From bc64ecbf152b7cec8c3e385d41c29b8218740fd3 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Mon, 16 Dec 2024 12:56:57 -0500 Subject: [PATCH 01/71] initial commit simplifying TimeIndependentMDCObjectiveFunction --- pygsti/objectivefns/objectivefns.py | 642 +++++++--------------------- 1 file changed, 154 insertions(+), 488 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index 208bdb46d..db7c7b665 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -4353,7 +4353,8 @@ def __del__(self): self.layout.free_local_array(self.obj) self.layout.free_local_array(self.jac) - #Model-based regularization and penalty support functions + # Main public instance functions + def set_penalties(self, regularize_factor=0, cptp_penalty_factor=0, spam_penalty_factor=0, errorgen_penalty_factor=0, forcefn_grad=None, shift_fctr=100, prob_clip_interval=(-10000, 1000)): @@ -4436,390 +4437,7 @@ def set_penalties(self, regularize_factor=0, cptp_penalty_factor=0, spam_penalty return ex - def _lspenaltyvec(self, paramvec): - """ - The least-squares penalty vector, an array of the square roots of the penalty terms. - - Parameters - ---------- - paramvec : numpy.ndarray - The vector of (model) parameters to evaluate the objective function at. - - Returns - ------- - numpy.ndarray - """ - if self.forcefn_grad is not None: - force_vec = self.forceShift - _np.dot(self.forcefn_grad, self.model.to_vector()) - assert(_np.all(force_vec >= 0)), "Inadequate forcing shift!" - forcefn_penalty = _np.sqrt(force_vec) - else: forcefn_penalty = [] - - if self.regularize_factor != 0: - paramvec_norm = self.regularize_factor * _np.array([max(0, absx - 1.0) for absx in map(abs, paramvec)], 'd') - else: paramvec_norm = [] # so concatenate ignores - - if self.cptp_penalty_factor > 0: - cp_penalty_vec = _cptp_penalty(self.model, self.cptp_penalty_factor, self.opBasis) - else: cp_penalty_vec = [] # so concatenate ignores - - if self.spam_penalty_factor > 0: - spam_penalty_vec = _spam_penalty(self.model, self.spam_penalty_factor, self.opBasis) - else: spam_penalty_vec = [] # so concatenate ignores - - if self.errorgen_penalty_factor > 0: - errorgen_penalty_vec = _errorgen_penalty(self.model, self.errorgen_penalty_factor) - else: - errorgen_penalty_vec = [] - - return _np.concatenate((forcefn_penalty, paramvec_norm, cp_penalty_vec, spam_penalty_vec, errorgen_penalty_vec)) - - def _penaltyvec(self, paramvec): - """ - The penalty vector, an array of all the penalty terms. - - Parameters - ---------- - paramvec : numpy.ndarray - The vector of (model) parameters to evaluate the objective function at. - - Returns - ------- - numpy.ndarray - """ - return self._lspenaltyvec(paramvec)**2 - - def _fill_lspenaltyvec_jac(self, paramvec, lspenaltyvec_jac): - """ - Fill `lspenaltyvec_jac` with the jacobian of the least-squares (sqrt of the) penalty vector. - - Parameters - ---------- - paramvec : numpy.ndarray - The vector of (model) parameters to evaluate the objective function at. - - lspenaltyvec_jac : numpy.ndarray - The array to fill. - - Returns - ------- - None - """ - off = 0 - - # wrtslice gives the subset of all the model parameters that this processor is responsible for - # computing derivatives with respect to. - wrtslice = self.layout.global_param_slice if isinstance(self.layout, _DistributableCOPALayout) \ - else slice(0, len(paramvec)) # all params - - if self.forcefn_grad is not None: - n = self.forcefn_grad.shape[0] - lspenaltyvec_jac[off:off + n, :] = -self.forcefn_grad - off += n - - if self.regularize_factor > 0: - n = len(paramvec) - lspenaltyvec_jac[off:off + n, :] = _np.diag([(self.regularize_factor * _np.sign(x) if abs(x) > 1.0 else 0.0) - for x in paramvec[wrtslice]]) # (N,N) - off += n - - if self.cptp_penalty_factor > 0: - off += _cptp_penalty_jac_fill( - lspenaltyvec_jac[off:, :], self.model, self.cptp_penalty_factor, self.opBasis, wrtslice) - - if self.spam_penalty_factor > 0: - off += _spam_penalty_jac_fill( - lspenaltyvec_jac[off:, :], self.model, self.spam_penalty_factor, self.opBasis, wrtslice) - - if self.errorgen_penalty_factor > 0: - off += _errorgen_penalty_jac_fill( - lspenaltyvec_jac[off:, :], self.model, self.errorgen_penalty_factor, wrtslice) - - assert(off == self.local_ex) - - def _fill_dterms_penalty(self, paramvec, terms_jac): - """ - Fill `terms_jac` with the jacobian of the penalty vector. - - Parameters - ---------- - paramvec : numpy.ndarray - The vector of (model) parameters to evaluate the objective function at. - - terms_jac : numpy.ndarray - The array to fill. - - Returns - ------- - None - """ - # terms_penalty = ls_penalty**2 - # terms_penalty_jac = 2 * ls_penalty * ls_penalty_jac - self._fill_lspenaltyvec_jac(paramvec, terms_jac) - terms_jac[:, :] *= 2 * self._lspenaltyvec(paramvec)[:, None] - - #Omitted-probability support functions - - def _omitted_prob_first_terms(self, probs): - """ - Extracts the value of the first term for each circuit that has omitted probabilities. - - Nonzero probabilities may be predicted for circuit outcomes that - never occur in the data, and therefore do not produce "terms" for - the objective function sum. Yet, in many objective functions, zero- - frequency terms that have non-zero probabilities still produce a - non-zero contribution and must be included. This is performed by - adding these "omitted-probability" contributions to the first - (nonzero-frequncy, thus present) term corresponding to the given - circuit. This function computes these omitted (zero-frequency) - terms and returns them in an array of length equal to the number - of circuits with omitted-probability contributions. - - Parameters - ---------- - probs : numpy.ndarray - The (full) vector of probabilities. Length is equal to the - total number of circuit outcomes (not the length of the - returned array). - - Returns - ------- - numpy.ndarray - """ - omitted_probs = 1.0 - _np.array([probs[self.layout.indices_for_index(i)].sum() - for i in self.indicesOfCircuitsWithOmittedData]) - return self.raw_objfn.zero_freq_terms(self.total_counts[self.firsts], omitted_probs) - - def _update_lsvec_for_omitted_probs(self, lsvec, probs): - """ - Updates the least-squares vector `lsvec`, adding the omitted-probability contributions. - - Parameters - ---------- - lsvec : numpy.ndarray - Vector of least-squares (sqrt of terms) objective function values *before* adding - omitted-probability contributions. This function updates this array. - - probs : numpy.ndarray - The (full) vector of probabilities. Length is equal to the - total number of circuit outcomes. - - Returns - ------- - None - """ - # lsvec = sqrt(terms) => sqrt(terms + zerofreqfn(omitted)) - lsvec[self.firsts] = _np.sqrt(lsvec[self.firsts]**2 + self._omitted_prob_first_terms(probs)) - - def _update_terms_for_omitted_probs(self, terms, probs): - """ - Updates the terms vector `terms`, adding the omitted-probability contributions. - - Parameters - ---------- - terms : numpy.ndarray - Vector of objective function term values *before* adding - omitted-probability contributions. This function updates this array. - - probs : numpy.ndarray - The (full) vector of probabilities. Length is equal to the - total number of circuit outcomes. - - Returns - ------- - None - """ - # terms => terms + zerofreqfn(omitted) - terms[self.firsts] += self._omitted_prob_first_terms(probs) - - def _omitted_prob_first_dterms(self, probs): - """ - Compute the derivative of the first-terms vector returned by :meth:`_omitted_prob_first_terms`. - - This derivative is just with respect to the *probabilities*, not the - model parameters, as it anticipates a final dot product with the jacobian - of the computed probabilities with respect to the model parameters (see - :meth:`_update_dterms_for_omitted_probs`). - - Parameters - ---------- - probs : numpy.ndarray - The (full) vector of probabilities. Length is equal to the - total number of circuit outcomes. - - Returns - ------- - numpy.ndarray - Vector of the derivatives of the term values with respect - to the corresponding probability. As such, this is a 1D - array of length equal to the number of circuits with omitted - contributions. - """ - omitted_probs = 1.0 - _np.array([_np.sum(probs[self.layout.indices_for_index(i)]) - for i in self.indicesOfCircuitsWithOmittedData]) - return self.raw_objfn.zero_freq_dterms(self.total_counts[self.firsts], omitted_probs) - - def _update_dterms_for_omitted_probs(self, dterms, probs, dprobs_omitted_rowsum): - # terms => terms + zerofreqfn(omitted) - # dterms => dterms + dzerofreqfn(omitted) * domitted (and domitted = (-omitted_rowsum)) - """ - Updates term jacobian to account for omitted probabilities. - - Parameters - ---------- - dterms : numpy.ndarray - Jacobian of terms before and omitted-probability contributions are added. - This array is updated by this function. - - probs : numpy.ndarray - The (full) vector of probabilities. Length is equal to the - total number of circuit outcomes. - - dprobs_omitted_rowsum : numpy.ndarray - An array of shape `(M,N)` where `M` is the number of circuits with - omitted contributions and `N` is the number of model parameters. This - matrix results from summing up the jacobian rows of all the *present* - probabilities for the circuit corresponding to the row. That is, the - i-th row of this matrix contains the summed-up derivatives of all the - computed probabilities (i.e. present outcomes) for the i-th circuit with - omitted probabilities. These omitted probabilities are never computed, but - are inferred as 1.0 minus the present probabilities, so this matrix gives - the negative of the derivative of the omitted probabilities. - - Returns - ------- - None - """ - dterms[self.firsts] -= self._omitted_prob_first_dterms(probs)[:, None] * dprobs_omitted_rowsum - - def _update_dlsvec_for_omitted_probs(self, dlsvec, lsvec, probs, dprobs_omitted_rowsum): - """ - Updates least-squares vector's jacobian to account for omitted probabilities. - - Parameters - ---------- - dlsvec : numpy.ndarray - Jacobian of least-squares vector before and omitted-probability contributions - are added. This array is updated by this function. - - lsvec : numpy.ndarray - The least-squares vector itself, as this is often helpful in this computation. - Length is equal to the total number of circuit outcomes. - - probs : numpy.ndarray - The (full) vector of probabilities. Length is equal to the - total number of circuit outcomes. - - dprobs_omitted_rowsum : numpy.ndarray - An array of shape `(M,N)` where `M` is the number of circuits with - omitted contributions and `N` is the number of model parameters. This - matrix results from summing up the jacobian rows of all the *present* - probabilities for the circuit corresponding to the row. That is, the - i-th row of this matrix contains the summed-up derivatives of all the - computed probabilities (i.e. present outcomes) for the i-th circuit with - omitted probabilities. These omitted probabilities are never computed, but - are inferred as 1.0 minus the present probabilities, so this matrix gives - the negative of the derivative of the omitted probabilities. - - Returns - ------- - None - """ - # lsvec = sqrt(terms) => sqrt(terms + zerofreqfn(omitted)) - # dlsvec = 0.5 / sqrt(terms) * dterms = 0.5 / lsvec * dterms - # 0.5 / sqrt(terms + zerofreqfn(omitted)) * (dterms + dzerofreqfn(omitted) * domitted) - # so dterms = 2 * lsvec * dlsvec, and - # new_dlsvec = 0.5 / sqrt(...) * (2 * lsvec * dlsvec + dzerofreqfn(omitted) * domitted) - - lsvec_firsts = lsvec[self.firsts] - updated_lsvec = _np.sqrt(lsvec_firsts**2 + self._omitted_prob_first_terms(probs)) - updated_lsvec = _np.where(updated_lsvec == 0, 1.0, updated_lsvec) # avoid 0/0 where lsvec & deriv == 0 - - # dlsvec => 0.5 / updated_lsvec * (2 * lsvec * dlsvec + dzerofreqfn(omitted) * domitted) memory efficient: - dlsvec[self.firsts] *= (lsvec_firsts / updated_lsvec)[:, None] - dlsvec[self.firsts] -= ((0.5 / updated_lsvec) * self._omitted_prob_first_dterms(probs))[:, None] \ - * dprobs_omitted_rowsum - - def _clip_probs(self): - """ Clips the potentially shared-mem self.probs according to self.prob_clip_interval """ - if self.prob_clip_interval is not None: - if isinstance(self.layout, _DistributableCOPALayout): - if self.layout.resource_alloc('atom-processing').is_host_leader: - _np.clip(self.probs, self.prob_clip_interval[0], self.prob_clip_interval[1], out=self.probs) - self.layout.resource_alloc('atom-processing').host_comm_barrier() - else: - _np.clip(self.probs, self.prob_clip_interval[0], self.prob_clip_interval[1], out=self.probs) - - #Objective Function - - def lsvec(self, paramvec=None, oob_check=False): - """ - Compute the least-squares vector of the objective function. - - This is the square-root of the terms-vector returned from :meth:`terms`. - This vector is the objective function value used by a least-squares - optimizer when optimizing this objective function. Note that the existence - of this quantity requires that the terms be non-negative. If this is not - the case, an error is raised. - - Parameters - ---------- - paramvec : numpy.ndarray, optional - The vector of (model) parameters to evaluate the objective function at. - If `None`, then the model's current parameter vector is used (held internally). - - oob_check : bool, optional - Whether the objective function should raise an error if it is being - evaluated in an "out of bounds" region. - - Returns - ------- - numpy.ndarray - An array of shape `(nElements,)` where `nElements` is the number - of circuit outcomes. - """ - - #DEBUG REMOVE - used for memory profiling - #import os, psutil - #process = psutil.Process(os.getpid()) - #def print_mem_usage(prefix): - # print("%s: mem usage = %.3f GB" % (prefix, process.memory_info().rss / (1024.0**3))) - - tm = _time.time() - if paramvec is not None: - self.model.from_vector(paramvec) - else: - paramvec = self.model.to_vector() - lsvec = self.obj.view() - - # Whether this rank is the "leader" of all the processors accessing the same shared self.jac and self.probs mem. - # Only leader processors should modify the contents of the shared memory, so we only apply operations *once* - # `unit_ralloc` is the group of all the procs targeting same destination into self.obj - unit_ralloc = self.layout.resource_alloc('atom-processing') - shared_mem_leader = unit_ralloc.is_host_leader - - with self.resource_alloc.temporarily_track_memory(self.nelements): # 'e' (lsvec) - self.model.sim.bulk_fill_probs(self.probs, self.layout) # syncs shared mem - self._clip_probs() # clips self.probs in place w/shared mem sync - - if oob_check: # Only used for termgap cases - if not self.model.sim.bulk_test_if_paths_are_sufficient(self.layout, self.probs, verbosity=1): - raise ValueError("Out of bounds!") # signals LM optimizer - - if shared_mem_leader: - lsvec_no_penalty = self.raw_objfn.lsvec(self.probs, self.counts, self.total_counts, self.freqs) - lsvec[:] = _np.concatenate((lsvec_no_penalty, self._lspenaltyvec(paramvec))) \ - if self._process_penalties else lsvec_no_penalty - - if self.firsts is not None and shared_mem_leader: - self._update_lsvec_for_omitted_probs(lsvec, self.probs) - unit_ralloc.host_comm_barrier() # have non-leader procs wait for leaders to set shared mem - - self.raw_objfn.resource_alloc.profiler.add_time("LS OBJECTIVE", tm) - assert(lsvec.shape == (self.nelements + self.local_ex,)) - return lsvec - - def terms(self, paramvec=None): + def terms(self, paramvec=None, profiler_str="TERMS OBJECTIVE"): """ Compute the terms of the objective function. @@ -4839,39 +4457,43 @@ def terms(self, paramvec=None): of circuit outcomes. """ tm = _time.time() - if paramvec is not None: self.model.from_vector(paramvec) - else: paramvec = self.model.to_vector() + if paramvec is None: + paramvec = self.model.to_vector() + else: + self.model.from_vector(paramvec) terms = self.obj.view() - # Whether this rank is the "leader" of all the processors accessing the same shared self.jac and self.probs mem. - # Only leader processors should modify the contents of the shared memory, so we only apply operations *once* - # `unit_ralloc` is the group of all the procs targeting same destination into self.obj unit_ralloc = self.layout.resource_alloc('atom-processing') shared_mem_leader = unit_ralloc.is_host_leader with self.resource_alloc.temporarily_track_memory(self.nelements): # 'e' (terms) self.model.sim.bulk_fill_probs(self.probs, self.layout) - self._clip_probs() # clips self.probs in place w/shared mem sync - + self._clip_probs() if shared_mem_leader: terms_no_penalty = self.raw_objfn.terms(self.probs, self.counts, self.total_counts, self.freqs) - terms[:] = _np.concatenate((terms_no_penalty, self._penaltyvec(paramvec))) \ - if self._process_penalties else terms_no_penalty + m = terms_no_penalty.size + terms[:m] = terms_no_penalty + if self._process_penalties: + terms[m:] = self._penaltyvec(paramvec) if self.firsts is not None and shared_mem_leader: - self._update_terms_for_omitted_probs(terms, self.probs) - unit_ralloc.host_comm_barrier() # have non-leader procs wait for leaders to set shared mem + omitted_probs = 1.0 - _np.array([probs[self.layout.indices_for_index(i)].sum() for i in self.indicesOfCircuitsWithOmittedData]) + omitted_probs_firsts_terms = self.raw_objfn.zero_freq_terms(self.total_counts[self.firsts], omitted_probs) + terms[self.firsts] += omitted_probs_firsts_terms + + unit_ralloc.host_comm_barrier() - self.raw_objfn.resource_alloc.profiler.add_time("TERMS OBJECTIVE", tm) + self.raw_objfn.resource_alloc.profiler.add_time(profiler_str, tm) assert(terms.shape == (self.nelements + self.local_ex,)) return terms - # Jacobian function - def dlsvec(self, paramvec=None): + def dterms(self, paramvec=None): """ - The derivative (jacobian) of the least-squares vector. + Compute the jacobian of the terms of the objective function. - Derivatives are taken with respect to model parameters. + The "terms" are the per-circuit-outcome values that get summed together + to result in the objective function value. Differentiation is with + respect to model parameters. Parameters ---------- @@ -4886,72 +4508,48 @@ def dlsvec(self, paramvec=None): of circuit outcomes and `nParams` is the number of model parameters. """ tm = _time.time() - dprobs = self.jac[0:self.nelements, :] # avoid mem copying: use jac mem for dprobs + dprobs = self.jac[0:self.nelements, :] dprobs.shape = (self.nelements, self.nparams) - if paramvec is not None: - self.model.from_vector(paramvec) - else: + if paramvec is None: paramvec = self.model.to_vector() + else: + self.model.from_vector(paramvec) - # Whether this rank is the "leader" of all the processors accessing the same shared self.jac and self.probs mem. - # Only leader processors should modify the contents of the shared memory, so we only apply operations *once* - # `unit_ralloc` is the group of all the procs targeting same destination into self.jac unit_ralloc = self.layout.resource_alloc('param-processing') shared_mem_leader = unit_ralloc.is_host_leader - with self.resource_alloc.temporarily_track_memory(2 * self.nelements): # 'e' (dg_dprobs, lsvec) - #OLD jac distribution method - # Usual: rootcomm -this_fn-> atom_comm (sub_comm) -> block_comm - # New: rootcomm -> jac_slice_comm -this_fn-> atom_comm (sub_comm) -> block_comm (??) - # it could be that the comm in resource_alloc is already split for jac distribution, but this - # would mean code that doesn't compute jacobians would be less efficient. - # maybe hold a jac_slice_comm that is different? - # break up: N procs = Natom_eaters * Nprocs_per_atom_eater - # Nprocs_per_atom_eater = Nparamblk_eaters * Nprocs_per_paramblk_eater - #wrtSlice = resource_alloc.jac_slice if (resource_alloc.jac_distribution_method == "columns") \ - # else slice(0, self.model.num_params) - - self.model.sim.bulk_fill_dprobs(dprobs, self.layout, self.probs) # wrtSlice) - self._clip_probs() # clips self.probs in place w/shared mem sync - + with self.resource_alloc.temporarily_track_memory(2 * self.nelements): + self.model.sim.bulk_fill_dprobs(dprobs, self.layout, self.probs) + self._clip_probs() if shared_mem_leader: if self.firsts is not None: for ii, i in enumerate(self.indicesOfCircuitsWithOmittedData): self.dprobs_omitted_rowsum[ii, :] = _np.sum(dprobs[self.layout.indices_for_index(i), :], axis=0) - - #if shared_mem_leader: # Note: no need for barrier directly below as barrier further down suffices - dg_dprobs, lsvec = self.raw_objfn.dlsvec_and_lsvec(self.probs, self.counts, self.total_counts, - self.freqs) - dprobs *= dg_dprobs[:, None] - # (nelements,N) * (nelements,1) (N = dim of vectorized model) - # this multiply also computes jac, which is just dprobs - # with a different shape (jac.shape == [nelements,nparams]) - - if shared_mem_leader: # only "leader" modifies shared mem (dprobs & self.jac) + dg_probs = self.raw_objfn.dterms(self.probs, self.counts, self.total_counts, self.freqs) + dprobs *= dg_probs[:, None] + + if shared_mem_leader: if self.firsts is not None: - #Note: lsvec is assumed to be *not* updated w/omitted probs contribution - self._update_dlsvec_for_omitted_probs(dprobs, lsvec, self.probs, self.dprobs_omitted_rowsum) - - if self._process_penalties and shared_mem_leader: - self._fill_lspenaltyvec_jac(paramvec, self.jac[self.nelements:, :]) # jac.shape == (nelements+N,N) - unit_ralloc.host_comm_barrier() # have non-leader procs wait for leaders to set shared mem - - # REMOVE => unit tests? - #if self.check_jacobian: _opt.check_jac(lambda v: self.lsvec( - # v), paramvec, self.jac, tol=1e-3, eps=1e-6, errType='abs') # TO FIX + omitted_probs = 1.0 - _np.array([_np.sum(probs[self.layout.indices_for_index(i)]) for i in self.indicesOfCircuitsWithOmittedData]) + omitted_dprobs_firsts_dterms = self.raw_objfn.zero_freq_dterms(self.total_counts[self.firsts], omitted_probs) + dterms[self.firsts] -= omitted_dprobs_firsts_dterms[:, None] * self.dprobs_omitted_rowsum - # dpr has shape == (nCircuits, nDerivCols), weights has shape == (nCircuits,) - # return shape == (nCircuits, nDerivCols) where ret[i,j] = dP[i,j]*(weights+dweights*(p-f))[i] + if self._process_penalties: + self._fill_dterms_penalty(paramvec, self.jac[self.nelements:, :]) # jac.shape == (nelements+N,N) + + unit_ralloc.host_comm_barrier() self.raw_objfn.resource_alloc.profiler.add_time("JACOBIAN", tm) return self.jac - def dterms(self, paramvec=None): + def lsvec(self, paramvec=None, oob_check=False): """ - Compute the jacobian of the terms of the objective function. + Compute the least-squares vector of the objective function. - The "terms" are the per-circuit-outcome values that get summed together - to result in the objective function value. Differentiation is with - respect to model parameters. + This is the square-root of the terms-vector returned from :meth:`terms`. + This vector is the objective function value used by a least-squares + optimizer when optimizing this objective function. Note that the existence + of this quantity requires that the terms be non-negative. If this is not + the case, an error is raised. Parameters ---------- @@ -4959,57 +4557,125 @@ def dterms(self, paramvec=None): The vector of (model) parameters to evaluate the objective function at. If `None`, then the model's current parameter vector is used (held internally). + oob_check : bool, optional + Whether the objective function should raise an error if it is being + evaluated in an "out of bounds" region. + Returns ------- numpy.ndarray - An array of shape `(nElements,nParams)` where `nElements` is the number - of circuit outcomes and `nParams` is the number of model parameters. + An array of shape `(nElements,)` where `nElements` is the number + of circuit outcomes. """ - tm = _time.time() - dprobs = self.jac[0:self.nelements, :] # avoid mem copying: use jac mem for dprobs - dprobs.shape = (self.nelements, self.nparams) - if paramvec is not None: - self.model.from_vector(paramvec) - else: - paramvec = self.model.to_vector() + if oob_check: warnings.warn('oob_check ignored') + lsvec = self.terms(paramvec, "LS OBJECTIVE") + lsvec **= 0.5 + return lsvec - # Whether this rank is the "leader" of all the processors accessing the same shared self.jac and self.probs mem. - # Only leader processors should modify the contents of the shared memory, so we only apply operations *once* - # `unit_ralloc` is the group of all the procs targeting same destination into self.jac + def dlsvec(self, paramvec=None): + """ + It's possible that lsvec.shape[0] == terms.shape[0] > self.nelements + if regularization is used. The relationship that lsvec = sqrt(terms) + holds regardless of whether or not regularization is used. + + dlsvec(mdl; self)) = Jac(sqrt(terms(mdl; self))) + = diag(0.5 / sqrt(terms(mdl; self))) Jac(terms(mdl; self)) + = diag( 0.5 / lsvec ) Jac(terms(mdl; self)) + + This implementation is valid for any objective function. We need it here + (instead of the default implementation) since the default makes a call to + the raw objective function, and the raw objective function doesn't use the + weighting we want. + """ + jac = self.dterms(paramvec) unit_ralloc = self.layout.resource_alloc('param-processing') - shared_mem_leader = unit_ralloc.is_host_leader + if unit_ralloc.is_host_leader: + lsvec = self.lsvec(paramvec) + p5over_lsvec = 0.5/lsvec + p5over_lsvec[lsvec < 1e-100] = 0.0 + jac *= p5over_lsvec[:, None] + return jac - with self.resource_alloc.temporarily_track_memory(2 * self.nelements): # 'e' (dg_dprobs, lsvec) - self.model.sim.bulk_fill_dprobs(dprobs, self.layout, self.probs) - self._clip_probs() # clips self.probs in place w/shared mem sync + # Helpers, supporting main public instance functions - if shared_mem_leader: - if self.firsts is not None: - for ii, i in enumerate(self.indicesOfCircuitsWithOmittedData): - self.dprobs_omitted_rowsum[ii, :] = _np.sum(dprobs[self.layout.indices_for_index(i), :], axis=0) + def _penaltyvec(self, paramvec): + blocks = [_np.zeros(shape=(0,))] - #if shared_mem_leader: # Note: barrier below work suffices for this condition too - dprobs *= self.raw_objfn.dterms(self.probs, self.counts, self.total_counts, self.freqs)[:, None] - # (nelements,N) * (nelements,1) (N = dim of vectorized model) - # this multiply also computes jac, which is just dprobs - # with a different shape (jac.shape == [nelements,nparams]) + if self.forcefn_grad is not None: + forcefn_penalty = self.forceShift - _np.dot(self.forcefn_grad, self.model.to_vector()) + assert(_np.all(forcefn_penalty >= 0)), "Inadequate forcing shift!" + blocks.append(forcefn_penalty) - if shared_mem_leader: - if self.firsts is not None: - self._update_dterms_for_omitted_probs(dprobs, self.probs, self.dprobs_omitted_rowsum) + if self.regularize_factor != 0: + paramvec_norm = self.regularize_factor * _np.array([max(0, absx - 1.0) for absx in map(abs, paramvec)], 'd') + paramvec_norm **= 2 + blocks.append(paramvec_norm) - if self._process_penalties: - self._fill_dterms_penalty(paramvec, self.jac[self.nelements:, :]) # jac.shape == (nelements+N,N) - unit_ralloc.host_comm_barrier() # have non-leader procs wait for leaders to set shared mem + if self.cptp_penalty_factor > 0: + cp_penalty = _cptp_penalty(self.model, self.cptp_penalty_factor, self.opBasis) ** 2 + blocks.append(cp_penalty) - # REMOVE => unit tests - #if self.check_jacobian: _opt.check_jac(lambda v: self.lsvec( - # v), paramvec, self.jac, tol=1e-3, eps=1e-6, errType='abs') # TO FIX + if self.spam_penalty_factor > 0: + spam_penalty = _spam_penalty(self.model, self.spam_penalty_factor, self.opBasis) ** 2 + blocks.append(spam_penalty) - # dpr has shape == (nCircuits, nDerivCols), weights has shape == (nCircuits,) - # return shape == (nCircuits, nDerivCols) where ret[i,j] = dP[i,j]*(weights+dweights*(p-f))[i] - self.raw_objfn.resource_alloc.profiler.add_time("JACOBIAN", tm) - return self.jac + if self.errorgen_penalty_factor > 0: + errorgen_penalty = _errorgen_penalty(self.model, self.errorgen_penalty_factor) ** 2 + blocks.append(errorgen_penalty) + + return _np.concatenate(blocks) + + def _fill_dterms_penalty(self, paramvec, terms_jac): + wrtslice = self.layout.global_param_slice if isinstance(self.layout, _DistributableCOPALayout) else slice(0, len(paramvec)) # all params + off = 0 + + if self.forcefn_grad is not None: + n = self.forcefn_grad.shape[0] + terms_jac[off:off + n, :] = -self.forcefn_grad + force = self.forceShift - _np.dot(self.forcefn_grad, self.model.to_vector()) + assert(_np.all(force >= 0)), "Inadequate forcing shift!" + terms_jac[off:off + n, :] *= 2*_np.sqrt(force)[:, None] + off += n + + if self.regularize_factor > 0: + n = len(paramvec) + terms_jac[off:off + n, :] = _np.diag([(self.regularize_factor * _np.sign(x) if abs(x) > 1.0 else 0.0) for x in paramvec[wrtslice]]) # (N,N) + paramvec_norm = self.regularize_factor * _np.array([max(0, absx - 1.0) for absx in map(abs, paramvec)], 'd') + terms_jac[off:off + n, :] *= 2*paramvec_norm[:, None] + off += n + + if self.cptp_penalty_factor > 0: + n = _cptp_penalty_jac_fill(terms_jac[off:, :], self.model, self.cptp_penalty_factor, self.opBasis, wrtslice) + cp_penalty = _cptp_penalty(self.model, self.cptp_penalty_factor, self.opBasis) + terms_jac[off:off+n, :] *= 2*cp_penalty[:, None] + off += n + + if self.spam_penalty_factor > 0: + n = _spam_penalty_jac_fill(terms_jac[off:, :], self.model, self.spam_penalty_factor, self.opBasis, wrtslice) + spam_penalty = _spam_penalty(self.model, self.spam_penalty_factor, self.opBasis) + terms_jac[off:off+n, :] *= 2*spam_penalty[:, None] + off += n + + if self.errorgen_penalty_factor > 0: + n = _errorgen_penalty_jac_fill(terms_jac[off:, :], self.model, self.errorgen_penalty_factor, wrtslice) + errorgen_penalty = _errorgen_penalty(self.model, self.errorgen_penalty_factor) + terms_jac[off:off+n, :] *= 2*errorgen_penalty[:, None] + off += n + + assert(off == self.local_ex) + return + + def _clip_probs(self): + """ Clips the potentially shared-mem self.probs according to self.prob_clip_interval """ + if self.prob_clip_interval is not None: + if isinstance(self.layout, _DistributableCOPALayout): + if self.layout.resource_alloc('atom-processing').is_host_leader: + _np.clip(self.probs, self.prob_clip_interval[0], self.prob_clip_interval[1], out=self.probs) + self.layout.resource_alloc('atom-processing').host_comm_barrier() + else: + _np.clip(self.probs, self.prob_clip_interval[0], self.prob_clip_interval[1], out=self.probs) + + # Hessians, public and private instance functions def hessian_brute(self, paramvec=None): """ From b6e540f4c180a740a0fbeaca35e8e765ab2efcc7 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Mon, 16 Dec 2024 16:39:29 -0500 Subject: [PATCH 02/71] bugfixes --- pygsti/objectivefns/objectivefns.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index db7c7b665..a609b6848 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -14,6 +14,7 @@ import sys as _sys import time as _time import pathlib as _pathlib +import warnings as _warnings import numpy as _np @@ -4477,7 +4478,7 @@ def terms(self, paramvec=None, profiler_str="TERMS OBJECTIVE"): terms[m:] = self._penaltyvec(paramvec) if self.firsts is not None and shared_mem_leader: - omitted_probs = 1.0 - _np.array([probs[self.layout.indices_for_index(i)].sum() for i in self.indicesOfCircuitsWithOmittedData]) + omitted_probs = 1.0 - _np.array([self.probs[self.layout.indices_for_index(i)].sum() for i in self.indicesOfCircuitsWithOmittedData]) omitted_probs_firsts_terms = self.raw_objfn.zero_freq_terms(self.total_counts[self.firsts], omitted_probs) terms[self.firsts] += omitted_probs_firsts_terms @@ -4530,9 +4531,9 @@ def dterms(self, paramvec=None): if shared_mem_leader: if self.firsts is not None: - omitted_probs = 1.0 - _np.array([_np.sum(probs[self.layout.indices_for_index(i)]) for i in self.indicesOfCircuitsWithOmittedData]) + omitted_probs = 1.0 - _np.array([_np.sum(self.probs[self.layout.indices_for_index(i)]) for i in self.indicesOfCircuitsWithOmittedData]) omitted_dprobs_firsts_dterms = self.raw_objfn.zero_freq_dterms(self.total_counts[self.firsts], omitted_probs) - dterms[self.firsts] -= omitted_dprobs_firsts_dterms[:, None] * self.dprobs_omitted_rowsum + dprobs[self.firsts] -= omitted_dprobs_firsts_dterms[:, None] * self.dprobs_omitted_rowsum if self._process_penalties: self._fill_dterms_penalty(paramvec, self.jac[self.nelements:, :]) # jac.shape == (nelements+N,N) @@ -4567,7 +4568,7 @@ def lsvec(self, paramvec=None, oob_check=False): An array of shape `(nElements,)` where `nElements` is the number of circuit outcomes. """ - if oob_check: warnings.warn('oob_check ignored') + if oob_check: _warnings.warn('oob_check ignored') lsvec = self.terms(paramvec, "LS OBJECTIVE") lsvec **= 0.5 return lsvec From 8f598916dd48c6ac0f2a7076284292431358ccc7 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Mon, 16 Dec 2024 17:02:04 -0500 Subject: [PATCH 03/71] improve readability --- pygsti/objectivefns/objectivefns.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index a609b6848..a864cdbb0 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -4419,8 +4419,10 @@ def set_penalties(self, regularize_factor=0, cptp_penalty_factor=0, spam_penalty self.prob_clip_interval = prob_clip_interval # not really a "penalty" per se, but including it as one # gives the user the ability to easily set it if they ever need to (unlikely) - self._process_penalties = self.layout.part_of_final_atom_processor \ - if isinstance(self.layout, _DistributableCOPALayout) else True + if isinstance(self.layout, _DistributableCOPALayout): + self._process_penalties = self.layout.part_of_final_atom_processor + else: + self._process_penalties = True ex = 0 # Compute "extra" number of terms/lsvec-element/rows-of-jacobian beyond evaltree elements @@ -4454,8 +4456,7 @@ def terms(self, paramvec=None, profiler_str="TERMS OBJECTIVE"): Returns ------- numpy.ndarray - An array of shape `(nElements,)` where `nElements` is the number - of circuit outcomes. + An array of shape `(nElements,)` where `nElements = self.nelements + self.ex_local`. """ tm = _time.time() if paramvec is None: @@ -4505,8 +4506,7 @@ def dterms(self, paramvec=None): Returns ------- numpy.ndarray - An array of shape `(nElements,nParams)` where `nElements` is the number - of circuit outcomes and `nParams` is the number of model parameters. + An array of shape `(nElements,nParams)` where `nElements = self.nelements + self.ex_local`. """ tm = _time.time() dprobs = self.jac[0:self.nelements, :] @@ -4565,8 +4565,7 @@ def lsvec(self, paramvec=None, oob_check=False): Returns ------- numpy.ndarray - An array of shape `(nElements,)` where `nElements` is the number - of circuit outcomes. + An array of shape `(nElements,)` where `nElements = self.nelements + self.ex_local`. """ if oob_check: _warnings.warn('oob_check ignored') lsvec = self.terms(paramvec, "LS OBJECTIVE") From 986e52867f1710136b2488c018db04a9f6c7e208 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Mon, 16 Dec 2024 18:04:11 -0500 Subject: [PATCH 04/71] inline comment --- pygsti/objectivefns/objectivefns.py | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index a864cdbb0..c3b548024 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -4473,10 +4473,9 @@ def terms(self, paramvec=None, profiler_str="TERMS OBJECTIVE"): self._clip_probs() if shared_mem_leader: terms_no_penalty = self.raw_objfn.terms(self.probs, self.counts, self.total_counts, self.freqs) - m = terms_no_penalty.size - terms[:m] = terms_no_penalty + terms[:self.nelements] = terms_no_penalty if self._process_penalties: - terms[m:] = self._penaltyvec(paramvec) + terms[self.nelements:] = self._penaltyvec(paramvec) if self.firsts is not None and shared_mem_leader: omitted_probs = 1.0 - _np.array([self.probs[self.layout.indices_for_index(i)].sum() for i in self.indicesOfCircuitsWithOmittedData]) @@ -4536,7 +4535,7 @@ def dterms(self, paramvec=None): dprobs[self.firsts] -= omitted_dprobs_firsts_dterms[:, None] * self.dprobs_omitted_rowsum if self._process_penalties: - self._fill_dterms_penalty(paramvec, self.jac[self.nelements:, :]) # jac.shape == (nelements+N,N) + self._fill_dterms_penalty(paramvec, self.jac[self.nelements:, :]) # jac.shape == (nelements + local_ex, nparams) unit_ralloc.host_comm_barrier() self.raw_objfn.resource_alloc.profiler.add_time("JACOBIAN", tm) @@ -4567,8 +4566,15 @@ def lsvec(self, paramvec=None, oob_check=False): numpy.ndarray An array of shape `(nElements,)` where `nElements = self.nelements + self.ex_local`. """ - if oob_check: _warnings.warn('oob_check ignored') lsvec = self.terms(paramvec, "LS OBJECTIVE") + if oob_check and _np.any(lsvec < 0): + bad_locs = _np.where(lsvec < 0)[0] + msg = f""" + lsvec is only defined when terms is elementwise nonnegative. + We encountered negative values for terms[i] for indices i + in {bad_locs}. + """ + raise RuntimeError(msg) lsvec **= 0.5 return lsvec From ccf17ef613bcd32dd881a23cef0cc902973d750e Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Tue, 17 Dec 2024 07:23:39 -0500 Subject: [PATCH 05/71] add pytest dispatcher for MPI test in test_packages --- test/test_packages/mpi/run_me_with_mpiexec.py | 340 +++++++++++++++++ test/test_packages/mpi/test_mpi.py | 351 +----------------- 2 files changed, 355 insertions(+), 336 deletions(-) create mode 100644 test/test_packages/mpi/run_me_with_mpiexec.py diff --git a/test/test_packages/mpi/run_me_with_mpiexec.py b/test/test_packages/mpi/run_me_with_mpiexec.py new file mode 100644 index 000000000..04a781d28 --- /dev/null +++ b/test/test_packages/mpi/run_me_with_mpiexec.py @@ -0,0 +1,340 @@ +# This file is designed to be run via: mpiexec -np 4 python -W ignore run_me_with_mpiexec.py +# This does not use nosetests because I want to set verbosity differently based on rank (quiet if not rank 0) +# By wrapping asserts in comm.rank == 0, only rank 0 should fail (should help with output) +# Can run with different number of procs, but 4 is minimum to test all modes (pure MPI, pure shared mem, and mixed) + +import os + +import numpy as np +from mpi4py import MPI + +import pygsti +from pygsti.modelpacks import smq1Q_XYI as std + +wcomm = MPI.COMM_WORLD + + +class ParallelTest(object): + # No setup here, must be defined in the derived classes + + def test_simulate_data(self): + comm = self.ralloc.comm + + exp_design = std.get_gst_experiment_design(4) + mdl_datagen = std.target_model().depolarize(op_noise=0.1, spam_noise=0.01) + + ds_serial = pygsti.data.simulate_data(mdl_datagen, exp_design, 1000, seed=1234, comm=None) + ds_parallel = pygsti.data.simulate_data(mdl_datagen, exp_design, 1000, seed=1234, comm=comm) + + if comm is None or comm.rank == 0: + assert (set(ds_serial.keys()) == set(ds_parallel.keys())) + for key in ds_serial.keys(): + assert (ds_serial[key].to_dict() == ds_parallel[key].to_dict()) + + def test_gst(self): + comm = self.ralloc.comm + + exp_design = std.get_gst_experiment_design(4) + mdl_datagen = std.target_model().depolarize(op_noise=0.1, spam_noise=0.01) + ds = pygsti.data.simulate_data(mdl_datagen, exp_design, 1000, seed=1234, comm=comm) + data = pygsti.protocols.ProtocolData(exp_design, ds) + + initial_model = std.target_model("full TP") + proto = pygsti.protocols.GateSetTomography(initial_model, verbosity=1, + optimizer={'maxiter': 100, 'serial_solve_proc_threshold': 100}) + + results_serial = proto.run(data, comm=None) + results_parallel = proto.run(data, comm=comm) + + # compare resulting models + if comm is None or comm.rank == 0: + best_params_serial = results_serial.estimates["GateSetTomography"].models['stdgaugeopt'].to_vector() + best_params_parallel = results_parallel.estimates["GateSetTomography"].models['stdgaugeopt'].to_vector() + + assert np.allclose(best_params_serial, best_params_parallel) + + def test_MPI_probs(self): + comm = self.ralloc.comm + + #Create some model + mdl = std.target_model() + mdl.kick(0.1,seed=1234) + + #Get some operation sequences + maxLengths = [1, 2, 4] + circuits = pygsti.circuits.create_lsgst_circuits( + list(std.target_model().operations.keys()), std.prep_fiducials(), + std.meas_fiducials(), std.germs(), maxLengths) + + #Check all-spam-label bulk probabilities + def compare_prob_dicts(a,b,indices=None): + for opstr in circuits: + for outcome in a[opstr].keys(): + if indices is None: + assert (np.linalg.norm(a[opstr][outcome] - b[opstr][outcome]) < 1e-6) + else: + for i in indices: + assert (np.linalg.norm(a[opstr][outcome][i] - b[opstr][outcome][i]) < 1e-6) + + # non-split tree => automatically adjusts wrt_block_size to accomodate + # the number of processors + serial = mdl.sim.bulk_probs(circuits, clip_to=(-1e6,1e6)) + parallel = mdl.sim.bulk_probs(circuits, clip_to=(-1e6,1e6), resource_alloc=self.ralloc) + if comm is None or comm.rank == 0: # output is only given on root proc + compare_prob_dicts(serial, parallel) + + serial = mdl.sim.bulk_dprobs(circuits) + parallel = mdl.sim.bulk_dprobs(circuits, resource_alloc=self.ralloc) + if comm is None or comm.rank == 0: # output is only given on root proc + compare_prob_dicts(serial, parallel) + + serial = mdl.sim.bulk_hprobs(circuits) + parallel = mdl.sim.bulk_hprobs(circuits, resource_alloc=self.ralloc) + if comm is None or comm.rank == 0: # output is only given on root proc + compare_prob_dicts(serial, parallel, (0, 1, 2)) + + def test_MPI_products(self): + comm = self.ralloc.comm + + #Create some model + mdl = std.target_model() + + mdl.kick(0.1, seed=1234) + + #Get some operation sequences + maxLengths = [1,2,4,8] + gstrs = pygsti.circuits.create_lsgst_circuits( + std.target_model(), std.fiducials(), std.fiducials(), std.germs(), maxLengths) + + #Check bulk products + + #bulk_product - no parallelization unless layout is split + serial = mdl.sim.bulk_product(gstrs, scale=False) + parallel = mdl.sim.bulk_product(gstrs, scale=False, resource_alloc=self.ralloc) + assert(np.linalg.norm(serial-parallel) < 1e-6) + + serial_scl, sscale = mdl.sim.bulk_product(gstrs, scale=True) + parallel, pscale = mdl.sim.bulk_product(gstrs, scale=True, resource_alloc=self.ralloc) + assert(np.linalg.norm(serial_scl*sscale[:,None,None] - + parallel*pscale[:,None,None]) < 1e-6) + + #bulk_dproduct - no split tree => parallel by col + serial = mdl.sim.bulk_dproduct(gstrs, scale=False) + parallel = mdl.sim.bulk_dproduct(gstrs, scale=False, resource_alloc=self.ralloc) + assert(np.linalg.norm(serial-parallel) < 1e-6) + + serial_scl, sscale = mdl.sim.bulk_dproduct(gstrs, scale=True) + parallel, pscale = mdl.sim.bulk_dproduct(gstrs, scale=True, resource_alloc=self.ralloc) + assert(np.linalg.norm(serial_scl*sscale[:,None,None,None] - + parallel*pscale[:,None,None,None]) < 1e-6) + + def test_objfn_generator(self): + params = [ + ("map", "logl", 1), ("map", "logl", 4), + ("map", "chi2", 1), ("map", "chi2", 4), + ("matrix", "logl", 1), ("matrix", "logl", 4), + ("matrix", "chi2", 1), ("matrix", "chi2", 4) + ] + for sim, objfn, natoms in params: + yield self.run_objfn_values, sim, objfn, natoms + + def run_objfn_values(self, sim, objfn, natoms): + comm = self.ralloc.comm + + mdl = std.target_model() + exp_design = std.get_gst_experiment_design(1) + mdl_datagen = mdl.depolarize(op_noise=0.01, spam_noise=0.01) + ds = pygsti.data.simulate_data(mdl_datagen, exp_design, 1000, seed=1234, comm=comm) + + builder = pygsti.objectivefns.ObjectiveFunctionBuilder.create_from(objfn) + builder.additional_args['array_types'] = ('EP', 'EPP') # HACK - todo this better + + if sim == 'map': + mdl.sim = pygsti.forwardsims.MapForwardSimulator(num_atoms=natoms) + elif sim == 'matrix': + mdl.sim = pygsti.forwardsims.MatrixForwardSimulator(num_atoms=natoms) + else: + raise RuntimeError("Improper sim type passed by test_objfn_generator") + + circuits = exp_design.all_circuits_needing_data[0:10] + objfn_parallel = builder.build(mdl, ds, circuits, self.ralloc, verbosity=0) + objfn_serial = builder.build(mdl, ds, circuits, None, verbosity=0) + + #LSVEC TEST + v_ref = objfn_serial.lsvec() + v = objfn_parallel.lsvec() + globalv = objfn_parallel.layout.gather_local_array('e', v) + + if comm is None or comm.rank == 0: + finalv = np.empty_like(globalv); off = 0 + for c in circuits: + indices, outcomes = objfn_parallel.layout.global_layout.indices_and_outcomes(c) + assert(outcomes == (('0',), ('1',))) # I think this should always be the ordering (?) + finalv[off:off + len(outcomes)] = globalv[indices] + off += len(outcomes) + + finalv_ref = np.empty_like(v_ref); off = 0 + for c in circuits: + indices, outcomes = objfn_serial.layout.indices_and_outcomes(c) + assert(outcomes == (('0',), ('1',))) # I think this should always be the ordering (?) + finalv_ref[off:off + len(outcomes)] = v_ref[indices] + off += len(outcomes) + + assert np.allclose(finalv, finalv_ref) + + #TODO: DLSVEC? + + #HESSIAN TEST + hessian_ref = objfn_serial.hessian() + hessian = objfn_parallel.hessian() # already a global qty, just on root proc + bhessian_ref = objfn_serial.hessian_brute() + bhessian = objfn_parallel.hessian_brute() + + if comm is None or comm.rank == 0: + assert np.allclose(bhessian_ref, hessian_ref) + assert np.allclose(hessian, hessian_ref) + assert np.allclose(bhessian, bhessian_ref) + + def test_fills_generator(self): + sims = ["map", "matrix"] + # XYI model with maxL = 1 has 92 circuits and 60 parameters + layout_params = [(1, None), (4, None), + (1, 15), (4, 15) + ] + for s in sims: + for natoms, nparams in layout_params: + yield self.run_fills, s, natoms, nparams + + def run_fills(self, sim, natoms, nparams): + comm = self.ralloc.comm + + #Create some model + mdl = std.target_model() + mdl.kick(0.1,seed=1234) + + #Get some operation sequences + maxLengths = [1] + circuits = pygsti.circuits.create_lsgst_circuits( + list(std.target_model().operations.keys()), std.prep_fiducials(), + std.meas_fiducials(), std.germs(), maxLengths) + nP = mdl.num_params + + if sim == 'map': + mdl.sim = pygsti.forwardsims.MapForwardSimulator(num_atoms=natoms, param_blk_sizes=(nparams, nparams)) + elif sim == 'matrix': + mdl.sim = pygsti.forwardsims.MatrixForwardSimulator(num_atoms=natoms, param_blk_sizes=(nparams, nparams)) + else: + raise RuntimeError("Improper sim type passed by test_fills_generator") + + serial_layout = mdl.sim.create_layout(circuits, array_types=('E','EP','EPP'), derivative_dimensions=(nP,)) + + nE = serial_layout.num_elements + nC = len(circuits) + + vp_serial = np.empty( nE, 'd') + vdp_serial = np.empty( (nE,nP), 'd') + vhp_serial = np.empty( (nE,nP,nP), 'd') + + mdl.sim.bulk_fill_probs(vp_serial, serial_layout) + mdl.sim.bulk_fill_dprobs(vdp_serial, serial_layout) + mdl.sim.bulk_fill_hprobs(vhp_serial, serial_layout) + + #Note: when there are multiple atoms, the serial_layout returned above may not preserve + # the original circuit ordering, i.e., serial_layout.circuits != circuits. The global + # layout does have this property, and so we use it to avoid having to lookup which circuit + # is which index in serial_layout. No gathering is needed, since there only 1 processor. + global_serial_layout = serial_layout.global_layout + + #Use a parallel layout to compute the same probabilities & their derivatives + local_layout = mdl.sim.create_layout(circuits, array_types=('E','EP','EPP'), derivative_dimensions=(nP,), + resource_alloc=self.ralloc) + + vp_local = local_layout.allocate_local_array('e', 'd') + vdp_local = local_layout.allocate_local_array('ep', 'd') + vhp_local = local_layout.allocate_local_array('epp', 'd') + + mdl.sim.bulk_fill_probs(vp_local, local_layout) + mdl.sim.bulk_fill_dprobs(vdp_local, local_layout) + mdl.sim.bulk_fill_hprobs(vhp_local, local_layout) + + + #Gather data to produce arrays for the "global" layout (global_parallel_layout should be the same on all procs) + # but only on proc 0 + global_parallel_layout = local_layout.global_layout + vp_global_parallel = local_layout.gather_local_array('e', vp_local) + vdp_global_parallel = local_layout.gather_local_array('ep', vdp_local) + vhp_global_parallel = local_layout.gather_local_array('epp', vhp_local) + + #Free the local arrays when we're done with them (they could be shared mem) + local_layout.free_local_array(vp_local) + local_layout.free_local_array(vdp_local) + local_layout.free_local_array(vhp_local) + + #Compare the two, but note that different layouts may order the elements differently, + # so we can't just compare the arrays directly - we have to use the layout to map + # circuit index -> element indices: + if comm is None or comm.rank == 0: + for i,opstr in enumerate(circuits): + assert(np.linalg.norm(vp_global_parallel[ global_parallel_layout.indices_for_index(i) ] - + vp_serial[ global_serial_layout.indices_for_index(i) ]) < 1e-6) + assert(np.linalg.norm(vdp_global_parallel[ global_parallel_layout.indices_for_index(i) ] - + vdp_serial[ global_serial_layout.indices_for_index(i) ]) < 1e-6) + assert(np.linalg.norm(vhp_global_parallel[ global_parallel_layout.indices_for_index(i) ] - + vhp_serial[ global_serial_layout.indices_for_index(i) ]) < 1e-6) + + def test_MPI_printer(self): + comm = self.ralloc.comm + + #Test output of each rank to separate file: + pygsti.baseobjs.VerbosityPrinter._comm_path = "./" + pygsti.baseobjs.VerbosityPrinter._comm_file_name = "mpi_test_output" + printer = pygsti.baseobjs.VerbosityPrinter(verbosity=2, comm=comm) + printer.log("HELLO!") + # + # + # #Note: no need to test "wrtFilter" business - that was removed + # + # + ## There are other tests within testmpiMain.py that don't need much/any alteration and would be + ## good to transfer to the new MPI unit tests, but: + ## - test_MPI_mlgst_forcefn(comm) -- this is never used anymore, you can ignore this test + ## - test_MPI_derivcols(comm) -- this is essentially tested by varying the layout types + + +class PureMPIParallel_Test(ParallelTest): + @classmethod + def setup_class(cls): + # Turn off all shared memory usage + os.environ['PYGSTI_USE_SHARED_MEMORY'] = "0" + cls.ralloc = pygsti.baseobjs.ResourceAllocation(wcomm) + +#class OnePerHostShmemParallel_Test(ParallelTest): +# @classmethod +# def setup_class(cls): +# # Use 1 host per shared memory group (i.e. no shared mem communication) +# os.environ['PYGSTI_MAX_HOST_PROCS'] = "1" +# cls.ralloc = pygsti.baseobjs.ResourceAllocation(wcomm) +# +#class TwoPerHostShmemParallel_Test(ParallelTest): +# @classmethod +# def setup_class(cls): +# # Use 2 hosts per shared memory group (i.e. mixed MPI + shared mem if more than 2 procs) +# os.environ['PYGSTI_MAX_HOST_PROCS'] = "2" +# cls.ralloc = pygsti.baseobjs.ResourceAllocation(wcomm) +# +#class AllShmemParallel_Test(ParallelTest): +# @classmethod +# def setup_class(cls): +# # Set as many procs per host as possible to use shared memory +# os.environ['PYGSTI_MAX_HOST_PROCS'] = str(wcomm.size) +# cls.ralloc = pygsti.baseobjs.ResourceAllocation(wcomm) + + +if __name__ == '__main__': + tester = PureMPIParallel_Test() + tester.setup_class() + tester.ralloc = pygsti.baseobjs.ResourceAllocation(wcomm) + tester.run_objfn_values('matrix','logl',4) + tester.run_fills('map', 1, None) + tester.run_fills('map', 4, None) + tester.run_fills('matrix', 4, 15) diff --git a/test/test_packages/mpi/test_mpi.py b/test/test_packages/mpi/test_mpi.py index 995b3a92f..b012fdd54 100644 --- a/test/test_packages/mpi/test_mpi.py +++ b/test/test_packages/mpi/test_mpi.py @@ -1,340 +1,19 @@ -# This file is designed to be run via: mpiexec -np 4 python -W ignore testMPI.py -# This does not use nosetests because I want to set verbosity differently based on rank (quiet if not rank 0) -# By wrapping asserts in comm.rank == 0, only rank 0 should fail (should help with output) -# Can run with different number of procs, but 4 is minimum to test all modes (pure MPI, pure shared mem, and mixed) +import subprocess +import pytest +try: + from mpi4py import MPI +except ImportError: + MPI = None -import os -import numpy as np -from mpi4py import MPI +class MPITester: -import pygsti -from pygsti.modelpacks import smq1Q_XYI as std + @pytest.mark.skipif(MPI is None, reason="mpi4py is not installed.") + def test_all(self, capfd: pytest.LogCaptureFixture): + result = subprocess.run("mpiexec -np 4 python -W ignore core_test_mpi.py".split(' '), capture_output=True, text=True) + out, err = capfd.readouterr() + if len(out) + len(err) > 0: + msg = out + '\n'+ 80*'-' + err + raise RuntimeError(msg) + return -wcomm = MPI.COMM_WORLD - - -class ParallelTest(object): - # No setup here, must be defined in the derived classes - - def test_simulate_data(self): - comm = self.ralloc.comm - - exp_design = std.get_gst_experiment_design(4) - mdl_datagen = std.target_model().depolarize(op_noise=0.1, spam_noise=0.01) - - ds_serial = pygsti.data.simulate_data(mdl_datagen, exp_design, 1000, seed=1234, comm=None) - ds_parallel = pygsti.data.simulate_data(mdl_datagen, exp_design, 1000, seed=1234, comm=comm) - - if comm is None or comm.rank == 0: - assert (set(ds_serial.keys()) == set(ds_parallel.keys())) - for key in ds_serial.keys(): - assert (ds_serial[key].to_dict() == ds_parallel[key].to_dict()) - - def test_gst(self): - comm = self.ralloc.comm - - exp_design = std.get_gst_experiment_design(4) - mdl_datagen = std.target_model().depolarize(op_noise=0.1, spam_noise=0.01) - ds = pygsti.data.simulate_data(mdl_datagen, exp_design, 1000, seed=1234, comm=comm) - data = pygsti.protocols.ProtocolData(exp_design, ds) - - initial_model = std.target_model("full TP") - proto = pygsti.protocols.GateSetTomography(initial_model, verbosity=1, - optimizer={'maxiter': 100, 'serial_solve_proc_threshold': 100}) - - results_serial = proto.run(data, comm=None) - results_parallel = proto.run(data, comm=comm) - - # compare resulting models - if comm is None or comm.rank == 0: - best_params_serial = results_serial.estimates["GateSetTomography"].models['stdgaugeopt'].to_vector() - best_params_parallel = results_parallel.estimates["GateSetTomography"].models['stdgaugeopt'].to_vector() - - assert np.allclose(best_params_serial, best_params_parallel) - - def test_MPI_probs(self): - comm = self.ralloc.comm - - #Create some model - mdl = std.target_model() - mdl.kick(0.1,seed=1234) - - #Get some operation sequences - maxLengths = [1, 2, 4] - circuits = pygsti.circuits.create_lsgst_circuits( - list(std.target_model().operations.keys()), std.prep_fiducials(), - std.meas_fiducials(), std.germs(), maxLengths) - - #Check all-spam-label bulk probabilities - def compare_prob_dicts(a,b,indices=None): - for opstr in circuits: - for outcome in a[opstr].keys(): - if indices is None: - assert (np.linalg.norm(a[opstr][outcome] - b[opstr][outcome]) < 1e-6) - else: - for i in indices: - assert (np.linalg.norm(a[opstr][outcome][i] - b[opstr][outcome][i]) < 1e-6) - - # non-split tree => automatically adjusts wrt_block_size to accomodate - # the number of processors - serial = mdl.sim.bulk_probs(circuits, clip_to=(-1e6,1e6)) - parallel = mdl.sim.bulk_probs(circuits, clip_to=(-1e6,1e6), resource_alloc=self.ralloc) - if comm is None or comm.rank == 0: # output is only given on root proc - compare_prob_dicts(serial, parallel) - - serial = mdl.sim.bulk_dprobs(circuits) - parallel = mdl.sim.bulk_dprobs(circuits, resource_alloc=self.ralloc) - if comm is None or comm.rank == 0: # output is only given on root proc - compare_prob_dicts(serial, parallel) - - serial = mdl.sim.bulk_hprobs(circuits) - parallel = mdl.sim.bulk_hprobs(circuits, resource_alloc=self.ralloc) - if comm is None or comm.rank == 0: # output is only given on root proc - compare_prob_dicts(serial, parallel, (0, 1, 2)) - - def test_MPI_products(self): - comm = self.ralloc.comm - - #Create some model - mdl = std.target_model() - - mdl.kick(0.1, seed=1234) - - #Get some operation sequences - maxLengths = [1,2,4,8] - gstrs = pygsti.circuits.create_lsgst_circuits( - std.target_model(), std.fiducials(), std.fiducials(), std.germs(), maxLengths) - - #Check bulk products - - #bulk_product - no parallelization unless layout is split - serial = mdl.sim.bulk_product(gstrs, scale=False) - parallel = mdl.sim.bulk_product(gstrs, scale=False, resource_alloc=self.ralloc) - assert(np.linalg.norm(serial-parallel) < 1e-6) - - serial_scl, sscale = mdl.sim.bulk_product(gstrs, scale=True) - parallel, pscale = mdl.sim.bulk_product(gstrs, scale=True, resource_alloc=self.ralloc) - assert(np.linalg.norm(serial_scl*sscale[:,None,None] - - parallel*pscale[:,None,None]) < 1e-6) - - #bulk_dproduct - no split tree => parallel by col - serial = mdl.sim.bulk_dproduct(gstrs, scale=False) - parallel = mdl.sim.bulk_dproduct(gstrs, scale=False, resource_alloc=self.ralloc) - assert(np.linalg.norm(serial-parallel) < 1e-6) - - serial_scl, sscale = mdl.sim.bulk_dproduct(gstrs, scale=True) - parallel, pscale = mdl.sim.bulk_dproduct(gstrs, scale=True, resource_alloc=self.ralloc) - assert(np.linalg.norm(serial_scl*sscale[:,None,None,None] - - parallel*pscale[:,None,None,None]) < 1e-6) - - def test_objfn_generator(self): - params = [ - ("map", "logl", 1), ("map", "logl", 4), - ("map", "chi2", 1), ("map", "chi2", 4), - ("matrix", "logl", 1), ("matrix", "logl", 4), - ("matrix", "chi2", 1), ("matrix", "chi2", 4) - ] - for sim, objfn, natoms in params: - yield self.run_objfn_values, sim, objfn, natoms - - def run_objfn_values(self, sim, objfn, natoms): - comm = self.ralloc.comm - - mdl = std.target_model() - exp_design = std.get_gst_experiment_design(1) - mdl_datagen = mdl.depolarize(op_noise=0.01, spam_noise=0.01) - ds = pygsti.data.simulate_data(mdl_datagen, exp_design, 1000, seed=1234, comm=comm) - - builder = pygsti.objectivefns.ObjectiveFunctionBuilder.create_from(objfn) - builder.additional_args['array_types'] = ('EP', 'EPP') # HACK - todo this better - - if sim == 'map': - mdl.sim = pygsti.forwardsims.MapForwardSimulator(num_atoms=natoms) - elif sim == 'matrix': - mdl.sim = pygsti.forwardsims.MatrixForwardSimulator(num_atoms=natoms) - else: - raise RuntimeError("Improper sim type passed by test_objfn_generator") - - circuits = exp_design.all_circuits_needing_data[0:10] - objfn_parallel = builder.build(mdl, ds, circuits, self.ralloc, verbosity=0) - objfn_serial = builder.build(mdl, ds, circuits, None, verbosity=0) - - #LSVEC TEST - v_ref = objfn_serial.lsvec() - v = objfn_parallel.lsvec() - globalv = objfn_parallel.layout.gather_local_array('e', v) - - if comm is None or comm.rank == 0: - finalv = np.empty_like(globalv); off = 0 - for c in circuits: - indices, outcomes = objfn_parallel.layout.global_layout.indices_and_outcomes(c) - assert(outcomes == (('0',), ('1',))) # I think this should always be the ordering (?) - finalv[off:off + len(outcomes)] = globalv[indices] - off += len(outcomes) - - finalv_ref = np.empty_like(v_ref); off = 0 - for c in circuits: - indices, outcomes = objfn_serial.layout.indices_and_outcomes(c) - assert(outcomes == (('0',), ('1',))) # I think this should always be the ordering (?) - finalv_ref[off:off + len(outcomes)] = v_ref[indices] - off += len(outcomes) - - assert np.allclose(finalv, finalv_ref) - - #TODO: DLSVEC? - - #HESSIAN TEST - hessian_ref = objfn_serial.hessian() - hessian = objfn_parallel.hessian() # already a global qty, just on root proc - bhessian_ref = objfn_serial.hessian_brute() - bhessian = objfn_parallel.hessian_brute() - - if comm is None or comm.rank == 0: - assert np.allclose(bhessian_ref, hessian_ref) - assert np.allclose(hessian, hessian_ref) - assert np.allclose(bhessian, bhessian_ref) - - def test_fills_generator(self): - sims = ["map", "matrix"] - # XYI model with maxL = 1 has 92 circuits and 60 parameters - layout_params = [(1, None), (4, None), - (1, 15), (4, 15) - ] - for s in sims: - for natoms, nparams in layout_params: - yield self.run_fills, s, natoms, nparams - - def run_fills(self, sim, natoms, nparams): - comm = self.ralloc.comm - - #Create some model - mdl = std.target_model() - mdl.kick(0.1,seed=1234) - - #Get some operation sequences - maxLengths = [1] - circuits = pygsti.circuits.create_lsgst_circuits( - list(std.target_model().operations.keys()), std.prep_fiducials(), - std.meas_fiducials(), std.germs(), maxLengths) - nP = mdl.num_params - - if sim == 'map': - mdl.sim = pygsti.forwardsims.MapForwardSimulator(num_atoms=natoms, param_blk_sizes=(nparams, nparams)) - elif sim == 'matrix': - mdl.sim = pygsti.forwardsims.MatrixForwardSimulator(num_atoms=natoms, param_blk_sizes=(nparams, nparams)) - else: - raise RuntimeError("Improper sim type passed by test_fills_generator") - - serial_layout = mdl.sim.create_layout(circuits, array_types=('E','EP','EPP'), derivative_dimension=nP) - - nE = serial_layout.num_elements - nC = len(circuits) - - vp_serial = np.empty( nE, 'd') - vdp_serial = np.empty( (nE,nP), 'd') - vhp_serial = np.empty( (nE,nP,nP), 'd') - - mdl.sim.bulk_fill_probs(vp_serial, serial_layout) - mdl.sim.bulk_fill_dprobs(vdp_serial, serial_layout) - mdl.sim.bulk_fill_hprobs(vhp_serial, serial_layout) - - #Note: when there are multiple atoms, the serial_layout returned above may not preserve - # the original circuit ordering, i.e., serial_layout.circuits != circuits. The global - # layout does have this property, and so we use it to avoid having to lookup which circuit - # is which index in serial_layout. No gathering is needed, since there only 1 processor. - global_serial_layout = serial_layout.global_layout - - #Use a parallel layout to compute the same probabilities & their derivatives - local_layout = mdl.sim.create_layout(circuits, array_types=('E','EP','EPP'), derivative_dimension=nP, - resource_alloc=self.ralloc) - - vp_local = local_layout.allocate_local_array('e', 'd') - vdp_local = local_layout.allocate_local_array('ep', 'd') - vhp_local = local_layout.allocate_local_array('epp', 'd') - - mdl.sim.bulk_fill_probs(vp_local, local_layout) - mdl.sim.bulk_fill_dprobs(vdp_local, local_layout) - mdl.sim.bulk_fill_hprobs(vhp_local, local_layout) - - - #Gather data to produce arrays for the "global" layout (global_parallel_layout should be the same on all procs) - # but only on proc 0 - global_parallel_layout = local_layout.global_layout - vp_global_parallel = local_layout.gather_local_array('e', vp_local) - vdp_global_parallel = local_layout.gather_local_array('ep', vdp_local) - vhp_global_parallel = local_layout.gather_local_array('epp', vhp_local) - - #Free the local arrays when we're done with them (they could be shared mem) - local_layout.free_local_array(vp_local) - local_layout.free_local_array(vdp_local) - local_layout.free_local_array(vhp_local) - - #Compare the two, but note that different layouts may order the elements differently, - # so we can't just compare the arrays directly - we have to use the layout to map - # circuit index -> element indices: - if comm is None or comm.rank == 0: - for i,opstr in enumerate(circuits): - assert(np.linalg.norm(vp_global_parallel[ global_parallel_layout.indices_for_index(i) ] - - vp_serial[ global_serial_layout.indices_for_index(i) ]) < 1e-6) - assert(np.linalg.norm(vdp_global_parallel[ global_parallel_layout.indices_for_index(i) ] - - vdp_serial[ global_serial_layout.indices_for_index(i) ]) < 1e-6) - assert(np.linalg.norm(vhp_global_parallel[ global_parallel_layout.indices_for_index(i) ] - - vhp_serial[ global_serial_layout.indices_for_index(i) ]) < 1e-6) - - def test_MPI_printer(self): - comm = self.ralloc.comm - - #Test output of each rank to separate file: - pygsti.baseobjs.VerbosityPrinter._comm_path = "./" - pygsti.baseobjs.VerbosityPrinter._comm_file_name = "mpi_test_output" - printer = pygsti.baseobjs.VerbosityPrinter(verbosity=2, comm=comm) - printer.log("HELLO!") - # - # - # #Note: no need to test "wrtFilter" business - that was removed - # - # - ## There are other tests within testmpiMain.py that don't need much/any alteration and would be - ## good to transfer to the new MPI unit tests, but: - ## - test_MPI_mlgst_forcefn(comm) -- this is never used anymore, you can ignore this test - ## - test_MPI_derivcols(comm) -- this is essentially tested by varying the layout types - - -class PureMPIParallel_Test(ParallelTest): - @classmethod - def setup_class(cls): - # Turn off all shared memory usage - os.environ['PYGSTI_USE_SHARED_MEMORY'] = "0" - cls.ralloc = pygsti.baseobjs.ResourceAllocation(wcomm) - -#class OnePerHostShmemParallel_Test(ParallelTest): -# @classmethod -# def setup_class(cls): -# # Use 1 host per shared memory group (i.e. no shared mem communication) -# os.environ['PYGSTI_MAX_HOST_PROCS'] = "1" -# cls.ralloc = pygsti.baseobjs.ResourceAllocation(wcomm) -# -#class TwoPerHostShmemParallel_Test(ParallelTest): -# @classmethod -# def setup_class(cls): -# # Use 2 hosts per shared memory group (i.e. mixed MPI + shared mem if more than 2 procs) -# os.environ['PYGSTI_MAX_HOST_PROCS'] = "2" -# cls.ralloc = pygsti.baseobjs.ResourceAllocation(wcomm) -# -#class AllShmemParallel_Test(ParallelTest): -# @classmethod -# def setup_class(cls): -# # Set as many procs per host as possible to use shared memory -# os.environ['PYGSTI_MAX_HOST_PROCS'] = str(wcomm.size) -# cls.ralloc = pygsti.baseobjs.ResourceAllocation(wcomm) - - -if __name__ == '__main__': - tester = PureMPIParallel_Test() - tester.setup_class() - tester.ralloc = pygsti.baseobjs.ResourceAllocation(wcomm) - #tester.run_objfn_values('matrix','logl',4) - tester.run_fills('map', 1, None) - tester.run_fills('map', 4, None) - tester.run_fills('matrix', 4, 15) From 5959a5325dad41544614f47cc788b893e813dced Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Tue, 17 Dec 2024 15:18:11 -0500 Subject: [PATCH 06/71] move simple MPI test from test/test_packages into test/unit, and rename a file in test/performance so that it doesnt get discovered by testing frameworks like pytest --- test/performance/mpi_2D_scaling/run.sh | 2 +- .../{mpi_test.py => run_me_with_mpirun.py} | 0 .../{test_packages => unit}/mpi/run_me_with_mpiexec.py | 0 test/{test_packages => unit}/mpi/test_mpi.py | 10 ++++++++-- 4 files changed, 9 insertions(+), 3 deletions(-) rename test/performance/mpi_2D_scaling/{mpi_test.py => run_me_with_mpirun.py} (100%) rename test/{test_packages => unit}/mpi/run_me_with_mpiexec.py (100%) rename test/{test_packages => unit}/mpi/test_mpi.py (54%) diff --git a/test/performance/mpi_2D_scaling/run.sh b/test/performance/mpi_2D_scaling/run.sh index b5c77dd13..d1b1a0d3e 100755 --- a/test/performance/mpi_2D_scaling/run.sh +++ b/test/performance/mpi_2D_scaling/run.sh @@ -29,4 +29,4 @@ export MKL_NUM_THREADS=1 # Note: This flags are useful on Kahuna to avoid error messages # But the --mca flags are not necessary for performance mpirun -np ${NUM_PROCS} --mca pml ucx --mca btl '^openib' \ - python ./mpi_test.py &> ${PREFIX}.out + python ./run_me_with_mpirun.py &> ${PREFIX}.out diff --git a/test/performance/mpi_2D_scaling/mpi_test.py b/test/performance/mpi_2D_scaling/run_me_with_mpirun.py similarity index 100% rename from test/performance/mpi_2D_scaling/mpi_test.py rename to test/performance/mpi_2D_scaling/run_me_with_mpirun.py diff --git a/test/test_packages/mpi/run_me_with_mpiexec.py b/test/unit/mpi/run_me_with_mpiexec.py similarity index 100% rename from test/test_packages/mpi/run_me_with_mpiexec.py rename to test/unit/mpi/run_me_with_mpiexec.py diff --git a/test/test_packages/mpi/test_mpi.py b/test/unit/mpi/test_mpi.py similarity index 54% rename from test/test_packages/mpi/test_mpi.py rename to test/unit/mpi/test_mpi.py index b012fdd54..ecbaa7eef 100644 --- a/test/test_packages/mpi/test_mpi.py +++ b/test/unit/mpi/test_mpi.py @@ -1,16 +1,22 @@ import subprocess import pytest +import os +from pathlib import Path + try: from mpi4py import MPI except ImportError: MPI = None - class MPITester: @pytest.mark.skipif(MPI is None, reason="mpi4py is not installed.") def test_all(self, capfd: pytest.LogCaptureFixture): - result = subprocess.run("mpiexec -np 4 python -W ignore core_test_mpi.py".split(' '), capture_output=True, text=True) + current_filepath = Path(os.path.abspath(__file__)) + to_run = current_filepath.parents[0] / Path('run_me_with_mpiexec.py') + subprocess_args = (f"mpiexec -np 4 python -W ignore {str(to_run)}").split(' ') + + result = subprocess.run(subprocess_args, capture_output=False, text=True) out, err = capfd.readouterr() if len(out) + len(err) > 0: msg = out + '\n'+ 80*'-' + err From 19316be58503a96506e4ea3ff105929405ea0c94 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Tue, 17 Dec 2024 15:23:42 -0500 Subject: [PATCH 07/71] implement changes discussed in 12/17 dev meeting --- pygsti/objectivefns/objectivefns.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index c3b548024..485f3c2ad 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -4440,7 +4440,7 @@ def set_penalties(self, regularize_factor=0, cptp_penalty_factor=0, spam_penalty return ex - def terms(self, paramvec=None, profiler_str="TERMS OBJECTIVE"): + def terms(self, paramvec=None, oob_check=False, profiler_str="TERMS OBJECTIVE"): """ Compute the terms of the objective function. @@ -4471,6 +4471,11 @@ def terms(self, paramvec=None, profiler_str="TERMS OBJECTIVE"): with self.resource_alloc.temporarily_track_memory(self.nelements): # 'e' (terms) self.model.sim.bulk_fill_probs(self.probs, self.layout) self._clip_probs() + + if oob_check: # Only used for termgap cases + if not self.model.sim.bulk_test_if_paths_are_sufficient(self.layout, self.probs, verbosity=1): + raise ValueError("Out of bounds!") # signals LM optimizer + if shared_mem_leader: terms_no_penalty = self.raw_objfn.terms(self.probs, self.counts, self.total_counts, self.freqs) terms[:self.nelements] = terms_no_penalty @@ -4566,8 +4571,8 @@ def lsvec(self, paramvec=None, oob_check=False): numpy.ndarray An array of shape `(nElements,)` where `nElements = self.nelements + self.ex_local`. """ - lsvec = self.terms(paramvec, "LS OBJECTIVE") - if oob_check and _np.any(lsvec < 0): + lsvec = self.terms(paramvec, oob_check, "LS OBJECTIVE") + if _np.any(lsvec < 0): bad_locs = _np.where(lsvec < 0)[0] msg = f""" lsvec is only defined when terms is elementwise nonnegative. From 9b73727887294fb84bb7ebb25c69781bf5bf2afb Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 18 Dec 2024 07:52:36 -0500 Subject: [PATCH 08/71] expand scope of exception handling --- test/unit/mpi/test_mpi.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/test/unit/mpi/test_mpi.py b/test/unit/mpi/test_mpi.py index ecbaa7eef..e4e285148 100644 --- a/test/unit/mpi/test_mpi.py +++ b/test/unit/mpi/test_mpi.py @@ -5,12 +5,13 @@ try: from mpi4py import MPI -except ImportError: +except (ImportError, RuntimeError): MPI = None + class MPITester: - @pytest.mark.skipif(MPI is None, reason="mpi4py is not installed.") + @pytest.mark.skipif(MPI is None, reason="mpi4py could not be imported") def test_all(self, capfd: pytest.LogCaptureFixture): current_filepath = Path(os.path.abspath(__file__)) to_run = current_filepath.parents[0] / Path('run_me_with_mpiexec.py') From fb4818cf55077edbea70741f564b0a7c4cdad6a9 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 18 Dec 2024 14:36:36 -0500 Subject: [PATCH 09/71] add separate TimeIndpendentMDCObjectiveFunction.dlsvec back --- pygsti/objectivefns/objectivefns.py | 111 +++++++++++++++++++++++----- 1 file changed, 94 insertions(+), 17 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index 485f3c2ad..b044ae704 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -4583,29 +4583,97 @@ def lsvec(self, paramvec=None, oob_check=False): lsvec **= 0.5 return lsvec + # def dlsvec(self, paramvec=None): + # """ + # It's possible that lsvec.shape[0] == terms.shape[0] > self.nelements + # if regularization is used. The relationship that lsvec = sqrt(terms) + # holds regardless of whether or not regularization is used. + + # dlsvec(mdl; self)) = Jac(sqrt(terms(mdl; self))) + # = diag(0.5 / sqrt(terms(mdl; self))) Jac(terms(mdl; self)) + # = diag( 0.5 / lsvec ) Jac(terms(mdl; self)) + + # This implementation is valid for any objective function. We need it here + # (instead of the default implementation) since the default makes a call to + # the raw objective function, and the raw objective function doesn't use the + # weighting we want. + # """ + # jac = self.dterms(paramvec) + # unit_ralloc = self.layout.resource_alloc('param-processing') + # if unit_ralloc.is_host_leader: + # lsvec = self.lsvec(paramvec) + # p5over_lsvec = 0.5/lsvec + # p5over_lsvec[lsvec < 1e-100] = 0.0 + # jac *= p5over_lsvec[:, None] + # return jac + + + # Jacobian function def dlsvec(self, paramvec=None): """ - It's possible that lsvec.shape[0] == terms.shape[0] > self.nelements - if regularization is used. The relationship that lsvec = sqrt(terms) - holds regardless of whether or not regularization is used. + The derivative (jacobian) of the least-squares vector. - dlsvec(mdl; self)) = Jac(sqrt(terms(mdl; self))) - = diag(0.5 / sqrt(terms(mdl; self))) Jac(terms(mdl; self)) - = diag( 0.5 / lsvec ) Jac(terms(mdl; self)) + Derivatives are taken with respect to model parameters. - This implementation is valid for any objective function. We need it here - (instead of the default implementation) since the default makes a call to - the raw objective function, and the raw objective function doesn't use the - weighting we want. + Parameters + ---------- + paramvec : numpy.ndarray, optional + The vector of (model) parameters to evaluate the objective function at. + If `None`, then the model's current parameter vector is used (held internally). + + Returns + ------- + numpy.ndarray + An array of shape `(nElements,nParams)` where `nElements` is the number + of circuit outcomes and `nParams` is the number of model parameters. """ - jac = self.dterms(paramvec) + tm = _time.time() + dprobs = self.jac[0:self.nelements, :] # avoid mem copying: use jac mem for dprobs + dprobs.shape = (self.nelements, self.nparams) + if paramvec is not None: + self.model.from_vector(paramvec) + else: + paramvec = self.model.to_vector() + unit_ralloc = self.layout.resource_alloc('param-processing') - if unit_ralloc.is_host_leader: - lsvec = self.lsvec(paramvec) - p5over_lsvec = 0.5/lsvec - p5over_lsvec[lsvec < 1e-100] = 0.0 - jac *= p5over_lsvec[:, None] - return jac + shared_mem_leader = unit_ralloc.is_host_leader + + with self.resource_alloc.temporarily_track_memory(2 * self.nelements): # 'e' (dg_dprobs, lsvec) + self.model.sim.bulk_fill_dprobs(dprobs, self.layout, self.probs) # wrtSlice) + self._clip_probs() # clips self.probs in place w/shared mem sync + + if shared_mem_leader: + if self.firsts is not None: + for ii, i in enumerate(self.indicesOfCircuitsWithOmittedData): + self.dprobs_omitted_rowsum[ii, :] = _np.sum(dprobs[self.layout.indices_for_index(i), :], axis=0) + + dg_dprobs = self.raw_objfn.dlsvec(self.probs, self.counts, self.total_counts, self.freqs) + dprobs *= dg_dprobs[:, None] + + if shared_mem_leader: # only "leader" modifies shared mem (dprobs & self.jac) + if self.firsts is not None: + #Note: lsvec is assumed to be *not* updated w/omitted probs contribution + omitted_probs = 1.0 - _np.array([self.probs[self.layout.indices_for_index(i)].sum() for i in self.indicesOfCircuitsWithOmittedData]) + omitted_dprobs_firsts_dterms = self.raw_objfn.zero_freq_dterms(self.total_counts[self.firsts], omitted_probs) + + lsvec = _np.sqrt(self.raw_objfn.terms(self.probs, self.counts, self.total_counts, self.freqs)) + lsvec_firsts = lsvec[self.firsts] + temp = self.raw_objfn.zero_freq_terms(self.total_counts[self.firsts], omitted_probs) + updated_lsvec = _np.sqrt(lsvec_firsts**2 + temp) + updated_lsvec = _np.where(updated_lsvec == 0, 1.0, updated_lsvec) # avoid 0/0 where lsvec & deriv == 0 d + omitted_dprobs_firsts_dterms = (0.5 / updated_lsvec) * omitted_dprobs_firsts_dterms + dprobs[self.firsts] *= (lsvec_firsts / updated_lsvec)[:, None] + + dprobs[self.firsts] -= omitted_dprobs_firsts_dterms[:, None] * self.dprobs_omitted_rowsum + + + if self._process_penalties and shared_mem_leader: + self._fill_lspenaltyvec_jac(paramvec, self.jac[self.nelements:, :]) # jac.shape == (nelements+N,N) + unit_ralloc.host_comm_barrier() # have non-leader procs wait for leaders to set shared mem + + self.raw_objfn.resource_alloc.profiler.add_time("JACOBIAN", tm) + return self.jac + # Helpers, supporting main public instance functions @@ -4874,6 +4942,15 @@ def _hessian_from_block(self, hprobs, dprobs12, probs, counts, total_counts, fre return _np.sum(hessian, axis=0) +""" +PLAN: have Chi2Function override dlsvec, and _maybe_ lsvec, so that these functions can +make calls to raw_objfn.[d]lsvec. Then have FreqWeightedChi2Function and CustomWeightedChi2Function. + +Note ... TimeDependentMDCObjectiveFunction classes don't have access to a default implementation of +dlsvec and lsvec; subclasses based on RawChi2Function have very simple implementations. How's that +possible? +""" + class Chi2Function(TimeIndependentMDCObjectiveFunction): """ Model-based chi-squared function: `N(p-f)^2 / p` From 122fd11f08673bccb87694d3f7b20841efbf8afa Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Tue, 24 Dec 2024 17:31:12 -0500 Subject: [PATCH 10/71] resolve an infuriating sign error. Will probably want to clean this up. --- pygsti/objectivefns/objectivefns.py | 370 +++++++++++-------------- test/unit/objects/test_objectivefns.py | 10 +- 2 files changed, 165 insertions(+), 215 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index b044ae704..2ef61bed4 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -592,8 +592,11 @@ def dterms(self, probs, counts, total_counts, freqs, intermediates=None): """ if intermediates is None: intermediates = self._intermediates(probs, counts, total_counts, freqs) - return 2 * self.lsvec(probs, counts, total_counts, freqs, intermediates) \ - * self.dlsvec(probs, counts, total_counts, freqs, intermediates) + lsvec = self.lsvec(probs, counts, total_counts, freqs, intermediates) + # NOTE: this function is correct under the assumption that terms == lsvec**2, + # independent of whether or not lsvec is nonnegative. + # lsvec = _np.abs(self.lsvec(probs, counts, total_counts, freqs, intermediates)) + return 2 * lsvec * self.dlsvec(probs, counts, total_counts, freqs, intermediates) def dlsvec(self, probs, counts, total_counts, freqs, intermediates=None): """ @@ -628,7 +631,9 @@ def dlsvec(self, probs, counts, total_counts, freqs, intermediates=None): A 1D array of length equal to that of each array argument. """ # lsvec = sqrt(terms) + # NOTE: ^ That's only correct if lsvec is >= 0, and some classes don't satisfy that. # dlsvec = 0.5/lsvec * dterms + # if intermediates is None: intermediates = self._intermediates(probs, counts, total_counts, freqs) lsvec = self.lsvec(probs, counts, total_counts, freqs, intermediates) @@ -1814,7 +1819,8 @@ def lsvec(self, probs, counts, total_counts, freqs, intermediates=None): numpy.ndarray A 1D array of length equal to that of each array argument. """ - return (probs - freqs) * self._weights(probs, freqs, total_counts) # Note: ok if this is negative + out = (probs - freqs) * self._weights(probs, freqs, total_counts) # Note: ok if this is negative + return out def dlsvec(self, probs, counts, total_counts, freqs, intermediates=None): """ @@ -1849,7 +1855,8 @@ def dlsvec(self, probs, counts, total_counts, freqs, intermediates=None): A 1D array of length equal to that of each array argument. """ weights = self._weights(probs, freqs, total_counts) - return weights + (probs - freqs) * self._dweights(probs, freqs, weights) + out = weights + (probs - freqs) * self._dweights(probs, freqs, weights) + return out def hlsvec(self, probs, counts, total_counts, freqs, intermediates=None): """ @@ -4441,23 +4448,6 @@ def set_penalties(self, regularize_factor=0, cptp_penalty_factor=0, spam_penalty return ex def terms(self, paramvec=None, oob_check=False, profiler_str="TERMS OBJECTIVE"): - """ - Compute the terms of the objective function. - - The "terms" are the per-circuit-outcome values that get summed together - to result in the objective function value. - - Parameters - ---------- - paramvec : numpy.ndarray, optional - The vector of (model) parameters to evaluate the objective function at. - If `None`, then the model's current parameter vector is used (held internally). - - Returns - ------- - numpy.ndarray - An array of shape `(nElements,)` where `nElements = self.nelements + self.ex_local`. - """ tm = _time.time() if paramvec is None: paramvec = self.model.to_vector() @@ -4480,9 +4470,12 @@ def terms(self, paramvec=None, oob_check=False, profiler_str="TERMS OBJECTIVE"): terms_no_penalty = self.raw_objfn.terms(self.probs, self.counts, self.total_counts, self.freqs) terms[:self.nelements] = terms_no_penalty if self._process_penalties: - terms[self.nelements:] = self._penaltyvec(paramvec) + terms[self.nelements:] = self._terms_penalty(paramvec) if self.firsts is not None and shared_mem_leader: + """TODO: remove this comment, probably. + Logic formerly handled by self._update_terms_for_omitted_probs(...), which called self._omitted_prob_first_terms(...). + """ omitted_probs = 1.0 - _np.array([self.probs[self.layout.indices_for_index(i)].sum() for i in self.indicesOfCircuitsWithOmittedData]) omitted_probs_firsts_terms = self.raw_objfn.zero_freq_terms(self.total_counts[self.firsts], omitted_probs) terms[self.firsts] += omitted_probs_firsts_terms @@ -4493,84 +4486,7 @@ def terms(self, paramvec=None, oob_check=False, profiler_str="TERMS OBJECTIVE"): assert(terms.shape == (self.nelements + self.local_ex,)) return terms - def dterms(self, paramvec=None): - """ - Compute the jacobian of the terms of the objective function. - - The "terms" are the per-circuit-outcome values that get summed together - to result in the objective function value. Differentiation is with - respect to model parameters. - - Parameters - ---------- - paramvec : numpy.ndarray, optional - The vector of (model) parameters to evaluate the objective function at. - If `None`, then the model's current parameter vector is used (held internally). - - Returns - ------- - numpy.ndarray - An array of shape `(nElements,nParams)` where `nElements = self.nelements + self.ex_local`. - """ - tm = _time.time() - dprobs = self.jac[0:self.nelements, :] - dprobs.shape = (self.nelements, self.nparams) - if paramvec is None: - paramvec = self.model.to_vector() - else: - self.model.from_vector(paramvec) - - unit_ralloc = self.layout.resource_alloc('param-processing') - shared_mem_leader = unit_ralloc.is_host_leader - - with self.resource_alloc.temporarily_track_memory(2 * self.nelements): - self.model.sim.bulk_fill_dprobs(dprobs, self.layout, self.probs) - self._clip_probs() - if shared_mem_leader: - if self.firsts is not None: - for ii, i in enumerate(self.indicesOfCircuitsWithOmittedData): - self.dprobs_omitted_rowsum[ii, :] = _np.sum(dprobs[self.layout.indices_for_index(i), :], axis=0) - dg_probs = self.raw_objfn.dterms(self.probs, self.counts, self.total_counts, self.freqs) - dprobs *= dg_probs[:, None] - - if shared_mem_leader: - if self.firsts is not None: - omitted_probs = 1.0 - _np.array([_np.sum(self.probs[self.layout.indices_for_index(i)]) for i in self.indicesOfCircuitsWithOmittedData]) - omitted_dprobs_firsts_dterms = self.raw_objfn.zero_freq_dterms(self.total_counts[self.firsts], omitted_probs) - dprobs[self.firsts] -= omitted_dprobs_firsts_dterms[:, None] * self.dprobs_omitted_rowsum - - if self._process_penalties: - self._fill_dterms_penalty(paramvec, self.jac[self.nelements:, :]) # jac.shape == (nelements + local_ex, nparams) - - unit_ralloc.host_comm_barrier() - self.raw_objfn.resource_alloc.profiler.add_time("JACOBIAN", tm) - return self.jac - def lsvec(self, paramvec=None, oob_check=False): - """ - Compute the least-squares vector of the objective function. - - This is the square-root of the terms-vector returned from :meth:`terms`. - This vector is the objective function value used by a least-squares - optimizer when optimizing this objective function. Note that the existence - of this quantity requires that the terms be non-negative. If this is not - the case, an error is raised. - - Parameters - ---------- - paramvec : numpy.ndarray, optional - The vector of (model) parameters to evaluate the objective function at. - If `None`, then the model's current parameter vector is used (held internally). - - oob_check : bool, optional - Whether the objective function should raise an error if it is being - evaluated in an "out of bounds" region. - - Returns - ------- - numpy.ndarray - An array of shape `(nElements,)` where `nElements = self.nelements + self.ex_local`. - """ lsvec = self.terms(paramvec, oob_check, "LS OBJECTIVE") if _np.any(lsvec < 0): bad_locs = _np.where(lsvec < 0)[0] @@ -4581,130 +4497,118 @@ def lsvec(self, paramvec=None, oob_check=False): """ raise RuntimeError(msg) lsvec **= 0.5 + self._compensate_for_dlsvec_sign(lsvec) + # ^ That wouldn't be needed if we computed dlsvec using dlsvec_generic(...), + # which only triggers indirect calls to raw_objfn.dterms, raw_objfn.terms. return lsvec - - # def dlsvec(self, paramvec=None): - # """ - # It's possible that lsvec.shape[0] == terms.shape[0] > self.nelements - # if regularization is used. The relationship that lsvec = sqrt(terms) - # holds regardless of whether or not regularization is used. - - # dlsvec(mdl; self)) = Jac(sqrt(terms(mdl; self))) - # = diag(0.5 / sqrt(terms(mdl; self))) Jac(terms(mdl; self)) - # = diag( 0.5 / lsvec ) Jac(terms(mdl; self)) - - # This implementation is valid for any objective function. We need it here - # (instead of the default implementation) since the default makes a call to - # the raw objective function, and the raw objective function doesn't use the - # weighting we want. - # """ - # jac = self.dterms(paramvec) - # unit_ralloc = self.layout.resource_alloc('param-processing') - # if unit_ralloc.is_host_leader: - # lsvec = self.lsvec(paramvec) - # p5over_lsvec = 0.5/lsvec - # p5over_lsvec[lsvec < 1e-100] = 0.0 - # jac *= p5over_lsvec[:, None] - # return jac - - - # Jacobian function - def dlsvec(self, paramvec=None): + + def _compensate_for_dlsvec_sign(self, lsvec): """ - The derivative (jacobian) of the least-squares vector. - - Derivatives are taken with respect to model parameters. + Assumes that lsvec has been computed as sqrt(terms), so it's nonnegative. - Parameters - ---------- - paramvec : numpy.ndarray, optional - The vector of (model) parameters to evaluate the objective function at. - If `None`, then the model's current parameter vector is used (held internally). + Assumes that lsvec is later used in least squares with a Jacobian matrix + "dlsvec", where dlsvec has been computed by a formula derived with a different + convention for lsvec (namely, the convention that terms == lsvec**2). - Returns - ------- - numpy.ndarray - An array of shape `(nElements,nParams)` where `nElements` is the number - of circuit outcomes and `nParams` is the number of model parameters. + On exit, lsvec has signs expected by the aforementioned dlsvec implementation. """ - tm = _time.time() - dprobs = self.jac[0:self.nelements, :] # avoid mem copying: use jac mem for dprobs - dprobs.shape = (self.nelements, self.nparams) - if paramvec is not None: - self.model.from_vector(paramvec) - else: - paramvec = self.model.to_vector() + unit_ralloc = self.layout.resource_alloc('atom-processing') + shared_mem_leader = unit_ralloc.is_host_leader + if shared_mem_leader: + raw_lsvec = self.raw_objfn.lsvec(self.probs, self.counts, self.total_counts, self.freqs) + lsvec[:self.nelements][raw_lsvec < 0] *= -1 + return + def dterms(self, paramvec=None): + tm = _time.time() unit_ralloc = self.layout.resource_alloc('param-processing') shared_mem_leader = unit_ralloc.is_host_leader - with self.resource_alloc.temporarily_track_memory(2 * self.nelements): # 'e' (dg_dprobs, lsvec) - self.model.sim.bulk_fill_dprobs(dprobs, self.layout, self.probs) # wrtSlice) - self._clip_probs() # clips self.probs in place w/shared mem sync - - if shared_mem_leader: - if self.firsts is not None: - for ii, i in enumerate(self.indicesOfCircuitsWithOmittedData): - self.dprobs_omitted_rowsum[ii, :] = _np.sum(dprobs[self.layout.indices_for_index(i), :], axis=0) - - dg_dprobs = self.raw_objfn.dlsvec(self.probs, self.counts, self.total_counts, self.freqs) - dprobs *= dg_dprobs[:, None] + if paramvec is None: + paramvec = self.model.to_vector() + else: + self.model.from_vector(paramvec) - if shared_mem_leader: # only "leader" modifies shared mem (dprobs & self.jac) - if self.firsts is not None: - #Note: lsvec is assumed to be *not* updated w/omitted probs contribution - omitted_probs = 1.0 - _np.array([self.probs[self.layout.indices_for_index(i)].sum() for i in self.indicesOfCircuitsWithOmittedData]) - omitted_dprobs_firsts_dterms = self.raw_objfn.zero_freq_dterms(self.total_counts[self.firsts], omitted_probs) - - lsvec = _np.sqrt(self.raw_objfn.terms(self.probs, self.counts, self.total_counts, self.freqs)) - lsvec_firsts = lsvec[self.firsts] - temp = self.raw_objfn.zero_freq_terms(self.total_counts[self.firsts], omitted_probs) - updated_lsvec = _np.sqrt(lsvec_firsts**2 + temp) - updated_lsvec = _np.where(updated_lsvec == 0, 1.0, updated_lsvec) # avoid 0/0 where lsvec & deriv == 0 d - omitted_dprobs_firsts_dterms = (0.5 / updated_lsvec) * omitted_dprobs_firsts_dterms - dprobs[self.firsts] *= (lsvec_firsts / updated_lsvec)[:, None] + self._dvecmap_fill_core('terms', shared_mem_leader) + if shared_mem_leader and self._process_penalties: + self._dterms_fill_penalty(paramvec, self.jac[self.nelements:, :]) - dprobs[self.firsts] -= omitted_dprobs_firsts_dterms[:, None] * self.dprobs_omitted_rowsum + unit_ralloc.host_comm_barrier() + self.raw_objfn.resource_alloc.profiler.add_time("JACOBIAN", tm) + return self.jac + + def dlsvec(self, paramvec=None): + tm = _time.time() + unit_ralloc = self.layout.resource_alloc('param-processing') + shared_mem_leader = unit_ralloc.is_host_leader + if paramvec is None: + paramvec = self.model.to_vector() + else: + self.model.from_vector(paramvec) - if self._process_penalties and shared_mem_leader: - self._fill_lspenaltyvec_jac(paramvec, self.jac[self.nelements:, :]) # jac.shape == (nelements+N,N) - unit_ralloc.host_comm_barrier() # have non-leader procs wait for leaders to set shared mem + self._dvecmap_fill_core('lsvec', shared_mem_leader) + if shared_mem_leader and self._process_penalties: + self._dterms_fill_penalty(paramvec, self.jac[self.nelements:, :]) + lsvec_penalty = _np.sqrt(self._terms_penalty(paramvec)) + p5over_lsvec_penalty = 0.5/lsvec_penalty + p5over_lsvec_penalty[lsvec_penalty < 1e-100] = 0.0 + self.jac[self.nelements:, :] *= p5over_lsvec_penalty[:, None] + unit_ralloc.host_comm_barrier() self.raw_objfn.resource_alloc.profiler.add_time("JACOBIAN", tm) return self.jac - - + # Helpers, supporting main public instance functions - def _penaltyvec(self, paramvec): - blocks = [_np.zeros(shape=(0,))] - - if self.forcefn_grad is not None: - forcefn_penalty = self.forceShift - _np.dot(self.forcefn_grad, self.model.to_vector()) - assert(_np.all(forcefn_penalty >= 0)), "Inadequate forcing shift!" - blocks.append(forcefn_penalty) - - if self.regularize_factor != 0: - paramvec_norm = self.regularize_factor * _np.array([max(0, absx - 1.0) for absx in map(abs, paramvec)], 'd') - paramvec_norm **= 2 - blocks.append(paramvec_norm) - - if self.cptp_penalty_factor > 0: - cp_penalty = _cptp_penalty(self.model, self.cptp_penalty_factor, self.opBasis) ** 2 - blocks.append(cp_penalty) - - if self.spam_penalty_factor > 0: - spam_penalty = _spam_penalty(self.model, self.spam_penalty_factor, self.opBasis) ** 2 - blocks.append(spam_penalty) + def _dvecmap_fill_core(self, vecmap, shared_mem_leader): + if vecmap == 'lsvec': + dprobs_scaling_vec_callable = self.raw_objfn.dlsvec + elif vecmap == 'terms': + dprobs_scaling_vec_callable = self.raw_objfn.dterms + else: + raise ValueError() + + dprobs = self.jac[0:self.nelements, :] + dprobs.shape = (self.nelements, self.nparams) - if self.errorgen_penalty_factor > 0: - errorgen_penalty = _errorgen_penalty(self.model, self.errorgen_penalty_factor) ** 2 - blocks.append(errorgen_penalty) + with self.resource_alloc.temporarily_track_memory(2 * self.nelements): + self.model.sim.bulk_fill_dprobs(dprobs, self.layout, self.probs) + self._clip_probs() + if shared_mem_leader: + if self.firsts is not None: + for ii, i in enumerate(self.indicesOfCircuitsWithOmittedData): + self.dprobs_omitted_rowsum[ii, :] = _np.sum(dprobs[self.layout.indices_for_index(i), :], axis=0) + dg_probs = dprobs_scaling_vec_callable(self.probs, self.counts, self.total_counts, self.freqs) + dprobs *= dg_probs[:, None] + + if shared_mem_leader and self.firsts is not None: + """TODO: remove this comment, probably. + Logic formerly handled by self._update_dterms_for_omitted_probs(...), which called self._omitted_prob_first_dterms(...). + """ + total_counts_firsts = self.total_counts[self.firsts] + omitted_probs = 1.0 - _np.array([_np.sum(self.probs[self.layout.indices_for_index(i)]) for i in self.indicesOfCircuitsWithOmittedData]) + omitted_dprobs_firsts_dterms = self.raw_objfn.zero_freq_dterms(total_counts_firsts, omitted_probs) + + if vecmap == 'lsvec': + """TODO: remove this comment, probably. + Logic formerly handled by self._update_dlsvec_for_omitted_probs(...), which called + self._omitted_prob_first_terms(...) and self._omitted_prob_first_dterms(...). + """ + lsvec_firsts = _np.sqrt(self.raw_objfn.terms( + self.probs[self.firsts], self.counts[self.firsts], total_counts_firsts, self.freqs[self.firsts] + )) + temp = self.raw_objfn.zero_freq_terms(total_counts_firsts, omitted_probs) + updated_lsvec = _np.sqrt(lsvec_firsts**2 + temp) + updated_lsvec = _np.where(updated_lsvec == 0, 1.0, updated_lsvec) # avoid 0/0 where lsvec & deriv == 0 + omitted_dprobs_firsts_dterms *= (0.5 / updated_lsvec) + dprobs[self.firsts] *= (lsvec_firsts / updated_lsvec)[:, None] - return _np.concatenate(blocks) + dprobs[self.firsts] -= omitted_dprobs_firsts_dterms[:, None] * self.dprobs_omitted_rowsum - def _fill_dterms_penalty(self, paramvec, terms_jac): + return + + def _dterms_fill_penalty(self, paramvec, terms_jac): wrtslice = self.layout.global_param_slice if isinstance(self.layout, _DistributableCOPALayout) else slice(0, len(paramvec)) # all params off = 0 @@ -4744,6 +4648,33 @@ def _fill_dterms_penalty(self, paramvec, terms_jac): assert(off == self.local_ex) return + def _terms_penalty(self, paramvec): + blocks = [_np.zeros(shape=(0,))] + + if self.forcefn_grad is not None: + forcefn_penalty = self.forceShift - _np.dot(self.forcefn_grad, self.model.to_vector()) + assert(_np.all(forcefn_penalty >= 0)), "Inadequate forcing shift!" + blocks.append(forcefn_penalty) + + if self.regularize_factor != 0: + paramvec_norm = self.regularize_factor * _np.array([max(0, absx - 1.0) for absx in map(abs, paramvec)], 'd') + paramvec_norm **= 2 + blocks.append(paramvec_norm) + + if self.cptp_penalty_factor > 0: + cp_penalty = _cptp_penalty(self.model, self.cptp_penalty_factor, self.opBasis) ** 2 + blocks.append(cp_penalty) + + if self.spam_penalty_factor > 0: + spam_penalty = _spam_penalty(self.model, self.spam_penalty_factor, self.opBasis) ** 2 + blocks.append(spam_penalty) + + if self.errorgen_penalty_factor > 0: + errorgen_penalty = _errorgen_penalty(self.model, self.errorgen_penalty_factor) ** 2 + blocks.append(errorgen_penalty) + + return _np.concatenate(blocks) + def _clip_probs(self): """ Clips the potentially shared-mem self.probs according to self.prob_clip_interval """ if self.prob_clip_interval is not None: @@ -4754,6 +4685,32 @@ def _clip_probs(self): else: _np.clip(self.probs, self.prob_clip_interval[0], self.prob_clip_interval[1], out=self.probs) + # Temp + + def dlsvec_generic(self, paramvec=None): + """ + It's possible that lsvec.shape[0] == terms.shape[0] > self.nelements + if regularization is used. The relationship that lsvec = sqrt(terms) + holds regardless of whether or not regularization is used. + + dlsvec(mdl; self)) = Jac(sqrt(terms(mdl; self))) + = diag(0.5 / sqrt(terms(mdl; self))) Jac(terms(mdl; self)) + = diag( 0.5 / lsvec ) Jac(terms(mdl; self)) + + This implementation is valid for any objective function. We need it here + (instead of the default implementation) since the default makes a call to + the raw objective function, and the raw objective function doesn't use the + weighting we want. + """ + jac = self.dterms(paramvec) + unit_ralloc = self.layout.resource_alloc('param-processing') + if unit_ralloc.is_host_leader: + lsvec = self.lsvec(paramvec) + p5over_lsvec = 0.5/lsvec + p5over_lsvec[lsvec < 1e-100] = 0.0 + jac *= p5over_lsvec[:, None] + return jac + # Hessians, public and private instance functions def hessian_brute(self, paramvec=None): @@ -4942,15 +4899,6 @@ def _hessian_from_block(self, hprobs, dprobs12, probs, counts, total_counts, fre return _np.sum(hessian, axis=0) -""" -PLAN: have Chi2Function override dlsvec, and _maybe_ lsvec, so that these functions can -make calls to raw_objfn.[d]lsvec. Then have FreqWeightedChi2Function and CustomWeightedChi2Function. - -Note ... TimeDependentMDCObjectiveFunction classes don't have access to a default implementation of -dlsvec and lsvec; subclasses based on RawChi2Function have very simple implementations. How's that -possible? -""" - class Chi2Function(TimeIndependentMDCObjectiveFunction): """ Model-based chi-squared function: `N(p-f)^2 / p` diff --git a/test/unit/objects/test_objectivefns.py b/test/unit/objects/test_objectivefns.py index b7116f75e..d8dd728b1 100644 --- a/test/unit/objects/test_objectivefns.py +++ b/test/unit/objects/test_objectivefns.py @@ -299,10 +299,12 @@ def test_derivative(self): places=3) # each *element* should match to 3 places if self.computes_lsvec: - lsvec = objfn.lsvec().copy() - dlsvec = objfn.dlsvec().copy() - self.assertArraysAlmostEqual(dterms / nEls, 2 * lsvec[:, None] * dlsvec / nEls, - places=4) # each *element* should match to 4 places + arg1 = dterms / nEls + lsvec = objfn.lsvec(v0).copy() + dlsvec = objfn.dlsvec(v0).copy() + arg2 = 2 * lsvec[:, None] * dlsvec / nEls + self.assertArraysAlmostEqual(arg1, arg2, places=4) # each *element* should match to 4 places + return def test_approximate_hessian(self): if not self.enable_hessian_tests: From 2ef13e2d05de2396a5bba03348e2273081a30d18 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 25 Dec 2024 21:24:12 -0500 Subject: [PATCH 11/71] check in slightly simpler (but still complicated) implementation. Next commit will simpify further, which may have consequences from the perspective of floating point arithmetic. Checking in NOW in case we want to revert to this version for numerical reasons. --- pygsti/objectivefns/objectivefns.py | 62 +++++++++++++++-------------- 1 file changed, 33 insertions(+), 29 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index 2ef61bed4..9a00f28e1 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -595,7 +595,6 @@ def dterms(self, probs, counts, total_counts, freqs, intermediates=None): lsvec = self.lsvec(probs, counts, total_counts, freqs, intermediates) # NOTE: this function is correct under the assumption that terms == lsvec**2, # independent of whether or not lsvec is nonnegative. - # lsvec = _np.abs(self.lsvec(probs, counts, total_counts, freqs, intermediates)) return 2 * lsvec * self.dlsvec(probs, counts, total_counts, freqs, intermediates) def dlsvec(self, probs, counts, total_counts, freqs, intermediates=None): @@ -1729,6 +1728,8 @@ def _gather_hessian(self, local_hessian): # = (p - f)^2 * ( ((1-p) + p)/(p*(1-p)) ) # = 1/(p*(1-p)) * (p - f)^2 + +# negative lsvec possible class RawChi2Function(RawObjectiveFunction): """ The function `N(p-f)^2 / p` @@ -2376,6 +2377,7 @@ def _zero_freq_dterms_relaxed(self, total_counts, probs): return total_counts * _np.where(probs > p0, 1.0, 2 * c1 * probs) +# negative lsvec possible class RawFreqWeightedChi2Function(RawChi2Function): """ @@ -2570,6 +2572,7 @@ def zero_freq_hterms(self, total_counts, probs): return 2 * total_counts / self.min_freq_clip_for_weighting +# negative lsvec possible class RawCustomWeightedChi2Function(RawChi2Function): """ @@ -2790,6 +2793,8 @@ def zero_freq_hterms(self, total_counts, probs): # terms, where each term == sqrt( N_{i,sl} * -log(p_{i,sl}) ) # # See LikelihoodFunction.py for details on patching + + class RawPoissonPicDeltaLogLFunction(RawObjectiveFunction): """ The function `N*f*log(f/p) - N*(f-p)`. @@ -4201,6 +4206,13 @@ def zero_freq_hterms(self, total_counts, probs): raise NotImplementedError("Derivatives not implemented for TVD yet!") +###################################################### +# +# Start MDCObjectiveFunction subclasses +# +###################################################### + + class TimeIndependentMDCObjectiveFunction(MDCObjectiveFunction): """ A time-independent model-based (:class:`MDCObjectiveFunction`-derived) objective function. @@ -4486,7 +4498,7 @@ def terms(self, paramvec=None, oob_check=False, profiler_str="TERMS OBJECTIVE"): assert(terms.shape == (self.nelements + self.local_ex,)) return terms - def lsvec(self, paramvec=None, oob_check=False): + def lsvec(self, paramvec=None, oob_check=False, raw_objfn_lsvec_signs=True): lsvec = self.terms(paramvec, oob_check, "LS OBJECTIVE") if _np.any(lsvec < 0): bad_locs = _np.where(lsvec < 0)[0] @@ -4497,27 +4509,11 @@ def lsvec(self, paramvec=None, oob_check=False): """ raise RuntimeError(msg) lsvec **= 0.5 - self._compensate_for_dlsvec_sign(lsvec) - # ^ That wouldn't be needed if we computed dlsvec using dlsvec_generic(...), - # which only triggers indirect calls to raw_objfn.dterms, raw_objfn.terms. + if raw_objfn_lsvec_signs: + if self.layout.resource_alloc('atom-processing').is_host_leader: + raw_lsvec = self.raw_objfn.lsvec(self.probs, self.counts, self.total_counts, self.freqs) + lsvec[:self.nelements][raw_lsvec < 0] *= -1 return lsvec - - def _compensate_for_dlsvec_sign(self, lsvec): - """ - Assumes that lsvec has been computed as sqrt(terms), so it's nonnegative. - - Assumes that lsvec is later used in least squares with a Jacobian matrix - "dlsvec", where dlsvec has been computed by a formula derived with a different - convention for lsvec (namely, the convention that terms == lsvec**2). - - On exit, lsvec has signs expected by the aforementioned dlsvec implementation. - """ - unit_ralloc = self.layout.resource_alloc('atom-processing') - shared_mem_leader = unit_ralloc.is_host_leader - if shared_mem_leader: - raw_lsvec = self.raw_objfn.lsvec(self.probs, self.counts, self.total_counts, self.freqs) - lsvec[:self.nelements][raw_lsvec < 0] *= -1 - return def dterms(self, paramvec=None): tm = _time.time() @@ -4547,13 +4543,21 @@ def dlsvec(self, paramvec=None): else: self.model.from_vector(paramvec) - self._dvecmap_fill_core('lsvec', shared_mem_leader) - if shared_mem_leader and self._process_penalties: - self._dterms_fill_penalty(paramvec, self.jac[self.nelements:, :]) - lsvec_penalty = _np.sqrt(self._terms_penalty(paramvec)) - p5over_lsvec_penalty = 0.5/lsvec_penalty - p5over_lsvec_penalty[lsvec_penalty < 1e-100] = 0.0 - self.jac[self.nelements:, :] *= p5over_lsvec_penalty[:, None] + if False: + self._dvecmap_fill_core('lsvec', shared_mem_leader) + if shared_mem_leader and self._process_penalties: + self._dterms_fill_penalty(paramvec, self.jac[self.nelements:, :]) + lsvec_penalty = _np.sqrt(self._terms_penalty(paramvec)) + p5over_lsvec_penalty = 0.5/lsvec_penalty + p5over_lsvec_penalty[lsvec_penalty < 1e-100] = 0.0 + self.jac[self.nelements:, :] *= p5over_lsvec_penalty[:, None] + else: + jac = self.dterms(paramvec) + if shared_mem_leader: + lsvec = self.lsvec(paramvec) + p5over_lsvec = 0.5/lsvec + p5over_lsvec[_np.abs(lsvec) < 1e-100] = 0.0 + jac *= p5over_lsvec[:, None] unit_ralloc.host_comm_barrier() self.raw_objfn.resource_alloc.profiler.add_time("JACOBIAN", tm) From 1c23251ce0454bd9846b337c9f240c888b0c021c Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 25 Dec 2024 21:26:13 -0500 Subject: [PATCH 12/71] simplifications complete --- pygsti/objectivefns/objectivefns.py | 114 ++++++---------------------- 1 file changed, 25 insertions(+), 89 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index 9a00f28e1..7f9ad93a2 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -4525,7 +4525,25 @@ def dterms(self, paramvec=None): else: self.model.from_vector(paramvec) - self._dvecmap_fill_core('terms', shared_mem_leader) + dprobs = self.jac[0:self.nelements, :] + dprobs.shape = (self.nelements, self.nparams) + + with self.resource_alloc.temporarily_track_memory(2 * self.nelements): + self.model.sim.bulk_fill_dprobs(dprobs, self.layout, self.probs) + self._clip_probs() + if shared_mem_leader: + if self.firsts is not None: + for ii, i in enumerate(self.indicesOfCircuitsWithOmittedData): + self.dprobs_omitted_rowsum[ii, :] = _np.sum(dprobs[self.layout.indices_for_index(i), :], axis=0) + dg_probs = self.raw_objfn.dterms(self.probs, self.counts, self.total_counts, self.freqs) + dprobs *= dg_probs[:, None] + + if shared_mem_leader and self.firsts is not None: + total_counts_firsts = self.total_counts[self.firsts] + omitted_probs = 1.0 - _np.array([_np.sum(self.probs[self.layout.indices_for_index(i)]) for i in self.indicesOfCircuitsWithOmittedData]) + omitted_dprobs_firsts_dterms = self.raw_objfn.zero_freq_dterms(total_counts_firsts, omitted_probs) + dprobs[self.firsts] -= omitted_dprobs_firsts_dterms[:, None] * self.dprobs_omitted_rowsum + if shared_mem_leader and self._process_penalties: self._dterms_fill_penalty(paramvec, self.jac[self.nelements:, :]) @@ -4543,74 +4561,18 @@ def dlsvec(self, paramvec=None): else: self.model.from_vector(paramvec) - if False: - self._dvecmap_fill_core('lsvec', shared_mem_leader) - if shared_mem_leader and self._process_penalties: - self._dterms_fill_penalty(paramvec, self.jac[self.nelements:, :]) - lsvec_penalty = _np.sqrt(self._terms_penalty(paramvec)) - p5over_lsvec_penalty = 0.5/lsvec_penalty - p5over_lsvec_penalty[lsvec_penalty < 1e-100] = 0.0 - self.jac[self.nelements:, :] *= p5over_lsvec_penalty[:, None] - else: - jac = self.dterms(paramvec) - if shared_mem_leader: - lsvec = self.lsvec(paramvec) - p5over_lsvec = 0.5/lsvec - p5over_lsvec[_np.abs(lsvec) < 1e-100] = 0.0 - jac *= p5over_lsvec[:, None] + jac = self.dterms(paramvec) + if shared_mem_leader: + lsvec = self.lsvec(paramvec) + p5over_lsvec = 0.5/lsvec + p5over_lsvec[_np.abs(lsvec) < 1e-100] = 0.0 + jac *= p5over_lsvec[:, None] unit_ralloc.host_comm_barrier() self.raw_objfn.resource_alloc.profiler.add_time("JACOBIAN", tm) return self.jac # Helpers, supporting main public instance functions - - def _dvecmap_fill_core(self, vecmap, shared_mem_leader): - if vecmap == 'lsvec': - dprobs_scaling_vec_callable = self.raw_objfn.dlsvec - elif vecmap == 'terms': - dprobs_scaling_vec_callable = self.raw_objfn.dterms - else: - raise ValueError() - - dprobs = self.jac[0:self.nelements, :] - dprobs.shape = (self.nelements, self.nparams) - - with self.resource_alloc.temporarily_track_memory(2 * self.nelements): - self.model.sim.bulk_fill_dprobs(dprobs, self.layout, self.probs) - self._clip_probs() - if shared_mem_leader: - if self.firsts is not None: - for ii, i in enumerate(self.indicesOfCircuitsWithOmittedData): - self.dprobs_omitted_rowsum[ii, :] = _np.sum(dprobs[self.layout.indices_for_index(i), :], axis=0) - dg_probs = dprobs_scaling_vec_callable(self.probs, self.counts, self.total_counts, self.freqs) - dprobs *= dg_probs[:, None] - - if shared_mem_leader and self.firsts is not None: - """TODO: remove this comment, probably. - Logic formerly handled by self._update_dterms_for_omitted_probs(...), which called self._omitted_prob_first_dterms(...). - """ - total_counts_firsts = self.total_counts[self.firsts] - omitted_probs = 1.0 - _np.array([_np.sum(self.probs[self.layout.indices_for_index(i)]) for i in self.indicesOfCircuitsWithOmittedData]) - omitted_dprobs_firsts_dterms = self.raw_objfn.zero_freq_dterms(total_counts_firsts, omitted_probs) - - if vecmap == 'lsvec': - """TODO: remove this comment, probably. - Logic formerly handled by self._update_dlsvec_for_omitted_probs(...), which called - self._omitted_prob_first_terms(...) and self._omitted_prob_first_dterms(...). - """ - lsvec_firsts = _np.sqrt(self.raw_objfn.terms( - self.probs[self.firsts], self.counts[self.firsts], total_counts_firsts, self.freqs[self.firsts] - )) - temp = self.raw_objfn.zero_freq_terms(total_counts_firsts, omitted_probs) - updated_lsvec = _np.sqrt(lsvec_firsts**2 + temp) - updated_lsvec = _np.where(updated_lsvec == 0, 1.0, updated_lsvec) # avoid 0/0 where lsvec & deriv == 0 - omitted_dprobs_firsts_dterms *= (0.5 / updated_lsvec) - dprobs[self.firsts] *= (lsvec_firsts / updated_lsvec)[:, None] - - dprobs[self.firsts] -= omitted_dprobs_firsts_dterms[:, None] * self.dprobs_omitted_rowsum - - return def _dterms_fill_penalty(self, paramvec, terms_jac): wrtslice = self.layout.global_param_slice if isinstance(self.layout, _DistributableCOPALayout) else slice(0, len(paramvec)) # all params @@ -4689,32 +4651,6 @@ def _clip_probs(self): else: _np.clip(self.probs, self.prob_clip_interval[0], self.prob_clip_interval[1], out=self.probs) - # Temp - - def dlsvec_generic(self, paramvec=None): - """ - It's possible that lsvec.shape[0] == terms.shape[0] > self.nelements - if regularization is used. The relationship that lsvec = sqrt(terms) - holds regardless of whether or not regularization is used. - - dlsvec(mdl; self)) = Jac(sqrt(terms(mdl; self))) - = diag(0.5 / sqrt(terms(mdl; self))) Jac(terms(mdl; self)) - = diag( 0.5 / lsvec ) Jac(terms(mdl; self)) - - This implementation is valid for any objective function. We need it here - (instead of the default implementation) since the default makes a call to - the raw objective function, and the raw objective function doesn't use the - weighting we want. - """ - jac = self.dterms(paramvec) - unit_ralloc = self.layout.resource_alloc('param-processing') - if unit_ralloc.is_host_leader: - lsvec = self.lsvec(paramvec) - p5over_lsvec = 0.5/lsvec - p5over_lsvec[lsvec < 1e-100] = 0.0 - jac *= p5over_lsvec[:, None] - return jac - # Hessians, public and private instance functions def hessian_brute(self, paramvec=None): From 6e9d366d062caaed84fb2e46332364afbaa68e3c Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Thu, 2 Jan 2025 14:47:47 -0500 Subject: [PATCH 13/71] add comment --- pygsti/objectivefns/objectivefns.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index 7f9ad93a2..1b651924b 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -630,7 +630,8 @@ def dlsvec(self, probs, counts, total_counts, freqs, intermediates=None): A 1D array of length equal to that of each array argument. """ # lsvec = sqrt(terms) - # NOTE: ^ That's only correct if lsvec is >= 0, and some classes don't satisfy that. + # NOTE: ^ That's only correct if lsvec is >= 0. + # Any class that doesn't ensure lsvec >= 0 must override this function. # dlsvec = 0.5/lsvec * dterms # if intermediates is None: From 7d20e5332e49f2837acecfb2c4812f6aee007662 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 20 Nov 2024 10:46:35 -0500 Subject: [PATCH 14/71] initial changes to RawTVDFunction --- pygsti/algorithms/core.py | 8 +-- pygsti/objectivefns/objectivefns.py | 78 ++++++++++++++++------------- 2 files changed, 49 insertions(+), 37 deletions(-) diff --git a/pygsti/algorithms/core.py b/pygsti/algorithms/core.py index dd0a21ef7..3111314b5 100644 --- a/pygsti/algorithms/core.py +++ b/pygsti/algorithms/core.py @@ -1007,11 +1007,13 @@ def _do_runopt(objective, optimizer, printer): tm = _time.time() nDataParams = objective.num_data_params() # TODO - cache this somehow in term-based calcs... profiler.add_time("run_gst_fit: num data params", tm) - - chi2_k_qty = opt_result.chi2_k_distributed_qty # total chi2 or 2*deltaLogL desc = objective.description + chi2_k_qty = opt_result.chi2_k_distributed_qty # total chi2 or 2*deltaLogL + if chi2_k_qty > 0: # reject GST model if p-value < threshold (~0.05?) - pvalue = 1.0 - _stats.chi2.cdf(chi2_k_qty, nDataParams - nModelParams) + pvalue = 1.0 - _stats.chi2.cdf(chi2_k_qty, nDataParams - nModelParams) + else: + pvalue = 0.0 printer.log("%s = %g (%d data params - %d (approx) model params = expected mean of %g; p-value = %g)" % (desc, chi2_k_qty, nDataParams, nModelParams, nDataParams - nModelParams, pvalue), 1) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index 1b651924b..d50c5ca16 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -558,6 +558,8 @@ def lsvec(self, probs, counts, total_counts, freqs, intermediates=None): """ return _np.sqrt(self.terms(probs, counts, total_counts, freqs, intermediates)) + # Infinite loop in evaluation of "dterms" and "dlsvec". + def dterms(self, probs, counts, total_counts, freqs, intermediates=None): """ Compute the derivatives of the terms of this objective function. @@ -592,10 +594,11 @@ def dterms(self, probs, counts, total_counts, freqs, intermediates=None): """ if intermediates is None: intermediates = self._intermediates(probs, counts, total_counts, freqs) - lsvec = self.lsvec(probs, counts, total_counts, freqs, intermediates) + u = self.lsvec(probs, counts, total_counts, freqs, intermediates) + v = self.dlsvec(probs, counts, total_counts, freqs, intermediates) # NOTE: this function is correct under the assumption that terms == lsvec**2, # independent of whether or not lsvec is nonnegative. - return 2 * lsvec * self.dlsvec(probs, counts, total_counts, freqs, intermediates) + return 2 * u * v def dlsvec(self, probs, counts, total_counts, freqs, intermediates=None): """ @@ -2765,37 +2768,37 @@ def zero_freq_hterms(self, total_counts, probs): return 2 * _np.ones(len(probs)) -# The log(Likelihood) within the Poisson picture is: # noqa -# # noqa -# L = prod_{i,sl} lambda_{i,sl}^N_{i,sl} e^{-lambda_{i,sl}} / N_{i,sl}! # noqa -# # noqa -# Where lamba_{i,sl} := p_{i,sl}*N[i] is a rate, i indexes the operation sequence, # noqa -# and sl indexes the spam label. N[i] is the total counts for the i-th circuit, and # noqa -# so sum_{sl} N_{i,sl} == N[i]. We can ignore the p-independent N_j! and take the log: # noqa -# # noqa -# log L = sum_{i,sl} N_{i,sl} log(N[i]*p_{i,sl}) - N[i]*p_{i,sl} # noqa -# = sum_{i,sl} N_{i,sl} log(p_{i,sl}) - N[i]*p_{i,sl} (where we ignore the p-independent log(N[i]) terms) # noqa -# # noqa -# The objective function computes the negative log(Likelihood) as a vector of leastsq # noqa -# terms, where each term == sqrt( N_{i,sl} * -log(p_{i,sl}) + N[i] * p_{i,sl} ) # noqa -# # noqa -# See LikelihoodFunctions.py for details on patching # noqa -# The log(Likelihood) within the standard picture is: -# -# L = prod_{i,sl} p_{i,sl}^N_{i,sl} -# -# Where i indexes the operation sequence, and sl indexes the spam label. -# N[i] is the total counts for the i-th circuit, and -# so sum_{sl} N_{i,sl} == N[i]. We take the log: -# -# log L = sum_{i,sl} N_{i,sl} log(p_{i,sl}) -# -# The objective function computes the negative log(Likelihood) as a vector of leastsq -# terms, where each term == sqrt( N_{i,sl} * -log(p_{i,sl}) ) -# -# See LikelihoodFunction.py for details on patching - - +""" +The log(Likelihood) within the Poisson picture is: # noqa + # noqa +L = prod_{i,sl} lambda_{i,sl}^N_{i,sl} e^{-lambda_{i,sl}} / N_{i,sl}! # noqa + # noqa +Where lamba_{i,sl} := p_{i,sl}*N[i] is a rate, i indexes the operation sequence, # noqa + and sl indexes the spam label. N[i] is the total counts for the i-th circuit, and # noqa + so sum_{sl} N_{i,sl} == N[i]. We can ignore the p-independent N_j! and take the log: # noqa + # noqa +log L = sum_{i,sl} N_{i,sl} log(N[i]*p_{i,sl}) - N[i]*p_{i,sl} # noqa + = sum_{i,sl} N_{i,sl} log(p_{i,sl}) - N[i]*p_{i,sl} (where we ignore the p-independent log(N[i]) terms) # noqa + # noqa +The objective function computes the negative log(Likelihood) as a vector of leastsq # noqa + terms, where each term == sqrt( N_{i,sl} * -log(p_{i,sl}) + N[i] * p_{i,sl} ) # noqa + # noqa +See LikelihoodFunctions.py for details on patching # noqa +The log(Likelihood) within the standard picture is: + +L = prod_{i,sl} p_{i,sl}^N_{i,sl} + +Where i indexes the operation sequence, and sl indexes the spam label. + N[i] is the total counts for the i-th circuit, and + so sum_{sl} N_{i,sl} == N[i]. We take the log: + +log L = sum_{i,sl} N_{i,sl} log(p_{i,sl}) + +The objective function computes the negative log(Likelihood) as a vector of leastsq + terms, where each term == sqrt( N_{i,sl} * -log(p_{i,sl}) ) + +See LikelihoodFunction.py for details on patching +""" class RawPoissonPicDeltaLogLFunction(RawObjectiveFunction): """ The function `N*f*log(f/p) - N*(f-p)`. @@ -4032,6 +4035,9 @@ def __init__(self, regularization=None, resource_alloc=None, name='tvd', description="Total Variational Distance (TVD)", verbosity=0): super().__init__(regularization, resource_alloc, name, description, verbosity) + def chi2k_distributed_qty(self, objective_function_value): + return -1 + def terms(self, probs, counts, total_counts, freqs, intermediates=None): """ Compute the terms of the objective function. @@ -4097,7 +4103,11 @@ def dterms(self, probs, counts, total_counts, freqs, intermediates=None): numpy.ndarray A 1D array of length equal to that of each array argument. """ - raise NotImplementedError("Derivatives not implemented for TVD yet!") + _warnings.warn('This derivative is discontinuous and does not return a full subgradient.') + t = self.terms(probs, counts, total_counts, freqs, intermediates) + d = 0.5*_np.ones_like(t) + d[t < 0] *= -1 + return d def hterms(self, probs, counts, total_counts, freqs, intermediates=None): """ From d15110e0073816ec69d703841604d0353233dbf8 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 20 Nov 2024 10:56:51 -0500 Subject: [PATCH 15/71] revert unncessary change --- pygsti/algorithms/core.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/pygsti/algorithms/core.py b/pygsti/algorithms/core.py index 3111314b5..dd0a21ef7 100644 --- a/pygsti/algorithms/core.py +++ b/pygsti/algorithms/core.py @@ -1007,13 +1007,11 @@ def _do_runopt(objective, optimizer, printer): tm = _time.time() nDataParams = objective.num_data_params() # TODO - cache this somehow in term-based calcs... profiler.add_time("run_gst_fit: num data params", tm) - desc = objective.description + chi2_k_qty = opt_result.chi2_k_distributed_qty # total chi2 or 2*deltaLogL - if chi2_k_qty > 0: + desc = objective.description # reject GST model if p-value < threshold (~0.05?) - pvalue = 1.0 - _stats.chi2.cdf(chi2_k_qty, nDataParams - nModelParams) - else: - pvalue = 0.0 + pvalue = 1.0 - _stats.chi2.cdf(chi2_k_qty, nDataParams - nModelParams) printer.log("%s = %g (%d data params - %d (approx) model params = expected mean of %g; p-value = %g)" % (desc, chi2_k_qty, nDataParams, nModelParams, nDataParams - nModelParams, pvalue), 1) From b0e72b5648c12835f86ec103c57526b8d6267cb5 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Thu, 2 Jan 2025 17:17:55 -0500 Subject: [PATCH 16/71] fix a silly bug and implement per-gate weighting for TVD --- pygsti/objectivefns/objectivefns.py | 195 +++++++++++++++------------- 1 file changed, 104 insertions(+), 91 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index d50c5ca16..f69f5b033 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -1470,96 +1470,6 @@ def approximate_hessian(self, paramvec=None): """ raise NotImplementedError("Derived classes should implement this!") - #MOVED - but these versions have updated names - #def _persistent_memory_estimate(self, num_elements=None): - # # Estimate & check persistent memory (from allocs within objective function) - # """ - # Compute the amount of memory needed to perform evaluations of this objective function. - # - # This number includes both intermediate and final results, and assumes - # that the types of evauations given by :meth:`_evaltree_subcalls` - # are required. - # - # Parameters - # ---------- - # num_elements : int, optional - # The number of elements (circuit outcomes) that will be computed. - # - # Returns - # ------- - # int - # """ - # if num_elements is None: - # nout = int(round(_np.sqrt(self.mdl.dim))) # estimate of avg number of outcomes per string - # nc = len(self.circuits) - # ne = nc * nout # estimate of the number of elements (e.g. probabilities, # LS terms, etc) to compute - # else: - # ne = num_elements - # np = self.mdl.num_params - # - # # "persistent" memory is that used to store the final results. - # obj_fn_mem = FLOATSIZE * ne - # jac_mem = FLOATSIZE * ne * np - # hess_mem = FLOATSIZE * ne * np**2 - # persistent_mem = 4 * obj_fn_mem + jac_mem # 4 different objective-function sized arrays, 1 jacobian array? - # if any([nm == "bulk_fill_hprobs" for nm in self._evaltree_subcalls()]): - # persistent_mem += hess_mem # we need room for the hessian too! - # # TODO: what about "bulk_hprobs_by_block"? - # - # return persistent_mem - # - #def _evaltree_subcalls(self): - # """ - # The types of calls that will be made to an evaluation tree. - # - # This information is used for memory estimation purposes. - # - # Returns - # ------- - # list - # """ - # calls = ["bulk_fill_probs", "bulk_fill_dprobs"] - # if self.enable_hessian: calls.append("bulk_fill_hprobs") - # return calls - # - #def num_data_params(self): - # """ - # The number of degrees of freedom in the data used by this objective function. - # - # Returns - # ------- - # int - # """ - # return self.dataset.degrees_of_freedom(self.ds_circuits, - # aggregate_times=not self.time_dependent) - - #def _precompute_omitted_freqs(self): - # """ - # Detect omitted frequences (assumed to be 0) so we can compute objective fn correctly - # """ - # self.firsts = []; self.indicesOfCircuitsWithOmittedData = [] - # for i, c in enumerate(self.circuits): - # lklen = _slct.length(self.lookup[i]) - # if 0 < lklen < self.mdl.compute_num_outcomes(c): - # self.firsts.append(_slct.to_array(self.lookup[i])[0]) - # self.indicesOfCircuitsWithOmittedData.append(i) - # if len(self.firsts) > 0: - # self.firsts = _np.array(self.firsts, 'i') - # self.indicesOfCircuitsWithOmittedData = _np.array(self.indicesOfCircuitsWithOmittedData, 'i') - # self.dprobs_omitted_rowsum = _np.empty((len(self.firsts), self.nparams), 'd') - # self.raw_objfn.printer.log("SPARSE DATA: %d of %d rows have sparse data" % - # (len(self.firsts), len(self.circuits))) - # else: - # self.firsts = None # no omitted probs - # - #def _compute_count_vectors(self): - # """ - # Ensure self.cache contains count and total-count vectors. - # """ - # if not self.cache.has_count_vectors(): - # self.cache.add_count_vectors(self.dataset, self.ds_circuits, self.circuit_weights) - # return self.cache.counts, self.cache.total_counts - def _construct_hessian(self, counts, total_counts, prob_clip_interval): """ Framework for constructing a hessian matrix row by row using a derived @@ -4104,7 +4014,7 @@ def dterms(self, probs, counts, total_counts, freqs, intermediates=None): A 1D array of length equal to that of each array argument. """ _warnings.warn('This derivative is discontinuous and does not return a full subgradient.') - t = self.terms(probs, counts, total_counts, freqs, intermediates) + t = probs - freqs d = 0.5*_np.ones_like(t) d[t < 0] *= -1 return d @@ -5308,6 +5218,109 @@ def create_from(cls, model, dataset, circuits, regularization=None, penalties=No def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0): raw_objfn = RawTVDFunction(regularization, mdc_store.resource_alloc, name, description, verbosity) super().__init__(raw_objfn, mdc_store, penalties, verbosity) + self.num_circuits = 0 + self.terms_weights = _np.array([]) + self._update_terms_weights() # <-- properly computes the two properties above. + + def _update_terms_weights(self): + # if self.num_circuits == self.layout.num_circuits: + # # Assume there's nothing to do. + # return + # else, assume that self.layout has been updated. + self.num_circuits = self.layout.num_circuits + num_elements = self.layout.num_elements + circuit_sizes = _np.zeros((num_elements,)) + for elind, circuit, _ in self.layout: + circuit_sizes[elind] = 1 + circuit.num_gates + assert _np.all(circuit_sizes > 0) + self.terms_weights = 1 / circuit_sizes + self.terms_weights /= _np.mean(self.terms_weights) + # ^ Make the weights mean-1 to avoid unintended changes to Levenberg-Marquardt + # stopping criteria. + return + + # QUESTION: could I just call the base class's implementation of terms and then scale the + # result? If so then I can avoid a ton of code duplication. + def terms(self, paramvec=None, oob_check=False, profiler_str="TERMS OBJECTIVE"): + tm = _time.time() + if paramvec is None: + paramvec = self.model.to_vector() + else: + self.model.from_vector(paramvec) + terms = self.obj.view() + + unit_ralloc = self.layout.resource_alloc('atom-processing') + shared_mem_leader = unit_ralloc.is_host_leader + + with self.resource_alloc.temporarily_track_memory(self.nelements): # 'e' (terms) + self.model.sim.bulk_fill_probs(self.probs, self.layout) + self._clip_probs() + + if oob_check: # Only used for termgap cases + if not self.model.sim.bulk_test_if_paths_are_sufficient(self.layout, self.probs, verbosity=1): + raise ValueError("Out of bounds!") # signals LM optimizer + + if shared_mem_leader: + terms_no_penalty = self.raw_objfn.terms(self.probs, self.counts, self.total_counts, self.freqs) + terms[:self.nelements] = terms_no_penalty + if self._process_penalties: + terms[self.nelements:] = self._terms_penalty(paramvec) + + if self.firsts is not None and shared_mem_leader: + omitted_probs = 1.0 - _np.array([self.probs[self.layout.indices_for_index(i)].sum() for i in self.indicesOfCircuitsWithOmittedData]) + omitted_probs_firsts_terms = self.raw_objfn.zero_freq_terms(self.total_counts[self.firsts], omitted_probs) + terms[self.firsts] += omitted_probs_firsts_terms + + self._update_terms_weights() + terms[:self.nelements] *= self.terms_weights + + unit_ralloc.host_comm_barrier() + + self.raw_objfn.resource_alloc.profiler.add_time(profiler_str, tm) + assert(terms.shape == (self.nelements + self.local_ex,)) + terms *= self.terms_weights + return terms + + # QUESTION: could I just call the base class's implementation of dterms and then scale the + # leading rows of the result? If so then I can avoid a ton of code duplication. + def dterms(self, paramvec=None): + tm = _time.time() + unit_ralloc = self.layout.resource_alloc('param-processing') + shared_mem_leader = unit_ralloc.is_host_leader + + if paramvec is None: + paramvec = self.model.to_vector() + else: + self.model.from_vector(paramvec) + + dprobs = self.jac[0:self.nelements, :] + dprobs.shape = (self.nelements, self.nparams) + + with self.resource_alloc.temporarily_track_memory(2 * self.nelements): + self.model.sim.bulk_fill_dprobs(dprobs, self.layout, self.probs) + self._clip_probs() + if shared_mem_leader: + if self.firsts is not None: + for ii, i in enumerate(self.indicesOfCircuitsWithOmittedData): + self.dprobs_omitted_rowsum[ii, :] = _np.sum(dprobs[self.layout.indices_for_index(i), :], axis=0) + dg_probs = self.raw_objfn.dterms(self.probs, self.counts, self.total_counts, self.freqs) + dprobs *= dg_probs[:, None] + + if shared_mem_leader and self.firsts is not None: + total_counts_firsts = self.total_counts[self.firsts] + omitted_probs = 1.0 - _np.array([_np.sum(self.probs[self.layout.indices_for_index(i)]) for i in self.indicesOfCircuitsWithOmittedData]) + omitted_dprobs_firsts_dterms = self.raw_objfn.zero_freq_dterms(total_counts_firsts, omitted_probs) + dprobs[self.firsts] -= omitted_dprobs_firsts_dterms[:, None] * self.dprobs_omitted_rowsum + + self._update_terms_weights() + dprobs[:self.nelements] *= self.terms_weights[:, None] + + if shared_mem_leader and self._process_penalties: + self._dterms_fill_penalty(paramvec, self.jac[self.nelements:, :]) + + unit_ralloc.host_comm_barrier() + self.raw_objfn.resource_alloc.profiler.add_time("JACOBIAN", tm) + return self.jac class TimeDependentMDCObjectiveFunction(MDCObjectiveFunction): From f8fea8ed8c15a1950ecbc6f658602dd439d589f2 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Thu, 9 Jan 2025 15:33:02 -0500 Subject: [PATCH 17/71] add option handling for TVD objective in user-facing GST functions. Separately, modify docstring for run_long_sequence_gst to be consistent with its implementation (including leaving notes for later). --- pygsti/drivers/longsequence.py | 19 +++++++++++-------- pygsti/protocols/gst.py | 4 +++- 2 files changed, 14 insertions(+), 9 deletions(-) diff --git a/pygsti/drivers/longsequence.py b/pygsti/drivers/longsequence.py index e6de76803..9785dff8b 100644 --- a/pygsti/drivers/longsequence.py +++ b/pygsti/drivers/longsequence.py @@ -385,7 +385,13 @@ def run_long_sequence_gst(data_filename_or_set, target_model_filename_or_object, Specifies advanced options most of which deal with numerical details of the objective function or expert-level functionality. The allowed keys and values include: - - objective = {'chi2', 'logl'} + + HISTORICAL NOTE: "XX" indicates that we've at least _intended_ for the + keyword argument to be removed. I've removed documentation for options + that we never reference in the code (truncScheme, estimate_label, and + missingDataAction). + + - objective = {'chi2', 'logl', 'tvd'} - op_labels = list of strings - circuit_weights = dict or None - starting_point = "LGST-if-possible" (default), "LGST", or "target" @@ -401,19 +407,16 @@ def run_long_sequence_gst(data_filename_or_set, target_model_filename_or_object, - prob_clip_interval = tuple (default == (-1e6,1e6) - radius = float (default == 1e-4) - use_freq_weighted_chi2 = True / False (default) - - XX nested_circuit_lists = True (default) / False - - XX include_lgst = True / False (default is True) + - XX nested_circuit_lists = True (default) / False -- passed to StandardGSTDesign; seems to be functional + - XX include_lgst = True / False (default is True) -- passed to StandardGSTDesign; seems to be functional - distribute_method = "default", "circuits" or "deriv" - profile = int (default == 1) - check = True / False (default) - - XX op_label_aliases = dict (default = None) + - XX op_label_aliases = dict (default = None) -- passed to StandardGSTDesign and used to set a GateSetTomography protocol object's oplabel_aliases field. - always_perform_mle = bool (default = False) - only_perform_mle = bool (default = False) - - XX truncScheme = "whole germ powers" (default) or "truncated germ powers" or "length as exponent" - appendTo = Results (default = None) - - estimateLabel = str (default = "default") - - XX missingDataAction = {'drop','raise'} (default = 'drop') - - XX string_manipulation_rules = list of (find,replace) tuples + - XX string_manipulation_rules = list of (find,replace) tuples -- passed to _proto.StandardGSTDesign construct as its "circuit_rules" argument. - germ_length_limits = dict of form {germ: maxlength} - record_output = bool (default = True) - timeDependent = bool (default = False) diff --git a/pygsti/protocols/gst.py b/pygsti/protocols/gst.py index dba21e8e6..b708589b1 100644 --- a/pygsti/protocols/gst.py +++ b/pygsti/protocols/gst.py @@ -785,7 +785,6 @@ def create_from(cls, objective='logl', freq_weighted_chi2=False, always_perform_ if objective == "chi2": iteration_builders = [chi2_builder] final_builders = [] - elif objective == "logl": if always_perform_mle: iteration_builders = [mle_builder] if only_perform_mle else [chi2_builder, mle_builder] @@ -793,6 +792,9 @@ def create_from(cls, objective='logl', freq_weighted_chi2=False, always_perform_ else: iteration_builders = [chi2_builder] final_builders = [mle_builder] + elif objective == "tvd": + iteration_builders = [chi2_builder] + final_builders = [_objfns.ObjectiveFunctionBuilder.create_from('tvd')] else: raise ValueError("Invalid objective: %s" % objective) return cls(iteration_builders, final_builders) From a67beceeef54a0805d20dad4fcbcb3f323efd846 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Thu, 9 Jan 2025 15:36:52 -0500 Subject: [PATCH 18/71] add string-casting to the setter method of ExplicitOpModel.default_gauge_group. Having the casting occur here makes sense, since a model has access to the associated StateSpace, Basis, and Evotype. --- pygsti/models/explicitmodel.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/pygsti/models/explicitmodel.py b/pygsti/models/explicitmodel.py index b1f601298..75dc6e906 100644 --- a/pygsti/models/explicitmodel.py +++ b/pygsti/models/explicitmodel.py @@ -234,6 +234,15 @@ def default_gauge_group(self, value): """ The default gauge group. """ + if isinstance(value, str): + value = value.lower() + if value == 'unitary': + value = _gg.UnitaryGaugeGroup(self.state_space, self.basis, self.evotype) + elif value == 'tp': + value = _gg.TPGaugeGroup(self.state_space, self.basis, self.evotypes) + else: + msg = f'string "{value}" cannot be used to set this model\'s gauge group.' + raise ValueError(msg) self._default_gauge_group = value @property From 2ed1c21ed1c6545a0b8774c13362d34768d227a3 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Thu, 9 Jan 2025 15:41:16 -0500 Subject: [PATCH 19/71] add the option of skipping report sections in construct_standard_report --- pygsti/report/factory.py | 62 +++++++++++++++++++++++++++++++--------- 1 file changed, 48 insertions(+), 14 deletions(-) diff --git a/pygsti/report/factory.py b/pygsti/report/factory.py index 8d2f675d7..94390ae6f 100644 --- a/pygsti/report/factory.py +++ b/pygsti/report/factory.py @@ -1172,6 +1172,32 @@ def construct_standard_report(results, title="auto", - idt_idle_oplabel : Label, optional The label identifying the idle gate (for use with idle tomography). + - skip_sections : tuple[str], optional + Contains names of standard report sections that should be skipped + in this particular report. Strings will be cast to lowercase, + stripped of white space, and then mapped to omitted Section classes + as follows + + { + 'summary' : SummarySection, + 'goodness' : GoodnessSection, + 'colorbox' : GoodnessColorBoxPlotSection, + 'invariantgates' : GaugeInvariantsGatesSection, + 'invariantgerms' : GaugeInvariantsGermsSection, + 'variant' : GaugeVariantSection, + 'variantraw' : GaugeVariantsRawSection, + 'variantdecomp' : GaugeVariantsDecompSection, + 'varianterrorgen' : GaugeVariantsErrorGenSection, + 'input' : InputSection, + 'meta' : MetaSection, + 'help' : HelpSection + } + + A KeyError will be raised if skip_sections contains a string + that is not in the keys of the above dict (after casting to + lower case and stripping white space). + + verbosity : int, optional How much detail to send to stdout. @@ -1240,20 +1266,28 @@ def construct_standard_report(results, title="auto", flags.add('CombineRobust') # build section list - sections = [ - _section.SummarySection(), - _section.GoodnessSection(), - _section.GoodnessColorBoxPlotSection(), - _section.GaugeInvariantsGatesSection(), - _section.GaugeInvariantsGermsSection(), - _section.GaugeVariantSection(), - _section.GaugeVariantsRawSection(), - _section.GaugeVariantsDecompSection(), - _section.GaugeVariantsErrorGenSection(), - _section.InputSection(), - _section.MetaSection(), - _section.HelpSection() - ] + possible_sections = { + 'summary' : _section.SummarySection(), + 'goodness' : _section.GoodnessSection(), + 'colorbox' : _section.GoodnessColorBoxPlotSection(), + 'invariantgates' : _section.GaugeInvariantsGatesSection(), + 'invariantgerms' : _section.GaugeInvariantsGermsSection(), + 'variant' : _section.GaugeVariantSection(), + 'variantraw' : _section.GaugeVariantsRawSection(), + 'variantdecomp' : _section.GaugeVariantsDecompSection(), + 'varianterrorgen' : _section.GaugeVariantsErrorGenSection(), + 'input' : _section.InputSection(), + 'meta' : _section.MetaSection(), + 'help' : _section.HelpSection() + } + + skip_sections = advanced_options.get('skip_sections', tuple()) + if skip_sections: + skip_sections = [s.lower().replace(' ','') for s in skip_sections] + for s in skip_sections: + possible_sections.pop(s) + sections = list(possible_sections.keys()) + # ^ This whole process won't affect ordering of objects in "sections". if 'ShowScaling' in flags: sections.append(_section.GoodnessScalingSection()) From d028a77e9722a7cccc17f3d0a5f6d8560abb48e3 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Thu, 9 Jan 2025 15:42:49 -0500 Subject: [PATCH 20/71] fix LIKELY bug in SummarySection.final_model_fit_histogram, where switchboard.objfn_builder was used when switchboard.objfn_builder_modvi should have been used --- pygsti/report/section/summary.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pygsti/report/section/summary.py b/pygsti/report/section/summary.py index 9ac7263f4..90a47b74a 100644 --- a/pygsti/report/section/summary.py +++ b/pygsti/report/section/summary.py @@ -28,7 +28,8 @@ def final_model_fit_progress_bar_plot_sum(workspace, switchboard=None, max_lengt def final_model_fit_histogram(workspace, switchboard=None, linlog_percentile=5, comm=None, bgcolor='white', **kwargs): return workspace.ColorBoxPlot( - switchboard.objfn_builder, switchboard.circuits_final, + switchboard.objfn_builder_modvi, # NOTE: this should objfun_builder_modvi + switchboard.circuits_final, switchboard.modvi_ds, switchboard.mdl_current_modvi, linlg_pcntle=linlog_percentile / 100, typ='histogram', comm=comm, bgcolor=bgcolor From 48fb7aeb0e71b612e2609bd8074c0db3f13874a6 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Thu, 9 Jan 2025 16:10:33 -0500 Subject: [PATCH 21/71] bugfix --- pygsti/report/factory.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pygsti/report/factory.py b/pygsti/report/factory.py index 94390ae6f..7b36c3d54 100644 --- a/pygsti/report/factory.py +++ b/pygsti/report/factory.py @@ -1286,7 +1286,7 @@ def construct_standard_report(results, title="auto", skip_sections = [s.lower().replace(' ','') for s in skip_sections] for s in skip_sections: possible_sections.pop(s) - sections = list(possible_sections.keys()) + sections = list(possible_sections.values()) # ^ This whole process won't affect ordering of objects in "sections". if 'ShowScaling' in flags: From 14c1a51ca5fbecb0a2464bd5f1a604a8a6d5a1f4 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Mon, 17 Feb 2025 16:00:46 -0500 Subject: [PATCH 22/71] check in chnages for example (and getting example to run after merging in develop) --- pygsti/layouts/copalayout.py | 12 +- pygsti/report/factory.py | 12 +- .../case0-gst-with-outliers.ipynb | 318 ++++++++++++++++++ 3 files changed, 334 insertions(+), 8 deletions(-) create mode 100644 wip_notebook_sharing/case0-gst-with-outliers.ipynb diff --git a/pygsti/layouts/copalayout.py b/pygsti/layouts/copalayout.py index 34e907d8f..a74ff89b5 100644 --- a/pygsti/layouts/copalayout.py +++ b/pygsti/layouts/copalayout.py @@ -207,12 +207,16 @@ def __init__(self, circuits, unique_circuits, to_unique, elindex_outcome_tuples, self._outcomes = dict() self._element_indices = dict() + self._element_inditer = dict() + ind_array = _np.arange(self._size) sort_idx_func = lambda x: x[0] for i_unique, tuples in elindex_outcome_tuples.items(): sorted_tuples = sorted(tuples, key=sort_idx_func) # sort by element index elindices, outcomes = zip(*sorted_tuples) # sorted by elindex so we make slices whenever possible self._outcomes[i_unique] = tuple(outcomes) - self._element_indices[i_unique] = _slct.list_to_slice(elindices, array_ok=True) + s = _slct.list_to_slice(elindices, array_ok=True) + self._element_indices[i_unique] = s + self._element_inditer[i_unique] = ind_array[s] # def hotswap_circuits(self, circuits, unique_complete_circuits=None): # self.circuits = circuits if isinstance(circuits, _CircuitList) else _CircuitList(circuits) @@ -744,7 +748,11 @@ def indices_and_outcomes_for_index(self, index): def __iter__(self): for circuit, i in self._unique_circuit_index.items(): - for element_index, outcome in zip(self._element_indices[i], self._outcomes[i]): + try: + iterator = zip(self._element_indices[i], self._outcomes[i]) + except TypeError: + iterator = zip(self._element_inditer[i], self._outcomes[i]) + for element_index, outcome in iterator: yield element_index, circuit, outcome def iter_unique_circuits(self): diff --git a/pygsti/report/factory.py b/pygsti/report/factory.py index 7b36c3d54..15f5670e4 100644 --- a/pygsti/report/factory.py +++ b/pygsti/report/factory.py @@ -184,8 +184,7 @@ def _create_master_switchboard(ws, results_dict, confidence_level, Ls = None for results in results_dict.values(): - est_labels = _add_new_estimate_labels(est_labels, results.estimates, - combine_robust) + est_labels = _add_new_estimate_labels(est_labels, results.estimates, combine_robust) loc_Ls = results.circuit_lists['final'].xs \ if isinstance(results.circuit_lists['final'], _PlaquetteGridCircuitStructure) else [0] Ls = _add_new_labels(Ls, loc_Ls) @@ -305,10 +304,9 @@ def _create_master_switchboard(ws, results_dict, confidence_level, else: est_modvi = est - switchBd.objfn_builder[d, i] = est.parameters.get( - 'final_objfn_builder', _objfns.ObjectiveFunctionBuilder.create_from('logl')) - switchBd.objfn_builder_modvi[d, i] = est_modvi.parameters.get( - 'final_objfn_builder', _objfns.ObjectiveFunctionBuilder.create_from('logl')) + switchBd.objfn_builder[d, i] = _objfns.ObjectiveFunctionBuilder.create_from('logl') # est.parameters.get('final_objfn_builder', _objfns.ObjectiveFunctionBuilder.create_from('logl')) + switchBd.objfn_builder_modvi[d, i] = _objfns.ObjectiveFunctionBuilder.create_from('logl') + # est_modvi.parameters.get('final_objfn_builder', _objfns.ObjectiveFunctionBuilder.create_from('logl')) switchBd.params[d, i] = est.parameters switchBd.clifford_compilation[d, i] = est.parameters.get("clifford compilation", 'auto') @@ -1283,6 +1281,8 @@ def construct_standard_report(results, title="auto", skip_sections = advanced_options.get('skip_sections', tuple()) if skip_sections: + if isinstance(skip_sections, str): + skip_sections = [skip_sections] skip_sections = [s.lower().replace(' ','') for s in skip_sections] for s in skip_sections: possible_sections.pop(s) diff --git a/wip_notebook_sharing/case0-gst-with-outliers.ipynb b/wip_notebook_sharing/case0-gst-with-outliers.ipynb new file mode 100644 index 000000000..f3e993ad8 --- /dev/null +++ b/wip_notebook_sharing/case0-gst-with-outliers.ipynb @@ -0,0 +1,318 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import pygsti\n", + "from pygsti.modelpacks import smq1Q_XYI\n", + "import numpy as np\n", + "from pprint import pprint\n", + "import copy\n", + "\n", + "\n", + "def make_depolarized_dataset(modelpack, depol_level=0.01, max_max_len=128):\n", + " ideal_model = modelpack.target_model()\n", + " prep_fids = modelpack.prep_fiducials()\n", + " meas_fids = modelpack.meas_fiducials()\n", + " germs = modelpack.germs()\n", + " max_lens = [2**p for p in range(1+int(np.log2(max_max_len)))]\n", + " lsgst_circuit_lists = pygsti.circuits.create_lsgst_circuit_lists(ideal_model, prep_fids, meas_fids, germs, max_lens)\n", + " all_circuits = lsgst_circuit_lists[-1]\n", + " shots_per_circuit = 1000\n", + " rng_state = np.random.default_rng(0)\n", + " depol_model = ideal_model.depolarize(op_noise=depol_level)\n", + " ds = pygsti.data.simulate_data(depol_model, all_circuits, shots_per_circuit, rand_state=rng_state)\n", + " return ds\n", + "\n", + "\n", + "def corrupt_dataset(ds, prop_corrupt, rng=0):\n", + " dsc = ds.copy_nonstatic()\n", + " rng = np.random.default_rng(rng)\n", + " num_circs = len(dsc)\n", + " selected = rng.choice(np.arange(num_circs), size=int(num_circs*prop_corrupt), replace=False)\n", + " circuits = list(dsc.keys())\n", + " selected = [circuits[i] for i in selected]\n", + " for c in selected:\n", + " num_shots = dsc[c].total\n", + " old_row = dsc[c].to_dict()\n", + " distn = rng.random(len(old_row))\n", + " distn /= np.sum(distn)\n", + " new_row = {k: num_shots * distn[i] for i,k in enumerate(old_row.keys())}\n", + " dsc[c] = new_row\n", + " dsc.comment = 'corrupt'\n", + " return dsc, selected\n", + "\n", + "\n", + "def cptp_gst(ds, fids, germs, target_model, final_objective: str, verbosity: int, mode='CPTPLND'):\n", + " \"\"\"\n", + " In the context of this notebook, `ds` is produced by either make_depolarized_dataset or corrupt_dataset.\n", + " final_objective can be 'tvd', 'chi2', or 'logl'.\n", + "\n", + " This function wraps up three steps of a GST pipeline.\n", + "\n", + " 1. Construct a StandardGSTDesign based on (target_model, ds, fids, germs).\n", + " * processor_spec is the value returned from target_model.create_processor_spec.\n", + " * max_lens list is all powers of two that are <= the depth of the longest circuit in ds.\n", + " * circuits in the design are filtered to only include circuits that appeared in ds.\n", + "\n", + " 2. Construct a StandardGST protocol object based on (final_objective, mode, verbosity).\n", + " * The gauge optimization suite is 'stdgaugeopt', minus the TPSpam optimization step.\n", + " * objfn_builders, optimizer, and badfit_options are all set so the final \n", + " iteration's objective function is based on final_objective.\n", + "\n", + " 3. Run GST with checkpointing turned off. \n", + " We dot NOT save the results to disk! The calling function is responsible for that.\n", + " \"\"\"\n", + " max_exp = int(np.log2(np.max([len(c) for c in ds.keys()])))\n", + " max_lens = [2**p for p in range(1 + max_exp)]\n", + " prep_fids, meas_fids = fids\n", + "\n", + " target_model = target_model.copy()\n", + " target_model.default_gauge_group = 'unitary'\n", + "\n", + " gos = pygsti.protocols.gst.GSTGaugeOptSuite.cast('stdgaugeopt')\n", + " gop_params = gos.to_dictionary(target_model)\n", + " # ^ a dict with one key, 'stdgaugeopt', whose corresponding value is a list of dicts.\n", + " # The internal dicts will indicate Frobenius-based losses for gates and SPAM,\n", + " # along with varying weights. Additional elements can be added to any one of these\n", + " # internal dicts to be passed to gaugeopt_to_target.\n", + " gop_params['stdgaugeopt'] = gop_params['stdgaugeopt'][:-1]\n", + " # ^ drop the 1-dimensional TPSpam gauge optimization step.\n", + "\n", + " exp_design = pygsti.protocols.StandardGSTDesign(\n", + " target_model.create_processor_spec(),\n", + " prep_fids, meas_fids, germs, max_lens,\n", + " None, # germ_length_limits\n", + " None, 1, None, # fidPairs, keepFraction, keepSeed\n", + " True, True, # include_lgst, nested_circuit_lists\n", + " None, # string_manipulation_rules\n", + " None, # op_label_aliases\n", + " ds, 'drop', verbosity=verbosity\n", + " )\n", + " data = pygsti.protocols.ProtocolData(exp_design, ds)\n", + " \n", + " from pygsti.drivers.longsequence import _get_gst_builders, _get_optimizer, _get_badfit_options\n", + " advanced_options = {'objective': final_objective}\n", + " proto = pygsti.protocols.StandardGST(\n", + " (mode,), gop_params, target_model, None, \n", + " objfn_builders = _get_gst_builders(advanced_options),\n", + " optimizer = _get_optimizer(advanced_options, target_model),\n", + " badfit_options = _get_badfit_options(advanced_options),\n", + " verbosity = verbosity\n", + " )\n", + " results = proto.run(data, disable_checkpointing=True)\n", + " return results\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "mp = smq1Q_XYI\n", + "target = mp.target_model()\n", + "fids = (mp.prep_fiducials(), mp.meas_fiducials())\n", + "germs = mp.germs()\n", + "ds = make_depolarized_dataset(mp, depol_level=0.01, max_max_len=128)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (full TP) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/objectivefns/objectivefns.py:4483: RuntimeWarning: divide by zero encountered in divide\n", + " p5over_lsvec = 0.5/lsvec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- Std Practice: [##################################################] 100.0% (full TP) --\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (full TP) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/objectivefns/objectivefns.py:4014: UserWarning: This derivative is discontinuous and does not return a full subgradient.\n", + " _warnings.warn('This derivative is discontinuous and does not return a full subgradient.')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- Std Practice: [##################################################] 100.0% (full TP) --\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (full TP) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/objectivefns/objectivefns.py:4483: RuntimeWarning: divide by zero encountered in divide\n", + " p5over_lsvec = 0.5/lsvec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- Std Practice: [##################################################] 100.0% (full TP) --\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [##################################################] 100.0% (full TP) --\n" + ] + } + ], + "source": [ + "fit_mode = 'full TP'\n", + "\n", + "results = cptp_gst(ds, fids, germs, target, 'logl', verbosity=1, mode=fit_mode)\n", + "results.estimates['fit-true-logl'] = results.estimates.pop(fit_mode)\n", + "tvd_res = cptp_gst(ds, fids, germs, target, 'tvd', verbosity=1, mode=fit_mode)\n", + "results.estimates['fit-true-tvd'] = tvd_res.estimates.pop(fit_mode)\n", + "\n", + "dsc, selected = corrupt_dataset(ds, prop_corrupt=0.025)\n", + "logl_res = cptp_gst(dsc, fids, germs, target, 'logl', verbosity=1, mode=fit_mode)\n", + "results.estimates['fit-corrupt-logl'] = logl_res.estimates.pop(fit_mode)\n", + "tvd_res = cptp_gst(dsc, fids, germs, target, 'tvd', verbosity=1, mode=fit_mode)\n", + "results.estimates['fit-corrupt-tvd'] = tvd_res.estimates.pop(fit_mode)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running idle tomography\n", + "Computing switchable properties\n", + "Found standard clifford compilation from smq1Q_XYI\n", + "Found standard clifford compilation from smq1Q_XYI\n", + "Found standard clifford compilation from smq1Q_XYI\n", + "Found standard clifford compilation from smq1Q_XYI\n", + "Found standard clifford compilation from smq1Q_XYI\n", + "Found standard clifford compilation from smq1Q_XYI\n", + "Found standard clifford compilation from smq1Q_XYI\n", + "Found standard clifford compilation from smq1Q_XYI\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/tools/optools.py:164: UserWarning:\n", + "\n", + "\n", + " Input matrix is not PSD up to tolerance 1.8189894035458565e-12.\n", + " We'll project out the bad eigenspaces to only work with the PSD part.\n", + " \n", + "\n", + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/tools/optools.py:172: UserWarning:\n", + "\n", + "\n", + " The PSD part of the input matrix is not trace-1 up to tolerance 3.637978807091713e-12.\n", + " Beware result!\n", + " \n", + "\n", + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/forwardsims/mapforwardsim.py:732: UserWarning:\n", + "\n", + "Generating dense process matrix representations of circuits or gates \n", + "can be inefficient and should be avoided for the purposes of forward \n", + "simulation/calculation of circuit outcome probability distributions \n", + "when using the MapForwardSimulator.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Statistical hypothesis tests did NOT find inconsistency between the data at 5.00% significance.\n", + "The data are INCONSISTENT at 5.00% significance.\n", + " - Details:\n", + " - The aggregate log-likelihood ratio test is significant at 167.04 standard deviations.\n", + " - The aggregate log-likelihood ratio test standard deviations signficance threshold is 2.00\n", + " - The number of sequences with data that is inconsistent is 21\n", + " - The maximum SSTVD over all sequences is 0.78\n", + " - The maximum SSTVD was observed for Qubit 0 ---|Gxpi2|-|Gxpi2|-|Gypi2|-|Gypi2|-|Gypi2|-|Gypi2|---\n", + "\n", + "The data are INCONSISTENT at 5.00% significance.\n", + " - Details:\n", + " - The aggregate log-likelihood ratio test is significant at 167.04 standard deviations.\n", + " - The aggregate log-likelihood ratio test standard deviations signficance threshold is 2.00\n", + " - The number of sequences with data that is inconsistent is 21\n", + " - The maximum SSTVD over all sequences is 0.78\n", + " - The maximum SSTVD was observed for Qubit 0 ---|Gxpi2|-|Gxpi2|-|Gypi2|-|Gypi2|-|Gypi2|-|Gypi2|---\n", + "\n", + "Statistical hypothesis tests did NOT find inconsistency between the data at 5.00% significance.\n" + ] + } + ], + "source": [ + "moar_results = results.copy()\n", + "dsc.comment = 'corrupt'\n", + "moar_results.data.dataset = dsc\n", + "report = pygsti.report.construct_standard_report(\n", + " {'eval-true' : results,\n", + " 'eval-corrupt' : moar_results\n", + " },\n", + " advanced_options={'skip_sections': ('colorbox',)},\n", + " title=\"GST Example Report\", verbosity=2\n", + ")\n", + "# NOTE: can reach in and change the entry in report.switchboard.objfn_builder_modvi,\n", + "# or anything else in the switchboard, according to my whims.\n", + "report.write_html(\"case0_reports_250217/exampleReport\", auto_open=True, verbosity=1)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rogst", + "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": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 5de61b8dc90de150286350f4b61cb32cfea5f583 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 16 Apr 2025 18:02:49 -0700 Subject: [PATCH 23/71] make entry point for final-iteration objective functions more flexible --- pygsti/drivers/longsequence.py | 3 ++- pygsti/protocols/gst.py | 20 ++++++++++++++------ 2 files changed, 16 insertions(+), 7 deletions(-) diff --git a/pygsti/drivers/longsequence.py b/pygsti/drivers/longsequence.py index 9785dff8b..5c25d1bcf 100644 --- a/pygsti/drivers/longsequence.py +++ b/pygsti/drivers/longsequence.py @@ -391,7 +391,8 @@ def run_long_sequence_gst(data_filename_or_set, target_model_filename_or_object, that we never reference in the code (truncScheme, estimate_label, and missingDataAction). - - objective = {'chi2', 'logl', 'tvd'} + - objective = typically, a string in {'chi2', 'logl', 'tvd'}. But this can + be anything accepted by the `ObjectiveFunctionBuilder.create_from` function. - op_labels = list of strings - circuit_weights = dict or None - starting_point = "LGST-if-possible" (default), "LGST", or "target" diff --git a/pygsti/protocols/gst.py b/pygsti/protocols/gst.py index b708589b1..10aa51e17 100644 --- a/pygsti/protocols/gst.py +++ b/pygsti/protocols/gst.py @@ -760,7 +760,7 @@ def create_from(cls, objective='logl', freq_weighted_chi2=False, always_perform_ Parameters ---------- - objective : {'logl', 'chi2'}, optional + objective : {'logl', 'chi2', 'tvd'}, optional Whether to create builders for maximum-likelihood or minimum-chi-squared GST. freq_weighted_chi2 : bool, optional @@ -782,7 +782,13 @@ def create_from(cls, objective='logl', freq_weighted_chi2=False, always_perform_ chi2_builder = _objfns.ObjectiveFunctionBuilder.create_from('chi2', freq_weighted_chi2) mle_builder = _objfns.ObjectiveFunctionBuilder.create_from('logl') - if objective == "chi2": + if not isinstance(objective, str): + import warnings + warnings.warn(f'Trying to create an objective function from non-string specification, "{objective}". \ + \nSupport for this kind of specification is experimental!') + iteration_builders = [chi2_builder] + final_builders = [_objfns.ObjectiveFunctionBuilder.create_from(objective)] + elif objective == "chi2": iteration_builders = [chi2_builder] final_builders = [] elif objective == "logl": @@ -792,11 +798,13 @@ def create_from(cls, objective='logl', freq_weighted_chi2=False, always_perform_ else: iteration_builders = [chi2_builder] final_builders = [mle_builder] - elif objective == "tvd": - iteration_builders = [chi2_builder] - final_builders = [_objfns.ObjectiveFunctionBuilder.create_from('tvd')] else: - raise ValueError("Invalid objective: %s" % objective) + iteration_builders = [chi2_builder] + try: + final_builders = [_objfns.ObjectiveFunctionBuilder.create_from(objective)] + except Exception as e: + raise ValueError("Invalid objective: %s" % objective) + return cls(iteration_builders, final_builders) def __init__(self, iteration_builders, final_builders=()): From 99619f1b1830710bf9f65c84d947acd0532f1a18 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 16 Apr 2025 18:06:14 -0700 Subject: [PATCH 24/71] remove commented-out code that was trying to be efficient at the expense of being possibly-incorrect --- pygsti/objectivefns/objectivefns.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index a9d645584..608100eb1 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -5225,10 +5225,6 @@ def __init__(self, mdc_store, regularization=None, penalties=None, name=None, de self._update_terms_weights() # <-- properly computes the two properties above. def _update_terms_weights(self): - # if self.num_circuits == self.layout.num_circuits: - # # Assume there's nothing to do. - # return - # else, assume that self.layout has been updated. self.num_circuits = self.layout.num_circuits num_elements = self.layout.num_elements circuit_sizes = _np.zeros((num_elements,)) From 8845e1209da721fd4bb77128be163064cff7e5af Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Thu, 17 Apr 2025 10:41:39 -0700 Subject: [PATCH 25/71] replace unused implementation of create_from in TimeIndependentMDCObjectiveFunction with an implementation that works for five of its eight derived classes. Removed the separate implementations for those five derived classes. Introduce a templated docstring for TimeIndependentMDCObjectiveFunction and a decorator to reduce duplication among the nine total __init__ functions of this class and its eight derived classes . --- pygsti/objectivefns/objectivefns.py | 494 ++++++---------------------- 1 file changed, 109 insertions(+), 385 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index 608100eb1..6db70f7b6 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -29,6 +29,13 @@ from pygsti.models.model import OpModel as _OpModel +def set_docstring(docstr): + def assign(fn): + fn.__doc__ = docstr + return fn + return assign + + def _objfn(objfn_cls, model, dataset, circuits=None, regularization=None, penalties=None, op_label_aliases=None, comm=None, mem_limit=None, method_names=None, array_types=None, @@ -4133,15 +4140,27 @@ def zero_freq_hterms(self, total_counts, probs): class TimeIndependentMDCObjectiveFunction(MDCObjectiveFunction): + + TEMPLATE_FIELDS = ( + # Class description """ A time-independent model-based (:class:`MDCObjectiveFunction`-derived) objective function. - - Parameters - ---------- + """ + # Constructor has ONE custom leading arguments. + """ raw_objfn : RawObjectiveFunction The raw objective function - specifies how probability and count values are turned into objective function values. + """, + # Constructor has ZERO custom trailing arguments. + "" + ) + DOCSTR_TEMPLATE = \ + """ + %s + Parameters + ----------%s mdl : Model The model - specifies how parameter values are turned into probabilities for each circuit outcome. @@ -4177,6 +4196,7 @@ class TimeIndependentMDCObjectiveFunction(MDCObjectiveFunction): Whether hessian calculations are allowed. If `True` then more resources are needed. If `False`, calls to hessian-requiring function will result in an error. + %s """ @classmethod @@ -4220,11 +4240,10 @@ def _create_mdc_store(cls, model, dataset, circuits, resource_alloc, return ModelDatasetCircuitsStore(model, dataset, circuits, resource_alloc, array_types, None, verbosity) @classmethod - def create_from(cls, raw_objfn, model, dataset, circuits, resource_alloc=None, penalties=None, - verbosity=0, method_names=('fn',), array_types=()): - mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, - array_types, verbosity) - return cls(raw_objfn, mdc_store, penalties, verbosity) + def create_from(cls, model, dataset, circuits, regularization=None, penalties=None, resource_alloc=None, + name=None, description=None, verbosity=0, method_names=('fn',), array_types=()): + mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, array_types, verbosity) + return cls(mdc_store, regularization, penalties, name, description, verbosity) @classmethod def _array_types_for_method(cls, method_name, fsim): @@ -4255,6 +4274,7 @@ def compute_array_types(cls, method_names, fsim): return array_types + @set_docstring(DOCSTR_TEMPLATE % TEMPLATE_FIELDS) def __init__(self, raw_objfn, mdc_store, penalties=None, verbosity=0): super().__init__(raw_objfn, mdc_store, verbosity) @@ -4763,113 +4783,40 @@ def _hessian_from_block(self, hprobs, dprobs12, probs, element_slice, counts, to class Chi2Function(TimeIndependentMDCObjectiveFunction): - """ - Model-based chi-squared function: `N(p-f)^2 / p` - - Parameters - ---------- - mdl : Model - The model - specifies how parameter values are turned into probabilities - for each circuit outcome. - - dataset : DataSet - The data set - specifies how counts and total_counts are obtained for each - circuit outcome. - circuits : list or CircuitList - The circuit list - specifies what probabilities and counts this objective - function compares. If `None`, then the keys of `dataset` are used. - - regularization : dict, optional - Regularization values. - - penalties : dict, optional - Penalty values. Penalties usually add additional (penalty) terms to the sum - of per-circuit-outcome contributions that evaluate to the objective function. - - resource_alloc : ResourceAllocation, optional - Available resources and how they should be allocated for computations. - - name : str, optional - A name for this objective function (can be anything). - - description : str, optional - A description for this objective function (can be anything) - - verbosity : int, optional - Level of detail to print to stdout. - - enable_hessian : bool, optional - Whether hessian calculations are allowed. If `True` then more resources are - needed. If `False`, calls to hessian-requiring function will result in an - error. + TEMPLATE_FIELDS = ( """ - @classmethod - def create_from(cls, model, dataset, circuits, regularization=None, penalties=None, resource_alloc=None, - name=None, description=None, verbosity=0, method_names=('fn',), array_types=()): - mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, - array_types, verbosity) - return cls(mdc_store, regularization, penalties, name, description, verbosity) + Model-based chi-squared function: `N(p-f)^2 / p` + """, "", "" + ) + @set_docstring(TimeIndependentMDCObjectiveFunction.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0): raw_objfn = RawChi2Function(regularization, mdc_store.resource_alloc, name, description, verbosity) super().