+EgzWiJqnMYqWYjMr7Qdaqnpwetra3QarVYu3Yt9u/fjw0bNmDz5s3IzMxkgjBdLpeg2Th07Zk/fz5qa2sjDg2Emg4T2O126PX6EHotFyR6YaY3JfHSiOnzlyvFix4A8gnyiwYyHItliyt0o0aDzdqgzQPYDlOJfmYiIRwNOBAIQKvVQiKRxK25CQaDePvtt7Fjxw6sX78e27ZtS5oF8DlEqtmIB59++ik8Hg/WrVsH4OwlcufOnfjggw9w991348ILL4REIolofcYXdK4DXx/wRHnvNJcxET9uvlQg+rARSgPCpXEhvuDkWYU4vNmvT6b7bE4rO0NEzMAhti2y3+/nrAcRCsFgEGazGQaDAWlpaVi4cOEXKVFVKKSajcj4QtcpnU4HnU7HePK7XC488MAD+Pvf/47bb78d3/72tyGVShlNVlpaGjQaTdzTVDrXgTjVcT1HEuW901P5RNyF+FKBomUuxQuujQupzUKnbxNEGo7RdwqxjWhoW2Sy6TYYDKJswyKBNMRnzpwBADQ1NaGioiKu1zp16hR++ctforCwEPfddx/UarWQjzqTkWo2hIROp8Odd94Jh8OBtrY2JmyJTHGJJSnXRoGedCTawfNNAScCbCFte2PZpgKhRUes6ToJOWRrEehCE251KiRoXQkR5o2OjsadsB4vBgcH0dXVBSCxQ5Qv6NwalUoFr9eLnp4e3lukWZyoKhRSzUZkpOpUGAwMDOCXv/wltFot2trasHLlSsbcQ6fT8Q6OpZ2FEt0C89X8EQ0fW4CdCGLZpgLC0Gtjgf5d0AJuobSAXEDrSgoKCpCRkRGWvi02LBYLOjo6mHyQmpqapNRImk1C7P1NJlNYGnA0DA4O4t5774VWq8WuXbuwbt26uWRSwgWpZkNoBINBfPTRR2hpaUFTUxMTthQIBNDT04P+/v6Ykxu2Q0O0NTRfkCYikjiPDnYSK/Qm3LoYALN6T1ZGB5mGeb1eSKVSxm0pWX7W5PdgMpkQDAYhl8tFCZYKB1KwyDoYAEPdEqpohwNNsZDL5aiqqgqhcbGT3yNdWuZAoqpQ+EL+0ByRqlNRcPz4cWzZsgUFBQXYvn076urqQoJjY03q6eGR0JkJtOavvr5+2plMBkYul0twCg8BTdchTklSqZQZEiUro4P8rE6nE+np6QzVS6wzmg3yeyCbo8rKyrANmBigw3vr6+uRkZERIrwXw14emE7vI3978v9j04Ajfe5dLhceeeQRvPLKK7jzzjvx3e9+d64Ivvki1WyIhUAggL/85S/YvXs3rrzySvz85z9HdnZ2yLSivr4+hKvH57KVKNjiPJ/PB4PBAI/HA7VanRQOIVnHDgwMAACqqqqSmtFBtj0TExMAwNDdxBZlhbOxlUgkom90ADA8bafTGTbsi/jBCx12ROhjBoMhJlUrWrM9UxNV9+/fj5tvvhl+vx8//vGPGSegJCDVbERGqk7FQDAYxOuvv47W1lZccMEFuO2221BQUBBC5WHTb8lly2QyobCwEAqFQjTnQ7JtJlouiUTC2LWyAzvFAtm29/T0AADKy8uTmtFB7gxjY2OQSqXIyMgQVAsaCbQehNwT0tLSRN/oAKEOU2TDRf+dib28GPcVYhMdi6rFZifQzbbf78fzzz+PRx55BD/4wQ9w4403Ji1oOBpmYp1KNRsCwePx4KGHHsKzzz6LW265BVdffTWkUimcTmfIKnRiYgLd3d2CiZG5IBgMoqenhxGDNTY2JiX9mrw30S7k5+fD7/cztCaxG51w2g0AIVqNeB0mYoEOxgu3hhVCaBgO9OtycaChw474CC0jvRYJwOTjKU82Py+//DJGRkawdOlSPPTQQzMuUdXv96OhoQFvvvkmampqsGrVKjz33HNobm5Oxtunmo3ISNUpjvD5fHjyySfx29/+Fps2bcKmTZuQnp4+jX47OTkJk8mE/Px8KBSKpFyg2Jz5+vr6pKRfE5C6kJ2dDZlMxtCaxK6VkWqBzWYLacDEGI7F0q6IoVUBEMIA4eIwJSQTg86Tqq+v50yjDgQCGBwcxFtvvYUjR47g61//Ovbu3YuVK1fi7rvvRllZWdzPJCRmap1KNRsCY3h4GPfccw+OHj2KHTt2YN26dQgEAujq6sLg4CBycnKwePFi0RNFCUjRsNlsqKurg9vtxuDgoKAWcpFApiV5eXkh9rmkAeNrvccVXFyp+IbnccXExESICDPWQRaPhWI4xEocjwViAZiVlcVbxxKLCsEFwWAQBw4cQFtbG06cOIEbbrgBLS0tM8oS8JNPPsE999yDf/7znwCAXbt2AQDuvPPOZLx9qtmIjFSd4gm73Y7du3dj//792Lp1Ky655BIAZ/WIfX19yMjIwKJFi0Sh14YDbRRSW1vLDCDitTLnA3LhzsjICMlcECp/KBK4uFKF2zoIMRyjnTS51GC6ppIcp3jqVKzE8VhIRGNKu2qF2/ZzxcmTJ3HXXXfh8OHDuPbaa7Fz586kfU+4YKbWqTnTbJzDtVFYdHZ2YsuWLbDb7RgcHMSNN96Ia6+9FlarNSw/UGgQYS5xd6DFyGIH8nH1PqdX53xsdiMhHkvBeMKRwoFcuL1eL+rr63nTkuLJFwH40Za4vBaxPuaSBC5UyFJfXx/uueceDA4O4v7770djYyN+97vf4bXXXptRYUd//etfsX//fjz55JMAgGeeeQYHDx7Eo48+moy3TzUbkZGqU3HCbDZj69at0Gq1cDqduOKKK3DTTTcxguySkpK4c3C4gBbmsmmlxDlPrEA+rnkTRPcGnN22JDooDKcRiVVzaCpPIud8oja2iQzHuNKWuICrbS9w9nPU3d2N4eHhhD5HFosFu3btwtGjR3Hfffdh3bp1eD7wtMwAACAASURBVPrpp/Hkk0/i3XffTdoAORZmap2aE83GOV4bTUMwGMRrr72Ge++9FxUVFRgbG8PSpUvR0tKC4uJi5oAdGhoS3JWIrGTpqX6ky5oQE2kaRAcAgPPUgZ2uGo8gjZ6WECcwvgcxXfT4bH2EbtxI9obX643pJkYsl4VOkmc7k7BpYHRTxxZ/8wE7UfVb3/pWyGc1EAjMmEYDAF588UX885//DDnE29vb8cgjjyTj7VPNRmSk6lSceO+997B9+3ZkZWVhcnIS1dXVaG1tRVVVVcggRmh9GR+bd6Em0gREBzA5OcnLJMNqtUKn08XMT4oEmlIcb5I4vcHms/UROkmdtlQnTUuk14tlVpMIyHAs3EaebuqqqqpQW1sb1+fX4/HgiSeewHPPPYdf/OIX+K//+q+Qz2qqToUg4ofq3CsuBUB7ezs0Gg1UKhUA4Pvf/z5eeeWVc3qIHz16FC+88ALkcjn8fj/+9Kc/4dJLL8WGDRtw/fXXQ6lUorq6GgaDAT09PQkforSVbE1NDdasWRPzC5CdnY3FixdjfHwcZ86ciTtojc8FmQ2JRIKysjKUlpZiYGAAhw8f5ixIYzcqK1asiHtaIpPJoFQqUVNTA5PJhPb29qiTG3paolAo0NDQIEjDSGh2dOOm0WhCtBS0DmjBggWCCwjpv8nw8DCOHj2KkpISyOVy2Gw2GI1GlJeXY/Xq1XFtgUii6hNPPIHrrrsOBw4cCPt3m0kHOADU1NQw4lEAjKNaCilwwUysU+3t7XjsscfQ1NSEQCCAV155BVdffTUuueQS3HLLLaipqcH8+fNhMplw6NChsCJePmCbQnA5QzIzM7FgwQI4nc6EgtZI08LlghwORUVFWLlyJUZGRnDs2DFeuku6UVm6dGncGhipVMo0fmazGQcPHow6HGNv+1evXi3IuZqVlYUFCxYwjZvJZJrWuPG14Y8HxcXFKCoqgsViwcmTJ5GXlweVSsWENxYWFmLlypVxaWMDgQBeeukl7N27F1deeSUOHDgQtlFK1SlumBObjXO8NuIMp9OJBx54AC+99BK2bNmCyy+/nBGRE8ES386fPkwSoQHRF3fiuBDrC0pTf4g/eKKgm6ZoU3OiMcjOzo47iToaItGa4p0sxQs60KmmpgYDAwNwOBxJEdgTBINB6PV6dHd3Izc3F4sWLYqrwaETVS+88EJs3bp1ViWq+nw+NDQ04O2330Z1dTVWrVqFZ599FgsXLkzG26c2G5GRqlMCwuv14ne/+x2eeuop/PSnP8WGDRuQlpYGj8cTQhXlswmnMzoStZIlF3euIbokMG5sbCwhXRwNNv020tScTPVJbReaahOJ1kTTriJlXQkJOhNEoVBgdHSUyVZKhpMYEGqEk5GRgYULF8Y1wA0Gg2hvb0drayuamprQ1tY2q8JjZ2qdmhPNxjleG/FGf38/7rrrLuh0OrS1tWHFihWQSCQhnEaVShX1MBYro4N+3UiXaaFEzdHADoMjB5bYhzcbZEIyNTWFkpISDA4OJjV9Gzj7u+jq6sLQ0BAKCwvR3NycNOE0LSRUq9Ww2+3MZ46PuI8kqhYVFeG+++5jpruzDfv27cMtt9wCv9+PTZs2Ydu2bcl661SzERmpOiUCrFYrdu7ciffffx933303LrzwQkgkEtjtdmi1Wk6bcLEyOugMhEiXaSJqHhoaEs0QhR0GR8xIyLaf5BmJLSCmh2NlZWUYHh5Oavo2cPbuoNVq0dfXh9zcXCxcuFB0214CYvXucrmg0Wjg8XiYzxyf4Mru7m60trbC4XDg/vvvn7XhsTOxTs2JZuMcq+/jxvHjx3HHHXcwYUtyuTyEZxjusk9PLMS0zw0nziPUIaHtWqOBrGIdDgdzuRdrJRsJY2Nj6OrqwtTUFPLz89HQ0CBq6jgBu/Grrq6GxWJJiPPLFVNTUzAYDBgfH4dGowkREvr9fvT19XHaptGJqrt378batWuTHsqnUCgwb948yGQypKWl4fDhwxgbG8P3vvc9mEwmKBQKvPDCCzN9y5JqNiIjVadEhE6nw5133gmHw4G2tjY0NzdPM6ZQKBQhZ0As3ZdQCKcfI/+b0Hat0UC0e1arFZmZmUyTIbT5SjTY7XacPn0aHo8HOTk5aGxsTIptON1QksaPCLjj1bdwBX1PYdsUE6taersTaUBos9nwwAMP4IMPPjin4bFzoFbN7WbjHK+NEgIdtrR+/Xr84he/QEFBwTQHhbKyMoyMjMBkMol+0aRBJgYWiwUSiQR1dXVJObzp9yeHuEwmQ2ZmZlI2GkB4G1sSkjhv3jyoVCpRCihdyEtLS6e5wbApCUJuWfj4n0dzkplJiaoKhQKHDx9mclYAYMuWLSguLkZLSwt2794Nq9WKPXv2nJPn44hUsxEZqTolMoLBID766CO0tLSgqakJ27ZtQ0VFxbTLfmVlJaxWK2dHO6FAnPGGhoYAnOWt87VVTfT9yaU3PT0dEokE9fX1SRmKke2zz+dj6G20nW80R8hEQedJsTOraEF8YWGhoFuWYDCI/v5+mM3mmHRmmvrM1oNOTU0x4bE33HDDOQ+PnQO1am43G8A5XRsJAp/Phz/84Q947LHH8KMf/Qg//OEPmbCljo4O2Gw2lJSUoLGxMWkJlfRkvaSkBE6nM2n0pampKXR3d2N0dDTEPSNRRxAucLvd0Ov1mJycDLtFoVf4Qm+XSJATl2A8+hBNRK8DhP5MxNGL62sRj/w///nPKC4uRkFBAR577DH84Ac/wE033SRa6jBXhDvAGxsb8e6776KyshIDAwNYv349urq6zuFTxkSq2YiMVJ1KEgKBAP7yl79g9+7duPLKK/Hzn/8c2dnZmJqawunTpzEyMoKCggIsWLAgKdtfIHSyXlhYCK/Xyzgs8rUg5wu2xrCyshJSqZR33lI8IIM4u90+bftMQIZjkcJl4wUJxiNhsNHuA0LZ9hJYLBbodDoUFxfzokgRB7S///3vcDqdWLRoER544AF885vfxB133CH6Z4UL5kCtmvvNRjRs2rQJr732GsrLy3Hy5EkAiLiaCgaDuPnmm7Fv3z7k5OTg6aefxnnnnZe0Zx0fH8fu3bvxxhtv4PLLL8err76KPXv2oKGhAT09PczhJea0iH040GI+vuI8vqAP70h2i+zJv1CXfbaNbSy3kkAgwGwYuDpoRQIddFhfX8+L68rldxYNxEI3kb9pMBjEP/7xD+zduxcmkwk7d+7Ej370oxnh1KFUKlFUVASJRILNmzfj+uuvR2FhIWw2G/PfFBUVwWq1nsOnjIlUsxEZqTqV5Drl8Xjw8MMP49lnn8UVV1yB/fv345ZbbsHatWsxMDDATNnF5OwTqpbRaJymTyCX/fT0dFHqJT2Ii3b205N/oS77fG1shRyOJRJ0yPV3FglC/E2DwSA+/vhjtLW14dSpU9iyZQtuvfVWUajo8WAO1KovdrPx/vvvIy8vDxs3bmQO8UirqX379uGRRx7Bvn37cPDgQdx88804ePBgUp/3wIEDuP3225kcjh07dmDx4sWQSCRxd/VcQPNso609uYjz+CKeg4irI0gs0JS1eLQoiVz2vV4vDAZD1OkUV9DbIC4/h8vlglarRTAYTCis6syZM2htbYVUKsWePXtQUFCAXbt2YXh4GM8991y8P45g6O/vR1VVFYaHh/G1r30NjzzyCC6//PLZdIADqWYjGlJ16hzUqZMnT+KOO+7A6dOnIZfLsXXrVqxbtw4SiQTj4+PQarXIyckRNAOIYGxsDHq9ntkAR7p4knoZb4YTG/Fc3OnNSyKT/XhCa9n/Pt7Lvs/ng8lkwujo6DRtBF+wbfpj/Rx0g5MINa2vrw/bt29Hf38/fvWrX0GlUuHBBx/ERx99hLfeeuucaDTYmAO16ovdbACAyWTCZZddxhzikVZTmzdvxvr163HNNddM+++SgY6ODmzdupVpMA4fPowtW7ZALpejtbUVlZWVIXxFoQRw5PDmQ08SItxNiKlLJEcQLs8fy3mLDyJRvyI9M53TIWSwI9nQ2Gy2sNaD9AYnkQaHJKoeO3YM9913Hy644IKQ95mampoxEyOCe+65B3l5efjDH/4wm1bTQKrZiIZUnUpynRoZGcEPfvADtLa2Yt26dejs7MQdd9wBqVSKHTt2QKPRiDKUstvt0Ol0nJKjCWjTlcrKSsjlct7nfDAYZGpkvFsKmvbKp26zBdjxhNbSiET9ivTMpMYLHexIb2jCDcfo/38iDY7D4cCDDz6If/7zn2htbWUiBwhmYp0CZm2tSjUb7EM80mrqsssuQ0tLC7785S8DAC6++GLs2bMHK1euPCfPDZz9wr/88stoa2vDpZdeiptvvhl5eXmCfBlpIZlKpYpruh3JpjYWhOaT0pdokvsR7jnoIiiGjS3NpWU7kgjd4EQDOyukoKCAKTJ8mrJwrxstUXUmwel0IhAIYN68eXA6nfja176G1tZWvP322ygpKWEmxmNjY7j//vvP9eNGQ6rZiIxUnZoBdSoYDOJf//oXtm3bhpUrV6KlpQXFxcW8TCciweFwQK/XIxAIQKPRxOWyRIZSxAqX6/lHamRmZua0lOp4QNftWIMmUiPFsLGNNhwTu0bSYFvpl5SUYGBgAD09PQkNU30+H/785z/j8ccfx49//GP85Cc/SZoNcDyYI7VqbieIC4lwzde5Xq9JpVJcccUVuOyyy/DYY4/hq1/9Kn72s59hw4YNUKvVqK6uhl6vZ5LIuQid6BCehoaGhCzy0tPTUV9fz4Q+mc3mqM9BNziLFi0STDyXkZGBxsZGRuDd3d09TeBNplN5eXlYvny5KOLlzMxMNDU1MV7rJF11amoKer0eJSUlcaea8kFWVhaam5vhcDhw6tQpOJ1OVFVVxZ38zTVRdSZhaGgI3/nOdwCcLT7XXnstLrnkEqxatQpXX301nnrqKcjlcrz44ovn+ElTSIE7ZmKdkkgkuPjii7F+/Xr86U9/wqWXXooNGzbg+uuvR11dHaqqqmAwGNDb28t5o0rOco/HA7VanZDlp0wmg1KpRHV1NYxGI3p6eqI+B93gJFojaZCtTE1NDYxGI8xm87TnIBuc9PR0LF68WBSNJtE+1NbWwmAwwGw2Q61WQyKRMLpMsWokjYyMDDQ0NMDtdqOzsxMnT55EaWlp3DWSNL07duzABRdcgPfee28mW8UymOu16gu72Zip62kuGBsbQ1tbGz766CPcfffdWL9+PRN4p9VqkZGRAY1GE5YK5XK5YDAYMDk5CbVaHVfCZixEEnLRIuh4p1PxPEdaWhoqKyvR29vLyT1DaAwMDKCrqwsymQwLFy5MSJfBF1arFVqtFvn5+SgvL0d3dzekUinUajVn8eZsT1SdI0htNiIjVadmYJ1yOp144IEH8NJLL2HLli0MfYWLVozeDkfbUicCOrCUFrO73W4YDAa43e6EGxw+z+Hz+VBbW8sI7DUaTVIdkkZHR9HR0YFgMIimpiZUVFQk7b3pu0t1dTV6e3vh9Xp5Z2p1dHTgl7/8JfLz87Fr1y6o1WoRnzqFMEjRqNiH+O233x52NfWPf/wDjz76KCO8u+mmm9De3n6Onz48dDodWlpa4HK50NbWhgULFiAYDDKrV5KemZaWBo/HA6PRiImJCU5OS0KAPEdeXh78fj88Hg80Gk1SpwxutxsdHR2w2+0oLi7GggULkrZKpYuZRqOBz+cT3baXwOl0QqvVQiKRTLMqttls0Ov1yMzMhFqtjjo1O1eJqrPJmSdJSDUbkZGqUzO4TvX39+Ouu+6CTqdDW1sbVqxYAYlEwrjgkcyijIwMhpJrsVg4OS0JASJmz8zMhEwmw8TEBC86sBDwer3o7OzE2NgY8vPz0dzcnJR8EuDzLC2n04n6+npIpVLeuph4QdgQk5OT09gQExMT0Ov1AAC1Wh11ODk0NIR7770XXV1d2L17N2NUIDZSdWoavtjNxjXXXIN3330Xo6OjqKiowPbt2/Gf//mfuPrqq2E2m5nVVHFxMYLBIG644Qbs378fOTk5+OMf/3hOebCxEAwG8eGHH6KlpQXNzc3Ytm0bysvLGW2A2WxmEk0TdZHgC6KhGBkZQTAYRE1NDerq6pJih8rWbxQXF2N4eDhhRxC+781ej9NBR0I5pLDf22AwYGJiImpjRwsewwUUnutE1dnmzJMEpJqNyEjVqRlepwDg+PHjuOOOO1BQUIDt27dDLpcz4mej0YjMzExMTk4yeopk2WYTDUV/fz8AYP78+VAqlUkJd2PrN8rLy5lzWeggvGjvHe5uEI9pDJ/3Ju5WarU6amNHhmPhAgpdLhceffRRvPzyy2hpacHVV1+dVLv1VJ2ahi92sxEvenp6sHHjRgwODkIqleL666/HzTffPCM710AggOeffx579uzBVVddhWuvvRZPPvkkLrzwQuTm5jIBdWKso9nw+XyMEI8IkQOBQFyOUXwRy5kqXkcQId6bBu2QMn/+/ITTbmnxIx93KyIENBgMeOutt7Bp0yb87//+74xIVJ1LlBIBkGo2IiNVp2ZJnQoGg3j99dfR2tqK9evX42c/+xn++Mc/YvXq1SgpKYHb7Ra1PtAIZyULQLT6wH7vaO9Dsq5MJpMg9YHPe9Ogh2NC2O3T783X3cpisTB16uqrr0Z7ezt+85vfYMOGDbjpppuSFnbMRqpOhSDil/bcJ27NYKSlpWHv3r3o7OzEgQMH8Nvf/hYdHR3YvXs3Lr74Ymi1Wlx88cXYvXs3AOD111+HVquFVqvF73//e/z0pz9N2rNKpVJce+21eO+993Dw4EGsWbMGg4ODWLFiBZYvX45ly5ZhcHAQR48excTEhCjPQFxHDh06hLS0NKxZs4ZxHiHivBUrVsBut+PQoUMYGxsT9L17e3vR3t4OmUwW8t40pFIpamtrsWrVKni9XrS3t2NoaCis4JIrgsEg+vr60N7eDqlUGvG9aUgkElRVVWHNmjWQyWRob29HT08PAoEA7/ceGBhAe3s7JBIJVq9ezatQSyQSVFRUYOXKlXA4HDj//PPxyiuvYP/+/bj++uvPWaMRDkNDQ8zBXFlZieHhYQBgbBkJampq0NfXd06eMYUUko3ZVKckEgkuvfRSfPjhhxgaGsLKlSvR2dnJ1KgVK1ZgfHwchw8fFi1LgFjHHzx4ED6fD6tWrWIymsLVh+Hh4YTqA/u9BwcH0d7eDq/XG/LeNCQSCSorK7FmzRpIpVK0t7ejt7eXd31gv/fw8HDM92Y/R3l5OVavXo3c3FwcPnwYRqMRfr+f93uPjIygvb0dk5OTWLVqFW8nxpKSEqxYsQIAcMkll+DRRx/FCy+8gC1btpyzRiMcUnUqPFLNRhRUVlYyE5958+ZhwYL/n707D6uyTv8H/j4HZJdVFtmXgwiyydpqaplFOY5mpelopU6jNZnlFfoty6VFrTFbtKbSdKpfZk2pU1qWZmWKcEAQRNn3RZB9Pevz+8M5z4CAAnLggO/XdXlddThynqP43Of+3J/PfQeirKwMBw4cwKJFiwAAixYtwv79+wEABw4cwMKFCyGRSHDTTTehvr4eFRUVg3a9ycnJmDx5MiZNmgS5XA5zc3PMmDEDJ0+ehKmpKYKDgyGTyZCdnY1z586hvb19QF634wdelUqF6OjoHnuZ6zpGBQcHo7S0FGfOnEFzc/N1vfbFixeRmJiI9vb2q752R7r9qBMnTkRdXV2/kh/dEMTExES0trYiKiqqz9vEpFIpPD09OwW3ysrKXgW32tpaJCUlobGxEZGRkfD29u5X//jU1FTMmjUL9fX1OHnyJB566CHce++9OHbsWJ++11AxxM48RINluMWp/Px83HrrrfD09MSZM2fg7++PmTNn4vDhwzA2Nsb48eMRFBSE4uJipKWlobW1dUBet+MH3ubmZkRGRsLX17fbBZWO8eHSpUtITk7u1IK4P2pqapCUlIT6+npERETAz8/vmos5UqkUXl5eiIqKQnt7e7+Tn/r6esjlcly6dAkTJ07s1Wt3pFsci4mJ6XPy09jYiJSUFFRVVSE8PBwymaxfi1g5OTmYP38+0tPTceTIETz77LOYO3cu9u3b1+fvNRRu9DhlOMuWBq6wsBBnzpxBbGxsnzPXwSqT6cpyug5TO3bsQGZmJuLj47F9+3asX78eMpkMERERuHTpElJTU+Ho6Ahvb+9+lWivnDgeERHR6/2lFhYWCA0NRUNDAy5cuABzc3P4+fn1aYViINrYdmxTm5ubi8LCQvj7+1+zU1ZjY6N4qDA0NPS6D/PpgpuHh4fYDrGnLizNzc3IycmBVCq9rtbBV05UjYiIEA+UP/roo1Aqldf1ngaas7MzKioqxPK0k5MTgMv/zkpKSsTnlZaWwtXVdaguk2jIDIc45enpiUOHDondjl5//XUsW7YM//d//4cdO3bg1VdfRUhICMLCwlBbW4uMjAzY2NjA19e331t46urqkJubCwsLC4SFhfU6zpiamortw3X3XH9//z7dczu2se3v/VrX2dHd3R35+fliW/drNVvRdYAUBAGBgYG97kDYEyMjI3h5ecHNzQ1FRUVITEzscduurm2xUqm8rtbBNTU12LRpE1JSUvDaa6+J3TcnTJiAhx9++LqTwIHGONU9Jhu90NzcjAceeADbtm27aiu6oc5cu7uRBAUF4eDBgzh69CiWLl2KmJgYxMfHw9HREQ4ODuL2n75OAK+rq0NeXh7Mzc2v68O2jY0NIiMjUV1djdTUVIwZM0bsoNWTpqYm5ObmwsjICEFBQQPSLaNj8pOdnd1jp6a2tjbk5uZCpVINaO91ne5mhehaFCuVSuTl5aG5uRn+/v79blt8rYmqAGBpaTmo7YF7409/+hP27NmD1atXY8+ePZg5c6b4+HvvvYe5c+fi9OnTsLGxGWn7YImuabjEKWNj4y5tVT09PfHpp59CLpfj+eefh6enJ1566SWMHTsW0dHRqKyshFwuh6ur6zW3/nSkixVSqfS6YoVuQUuX/FhbW4sdtHqim7OkUqkGrI2tbnaSLonQJR1Xxn6FQoH8/Hw0NzfrpQNkx8Ux3YwO3eKY7vB3bW2tONS2Pz9fCoUC//znP/H//t//w7PPPot33nmny6KoiYmJ+GHeUDBOdY/JxjWoVCo88MADmD9/PmbPng1g+GWuEokEd911F6ZMmYI9e/YgLi4OCxYswNKlS+Hh4QEXFxcUFhYiMTFRPETek4437/Hjx1/3Sonu+pycnDBmzBiUlZUhKSmp24Nrug/gusPufem/3Vs2NjaIiIhATU0Nzp49K3YEASB2mLpyKrg+mJubIzg4WFxR020lkMlkGD9+fL9u3ldOVE1ISDDYiaodO/O4u7tj/fr1YreRK4cbxcXF4dChQ5DJZGJnHqIbyUiJU9HR0Th69Cj279+POXPmIC4uDitWrMDYsWPh5OSE4uJiJCYmXrMtru6Dfn9mNVyNvb09oqOjcfHiRSQnJ3d7eFvXEbCxsVFvscLS0hJhYWGor6/vtDPA2NhY7DDl4+PT71jRWyYmJhg/fry4CJeVlQVBEODt7Y3o6Oh+Ha7XarXYv38/3nzzTcyePdugh8cyTvUeu1FdhSAIWLRoEezt7bFt2zbx8eHe+7ylpQVvvPEGDhw4gOeffx4zZsyAVCoVbxhqtbrTkCOg8zBAfX3Q17myJZ+NjQ0KCwtRX19/XSslfaU7+K3r9S2TyfpU/RmI19d1rLKzs0NTUxMsLCzg6+vbp0rSlRNVX3jhhWExUZU6uXE29/Yd49QIjFNKpRI7duzArl27sHz5csyfPx9GRkZXrfDqVvR186T0uSika4hSXl4OLy8vODo6ori4WGxj29uOgNdLdxblwoUL0Gq18Pb27tW5xYF+/fz8fFhbW6O9vV0cntuXxUhBECCXy7F27VqMGzcOGzduvKFW/kcItr7tjxMnTuD2229HSEiI+A/3tddeQ2xs7IjofV5eXo4XX3wR+fn52LBhgzhsSTfkyMLCAh4eHigrKxNXaQZjGKBOa2sr0tPT0dLSAk9PT/j5+Q3JB33dtOzKyso+t+vrL91ARF1lZdSoUZ0GNva2Bzsnqo4YTDZ6xjg1guNUbW0tNm7ciD/++AMvv/yyuGdfV/XVdTq8ePEiLl26NGjDAHUUCgUyMjJQX18PV1dXBAQEDMkHfQcHB5iamornbzw8PAasXW5POn5W6DiLo76+Hrm5uTA3N+/V4lhRURFefvllNDY2YsuWLQgNDdXrdZPeMNkYbO3t7Zg0aRIUCgXUajXmzJmD9evXo6CgAHPnzkVtbS0iIiLw6aefwsTEBAqFAgsXLkRycjIcHBzw5Zdfwtvbe1CuNTU1VRxEs379enh4eKCurg7FxcW4dOkSHB0dERQUNGhtUHUDCUtKSuDq6go7Ozvk5eVBKpV2mYatDzU1NcjNze0ydK/jdFt9DUjsGED9/f27vUnr2icWFRX1OKBwqCaq6nQ3WXXdunX46KOP4OjoCODyB6K4uDgAlw+J7ty5E0ZGRnjnnXcwffr0QbvWYYLJRs8Yp/ppOMWp3NxcrF69Gq2trdi4cSMCAwPR3NyMgoICVFVVwdbWFsHBwf1qFNIfuk6IukGxTk5OKCoqglKp7FWTkeul+0B/5Qd9jUaDkpISVFRUwMvL65pt2Puj49nFnt7rlQ1kulsca2howJtvvonffvsNGzZswD333MM4Nbwx2RhsgiCgpaUFVlZWUKlUuO222/D2229j69atmD17NubOnYu//e1vCAsLw7Jly7Bjxw6cPXsWH3zwAfbu3Ytvv/0WX3755aBe7/fff4+XXnoJLi4uyMrKwr/+9S+EhISgvLwcZWVlertxdbwG3SrNmDFj4OXl1an7SG1tLXJzc3t1OK8/dF1DdJNKe1qNaW9vR35+PlpaWq7roHZHCoUCeXl5aG1thb+/f6+2qXUckCSRSBAWFgZBEIZ0oqpOd5NV161bQP1ESgAAIABJREFUBysrK6xatarTczMzMzFv3jwkJiaivLwcd911F7Kzs/W+KjfMMNnoGeNUPw3HOHXixAnEx8dj9OjRyMnJwbZt2zB58mRUVVWhqKgIbm5ufZ7h0FdXi0UdOxVeLY70l+5wuFar7bLduSOVSoWCggLU1dXB19f3qlO6e6vj97zW+U6djotjEokEAQEBsLS0xK5du7Br1y489dRTWLx48ZDMdGKcGnAc6jfYJBKJeBNQqVRQqVSQSCQ4duwY5syZA6Br73NdT/Q5c+bg6NGjAzZIqDc0Gg0uXrwItVoNMzMzWFlZQS6XQxAEeHp6ikPfBnoYn05dXR3kcjlqamowceJEyGSyLm0OdYfzbG1tkZKS0q/hQt1pa2tDRkYGcnJyIJPJEBwcfNUAoesIEhgYiMLCQqSmpvZ7VohGo0F+fj7OnDmDMWPGIDIystfnYXQDqGJiYnD69GlERUUhJiYGZmZmSEhIwNy5c4ck0QCASZMmwd7evlfPPXDgAObOnQtTU1P4+PhAJpMZ5B5yopFmuMUp4HLVVqFQwNzcHHZ2dkhLS4NKpRLnQKjV6gEfxqfT1NSElJQUlJWVITg4GOPHj++y6GVtbY2IiAi4uLjg7NmzyMnJgUqluu7XViqVuHDhAs6dOwcPDw9MnDjxqmciRo0ahXHjxiE0NBQXL15ESkoKGhoa+vXaWq0WxcXFkMvlsLS0FCe+94ZuQGFMTAyys7Nx6623IioqCpWVlfjjjz/wxBNPDNnwWMapwcNkQ480Gg3Cw8Ph5OSEadOmiS1Mdf+wOk6Q7Nj73NjYGDY2NqipqRm0ay0rK0N+fj5+++03fPPNN/jtt99QUVGBqVOn4scff4SxsTHGjRsnDuNLTU1FS0vLdb9uc3Mzzpw5g+LiYvED/NXK4BKJBC4uLp2GC5WXl/crqKhUKmRnZ+Ps2bNwcXFBREREn9oTWllZITw8HN7e3mIQ6O2gxI5Tx42NjRETE9OvfcaCIODUqVM4ePAg7rrrLtx777345ptvDHYg33vvvYfQ0FA8/vjj4oTgG32yKtFQGk5xqrGxESdPnsT333+P/fv3448//sCoUaMwdepUfPnll5BIJPD19e00jK+/H7A7amtrQ3p6urggFRISctUOSRKJBGPGjEFMTAwsLCwgl8tRVFTUrwngugWplJQU2NraIjo6utcfkIH/dTYcN24c8vPzkZaW1uvY3XForlqtRkxMDNzc3PoVp9LT07Fv3z7ccsstmDlzJo4cOYL//Oc/g56s9gbj1MBjsqFHRkZGSE1NRWlpKRITE3H+/Pkuz9H9ox3q3udeXl549dVXxS1BNjY22LRpE7755ht8/fXXmDVrFtLT08V5FN7e3sjMzMSFCxf6NfxNV03IysqCr68vwsLC+nQWo+Nk1ebmZiQmJuLSpUu9unFpNBoUFhZCLpfDysoKMTEx11VitrW1RWRkJBwdHZGamnrNlayamppOU8f72zkkJycHjzzyCHbs2IFPPvkEH330EbZv345///vfBjfoCACWLVuGvLw8pKamYuzYsXjuuecADP3PPtGNbDjFKRsbG2zdulVs2mFmZobVq1fjp59+QlJSEu6++26cPHkSJiYmCAoKQkBAAPLy8pCeno62trY+v55SqURWVhbS09Ph6ura5wUpiUQCNzc3xMTEQKPRIDExEZWVlb2KU1qtVvw70S1Iubi49PvPe/To0Zg4cSI8PDyQmZmJ8+fPQ6FQ9Pj8hoYGJCcni7sNfH19+7VlqLy8XBzauHnzZnz22Wd488038dNPP6G9vd3g7vWMU/rBORuDwNbWFpMnT0ZCQgLq6+uhVqthbGzcqb+5rve5u7s71Go1Ghoa+rR6oS9eXl747LPPkJSUhOeffx7e3t7iuY6oqCixPOvs7Nyl33h3lEolCgsLUVdXNyBtbHWlYt0MjuLi4qseWKusrERhYaFYHRmo/ZYdZ4VUVFRALpd36QjS1NSEnJwcjBo16roGIfY0UVXHw8MDjzzyyIC8r4HUcZDX0qVLcf/99wMw3L7/RDeS4RynnJycsGPHDmRmZiI+Ph7bt2/H+vXrIZPJOs1Nsre3h4+PzzW37Wg0GhQVFaGqqgre3t4YN27cdcUpIyMj+Pr6ihPAS0pKehy21/Fgtb29PaKiovo9Ob079vb2sLOzQ1VVlbh9t+Mg3dbWVuTm5kKj0VzXLK3m5mZs27YNP/zwA9auXYuZM2d2WlQbM2YMFi9ePCDvaSAxTukHKxt6Ul1dLa4ut7W14eeff0ZgYCCmTJmCr7/+GgC6TJfcs2cPAODrr7/G1KlTDSZrlkgkiImJwbFjxzBjxgw88MADeO2119Da2ip+aJdIJEhMTERFRUW3KwAajQYFBQVITk4ekGrClXSlYplMhuzsbGRkZHRayaqpqUFSUhIaGxsRGRkJHx8fvRzskkql4kqWIAhITExEUVERzp07h6ysLPj5+SEkJKRfiYZCocA777yD++67D9HR0Thx4gSmTJliMD8n11JRUSH+97fffovg4GAAl3/29+7dC4VCgYKCAuTk5CAmJmaoLpPohjGS4hQABAUF4eDBg3jyySexdOlSxMfHo7a2Fg4ODoiJiYG5uTmSkpJQUlLS7ZYm3eyMgaomXEk3BC8oKAjFxcVdzvs1NDQgJSUFVVVVCAsLg7+//4AmGjoSiQTOzs6IiYmBqakpkpKSUFBQgKysLGRkZMDNze2aZ0J6olarsWfPHtx1111wdnZGQkICZs2aNWTnB/uKcUo/2I1KT86ePYtFixZBo9FAq9XioYcewksvvYT8/HyxpeDEiRPx2WefwdTUFO3t7fjLX/6CM2fOwN7eHnv37oWvr+9Qv41uKRQKbN++Hbt37+4ybEk3UEm3anNlG1t3d3e9d2/oOI/CysoK7e3tYoepwZxEqlarkZ+fj4qKChgZGSEgIKBfCdaVE1VXrVplsBNVdTpOVnV2dsb69etx/PhxpKamQiKRwNvbG//85z/FoU2vvvoqdu3aBWNjY2zbtg333nvvEL8Dg2M4n+gMD+NUP43kOKXRaLBnzx5s27YNCxYswNKlS2FqatppaKyfnx/GjBkDAKiqqkJBQUGP7cT1ob6+XuxcpdVqIQgCZDKZ3tvmdqTValFUVITi4mKxvXx/EixBEPDLL79g/fr1mDRpEl588UWDHx7LODXg2PqWBl5NTQ02btyIkydPYt26dbjjjjsgkUjQ0tKCnJwcKJVKqNVqODk5dWljq2/t7e3Izc1FY2MjtFotPDw84OHhMSirK7rD37rtBm5ubmJr275MYOdEVeqAyUbPGKeoRy0tLXjjjTdw4MABPP/885gxYwakUina29uRl5eHpqYmCIIgzlUarDkdAMQFupqaGgiCgLFjxw5aoiMIgphg6bZB6w6j64b49rbjVGZmJtauXQsrKyu8/vrrkMlker56MlBMNoYbjUaDqKgouLm54bvvvjPIIUs6OTk5WL16Ndrb27Fx40YUFhaitLQUUVFRaG9vF2/iAz0Xozu6wXu1tbVib3Fd276LFy/C29sbzs7Oein9d6yo2Nvbw9vbu0uC1dTUhNzcXBgZGcHPz6/HQ/FDOVG1pKQECxcuRGVlJaRSKf76179ixYoVqK2txcMPP4zCwkJ4e3tj3759sLOzgyAIWLFiBQ4dOgQLCwvs3r0bERERg3a9NwgmGz1jnBpCwyVWlZeX48UXX0R+fj42bNgAlUqF5ORk3HLLLVCr1bC0tISfn584HE+fNBoNiouLUVlZCW9vb7i4uIiLVKWlpfDw8ICrq6veFsd0FRUrKyv4+fl1ic2tra3Iy8uDSqWCTCbr8VB8x+Gxr7/+Om699dZB21bHOGWQOGdjuHn77bcRGBgo/n98fDxWrlyJnJwc2NnZYefOnQCAnTt3ws7ODrm5uVi5ciXi4+MH/Vr9/f3x9ddf48EHH8Q999yDjRs3Yty4cQgPD0dsbCysra2RnJyMwsLCfrX+6w1dKVgul8PCwgIxMTHihG8jIyP4+PggIiIC9fX1SEpKEtvZDZSmpiacOXMGlZWVV91rq+sI4u7u3m1HkIaGBqxduxYLFy7EY489hsOHDw9qogFcbmn5j3/8A+fPn0dCQgK2b9+OzMxMbNq0CXfeeSdycnJw5513YtOmTQCAw4cPIycnBzk5Ofjwww+xbNmyQb1eIho6wyVWubq6YteuXXj++ecxb948PPXUU/D09ERERASio6Ph5OSE1NRU5ObmQq1W6+UaOrY8l0qliI2NFQfl6uYmRUdHo729XS+zQlpbW5GWlobCwkKx1Xx3i4AWFhYICQmBTCZDbm5ul25ebW1tePPNN/HnP/8Zd955J3799Vfcdtttg3p+h3FqeGFlwwCVlpZi0aJFeOGFF7B161b85z//gaOjIyorK2FsbIxTp05h3bp1+PHHHzF9+nSsW7cON998M9RqNVxcXFBdXT3oh/aWL1+OCxcu4JVXXkFBQQG2bNmCBx98EMuWLYO5ubm4knPx4kX4+Pj0a6ZEd67sMNWbjli6Cay6/bH97bYBQCzFt7e3w9/fv09tEXVl7Pfffx8NDQ3w8fHB559/PqQTVbszc+ZMPPXUU3jqqadw/PhxjB07FhUVFZg8eTKysrLwxBNPYPLkyZg3bx4AICAgQHweDRhWNnrGODVEhlus2rBhAw4dOoT169dDqVRi/fr1mDJlCp577jlYW1tDq9V2qi70Z6ZEd3pT9b6SLra0tbXB39+/18Neu6NUKlFQUICGhgbIZLI+dxCrqanB559/jrS0NNx0003YtWsXHnnkEaxYsWJQKkG9wThlEFjZGE6eeeYZbNmyRSyh1tTUGOyQJZ0VK1bg6NGjuOWWWzB//nwkJCTAyMgIU6dOxb59+yCRSMTqQm1tLeRy+XXPgqitrUVSUhIaGhr61GHK0tISYWFh8Pb2xvnz55GZmXnVfuPdUavVyM3NRWpqKpycnPrcfx243BHE0dERERERyMjIwFtvvYVFixbhscceM5hEo7CwEGfOnEFsbCwuXrwo3pjHjh2LqqoqABx2RHSjGm6xau7cuTh58iSmT5+OGTNm4NSpU/Dx8cG0adPw0UcfQaPRdKkuXLp06bpes7GxESkpKaisrERoaGivO0yZmZlhwoQJCAgIQH5+Ps6ePYvW1tY+vbZuplRycjKsra37PBBQx97eHuHh4aioqMCrr76K++67D0899ZTBJBqMU4aPyYaB+e677+Dk5ITIyEjxsasNkzGUQTMBAQGdXtfc3Bxr1qzBkSNHkJCQgOnTp+PUqVMYNWoUAgMDERgYiIKCApw9e7bPw5Z0U8dLS0sRHByM8ePH9+s8iK2tLaKiouDg4IAzZ84gLy/vmuVzXWvEpKQkmJmZddqu1ReCICAtLQ0zZ87E999/jy+++EJcxXrggQf6/F70obm5GQ888AC2bdt21UTKUH4GiWjwDMdYNW7cuE7nIEaNGoXly5fjxIkTqKiowNSpU/Hjjz/CyMgIMpkMoaGhqKysREpKCpqamvr0Wrqp47m5uRg3bhyCg4P71fJct/XWzc1NHIR7rUG6uop/YmIiACAmJkbcrtVXubm5mD9/Pnbs2IH3338fJSUlcHFxwd1336237WZ9wTg1PBjG8imJ/vjjDxw8eBCHDh1Ce3s7Ghsb8cwzzwy7IUs6zs7O+OCDD3Du3DnEx8fDxMQE69evh5+fHyZOnIja2lqkp6fD1tYWPj4+V13xGciyso6u37ijoyPKysqQlJTU7eE83aClvLw8jBkzBtHR0f2uPpSXl2PDhg0oKSnBG2+8gcjISPGmt3btWmg0mut+X9dLpVLhgQcewPz58zF79mwAl/8uKyoqxPK0k5MTAA47IroRjaRYZWNjg02bNomTrt9//31s3LgRISEhCA4ORkNDA7Kzs2Fubg4/P7+rdqzquGWpLx2drsXBwQH29vaorKxEcnJyl6GxOnV1dcjNzcXo0aMRGRnZ78YstbW12LRpE+RyOV577bVOM52efvppPPnkk3pvY38tjFPDBysbBub1119HaWkpCgsLsXfvXkydOhWff/75sB2ypDNhwgT85z//wbJly7BkyRKsXr0adXV1sLe3R3R0NKysrCCXy1FcXNzlELlKpUJubi7S0tLElbSBSDQ6utrhPF0ZvKqqCuHh4ZDJZP1KNJqbm/HKK6/goYcewqxZs3D06FFERUV1+fsa6hu4IAhYvHgxAgMD8eyzz4qPd/xZu/Jn8F//+hcEQUBCQgJsbGy4D5ZohBuJscrLywufffYZXnnlFcTHx2P58uWorKyEjY0NIiIiMGbMGLEKfuWiUHdblgYq0dCRSCQYO3asOEwuMTER5eXlEAQBLS0tSE1NRXFxMYKCgvpd8VcoFHj33XcRFxeHyMhI/PHHH93+XTFOUV/wgLgBO378ON5880189913I2LIko5Go8Hu3bvx9ttvY+HChViyZAlMTEyg0WhQVFSEqqoq+Pr6wsHBAWVlZeJeS322ArxSe3s7srOzUVdXBzMzMwQFBfV70JJarcbnn3+O999/H4sXL8ayZcsGpQ1wf504cQK33347QkJCxD/v1157DbGxsXjooYdQXFwMT09PfPXVV7C3t4cgCHjqqafwww8/wMLCAp988gmioqKG+F2MOIb1qcywME4NsZEYq7RaLb799lu88soruO+++7BixQpYWlpCq9WitLQUZWVl8PLygouLCyorK1FUVNRjtUFflEolcnNzUV1dLW5R7u8gPa1Wi4MHD2LLli2YNWsWVq1a1WNrdkPAOGWQOGfjRuLt7Y3Ro0fDyMgIxsbGkMvlBtl7uqWlBVu2bMHBgwcRHx+P+++/Xxy2lJGRgcbGRri4uCAgIGBQV1HUajUKCgpQW1sLV1dXVFdXw9jYGDKZrE+Tu4fjRFUyWEw2esY4NQwNlzilUCiwfft27N69G8uXL8f8+fNhZGQElUqFzMxM1NTUwMHBAUFBQYM6uLbjrA43Nzdx+5q/v3+fFsYEQUBycjLWrl0LmUyGjRs3cnsR9Re7Ud1ofvnlF6SmpkIulwOAQfaetrS0xPr16/Hdd9/hxx9/xP33348PP/wQcXFxMDY2xsSJE6FQKJCZmdnnQ+T90fHwt7m5OaKjo+Hh4YGIiIg+Hc4DLk9UnTNnDvbs2YMvvvgC//jHPwYl0SgpKcGUKVMQGBiICRMm4O233wYArFu3Dm5ubggPD0d4eDgOHTok/h7dxNeAgAD8+OOPer9GIiJgeMQpU1NTPPvss/jll19w4cIF3Hnnnfj4448xffp01NfXY+LEiZBIJMjIyEBzc7Per0cQBFRUVCAxMRESiQSxsbHw9PREaGgo/Pz8kJ2djXPnzqG9vf2a36u4uBiPP/44XnvtNbzzzjvYuXPnoCQajFM3HlY2RiBvb2/I5XKMGTNGfKxjT2lD7D197tw5LF++HMXFxYiJicGGDRvENnU1NTXIzc3tdX/yvhIEAdXV1cjPz4ejoyO8vLy6PZPRm5keQzlRFQAqKipQUVGBiIgINDU1ITIyEvv378e+fftgZWWFVatWdXp+ZmYm5s2bJ+79veuuu5CdnT3k+3GpC1Y2esY4NQwNxzhVVFSEp59+GikpKQgLC8OGDRswfvx4ABAPkfc0lXsg1NbWIjc3FzY2NvDx8en2NTo2M3FwcOg2ZjY0NOAf//gHjh8/jg0bNuDee+9lnKKBwMqGofL29sZnn302oN9TIpHg7rvvRmRkJD788EMAMOje0ydPnsSTTz6JTZs2IS8vDwsWLMAjjzyCl19+GY2NjXBwcEBMTAwsLCwgl8tRUlIyYJPIGxoakJycjEuXLiE8PBx+fn49Hv7ueDhPIpEgMTERycnJ0Gg0BjFRFbj8d6vbXjB69GgEBgZe9e/zwIEDmDt3LkxNTeHj4wOZTCa2SyQiAhinACA/Px8PPfQQli9fjqKiIjz//PP4+9//jpUrV6K6uho2NjaIioqCvb09UlJSUFBQMGCdBXWHv0tKShAcHIyAgIAekxnd/KaYmBiYm5tDLpdDLpejvb0dKpUKH374Ie6++274+fkhISEBcXFxjFOkd0w2RqA//vgDKSkpOHz4MLZv347ffvutx+caQu/pm266Cb/88gtuvvlmSKVSzJgxAwkJCfD29sa0adPw8ccfQ61Ww83NDdHR0VAqlUhMTER1dXW3198buh7oeXl5CAgIQFBQUK8HFBkZGcHb21tcjYmMjERsbCxMTU1x+vRpzJs3b9AOsl9Nx0FHAPDee+8hNDQUjz/+OOrq6gAYRhAnohvPcItTvr6+OHXqFKZPnw6pVIpJkybh999/x6RJk/CnP/0JW7duRXt7O5ydnRETEwOpVIrExERUVFT0O04plUpx8Ky3tzfCwsJ6fW5QKpXC3d0dMTEx+OOPPxAdHY2YmBhUVlbixIkTWLZsmUEMj2WcujEM/SciGnC6PZdOTk6YNWsWEhMTxd7TAAyu97RUKu0SOEaNGoUnn3wSv//+O8rKynDnnXfiyJEjMDIygp+fH8LDw1FVVYWUlBQ0Njb2+rVUKhVycnJw9uxZuLq6IiIiol9dpgRBQFJSEk6dOoVbbrkFYWFh+PXXX5Gdnd3n76UPVw46WrZsGfLy8pCamoqxY8fiueeeA2AYQZyIbjzDLU4B6LKIJJVKMX/+fCQkJMDIyAhTp07Fvn37AFxuoxsVFYXGxkYkJSWJH5x7Q6PRoKCgACkpKbC3t0dUVBRsbW37fL2CIODcuXP4+eefxWTj5MmTyMjI6PP30gfGqRsHkw0D0traipkzZ+K+++5DS0tLv75HS0uLOOm0paUFR44cQXBw8LDtPW1ra4vNmzfj66+/xr59+zBr1iykp6fDzMwMEyZMwLhx45CTk4OMjIyrHojTarUoLi6GXC6HhYUFYmJi+t0DXTdR9d1338XOnTuxe/du/Pvf/8aGDRtw+vTp/r7VAdPToCMjIyNIpVIsXbpULEEPZBD39vbGK6+8gilTpsDKygohISE4e/YsvvjiC8hkMtjY2GDJkiUGMXWWiPqHcaorc3NzrFmzBkeOHEFCQgKmT5+OkydPwtjYGAEBAQgODkZxcTFSU1Ov+mcmCALKy8uRmJgIqVSKmJgYODs79+uDdXl5OZYvX441a9Zg06ZN+OKLL/Dpp5/io48+MogtSIxTNxhBEK72i/TMy8tL+PTTT4WKigohKipK+Nvf/iao1ep+f7+8vDwhNDRUCA0NFYKCgoRXXnlFEARBuHTpkjB16lRBJpMJU6dOFWpqagRBEAStVissX75c8PX1FYKDg4WkpKQBeV/6oNVqhYSEBGHSpEnCokWLhLy8PKGlpUVobm4WCgsLhWPHjglpaWlCQ0OD0NLSIn6toKBAOHr0qJCeni40NjaKX+vrr+LiYuHJJ58UYmNjhaNHjwparXao/0i60Gq1wl/+8hdhxYoVnR4vLy8X/3vr1q3Cww8/LAiCIGRkZAihoaFCe3u7kJ+fL/j4+PT758/Ly0uQyWRCZmamoFQqhfnz5wu+vr7C0qVLhebmZqGoqEhwdHQUPv/88/6/wRvXte7VN/Iv0jPGqb7JyMgQ7rvvPmHWrFnC2bNnxRhSVlYmHD9+XEhOThbq6uo6xZeSkhLhl19+Ec6cOSPU19f3O05dvHhRWLNmjRAeHi78+9//FjQazVD/cXTBODVi9Xif5k18iHl5eQlr1qwRfHx8hE2bNg315QwLGo1G+Oqrr4SwsDDhxRdfFKqqqoSWlhahqalJyMrKEn7++WchKytLKC0tFX799VchKSlJqK2t7ffNu7a2Vti0aZMQEhIi7Nq167qCrL79/vvvAgAhJCRECAsLE8LCwoTvv/9eWLBggRAcHCyEhIQIM2bM6HRTf+WVVwRfX19h3LhxwqFDh/r92l5eXsKWLVvE///+++8FAEJVVZX42IMPPig888wz/X6NG9hQf6A35F+kZ4xTfafVaoUjR44IMTExwt///nehpKREXADLy8sTjh49Kpw7d06oqKgQTpw4IZw6dUqorq7ud5xqaGgQduzYIQQHBwtvvfWWoFAohvqPoEeMUyNWj/dptr4dYt7e3mhvb8eYMWOQkJAAKyurIb2e+vp6LFmyBBkZGZBIJNi1axcCAgIMbtAScHnY0nvvvYc9e/bgySefxCOPPAIjIyPU1NQgKysLCoUCMpms08GyvtBqtThw4ADeeOONYTFRdajpytMLFiwAcHmq8F133dWpHP3oo4/C2NgYH3/88VBd5nDFDco9Y5zSM8ap/tNoNNi9eze2bduGhQsXYunSpTAxMUF9fT2ysrLQ0tICb29v+Pj49Gu7lCAIOH78ONatW4fbbrsNa9euhb29vR7eycjAOKVXbH1ryDZt2oSQkBDcddddfTpEpg8rVqzAPffcgwsXLiAtLQ2BgYEGOWgJuDxs6bnnnsMvv/yCzMxM3HHHHZg/fz7+8pe/ICAgADfffDMaGhqQkpIi7g/uDUG4fPg7Li4Ox44dw6FDh/Dyyy8z0SCiGxbjVP8YGRlh8eLFSEhIQHNzM+644w48/vjjiIuLg6enJ2699VYolUrI5XLU19f36XufP38eDz74ID755BN88cUXeOutt5hokEFismEAjI2N8fnnnyMkJASTJ0/GxYsXh+Q6Ghsb8dtvv2Hx4sUAABMTE9ja2uLAgQNYtGgRAGDRokXYv38/gMu9rxcuXAiJRIKbbroJ9fX1YieRwTR69Gj4+PigtbUVtbW1sLS0RHV1NczMzBAcHAyZTIbs7GxkZmZCoVBc9XsVFxfjsccew2uvvSYeAB/Mrift7e2IiYlBWFgYJkyYgJdffhkAUFBQgNjYWPj7++Phhx8Wp5grFAo8/PDDkMlkiI2NRWFh4aBdKxHdOBinro+ZmRl8fHygUqlQUVEBW1tblJeXw8TEBOPHj0dQUBAKCwuRlpbx2h0QAAAgAElEQVSG1tbWq36vqqoqrFixAk8//TTWrFmDr776CjKZbJDeCeMU9R2TDQMhlUrx0Ucf4c4778SkSZNQXFw86Negm6D92GOPYeLEiViyZAlaWloMetASAOzcuROtra1ISUnB8ePH8fzzz+Opp54Shy1ZW1sjIiICY8aMwZkzZ5CXl9dl2FJDQwPWrl2LBQsW4NFHH8UPP/yAsLCwQX8vpqamOHbsGNLS0pCamooffvgBCQkJiI+Px8qVK5GTkwM7Ozvs3LlTfO92dnbIzc3FypUrER8fP+jXTEQ3Bsap/tu/fz8yMzNx8uRJHD9+HG+99RY2bNiAJUuWoKSkBJaWlggPD4eHhwcyMjKQlZUFlUrV6XvohsfOnDkTU6ZMwe+//47bb7990NvAMk5Rn13tQMdgnyyhoZWUlCQYGRkJCQkJgiAIwtNPPy28+OKLgo2NTafn2draCoIgCHFxccLvv/8uPj516lRBLpcP3gVfhUajET799FMhJCRE2Lhxo1BTUyMeIj9//rxw6NAh4c033xQuXbokvPXWW0JwcLCwY8cOQaVSDfWli1paWoSJEycKCQkJgoODg3htJ0+eFO6++25BEATh7rvvFk6ePCkIgiCoVCrBwcHBILtk0XUb6kPYhvyLbiAjLU4dPHhQiIiIEFatWiVUVFSIh8hzc3OFI0eOCJs2bRKqq6uFnTt3CiEhIcLrr78utLW1DfWlixinqIMe79OsbJDI3d0d7u7u4iTPOXPmICUlxeAHLXVHKpViwYIFOH36NKRSKaZMmYKvvvoKAODp6YnQ0FCcOHECQUFBOH36tEFNVNVoNAgPD4eTkxOmTZsGPz8/2NraitfWcWWu46qdsbExbGxsUFNTM2TXTkSkTyMtTs2YMQMJCQnw9vbGtGnT8PHHH0OtVmPs2LGIiIhAZmYmJkyYgC+//BI//fQTVq9eDTMzs6G+dMYp6hMmGyRycXGBh4cHsrKyAABHjx5FUFDQsB20BFwetvR///d/OHLkCE6dOoXp06fjs88+w8KFC2FpaYlvvvkGAPDII4+gtLR0iK/2MiMjI6SmpqK0tBSJiYk4f/58l+foyuZCN93kOFmViEaqkRinRo0ahSeffBK///47ysrKcOedd2L37t1YsmQJamtr8dVXX8HHxwcPPfRQt/FgKDBOUV8M/TIuGZR3330X8+fPh1KphK+vLz755BNotVo89NBD2LlzJzw9PcUKQVxcHA4dOgSZTAYLCwt88sknQ3z1PXN2dsYHH3yAjIwMzJs3Dx999BFiY2MhkUhw66234vTp0xg9evRQX2Yntra2mDx5MhISElBfXw+1Wg1jY+NOK3O6VTt3d3eo1Wo0NDSwGwkRjWgjNU7Z2tpi8+bNKCwsxOzZs7F+/Xrcf//9kEgkmDRpEjIzMxmnaFjinA0iA1JdXY1Ro0bB1tYWbW1tuPvuuxEfH489e/bggQcewNy5c/G3v/0NoaGhWL58ObZv34709HR88MEH2Lt3L7755hvs27dvqN8GDTwuA/aMcYpoEDFOUQ84Z4P0LysrC+Hh4eIva2trbNu2DbW1tZg2bRr8/f0xbdo0sUe7IAh4+umnIZPJEBoaipSUlCF+B0OvoqICU6ZMQWhoKKKjozFt2jTcf//92Lx5M7Zu3QqZTIaamhqx7ePixYtRU1MDmUyGrVu3ir3liYioK8ap68c4RX3FygbphUajgZubG06fPo3t27fD3t4eq1evxqZNm1BXV4fNmzfj0KFDePfdd3Ho0CGcPn0aK1aswOnTp4f60okMESsbPWOcon5hnCIaUKxs0OA6evQo/Pz84OXlZfDDloiI6MbDOEU0OJhskF7s3bsX8+bNAwCDH7Y0EHqaqProo4/Cx8dHLNmnpqYCYGmeiGio3WhxCmCsoqHBblQ04JRKJQ4ePIjXX3/9qs8bSe3wdBNVraysoFKpcNttt+Hee+8FALzxxhuYM2dOp+cfPnwYOTk5yMnJwenTp7Fs2TKW5omIBsmNGKcAxioaGqxs0IA7fPgwIiIi4OzsDADDcthSX0kkElhZWQEAVCoVVCrVVQMSS/NEREPnRoxTAGMVDQ0mGzTgvvjiC7E0DWBYD1vqiysnquom3L7wwgsIDQ3FypUroVAoAIy80jwR0XByo8YpgLGKBh+TDRpQra2t+OmnnzB79mzxsdWrV+Onn36Cv78/fvrpJ6xevRrA5WFLvr6+kMlkWLp0KXbs2DFUlz0grpyompGRgddffx0XLlxAUlISamtrsXnzZgAjrzRPRDRc3MhxCmCsosHHMxs0oCwsLFBTU9PpMQcHBxw9erTLcyUSCbZv3z5YlzZodBNVf/jhB6xatQrA5X2yjz32GN58800AI680T0Q0XDBOXcZYRYOFlQ0yGG+99RYmTJiA4OBgzJs3D+3t7SgoKEBsbCz8/f3x8MMPQ6lUAgAUCgUefvhhyGQyxMbGorCwcEivvbq6GvX19QCAtrY2/Pzzzxg/fry4t1UQBOzfvx/BwcEARmZpnohopBvOcQpgrKKhwWSDDEJZWRneeecdyOVyZGRkQKPRYO/evYiPj8fKlSuRk5MDOzs77Ny5EwCwc+dO2NnZITc3FytXrkR8fPyQXn9PE1Xnz5+PkJAQhISE4NKlS3jxxRcBjMzSPBHRSDbc4xTAWEVDgxPEySCUlZXhpptuQlpaGqytrfHnP/8Zf//73zF//nxUVlbC2NgYp06dwrp16/Djjz9i+vTpWLduHW6++Wao1Wq4uLigurqae0lppOIPds8Yp2hQME4RXRUniJNhc3Nzw6pVq+Dp6YmxY8fCxsYGkZGRsLW1hbHx5aNFHbtgdOyQYWxsDBsbmy57cImIiAYK4xRR/zDZIINQV1eHAwcOoKCgAOXl5WhpacHhw4e7PE+3IqSPDhkajQYTJ07E/fffDwDDah8uERHpF+MUUf8w2SCD8PPPP8PHxweOjo4YNWoUZs+ejZMnT6K+vh5qtRpA5y4YHTtkqNVqNDQ0wN7e/rqu4e2330ZgYKD4/8NpHy4REekX4xRR/zDZIIPg6emJhIQEtLa2QhAEHD16FEFBQZgyZQq+/vprAF0HLekGMH399deYOnXqda0YlZaW4vvvv8eSJUsAXF6ROnbsGObMmQMAWLRoEfbv3w/g8kTVRYsWAQDmzJmDo0ePdruCRUREIwfjFFH/MNkggxAbG4s5c+YgIiICISEh0Gq1+Otf/4rNmzdj69atkMlkqKmpweLFiwEAixcvRk1NDWQyGbZu3YpNmzZd1+s/88wz2LJlC6TSy/8kampquA+XiIhEjFNE/XOtblREI55EIrkfQJwgCMslEslkAKsAPAbglCAIsv8+xwPAIUEQQiQSyTkA0wVBKP3v1/IAxAiCwDs5ERENOMYpGs44QZwIuBXAnyQSSRwAMwDWALYBsJVIJMaCIKgBuAMo/+/zSwF4ACiVSCTGAGwA1A7+ZRMR0Q2CcYqGLW6johueIAhrBEFwFwTBG8BcAMcEQZgP4BcAc/77tEUADvz3vw/+9//x368fE1giJCIiPWGcouGMyQZRz+IBPCuRSHIBOADY+d/HdwJw+O/jzwJYPUTXR0RENzbGKTJ4PLNBRERERER6wcoGERERERHpBZMNIiIiIiLSCyYbRERERESkF0w2iIiIiIhIL5hsEBERERGRXjDZICIiIiIivWCyQUREREREesFkg4iIiIiI9ILJBhERERER6QWTDSIiIiIi0gsmG0REREREpBdMNoiIiIiISC+YbBARERERkV4w2SAiIiIiIr1gskFERERERHrBZIOIiIiIiPSCyQYREREREekFkw0iIiIiItILJhtERERERKQXTDaIiIiIiEgvmGwQEREREZFeMNkgIiIiIiK9YLJBRERERER6wWSDiIiIiIj0gskGERERERHpBZMNIiIiIiLSCyYbRERERESkF0w2iIiIiIhIL5hsEBERERGRXjDZICIiIiIivWCyQUREREREesFkg4iIiIiI9ILJBhERERER6QWTDSIiIiIi0gsmG0REREREpBdMNoiIiIiISC+YbBARERERkV4w2SAiIiIiIr1gskFERERERHrBZIOIiIiIiPSCyQYREREREekFkw0iIiIiItILJhtERERERKQXTDaIiIiIiEgvmGwQEREREZFeMNkgIiIiIiK9YLJBRERERER6wWSDiIiIiIj0gskGERERERHpBZMNIiIiIiLSCyYbRERERESkF0w2iIiIiIhIL5hsEBERERGRXjDZICIiIiIivWCyQUREREREesFkg4iIiIiI9ILJBhERERER6QWTDSIiIiIi0gsmG0REREREpBdMNoiIiIiISC+YbBARERERkV4w2SAiIiIiIr1gskFERERERHrBZIOIiIiIiPTC+BpfFwblKoiI6GokQ30BBoxxioho6PUYp1jZICIiIiIivWCyQUREREREesFkg4iIiIiI9ILJBhERERER6QWTDSIiIiIi0gsmG0REREREpBdMNoiIiIiISC+YbBARERERkV4w2SAiIiIiIr1gskFERERERHrBZIOIiIiIiPSCyQYNWx4eHkhJSen2a2vWrMG2bdt69X1iYmJw7ty5gbw0IiIixikiMNmgYaqurg7l5eUYP358l69VV1fjX//6F5544gnxsQULFmDs2LGwtrbGuHHj8PHHH4tfW7VqFV566aVBuW4iIroxME4RXcZkg4al9PR0+Pj4wMLCosvXdu/ejbi4OJibm4uPrVmzBoWFhWhsbMTBgwfx4osvIjk5GQDwpz/9Cb/88gsqKioG7fqJiGhkY5wiuozJBg1LZ8+ehZ+fH1asWAFHR0e4urrip59+AgAcPnwYd9xxR6fnT5gwAaampgAAiUQCiUSCvLw8AICZmRkiIyNx5MiRwX0TREQ0YjFOEV3GZIOGpbNnz0IulyMuLg4XL17EE088gc2bNwO4vJoUEBDQ5fcsX74cFhYWGD9+PMaOHYu4uDjxa4GBgUhLSxu06yciopGNcYroMiYbNCylp6fjhRdewPTp0yGVShEUFCR+rb6+HqNHj+7ye3bs2IGmpib8/vvvmD17triCBACjR49GfX39oFw7ERGNfIxTRJcx2aBhRxAEZGRkYMaMGeJjGRkZ4o3czs4OTU1N3f5eIyMj3HbbbSgtLcX7778vPt7U1ARbW1v9XjgREd0QGKeI/ofJBg07BQUFAACZTCY+dubMGYSHhwMAQkNDkZ2dfdXvoVarxb2wAHD+/HmEhYXp4WqJiOhGwzhF9D9MNmjYOXv2LEJCQiCRSMTHzpw5I96E4+Li8Ouvv4pfq6qqwt69e9HQ0ICWlhbs378fX3zxBSZPngwAUCgUSE5OxrRp0wb1fRAR0cjU3zjV2NjYJU4JgsA4RcOa8VBfAFFfpaend1rdqampQWVlJYKDgwEACxcuRHh4ONra2mBmZga1Wo23334bTzzxBLRaLTw9PbFlyxbcc889UCgU2L9/PyZPngxXV9ehektERDSC9DVOaTQavPPOO3jiiScgCAI8PDzEOKVUKvHtt98yTtGwJREE4Wpfv+oXiQzVmjVr4ODggL/+9a/QarXIycmBi4sLbG1tYWRkBCMjI+h+9m+//XZ88MEHCA0NhbGxcaeVKCIDwR/KnjFO0bCki1NPPPEE1Go1iouLYWlpCWdnZ0ilUhgbGzNO0XDS4w8lkw0aMQRBgFarhUKhgFKpBHC5V7lUKkVWVhZcXFxgZmaG2tpaODk5dRqmJAiCeFM3MjISb+a8oZOB4A9izxinaFjRaDRQKpVQKpXQarWQSqWQSCQoKiqChYUF7OzscOnSJTg4OMDKykr8fYxTZOCYbNDIJQgCVCoVFAoFNBoNAIg3b93X09PTodFooNFo4OTkhJqaGowaNQqenp5wcHDo9NyWlhaUlZUhICBArILwZk5DjD+APWOcIoMnCALUajUUCgXUajUEQegSW7KystDa2gq1Wg0nJyc0NDRAEAR4enrCycmpU5xSq9U4f/48QkJCYGxszDhFhqDHH0Ce2aBhS6vVQqlUQqFQQBAEsYqhu+FqNBpcvHgR5eXlUKlU8PT0hKurK4yNjSGTydDY2Iji4mJkZ2fDzc0Nbm5uMDa+/E+ivb1dTGLUarWYdEil7KlARES9o9VqxcUwrVYLoPNimFarRXV1NcrKyqBUKuHk5AQfHx+xctHS0oLi4mLk5uZi7NixcHd3h4mJCSQSCdra2gAAKpUKKpVKTDoYp8jQMNmgYUUQBGg0GigUCqhUKgCXb9wdb65tbW0oKytDTU0NnJycEBoaioKCAlhZWXVa+bG2tkZwcDCUSiXKyspw+vRp2NnZYcyYMQAglqd1q0hqtVrcR9sxWBAREen0tKXXyMhIfI5CoUB5eTmqqqrg4OCAwMBAXLx4ERYWFp1ii6WlJQIDA6FWq1FeXg65XI7Ro0fDzc1N/L6MU2TomGzQsCAIgrjHVa1Wd6liCIKA2tpalJWVQa1Ww93dHb6+vr1a4TExMYGPjw+8vb1RXV2NgoICtLS04OLFi2LpWncz11VTJBIJS9dERCTqzZbehoYGlJaWor29HW5uboiKiuqUhPTE2NgYnp6e8PDwQG1tLQoLC9HU1ITy8nK4uLiIr6M716GLUx3PdhANFSYbZNA0Gg3a2tpQXV0NW1tbSKXSTh/w1Wo1KioqUFFRAWtra/j6+nY6UNcXEolEPDiek5OD2trabkvXwP+CCkvXREQ3Nt0iVFlZmXgGsKctvebm5vDw8IC1tXW/EgCJRAIHBwdYW1tDLpejubkZJ0+ehLOzMzw8PGBmZiY+r2O1gwfKaSgx2SCDc+VBOoVCgdLSUnF7EwA0NzejrKwMDQ0NGDt2LCZOnIhRo0YN2DUYGxt3W7r29PSEjY0NS9dERDew7rb0FhYWwsnJSXxOd1t6TUxMBuwajIyMMG7cOMhkMlRWViI1NRWmpqbw8vKCnZ1dpzila5CiW7BjVZ4GE5MNMhg9HaTTHdrWarW4dOkSysrKIJVK4e7ujnHjxun1hnll6To/Px9KpRIeHh7dlq51VRh3d3eWromIRhjdFiXdVqmOVQxdLOjvlt7+kkqlcHV1haurKxoaGlBUVIQLFy7A3d1dbIrSMU6pVCoUFRXB09OTVXkaFEw2aEj15iCdUqlEW1sb5HI57OzsEBAQAAsLi0G9Tl3p2sHBAW1tbSgpKem2dC0IAsrKyuDi4sLSNRHRCKGbjaHrftjdll6lUomkpKTr3tJ7PWxsbBAaGgqlUonS0lKcPn0a9vb28PT0hKWlJYDL8ay4uBiurq6sytOgYLJBQ+JaB+kAoLGxEaWlpWhuboZEIkFkZGSvDtLpm7m5eY+la10nEalUytI1EdEwpotTSqUSKpWqy1kMoPOWXgADvqX3aq4WS0xMTODr6ys2PsnMzIREIoGnpyccHR0BQIxTbHxC+sZkgwZVb2ZjVFVVoby8HKampnB3d4eFhQXOnTtnEIlGR92VrpuamsRzHN2VrnmgnIjIsHUXpzp+AO9pS69cLh+0ROMaA5lFUqkUzs7OcHZ2RnNzM4qLi5GTkyN2d2TjExoMTDZI73ozG6O9vR1lZWW4dOkSHB0dERwcDFNTUwAQf89gXm9fV3V0pevGxkakpaX1WLrmgXIiIsPT2y29utkYQ7Wl93pYWVkhKCgIarUaJ06cgFwuh7W1NTw9PcXuWIxTpA9MNkhvdAfpKioqxA5OV/Ycr6urEyenurm5wcfHp8tKiu7mNxyYmJjA0tIS4eHh3ZauObODiMhw6FbzKysrYWVlJS6Edbelt6WlBa6urnrd0jsYsc7Y2BgmJia4+eabUVNTg9zcXKhUKnh6esLZ2ZkzO2jAMdmgAXflQbq8vDxER0d3OkhXWVmJiooKWFlZwdvbG6NHj9brNQ3WzVEXKHoqXbu6usLd3R2jRo3qtnQtkUgwatQoriIREenRlVulCgoKEBwcLCYR3W3p1S2a6ctgxymJRIIxY8ZgzJgxaG1tRUlJCfLy8uDi4gIPDw9xd8GV1Q7g8sIa4xT1FpMNGhBXO0in+9XS0oKysjLU19fD2dkZ4eHhvd7fOlwqG0DXgNGxdF1WViZ2K+mudH3q1CnExsaKK0i8mRMRDYyrbenV3YevtqV3JLkyrlhYWCAgIAAymQwVFRVISUmBhYUFPD09YWtr2ylOpaSkYPz48bC0tGTjE+oVJht0Xa51kE63GpKamgoAcHd3h7+/f59uTENxE+vva14tKTI2NoaXlxc8PT17LF0D/+sQwtI1EdH16zgbo+MMpyvjVGZmJtRqdY9beg2NPuKUkZER3N3d4ebm1mlmh4eHB8aOHStWfnRxigfKqTeYbFCf9eYgnUqlQnl5OS5evAi1Wg1/f3/xoLShu94qyrUCwNVK11qtVvz9V5auObODiKj3dFUMpVLZaTaGTsctvUqlEr6+vnBwcBjCK+69wYhTtra2sLW1hUKhQGlpKU6dOgVHR0fx4DgPlFNvMQWlXtOtDjU3N6OpqQlKpVK8eetWM5qamnD+/HmkpqbCyMgIERERMDMzu66OHQN1QHwwtmL19TV0peubb74ZZmZmaGtrQ1paGurq6rq0BtYFToVCAbVaPay2lhERDYaOcaqxsVGMU7oPwADQ0tKC7OxspKSkQKPRIDw8HKNHjx6SIXxDoa+xw9TUFH5+frjllltgY2ODpqYmpKen49KlS13iVMfdDoxTpMPKBl3TtWZjaLVaVFVVoaysDCYmJnB3dxf3eN6I+vO+daXroqIieHl5dVu67ph0sXRNRPQ/HeOUVqvtMuFbEATU1NSgtLQUQPdbem+kD8b9iVNSqRQuLi4oKSmBr68vysvLkZ2dDVdXV7i5ufXY+KRjt8X/z96bx1h25fd9n3POXd5a+772Vs2lOSRnEcayDduxYkhyMonsOBISGRYiw1YcRZYQ2bEkBDAQeBGcQNJYtixbHmlG9sixIkWGMRrb2mxZmiEpUiSHy5DsHjbZtXbty1vufk7+eEu/qnr16r2uIlndfT9AA2R3vfvurXff+d7f+f6WR/W54FEnDTZS2tIopNvZ2cFxHOD4bIwgCFhZWWFzc5ORkRFu3LhBJpM5dqzGQ/KjsMich7V91Lp+/vnnGRkZYXZ2tukQpdZ1SkrKo05Dp/b29poPs421sEFrSu/AwMCJKb0PUov1s3LW6zTGUCwWeeqpp4iiiJWVFf7gD/6AgYEB5ubmKBaLaYpVyiHSYCPlEEcL6V5//fVDbWuNMezv77O8vEwQBExNTfGpT32qY8/xsy7iD5oInNci2rCuL1++zMbGBm+88QZKKebn5xkeHj42s2NnZ4cgCJiYmEi7g6SkpDy0NHbNgyAgSRLeeOMNnn766UNBRqlUYnl5mXK5zOTkJJ/4xCcO/ftRHjSdOesG3lle2/retm1z6dIl5ufn2dra4ubNm2itmZ2dZWxs7NjMjoODA/b395menk4bnzxCpMFGCtC+kK61ACxJkmYhXaMdXl9f30d92h8YH0SXj/ulYV1PTExQKpW4c+dOW+va8zwODg4YHh5OreuUlJSHjpNSehuOe5rS2x1nDVTavV4IwejoKKOjo1QqlUONT2ZmZprtg6MoYnt7m/Hx8bTxySNEGmw8wnSajdFAa82tW7fY3d1lbGyMp59+uplW1S0P4o7RWfggF8xO1nVrkJha1ykpKQ8DjVQp3/ebBcdH69SMMbz//vvs7Ox0TOntxIOmU2fhPNKoOmlJPp/n8ccfJ47j5syOfD7P3NwcQPNZo/HZJkmCEOLQBlnKw0UabDyCdFNIt7Ozw/LyMr7v09/fz9WrV++7CPk80qgeFD4ssWpnXXueR7FYbH6mrdZ1OrMjJSXlQaKb2RiNlN5yuczo6OipKb2deJSCjQ8Ly7KYnZ1lZmaG3d1d3n//fcrlMo7jkCRJ2vjkESINNh4RGjsIYRg2Z2O0K6RbW1vj7t27zQDjrbfeYnR0NH047YEP83fVal2/9957bGxs8Nxzzx2zrtOZHSkpKQ8Cp83GaJfSq7VmdHT0vgONR5HzTqM67b2GhoYYGhri7t27vPfeezz33HOMjY0xOztLNptt/lzqyj+cpMHGQ87RQjrg2Be3XC6zvLzMwcHBsUK68+gk9SjtGH2U1+k4DuPj48zMzByzrht5y43PIrWuU1JSLgqtOhXHcduU3mq1ysrKStuU3vPQmEdNp8663t/v623bZmhoiIWFBe7evctrr72G4zjMzc0xNDR0rPFJw5VPderBJg02HlK6mY2xtbXF8vIylmUxPT3NY4891rbo61FbxD/KLh9noZE+1c669n2f2dnZQ52qUus6JSXlo6SXlF6tNdPT021TelOd6v21HxVa6+bzyNTUFFNTU+zv77O4uMg777zDzMwMU1NTh9J905kdDz5psPEQ0VpIVy6Xef/993nyyScPLcxhGDZnYwwNDfHEE080Lcx2nNci/qjwUS7iR8Wn1br2fZ+lpaWO1vWbb77J5OQkAwMDqXWdkpLygdCa0hsEAa+//jrPPvvsqSm9naZ7NyZXn4WLstY9SAHP/dAuSOrv7+djH/tY8/nkhRdeYGhoiNnZWQqFwiG34xvf+Ab5fJ7x8fFUpx4g0mDjIaBdIZ1Sqpkm0+htvby8jOd5TE1N8clPfrKr/Nbz2u152BfQVj6qha+R39yOTCbDwsICV69ePdG6br1f0oLylJSU86SxroRheChVqvHf0DmltxMPi0592PV+HwWdHBnHcbh8+TKXLl1ic3OTt99+G4C5ublm7WgjHTzVqQeLNNh4gOlUSNfIyV9bW2NlZYVsNsvMzAx9fX09F3Y9avb0WbhIzkY7OlnXjYK8dpNfU+s6JSXlfkiSpFmP0VijjqZKbWxsnJrS24lUp3rjPGo2Psj3FkIwNjbG2NgY5XKZpaUlbt26xdTUVEedSsuUOrAAACAASURBVBufXFzSYOMBo5tCOs/zWFpa4uDggIGBAT72sY81uxL1SqMX9lk4j0X8QRKBi7yIt3LUut7a2kII0UxZaLeYNwLatFAvJSXlJBprRkOn2s3GaKw71WqVg4ODU1N6O3FRdOpB4aPeFOulLrBQKPDEE08QxzGrq6usra1RqVRwXbe5eZo2Prn4pMHGA0I3hXS7u7ssLy8TxzGTk5McHBxw+fLlM73veeTCXhQ+jAW2Er9GkPm33Ny8jTF7QAlMDEJjmRgpEqSwkcJFiiJKjqPkPLa1gGs/A0T3/d69LuINGtb17u4uQ0NDba3r1pkdURQd20VKSUlJ0Vo3N8NOmo1xNKU3l8tx7dq1M73vo1hbeBZ3ohrdJCn8Eje3/h5abyKogglBJCijUSJGIFEyjxR5pBjFUrPY1nVc62kg/NDP27Is5ubmKJfL5HI53n33XcIwbDY+aZ0tBWnjk4tGGmxcYLqZjRHHcbPneLFY5PLly83BbisrK2c+h4tiT3+YQtDLYhjFFVaqv8i+/5+RZgVhKliZhNiANhGOyCKFh4UAqRDUp6+bGGM2iPUysXmBKIBQwMKCYHNrAKWmcew/STb73Sg11dW5NLp83C/GGEZHR5mdnT1mXU9PTx9rM5la1ykpKXA4pRdopkq1/vvGxkbblN6LolPwYDnovZAkEavVf8WO9x+QZhVhSmTyEbGWaBNhiwyqrlNCKsABBJgIYzZJWCGJXyIMIKjr1MZWAUvNYFt/lEz2u7GtK12dy3no1MDAAJcuXWpmcXz1q19lfHyc2dnZ5uT4dGbHxSINNi4gJxXStX5BKpUKy8vL7O/vMzExwbPPPott281/P6/F96IEG2dFCEEQBNy6dYvt7W2Gh4eZn5/v2OGkEzvVr7Ja/QJB/BaWCBFmHyVdYlNFCEAbbGFwhAQTIjBIA7JljUuMQCHICAlCosmQJAmWKkHydaLkTQLvZ0AMoKyncd3/Bsf5DEo5bc/prHm4ra8/al2/9NJLFItF5ufnT7Suq9UqhUIBx3HSxTwl5SGnmxlOnuexsrLC9vY2Y2NjZ0rp7cTDolNQ20C8ffs2m5ub9Pf3Mz8/T39//30da89/jbvVz1ENX0USICkhpU1iqiAN0hhsBA4GiBAYhDFIoPExaiMQdZ0SQqDJ1qd/B5DcJE7epuT/PEYUUNYNHOfP4rp/AaVybc/pPHUqm81y/fr1ZuOTV199lUwmw9zcHIODg8dc+TAM8X0f13XJZrOpTn2IpMHGBaJRSHfnzh2GhoZwHOdYqlRjNoaUkunpaa5fv972C3NeX6KLsgDfL8YY9vb22NraYnd3l0uXLnH9+nX29vaa6ULz8/OMjIyc+juLdImlg59lP/wK6A0yokRRVmvvg0EbRU5ociLm3qFKcCjAgMQobGGwhQSjiTHYJsESFSzVOG/QwgWhMGaPJPpdvOh38cTfRIoZLOtxbPd7cDN/7NC1nuVzb6TntdKwrmdnZ9nZ2TnRuga4desWCwsL5HK51LpOSXlIaaT0NlyKfD5/LFWqNaV3enqaK1eufKBrwcMQbJRKJdbX14njmEuXLvHpT3+acrnM7du3CcOQ+fl5xsbGTv09JtpjufQL7Aa/A3oVR1QoyDJQz5bQCdm6Tt3b/DqcvqsNxEZhYbCFgzEhGoNlTBudckBYGFMhiZ7Hj57Hr/5thJjEVtex3O/EzXz7ofvjvHVKKcX09DTT09Ps7e0dm9nRcNmEELz//vuMjY0dq+tIA48PljTY+IhpV0i3u7tLf39/8wsVhiFra2usr68zODjIY489Ri7XftfgvHlQF3GtNevr66ysrJDL5ejr62NqaorBwUGklIyOjjI6Okq5XObOnTvcunWLmZkZpqenjx1r2/tPrJb+OZF+D4HGEhGhqRLggraxhMHCYAmQ5Ah0jJQGSYIiQghDbGwEmqzQSNmogUmagYg2EBqBMbW/soRBEQBgAEQOS1hgPDDLiGgFHf8OlUoWYT2DnfmLGHPp3HaMjiKEYHh4mOHh4ROta611M50qta5TUh4e2qX0HhwcNL/bcHJK74fBRx0o3C/GGDY3N1leXsa2bfr6+ujr62NychIpZXNOUrVaZXFxkXfffZepqSlmZmaOHWvXf4mV0j8lTN5GkKBESGR8fCRC29hCowBHWCgyBDpESZAkSEJkXaeo61RONHQqOKRTkRHoQzoVAmEt64osSjgI44G5C/E6Jvl9qtUMqCewM9+JMZ9EyvbufLe/s05aMjAwwMDAAEEQsLy8zPPPP8/w8DBzc3PkcjmMMW11Km188sGSBhsfEZ0K6aSUaK2bhXSVSqWn2RjnyYMWbLQOLRwZGWna9rdu3Wr784VCgRs3bhCGYXNhKhQKGFnh3b2/z37wFYTZwRUHuNIDaoudEQ6uCChKv8XFOFw0FxtJxWQoCoEQCYmx8Y3CIcASh3eSpAAX01zUjYHIKEAjkWQIa4u6AJAYIDGy5nxEzxHHzzM9ZJPwJKH3XVjuf4eUvd0r7XaM2nGSdR0EQXOH6Kh1nfZCT0l58OiU0tvQqdNSej8MHjSdah1aODg42OzEtbi42HZ9zOVyPP7448RxzMrKCi+++CKFQoEorvD+/mfZCf4TUm9gixIFWQHq9SfCwUbSrwKEaFzbUZ0SVI1LUVj1AMXBx8I1EZYIDv2sFNRSrlpOMcZGmxiFxBURFlFTpwASJGBB/DJx+WVG8haRvkbo/QUs939Eyt7S6rrVKdd1uXr1KpcvX2ZjY4M333yzec+206m08ckHSxpsfMh0mo0BtS+S7/u89dZbzUK6gYGBj+zGf1AW8VKpxNLSEtVq9b4CM8dxuHLlCn0Td7i9/X8Cd9j3AvLSR9SdiNjYGHLkRYk+6Z94rKp2ECgGVUj/ocX6XoDha0VMH5Gp4AiFK3wEBmNqQYolBAUBQtQWVWMgMLWdJSXAEaCEBsogBBqBEQGKV4grXyOu/m1QT2C5n+l6Qe/V3j5qXb/88sv84R/+IbOzs8es67SgPCXlwaHVxThpNobneWxubpLJZDqm9H4YPCg6Va1WWV5eZm9vr6ehhQ0sy6rVcIxt8e7OP8Dod9iu+ORVgJS1upnYWMQmQ1F49KmTdcrTNgaLARXSL+ruRO0IUHfVA6OIKRLqKo5QOMJHNnVKoIQkLwxC3tOp0NTShZs6xWGd0iZCybeIK3+HuPrjoBZQzrdhZ74HKU93wnrVKSklExMTTExMUCqVeOWVV3jttdeYnZ1lenq6GRinOvXBkgYbHwLdFNL5vs/q6iqbm5tIKbl06RJjY2Mf1Sk3ucj2dGsNi1KK2dnZ+wrMEl1l8eCn2fN/E2N2yFgWGEOMpJIUaqlSygdiHKmo6AwVLBSajBQofAwST2foV4JRy6O2YJ9MRmpgr/5/Eb5WBMZCGklWSTJ4tF6GEOBCc0cpMhCb2t6RI0AKg2wJTLQJMfGrmPg14ur/BeoGyv4z2Nm/3NHxuN9FdWBggFwuxzPPPMPq6uox67pdQXlqXaekXBy6nY3RSOlVSjExMcH8/Py5vPdZ1oCLpFNHr6WRGr20tITWmtnZWRYWFnq+Xq0jlkv/jG3/Sxi9ga1sLJGQCEk1KYJWWCoAIhzp4BtJNVF1nZJIfARQ1Vn6lMWIVQaSju/pCo3LPnkFEBFoSUXbtcYmSpHFb3FMajrlQFOnYlPTKgG4bXUqxsRfx8Rvk3g/DeoJlPWnsHPfh5TtZ65062y0o1gs0tfX12z1/uKLL9Lf38/c3BzFYrGtTqUzO86HNNj4AGk3G+NoId3e3h7Ly8uEYcj09DTf9E3fxO3bt3va7fgguYg7RnEcs7a2xtraGgMDA/ddw7Lvv8xq5ecI4rdBb1GQfsvukMIyg2Rkmay92/Kq6qFjBKaIbyR9MkJLQ2iyhElCTkY4ovM1GyMoawtbCPpEgpD3ApTQSELThzEBWRkgOTzrxBa1P1DrFhJo0/x7JUCiMDgofCBEx69gklcJ/Z8G65NY2e/Hcr6p599ZJ7TWZDKZttb13Nxcswg/ta5TUi4Op83GANqm9K6urp6LTjVSW86SInwRdSpJkmbdYKFQaA5L7ZVyeJOV8j/Bi14DvVnTKVXTikQIYoZx8cjZpZZXHdap0OTwTZb+uk5FxmE3cciJCFeeplNQ0TYSSb+M6bMaOhURGYHf1KkIdSR4sUTtD4BBEJiaK9L4e4nEkEHiIUjQ8evo5E3C4J+B9TRW5q9iuX/6yPmcvcDccRwuXbrE/Pw829vb3Lx5kyRJmJubaxbhtz6npTM7zs7FeKJ9iOh2Nsb6+jqrq6vk8/lmO9EGjcX3InCRFvGzWtAAWies+7/AjverGLOJKwxFcQB1nYtMnlhbDKgDlNxoe4zECEo6R1ZqhlSl+fdZEhr2c22Btmo1GiKHpES23v0j0IrA2PRLwYhqPxzJEQaHfRC11KmysQCHjIyxjuTcSmHI1Nfe0ECsBVIYXBGg6rtIiPrOJR7Ef0Bc+gqxGEM634aV++tIeX+tFY/SWKCPWteLi4uHZna0s663trbIZDL09/en1nVKygeIMQatdcfZGFrr5mwMx3GOpfSel05dFI05L50KguBQu99nnnmmOaOoW4wxbFR/mS3/iyTJKq4UFNhDNHUqS6IzFOU+9gk6pY2gpLM4AobVvdrCVp0C8LTCMxaWyCPNPjmZIAVERuJpm6KUDKsQOP5Z28Jgc9DUqYqx0NhkpMbmcL2HwOAKQNTcjlALEAZX+FjHdCqE+E3i8vcRVwaR9p/Gyv0QUk2cydmAw86IEIKRkRFGRkaoVqssLS3x7rvvMjExwczMDK7rHtogi+OY3d1dpJTNZjOpTnVHGmycE8YYfN8nDMNmAdLRG7FarbKyssLu7i7j4+MnLkIXyRL+qIclNdyfarXabGV3PxZ0IjdYT/4Rq3uvgCmTlzFK1kRWG4gYQhmPflVq1LUdw9M2VZ1lwg7pU17H9xMC8iImX89/NQYOtIMwtXsiJ2Ic0Z1QSwF9IgFq71nVipgMjjTYpooRkthobCHItwzyMMbga4MBMqL2WdoIIEYbSMw6if+L6OCLoJ5gsPhHMeabz33xLBaL3LhxgyiKmsWN7azrzc1NBgcHyWQyqXWdkvIBYIwhCAJ8/14uf6eU3tHRUW7cuNEclNaKEKKZFnwWLkqgcNZjHBwc4Hkeb7zxBjMzM1y+fLnnh+KEXXblF9nefQFMmZyM66lR9WJ9MwzGp1+WEVal7TF8rajoPKN2wtQpOgWQlcmhAKSUOGgja7M1ZEJGdDctXAooioRGapZvFKFxsaXAMZWmTlkIck2dEod0qpZq1dCpoK5TOyTBr6DDXwN1jaHip4FPdnVO7TgpWMnlcjz22GNcu3aNtbU1Xn75ZXK5XHPOSUOn9vb2kFKSz+fTxic9kAYbZ6TVxVhcXCSTyTA+Pn7Igtve3mZ5eRljDDMzM1y7dq3jjfkwOhu90rCgG+6P4zh8/OMf7/k4O/6/Z8P/PHH/+1gypCg8RN02jo1DQpGc2KMgtk4+RpzHkS6jaq+e59o9kZGEZhCXEmPq3kIM4BmHqtbYGAr13aRuyEkNVClrRYiDKwrYwscRh4sBhRBNxyMxhkAbFOBKgRQCiQABiYlJ4te5NPkmwc6/Qjh/HDv7vyGtSz1d62nYtt3Rum60zpVSptZ1Sso50prSu76+ThiGzM3NnZrS2+k7J6UkjjvXpnXDeejdR7VBp7Vma2uLpaUlXNdt6lSvKWH7wXPc9X6WKHcLJXyK4l5jksTYRKaPnCiRlyfr1G6SAxwmrBLDPepUbAS+GcChwsgRnfKNoqolEk1RJqgudSojNBnhUdWKCg4OeSwR4RxJ8WrVKW0MnjYtNR6tOqXR8TvMjN0i3v8VEvvTWLkfQlk3errW05wRpVSzDf7e3h537tzB8zxmZmaYnJxEa41t202dSgvKuyMNNu6DkwrplFLNfMLW1nYDAwMsLCyQz+e7Ov55BhsXofCul2M0LOjNzU3GxsZ4+umncRyHF198sev3S3SV1eo/5iD8HWAHWzgoNLFJqJBDUUCaCpYIcERCYPoJjEVVV3CEizIGYWIS6dInPWadCg1XoVsqSQFDjn65R0Hutf2ZrAjJNm1xwX5S+5+CjJv1GEeJjaBiLLLCMKgMNWv7AKjZ4TE5XOFhicMPAUoIcs3iclPveFVzQ5SQqLp9nXCADr5MGP57kPMo98+hMn8FKc+vleVJ1nVjlkfjZ452B0lndqSkdE8jpTcIAqKo1gmv8R1qtKk+LaW3ExctjerDDFiiKGJ1dZX19XWGhoZ48sknyWazvPTSS12/n9YRd73PsRf8OoZNbGFjkRCjqegMShQRVLGFjysTQpMn0AN4uoIrsggShIlIcMjLkBm7DJzcfaodns4SU6RfHJCXB21/JiMSMqpRyyjYS2r9pfIyrqVFtSGpp1Q5QtCvNDWdqtWU+FoRmiyuCLHF0ZRgQbZZXF6b+yEJcYRA1f/UdKpKEv5HTPS7RHIS6fxXWNnvR8rTn7G6TcMSQjA4OMjg4CC+77O8vMxzzz2HbdtMTk42fyZtfNIdabDRA6cV0kkpqVarvPXWW5TL5fuuKzivRfyiFN7B6WlUjQLEarXa1a5aO0r+G6wHP4eXvIYyBxRkgKw/dCdKEJl+cjIgL9daXnUvr3RQgaehkmSYsD0sUWYvybES9oEwFGVIUQactH4kRrATDWPJgAmrSi8Bii0Mg/WiP23gQCsMWRwqZKWhqhUJkj6pGZHt742sNEClXi/iIoWDQxl5pFDdFgJbmHo7XYM2BldIpBBY9V0kbQyJfo/Y+yyJ9zMI61lU5gew3E93fU3d0Gpdv/jii9y8eZO1tbVj1nU6syMlpTsa35NG98OjKb1SSoIg4NatW6em9HbivHXqLHxYm2KNmSIHBwdt9b2b9aga3eFu9WeoJC8hzR55GaHqD91aQWj6ycqEwgk6hQJfJ5SSPGNWSEZU2EsyrEZ9aGPoVwl5WT3RKdcGtuJBJAlTVgUhtk895waWMAzUdcoYKGlFbGozp3JS42lJhEWfTBiWhvpI2kNkpCFTdzcq2gaRxaFcb+Xe+l6iNrMDQWgMiTHNoKOhU8YYYr1C4n8O7X8eYd1Aun8VO/NnOl5Hr7qRyWS4du0aV65c4Wtf+xqLi4tsbW0xNzfH8PBw2vikC9Jg4xS6LaTb3NxkaWkJIQQLCws8/vjj932DnccuTeM4H3Uea+MY7WhY0I3pqbOzs80HzG4xxrDp/wrb/r9E61VsoekXFRr1ZrFxiYxLvywxoHZOOAbsJYNYQjKm9hhW92zeIavKUMvPVrVNKRmgkkQ4ImbE8giNZDcuMG55zDi7x9+gRxo1GtpUKBuF1rX+5DmRNDt7dEIIKIgIiEiMoKpzKGFwqB5ppyvq7XQF2hj8+i3nilqaFUag0QgCTPQCJn6JsDqOcD6Dyv5Az8OYOqGUIpvNcuXKFeI4PmZdpzM7UlI605rS29i9PTobY3t7m/fee48oilhYWDg1pbcTD5tOnYQxhp2dnWYq9Ozs7H3NFNnxf4MN73Mk+j0skdAvqs2U3sQ4RCZLQZboVydryH7Sj8RlVO0wpFo2yiyfwRZXw9cW+3E/FZ1gi4RBy0Ng2Ir6GLF8Zu32LkYviHqNhjYeZWNR0QqNICv0ic78UfIyAcpoU2vJq4TCpnwsUHKEgPpn7Nc7WmXqa75V1ynQmOhVTPwDhN4gwv42VO6HkbL3DmAnIaUkl8sxNzeHbdssLi5y8+bN5rypRuDZzpW3bfuR1qk02DiBbmZjBEHQLKQbHh5mZmaGOI4ZGho66bBdcV65sBdlET96jJOmp/ZCrEusVD5LOfpNjCmTkxq7pRiu1jY2YkhVkKK9wxAZi714kH5VYbrLxTcnY3JyCyxYj/IcJEM4IsASgsAoikSnH+QUAmPjGUG/NAxLTWN3qFZkLkmMoCi7CzyUgEL9+kOjCE0Om6CtfZ2p7xT5xoAR2ELgHO0Solch+BckwRdIrE+hcn8D2WPO7Ek0ajaKxeIx63p0dJS5uTmy2WxqXaek1Dma0gvHux8eTemdnZ1lf3+f0dHRM713I2f9rFwknWoNnpIk4e7du6yurlIsFrl27dqpqdBHz0PrgNXqP2E//BLG7JKVhnzLZlakiyQkDKkKSrRPgUqMYicZoCBCJq0ycHo9RkYmZGRtc207znIQ95OXYU2ntIWR4YkOfbeERlHViqLUDKvDLkZZS8K6TnUTeEgBhfoA3MhIPJNHEbWvQwQMhtAYjKk5He4xndqC8FdJwl8mUU8jcz+Ess/HlW/oTV9fH0899RRhGLKyssILL7zA4OAgc3NzFAqFQzrV2LB+lBufpMHGEVoL6Rr1Dkd7LjdSfjzPY3p6ujmtemtri4ODs+8YPGz2NNR+b438/P39faampu4rxawcvsma99NEyRsIAgrCQ8pG+pEgMoO4osywal8nAVDVRUqJYsqqMu+0dztOIjaC7XgUV3jMO1WO5siWkixbiY1DwqhVRZ0ya6OVjTgHSCYtj0KbDDIhoE80ppnDbqJQGIpSdyUctXa6tQ4mvsmjhcIyJSxhiI0hMQJXKIot20q+0WhTcztq3awUEBAbjYm+AgfPkYgZhPtdwJNdX2s7Got4g1bren19nddffx3Lsk60rqvVKpubm8zNzaUF5SkPNY2UXt/32+oUQKlUYmVlhVKpdCjlp1Qqsbt7Dg7sQ6ZTjWM0Njl2dnYYGxvj2Wefbbbq7hYvep9V77ME8UsIfPLCR8naRpQ2EJlBHOEzZO2feAxfZ9mPM4zbVebtk/WsHdrAejyMTcSMU6ExHXy8ntpbTly2YgeJYcyqYPegU1txhtjYTNoeeat9N7JCPdVXG9hLFGDok7qrJii2ALuuU4HJkAgHZcrYQjd1yhHqUOfFwGhic8+Vr+lUSGIgiV+C0veQiDGk8+c4SycrOF7z4TgOly9f5tKlS2xubvL2229jjGk2Pml1MxopjktLS1y5cuWR0qk02ODkQrrWm6C1O1I2m2VmZoa+vr5Di/t5Fsw9LPa0MQbP89ja2sKyrPuyoI0xiOLv8tbu30HrNVypKIjd5gN2bFxCYzMgK/Sr9t06jIHtuA8lYMqqMNJjGUspsSnrEYbVLvPOyTmuReVTVLUAxNeKu1EWJWz65F69i9RhQi3ZTPL0qZhpOzj27ydhCRisF+3VprpCRhhypwxoapARHtpASVvYMotFRE4Exz6XjJD1TiC19oRKCGxBsy+6MYbY3AHvJ/jEgkVU+hOo7I8irZmur6XBSYV7UkomJyeZnJzk4ODgROs6iiK2traYmppKC8pTHjp6SeldWVnBsixmZmZ47LHHPhCdumgF4mc9RkOnNjY2mJmZ4cqVKz0/CJrsi9wq/Thav48jFQWxR2PCdmIcQpOhKMv0q5M1ZDcukhjFjF1myO2u7WzzGrTiQE9QFLtcdk4OUAoqpFCf8RQZyVqcRRuLAVVrC3+U2Eg24hw5qZm0a1PKu0EKGKjrVGgE1cTCEjGFLnXKFSHGhJS0IJZFLKHJ4tXSfA/9nMRt0SkhaoGHEqBQdZ1aQ/v/lE9eV0QHfwSV+9+R1mNdnUcrJ+mUEIKxsTHGxsaoVCosLi7yjW98g8nJSWZmZnAcp3mfrq+vMzc390jp1CMdbDSizLt37+I4Dtls9tgH7nkeKysr7OzsMDo62uyO1I7zXMTPy57+qHaMWi1ogLGxMebn53s6RqxLrFb/IaXwN3BHSriYw6lSuogmYlhVO6RKKXbjfvqVz5zTvi95J9bCAlIUmbI3GbXaD086iYxMmHNqlrc2NefCN4qiDMAIysZlyvK55FRPOVJnXKlpVE+UtSQykJca54R1KzCC0BTJiBKDSkN9FynUiljksE35mPVd62ZV+0vfaBB5bFNtcTsMsQxIwt+C6LeJ5VVU9i+j3D/f9XV00yWkk3XdaIbQ+P40XMpH2bpOefBppPRubm4CkM/nT03p7ZSaetE2xT5KZ6N1cKExhoGBAa5fv97jMQJWvZ9lP/gSzug2DuC0pEqFukCCZrhjqpRkNxkiK3ym7d71YCvKkTDEuLXBoH23p9faQjNj1zTAGNhMslS0RUGE2EKzp7NMWiHzTm8dGY/iCIOjakFKVQt8I8nJpNn29iihEfim1l1xQBkaU9EjJD55LFM+pnGtOhUYjREFLFNpcTsgISKJfg8OvkIsZ5HO/4DK/k9da0M3OpXP53niiSeI45jV1VVeeuklisViMxW4VacelcYnj2SwcbSQbmdnh4GBAQqFWiFRoyBsZWWFJEmYnp7uapfjou0YnUfQ0qugBEHA8vLyoempjWCuWxqpUmHyNQQRBeEjrUaqlCQwRTKiwnAHC7qSZClrl2nbp985+efakRjBZjiOqxIuuzv02va2HVLAuOVxN84TGxdHaKQRJJzvotJqX+8nEgP0SY0ASloCgj6pycrSsdc6UuNQxgCeySGIcTnB7cAjAXytsYTEanE7tDHo5CZJ5f9AV/8+wv4WZO5vIeVgx3PvZTJsO+s6DEMcx2mmlbRa1+nMjpQHjaMpvfv7+81cceic0tuJi6Qv8NE4G40BoxsbGwwPD3Pjxg329/cPDTs8DT++w0r1p/DiF5AE5EWIsmoP07VOf/1YosqwdXJqta9d9pIcU1bAnN1bapsxsBGOIgTMODtIsXb6i05BCBizfNbjbF2nYiSC5KRJt/dJThpyJC11iDWdUgLKiWh2XszI44FXbWp5GQR4JotA4+C3dTugiqamU1JIHEGt1Tt1Vz55n8T/vzH+T4P9x1G5H0WqqY7n3kuHz0ba7+zsLLu7u9y+fRvP85rHaZ1k/rA3Pnlkgo1OhXSN+RhxHLO2tsba2hp9fX1cuXKlGYB0w0ULNj7MRfzg4IClpSV832d6evrY9NTTjlHrKvWrbPn/D4lewqqn68Q6wsNB1KbwvgAAIABJREFUxzmEhKKqkMEDCmzGhsjUPsuszAIxYRJjSZi2KoxavVnQvs6wEQ0yaZe5lNns6bWdCI1iPcrRr2Jm7XtiNkpAYgRrUQ5NLRixesid7YQU0K80kYF9bSFopFmdfl8JAdn6LlJoLBIyWKZygttRW3QDo0mMwZWyPohJAZpY76ODf4MJ/y2JegKZ+V9R7n/R4b17W1hbreuVlRXu3LnDV7/61WPWdTqzI+VBoFNKr1IKrXVXKb2deJR1qlwus7y8TKlUYmpq6lBwdnBw0NUxdvzfYN37RWL9DSxhcIQk1gk+FibOYSQUZQVXVEHk2I5zBE2dylHTqQgpBDN2hSHVW51naGw2o1GGrSrzmd5qDjsRG8HdqEBOJkxbAY06j2EV1mtAckRaMGZXcc5Jpxp1iEk9pRdqDkhfFzoFkK1nNMRG4ZNr63bINjrlCIUU1N0OTWIq6PA3Ifqtuiv/V1Duf9v2PY/WFnZ3nYKhoSGGhobY3t7mrbfe4qtf/Srj4+PMzs6SyWQe+sYnD32wcbSQDo53lYrjmOXlZd577z0mJyf5+Mc/3nNBWOO4F8me/qDTqBr5wcvLy7iuy+zsbFvR6/QliZISq9V/TCn6XYzZIi/jw6lSolCreXC2W4rLIloLs2Oj2IokAypm2vVri3E8SGwcoMKIVcXtsHhtRgXKSYar7gHXM72lSnXiILHZTXJMWz6XT0iVUsIwbdeu19OKtcglIxNGrd6GMx3F07UO5f3SMGbd+/wOtCLS1Iv1ThcMRyRApcXtCHGJjn2mbmtth9FYQmEJ0+J2JCTxa5jK95NUB5HOn0Vmf6irIUzdYts2o6OjXL58+Zh1nc7sSLnItM7GaDfDCWoPOZubm6yurp6a0tuJixZsfNBpVI2Wv8vLywgh2taxnEaS+Kx5P8d++Btos05OJhTUvbTcSOYQZCg620c2jAIapoA2ks1YkpcJ065HZCx2kn48bSPwGbaq5GT7gmuAnSjLge5j3tnnqrve9bmfRkUrtuIC4x1SeqWASaumU4GWLMcZbJEwpvwzdbUKjMI3gj5pGG3RqbJW+BqKUne1AWcJjdWt21GfI+VrXWu1K0zT7dBGo5N3SCo/hq7+XYT9XyJzf/OQK9+LA98O27bp7+/nxo0b3L17l6997Ws4jsP8/DyDg4PHdKp1ZseD7Mo/lMFGt4V0W1tbrKysEIYhg4ODLCwsnOmB46LZ0+eVRnX0GEenp964cYNMJtPTMSrhTda8z+Inf4gkpiB8lLw3LCgwfdjC62hBe9phv25BX3HvtQN0RMR4iyWdGMF6lCfSRWIChqyIgqywGAzQb9nMOtucR6pUg/UoR4zFjFVhSJ3eprBBVib1Dlewk7gcJDbDlt+2YK8d2sBGksVC1Iv4jtMnDchaLcueNrjo+jDAzhx2O+yaxU7lWPvdhtthjKkPYqLudoBEYUxCYjZJ/H+JDv4NqI9h5X4QaT/T1TV2vP66CLSzrsMwZHZ2lomJiUfKuk652DRcjDAMMcY0XYzWFMBGSq/neeTzeZ599tkzPXBcxE2xs+pUO62L45i7d+82MxUWFhY6tq5tdx5+vMJq9afw4q+ACWoD+GTtmcIYQ2D6kcJnWJ3ckjbQNrtJngkr5rJz72dsETNq3Svi1ga2oiy+7iMkZEjF9KsSy+EAOZVl0t5kSrRvgHI/bEdZqsZmxq5y1elep1ypmaunN+0nDruJU9fU7hucbMQZQDFp+W2LxQvSUJA1t2VfSyw0+S6LyhtuR2QUAVksUz3WbavV7QiNqXWyatEpSIj1Hknwa8jwPyDUE6jsX0M6f+zMwUaSJM2gYWpqiqmpKfb391lcXOSdd95henqaqampjjM7HkRX/qEKNrqZjRGGIaurq2xsbDA4OMhjjz3G3t5ec9LqWbhIOz1w/s5G6/TUoxb0aTSOseP/OuveL6D1HSwBfaLc0q3DwjcZ+mWVvg6ta3fjHImxmbYrDKvjtQdHUcIwbleACp5WrISDxMoGLCJtiI3EEmf7PcVGsBIWa7tWts+hia/3wZAKGWra11liIxizvLYtCn2t2NN99Mkqs3Z3qWO20AwrMEZQMpLYGIqiu9aEjohxiDEGPPII4+Fy+PtTGxgoDrkdUthIkUGaWpCSmBJJ/BV06QW0mGJ84Jsx5pvv+3vYWMRbz6FhXfu+z9LS0qnW9d27d8nn8/T39z8U1nXKxaOhU2EYEkVR27a1jZTeu3fvUiwWuXLlCmEYsrOzc+adzfO6p89zU+w8daq1qcv4+HjXrWtbj7EX/B7r3s8SJzdRwlAQVWTdHU+MIjA58rLKaAed2o+zBMZl2q4w2IVOSQEjtgd4hFqxFA5icNBGkGhDbBSOONv8LW1gJSriCOqF6L2lGh+lX0X0q6hWPxJn8Y1gTHm4bQKD0Aj2kiGyssK0XRs6exqWgCFlAEFJSyJtKNRrO07DFhq77sr7JgtEbV15p17LoZs6pZAih6SKjcGYCnH8Irr8KlqMMDX0KeBTcJ91LO2Clf7+fj72sY8RhiHLy8u88MILDA0NMTc3Rz6fPzazY319HSklIyMjD4xOPRTBxmmzMYBmIV21Wj32oFwqlZr5sWfhIgUJjfM5DzGoVCq8+uqrAPfXulZElNUv8fXd/wimjCsinFYL2uSINAxbFQZE+8VPG8FmVCQnTX2R7O1hfjfKspMUuOwe8FS25no0ysACbbMajSOQjFhbZHvYoSknNSt8VFW46vbe7eo0avZ1o5Vuzb52RMyYFbAXO1SMw5TtM9tj3m+DWs5s7R4JjWRPCzJoMl27HRUQEBmbMJG4sjbgsIExhsjUWuY6JASmjKnvHSkhUAiMSYjZZHbsV9C7v46x/yQy/2NIOdLTtXTKpc1kMiwsLHD16tWO1vXu7i5KKXK5XFpQnnKutOpUuwnfUKspWFlZYX9/n8nJyUMPynt7e+eiC+fFRarZAPB9n9dff50oiu6rda3B4Nv/H1/f/W3QezgyItfiTMcmQ6AlQ1aVAdG+6YgxsBUXsIVkyr4336JbDmKH7XiAaeeAG3WdmqzHSZFRLIXjaGMxbO1QUN278Z5W7CTD9MnSiSm9Z0EIGK/rVGQEK1EWhWHM8ihri5LOMG4FTPc4L6SVYtOVr+mUg+6q1bsQ1Os8ITYWQWLhSB/7iE6FRiOFwMUQmjKxkSgpkICDwJiY2GwxOfRlzN5vEVt/BJn7MaQ119N1HN0Ua8VxHK5cucLly5fZ2Njg61//OkII5ubmGB0dbepUqVTCsiz6+/sfGJ16YIONbmdjbGxssLq6iuu6zMzMNHO3W5FSNp2Qs3DR0qjOsog3dnkXFxdRSnHjxo1Tp6ceJYhXWKn+JFX395FCkyNA1R/ka1/uPoQIahb0CQZJqC12kgIjKuDyfTzML4d9GGNzxd1n8oQBfq6MmHNqnTwSI1kORvC1xaC1y/AJ6Ug78QC+zjFlbzHQYXfrPMlIzayocjfJshYV0AjGLP/civUcAcOqNpX1wEiSHtwOW8TYqhYU+uTAVDFG4whBpnVQX737VmQMkdFkpFUv1KvlNiemjA7/HSL6DWL1ODL715HOn+jq/LXWp+5gnmZdJ0nStKgfBus65aOloVON7odwfMJ3a0qvlJKZmZm2GzpKqXPRqfPivL4HZ9GpRuvaxcVF4jjmqaeeanbr6pZI77JS+UlK4reQ2YgcMVbLg3ygi5hGi/UTdCoyiu24wICM7qtF7HpYwNMZrmb2GbPbz+CwRcKsU6vV0EawEg5Rji0GrDLjJwQQ+0mRctLHhLXN/AnHPW9sYZi1PdbjDOtxTaeGVUC2y6Lv049PfWJ5ze0I6zrVjdthiQRLJRgjCMihdRVDgiMkmZYPt+HKx3WdcoSNEhqbBBQkxkNHv4s4+D1ieQWZ/T6k+193df7dpGEJIRgfH2d8fJxyuczi4iK3bt1iamqKmZkZkiQhk8k8UDr1wAUbrYV0b7/9NtevXz/2i/V9n5WVFba3txkZGeGpp57Cdd0Tj9no8nFWLpKtDPfnkLROTx0fH+fKlSuUy+WeAo394Cvc9X6mbkHXOkhJ0WjJKvFMnoLwGOnQuraaFChpi2mrwoBzugXdSmwEa+EYWelzze0+FxVACc2Mey8vdi3MEzJAVlQZtva4WR3GFYbruT2gt/M6C6ERrMe1jlYzLQFQbARLUZaskIxY5+OsCAH99QAmMJIDLcgKjXtKUGMMlI1EmoA+KYlEhthoMAHqyMJnC4EtFNpoPGNQwsIRuqVQL0bHr6PL/wtaDCGcP4/Ifj9SnhxMNBbgbmlnXWutGR0drf8eDlvX6cyOlG5pTel96623WFhYOHbPtEvpzeVyJx7zvHThonE/19WYs9OYK7KwsMDq6mpPgUY5fI017x8SJq8jSeiTHlLUgjltBIEp4Aq/o075Osd+7DBhV7nSQ91D7T1gIxoHQi65B/RSNyiFYdrZgXp/gO04z0HSR1aGjFnbvOsNYYzi8dwuw+r8nYyTSIxgLc6Sk5qplpRebWA5yuJKhxG5f6ai8laK8p4rv193O7qpQawYiTYB/VKS4BAZCcY75MoDWEJgiVqtoW9qnrzTUlBuTEKcvIOu/Ai6+nfB/nZk7oc7Nj7p5Gy0o1Ao8OSTTxLHMSsrK7z44osYY5iZqQ3OfVAanzxQwUYcx3znd34nn/vc55BSUiqVmh+aMYa9vT2Wl5eJoqht+9WTuGiL+IftkDT6tS8tLREEwSELemdnp6tzMcaw7n2eneBfo/UmGSkOWdCRdgmNYsiq0n+CBQ2wFRVwhMO4tc9Ij3dnJbFYiwaYsassZM6nkG7SqeDrkHcqwyTOOKEWjPYoKmehoh22E5sJK+BSmx0zq76LBLAdO1S0Ytzy2+bM3g+uAFcZtBEcaImhtovUun5FBipGkROGQQVQe2+HEEfUanFCMmDKOEddRSHICgFo/MTGiJCMFPX2uQ3reh0T/HNk8Hli69PI3I8irUvHzvV+C/darevnn3+e27dvs7i4yPz8PCMjI21ndsRxjOu6F2YhT7k4GGP4ju/4Dr7whS8ghKBSqRy6hzql9HbivBz4i0Yvzka5XGZpaYlyuXxorki1Wu1a67b8X2XL/xckegVXCoqi1FzPYm0TGIcBVaVfnaxTO3EOcJm0Sgy5vaX0+vW6wTHL55J7Pjo1bFXokz5vVYbR9jheIhi1va46Dp4HvrHZiB1GrZB553gnRSlgpl6Psp/Y7CU2o5bfVSv2bnDqtR2dahBjA2WjyAjoV4aGTklibAG6XlBuTBm3TUfNmitvCLUiIcKV99rnGhOTmB1M8K8x4f9LrD6OzP8I0nry2LneT+tcqM3smJ+fZ25ujldeeYW7d++yvr7O7Ows4+PjJzY+cV33QqRXPVDBhlKK27dvH7KgWztOFAoFLl26RLFY7Pm4F2kR/7Ba37ZOT81kMszOztLf33/sGJ0W8Ujvslr5KUrRbyOMR04mWOreYhPoQt2C9k5Mx4mNZDsu0i9j5pzagtQLW1GOUpLjSks9xnlwEOdZ9Ae4nt3lk8Xa3I1Zt7ZL8643SCVRXMvuklPnf+9sxi4hFtOWT5/qrihw2AoZBkItWQwz5GTCSI+zRk5CCupTXGu1IxUtsIRDYkL6pWG4Q3CjhCGLB0Lhmwza+Dgcb8iQUREgSOr5s7awsYTGRgAJiYnQ0X9GHPw+sbyEzP7PSPczzdf3umN0FCEEjuPw1FNPEUURi4uL3Lx5s2ld27bdfGjUWjfrw1JSWhFCcOfOnabjrpQiiqKuUno7cV4OfIOLcv+epjGN1rVLS0sopZiZmeHxxx8/1pCi0zESXWXV+0ccBF8GDsgKsA9N+c4RoxlRHkq0Dx60EWzGRfJS1x+cewsy9uIM23GBS06pWY9xHlSTDHf8UebcXT5RrKVKzdUN3uVgkK3I4mpml6J1tuLyduzU6wZnbJ/LXaaPNYrKG668IxLGz0mn2tUg2sIlNgF9p+iUFDR1KjAuiQmxiY+1z3VkAki00VTrOmULTW2ala63eX8BcfDfE8tppPuXEJnvbt6v3aT7dr7Gmk5dvXoVx3FYXFzk3XffZWJigtnZ2eYm2Hk9R54XD1Sw0UqlUiEIAl5++WUmJia67jjRjovobJzXcdotwA37fn19nZGRkY6ta086xj0L+jUkmqLwkPUe4Y3pqa4IGOnYutbmIMkzbnmHWgJ2y1LQj0Rx2d1HOmfr/tTKSjBArHPMuxs8W7h77N+lgKt1sagkFm97E1h4XMv2Nqn8KNoI1uM+LBEyYQd0062jHY7U9aANNmMHzygmLe/YUL77O0fwjcARkBchBwYqGvq6fMbPCL+eC+sSIFFtrGslBNkW61q0ta5voSs/hq7+PbC/DZn7G2duSQg0W+C6rnvMuu7r62Nubq6ZqnERHtRSLja+7+P7Pq+++ipjY2OnpvR24jydjcbD+UW4h0/S39YhuwMDAx3TzE4KNrzoNqveT+LHLyEIa1O+RWPKt8E3/bgiZtg6WX8CbbGXFBhRPped3lNV18I+onrd4ESPU8I7sR4WqcRF5twtPpZfbfszM+4uM25tNsY3/Eli7bGQ3euqvqHje0d9GJEwZXmM3meHrFZXfiexKSUW45bfVXOS0zAGPC2wBBSFz4ERVHRtnkc3t7wrgnonRZsAG2Wqx3Sq0T7XmITAGAwSV9T+3mk0PkneR3s/jvA+i3H+FDL3Iz2n+7ajsbGWzWZ57LHHuHbtGmtra7zyyitks1nm5uYYGBg403ucNw9UsJEkCbZt8y3f8i388A//MMPDw/c9gK+Vi+ZsnBdHF+BWC3pqaopPfepTp+4Etx7jNAs6MTaedhhQHn3q5EW119a1rYRashQOMm5rFjL314GpHdoIloJJHJEw727RbT1GXsU8nq0FJGthjrthnnm3xJDd/VC+SqK47Q8y5XrM9FifchqNKeq+lqxGGfpkxKDVexDjaUnV9FGQJQZanJyB+u1T0YLA1IYIdleoF2MBmu6s66je1SojWye/xsR6BxP8Mib8FaYHFpDibwGf7vn6GhwNWFqt652dHb7xjW8QxzGXL19menr6vt8n5eHFGIPjOHzmM5/hu77ru3jyySe5fv16z477Uc7T2Wg84F+E9IqjOuV5HsvLy+zu7jIxMdGVxh89xm7wm6x7P0eSvIclDEVRaWmxrvBNlqL0GOvQ3OMgzhCYDJNdtq5tJTGCxXCQfgmX3bNtQh1lyR9HCsWss47ssrOUKzXXMrUmKFtRhqWgyIzj9ZQSHGjJLX+QEStkusdayNMYUhFDKiI0gsUwS1YmTe3qhdAIDnQ/eVmhX93Tuf66K+/pWhBSlKarzTdVb2xj6jqldQVHmGOumltvfNIoKHeljRS6rlMJiTlAB7+OCP8d44UrhOKvAd/e8/U10FofenZrOH7T09Ps7+9z584d3n77bebn55mfn7/v9zlPzjXY+N7v/V6+9KUvMTY2xhtvvHHs340x/OAP/iBf/vKXyeVyfP7zn+cTn/jEqcfd2dnhc5/7HF/84hfZ2dnhl37pl3jmmWf42te+dqHmWlw0hBAkScLW1hZLS0tIKZmdnT1mQZ92DG08lsr/gIPwyxhK5IQ5YkHn6xZ0lQF1sgW9FRfJivtrXVtKHNajPuadMjey59f9ydcWt71RphyPq5mzTWWddKpMOlUSI3jHGyA2koXMLs4JOzWbYYYDM8y0vcmzhfadss6LjNTNgYF3Y5fYSCYt79TAYCe2KRubCctjWJ78e89LQ55a/cZ+IsgKQ7aL55hW69rXNhqw20x+rRWUi3pBucYSDrZI6hPKa9Z11nkTqb+XeG8K6f5FROYv9bxz2xisdhQhBMPDwwwPD+N5Hr5/tgnvKR8tH4RWVSoVvvCFL/DzP//zbGxs8BM/8RN867d+K2+//fa5dRc8Ly6S5jUcm93dXZaWlojjmNnZWa5du9ajTkWsVn6GvfDXMGaHjID8oRbrWUIjGFZVBsTJG1WbURFbCCatCkL0tjFTrdcNTtpVnsicX5ARack71RHG7JjLZ6xHHLF9RuqbYe/6/VQTi6uZvRPTgXdjh91knDFrk2fy5+fMtMMRpo0r77edL9XKQWKxqx3Glc9IhwAyKyFLbYjfTiLICEOuC51qts9VkkBbJFhYpnKs8UmjoFybpN745P9n782CI8vu887f3W/e3BP7XijUvnZ3dTebFKXxjGNiYvigiImYcEgRY9oPfDBHimDEREiheZgHRmgm5AnZomUqxqLGomzZlinJGomSSWqxRFFis1tcmlXd1dW1ooDEjgRyz7vfMw+ZQAFVACoTSKBQNL43IBPnXuRy/ue/fN+noUthqysviESArt7F5OcIir+EpP/PSLH/FXk3mbNdsNGBf/Y+JTKZDJlMBtd1qVarx6J7CV1ONv7xP/7H/OzP/iyf/vSnd3z861//Ovfv3+f+/fu8++67fPazn+Xdd9997rrT09PE43H+5m/+hh//8R/nypUrQPc6Et3ubByH9nQQBJRKJUqlEr29vZw7d65j6Vrbf8SC/88I+r5L1QufaUG7IoUiufSou1d93EihFCbpUVxO7aMFvey3JAH1Mn1dnHNd80zWgj4mjDWuxVe6ti40OQrnWwlRNdS4U0+TVTzGW52YR04aX6icj5UYlBa7eu12MKg2E71GpLLq62QUj/QWXogQsBg0ewpDmkOug3EubZOo10w6gLZb16bcvM5G61oWdTRpeySQWyNWEOKKCCEUDFk0W9fSRut6hsj+ZST7XyK0v4cc/9+R5Z62/4fn3qdp7nsU5gTHA4cRq1ZWVqjX6/zpn/4p//Af/kMuXrwIHM/OeTeTjYPEuyiKKBaLLC0t4Xkek5OTHXeAvHCZvP3PUEb+mqITYMkB6havJEckkYRHTqm3xRuc2IcPxZofoxzGOW1UusrHKAc6q/4gg9o6ryS6L1071UqI7Ejh/XoPKVUwpq8jS5B3E1RDk/OxEr3afNev/TxsdDZcoTLraSTl4Jmu/HJgEAiJoQ64jbDVLBAqkUQkmnGqHZl3Qw6AgAgVF6MlfLJTnGpyDT0REQoZU946YhUQREvgfgnc3yRQP97y7Bht6/7b6UoahnHgqZ9uoqvJxk/8xE/w+PHjXR//oz/6Iz796Wa18a233qJUKrG4uMjQ0NCe6964cYMbN24AEI/HaTQaJJPJY+ePsXWtgxBVD4KtLWjLshgeHmZycrKjNYruX7Bsf4kwfIQiScQlj5AQV6hIIo4vQlTJJyHbmLvMa1ZDEzsyGVbrZDscDYoE5L0MhgSnjCqdEsb3woydphEZXIgVGdB3nnPtJpKKz/V4gVBIfL86gEDmcrxA4hBI5Z3CkgMm9IBIwIJvEomIEIW0EjDcwRjYTpCkJ63rRgTOvlrXKi4mUdR4pnUNYEgySIJACDwh0FFQ5ScjVqHwEN7XEP43Wp4d/xuy/okD/V9P/j/phRcUTrB/HEasmpyc5Od+7ueAplxlo9E8tP4oJxsbJNRO492G3PTq6irJZJLe3l4uXLjQ0RoV7+9Ysn8NL/wQBYEl+0RShCdkfJEmEAIZn4TcwFR29gtqhDrVyGJQbeyLNzjnpgCV00aZEb07JGeABTdBNUgxFSvQEzv8g35MDrkaXyMScKveT4DCObPIKfNwO+7twJACxvXmOWM5MPCEhBBgyRED6sG5mqnW5IEjoBFKJGSB3sbWLkuCGA5IKq4wCYSNQbSjQ3mT/9EUPlHR0OQN4ZNWnPL/ClH5Vsuz47PIxt4jVgcVQ3kROFLOxvz8PGNjY5s/j46OMj8//9xkYyssy6Jer5NMJrs2w3oY7emj/CAIISiXy+Tz+U331KmpKQqFwmbAex6iyGfJ/k1K3tcIowUScoSxVbpWmNhCJqeU0KTtr7kv5FaLWqMSKKiSwJIjhlQXvQNpOy9SyXtp+lWX82b3uAuRkJr+GHLE2VgZOELp2lDlkZNl3HB4K9WsTDmRwgf1LGnFYcw8unvZDY1IIRAKg2qAK6AYahhS2DUTJksGa9+tawcUuelQLkJU/F1a1xJCRDiRQG61rmVkIgKcKCSMbqJG/xeq99+gWj+DLO/uZ3CCExw0Vm0UxeB4Sta+KAPaarVKPp+n0WgwMjLC66+/Tq1WY2npWSGOndCUWP8PrHt/SBjOYMkROflJrAiETkOoZOQKhrz9NW/GKRNfaFRDHZkAQ2p2emNy++9PIGRm3SwZxeeM2R1vow08aGQRKJyPFZGN7hXZngcvkrnn9DCsB7yRXN/83Yf1HkzZ53Sse/zI/aIRNUeY+lQPARQCnXoUEu/gvdsLpgSmIggFFEMJTRIk2qQ0GZKDIUlN4RMBCm4bwidNb6lmnIpwIp8wuo3a+Beo3ruo1ueQleyu13zeubUbo5vdxJEmGzv9850e9Ldu4j/KFaN2EEURy8vLzM/PE4vFmJiY2GZq1I5+uReuMF//FRrB3yCJBgk5QN3Cu7CjBBEBPYpNepe3SgaqoUpOkRg2n8xLRgIKQQw7iiMLibhSIbNDJaIUGKwFSU4b9a7yMexQ4b7dy6jucuWQZ02fxppvsuClOBer8Fpi+7VNOeRavLmhz3u9rPsRZ2OljhKzbqAYmNSF3JLXbQY2k4i0EuAJibwXJy775LokS3iQ1rUm+S0tdBUXHUT9mda1JEmYkoQrfJaDEFcyiavXUMQ8IponDG/jhrfRwg9JpL/8zDWO2+Z8gheHg8Yqy7Ko1ZqFhOMYp7ppZPu8dYQQm7xBVVUZGxsjk8lsvp7txCk/rLDY+FUq/p8jiypxOULb5vIdxxcRPapNahfpWgVBMVRJKSpDxvaKfTEwqUUWklCwlBo59dmDfi3UWPbTnNIdLnUxTnmRzL1GD71awAXraA/1lUDjsZvltFnj1adipC5Hm3Fzxc+x6ElMmUUs5WjjVDUyKQUKQ5qzzWsqodsEQmLOtzCkkL4udDm8ZRuqAAAgAElEQVQAFAmyrThViyT8VpxqW/hEAiEUXExEVEd/KsBtCJ94ImA1DLGFiqVdQxNriGiaMLyLG95F8b9LMvvHSNLBRByOSwf+SJON0dFR8vn85s9zc3MMDw93tMZGZwOO5yZ+FLOwG+6pKysr9PX1cfXq1R1nyPfaxKve91m0v4gffIAqRaS3SNdGAhoiifEcPoYdqZTDBAOqs2MLWpagV93um1ENLcphikbkoRMgS92XBFz3Y8y5ac6aJV5LdMc0qV3MOkk8keCMWWBQf34LekRfZ0SHcqDxkZ1kUKvRv4MpUjex6JvISAxpLrsxGXRJMNHa2JeCGIGAQXV3r5ROcbDWtdtqXRsEwkZvmTPVREQlCnFbPyMalPzvAoK4ehVTAhG+j+9/kzCcRVHGt619XNR5TvDicdBY9XRRrFsxYWNs6aCf0252NnZbJwgCFhYWWFpaIpvNcvHiRWKxWEdr1L27LNpfwA1/gEJAWnJR5Ce8wYZIo0ou2T2laxWKYZLePXiDWdUhy5N9tx6alKM0tTBAFz5C0pkyyvR2kY9RCwweORlOGdVDFwh5GoueRT3McsoscKONa/drJfq1Zqf+/XqKHq3B8D64LZ1gJTAIhMKw6pDWd36OKgnGWyO/q4GJHckMajZql8wME5sO5U0OoiUJzI668gqe0AmEh0aIRCtOtbobTXhU/O8DAku9QEyOIYJbhNGHBMH30LQ3933/xyXRgCNONn7yJ3+SL37xi/zUT/0U7777Lul0uqMRKmhu4v81JBs76aBXq1Xm5uY23VOfJ1379L00pWt/j4Lz74iieUxZIi5vl66tRwZpxaZf2b3CUg5MPGEyvA/pWku2WfMNMkrEuF5n1Y8z5w2jSVVG9INVdWbtFLXQ5KJVpD+xeqC1OsU9O4eKxHmrzH44JmnV5xV1nVDAPTsLCM6Ypa4d7gMhsRjESMghI1pnnYoNQnklbLqZ96l215xfn25dG7KBJbWXbBmSSyhUCoxgi0qTcLcjJOrBB9QBXR4hoY7guv8Zy/rstmd1Mgd7nDbxE3QfB41ViURiW5zy/f155jyNbknWdmu0a6eCVqPRYG5ujlKpxNDQEK+99to2I9521lh3vsGK868Jw8cYEmSk6jbp2oawiMs2fXu4fNdCg3oUY1htdMwbjCsO64FBQpY5bdRZD0IW/EEUGozoB9uXF904a36CC1bxyIthj+wMvtC4aBVRpM7VF+NKwPVWV/6BncEVMmdjxa4d7iMBi0EMQxIMqh6deE1tEMrrkcqCr5NTnK5xIzccyiMBpVBCk2PEpfaSLV3yiJBYE2PYePhRfpdnSjSCuzQAVe4lqZ7Gc//0QMnGcUJXk42f/umf5pvf/CaFQoHR0VE+//nPb26y/+Sf/BM+9alP8bWvfY0zZ85gWRZf/vKzowzPw9Ob+I9qsrGxjiRJFAoF5ubmdnVP3Q0bm3gYNVho/CoV7+uo+MQkG32rdK2I4UYSPWqDpLLzQVQIKARNScBhrU6nhnN2pLDgZRnUbC5smf/s0+r00Xw/C77Jkp8gLnuMG5W22paRoMXHgLOxEtDdGdq94EUyd+0ehvWIy1Z3ZA4VCS60zAGXvTilKMWgurpv99dqqDLtZJg060wckPSdUgJSSoAvJOY8i4QEGa071a0nrWvnua1rIWAtkpkNFAqRDDQTS8u7yHBSJQjf3/U6XrTAureAGSkHSjZO8HLjsGPVYY37HuX4UyfrCCEoFovMzc0RhiGjo6OcPXu2ozgVRT6L9q9T9v4/ZNHAkLyneIMGdqSSUxsk95CuXQviSKgMq3X69+jM7wS/xcfoUT3ObuHT5VSHnNoUFKkEBnkvgSEHTBjlts1S7zeyCKFwwSoyZhyddHbQ4iz26jLnre51UM604vh6YLIW5sjKBXLa/kaZGqHMQyfLiO5sGv3tF3E5JK7bhALmfQsTmR6tO9xIWYKMIoDGprdUShaou3wGiqHEbKiyEsoI1gFBzD/PcMIiDG8Cu3QFowJFr4AWLBNP/B/bHntZx327mmz8zu/8zp6PS5LEr/3arx3oGk8T77pVMepWe7qbLfN8Ps/q6upz3VN3gy/yNOJf4KPShyj4pCQPRXqSTDSiJBI+OaWBtMsZKxAyBT9JRt2fJGApMFkLEkwa1edKAm7V/66FMfJuChmP02YJ7SmvCjtUeGD3MKjZXIl3b362HVQCjUV/kCFt/Rk+RjcxoNsMYONGMrcbPSRkj4k2ifPLXoxV3+JcrMIr8e626DVJMN4a9VoOTDwhMdiGZ0e72K11HQhYCBVmA4WGePZ72tCXeOBCUrlAn5ZBhLcQYufPbCCelRw+6Wz814PDjlXxeJxqtfld7WaycdwUGCVJYnl5mbW1NRKJBKdPnyaRSHS0Rsgafvpfcqd0C0m4JOQQVX5yGHeiBOEGb3CXr2fY8nFKyNG+DqvVUGPFTzOmP583mFJdLre6vXZk8MjOEImAyVgR86mOr9/iY2RUn4tHzMdohCoL3hBZtcT1xOHFyJzqklMXCYTER40cqhRyJtZe8W3dN5j3kkyZla57eCgSjLXOE4XAoCEUBlT7uZ4d7WKrt9RW4ZNQwFIrTlWfiVMStrbCQxcsZZIBbQApvE0kdn69gmjpmQmXlzVOvVQO4vDsGFW3zLWOS3t6Q7q2Wq2SyWSe24LeCUX3vzSla8UDTAPiUh259QWLhEw9imPJDr3q7pufHWpUogQDaoPT+3ALXfSS+ELfNx8jodhctJpBoxZq3K2nkRHkVJtymGNML/DqIeiO74UVL86Sl+CiVeKKdXT+GIYccdVqvoYzbopKy4Tp6cAGMO0k8YTKObPMkN4dwtxeGGi1rquRxpqv0as6XRux2mhd1yOJDzyF5VBpTb3ujWq4TDVcRpXSDOmvoosZwmhu23PCcHHfm/hx8NE5wfFGIpFgebk5ptJNNaqj4Fq0A9d1mZ+fp1Ao0N/fz/Xr19H1XQbrd0HV+y6L9hfxgg+JWSEJyXmGN6hLLrnn+DgVwyT9isPkPnycVvw49TDGlLE/H6eY7HLear7PTqTwYb2HCOjTGqz5GUb0CtePmI+x7pnkvTTnYhUuHmGcUiXBJauZ1Cx4CVZ9g9NmifgOo0x5N041NDgfK9OrHf7r06v6gE8jUlj0dbKKS7JLI1Yb3lKOgDueylKo4LcRpxrhOtPhOgoGg8YniLFEED7a9hyBSyTWUaQn7MqXtQP/0icb3eoibFSfOj3YP439bOJCCEql0jb3VNu2GRoaavt+oihkyfnXlNzfQ0TrxGQwt7SgA2HQiFSyis2AtnuSUQpiBMJgWKvRs0cyshNCIZF3M1hKxKRRA7ozYpNQfNKKSz20yKgS9UBQDCxSavccWvfCjJ2jHqlciRcZMY42wXkaE63ErxpqvF9P0K816NUcZt1+FMlm6gVJ6SblkKQeEgiJed/CkAJ6D6hiVQwlZgKVlUiGNjbvpxEIh7z7ISDo118nJXsEwS0ABB6hWEOVejef/7Ju4ic4fjjMMaoX2dmoVCrk83ls22ZkZISBgQEGBgbaTjSEEKw6/5E1598TRYuYskR2C28wEBqNNniD1cDEFiYjWp1sh7xBgLybRpFkThsVoDsFS1MOyWkOa36StCJTC6AYxMiqTte4d3shb6cohTGuxIsMvOA4Naw3CeSNUOGDeoaM6jBq1Jnz+nEjj7NmhW76Z7ULS444pTstb6kYinRwr45qJDETKCyGCmIfcSrEZ969Awh6tFfIKjJBcBNofs+DcBFF3l+ycZyKYi9dspFIJDY1ubvdnj7qilEYhiwvL7OwsIBlWdvcU1dWVtpax4/WmK//c2r+XyELh7gcoilbWtBhnECK6FFsUsrOm2pTojaFKQmGtQbQ2SHRjVTmvMwzfIyDIhLwwO5Bl2TOWyU2gkI60XT8nrbTFLwYZ6w1Ump3xuk2EAqJ+41+0iqcsV7sxr0TkorPuViZe3aGtcAiqQjG9mFK1W2okthsXa8EJm6HI1aRkLjnJfiukyWuT9GnliH68IB3JbHiPWQFSCjn6ddyiPAWQbiAKm9PNk7UqE7QDRyWGlU341QQtMcDi6JokzeoaRpjY2Ok02kkSaJer7d1P2FU3+QNSqKGJYttvEE3jOFBU7p2F94gQMFPoEgyw2odSepszw+EwoybeYaP0Q1M21mCFvF6onXQT6tN8ve8k2TejXPKLNFzCEqDj+wBdFnltLVMtwp83YKlhFy0inxkZ6k0DOKK0krwXixkCUZb/JK10KAWyQyqTtsjVkLAIz/Od50ssjLFkO4geJ/dOBjtQWLNn2HNh5g8zoA+hBx+SBAtYHBl81mddOCPE166ZOOwKkZHOQu70YJeXV2lv7+fa9euPVMZ2uCQ7Iaad5NF+1fxwvdRichINrLcDB5CCOoihSFF5PYgRnlCYT1IklO8XSUB98IGH+N0G3yMTuBFMvftPvo1l6vx3TemyViVyVgVJ1K4VR1Al33OWesHqiK5kcp0Y5hevcLVIx7TahelQGPGTTJpVjeVQaApZ7gamJw1S10z5DsI+ludjVprxCqnOsR3uS83krnlppkXH+Ou/bj5y2CR+8CA9iqTMQ0/vEUkDhasa+EKtXAFVUqSDSuY2pPHutHZPMEJoBmnDsNn4yg7G77vs7CwwPLyMrlcbkfp2uetY/sPWbB/BSf4LgoBqS3StQCNKIEsCbJqjd2KsIGQWQuSpOSAcX3/fIwJvdFVH6dQSNxr9JJSQi5Yu3dXRsw6I2YdP5K4XetDCMHFxBrKAbgDQaQwbQ+TUhucP4bFMIB6qPDASTOq1zfHgAEKvsG8F+e0WSGp7E/4pJvoUXx6lKaITd7XSSseqV3uyxcSt90Us9Hr3LFbzu7+MtMOZNXLnLUSROH7hOJgyawdlXjslJDQsIIG8S3OBiedjSPCVjWqbs7CHoXKx9PuqW+88cauldSdnFmb0rV/QMH5t0TRHIYstSQBm4+HQqEuYiRlZ88WdC3UqUcWQ2qD0x1KAsLB+Ri7oRLozDg5pswar3Uw52rKIa+mmopEK16ax40YE7ESvR1UkcpenBVvgPHYCleTCx3f+1FgyTNZ8S3OxUrbkowNDOkNhvQGtVDlZiPDsFqn7wh4G89DQg5J7DJiVQ5Vvu/kuOmm8YTCqPmsCMKyX2TZh5g8wcV4L5p4gBd1Ltu4FYFw8cX216YTztZx2sRPcPyQSCQOrSh22HGqXq8zNzdHuVxmeHh4T97gbl5OTd7grxOGj9AlSEu1LbxBibqIY0kuvW35ONk7+jg9DwflY+yGeqjyyO5lzGjwSgfEa00WXEs2E4N1P86DRoJho8qQ0X6hrx4YzDsjDJvrXEocHR+jE6z7OnkvwRmzsmOc6tVcejUXJ1J4v5GlR3UP3bOjHcTkiInWiNWiH0MiYrDV/ahHCu85WX7gZLCFyoiZAua3/X0xqPJ3lSqaNMil+CAxaQY33E3mtj0IAtynRE5e1g78S5dsHGZ7+jAqRkIIVldXN1vQo6Oj29xT21knjBwW7H9Jxf3PSKLaakE/2aB8YWILmR7FJintvnmvBxZCaAxrnUsCHhYfA2DJi+NEvUwYy7yePFiVpl+v0q9XCUSzimTICpOxFRRp58/Jop2lGqQ5m1ik15g90LUPC4+dOI1I43ysxGAbCVRCCbhurRMKiTt2Bk0KOdOmitVhYuuI1R03xYdenPtequ05Vzty+UF1HohxwfoEOXUdO/ho3/cTie3jGi9rxegExw9bk41uGr0eVmdDCMH6+jpzc3MIIRgdHeXcuXMdxakmb/A3KLm/j4iKe/MG9+BZbPg4jWi1jn2c4HD4GABrXoxK1M+ovnLgOJXTGryZbhAJ+Kjeg8BgKraMJu/83q55KQpuL1PxRS4nj2ecmvcs1gODC2Z5xyTjaZhyuNnxuG+niJA4a5aPhN+yF2QJRlpJxiMvwW03wR0vRUh7B3xfBNyszQEyU7G3GNDq2MEHwP46WUJsHxeMouil7MC/dHf8MuiXB0GA7/ssLi4+1z11r3WcYJaHlc/jBN9BFj5J2UOVnxyQ7CiBICCn2KR3+YJGLUlAS44Y1Wygs0r3bv4Y3cAjO0vUmnOVpe52E1TpSRWp6MW5W00yHKswEmsGv8f1QRqh4GpqDXmPBO1FIRJwz04TV1VOmfsLbIokuNgaG8i7ccqhzlmzhCG/uFnOh57FO3aO2WBnGed250w/ajQ/L0P6a5wyFdzgJqJDrlH0lFdMGIZomrbLs09wgvaxNU51MzHtdmcjDEOWlpZYWFggmUwyNTXVkXStLMv40RrT1X9B3f8msnCa0rVb+IFuFMdnb96gEFAIk+hIDO3Dx2k3f4xuYNZJ0YhiXLaKDHc5TskSXGrJp9cCk5vFDL16nVPxpvjJTL2Xiq9xPVNg8JgWwx7YSTQlxpi2wsg+OxRnW2eLZS/Gkh/jjFneUcXqqJD3Td6xczzw4+wkTtLed1Diob3EQxt61GucsWKE4S3CXeTYd8NOcepl7MC/lMnGYZj6dWsT932flZUVlpaW2nJP3Qkl929oJL+AI82gB5CW6sitWfcNSUBDcul5niRgkKRXdQ/Ex2jHH6MTBELifqOPtBpwcY85124iq9d5q6eOF8ncLJ2n6jv8eO8C6gs8dO8GX8DdRpaM6nGhS0aBAGNGnTHqlAONj+wkY3qNXIdO4vtFKOCOl+QdO8dqaOz53Gq1Ch0oaC566yx6YEmTTMgWyVgeP2rPlTcSz27ipmm2f/ETnGAXbB337Sa61YEPw5Biscj3vvc9BgYG9ild+x5l45+B/BGaL1q8wea9CSFoiBSq5JFVdz/8+0JmLUiRVTwm9mEQ2ok/RifYECcxJIlzVpmjUE5KqA4fyy0RCbhZPkfJ8/hk7yLGLt2OF4lQwF07Q0wOOROrAt2J5QO6zYBuUw9VbtVzDGgNBg6BVL8ThID7fpx37Bzzwd6F4Xqt3lGcWgvKrFXK6NII48KiN7mKG7aXuP6odOBfumTj6fZ0txj3B0lcNtxT8/k8rutiWRaXL1/u6I0WQrBs/ybr7u8iolUsFawto1Kh0KhHOmnleXwMEzuyGFKrZI3jw8eohxqP7B4mjDqvHKIR3k5ohBoflHs5ZVV5PfsQgHUvwb1qgoupEukDOmt3A41Q4b6TZkSvc6XL5kZbkVZ9rqvr+ELint2LJjlMHpJcriskbjppvutkqUTtdQxSqRSVfbwdDeFyJ3SRaknOW+fIqQXs4N6ef/N0e/pl3cRPcPwQi8W65gG1FYqiHMjItlwub/IGFUXh1Vdf7Wj+u8kb/H0Kzm8TRfOYCsTk2o68wb494lQj0miEafrUyr54g4fFx3AjhQeNXgb0vcVJDgPNYlg/Q2aDG5mm30LFN3mv2MfZRJWeDrgdhwU3krlrZ+jX7E1PjcNAXAm4Fl8nFDDt9hJE3mb3o9sIhMQHbpJ37RzrUXsZRDKV3Fec8oTPA8o8qGqcsT5Ov1ptjVjtDsF2svrLKtH+0iUbWzsb3cR+Ohsb0rXz8/MkEgmmpqbwPI/19fW2DyN+VGKh/itU/f+CLBrE5QhNeVJF8SILF9HkY+whCbgWJJBQGFbryB36TxwmH6PgxVjy0lywygeec+0UK06M6XqWa+l13sptJxTn9Bpv9dRwQ4UPKiOkNYex2NGrehQ9i7vVFJdTa23NuXYLmiQ4F2t2AB67CeqhyrlYCa0LZ+hGZPKe28Pf2SaOONpNUSA2R6xGjNcZNwRucAuxw1jGTu3pl3ETP8HxQzcLYU+v22lRLIqiTd6gYRiMjY2hqirT09NtJxph1GCh8UUq3gZvkH3xBouBRSg0RrQGvUrnScJh8TFKgU7eyXEmVuW15NGa8JU8nbvVXi6lKnwst7LtsZTm8FbPEkEkc7c2giH5nIqv7LLS4aHqG9yuZDmbLHHtCOOUIsGk0ZIQbnFCzpolzC5MJTiRxm2vn283DOriqI/CEg8aizwA+rVXOB0zWmqLz3bQdurAG8beEwLHES9dsrG1s9FNKIrStu6467rMzc3t6J5aLpfbSlrq3m0W7C/ghTdRCElLDsoW6dpGlESRfXLq7olVKCQKQYqkHDKmdd7mPUw+xoyTxglNLr0AI7zH9RRrnsX1dIFBc2nP5xpKyJVUU1Ui3+jHCQwmE3OHPmL1uJ5k1bV4JVPgrZ697/GwcaplFLgeGOTdOKeMKul9+JbMeRZ/sH6av6iMIFD5RC5LRluiFLyYWeN5t8C8Cwn5LBfiGWTxEX70JFAepLNxghO0gw23+Q0p84OqyHTCLfR9n/n5eVZWVsjlcly+fHlzTNBxnLbWsf3HLenad3fkDTaiOIKQnj15g1AIksQkGNEadMobPEw+xqKboBgkuGQVGTjiYtiCHWfeTnE1vcbHnxMDVDnifKIZpxbtHsp+nMn4PMYh8xoWbYvZRorrmbUXHqdG9AYjeoNKqHG3nmRUr9Ojda62WPBN/rB4iq+VxnGEypvZLIPmOkX/4SHc9fOx4pdY8cGUxrgY70PnIV705LU+yBjVccJLl2zout41nsZWtNPZ2OqeOjo6yuTk5DPB43mVp4LzVVadLxOFeQwJMlINaVMSUKYexYnLDn3a7tUhJ1IphQn6FWdfkoCHxccIhcSDRg+mAudiZY7aYOh2uQdLNZiKL3E63vnrMmY1K0ZFN85sLcdEskBmH7rue+FOJYsbKVxLFzgVP17E9JzqklNd3Ejm/UaWrOIyajz/PfzITvP766d5uza4RVlK8K21dUDncuI1ziYdSv5dIo5+/rgWNfhetYFChgvxi6TkZZzwwY4Vo5dxEz/B8cNGgrGBjbhw0GSjnThVr9fJ5/NUq1WGh4e5cePGM5/r561TdL/Jiv2vCMKHaJLYlTfYuwcfY8PHqUfxOLUP4vBh8TEAHjRySJLChViJU+bRyoPfq2ZASnDGWmQ01nl8GYqtMRRbo+ab3CsNMxwv0dNltcGHtTRFz+B6ZpWh2IuXpd2KlOJzPb5OICQ+bGQw5ZDTbfz/M26C318/zV9VhrcpS71bLAISU9YrXElHVII7hGLvYtthDNE6wuO92jxgcs76OL1qCTu480xXvt049fQe9KLx0iUbO2GjenQQ7MbZ2HBPzefz6Lq+zT11J+y0iUeRy4L9/1B2/whEGUuWMLZIAvrCwN6QBNR27zBsSAIOa3Vy+5AEPCw+htOacx3SHa4lukdqbgd+JHGr3E9W87ia7s7/lDXqZI06XqjwUXkcRapwNrX/YBcKuFcbQsLhYupo+Sr7gSFHm5KED5wUvpA4Z5a3uYBHAu66E3x5ZZD37Z4917tdq3C7Bv36Bd7IGnjiHm509G7nIRG3683q4JjxBiOktj9+wtk4QZexEZu6pXa4W5zakK7N5/NIksTo6Cjnz5/vME6FLDu/SdH9PURUICbLJLfEmVBo1CKDjGK34eMUZ0it74uPsdriY5zuMh/Dj2Tu271k1YDLR1zoiQS8X+7DlKNWDDj4JEFCc7jak2+aC1ZG8EOHy9mDdWce1AaxQ5+r6eNpFLgVqiQ2eSMzboJqqHHOLKE/NZXw0B3i364O83f1fvZKEx42ajxsQEY7y8dzFoKHNMIXE6/vNRa5R9PQtlf0b3vsxEH8CCGE2NzENzbNg1Ykn958n3ZPvXTpUlvStVvXcYN55hv/HDt4G1m4JOUAVX5SRXGiOEFLEjC9iyQgQMFPokoSQ2odSepsvCUSMOtmD4WPUfINVv1+xox1bhzxnGstUPmw0seEVeWN7OqhXENXQi6kmyNAd8tZ7NDgUmb5mc1sN7ihzM1SH0OxOheTx9OA6Xk4YzaD4opvsuRbjOtVvlvv5/fWpkiZ17htP2p7rRXP5T8vuxjSMJ/oSRNX56kEL8ZAMe+uMuxHXNjyu5POxgm6CV3X8TwP0zQPzR8jDEMWFxdZXFwklUpx9uxZ4vF4R+v4UbHFG/zLXXiDMVwkepTGnrzB9SCOECojWo1+tfPDdN5NoyBz2uwuH6MWaCz7Q/RrRV49YnESN5S5Ve5n0LR5NXM4B3hFEpxrjQLP1jMs2iZXM6tYanuftyCCm6V+0rrLmcSLHZXaLyZao8DFQOeOnWDMqPGhneX31qZQtVf5sD7d9lol3+Pryx4KvXw8d4YeY5Wi//iQ7nxvLPtF5j2P61t+97LGqZcu2Xi6UrOxiR/0xd+oPHXinroTZFkmUG9yt/RPCaK7aESkpcZmC1q0WtCq5JHbowUdCJm1IElaDhjfRwt6g48xcAh8jHk3SSWIN+dc9aPdnFadGCtePxOx5WdI34eJ862uyaoT42E1w4V0mcwu78smmS5R4s2eo7vHw0RK8fl2NcU/XXiFQksW8Oo+VWJdEfFXhSIQ45XU65yK11j3Pjqc3vQekJ4yaXpZN/ETHE9YlkWtVutqsrGxjuM4zM3Nsb6+zsDAAK+88kpHHjGSJBFpD7hf/vVdeYO2SCHhk1Ma7NbIi4TEapAkvunj1Bl8IZN3s+QOgY+x6lmseCkuWGUuWUdb7Cl5OgvuMIP6yjOk78PEeLzEeBzKvs47q/2cSdXo3UWV0g5VbpV6GLNq3DjCezxMJBSfRd/iXyxdZd5v+sVc6UzReRMhgr9dLwIqFxOvcj7pU/Q+ROxiEHxYOEicOk4d+Jcu2YAn86+qqnZFd1wIQbVaZXV1lUaj0bZ76tNrrNi/zZrzO2h9S6hCISFVt0gCqtSFSUq2nyMJqFINEwyo9rHiY0BzzlWWFM7HSsjm0crFPq6nWHMtrmcKDJj5I732VvSZNn2mjR8pfH9tgITqbSYiS7bF42NCpusWKqHGV4un+MP1U1SfkQU8aJtW4oeVEj+sQK90no8PWM+Qtg8T0lPZTTsE3uPWmj7B8cWGcmJvb29Xkg0hBLVajUqlwu3btxkdHeX06dMd80AKzh+x6nwZa2QWKZJ34A1aWLJL7x7dCTdSKIZJ+hYvZUYAACAASURBVBSHyX34OG3lY1zqMh/jsZ3GEyYXrSKjRyxOsmhb5O0011JrXHmBTt9pzeOtvkUiIfHDYh+qFHGl1VlZcw3uV7NcTpf42I9IMcyOFL5WGucP1ic3i2Gb6MKWfadW5U4N0tIZfqw/QaemkwfB03HqZS2KvZTJxoY7ayqVOtAmvtU9NRaLkUgkuH79+vP/cAuCqMx841epeX+GJOpYskBXn1S8PRHDEdJzJQFLQYxAGAxrNXr3wcdYC3LUQ6nrfAw/krnf6CWn+Uc+5wrwYbmHCJkrqbV9kb4PC5occqO1Ub9fmWC5EfFjffO8dczIdPvFqm/yB+uTm4odh42CCPnjpSrXU6foM2Lo8mNq4eGMx21Akp49pLVTYDhuxLsTHE887Qm13zgVRRErKyvMzc1hmiaapnHjxo2O1ggjh0X71yi7f9LiDYKxRekwEAaNTd7g7vt8NTSwoxjDWp3sPuJUKUhTCrSu8zFCIXHf7iUuR5y3Khy1OMn9WoZaYHA9VWAkdnwO8LIkeKU1Zny3OkK+JnOjZ5m3en90imF/uH6KrxZ3Koa10MWtuiwivrZc4WrqFDndJKUuUA7mu3eBHSBLPxod+Jcy2bAsi3q9vplsdEq829qC7u/v55VXXiEMQ+7d29sEbCvq/l0WG7+CHf4QhCAmKwRALQIR9hEqGk7k0Kc2yCoBKs/eoxBQCJPowLDWADpzdN7KxxjXy9B+F/25qAYaj50eJs0aryaPds41aJG+05rHlS6Rvg8Dt8s5wkjmWnaGqylYdixuFlNcyRRJqEercNIt5N04v79+mr8ojxJwMOWc/SAQEn+xWgRSvJk9xbBZZN1/cCjXero9fYITdBOWZW0mG/spinmet8kb7O3t5erVq+i6zve+972213CDOeYb/5xG8A6RCLBkjRCJehRSC3sRikE9sulVbDKKj77LiEjBjyNLKsNqDVntPMnYyscY7GKcskOVh3YvI4bN9fjhmczthEjAB+VedBkupY6Ws9gJ7lczVH2dV7ILnE8Kip7BO4VBzqfKZLustnhUWPVN/lOrGOY+pxh2GGWhSEj8daEImE9Ggf27dKWN8hT2M0Z1HDvwL2WysdHZgM428Q33VNd1n2lBCyHaSlpK7rdYcb9K2fsAT0oT8CbL3iMUySSpnmHFX6UelpGQ6dXOcs92WfWXkInoVU161QT1YA5DqtOvOlwyihhyZy25w+RjrHhxVv0kF2OlIzfh2yB9j8dqvH5IpO+DIhRws9hPnym4nN5+jwNmgwGzgRNqvLs2yEisxqh1fLoxe+G+k+I/rp3h29Wt8rV743C3M4m/KzYTzU4kCTuB03AQ2YMr2Z3gBDthqwFtJ0WxWq1GPp+nVqsxMjLC66+/3nEls+J9nxXnDyj7N7GFipDfZNmbBmRS2jnW/RLlsLm/92pnmA5g0csjIcgpGgNahka4iEaZnOJzxVzDkjvbyw6Tj7Humyy4Gc7FKtw44jjlhjLvl/vpN2xeyRzfJOP9Ui8JTeVscnsXI6u7vNW7hBcpfHd9gF7dZjJxtG7p+0XejfO761P8ZXnkhRTDnsWTUeBR8yqvZWTq4Uf4ontj5o7tbFNc7UR99TjFtpcy2djobMDzk42NFvT8/Pyme2o6nX7mec9bp+zdYt7+Y5b8dzHlMepignV/FkuOkVKvMu/NUAgeoEkWMXcc23CYdmYAyGmjSMSZdmZ45DaAHIP6qzx2ZP5TKc+QWmfKKHLJdBhUl9Hlnc0FD5OPMW1nCITBRWudsSOecy24JjONHs4l1o6U9N0J3FDmVrGfwVid155DpjMVn4/1LBEKifeKfZhKcGwlb39Y7+Era1P8oNH3om8FaDqAP40NScJsS5IwFPexo4NLLFeqVd555x3GxsYYGhpq+++O0wZ+guOLp5ONveKLEIK1tTXy+TyyLDM2NsaFCxc6/qzV/AfM219l0ftbVKkXT0yw6j/ClFdJqpdY8OZYcx6iSgYxdwzfiHjszAGQVgcxpBwzziwzXh1I0addYi40+Go5T59aY0ovcSnmMKwuY8o7d+IPk48x56SohhaXrCJD+tHGqbKv87g+wFhsjTePKaE6iJrFsJzhczVT2PO5uhzyRiveflDuIRJwJb2GfAy3t7t2mt9dn+qoGHbUmHMazC1BQjnFJ3qSqNI0tXDv96AdOI7L22+/zcjICKOjo0D7477HCS9lsrF1E99tFnbDPXWjBb3VPXUn7GZyVPI+4LH9u6x5PySmTFGPkqz606TVKWLKZRa8aaBGQunDlAeYc2cJ1BWkUGJAP0MtDJh1loACCiqDxiRrXpUHjaY6hi7F0NTzvFsv8Y1yBZnLTOgVbsR9hpQ5xoxV1oL4ofhjhELifqOXhBJxwaoAnRP9DoLZepIVL8H11CpvZI+nNGw9MHh/PcfZdIk3OpxzVSTBq60OzUyjh4KjcC2zgvaCCzKRgG8WTvF998f4gStY94/WG2W/KPoeX1v20KR+PtFzvjUvO7fv9YYGh7h++jXy+Tzf+c538DwPx3H23CdOcIJ2kUgktiUbnvfs4TwIgk3p2nQ6zfnz57Esq+NrVf2HzNi/y7L7NjHlNHaUohLOkVTGSShXmfemEdwnJmfoU6dY8OYoqasQQr9+GjeSmHfngGZXfkSfohy4TNvNQ7WCgqm+wi2nzl9U1pG4xIhW4Y1EyJA8x7i5TDU0D8UfA+BeowdNkrlglYCjHf1ZsC3m7TRXUmu8mjnc+fz9wg0Vfrjex6lknRs9nSdCV1q+GotOlrmGztX0KqZytKpLO+E768N83/kJ/s7RWfaOj/fHXsf4WhjwZytFJDK8lZ2k31yj6LcvEf80enI9vHn6Tebm5nj33XdxXZdGo7GvfeJF4qVMNp7exLcmG7Vajbm5uU331HZb0E9ngZHw+bD6qxS8d1GlUUqhwnrwkLR2HjtKM+M2D8c5dZIQg3n3MVBFl0wS7ih1w+Oh3TwIWXKSnDbCrL3I3Xqz25FS0mTUAabtJT6sNVUrDNlgxBhl3ilzu1ABRlAJuWat8snECpGoQ4c+GzuhEao8cnoY1W2uJ452zhXgTqWHIFK4mi5wKn60CU67WHcsFpw+phKLvNV/8ERowlpjwoIlx+JxPcWl1Bop7egULQC8SOari1P8+uNrPKxnyBoxyp7NW8NDCBYpKJ0lHYcxF9rOkr4Q/HVhHTC4lrrB6XijJZ3b2f3IyBiGwZkzZxgfH+edd97hvffeIx6PMzExsWMH9AQnaBd7jfvats3c3BzFYpHBwUFeffXVjqRrNyBExIP6vyHvfBVDnqQSmhSCh2S186giTd6bAwpk1FEkksy5j1n1H6BKOglvBFcXPLKbXjeGZNGnj7PgFrjbaCr+WXKcPm2EGWeVO7Xm71RJZcwcp+DV+feFIjCITMRFc41PJpeZoAEcnLPmtUz4+lSfq/GjH/N5UM1QDQyupwuMHiPS91ZUPIN8Y5Cx+Aof6zs46XvILDJkwppn8sNShrOJIj3G0fIPIwF/tnKKfzV9jVuVPk4nszyqrnNj8ByGUmBVOb6ja1shgO8Ui4DMufirXEoFFL3bHUvnSshomsbk5CSnTp3iW9/6Fh988AGqqnLq1Cmy2eyx62LshJcy2dhpEy8UCszNzbXlnvo8OEGBO/UvUfIXWPFr6NICSfUCi94C6840Mir9+kXKfp1Zt/kFTyq9WMoAs84cvlqAEHLqALqcZroxy4rXzGz7tSEUKc6jxhyLbjPxSKtpcmofD+0lPqhtl3Xt0QdYi87wfy8sIuPxenyJTybneDW+jCF39qFd80wWvSznrDI3Ekf7hQ0iiffL/SQ1n8vHdKQIYLmRoeAmOJde5Io50/X1B80Gg2aDWqDx7vogU/H6rjro3UIjVPnK3Dn+9cxVFpzE5u+FEEQC3p4vATHOZXuZ6oPH7mMCcXBPgP1gpzGq3SFxq1LmVgX65PN8rM/Eie7iifYqn1vVqIQQWJbFjRs3KBaLPHr0CM/zmJiYoL+/f5u86MuwsZ/gxSMej1MuNxN4WZYJgoBiscjc3By+7zM6OsrU1FTH0rUb8KMqd2pfoujPUvB9FGmGpHqWhl9g2plBQqJXO089Cphz54EClpwirY6SdxcoKWsQQkrJkVD6mLbnWPOb5mc5rZeYnOVRY55Vr7kPxpU4A/oQj+1Vbte2dxQzWg6bM3xxaYkw8nglvsyPJed5M7FIbJex4N1QCXRmnBynzRqvHbEJXyTgg0ofmiS4fIxJ3+tOikU7w1RykcuZ7sepHt2hp2cJJ2zyOkYtjyHzcN8LL5L5o8Upfn36Go8amc3fNyOCxPeXKoDOROo0lwZl5rxZnKgdUZ3u79edltnu1avcq0NaOsvHe2JE8kOcqL24vzVOSZKEpmm8+eablMtlZmZmuHv3LuPj4wwNDR3rOPXSJhv1ep0gCCiVSqyvr9PX19e2e+peKHmPeaf0S5TDWRLKCEn1KvPuY1aC+xhykn7tCkveIo/s5qbcq40TCZNZdxZoJhQpvw/ZjDPrzANFJCTGjElqQci0vQQ0N7FBfRBVsrjfmGPR3a7JPW6MUKo7PPTLwEZVR+Xt2ihv10YxJZ9Pplb571IrnDFnUPfIlvNOinoY45JVZNg4+AxhJ6gHCrcr/YzHatw4pqRvgLnaAF6ocSo1z4B1+N2ehOrzsVyT1/GDYh+W6nMh2d3rlnyd3569xG/NXqboPzsaFETbE4p7xTr3ipAzR3hjNMZaNE85eDkI7qtRwJ8s17DkcX6sJ4XWhnTuVpWPDYUPSZLI5XLkcjkajQazs7M8fPiQ4eFhRkdHN59zghM8D4lEgsXFRaIoolQqsby8jO/7TExMkEql9r2uJEnU/BXeKf2frAX3sOSBFm9wtsUbjNGvX2bVW+Ox04wrWXUYRUoz68yy0hrpSIa96HqSWXceQfN7PmyM40UyM/YC0DwM9Wq9xJU09xvzrHrb49SoMUSt5pP3K8xuys3KfK8+xPfqQ+hSyBuJVf77dIEL5vSufESAZS9OwU9y0Tp6cRIvkrlVapG+08dnXOdpLNVzVL0Ek+k5cubhd3tMpcnriAS8X+4BBFfT3U3C6oHKV+bP8/8+vsqS++z5zfO3TwDMVBrMVCCh9fPWaIKavMjaEY8C7zcClEXINwo1NGmQT/ZcIKHOUw4WnnOtJ3EqiqLN+JNOp7l27Rqu6zI7O8vbb7/NwMAA4+Pj6Po+nQwPEdJzRiGOn34W8PnPf5533nmHbDbL5z73OTRN4+zZswde9+0f/BmFkX+HIfdgR7DszwISKWUQVcox784QEiKj0K9PUQ4cVv3mfKQqaQzqk6y6ZdaCZgVAlwyGjHEWnSLFoLlxS0iMmxPUAp+8u/0wpEkqE+YYi06NZW/3L09WS9Kr9nG7XMCOfK4l04yoP+C/zczwSmIZRWpWrB/YPeiSxHnr6Gfy11yTB/UsF5NFMlpnkr5HhUjAndV+LAMm0y+e8He3mqEeaFxLr6IegNex5Fj85swVfmfuPPVw900nrqjUw92DvypJfGIsjWwUmXefHSO4nJzgTu3x/m90B5xPjHO3dnAzLAl4M5tl0Fyn6D/c8Tl/v/czvJr5HwGoVqtMT09z7dq1Z54XBAHz8/PMzc2RyWS4cOHCi5iXPclwdsexjFO/9Vu/xVe+8hUkSeLzn/88QRBw5cqVA6/73ff+ltWR30aWNDxhsug9BiCu9BCTh5h38/jCBSQG9NM0wohFb4HmsUVmyDhNybdZ9prxR5VURoxTrLo1Vv0n1esxcxQvUpi2m3+7ARmZU7Exip5H3tn9YJ5ULIaMQe5W1qkELhNmnCnzNn8vPcMbyUX0Vmf+kZ0lFBoXrSLKEX/KK77GnUovZxIV+ozjKwN7p9CDImucy714f4zpeoqCa3Its4oh7/+rV/QM/m3+Ev9m9hKlHYphGxg24iy4u49bSwg+NpIlEa8y4zzLqbmamuR2df+ciZ1wOTnJ7ep0F1YSvJrOMmFVWffvsdNW9mbmf+Inev8XoMlFfu+993jzzTefeV4YhiwuLpLP54nH45w/f/5ARY19Ytdv8KF1Nr7xjW/wuc99jjAM+cxnPsMv/MIvbHt8dnaWf/SP/hGlUokwDPmlX/olPvWpT+255re//W2+8IUvcOvWLT7xiU/wy7/8y9TrdQqFg1fr7bDEcuovcKIYi35zlKlHO4MXSSx4eaCEIcfpVydY9FZ4YDcPRHE5RUYdZtZZ4KP6YwBikUWvOcJjZwsfQ9IZMceZd0rcqW/PZBNKnEF9kAf1VX5Y2Z2ANmL0oRLng/Iyj3nCI3CFxdeLU3y9OEVGcfhkKs+Pp5f4sfTRb0wz9SQFN861dIGPH1NlKS+UeLA+TNqsc7n/xScZGzjf6mws2HHydoJLqTWSavsjCNP1FF96fJU/WDiLL9qQytzB1G4rAiH41mzznq70nmU05zPtzBJtesYcBmejO2sK4N1iEZA4E3+Fy6mQsn+HiCev59b29F7a5aqqMjExwfj4OCsrKy+lodIJdsZhxKmbN2/yhS98ge985zucO3eOL33pS0iSxKNHBz/w+JHNUvpPaYQKpbC5v+fUU4SYLd7gAzTJYEi/yKpX5KHdjCemHKdXG2PeWeVuvRWThEmfPsKct8aHO/Ax7tY39sbm+SEmG4wYI8zYRW5Vduex9etZEnKWD8rLzNWfPC+STP6ydIq/LJ0iLnt8IpXnreQq/0Muv+tah4Ulx2K2keZqao2PH1MX7TCCB8UhVNnlYu/x6bZMxitMxisUXJP3ahnOJYvk9PZ5HYubxbALNMLn85RUTd2TAiSQeGe+GafOZqc40wcz7gy+6Gx878VA4r1yiffKMGZe49WMRC28QyCe/MNym3FKURRGR0cZGRlhbW3t2MWpQ0k2wjDkZ37mZ/jzP/9zRkdHeeONN/jJn/xJLl26tPmcX/zFX+Qf/IN/wGc/+1k+/PBDPvWpT/H48eM91/32t7/Nz//8zzM9Pc3NmzfRdR3HcfbtzLoBIQR/Vfw1VsxHKKFOv36Jda+0mSWnlH5MuZdZN89aqwXdow2ikmTanmW5xccY0IeRsXhYz1NoNDf07XyM7RXbfr0HS07zUW2BJWfnDVdC4nRslIon+LBcAJ4da5G3tNlKocmfFM/yl8VLfDIV8Hri+/z9numO+R2d4k4lRyBUrqYKTB5T0nfdU7m73s9IssSlvuOpKgIwHKszHKtT9TU+qIzTq68yaO5edfug0sMfzn+ML+c7kwVsnx8h8UGhygcFGIiP8dqwwWKQP5RycmecjfbwoF7jQR16tHN8LGcRinvYUQVpy2vVjlGSJEn09fXte8b+BMcLhxmnPv3pT/OZz3yG3/iN3yAej+O67oHjFMDb5X/Dgn4HKVTo0y5QDe1N3mBcyZJUhsg789wPmlXXtNpLXO7hkZ2n4DV/16P1YUoZHjTyrLdETPbiY2TV9P/P3nsGyZafZZ6/Y9J7Xy7L3FvX17V9b3dLLVAIUAuGQSxiFiSxMAokbcQgscEGERDExhCKYCcgJlYwWtgRoNGgXRg5CKAFAz0tT0tqe0157326Su8zz37IyqqsqqyqdHW7bqueL92V9+Q/T548533/r3meF5vKyVR8g0eZw9XfenXt5PIqxqI+4OCcAbli05Qoqvl6+Dz/Er7Iv2wJ3DU+5HnnDAbpZDeIs3ELkZyOmxY/HdrTGWRk8iKToXbs2iiXHKdTpRHAqUnj1GyQKkiMxbwYxS26DYe33c4lLPz39Wf5k/n22pJh26gn/zS9lWB6C2yadu516dlSVjmRpNgJrLmcTrC8ASa5j3faTYjCHIlCEBrwU3a7HVk+XSyJEzmb119/nf7+fs6dOwfABz/4QV544YU9RlwQBKLRUs9hJBKho6Pj2HV/67d+CwC/33+s9G09mEx+i0B2Hl2mh6gqwlyqFDy4VL3kFZnlzDKl/lWBdnUfyUJxu6c1tMPHiOXzzCV9QBAE8KjcqEQD08nVg3wMbSfZgshMYoNyX+x+qAUZR95KWBJ5sFWu3FTfSIoVr9sFPRbJxkg0zFcTMl/lOazzT/Fz7hl+oW0KbwuHK+UVgeGwC5MqzzXz41e1qhXBpJaZsJ3LjgB32o7ujzxNMKlyDKiWyBUF7m+5MKmyXDTutsS9EmrnT+du8HKwi369GYX6eniLDVQRNhMZ/nk6g1a20t1jx6OOnypJwqMQ3JHO9fBOx2WKym5/cC1GvIwzzsbbAyflp37t134NgKGhoaYmiO/HcvoR88lXMeT6iElR5tMLADhUXYCBpfQim5QTX93kizKL6WXKnL/ObT7GQmpt5zW7ZMekslXlY3Rq2hDRMhFfYzlVPRkmCSLugo2kpGZwq+wDqj8fUkWwYUaDU+VgPBbjhUSeFzaf5vfnb/Mz7ll+0TPFxRZPAx+OOJEEGDCHOMznvtWIZVWMB1ycs25xw316k2H7oZMKXDUtU1RgMOJEoshABa9jOOLkT+dv8OJmL1dNDnJKfZyPYgMb+61MjpdmI0iCEU+flU6Np2or8GlELJ/nf/i2ELHxjO0csKuKWI+fOm04kWBjdXUVr9e783dXVxevvfbanmM+9alP8fzzz/PHf/zHJBIJvvGNb9S8/n41qlons1ZDLB9iJPY9ArkMiryJpMi0qy8RyiWYT5cyRipBg0fdy2YmzHSyZAQ0gpY2TQ/r6RAT221RJT5GN8FEgvnCFrBrMGVBokfbjS+TYCxW5mocNMpm2UCbysNELMhEPgE1qKPG43E6RSuKoGcsGqJMQC8jnNfyF2sD/MXaNd5lXeMX2yb5UdsqUp1SoWUkt0nfPfoET9keL+G8HqzGjGwmzFx1bvJMx5MTZOyHSlR2yPVjUTujUQdfWrnMo4h755hGfslmWpbSeYWlMDwKytx2nsNijLOuNN+S1ohjqRdl6dzblt0+4bNg44cPJ+2njEbjjp9qNimWKSS5H32RSAHy4jqCItKmvkCskGNx3xynUDbBbLLku2RBTaemB38mzlSivNkS8Gq9xFJZVrIhyO4moAQEenVeItk8k/HDbbte1NKlbWcmFmEil4QdcvjhSCdTeAQTGsnMSCTEEnvVjZJFFV/ZuMxXNi5zx7TJB9sm+QnH0g63o15kiyJDERcudYabLSY1txL+pI75sJWrzgBPP8F+ShTgpqV0z8zGLQxFHfzt6kW+H+psat1m/FRBgY2YyJt+uOY4j8sQY1PwPxY/0yyKlKRzzxsb81OnDScSbFS7OfY76C996Ut85CMf4Td/8zd55ZVX+OVf/mVGRkZqalE4Sr+8XrwS+VsWM9PoRBOqjJ0tOcZ0qiQlZ5QsWKQ2FtPrO3wMi2zFIrmZS60zFi8dt5ePUS57lr6vQdLTrm5nNhlg8Ag+RpvagU40MxLZZFmprXQqItAtO9FJFl4JrVJqbDxqIyTwvXAn3wt30q6J8wueKT7gnsGhPljyroZgRstM3MYV8xbPnFI+BsDslpVoRsN1t49O05OhpnQc8kWBf9w4x2fnbjKdsB08oAGD3KzBLT3nAg8DSQiI9FvPcdEjsNREv+xJzO44DLKwa7QLhcJZe9QPGR6nnxJFsal7+37sRWZTI6gFLdZCF2Ehwcx2tUEnGrCrulhN+3bmOBkkI05VB4sp3yF8jN3AA0AraujSdLKUCjMcPZzr51BZsMoOxiJ+1pO1cQIFwCvZMci2bT91/Mb/QczDg5gHx0KKD7hn+J/bpujQ1NaeG82pGI85OW+I8vQpVkBcjprYTBi44fbxdMfpbZeqB0UFvuHr5rPzNxmsSIaV0cgz0LRHUAAERoMJCIp0GXu52aFiJbdQo3RulSUfo59S7fNTT2pS7ESCja6uLpaXd8uuKysrB8rPn//853nxxRcBeMc73kE6nSYQCOB2H7xB96NVwUYwt8ZKegq36jKL6WUK4iYUwalqR8TAQmqZDUp9rh51BwI65pIrrFExH0PlYjZ5kI9hl6xYVHYm4utsHsLHAOjTdZLKi0xG/dSSHQLQINElu1lMpXmUiHPbWv/wsfWMkc8s3eHvAz/K+6xrvMPyKvds1Z3HUsKEL2Mskb5PKZkOYMTvRCNJXDjFgVC9yBYl/nrlAn+2cIOVlOnQ4xqrbDR+XnBQOncmnGQmDHZdB0936gkqy0Tz9fF3TqIX9jDI4q75KxaLp67H9Qwni5P2U5XDZ5tBshBlMvEqHvVVltIrhNgABWyyC61oYz61jH+Hj+FGJ1iYSa7gyxw/H8MsGnFpPEzFN4/kY3i1HoSijpHoBgq1bYxlRLplF+uZAkOJBNctVuoVVQvmdHxu9TpfCzzLT9kDPGN6leccy4hVlimTvgfMoVMrTgIwFbKhoOOCbQ2v+XS2dNWLgiLwD+tHJMOaQLMb+/w+xcWVeIqVqRRGlZt3eo3ExHVCdUrnPs66iCzs+qWzYGMf7t27x/T0NPPz83R2dvLlL3+ZL37xi3uO6e7u5pvf/CYf+chHGB8fJ51O43K5alq/0oiLothwG9Ur4ZdYzgQpcy9sxXZyoshCqsS9EBHxavqI5nPbfIwS2tSe7fkY1fkY8WSOpdwWpKsHD7Ig0afzsp5M8WirnOU5/sao7HO9X9ht0WrkprpodFHMqRjZDDC56eL/5me4YAzxS95x/qeOaYxyjvGonVxR5sYpnvSdL8KQz4NVk2HAdXpbuupFLK/ii0uX+UHkPbzsO5lr3whnoxLJZHXSeiiV5cWZLGrRwnNeL0VNgI1Mbb/NW5kxqjXYOG1G/AyN4aT9lFarJZ2urWp8FF6PfoulzG57rE1poyCoWE5vAKVNUqemh0xB2OYSll5zqhwYJGtVPka7xk0xIzGXCbKWOVyc5LzeSyiTZyS8RYnncfy9r0dFh8rFVDzBg8Quj0yoM9AA6NPb0Sg6hnwB/jRg5U/5Sbp1UT7oHeffdE5iV2eYi1sIn3LSd1GBEb8LtVTkMlrjtQAAIABJREFUsiMInN7BtvUgU5D4m9UL/CDyXv55rbZkab1otgJ/2DMYz+V5aS6MKOh4Z2c7GkOElXRtgfTjTYo1Vtk4bTiRYEOWZf7kT/6E973vfRQKBX71V3+Va9eu8bu/+7vcvXuX97///Xz605/m4x//OH/0R3+EIAh84QtfqNmJVwYbgiA0tEFJFGI8ir2KWtDhVvewkQmyXAxCETSijjZ1N6vpEBOJUutTmY+RKOSZS/moNBaSINGr9eLPpI7kYxglHR2adqaiW7yZqt0ougUT2kP6XKH2DZokCFw1thOI5xhaLzuB3fOcjtv51Phz/Mepp/lJzzyfPDdIr/l0buBTeYkRn5tOU4w7bafTwTSCrZSWvxy+zveCzyFYzWTlAnB8sNGI8WvWiBsMhj393vuRLRb59mIYkHiq7SIuS5r59NEzNB5nL+3+NqqzysYPF07aT7WiLS+v5Hgj8h0kZNo1fQSyMZbzpaCjzMfwZWJMJcpVaYEubRf5osRcxWC+0r+U+BixXIHpuI/DAgetqKZb28lCPMb9UO1tSHb0WOSSOMmactBP1RNqXDW1kUjB+GaI/fZvKWXmP049w3+aeYofdy3yyfND3LWdHgnzSuSKAkObHhz6FDfcp7elq17Esyq+MnaNl9beiWhzoWigls6MhtqomnQJBoMBUodXLooKfG9lV+Ld68izkF6skHivelbNnVQd2O+nntRg44kc6pfNZnn22Wf57ne/C8Abb7zBvXv36lrj1ci3GIndZyG9RqZY0jQ2YMIsOlnO+ckoJWZ2iY/hZS0dJpTfW/LUi1o6NJ3MJ4NE8oc/aC61FbNoZyTqI1usreVLANqKJgqykanYFkeZ6tvWTh6GD+eD6ESZS8Y2ZkMx/KnaM20CCu91L/Kx3hHu2k7Hhj6SVjMVctJvC2HTNZ81PC3YiBv4wtBN/nrsKqn8rvb4Da+bohG2VElmY4f3OvdqjSxkHi8/5arNxdhWfU6+z6LnapvIYmahKq/Dq3OznHo8G4f/cOV/5brlPACTk5M4HA6cTueR7ykWi2g0mreC33FWTjkcp9JPKYrCrVu3ePnllxEEgTfeeIO7d+/WVRkbjr/Ba5HvspLeIFEo+RgdeiyCm43iFolCyQZW8jE2s3s3+hpBjVfbxUoqQiB3eNuOVTbhUrkYiwZIFmpQJtmGu2hEkk2MH+Onblo6GIwcToBWCSJXTB2sRVKsxOuzZe9yrPLR3mHe7TwdKk6JnMRUsI0OUxjPKe0KaARbKS1/NXKd/zZ8nWhWs/P6ne52Mto8CV2WiejhQdUVg5XxZH1KY06tnsAhXSK14I6znQeB+oj3HUYttzrUrOUWSRUPDvm4aPQyFX8882H+9/O/yHtcdwBYWFhAlmW6urqOfE+xWEStVr8VgcnjH+p3klCpVE0pUAF8b+v7bGZLJbM2dSegZTa5jI/STWmRzdhV7m0+xt6byqGyYZFsTMQ38B1Sggbo1rZRKGoYi25CXX2uTjayRcZTCUqKVkc7p8P+1aHW4ywamIomeDVSf1ZFQeAlXy8v+Xq5afHx8d5hnncvIjcxNbRRbMb1LEYtDLj83HuCFTv2YyNh5/954zpfm7pErnjQMBTyCqMTpd/uUrsLo1vFaNxH+ojJ37VAoBXEu/pXmI8kmY+UdNCf8eoJKatE8rsbi2Zbu+qB9DYpT5/hdEIQhD2BRbnlt5777NXwq0wlS3K2TpUbjWBhJrlMYNufHMXHsMgmXCo3Uwkfj6KH8zE6NC5UGBmJbLBYo58SEeiWHIQLIlOpGDX5qUP+2Sxr6ZIsTIfjvH7ERvUofC/YyfeCnVw0hvhozwg/2zHbsIpVMwilNExvObhsD3K77XQEPq1AKGXmzx8OHEiGlaEoMDpd+u3Ouew4O3SMJvwk8nsJ2I1Y92Z9QiPVlLV4mrWpNAaVk3d6TSTEDYK53SDprRIyqdV+7Lc9pwFPZLDRLDYz6/izm3g154jks8wmd1ufnKIDncrCVHLlAB+jS9OOoqiZTKxxWGuLiECX5CGUKzAcLpfuautzbVc5mUkkeZCojzS2/7bv1tkwCgaGfAFWlfpmLxyGxaSXLyzc5k+GFviF3gf8m75JjKras1+NYiFiJpjSc8Ptw2M8mZ7QtwITAQd//uAOgdy7eHP5cFWXyt92cT0C62DQarh5ro1Voqwkt3/fOo2fiEChyXCjGSewlcnx4kwElWjittOJxhTFlw8dU7puLd4uKh9nON1QFAVBEHbETGq9zxKFOLPJKTo1PaT38TGsggWLxlmVj9GmdqERjIzH11g9ZD6GgIBX9hDNFhmPRKk2LLYaSuIkLhbTGR41SX5v05pwSVaGfAF8xdbM1VhNufnr1Q/yX0bX+FnvAz50fgybpvbp1g1/bszIWszEgNv3RMus78di2MJ/eXSLpeS7eWOptkBwzR9nzR9Ho5K4d76ToJxkLt44R+WtLFsmcgW+PhdGQMNTrnMYjFE2ioFTz9l4nMFQrXhigw1FUXaMuCAIFIvFmlsbxuNTiNgO8DHC6TRLuRBUlJolRHp1XoLZDBPx8vCyg5sNnaihW9vJbDzCcLp2ZYNyn+toLMxaojmDe9noIZsRGfMFqVXZ6jh06c2YczLTwSgPApuAjv/z0XN8ZvQu/+7qHO/3vkmbvvVBwHjATr4ocd3tp9fSmoDpNOD+ehufe3CHf1nqAeCp7qPv2Wrb2kQ6x+CYDwWFgW4Psk0knK7vGgmC0HQzbCvCglxR4XVfGnwq7nguYtEXWefxDAl8u/TCnuH0QqPRkMlk0Ol0dSsnTsSnUAvOPXwMr7aLVFZhKedjLb8bSAgI9Gi7SBQUZhKblIOS/VAJMn26LpaTSYbjtSfDTGhwq5yMx6LcL9Sn3LOL0uecNziRCxqG/H4WaE3LpFtrwIOOmUCUByEfIPOHkaf5z+O3+aX+WT587iE9ptb7kbktK5G3mcw67CbDXpo7R1ERudNd/1YxkyswPOFHQeFyuwuDW0UiV3+A2uzGuRXbbgWBN/1J8MtcdfSj0asQWX8sybG3i596IoON/ZnF8sCkWoONwdgokXx8Dx9j/3wMnaihU9PJYirEYPTw0rJdZcYuOxmN+NlI1q6V3SFaEAQDo1WG8NUDSRDRCxrcOHm0fvQE13pw0exAlZMZWw+wVmW9WE7Dl+ef5Q+HLvKvu2f56KVBrlibG5xUVGDI50avynHFeXqHMDWChxvn+cNXB7i/fvwE4kocZSgFBGaWtmAJztv13OvoZDoXJJw9nssitqCPqrXZE4EHm1E2YiY0ch9X2iSWc4tkiydXPas3Y3Qas0VnON3Q6/XE4/GGgo2R+DiBXBhJkOjW9mzPxyhvzks2WSWo6NF5WUtFGYntnZ1RCbNsoE3tYSIa4o26xEmM6CQLw5EQy034KQHQC2q8kofRjTAl4nrzfqrXYMWCjtF1PwHlYAUjXVDx1/M3+K+Tl/iJzgU+dmmQp5zN8w9H/SVu17W3kQIiwGSomz96ZWAnGVY7DreNAgIL21X5K04zT7naWSaKL11b4PFWtFEdhbFgnGLBRTjn5U6HhvXCEsnCyfFHGw02TlsF/okMNmA3wJBlua4p4nklTzAXok/Xz1wVPoZZMOLUupmMb+A/Qne8S+tGLOoZjW4yX0efq1dyEClIjMWiQJZGDa5BUnNB72EmFCOcUFiKNa/XLQADVg/JWJ6Z5cj2a4efnwDkFYm/X7zI3y9e5J3uFT56aYh3t9dHnMoWBIZ8HjyGBLc8p1NVpBEUigJfnzvH5x7cJs8FpoOtaRWohmxWYXbEjywJ3OvvJKg+unQtCSLN1iZOopRcUBTmoynmo2DTtPGM19DQvI5a8HbRLz/D6YXBYCCRSOByueoONlbT65zT9VflY+jR0aHrYCbh41HkcD/lUdvRixZGIz6W47Unw7yinQxqpmJhmkmGqUWJK8Z2lsIJIgmF6XBrbOBVswslIzCxFkQ4tgVMQEHg66t9fH21j1v2TT56aYj3ds7XxT/MF2HY58Gkyb7tgoyXF7187sEdkoUBxvyN/N612cR8XmF6PIggwJ3zHST1OSZiR7dnNT18tql3V0dRKbKRyPBP0xkMKsc2r2OdYJ3zOmqBSmzMT502PLHBRln+1mKx1GXEV9IbrKTjFNm7Oe/StJHLScxlfKxlqxtvAYFzui4i2SKj4SBQ2wZfjYRXdrGUzjLYZJ+rU22gQ21n2B/i1XBpY95jaG6IjkaUuGZ2sxFKMrpYewvLfvPyA18XP/B1ccEc4lcvDfGz3dOopcM3tLGsivGAk35bhLvttU2jfRKQL0q8MHmBzz+4zWLECsA5e+NT7mtBOXmTLygMT5ZK11e73GicEkPRDQr7sjuNaN7vx0mQuQvK7v1S4nWEUYlm3lXnvI5a0EjG6CzQOEM9MBqNe6aI1+qnIrkYc6kQeWXv8R61E7WiZzK5gS93eFKnV9tBpiAzEfUB1efh7IeEQLfsYjOrMJyIb7+vsfvdImvp1bmYCIR5bVucpENb//DZPecnCFy3eNiKZJhYLiVSarFj+x/ZRyEPv/7Ke+kyRPnIheFj+YfpvMiwz0OnKcbtt5HMelER+PpsH3/+4A4TgdLsmMuuk20LKnsMRYGxmZIt7/c4sLZrGY5tkKmi1nka2qj2o9L3lXkdO/M69GFWMq3bz5y1Ub3F0Ov1DQUbvkxwJ1IWt/kY4WyOiXh5E3PQeGlEFb3aLhYScR5sHX7cfpjQ4JYdjMfjTfS5ltCts2HAwJDfz4rSmuy/RaXhgsHB7EaYR1ut0wCfjtr5i+kP8E+zKZ62/wsfujqKVbtb4g4kdcyFrVx1Bni6o/Zs22lHMifzt2NXeMP3PN+Y3stj0Wq11Bqc7kGNlnJ/lUFAYG4lDCvgNpvp7jUzmfHvtFhVm8BbL07CLWWy2QOv5YrKzryOu+2XcJiTLKSblx2sNOKKorwVcrZneJtDr9fvBBv1VOB92eCeQKNH6yVdUJhNbFIarncQkiByTudlM5lmMFyuah7/oOtQ0SE7mU0meVinOMl+tGvNOCULg74Am3XKYh8GvaTiisnFsj/G0GL9yYbDrsBKwsxfzf0rvrX0r7hl/i6/dG1kj0xtJK1mIuTkgi3EvbeRn8oVRP5x8iI/WH8v/zS5V9VQp9VRUharEzUHBAePW9mMsbIZw6zTcq7fzlw+hL+ixeq0tVEBJFIHA/jKeR3XXRfpsme353U09/n1Bhuntd33iQ02DAbDHiNea7CxmQ2iFTV0aTpZToUZOoKPYZGNuFVuxqMBXk/WHqk6MSAXdUynEyxXGW5UDy7qXeRzcktJ3206I50qM2NrAR4EGncI+7O8AjDgcJOJ5ZhfCDMPvDr3DH/+8A4/d2mSn+mfoqAIXHf531ZBRiKr5a8Gr/KXgzcIp3Xc7rFw4Lc65PlPJI+pdLUgKNiKptgaSm23WHURUCcIZJpvSzoJo6aI4hFRjMCb272//dZzXPQILGYWDmR/a4VKfDIzRGd4clBuo4L6/JQvG0QlyHRrvWyk44wewccwSFq6NB1MxcK8GardntvQoSvomcmmWW9SDapXY0UjlJJh8xyi/lSnLbNrdPRp7UxvhHhYx/c68LFVqpFXbA7EjMDkUpBVBN7gDn8xeJOf7p/h5y+NoSBwzRV4WylLpfMq/mb0Ml94eIuNuJFbXiewd19zmEVPxI/zUzX+uEe4jFgqx+DwZqnFqr+DpC7LRCzQdGXiJNp9ZZUKcodXDIf9UYb90GXq4Ua7ipXcIpniwURaTZ/VgBrVmfRtC9GoEY/lc8RzMo+O4GO0axxoMTMc3WRJqX1T3CXayKFlMrZFLVOfD4MsiFw1tbMcjDG0eXDS937U+jCdM9owKRpG1gP4lNYRmtSixIDdhS+QYGL2YOYpXVDxpbEBvjx2jfedm+VjNx++LXpeAwkd/9+jm4yH38Mr88c460MefJV8ULO8ErXu52s5rtRiVVKxutzjRLDBUGSj4czL/tasVqCyjeoozISTzITBoevkhgPCso9knfd0JWejVpw2A36G041G/VQ4lyJX0DMYPXxWg1NlxSLZGI36WUvU7qfaBTOiaNwWJ2k8gSUAV03tBMJpJgJJmmm7qkSX3oxLMjCyFuBhsfm2pfIZSYLAdbubaCTDzPzBNqy8IvHC9CVemL7Ej3gX+fjNh2+LikYkrebLwwO8vvEeXlus/L0P2u/DzJtGo6n+D0es1ehRigJj06X9wYU2J5Y2NY+iG+Rr9A3V1ms1lBpv85VYmpVYGpPazV23mpi8QUyp75lrlFt42vDEBhvlNiqorxd2JRUkfYjCTY+mnXAiz0Q6Rq1GWEKgW3LhyxUZSSSANM2SvqdDUV5frX0zftz+p19rQSqomVoNtaRXvwxZFLjrbGdhLczwzPGZJwWBF+f6eXGun3d2LvPxWw94tvPJyxytREx8c+HdfOb77WQLMre7tTW8q7rFU6vVrT25GiAgsOFLEF5K02Gz4HDLTOW2SNU43b6Mk6hs1OtQgqks314BjWTjXV4LWZUPX7Y2gmNlefoMZzgJ7A828