__init__(raw_objfn, mdc_store, penalties, verbosity) class ChiAlphaFunction(TimeIndependentMDCObjectiveFunction): + + TEMPLATE_FIELDS = ( """ Model-based chi-alpha function: `N[x + 1/(alpha * x^alpha) - (1 + 1/alpha)]` where `x := p/f`. - - Parameters - ---------- - mdl : Model - The model - specifies how parameter values are turned into probabilities - for each circuit outcome. - - dataset : DataSet - The data set - specifies how counts and total_counts are obtained for each - circuit outcome. - - circuits : list or CircuitList - The circuit list - specifies what probabilities and counts this objective - function compares. If `None`, then the keys of `dataset` are used. - - regularization : dict, optional - Regularization values. - - penalties : dict, optional - Penalty values. Penalties usually add additional (penalty) terms to the sum - of per-circuit-outcome contributions that evaluate to the objective function. - - resource_alloc : ResourceAllocation, optional - Available resources and how they should be allocated for computations. - - name : str, optional - A name for this objective function (can be anything). - - description : str, optional - A description for this objective function (can be anything) - - verbosity : int, optional - Level of detail to print to stdout. - - enable_hessian : bool, optional - Whether hessian calculations are allowed. If `True` then more resources are - needed. If `False`, calls to hessian-requiring function will result in an - error. - + """, + "" # no custom leading arguments. + """ alpha : float, optional The alpha parameter, which lies in the interval (0,1]. - """ + """ # one custom trailing argument. + ) @classmethod def create_from(cls, model, dataset, circuits, regularization=None, penalties=None, resource_alloc=None, name=None, description=None, verbosity=0, method_names=('fn',), array_types=(), alpha=1): - mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, - array_types, verbosity) + mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, array_types, verbosity) return cls(mdc_store, regularization, penalties, name, description, verbosity, alpha) + @set_docstring(TimeIndependentMDCObjectiveFunction.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0, alpha=1): raw_objfn = RawChiAlphaFunction(regularization, mdc_store.resource_alloc, name, description, verbosity, alpha) @@ -4877,117 +4824,41 @@ def __init__(self, mdc_store, regularization=None, penalties=None, name=None, de class FreqWeightedChi2Function(TimeIndependentMDCObjectiveFunction): - """ - Model-based frequency-weighted chi-squared function: `N(p-f)^2 / f` - Parameters - ---------- - mdl : Model - The model - specifies how parameter values are turned into probabilities - for each circuit outcome. - - dataset : DataSet - The data set - specifies how counts and total_counts are obtained for each - circuit outcome. - - circuits : list or CircuitList - The circuit list - specifies what probabilities and counts this objective - function compares. If `None`, then the keys of `dataset` are used. - - regularization : dict, optional - Regularization values. - - penalties : dict, optional - Penalty values. Penalties usually add additional (penalty) terms to the sum - of per-circuit-outcome contributions that evaluate to the objective function. - - resource_alloc : ResourceAllocation, optional - Available resources and how they should be allocated for computations. - - name : str, optional - A name for this objective function (can be anything). - - description : str, optional - A description for this objective function (can be anything) - - verbosity : int, optional - Level of detail to print to stdout. - - enable_hessian : bool, optional - Whether hessian calculations are allowed. If `True` then more resources are - needed. If `False`, calls to hessian-requiring function will result in an - error. + TEMPLATE_FIELDS = ( """ + Model-based frequency-weighted chi-squared function: `N(p-f)^2 / f` + """, "", "" + ) - @classmethod - def create_from(cls, model, dataset, circuits, regularization=None, penalties=None, - resource_alloc=None, name=None, description=None, verbosity=0, - method_names=('fn',), array_types=()): - mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, - array_types, verbosity) - return cls(mdc_store, regularization, penalties, name, description, verbosity) - + @set_docstring(TimeIndependentMDCObjectiveFunction % TEMPLATE_FIELDS) def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0): raw_objfn = RawFreqWeightedChi2Function(regularization, mdc_store.resource_alloc, name, description, verbosity) super().__init__(raw_objfn, mdc_store, penalties, verbosity) class CustomWeightedChi2Function(TimeIndependentMDCObjectiveFunction): + + TEMPLATE_FIELDS = ( """ Model-based custom-weighted chi-squared function: `cw^2 (p-f)^2` - - Parameters - ---------- - mdl : Model - The model - specifies how parameter values are turned into probabilities - for each circuit outcome. - - dataset : DataSet - The data set - specifies how counts and total_counts are obtained for each - circuit outcome. - - circuits : list or CircuitList - The circuit list - specifies what probabilities and counts this objective - function compares. If `None`, then the keys of `dataset` are used. - - regularization : dict, optional - Regularization values. - - penalties : dict, optional - Penalty values. Penalties usually add additional (penalty) terms to the sum - of per-circuit-outcome contributions that evaluate to the objective function. - - resource_alloc : ResourceAllocation, optional - Available resources and how they should be allocated for computations. - - name : str, optional - A name for this objective function (can be anything). - - description : str, optional - A description for this objective function (can be anything) - - verbosity : int, optional - Level of detail to print to stdout. - - enable_hessian : bool, optional - Whether hessian calculations are allowed. If `True` then more resources are - needed. If `False`, calls to hessian-requiring function will result in an - error. - + """, "", + """ custom_weights : numpy.ndarray, optional One-dimensional array of the custom weights, which linearly multiply the *least-squares* terms, i.e. `(p - f)`. If `None`, then unit weights are used and the objective function computes the sum of unweighted squares. """ + ) @classmethod def create_from(cls, model, dataset, circuits, regularization=None, penalties=None, resource_alloc=None, name=None, description=None, verbosity=0, method_names=('fn',), array_types=(), custom_weights=None): - mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, - array_types, verbosity) + mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, array_types, verbosity) return cls(mdc_store, regularization, penalties, name, description, verbosity, custom_weights) + @set_docstring(TimeIndependentMDCObjectiveFunction @ TEMPLATE_FIELDS) def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0, custom_weights=None): raw_objfn = RawCustomWeightedChi2Function(regularization, mdc_store.resource_alloc, name, description, @@ -4996,56 +4867,14 @@ def __init__(self, mdc_store, regularization=None, penalties=None, name=None, de class PoissonPicDeltaLogLFunction(TimeIndependentMDCObjectiveFunction): - """ - Model-based poisson-picture delta log-likelihood function: `N*f*log(f/p) - N*(f-p)`. - - Parameters - ---------- - mdl : Model - The model - specifies how parameter values are turned into probabilities - for each circuit outcome. - - dataset : DataSet - The data set - specifies how counts and total_counts are obtained for each - circuit outcome. - circuits : list or CircuitList - The circuit list - specifies what probabilities and counts this objective - function compares. If `None`, then the keys of `dataset` are used. - - regularization : dict, optional - Regularization values. - - penalties : dict, optional - Penalty values. Penalties usually add additional (penalty) terms to the sum - of per-circuit-outcome contributions that evaluate to the objective function. - - resource_alloc : ResourceAllocation, optional - Available resources and how they should be allocated for computations. - - name : str, optional - A name for this objective function (can be anything). - - description : str, optional - A description for this objective function (can be anything) - - verbosity : int, optional - Level of detail to print to stdout. - - enable_hessian : bool, optional - Whether hessian calculations are allowed. If `True` then more resources are - needed. If `False`, calls to hessian-requiring function will result in an - error. + TEMPLATE_FIELDS = ( """ + Model-based poisson-picture delta log-likelihood function: `N*f*log(f/p) - N*(f-p)`. + """, "", "" + ) - @classmethod - def create_from(cls, model, dataset, circuits, regularization=None, penalties=None, - resource_alloc=None, name=None, description=None, verbosity=0, - method_names=('fn',), array_types=()): - mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, - array_types, verbosity) - return cls(mdc_store, regularization, penalties, name, description, verbosity) - + @set_docstring(TimeIndependentMDCObjectiveFunction % TEMPLATE_FIELDS) def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0): raw_objfn = RawPoissonPicDeltaLogLFunction(regularization, mdc_store.resource_alloc, name, description, verbosity) @@ -5053,112 +4882,35 @@ def __init__(self, mdc_store, regularization=None, penalties=None, name=None, de class DeltaLogLFunction(TimeIndependentMDCObjectiveFunction): - """ - Model-based delta log-likelihood function: `N*f*log(f/p)`. - - Parameters - ---------- - mdl : Model - The model - specifies how parameter values are turned into probabilities - for each circuit outcome. - - dataset : DataSet - The data set - specifies how counts and total_counts are obtained for each - circuit outcome. - - circuits : list or CircuitList - The circuit list - specifies what probabilities and counts this objective - function compares. If `None`, then the keys of `dataset` are used. - regularization : dict, optional - Regularization values. - - penalties : dict, optional - Penalty values. Penalties usually add additional (penalty) terms to the sum - of per-circuit-outcome contributions that evaluate to the objective function. - - resource_alloc : ResourceAllocation, optional - Available resources and how they should be allocated for computations. - - name : str, optional - A name for this objective function (can be anything). - - description : str, optional - A description for this objective function (can be anything) - - verbosity : int, optional - Level of detail to print to stdout. - - enable_hessian : bool, optional - Whether hessian calculations are allowed. If `True` then more resources are - needed. If `False`, calls to hessian-requiring function will result in an - error. + TEMPLATE_FIELDS = ( """ + Model-based delta log-likelihood function: `N*f*log(f/p)`. + """, "", "" + ) - @classmethod - def create_from(cls, model, dataset, circuits, regularization=None, penalties=None, - resource_alloc=None, name=None, description=None, verbosity=0, - method_names=('fn',), array_types=()): - mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, - array_types, verbosity) - return cls(mdc_store, regularization, penalties, name, description, verbosity) - + @set_docstring(TimeIndependentMDCObjectiveFunction.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0): raw_objfn = RawDeltaLogLFunction(regularization, mdc_store.resource_alloc, name, description, verbosity) super().__init__(raw_objfn, mdc_store, penalties, verbosity) class MaxLogLFunction(TimeIndependentMDCObjectiveFunction): - """ - Model-based maximum-model log-likelihood function: `N*f*log(f)` - - Parameters - ---------- - mdl : Model - The model - specifies how parameter values are turned into probabilities - for each circuit outcome. - - dataset : DataSet - The data set - specifies how counts and total_counts are obtained for each - circuit outcome. - - circuits : list or CircuitList - The circuit list - specifies what probabilities and counts this objective - function compares. If `None`, then the keys of `dataset` are used. - - regularization : dict, optional - Regularization values. - penalties : dict, optional - Penalty values. Penalties usually add additional (penalty) terms to the sum - of per-circuit-outcome contributions that evaluate to the objective function. - - resource_alloc : ResourceAllocation, optional - Available resources and how they should be allocated for computations. - - name : str, optional - A name for this objective function (can be anything). - - description : str, optional - A description for this objective function (can be anything) - - verbosity : int, optional - Level of detail to print to stdout. - - enable_hessian : bool, optional - Whether hessian calculations are allowed. If `True` then more resources are - needed. If `False`, calls to hessian-requiring function will result in an - error. + TEMPLATE_FIELDS = ( """ + Model-based maximum-model log-likelihood function: `N*f*log(f)` + """, "", "" + ) @classmethod def create_from(cls, model, dataset, circuits, regularization=None, penalties=None, resource_alloc=None, name=None, description=None, verbosity=0, method_names=('fn',), array_types=(), poisson_picture=True): - mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, - array_types, verbosity) + mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, array_types, verbosity) return cls(mdc_store, regularization, penalties, name, description, verbosity, poisson_picture) + @set_docstring(TimeIndependentMDCObjectiveFunction.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0, poisson_picture=True): raw_objfn = RawMaxLogLFunction(regularization, mdc_store.resource_alloc, name, description, verbosity, @@ -5166,79 +4918,26 @@ def __init__(self, mdc_store, regularization=None, penalties=None, name=None, de super().__init__(raw_objfn, mdc_store, penalties, verbosity) -class TVDFunction(TimeIndependentMDCObjectiveFunction): +class TermWeighted(TimeIndependentMDCObjectiveFunction): """ - Model-based TVD function: `0.5 * |p-f|`. - - Parameters - ---------- - mdl : Model - The model - specifies how parameter values are turned into probabilities - for each circuit outcome. - - dataset : DataSet - The data set - specifies how counts and total_counts are obtained for each - circuit outcome. - - circuits : list or CircuitList - The circuit list - specifies what probabilities and counts this objective - function compares. If `None`, then the keys of `dataset` are used. - - regularization : dict, optional - Regularization values. - - penalties : dict, optional - Penalty values. Penalties usually add additional (penalty) terms to the sum - of per-circuit-outcome contributions that evaluate to the objective function. - - resource_alloc : ResourceAllocation, optional - Available resources and how they should be allocated for computations. - - name : str, optional - A name for this objective function (can be anything). + Represents an objective whose term function takes the form + f(params) = w * g(params), + where w is constant and g(params) is a black-box term function. - description : str, optional - A description for this objective function (can be anything) - - verbosity : int, optional - Level of detail to print to stdout. - - enable_hessian : bool, optional - Whether hessian calculations are allowed. If `True` then more resources are - needed. If `False`, calls to hessian-requiring function will result in an - error. + This class' implementation of terms() and dterms() has a substantial amount of code + duplication with the implementations in TimeIndependentMDCObjectiveFunction """ - @classmethod - def create_from(cls, model, dataset, circuits, regularization=None, penalties=None, - resource_alloc=None, name=None, description=None, verbosity=0, - method_names=('fn',), array_types=()): - mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, - array_types, verbosity) - return cls(mdc_store, regularization, penalties, name, description, verbosity) - - def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0): - raw_objfn = RawTVDFunction(regularization, mdc_store.resource_alloc, name, description, verbosity) - super().__init__(raw_objfn, mdc_store, penalties, verbosity) - self.num_circuits = 0 - self.terms_weights = _np.array([]) - self._update_terms_weights() # <-- properly computes the two properties above. + @property + def terms_weights(self) -> _np.ndarray: + if not hasattr(self, '_terms_weights'): + self._update_terms_weights() + return self._terms_weights def _update_terms_weights(self): - self.num_circuits = self.layout.num_circuits - num_elements = self.layout.num_elements - circuit_sizes = _np.zeros((num_elements,)) - for elind, circuit, _ in self.layout: - circuit_sizes[elind] = 1 + circuit.num_gates - assert _np.all(circuit_sizes > 0) - self.terms_weights = 1 / circuit_sizes - self.terms_weights /= _np.mean(self.terms_weights) - # ^ Make the weights mean-1 to avoid unintended changes to Levenberg-Marquardt - # stopping criteria. + self._terms_weights = _np.ones(self.layout.num_elements) return - # QUESTION: could I just call the base class's implementation of terms and then scale the - # result? If so then I can avoid a ton of code duplication. def terms(self, paramvec=None, oob_check=False, profiler_str="TERMS OBJECTIVE"): tm = _time.time() if paramvec is None: @@ -5279,8 +4978,6 @@ def terms(self, paramvec=None, oob_check=False, profiler_str="TERMS OBJECTIVE"): terms *= self.terms_weights return terms - # QUESTION: could I just call the base class's implementation of dterms and then scale the - # leading rows of the result? If so then I can avoid a ton of code duplication. def dterms(self, paramvec=None): tm = _time.time() unit_ralloc = self.layout.resource_alloc('param-processing') @@ -5321,6 +5018,33 @@ def dterms(self, paramvec=None): return self.jac +class TVDFunction(TermWeighted): + + TEMPLATE_FIELDS = ( + """ + Model-based TVD function: `0.5 * |p-f|`. + """, "", "" + ) + + @set_docstring(TermWeighted.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) + def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0): + raw_objfn = RawTVDFunction(regularization, mdc_store.resource_alloc, name, description, verbosity) + super().__init__(raw_objfn, mdc_store, penalties, verbosity) + self._update_terms_weights() + + def _update_terms_weights(self): + num_elements = self.layout.num_elements + circuit_sizes = _np.zeros((num_elements,)) + for elind, circuit, _ in self.layout: + circuit_sizes[elind] = 1 + circuit.num_gates + assert _np.all(circuit_sizes > 0) + self._terms_weights = 1 / circuit_sizes + self._terms_weights /= _np.mean(self.terms_weights) + # ^ Make the weights mean-1 to avoid unintended changes to Levenberg-Marquardt + # stopping criteria. + return + + class TimeDependentMDCObjectiveFunction(MDCObjectiveFunction): """ A time-dependent model-based objective function From 4d09819d580eda4b83406cf8898fb07f76d029d4 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Thu, 17 Apr 2025 14:25:01 -0700 Subject: [PATCH 26/71] fix silly bugs --- pygsti/objectivefns/objectivefns.py | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index 6db70f7b6..cc9794437 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -4142,18 +4142,14 @@ def zero_freq_hterms(self, total_counts, probs): class TimeIndependentMDCObjectiveFunction(MDCObjectiveFunction): TEMPLATE_FIELDS = ( - # Class description """ A time-independent model-based (:class:`MDCObjectiveFunction`-derived) objective function. - """ - # Constructor has ONE custom leading arguments. + """, """ raw_objfn : RawObjectiveFunction The raw objective function - specifies how probability and count values are turned into objective function values. - """, - # Constructor has ZERO custom trailing arguments. - "" + """, "" ) DOCSTR_TEMPLATE = \ @@ -4802,7 +4798,7 @@ class ChiAlphaFunction(TimeIndependentMDCObjectiveFunction): """ Model-based chi-alpha function: `N[x + 1/(alpha * x^alpha) - (1 + 1/alpha)]` where `x := p/f`. """, - "" # no custom leading arguments. + "", # no custom leading arguments. """ alpha : float, optional The alpha parameter, which lies in the interval (0,1]. @@ -4831,7 +4827,7 @@ class FreqWeightedChi2Function(TimeIndependentMDCObjectiveFunction): """, "", "" ) - @set_docstring(TimeIndependentMDCObjectiveFunction % TEMPLATE_FIELDS) + @set_docstring(TimeIndependentMDCObjectiveFunction.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0): raw_objfn = RawFreqWeightedChi2Function(regularization, mdc_store.resource_alloc, name, description, verbosity) super().__init__(raw_objfn, mdc_store, penalties, verbosity) @@ -4858,7 +4854,7 @@ def create_from(cls, model, dataset, circuits, regularization=None, penalties=No mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, array_types, verbosity) return cls(mdc_store, regularization, penalties, name, description, verbosity, custom_weights) - @set_docstring(TimeIndependentMDCObjectiveFunction @ TEMPLATE_FIELDS) + @set_docstring(TimeIndependentMDCObjectiveFunction.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0, custom_weights=None): raw_objfn = RawCustomWeightedChi2Function(regularization, mdc_store.resource_alloc, name, description, @@ -4874,7 +4870,7 @@ class PoissonPicDeltaLogLFunction(TimeIndependentMDCObjectiveFunction): """, "", "" ) - @set_docstring(TimeIndependentMDCObjectiveFunction % TEMPLATE_FIELDS) + @set_docstring(TimeIndependentMDCObjectiveFunction.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0): raw_objfn = RawPoissonPicDeltaLogLFunction(regularization, mdc_store.resource_alloc, name, description, verbosity) From 8d3bdefedbd3acb1816b90bb30007d5cca9780ea Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Fri, 18 Apr 2025 09:48:10 -0700 Subject: [PATCH 27/71] correct log message in simplerlm.py. Add a comment about a TODO. --- pygsti/optimize/simplerlm.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/pygsti/optimize/simplerlm.py b/pygsti/optimize/simplerlm.py index 7d3fd8913..db7e64fda 100644 --- a/pygsti/optimize/simplerlm.py +++ b/pygsti/optimize/simplerlm.py @@ -287,6 +287,8 @@ def run(self, objective: TimeIndependentMDCObjectiveFunction, profiler, printer) objective_func, jacobian, x0, max_iter=self.maxiter, num_fd_iters=self.fditer, + # NOTE: I think the fallback values below will NEVER be triggered. + # Should probably remove them. See __init__ instead. f_norm2_tol=self.tol.get('f', 1.0), jac_norm_tol=self.tol.get('jac', 1e-6), rel_ftol=self.tol.get('relf', 1e-6), @@ -583,7 +585,7 @@ def simplish_leastsq( if norm_JTf < jac_norm_tol: if oob_check_interval <= 1: - msg = "norm(jacobian) is at most %g" % jac_norm_tol + msg = "norm(J'f) is at most %g" % jac_norm_tol converged = True break else: From ac67824e5b538c91d74741c32bc04d87f66b054c Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Fri, 18 Apr 2025 10:52:30 -0700 Subject: [PATCH 28/71] make classmethods of GSTObjFnBuilders static methods, since GSTObjFnBuilders has no derived classes. --- pygsti/protocols/gst.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/pygsti/protocols/gst.py b/pygsti/protocols/gst.py index 10aa51e17..73db5bb05 100644 --- a/pygsti/protocols/gst.py +++ b/pygsti/protocols/gst.py @@ -731,8 +731,9 @@ class GSTObjFnBuilders(_NicelySerializable): on the final GST iteration. """ - @classmethod - def cast(cls, obj): + # This used to be a class method, but this class has no derived classes. + @staticmethod + def cast(obj): """ Cast `obj` to a :class:`GSTObjFnBuilders` object. @@ -747,14 +748,16 @@ def cast(cls, obj): ------- GSTObjFnBuilders """ + cls = GSTObjFnBuilders if isinstance(obj, cls): return obj elif obj is None: return cls.create_from() elif isinstance(obj, dict): return cls.create_from(**obj) elif isinstance(obj, (list, tuple)): return cls(*obj) else: raise ValueError("Cannot create an %s object from '%s'" % (cls.__name__, str(type(obj)))) - @classmethod - def create_from(cls, objective='logl', freq_weighted_chi2=False, always_perform_mle=False, only_perform_mle=False): + # This used to be a class method, but this class has no derived classes. + @staticmethod + def create_from(objective='logl', freq_weighted_chi2=False, always_perform_mle=False, only_perform_mle=False): """ Creates a common :class:`GSTObjFnBuilders` object from several arguments. @@ -805,7 +808,7 @@ def create_from(cls, objective='logl', freq_weighted_chi2=False, always_perform_ except Exception as e: raise ValueError("Invalid objective: %s" % objective) - return cls(iteration_builders, final_builders) + return GSTObjFnBuilders(iteration_builders, final_builders) def __init__(self, iteration_builders, final_builders=()): super().__init__() From 5152605d01348cdb5ce59077b300fd310f7538a5 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Fri, 18 Apr 2025 10:54:29 -0700 Subject: [PATCH 29/71] Objective functions: new RawAbsPower and LpNormToPowerP classes. Make classmethods of ObjectiveFunctionBuilder static, since ObjectiveFunctionBuilder has no derived classes. --- pygsti/objectivefns/objectivefns.py | 93 +++++++++++++++++++++++++---- 1 file changed, 80 insertions(+), 13 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index cc9794437..fda264b56 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -150,8 +150,9 @@ class ObjectiveFunctionBuilder(_NicelySerializable): Penalty values (allowed keys depend on `cls_to_build`). """ - @classmethod - def cast(cls, obj): + # This was a classmethod, but I made it static because this class has no derived classes. + @staticmethod + def cast(obj): """ Cast `obj` to an `ObjectiveFunctionBuilder` instance. @@ -167,6 +168,7 @@ def cast(cls, obj): ------- ObjectiveFunctionBuilder """ + cls = ObjectiveFunctionBuilder if isinstance(obj, cls): return obj elif obj is None: return cls.create_from() elif isinstance(obj, str): return cls.create_from(objective=obj) @@ -174,15 +176,16 @@ def cast(cls, obj): elif isinstance(obj, (list, tuple)): return cls(*obj) else: raise ValueError("Cannot create an %s object from '%s'" % (cls.__name__, str(type(obj)))) - @classmethod - def create_from(cls, objective='logl', freq_weighted_chi2=False): + # This was a classmethod, but I made it static because this class has no derived classes. + @staticmethod + def create_from(objective='logl', freq_weighted_chi2=False): """ Creates common :class:`ObjectiveFunctionBuilder` from a few arguments. Parameters ---------- - objective : {'logl', 'chi2'}, optional - The objective function type: log-likelihood or chi-squared. + objective : {'logl', 'chi2', 'tvd'}, optional + The objective function type: log-likelihood, chi-squared, or TVD. freq_weighted_chi2 : bool, optional Whether to use 1/frequency values as the weights in the `"chi2"` case. @@ -190,18 +193,34 @@ def create_from(cls, objective='logl', freq_weighted_chi2=False): Returns ------- ObjectiveFunctionBuilder + + Notes + ----- + This function's implementation calls relies on various "builder" classmethods of other classes. + There is a default implementation of `.builder` that's triggered in most codepaths. That + default is to just return + + ObjectiveFunctionBuilder(cls, name, description, regularization, penalties, **kwargs). + + In these cases, the kwargs that we pass to `.builder` functions get seen as **kwargs + for a call to the ObjectiveFunctionBuilder constructor. Those kwargs are stored in the + `.additional_args` member of the returned ObjectiveFunctionBuilder object. That member + is passed as **kwargs to the constructor for the input `cls` when we call `.build()` on + the ObjectiveFunctionBuilder instance. """ if objective == "chi2": if freq_weighted_chi2: builder = FreqWeightedChi2Function.builder( name='fwchi2', description="Freq-weighted sum of Chi^2", - regularization={'min_freq_clip_for_weighting': 1e-4}) + regularization={'min_freq_clip_for_weighting': 1e-4} + ) else: builder = Chi2Function.builder( name='chi2', description="Sum of Chi^2", - regularization={'min_prob_clip_for_weighting': 1e-4}) + regularization={'min_prob_clip_for_weighting': 1e-4} + ) elif objective == "logl": builder = PoissonPicDeltaLogLFunction.builder( @@ -210,16 +229,19 @@ def create_from(cls, objective='logl', freq_weighted_chi2=False): regularization={'min_prob_clip': 1e-4, 'radius': 1e-4}, penalties={'cptp_penalty_factor': 0, - 'spam_penalty_factor': 0}) + 'spam_penalty_factor': 0} + ) elif objective == "tvd": - builder = TVDFunction.builder( - name='tvd', - description="Total Variational Distance (TVD)") + builder = TVDFunction.builder(name='tvd', description="Total Variational Distance (TVD)") + + elif isinstance(objective, tuple) and objective[0] == 'Lp^p': + power = objective[1] + builder = LpNormToPowerP.builder(name='Lp^p', description=f"L_{power} norm to the power {power}.", power=objective[1]) else: raise ValueError("Invalid objective: %s" % objective) - assert(isinstance(builder, cls)), "This function should always return an ObjectiveFunctionBuilder!" + assert(isinstance(builder, ObjectiveFunctionBuilder)), "This function should always return an ObjectiveFunctionBuilder!" return builder def __init__(self, cls_to_build, name=None, description=None, regularization=None, penalties=None, **kwargs): @@ -4132,6 +4154,30 @@ def zero_freq_hterms(self, total_counts, probs): raise NotImplementedError("Derivatives not implemented for TVD yet!") +class RawAbsPower(RawObjectiveFunction): + + def __init__(self, power: float, regularization=None, resource_alloc=None, + name='Lp^p', description="Elementwise absolute value and raising to a power.", verbosity=0): + super().__init__(regularization, resource_alloc, name, description, verbosity) + assert power >= 1 + self.power = power + + def chi2k_distributed_qty(self, objective_function_value): + return -1 + + def terms(self, probs, counts, total_counts, freqs, intermediates=None): + return 0.5 * _np.abs(probs - freqs) ** self.power + + def dterms(self, probs, counts, total_counts, freqs, intermediates=None): + t = probs - freqs + d = (0.5 * self.power) * _np.abs(t) ** (self.power - 1) + d[t < 0] *= -1 + return d + + def zero_freq_terms(self, total_counts, probs): + return 0.5 * _np.abs(probs) ** self.power + + ###################################################### # # Start MDCObjectiveFunction subclasses @@ -5041,6 +5087,27 @@ def _update_terms_weights(self): return +class LpNormToPowerP(TermWeighted): + + TEMPLATE_FIELDS = ( + """ + Model-based loss function: `0.5 * |p-f|^power`. + """, "", + """ + power : float, optonal + Must be >= 1. + """ + ) + + @set_docstring(TermWeighted.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) + def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, + verbosity=0, power=2): + raw_objfn = RawAbsPower(power, regularization, mdc_store.resource_alloc, name, description, verbosity) + super().__init__(raw_objfn, mdc_store, penalties, verbosity) + self.power = raw_objfn.power + self._update_terms_weights() + + class TimeDependentMDCObjectiveFunction(MDCObjectiveFunction): """ A time-dependent model-based objective function From 74fcb68a17505523ee1fa39884daf86576c4bd65 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Tue, 22 Apr 2025 16:50:29 -0700 Subject: [PATCH 30/71] support for both TVD and normalized TVD --- pygsti/objectivefns/objectivefns.py | 35 +++++++++++++++++++---------- 1 file changed, 23 insertions(+), 12 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index fda264b56..863bf03f3 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -232,8 +232,11 @@ def create_from(objective='logl', freq_weighted_chi2=False): 'spam_penalty_factor': 0} ) - elif objective == "tvd": - builder = TVDFunction.builder(name='tvd', description="Total Variational Distance (TVD)") + elif 'tvd' in objective: + descr = "Total Variational Distance (TVD)" + if 'normalized' in objective: + descr = descr + ', normalized by circuit depth' + builder = TVDFunction.builder(name=objective, description=descr) elif isinstance(objective, tuple) and objective[0] == 'Lp^p': power = objective[1] @@ -5064,7 +5067,10 @@ class TVDFunction(TermWeighted): TEMPLATE_FIELDS = ( """ - Model-based TVD function: `0.5 * |p-f|`. + Model-based TVD function: `0.5 * w * |p-f|`, where w is a vector of weights. + + If `name == 'normalized tvd'`, then `w[i]` will be 1/(length of circuit associated with i). + Otherwise, w[i] will equal 1. """, "", "" ) @@ -5072,18 +5078,23 @@ class TVDFunction(TermWeighted): def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0): raw_objfn = RawTVDFunction(regularization, mdc_store.resource_alloc, name, description, verbosity) super().__init__(raw_objfn, mdc_store, penalties, verbosity) + self.normalized = name == 'normalized tvd' + self._terms_weights = None self._update_terms_weights() def _update_terms_weights(self): - num_elements = self.layout.num_elements - circuit_sizes = _np.zeros((num_elements,)) - for elind, circuit, _ in self.layout: - circuit_sizes[elind] = 1 + circuit.num_gates - assert _np.all(circuit_sizes > 0) - self._terms_weights = 1 / circuit_sizes - self._terms_weights /= _np.mean(self.terms_weights) - # ^ Make the weights mean-1 to avoid unintended changes to Levenberg-Marquardt - # stopping criteria. + if self.normalized: + num_elements = self.layout.num_elements + circuit_sizes = _np.zeros((num_elements,)) + for elind, circuit, _ in self.layout: + circuit_sizes[elind] = 1 + circuit.num_gates + assert _np.all(circuit_sizes > 0) + self._terms_weights = 1 / circuit_sizes + # self._terms_weights /= _np.mean(self.terms_weights) + # ^ Make the weights mean-1 to avoid unintended changes to Levenberg-Marquardt + # stopping criteria. + elif self._terms_weights is None: + self._terms_weights = _np.ones(self.layout.num_elements) return From 5218e9f3f05468785d0858aa106db2f002744336 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Tue, 22 Apr 2025 16:50:42 -0700 Subject: [PATCH 31/71] check in --- wip_notebook_sharing/experiment_helpers.py | 182 +++++++++++++++++++++ 1 file changed, 182 insertions(+) create mode 100644 wip_notebook_sharing/experiment_helpers.py diff --git a/wip_notebook_sharing/experiment_helpers.py b/wip_notebook_sharing/experiment_helpers.py new file mode 100644 index 000000000..2f93acda5 --- /dev/null +++ b/wip_notebook_sharing/experiment_helpers.py @@ -0,0 +1,182 @@ +import pygsti +import numpy as np +from typing import Union, List +from pygsti.algorithms import run_gst_fit +from pygsti.drivers.longsequence import _get_optimizer, _get_badfit_options, _update_objfn_builders +from pygsti.objectivefns import ObjectiveFunctionBuilder, ModelDatasetCircuitsStore +import pygsti.objectivefns +from pygsti.optimize import SimplerLMOptimizer + + +def make_depolarized_dataset(modelpack, depol_level=0.01, max_max_len=128): + ideal_model = modelpack.target_model() + prep_fids = modelpack.prep_fiducials() + meas_fids = modelpack.meas_fiducials() + germs = modelpack.germs() + max_lens = [2**p for p in range(1+int(np.log2(max_max_len)))] + lsgst_circuit_lists = pygsti.circuits.create_lsgst_circuit_lists(ideal_model, prep_fids, meas_fids, germs, max_lens) + all_circuits = lsgst_circuit_lists[-1] + shots_per_circuit = 1000 + rng_state = np.random.default_rng(0) + depol_model = ideal_model.depolarize(op_noise=depol_level) + ds = pygsti.data.simulate_data(depol_model, all_circuits, shots_per_circuit, rand_state=rng_state) + return ds, depol_model + + +def make_tweaked_dataset(modelpack, depol_level=0.01, rand_unitary_scale=0.001, max_max_len=128): + ideal_model = modelpack.target_model() + prep_fids = modelpack.prep_fiducials() + meas_fids = modelpack.meas_fiducials() + germs = modelpack.germs() + max_lens = [2**p for p in range(1+int(np.log2(max_max_len)))] + lsgst_circuit_lists = pygsti.circuits.create_lsgst_circuit_lists(ideal_model, prep_fids, meas_fids, germs, max_lens) + all_circuits = lsgst_circuit_lists[-1] + shots_per_circuit = 1000 + rng_state = np.random.default_rng(0) + depol_model = ideal_model.depolarize(op_noise=depol_level, spam_noise=depol_level/2) + final_model = depol_model.randomize_with_unitary(scale=rand_unitary_scale) + ds = pygsti.data.simulate_data(final_model, all_circuits, shots_per_circuit, rand_state=rng_state) + return ds, final_model + + + +def corrupt_dataset(ds, prop_corrupt, rng=0): + dsc = ds.copy_nonstatic() + rng = np.random.default_rng(rng) + num_circs = len(dsc) + selected = rng.choice(np.arange(num_circs), size=int(num_circs*prop_corrupt), replace=False) + circuits = list(dsc.keys()) + selected = [circuits[i] for i in selected] + for c in selected: + num_shots = dsc[c].total + old_row = dsc[c].to_dict() + distn = rng.random(len(old_row)) + distn /= np.sum(distn) + new_row = {k: num_shots * distn[i] for i,k in enumerate(old_row.keys())} + dsc[c] = new_row + dsc.comment = 'corrupt' + return dsc, selected + + +def run_gst(ds, fids, germs, target_model, final_objectives: List[Union[str, tuple]], verbosity: int, mode='CPTPLND', + iteration_objective='chi2'): + """ + In the context of this notebook, `ds` is produced by either make_depolarized_dataset or corrupt_dataset. + final_objective can be anything accepted by `ObjectiveFunctionBuilder.create_from`. + + This function wraps up three steps of a GST pipeline. + + 1. Construct a StandardGSTDesign based on (target_model, ds, fids, germs). + * processor_spec is the value returned from target_model.create_processor_spec. + * max_lens list is all powers of two that are <= the depth of the longest circuit in ds. + * circuits in the design are filtered to only include circuits that appeared in ds. + + 2. Construct a StandardGST protocol object based on (final_objective, mode, verbosity). + * The gauge optimization suite is 'stdgaugeopt', minus the TPSpam optimization step. + * objfn_builders, optimizer, and badfit_options are all set so the final + iteration's objective function is based on final_objective. + + 3. Run GST with checkpointing turned off. + We dot NOT save the results to disk! The calling function is responsible for that. + """ + if isinstance(final_objectives, str): + final_objectives = [final_objectives] + assert isinstance(final_objectives, list) + + max_exp = int(np.log2(np.max([len(c) for c in ds.keys()]))) + max_lens = [2**p for p in range(1 + max_exp)] + prep_fids, meas_fids = fids + + target_model = target_model.copy() + target_model.default_gauge_group = 'unitary' + + gos = pygsti.protocols.gst.GSTGaugeOptSuite.cast('stdgaugeopt') + gop_params = gos.to_dictionary(target_model) + # ^ a dict with one key, 'stdgaugeopt', whose corresponding value is a list of dicts. + # The internal dicts will indicate Frobenius-based losses for gates and SPAM, + # along with varying weights. Additional elements can be added to any one of these + # internal dicts to be passed to gaugeopt_to_target. + gop_params['stdgaugeopt'] = gop_params['stdgaugeopt'][:-1] + # ^ drop the 1-dimensional TPSpam gauge optimization step. + + exp_design = pygsti.protocols.StandardGSTDesign( + target_model.create_processor_spec(), + prep_fids, meas_fids, germs, max_lens, + None, # germ_length_limits + None, 1, None, # fidPairs, keepFraction, keepSeed + True, True, # include_lgst, nested_circuit_lists + None, # string_manipulation_rules + None, # op_label_aliases + ds, 'drop', verbosity=verbosity + ) + data = pygsti.protocols.ProtocolData(exp_design, ds) + + # + # Run long-sequence GST where the final objective is the first entry + # in the final_objectives list. + # + final_objective = final_objectives[0] + builders = pygsti.protocols.GSTObjFnBuilders( + [ObjectiveFunctionBuilder.create_from(iteration_objective)], + [ObjectiveFunctionBuilder.create_from(final_objective)] + #[ObjectiveFunctionBuilder.create_from(fo) for fo in final_objectives] + ) + advanced_options = { + 'extra_lm_opts': {'tol': + {'relx': 1e-8, 'relf': 1e-6, 'f': -1.0, 'jac': -1, 'maxdx': 1.0}, + } + } + _update_objfn_builders(builders.iteration_builders, advanced_options) + _update_objfn_builders(builders.final_builders, advanced_options) + optim_iter = SimplerLMOptimizer.cast( + _get_optimizer(dict(), target_model) + ) + optim_last = SimplerLMOptimizer.cast( + _get_optimizer(advanced_options, target_model) + ) + bfops = _get_badfit_options(advanced_options) + proto = pygsti.protocols.StandardGST( + (mode,), gop_params, target_model, None, + objfn_builders = builders, + optimizer = optim_iter, + badfit_options = bfops, + verbosity = verbosity + ) + modelest_results = proto.run(data, disable_checkpointing=True) + modelest_results.rename_estimate(mode, str(final_objective)) + + # + # Run one GST fit for each entry in final_objectives[1:]. + # Initialize the fit at the last model that fit with iteration_objective. + # + est = modelest_results.estimates[str(final_objective)] + seed_name = f'iteration {est.num_iterations - 1} estimate' + seed_model = est.models[seed_name] + circuits = exp_design.all_circuits_needing_data + # array_types = optim.array_types + builders.final_builders[0].compute_array_types( + # optim.called_objective_methods, seed_model.sim + # ) + seed_mdc_store = est.final_mdc_store() + comm = seed_mdc_store.resource_alloc.comm + printer = pygsti.VerbosityPrinter.create_printer(verbosity, comm) + import time + import copy + from pygsti.algorithms.core import _do_runopt + from pygsti.algorithms.gaugeopt import gaugeopt_to_target + for final_objective in final_objectives[1:]: + builder = ObjectiveFunctionBuilder.create_from(final_objective) + array_types = optim_last.array_types + \ + builder.compute_array_types(optim_last.called_objective_methods, seed_model.sim) + mdc_store = ModelDatasetCircuitsStore(seed_model, data.dataset, circuits, seed_mdc_store.resource_alloc, array_types=array_types) + printer.log('') + out = run_gst_fit(mdc_store, optim_last, builder, verbosity - 2) + # ^ Would be nice if that accepted a printer. + fobjstr = str(final_objective) + modelest_results.add_estimate(copy.deepcopy(est), fobjstr) + modelest_results.estimates[fobjstr].models['final iteration estimate'] = out[1].model + modelest_results.estimates[fobjstr].models.pop('stdgaugeopt') + modelest_results.estimates[fobjstr].add_gaugeoptimized(gop_params['stdgaugeopt'], label='stdgaugeopt') + pass + + return modelest_results + From b67161d6a7ba6739f7a99fa77d58993c5a912226 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 23 Apr 2025 13:22:49 -0700 Subject: [PATCH 32/71] add the option for stateless evaluation of an MDCObective.fn(...). Update experiment_helpers.run_gst to make some necessary copies. Update notebook to include computing model violation and all-pairs objective values having fitted to multiple final objectives. --- pygsti/objectivefns/objectivefns.py | 17 +- pygsti/protocols/gst.py | 3 + .../case0-gst-with-outliers-beyond-tvd.ipynb | 977 ++++++++++++++++++ wip_notebook_sharing/experiment_helpers.py | 53 +- 4 files changed, 1022 insertions(+), 28 deletions(-) create mode 100644 wip_notebook_sharing/case0-gst-with-outliers-beyond-tvd.ipynb diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index 863bf03f3..0867bcaf9 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -236,6 +236,9 @@ def create_from(objective='logl', freq_weighted_chi2=False): descr = "Total Variational Distance (TVD)" if 'normalized' in objective: descr = descr + ', normalized by circuit depth' + assert objective == 'normalized tvd' + else: + assert objective == 'tvd' builder = TVDFunction.builder(name=objective, description=descr) elif isinstance(objective, tuple) and objective[0] == 'Lp^p': @@ -1392,7 +1395,7 @@ def dlsvec_percircuit(self, paramvec=None): # Note: don't need paramvec here since above call sets it return (0.5 / denom)[:, None] * self.dpercircuit() - def fn_local(self, paramvec=None): + def fn_local(self, paramvec=None, stateless=False): """ Evaluate the *local* value of this objective function. @@ -1411,9 +1414,15 @@ def fn_local(self, paramvec=None): ------- float """ - return _np.sum(self.terms(paramvec)) + if paramvec is None or not stateless: + return _np.sum(self.terms(paramvec)) + else: + old_paramvec = self.model.to_vector() + val = _np.sum(self.terms(paramvec)) + self.model.from_vector(old_paramvec) + return val - def fn(self, paramvec=None): + def fn(self, paramvec=None, stateless=False): """ Evaluate the value of this objective function. @@ -1428,7 +1437,7 @@ def fn(self, paramvec=None): float """ result, result_shm = _smt.create_shared_ndarray(self.resource_alloc, (1,), 'd') - local = _np.array([self.fn_local(paramvec)], 'd') + local = _np.array([self.fn_local(paramvec, stateless)], 'd') unit_ralloc = self.layout.resource_alloc('atom-processing') # proc group that computes same els self.resource_alloc.allreduce_sum(result, local, unit_ralloc) global_fnval = result[0] diff --git a/pygsti/protocols/gst.py b/pygsti/protocols/gst.py index 73db5bb05..6c4bc392b 100644 --- a/pygsti/protocols/gst.py +++ b/pygsti/protocols/gst.py @@ -3121,6 +3121,9 @@ def add_estimate(self, estimate, estimate_key='default'): _warnings.warn("Re-initializing the %s estimate" % estimate_key + " of this Results object! Usually you don't" + " want to do this.") + + if estimate.parent is None: + estimate.set_parent(self) self.estimates[estimate_key] = estimate diff --git a/wip_notebook_sharing/case0-gst-with-outliers-beyond-tvd.ipynb b/wip_notebook_sharing/case0-gst-with-outliers-beyond-tvd.ipynb new file mode 100644 index 000000000..848484411 --- /dev/null +++ b/wip_notebook_sharing/case0-gst-with-outliers-beyond-tvd.ipynb @@ -0,0 +1,977 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import pygsti\n", + "from pygsti.modelpacks import smq1Q_XYI, smq2Q_XYCNOT\n", + "import numpy as np\n", + "from pprint import pprint\n", + "from experiment_helpers import make_depolarized_dataset, run_gst, corrupt_dataset, make_tweaked_dataset\n", + "from scipy import linalg as la\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "mp = smq1Q_XYI\n", + "target = mp.target_model()\n", + "fids = (mp.prep_fiducials(), mp.meas_fiducials())\n", + "germs = mp.germs()\n", + "maxmaxlen = 64\n", + "ds, m_datagen = make_tweaked_dataset(mp, depol_level=0.001, rand_unitary_scale=0.001, max_max_len=maxmaxlen)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Circuit Creation ---\n", + " 952 circuits created\n", + " Dataset has 952 entries: 952 utilized, 0 requested circuits were missing\n", + "-- Std Practice: Iter 1 of 1 (CPTPLND) --: \n", + " Precomputing CircuitOutcomeProbabilityArray layouts for each iteration.\n", + " Layout for iteration 0\n", + " Num Param Processors (1,)\n", + " MapLayout: 1 processors divided into 1 x 1 (= 1) grid along circuit and parameter directions.\n", + " 1 atoms, parameter block size limits (None,)\n", + " *** Distributing 1 atoms to 1 atom-processing groups (1 cores) ***\n", + " More atom-processors than hosts: each host gets ~1 atom-processors\n", + " Atom-processors already occupy a single node, dividing atom-processor into 1 param-processors.\n", + " *** Divided 1-host atom-processor (~1 procs) into 1 param-processing groups ***\n", + " Layout for iteration 1\n", + " Num Param Processors (1,)\n", + " MapLayout: 1 processors divided into 1 x 1 (= 1) grid along circuit and parameter directions.\n", + " 1 atoms, parameter block size limits (None,)\n", + " *** Distributing 1 atoms to 1 atom-processing groups (1 cores) ***\n", + " More atom-processors than hosts: each host gets ~1 atom-processors\n", + " Atom-processors already occupy a single node, dividing atom-processor into 1 param-processors.\n", + " *** Divided 1-host atom-processor (~1 procs) into 1 param-processing groups ***\n", + " Layout for iteration 2\n", + " Num Param Processors (1,)\n", + " MapLayout: 1 processors divided into 1 x 1 (= 1) grid along circuit and parameter directions.\n", + " 1 atoms, parameter block size limits (None,)\n", + " *** Distributing 1 atoms to 1 atom-processing groups (1 cores) ***\n", + " More atom-processors than hosts: each host gets ~1 atom-processors\n", + " Atom-processors already occupy a single node, dividing atom-processor into 1 param-processors.\n", + " *** Divided 1-host atom-processor (~1 procs) into 1 param-processing groups ***\n", + " Layout for iteration 3\n", + " Num Param Processors (1,)\n", + " MapLayout: 1 processors divided into 1 x 1 (= 1) grid along circuit and parameter directions.\n", + " 1 atoms, parameter block size limits (None,)\n", + " *** Distributing 1 atoms to 1 atom-processing groups (1 cores) ***\n", + " More atom-processors than hosts: each host gets ~1 atom-processors\n", + " Atom-processors already occupy a single node, dividing atom-processor into 1 param-processors.\n", + " *** Divided 1-host atom-processor (~1 procs) into 1 param-processing groups ***\n", + " Layout for iteration 4\n", + " Num Param Processors (1,)\n", + " MapLayout: 1 processors divided into 1 x 1 (= 1) grid along circuit and parameter directions.\n", + " 1 atoms, parameter block size limits (None,)\n", + " *** Distributing 1 atoms to 1 atom-processing groups (1 cores) ***\n", + " More atom-processors than hosts: each host gets ~1 atom-processors\n", + " Atom-processors already occupy a single node, dividing atom-processor into 1 param-processors.\n", + " *** Divided 1-host atom-processor (~1 procs) into 1 param-processing groups ***\n", + " Layout for iteration 5\n", + " Num Param Processors (1,)\n", + " MapLayout: 1 processors divided into 1 x 1 (= 1) grid along circuit and parameter directions.\n", + " 1 atoms, parameter block size limits (None,)\n", + " *** Distributing 1 atoms to 1 atom-processing groups (1 cores) ***\n", + " More atom-processors than hosts: each host gets ~1 atom-processors\n", + " Atom-processors already occupy a single node, dividing atom-processor into 1 param-processors.\n", + " *** Divided 1-host atom-processor (~1 procs) into 1 param-processing groups ***\n", + " Layout for iteration 6\n", + " Num Param Processors (1,)\n", + " MapLayout: 1 processors divided into 1 x 1 (= 1) grid along circuit and parameter directions.\n", + " 1 atoms, parameter block size limits (None,)\n", + " *** Distributing 1 atoms to 1 atom-processing groups (1 cores) ***\n", + " More atom-processors than hosts: each host gets ~1 atom-processors\n", + " Atom-processors already occupy a single node, dividing atom-processor into 1 param-processors.\n", + " *** Divided 1-host atom-processor (~1 procs) into 1 param-processing groups ***\n", + " --- Iterative GST: Iter 1 of 7 92 circuits ---: \n", + " --- chi2 GST ---\n", + " --- Outer Iter 0: norm_f = 3483.29, mu=1, |x|=0, |J|=1553.28\n", + " --- Outer Iter 1: norm_f = 2632.76, mu=304.068, |x|=0.0132745, |J|=12846.5\n", + " --- Outer Iter 2: norm_f = 899.905, mu=810.848, |x|=0.138517, |J|=1492\n", + " --- Outer Iter 3: norm_f = 97.8796, mu=270.283, |x|=0.0435912, |J|=1755.73\n", + " --- Outer Iter 4: norm_f = 79.08, mu=540.565, |x|=0.0702357, |J|=1624.07\n", + " --- Outer Iter 5: norm_f = 63.3538, mu=469.671, |x|=0.0726337, |J|=1645.58\n", + " --- Outer Iter 6: norm_f = 61.0621, mu=978.633, |x|=0.0705162, |J|=1652.27\n", + " --- Outer Iter 7: norm_f = 58.1969, mu=880.157, |x|=0.0649962, |J|=1663.25\n", + " --- Outer Iter 8: norm_f = 58.0818, mu=1044.49, |x|=0.0659669, |J|=1663.6\n", + " --- Outer Iter 9: norm_f = 57.8849, mu=1024.43, |x|=0.0651468, |J|=1667.45\n", + " --- Outer Iter 10: norm_f = 57.8503, mu=1024.26, |x|=0.065333, |J|=1669.48\n", + " --- Outer Iter 11: norm_f = 57.8363, mu=1024.24, |x|=0.0652145, |J|=1669.37\n", + " --- Outer Iter 12: norm_f = 57.8335, mu=1049.24, |x|=0.0652728, |J|=1669.41\n", + " --- Outer Iter 13: norm_f = 57.832, mu=1066.25, |x|=0.0652502, |J|=1669.34\n", + " --- Outer Iter 14: norm_f = 57.8319, mu=1953.35, |x|=0.0652778, |J|=1669.33\n", + " --- Outer Iter 15: norm_f = 57.8306, mu=2112.42, |x|=0.0652695, |J|=1669.31\n", + " --- Outer Iter 16: norm_f = 57.8299, mu=2149.84, |x|=0.0652698, |J|=1669.3\n", + " --- Outer Iter 17: norm_f = 57.8294, mu=2148.38, |x|=0.0652685, |J|=1669.28\n", + " --- Outer Iter 18: norm_f = 57.8289, mu=1874.06, |x|=0.0652692, |J|=1669.27\n", + " --- Outer Iter 19: norm_f = 57.8285, mu=1058.62, |x|=0.0652703, |J|=1669.24\n", + " --- Outer Iter 20: norm_f = 57.8278, mu=707.388, |x|=0.065274, |J|=1669.18\n", + " --- Outer Iter 21: norm_f = 57.8274, mu=864.668, |x|=0.0652824, |J|=1669.09\n", + " --- Outer Iter 22: norm_f = 57.8267, mu=2194.64, |x|=0.0652825, |J|=1669.12\n", + " --- Outer Iter 23: norm_f = 57.8258, mu=2235.16, |x|=0.0652794, |J|=1669.1\n", + " --- Outer Iter 24: norm_f = 57.8253, mu=2231.91, |x|=0.0652778, |J|=1669.08\n", + " --- Outer Iter 25: norm_f = 57.8249, mu=1771.33, |x|=0.0652781, |J|=1669.06\n", + " --- Outer Iter 26: norm_f = 57.8244, mu=757.668, |x|=0.0652796, |J|=1669.03\n", + " --- Outer Iter 27: norm_f = 57.8234, mu=579.355, |x|=0.0652868, |J|=1668.92\n", + " --- Outer Iter 28: norm_f = 57.823, mu=1216.24, |x|=0.0652864, |J|=1668.92\n", + " --- Outer Iter 29: norm_f = 57.8223, mu=2432.66, |x|=0.0652855, |J|=1668.92\n", + " --- Outer Iter 30: norm_f = 57.8219, mu=1975.32, |x|=0.0652851, |J|=1668.9\n", + " --- Outer Iter 31: norm_f = 57.8214, mu=658.44, |x|=0.0652867, |J|=1668.87\n", + " --- Outer Iter 32: norm_f = 57.8203, mu=388.144, |x|=0.0652961, |J|=1668.74\n", + " --- Outer Iter 33: norm_f = 57.8198, mu=858.823, |x|=0.065298, |J|=1668.73\n", + " --- Outer Iter 34: norm_f = 57.8192, mu=2091.22, |x|=0.0652993, |J|=1668.74\n", + " --- Outer Iter 35: norm_f = 57.8183, mu=2134.93, |x|=0.0652974, |J|=1668.72\n", + " --- Outer Iter 36: norm_f = 57.8178, mu=2134.13, |x|=0.0652965, |J|=1668.7\n", + " --- Outer Iter 37: norm_f = 57.8174, mu=1858.38, |x|=0.0652969, |J|=1668.68\n", + " --- Outer Iter 38: norm_f = 57.8169, mu=1011.97, |x|=0.0652982, |J|=1668.66\n", + " --- Outer Iter 39: norm_f = 57.8161, mu=669.726, |x|=0.0653026, |J|=1668.6\n", + " --- Outer Iter 40: norm_f = 57.8158, mu=877.972, |x|=0.0653121, |J|=1668.53\n", + " --- Outer Iter 41: norm_f = 57.815, mu=2121.74, |x|=0.065311, |J|=1668.56\n", + " --- Outer Iter 42: norm_f = 57.8142, mu=2162.41, |x|=0.0653081, |J|=1668.55\n", + " --- Outer Iter 43: norm_f = 57.8137, mu=2160.74, |x|=0.0653069, |J|=1668.54\n", + " --- Outer Iter 44: norm_f = 57.8132, mu=1797.31, |x|=0.0653073, |J|=1668.52\n", + " --- Outer Iter 45: norm_f = 57.8128, mu=867.323, |x|=0.0653088, |J|=1668.5\n", + " --- Outer Iter 46: norm_f = 57.812, mu=615.008, |x|=0.0653142, |J|=1668.44\n", + " --- Outer Iter 47: norm_f = 57.8115, mu=1230.14, |x|=0.065314, |J|=1668.45\n", + " --- Outer Iter 48: norm_f = 57.8112, mu=1696.6, |x|=0.0653186, |J|=1668.43\n", + " --- Outer Iter 49: norm_f = 57.8107, mu=2034.52, |x|=0.0653204, |J|=1668.42\n", + " --- Outer Iter 50: norm_f = 57.81, mu=2075.77, |x|=0.0653187, |J|=1668.41\n", + " --- Outer Iter 51: norm_f = 57.8096, mu=2075.22, |x|=0.0653179, |J|=1668.41\n", + " --- Outer Iter 52: norm_f = 57.8092, mu=1850.93, |x|=0.0653182, |J|=1668.4\n", + " --- Outer Iter 53: norm_f = 57.8088, mu=1100.56, |x|=0.0653193, |J|=1668.39\n", + " --- Outer Iter 54: norm_f = 57.8082, mu=717.441, |x|=0.0653224, |J|=1668.36\n", + " --- Outer Iter 55: norm_f = 57.8078, mu=750.118, |x|=0.0653291, |J|=1668.31\n", + " --- Outer Iter 56: norm_f = 57.8075, mu=2190.32, |x|=0.0653298, |J|=1668.34\n", + " --- Outer Iter 57: norm_f = 57.8066, mu=2229.69, |x|=0.065328, |J|=1668.34\n", + " --- Outer Iter 58: norm_f = 57.8062, mu=2223.96, |x|=0.0653263, |J|=1668.33\n", + " --- Outer Iter 59: norm_f = 57.8059, mu=1621.18, |x|=0.0653268, |J|=1668.33\n", + " --- Outer Iter 60: norm_f = 57.8056, mu=588.951, |x|=0.0653284, |J|=1668.32\n", + " --- Outer Iter 61: norm_f = 57.8047, mu=512.066, |x|=0.0653372, |J|=1668.24\n", + " --- Outer Iter 62: norm_f = 57.8046, mu=1617.52, |x|=0.0653362, |J|=1668.29\n", + " --- Outer Iter 63: norm_f = 57.8042, mu=2225.15, |x|=0.0653402, |J|=1668.28\n", + " --- Outer Iter 64: norm_f = 57.8035, mu=2253.82, |x|=0.0653377, |J|=1668.28\n", + " --- Outer Iter 65: norm_f = 57.8032, mu=2236.55, |x|=0.0653367, |J|=1668.28\n", + " --- Outer Iter 66: norm_f = 57.8029, mu=1426, |x|=0.0653376, |J|=1668.27\n", + " --- Outer Iter 67: norm_f = 57.8026, mu=475.334, |x|=0.0653402, |J|=1668.26\n", + " --- Outer Iter 68: norm_f = 57.8018, mu=459.22, |x|=0.0653545, |J|=1668.16\n", + " --- Outer Iter 69: norm_f = 57.8013, mu=3337.35, |x|=0.065345, |J|=1668.24\n", + " --- Outer Iter 70: norm_f = 57.8012, mu=1112.45, |x|=0.0653479, |J|=1668.23\n", + " --- Outer Iter 71: norm_f = 57.8007, mu=370.816, |x|=0.0653548, |J|=1668.19\n", + " --- Outer Iter 72: norm_f = 57.7994, mu=210.304, |x|=0.0653842, |J|=1667.99\n", + " --- Outer Iter 73: norm_f = 57.7983, mu=213.387, |x|=0.0654752, |J|=1667.4\n", + " --- Outer Iter 74: norm_f = 57.7958, mu=1351.61, |x|=0.0654521, |J|=1667.75\n", + " --- Outer Iter 75: norm_f = 57.7952, mu=1351.68, |x|=0.065483, |J|=1667.6\n", + " --- Outer Iter 76: norm_f = 57.7947, mu=1404.86, |x|=0.065512, |J|=1667.48\n", + " --- Outer Iter 77: norm_f = 57.7941, mu=1635.13, |x|=0.0655415, |J|=1667.35\n", + " --- Outer Iter 78: norm_f = 57.7934, mu=1785.18, |x|=0.065567, |J|=1667.23\n", + " --- Outer Iter 79: norm_f = 57.7926, mu=1813.89, |x|=0.06559, |J|=1667.12\n", + " --- Outer Iter 80: norm_f = 57.7918, mu=1813.89, |x|=0.0656141, |J|=1667\n", + " --- Outer Iter 81: norm_f = 57.7912, mu=1783.83, |x|=0.06564, |J|=1666.88\n", + " --- Outer Iter 82: norm_f = 57.7905, mu=1576.36, |x|=0.0656675, |J|=1666.75\n", + " --- Outer Iter 83: norm_f = 57.7898, mu=1221.34, |x|=0.0656998, |J|=1666.6\n", + " --- Outer Iter 84: norm_f = 57.7889, mu=1082.51, |x|=0.065743, |J|=1666.4\n", + " --- Outer Iter 85: norm_f = 57.7882, mu=1084.46, |x|=0.0657927, |J|=1666.19\n", + " --- Outer Iter 86: norm_f = 57.7874, mu=2168.92, |x|=0.0658139, |J|=1666.1\n", + " --- Outer Iter 87: norm_f = 57.7868, mu=1820.32, |x|=0.0658361, |J|=1665.99\n", + " --- Outer Iter 88: norm_f = 57.7863, mu=744.391, |x|=0.0658646, |J|=1665.87\n", + " --- Outer Iter 89: norm_f = 57.785, mu=466.905, |x|=0.0659369, |J|=1665.53\n", + " --- Outer Iter 90: norm_f = 57.7844, mu=950.44, |x|=0.0659875, |J|=1665.35\n", + " --- Outer Iter 91: norm_f = 57.7837, mu=1937.22, |x|=0.06601, |J|=1665.26\n", + " --- Outer Iter 92: norm_f = 57.7831, mu=1937.14, |x|=0.0660316, |J|=1665.16\n", + " --- Outer Iter 93: norm_f = 57.7827, mu=1825.35, |x|=0.0660543, |J|=1665.07\n", + " --- Outer Iter 94: norm_f = 57.7822, mu=1328.02, |x|=0.0660781, |J|=1664.96\n", + " --- Outer Iter 95: norm_f = 57.7817, mu=890.704, |x|=0.0661107, |J|=1664.83\n", + " --- Outer Iter 96: norm_f = 57.7811, mu=883.836, |x|=0.0661582, |J|=1664.63\n", + " --- Outer Iter 97: norm_f = 57.7806, mu=1775.8, |x|=0.0661778, |J|=1664.56\n", + " --- Outer Iter 98: norm_f = 57.7803, mu=1775.79, |x|=0.0661981, |J|=1664.48\n", + " --- Outer Iter 99: norm_f = 57.7799, mu=1757.94, |x|=0.0662182, |J|=1664.4\n", + " Least squares message = Maximum iterations (100) exceeded\n", + " Sum of Chi^2 = 57.7796 (92 data params - 60 (approx) model params = expected mean of 32; p-value = 0.00346544)\n", + " Completed in 1.6s\n", + " Iteration 1 took 1.8s\n", + " \n", + " --- Iterative GST: Iter 2 of 7 168 circuits ---: \n", + " --- chi2 GST ---\n", + " --- Outer Iter 0: norm_f = 197.313, mu=1, |x|=0.0662378, |J|=2473.45\n", + " --- Outer Iter 1: norm_f = 146.159, mu=689.768, |x|=0.0570769, |J|=2304.6\n", + " --- Outer Iter 2: norm_f = 142.417, mu=479.065, |x|=0.0536076, |J|=2323.12\n", + " --- Outer Iter 3: norm_f = 142.19, mu=485.598, |x|=0.0542792, |J|=2321.46\n", + " --- Outer Iter 4: norm_f = 141.856, mu=485.482, |x|=0.0535151, |J|=2322.08\n", + " --- Outer Iter 5: norm_f = 141.562, mu=405.803, |x|=0.0528087, |J|=2326.02\n", + " --- Outer Iter 6: norm_f = 141.51, mu=811.601, |x|=0.0524609, |J|=2327.69\n", + " --- Outer Iter 7: norm_f = 141.499, mu=859.672, |x|=0.0524809, |J|=2328.14\n", + " --- Outer Iter 8: norm_f = 141.493, mu=895.439, |x|=0.0523328, |J|=2328.75\n", + " --- Outer Iter 9: norm_f = 141.491, mu=1057.57, |x|=0.0523946, |J|=2328.66\n", + " --- Outer Iter 10: norm_f = 141.49, mu=1166.05, |x|=0.0523309, |J|=2328.87\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " --- Outer Iter 11: norm_f = 141.49, mu=2050.66, |x|=0.0523643, |J|=2328.78\n", + " --- Outer Iter 12: norm_f = 141.489, mu=2153.65, |x|=0.052346, |J|=2328.83\n", + " --- Outer Iter 13: norm_f = 141.489, mu=2256.61, |x|=0.0523475, |J|=2328.83\n", + " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", + " Sum of Chi^2 = 141.489 (168 data params - 60 (approx) model params = expected mean of 108; p-value = 0.0168947)\n", + " Completed in 0.3s\n", + " Iteration 2 took 0.3s\n", + " \n", + " --- Iterative GST: Iter 3 of 7 285 circuits ---: \n", + " --- chi2 GST ---\n", + " --- Outer Iter 0: norm_f = 311.393, mu=1, |x|=0.0523475, |J|=3402.26\n", + " --- Outer Iter 1: norm_f = 308.229, mu=4856.95, |x|=0.061184, |J|=3305.72\n", + " --- Outer Iter 2: norm_f = 270.928, mu=1618.98, |x|=0.0532447, |J|=3357.59\n", + " --- Outer Iter 3: norm_f = 269.767, mu=1523.14, |x|=0.0540491, |J|=3354.45\n", + " --- Outer Iter 4: norm_f = 268.868, mu=598.973, |x|=0.0525103, |J|=3364.86\n", + " --- Outer Iter 5: norm_f = 268.841, mu=830.643, |x|=0.0533074, |J|=3360.17\n", + " --- Outer Iter 6: norm_f = 268.207, mu=1487.83, |x|=0.0532672, |J|=3360.65\n", + " --- Outer Iter 7: norm_f = 267.934, mu=1272.14, |x|=0.053316, |J|=3358.56\n", + " --- Outer Iter 8: norm_f = 267.799, mu=1176.36, |x|=0.0538154, |J|=3353.21\n", + " --- Outer Iter 9: norm_f = 267.733, mu=1180.63, |x|=0.0543916, |J|=3347.56\n", + " --- Outer Iter 10: norm_f = 267.664, mu=1185.17, |x|=0.0547029, |J|=3344.42\n", + " --- Outer Iter 11: norm_f = 267.578, mu=1087.75, |x|=0.0546495, |J|=3344.54\n", + " --- Outer Iter 12: norm_f = 267.551, mu=698.869, |x|=0.0545473, |J|=3345.29\n", + " --- Outer Iter 13: norm_f = 267.549, mu=698.864, |x|=0.0545321, |J|=3345.3\n", + " --- Outer Iter 14: norm_f = 267.548, mu=1570.68, |x|=0.0545212, |J|=3345.46\n", + " --- Outer Iter 15: norm_f = 267.547, mu=1708.77, |x|=0.0545212, |J|=3345.4\n", + " --- Outer Iter 16: norm_f = 267.547, mu=1770.1, |x|=0.0545189, |J|=3345.44\n", + " --- Outer Iter 17: norm_f = 267.547, mu=1772.85, |x|=0.0545185, |J|=3345.43\n", + " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", + " Sum of Chi^2 = 267.547 (285 data params - 60 (approx) model params = expected mean of 225; p-value = 0.0272684)\n", + " Completed in 0.4s\n", + " Iteration 3 took 0.4s\n", + " \n", + " --- Iterative GST: Iter 4 of 7 448 circuits ---: \n", + " --- chi2 GST ---\n", + " --- Outer Iter 0: norm_f = 452.138, mu=1, |x|=0.0545185, |J|=5274.29\n", + " --- Outer Iter 1: norm_f = 416.766, mu=3443.72, |x|=0.0559625, |J|=5234.27\n", + " --- Outer Iter 2: norm_f = 413.511, mu=3121.53, |x|=0.0551826, |J|=5241.1\n", + " --- Outer Iter 3: norm_f = 412.638, mu=3067.47, |x|=0.0547291, |J|=5244.38\n", + " --- Outer Iter 4: norm_f = 412.205, mu=2542.54, |x|=0.0542903, |J|=5248.18\n", + " --- Outer Iter 5: norm_f = 412.109, mu=1837.11, |x|=0.0540098, |J|=5250.06\n", + " --- Outer Iter 6: norm_f = 412.084, mu=1581.39, |x|=0.0538142, |J|=5250.54\n", + " --- Outer Iter 7: norm_f = 412.062, mu=1566.05, |x|=0.0536469, |J|=5250.84\n", + " --- Outer Iter 8: norm_f = 412.037, mu=1553.18, |x|=0.0535164, |J|=5251.27\n", + " --- Outer Iter 9: norm_f = 412.013, mu=1553.13, |x|=0.0534733, |J|=5251.44\n", + " --- Outer Iter 10: norm_f = 411.986, mu=1551.52, |x|=0.0535262, |J|=5251.45\n", + " --- Outer Iter 11: norm_f = 411.957, mu=1460.74, |x|=0.0536341, |J|=5251.56\n", + " --- Outer Iter 12: norm_f = 411.94, mu=1222.67, |x|=0.0537467, |J|=5251.67\n", + " --- Outer Iter 13: norm_f = 411.934, mu=1000.14, |x|=0.0538424, |J|=5251.66\n", + " --- Outer Iter 14: norm_f = 411.932, mu=770.797, |x|=0.0539027, |J|=5251.62\n", + " --- Outer Iter 15: norm_f = 411.931, mu=628.278, |x|=0.0539405, |J|=5251.56\n", + " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", + " Sum of Chi^2 = 411.931 (448 data params - 60 (approx) model params = expected mean of 388; p-value = 0.193286)\n", + " Completed in 0.6s\n", + " Iteration 4 took 0.6s\n", + " \n", + " --- Iterative GST: Iter 5 of 7 616 circuits ---: \n", + " --- chi2 GST ---\n", + " --- Outer Iter 0: norm_f = 585.139, mu=1, |x|=0.0539405, |J|=8813.75\n", + " --- Outer Iter 1: norm_f = 555.884, mu=5905.76, |x|=0.0561539, |J|=8796.71\n", + " --- Outer Iter 2: norm_f = 553.618, mu=3485.3, |x|=0.0562766, |J|=8802.55\n", + " --- Outer Iter 3: norm_f = 553.211, mu=3117.32, |x|=0.0562064, |J|=8805.34\n", + " --- Outer Iter 4: norm_f = 553.025, mu=2900.4, |x|=0.0560686, |J|=8806.35\n", + " --- Outer Iter 5: norm_f = 552.903, mu=2872.13, |x|=0.0559834, |J|=8805.33\n", + " --- Outer Iter 6: norm_f = 552.797, mu=2875.53, |x|=0.055949, |J|=8802.51\n", + " --- Outer Iter 7: norm_f = 552.514, mu=2505.7, |x|=0.0558017, |J|=8803.05\n", + " --- Outer Iter 8: norm_f = 552.375, mu=5019.47, |x|=0.0558568, |J|=8800.05\n", + " --- Outer Iter 9: norm_f = 552.086, mu=4824.25, |x|=0.0557519, |J|=8800.95\n", + " --- Outer Iter 10: norm_f = 551.9, mu=2071.66, |x|=0.0555706, |J|=8803.02\n", + " --- Outer Iter 11: norm_f = 551.853, mu=1609.26, |x|=0.0554713, |J|=8803.51\n", + " --- Outer Iter 12: norm_f = 551.832, mu=1128.16, |x|=0.055309, |J|=8804.73\n", + " --- Outer Iter 13: norm_f = 551.823, mu=1122.11, |x|=0.0551568, |J|=8805.32\n", + " --- Outer Iter 14: norm_f = 551.812, mu=848.588, |x|=0.0550168, |J|=8806.21\n", + " --- Outer Iter 15: norm_f = 551.807, mu=788.134, |x|=0.0549077, |J|=8806.76\n", + " --- Outer Iter 16: norm_f = 551.802, mu=550.146, |x|=0.0548286, |J|=8807.31\n", + " --- Outer Iter 17: norm_f = 551.8, mu=473.662, |x|=0.0547768, |J|=8807.6\n", + " --- Outer Iter 18: norm_f = 551.799, mu=278.631, |x|=0.0547472, |J|=8807.84\n", + " --- Outer Iter 19: norm_f = 551.799, mu=226.867, |x|=0.0547359, |J|=8807.91\n", + " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", + " Sum of Chi^2 = 551.799 (616 data params - 60 (approx) model params = expected mean of 556; p-value = 0.54233)\n", + " Completed in 0.6s\n", + " Iteration 5 took 0.6s\n", + " \n", + " --- Iterative GST: Iter 6 of 7 784 circuits ---: \n", + " --- chi2 GST ---\n", + " --- Outer Iter 0: norm_f = 783.551, mu=1, |x|=0.0547359, |J|=15826.7\n", + " --- Outer Iter 1: norm_f = 750.942, mu=15202.5, |x|=0.0547454, |J|=15777.2\n", + " --- Outer Iter 2: norm_f = 749.281, mu=14465.9, |x|=0.0537542, |J|=15776.8\n", + " --- Outer Iter 3: norm_f = 747.511, mu=9148.89, |x|=0.053007, |J|=15788.4\n", + " --- Outer Iter 4: norm_f = 746.793, mu=9065.67, |x|=0.0526032, |J|=15794.9\n", + " --- Outer Iter 5: norm_f = 746.121, mu=8116.74, |x|=0.0523044, |J|=15806\n", + " --- Outer Iter 6: norm_f = 745.85, mu=8115.52, |x|=0.0523011, |J|=15810.4\n", + " --- Outer Iter 7: norm_f = 745.547, mu=7630.24, |x|=0.0524001, |J|=15812.9\n", + " --- Outer Iter 8: norm_f = 745.348, mu=7254.76, |x|=0.0525984, |J|=15813.1\n", + " --- Outer Iter 9: norm_f = 745.191, mu=6816.28, |x|=0.0528335, |J|=15812.5\n", + " --- Outer Iter 10: norm_f = 745.066, mu=6448.2, |x|=0.0530809, |J|=15811.6\n", + " --- Outer Iter 11: norm_f = 744.963, mu=6047.94, |x|=0.0533136, |J|=15810.7\n", + " --- Outer Iter 12: norm_f = 744.879, mu=5656.39, |x|=0.0535221, |J|=15810.1\n", + " --- Outer Iter 13: norm_f = 744.814, mu=5237.96, |x|=0.0536972, |J|=15809.7\n", + " --- Outer Iter 14: norm_f = 744.765, mu=4798.89, |x|=0.0538356, |J|=15809.6\n", + " --- Outer Iter 15: norm_f = 744.731, mu=4253.1, |x|=0.0539351, |J|=15809.9\n", + " --- Outer Iter 16: norm_f = 744.711, mu=3424.31, |x|=0.0540046, |J|=15810.5\n", + " --- Outer Iter 17: norm_f = 744.702, mu=2640.2, |x|=0.0540601, |J|=15811\n", + " --- Outer Iter 18: norm_f = 744.698, mu=1868.55, |x|=0.0540998, |J|=15811.4\n", + " --- Outer Iter 19: norm_f = 744.696, mu=1164.1, |x|=0.054132, |J|=15811.7\n", + " --- Outer Iter 20: norm_f = 744.693, mu=891.762, |x|=0.0541716, |J|=15811.8\n", + " --- Outer Iter 21: norm_f = 744.689, mu=297.254, |x|=0.0542196, |J|=15811.8\n", + " --- Outer Iter 22: norm_f = 744.684, mu=792.677, |x|=0.0542247, |J|=15812.3\n", + " --- Outer Iter 23: norm_f = 744.676, mu=1475.78, |x|=0.0542658, |J|=15811.2\n", + " --- Outer Iter 24: norm_f = 744.669, mu=1653.86, |x|=0.0543124, |J|=15809.7\n", + " --- Outer Iter 25: norm_f = 744.633, mu=1164.12, |x|=0.0543028, |J|=15810.7\n", + " --- Outer Iter 26: norm_f = 744.62, mu=475.259, |x|=0.0543026, |J|=15811.5\n", + " --- Outer Iter 27: norm_f = 744.618, mu=462.416, |x|=0.0543497, |J|=15812\n", + " --- Outer Iter 28: norm_f = 744.616, mu=369.25, |x|=0.0543912, |J|=15812.7\n", + " --- Outer Iter 29: norm_f = 744.616, mu=369.187, |x|=0.0544509, |J|=15813.2\n", + " --- Outer Iter 30: norm_f = 744.615, mu=317.41, |x|=0.0545062, |J|=15813.7\n", + " --- Outer Iter 31: norm_f = 744.614, mu=314.481, |x|=0.0545751, |J|=15814.2\n", + " --- Outer Iter 32: norm_f = 744.613, mu=616.178, |x|=0.0546056, |J|=15814.6\n", + " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", + " Sum of Chi^2 = 744.613 (784 data params - 60 (approx) model params = expected mean of 724; p-value = 0.289745)\n", + " Completed in 1.1s\n", + " Iteration 6 took 1.1s\n", + " \n", + " --- Iterative GST: Iter 7 of 7 952 circuits ---: \n", + " --- chi2 GST ---\n", + " --- Outer Iter 0: norm_f = 951.549, mu=1, |x|=0.0546056, |J|=29521.4\n", + " --- Outer Iter 1: norm_f = 922.257, mu=52763.7, |x|=0.0547357, |J|=29496.4\n", + " --- Outer Iter 2: norm_f = 919.796, mu=17587.9, |x|=0.0547233, |J|=29502.2\n", + " --- Outer Iter 3: norm_f = 918.843, mu=17587.9, |x|=0.0545352, |J|=29481.9\n", + " --- Outer Iter 4: norm_f = 917.113, mu=12599.1, |x|=0.0539607, |J|=29504.8\n", + " --- Outer Iter 5: norm_f = 916.395, mu=9575.77, |x|=0.0535451, |J|=29514.9\n", + " --- Outer Iter 6: norm_f = 916.101, mu=3593.81, |x|=0.0532769, |J|=29524.3\n", + " --- Outer Iter 7: norm_f = 916.055, mu=2588.9, |x|=0.0531642, |J|=29527.4\n", + " --- Outer Iter 8: norm_f = 916.039, mu=2089.85, |x|=0.0531501, |J|=29528.9\n", + " --- Outer Iter 9: norm_f = 916.029, mu=1500.37, |x|=0.0531624, |J|=29529.5\n", + " --- Outer Iter 10: norm_f = 916.024, mu=725.547, |x|=0.0531951, |J|=29529.9\n", + " --- Outer Iter 11: norm_f = 916.022, mu=400.97, |x|=0.0532904, |J|=29530.2\n", + " --- Outer Iter 12: norm_f = 916.02, mu=358.522, |x|=0.0533858, |J|=29530.4\n", + " --- Outer Iter 13: norm_f = 916.019, mu=702.794, |x|=0.0535076, |J|=29530\n", + " --- Outer Iter 14: norm_f = 916.018, mu=1006.26, |x|=0.0536529, |J|=29529\n", + " --- Outer Iter 15: norm_f = 916.014, mu=1006.21, |x|=0.0537709, |J|=29528.5\n", + " --- Outer Iter 16: norm_f = 916.007, mu=853.296, |x|=0.0538329, |J|=29528.9\n", + " --- Outer Iter 17: norm_f = 916.004, mu=1701.12, |x|=0.0539266, |J|=29527.8\n", + " --- Outer Iter 18: norm_f = 915.999, mu=1701.12, |x|=0.054028, |J|=29526.6\n", + " --- Outer Iter 19: norm_f = 915.993, mu=1705.36, |x|=0.0541384, |J|=29525.1\n", + " --- Outer Iter 20: norm_f = 915.986, mu=1712.58, |x|=0.0542542, |J|=29523.6\n", + " --- Outer Iter 21: norm_f = 915.977, mu=1714.21, |x|=0.0543715, |J|=29522\n", + " --- Outer Iter 22: norm_f = 915.967, mu=1714.21, |x|=0.0544866, |J|=29520.4\n", + " --- Outer Iter 23: norm_f = 915.957, mu=1712.87, |x|=0.0545963, |J|=29518.9\n", + " --- Outer Iter 24: norm_f = 915.947, mu=1702.15, |x|=0.0546984, |J|=29517.5\n", + " --- Outer Iter 25: norm_f = 915.939, mu=1674.29, |x|=0.0547924, |J|=29516.2\n", + " --- Outer Iter 26: norm_f = 915.933, mu=1627.91, |x|=0.0548788, |J|=29515\n", + " --- Outer Iter 27: norm_f = 915.928, mu=1564.91, |x|=0.0549585, |J|=29513.9\n", + " --- Outer Iter 28: norm_f = 915.924, mu=1489.15, |x|=0.0550322, |J|=29512.9\n", + " --- Outer Iter 29: norm_f = 915.92, mu=1405.14, |x|=0.0551008, |J|=29511.9\n", + " --- Outer Iter 30: norm_f = 915.918, mu=1317.04, |x|=0.0551647, |J|=29511\n", + " --- Outer Iter 31: norm_f = 915.915, mu=1228.17, |x|=0.0552246, |J|=29510.2\n", + " --- Outer Iter 32: norm_f = 915.913, mu=1140.9, |x|=0.0552808, |J|=29509.5\n", + " --- Outer Iter 33: norm_f = 915.912, mu=1056.82, |x|=0.0553335, |J|=29508.8\n", + " --- Outer Iter 34: norm_f = 915.911, mu=976.884, |x|=0.0553832, |J|=29508.2\n", + " --- Outer Iter 35: norm_f = 915.91, mu=901.637, |x|=0.0554299, |J|=29507.6\n", + " --- Outer Iter 36: norm_f = 915.909, mu=831.234, |x|=0.0554739, |J|=29507.1\n", + " --- Outer Iter 37: norm_f = 915.908, mu=765.757, |x|=0.0555153, |J|=29506.6\n", + " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", + " Sum of Chi^2 = 915.908 (952 data params - 60 (approx) model params = expected mean of 892; p-value = 0.282035)\n", + " Completed in 1.6s\n", + " Iteration 7 took 1.6s\n", + " \n", + " Last iteration:\n", + " --- dlogl GST ---\n", + " --- Outer Iter 0: norm_f = 472.363, mu=1, |x|=0.0555153, |J|=20866.1\n", + " --- Outer Iter 1: norm_f = 468.633, mu=26136.5, |x|=0.0539846, |J|=20902.6\n", + " --- Outer Iter 2: norm_f = 467.799, mu=8712.18, |x|=0.0529566, |J|=20895.5\n", + " --- Outer Iter 3: norm_f = 467.539, mu=2904.06, |x|=0.052252, |J|=20890.8\n", + " --- Outer Iter 4: norm_f = 467.506, mu=968.02, |x|=0.051971, |J|=20889.3\n", + " --- Outer Iter 5: norm_f = 467.504, mu=322.673, |x|=0.0519071, |J|=20888.8\n", + " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", + " 2*Delta(log(L)) = 935.008 (952 data params - 60 (approx) model params = expected mean of 892; p-value = 0.154326)\n", + " Completed in 0.3s\n", + " Final optimization took 0.3s\n", + " \n", + " -- Performing 'stdgaugeopt' gauge optimization on CPTPLND estimate --\n", + "\n", + "--- normalized tvd GST ---\n", + " --- Outer Iter 0: norm_f = 0.261786, mu=1, |x|=0.0519071, |J|=3533.75\n", + " --- Outer Iter 1: norm_f = 0.260246, mu=2645.95, |x|=0.0518531, |J|=4056.71\n", + " --- Outer Iter 2: norm_f = 0.258875, mu=2645.47, |x|=0.0518144, |J|=5596.61\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/objectivefns/objectivefns.py:4055: UserWarning: This derivative is discontinuous and does not return a full subgradient.\n", + " _warnings.warn('This derivative is discontinuous and does not return a full subgradient.')