vnahnFuZCLED1G48Wo8xOMF5tJxFGqruJfFSaIFifF4FDh62vdR2CV9J7m/1jrVwG61AZNk3FZAbJ2cuiyKPOVsY90XZ3S2tnbhl5d7eHm5h1vuDT526yHv6VloSevp40QgoeOl+ef4zPd6SOTUXG03sHdfc/ALHWbSZdXRW8Wak2K1HbaD5Y0oa34Rh8FAR6ee6VyIeB3T7aF5gnk11Jtoi2XzfHslj4iZd3V3I2lDrGVqq9bt91NPasLriQ02ygRxqC9jlNhnwFWCRJ/Oy2oyxVCkdulYHTIdsovZZIqHueb6XKuRvpuFLIhct3jwhRLMbSaARMsCDadOT6/OQiqWY9DfWObpB6tefrDq5bprk//t3gjv7Jw69cZ8ftPGP9x/lm9PXsPb7eSGO0swU1s2r/Fr37qMUSXK19q/lcS/BVq1mhsXHMwVtvb0yx6FkzDi+Rp72vcjU1D45kIYUPFMx2UsphiL6aMn+JYUueoLmp5UQ3+GtwaVBHFJkshkahswlyjsbdEQETin9xJM5xmJ1M7HKImTOFlO55oWJ7GotPRpXYxXkL7rQpXHTACuWz3EIlkW/TEg0zI/ZdFouGi0EwgmGVprzK8+8rXxyZd+ivPWEP/uzhDv7Zs8UvTkNGA9ZOSloef42oMrdHjdXHXmCeXSiDU42BO3bg0kqIqKQjiWITyRQSWJPH2xi1UhymqytrkpJ5EUKzTop4rAvyyFAYFbnou0WTMspJeO7E55u7T7PrHBxn7ORq3Eu7IRN0o6OtTtTMXq0x23osMm2xmLhZvuc+3W2TBiYLCFpG+DrOKy0cWiL8rgYutI3wBekxmnqGdsycegkuac3dr0msN+D3/66CZferjGT/R8l399bQrVKTPmY0suXhp6N1/5roN2l4WNQBS3yczEdKkq03ZBwy2DC8ksMx8KVDXYjZq7+HG9sg1+wn6Hns4WeDTqQxTgqf5OwpoUs/Gjs5YnoUbV/IoCr62VnstL9n7Ou4osZBYOtGfJgrQTOJSHgx57bqeUeHeG0wuDwcDWVik4qKcCH8+X/JRWVNOt7WQ+HuN+qHZ7bkSNW3YyGY9zv9DcMLs9pO9ia/xUpQLiSB0KiLXAozfQpTExsRTg0eYmDr2u6TVnw3Y+P/zz/ONYlHd1fJsP3BhHp66tSvW4ML9p49uj7+a//g8357xu1jYCWPRpxudLv9nFbic3dA7UFjWLkeq2vVELF43WuPFvYO1KP5MrKAyOl1qBb51rJ2PMMx49+rk4Cc5Goy1duxB4tBmFTeg193G1TWI5t1B1rlQjFfjTmBR7ooONyspGtoqKTTVoRQ3ntecYjmyyWkefa5tgRhaNjERDLDShOw4nM+nbqdHTq7UxsR7kYbC1syrOGc3IaZhbjrFRoWmeyzc+cE0SBK65XUSDKcKbSYaDIt8bfw//+Xv3+JV7g3zg5jgG9ckNdKsFk6t9/N2rz/K1Vyz0dzuBwG7LWuXGU4GZyd1rbtBLPO1qJ1TMMBMs3SuNPvp6gx4Sx0tX1rsPPizJVVRgZHpXOlflEBmKbFQ117XyK2qFLIgtMOK7mAzFmQyBW+/lbufe4Uuqt8mgpDOcbhiNRlZXSxW2epJiIiIXdOcYjQR4ox5xEsGAQbIyEg2x0qQ4yXmDE6mgZsgfOJz0XSdOSgERoNtoQpcRmF+PE1J2bWajWegyrrmdZGMF/CtxZlM5fjDxLv7sB3f58FNDfPDOCFZda65No1jY7ODLL9/ja6+2MdDfTqG4sZtM2me456Z327MNksDTjnaiYo6pQJCiotTM894Pk8kEseP3MvUmbA47WkBgfK4UpF7wODG1qRk6hNdxMhLtrVtzIZpkoWKuVEhZIZLf3We9Xdp9n+hgoxE1qolIlEi+NiKpAHRLDmIFmYkm+1xlQeSKsQ1fLNvSSd9evQVDQUPEn+WB0joN8LKyVDqWY2Gl+oC/Rp43jSxxzelifT3KxPYGvduxq7/uixv5v779HH/+ylN86M4IH7wzjF1/ctM596OoCAzO9fPf33gnq5FzxJIZILTzU+1kwyvftO86FNJ5xkZKgWyXVUdHrw1BbsxgnNTmVhSOlnkVEJjdls712m20eQ0MxzdIFXazea024ipRJF9ofVXLlywNX9LLdt7pNZOSNsgou8mJJ5l0d4bTjUb91Hw8xVyy9qTRXnGS5iZ9XzW1E00Wtyd9l19tDm06IyY0ZLaUphQQq+GKzYmYhsmVYNUWrEYqkrIoMuByEQokmJ4qbdDNul2SdCSt5bPff5ovvH6bn785xi/fHaTN3PrBo0dhfLmHv3vlGeb8l/FvlVqld/1U6b/KvqRYJYr5ImOPSoGs26jBe86ORlQhCkLdtv2k/JQkwHFPzNJ2hcBlNtLda2E85SOa2w0AiydQkG603fco7M6VMvGctwtFHWAjG0DeHur3pLf7PrHBhtFoZGWlpChVT3k6X8MPpkLEK7tYSecZTDRXeTBIavr1HmZCUd5Ya125+JLZiZyVGFsP4PGYKLTogVKJItftbvyHKEtVQq06WkmpEnpZ4rLNwcJKmJHAvkxdlecimtbyZz+4y//7+k0+cGOcX743SIfluEmxjaNQFBlcusG3HvwoY0sGFEFAo9l9wMtOrHyqlad8lDJFOJwi/CjFlT43PUkdnl4rq+k4q9HmJ75Xot6fvx5jtBlKsBlKYNBquHG+nbl8EH8m2fJgQz4mAGoWyXyRb8yHEdDwYz3dO6+fBRtnOCk0GmwUa6jwlcVJ/HmFkVicZsRJNKLMZWNby0nffUYb5m0FRK+7QLZFyQRxW1kqFskwM1+uHlf/7nIdz7ZGErlsd7CxEWNscm/yrprJTOVU/NWbN/nygwF++uo0H3nmIeccrZmSXg1FRWB89TLfevBuHs7ZSWdz6PRKxUZ0X0Kw4u+j/FQ8nmF8aJ2bF9ppD6vpOm9ns5BiYau136VuP4VQ87tC0TShoTQalcTT/V2sEGEtFTuRNqpWV/UrkSsqfKdirpRIyS8Wi8WaZkHV2hb8uPHEBhuNcjaO0uQ3osacN7KYz/OgyT5Xi6Cmx+Bh2B/itRaRvkUEBqxu4rE808slI9AqMp1RpeKyxclijcpSpQ8//rNdBj3dehPjcz5GDsloHbVKOq/iiw9u8JWHA/zU1Wk++uwg5xytC9pyeZlXxm/x8sx7iCScKJkCENsu2+yaKWF/ZaPSftWk5wehYJJQMIkCXO22oXfrjv39ape+rc+gNkLGT6RzPBrd3OF1JLTpnSGBrYD0mIbqKQiM+yvaLM6CjTOcECrbfetJiuWO2MxokXEUTKwWlKbFSQyCzAVjO2OBrcZI34fgqsVFMQWTFQqIrdj0aSSJazYXG744YzUqS9UCi1ZDv8XG9JyP8WD1dY+y1fmixAsjl3lh5BI/dmGeX33mIdc7WlfBKRQFHswM8J2JH8Mf60BJlfyUoiig7F7b3WTYTmljd5EaL380mmbsYYmPeKHdgrXTgNSiVh6lzoqAJArk6yxNZHIFBsd9gMKtcx1IMszTXEvhfpxEZeMgBB5sRHcI4k+6n3pig41G1aiq9fTt9rlusaIc3x9/FEqkbz2PfH42o60j0w2YPawF4y0n0zl1Onp1VqaWgw0rS1WD12LGIWmZWPAxXGyel1JURKZ8T/Pv/+4dtGnv829/9CE3uhs/30RaxQ/Gn+Ybj54lljCh06tRaUolW0Whog69931CtdLGPlR1ShWBmQAsL23B0ha3LnbwlMlNQlNgInDwt601Jqi7ytBE5qPM69CrZQba2kioYswXmm8hkB6jHJm6gUFJcDrL02c4vWjUTxWqJMWsaLfFSaJsFJsL8suk74cbPl5tkZ+SBZEBi5tQOMPE0sE5Fs08OWa1mosmB3OrWwy3kJPoMRro1BmZnPczvFE7N+ZwCMwFbvIf/vEp7KphfuVdD3n2wuEzvY5DNifx6uQt/un1dxBLudDq5B0/BSX3pFAl2VTFTx0M9o7/RTbWI2ysR7jZ384tnYuCSWDU5zt87uoxeLztWQLjcwG6rGYu6dwUdEnmiomWqCi2klt4FBr1U6cRT2yw0Uh5WlGUPeWvLtFGHi0TTfa5Qon0ncmIjO+QvpvflJgkFb1qM0tbKR5uHW5gG/mkLqMJt2xgdMnHYAsGJ5VxwWFHkxOYXAyySW3VoaMMiiyKXOtw4VsNszgbxmLQMp3o5eWJXm73rvErP/KQ5y4t13x+4biWv//+Db7yrav0dfehAAIKFBWU/K4JL5ujsnEsn2NNlSShdmOmKDA5Vrr+7WYV3gsuZuJhQsnU9pnUBrFOoyy1YNNcLML09qbinMuBvUPLYGydXINZH5nHZ0jV0lmwcYaTx37p29or8LvHlcVJRqMhFprM0J4E6VsnyfTJJvwJJTaqAAAgAElEQVTxPEOLh7feNrLFc+sNdGvNjC/5GfS1zk/1WC1YBTUTC362lNoSJUc9+qIgcLXdRSyYYH2x5PcWhE7enO3kYnuAf/ujD/mxa3NIYm1XIZlW8fffv8LXXnkKm7WHXKGIRoZiAYq5kp9SlHLhQmHndjnA2ahYdF8fVb2WbGaqtAdx6iV6L3tYTEXZ3FZLrNlP1Vm9bo2fUlhYL/0mHVYrHT0H+Yf1QISGg616cRZsnALUG2zkcjmWVleQEPBKToJ5mu5zlQWRq8Z2NmOZlpK+23RGOlVmRlb9jBYjxx5fjxG/aLWjzUmMrQTYbGLK+X4MuF1kojkWZxtQX6ryBXRqmStuJ6srYSbGdjNOles+XOjg4UIHF9oC/MqPPOLHB2aRpepXwxc28M+v3+MvXzqHIOpJpXPb6ynstIUWK/pDD0kU7Z7z7gEHPrFqc+zxv1IsmmPs/hqiKHD7oousHvI1mrW3QpS1MnBf9cdZ9cexm4z09FoYT28Sy9WmEFfG46xsZJMppqen6e7ufuKN+BlOLyr9lCiKxwYbhUKB9fV1MtkM3aKDRFHFRDxCM+IkJ0X6dmh09GltTKyFmMi3loPWY7ZgQ8vYsp+tJrsNKnHJ6UDKKEwvhGjFbHCVJHKt3UVgI8b0xN5gqOwiptad/B9feS9d9gj/y4884qdvTaFRVd+vhONa/uGVm3zxG1eIpTToNDI2W0WholgEpWSrdlt6hYPttlUcwv7KRqNtbclkgbEHayjAjQtOBIt0YvM5jhMyqQWV1RR/OIk/nMSg1XGj38p0LkAoU9/9JYsi2cfSRgVCocjExAQ9PT1PfFLsbR9spFIpVlZW2NrawtHmppiz8ijRHNHYIKm5YPAwHYzy+lrtk76PwzmjDZOiYXjNj4/ay+S13FYDDhfZWIH5hRZyPRSFWx4PIX+Cqakm+mcrTsWi03DeYWNuLsBo4KA7qPYQTW84+fd//RP82Tef4cPPPeRn7kzuGPPNsJ2/+OcBXnz9Ih6nlXQ2hl5XucYu8XtX+ULYKU3vlKf3Z4z2rHC8pa/neheLCtMTpSzSwKU27nnamQyHiB4xEKzRoX7NoFillzYcyxAe9qFRyzzT72FR2WIjVdvz1oosVq2wmy3odDru37+PLMtYLJbj33SGM9SJyjaqozYA2WyWlZUV/H4/Ho8HGQdDsebIuSdF+u7Sm3FJRkbW/DyoZ+5GDUbqss2BlBGZXAqwRusCmOseF8lwlvmZUqtq42FbCXqNissuB8vLW4yPVW+/2v91V0IW/uCFd/O5b97jI+8e5advD2PUlhIyoZiR//aN67zw/Sto1EZiqZKtLxZLFG9B2PYySom7WVn5UpTibgW+fKYVM4SO/zaNQWBXSvfW5Q6edrUzmwgTTLYuOGyFS6jWupVIZ3k04kMSBe5d8LIpR1lKHJ/YhZIUbfYx1TYMWi1Wq5XBwUFEUUStVtf0vrNgo4WoNOLViHfRaJSlpSUymQxer5f+/n5ySoGtycZ7XcuTvod8wZZN+ga4ZnGTTypM7ZDp6rxRDrmxZFFkwOpkfSPC5GzruB46lcxVu5NissDoXGtK226zgU6DkalZH6Obh+ecjnqGYhkXXx/6Bb760hI/de8RCxs2NmLvYHS6FAiVW432txwJsN34KiBQ3P5jr7HeUaOqcgIHbFkLH/R0IsfMVBCVSuTeZQ8+Ic3iVm1G8SgINJ8xOkprPJMt8GjMhyDAU+c7CWuPHxLYiixWrVBLMl1dXXR2djI+Po7P5yMej9PX14fNZjvUWJ9GI36G0wuNRnPk1PBEIsHS0hLxeJyuri7u3buHKIpsLTTe4mRRaenVuhhvMen7otmBOiczuh5grYH5UIepIYmCwIDdRXAzyuz8Qa5Ho1CJIgNuF+lojsnJ5q+DANgMWvqsVubm/Iz6G6uNJLNmvj32fv7mG/f48ZsPiCS0TK69g6nFUnCp3VXY3baxwk5CSwFERdjjYxRFOFilqHL59gceSisSjtvIZ4uMTW4gigJPXXYTVeeZDh6093XPg2qBaMhRylGFosLQZGlI4M3eDnLmPGPHcJhkUTxej7dFUEsybW1teDweFhYWWF5e5vXXX6e3txeXy/VE+aO3TbABpYcpEAiwvLyMSqWiu7t7T8ZSOEr77Qj06G0YlNZO+pYFkesWD4FwmvGlo6X7jsW+J9igUnFlW1lqdK51lZeyYsfsYpBh/wa9ruYniHvtZjp0Bh7MrRJRasiAV3m4nGY9XRYzi/MBRtfXARWf/6d7AFztP2isxJ20voIoimgkqVTFyBd3Uv4K2yof+xQFq/XCHmyjOv5r1Itcrsj4cGm43kCvA8mpYsTn29nw1/uR2Wxz/dq19q0qCozMBFBQuNbVhuSAoUj1TODjrGyUORuCIKDX67FYLJhMJhYWFpiamqKnpwePx9MSZ3eGH15Uu38URSEcDrO0tISiKHi9Xi5fvrxn49DIk3ASk74FYMDqIRnLM7Ncfd5SPWtVQiNJDNhcbPjjjB8js14PDGoVl20OllbDjE5sYjc0P0HcYzbQYzYzOrm+7WPqh1mv4ZzLxvxMgIWZAPGEwl994zYA57t322Mq7wOlWOYMllqLlUIRpVBKiAnCTkrsgPTtjofb46ia42wchfJaxaKyw0G81GXF0KZjJOAnW6Mwwn4U8s1Paa9Wgd8PAYGJhfKQQBemNhWD0fWqCTX5MfqEMmdDEAR0Oh2dnZ20tbWxsLDAzMwMXq+Xjo6OJ6IN+IkNNgwGA6lUqVxXKBTIZrO88cYbWCwWLl++jF6vP/Ceeo3kzqTvzdZN+tbLKq4YXSz6owwutirrVPpedq2Oc3or0yutVZZyGXQ4kFneTOxR7GjGWPW77WgLAlPzAfRtYs3SrZXtPx0OE06NjskpH2MrCdSqgw9c5f61/Bn5fA6bSYMWKCQL5CmgM2lKu2eRPbSNw07rYCPWUS+UV2seArCwEIQFaLfq6DhvYyq6BXWqY6hUKqDxcrdUQ/95JQQEZla2YAV6nQ6cnVqGYhtki7tOSHqclY19xDutVovZbObGjRuk02kWFxeZm5ujs7OTrq4uZPmJNZVnOAUot2Tmcjnu37+PXq/n/PnzGI3GqsfXk7HsNzgR860lfatFiQGLm41gktEWKSCWbalJreayycHcWpihFipL2XRa3ILERjDDsK8iodGEo+p2WLBKaqZm/SxHi+RrHGhVORjPZTHQYTYyPeljbKMUqBh0e9thDgs09VoJjaBQyEI2nUFnUpf0R8r0QmHbTx26oa4IXPYLl7Qw2qj26asrYVgJYzOq6bnoZj4ZQamz/Ugly9BkwFHvtO/ykECPxUxXj5mR5AbJfG7n30+OoXIQ1QjiBoOBa9eukc1mWVpa4pVXXqGtrY3u7u6dNqvTWPF4Yj2oLMtIksTv/M7v8L73vQ9Zlrl9+/b2Jqo6arn+EgKXDR78iVxLSd87ZLr1EA9baGAB1KLIHWsbY0v+lipLdVvN2EUt4/M+IlUJZ/XjSruTQjzPYhO9s70eG3pFYnrGT6BC8arqA1aRzSnk83S4TLjNBkaHVtmSZRBAr9NBXo2kEihsa5aLgoggVOqX722j2iPyUdOFaOweOmrtcDhF+H4KSRLo6DVhtOlqbrGSG5xoXoYoCg1LcqwH4qwH4lgMBnp6jIynfSSK+boVtZqBRto1fcVicU9mSKvVcunSJc6fP8/KygqvvfYaTqcTr9eLVqt9bOd4hicfgiAgyzK/93u/x927dzGbzVy7dg2d7uhs+3FPggBc1LlIZAVGNiIVrzYHs0rDRYOT2Y0tHm21rgULQBYE7tramVgO8KiFylJtJgMdWiMTcz7mqwQDjcweveC2o8rDzHyQ8pnWdXUF6LSbsWu0TE36DlTt9yfXKtcuFgu4bHo8ZiNT4xtEdTHyhSI6rRZtTkbSSpQ9kCgI1Sd+V+Ns7G+jqnJhGm9fO/wiJ+JZxh6sIQgCHX1GdA571RaramiFaEgtlY1qCEbSBIfS6DUaBs57mExvEinmHts8KChV/8rYTxBXq9X09/fT19fH2toab775JhaLhe7ubjQaTbXl3lI8kcHG5OQkn/70p5mfn+fnfu7nePbZZxkeHj4y0ICjH6Qy6XtiM8T9WOsGwHj1ZpyNkOlqwHmLDUNBphAtMrzaurUvOuyociJTCwE2apSvPQqSINBnNZJNFJmr0jtb6x6zz23GIusYndioymyptk5iWwyg02XAqdcxOrpJwZYFBQxGI0qxQDqdppDLYDIbEPQaNMkihXQBWStgSCpcMJixyypu9bWjyGXOx2PSf6rh2hQKCqGNNFvJHAN9DrBJjPp8R56h2OTGRBZEMk02rkYTGYbHMqgkmacvtqPIj0tQcG/GKJ/PV213kWWZ3t5euru72dzcZHNzE6u1+dbBM/xwYHl5mc985jPMzc3x/PPP89xzzzE3N1dTlewwX1Umfc/5IwzGmrfNZXi0BrrUFsbWAjwItC4QAOg2mXGIOojB4Frr1u61WbCgYXzBR6hG+drjcN5uQkkpLFVJhtXqpzrtBqwqHVPTfvxUT/7s51ik06Uqc5tdj12vYXoyQIQYigIGg5F8Pk86nSaylQGrEUGnQZuG/FYOSS1giil0WI1YBJnbfW2oVKoDm+KDsUUV/mHDfu34i6MoComtPOvzcS53WdF6NAz7/RSOqJC3gsfX7LTvZCbH4JgfURC5e6GLgqZQs+hJs9gv0V4tiJAkCa/XS1dXF36/n42NDex2+2M5v3pwoiHaiy++yKVLl+jv7+cP/uAPqh7z1a9+latXr3Lt2jU+/OEPH7vmSy+9xK//+q/zgQ98gI6ODj75yU/uZBuPa8Wp9ji41AZuGr0kYjKvrvoIt6BHEKBbZeCqxsXKeoLBFR+FBqPrarhmd3FJ42BxMcz4SrA1ylKU5GsvaK0szIaYXgocv+oxX0ktS9zu8uARtCwvRtkM1P+AioLANa+bLq2e1ZkIfl/s0POqlhm3m0302s345qMkohkEKvqoFQVJljAajZhNRtLpNNHNAIVkHoqlnthcJs/KfJBkIMXUa0sU1lNYogrmosTtvnbaHaaDBrrKdWk4aV/HbSMA8/NB5h/46MkYuOtpR3vYxqbJW0ZsoUxtrqAwOO4jtwK35Q4umBwtW/swVAYbxWLxyA2gKIq0t7fT09Nz4ud1hsePk/BTw8PDfOhDH+KZZ57h4sWL/MZv/AYWi6XmmVD7bZlFpeWWyYuS0vLaqh9/tj5Z6cPQqdZzTeMi4MvwcNlHptC6gP+i1c6AwcXacoyRRX/L0jOXnA6umhyszkcYn/fVYCOPPkASBG50uunRGFlZiLK62ZgK1qVOJ30mE/6FOPPzwSNN7P79r9Vk4ILHRmApRjiQLKlO7QQLyk77jNlkJpPOEPYFycXT2+ImCplknrXFLTJbaSZfW6bgS2EMFXCg4nZfO153bWp7jVv1+n7d5ZUw0/c3aYtquOdux6iprrLUinagVu29igoMT/mJLOS4rrRz1exuybpHQS3u+qXjpG8FQcDtdnP+/PkTP69GcGKVjUKhwCc+8Qm+/vWv76hsvP/97+fq1as7x0xPT/P7v//7fP/738dms+HzHZ+df+9738vzzz8PwG//9m/vvF5WpDpq01B541aSvpdbRPoWgOtWD4lYnvlABMi0LBCQBIHrdjfhUIqpuXLWpbR2OtO4wpa8rdjRkHztIV/NoFFxyWVnaXGLsUNkAfcuc3AhlSRypctFYD3O9OhuNuxo47NdWhYFLnU7iPqTFBMFVsvTbPeTvKGkQCUIiKKI1WIhm8jgj2+RyWQRZPGApCBAOpUjE84yOVX6bp0DOm5724gVcsyuBw9RAjn2MhzyjWp74/6j/P4Efn8Cs17NjUvtzCUjBBK7vKNmKxsnUUoWRZHRmZIyyNUuD7JDOJRM3iz2Z4yOI4LXyik6w5OFk/JTAwMDvPzyywiCwGc/+1ni8Th6vb6uKeJwMqRvgKsWF8WUwKQ/yDqtCVxgm1DucJOO5Q7IrB+lylXTui2Rr92FRpa40uZiczXM5PjxFZdqfkoUBK54XcSDKebH/XtePwr57XvggtdOPp6nGCuwMBvYrtiXW3Z3PxmhpB0lSRIWrRldJkcwESaTySIr1eVds9k8sWCKhaXS9errd3G7y0OSIjNrgaq+pVErV6t53H9cOJwi/CCFWiVx72o7q7k4a9HdYK8VOa2jKieNQCVKTC2W2sCOI5M3i/2cjVoFS36oOBuvv/46/f39nDt3DoAPfvCDvPDCC3uM+Oc+9zk+8YlPYLPZAHC7j48UKy+iJEnk8/lSybDG6axXTG2k0zC+GaJVpG+NKDFg9rAWijPSIjLd7toiA3YPqxtRRmerBwONBDQ6Weaq08nKapixicZK2/s3YGVZwNk5P6P+xjaJOrXMpQ4nqwtbTAwfXOOobyqKAtf63PhXI8yMlL6Ty75LwNzPu9CoZbSSSCFXRERAKSjIgohKVqEgUyjkdxzkjtbHTqCy+90LmQITo2sAWPQyZkHmYqeT6bXAjnFt9Nlv1mQkkllGH64jinDnsoewnGUutNX0wiehHCXs/FdgdiW8QyZ3dGoZamIyeTWoGpzMehqN+Bkax+PwUwaDYY9yYi1+qkdvx4q5paRvSRC4bvGwFckwsdQ6iVko8TFuODz4AwkmDlGWaiRgL8vX+jfjDcvX7v9Yo0bNRZedxcUg42ONKUvtJsNiTI/U5z9FUeBCl4NoIMnCdoByqX/3nirfOuXqsSwJaFUqCpk8KlEARUBUBNQaNbl8aTNd2K5I7Ui1V+Fs5NN55rb3EAaNhEVQcdXrZnI1ULEhb1BprEm7mM0VGBtcRwFuXXSR1itMBAItkZFvdRBQGUiWyeRtFgudPUaGE41PJq+GyqTYfm7hk4YTCzZWV1fxer07f3d1dfHaa6/tOWZqagqA5557jkKhwKc+9Sl+8id/subPKMvfWq3WmjNGw+uR7YE4rSXTPdxqLR/DqtFw3mBjainAUOhoY1YPGagsXzu3GGQ40JqsscdioENvYGrG17AsoEmv4bzLxsJsgLGhI9aoYny0GokLnQ62NuNMDe99r1DlD4tJi9OsJxdJo0gigiigEtWIAuS35W4FQKvVoZLzQI5YrJRt2S8xCHuzQZlknkwow8rYBm1WHR3nHQRzjW8WWmUni0WYHNtEAa722LBrDEwLoaoZsVpwElvuatnAMpm8mcnk1bC/slGLET8LNN5+eFx+qpYBtJWYC6RYTUZpxZOml1RcMblY9scYWmydxCzsyqzPLgUYPsYH1uOn9CoVVxwOllZK8rWtgN2go9diZqbRGRlCKRl2sd3J6mL1ZNjOoVVshUoWueh14F+NMj/ho1BJZq9Uo9r+f4fNgNNigFSBVCIHCEiigKQGQdk9tpKrGt32U7sB7e5nVJr6XKZAJphmYXIdu1FN18U24sVdxaV60arKrwBMb3dYXOy04DAa8cUT5BtMNJ0Eu7JaVT8QSREYSmHQarnRb2toMnk1VFOjelJxYpyNqkoH+x7AfD7P9PQ03/nOd/jSl77Exz72McLh2qem6vX6nYxRrUa8FRrJbTojT5k7yG0pPFjcJJJpXRm6zWDgKVsb2UCBoTkf6Xxrsrluo4GnXB7ygRzDkxsk0o0bljJMGg03XC7CK3HGJjdrlgWshMOkx2PQo4SyjA2vk0wefV6Vd5BRr+J6nxtVosjE0DqpKu+tPCOtWuZit4NUMMnUyNredRVQioXtTJ+ComzLVW7/u9lkAtjZNOzJTh5iaCPhFOP3V/AN+TFnRe70tmM1noyaUa22XgAWF7dILCfpTpZ4HaoGatWFfOunGh21lw/HMgwO+xAXZZ7We3FrDU19lqaOXtgzvH3xuPxUvcGG3IL+EZtay11LB3Jc5uGij0ALJzvbtTru2tuRIwKDM5vEs8d/p1pMlE2n5SlPG6qowvDEBpF44y3CZVj1Wm61uUn7UoxObJCp4Vz3w6zX0Oewok7A+PA60ejR51V5C2nVEgN9box5kanhDbZCySPttU4rc6XPTcKXYHxwpXSPCpScVBGEooJQUZMqsmv/LWYzAIl4iR+5V4mpuvRtIp5l8sEKq4820SeL3Olpx2Vtzr4ehnp2CKurEYKzMdrCap52d6BrwEafxAb3qEczkc7xaMRHZkbhnqaLLr25qc+qNyl2mtt9T6yy0dXVxfLy8s7fKysrdHR0HDjm2WefRaVS0dfXx6VLl5ienubevXs1fYbBYKjbiDfT/tFntGFWtIys+/EpzRvBPWubrZgVDWPLPoJ1zj84qq/fazGjSeVZXk0QbpFixwWPHVUW0oEUy/7GFFHa7SZcOh1TUz62RA3ZbG1BVSaTwWbS0OWwMDPpY8K3m6Gqlk0SgP4+J+loilwsw8L8Fh0dJUUhRdmeyEqp51WFSEEpIgi7I+t2ytLbFsZoNMJmknh8l+xey+OdTeSZmN5AEAWuX3JT0IlMrLZWWrIeiIJAIBAnEIhj0Yicu9bGVGyLcLq2+1qnVbPVIpLqzjnVkMVNZwsMjvkQBXiqvwu/FGUpXf892Ehl4wxvPzwuP1VvUqyZmTOdetP/z96bxTiWZ+edv3t5ue87GYx9zdiyqrKystpSuyWN1Gp3S91q9WK524OBLBgDeCxAgjHA6GFgePTQEGBDGBh+8/NIxkADYeahIciAF0ijVrtryyUWBmMlI7gH932780AykhHBiCAZzFJmTX79kNWZ5OUl773n/z/fOef7cCmMbL0CBUSfwYhb0o8ks37bRsht0GNuwGm8xPPIeCru03YzFlFFNJYlUhhNYdJu1DFlNbK/Fydcy1AuD0bStVotDDolc24rx4EE/isV96u+GIIAM5M2xEaDerZKKHqOXt9bCerExpbcTjaE7t/JfSXIjSYTnBUuvMjgOiHV73K0ajK7T8/aFfAlJwqzkt2z8VbDhoEoCmSyFTIfh9EoBTbWPBxXcySKg7XAS0POSA10TgPsIRtNmWf+RNuZfH6CkrZCoDiY3G8vRqlsCILwWlbhX1my8cEHHxAIBDg6OsLn8/Ef/sN/4E/+5E8uvebb3/42f/qnf8pv//Zvk0wm2dvbu+idHQRXe2FfVRBvD9OB/yyFwGhKFTdh1epAqMjsBVMdrmKUm+T6e5btNlR1Af/J7aoYw2DV66BRaBAMtOdSBlW46MW0y4xRVBIIJDiXh9sk2s1qZl02nn92xm7kerDpfb4EAZbnXWgQ2O04ms7Pd1SOLl7XjraSJCLIMs1aA4SOq5/ARXXj8oe0/2ibcbXPoVIePDmUWzIHnWFEl0WLSaHEZdETz/RPBAflKYYmNHp+q0q1xfYnEZSSyJM1L6fNy0N6/TAOScLr5zT4ndqS4UWgHcyX3DZUbomt3OAbq94g3v7ouz/7dQzgb3E/fN7r1KskxZaMNjQNJS8iSSL3MOzsh67M+vZpkjjjIa0AZixmLIKK3eME6TGRsl0yrOuRYdIP7znQS4Ztnw33fU16JXNuG3s7sUtkWC96v+rirAOjUkkgEEYAJibabPhVxT+l1I65cqMF4ktruQsvqN71r/Nn28+l3cY7zIC+ABwH2mSYWa/CJKrwOUycJccntzz4mbRRr8vsPI0iivB41U1CrHCSud1XSiEK3FOh/RqG8YMSENjtCPrM2kyYp/Q8y0YGXte/SKTYK0s2JEni3/27f8fXvvY1ms0mv/M7v8P6+jr/8l/+Sx4/fsy3vvUtvva1r/GXf/mXrK2toVAo+Nf/+l9jtw8ue6nT6S4Y5kEHxAdto5IEkQ2zi3Rm/MN0oiCwaXOSz9bYP0qN9djrLif1fJ3jg+5x7weFILA+4SR+lr7ukTHE4jBl06NpSRzvZxhu7BsmnAa0gsjJYZpEU3kjQyYIAgqFwMq8i3Q8z8GLCOvrPSxld/BOEFCpFJiNWmrFCq1SC9GgRqGQkGUZrSSiRkavU2GYtFNw1C8CTL/rpLimgNZP+/b6X+UyZWrnFQr7OVYX7VSkJsfn+ZHmNIbONfoEzHqj1Z6XEeC9FRcFdYNAqj8b8yp8jUZLswWCsSLEYNHtxDSgMsjVZOMt/v+Jz2OdGoUUkwZM5gVgw+ymUmy2RRUY31oCbZn1ZrHF/kmq07oznmOvOOwoqjKB4xSjTfldx5rXQS6evyDDrnBKA2HCqscgSByfZK6TYXd8dadVh12r5nD/nJN8kkb95uvcJcPK2QrHOzE0q56Xh+8ZEBdFAZ1WRbNSo1GqI+pVSKJEXZBRKwQESYFar8Nu1lFttlAoxEvH6P3q0ogb1VKxRjVdIbWT5sG8jaqqSTBdGrvKUz/0a1lqtWB3qz1/+HDRQcMksJ3o3yXwSlQTRySdIqkqkVSVKZsV15Se5/kI1dbtsWAY6dvXHa/U1O8b3/gG3/jGNy793R/+4R9e/LcgCPzxH/8xf/zHfzzS8UcZvLuLMdKKEvMqI/F8Y+zDdGqFgg2rk0i8wPYNih2jQeZdj5tUongxXHVfqCQF6x4HsXBuIFnAm/Bg0kEj3yB43E3YBseM14yqKXJ02JUEvJn8VikVzPgsnB422XsW7vsaAdBolbgdBgrxPNl4HqVKgUIhIrSgRRNaMrV8nVqjRaMKiViOXL7C2qaPKasRm0HD4pzzkgSd8kqyUSgMyfzJcNJZICccetxzVg4SabLFwVv1hjVjuvU6yBDYbVcJ1metKOxKnl8xCXwllY17ItRRBnGbTUzOGHlRilFq9G97UI0QtN9WNr6Y+LzXqdoA7Yd3bZJUosi8ZCRfFdgKjlcBUSEIPLS5SPeRWb8PZBk2XU7K2fHJ13bJsGyyxMGIalUAC14byjocHJ4z7Mo84TRgVqkJ+ONkOp0PN8WKLhmW6ZBhL9E7IA5KpYKZKRvh4yTldAlBbB9TlKHVbIIAjWKNUrFGPl0lGsmiUCpYWfMyYdRhVCtZWXBR6w4AOgoAACAASURBVPFNEa/EvN5W4EEgAMHDNvnkMGvwLbkJZrIks6VXVoG/LeYKwOF++2otT5gwTOh4mohfSoIGlYod1zkNgliqRCxVwqTTMbdgwV9JkK33X+uv+kG9iu/zeeGNdBDvYpSZjZsYI5tay5zGhj98zk5jvK1SRpWKFaOdw7M0z87H10OrlSRWHQ7UFYGPD8bDD+nUSh447QSDqTs9Mm6KG6IgsDblJJcoc7g9yAJw+UgLk1aapSZngevVmauPuVarZHHaQeggQeQoTSbVv5dTp1Oi10hI5TrnkSz5bBm3x9ztmKLVbKEQFdBoAjKCDALyxYCdIAhEgikMehUnu1GWVjyszjgR1Ypr9ItGM3zZvot0skg6WUShEHl3zQ3aATfF92ijug0nx2k4hjmXAdu0kWfJOLVm85WoUY1rL3+erXD+rIJOrWZzydNXGWTYysbrPHj3Fq83DAYDyWR7U3TfdcogqXhgcHIQTbNXHV87E3Tk0C1OzqI5Xtwgsz4KJFFkw+lE0xD5ZEzrlEpSsOZxEDvL3kmG3fbkXpBh/uGTn14yLHH1vVcOpFIqWJ53EgumOdiKXkjVXoVarcBsUFOMlwgfJEining9losNbqvRQhQUCMggC5e/n9CuPMXDWRwOA4fPz5iYtLIyaUepV1GpX5ZkbbdYjbbXyWcr7H50CgJsPnCj7VHEGicGvR7hcA7COSasWjwLFrZSSUr1+isaEB/PQpUr1Xj6PI5KKfJkcYoQGSLly9fjKin2Jrf7vrlpEiP2wl7JDCd1Jt4zesknG3wajFEak4M4gF2l5rHNg5ySeXoQI18Zz0CtSa3msduDpijyYjdKvX7/cqZFp+GRz4Mq12JrO0K+MLxcq1Ih8nDGjVvSsvciRjQ2WH9npVJBFGBl2s6kUU9wN3lhxHcd7QfJaFCzueJFKjfZ+eyUQr7aNzIpJJH1Bx6a+SqVXJVy+WVLlCzLSEoFUzN2lJKIQOsS9dKSeakx2JUa7Dn2/oswex+HaGTKLPtsrC97UKsUfY0lG/Xh7qtms8Xe8wiV0xJLWjMPZzy3sp3D5xrDBaR4vMDuRxHs5xJPXBPoVa9icRlvkCxV28oglYDMY/UkU7qXM0ZqRfsaDZNEvK5B/C1eb4wkZHLlWXdp9Dw2+WhmhbErIBolJY/tXtR5Bc/2Y5wXxjPvoVVKvO/xYK+r2PbHKA04XH0b9GoljyY9mKoKdrajpLLDn6soCKxPu5jWGjncThAMDTY8Xq+3z3/BZ2XOaiIcSHHcqbpfxcXMhEbJ5ooHPSK7n52RTpX6r1OiwMaqF6napJgqkc+WX3plICOKAlPTdlRqCUGQ23MI3VapTgzr5bx64/vhThT/R0HKsTyLHgubK150WqkvS14bVlZchoOdGI1YhVlJz7uzXtTK8bX6CEOqsqXTZXY+iqCLyDxxeLHptGM7l4tzGvPxavUWT3fipHaqPJJ8LBhsF/+m/gK1+36hKhuDDEB1GaNlox1VQ2IrkiQ8JnO/LqaNJkxNib1IhqcDuM0OCqdex7TOiP84wbPYeBQ7XCY9Pr2evYMEW9HRWKeuEd/pDUZ8t0EhCritJlq5Fofbd/9WapWCjWUPBztRdhKnl/6tNy45HAZcNj1yqc7us1OuwmrT89VvPOQXfmUNg1HDj//8Yz7720NEUUQlglIpoDeoMeicZPOVaxHm8h5V4MjfZtcEhYBeIbG65CZwlKDRkS4eOoj34OwkBSftgfKJJTuBRIpc6X5GX6Pum/P5Ktsfh5mfsPHE5eWwlCZZHpcy26upHjRbMs+7yiBzXqqGBsrOQvum98G+xeuP0Srw7Qd0Rm/BKmh5EU6QlMfjN9GFR6/HLerYOzvnaXJ8x+56OR2cnPO8x9z1PtXBy4axQ65Tnc/tGvElwsMb8QkCTDhM6OUSQf/djVZajZJZn5Vjf5ydz84u/ZsoCBczy2azlkmvGbHeYPvp5dcJYnsN+ntfXuIXf3UDq03P3/znHf7LX7xAFASUImglAZtZj8tppFRtXNuc9/7koiByst+pWAlgmJNYX/ZwcJKkUm2TYcOSYi9PFuLhHPFwDpNexfSql+NMlvPc/fZWo27sK+UG259GcFp0fDDp4ayZJzxse/NN5/TKOCeBrf22s/vapAeFXb5kPjvwUV5TUuyNTjYMBgPpdJuVGHTwbtFgQ6pI7L+CYboViw1lXcFuKElkjMedNJtwSBp2j+I8b11/eEeJ4VM2EzZJg/8gTlYernezC0kUeG/Gw9FdRnx9oFKKLE3aiYaylNJVzs9vDwR2qw6LXolcarC72z8pEQQBj8eMRa9mfztMOpRm7eHkpdc43Sa+8e33+JWvP0Slfnn7r25M8snfBJCbLRqVBrVai2qpSSySpVqus/H+DCuLLkxGLWazlt6Nce+Vlpsy1UKV/edn6AwqZpc8ZCs1dDo1MIKSR8/FzWbKZH92iqRU8N66m0Stwmky23nZ59vmoxAEtj9rD5M/XLSS1bTuVAa5G682SAoI7HYEGTTr7crMm+7K+havP0ZJNnwaE2uaOjvhc0KMFp9vwpzJgllWs32a4Fwen2qVS69jUmfEf5TgefQ66TRKiHKb9Hj1BgL7sZENYyVJwbuznpHJsOXptmFsNlokFr+97chs0uAwqSik69eSjC4EQWiTYXY9+1sRdqM5Vjd9l49j0fFrv/kev/btR+h61LQ2Hs3wn3/8FGSRZrVBtVAjGy9zniyg1amYe+Bhcc6BXqfE7jC02606uDTXJ0O1UGVvJ4JKLbGx4qbcanbWqcE9ZC4O13PoYrHGzkenCKLAu+seckKDw2iq+7FD4b77M1EQ2XkR7ZjZmmhYleyd32/G6VVv5gUEDk7TcArayeE6CGRZfptsvApcNfUbRI3qLFHkMDX8w3QTBGDD7qKca3B4PN4EZtFmpZWrEDrJEb91ozr4I7zgtKJpiQSOksQZbXNoM2qZtpoppCpsBYZbALRqiYUJK6GjFHsv2oHfcYuB0ITHjFmnJrAdJSvLTE5a+75uatKKzaTh+cdBLncbt38bp9vAV35liX/wzceo1aprG0yPz4rQasvkdZ9VuSVfmNfJskzg+RkrGz5ykRwzk1Y2NiYIR27+DcuFGjuftqsq774/w8MVL4enKQrFl1WJ6h3Dov2ubKPexP9ZuB1Al53UNTKh9HC9t/cOSBc/EhwG0sjAO4sOqkbYTY4mflAZ0ONjHOi2UQ1T2Xhdg/hbvN4YRY3qPFNlN5YaKxnWllmHveA5p2M87pTZhKbSJBgukpFvZrJbQ6xTbTJMjf8gQWZEMsyoVbHgshE/yw1NhnXdvmOnWfa3OlLlTuONr3faDbisegLbUU5ixQ4hdR1utwmHRYv/2RnpPu1bFpuOL31lju/8w59Hq1Nfa8k1mXUolBKtSqPDzQgX8x8KhYgsyxxuR9CqJVLBNMsbE2xs+Egk8jfeS7VqA/+zdmL0zvvTvLPsJRjLks6+vJajxGa5JbPX8RdZmbWhsCqJD1tduGejf1c+WACCJzk4gbUZKwpHW/RkFFTGVsm/G911qtVqvfHrzxudbBiNxr8TB3EApSiyaXORSBbZHauyFKw5HTSLTY4OB1NwGiSEr7htyOUWJ/eQxHVbDXj0evb8MbbPSky4BnfH1GslJm0Gzk4y+F9cYZf6nMz0pBWtqGDfH6M3JFx93mZn7SiaLY724jQ85muHstkN/Iv/9Rt4p1QYDHokqf/GUqtToVQBTQGE9uc0Gy2aja6538vPF4BStsLhflt+b9JtZGPdx0nonPwNzrLVcp2d7TAKpYL1VQ/lVpPD4Pm9DIcE4LijPub26rHMONk6S1B/Be7eV3G1lVYADjrKIA+mLGjcap7F47SGoDM/z+qMunMfNJvNgRU+3vRg/xZ/NzAYDEOTYsoxrVNtmXUX+Wx17DLrQ3s5DfB4z9nN6GSJwHGSURuQbUYtU1YTB3sJtmMRdNrB2WGNSsGU00DirHBBhnXR7zt63aZ2JX0nRvr0JfF0NVb4fBYMKiWB7TAFjfKaqZ/BqOGf/Yuv8mDTSqvVRK25+Zy1agWFUr2ba1wkG6JCvPQbC0A2VSS+3SamFlc92Nd9hCMZUqn+m/5mrcX283ZV4sGqh5YkEDi+v1hA6DgFxzAxaWB6xstONEmpevcMzyBGr7e+v0/MDp6k4QQW3EYsU3qeJuI0hpHx/RyXAbX0Mtl4k5Wo4A1PNkYpT983iOuVSlbNDk7CGZ7vj28eQyEIbLicZM8r7AeGS15u2qOJHVnA80iW48DopcNJhxmpWiMczJOWXzLog+wNLUY1k3YT+7txDpN3n8P8jB2x3uIo8FLu9hI6wWNxwUmjVCO0014QrsriLq96+d4/fsKjJ3MIgsDx8fGdn60UBGrQSUgUFOu1S0lG+8+OKkjXWRwo5ioc7ScQFAITk0Z0GhWSpKDRZ9PfrDfZ67BIE14zLrOJiK5IsXS/gc/seYVE9BSbUc3kiodAMn3rXMd94+VtG+/TUAZCMO004Jgx8jwZpzrAs6lWj67iNSyUipfJxts2qrd4lRhFol2puN86pVYoWLc6icYLbI9RWQpg3eWglm8O7eV0G5mw6nVQOi8SPup2BwwPl8WAQW4RCZfYOeupsAywThm0SuY8Vg4DcY737nZ6nvJZ0Ckk9v0x+v66nS8wM2NHkmWO/LGX8u09TM30rJ3v/OAJP/cLyygUItFolFrtLu8FEYQ2a69SvUxeFX3umQs1RSCXLpHYbRNkbp8Bg0GNWqOkWrm+6ZdbMgdb7aqE067HaTKRMJXJ5O7XdlcpNgj/LIRWK7G65uE4m7v3XMdtuM3qIBbLE4vlcZs1+JasbKWTFGt3J0AajRYYzZF+WHTXqUaj0Vd45k3CG332Iw3ejZhs2DRa5nUW9kLnPE2Mb5hOpVCw4XQSieTY8Y+avFyOpkqFyLrXSSKSZ+8eHhnzHivqlsj+fn/FjduiuMOixW3SE/DH8UfvDibL805qhRrBzjxGX4UPAaxmLbLDwNGL614aggAb707xvR8+YePdqWsb4ruYc4WkQJDrtEpVauUmKllgZcWNQq282JD2tlh10U085KZM7CSHw2pG22wx88BLulDh7LR/YEpEsricRuRUlQdLdlKlCvHzK7/VgGR/tx+3kK+y+1F7ruP9DQ/hcolIarxSzjAYy59MFEgmCtiMaqaX3Wxnk+THqKJzH3QZo7fJxlu8anye61SvzPrzMcqsK0SBTaeLVLJIYG+0Sv4VMv+CDMunytcNY4fApMOERakmsJcg0yfG3xZCzQY1U3YTB4EEu8k7Wq0EmJu2IzVlDvcT/cmwDjwuE1atkuDuSzKs5zAsLLv57g+f8MHfW7jmFH4XlCqpLddeqtLI11C1mkwsudCZtBcl5271Sm72rFPNl4lH8qyA3VJALNZYX/VSrDU4Pu5/XdPnRcrpMuVIgZUVJ7lqlUjiSmVk0Kp052WVcoPdj9sVlPfWvaTkGifx6+3t95WZHUTNKputkP0oglYrsfHAy34pw3np5qTq86xvdyvww1Q2XtcK/BudbPQyRoP2wg473e8zGHFLeraDCZ62xpdkGFQqVqw2TkJpXuzeT1mq+5xrVRKrLjuhUIadOzwybsOKz0Gr1ORk73bmql988Tr0mNUa9vfiZM9u77UVBFhZcKERFLx4el0xqgtRFJifs5GJ5sjH80T69Lq+/+Ec3/nBEx70OoZf+qy7H0CFQoRGq91GJUOpWCV0mEStUbKwPsH0hBmDToXDZbzUHiRfve1kKBWq7Hx8AsDUrB2jToVWq6R8Vf5RlqnXGhx0HFEXFp2oTGr2DuM0W/JACmvdz+xFo95k99OztsvqmoeSssV+5CVjd//KxuCvzeerbH0cRq1W8OGql8NKlkTxegI6TMvVfaFSvFWjeovPB73rlCAIA7ULDrtOObU6ZrQmdoPnPI2Pb53SSBLrDgdn4Szb/vsdt0uIdMmweCR3LzJszmNF0xTZP7i95arf790mwwwE/DH8sbvJsMU5J1pRZOd5m+S6KfwtzNspnBdIhlKk+4ierG76+O4PnvDu45mRN4WSJCDXmwiiiCAIVEt19p+GcPksWB0GfC4jOq0Sr89yab252r4ny1Cr1Nn9NAiAd8KCXqvEYNRQyF9tB5ZpNVscbrev18y0FaPTgP8oTr3RolQerOJx9Uq05zrabV7rS05aRomd057E854L1TDJSqXcYOvTCAqFwAfrHsLNIme560Td59nuqxqhAv822XgFuOqzMVAv7IDl6UWzFV1TyfZpgjjjM0+y67TMGswEjhM8j49HvlYQ4NGkh8PDJFvJ0Y4pCLA65aKUqnC0MxjL1PvQTbuNaAQlhwcJkuRujRFtF1U3qUiO/WcR5hecfV8nSSLLiy4SZ2mOOv2zRsPLwTtBgC99eYnv/PAJ84uugc75NrxkqjqtUh0mSFS0g/rpfhyTScv5cZKVR9OsP5zkNJS6KFV3cdXNO3x8jsmoRSjVWX/gJVOocHaWfvklej4/2JEmtFh1TC47SBUGKzHfFP8E4KCTeC7P2lA7tGyF4vcO4qO8vVptsvVZBFGEx2tu4kKFYI+C1eepp6UaobLxugbxt3i9oVKpLpEGg9xHg7b7ThtN2EVtmwyTxze4alSrWLHYOAimeD7imnIVAgKPJj2Egul7kWHLPgdyqcnxXurW6kIXvbHRY9dj1Q5Ohi3PuyilyxxvRfF4+s8oiqLA8pKLbCLP8Vb7e9nsl0VP3n08w3d/+IS1zcl+hxgKgiBAq4ksd5j7zv3UZr4FIsdJbHY9sUCMyWU3vulJopHstbbeq5vmeDiD02Wiliqxttaudpyc9G99DgfTEEyjM6iZWfFSadQZ1SAQOvOHgfbaNzdhwuwzsXUav3fMHdanA6DZlNl51lawem/FSV7TYD/VQ3B+jsuASvritPu+0clG7+CdKIpjGbzbsDnJJQscn2QQOv8bByZMBtwqfVu+NjweuUGHUceU0QilFlvHo8kCKkSBtSkXqWiB/SG1x2u1GnMTFoSqTOjw7v5dSRJZmXcRD6bZe9pfFhBApZJYWnASPkri/zR0+R87vap//797wHf+0QdMztiHOufboFQoOkZ/Ak2VgtbF4N1LNrI7IJ5PFYkEUyDAlNeMYX2Cw/04tWp/nXJBaA+J734aRAZm5p3orNobKwS5dIntj4JMzdjZmHOTLBSJJm5eHK8mOP1w1hnS8zn1mCUVGqV0zVV2UNynvN1qwe6LdiVncUKLwq3Dnzy/lrS9Sqjfzmy8xeeEUQY776psrFhs1DNVgqE8kTFK4zr0Omb0JvxH8bF5OZm1ahZsVsq5ClsjOogLAqxOuiinKxx3yLBBI1Cr1WLKbUQrSBweJDm/Q4JcFAVWFlxkY3kOnt98vooOGXZ+liHw2ZXKfCc+Pvn5Bb77gycsrngGPNu7IUlt8kshCShViovfQRCFixjajaTpaI7T/TgyML/qxTdl4+ggQfmWGcFmvYm/02ngm7JhcRtvvIfbFfwgS8se1macFOo1guH7SaB3/TrsZg0OlRqDVkWhPFr77X3WKQEI+BPt386tRTuhZyuZHEnCeVR8kdapNzrZ0Ol0lDvlu0Ez4H69sIqOYkcmVcZ/2M7kx5VkzFktGGUlO8cJkmNaFCasRlxqLbv7cbbPiixPOoY+hlqp4IHPSSSYwT+k9rgALExa0SDh99/9Xo1aybTXSCyYxX+D9jiAVqtkYdZBMBBj95PgtX9XKATeeTTD//K/fQuP1zLUOQ8CSQEiMs1SnVqugsmgYnLW3jY8uhJgLnphZUjF8kRDKTR6FZ5pE5q+SiKXKxinh+0Fc/P9GTbXfATPzslmrzOTzUYL/7N2O9TysoumEg6C1wcYhwmAqUSRCXsFU6bFg2UngWSa4g1J0s24//MhANFwGcJlVqetmCU1Ap9PheOLxBi9xZuBYTTw+61TAq9OZn3SbMQhaW/0choFTqOOSaORvf04W7EIPsfg6oVddMmwdLRwIUE7DOYmzKhaCg4P7k5QJElkfspG8ixL4On1mcAulEoFS4tOoscp9q6SYbTzjHceTfPN773PzNzwa/NdkBQCAi1a5Rqtch27TcvsshtBFKnVOnG8E0S7BKwAxEIpSjtVlGoJ37QRo+G6IMfV2zMaShENpVh7d4rN1QkiiRzJ5PV9jAzsv2gnZnNzdpQGJfsnyWtzOsO0IOWzFUqxEqpojUdrLvZTaXJDutCPoxgtAPFYGWJlFj0mTIIKhSjSHEbBakSMsk69rhX4NzrZkCRp6P65XsZIK0msWRycRnNsjVmx44HDTjlV5PQoM1CpdxDMOSzokdg7TFxiZ4b5DfQaJUtuO8Gjc3aG1B4XRViatJM/L3PiT96qOw6g16mYn7Jz7I9zuH3z7ysKAhurXo53Iuz0STJUKgV//5eXefL3fTz+YHOocx4GCqWI3GgCMoIokgynCQViWJ1GfAsu1t6dvDAC7GXhZbkddCrFGqc7SYxqLRMOA1avhaPDOKVirT0E0getZovtT05BEJhbsoNS4ujo+qCeABzttTuTfV4TJpeeQDBJo9FhsoZ8DgShbb4U+DSCQhJ5f9NLqFggnhmsZXDc8SwUTKNtKZirGbBOG4aXIxwCAi83c81mc2AVrNc1iL/F6w1BEPqKVdx2P/W2+ypFkQ2bi+QrkFlfsFmRcxVCJ3ni92iD6YXPasSh0uI/aJNhXQwzk6VWKngw4SQSGp0MqxcanAZSqFS3b9LUKomlWQfh4xSHtyQ0giiwvurl9CCO/5PrSYZCIfJzv7jIex86+YVfejLUOQ8DpUqiVW8B7Xsony4SP83gnrSi0qlYe+hD05H7bTVextBu4lGvNogE0mgUapwmDa4ZB6HgObls+VbSavvjdlV+as6CxqQnEOj/W50etQlbh0OPc8rCfjBJtdb1qxruuwoCVKsN/J+GEQR4b3OCRKPCaXIwg9xx+tQARKM5nDodvpwa97yFF+kE5VFd1weAagQ/qNcVb3Sy0cUwjJFSFLGo1SwabOyfpniWHJ9ih9iRry2kKxzudysk98eiw4LUEDg87N+jOkj3iVGrYsFp42g/wXZ8uCRDUgj47FpK2SaHOz2/1w2Rw2zSMOW2cOSPXZja9X2dWcuk10yzXGfn+fWKh0ar5B986x2++d1HtOQqqdTdkoT3gVrZVvmQVCJa3csNqKgQadQa7H5ywvqH8zidOryTZuqNBulEAfnKnliWIRpMEQ2mUKolVjd8aNT9ddPl7tWUZYIdlZfJaSsmp5H9g1jfJCIeyRGP5DAY1Uwu2DkMpyhVRg94zUaL3U/PEASB9zY8nLfqBBN3GV+Of+PdkmXi8QLxeAGPWYN3sS1HWKoPx2bdBZWkuIgXX4Qg/hZvBrrrVFeR6jYpS6UoXpJZfzFGmXVoezm1ik0ODtNjI8OmrAbMCg17h+25vasYhBDRqCQeTDgIHabYuaWFqR9EAaaceuqFFif+nqTsho/VaVUsTNkI7ifYvaXirtermJ22UcqU+lbclUoFv/wPNviN33qM3qAYSGb9PtAole11SqkAWUChEFFIYruNqt5k++kBqx/MYjWrmZh3kUgUiIczFzOIF5DhPJrjPJpDlEQebEyi1dxwT3Yl4KEjT5zB7TXj9Jk5OEr0vbbpZJF0sohGK7G07CIYz4zgK/XyzpRl2HvWHibfXHVTVsmXRE/64b5qVn0hQypdJvVxGZNexeaKF38+RbYyoJjLEPgiVeDf6GSjX4JxV+LhlHTUkk2exsYpXyuy7nQSjxbYHVm+9jIEYG3CST6RJ3Snud8tErQmHQ61kuBJlu3YcMFbpRRZ8tmJhjKEj/qoMlz5/zarjgm7kf2dGLvRm4O3zabH4zSyvxVmN5pjfunycLfBqObXfvM9vv4b72E0aQBIp2uvXAVCUknIjRbNcp1GpYnFomF+1Uu12nz52bJM4jRNs94gHS8wt+7DaNOTTuapX+ijvzzPerWB/+MTVh/NMOk2Y3Qa2PdHqdfbr+13TSPBNJFgGr1RzYTbTKlUI5u9PudTzFfxfxZGEGFxyU66XOU8M+iQaP9nZ+95BBnYWHZR0wnshfsPCL6KGN5bls5kK2Q+jmDQKdlY8RIopkmPybm1yxbBFyOIv8XrD61WS7lcRq/XD5RsWEUNUlYYq8y6KAhsupzkUuULL6dxPMYrHjuNnurITce8bSbLpFMzadITOskM7fYtKQSWpuykIgXCR32SnKufZdAwM9Ehw25JMkwmDdM+C4fbUXY/CeH2mi/9u1oj8au/9pBvfe99bA4DAKVS6XNYpxTIjQb1UpVGvYXJrGFixo4sCBQ664TckknHc2SSeeSWzPSKF71Vz9FenEpn/qH3PFuNFnufBVl7fwa3TYfDZ+NgP0al07bU7xslI1mSkSxqrYTdpCXrMPRtsaqUG/ifhkGA+Qd2sroG0eSgFfTrd5MAHHZUzFbmbEg2TVv0ZMD33xe9Fbpiscb2JxGUSpEn616Oq7nhXdJvwShqVK8r3uhkAy4/MF1ZwdtusEqlQb05nvYMvUrJA5udk1Card3xLAoKUWDd6yITL7C/O1ji0q887bUZcWi17Pnj5IYcvNWqJRa8VoJH5+xt3VzC7n6sy2HEYdK2k4yzm0vxNpsWt91E4MUZ2bPrzLnZouOb333E1775EJ3+8zN460IUBeRWE0HRbnvIJgtEjpJ4Zx3YvBbWH8+iUHZ0rxsyyDJHL06xuU0o5SYLmxPEopmX1YorODtKwFECvUnDyqaPcCTLbYliMV8ldnxOOV5gfdNHulAhHL7+u8ktOPGft+c6HrgoNGqE43fNB938ub0tW8vTVlQuLduhxCuXpu3XNVUq1dn+NIJSEvlgzUOoUSCav9/sk7rHRX6QIP55Sh2+xRcTOp2OYrGIXq8fSKa9Xm9Rqo2nPaPt5eQgGs3fw8vpMgRghc3dCwAAIABJREFUfcJJOVPleG+wxKVf/HCYdPgsRvb9cfajw5EJKklkacpGJJi9dZ6j+/xaLTp8TiMHOzF24jcnGUajiukJGwfbYXZi19cznU7F17/9Lr/+nUeYzNo+R3i1UChF5KbcVklsyFQKFdKxLHaPBUkUWP9gFknZ3tp1/aCC/giCKKBUK3mwOUkikb2xpSl+miF+mkGtU7G2PsF5qnhr/1O13CAbz5MOpnmw6aPcaPZXsZIhcVogl6+ysOCgoWxxEr6rHer22Ht6lIIjmPWYME8Z2TpN0OjZ372Swkaf36Jeb7HdVVpcdRMXLystjoreyoZKpRroPa9ru+8bn2xIkkS9XkelUl0wRrepf9zXQRzAqtUwb7Swf5Icm3ytWlKw5nESOcvg3xnumL33/rTLjFFQsReI36m6cRUGnZI5l4XD/QT+W5KMLsxGDQ6jlv2dKCn55rYbr9eMVilwspcge3b9nDRaJb/zP/0iv/L1DdR9h6s/H6g0EsgyzUodhSiysDZBIV+hVm1QK9cIPA2x9mSe+WU3WqOWfLZMs9Gk1WxRylfZ/dkRAJOTNh489LG/E6XRp4JRzFXY+ulRm+mZczC34OTohpkhQRQ6bU6htnrTAzeoJPYPXm4aBKF9DwjAUSdBnZ2xIegEjsP9F5VBA9JZR+Jw0mXAPmPhxVmCeqM59l5Y4NaBu3qjxc6zKIIA76+6SUlVjtJ3tXr1h4KXFdBWq/XGD969xesPvV5PoVDA5XINJNOuHAOLaVCpWLHZOAmmeTEmMkwSRdYnnKRiBQIDkmFd9HJeXpsRp1aL3x9n53Q4JlirVrAwYSN0lGJvAAVFu1WPw6RjfyfKbvhmMszhMGAzqznaibEbv35OarXED3775/j6b7yD3qAZ6pzHCUklIggyzWodWjLuSSvVSp1GvUm5WGX/WYild6eZmXegM+s43otRLlaRW3LbV6OzTtnfM7L27hQH/ijV7uB1zzWqlmrs/OwYGZjwmFhacRO40WtFABkCz9pJ3OyCA7VJS2A/dmW+sSPx3pk9mpwwobGqODzL9q18DRpzE9EciWgOp1WHd9HObjRJqVp/NZWNW8jbVgt2O55Z7y47Keta+M/7dwfcBVEQ6O5W31Y2XgN0vTZ6kw2l8vqGtdVqkUgkSMZHZ3Y8Rj0TakNbsSMyniTDoFax7LBxcnLO9vZosoCyLLM4YUOqwcH+OVGGK49bjGom7Sb2/XF2E3d/r2mfFa0okokVOO9jXNTF1KQVraRgfyfS93xcHhO/+Y8+4Je+uoZSdfutOKgZ1n2gVCkQBRmlSgEtkdNAlGKuzNzGJHqLHrunXUY/fB5Cb9SgUYrMvDNDqVAl0/M7VIs19p6F0Ju1rLw7STSc7X9BZKgUKpw8O8Xla7dYnRynLxIUuDzg1k4m2sF+YtKCxWNiLxCj1WpdSyjOTtq9rG6nAaNLy1EkczFMDsMP6p3HC5zHC9hNGnwrHlB8vkG8C1kG/3Y7mD9cdJAWi4QGNJTqQoHMT37yE6anp6nX6wNJk75NNN7iPug19rvNRVyWZc7Pz4mc3Tzrdhe6Xk77x+MjwzRKiVW3nfBplt0RPTLklsy0y4xJVOHfG4EM0yqZ9Vg5DsTxv7j7HDwuEza9hsO9OOcnNxMTbrcJm1FDYCtMOnQ9BlmsOn7j+4/56q9votXezi5/HuuUVq1CBCSliNyCVDhNpVTDOWVDZzJTrzUQgJOdtqKWyaZn7r1pspkSkVD6QtK9nCsT+vQEjU7N2sNJzs9L9KskCLTXtIPPQjjcBsweE6enOaqVl7N0V8NjqJNM2N1G3NM29g/jVCqNa79NLJyDMFgtWhwzRo4jGaq10TtPcukSuZ+V0GqVrK55QDn+DfogFX4B2N9rE4hrM1bK6gpHQ65TSlHkJz/5CT6fj3q9/jbZ+LtGN9mwWq19g3iz2SQSiRAOh7Farfi8HggNx/LMWMxYBBU7xwlS8nj68ax6LXMWM/sHcbYSoyUZAA8mHeiReN5JVIbZErVdVPUE/HH80bvlDuembCgaMkeBJAJgv2Jc1MXsrB1FU+ZoL9Z3+HBiysp3f/CEL//SCpL0+jxAKklBo9akUakjN9uB0jfvolSqko5mSIaSTC26WH53mrOjBMVMie2/CaAzqpldcCLpVBxuhy82zcVsma2f7IMgMLvoZH7FzeFVZqgTt5JnWZJnWYxWHVObPk6CKfLZ8o0XNHaaIXaawWTRYp82EU6U2qpXV3CeKHCeKGAwqXFPmwjGcpSrTa5NtQ+IfK7C7s9Oebjh4/0pD/54ksLQsrn9MYyUoAAc7rcXtNUpCyqXmmfx2ECyuQadjsePH3NycsL5+TkGg4G5ublbe+jf4i3uA51Od2uy0Wq1iMfjhEIhjEYj0z4fBK4rHt2GCaMBj1rPzhi9nAyaDhl2fM72PYz4Frw2DEhs+2NDk2Fmg4oph5mDvQT+5N1r5YTHhFGlYn83RpJ2e2w/+HwWDCqJwHaEfhpfdqeBb//DD/jlr2+gVr8+sUHSKGk2GjRqdVq1FnqTBpvbTK3WIHIQJxZM4vSaefD+LAcvTqlVG2z/7T4A02s+dCYdga3Ti+papVRl+6cHAEz4zCytTXCwG+lL/qRiBVKxAjq9mqWNCcLRHKlk4cYLmorlScXy6PQqpuZtpHNVin3WqVymTC5TRqOVmFuwcZrMUShdT04GRaVcZ/fjU9ZWPDyacHOUSZEujUdoZNh24uBJe+Z2wW3EMqUfWGlRo5T48MMPCYVCRCIRms0mRqPxxnaq173d9/V5gkaEXq+/COK9vbD1ep2zszNisRhut5v33nsPpVLJ32SeDnzsFYcdRVUmcJxi9HTgMtxmPRM6fVt7PDKanrkoCKxOOSkkyxxuJ/C5zXe/qQceux6rRkNgABdVgKU5J4XzHCH/Za3yq/f2wryTZrlGqNMGdjX+eHwmfvhPvsyXvryEYkAn9y7GyRjdVJJUqhXQalKv1TAY9AiigrP9KBaXCYNVz/z6JJVCGf/PjjA7jKx/MEc0lKKYL3PcYZG0ZjVmixaLw0CmOywny1RyFY4+C+KZtmGbsLK/G+1rAJhPl9j+20MUSgVr70zSvGNZzmXKFPIVFEqRzQ0f4Xie8/Pr17SYq3L4IoFSpWBl0UaxcE/PlybsfnyKKAo8euglXC4RTd/vmKPqlodCGQjBnMuAddrIs2Ts1rkslaRApVKxtLREOp1GFEV++tOf4nQ6mZmZGVgK9y3eYlB0STG4nGw0m03C4TCRSAS73c7Dhw9Rq9Uc7+4PfOy2l5PE7kmSpDweLyebXsus2cT+YYKtIdULe/Fg0kEj3yDoP8egUw2VZNjNGjxmIwF/DH/s7uRpbspGo1wlfJgmzs0JzfS0DZUgcLgb7UuGWe1a/tH/8PP8wlfXUA7JjI9znWpXrK/PoCo1ErTabVSSSolWqyIZzaAzarC59JisOkr5CgfPguiMGhY3J4mfpYmFUsSD51RKEVRaJQ63iVq1QaJnDrBaqLL/yQk2twnPvIujQJxSsXptrS8Vq2z/9AhBFFh5OIlac3vFp1Sscfg8hsGiZn3Fw3m2TDR6faahUm5w8CKOIAosLzuoVe7n+SIgtL29BHh33UNKrhNM3G+WYlTz2VgsTyyWx2PR4l24WzZXLUlIksTc3By5XA6tVstHH32ExWJhbm4Orfb6vFA/me3XBV+IZKM3iJfLZWKxGOl0Gp/Px+PHjy9tLFV3lKIEYMlsol5scTRG+dppmwmLpGbvIEFmxAVBUoisTTpJRvIEenpVBw1uky4jOlHJ4UHbp+O27yUIsDzvopQuc3TD/Eb3c5cWXVRyZY63+hshLSy7+dVvrjKzYGRpaXGgc30VaLVa1Ov1C/lJURQvPZgagw5ZkBFEAUEUqBYqKFUiVoeBSqXO4WfHbPzcMusfLnC8G76oWiy+MwUKBfvPTylnq6RiWbKxLMvvTFGrtzjuMT6MBVPEgil0RjVL707eWNlp1pvsfnTC5LydxTk7LUnBYeCGFkBZpl5rsv1JEEEUWNvwkS3VODtLX3tpvdbkcDvBxqaPpQkFqWKZ8+wIkn1C9zeV8X8W7gRzL2nqnMRHm6VoXpVmHBJd2VyXScPE0s2yueqeCkar1WJ2dpa5uTkikQgff/wxZrOZubk5dDrdxete1wD+Fm8Grq5T1WqVw8NDEokEHo+HR48eXaqsDTKzsWAxIZRkTjpeTuOAx2zAo9WxdzAGMuy8fMlfaVBG2G3TYdPp2N+LkQvf3UmwMOtArjQI+vsb93U3h/NzDqg1Ofb3r7j7pmz82nceMjGjZHNzY6BzfRVotVrUajUEQUAUxYv9SzcG6Q0aWq06giCjEAVqlRqC3EJv1JLLFAh8dMziuzNsfLhAaD9GcCdM9rzA7NoEWrMe/2dBauU60aMEiVCK+c0pBEnkYCuM3KkNp2I5UrEcKq2Stc2p9jxjH8gtmcBnIZY2J5jxmVEbtQT80RvbdOWWzO5n7fnD5bUJ6sh9faXklszRboK1dS+LbjP5RpXY+fBqhBfXWIbAi2hbNnfFRUUjEIiMNktxX6GUTKZM5uMyJp2KzQde9vJpMpXr303VszeQZRmv18vCwgLxeJynT5+i1WqZn5/HaLzd7+x1wRufbOh0OgqFAvl8nnQ6TSqVYm5ujsXFxb4bhJuCuCSKbLqcJOMFjk+G6yW9DQtOK+qmyP5xklFH9FSSyOqki/Bxht0+Bkd3ZdpTLgNUW0SPuq6zN0MUBVbmXWTjeQ5u0TkXRQGf14xGgMM+HhkAqxsTfPeHH/Lu4xlSqRSZzGib0PuiyyImk0msVitarZZWq0Wr1UIUxYukQ1IJiDIoWi1oNhFkGYvdQLPZIhvPMr3sRqNT8tl/3UUQBR68P0cuXeLgWQi5JWPzWjC6dCiVCuRmi71PTgDwzjrR6lQoVYoLedxSvsr2Tw5Y/2CWlVUPmVyJWJ/heUEUONxuJ3G+GTsmp4m93SjNHua+9+rLLRn/s1NkYGnFTUup4OCG4fNjfxIZmJ4xURNlosl7tF7IEHjRkc1dcVHT3iybexPGZeKXzVXIfhxBr1Wy8eC6bK6qp6rWvQcEQcDn8zExMUEikeD58+eo1Wrm5uYwmYZ3Pn6Lt+hFN9koFoskk0nK5TJzc3N88MEHfWeGbiLFBGDT7aKYqRI6zl783X0xaTVikzT4D+Ok5dHM/RSiwPqUi2SkcIkM6+Kaz8MVTDj0qJoyp6E8qQEMBpfnXVRyFU62b19ZFxec1ItVTm5oNZ6dd/K9f/yEJz+/SLlcIhi87qUxKO5DSnQrGel0GpfLhV6vR5ZlGo3GBUEGoNIqL4xJ5UYTQalAb1SD3CR1lsYzY8dk1bH1twfU6w3Wf24Fq8vE8XYYk02PUa/CNKFHQbsacfi83a7n8FnQG9Ro9CoqnVanWrnOzn87ZPmdSRaXnFSbTUKH170tBARCHS8Yp9eMY8rOvj9KrXZlNqnz+wjAQWddm513oDZq2AvEriUpgiBy0iF9J7x6RL3EaWyIVvYr10MADjuKbA/m7ChsKrZCwxk6N8ekZlos9cjmrnk4qeWJ9cjm9saAbjeGIAi43W5cLhfpdBq/348gCMzNzWGxWF5rUuyNTzYKhQL/6l/9K77yla/w/e9/H7vdjtvtvvH1yivtO1qlxJrdwelZZmzytQCrXgeFZIHTg/aDOcotoNeomLUZOD3J3ur23S/TfumiWid8dHfZUJJEVuZdJE4zBJ71r1BA2yV1eclFKpwhcpgkk77Ofj18NM33fvgh6+9Mvjyfe5aXR3l/s9m86Ev1+Xyo1WqeP3+OzWZjZmYGlUp1KemYXpnky7/5hJ/950/IRooo1QqajRa0mshyE6PVTKVYQaNXMbPqI3KSJBXNMrXiRWfWcfAsRCqSYeaBl9VHM6QSeWKhFJHjBDa3EZUEy5szhI6S5FLtoCLLsPfJCTLgW3CgtxnZ3+7tl31550ROzomcnGNxGJhY8HIQSFAuX+9/7b6rOx8yNW3DYNfj34tdS0wFINJJrqenzMhqCA0QzG+6FgJw1AnmK7M2JPvNGuhXMa4g3kW53JbNlSSBJ2teQo08kXwB1ZXZjN4ALQgCLpfrIpjv7+/TarXY3Nx82171FiMjl8vx4x//mL/+67/md3/3d3E4HExMTNz4+qvrlFIU2XA5ScTG5+UEsOiyIReqhE5yJIYc2O5CrZRYdFuIBPuTYV3cxAjPek0oGgInx/1Na3shCgIrCy5yiSKHL25eEwUBlpfcFFNFjl70X8+WHnj43j/+kPc/nLuIAeNogxplnWo2mxexR6lUsrOzczFLptFoLiUddo+Vr3znQ57/t13CO3EUooAsCNCSadXqWH02SvkyoiDz4PEcuWSOk90I3nknjkk7/o+PyT7LY/OaWHlnimKhyulBnORZBofXArUaa4+miZ1lOI917wmBg2ftpMQ1acbucxDYDtPoOpT3XLSu/4bepGH5oY+T43PyucrVl10gdNiubLi9ZuwTFvx70Yvj9u6dE5H2ujThMaIyKQhGC3caGt+29w4dncMRzE+YMfoMvAjFaQ7QItUc82xEvd5i+2lbafHxmpuEWOEkk71U2bja+i0IAjabDZvNRi6X4+joiL29PdbW1tBo/u6U0m7D55Js/MVf/AW/93u/R7PZ5J/+03/KH/zBH/R93Z/92Z/x/e9/n5/97Gc8fvz4xuPJssyPf/xj/s2/+TckEgl+67d+i9///d8nFArdKSnYzRbNGjWLZiuHJ+c8H0CBaRCIgsD6hJN8qsyhf7hsuRcWg4ZZm4WDQJxA/G52uDe4iQIsTdnIpyqXXVRvgEqlYHnOSfg41e5tvAGSJLAw7+T8NMvep+2gY7boLr3m/S/N8b0ffMjymvfa+z8PlY4ueoO3QqG4aJnqMtfRaJRPP/0Uq9XK7OzsRdJhsOj48nef4Nq0oFHo+Pj/ec6n/2mrXfUQFNCSaTSaeKcd5FMFKrkiK49mKBWr7H9yhN6iw+Yzo1Kq2OkM5M09nELsBI1ipszWTwIolApW3pkmn3vZviQA4YMkHCSxecx45pwc+KN9g2UmWSCTLKDRqdjYnOQkmCSfv7kVKhxMQTCF3WXEPWVj7wY34kionZR63UbUZgXBWHHk/lSA0+MUHMPchAmTz8iLUOJ2edsxJxtdNBpy2yhMgEcPXHh1hoHeZ7Vaef/998nlcn0V7t7ii4Vxr1MAf/VXf8Uf/dEfEQwG+cpXvsKPfvQjksnkxZzhTehW4HVKJat2O8HT8ZNhjUKD4D1ahfUaFUtuGyeH5wS2717vrsaShUkLzWKLs/27TGvbLcTL8y7OI9lbyTBRFJifs1E4L7P/tL+i19qmj+/99x/y8L3pa0zw571OdfcrkiRdrFNd5jqZTPL8+XMMBgOzs7NotVpkWUZSSfz8dz5k6rGXZq2F/78c8dFfPoeWjEolISBTLVfxzjqpFCpU8iUWH07SbEE4EEWpEPCsOhFbCvw/OwRg6oEXram9nleKtfZAuSCw9M40tUbr0m+SOM2SOM1isumZ3PBxckPlvJirsP3TQ5RqifXNKcLRDPXGzf4yiUiWRCSLyapjcsXLwVH/4yaieYiC3abD6NIQjBVeJj0jIBbOEgtn8Tr0OOesbJ0lqN1ynuMmxbqQ5Zeyue8sOpgwWXo+82bpW5PJxDvvvEOpVHqtFateebLRbDb55//8n/Mf/+N/ZHJykg8++IBvfetbrK2tXXpdPp/n3/7bf8uHH3440HFfvHjBv//3/54///M/v+hZG8QsSSspeN/pZvcowfPoeJIMpUJk3eskEcmztzP6guA065kwGQj4Y2yHBx/Kq1Zrl1xUD3fuDvxarZLFKTvB/SS7n96cZKjVEosLToL+CPtPL7+uPbwGX/ryEt/54RPmF103HGU8uGsR6E0yeoN3LwRBwOv14vF4iMViF0lHt9IRjUY5Pz/n4UMf3/uff51f/2df5b/+nz/hP/0f/y9ysz3MpZAEKokynik7tFqc7Z4xvepD0ig5eHbKxLyTB+/PkjkvcNRhg975ygprj2cJPD9tu4p/1NY6d3lWWNzwsf/i5W+bimZJRbNo9WqcDiO5dIlM6nq1oVKqsf3fDkEhsLzhI1eqE+1j+ndx3HieVDyP3qhGr1FiMKgpFK4nKclYHmJgs2oxebSE4gXq9wjm8XCOeDiH167HNW9lK5ygWr/+nL6qIH4BGfZ24swZLHe/tgcGg+GtUtUXHK9qnfrss8/40Y9+xM7ODltbWxc9+HetUypB5H23py1fO24y7PyeZJhew6zdwmEgwfYQw+PNZgtRgMUpG6V0laD/biJNpWyTYdGTNHtPb16nFJLIyqKL6EmSoz6yuIIADx/N8N0fPmH94WSfI7zEfSvwd6E3yeglw64ex+l04nA4SCaTbG1todPpLiodyWSSSCTC8vIy3/gff5lf/Se/yE/+74/4v/73H9OsNxABURIpZstYHEZEScHJZyfMbU4hSlYCT0/QGTUsvTNFtVInuNu+jqsfzrP2eJaj3QjlQpXAZ+1W4I0vLbDy7hSBZ6cXSWMuVWT7J/so1RJmow+nx0Qier06Vq822Pmo7Su19M4EJZuBUD/Tvw5y6RLbHx2j1irRa5RYLTrSmevJeSZVIpMq8f+x96bRjebndefvxb4DxEasJLgWi2TtS7csqdW29larZUkteZHssZVknHHsJJ6TxMmcKD7HH5LjSE4yHh97Zs5J7JnEHltS7ESSl2MntjxSq7urq6uqu4orSGLfiZUAsQPzAQSKC8CtWK3unrrfqggCL8AXz/O/z3KvRivH7NQR3ixRqe79Xp3EDyq7WSK7WWJIK8d1zs5KMk2xcnBy4Djdj0eBAKyvbaIXHi7dt9vtIyXalUrl/7/HqG7dusXk5CTj4+MA/PiP/zj/9b/+1wNB/Mtf/jL/5J/8E7761a8e+ZyCIPDLv/zLwMHFu1qt/1hJF+KmiPves6kQqWRSZqwmQqEsS48gC+gwaTHLOwZH+RMuj8skIjy2IcL+7KEuql1o1HLGnEP4V5MsHdLJUCqlTIyZCawkWH794AyrSCRw7ekxPvW567hHTUe+7lmMUfVDd871KJLR7/lsNhvDw8MkEglu375No9HAYrFw9epVZDIZjUYDqULCR3/2WX7ki+/j5f9ym+/90S2yyQJisYBcKaVcqqLRKtHoVGw8CKJSS7A4jKzeC7BdKDN2wY1YIqFea7B4awO1Xsn0DQ+h9RSFTInadpW1O36GbFpMTiOBlWRvr6NcqpKOZikmCsxcdpPLlYn3Wfqm2WbtjZ09jQtO6m3w91m666K0VaWcK9MsVJkcHyKVLZPPH1xQy2fL5LNllGoZo+69wfw0f8psukQ2XUKvkeM+b2c5vkmp+nCB+3E7lHchewza60/wzsbjyFMAv/iLvwhAMBjsq5o4CDJBzP2Vsy2GJWOFMyqGJVmMnUyhSiwSmHIbycaL+I5RDFPIpUyOmolsbLJ8SJ6SSsVMT1qJ+dOs3O0vFXzjPeN85iduMH1+8NhaF2dxWBuU545DMvpdz27Sce/ePWq1GkNDQ1y4cAGVStUxMhYLvP/Fp3jvZ25w5y/u8+e/+x2ysRxiiYBUKqGyXUMmFaPSKPA9CCERtxmdsRNcSVBIF3ujwM1Gi9XXvMiVUmave0iEs6TjeSqlKmv3w+jMaqwjJsIbGSrbnbNWvdpgK11k059m+rKbcrVBqF/+aUPcl6WQLzM2Y0OskLF2yFmsWq5TyVcoJbeYmraQLpTJZA7uFZa2qpSWq8jkEibH9MSzJYrbjZ2XPHlOKW5VWb4dQq6QcG3OxnomR670MD+eVjXxpJBJT348fzuTjUe30z4CkUgEt9vd+7fL5SIS2Rs87t69SygU4vnnnz/x8x/XLKkL6Rn4OuiUcq66bMiKbRYWYxS2Tq6SADBqNTA7bCblK7C8nDjRYVwpFzM/ZkHZEIj4sr2ZyEHQqKVcmLLRzFdZuhehPEBzWqORM3/ejrhcZ+n1INv7Kt9isYgPfmye3/gPP8Mv/KOPHotowNm3p1utVq+T0Wq1kEgkyGQypFLpsUzaoJMU4vE4fr8fi8XC5OQkxWIRn89HtVrtXXOj0UAkEnjmc0/zz37/F/mJf/Yphqxa2q1OpU6lU9BqNGhWG5gcerbzJRqVKrM3x9lKb7F214dIBOevj1Er11n4vpdSZouZK25kOyZR2fgWa68HUEgE5q6NotEre59bs9Fk5baf+FqCqalhPAM6SAKwfj9C8EEEj8PA1Dnboe+/VqnjW0yynSwyN21jeLj/InS5VCOwnEFUaDLp0KFVS6nVTqFgtYNSscryayFkiTpzZgNGrRLJYzAJHITuLGyr1XpbB+cneOvwuPPUbon2typPqWRSrrpsGOpSlhbjpPvs1x0HDqOWiw4rhUiJpYX4iUZWpBIRsx4zQ2Ip64tJskdcg0Iu5sK0HWm1xfK98MC8plBImT9vRyXA8p0g+X2dX0GAH/rANL/+v3+Rf/qrnzoW0ej83tmPUTWbTer1Oq1WC7FYfKo8tbm5ic/nQ6/XMz09Ta1WY2Njg+3t7V4M695T1z92iS//4T/kf/zKF7G6jNBuIRLaqLRy2s0WlWIFs1NPMV2klCkyc91DvVpn5dY6QrvF3M1xBEFg8WUvmUia6Qsu1Dv5qLBZYu1OEKFeZ/bqCAazZudzA9ptvHeDhBajeDwmJs/3G6fu5KnAcpyNe0EcZg0zs/aBXigAzUaLjcUE+UiBmQkrLtdQ38fVqg0Cy2nqmzUm7ToMOhnV6unzVLXSYPn1MM3gNjMGLbYhzc7n/NaQjZPGgH4yyW8nPPbORr8v7u4PpNVq8Uu/9Ev87u/+7qmeX6VS9dUvHwT5IwRxs1aFW6vFu55kIXF67fEphwlRtc3GWvrIhbj90CileIYN+NdSPRdVjXrw4qrZqMZFNIqGAAAgAElEQVSsU7KxkmQpNbhCpNcrcdn1bCzFWLpzsJMhkYh4/wen+bGffi8W6w9Gnafdbvc6GSetEO1/nng8TiAQwGg0cuXKld7yr9Pp7KkRDVrQu/qhC1z90AW8r2/wR//uz9i4H0SgjVIjQ6DNVmaLkWk7pfw2m+EMU1c9SCVi7n93Ce2QmukrI/iXYyzf2mDmuofxGRvlao2YL8NWbpuFl71IZRJmr4yyy/S740q6M5blnhpGqVfiXYz27TIEdypGNvcQQzYDqyvxgUGy1ezIESLAzLyTUrVBKHxQcaReaxBYySCIBOTWTjDPFQ7vJB6GWq2Bf3ETsVjEpQt2opVtYpnTKeGcBF2ycdgc7H68nYP4Ezw6Hnee2t+BP3K38BHylF4pZ8I4xIYvxcIxTPAGYdRqQCNIWPWm2Dzh8rhCJmbCaSTiy+Dd1XEXiYS+Etd6nQKXVcfGcoKle4Pd01UqGeOjJgIr8b55SiQSuP6eUb7ws8/gOmYhbDfOUshk0O7gcbGbZGg0Gi5cuNDzVrDb7WSzWZaWlnqqeWq1uve6giAwfWOcf/61f0hoOcof/8afkYlnEWihUHWITi6Rw31umEalTnw9wfilEZQqOXf/ZhmlRs7szXGivhSrd3yMz7twT5gRJAKh1RTlUpXFV9YQSUScuzyKVPbwKNlxL+/cd/ZRE4ZhPauL0b75Jx5IEw+kMQ5rGfZY8HoTff2nOh8IeO9HekqLTYmYjT57Ha1mi8BqpqO0aNJiHpKzmT096Wg1W4RWsz159y1xk8L26Z/vuHiUGPB2xGMnGy6Xi1DoYXszHA7vUeHY2triwYMHPPvsswDE43FeeOEFvvnNbx65fAeg1WpPRDaOo1++H44hLRaZgpX1FIuR0zuIz7gs1As1AsudFuOJXVRNeta9KVY297bX+wVHm1WLUaPEuxgnFx6cKIxGNXaLBu9CjOU+85ZyhYSPfOIiF6+b8Iw7MBpPRzQeNYi32+3euBScjmS0Wi0SiUSPZHTHpfZfp9VqxWKx9F3QA3qkY/LqGL/8H/8esfUE3/ztvySyFoN2GxFtZHIxm5EMrgkrQrtNuVDGPTWMQi3nwfdWkcolnL/uQaaQsXx7FQCbx4zeqsf7ZrgzdvXqOmNzDqZm7ZQrDcIbDwNreIdMWBxDyIekxEJbNPrsQSRDWZKhLAaLFse4Be9akmplgJPqrmA+OWOjJRGxsXEwmLdbbYSGmGJkG5dbQ0VoP1IwbzZbhFZTFCs1Ls3ayNE4tVfHcdBtTz8hG0/QxePOUxqN5kR56ig/qH4wa1W4NVq8G49WDJt0mBBXYWNt88TFMLVSwphtiMD6Jqt9difE4r0jZMYhFQ6TjrWlOCvxwblVq1Uw6jKwsdSfZEgkIp79yCxPP+PC5jBgt5+caJwV2u12zyfjuGO9+38/lUrh9/sPkIwudqsRZTIZVlZWkEqljI+Po9Fo9hTmnNM2/v5v/S3S0Sx//h++g/9BCFpNaLc6hoCxHMNuI3K5lK3MFvZRI3qzriNyIsDM1TEkSikPvt8RPTHa9QyPWli7H6Fe6+wfnrs6ytiUFUQifLvG/7pkwmDRYrBryOf654mu07hap2B63onPv0mpzz4h7FVa9Iybke3I5vZ7nLgtIRcsYXcoacslZyLvLpVLmPMYqSoE1k7p1XEcyHfGfd/uzuDHxWMnGzdu3MDr9eLz+XA6nfzBH/wBv//7v9/7uV6vZ3Pz4Xzfs88+y1e/+tVjBXA4TXv6+F96j9mAFgkrGx0TvNNAEGDObSWfKrOxeHLJQqNOjl4uIRLcGuiiurtS5LTp0SpkrC3F2WwPPrBZLFosQyq8D6LkIwcfp1TJ+PinLvP8Z66gN6hYX19/y6Vr4eFOhkgkolAoEI/HcblcJ1rYbbVaxONxgsEgJpOpL8nod72DFvS6gb9bQbKNW/m5r36Rj//dZ/nGv/s267dC0GztGC3Jyae3MNkNaHUKvPeCWBwGzE4jq6/7mLgwwtRFN7ncFnH/JnF/V43KyvpCFEEQ8N7tLOl5Zh2I5VLWFx8eJFLRLERBa1ThnnOzsZqgvH2w25BLbZFLbaHSyJmadyIc0rYWAN9yJ2GMeEwoDUpWVvcF83YbAUiEirQBz6iBiqh56mAuEYs7wXwh3vPqqMgfTzDfPUb1dlbveIK3Do87T5183Pf4ecoxpMUqU7K8nmSx9SjFMDP1rQbBUxTDdGoZJpWERGT7QDFsN7rjMlazBotehXcxwXJkcDfTYFDitOlZX4iylDz4OKlUzIeeu8CnPn8Ni1VHOBz+geapZrNJrVYjEokwMjJyIhW73SRDq9X2JRn90CUdXd8FiUTSM3vbTTqGbHq+8M8/zcf+zgf4T7/2de796TLtVot2o4l2WEMxV0SukGK06ti4H0ZnUOKctLH+IMzwiJHx8w6q9RqRtU0ysXynS3/Jg38ljkgkwvegQwKdk1bUBg3eB5HeZ9nNP1aXgfnLbgK+TbbyffYvdhSsZAopcxddiI/4HnRlc+0OPUM2Pcur8b2qZ+3OfbwZ7byWy6mjrYBI8vTO5I1GE9/qjrz7mAmRQcZS+PSCC4Mg3ZWnTkJW36547GRDIpHwm7/5m3z0ox+l2WzypS99ibm5Of7Fv/gXXL9+nRdeeOGRnn832TjW4t0xDhdT1iHq+QrRjeyJKztdSMUixi06cskyq30Mjo5Cx0VVydpqkq0jYl+r3WLUZUSOwPpq8lDzQJtNj0EtZ20xSiZ4cFRGo5XziU9f5bkfvYxG+1Cv+a32ydi/+K1Wq7l58ybhcJjXXnsNl8uF0+k89Et4GpLR77q7pCOdTrOwsIBSqdzjMN29Rq1Jzcd+/hlG/+UYf/E73+Hb/8d/Azra51KpmNxmEZlcjNVpwL8YQqmUoh1SsnjLx3ahjHvGjkKtYO3NEJl4HpVWwZBZi3ZIxVZ2G/+OCZJ9zILeomP1QYTWTmt6K7PN4svrKNUy5i+5CQbSFPooeGwXqyy+usH8tVHm5xwEw2kKhziIR/ydw77docdo17O8mqDZbO0Z3RKASKBDWE8bzMW7djZ2Gy89jmDeXRA/SWfjCd7deCvz1Fl1NsbMBihWiAQKpy6GiQSBKZuBrc3KHrfv48KoU2Af0rK2kiScPLrQYLfqUIgleBdjZIKD/Z9MJjXDZg1rD6Isxw6+N4VCykc+eZEXPnuVIdNDKesfdJ6SyWTcvHmTWCzG66+/js1mw+12H1oc208yLl68eCqvhK5Udy6Xw+v1IhKJGB8fR6fT7SEdSq2C93/xBj//r77Ef/+97/HHv/FnCK0WrXoDqU5JdbtKu9nAOeYivBpF1GpiHNaz8SBMfrPI8JiZIase770gC9/3IlNIUWtlGId1ZBIFImtJIInZMYR1xIR3IdITPamV6yy8vI5ULmH20giJeJ506iCJrFXqLN3yMXdtlNkZG/HNLTKbg4l0MponGc1jNGuweUysriWp1Q6OY3WNc+3DGqRaMaFk6URiJ206nf0uQr5Ofhx3GlDb1CyEkmcmdHKaPPV27sC/JXqOzz33HM8999ye//vVX/3Vvo/9zne+c6Ln3l8xOu0srADMOixU8jWCa6c34lPIJMw4zET8WXyrfZSDjoDTokEjkbG+ljqWi+r4qAlxo83GaupQYmQ2KzHq1KwtxOiXUvQGFS+8eJWPfvISStXBA/lbpT/eNdnrLu92F+m6pGJ8fBy3200oFOLVV1/tSzparRaxWIxQKHRqkrEfgiBgNpsxmUxkMhmWlpZQKBQ90lGpVAiFQigUClRaBZ/+Bx/n+b/7If76//k+f/S//intVpNmrYbBpKZWrlGv1Ji84qFarNAsV/HMD5ONbxFajmEbszA0rMf7RpBcIk85V2L2qodELEc6lifmSxHzpXp+HKv3wzQbHQJQLtVYeGUdiUzC7GU3yeQWm4mDybrVarN4x48gFpiec5Av1kjEByf/bjAfMmmwjZkGemLsDuYSrYhwcvtYwVws7k8ae8HcoUdj7xgvPWowl55iZ+MJ3v14q/LUcWLpYfPa08MmRNUWGxtHm+ANglQs4rzLQiq6hW/lYNHpKFiHlJg1atZWkqzGju6muJ1DqMRiYsEM2wPESQAMBjl2ix7vgyi58MGOu0ol4+M/epnnP3MVnf5g1V8QhCPPAEfhOHlu/+7g/nGpkZERnE4nkUiE1157Dbvdjtvt3hNv2u02yWQSv9+PXq8/NcnYD4PBwNWrV8nlcqyvryMIQo90VKtVAoEAUqkUsVTMx770w3zkf/gAL/3xa3z9178N7RatWgPdkJpmvcF2rsTE5U6eKqaLeOZtbKXLLL+6jsk+hG3MjPfNEKXsNtlYlpkrHnLZbeLBNJvRLJvRbKcDMu9kbSnac9mrVxss3dpAEIuYuewmX6gQC/c5L7Vh+W4QBBifGabWEgiHBt+v2c0i2c0iap2Cc7OOga5+m4kiJMBsUqE2ygilSn13ifZDKhao95G+jUdyEMnhtGgwjRpYCCcf2Tfq3bZb+I4Xj9+/eHdkxWhfEBeLBObsVnKpImvLp3dm1ShlTFqN+NfTHQOxE2LUpkPaEuH3pUlydAKZGrdQ36oRXEoiEg1Wkx4dMSJut/GvJslSOPA4o0nNpz5/nQ8/dwG5YnDL93FXjHaTDJFIdIBk7EZ3NnU36XC73T3vjFAohNlsPhOS0e99mEymXtt6YWGBer2OIAhMTExgsVgeLgVKxXzkZz7Ah376GV7707v8/r/6LwhAs1ZHP6Si1WjSqNXxzDlJRdNsbRaZuTZObnOLpVfWMFh1GExqwmIRi6+uIYgEzl31UMhXiPk3e34cCrWM6Stu/N4U2zvKaI1ag6Vbvs7vXB5hq1TtmPvtQ7vZZu3NCAhw7qKLUqVxeDBPF8mmi8xedHF+ehhfME2lcrCCtDuYq4xSwqntQ4O5+Ig2cTyah2j+TIL57iB+3Pb02zmIP8HbH3K5vCfLfpx7SbKPfO8uhgW8Jx9z6kIhk3DOYSbqzx7q9j0IDrMGnUzGmjdFlqNl2sdGjEgasOHtFMN0OiVwkGw47Hq0KhnehShbsYPPq9Eq+ORnr/LxT11CrRl8IH9cEutdnESgRCwW7yEdt27dwm6343K5SKfTPZJx6dKlx+L6bDAYuHLlCvl8Hq/XS7lcRhAEPB4PNput914EoSOb+/4Xn+KNv17gD37tm5S3oFVvoNEpEAltirkiY3MOtvIl0juiJ5XtjrKixqBCrVOgUstZvt0xCZy8NEK92SawEmcrW2Jhx4/DNWen3W6TT3fObO1mi5XXA509wYsu6m0I9DOdbYN/qWN4N3HeTksixrc++LxWKlRYvB3gwmU38zM2NoLpviQ3l94ml95Gr5OjtyuJbG5Tqw/OK2KxmHprwAI7kE4VSaeKmA1K7JPDLMU2KdcGk+vDIJM83C18Mkb1NsBJF++6C+JyiZhZm5lYJM/K0un1zIc0SkaNeta9SRZPsZQ34TTQLLeIrB/toioIcG7cSimzvce4qJ/L8/iYGWpN/CuJvtUvq03Hp3/sBj/8kdk9ShKDX/vxkI0uwegGPZlMhiAIx/pydUmH0+lkcXGRlZUVhoaG9qhLPS7UajVSqRTtdhubzUY2myWRSKBWqw8s6AmCwFPPX+Wp569y57/d5+tf/TbbYhGtehOEjklgIbXF6IyDcmGb+HqCySse6vUm+VQBMS1mb4wRWI33zADHL7pptQX8yzEqpRorr/mQSCXMXh0hEkg/DOatNqt3OsF8Yt5FUwD/Wh862wbvjlfH5KyDpljAN8AZFjrKVN7lOFK5mAsXXATCWQqFg2MUnWDOkcFcdEzp224wN+mVGJwa/PltKn3a5YdB/mSM6gne5hAEAZlETLPVYs5uIZcsnVkxbOkUxbCRYR1yRPg20qQ4muhMjlloluqEljsxpPv4/WHd7RpCIRGxvhQn0ed5DUMqXvjcNT7y/EWUyqMLR48zT51WBbFLOhwOB8vLy/zN3/wNOp2u55PxOFGv10mlUtRqNWw2G1tbW708ZTAYDuSpSz88x+UfmWfx5VW+/uvfJh3L0m40odVEJhOTS+RxTpppNZqElsJ45l2IpVJK2RK1YpnZ62NE/ZusvdHZ3xiZsSNTKVjbMbONrqUoZkqcvzLKZnKLVKzTTReA9Tc7KmSj0zakGjlrS7EDN4QA+HaUrkYnrci0CryH+NE0W22W7oURS0TMX3QRS22RTh8ks1uFKluFKkqVFJdLSyxbptyniCaViBikr7Ib+VyZ/O0QarWMMY+BYKFE8Ti/uAu7x6jeDaay7/h30B1hgeNVjKQSEVddNgKBNIuPYMRnNaixazR4VxMsRk82ny4IMOkyUs5XCa4evQArCDAzOUwuscXa/f6JQhA6RmuTkxbqxSqBnSXi/Z+IwzXEZ37iBu//kRkkJ5BWexw+GbuDnFQqPTbJ2P0c0WiUUCiExWLh3LlzxGIx7ty5g9vtxuFwnHlFoFqt4vf7yeVyjI6OMj093bvvjlrQ68rmXvvwxV4wL2ZLtBsNpFIRMrmE8GqckXN2aLcJPAhx5YNz2D0WFl5aRSqXMnt9jFgwzcaOBK5r2kZTaLEZ3qJarrH48hpiqbhvMN940AnmI1PDKJX9v/oCsLGzGzI6ZUWmluPdvxy+C/Vqk8XbASQyMfPzTmKpAun0wbGKbjBX9IL5NuXKw8LAoDGqQSjkyxTyZVRKCXOzdrybmWPLET4Zo3qCHxROooV/1WXD50+z8ghGfAaNAo/RcOpi2LhDD9U2oY2ji2EA0xNWKrkK/oX+ubUbjz0eE+JmG99q/2KY0azh0z92nQ9+/AJy+fGPKWdNNvqZxg7quA9Cu93uqSAaDAbe8573kEqleOONN/qOV50F6vU6wWCQZDKJ2+3mqaee6l3z1tYWGxsbbGxsMD4+3pd0nH96il/5xv+M70GQb/z6n1Ba3IZWR2lRpZaTCmewe8zIFVK8d/xcenaWsTknS694O6NR18bIJLd6zuTDoyZkGhnbhTrNerMzQiUSmL48SrFYJbrLUTy42rl37KMmlAopIrFAq09XPLTTAXG4jeiGdawux/oWXqHj1bF0J4ggFpidd5HZKhPvMzZc3q4TWs0ilYmZ8OhIFCoUSw9Jwkn/TtulGv6FTaRSMVfn7QQKBdKF450XT7Mg/nbuwL/jyYZYLD5RcJFLJCwsnl4W0GnSYZQrWF1Jkjuh23fXRTWX3Ma/fPQynkQi4ty4lchGEu8b0YGPEwSYntrpeNzv/ziLTc0Xv/QB3vPM1IkPdp3XOJsg3jXj6/7fo5IMq9XK9evXe6ofExMTjIyMEAwGe+NVZ0E6uiQjm83i8Xj2kIwu9i/oicVixsbGDizo7Q7m/gchvv7VbxFYDkOr2VGw0imI+1MMjxg7h/97fuweCzqzlqVXvCASmLk2TnZzi/BOYHZMWNGZday+EeoFcwSB6SsHg3nIm0CrU2K3apGoxYQD+T1Lbw8fNziY778VGrVmL5jPXXCxmd8m0WdXpLITzGVyCaNOJelSg+J2HdEpg2Sl3GDp9RAymYSrc7ZjBfMn0rdP8FZDEIQD99BRxMO/kSZXPJ26m8WgxqHV4F1JsBg9Wb4TgAnXELVCnZD36H0OkSBwbsJKMpxmY0AxrAuHXY9OLiG0M02w/93rh+T8xM+8n2c/dP5YHfcD135GRbH9JOOwsd5B6JIMv9/P0NAQly9f7nXcR0dHcblchMNhbt26hcPhwOVyPTLpOIxkdKHVarl06VKPdKyvrzM+Ps7Q0FAvT3UPuJ45N//4d/4nYusJ/vO/+1P8DwIItGnW6uiMJrKxHAarFolUxMYbAYxWLZYRM947Ppr1FlNXPZS3a4R3Rp48sw6GXR68O52O1Tt+ACYuumm0ILBLwjYeSGOy6hjSyNFa1YR92b6GkvFQhngog2lYh2XUxOpK/KEM/L5bod1ss/JGiDY7nlK1QZ5STYKrWURiEaMjGnKVOvli/dgd+IN/lybLd8OIxCKuztuIVcpHekp1PeEajca7oij2jicbXRy3YiQWCb0uwEngGR5C1RbjXUuRYvAibT9IJSKmXEZS0S3WjyF/K5dJmPKYiQYyrNwbbMQnEglMT1nJJ7YILsepVQ+OkI1PWfnsT95ELMtx4+a5E133bpyFT0ar1dpx4xb1FupOglarRSQSIRwOHyAZuyGVSs+MdNRqNfx+P5lM5kAnYxAOW9DbTzpG51z849/9ef77t/6aN7/tJbKaQGi3aVRqWEdMVLerGMwahixaVu/6MVp1nWB+10ej1mTqqodstsBmJEt0PdmTzd0fzMcvuGgiEFjdG8wBjMM6hj1mVpdifb06+gXzQWjvMwgsVuuE+yz+1aoNohtbiMQixsd0tNqnNweEjkFgN5hfmbeRqJSJDgjmp1m8e4IneFR0l5e74zdHSS+fRP62i93FsPwJi2EiEUy5TBTS2wRWNo98vEQsYnrcSjqWx/vm4GIYwNSUlWq+TDqUJdmnCOFwD/HZn7iJQrvF009fONF178ZZ56nTkoyuaexhY71isbhHOkKh0CORjkajQTAYJJFI4HK5+pKM/eiSjmKx2Ot0jI2NYTQaD5AO27iVX/jffpYbn5vntf98n+hqHJEAte0KFreJeqWGQilleMTE+ptB1GoZzmkH6/dDVLdrjF9wU65VKeY6qoraITVTFzz4lmKUS1XWdzr1I+fsyNRy1hYe3k+ZeIFMvIB2SIV7zsnaSryvV1Q6USCdKKA3qnHO2Fjrt/uxAwE60rzA1IyNplhgw3fwnm81W0R9BRBgbMxATdTi5HIKe59v+Y0oCHBx1kb+EE+p7hjVu0Wi/R1PNk5acezOwlb7HKr6YcphQqi08HlPrvwhl4pwm9Wk4+U9LqqDoFRKmXSbCK5tsnwIyRBLRJybtJIKZ/HuuK0qlFLg4Xs6N2vnxS88xZUbHgBu3759gis/iEfRH+9Wh7oH9/Hx8RN9eY5LMvZjN+kIBAK8+uqrjIyMYLfbjwzEtVqNQCBAOp1mdHSUqampE99ruxf01tfXgY6all6vP0A6jC4DP/dvv8hPZSv88W/8GcHFECKgXa9jGtaRT+ZRqmR7grnrioP1B2EqpSpT1zw0W+BbiJCJ5w8E8437nfvEfc6OfJ/jfCZRIJMooDdpcM46jhXMDTolMrnkeG6vMzYaIgGfv38wD63l8HiMjFs05Ot10gOMn46DVrPFyk4wH2QQuJtsnLWAwBM8wSAolUrK5TIajeZYZKN72DgOPMMGVG3JqYphErGA26yilGuwsXR0MUwmFTM9ZiEeyLL6xuA81em4D1PcLLLxZudxw7a9prAjY2Ze/MJNnn5fp+N+69atE137wdc8fZ7qxmOpVMra2hpjY2Mn2v3bTTKMRuOxdwfFYjEej6cneHIS0tFoNAiFQj3/qeOQjP3QaDRcvHiRYrGIz+fbQzqAPaRDa1bzk7/yo/zUlz/Ht377LwkvRxCLBVq1BmabnlK2iNBu4Zq0EVgMIxbazD09QWA5RjG3zcxTExitOlbvBVl82YtSI2fuuofgepKt7DbBlU5nzOYxo7fsvVe2stssvrKOWqtg8pIb33qS7T7Gf/lMifyrPlQaORqVFLVaRqnUv5glABs7nlJjExYkGjnePgaBtCG8kcNi0eAxatimQTJTOdHnvP/51nY8peamrdSUAt7o3pH6d9u47zuebMBBqbqjuhzSY5CNkSEV4qbkVG7fKoWEcXvHRTWwdrQTskYtZ8w5hH81ydIhJEMqFTM9aSXmT7NyN7TnZ12fgvnLbl78wlPMX3Kd6ejHSSUFuySja8inVCp56qmnSCaT3Lt3D4PBgMfjOTQYN5tNotEo4XCY4eHhY5OM/ZBKpUxOTjI6Onok6dhNMkZGRpiYmHjkESy9Xs+VK1coFApsbGzQarX2zMoWi0WKxU4V0uQY4u/82hf4/D/6JN/8rb9g5fYGrWab8tY2ZpuBYqYbzIcJLIQR0cJ93kw6miOTyOOetqHUKvG+EeobzEM7wfzyM9Ocu+hi9X641+XLp4vk0x3ZwKnLbjbWBgfzXDyPtNZgZNJENFZku3SI22s3mE9akKhkePtUnKRSKX5vR21kzKOnRPORg7l3QDA/iX75u8W99Ql+8FCpVJRKJTQaTU/M5LB4Jj3GTp1rSIUSOf5TFMNkUhFTLhNRf5bwxtE+HQq5lMlRM5GNw4thIpHAualhMvE8a2+E9/xM2BnhnZge5sUvPMX1p8d7Rn9ngdP4ZOzfHbxy5Qq5XI6lpSVUKhXj4+OHmuvt9nMyGo2nVkE8CenYTTKcTic3b9585ANp1628VCr1SMf4+HiPdJTLZQqFAjabDZ1Jwxe//Bk+8w8+zp/9+7/iwUsrSKRitrJFzDY91VKFaqnM1NVxot441VIZ96yF7cI2wZU4w6NmjDYDq/eCLLzsRSqTMnttlFgoSzZZ6BncXnzfFLNXRljZkXcHKG1VWHxlHblSxtwlF6FghkK2v6dUKbNNo1BhbNJIJlsj38d7qovgjiCKa2QIrUnD8mr8wASMVCoh6s/RBkbdOuqyNtHU6V3JBegZBE57jEiG5CzueEq928Z93xVkQyqVUqvVkMvlvSB+2Pb+oCAuEgnMuq3kkyXigaM9LvZDq5YyajHg86ZYSR+9fG7QK3FZ9Wwsx1lKHUYyRJybHCa0nmT5TrDvYy5eHeX5T19hZt554us+Do4bxPvtZOz+ogwPD2O1WkkkEty9exeTycTo6Oie4NxsNolEIkQiEYaHh7lx48aZqDHsJx23bt3C7XZjt9tpNpsEAgE2NzfPjGTsh06n4/Lly71ZWa/Xi0QiodFoMD09jVQq7bXvdWYtP/UrL1LKb/Mn/+df8c3f/gukcjH5VAGz3UClWKG63QnmgZUIUpmM2ZvjRH0pQqtxbB4zhq7xUp9gXtuusXLbj9aswjlhx7sQfRjMCxUWXmqbLn0AACAASURBVN4VzAOZvgaB5VKNjTdjyFUy5uedBEMZCn1cYbsIrnWCqHvUiNqoZnk13pup7cZIAYj487SBkREddUmb2CldybvP13N79RgRG+U9Rbp3SxB/gncGTirTLj3k3jzvslAt1AgHckDpRCRDKZcwYR8i6Euz+uDoPKVWyRh3m/CvJFm+Fx74OLG4QzKS4Syr90J9HzM+aeFv/70f5vL10b7fqUf9nj1Kntq9O9iVN9/c3OT+/ftotVrGxsb2SNSehWlsP3RJx+6dDqfTidPppN1unznJ2A+1Ws38/HyPdKytrSGXy6lWq3g8HjQaTS9PKbUKPvtLn+D5v/th/vL//n/5xr/9U9RaJZViGdOwnkatzla6wNTVMSL+JFq9hvPXx9iM5Vh6dQ2TzYBtzNIpjr2yhkgiYuaKh2y6RCKUoVltsPiaD7VBgWvOiW850eumV8s1Fl/ZQCwVM3tlhES80NcgsF5tEFhIIpaKmbvkJp4skN4cPGIYC2aJBbNYbXrMTgPLq7GebHt331UAYqEOQXfaNQjqjqfUoyDsz4D/oaeUbOe1jipKvFPwriAb3SB+XLKx32tDKhEz6zSTjBRYPYX2+JBOjmNIx/pKkpXk0b9vGlJjN2nwLsZZ7qMp3oVKJWPcY2JjMcrSAJJx870TvPiTTzExPXzi6z4JjuOT0Q3eIpGoJw046LlsNhvDw8M99Siz2YzL5SKZTBKJRLDZbGdGMvajSzpGRkbw+Xx897vfRSQSMTY2xs2bNx+7prVUKkUqlbK9vd3r/HTnuPfPyqp0Sj7/j5/nhZ//MH/+O9/hj3/jz9EY1FS2yphsehrVOuV8mbH3uEn4U2ylCpy/Pk4qmmP51jomuwGbx9JpW+8K5pKdqsnW5jbLm+sYLDock8N4F6PU9wXzrkFgPF4gsxPMd98K1e0ai7d8SGRi5i66iSUKZPrIC3YRDWQgkGHIqMDusbDiTR74zAUgHjzbYB7aCebyn+wcaI5LNp4QjSc4C5zYE2rfGJVIJDDrspBPlY+1+7cfGpWUMauBjbUUKwPUonZDp1UwajewsZxg6e5gkiGTSZiasBBcjR3ouHdx4Uqn4z538fCO+6N2Eo+bp7px97DdQUEQsFgsmM3mnnqUXq/H4/GQyWTOnGTsh0Qi6ZGOYDDI9773PaCzXP44SMZ+SKVSZDIZhUKBRqPRI2PdWL07T8kUUp7/uQ/x0Z99lr/6/Zf4xr/5E1RaJbVSBYNJA602W6kiY+edpMI5NoObnLs+RiFXZuFlLzqThunLI6w/CLP8WserY/qKB9mO91cpV2Hl1Q3UBiVTV0fYWElQ3ummdwRRfDsGgSPk8tsdkz1g94Z4s95k6XbHyPb8JTeZXPlQI9vNeJ7NeB6NTobjnI1132bfBfHUzhnOblUj1YsJp8oDVbGOg66nlPwj1zrX3Ww+Fh+WtxrvKrJhNBoRiUTHNvZTyiRM281EAlmWTkEyLAYlVp0a73KS1djRB6FhixaTVol3MU4+MrhtrdUqGHUN4VuKsfT6QZIhCPBDHzjHZ3/yJqNj5hNf92lwmP74cUlGv+d0OBxYLBaWlpZ46aWXMBgMXL58+dC29VmgXq8TCoXIZDJ4PB5qtRrhcLizDGezPRbCUa1W8fl8FAoFPB4P58+fRxCEYy3oyVUyfvQXPspzf/tH+O+/9z2+/m/+BK1RQ7VYRq2T0W62yMSzTFz2kI3lSIc2mbk+Ti5bYuFlL3qzBtf0KOv3O8F89uY40xecpFI5svESuVSBXKqAxqBi6soIGytxKtudOdeeQaBYxPkrI2Sy2xyQ+WBHkeq2H5FYxPlLbtLZUt9l0C4KmQqFTIghkwa9Ro5MJqZWO/jd7QZzm1WN7AyCuVzeSWDvllnYJ3hnQK1W91zEj9XZ2MlTHbdvK6logdUHJ5fBNWjluIw61laTLKeOznNDBhVOs461pThLicEdd4VSyoTHTHhtcMf96s0xXvzCzY6b81uAbsFmP/aTjP0d98MgCAJWqxWTycTq6iovvfQSGo2G+fl5NBrNWb+FPWg2m4TDYRKJBG63m3a7TSwWQyQS4XQ6H0v8qtfre7r83X3FcrmMz+fD5/Ph8XiwWCzAXtIhkYr56M98gA998X18949u8bWvfBuFUkatXEWplkAbksEU4/MutvNlYquxjkFgtcGD76+i0imYuzmOfyXG6l0/52+OMTYzTGm7QjKYp5Qrs/B9LwqVnLkrIwQ3UmztdNM7BoF+2sD0ZTfblUZfIaB2s83KnWDPyLZYqRMJ9XEv30GxUGP1bgi1ToF1SEMysUW5fHAHJJ0sQRJMRiVqq4xwqkzjEVzEFSfIU++Ecd93DdnYHcSP2i0waBSYVUp865ssHSHV1w92sxqDXIF3NUkucrTih8OmRy+X4V2Ok24P3uHQ65W47XrWFmMsJQ+2A0UigWc+eJ7P/MQNnG7jia/7UdBPf/y0JKOLbiCNRqPY7XZmZmZ6Ox3Dw8OMjIyceWfjMGnA3eNVIyMjZ0Y6arUaPp+PXC6Hx+Ph3Llze6p7J1nQk8olfPxv/TAf/un38zdfe4Wvf/VPkCkl1Cs1dHo1YpFAKrTJxAU3pVyJuDfO1LUxKuUaC99fRa1XMndzHEEk7JIdHKHeaBH0Jijmth/uelwdwb+WpFTo7E60my2Wb/tBELjyvimKdi2p2MH7tNVssXIn0FGkuuSmsF0j2keRqotsukgxs42qLXBu1sGaL0W5fHBBPbMrmKssso4r+SlIx7tt8e4J3hnY3dk4TlFMo5BxadRGJJBl+RR5yrxTDFtbSbISP7oYZjFpsBrUnY57dPAYsVotY2zEhH85PpBkPPXeSV78wk3Gp07Wce/mmdN2EwflqdOQjN3PEYvFCIVCmM1m3ve+95HNZnnw4EHfMeCzwP7cuLuTMTo62tvp6I5XnUUc250bR0ZGDnT5lUols7OzPdLh9/sHkg6RWMSzn38Pz7z4FK986w5f++q3kcnFtOoNlCoZcoWU8GoUz6ydRq1BaCHM2AU3gljMg++vIlNImb0xhkQqYelWx8jWPWNHppCzvhChsl3tjAfLJcxe8RANZ8ntjEYJgPdeR9722gemcY4OEQn0yT+7jWznHNSBQB9Fqi5KhQq5ZAFxucaFWQf+UIatrYN7hflMmXymjE4nR29TE94sU+8j2XsUpCfc2egnr/12wruGbJykPS0Umiz4jva52A/3sBYFEnwbm2xSOHJOdsQ5RKtcIbaR4bCmt8mkZtik6ZCM+MFKsEgs8MGPzfPpH7vBsF1/4us+CwzyyTiNhO3+QLp7XMrlcuFwOIhEIrz22mtnZnq0WxpwkP64TCZjamrqzEjHfkWro2Rzj1rQ2x3MxRIxH/zC+3j2x3+I3/3Xv8fSfwugVMtoVOqoNTJkcjGBpSRjcw7qlRqhxQhjF90gEvHg+6vM/9AUszfG8K1EWX+zc2DwzDkRJBJ8i1HKxSoL3/d2gv7lUSKhTM+VnHabbCJPaiPN6IyVtlhKcKPP96kNqztBf2reSbXVIujvb2IpCLCVL7N4O4BKLePCrANfME2x74J6J5hr1BKMTjWRdLmvK/kgyHb0+48rKfh2DuBP8M7B/jx1VFFMUm6fqhhmM6kZUirwriTJH6MYZh/WIWk1iIcKZEODx0p0OgUjTgMbi/G+Y72CAE+/f5LPf/E9jJyy434WZKMbJw/bHTwO9pvGXrt2rTc73x0DjsfjvTHg0dHRR56t372vuJ9kdCGRSBgbG9uzSN7Nm6fJk7uXzQflxt3oko5KpbKn02G1WoF9pEMk4oc+dZ33vHCN//QbX8P/Uhy5XEqz1kAiFqHSKAksRXBNWhGLBdbu+XHN2FFqlSy87GXu6UnOX/MQ9icI7RgEOieHUenVeN8MUa82WHzloZFtKllkM/7QyLaULxNdTmAfN6LSa1hfPtjZE4CNhWhHnOScDZFCwrq3/4lNEDrGf4u3A0jlEubnHETiebJ9dhqLhSrFQhW5XIxrVEs8W6E8SL2xD04iZPJOwLuCbKhUqp6az3HIxkmcswHG7HqEOoQ2OgrLR4XB8VEToloLn3fzUIUQq1WLSa/C+yBCLnyw4yGTifnQcxcYPy/nmQ889QO/4RqNxgGTo5Og2WwSCoWIxWIDAyl0qn5dX4xIJPKW648/KunY34Y+6bL5/gW9bqfDZDIBB4P5+Q9M8LO//AVe+7M3+MOvfAuFUk6z1kQmlaBUK/AthHFPDyMSBNbvBXDP2JErpLz+V4uIJCJmb4wTC2TwL3RGJlzTNpQaJd77YWqV+sNgftVDMt6Rv223O/d1cDnZCdLnHSAT4+vjNi4A6zua5uMzdpCJ2dinSCXs+pZsl2os3g4gU0i5MGcnGM2R77N4Xi41iKzmUaqlOJ1qYtkKlT5eM/shfZeZJT3BOwMnXhA/gfQtgMuqRSWSsLG+SfoYxTCXw4BaImFtOXHoY4eGVDiGdawvRFlK9O+4f+BD55m/ZuDm0xdQqVQnuu69zyU6kWPyfnSN+JrN5qk77vtJxiAVREEQsNvtvd3D119/HYvFwsjIyIlJx26Scdx9xUGkw+l0Huvz252PT7NsrlAoOH/+PJVKBb/fj9/vZ3R0lOHhTjdrf56auOHmi3//8zz43gp/+K+/hUwuod1o0m400RnVhL0JbKMm1GoFq7d92McsKNUy7vz1Em3g3LUxsptFImudHDM8asJgNeB9M7THlfzc1VHy+SrxUKY37RvfyAAZXBMWlAYV3j7GzgIQ2PGQco+bUepVBzylBB5+rvVqg6U7QcQSEfMXnCQyJVJ9FtRr1SaR1RwyuYTxUQ3JQpXi9sHO/X48IRtvQ5x0Fva4+uXjDh3N7RbhtePZuEyNWWiUagR3tMoHBXC7XY9eLcP7IEq6ffC5FQopH33hIi+8eA3DkJq7d+/+wIxdugZHCoWCTCbTW1o7STLYHdQcDsexg5pYLGZkZASn03nitvFZ6I/3Ix2jo6PYbLa+lbdGo0EgEOiNaD3qsnmXdGxvb++pIJnNnaphq9WiWq3SarUQBIGnPnGFm89d5u5fLfCHv/ZNFEoprXoDod1Ga1ARWo1jHzOjVMmpFLZxjJhADouvrHeW5q6NsRnL91zJ7WMWtGYt3jfCnWD+6jqCSGDmqofWrvclAP6ljgmTe2oYuVbB2lL/YO7fqU6NTFioCw1i0dLDH+5DrVJn8fUgYqmY+QtO4ptbbPZRESmX6kRW871gnshXKfUZw+pit1nScf4+TzobT3AWOHGeOmZRzG3VIGuLCfqOVwzzuI1I27Cxmjq0GGY2a7Ca1Kw9iLIcPdjxkEjF/MhH5/j0j13HatOzuLh4Inn0fnhUPyeJREK5XD61n1OXZJzEz6m7P2G324lGo9y+ffvYY8C7PaROK4qym3R0TWwPIx27ic1J8vEgKBQKZmZmqFQqBAKBHumw2Wy991iv13v3+4X3z3Dh/TMsv7rGH37l20gkImi1qVdqDE1YiQc2Mdn16M0aKoUyJosWuUGG966fVrPN1FUPxUKFmC9FIpDG5BjCOmJi9Y0wzUaTldt+AKYujyKT7/0sIzvytrYRI/phPd7FaN/9v8hGZ5zK4TYiqARikSKtVhuhj0xzs9Fi6W4IQSRwft5Jrlgh1mfxvFZtEF7NIZZ0jGzTpRr54mAzW6ns5NK3b2e8K8iGVqs9M7IhEmDKbSKbKBDyDp4z70IQYHrcyna2jO8IhQ+XawiVVMzaYqzvWJVKLee5H73M85+5glb3cEG6W+15K9ElGV0FCp1Ox9NPP000GuW1117DZrMxMjJy6Jeg0WgQDodPTDL2Y7cU4FEVnMehP76bdPj9fgKBwB7ScRbE5jCoVCrm5ub2LOi53W7K5TKJRILR0dGeFKFIJOLqB+e5+sH5TgXpK99CKhXTbnZcyQ0mGzF/itFZJzqjmpXXfVjdJobsQyy/ttGrIOXTJWK+FDFfCqvLiNFp7FSQGi2Wb/sYmR7G5THQaEI89LArF94xRHJ4TGjNWlYXo32X9MI7Qd8xakJn1uzpbOxHs95k6U4QQSwwO+8is1UmfoxgvlmqUdgXzMViUU++sDvHfRjeCYt3T/DOgFarJR7v5AixWEy9fnh1s6uzPwgTLgOVXJW473gmfhMeM+1Kg+BK57s36Bs3bNVi1CnxLkTJ9lmclcnEfPgTF/nU565hsmh7/38WeWrQgvcg7N8dVKlUPPXUUyQSiWOPN53WNLbfte8fAx6UJ3e/5lnJu0skEsbHxxkZGelLOs6C2BwGhULBuXPnqFarvTzpdrtpNBpEo1GcTueePDXz1CS/8o1/yNo9P1/7yrcR0elwV4tl3OedpCMZhj1mLM4hVu740Jm02CeG8d4LUq81mLw8Sq3aIORNkI5mMVi0OCbdeO+HqVcbeO8FmL7sxjmiB7GYiO9hYTcRzJAIZjA7DFjcRlYWojT7LHPHQ53fMdv0mN1Gmu3B93e71Wb1zc4OyLl5B9u1JqHwwWJys9Ei7M2BIDA+ridXq5PJHxwXlp1wt/DtXhR7V5ANlUrVIxvHWbyT9gniErHAlMtEOr51LBdVkUhgZsJKLrHF+hFztaOjO5WklUTfSpJWp+D5z1zl45+6hFpzUOLstNWe/TjOLOzu4C0Wi5FIJHsOZC6XC7vd3htv6tdpeBwHfuhfwemOW3X1x0/bEj4OZDIZ09PTPSd0v9+PRqOhWCw+ttfcDaVSyblz5/D5fCwvLyORSJiamhrYtp5/3znm33eOldfW+dpOBUmgTbVYQSoVE16NodbLMNr0eF/fYMiiZdhjZfVegEa9ydRVD+VSjch6kmQ4g9Gmx+ax4t1xIw8vd4jF5KURqo0WofWHexsxf5qYP43FZcDoGGJ1MUqrefAejgfSxANpLt4cY3bOycpyrG/Qhx0VkTdCHRWRWTvpQonN9MHxqm4wF0QC42N6stU62UInmB/HKG0/3u5B/AneGTiLDrxIgEm3ke1sleBK//2n/Zget1ApVAksHq5kZbfr0StleBejbPZJNwqllI998hKffPEqhiH1wWs7IVHoh+Oaxx61O2iz2bBarcTjcV5//XWsVuuBTsP+A/9pScZ+HDYGLAhCr3tylq+5G13S0R2veuWVV9BqtRSLxTP1rRoEuVzO1NQUoVAIr9eLIAiMj4/jdDr37NR089TkZQ//y+/9AoGlCF//6rfx3w8gFglsZ0vIpuxEw0lkMhF2j4X1O35UahmuK6OsvRmkWq4zPu+iLRLhX4ySS22hHVIzfcHD+mIUEIjs+CyNnrcjUchZ39V134zm2IzmMFi02CcseJfi1GsH9yq6Mrgzl1zMzTvxrsb7KidC53y39qCzAzI1YyNf2SaZ7OMV1W4TXu8YBI6NGSi2GqSyDxfOpbKHZONxy/G/FXhXkI39s7CNxuFLODLpwz+cTCJiym0kFsyzdkQwBpBIRJwbt5IK51h9I3roY8fHzWxnC4SW+5MMw5CKT33uOh9+/gJK5WA1i7OoGB21eLe/QrSfZOzG7vGmYDDYM8ezWq1EIpHHajgEe4Opz+fr6Y93FTQed8tRLBajUChot9tUqzuH2FPssJwE+6tSzzzzDPV6HZ/PRyAQOHRB79yNCb78tX/AxptBvvaVbyG02wjtNuX8NganjnQkg1qnYHjEjPd1Hxq9EseUDe8bQerVBhMXR2g22wRWYmTieXQmDSarjlhgk3q1ydobnWXRsTknSCT4ds25psI5UuEcxmEdw2MWVhejNOoHg3Sz3mT5bpAhixbbmJnVlTj1Po8DoA1rC7FOMD9vow74AwcPXu3WrmA+bqDYbFCutR5pAfUJnuC02E02jlcUexjHxCKBKbeRXKKEb+locRORIHBuwsrWZomNI4z73O4hWpUKsY3NgR33T3z6Mp/49N6O+34clygchuP4ZHQ77kftDopEIhwOBzabbY/giMPhIJFInDnJ2I/9efKll14CwOFwPLbX3P/6CoUCQRCoVCq0221kMtljzVPtdpt4PE4gEMBsNvPe976Xdrt9wES3H+kYPe/kH/37nyO6Fucb/+ZP2XgzgCC0KGWK6Kwa8qk8ErGAa8rGxr0AEomIyacm2FgIUy5WGZlxIFPIWH8QZuFlL0qtHJ1BiVIto1yqEdghGa6pYZR6Nd4HD2Wdc6mtHaKiYvqSi/XVBJU+o7jtVpvlWz60BiXT807W11N9ZXBhZ/F8ZyF9bNKCWCVjbe3gN0wAIr4dV/JRHVVRm3i6jET80M/kSWfjbQKNRkMq1QnAYrG4dwDcj3a7zebmJvlcFoVczKTDSMiXOZZ2uUwmZnrMStSfZuXeYO1xgMlJC/VilcBC5+befwuYLBp+9PM3+ODH55HLj/4TnFV7ut+M+klIxn6IxWLGxsaw2WwsLCywsrKC1Wp97JUT6LD9aDRKOp3utWfj8ThyuXzgTsWjYv+B/+bNm0gkEqrVKoFAoHfoHx4ePrPX7+qqB4PBA8uKYrH4RAt64xdH+Kf/8e8RXI7wl//Xd3nw0goiEZSyJRyTw2RjWeRKCc4JK2t3/SgUUqYvj7J2P0R1u4ZnzoVYImZjIUI6kkFMi4mrbgIrScqlKr6dBXP3OTsytZz1XUt4mUSBTKKA3qTBOetgbSVGtXKwKJBNbZFNbaEbUuG84GTNm6Ra6T9uIgAbS/HO4vmUFZFCytr6gGC+0QnmE9NGXn75ZTwez5MRqSd4S3FcifZ2u002myW9mUQqETHtMpKIFI5l5CcWC5wbt5KObuF98/BimMdjQtxs41sd3HH/5Gev8bFPXUKtlh/52meZp/Zj/1jvSYo73U7D8PAwi4uLrK+vYzQauXbt2mMx49t/3bFYjHg83jtkp1Ip5HL5sRe5T4rdB36TydR7n11p290TAWf1+u12m1Qqhc/nw2AwHDA67I4h73/9fqTDMWnj7//Wl/jxf/oCf/kfv8vC91aQiFUUN7cYHjGxld6i3WzgueAhcD8EzSZzT40TWI5RzJdxTg6j1qvxvhGksLlFo1xl+pKL+P/H3ptHOXqXZ6KP9l0qLaV9r72qt+rqdttgcLyADQYTgk0AJ5OECYFsA2GSm9yce3NI7klOIMAkOdyEyRCGuSEEAwPBxo6JTWywsem93d21apdKVSrt+77cP9S/r79SSSqpJLW7+/RzDucArfo+re/7e97lefxJpBM5atRXa1VBppZh40