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " --- Outer Iter 3: norm_f = 0.257933, mu=2644.87, |x|=0.0517754, |J|=13214.3\n", + " --- Outer Iter 4: norm_f = 0.257274, mu=2644.2, |x|=0.0517611, |J|=22886.5\n", + " --- Outer Iter 5: norm_f = 0.25666, mu=2642.01, |x|=0.0517413, |J|=20782\n", + " --- Outer Iter 6: norm_f = 0.256166, mu=2632.83, |x|=0.051721, |J|=34502.7\n", + " --- Outer Iter 7: norm_f = 0.255812, mu=2606.21, |x|=0.0517133, |J|=20646\n", + " --- Outer Iter 8: norm_f = 0.25547, mu=2570.95, |x|=0.0516964, |J|=15219.5\n", + " --- Outer Iter 9: norm_f = 0.25513, mu=2512.1, |x|=0.0516728, |J|=12524.5\n", + " --- Outer Iter 10: norm_f = 0.254784, mu=2441.21, |x|=0.0516498, |J|=11242.5\n", + " --- Outer Iter 11: norm_f = 0.254466, mu=2356.32, |x|=0.0516292, |J|=13402.4\n", + " --- Outer Iter 12: norm_f = 0.254264, mu=2324.44, |x|=0.0516215, |J|=12752.6\n", + " --- Outer Iter 13: norm_f = 0.254063, mu=2232.63, |x|=0.0516154, |J|=13307.6\n", + " --- Outer Iter 14: norm_f = 0.253835, mu=2066.01, |x|=0.0516039, |J|=16373.7\n", + " --- Outer Iter 15: norm_f = 0.253594, mu=1873.36, |x|=0.0515917, |J|=20361.5\n", + " --- Outer Iter 16: norm_f = 0.253377, mu=1637.44, |x|=0.051583, |J|=27178.9\n", + " --- Outer Iter 17: norm_f = 0.253192, mu=1427.12, |x|=0.051578, |J|=35323\n", + " --- Outer Iter 18: norm_f = 0.253008, mu=1257.13, |x|=0.0515717, |J|=43863.2\n", + " --- Outer Iter 19: norm_f = 0.252823, mu=1073.14, |x|=0.051565, |J|=63678\n", + " --- Outer Iter 20: norm_f = 0.25262, mu=884.002, |x|=0.0515565, |J|=82730.8\n", + " --- Outer Iter 21: norm_f = 0.252407, mu=691.286, |x|=0.0515473, |J|=52740.5\n", + " --- Outer Iter 22: norm_f = 0.252192, mu=519.253, |x|=0.0515384, |J|=73894.1\n", + " --- Outer Iter 23: norm_f = 0.252006, mu=444.448, |x|=0.0515325, |J|=63255.7\n", + " --- Outer Iter 24: norm_f = 0.251886, mu=442.835, |x|=0.0515324, |J|=121698\n", + " --- Outer Iter 25: norm_f = 0.251752, mu=435.726, |x|=0.05153, |J|=126012\n", + " --- Outer Iter 26: norm_f = 0.251606, mu=421.38, |x|=0.0515255, |J|=128653\n", + " --- Outer Iter 27: norm_f = 0.251452, mu=401.417, |x|=0.0515201, |J|=87338.7\n", + " --- Outer Iter 28: norm_f = 0.251294, mu=377.874, |x|=0.0515138, |J|=69765.6\n", + " --- Outer Iter 29: norm_f = 0.251137, mu=352.729, |x|=0.0515068, |J|=78886.4\n", + " --- Outer Iter 30: norm_f = 0.250985, mu=327.883, |x|=0.0514995, |J|=206210\n", + " --- Outer Iter 31: norm_f = 0.250846, mu=306.023, |x|=0.0514918, |J|=110789\n", + " --- Outer Iter 32: norm_f = 0.250707, mu=282.683, |x|=0.0514823, |J|=78450.6\n", + " --- Outer Iter 33: norm_f = 0.250571, mu=258.194, |x|=0.0514718, |J|=68640.1\n", + " --- Outer Iter 34: norm_f = 0.25044, mu=234.015, |x|=0.051461, |J|=79565\n", + " --- Outer Iter 35: norm_f = 0.250323, mu=213.632, |x|=0.0514502, |J|=78465.3\n", + " --- Outer Iter 36: norm_f = 0.250229, mu=202.389, |x|=0.0514398, |J|=82866.3\n", + " --- Outer Iter 37: norm_f = 0.250154, mu=196.676, |x|=0.0514304, |J|=78610\n", + " --- Outer Iter 38: norm_f = 0.250086, mu=189.925, |x|=0.0514209, |J|=76114.9\n", + " --- Outer Iter 39: norm_f = 0.250021, mu=179.983, |x|=0.0514114, |J|=77884.5\n", + " --- Outer Iter 40: norm_f = 0.249959, mu=163.35, |x|=0.0514018, |J|=85424\n", + " --- Outer Iter 41: norm_f = 0.249902, mu=133.09, |x|=0.0513922, |J|=136371\n", + " --- Outer Iter 42: norm_f = 0.249851, mu=82.9424, |x|=0.051384, |J|=120933\n", + " --- Outer Iter 43: norm_f = 0.249808, mu=27.6475, |x|=0.0513819, |J|=150029\n", + " --- Outer Iter 44: norm_f = 0.249776, mu=9.21583, |x|=0.0513847, |J|=327000\n", + " --- Outer Iter 45: norm_f = 0.249747, mu=3.07194, |x|=0.0513989, |J|=525508\n", + " --- Outer Iter 46: norm_f = 0.249718, mu=1.02398, |x|=0.051425, |J|=162859\n", + " --- Outer Iter 47: norm_f = 0.249692, mu=0.572186, |x|=0.0514684, |J|=126786\n", + " --- Outer Iter 48: norm_f = 0.249661, mu=0.243615, |x|=0.0515189, |J|=119105\n", + " --- Outer Iter 49: norm_f = 0.249626, mu=0.124945, |x|=0.0515615, |J|=184907\n", + " --- Outer Iter 50: norm_f = 0.249589, mu=0.0579048, |x|=0.0516054, |J|=286152\n", + " --- Outer Iter 51: norm_f = 0.24955, mu=0.0298822, |x|=0.0516478, |J|=192511\n", + " --- Outer Iter 52: norm_f = 0.249511, mu=0.0157943, |x|=0.0516873, |J|=177999\n", + " --- Outer Iter 53: norm_f = 0.249471, mu=0.00677886, |x|=0.0517327, |J|=170737\n", + " --- Outer Iter 54: norm_f = 0.24943, mu=0.00225962, |x|=0.0517942, |J|=196463\n", + " --- Outer Iter 55: norm_f = 0.249388, mu=0.000753207, |x|=0.0519122, |J|=172608\n", + " --- Outer Iter 56: norm_f = 0.249348, mu=0.000251069, |x|=0.0521928, |J|=67249.5\n", + " --- Outer Iter 57: norm_f = 0.249312, mu=8.36897e-05, |x|=0.0527623, |J|=84645.9\n", + " --- Outer Iter 58: norm_f = 0.249257, mu=2.78966e-05, |x|=0.0551668, |J|=21157.2\n", + " --- Outer Iter 59: norm_f = 0.249107, mu=9.29885e-06, |x|=0.0761846, |J|=5181.81\n", + " --- Outer Iter 60: norm_f = 0.249086, mu=0.000127291, |x|=0.097117, |J|=4897.21\n", + " --- Outer Iter 61: norm_f = 0.248929, mu=0.000130547, |x|=0.111732, |J|=5732.09\n", + " --- Outer Iter 62: norm_f = 0.248915, mu=0.000477259, |x|=0.131355, |J|=5287.56\n", + " --- Outer Iter 63: norm_f = 0.248727, mu=0.000481886, |x|=0.138182, |J|=7797.22\n", + " --- Outer Iter 64: norm_f = 0.248614, mu=0.000526057, |x|=0.145611, |J|=10648.8\n", + " --- Outer Iter 65: norm_f = 0.248548, mu=0.000656308, |x|=0.153162, |J|=12010.4\n", + " --- Outer Iter 66: norm_f = 0.2485, mu=0.000889541, |x|=0.159021, |J|=15090.2\n", + " --- Outer Iter 67: norm_f = 0.248465, mu=0.00129096, |x|=0.163023, |J|=21155.3\n", + " --- Outer Iter 68: norm_f = 0.248441, mu=0.00197409, |x|=0.165693, |J|=30149.5\n", + " --- Outer Iter 69: norm_f = 0.248419, mu=0.00308786, |x|=0.167412, |J|=46651.1\n", + " --- Outer Iter 70: norm_f = 0.248398, mu=0.00482215, |x|=0.168486, |J|=78064.4\n", + " --- Outer Iter 71: norm_f = 0.248376, mu=0.00728337, |x|=0.169178, |J|=225298\n", + " --- Outer Iter 72: norm_f = 0.248347, mu=0.0100607, |x|=0.169629, |J|=330722\n", + " --- Outer Iter 73: norm_f = 0.248321, mu=0.0143503, |x|=0.169917, |J|=182343\n", + " --- Outer Iter 74: norm_f = 0.2483, mu=0.0214009, |x|=0.170074, |J|=157534\n", + " --- Outer Iter 75: norm_f = 0.248287, mu=0.0343809, |x|=0.170106, |J|=104164\n", + " --- Outer Iter 76: norm_f = 0.248282, mu=0.0613299, |x|=0.170058, |J|=82096.1\n", + " --- Outer Iter 77: norm_f = 0.248281, mu=0.11986, |x|=0.170058, |J|=70862.8\n", + " --- Outer Iter 78: norm_f = 0.24828, mu=9.65239e+06, |x|=0.170058, |J|=104551\n", + " --- Outer Iter 79: norm_f = 0.24828, mu=2.89572e+06, |x|=0.170058, |J|=162658\n", + " --- Outer Iter 80: norm_f = 0.24828, mu=6.94972e+06, |x|=0.170058, |J|=279902\n", + " --- Outer Iter 81: norm_f = 0.24828, mu=4.16983e+06, |x|=0.170058, |J|=522045\n", + " --- Outer Iter 82: norm_f = 0.24828, mu=1.00076e+07, |x|=0.170058, |J|=887854\n", + " --- Outer Iter 83: norm_f = 0.24828, mu=3.00228e+06, |x|=0.170058, |J|=1.49401e+06\n", + " --- Outer Iter 84: norm_f = 0.248279, mu=7.20547e+06, |x|=0.170057, |J|=2.49321e+06\n", + " --- Outer Iter 85: norm_f = 0.248279, mu=2.16164e+06, |x|=0.170057, |J|=4.06279e+06\n", + " --- Outer Iter 86: norm_f = 0.248279, mu=1.29698e+06, |x|=0.170057, |J|=5.7961e+06\n", + " --- Outer Iter 87: norm_f = 0.248279, mu=3.11276e+06, |x|=0.170057, |J|=8.30535e+06\n", + " --- Outer Iter 88: norm_f = 0.248279, mu=1.86766e+06, |x|=0.170057, |J|=9.14273e+06\n", + " --- Outer Iter 89: norm_f = 0.248278, mu=4.48238e+06, |x|=0.170057, |J|=1.16061e+07\n", + " --- Outer Iter 90: norm_f = 0.248278, mu=1.07577e+07, |x|=0.170057, |J|=1.40298e+07\n", + " --- Outer Iter 91: norm_f = 0.248278, mu=3.22731e+06, |x|=0.170057, |J|=1.46472e+07\n", + " --- Outer Iter 92: norm_f = 0.248278, mu=7.74555e+06, |x|=0.170057, |J|=1.86242e+07\n", + " --- Outer Iter 93: norm_f = 0.248278, mu=1.85893e+07, |x|=0.170057, |J|=2.55751e+07\n", + " --- Outer Iter 94: norm_f = 0.248278, mu=4.46144e+07, |x|=0.170057, |J|=3.6027e+07\n", + " --- Outer Iter 95: norm_f = 0.248278, mu=2.67686e+07, |x|=0.170057, |J|=4.1885e+07\n", + " --- Outer Iter 96: norm_f = 0.248278, mu=6.42447e+07, |x|=0.170057, |J|=5.50157e+07\n", + " --- Outer Iter 97: norm_f = 0.248278, mu=1.54187e+08, |x|=0.170057, |J|=7.51481e+07\n", + " --- Outer Iter 98: norm_f = 0.248278, mu=9.25123e+07, |x|=0.170057, |J|=8.46366e+07\n", + " --- Outer Iter 99: norm_f = 0.248278, mu=2.2203e+08, |x|=0.170057, |J|=1.10043e+08\n", + " Least squares message = Maximum iterations (100) exceeded\n", + "Total Variational Distance (TVD), normalized by circuit depth = -1 (952 data params - 60 (approx) model params = expected mean of 892; p-value = 1)\n", + "Completed in 8.6s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- tvd GST ---\n", + " --- Outer Iter 0: norm_f = 8.78668, mu=1, |x|=0.0519071, |J|=3533.75\n", + " --- Outer Iter 1: norm_f = 8.71865, mu=882.058, |x|=0.0517526, |J|=4843.52\n", + " --- Outer Iter 2: norm_f = 8.70297, mu=294.019, |x|=0.0516355, |J|=5380\n", + " --- Outer Iter 3: norm_f = 8.69, mu=98.0064, |x|=0.0513986, |J|=7211.6\n", + " --- Outer Iter 4: norm_f = 8.68104, mu=32.6688, |x|=0.0510276, |J|=9671.75\n", + " --- Outer Iter 5: norm_f = 8.6741, mu=10.8896, |x|=0.0508288, |J|=13581.2\n", + " --- Outer Iter 6: norm_f = 8.67139, mu=21.7785, |x|=0.0507586, |J|=16988.3\n", + " --- Outer Iter 7: norm_f = 8.66352, mu=7.2595, |x|=0.0505225, |J|=25195.7\n", + " --- Outer Iter 8: norm_f = 8.66152, mu=2.41983, |x|=0.0504138, |J|=26204.5\n", + " --- Outer Iter 9: norm_f = 8.66128, mu=5.28878, |x|=0.0505487, |J|=45952.7\n", + " --- Outer Iter 10: norm_f = 8.66046, mu=5.10978, |x|=0.0506781, |J|=77666.6\n", + " --- Outer Iter 11: norm_f = 8.65947, mu=1.70326, |x|=0.0507142, |J|=93340.2\n", + " --- Outer Iter 12: norm_f = 8.65902, mu=1.17013, |x|=0.0507437, |J|=176298\n", + " --- Outer Iter 13: norm_f = 8.65873, mu=0.77144, |x|=0.050751, |J|=290538\n", + " --- Outer Iter 14: norm_f = 8.65847, mu=0.43221, |x|=0.0507501, |J|=161232\n", + " --- Outer Iter 15: norm_f = 8.65827, mu=0.14407, |x|=0.0507653, |J|=168779\n", + " --- Outer Iter 16: norm_f = 8.65809, mu=0.0480233, |x|=0.0507796, |J|=147152\n", + " --- Outer Iter 17: norm_f = 8.65802, mu=0.0404085, |x|=0.0508331, |J|=166650\n", + " --- Outer Iter 18: norm_f = 8.65795, mu=0.0382692, |x|=0.0509005, |J|=169199\n", + " --- Outer Iter 19: norm_f = 8.6579, mu=0.0324028, |x|=0.0509892, |J|=177459\n", + " --- Outer Iter 20: norm_f = 8.65783, mu=0.0281327, |x|=0.0510943, |J|=203554\n", + " --- Outer Iter 21: norm_f = 8.65778, mu=0.0249417, |x|=0.0512223, |J|=198388\n", + " --- Outer Iter 22: norm_f = 8.65771, mu=0.0193391, |x|=0.0513644, |J|=141927\n", + " --- Outer Iter 23: norm_f = 8.65766, mu=0.0185955, |x|=0.0515618, |J|=208600\n", + " --- Outer Iter 24: norm_f = 8.65758, mu=0.0148754, |x|=0.0517749, |J|=126175\n", + " --- Outer Iter 25: norm_f = 8.65755, mu=0.0148696, |x|=0.0520519, |J|=115782\n", + " --- Outer Iter 26: norm_f = 8.65747, mu=0.0129986, |x|=0.0523393, |J|=122712\n", + " --- Outer Iter 27: norm_f = 8.65746, mu=0.0137916, |x|=0.0526814, |J|=103789\n", + " --- Outer Iter 28: norm_f = 8.6574, mu=0.0136512, |x|=0.0530078, |J|=114060\n", + " --- Outer Iter 29: norm_f = 8.65739, mu=0.0168338, |x|=0.0533236, |J|=134665\n", + " --- Outer Iter 30: norm_f = 8.65736, mu=0.016854, |x|=0.0535883, |J|=167954\n", + " --- Outer Iter 31: norm_f = 8.65735, mu=0.0252614, |x|=0.0538599, |J|=162223\n", + " --- Outer Iter 32: norm_f = 8.65733, mu=0.025474, |x|=0.0540612, |J|=248203\n", + " --- Outer Iter 33: norm_f = 8.65732, mu=0.0256207, |x|=0.0542709, |J|=217091\n", + " --- Outer Iter 34: norm_f = 8.65731, mu=0.028703, |x|=0.054487, |J|=228723\n", + " --- Outer Iter 35: norm_f = 8.6573, mu=0.0300427, |x|=0.0546843, |J|=246173\n", + " --- Outer Iter 36: norm_f = 8.65729, mu=0.0324868, |x|=0.0548768, |J|=246665\n", + " --- Outer Iter 37: norm_f = 8.65729, mu=0.0352858, |x|=0.0550583, |J|=266147\n", + " --- Outer Iter 38: norm_f = 8.65728, mu=0.0470822, |x|=0.0552287, |J|=277589\n", + " --- Outer Iter 39: norm_f = 8.65728, mu=0.0512434, |x|=0.0553594, |J|=376186\n", + " --- Outer Iter 40: norm_f = 8.65727, mu=0.0548577, |x|=0.0554804, |J|=393093\n", + " --- Outer Iter 41: norm_f = 8.65727, mu=0.0855496, |x|=0.0555958, |J|=425220\n", + " --- Outer Iter 42: norm_f = 8.65727, mu=0.0997164, |x|=0.05567, |J|=670533\n", + " --- Outer Iter 43: norm_f = 8.65726, mu=0.112366, |x|=0.0557348, |J|=735990\n", + " --- Outer Iter 44: norm_f = 8.65726, mu=0.143338, |x|=0.0557923, |J|=846488\n", + " --- Outer Iter 45: norm_f = 8.65726, mu=0.177549, |x|=0.0558381, |J|=1.07287e+06\n", + " --- Outer Iter 46: norm_f = 8.65726, mu=0.0532647, |x|=0.055875, |J|=1.28598e+06\n", + " --- Outer Iter 47: norm_f = 8.65726, mu=0.157908, |x|=0.0559378, |J|=783920\n", + " --- Outer Iter 48: norm_f = 8.65725, mu=0.223373, |x|=0.0559802, |J|=1.21034e+06\n", + " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", + "Total Variational Distance (TVD) = -1 (952 data params - 60 (approx) model params = expected mean of 892; p-value = 1)\n", + "Completed in 2.0s\n", + "\n", + "--- Lp^p GST ---\n", + " --- Outer Iter 0: norm_f = 4.54032e-13, mu=1, |x|=0.0519071, |J|=0.000774409\n", + " --- Outer Iter 1: norm_f = 4.22989e-13, mu=1.76429e-07, |x|=0.0519089, |J|=0.00139413\n", + " --- Outer Iter 2: norm_f = 4.08546e-13, mu=1.19564e-07, |x|=0.05191, |J|=0.00114735\n", + " --- Outer Iter 3: norm_f = 4.01246e-13, mu=4.66244e-08, |x|=0.0519112, |J|=0.00115444\n", + " --- Outer Iter 4: norm_f = 3.94983e-13, mu=3.80545e-08, |x|=0.0519141, |J|=0.00123205\n", + " --- Outer Iter 5: norm_f = 3.90037e-13, mu=1.40786e-08, |x|=0.0519143, |J|=0.00118648\n", + " --- Outer Iter 6: norm_f = 3.81598e-13, mu=5.1136e-09, |x|=0.0519138, |J|=0.00121366\n", + " --- Outer Iter 7: norm_f = 3.70547e-13, mu=2.40967e-09, |x|=0.0518977, |J|=0.00117361\n", + " --- Outer Iter 8: norm_f = 3.60039e-13, mu=9.50923e-10, |x|=0.0518498, |J|=0.00116702\n", + " --- Outer Iter 9: norm_f = 3.4658e-13, mu=3.73005e-10, |x|=0.0518059, |J|=0.00111234\n", + " --- Outer Iter 10: norm_f = 3.29304e-13, mu=1.54748e-10, |x|=0.0520676, |J|=0.00111376\n", + " --- Outer Iter 11: norm_f = 3.22645e-13, mu=1.58458e-10, |x|=0.0535845, |J|=0.00139206\n", + " --- Outer Iter 12: norm_f = 3.12353e-13, mu=1.19186e-10, |x|=0.0543886, |J|=0.00105821\n", + " --- Outer Iter 13: norm_f = 3.1022e-13, mu=8.55255e-11, |x|=0.0555549, |J|=0.00107934\n", + " --- Outer Iter 14: norm_f = 3.09153e-13, mu=5.58402e-11, |x|=0.0563069, |J|=0.00102567\n", + " --- Outer Iter 15: norm_f = 3.08472e-13, mu=3.52505e-11, |x|=0.0565967, |J|=0.00104656\n", + " --- Outer Iter 16: norm_f = 3.08031e-13, mu=2.48036e-11, |x|=0.0566524, |J|=0.00102207\n", + " --- Outer Iter 17: norm_f = 3.07789e-13, mu=1.71581e-11, |x|=0.056319, |J|=0.00104161\n", + " --- Outer Iter 18: norm_f = 3.07604e-13, mu=1.01037e-11, |x|=0.0558281, |J|=0.00102815\n", + " --- Outer Iter 19: norm_f = 3.07431e-13, mu=5.7762e-12, |x|=0.0552678, |J|=0.00104054\n", + " --- Outer Iter 20: norm_f = 3.07246e-13, mu=2.10893e-12, |x|=0.0548908, |J|=0.00103169\n", + " --- Outer Iter 21: norm_f = 3.07089e-13, mu=4.21163e-12, |x|=0.0547622, |J|=0.00103832\n", + " --- Outer Iter 22: norm_f = 3.06708e-13, mu=3.69084e-12, |x|=0.0547342, |J|=0.00103226\n", + " --- Outer Iter 23: norm_f = 3.06586e-13, mu=3.24647e-12, |x|=0.0543116, |J|=0.00103641\n", + " --- Outer Iter 24: norm_f = 3.06514e-13, mu=3.07967e-12, |x|=0.0537928, |J|=0.00103397\n", + " --- Outer Iter 25: norm_f = 3.06474e-13, mu=2.97911e-12, |x|=0.0533374, |J|=0.00103714\n", + " --- Outer Iter 26: norm_f = 3.06448e-13, mu=2.89797e-12, |x|=0.0529933, |J|=0.00103571\n", + " --- Outer Iter 27: norm_f = 3.0643e-13, mu=2.82251e-12, |x|=0.0527225, |J|=0.00103758\n", + " --- Outer Iter 28: norm_f = 3.06419e-13, mu=2.74997e-12, |x|=0.0525218, |J|=0.00103664\n", + " --- Outer Iter 29: norm_f = 3.06411e-13, mu=2.6733e-12, |x|=0.0523582, |J|=0.0010377\n", + " --- Outer Iter 30: norm_f = 3.06406e-13, mu=2.59234e-12, |x|=0.0522307, |J|=0.00103711\n", + " --- Outer Iter 31: norm_f = 3.06403e-13, mu=2.49734e-12, |x|=0.0521202, |J|=0.00103771\n", + " --- Outer Iter 32: norm_f = 3.064e-13, mu=2.38453e-12, |x|=0.0520269, |J|=0.00103737\n", + " --- Outer Iter 33: norm_f = 3.06399e-13, mu=2.23697e-12, |x|=0.0519411, |J|=0.0010377\n", + " --- Outer Iter 34: norm_f = 3.06397e-13, mu=2.04443e-12, |x|=0.0518621, |J|=0.00103751\n", + " --- Outer Iter 35: norm_f = 3.06396e-13, mu=1.78318e-12, |x|=0.0517846, |J|=0.00103769\n", + " --- Outer Iter 36: norm_f = 3.06395e-13, mu=1.45314e-12, |x|=0.0517052, |J|=0.00103759\n", + " --- Outer Iter 37: norm_f = 3.06395e-13, mu=1.06591e-12, |x|=0.0516184, |J|=0.00103769\n", + " --- Outer Iter 38: norm_f = 3.06394e-13, mu=6.97481e-13, |x|=0.0515132, |J|=0.00103766\n", + " --- Outer Iter 39: norm_f = 3.06393e-13, mu=4.18659e-13, |x|=0.051379, |J|=0.0010377\n", + " --- Outer Iter 40: norm_f = 3.06392e-13, mu=2.69945e-13, |x|=0.0512023, |J|=0.00103773\n", + " --- Outer Iter 41: norm_f = 3.06391e-13, mu=2.15137e-13, |x|=0.0510285, |J|=0.00103772\n", + " --- Outer Iter 42: norm_f = 3.0639e-13, mu=2.13101e-13, |x|=0.0509237, |J|=0.00103783\n", + " --- Outer Iter 43: norm_f = 3.0639e-13, mu=7.19687e-13, |x|=0.0509016, |J|=0.00103765\n", + " --- Outer Iter 44: norm_f = 3.0639e-13, mu=9.95405e-13, |x|=0.0508962, |J|=0.00103785\n", + " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", + "L_10 norm to the power 10. = -1 (952 data params - 60 (approx) model params = expected mean of 892; p-value = 1)\n", + "Completed in 1.8s\n" + ] + } + ], + "source": [ + "fit_mode = 'CPTPLND'\n", + "\n", + "Lpnorm_spec = ('Lp^p', 10)\n", + "verb = 4\n", + "\n", + "results = run_gst(ds, fids, germs, target, ['logl', 'normalized tvd', 'tvd', Lpnorm_spec], verbosity=verb, mode=fit_mode)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# GST model, fit using logl as the final iteration\n", + "logl_est = results.estimates['logl']\n", + "final_logl_model = logl_est.models['stdgaugeopt']\n", + "# GST model, fit using Lp^p for the final iteration\n", + "Lp_est_name = str(Lpnorm_spec)\n", + "Lp_est = results.estimates[Lp_est_name]\n", + "final_LpP_model = Lp_est.models['stdgaugeopt']\n", + "# data generating model.es\n", + "results.estimates['datagen'] = logl_est.copy()\n", + "to_replace = [k for k in results.estimates['datagen'].models.keys() if k != 'target' ]\n", + "m_datagen.convert_members_inplace(fit_mode)\n", + "for k in to_replace:\n", + " results.estimates['datagen'].models[k] = m_datagen\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running idle tomography\n", + "Computing switchable properties\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/report/factory.py:1228: UserWarning: You should really specify `title=` when generating reports, as this makes it much easier to identify them later on. Since you didn't, pyGSTi has generated a random one for you: 'rebellious laser beams'.\n", + " _warnings.warn((\"You should really specify `title=` when generating reports,\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found standard clifford compilation from smq1Q_XYI\n", + "Found standard clifford compilation from smq1Q_XYI\n", + "Found standard clifford compilation from smq1Q_XYI\n", + "Found standard clifford compilation from smq1Q_XYI\n", + "Found standard clifford compilation from smq1Q_XYI\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/tools/optools.py:164: UserWarning:\n", + "\n", + "\n", + " Input matrix is not PSD up to tolerance 1.8189894035458565e-12.\n", + " We'll project out the bad eigenspaces to only work with the PSD part.\n", + " \n", + "\n", + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/tools/optools.py:172: UserWarning:\n", + "\n", + "\n", + " The PSD part of the input matrix is not trace-1 up to tolerance 3.637978807091713e-12.\n", + " Beware result!\n", + " \n", + "\n", + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/forwardsims/mapforwardsim.py:732: UserWarning:\n", + "\n", + "Generating dense process matrix representations of circuits or gates \n", + "can be inefficient and should be avoided for the purposes of forward \n", + "simulation/calculation of circuit outcome probability distributions \n", + "when using the MapForwardSimulator.\n", + "\n" + ] + } + ], + "source": [ + "report = pygsti.report.construct_standard_report(\n", + " {'eval-true' : results\n", + " },\n", + " # advanced_options={'skip_sections': ('colorbox',)},\n", + "# # title=\"GST Example Report\", verbosity=2\n", + ")\n", + "# NOTE: can reach in and change the entry in report.switchboard.objfn_builder_modvi,\n", + "# or anything else in the switchboard, according to my whims.\n", + "report.write_html(f\"case0_reports_250422/exampleReport_mml{maxmaxlen}_{int(np.log2(m_datagen.dim))}Q\", auto_open=True, verbosity=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "repdata = ds.repData\n", + "cirindex = ds.cirIndex\n", + "empiri_probs = []\n", + "mprobs_logl = []\n", + "mprobs_LpP = []\n", + "for circ, indices in cirindex.items():\n", + " counts = repdata[indices]\n", + " empirical_probs = counts / np.sum(counts)\n", + " empiri_probs.append(empirical_probs)\n", + " model_probs_LpP = np.array(list(final_LpP_model.probabilities(circ).values()))\n", + " mprobs_LpP.append(model_probs_LpP)\n", + " model_probs_logl = np.array(list(final_logl_model.probabilities(circ).values()))\n", + " mprobs_logl.append(model_probs_logl)\n", + "empiri_probs = np.array(empiri_probs)\n", + "mprobs_logl = np.array(mprobs_logl)\n", + "mprobs_LpP = np.array(mprobs_LpP)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "bins = 100\n", + "x = np.array([empiri_probs.ravel(), mprobs_logl.ravel(), mprobs_LpP.ravel()]).T\n", + "plt.hist(x, bins=bins)\n", + "plt.legend(['empirical', 'logl', Lp_est_name ])\n", + "plt.yscale('log')\n", + "plt.xlim([0.3, 0.7])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bins = 50\n", + "x2 = x[:,1:3].copy()\n", + "x2[:,0] = np.abs(x2[:,0] - x[:,0])\n", + "x2[:,1] = np.abs(x2[:,1] - x[:,0])\n", + "plt.hist(x2, bins=bins)\n", + "plt.legend(['logl', Lp_est_name ])\n", + "plt.yscale('log')\n", + "plt.title('Histogram of abs(model_probs - empirical_probs)\\nwhere model_probs uses one of two final objectives')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "['logl', 'normalized tvd', 'tvd', \"('Lp^p', 10)\", 'datagen']\n", + "[0.32639205508473434, 3.7837270451549756, 1.1055924120918392, 8.277176250617178, 0.878729720001016]\n", + "[5, 4, 5, 4, 5]\n", + "[[4.67504133e+02 2.61786237e-01 8.78667563e+00 5.83053781e-02]\n", + " [5.41695997e+02 2.48277943e-01 9.28564031e+00 6.73683489e-02]\n", + " [4.84225199e+02 2.53637734e-01 8.65725433e+00 5.84214073e-02]\n", + " [6.38122108e+02 2.85037849e-01 9.57856631e+00 5.60566961e-02]\n", + " [1.83349018e+06 1.33844594e+01 4.67173129e+02 1.26554588e+02]]\n" + ] + } + ], + "source": [ + "Nsigs = []\n", + "ratings = []\n", + "from pygsti.report.plothelpers import rated_n_sigma\n", + "circuitlist = list(ds.cirIndex.keys())\n", + "pvecs = []\n", + "objectives = []\n", + "for estname, est in results.estimates.items():\n", + " model = est.models['stdgaugeopt']\n", + " Nsig, rating = rated_n_sigma(ds, model, circuitlist, 'logl')\n", + " Nsigs.append(Nsig)\n", + " ratings.append(rating)\n", + " objective = est.final_objective_fn()\n", + " objectives.append(objective)\n", + " # pvecs.append(objective.model.to_vector())\n", + " #\n", + " # PROBLEM: the to_vector() might be based on full TP for some models and CPTPLND for others, since gauge optimization uses full TP.\n", + " # I think that objectives will always be with respect to CPTPLND parameterization, at least when fit_mode == 'CPTPLND'.\n", + " # Note that objective.model is different from model in general, since the latter is obtained by gauge optimizing the former to target.\n", + " # HOWEVA, there's an issue where objective.model.to_vector() is returning the same value for (very!) different objectives.\n", + " # I'm not sure what that's about!\n", + " # \n", + " # This persists even when I call est.models['final iteration estimate'] instead of relying on objective.model.\n", + " #\n", + " pvecs.append(est.models['final iteration estimate'].to_vector())\n", + "\n", + "print(list(results.estimates.keys()))\n", + "print(Nsigs)\n", + "print(ratings)\n", + "\n", + "objvals = np.zeros((len(pvecs),len(objectives)-1))\n", + "for i,pvec in enumerate(pvecs):\n", + " for j,objective in enumerate(objectives[:-1]):\n", + " val = objective.fn(pvec, stateless=True)\n", + " if val < 1e-8:\n", + " val = val ** 0.1\n", + " objvals[i,j] = val\n", + "\n", + "np.set_printoptions(linewidth=200)\n", + "print(objvals)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "13495330512\n", + "13494092560\n", + "13490937360\n", + "13512444880\n", + "13536979536\n", + "\n", + "6109039248\n", + "6109039248\n", + "6109039248\n", + "6109039248\n", + "13517386256\n", + "\n", + "8.277176250618385\n", + "-21.482563694498033\n", + "-21.482563694498033\n", + "-21.482563694498033\n", + "8.249399003910483\n" + ] + } + ], + "source": [ + "\n", + "print()\n", + "for est in results.estimates.values():\n", + " print(id(est.final_objective_fn()))\n", + " \n", + "print()\n", + "for est in results.estimates.values():\n", + " print(id(est.final_objective_fn().model))\n", + "\n", + "print()\n", + "for est in results.estimates.values():\n", + " print(est.misfit_sigma())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rogst", + "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": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/wip_notebook_sharing/experiment_helpers.py b/wip_notebook_sharing/experiment_helpers.py index 2f93acda5..33ec85228 100644 --- a/wip_notebook_sharing/experiment_helpers.py +++ b/wip_notebook_sharing/experiment_helpers.py @@ -32,9 +32,9 @@ def make_tweaked_dataset(modelpack, depol_level=0.01, rand_unitary_scale=0.001, lsgst_circuit_lists = pygsti.circuits.create_lsgst_circuit_lists(ideal_model, prep_fids, meas_fids, germs, max_lens) all_circuits = lsgst_circuit_lists[-1] shots_per_circuit = 1000 + depol_model = ideal_model.depolarize(op_noise=depol_level, spam_noise=depol_level/2, seed=1997) + final_model = depol_model.randomize_with_unitary(scale=rand_unitary_scale, seed=250422) rng_state = np.random.default_rng(0) - depol_model = ideal_model.depolarize(op_noise=depol_level, spam_noise=depol_level/2) - final_model = depol_model.randomize_with_unitary(scale=rand_unitary_scale) ds = pygsti.data.simulate_data(final_model, all_circuits, shots_per_circuit, rand_state=rng_state) return ds, final_model @@ -119,18 +119,17 @@ def run_gst(ds, fids, germs, target_model, final_objectives: List[Union[str, tup builders = pygsti.protocols.GSTObjFnBuilders( [ObjectiveFunctionBuilder.create_from(iteration_objective)], [ObjectiveFunctionBuilder.create_from(final_objective)] - #[ObjectiveFunctionBuilder.create_from(fo) for fo in final_objectives] + ) + _update_objfn_builders(builders.iteration_builders, dict()) + optim_iter = SimplerLMOptimizer.cast( + _get_optimizer(dict(), target_model) ) advanced_options = { 'extra_lm_opts': {'tol': {'relx': 1e-8, 'relf': 1e-6, 'f': -1.0, 'jac': -1, 'maxdx': 1.0}, } } - _update_objfn_builders(builders.iteration_builders, advanced_options) _update_objfn_builders(builders.final_builders, advanced_options) - optim_iter = SimplerLMOptimizer.cast( - _get_optimizer(dict(), target_model) - ) optim_last = SimplerLMOptimizer.cast( _get_optimizer(advanced_options, target_model) ) @@ -152,30 +151,36 @@ def run_gst(ds, fids, germs, target_model, final_objectives: List[Union[str, tup est = modelest_results.estimates[str(final_objective)] seed_name = f'iteration {est.num_iterations - 1} estimate' seed_model = est.models[seed_name] + seed_vec = seed_model.to_vector() circuits = exp_design.all_circuits_needing_data - # array_types = optim.array_types + builders.final_builders[0].compute_array_types( - # optim.called_objective_methods, seed_model.sim - # ) - seed_mdc_store = est.final_mdc_store() - comm = seed_mdc_store.resource_alloc.comm - printer = pygsti.VerbosityPrinter.create_printer(verbosity, comm) - import time + printer = pygsti.VerbosityPrinter.create_printer(verbosity, None) import copy - from pygsti.algorithms.core import _do_runopt - from pygsti.algorithms.gaugeopt import gaugeopt_to_target for final_objective in final_objectives[1:]: builder = ObjectiveFunctionBuilder.create_from(final_objective) + curr_seed_model = copy.deepcopy(seed_model) + # ^ A copy is needed because this will be used as the foundational of a ModelDatasetCircuitStore, + # which in turn will be the foundation for an MDCObjective. + curr_seed_model.from_vector(seed_vec) array_types = optim_last.array_types + \ - builder.compute_array_types(optim_last.called_objective_methods, seed_model.sim) - mdc_store = ModelDatasetCircuitsStore(seed_model, data.dataset, circuits, seed_mdc_store.resource_alloc, array_types=array_types) + builder.compute_array_types(optim_last.called_objective_methods, curr_seed_model.sim) + mdc_store = ModelDatasetCircuitsStore(curr_seed_model, data.dataset, circuits, None, array_types=array_types) printer.log('') - out = run_gst_fit(mdc_store, optim_last, builder, verbosity - 2) - # ^ Would be nice if that accepted a printer. + _, outobjective = run_gst_fit(mdc_store, optim_last, builder, verbosity - 2) + fobjstr = str(final_objective) - modelest_results.add_estimate(copy.deepcopy(est), fobjstr) - modelest_results.estimates[fobjstr].models['final iteration estimate'] = out[1].model - modelest_results.estimates[fobjstr].models.pop('stdgaugeopt') - modelest_results.estimates[fobjstr].add_gaugeoptimized(gop_params['stdgaugeopt'], label='stdgaugeopt') + + curr_est = copy.deepcopy(est) + curr_est._final_mdc_store = outobjective + curr_est._final_objfn = outobjective + curr_est._final_objfn_cache = None + curr_est._final_objective_fn_cache = None + modelest_results.add_estimate(curr_est, fobjstr) + + curr_est = modelest_results.estimates[fobjstr] + curr_est.models['final iteration estimate'] = outobjective.model + curr_est.models.pop('stdgaugeopt') + curr_est.add_gaugeoptimized(gop_params['stdgaugeopt'], label='stdgaugeopt') + pass return modelest_results From b011a2d237a34ce595b30423fafbb2c18dfaef93 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 23 Apr 2025 15:01:49 -0700 Subject: [PATCH 33/71] dataframe --- .../case0-gst-with-outliers-beyond-tvd.ipynb | 222 +++--------------- 1 file changed, 33 insertions(+), 189 deletions(-) diff --git a/wip_notebook_sharing/case0-gst-with-outliers-beyond-tvd.ipynb b/wip_notebook_sharing/case0-gst-with-outliers-beyond-tvd.ipynb index 848484411..a0a95dd10 100644 --- a/wip_notebook_sharing/case0-gst-with-outliers-beyond-tvd.ipynb +++ b/wip_notebook_sharing/case0-gst-with-outliers-beyond-tvd.ipynb @@ -14,7 +14,8 @@ "import numpy as np\n", "from pprint import pprint\n", "from experiment_helpers import make_depolarized_dataset, run_gst, corrupt_dataset, make_tweaked_dataset\n", - "from scipy import linalg as la\n" + "from scipy import linalg as la\n", + "import pandas as pd" ] }, { @@ -205,8 +206,8 @@ " --- Outer Iter 99: norm_f = 57.7799, mu=1757.94, |x|=0.0662182, |J|=1664.4\n", " Least squares message = Maximum iterations (100) exceeded\n", " Sum of Chi^2 = 57.7796 (92 data params - 60 (approx) model params = expected mean of 32; p-value = 0.00346544)\n", - " Completed in 1.6s\n", - " Iteration 1 took 1.8s\n", + " Completed in 1.8s\n", + " Iteration 1 took 2.0s\n", " \n", " --- Iterative GST: Iter 2 of 7 168 circuits ---: \n", " --- chi2 GST ---\n", @@ -219,8 +220,7 @@ " --- Outer Iter 6: norm_f = 141.51, mu=811.601, |x|=0.0524609, |J|=2327.69\n", " --- Outer Iter 7: norm_f = 141.499, mu=859.672, |x|=0.0524809, |J|=2328.14\n", " --- Outer Iter 8: norm_f = 141.493, mu=895.439, |x|=0.0523328, |J|=2328.75\n", - " --- Outer Iter 9: norm_f = 141.491, mu=1057.57, |x|=0.0523946, |J|=2328.66\n", - " --- Outer Iter 10: norm_f = 141.49, mu=1166.05, |x|=0.0523309, |J|=2328.87\n" + " --- Outer Iter 9: norm_f = 141.491, mu=1057.57, |x|=0.0523946, |J|=2328.66\n" ] }, { @@ -235,6 +235,7 @@ "name": "stdout", "output_type": "stream", "text": [ + " --- Outer Iter 10: norm_f = 141.49, mu=1166.05, |x|=0.0523309, |J|=2328.87\n", " --- Outer Iter 11: norm_f = 141.49, mu=2050.66, |x|=0.0523643, |J|=2328.78\n", " --- Outer Iter 12: norm_f = 141.489, mu=2153.65, |x|=0.052346, |J|=2328.83\n", " --- Outer Iter 13: norm_f = 141.489, mu=2256.61, |x|=0.0523475, |J|=2328.83\n", @@ -288,8 +289,8 @@ " --- Outer Iter 15: norm_f = 411.931, mu=628.278, |x|=0.0539405, |J|=5251.56\n", " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", " Sum of Chi^2 = 411.931 (448 data params - 60 (approx) model params = expected mean of 388; p-value = 0.193286)\n", - " Completed in 0.6s\n", - " Iteration 4 took 0.6s\n", + " Completed in 0.4s\n", + " Iteration 4 took 0.4s\n", " \n", " --- Iterative GST: Iter 5 of 7 616 circuits ---: \n", " --- chi2 GST ---\n", @@ -315,8 +316,8 @@ " --- Outer Iter 19: norm_f = 551.799, mu=226.867, |x|=0.0547359, |J|=8807.91\n", " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", " Sum of Chi^2 = 551.799 (616 data params - 60 (approx) model params = expected mean of 556; p-value = 0.54233)\n", - " Completed in 0.6s\n", - " Iteration 5 took 0.6s\n", + " Completed in 0.7s\n", + " Iteration 5 took 0.7s\n", " \n", " --- Iterative GST: Iter 6 of 7 784 circuits ---: \n", " --- chi2 GST ---\n", @@ -420,8 +421,7 @@ "\n", "--- normalized tvd GST ---\n", " --- Outer Iter 0: norm_f = 0.261786, mu=1, |x|=0.0519071, |J|=3533.75\n", - " --- Outer Iter 1: norm_f = 0.260246, mu=2645.95, |x|=0.0518531, |J|=4056.71\n", - " --- Outer Iter 2: norm_f = 0.258875, mu=2645.47, |x|=0.0518144, |J|=5596.61\n" + " --- Outer Iter 1: norm_f = 0.260246, mu=2645.95, |x|=0.0518531, |J|=4056.71\n" ] }, { @@ -436,6 +436,7 @@ "name": "stdout", "output_type": "stream", "text": [ + " --- Outer Iter 2: norm_f = 0.258875, mu=2645.47, |x|=0.0518144, |J|=5596.61\n", " --- Outer Iter 3: norm_f = 0.257933, mu=2644.87, |x|=0.0517754, |J|=13214.3\n", " --- Outer Iter 4: norm_f = 0.257274, mu=2644.2, |x|=0.0517611, |J|=22886.5\n", " --- Outer Iter 5: norm_f = 0.25666, mu=2642.01, |x|=0.0517413, |J|=20782\n", @@ -535,7 +536,7 @@ " --- Outer Iter 99: norm_f = 0.248278, mu=2.2203e+08, |x|=0.170057, |J|=1.10043e+08\n", " Least squares message = Maximum iterations (100) exceeded\n", "Total Variational Distance (TVD), normalized by circuit depth = -1 (952 data params - 60 (approx) model params = expected mean of 892; p-value = 1)\n", - "Completed in 8.6s\n" + "Completed in 8.4s\n" ] }, { @@ -653,7 +654,7 @@ " --- Outer Iter 44: norm_f = 3.0639e-13, mu=9.95405e-13, |x|=0.0508962, |J|=0.00103785\n", " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", "L_10 norm to the power 10. = -1 (952 data params - 60 (approx) model params = expected mean of 892; p-value = 1)\n", - "Completed in 1.8s\n" + "Completed in 1.9s\n" ] } ], @@ -679,7 +680,7 @@ "Lp_est_name = str(Lpnorm_spec)\n", "Lp_est = results.estimates[Lp_est_name]\n", "final_LpP_model = Lp_est.models['stdgaugeopt']\n", - "# data generating model.es\n", + "# data generating model.\n", "results.estimates['datagen'] = logl_est.copy()\n", "to_replace = [k for k in results.estimates['datagen'].models.keys() if k != 'target' ]\n", "m_datagen.convert_members_inplace(fit_mode)\n", @@ -690,79 +691,7 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running idle tomography\n", - "Computing switchable properties\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/report/factory.py:1228: UserWarning: You should really specify `title=` when generating reports, as this makes it much easier to identify them later on. Since you didn't, pyGSTi has generated a random one for you: 'rebellious laser beams'.\n", - " _warnings.warn((\"You should really specify `title=` when generating reports,\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found standard clifford compilation from smq1Q_XYI\n", - "Found standard clifford compilation from smq1Q_XYI\n", - "Found standard clifford compilation from smq1Q_XYI\n", - "Found standard clifford compilation from smq1Q_XYI\n", - "Found standard clifford compilation from smq1Q_XYI\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/tools/optools.py:164: UserWarning:\n", - "\n", - "\n", - " Input matrix is not PSD up to tolerance 1.8189894035458565e-12.\n", - " We'll project out the bad eigenspaces to only work with the PSD part.\n", - " \n", - "\n", - "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/tools/optools.py:172: UserWarning:\n", - "\n", - "\n", - " The PSD part of the input matrix is not trace-1 up to tolerance 3.637978807091713e-12.\n", - " Beware result!\n", - " \n", - "\n", - "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/forwardsims/mapforwardsim.py:732: UserWarning:\n", - "\n", - "Generating dense process matrix representations of circuits or gates \n", - "can be inefficient and should be avoided for the purposes of forward \n", - "simulation/calculation of circuit outcome probability distributions \n", - "when using the MapForwardSimulator.\n", - "\n" - ] - } - ], - "source": [ - "report = pygsti.report.construct_standard_report(\n", - " {'eval-true' : results\n", - " },\n", - " # advanced_options={'skip_sections': ('colorbox',)},\n", - "# # title=\"GST Example Report\", verbosity=2\n", - ")\n", - "# NOTE: can reach in and change the entry in report.switchboard.objfn_builder_modvi,\n", - "# or anything else in the switchboard, according to my whims.\n", - "report.write_html(f\"case0_reports_250422/exampleReport_mml{maxmaxlen}_{int(np.log2(m_datagen.dim))}Q\", auto_open=True, verbosity=1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -786,12 +715,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -804,30 +733,6 @@ "from matplotlib import pyplot as plt\n", "bins = 100\n", "x = np.array([empiri_probs.ravel(), mprobs_logl.ravel(), mprobs_LpP.ravel()]).T\n", - "plt.hist(x, bins=bins)\n", - "plt.legend(['empirical', 'logl', Lp_est_name ])\n", - "plt.yscale('log')\n", - "plt.xlim([0.3, 0.7])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ "bins = 50\n", "x2 = x[:,1:3].copy()\n", "x2[:,0] = np.abs(x2[:,0] - x[:,0])\n", @@ -842,58 +747,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "['logl', 'normalized tvd', 'tvd', \"('Lp^p', 10)\", 'datagen']\n", - "[0.32639205508473434, 3.7837270451549756, 1.1055924120918392, 8.277176250617178, 0.878729720001016]\n", - "[5, 4, 5, 4, 5]\n", - "[[4.67504133e+02 2.61786237e-01 8.78667563e+00 5.83053781e-02]\n", - " [5.41695997e+02 2.48277943e-01 9.28564031e+00 6.73683489e-02]\n", - " [4.84225199e+02 2.53637734e-01 8.65725433e+00 5.84214073e-02]\n", - " [6.38122108e+02 2.85037849e-01 9.57856631e+00 5.60566961e-02]\n", - " [1.83349018e+06 1.33844594e+01 4.67173129e+02 1.26554588e+02]]\n" + " LogL(m) nTVD(m) TVD(m) L_{10}(m) N_{\\sigma}\n", + "m_logl 4.675041e+02 0.261786 8.786676 0.058305 0.326392\n", + "m_{ntvd} 5.416960e+02 0.248278 9.285640 0.067368 3.783727\n", + "m_tvd 4.842252e+02 0.253638 8.657254 0.058421 1.105592\n", + "m_{L_{10}^10} 6.381221e+02 0.285038 9.578566 0.056057 8.277176\n", + "m_datagen 1.833490e+06 13.384459 467.173129 126.554588 0.878730\n" ] } ], "source": [ "Nsigs = []\n", - "ratings = []\n", "from pygsti.report.plothelpers import rated_n_sigma\n", "circuitlist = list(ds.cirIndex.keys())\n", "pvecs = []\n", "objectives = []\n", "for estname, est in results.estimates.items():\n", " model = est.models['stdgaugeopt']\n", - " Nsig, rating = rated_n_sigma(ds, model, circuitlist, 'logl')\n", + " Nsig, _ = rated_n_sigma(ds, model, circuitlist, 'logl')\n", " Nsigs.append(Nsig)\n", - " ratings.append(rating)\n", " objective = est.final_objective_fn()\n", " objectives.append(objective)\n", - " # pvecs.append(objective.model.to_vector())\n", - " #\n", - " # PROBLEM: the to_vector() might be based on full TP for some models and CPTPLND for others, since gauge optimization uses full TP.\n", - " # I think that objectives will always be with respect to CPTPLND parameterization, at least when fit_mode == 'CPTPLND'.\n", - " # Note that objective.model is different from model in general, since the latter is obtained by gauge optimizing the former to target.\n", - " # HOWEVA, there's an issue where objective.model.to_vector() is returning the same value for (very!) different objectives.\n", - " # I'm not sure what that's about!\n", - " # \n", - " # This persists even when I call est.models['final iteration estimate'] instead of relying on objective.model.\n", - " #\n", " pvecs.append(est.models['final iteration estimate'].to_vector())\n", "\n", - "print(list(results.estimates.keys()))\n", - "print(Nsigs)\n", - "print(ratings)\n", "\n", "objvals = np.zeros((len(pvecs),len(objectives)-1))\n", "for i,pvec in enumerate(pvecs):\n", @@ -902,54 +785,15 @@ " if val < 1e-8:\n", " val = val ** 0.1\n", " objvals[i,j] = val\n", + "objvals = np.concatenate((objvals, np.array(Nsigs).reshape(-1,1)), axis=1)\n", "\n", - "np.set_printoptions(linewidth=200)\n", - "print(objvals)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "13495330512\n", - "13494092560\n", - "13490937360\n", - "13512444880\n", - "13536979536\n", - "\n", - "6109039248\n", - "6109039248\n", - "6109039248\n", - "6109039248\n", - "13517386256\n", - "\n", - "8.277176250618385\n", - "-21.482563694498033\n", - "-21.482563694498033\n", - "-21.482563694498033\n", - "8.249399003910483\n" - ] - } - ], - "source": [ - "\n", - "print()\n", - "for est in results.estimates.values():\n", - " print(id(est.final_objective_fn()))\n", - " \n", - "print()\n", - "for est in results.estimates.values():\n", - " print(id(est.final_objective_fn().model))\n", + "df = pd.DataFrame(\n", + " objvals,\n", + " index=['m_logl', 'm_{ntvd}', 'm_tvd', 'm_{L_{10}^10}', 'm_datagen'],\n", + " columns=['LogL(m)', 'nTVD(m)', 'TVD(m)', 'L_{10}(m)', 'N_{\\sigma}']\n", + ")\n", "\n", - "print()\n", - "for est in results.estimates.values():\n", - " print(est.misfit_sigma())" + "print(df)" ] } ], From 08ef8beffa2d9a588896b0d622fe7eb22da7a494 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 23 Apr 2025 15:07:20 -0700 Subject: [PATCH 34/71] minor change --- pygsti/objectivefns/objectivefns.py | 8 +- .../case0-gst-with-outliers-beyond-tvd.ipynb | 180 +++++------------- 2 files changed, 53 insertions(+), 135 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index 0867bcaf9..5d5018a6e 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -5099,9 +5099,13 @@ def _update_terms_weights(self): circuit_sizes[elind] = 1 + circuit.num_gates assert _np.all(circuit_sizes > 0) self._terms_weights = 1 / circuit_sizes - # self._terms_weights /= _np.mean(self.terms_weights) + self._terms_weights /= _np.mean(self.terms_weights) # ^ Make the weights mean-1 to avoid unintended changes to Levenberg-Marquardt - # stopping criteria. + # stopping criteria. This has an undesirable side effect of affecting how + # one would interpret individual circuits' contributions to the terms vector. + # However, this function is first and foremost a sum of terms, and the sum + # never has an interesting interpretation. + # elif self._terms_weights is None: self._terms_weights = _np.ones(self.layout.num_elements) return diff --git a/wip_notebook_sharing/case0-gst-with-outliers-beyond-tvd.ipynb b/wip_notebook_sharing/case0-gst-with-outliers-beyond-tvd.ipynb index a0a95dd10..9db5e6cde 100644 --- a/wip_notebook_sharing/case0-gst-with-outliers-beyond-tvd.ipynb +++ b/wip_notebook_sharing/case0-gst-with-outliers-beyond-tvd.ipynb @@ -206,7 +206,7 @@ " --- Outer Iter 99: norm_f = 57.7799, mu=1757.94, |x|=0.0662182, |J|=1664.4\n", " Least squares message = Maximum iterations (100) exceeded\n", " Sum of Chi^2 = 57.7796 (92 data params - 60 (approx) model params = expected mean of 32; p-value = 0.00346544)\n", - " Completed in 1.8s\n", + " Completed in 1.7s\n", " Iteration 1 took 2.0s\n", " \n", " --- Iterative GST: Iter 2 of 7 168 circuits ---: \n", @@ -219,8 +219,7 @@ " --- Outer Iter 5: norm_f = 141.562, mu=405.803, |x|=0.0528087, |J|=2326.02\n", " --- Outer Iter 6: norm_f = 141.51, mu=811.601, |x|=0.0524609, |J|=2327.69\n", " --- Outer Iter 7: norm_f = 141.499, mu=859.672, |x|=0.0524809, |J|=2328.14\n", - " --- Outer Iter 8: norm_f = 141.493, mu=895.439, |x|=0.0523328, |J|=2328.75\n", - " --- Outer Iter 9: norm_f = 141.491, mu=1057.57, |x|=0.0523946, |J|=2328.66\n" + " --- Outer Iter 8: norm_f = 141.493, mu=895.439, |x|=0.0523328, |J|=2328.75\n" ] }, { @@ -235,6 +234,7 @@ "name": "stdout", "output_type": "stream", "text": [ + " --- Outer Iter 9: norm_f = 141.491, mu=1057.57, |x|=0.0523946, |J|=2328.66\n", " --- Outer Iter 10: norm_f = 141.49, mu=1166.05, |x|=0.0523309, |J|=2328.87\n", " --- Outer Iter 11: norm_f = 141.49, mu=2050.66, |x|=0.0523643, |J|=2328.78\n", " --- Outer Iter 12: norm_f = 141.489, mu=2153.65, |x|=0.052346, |J|=2328.83\n", @@ -266,8 +266,8 @@ " --- Outer Iter 17: norm_f = 267.547, mu=1772.85, |x|=0.0545185, |J|=3345.43\n", " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", " Sum of Chi^2 = 267.547 (285 data params - 60 (approx) model params = expected mean of 225; p-value = 0.0272684)\n", - " Completed in 0.4s\n", - " Iteration 3 took 0.4s\n", + " Completed in 0.5s\n", + " Iteration 3 took 0.5s\n", " \n", " --- Iterative GST: Iter 4 of 7 448 circuits ---: \n", " --- chi2 GST ---\n", @@ -289,8 +289,8 @@ " --- Outer Iter 15: norm_f = 411.931, mu=628.278, |x|=0.0539405, |J|=5251.56\n", " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", " Sum of Chi^2 = 411.931 (448 data params - 60 (approx) model params = expected mean of 388; p-value = 0.193286)\n", - " Completed in 0.4s\n", - " Iteration 4 took 0.4s\n", + " Completed in 0.7s\n", + " Iteration 4 took 0.7s\n", " \n", " --- Iterative GST: Iter 5 of 7 616 circuits ---: \n", " --- chi2 GST ---\n", @@ -356,8 +356,8 @@ " --- Outer Iter 32: norm_f = 744.613, mu=616.178, |x|=0.0546056, |J|=15814.6\n", " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", " Sum of Chi^2 = 744.613 (784 data params - 60 (approx) model params = expected mean of 724; p-value = 0.289745)\n", - " Completed in 1.1s\n", - " Iteration 6 took 1.1s\n", + " Completed in 1.2s\n", + " Iteration 6 took 1.2s\n", " \n", " --- Iterative GST: Iter 7 of 7 952 circuits ---: \n", " --- chi2 GST ---\n", @@ -420,8 +420,9 @@ " -- Performing 'stdgaugeopt' gauge optimization on CPTPLND estimate --\n", "\n", "--- normalized tvd GST ---\n", - " --- Outer Iter 0: norm_f = 0.261786, mu=1, |x|=0.0519071, |J|=3533.75\n", - " --- Outer Iter 1: norm_f = 0.260246, mu=2645.95, |x|=0.0518531, |J|=4056.71\n" + " --- Outer Iter 0: norm_f = 16.3355, mu=1, |x|=0.0519071, |J|=3533.75\n", + " --- Outer Iter 1: norm_f = 15.8436, mu=882.058, |x|=0.0516419, |J|=4309.22\n", + " --- Outer Iter 2: norm_f = 15.6493, mu=294.019, |x|=0.0513698, |J|=5816.59\n" ] }, { @@ -436,121 +437,34 @@ "name": "stdout", "output_type": "stream", "text": [ - " --- Outer Iter 2: norm_f = 0.258875, mu=2645.47, |x|=0.0518144, |J|=5596.61\n", - " --- Outer Iter 3: norm_f = 0.257933, mu=2644.87, |x|=0.0517754, |J|=13214.3\n", - " --- Outer Iter 4: norm_f = 0.257274, mu=2644.2, |x|=0.0517611, |J|=22886.5\n", - " --- Outer Iter 5: norm_f = 0.25666, mu=2642.01, |x|=0.0517413, |J|=20782\n", - " --- Outer Iter 6: norm_f = 0.256166, mu=2632.83, |x|=0.051721, |J|=34502.7\n", - " --- Outer Iter 7: norm_f = 0.255812, mu=2606.21, |x|=0.0517133, |J|=20646\n", - " --- Outer Iter 8: norm_f = 0.25547, mu=2570.95, |x|=0.0516964, |J|=15219.5\n", - " --- Outer Iter 9: norm_f = 0.25513, mu=2512.1, |x|=0.0516728, |J|=12524.5\n", - " --- Outer Iter 10: norm_f = 0.254784, mu=2441.21, |x|=0.0516498, |J|=11242.5\n", - " --- Outer Iter 11: norm_f = 0.254466, mu=2356.32, |x|=0.0516292, |J|=13402.4\n", - " --- Outer Iter 12: norm_f = 0.254264, mu=2324.44, |x|=0.0516215, |J|=12752.6\n", - " --- Outer Iter 13: norm_f = 0.254063, mu=2232.63, |x|=0.0516154, |J|=13307.6\n", - " --- Outer Iter 14: norm_f = 0.253835, mu=2066.01, |x|=0.0516039, |J|=16373.7\n", - " --- Outer Iter 15: norm_f = 0.253594, mu=1873.36, |x|=0.0515917, |J|=20361.5\n", - " --- Outer Iter 16: norm_f = 0.253377, mu=1637.44, |x|=0.051583, |J|=27178.9\n", - " --- Outer Iter 17: norm_f = 0.253192, mu=1427.12, |x|=0.051578, |J|=35323\n", - " --- Outer Iter 18: norm_f = 0.253008, mu=1257.13, |x|=0.0515717, |J|=43863.2\n", - " --- Outer Iter 19: norm_f = 0.252823, mu=1073.14, |x|=0.051565, |J|=63678\n", - " --- Outer Iter 20: norm_f = 0.25262, mu=884.002, |x|=0.0515565, |J|=82730.8\n", - " --- Outer Iter 21: norm_f = 0.252407, mu=691.286, |x|=0.0515473, |J|=52740.5\n", - " --- Outer Iter 22: norm_f = 0.252192, mu=519.253, |x|=0.0515384, |J|=73894.1\n", - " --- Outer Iter 23: norm_f = 0.252006, mu=444.448, |x|=0.0515325, |J|=63255.7\n", - " --- Outer Iter 24: norm_f = 0.251886, mu=442.835, |x|=0.0515324, |J|=121698\n", - " --- Outer Iter 25: norm_f = 0.251752, mu=435.726, |x|=0.05153, |J|=126012\n", - " --- Outer Iter 26: norm_f = 0.251606, mu=421.38, |x|=0.0515255, |J|=128653\n", - " --- Outer Iter 27: norm_f = 0.251452, mu=401.417, |x|=0.0515201, |J|=87338.7\n", - " --- Outer Iter 28: norm_f = 0.251294, mu=377.874, |x|=0.0515138, |J|=69765.6\n", - " --- Outer Iter 29: norm_f = 0.251137, mu=352.729, |x|=0.0515068, |J|=78886.4\n", - " --- Outer Iter 30: norm_f = 0.250985, mu=327.883, |x|=0.0514995, |J|=206210\n", - " --- Outer Iter 31: norm_f = 0.250846, mu=306.023, |x|=0.0514918, |J|=110789\n", - " --- Outer Iter 32: norm_f = 0.250707, mu=282.683, |x|=0.0514823, |J|=78450.6\n", - " --- Outer Iter 33: norm_f = 0.250571, mu=258.194, |x|=0.0514718, |J|=68640.1\n", - " --- Outer Iter 34: norm_f = 0.25044, mu=234.015, |x|=0.051461, |J|=79565\n", - " --- Outer Iter 35: norm_f = 0.250323, mu=213.632, |x|=0.0514502, |J|=78465.3\n", - " --- Outer Iter 36: norm_f = 0.250229, mu=202.389, |x|=0.0514398, |J|=82866.3\n", - " --- Outer Iter 37: norm_f = 0.250154, mu=196.676, |x|=0.0514304, |J|=78610\n", - " --- Outer Iter 38: norm_f = 0.250086, mu=189.925, |x|=0.0514209, |J|=76114.9\n", - " --- Outer Iter 39: norm_f = 0.250021, mu=179.983, |x|=0.0514114, |J|=77884.5\n", - " --- Outer Iter 40: norm_f = 0.249959, mu=163.35, |x|=0.0514018, |J|=85424\n", - " --- Outer Iter 41: norm_f = 0.249902, mu=133.09, |x|=0.0513922, |J|=136371\n", - " --- Outer Iter 42: norm_f = 0.249851, mu=82.9424, |x|=0.051384, |J|=120933\n", - " --- Outer Iter 43: norm_f = 0.249808, mu=27.6475, |x|=0.0513819, |J|=150029\n", - " --- Outer Iter 44: norm_f = 0.249776, mu=9.21583, |x|=0.0513847, |J|=327000\n", - " --- Outer Iter 45: norm_f = 0.249747, mu=3.07194, |x|=0.0513989, |J|=525508\n", - " --- Outer Iter 46: norm_f = 0.249718, mu=1.02398, |x|=0.051425, |J|=162859\n", - " --- Outer Iter 47: norm_f = 0.249692, mu=0.572186, |x|=0.0514684, |J|=126786\n", - " --- Outer Iter 48: norm_f = 0.249661, mu=0.243615, |x|=0.0515189, |J|=119105\n", - " --- Outer Iter 49: norm_f = 0.249626, mu=0.124945, |x|=0.0515615, |J|=184907\n", - " --- Outer Iter 50: norm_f = 0.249589, mu=0.0579048, |x|=0.0516054, |J|=286152\n", - " --- Outer Iter 51: norm_f = 0.24955, mu=0.0298822, |x|=0.0516478, |J|=192511\n", - " --- Outer Iter 52: norm_f = 0.249511, mu=0.0157943, |x|=0.0516873, |J|=177999\n", - " --- Outer Iter 53: norm_f = 0.249471, mu=0.00677886, |x|=0.0517327, |J|=170737\n", - " --- Outer Iter 54: norm_f = 0.24943, mu=0.00225962, |x|=0.0517942, |J|=196463\n", - " --- Outer Iter 55: norm_f = 0.249388, mu=0.000753207, |x|=0.0519122, |J|=172608\n", - " --- Outer Iter 56: norm_f = 0.249348, mu=0.000251069, |x|=0.0521928, |J|=67249.5\n", - " --- Outer Iter 57: norm_f = 0.249312, mu=8.36897e-05, |x|=0.0527623, |J|=84645.9\n", - " --- Outer Iter 58: norm_f = 0.249257, mu=2.78966e-05, |x|=0.0551668, |J|=21157.2\n", - " --- Outer Iter 59: norm_f = 0.249107, mu=9.29885e-06, |x|=0.0761846, |J|=5181.81\n", - " --- Outer Iter 60: norm_f = 0.249086, mu=0.000127291, |x|=0.097117, |J|=4897.21\n", - " --- Outer Iter 61: norm_f = 0.248929, mu=0.000130547, |x|=0.111732, |J|=5732.09\n", - " --- Outer Iter 62: norm_f = 0.248915, mu=0.000477259, |x|=0.131355, |J|=5287.56\n", - " --- Outer Iter 63: norm_f = 0.248727, mu=0.000481886, |x|=0.138182, |J|=7797.22\n", - " --- Outer Iter 64: norm_f = 0.248614, mu=0.000526057, |x|=0.145611, |J|=10648.8\n", - " --- Outer Iter 65: norm_f = 0.248548, mu=0.000656308, |x|=0.153162, |J|=12010.4\n", - " --- Outer Iter 66: norm_f = 0.2485, mu=0.000889541, |x|=0.159021, |J|=15090.2\n", - " --- Outer Iter 67: norm_f = 0.248465, mu=0.00129096, |x|=0.163023, |J|=21155.3\n", - " --- Outer Iter 68: norm_f = 0.248441, mu=0.00197409, |x|=0.165693, |J|=30149.5\n", - " --- Outer Iter 69: norm_f = 0.248419, mu=0.00308786, |x|=0.167412, |J|=46651.1\n", - " --- Outer Iter 70: norm_f = 0.248398, mu=0.00482215, |x|=0.168486, |J|=78064.4\n", - " --- Outer Iter 71: norm_f = 0.248376, mu=0.00728337, |x|=0.169178, |J|=225298\n", - " --- Outer Iter 72: norm_f = 0.248347, mu=0.0100607, |x|=0.169629, |J|=330722\n", - " --- Outer Iter 73: norm_f = 0.248321, mu=0.0143503, |x|=0.169917, |J|=182343\n", - " --- Outer Iter 74: norm_f = 0.2483, mu=0.0214009, |x|=0.170074, |J|=157534\n", - " --- Outer Iter 75: norm_f = 0.248287, mu=0.0343809, |x|=0.170106, |J|=104164\n", - " --- Outer Iter 76: norm_f = 0.248282, mu=0.0613299, |x|=0.170058, |J|=82096.1\n", - " --- Outer Iter 77: norm_f = 0.248281, mu=0.11986, |x|=0.170058, |J|=70862.8\n", - " --- Outer Iter 78: norm_f = 0.24828, mu=9.65239e+06, |x|=0.170058, |J|=104551\n", - " --- Outer Iter 79: norm_f = 0.24828, mu=2.89572e+06, |x|=0.170058, |J|=162658\n", - " --- Outer Iter 80: norm_f = 0.24828, mu=6.94972e+06, |x|=0.170058, |J|=279902\n", - " --- Outer Iter 81: norm_f = 0.24828, mu=4.16983e+06, |x|=0.170058, |J|=522045\n", - " --- Outer Iter 82: norm_f = 0.24828, mu=1.00076e+07, |x|=0.170058, |J|=887854\n", - " --- Outer Iter 83: norm_f = 0.24828, mu=3.00228e+06, |x|=0.170058, |J|=1.49401e+06\n", - " --- Outer Iter 84: norm_f = 0.248279, mu=7.20547e+06, |x|=0.170057, |J|=2.49321e+06\n", - " --- Outer Iter 85: norm_f = 0.248279, mu=2.16164e+06, |x|=0.170057, |J|=4.06279e+06\n", - " --- Outer Iter 86: norm_f = 0.248279, mu=1.29698e+06, |x|=0.170057, |J|=5.7961e+06\n", - " --- Outer Iter 87: norm_f = 0.248279, mu=3.11276e+06, |x|=0.170057, |J|=8.30535e+06\n", - " --- Outer Iter 88: norm_f = 0.248279, mu=1.86766e+06, |x|=0.170057, |J|=9.14273e+06\n", - " --- Outer Iter 89: norm_f = 0.248278, mu=4.48238e+06, |x|=0.170057, |J|=1.16061e+07\n", - " --- Outer Iter 90: norm_f = 0.248278, mu=1.07577e+07, |x|=0.170057, |J|=1.40298e+07\n", - " --- Outer Iter 91: norm_f = 0.248278, mu=3.22731e+06, |x|=0.170057, |J|=1.46472e+07\n", - " --- Outer Iter 92: norm_f = 0.248278, mu=7.74555e+06, |x|=0.170057, |J|=1.86242e+07\n", - " --- Outer Iter 93: norm_f = 0.248278, mu=1.85893e+07, |x|=0.170057, |J|=2.55751e+07\n", - " --- Outer Iter 94: norm_f = 0.248278, mu=4.46144e+07, |x|=0.170057, |J|=3.6027e+07\n", - " --- Outer Iter 95: norm_f = 0.248278, mu=2.67686e+07, |x|=0.170057, |J|=4.1885e+07\n", - " --- Outer Iter 96: norm_f = 0.248278, mu=6.42447e+07, |x|=0.170057, |J|=5.50157e+07\n", - " --- Outer Iter 97: norm_f = 0.248278, mu=1.54187e+08, |x|=0.170057, |J|=7.51481e+07\n", - " --- Outer Iter 98: norm_f = 0.248278, mu=9.25123e+07, |x|=0.170057, |J|=8.46366e+07\n", - " --- Outer Iter 99: norm_f = 0.248278, mu=2.2203e+08, |x|=0.170057, |J|=1.10043e+08\n", - " Least squares message = Maximum iterations (100) exceeded\n", + " --- Outer Iter 3: norm_f = 15.5993, mu=98.0064, |x|=0.0515442, |J|=8849.06\n", + " --- Outer Iter 4: norm_f = 15.4789, mu=32.6688, |x|=0.0511876, |J|=10740.7\n", + " --- Outer Iter 5: norm_f = 15.4365, mu=10.8896, |x|=0.0508289, |J|=7197.58\n", + " --- Outer Iter 6: norm_f = 15.409, mu=7.25974, |x|=0.050839, |J|=18949.8\n", + " --- Outer Iter 7: norm_f = 15.3957, mu=7433.97, |x|=0.0505789, |J|=13428.1\n", + " --- Outer Iter 8: norm_f = 15.3693, mu=2477.99, |x|=0.0505844, |J|=13077.8\n", + " --- Outer Iter 9: norm_f = 15.3649, mu=47577.4, |x|=0.0505742, |J|=17215.9\n", + " --- Outer Iter 10: norm_f = 15.3634, mu=114186, |x|=0.0505784, |J|=81869.3\n", + " --- Outer Iter 11: norm_f = 15.3587, mu=114154, |x|=0.0505777, |J|=70532\n", + " --- Outer Iter 12: norm_f = 15.3539, mu=273971, |x|=0.0505777, |J|=107728\n", + " --- Outer Iter 13: norm_f = 15.3525, mu=164382, |x|=0.050581, |J|=83229.6\n", + " --- Outer Iter 14: norm_f = 15.3507, mu=394518, |x|=0.05058, |J|=71635.8\n", + " --- Outer Iter 15: norm_f = 15.3489, mu=236711, |x|=0.0505776, |J|=64967.4\n", + " --- Outer Iter 16: norm_f = 15.3467, mu=568106, |x|=0.0505762, |J|=55143.7\n", + " --- Outer Iter 17: norm_f = 15.3444, mu=1.36345e+06, |x|=0.0505755, |J|=93355\n", + " --- Outer Iter 18: norm_f = 15.3434, mu=409036, |x|=0.0505737, |J|=113623\n", + " --- Outer Iter 19: norm_f = 15.3431, mu=7.85349e+06, |x|=0.0505735, |J|=234545\n", + " --- Outer Iter 20: norm_f = 15.3426, mu=2.35605e+06, |x|=0.0505729, |J|=191090\n", + " --- Outer Iter 21: norm_f = 15.3426, mu=4.52361e+07, |x|=0.0505729, |J|=288643\n", + " --- Outer Iter 22: norm_f = 15.3426, mu=1.35708e+07, |x|=0.0505728, |J|=224014\n", + " --- Outer Iter 23: norm_f = 15.3426, mu=3.257e+07, |x|=0.0505728, |J|=321322\n", + " --- Outer Iter 24: norm_f = 15.3426, mu=9.771e+06, |x|=0.0505728, |J|=237417\n", + " --- Outer Iter 25: norm_f = 15.3426, mu=2.34504e+07, |x|=0.0505727, |J|=690149\n", + " --- Outer Iter 26: norm_f = 15.3425, mu=7.03512e+06, |x|=0.0505726, |J|=321702\n", + " --- Outer Iter 27: norm_f = 15.3424, mu=1.35074e+08, |x|=0.0505726, |J|=457133\n", + " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", "Total Variational Distance (TVD), normalized by circuit depth = -1 (952 data params - 60 (approx) model params = expected mean of 892; p-value = 1)\n", - "Completed in 8.4s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Completed in 2.8s\n", "\n", "--- tvd GST ---\n", " --- Outer Iter 0: norm_f = 8.78668, mu=1, |x|=0.0519071, |J|=3533.75\n", @@ -604,7 +518,7 @@ " --- Outer Iter 48: norm_f = 8.65725, mu=0.223373, |x|=0.0559802, |J|=1.21034e+06\n", " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", "Total Variational Distance (TVD) = -1 (952 data params - 60 (approx) model params = expected mean of 892; p-value = 1)\n", - "Completed in 2.0s\n", + "Completed in 2.2s\n", "\n", "--- Lp^p GST ---\n", " --- Outer Iter 0: norm_f = 4.54032e-13, mu=1, |x|=0.0519071, |J|=0.000774409\n", @@ -747,19 +661,19 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " LogL(m) nTVD(m) TVD(m) L_{10}(m) N_{\\sigma}\n", - "m_logl 4.675041e+02 0.261786 8.786676 0.058305 0.326392\n", - "m_{ntvd} 5.416960e+02 0.248278 9.285640 0.067368 3.783727\n", - "m_tvd 4.842252e+02 0.253638 8.657254 0.058421 1.105592\n", - "m_{L_{10}^10} 6.381221e+02 0.285038 9.578566 0.056057 8.277176\n", - "m_datagen 1.833490e+06 13.384459 467.173129 126.554588 0.878730\n" + " LogL(m) nTVD(m) TVD(m) L_{10}(m) N_{\\sigma}\n", + "m_logl 4.675041e+02 16.335460 8.786676 0.058305 0.326392\n", + "m_{ntvd} 5.000476e+02 15.342389 8.720995 0.058255 1.842914\n", + "m_tvd 4.842252e+02 15.826993 8.657254 0.058421 1.105592\n", + "m_{L_{10}^10} 6.381221e+02 17.786360 9.578566 0.056057 8.277176\n", + "m_datagen 1.833490e+06 835.190192 467.173129 126.554588 0.878730\n" ] } ], From 1505fc2d1697d945ab20a167180f8ec72f425817 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Mon, 28 Apr 2025 09:19:57 -0700 Subject: [PATCH 35/71] leave comments about report generation and add a helper function --- pygsti/data/datasetconstruction.py | 18 ++++++++++++++++++ pygsti/report/workspaceplots.py | 5 +++++ 2 files changed, 23 insertions(+) diff --git a/pygsti/data/datasetconstruction.py b/pygsti/data/datasetconstruction.py index fa361eedf..82299e2b3 100644 --- a/pygsti/data/datasetconstruction.py +++ b/pygsti/data/datasetconstruction.py @@ -209,6 +209,24 @@ def simulate_data(model_or_dataset, circuit_list, num_samples, return dataset +def mix_datasets(dsa, dsb, p): + dsc = dsa.copy_nonstatic() + # arr = _np.array(dsc.repData).ravel() + # print((arr, arr.size)) + # print((dsb.repData, dsb.repData.size)) + for i, (_, dsrow) in enumerate(dsb.items()): + interpolated = p*dsc.repData[i] + (1-p)*dsrow.reps + total = int(_np.ceil((_np.sum(interpolated)))) + j = _np.argmin(interpolated) + interpolated[j] = _np.ceil(interpolated[j]) + interpolated[1 - j] = total - interpolated[j] + dsc.repData[i][:] = interpolated + dsc.done_adding_data() + # arr = np.array(dsc.repData).ravel() + # print((arr, arr.size)) + return dsc + + def _adjust_probabilities_inbounds(ps, tol): #Adjust to probabilities if needed (and warn if not close to in-bounds) # ps is a dict w/keys = outcome labels and values = probabilities diff --git a/pygsti/report/workspaceplots.py b/pygsti/report/workspaceplots.py index 1acbd017f..b859ec63a 100644 --- a/pygsti/report/workspaceplots.py +++ b/pygsti/report/workspaceplots.py @@ -3175,10 +3175,15 @@ def _create(self, x_names, circuits_by_x, model_by_x, dataset_by_x, objfn_builde if isinstance(dataset_by_x, _DataSet): dataset_by_x = [dataset_by_x] * len(model_by_x) + # TODO: figure out why the report shows N_sigma values that are different than if I + # call _ph.rated_n_sigma directly. + self.ws.smartCache.ineffective.add('rated_n_sigma') for X, mdl, circuits, dataset, Np in zip(x_names, model_by_x, circuits_by_x, dataset_by_x, np_by_x): if circuits is None or mdl is None: Nsig, rating = _np.nan, 5 else: + # from pygsti.baseobjs.smartcache import _get_fn_name_key + # self.ws.smartCache.ineffective.add(_get_fn_name_key(_ph.rated_n_sigma)) Nsig, rating, _, _, _, _ = self._ccompute(_ph.rated_n_sigma, dataset, mdl, circuits, objfn_builder, Np, wildcard, return_all=True, comm=comm) From 01d15cc88dfff5daee574afc8616ec3d2277f6e1 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Mon, 28 Apr 2025 09:20:16 -0700 Subject: [PATCH 36/71] temporarily cache results --- ...unch-of-data-fix-embarassing-oversight.txt | 238 ++++++++++++++++++ 1 file changed, 238 insertions(+) create mode 100644 wip_notebook_sharing/bunch-of-data-fix-embarassing-oversight.txt diff --git a/wip_notebook_sharing/bunch-of-data-fix-embarassing-oversight.txt b/wip_notebook_sharing/bunch-of-data-fix-embarassing-oversight.txt new file mode 100644 index 000000000..8c944178e --- /dev/null +++ b/wip_notebook_sharing/bunch-of-data-fix-embarassing-oversight.txt @@ -0,0 +1,238 @@ + +I used the following function to generate noisy models and 1000-shot datasets for GST. + + def make_tweaked_dataset(modelpack, depol_level=0.01, rand_unitary_scale=0.001, max_max_len=128): + ideal_model = modelpack.target_model() + prep_fids = modelpack.prep_fiducials() + meas_fids = modelpack.meas_fiducials() + germs = modelpack.germs() + max_lens = [2**p for p in range(1+int(np.log2(max_max_len)))] + lsgst_circuit_lists = pygsti.circuits.create_lsgst_circuit_lists(ideal_model, prep_fids, meas_fids, germs, max_lens) + all_circuits = lsgst_circuit_lists[-1] + shots_per_circuit = 1000 + depol_model = ideal_model.depolarize(op_noise=depol_level, spam_noise=depol_level/2, seed=1997) + final_model = depol_model.randomize_with_unitary(scale=rand_unitary_scale, seed=250422) + rng_state = np.random.default_rng(0) + ds = pygsti.data.simulate_data(final_model, all_circuits, shots_per_circuit, rand_state=rng_state) + return ds, final_model + +Here is how I used it; note that Model A has more noise than Model B. + + mp = smq1Q_XYI + target = mp.target_model() + fids = (mp.prep_fiducials(), mp.meas_fiducials()) + germs = mp.germs() + maxmaxlen = 64 + dsa, m_dga = make_tweaked_dataset(mp, depol_level=0.001, rand_unitary_scale=0.001, max_max_len=maxmaxlen) + dsb, m_dgb = make_tweaked_dataset(mp, depol_level=0.010, rand_unitary_scale=0.020, max_max_len=maxmaxlen) + + +In the tables below, rows labeled "argmin(func)" show results for the model obtained by fitting "func" to the mixed dataset. + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The mixture dataset had weight 1.00 on dataset A and weight 0.00 on dataset B. argmin(...) models were fit to that dataset. + + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.058305 0.043682 0.058305 4.675041e+02 6.854094e+04 4.675041e+02 0.326392 3172.543057 0.326392 8.786676 60.511077 8.786676 16.335460 123.354424 16.335460 +argmin(nTVD) 0.058255 0.043691 0.058255 5.000476e+02 6.956110e+04 5.000476e+02 1.842914 3220.082562 1.842914 8.720995 60.394434 8.720995 15.342389 124.476252 15.342389 +argmin(TVD) 0.058421 0.044154 0.058421 4.842252e+02 6.921505e+04 4.842252e+02 1.105592 3203.956620 1.105592 8.657254 60.464797 8.657254 15.826993 123.250965 15.826993 +argmin(L10^10) 0.056057 0.049538 0.056057 6.381221e+02 6.964097e+04 6.381221e+02 8.277176 3223.804647 8.277176 9.578566 60.363776 9.578566 17.786360 122.383633 17.786360 +Model A 126.554588 60.941755 126.554588 1.833490e+06 1.750514e+06 1.833490e+06 0.878730 3152.059162 0.878730 467.173129 465.279406 467.173129 835.190192 868.058186 835.190192 +Model B 60.353297 36.518298 60.353297 7.812251e+05 7.029310e+05 7.812251e+05 1764.118964 2.060910 1764.118964 393.395690 362.433941 393.395690 769.413642 713.138467 769.413642 + + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The mixture dataset had weight 0.99 on dataset A and weight 0.01 on dataset B. argmin(...) models were fit to that dataset. + + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.058280 0.039962 0.057080 5.073249e+02 6.364611e+04 4.180941e+02 2.182037 2944.444398 -1.976113 8.942991 59.846071 8.432625 16.504940 121.838636 15.728875 +argmin(nTVD) 0.058199 0.040237 0.057029 5.238449e+02 6.480701e+04 4.508641e+02 2.951869 2998.542071 -0.449034 8.881305 59.807557 8.380632 15.663741 122.889098 14.786347 +argmin(TVD) 0.058479 0.039820 0.057338 5.092324e+02 6.398873e+04 4.319471e+02 2.270928 2960.410479 -1.330563 8.828511 59.859282 8.314256 16.191643 121.921252 15.361203 +argmin(L10^10) 0.056076 0.048398 0.054859 6.503304e+02 6.860767e+04 6.385727e+02 8.846081 3175.652652 8.298174 9.610355 60.149631 9.356175 17.971954 121.908982 17.208866 +Model A 126.554588 60.941755 124.595602 1.833490e+06 1.750514e+06 1.831321e+06 0.878730 3152.059162 0.355726 467.173129 465.279406 466.950288 835.190192 868.058186 835.054746 +Model B 60.353297 36.518298 59.638173 7.812251e+05 7.029310e+05 7.790110e+05 1764.118964 2.060910 1696.546001 393.395690 362.433941 392.844547 769.413642 713.138467 768.335640 + + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The mixture dataset had weight 0.95 on dataset A and weight 0.05 on dataset B. argmin(...) models were fit to that dataset. + + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.058553 0.028187 0.054809 8.739089e+02 5.279031e+04 3.702706e+02 19.264823 2438.565156 -4.204682 10.142951 57.797471 8.212986 18.901034 117.555414 14.999158 +argmin(nTVD) 0.059798 0.028166 0.055133 8.835317e+02 5.303729e+04 3.918073e+02 19.713245 2450.074142 -3.201075 10.156287 57.551119 8.186089 17.928632 117.965291 14.160187 +argmin(TVD) 0.058915 0.027390 0.055142 8.848670e+02 5.272912e+04 3.792575e+02 19.775468 2435.713379 -3.785893 10.053967 57.813796 8.103999 19.094600 117.175958 14.728985 +argmin(L10^10) 0.058181 0.022799 0.052683 1.095965e+03 5.199359e+04 5.419852e+02 29.612631 2401.437971 3.797205 10.804595 57.729039 9.199417 17.573569 120.776182 16.172637 +Model A 126.554588 60.941755 119.813465 1.833490e+06 1.750514e+06 1.825516e+06 0.878730 3152.059162 17.179587 467.173129 465.279406 466.693855 835.190192 868.058186 835.189767 +Model B 60.353297 36.518298 58.332841 7.812251e+05 7.029310e+05 7.735094e+05 1764.118964 2.060910 1518.501283 393.395690 362.433941 391.381852 769.413642 713.138467 765.808000 + + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The mixture dataset had weight 0.90 on dataset A and weight 0.10 on dataset B. argmin(...) models were fit to that dataset. + + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.069575 0.017990 0.053253 1.707234e+03 4.291860e+04 3.482229e+02 58.097710 1978.544133 -5.232105 12.207427 55.203937 8.132309 23.235284 112.218973 14.596372 +argmin(nTVD) 0.079953 0.017285 0.058557 1.705770e+03 4.306758e+04 3.637916e+02 58.029501 1985.486770 -4.506605 12.338981 54.893533 8.109634 21.600853 113.270335 13.542902 +argmin(TVD) 0.072924 0.017074 0.053595 1.713855e+03 4.287397e+04 3.530817e+02 58.406262 1976.464261 -5.005686 12.164062 55.122870 7.974263 23.594074 111.497258 14.229698 +argmin(L10^10) 0.094878 0.011530 0.050696 2.031710e+03 4.191370e+04 5.366406e+02 73.218257 1931.715827 3.548147 12.693870 55.157799 9.248214 20.035872 115.901433 16.354479 +Model A 126.554588 60.941755 114.261294 1.833490e+06 1.750514e+06 1.819040e+06 0.878730 3152.059162 65.570121 467.173129 465.279406 466.614406 835.190192 868.058186 836.788448 +Model B 60.353297 36.518298 56.715375 7.812251e+05 7.029310e+05 7.672737e+05 1764.118964 2.060910 1321.774150 393.395690 362.433941 389.555690 769.413642 713.138467 762.774974 + + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The mixture dataset had weight 0.80 on dataset A and weight 0.20 on dataset B. argmin(...) models were fit to that dataset. + + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 1.280690e-01 0.006850 0.058235 4.073798e+03 2.950145e+04 3.447751e+02 168.379391 1353.305782 -5.392771 16.805776 49.914257 8.147237 32.461797 101.228335 13.417375 +argmin(nTVD) 1.460365e-01 0.005945 0.094634 4.183566e+03 2.918867e+04 3.725061e+02 173.494602 1338.730370 -4.100509 17.372651 49.184236 8.021515 32.596331 100.884726 12.828388 +argmin(TVD) 1.466788e-01 0.005582 0.091479 4.232062e+03 2.899532e+04 3.733013e+02 175.754504 1329.720514 -4.063453 17.390356 49.195911 7.972158 33.085064 100.576929 13.119409 +argmin(L10^10) 2.345534e-08 0.003162 0.048265 4.352138e+03 2.963231e+04 6.006732e+02 181.350043 1359.404108 6.532062 16.844040 50.229908 9.723182 28.929435 104.827273 15.294466 +Model A 1.265546e+02 60.941755 104.583029 1.833490e+06 1.750514e+06 1.808010e+06 0.878730 3152.059162 226.530439 467.173129 465.279406 466.486253 835.190192 868.058186 840.349965 +Model B 6.035330e+01 36.518298 53.755875 7.812251e+05 7.029310e+05 7.564658e+05 1764.118964 2.060910 994.843938 393.395690 362.433941 385.923879 769.413642 713.138467 756.665756 + + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The mixture dataset had weight 0.70 on dataset A and weight 0.30 on dataset B. argmin(...) models were fit to that dataset. + + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 7.003153e-08 0.002223 0.074748 7.091943e+03 2.049327e+04 3.748087e+02 309.024731 933.525551 -3.993207 21.834864 44.494855 8.290449 42.852141 90.101863 12.886797 +argmin(nTVD) 1.308041e-07 0.001822 0.101387 7.261664e+03 2.014795e+04 3.914457e+02 316.933697 917.433559 -3.217924 22.304388 43.941396 8.081331 42.897418 89.962380 12.315999 +argmin(TVD) 2.240636e-07 0.001572 0.124270 7.352556e+03 2.005034e+04 4.280172e+02 321.169276 912.884917 -1.513698 22.608459 43.610406 7.988594 43.227342 89.623360 12.475021 +argmin(L10^10) 8.068013e-07 0.000791 0.051606 7.656646e+03 2.035537e+04 7.273908e+02 335.339826 927.099577 12.437089 22.360088 44.437987 10.407362 40.644331 92.165539 15.493826 +Model A 1.265546e+02 60.941755 96.185813 1.833490e+06 1.750514e+06 1.797466e+06 0.878730 3152.059162 454.592147 467.173129 465.279406 466.320765 835.190192 868.058186 843.676595 +Model B 6.035330e+01 36.518298 50.947670 7.812251e+05 7.029310e+05 7.465651e+05 1764.118964 2.060910 729.831768 393.395690 362.433941 382.212791 769.413642 713.138467 750.519271 + + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The mixture dataset had weight 0.60 on dataset A and weight 0.40 on dataset B. argmin(...) models were fit to that dataset. + + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.000001 0.000577 0.091391 1.057215e+04 1.407142e+04 4.205712e+02 471.201894 634.267962 -1.860679 27.077488 38.959680 8.587280 53.755140 78.694803 13.186374 +argmin(nTVD) 0.000002 0.000464 0.111901 1.064063e+04 1.400078e+04 4.350772e+02 474.393338 630.976212 -1.184701 27.306314 38.733142 8.396607 51.752982 80.636663 12.291792 +argmin(TVD) 0.000005 0.000310 0.154856 1.093002e+04 1.377477e+04 5.207620e+02 487.878686 620.443965 2.808205 27.986584 38.230402 8.195175 53.533009 78.948949 12.597676 +argmin(L10^10) 0.000010 0.000173 0.056281 1.142365e+04 1.383654e+04 8.348078e+02 510.881935 623.322260 17.442714 28.142159 38.727617 11.162597 54.015160 78.750509 16.638443 +Model A 126.554588 60.941755 89.013251 1.833490e+06 1.750514e+06 1.789088e+06 0.878730 3152.059162 732.687654 467.173129 465.279406 466.176419 835.190192 868.058186 847.274243 +Model B 60.353297 36.518298 48.387892 7.812251e+05 7.029310e+05 7.382813e+05 1764.118964 2.060910 517.410633 393.395690 362.433941 378.560568 769.413642 713.138467 744.458052 + + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The mixture dataset had weight 0.50 on dataset A and weight 0.50 on dataset B. argmin(...) models were fit to that dataset. + + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.000019 0.000103 0.103135 1.445346e+04 9.389479e+03 4.813178e+02 652.070827 416.089708 0.970108 32.566106 33.543358 9.169555 65.724820 67.846493 16.004581 +argmin(nTVD) 0.000027 0.000077 0.122024 1.457978e+04 9.296960e+03 4.992725e+02 657.957229 411.778345 1.806797 32.851237 33.297344 8.877317 64.277568 68.784671 14.620929 +argmin(TVD) 0.000046 0.000047 0.153857 1.491067e+04 9.096656e+03 5.650716e+02 673.376719 402.444189 4.873029 33.516252 32.955458 8.697918 66.899668 66.687181 15.806218 +argmin(L10^10) 0.000066 0.000032 0.058901 1.534189e+04 9.342669e+03 9.002678e+02 693.471719 413.908394 20.493147 33.948918 33.010908 11.802510 65.332360 68.314439 17.769593 +Model A 126.554588 60.941755 82.856783 1.833490e+06 1.750514e+06 1.780313e+06 0.878730 3152.059162 1049.968900 467.173129 465.279406 466.029406 835.190192 868.058186 850.583717 +Model B 60.353297 36.518298 46.079898 7.812251e+05 7.029310e+05 7.303883e+05 1764.118964 2.060910 348.353651 393.395690 362.433941 375.034359 769.413642 713.138467 738.464676 + + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The mixture dataset had weight 0.40 on dataset A and weight 0.60 on dataset B. argmin(...) models were fit to that dataset. + + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.000171 0.000010 0.108947 1.886757e+04 5.752944e+03 4.746497e+02 857.768204 246.627478 0.659375 38.293271 27.854670 9.021935 76.723239 55.628956 14.741397 +argmin(nTVD) 0.000241 0.000006 0.129251 1.903001e+04 5.674293e+03 4.941562e+02 865.337660 242.962347 1.568377 38.541429 27.808615 8.754129 75.034583 57.196161 13.549772 +argmin(TVD) 0.000286 0.000005 0.142902 1.893380e+04 5.795962e+03 5.309794e+02 860.854538 248.632108 3.284335 38.486363 28.009825 8.653013 75.854167 56.356211 13.840286 +argmin(L10^10) 0.000332 0.000004 0.058838 1.965420e+04 5.884199e+03 8.633228e+02 894.425086 252.743956 18.771513 39.638916 27.580884 11.777146 78.797379 54.682824 18.139113 +Model A 126.554588 60.941755 77.160462 1.833490e+06 1.750514e+06 1.773474e+06 0.878730 3152.059162 1414.486997 467.173129 465.279406 465.876503 835.190192 868.058186 854.223189 +Model B 60.353297 36.518298 43.777898 7.812251e+05 7.029310e+05 7.236160e+05 1764.118964 2.060910 214.038392 393.395690 362.433941 371.769559 769.413642 713.138467 732.147248 + + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The mixture dataset had weight 0.30 on dataset A and weight 0.70 on dataset B. argmin(...) models were fit to that dataset. + + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.001147 4.108738e-07 0.102495 2.357782e+04 3.236178e+03 4.624121e+02 1077.265150 129.346379 0.089104 44.115671 22.393879 9.130840 88.726527 44.168714 15.734786 +argmin(nTVD) 0.001229 3.566033e-07 0.108070 2.351664e+04 3.269966e+03 4.686338e+02 1074.414076 130.920879 0.379032 44.009629 22.560514 9.030624 86.514322 46.236778 14.452820 +argmin(TVD) 0.001369 2.912033e-07 0.127903 2.331235e+04 3.401875e+03 5.032436e+02 1064.894275 137.067856 1.991849 43.854686 22.950017 8.905500 88.963584 43.867608 15.718299 +argmin(L10^10) 0.001462 2.406509e-07 0.055216 2.400751e+04 3.469828e+03 7.530523e+02 1097.288687 140.234437 13.632915 44.771370 22.378776 11.375579 89.778594 43.822841 17.980791 +Model A 126.554588 6.094176e+01 72.244649 1.833490e+06 1.750514e+06 1.766168e+06 0.878730 3152.059162 1806.872182 467.173129 465.279406 465.715841 835.190192 868.058186 857.751642 +Model B 60.353297 3.651830e+01 41.695823 7.812251e+05 7.029310e+05 7.171513e+05 1764.118964 2.060910 115.494432 393.395690 362.433941 368.849937 769.413642 713.138467 726.102011 + + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The mixture dataset had weight 0.25 on dataset A and weight 0.75 on dataset B. argmin(...) models were fit to that dataset. + + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.002629 5.180830e-08 0.093984 2.599593e+04 2.326897e+03 4.509724e+02 1189.949238 86.973972 -0.443988 46.982771 19.751013 9.242001 94.654726 38.596659 16.399717 +argmin(nTVD) 0.002805 4.450868e-08 0.104821 2.585265e+04 2.390805e+03 4.646910e+02 1183.272330 89.952041 0.195299 46.761698 20.078455 9.142028 92.172305 40.877737 14.922437 +argmin(TVD) 0.002720 4.938478e-08 0.110849 2.561716e+04 2.481830e+03 4.760788e+02 1172.298145 94.193840 0.725971 46.565040 20.348602 9.081771 95.040781 38.108054 16.559203 +argmin(L10^10) 0.002901 3.913473e-08 0.052445 2.626039e+04 2.534471e+03 6.706137e+02 1202.272988 96.646897 9.791280 47.301903 19.848731 11.065213 95.153098 38.625469 18.135336 +Model A 126.554588 6.094176e+01 70.105753 1.833490e+06 1.750514e+06 1.763134e+06 0.878730 3152.059162 2012.002268 467.173129 465.279406 465.653406 835.190192 868.058186 859.393352 +Model B 60.353297 3.651830e+01 40.782671 7.812251e+05 7.029310e+05 7.143812e+05 1764.118964 2.060910 78.510734 393.395690 362.433941 367.455068 769.413642 713.138467 723.275767 + + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The mixture dataset had weight 0.20 on dataset A and weight 0.80 on dataset B. argmin(...) models were fit to that dataset. + + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.005422 0.146681 0.082374 2.846521e+04 1.616355e+03 4.361275e+02 1305.017377 53.862765 -1.135755 49.812296 17.179430 9.393956 100.692317 32.977846 16.957784 +argmin(nTVD) 0.005479 0.146755 0.089686 2.830506e+04 1.668395e+03 4.469013e+02 1297.554226 56.287818 -0.633697 49.607226 17.440040 9.315584 98.327986 35.191299 15.409819 +argmin(TVD) 0.005141 0.150556 0.094477 2.808630e+04 1.726544e+03 4.514475e+02 1287.359948 58.997535 -0.421848 49.446819 17.645242 9.251400 100.983947 32.606542 16.987316 +argmin(L10^10) 0.005477 0.146093 0.049514 2.853509e+04 1.795086e+03 5.921762e+02 1308.273646 62.191601 6.136102 49.858494 17.422069 10.711664 100.412987 33.630635 18.473805 +Model A 126.554588 60.941755 67.948218 1.833490e+06 1.750514e+06 1.760483e+06 0.878730 3152.059162 2228.883735 467.173129 465.279406 465.562802 835.190192 868.058186 861.097728 +Model B 60.353297 36.518298 39.808559 7.812251e+05 7.029310e+05 7.118039e+05 1764.118964 2.060910 48.573929 393.395690 362.433941 366.064099 769.413642 713.138467 720.476766 + + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The mixture dataset had weight 0.10 on dataset A and weight 0.90 on dataset B. argmin(...) models were fit to that dataset. + + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.017872 0.076564 0.056305 3.341963e+04 7.478670e+02 4.293123e+02 1535.892671 13.391279 -1.453343 55.170456 12.812219 9.801565 112.532181 23.162979 18.171220 +argmin(nTVD) 0.017808 0.078943 0.062971 3.326938e+04 7.765805e+02 4.407960e+02 1528.891367 14.729328 -0.918204 54.977003 13.018956 9.790191 109.947903 25.628907 16.632490 +argmin(TVD) 0.017182 0.079781 0.062700 3.328002e+04 7.740075e+02 4.402553e+02 1529.387062 14.609425 -0.943400 54.969167 12.971492 9.714384 112.902957 22.935576 18.372205 +argmin(L10^10) 0.015123 0.085642 0.047752 3.305360e+04 8.633935e+02 4.973585e+02 1518.836111 18.774804 1.717605 54.851380 13.490325 10.493062 110.463381 25.261768 19.602431 +Model A 126.554588 60.941755 64.087331 1.833490e+06 1.750514e+06 1.754870e+06 0.878730 3152.059162 2682.519826 467.173129 465.279406 465.431406 835.190192 868.058186 864.637888 +Model B 60.353297 36.518298 38.024661 7.812251e+05 7.029310e+05 7.068148e+05 1764.118964 2.060910 10.911265 393.395690 362.433941 363.716712 769.413642 713.138467 715.982743 + + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The mixture dataset had weight 0.05 on dataset A and weight 0.95 on dataset B. argmin(...) models were fit to that dataset. + + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.029174 0.057311 0.053609 3.590183e+04 5.511987e+02 4.447755e+02 1651.563288 4.226552 -0.732762 57.715396 11.268873 10.094609 118.373585 19.866242 18.893372 +argmin(nTVD) 0.029059 0.060941 0.057731 3.576865e+04 5.659644e+02 4.525418e+02 1645.356888 4.914632 -0.370850 57.528568 11.375743 10.084489 115.798353 21.208465 17.470876 +argmin(TVD) 0.029017 0.059799 0.054853 3.561255e+04 5.763195e+02 4.559021e+02 1638.082959 5.397179 -0.214260 57.334562 11.389517 10.013522 116.983272 20.113419 18.207249 +argmin(L10^10) 0.023781 0.059954 0.049035 3.540689e+04 6.282318e+02 4.938562e+02 1628.498898 7.816287 1.554395 57.298475 12.048697 10.697845 115.475494 21.996857 20.429503 +Model A 126.554588 60.941755 62.314294 1.833490e+06 1.750514e+06 1.752496e+06 0.878730 3152.059162 2920.109931 467.173129 465.279406 465.367885 835.190192 868.058186 866.340832 +Model B 60.353297 36.518298 37.166768 7.812251e+05 7.029310e+05 7.046459e+05 1764.118964 2.060910 2.774278 393.395690 362.433941 362.896207 769.413642 713.138467 714.317761 + + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The mixture dataset had weight 0.01 on dataset A and weight 0.99 on dataset B. argmin(...) models were fit to that dataset. + + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.041740 0.055933 0.054759 3.790410e+04 4.925380e+02 4.717126e+02 1744.868852 1.492969 0.522508 59.687372 10.723672 10.424930 122.997044 19.884425 19.506542 +argmin(nTVD) 0.041821 0.062239 0.061105 3.783496e+04 5.065915e+02 4.852535e+02 1741.647075 2.147859 1.153510 59.558941 10.707916 10.409266 120.845389 18.528914 17.960292 +argmin(TVD) 0.044013 0.057337 0.056173 3.802370e+04 5.022734e+02 4.824273e+02 1750.442090 1.946636 1.021811 59.667143 10.617281 10.308995 122.850117 19.403571 18.992379 +argmin(L10^10) 0.035787 0.051705 0.050511 3.740704e+04 5.414413e+02 5.156873e+02 1721.705991 3.771859 2.571722 59.240605 11.358666 10.993873 119.617487 21.591750 21.098547 +Model A 126.554588 60.941755 60.998233 1.833490e+06 1.750514e+06 1.750807e+06 0.878730 3152.059162 3115.145026 467.173129 465.279406 465.300180 835.190192 868.058186 867.716656 +Model B 60.353297 36.518298 36.532100 7.812251e+05 7.029310e+05 7.030719e+05 1764.118964 2.060910 1.307916 393.395690 362.433941 362.446148 769.413642 713.138467 713.134395 + + +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The mixture dataset had weight 0.00 on dataset A and weight 1.00 on dataset B. argmin(...) models were fit to that dataset. + + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.045918 0.056020 0.056020 3.832014e+04 4.904003e+02 4.904003e+02 1764.256112 1.393350 1.393350 60.127224 10.679844 10.679844 123.975368 19.966883 19.966883 +argmin(nTVD) 0.046466 0.063619 0.063619 3.818218e+04 5.027886e+02 5.027886e+02 1757.827150 1.970647 1.970647 59.933777 10.644054 10.644054 122.274428 18.476274 18.476274 +argmin(TVD) 0.049318 0.057454 0.057454 3.851677e+04 5.008840e+02 5.008840e+02 1773.419021 1.881892 1.881892 60.222429 10.548472 10.548472 123.836231 19.332965 19.332965 +argmin(L10^10) 0.040487 0.051594 0.051594 3.801835e+04 5.285188e+02 5.285188e+02 1750.192803 3.169672 3.169672 59.861629 11.253837 11.253837 121.219802 21.526994 21.526994 +Model A 126.554588 60.941755 60.941755 1.833490e+06 1.750514e+06 1.750514e+06 0.878730 3152.059162 3152.059162 467.173129 465.279406 465.279406 835.190192 868.058186 868.058186 +Model B 60.353297 36.518298 36.518298 7.812251e+05 7.029310e+05 7.029310e+05 1764.118964 2.060910 2.060910 393.395690 362.433941 362.433941 769.413642 713.138467 713.138467 + From 816663f92cb8dc36de19610d5e7f3b8367b063b5 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Mon, 28 Apr 2025 09:20:42 -0700 Subject: [PATCH 37/71] cache notebook that generated previous results --- .../objectives-handling-mixtures.ipynb | 616 ++++++++++++++++++ 1 file changed, 616 insertions(+) create mode 100644 wip_notebook_sharing/objectives-handling-mixtures.ipynb diff --git a/wip_notebook_sharing/objectives-handling-mixtures.ipynb b/wip_notebook_sharing/objectives-handling-mixtures.ipynb new file mode 100644 index 000000000..7b82b880c --- /dev/null +++ b/wip_notebook_sharing/objectives-handling-mixtures.ipynb @@ -0,0 +1,616 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import pygsti\n", + "from pygsti.modelpacks import smq1Q_XYI, smq2Q_XYCNOT\n", + "import numpy as np\n", + "from pprint import pprint\n", + "from experiment_helpers import make_depolarized_dataset, run_gst, corrupt_dataset, make_tweaked_dataset\n", + "from scipy import linalg as la\n", + "import pandas as pd\n", + "from pygsti.data.datasetconstruction import mix_datasets\n", + "from pygsti.report.plothelpers import rated_n_sigma" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "mp = smq1Q_XYI\n", + "target = mp.target_model()\n", + "fids = (mp.prep_fiducials(), mp.meas_fiducials())\n", + "germs = mp.germs()\n", + "maxmaxlen = 64\n", + "dsa, m_dga = make_tweaked_dataset(mp, depol_level=0.001, rand_unitary_scale=0.001, max_max_len=maxmaxlen)\n", + "dsb, m_dgb = make_tweaked_dataset(mp, depol_level=0.010, rand_unitary_scale=0.020, max_max_len=maxmaxlen)\n", + "mixture_weights = np.array([1, 0.99, 0.95, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.25, 0.2, 0.1, 0.05, 0.01, 0.0])\n", + "num_mixtures = mixture_weights.size\n", + "\n", + "fit_mode = 'CPTPLND'\n", + "Lpnorm_spec = ('Lp^p', 10)\n", + "verb = 1\n", + "\n", + "m_dga.convert_members_inplace(fit_mode)\n", + "m_dgb.convert_members_inplace(fit_mode)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/objectivefns/objectivefns.py:4055: UserWarning: This derivative is discontinuous and does not return a full subgradient.\n", + " _warnings.warn('This derivative is discontinuous and does not return a full subgradient.')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", + "\n", + "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", + "\n", + "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/objectivefns/objectivefns.py:4557: RuntimeWarning: divide by zero encountered in divide\n", + " p5over_lsvec = 0.5/lsvec\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", + "\n", + "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", + "\n", + "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", + "\n", + "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", + "\n", + "\n", + "--- Circuit Creation ---\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", + "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Circuit Creation ---\n", + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", + "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Circuit Creation ---\n", + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", + "\n", + "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", + "\n", + "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", + "\n", + "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", + "\n", + "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/optimize/customsolve.py:94: LinAlgWarning: Ill-conditioned matrix (rcond=3.1334e-17): result may not be accurate.\n", + " x[:] = _scipy.linalg.solve(a, b, assume_a='pos')\n", + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/optimize/customsolve.py:94: LinAlgWarning: Ill-conditioned matrix (rcond=6.26613e-17): result may not be accurate.\n", + " x[:] = _scipy.linalg.solve(a, b, assume_a='pos')\n", + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/optimize/customsolve.py:94: LinAlgWarning: Ill-conditioned matrix (rcond=4.10509e-18): result may not be accurate.\n", + " x[:] = _scipy.linalg.solve(a, b, assume_a='pos')\n", + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/optimize/customsolve.py:94: LinAlgWarning: Ill-conditioned matrix (rcond=8.18734e-18): result may not be accurate.