qQygWZRB4rP3NDJOFj4agJHlcY+V2KiM1fSSZZwMoZD4QiLg4dNsDjiSLXRjmRwO9snk9NZgWEChE2HKE948UMANu+phqpySjBubOnYTabeyqO3QoFNNZnPvOZbv/e9R9vFjidTni9Xrz97W9HsVhEoVCg5EKB5g9+Z2cHa2traDQayOfZcK3uYGcr3bl6eg0CPgdzdg0KsTy2fIm2BySgubsxM62BgMFAwBFBOrm3babWSvHLH3sbfvu/vhOzC/rmYlQPiEajkEql4PH2D/idEAqFoFarKYZMgjddf5zNZvf1hSWqSy6XC3q9HrOzs8jn83C5XOBwOBCJREP/AZBl87W1NYjFYszPz0OpVEKlUkGtViMcDsPpdILNZg/t/mRxcGVlBXw+n7onCdRsNhtKpRLj4+PY2dmBy+Ua+P6NRgM7OztYXl4Gm83G3NwcxsfH21Y42Gw2VCoVVCoVdnZ2qNcvkUh2SRYDzerH2LgUxx86hLc8toQrP1tBzJ+AUjeG6GYcWrMKuVQepXwR1nkjPFcCYDGAmeM2bDp3EPbHYJpUY2xcii13BNFgAhx2UwEkny2hUqoiHcsisZ2EwaKE1qJCnOYZUyqUEdlskn2tRYpyuYFqpQa1ToYIbQ+jVKwgGkyCz2Zi+pAB2Vyp42+VASAVzyEZzsBkUEBrGEOMyPS2PI7P5eJjH3sE4XAYkUgEXC6Xep86fQ7E3f1Nxp++2U/gJsZn3uwn0AsikQheffVVPProo6hWq0gmk5RHAXC9GLa2toZSqYRaXQDnyg62g6mOeYeAw2FhblKDeraCoDeOQr7z4unExDhkfA4CjjCSsdzejrtCiF/8T/fgk3/0Lhw5bqakovdDKpWi4v5BEY1GIZPJqIMqWS4meYrL5YLD4fQVV4nU+vr6OlQqFebn51GtVqkRH7FYPPQ8Rc8ZPB4P8/PzGB8fh0KhgE6nQyKRwPr6+lDv32g0EA6Hsby83FQpnJvblfNZLBYUCgW0Wi3i8Tg2NjYGvn+j0UAsFsPy8jJqtRpmZ2eh1Wrb5in6/WOxGHX/TnlKPCbCkbfP4YEPvxUr59cR2ghDqZcjHkpBqR1DtVJDMpLC5FELAmvbKOVLmD1hR2QriS3XDrRmBVQ6ObY8EcSCSdQrNcwuWVCp1FDMl5FN5hELJjCuk8A8rUU8kqEIQKVcRWQzAVYD0FmlAFgoFSsY10oRpRnVVio1RIJJsOoNzB4yolSpdv2tZlIFxENpaMYlMNtUiMVzbXcaK8Uafu/T70cikcDOzg6YTCYkEknHPER+HzfBInnHPHVbkI3NzU0sLy/jwQcfRLlcRjabhVKppH7w6+vrYLFYmJmZgVqtxoVLAVxpI31Gh1DAgVUvRT5WxHYg2XE+j8lkYHZaC1a1jk1nBNn0XpMXvVGOX/3EffjN33sHJme01IJqr4jH4xCJRANZ1u/s7GB8fJxaaB6UZHi9XjgcDsjlcszNzWFsbGzXoXt7exsejwdcLhdCoXDgYEoSBiEZc3NzlGM8AYvF2nPoH4T0kK7CysoKuFwu5ufnoVKpOv7gyaGf3P8gpIccOK5evQoAexJGN3S6P0kmrcFcohBDYGLhyU89ji1HCO7LXij1csS24hg3KFHMl5CJZ2E/bIZvJYhauYqZE3bsBGJgNBpgcQDjpBaRzQTCgRgYjTpml6woFSsoFSrIJHKIbyWgMchhmlTvDualKtKRPDgMYPqICUw2E+E2ijeVcjOYswHMHDKgUKyghawx0AAAIABJREFU3EWrPJPMIx5KQ6eRwmhRId4SzJVKMd7380uQy+WIRCLg8/lYXV1FvV6HWCze89k2Go2+un0jxB2y0RmfebOfQC9IpVL44Q9/iPe///2o1WqIxWLQaDS7imH1eh2Tk5PQ6/VwOCP42VlP12vyuCxY9VLUMlVs+RJtRz8IpqbUELOZCDjCSCf2+gIoVWI8+dG34nf+4BEsHDGC3af07rDIhlQqBYfDofIUi8UCh8M5EMkghSmRSIS5uTkolUqw2WzI5XLq0OtwOMBisYZy6CedDHrOaC0SMZlM6v7xeBwOhwNMJnOgPBWJRLC8vIxGo4G5uTloNJqOOWNYpCMej2NlZQXFYhGzs7PQ6XQ9TTS0uz8ASKXStnlKKBWAqwV+7Y8/glgwjtXTDqj0cqTCGchUTYGCaDCGqUUrtt1h5BI5zJ60IRXLgs1hoVIuwzStRSKcRtgfQ6VUwexxKxoNoJAtIZ8uIroZh1wpgnVOh2QsRylU1ao1pKN5NCpVzBw1gsPlYGdrr01BrVpHZCuJeqmK2UNG1OqNjuNVAJDLlBDbTkE5JoBtQoNE8vo9AUAg4OJDH7obCoUCoVAIUqkUq6urKJfLEIvFez7bO2TjBiEUCuH06dN417vehUqlgkQigWKxiPX1dUohgf6Dv7q6jeW19mRDJhVg2qxCPJhGbCeHagezMBabiblpDRqFCjbd0ZZWWxNmmwoPvNuCT/+fj2FiWgtmG9m0XpBIJCAQCCAQdJ6X3Q87OzuQyWRUq3KYJKP1GuTQq1QqEQwG4fP5wOfzIRAI+g6mdJJBEkYryWgFvdIfCoX6Jj39dBW63b+fTkej0UA8Hsfy8jIqlQrm5uag1WoPNI5Gvz/p9NCTKQnmJDHOHprByUeO4v4PvQUhbxiO826o9HIkQikoNDLUajUkdpKYOmbB5kYIhUwBtgUjdgIxhDxRqI1ymKZ0iASbpKNWqWJ2yYpKtY5ivoxcKo9oMAHluASWGS0S0Swa1wJrtVJDZDMBtVaKcZ0M5XK17dhU9VoFCZUa5g4b9q0g5dJFxLZTGJeLYJ1QU8FcrZbiXe86gmq1ikgkgsOHD0Ov1yOVSmF1dRWVSgUSiYT6rO90Nm4JfObNfgK9oFAo4Lvf/S4++MEPUjGm0WhgbW0NLBYL09PT0Gg01JikxxfF2Qu+ttcSCbmYtY0jE84hGsp1JOCk485rAJvOCDJt/Go0OhkeenQCv/t/PIJDxyw9d9xbkU6nwWQyIRaLD/T3ABCLxcDn86ndgoPkqdbuNyEZrb9hcuglHfFBilN0ktFrzqDfPxKJ7InT+4EUplq7Cr3mjNZDf6+dnlQqhZWVFWSzWUxPT8NoNB5o/4R+f9LpaTQaVAWfTjo2NzcxPTeFYw8s4OFfuQ/JcApXf7oOlU6OTDwLsUwINoeFbU8E08etiPjjSOykYJ3XI76TwpYzgjGVGLZ5I+I7KYT9MRRzRcwuWsBksZDLFFHIlhDdjEMi5WHykBGpeI4SK6nX6ogGk1AqRRjXytAAA/nc3jNfvdZAdCuJcq6E2cMGNJgM5HOdSUchV0Z0OwWpkIfJGS1S6QJqtTpEIj4+8IET1Gs/fPgwjEYj8vk8VldXUSgUIJFIqM/6Dtm4QYjH43jppZfwzne+E8FgEJFIBGNjY5iZmYFSqdzzAaxuhHB5effehUIuxIRRgbAvgfB2uq1yDnCtXT2jRSVTQtATbduunpjW4Df+y4P4tU/chwYzh3F17wfVdkgmk+DxeAcySyJt6GKxiFAoBJlM1ncwpZMMhULRkWS0gs1mY3x8HHK5HIFAAJubmz2Tpnq9js3NTayuru6qSvVz6COHboVCgWAwCK/XCx6P15H00NvQDAajr65Ct/vv12lJJBJYXl5GsVjEzMwM9Hr9UHZeSKeJnsxIOzafz2NtbQ1CoRBKpRIMBgMiqRBL7ziCh37pbYhuXqsgGa5VkMYlYAAIB6KYWrQin8ojEohj9qQN6XgOgY1tKLUyWOcMiG0nmxWkYhmzxy1o4FoFKdOsII2NCWGZ1SIZy1GkY1wnw8oZL6rFCmaPmlGr19v+tuq1ZgWpWqjAOqEAi8NBvsvISD5XQnQ7BZmIj4lpDfgCDh58aAHlchmxWAw6nY6qNBoMBuRyOayurqJYLFIVpDtk46bHZ97sJ9ALqtUqvv71r+OJJ56g5vhFIhFVDGv9zfv8cfzsmmcAgVTMx7RVhcR2GqHNVMdiGJPJwOyMFsxKDZuuCHKZvR13g0mBX/vN+/CJTz0EvqgK2ZhsoFHdTKY5LimRSPZ55F6Qsd5yuYzNzU1IJJKu443t0I5k7FeYAnZ3xPvtyJP9CDrJ6DdnkPv3OgZML0yVy+W+ugqd7q9QKKDRaBCNRjuSjkwmg9XVVSSTSUxOTsJsNg9l54XFYkEul0On0yGZTO4iHeVyGRsbG2AymdQuJF/Iw5H75vDIR+9HNpHD5R+vQqkdQz5VAF/EA1/IRdARwvRxK2rVGgLrIUwumlEt1+BdCUIyJsDEERNS0Sx2/DHkUnnMLJrB5XOQTRVQypcRCcQhEHAwediEZCJLnQfH9WNYPe9FMVPAzBETWBxW299Wo9FAdDuFQroIk3UMQrEQ2TaPIygVK4hupSDgsDA9pweDxcCj7zmKRqOBzc1NmEwmMBgMyGQyGI1GVCoVrK6uIpPJQCwWU2psN3OeYuyzJHlLbFCePn0an/70p1EqlfDVr34V6XQaR48e7fj47z5zCV/959cBAGqVBCqpAI6VEHXwaQcej40p+zgCrjCyqfZfmtkFPR5/8hSOnbBQP9LLly9jenp6oBEot9sNqVRKmbn1gtaFOjabjUwmQ1XZJyYm9m13k52MSCQylIWybDYLt9uNarWKiYkJyGSyts+bLkfYizFSrygUCnC73cjn87Db7VAoFFSlPxKJwOv1QiqVwmq1DvR5dQJZ5E6lUtQ9XC4XWCwWJiYmBqoI9oJyuQyXy4VQKAQul4vZ2VkoFApqOY50vch3NxXN4Om/+3c8+z/+A6YZPVKxLJgsJsQKEUqFCmqMGkrpKuLhFGZO2BHZTiG6lYBcI4POrobjkh/VSg0MFhMzx62IR7MIb17X7RfKeLAumOFa3cbEvAEr573UvzHZTEwfsyAayyASSqMTGEwGpo+ZEE8VsLO9v9/A4kkb/uzzH0Q2m4XL5WobJ0iVkixYLiws3AwVo5t7++/NxS2Rp8LhMN797ncjl8vhG9/4BuLxOO66666Oj//xaw781d++CACQjwlhGJfCtRpCpcNIL9BUS5yeUiMcSCAR2WuACQAWuwqPf+QUTt07SY30rq+vQ6vVto3JvWJrawv1eh1Go7Hnv2knUFIoFOByuVCtVjE5OQmpVNr1GnTTWJ1OB5PJNNDvlfgZ5XI5TExM7Nr/JKAvYSsUClit1qEtmxOfimQySakMkpgcj8fhdrvB5/Nht9sHcmvvBJL3o9EoTCYTpFIpPB5P17w9TFSrVXg8HgSDQarjR99tas1T+UwB//aVl/C9v30e42YVysUKivkyVEYFavU68vkCqsU6dvxxTC/ZkEkXsO2JQCIXwTJngOvqJorXClZTixYUChXKBwoAeEIOJo9Z4XWEYZnSYIXebWQ0808mV8ZWoLP5cwPA1GEDCpUaAv79TaItNhW+9LX/jGq1ivPnz+PUqVN7r3mtOOrxeCASiXDs2LGRC/P0gI556oaRjeeffx6f/OQnUavV8Ou//uv4oz/6o13//sUvfhFf+cpXqGr4V7/6VVgslq7XDAQC+PznP4+XX34ZfD4fzz33HBgMBq5evYrFxcWOf/fM81fwzPNXIONz4VgNdX2VQiEXdqsSvvUQ8h3cHg8vmvD4k6ewcMS4pxJx9erVgYOC1+uFUCikpE27gU4yOlVl4/E4XC4XhEIh7Hb7nk7DsElGKzKZDJxOJxgMBiYmJiCRSPa4t5rN5pFJA+ZyOXg8HhQKBahUKkQiEYjFYthstoFG1XpFLBajRnasViusVuvIlSRKpRI8Hg/S6TTMZjMymQyVTIgqSCfSkU3m8cyXX8DTf/cC9BMa5DNFjGmkSCbTCDnjmDpuRSKcQSQYx8wJOxLRDHYCcchUYhindHBe2US5WGkG5uMWZFJFbNOkakUyAebvnsDVc34UWlrTDCYD04tmJFNFhIJdTDYZwNQRI7KFCoKbnR93/C4b/vSvPohUKoVAIIBDhw51fGyj0UAmk+m7ozYi3CEbnTFUsjHsXBWNRvHXf/3X+MEPfoBCoYCXX34ZYrEYZ8+excmTJzv+3etnPfiHr70K9ZgIztUQatXOSk9cLhtTE+PY8kTb7mMAwOSMBo8/eQon7rbviTcOhwMqlQpyubzjPfbD9vY2qtUqTCbTvo/tRQUxnU7D5XKByWS2LcbQSQbxsRhmUSCXy8HtdqNcLmNiYgJjY2PU+JvX6x06yWgFKU6l02mo1WrEYjFwOJyeCoXDQDqdxvLyMgqFAoxGIyYnJ0ceB+lEx2w2o1QqUUpeJpNpl5dLa54q5kv44f/8Mf73f3sOcu0YGg1AohQjlUojuBbBxFEz8tkSgs4dTC3ZUMiXsencgUgmgG3BBM/qFvLXug8TR8yo1Orwb1xXKuUJuDh87zQ2rgb3/MYaaOafYrWOgCfa8fU1AEzM61FlMuB1d5awJmSjVCrhypUrOHHiROdrNhpIp9Ntp3jeBLy5ZKNWq2F6ehovvPACjEYjTp48iX/5l3/B/Pw89ZiXXnoJp06dglAoxN///d/j5ZdfxlNPPdX1uj/84Q+RzWbx0EMP4eGHH8YLL7yAer2OCxcudP1wfvSjVfzt3/yoa/YWi3kYV/AQ9qc7Lt0dv8uGx5+8CzPz+o7XWVlZgdlsHqhq7ff7weVyodVqOz6mVquhVqt1JRl0kHlPt9uNsbEx2Gw26l6RSARms5kaMRkVkskkXC4XKpUKarUa5bY6av1x0oZ2Op0ol8vg8XiYnp7G2NjYSO/bmrz4fP6uTge9gjUs0IN3qywvkUIMh8OU02w30pFPF/DcV/4D3/vb52E/ZsWmexsssKA0KLBxwYvJRSvS8RxCvuiuCpJ4TATr/O4Kku2awlRks9mNWLjLDvdKEPYjZnidYeRahBYaAKaPmZArVhFs46lBf9zkggGlRh1+797HnXrrJP6vv/gAEokEQqEQ5ubmur5/9XodPB7vDtm4uTE0sjGKXHX69Gmsrq7iIx/5CE6ePEn5Au1HNi5c8OP/+bNnunbc+XwONOM8JEOFtuIkADB3SI/Hn7wbR5fMHeOLy+XC2NgYlEplx3vth52dHRSLxa7E6yBS64lEAi6XCwKBAHa7HRwOhzKNHQXJaEU2m4XT6USxWES9XodSqYTVah1o5KxXpFIpOBwOFAoFysSVjL2OCsViER6PB5lMBna7HRKJBIFAgCIAWq126PGwVqvB7/cjFArtKXCSvc2trS1otVqqc9Wap4DmQnm5WMEL//QKvvPFZ2GeNWA7EEYhXYZhSouNC15Y5g2olmvwr2/DftSCaq0O//o2+CIeJo+a4dvYQTbZJBPmWR3KtSpC3uZS+PxJGxxXNjG1aMGWP4FkbHf3sAHAvqBHncmEt42nBh2WaQ2YAg5cjr2Pm5jW4K//x68in89jfX29a/EcaP6uuFzuTU02bkjP5cyZM5icnITdbgcAfOhDH8L3v//9XQH8/vvvp/773Xffja9//ev7Xvfhhx8G0Gy7FYvNQNuL2zaTyej4jshkAph0MjiXt+Hv0IY+9dZJPP7kXbBPafZ9jqPUHgeaP0Tyb/3M7TEYDIyPj0OlUiEYDOL115tjZTabDadOnRr54aperyOXy6FcLlM7BMViEdVqdaRkgyQuPp+PQ4cOQSQSUSM1brd7JG1i+vhWa1t+dnZ2l0+GzWajVMMGAT14E7PD1s+UVMnMZjMCgQBOnz5NOcKT7xxd/1woFeDxTz+K93z8IbzwT69g4y+c0Fm0yKfyGFOKwWQCYX8EU0fNyCVz2HbuYGrJikK+gquvbUAo5WPhLjs8q1vwXDNUsh82otZgNJVBcmUsv+4El8/B/KIZm94YVUFiAHBcCjTJxGEjcsUStjf3jk0xALiWg82gP6sFuGy4aUGfdW35tVqt3gyB+Q5uMowiV506dQqnTp2i8lKvmvgcNrMj0RCJuLCZlfCubWNzo30n48hxMx7/yCksHN1/rGnUeardWG+vOUYul2NpaQnhcBhnz55FvV6/YQaujUYDuVwOpVIJIpEI5XIZxWIRlUplpGSDdHUYDAamp6chlUqpPOLxeHaNAQ8L5XIZHo8HyWQSNpsNs7Oz1PXJjgYx5xsW6aCPTev1+rafKYvFgsVigdFopMwZ6aSD5ClSaOXw2Hj0Yw/gnb/ydvzk2z/D//rTb0MsE6OcK0Ek5oEv4MKx5oVt3oB6pQr/ShDWwyYwmExcfc3RzD8nbdh0heG/JiRkntGBK+QBjKaS4srPXGBxWJhbtCC8nUYs3Bz1ZQDwLDf9bSwzWjQ4DPhd7YtjvmtdE5N9HAKZABs0M1wi0lCr1Xr+jt/sPhs3hGwEg8FdrVWj0YjTp093fPw//uM/4l3velfP16d/GL284VzO3petVIqgUYrhXNnGapsZcSaTgbfcN40PfPgumG29704M0/2bDjrJOOgSa7lchs/nQywWw8TEBIDmaFqtVoPZbB5JICfz8IFAACqVijKpI3rdV65cgUQigc1mG+reBOmicDgczM7O7uo0icViHD16lNppaTQamJiY2HdWeD/Q2+A2mw0qlart95PP51Okw+PxwOv1wmq1Hoh0kMX6YDAIg8HQE3HkcDiw2+0U6Thz5kxX0sEX8fDeTzwE6QwXmY0yvv2FZyFTSVDMFCG5pgoS8uxg8ogRxWwJm7QK0tXXNsAX8TB1zIigKwL3lU0AwPEH5mGb08Gzuo1ysYKVn7nA5rIxf9yCrUACyWiT+DMAuK5sogHAOKUCRyiAZ2OvKScDgPeae6tlUg2umAfHxg7Y7Os+M7dLEL+D4WGUuYq4XvcKDnfv91Mq5cNsGIN7JYTVC/62f7d0yoYPfKR7x70VLBZrX0fz/dAu17WO9R5ERrparVKdDIvFAi6XC5/Ph1qtBqvVOpLiFJmH93q9kMlkOHr0KJWPyBIzl8uF3W4f6khTJpOB2+1GrVbbU/gSCARYWFhAPp/fQzoGARGAicVisFgsmJ6ebvs95XK5lDnfoKSDyMr7fD5oNJqejIDpjuyEdGg0GphMJrDZbDQajd3fNQ4LDz55L4Q2FrLuCr7z+ecglgpQzpfA43EgkPDgesMPy6wWTADuN3wwz+nBEXCx/LoDbC4b04smbHuj8K83ScfR+2YxdcQIx+VN1Co1rJ5xg8FiYpbsJNJkcf3XyIPaPAbZuAyODnYLwWvjVHqTAlKNBBtrISpP9UM2bnbcELLRrtPQKeh+/etfx7lz5/DjH//4QPfpqWJEC+JqtQRKmRCOq0EkN/fqJzOZDNz30Bx+4cN3QW/sf551WBWjarUpbThskmE2mzExMUFdQ6fTYXNzE2fOnKFGa4bR5ajX6wiFQvD7/VAqlXscRhkMBiWXG4lE8MYbb1DjXYPMxKZSKbjdbjCZTExPT3dVSpFIJDh27NiuqhLZKekH5XIZXq8X8XgcNpsNMzMzPX0v+Xw+5QjeL+k4SPBuBZvNhs1mg8lkokgHGVNoRzo4PDbe8/G34+FfvQ8v/vOr+M4XnoVkTIBStgSRmA8ujwPvShC2Bf2eCtLGOW+TTJy0IeiOoJgpwvOG75r7OB+uq0FUy7QK0vFrFaSd6xWkoKM5G2ue1oAr4sPZQc46cK2zYbAoMH5Nl71Wq90Mo1F3cJNh1LmKOCbTzVU7fQ/pRTG5XAi9RgrX8hZWdzJtH3/32ybxgY+cgn1y/92+Vgy7KEYnGQdVyqlWq/D7/djZ2YHBYNhV9dZoNNje3sa5c+eoKvcwlmPpTtitJINgbGwMS0tLlM9Ep93HfpDL5aiRYrIf0glCoRCHDh2i/sbj8ez7N+1Af39bzwHdMAjpoL+/pGPVb34npIN0Os6dO0ftebYjHWAAD37kXjzw4Xvx6nfP4KnPPgOxlI9KoQw2s6nC6LkagHFSDS6PA+cFL4zTOghkAqyf9TTJxDWzwEqxDMc593X38Tc20ajVsXbWDTCa5rapVBEh2rJ4xJ9ExJ+EzqKEVC3FxnKwrZFfKBBHKBCHSieDwdA8a95OeeqGkA2j0YhAIED9b9Iya8WLL76IP//zP8ePf/zjvlqU/VYeuRwWdDoZZCIuHFe3EGvsVQdgc1g4vDiO3/jdd0OtPfhYDZPJHErFqFaroVJp7o4MQjLIIbhTcCEtS4PBAL/fj9OnT8NisUCn0x2owrsfyWgFg8GAWq2m5GIvXLgAlUoFi8XSVwVrkC6FVCrF4uIikskkZfjUi1oUfT/CYrFgamrqQO9ZP6SDrkihVCqpTtEgIKSD3umgL+jV63VkMhnU6/Wm6R2XjXd99H6845ffhpf+5TV86/M/gHhMgFK+DL6AC4GID+cbPphndWA0GvC84YN+Sg0ml01VkIRiE+RqKQLXKkiGCTWEcvH1CtJpUkGyIB7N7aogBa51Noz2cQjlImysbLV9Xdu+OLLxprP47VQxuoPhYdS5SigUIp/PU14u3cgGh8uCSiWGWimC8+oW1tqYXjKZDMweVuFjv/MIzNbeO+57rzOcolitVkO5XB6IZFQqFQQCAezs7MBoNLYdrWEymTAYDFRx7OzZszAYDFRhpF/QFQklEklbktEKhUIBuVyOaDSKK1euQCqVwmaz9fV9IF2KYrHYd5dCJBLhyJEjfY8B0xfrjUbjgcem25EOi8UCrVbbNk8RYZpe39/9wGQyYTKZYDAYsLW1hXPnzmF8fBwWi4UiHdlsFuVy+drjGbjvibvxtg/chde+fx5Pfe4ZCCV81MpVMBoNSBVi+FaD0NlUEIq42DjrhtqqAk/CxdoZF8AAbLNaqI0KhLxRhLxRqI0KKAwKbFzeRL1Wx/o1RcWpRQvy+QqC3uvL4iFfDCFfDOPGMSgNCqwvB9taLES3U4hfy2+1Wu1mUJgaCm7Iqzh58iQcDgc8Hg8MBgO++c1v4hvf+Maux1y8eBEf//jH8fzzz/ekutSKRqOxq7PRrcvB47Cw446i3foOl8vCOx49gvc9sQS3d20gogGASigHBVn6jkajUCqVkEqlfQeGUqkEn8+HeDze8yGYzWbDbrfDZDLB6/Xi9OnTsNlsPS8xt0oD7kcyWsFgMKDVaqFWq7G9vY3z58/vql50Agm87drQ/WJsbAzHjx+nDIc6tc3pbX6TydR2P+IgIKSjUChQOx2EdABNVSsii7y4uDj0GWIWiwWr1QqTyUR1u1QqFUqlEgqFAmZmZnYrn7FZeMd/ejse+Mhb8eNvn8a3/uoZSGSCZgWJxYRYKoD7SgDGKQ0YbAYCy1swTGshlAqRTeWQCqcwt2RDeCuJoCsMINymguQBGAyY59QoFusIB68fwJrt6Ai0ZgVkGhkcK1u7XFmB3bOw+5Gy/Xa/7uD2w6hzlVAoRC6Xo8hGt8MEl81EMphEIrBXXY3FYuK+h+bw/g+dRHDbORDRAHZ3zw8CkqcSiQTUanVP/hataBWs6OUQzGQyYTabodfrqb2zfsRNiFCKx+OBWCzG4cOH++pQ0Hcfw+EwLl26BLlcvq9KVScZ9oOg1zFg+n6ETqcb2s5LK+kgBUpCOsgIM4/Hw6FDh4Yu18tkMmE0GqHX6ynSoVAoUK/XkU6nMT09vUs8h8Fg4N73n8Rb3reEM89dwjc/+zQEYh7q5Spq5Srk41L417egMSsgkvLhubIJtVkFuW4MhWwRYV/TODCdLCDkiyK8GYdSNwa1dRwbb2yiVq3BcbEpj2uaUaPaYGDbd/03HNlMIrKZhEIjhcamwsbKNqqV3QVpslvYrRjRipt93PeGkA02m40vfelLePjhh1Gr1fDRj34UCwsL+JM/+ROcOHECjz32GP7gD/4A2WwWTzzxBADAbDbj6aef7vkeXC4X5XIZfD6fqtJ0+iFxOKw9C+J8AQePvPco3vv4cYzJm4dJt/cgr3Y3DloxoqtLqVQq8Hg8OJ1OSpGjl4BI9LoTicSBK+0cDgdTU1MwmUzweDzw+XzUknMnw6FBSEYr6BUsMqfZTkudrvRkt9sHknBsBWn3xuNxrK6uUhrnPB6PUslobfMPEwKBgCIdHo+Hkg2WSCR9J8eDgMViQafTUcaQAGAwGCCVSqnRCTrpYLKYeODDb8F9H7wbP/3eWXzzs09DLBOgWqqAiQakchHcV/1Qm+UQivhwnHNj8YF5TBw2YvWMC2Aymr4c4QxVQRo3KaDUX68g+Vea3YzJY2YUilUEaXKDO/44dvxxjOvHoDQpsLG8RTnBsjnXZ2F7razd7EH8DoaHUecqkUiEXK7ZXdtvT4LLZe9ZEGdzWHjwkQX8/AdPUIWw4HbvI8SdMEieIn9H4pHL5UIwGOzZO+ggJKMVpBtrNBrh9Xpx5syZrsWxQUlGKxgMBjQaDdRqNUKhUMeOfLFYhNvtRjabhd1uH6qyVKcxYJFIhO3tbfj9/gOP2PYCOunwer1wu91gsVjg8/mYmZkZuY8Uk8mEVqtFqVRCMBikPhO6ATFdpIDBYODu9xzHqUcXcf7fL+Obf/k0BCIu6tUaqsUKlNNj8Du2IBsXQa6WYuOsG4fvncbskhUbl3yoVeuYOm5FIVtG0B1GbDuJMbUU+gkjHFeCqJSrCKw3S9q2BQMaTCa8tP3C+E4a8Z00ZCoxDAt6bKxso1JuEn6Sp24nIZPbwtQPAH7u534OX/va16BUKnHx4kUsLCx0POT6PFF8+jf+CQAgFPHw6PuP4dH3L0Ii3R1s9pMm7AXBYFN1x2Aw7PtYMpbSScKWLFETmcJO+wx0ktEqdzooSNu3VCoPIDROAAAgAElEQVTtmhOl64+Tys4o1DroMngGgwEKhQJer/dAbeiDgCSp9fV1VCoVGI1G2O32GxIQiD9Jo9EAi8VCqVTqung+DNRqNfh8Puzs7FDjdKRCFgwG9yzoAbg+K4vrM+qvP3MBT332aeRSBagMCnhWNmGa1yHkjkEsF8IwqcOF/1iB2qLEmGYMjks+NBrA9JIV6XgeIX9T0UOpG4PaMo71NwKo164fjiaOGFGpNuB37dUuV6gl0NjHsbGyjff84l34tU8+hI2NDcjl8l1mUa0gr2cUBo8HwB3G0xm3TJ568skn8Vu/9Vs4cuQIVldXYTQaO+6EZTNF/Mov/D0AgMtj452PHsZjT5yAUrX70Hbu3DkcP358oG5qNBpFOp2mVLj2w367g8lkct/iGJ1kDNvPie4rRD/UkxxKjNBG5a1EhFD8fj/VoQ8EAkilUkNTHNwPiUQCa2trKBaL0Gg0mJqaGrmsPHC98FcqlcDj8ZDL5TqOVw0LrcIoxFiSjHATTxTy+gk5J6SDfDcuvbSCb/7l97HtCUM/qYXrsh+GGQ2SO1kwWEzYD5tx8aUVyMal0NrU2LjkQ7VSw+RRM8rlGgKOJpmQKsUwTeuxcTWASul6QcEyqwNbwIWrzbK4RC6EaVYH13oIJ982jd//81+A3+8Hg8HY17umXq+Dz+ffDIWxN1f69kaAVIyIsUm3ihGHw4JEysd7P7CER953FCLR6CTsemlPE5Kxn4QtfYmaVE/oM4p059FuqhKDgCyn0edEFQoFdnZ2MDY2NpJxHjrITolSqcTy8jIcDgeMRiPm5uZGfuCn75/odDqIxWL4fD6sr6/DbreP7FBKFgFbHVxJp8Pj8QyddNBb7q2qVv0u6DEYDLz1fSfwlseuta0/9zR4Ajbq1TqqpQoUahmK2QJUehnkKgk2zrqg1MuhNCmxcdGHeq2O6eNW5DIlbHkiiG0nIZYLYJg2wb3crAa5LjdVrWwLBjRYLHhpMoLxcAbxcAYyhQhjsubB4s7Oxh28GRCJRMjnm1K1veQpvoCDRx47isc+sASZvP34CbnOIAf1XjsbvQqUkCXqaDSKy5cv7yqOtZrGjkJqncfjYXZ2dpdc7Pj4OCKRCIRCIRYWFkbivk1AOvJKpRJXr16F2+2GRqPB0tLSyGfw6UvYZK8kEAhgbW1t6OpZdJCuTavzOhnj9vl8Qycd9EkKtVq9p2uj1+uh1Wqp/c/WETdS2CWkY/GBBRy7fx5XX13HN//y+2CzmWAAyKfysB02o1woQyIXQmtWYuOcC+IxEfRTWjgu+VEpV2E/bEK9AfjWtrH8+gYEEi5sxwzwb0RQzJfhuyZiYpzUQHBNmIggk8hj5XUXhBIeVOPXhUz2O0/dKuO+txXZoAfxboFTpZbg77/+nyEQ7D/eM8r2dCvJ6HXxm8FgQKfTQaPRIBgM4vTp0+ByuZQU4ChIRitEIhG0Wi1cLhcCgQDEYjGMRuPIjY5aDYdkMhn8fj/Onj07suoJvWvTuuSuVqsRiURw+fJlSKVSWK3WoZGOTsGbQCAQYH5+fqikY7/gTUcvC3q7VEEAnHp0EXe9+xie/trzeOl//gx8AQeNWh1oMKDSy7HtDkOukUKlH8P6GRfkGhnU1nE4qAqSBaVSFZvOHayfdkGmFMNw2ALn8hbKxQo8y83gbZ7RgSPcXUFKxXMoXnMov0M27uDNAH2Mar8DPofLxpe//uuQSLvHk1F7ZAD9m8YCu/cZQqEQzp8/DzabTbmM3wg/Jz6fD61WC4fDgUAgAIFAAJPJNFKiAexWfLRYLDh27Bi1yD5MlUc6SNfG7XbvWcJWq9V7xoCH9R60+nPMzc3tyj/ENJcUQ30+38ATF3RTYrlc3nVcm8lkQqfTQavVIhQK4eLFix1JByEeh982i8Nvm8V/fO8VPP/ffww2iwkmgHq5Co1JiUggBpGIB719HI5zHgjEfEwfM8N5JYBSoQLrghFMFhOelS1snPFAKOFj4YQFnvUd5DNFbDqbHRCdTQWJUgrH1SBFGvKZUt9CJv3Kar8ZuG3Jxn6zsL2AtNaGTTYOSjJaUS6XkcvlwGKxwOPxkM1mB046+6FVf/z48ePg8/mUDKBIJBpJlZ+0xUkbup3hkNfrhd/vh3VIbtytMojtujZ09axwOExJ9g4yRkaCN3mtrcG7FXTS4Xa7KXPAfuaBWx3l+9m1abegNz4+vssNvnVWduYeG9763pPYvLKDb372GTCZDNRqDRQyBdiPmBHyRDCmFEFjUmDjrAtihRj6SU2zbV2uQTuhBI8ngH8jhNTrDojHhJg6YYVrZQvFfJnSRTdMqiGUiagKUr9mSTd7AL+DWwv95Ckmk7Ev0ejlOr2gE9mgk4xB1KUIweLz+ZSS3ShB1I/cbjcEAgGOHDkCoVCIdDoNp9MJNptN7TMME3RFwlbFR6vVCqPRSKk89rPIvh/or7XT/gnpcsTjcSwvLw8s2UvG4CKRSE+TFDweDzMzMxTpIIIn/ZIOuqN8P6pWpEhLOh2XLl2CTCajCoTkTEYfsbIvmvDJf/w1ZLeKeOpzz6CUL4PFZCAby8A8b0AsGAdPwIZpWgPnBS/YPDYmT9nhurKJYr4MtUUOkUwM78o2ll9zgC/iYuGEFX5nGJlkHtueKLY9UahNCih0cmxc3US91jiQqd/NjtuGbAiFQmSzTeOvYQRf+nWG1Z5u3ckgBkf9Xp9U99PpNKxWK+XjQKRtz5w5A7vdPtSxmv2kAekygMPyyAB696zgcrmYnp6mjPR8Pt+Bq/z0RCUSiXoKaPQFwZ2dHVy8eJGaE+31PaAnqoN0qOimT/ROx36kgz5ffeTIkQMnHzrp2N7exoULF6BUKnctSZLvPgmix+5fwLH7F3Dl1XU89dmnweGQtnUOtkMm7PgiEMsE0FtVcJxzQyThw3jMgvVLPlTLMdgWDGCwWPCsbGH5tY1rFSQrPGvbyGdLCF7z2NBaVZCOS8Hi3H5mSXdw60AkEg09Tw27s9EuTw3q50TvZBB/h25SqYOAxG4+n4/5+fldhEIqleL48eND9cgAmsUUn89H7Z90UiSkqzwSudhBqvypVAoulwtsNhtzc3P7kicGgwGlUgmFQkGZ6IrF4r4KhHTp3IOoL7aSDjJetd97QHYWmUzmHmPefkCULjUaDVUgJLLFraSjWq02n+/JCfzJtz8F5yUvvvW5ZwA0wGIxkYlmYJjWIbaVAJvNgHVOD9dFH5gsBhZO2eG8EkDYl4BxUgOhTAjHGwEsv7YBDo+D+SUrNj1RpOM5hANxhAPXVK0sSnCuFcRvpzx125CN1vb0zRTESXWIBG8OhzMwyWit7gO7D9wul4tSjhpEmamVZBw5cqRjUGptmx/UIwPY24buVUmLuHHTq/ztRpA6gVRN+Hz+geZ66YGs1/dg0ODdCjKTvB/pGFbwbgVdQYy8B4R4sVgsBINBSoa5Xq+DwWDg8L0zOHzvH2DldQee+tzTzbY1g4FcIgfLvBGRzRgEQg4Mk1o4znvA4TIwc2qi2bbOl2Ge1YPL58J1dRPLr21cqyBZ4HOEkU0VKFWre985D+BOZ+MO3hz0o0bVKwaVVwfa56lhkIx2fk7kwG00GuHxeOD3+4dSHCMkg8fj7XvwbvXIIBXufrvRdNnzfpS0OBwO1ZEnKo/9LI6T2M1gMDA1NdW38WyriW4vY8DDls5tRzraTSXk83m4XC6Uy2VMTk4OJGdPB71ASN4DiURCvQehUAihUAgLCwtU0XnymBV//I3fhedqAE997hnUq1VwOCykI2noJzRIhJJgoAHbvAmeN/xoVKuYO2lFYGMHm84d6OzjkCqlcFzyY+Wax9T8cSu2AgkkIxnEtpOIbScxvdAUFLqd8tRtQzYkEknPOxu9YtBkQNhxPp9HtVoFl8s9EMkgPguZTAZWq3UPyWgFOSjncjk4nU54vV5MTk72FZAGkQak75SQsZp2crXtQG/N9uNq2gpS5SfKGPu5rJIKEYvFGsrBm/4ekHnl1tEievDW6/VDl87tRDqEQiGlFjLM4N0KJpNJLeiFQiGcPn0atVoNOp2O2gVpXdCbv2cKf/q9/4qNc25887NPg9logMViIBvNwDTXbFtzeWzIDRI4L3jA5rIxeWoC7uVNFLIlGKe0EEgEcF4OYPk1B7h8LuaXLFQFiby/vQTxW2Xx7g5uHUgkEmxtNU0nmUwmZdQ6CAYtrpFcWSwWUalUwOVyR0IyWsHlcjEzM0MVhg5aHEskEnC73eBwOH3FbnpxjHSjey2O0QtEg8iek/eAbuDaTRK3k1jIQdHLGDB9j298fHzo0rmdxqtkMhm8Xi/S6XRXuf1BQX8PIpEILl68iEqlAqVSiaWlJXA4nD3jVbZDJvzR//db8K0G8a2/egauS15weWxsu9PQ28aRiqZRq1ShmVDAvxxEtVzDwl12+B072HZHoDErIdeONUnHz5xgcViYO25DeDuFWCgFFuv2G6O6baRvv/CFL0AgEOCXf/mXEQgEwGazodPpBrrmysoKzGZz3wfPer1O/YfBYCAQCCAWi/VligdcVxvKZrOwdnCQ7gWpVApOpxNcLhcTExNdq/WtJGMY0oB0udpOy3Gktb6zszN0GUTgutlfvV7fZXg0iNN4P6jX69ja2kIgEIBaraY8OnoxKhwWkskkVlZWUCwWYbPZYLFYRr6gCTQPA06nE2KxGCKRCFtbW7vkkemGnHTiAQDOi1489blncPmVVdiPWOC5ugnjtA6RnTgquSqsh01wX9kEgwHYjljgWQsiny5Cbx+HRCHBxiU/AIDNZWP6uBUPPnEXHnz8JF577TXcc889XX9P9XodbDb7hshF9oCbv3T15uGWyVPf+ta3cPnyZfzhH/4hwuEwcrkcbDbbQNckUuhKpbKvv6PvDpIDJTEm7Sf+0kddyTjMQeIKPUb3Uhyjkwy73T5wgahVrtZsNu856NVqNUr2u9cCWj8gxKtQKOyScqc7jQ86rdANrRL2EokEgUCgJ6PCYSGbzWJ5eRnZbBZGoxFTU1M3JE/R93kUCgW2trYoeWShUNg1T21ubOPbn/8BXnvmPCaO2bC5sQ2VUYlMJot0OIepJRv869so5kuYWrIj6I4gFc1AZZBj3KiE4w0/atU6GCwmZpasOPnQIXzg4/fj/PnzWFhY6DriRp7TqMV5ekTHPHXbkI0vf/nLyGQy+MQnPoGtrS3U63VKa/mgWF9fh06n6/kASggG+fDZbDb1pSQLzplMZt+xHjrJGJakKdlDcLlc1Hwi/ctJ1x8f1hxrK8hcK+laEM8Gv99PtaFHodJBBzE8IkSQkIxOHY9hggRyh8OBWq0Go9EIq9U6cqLRugsikUjg8Xh2JbRRVIxIq5/FYu1axiTvg8/n27Og1ymYk7b1xR9dgf2oFc43fDDPGFDIlxAPJZtEZCWIWrWGyUUr/I4QMok8NBYV5BoZHJf8qNcb+O3P/iLe8aG78dprr+Etb3lL1+d/h2zcMrhl8tSzzz6LF198EX/2Z3+GWCyGZDKJiYmJga7p9XohFAp7djPvJlBCjxV2u71rgYtImhKSMazdC1Ic4/F4mJiY2JOHkskk3G43NY41bLM4OqGgezaQLrRWq6W8hUYF0sEolUrgcDiUWe0wTQC7IRaLYW1tDZVKBRqNBhMTEyMnGq3jxEqlEj6fD6lUamiiL+1AxrQqlQomJyep8x79TCQQCGCz2SASibrmqS3XDr7zhWfxynfPYGLRCu/qJtSmcTCYDGw6Q5g+bkfQ1RztnTlhw7Y/hkQ4DYVWBq1VjY03/KhVavjQ7z2MD33qEZw5cwaLi4tdc1Cj0QCTybwhRLAH3P4+G2KxGDs7TTkxFot1Q9vTrSSDw+FQX0ICovudz+fhdDrh8/n2VG9aScZ+KkT9gL4YFg6HqZax2WxGJpOB2+0euf44UQAxmUxwu93UzKnZbB6Z+3YrOBwOuFwu0uk0GAwGRCLRyH+kdKInkUhw8uRJcDicro7owwC9o9S6C3Lo0CHkcjl4PB643e6hko5CoUAlynZjWv0s6HVqW9fPVcDlsRHyhqGzjSMdy6BWKmNy0Qbf8iZKxTLmT9qx6Q5jxxfFuEEBlVFBObPewR28GehHjapX9Lpb2ItACdklMBqNcLvd8Pv9mJyc3FWMoZvG9rNP1yuIymE8HseVK1eo2ECq/iwW60B7Cr2C+Ajp9Xr4fD68+uqrYDAY0Ov1OHHixA0pPrDZ7F0KkxwOBzweb+REI5lMwuVygcvlYnFxkdpdIJ5e9DHgYYHeUWrdBZmdnaVEX8h41bBIR7lchtvtpsa0WjuD9L0WIijQSjpa85R+QoP/8ncfxS/+4WP4zhefxcY5F4QiHkK+KPTWcRSyBWQTGcwsTWDbvYN0PIe5E3aEgwms/MyBsXEJDJNacHnXBVV6Gfe9s7NxAzGKxbv9gni9Xqfu04lktEIoFOLIkSNIpVLY2NgAj8ejfApIS32YJKMVZClKpVLB6XTilVdegUgkwsLCwsiCNx3EGC+RSECv16NUKiEcDkMsFo+swg7s9uew2WyYn58Hg8GgZABH5SZLgjePx8OhQ4d2ETmz2QyDwYBAIIAzZ87AYDDAYDAMTDq6BW86RCLRLtJBdjoO+jmQcYpEIkEF727X6bagJxAI2gZzy5wBv/+PH8cPvvVvcP4oCOclD3h8LiL+bejtamQTWZTyRUwdt2NzfQv5TBHzJ+3Y9kWxetqJR36pezej3XO8gzsYFka1IN7tOq1jvb0IlBAlJ/ren9lsRiQSQSKRgHXEfk704pjH48FPf/pT8Hg8LCws3LAudDgcRiQSgVarRb1eRyQSgVgsHlmFHWh2ob1eL2Kx2K73OJVKweFwjKybQ8aJAWB6enrXWYDs3ZH9S41GM5TRX/IeezweqFSqjkSOiL7QSccgLuxkZDscDsNKU/PsBPp3kXiV8Hg82Gw2iMViKk/V63Xqd6WxqPDbf/MrmLhfB+8rO3Bd8kJjViKyGYfOMo5yoYRUOI2Zk3aEvRHEw2nMnrAhHs5g+XUHlu6fo96j2yUH3TZkQywWUxWjYalRdQrihGSQ9hVR7egHMpkMs7OzWFtbo2RSjx49OvK5O1Jl93g84PP5uOuuuxCLxXD16tWh6n63gr6zoNFodi2ZkXlUohw1zGRCPwC3W65vVSVpN2J2ENCVnmZmZjomBxaLBes1/XVCOg46TkYP3kqlsucqXDvSYbfbIZfLewp0tVqNGoWzWq19VzpbF/SIHCMhf62kAwBUZjne+w/vxi+6HsN3vvAsXBc94Au42PaEobeqkU/nkU/lML1kx5YjhEwyh/kTdvD4/VXlbpdAfwc3B+h5aphFsWq1uuf/p5MMJpN5IBVEkUiEmZkZrK+v49KlS5BKpTh69OjQizLtkEql4Ha7wWAwsLS0hEwmg9XVVej1ehiNxpF0wrsZuJZKpV2L7MMsjtF3Ftst15NuTyKRwNra2tCM+ehKT91yL13WnHTkBxkni8ViVKe/nX9VO9BJB1mm72eXlS7IYjQa+1Z9pJOORCKB9fX1XftC7UiHVC3GJ77wTjzx++/Bd//mebjf8EFlkCO+nYLWrES1VEF8O4GZE3bEgglEt5r/XSjm77rv7YDbhmy0VoyGoUbV2tloJRkcDudAAY8crguFAlVJJq3KTotpw0AnaUCJRAKDwUB5dPS7yN4N9Cq7Wq1uewAWCoU4dOgQstksnE4nAGBiYmKgTgt99ni/Vj9dlSQcDuPSpUsHXogj6leVSqUvtRA2mw2bzQaTyUSZPvWzqEnGtMRicc/BuxV00uF2u3eNV7UDnUDq9fqBHYHppIOQv9YFvXq9jlKpBCaTiUajsadt7brkhc6mRsgXhc46jmKuiHQsjZkTEwi5w0C9cUdl6g7eNIjF4pF0NkqlEvW/W8d6uVzuvh33diCVZDIzf/jwYcRiMVy+fJmSsh7FSBHZq2MwGLtEO2QyGXQ63S6PDp1ON5Q8RTerHRsbaxtDSd4cZnGMvqfQi3SuXC7H0tLSro78QUx0i8Ui3G43crkctQvSC5hMJpWXDjIGTBerae309wo+n4+5ubmeSQedQA5DTYvBYEChUOwiHaTjJJFIqDxVqVSo/Q6lTo6P/eWH8fjvvRvf+9vn8W9ffQnWQyakIhlojArUazVEN2OYXrIhGUqiWh58DeBmw22zIP7GG2/gL/7iL/CVr3wF2WwWfr8f8/PzA10zGGy6Dut0uoE7GQCoynGnxVz6jP0wuwxEtYPL5cJut3fVHyc/4Gw225c/RSvocnn9em30o57VCrru+UFVrQ7y3OnBe5D3jYAuAWwymTp+F4iCBofD6fu92g+EdJRKpV2kg3ivuN3uA/uo9IJW0QKz2Yx4PI6trS3Y7XaKENMX9CKbMfzv//YcfvTPP4X9qAWRzQT4Yh74Yj48Vzfxx//0m1h8YB7nz5/HqVOnut6/Xq9TMqA3AW6P8tZocMvkqXg8jve973147rnnUKlUcOXKFRw/fnyga0ajUcrgtZNAST9oJRmthSd6AWmYXYZ0Og23241GowG73d61UEMfN9pvkb0b6D5S+/lMtKJf9Sw66vU6Njc3B1K1IsqRbrcbMpmsp448vdM/yCgSQasEcKcxYDKOd5D3aj+0WgPQX1M8HofT6RzaxEInJBIJeDwesFgs2Gw2ZDIZ+P3+XWcQep5KRdL4/v/77/jBP/wI5jkDMok86vU65NoxbFzw4re++CTe+Utv61nIhMPh3BBFyx5w+6tRuVwufOpTn8I3vvENFAoFOJ1OHD58eKBrbm1toVwuw2Aw7DI56hf7kYxWkEBKXLMPGhAGkQak63nTFRr2A72K0K+DditisRjl4r1f9YZO1IiCyKBEjV6579QyLpfL8Hg8SCaTQ3dtB/Z2aIjiC/l8arXaSCV7gWZS9Xg8KJfLVPeHuM7eCLm9er0Ol8sFv98PkUiE+fl5SKVSqmoEYE8wj20l8N2/+Tf8+//6CWxHzIhvp8DmsfHxz30E82+dxJUrV3DixIl973uHbNwSuGXyVLlcxj333IOXX34Z9XodFy5c2Pd7uB9isRilHjUoySCmsb0s4tIPmoN0Gejy4/uRjHbP+SBFHlLIcLvdA0u801286ap77UAnaqPYfVAqlbBYLHtyLl0JchSu7a3mhmQMmP75TE5OjkyyF9hNOjQaDWKxGNhsNiYnJ0cmekNHo9GAz+ejCrvz8/NQKBRd81QmnsX3/+4FPPPlF2CY0qGQLaFcrOCX/u+fx889cTdef/31nsjGrZCnbhuyEQqF8OSTT+Jf//VfUS6XsbKygmPHjh3oWmRcKpPJwOFwwGazHejHSa8MH2TxlvxQ8/n8HkWQbiBLycPQH6drT3cLpK1taOsB3Fg7XTcSicDj8bQdbWp1NR2FqhO9CkUqeUSyd1TBuxWkIhWNRsHlcinJ3kE7KL0im81ibW0N+XwefD4fU1NTI00cBKQyRSRyCfFpXdDrFMwTO6nrbesFE37pT34BUycsWF9fx+LiYtd7NxoNyojzJsAdstEZt0yeajQaOHbsGF599VUAwNmzZ3Hy5MkDXYvkqUKhQHlCHWTXiy6e0c8MPEGlUoHH4+lZGIKAqCDWajXY7faBxpF6LY6RnUWivjhMiXcyytquODZIp79XNBoNisgQ5Sgmk7mvx9UwQXZPQqEQeDweNU487CJcJxQKBayvryOdTlPKajfi3mQag8/nY2JigtrvAUAR6G55KpvM4wf//UX865d+CJ1dg5//nYfxtg+cxOnTp3HPPfd0vfcdsnGDkc1m8eCDD+LFF19ErVbDpUuXsLS01Nc16OpSTCaTmql1u93IZDI9M3NyICLjJ70u2na7HtllmJyc7EgeiP44i8Wi5geHhU6BlG4CKJFIKPnSYaPVxdRkMiEajcLn80GtVsNisYy8jVir1eDz+RAIBMBgMGCz2UYevAlIQo/FYuDz+RSBHaUyCnDdZKpYLFIyttlsltpLId/vYYPu0dGuMkVEDloX9DoF81QkjX/90g9xz3uXoJsZh8fjwZEjR7o+h3q9Dh6Pd4ds3Py4ZfLUMMhGO4GSer1OuXf32mHtNn5yEOwneU0wTJLRCnpxrDVuJBIJuFwu8Pl8Sr502KCPNpGiWyqVgsfjwdjYGGw228il1kkBzuPxoNFowGQywWKx3JDDKMmRoVAIQqEQ+Xye6nqNMo6SCYNUKkUR3lH4lbWCWBlUq9W2cszks6d37brlqXy6gGf/4UewHjLh2IPzuHjxIu66666uz+EO2bjBqNVqWFpawiuvvIJGo4Fz5871HMTbkYzWD47MHDYaDUxNTbUNVPRDGOlkDBPJZBJOp5OqyJBDPWnjjoJk0EGf1VcoFJDJZNRoyyhkY9uhVqthfX0d29vbkMlkOHTo0EjITSvoHRSNRkNJIY5SwQvYrfREH1Wgz1UPY/a2Fb1UK0dBOorFIlwu1y5y0w30UUGbzUYt6HUK5kDzdxQMBrGwsND12nfIxi2DWyZPEbLxyiuvgMFg9EU2elFB7OX3Qz+EDYNktKKTmSeJF9VqdegkoxWkIyqRSKBSqeD3+4fmNN4L6vU6nE4nAoEAJbxxI+5LL8oplUqw2WyEQqGhyap3Aj0/0seY6WPAJFcO87tGyA2RsdVoNHuuPwrS0Y7cdAPZR6rX69R3f788VSwWsby8vG/R/FbJU7cN2Wg0GlhcXMQrr7wCoLeKUatPRi87GclkEg6HY9fM+qhJBh30A79YLEapVKKC+o3wyQCaC4nr6+vUPsvExMTIWTV9nGpsbAwmkwnhcJhyGz3IIniv96V3VOgdlFZN9HaB7qDoFLxbMexA2oncdAP9+39QdRb6ntJBdl/adfU6BfNYLIZIJILZ2dmu16zX6+Dz+TeL9OBN8SRuUtwyeQoAjh07hp/85Cc9k42D5CkyAkxGSQQCwQ2p9NJBuuE8Ho96DaPqhH/etaMAACAASURBVLZDKpXC6uoqCoUC1Go1pqenb4ghHxnTEggEsFqtSCQSCAQCIxvzBfZ2VOgdFHpMH3aupOdHtVrdcQeFjAHH4/Gh5Mpe8yMdw/j+t8q89zs+Te/q2Ww2yOXyjnkql8vB4XDsuw5wh2zcYBCy0UsQr9frqFarlN080R/v517hcJhywCaqSTcyiJJdkGq1CpPJBJPJNPIvW6uqFZ/P3+VQPYoDP33Gtt0s7KgW3/bbFaGDviQ+aJehG7npBjLulM/nD+QG3ipjexCFmYOQDvo+zDC6RIR0MJlM2O32tovksVgMqVQKU1NT+z63O2TjlsAtk6cA4Pjx4/jRj34ENpuNs2fP4sSJE22/Y7103PdDLBaDw+FAvV6niPiNmp8nexS5XA61Wg06ne6GjLvSF84nJiYgFot3mZyO6sBP35Vs3W+kC5gMe3ei264IHXSFw0G7DN3ITTcQ9/lkMtmTCEG7+5Jl+H7yIx30XGmz2XraMSL7MD6fbygKbJlMBh6PZ1eBujVPEVXV/YSO7pCNNwH7VYwGJRkEhJ1Wq1WIRCIkEomRVtcJ6NKARIHoIJXofkEOcJ3cS+mt0mFW+OkztvsZGA1T0q8fFSw66IuWRLu81+dAV0chMoYHme0lGvDFYrGrRwb9vsOWse1lJpuuWkZ2boZ5ACCEHNi9oFev13cptHX7fO6QjVsGt1Seuvfee/HUU09BKpXi/PnzOHbs2K7v/jBIBtCMBR6PB7lcDmNjY4hGo9Dr9SM7bBO0k8w+SCW6XxApWqLQ1zpCRlfPGma+JmNjxBek24QBvTg26IGfLhG/n6Q9HeVymdrvOUi+JuPcAoHgwAv2BxkDHraMLfl9dCMdJC+7XC7I5XLYbLahdsdIga5cLu/q+pHfSz6fx/z8/L556g7ZuME4evQofvKTn4DJZO4iG8MmGa2HqGq1ukvze9iVo16kAQ+qCNINreZK+41plUolSjpxEIfV/5+9Mw9r6szb/52ERZAd2bcAYXcDgdZ2nHHpZqevrbW121RbO63tNVa7uCE/QcUFu/hO1b6203Gmq61OZ6qdVu20dhynCwIiWlQkJASQfQmELYQk5/dHr+f0ISQkJzkJoPn89b69GM4B4vN9vtt901KCXGdsbfG7IJUpa/w9aEjlhJY6NvdcWw9vQ0jANzzEaJRKJaqrq602hjKHqX8vdNCIi4uz68IkvaA3ZcoUdtcnNjYWLi4uRmdlAbBVJmeyMSGYUHHqjjvuwP79+xEWFoby8nKkpqbCzc2N9yTD8BJFX7atGQExB5F4J4UOY8IoRK1otBl7a55r7qyjoYtjtohs0DsoXAxcAduKY7b4e9CQeN3d3W3RvYUkVUKhkO0Y2QpdoDM12kRGAu0lY2vq34tKpYJUKoW7uzvi4+PtupNKJ+jBwcFobW1l7wMkkTAWp4CJUxS7rpKN7OxsfPHFF/Dw8EBJSQnS09OHtaXI5YIrlqpncF1u5fJcSw8zSxVBzD2XbkNz9XAg1XWu72Drcw3fQSaTQaPRmH0H8lzAdudyGlqO0djfz1KVMVswtpTZ29sLqVQKoVAIiURiF1UWGvI5Ji7HRB7Q3s8l9Pb2srPbHh4eSEhIMLugR/67I8QHLGRcRJJxyoSKU4sXL0ZeXh4kkp/9XuiE2xY/J3LuDgwMjDoeQi+3SiQSm3cM6edaOsKp0Wggl8vZwpSlDtbGnmtpF9cQw+KYpe9AP9fW8WnaRNfc746Oq7Y6lxu+A+0mbvgOfNwpzGFsDJjcp+z5XBqSdPT09LBeNQkJCXb1sKIZGBhAZWUlenp64ObmBolEwn4mR1skdyYbY8C8efPwl7/8BQEBATh//jx7gbM2yTCmIGAJxpbzuMCHNKApRZDR4FstxNJKCLmU27JgPNo7kETC8EJPJyR8P9fYOzAMA4lEAhcXF/YQtedzDd+hqqoKfX19cHNzQ3JyskOeC/yShPf29kIkErGfB3sHj8HBQchkMvT39yMhIQG+vr4WL+gRnMnGhGBCxanf/e53ePbZZzFt2jRcunQJoaGh8Pf3t7rjztU0lkBkO3U6HRISEjgXO+iKsDV7YuR7kLPfUvNYW/fTTL2DucIUuZT39vba1Lk39Q6mEgni2dDT08P7c429A0miPD09eZlU4PoO1dXV6OrqgouLC5KSkhzyXODnjpdcLkdnZyfbaeTj82XJc8nyPEkw6H9bYrEYU6ZMAWA86XAmG2PAXXfdhd27dyMqKgoqlQo1NTUICAhgRyYshd6N4OpoSkNm/Xx9fREXF2d21o+eOeVLGpAsj43m+My1Dc0Vsnvh4eGB+Ph49gJHBw17G9SRESl3d3dERESgqamJreRYU1Wzhvb2dly5cgU6nQ4SiYR1prc39IhdWFgY2tvb2Qu/PRXMyHghcTcmowLkwk8Oc76TDloOkX4ujTEFOTrpIPLCjvpsWMC4iCTjlAkVp5566iksWbIEN998M3v2enl5IT4+ntNIobVJhiHd3d2QSqUjzmdTWLNgaw7ijzFagc6SkRtboItjdMeXXmq2h8w4Del2MwyDmJgYdHR0oLOz0+7Ppenq6sLly5ehVqshFosRGxvrkOfS53ZoaChUKpVdvZzo55LxQnrv1ZqOHRcMxVHCw8NHfP/+/n4oFIphClrA8KSjv7+f/e/jgBsj2fjVr36FSZMm4dVXX0VSUtIwhR1LFCDIjgIA3i5BRF1IoVCYVMKwt0maKQUHOnsm7WB7HSq0egWRJaUv+444zAYHB1FZWYmOjg74+fkhJSXFId4g9FIgUbWSy+UWLb7bwmjiAbQ3C1/ztwT6EB1tEZOvpJ7AMAwaGxtRV1dn8RIqbcDp7u6OtLQ0XLt2DVu2bIG3tzfeeecdm96JR5zJhmkmVJx68MEHIZfL8cYbb7Au9uR8DgkJQXR09KhjVPR8N0mUbT0/yfksk8kwZcoUiMXiEQU6R0jn0gU6IpJB7xY44tJNdtk8PDwgEonQ3d3Nu7T5aGi1WlRVVaG5uRleXl5IS0tzyMip4QK9t7c35HK5xTub1kLf0wzPbXt6s9AKU6OplPGV1NPPJf/eLRVHof/tubq6YsaMGWhvb0dhYSEaGxvx+eefW/0+PHNjJBsMw+D06dPYtGkT0tPTkZOTg8DAwGEV1vj4+BGHpDHlGr4xtpxHgoYjsnfgF4UDYnAEwKGXfTIr3NbWBoZhEB4e7hApREN53JCQENb13JysrS3QcoeGl25zkr62oNfrWalHczJ9o8k1csVaWUI+ko6Ojg5UV1cjICAAYrGYs2JIX18fVq5ciUuXLkGv1+P111/HPffcM14UPgBnsjEaEy5OlZWVYf369QgNDcWWLVsQEREBvV6Puro6kwvcdGJsagHbVowV6Mhl31H+HKRAV1NTw7qjO/qyX1tbi8bGRgBAcHCwRZMJtmJMHpecz/YS8QCGxwtjl25ajZLPXTsu8cISkRwuGEtqzUELIFib5JMuoqenJ+Lj4zkrag0MDGDTpk04deoUhoaGsG3bNvzud78bL+7hwI2SbBB0Oh0+/PBDvP7663jkkUewcuVKTJo0adgCd2JiIiuDKRAIWE1+e6PRaFBVVYX29nZMmjQJiYmJdh0foiFtaJVKBU9PTzZ42PsQp9U/iBcGwzBs9Ztv3XGCOW1zUtmoq6uzWrPbGPThHRoaOmqlkvbzIAeftZJ+tIxtYGAgp0s3SXys7baQauBo43rmsCbpsHU/Cvj58/nee+/hnXfewf3334+amhrU1tZi9+7duPnmmzl/PzvhTDZMMyHjlF6vxz//+U9s3boVt99+O1566SV4e3sPW+BOSEiAq6urw0xjCVqtFjKZDE1NTXB1dUVCQoLDxnhoaVYvLy+oVCp2zMSeyT9dECTxQiAQsNVvc2e5tdAJnrFnGBraWiuLbgh92bckXtBmhbaqJloy3m0MrgqZhvARLyxRXTPE1v0o4OfPyd///nfs2bMHd911F1QqFcrKyrBt2zbceeednL+fnbixkg1Cf38/Xn/9dfz973/Hyy+/jMWLF0MoFLKXCZFIhJSUFIfNu9Ef0vDwcLS1tUGv11v94bMUU0Y6fCiCjAaROWxpaTE5TkNLIfJlykcf3paYONEa8LYY9thy2ad9JwIDAxETE8MpoBBne1sqYHS3xdPT06KAQmaMBQIBb8pWliQdpHAwMDDALn9zhWEYfP3119i+fTvmz5+PTZs2sW366upq9PX1YcaMGTb/PDzhTDZMM6HjlEajwVtvvYV33nkHzz77LB5//HG4uLigvr4eNTU10Ov1SExMRHh4uEPeh96NINV1IrJgT1EJQ9O50NBQCIXCYZMJtkjVmoIe+zR1/tNfw1dxjD7zLfE4stbw1RjWXvZpPyhvb2/ExsZyijXWCNcYg+sYMK2oxZfClDE/GUPI0nl3d7fVdyyGYVBUVIS8vDxMmzYNW7duRUhICACwY8MToSh2XScbhKamJuTl5eHcuXMQCAS4+eabsXnzZlatxtSMKl/QKg+G7TdizEPkQPlskxIt787OzlHb0CTr1mq1SEhIsHk2k+4oWDo7T7twW+tVYngYR0dHc2p922L6RB/eXA9gGr1ej+bmZrbbYu5nsMdlnw4o5OcxTDrIv52+vj67XULoHSoihUyPxJla/raEiooKbN68GQEBAdi1axfEYjHPb887zmTDNNdFnFIqldixYwdOnDgBPz8/REZGYs+ePQB+Tn69vb3t6kszmtGaPc4ZgiVFKeAXVabe3l5IJBKbx47pDjTZlTF3BzAcybWmOEb2Y+huNpe/KddiGg29iG+LlxTXbou95HPNjQHbw3/MEFMmlvX19WhsbLTJbFkulyM/Px9qtRq7d+/G1KlTeX13O3BjJxs//vgjtm3bhqGhIYhEIvj4+GDbtm2IiYkZVtWOioriVSHIUmlAenmaVMRtSXzosSUuLqW2mtjQv0uuhyCBNsSTSCQWXWLp3x8fbWZjY1+mfn/k8OZ7ntVca52u7Fv6e+IKHRRJFcvFxYWXyz4XSBVrcHAQer0e0dHRVlcWm5ubUVBQALlcjt27d+Omm24aL5KB5pgQLzlGXBdx6qeffsK2bdvQ3NwMX19fDA0NYfv27UhLSxshNBIdHc3bSBG9gE13vo1BxiX5SHxoAQsu3YK+vj5UV1dbPRVAF6Us6SgYgx5147L3SIpSlnaOR4NLcYzIyxPPLr5Gxg078obdfGt/T1yhx4BJcYyPyz4XyDJ7X18fdDodIiIirB67UyqVePXVV/HDDz+goKAAd9xxx4SPUzdEsvH2229j9uzZmD59OhiGwcmTJ5GXl4c5c+Zg7dq18PPzG6Z1TJbIrcWUI6U5uKpnGULa0K2trWySwTUgkVEaLstTpBpfW1vLqivY2iWy1PCOvKs9FuhG67bQhn2W6sNbg06nQ0NDAxoaGhAREYHg4GDU19ejs7PTLm71xiCzvVVVVdDpdIiJiUFMTIxDlqdJl4X8jQcHByESiTjvWPX19WHv3r345z//idzcXCxZsmQ8LX9bwoSINGPEdRGnDh06hJiYGNx6661gGAY//PADcnJykJCQgNzcXISGhrIX9JaWFpuXpk2N15qDvmBaop5lCN35tmVstauri1WNsmQqgJxjCoWCN2EQumI/mmeSvYpSwOjdFtqwz57y8oZdooiICDQ2NqK5udmo2IE9ILHi6tWr0Gg0iIiIQHx8vMOWp8kos7u7O/R6PWthwKUDp9FocPDgQbz77rtYvXo1nnzySbsL6PDMjZ1sGEOr1eLgwYPYv38/nnzySTz11FNwdXXFwMAAqqurMTQ0xHmkiFTlbZV0pQ8PS2ZULW1Dc4Gu/pgKKOaqGnxA+2PQ3RYyfubm5oa4uDi7SgPSB3ZUVBQ6OzvtfngbotFoUFFRAaVSiZCQEKSkpDjkEKWXCKdMmcJWjIjqk71GOoBflvmImyq5TBD1OEuWBHU6HQ4dOoQ333wTy5cvx6pVq6xewB9jnMmGaa7bOKXX6/G3v/0Nu3btwr333ovnn38enp6e7L5dT08P5zFGkmQolUqbEhZLfAJMfb21nW9D6K62KSU6W/cMLMFUcYx4Z+n1ekgkErv6GpGxaaVSiaioKPT29kKpVDrUo0Or1eLKlStobW1FYGAg0tLS7K7iRSBFKR8fH/j5+aGurs7kGDCf9Pf3QyqVgmEYJCQksHcRLpYGer0eX375JQoLC3HPPfdgw4YNdt3jtSPOZMMUKpUKu3fvxokTJ5CTk4OFCxdCKBRykiizl/44PaNqLKDQbdSIiAhERETYRSnD0DNBIBAMm9cUi8V2vcDRwcLDwwNarRYA7H540wwNDUEqlaKlpQUeHh4Oc+CmvSPCwsIQGhqKhoYGdonSnu1heumc/jdAJ6LWLLObg1aNG22ZjyQdQqEQcXFxwz4LDMPgzJkz2LJlC26++Wbk5eWNJ4M+a3AmG6a57uOUWq3Gvn378MEHH+D555/Hww8/DJFIhL6+PkilUtaIbrQZfHqHjy8xDgAjpgIMi2x0x97S3QiumPJM4FNByRJIt8XFxQUCgQBDQ0Osh5Uj0Ol0kMlkaGhogJubG5KTkx1y7hkWpaKiotDc3Mxpb9NaSFHKxcVl2L8BY2PA9ph86O7uhkQiMVl4HC3pYBgG58+fx+bNmxEdHY0dO3YgMjKSt3ccA5zJhjnq6+uxadMmNDQ0YPv27awKDVEXMma+4giTIwBsQCHLeZMmTeKlDc0FElBaWlogEAjg7+9vN91vY/T390Mmk6GnpwcMwyA0NNQhHh3GjPFIFYu4cNujAkEv4Bmr2NHykHxr0JOZaIZhRh1ho5fZrZ19pjHnh2MKogNfWVnJHuabN2+Gq6srdu/ejcTERKvfaRzhTDZMc8PEqba2NmzduhWlpaXYunUrfvWrX0EgEKCzsxPV1dXw9fUd4QlhryTDEEPFHy8vL3a0Jjg4mLNghzWQAty1a9cgFApZZ3ZHmOIBvxQIlUolGIZBYGCgXZf6CfS+JLnck6KNRqPhdSnbkNGUrbRaLerr69Hc3Mzb1AWBLkqN9vPxKS8PGPdMs+TfE0k6pFIpvL29kZqaii1btqC1tRWvvPIK0tPTJ8pexmg4kw1LYBgG586dw/r16xEeHo78/HzWbIlU92NiYuDv7w+FQoGenh6IxWKHtSg7Ojpw5coVaLVaREZG2lVByxClUsmqPggEAlZVwt4VG3J49/T0sGNLDMMMk6qNiorivWpiiRwuqWIRJTG+qmbk+5IlwtESOrLcqVKpbE54rVV7oSuX1lwq6P+9LQHpxIkT2LJlC1paWrBr1y6sWLHieji8CdfND2IHbqg4BQCVlZXYsGEDGIbBtm3bkJiYOKy6T+93dXR0DJOStTcqlQqXLl2CWq1GaGgoJBKJw0ZpiLQqALi5uaG3t9chu22kGNfR0cGOPgPgTarWFJbI4RJDPGD0/UeucJGx5SK6Yg66k8blb0uPfVszBkz+9zU1NTZ5rpw9exbr169HTU0N1q1bh3Xr1k20/cHRcCYbXNDr9Th27BgKCgpw55134sUXX4SXlxfa29tRXV2N/v5+iMVixMbGOuQyY3iZmzRpEvt/W2J1bwtECcjFxQVxcXHsQWWrIog5SOW+s7PT5Mypsa6DrX8P+kCyJEDQC/VEocXaqomlS/HGoPdKRlM+MwbXHSFT0AlaaGgooqKizP7u2tvbbZafVqvVOHDgAA4fPox169YhNjYWO3bsQEREBP785z9z/n7jFGeyYZobMk4xDIPTp09j06ZNSE9PR05ODgIDA9nxR5VKhYiICCQkJDhsv4u+zPn4+KC2thb+/v6IjY21a8JBqsZarRbx8fFsldveqn30viS5RBteHI11HfjYqeSqwEjvP1pj3EqwRcaW3ivhIkoAjNwRskYAB7BuDJgPFTatVov3338fb7/9NlauXIlbb70Vu3btwsDAAI4dO3a9JBzOZMMaNBoNDhw4gAMHDiA6OhpKpRKHDx+Gj4+PzW6QlkCrPJGLLzmwuS7ncYVUQ8gojandCK6KIOYwPLwtSSDoSoe1y3D0Xog12udc3Vhp+AyItKeLuVlhW9XPTEEraJlaBCUyy7b4y9COqkuXLsVLL700rLvU1NSEsLAwm3+ecYIz2TDNDR2ndDodPvzwQ+zevRsxMTGoqanBP/7xD4SGhkIul7OGfPYao6FVngx3+Ohuiz260Jaed729vcN2W2wdq6Ljr6UJBF/FMTrmct1DMSyOcdljoNUZbfWs4NKRp5NYPguslowBk6IqAKs/NwzD4JtvvkFBQQHmzZuH3NzcYTH+RolTzmRjFJqbm1FYWIgzZ85ALBajrq4O+fn5mD9/PgQCAVtBInOhfM1lcsm8zS3ncYWWdI2Li+Psc2FKEcQctByitRdfurrPZQzIlOKVNdDB1dwypLXtYEswVekj70h2kexpaGlM4nJoaAjV1dXQaDRWG0iO5qh6HeNMNkxzQ8eprq4u7NmzB0ePHkViYiJkMhleeuklLF68GEKhkL1okwVavsY9uSzg8inZC1jfySUVakPRC0uhJV6tHaWhq/tczn1bOt+G0HsM5mSA6SSJbxlbouDZ399vVN2R7CL5+PjYbffF2BgwwzDs+LYt4+KXLl1Cbm4uAgMDsWPHDsTFxfH89uMOZ7JhDf/+97/R1taGBx54AEKhEDKZDDk5OVCpVNi+fTtSU1OHVbSt0Rynob8X15lCw+U8rr4PtJkeObyteX9jiiCjYYsbqiloA6PRujJ8Ht6GGLbOaaUwesHMHl0pGuLCTWRidTqdxfsgfKHT6aBQKFBfXw+RSITk5GSrE6sJ6KjKF85kwzQ3dJwqLy9HaWkpli9fDldXVzQ3N2Pz5s2orKzEtm3bkJ2dDYFAgI6ODlRXV1tdECKQLnBNTQ0mT57MSVqUSPaqVKpRFXxMQXuD2NLF5lpsoavrfIhhAJYXxyz18rAGErPr6upGjA3Tsdleu5EE4sKt0WgQHx8PFxcXSKVSiEQisyprfEFcvxUKBYCf7wTWxubm5mZs374dMpkMhYWFuPnmm6+n/cHRcCYbfEHMljZu3IikpCTk5uYiJCRkmD091+yfT7UE2pvAkiq9Wq1GTU0Nenp6eHP4pC/TptrF9ji8DSH7Jq6uroiPj2cPLHJ4ExULe0rY0lWhqKgoAOA1sbKU5uZmXL16FQCQmpqKoKAghzzXMOkaGhpCa2sr50Vw2lF1+/btuP3222+Uw5twQ/2wHHHGKSP89NNPWL9+Pby8vLBt2zbExMQMk9K2poNMRnDICI+1l8D+/n5Oo8j0AjZf6nv0ZdrUKBQ9Xuvj42OzkpEx6P1HujhGjxrZ030bGH5Oh4WFYdKkSXaNzabo6OjA5cuX2c9FRESEQ55LpkkUCgV7dyPjTVzidH9/P/bu3YvPP/8cmzZtYgvVNxDOZINv9Ho9jhw5gsLCQtx3331YtWoVPD09MTQ0BLlcju7ubiQkJIzafrOnDjSpYplazqPnL+1l+jM0NISampph7WIA7M9szW6ENRCzH09PT3aswN6HNw05yKqqqgDYVjHhimElEQD7u7Cn7jxdPTSsmHFxuieOqu+99x5Wr16NJ554YqI5qvKFM9kwjTNOmYBhGHz11VfYvHkz5syZg7Vr18LPz2+YKERcXJzZ81+pVEIul/Nuokor7xkba6Lf016KWjqdDrW1tWhtbR2mlEQUGB3l0UGMal1cXODu7s46u/Mpa26O9vZ2VvFSLBYjJibGIZdlWvaciKwQDyV7ycsTaNlo+j7Cxelep9Ph448/xv79+7Fs2TI8//zzE9U81lacyYa9UKvV2Lt3Lz766COsXr0aS5cuhUgkMukqCfyypCWXy+166TO2nEcOVj4k6CyFLED39PQAAKvoYO/Dm0AOsqamJgBAaGioXdzOjWG4QC8QCKBQKNDd3W1XbxY6gBoGLHs76pKgaW75m5ZDNDQoJI6qu3fvxj333MNWaMeakydPYs2aNdDpdPj973+PjRs3OurRzmTDNM44ZQatVou//OUv2L9/P5544gk89dRTcHV1NWugSTxsiLypPT2F6MKEQCBgu+N8ileMBinAdXZ2QigUsme2ozw6yOW2vr4eDMNgypQpNqkbcsFwgd7Nzc0h9wRjpsH035nsU/Kd5ALD/csSEhJMdukMzZPpDhjDMPjvf/+L/Px83HTTTcjLy2OLqmPJeIxTzmSDJ4jZ0rlz57B161bceuutbHWElkwji7uTJk2yqQ3NBTIzT4yOYmNjeTXXMYdKpWJ3I3Q6Hdzc3HhRBDEHPdpGAhYA3ndEjGFuH4TekeHTYdaS0QACPb5nqXziaAwMDKC6uhpDQ0Oclr+JzPGZM2eg0+kwc+ZMbN26FTExMayE7XhAp9MhMTERX3/9NSIjI5GVlYWPP/4Yqampjni8M9kwjTNOWYhKpcLu3btx4sQJ5OTkYOHChRAKhcPGbyUSCTQazbBdL2uEHLhCz8wLBAJER0c7dNSU7PppNBoIhUK2UMh1/5ErxpbOhULhMEdue40ymZOxpScg+BQxofdTLVGYIuN7fBRnyWe7t7cXCQkJFo9RE6XM4uJiXLt2DXfeeSd27NgBkUiEwsJCJCUlWf1OfDJe45Qz2eCZyspKbNy4kTVbSkhIAMMwqK6uRn19PTw8PJCWlmb3A4xAtwJDQ0OhVqtZhQVrlsC50NvbC5lMNmIWlSRgxhxH+YDu6JhSDKGrFXw6m5Klv/7+fov2QWj1L4lEYvXnwhZFMFr9zJrARqtqSSQSBAYGWvUznDt3Drm5uaioqMCGDRuwdu1ah100LOHHH3/Eli1b8NVXXwEAdu3aBQDIyclxxOOdyYZpnHGKI/X19di0aRMaGhqwfft2zJgxg/3vZM8tLS3N7qatBLpIEhQUxHZfLRnxshVThR9SJDPc+eMLuqNjSi7dHgIqwHBFLEtULOlldmOqUVywAY8ohgAAIABJREFUNv7TY+c+Pj4Qi8WcOvK0IpotflJSqRTr1q1DcXExnn76aezYscPuo+BcGK9x6rpJNsawbTQCYraUk5ODuLg4yGQyLF68GMuXL4dSqWQl5Ow5i2m48EUfUlyX87hCL2CbqtrTlQ2+5FctObwNoUd5bDmA6AVGawIkUY2yZka1u7sbUqnUZq8TusJmTraXfD0fXi8qlQp79uzBqVOnkJ+fj1mzZmH37t346aefcOrUqXGzYPfpp5/i5MmTrFHgBx98gLNnz2L//v2OeLwz2TCNM05ZAcMwKCsrw7p16+Dr6wulUolZs2bhpZdeQl9fH+rr63ktxJh6B1OFDrpwYw+fEEsXsMnOH5/yq6RS7+XlZdEYq6GCobWGdrZ6fdDFMUNJdXOQbr9AILBpsoH2dTEn20u+ng+vF0Pz2IULF+KNN97A8ePHcebMGYeN25ljvMap62LTUqfT4Q9/+MOwttGiRYsc1TYagUAggJ+fH4KCglBZWclm7pMmTYJYLEZ4eDjkcjnq6+s5tfEswfDCmJWVNeLC6OnpienTp6O7uxuVlZUml/O4wkU9QyAQICQkBEFBQWhsbERJSYlN7qrk8J48eTJmzpxp8YXb1dUVEokEUVFRkMvlqK2tZSs9lmAYBOLj4616fx8fH6Snp0OpVKKystKipUQ6aUxKSrJ51EEoFCIiIgJhYWFoaGhASUmJ0WoandQFBwcjOzvbqmqbVqvFe++9hz/96U9YuXIlioqK2IvG3r170dfXN24SDeDnn9uQG0wRy4kNjMc45e3tjbCwMFRUVMDLywuurq5wcXFBZGQkQkNDoVAoUFJSwouHE43hhTEjI2PEhXHSpElITU1l9wn4kkGlC0xisRhJSUmj/lyBgYEICAhAS0sLysrKWC8Ga4pjpFvi4uKC1NRUiy+oIpEIYrEYERERUCgUKC4u5lQcM5SxtfbMnjx5MqZPn46enh5UV1dbVBwbHByETCZDX18fL/cdcncIDg5Gc3MzysrKTHbkyfK3n58fMjMzrRpF0+v1+Mc//oHXX38dDz74IIqKitjPYEFBATZu3DhuEg1g/Map6yLZKC4uhkQiYQ1THn74YRw7dmzMDnG9Xo8DBw4gLy8PWVlZ6O/vx2uvvYZ58+Zh3bp1uPfee5GcnGzxgpIlGBoBWvIPy9fXF7NmzUJbWxvOnz8/QjXIUuiqfmxsrNnDm0YoFCIyMhJhYWGora1FcXExp4U0aw9vQ9zd3ZGSkoL+/n7IZDIoFAqjM6wEQ08Raw9vQ/z9/TFr1ix0dHTgp59+Miq3aKtWvTmEQiFb0bx27RqKi4vZRJDMdnt6eiI9Pd2qBJVhGHz99dfYvn075s+fj//+979GA9B4OsABIDIyEvX19ez/f+3aNYSHh4/hGzmZSIy3OAUAb731Fp566inMnz8fGo0Gb731Fm677TY8++yzePzxxyGRSBAZGYnq6mrU1dVZbcRJMByFmTFjhtnCkJeXF9LT09HZ2YmKigqrOwx0VT86OhrZ2dkWFzMEAgFCQ0MRHByMa9euoaSkhNPiOl/jsq6urkhISEB0dDTkcjnq6upGHWsy9BSx9sJtiLe3N9LT09HV1YWrV68aNcQ1VJhKSUnh9dIrEAgQFhaGkJAQNDY2orS0FKGhoYiKioJarWYT1KlTp1p1t2IYBmfPnkVeXh7S0tLw1VdfITQ0dMTXOeOUZVwXY1Rj3DaymKamJmzevBlXr15FQUEBsrKyIBAIhkmvxcXFcToM+FrypceuLD1EycJUS0sLmyDYWokmC2nd3d2jdhhIVd/Ww9sUpHJDVFjIgcKHW7ql0KNmgYGBiIqKQlNTk12cXEdDq9VCJpOhsbERkyZNsmnnqKKiArm5uZgyZQp27tyJ2NhYnt/Wfmi1WiQmJuLUqVOIiIhAVlYWDh06hLS0NEc8fuxLU+MXZ5ziEaVSiR07duD06dPIy8vDggULIBAIoFKpIJVKzarMmYKPJV+6qGapiS497mlL95yGluQdrcPA566DMUaLg7Rbuj0NXIm6pkwmg7e3N8RiMdrb2zndJfhAr9dDoVCgtrYWrq6uSE1Ntfr3XVNTg7y8PAwMDOCVV16ZUOax4zVOXRfJxt/+9jd89dVXww7x4uJi7Nu3b4zfzDgXL17E+vXr4e3tPcxsictcob3kSy3RX7fH4W0IrWxEH6LEhLC3t9cuh7ch9HjWlClTUFdXZ/NuBFd0Oh0qKyvR3NyMgIAApKWlOWwhjXilEM13lUpllRxic3MzCgoKIJfLJ7Sj6vHjx/HCCy9Ap9NhxYoVyM3NddSjJ94vy3E445QdkMlkyMnJgUqlwvbt25GamsoWW2QyGYKCgiAWi81e9u0hX0orDZraPdDr9SMSE749egYHByGXy0fEI1t3+LhCd/hDQ0PR0NDAytg6qvKu1+tZIRxvb2+rOwrWYOiVolar0dzczDnZoc1jCwoKcMcddzjjFDeu72RjjLfvrYJhGJw8eRJ5eXnDzJbofzSmKibEbMie8rnGlvOMSfTZWy2IHKIikQiurq7sPoi9D2+a3t5eXL58GX19fQgODkZiYqJDPDpIQimTyRAQEIDo6Gi0tLTYNckjjBbMaSUTsVg86txwX18f9u3bdyM7qvLFxIt4jsMZp+wEwzD44YcfsHHjRiQlJSE3NxchISHDOuGmxCGI6IU95XPpYgjphBt2hB3hqUSPSXl6ekKpVFq1gG0LarUaly9fRnd3N/z9/ZGcnOywglhXVxekUinbRens7ERdXZ1N+y2WwDAMGhsbUVdXNyIm0pMXlpjH/vnPf2bNY5988skb1TzWVq7vZGOM20Y2odVqcfDgQezfvx8rVqzAihUr4Orqyi5V0Zf97u5uyGQyuLi4OMxsqLe3F1VVVdBqtdBqtawmtiMu28Avc7YNDQ0Afl7Wi4+Pd0hln26BExlbc5K6fEFGFozNwhrOHlurTGIMOlCbG1NQq9VQKBRQqVQjDArHk6OqWCyGt7c3RCIRXFxcUFpais7OTjz00ENQKBQQi8U4cuSIw2Q+rcSZbJjGGafsjF6vx5EjR7Br1y4sXrwYq1atgqenp1HZayJ5rtPpOCsWWcvAwACkUin6+/vBMAz8/f1H7LrZE5J81dbWQiAQwMfHBxKJxCHGtSThUiqVrDALGfW1d7LV19eH6upqMAwzwk+KTkjNOXBbQ0dHB6qrq9m/tamfke4yGXbkiXlsYWEh7rnnHmzYsGFMzWOvg1h1fScbwJi2jXhBpVKhsLAQJ0+exKZNm3DXXXdBKBSit7cXV65cwcDAACZPnozExESHGCwBw0e13N3dMTAwAD8/P97k/0bDUD2DVCxaWlqgUCgsMgKyFnMtcHqMjO+ZVC7GeIaL+dbK9hJIdYqr/jnRqf/mm28QGxsLPz+/ceWoKhaLUVpaOuw91q9fj4CAAGzcuBGFhYVQKpXYvXv3GL6lWZzJhmmcccpBqNVq7N27Fx9++CFWr16Nhx56CCKRCAMDA6isrERPTw/c3d2RmJjo0AtRV1cX2wEfGhqCp6cnJBKJ3Sv79A4JEVgRiURsR9rcRdgWzMnYWuI3ZS30+JhEIhn1b813cYwUQImSpKUJHdkH/c9//sPuHubn5yMyMhI7d+5EZGSk1e/EF9dBrLr+k43RWLFiBb744gsEBwejoqICAExmiwzDYM2aNTh+/Dg8PT3x7rvvIiMjw2HvWldXh9zcXDQ0NGDFihX46KOP8PzzzyMuLg5NTU12PbxoyKgWLcHKMAx72bd0OY8r9DNMqWPZ67JPGyBacijS+y22+qZYuhhvDC6Sw8bo7++HVCplHXOt7ZgdP34cO3bsQFNTE/73f/8XjzzyiFXfh2+MHeBJSUk4ffo0wsLC0NTUhLlz5+Lq1atj+JZmcSYbpnHGKQfHqba2NmzduhWlpaX4wx/+gM8++wz3338/MjMz0dLSgsmTJ/Mip24OYxKshuOn9qjs04U4X19fo8Is9rrs0+PMlnQM6Lhmq2+KpYvxxjCUHOYaL41Ne1jD2bNnkZubi6tXr2LLli34wx/+MG5Ge6+DWHVjJxtnzpyBl5cXli1bxh7iprLF48ePY9++fTh+/DjOnj2LNWvW4OzZsw59X7lcjjVr1qCkpARz5szBrl27EB4ePup8Il/Qi2amRrUsWc7jCn14WyptSEvr2VLZtzUo0IkC18s+7dNhq8KUKSdcS97bFgnd9vZ27Ny5ExcvXsTOnTsRGBiILVu2wN/fn12GHUtiY2Ph7+8PgUCAlStX4plnnoGfnx+6urrYr/H394dSqRzDtzSLM9kwjTNOjUGcam5uxvr163Hy5ElkZWWhoKAAycnJw0Yx7dWBtsRczl7xknRRiCqXuco6X4Z8tPKkNUmULcUxetLA1gLf4OAgFAoFuru7R4zfmntvW/Y1e3p6sGfPHnz99dfIz8/H1KlTsX37drS2tuKLL74YF4vg10GsurGTDQBQKBS455572EPcVLa4cuVKzJ07l63K0l/nCC5fvozf//73yMvLw+23347PP/8c27Ztw1133YUXX3wRXl5e0Ol0UCgUNv/joyGHN5mztUTa1NhynjUQtRJj+wmWYEoRxBy0jC0fHSPinD44OIj4+PhRzYsMfToMjfNsgcxM6/V6o39LvV6Puro6mxMcQ0fVRx99dNjPoFQqx8VsaWNjI8LDw9Ha2orbb78d+/btw6JFiybSAQ44k43RcMYpB8eptrY23H333Vi3bh2WLFmCM2fOICcnB7NmzcLGjRsRGBg4rAPNV1GKFFT6+/stPutp0RVbOtDEARvAiP0ES7BFoYpPGVu6E05+h6bew9CnQywW87Y4ba44xjAMGhoabE5wtFot3n//fbz99tt45pln8Oyzzw6L8+MlTgHXRay6vh3EraGlpYU9mMPCwtDa2goAaGhoQFRUFPt1kZGRaGhocNghnpKSgu+//579x7948WL89re/xYEDB7BgwQI899xz+N3vfof4+HhERERAJpOxTuTWeB9wrYbTuLq6IjExkd0zqK2t5WT6RB/eSUlJVi9mEUM+sqxWW1sLiUQy6nt0d3ez1SlLjKUswcPDA1OnTjUblIh0pL+/P28mSzReXl6YMWPGiC6Vp6fnsDE4a40IR3NUpRkvBzgxNAoODsbixYtRXFyMkJAQNDU1sZe44ODgMX5LJ05GMl7jVFBQEIqLi9k4NW/ePHz//ff44IMP8Nvf/haPPvooVq5cyVby5XI5rl27ZnUHle7CWlINpxGJRIiLi0NkZCTkcjnq6+vN7hnQkCKSWq1mhUKsgRjyRUVFQSaToa6uzuz3I7FEIBDYZFpL4+7ujuTkZPbnMmVgSzo4np6emDlzJu/7Lx4eHkhLS2MLnTU1NWzcpsfgrI2RDMPgm2++QUFBAebNm4czZ84Y/ZuPlzgFXN+x6oZNNkwx1lbvxp7l5uaGNWvWYNmyZazrcn5+PubPn4+0tDTW1dnNzc3ipThb5/xpPDw8MG3aNKhUKlRVVcHd3X3U96ATHFsOb0MmT56MGTNmoLu7G1VVVaw0MN0pIQmJXq+3KcEZDS8vL8ycORNdXV2orKxk916GhoZQXV0NNzc3TJ8+3e5KJT4+PsjIyIBSqcTFixeh0WgQGBiIjIwMqxb8LXVUHU/09fVBr9fD29sbfX19+Ne//oW8vDwsWrQI7733HjZu3Ij33nsP995771i/qhMnFjPWccrY80QiEZ544gksXboUr732GubNm4d169bh3nvvRVJSEmtAR5zILbk4G875JyYmWv1zurm5ITk5eVhRarT3sGU8djTIcjJdlDJ8D7VaDZlMxnuMpCHFMcO9F4FAwMbI5ORku6szTZ48GdOnT4dKpcLVq1fR398PHx8fm2LkpUuXkJubi4CAABw5cgRxcXE8vzX/XO+xyjlGNc7a05ZQXV2NnJwc9Pb2oqCggDVbItUAIndnrN1JH958KBgZMtpyHjm86bErewVIwxGp8PBw1NXVsTK2jqpmkLlhqVQKkUiE1NRUq8fNrIGWJgwMDERTUxN8fHw4y0JOVEdVuVyOxYsXA/i5nf7oo48iNzcXHR0dWLp0Kerq6hAdHY2//e1vdjeItBHnGJVpnHFqHMappqYmbN68GVevXkVBQQGysrIgEAiGqd6ZkjGnFYxsXWo2hSn1PT6FPyyBHpGKiopCc3MzOjs7ERcXx6mDYyutra2orKwEwzBISkpyaCGJJFdqtRohISFobm62eCeGprm5GTt27IBUKkVhYSFmz549LnYxLOE6iVXOnQ3DQ3zdunUIDAxkF+86Ozvxyiuv4Msvv8T+/fvZxbvVq1ejuLh4jN9+JAzD4Pvvv8fGjRuRnJw8zGzJ2CKXPb0ZTL0fWc4LCwuDVqu1ynnaVoaGhlBRUYHOzk6EhIQgOTnZYWY9tP55fHw8NBoN6urqTKps8YlGo4FcLkdPT8+w5IpW+5oyZYpZz5TrxVH1OsD5SzeNM06N0zgFABcvXsT69evh7e2Nbdu2ISYmxqSyob29GQwxNAB0cXGxynnaVrRaLSorK9HS0oKAgACkpaU5xEsKGLnTIhQKUVNTAz8/P6MqW3xCi7zEx8ezyZWhYIy54lh/fz/27duHY8eOIScnBw8++OC4UZi6wbixk41HHnkEp0+fRnt7O0JCQrB161bcd999RrNFhmGwatUqnDx5Ep6envjrX/+KzMzMsf4RTKLX63H48GEUFhbi/vvvx6pVq+Dh4THsH7GPjw+6u7vt7jpt7N3q6upYoyOJROIwR1VaMSsqKgohISFoaGhgFUGMOd7yBa0+YrgYSQdTe/w96GfHxsaarMqZc4PXaDQ4ePAg3n333TFxVJ1IMqAOwplsmMYZp8Z5nGIYBidPnkReXh7mzJmDtWvXws/Pj40RjY2N8PPzQ3d3N5t8OOq8IYvIcrkcDMMgNjbWYXHSsCgXERGBlpYW1l8qKirKbu9BFyYNYxHxD6GTQT7/HpaqW5lzg9fpdPjkk0+wb98+PP7441i9erVDzWOdcWoEN3ayYS319fVYtmwZmpubIRQK8cwzz2DNmjXj8sOkVqvxxhtv4KOPPsKaNWtw33334fDhw0hISIBQKISrqyuSkpIcYghoTEpWp9MZrbQ74tn0JZp2vOW7TU0f0OYUprRaLerq6tDS0sKLUguXZ9PodDo0NDSgoaEBlZWVeOihh3Dq1Kkxd1SdaDKgDsCZbJjGGacmSJzSarU4ePAg9u/fjxUrVuCJJ57Ap59+iqioKLaCnpSU5JAxV1ppiVxkBQLBMMUoe40xmZOxpScRbJVEN/ZsMmJsTkKXVhTjozhGP3u0cW9j70HME6uqqrBo0SL89NNP2LJlC7KyspCfnz8m5rHOODUCZ7JhDU1NTWhqakJGRgZ6enowa9YsHD16FO++++64/TC1tLRg+fLlKC8vx5w5c7B//354e3tDpVJBKpWyc5D2cFalD1B/f3+IxeIRLVh6h8AWAzljz6ZdW409m4bvBTzybFMGT6bQaDRQKBRQKpVWB7fOzk5UV1dzfjbN4OAgXnzxRZw8eRLx8fE4dOgQYmJiOH8fPrme5td5wJlsmMYZpyZYnOru7sazzz6Lb7/9Funp6Thw4ABCQkLY+AD8rOTHV3wwpLOzEzKZzKSULL1DYEypyRZolSdzMrZ8L6nTKoxc7gGkONba2mr1KDQfdxC9Xo+8vDx88sknCAwMxKFDhzBt2jTO34dPnHFqGCY/FM6htlEICwtjKz7e3t5ISUlBQ0MDjh07huXLlwMAli9fjqNHjwIAjh07hmXLlkEgEODmm29GV1cXmpqaHPa+ly5dwuLFi5GQkIAjR45gcHAQzzzzDKRSKatMFBwcjPLyctYQiS+USiVKS0vR3t6OGTNmIDEx0eillyhGicViXL58GVeuXMHg4KBNz+7u7kZZWRlaWlowffp0k8+mIYogycnJUCgUuHDhAvr6+jg/u6enB2VlZWhsbMS0adOQlJTE6bLv5uaGxMREzJgxA62trTh37pzFGtp9fX0oLy9HfX09pk6dyvnZhIaGBqxatQr19fU4dOgQfvWrX2HJkiUoKiri/L3sCVcZUCdObgQmWpyqr6/H/fffD1dXV/z9739HUFAQnnrqKVy4cAGenp6YMWMGoqOjcenSJVy9ehUajYa3Z6tUKpSVleHatWtITU1Famqq0UsviQ+JiYmQy+W4ePEiBgYGbHp2b28vysvLUVtbi+TkZJPPpnFzc0NSUhKmTZuG5uZmlJWVobu7m/Oz+/v7cfHiRcjlciQmJiItLY3TZd/FxQVxcXHIyMiASqVCcXEx2trajKqiGaJWq1FRUQGpVGrVswnt7e1Yt24dfvzxRxw8eBCPPPIIHn/8cZw8eZLz97InzjhlHKf0rYUoFAqcP38eN91007jVPg8JCcGhQ4cgFosBAHPmzMG3336LlStXIjMzExs2bEBQUBACAwPR0NCAkpISm/cXiGweUVqytBLl5+eHzMxMtLa24vz581YtTdMytomJiVaNiBGZWqVSicuXL1tsmqRWq1FdXY3BwUFeKl+0HCKtfW7sZ9JoNJDJZOjt7UVCQoLVXRlDR9X/+Z//gVAoxPz589HU1AS9Xm/Tz+QoxoMMqBMn44GJEKemTJmCvXv3Ii0tDQBw66234ty5c1i3bh0iIyORn5+P8PBwZGVlobm5GefOnUNYWBiio6OtHuGhHcclEonFnlTe3t5IT09HR0cHfvrpJ/j6+iIuLo6T7wMfXXRaplYqlcLFxQUSicSovxENEQpRqVRWe5zQkOSHyNcTTytjP5NWq0VNTQ06OzttUp9Uq9V4++238fHHH+Pll1/G/v37IRKJsHDhQjz33HPo7Oy06WdyFDd6nHImGxbQ29uLJUuW4I9//OOoh9RYf5imTJkybG5RIBBgwYIFmDt3Lt5//3389re/xWOPPYZnnnkGUVFRCA0NhUKhQHFxMSQSCSdJ1v7+fshkMgwNDSE+Pt6qy7ZAIEBISAiCgoLY5McSmUPaMZyv/Q9isNfW1oby8nKTSk30zoc95HuJIR/xCqFd1ckcb0tLC2JjY5GcnGzVsw0dVc+ePTvi5xyPrV1T5kaRkZGor69nv44o2ThxciMxUeIUMXOjn52ZmYlTp07h2LFjePDBB3HXXXfhxRdfRFhYGIKDg1FXV4fi4mLOcu1qtRpyuRx9fX0WO44bIzAwEAEBAWhubkZpaalFe3H22A/09vZGRkYGOjs7UVFRYVKpyXDnIykpide/MfkbkuIYbchHi6BERUUhKyvLqiRRr9fjs88+w2uvvYYHHnjAqHmsv7//uDLlA5xxyhTOMSozDA0NYcmSJXjsscdw//33A/jlwwRgQnyYRCIRnnzySfz4449Qq9WYN28ePvvsM4hEIiQkJGD69OlobGxEWVkZent7R/1eg4ODuHLlCioqKhAeHo6MjAybq/pCoZA9lNRqtckWrVarRXV1Nc6fP4/AwEBkZmbyetAIBAIEBwcjOzsbkyZNQmlpKWpra6HX66HX61FbW4vS0lJ4enoiOzvbrhrovr6+yMjIQFhYGC5evIhz587h7NmzEAqFyM7Otkr7nWEYfP3111iwYAFqa2tx5swZPP/887w7mNsLYm4EYJi50aJFi/D++++DYRgUFRXB19d3XCZLTpzYi+shTgmFQixevBhnz55FaGgoFixYgHfffZdViCKX7NLSUnR1dY36vYaGhiCVStnCUWZmps1VfYFAgLCwMGRnZwMAiouL0dTUNCJO6XQ6KBQKlJaWYvLkycjOzkZQUBCvsSIgIABZWVnw8/PD+fPn2a4NUdYqLi6GUCjETTfdZFcFSFIci42NxdWrV1FaWoqioiJoNBpkZWVZtVBOzGMXLlyIM2fO4KuvvsLmzZvNdnHGC844ZRzngvgoMAyD5cuXIyAgAH/84x/Z/z7Rtc8bGxuxefNmSKVSFBQUIDMzEwKBAN3d3ZBKpfD09ER8fPywagmp0nR0dNjFDJDGcDnP29t7mIytPQyejEH0x69duwaBQICIiAjExMTYVffdELL87erqCrVajaCgIIsVPGhoR9WdO3eOe0fV61kG1EpunH47d5xx6jqMU0qlEtu3b8d//vMf5OfnY/78+RAIBOjt7WVNUg1HiRzpJ0Uvb5OuCVFC5KIIaCtEMaq2thbAz0lmbGysQ4tI5O4gFAqh0Wjg5+eHuLg4zjuECoUCeXl56OvrwyuvvDLmy9/mcMapETjVqKzhu+++w5w5czBt2jT2wNq5cyduuumm6+LDdOHCBaxfvx6+vr7Ytm0boqOjh8kBBgcHIzIyEo2Njaw/hb3NAGlUKhUuXboEtVqNiIgIxMfHj8lFn0i/9vb28qIIYgnGAiqtTW6p4dVEdlR1MgznH800zjh1Hcep6upq5OTkoLe3FwUFBUhNTQXwy/ns5+eHmJgYtLa2OswMkKa/vx+XLl1CT08PQkJCkJiY6PCLfnV1Ndzc3ODm5galUukQ53MAGBgYQHV1NYaGhpCQkABvb+9hho2W7mJ2dXXh1VdfxXfffYeCggLceeedzjg1MXEmG06MwzAMTpw4gby8PPzmN7/B2rVr4evrC41Gg8rKSrS1tSEoKAipqakONVkiUrJ+fn7w8/NDbW2tTdKuXKCX3unK2cDAAGQyGW9L4cYYHByETCZDf3+/ycU7S6p3Y+2oaszsaMuWLXjnnXcQFBQE4OcL0d133w0A2LVrFw4ePAiRSIS9e/fizjvvdNi7ThCckdc0zjh1ncMwDL7//nvk5OQgKSkJubm5CAkJgVarRVVVFZqamuDv74+pU6c6zHkb+EXG1sPDg90tsae8PI2xiz7wcwypqanhbSncGIa7i8Y8Lixxg6fNY59//nmsWLFizM1jnXHKJpzJhqNRq9X49a9/jcHBQWi1WjzwwAPYunUrampq8PDDD6OzsxMZGRm16G0aAAAgAElEQVT44IMP4ObmhsHBQSxbtgznzp1DYGAgDh8+zKpKOQKtVot33nkHb775JmbPno3//Oc/eOuttzBjxgzU19ejq6vLbgcXDanS0IvRgPWmdVygVUMSEhJMJhNcFUEsgYxstba2WjymRo+2TZkyBXFxcWAYZkwdVQnGzI62bNkCLy8vrF27dtjXXr58GY888giKi4vR2NiI2267DVVVVQ7tYk0AnMmGaZxxykomWpzS6/U4fPgwdu3ahaysLPz444/Iz8/HbbfdhqamJrS2tiIuLo73HQlDent7h/mBkO63taZ1XDAc3zIl7NLf34/q6mpotdphyYgt0CZ/lipZ0sWxgIAAxMfHQygU4sSJE9i1axcWLlyIDRs2WKwQxifOOMU7Tp8NR+Pu7o5vv/0WFy5cQHl5OU6ePImioiJs2LABL774IqRSKfz9/XHw4EEAwMGDB+Hv74/q6mq8+OKL2LBhg0PfVyQSIT4+Hp6enrh48SICAgKgVCrh7u6OxMRETJ06FdeuXcP58+et8qMwR19f3zAd8KlTp7KJBmD5cp410MuEwcHBmDVr1qhdC6IIEhkZiYqKClRWVlqtBc8wDBobG1FcXAwXFxdOy9+urq5ISEjAzJkz8dFHH+Gmm27CLbfcgosXL+Lbb7/FunXrxiTRAIBf//rXFiemx44dw8MPPwx3d3fExsZCIpGMyxlyJ06uNyZanBIKhYiNjUVgYCAuXLgALy8v9Pb2ws3NDXFxcUhPT0d7ezvOnTtnlR+FOdRqNS5fvozKykqIxWLMnDmTTTSAn+NUUFAQsrKyMGnSJJSUlKCuro4XGXGdToeamhqcO3cOPj4+yMrKGlVB0tPTE9OnT0d8fDyqqqpQUVFhtVcIwzBobW1FcXExtFotsrOzERERYVGcEolEiI2NZRXHMjMzMW/ePBw7dgzHjh3Djh07xiTRAJxxypE4kw07IRAI2ENoaGgIQ0NDEAgE+Pbbb/HAAw8AGGm0RAyYHnjgAZw6dYqXi7SlyOVyfPjhhzh8+DDOnj2LI0eO4LPPPsO9996LCxcuwMPDA9OnT0dsbCxrxseH2RJRt7p8+TIiIyORnp4+agVGJBJBLBZj1qxZUKlUKCkpQUdHh1XP1uv1qKurQ2lpKTw8PDirhtCKIGVlZZyNEjs6OlBcXIze3l5kZmZarSNfV1eHy5cvIy4uDtHR0aiurkZdXR3n7+MI9u/fj+nTp2PFihWseeGNbnbkxMlYMdHiVFdXF/bs2YN9+/ahtLQUX375JcrKynDHHXfgu+++g5ubG1JTU5GcnAyZTIaffvrJZjM+4JeC1IULFxAUFIRZs2aN6pchFAoRGRmJ7OxsDA0Nobi4GC0tLVb9ruiClEAgQHZ2NieFKaJsGBoaiosXL3I2Suzu7sa5c+fQ3t6O9PR0xMXFWVXNb21tRXl5OaKjoxEVFYW6ujrI5XLO38cROOMU/ziTDTui0+kwc+ZMBAcH4/bbb0d8fDz8/PzYtir9YaU/yC4uLvD19bX6Em0N8fHxeP/99xEbGwsAiI6OxgcffIDCwkJs2rQJzz33HBobG1kzvoCAAJSVlaGmpgY6nY7z82gZ24CAAM7ShLSzKpHt7enpseh/S0ay6CqNNRJ9wM/BOjQ0FNnZ2XBzc0NJSQnq6+tHrWT19vayruPE8dyahcL29na8/PLLeO655/Dyyy/j888/x5dffokdO3bg3//+N+fvZ2+ee+45yGQylJeXIywsDC+//DKAsdf9d+LkRmYixSk/Pz8cOXIEU6dOBQAEBQXhzTffxF/+8hfs378fjz76KKRSKby8vJCRkYHw8HBcvHgRUqkUQ0NDnJ9Hy9gSyXMuBSkyMZCenm6xbC+BjGTRBSmxWGzVRV8gEGDKlCnIzs6Gj48Pzp07ZzZ2DwwMsNMGxPHcmk55T08Ptm3bhoceeggPPPAA/vWvf+Ef//gH/vznP+P06dMOTVYtwRmn7IPT1M+OiEQilJeXo6urC4sXL8aVK1dGfA35sI7HD7JAIEBWVhZOnTqFo0eP4sEHH8TChQvxwgsvsGZ8xGxJLBYjNDTU7DvTM59RUVHIzs62aXHZw8MD06ZNY03wzC3ndXZ2QiaTsaNQfC0TEq+QsLAw1NbWGjWgope/R9sJMYdarcZbb72FTz75ZJijKiEjIwMZGRm8/Fx8EhISwv7fTz/9NO655x4A41f334mTG4GJHqcAICUlBceOHcO3336LlStXYtasWdi4cSNrxtfY2IjS0lJERkYiIiLCbMxhGGaYjG12drZNs/nu7u5ISUlBX18fpFIpBAIBJBIJJk+ebPTrVSoVqzA1ffr0YSPFtkDGkUNCQlBfX4/i4uIRcvJDQ0OoqalBV1fXqDsh5tBqtfjggw/w1ltv4emnn0ZRUdGweJuYmIj8/Hxefi4+ccYp++DsbDgAPz8/zJ07F0VFRejq6mJHbegPK/1B1mq16O7utvsytqUIhULcf//9OHv2LIKDg7FgwQK899570Ov1I0aaSMvREHJ4FxcXQ6fT2dRNMAZpFQcHB+PChQvsYhyht7cX58+fR319Pdtmt4dqiYuLC1vJUiqVKCkpQVtbG2QyGc6fP48pU6aY3QkxhV6vx6effop58+ZBp9OhqKgIy5cvnzALasRgDAA+++wztjq5aNEifPLJJ6yKilQqZXdznDhx4hgmepwSCARYsGABvv/+e2RmZuLuu+/G3r17odFoEBERgaysLGg0GpOmsQBY6ffi4mL09fXZ1E0wxuTJkzFz5kxER0cbHUceGBhARUUFpFIpJBLJiN1FvhAKhYiJiUFmZiZrpNvc3Mwa13p5eZndCTEFwzD45ptvMH/+fNTU1ODMmTNYvXq1Q1XCbMEZp+yDs7NhJ9ra2uDq6go/Pz8MDAzgm2++wYYNGzBv3jx8+umnePjhh0e4S7733nuYPXs2Pv30U9bAaDzh5uaGF154AcuWLUNBQQEWLFiA/Px8zJs3D0lJSejr62P3BUjVhsjYyuVyNiGw16FDlvMCAwPR2NiIkpIShISEYGBgAAMDAyalZO2Bu7s7kpKSoFAoUFFRAXd3d6SlpVmVZBBH1by8PKSkpOCrr75CaGioHd6aP2izo8jISGzduhWnT59GeXk5BAIBxGIx3n77bQBAWloali5dysorv/nmmxMmgXLiZCJzPcYpkUiEJ598EkuXLsVrr72GuXPnYv369bj33nsRHx+PiIgIyGQy1NXVISEhgV1OpmVsZ8yYYVfpWn9/f2RmZqK1tRVlZWUIDAyEXq8f1k1wxO/V1dWVNfCtrKyESCRCSkqKUSlbS7h06RL+3//7f/D19cXhw4cRHx/P8xvzizNOOQ6n9K2duHjxIpYvXw6dTge9Xo+lS5ciLy8PcrmclRRMT0/Hhx9+CHd3d6jVajz++OPsDsMnn3wy7l2eq6ursXHjRvT19Q0zW1IqlWwLWKPRwMPDY5iMrSPQarWoqalBY2MjhEIhEhISHGJyRGhvb4dMJkNAQADEYjErQ2go6WsOhUKBzZs3o7+/f0I4qjqxG+PrRje+cMYpK7kR4lRjYyM2b94MqVSKgoICZGZmQiAQoKenh5Uu1ev1EAqFw2RsHQGRhSXdori4OItVnvigq6uL3XGJi4uDVquFVCoFAE6/C2IeW1VVhcLCQtxyyy3jLgl14hCcPhsTDZ1Oh8zMTEREROCLL74Yt7rnDMPgu+++w8aNG5GSkoLc3Fy0trZCKpUiPDwcWq0WkZGRVistcYU2EiKzqENDQ5DL5ejt7UVCQoJduxvEg8PNzW1EUmFoVjiaQeFYOqrW19dj2bJlaG5uhlAoxDPPPIM1a9ags7MTDz30EBQKBcRiMY4cOQJ/f38wDIM1a9bg+PHj8PT0xLvvvjsud0YmOM7IbRpnnBpDJkqsunDhAtavXw9fX19s3boVOp0OJSUliIqKAsMwCAkJsYsvhjGM+Ubp9XrWKC8uLg5Tpkyx25lPil86nQ4JCQkjkgq6yzPaDiQxjz169ChycnKwdOlSh5nHOuPUuMTpszHReOONN5CSksL+/+NV91wgEGDOnDn473//i4yMDNxyyy1YtmwZJk+ejJtvvhmzZ88GwzDsTKi9lCcYhkFLSwuKi4uh0WiQlZXF7oSQ5bzU1FQoFApcuHCBd68QtVqNS5cuoaqqyuSsLa0I4u3tbVQRRKPR4MCBA7jzzjuRkpKCH3/8EXfddZdDq0QuLi54/fXXceXKFRQVFeHNN9/E5cuXUVhYiAULFkAqlWLBggUoLCwEAJw4cQJSqRRSqRR/+tOf8NxzzznsXZ04cTK2TJRYNWPGDJw8eRIPPPAAbrvtNixatAg6nQ6zZ8/G7Nmz4e7ujpKSEly7do0XXwxTdHR0oKSkBCqVCrNmzWJ3Qohv0owZM9DS0oKysjKoVCpenz00NISrV6+ioqKClZo31r3w8/PDrFmzEBwcjPLy8hFqXjqdDh999BHmzZuHyZMn4+zZs3j44YcdlmgAzjg10XB2NsYh165dw/Lly5Gbm4s9e/bgn//8J4KCgtDc3AwXFxf8+OOP2LJlC7766ivceeed2LJlC2bPng2tVovQ0FC0tbU5vIVZUFCAo0ePYu3atVAoFPj444/xwgsvsJUOjUYDmUxml+4CGdsirWBz8nz018fHx9u0Q6LVaqFQKNDe3o74+HhO1SidTodr167h0KFD7HjVK6+8grvvvnvMHFWNce+992LVqlVYtWoVTp8+jbCwMDQ1NWHu3Lm4evUqVq5ciblz5+KRRx4BACQlJbFf54Q3nJ0N0zjj1Bgx0WLVW2+9hQMHDmDVqlUYHBzE22+/jRUrVmDFihVwdXVlz/OOjg72POcL0vV2dXWFRCIxO0rL9etHg3hKNTU1WawcSf9vm5qacPToUbS2tmLOnDnYuXMnMjMzkZ+fj6CgIKvfi0+ccWpc4OxsTCReeOEFvPLKK2yVoKOjY9zqnhN+/etfo6ioCI888ghycnLwzTffoLS0FLfffju+//57uLq6DusuXLx4Ef39/TY9s6+vD+Xl5airq0NqaipSUlIs0gEny3nEK0Qul3P2CiESviUlJXB3d+esvw78vMgYHR2NW265BSdOnMDq1auxatUqbN++fdwkGgqFAufPn8dNN92ElpYW9mAOCwtDa2srAKfZkRMnNyoTLVZNmzYNRUVFePrpp7Fq1Sp89913aG1txbx583D8+HF2b2P69Olobm7m5N9kCqIwRbre06ZNsyhxIPLsxCukqqqKs1cI7SnFMAxnQ0DgZ+WqiIgI3HrrrTh37hyeeOIJ3HfffXjjjTfGTaLhjFPjH2eyMc744osvEBwcjFmzZrH/bTRt8/Gie/6b3/xmmDFdcHAw/u///g8HDx7E3r178dhjj6G6upqV/ouMjERFRQWuXr3K+QAdHBzE5cuXcfnyZYjFYsyYMcOkXrkpBAIBQkJCkJ2dDRcXFxQXF6OhocHsmBeRRiwpKYFarUZWVhaioqKsah83NDTgmWeewauvvoq9e/fi/PnzKCoqwv3338/5e9mD3t5eLFmyBH/84x9HTX7Gy2fQiRMnjmMixqpbb7112EXf19cXu3btwtGjR3H06FEsWrQIFy5cwKRJkzB16lRIJBJUVVXh0qVLUKvVnJ5FXMcvXryI0NBQZGRkWFVECgwMRHZ2Nry8vFBaWora2lqLxryUSiVrIpiRkYHY2Fir1JM6Ojqwdu1avPTSS+yifUdHB+bOnTtMXn6scMapiYFT+nac8f333+Pzzz/H8ePHoVaroVKp8MILL7C65y4uLkZ1zyMjI8ed7jkApKam4vPPP8epU6fw9NNPIysrCxs3bkRAQACysrLQ1NSE0tJSREREmPXd0Gq1qK2tRVtbG2JjY5GSkmLzYSEUChEdHY2wsDAoFAoUFxebXM5TqVSQSqVwd3e3SRqxp6cHe/bswddff428vDwsWrSI/bnfeecdmytpfDA0NIQlS5bgscceY5OfkJAQNDU1se3p4OBgAE6zIydObkSup1gVHR2NDz74AKWlpVi3bh2io6ORl5eH8PBwZGRkoK2tDeXl5QgKCjLru6HX61FfX4/GxkZER0dDIpHYHKcEAgHCw8MREhLCGunGxMQYHYciEvQMwyA1NZVzIY6gVqvx9ttv49ChQ3j55Zexb98+9ud+9dVX0dPT45Bl+tFwxqmJg7OzMc7YtWsXrl27BoVCgU8++QTz589nF7E+/fRTADCqew5g3OqeCwQC3Hbbbfjhhx+QkZGBhQsXYt++fdBoNAgPD0d2dja0Wi2Ki4vR0tIyogJBDu+SkhK4ubkhOzubdxnb0Zbz1Go1a7SUmJiIqVOnWpVoaLVa/PWvf8Vtt92GiIgIFBUV4b777huRYHl7e/PyM1kLwzB46qmnkJKSgpdeeon97/RnzfAz+P7774NhGBQVFcHX19c5B+vEyXXO9RarBAIBsrKy8O233+K+++7Dgw8+iO3bt6Ovrw/BwcHIzs6Gq6uryS44Ma49e/Ys9Ho9srOzeZexFYlE+P/t3XtU1HX+x/Hn4GhGJrfQvHJxEC8MAiq2+6sNUKxcNy+xmuHJC7abHNNoPYInD9lqClpeo/VoqHUsXW1dZFfQFaz1CsiyoGi6gCjeUAG5OMj98/tDnSMZpQTMAO/HOZwT4xfmM5Lz4vP9fD7vt5OTE15eXsZGukVFRcDd4iJnz57l9OnT9O3bFw8Pj0ZNNOrq6vjb3/6Gr68v1dXVJCcnM3369IcmWJJT4nHIAXEz9t133/Hxxx/zz3/+s03VPTcYDKxcuZKYmBgWLFhgvLNfWVlJTk4O5eXlxmZLN2/e5Pz589jb2+Pg4NBid1LKyso4d+4cVVVVaDQaXFxcGt1oSSlFYmIif/7zn/Hx8eH999/HxsamGUbdNI4cOcILL7yAXq83ToSWLVvGiBEjmDRpEnl5efTt25ddu3Zha2uLUoo5c+awb98+LC0t2bJlC8OGDTPxq2hzzOe3MvMjOWVibTGrqqqqjFuBg4ODCQwMRKvVUl1dTW5uLrdu3UKn02FnZ0dRURHZ2dlYWVn9ZEnzplZeXk5WVha3b98G7vbpeJzD3w+6XzUyPDwcV1dXlixZYta/jEtOmSXpsyHMz9WrV1m0aBE5OTksWbKEoUOHotFouH37NmfOnKG8vBw7Ozv69+//SAe/m8qDvTpsbW0pLi7GxsYGJyeneudSHsWDHVWXL19u9h1VhdmSyUbDJKdEsykqKmLJkiUcOXKEDz74AF9fXzQaDXfu3OHMmTOUlZXRtWtXBgwYgKWlZYuN68FeHTY2NpSVlfHUU0/h7Oz82CvvFy9eJDw8nLKyMlasWIG7u3szjVq0cVKNqj1xdHREr9fj4eFhnLkXFRXh7++Pi4sL/v7+3Lp1C7j7hjV37lxjBY60tLQWG2fPnj3ZvHkza9euZenSpcycOZOEhARmzJiBUgqdTkd5eTl5eXmPfYi8MZRS3Lhxo16vDldX13qH8y5cuPBIh/Py8/N55513eO+99wgPD+evf/1ri0w0Ll26hK+vLwMHDmTw4MGsXbsWgMWLF9OrVy88PDzw8PAgLi7O+DXLly9Hp9Ph6urK/v37m32MQgjRWnLK1taW1atXs337drZu3UpAQACJiYkEBQVRUFCAi4uLsSFfZWVli4ypqKioXq+OAQMGMGzYMJ555hnS09PJzs5+pMPbxcXFLFq0iDfffJOZM2cSHx/fIhMNyan2R1Y22iBHR0dSU1Pr1QhfsGABtra2hIWFERERwa1bt4iMjCQuLo7169cTFxdHcnIy8+bNIzk5ucXHnJ+fT1BQEKmpqYwdO5alS5diZWWFUoqrV6+Sl5dH79696dWrV7M0DiopKSE7O5vOnTs32DG1traWvLw8rl+/3uDhPFN2VAW4du0a165dw8vLi7KyMoYOHUpMTAw7d+6kS5cuzJ8/v971Z86cYcqUKaSkpHD16lVGjRrF//73v0ZVLRHNSlY2GiY51Qq1xpwqLi5mzpw5xMfH4+fnR0REBD169DDeqMrNzaV79+707du3Wd5DDQYDWVlZaDQadDrdj57JeHBlvqHMrK6uJjo6mi1btjBnzhyCgoJa9LC35FSbJSsb5srR0ZFt27Y1+/Ps2bOHadOmATBt2jRiYmKMj7/55ptoNBqee+45iouLuXbtWrOP50EpKSmMGTOGGTNmcOnSJYYOHcro0aPZuHEjNTU19OrVi+HDh1NVVUVKSgo3b95ssk7kd+7c4dSpU+Tk5NC/f38GDx7c4BL0jx3Ou1/dwhw6qsLduuJeXl7A3QN8AwcO/Mla4nv27OH111/niSeewMnJCZ1OR0pKSksNVwjRCkhOwfnz53nxxRfx8fHh2rVrTJgwgYkTJ/Lxxx9TUVFhLKVuYWFh/KW4qXKqqqqK77///pHKvVtYWNCnT596mZmbm0tdXR11dXXs3bsXX19fCgoKOHr0KH/84x9bvKqU5FT7I5ONNkij0TB69GiGDh3Kxo0bAcy60Y2npydJSUkEBATQqVMngoODOXLkCPn5+fj5+REfH4+FhQX9+vXDw8ODGzdu1KsW1RgP1kC//8b3qNU1OnXqhKurK25ubqxcuRIfHx9eeOEFMjIyOHjwIAsWLGh0Wdym9GCjI4BPP/0Ud3d3Zs6cadyeYA4/fyFE+9PacsrJyYnjx48za9YsOnXqxBtvvEFycjKdOnXC19eXHTt2AODg4MCwYcMoKyurVy2qMWpra8nNzSUtLc3YjNba2vqRvlar1dKvXz88PT356quv+PWvf82oUaOIjY0lJiaGZcuWmUXzWMmp9kEmG23Q0aNHSUtLIz4+nqioKA4dOtTgtebQ6KZjx44PVe+wsrIiIiKC3bt3s3v3bsaPH8/Jkyfp3LkzgwcPpn///mRnZ5OZmflYzZbq6urIy8sjNTUVS0tLvL296y3jP47Lly9z48YNbG1teeKJJ6isrGyRsyWP4oeNjmbPnk1OTg7p6en06NGDP/3pT4B5/PyFEO1Pa8spjUbz0AHwzp07ExYWRkJCAqmpqfj7+3P06FG0Wq3xhtSlS5dIT0/HYDA88nPd3z6ckpKChYUF3t7eja4yVVBQQF5eHs888wydOnWisrKSqqqqx/4+zUFyqv2QyYYZKS8vZ9y4cfz2t799rDemH7rfqKZbt25MmDCBlJQUY6MboFU1unFwcGDbtm0sW7aMsLAwZs+ezbVr13j66afx9PSke/fuZGRk/OyBuAcPf1dXV/+iGuj3O6q+/fbbhISEEB8fT1JSEmPHjjXW9zalhhoddejQAQsLC9566y3jEnRT/vwdHR1ZunQpvr6+dOnSBb1ez8mTJ9m+fTs6nQ4rKytmzZplFl1nhRCNIzn1sG7duhnL5K5bt47AwECys7OxtLRkyJAhODo6cubMGc6ePfuzv+jfP/xdVlbGsGHDcHBwaNR23Nu3b7NkyRImTZrEa6+9RkJCAocOHSI4OJhNmzY19qU2Gcmp9kUmG2YiPz+fF198kZ49exIbG9vorp8Gg8HYgdpgMPCvf/0LNze3Vt3oRqPR4O3tzcGDB3n11VcJCAjgo48+wmAwYG9vz/Dhw+ncuTMnTpzg8uXLD1WLKikp4T//+Q8FBQV4enrSr1+/Rh0sq6ioYO3atYwZMwZvb2+OHj1qLIOo0WgYN24cCxcubKqX3SgNNTp6cH/z3//+d9zc3IC7P/8dO3ZQWVlJbm4uWVlZeHt7N/r5v/jiCz777DNu3brFkCFDmDBhAt9++y0ZGRmcOnWK2NhYdu7c2fgXKIQwGcmpnzZo0CBiY2OZM2cOb731FgsWLKCoqAhra2vjFqi0tDQuXLhAbW1tva+9ffs2//3vf7l8+TJubm64uro+dql1uNs8duvWrYwcOZJnn32W5ORkJkyYYJyw+Pj4EBkZ2SSvt7Ekp9ohpdRPfYhm5uDgoBYuXKicnJxURETEL/5+OTk5yt3dXbm7u6tBgwappUuXKqWUKigoUH5+fkqn0yk/Pz9VWFiolFKqrq5OBQcHK2dnZ+Xm5qZOnDjxi8fQ3CoqKtQnn3yi3Nzc1F/+8hdVWlqqDAaDKikpURkZGergwYPq4sWL6ubNm+r48ePqyJEj6vr168pgMDTqo6ysTH355ZfK3d1dffjhh8pgMJj6r6BBhw8fVoDS6/VqyJAhasiQIWrv3r1q6tSpys3NTen1evW73/1OXb161fg1S5cuVc7Ozqp///4qLi6u0c/t4OCgVqxYYfx87969ClA3btwwPvb73/9evfvuu41+jnbs596r2/OHaGaSU4+vpqZGRUdHKzc3NxUREaGKioqUwWBQpaWl6vTp0yohIUFlZ2erwsJClZqaqr777jt15cqVRufU7du3VWxsrBo6dKh67733VFFRkan/ChokOdVmNfg+LaVvTczR0ZGKigqeeeYZkpKS6NKli0nHU1xczKxZs8jMzESj0bB582ZcXV2ZPHkyFy5cwNHRkZ07d2JjY4NSinnz5hEXF4elpSVbt241VphoCYWFhSxZsoSjR4+yePFifHx80Gg0lJaWkpmZSWVlJS4uLvTu3btR31+1so6qpnZ/eXrq1KnA3a7Co0aNqrccPX36dLRaLZ9//rmphtlayQblhklONTPJqcYzGAysXLmSmJgYFixYwKuvvoqFhQXl5eWcOnUKg8GAo6MjTk5OjT6HcObMGRYtWsTTTz9t7EchfpzkVLOS0rfmLCIiAr1ez6hRo4zVF0xl3rx5vPzyy5w9e5aMjAwGDhxIREQEI0eOJCsri5EjRxIREQFAfHw8WVlZZGVlsXHjRmbPnt2iY7Wzs2PNmjV8/fXXbN68mddee43Q0FCmTJmCo6MjXl5eXL9+ndOnTz/WIXK421F1+vTpREZGEhUVxeeffy4TDSFEuyU51ThPPfUUixcvJi4ujoSEBMaMGcMHH3zAyy+/TDULURoAAAt0SURBVPfu3Rk+fDilpaWcPHmS8vLyx/re169fZ+7cuYSEhLBo0SJ27twpEw1hlmSyYQa0Wi1fffUVer0eHx8frl+/bpJxlJaWcujQIYKCgoC7JV6tra3NuvY5gE6nY8qUKZw/f57Dhw/j7OyMVqvFysoKLy8v7O3tSU9PJycn56F9sj9kqo6q91VUVODt7c2QIUMYPHgwH3zwAQC5ubmMGDECFxcXJk+ebDxkWFlZyeTJk9HpdIwYMYILFy602FiFEO2H5NQv07NnT9544w2Ki4vZt28fTk5OWFhY8PTTT+Ph4UGfPn3IzMzk3LlzP1vVsLy8nBUrVjB+/HhGjhzJv//9b55//vkWq9AkOSUel0w2zISFhQWbNm1i5MiR/OY3vyEvL6/Fx3D+/Hns7e2ZMWMGnp6ezJo1C4PBYNa1zwHWr19PYmIihw8fJi0tDT8/P8aNG8cnn3xCRUUF3bp1w9vbm06dOpGSksKVK1ceKqVXXV3Nhg0beOmll3B1deX48eO88sorLV5e74knnuDgwYNkZGSQnp7Ovn37SEpKIjQ0lJCQELKysrCxsSE6OhqA6OhobGxsyM7OJiQkhNDQ0BYdrxCi/ZCcarxdu3axefNm9uzZw8mTJ5k2bRqBgYGEh4dTUlKCra0tw4cPp2vXrqSmpnLx4sWHip3U1tayfft2/Pz8ePLJJ0lOTmbKlCkt3jxWcko8tp860NHSJ0uEaZ04cUJ16NBBJSUlKaWUmjt3rlq0aJGysrKqd521tbVSSqkxY8aow4cPGx/38/NTqampLTfge+rq6h56rLy8XC1btkzp9Xq1efNmVVZWpgwGgyouLlbp6enqwIEDateuXaqsrEzt3LlTeXp6qoULF6qSkpIWH39DDAaD8vT0VElJScrOzk5VV1crpZQ6duyYGj16tFJKqdGjR6tjx44ppZSqrq5WdnZ2P/r3IVo9Ux/CNucP0Y60pZyqqqpSUVFRys3NTa1evVoVFxcbD5FnZmaqxMRE9fXXX6vS0lK1f/9+9dxzz6ng4OB6h5lNTXJKPKDB92lZ2RBGvXv3pnfv3sZOngEBAaSlpZl97fMfW3148sknWbhwIQcOHCA5OZnRo0dz7NgxtFotLi4uODs78+mnn6LX6/nyyy/NqqNqbW0tHh4edOvWDX9/f/r164e1tTVarRaof2fuwbt297eOFRYWmmzsQgjRnNpSTnXs2JHg4GCOHDlCfn4+fn5+xMfHo9FocHJyws3Nje3bt6PX61myZAmbN28mKioKe3v7Fh//D0lOicchkw1h9Oyzz9KnTx/OnTsHQGJiIoMGDWrVtc+7d+/Ohg0b2LRpE2vWrGHq1KkcO3aM8PBwNBoN4eHhlJWV8dFHH1FQUGDq4QLQoUMH0tPTuXz5MikpKXz//fcPXXM/uNSPVJOTzqpCiLaqLeaUlZUVERER7N69m927dzN+/HgOHTrEhx9+yI0bNwgPD8fKyooPP/zQJFvXfozklHgcWlMPQJiX9evXExgYSFVVFc7OzmzZsoW6ujomTZpEdHQ0ffv2ZdeuXQCMGTOGuLg4dDodlpaWbNmyxcSjb9jgwYP5xz/+QUJCAjNmzGD16tVMnDgRCwsLpk+fzu7du82uY6i1tTU+Pj4kJSVRXFxMTU0NWq223p25+3ftevfuTU1NjXHvrxBCtFVtNaccHBzYtm0bJ06cYNKkSYSGhrJu3To6dOjAzJkzSUhI4M6dO6YeZj2SU+JRSJ8NIczIzZs36dixI9bW1ty5c4fRo0cTGhrKF198wWuvvcbrr7/O22+/jbu7O8HBwURFRXHq1Ck2bNjAjh072L17t3Q+bZvkNmDDJKeEaEGSU6IB0mdDNL9z587h4eFh/OjatStr1qyhqKgIf39/XFxc8Pf3N9ZoV0oxd+5cdDod7u7upKWlmfgVmN61a9fw9fXF3d2d4cOH4+/vz9ixY4mMjGTVqlXodDoKCwuNZR+DgoIoLCxEp9OxatUqY215IYQQD5Oc+uUkp8TjkpUN0Sxqa2vp1asXycnJREVFYWtrS1hYGBEREdy6dYvIyEji4uJYv349cXFxJCcnM2/ePJKTk009dCHMkaxsNExySjSK5JQQTUpWNkTLSkxMpF+/fjg4OJh9syUhhBDtj+SUEC1DJhuiWezYsYMpU6YAmH2zpabQUEfV6dOn4+TkZFyyT09PB2RpXgghTK295RRIVgnTkGpUoslVVVURGxvL8uXLf/K6tlQO735H1S5dulBdXc3zzz/PK6+8AsDKlSsJCAiod318fDxZWVlkZWWRnJzM7NmzZWleCCFaSHvMKZCsEqYhKxuiycXHx+Pl5UX37t0BzL7ZUlPQaDR06dIFgOrqaqqrq38ykGRpXgghTKc95hRIVgnTkMmGaHLbt283Lk0DrbrZ0uP4YUfV+x1u33//fdzd3QkJCaGyshJoe0vzQgjRmrTXnALJKtHyZLIhmlR5eTkHDhxg4sSJxsfCwsI4cOAALi4uHDhwgLCwMOBusyVnZ2d0Oh1vvfUWn332mamG3SR+2FE1MzOT5cuXc/bsWU6cOEFRURGRkZFA21uaF0KI1qI95xRIVomWJ2c2RJOytLSksLCw3mN2dnYkJiY+dK1GoyEqKqqlhtZi7ndU3bdvH/Pnzwfu7pOdMWMGH3/8MdD2luaFEKK1kJy6S7JKtBRZ2RBmY/Xq1QwePBg3NzemTJlCRUUFubm5jBgxAhcXFyZPnkxVVRUAlZWVTJ48GZ1Ox4gRI7hw4YJJx37z5k2Ki4sBuHPnDgkJCQwYMMC4t1UpRUxMDG5ubkDbXJoXQoi2rjXnFEhWCdOQyYYwC1euXGHdunWkpqaSmZlJbW0tO3bsIDQ0lJCQELKysrCxsSE6OhqA6OhobGxsyM7OJiQkhNDQUJOOv6GOqoGBgej1evR6PQUFBSxatAhom0vzQgjRlrX2nALJKmEa0kFcmIUrV67w3HPPkZGRQdeuXRk/fjzvvPMOgYGB5Ofno9VqOX78OIsXL2b//v289NJLLF68mF/96lfU1NTw7LPPcvPmTdlLKtoq+R+7YZJTokVITgnxk6SDuDBvvXr1Yv78+fTt25cePXpgZWXF0KFDsba2Rqu9e7TowSoYD1bI0Gq1WFlZPbQHVwghhGgqklNCNI5MNoRZuHXrFnv27CE3N5erV69iMBiIj49/6Lr7d4Sao0JGbW0tnp6ejB07FqBV7cMVQgjRvCSnhGgcmWwIs5CQkICTkxP29vZ07NiRiRMncuzYMYqLi6mpqQHqV8F4sEJGTU0NJSUl2Nra/qIxrF27loEDBxo/b037cIUQQjQvySkhGkcmG8Is9O3bl6SkJMrLy1FKkZiYyKBBg/D19eWbb74BHm60dL8B0zfffIOfn98vumN0+fJl9u7dy6xZs4C7d6QOHjxIQEAAANOmTSMmJga421F12rRpAAQEBJCYmPijd7CEEEK0HZJTQjSOTDaEWRgxYgQBAQF4eXmh1+upq6vjD3/4A5GRkaxatQqdTkdhYSFBQUEABAUFUVhYiE6nY9WqVURERPyi53/33XdZsWIFFhZ3/0kUFhbKPlwhhBBGklNCNM7PVaMSos3TaDRjgTFKqWCNRuMDzAdmAMeVUrp71/QB4pRSeo1Gcxp4SSl1+d6f5QDeSil5JxdCCNHkJKdEayYdxIWA/wNe1Wg0Y4DOQFdgDWCt0Wi0SqkaoDdw9d71l4E+wGWNRqMFrICilh+2EEKIdkJySrRaso1KtHtKqYVKqd5KKUfgdeCgUioQ+BYIuHfZNGDPvf+Ovfc59/78oJIlQiGEEM1Eckq0ZjLZEKJhocB7Go0mG7ADou89Hg3Y3Xv8PSDMROMTQgjRvklOCbMnZzaEEEIIIYQQzUJWNoQQQgghhBDNQiYbQgghhBBCiGbx//Bp5Miq7h2QAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "## plot