\n", + " x[:] = _scipy.linalg.solve(a, b, assume_a='pos')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", + "\n", + "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "reslist = []\n", + "for i,p in enumerate(mixture_weights):\n", + " ds = mix_datasets(dsa, dsb, p)\n", + " results = run_gst(ds, fids, germs, target, ['logl', 'normalized tvd', 'tvd', Lpnorm_spec], verbosity=verb, mode=fit_mode)\n", + " reslist.append((results,ds))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "dflists = []\n", + "for results, dsc in reslist:\n", + " currdfs = []\n", + " for ds in [dsa, dsc, dsb]:\n", + " circuitlist = list(ds.cirIndex.keys())\n", + " pvecs = []\n", + " objectives = []\n", + " Nsigs = []\n", + " for estname, est in results.estimates.items():\n", + " model = est.models['stdgaugeopt']\n", + " try:\n", + " Nsig, _ = rated_n_sigma(ds, model, circuitlist, 'logl')\n", + " except Exception:\n", + " Nsig = np.NaN\n", + " Nsigs.append(Nsig)\n", + " objective = est.final_objective_fn()\n", + " objective.dataset = ds\n", + " objective.add_count_vectors(force=True)\n", + " objective.add_omitted_freqs(force=True)\n", + " objectives.append(objective)\n", + " pvecs.append(est.models['final iteration estimate'].to_vector())\n", + " pvecs.append(m_dga.to_vector())\n", + " Nsigs.append(rated_n_sigma(ds, m_dga, circuitlist, 'logl')[0])\n", + " pvecs.append(m_dgb.to_vector())\n", + " Nsigs.append(rated_n_sigma(ds, m_dgb, circuitlist, 'logl')[0])\n", + " Nsigs = np.array(Nsigs).reshape((-1,1))\n", + "\n", + " objvals = np.zeros((len(pvecs), len(objectives)))\n", + " for i,pvec in enumerate(pvecs):\n", + " for j,objective in enumerate(objectives):\n", + " try:\n", + " val = objective.fn(pvec, stateless=True)\n", + " if val < 1e-8:\n", + " val = val ** 0.1\n", + " except Exception:\n", + " val = np.NaN\n", + " objvals[i,j] = val\n", + " objvals = np.concatenate((objvals, Nsigs), axis=1)\n", + "\n", + " df = pd.DataFrame(\n", + " objvals,\n", + " index=['argmin(LogL)', 'argmin(nTVD)', 'argmin(TVD)', 'argmin(L10^10)', 'Model A', 'Model B'],\n", + " columns=['LogL', 'nTVD', 'TVD', 'L10', 'N_sigma'],\n", + " )\n", + " df.rename_axis(index='model')\n", + " currdfs.append(df)\n", + " dflists.append(currdfs)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "dflist = []\n", + "for dfl in dflists:\n", + " dfa, df, dfb = dfl\n", + " temp = dfa.join(df, lsuffix='(A)', rsuffix='(mix)')\n", + " dfb = dfb.copy()\n", + " dfb.columns = dfb.columns.map(lambda x: str(x) + '(B)')\n", + " temp = temp.join(dfb)\n", + " temp = temp.sort_index(axis=1)\n", + " dflist.append(temp)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 1.00 on dataset A and weight 0.00 on dataset B. argmin(...) models were fit to that dataset.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.058305 0.043682 0.058305 4.675041e+02 6.854094e+04 4.675041e+02 0.326392 3172.543057 0.326392 8.786676 60.511077 8.786676 16.335460 123.354424 16.335460\n", + "argmin(nTVD) 0.058255 0.043691 0.058255 5.000476e+02 6.956110e+04 5.000476e+02 1.842914 3220.082562 1.842914 8.720995 60.394434 8.720995 15.342389 124.476252 15.342389\n", + "argmin(TVD) 0.058421 0.044154 0.058421 4.842252e+02 6.921505e+04 4.842252e+02 1.105592 3203.956620 1.105592 8.657254 60.464797 8.657254 15.826993 123.250965 15.826993\n", + "argmin(L10^10) 0.056057 0.049538 0.056057 6.381221e+02 6.964097e+04 6.381221e+02 8.277176 3223.804647 8.277176 9.578566 60.363776 9.578566 17.786360 122.383633 17.786360\n", + "Model A 126.554588 60.941755 126.554588 1.833490e+06 1.750514e+06 1.833490e+06 0.878730 3152.059162 0.878730 467.173129 465.279406 467.173129 835.190192 868.058186 835.190192\n", + "Model B 60.353297 36.518298 60.353297 7.812251e+05 7.029310e+05 7.812251e+05 1764.118964 2.060910 1764.118964 393.395690 362.433941 393.395690 769.413642 713.138467 769.413642\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.99 on dataset A and weight 0.01 on dataset B. argmin(...) models were fit to that dataset.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.058280 0.039962 0.057080 5.073249e+02 6.364611e+04 4.180941e+02 2.182037 2944.444398 -1.976113 8.942991 59.846071 8.432625 16.504940 121.838636 15.728875\n", + "argmin(nTVD) 0.058199 0.040237 0.057029 5.238449e+02 6.480701e+04 4.508641e+02 2.951869 2998.542071 -0.449034 8.881305 59.807557 8.380632 15.663741 122.889098 14.786347\n", + "argmin(TVD) 0.058479 0.039820 0.057338 5.092324e+02 6.398873e+04 4.319471e+02 2.270928 2960.410479 -1.330563 8.828511 59.859282 8.314256 16.191643 121.921252 15.361203\n", + "argmin(L10^10) 0.056076 0.048398 0.054859 6.503304e+02 6.860767e+04 6.385727e+02 8.846081 3175.652652 8.298174 9.610355 60.149631 9.356175 17.971954 121.908982 17.208866\n", + "Model A 126.554588 60.941755 124.595602 1.833490e+06 1.750514e+06 1.831321e+06 0.878730 3152.059162 0.355726 467.173129 465.279406 466.950288 835.190192 868.058186 835.054746\n", + "Model B 60.353297 36.518298 59.638173 7.812251e+05 7.029310e+05 7.790110e+05 1764.118964 2.060910 1696.546001 393.395690 362.433941 392.844547 769.413642 713.138467 768.335640\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.95 on dataset A and weight 0.05 on dataset B. argmin(...) models were fit to that dataset.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.058553 0.028187 0.054809 8.739089e+02 5.279031e+04 3.702706e+02 19.264823 2438.565156 -4.204682 10.142951 57.797471 8.212986 18.901034 117.555414 14.999158\n", + "argmin(nTVD) 0.059798 0.028166 0.055133 8.835317e+02 5.303729e+04 3.918073e+02 19.713245 2450.074142 -3.201075 10.156287 57.551119 8.186089 17.928632 117.965291 14.160187\n", + "argmin(TVD) 0.058915 0.027390 0.055142 8.848670e+02 5.272912e+04 3.792575e+02 19.775468 2435.713379 -3.785893 10.053967 57.813796 8.103999 19.094600 117.175958 14.728985\n", + "argmin(L10^10) 0.058181 0.022799 0.052683 1.095965e+03 5.199359e+04 5.419852e+02 29.612631 2401.437971 3.797205 10.804595 57.729039 9.199417 17.573569 120.776182 16.172637\n", + "Model A 126.554588 60.941755 119.813465 1.833490e+06 1.750514e+06 1.825516e+06 0.878730 3152.059162 17.179587 467.173129 465.279406 466.693855 835.190192 868.058186 835.189767\n", + "Model B 60.353297 36.518298 58.332841 7.812251e+05 7.029310e+05 7.735094e+05 1764.118964 2.060910 1518.501283 393.395690 362.433941 391.381852 769.413642 713.138467 765.808000\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.90 on dataset A and weight 0.10 on dataset B. argmin(...) models were fit to that dataset.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.069575 0.017990 0.053253 1.707234e+03 4.291860e+04 3.482229e+02 58.097710 1978.544133 -5.232105 12.207427 55.203937 8.132309 23.235284 112.218973 14.596372\n", + "argmin(nTVD) 0.079953 0.017285 0.058557 1.705770e+03 4.306758e+04 3.637916e+02 58.029501 1985.486770 -4.506605 12.338981 54.893533 8.109634 21.600853 113.270335 13.542902\n", + "argmin(TVD) 0.072924 0.017074 0.053595 1.713855e+03 4.287397e+04 3.530817e+02 58.406262 1976.464261 -5.005686 12.164062 55.122870 7.974263 23.594074 111.497258 14.229698\n", + "argmin(L10^10) 0.094878 0.011530 0.050696 2.031710e+03 4.191370e+04 5.366406e+02 73.218257 1931.715827 3.548147 12.693870 55.157799 9.248214 20.035872 115.901433 16.354479\n", + "Model A 126.554588 60.941755 114.261294 1.833490e+06 1.750514e+06 1.819040e+06 0.878730 3152.059162 65.570121 467.173129 465.279406 466.614406 835.190192 868.058186 836.788448\n", + "Model B 60.353297 36.518298 56.715375 7.812251e+05 7.029310e+05 7.672737e+05 1764.118964 2.060910 1321.774150 393.395690 362.433941 389.555690 769.413642 713.138467 762.774974\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.80 on dataset A and weight 0.20 on dataset B. argmin(...) models were fit to that dataset.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 1.280690e-01 0.006850 0.058235 4.073798e+03 2.950145e+04 3.447751e+02 168.379391 1353.305782 -5.392771 16.805776 49.914257 8.147237 32.461797 101.228335 13.417375\n", + "argmin(nTVD) 1.460365e-01 0.005945 0.094634 4.183566e+03 2.918867e+04 3.725061e+02 173.494602 1338.730370 -4.100509 17.372651 49.184236 8.021515 32.596331 100.884726 12.828388\n", + "argmin(TVD) 1.466788e-01 0.005582 0.091479 4.232062e+03 2.899532e+04 3.733013e+02 175.754504 1329.720514 -4.063453 17.390356 49.195911 7.972158 33.085064 100.576929 13.119409\n", + "argmin(L10^10) 2.345534e-08 0.003162 0.048265 4.352138e+03 2.963231e+04 6.006732e+02 181.350043 1359.404108 6.532062 16.844040 50.229908 9.723182 28.929435 104.827273 15.294466\n", + "Model A 1.265546e+02 60.941755 104.583029 1.833490e+06 1.750514e+06 1.808010e+06 0.878730 3152.059162 226.530439 467.173129 465.279406 466.486253 835.190192 868.058186 840.349965\n", + "Model B 6.035330e+01 36.518298 53.755875 7.812251e+05 7.029310e+05 7.564658e+05 1764.118964 2.060910 994.843938 393.395690 362.433941 385.923879 769.413642 713.138467 756.665756\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.70 on dataset A and weight 0.30 on dataset B. argmin(...) models were fit to that dataset.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 7.003153e-08 0.002223 0.074748 7.091943e+03 2.049327e+04 3.748087e+02 309.024731 933.525551 -3.993207 21.834864 44.494855 8.290449 42.852141 90.101863 12.886797\n", + "argmin(nTVD) 1.308041e-07 0.001822 0.101387 7.261664e+03 2.014795e+04 3.914457e+02 316.933697 917.433559 -3.217924 22.304388 43.941396 8.081331 42.897418 89.962380 12.315999\n", + "argmin(TVD) 2.240636e-07 0.001572 0.124270 7.352556e+03 2.005034e+04 4.280172e+02 321.169276 912.884917 -1.513698 22.608459 43.610406 7.988594 43.227342 89.623360 12.475021\n", + "argmin(L10^10) 8.068013e-07 0.000791 0.051606 7.656646e+03 2.035537e+04 7.273908e+02 335.339826 927.099577 12.437089 22.360088 44.437987 10.407362 40.644331 92.165539 15.493826\n", + "Model A 1.265546e+02 60.941755 96.185813 1.833490e+06 1.750514e+06 1.797466e+06 0.878730 3152.059162 454.592147 467.173129 465.279406 466.320765 835.190192 868.058186 843.676595\n", + "Model B 6.035330e+01 36.518298 50.947670 7.812251e+05 7.029310e+05 7.465651e+05 1764.118964 2.060910 729.831768 393.395690 362.433941 382.212791 769.413642 713.138467 750.519271\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.60 on dataset A and weight 0.40 on dataset B. argmin(...) models were fit to that dataset.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.000001 0.000577 0.091391 1.057215e+04 1.407142e+04 4.205712e+02 471.201894 634.267962 -1.860679 27.077488 38.959680 8.587280 53.755140 78.694803 13.186374\n", + "argmin(nTVD) 0.000002 0.000464 0.111901 1.064063e+04 1.400078e+04 4.350772e+02 474.393338 630.976212 -1.184701 27.306314 38.733142 8.396607 51.752982 80.636663 12.291792\n", + "argmin(TVD) 0.000005 0.000310 0.154856 1.093002e+04 1.377477e+04 5.207620e+02 487.878686 620.443965 2.808205 27.986584 38.230402 8.195175 53.533009 78.948949 12.597676\n", + "argmin(L10^10) 0.000010 0.000173 0.056281 1.142365e+04 1.383654e+04 8.348078e+02 510.881935 623.322260 17.442714 28.142159 38.727617 11.162597 54.015160 78.750509 16.638443\n", + "Model A 126.554588 60.941755 89.013251 1.833490e+06 1.750514e+06 1.789088e+06 0.878730 3152.059162 732.687654 467.173129 465.279406 466.176419 835.190192 868.058186 847.274243\n", + "Model B 60.353297 36.518298 48.387892 7.812251e+05 7.029310e+05 7.382813e+05 1764.118964 2.060910 517.410633 393.395690 362.433941 378.560568 769.413642 713.138467 744.458052\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.50 on dataset A and weight 0.50 on dataset B. argmin(...) models were fit to that dataset.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.000019 0.000103 0.103135 1.445346e+04 9.389479e+03 4.813178e+02 652.070827 416.089708 0.970108 32.566106 33.543358 9.169555 65.724820 67.846493 16.004581\n", + "argmin(nTVD) 0.000027 0.000077 0.122024 1.457978e+04 9.296960e+03 4.992725e+02 657.957229 411.778345 1.806797 32.851237 33.297344 8.877317 64.277568 68.784671 14.620929\n", + "argmin(TVD) 0.000046 0.000047 0.153857 1.491067e+04 9.096656e+03 5.650716e+02 673.376719 402.444189 4.873029 33.516252 32.955458 8.697918 66.899668 66.687181 15.806218\n", + "argmin(L10^10) 0.000066 0.000032 0.058901 1.534189e+04 9.342669e+03 9.002678e+02 693.471719 413.908394 20.493147 33.948918 33.010908 11.802510 65.332360 68.314439 17.769593\n", + "Model A 126.554588 60.941755 82.856783 1.833490e+06 1.750514e+06 1.780313e+06 0.878730 3152.059162 1049.968900 467.173129 465.279406 466.029406 835.190192 868.058186 850.583717\n", + "Model B 60.353297 36.518298 46.079898 7.812251e+05 7.029310e+05 7.303883e+05 1764.118964 2.060910 348.353651 393.395690 362.433941 375.034359 769.413642 713.138467 738.464676\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.40 on dataset A and weight 0.60 on dataset B. argmin(...) models were fit to that dataset.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.000171 0.000010 0.108947 1.886757e+04 5.752944e+03 4.746497e+02 857.768204 246.627478 0.659375 38.293271 27.854670 9.021935 76.723239 55.628956 14.741397\n", + "argmin(nTVD) 0.000241 0.000006 0.129251 1.903001e+04 5.674293e+03 4.941562e+02 865.337660 242.962347 1.568377 38.541429 27.808615 8.754129 75.034583 57.196161 13.549772\n", + "argmin(TVD) 0.000286 0.000005 0.142902 1.893380e+04 5.795962e+03 5.309794e+02 860.854538 248.632108 3.284335 38.486363 28.009825 8.653013 75.854167 56.356211 13.840286\n", + "argmin(L10^10) 0.000332 0.000004 0.058838 1.965420e+04 5.884199e+03 8.633228e+02 894.425086 252.743956 18.771513 39.638916 27.580884 11.777146 78.797379 54.682824 18.139113\n", + "Model A 126.554588 60.941755 77.160462 1.833490e+06 1.750514e+06 1.773474e+06 0.878730 3152.059162 1414.486997 467.173129 465.279406 465.876503 835.190192 868.058186 854.223189\n", + "Model B 60.353297 36.518298 43.777898 7.812251e+05 7.029310e+05 7.236160e+05 1764.118964 2.060910 214.038392 393.395690 362.433941 371.769559 769.413642 713.138467 732.147248\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.30 on dataset A and weight 0.70 on dataset B. argmin(...) models were fit to that dataset.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.001147 4.108738e-07 0.102495 2.357782e+04 3.236178e+03 4.624121e+02 1077.265150 129.346379 0.089104 44.115671 22.393879 9.130840 88.726527 44.168714 15.734786\n", + "argmin(nTVD) 0.001229 3.566033e-07 0.108070 2.351664e+04 3.269966e+03 4.686338e+02 1074.414076 130.920879 0.379032 44.009629 22.560514 9.030624 86.514322 46.236778 14.452820\n", + "argmin(TVD) 0.001369 2.912033e-07 0.127903 2.331235e+04 3.401875e+03 5.032436e+02 1064.894275 137.067856 1.991849 43.854686 22.950017 8.905500 88.963584 43.867608 15.718299\n", + "argmin(L10^10) 0.001462 2.406509e-07 0.055216 2.400751e+04 3.469828e+03 7.530523e+02 1097.288687 140.234437 13.632915 44.771370 22.378776 11.375579 89.778594 43.822841 17.980791\n", + "Model A 126.554588 6.094176e+01 72.244649 1.833490e+06 1.750514e+06 1.766168e+06 0.878730 3152.059162 1806.872182 467.173129 465.279406 465.715841 835.190192 868.058186 857.751642\n", + "Model B 60.353297 3.651830e+01 41.695823 7.812251e+05 7.029310e+05 7.171513e+05 1764.118964 2.060910 115.494432 393.395690 362.433941 368.849937 769.413642 713.138467 726.102011\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.25 on dataset A and weight 0.75 on dataset B. argmin(...) models were fit to that dataset.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.002629 5.180830e-08 0.093984 2.599593e+04 2.326897e+03 4.509724e+02 1189.949238 86.973972 -0.443988 46.982771 19.751013 9.242001 94.654726 38.596659 16.399717\n", + "argmin(nTVD) 0.002805 4.450868e-08 0.104821 2.585265e+04 2.390805e+03 4.646910e+02 1183.272330 89.952041 0.195299 46.761698 20.078455 9.142028 92.172305 40.877737 14.922437\n", + "argmin(TVD) 0.002720 4.938478e-08 0.110849 2.561716e+04 2.481830e+03 4.760788e+02 1172.298145 94.193840 0.725971 46.565040 20.348602 9.081771 95.040781 38.108054 16.559203\n", + "argmin(L10^10) 0.002901 3.913473e-08 0.052445 2.626039e+04 2.534471e+03 6.706137e+02 1202.272988 96.646897 9.791280 47.301903 19.848731 11.065213 95.153098 38.625469 18.135336\n", + "Model A 126.554588 6.094176e+01 70.105753 1.833490e+06 1.750514e+06 1.763134e+06 0.878730 3152.059162 2012.002268 467.173129 465.279406 465.653406 835.190192 868.058186 859.393352\n", + "Model B 60.353297 3.651830e+01 40.782671 7.812251e+05 7.029310e+05 7.143812e+05 1764.118964 2.060910 78.510734 393.395690 362.433941 367.455068 769.413642 713.138467 723.275767\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.20 on dataset A and weight 0.80 on dataset B. argmin(...) models were fit to that dataset.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.005422 0.146681 0.082374 2.846521e+04 1.616355e+03 4.361275e+02 1305.017377 53.862765 -1.135755 49.812296 17.179430 9.393956 100.692317 32.977846 16.957784\n", + "argmin(nTVD) 0.005479 0.146755 0.089686 2.830506e+04 1.668395e+03 4.469013e+02 1297.554226 56.287818 -0.633697 49.607226 17.440040 9.315584 98.327986 35.191299 15.409819\n", + "argmin(TVD) 0.005141 0.150556 0.094477 2.808630e+04 1.726544e+03 4.514475e+02 1287.359948 58.997535 -0.421848 49.446819 17.645242 9.251400 100.983947 32.606542 16.987316\n", + "argmin(L10^10) 0.005477 0.146093 0.049514 2.853509e+04 1.795086e+03 5.921762e+02 1308.273646 62.191601 6.136102 49.858494 17.422069 10.711664 100.412987 33.630635 18.473805\n", + "Model A 126.554588 60.941755 67.948218 1.833490e+06 1.750514e+06 1.760483e+06 0.878730 3152.059162 2228.883735 467.173129 465.279406 465.562802 835.190192 868.058186 861.097728\n", + "Model B 60.353297 36.518298 39.808559 7.812251e+05 7.029310e+05 7.118039e+05 1764.118964 2.060910 48.573929 393.395690 362.433941 366.064099 769.413642 713.138467 720.476766\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.10 on dataset A and weight 0.90 on dataset B. argmin(...) models were fit to that dataset.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.017872 0.076564 0.056305 3.341963e+04 7.478670e+02 4.293123e+02 1535.892671 13.391279 -1.453343 55.170456 12.812219 9.801565 112.532181 23.162979 18.171220\n", + "argmin(nTVD) 0.017808 0.078943 0.062971 3.326938e+04 7.765805e+02 4.407960e+02 1528.891367 14.729328 -0.918204 54.977003 13.018956 9.790191 109.947903 25.628907 16.632490\n", + "argmin(TVD) 0.017182 0.079781 0.062700 3.328002e+04 7.740075e+02 4.402553e+02 1529.387062 14.609425 -0.943400 54.969167 12.971492 9.714384 112.902957 22.935576 18.372205\n", + "argmin(L10^10) 0.015123 0.085642 0.047752 3.305360e+04 8.633935e+02 4.973585e+02 1518.836111 18.774804 1.717605 54.851380 13.490325 10.493062 110.463381 25.261768 19.602431\n", + "Model A 126.554588 60.941755 64.087331 1.833490e+06 1.750514e+06 1.754870e+06 0.878730 3152.059162 2682.519826 467.173129 465.279406 465.431406 835.190192 868.058186 864.637888\n", + "Model B 60.353297 36.518298 38.024661 7.812251e+05 7.029310e+05 7.068148e+05 1764.118964 2.060910 10.911265 393.395690 362.433941 363.716712 769.413642 713.138467 715.982743\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.05 on dataset A and weight 0.95 on dataset B. argmin(...) models were fit to that dataset.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.029174 0.057311 0.053609 3.590183e+04 5.511987e+02 4.447755e+02 1651.563288 4.226552 -0.732762 57.715396 11.268873 10.094609 118.373585 19.866242 18.893372\n", + "argmin(nTVD) 0.029059 0.060941 0.057731 3.576865e+04 5.659644e+02 4.525418e+02 1645.356888 4.914632 -0.370850 57.528568 11.375743 10.084489 115.798353 21.208465 17.470876\n", + "argmin(TVD) 0.029017 0.059799 0.054853 3.561255e+04 5.763195e+02 4.559021e+02 1638.082959 5.397179 -0.214260 57.334562 11.389517 10.013522 116.983272 20.113419 18.207249\n", + "argmin(L10^10) 0.023781 0.059954 0.049035 3.540689e+04 6.282318e+02 4.938562e+02 1628.498898 7.816287 1.554395 57.298475 12.048697 10.697845 115.475494 21.996857 20.429503\n", + "Model A 126.554588 60.941755 62.314294 1.833490e+06 1.750514e+06 1.752496e+06 0.878730 3152.059162 2920.109931 467.173129 465.279406 465.367885 835.190192 868.058186 866.340832\n", + "Model B 60.353297 36.518298 37.166768 7.812251e+05 7.029310e+05 7.046459e+05 1764.118964 2.060910 2.774278 393.395690 362.433941 362.896207 769.413642 713.138467 714.317761\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.01 on dataset A and weight 0.99 on dataset B. argmin(...) models were fit to that dataset.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.041740 0.055933 0.054759 3.790410e+04 4.925380e+02 4.717126e+02 1744.868852 1.492969 0.522508 59.687372 10.723672 10.424930 122.997044 19.884425 19.506542\n", + "argmin(nTVD) 0.041821 0.062239 0.061105 3.783496e+04 5.065915e+02 4.852535e+02 1741.647075 2.147859 1.153510 59.558941 10.707916 10.409266 120.845389 18.528914 17.960292\n", + "argmin(TVD) 0.044013 0.057337 0.056173 3.802370e+04 5.022734e+02 4.824273e+02 1750.442090 1.946636 1.021811 59.667143 10.617281 10.308995 122.850117 19.403571 18.992379\n", + "argmin(L10^10) 0.035787 0.051705 0.050511 3.740704e+04 5.414413e+02 5.156873e+02 1721.705991 3.771859 2.571722 59.240605 11.358666 10.993873 119.617487 21.591750 21.098547\n", + "Model A 126.554588 60.941755 60.998233 1.833490e+06 1.750514e+06 1.750807e+06 0.878730 3152.059162 3115.145026 467.173129 465.279406 465.300180 835.190192 868.058186 867.716656\n", + "Model B 60.353297 36.518298 36.532100 7.812251e+05 7.029310e+05 7.030719e+05 1764.118964 2.060910 1.307916 393.395690 362.433941 362.446148 769.413642 713.138467 713.134395\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.00 on dataset A and weight 1.00 on dataset B. argmin(...) models were fit to that dataset.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.045918 0.056020 0.056020 3.832014e+04 4.904003e+02 4.904003e+02 1764.256112 1.393350 1.393350 60.127224 10.679844 10.679844 123.975368 19.966883 19.966883\n", + "argmin(nTVD) 0.046466 0.063619 0.063619 3.818218e+04 5.027886e+02 5.027886e+02 1757.827150 1.970647 1.970647 59.933777 10.644054 10.644054 122.274428 18.476274 18.476274\n", + "argmin(TVD) 0.049318 0.057454 0.057454 3.851677e+04 5.008840e+02 5.008840e+02 1773.419021 1.881892 1.881892 60.222429 10.548472 10.548472 123.836231 19.332965 19.332965\n", + "argmin(L10^10) 0.040487 0.051594 0.051594 3.801835e+04 5.285188e+02 5.285188e+02 1750.192803 3.169672 3.169672 59.861629 11.253837 11.253837 121.219802 21.526994 21.526994\n", + "Model A 126.554588 60.941755 60.941755 1.833490e+06 1.750514e+06 1.750514e+06 0.878730 3152.059162 3152.059162 467.173129 465.279406 465.279406 835.190192 868.058186 868.058186\n", + "Model B 60.353297 36.518298 36.518298 7.812251e+05 7.029310e+05 7.029310e+05 1764.118964 2.060910 2.060910 393.395690 362.433941 362.433941 769.413642 713.138467 713.138467\n", + "\n" + ] + } + ], + "source": [ + "pd.set_option('display.max_columns', 100)\n", + "pd.set_option('display.width', 300)\n", + "for i,df in enumerate(dflist):\n", + " print( '\\n' + 250*'-')\n", + " p = mixture_weights[i]\n", + " print(f'\\nThe mixture dataset had weight {p:1.2f} on dataset A and weight {1-p:1.2f} on dataset B. argmin(...) models were fit to that dataset.\\n')\n", + " print(df)\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rogst", + "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": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 5566b378bb28d812e1b4b08142676310ae6d9539 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Mon, 28 Apr 2025 09:25:17 -0700 Subject: [PATCH 38/71] leave note about error in table generation --- .../bunch-of-data-fix-embarassing-oversight.txt | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/wip_notebook_sharing/bunch-of-data-fix-embarassing-oversight.txt b/wip_notebook_sharing/bunch-of-data-fix-embarassing-oversight.txt index 8c944178e..81dc3d25d 100644 --- a/wip_notebook_sharing/bunch-of-data-fix-embarassing-oversight.txt +++ b/wip_notebook_sharing/bunch-of-data-fix-embarassing-oversight.txt @@ -29,6 +29,10 @@ Here is how I used it; note that Model A has more noise than Model B. In the tables below, rows labeled "argmin(func)" show results for the model obtained by fitting "func" to the mixed dataset. +PROBLEM: the L10(...) objectives occasionally have values < 1e-6, even though the vast majority are O(1e-2). +This happens because of a stupid way that I wrote code to infer when we were looking at the result of the L10^10 +objective as opposed to other objectives. In reality, any obective value < 1e-6 needs to be raised to the power 1/10. + ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The mixture dataset had weight 1.00 on dataset A and weight 0.00 on dataset B. argmin(...) models were fit to that dataset. From 6e27fe967583f52921a2ba1e505d932de4578cf6 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Mon, 28 Apr 2025 13:42:08 -0700 Subject: [PATCH 39/71] new fn_from_model function --- pygsti/objectivefns/objectivefns.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index 5d5018a6e..7bf68cf5d 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -1444,6 +1444,13 @@ def fn(self, paramvec=None, stateless=False): _smt.cleanup_shared_ndarray(result_shm) return global_fnval + def fn_from_model(self, model): + m = self.model + self.model = model + val = self.fn() + self.model = m + return val + def jacobian(self, paramvec=None): """ Compute the Jacobian of this objective function. From c60fd33ccde6a89f91674c4011a52d6ae333ce06 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Mon, 28 Apr 2025 13:44:28 -0700 Subject: [PATCH 40/71] fix scripting error --- ...unch-of-data-fix-embarassing-oversight.txt | 256 ++++++------- .../objectives-handling-mixtures.ipynb | 349 ++++++++---------- 2 files changed, 270 insertions(+), 335 deletions(-) diff --git a/wip_notebook_sharing/bunch-of-data-fix-embarassing-oversight.txt b/wip_notebook_sharing/bunch-of-data-fix-embarassing-oversight.txt index 81dc3d25d..37e908293 100644 --- a/wip_notebook_sharing/bunch-of-data-fix-embarassing-oversight.txt +++ b/wip_notebook_sharing/bunch-of-data-fix-embarassing-oversight.txt @@ -29,214 +29,198 @@ Here is how I used it; note that Model A has more noise than Model B. In the tables below, rows labeled "argmin(func)" show results for the model obtained by fitting "func" to the mixed dataset. -PROBLEM: the L10(...) objectives occasionally have values < 1e-6, even though the vast majority are O(1e-2). -This happens because of a stupid way that I wrote code to infer when we were looking at the result of the L10^10 -objective as opposed to other objectives. In reality, any obective value < 1e-6 needs to be raised to the power 1/10. ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -The mixture dataset had weight 1.00 on dataset A and weight 0.00 on dataset B. argmin(...) models were fit to that dataset. +The mixture dataset had weight 1.00 on dataset A and weight 0.00 on dataset B. - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.058305 0.043682 0.058305 4.675041e+02 6.854094e+04 4.675041e+02 0.326392 3172.543057 0.326392 8.786676 60.511077 8.786676 16.335460 123.354424 16.335460 -argmin(nTVD) 0.058255 0.043691 0.058255 5.000476e+02 6.956110e+04 5.000476e+02 1.842914 3220.082562 1.842914 8.720995 60.394434 8.720995 15.342389 124.476252 15.342389 -argmin(TVD) 0.058421 0.044154 0.058421 4.842252e+02 6.921505e+04 4.842252e+02 1.105592 3203.956620 1.105592 8.657254 60.464797 8.657254 15.826993 123.250965 15.826993 -argmin(L10^10) 0.056057 0.049538 0.056057 6.381221e+02 6.964097e+04 6.381221e+02 8.277176 3223.804647 8.277176 9.578566 60.363776 9.578566 17.786360 122.383633 17.786360 -Model A 126.554588 60.941755 126.554588 1.833490e+06 1.750514e+06 1.833490e+06 0.878730 3152.059162 0.878730 467.173129 465.279406 467.173129 835.190192 868.058186 835.190192 -Model B 60.353297 36.518298 60.353297 7.812251e+05 7.029310e+05 7.812251e+05 1764.118964 2.060910 1764.118964 393.395690 362.433941 393.395690 769.413642 713.138467 769.413642 + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.058305 0.731190 0.058305 467.504133 68540.938167 467.504133 0.326392 3172.543057 0.326392 8.786676 60.511077 8.786676 16.335460 123.354424 16.335460 +argmin(nTVD) 0.058255 0.731205 0.058255 500.047586 69561.100943 500.047586 1.842914 3220.082562 1.842914 8.720995 60.394434 8.720995 15.342389 124.476252 15.342389 +argmin(TVD) 0.058421 0.731976 0.058421 484.225199 69215.050104 484.225199 1.105592 3203.956620 1.105592 8.657254 60.464797 8.657254 15.826993 123.250965 15.826993 +argmin(L10^10) 0.056057 0.740446 0.056057 638.122108 69640.974153 638.122108 8.277176 3223.804647 8.277176 9.578566 60.363776 9.578566 17.786360 122.383633 17.786360 +Model A 0.058701 0.731059 0.058701 479.867127 68138.580337 479.867127 0.878730 3152.059162 0.878730 8.853287 60.327248 8.853287 16.534238 123.431015 16.534238 +Model B 0.731329 0.059377 0.731329 38338.240487 505.249618 38338.240487 1764.118964 2.060910 1764.118964 60.134177 10.943588 60.134177 125.523234 21.227815 125.523234 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -The mixture dataset had weight 0.99 on dataset A and weight 0.01 on dataset B. argmin(...) models were fit to that dataset. +The mixture dataset had weight 0.99 on dataset A and weight 0.01 on dataset B. - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.058280 0.039962 0.057080 5.073249e+02 6.364611e+04 4.180941e+02 2.182037 2944.444398 -1.976113 8.942991 59.846071 8.432625 16.504940 121.838636 15.728875 -argmin(nTVD) 0.058199 0.040237 0.057029 5.238449e+02 6.480701e+04 4.508641e+02 2.951869 2998.542071 -0.449034 8.881305 59.807557 8.380632 15.663741 122.889098 14.786347 -argmin(TVD) 0.058479 0.039820 0.057338 5.092324e+02 6.398873e+04 4.319471e+02 2.270928 2960.410479 -1.330563 8.828511 59.859282 8.314256 16.191643 121.921252 15.361203 -argmin(L10^10) 0.056076 0.048398 0.054859 6.503304e+02 6.860767e+04 6.385727e+02 8.846081 3175.652652 8.298174 9.610355 60.149631 9.356175 17.971954 121.908982 17.208866 -Model A 126.554588 60.941755 124.595602 1.833490e+06 1.750514e+06 1.831321e+06 0.878730 3152.059162 0.355726 467.173129 465.279406 466.950288 835.190192 868.058186 835.054746 -Model B 60.353297 36.518298 59.638173 7.812251e+05 7.029310e+05 7.790110e+05 1764.118964 2.060910 1696.546001 393.395690 362.433941 392.844547 769.413642 713.138467 768.335640 + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.058280 0.724711 0.057080 507.324913 63646.108882 418.094069 2.182037 2944.444398 -1.976113 8.942991 59.846071 8.432625 16.504940 121.838636 15.728875 +argmin(nTVD) 0.058199 0.725208 0.057029 523.844937 64807.005109 450.864053 2.951869 2998.542071 -0.449034 8.881305 59.807557 8.380632 15.663741 122.889098 14.786347 +argmin(TVD) 0.058479 0.724452 0.057338 509.232448 63988.729224 431.947107 2.270928 2960.410479 -1.330563 8.828511 59.859282 8.314256 16.191643 121.921252 15.361203 +argmin(L10^10) 0.056076 0.738725 0.054859 650.330393 68607.667786 638.572710 8.846081 3175.652652 8.298174 9.610355 60.149631 9.356175 17.971954 121.908982 17.208866 +Model A 0.058701 0.731059 0.057534 479.867127 68138.580337 468.637758 0.878730 3152.059162 0.355726 8.853287 60.327248 8.592865 16.534238 123.431015 16.151412 +Model B 0.731329 0.059377 0.723682 38338.240487 505.249618 36887.387442 1764.118964 2.060910 1696.546001 60.134177 10.943588 59.237540 125.523234 21.227815 123.602545 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -The mixture dataset had weight 0.95 on dataset A and weight 0.05 on dataset B. argmin(...) models were fit to that dataset. +The mixture dataset had weight 0.95 on dataset A and weight 0.05 on dataset B. - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.058553 0.028187 0.054809 8.739089e+02 5.279031e+04 3.702706e+02 19.264823 2438.565156 -4.204682 10.142951 57.797471 8.212986 18.901034 117.555414 14.999158 -argmin(nTVD) 0.059798 0.028166 0.055133 8.835317e+02 5.303729e+04 3.918073e+02 19.713245 2450.074142 -3.201075 10.156287 57.551119 8.186089 17.928632 117.965291 14.160187 -argmin(TVD) 0.058915 0.027390 0.055142 8.848670e+02 5.272912e+04 3.792575e+02 19.775468 2435.713379 -3.785893 10.053967 57.813796 8.103999 19.094600 117.175958 14.728985 -argmin(L10^10) 0.058181 0.022799 0.052683 1.095965e+03 5.199359e+04 5.419852e+02 29.612631 2401.437971 3.797205 10.804595 57.729039 9.199417 17.573569 120.776182 16.172637 -Model A 126.554588 60.941755 119.813465 1.833490e+06 1.750514e+06 1.825516e+06 0.878730 3152.059162 17.179587 467.173129 465.279406 466.693855 835.190192 868.058186 835.189767 -Model B 60.353297 36.518298 58.332841 7.812251e+05 7.029310e+05 7.735094e+05 1764.118964 2.060910 1518.501283 393.395690 362.433941 391.381852 769.413642 713.138467 765.808000 + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.058553 0.699850 0.054809 873.908924 52790.312819 370.270628 19.264823 2438.565156 -4.204682 10.142951 57.797471 8.212986 18.901034 117.555414 14.999158 +argmin(nTVD) 0.059798 0.699797 0.055133 883.531715 53037.287180 391.807285 19.713245 2450.074142 -3.201075 10.156287 57.551119 8.186089 17.928632 117.965291 14.160187 +argmin(TVD) 0.058915 0.697846 0.055142 884.866988 52729.115781 379.257525 19.775468 2435.713379 -3.785893 10.053967 57.813796 8.103999 19.094600 117.175958 14.728985 +argmin(L10^10) 0.058181 0.685159 0.052683 1095.965264 51993.590764 541.985234 29.612631 2401.437971 3.797205 10.804595 57.729039 9.199417 17.573569 120.776182 16.172637 +Model A 0.058701 0.731059 0.056414 479.867127 68138.580337 829.861371 0.878730 3152.059162 17.179587 8.853287 60.327248 9.539133 16.534238 123.431015 17.309003 +Model B 0.731329 0.059377 0.694352 38338.240487 505.249618 33064.605213 1764.118964 2.060910 1518.501283 60.134177 10.943588 56.927442 125.523234 21.227815 118.767794 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -The mixture dataset had weight 0.90 on dataset A and weight 0.10 on dataset B. argmin(...) models were fit to that dataset. +The mixture dataset had weight 0.90 on dataset A and weight 0.10 on dataset B. - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.069575 0.017990 0.053253 1.707234e+03 4.291860e+04 3.482229e+02 58.097710 1978.544133 -5.232105 12.207427 55.203937 8.132309 23.235284 112.218973 14.596372 -argmin(nTVD) 0.079953 0.017285 0.058557 1.705770e+03 4.306758e+04 3.637916e+02 58.029501 1985.486770 -4.506605 12.338981 54.893533 8.109634 21.600853 113.270335 13.542902 -argmin(TVD) 0.072924 0.017074 0.053595 1.713855e+03 4.287397e+04 3.530817e+02 58.406262 1976.464261 -5.005686 12.164062 55.122870 7.974263 23.594074 111.497258 14.229698 -argmin(L10^10) 0.094878 0.011530 0.050696 2.031710e+03 4.191370e+04 5.366406e+02 73.218257 1931.715827 3.548147 12.693870 55.157799 9.248214 20.035872 115.901433 16.354479 -Model A 126.554588 60.941755 114.261294 1.833490e+06 1.750514e+06 1.819040e+06 0.878730 3152.059162 65.570121 467.173129 465.279406 466.614406 835.190192 868.058186 836.788448 -Model B 60.353297 36.518298 56.715375 7.812251e+05 7.029310e+05 7.672737e+05 1764.118964 2.060910 1321.774150 393.395690 362.433941 389.555690 769.413642 713.138467 762.774974 + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.069575 0.669120 0.053253 1707.234071 42918.600355 348.222883 58.097710 1978.544133 -5.232105 12.207427 55.203937 8.132309 23.235284 112.218973 14.596372 +argmin(nTVD) 0.079953 0.666450 0.058557 1705.770360 43067.584239 363.791579 58.029501 1985.486770 -4.506605 12.338981 54.893533 8.109634 21.600853 113.270335 13.542902 +argmin(TVD) 0.072924 0.665629 0.053595 1713.855390 42873.967832 353.081662 58.406262 1976.464261 -5.005686 12.164062 55.122870 7.974263 23.594074 111.497258 14.229698 +argmin(L10^10) 0.094878 0.640004 0.050696 2031.709888 41913.699383 536.640612 73.218257 1931.715827 3.548147 12.693870 55.157799 9.248214 20.035872 115.901433 16.354479 +Model A 0.058701 0.731059 0.073987 479.867127 68138.580337 1868.850198 0.878730 3152.059162 65.570121 8.853287 60.327248 11.344055 16.534238 123.431015 20.830502 +Model B 0.731329 0.059377 0.657746 38338.240487 505.249618 28840.694551 1764.118964 2.060910 1321.774150 60.134177 10.943588 53.972022 125.523234 21.227815 112.825645 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -The mixture dataset had weight 0.80 on dataset A and weight 0.20 on dataset B. argmin(...) models were fit to that dataset. +The mixture dataset had weight 0.80 on dataset A and weight 0.20 on dataset B. - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 1.280690e-01 0.006850 0.058235 4.073798e+03 2.950145e+04 3.447751e+02 168.379391 1353.305782 -5.392771 16.805776 49.914257 8.147237 32.461797 101.228335 13.417375 -argmin(nTVD) 1.460365e-01 0.005945 0.094634 4.183566e+03 2.918867e+04 3.725061e+02 173.494602 1338.730370 -4.100509 17.372651 49.184236 8.021515 32.596331 100.884726 12.828388 -argmin(TVD) 1.466788e-01 0.005582 0.091479 4.232062e+03 2.899532e+04 3.733013e+02 175.754504 1329.720514 -4.063453 17.390356 49.195911 7.972158 33.085064 100.576929 13.119409 -argmin(L10^10) 2.345534e-08 0.003162 0.048265 4.352138e+03 2.963231e+04 6.006732e+02 181.350043 1359.404108 6.532062 16.844040 50.229908 9.723182 28.929435 104.827273 15.294466 -Model A 1.265546e+02 60.941755 104.583029 1.833490e+06 1.750514e+06 1.808010e+06 0.878730 3152.059162 226.530439 467.173129 465.279406 466.486253 835.190192 868.058186 840.349965 -Model B 6.035330e+01 36.518298 53.755875 7.812251e+05 7.029310e+05 7.564658e+05 1764.118964 2.060910 994.843938 393.395690 362.433941 385.923879 769.413642 713.138467 756.665756 + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.128069 0.607533 0.058235 4073.797769 29501.445680 344.775113 168.379391 1353.305782 -5.392771 16.805776 49.914257 8.147237 32.461797 101.228335 13.417375 +argmin(nTVD) 0.146037 0.598981 0.094634 4183.566412 29188.668078 372.506101 173.494602 1338.730370 -4.100509 17.372651 49.184236 8.021515 32.596331 100.884726 12.828388 +argmin(TVD) 0.146679 0.595218 0.091479 4232.062255 28995.323212 373.301298 175.754504 1329.720514 -4.063453 17.390356 49.195911 7.972158 33.085064 100.576929 13.119409 +argmin(L10^10) 0.172593 0.562341 0.048265 4352.138395 29632.311270 600.673234 181.350043 1359.404108 6.532062 16.844040 50.229908 9.723182 28.929435 104.827273 15.294466 +Model A 0.058701 0.731059 0.145681 479.867127 68138.580337 5324.814797 0.878730 3152.059162 226.530439 8.853287 60.327248 15.643033 16.534238 123.431015 29.515782 +Model B 0.731329 0.059377 0.584742 38338.240487 505.249618 21821.205206 1764.118964 2.060910 994.843938 60.134177 10.943588 48.146528 125.523234 21.227815 100.913491 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -The mixture dataset had weight 0.70 on dataset A and weight 0.30 on dataset B. argmin(...) models were fit to that dataset. +The mixture dataset had weight 0.70 on dataset A and weight 0.30 on dataset B. - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 7.003153e-08 0.002223 0.074748 7.091943e+03 2.049327e+04 3.748087e+02 309.024731 933.525551 -3.993207 21.834864 44.494855 8.290449 42.852141 90.101863 12.886797 -argmin(nTVD) 1.308041e-07 0.001822 0.101387 7.261664e+03 2.014795e+04 3.914457e+02 316.933697 917.433559 -3.217924 22.304388 43.941396 8.081331 42.897418 89.962380 12.315999 -argmin(TVD) 2.240636e-07 0.001572 0.124270 7.352556e+03 2.005034e+04 4.280172e+02 321.169276 912.884917 -1.513698 22.608459 43.610406 7.988594 43.227342 89.623360 12.475021 -argmin(L10^10) 8.068013e-07 0.000791 0.051606 7.656646e+03 2.035537e+04 7.273908e+02 335.339826 927.099577 12.437089 22.360088 44.437987 10.407362 40.644331 92.165539 15.493826 -Model A 1.265546e+02 60.941755 96.185813 1.833490e+06 1.750514e+06 1.797466e+06 0.878730 3152.059162 454.592147 467.173129 465.279406 466.320765 835.190192 868.058186 843.676595 -Model B 6.035330e+01 36.518298 50.947670 7.812251e+05 7.029310e+05 7.465651e+05 1764.118964 2.060910 729.831768 393.395690 362.433941 382.212791 769.413642 713.138467 750.519271 + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.192544 0.542873 0.074748 7091.943198 20493.271003 374.808710 309.024731 933.525551 -3.993207 21.834864 44.494855 8.290449 42.852141 90.101863 12.886797 +argmin(nTVD) 0.204957 0.532177 0.101387 7261.663790 20147.948696 391.445729 316.933697 917.433559 -3.217924 22.304388 43.941396 8.081331 42.897418 89.962380 12.315999 +argmin(TVD) 0.216290 0.524384 0.124270 7352.556204 20050.338197 428.017151 321.169276 912.884917 -1.513698 22.608459 43.610406 7.988594 43.227342 89.623360 12.475021 +argmin(L10^10) 0.245854 0.489594 0.051606 7656.645776 20355.374322 727.390771 335.339826 927.099577 12.437089 22.360088 44.437987 10.407362 40.644331 92.165539 15.493826 +Model A 0.058701 0.731059 0.218786 479.867127 68138.580337 10221.507329 0.878730 3152.059162 454.592147 8.853287 60.327248 20.452823 16.534238 123.431015 39.614498 +Model B 0.731329 0.059377 0.511604 38338.240487 505.249618 16131.152604 1764.118964 2.060910 729.831768 60.134177 10.943588 42.329793 125.523234 21.227815 89.111709 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -The mixture dataset had weight 0.60 on dataset A and weight 0.40 on dataset B. argmin(...) models were fit to that dataset. +The mixture dataset had weight 0.60 on dataset A and weight 0.40 on dataset B. - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.000001 0.000577 0.091391 1.057215e+04 1.407142e+04 4.205712e+02 471.201894 634.267962 -1.860679 27.077488 38.959680 8.587280 53.755140 78.694803 13.186374 -argmin(nTVD) 0.000002 0.000464 0.111901 1.064063e+04 1.400078e+04 4.350772e+02 474.393338 630.976212 -1.184701 27.306314 38.733142 8.396607 51.752982 80.636663 12.291792 -argmin(TVD) 0.000005 0.000310 0.154856 1.093002e+04 1.377477e+04 5.207620e+02 487.878686 620.443965 2.808205 27.986584 38.230402 8.195175 53.533009 78.948949 12.597676 -argmin(L10^10) 0.000010 0.000173 0.056281 1.142365e+04 1.383654e+04 8.348078e+02 510.881935 623.322260 17.442714 28.142159 38.727617 11.162597 54.015160 78.750509 16.638443 -Model A 126.554588 60.941755 89.013251 1.833490e+06 1.750514e+06 1.789088e+06 0.878730 3152.059162 732.687654 467.173129 465.279406 466.176419 835.190192 868.058186 847.274243 -Model B 60.353297 36.518298 48.387892 7.812251e+05 7.029310e+05 7.382813e+05 1764.118964 2.060910 517.410633 393.395690 362.433941 378.560568 769.413642 713.138467 744.458052 + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.261164 0.474408 0.091391 10572.145717 14071.423474 420.571207 471.201894 634.267962 -1.860679 27.077488 38.959680 8.587280 53.755140 78.694803 13.186374 +argmin(nTVD) 0.272057 0.464147 0.111901 10640.631764 14000.784948 435.077184 474.393338 630.976212 -1.184701 27.306314 38.733142 8.396607 51.752982 80.636663 12.291792 +argmin(TVD) 0.293808 0.445746 0.154856 10930.017392 13774.770683 520.762005 487.878686 620.443965 2.808205 27.986584 38.230402 8.195175 53.533009 78.948949 12.597676 +argmin(L10^10) 0.315435 0.420561 0.056281 11423.650183 13836.536763 834.807801 510.881935 623.322260 17.442714 28.142159 38.727617 11.162597 54.015160 78.750509 16.638443 +Model A 0.058701 0.731059 0.291888 479.867127 68138.580337 16192.471084 0.878730 3152.059162 732.687654 8.853287 60.327248 25.670594 16.534238 123.431015 50.763009 +Model B 0.731329 0.059377 0.438699 38338.240487 505.249618 11570.277416 1764.118964 2.060910 517.410633 60.134177 10.943588 36.631982 125.523234 21.227815 77.203040 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -The mixture dataset had weight 0.50 on dataset A and weight 0.50 on dataset B. argmin(...) models were fit to that dataset. +The mixture dataset had weight 0.50 on dataset A and weight 0.50 on dataset B. - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.000019 0.000103 0.103135 1.445346e+04 9.389479e+03 4.813178e+02 652.070827 416.089708 0.970108 32.566106 33.543358 9.169555 65.724820 67.846493 16.004581 -argmin(nTVD) 0.000027 0.000077 0.122024 1.457978e+04 9.296960e+03 4.992725e+02 657.957229 411.778345 1.806797 32.851237 33.297344 8.877317 64.277568 68.784671 14.620929 -argmin(TVD) 0.000046 0.000047 0.153857 1.491067e+04 9.096656e+03 5.650716e+02 673.376719 402.444189 4.873029 33.516252 32.955458 8.697918 66.899668 66.687181 15.806218 -argmin(L10^10) 0.000066 0.000032 0.058901 1.534189e+04 9.342669e+03 9.002678e+02 693.471719 413.908394 20.493147 33.948918 33.010908 11.802510 65.332360 68.314439 17.769593 -Model A 126.554588 60.941755 82.856783 1.833490e+06 1.750514e+06 1.780313e+06 0.878730 3152.059162 1049.968900 467.173129 465.279406 466.029406 835.190192 868.058186 850.583717 -Model B 60.353297 36.518298 46.079898 7.812251e+05 7.029310e+05 7.303883e+05 1764.118964 2.060910 348.353651 393.395690 362.433941 375.034359 769.413642 713.138467 738.464676 + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.336724 0.399223 0.103135 14453.459856 9389.478776 481.317795 652.070827 416.089708 0.970108 32.566106 33.543358 9.169555 65.724820 67.846493 16.004581 +argmin(nTVD) 0.348744 0.387786 0.122024 14579.777698 9296.960102 499.272540 657.957229 411.778345 1.806797 32.851237 33.297344 8.877317 64.277568 68.784671 14.620929 +argmin(TVD) 0.368227 0.369535 0.153857 14910.668610 9096.655991 565.071618 673.376719 402.444189 4.873029 33.516252 32.955458 8.697918 66.899668 66.687181 15.806218 +argmin(L10^10) 0.381766 0.355110 0.058901 15341.892516 9342.669387 900.267841 693.471719 413.908394 20.493147 33.948918 33.010908 11.802510 65.332360 68.314439 17.769593 +Model A 0.058701 0.731059 0.364902 479.867127 68138.580337 23004.788336 0.878730 3152.059162 1049.968900 8.853287 60.327248 31.004211 16.534238 123.431015 61.974967 +Model B 0.731329 0.059377 0.366102 38338.240487 505.249618 7940.470086 1764.118964 2.060910 348.353651 60.134177 10.943588 31.177551 125.523234 21.227815 65.981165 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -The mixture dataset had weight 0.40 on dataset A and weight 0.60 on dataset B. argmin(...) models were fit to that dataset. +The mixture dataset had weight 0.40 on dataset A and weight 0.60 on dataset B. - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.000171 0.000010 0.108947 1.886757e+04 5.752944e+03 4.746497e+02 857.768204 246.627478 0.659375 38.293271 27.854670 9.021935 76.723239 55.628956 14.741397 -argmin(nTVD) 0.000241 0.000006 0.129251 1.903001e+04 5.674293e+03 4.941562e+02 865.337660 242.962347 1.568377 38.541429 27.808615 8.754129 75.034583 57.196161 13.549772 -argmin(TVD) 0.000286 0.000005 0.142902 1.893380e+04 5.795962e+03 5.309794e+02 860.854538 248.632108 3.284335 38.486363 28.009825 8.653013 75.854167 56.356211 13.840286 -argmin(L10^10) 0.000332 0.000004 0.058838 1.965420e+04 5.884199e+03 8.633228e+02 894.425086 252.743956 18.771513 39.638916 27.580884 11.777146 78.797379 54.682824 18.139113 -Model A 126.554588 60.941755 77.160462 1.833490e+06 1.750514e+06 1.773474e+06 0.878730 3152.059162 1414.486997 467.173129 465.279406 465.876503 835.190192 868.058186 854.223189 -Model B 60.353297 36.518298 43.777898 7.812251e+05 7.029310e+05 7.236160e+05 1764.118964 2.060910 214.038392 393.395690 362.433941 371.769559 769.413642 713.138467 732.147248 + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.419938 0.316692 0.108947 18867.574117 5752.944088 474.649704 857.768204 246.627478 0.659375 38.293271 27.854670 9.021935 76.723239 55.628956 14.741397 +argmin(nTVD) 0.434744 0.302557 0.129251 19030.009064 5674.293087 494.156212 865.337660 242.962347 1.568377 38.541429 27.808615 8.754129 75.034583 57.196161 13.549772 +argmin(TVD) 0.442236 0.295717 0.142902 18933.804562 5795.961973 530.979411 860.854538 248.632108 3.284335 38.486363 28.009825 8.653013 75.854167 56.356211 13.840286 +argmin(L10^10) 0.448831 0.288644 0.058838 19654.203801 5884.199204 863.322840 894.425086 252.743956 18.771513 39.638916 27.580884 11.777146 78.797379 54.682824 18.139113 +Model A 0.058701 0.731059 0.438333 479.867127 68138.580337 30831.323781 0.878730 3152.059162 1414.486997 8.853287 60.327248 36.680717 16.534238 123.431015 74.100404 +Model B 0.731329 0.059377 0.293411 38338.240487 505.249618 5056.599171 1764.118964 2.060910 214.038392 60.134177 10.943588 25.803049 125.523234 21.227815 54.630480 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -The mixture dataset had weight 0.30 on dataset A and weight 0.70 on dataset B. argmin(...) models were fit to that dataset. +The mixture dataset had weight 0.30 on dataset A and weight 0.70 on dataset B. - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.001147 4.108738e-07 0.102495 2.357782e+04 3.236178e+03 4.624121e+02 1077.265150 129.346379 0.089104 44.115671 22.393879 9.130840 88.726527 44.168714 15.734786 -argmin(nTVD) 0.001229 3.566033e-07 0.108070 2.351664e+04 3.269966e+03 4.686338e+02 1074.414076 130.920879 0.379032 44.009629 22.560514 9.030624 86.514322 46.236778 14.452820 -argmin(TVD) 0.001369 2.912033e-07 0.127903 2.331235e+04 3.401875e+03 5.032436e+02 1064.894275 137.067856 1.991849 43.854686 22.950017 8.905500 88.963584 43.867608 15.718299 -argmin(L10^10) 0.001462 2.406509e-07 0.055216 2.400751e+04 3.469828e+03 7.530523e+02 1097.288687 140.234437 13.632915 44.771370 22.378776 11.375579 89.778594 43.822841 17.980791 -Model A 126.554588 6.094176e+01 72.244649 1.833490e+06 1.750514e+06 1.766168e+06 0.878730 3152.059162 1806.872182 467.173129 465.279406 465.715841 835.190192 868.058186 857.751642 -Model B 60.353297 3.651830e+01 41.695823 7.812251e+05 7.029310e+05 7.171513e+05 1764.118964 2.060910 115.494432 393.395690 362.433941 368.849937 769.413642 713.138467 726.102011 + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.508091 0.229811 0.102495 23577.816968 3236.178060 462.412103 1077.265150 129.346379 0.089104 44.115671 22.393879 9.130840 88.726527 44.168714 15.734786 +argmin(nTVD) 0.511618 0.226578 0.108070 23516.635010 3269.965680 468.633753 1074.414076 130.920879 0.379032 44.009629 22.560514 9.030624 86.514322 46.236778 14.452820 +argmin(TVD) 0.517180 0.222034 0.127903 23312.347102 3401.875282 503.243620 1064.894275 137.067856 1.991849 43.854686 22.950017 8.905500 88.963584 43.867608 15.718299 +argmin(L10^10) 0.520573 0.217841 0.055216 24007.507335 3469.827760 753.052320 1097.288687 140.234437 13.632915 44.771370 22.378776 11.375579 89.778594 43.822841 17.980791 +Model A 0.058701 0.731059 0.511825 479.867127 68138.580337 39256.191000 0.878730 3152.059162 1806.872182 8.853287 60.327248 42.327993 16.534238 123.431015 86.193286 +Model B 0.731329 0.059377 0.221235 38338.240487 505.249618 2940.770627 1764.118964 2.060910 115.494432 60.134177 10.943588 20.712529 125.523234 21.227815 43.896415 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -The mixture dataset had weight 0.25 on dataset A and weight 0.75 on dataset B. argmin(...) models were fit to that dataset. +The mixture dataset had weight 0.20 on dataset A and weight 0.80 on dataset B. - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.002629 5.180830e-08 0.093984 2.599593e+04 2.326897e+03 4.509724e+02 1189.949238 86.973972 -0.443988 46.982771 19.751013 9.242001 94.654726 38.596659 16.399717 -argmin(nTVD) 0.002805 4.450868e-08 0.104821 2.585265e+04 2.390805e+03 4.646910e+02 1183.272330 89.952041 0.195299 46.761698 20.078455 9.142028 92.172305 40.877737 14.922437 -argmin(TVD) 0.002720 4.938478e-08 0.110849 2.561716e+04 2.481830e+03 4.760788e+02 1172.298145 94.193840 0.725971 46.565040 20.348602 9.081771 95.040781 38.108054 16.559203 -argmin(L10^10) 0.002901 3.913473e-08 0.052445 2.626039e+04 2.534471e+03 6.706137e+02 1202.272988 96.646897 9.791280 47.301903 19.848731 11.065213 95.153098 38.625469 18.135336 -Model A 126.554588 6.094176e+01 70.105753 1.833490e+06 1.750514e+06 1.763134e+06 0.878730 3152.059162 2012.002268 467.173129 465.279406 465.653406 835.190192 868.058186 859.393352 -Model B 60.353297 3.651830e+01 40.782671 7.812251e+05 7.029310e+05 7.143812e+05 1764.118964 2.060910 78.510734 393.395690 362.433941 367.455068 769.413642 713.138467 723.275767 + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.593498 0.146681 0.082374 28465.212070 1616.355278 436.127541 1305.017377 53.862765 -1.135755 49.812296 17.179430 9.393956 100.692317 32.977846 16.957784 +argmin(nTVD) 0.594112 0.146755 0.089686 28305.058345 1668.395129 446.901333 1297.554226 56.287818 -0.633697 49.607226 17.440040 9.315584 98.327986 35.191299 15.409819 +argmin(TVD) 0.590347 0.150556 0.094477 28086.296649 1726.543671 451.447459 1287.359948 58.997535 -0.421848 49.446819 17.645242 9.251400 100.983947 32.606542 16.987316 +argmin(L10^10) 0.594091 0.146093 0.049514 28535.089216 1795.085966 592.176231 1308.273646 62.191601 6.136102 49.858494 17.422069 10.711664 100.412987 33.630635 18.473805 +Model A 0.058701 0.731059 0.584358 479.867127 68138.580337 48317.163314 0.878730 3152.059162 2228.883735 8.853287 60.327248 48.226359 16.534238 123.431015 98.409177 +Model B 0.731329 0.059377 0.151299 38338.240487 505.249618 1503.926489 1764.118964 2.060910 48.573929 60.134177 10.943588 16.043020 125.523234 21.227815 34.029001 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -The mixture dataset had weight 0.20 on dataset A and weight 0.80 on dataset B. argmin(...) models were fit to that dataset. +The mixture dataset had weight 0.10 on dataset A and weight 0.90 on dataset B. - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.005422 0.146681 0.082374 2.846521e+04 1.616355e+03 4.361275e+02 1305.017377 53.862765 -1.135755 49.812296 17.179430 9.393956 100.692317 32.977846 16.957784 -argmin(nTVD) 0.005479 0.146755 0.089686 2.830506e+04 1.668395e+03 4.469013e+02 1297.554226 56.287818 -0.633697 49.607226 17.440040 9.315584 98.327986 35.191299 15.409819 -argmin(TVD) 0.005141 0.150556 0.094477 2.808630e+04 1.726544e+03 4.514475e+02 1287.359948 58.997535 -0.421848 49.446819 17.645242 9.251400 100.983947 32.606542 16.987316 -argmin(L10^10) 0.005477 0.146093 0.049514 2.853509e+04 1.795086e+03 5.921762e+02 1308.273646 62.191601 6.136102 49.858494 17.422069 10.711664 100.412987 33.630635 18.473805 -Model A 126.554588 60.941755 67.948218 1.833490e+06 1.750514e+06 1.760483e+06 0.878730 3152.059162 2228.883735 467.173129 465.279406 465.562802 835.190192 868.058186 861.097728 -Model B 60.353297 36.518298 39.808559 7.812251e+05 7.029310e+05 7.118039e+05 1764.118964 2.060910 48.573929 393.395690 362.433941 366.064099 769.413642 713.138467 720.476766 + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.668679 0.076564 0.056305 33419.625902 747.866994 429.312337 1535.892671 13.391279 -1.453343 55.170456 12.812219 9.801565 112.532181 23.162979 18.171220 +argmin(nTVD) 0.668440 0.078943 0.062971 33269.383067 776.580541 440.796012 1528.891367 14.729328 -0.918204 54.977003 13.018956 9.790191 109.947903 25.628907 16.632490 +argmin(TVD) 0.666052 0.079781 0.062700 33280.020316 774.007514 440.255326 1529.387062 14.609425 -0.943400 54.969167 12.971492 9.714384 112.902957 22.935576 18.372205 +argmin(L10^10) 0.657603 0.085642 0.047752 33053.604675 863.393466 497.358545 1518.836111 18.774804 1.717605 54.851380 13.490325 10.493062 110.463381 25.261768 19.602431 +Model A 0.058701 0.731059 0.657304 479.867127 68138.580337 58057.143232 0.878730 3152.059162 2682.519826 8.853287 60.327248 54.174145 16.534238 123.431015 110.783513 +Model B 0.731329 0.059377 0.084357 38338.240487 505.249618 695.274792 1764.118964 2.060910 10.911265 60.134177 10.943588 12.104857 125.523234 21.227815 25.858820 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -The mixture dataset had weight 0.10 on dataset A and weight 0.90 on dataset B. argmin(...) models were fit to that dataset. +The mixture dataset had weight 0.05 on dataset A and weight 0.95 on dataset B. - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.017872 0.076564 0.056305 3.341963e+04 7.478670e+02 4.293123e+02 1535.892671 13.391279 -1.453343 55.170456 12.812219 9.801565 112.532181 23.162979 18.171220 -argmin(nTVD) 0.017808 0.078943 0.062971 3.326938e+04 7.765805e+02 4.407960e+02 1528.891367 14.729328 -0.918204 54.977003 13.018956 9.790191 109.947903 25.628907 16.632490 -argmin(TVD) 0.017182 0.079781 0.062700 3.328002e+04 7.740075e+02 4.402553e+02 1529.387062 14.609425 -0.943400 54.969167 12.971492 9.714384 112.902957 22.935576 18.372205 -argmin(L10^10) 0.015123 0.085642 0.047752 3.305360e+04 8.633935e+02 4.973585e+02 1518.836111 18.774804 1.717605 54.851380 13.490325 10.493062 110.463381 25.261768 19.602431 -Model A 126.554588 60.941755 64.087331 1.833490e+06 1.750514e+06 1.754870e+06 0.878730 3152.059162 2682.519826 467.173129 465.279406 465.431406 835.190192 868.058186 864.637888 -Model B 60.353297 36.518298 38.024661 7.812251e+05 7.029310e+05 7.068148e+05 1764.118964 2.060910 10.911265 393.395690 362.433941 363.716712 769.413642 713.138467 715.982743 + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.702262 0.057311 0.053609 35901.832160 551.198688 444.775466 1651.563288 4.226552 -0.732762 57.715396 11.268873 10.094609 118.373585 19.866242 18.893372 +argmin(nTVD) 0.701985 0.060941 0.057731 35768.647398 565.964380 452.541828 1645.356888 4.914632 -0.370850 57.528568 11.375743 10.084489 115.798353 21.208465 17.470876 +argmin(TVD) 0.701885 0.059799 0.054853 35612.554235 576.319495 455.902128 1638.082959 5.397179 -0.214260 57.334562 11.389517 10.013522 116.983272 20.113419 18.207249 +argmin(L10^10) 0.688055 0.059954 0.049035 35406.887349 628.231765 493.856172 1628.498898 7.816287 1.554395 57.298475 12.048697 10.697845 115.475494 21.996857 20.429503 +Model A 0.058701 0.731059 0.694001 479.867127 68138.580337 63158.419146 0.878730 3152.059162 2920.109931 8.853287 60.327248 57.151030 16.534238 123.431015 116.958974 +Model B 0.731329 0.059377 0.060244 38338.240487 505.249618 520.566267 1764.118964 2.060910 2.774278 60.134177 10.943588 10.952796 125.523234 21.227815 22.906553 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -The mixture dataset had weight 0.05 on dataset A and weight 0.95 on dataset B. argmin(...) models were fit to that dataset. +The mixture dataset had weight 0.01 on dataset A and weight 0.99 on dataset B. - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.029174 0.057311 0.053609 3.590183e+04 5.511987e+02 4.447755e+02 1651.563288 4.226552 -0.732762 57.715396 11.268873 10.094609 118.373585 19.866242 18.893372 -argmin(nTVD) 0.029059 0.060941 0.057731 3.576865e+04 5.659644e+02 4.525418e+02 1645.356888 4.914632 -0.370850 57.528568 11.375743 10.084489 115.798353 21.208465 17.470876 -argmin(TVD) 0.029017 0.059799 0.054853 3.561255e+04 5.763195e+02 4.559021e+02 1638.082959 5.397179 -0.214260 57.334562 11.389517 10.013522 116.983272 20.113419 18.207249 -argmin(L10^10) 0.023781 0.059954 0.049035 3.540689e+04 6.282318e+02 4.938562e+02 1628.498898 7.816287 1.554395 57.298475 12.048697 10.697845 115.475494 21.996857 20.429503 -Model A 126.554588 60.941755 62.314294 1.833490e+06 1.750514e+06 1.752496e+06 0.878730 3152.059162 2920.109931 467.173129 465.279406 465.367885 835.190192 868.058186 866.340832 -Model B 60.353297 36.518298 37.166768 7.812251e+05 7.029310e+05 7.046459e+05 1764.118964 2.060910 2.774278 393.395690 362.433941 362.896207 769.413642 713.138467 714.317761 + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.727873 0.055933 0.054759 37904.100877 492.538014 471.712639 1744.868852 1.492969 0.522508 59.687372 10.723672 10.424930 122.997044 19.884425 19.506542 +argmin(nTVD) 0.728014 0.062239 0.061105 37834.963909 506.591477 485.253476 1741.647075 2.147859 1.153510 59.558941 10.707916 10.409266 120.845389 18.528914 17.960292 +argmin(TVD) 0.731742 0.057337 0.056173 38023.698465 502.273367 482.427318 1750.442090 1.946636 1.021811 59.667143 10.617281 10.308995 122.850117 19.403571 18.992379 +argmin(L10^10) 0.716759 0.051705 0.050511 37407.042940 541.441313 515.687253 1721.705991 3.771859 2.571722 59.240605 11.358666 10.993873 119.617487 21.591750 21.098547 +Model A 0.058701 0.731059 0.723362 479.867127 68138.580337 67346.000215 0.878730 3152.059162 3115.145026 8.853287 60.327248 59.503700 16.534238 123.431015 121.896791 +Model B 0.731329 0.059377 0.058335 38338.240487 505.249618 489.082140 1764.118964 2.060910 1.307916 60.134177 10.943588 10.690820 125.523234 21.227815 21.075044 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- -The mixture dataset had weight 0.01 on dataset A and weight 0.99 on dataset B. argmin(...) models were fit to that dataset. +The mixture dataset had weight 0.00 on dataset A and weight 1.00 on dataset B. - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.041740 0.055933 0.054759 3.790410e+04 4.925380e+02 4.717126e+02 1744.868852 1.492969 0.522508 59.687372 10.723672 10.424930 122.997044 19.884425 19.506542 -argmin(nTVD) 0.041821 0.062239 0.061105 3.783496e+04 5.065915e+02 4.852535e+02 1741.647075 2.147859 1.153510 59.558941 10.707916 10.409266 120.845389 18.528914 17.960292 -argmin(TVD) 0.044013 0.057337 0.056173 3.802370e+04 5.022734e+02 4.824273e+02 1750.442090 1.946636 1.021811 59.667143 10.617281 10.308995 122.850117 19.403571 18.992379 -argmin(L10^10) 0.035787 0.051705 0.050511 3.740704e+04 5.414413e+02 5.156873e+02 1721.705991 3.771859 2.571722 59.240605 11.358666 10.993873 119.617487 21.591750 21.098547 -Model A 126.554588 60.941755 60.998233 1.833490e+06 1.750514e+06 1.750807e+06 0.878730 3152.059162 3115.145026 467.173129 465.279406 465.300180 835.190192 868.058186 867.716656 -Model B 60.353297 36.518298 36.532100 7.812251e+05 7.029310e+05 7.030719e+05 1764.118964 2.060910 1.307916 393.395690 362.433941 362.446148 769.413642 713.138467 713.134395 - - ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The mixture dataset had weight 0.00 on dataset A and weight 1.00 on dataset B. argmin(...) models were fit to that dataset. - - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.045918 0.056020 0.056020 3.832014e+04 4.904003e+02 4.904003e+02 1764.256112 1.393350 1.393350 60.127224 10.679844 10.679844 123.975368 19.966883 19.966883 -argmin(nTVD) 0.046466 0.063619 0.063619 3.818218e+04 5.027886e+02 5.027886e+02 1757.827150 1.970647 1.970647 59.933777 10.644054 10.644054 122.274428 18.476274 18.476274 -argmin(TVD) 0.049318 0.057454 0.057454 3.851677e+04 5.008840e+02 5.008840e+02 1773.419021 1.881892 1.881892 60.222429 10.548472 10.548472 123.836231 19.332965 19.332965 -argmin(L10^10) 0.040487 0.051594 0.051594 3.801835e+04 5.285188e+02 5.285188e+02 1750.192803 3.169672 3.169672 59.861629 11.253837 11.253837 121.219802 21.526994 21.526994 -Model A 126.554588 60.941755 60.941755 1.833490e+06 1.750514e+06 1.750514e+06 0.878730 3152.059162 3152.059162 467.173129 465.279406 465.279406 835.190192 868.058186 868.058186 -Model B 60.353297 36.518298 36.518298 7.812251e+05 7.029310e+05 7.029310e+05 1764.118964 2.060910 2.060910 393.395690 362.433941 362.433941 769.413642 713.138467 713.138467 + L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) +argmin(LogL) 0.734850 0.056020 0.056020 38320.137210 490.400273 490.400273 1764.256112 1.393350 1.393350 60.127224 10.679844 10.679844 123.975368 19.966883 19.966883 +argmin(nTVD) 0.735721 0.063619 0.063619 38182.176420 502.788631 502.788631 1757.827150 1.970647 1.970647 59.933777 10.644054 10.644054 122.274428 18.476274 18.476274 +argmin(TVD) 0.740118 0.057454 0.057454 38516.766492 500.884024 500.884024 1773.419021 1.881892 1.881892 60.222429 10.548472 10.548472 123.836231 19.332965 19.332965 +argmin(L10^10) 0.725658 0.051594 0.051594 38018.348946 528.518821 528.518821 1750.192803 3.169672 3.169672 59.861629 11.253837 11.253837 121.219802 21.526994 21.526994 +Model A 0.058701 0.731059 0.731059 479.867127 68138.580337 68138.580337 0.878730 3152.059162 3152.059162 8.853287 60.327248 60.327248 16.534238 123.431015 123.431015 +Model B 0.731329 0.059377 0.059377 38338.240487 505.249618 505.249618 1764.118964 2.060910 2.060910 60.134177 10.943588 10.943588 125.523234 21.227815 21.227815 diff --git a/wip_notebook_sharing/objectives-handling-mixtures.ipynb b/wip_notebook_sharing/objectives-handling-mixtures.ipynb index 7b82b880c..9e82574b8 100644 --- a/wip_notebook_sharing/objectives-handling-mixtures.ipynb +++ b/wip_notebook_sharing/objectives-handling-mixtures.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ "maxmaxlen = 64\n", "dsa, m_dga = make_tweaked_dataset(mp, depol_level=0.001, rand_unitary_scale=0.001, max_max_len=maxmaxlen)\n", "dsb, m_dgb = make_tweaked_dataset(mp, depol_level=0.010, rand_unitary_scale=0.020, max_max_len=maxmaxlen)\n", - "mixture_weights = np.array([1, 0.99, 0.95, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.25, 0.2, 0.1, 0.05, 0.01, 0.0])\n", + "mixture_weights = np.array([1, 0.99, 0.95, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.05, 0.01, 0.0])\n", "num_mixtures = mixture_weights.size\n", "\n", "fit_mode = 'CPTPLND'\n", @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -128,25 +128,6 @@ "\n", "\n", "--- Circuit Creation ---\n", - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "\n", - "--- Circuit Creation ---\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", @@ -169,7 +150,9 @@ "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", - "\n" + "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" ] }, { @@ -184,7 +167,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "--- Circuit Creation ---\n", "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", @@ -208,9 +190,7 @@ "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" + "\n" ] }, { @@ -225,6 +205,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "--- Circuit Creation ---\n", "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", @@ -282,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -308,22 +289,17 @@ " objective.add_omitted_freqs(force=True)\n", " objectives.append(objective)\n", " pvecs.append(est.models['final iteration estimate'].to_vector())\n", - " pvecs.append(m_dga.to_vector())\n", " Nsigs.append(rated_n_sigma(ds, m_dga, circuitlist, 'logl')[0])\n", - " pvecs.append(m_dgb.to_vector())\n", " Nsigs.append(rated_n_sigma(ds, m_dgb, circuitlist, 'logl')[0])\n", " Nsigs = np.array(Nsigs).reshape((-1,1))\n", "\n", - " objvals = np.zeros((len(pvecs), len(objectives)))\n", - " for i,pvec in enumerate(pvecs):\n", - " for j,objective in enumerate(objectives):\n", - " try:\n", - " val = objective.fn(pvec, stateless=True)\n", - " if val < 1e-8:\n", - " val = val ** 0.1\n", - " except Exception:\n", - " val = np.NaN\n", + " objvals = np.zeros((len(pvecs)+2, len(objectives)))\n", + " for j,objective in enumerate(objectives):\n", + " for i,pvec in enumerate(pvecs):\n", + " val = objective.fn(pvec, stateless=True)\n", " objvals[i,j] = val\n", + " objvals[i+1,j] = objective.fn_from_model(m_dga)\n", + " objvals[i+2,j] = objective.fn_from_model(m_dgb)\n", " objvals = np.concatenate((objvals, Nsigs), axis=1)\n", "\n", " df = pd.DataFrame(\n", @@ -338,24 +314,7 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "dflist = []\n", - "for dfl in dflists:\n", - " dfa, df, dfb = dfl\n", - " temp = dfa.join(df, lsuffix='(A)', rsuffix='(mix)')\n", - " dfb = dfb.copy()\n", - " dfb.columns = dfb.columns.map(lambda x: str(x) + '(B)')\n", - " temp = temp.join(dfb)\n", - " temp = temp.sort_index(axis=1)\n", - " dflist.append(temp)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -365,231 +324,223 @@ "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 1.00 on dataset A and weight 0.00 on dataset B. argmin(...) models were fit to that dataset.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.058305 0.043682 0.058305 4.675041e+02 6.854094e+04 4.675041e+02 0.326392 3172.543057 0.326392 8.786676 60.511077 8.786676 16.335460 123.354424 16.335460\n", - "argmin(nTVD) 0.058255 0.043691 0.058255 5.000476e+02 6.956110e+04 5.000476e+02 1.842914 3220.082562 1.842914 8.720995 60.394434 8.720995 15.342389 124.476252 15.342389\n", - "argmin(TVD) 0.058421 0.044154 0.058421 4.842252e+02 6.921505e+04 4.842252e+02 1.105592 3203.956620 1.105592 8.657254 60.464797 8.657254 15.826993 123.250965 15.826993\n", - "argmin(L10^10) 0.056057 0.049538 0.056057 6.381221e+02 6.964097e+04 6.381221e+02 8.277176 3223.804647 8.277176 9.578566 60.363776 9.578566 17.786360 122.383633 17.786360\n", - "Model A 126.554588 60.941755 126.554588 1.833490e+06 1.750514e+06 1.833490e+06 0.878730 3152.059162 0.878730 467.173129 465.279406 467.173129 835.190192 868.058186 835.190192\n", - "Model B 60.353297 36.518298 60.353297 7.812251e+05 7.029310e+05 7.812251e+05 1764.118964 2.060910 1764.118964 393.395690 362.433941 393.395690 769.413642 713.138467 769.413642\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.99 on dataset A and weight 0.01 on dataset B. argmin(...) models were fit to that dataset.\n", + "The mixture dataset had weight 1.00 on dataset A and weight 0.00 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.058280 0.039962 0.057080 5.073249e+02 6.364611e+04 4.180941e+02 2.182037 2944.444398 -1.976113 8.942991 59.846071 8.432625 16.504940 121.838636 15.728875\n", - "argmin(nTVD) 0.058199 0.040237 0.057029 5.238449e+02 6.480701e+04 4.508641e+02 2.951869 2998.542071 -0.449034 8.881305 59.807557 8.380632 15.663741 122.889098 14.786347\n", - "argmin(TVD) 0.058479 0.039820 0.057338 5.092324e+02 6.398873e+04 4.319471e+02 2.270928 2960.410479 -1.330563 8.828511 59.859282 8.314256 16.191643 121.921252 15.361203\n", - "argmin(L10^10) 0.056076 0.048398 0.054859 6.503304e+02 6.860767e+04 6.385727e+02 8.846081 3175.652652 8.298174 9.610355 60.149631 9.356175 17.971954 121.908982 17.208866\n", - "Model A 126.554588 60.941755 124.595602 1.833490e+06 1.750514e+06 1.831321e+06 0.878730 3152.059162 0.355726 467.173129 465.279406 466.950288 835.190192 868.058186 835.054746\n", - "Model B 60.353297 36.518298 59.638173 7.812251e+05 7.029310e+05 7.790110e+05 1764.118964 2.060910 1696.546001 393.395690 362.433941 392.844547 769.413642 713.138467 768.335640\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.058305 0.731190 0.058305 467.504133 68540.938167 467.504133 0.326392 3172.543057 0.326392 8.786676 60.511077 8.786676 16.335460 123.354424 16.335460\n", + "argmin(nTVD) 0.058255 0.731205 0.058255 500.047586 69561.100943 500.047586 1.842914 3220.082562 1.842914 8.720995 60.394434 8.720995 15.342389 124.476252 15.342389\n", + "argmin(TVD) 0.058421 0.731976 0.058421 484.225199 69215.050104 484.225199 1.105592 3203.956620 1.105592 8.657254 60.464797 8.657254 15.826993 123.250965 15.826993\n", + "argmin(L10^10) 0.056057 0.740446 0.056057 638.122108 69640.974153 638.122108 8.277176 3223.804647 8.277176 9.578566 60.363776 9.578566 17.786360 122.383633 17.786360\n", + "Model A 0.058701 0.731059 0.058701 479.867127 68138.580337 479.867127 0.878730 3152.059162 0.878730 8.853287 60.327248 8.853287 16.534238 123.431015 16.534238\n", + "Model B 0.731329 0.059377 0.731329 38338.240487 505.249618 38338.240487 1764.118964 2.060910 1764.118964 60.134177 10.943588 60.134177 125.523234 21.227815 125.523234\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.95 on dataset A and weight 0.05 on dataset B. argmin(...) models were fit to that dataset.\n", + "The mixture dataset had weight 0.99 on dataset A and weight 0.01 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.058553 0.028187 0.054809 8.739089e+02 5.279031e+04 3.702706e+02 19.264823 2438.565156 -4.204682 10.142951 57.797471 8.212986 18.901034 117.555414 14.999158\n", - "argmin(nTVD) 0.059798 0.028166 0.055133 8.835317e+02 5.303729e+04 3.918073e+02 19.713245 2450.074142 -3.201075 10.156287 57.551119 8.186089 17.928632 117.965291 14.160187\n", - "argmin(TVD) 0.058915 0.027390 0.055142 8.848670e+02 5.272912e+04 3.792575e+02 19.775468 2435.713379 -3.785893 10.053967 57.813796 8.103999 19.094600 117.175958 14.728985\n", - "argmin(L10^10) 0.058181 0.022799 0.052683 1.095965e+03 5.199359e+04 5.419852e+02 29.612631 2401.437971 3.797205 10.804595 57.729039 9.199417 17.573569 120.776182 16.172637\n", - "Model A 126.554588 60.941755 119.813465 1.833490e+06 1.750514e+06 1.825516e+06 0.878730 3152.059162 17.179587 467.173129 465.279406 466.693855 835.190192 868.058186 835.189767\n", - "Model B 60.353297 36.518298 58.332841 7.812251e+05 7.029310e+05 7.735094e+05 1764.118964 2.060910 1518.501283 393.395690 362.433941 391.381852 769.413642 713.138467 765.808000\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.058280 0.724711 0.057080 507.324913 63646.108882 418.094069 2.182037 2944.444398 -1.976113 8.942991 59.846071 8.432625 16.504940 121.838636 15.728875\n", + "argmin(nTVD) 0.058199 0.725208 0.057029 523.844937 64807.005109 450.864053 2.951869 2998.542071 -0.449034 8.881305 59.807557 8.380632 15.663741 122.889098 14.786347\n", + "argmin(TVD) 0.058479 0.724452 0.057338 509.232448 63988.729224 431.947107 2.270928 2960.410479 -1.330563 8.828511 59.859282 8.314256 16.191643 121.921252 15.361203\n", + "argmin(L10^10) 0.056076 0.738725 0.054859 650.330393 68607.667786 638.572710 8.846081 3175.652652 8.298174 9.610355 60.149631 9.356175 17.971954 121.908982 17.208866\n", + "Model A 0.058701 0.731059 0.057534 479.867127 68138.580337 468.637758 0.878730 3152.059162 0.355726 8.853287 60.327248 8.592865 16.534238 123.431015 16.151412\n", + "Model B 0.731329 0.059377 0.723682 38338.240487 505.249618 36887.387442 1764.118964 2.060910 1696.546001 60.134177 10.943588 59.237540 125.523234 21.227815 123.602545\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.90 on dataset A and weight 0.10 on dataset B. argmin(...) models were fit to that dataset.\n", + "The mixture dataset had weight 0.95 on dataset A and weight 0.05 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.069575 0.017990 0.053253 1.707234e+03 4.291860e+04 3.482229e+02 58.097710 1978.544133 -5.232105 12.207427 55.203937 8.132309 23.235284 112.218973 14.596372\n", - "argmin(nTVD) 0.079953 0.017285 0.058557 1.705770e+03 4.306758e+04 3.637916e+02 58.029501 1985.486770 -4.506605 12.338981 54.893533 8.109634 21.600853 113.270335 13.542902\n", - "argmin(TVD) 0.072924 0.017074 0.053595 1.713855e+03 4.287397e+04 3.530817e+02 58.406262 1976.464261 -5.005686 12.164062 55.122870 7.974263 23.594074 111.497258 14.229698\n", - "argmin(L10^10) 0.094878 0.011530 0.050696 2.031710e+03 4.191370e+04 5.366406e+02 73.218257 1931.715827 3.548147 12.693870 55.157799 9.248214 20.035872 115.901433 16.354479\n", - "Model A 126.554588 60.941755 114.261294 1.833490e+06 1.750514e+06 1.819040e+06 0.878730 3152.059162 65.570121 467.173129 465.279406 466.614406 835.190192 868.058186 836.788448\n", - "Model B 60.353297 36.518298 56.715375 7.812251e+05 7.029310e+05 7.672737e+05 1764.118964 2.060910 1321.774150 393.395690 362.433941 389.555690 769.413642 713.138467 762.774974\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.058553 0.699850 0.054809 873.908924 52790.312819 370.270628 19.264823 2438.565156 -4.204682 10.142951 57.797471 8.212986 18.901034 117.555414 14.999158\n", + "argmin(nTVD) 0.059798 0.699797 0.055133 883.531715 53037.287180 391.807285 19.713245 2450.074142 -3.201075 10.156287 57.551119 8.186089 17.928632 117.965291 14.160187\n", + "argmin(TVD) 0.058915 0.697846 0.055142 884.866988 52729.115781 379.257525 19.775468 2435.713379 -3.785893 10.053967 57.813796 8.103999 19.094600 117.175958 14.728985\n", + "argmin(L10^10) 0.058181 0.685159 0.052683 1095.965264 51993.590764 541.985234 29.612631 2401.437971 3.797205 10.804595 57.729039 9.199417 17.573569 120.776182 16.172637\n", + "Model A 0.058701 0.731059 0.056414 479.867127 68138.580337 829.861371 0.878730 3152.059162 17.179587 8.853287 60.327248 9.539133 16.534238 123.431015 17.309003\n", + "Model B 0.731329 0.059377 0.694352 38338.240487 505.249618 33064.605213 1764.118964 2.060910 1518.501283 60.134177 10.943588 56.927442 125.523234 21.227815 118.767794\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.80 on dataset A and weight 0.20 on dataset B. argmin(...) models were fit to that dataset.\n", + "The mixture dataset had weight 0.90 on dataset A and weight 0.10 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 1.280690e-01 0.006850 0.058235 4.073798e+03 2.950145e+04 3.447751e+02 168.379391 1353.305782 -5.392771 16.805776 49.914257 8.147237 32.461797 101.228335 13.417375\n", - "argmin(nTVD) 1.460365e-01 0.005945 0.094634 4.183566e+03 2.918867e+04 3.725061e+02 173.494602 1338.730370 -4.100509 17.372651 49.184236 8.021515 32.596331 100.884726 12.828388\n", - "argmin(TVD) 1.466788e-01 0.005582 0.091479 4.232062e+03 2.899532e+04 3.733013e+02 175.754504 1329.720514 -4.063453 17.390356 49.195911 7.972158 33.085064 100.576929 13.119409\n", - "argmin(L10^10) 2.345534e-08 0.003162 0.048265 4.352138e+03 2.963231e+04 6.006732e+02 181.350043 1359.404108 6.532062 16.844040 50.229908 9.723182 28.929435 104.827273 15.294466\n", - "Model A 1.265546e+02 60.941755 104.583029 1.833490e+06 1.750514e+06 1.808010e+06 0.878730 3152.059162 226.530439 467.173129 465.279406 466.486253 835.190192 868.058186 840.349965\n", - "Model B 6.035330e+01 36.518298 53.755875 7.812251e+05 7.029310e+05 7.564658e+05 1764.118964 2.060910 994.843938 393.395690 362.433941 385.923879 769.413642 713.138467 756.665756\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.069575 0.669120 0.053253 1707.234071 42918.600355 348.222883 58.097710 1978.544133 -5.232105 12.207427 55.203937 8.132309 23.235284 112.218973 14.596372\n", + "argmin(nTVD) 0.079953 0.666450 0.058557 1705.770360 43067.584239 363.791579 58.029501 1985.486770 -4.506605 12.338981 54.893533 8.109634 21.600853 113.270335 13.542902\n", + "argmin(TVD) 0.072924 0.665629 0.053595 1713.855390 42873.967832 353.081662 58.406262 1976.464261 -5.005686 12.164062 55.122870 7.974263 23.594074 111.497258 14.229698\n", + "argmin(L10^10) 0.094878 0.640004 0.050696 2031.709888 41913.699383 536.640612 73.218257 1931.715827 3.548147 12.693870 55.157799 9.248214 20.035872 115.901433 16.354479\n", + "Model A 0.058701 0.731059 0.073987 479.867127 68138.580337 1868.850198 0.878730 3152.059162 65.570121 8.853287 60.327248 11.344055 16.534238 123.431015 20.830502\n", + "Model B 0.731329 0.059377 0.657746 38338.240487 505.249618 28840.694551 1764.118964 2.060910 1321.774150 60.134177 10.943588 53.972022 125.523234 21.227815 112.825645\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.70 on dataset A and weight 0.30 on dataset B. argmin(...) models were fit to that dataset.\n", + "The mixture dataset had weight 0.80 on dataset A and weight 0.20 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 7.003153e-08 0.002223 0.074748 7.091943e+03 2.049327e+04 3.748087e+02 309.024731 933.525551 -3.993207 21.834864 44.494855 8.290449 42.852141 90.101863 12.886797\n", - "argmin(nTVD) 1.308041e-07 0.001822 0.101387 7.261664e+03 2.014795e+04 3.914457e+02 316.933697 917.433559 -3.217924 22.304388 43.941396 8.081331 42.897418 89.962380 12.315999\n", - "argmin(TVD) 2.240636e-07 0.001572 0.124270 7.352556e+03 2.005034e+04 4.280172e+02 321.169276 912.884917 -1.513698 22.608459 43.610406 7.988594 43.227342 89.623360 12.475021\n", - "argmin(L10^10) 8.068013e-07 0.000791 0.051606 7.656646e+03 2.035537e+04 7.273908e+02 335.339826 927.099577 12.437089 22.360088 44.437987 10.407362 40.644331 92.165539 15.493826\n", - "Model A 1.265546e+02 60.941755 96.185813 1.833490e+06 1.750514e+06 1.797466e+06 0.878730 3152.059162 454.592147 467.173129 465.279406 466.320765 835.190192 868.058186 843.676595\n", - "Model B 6.035330e+01 36.518298 50.947670 7.812251e+05 7.029310e+05 7.465651e+05 1764.118964 2.060910 729.831768 393.395690 362.433941 382.212791 769.413642 713.138467 750.519271\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.128069 0.607533 0.058235 4073.797769 29501.445680 344.775113 168.379391 1353.305782 -5.392771 16.805776 49.914257 8.147237 32.461797 101.228335 13.417375\n", + "argmin(nTVD) 0.146037 0.598981 0.094634 4183.566412 29188.668078 372.506101 173.494602 1338.730370 -4.100509 17.372651 49.184236 8.021515 32.596331 100.884726 12.828388\n", + "argmin(TVD) 0.146679 0.595218 0.091479 4232.062255 28995.323212 373.301298 175.754504 1329.720514 -4.063453 17.390356 49.195911 7.972158 33.085064 100.576929 13.119409\n", + "argmin(L10^10) 0.172593 0.562341 0.048265 4352.138395 29632.311270 600.673234 181.350043 1359.404108 6.532062 16.844040 50.229908 9.723182 28.929435 104.827273 15.294466\n", + "Model A 0.058701 0.731059 0.145681 479.867127 68138.580337 5324.814797 0.878730 3152.059162 226.530439 8.853287 60.327248 15.643033 16.534238 123.431015 29.515782\n", + "Model B 0.731329 0.059377 0.584742 38338.240487 505.249618 21821.205206 1764.118964 2.060910 994.843938 60.134177 10.943588 48.146528 125.523234 21.227815 100.913491\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.60 on dataset A and weight 0.40 on dataset B. argmin(...) models were fit to that dataset.\n", + "The mixture dataset had weight 0.70 on dataset A and weight 0.30 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.000001 0.000577 0.091391 1.057215e+04 1.407142e+04 4.205712e+02 471.201894 634.267962 -1.860679 27.077488 38.959680 8.587280 53.755140 78.694803 13.186374\n", - "argmin(nTVD) 0.000002 0.000464 0.111901 1.064063e+04 1.400078e+04 4.350772e+02 474.393338 630.976212 -1.184701 27.306314 38.733142 8.396607 51.752982 80.636663 12.291792\n", - "argmin(TVD) 0.000005 0.000310 0.154856 1.093002e+04 1.377477e+04 5.207620e+02 487.878686 620.443965 2.808205 27.986584 38.230402 8.195175 53.533009 78.948949 12.597676\n", - "argmin(L10^10) 0.000010 0.000173 0.056281 1.142365e+04 1.383654e+04 8.348078e+02 510.881935 623.322260 17.442714 28.142159 38.727617 11.162597 54.015160 78.750509 16.638443\n", - "Model A 126.554588 60.941755 89.013251 1.833490e+06 1.750514e+06 1.789088e+06 0.878730 3152.059162 732.687654 467.173129 465.279406 466.176419 835.190192 868.058186 847.274243\n", - "Model B 60.353297 36.518298 48.387892 7.812251e+05 7.029310e+05 7.382813e+05 1764.118964 2.060910 517.410633 393.395690 362.433941 378.560568 769.413642 713.138467 744.458052\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.192544 0.542873 0.074748 7091.943198 20493.271003 374.808710 309.024731 933.525551 -3.993207 21.834864 44.494855 8.290449 42.852141 90.101863 12.886797\n", + "argmin(nTVD) 0.204957 0.532177 0.101387 7261.663790 20147.948696 391.445729 316.933697 917.433559 -3.217924 22.304388 43.941396 8.081331 42.897418 89.962380 12.315999\n", + "argmin(TVD) 0.216290 0.524384 0.124270 7352.556204 20050.338197 428.017151 321.169276 912.884917 -1.513698 22.608459 43.610406 7.988594 43.227342 89.623360 12.475021\n", + "argmin(L10^10) 0.245854 0.489594 0.051606 7656.645776 20355.374322 727.390771 335.339826 927.099577 12.437089 22.360088 44.437987 10.407362 40.644331 92.165539 15.493826\n", + "Model A 0.058701 0.731059 0.218786 479.867127 68138.580337 10221.507329 0.878730 3152.059162 454.592147 8.853287 60.327248 20.452823 16.534238 123.431015 39.614498\n", + "Model B 0.731329 0.059377 0.511604 38338.240487 505.249618 16131.152604 1764.118964 2.060910 729.831768 60.134177 10.943588 42.329793 125.523234 21.227815 89.111709\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.50 on dataset A and weight 0.50 on dataset B. argmin(...) models were fit to that dataset.