copula \n", + "\n", + "cdf=EX3SS['Copula']['value'].reshape(4,30,30) # important: 4,30,30 not 30,30,4? \n", + "\n", + "fig = plt.figure(figsize=(14,14))\n", + "fig.suptitle('Copula of m and k(for different h)',\n", + " fontsize=(13))\n", + "for hgrid_id in range(EX3SS['mpar']['nh']):\n", + " ## plots \n", + " ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d')\n", + " ax.plot_surface(mmgrid,kkgrid,cdf[hgrid_fix,:,:], rstride=1, cstride=1,\n", + " cmap='viridis', edgecolor='None')\n", + " ax.set_xlabel('m',fontsize=13)\n", + " ax.set_ylabel('k',fontsize=13)\n", + " ax.set_title(r'$h({})$'.format(hgrid_fix))\n", + " ax.set_xlim(0,400)\n", + " ax.view_init(30, 45)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given the assumption that the copula remains the same after aggregate risk is introduced, we can use the same copula and the marginal distributions to recover the full joint-distribution of the states. " + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1130,7 +1226,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.3" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/HARK/BayerLuetticke/DCT-Copula-Illustration.py b/HARK/BayerLuetticke/DCT-Copula-Illustration.py index 4517c4a33..315dbdb05 100644 --- a/HARK/BayerLuetticke/DCT-Copula-Illustration.py +++ b/HARK/BayerLuetticke/DCT-Copula-Illustration.py @@ -36,10 +36,7 @@ # * $c_a(s)$ - adjusters # # The usual envelope theorem applies here, so marginal value wrt the liquid asset equals marginal utility with respect to consumption: -# \[ -# \frac{d v}{d m} = \frac{d u}{d c} -# \] -# +# $[\frac{d v}{d m} = \frac{d u}{d c}]$. # In practice, the authors solve the problem using the marginal value of money $\texttt{Vm} = dv/dm$, but because the marginal utility function is invertible it is trivial to recover $\texttt{c}$ from $(u^{\prime})^{-1}(\texttt{Vm} )$. The consumption function is therefore computed from the $\texttt{Vm}$ function # %% {"code_folding": [0, 6, 17, 21]} @@ -123,7 +120,7 @@ def in_ipynb(): ' * '+str(EX3SS['mpar']['nk'])+ ' * '+str(EX3SS['mpar']['nh'])+ ' = '+ str(EX3SS['mpar']['nm']*EX3SS['mpar']['nk']*EX3SS['mpar']['nh'])) - + # %% [markdown] # ### Dimension Reduction @@ -175,7 +172,7 @@ def in_ipynb(): '\n state variables are below the corresponding point.') -# %% {"code_folding": [0]} +# %% {"code_folding": []} ## Import necessary libraries from __future__ import print_function @@ -204,6 +201,8 @@ def in_ipynb(): from mpl_toolkits.mplot3d import Axes3D from matplotlib.ticker import LinearLocator, FormatStrFormatter +import seaborn as sns + # %% {"code_folding": [0]} ## State reduction and discrete cosine transformation @@ -411,7 +410,7 @@ def do_dct(self, obj, mpar, level): #EX3SS['par']['rhoS'] = 0.84 # Persistence of variance #EX3SS['par']['sigmaS'] = 0.54 # STD of variance shocks -# %% {"code_folding": []} +# %% {"code_folding": [0]} ## Choose an accuracy of approximation with DCT ### Determines number of basis functions chosen -- enough to match this accuracy ### EX3SS is precomputed steady-state pulled in above @@ -425,7 +424,7 @@ def do_dct(self, obj, mpar, level): EX3SR=StateReduc_Dct(**EX3SS) # Takes StE result as input and get ready to invoke state reduction operation SR=EX3SR.StateReduc() # StateReduc is operated -# %% {"code_folding": [10, 12]} +# %% {"code_folding": [7, 10, 12]} print('What are the results from the state reduction?') #print('Newly added attributes after the operation include \n'+str(set(SR.keys())-set(EX3SS.keys()))) @@ -502,66 +501,6 @@ def do_dct(self, obj, mpar, level): kgrid_rdc = mut_rdc_idx[1][(mut_rdc_idx[0]==mgrid_fix) & (mut_rdc_idx[2]==hgrid_fix)] hgrid_rdc = mut_rdc_idx[2][(mut_rdc_idx[0]==mgrid_fix) & (mut_rdc_idx[1]==kgrid_fix)] -# %% {"code_folding": [0]} -## 2D graph: compare consumption function before and after dct - - -fig=plt.figure(figsize=(15,8)) -fig.suptitle('Consumption at grid points of states') - -## for non-adjusters - -#c_n(m) -plt.subplot(2,3,1) -plt.plot(mgrid,cn_StE[:,kgrid_fix,hgrid_fix],'x',label='StE(before dct)') -plt.plot(mgrid[mgrid_rdc],cn_StE[mgrid_rdc,kgrid_fix,hgrid_fix],'r*',label='StE(after dct)') -plt.xlabel('m',size=15) -plt.ylabel(r'$c_n(m)$',size=15) -plt.legend() - -## c_n(k) -plt.subplot(2,3,2) -plt.plot(kgrid,cn_StE[mgrid_fix,:,hgrid_fix],'x',label='StE(before dct)') -plt.plot(kgrid[kgrid_rdc],cn_StE[mgrid_fix,kgrid_rdc,hgrid_fix],'r*',label='StE(after dct)') -plt.xlabel('k',size=15) -plt.ylabel(r'$c_n(k)$',size=15) -plt.legend() - -## c_n(h) - -plt.subplot(2,3,3) -plt.plot(hgrid,cn_StE[mgrid_fix,kgrid_fix,:],'x',label='StE(before dct)') -plt.plot(hgrid[hgrid_rdc],cn_StE[mgrid_fix,kgrid_fix,hgrid_rdc],'r*',label='StE(after dct)') -plt.xlabel('h',size=15) -plt.ylabel(r'$c_n(h)$',size=15) -plt.legend() - - -### for adjusters -## c_a(m) -plt.subplot(2,3,4) -plt.plot(mgrid,ca_StE[:,kgrid_fix,hgrid_fix],'x',label='StE(before dct)') -plt.plot(mgrid[mgrid_rdc],ca_StE[mgrid_rdc,kgrid_fix,hgrid_fix],'r*',label='StE(after dct)') -plt.xlabel('m',size=15) -plt.ylabel(r'$c_a(m)$',size=15) -plt.legend() - -## c_a(k) -plt.subplot(2,3,5) -plt.plot(kgrid,ca_StE[mgrid_fix,:,hgrid_fix],'x',label='StE(before dct)') -plt.plot(kgrid[kgrid_rdc],ca_StE[mgrid_fix,kgrid_rdc,hgrid_fix],'r*',label='StE(after dct)') -plt.xlabel('k',size=15) -plt.ylabel(r'$c_a(k)$',size=15) -plt.legend() - -## c_a(h) -plt.subplot(2,3,6) -plt.plot(hgrid,ca_StE[mgrid_fix,kgrid_fix,:],'x',label='StE(before dct)') -plt.plot(hgrid[hgrid_rdc],ca_StE[mgrid_fix,kgrid_fix,hgrid_rdc],'r*',label='StE(after dct)') -plt.xlabel('h',size=15) -plt.ylabel(r'$c_a(h)$',size=15) -plt.legend() - # %% {"code_folding": []} ## 3D scatter plots of consumption function ## at all grids and grids after dct for both adjusters and non-adjusters @@ -586,6 +525,8 @@ def do_dct(self, obj, mpar, level): mut_n_rdc= mut_n_StE[rdc_id] c_n_rdc = cn_StE[rdc_id] c_a_rdc = ca_StE[rdc_id] + mmax = mmgrid_rdc.max() + kmax = kkgrid_rdc.max() ## plots ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d') @@ -596,6 +537,9 @@ def do_dct(self, obj, mpar, level): ax.set_xlabel('m',fontsize=13) ax.set_ylabel('k',fontsize=13) ax.set_zlabel(r'$c_a(m,k)$',fontsize=13) + + ax.set_xlim([0,mmax*1.1]) + ax.set_ylim([0,kmax*1.2]) ax.set_title(r'$h({})$'.format(hgrid_fix)) ax.view_init(20, 240) ax.legend(loc=9) @@ -617,16 +561,20 @@ def do_dct(self, obj, mpar, level): mut_n_rdc= mut_n_StE[rdc_id] c_n_rdc = cn_StE[rdc_id] c_a_rdc = ca_StE[rdc_id] + mmax = mmgrid_rdc.max() + kmax = kkgrid_rdc.max() ## plots ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d') - ax.scatter(mmgrid,kkgrid,ca_StE[:,:,hgrid_fix],c='yellow',marker='.', + ax.scatter(mmgrid,kkgrid,ca_StE[:,:,hgrid_fix],marker='.', label='StE(before dct): adjuster') - ax.scatter(mmgrid_rdc,kkgrid_rdc,c_a_rdc,c='blue',marker='*', + ax.scatter(mmgrid_rdc,kkgrid_rdc,c_a_rdc,c='red',marker='*', label='StE(after dct):adjuster') ax.set_xlabel('m',fontsize=13) ax.set_ylabel('k',fontsize=13) ax.set_zlabel(r'$c_n(m,k)$',fontsize=13) + ax.set_xlim([0,mmax*1.1]) + ax.set_ylim([0,kmax*1.2]) ax.set_title(r'$h({})$'.format(hgrid_fix)) ax.view_init(20, 240) ax.legend(loc=9) @@ -669,11 +617,84 @@ def do_dct(self, obj, mpar, level): ax.legend(loc=9) # %% [markdown] -# #### Observation +# ##### Observation # # - For a given grid value of productivity, the remaining grid points after DCT to represent the whole consumption function are concentrated in low values of $k$ and $m$. This is because the slopes of the surfaces of marginal utility are changing the most in these regions. For larger values of $k$ and $m$ the functions become smooth and only slightly concave, so they can be represented by many fewer points # - For different grid values of productivity (2 sub plots), the numbers of grid points in the DCT operation differ. From the lowest to highest values of productivity, there are 78, 33, 25 and 18 grid points, respectively. They add up to the total number of gridpoints of 154 after DCT operation, as we noted above for marginal utility function. +# %% [markdown] +# #### Distribution of states +# +# - We first plot the distribution of $k$ fixing $m$ and $h$. Next, we plot the joint distribution of $m$ and $k$ only fixing $h$ in 3-dimenstional space. +# - The joint-distribution can be represented by marginal distributions of $m$, $k$ and $h$ and a copula that describes the correlation between the three states. The former is straightfoward. We plot the copula only. Copula is essentially a multivariate cummulative distribution function where each marginal is uniform. +# + +# %% {"code_folding": [0]} +### Marginalize along h grids + +joint_distr = EX3SS['joint_distr'] +joint_distr_km = EX3SS['joint_distr'].sum(axis=2) + +### Plot distributions in 2 dimensional graph + +fig = plt.figure(figsize=(10,10)) +plt.suptitle('Marginal distribution of k at different m') + +for hgrid_id in range(EX3SS['mpar']['nh']): + ax = plt.subplot(2,2,hgrid_id+1) + ax.set_title(r'$h({})$'.format(hgrid_fix)) + ax.set_xlabel('k',size=12) + for id in range(EX3SS['mpar']['nm']): + ax.plot(kgrid,joint_distr[id,:,hgrid_id]) + +# %% {"code_folding": [0]} +## Plot joint distribution of k and m in 3d graph + +fig = plt.figure(figsize=(14,14)) +fig.suptitle('Joint distribution of m and k(for different h)', + fontsize=(13)) +for hgrid_id in range(EX3SS['mpar']['nh']): + ## plots + ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d') + ax.plot_surface(mmgrid,kkgrid,joint_distr[:,:,hgrid_fix], rstride=1, cstride=1, + cmap='viridis', edgecolor='none') + ax.set_xlabel('m',fontsize=13) + ax.set_ylabel('k',fontsize=13) + #ax.set_zlabel(r'$p(m,k)$',fontsize=10) + ax.set_title(r'$h({})$'.format(hgrid_fix)) + ax.set_xlim(0,400) + ax.view_init(20, 40) + +# %% +copula_value = EX3SS['Copula']['value'] +fig=plt.plot(copula_value) +plt.title("Commulative probability distribution function in StE") + +# %% [markdown] +# Notice the cdfs in StE copula have 4 modes, corresponding to the number of $h$ grids. Each of the four parts of the cdf is a joint-distribution of $m$ and $k$. It can be presented in 3-dimensional graph as below. + +# %% {"code_folding": []} +## plot copula + +cdf=EX3SS['Copula']['value'].reshape(4,30,30) # important: 4,30,30 not 30,30,4? + +fig = plt.figure(figsize=(14,14)) +fig.suptitle('Copula of m and k(for different h)', + fontsize=(13)) +for hgrid_id in range(EX3SS['mpar']['nh']): + ## plots + ax = fig.add_subplot(2,2,hgrid_id+1, projection='3d') + ax.plot_surface(mmgrid,kkgrid,cdf[hgrid_fix,:,:], rstride=1, cstride=1, + cmap='viridis', edgecolor='None') + ax.set_xlabel('m',fontsize=13) + ax.set_ylabel('k',fontsize=13) + ax.set_title(r'$h({})$'.format(hgrid_fix)) + ax.set_xlim(0,400) + ax.view_init(30, 45) + +# %% [markdown] +# Given the assumption that the copula remains the same after aggregate risk is introduced, we can use the same copula and the marginal distributions to recover the full joint-distribution of the states. + # %% [markdown] # ### Summary: what do we achieve after the transformation? # From fa70ff9e1d1915f7da35f35bceca4d1b3e4727d2 Mon Sep 17 00:00:00 2001 From: llorracc Date: Fri, 14 Jun 2019 08:52:57 +0200 Subject: [PATCH 76/77] After talk with Luetticke at PASC19 --- HARK/BayerLuetticke/TwoAsset.ipynb | 45 +++++++++++------------------- HARK/BayerLuetticke/TwoAsset.py | 22 +++++++++------ 2 files changed, 30 insertions(+), 37 deletions(-) diff --git a/HARK/BayerLuetticke/TwoAsset.ipynb b/HARK/BayerLuetticke/TwoAsset.ipynb index 2515daae8..3ad2b453f 100644 --- a/HARK/BayerLuetticke/TwoAsset.ipynb +++ b/HARK/BayerLuetticke/TwoAsset.ipynb @@ -201,23 +201,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "code_folding": [] }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'os' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode_dir\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Go to the directory with pickled code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m## EX3SS_20.p is the information in the stationary equilibrium (20: the number of illiquid and liquid weath grids )\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'os' is not defined" - ] - } - ], + "outputs": [], "source": [ "## Load Stationary equilibrium (StE) object EX3SS_20\n", "\n", @@ -225,7 +213,8 @@ "os.chdir(code_dir) # Go to the directory with pickled code\n", "\n", "## EX3SS_20.p is the information in the stationary equilibrium (20: the number of illiquid and liquid weath grids )\n", - "EX3SS=pickle.load(open(\"EX3SS_20.p\", \"rb\"))" + "EX3SS=pickle.load(open(\"EX3SS_20.p\", \"rb\"))\n", + "\n" ] }, { @@ -301,9 +290,7 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "code_folding": [ - 0 - ], + "code_folding": [], "lines_to_end_of_cell_marker": 0, "lines_to_next_cell": 1 }, @@ -602,9 +589,7 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "code_folding": [ - 0 - ] + "code_folding": [] }, "outputs": [], "source": [ @@ -670,7 +655,7 @@ "# invmutil = lambda x : (1./x)**(1./par['xi'])\n", " invmutil = lambda x : np.power(1./x,1./par['xi'])\n", " \n", - " # Generate meshes for m,k,h # Question: m not b?\n", + " # Generate meshes for m,k,h\n", " \n", " # number of states, controls in reduced system\n", " nx = mpar['numstates'] # number of states \n", @@ -706,7 +691,7 @@ " marginal_mind = range(mpar['nm']-1)\n", " marginal_kind = range(mpar['nm']-1,mpar['nm']+mpar['nk']-2) # probs add to 1\n", " marginal_hind = range(mpar['nm']+mpar['nk']-2,\n", - " mpar['nm']+mpar['nk']+mpar['nh']-4) # Question: Why 4?\n", + " mpar['nm']+mpar['nk']+mpar['nh']-4) # Question: Why 4? Awesome guy not perturbed\n", " \n", " # index for the interest rate on government bonds = liquid assets\n", " RBind = NxNx \n", @@ -777,6 +762,7 @@ " B = np.exp(Control[Bind])\n", " \n", " # Aggregate Controls (t) # Question: Why are there more here than for t+1?\n", + " # Only include t+1's that show up in eqbm conditions (Envelope thm)\n", " PIminus = np.exp(Controlminus[PIind])\n", " Qminus = np.exp(Controlminus[Qind])\n", " Yminus = np.exp(Controlminus[Yind])\n", @@ -808,7 +794,7 @@ " \n", " ## States\n", " ## Marginal Distributions (Marginal histograms)\n", - " #LHS[distr_ind] = Distribution[:mpar['nm']*mpar['nh']-1-mpar['nh']].copy() Question: Why commented out\n", + "\n", " LHS[marginal_mind] = Distribution[:mpar['nm']-1]\n", " LHS[marginal_kind] = Distribution[mpar['nm']:mpar['nm']\n", " +mpar['nk']-1]\n", @@ -959,12 +945,15 @@ " c_n_star = result_EGM_policyupdate['c_n_star']\n", " m_n_star = result_EGM_policyupdate['m_n_star']\n", " \n", - " # Question: Is this max value of ind pty? Why needed?\n", + " # Question: Is this max value of ind pty? Why needed? Victor \"Awesome\" state\n", " meshaux = meshes.copy()\n", " meshaux['h'][:,:,-1] = 1000.\n", " \n", " ## Update Marginal Value of Bonds\n", " # Question: Marginal utility is weighted average of u' from c and u' from leisure?\n", + " # GHH preferences (can write optimization problem for the composite good)\n", + " # Just to make everybody have the same labor supply (it's about eqbm prices)\n", + " # easier to do the steady state\n", " mutil_c_n = mutil(c_n_star.copy())\n", " mutil_c_a = mutil(c_a_star.copy())\n", " mutil_c_aux = par['nu']*mutil_c_a + (1-par['nu'])*mutil_c_n\n", @@ -1030,7 +1019,7 @@ " ## Liquid assets of the k-adjusters\n", " ra_genweight = GenWeight(m_a_star,grid['m'])\n", " Dist_m_a = ra_genweight['weight'].copy()\n", - " idm_a = ra_genweight['index'].copy() # Question: idm_a is index of original exogenous m grid \n", + " idm_a = ra_genweight['index'].copy() # idm_a is index of original exogenous m grid \n", " \n", " ## Liquid assets of the k-nonadjusters\n", " rn_genweight = GenWeight(m_n_star,grid['m'])\n", @@ -1202,9 +1191,7 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "code_folding": [ - 0 - ], + "code_folding": [], "lines_to_next_cell": 2 }, "outputs": [], diff --git a/HARK/BayerLuetticke/TwoAsset.py b/HARK/BayerLuetticke/TwoAsset.py index f3ac12da2..3c7dd10cc 100644 --- a/HARK/BayerLuetticke/TwoAsset.py +++ b/HARK/BayerLuetticke/TwoAsset.py @@ -178,6 +178,8 @@ def in_ipynb(): ## EX3SS_20.p is the information in the stationary equilibrium (20: the number of illiquid and liquid weath grids ) EX3SS=pickle.load(open("EX3SS_20.p", "rb")) + + # - # #### Compact notation (Schmitt-Grohe and Uribe, 2004) @@ -234,7 +236,7 @@ def in_ipynb(): # # The approach follows the insight of KS in that it uses the fact that some moments of the distribution do not matter for aggregate dynamics -# + {"code_folding": [0]} +# + {"code_folding": []} ## Import necessary libraries from __future__ import print_function @@ -499,7 +501,7 @@ def do_dct(self, obj, mpar, level): # - Optimal policy for adjusters and nonadjusters are $c^*_a$, $n^*_a$ $k^*_a$ and $b^*_a$ and $c^*_n$, $n^*_n$ and $b^*_n$, respectively # -# + {"code_folding": [0]} +# + {"code_folding": []} ## Construct the system of equations (including decoding): The F system def Fsys(State, Stateminus, Control_sparse, Controlminus_sparse, StateSS, ControlSS, Gamma_state, indexMUdct, indexVKdct, par, mpar, grid, targets, Copula, P, aggrshock): @@ -562,7 +564,7 @@ def Fsys(State, Stateminus, Control_sparse, Controlminus_sparse, StateSS, Contro # invmutil = lambda x : (1./x)**(1./par['xi']) invmutil = lambda x : np.power(1./x,1./par['xi']) - # Generate meshes for m,k,h # Question: m not b? + # Generate meshes for m,k,h # number of states, controls in reduced system nx = mpar['numstates'] # number of states @@ -598,7 +600,7 @@ def Fsys(State, Stateminus, Control_sparse, Controlminus_sparse, StateSS, Contro marginal_mind = range(mpar['nm']-1) marginal_kind = range(mpar['nm']-1,mpar['nm']+mpar['nk']-2) # probs add to 1 marginal_hind = range(mpar['nm']+mpar['nk']-2, - mpar['nm']+mpar['nk']+mpar['nh']-4) # Question: Why 4? + mpar['nm']+mpar['nk']+mpar['nh']-4) # Question: Why 4? Awesome guy not perturbed # index for the interest rate on government bonds = liquid assets RBind = NxNx @@ -669,6 +671,7 @@ def Fsys(State, Stateminus, Control_sparse, Controlminus_sparse, StateSS, Contro B = np.exp(Control[Bind]) # Aggregate Controls (t) # Question: Why are there more here than for t+1? + # Only include t+1's that show up in eqbm conditions (Envelope thm) PIminus = np.exp(Controlminus[PIind]) Qminus = np.exp(Controlminus[Qind]) Yminus = np.exp(Controlminus[Yind]) @@ -700,7 +703,7 @@ def Fsys(State, Stateminus, Control_sparse, Controlminus_sparse, StateSS, Contro ## States ## Marginal Distributions (Marginal histograms) - #LHS[distr_ind] = Distribution[:mpar['nm']*mpar['nh']-1-mpar['nh']].copy() Question: Why commented out + LHS[marginal_mind] = Distribution[:mpar['nm']-1] LHS[marginal_kind] = Distribution[mpar['nm']:mpar['nm'] +mpar['nk']-1] @@ -851,12 +854,15 @@ def Fsys(State, Stateminus, Control_sparse, Controlminus_sparse, StateSS, Contro c_n_star = result_EGM_policyupdate['c_n_star'] m_n_star = result_EGM_policyupdate['m_n_star'] - # Question: Is this max value of ind pty? Why needed? + # Question: Is this max value of ind pty? Why needed? Victor "Awesome" state meshaux = meshes.copy() meshaux['h'][:,:,-1] = 1000. ## Update Marginal Value of Bonds # Question: Marginal utility is weighted average of u' from c and u' from leisure? + # GHH preferences (can write optimization problem for the composite good) + # Just to make everybody have the same labor supply (it's about eqbm prices) + # easier to do the steady state mutil_c_n = mutil(c_n_star.copy()) mutil_c_a = mutil(c_a_star.copy()) mutil_c_aux = par['nu']*mutil_c_a + (1-par['nu'])*mutil_c_n @@ -922,7 +928,7 @@ def Fsys(State, Stateminus, Control_sparse, Controlminus_sparse, StateSS, Contro ## Liquid assets of the k-adjusters ra_genweight = GenWeight(m_a_star,grid['m']) Dist_m_a = ra_genweight['weight'].copy() - idm_a = ra_genweight['index'].copy() # Question: idm_a is index of original exogenous m grid + idm_a = ra_genweight['index'].copy() # idm_a is index of original exogenous m grid ## Liquid assets of the k-nonadjusters rn_genweight = GenWeight(m_n_star,grid['m']) @@ -1090,7 +1096,7 @@ def Fsys(State, Stateminus, Control_sparse, Controlminus_sparse, StateSS, Contro 'k_a_star':k_a_star,'c_n_star':c_n_star,'m_n_star':m_n_star,'P':P} -# + {"code_folding": [0]} +# + {"code_folding": []} ## Update policy in transition (found in Fsys) def EGM_policyupdate(EVm,EVk, Qminus, PIminus, RBminus, inc, meshes,grid,par,mpar): From 0d9cba0d8779f4e5c56825124e6af7dc28689264 Mon Sep 17 00:00:00 2001 From: llorracc Date: Fri, 14 Jun 2019 10:03:09 +0200 Subject: [PATCH 77/77] DCT-Copula tiny edits --- HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb | 14 +++++++------- HARK/BayerLuetticke/DCT-Copula-Illustration.py | 6 +++--- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb b/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb index 124661fa4..b56d27dcb 100644 --- a/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb +++ b/HARK/BayerLuetticke/DCT-Copula-Illustration.ipynb @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "code_folding": [ 0, @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "code_folding": [ 0 @@ -123,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "code_folding": [ 0 @@ -177,16 +177,16 @@ "source": [ "### Dimension Reduction\n", "\n", - "The authors use different reduction methods for the consumer's problem and the distribution" + "The authors use different dimensionality reduction methods for the consumer's problem and the distribution across idiosyncratic states" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### The consumer's problem: Discrete Cosine Transformation\n", + "#### The consumer's problem: Basis Functions\n", "\n", - "The idea is to find an efficient \"compressed\" representation of our functions (e.g., the consumption function). The analogy to image compression is that nearby pixels are likely to have identical or very similar colors, so we need only to find an efficient way to represent the way in which the colors change from one pixel to another. The analogy is that consumption at a given point $s_{i}$ is likely to be close to consumption point another point $s_{j}$ that is \"close\" in the state space (similar wealth, income, etc), so a function that captures that similarity efficiently can preserve most of the information without keeping all of the points.\n", + "The idea is to find an efficient \"compressed\" representation of our functions (e.g., the consumption function). The analogy to image compression is that nearby pixels are likely to have identical or very similar colors, so we need only to find an efficient way to represent the way in which the colors change from one pixel to another. Similarly, consumption at a given point $s_{i}$ is likely to be close to consumption point another point $s_{j}$ that is \"close\" in the state space (similar wealth, income, etc), so a function that captures that similarity efficiently can preserve most of the information without keeping all of the points.\n", "\n", "Like linear interpolation, the [DCT transformation](https://en.wikipedia.org/wiki/Discrete_cosine_transform) is a method of representing a continuous function using a finite set of numbers. It uses a set of independent basis functions to do this.\n", "\n", @@ -1226,7 +1226,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.7" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/HARK/BayerLuetticke/DCT-Copula-Illustration.py b/HARK/BayerLuetticke/DCT-Copula-Illustration.py index 315dbdb05..add78f14f 100644 --- a/HARK/BayerLuetticke/DCT-Copula-Illustration.py +++ b/HARK/BayerLuetticke/DCT-Copula-Illustration.py @@ -125,12 +125,12 @@ def in_ipynb(): # %% [markdown] # ### Dimension Reduction # -# The authors use different reduction methods for the consumer's problem and the distribution +# The authors use different dimensionality reduction methods for the consumer's problem and the distribution across idiosyncratic states # %% [markdown] -# #### The consumer's problem: Discrete Cosine Transformation +# #### The consumer's problem: Basis Functions # -# The idea is to find an efficient "compressed" representation of our functions (e.g., the consumption function). The analogy to image compression is that nearby pixels are likely to have identical or very similar colors, so we need only to find an efficient way to represent the way in which the colors change from one pixel to another. The analogy is that consumption at a given point $s_{i}$ is likely to be close to consumption point another point $s_{j}$ that is "close" in the state space (similar wealth, income, etc), so a function that captures that similarity efficiently can preserve most of the information without keeping all of the points. +# The idea is to find an efficient "compressed" representation of our functions (e.g., the consumption function). The analogy to image compression is that nearby pixels are likely to have identical or very similar colors, so we need only to find an efficient way to represent the way in which the colors change from one pixel to another. Similarly, consumption at a given point $s_{i}$ is likely to be close to consumption point another point $s_{j}$ that is "close" in the state space (similar wealth, income, etc), so a function that captures that similarity efficiently can preserve most of the information without keeping all of the points. # # Like linear interpolation, the [DCT transformation](https://en.wikipedia.org/wiki/Discrete_cosine_transform) is a method of representing a continuous function using a finite set of numbers. It uses a set of independent basis functions to do this. #