\n", + "The mixture dataset had weight 0.60 on dataset A and weight 0.40 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.000019 0.000103 0.103135 1.445346e+04 9.389479e+03 4.813178e+02 652.070827 416.089708 0.970108 32.566106 33.543358 9.169555 65.724820 67.846493 16.004581\n", - "argmin(nTVD) 0.000027 0.000077 0.122024 1.457978e+04 9.296960e+03 4.992725e+02 657.957229 411.778345 1.806797 32.851237 33.297344 8.877317 64.277568 68.784671 14.620929\n", - "argmin(TVD) 0.000046 0.000047 0.153857 1.491067e+04 9.096656e+03 5.650716e+02 673.376719 402.444189 4.873029 33.516252 32.955458 8.697918 66.899668 66.687181 15.806218\n", - "argmin(L10^10) 0.000066 0.000032 0.058901 1.534189e+04 9.342669e+03 9.002678e+02 693.471719 413.908394 20.493147 33.948918 33.010908 11.802510 65.332360 68.314439 17.769593\n", - "Model A 126.554588 60.941755 82.856783 1.833490e+06 1.750514e+06 1.780313e+06 0.878730 3152.059162 1049.968900 467.173129 465.279406 466.029406 835.190192 868.058186 850.583717\n", - "Model B 60.353297 36.518298 46.079898 7.812251e+05 7.029310e+05 7.303883e+05 1764.118964 2.060910 348.353651 393.395690 362.433941 375.034359 769.413642 713.138467 738.464676\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.261164 0.474408 0.091391 10572.145717 14071.423474 420.571207 471.201894 634.267962 -1.860679 27.077488 38.959680 8.587280 53.755140 78.694803 13.186374\n", + "argmin(nTVD) 0.272057 0.464147 0.111901 10640.631764 14000.784948 435.077184 474.393338 630.976212 -1.184701 27.306314 38.733142 8.396607 51.752982 80.636663 12.291792\n", + "argmin(TVD) 0.293808 0.445746 0.154856 10930.017392 13774.770683 520.762005 487.878686 620.443965 2.808205 27.986584 38.230402 8.195175 53.533009 78.948949 12.597676\n", + "argmin(L10^10) 0.315435 0.420561 0.056281 11423.650183 13836.536763 834.807801 510.881935 623.322260 17.442714 28.142159 38.727617 11.162597 54.015160 78.750509 16.638443\n", + "Model A 0.058701 0.731059 0.291888 479.867127 68138.580337 16192.471084 0.878730 3152.059162 732.687654 8.853287 60.327248 25.670594 16.534238 123.431015 50.763009\n", + "Model B 0.731329 0.059377 0.438699 38338.240487 505.249618 11570.277416 1764.118964 2.060910 517.410633 60.134177 10.943588 36.631982 125.523234 21.227815 77.203040\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.40 on dataset A and weight 0.60 on dataset B. argmin(...) models were fit to that dataset.\n", + "The mixture dataset had weight 0.50 on dataset A and weight 0.50 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.000171 0.000010 0.108947 1.886757e+04 5.752944e+03 4.746497e+02 857.768204 246.627478 0.659375 38.293271 27.854670 9.021935 76.723239 55.628956 14.741397\n", - "argmin(nTVD) 0.000241 0.000006 0.129251 1.903001e+04 5.674293e+03 4.941562e+02 865.337660 242.962347 1.568377 38.541429 27.808615 8.754129 75.034583 57.196161 13.549772\n", - "argmin(TVD) 0.000286 0.000005 0.142902 1.893380e+04 5.795962e+03 5.309794e+02 860.854538 248.632108 3.284335 38.486363 28.009825 8.653013 75.854167 56.356211 13.840286\n", - "argmin(L10^10) 0.000332 0.000004 0.058838 1.965420e+04 5.884199e+03 8.633228e+02 894.425086 252.743956 18.771513 39.638916 27.580884 11.777146 78.797379 54.682824 18.139113\n", - "Model A 126.554588 60.941755 77.160462 1.833490e+06 1.750514e+06 1.773474e+06 0.878730 3152.059162 1414.486997 467.173129 465.279406 465.876503 835.190192 868.058186 854.223189\n", - "Model B 60.353297 36.518298 43.777898 7.812251e+05 7.029310e+05 7.236160e+05 1764.118964 2.060910 214.038392 393.395690 362.433941 371.769559 769.413642 713.138467 732.147248\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.336724 0.399223 0.103135 14453.459856 9389.478776 481.317795 652.070827 416.089708 0.970108 32.566106 33.543358 9.169555 65.724820 67.846493 16.004581\n", + "argmin(nTVD) 0.348744 0.387786 0.122024 14579.777698 9296.960102 499.272540 657.957229 411.778345 1.806797 32.851237 33.297344 8.877317 64.277568 68.784671 14.620929\n", + "argmin(TVD) 0.368227 0.369535 0.153857 14910.668610 9096.655991 565.071618 673.376719 402.444189 4.873029 33.516252 32.955458 8.697918 66.899668 66.687181 15.806218\n", + "argmin(L10^10) 0.381766 0.355110 0.058901 15341.892516 9342.669387 900.267841 693.471719 413.908394 20.493147 33.948918 33.010908 11.802510 65.332360 68.314439 17.769593\n", + "Model A 0.058701 0.731059 0.364902 479.867127 68138.580337 23004.788336 0.878730 3152.059162 1049.968900 8.853287 60.327248 31.004211 16.534238 123.431015 61.974967\n", + "Model B 0.731329 0.059377 0.366102 38338.240487 505.249618 7940.470086 1764.118964 2.060910 348.353651 60.134177 10.943588 31.177551 125.523234 21.227815 65.981165\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.30 on dataset A and weight 0.70 on dataset B. argmin(...) models were fit to that dataset.\n", + "The mixture dataset had weight 0.40 on dataset A and weight 0.60 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.001147 4.108738e-07 0.102495 2.357782e+04 3.236178e+03 4.624121e+02 1077.265150 129.346379 0.089104 44.115671 22.393879 9.130840 88.726527 44.168714 15.734786\n", - "argmin(nTVD) 0.001229 3.566033e-07 0.108070 2.351664e+04 3.269966e+03 4.686338e+02 1074.414076 130.920879 0.379032 44.009629 22.560514 9.030624 86.514322 46.236778 14.452820\n", - "argmin(TVD) 0.001369 2.912033e-07 0.127903 2.331235e+04 3.401875e+03 5.032436e+02 1064.894275 137.067856 1.991849 43.854686 22.950017 8.905500 88.963584 43.867608 15.718299\n", - "argmin(L10^10) 0.001462 2.406509e-07 0.055216 2.400751e+04 3.469828e+03 7.530523e+02 1097.288687 140.234437 13.632915 44.771370 22.378776 11.375579 89.778594 43.822841 17.980791\n", - "Model A 126.554588 6.094176e+01 72.244649 1.833490e+06 1.750514e+06 1.766168e+06 0.878730 3152.059162 1806.872182 467.173129 465.279406 465.715841 835.190192 868.058186 857.751642\n", - "Model B 60.353297 3.651830e+01 41.695823 7.812251e+05 7.029310e+05 7.171513e+05 1764.118964 2.060910 115.494432 393.395690 362.433941 368.849937 769.413642 713.138467 726.102011\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.419938 0.316692 0.108947 18867.574117 5752.944088 474.649704 857.768204 246.627478 0.659375 38.293271 27.854670 9.021935 76.723239 55.628956 14.741397\n", + "argmin(nTVD) 0.434744 0.302557 0.129251 19030.009064 5674.293087 494.156212 865.337660 242.962347 1.568377 38.541429 27.808615 8.754129 75.034583 57.196161 13.549772\n", + "argmin(TVD) 0.442236 0.295717 0.142902 18933.804562 5795.961973 530.979411 860.854538 248.632108 3.284335 38.486363 28.009825 8.653013 75.854167 56.356211 13.840286\n", + "argmin(L10^10) 0.448831 0.288644 0.058838 19654.203801 5884.199204 863.322840 894.425086 252.743956 18.771513 39.638916 27.580884 11.777146 78.797379 54.682824 18.139113\n", + "Model A 0.058701 0.731059 0.438333 479.867127 68138.580337 30831.323781 0.878730 3152.059162 1414.486997 8.853287 60.327248 36.680717 16.534238 123.431015 74.100404\n", + "Model B 0.731329 0.059377 0.293411 38338.240487 505.249618 5056.599171 1764.118964 2.060910 214.038392 60.134177 10.943588 25.803049 125.523234 21.227815 54.630480\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.25 on dataset A and weight 0.75 on dataset B. argmin(...) models were fit to that dataset.\n", + "The mixture dataset had weight 0.30 on dataset A and weight 0.70 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.002629 5.180830e-08 0.093984 2.599593e+04 2.326897e+03 4.509724e+02 1189.949238 86.973972 -0.443988 46.982771 19.751013 9.242001 94.654726 38.596659 16.399717\n", - "argmin(nTVD) 0.002805 4.450868e-08 0.104821 2.585265e+04 2.390805e+03 4.646910e+02 1183.272330 89.952041 0.195299 46.761698 20.078455 9.142028 92.172305 40.877737 14.922437\n", - "argmin(TVD) 0.002720 4.938478e-08 0.110849 2.561716e+04 2.481830e+03 4.760788e+02 1172.298145 94.193840 0.725971 46.565040 20.348602 9.081771 95.040781 38.108054 16.559203\n", - "argmin(L10^10) 0.002901 3.913473e-08 0.052445 2.626039e+04 2.534471e+03 6.706137e+02 1202.272988 96.646897 9.791280 47.301903 19.848731 11.065213 95.153098 38.625469 18.135336\n", - "Model A 126.554588 6.094176e+01 70.105753 1.833490e+06 1.750514e+06 1.763134e+06 0.878730 3152.059162 2012.002268 467.173129 465.279406 465.653406 835.190192 868.058186 859.393352\n", - "Model B 60.353297 3.651830e+01 40.782671 7.812251e+05 7.029310e+05 7.143812e+05 1764.118964 2.060910 78.510734 393.395690 362.433941 367.455068 769.413642 713.138467 723.275767\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.508091 0.229811 0.102495 23577.816968 3236.178060 462.412103 1077.265150 129.346379 0.089104 44.115671 22.393879 9.130840 88.726527 44.168714 15.734786\n", + "argmin(nTVD) 0.511618 0.226578 0.108070 23516.635010 3269.965680 468.633753 1074.414076 130.920879 0.379032 44.009629 22.560514 9.030624 86.514322 46.236778 14.452820\n", + "argmin(TVD) 0.517180 0.222034 0.127903 23312.347102 3401.875282 503.243620 1064.894275 137.067856 1.991849 43.854686 22.950017 8.905500 88.963584 43.867608 15.718299\n", + "argmin(L10^10) 0.520573 0.217841 0.055216 24007.507335 3469.827760 753.052320 1097.288687 140.234437 13.632915 44.771370 22.378776 11.375579 89.778594 43.822841 17.980791\n", + "Model A 0.058701 0.731059 0.511825 479.867127 68138.580337 39256.191000 0.878730 3152.059162 1806.872182 8.853287 60.327248 42.327993 16.534238 123.431015 86.193286\n", + "Model B 0.731329 0.059377 0.221235 38338.240487 505.249618 2940.770627 1764.118964 2.060910 115.494432 60.134177 10.943588 20.712529 125.523234 21.227815 43.896415\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.20 on dataset A and weight 0.80 on dataset B. argmin(...) models were fit to that dataset.\n", + "The mixture dataset had weight 0.20 on dataset A and weight 0.80 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.005422 0.146681 0.082374 2.846521e+04 1.616355e+03 4.361275e+02 1305.017377 53.862765 -1.135755 49.812296 17.179430 9.393956 100.692317 32.977846 16.957784\n", - "argmin(nTVD) 0.005479 0.146755 0.089686 2.830506e+04 1.668395e+03 4.469013e+02 1297.554226 56.287818 -0.633697 49.607226 17.440040 9.315584 98.327986 35.191299 15.409819\n", - "argmin(TVD) 0.005141 0.150556 0.094477 2.808630e+04 1.726544e+03 4.514475e+02 1287.359948 58.997535 -0.421848 49.446819 17.645242 9.251400 100.983947 32.606542 16.987316\n", - "argmin(L10^10) 0.005477 0.146093 0.049514 2.853509e+04 1.795086e+03 5.921762e+02 1308.273646 62.191601 6.136102 49.858494 17.422069 10.711664 100.412987 33.630635 18.473805\n", - "Model A 126.554588 60.941755 67.948218 1.833490e+06 1.750514e+06 1.760483e+06 0.878730 3152.059162 2228.883735 467.173129 465.279406 465.562802 835.190192 868.058186 861.097728\n", - "Model B 60.353297 36.518298 39.808559 7.812251e+05 7.029310e+05 7.118039e+05 1764.118964 2.060910 48.573929 393.395690 362.433941 366.064099 769.413642 713.138467 720.476766\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.593498 0.146681 0.082374 28465.212070 1616.355278 436.127541 1305.017377 53.862765 -1.135755 49.812296 17.179430 9.393956 100.692317 32.977846 16.957784\n", + "argmin(nTVD) 0.594112 0.146755 0.089686 28305.058345 1668.395129 446.901333 1297.554226 56.287818 -0.633697 49.607226 17.440040 9.315584 98.327986 35.191299 15.409819\n", + "argmin(TVD) 0.590347 0.150556 0.094477 28086.296649 1726.543671 451.447459 1287.359948 58.997535 -0.421848 49.446819 17.645242 9.251400 100.983947 32.606542 16.987316\n", + "argmin(L10^10) 0.594091 0.146093 0.049514 28535.089216 1795.085966 592.176231 1308.273646 62.191601 6.136102 49.858494 17.422069 10.711664 100.412987 33.630635 18.473805\n", + "Model A 0.058701 0.731059 0.584358 479.867127 68138.580337 48317.163314 0.878730 3152.059162 2228.883735 8.853287 60.327248 48.226359 16.534238 123.431015 98.409177\n", + "Model B 0.731329 0.059377 0.151299 38338.240487 505.249618 1503.926489 1764.118964 2.060910 48.573929 60.134177 10.943588 16.043020 125.523234 21.227815 34.029001\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.10 on dataset A and weight 0.90 on dataset B. argmin(...) models were fit to that dataset.\n", + "The mixture dataset had weight 0.10 on dataset A and weight 0.90 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.017872 0.076564 0.056305 3.341963e+04 7.478670e+02 4.293123e+02 1535.892671 13.391279 -1.453343 55.170456 12.812219 9.801565 112.532181 23.162979 18.171220\n", - "argmin(nTVD) 0.017808 0.078943 0.062971 3.326938e+04 7.765805e+02 4.407960e+02 1528.891367 14.729328 -0.918204 54.977003 13.018956 9.790191 109.947903 25.628907 16.632490\n", - "argmin(TVD) 0.017182 0.079781 0.062700 3.328002e+04 7.740075e+02 4.402553e+02 1529.387062 14.609425 -0.943400 54.969167 12.971492 9.714384 112.902957 22.935576 18.372205\n", - "argmin(L10^10) 0.015123 0.085642 0.047752 3.305360e+04 8.633935e+02 4.973585e+02 1518.836111 18.774804 1.717605 54.851380 13.490325 10.493062 110.463381 25.261768 19.602431\n", - "Model A 126.554588 60.941755 64.087331 1.833490e+06 1.750514e+06 1.754870e+06 0.878730 3152.059162 2682.519826 467.173129 465.279406 465.431406 835.190192 868.058186 864.637888\n", - "Model B 60.353297 36.518298 38.024661 7.812251e+05 7.029310e+05 7.068148e+05 1764.118964 2.060910 10.911265 393.395690 362.433941 363.716712 769.413642 713.138467 715.982743\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.668679 0.076564 0.056305 33419.625902 747.866994 429.312337 1535.892671 13.391279 -1.453343 55.170456 12.812219 9.801565 112.532181 23.162979 18.171220\n", + "argmin(nTVD) 0.668440 0.078943 0.062971 33269.383067 776.580541 440.796012 1528.891367 14.729328 -0.918204 54.977003 13.018956 9.790191 109.947903 25.628907 16.632490\n", + "argmin(TVD) 0.666052 0.079781 0.062700 33280.020316 774.007514 440.255326 1529.387062 14.609425 -0.943400 54.969167 12.971492 9.714384 112.902957 22.935576 18.372205\n", + "argmin(L10^10) 0.657603 0.085642 0.047752 33053.604675 863.393466 497.358545 1518.836111 18.774804 1.717605 54.851380 13.490325 10.493062 110.463381 25.261768 19.602431\n", + "Model A 0.058701 0.731059 0.657304 479.867127 68138.580337 58057.143232 0.878730 3152.059162 2682.519826 8.853287 60.327248 54.174145 16.534238 123.431015 110.783513\n", + "Model B 0.731329 0.059377 0.084357 38338.240487 505.249618 695.274792 1764.118964 2.060910 10.911265 60.134177 10.943588 12.104857 125.523234 21.227815 25.858820\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.05 on dataset A and weight 0.95 on dataset B. argmin(...) models were fit to that dataset.\n", + "The mixture dataset had weight 0.05 on dataset A and weight 0.95 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.029174 0.057311 0.053609 3.590183e+04 5.511987e+02 4.447755e+02 1651.563288 4.226552 -0.732762 57.715396 11.268873 10.094609 118.373585 19.866242 18.893372\n", - "argmin(nTVD) 0.029059 0.060941 0.057731 3.576865e+04 5.659644e+02 4.525418e+02 1645.356888 4.914632 -0.370850 57.528568 11.375743 10.084489 115.798353 21.208465 17.470876\n", - "argmin(TVD) 0.029017 0.059799 0.054853 3.561255e+04 5.763195e+02 4.559021e+02 1638.082959 5.397179 -0.214260 57.334562 11.389517 10.013522 116.983272 20.113419 18.207249\n", - "argmin(L10^10) 0.023781 0.059954 0.049035 3.540689e+04 6.282318e+02 4.938562e+02 1628.498898 7.816287 1.554395 57.298475 12.048697 10.697845 115.475494 21.996857 20.429503\n", - "Model A 126.554588 60.941755 62.314294 1.833490e+06 1.750514e+06 1.752496e+06 0.878730 3152.059162 2920.109931 467.173129 465.279406 465.367885 835.190192 868.058186 866.340832\n", - "Model B 60.353297 36.518298 37.166768 7.812251e+05 7.029310e+05 7.046459e+05 1764.118964 2.060910 2.774278 393.395690 362.433941 362.896207 769.413642 713.138467 714.317761\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.702262 0.057311 0.053609 35901.832160 551.198688 444.775466 1651.563288 4.226552 -0.732762 57.715396 11.268873 10.094609 118.373585 19.866242 18.893372\n", + "argmin(nTVD) 0.701985 0.060941 0.057731 35768.647398 565.964380 452.541828 1645.356888 4.914632 -0.370850 57.528568 11.375743 10.084489 115.798353 21.208465 17.470876\n", + "argmin(TVD) 0.701885 0.059799 0.054853 35612.554235 576.319495 455.902128 1638.082959 5.397179 -0.214260 57.334562 11.389517 10.013522 116.983272 20.113419 18.207249\n", + "argmin(L10^10) 0.688055 0.059954 0.049035 35406.887349 628.231765 493.856172 1628.498898 7.816287 1.554395 57.298475 12.048697 10.697845 115.475494 21.996857 20.429503\n", + "Model A 0.058701 0.731059 0.694001 479.867127 68138.580337 63158.419146 0.878730 3152.059162 2920.109931 8.853287 60.327248 57.151030 16.534238 123.431015 116.958974\n", + "Model B 0.731329 0.059377 0.060244 38338.240487 505.249618 520.566267 1764.118964 2.060910 2.774278 60.134177 10.943588 10.952796 125.523234 21.227815 22.906553\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.01 on dataset A and weight 0.99 on dataset B. argmin(...) models were fit to that dataset.\n", + "The mixture dataset had weight 0.01 on dataset A and weight 0.99 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.041740 0.055933 0.054759 3.790410e+04 4.925380e+02 4.717126e+02 1744.868852 1.492969 0.522508 59.687372 10.723672 10.424930 122.997044 19.884425 19.506542\n", - "argmin(nTVD) 0.041821 0.062239 0.061105 3.783496e+04 5.065915e+02 4.852535e+02 1741.647075 2.147859 1.153510 59.558941 10.707916 10.409266 120.845389 18.528914 17.960292\n", - "argmin(TVD) 0.044013 0.057337 0.056173 3.802370e+04 5.022734e+02 4.824273e+02 1750.442090 1.946636 1.021811 59.667143 10.617281 10.308995 122.850117 19.403571 18.992379\n", - "argmin(L10^10) 0.035787 0.051705 0.050511 3.740704e+04 5.414413e+02 5.156873e+02 1721.705991 3.771859 2.571722 59.240605 11.358666 10.993873 119.617487 21.591750 21.098547\n", - "Model A 126.554588 60.941755 60.998233 1.833490e+06 1.750514e+06 1.750807e+06 0.878730 3152.059162 3115.145026 467.173129 465.279406 465.300180 835.190192 868.058186 867.716656\n", - "Model B 60.353297 36.518298 36.532100 7.812251e+05 7.029310e+05 7.030719e+05 1764.118964 2.060910 1.307916 393.395690 362.433941 362.446148 769.413642 713.138467 713.134395\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.727873 0.055933 0.054759 37904.100877 492.538014 471.712639 1744.868852 1.492969 0.522508 59.687372 10.723672 10.424930 122.997044 19.884425 19.506542\n", + "argmin(nTVD) 0.728014 0.062239 0.061105 37834.963909 506.591477 485.253476 1741.647075 2.147859 1.153510 59.558941 10.707916 10.409266 120.845389 18.528914 17.960292\n", + "argmin(TVD) 0.731742 0.057337 0.056173 38023.698465 502.273367 482.427318 1750.442090 1.946636 1.021811 59.667143 10.617281 10.308995 122.850117 19.403571 18.992379\n", + "argmin(L10^10) 0.716759 0.051705 0.050511 37407.042940 541.441313 515.687253 1721.705991 3.771859 2.571722 59.240605 11.358666 10.993873 119.617487 21.591750 21.098547\n", + "Model A 0.058701 0.731059 0.723362 479.867127 68138.580337 67346.000215 0.878730 3152.059162 3115.145026 8.853287 60.327248 59.503700 16.534238 123.431015 121.896791\n", + "Model B 0.731329 0.059377 0.058335 38338.240487 505.249618 489.082140 1764.118964 2.060910 1.307916 60.134177 10.943588 10.690820 125.523234 21.227815 21.075044\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.00 on dataset A and weight 1.00 on dataset B. argmin(...) models were fit to that dataset.\n", + "The mixture dataset had weight 0.00 on dataset A and weight 1.00 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.045918 0.056020 0.056020 3.832014e+04 4.904003e+02 4.904003e+02 1764.256112 1.393350 1.393350 60.127224 10.679844 10.679844 123.975368 19.966883 19.966883\n", - "argmin(nTVD) 0.046466 0.063619 0.063619 3.818218e+04 5.027886e+02 5.027886e+02 1757.827150 1.970647 1.970647 59.933777 10.644054 10.644054 122.274428 18.476274 18.476274\n", - "argmin(TVD) 0.049318 0.057454 0.057454 3.851677e+04 5.008840e+02 5.008840e+02 1773.419021 1.881892 1.881892 60.222429 10.548472 10.548472 123.836231 19.332965 19.332965\n", - "argmin(L10^10) 0.040487 0.051594 0.051594 3.801835e+04 5.285188e+02 5.285188e+02 1750.192803 3.169672 3.169672 59.861629 11.253837 11.253837 121.219802 21.526994 21.526994\n", - "Model A 126.554588 60.941755 60.941755 1.833490e+06 1.750514e+06 1.750514e+06 0.878730 3152.059162 3152.059162 467.173129 465.279406 465.279406 835.190192 868.058186 868.058186\n", - "Model B 60.353297 36.518298 36.518298 7.812251e+05 7.029310e+05 7.029310e+05 1764.118964 2.060910 2.060910 393.395690 362.433941 362.433941 769.413642 713.138467 713.138467\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.734850 0.056020 0.056020 38320.137210 490.400273 490.400273 1764.256112 1.393350 1.393350 60.127224 10.679844 10.679844 123.975368 19.966883 19.966883\n", + "argmin(nTVD) 0.735721 0.063619 0.063619 38182.176420 502.788631 502.788631 1757.827150 1.970647 1.970647 59.933777 10.644054 10.644054 122.274428 18.476274 18.476274\n", + "argmin(TVD) 0.740118 0.057454 0.057454 38516.766492 500.884024 500.884024 1773.419021 1.881892 1.881892 60.222429 10.548472 10.548472 123.836231 19.332965 19.332965\n", + "argmin(L10^10) 0.725658 0.051594 0.051594 38018.348946 528.518821 528.518821 1750.192803 3.169672 3.169672 59.861629 11.253837 11.253837 121.219802 21.526994 21.526994\n", + "Model A 0.058701 0.731059 0.731059 479.867127 68138.580337 68138.580337 0.878730 3152.059162 3152.059162 8.853287 60.327248 60.327248 16.534238 123.431015 123.431015\n", + "Model B 0.731329 0.059377 0.059377 38338.240487 505.249618 505.249618 1764.118964 2.060910 2.060910 60.134177 10.943588 10.943588 125.523234 21.227815 21.227815\n", "\n" ] } ], "source": [ + "dflist = []\n", + "for dfl in dflists:\n", + " dfa, df, dfb = dfl\n", + " temp = dfa.join(df, lsuffix='(A)', rsuffix='(mix)')\n", + " dfb = dfb.copy()\n", + " dfb.columns = dfb.columns.map(lambda x: str(x) + '(B)')\n", + " temp = temp.join(dfb)\n", + " temp = temp.sort_index(axis=1)\n", + " for colname in ['L10(A)', 'L10(B)', 'L10(mix)']:\n", + " temp[colname] = temp[colname]**0.1\n", + " dflist.append(temp)\n", + "\n", "pd.set_option('display.max_columns', 100)\n", "pd.set_option('display.width', 300)\n", "for i,df in enumerate(dflist):\n", " print( '\\n' + 250*'-')\n", " p = mixture_weights[i]\n", - " print(f'\\nThe mixture dataset had weight {p:1.2f} on dataset A and weight {1-p:1.2f} on dataset B. argmin(...) models were fit to that dataset.\\n')\n", + " print(f'\\nThe mixture dataset had weight {p:1.2f} on dataset A and weight {1-p:1.2f} on dataset B.\\n')\n", " print(df)\n", " print()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 3290f81a803c20174c5efec1b7eaf4eb815dc472 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Mon, 28 Apr 2025 15:17:29 -0700 Subject: [PATCH 41/71] plots --- .../objectives-handling-mixtures.ipynb | 127 ++++++++++++++++++ 1 file changed, 127 insertions(+) diff --git a/wip_notebook_sharing/objectives-handling-mixtures.ipynb b/wip_notebook_sharing/objectives-handling-mixtures.ipynb index 9e82574b8..a0c5c24d2 100644 --- a/wip_notebook_sharing/objectives-handling-mixtures.ipynb +++ b/wip_notebook_sharing/objectives-handling-mixtures.ipynb @@ -541,6 +541,133 @@ " print(df)\n", " print()" ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "fig, outer_axs = plt.subplots(4, 4, figsize=(12,12))\n", + "for i,metricname in enumerate(['LogL', 'nTVD', 'TVD', 'L10']):\n", + " axs = outer_axs[i,:]\n", + " for modelname, ax in zip(['argmin(LogL)', 'argmin(nTVD)', 'argmin(TVD)', 'argmin(L10^10)'], axs):\n", + " rows = []\n", + " datanames = ['(A)', '(mix)', '(B)'] \n", + " for df in dflist:\n", + " row = [ df[metricname + dataname][modelname] for dataname in datanames ]\n", + " rows.append(row)\n", + " y = np.array(rows)\n", + " x = mixture_weights.copy()\n", + " ax.plot(x,y)\n", + " if metricname == 'LogL':\n", + " ax.set_yscale('log')\n", + " ax.legend(datanames)\n", + " ax.set_title(modelname)\n", + " if modelname == 'argmin(LogL)':\n", + " ax.set_ylabel(metricname)\n", + "fig.supxlabel('proportion of mixed dataset taken from dataset A')\n", + "fig.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "datanames = [ '(mix)', '(A)', '(B)'] \n", + "modelnames = ['argmin(LogL)', 'argmin(nTVD)', 'argmin(TVD)', 'argmin(L10^10)']\n", + "lossnames = ['LogL', 'nTVD', 'TVD', 'L10']\n", + "losscolors = ['b', 'r', 'y', 'k']\n", + "fig, outer_axs = plt.subplots(len(lossnames), len(datanames), figsize=(12,12))\n", + "for i,metricname in enumerate(lossnames):\n", + " axs = outer_axs[i,:]\n", + " for dataname, ax in zip(datanames, axs):\n", + " rows = []\n", + " for df in dflist:\n", + " row = [ df[metricname + dataname][modelname] for modelname in modelnames ]\n", + " rows.append(row)\n", + " y = np.array(rows)\n", + " x = mixture_weights.copy()\n", + " for i,yi in enumerate(y.T):\n", + " ax.plot(x,yi,losscolors[i])\n", + " if metricname == 'LogL':\n", + " pass\n", + " #ax.set_yscale('log')\n", + " ax.legend(lossnames)\n", + " ax.set_title(dataname)\n", + " if dataname == '(mix)':\n", + " ax.set_ylabel(metricname)\n", + "fig.supxlabel('proportion of mixed dataset taken from dataset A')\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# datanames = [ '(mix)', '(A)', '(B)'] \n", + "modelnames = ['argmin(LogL)', 'argmin(nTVD)', 'argmin(TVD)', 'argmin(L10^10)']\n", + "lossnames = ['LogL', 'nTVD', 'TVD', 'L10']\n", + "losscolors = ['b', 'r', 'y', 'k', 'd','.']\n", + "dataname = '(mix)'\n", + "fig, outer_axs = plt.subplots(1, len(lossnames), figsize=(15,4))\n", + "for metricname,ax in zip(lossnames, outer_axs):\n", + " rows = []\n", + " for df in dflist:\n", + " row = [ df[metricname + dataname][modelname] for modelname in modelnames ]\n", + " rows.append(row)\n", + " y = np.array(rows)\n", + " x = mixture_weights.copy()\n", + " for i,yi in enumerate(y.T):\n", + " ax.plot(x,yi,losscolors[i])\n", + " ax.legend(modelnames)\n", + " # ax.set_title(dataname)\n", + " ax.set_title(metricname)\n", + " # if dataname == '(mix)':\n", + " # ax.set_ylabel(metricname)\n", + "fig.suptitle('Loss functions L(fit(data(p), L), data(p)), where...\\ndata(p) is the mixed dataset with weight p on dataset A, and\\nfit(ds, L) is the model from fitting loss L to data d')\n", + "fig.supxlabel('proportion of mixed dataset taken from dataset A')\n", + "fig.tight_layout()" + ] } ], "metadata": { From 661f5d31479cf33a166a520376c30287d917a024 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Tue, 13 May 2025 12:36:34 -0700 Subject: [PATCH 42/71] dataset and noisy model generation --- pygsti/data/datasetconstruction.py | 12 ++++++----- pygsti/models/explicitmodel.py | 34 +++++++++++++++++++++--------- 2 files changed, 31 insertions(+), 15 deletions(-) diff --git a/pygsti/data/datasetconstruction.py b/pygsti/data/datasetconstruction.py index 82299e2b3..878c23a08 100644 --- a/pygsti/data/datasetconstruction.py +++ b/pygsti/data/datasetconstruction.py @@ -209,17 +209,19 @@ def simulate_data(model_or_dataset, circuit_list, num_samples, return dataset -def mix_datasets(dsa, dsb, p): +def mix_datasets(dsa, dsb, p, integral=True): dsc = dsa.copy_nonstatic() # arr = _np.array(dsc.repData).ravel() # print((arr, arr.size)) # print((dsb.repData, dsb.repData.size)) for i, (_, dsrow) in enumerate(dsb.items()): interpolated = p*dsc.repData[i] + (1-p)*dsrow.reps - total = int(_np.ceil((_np.sum(interpolated)))) - j = _np.argmin(interpolated) - interpolated[j] = _np.ceil(interpolated[j]) - interpolated[1 - j] = total - interpolated[j] + if integral: + assert interpolated.size == 2 + total = int(_np.ceil((_np.sum(interpolated)))) + j = _np.argmin(interpolated) + interpolated[j] = _np.ceil(interpolated[j]) + interpolated[1 - j] = total - interpolated[j] dsc.repData[i][:] = interpolated dsc.done_adding_data() # arr = np.array(dsc.repData).ravel() diff --git a/pygsti/models/explicitmodel.py b/pygsti/models/explicitmodel.py index 75dc6e906..50e218d3d 100644 --- a/pygsti/models/explicitmodel.py +++ b/pygsti/models/explicitmodel.py @@ -1101,7 +1101,7 @@ def rotate(self, rotate=None, max_rotate=None, seed=None): newModel._clean_paramvec() # rotate may leave dirty members return newModel - def randomize_with_unitary(self, scale, seed=None, rand_state=None): + def randomize_with_unitary(self, scale, seed=None, rand_state=None, transform_spam=False): """ Create a new model with random unitary perturbations. @@ -1142,16 +1142,30 @@ def randomize_with_unitary(self, scale, seed=None, rand_state=None): mdl_randomized = self.copy() + def rand_unitary_as_superop(): + rand_mat = rndm.randn(unitary_dim, unitary_dim) + 1j * rndm.randn(unitary_dim, unitary_dim) + rand_herm = rand_mat.T.conj() + rand_mat + rand_herm /= _scipy.linalg.norm(rand_herm) + rand_herm *= scale * _np.sqrt(unitary_dim) + rand_unitary = _scipy.linalg.expm(-1j * rand_herm) + rand_op = _ot.unitary_to_superop(rand_unitary, self.basis) + return rand_op + for opLabel, gate in self.operations.items(): - randMat = scale * (rndm.randn(unitary_dim, unitary_dim) - + 1j * rndm.randn(unitary_dim, unitary_dim)) - randMat = _np.transpose(_np.conjugate(randMat)) + randMat - # make randMat Hermetian: (A_dag + A)^dag = (A_dag + A) - randUnitary = _scipy.linalg.expm(-1j * randMat) - - randOp = _ot.unitary_to_superop(randUnitary, self.basis) - - mdl_randomized.operations[opLabel] = _op.FullArbitraryOp(_np.dot(randOp, gate)) + rand_op = rand_unitary_as_superop() + mdl_randomized.operations[opLabel] = _op.FullArbitraryOp(rand_op @ gate) + + if transform_spam: + from pygsti.modelmembers.states import FullState + for preplbl, rho in self.preps.items(): + rand_op = rand_unitary_as_superop() + mdl_randomized.preps[preplbl] = FullState(rand_op @ rho) + from pygsti.modelmembers.povms import create_from_dmvecs + for povmlbl, M in self.povms.items(): + rand_op = rand_unitary_as_superop() + dmvecs = {elbl: rand_op @ e.to_dense() for elbl, e in M.items()} + mdl_randomized.povms[povmlbl] = create_from_dmvecs(dmvecs, 'full') + #Note: this function does NOT randomize instruments From f2268225e4ddd81249297fb4e0dbe136c38d49dd Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Tue, 13 May 2025 12:37:42 -0700 Subject: [PATCH 43/71] gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 3b0f1ab15..956606abc 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ +wip_notebook_sharing/case0_reports_* *~ *.tmp *.bak From b789eeb3519e0179385eb1c3ace123da35d150e0 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Tue, 13 May 2025 12:39:38 -0700 Subject: [PATCH 44/71] notebook --- .../objectives-handling-mixtures.ipynb | 981 ++++++++++++++---- 1 file changed, 752 insertions(+), 229 deletions(-) diff --git a/wip_notebook_sharing/objectives-handling-mixtures.ipynb b/wip_notebook_sharing/objectives-handling-mixtures.ipynb index a0c5c24d2..ccb8ba32c 100644 --- a/wip_notebook_sharing/objectives-handling-mixtures.ipynb +++ b/wip_notebook_sharing/objectives-handling-mixtures.ipynb @@ -2,9 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -13,16 +22,20 @@ "from pygsti.modelpacks import smq1Q_XYI, smq2Q_XYCNOT\n", "import numpy as np\n", "from pprint import pprint\n", - "from experiment_helpers import make_depolarized_dataset, run_gst, corrupt_dataset, make_tweaked_dataset\n", + "from experiment_helpers import make_depolarized_dataset, run_gst, corrupt_dataset, make_tweaked_dataset, make_tweaked_dataset_pairs\n", "from scipy import linalg as la\n", "import pandas as pd\n", "from pygsti.data.datasetconstruction import mix_datasets\n", - "from pygsti.report.plothelpers import rated_n_sigma" + "from pygsti.report.plothelpers import rated_n_sigma\n", + "from pprint import pprint\n", + "from pygsti.tools.optools import fidelity, povm_fidelity, entanglement_fidelity\n", + "\n", + "from matplotlib import pyplot as plt" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -30,11 +43,28 @@ "target = mp.target_model()\n", "fids = (mp.prep_fiducials(), mp.meas_fiducials())\n", "germs = mp.germs()\n", - "maxmaxlen = 64\n", - "dsa, m_dga = make_tweaked_dataset(mp, depol_level=0.001, rand_unitary_scale=0.001, max_max_len=maxmaxlen)\n", - "dsb, m_dgb = make_tweaked_dataset(mp, depol_level=0.010, rand_unitary_scale=0.020, max_max_len=maxmaxlen)\n", - "mixture_weights = np.array([1, 0.99, 0.95, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.05, 0.01, 0.0])\n", + "maxmaxlen = 32\n", + "sps = 10_000\n", + "dsa, m_dga, dsb, m_dgb = make_tweaked_dataset_pairs(\n", + " mp, depol_level=0.001, rand_unitary_scale=0.03, max_max_len=maxmaxlen,\n", + " sample_error='multinomial', seed=5000009, shots_per_circuit=sps, gaugeopt=False\n", + ")\n", + "\n", + "import pygsti.algorithms\n", + "\n", + "mods = []\n", + "for model in [m_dga, m_dgb]:\n", + " model = model.copy()\n", + " model.convert_members_inplace('full')\n", + " model.default_gauge_group = 'unitary'\n", + " model = pygsti.algorithms.gaugeopt.gaugeopt_to_target(model, target)\n", + " mods.append(model)\n", + "\n", + "m_dga_gopped, m_dgb_gopped = mods\n", + "\n", + "mixture_weights = np.array([1, 0.95, 0.9, 0.85, 0.8, 0.7, 0.6, 0.575, 0.55, 0.525, 0.51, 0.5, 0.49, 0.475, 0.45, 0.425, 0.4, 0.3, 0.2, 0.15, 0.1, 0.05, 0.0])\n", "num_mixtures = mixture_weights.size\n", + "integer_counts_in_mixed = False # older runs effectively had this == True\n", "\n", "fit_mode = 'CPTPLND'\n", "Lpnorm_spec = ('Lp^p', 10)\n", @@ -46,13 +76,31 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "--- Circuit Creation ---\n", + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/objectivefns/objectivefns.py:4062: UserWarning: This derivative is discontinuous and does not return a full subgradient.\n", + " _warnings.warn('This derivative is discontinuous and does not return a full subgradient.')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", "--- Circuit Creation ---\n", "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" ] @@ -61,6 +109,8 @@ "name": "stderr", "output_type": "stream", "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", "\n", "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" ] @@ -70,31 +120,65 @@ "output_type": "stream", "text": [ "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n" + "\n", + "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/objectivefns/objectivefns.py:4055: UserWarning: This derivative is discontinuous and does not return a full subgradient.\n", - " _warnings.warn('This derivative is discontinuous and does not return a full subgradient.')\n" + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", "--- Circuit Creation ---\n", - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", "--- Circuit Creation ---\n", - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", "--- Circuit Creation ---\n", @@ -105,8 +189,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/objectivefns/objectivefns.py:4557: RuntimeWarning: divide by zero encountered in divide\n", - " p5over_lsvec = 0.5/lsvec\n" + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" ] }, { @@ -116,28 +204,79 @@ "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", - "\n", "--- Circuit Creation ---\n", - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", "--- Circuit Creation ---\n", - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", - "\n" + "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", "\n", "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" ] @@ -146,11 +285,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--- Circuit Creation ---\n", "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", - "\n", "--- Circuit Creation ---\n", "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" ] @@ -159,6 +296,10 @@ "name": "stderr", "output_type": "stream", "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", "\n", "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" ] @@ -170,7 +311,6 @@ "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", - "\n", "--- Circuit Creation ---\n", "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" ] @@ -179,6 +319,8 @@ "name": "stderr", "output_type": "stream", "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", "\n", "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" ] @@ -190,13 +332,16 @@ "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", - "\n" + "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", "\n", "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" ] @@ -205,50 +350,165 @@ "name": "stdout", "output_type": "stream", "text": [ + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", + "\n", "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", "\n", "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", "\n", "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", - "\n" + "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/optimize/customsolve.py:94: LinAlgWarning: Ill-conditioned matrix (rcond=3.1334e-17): result may not be accurate.\n", - " x[:] = _scipy.linalg.solve(a, b, assume_a='pos')\n", - "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/optimize/customsolve.py:94: LinAlgWarning: Ill-conditioned matrix (rcond=6.26613e-17): result may not be accurate.\n", - " x[:] = _scipy.linalg.solve(a, b, assume_a='pos')\n", - "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/optimize/customsolve.py:94: LinAlgWarning: Ill-conditioned matrix (rcond=4.10509e-18): result may not be accurate.\n", - " x[:] = _scipy.linalg.solve(a, b, assume_a='pos')\n", - "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/optimize/customsolve.py:94: LinAlgWarning: Ill-conditioned matrix (rcond=8.18734e-18): result may not be accurate.\n", - " x[:] = _scipy.linalg.solve(a, b, assume_a='pos')\n" + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", "\n", "--- Circuit Creation ---\n", "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", "\n", "--- Circuit Creation ---\n", "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", "\n", "\n", + "--- Circuit Creation ---\n", + "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", + "\n", "\n" ] } @@ -256,14 +516,14 @@ "source": [ "reslist = []\n", "for i,p in enumerate(mixture_weights):\n", - " ds = mix_datasets(dsa, dsb, p)\n", - " results = run_gst(ds, fids, germs, target, ['logl', 'normalized tvd', 'tvd', Lpnorm_spec], verbosity=verb, mode=fit_mode)\n", + " ds = mix_datasets(dsa, dsb, p, integral=integer_counts_in_mixed)\n", + " results = run_gst(ds, fids, germs, target, ['logl', 'normalized tvd', Lpnorm_spec], verbosity=verb, mode=fit_mode)\n", " reslist.append((results,ds))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -304,8 +564,8 @@ "\n", " df = pd.DataFrame(\n", " objvals,\n", - " index=['argmin(LogL)', 'argmin(nTVD)', 'argmin(TVD)', 'argmin(L10^10)', 'Model A', 'Model B'],\n", - " columns=['LogL', 'nTVD', 'TVD', 'L10', 'N_sigma'],\n", + " index=['argmin(LogL)', 'argmin(nTVD)', 'argmin(L10^10)', 'Model A', 'Model B'],\n", + " columns=['LogL', 'nTVD', 'L10', 'N_sigma'],\n", " )\n", " df.rename_axis(index='model')\n", " currdfs.append(df)\n", @@ -314,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -326,195 +586,276 @@ "\n", "The mixture dataset had weight 1.00 on dataset A and weight 0.00 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.058305 0.731190 0.058305 467.504133 68540.938167 467.504133 0.326392 3172.543057 0.326392 8.786676 60.511077 8.786676 16.335460 123.354424 16.335460\n", - "argmin(nTVD) 0.058255 0.731205 0.058255 500.047586 69561.100943 500.047586 1.842914 3220.082562 1.842914 8.720995 60.394434 8.720995 15.342389 124.476252 15.342389\n", - "argmin(TVD) 0.058421 0.731976 0.058421 484.225199 69215.050104 484.225199 1.105592 3203.956620 1.105592 8.657254 60.464797 8.657254 15.826993 123.250965 15.826993\n", - "argmin(L10^10) 0.056057 0.740446 0.056057 638.122108 69640.974153 638.122108 8.277176 3223.804647 8.277176 9.578566 60.363776 9.578566 17.786360 122.383633 17.786360\n", - "Model A 0.058701 0.731059 0.058701 479.867127 68138.580337 479.867127 0.878730 3152.059162 0.878730 8.853287 60.327248 8.853287 16.534238 123.431015 16.534238\n", - "Model B 0.731329 0.059377 0.731329 38338.240487 505.249618 38338.240487 1764.118964 2.060910 1764.118964 60.134177 10.943588 60.134177 125.523234 21.227815 125.523234\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.017095 1.143082 0.017095 356.490631 870722.190464 356.490631 -1.031219 44854.838049 -1.031219 3.915744 137.589484 3.915744\n", + "argmin(nTVD) 0.017287 1.142589 0.017287 360.248995 870045.936046 360.248995 -0.837525 44819.986056 -0.837525 3.704271 137.461658 3.704271\n", + "argmin(L10^10) 0.015880 1.141510 0.015880 548.027542 865450.799571 548.027542 8.839982 44583.167393 8.839982 4.292753 137.443425 4.292753\n", + "Model A 0.017573 1.143172 0.017573 374.434906 872019.805514 374.434906 -0.106428 44921.712986 -0.106428 4.037776 137.616702 4.037776\n", + "Model B 1.142056 0.019150 1.142056 873945.399412 411.929169 873945.399412 45020.951948 1.825906 45020.951948 137.853377 3.774255 137.853377\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.99 on dataset A and weight 0.01 on dataset B.\n", + "The mixture dataset had weight 0.95 on dataset A and weight 0.05 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.058280 0.724711 0.057080 507.324913 63646.108882 418.094069 2.182037 2944.444398 -1.976113 8.942991 59.846071 8.432625 16.504940 121.838636 15.728875\n", - "argmin(nTVD) 0.058199 0.725208 0.057029 523.844937 64807.005109 450.864053 2.951869 2998.542071 -0.449034 8.881305 59.807557 8.380632 15.663741 122.889098 14.786347\n", - "argmin(TVD) 0.058479 0.724452 0.057338 509.232448 63988.729224 431.947107 2.270928 2960.410479 -1.330563 8.828511 59.859282 8.314256 16.191643 121.921252 15.361203\n", - "argmin(L10^10) 0.056076 0.738725 0.054859 650.330393 68607.667786 638.572710 8.846081 3175.652652 8.298174 9.610355 60.149631 9.356175 17.971954 121.908982 17.208866\n", - "Model A 0.058701 0.731059 0.057534 479.867127 68138.580337 468.637758 0.878730 3152.059162 0.355726 8.853287 60.327248 8.592865 16.534238 123.431015 16.151412\n", - "Model B 0.731329 0.059377 0.723682 38338.240487 505.249618 36887.387442 1764.118964 2.060910 1696.546001 60.134177 10.943588 59.237540 125.523234 21.227815 123.602545\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.046194 1.105109 0.035502 2819.767774 766652.039453 1226.076815 125.918220 39491.394741 43.784477 8.450762 133.934899 6.581532\n", + "argmin(nTVD) 0.061438 1.105614 0.059253 3185.092343 763352.874618 1408.176914 144.745882 39321.366319 53.169334 8.675401 133.224769 5.669143\n", + "argmin(L10^10) 0.055301 1.093434 0.023177 5100.881754 755038.940319 2812.480028 243.479552 38892.892690 125.542633 10.354776 134.852384 7.933155\n", + "Model A 0.017573 1.143172 0.057863 374.434906 872019.805514 4171.398954 -0.106428 44921.712986 195.576976 4.037776 137.616702 8.418903\n", + "Model B 1.142056 0.019150 1.084884 873945.399412 411.929169 790483.410656 45020.951948 1.825906 40719.587524 137.853377 3.774255 130.963634\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.95 on dataset A and weight 0.05 on dataset B.\n", + "The mixture dataset had weight 0.90 on dataset A and weight 0.10 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.058553 0.699850 0.054809 873.908924 52790.312819 370.270628 19.264823 2438.565156 -4.204682 10.142951 57.797471 8.212986 18.901034 117.555414 14.999158\n", - "argmin(nTVD) 0.059798 0.699797 0.055133 883.531715 53037.287180 391.807285 19.713245 2450.074142 -3.201075 10.156287 57.551119 8.186089 17.928632 117.965291 14.160187\n", - "argmin(TVD) 0.058915 0.697846 0.055142 884.866988 52729.115781 379.257525 19.775468 2435.713379 -3.785893 10.053967 57.813796 8.103999 19.094600 117.175958 14.728985\n", - "argmin(L10^10) 0.058181 0.685159 0.052683 1095.965264 51993.590764 541.985234 29.612631 2401.437971 3.797205 10.804595 57.729039 9.199417 17.573569 120.776182 16.172637\n", - "Model A 0.058701 0.731059 0.056414 479.867127 68138.580337 829.861371 0.878730 3152.059162 17.179587 8.853287 60.327248 9.539133 16.534238 123.431015 17.309003\n", - "Model B 0.731329 0.059377 0.694352 38338.240487 505.249618 33064.605213 1764.118964 2.060910 1518.501283 60.134177 10.943588 56.927442 125.523234 21.227815 118.767794\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.088179 1.071385 0.068067 8898.892335 691037.753082 3522.021369 439.216891 35594.475843 162.110128 14.948660 130.731460 10.957710\n", + "argmin(nTVD) 0.134362 1.065096 0.131374 12354.307495 669445.516092 4472.671155 617.297956 34481.680888 211.103583 17.952717 129.549450 9.711630\n", + "argmin(L10^10) 0.108957 1.041488 0.040869 14024.546660 672165.545097 6247.889390 703.376749 34621.862498 302.592663 16.774763 131.534909 12.274848\n", + "Model A 0.017573 1.143172 0.114482 374.434906 872019.805514 13948.215634 -0.106428 44921.712986 699.442892 4.037776 137.616702 14.612190\n", + "Model B 1.142056 0.019150 1.027713 873945.399412 411.929169 713001.270999 45020.951948 1.825906 36726.405431 137.853377 3.774255 124.078971\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.90 on dataset A and weight 0.10 on dataset B.\n", + "The mixture dataset had weight 0.85 on dataset A and weight 0.15 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.069575 0.669120 0.053253 1707.234071 42918.600355 348.222883 58.097710 1978.544133 -5.232105 12.207427 55.203937 8.132309 23.235284 112.218973 14.596372\n", - "argmin(nTVD) 0.079953 0.666450 0.058557 1705.770360 43067.584239 363.791579 58.029501 1985.486770 -4.506605 12.338981 54.893533 8.109634 21.600853 113.270335 13.542902\n", - "argmin(TVD) 0.072924 0.665629 0.053595 1713.855390 42873.967832 353.081662 58.406262 1976.464261 -5.005686 12.164062 55.122870 7.974263 23.594074 111.497258 14.229698\n", - "argmin(L10^10) 0.094878 0.640004 0.050696 2031.709888 41913.699383 536.640612 73.218257 1931.715827 3.548147 12.693870 55.157799 9.248214 20.035872 115.901433 16.354479\n", - "Model A 0.058701 0.731059 0.073987 479.867127 68138.580337 1868.850198 0.878730 3152.059162 65.570121 8.853287 60.327248 11.344055 16.534238 123.431015 20.830502\n", - "Model B 0.731329 0.059377 0.657746 38338.240487 505.249618 28840.694551 1764.118964 2.060910 1321.774150 60.134177 10.943588 53.972022 125.523234 21.227815 112.825645\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.129666 1.039715 0.102642 18086.814606 626537.235812 6902.941809 912.733069 32270.325120 336.351973 21.837154 127.613544 15.383077\n", + "argmin(nTVD) 0.207047 1.024162 0.202889 27096.400336 592385.654457 9438.352505 1377.058352 30510.261656 467.018945 26.981130 126.110105 13.618625\n", + "argmin(L10^10) 0.156970 0.995362 0.059257 28018.837079 608243.064487 12601.035167 1424.597797 31327.501939 630.013512 24.954749 129.572462 18.155908\n", + "Model A 0.017573 1.143172 0.171491 374.434906 872019.805514 28669.805304 -0.106428 44921.712986 1458.146621 4.037776 137.616702 21.280094\n", + "Model B 1.142056 0.019150 0.970542 873945.399412 411.929169 640463.962930 45020.951948 1.825906 32988.064170 137.853377 3.774255 117.194751\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", "The mixture dataset had weight 0.80 on dataset A and weight 0.20 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.128069 0.607533 0.058235 4073.797769 29501.445680 344.775113 168.379391 1353.305782 -5.392771 16.805776 49.914257 8.147237 32.461797 101.228335 13.417375\n", - "argmin(nTVD) 0.146037 0.598981 0.094634 4183.566412 29188.668078 372.506101 173.494602 1338.730370 -4.100509 17.372651 49.184236 8.021515 32.596331 100.884726 12.828388\n", - "argmin(TVD) 0.146679 0.595218 0.091479 4232.062255 28995.323212 373.301298 175.754504 1329.720514 -4.063453 17.390356 49.195911 7.972158 33.085064 100.576929 13.119409\n", - "argmin(L10^10) 0.172593 0.562341 0.048265 4352.138395 29632.311270 600.673234 181.350043 1359.404108 6.532062 16.844040 50.229908 9.723182 28.929435 104.827273 15.294466\n", - "Model A 0.058701 0.731059 0.145681 479.867127 68138.580337 5324.814797 0.878730 3152.059162 226.530439 8.853287 60.327248 15.643033 16.534238 123.431015 29.515782\n", - "Model B 0.731329 0.059377 0.584742 38338.240487 505.249618 21821.205206 1764.118964 2.060910 994.843938 60.134177 10.943588 48.146528 125.523234 21.227815 100.913491\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.171381 1.008515 0.138944 30344.751476 568691.363953 11141.617709 1544.467999 29289.133574 554.799796 29.147456 124.504348 19.811381\n", + "argmin(nTVD) 0.292415 0.972151 0.287453 47825.067226 523759.961906 16139.589932 2445.347661 26973.512569 812.379327 35.077023 121.829965 17.255425\n", + "argmin(L10^10) 0.203040 0.951608 0.077583 46358.458594 553271.933719 20868.697414 2369.763335 28494.465371 1056.102427 33.600519 127.437909 24.307347\n", + "Model A 0.017573 1.143172 0.228585 374.434906 872019.805514 47831.053085 -0.106428 44921.712986 2445.656153 4.037776 137.616702 27.991667\n", + "Model B 1.142056 0.019150 0.913371 873945.399412 411.929169 572366.257601 45020.951948 1.825906 29478.525860 137.853377 3.774255 110.311423\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", "The mixture dataset had weight 0.70 on dataset A and weight 0.30 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.192544 0.542873 0.074748 7091.943198 20493.271003 374.808710 309.024731 933.525551 -3.993207 21.834864 44.494855 8.290449 42.852141 90.101863 12.886797\n", - "argmin(nTVD) 0.204957 0.532177 0.101387 7261.663790 20147.948696 391.445729 316.933697 917.433559 -3.217924 22.304388 43.941396 8.081331 42.897418 89.962380 12.315999\n", - "argmin(TVD) 0.216290 0.524384 0.124270 7352.556204 20050.338197 428.017151 321.169276 912.884917 -1.513698 22.608459 43.610406 7.988594 43.227342 89.623360 12.475021\n", - "argmin(L10^10) 0.245854 0.489594 0.051606 7656.645776 20355.374322 727.390771 335.339826 927.099577 12.437089 22.360088 44.437987 10.407362 40.644331 92.165539 15.493826\n", - "Model A 0.058701 0.731059 0.218786 479.867127 68138.580337 10221.507329 0.878730 3152.059162 454.592147 8.853287 60.327248 20.452823 16.534238 123.431015 39.614498\n", - "Model B 0.731329 0.059377 0.511604 38338.240487 505.249618 16131.152604 1764.118964 2.060910 729.831768 60.134177 10.943588 42.329793 125.523234 21.227815 89.111709\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.258163 0.943361 0.214229 64828.989566 465280.154088 21418.368308 3321.675529 23959.649989 1084.430710 44.730951 117.662152 27.910551\n", + "argmin(nTVD) 0.408399 0.854345 0.399343 102098.612145 412311.588812 31616.533349 5242.432882 21229.819252 1610.011576 53.125580 112.177459 23.679532\n", + "argmin(L10^10) 0.291917 0.869233 0.113762 95658.312517 459932.703537 41394.658872 4910.520402 23684.059461 2113.944946 50.856084 121.829809 34.952054\n", + "Model A 0.017573 1.143172 0.342854 374.434906 872019.805514 98322.081572 -0.106428 44921.712986 5047.802554 4.037776 137.616702 41.533669\n", + "Model B 1.142056 0.019150 0.799030 873945.399412 411.929169 448339.370775 45020.951948 1.825906 23086.576105 137.853377 3.774255 96.555043\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", "The mixture dataset had weight 0.60 on dataset A and weight 0.40 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.261164 0.474408 0.091391 10572.145717 14071.423474 420.571207 471.201894 634.267962 -1.860679 27.077488 38.959680 8.587280 53.755140 78.694803 13.186374\n", - "argmin(nTVD) 0.272057 0.464147 0.111901 10640.631764 14000.784948 435.077184 474.393338 630.976212 -1.184701 27.306314 38.733142 8.396607 51.752982 80.636663 12.291792\n", - "argmin(TVD) 0.293808 0.445746 0.154856 10930.017392 13774.770683 520.762005 487.878686 620.443965 2.808205 27.986584 38.230402 8.195175 53.533009 78.948949 12.597676\n", - "argmin(L10^10) 0.315435 0.420561 0.056281 11423.650183 13836.536763 834.807801 510.881935 623.322260 17.442714 28.142159 38.727617 11.162597 54.015160 78.750509 16.638443\n", - "Model A 0.058701 0.731059 0.291888 479.867127 68138.580337 16192.471084 0.878730 3152.059162 732.687654 8.853287 60.327248 25.670594 16.534238 123.431015 50.763009\n", - "Model B 0.731329 0.059377 0.438699 38338.240487 505.249618 11570.277416 1764.118964 2.060910 517.410633 60.134177 10.943588 36.631982 125.523234 21.227815 77.203040\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.359033 0.864997 0.291294 116853.651135 369164.727544 32673.987836 6002.860482 19006.167782 1664.509404 62.806959 108.603184 33.807731\n", + "argmin(nTVD) 0.495681 0.721576 0.472984 169481.141550 323578.543744 46016.010303 8715.113309 16656.801416 2352.114643 70.425665 100.552823 27.932039\n", + "argmin(L10^10) 0.374371 0.794443 0.149206 158763.759223 389876.384712 66104.715510 8162.773642 20073.580464 3387.422371 69.509902 116.623167 45.652330\n", + "Model A 0.017573 1.143172 0.457162 374.434906 872019.805514 163928.489876 -0.106428 44921.712986 8428.947370 4.037776 137.616702 55.164323\n", + "Model B 1.142056 0.019150 0.684692 873945.399412 411.929169 339427.932234 45020.951948 1.825906 17473.628295 137.853377 3.774255 82.807048\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.57 on dataset A and weight 0.43 on dataset B.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.390306 0.840704 0.311146 134223.116635 344441.396690 35340.592648 6898.027276 17732.006246 1801.937702 68.084280 105.338935 34.629188\n", + "argmin(nTVD) 0.513494 0.684431 0.480013 187868.127553 301789.312738 48059.337541 9662.719861 15533.854019 2457.421205 74.471237 97.559244 28.703496\n", + "argmin(L10^10) 0.395285 0.775506 0.157910 176215.496621 378364.633413 73903.586249 9062.180468 19480.301557 3789.351268 74.775241 115.906125 48.917220\n", + "Model A 0.017573 1.143172 0.485742 374.434906 872019.805514 182598.432178 -0.106428 44921.712986 9391.136587 4.037776 137.616702 58.589446\n", + "Model B 1.142056 0.019150 0.656108 873945.399412 411.929169 314468.367672 45020.951948 1.825906 16187.292028 137.853377 3.774255 79.370582\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.55 on dataset A and weight 0.45 on dataset B.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.426815 0.812684 0.331646 154505.580212 318389.229806 37799.967040 7943.320691 16389.360744 1928.686006 73.960071 101.352361 35.006462\n", + "argmin(nTVD) 0.539447 0.645818 0.492305 210353.569393 280080.149616 51277.275693 10821.547742 14415.033064 2623.263463 79.495787 93.217978 29.085771\n", + "argmin(L10^10) 0.415820 0.757163 0.166849 193983.025885 366917.672213 81350.361397 9977.862198 18890.361724 4173.134283 80.146052 114.870936 51.898359\n", + "Model A 0.017573 1.143172 0.514322 374.434906 872019.805514 202161.589751 -0.106428 44921.712986 10399.359267 4.037776 137.616702 62.015117\n", + "Model B 1.142056 0.019150 0.627524 873945.399412 411.929169 290402.095104 45020.951948 1.825906 14946.993179 137.853377 3.774255 75.934306\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.53 on dataset A and weight 0.47 on dataset B.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.472159 0.779173 0.353226 179224.550857 289634.864350 39877.511959 9217.257516 14907.452555 2035.756038 81.100340 95.811670 34.693401\n", + "argmin(nTVD) 0.557663 0.618013 0.490492 225286.910115 262052.835981 50958.785359 11591.164444 13485.962877 2606.849488 82.691929 90.368760 29.254841\n", + "argmin(L10^10) 0.435855 0.738712 0.175734 212313.530212 358592.508767 90004.106799 10922.557865 18461.309381 4619.120692 85.794787 114.737510 55.982184\n", + "Model A 0.017573 1.143172 0.542904 374.434906 872019.805514 222614.047342 -0.106428 44921.712986 11453.413631 4.037776 137.616702 65.441796\n", + "Model B 1.142056 0.019150 0.598940 873945.399412 411.929169 267225.050555 45020.951948 1.825906 13752.522302 137.853377 3.774255 72.498645\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.51 on dataset A and weight 0.49 on dataset B.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.503365 0.752448 0.367487 196444.716929 271235.366777 40922.799531 10104.729895 13959.201196 2089.626822 85.318313 92.114354 34.638214\n", + "argmin(nTVD) 0.578105 0.594765 0.497356 239971.288974 250199.949013 52813.996406 12347.950446 12875.102927 2702.461142 85.272729 87.884260 29.272573\n", + "argmin(L10^10) 0.451079 0.726472 0.181289 222806.063613 352695.013608 94282.313427 11463.309536 18157.371310 4839.605802 88.734141 113.549035 56.793083\n", + "Model A 0.017573 1.143172 0.560053 374.434906 872019.805514 235311.336294 -0.106428 44921.712986 12107.791363 4.037776 137.616702 67.498546\n", + "Model B 1.142056 0.019150 0.581790 873945.399412 411.929169 253744.656430 45020.951948 1.825906 13057.785829 137.853377 3.774255 70.437555\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", "The mixture dataset had weight 0.50 on dataset A and weight 0.50 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.336724 0.399223 0.103135 14453.459856 9389.478776 481.317795 652.070827 416.089708 0.970108 32.566106 33.543358 9.169555 65.724820 67.846493 16.004581\n", - "argmin(nTVD) 0.348744 0.387786 0.122024 14579.777698 9296.960102 499.272540 657.957229 411.778345 1.806797 32.851237 33.297344 8.877317 64.277568 68.784671 14.620929\n", - "argmin(TVD) 0.368227 0.369535 0.153857 14910.668610 9096.655991 565.071618 673.376719 402.444189 4.873029 33.516252 32.955458 8.697918 66.899668 66.687181 15.806218\n", - "argmin(L10^10) 0.381766 0.355110 0.058901 15341.892516 9342.669387 900.267841 693.471719 413.908394 20.493147 33.948918 33.010908 11.802510 65.332360 68.314439 17.769593\n", - "Model A 0.058701 0.731059 0.364902 479.867127 68138.580337 23004.788336 0.878730 3152.059162 1049.968900 8.853287 60.327248 31.004211 16.534238 123.431015 61.974967\n", - "Model B 0.731329 0.059377 0.366102 38338.240487 505.249618 7940.470086 1764.118964 2.060910 348.353651 60.134177 10.943588 31.177551 125.523234 21.227815 65.981165\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.525808 0.730174 0.378125 209376.504570 258051.435824 41470.278853 10771.192941 13279.743489 2117.842158 88.241723 89.238192 34.565610\n", + "argmin(nTVD) 0.587828 0.583887 0.498207 246991.525604 243288.824535 52896.483725 12709.751028 12518.925637 2706.712276 86.384073 86.606059 29.369697\n", + "argmin(L10^10) 0.459907 0.718566 0.184843 230385.564671 349647.368233 97772.775107 11853.932821 18000.305398 5019.493053 91.093991 113.339135 58.078814\n", + "Model A 0.017573 1.143172 0.571485 374.434906 872019.805514 243953.428865 -0.106428 44921.712986 12553.177222 4.037776 137.616702 68.869768\n", + "Model B 1.142056 0.019150 0.570357 873945.399412 411.929169 244934.972946 45020.951948 1.825906 12603.762870 137.853377 3.774255 69.063499\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.49 on dataset A and weight 0.51 on dataset B.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.731121 0.506288 0.378186 271618.467034 197098.739531 41437.127109 13978.944960 10138.436131 2116.133623 93.255994 84.550062 34.316787\n", + "argmin(nTVD) 0.599226 0.571129 0.495832 255120.033760 234443.285410 52398.612820 13128.668386 12063.054792 2681.053599 87.814232 85.151958 29.407567\n", + "argmin(L10^10) 0.728272 0.446160 0.181194 336564.979937 229597.292572 89835.180582 17326.080874 11813.307794 4610.414774 111.702385 90.372357 57.801162\n", + "Model A 0.017573 1.143172 0.582918 374.434906 872019.805514 252737.288429 -0.106428 44921.712986 13005.869299 4.037776 137.616702 70.241001\n", + "Model B 1.142056 0.019150 0.558923 873945.399412 411.929169 236267.057198 45020.951948 1.825906 12157.046167 137.853377 3.774255 67.689445\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.47 on dataset A and weight 0.53 on dataset B.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.762886 0.474621 0.362197 290881.822487 179131.754463 40403.743946 14971.716796 9212.475086 2062.876354 97.190539 80.213858 35.046510\n", + "argmin(nTVD) 0.613784 0.553874 0.487832 264649.030693 222741.122203 50838.085161 13619.762463 11459.962663 2600.628986 90.127751 82.920699 29.368744\n", + "argmin(L10^10) 0.739535 0.433916 0.175608 343518.528432 217472.313477 85534.972045 17684.444563 11188.425084 4388.795755 112.545038 86.906840 56.241001\n", + "Model A 0.017573 1.143172 0.600068 374.434906 872019.805514 266178.950531 -0.106428 44921.712986 13698.609647 4.037776 137.616702 72.297864\n", + "Model B 1.142056 0.019150 0.541774 873945.399412 411.929169 223531.033784 45020.951948 1.825906 11500.672184 137.853377 3.774255 65.629648\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.45 on dataset A and weight 0.55 on dataset B.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.800393 0.429306 0.339442 319702.562793 154300.301009 38243.368898 16457.045733 7932.741257 1951.537522 102.461053 73.529709 35.804160\n", + "argmin(nTVD) 0.648239 0.528095 0.488898 286300.646306 203776.843787 50424.606529 14735.617603 10482.604303 2579.319617 93.590802 78.858126 29.107831\n", + "argmin(L10^10) 0.757706 0.414236 0.166320 354884.230784 199168.101002 78752.409352 18270.196573 10245.084414 4039.244130 113.754067 81.384610 53.516701\n", + "Model A 0.017573 1.143172 0.628650 374.434906 872019.805514 289291.449866 -0.106428 44921.712986 14889.754069 4.037776 137.616702 75.726209\n", + "Model B 1.142056 0.019150 0.513191 873945.399412 411.929169 203014.033124 45020.951948 1.825906 10443.291477 137.853377 3.774255 62.199861\n", + "\n", + "\n", + "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "\n", + "The mixture dataset had weight 0.42 on dataset A and weight 0.57 on dataset B.\n", + "\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.830263 0.392070 0.318209 345943.430101 133872.611129 35725.412999 17809.416252 6879.963342 1821.770114 106.447432 67.621482 35.758309\n", + "argmin(nTVD) 0.678813 0.507580 0.484278 306083.895608 186322.628853 48943.869399 15755.183108 9583.069793 2503.007153 96.695675 75.174431 28.661775\n", + "argmin(L10^10) 0.775628 0.394558 0.157157 368138.331239 180920.217298 72210.619555 18953.270593 9304.646750 3702.101170 114.985070 75.934725 50.594172\n", + "Model A 0.017573 1.143172 0.657233 374.434906 872019.805514 313293.215949 -0.106428 44921.712986 16126.728460 4.037776 137.616702 79.157890\n", + "Model B 1.142056 0.019150 0.484609 873945.399412 411.929169 183386.365138 45020.951948 1.825906 9431.744136 137.853377 3.774255 58.770576\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", "The mixture dataset had weight 0.40 on dataset A and weight 0.60 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.419938 0.316692 0.108947 18867.574117 5752.944088 474.649704 857.768204 246.627478 0.659375 38.293271 27.854670 9.021935 76.723239 55.628956 14.741397\n", - "argmin(nTVD) 0.434744 0.302557 0.129251 19030.009064 5674.293087 494.156212 865.337660 242.962347 1.568377 38.541429 27.808615 8.754129 75.034583 57.196161 13.549772\n", - "argmin(TVD) 0.442236 0.295717 0.142902 18933.804562 5795.961973 530.979411 860.854538 248.632108 3.284335 38.486363 28.009825 8.653013 75.854167 56.356211 13.840286\n", - "argmin(L10^10) 0.448831 0.288644 0.058838 19654.203801 5884.199204 863.322840 894.425086 252.743956 18.771513 39.638916 27.580884 11.777146 78.797379 54.682824 18.139113\n", - "Model A 0.058701 0.731059 0.438333 479.867127 68138.580337 30831.323781 0.878730 3152.059162 1414.486997 8.853287 60.327248 36.680717 16.534238 123.431015 74.100404\n", - "Model B 0.731329 0.059377 0.293411 38338.240487 505.249618 5056.599171 1764.118964 2.060910 214.038392 60.134177 10.943588 25.803049 125.523234 21.227815 54.630480\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.855801 0.360114 0.297807 370730.448504 116456.778037 32992.899051 19086.860041 5982.406909 1680.945070 109.604491 62.296657 35.106070\n", + "argmin(nTVD) 0.713211 0.485344 0.472518 327255.914145 167571.984950 46272.210114 16846.321345 8616.721473 2365.318364 100.035958 71.023627 28.071218\n", + "argmin(L10^10) 0.793517 0.374744 0.148114 383041.334238 163095.121592 65900.260130 19721.323785 8385.998229 3376.885393 116.300822 70.624216 47.582292\n", + "Model A 0.017573 1.143172 0.685817 374.434906 872019.805514 338188.321863 -0.106428 44921.712986 17409.742734 4.037776 137.616702 82.593045\n", + "Model B 1.142056 0.019150 0.456027 873945.399412 411.929169 164651.972550 45020.951948 1.825906 8466.233357 137.853377 3.774255 55.342108\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", "The mixture dataset had weight 0.30 on dataset A and weight 0.70 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.508091 0.229811 0.102495 23577.816968 3236.178060 462.412103 1077.265150 129.346379 0.089104 44.115671 22.393879 9.130840 88.726527 44.168714 15.734786\n", - "argmin(nTVD) 0.511618 0.226578 0.108070 23516.635010 3269.965680 468.633753 1074.414076 130.920879 0.379032 44.009629 22.560514 9.030624 86.514322 46.236778 14.452820\n", - "argmin(TVD) 0.517180 0.222034 0.127903 23312.347102 3401.875282 503.243620 1064.894275 137.067856 1.991849 43.854686 22.950017 8.905500 88.963584 43.867608 15.718299\n", - "argmin(L10^10) 0.520573 0.217841 0.055216 24007.507335 3469.827760 753.052320 1097.288687 140.234437 13.632915 44.771370 22.378776 11.375579 89.778594 43.822841 17.980791\n", - "Model A 0.058701 0.731059 0.511825 479.867127 68138.580337 39256.191000 0.878730 3152.059162 1806.872182 8.853287 60.327248 42.327993 16.534238 123.431015 86.193286\n", - "Model B 0.731329 0.059377 0.221235 38338.240487 505.249618 2940.770627 1764.118964 2.060910 115.494432 60.134177 10.943588 20.712529 125.523234 21.227815 43.896415\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 0.937163 0.258018 0.219063 466431.832777 64637.806907 21493.254655 24019.003831 3311.822585 1088.290113 118.372641 44.406570 29.397749\n", + "argmin(nTVD) 0.848551 0.397227 0.398948 416091.319302 100522.705394 31510.528669 21424.614494 5161.215679 1604.548433 111.154663 54.754358 24.326325\n", + "argmin(L10^10) 0.867410 0.293123 0.112546 455613.399323 97904.546934 41534.441958 23461.456312 5026.284152 2121.148920 121.362614 50.457551 35.420632\n", + "Model A 0.017573 1.143172 0.800152 374.434906 872019.805514 446843.419194 -0.106428 44921.712986 23009.479537 4.037776 137.616702 96.340620\n", + "Model B 1.142056 0.019150 0.341709 873945.399412 411.929169 98789.215839 45020.951948 1.825906 5071.877163 137.853377 3.774255 41.638390\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", "The mixture dataset had weight 0.20 on dataset A and weight 0.80 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.593498 0.146681 0.082374 28465.212070 1616.355278 436.127541 1305.017377 53.862765 -1.135755 49.812296 17.179430 9.393956 100.692317 32.977846 16.957784\n", - "argmin(nTVD) 0.594112 0.146755 0.089686 28305.058345 1668.395129 446.901333 1297.554226 56.287818 -0.633697 49.607226 17.440040 9.315584 98.327986 35.191299 15.409819\n", - "argmin(TVD) 0.590347 0.150556 0.094477 28086.296649 1726.543671 451.447459 1287.359948 58.997535 -0.421848 49.446819 17.645242 9.251400 100.983947 32.606542 16.987316\n", - "argmin(L10^10) 0.594091 0.146093 0.049514 28535.089216 1795.085966 592.176231 1308.273646 62.191601 6.136102 49.858494 17.422069 10.711664 100.412987 33.630635 18.473805\n", - "Model A 0.058701 0.731059 0.584358 479.867127 68138.580337 48317.163314 0.878730 3152.059162 2228.883735 8.853287 60.327248 48.226359 16.534238 123.431015 98.409177\n", - "Model B 0.731329 0.059377 0.151299 38338.240487 505.249618 1503.926489 1764.118964 2.060910 48.573929 60.134177 10.943588 16.043020 125.523234 21.227815 34.029001\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 1.004504 0.171110 0.142458 568859.462809 30466.157132 11076.047899 29297.796852 1550.724858 551.420537 124.901431 28.928334 21.006062\n", + "argmin(nTVD) 0.968066 0.289869 0.292726 523975.091866 48816.891215 16779.761102 26984.599681 2496.463103 845.371705 120.357413 37.390472 18.339919\n", + "argmin(L10^10) 0.948358 0.207571 0.076269 547841.062685 47906.618275 20824.736447 28214.575618 2449.550542 1053.836819 126.383526 33.201476 24.243971\n", + "Model A 0.017573 1.143172 0.914490 374.434906 872019.805514 570621.966009 -0.106428 44921.712986 29388.630640 4.037776 137.616702 110.094111\n", + "Model B 1.142056 0.019150 0.227420 873945.399412 411.929169 48049.854110 45020.951948 1.825906 2456.932460 137.853377 3.774255 28.025354\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.10 on dataset A and weight 0.90 on dataset B.\n", + "The mixture dataset had weight 0.15 on dataset A and weight 0.85 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.668679 0.076564 0.056305 33419.625902 747.866994 429.312337 1535.892671 13.391279 -1.453343 55.170456 12.812219 9.801565 112.532181 23.162979 18.171220\n", - "argmin(nTVD) 0.668440 0.078943 0.062971 33269.383067 776.580541 440.796012 1528.891367 14.729328 -0.918204 54.977003 13.018956 9.790191 109.947903 25.628907 16.632490\n", - "argmin(TVD) 0.666052 0.079781 0.062700 33280.020316 774.007514 440.255326 1529.387062 14.609425 -0.943400 54.969167 12.971492 9.714384 112.902957 22.935576 18.372205\n", - "argmin(L10^10) 0.657603 0.085642 0.047752 33053.604675 863.393466 497.358545 1518.836111 18.774804 1.717605 54.851380 13.490325 10.493062 110.463381 25.261768 19.602431\n", - "Model A 0.058701 0.731059 0.657304 479.867127 68138.580337 58057.143232 0.878730 3152.059162 2682.519826 8.853287 60.327248 54.174145 16.534238 123.431015 110.783513\n", - "Model B 0.731329 0.059377 0.084357 38338.240487 505.249618 695.274792 1764.118964 2.060910 10.911265 60.134177 10.943588 12.104857 125.523234 21.227815 25.858820\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 1.036579 0.129418 0.105549 626389.074367 18283.709974 6831.379668 32262.689352 922.880428 332.663889 127.828492 21.635788 16.420418\n", + "argmin(nTVD) 1.018728 0.222629 0.224893 590476.853618 28553.406634 10173.788544 30411.888155 1452.147805 504.920971 124.826776 28.161849 14.657706\n", + "argmin(L10^10) 0.992331 0.160923 0.057869 603381.642140 29005.135822 12493.477071 31076.959753 1475.428485 624.470311 128.778043 24.851508 18.476589\n", + "Model A 0.017573 1.143172 0.971660 374.434906 872019.805514 638604.877952 -0.106428 44921.712986 32892.252866 4.037776 137.616702 116.971717\n", + "Model B 1.142056 0.019150 0.170307 873945.399412 411.929169 28773.815889 45020.951948 1.825906 1463.506994 137.853377 3.774255 21.283896\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.05 on dataset A and weight 0.95 on dataset B.\n", + "The mixture dataset had weight 0.10 on dataset A and weight 0.90 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.702262 0.057311 0.053609 35901.832160 551.198688 444.775466 1651.563288 4.226552 -0.732762 57.715396 11.268873 10.094609 118.373585 19.866242 18.893372\n", - "argmin(nTVD) 0.701985 0.060941 0.057731 35768.647398 565.964380 452.541828 1645.356888 4.914632 -0.370850 57.528568 11.375743 10.084489 115.798353 21.208465 17.470876\n", - "argmin(TVD) 0.701885 0.059799 0.054853 35612.554235 576.319495 455.902128 1638.082959 5.397179 -0.214260 57.334562 11.389517 10.013522 116.983272 20.113419 18.207249\n", - "argmin(L10^10) 0.688055 0.059954 0.049035 35406.887349 628.231765 493.856172 1628.498898 7.816287 1.554395 57.298475 12.048697 10.697845 115.475494 21.996857 20.429503\n", - "Model A 0.058701 0.731059 0.694001 479.867127 68138.580337 63158.419146 0.878730 3152.059162 2920.109931 8.853287 60.327248 57.151030 16.534238 123.431015 116.958974\n", - "Model B 0.731329 0.059377 0.060244 38338.240487 505.249618 520.566267 1764.118964 2.060910 2.774278 60.134177 10.943588 10.952796 125.523234 21.227815 22.906553\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 1.069202 0.088090 0.069850 690832.458850 9104.593239 3466.121932 35583.895633 449.818059 159.229250 130.631553 14.746141 11.733843\n", + "argmin(nTVD) 1.063294 0.143161 0.144484 668764.889270 12913.902284 4687.742944 34446.603555 646.137684 222.187696 129.223049 18.425271 10.630982\n", + "argmin(L10^10) 1.039019 0.111528 0.039468 669006.429825 14479.521363 6120.954140 34459.051784 726.824693 296.050825 131.115027 16.941541 12.999624\n", + "Model A 0.017573 1.143172 1.028830 374.434906 872019.805514 711043.993203 -0.106428 44921.712986 36625.533583 4.037776 137.616702 123.849738\n", + "Model B 1.142056 0.019150 0.113260 873945.399412 411.929169 13954.019078 45020.951948 1.825906 699.741983 137.853377 3.774255 14.637279\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", - "The mixture dataset had weight 0.01 on dataset A and weight 0.99 on dataset B.\n", + "The mixture dataset had weight 0.05 on dataset A and weight 0.95 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.727873 0.055933 0.054759 37904.100877 492.538014 471.712639 1744.868852 1.492969 0.522508 59.687372 10.723672 10.424930 122.997044 19.884425 19.506542\n", - "argmin(nTVD) 0.728014 0.062239 0.061105 37834.963909 506.591477 485.253476 1741.647075 2.147859 1.153510 59.558941 10.707916 10.409266 120.845389 18.528914 17.960292\n", - "argmin(TVD) 0.731742 0.057337 0.056173 38023.698465 502.273367 482.427318 1750.442090 1.946636 1.021811 59.667143 10.617281 10.308995 122.850117 19.403571 18.992379\n", - "argmin(L10^10) 0.716759 0.051705 0.050511 37407.042940 541.441313 515.687253 1721.705991 3.771859 2.571722 59.240605 11.358666 10.993873 119.617487 21.591750 21.098547\n", - "Model A 0.058701 0.731059 0.723362 479.867127 68138.580337 67346.000215 0.878730 3152.059162 3115.145026 8.853287 60.327248 59.503700 16.534238 123.431015 121.896791\n", - "Model B 0.731329 0.059377 0.058335 38338.240487 505.249618 489.082140 1764.118964 2.060910 1.307916 60.134177 10.943588 10.690820 125.523234 21.227815 21.075044\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 1.103517 0.046503 0.035750 767236.366039 2970.730490 1215.864059 39521.509068 133.698357 43.258144 134.203725 8.265166 7.070433\n", + "argmin(nTVD) 1.104745 0.064443 0.064720 764736.362288 3337.516587 1439.310732 39392.666856 152.601341 54.773872 133.247723 9.193885 6.425464\n", + "argmin(L10^10) 1.092544 0.055558 0.022420 755081.775260 5633.096483 3137.382090 38895.100266 270.908199 142.287048 135.402631 10.913877 9.100373\n", + "Model A 0.017573 1.143172 1.086001 374.434906 872019.805514 788469.374009 -0.106428 44921.712986 40615.790506 4.037776 137.616702 130.729944\n", + "Model B 1.142056 0.019150 0.056531 873945.399412 411.929169 4120.454663 45020.951948 1.825906 192.951470 137.853377 3.774255 8.316892\n", "\n", "\n", "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", "\n", "The mixture dataset had weight 0.00 on dataset A and weight 1.00 on dataset B.\n", "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.734850 0.056020 0.056020 38320.137210 490.400273 490.400273 1764.256112 1.393350 1.393350 60.127224 10.679844 10.679844 123.975368 19.966883 19.966883\n", - "argmin(nTVD) 0.735721 0.063619 0.063619 38182.176420 502.788631 502.788631 1757.827150 1.970647 1.970647 59.933777 10.644054 10.644054 122.274428 18.476274 18.476274\n", - "argmin(TVD) 0.740118 0.057454 0.057454 38516.766492 500.884024 500.884024 1773.419021 1.881892 1.881892 60.222429 10.548472 10.548472 123.836231 19.332965 19.332965\n", - "argmin(L10^10) 0.725658 0.051594 0.051594 38018.348946 528.518821 528.518821 1750.192803 3.169672 3.169672 59.861629 11.253837 11.253837 121.219802 21.526994 21.526994\n", - "Model A 0.058701 0.731059 0.731059 479.867127 68138.580337 68138.580337 0.878730 3152.059162 3152.059162 8.853287 60.327248 60.327248 16.534238 123.431015 123.431015\n", - "Model B 0.731329 0.059377 0.059377 38338.240487 505.249618 505.249618 1764.118964 2.060910 2.060910 60.134177 10.943588 10.943588 125.523234 21.227815 21.227815\n", + " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", + "argmin(LogL) 1.143018 0.019721 0.019721 878354.400236 391.057008 391.057008 45248.177773 0.750222 0.750222 138.090204 3.689869 3.689869\n", + "argmin(nTVD) 1.143098 0.019853 0.019853 879427.393095 393.861521 393.861521 45303.476399 0.894757 0.894757 138.238434 3.572134 3.572134\n", + "argmin(L10^10) 1.138419 0.017489 0.017489 866835.723106 635.495770 635.495770 44654.541930 13.347815 13.347815 137.827680 4.678087 4.678087\n", + "Model A 0.017573 1.143172 1.143172 374.434906 872019.805514 872019.805514 -0.106428 44921.712986 44921.712986 4.037776 137.616702 137.616702\n", + "Model B 1.142056 0.019150 0.019150 873945.399412 411.929169 411.929169 45020.951948 1.825906 1.825906 137.853377 3.774255 3.774255\n", "\n" ] } @@ -544,14 +885,14 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -559,40 +900,139 @@ } ], "source": [ - "from matplotlib import pyplot as plt\n", + "datanames = [ '(mix)', '(A)', '(B)'] \n", + "modelnames = ['argmin(LogL)', 'argmin(nTVD)', 'argmin(L10^10)']\n", + "legendnames = [ 'f = ' + name.strip('argmin(').strip(')') for name in modelnames ]\n", + "lossnames = ['LogL', 'nTVD', 'L10', 'N_sigma']\n", + "losscolors = ['b', 'r', 'y', 'k', 'd','.']\n", + "dataname = '(mix)'\n", + "fig, outer_axs = plt.subplots(1, len(lossnames), figsize=(15,5))\n", + "for metricname,ax in zip(lossnames, outer_axs):\n", + " rows = []\n", + " for df in dflist:\n", + " row = [ df[metricname + dataname][modelname] for modelname in modelnames ]\n", + " rows.append(row)\n", + " y = np.array(rows)\n", + " if metricname in {'LogL', 'chi2', 'N_sigma'}:\n", + " ax.set_yscale('log')\n", + " for i,yi in enumerate(y.T):\n", + " x = mixture_weights\n", + " if metricname in {'LogL', 'chi2', 'N_sigma'}:\n", + " ind = yi > 0\n", + " x = x[ind]\n", + " yi = yi[ind]\n", + " ax.plot(x, yi,losscolors[i])\n", + " ax.legend(legendnames)\n", + " ax.set_title( 'g = ' + metricname)\n", + "fig.suptitle('For various loss functions g and f, plot\\n\\ng(argmin(f, data(p)), data(p)) over 0 <= p <= 1, where\\n\\n'\n", + "'data(p) is the dataset with weight p on dataset A, and\\nargmin(f, ds) is the model from fitting loss f to dataset ds')\n", + "fig.supxlabel('proportion of dataset taken from dataset A')\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## View into what models look like\n", + "```\n", + "rho0\n", + "Mdefault = Lindblad-parameterized POVM of length 2\n", + "[] = Exponentiated operation map with dim = 4, num params = 12\n", + "Gxpi2:0 = Exponentiated operation map with dim = 4, num params = 12\n", + "Gypi2:0 = Exponentiated operation map with dim = 4, num params = 12\n", + "```\n", + "Could make a 5-by-3 figure, each of 15 panels plotting two measures of fidelity.\n", + " * Top row is SPAM fidelity, bottom row is POVM fidelity, middle rows are gate fidelity.\n", + " * First column is f = LogL, second column is f = nTVD, third column is f = L10^10.\n", + " \n", + "The two measures of fidelity are mixed-to-A and mixed-to-B." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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BoN69e1NDQ4Pf8SuuuIIyMjL8Bpo+n4/Gjx+vemNKT0+nAwcOyMe2bt1KAOjYY4+l9vZ2+XhWVlbQP6xaN6a0tDTat2+ffMzpdFK/fv3oj3/8o3yMCcWGDRt0XYtwN6bGxkZKT0+n+fPn+x3fv38/paam0tVXXy1fi8GDB9OZZ57pd96+ffsoOTnZ0kmQBx54gARBoK1bt/odP++880K2D0mSyOv10r59+4La+XPPPRdUr6HKyMvLIwC0bds2+W+33XYbpaenBw2cWB84/fTT/f62d+9eSk5OpgULFgTFOfvss4OurRY8Hg8JgkAPPPCArvMTEVxDuYYSRUdDZ82apdoWxo8fT+eff76qT7hJEFbHL774onysvr6eUlNT6ZprrvE7N9wkiNPpJAB04YUX6uKjRCTtNxCiKFJbWxtlZGTQK6+8Ih//9NNPdbW5UPr8zDPPEAA6fPiwnw9r88cdd5zfP0EtLS3Ur18/+tWvfhUU55prrqFBgwaFzEWJIUOG0JVXXqn7/K4MrpNcJ4liM9YMRKhJkJ9++okA0Mcffxz0NzbJevDgQd2x9CDUJEhycrLftWEoKCggAPTRRx9FHO/KK6+k9PR0P40TRZHGjh0bcmwaakz6pz/9Kai+1eDz+cjr9dKKFSvIbrf7tdELL7yQxo4dG+TD7mmXXnqp33FWV48//niQTyRaWlFRQQDo9ddf13W+EfDHYboYfv3rX/t9HjduHARBwIUXXigfS0pKwujRo/2WpObm5mL8+PE444wz/PxvvPFGEJG8Wc3atWvRq1cvXHzxxX7nXX311X6fd+/ejR07duCaa64B0LFUjf3Mnz8fhw4dinijxuOOOw6bNm3S9TN58uSw5X366ac4++yz0bNnTyQlJSE5ORnLly9XfaRj7ty56Nu3r9+xvLw8zJ07FwMGDJCP2Ww2/O53v1ONN2nSJL/n5caNGwegY+l0jx49go4r60cLkyZNwrBhw+TPaWlpGDNmjJ/vwYMHASDk84uRYP369XA6nbjxxhv9jg8dOhRz586Vlxjv3LkThw8fDroew4YNw9lnnx2VXLSwdu1anHzyyUHPyAe2U6BjB+9bb70VQ4cOldvB8OHDAUC1LaihsrISV199NQYPHgy73Y7k5GTMmjUrqIyDBw/KO5yr4eqrr/b72/Dhw3HWWWepPvYycOBA3Usqk5OT0adPH8NLMBMJXEO5hkZDQwcPHhzUFk499VRdOalh1qxZGDVqlN8jMR9++CHcbrfuR2EYyMTyYb3tFwDa2trwwAMPYPTo0UhKSkJSUhJ69uyJ9vZ23dqqV58PHjwIQRD82pISl112GdLS0uTPvXr1wm9+8xvk5+cHbTg7cOBA1NbWQhRFXTlGosXdBVwnuU5aPdY0glCPzph9602kiHYua9euxbnnnotBgwbJx+x2O6688sqgc/WOSUOhqKgIF198Mfr37y+Xcf3118Pn82HXrl3yeQcPHgxZ56xvMpx11lkYPny46XEti2ml9iZZVjKHJejXr5/f55SUFPTo0cPv5s+Ot7S0yJ/r6+tV92g47rjj5L+z38oOyDB48GC/z0eOHAHQ8SaLe++9VzXXwGd6wyElJQWTJk3Sda7dbg/5988//xy/+93vcMUVV+C+++7D4MGDkZSUhNdff131Ocdjjz026JjWtVA7BqjXTajjLpcrJAcA6N+/f9Cx1NRUv820mB3YBoyCtQW1a3LcccdhzZo1fudpXaNQO3BHI8eRI0cGHQ9sp5IkYd68eTh48CAefvhhnHLKKcjIyIAkSZg2bZquTcna2towY8YMpKWl4fHHH8eYMWPQo0cPVFdX47LLLguqi1D1EJgfO6b2Joe0tLSINk2L9PxEBdfQDnANNaehesqNBIIg4Oabb8bf/vY3/Pzzz5gyZQreeecdjBw5EnPmzImoLPaPC2ubkUBv+wU6/mH9/vvv8fDDD2Pq1KnIzMyEIAiYP3++rusQiT47nU4kJydrtlstbfV4PGhra0Pv3r3l42lpaSAiuFwuXa+LT0Rt5TrZAa6T1o01IwHLT+1tIQ0NDRAEIap74unJRysXILgu9KC+vl5Tx5SIZEyqhf3792PGjBk46aST8Morr2DEiBFIS0tDYWEh/vSnPwXVu1ZbVMuPHVO7PpFoKWtnVmovnwRJEPTv3x+HDh0KOs5mdtkMdP/+/VFYWBh0XuBmVez8hx56SHOn9pNOOimiHPfu3av6j60a1q5dG/L93R988AFGjhyJTz75xG9GVmu3fLVZ2/79+8s3YCW0Nu6KF1hdNDQ0qN5gIwW72Wi1F2VbARCXa9S/f3/VGIHHSkpKsG3bNrz77ru44YYb5OO7d+/WHSs3NxcHDx7EunXr5Jl2AKqv0h0wYAC2bNmiWZZWzmoDkIaGBs1vPdXQ2NgY0fkckYFraAe4hlqHG2+8EY888gjefvttJCcno6ioCI899ljE3yp++eWXABCyfrWgt/02Nzfj66+/xqJFi/Dggw/Kx91ut/yPQDhEos8DBgyAx+NBe3s7MjIywubHjqWkpARNdDQ0NCA1NVXXBAg73+qNvrsLuE52gOtkdDFq1Cikp6dj+/btQX/bvn07Ro8eHdPJmVNOOUUzF6DjLWCRQu+4NpIxqRaysrLQ3t6Ozz//XF55BwBbt24NOnfAgAEhNV0r59GjRwcdj0RLWUwrx7X8cZgEwbnnnouysrKgf9BWrFgBQRDkb5rmzJmD1tZWeRDF8NFHH/l9Pumkk3DiiSdi27ZtmDJliupPpO8Qj+YSRUEQkJKS4nfDOXz4sOqO3VqYNWsWcnNz/b5lkCQJn376aUS8rMbYsWMBIGq710+fPh3p6en44IMP/I4fOHAAubm5OPfccwF0tIHBgwcH7dC+f/9+FBQURCUXLcyZMwelpaVBKygC2ymr/9TUVL/jaq/dYucEzjpHUsbYsWNRX1+P5uZm1bw//vhjv6Xq+/btQ0FBgeogq7KyEuPHj1ctJxAHDx6Ey+XSfT5H5OAayjXUahx33HG44IIL8PHHH2Pp0qWw2Wx+kwN6sG3bNjz55JMYMWKE5nL6UNDbfgVBABEF6eJbb70V9PhJtLQV0K6jzz//3O8b79bWVnz11VeYMWNG0Lf5kWirKIqorq7m2qoTXCe5TlqBpKQk/OY3v8Hnn3/u9xa8/fv3Y+3atYZfm20Ul156KXbs2IGNGzfKx0RRxAcffIAzzzzT0Cq8OXPm4Pvvv/ebEPP5fEFv+bJqXEtEePPNN4PKGDt2rOrbXhg+/PBDv88FBQXYt29f0Lg2Ui1lMa3UXr4SJEHwl7/8BStWrMBFF12ERx99FMOHD8c333yD1157DbfddhvGjBkDALj++uvx0ksv4frrr8cTTzyBE088EdnZ2fj222+DyvzXv/6FCy+8EOeffz5uvPFGDBkyBA0NDSgvL8eWLVsiFvCUlBRMmTIlKnx//etf4/PPP8ftt9+O3/72t6iursZjjz2GY489FhUVFbrK+Nvf/oavvvoK5557Lv72t78hPT0dy5Ytk19BpfdVWlbjzDPPRHp6OjZs2BD0fK0WfD4f/vvf/wYdz8jIwIUXXoiHH34Yf/3rX3H99dfjqquuQn19PZYsWYK0tDQsWrQIQAf/JUuW4I9//CN++9vf4uabb0ZTUxOWLFmCY489VvX67NmzRzXu+PHjMX78eLz77ru46aab8M477wTtSaLE3XffjbfffhsXXXQRHn/8cQwaNAgffvghduzY4Xfe2LFjMWrUKDz44IMgIvTr1w9fffWV/EiPEqeccgoA4JVXXsENN9yA5ORknHTSSTjrrLPQt29f3HrrrVi0aBGSk5Px4Ycfqj7CMnv2bBARNm7ciHnz5gX9vba2Fpdeein+8Ic/oLm5GYsWLUJaWhoeeughv/Pq6+tRUVGBP//5z5rXQIkNGzYAQMTL5jn0g2so19BY4JZbbsE333yDt956C+effz6GDh2qee7mzZvRu3dveL1eHDx4EN9//z3ef/99DBw4EF999ZW8HB7oeE33nDlzsGjRIixevFizTL3tNzMzEzNnzsRzzz2HAQMGYMSIEcjLy8Py5cuDlqWzb0XfeOMN9OrVC2lpaRg5cmRE+swG1Bs2bMCpp54a9He73Y7zzjsPCxcuhCRJeOaZZ9DS0oIlS5b4nSdJEgoLC3HLLbdoXgMliouL4XA4uLbqBNdJrpNKhBtrAsCqVavQ3t4uT26UlZXJPvPnz5f3OVmyZAmmTp2KX//613jwwQfhcrnwyCOPYMCAAbjnnnuiQRE///yz/ArZlpYWEJGcy9SpU+VVEzfffDOWLl2KK664Ak8//TQGDhyI1157DTt37sR3333nV+bixYuxZMmSsKuK/v73v+PLL7/E3Llz8cgjj6BHjx5YunSp32tvAUQ0JmXj2meeeQYXXngh7HY7Tj31VJx33nlISUnBVVddhfvvvx8ulwuvv/46Ghsbg8qYPXs23n77bezatUvuv4HXbMGCBbjiiitQXV2Nv/3tbxgyZAhuv/12v/Mi1dINGzbAbrdj5syZus43BMu2XOWIKrR2g9fafXnWrFl08skn+x3bt28fXX311dS/f39KTk6mk046iZ577jl5Z22GAwcO0OWXX049e/akXr160eWXXy7veBy4M/y2bdvod7/7HQ0cOJCSk5Np8ODBNHfuXFq2bJl8TiSvLYsmnn76aRoxYgSlpqbSuHHj6M0331TdGRsh3lryww8/0Jlnnkmpqak0ePBguu++++Rd6puamuTzhg8fThdddFGQv1rZbGfy5557Tj6mtWO3WpmzZs0K2rH6uuuuo/Hjx6tfiADccMMNmjuhK9/o8tZbb9Gpp55KKSkp1Lt3b7rkkkuotLQ0qLw33niDRo8eTSkpKTRmzBh6++236ZJLLlHdGV3rh+0G/uqrrxIAWr16dVgeZWVldN5551FaWhr169ePbrnlFlq5cmVQW2Pn9erVi/r27UtXXHEF7d+/X3UX8oceeoiOO+44stlsfuUUFBTQ9OnTqUePHnTMMcfQggULaMuWLUF9wufz0YgRI+j222/3K5f1gffff5/uvPNOOuaYYyg1NZVmzJhBP//8cxC35cuXU3JyctCbELRw3XXX0SmnnKLr3EQF19DIwTVUHWptg6ijLQW+FYsh3NthGDweDw0aNIgA0H/+8x/VcxhX9pOamkrHHnsszZs3j1555RVqaWkJ8vnqq68IgF+70oLe9svO69u3L/Xq1YsuuOACKikpoeHDh9MNN9zgV+bLL79MI0eOJLvd7ldOJPo8Y8aMoLeWsbbwzDPP0JIlS+j444+nlJQUOu2001RfU/n9998TANq8eXPY60BE9PDDD9OAAQPk10p2d3CdjBxcJ9Whd6w5fPhwzfMC34jy888/07nnnks9evSgzMxM+n//7//R7t27deVjNufANnn48GG6/vrrqV+/fpSWlkbTpk2jNWvWBJV5zz33kCAIVF5eHjb+Tz/9RNOmTfNrB2+88UbQtdA7JnW73bRgwQI65phjSBAEv3K++uormjhxIqWlpdGQIUPovvvuo1WrVgX1oebmZurZsyc9++yzfrmye1pOTg5dd9111KdPH/nNkhUVFUHcItXSGTNm0G9+8xtd5xoFnwTh4IgQ5513Hp144onxTsMPmzZtiujVZVaisbGRjjnmGPrDH/4Qse8VV1xBU6ZMsSCr2OH555+nvn37ksPhkI+xwdmnn36qq4xzzjlHfhVxODQ3N1NGRga98cYbhvLl4Ig1uIbGHvfddx8df/zxfq+R7Wr473//S3a73e/1oGr/6IXCtddeS2eddZauc0VRpBEjRtBf//pXQ/lycJgB18nugalTp9Jvf/vbeKdhCnfccQeNGzeOJEmSj+md2CeKXEt3795NgiBQTk6O4Zz1oHOsseLg6KRYuHAh3n//faxbtw6ff/45Lr/8cqxZs8ZvI7jOgClTpuB3v/sdHnvssZjGPXz4MP785z/j888/R15eHlasWCE/63vXXXdFVBYRYd26dXjiiScsyjY2+NOf/oTevXtj6dKlhvzz8/OxadMm3XX50ksvYdiwYbjpppsMxePgsBJcQzsH1q5di4cffjgub3aIFi677DJMnToVTz31lCH/PXv24JNPPsEzzzyj6/wPPvgAbW1tuO+++wzF4+DQC66T3RMtLS3Ytm0bHn300XinYgp///vfUVNTg88++8yQf6Ra+vjjj+Pcc8/FeeedZyieXvA9QTg4QsDn8+GRRx7B4cOHIQgCxo8fj/fffx/XXnttvFMLwgsvvIDly5ejtbU14o3CjCI1NRV79+7F7bffjoaGBvTo0QPTpk3DsmXLcPLJJ0dUliAIqK2ttSjT2CEtLQ3vv/8+ioqKDPnX19djxYoVOOGEE3Sdn5mZiXfffRdJSVzOOTofuIZ2DmzatCneKZiGIAh488038eWXX0KSpIj3Sti/fz/++c9/4pxzztF1viRJ+PDDD2P66k2OxATXye6JzMxMzTcFdSWwfffU9gzRg0i0VBRFjBo1KmivPCsgECleVcDBwcHBwcHBwcHBwcHBwcHRTcEfh+Hg4ODg4ODg4ODg4ODg4EgI8EkQDg4ODg4ODg4ODg4ODg6OhACfBOHg4ODg4ODg4ODg4ODg4EgI8J30DEKSJBw8eBC9evWCIAjxToeDg4MjpiAitLa24rjjjot4g8JAcD3l4OBIZERTTwGuqRwcHIkLvXrKJ0EM4uDBgxg6dGi80+Dg4OCIK6qrq3H88cebKoPrKQcHB0d09BTgmsrBwcERTk/5JIhBsNdCVVdXIzMzMyJfr9eLnJwczJs3D8nJyVak1ynAeXYvJApPIHG4muHZ0tKCoUOHRuUVeVxPw4Pz7H5IFK6cZ3hEU08B45rK66r7IVG4cp7dC7HQUz4JYhBseWFGRoahQXuPHj2QmZkZUcX6fD4UFxfj1FNPhd1ujyimUV8zMY3yjFe+Rn05z86ZbzzabqLwZHEBRGWpdTz0FOD9zyo/M75cZ6yN2ZV4xivfeLVdIDp6qiwnUk3lddU58+U6Ex687VoXsyvyBMLrKd8YtYshPT095r5mYppBPPKNB1fO0zpfzpMjHHj/s8bPrG88Ynal/pcoPM3E7WpttzsgUeoqUfpfovA0EzdR2m5X46kHfCWISUQ6I2Y21tixY2PqayamGcQj33hw5Tyt8+U8rYUV2hdLPWXxeP+Lvp9ZX6NIlP6XKDzNxO2KbbcrlasVK1HqKhH6X6LwNBM3UdpuV+SpB3wliEmIohjTWJs2bTIU06ivmZhmEI9848GV87TOl/O0FlbE4zpjDThP63w5T2uRSG23K5WrFStR6ioR+l+i8DQTN1HablfkqQd8JYhJSJIE4H/PH9ntdj9bFEUIgiDbylf1MF923Gazwev1wm63y3ZSUhIEQYDX64UgCOjTpw9EUZRnuURRRHJyMohItiVJgs/nk21JkmRfn8+HpKQk+XhSUhJ8Ph+ISLaVPCRJQt++feUytTjZbLYgW8lPi1NSUpJ8jtK22WwyV5ZvIKdAHuwcli/z1aqbwHpiXH0+nyYnrXpiCMVJq5769u0rH1fjpFZPkiShd+/eEARBd9sjIvh8Png8HvTq1Qtutxsul0tX20tKSpJ/O51OiKKou+0x3549e8LtdsPj8ehqe8z2eDyyr8vl0tX2bDYbiAg9e/aE0+lEcnKy7raXnJwMj8eDpKQkuFwueL1eXW2P9bOePXvC4XAgJSVFd9tjzy0q89Xb9gRBQGZmJtrb25GSkhJR2wOApKQktLe3Iy0tzY+T3W6HIAia9WTFaxdjqafs+rDn5ZXXxyo9Zdetd+/efvlyPY2vnmrd60K1PZaHUT0FAJfLJWuqy+XieqpiK/N1u9262p4ZPWXxe/bsCZfLJZfZFfUUiFxT1XTG6jFquH5r1RiVQU9fDWwDffv2lX1D9dVoaapyzBeur0ZLU4kImZmZEARBV1+NxhiV9Xe1ftsZx6gA/MZ8etueGU0FOnSmra0N6enputuemTGqJEnIzMyEw+FAUlJSRG3P6BiV3TecTifcbrfMg4iQlpYmt0ut+7ke8EmQCLF06VIsXbpUbiDl5eWYMWMGysvLAQATJkxAcXEx0tPTMXbsWBQVFaFv374YPXo0CgsLMWTIEBx33HEAgCNHjmDYsGHIz8/HhAkTMHDgQOTm5mLq1Kno168fcnJyMGPGDGRmZiI7Oxvz5s3DiBEjkJ2djfnz50MUReTk5OCSSy5Ba2srfvjhB1x00UVoamrCpk2bcP7556Ourg4lJSWYO3cuevTogY0bN2LmzJmorq5GTU0NzjrrLFRVVaGxsRFTp05FRUUFnE4nTjvtND9ORUVFITkNHz4cBQUFGDVqFIYMGYL8/Hx5+VN+fn5ITklJSaqcBg0ahO+//z4kp0OHDmHPnj1BnHbv3h2Wk1Y9FRQUaHJSq6e1a9fK7SMcJ616qq2tDctJWU+VlZVwOp2w2+0oKSkJyWnUqFHYvHkz0tPTkZycLA+89+/fLw+87XY7nE4nUlNTYbPZ/GyHw4H09HQQEQYPHozq6moIggCn04kePXpAkiS43W5ZjJnt8/ng9XqRlpYm35D2798PURTh8/mQmpoKr9cLSZJkm4iQkpICj8cDALItCAL2798Pt9sNm82G5ORkuN1u2O12eXCdlJQk20pO+/fv1+QEAE6n089mnBhXIgrJidlKTpWVlbo4MR5KTnv37g3LSa2edu/eHZZTYD2lpaVh8ODBqKqqCuLk9XrhdDpxxhlnqGrEqFGjuryeJiUlYceOHTjhhBPgcrlipqft7e2orKzketpJ9FTJKZyesnrq2bOnKT1lfZPpjMvl4nqqoaeSJGH//v0x1VOHw4H9+/d3KT2NhqYOHjwYAFBYWIjRo0eH7KvRHqPW1NRE1FfNjFELCgrka6bnPqHGqaWlJSynaGnqtm3b0LdvX9jt9phpamtrK/bu3YuTTjop5mNUZb/tCmPUvXv3quqPFZqanp4u64weTtEco1ZWVuriFK0x6uDBg2XtD+QEAI2NjaoacfToUV16KRAR6ZdXDoaWlhb07t0btbW1OOaYYyL65tLn8yE7OxsXXHABUlNTdc90Ah03ptNPP11uAHpnOgFg48aNmDx5ctA3FKFmOkVRxObNmzF58mT52wy9M50+nw+rV6/GvHnz5E6gd6ZTEARs3LgRp59+ekQznR6PB1u2bMHkyZPljqp3ptPn82Hz5s04/fTTkZKSonum0+FwYM2aNZg/fz4ARPTNJct3ypQpsNlsumfZ3W43fv75Z5x55pnyN0haba+2thaNjY0YOHAgMjIyQESy6DCwGVVBEDRtIkJbWxt69uwpf2az2Vq2smy1mMpztGyjvgDkG4nNZlPlBHSsAFDa7FwtrpHE1MMvUt9AHsD/biihOKnVEwCZpzImALS3t6O2thZ9+/bFwIEDgzSiubkZ/fv3R3Nzc8RvdAlEPPQ0KSkJoihi48aNOPPMM+XPVuopq4+NGzdiypQpcr5cT+Orp1r3OrW2Z7PZsHfvXvh8PnkCzoieAh3f0re3t6Nnz54AwPXUwnz16inLnfmy87uSngLGNVVNZ6weo4brt1aMUZ1OJ3JycmSdiWQliMvlQlFREaZOnQoAEa0EMaqpyjEfgIhWghjVVI/Hg8LCQkybNk3ux1aPUXv06CGPZ9R0r7ONUYHojfn0airQoTMZGRmw2+2anKKZL/u/gWliOM2O1hhVWZ+BudTW1iIzMxNDhgwJ0oiGhgYMHDgwrJ7ylSAmwV7bw5b+BdpMYJQ2EwGbzRZ0jvI1QIG2JEk4/vjjkZqaKjcedg6bsWPlsrKZzXzZUiTlOVq5s+WbQ4YMQXJysmq+oWzWKdjSqXD8tLhqcVKzU1JSgvINxU+Zr81mw5AhQ+QBeyh+enkE2oH1xPJVXiMtfsp8k5OTcfzxx/v9XY2Tz+dDU1MTBg0ahP79+wPoEBy73Y6UlBS5HekBu4GnpaX5xdQDozHN+DIxNRLTKFczMY36mrm2oXiyG1ZtbS0GDhwot3/WxiJtA3oQSz1lZQ8dOlTWOqv1FIDsy8rnehp/PdW616m1Pa/XC5fLheOOOw49evQw3f/YN1yR6kwi6Gm88jXq29n0FIhcU9V0xuoxarh+a9UYVQ8nNTs1NRVDhgyRJxnUOEVbU7XGfFZqalJSEoYOHSrHjMUY1arxTDh0RZ1hExmxiBmPe06o+mSTVbW1tRg8eHBQv1G27VDgkyAmYdWNSyvW8OHDY+prJqYZxCPfeHC1mif7diZwdpv9MxQrmIlp1JfzNA/Wbti3SkpYoX2x1FMWj+tM9P3M+hpFLPJlk25sAozrjLWIR75WcY21nlpZrlYsrjPW+HZXnoFjVK4z1qI78YyGnvK3w5iEcnOlWMTKz883FNOor5mYZhCPfOPBNVY8lbOvRITW1la/5XFWw0xMo76cp3mEmrW3op9wnbEGnGf0fZVLebnOWId45GsV11jrqZXlasXiOmONb3fnGU89NRO3M+qMFTE7I89o6CmfBDGJWM+yjxo1ylBMo75mYppBPPKNB9d41Us8ZtrNxDTqy3lah+6yEoTrTPT9zPoaRbzy5TpjLeKRb6y5dpeVIFxnrPFNFJ4A1xmrkQg8+UqQGCHWgjRkyBDDgmTE10xMM4hHvvHgGg+egiAYeq7PDIzEnD17Nu6+++6IfHfs2IFp06YhLS0Np512Gg4ePAibzYatW7dq+qxbtw6CIKCpqUl3blowc22N+sajPoHuMwnCdSb6fmZ9jSIe+XI99Uc09RSIjy7Go067yyQI1xlrfBOFZ7zGM5HGNaunkyZNwr59+5Camopt27Zp+nA9NQY+CRIjxHppWm5uruGlaUZ8zcQ0g3jkGw+u8eBJRGhpaYn5ErxIY37++ed47LHHIvJdtGgRMjIysHPnTnz33Xfo3bs3Dh48iAkTJphJXzdC5cpueC0tLSF9582bB7vdjg0bNpiOaSW6y+MwXGei72fW1yjikS/XU2uhla9VehoqppXoLo/DcJ2xxjdReMZrPBNpXLN6+v333+P444/Hrl27cPLJJ5tNXze4nvqDT4KYBKtUn88nb5imtNkrx5jNXhcEQLaVx9k7qZnNyvd6vRAEASeffLL8KiAikjcVUtpsx3lms9dmjR8/Xi6bHWf5Km0lDyLChAkTQERhOemxAzkpeShtm80mc9XipGZLkiTnq8VJq54YV+VrMEPxUNoMoTip1RPLl5Ufip+ynogI48aNg81m09X2WB7MZu/nVh6XJCmszUBEMv9QttKPvTIvMBctu2/fvujVq5dmvmr2nj17cPbZZ2PYsGHo378/MjIyMHjwYPlVboGctHIPxTVc7ux99YHHs7KyMHv2bGRmZmr61tbWYv369bjjjjvw1ltvqZ4TyEMZU8kpFL9QXNViqmmEFd9IxVJPiTpe0TZ27Fj51WtW6ym7buPGjZNz4Xoafz3VutdZqaeB+sj1VNtW42qVnjJbGbOr6imLq4wTaEeqP1aMUcP1W6vGqAzh7hOB+uPz+TBhwgQIghDyPqFmG9VUSZIwfvx42Gw2Q/Wk5KpXUwHI48xQnKI9RmV9L/B4Zxqj9u3bV361eVpami4d27NnD8455xwMGzYM/fr1g91ux/Dhw+W3+mhpqrIspc4o/2Z2jLpy5UrMnj0bvXr10vQ9evQo1q9fjz/96U9Yvny5ZvnRGqMG1mcoTmr3cz3gkyARYunSpRg/frz8fvCysjIAQHl5OcrLywEAxcXFqKioAAAUFRWhqqoKQMf706urq+Wyjhw5AgDIz89HXV0dACA3N1de9pSTk4PW1lYAQHZ2NjweD/r164fVq1dDFEW4XC5kZ2cDAFpbW5GTkwMAaGpqQm5uLgCgrq4O+fn5shCx2bvq6moUFhYCAKqqqlBUVAQAqKioQHFxscxp586dGDhwIEpKSsJyKigowKFDh2RO9fX1sq3FyeVyQRRFZGdn+3Gy2WxIT0/Hd999p8kJAA4dOoSCggKZ088//4yBAwdi3759mpy06mnfvn0YOHAgfv75Z01OavW0du1auU5DcVKrp7Vr12LgwIFoaGjQ5KRWT3v27MHhw4dhs9lCcjpw4ACAjveCezweAEB7ezuIOv4BbG1tlYWjtbVVviG1tLTIYsRmcAOFh80YS5Ik2z6fT65fURRl2+v1wu12QxAEeDwetLe3AwDcbjccDgcAwOVywel0yvasWbNw9913y9dz5MiRWLJkCW688Ub06tULw4YNw2uvvQag453pNpsNmzdvxmOPPQabzYYlS5Zgx44d8vJtxik7OxtjxoxBjx49MHfuXOzYsUPmpeTEEMhpw4YNsNlsOHToENra2tDY2Ai73Y7LLrsMgiDA5/Phsccew/Tp0/04ZWVl4YILLgDQ8c50l8sl1w27Nu+88w4uvPBC3HbbbfjPf/4jt7Fw9ZScnOxXT4F2qHpi14/VE7M9Ho9cNwcOHAjqT9EYtMdTT10uFyRJwqZNmyBJUkz0tLy8HDabDYcPH8aePXtCcuJ6Gjs9raioQElJCQYOHIidO3eGbXsej0f+R8GMnrK643qqrqfsWj/11FM466yzYqangiDA4XB0OT0FzGtqTU2NbIfrq9Eao/74448YOHAgjhw5oquvRmOMyuKE46SmP6tXr8bAgQPR3t4e8j4BRE9Tt23bJvfLUPeJaGpqS0uL3DZDcYrmGFUQBLjdbllfO+sYddasWfjzn/8MQRBkPX3yySdxww03IDMzE8OGDcOrr74qj7uZnj766KOw2Wx45JFH5NUXmzdvlnl8/fXXGDNmDDIyMjB37ly5HQdyYtcjkFNRURHsdjvq6urg9XpRXV0Nm82G3/72t2hvb4cgCHj66acxffp0P04rV67EBRdcIOtooKZ6PB588MEHuOCCC7BgwQJ88sknOHLkiO56MjNGDawnpb6y6xuoEUz3woI4DKG5uZkA0JEjR4iISBRFEkUxyPZ6vX62z+cjj8dDWVlZ5HK5/I4TEXk8Hj9bkiTZdrvdtGrVKmpvbydJkkiSJPJ4PEREfjaLwWyv10sej4dWrVpFDofD7zjLV2kreTidTlq9ejU5nc6QnNRsl8tFWVlZ5HA4NDkpeShtlm97e7smJzXb4XDI+Wpx0qonxlWZrxY/ZT21t7dTVlaWnLcWJ7V6Yvm6XC5NTmr15HQ6adWqVeTxeEJyamtro7KyMnI4HPJ1F0Uf1dQ0UUuLj1pbJWptlaitjeTPWnZLi48OHGj0+9zWRiFtZRk1NU0kij75mrDroWXPmjWL7rrrLvL5fNTU1ETDhw+nfv360T//+U+qqKigJ598kmw2G5WXl5MkSXTw4EE6+eSTaeHChXTw4EFqbm6mbdu2EQAqKioin89H+/bto9TUVLrzzjupvLyc3n//fRo0aBABoIaGBrlOfT4fNTY2ks/XkS87zuwBAwbQp59+SpIkUVZWFg0YMIAGDhwo53reeefRAw88IHNqbGyk5ORk2rdvnyZvURRp6NCh9OWXXxIR0eTJk2n58uVB57OcmM1iiqIon8fOUctdaSt5quXlcDiorKyM2tragvpTXV0dAaDm5mYyi3joqSRJsqa63e6Y6KkoirKv0+nU5KRmcz21Tk+17nVqbc/pdFJpaalc90b1tLVVouZmUdZUrqfBesp8586dS/fff39M9JTl1NTU5Pe5K+kpkXFNVdMZq8eo4fqtFWNUh8PhpzORaGp7ezutXr2a3G53yPuEmm1UU5VjvlD3iWhqqsvlkmOG4hTNMapST9V0z8oxamNjk19fZ9dDzZ41axbdeeedqnq6a9cueuqpp8hms1FZWRkRkayn99xzDx08eJBaWlpoz549BIA2b95MRER79+6l1NRUuuuuu6isrCxITwN1hl1HpRYxPf3vf/9LkiTRF198IespO4/pKfNtaGiQ9VSLt1JPJUmS9VTt/GiOUZX3jcA4DoeDSktL5b6vbIdHjhzRpad8EsQg2A2msbExYl82aGdCoxc+n4/q6+vlRhILXzMxjfI0Ezce16gz83Q6nVRWVib/w0XUIfwAxeWntVXSzZEN2iVJIq/XS8OHD6drr71W/rskSTRw4EB6/fXX5WMTJ06kRYsWyX+vqKiQB+1ERA899BCNGzdOFlEiogceeCCoLwcOZgNx2WWX0R133EFERHfffTfdc889NGDAACopKSGn00k9e/akVatWyed/+OGHdPrpp4fk++2339Ixxxwjt6OXXnqJzj777LDXiV0fJSe9CMdTrf0wNDY2Rn0SJJZ6SsR1xio/M77x4BmJb2Cf4HpqnZ6WlpaSx+Ohnj17UnZ2tny+lXrKuBrR1M6ip0TGNZXrjHUxzfjGYyyeaHra1KS/z5vVUyKiyspKAkBbtmwhIq6ngYiFnvLHYUwi1js19+vXz/BOzUZ8zcQ0g3jkGw+u8eAZ6123zcYWBEF+ZvLUU0/1Oz548GDU1taG9WMoLy/HtGnT/PJgywIjwezZs7Fu3ToAQF5eHubMmYOZM2ciPz8fRUVFcDqdOPvss+XzV65ciYsvvjhkmW+//TauvPJKJCcnAwCuuuoqbNy4ETt37gzpx3jyt8MYi8d1Jvp+Zn2NIh75cj21Tk/z8vLw888/w+l04pxzzpHPt1JPgfhoand5OwzXGWt8E4VnPPXUSJ83qqfsHOVvrqfRA387TIyg3EAoFrG++eYbQzGN+pqJaQbxyDceXOPBMy1NwoEDTWhpkdDWBt0/LS3G/JS+aWnBm8eFgyRJ8rOtTIAZBEFQ3ZCO+QXudE06N0sKh9mzZ6O0tBS7d+9GSUkJZsyYgVmzZmHdunVYtWoVJk+eLG8w5fV6sXr1alxyySWa5TU0NCArKwuvvfYakpKSkJSUhCFDhkAURbz99tshc2HXR+s6WAUr+gnXGWvAeVrna1RPzWhqouhpXl4e1q5di0mTJiEjIwOA9XoKxEdTreonXaH/cZ2xNqZRJIqeMl+PJ/I+b1RPma/yN9fT6EFvu4v7JMhrr72GkSNHIi0tDZMnT8YPP/wQ8vy8vDxMnjwZaWlpOOGEE7Bs2TK/v7/55puYMWMG+vbti759++JXv/qVvGmPmbhaCPxmxEokJSVhxowZhmIa9TUT0wzikW88uMaDp80mYPDgXujZU0BGBmLy07NnR0ybzdg3l2xCIVI/JvQM48ePD3q1VySv+mKYMGEC+vfvj8cffxwTJ05EZmYmZs2ahfz8fGzYsAEzZ86Uz127di369OmDSZMmaZb34Ycf4vjjj8eWLVtQVFSErVu3YuvWrXj55Zfx3nvvhXzdF7s+sf4GxYp+wnXGGnCe1vlyPbVOT/Py8pCXl4fZs2fL+ma1njKusdZUq/pJV+h/XGesjWkUiaKnTFMzMyPv80b1lPkqf3M9jR70tru4ToJ88sknuPvuu/G3v/0NRUVFmDFjBi688ELs379f9fyqqirMnz8fM2bMQFFREf7617/izjvvxGeffSafs27dOlx11VVYu3Yt1q9fj2HDhmHevHnyjtdG4oZCLCtVEARkZmYaXgJrxNdMTDOIR77x4Bovnna7PeY8jcZkvtHwu/XWW7Fnzx4sXLgQO3fuxEcffYR3333X75yamhqMHz9e3rFbq+yZM2figw8+wOzZswF0LIX0eDzIzc3FnDlz5HO//PLLsEsNly9fjt/+9reYOHEiTjnlFEyYMAETJkzAzTffjKamJnzzzTdhecajj3aFMsPF4zoTfT+zvkYRr3sk11Pr9PT777/H3LlzZa5W66mSa6zrtCuVqxWL64w1vonEM17jGSNxjeop81X+5noaPeiNFddJkBdffBG33HILFixYgHHjxuHll1/G0KFD8frrr6uev2zZMgwbNgwvv/wyxo0bhwULFuDmm2/G888/L5/z4Ycf4vbbb8ekSZMwduxYvPnmm5AkCd9//73huKEQ66VpK1euNLw0zYivmZhmEI9848E1HjzjsTTNTEzlcsNI/QKXbw8bNgyfffYZvvrqK0ycOBHLli3Dk08+6XeO1+vFzp075VehaWHOnDnw+XzyTUYQBPk5y7POOks+78svvwy51HDz5s3Ytm0bLr300qBr1KtXL8ybNw/Lly8PyZM/DmM8HteZ6PuZ9TWKeOTL9dQ6PZ0xYwaAjm82GVer9ZRx7azLtztLuVqxuM5Y45soPOM1njEa16ieMl/lb66n0YPedidQtB5CihAejwc9evTAp59+iksvvVQ+ftddd2Hr1q3Iy8sL8pk5cyZOO+00vPLKK/KxL774Ar/73e/gcDiCnscCOt5XPHDgQHz66af49a9/bSgu0PGuaLfbLX9uaWnB0KFDcfToUfTu3Tsi7l6vF2vWrMF5552nmrMWiAhutxupqakRz6gZ9TUT0yjPeOVr1Lcz83S5XKiursaIESOQlpbm52/k+rS2thpe1mYkpllfM35GuSpjbtmyBb/61a9w5MgRXW2js/F0uVzYu3cvhg4d6td+AKC5uRnHHHMMmpubkZmZGVHczqCnANcZq/zM+MaDZyS+apoab50xErcz6YzeuLHSU6O+8dJTIHqaynWmc+Ybj7F4Iump2bidSWf0xuR6CsT2AWwF6urq4PP5MGjQIL/jgwYNwuHDh1V9Dh8+rHq+KIqoq6vDscceG+Tz4IMPYsiQIfjVr35lOC4APPXUU1iyZEnQ8TVr1qBHjx6afqGwZs0aQ35dDZxn/JCUlITBgwejra0NHo8nKmW2trZGpZyuALNcm5ub8fTTT8PpdIaduY8ntHh6PB44nU7k5+cHPffpcDgMx+N6ahycZ3zBNdU4uJ5ao6dA9DW1s/a/aCNReAKdkyvXU+PgempeT+M2CcIQOLsTbrZI7Xy14wDw7LPP4uOPP8a6deuCZokijfvQQw9h4cKF8mc2yz5nzhz0799f008NRmdlzcxcd6WYXS3fzhyTzbL37NnTb5bdyCyy2W8tY+3bGWLOmTPHb3+QWMSMBHpm2tPT0zFz5swgDa2vr48olhKdQU/N+PKY3StmJL6BmtoZdKa7xgz0jYWemvGNl54C0dPUROnznV1nEiVmZ9BTM75dOSbX0zhOggwYMAB2uz1o9UVtbW3QKg2GwYMHq56flJQUJPLPP/88nnzySXz33Xd+7242EhcAUlNTkZqaGnQ8PT09YnFgSE5Ojsg3KSkJ8+fPN/S+ZaO+ZmIyRMrTTNx4XCOGzsjT5/NBEATYbDb5vdlEJG9YFUlM9jwfKy8SGI1pxtdMTKNcuxtPm80GQRBU23Z6enpEsZToDHoKcJ2xys+sLxBbnpH4Bmoq15nwiMe9Ix7XKF56CkRfU7nOWBOzK43FE0VPge6lM1bE7Iw8o6GncdsYNSUlBZMnTw5anrVmzRq/TQWVmD59etD5OTk5mDJlit8FeO655/DYY49h9erVmDJlium4nQnhXkVkha+ZmGYQj3zjwbWr1QsHR3cC1xlr/Mz6xiMm11QODnPgOmOdb6Lw5OCIFeL6dpiFCxfirbfewttvv43y8nL85S9/wf79+3HrrbcC6Fjed/3118vn33rrrdi3bx8WLlyI8vJyvP3221i+fDnuvfde+Zxnn30Wf//73/H2229jxIgROHz4MA4fPoy2tjbdcSNBLDu5KIrIyckxFNOor5mYZhCPfOPBNR48iQgtLS3yo2SxgJmYRn05T2thRT/hOmMNOE/rfLnOWIt45BsPrlb1k67Q/7jOWBvTKBJFT83E7Wo6kyg8dbc7ijOWLl1Kw4cPp5SUFDr99NMpLy9P/tsNN9xAs2bN8jt/3bp1dNppp1FKSgqNGDGCXn/9db+/Dx8+nAAE/SxatEh3XD1obm4mANTc3ByRHxGRx+OhrKws8ng8Eft2JXCe8YfT6aSysjJyOp2my/L5fNTY2Eg+ny8KmXVuJArXcDxDtR8zGhjNsjpz/4smOM/OAa6pkYPz7ECs9NRMeZ29/0ULicKTqHNz5XoaOTjPDkRDT+O+Mertt9+O22+/XfVv7777btCxWbNmYcuWLZrl7d2713TcSEAxnsHrbBvTWIV45BsPrvHiKUmS/DxdLGAmplFfztNaWKF9sdRTFo/rTPT9zPoaRbzukVxnrEM88o1XnXalcrVicZ2xxjeReHKdsQ6JxFMP4vo4THdArJem/fDDD4aXphnxNRPTDOKRbzy4xoMnuznFY3BkJKZRX87TWnSXx2G4zkTfz6yvUcQjX64z1iIe+caDa3d5HIbrjDW+icKT64y1SBSeetsdnwQxCbZjrc/ng8/nC7JFUfSz2W63wP92vlUe93q9fjZrNF6vV95tGehoVEQEr9crf2a2JEl+tiiKSE5OxoUXXijPwrHjLF+lreRhs9lw0UUXwWazheWkxw7kpOShtJOTk2WuWpzUbEEQ5Hy1OGnVE+MqCIIuHkqbIRQntXoCgIsuugh2u12Tk1o92Ww2XHDBBUhOTtbV9lgeQMdOy71795Z34mbHJUkKazOwmd1wdriYemyjvjabTd6RWosTy1dpB0KLk1bM3r17yzEjzVePbyAPQRDQp08fv/PUOGnZgVzVYqpphNG3uIRCLPWUiJCUlIR58+YhKSkpJnrKrtsFF1wgc+V6Gn891brXWamnynOV+XM9jV6+RvQ0kGtX1lPAuKbq7atK/TE6RmU6o9VvrRqjMoS7TwTqD9ChM0lJSSHvE2q2UU0VBEEe8xmpJyVXvZpqt9sxb948JCcnh+QUzTFqqH7bGceoZsd8ZjRVWZ7VY1Q2zmSaGOr8aI1RA+szVEy1+7ke8EmQCLF06VKMHz8eU6dOBQCUlJQAAMrLy1FeXg4AKC4uRkVFBQCgqKgIVVVVAIDCwkJUV1fLZR05cgQAkJ+fj7q6OgBAbm4umpqaAHS8+aa1tRUAkJ2dDYfDgaNHjyI7OxuiKMLlciE7OxsA0NraipycHABAU1MTcnNzAQB1dXXIz8+HJEmoqKhAQUEBAKC6uhqFhYUAgKqqKhQVFQEAKioqUFxcLHMqKytDQ0ODLk4FBQU4dOiQzIm9pzk/P1+Tk8vlgiiKQZwkScKBAwdCcgKAQ4cOBXFqaGhAZWWlJieteqqsrERDQ0NITmr1tHbtWrlOQ3HSqqeGhgbU1taG5BRYT7t27cKmTZsgSVJITgcOHAAAOBwOeDweAEBbWxvcbjeIOmZnmXC0trbKN6SWlhZZjNiGRoHC09LSAqBD5Jjt8/nk+hVFUba9Xq88E+zxeNDe3g4AcLvdcDgcADre+e10OmV71qxZuPvuu+F0OuF0OkFEcDgccLvdAID29nY/Tl6vFzt27MAZZ5yB9PR0TJo0CSUlJbDZbNi6dasmp+zsbNhsNjQ2NvpxYgjFiW24zDgREZxOpyYnl8sFAHA6nbLNOBER2tragjixutGqJyb+Sk6Bdqh6YrHUODEeBw4cCOpPaoP+SBFPPXW5XPB4PMjOzobH44mJnpaXl0OSJGzatAm7du0KyYnraez0lHFqaGhAWVlZ2Lbn8XjkvmlGT1ndcT1V11PGiWlrLPSUDc4D7a6gp4B5Ta2pqZHtcH01WmPUvLw8NDQ04ODBgzEbo7I44Thp6U9DQwNaWlpipqlbtmxBaWkpJEmKmaY2NDTgu+++gyRJYTkF1pPRMSrTRb19ldmxHqPOmjULf/7zn0FEaG9vD9IfIFhTt2/fjmnTpsl6WlVVBbvdjp9//lmT09q1a2G329HY2OjHiV2PcJyiOUYVRVH1PhGunsyOUbU4eb1eOZdAjWC6FxbEYQhs05UjR44QEZEoiiSKYpDt9Xr9bJ/PJ29S5HK5/I4TdWxgpLQlSZJtt9tNq1atovb2dpIkiSRJkjc6UtosBrO9Xi95PB5atWoVORwOv+MsX6Wt5OF0Omn16tXkdDpDclKzXS4XZWVlkcPh0OSk5KG0Wb7t7e2anNRsh8Mh56vFSaueGFdlvlr8lPXU3t4ubzoVipNaPbF8XS6XJie1enI6nbRq1SryeDwhObW1tVFZWRk5HA75uvt8PmpqaiKfzyfnx46HspWbFLHPjJOWHS5mKLuuro5aWloi8v3d735Hc+fOpaqqKqqtraX6+nqqqamR6y6QkyRJ9P333xMAamhokHMPxTVUfGWugedUVVVRSkoKNTc3B/lWVlYS8L+NnJOTk2nUqFH06KOPyudq1Q2LKYqiXx5KW6uelDzVODkcDiorK6O2trag/lRXV0dAdDdGjaWeSpIka6rb7Y6JnoqiKPuyzby4nsZfT7XudWptz+l0UmlpqV/dG9FTSZJIFEW5/3E9Vbe18rVKTwN1vCvqKZFxTVXTGavHqOH6rRVjVIfD4aczkWhqe3s7rV69mtxud8j7hJptVFOVY75Q94loaqrL5ZJjhuIUzTFqqDFUZxqj1tXVUXNzc8h8A22lnh49epQ8Hg/t3LmT3G63Jqfc3FxZTwN1hl3HSDRVK9+9e/dSSkoKNTU1BfmG0lM99WRmjKqsz8A4DoeDSktL5b6vbIdHjhzRpadx3xi1qyMtLQ1Ax7IxBqWdlJQUZPt+mSFjy3WU5yiXRKrZF1xwgV98dlwQBNm22Wxy2Upb6as8rpW73W6H3W7H+eef7xdTjZOaTb/MyiYlJcmxwvHT4qrFKdBOT08PyjcUP2W+SUlJurlGwkNpB9aTWr5a/JT5pqWlBbUFNU5sZlYQBHn5G1sOFwjl8jE1m9VnYHmhbGUZajGV5wTa/fv3lz/r9d2zZw8uuugijBgxIuh8JdT4CYKguoROi5OazZZUqp3z5ZdfYvbs2cjMzNQs/7vvvsPJJ58Mt9uNH3/8EQsWLMBxxx2HW265JWTugddHK7dAWwr49jHQj3222+1y+2dtzIrl27HW05SUFL9+ZLWest9KX66n8ddTrXudWtvzer1R0VOWf+Bxrqf6uFqpp6F0vCvpKRC5pqrpjNVj1HD91qoxql5OgXaPHj1060y0NFVrzGelpqampgbdN0Lxi8YYNVTf60xjVC09DaVpano6ZsyYkJwC81bqTDhOkYxRV65cidmzZ8t/U/MNpafh6snIGDWwPtX8lJrKYLfbZd0LB/44jEkE3vSsjlVbW2soplFfMzHNIB75xoNrPHiS4lnQWMFIzNmzZ+Puu++WfUeMGIEnn3wSN998M3r16oVhw4bhjTfekM8XBAGbN2/Go48+CkEQsGjRIlRUVEAQBGzdulU+Lzs7G2PGjEF6ejrmzJmj+41SDNu3b4fNZpOXnTY2NsJms+GKK66Qc33yyScxffp0P7+VK1fi4osvDll2ZmYmBg0ahOHDh+Oaa67BWWedFfJtWEB86hOwRvu4zlgDztM6X66n1ukp4/r444/HTE9ZzFjXqVX9pCv0P64z1sY0ikTRUyNxzerp4sWLUVVVBUEQ5Mc4AK6n0YLedscnQUwi1oJUUlJiWJCM+JqJaQbxyDceXONSL0Rw1tUB7e0x/XHW1QEGRZA9s/jCCy9gypQpKCoqwu23347bbrsNO3bsANDxnOrJJ5+Me+65B4cOHcK9994rP9PIUF1djcsuuwzz58/H1q1bsWDBAjz44IMR5TJhwgT0798feXl5ADqew+3fv7/8vKzT6UReXh5mzZol+zQ1NeGHH34Ie5Nhz5ICwM8//4wtW7bgzDPPDJsTuz6xRHeZBOE6E30/s75GEZd8uZ5aqqdAx54GM2fOlD9bradA7DW1u0yCcJ2xxjdReMZLT9HeDucv+2RECqN6Ggiup9GD7nYX8mEZDk2w5y2NPL/JnmFnz911V3Ce8YfT6aSysjJ5/wEiImprI+oYPsf+p61Nd+6zZs2iu+66S/48fPhwuvbaa+XPkiTRwIED6fXXX5ePTZw4kRYtWiR/rqqqIgBUVFREREQPPfQQjRs3Tn6mkIjogQceIADU2NgoHwt8tjsQl112Gd1xxx1ERHT33XfTPffcQwMGDKDS0lLyer3Us2dPWrVqlXz+hx9+SKeffromV5Zneno6ZWRkUHJyMgGg//u//wt5jcwiHE/V9vMLzGhgNMvqzP0vmuA8OweC+gTXU66nv6Cz6KmZ8jp7/4sWEoUnUefmyvWU66kWYqGnfCWIScR6VrampsbwrKwRXzMxzSAe+caDazx4UoyXGZqNTUTyDtCnnnqqfFwQBAwePBi1tbVh/RjKy8sxbdo0v+cKA5cF6sHs2bOxbt06AEBeXh7mzJmDmTNnYt26dSgoKIDT6cTZZ58tn69nqSEAfPDBBygqKsK2bdvwySefYOXKlWG/CWA8Y12v3WUlCNeZ6PuZ9TWKeOTL9dQ6Pc3Ly0NhYSGcTifOOuss+Xwr9RSIj6Z2l5UgXGes8U0UnvHUUyN93qieMl/lb66n0YPedsc3RjWJWAvSnj17MGjQIN3vQDbrayamGcQj33hwjUu99OiB1kOH0LNnTz+x1ROzpaUFmZmZEcck6nhdYc8ePSLL9RewZXiBm8epbUanROCgPVoiPHv2bNx1113YvXs3SkpKMGPGDOzZswf5+fmora3F5MmT0atXLwAdGymuXr0aDz30UNhyBw0ahNGjR0MQBIwbNw6VlZV4+OGHsXjx4pAbPbndbss21tNCd5kE4ToTfT+zvkYRl3wN6imLaURTE0VP8/Ly0NjYiEmTJsVUT4HYa2p3mQThOmONb6LwjIeeAh1a5pYkGOnxRvVULYdogOspnwSJGVgnZW8osNvtfrYoihAEQbaVHZNVEjtus9ng9Xpht9tlOykpCYIgyPaMGTMgiqLcWURRRHJyMohItiVJgs/nk21JkpCUlIRzzjlHjqk87vP5QESyreQhCAJmzpwJn88Hn8+nyclmswXZSn6hOLFzlHZycrLMleWrxinQBiDny8rUqhu1epo5c6acuxonrXpiCMcpsJ6ICDNnzoQkSfL5WvyU9SQIAs4+++ywnNhnoo73cQuCAAgCeg4a5Lf7MhNr5e7TgTaIAJ8PyMgA/eJrs9nkstVsuY8QoWdGRlDMcDZDz549g3gozwm02Tk9FP8kSJKEcePGYeXKlf/jBGD9+vWyH6t3JdQ4secuH3/8cUycOBG9evXCzJkz8dRTT6GxsVF+3pKIkJubiz59+mDixIlBuTObfVbmy2KKogi3243U1FTNuunVq5ef6Mt1rShHrZ4CoZWXWn9S7kQfLcRaT+12O6ZPnw673e7XN63SU3bdlKuEuJ7GX0+17nVqnAD49x+jegqAJEnWVAngeqqhp3PmzJFzjIWeCoKAjIyMIP7KcrqCnrLYLCYQXlPVdMbqMSqAkP3WqjEqQzhOjAezmc4QdWz4GOo+ES1NBSCP+UJxiqam2mw2TJ8+PaSORn2MarOh56BBrPHGdowKfTqq1AJBEGQ9ZccCz1H+VjuHgekpAJmTUk8DdUZZtpLTySef7KenmZmZQXrKwPR00qRJYXkrNVEQBLnNeTwepKamhqwnI2NUJcJpvdr9XA/44zARYunSpRg/fjymTp0KACgpKQHQsYypvLwcAFBcXIyKigoAQFFREaqqqgAAhYWFqK6ulss6cuQIgI6Na9hOvrm5uWhqagIA5OTkoLW1FUDHjsEOhwNVVVXIzs6GKIpwuVzIzs4GALS2tiInJwdAxyY3ubm5AIC6ujrk5+dDkiRs374dBQUFADo24CksLAQAVFVVybsTV1RUoLi4WOZUVlaGffv26eJUUFCAQ4cOyZzq6+tlW4uTy+WCKIpBnCRJws6dO0NyAjo2GwrktG/fPlRWVmpy0qqnyspK7Nu3LyQntXpau3atXKehOGnV0759+1BbWxuSU2A97dq1CwUFBZAkKSSnAwcOAAAcDof8LV5bWxucTieICK2trfJgoLW1Vb4htbS0yILV0tLiJzbMbmlpAfC/2XegQ4hY/YqiKNterxetra0g6lgW197eDqBjdtjxy2ZULpdL3jzJ5XLJ8Z1Op3y+1+uVZ93b29vlnNra2uQbvcfjkQdhbW1tct20tLTg//7v/7Bnzx78+c9/xo4dO/Dhhx/i3XfflXm1tLSgpqYG48ePx+bNmzU5CYKAc845Bx988AFmz54Nj8eDUaNGwePx4Pvvv5eXMLpcLnzxxRe4+OKL4XK55I0FnU6nbCvrprq6Gvv378eBAwfw+eef4x//+If8D4BWPfl8Prjdbr96CrRD1RO7fuz6Mtvj8ch1c+DAgaD+FI1vGOOppy6XCx6PB9nZ2fB4PDHR0/LyckiShIKCAuzatSskJ66nsdNTxmnfvn0oKysL2/Y8Ho+sN2b0lNUd19PQenrmmWeCiGKip2wyLdDuCnoKmNfUmpoa2Q7XV6M1Rs3Ly8O+fftw8ODBmI1RWZxwnLT0Z9++fWhpaYmZpm7ZsgVbtmyBJEkx09SGhgasWbMGkiSF5RRYT0bHqEwXmf501jEqm/RiushiOxwOWVPZOQD8zmGclBOFAHD11Vdjz549WLhwITZv3owPP/wQ7733nh+nHTt2yHrKcg/k1NbWhpkzZ+KDDz6QH3sZO3Ys3G63PD5lnL744gvMnz9f5qemqYzzoUOHUFlZicrKSqxatQqvvPIKZv/ymvJQ9WR2jBpYT0p9ZVofqBFM98KCOAyBbbpSW1tLRESiKJIoikG21+v1s30+n7xJkcvl8jtO1LGBkdKWftkgx+PxkMfjoR9//JEcDgdJkkSSJMkbHSltFoPZXq+XvF4v/fjjj/IGMuw4y1dpK3m4XC766aefyOVyheSkZrtcLsrKyiKHw6HJSclDabN8HQ6HJic12+l0yvlqcdKqJ8bV6XRqclKrp/b2dnnTqVCc1OqJ5et2uzU5qdWTy+WiH3/8Ua4LLU5tbW1UVlYmtxlWZmtrq5yb8ngoW7lJEfvMOGnZ4WKGstnGU8x3+PDh9OKLL/qdwzaaYr4TJ06kRx55RP68fft2eeMplvdXX31Fo0ePptTUVJoxYwa99dZbBIAaGhrI5/PJm0B99dVXqlxZ/H/84x8EgL7++mv5+CWXXEJ2u13exEqSJBo6dCitWbMmJNfKykoCIP/Y7XY6/vjjacGCBVRbWxuyntj1YcdZvkpbq54CN54KzMvhcFBZWRm1tbUF9af6+npdG0/pQTz0lPXFH374we8z426FnrK//fjjj375cj2Nr55q3evU2p7T6aTS0lK/a2lETyVJIlEU5f7H9VRbT2tqauRjVusp+1tLS4tfvl1JT4mMa6qazlg9Rg3Xb60YozocDj+diURTHQ4H/fTTT7KvGqdoa6pyzBfqPhFNTXW73fTDDz/I5cZijCpJkjyeYfno1dRYjlFnzZpFd955p5zv8OHD6aWXXvI7h+kn8w3UV6ZTmzdvlnP48ssv/fR0+fLlsp5KkkR79uyR9ZRdRzVOr776qnweO67UU3b+0KFDKScnJyTXUHp65MiRsPVkZoyqrM/AOA6Hg0pLS8npdAZpRG1trS49FYhU1vBxhEVLSwt69+6N5uZmZGZmRuTr9XqRnZ2N+fPnx/w5/liC84w/XC4XqqqqMHLkyLDP7IWDZOJ5y66GaHHdsmUL5s6di6NHj3a6tgGE5xmq/ZjRwEBwPQ0PzrNzgGtq5OB62oFY6amZ8jp7/4sWEoUn0Lm5cj2NHFxPOxANPe2+rSRG8P2y5CdWsXbv3m0oplFfMzHNIB75xoNrPHjSL8uKYzn/aSamUd/OwFMURbz66qu6bjBdiSdgjfZxnbEGnKd1vp1BZ2LhGy+dUcaNhZ6a9TUKq/pJV+h/XGesjWkUiaKnZuJ2NZ3heuoPPgliErFuvI2NjYYboRFfMzHNIB75xoNrvOol1v9smo1p1DfePM844wxcd911hnyNxowVrOgnXGesAedprW+8dSZWvvHgqYwbKz0162sEVvWTrtD/uM5YG9MoEklPzcTtSjqjjMn1lL8dxjSs2tFbKxbb7CpWvmZimkE88o0H13jwVO58HyuYiWnUl/O0FlZoXyz1lMXjOhN9P7O+RhGPfLnOWIt45BsPrlZpX1cYo3KdsTamUSSKnpqJ29V0JlF46tU9vhLEJGK9NG3Hjh2Gl6YZ8TUT0wzikW88uMaDJxHJbzOIFczENOrLeVqL7vI4DNeZ6PuZ9TWKeOTLdcZaxCPfeHDtLo/DcJ2xxjdReHKdsRaJwpM/DtNNwV5VFEtfMzHNIB75xoNrPHjG+gZjNqZRX86TIxy4zljjZ9Y3HjGN+nKdsRbxyJdrqjFwnbHON1F4cp2xFonCUw/4JEiU4PP55JknpS2Kop8tKd4Fz2zlca/X62ezhuP1emGz2TBp0iT53fVEJL9LW2lLkuRni6IIu92OiRMnyuWx4yxfpa3kAQCnnXaa32ctTnrsQE5KHkrbbrfLXLU4qdlEJOerxUmrnhhXItLFQ2kzhOKkVk+SJOG0006DIAianNTqCQBOPfVU2O12XW2P5cGQnp4OQRD8jrPrF8pmUF6jUHa4mHpso76CIPjtGK3GieWrtAOhxUkrZnp6up9vJPnq8Q3kAQA9evSQz9HipGUHclWLqaYRdrs9yD9aiIWeEhFsNhsmTJgAm80WEz1l1+3UU0+V8+Z6Gn891brXWamngWVwPY1+vkb0NJBrd9BTZZxAO1L9sWKMynRGq99aNUZlCHefCNQfpjM2my3kfULNNqqpRCSP+YzUk5KrXk0VBAETJkyA3W4PySmaY9RQ/bYzjlHNjvnMaKqyPKvHqEDHODMwrh5NZb7sHL2aGlifoWKq3c/1gE+CRIilS5di/Pjx8rNu27dvBwCUl5ejvLwcAFBcXIyKigoAQFFREaqqqgAAhYWFqK6ulss6cuQIACA/Px91dXUAgNzcXDQ1NQEAcnJy0NraCgDIzs5Ge3s7tm/fjuzsbIiiCJfLhezsbABAa2srcnJyAABNTU3Izc0FANTV1SE/Px8+nw+FhYX46aefAADV1dUoLCwEAFRVVaGoqAgAUFFRgeLiYplTaWkpSkpKsG3btrCcCgoKcOjQIZlTfX29bGtxcrlcEEUxiJPP50NRUVFITgBw6NAhFBQU+HEqKSlBZWWlJieteqqsrERJSUlITmr1tHbtWrlOQ3HSqqeSkhIcOXIkJKfAetq5cyfy8vLg8/lCcjpw4AAAwOFwwOPxAADa2trQ3t4OIkJra6ssHK2trbJ4tLS0yGLU0tKiKjwtLS0A/vcaK6BDfFj9iqIo216vVy7H4/Ggvb0dAOB2u+FwOAB0vO6KfXPgcrngcrnk3Nva2kBEcDgccLvdAID29nY/TuzGzTixHBk/LU6BNuPEEIpTW1sbAMiciAhtbW1hOTmdTj9+brdbjh+Kk1o9+Xw+OJ1OXZzU6onFUuPEeBw4cCCoP0VjWW489dTlcsHtdiM7OxtutzsmelpeXg6fz4e8vDzs3LkzJCeup7HT04qKCmzbtg0lJSUoLS0N2/Y8Ho/cN83oKas7rqfqeso4tba2gohioqdscB5odwU9Bcxrak1NjWyH66vRGqPm5eWhpKQENTU1uvpqNMaoLE44Tlr6U1JSgubm5php6pYtW7B+/Xr53hMLTW1oaMC3334Ln88XllNgPRkdo7I+prevMjteY1Sm44H6A1irqayccJyiOUZ1Op1hOanVk9kxqhYnr9cr5xKoEUz3woI4DKG5uZkA0NGjR4mISBRFEkUxyPZ6vX62z+cjj8dDWVlZ5HK5/I4TEXk8Hj9bkiTZ9nq9VFxcTE6nkyRJIkmSyOPxEBH52SwGs1kOxcXFckx2nOWrtJU83G43bd++ndxud0hOarbL5aKsrCxyOByanJQ8lDbL1+l0anJSs10ul5yvFietemJcXS6XJie1empvb6esrCzyeDwhOanVE8uX1W8ofsp6crvdtG3bNpmDFqe2tjYqKysjh8MhX3efz0ft7e1ybsrjoWyfz0eNjY1+nxknLTtcTD22UV9Jkqi9vV3OR40Ty1dps79rcY0kppl8Q12PQJv1sVCc1GwlT7WYDoeDysrKqK2tLag/NTQ0EABqbm4ms4iHnkqSRF6vl7Zt20Zerzcmesp+tm3bRm63W5OTms311Do91brXqbU9p9NJpaWl5HA45DKN6KkkSSSKotz/uJ5am28kesp8u6qeEhnXVDWdsXqMGq7fWjFGdTgcfjoTiaY6nU7avn07eb3ekPcJNduoprrdbiouLpavRSw01ePxyOPMUJyiOUYN1W874xhVr85EU1MZT3YdI9FUo/n6fD6/cWao86M5RlXWZ2Ach8NBpaWl5HQ6gzTi6NGjuvSUvx3GJFJSUgDAbymj0lbuUMts3y8zZDabLegc5fua1exTTjnFLz47LgiCbNtsNrlspa30VR7Xyt1ut8Nut2PChAl+MdU4qdn0y6xsUlKSHCscPy2uWpwC7dTU1KB8Q/ELzF0v10h4KO3AelLLV4ufMt+UlBS/JfVanNjMrCAIEARBLpMtawuMG8pm9RlYXihbWYZaTOU5WrYZX6WfFj8tX+UxNU56Yuo5P1JfNR7K5Y1avmr1JAUs4Q70Y5/tdrvc/lkbs2L5dqz1NCkpya8fWa2nDMqYXE/jr6da9zo1Tl6vNyp6yvIPPM711Jp8I9FTPb5dQU+ByDVVTWesHqOG67dWjVH1cgq009LSdOtMtDQ1JSUl6NpGk5NaPSUnJ/vdq8Lxi9YYVavvddYxarTGfHo1lelMOE7RzDfwMZpQ50drjBpYn1p5AcH9huleOPDHYUzCF6UljHpjFRUVGd6p2YivmZhmEI9848E1Hjzpl2XQSoGxGkZizp49G3fffXdEvjt27MC0adOQlpaGSZMmoby8HIIgYOvWrZo+69atgyAI8tJRMzB6bVkOBw8e1PR999130adPn6jFZGUOHz48Yj+g+7wdhutM9P3M+hpFPPLleuqPaOopYIyrGT01GpOV2Zn01MpytWJxnbHGN1F4xkNPjcSNhp5WVVVBEAT5MQ41cD21Vk/5SpAuhsDZtFj4molpBvHINx5c48FT7Rs6qxFpzM8//9zv2wk9WLRoETIyMrBz505kZGQgLS0NBw8exDHHHBNxvkahleu+ffswZswYHD16FJmZmX5/O+uss3Dw4EH07t07qjE5woPrjDV+Zn3jEdOoL9dTa6GWr5V6qhWTIzy4zljnmyg849X3IolrVk979uyJ3r17o7KyEscff7yhfI2C6+n/wCdBTMLqHb0DY40dOzamvmZimkE88o0H13jwVFvWZjWMxOzXr59s6/Xds2cPLrroIr/Z4549e0YU1wxC8Vy5ciVmz54ddIMBOpaOHnvssVGPaSWs0L5Y6imLx3Um+n5mfY0iHvlyPbUWWlyt0tNQMa2EVdrXFcaoXGesjWkUiaKnRuJGS09HjhypP8kogOupP/jjMCahfB1ULGJt2rTJUEyjvmZimkE88o0H13jwJCJ5l+hYwUhM5XLD9vZ2jBgxAk8++SRuvvlm9OrVC8OGDcMbb7whny8IAjZv3oxHH30UgiBg0aJFKCsrC1q+nZ2djTFjxiA9PR1z5szB3r17I+Kyfft22Gw2eRf2xsZG2Gw2XHHFFXKuTz75JKZPn+7nt3LlSlx88cWqZbLlhjU1NfI1evfddzFs2DD06NEDl156qfx2ECW++uorTJ48GWlpaTjhhBOwZMkSvzbx4osv4pRTTkFGRgaGDh2K22+/Xd5Z2yys6CdcZ6wB52mdL9dT6/SUcV2yZEnM9XTkyJFYvHhxl9ZTK8vVisV1xhrfROEZDz01Etesni5evFj1cRiup7HVUz4JYhKxXOIjCAL69u1rKKZRXzMxzSAe+caDazx4doiYCz5fe0x/AJfhGxub1X3hhRcwZcoUFBUV4fbbb8dtt92GHTt2AOh4bdvJJ5+Me+65B4cOHcK9994bNBtcXV2Nyy67DPPnz8fWrVuxYMECPPjggxHlMmHCBPTv3x95eXkAOl5L179/f/n1cXa7HXl5eZg1a5bs09TUhB9++EHzJhPIc+PGjbj55ptx++23Y+vWrZgzZw4ef/xxv3O//fZbXHvttfjzn/+MoqIiLFu2DO+++y6eeOIJ+RybzYZ//OMfKCkpwXvvvYfc3Fzcf//9EfHVghX9hOuMNeA8rfPlemqtngLAjz/+iJkzZ8qfrdTTO++8E6WlpfjnP/+J9957r0vrqZXlasXiOmONb6LwjJee+nztfht6RgKjehoIrqex11P+OEyUwDZhsdvtfrYoihAEQbaVnYzt8MuO22w2eL1e2O122U5KSoIgCLI9atQov3JEUURycjKISLYlSYLP55NtSZKQlJSEE044QY6pPO7z+UBEsh3IY/To0fD5fPD5fJqcbDZbkK3kF4oTO0dpJycny1ztdrsmp0CbiOR8WZladaNWT6NHj5ZzV+OkVU8M4TgF1pMkSRg9ejQkSZLP1+KnrCegYxldOE7sM/3yPm5BECBJDmzaNMBEazeOGTPaQNSxM7Vy92ctmyE1NRUAMH/+fNx2220QBAH3338/XnrpJaxbtw4nnXQSBg0ahKSkJGRkZGDQoEEQBMFv93NJkvDaa6/hhBNOwAsvvACbzYYxY8aguLgYzz77LIhIrncl2LWz2WxyXjNnzsTatWtx2WWXYd26dbj++uuxYsUKlJeX48QTT0RBQQH+8pe/yOdnZ2fjlFNOkZ/9DOTKPjOer7zyCs4//3w88MADEARBLnP16tWQJAmCIOCJJ57AAw88gBtuuAGCIOCkk07Co48+igceeAAPP/wwBEHAXXfdJec+YsQILFmyBH/605+wdOlS1X+gtPLS6k9WIVZ6arPZMHz4cLluY6Gndrvdbwks19P466nWvU6NEwC/vsH11Do9LSsrw4knnogNGzbgnnvuiYmeXn/99RAEASNHjuw2espisjihNFVNZ6weozKd0eq3Vo1RGcJxYjyYzXSGiOD1ekPeJ6KlqUQkj/lCcYqmpgqCgOHDh8v3DC1O0RyjEjnjpqfnnNOqS0eV9wFBEIL0FAAeeOABvPTSS1i7di3Gjh0r62nPnj0xaNAgAJBXazAwPX3ppZdAREF6qtQQFp/9ZroEdOjpunXrcNlll2Ht2rW44YYb8N5776G0tBQnnXQSNmzYgIULF8q+33zzjaynjJMa77S0NAD+egoAY8aMkfUU6OgfTzzxBB588EFcd911EAQBo0aNQn19PR588EE88sgjICLcddddcg5Mb2+//Xa89tprQXqqlZdSUwM1Qg/4SpAIsXTpUowfPx5Tp04F0LH0CADKy8tRXl4OACguLkZFRQUAoKioCFVVVQCAwsJCVFdXy2UdOXIEQMdMHesMubm58i7AOTk5aG1tBdBx029ra8NPP/2E7OxsiKIIl8uF7OxsAEBraytycnIAdMzq5ebmAujoZPn5+RBFEXl5efjpp58AdMw4FhYWAgCqqqrk5VgVFRUoLi6WOZWWlqKgoADbtm0Ly6mgoACHDh2SObHlUfn5+ZqcXC4XRFEM4iSKIn744YeQnICO2dWCggKZ08aNG1FQUIA9e/ZoctKqpz179qCgoAAbN27U5KRWT2vXrpXrNBQnrXoqKCjAkSNHNDmp1dPOnTvx3XffQRTFkJwOHDgAAHA4HPB4PAAQteVmRkBE8Hg8aG9vBwC43W44HA4AgMvlgtPplG02GHM4HHKbGTt2LNxut3x80KBBqK2tRVtbm3yj93g8EEURROTHtaWlBeXl5Zg2bRpaW1tlAZ04caKcW0tLC4D//UMNdIgriy+KIlpbWzF79mysW7cObW1tyMvLw4wZM3DWWWdh3bp1yMvLg9PpxNlnny1zWrlyJS688EK4XC4AgNPplG2HwyHn3tLSAo/Hg/Lycpx++uny8dbWVpxxxhmy7fP5sHnzZjz22GPo1asXevbsiczMTPzf//0fDh06hMOHD4OIkJubi3PPPRdDhgxBZmYmbrzxRtTX16OlpUXmBPyvTXi9Xtn2eDxy3Rw4cCCoP0VjWW489dTlcsl9U2mza2yFnpaXl0MURXz33XfYuXNnSE5cT2OnpxUVFdi2bRsKCgpQWloatu15PB65b3I9tU5PWR9zOp0466yzuJ7qgFlNrampke1wfTVaY9S8vDwUFBSgpqZGV1+NxhiVxQnHSUt/CgoK0NTUFDNN3bx5M9auXQtRFEPeJ6KpqfX19Vi9ejVEUQzJKZpj1Fg/BqNEc3Ozbk1lk15M54gIp556KhwOB9xuNwRBwMCBA+W6aWtrk7m1trbKmgr879GN7du348wzz4QgCHKZ06ZNA/C/iQ6mqawcIFhTp02bhnXr1sHr9WLdunWYM2cOzjnnHKxZswaFhYVwOp2YNGmSzOmLL77AxRdfLI+DAH9NZb/b2trQ3t6OsrIyTJ8+3e8+cdppp/nlxR79yczMlDX1j3/8Iw4dOoT29na0tLQgNzcX5513Ho4//nj06tULN9xwA+rr69He3g6fzyfHZfcJwF9TvV6v/H9NoEYw3QsL4jCE5uZmAkB1dXVERCSKIomiGGR7vV4/2+fzkcfjoaysLHK5XH7HiYg8Ho+fLUmSbIuiSFVVVeRyuUiSJJIkiTweDxGRn81iMJuVX1VVRW632+84y1dpK3l4PB7au3evHF+Lk5rtcrkoKyuLHA6HJiclD6XN8mXXSI2Tmu12u+V8tThp1RPj6na7NTmp1VN7eztlZWWRx+MJyUmtnli+Xq9Xk5NaPXk8HqqsrCSfzxeSU1tbG5WVlZHD4ZCvuyiK1N5eT15vq/wjim3k8bSEtD2eFqqrO+D3WRTbQtqsDK+3ldrb68nn88nXhF0PLXvWrFl01113kc/nI5fLRcOHD6cXX3zR75yJEyfSokWLZN+JEyfSI488In/esWMHAaCioiLy+Xx0ySWX0E033eSXx+eff04AqKGhQa5Tn89HjY2N8nnsOPMpLi4mQRBo165dlJycTE1NTfTyyy/TlVdeSY8++iidccYZ8vlut5syMzNpy5Ytmlxzc3MJAB0+fFjmsXjxYr9zXnrpJerdu7ecU1paGj399NO0a9cuKi0tpZ07d9KuXbto165d5PV6qaqqitLS0uiuu+6i9evX044dO+jNN9/047p8+XLKzMwM4sdsh8NBZWVl1NbWFtSfGhsbCQA1NzeTWcRDTyVJIlEUac+ePSSKYkz0VBRF8vl8VFlZKZfJ9TT+eqp1r1Nre06nk0pLS8nhcMjnGNFTr7eV3O5mWVO5nqrr6RNPPEFTp06Vz42FnjJN7ap6SmRcU9V0xuoxarh+a8UY1eFw+OlMJJrqcrlo7969cnw1TtHWVLfbLY/5Qt0noqmpXq+X9uzZQz6fLySnaI5RfT6frKeBumf1GNXpdMrXI5ymzpo1i+688065PQwfPpxeeuklv3OYfjLfQH2trKwkALRlyxYiIrr44ovpxhtvlK+rJEn0xRdfyBrDrg/TU3YdAzV127Ztfnra3NwcpKfsfKanRUVFmlyVeurz+WjixIm0ZMkSv3OYnrLc09LS6JlnnpHHpcoxqiiKVFlZKevpTz/9RDt37qS33nqLAFBjYyNJkuSnp2p5ORwOKi0tJafTGaQRdXV1uvSUPw5jEmx5qHIpo9Jmy8KUNluqw5YuKc9RLjdVs0eMGKEaX7lUlS2HC7SVvsrjWrnb7XbY7fag9zSrcVKz6ZdZTrbsXA8/La5anALtlJSUoHxD8QvMXS/XSHgo7cB6UstXi58y3+TkZNVdpQM5sRlmQRDkJXx2ux09evRT8Q1tS5IEu90Huz3D0LOTSSpqo3xuT8u22WzyckMlj1C+7DPzY+WMHz8eWVlZfvmzbzEEQVDlpRaTPXf5xBNPYOLEiejduzdmz56Np59+Go2NjfJ+IIIgYN26dejTp4/fTHmofAVBwPjx47Fx40a/uBs3bpR5AMDpp5+OXbt24cQTTwy+sOj45kgURbz44ouyz6effhqWq1pedrtdbv+sjRl9fjYUYq2ndrsdJ5xwQtBxq/SUQdl3uZ7GX0+17nVqnLxeb1T0FGCaKhnS1ETS09mzZ8vncj2NDJFqqprOWD1GDddvrRqj6uUUaKempsoxlfVnpaampKTI9w1lW7FSU9ljnwzh+EVjjCoIgqqedviGtmM5RlXmrNRFtXO0ygn8ffLJJyMrKwvA/6410ynl9ZF+WVmn9Ffap5xyip+eZmZmBukpK5/pKVsZEop3amqqrPsbNmzwO4flyXI//fTTsXPnTowZMybomgLAli1bgvT0P//5T8TXUampDHa73a9thwJ/HMYkorWEUW8sthQ7Vr5mYppBPPKNB9d48CQieflyrGAmJvM14he4VP3WW2/Fnj17sHDhQuzcuRMfffQR3n33Xb9zampqMH78eGzevFmzbEEQMHPmTHzwwQeYPXs2AODUU0+Fx+PB999/77cp6pdffhl2wykGdo3uvPNOrF69Gs8++yx27dqFf/7zn/LzlgyPPPIIVqxYgUWLFqGwsBBlZWX45JNP8Pe//x0A5GezX331VVRWVuL999/HsmXLdOWhB1b0E64z1oDztM6X66n1enrGGWfIXK3W08WLF6OkpAQ///wz/v3vf3dpPbWyXK1YXGes8U0UnvHQUzNxjeop81X+5noaez3lkyAmYdXsvVasUaNGGYpp1NdMTDOIR77x4BqvelHOXMcKZmIa9U1JSfH7PGzYMHz22Wf46quvMHHiRCxbtgxPPvmk3zlerxc7d+6Un/3Uwpw5c+Dz+eSbjCAImDFjBgDgnHPOkc/78ssvcckll+jKl/GcNm0a3nrrLbz66quYNGkScnJy5JsHw/nnn4+vv/4a3333HWbPno3p06fjxRdflL9lmjRpEl588UU888wzmDBhAj788EM89dRTuvLQAyv6CdcZa8B5WuvL9dRaPWXHAOv1dM2aNTjjjDNw7rnn4qWXXurSempluVqxuM5Y45soPIH46KmZuNHKl+tpHPQ05MMyMcDSpUtpxIgRlJqaSqeffjrl5+eHPH/dunV0+umnU2pqKo0cOZJef/11v7+XlJTQZZddRsOHDycA9NJLLwWVsWjRIgLg9zNo0KCI8mbPWxp5fpM9w86eu+uu4DzjD6fTSWVlZeR0Ok2XpXyuu7sjWlw3b95MvXv37pRtgyg8z1Dtx4wGRrOsztz/ognOs3OAa2rk4HragVjpqZnyOnv/ixYShSdR5+bK9TRycD3tQDT0NK4rQT755BPcfffd+Nvf/oaioiLMmDEDF154Ifbv3696flVVFebPn48ZM2agqKgIf/3rX3HnnXfis88+k89xOBw44YQT8PTTT2Pw4MGasU8++WQcOnRI/tG9k2wAYr00LTc31/DSNCO+ZmKaQTzyjQfXePAkxW7WsYKZmEZ9OwNPttxPz/OJXYkn0H0eh+E6E30/s75GEY98O4POxMI3XjqjjBsLPTXraxTd5XEYrjPW+CYKz86gM7HwM+trFFxP/RHXjVFffPFF3HLLLViwYAEA4OWXX8a3336L119/XXVZzLJlyzBs2DC8/PLLAIBx48bh559/xvPPP4/LL78cADB16lT51WAPPvigZuykpKSQkyR6EeulaRMmTDC8NM2Ir5mYZhCPfOPBNV71kp6ebsjPDMzENOobb55nnHGG/CrGSH2NxowVusvjMFxnou9n1tco4pVvvHUmVr7x4KmMGys9NetrBN3lcRiuM9b4JgpPIP46Eys/s75mY3I9jeMkiMfjwebNm4MmKubNm+f3/m4l1q9fj3nz5vkdO//887F8+XJ4vV7du8ECHe8RPu6445CamoozzzwTTz75pN8uyIFwu91wu93yZ/auZp/PJ78nWS/Y+ZH6AUDfvn3h8/nkNyLEwteonxmeZuLG+hp1Zp5erxdEBEmS5B2lgY7dk+mX947rBTuXlRcpjMQ062vUzwzX7sRTkiQQEbxer9/u2wAM9S+GzqKnANcZq/yM+saLp15fNU3lOhMa8bp3xPoaxUtPgehpKtcZ62Ka9Y3HWDxR9NRM3M6mM1bENONn1DcWehq3SZC6ujr4fD4MGjTI7/igQYNw+PBhVZ/Dhw+rni+KIurq6nDsscfqin3mmWdixYoVGDNmDI4cOYLHH38cZ511FkpLS9G/f39Vn6eeegpLliwJOp6Tk4MePXroihuINWvWGPLrauA84we24qmtrQ0ejycqZRrdCbsrIlG4avH0eDxwOp2qu7w7HA7D8bieGgfnGV9wTTWOROdplZ4C0dfUztr/oo1E4Ql0Tq5cT40j0XlGQ0/j+jgMEPwuYCIKOhbufLXjoXDhhRfK9imnnILp06dj1KhReO+997Bw4UJVn4ceesjvby0tLRg6dCh+9atfoU+fPrpjAx0zn2vWrMF5550X0eoVSZLQ3NyM3r17R7zEzKivmZhGecYrX6O+nZmn2+3G/v37kZGR4bcczefzBc2chgP98iqwXr16RdTfzMQ062vUzwzX7sTT6XQiPT0ds2bNCtoBvampKeJ4DJ1BTwGuM1b5mfGNB89IfNU0letMaMTr3hHraxQvPQWip6lcZzpnvvEYiyeSnpqJ29l0xoqYZvyM+sZCT+M2CTJgwADY7fagVR+1tbVBqz0YBg8erHp+UlKS5goOPcjIyMApp5yCiooKzXNSU1NVX4OUmpoasYAyJCcnR+w7cOBAQ7HM+JqJCRjjaSZuPK4R0Dl52mw2CIIAl8uFjIwMv+ORgi1HEwTB8DOiRmHU16ifGa7diafL5YIgCEhPTw+6gZl9RWdn0FOA64xVfmZ9Y80zEl9BECCKotxnuM6ERrzuHbG+RvHSU+YfTU3lOmNdzK40Freap9oYletMaHCeHYiGnsZtEiQlJQWTJ0/GmjVrcOmll8rH16xZo/nO4unTp+Orr77yO5aTk4MpU6YYHjgDHTOR5eXl8juUI4HR5wmNwOv1IicnB/PmzYuYr1FfMzHNIB75xoOr1Tztdjv69OmD2tpaAECPHj1ARGhra0PPnj0j/ibC4/HA5XIZ+lbASEwzvmZjGuHaXXgSERwOB2pra9GnTx/VGXwrtC+WesricZ2Jvp9ZX6OIRb5JSUno0aMHjh49Kp/HdSa8b6zvHfG6Rp1JT60sVysW1xlrfLsrz8AxalpaGtrb27nOhPFLZJ7R1NO4Pg6zcOFCXHfddZgyZQqmT5+ON954A/v378ett94KoGN5X01NDVasWAEAuPXWW/HPf/4TCxcuxB/+8AesX78ey5cvx8cffyyX6fF4UFZWJts1NTXYunUrevbsidGjRwMA7r33XvzmN7/BsGHDUFtbi8cffxwtLS244YYbIuaQlBS7S5iUlIQZM2YYimnU10xMM4hHvvHgGgue7C1I7CYDdIhLfX19RDGJSF5+ZmQJnpGYZn2N+pnh2p149unTR/MtWlb0E64z1oDzjJ6vIAg49thjUVVVhX379gHgOhMO8bp3xPoadTY9tbJcrVhcZ6zx7c48A8eoXGdCg/PsQDT0NK6TIFdeeSXq6+vx6KOP4tChQ5gwYQKys7MxfPhwAMChQ4ewf/9++fyRI0ciOzsbf/nLX7B06VIcd9xx+Mc//iG/HhcADh48iNNOO03+/Pzzz+P555/HrFmzsG7dOgDAgQMHcNVVV6Gurg7HHHMMpk2bhg0bNshxI4GRBmgUgiAgMzMzpr5mYppBPPKNB9dY8GSD9oEDB5r6Vsjr9SI/Px8zZ86M6aqgeCBRuIbimZycHPIZTiu0L5Z6yuJxnYm+n1lfo4hVvikpKTjxxBNNb+THdaZ7obPpqZXlasXiOmONb3fmyceokYHzjKKeEochNDc3EwCqq6uL2Nfj8VBWVhZ5PJ6Y+HW1mGZ8eczuFdOML49prW9dXR0BoObm5oh9AxEPPTXjy2N2r5hmfHnMzunb1WJGU0+JjGtqV7tuXSmmGV8es3P68pidM6ZePTW+ywkHgNgvNZw3b57hpWlGfM3ENIN45BsPrpyndb6cp7XoLo/D8P4XfT+zvkaRKP0vUXiaidsV225XKlcrVqLUVSL0v0ThaSZuorTdrshTD/gkiEn4fD75t5otiqKfzXa7Bf63863yuNfr9bPpl1cAM9tut8s2EclLx5S2JEl+Nnt/ss1mk23lcZ/P52cH8khKStLFSY+txonlHsiJcQ3FSc1m+YbipFVPSUlJunkobYZwnALrieUbjpNaPbHlXpG2PeVbDSJpe+yzkbYX2O71tr1ArpG2PWXsSNoeOy8cJzWbvTkiHCe1elLmq7ftERGSkpIibnvKdhtp21PmG03EWk8Df2Khp0BHG+F62rn0VO1eZ7WeqvHgehrMKdy9Ltp6ynLu6nrKyg+MY2Y8Z9UYNVS/tWqMyqC3ryrtpKQk3X01Wpqq7Lex0lSGWI5RlbG7whjVzP1cGTsSTWXnhOMUzTGq0fu50TFqOB7R0FQ+CRIhli5divHjx2Pq1KkAgO3btwMAysvLUV5eDgAoLi6WX7dbVFSEqqoqAEBhYSGqq6vlso4cOQIAyM/PR11dHQAgNzdXfr9xTk4OWltbAQDZ2dloa2vDqlWrsGrVKoiiCJfLhezsbABAa2srcnJyAHS8Hzk3NxcAUFdXh/z8fIiiiNWrV6OgoAAAUF1djcLCQgBAVVUVioqKAAAVFRUoLi6WOZWWliI7Oxvbtm0Ly6mgoACHDh2SObFNcPLz8zU5uVwuiKKI7OxsP06iKGLVqlVYs2aNJiegY9+YQE7Z2dnYs2ePJietetqzZw+ys7NDclKrp7Vr18p1GoqTVj1lZ2fjyJEjITkF1tPOnTvx7bffQhTFiNteQUEBVq9eDVEUdbc9xglAxG0PAGpqapCTkwNRFHW3PcZp27ZtMle9ba+urg6iKCInJ0duh3rbHtDxtgeGSNqeKIr49ttvsWXLlpCc1OqJ5cs2W9TT9nJyctDU1ITs7GysWrUq4rbHwHjoaXuMk3IQaRTx1FOXywWXy4U1a9bIttV6Wl5eLreRnTt3huTE9TR2elpRUYFt27YhOzsbpaWlMdNTVh9cT7Xrac+ePXK+sdDT1tZWuW7a2tq6lJ4C5jW1pqZGtvVeL8D8GDU7Oxs1NTUxG6OyOOE4qbXrVatWITs7G01NTWH7arQ0dcuWLXI/iJWm1tfXyzqjp69GY4zK+i1rh519jGr0fm5WU5X1GosxqtH7uZkxKuNRX18f8f2c6V5YhHxYhkMT7HnL+vp6IiISRZFEUQyyvV6vn+3z+eTnnFwul99xoo5noJS2JEl+x91uN7ndbpIkiSRJkp+VUtosBrO9Xi9JkkQulyvoOMtXaSt5eL1e8ng8QTwCOanZLpeLsrKyyOFwaHJS8gi0GVctTmq2x+OR89XipFVPjKuyDrT4Kc9pb2+Xn1sLxUmrnjwej18daPFTnuP1esnpdJIkSRG1PZa7y+UiSZJ0tz1WH1lZWXL709v2WF4Oh4MkSdLd9pS5M656257P5yNJksjhcMjn6217RCRzZddET9tj/czpdMrn6217oigG5aun7SnzC9QFvW2P9VG9bY/l3tTUFPU9QWKpp6wttre3y+3Faj1l9ex0Ov36MtfT+Oqp1r3OSj2VJEnm6na7uZ5q2HruddHUU2XdKHWhK+kpkXFNVdMZq8eo4fqtFWNUh8PhpzORaCrrr2o8AnOPlqZ6PB6/fhsLTdXSGSvHqKH6bWccoxq9n5vRVKYz7e3tEbU9M2NUo/dzM2NU1kddLlfE9/P6+npdehrXt8N0B7DdaZW71Cpt5XNJzGbLdNgSWuU5yh1wA20igs/nQ1pamrzMjJ0jCIJs22w2uWxmExEkSUJKSkrQOVq52+12EBFcLpdfTDVOajb9sqwrKSlJjhWKnxZXLU5qtiAIQfmG4heYr16uenkE2oH1pJavFr/A3L2KZe5anLRsl8sVMSeWnyAIfjzCtT1mM+hte2pc9bY94H9LsCNte0qu4Tip9TP6ZemfXn5a+epte0QEURRVdSGQU2A9BV5XvW3PKsRSTxlYe46kTRvVU+B/S3XZZ66nnUNP1fK1Uk+1eHA9DeYU7l4XTT1lvmq6EMips+upsny9mqqmM1aPUcP1W6vGqHo4adkulwtJSUm6+mq0NFX8ZaWDVZyA4Hpi0NtXlbkbGaOG6redcYxq9H6uxlVvPbHrGmnbMzNGNXM/NzpGVepopJqqV1f54zAmIUZpCaPeWGy5Vqx8zcQ0g3jkGw+unKd1vpyntbAiHtcZa8B5WufLeVqLRGq7XalcrViJUleJ0P8ShaeZuInSdrsiTz0QiE1FcUSElpYW9O7dG83NzRG/R9vr9SI7Oxvz58/v9u945jy7DxKFJ5A4XM3wNKOB0SyL11X3QqLwBBKHK+cZHtHUUzPl8brqfkgUrpxn90Is9JSvBDGJWM4hERFaWloMxTTqayamGcQj33hw5Tyt8+U8rYUV8bjOWAPO0zpfztNaJFLb7UrlasVKlLpKhP6XKDzNxE2UttsVeeoBnwQxiVgv7/nhhx8ML0cy4msmphnEI994cOU8rfPlPK1Fd3kchve/6PuZ9TWKROl/icLTTNyu2Ha7UrlasRKlrhKh/yUKTzNxE6XtdkWeesAfhzEIvnw7PDjP7oVE4QkkDtfOsnyb62l4cJ7dD4nClfMMD/44TGyRKDyBxOHKeXYv8MdhugDYLr0+n09+S4HSFkXRz5YkSfZltvK41+v1s9kcldfrhc/nQ319Pdxut7zDL4uvtCVJ8rNZ+XV1dfB4PH7HWb5KW8nD6/WioaFBjh+Kkx47kJOSh9KWJEnmqsVJzfZ4PHK+Wpy06olx9Xg8ungobWV70OKkVk8sX1EUNTmp1ZPX68XRo0chSVLEbc/j8aCurs6vbYRre4wH4xBJ22PxWb562x6zvV6vX756254kSaitrfW7ZnraHuPEoLftqfUzvW3P5/NBkiQcPXrU75rpaXs+nw8NDQ1BuqC3nlj5ofip1ZNSy6KFWOopUcebDGpra+Hz+WKip8p6ZmVyPY2/nmrd66zWUzUeXE/9bT33umjqKRHJXJW60BX1lHEKvEZ62nUk14vZRseo4fqtVWNU5TWKRFPdbjcaGhrk+Gqcoq2pWjpjpaaKooja2tognbFyjBqq33bGMaqZ+7kZTVXWayzGqGbu50bHqOHaW7j7uR7wSZAIsXTpUowfPx5Tp04FAJSUlAAAysvLUV5eDgAoLi5GRUUFAKCoqAhVVVUAgMLCQlRXV8tlHTlyBACQn5+Puro6AEBubi6ampoAADk5OWhtbQUAZGdno729HZs2bcLq1ashiiJcLheys7MBAK2trcjJyQEANDU1ITc3FwBQV1eH/Px8+Hw+FBYWYv369QCA6upqFBYWAgCqqqpQVFQEAKioqEBxcbHMqaysDJs2bdLFqaCgAIcOHZI51dfXy7YWJ5fLBVEUkZ2d7ceJ5fvdd99pcgKAQ4cOoaCgQOa0adMmbNq0CZWVlZqctOqpsrJS9tfipFZPa9eules0FCe1elq7di02bdqE2tpaTU5q9bRr1y5s3LgRPp8v4ra3fv16FBYWwufz6W57jBOAiNseABw8eBDr16+Hz+fT3faUnDZs2ACfz6e77dXV1cHn82H9+vVoaGiIqO0BQFtbm1ynetseu6YbN26MuO1VVVXB5/Nhw4YN2L9/vyYntXpqbm6WdSGStsc4sVhanLTqid0gzSCeeupyueB2u7F+/Xq43e6Y6Gl5ebncRnbt2hWSE9fT2Okp47Rp0yaUlZXFTE/XrFkDAFxPQ9RTZWWlnG8s9LS1tVXm2t7e3qX0FDCvqTU1NbKt93oB5saoP/zwAzZt2oSDBw/qvl6Mk9ExKosTjpNau169ejU2bdqE5ubmsH01mprKxnzh+mogJ6Oa2tDQIOuMnr4ajTEq63sHDx4MyamzjFGN3s/NaqqyXmMxRjV6PzczRmU86uvrI76fM90LC+IwhObmZgJADQ0NREQkiiKJohhke71eP9vn85HH46GsrCxyuVx+x4mIPB6Pny1Jkp8tSVKQTUR+NovBbK/XG9IWRdHPVuMRjpOa7XK5KCsrixwOR7fhpFZP7e3tlJWVRR6Pp9twUqsnt9tNWVlZ5Ha7uw0nrXpiXJl/d+CkVk9MixwOR8ScmAY2NzeTWXA95XqaaHoqSZLM1e12dxtOXE/jr6dExjVVTWe6o6Y6HA4/nekOnBJdUxNljMp0pr29vdtwUqsn1kddLlfEnBoaGnTpKV8JYhKCIAAA7HY77HZ7kJ2UlORn22z/u+TMVh5PTk72s1n5ycnJICIcPXoUdrsdgiBAEAT5OSmlbbPZ/OykpCR5CRQrmx1n+SptJQ9BEFBbWwtBEMJy0mMHclLyUNpsiRmLo8ZJzbbZbHK+Wpy06olxZeWE46G0GUJxUqsnu92O2tpaufxQ/JT1JAgC6uvrIUlSxG3PZrPJyyr1tj3Gg8WOpO0xNDQ0QJIk3W2P2UqukbQ9SZLQ2NgYVDd664lBb9tj/ay+vl7ORW/bs9vtkCRJ/kYgFL/AeiIi1NbWBumC3npi5Yfip1ZPbLljNBFLPRUEAUSExsZGEFFM9JRdt/r6ejkXrqfx11Ote53VeqrGg+upvy0Igsw1FnoqCILMVakLXVFPWZ7KOIF2pPpjxRiV6QyLxY5bPUZl0NtXmc10hojC9tVoaarNZvPrt7HQVABobGwM0hkrx6ih+m1nHKOauZ+b0VRlvcZijGrmfm50jBquvYW7n+sBnwQxCatuXFqxSkpKDMU06msmphnEI994cOU8rfPlPK2FFfG4zlgDztM6X87TWiRS2+1K5WrFSpS6SoT+lyg8zcRNlLbbFXnqQVL4UzhCQTmLGItYc+fOjamvmZhmEI9848GV87TOl/O0FlZoXyz1lMXj/S/6fmZ9jSJR+l+i8DQTtyu23a5UrlasRKmrROh/icLTTNxEabtdkace8JUgJhHrma2amhrDM3FGfM3ENIN45BsPrpyndb6cp7XoLitBeP+Lvp9ZX6NIlP6XKDzNxO2KbbcrlasVK1HqKhH6X6LwNBM3UdpuV+SpB3wSxCRiXal79uwx3AiN+JqJaQbxyDceXDlP63w5T2vRXSZBeP+Lvp9ZX6NIlP6XKDzNxO2KbbcrlasVK1HqKhH6X6LwNBM3UdpuV+SpBxGvkyMifPfddygoKMDhw4chCAIGDRqEs88+G+eee67uzUi6C2K91HDmzJkx9TUT0wzikW88uHKe1vlyntaiuzwOw/tf9P3M+hpFovS/ROFpJm5XbLtdqVytWIlSV4nQ/xKFp5m4idJ2uyJPPYhoJUhNTQ1OP/10XHjhhfjiiy9QWVmJ3bt344svvsAFF1yAKVOmyO8YTxR4vV4AgM/nk9/zrrRFUfSzlbNTzFYe93q9fjYRybbP58PevXvhdrtBRCAiOb7SliTJz2bl7927Fx6Px+84y1dpK3l4vV7s27dPjh+Kkx47kJOSh9Jm+brdbk1OarbH45Hz1eKkVU+Mq8fj0cVDaSvbgxYntXpi+YqiqMlJrZ68Xi+qqqogSVLEbc/j8WDv3r1+bSNc22M8GIdI2h6LX1lZCUmSdLc9Ziu5RtL2JElCZWWl3zXT0/YYJwa9bY/FraqqkvuZ3rbn8/mC8tXb9nw+H/bt2xekC3rriZUfip9aPVkxqx9LPSUi+Hw+VFZWwufzxURP2XWrqqqSy+R6Gn891brXWa2najy4nvrbeu510dRTIpJ9lbrQFfWUcQq8RnradSTXi9lGx6jh+q1VY1TlNYpEU91uN/bt2yfHV+MUbU31eDx+/TYWmqqlM1aOUUP12844RjVzPzejqcp6jcUY1cz93OgYNVx7C3c/14OIJkFuv/129OvXD9XV1di6dSu+/fZb5OTkYOvWraiurkafPn3wpz/9KZIiuxyWLl2K8ePHY+rUqQCA0tJSAEB5eTnKy8sBAMXFxaioqAAAFBUVoaqqCgBQWFiI6upquawjR44AAPLz81FXVwcAyM3NRVNTEwAgJycHra2tAIDs7Gw4HA4cOHAAq1evhiiKcLlcyM7OBgC0trYiJycHANDU1ITc3FwAQF1dHfLz8+WGv379egBAdXU1CgsLAQBVVVUoKioCAFRUVKC4uNiPU01NDbZv3x6WU0FBAQ4dOiRzqq+vl20tTi6XC6IoIjs724+TJEnYv38/vvvuO01OAHDo0CEUFBTInDZt2oSampqwnLTqqaamBps2bdLkpFZPa9eules0FCe1elq7di1qampw9OhRTU5a9bRr1y5IkhRx21u/fr18c9Lb9hgnABG3PcapvLwckiRF1PYAYPv27di9ezckSdLd9tgrK0tLS+VXkOltewDQ1tYm16netldYWAhJ6lj2Z6TtSZKEHTt2YP/+/Zqc1OqppaUFNTU1WL16dURtj3FisbQ4adVTNAbt8dRTl8sFj8eD7du3w+PxxExPJUnCrl27uJ52Mj3dvn07ampqYqqna9asAQCup2HqaceOHZAkKSZ62traCkmSsH37djgcji6lp4B5TWVfZBYWFuq+XoC5MeoPP/yAmpqaiK6XkpORMSqLE46TWrtevXo1ampq0NLSoquvRktT2SMF4fpqICejmtrY2IjS0lJIkqS7r5odo7LzI2178RqjGr2fm9VUZb3Gaoxq5H5uZozKeNTX10fc9pjuhQVFgIyMDNq6davm37ds2UIZGRmRFNll0dzcTACooaGBiIhEUSRRFINsr9frZ/t8PvJ4PJSVlUUul8vvOBGRx+PxsyVJ8rMlSQqyicjPZjGY7fV6Q9qiKPrZajzCcVKzXS4XZWVlkcPh6Dac1Oqpvb2dsrKyyOPxdBtOavXkdrspKyuL3G53t+GkVU+MK/PvDpzU6olpkcPhiJgT08Dm5mYyC66nXE8TTU8lSZK5ut3ubsOJ62n89ZTIuKaq6Ux31FSHw+GnM92BU6JraqKMUZnOtLe3dxtOavXE+qjL5YqYU0NDgy49jWglSHp6OhoaGjT/3tjYiPT09EiK7Daw2+2w2+1BdlJSkp9ts/3vkjNbeTw5OdnPZnusJCcnyzN4NpsNgiBAEAQkJycDgJ9ts9n87KSkJHnZNyuPHWf5Km0lDwDYvXu332ctTnrsQE5KHkrb5/PJXLU4qdmCIMj5anHSqifGVRAEXTyUNkMoTmr1ZLPZsHv3bhCRJie1egI6Zj59Pl/EbU8QBHmpr962x3gw/0jaHtCxzG3v3r3w+Xy62x6zlVwjaXu+X5bg0S/LC/W2PcaJQW/bY/2sqqpK9tfb9ux2u7yUmOWrt+1JkoTdu3cH6YLeemLlh+KnVk++X5ZKWoFY6KkgCJAkCfv27YMkSTHRU3bd2DdFoThxPY2dnmrd66zWUzUeXE/9bQAy11joqSAIMlelLnRlPVXGCbQj1R8rxqhMZ7T6rVVjVAa9fZXZTGckSQrbV6OlqYIg+PXbWGgqEcmP/ejpq9EYo4bqt51xjGrmfm5GU5X1GosxKmD8fm50jBquvYW7n+tBRJMgv//973HDDTfgv//9L5qbm+Xjzc3N+O9//4ubbroJV199dSRFdnmwxhurWI2NjYZiGvU1E9MM4pFvPLhyntb5cp7Wwop4XGesAedpnS/naS0Sqe12pXK1YiVKXSVC/0sUnmbiJkrb7Yo89SCibaNfeOEFiKKIa665BqIoIiUlBUDHhj1JSUm45ZZb8Nxzz0WebReG3h1ooxWLPecZK18zMc0gHvnGgyvnaZ0v52ktrNC+WOopi8f7X/T9zPoaRaL0v0ThaSZuV2y7XalcrViJUleJ0P8ShaeZuInSdrsiTz2IaCVISkoKXn/9dRw9ehTfffcd3n77bbz99tv47rvvcPToUbz22mvyxEiiwOoljIGxduzYYSimUV8zMc0gHvnGgyvnaZ0v52ktrIjHdcYacJ7W+XKe1iKR2m5XKlcrVqLUVSL0v0ThaSZuorTdrshTDwxNEWdmZmLOnDlGXDlMwul0xtzXTEwziEe+8eDKeVrny3lyhAPvf9b4mfWNR8yu1P8ShaeZuF2t7XYHJEpdJUr/SxSeZuImStvtajz1wPA6uby8PDz//PMoLy+HIAgYN24c7rvvPsyYMSOa+XV6RLIBSzRinXbaaTH1NRPTDOKRbzy4cp7W+XKe1sIK7YulnrJ4vP9F38+sr1EkSv9LFJ5m4nbFttuVytWKlSh1lQj9L1F4mombKG23K/LUg4geh2H44IMP8Ktf/Qo9evTAnXfeiTvuuAPp6ek499xz8dFHHxkpssvC4/EA6Fh6w5bfKG1RFP1s5bvgma087vV6/Wy2uYvX64Uoiti+fTtcLheICEQEr9cLAH62JEl+Nsth+/btcLvdfsdZvkpbycPj8aCkpAQejycsJz12ICclD6XN8nW5XJqc1Gy32y3nq8VJq54YV7fbrYuH0mYIxUmtnli+rH5D8VPWk8fjQXFxscwhkrbndruxfft2uTw9bY/xYBwiaXusDJav3rbHbI/H45ev3rbn8/lQXFws56O37TFODHrbnrKfhdMFtXoKzFdv2xNFESUlJUG6oLeeWPmh+KnVkxVLG2Opp0QEURRRXFwMURRjoqfKemZcuZ7GX0+17nVW66kaD66n/raee1009ZSIZF+lLnRFPQWMa2ok14vZRseo4fqtVWNUBr19ldkulwslJSUQRTFsX42Wpgb221hoqpbOWDlGDdVvO+MY1cz93IymKus1FmNUM/dzo2PUcO0t3P1cDwxNgjzxxBN49tln8cknn+DOO+/EXXfdhU8++QRPP/00HnvsMSNFdhksXboU48ePlzd5KSsrAwCUl5ejvLwcAFBcXIyKigoAQFFRkfz6pMLCQlRXV8tlHTlyBACQn5+Puro6AEBubi6ampoAADk5OWhtbQUAZGdnyw3o22+/hSiKcLlcyM7OBgC0trYiJycHANDU1ITc3FwAQF1dHfLz8wEA7e3t2LBhAwCguroahYWFADpeY1VUVAQAqKioQHFxscxpx44dAICSkpKwnAoKCnDo0CGZU319vWyH4iSKIrKzs4M4eb1efP/99yE5HTp0CAUFBTKnzZs3AwD27dunyUmrnvbt2wcA2Lx5syYntXpau3atXKfhOAXW07p168JyUqun3bt3y7lE2vY2bNiA9vb2kJy06gmAobZ3+PBhHDx4MCQnrXoqKSlBY2NjSE5a9bR//375TVaRtL22tja5TvW2PcapubkZW7duDclJq54OHz6MAwcOhOQUWE8s12+//Taitsc4sVihOGnVk1nEW09FUURVVVXM9JRxqqurk1+TyPU0/npaUVGBkpISAMCOHTtipqdr1qwBAK6nIepp3759qK2tDckpmnrKOFVVVXU5PQXMa2pNTY1sR3K9zIxRf/zxRwAd9RXJ9TIzRmVx9HAKbAPffvstgI52Ha6vRktTt27dKvc9PX01Gpra3NyM/fv3h+UU7THqwYMHcfjw4ZCcOtMY1cj93KymKus1FmNUo/dzM2NUxqO+vj7i+znTvbAgA0hJSaGKioqg4xUVFZSammqkyC6H5uZmAkANDQ1ERCSKIomiGGR7vV4/2+fzkcfjoaysLHK5XH7HiYg8Ho+fLUmSny1JUpBNRH42i8Fsr9cb0hZF0c9W4xGOk5rtcrkoKyuLHA5Ht+GkVk/t7e2UlZVFHo+n23BSqye3201ZWVnkdru7DSetemJcmX934KRWT0yLHA5HxJyYBjY3N5NZcD3leppoeipJkszV7XZ3G05cT+Ovp0TGNVVNZ7qjpjocDj+d6Q6cEl1TE2WMynSmvb2923BSqyfWR10uV8ScGhoadOmpoZUgQ4cOlb9RUuL777/H0KFDjRTZ5WG32+VnkJR2UlKSn22z/e+SM1t5PDk52c8WBEG2JUnC1q1bYbPZIAgCBEFAcnIyAPjZNpvNz05KSoLP58O2bdvk8thxlq/SVvIAIM+uheOkxw7kpOShtH0+n8xVi5OaLQiCnK8WJ616YlwFQdDFQ2kzhOKkVk82mw1FRUUgIk1OavUEQF66F2nbEwQB27Ztg8/n0932GA/mH0nbAzqWubGlnHrbnvKZPsY1krbn8/lQUlIiLy/U2/YYJwa9bY/1s+LiYtlfb9uz2+3yMkWWr962J0kSioqKgnRBbz2x8kPxU6snn0XLtwOvj1V6KggCJElCSUkJJEmKiZ6y66b85pfrafz1VOteZ7WeqvHgeupvA5C5xkJPBUGQuSp1oSvrqTJOoB2p/lgxRmU6o9VvrRqjMujtq8xmOiNJUti+Gi1NFQTBr9/GQlOJCCUlJUE6Y+UYNVS/7YxjVDP3czOaqqzXWIxRAeP3c6Nj1HDtLdz9XA8MbYx6zz334M4778TWrVtx1llnQRAE/Pjjj3j33XfxyiuvGCmSQyfS09Nj7msmphnEI994cOU8rfPlPDnCgfc/a/zM+sYjZlfqf4nC00zcrtZ2uwMSpa4Spf8lCk8zcROl7XY1nnpgaCXIbbfdhn//+9/Yvn077r77btx1110oKSnBJ598gj/+8Y8RlfXaa69h5MiRSEtLw+TJk/HDDz+EPD8vLw+TJ09GWloaTjjhBCxbtszv76Wlpbj88ssxYsQICIKAl19+OSpxtRDJjJNZ2O12jB071lBMo75mYppBPPKNB1fO0zpfztNaWBGP64w14Dyt8+U8rUUitd2uVK5WrESpq0Tof4nC00zcRGm7XZGnHhiaBAGASy+9FD/++CPq6+tRX1+PH3/8EZdccklEZXzyySe4++678be//Q1FRUWYMWMGLrzwQnkjnkBUVVVh/vz5mDFjBoqKivDXv/4Vd955Jz777DP5HIfDgRNOOAFPP/00Bg8eHJW4oSAqdpi2GqIoYtOmTYZiGvU1E9MM4pFvPLhyntb5cp7Wwop4XGesAedpnS/naS0Sqe12pXK1YiVKXSVC/0sUnmbiJkrb7Yo89cDwJAjQ8QqkAwcOYP/+/X4/evHiiy/illtuwYIFCzBu3Di8/PLLGDp0KF5//XXV85ctW4Zhw4bh5Zdfxrhx47BgwQLcfPPNeP755+Vzpk6diueeew6///3vkZqaGpW4oaB81tVqCIKAvn37Gopp1NdMTDOIR77x4Mp5WufLeVoLK+JxnbEGnKd1vpyntUikttuVytWKlSh1lQj9L1F4mombKG23K/LUA0N7glRUVODmm2/2e8UU0LGxjCAIujZ48ng82Lx5Mx588EG/4/PmzQsql2H9+vWYN2+e37Hzzz8fy5cvh9fr9dswJppxQyHWy3tGjx4dU18zMc0gHvnGgyvnaZ0v52ktusvjMLz/Rd/PrK9RJEr/SxSeZuJ2xbbblcrVipUodZUI/S9ReJqJmyhttyvy1ANDkyA33ngjkpKS8PXXX+PYY481NLtTV1cHn8+HQYMG+R0fNGiQ/H7oQBw+fFj1fFEUUVdXh2OPPdaSuADgdrvhdrvlzy0tLQAAp9MZ8YYvXq/X77deiKKIzZs3Y/LkyX47MFvpayamUZ5m4sbjGnGe1sU04xuPtpsoPIEO7TOKzqCnAO9/VvmZ8eU6Y23MrsTTTNyu1nbN6CkQPU3ldWVdTDO+XGfCg7dd62J2NZ569VQg9m6eCJCRkYHNmzdj7NixESfGcPDgQQwZMgQFBQWYPn26fPyJJ57A+++/jx07dgT5jBkzBjfddBMeeugh+dhPP/2Ec845B4cOHQraA2TEiBG4++67cffdd5uKCwCLFy/GkiVLgo5/9NFH6NGjh27eHBwcHN0BDocDV199NZqbm5GZmRmRL9dTDg4Ojv/BjJ4CXFM5ODg4GPTqqaGVIOPHj0ddXZ3h5ABgwIABsNvtQasvamtrg1ZpMAwePFj1/KSkJPTv39+yuADw0EMPYeHChfLnlpYWDB06FPPmzYv4huX1erFmzRqcd955uh7h6argPLsXEoUnkDhczfBk3zQaAdfTyMF5dj8kClfOMzzM6CkQPU3lddX9kChcOc/uhVjoqe5JEGWBzzzzDO6//348+eSTOOWUU4KS0yO4KSkpmDx5MtasWYNLL71UPr5mzRrNt8xMnz4dX331ld+xnJwcTJkyRfcFMhIXAFJTU1U3WhUEwXAjTE5OjshXFEUUFBTgrLPOMrQcyYivmZgMkfI0Ezce14iB87QmZldqu4nCEzC34V5n0FOA9z+r/Mz6AlxnrIrZlXiaidvV2q7ZTQOjram8rqyJ2ZX6X6LwNBM3UdpuV+MZ9Y1R+/Tp41coEeHcc8/1OyeSjVEBYOHChbjuuuswZcoUTJ8+HW+88Qb279+PW2+9FUDHzHZNTQ1WrFgBALj11lvxz3/+EwsXLsQf/vAHrF+/HsuXL8fHH38sl+nxeFBWVibbNTU12Lp1K3r27ClvzBIubiSw2Uy9YCfiWKNGjTIU06ivmZhmEI9848GV87TOl/O0FlbE4zpjDThP63w5T2uRSG23K5WrFStR6ioR+l+i8DQTN1HablfkqQe6J0HWrl0LoGPzpSeffBJXXXWVqT1BAODKK69EfX09Hn30URw6dAgTJkxAdnY2hg8fDgA4dOiQ3yt3R44ciezsbPzlL3/B0qVLcdxxx+Ef//gHLr/8cvmcgwcP4rTTTpM/P//883j++ecxa9YsrFu3TlfcSBDrSh0yZEhMfc3ENIN45BsPrpyndb6cp7XoLpMgvP9F38+sr1EkSv9LFJ5m4nbFttuVytWKlSh1lQj9L1F4mombKG23K/LUdZ7eAmfNmoVZs2Zh3rx5KC0txdy5c+VjgT+R4Pbbb8fevXvhdruxefNmzJw5U/7bu+++K09cKPPYsmUL3G43qqqqglZvjBgxAkQU9BNYTqi4kUAURUN+RmPl5uYaimnU10xMM4hHvvHgynla58t5Wgsr4nGdsQacp3W+nKe1SKS225XK1YqVKHWVCP0vUXiaiZsobbcr8tQDQ1PE119/PZYvX27Etdsh1rPsEyZMMLwcyYivmZhmEI9848GV87TOl/O0Ft1lJQjvf9H3M+trFInS/xKFp5m4XbHtdqVytWIlSl0lQv9LFJ5m4iZK2+2KPPXA0K4zHo8Hb731FtasWYMpU6YgIyPD7+8vvviikWK7JGJdqQMHDoypr5mYZhCPfOPBlfO0zpfztBbdZRLk/7P35eFRHGf6bx8zuoWQAEmIG8whsM0lY/AZO4BD4pzeOPGuvfEmjh3ncOx4s3EOHxvnl83mWMcJThzH2Zw+NnEsx0aAhJER5hKHjAAJEFiALiSNpNExZx/f74+ebvVIM5qeac2IYfp9nn7mm56u/urt+uqt6prqaqv+jX86s2ljRarUv1ThacZvMsZuMp03nK9UKatUqH+pwtOM31SJ3WTkaei4WE5+/PhxrFy5Erm5uTh9+jTq6uq07b333ovllEkLr9cLAJAkSVsQVm+Lohhky7KspVVt/X5BEIJsItJsv9+Pbdu2we12a4/5CIIAAEG2LMtBtiiKEAQB27Ztg8fjCdqv5ldv63l4vV5s374dXq83Iicj9khOeh56W82v2+0OyymU7fF4tPyG4xSunFSuHo/HEA+9rWIsTqHKSc2vz+cLyylUOXm9Xmzbtg2CIEQdex6PR0trNPZUHiqHaGIPUNYSUn0ajT3V1nONJvbUGPL5fFHFnspJhdHY09ezSLoQqpxG5tdo7Pn9fmzfvn2ULhgtJ/X8Y/ELVU769OOFROopEWma6vf7E6Kn+nJWuVp6OvF6Gq6ti7eehuJh6WmwbaStG089VfM9UheSUU+B2DU1muul2rH2USPV23j1UVUYrauq7Xa7sX37dvj9/oh1dbw0dWS9TYSmhtOZePZRx6q3l2If1Ux7bkZT9eWaiD6qmfY81j5qpHiL1J4bQUyDINXV1WG3nTt3xnLKpMHmzZtRWlqKsrIyAMCpU6cAAI2NjWhsbAQA1NfXo6mpCQBQV1eH5uZmAEBtbS1aWlq0c3V2dgIAampq4HA4AAA7d+6E0+kEoLz+d3BwEABQUVEBQRCwcuVKVFVVaUFZUVEBABgcHERlZSUAwOl0auXgcDhQU1MDjuMwd+5cHDx4EADQ0tKC2tpaAEBzczPq6uoAAE1NTaivr9c4NTU1oaysTLPH4rR37150dHRonHp6ejQ7HCe1IaioqAjixHEcli5dqi3IG4oToCyeu3fvXo1TXV0dysrKNDsUp3Dl1NLSgrKyMs0OxSlUOal5jMQpVDnV1NSgrKwMfX19YTmFKqfm5mbk5eWB47ioY+/gwYOYO3cuOI4zHHv6Bjva2AOA7u5u2O12cBxnOPZUTo2NjSgsLATHcYZjz+FwgOM4EJHGw2jsAcDQ0JBWpkZjr7a2FhzHYfr06RoPo7HX3NwMjuOQkZGB9vb2sJxClZPH40FZWRmqqqqiij29Vqs8jMSeyonjOJjFROqp1+sFEcHn84GIEqKnjY2N4DgOeXl5Gg9LTydeT5uamtDY2IiysjLNDsVpvPW0qqoKACw9HaOcWlpakJOTA47jEqKng4OD4DgOPp9Pu+FMFj0FzGtqW1ubZhu9XoC5PurevXtRVlaG7u5uw9dL5RRrH1X1E4lTqLiuqqpCWVkZPB5PxLo6XppaX1+P6dOng+O4iHV1JKdYNXVwcBBEBI7jDNXV8eijchwHu92O7u7uMTldKn3UWNtzs5qqL9dE9FFjbc/N9FFVHj09PVG356ruRQRZiAn9/f0EgHp7e4mISBRFEkVxlC0IQpAtSRL5/X4qLy8nr9cbtJ+IyO/3B9myLAfZsiyPsokoyFZ9qLYgCGPaoigG2aF4ROIUyvZ6vVReXk5ut/uy4RSqnFwuF5WXl5Pf779sOIUqJ5/PR+Xl5eTz+S4bTuHKSeWqpr8cOIUqJ1WL3G531JxUDezv7yezsPTU0tNU01NZljWuPp/vsuFk6enE6ylR7JoaSmcuR011u91BOnM5cEp1TU2VPqqqMy6X67LhFKqc1Drq9Xqj5tTb22tITxP7APZlCHXKD8dx2ki+3uZ5PsjWP6ek2vr9NpstyGYYRrPFwOgZADAMA4ZhYLPZtO+qzbJskM3zPARBwNatW7WpVup+Nb96W89DlmVs2bIFsixH5GTEHslJz0NvC4IQNOIZilMom4i0/IbjFK6cVK5EZIiH3lYxFqdQ5QQAW7ZsgSRJYTmFKidZlrWpe9HGHhFh69atEATBcOypPFQO0cQeoExP2759OwRBMBx7qq3nGk3sCYKAyspKbUqf0dhTOakwGntqPdu2bZtWz4zGHsdxEAQB27dv1/JrNPZEUcSWLVtGlY3RclLPPxa/UOUkxGH6diL1lGEYiKKIyspKiKKYED1Vr9u2bds0rpaeTryehmvr4q2noXhYehpsy7KscU2EnjIMo3HV60Iy6ikQu6ZGc71UO9Y+qqoz4eptvPqoKozWVX0/aMuWLRBFMWJdHS9NJaKgepsITZUkCZWVlaN0Jp591LHq7aXYRzXTnpvRVH25JqKPaqY9j7WPGineIrXnRmANgpiEXkgT4euGG26IyWesac34NIOJyO9EcLV4xi+txTO+iIc/S2fiA4tn/NJaPOOLVIrdZDpvOF+pUlapUP9ShacZv6kSu8nI09Bxcc7HZQ/9PxyJ8JWbm5vQtGZ8msFE5HciuFo845fW4hlfxEP7Eqmnqj+r/o1/OrNpY0Wq1L9U4WnGbzLGbjKdN5yvVCmrVKh/qcLTjN9Uid1k5GkE1kwQk4jXFMZwvt54442YfMaa1oxPM5iI/E4EV4tn/NJaPOOLePizdCY+sHjGL63FM75IpdhNpvOG85UqZZUK9S9VeJrxmyqxm4w8jcAaBDGJRE/v2bBhQ8zTkWJJa8anGUxEfieCq8UzfmktnvHF5fI4jFX/xj+d2bSxIlXqX6rwNOM3GWM3mc4bzleqlFUq1L9U4WnGb6rEbjLyNAJrECTJYCaIYk2baDEy63cirpEZWDzjl9biaSESrPoXn3Rm006Ez2Sqf6nC04zfZIvdywGpUlapUv9ShacZv6kSu8nG0wisQRCT8Pl8AJSVhdUVfvW2KIpBtn7FWtXW7xcEIchWV/AVBEFb4d/j8YCIQETalB+9LctykC2KorZqt9frDdqv5ldv63n4fD5UVFTA5/NF5GTEHslJz0Nvq/n1eDxhOYWy1XdO+3y+sJzClZPK1ev1GuKht1WMxSlUOan59fv9YTmFKic1r2pZRBN7qk81LozEnspD5RBN7AGA3+/XfBqNPdXWc40m9tQY8vv9UcWeykmF0djT17NIuhCqnEbm12jshdMFo+Wknn8sfqHKSf0+nkiknqrXo6KiIug7ED891ZezytXS04nX03BtXbz1NBQPS0+DbSNt3XjqKRFpafW6kIx6CsSuqdFcL9WOtY8aqd7Gq4+qwmhdVW2Px6PFR6S6Ol6aOrLeJkJTw+lMPPuoY9XbS7GPaqY9N6Op+nJNRB/VTHseax81UrxFas+NwBoEiRKbN29GaWkpysrKAACnTp0CADQ2NqKxsREAUF9fj6amJgBAXV0dmpubAQC1tbVoaWnRztXZ2QkAqKmpgcPhAADs3LkTTqcTAFBZWYnBwUEA0Crn+vXrUVVVBVEUNTEFgMHBQVRWVgIAnE4ndu7cCQBwOByoqakBz/NYsWIFDh48CABoaWlBbW0tAKC5uRl1dXUAgKamJtTX12ucmpqasGnTJs0ei9PevXvR0dGhcerp6dHscJy8Xq9WKfWceF5ZTbi6ujosJwDo6OjA3r17NU51dXXYtGkTWltbw3IKV06tra3YtGkT6urqwnIKVU5qHiNxClVONTU12LRpk2aH4hSqnJqbmzFjxgzwPB917B08eBArVqwAz/OGY0/lBCDq2FPtvLw88DxvOPZUTo2NjViwYAF4njccew6HAzzPIz09HUNDQ1HFHgAtzVicQpUTz/NYvHixxsNo7DU3N4PneUyZMkXjYST2Kisr4fF4sGnTJlRVVUUVeyon1Vc4TuHKaTxG9idST9UBDBWJ0NPGxkbwPI8ZM2ZoPCw9nXg9bWpqQmNjIzZt2qTZoTiNt55WVVUBiC72VDsV9BQAWltbUVRUBJ7nE6Kng4ODmrbpbxqSQU8B85ra1tam2UavF2Cuj7pv3z5s2rQJDofD8PVSOcXaR1X9ROIUKq6rqqqwadMmeDyeiHV1vDS1vr4eixcvBs/zEevqSE6xaurQ0BDS09PB87yhujoefVSe55GXl6fxuNT7qLG252Y1VV+uieijxtqem+mjqjx6enqibs9V3YsIshAT+vv7CQD19PQQEZEoiiSK4ihbEIQgW5Ik8vv9VF5eTl6vN2g/EZHf7w+yZVkO2u9yucjn85EsyyTLMvn9fiKiIFv1odqCIJAsyzQ0NDRqv5pfva3nIQgCud3uUTxGcgple71eKi8vJ7fbHZaTnsdIW+UajlMo2+/3a/kNxylcOalc9WUQjp/+GJfLReXl5eT3+8fkFK6c3G53UBmE46c/RhAEGhwcJFmWo4o9Ne9DQ0Mky7Lh2JNlmXw+H5WXl2vxZzT21HwNDAyQLMuGY0+fd31+jcSeJEkkyzINDAxoxxuNPSLSuKrXxEjshapnRmNPFMVR+TUSe6rtdrtH6YKR2FO1yO12G449Ne9Op5MAUH9/P5nFROipGosDAwNavMRbT9VyHhwcDKrLlp5OrJ6Ga+viqadqmaiaaulpaNtIWzeeeqrmfaQuJJOeEsWuqaF0Jt591Ej1Nh59VLfbHaQz0Wiqz+fTrk+kujpemhpOZ+KpqeF0Jp591LHq7aXYR421PTejqarOuFyuqGLPTB811vbcTB9VraNerzfq9rynp8eQnlozQUyCAlOXOI4Dx3GjbJ7ng2yWHb7kqq3fb7PZgmz1NT82mw2SJKGqqgoMw2ibzWYDgCCbZdkgm+d5iKKIHTt2BPlW/3ngOC7I1vMgIlRWVoKIInIyYo/kpOeht8XAv2TqsaE4hbIBaPkNxylcOalcw5XNWOWkYixOocpJza8sy2E5hSonIsLbb78NURRjir0dO3ZAFEXDsafyUDlEE3uAMm1t586dEEXRcOypNhEF5ddo7ImiiJ07d2rT8ozGnspJhdHYC1XPjMYex3Gj8ms09iRJQmVl5ShdMFpO6vnH4heqnMQ4TN9OpJ4yDANJkrBz505IkpQQPVWv29tvv61xtfR04vU0XFsXbz0NxcPS02DbSFs3nnrKMIyWVq8LyainQOyaGs31Uu1Y+6jA2PU2Xn1UFUbrqp5XZWUlJEmKWFfHS1OB0DoTT00NpzPx7KOOVW8vxT6qmfbcjKbqyzURfVQz7XmsfdRI8RapPTcChoweaSEIAwMDmDRpEvr7+6N+/7EQeD5q06ZNQQF9ucHieXkhVXgCqcPVDE8zGjie57LK6vJCqvAEUoerxTMyxlNPzZzPKqvLD6nC1eJ5eSERemrNBDGJRI4hEREGBgZi8hlrWjM+zWAi8jsRXC2e8Utr8Ywv4uHP0pn4wOIZv7QWz/gilWI3mc4bzleqlFUq1L9U4WnGb6rEbjLyNAJrEMQk4jWFMZyv3bt3x+Qz1rRmfJrBROR3IrhaPOOX1uIZX8TDn6Uz8YHFM35pLZ7xRSrFbjKdN5yvVCmrVKh/qcLTjN9Uid1k5GkE1uMwMcKavh0ZFs/LC6nCE0gdrpfK9G1LTyPD4nn5IVW4Wjwjw3ocJrFIFZ5A6nC1eF5esB6HSQKoC9okyldvb29MPmNNa8anGUxEfieCq8UzfmktnvFFPPxZOhMfWDzjl9biGV+kUuwm03nD+UqVskqF+pcqPM34TZXYTUaeRmANgpiE3+8HAEiSBEmSRtmiKAbZ+oJRbf1+QRCCbHWijiAIEEURtbW18Hq9ICIQEQRBAIAgW5blIFvNQ21tLXw+X9B+Nb96W8/D7/fj4MGD8Pv9ETkZsUdy0vPQ22p+vV5vWE6hbJ/Pp+U3HKdw5aRy9fl8hnjobRVjcQpVTmp+1fIdi5++nPx+P2prazUO0cSez+fT0hqNPZWHyiGa2FPPofo0GnuqrecaTeypMaTmx2jsqZxUGI09fT2LpAuhymlkfo3GniiKOHjw4ChdMFpO6vnH4heqnNT8jycSqadEpGmqKIoJ0VN9OatcLT2deD0N19bFW09D8bD0NNg20taNp54SkZZWrwvJqKdA7JoazfVS7Vj7qJHqbbz6qCqM1lXV9nq9OHjwIERRjFhXx0tTR9bbRGhqOJ2JZx91rHp7KfZRzbTnZjRVX66J6KOaac9j7aNGirdI7bkRWIMgUWLz5s0oLS1FWVkZAODUqVMAgMbGRjQ2NgIA6uvr0dTUBACoq6tDc3MzAKC2thYtLS3auTo7OwEANTU1cDgcAICdO3fC6XQCUF6jNTg4CACoqKiAJEm49dZbUVVVBVEU4fV6UVFRAQAYHBzUXl/kdDqxc+dOAIDD4UBNTQ1sNhuuvPJKHDx4EADQ0tKC2tpaAEBzczPq6uoAAE1NTaivr9c4nTlzBhs3bkRjY2NETnv37kVHR4fGqaenR7PDcfJ6vRBFERUVFUGcbDYb1q1bh+rq6rCcAKCjowN79+7VONXV1WHjxo1obW0NyylcObW2tmLjxo2oq6sLyylUOal5jMQpVDnV1NRg48aN6O/vD8spVDmdO3cOhYWFsNlsUcfewYMHceWVV8JmsxmOPZUTgKhjT7UzMzNhs9kMx57KqbGxEXPmzIHNZjMcew6HQ3sFl8vliir2AGBoaEgrU6OxV1tbC5vNhgULFuDYsWNjcgpVTjabDbm5ubh48WJYTqHKyev1YuPGjaiqqooq9lROqq9wnMKV03hMw5xIPfV6vWAYBj6fDwzDJERPGxsbYbPZUFhYiHPnzo3JydLTxOlpU1MTGhsbsXHjRpw5cyZhelpVVQUAlp6OUU6tra0oKCiAzWZLiJ4ODg7CZrPB5/NpHetk0VPAvKa2tbVpttHrBZjro+7btw8bN26Ew+EwfL1UTrH2UVU/kTiFiuuqqips3LgRXq83Yl0dL009duwYFixYAJvNFrGujuQUq6a6XC7ttaVG6up49FFtNhsyMzM1Hpd6HzXW9tyspurLNRF91FjbczN9VJVHT09P1O25qnsRQRZiQn9/PwEgh8NBRESiKJIoiqNsQRCCbEmSyO/3U3l5OXm93qD9RER+vz/IlmVZs0VRpIsXL5LX6yVZlkmWZfL7/UREQbbqQ7XV83d0dJDP5wvar+ZXb+t5+P1+6uzs1PyH4xTK9nq9VF5eTm63OywnPQ+9LUmSxjUcp1C2z+fT8huOU7hyUrn6fL6wnEKVk8vlovLycvL7/WNyClVOan4FQQjLKVQ5+f1+am9vJ0mSooo9IiKfz0cdHR1BsREp9mRZJp/PR+Xl5eTz+aKKPdW/ml+jsafafr8/KL9GYk/1097eHnTNjMSeeo3UMjUae6HqmdHYE0VxVH6NxJ5aLzs7O0fpghGNULXI7XYbjj017319fQSA+vv7ySwmQk9lWSZRFKmtrY1EUUyInurLWT2npacTr6fh2rp46qksyxpXn89n6WkY20hbN556KssySZI0SheSSU+JYtfUUDoT7z5qpHobjz6q2+0O0ploNNXr9VJnZ6fmPxSn8dbUcDoTT00NpzPx7KOOVW8vxT5qrO25GU1VdcblckUVe2b6qLG252b6qGod9Xq9UbfnDofDkJ5aM0FMgmEYAADHceA4bpTN83yQzbLDl1y19fttNluQrZ7fZrOBiHDixAlwHAeGYbQRWjUfqs2ybJDN8zxkWUZDQ4N2bnW/ml+9refBMAyOHz8OhmEicjJij+Sk56G3ZVnWuIbjFMpmWVbLbzhO4cpJ5aqeJxIPva1iLE6hyknNr3r+sfjpy4lhGDQ2NkKW5ahjj2VZNDQ0QJZlw7Gn8lB9RxN7KtT8Go091WYYJii/RmNPlmVtVHssTuHKSYXR2AtVz4zGHsdxo/JrNPaICMePHx+lC0bLST3/WPxClZM63XE8kUg9ZRgGRISTJ0+CiBKip/pyVvNi6enE62m4ti7eehqKh6WnwbaRtm489ZRhGMiyPEoXklFP1Xzq/Yy0o9WfePRRI9XbePVRVRitq6rNcRyOHz8OIopYV8dLU8PpTDw1FQitM/Hso45Vby/FPqqZ9tyMpurLNRF9VDPteax91EjxFqk9NwI+8iEWxoK+AiXC1y233JLQtGZ8msFE5HciuFo845fW4hlfxEP7Eqmnqj+r/o1/OrNpY0Wq1L9U4WnGbzLGbjKdN5yvVCmrVKh/qcLTjN9Uid1k5GkE1kwQk4jX6H04X21tbTH5jDWtGZ9mMBH5nQiuFs/4pbV4xhfx8GfpTHxg8YxfWotnfJFKsZtM5w3nK1XKKhXqX6rwNOM3VWI3GXkagTUIYhKJLtSzZ8/GHISxpDXj0wwmIr8TwdXiGb+0Fs/44nIZBLHq3/inM5s2VqRK/UsVnmb8JmPsJtN5w/lKlbJKhfqXKjzN+E2V2E1GnkZgPQ5jEomeanjjjTcmNK0Zn2YwEfmdCK4Wz/iltXjGF5fL4zBW/Rv/dGbTxopUqX+pwtOM32SM3WQ6bzhfqVJWqVD/UoWnGb+pErvJyNMIrJkgJpHoka3z58/HPBIXS1ozPs1gIvI7EVwtnvFLa/GMLy6XmSBW/Rv/dGbTxopUqX+pwtOM32SM3WQ6bzhfqVJWqVD/UoWnGb+pErvJyNMIrEEQkxAEAQAgSRIkSRpli6IYZOsLRrX1+wVBCLKJSLMlSUJrayt8Ph+ICESk+dfbsiwH2er5W1tb4ff7g/ar+dXbeh6CIKCtrU3zPxYnI/ZITnoeelvNr8/nC8splO33+7X8huMUrpxUrn6/3xAPva2Ph3CcQpWTml9RFMNyClVOgiCgtbUVsixHHXt+v19LazT2VB4qh2hiT/Wv+jQae6qt5xpN7MmyjJaWlqBrZiT2VE4qjMaevp5F0oVQ5aSm1V8zI7EnSRLa2tpG6YLRclLPPxa/UOUUjwYtkXpKRJAkCS0tLZAkKSF6qi9n9ZyWnk68noZr6+Ktp6F4WHoabBtp68ZTT4lI46rXhWTUU5XTyGtkJK6juV6qHWsfNVK9jVcfVX+NotFUn8+HtrY2zX8oTuOtqSPrbSI0VRRFtLS0jNKZePZRx6q3l2If1Ux7bkZT9eWaiD6qmfY81j5qpHiL1J4bgTUIEiU2b96M0tJSlJWVAQBOnjwJQHm1kvqqo/r6ejQ1NQEA6urq0NzcDACora1FS0uLdq7Ozk4AQE1NDRwOBwBg586dcDqdAIDKykoMDg4CACoqKiCKIq655hpUVlZCFEV4vV5UVFQAAAYHB1FZWQkAcDqd2LlzJwDA4XCgpqYGPM9jzpw5qK2tBQC0tLRodnNzM+rq6gAATU1NqK+v1zg1NTVh3bp1aGhoiMhp79696Ojo0Dj19PRodjhOXq8Xoihq/FROPM/jyiuv1HiE4gQAHR0d2Lt3r8bpyJEjWLduHVpaWsJyCldOLS0tWLduHY4cORKWU6hyqq6u1sp0LE6hymnXrl1Yt24dnE5nWE6hyqm5uRmZmZngeT7q2KutrcWcOXPA87zh2FM5AYg69gCgu7sbkiSB53nDsadyamhoQEFBAXieNxx7DocDPM/D5XJhaGgoqtgDoKUZi1OocuJ5HoWFhRoPo7HX3NysvYZM5WEk9iorK+HxeLBu3TpUVlZGFXsqJ9VXOE7hymk8pllPpJ56vV4A0HQqEXra2NgInueRmZmp8bD0dOL1tKmpCQ0NDVi3bh2ampoSpqdVVVUAoos9IHX0VLXtdjt4nk+Ing4ODoLnefT09Gg35cmip4B5TW1ra9Nso9cLMNdH3bt3L9atW4fu7m7D10vlFGsfVfUTiVOouK6srMS6devg8Xgi1tXx0tT6+noUFhaC5/mIdXUkp1g1dWhoCC6XCzzPG6qr49FH5XkekiShu7t7TE6XSh811vbcrKbqyzURfdRY23MzfVSVR09PT9Ttuap7EUEWYkJ/fz8BoO7ubiIiEkWRRFEcZQuCEGRLkkR+v5/Ky8vJ6/UG7Sci8vv9QbYsy5otCAKdPn2aPB4PybJMsiyT3+8nIgqyVR+qrebh9OnTmk91v5pfva3n4fP5qKmpiXw+35icQtler5fKy8vJ7XaH5aTnobfV/Ho8nrCcQtler1fLbzhO4cpJ5er1esNyClVOLpeLysvLye/3j8kpVDmp+VXLdyx++nLy+Xx06tQpjYPR2CMi8nq9dPr0ae18RmJPlmXy+XxUXl5OPp8vqthTz6Hm12jsqbaeq9HYkySJRFGkkydPavkxGnuqT7VMjcaemrdTp06Rz+eLKvbU76dOndJ8GYk9NW6amppG6YIRjVC1yO12G449Na+9vb0EgPr7+8ksJkJPZVkmQRDo5MmTJAhCQvRUX85qjFh6OvF6Gq6ti6eeyrKscfX5fJaehrGNtHXjqaeyLGtc9bqQTHpKFLumhtKZePdRI9XbePRR3W53kM5Eo6kej4eamppIEISIdXW8NHVkvU2Epvr9fjp58uQonYlnH3Wsensp9lFjbc/NaKqqMy6XK6rYM9NHjbU9N9NHVeuo1+uNuj3v7u42pKfWTBCTYFnlEnIcB47jRtk8zwfZ6vH6tPr9NpstyGYYRrMBZXRM/YeDYRhtv95mWTbI5nkeRASn06nlRd2v5ldv63mwLIu+vj6wLBuRkxF7JCc9D72t5lfNVyhOoWyO47T8huMUrpxUrirvSDz0toqxOIUqJzW/DMOE5RSqnFiWRX9/P4go6tjjOA5OpxNEZDj2VB4qh2hiTz1Gza/R2NPHqpo2mtgjIgwMDIziYbScVBiNPbWe9ff3R9SFUOWkplV9RxN7fX19o3TBaDmp5x+LX6hyIt0U9/FCIvVUtQcGBgAgqpiOVU/15azPr6WnE6un4dq6eOtpKB6Wno62I7V146mnDMNoXPXlkYx6qvrV+xlpR6s/8eijRqq38eqjqjBaV1Wb53n09fUF8Y63po6st4nQVIZhMDAwMEpn4tlHHaveXop9VDPtuRlN1ZdrIvqoZtvzWPqoRtqGsdpzI7DeDmMSeiFNhC91imOi0prxaQYTkd+J4GrxjF9ai2d8EQ/tS6Seqv6s+jf+6cymjRWpUv9ShacZv8kYu8l03nC+UqWsUqH+pQpPM35TJXaTkacRTPhMkOeeew5z585Feno6Vq1ahd27d495/K5du7Bq1Sqkp6dj3rx5+PWvfz3qmNdeew2lpaVIS0tDaWkpXn/99aDfn3zySW0ESt2Kiopiyr+6cEwiIEkSTp48GZPPWNOa8WkGE5HfieBq8YxfWotnfBEPf5bOxAcWz/iltXjGF6kUu8l03nC+UqWsUqH+pQpPM35TJXaTkacRTOggyKuvvoqvf/3r+M53voO6ujrccMMN+NCHPoQLFy6EPL65uRmbNm3CDTfcgLq6Onz729/G1772Nbz22mvaMfv27cOdd96Ju+++G0ePHsXdd9+NT3/60zhw4EDQuZYuXYqOjg5tO3bsWFy5jhc8Hk/C05rxaQYTkd+J4GrxjF9ai6eFSLDqX3zSmU07ET6Tqf6lCk8zfpMtdi8HpEpZpUr9SxWeZvymSuwmG09DGHPFkDjjmmuuoQceeCBo3+LFi+lb3/pWyOO/+c1v0uLFi4P23X///XTttddq3z/96U/TbbfdFnTMxo0b6TOf+Yz2/YknnqCrr77aVN7VRadiWcRKXdRGXQDmcoXF8/JCqvAkSh2uZnia0cDxPJdVVpcXUoUnUepwtXhGxnjqqZnzWWV1+SFVuFo8Ly8kQk8nbCaI3+/H4cOHsWHDhqD9GzZsCHp1lR779u0bdfzGjRtx6NAh7Z3A4Y4Zec6mpiZMnz4dc+fOxWc+8xm8//77MfFI9PSe48ePxzwdKZa0ZnyawUTkdyK4Wjzjl9biGV9cLo/DWPVv/NOZTRsrUqX+pQpPM36TMXaT6bzhfKVKWaVC/UsVnmb8pkrsJiNPI5iwhVEdDgckSUJhYWHQ/sLCQly8eDFkmosXL4Y8XhRFOBwOFBcXhz1Gf841a9bgj3/8IxYuXIjOzk48/fTTWLduHU6cOIGCgoKQvn0+H3w+n/ZdXTlcEARtAMYo1OOjTSdJEmRZhiAIkGU5IWnN+IyVpxm/E3GNLJ7x82km7UTEbqrwjDWNiktBTwGr/sUrnZm0ls7E12cy8TTjN9li14yeAuOnqVZZxc+nmbRJoTPnzgE5OUBBQVLxNOM3VWI32XgaTcMQxem9XBHQ3t6OkpIS7N27F2vXrtX2/+AHP8Cf/vQnnDx5clSahQsX4t5778Vjjz2m7duzZw+uv/56dHR0oKioCHa7HX/4wx/w2c9+VjvmL3/5Cz7/+c/D6/WGzIvL5cL8+fPxzW9+E4888kjIY5588kk89dRTo/a/9NJLyMzMNMzbggULFi4HuN1u3HXXXejv70dubm5UaS09tWDBgoVhmNFTwNJUCxMH1u/H4pdewoJ//AODM2ag+tlnJzpLFlIcRvV0wmaCTJkyBRzHjZr10dXVNWomh4qioqKQx/M8r83gCHdMuHMCQFZWFq688ko0NTWFPeaxxx4LGiAZGBjAzJkzceutt2Ly5Mlh04WCIAioqqrC+vXrg975HAmSJOHEiRNYunSp9v7meKc14zNWnhOV31jTWjwvzfxOROymCk9Aee97rLgU9BSw6l+80plJa+lMfH0mE8+Jyu9ExK4ZPQXGT1Otsro083up6gxz8CC4z38eTOCP65zWVmy67TZIREnD04zfVIndZONpVE8nbBDEbrdj1apVqKqqwic+8Qltf1VVFT72sY+FTLN27Vq8+eabQfsqKyuxevVq7QKtXbsWVVVVePjhh4OOWbduXdi8+Hw+NDY24oYbbgh7TFpaGtLS0kbtt9lsURdOrGlZlkVWVhZsNlvUQRhrWjM+VcRyjSYiv2a5Wjzj4zOZYjdVeKppYsWloKeAVf/ilc5sWsDSmXj5TCaeZvwmW+ya0VNg/DXVKqv4+Eym+jemT58PeOop4Ec/AmQZKCwEOjvByDJsHg/YnJyk4WnGb6rEbrLxNHr8hA2CAMAjjzyCu+++G6tXr8batWvxm9/8BhcuXMADDzwAQBnZbmtrwx//+EcAwAMPPIBf/vKXeOSRR3Dfffdh3759ePHFF/Hyyy9r53zooYdw44034kc/+hE+9rGP4Y033sCOHTvw7rvvasc8+uijuP322zFr1ix0dXXh6aefxsDAAP71X/81ag6xVtJYwHEcFi9enNC0ZnyawUTkdyK4Wjzjl9biGV/EQ/sSqaeqP6v+jX86s2ljRarUv1ThacZvMsZuMp03nK9UKatUqH9hfVZXAw88AJw+rXy/6y7g2WeBWbMAtxvo6wOXl5c0PM34TZXYTUaeRjBhb4cBgDvvvBPPPPMM/vM//xPLly9HTU0NKioqMHv2bABAR0cHLly4oB0/d+5cVFRU4J133sHy5cvx/e9/H88++yw+9alPacesW7cOr7zyCv73f/8XV111FX7/+9/j1VdfxZo1a7RjWltb8dnPfhaLFi3CJz/5Sdjtduzfv1/zGw1EUTRxBaL3dfDgwZh8xprWjE8zmIj8TgRXi2f80lo844t4+LN0Jj6weMYvrcUzvkil2E2m84bzlSpllQr1b5TP7m7gnnuAW25RBkCKioDXXgP+8hegoABQH7vq7U0qnmb8pkrsJiNPI5jQmSAA8OCDD+LBBx8M+dvvf//7UftuuukmHDlyZMxz3nHHHbjjjjvC/v7KK69ElcexwDDMuJ3LiK/JkyfH5DPWtGZ8msFE5HciuFo845fW4hlfxMOfpTPxgcUzfmktnvFFKsVuMp03nK9UKatUqH+aTyLgt78FvvlNoK8PYBjgS18CfvADIC9vOEF+PtDWBvT2JhVPM35TJXaTkacRTPggSLIj0VMNFyxYkNC0ZnyawUTkdyK4Wjzjl9biGV9cLo/DWPVv/NOZTRsrUqX+pQpPM36TMXaT6bzhfKVKWaVC/eM4Dgt8PmXmh7qkwNVXA88/D+hm12vIz1c+e3uTiqcZv6kSu8nI0wgm9HGYywGJnt6zd+/emKcjxZLWjE8zmIj8TgRXi2f80lo844vL5XEYq/6NfzqzaWNFqtS/VOFpxm8yxm4ynTecr1Qpq8u+/kkSpCefhHz11coASFYW8NOfAocOhR4AAYIGQZKGp0m/qRK7ycjTCKxBEJMgIgDKK4AkSRpli6IYZMuyrKVVbf1+QRCCbPX8giCAYRhMnz4dkiSBiEBEEARBy4dqy7IcZIuiCJZlMX36dO3c6n41v3pbz4OIUFJSAiKKyMmIPZKTnofeVvOr+gnFKZQty7KW33CcwpWTylU9TyQeelvFWJxClZOaX/X8Y/HTlxMRobi4GCzLRh17sixj+vTpYFnWcOypPFTf0cSeiqKiIrAsazj2VFvPNZrYY1kWRUVFo8rGaDmpMBp7aj0rLi7W8mI09iRJGpVfo7HHMAxKSkpG6YLRclLPPxa/UOXEsuPffCRST4kIDMOgqKgIDMMkRE/V61ZcXKzlxdLTidfTcG1dvPU0FA9LT4NtI23deOopEWlp9bqQjHqq5lPvZ6Qdrf6MFdeSNITi4mlR91Ej1dt49VFVGK2rqi1JEkpKSsAwTMS6Ol6aKstyUL2Nq6a2tQG33QbuqafAShLoox+FfPw4xK99DeD58PzUNUH6+mLuo45Vby/FPqqZ9tyMpurLNRF9VDPteax91Eg6Gqk9NwJrECRKbN68GaWlpSgrKwMANDQ0AAAaGxvR2NgIAKivr0dTUxMAoK6uDs3NzQCA2tpatLS0aOfq7OwEANTU1MDhcAAAdu7cCafTCUB5te/g4CAAoKKiAn6/HyUlJdi2bRtEUYTX60VFRQUAYHBwEJWVlQAAp9OJnTt3AgAcDgdqamrAsixsNhv2798PAGhpaUFtbS0AoLm5GXV1dQCApqYm1NfXa5xOnTqF2bNn4/jx4xE57d27Fx0dHRqnnp4ezQ7Hyev1QhRFVFRUBHFiWRb5+fnYsWNHWE6Asnju3r17NU6HDh3C7Nmzcf78+bCcwpXT+fPnMXv2bBw6dCgsp1DlVF1drZXpWJxClVN1dTVmz56N3t7esJxCldPZs2fhdDrBsmzUsbd//37YbDawLGs49lROAKKOPUCJ9dbWVrAsazj2VE7Hjx+Hz+cDy7KGY8/hcIBlWTQ1NWFgYCCq2AOAoaEhrUyNxl5tba3W4B89enRMTqHKiWVZXLx4EW1tbWE5hSonl8uF2bNnY9u2bVHFnspJ9RWOU7hyGo9O+0TqqdfrhSzLOHbsGGRZToieNjY2gmVZOJ1OnD17dkxOlp4mTk+bmppw/PhxzJ49G6dOnUqYnlZVVQGApadjlNP58+eD/MdbTwcHB8GyLI4dOwa/359UegqY11T1etXW1hq+XgDw5pu/wJ4909He/p2o+6jvvvsuZs+ejc7OTsPXS+UUax9V9TMWp3BxvW3bNsyePRsulytiXR0vTT169Kh20x2pro7kFI2muisrIV11FbBjBygzE/WPPgrmjTfgyMyMWFf71Rju7Y25j8qyLFpbW2Nqzyeijxpre25WU/Xlmog+aqztuZk+qsqjp6cn6vZc1b2IIAsxob+/nwBQV1cXERGJokiiKI6yBUEIsiVJIr/fT+Xl5eT1eoP2ExH5/f4gW5Zlzfb7/fTOO++Q2+0mWZZJlmXy+/1EREG26kO1BUEgQRDonXfeIY/HE7Rfza/e1vPwer20a9cu8nq9Y3IKZXu9XiovLye32x2Wk56H3lbz63a7w3IKZXs8Hi2/4TiFKyeVq8fjCcspVDm5XC4qLy/Xyigcp1DlpObX5/OF5RSqnLxeL73zzjtaWRiNPSIij8ejpTUae7Isk8/no/LycvL5fFHFHhGRz+fTfBqNPdXWczUae6qPd955h3w+X1Sxp+ZXLVOjsaevZ2rdNhp76nXQ59dI7Kkxt2vXrlG6YEQjVC3S17NIsafmvaenhwBQf38/mcVE6Kl6Pd55552g7+Gulf6axKqn+nLW59fS04nV03BtXTz1VJZljavP57P0NIxtpK0bTz2VZVlLq9eFZNJTotg1NZTORLpeBw4Q/epXt1B1Nai6GnT48MGo+qiR6m08+qhutztIZ6LRVLfbTbt27dLShuI03po6st6Ou6b6fEQ//jHJHEcEEC1eTP66upA6E9Z++mkl7b33xtxHHaveXop91FjbczOaquqMy+WKKvbM9FFjbc/N9FHVOqqvK0Y1taury5CeWgujmoTNZgMQvAiL3uZ5fpStTjFSR/71x6jnC2XLsowFCxYgLS1NW/lWPYZhGM1mWVY7t2qrae12+6hjwuWd4zgwDIP58+dr/3SF4xTKpsB0JJ7ntbRj8QvHNRynULbdbh+V37H46fPLsizmz58Pu90ekatRHiPtkeWk5ld/jcLx0+fXZrNhwYIFQb+H4hTKttvtIdMaybv6qecRKfZU/3qf0ZRNOK5G4nDBggXa92jLSYXR2NPXs0i6EKqc1LTqd6OxJ8sy5s+fH1IXRnIaWU5CYOqhen4jsafa8Zi+nUg9Vc+9YMECTeviracARsWIpacTr6fh2rp46mk4HpaeBttG2rrx1FPVfyhdGMnpUtdTfX6NamoonQl3vYhsePxxYOvWnfjJT4Znwmzf/gvMnfsHTJ5srI8aqd7Gq48aipMROy0tDfPnzwfHcVHFshlNjbbeRsXJ6YTtc58D3ngDDADcdRfw/PPgMjOxoKNjlM6EtadMUc7X22uqjxqu3l6KfdRY2/NQXI2WmaozkTiNZx/VTHseax9Vr6PRaqr+/GPBehzGJOLVcIXzVVJSEpPPWNOa8WkGE5HfieBq8YxfWotnfBEPf5bOxAcWz/iltXjGF6kUu8l03vp64JprgKefJtx773cAADbbBwAAZWUv4fOfb4Fu+YKIeUy2srps6t+RI8DKlcAbbwB2O/CrXwF//jOQnR29T93CqJcczzj5TZXYTUaeho6Lcz4ue6jPoCXK186dO2PyGWtaMz7NYCLyOxFcLZ7xS2vxjC/i4c/SmfjA4hm/tBbP+CKVYjcZziuKwH/9F7B6NXD0KLB+fQWWLt0Pls3AihV/hNu9EjwvorDw5/j6143nMdnKKunrnywrb3tZuxZobgbmzAH27AEeeAAI/Fsftc8Rb4e5JHjG2W+qxG4y8jQCaxDEJBI9srVs2bKYR+JiSWvGpxlMRH4ngqvFM35pLZ7xxeUyE8Sqf+OfzmzaWJEq9S9VeJrxm4yxe6mf9/Rp4IYbgMceAwQB+OhHZTz11HcBACUlX0V6+nQsWvQNAMDttz+PP/zBic2bjeUx2coqqetfayuwfj3w6KOA3w987GPKjJDVq835VAdB+vouDZ4J8JsqsZuMPA0dF+d8XPZIdKFOmzYt5iCMJa0Zn2YwEfmdCK4Wz/iltXjGF5fLIIhV/8Y/ndm0sSJV6l+q8DTjNxlj91I9rywDv/gFsHw5sH8/kJsL/P73wAsv/B0+33vguBzMmvVNsCyLefM+i6ysZcjMHMLttz+Phx4CAi98GDOPyVZWSVv//vpX4KqrgJ07gcxM4PnngddfB9TX25rxqZ4j8DiMpTPxSxsrUomnoePinI/LHoLRhx7Hydf27dtj8hlrWjM+zWAi8jsRXC2e8Utr8Ywv4uHP0pn4wOIZv7QWz/gilWL3Ujzv+fPKpIGvfQ3weIBbbwWOHQPuuUfCuXOPAwBmzHgENlsBBEFAZWUlpk9/GABw993PgGV9+PSngZMnx85jspVVstU/3u0G9/nPA5/+NNDXp8z6qKsDvvhF7fEX0z7VmSBeL4SBAUtn4pg2ViScpyxDfP11HH3sMQjvvx9dWhMwmk9rEGScIEmS9pYCvS2KYpAty7KWRrX1+wVBCLLVlboFQQDLsli9ejVkWQYRgYi0gtbbsiwH2aIoguM4rFq1Sjuful/Nr97W8wCgvW8+Eicj9khOeh56m+M4jWs4TqFsItLyG45TuHJSuRKRIR56W8VYnEKVk5pfhmHCcgpVTgCwcuVKcBwXdewREVatWgWO4wzHnspDTR9N7AHKqs9qfo3GnmoDCMqv0djjOA4rVqzQVqM2GnsqJxVGYy9UPTMae5IkgeM4rFy5Usuv0dhjWRZlZWWjdMFoOannH4tfqHLSr8Y93kiEnhIRWJbFihUrwLJsQvRUX84qLD2deD0N19bFW09D8bD0NNgGIrd146mnRKRx1etCMuup3s9IO1wMCIKI3/5WxpVXqpMGCL/8JbBli4AZM2R0dv4FbncjeD4fM2c+EtRHzc//J9jtJcjMvIgHHvgj+vuBj3yEcPFi6LiOVG/j1UdVYbSuqrYsyygrKwPLshHr6nhpajidMRLX0u7duPnhh8H+6U8ghgE99hhozx4Ic+eOGdcMw2DFihWjdCZsXGdmgtQ47u2NqY86Vr29FPuoZtpzM5qqHjMWp/HsowIxtOfNzZBvuQX8Jz+Jq//rv2CbPx80axborrsgPfss6L33QKIYtpwixVuk9twIrEGQKLF582aUlpZqItbQ0AAAaGxsRGNjIwCgvr4eTU1NAIC6ujo0NzcDAGpra9HS0qKdq7OzEwBQU1MDh8MBANi5cyecTicAoLKyEoODgwCAiooK+P1+5ObmYtu2bRBFEV6vFxUVFQCAwcFBVAbmHDqdTuzcqbyuzOFwoKamBizLwufzYf/+/QCAlpYW1NbWAgCam5tRV1cHAGhqakJ9fb3G6dSpU8jPz8fx48cjctq7dy86Ojo0Tj09PZodjpPX64UoiqioqAjixLIsbDYbduzYEZYTAHR0dGDv3r0ap0OHDiE/Px/nz58PyylcOZ0/fx75+fk4dOhQWE6hyqm6ulor07E4hSqn6upq5Ofno7e3NyynUOV09uxZtLa2gmXZqGNv//798Pl8YFnWcOypnABEHXuAEuuNjY1gWdZw7Kmcjh8/DofDAZZlDcee/viBgYGoYg8AhoaGtDI1Gnu1tbVgWRZOpxNHjx4dk1OocmJZFk1NTWhrawvLKVQ5uVwu5OfnY9u2bVHFnspJ9RWOU7hyGo+pjROpp16vF7IsY9++fZBlOSF6qtaB1tZWnD17dkxOlp4mTk+bmppw/Phx5Ofn49SpUwnT06qqKgCw9HSMcjp//jzOnTun6WO89XRwcBAsy2Lfvn3w+/1JpaeAeU1ta2tDb28aPvhBN+67j8XgILB0qRNvv92LL38ZeOednejr68a5c08CAKZNewg8nxvUR62s3Inp078GALj99u9j7lwZZ88y2LBhAH7/6Bh49913kZ+fj87OTsPXS+UUax9V9QMYq6v6GNi2bRvy8/Phcrki1tXx0tSjR4/C6XSCZdmIdVXlNLhvH3DXXUhbvx5ZnZ2g2bPx7tNPw/u970FkmIhxPTAwgLq6OrAsa6iuNp87BzE3FwDQWl8fUx9VPT6W9nwi+qixtudmNRUYbisT0UeNqj1/+214/ud/gCuvBLtrF6TcDNCqFZBZFkxLC5iXXwb30ENgVqwACgrQu2YN8P3vw/3WW3j7H//QOKk8enp6om7PVd2LCLIQE/r7+wkAdXZ2EhGRKIokiuIoWxCEIFuSJPL7/VReXk5erzdoPxGR3+8PsmVZ1myfz0dvvvkmuVwukmWZZFkmv99PRBRkqz5UWxAE8vv99Oabb5Lb7Q7ar+ZXb+t5eDweeuutt8jj8YzJKZTt9XqpvLyc3G53WE56Hnpbza/L5QrLKZTtdru1/IbjFK6cVK76/Ibjpy8nl8tF5eXlWr7DcQpVTmp+vV5vWE6hysnj8dCbb75Jfr8/qtgjInK73Vpao7EnyzL5fD4qLy8nn88XVewREXm9Xs2n0dhTbT1Xo7Gn5uPNN9/U6pnR2CMijauaXyOxp69nHo8nqtgTRXFUfo3EnqoLb7311ihdMKIRqhaF0oVI5eRwOAgA9ff3k1lMhJ6qMf3mm29GHdOx6qm+nNUYsfR04vU0XFsXTz2VZVnj6vP5LD0NYxtp68ZTT9V8j9SFZNJTotg19U9/8lNOjo8AIrtdph/9iMjrDb5eLS3PUXU16N13C0kQBrX9+j6q3++kmppcqq4GHTr0BmVnywQQ3XcfkShKUdXbePRR3W53kM5Eo6kul4veeuutoHobb00dWW/H1NRDh0j66EeJAG27cNNN5O/ujkpTw+nMWHEtL1pEBJC3qiqmPupY9fZS7KPG2p6b0VRVZ6Jtz830UQ235+fPk3TrrVrcDX10Ne1+J5927FhEAx3NJFdVkfzkkyStX09yTk5QjBJAMscRrV5N8kMPkfcvf6Gtv/tdTO15Z2enIT21BkFihNrAOJ3OqNOqAawGrVHIskz9/f1aJUlEWjM+Y+Vpxu9EXCOLZ/x8mkk7EbGbFDwliejUKaKXXiLx4Yfp/Ac+EFOZOp3OcR8ESaSeEln1L17pzKS1dCa+PpOJpxm/Cb1GTidRXR0Jr75Kxz73OfJfvBi1z/HUU6LYNNXhIJo0SRmsWL5cpmPHRh8jih7as6eEqqtBLS3PBv028rqdOfMfVF0NOnLkenrrLSKGUe51nnmGxkwXDSyd0eHdd4luu234ppJhiO64g/y1tYnTmbVrlZvZv//d0pk4pb0kecoy0W9/S6QObGRkkPzMz+jIkRupuhpUXQ06ceKu4PSiSFRXR/SLXxB95jNEM2aMGhQhgMRHHomap1E95Y3NF7EQDkyYBYXCobsbqKlh0NKSi/5+YMqU6HzlBqaaRYtY05rxaQYTkd+J4GrxjF9aiyeUZf3PnAEOHx7ejhwBAtMvOQAzGQbi4ODwomZR+B1vRHtOWVYW3OvtTYPbrby1IJpTWPUvPunMpo0Vl1z9ixNShacZv+N6jQQBuHABeP99oLlZ+dTbvb0AAB7AMgDi5z4HFBZG7TMeiOa8BQXAM89I2LbtDF58cT6ysmyjjmlv/zX8/jakpc3E9OlfHOVLf91mzPgaWlt/hv7+d3H99fvwk5+sxTe+ATzyCLBwIfChD4VOFy2/lK5/REB1NfD97wPvvKPsY1ngrruUdxmXlirx296emLwG3hDD9PVZOhPHtLEiLjxbW4H77gO2bVO+r1sH/O//ojWjAv1na8CymZBlH7q6XkJOThlmzvy6chzHKa+cWr4c+MpXlH0XLgB79gDvvgt6913g2DHQkiUx5dcIrDVBTEK/SI0R7NsH3HEHj4cf/gCmTrUhPx9YuRL45CeBb3xDeQ3Zm28Cx48DusdoNV9vvPFGzKsQx5LWjE8zmIj8TgRXi2f80qYcT58POH0aePll4NFHgQ98QOmQLFqkdIh++lOlkzQwAKSnA2vWQHrgAbz3la9EN3Kg8zveiPacTidw1VU2/Nu/3Ya8PBvsdqUjP28esGIFcPPNwEc/Ctx9t9LGfuc7wH//N/DrXyuX6a23RDz33A643Vb9G890ZtPGCktn4pc2JfoCXV3A1q2Qnn4a59evh/yBDwBz5ih6uWABsGEDcP/9wI9+pLxq9NAhbQAEU6dCLitD6w03gDIyosqrmt94INrz/vM/Ez772VOw20f/JopDuHDh/wEAZs9+HCybNsqX/pqnpU1HYeHdAICWlh/j4YeBz39eGby+807gxInQ6aJBytY/ImDLFuC665RX9rzzDmCzAV/4gtIP+NOflAEQE4gpr4E/U6Tubktn4pg2VowrTyLgD38Ali1TBkDS0oAf/xioqYF7BqG5+TEAwNy5P4bH8zkAwNmzj6Kv753wjmbNAj77WWDzZoiHDqHiz38GfepTUbKMQveinmNigYhin769ZQvRqlUS5eZ6Q836GbUVFBCtWkV0xx1E3/iGTM8+66PDh2UKPPpkGLIsk9vtjmkKVCzpiMxP2Up0fmNNa/GMn08zaScidhPKs7+faOtWkh97jMQbbyQ5Nze0iKSnE61ZQ/Tgg0S/+x3R0aNEAV5myvRSeBzmwgWivDyZWFY2pKfhNp6XaeFCoo98hOgb3yB6/nmi6mqi9nZlludIpEr9SxWeZtJe9jozDj4vydjt6CB66y2ip54i+tjHwk7FDtLR0lKiD3+Y6KtfJfrZz4jKy4nq64kGBkzzvBQehyEam8O5cz+g6mrQ/v0LSJJG/x7qmg8NNQSmwzPkcp0in4/oxhuVSzp3LlF3t6UzUaUTRaK//Y1oxYrh2ExLI/rKV4jOnw+ZNqE687WvKY/DfOtbls7EKe0lwbO9XekwqTF4zTVEDQ1ERCRJAh06tIaqq0HvvbeBJEkil8tFJ078MylrCU0lj+dCXHlaj8Ncoti0CVi/XkJFxTbccMMmtLfbcO4ccO6cMrNStc+dU/5g6OlRtsOHAYABoAzPZ2UBZWXA2rXDW6RHa3g+tuKONZ1ZTER+J4KrxTN+aS8rnt3dwO7dylZTA7z3HiDLYKA81gJA+cfy6quBVauA1auVzyVLlH+ILkPMnAl0dYnYsqUCN964CW63Df39ymSX/v7gbeS+gQHA4SCcPQu43QxOn1b+QHvrrWAf2dnK1O1Fi5RPdZs7l0d6emz5Tqa4tHQmvj5jRarwNONXS9fePvwooPpYYKjHAxgGWLQItHIl5CuuADt/Ppj584G5c4GiophmzF0uEAQnWlp+DACYM+dJsGzoNmVkWWVlLUFBwe3o6XkTLS0/xaJFz+O114A1a5QniT75SaCqytKZiBBF2P7v/5SpjOqbL7KygAcfVJ4vKiqKOT9jwUheBwfr0N7+PLq6XkHeDUW48lkAfX2ppzMJTptonzzPK0MeL7+sTK3t6wPsduCpp5TZx4HztrT8GIODB8Bxk7Bo0W/BMAxsNhsWLnwebvcJDA29hxMnPoXly2vAcTF2osYJ1uMwJqG+kikW5OQos4g+8hElnn76U+C115T2uadH6agfPQq88Qbw858DDz0kYfnyLuTmElwuZfbbD3+oTPeeOhW44grgnnuAX/1KuT/SZ03/mqVo+cWSziwmIr8TwdXiGb+0Sc/zwgXgz39Wpl8vWQJMmwZ86lPAM88onXlZBubNg3z33aj78pchHDqk3Nnv3w9s3gzcey9w1VVxGwCJx3WN9ZwMo+jpjBnA0qXKoPBttylTrr/4ReDf/115ZPrZZ5XZm+XlwM6dwOHDIv7853/g/fcFvP028NxzwNe/rgxWz5+vPFo9NKRc7pdfVtr6f/5noKyMwZQpNkybBlx7rbLv8ceVc7/7LtDRofQVwnFMlri0dCa+PmNFqvCMyq/6OOD27cCvfw353/8dPevWAdOnAyUlSkfpySeV543b2xXRWLIE+Jd/Af7nf5SB5f5+oLER4u9/j7dWrID4mc8oz7cXF8d9ACRe13W8ztva+lOIohOZmUsxbdpnwvoKVVYzZ/47AODixT/A7+/ElClKMeTmKmP6999P2LLF0pmQ8PuBF18EliwB/7nPgWloACZNAr73PeD8eWVQJE4DIGPlVZI86Oh4EYcPX4PDh1eio+N5SFI/eqacgpgOUE/P5akz45TObNpYYYbn2y+9BPrkJ5UOT1+fspbD4cPAt76lDYAMDR3DuXNPAACuuOLnSE+fqfkksmPp0r+D5/MxOHgQTU1fAYXrKJmEUX7WTBCTUBdfkSQJAMBxXJAtiiIYhtFs/bvgZVkGAG0/y7IQBAEcx4FlWWRkCLjySh5XXcVo+0UxD7Is4MwZG/btA/buJRw4wKKxUVn/8MwZ5VFAQBkkXrOGsGYNYd06HqtW3RbkW5Zl8DwPSZJARJqt58EwDDZt2gSGYSBJUlhOLMuOsvX8VE6CIIDneTAMo9nqMSPtD33oQ1oFkWUZkiTBZrMF5X2kDUDLr3qecGUTqpw2bdqk+QvFKVQ56Z89G4uTzWYDEWm2Pr/q+UNxClVODMNg48aNY/ILV04AcNttt4Hn+TE5jSwntSxIeatUWE6hyollWWzYsGFMTuF46LkajT2WZcHzPDZs2KBxNhp7Kid9XTESe6q9ceNGLa3h2CMC39SED7W0gLv3XqVneOECRmHZMkjXXQfmxhvB3nQThGnTwHEclgWuIwVG6o3EnpoH/TFGYk9NF49/MOKpp6HKn+M4bNy4HunpHObMIdxwQ/C1IrKhqUnGyZMy3n+fx8mTpM0Y6epi4HAwcDiAAwdGc8nMBObNI8ybByxYwGDuXBnz5hGuuILHrbdu1LhaejrxehqurYunnqr8AEVTBUGw9DSEreWXYSA1NwPNzeAuXIB89ixw7hzYc+dAzc1AezsYnR8OgHZryLLK4norV4JZvRriVVeBXbkSbG7u6HIKXL/169eD4zitPJJRT4HoNTWUzng8HWhtfQYAMGvW4yBiwDCjY4DjOE1n1DIXRRGTJl2PnJxrMTi4H62tv8CcOf+JK66Q8OqrNnz4w4Q//IFFaelHcPvtCFlv49VHVWGkrupjgIiwadMmcByn1dtx11S/H/Jvfwvmxz8G09ICBgAVFAAPPwzpgQfATp4cd00NpTOAG21tv0Jr6/9AEDoDMWbDlCmfRF9fFUSxF+7ZQHZfX0x91JE6c6n3UdU+X7TtuRlN1ZdlNLEXdR+VCFxrK6TaWnCHD2Pjiy+C6ekB2WxgHn8c4je+ATYtDayWLxEnT/4riATk538EhYX3aGW2adMmEBHS0+dgyZKXcezYh3Dx4ovIyVmNadM+H7KcVK6qHW17bgTWTJAosXnzZpSWlqKsrAwAcCKwslNjYyMaGxsBAPX19WhqagIA1NXVobm5GQBQW1uLlpYW7VydnYqA1NTUwOFwAAB27twJp9MJAKisrMTg4CAAoKKiAl6vFz6fD9u3b8WiRSL+5V+8+PjH30RDA3Du3ACeeqoWjz8O3HyzgMxMES4XsHMngx/+kMXttzOYOTMNJSUyPvpR4OGHB/DTnzYFFjxvRl1dHQCgqakJ9fX1QZxEUcSxY8cictq7dy86Ojo0Tj09PZo9Fif9yKTX60VFRQUAoL+/H1VVVQAAp9OJnTt3AgAcDgdqamoAAB0dHdi7dy8AoKWlBQcPHoQoijh37tyYnEKV07lz5yCKIg4ePBiWU6hyqq6u1so0EqfBwUFUVlYGcRJFcUxOtbW1AIDm5uByOn78eEyxt2/fPrQHpgJHE3uqIBnhNLKcLl68iAOBO8WxOIUqp1hiT+W0a9eumGJvSLcqsdHYi4bTiV270P7CC8D3vgfX9dcDBQVgli6F/atfBfuXvwAXLoA4Dv7ly4FvfAP13/8+uhsbgWPHsOMTn4DzttuAkhKNkyiK2Lp1a9Sxp0LlES0ns5hoPRVFEVVVVWGvld0OFBc7kZ39Nv7934Ef/rAbTz1VjYsXgaNHz+G3vz2Mv/0N+Na3+vCxj13ErbcCM2YIYFmC2w0cP87gH/9g8LOfAV/9KosPf5jDwoUMCgrsWLZMxD//M/DQQxfx29924uxZYP9+S08nSk+PHTsGURQTqqdqeaS8nooiOg8fxrEXXwTKy9H3gx/g4uc/D9x7L/w33aQsSpqZCW7ePHC33grcey/Yp58G++c/A+++C6atTRkAycyEe948uG6+GfTlL+PsQw+h429/AwYGsPu559D+wx8CX/saamQZDq93zHKqqqpKOj0FzGtqW1ubZqsxsH//Q5CkIWRnr8LRo7mhY2DLFvhrauBrb8fWrVuDrhfDMJgy5csAgPb259DT04qdO3fittuA//xPJTb+4z84zJ9P2LixFz/5CfDXv3ahuvrQmNfLTB9VLRfAWF3Vx4DKz0hdjVZTTx0+jK5vfQuYNw/s174GpqUFKCrChYcewqlt24Bvfxu1p04lTFN37doFALh48RR2774X+/fPRnPztyAInUhLm4XJkx8Dz7+OpUtfAcctAgC45gC+jo6Y+6gHDhzAxYsXx+R0KfVRjx8/nnBN1ZfruPRRiXBmxw60Pfss8O1vw3X99aCpU4G5c8HdeSfY//5vMD09cC1YgI433gC++13sPXgwiNPJk9/F0FAdiHJQWPjfYBgmZB81N/cD8Hj+BQBw5szXsH37z0KWk8qjp6cnak6q7kVE1KuNWCCi4UWnOjs7iYhIFEUSRXGULQhCkC1JkrbYi9frDdpPpCwEo7fVRWj8fj/5fD4qLy8nl8tFsiyTLMvagjF6W5Ik8nj8VF9P9KtfSXTPPRItXCgTw4RePLCgQKZbb5Xom98k+stfJGpoEEmSFB4ej4fKy8vJ4/GMySmU7fV6qby8nNxud1hOeh56W71GLpdL46TnJwRWhh1pu91uLb/q/nBlM7KcVK76/Ibjpy8nl8ulLd4zFqdQ5aTm1+v1huWk56Haal79fn9UsUdEmk+/32849mRZ1uLP5/ONySlUOamxoPoIxSkcDz1Xo7EXqp4ZjT0i0riq+TUSe4IgaD49Hs8wD5eL6MABkv7nf0j67GeJ5s8PufCenJFB3UuXkvDYY0RVVST09UWMvbF0IVLs6euZ2+02HHtq2TgcjnFfGDWRemompse6bqIokssl0OnTRG+9JdKzz0r08MNEt98u09KlMmVkhF/ENSNDplWrZLrnHqIf/lCkN9+U6Nw5Ir/f0tN46mm4ti6eeirLssbV5/Ndnnrq8xH19ZH/0CHa+73vkfD88yQ9+SSJ991HdPvtJK9cSXJRERHDGFrBWOZ5kufPJ/rgB0n6whdIevppopdfJnHPHhIDqxirnMbShUjlpKbV60Iy6SlR7Jo6Ume83jbatSudqqtBDsfW0HEtSSR+5StEAHkmTyb3/v2jrpckCbRv3wKqrgZduPA/2n5RlOjhh/0hi5xhZFqyhOjuuyV65hmR9u0jGhoK5hFrH1Vfb43U1Uj11rSm9vQQPf00yQUFw/E+cyZJzz5L5HaPqreJ0FSv10tvvPEinTr1EO3alRlY4Ba0f/9iam39HUlSsP6cOvUVqq4GnbkfJM2eHVMf1Ux7Pjjoo7/+9Y2E9lFH9vkS0UeNtT3X+i6vv06ekyeJ/vY3kv7jP0hev155C0foVeNJXr6chHvvpSNf/Sq5nc6QnPr69lN1NUfV1aC2tj9H7KP6fD46duyOwEKpxeT1to9re97Z2WlITxmiOD2Qc5ljYGAAkyZNQn9/f9TvXBYEARUVFdi0aRNsCVywcHAQqK9Xnm+vq1M+T5wIXjtERXa28urmFSuUbfly5fHZaBYCnCieiYbF8/JDzFyJlFWN9+9XnpE4cECpaH7/6GMXL1ZWh7v2WuVz2bKEL2BqpkzNaOB4nivZ4lKWlRA5cUJ5Fbr6efKksqxBKGRnA6WlMrKyWrFmTQlmzeIwY4ay1MGMGcqi2OxlMq8z2crTDJKeq98PtLUpj+61tCifI7fAP5sRwXHK2hvqGh7Tpw/bc+Yoi5OWlCjHXaK4VPTUzPlGcjh9+stob38OubnXYcWK3aOnmQsC8G//pqxfpSI3F/jHP4Cbbgo6tL39Nzh9+n6kpc3CmjVnghZX7etTlhc4dAg4eFDZdBP9NPA8cOWVyrrfZWXKtnRp9E3nJVP3HA5l0b9f/EJZkwZQFqT69reV9WpCvas4SsTC1e0+hQsXfozOzj+CSHmcJjt7JWbP/jamTPkEGGZ0g9PW9ms0NX0J+fuBud/JxTUL+7FihbJWuyAAra3DG6DcU5SWKuVXWqpM+Ir2qTCfT3k768svA2++SfD7ZSxfzqCsjNXiY8mSS1o2okbU5dnWNlyxDh1StsDM0iDYbEo/VL+w/pVXKq++HQOS5MXhw6vgdjdg6tRPY+nSVw3xEMVBHDlyLdzuBuTmXofly3eCZYfjPRF6aq0JYhKJHEMiIgwODiInJ8fw8076tESDWLcuB9ddN5zW51M64PqBkfp6ZTHAd99VNhUcp9y3XX21sl11lfIZj0XTY+Vq9hrFmjZWWDzjlzbuPLu6hu9gAxudOAFG7cjoUVAQPOBRVgZMnjw6vwMDlx7PMfwmwzkj+Ut0bDEMYcqUQXzkIzm4/fbhtKKovC1BHRhRQ+vUKUWPa2tZALOgm9mswWZT7g/VQZHRNiE7exAFBZbOjHfay5YnkdJRDgxm0IUL8J85A3tnJxh1gGOsFYD1pyoowEBODnIWLwarBufIwY6pU0PeqaRSG3mpndfjOYeOjhcAAPPm/WD0tfB4lNWn33wT4DjQL34B6c9/Br93L7BxI/DSS8rrXwIoLLwHzc3fg893Ad3df0Vh4V1aHjluELfemoMPfnDYR2dn8KDIwYPKS9Lq6pTtBSVrSEsjXH01sHo1o92/lZZGf0NtFOMSWy4XmJ/9THmTgcul/Fhaqgx+3HnnqMwnKiYHBg7iwoX/gsPxOgAldiZNuhGzZ38bkydvGNN3VtZSAMrjMDnyAJpOijh5ksfLL4c+/ujR4O82G7BoEeGKK0QsXcpj8WJGeyub/j5WFIHqauCVV4C//x0IPE0CBN6Vp97n/+pXyt7MTGUNT3VQZPVqZcBFT+Wy0RmHY3iwQ/0MPLIS5NtmAxPFgMdYeT137gm43Q2w2abhiis2G07L8zlYtux1HD5choGBPThz5hEsXPhLkxdg2KcRWIMgJiGGmkYRR1+7d+/Ghg0boh4VC5c2LU2J/VWr9McqHe+6OuDQIQk7dzrR1paP3l5G65y/9NLw8VOnBg+KXH21MvJqpk7HyjUe1yiesHjGL+248XQ6R/9tf/y40hsbAQaAzPPA8uVgr712eNBj/vyIFWLCeUaJeGhfIvVU9Xep1D+eH379ru6+AYIANDUB770noqLiNHJyFqG9nUNbm/KPWleXcoz6avXQYADkorCQRg2QjPzMyYkvz3giqXUmCowLzxtvhO3ixeEZG6Fmcng8WjoGQMjusd0OzJqlvK961qzR28yZEO12vBP4R49NgnKZqDK91M57/vxTIBIwefJ65OUFz+pAfz9w++3KIt7p6cDf/gZxwwbsKCzExilTwP7jH8Addyiv3HrgAQAAx6Vjxoyvobn5u7hw4b8xbdpntQVCQ13vwkLgwx9WNkAZb2tpGR4QUW52Cf39DGprgcDyAACULC1fPvpt8eMxMGIqtt5/H71f/zpyqqqGp/6tWAF897vAxz8edkpfPGOSiNDXV4ULF34Ep3N4bZvJkz+M9vabcN11XzfkMyurFADgKwLEdOBzG49gzupVaGjgkJk53M7MmKHcZzQ0KNuJE0BjI7R1tI4ft+H114PPXVSktI0lJcDbbyvtnorp05Vxo3/6JxH19dXIzv4A3nuPx8GDyuyiUH/q5uUpcaHOKFq+XERDw25s3JhEOtPfr/xrrR8pPH9+9HEcp0y3KSuDtGIF9gkC1nzhC7BlZ5vOa3//Xu3V2YsWvQC7fUpUPDMzF2LJkr/g+PHb0d6+Gbm5ZSgq+lfD+Rorv0ZgPQ4TI1Jp+jagND5tbcrI7dGjSr07elR5S0Jgwesg8DyweDEhN7cda9YUYfZsTusnzZypvO0zgX9axxXJWJ6x4LLm6XYrd5CBtw9IZ87AUVODad3dYNS5myPBMMC8ecr0waVLlc9ly5SWOsL0wUsFl8r07VTT01gQjqcgKH/0tLYqGq0Ojug/29pCP5EVCuqrhvUDI0VFypMKRUXDdmZmYnlejogLV0lS7hDa25XA0H+qdktL8F3EWCgsDBrQGDXIMXVqxGexUqVMLxU9NXM+lcPNN89HXd3VAGSsXHkAubnXDB/U2am8g/y995S/6N96C7jhhuHfJQl48EHgN79Rvj/xhLIxDAShF/v2zYIsu3DVVZXIz19viqcsA2fPDj9Kc/iwsoV6CisjY3hgZPlyES5XDe677wZkZMQxJltagL17lW3fPmW6tfomoWuvVV51+6EPxbVDHC4uZdmP7u7X0NLyYwwNKYtKMgyPadPuwsyZ/47s7GVR+9q5swgs24mVDwDpfzsF+7KFhtLJsjLuqg6InDql3F+cOqWE20gUFChjbJ/9LHD99cp9fiiekqScRz+b6L33Qj96WlgY/JjV6tXKvcolAb9fuek6cADyvn1wv/MOsgMLco/CokXDBJQRnrg02JLkxqFDy+HxNKGw8B4sWfKHmM/V3Pwkzp9/CgyThpUr9yAnZ5X1OEwyQA41AhBHX06nE3l5eUGvhoxnWn26GTNYzJgxPCoPKPeOJ04MD4qoW3+/MqILlEC3CLeGtDSlg63vV6kDJLNmASUlMmTZXH4TdY1igt8POByQOzsx4PMhd/58sAUFhh/s7+qS8be/uZGengmeZ8FxylRCdeN55ebI5wve/H7A65Uhyx6UlGRgyhQWBQVAfr6y5eXF59nJiY5dVpKUFjYwyBG0nTs3qpXlABTqd8ycOXqwY8mSUQ2L5jPwermE80zgwhDx0L5E6qnqL9HXfLzLy2Ybvh8NB0mScfZsP4aGJqG9nQ07WNLfr9w8NDYq21jIyRk9MKK3p02TkZHRj/nzJ8FmS0xcTrjOxLP+6QY35LY2eM6eRUZ/P9iRAx2dnaH/mQiFzMyQMzc0e8YMbSGwidSZZK+jRn1eSudtaflPADIKCj4aPABy7hywfj1w5oxy17htm3KThRHX7de/VoTgqaeU7eJFYPNm2Gz5KC7+Atrafo6Wlv9Gfv56k9dbRkGBE5/+dB4+8xk2kA8leyMHRoaGlHGIffsA5fbnFvzHfxCuukqZjLFypfJ55ZVjr38XNr9+v3KHrQ567N2rCOsICNdfD+6JJ8DeeqvhwY/xjEmPpxkdHb9BR8eLEARlRivLZqK4+D7MnPkI0tNnxezzwoVSzJnTCfccgNqawZcuMJSWZZWlf2bNkrF2bbBPp1OZDXnqlDLRYcUKJQSN3BdznNJVW7IEuOceZZ8gKJN59TOKjh0jdHYy2LIF2LJlOP2sWcGDIqtXA5MmDf8eF50JtbZcXZ02csMC0OZwzJkTPHKzcmVwBo36jCGv77//GDyeJtjtJViw4OemfM6Z8ziGhg6jp+ctHD/+SaxadQgMk2c4f6F8GoE1CGIS+nfDG4HX24r6+k1IS1sGt3seJk26MipfBw8exC233BJ1ZYs1baR0mZnDdU8FkXK/efiwiC1bGpGbW4rWVk6bbXvxolKXz55VttBgkZWVgzlzmFF9M3WwZMaM0X+4T8Q1AqC0ug6HsrpXd7fSWQ23dXcrx0ERszz1HBynDG9PnaqsdDh1qjLVeHAQGBhQNpYFfvtb3PXoVXj7beNT2YLBAsgK+2tBQfC0RXUrKmLQ0pIDp1PJXjR/XMS9XGRZuQHQD268/z5QV6es0dHaGvnGYNIkZfG9uXMhzZ6NY34/lt15J/irrx6zUUkoz3H2aQbRal+8znnq1D2w2+1wu+cgN/eqqJ6tnYhrPhHlJcsSmptrccstt2DlyvA+h4aCZ5NcuCChtrYNPD8DXV0sOjqUe22PR5GlwUHlX7bQYAFMBscRpk0LPVAy0s4KL0uGkJT1T5KUi+pwBM/WGDmDQze4MbaCQ2knCguHFxpVP6dPhzh1Kmrb21H2qU/BVlhoWMgnUmdSoY7GQ09jPS/LNsPh+CsAYO7c/xz+oaFBuftsb1duvqqqlMUVdL6062azAU8+qVTsBx8Enn9e6fv85S+YOfNhtLX9En19OzA4WIf09GXjWlYsO/xo4Wc/qxwny8qNtDoocvCgjEOHZHi9/KhHadQbZ3VQRH05gNoN0HwuXQr20CFlVGXvXuXkgVcvB53s6quBdeuAdesglJVh59mzuOWmm8AmoK3SnQE9Pf9AV9dv0du7Hep6H3b7dEyf/kWUlHwFNluBKZ9NTcCRI0sxZ041XHOAttpaXDUO9TYvb/R9hhnYbMPl+sUvKvsGBkS8+OJhMEwZjhzhcPCgMuiiPhn42mvD6RcuHB53WLFChtN5BJs23RR77K5cCfa994YHPA4cCPm4NfLzgWuugbR6NWoZBqsfeAC2wbYs0wABAABJREFU6dNj82myXPr6qtHW9iwAYPHiF2Gz5YVNe/q0hPfeO4pPfer6sD4ZhsXixX/CkSPXwONpQkPDZ1Ba+lZU3Ebm1wisx2FihDrVpre3F5MnT9YuOMdxQbYoimAYRrPb23+B999/RDtPVtYyFBR8ClOn3oGcnGUQBAEcx4FlWQiCAJ7nwTCMZgPKs05622azgYg0W5ZlSJKk2bIsg+f5sLYkSSAizQ7FYyxOLMuCZdlRtiRJ2LZtGzZs2IC0tDSNkyzzaG9n0Nwsor2dC4iMjNZWFi0tyuzBvj5jjUNhIQUGRxjMmEGYMUPGrFkcCgokTJtGmD6dx6RJElg2Bk6SBLGjA2x3N9jubkjt7WAcDrBdXZA7OsB0dYHp6gJdvAhyOMBG+Y8LBQY9GJ9veFVwA3BeuxGT928DzxPWr2cgSQRRBCSJgd9PEATF5nlCWhqQns7AZpORlsYgPZ0Bz8vw+Rj09THo6SH09QE9PYzhRfwBID2dUFwMFBczmD6dUFgoo6SEQ1GRjMJCGTNn8igslDF5sgybbRxijwhcXx/EpiawFy6APX8e8tmzYM6dA3PuHOj8eTAR5vtTejowZw6YwCAHO28eMHcuxFmzwC9YAEyerNUhv9+PrVu3YtOmTVoeJro+qfZ4agSgvI9+w4YNyMjIiIqTy+Ua98dhotVTr/cMDh1aoovLecjP/zAKCj6CyZNvhiQxl72eJpqTKEoYGgK6uji0tUno7GTQ2cmivV3GxYtAZyeLjg7CxYtAdzdjZN1MDdnZQFGRDLu9F4sX52PaNEJhIYtp04ApUyQUF3OYNg3IzxcxZYoNwCVUTgDEri6wDgdYhwNiWxvYnh6wDgckpxPs4CCYgQHITieYwUFF851OwOEAY7DtIJYFU1gIKi4GFReDLSkBFRVBLioCN2MGpMBvfHExpMAN1qWgPZaeJlZPgdg1VZIk1NTcCJutVnvTgyiKYA8eBPuRjwC9vaClS8Fs3w5h2jRj1+sf/wDddZfSRt94I+TXX0dD+5fhcLyCqVM/g0WL/pTwGPB4PNi2rRILF27Ce+8xOHqUw3vvMairIzgco/ufLCR8aMYxfGzqXqyR92F+1z5kdYT4Fy8/H7R2LeRrrwV3/fWQV62CnJExIXENeDA0dATd3dvR2voCWHb4jSCTJ29EYeEXMG3ax8Aw/Li0E488IuPs2efx8MMPIn8fsGzGH8HefXfC6qogCNi6dSs+9KEPwWazmebkdvM4eFAKDJxxOHSI0Nwc+t5k1izC4sUMFi6UsWgRUFrKYsECESUlLDhOx0OSINbVKfXp4EHQ/v1gTp0adT6y2ZSRt8CgB7duHbBgAUSdzqxfvx6ZmZkJb/tk2YXDh6+Gz3ceRUX3YfHi34wqJyIO5eUsnnlGxr59LHge+PSnZTz6KIPly8Nr6sDAURw9ej1k2YXi4q/j5Mmbcdttt4HjuKg4DQwMID8/P7KejvkCXQuj8Mtf/pKWLFlCCxcuJABUU1NDRETHjh2jY8eOERHRkSNHqLGxkYiIamtrqampiYiI9uzZQ2fPvkctLS9QZeVKqq62ae/drq4GHThQSlVV/0xtbXuIiOitt97S3nFcXl5OQ0ND1NbWpr3LWn2HMpHyTvi33nqLiIh6enpo27ZtRETU2dlJb7/9NkmSRA0NDbRr1y4iIjp37hzt2aP4aWpqotraWiIiamxspCNHjmic6uvrqbOzc0xO586dIyKiXbt2UWtrKxERvf3221pet23bRj09PSE5ud1u7Z3Xek79/RJVVbXSU0/tpxdeIHr0UTetX99Kt95KNG+eQHa7GPKV1qE2jpOpoMBPV11FdOM13fSvHzhKP/vMAXr+I/9Lb378J9R49w/oxAf/hS5+4CPkuXYduebMIXHyZGMnH7H5srNJWriQpOuvp7a1a0m8/37yP/YYvffFLxL99a80tGULvfOrXxE5HNTT3U3btm2jzs5O6jh/nna/+irR0aPU/cor1PD440TPPks93/0uNf3HfxC9+ip1PPMMyRxHBNByHKFNmzpJkiTDsacvp4aGBpIkid5++23q7OwMlM12Onmyl+rriZ566gA9+6ybnnySaP36Zlq/XqTSUomysnxRXRKbTaJZs4hWrPDS9dd30pe+JNM3vtFH3/pWE1VUEL35t9NU/ccKkg4cpAu//jWde+IJoh/+kLrvvpv6Nm0iuvVWcs+fT2JmZkRnMscRzZ1LPStWkPuuu0j6/vep7t//nfoqKoja2+mtN980FHtqHVL3h6pPREStra2j6pMkSVRXV0cHDhwIWZ/GKidJkqi6upref//9kPVJLaeR9cnpdFJnZ2dETqpGyLJE7e2HqLLye3TmzPdo27ZNtGPHjrCcwmlEX1+fofewj4Xx0NPz558N6Kk9SE9ranJpx44b6MyZzeTzdYfUHq/Xq72HPhF6euzYMZIkifbu3UsNDQ0hOcVDTyVJoubm5jE5hSv/zs5OOn369JicQpXT6dOnqa2tk954o5beequdtmwh+uY3T9E3v+mkr3yF6IYbOumaa3w0fz5RWpoQtdzabETFxRLNm+ek9euJ7rjDS5/4RDM9+eQA/e//9tKLL+4ntzu6mO7s7KT6+nqFkyzTezU1dLaigmjnTjrzgx9Q1+OPEz3xBLV/8pM09OEPE33gAzQ4bx4JBQWaNseyySxLnsmTiVauJP9tt9G5224jeuIJGvrZz+jwk08SHTpE3fX1tLOykoiIWlpaaMeOHSRJUlSxp5bToUOHSJIkw7HX2am0NRUVFdTd3R1V7KnxFoueEhGdPn2aampqSJKkhOhpf38/SZKk9bki6anCo4m2b/8fam39HW3d+mnaseO1MTnFS0+JzGtqQ8NrAQ1l6f33lbwf+dGPSAq0wX2LF1PvmTNB14soch91aMsW8gfOIZSWUvWrPwj44WjHjj9TZ2cntbS0JKyPumPHDi2f+hh48823qKFhgCpe6qMf3vQbevmK71BN+gdpANkh624DV0pbZ95Hr2z8NT3zpb/T+2dlunhx/DX1wIEDVFdXR5IkheQkyzK9/fbv6dSpX9CpUw/Q22/Pp+pqNqhN3L17Cm3d+knq7T1uKK67u7upoqKCJEkKW1f37m2hJ544RfffT5SVJdFVV+2i6mrQvpdA7z/8cNR9VEmSaMeOHdTS0kJEkeuqGntut5tcLheVl5eTy+WKqj0nMq6pb799lLZuJfryly/SzTf3U3GxPIa0y7Q4s5m+Nf//6K+zH6D3Z6wlwZ4R+uB586j1ppvI81//RbRvH/3j//4vrKaq+2Jpz2Pto54+fZo6Oztpz549dOjQZwMxVUQXLpwMKqeeHqJ/+7dTVFIyfI/GccHX6AMfkOh739tLPl/o2Ovs/KsWs1u2PEptbW0R24mR5VRTU2NIT62ZIDFCHWXv6urC1KlToxrplCQJFRUV+OAH12BgYBu6uv4PTucOqO/iBoDMzCUoKPgkCgvvRFbWMm2l25qaGqxbtw7pgQcWjY50AsCuXbtw3XXXIT093fCooCiK2LNnD6677jrwPJ+wfy4ZhsGuXbuwbt067R8VPSdJkuF08rhw2o3OBgecZ50YaHZg6EIPvC0tmOQdQNZQJyb5ulCITkyD8jkJA1GVswQWQxlT4c4phD9vGuRpheCKC8FPn4KseUXInl8M/+Rc1Jw8iVvuvBOw26P698jv92Pv3r24/nplmlikkc7Bj/4L8ipewSu4E+4Xv4577lmtTf83Osru9XqxZ88e3HTTTQAQ9Sj7zTd/CD09NrS2Sujq4tHeTmhrk9HZyaG9TUZ/2xDoYhdsfcPXXV8GejsPxmfAAIBUWAxm3hyw8+ZBnj0bmDsX7Pz5EGfOBDtrFli7XeMqyzJ27dqFG264AXa7PSH/XKr17Prrr0daWlpUo+xEFJRfo/+GAMDu3buxdu3aIF1QyqwPAwPvwettgMt1FENDx+ByHYcsu7RrSsSgrKwb2dkFUf1z0N/fj4KCgnGdCWJGT9evvx4u1244HG+gt3crBEG/zguLnJxrMWXKRzBlykdhs12h/VNUU1ODG2+8MTDLIb56qlxvCoqRS0FPw8W0qk/XXXed9k+M0ZiWJAl79uzBunXrYLfbx/yHz+8X4PFw6Opi0dzsRVXVMcyYsTLw9CCL7m6gs5PQ3c2gsxMYHNT/G0fIxhAmow95cMIGASJ4iOAhwIapRRymz+Yxe74dM4t8KPSdxzRvG/IGzmPSwAVMkbuR4+0G+nrhGRhARno6GFEE09k5/OrKKED5+WCmTYM8dSqYadPATJsGKScHbF4emLw8iJmZ4CZPBiZNgtdux66TJ3HLZz4DhucN/2vp9/uxe/du3HTTTZrWGf2Hz+fz4d1339X03+g/sROhpzzPB+U3XFs3nnqqXrtdu3YF6QLggsfThKGhk/D7m+F2N8HtPg2vtwmi2BcUA0uWVKKwcP2E6SkQu6YeO3Yb+vvfxpQp/4LS0j+A/fvflVkcggCsXw/h1VfBTZoUUn+ACH3Uw4dhu/124OJF0OzZqH+pBH3+vSgqehDt7Z8K2w+KRx/V4/GgsrISmzZtUmaanj0Ldv9+yO++C+bAATAnToy6pmJGNlpLrsUh+7X4R/cqvNV9I/qQP+q4vDzC8uWElStZXH21jKuvlrF0KQ+WjV1TR9ZbWR6Cy3UYfX17MDS0HwMDByCKvaPykpY2E1lZZWhtnYNbb30KLJtmuI/a2+vHa6/VoaRkNTo7GXR0EDo7OXR0EDo6gPPnGbS0BPtbtaobP/mJsprojD99FnOe/2NUfdSROpOomSCxaioR4R//2IP8/LW4cMIH964DyDhWi5KWWix1HUAxLo4qEycm4SCuwen8a9A8bQkyb74Vc8qKsGCBiKVLORQUjK2pQOwzQYDY+qhqe15aOoSTJz8GAFi2bAfy8z8AlmVx9KiIzZs5/PnPjPZSsalTgfvuk3D//TK2bHkP77yzAq+9xkGSlHgoLSU8/DBw550icnKCy+ns2f9AS8t/gygNV1+9B7m5V0c1E6S3txfTpk2LqKfWIEiMGO+3GQiCEz09/0B391/R21sJouGp/ZmZizF16h2YOvWfkJV1ZcLeW28WUa3sK0nK1OCentFbb2/4fW531PmSOBsGMwvRZy+Egy3ERXkaWvyFaHYXok1Sb9ML0YVp6EEBZIRfKZRlgalTCWlpg5gzJxsFBcMLjeo/J00a3nJzlc9YXiDyxMeP4qk3lkMCC+7MaeXVq/GELCsP/vf1QejuRu22bVgzdy74nh5lfZPOTmXT2yOfiY0AATy6MC3ouus/O1GIC5iF85gNLzK0pVMKCpS1ScJ96u28PMNrzip5SrK3GciyAI/nNIaG6uFy1Qc+j8Hnawl5PMOkIStrKTIzl+H8eQ433/wTZGSM7syNhUv57TBEMgYHD6Gn5004HG/C5ToalCY9fS4KCj6C/PxNyMiYC54vgM02GQwTh1WBxwnJFpNjQpaVAQZ1YZGhIc0WnU4c37cPV86ZA05dfET9PfApO/sh9ziBfie4Qafhx0ligduWi4GsYgxmFmIwfSqG0qdiKH0KBtOnBr4r9kBmIYbSp4A4pWzUZprjoK2Jot8KCwGWvYzKdAwkQ+xKkhd+fwf8/nb4fG3weM7A42mC290Ej6cJgjD223Ts9hKkpy+Aw2HHNdf8F/LyVkbl/1J4O4wo9qOu7kYMDTVg9eoG5LzyDnD//cofuP/0T8Cf/mT+zWfNzcDGjUBTE3o/kIP6xwfBsplYu/bCqDUpxh1DQ9paO+KFC2javh2L+vrA7t+v9ClHYv58bS0PrFunLIyuWzl+aEh5KUBd3fB27Jiy8OZIZGQAV12lLA8yZYqy/pHRLSNDhtd7CgMD+zEwsA8DA/vhch2Huq6HCoZJQ07OakyatBa5udciN/dapKWVRKx/TqeyELb6ylp1u3Ah8iXlOGXtlBtuAG68EbjlFqB+bxaENDdWVt+J3KdeiXyScULcdcbtVtbqGLk1NiqLmDY0KHVFB+J5OGddhfenrMFhfg2qBtag8txCDAyF75BOnQosXjx6mz07/FtwEgFB6MPBg1fC729DScnXMH/+z1FRAfz858COHcPHLV8OPPQQ8JnPjF5g+MIF5fgXXhh+g9O0acBXvwp86UtKnx0AiCQcPXobnM4dmDr1Lixd+peo8mq9HSZBkMep82Wz5aGo6B4UFd0DUeyHw6EOiGyH230S588/jfPnnwbPz0BW1hVITy+B3T4daWklSEsbtu32YrDs6EohyzI6OjpQXFwc1WI4UaUjUm6A1UU8e3sx9ehRMENDyvPPYw1qOJ2jxMMwOE4bbaD8fHizs5E+axYYtaepbtOmAYWF4PLykMcwyAMwV3caSZLR1HQRLFsEh4PV7u31m35fT4/Sl+/sZADkGmow9EhLAyZNIuTni5gzh8fMmcyoxUhzc5UG1e9XtPb/bbkaa/AhbMJWDD31FDJ//3vj5SkIgMsFeWAA3adOYaokgVX+Yg3e1AVenU6l3AIxbgNwnVFyWVlB15ymTcNgZiay588HO6JMmJzJQBcDuQMQOwBvO+DuAAY7AGc70HeR4O6UwPdxwNDwSxKMvuERUAZA1AGpSAMmBQXKdRfF6AcbY61nkdKK4hB8vhb4fBfg9V6Az9cS+LwAr1fZrx841SMtbTays69CVtZVyM6+EllZVyEj4wqwrPLPSVNTBXg+Jyau443xOifDsMjNvQa5uddg7tzvw+ttQU/PW+jpeQt9fW/D621GW9sv0Nb2C30q8HwebLaCwKCIuk0ZtY/jJqO3V0RJySLwfE5UA9NmYiRWmNb/qVPBut2jBizGsmlgAD6HA2miqKyBoT9mjBkWPIDlEfLFBrYg2GygyZMhcRw4IkAUQYII8guAKIIRRcgMi56MGehKn40O+yy0YBZO9BSh3V+AXuRDgA0yWIjg0YlCdKAYbiELcELZxhl5eTxstvXIz+eRmaksNJ6REfkzPV2Gz+dEcXEesrPZMY+124PXPk1IX2AcYTa/hYX5EMUu+HztgQGOdvj9HaO+h/oXfSRstkJkZl6BjIwrkJGxUGfPB8dlaTcnWVnGF7zX5zceiOa8PD8JV19di+3bf4nMZ/8P+O53lR/uvx/YvDniq+MMldXcucCePcCmTZhcfQjZ/8xgaL4bJ0/+CEuX/ldM7ebFM2dQJMtgOztDvxJa/dQtfMYDWKI/UXq6stLl2rXKgMfatWHfj6rnuW4di3Xrhn/z+5V7YXVQ5MgR5Y2JQ0PDa15GQlaWE6WlB7BkyX4sXboPS5YcQE6Oc9RxfX1zcPHiWvT0XIv+/rXw+a5GZqZ91CCK3c5g9+5ZOHaMhcMxusvX0zM6DyomT5YwYwaLoiImsA7c8GLW06crN7w5I7oPWb5iONPOok88g2xZvjR1hkhphwIvMpA7O9F/5gwmCQJYhyN4kEN9oYGRP11nzVJefbxmDbBmDZiVKzE5IwOrAKwC8MWA644OoKFBRm3tADo6JuH0aQYnTyqDBKrb3buDT52WBlxxBbBwIQegFA0NLKZOVfq2kycPv+Vx8mSl3EN1S8zoaX39ffD725CWdgW2bfshnn1WeQsToPSxP/5xZfDjhhvCtzmzZrH46U+Bxx9XBkJ+/nNlEfbvfQ/4f/8P+NzngIcfBq64gsPChX9CTc1DWLDgN4bzqfdpBBM+CPLcc8/hxz/+MTo6OrB06VI888wzuEH/vvER2LVrFx555BGcOHEC06dPxze/+U088MADQce89tpr+N73voezZ89i/vz5+MEPfoBPfOITpvyGQzwaLp6fhKKiu1FUdHdgQOTNwIDINohiK/r7W8dYR5OBzTZ11OAIzxfh7NleZGRcj/T0QtikbPB+OxivV6nYHs/wp86mwUF4GxpAU6YoKq4OcITbpOEVeW0A1oXLZjjk5ioLS+Xno4/jkDd/PtgpU4bvYPWbui83V/ubXxJFHNy7F+vWrdOmjxkFkYyurjNYt24aFi6MLA6CoDQera0CKioOYsGCa9DfzweN86if/f3D29CQkt7nA7q6GHR12XDypPF8bl/+LWx6bysyX31VeReuzaao6tBQ8OZyBX8PLBzKYsTrX40gPR00eTIGeR7Z8+aNGsgY9TniNQ+SKOK9QLmwI8qFh/I2mpKS0K5FUcLeQFpJUq6vw4GInw4HoatLgsvFQ5bVfcqK35FhA/BRpKcTcnKUEMvNhWaH25eVRWhp6cXq1YXIyWGDbkhUW9+PVKb/9UMQOuHxtOPMmWoIQgb8/taggY6R061DgeOyAwMdVyErSxnsyMpaNuaq3WZwKQ+CjER6+kyUlHwJJSVfgiS50Nf3Nnp63kRf3zvwejsAuKAsstkXuNZnDJ333DmAYXjwfB54fnJgU2ybbXLI/SybgzNnmlFQcCvS0grAMCY7eoKg6PUYGw0Own38OKi4WBmoDnWcyzVqH+N2o2hwEGyERYdDgQEwxlsmFbCsUnGys5XPnBzIWVnodLtROH8+WLViqb9nZytbXt7oLSMDkjSsFTzPY2QfUNW+QgDqbaosKy+Sqq+XcORIE2bMuAKCwEFQxk4gCMH/7DLMcAePYZR/rC5caMGsWTO1mUT68Xy/X+lDX7yIwMKxyqffDzidDIDMkC8CiHDhgBDT8EMeyY4cHGEgSbkoKGACAyrBW6h96emAzUZobR3ElVcWISvLWLoI98yGIEkizpw5ikmTfACGIIr9gc0JSVI+1e/630SxHx5PB5qajD9yybLpsNunw24vwsBANmbNug7Z2YsCAx0LwPPmZ2iEw6UwCAIADBgs/t274MrLlR3f/jbw9NOG3iIkyzLOnj2LwsLCsW+wpk4FqqvBfOpTmPlSJRq/B/S1/xKdPZlI99rBD7GwDTCw9Urg+j1K37K/f7ifqbOZ/n5MV+ffG0F2NjB9OuTiYrQBmH777eBuuEG5m7fbDZ1iLJ52u3Kq5cuBe+9Vj1duFuvqgPp6GSdPdiA3txgeDwuXS0ZmZgOmTt2P6dP3Yfbs/SgpaRjl0+vNwKlTZThxYi0aGq5FQ8O16OsrMkiaB7BizCNKSoDS0uDtiitEnDoVfX86k2bDibMYtHdAjmEQ5OzZsyjMyQHb2zv6n0iHQxGbyZMV3Z88edjOzkZWRweY/fuH39SoH8QYuelmLSvvMjMAu12J38AmFxSgledR8olPKIuXFkUuE4ZRBpCmTZNht9cHXV+XS3nj2smTwdupU8o9w/HjwPHjLIAr8Pe/h/dhs4UeHMnLA5xOCYsWEbKzla56Zqb6ScjI6EdGhgNpad2w2RzgeQcYpht+/1kMDr4GIhZf+9ofcOhQJgDlfF/4AvDlLysvjAqFUPVl0iTg0UeVQZO//hX46U+VAcNf/Qr49a+Bj30MeOihKfB67wLLGquXI30awYQ+DvPqq6/i7rvvxnPPPYfrrrsOzz//PH7729+ioaEBs2bNGnV8c3Mzli1bhvvuuw/3338/9uzZgwcffBAvv/wyPvWpTwEA9u3bhxtuuAHf//738YlPfAKvv/46Hn/8cbz77rtYs2ZNTH5DYbynb4eEJCnDgk1NQFMTxObjGBQb4U8fgi/TBV+mB/4sD3zZXviyffDn+EG88eJkRMDWH2YbAGzOYZsfBPghgHcBTKTYYhggJweUm4tBlkX2nDmRBzJU+xKdKjsWop2aJknKHxLqywEuXlRGQtVNfT1lS4siiHa7cllsNkVw/vwnwjUPX6e+7D568LxyrfUDGfpNLSu1gcnLA9LTk2JKcyj4/cpgVOiBktCDKE5n7P4YRkZOTi/y8zsxeXIn8vK6NHvy5E4UFChbXl4nJk3qgs3mM3Ren28ShoZmweWaBZdrJtzuWfB4ZsLjmQWfbzYyMmZi6lQ26A3Lqh1uBrOZMr2UH4cJC1FUKpb6fu4LFwBJgsxIEG1eCDYPBLsXgt0DUbVtHgg29bsn6DuxJm9cCOB9NvBeG3gPD5uXB+/mwXs42NwceDcH3sXC5mLBDQL+LhdyRB5cvw+c0wO2zwPWLY262Y8beF4brBg5eBGTnZEx6uYqWXUmGhApGtPSIqCyci9WrrwOfj8f9F+E/jPUvkifcXxCKBQjpKV5kJ7uQnq6O/DpQlaWC7m5LuTmDsFmc2LSJD/S093IyBjSjsnIGEJamiuwDcFud8FuV2ybzQWbzQ2GMddNlSQ7vN7p8HqL4fFMD9rc7ulwu4vhdk+H358HIiUeJ09W9HPatGEtVe1wr3K+VPQ05vOJIuT77gP7+98r33/yE+Ab3zCdl7Dw+yF//nM48OGX4Qtz78h6df3QgUDfVO2jBmxuCOApHVz2NHC5heAml4AvmAm2cBaY6SXBr4oOTFtIpM4QySASQSRCkoYwOHhI92hLLSRp9Fp16enzkZu7FhkZa8Gy10KSroTbbYPLhRg2GT5fN5YunYriYnZUl2/WrOHX/44H2ioeQFPm88hqtaGYPgJeSAOxBClNgmQXIdoEQJZh7+eQ5iDYO0XY2zywnxsE3x4YvDC4HhMBEHMBdwngKQGID9yrjNzcYe5d0tODK3moTf97To7h14qPJ9TbwZMngRMnJOzadQ55eXPhdLLo61P6uOomCADP+5GT04fs7D7k5vYG2ZMmOXRbd9B3nhfHzMdLL/0HXnjhv7B4MfC1rwF336005WZBBLzzjjIYsmXL8P4rrujDf/93Dj7+8ej+1E6Kx2F+9rOf4fOf/zy+8IUvAACeeeYZbN++Hb/61a/wwx/+cNTxv/71rzFr1iw888wzAIAlS5bg0KFD+MlPfqINgjzzzDNYv349HnvsMQDAY489hl27duGZZ57Byy+/HJPfsWB69F6WlTvewEBH0Pb++9q/94BSWGONVBIDCJMAXwHgn6p8+qYA/inKp5Cn/C5MAqRMRSz8BcoWDTgpHTxlgUcOeC4XNn4yeHs++LQC8JmF4DOmgrflg2GycfDgcVx77fWw2zPBMDYwjA0saw9jy2BIBsMoixK1tLRg5syZUY8ix5LObNpowHHDf17OnCkjL68F69dH45OB/NJL6H/hBUzKzFRezcsww/+SqsO7+u+6fTLPJ4SnHhNRLvp0RUXsmAP0siyCyA9Z9kGWffB4hrB9eyWWLVsKr9cFt3sAXm8/fL5+CMIARLEfstwPon4wzABYth82m7Klp/eD48ZuSEZiaCgXfX2FcDhK0N09E52ds9DVNQtdXTO1T7c79o5xTs7odl0Z62LR3j4Tt94a/fjjJTsTxO1WtFMd6NBv584pAyEjwAKwBzajIAByOiBkA2I2IOYEtoAdtF9nC4HvcgYABhDTBYjpApAXO2XWA3BegPUz4AQWrMCBE3lwEg9WtoOV7SCRg53PAsdkgGMzwbKZ4LgscLZssLZscLYcsGmTwKVNAps+CVzGZDDpeejq92L6wlKwk/LBjHzAdwwkSk/Hy2ci2xyGUW6ys7OB5mYnbriBoqp/kXwSqU8/EjweAS6XAI/HD4/HD7fbi46OVmRnp0MQPBAEF0TRBUlyQZLckGUXiJRNmSHlAsO4wbIuMMwg7HYPeN6lDVAogxZusGx8/0/z++1wuSZhaCgv5OfIfUNDkzAwUICenukYGMgHxnGoMDNz9P2R8upmRU+XL1ee348Gl8RMkIEBMHv2gFgW0vPPgw/0kaPxFVVdsNvB/uHPWPwzHq3n/woxL9BHzZYgZoggjiCnA750wBdx+qoXwIXAdjCwjwXHZYPjcsA7c8AN5oDjlI1ls5Ce7sCZM/8AwwwPUug3WRZC7icS4fd7wPMMiMIfo27A2GXAslmBxzeH1/Kw26eGuLbnUVoavbYJgoSKiv2BAZ/496ezc1cAIuCaIeAMXg9/YB6AEfWE9QD23sCgl5sBL2TAJmeBZ3KV2ZRpk+HjnPCkdcOT5YQnbwhihhTq7KPAyRngkQ2em6Tcs2RMUe5b+Dxw3CQMDQFTpsyGzZYfmLk5vHFcLlh29C1zrNeIiCBJPrS0nEVx8RQAAmTZCyIfZNkb6IsGf6ane3H11T6UlrqwbNkRzJs3DbLcD1HsgyD0Bmay9kIQ+qBf/D5aeDzZGBiYAqdzCvr6pmJgYAr6+6fg3LmlkOV7sHUrsGGD8XX2jFwjhgE+8AFla2wE/ud/gD/+kdDUNBkHD0r4+Mej43DJPw7j9/tx+PBhfOtb3wrav2HDBuzduzdkmn379mHDhg1B+zZu3IgXX3wRgiDAZrNh3759ePjhh0cdow6cxOJ3LETdcNXVgf3zn3HNnj3gv/1tpbM+1kKSdruyONMVV0CePx/niDD7iivApaUNPwrB8wDPg+F52APbyN9EIhxrasLqsjXgc3IgpbMQeTf8ch8EwRFi64YgOOD3d8Pt7gDLuiHLyvNwEueFBC98CDxIKAHwBDZncPazs5XpW9GBA8vaIMssWloywLK2wECJPYRtDwygKDbAo6/PicHBghGDtcMdtOHJTxT0OxGht7cX/f2TwbIMgidJhU4DKDGQmdmJEyeeAyDpGj5pjAZ12BZFAS0t9sC6AgwANozNBPwpN+rSei9YltHywTA8GIYHwAVsBhAYoI8F49Sfg4XPJ6KzcxJYNg0smxa4nmm676OvMcAhLe0Czp8/AI7jw+STBcAEpvgPf8qyjAsXWsEwswPnHH1sqHQAG0h7Ciw7HQwjQpaVwQqlsfAF7OEBDPW7JHnR3++Aw2EHkTBmmlCdlMJCBE1TV2fjGAXPT4bdXgibrRB2eyFYthBEhZDlQkhSIfz+Qvh8hfB4pkGSMsAwEvr63seCBfOwYAEHWVbGSCUJmh3quyBIeP/9dqSnl6C3lw2a7elwDM88GhxU5CYYHICV+OEPQ6zkFgGXxCBIdzfY557DipoacD/5iTLQ0dExdhq7HZg3D5g/H/KsWbjY24uioqLhxjnU5MgR+2RZxsWLF1FUUoJ0nld6A+rGccHf1c3PAX0spF7C+bY2zJw7A3K6ANHug8h7lZkovAci74bIeSBwLoisGyLrhsC6IDCD8AhO8GmABC8Iw2UmZwQGVUBQBFkCEP0jLMN8AbgDG4CzjYBSL9WBa7tuANuu2zf8HeDR3+/GwMC0gK4EHxvqXCxrhyxzsNlOorOzGzxvh6ItXEATOJ3NjvqUZUJLyylkZS0MaLgMpW5TwCaM1JfAcFZAh08hPf0KcCGf4wh9Ey1JElpaTiMtbT44jg3yNfwZ+uZIFH1ISzuOCxcOgWEkyLIfRP6AXvkDGqV+Ctp3WfZhaKgPnZ12KJ1n/bFC0HlGsQhMxY4XWDYdDJMJls0CoGyynIneXj8mT54BhskBURZkWd2yIctZkKQsSFI2JCkLopgFUcyGKGbB603H2bNOlJQsBsNwmv6xrLpopDJxUa+NkgSIoozW1naUlEwGxzHaY0wjN/Wa6DdJktDY2AmWLUJPDxs0o97nU8ZZz59XtmAoevqRj4jJOQiSnw9x61Yc/t//xap//deYfLW1taGkpMT4DSHLIufrv8Ng7QO45pprtMcClJvEwUB/tCewOSCKPUHf/X4HBgbakZ6uHK9sQ1DrtSQNQJIGEOqJvrQ05fG0WBFqAVRjmIVp025CXt51yM29FllZyyIuyh3TtTWJWH3mrv0CFvzqr+j1ngQ7mYWULoCRWHBeFryXAedhQByj/AmbK8Kf5YXPPgSZ9UHOALwlyhbcEI39zKCyMPF89PQMIT/frntUzglZVh6XklgPJHjgo27AB2UbgVDr46rguJzAoMgkbXCEZXPR0zOAoaGcgOaGH8QYOcih9t1H60hkpKcrS91EwvAjufmw2SaD4/LQ2yuhpKQUaWmFgTXPpsBmm6qtf8ZxGVp6v1/Ru/5+EcePH8HGjQyiXGkg6jhasgT4zW+Axx8X8eijZ/GlL80HxnhBRTifRjBhgyAOhwOSJKGwMHh4t7CwEBcvjn6lEABcvHgx5PGiKMLhcKC4uDjsMeo5Y/ELAD6fDz7fcI0ZGFCmrxGR9mowI2AaGsD/7Gco1u0jngfmzgVdcQVowQJgwQJQYMPMmUEP1s6E0qWLpblctmIFCNC6zizykI7po1bvDQdZ9geew1Wfve3TREbZ59R974cg9GJgoBtZWWmBm35B10ETwnbSAAmyrIzsimL0b38BlJu/WDGWCIaDzWbuEYoQf04bgpl+k9vdGnWa9HRl4lKsGH0Tbhxnz8aWTrcWmmEwTBokKQ0ZGVPA85PAcbng+Vxw3KTAp2pPCjSMk3S/TYLNNi3K5xjVejAHsdXwIgzf/A5DlpXHrpQBEUZ7BKi7W7G7ugjNzV1gmLyoO3RmnqQcLz2FywXbk09i5EOMNGkSMG8eSN3mzwfmzwfNm6c8/KxriKci1JWLjFjTAYqOA9HNQBEEAVVVVVh/w/rAa/9ESJIHsqz8e6/8i698V//RV2x1nytwrCfk78PHDJ+DSN9LpMDgYnSDK2MtuhcOmZnDC67FgugH3ofR2BhbumjWddIjPR2jXjNpFDG8HC0w2JQBls1SZgJxyqwg5Xumtl/5zBjxXf97ZojjM0PezKmxu3r1+hgfPZiG2HSxELHX0tE1nEhpT1Q9VZco6OpiAgtOEk6e7EVRUQ4EITp9NPtk+nhpqlBcjM6ysuh0WIeysrLodTxsugzw/Ezw/ExkZIRNOgpEckDD1EERl85WBkn8fieamo5hwYJF4Di79idS8MYFBnpH/waEOla1Q6dRf2PZ4OdTRdFYbMd6bdXjYynTWH0W3r816rXnRHEQgnARfn8nRLEPkqTeY+g/+2G3FyE9fT4yMhYgPX0+0tPng+MyNZ257rpgnZFlf9D6QcP3K/0j7mmG1xlSPvsDgyjKrAo1fkK9dS/6dZ2CocRFOhgmDSybrvtDUv89PfBnrx0XL/Zh1qylsNun6NYcy9etQTYZPD8p4uDaSCh/rg2XNcMML7A7ffqKmGIBiC2O8vMFfPazp5CfPytu/dMJXxh15Kr6RDRqX6TjR+43cs5o/f7whz/EU089NWp/ZWUlMjMzw6YbiayBAcz78IcxVFwM1/TpGCouhmfaNNDIf57UJaYbRi+QdGkhO7DNCHuEuhBoaCij9YAIQATDKDMowtsCGEbSjgekwG96W9+YMGHssX4f3hdcj0b/HvwbB+W2hgMRG/iu7CPidN+5Ed9VqP9UqjYC3ynwjxUF8mQDYAucY3iGCCAHro16PSnMOdR/iwUwjBD4FKHciIuBfWp5SAguG1F3DtL5pRH7R/LQ85EDeQnmOPbGBzjbAjavuw7Kb/rfh48buS84zXBa/e/qdY1mAEUG0BfYLl3w/PArOvWoqor+XO5Y7sACGC89hSxj+a23wl1YCFdRkbYJoZ7bdbmU9xceOxZzvi8FVBkurMzAZgbKjJJg/R2pyyP1IZKtplV1RgpzvKxtDCMHfVeg6KXym6oTqq2f6YGAHuvjQZ+O0f3OILS+G+lQMWG3YL0f2R7wAR1SbVW7uMB+vVbpj+eD7FD7Rtp6bTMHb2CL7h8D47GbHOC44XUV9GhuVrZoYEZPgXHU1AAut7JSwADIDWzTAZTixIlozyFDmWVnYqbdBCG5ypQDMCWwjYWWwDYMYzzV9rE40oFQ2iU3GMYFhlEfEVS3ITCMCCI7hvubtsB3HoBdt88W+B68X9min90zepF/AUBXYLt8EEvcGtXTCRsEmTJlCjiOGzX7oqura9QsDRVFRUUhj+d5HgWBlwuHO0Y9Zyx+AWVtkUceeUT7PjAwgJkzZ+KWW25Bfr6xVdpVCP/6r8o/euuj+1dEFEXU19fjqquuivrNJ7GmNeNT++cySp4Tld9Y01o8L838TkTspgpPAOiNZcpUAOOqpxs3WvUvTj5ThaeZtJbORIYVu5FhRk+B8dNUq6wuzfxaOhMZVuzGz2ey8TSqpxM2CGK327Fq1SpUVVUFvb62qqoKH/vYx0KmWbt2Ld58882gfZWVlVi9erV2gdauXYuqqqqgdUEqKyuxLvAi71j8AkBaWhrSQrxiISMjI+aVpW02W1RpbTab9oabWHzFktaMT/05or1GE5Ffs1wtnvHxmUyxmyo8AUX7YsWloKdqGqv+jX86s2nV9JbOjL/PZOJpxm+yxa4ZPQXGX1OtsoqPz2Sqf6nC04zfVIndZONpVE8Ts7pOGDzyyCP47W9/i9/97ndobGzEww8/jAsXLuCBBx4AoIxs33PPPdrxDzzwAM6fP49HHnkEjY2N+N3vfocXX3wRjz76qHbMQw89hMrKSvzoRz/CyZMn8aMf/Qg7duzA17/+dcN+o4EkxfKsaWyQJAknT56MyWesac34NIOJyO9EcLV4xi+txTO+iIc/S2fiA4tn/NJaPOOLVIrdZDpvOF+pUlapUP9ShacZv6kSu8nI0wgmdE2QO++8Ez09PfjP//xPdHR0YNmyZaioqMDswLLaHR0duHDhgnb83LlzUVFRgYcffhibN2/G9OnT8eyzz2qvxwWAdevW4ZVXXsF3v/tdfO9738P8+fPx6quvBo1CRfJ7KcPj8SQ8rRmfZjAR+Z0IrhbP+KW1eFqIBKv+xSed2bQT4TOZ6l+q8DTjN9li93JAqpRVqtS/VOFpxm+qxG6y8TSCCV8Y9cEHH8SDDz4Y8rff//73o/bddNNNOHLkyJjnvOOOO3DHHXfE7DcahH6VXnzAcRxWrFiR0LRmfJrBROR3IrhaPOOX1uIZX8RD+xKpp6o/q/6NfzqzaWNFqtS/VOFpxm8yxm4ynTecr1Qpq1Sof6nC04zfVIndZORpBBP6OMzlgERP7zl+/HjM05FiSWvGpxlMRH4ngqvFM35pLZ7xxeXyOIxV/8Y/ndm0sSJV6l+q8DTjNxljN5nOG85XqpRVKtS/VOFpxm+qxG4y8jQCaxDEggULFixYsGDBggULFixYsJASsAZBxgmSJGkjT3pbFMUgW5ZlLY1q6/cLghBkE5FmsyyLpUuXQpZlEBGICIIgAECQLctykC2KIjiOQ2lpqXY+db+aX72t5wEAy5YtC/oejpMReyQnPQ+9zXGcxjUcp1A2EWn5DccpXDmpXInIEA+9rWIsTqHKSZZlLFu2DAzDhOUUqpwAYMmSJeA4LurYIyKUlpaC4zjDsafyUNNHE3sAwDAMFi9eDI7jDMeefiRX5RpN7HEch0WLFoFhmDE5hSsnFUZjT61nS5Ys0dIbjT1JksBxHBYvXqzl12jssSyLZcuWjdIFo+Wknn8sfqHKKZ7TrBOhp0QElmWxaNEisCybED1Vr9uSJUu0fFt6OvF6Gq6ti7eehuJh6WmwDUDjmgg9JSKNq14XkllP9X5G2tHqTzz6qKrOhKu38eqjqjBaV1Vb1RmWZSPW1fHSVCIKqreJ0FSGYbBo0aJROhPPPupY9fZS7KOaac/NaKq+XBPRRwVib89j7aNGirdI7bkRTPiaIMmGzZs3Y/PmzdrF3r9/P6677jo0NDQAAEpLS3H06FFkZGRg4cKFOHz4MPLy8jB//nwcOHAAxcXFKC4uhtvtxpkzZzBz5kzs2rULpaWlmDp1Knbs2IGVK1ciPz8f27dvx9q1a5Gbm4stW7bgpptuQlNTE9rb27FhwwaIooidO3fiwx/+MAYGBrBv3z5s3LgRvb29OHLkCD74wQ+iu7sbDQ0NuP7663Hw4EGIoojrr78eFy5cQEdHB9asWYOzZ8/C6XRi1apVOH36NDweD66++mo0NDRAlmVIkgQiQmZmZlhOs2bNwp49ezB37lxMnz4du3btwsKFC+F2u7F169awnG655RbwPI/KysogTrfddhvq6urQ09MTltNNN92E9vZ2NDc347rrrtM4paenIzs7OyynscppaGgIXq83LKdw5SSKIgYGBlBZWRmWU7hymjp1KgoLC3Hq1KmwnEaW08mTJ9HR0YEbb7wRp06dMhx7s2bNwrvvvgue51FWVoZ3333XUOzdcsstICK43W709vaCYRjDsXfTTTehtbUVjY2NuOWWW9DW1mYo9lRO7733HgYGBnDdddfhvffeMxR7paWlyM/PR1VVFcrKylBQUGA49j784Q+jt7cXbrcbAwMDGBwcNBx7q1evxr59+5Ceno7Vq1dHFXtz5sxBdXU1FixYgDlz5hiOvTVr1uD8+fNobW2NOvZuuukmuN1uVFVV4eabbzYUeyqnWbNmAQi+wUkmPb3lllvAMAzefvtt3HrrrSCiuOspACxatAg1NTUoLi7G4sWLLT29BPT09OnTcLvdYBgGHMeBZdm466nKCQC6urqwe/duS0/DlNO5c+dw88034+zZs3HX07Vr1yIrKwvbtm3DTTfdhLS0tKTR0/HQ1GnTpsHtdqO6uhrz5s0zdL3M9lFPnDiByZMno6CgAOfPnzd0vcz2Ud955x0tJnft2hW1ps6YMQOzZ8/GgQMHEqKphw4dgtfrxdq1a3Ho0KGEaGpPTw8OHjyI9evXo7e311BdNdtHLSkpwc6dO7FkyRLMmDEjqvZ8IvqosbbnZjR1w4YNcLvd2LJlS9SxZ6aPGmt7bqaP6vP5cO7cOZw+fTqq9ry7u9uQnjJkVnFTFK2trZg5c+ZEZ8OCBQsWJhQtLS2YMWOGqXNYemrBggUL46OngKWpFixYsBBJT61BkBghy7I2QqZOZTKKgYEBzJw5Ey0tLcjNzY0qbVlZGQ4ePBhVGrNpY01nhqcZv4m+RhbP+Pk0m3YiYjdVeBKRNurOsuaerJwoPQWs+hevdLGmtXQmvj7NpLX6AmPjUtFTIHZNtcoqfj7NprV0ZmxYsRs/n2bSxZo2EXpqPQ4TI1iWhd1ux6RJk2I+R25ubtQFy3FcTEFvJq0Zn0BsPM34nYhrBFg84+kzmWI3VXgCgN1uH5cO+0TpKWDVv3ilM5vW0pn4+Uwmnmb8JlvsjpeeAuY11Sqr+PlMpvqXKjzN+E2V2E02nkb01FoY1QS+/OUvJ5XPWNNOBE8zfifiGpmBxTN+aS2e8cV4+k02DqlS/1KFp5m0Fs/4wordS+N88fSZSmWVTPUvVXia8ZsqsXs58rQeh5kADAwMYNKkSejv7zc12nmpw+J5eSFVeAKpw/Vy4Hk5cDACi+flh1ThavFMHlwOHIwgVXgCqcPV4nl5IRE8rZkgE4C0tDQ88cQTSEtLm+isxBUWz8sLqcITSB2ulwPPy4GDEVg8Lz+kCleLZ/LgcuBgBKnCE0gdrhbPywuJ4GnNBLFgwYIFCxYsWLBgwYIFCxYspASsmSAWLFiwYMGCBQsWLFiwYMGChZSANQhiwYIFCxYsWLBgwYIFCxYsWEgJWIMgFixYsGDBggULFixYsGDBgoWUgDUIEic899xzmDt3LtLT07Fq1Srs3r17zON37dqFVatWIT09HfPmzcOvf/3rBOXUHKLh+fe//x3r16/H1KlTkZubi7Vr12L79u0JzG3siLY8VezZswc8z2P58uXxzeA4IVqePp8P3/nOdzB79mykpaVh/vz5+N3vfpeg3JpDtFz/8pe/4Oqrr0ZmZiaKi4tx7733oqenJ0G5jR41NTW4/fbbMX36dDAMg/Ly8ohpLlUdsvR0NCw9TQ6kiqZe7noKXD6aaunpaFh6mhyw9DQ0LD2NEWRh3PHKK6+QzWajF154gRoaGuihhx6irKwsOn/+fMjj33//fcrMzKSHHnqIGhoa6IUXXiCbzUZ/+9vfEpzz6BAtz4ceeoh+9KMfUW1tLZ0+fZoee+wxstlsdOTIkQTnPDpEy1OF0+mkefPm0YYNG+jqq69OTGZNIBaeH/3oR2nNmjVUVVVFzc3NdODAAdqzZ08Ccx0bouW6e/duYlmWfv7zn9P7779Pu3fvpqVLl9LHP/7xBOfcOCoqKug73/kOvfbaawSAXn/99TGPv1R1yNJTS0+Jkk9PiVJHU1NBT4kuD0219NTSUyJLTy9lWHoaGvHQImsQJA645ppr6IEHHgjat3jxYvrWt74V8vhvfvObtHjx4qB9999/P1177bVxy+N4IFqeoVBaWkpPPfXUeGdtXBErzzvvvJO++93v0hNPPJEUjUy0PLdu3UqTJk2inp6eRGRvXBEt1x//+Mc0b968oH3PPvsszZgxI255HE8YaWAuVR2y9NTSU6Lk01Oi1NHUVNNTouTVVEtPLT2l/8/elYdXUZ3vd+6SnbCEJRD2TUAUlEVB2axCxar9uVatdam1lroVbcW2LrjWDVGLWpfWutZWbdQ2haAJCRIgMYQl5IYEEiAJgXCz3uSuM3N+f1zOMDe5c+/cmTv3EnLe58nDl2G++b53vnPeczJzZoYwPT2VwfQ0OIzQIvY4TJTh9XpRWlqKpUuXBmxfunQpioqKgvps3bq1x/7Lli3D999/D5/PZ1iueqCFZ3eIogiHw4FBgwYZkWJUoJXn3/72Nxw4cACPPfaY0SlGBVp4fvXVV5g9ezaef/55ZGVlYfLkyXjwwQfhcrlikbJmaOE6f/581NfXIycnB4QQHDt2DJ999hkuu+yyWKQcE5yKOsT0lOkp0Pv0FOg7msr0VBmnmhYxPWV6CjA9ZXraO2GEFlmikRjDSdjtdgiCgGHDhgVsHzZsGI4ePRrU5+jRo0H353kedrsdw4cPNyxfrdDCszteeukldHV14brrrjMixahAC8/q6mqsWrUKmzdvhsXSO7qYFp41NTX47rvvkJSUhH//+9+w2+1YsWIFWlpaTulnLrVwnT9/Pj766CNcf/31cLvd4HkeV1xxBV577bVYpBwTnIo6xPSU6Wlv1FOg72gq01NlnGpaxPSU6SnTU6anvRVGaBFbCWIQOI4L+J0Q0mNbuP2DbT/VEClPik8++QSPP/44Pv30UwwdOtSo9KIGtTwFQcCNN96I1atXY/LkybFKL2qIpJ6iKILjOHz00UeYO3culi9fjjVr1uC99947pa+0U0TCtaKiAvfeey8effRRlJaWYv369aitrcVdd90Vi1RjhlNVh5iehgbT01MXfUVTmZ4Gx6moRUxPQ4Pp6akLpqc9wfRUuxb1nsuAvQSDBw+G2WzuccWuqampxxUsiszMzKD7WywWZGRkGJarHmjhSfHpp5/i5z//Of71r3/h4osvNjJN3YiUp8PhwPfff4+ysjLcfffdAPxCTAiBxWJBbm4uLrroopjkHgm01HP48OHIyspC//79pW1Tp04FIQT19fWYNGmSoTlrhRauzz77LC644AL89re/BQCcffbZSE1NxYIFC/DUU0+dknfDIsWpqENMT5me9kY9BfqOpjI9VcappkVMT5meMj1letpbYYQWsZUgUUZCQgJmzZqFjRs3BmzfuHEj5s+fH9Rn3rx5PfbPzc3F7NmzYbVaDctVD7TwBPxX2G+99VZ8/PHHveJ5tUh5pqenY8+ePdi5c6f0c9ddd+GMM87Azp07cd5558Uq9YigpZ4XXHABjhw5gs7OTmlbVVUVTCYTRo4caWi+eqCFq9PphMkUKJdmsxnAySvRvR2nog4xPWV62hv1FOg7msr0VBmnmhYxPWV6yvSU6WlvhSFapPmVqgyKoJ83evfdd0lFRQW5//77SWpqKjl48CAhhJBVq1aRm2++WdqffvbnN7/5DamoqCDvvvtur/oEmVqeH3/8MbFYLGTdunWksbFR+mlra4sXBVWIlGd39Ja3b0fK0+FwkJEjR5JrrrmG7N27lxQUFJBJkyaRO+64I14UVCNSrn/729+IxWIhr7/+Ojlw4AD57rvvyOzZs8ncuXPjRSEsHA4HKSsrI2VlZQQAWbNmDSkrK5M+s9ZbdIjpKdNTOXqLnhLSdzS1L+gpIaeHpjI9ZXoqB9PTUw9MT2Onp+wiiEFYt24dGTNmDElISCDnnnsuKSgokP7vlltuIYsWLQrYf9OmTeScc84hCQkJZOzYseSNN96IccbaEAnPRYsWEQA9fm655ZbYJx4hIq2nHL1pkImUp81mIxdffDFJTk4mI0eOJCtXriROpzPGWWtDpFxfffVVMm3aNJKcnEyGDx9ObrrpJlJfXx/jrNUjPz8/ZH/rTTrE9JTpKUVv0lNC+o6mnu56Ssjpo6lMT5meUjA9PTXB9DQ2WsQRchqtlWFgYGBgYGBgYGBgYGBgYGBQAHsnCAMDAwMDAwMDAwMDAwMDQ58AuwjCwMDAwMDAwMDAwMDAwMDQJ8AugjAwMDAwMDAwMDAwMDAwMPQJsIsgDAwMDAwMDAwMDAwMDAwMfQLsIggDAwMDAwMDAwMDAwMDA0OfALsIwsDAwMDAwMDAwMDAwMDA0CfALoIwMDAwMDAwMDAwMDAwMDD0CbCLIAwMDAwMDAwMDAwMDAwMDH0C7CIIAwMDAwMDAwMDAwMDAwNDnwC7CMLAwMDAwMDAwMDAwMDAwNAnwC6CMDAwMDAwMDAwMDAwMDAw9AmwiyAMDAwMDAwMDAwMDAwMDAx9AuwiCAMDAwMDAwMDAwMDAwMDQ58AuwjCwMDAwMDAwMDAwMDAwMDQJ8AugjAwMDAwMDAwMDAwMDAwMPQJsIsgDAwMDAwMDAwMDAwMDAwMfQKWeCfQWyGKIo4cOYJ+/fqB47h4p8PAwMAQUxBC4HA4MGLECJhM+q6nMz1lYGDoy4imngJMUxkYGPou1OopuwiiEUeOHMGoUaPinQYDAwNDXFFXV4eRI0fqOgbTUwYGBobo6CnANJWBgYEhnJ6yiyAa0a9fPwDAwYMHMXDgwIh8fT4fcnNzsXTpUlitVtV+giCgvLwc06dPh9lsjiimVl89MbXyjFe+Wn0Zz1Mz33i03b7CEwBaW1sxduxYSQv1IB56CrD+Z5SfHl+mM8bG7E0845VvPNpuNPUU0K6prFanZr5MZ8KDtV3jYvY2nmr1lF0E0Qi6vDA9PR3p6ekR+fp8PqSkpCA9PT1iQRoyZAjS09M1NUItvnpiauUZr3y1+jKep2a+8Wi7fYUnjQsgKkut46GnAOt/Rvnp8WU6Y2zM3sQzXvnGq+0C0dFT+XEi1VRWq1MzX6Yz4cHarnExeyNPILyesosgOhFpY9Aba8qUKTH11RNTD+KRbzy4Mp7G+TKexsII7YulntJ4rP9F30+vr1b0lf7XV3jqidsb225vOq5SrL5Sq77Q//oKTz1x+0rb7Y081YB9HUYneJ6PaaySkhJNMbX66ompB/HINx5cGU/jfBlPY2FEPKYzxoDxNM6X8TQWfant9qbjKsXqK7XqC/2vr/DUE7evtN3eyFMN2EUQnYjlW7c5jsPAgQM1xdTqqyemHsQj33hwZTyN82U8jYUR8ZjOGAPG0zhfxtNY9KW225uOqxSrr9SqL/S/vsJTT9y+0nZ7I081YI/DRAn0+SOz2Rxg8zwPjuMkW/6pHlEUAUDabjKZ4PP5YDabJdtisYDjOMmeMGFCwHF4nofVagUhRLJFUYQgCJItiiIsFgvGjx8vxZRvFwQBhBDJ7s5j4sSJEAQBgiAocjKZTD1sOb9QnOg+cttqtUpczWazIqfuNiFEypceU6k2weo0ceJEKfdgnJTqRBGOU/c6iaKIiRMnQhRFaX8lfvI6AcC4ceNUcaI2IUQ6LyNGjIDP54PL5VLd9ui/LpcLPM9H1PZEUURmZiZ8Ph88Ho/qtkft4cOHS/mqaXvUHjZsGDweT8Rtz+v1wmKxwO12w+fzqWp71B4+fDhcLlfEbc9sNiMzM1PKV23bs1gsGDlyJJxOZ0Rtj+ZgsVjQ1dWFpKSkAB5msxkcx4Wsk1GIlZ6aTCaMGTMGJpMp4PwYqadmsxnjxo2T8mZ6Gn89VRrrQrU9modWPQUAt9staarb7WZ6qlAnytXtdqtue3r0lOM4DBs2DF6vV3XbO5X1FFCvqcF0xug5KtUZpX5r1ByVQk1flbcBqjOEEPh8vrB9NRqaSgiR5nxq+mo0NJXjOIwZM0YaM9T0VT1zVJPJBLPZjLFjx0p/xEbS9gghAPyfR410POc4DmPHju3B1ajxnHIdM2aMxFWtpsprGUnbo/a4ceOkcxWL8dxisWDixInw+XwBuhBOUylXreO5GrCLIBFi3bp1WLdunXSS9+zZg4ULF8JmswEApk+fjt27dyM5ORlTpkxBWVkZBg4ciIkTJ6K4uBhZWVkYMWIEAODYsWMYPXo0CgsLMX36dAwdOhR5eXmYM2cOBg0ahNzcXCxYsADp6enIycnBRRddhF27dqG5uRnLly8Hz/PIzc3FlVdeCYfDgc2bN+Oyyy5DW1sbSkpKsGzZMtjtdpSXl2PhwoXYvHkzTCYTFi1ahLq6OjQ0NGD+/Pmora1Fa2sr5syZg+rqarhcLpxzzjmw2WwQRRGdnZ1ISkpCSkqKIqcxY8agqKgIEyZMQFZWFgoLC6VnwAoLCxU5LV26FBaLBTk5OQGcLrvsMhQVFcHhcChyuuiii9DY2IgDBw5g4cKFqKurQ319PTiOw+DBg9He3h6Uk1Kd+vfvD7vdDkIIRo4cGZRTsDrl5+dL7SMUJ6U69evXD+PGjYPNZgvKKVid9u3bh7q6OvzgBz9AZWVlyLY3YcIElJaWIjk5GVarVZpIJiYmwuPxwGq1wmw2w+VyITExESaTKcB2Op1ITk4GIQSZmZmoq6sDx3FwuVxISUmBKIrweDxITk4OsAVBgM/nQ1JSEniel2w6WUlMTITP54MoipJNCEFCQoI0+aS2IAhISkqC1+uFyWSC1WqFx+OB2WyWJtcWi0WyrVZrAI/u/CgnAHC5XAE25US5EkIUOfE8L9mCICAhIQEejwccx4XlxHGcxMNkMgXkrsRJqU70D4twnLrXKSkpCZmZmaitre3Bif6RNHfu3KAaMWbMmF6tp0uXLgUA6e3j1DZSTwFgypQp+PbbbzFq1ChMmzaN6ekpoKfV1dVwOp1wu91IS0uDyWQK2/bS0tJ06Sntm1Rn3G4309Nuekp5yLfHQk/p+JaUlCTZvUFPo6GpmZmZAIDi4mJMnDgxZF+N1hx1z549SE5OxqhRo1BbWxu2r0ZjjlpUVCSds3DjRDD9GTJkCKZOnYqioqKYaGppaSkcDgcWL14ccpyIpqY2Nzdj27ZtuPTSS9HS0qLISe8cVV6nrKwsbNiwAWeffTZGjRoV0XhO/7Cn+qF2PL/ooovQ0NCA3bt3Y9myZWhoaDB8PJ8+fToGDRqE9evX4/zzz8eQIUNUt73ly5cDAPLz8yNqew0NDZg7dy42bdqEfv364bzzzjN8PM/NzcX8+fNhs9lw/PjxiMdzAGhubkZlZWVE43lTU5MqveQIbTUMEaGjo0NqFBkZGRFd6RQEATk5OfjhD3+IxMRE1Vc6TSYT6urqMHz4cCQkJABQvxLEZDLh8OHDGDFiBBISElRf6RRFEUeOHMGIESOkK5dqr3QKgoD169dj6dKl0iRD7ZVOs9mMw4cPY/jw4UhMTFR9pZPneTQ2NmLEiBEBd12C1aZ7nQghOHLkCIYPHy7dJVZzpdPpdGLjxo2SMEVy55Lmm5WVJfmqudLp8/lQX1+PMWPGSMKv1PaamprQ2tqKoUOHIjU1NSAHCo7jIIoiOI5TtAkh6OzsRFpamvQ7vYOuZNNjy++YyGN23yeYTc9TpL7AySvlSpwA/10DuW0ymaSJVTCuamLS9qOGn1K+oc5Hdx7yfqXEKVidAEg85fkCQFdXF5qamjBw4EAMHTq0h0Y4HA4MHDgQ7e3tEX/RpTvioae0fx06dEhaDWK0ntIaHjp0CCNHjoTVamV6egroqdJYF6ztmUwmHDx4EIIgSBfgtOgp4L/D1dXVhbS0NABgempgvpHoqdxXidOprKeAdk0NpjNGz1HD9Vsj5qgulyvgD8pIVoJ4vV4cPXoUI0eOhCiKEd2N16qpXq8XDQ0NGDNmjDQGGa2pPM/j8OHDGDt2LABEtBIkkjlq95WdBw8exOjRo2GxWCIaz30+H/73v//h0ksvlfJXuxJEiatR43kwrpGsBMnJycEll1wiXZBVuxLEZDLh0KFDyMrKQkJCguHjOdWF+vp6ZGZmBuhCuPHc4/EgNzcXP/zhD6UL9GrH87a2NulCYyg9ZStBdIIOkvKljHKbFllu08ZFO4F8H/lAH8ymHbT7dnoXhB6XHltuy33l25VyN5vN0nItOYJxCmZT4aMdRg0/Ja5KnLrbCQkJPfINxa977mq5RsJDbnevU7B8lfjJ87VarQFL8JQ4UTEYNmwYMjIyeuwfCURRhNfrRVJSkpRTJKB30mIJrTH1cNXDU6uvETyTk5Oli2hDhw6V2j9tY1raQDjEWk/NZjPGjx/fY7tRekoh77tMT+Ovp0pjXTBO9LGMESNGICUlBXogiqK0EiKWOqMV8dBTPYi1Fp9qegpErqnBdMboOWq4fmvUHFUtp+52YmKiFFNePyM1NSEhQRo35G3FSE21WCwB42M4fjRfj6cBO3acj/79LwTHfRyQb7jxHEBAzEg40QuV9KJmJOO5ElejxvNgXNXWiT7eFI6Tki3PN1bjeXdfNeM5PWZ3W814Lj9+KLAXo+qE/PmsWMQqLCzUFFOrr56YehCPfOPB1WieVCzlk3VCCBwOR8BdKqOhJ6ZWX8ZTP2i7kT9TTGFEP2E6YwwYz+j50otu9I4W0xljEY98jeIaaz018rhKsZjOGOPbm3jW178Kj6ceTU3/wNGjn8Qkpl6wtmuMn15frVAbi10E0YlY3tUwmUyYMGGCpphaffXE1IN45BsPrrHiSa+QUyQmJkYULxrQE1OrL+OpD93bjRxG9BOmM8aA8Yy+r7xvMJ0xFvHI1wiusdZTI4+rFIvpjDG+vYWnIDjR2Pi29Htt7QPg+XZDY0YDrO0a46fXVyvUxmKPw+hErItKn5mMla+emHoQj3zjwTUePDmOk+5ixgp6Ymr1ZTyNxelyEYTpTPT99PpqRTzyZTpjLOKRbzy4ni4XQZjOGOPbW3geO/YReL4VSUljwXEJcLmqUFv7R0ya9JphMaMB1naN8dPrqxVqdY+tBNGJWC/vycvL07wcSYuvnph6EI9848E1HjwJIejo6Ij58u1IYy5evBj3339/RL6VlZU4//zzkZSUhJkzZ2LPnj3gOA47d+5U9Nm0aRM4jkNbW5vq3JSg59xq9Y1HPYHT53EYpjPR99PrqxXxyJfpaSCiqadAfHQxHjU9XR6HYTpjjG9v4EkIQUOD/2JHZuYKdHX9AgDQ0PA6Ojq+NyRmtMDarjF+en21Qm0sthJEJ2J9lX369OmalyNp8dUTUw/ikW88uMarLr3hpXpffPGF9HIjtb6PPfYYUlNTsW/fPqSmpiItLQ1HjhzBkCFDIs5XK5RyPXToECZPnozjx48rvq06OTkZy5Ytw7fffostW7bg/PPP1xXTSJwuK0GYzkTfT6+vVsQrX6anxiJYvkbqqVJMI3G6rARhOmOMb2/g2dZWgK6uPTCZUjB8+O1ISRFgt5ehqeljVFbegnPP3QaLpV9UY0YLrO0a46fXVyvYSpAYgd4pEARBemGa3OZ5PsAWRVHypbZ8u8/nC7Dp8X0+HziOw5AhQ6RPAdHP5NE8qE3fOE9tnvd/umjw4MHSsel2mq/clvMghEifcwvHSY3dnZOch9w2mUwSVyVOwWxRFKV8lTgp1YlypccJx0NuU4TiFKxOgiBg6NCh0vFD8ZPXiRCCjIwMmEwmVW1P/gk/ANJnuOTbRVEMa1MQQiT+oexwMUPZAwcORL9+/SLyPXDgAC644AKMHj0agwcPRkJCAjIzM6XPaXXnpJR7KK6h4tNP3sl96T7Z2dlYvHgx0tPTFX2PHDmCrVu34u6778Y777wTNFZ3HgCkz4vJOYXiF4prsJjBNMKIAS2WekrP+cCBAwM+O0rzMEJP6XnLyMiQcmF6Gn89VRrrjNTT7sdgehrcVsrXKD2lvvRrA71ZT2lceZzudqT6Y8QcleoMjUW3Gz1HpQg3TnTXH6oz9FOmwThFW1NFUZTmfFrqJOeqVlMBYODAgTCZTCE5Ubu+/hUAQGbmz2CxDEBGRgYmTnwZCQnD4XRWoLLy1h616V4nk8mEQYMGSfEjGc+79zW14znFoEGDenA1cjw3mUwYOHBgj9qoqZO8rmrbHp27ZGRkSLnEYjznOA5Dhw7toQvh6hSuvYUbz9WAXQSJEOvWrcO0adMwZ84cAMDu3bsBADabDTabTdpWXV0NACgrK0NtbS0AoLi4GHV1ddKxjh07BgAoLCyE3W4HAOTl5UnLSHNzc+FwOAD4vwntcDiwYcMG5OTkgOd5uN1u5OTkAPB/Yz43NxcA0NbWhry8PACA3W5HYWEhfD4f1q9fjy1btgAA6urqUFxcDACora1FWVkZAKC6ujqAU3l5OTZs2ICdO3eG5VRUVITGxkaJU3Nzs2QrcXK73eB5vgcnmm8oTgDQ2NiIoqIiidP27duxYcMG7N+/X5GTUp3279+PDRs2YPv27YqcgtUpPz9fqmkoTkp12rBhA44eParIKVidKisr8b///Q8+ny8kp/r6egCA0+mE1+sFAHR2dqKjowOiKMLhcEjC4XA4JNGj/0/tYANMR0cHAL8QUVsQBKm+PM9LttfrRXt7O0TR/wnBrq4uAIDH44HT6QQAuN1uuFwuyV60aBHuv/9+OJ1OtLe3Y+zYsVi9ejVuvfVW9OvXD6NHj8brr78ucTKZTCgtLcWTTz4Jk8mExx57DOXl5TCZTNi5c6fEKScnB5MnT0ZKSgouuugiVFZWSrzknCi6c9q2bRtMJhMaGxvR2dmJ1tZWmM1mXHXVVRBFEe3t7Vi9ejXmzZsXwCk7Oxs//OEPAQAulwtut1uqjcfjgSiKePPNN3HppZfiV7/6Ff75z39KbSxUnQRBQHt7e0Cdutuh6kTPH+AfsKjt9Xql2tTX1/foT8G+cBAp4qmntDY5OTlSPYzWU5vNBp/Ph//9739Su2N6Gn89ra6uxs6dO7FhwwaUl5eHbXter1dq/3r0lNaO6WlwPaWcHnvssZjpKZ3kU7s36SmgX1MbGhokO1xfjdYctaCgABs2bEB9fb2qvhqNOSqNE46Tkv5s2LABra2tMdPU0tJSrF+/Hj6fL+Q4EU1Ntdvt0vgRilNxcTG6umxobv4KAJCVdbc0R+W4gUhOfh6ABXb7FygpuS9knXw+H3JycqS5ayTjOe3Tkf59BPj7JOUai/HcbrdLXCk/tW2PgtZVbdsrLi6W5gKlpaWq256e8Tw3Nxetra2SLkQynlMezc3NEY/nlFNYEAZNaG9vJwCI3W4nhBDC8zzheb6H7fP5AmxBEIjX6yXZ2dnE7XYHbCeEEK/XG2CLoijZPM8Tu91O3G43EUWRiKJIvF4vIYQE2DQGtenxjx8/TjweT8B2mq/clvPwer2kublZiq/EKZjtdrtJdnY2cTqdipzkPOS2IAgSVyVOwWyPxyPlq8RJqU6Uq8fjUeQUrE5dXV0kOzubeL3ekJyC1Ynm6/P5FDkFq5PX6yVNTU1EEISQnDo7O0lFRQVxOp3Seed5gbS2eonDIUo/nZ2EdHQIIe2ODoHU17cG/N7ZSULa9BgOh0haW71EEETpnNDzoWQvWrSI3HfffVLtx4wZQwYNGkT+/Oc/k+rqavLMM88Qk8lEbDYbEUWRHDlyhJx55plk5cqV5MiRI6Sjo4NUVVURAKSsrIwIgkAOHTpEEhMTyb333ktsNhv54IMPyLBhwwgA0tLSItVUEATS2tpKBEEgoihK26k9ePBg8q9//YuIokiys7PJ4MGDydChQ6X6XnLJJeShhx6SOLW2thKr1UoOHTqkyFsQBDJmzBjy9ddfE0IImTVrFnn33Xd77E9zktu0bcqPJbflucttOc9geTmdTlJRUUE6Ozt79KfW1lYCgLS3txO9iIeeiqJIeJ4nx44dIzzPx0RPeZ4ngiCQpqYm6ZhMT+Ovp0pjXbC253K5yN69e4nT6TyxjzY9dThE0t7OS5rK9LSnnlLfSy65hPzud7+LmZ52b2+9TU8J0a6pwXTG6DlquH5rxBzV6XQG6Ewkmup2u0lzc7MUPxinaGuqx+Mhx48fDxiDQvGLhqb6fD5y7NgxqW0rcfJ6vaSs7CKSnw+ya9flUnz5HLW+/i8kPx8kP58jTU1fKtaJjo/0+JGM5x6Ph2RnZxOPxxPReE7j0HxjMZ7TOMeOHQuY16sZz+mcp6urK6K2F2zuYvR4Tvtlc3NzD10IpxG0j7rd7ojHc7vdrkpP2TtBdII+Y0uXTna35UvjqS2cuOtAlz/K96HHU7IzMjKCxuc4TrJNJpN0bLk9ePBgyU++XSl3s9kMs9kcsDRNiVMwm5xYOWCxWKRY4fgpcVXi1N1OSEjokW8oft1zV8s1Eh5yu3udguWrxE+er9VqDfpcdndO9Mo4x3HS5/ncbhMGDgy2CMwUxuYADJBt52T/Ktnyfa3o7ARSU2VbZZ8MVLLl52D58uX49a9/DQBYtWoV1q5di02bNmHKlCkYPnw4LBYL+vXrh+HDhwOAdPecHufNN9/E+PHjsXbtWnAchylTpqC8vBzPPfccOI4LuiRZfu6ovXDhQhQUFOCaa67Bpk2bcMstt+Dvf/87bDYbJk+ejK1bt2LlypWSX05ODs466yyMHj1akes333wDp9Mp3d386U9/ir/+9a+4/fbbe5yP7ra8bSodPxgP+R3aYH70d7PZLMWgbcyI5dux1lOz2Swtw5ZvN0pPKeR9l+lp/PVUaawLxoku79WvpwBgRqCmAkxPT+ppRUVFXPQUCGxvvVVPgcg1NZjOGD1HDddvjZqjquXU3U5MTJQ+oSyvn5GampCQII058rZipKZaLJaA8VGJk93+Gdra8mAyJWHSpFek48nHuaysO9HVVYYjR95EZeXNSE0tQUrK5KC5K42PanKn/8p5qBnPLRZLQFyjx3OKYPOPcDZdNRaOk5q5S6zG8+6+asZzeszutprxXH78UGCPw+hEtJYwqo313//+V1NMrb56YupBPPKNB9d48Ow+UYsltMQWRVFaWnf22WdL2zmOQ2ZmJpqamhT96HJlCpvNhvPPPz9gcjpv3ryIc1q8eDE2bdoEACgoKMCSJUuwcOFC5OfnIy8vDy6XCxdccIG0/5dffokrrrgi5DHfeecd/PjHP5ZE/YYbbsD27duxb9++kH70/MS6rkb0E6YzxoDxNM6X6alxelpQUIDt27fD5XIFHNdIPQXio6lG9ZPe0P+YzhgbUyvUxOR5B/bv/w0AYPToVUhOHqfoO3HiK0hPvwCC0IHy8h9DENyaYhoB1naN8dPrqxVqY7GVIDrR/Q6s0bEWLFigKaZWXz0x9SAe+caDazx4pqZyaG/3v9BJPnkNBzoJTk9Pj/iuFTnx4rjU1Mivu3IcJ73Qr/vV3WB33+T/lyq/TQqofllSOCxevBj33Xcf9u/fj/LycixYsAAHDhzA5s2b0dbWhlmzZkk5+048g/nwww8rHq+lpQVffvklfD4f/vrXv0rbBUHAX//6Vzz33HOKvvT8RFLLaMCIfsJ0xhgwnsb5atVTQLum9hU9LSgoQGtrK84991zpCzBG6ykQH001qp/0hv7HdMbYmFqhJuahQ0/C6z2CpKTxGDXqdyF9TaYEnHnmZygtPQdOpw2NjW9h5Mh7I45pBFjbNcZPr69WqI0V95Ugr7/+OsaNG4ekpCTMmjULmzdvDrl/QUEBZs2ahaSkJIwfPx5vvvlmwP+//fbbWLBgAQYOHIiBAwfi4osvll6coieuEmI5SHIch/T0dE0xtfrqiakH8cg3HlzjwdNk4pCebkZaGofUVMTkJy3NH9Nk0lYX+VI3PX7Tpk3Dtm3bArZ1/10Npk+fjoyMDDz11FOYMWMG0tPTsWjRIhQUFKCwsBCLFi2S9s3Pz8eAAQMwc+ZMxeN99NFHGDlyJHbt2oWdO3dKP2vXrsXf//73kN88pzzj0Ud7wzHDxWM6E30/vb5aEY98mZ4aq6cFBQVYvHixVBej9VTONdZttzcdVykW0xljfE9Fnl1dNtTXvwwAmDTpVZjNyWF9ExMzMXbs4wCAQ4eegSA4I4ppFFjbNcZPr69WqI0V14sgn376Ke6//3784Q9/QFlZGRYsWIBLL70Uhw8fDrp/bW0tli9fjgULFqCsrAy///3vce+99+Lzzz+X9tm0aRNuuOEG5OfnY+vWrRg9ejSWLl0qvfFaS9xQiPXyHnp3I1a+emLqQTzyjQfXePCMx1JfPTHly7cj9eu+fPuuu+7CgQMHsHLlSuzbtw8ff/wx3nvvvYB9GhoaMG3aNOnt2cFAn2P/8MMPsXjxYgD+peVerxfffvstFi5cKO371VdfhV26/e677+Lqq6/GyJEjMW3aNEyfPh3Tp0/H7bffjra2Nvz3v/8NyZM9DqM9HtOZ6Pvp9dWKeOTL9NR4PZ09e7bE1Wg9pVzZ4zDaYjGdMcb3VONJCEF19d0ghEdGxuXIyLhMtW9m5m1IShoLn+8YGhpeV+1nJFjbNcZPr69WqI0V14sga9aswc9//nPccccdmDp1KtauXYtRo0bhjTfeCLr/m2++idGjR2Pt2rWYOnUq7rjjDtx+++148cUXpX0++ugjrFixAjNnzsSUKVPw9ttvQxRFfPvtt5rjhkKsl/csXbpU83IkLb56YupBPPKNB9d48OxNV5/lvlr80tLSAraNHj0an3/+Ob7++mvMmDEDb775Jp555pmAfXw+H/bt2yd9ilEJS5YsgSAI0qSd4zgsWLAAAKR/Af+k/corr1Q8TmlpKXbt2oWrr766xznq168fli5dinfffTckz3jcOTldHodhOhN9P72+WhGPfJmeGq+nS5culbgarac0dqxrero8DsN0xhjfU43n8eP/lF6GOnHiKxH5mkwJGDPmMQDA4cN/As87VPkZCdZ2jfHT66sVamPF7Z0gXq8XpaWlWLVqVcD2pUuXBny/W46tW7di6dKlAduWLVuGd999Fz6fL+jbYJ1OJ3w+n/RmWi1xAf+36j0ej/Q7vSPi8/k0XVGT/6sW5MR36n0+X8SDs1ZfPTG18tQTNx7n6FTm6fP5pOfH5Xe16LPckTzTLffRcocs0pj0u+eUa01NDYDAlwHu2LEjYFv338eMGSN9PYRuW758OZYvXx4Q65ZbbpH2GT16NHieh8PhCMl1xYoVWLFiRcCxv/jiCxBCpGfrd+zYgY6ODixYsEDxOOecc46UY7BzlJ2d3YN3d2ipZ3e/YMcXRVFqZ92Xwuu5qn8q6CnAdMYoPz2+8eAZiW8wTTWq/6n1VYPeqqfynEtLS2Oip0q+4RAvPQWip6lMZ4yLqcc3HnNxpZg8347qav/LULOyfguLZWSPY4fLNyPjeiQlPQO3uxqHD7+MUaMejhtPPXH7StvtbTzV+sTtIojdbocgCBg2bFjA9mHDhuHo0aNBfY4ePRp0f57nYbfbpc+4ybFq1SpkZWXh4osv1hwXAJ599lmsXr26x/aNGzciJSVF0S8UNm7cqMmvt4HxjB8sFgsyMzPR2dkJr9cblWM6HI7wO50m0Mu1vb0df/rTn+ByucLeCY0nlHh6vV64XC4UFhb2eI7e6XQG9VEDpqfawXjGF0xTtYPpqTF6CkRfU0/V/hdt9BWeQPS4JiW9jcTERgjCCJSXn4Xy8hxNx7Faf4SUlJdx6NDz2LNnAoC0sD5q0FdqyngqQ62exv3rMN2vCtG7qJHsH2w7ADz//PP45JNPsGnTJiQlJemK+/DDD2PlypXS7x0dHRg1ahQuueQS9O/fX9EvGHw+HzZu3IhLLrlE9beMaY48z8NisWi6EqfFV09MrTzjla9W31OZp9vtRl1dHdLS0gL6QLj2rhTT4XBofmu+lph6ffX4aeUqj7lkyRIsWbJEk6/WmJH6heLpdruRnJyMhQsX9tDQ9vb2iONRnAp6CjCdMcpPj288eEbiG0xT460zWuKeSjqjNm6s9FSrb7z0FIiepjKdOTXzjcdcPFjMzs4d2LXrfwCAs89+FwMG/EBzvoQsQ1nZerhcNkyfbsPo0Y+xvzlUgPEMD7V6GreLIIMHD4bZbO6x+qKpqanHKg2KzMzMoPtbLBZkZGQEbH/xxRfxzDPP4JtvvsHZZ5+tKy4AJCYmIjExscd2q9UacXG0+hJCIAgCrFarpkaoxVdPTAot5yge+erleiryFAQBHMfBZDJJn2CUXziMJCZd3kuPFwm0xtTjqyemVq6nG0/62c9gbVur7gGnhp4CTGeM8tPrC8SWZyS+3TWV6Ux4xGPsiMc5ipeeAtHXVKYzxsTsTXPx7jEJEVBTczcAEUOH3oAhQ36oM18rxo9/Env3XoMjR17F6NG/gdU6iP3NoRKMZ2gfNYjbi1ETEhIwa9asHstcNm7ciPnz5wf1mTdvXo/9c3NzMXv27ADCL7zwAp588kmsX78es2fP1h03FMJ9ai2a4Hkeubm5mmJq9dUTUw/ikW88uMaDJyEEHR0d0kQvFtATU6sv42ksjOgnTGeMAeNpnC/TGWMRj3zjwdWoftIb+h/TGWNjakX3mEeOvAmH43uYzemYMGFNRL5KGDz4/5CWNhOC4MDhwy+wvzkMRl/iqQokjvjHP/5BrFYreffdd0lFRQW5//77SWpqKjl48CAhhJBVq1aRm2++Wdq/pqaGpKSkkN/85jekoqKCvPvuu8RqtZLPPvtM2ue5554jCQkJ5LPPPiONjY3Sj8PhUB1XDdrb2wkA0t7eHjFvr9dLsrOzidfrjdi3N4HxjD9cLhepqKggLpdL97EEQSCtra1EEIQoZHZqo69wDcczVPvRo4HRPNap3P+iCcbz1ADT1MjBePoRKz3Vc7xTvf9FC32FJyHR4ep2N5LCwnSSnw9SX//nKGZHyPHjX5H8fJCCghTi8RzVfJy+UlPGMzzU6l9cP5F7/fXXY+3atXjiiScwc+ZMFBYWIicnB2PGjAEANDY24vDhw9L+48aNQ05ODjZt2oSZM2fiySefxKuvvoqrr75a2uf111+H1+vFNddcg+HDh0s/8s/ohosbCQi7+2MI4pFvPLjGi6cgCDHnqTWmVl/G01gYEY/pjDFgPI3zZTpjLOKRb7xq2puOqxSL6YwxvvHmeeDASghCB/r1m40RI+6KyDccMjJ+hH795kIUnTh06E/sbw4D0Zd4qkFcL4IA/s+iHTx4EB6PB6WlpVi4cKH0f++99x42bdoUsP+iRYuwY8cOeDwe1NbW4q67AjvjwYMHQQjp8fP444+rjhsJYr28Z/PmzZqXI2nx1RNTD+KRbzy4xoMnIUT6XGGsoCemVl/G01icLo/DMJ2Jvp9eX62IR75MZ4xFPPKNB9fT5XEYpjPG+MaTp92+AU1NnwAwYfLkN8FxZtW+avLlOA7jxj0JAGhsfBPffZfN5gIGoS/xVIO4fx2mt0Pvy6wijXXZZZfF1FdPTD2IR77x4BoPniaTCQMGDNDkqxV6Ymr1ZTyNhRHaF0s9pfGYzkTfT6+vVsQjX6YzxiIe+caDq1Ha1xvmqExnjI2pFVarFT/84Q/w/ff+j0tkZf0a/frNUu0bSb4DB16C/v0vRHv7d5g4sRhW68805awVrO0a46fXVytO+Rejni7w+XwA/G+MFwShh83zfIBN3x4OnHyTuHy7z+cLsOmdCJ/PB0EQ0NzcDI/HI61wofHltiiKATY9vt1uh9frDdhO85Xbch4+nw8tLS1S/FCc1NjdOcl5yG1RFCWuSpyC2V6vV8pXiZNSnShXr9eriofclrcHJU7B6kTz5XlekVOwOvl8Phw/fhyiKKpqezQP+bmUr5Si28PZFIQQiX8om/p1Px/y7eFspXzD2fKYSpxovnK7O0JxUoqpdv9Q+YY6H91t2jZDcVKyu3MNFjOYRgTz14tY6ikh/iXvTU1N0tJ3o/WUnrfjx49Lx2R6Gn89VRrrjNRT+b50Oz0209Po5xuJnnY/v71VTwHtmqq2r8r1R+scNVy/NWqOKj9HkWiqx+NBS0uLFD8Yp2hrqtfrhd1uDxiDIqmTnKtaTeV5HpWVj8LlqkZCQiZGjXosJD89c1RBEDBmzGoAQGPjW+jsPBCWU7A6UQ6RjOc0Ds03VuO5KIpoamoKOGdqxnN5LUNxCmZ3n7vEYjwXBAEtLS09dCFcncKN4eHGczVgF0EixLp16zBt2jTMmTMHAFBeXg4AsNlssNlsAIDdu3ejuroaAFBWVoba2loAQHFxMerq6qRjHTt2DABQWFgIu90OAMjLy0NbWxsA/5dvHA4HACAnJwddXV0oKSnB+vXrwfM83G43cnJyAAAOhwO5ubkAgLa2NuTl5QEA7HY7CgsLIQgCiouLsXXrVgBAXV0diouLAQC1tbUoKysDAFRXV2P37t0Sp4qKCpSUlKjiVFRUhMbGRolTc3OzZCtxcrvd4HkeOTk5AZxovt98840iJ8D/3piioiKJU0lJCUpKSlBTU6PISalONTU1kr8Sp2B1ys/Pl2oailOwOuXn56OkpARNTU2KnILVqaqqCtu3b4cgCCE51dfXAwCcTqckel1dXXA6nSDEv+SXCofD4ZBEr6OjQxIx+ixf9wGmo6MDgF+IqC0IglRfnucl2+v1oqurC4QQyQYAj8cDp9MJAHC73XC5XJK9aNEi3H///XC5XJKv0+mU/pDr6uqSOHV2dsLn86GyshJz585FcnIyzjnnHNhsNphMJuzcuVORU05ODkwmE1pbWwM4UShx8vl86Ozs7MGPnt9gnNxuNwDA5XJJNuVEfbtzorVRqpMgCOjq6grg1N0OVScaKxgnyqO+vr5Hf6I56EE89dTtdsPj8WDr1q3weDwx0VObzQZBELB9+3ZUVVWF5MT0NHZ6SjmVlJSgoqIibNvzer1S39Sjp7R2TE+D62l3rrHQU3rBo6urS7J7i54C+jW1oaFBssP11WjNUTdv3oySkhIcOXJEdV+lnLTOUWmccJyC6c/69etRUlKC9vb2kOMEEF1NpXO+UONENDW1sfF7NDWtBQAMGbIaRUW7gnLSM0eV18nhmID+/S8CIT7YbPeH5KRUJwAR/30EAEeOHMHWrVshCEJMxnO73Q5BELB161a0tLSE5BSsTvK6qm17xcXFUr6xGs9zc3PR3t4u6UIk4znl0dzcHPF4TnUvLAiDJtA3z7a0tBBCCOF5nvA838P2+XwBtiAI0htv3W53wHZC/G/DlduiKAbYoij2sAkhATaNQW2fzxfS5nk+wA7GIxynYLbb7SbZ2dnE6XSeNpyC1amrq0t6g/Gpxqmzs5NUVFQQp9MpnXeaU3dbEISQtiB78z39nR5DyQ4XM5Rtt9tJR0dHRL7XXXcdueiii0htbS2x2+3E6/WSI0eOSLXrzkkURfLtt99KfZnmHoqrVk61tbUkISGBtLe399inpqaGAJB+rFYrmTBhAnniiSekfdXWieYrt5XqJOcZLHen00kqKipIZ2dnj7ZnxNdhmJ4yPT2V9ZTneeJyucjevXuJ0+mUctCip6IoEp7npf7H9JTpabT0lBDtmhpMZ05HTXU6nQE6czpwiqamiqJIysouJvn5IDt3XhKQr5GcHI6dJD/fRPLzQZqbN0bU9jweD8nOziYej6dX1klt26Nznq6urtOGU7A60T7qdrsj5tTS0qJKT9lKEJ3gOA4AYDabYTabe9gWiyXANplOnnJqy7dbrdYAmx7farWCEILjx4/DbDaD4zhwHCc99yS3TSZTgG2xWKQlUPTYdDvNV27LeXAch6amJnAcF5aTGrs7JzkPuS2eWGJG4wTjFMw2mUxSvkqclOpEudLjhOMhtylCcQpWJ7PZjKamJun4ofjJ68RxHJqbmyGKoqq2R/Og4HkehJCA7SaTKaxNwXGcxD+UHS5mKDsjIwP9+vWLyPfAgQO48MILMXbsWAwaNAgAkJmZKdWuOyel3ENxDRWfEBKwzFa+z1dffYXFixcjPT1d8Tjr16/HkSNHUF1djdWrV+OZZ57BX//615B1Avx3HOk+NN9Q/EJxDZZXMI0QDVi+HUs9pfVqbW2V2pbRekrPW3Nzs5QL09P466nSWGeknnY/BtPT4LZSvkbpKdUF4cTKjN6spzSuPE53O1L9MWKOSnWGxqLbjZ6jUoQbJ7rrD9UZQkjIcSKYrVVTTSaTNOfTUic5VzWa2tT0CdravgHHJWLixNcCamDEHJXmnpJyFgYNuh0AUF19N0wmUfV43r2vqR3PKVpaWiCKYszGc1EU0dra2qM2auokr6vatkfnLs3NzVIusRjPCSFoamrqoQvh6hSuvYUbz9WAXQTRCaMGLqVY5eXlmmJq9dUTUw/ikW88uMarLnRJcSwRaczFixfj/vvvl3zHjRuHZ555Brfffjv69euH0aNH46233pL25zgOpaWleOKJJ8BxHB5//HFUVlbCZPIv36bIycnB5MmTkZycjCVLluDgwYMR5bVnzx6YTCZp6V9raytMJhOuvfZaKddnn30W8+bNC/D78ssvccUVV4Q8dmpqKjIzMzFmzBjcdNNNmD9/Pnbs2BE2p3jU04h+wnTGGDCexvoyPTVOTwHgqaeewvz58wP8jNRTIPY1Naqf9Ib+x3TG2JiRwudrwf79vwEACMKNSEycEPEx9PC02y+H1ToMLtc+1Ne/HHFsLWBt1xg/vb5aoTYWuwiiE/KriLGIddFFF2mKqdVXT0w9iEe+8eAaD54cgHSzGZzTCXR1xeSHczr9MSPO1j8ZT09PBwC89NJLmD17NsrKyrBixQr86le/QmVlJQD/s4JnnnkmHnjgATQ2NuK3v/2tdOeToq6uDldddRWWL1+OnTt34o477sCqVasiymf69OnIyMhAQUEBAP+zkBkZGSgsLJRyLSgowKJFiySftrY2bN68OeykPS0tTbqC/f3332PHjh0477zzVJ0ftVe+owUj+gnTGWPAeBrny/TUOD2l+W7bti1mekpjxlpTjeonvaH/MZ0xNmakqKl5CD5fE1JSpmLx4jdVxXQ6gV//GnjtNQBeLyx33YWLDhzQyPMKTJjwAgDg4MEn4HbXhfHSD9Z2jfHT66sVamOxiyA6EesrWw0NDZqvxGnx1RNTD+KRbzy4xoMn6eoC0tIi/jGlp2PAyJEwpadr8kdamj92pPmeePkfACxfvhwrVqzAxIkT8dBDD2Hw4MHYtGkTgJPLtNPS0pCZmYnU1FTJj+KNN97A+PHj8fLLL+OMM87ATTfdhFtvvTWifDiOw8KFC6W4mzZtwi233AJRFLF37144nU4UFRVh8eLFkk9OTg7OOussjBo1KuSx58+fj7S0NCQkJGDOnDm47rrr8LOf/UzV+SGEhNwv2jhdVoIwnYm+n15frYhHvlr1NBqaerrraUVFBXw+H4qKigIughipp0B8NPV0WQnCdMYY31jxbGvbjMbGdwAAkya9gcbG42Fj+nzAtdcCr78OrFwJuP69Hnj3XeDOOyGeeNGlWlCeQ4bciP79L4QoOqVVKUaCtV1j/PT6agVbCRIjxLqoBw4c0NwItfjqiakH8cg3Hlx7W13iBfoFg7PPPlvaxnEcMjMzpWeJg6H7pN1ms+H8888PuMPX/bEVNVi8eLE0aS8oKMCSJUuwcOFCFBQUoKioCC6XCxdccIG0v5ql2wDw3nvvoaysDLt27cKnn36KL7/8UtWdVXp+YonT5SII05no++n11Yrelm+80Jv0tKSkJOZ6CsReU0+XiyBMZ4zxjQVPUfSiquqXAIDhw+9Av34XhI0pisAttwD0oyU8DxzN3ib9P3frrcCJR93U5eDnSQjBpEnrAJhht3+OlpYNWihFHJe13ej66fXVCrWxYrv2+DRErJf3LFy4MKa+emLqQTzyjQfXePDkUlOBE5/tiwSi6P8kYHp6etCXwKmKnZISuQ/HScuw5S+Fov+nJHYcxyEtLS1gW7Tu7C1evBj33Xcf9u/fj/LycixYsAAHDhxAYWEh2tvbMWvWLClnn8+H9evX4+GHHw573MmTJ2PSpEkAgKlTp6KmpgaPPPIIHn/8cSQlJQX1kZ+fWOJ0eRyG6Uz0/fT6akU88tWqp4B+TT3d9bSgoABtbW2YNWuW9AiP0XoKxEdTT5fHYZjOGOMbC551dS/A6bTBah2C8eOfCxuTEOCee4BPPgEsFmDKFKC8HMCJT8fDYgHX2Aj8/OdAdjag4vEyecy0tLMxcuQ9qK9fi+rqezBnzh6YTIlRYBo6biz89PpqRV/iqQZsJYhO0O/OC4IgvU1cbvM8H2DLJxjUlm/3+XwBNp1k+Hw+CIKAgwcPwuPxSN+sp/HltiiKATY9/sGDB6U7OXQ7zVduy3n4fD4cOnRIih+Kkxq7Oyc5D7lN86V3Y4JxCmZ7vV4pXyVOSnWiXL1eryoeclveHpQ4BasTzZfneUVOwerk8/lQW1sLURRVtT2aBwCIhMBjsYCkpEg/SE2FmJwc1qY/JCUFYnJyWJv6kZQUf8xuuYSyKURRlNqB0j7BjkMICbibJ4oipk6dim3btkEURWmfrScGbEJI0D8A5NupD32O/amnnsKMGTPQr18/6c5lXl6eJPiEEOTl5WHAgAGYMWNGWN7yJdiEEJhMJvA8L/V5yqO77fF4Ajh1t4PxUOIaLK9gGmHEVf1Y6in9AkRNTQ0EQYiJntLzVltbKx2T6Wn89VRprDNST+X70u1MT3vqaX5+Pi688EJp31joKeXa2/UU0K6pavtqNOao4fqtUXNU+TmKRFM9Hg8OHTokxQ/GKdqa6vV6pTmfljrJuQbj1NFhw8GDTwIAxo59AVbrIPA8j5qaGoiiGJTTo4/6H4HhOIIPPgDuvluEGTwy64oBAE3PPw+SkAB89RXE119X1fZEUURNTY0Ua+TIPyIhIRMuVzUOHnw+bJ1oW4tkPKfxg3E1cjzvzlXteC6vZSRtj8atra2V5i6xGM8FQcChQ4d66EK4OoUbw8ON52rALoJEiHXr1mHatGmYM2cOAGDv3r0A/MtCbTYbAGD37t2orq4GAJSVlaG2thYAUFxcjLq6ky/4OXbsGAD/i8Dom9Hz8vLQ1tYGAMjNzYXD4QDgfwbW6XSivr4e69evB8/zcLvdyDmxBs3hcCD3xLN3bW1tyMvLAwDY7XYUFhZKDZ9OUOrq6lBc7Beq2tpalJWVAQCqq6uxe/fuAE4NDQ3Ys2dPWE5FRUVobGyUODU3N0u2Eie32w2e55GTkxPASRRFHD58GN98840iJ8D/8raioiKJU0lJCRoaGsJyUqpTQ0MDSkpKFDkFq1N+fr5U01CcgtUpPz8fDQ0NOH78uCInpTpVVVVBFMWQnOrr6wEATqdTEr2uri5pMutwOCThcDgckuh1dHRIItbR0RF0gOno6ABw8m4m4BcfWl+e5yXb5/NJb9v3er3oOvEsu8fjgdPpBAC43W5pH7fbLcV3uVxSvj6fT7K7urqknDo7OyXR83q9Eif5pL2jowN33nknDhw4gHvuuQeVlZX46KOP8N5770m8Ojo60NDQgGnTpqG0tFSRE8dxuPDCC/Hhhx9i8eLF8Hq9mDBhArxeL/Lz83H++edLPP7973/jiiuugNvthtvtljhRW16bo0eP4vDhw6ivr8cXX3yBV199FUuWLAHHcYp1ooIvr1N3O1Sd6Pmj55faXq9Xqk19fX2P/hSNSXs89dTtdsPr9WLPnj3wer0x01NRFFFVVcX09BTT0z179qChoUFV2/N6vZLe6NFTWjump8p6mpeXF1M9lZ8nevGjt+gpoF9TGxoaJDtcX43WHHXz5s1oaGhQ3VejMUelccJxCqY/69evR0NDAzo6OkKOE0B0NZU+UhBqnNCiqS6XC9u33wBCPOjXbwl27swA4P9S0969eyGKYg9ODz9cg6ee8h/zgQdq8JOfAOPG1WE6ypEsOiGkpaFsyhSITz/t3+m3vwUOHAg7ntM57ck67cKQIY8AAA4dehpNTXtC1glAxH8fUU50fI7FeG632yGK/vfI0c/kqh3PKWhd1ba94uJiiKL/0ZRYjee5ubnSOLB+/fqIxnPKo7m5OeLxnOpeWBAGTWhvbycASEtLCyGEEJ7nCc/zPWyfzxdgC4JAvF4vyc7OJm63O2A7IYR4vd4AWxTFAFsUxR42ISTApjGo7fP5Qto8zwfYwXiE4xTMdrvdJDs7mzidztOGU7A6dXV1kezsbOL1ek85Tp2dnaSiooI4nU7pvNOcutuCIIS0BUEgra2tAb/TYyjZ4WKGshctWkTuu+8+afuYMWPImjVrAvaZMWMGeeyxx6R9ZsyYQR599FFpnwMHDhAApKysTMr766+/JhMnTiSJiYlkwYIF5J133pH6siAIpLa2lgAgX3/9dVCu9NivvvoqAUD+85//SNuvvPJKYjabSVtbm7T/qFGjyMaNG0NyrampIQCkH7PZTEaOHEnuuOMO0tTUFFGdaL5yW6lO8poGy8vpdJKKigrS2dnZo+1RDWxvbyd6wfSU6Wlv0FOe54nL5SJ79+4lTqdTykGLnoqiSHiel/of01Omp9HSU0K0a2ownTkdNdXpdAbozOnASaumNja+T/LzQQoKkkhXV3VYTuXlAklOFglAyOrVJ7f7fDz5bdo6QgDSPPtif76CQMRFiwgBCFm4kPBeb8SceJ4nO3YsJPn5ILt3/1ixTh6Ph2RnZxOPx9Mr66S27dE5T1dX12nDKVidaB91u90Rc2ppaVGlp+wiiEZ0H2AiAW3AtNhqwfM8qa6ulhpjLHz1xNTKU0/ceJyjU5mny+UiFRUVxOVySdtEUSQul0sSJrXoPsGLBFpj6vHVE1Mr1+4xS0tLSf/+/VW1jVORZ7D2Q6F2kFGDeOgpIUxnjPLT4xsPnpH4du8Tp4LOxMI3Hjy7x42FnurxPVX0lBDtmsp0xriYenyNmot7vXby3XeDSX4+yMGDT4eN6XYTMmOG/5rG0qWEdG/q+WN+RghAChf/8aRvTQ0hqal+p1de0cTT4dhD8vPNJD8fxG7PiZhnOLC2a4yfHl89PNXqKXscRieI7DnaWMRqbW3VFFOrr56YehCPfOPBNV51EU4s/40l9MTU6htvnjzP47XXXuvxAkI1vlpjxgpG9BOmM8aA8TTWN946EyvfePCUx42Vnur11QKj+klv6H9MZ4yNGQoHDjwEn8+OlJRpGDXqwbAxf/97YNcuYPBg4L33gO7vc57h9D8iuqHj/JO+48YBL7zg32HVKuDEYxbBoMQzLW06Ro68DwCwf/99EEVvMHfNYG3XGD+9vlqhNhb7OoxOxPrN2/Q5z1j56ompB/HINx5c48GT4zikpqZq8tUKPTG1+p4KPOfOnYu5c+dq8tUaM1Y4Xb4Ow3Qm+n56fbUiHvmeCjoTC9946Yw8biz0VK+vVpwuX4dhOmOMrxE8W1vzcfTouwCAyZP/ApMpIWTM3FxgzRq//e67wPDh3Q7Y3IyBx/0XOD6omofHzxkEqfn98pfAZ58BeXnAbbcBBQWA2dwjp1A8x459DMeOfQSXqxr19a9i9OgHg+6nBaztGuOn11cr2NdhYoRY3i0QBAGVlZWaYmr11RNTD+KRbzy4xoMnIQQulyvmV2W1xtTqy3gaCyP6CdMZY8B4GufLdMZYxCPfeHA1qp/0hv7HdMbYmMGP58S+fXcAAIYP/yUGDLgwZMzjx4FbbvFv/9WvgCuuCHLQbdsAAFWmM3C4cxC+/LL2ZL4mk//KSVoasGUL8OqrCnkp87RY0jF+/LMAgEOHnoDXeyxC1spgbdcYP72+WqE2FrsI0stA3/YeS189MfUgHvnGg2s8eMZ6Iqs3plZfxpMhHJjOGOOn1zceMbX6Mp0xFvHIl2mqNjCdMc43mjxrax+B212DxMSRmDDh+ZAxCQF+/nPg6FFg6lTgxRcVdj5xEaRuhP9rTkVF3R5bGzv2pPPvfw+c+CpJsJhKyMy8BWlpsyAIDtTU/EFxPy1gbdcYP72+RoJdBNEJc5DlXEbGOuecczTF1OqrJ6YexCPfeHCNB0+O45CSkgKO4yL21Qo9MbX6Mp7Gwoh+wnTGGDCexvkynTEW8cg3HlyN6ie9of8xnTE2Znd0dGxHff1aAMDkyW/CYkkPGfOdd8z4+msgIQH45BMgJUXhwCc+GS/MnQcAOHBgZM9877wT+OEPAbcbuOYa4MRnprvHVOLJcSZMmuRfRXL06F/hcJSqYBwerO0a46fXVyvUxmIXQXSCfo9eEARp+Y3c5nk+wBZl34Kntny7z+cLsOndCJ/PB57nsWfPHrjdbumb9T6fDwACbFEUA2yaw549e+DxeAK203zltpyH1+tFeXk5vF5vWE5q7O6c5DzkNs3X7XYrcgpmezweKV8lTkp1olw9Ho8qHnKbIhSnYHWi+dL6huInr5PX68Xu3bslDuHaHs2DHtPpdErb5NvD2RSEEIl/KDtcTDW2Vl9CCJxOp5RPME40X7ndHUqc1MTUk2+o89HddrlcYTkp2d25BosZTCOMWNoYSz0lhIDneezevRs8z8dET+nP7t27Ja5MT+Ovp0pjnZF6Kt+XbqfHZnoa/Xwj0VPq29v1FNCuqWr7qlx/tM5Rw/Vbo+aoFOHGie7643a7UV5eDp7nQ44TwWytmur1erFnzx7pXGjVVI+nE5WVPwcgYsiQGzBo0HLFOvl8PmRn78PKlf7z8fTTAmbMUOAnCCDFxQCAkdf4V4L8978iKiq6zVFFEcJ77wGjRgHV1SC33QacGItFUZTGR5pDME79+8/HkCE3ACCorr4XXq83aF9TO57TY9M5dazG8+5c1Y7n8nYbSduTz13C6UI0x3Oe51FeXt5DF8LVKdwYHm48VwN2ESRCrFu3DtOmTZNe8lJRUQEAsNlssNlsAIDdu3ej+sTbj8vKylBbWwsAKC4uRl1dnXSsY8f8z7MVFhbCbrcDAPLy8tDW1gYAyM3NhcPhAADk5ORIDWjDhg3geR5utxs5OTkAAIfDgdzcXABAW1sb8vLyAAB2ux2FhYUAgK6uLmyjy9Xq6lB8QrBqa2tRVlYGAKiursbuE0vUbDYbKisrAQDl5eVhORUVFaGxsVHi1NzcLNmhOPE8j5ycnB6cfD4fvv3225CcGhsbUVRUJHEqLfVfFT506JAiJ6U6HTp0CABQWlqqyClYnfLz86WahuPUvU6bNm0KyylYnfbv3y/lEopTfX09AMDpdEqi0NXVJQmUw+GQhMPhcEii19HRIe3T0dERdIDpOHEFXxRFyRYEQaovz/MBNhU3r9eLrq4uAIDH44HT6QQAuN1uacmc2+2W/mBzuVxSXk6nU/rDs6urS+LU2dkpHV/OST4xUuLU3aacKJQ4+Xw+dHZ29uAkCEIADyVO1JZz8vl8Eg8lTt3rJLfDcQpWJxorGCdam/r6+qD9SS/irac8z6O2tjZmeko52e127N+/PyQnpqex09Pq6mqUl5cDACorK8O2Pa/XK/VNPXpKa8f0VFlP5X8sxkJPaa5er1e6+NFb9BTQr6kNDQ2SHa6vRmuO+t133wEAjh49qqqvRmOOSuOo4dRdfzZs2ADAX+dw40S0NHXnzp1ob28PyUmNpubn3wGncy+s1iE4cODSkHVqamrHypWZcLk4LF7swcyZBUE5FRcXA2++Cc7hAJ+aimnXTcfChQ7wvAkrV5pQUdGNU2sr8K9/QbRYwH3xBfDKKwGcjhw5gqNHj4bkdOjQMphMKejoKMI33zws1QmApvH86NGjOHLkSCAnGDeeU06HDx+W6hrJeC6vq9q2Rzm1t7dj586dITlFazzPzc2VNHDDhg0RjeeUR3Nzc8TjOdW9sCAMmtD9G+w8z0vfQJbbPp8vwBYEQfr2sdvtDthOiP+7yHJbPPGdemqLotjDJoQE2DQGtX0+X0ib5/kAOxiPcJyC2W63m2RnZxOn03nacApWp66uLulb1qcap87OTlJRUUGcTqd03mlO3W1BEELagiCQ1tbWgN/pMZTscDGNtsNxovnKbfr/Slx7K6dgtpxnsJhOp5NUVFSQzs7OHm2PamC477CrAdNTpqe9QU95nicul4vs3buXOJ1OKQet/ZTnean/MT3tXZxOZT0lRLumBtOZ01FTnU5ngM6cDpzCaWpraynZtMlC8vNBjh79JCyn++8XCUDI4MGE1NeH4LR9OyEJCYQAhH/hBUIIITYbT6xWv/+//63Ab+1aQgBCLBbi27QpIk5er5fU1j5J8vNBtmwZSXw+B/F4PCQ7O5t4PJ5eWSe1bY/Oebq6uk4bTsHqRPuo2+2OmFNLS4sqPWUrQaIEs9ksPYMkty0WS4Btkn1Um9ry7VarNcCmz6RarVaIooidO3fCZDKB4zhwHAer1f/iIbltMpkCbIvFAkEQsGvXLul4dDvNV27LeQCQrq6F46TG7s5JzkNuC4IgcVXiFMzmOE7KV4mTUp0oV47jVPGQ2xShOAWrk8lkQllZGQghipyC1QmAtHRPTdujeVDQN9/Lt9PzF8qmkJ+jUHa4mGpsrb7kxLJVimCcaL5yuzuUOCnFlL8AKtJ81fh25wH4737SfZQ4KdnduQaLGUwjBAPf9B0LPeU4DqIoory8HKIoxkRP6XmT3/llehp/PVUa64zU0+7HYHoa/Xy16Gl3rqeDnsrjdLcj1R8j5qhUZ5T6rVFzVIpw40R3/aE6I4piyHEimK1VUzmOk+Z8WuoECNi//04QwiMj40oMHXp9SE396itg7Vr/eXj7bQFZWQqcOjthufFGwOsFrrgC5gceAABMmgTcdFMTAOCBB8zw+YLwu/de4Cc/AXgelptugslulx7XICdWHofiNGrUA0hKGguvtx51dc/36Gtqx3PAvxqPPm4Uq/FcEASUl5dLXNWO5xSRtj06d9m9e3eAFhk9nouiiLKysh66EK5O4cbwcOO5GrCLIL0MycnJMffVE1MP4pFvPLjGg6d8ghkrRBpz8eLFuP/++yPyraysxPnnn4+kpCScc845OHz4MEwmk7T0Lxg2bdoEjuOk5Xt6oeXcbtq0CSaTSVoWGQzvvfceBgwYELWY9JhjxozR5Hu6gOmMMX56feMRU6sv09OTiLaeApFz1aunWmLSYzI9ZTpjlK+emAkJX6OzsxRmc39Mnvx6yPZ98ODJz+HecksLLr9cYUdC/C86PXAAGD0a+NvfANlx7767DSNGENTUAGvWBPHnOOCtt4ApU4CGBuDGGwFBUM3TbE7GhAn+r83U1b0At/ugKj8lsLZrjJ9eXyNhCb8LQyjE+m23U6ZMiamvnph6EI9848E1Hjw5jou5IGmJ+cUXX0hXi9X6PvbYY0hNTcW+ffuQlpaGAQMGoLGxEYMHD9aSdsQIleuhQ4cwefJkHD9+HOnpgW9jnz9/PhobGzFs2LCIJ9/xqCdw+nwdhulM9P30+mpFPPJlemoslPI1Sk9DxTQSRmlfb5ijMp0xNqbLtR9JSR8DACZOfAmJiSMU9/V6/Ysz2tqAuXOBt94aBMUm9PbbwKefAmYz8I9/AIMGBeQ7a9YZeP554Kc/BZ591v+Z3WHDuh2jXz/gs8/8wb79FuYnn8SUJ59UzW3w4KswYMAStLXl4+DBhwHcrNpXDtZ2jfHT66sV7OswMQJ9EU+sYpWUlGiKqdVXT0w9iEe+8eAaD56EEHR1dUlL8GIBLTEHDRqEfv36ReR74MABXHjhhRgzZgwGDRoEt9uNYcOGBSx9NRKhcv3yyy+xePHiHhN2AEhISMCwYcOkLxJEK6aRMKKfMJ0xBoyncb5MT42FUr5G6WmomEbCqH7SG/of0xnj/AgRsX//XeA4L/r3vwiZmbeH3H/VKmD7dmDAAOCjj3js2qUQc88e4L77/PYzzwDz5gXN97rreMyZA3R2AqtXKwQ980z/BRUAeOopVL3yimqeHMdh4sS1AExobv4cZvMeVX7dwdquMX56fbVCbSx2EUQnYrkMluM4DBw4UPOdDS2+emLqQTzyjQdXLTEFwYnm5myYTB/g6NF30dLyDTyeIxHFjfUddy0x5cu3zWYzxo0bh2eeeQa33347+vXrh9GjR+Ott96S9uc4DqWlpXjiiSfAcRwef/xx1NfX91i+nZOTg8mTJyM5ORlLlizBwYMHI8prz549MJlM0puwW1tbYTKZcO2110q5Pvvss5jXbVLw5Zdf4oorrgh6TLp8W/6Fgffeew+jR49GSkoK/u///k/6OogcX3/9NWbPno2MjAxMmDABq1evDhD/NWvW4KyzzkJqaipGjRqFFStWSG/q1gsj+gnTGWPAeBrry/TUOD0F/Do2f/78AD+j9HTWrFlITk7G9OnTe72eGnlcpVhMZ4zx1erX0PA6OjoKQUgiJk58I6T/l18CL7/st//+d2DcOIWYggDccQfgdgOXXgo8+KBiviYThxf9T6zgrbeAEx/z6YkbbwRWrAAATHj8cXCHD6vmmJZ2NkaM+CUAIDn5HYiius+jBsuXtd3o+un11Qq1sdhFEJ2I9VLDiRMnaoqp1VdPTD2IR77x4BpJzLa2QpSXX4MtW4bAZrsWDsdr2L//LuzefQm2bs3C7t2XoaVlAwgRwx7LahUgik4IQldMfkTRCatV24vfOI5DUlISAOCll17C7NmzUVZWhhUrVuBXv/qV9Im8xsZGnHnmmXjggQfQ2NiI3/72t0hMTAw4Vl1dHa666iosX74cO3fuxB133IFVq1ZFlM/06dORkZGBggL/5+IKCwuRkZGBwsJCKdeCggIsWrRI8mlra8PmzZsVJ+0USUlJ4DgO27dvx+23344VK1Zg586dWLJkCZ566qmAfTds2ICf/vSnuPfee1FRUYG//OUveO+99/D0009L+5hMJrz66qsoLy/H3//+d+Tl5eF3v/tdRHyVcLo8DsN0Jvp+en21Il75Mj01Rk9pvt99913M9fStt97C3//+916tp0YeVykW0xljfLX4dXXtxYED/gsUbvfNSEoap7jvwYPArbf67ZUrgSuuCBHz7beB4mL/oyzvvAMEeSmw3HfhQv/xBAF46KEQCa9ZA8yZA3NbG8zXX++/yKIS48Y9CYslA2bzIRw+rLTkRBms7Rrjp9dXK9TGYu8E0Qn6PXr6Zm/6tmBq8zwPjuMkW/4GcfHE9+jpdpPJBJ/PB7PZLNkWiwUcx0nfty8uLsa5554rTWJ4nofVagUhRLJFUYQgCJJN42zfvh2zZs1CUlKStJ2+LZi+lbs7D57nUVpailmzZklvPQ7GyWQy9bDl/JQ40WW1PM8H2HTicu655yI5OTkoJ4vF0sP2er3YsWMHZs2aJb0tWKk23eskCAJKS0tx7rnnIiEhISgnpTpRhOIUrE4039mzZ0tvOu7OqaOjBIcOPYbW1g1SnMTEMfB6z8SAAQRudy1crn1oaclBS0sOkpMnIyvrHgwZcgsEwd/eCCHS2/V5vhNbtvRcQhwLXHihA2ZzKgD/5JYuN1ayAX8/cTqdAIDly5fjV7/6FTiOw+9+9zu8/PLL2LRpE8444wxpmXZqaiqGnXjwVH5XUhRFvP766xg/fjxeeuklmEwmTJ48Gbt378bzzz8PQghEUezxln967kwmk5TXwoULkZ+fj6uuugqbNm3Cz372M7z//vvYu3cvsrKyUFRUhN/85jfS/jk5OTjrrLMwcuRI6ZhyrvT3zs5OpKen45VXXsGyZcvw0EMPgeM4TJo0CUVFRVi/fr30JZOnn34aDz30EG6++WY4nU6MHTsWTzzxBB566CE88sgj4DgO9913n5T72LFjsXr1avz617/GunXrgi71VsqLaoG8PxmxtDGWemqxWMDzPLZv347zzjtP+t1IPaXnePv27Zg9ezYSExOZnsZYT5XqFGysC9b2aA1p32B6apyeVlRUYOLEiSgqKsL9998fEz392c9+BgAYMmQIVq9ejVWrVvVaPQUi19RgOmP0HDVcvzVqjkoRbpygPKjtdrtRVlaGOXPmAIDiOBFNTfV4PPj+++9x3nnnAYAiJ2p7PJ2oqLgRhHjQv/8laG+/TOLanZPXC1x3HUFbmwnnnUfwxBM8ACu8Xi+Ki4tx/vnnw2Qy+Xk0N4OsWgUOAJ5+GmJmJkRZW6Z1kufLcRyeeQb473/N+OorID9fwJIlQepktUL85BOQc8+F9fvvgXvvBf/66yrH80GYMOF17Nt3PRoaXsTgwT9CWto8VeO5/xx4UVJSgvPOO+8kVwPHcxpj27ZtmDt3LhISElSP5/J2G0nbk89daL6xGs9LSkpwzjnnBOhCuPGccqV2JOM51b1wYCtBIsS6deswbdo0SfzoXRObzQabzQbA/+nS6upqAP5PCtXW1gLwDw51dXXSsY4dOwbAf+eDLgXNy8uT3qqem5srLefMycmB1+vF8OHDkZubC57n4Xa7kZOTAwBwOBzIzc0F4L9LkpeXBwCw2+0oLCyEyWRCWloaiouLAfjv4FC7trZW+mRXdXW19Lknm82GqqoqZGVloaKiIiynoqIiNDY2SpzoctPCwkJFTm63GzzPIycnJ4CTyWRCRkaGxCMYJ8B/t6qoqEjitGPHDmRlZeHw4cOKnJTqdPjwYWRlZWHHjh2KnILVKT8/X6ppKE7B6lRQUICsrCy0tLT04OR0VuH773+EnTvPP3EBxAKL5SrMmlWK/v1zYDKtwlln/Qepqf/CwIH/wciR9wNIg8tVhf3778H27fNx+PA+AP5PqHq9/iWCXV1diCe8Xq+Ug8fjkSbkbrdb+qSh2+2WBFv+WcYpU6ZI4uZ0OjFs2DA0NTWhs7NTEluv1xsgnhQdHR2w2Ww4//zz4XA4pEnpjBkzAPgHs46Ojh5+giBIbZbneTgcDixevBibNm1CZ2cnCgoKsGDBAsyfPx8FBQXYsWMHXC4XLrjgAonTl19+iUsvvVTi4nK5JNvpdEq5i6IIn88Hm82Gc889V9rucDgwd+5cyaYD0pNPPon09HRkZmYiPT0dd955JxobG3H06FEQQpCXl4cf/OAHyMrKQnp6Om699VY0Nzejo6MjYKk4XdLt8/kk2+v1SrWpr6/v0Z+6/3GjBfHUU9rGmpubIYpiTPTUZrNJk5+ampqQnJieRldPw9WpoqICWVlZqKqqCtv2vF6v1DeZnhqrp0VFRTHV0379+qFfv34YPnw4fvnLX/YqPQX0a2pDQ4Nkh+ur0ZqjbtmyBVlZWWhqalLVV6MxR6VxwnEKpj+5ubnIysqC0+kMOU4A0dPUXbt2SX98hhonaJ02b74FXV27YbUORmPjzwD/ZYugnB58UEBJiQkDBgDvvNOJTZv8nDo6OtDR0SE9rlZYWAg88AC49nY4zjgDWLFCsU41NTXSRVCbzQZCbLjzTv/5vu8+L0QxeJ1M48Zh96pVIBwHvP02qn//e9Xjef/+P4LX+wMABDbbzcjJ+VfYtkfr1NTUBKfTCZPJFJPx3G63w2QySedYbduj/Qk4OVaqbXvFxcUwmUxISEjArl27VLc9PeN5bm4unE4nsrKykJubG9F4Tnk0NzdHPJ5XKj531Q2EQRPa29sJANLS0kIIIYTnecLzfA/b5/MF2IIgEK/XS7Kzs4nb7Q7YTgghXq83wBZFMcAWRbGHTQgJsGkMavt8vpA2z/MBdjAe4TgFs91uN8nOziZOp/O04RSsTl1dXSQ7O5t4vd6ocTp27GtSUJBC8vNB8vM5UlFxM3E4qsJycrtbyeHDr5HNmweR/HyQkpLLyN695cTpdErn3R/LQXi+k/h8Dsn2ejtC2l5vB7Hb6wN+p8dQsrvHoeeD5hLKXrRoEbnvvvuk7WPGjCFr1qwJ2GfGjBnksccek/aZMWMGefTRR6V9Dhw4QACQsrIyIggCufLKK8ltt91GBEGQfL744gupL9OaCoJAWltbpf3oduqze/duwnEcqaqqIlarlbS1tZG1a9eS66+/njz99NNk7ty50v4ej4ekp6eTHTt2KHLNy8uTcqA8Hn/88YB9Xn75ZdK/f38pp6SkJPKnP/2JVFVVkerqarJv3z5SVVVFqqr87aS2tpYkJSWR++67j2zdupVUVlaSt99+O4Dru+++S9LT03vwo7bT6SQVFRWks7OzR9ujGtje3k70gukp01Mj9TSanFwuF9m7dy9xOp2yY0aupz6fg3g87ZKmMj1lehotPSVEu6YG05nTUVOdTmeAzpwOnKjd3LzxxNwR5Pjxr0Jq6hdfiMT/rVtCvvwyNCc+N5cQgIgcR3xbt0bM6ehRQtLS/PE+/jgMp9Wr/bGSkojw/feq2p7H4yHZ2Z+QoqKxJD8fpLz8p6d0ndRwCtb26Jynq6vrtOEUrE60j7rd7og5tbS0qNJTthJEJ8iJ5Y1ms1l6Bklu0+V51JZf7ae2fLvVag2w6fJbq9UKQRCwefNmcBwn/VitVgAIsE0mU4BNl3l/9913AbHpUiS6JC8YD0IICgsLQQgJy0mN3Z2TnIfc5nle4qrEKZgNQMpXiZNSnShXpdqEqhNFKE7B6sRxHAoLC6UlXQBw7Nj7qKj4MUTRiQEDFmP27N2YOvV9pKVNkvYhhGDLli3SklXKIzFxAEaNuhszZ+bBah0Mp7McXu8xECJI55LjOLhcBCZTCiyWNFgsaTCbU2G19gtr0x+LJQ1Wa7+wttzP5Tq5NFiei5ItB72bprRPsOMQQqQ7b/R8T5s2Ddu2bZPOPcdx0pVkjuOC3o2Tb6c+9Dn2p59+GjNmzED//v2xePFiFBQU4Ntvv8XChQul/Tdt2oQBAwbgnHPOCcub8pw2bRq2b98esM/27dsD2s25556LqqoqTJw4EcOGDcOkSZOkH4vFgtLSUvA8jzVr1uD888/HGWecgaNHj4blGiyvYBpB7w5HE7HUU47jIAgCtm7dCkEQYqKn9Lxt2bJF4sr01Fg9VeLXvU7Bxjqltte9n2jR0+76yPQ0uJ4WFBRg3rx50uOcRuvppEmTMGHCBAwfPhwTJ07s1XoKaNdUtX01GnNUACH7rVFzVIpw40R3/aE6Qx9BCMYp2poKQJrzheIkCK2orPQ/0jVixK8wePDlipp67JgVt9/u5/rAA/73dsjrJIoitm7d6n/UweeD+e67/bVcsQKW888PWadgc9Rhw4CHHvLH+/3vAZ+vZ514nkdRURHEVauAyy4D53bDdO21QGurqjoByZg8+T0AJhw//iGamj4LO55TrkVFRdJjHbEYz3mex9atW6WVcmrHc3ktI2l7dO6yZcsW6RixGM8FQZDemRfJeB5uDA83nqsBuwiiE9Fawqg21oQJEzTF1OqrJ6YexCPfeHCVxySE4NChp7Fv320ABAwbdjPOPnsD0tKmR5xrWtoMzJiRD4tlEAjxwu2uhSiefNa++wvuYgE9MbX6JiQkBPx+11134cCBA1i5ciX27duHjz/+GO+9917APg0NDZg2bRpKS0sVj8txHBYuXIgPP/wQixcvBgCcffbZ8Hq9KCgokLYBwFdffRX2BX4UlOe9996L9evX4/nnn0dVVRX+/Oc/Y/369QH7Pvroo3j//ffx+OOP48CBA7DZbPj000/xxz/+EQAwYcIE8DyP1157DTU1Nfjggw/w5ptvqspDDYzoJ0xnjAHjaawv01Pj9PTbb7/FkiVLpH1joad79+5FTU1Nr9dTI4+rFIvpjDG+avwIIdi37xfwehuRkjIFEya8GGJf4PbbgbY2YM4c4Nlnw8R8/nmgqgrIzARkLwuONN+VK4GsLP+LWB99NISfxQJ88AEwfjxQWwv89KeA7LG6UEhPn4/Rox8GAFRV/RIeT4PmfI3y0+urFX2Jp6r9DM4jLF5//XWMGzcOSUlJmDVrFjZv3hxy/4KCAulldOPHj+8xCO3duxdXX301xo4dC47jsHbt2h7HePzxxwOuSHEch8zMTE35x7qoWVlZmhuhFl89MfUgHvnGgyuNyXEE1dW/Rm2tf8I1evQqTJnyd5hMCSH9QuWaljYdU6a8D44zgRAPXK4qiKIPHMchISGhx91BI6EnJvWNht/o0aPx+eef4+uvv8aMGTPw5ptv4plnngnYx+fzYd++fdLz9EpYsmQJBEGQJu0cx2HBggUAIP0L+CftV155paqc6Tk6//zz8c477+C1117DzJkzkZubK03GKZYtW4b//Oc/+OabbzB//nzMmzcPa9aswZgxYwAAM2fOxJo1a/Dcc89h+vTp+Oijj/BssJmORpwuF0H6ks4wntH3ZXpqvJ4uWbJE4mq0nm7cuBFz587FwoUL8fLLL/dqPTXyuEqxmM4Y46vG7+jRv8Ju/zc4zoqpUz+C2ZyiuO8bbwAbNwJJScD77wOyBQY9Y9bUnLzw8fLLQP/+mvNNSQH+8he/vWYNsHVrCL+BA4HPP/cnmZMDdPuiUyiMHfsY0tJmgedbUFl5W9ivJrK2a4yfXl+tUB0r5MMyBuMf//gHsVqt5O233yYVFRXkvvvuI6mpqeTQoUNB96+pqSEpKSnkvvvuIxUVFeTtt98mVquVfPbZZ9I+xcXF5MEHHySffPIJyczMJC+//HKP4zz22GPkzDPPJI2NjdJPU1NTRLnT5y2bm5sj8iOESM9z0Wef1MLn85Fvv/1Weu4pFr56YmrlqSduPM6Rfp45ZNeuK6X3f9TVvRa1XF0uFykv30Xs9jLS0VFCOjv3EJ73kPb2duk5PbWQP9cdKURR1BRTj6+emFq5do9ZWlpK+vfvr6ptnIo8XS4XqaioIC6Xq8f/NTc3R/2dILHUU0L6ms4wntHw7d4nTgWdiYVvPHh2jxsLPdXje6roKSHaNZXpjHEx9fiG8+vqqpLeI3fo0HMB/9eda3U1ISkp/veArF0bJuY33xDhkkv8O198MSEq+0S4fG++2X/IM84g5MTrlZT9/vY3/84cR8j//qcYszvPzk4bKShIJvn5IHV1r+jKN9p+enxZHw0PtXoa15Uga9aswc9//nPccccdmDp1KtauXYtRo0bhjTfeCLr/m2++idGjR2Pt2rWYOnUq7rjjDtx+++148cWTS77mzJmDF154AT/5yU9CLvm0WCzIzMyUfoYMGaKJQ6yvbE2fPl3zlTgtvnpi6kE88o0HV0FoQ2rqY2hp+RIcl4gzz/wXRo68O6xfJLmaTFYkJY0Dx1khim64XFVISor917GTk5Nj7qsnplbIY9Ll09Zgt1nC+GqNGSucLitB+oLOMJ7G+sZbZ2LlGw+e8rix0lO9vlpwuqwEYTpjjG8oP1H0wWa76cR75JZg1KgHFY8jCMCttwJOJ7BkCXDPPaFjzqqpgWnjRiAxEXj9dUDl6rNwPNeu9T9Zs28f8PjjYfxuvRW4807/Mzw33eR/lkYFUlNPPhJUU/MQurr2as432n56fbWiL/FUtZ/BeSjC6/WitLQUS5cuDdi+dOnSgE9XybF169Ye+y9btgzff/+99OkztaiursaIESMwbtw4/OQnP5E+bxQpYl3UoUOHam6EWnz1xNSDeOQba64eTyN27VoIl6sEFssAzJixEUOGXK3KN9JczeZEpKScAY5LACFueL01IMSYF7EFg/xFSLHy1RNTK7rHnDt3Lm6++WZNvlpjxgqny0WQ011n9MTsKzz1+J4KOhML33jpjDxuLPRUr69WnC4XQZjOGOMbyu/gwdVwOPzzyClT/g6OUz72Sy8BW7YA/foBf/sbECoNU2cn+j/2mP+Xhx8GJk2KSr4AMGjQycdiXnwROPE+ZWW/V14BZs8GWlqAq67yX8VRgREjfoVBgy6FKLphs/0UoujVlK8S+krb7Y081SD2t4NPwG63QxAEDBs2LGD7sGHDpDdud8fRo0eD7s/zPOx2O4YPH64q9nnnnYf3338fkydPxrFjx/DUU09h/vz52Lt3LzIyMoL6eDweeDwe6Xf6XWen0xnxHQN6wSbSCzc+nw+FhYVYuHCh6jshen31xpT/G6lvPPLVGlP+rxrwfCv27FkKp9MGQgZjypT/ITV1hupjqM3V5/OBEHLi7dNJSE6eDJerCoS44XJVIzl5MtReCyUn3rZ88njqQQhBZ2cn0tLSIp5YavXVG5P+GwnX042nKIoghMDn8wW8pR5AwJciIsWpoKfU53TWmWjE7As8I/GVayrtH0xnwvvSf2M1dsTrHNF/Y6mnQPQ0lemMcTH1+Cr5tbd/h8OH/e/hmTDhdZjNmT040d/Lyng88ogFAIeXXuIxYgRBSPqrVsHa2Ahx4kQIK1ci9M6R87z0UuCGG8z45BMTbr2VYPt2Hmazgp/ZDPzjH7DMmweurAzirbdC+PDDgJUpSjWdMOFNdHSci87OnThw4A8YOzbwvUVq89XKM9q+rI+Gh1o95QhV7RjjyJEjyMrKQlFREebNmydtf/rpp/HBBx+gsrKyh8/kyZNx22234eGHH5a2bdmyBRdeeCEaGxt7vNx07NixuP/++3H//feHzKWrqwsTJkzA7373O6xcuTLoPo8//jhWr17dY/vHH3+MlBTllw8xMPSEB6mpj8FiqYQoDkRn57MgRNuLecOBPvY1atQo2UvtvDCZ6sFxIghJhigOxynwjmSGUxBerxd1dXU4evRoj084Op1O3HjjjWhvb0d6enpEx2V6ytBbEVxTGRjCwyg9BZim9k10ol+/38BkOg6v9yK4XPcq7unzcfjd7xaitnYAZs8+ij/8YXvIJ1sG2Wy48Pe/B0cIilavxvEZMwzIH+josOLeey9CW1sSrr66CjffbAu5/6C9e3HBo4/CJAiouOkmVF97rao4Fss2pKb+CYRw6Op6CoJwZjTSZzhFoVZP47YSZPDgwTCbzT1WfTQ1NfVY7UGRmZkZdH+LxaK4gkMNUlNTcdZZZ6G6ulpxn4cffjjgAklHRwdGjRqFpUuXRjxg+Xw+bNy4EZdccknEV9R6ExjPnhBFHyorr0ZrayXM5gGYOfMbpKaeZVhuHo8Hhw8fRmpqasDdIFFMgctVBY5zITGxFYmJ48IeixACh8OBfv36xXxJdKzRV7iG4+lyuZCcnIxFixb1eMcSvdOoBUxPIwfjeWpASVO1gOnM6YV46SkQPU091ftftHA68Ny372ew248jKWk8zjvvX7BY+gXdz+fz4dZb61BbOwAZGQRffJGBzMzlygd2uWD57W/BEQLxllswR3bj2QgkJXG47jrg3/+ehAceGI/Zs0Pcm1++HGTAAODXv8bUjz/G5KuvBvnRjwCEq+lyVFcfRVPTe8jI+AtmziyFxRL+KzenIk6HtqsGeniq1dO4XQRJSEjArFmzsHHjRvzf//2ftH3jxo2Knz+bN28evv7664Btubm5mD17tq6G4PF4YLPZAj5r2R2JiYmKL1rVGttqtUa8NCg3NxdLly7VtBxJi6+emBSR8tQTNx7niCIcT0JE2Gy3orV1PUymZJx99n+RknKWoTxNJhM4joPb7UZqaioA/5Lczk4eqakT4HbvB8+3guOsSEwcFXKCSpf3chwX8bN9oiiio6MD6enpMfPVGxOInOvpxtPtdoPjOCQnJ/dYvq0Hp4KeAqenzkQzZl/hGakvx3HgeR4mk4npjEpfILZjR7zOERB7PQWir6lMZ4yJGa1zdOzYR7Db/wHAjKlTP0Ry8iBF35ISDp9/7n+fxxtvcBg1KkzcP/4RqK4GGT4cGy+9FBch8jYUCc9rrwV+8hPgH//gcMcdZjz99HpcdtnFyn4rVgDl5eDeeAOWn/0M2LYNOPPkyg6lmk6e/Co6Ogrhdtfg4MGVmDr1fU35auUZTV+A9dFoIG4XQQBg5cqVuPnmmzF79mzMmzcPb731Fg4fPoy77roLgP/KdkNDA95/399Q77rrLvz5z3/GypUr8Ytf/AJbt27Fu+++i08++UQ6ptfrRUVFhWQ3NDRg586dSEtLw8SJEwEADz74IC6//HKMHj0aTU1NeOqpp9DR0YFbbrklYg4WS+xOocViwYIFCzTF1OqrJ6YexCNfI7kSQrB//31oavoYHGfBmWd+gf7954MQYihPs9mMAQMGoKmpCQCkZbFWqxU8bwIwAl5vA7zeJni9QELCUMVjiaIIr9cLt9sd8aSSEAKr1QqPx6Pp+WwtvnpiauV6uvAkhMDpdKKpqQkDBgwIOmE3op8wnTEGjGf0fC0WC1JSUnD8+HHp5ZlMZ0IjHmNHPM7RqaanRh5XKRbTGWN85X4u10FUVa0AAIwd+wj695+n6Od0ArfdZoYocrj+ehHXXhum/xUX+99UCgBvvol5ixfHhOdrrwHffgtUVHDYsuUiXHllGL9XXgFsNmDTJuDKK/159wu+EuZkTv0wdeoHKCtbgGPHPkBGxo8wdOh1mvI9ecy+0XZ7I09V+xmcR0hcf/31aG5uxhNPPIHGxkZMnz4dOTk5GDNmDACgsbERhw8flvYfN24ccnJy8Jvf/Abr1q3DiBEj8Oqrr+Lqq09+UePIkSM455xzpN9ffPFFvPjii1i0aBE2bdoEAKivr8cNN9wAu92OIUOG4Pzzz8e2bdukuJEg1m+E1/KsqB5fPTH1IB75Gsn14MHVaGj4MwAOU6a8j4yMH+qKGYkffVcOvRDSHTwvgOdbAdhhtR6D2ZwWdD9CiLSc93Re0gz0Ha7heA4YMKDHu5YojDgv8fjyxOmkM9GO2Vd4RuLLcRyGDx+O2tpaHDp0SFMsCqYzpxdONT018rhKsZjOGONL/QgRUFl5MwShA+np8zB69B8UfQgBfvELoKqKw8CBbrzyihkh3//m8QC33w6IInDTTeCuuAJaz2ykPAcPBt54A7jmGuDllxPxk58As2aFcLBagX/9C5gzBzhwALj+euCrr8LG6d9/PsaM+T0OHXoKVVV3oX//C5CYmMXarkF+en21Qq3uxfUiCACsWLECK1asCPp/7733Xo9tixYtwo4dOxSPN3bsWIR71+s//vGPiHIMBS1vrdUTKycnB8uXL9e0HEmLr56YehCPfI3iWl//Gg4d8r+wbNKkP2PYsBt0x4zEj07ahw4dCp/PF/RNzXV1a9DY+BYAEyZNeg0DB/4gaMxYvx1ajy+Lqc/XarWGXLJthPbFUk9pvNNFZ4yI2Vd4RuqbkJCASZMmwev1nlZ9/lSL2dvyPdX01MjjKsViOmOML/U766xdaG//DmZzP0yd+iFMJuU/4557Dvj4Y8BiIVi58nsMGnRe6CBPPQXs3QsMHQq88krMeV59NXD11SI+/9yEn/2MYMcODgpPePkxeDDw5ZfA/PnAN9/A9NBDwA96zl27Y8yYR9HSsh4Ox/eorLwVZ5+9ATwvsLZrgJ9eX61QrXuEQRPa29sJANLW1haxr9frJdnZ2cTr9UbkJ4oicTqdRBTFiGNq9dUTUytPPXHjcY5C8Tx69GOSnw+Snw9SW7s6ajGjzVMURWKz3U7y80EKCpJIW9uWHn7xqKce33i03b7CkxBC2traCADS3t4esW93xENPCTl1+p8aMJ7GxdTjy3QmPFjbDY9o6ikh2jWV1cq4mHp8RVEkTU0FJD/fTPLzQRob/x5y/6++IoTjCAEI+fOf+fBcd+wgxGz2O3z2ma5c9fgeOyaSIUNEAhCyapVKp88/9+cNkB13362qpl1dlaSgIJnk54PU1a1lbdcgPz2+sdBT9l3MXgY9z1Rp9Y31c/p648bjHAVDa+smVFb63zOTlXUPxox5JKoxo8mT4zhMnvwXZGRcDlF0Y8+eK+B0Kn8tKRoxY+Ebj7bbV3ieLjgV+l8swHga58t4Gou+0nZPB/SVWsU6X0HoxIEDPwcgYMiQ6zFs2M2K+5aXAzfe6L8qsGIFcOedYuiD+3z+x2AEwf88iuwVA7HmOWQIsG6dAAB4/nlg+3YVTlddBTz2GABgxptvgjvx6oNQSEk5AxMmvAQAOHDgIXR17WVt1yA/vb5Ggl0E0QmPxwMAEAQBgiD0sHmeD7Dp28OBk28Sl2/3+XwBNjnxaA99jCEnJwculwuEEBBCpCU/clsUxQCb53nwPI+cnBy43e6A7TRfuS3n4fF4kJOTA4/HE5aTGrs7JzkPuU3zdblcipyC2W63W8pXiZNSnShXt9utiofcpqA8OjvLUV7+YxDiw+DB12DMmBfAcVyPOtF8vV6vIqdgdaK50lpE0vZoTNou6HZBIJgy5SP06zcbPN+M3bsvhdd7PKA2tK1F0vYA/0uKaUy1bY/acq6RtD3ahrxeb0Rtj3KiUNv25P0snC4Eq1P3fNW2PSVdUFsneny1bY/mTn+PJmKpp/R85OTkBPwOGKen8jpTrkxPw+up2jatVU+Vxjo9ehqu7SnxYHoaaKsZ66Kpp4QQyVeuC71RTwHtmhrJ+aK21jlquH5r1ByVQm1fpbbL5ZLaR7i+Gi1N3bfvXrjd+5GQMAoTJrwm5did0/HjIq64AujsBJYsIVi7VoWm/ulPwM6dQEYGyGuvSfsr6YyRc1Se55GQ8F9cf70AUQRuuYWgq0uFpj7yCIRrroGJ52G+9lqQ8vKwbS8z804MHHgpCPGgouIm5OR8GbGm6hnP9Wgq3SeStqdnjqpnPNc6Rw3X3sKN52rALoJEiHXr1mHatGmYM2cOAGDfvn0AAJvNBpvNBgDYvXs3qqv9d9HLyspQW1sLACguLkZdXZ10rGPHjgEACgsLYbfbAQB5eXloa2sD4P/8r8PhAABJTC655BJs3LgRPM9LYgoADocDubm5AIC2tjbk5eUBAOx2OwoLC2GxWHDOOeegpKQEAFBXV4fi4mIAQG1tLcrKygAA1dXV2L17t8Spuroay5cvl+xQnIqKitDY2Chxam5ulmwlTm63W+qUck70bcL5+fmKnAD/y3OLiookTmVlZVi+fDnq6+sVOSnVqb6+HsuXL0dZWZkip2B1ojlSTh0dNdi9ezkEoR3p6Rdg3Li38L//rQ9ap8LCQixfvlyyg3EKVqfa2lqMHDkSFosl4rZXUlKCc845BxaLpQcnh8OHs876D0RxGNzuAygvvwI5Of+W6gQg4rZH7QEDBsBisahue5STzWbDxIkTYbFYVLc9u90Oi8WCpKQkdHZ2RtT2AEg+kbS94uJiWCwWTJkyReKhtu3V1tbCYrFg8ODBEg81bS83NxculwvLly/Hxo0bQ3JSqhONpbbtUU7RuLIfTz2lFzAoYqGnNpsNFosFI0eOlHgwPQ2tp5G0aa16Wl1dDZvNhuXLl0t2ME6R6mm4Om3cuBFAZG2P2n1BTwH/i+wzMzNhsVhioqcOh0PSNvkfDb1BTwH9mtrQ0CDZas8XoG+OunXrVixfvhx2u131+aKctM5RaZxwnIK1640bN2L58uVwuVxh+2o0NPX48S9w/Ph7ADhMnfp37NhRHZRTXt5m/PjHPGprgeHDnXjrrTZYrWE0dedO4Mkn/f/56qtwpKRInDo7O5GUlASLxaKqr0ZjjmqxWDBgwAD88Y/HMHw4sG8fh9/+1h2+Tl4vPH/5C5qnTgXX3g6yfDm+/fDDkG2vubkZzc23w2odDKdzN/r3/yJiTdU6nuvVVAARj+d65qhax3M9c1TKo7m5OeLxnOpeWIR8WIZBEfR5y+bmZkIIITzPE57ne9g+ny/AFgRBes7J7XYHbCfE/wyU3KbPUNHtXV1dxOPxEFEUiSiK0rNScpvGoLbP5yOiKJLOzs4e22m+clvOw+fzEafT2YNHd07BbLfbTbKzs4nT6VTkJOfR3aZclTgFs71er5SvEielOlGu8hoo8ZPv09XVJT235nTaSXHxDJKfD7Jt22Ti8djD1snpdAbUQImffB+fz0ccDgcRRTGitkdz7+zsJKIoKnJqa9tNNm8eSPLzQXbv/jERBB/xeDwkOztban9q2x7Nq6Ojg4iiqLrtyXOX56um7QmCQERRJB0dHdL+atseIUTiSs+JmrYXrJ+pbXs8z/fIV03bo7bT6eyhC2o0gmqR0+lU3fZo7ka8EySWekrbYkdHh9RejNZTWmeHwxHQl5meBtfTcP00WnqqNNZFS0+V6kS5ejwepqcKtpqxLpp6SnPvrgu9SU8J0a6pwXTG6DlquH5rxBzV6XQG6EwkmurxeKTzE66v6tXUrq5DZPPmQSQ/H8RmW9lDZ+T2L38pEICQfv0IKStToakuFxHnzCEEIMKPfkRIt5op6YyRc1R5v/36a/+rPjhOJJs3h9dUj8dD/vv++0ScNIkQgIgzZxJyIodQdWpq+jfxv7uPIy0t+TEZz/VoKtWZrq6uiNqenjmq1vFczxyV9lG32x3xeN7c3MzeCRILkBNLl8xms/Tmb7ltsVgCbPm346kt3261WgNs+pkfq9UKQRCwceNGcBwn/dA37cptk8kUYFssFvA8j2+++SYgNr3zYDabA2w5D0IIcnNzQQgJy0mN3Z2TnIfc5k/cJaP7BuMUzAYg5avESalOlKtSbULVCQBE0YeqqhvQ1bULVuswnH32eiQkZISsE81XFMUenDo6gJ07Tdixw4KtW4GiIjNKSy3wev3t7ttvvwXP85ra3jfffAOe5xU59e9/FqZPzwbHJaC5ORs1Nb+TahFp2/OfGxF5eXngeV5126M2ISQgX7Vtj+d55OXlScvy1LY9yolCbdsL1s/Utj2z2dwjX7VtTxAE5Obm9tAFtXWixw/FL1ideAOWb8dSTzmOgyAIyMvLgyAIMdFTet6+/fZbiSvT0+B6Go5TsDrRfIPpabg6BRvroqWnoerUnQfT00BbzVgXTT3lOE7yletCb9RTQLumRnK+qK11jgqE7rdGzVEp1PZVOa/c3FwIghC2r+rRVJOJQ1XV7eD5FqSmnoN9++b10Blqv/EG8Je/mMBxwEcfATNnqtDUt98GV1IC9O8P01/+AnSrmZLOqGnXWueo8n77ox8Bt94KEMLhttsArzd8nXzp6eC/+goYMgTczp3AddeBC1OnIUN+jKFDbwNAUFl5C0TRoart6R3P9WiqvK6xmKPqGc+1zlHDtbdw47kqhLxEwqAIepVdy1V7ehWPXvE6XdG3eP6b7N17M/F/XSWVtLeXaD6e00nIM8/4r+afeOF1wM+AAYQ8uWwz2XfdH4jw1NOEvP46Ibm5hGh4a3M4yL9uc/Dgy32inoT0tbarjaceDYzmsVitTi/0FZ6E9B2ujGd4RFNP9RyP1erUweHDL52YUyaTrq5Kxf3y8wmxWPzzw2ef7fn/Qbk2NJycZL7xRvSTjxLa2ggZOdKf5j33hN43gOe2bYQkJ/sd77wz7PzY5+sgW7eOJ/n5IBUVP40ig+ijN7TdaCAWespWgugEUXu1KUqxOjo6NMXU6qsnph7EI189vomJn6Cp6QMAZpx55j+Rnj474piiCHz4IXDGGcDvfw84HP43ZY8ZA4wfD0yeDCwYsAcftP0If9ywAJP/+TRMf/yD//XfS5cC11/vd4oiz2HDbsC4cc8CAGprV8JiUfOqbn0xo+Ubj7bbV3jSuL3hmOHi9Sad0QrG0zhfxtNY9KW225uOqxSrr9TK6Hw7O3ehpuZhAMDEiS8jOXlyUL+aGv/HXHgeuOkm4KGHVCbym9/454vnnQfceaeuXKPp292vf3/g3Xf9//faa4Ds9Sahcd55wCefABwHvPUW8NxzIXc3m9MwevRfAJhw7NiHaGr6VFO+kaA3td3eyFMN2EUQnTBqCaNSrM2bN2uKqdVXT0w9iEe+Wn2PHv0rkpL+CQCYPPkNZGQsjzhmXp6AuXOBm28G6uqAUaP8F0SOHgUOHgQOfN+KfUvuQkH7DPwI/4XAmfEP00/wLm7H/xKvhGi2AP/6l1/4KyujynP06IcwfPidAAhSUl6Cw1Gsmp/WmNHwjUfb7Ss8adzecMxw8XqLzugB42mcL+NpLPpS2+1Nx1WK1VdqZWS+guBCRcWNIMSLjIwrMHz4nUH9OjqAK64AWlqAOXOAt9/2/80fFuvXA//8J2AyAW++6f9XY65KiGZbWLr05HWa229Xda/PjyuvBF55xW8//DDw8cch45aVeTBy5CoAQFXVXXC76zXlqxa9qe32Rp6qEHKdCIMi2PLt8OgLPI8fzyb5+SaSnw9SXb0qYv/KSkKuuOLkoy79+vmXM554r5p/Cd8HHxAyZMjJna69lpB9+8iePYSceaZ/03xsIa5BI/y/pKUR8tlnhHi9hDQ2ElJeTsimTYR8/jkhb71FyJ/+REheXkR5CoKP7Nx5KcnPB9m8OYN0dtoi5tqb0BfaLiGnzvJtpqfhwXiefugrXBnP8GCPw8QWpzLPqqq7SX4+yJYtmcTjaQq6jyAQcvnl/inf8OGE1NcrHy+Aq9NJyPjxfsff/MYgBtFHRwchY8f60/7FL4Lvo1jTlSv9jgkJ/rlwCAiCl3z//RySnw9SVvYDIopClBhED6dy240m2OMwvQD0hTaxitXS0qIpplZfPTH1IB75Rurb1laIioqfABDh9V6E0aNXRxALWLMGOOssgq++AsxmghUrgP37gVWrgORkAFVVwMUX+5eHHD8OTJ0KFBQA//wnxIkTMWJEC7ZvF3HTTUAR5uNc7IBn3iL/B+KvuQZISACGDwemTwcWL/avmbzzTn+Aiy4CufNOoKtLVb4mkwVnnPEReH4SeL4Zu3cvVXWV/CTfvtF2+wpPGrc3HDNcvFNdZ6IBxtM4X8bTWPSlttubjqsUq6/Uyqh8m5tz0NDwZwDAGWf8DQkJQ4L6/fGPwNdfA4mJQHY2kJWlMoFnnvE/Q5OVBawOPWc9lXSmXz/gb3/zr3R5+23g3/+O4KAvvOCf/3q9wI9/DJz4DGywuIAZU6d+CJMpBW1t36K+/lVN+apBb2q7vZGnGrCLIDrh9XoBAIIgQBCEHjbP8wG2vDDUlm/3+XwBNjnxXJPP5wPP8yguLobb7QYhBIQQ+Hw+AAiwRVEMsGkOxcXF8Hg8AdtpvnJbzsPr9aKkpARerzcsJzV2d05yHnKb5ut2uxU5BbM9Ho+UrxInpTpRrh6PJyyPtrZS7NlzBUTRjQEDlsPl+rX/zdQhONE6HTniw+WXAw88APh8HObMOY7SUh9eeYXH0KEnOJWUAHPnAnl5IElJwDPPQCgtBT9/vtTuiouLkZAg4K23BMycSWBrGYYfJW6EsHKltCaScByQkQEyeTLIvHnA5ZdDuPxyEI4D9/bbIOeeC7GkJGzbI4TAZEqF0/kIkpMnw+Opw65dS+HztYRte/QYxcXFEARBddujNuVK91Xb9mgbovmobXu0ThRq2568n4XThWAa0T3fUH1IbvM8j5KSkh66oFYj6PFD8QtWJ5p/NBFLPSWESJrK83xM9FReZ8qV6WnPOlGo6adyHjRf2i9C8etep2BjnZq25/F4AvRJbdtT4sH0NNCW5xsLPSWESL5yXeiNegpo19RIzhe1tc5Rw/Vbo+aoFGr7KrXdbjdKSkrA83zYvhqJpnq9TaisvA0AkJV1LwYMuCRov/3gAwHP+l/VhrfeEjB7tkpNrawEOfFuDPLKK/AlJYWsk5LOqGnXavptsDqF6rcXXODDgw/6a3PHHQT19T3rRDkEcOI4+P76V2DePKCtDeTSS4GjRxU1NSlpIsaNex4AUFOzCu3tO4O2Pb3juR5Nldc1FnNUPeO51jlquPYWbjxXA3YRJEKsW7cO06ZNw5w5cwAA+/btAwDYbDbYTlxd3L17N6qrqwEAZWVlqK2tBQAUFxejrq5OOtaxY8cAAIWFhbDb7QCAvLw8tLW1AfB/Rstx4uG3nJwcCIKAH/zgB9i4cSN4nofb7UZOTg4AwOFwSJ8vamtrQ15eHgDAbrejsLAQVqsVZ511FkpO/MFbV1eH4mL/ux1qa2tRVlYGAKiursbu3bslTvv378eyZctgs9nCcioqKkJjY6PEqbm5WbKVOLndbvA8j5ycnABOVqsV8+fPR/6JtyAF4wQAjY2NKCoqkjiVlZVh2bJlqK+vV+SkVKf6+nosW7YMZWVlipzsdjtcrlqUlV0CQWhH//4XoqHhdgDmsJz8/+/EWWcJyMkBEhMJ7rlnL7ZvH4Lhw9skTse/+w7i0qVAezvc556LHe+/Dzz8MGobGiROBw8exLBhw2C1WlFTY8Pq1dVISQG+2WTF7/AQYLdjR24uDthsgN2OrX/7Gw5/8gnw1VfY8uCDsP/jH0BWFriqKnDz5wPPPYe8jRvD1omQdEye/CWs1hFwuWzYs+dytLcfC9n2qJ2SkgKr1aq67dE62Ww2jB07FlarVXXbs9vt0ie4uk6sdlHb9gCgs7MTFGrbXnFxMaxWKyZOnIg9e/ZE1PZqa2thtVqRnp6Oo0ePKnICemqE2+3GsmXLsHHjxpCclDSCxlLipFQn+afatCKeeup2u8FxHDweDziOi4me2mw2WK1WDBs2DAcPHgzJqa/oafc65cvefBeun3avU2FhIZYtW4b29vaQ/TRYnWw2G5YtW4b9+/dH1PZKSkpw1llnwWq1RtT2eN7/2WIAEbU9avcFPQWA+vp6ZGRkwGq1xkRPHQ4HrFYrPB6PNLHuLXoK6NfUhoYGyVZ7vgB9c9StW7di2bJlsNvtqs8X5aR1jkrjhOMUrF1v3LgRy5Ytg9vtDttX1Wrqrl27UFl5O3y+JpjNEzF+/HMBddqzZw8mTpyInTutuOMOf94PPQSMHbtFnaYSAvO994Lz+SD88IfgL788bLvu6uqSPluqpq/K6ySfo0YynlutVqSkpEg8unNaubIVs2YBLS0cfvpTAaIYWCcAwTlt3gx89RWE8ePBHToE/OhHaD50SFFTDx+eiUGDLgMhHuzadS1E0RPV8Vyvpkp1jaDt6Zmjah3P9cxRKY/m5uaIx3Oqe2ER8mEZBkXQ543sdjshhBCe5wnP8z1sn88XYAuCID3n5Ha7A7YT4n8GSm6LJz7r5PV6Cc/z5OjRo8TtdhNRFIkoitKzUnKbxqA2PX5jYyPxeDwB22m+clvOw+v1kmPHjknxlTgFs91uN8nOziZOp1ORk5yH3BYEQeKqxCmY7fF4pHyVOCnViXL1eDyKnFyuRrJt20SSnw+yfft04vW2kK6uLum5NSVOLpeXPPYYISaTSABCpkwhpKzsZL4+n8+f76FDRBw1yv/84qxZRGhtDVonr9dLjhw5QgRBkHi8847fzWIRyfbtynXyeDyksbGRCMePE+Gqq6R3jQgLFxLh4EHFOnk8HpKdnU08Hg9xOHaTwsIBJD8fZNeuy4jb3RWyTj6fT8pXbdujttfr9ed7wk9N26Nxjhw5EnDO1LQ9eo5oTdW2vWD9TG3b43m+R75K/LprBM/z5NixYz10QY1GUC1ynngJjRK/YHVqbW2N+jtBYqmnoigSnudJQ0MD4Xk+JnoqrzM9Zl/W02B1UqOnSnXqoach+HWvU7CxLlzbI0Smp7K2oabtiaIocfV4PKrbHo3fF/SUHrv7WBeu7enRU1EUiSAIPXShN+kpIdo1NZjOGD1HDddvjZijOp3OAJ2JRFPdbjc5duyYFD8Yp0g19fDhP5P8fJBNmxJJe3tZD64ej4eUlR0jI0b455CXXSYSnlevqd/ffz8hABGTk4l44ICqdq2kM2ratZp+G6xOofot5VRZSUhKiv88PP988DmqYtvbt4+Igwf7z8VllxGfy6XI1e1uJJs3Dyb5+SD79/82quO5Hk2lOtPVFXreHc05qtbxXM8clfZRt9sd8Xhut9vZO0FiAe7EYwdmsxlms7mHbbFYAmyT7C3M1JZvt1qtATY9vtVqBSEEe/fuhdlsBsdx0hVamge1TSZTgG2xWCCKIioqKqRj0+00X7kt58FxHMrLy8FxXFhOauzunOQ85LYoihJXJU7BbJPJJOWrxEmpTpQrPU7PmjlRXn4ZXK79SEwcgxkzNsBqHRhwBycYpyNHOCxbZsXq1YAocrjtNuD774GZM0/mCwCW5mbg4ovB1dX53/+xfj1MAwYErRPHcbDZbBBFUeJx++3AddcBPM/hhhsApzN4nUwmEyoqKiAOGADTZ58Bf/0rkJoKU2EhTDNnAv/8p2KdaOy0tLNw9tn/gcmUhJaW/6KmZsWJR2aC1wmAlK/atkdtjuP8+YpiRG1PFEXpqnYkbY/GpFDb9oL1M7Vtz2w298g3VB+S24QQlJeX99AFtRpBjx+KX7A60eWO0UQs9ZTjOBBCUFlZCUJITPRUXmeaS1/V01B1olDTT+U8AvQ0wjYdbKxT0/YkPT2hT2rbnhKPcG2Poi/oKfXtPtaF4qdXTzmOgyiKPXShN+opzVMep7sdqf4YMUcN12+NmqNSqO2r1DabzSgvLwchJGxfVaOpbncVamsfBACMH/8npKfP7MHV4zHhhhsSceQIh2nTgI8/5mA2q6xTZyem/+1v/jo88gi48eNVtWsguM6oaddq+m2wOoXqt5TTGWcAr7zir9Hvfw/s3t1zjqrY9iZPBvfVV0BSErj//heWlSv9twGDcE1MzMSUKe8AAOrqXkRHx+aojud6NJXuE0nb0zNH1TOea52jhmtv4cZzVQh5iYRBEexrBuFxOvEUBDcpK7uI5OeDfPfdYNLVtU/6v1A8168nJCPj5EdbPvxQIUBLCyFnn+3fcezY0K/6DoHWVkLGjPEfZtw4Ql57jZDOThWO1dWEzJ178gs0K1cScuKqKkUwnsePf03y883SlfLTBadT2w0FPTzZ12FiC8bz9ENf4cp4hgf7OkxscarwFAQ3KSmZSfLzQXbuXBr0aySiSMhNN/mnZoMGEbJ/f2Qx+F/8wr/yYcoUQk6sAOjtEEVC6ELmyZP989yIavr554RwnP8Azz0XctfKyjtIfj5IUdEo4vW2RoeADpwqbddoxEJP2UoQnTDq6r1SrIaGBk0xtfrqiakH8chXyZcQATbbT9HWlgezOQ1nnfU/pKRMDnksQoDnnweWLweam4FzzgF27ABuuqlnzCNVVSCXXQbs3g1kZgIbN4Z91bdSrgMGAJ9+CgwZAtTWAvfcA4weDTzyCNDUFMJ34kTgu+/831IH/J+u+dGPgBPP9Slh8OAf4Ywz6JXyF1BX91JE+apBb2q7fYUnjdsbjhku3qmiM0aC8TTOl/E0Fn2p7fam4yrF6iu1ila+tbWPoLNzJyyWDEyZ8h44ruefZS+8AHz0kf8rgp9+KmLChAgCFhbC/PbbAABh3Tr/VwM15hoJjG4L9CsxWVn+Dynef3+ECV51lX+eCwAPPQTx448V406Y8DKSkibA46lDdfXdmvINht7UdnsjTzVgF0F0ItZFPXDggOZGqMVXT0w9iEe+wXwJIaiuvgfHj38GjrPizDP/jfT02SGP09UF3HCD/6VVogj8/OdAUREwaVKQmC4Xkm+8EdzWrcDAgUBurv+ChA6e550HHDwIrFsHTJgAtLQATz3lvxjyy18ClZUKvlar//Np//yn/xu9Gzb4DxbmBUPDh9+K8eP9bxw/cOBBHD36fkT56uFqhJ8e9BWeNG5vOGa4eKeCzhgNxtM4X8bTWPSlttubjqsUq6/UKhr5trbmoa7uRQDAGWe8g8TE4T32/89/gFWr/PY99+zH4sURxDx+HLjxRgDAoR/8AGTBAs25RopYtIVBg4APPvBfEHnnHeCLL1Q+AkFx//3AffcBALjbbsPxL74IGtdiScPUqR8CMKOp6SMcO/YPTfl2R29qu72RpypoXKXS58GWb4fH6cCztvZxkp8Pkp/PkWPHPg26j5xnTc3Jp1osFkJef92/bC8ofD5C/u///DunphKybVvU8+d5Qj77jJDzzjv5pAvHEXLHHf7/U8SOHYTQF7T270/I//4Xsp6iKJLq6pUnzpWZ2O3/jTqXWOJ0aLtqcKos32Z6Gh6MZ+whCF5y9OjHpKxsCdmxYyGpqrqXHDnyN9LRUUYEQf+y8lOJq5FgPMODPQ4TW8Sbp9fbTLZsySL5+SCVlXcG3ae8nJB+/fzTsLvuijCAIBCybJn/MZjJk8nXn3xy2tb0oYf852jgQJG88876yHjy/Ml5+MCBhNhsirvW1DxK8vNBNm8eQFyuw1HIXBvi3XZjBfY4TC9ArK9sHTp0SPOVOC2+emLqQTzy7e7b0PAGDh58HAAwadKfMXTodSH98/I4zJ7tf6pl6FAgLw/41a/8V6l7gBDg7ruBf/8bJCEBYna2f9WFxlyVYDYDV18NbN0KFBYCl1/uD/3OO8CKFYS+D6onzjkHKCkBLrgAaG8HLrsMppdfhpIDx3GYMOEFDBv2UwAC9u69Bu3tWyPOVw/XaPnpQV/hSeP2hmOGixdvnYkFGM+ecDr3Yf/+B1FVtQL796/EgQN/wN69r8HprAHPd6Cubg22b58Im+1GtLXlo729EA0Nr2LfvttQWnoONm9Ow/ffnwOb7Vbs2fMEHI5dICQ2XPuazvSVttubjqsUq6/USk++Bw8exL59d8LrbUBy8mRMnLimx37NzcAVVwAOB7BoEbB2bYQxn3vOv5o3KQn8J59ASE7WlGtv0JknngBmzQJaWzmsXXsuTnwpVx3MZuDDD0HOOw9obQW59FLg2LGgu44Z80f06zcHPN+GyspbQYjYZ9pub+SpBuwiiE74fD4AgCAIEAShh83zfIAtLwy15dt9Pl+ATU780enz+SAIAurr6+HxeEAIASFEii+3RVEMsOnx6+vr4fV6A7bTfOW2nIfP50NDQ4MUPxQnNXZ3TnIecpvm6/F4FDkFs71er5SvEielOlGuXq8Xx459iurqXwMARo9+BFlZKxTr5PX68NVX47F8uRktLcDs2QQlJQTnn+9TrtOzzwJ/+QsIx2Hf44+DX7hQkVOwOvl8PtTX10MURVVtTxB4XHCBiK++Aj780AuOI3jrLQ6PPy4ot72hQ4Fvv4V4222AKML80EM459VXQVyuoJw4zoRJk97BgAHLIIou7NlzGTo6dku50HzVtj1qy7lG0vZEUURdXV3AOVPT9ignCrVtT97PwulCsDpRX3rMUH1IbguCgIaGhh66oFYj6PHVtj2auxEDWiz1lBACQRBQV1cHQRBioqfyOtNj9gU9VcNDbsvbg5o2LQhuNDfnYv/+3+HQod+ioeFttLZugyC4enDq6jqIfft+geLiM1Ff/xKOHHkD9fUvo67uGRw/fi+Kiyfgu+8G4sCBB+DxHIbVOhRjx67G5Ml/Q1bWfRgwYAnM5gEgxIfOzp04duzvaG5+DKWlM7FlyzCUl1+N+vo/o719Z4CGKdWpe3sL1/bouesLeto931joKSFE4irXhd6op5RT93OkRlMjOV/U1jpHpTrD87zq80VtPXNU+TmKRFM9Hg8aGhqk+ME4hdLUurq3Ybd/Do6zYOrUjwAkBXByuwVcdx1QUwOMG0fw2WcAENhvQ9Zp82aQP/7RT+7Pf4ZvypQArmrnCTzPo66urofOGDFHleuMUr9VqhPH+fDRRwSpqQTl5UPwhz+YItPUlBT4Pv8crqwscAcPglx+Ofj29h6cCDFh8uS/w2RKQVtbHurqXtY1nuvRVLpPJG1PzxxVz3iudY4arr2F0gj5eQoFdhEkQqxbtw7Tpk3DnDlzAACVlZUA/J9Wop862r17N6qrqwEAZWVlqK2tBQAUFxejrq5OOtaxE1cbCwsLYbfbAQB5eXloO/EyytzcXDgcDgBATk4OeJ7H3LlzkZubC57n4Xa7kZOTAwBwOBzIzc0FALS1tSEvLw8AYLfbUVhYCIvFgrFjx6K4uBgAUFdXJ9m1tbUoKysDAFRXV2P37t0Sp+rqasyfPx8VFRVhORUVFaGxsVHi1NzcLNlKnNxuN3iel/hRThaLBWeddZbEIxgnAGhsbERRUZHEaceOHZg/fz7q6uoUOSnVqa6uDvPnz8f3378Fm+1nAAiAK5CQcKdinVwu4PLLW/DXv54FUeSwZMlhbNjgxvDhPTnROu196CHgD3/w5/OrX2HKww+jra1NkVOwOtXW1iIlJQUWiyXitjdq1DY89ZRf4J94wow1azqV215iIr6+4gp4X3oJxGzG6Px8mC+5BO6DB4O2vY6OLhw79iv063ceeL4VpaUXw+2uw/HjxyEIAiwWi+q2RzlVVFQgIyMDFotFdduz2+2wWCzo6upCZ2dnRG0PgOQTSdsrLi6GxWLBsGHDJB5q215tba30GTLKIxinYHVyuVyYP38+cnNzQ3JS0ggaS23bo5zknxjUinjqqdvtBgBJp2KhpzabDRaLBSkpKRKP011Pd+zYEbKfdq9Tfn6+VFNlTv9FZ+du7N//NAoKzsOWLQOxZ88yHDnyEkTxXzhw4C7s2jUPmzf3w7ZtU/Ddd8tw6NCfsHPnnSgpOQONje8AEGA2X4AxYx5DauodSEi4Gv36zQFgASAiJWUqkpMfwaBBuRg79lE0NJwJjrsbM2fmwWT6D0aO/A7Tp2cDuBUpKYtgMqWA5+2w27/A/v33oKzsHBQVZWLv3uvx7bfPoKOjvQenjRs3Rtz2APQZPaV2QkICLBZLTPTU4XDAYrGgublZ+qO8t+gpoF9TGxoaJFvt+QL0zVGLioowf/58HD9+XPX5opy0zlFpnHCcgrXr3NxczJ8/Hy6XK2xf7d4GSku/AvAqAMBiuRPp6bN7cLrttjbk5QEpKQLWrq3F4MH+mg0bNgwWiyX0OGGzAT/5CThRhOe664Dbb1epqT3r1NnZia6uLlgsFlV9NRpzVIvFAkEQcPz48Yja3ujRbrz5pv+mxMsvm/Hhh77INBVA+XPPAYMGgSspQcdllwGC0KPt7d/vllbu1NT8Hm63TdN4rldTgZNjZSzmqFrHcz1zVMqjubk5orZXXV0t6V5YhHxYhkER9Hmj48ePE0II4Xme8CdesiC3fT5fgC0IgvSck9vtDthOiP8ZKLktnnihhNfrJT6fj1RVVRGXy0VEUSSiKErPSsltGoPaNIeqqiopJt1O85Xbch4ej4dUV1cTj8cTklMw2+12k+zsbOJ0OhU5yXnIbZqvy+VS5BTMdrvdUr5KnJTq5PF4yN69/yYFBWkkPx9kz56ridfrDsrP6/WSQ4cEMnu2/1FCk0kga9bwxONR5kQIIeKGDUS0WPzPaf72t1K+tL6h+Mnr5PF4yL59+yQO/8/eeYdHVW1t/DclnSKE3hGkdwQFFRQEwd4LVuwdCxbQ+6Eo9oJesVcs14aCYOgEAgQSSggkBAgQAoEU0stk2jnr++NkJglp09Ig7/Pkyc7krL3We9ba6+zZZxdXY09ExGw2y4EDB+Sll5QS21VZtKj62FNVVaxhYWJp1kyzvUsXsUZFVRl7VmumbN3aT8LDkaioflJUlOq019XYc5TLcnU19hRFEbvdLvv27XPa5mrsOXQ61iK6GnsO2/bv3y+WkmPoXI09x9/79+936qqK36l+stlskpiYWCEvuJIjHLnIZDK5HHsOW7Ozs32+J0hd5lNVVcVms8m+ffvEZrPVST4t62dHjJzO+TQxMVHM5spzaFV+Kioqcra9sjwKC1MkNXWhxMffIZs2dSjZf6j0Z/PmThIXd4ds2zZddu68RDZuDK1wjeNn585xkpW1oRwnh70mU64UFOwTVVXcyqdWq0mysjZIUtJrsmvXpbJhQ1A5nVu3niPHjs0Xk+mkk5ODq8VicTn2HPfrTMinp9pbF/lUVVUn17J5oTHlUxHPc2pleaa2+6g19YNqo49qMpnK5Rl3cmpxcbEkJiaKzWarsa2WLdtsZtm2bbSEhyM7dlwkVqu5AqdPP1Wce7f99VfV7bbStmqxiDppktZH69tXlJJYqiqn1hTXVqtV9u3bVyHP1GYftbp2W5OfLBaLXHfdAQGR4GBVduzwIKdu2CBqQIDWsX/sMbHbbBViT1VViY29oqR/O1j27dvj9vPcm5zqyDNFRUUux563fVRPn+fe9FEdbdRsNrsVe3a7XU6ePNm0J0hdQK/XbqHBYMBgMFQoG43GcmXH9WVly37u5+dXrqwr2VDCz88P0EbHHG84dDqd8/OyZb1eX65sNBoREXJzc522OD532Fu2XJaHXq8nJycHvV5fIydXyqdyKsujbNlhr8OuyjhVVjYYDE57q+JUlZ8slkNkZNyLqhZy1lmXMGDAz/j5BVTKIzraj9Gj9WzfDqGhwiuvbOHxx1X8/avmxK5d6G68EZ3dDtOmoXvrLae9Op2uSk6V+Umv15OXl4eIuB17BoOB3Nxc5sxReeABUFUd06ZBZGTVsafT6eDSS4l45x2kb190KSn4XXwx/P57pbHn5xfK0KErCQjogsm0j4SEa8jNTUNEXI69srHq4OpO7IkI+fn5FXnUEHuA8zp3Ys/RzvLy8mrMC5X5ySHr0F1dGypbBsjJyamQF1zNEY76XY09h+1S5YYynqMu86mjnJ+fD1An+bSsn8vaezrmU8ezw/EcqYnHqTGtqlaKiiJJSnqJHTvOZdu2LuzbdxcZGT9hs6Wh1wfTuvXl9Or1IaNGxTNmTAp9+36HyCMMHryKCy44yZgxKQwe/C89e75B27a3EBp6FYMHL2fYsPW0bj2uHCeHvX5+ITRr1hedTu9WPtXp/Gjdehw9erzM0KGrufDCHIYN20iXLk9hMLSguDiRgwefYtu2HiQmPobJlFBpvNUUe45rHDnxdM6np9pbF/lUp9M5uZb1R2PMpw69ZfWcWnY3/9RGH7WmflBt9VEdqOk5cWoMGI1GcnJyyvF2Ja6Tkl6gsDAaaEafPt/j5xdQjtOGDfDkk5odr78O111XdbutlNN776FbvRqCgtD9+Sf6Fi3K2Vgdp6pyUX5+foU8U5t91OrarSt+uuOOvVx6qYrJpOPmm/3IyXEzp44di+6nn7TN/BYswPDRRxViT6fT0a/fN/j5tcVk2kNm5tsePc+9yanuxp63fVRvn+ee9FFdeTZUlSPK9g2rRbVDJE2oEk2nGdSMxsTTbD4uW7b0kPBwZNu24WKzVe3XL78U8fPTBoqHDBHZv98FnkeOiHTsqAldcolIyRvk+obNJnLNNaWHwMTGVn2t058nT4pMnVp63Mx//qPtRF4JCgvjZePGVhIejsTGThVFafixINK4Ytcb1MXu27VdV5OvTh+oqiJpaX/LqlWjnTPyyv5s2zZcDh16UbKz14miNIwc6gpstgJJSflMoqIGnDIj5RJZtmyO863c6YozIXZFGk4+9aa+Jl/VDo4d+8jZ7tPT/6jw/8OHRUJDtS7VrbdWc6pgVVi/XkSv1yr49tty/zrTfJqaapUePbRbMXVqDSchVoX33ivt4/7+e6WXnDz5j9Onqak/eGe8GzjT/Fmb+bRpJoiXcGwcU1e69u3b55FOT2W90ekN6tJemy2X3bunYDYfwWDoxsCByzAaW1RynXagy4MPauUbb4TNm6FnzxoUZGfD1KmQmgqDB8Pff0NAgMf2eiN3qqzRCP/7H1x4oXYIzJQpcORIDRW0bAlLl8LMmdrfr72m3Ywy674dCAkZwODB/6LXB5GdvZx9+6bj7gkKjSl2z7Q22hjqrElffba/ukJD56mqNtLSFrJt2xASEq7Dzy8aVS3Ez68N7drdTr9+Cxk7No1zz93J2We/SatWl6DXB9Sbve7KGY3N6Nz5YUaNimPo0HW0aXMdoCcvL5yQkFeJi5tIbu7GWrfVG9kzoS/gC1lPUVu6mvKM7+GuzszMfzh48CkAevSYR3b2oHKyBQXaSTBZWdopJ998U/FUwWp1ZmTAbbeBqsJdd8E993jIzA2dtSTrC3+GhsJff0FgICxfDq+84oHeZ57ROvwAd96pdfZPQZs2V9G58zMA7Nt3L5mZS92yszHErrdy3sp6Cld1NQ2CNDIUFxfXuaw3Or1BXdirKMXExV1NUdEe/Pw6EBz8Cf7+7Stcd/IkTJoECxZof7/+Ovz+OzRrVoMCsxmuvRYSEqBLFwgL0wYRPLTXF3KnygYFwT//wKBB2jjNZZdByf5GVcNggHffhe+/B39/bWDnggsgObnCpS1bjqF//18BAxkZP3Po0HNuT/1tTLF7JrXR0wH13f7qCg2Rp91eyLFj84mK6sW+fXdjMsVjMLTAYrmWoUO3MnZsOgMG/ESHDndWmpfr2l5v5XQ6Ha1aXcKgQX9x/vmH6djxSUT8yM/fxK5d49i9eyoFBTtq1VZvZE/nvoAvZc9knCm+clVnfv529u69DRA6dnyALl2eLyerqtp37Lg46NABliyB4GA3dKoq3HGH1nnr3x8+/bTiCIoXaKx5Zvhw+Oorrfz667B4sZt6dTqYP18bnbJYtN8HDlSQ6dnzLfz8rgIU9u69mdzcCLfsbMix6ys5b2VrFR7OUjnj0TR9u2Y0dJ6KYpPdu6+W8HAkIqKFFBTsqvS6mBiR7t21WXHNm4ssWVL+/1XytNtFbryxdK3Jnj21QcNnSEkR6dZNM/f880VK9lxyokqekZEi7dtrgm3bimzcWGn9qak/OKcOJie/XUssfIOGHru+QkOZvt2UT2vG6cTTYsmQw4f/41wqp21s2kGSk98Sk+nkacOzJlitVlmy5GvZu/cBWb/e6LwXe/ZcL4WF8fVtns9wOsVudWgo+dSb+pp85TsUFx+RTZvaS3g4smvXZZUuB37pJa3rFBAgsnWrB0pee02rIChIJC6u0kvOZJ8++WRp3z0hwYNKi4pERo3SKjn7bJH09AqXnPpdIj8/xnMSLuBM9qeraFoOU0eo6+k9cXFxHk9H8kTWG53eoLbtFREOHHiIrKx/0OkCGDx4KUFBgyrI/v47jB2rTXDo3RuiorQB4RohAk89BX/+qc2UWLxYm2pRxzzdke3cGVasgLPOgq1b4fbbwaXqx4yBbdu0ofeTJ2HCBG0+5yk6MzNH0LPn2wAcPvwCqanfe2Vvbcl5gzOtjTaGOmvS11DaX22iofAsLk7iwIHH2bq1O8nJr2G35xAUdA59+nzJeecl0a3bCxiNLauptW7trW2dACJt6N17AaNH76N9+zsAHZmZf7Ft2yASEu6iuPiwT3U2xjzTEGK3tnG6LIc5U3xVk05tmfXl2GzphIQMYeDA39Hr/crJ/vorzJunXf/VV3DeeW7qXL8e5szRyp9+CgMHek+uJp21LOtrf773Howbpy05uu46KNnv2HW9wcHa8u+ePeHwYbjqKjCZysnt3buPvn1/pmXLcShKPrt3T8FkOlijbQ01dn0p562sp2haDlNHcNxoRVEqLdvt9nJlVS3dD8FRLvu5zWYrV5aSZQOOsohUKAPlyqqqlivb7XbnNY5y2c8VRSlX9pSTK2Vfc6qqXBOnpKSXSEv7FtAzYMCvNGs2thwnm01l9my45RYoLtaWiERG2unbt3I/OeDk8dZb8MkniE6HLFyI7YILKuVUE4/qODn0e+Inhw9O9U3fvir//AMBAcLixTBjBlitpb5xcKjgpw4dYONG5MYbtQ1T7r8fmTEDu9ns5KGqKl26PEuXLs8CsH///WRkLHEp9sra607sOfR6EnsOuBt7IlJjG6rKT2Xtra4N+bI9OeqvjlNV7cnXqI98qqpqnedTVVVPq3xak5/y83eyd+80oqLO4cSJBahqMc2anUv//r8zenQCbdveg07n7+ThgLucvMmn3jz3qsqnrvjJwcNo7Eb//j9y7rm7CQ29FhDS038kOrovBw48QnHxsXI8yuY4dzk1pnzqyrOuNvLpqXmhMeZTR/2n6vGmP1ebfdS6aKtlyw74mpPdbiYu7npMpr34+3diwIAlGI0tynGKjlaZPl3T8+yzKtOm1dxnKJdnUlOd+4BIyT4g1fmpLFd3OFWWZ2q7j1pVu3XFTw4ODh5+fvDbb0LnzsK+fXDXXYLFUnnsVZlTQ0Nh+XKkdWuIjobbb0exWsvlGfBn8OB/CAkZgs2Wzu7dkygqOlprObWsX+uyj1qXOcIXz/Oa4PIgyIgRI9z6GTlyJMePH3e1+kaDBQsWMGDAAEaNGgVAQkKC87ejvHv3bhITEwGIiYkhKSkJgOjoaI4dO+asKz09HYCIiAgySzZhWLduHbm5uQCsWrWKgoICAMLCwrDZbPTr14+VK1dit9sxm82EhYUBUFBQwKpVqwDtiLJ169YBkJmZSUREBAaDgdatWxMVFQXAsWPHiI6OBiApKYmYmBgAEhMT2b17t5PTgQMHGDRoEPHx8TVyioyMJDU11ckpKyvLWa6Kk9lsxm63ExYWVo6TwWCge/furF27tkpOAKmpqURGRjo57dixg0GDBnH06NEqOe3cOZujR98EoEWLV2jb9lpiYmI4evQogwYNYsOGXUyZYuZN7RIefriAf/+FPXsq91N4eLjTp2FhYdi+/hrd7NkAqO+/j/nqq6v00/r16xk0aBA5OTlVcqrMT4cPH0ZRFAwGg9uxFxUVRevWrTEYDJXG3kUXwdNP70SnExYsgAceOOD0E1B17IWEkPP55xy8804AdB9/TN5FF0FuLhkZGc4jRY3Gx9DrpwAKCQm3sn37D5X6ycEpPj4ePz8/DAaDy7GXmZmJwWAgNTXVGW+uxh5AYZlNXl2NvejoaAwGA0FBQcTGxlbLqTI/GQwGCgsLOXHiRJWcTo29VatWYTKZGDRoECtXrqyWU1U5wqHL1dhzcHIcn+YN6jOfms1mRISkpCREpE7yaUJCAgaDAUVROHz4cLWcGks+rcpPycnJdO58kq1bx7Nz50gyMv4HKAQHj2fo0HXk57+FTjcenc5QbT51J6Y9zaeJiYnEx8czaNAgDhw44NN8Wp2fVq9eDVCOk6p24+jR+xkxYhvNmk1AxM6JE58THd2HjRtvw2o9WS6fuhN70LjyKcDRo0exWCwYDIY6yacFBQUYDAaSkpKw2WyNKp+C9znV0W+Pjo52+X6Bd33UzZs3M2jQIDIyMly+Xw5OnvZRHXpq4lRZXK9cuZJBgwZhMpkqcBIR9uy5h7y8cAyGZnTo8B07dhx1ctqxYwehoYO47jodZrOOyy+H6dMP1BjXsbGxBAUFYTAYiN6yBcvNN0NaGkU9epD68svV+snTnFpQUEBqaioGg8GltuqLPqrjyPGMjAy3Yq+6PmpwcAHPPLMZf39YskTHQw8dccaeg1ONObVvX45+9BGqnx8sXkzu9Okk7N1b7nluNLZE5G2Mxu6YzUfYvv0SjhyJqxB7vsipZf1aF31Ux/ejHTt2VPucqMxPnvZRHTyysrLcfp47eNSIytbIVAadTiczZ86UV155pcafOXPmSEBAgBw6dMjV6hsdHOuNTp48KSIidrtd7CXnMJUt22y2cmVFUZzrnMwlx6Q6PhfR1kCVLasl52RZrVax2WyyY8cOKS4uFlVVRVVV51qpsmWHDkfZYcOOHTucOh2fO+wtWy7Lw2KxyM6dO8VisVTLqbKy2WyWxYsXi8lkqpJTWR5lyw57i4uLq+RUWdlsNjvtrYzTiRMLneuuDx9+rRwPi8Uif/0VL336KAIigYEiP/xgr5RfWT8VFRU5163Z/vlHVINBBMT+7LM1+slhr8O/1fEr6yeLxSLbt293cnM19kREzGaz7Nixw1lfVbH3wQeq84Swn35SxWKxyOLFi8VisdQce3/+KWpwsCbcp49Y9+xx2qsoilgsJomNvULCw5GNG1tJYWFclTwsFks5e12JPUVRxG63y/bt2522uRp7Dp0On7oae2XbmePIy6o4VeanU+2tit+pfrLZbLJz584KecGVHOHIRSaTyeXYc9ianZ3t8z1B6jKfqqoqNptNtm/fLjabrU7yaVk/O2KkMefTysqqapfU1N9k27ZzyxwHq5f4+NskJ2ebW/m0pnbqq3xa1bPOl/m0Mj85uFoslio5nTy5VnbuvNB5LyMimkli4mzZtm29M5+6GnsijSufOmRretb5Mp+qquqULZsXGlM+FfE8p1aWZ2q7j1pTu62NPqrJZCqXZ9zJqcXFxbJz506x2WwVOCUlvVrSVg2SmRlWIQZyc80yaFChgEj//qrk5rrWTyjbbu2vvKL1rYKDxRYbW2s51Wq1luu31UUftbp2W5OfauqjfvWVdtt0OlXCwsrbXhXXU2NP+eUX59G5ynvvVfo8LyxMlM2bO0p4OLJjx1ix2wt9mlMdeaaoZOO+uuijOtqZ2Wyu9jlRmZ887aM62qjZbHb7eX7y5EmX8qnRtaESDc899xzt2rVz6dr333/fnaobLRyj92VH8cuWjUZjhbJjmo5er69wjZ+fX5VlRVEIDg7Gz88PXcnuz45rdDqds6zX6511O8oOWYeustdUZbuj7Bh9dvxdGafKylIyBcpoNDp1VcevKq5VcaqsbDQaK9jr+J2VtYIDB+4FoHPnGfTo8ZLzPhqNRpYtU7j77r4UFOjp0kXbxmPkyMp9WZntum3bMN56q7aRxp13YnjnHecu3VX5yWGvXq932lkVv1N9ExISUunnVfmjbDm4ZPvx6jg9/TQcOwYffgjTp+to107v5FCWR6Wxd8MN6Hr1cu6obbzwQtq+/z4MG4Zer8ffP4iBA38nNvZS8vO3EBt7GSNGRBIY2K1STpXZW1NZURRCQkLcjj0HJwdcjb2y7aymvFCZn061typ+lbWVoKCgSvPCqZxO9ZNj6mFlecGVHOFr1GU+Be2ehISEuBbT+CafOvzc2PPpqWWwkZ7+I8eOvUtxcWLJZwF07Hgf3brNJCio/Fni7vipqrIv8ylUfNb5Mp+6yuNUTm3aTCA09BKys1eSlPQShYU7SUl5A51uAceOPU/XrjMwGqt/FjTWfOqwt6ZnnS/zqQOV5YVTOTX0fFq2fldzamV5prb7qDW129rqo7rCqapyUFAQOp2unK8zMn7iyBFtj44+fRYQGjrV+T/tHuh5/HE/4uICaNVK+OcfXclhgW602/BwDHPnah9++inGIUN8xkmzsbyfHG3P1bZa1l5P+qjVtVtX80xVz/P779e2r/vySx3TpsH27Xp69SrlURnXCrbfdhukpMDzz6N/7jmUbt0IGTy4XOwZjb0ZMmQVu3ZdRH5+JPHxNzFo0JIKnDzNqY484+7z3Js+qqOfWVVeqI0+atm25W5OdTWvurwcJikpibZt27p6OXv37qV79+4uX99YUdsPsFN19evXzyOdnsp6o9Mb+Nre/Pwo4uNvQMROu3bT6N37A2djFIG334arrzZQUGDgggtg+3btvHZXEXLiBIZrrtE2TJo8WdsYVF9z86oPv7gj+957cNNN2jYfN95o4MiR5q4rGjZMe+KMHYsuL49uDz6I4b//1W44YDAEM3jwMoKDB2C1Hic2djJWa8WzeRtT7J5pbbQx1FmTvobc/nyF2uRpt+dx9OjbREX15MCBBykuTsRobEX37i8zduxR+vZdUGEApLZwurY/nU5HaOgURo7czsCBfxIc3B+RPI4ceYmtW3uRkvJfVNVSq/aeLn2B2pb1FLWl63TJM7Uh6ykq05mTE87+/fcD0LXr83Tq9FA5GRF4+WX46Sc9BgP88YeO3r3d1NmqFYY779SOxZ0+He6+2yd8qtV5muXTjz+G88+H3Fxto9SiIg/0zpwJjzwCIhjuuot+2dkV5Jo1G8TgwWHo9cFkZy9n3757EFHLXdNQYrc25byV9RQ+HwTJy8srt7lVTcjPz3fp+k8//ZSePXsSGBjIyJEj2bhxY7XXb9iwgZEjRxIYGMjZZ5/N559/Xu7/8fHx3HDDDfTo0QOdTsf8+fN9orcq2MtsrlTbsNvtbNu2zSOdnsp6o9Mb+NLeoqIEdu++HFU10arVZfTr9x06nRb6JhNMmwYvvqg9pK67LoNVq+y0b++G0rQ0xrz6KrrMTG3k5M8/ocwop7v21qacu7J6PSxcCBddBPn5Ol57bQxltmGoGe3bw7p1qHffrT24n34a7r9fO3cd8PNrzZAhKwkI6Epx8X727LkCu72wXBWNKXbPtDbaGOqsSV9Dbn++Qm3wtFhSOXToBbZs6cbhwy9itaYRENCFXr0+4Pzzj9K16xxiY5MbBU9vZOvSnzqdjrZtb2D48Bj8/ecQENATmy2dgwefJCqqD6mp36Kq1dvRGHj6Qm9jbKONqd6qdJ0pviqrs6gogbi46xCx0bbtTZx99pvlrheBF16AN97Q/n722SOMH+/mPbJYyLvqKkhL006B+eQTn3CpVudpmE8DAmDRIq1rumeP1h0VcVOvTqeNplx5JZjN2KdOxb59e4XLWrYcw8CBi9DpjGRk/MLBgzPKfS9uCLFb23LeynoKV3W5PAgyfPhw58ZsrmDMmDEcPXq02mt+++03nnrqKV566SViYmK46KKLmDp1apVySUlJXH755Vx00UXExMQwe/ZsnnzySRYtWuS8xmQycfbZZ/PWW2/RoUMHn+itDmWnedY2dDodrVq18kinp7Le6PQGvrLXbD7G7t2Tsduzad58NAMH/oler51CcPQoXHgh/PorGI2wYIHKO+/kExDghs6sLIxXXklIejrSqxf8+y80d322RH34xV3ZwEBtaVC/fkJWVhCXXWakZC8k1xAQgHz9NSdnz0b0evj2W5g4EUo2vgoM7MKQISsxGltTUBBNfPyNqKrVY3u9lfMGZ1obbQx11qSvobc/X8CXPE2mA+zf/wBbt/bg2LF3UJR8goMH0K/f95x33iG6dn0ao7FZo+LpjWx98NTrjbRrdwfnnhvPOed8hr9/JyyWo+zffx/btg0kPf3XCm8dvbW3sfcF6krWU9SWrsbQ/hqjrxw6rdZ09uy5HEXJo0WLsfTr94PzJRtoX7CffhrefVf7+6OPVB54wO62vfo336Tltm1IcDD8/rt2dGst43TNp506ae8qjUat///BBx7oLRGWUaMw5udjmDxZm0J+CkJDp9Cv30JAx/Hjn5Cc/Jrzf/Udu3Uh562sp3BVl8t7gogI//nPf5xrSWuC1Wqt8ZoPPviA++67j/vv16aQzZ8/n5UrV/LZZ5/x5ptvVrj+888/p1u3bs7ZHf3792f79u2899573HDDDQCMGjXKuSv2iy++6BO91aGup/f0dmf+nA9kvdHpDXxhr82Wxe7dl2GxpBAU1JfBg//FaGwGQEQE3HgjnDwJbdtqCXHcOD3ghs7sbJg0Cd3u3ZjPOgvD0qX4uTWFpH784ols69awbJmdsWOtHDwYwoQJ2hH1rtI1GI20nTdPO7D9lltg82YYNQr++QeGDiUkpD9DhoSxa9cEcnJWsm/fdPr3/xGdTt+oYvdMa6ONoc6a9DWG9uctfMEzPz+ao0ffJjPzb0B7m9WixQV06/YCoaFXlOv4e6PTU9jtsHu3AVXtjaqCu6Hk8T0qLqb3yZOwf7/2TDj1R6/Xpk8PHep23a7Y2rnzw3TocDcnTnxKcvKbFBcfICHhNo4efZOePV8nNPTKch3CxphnzpQ22pjqrUrXmeKr3r17oygm9uy5CrP5CEFBvRk0aAkGQ5DzOlWFxx4Dx4T1L76ABx90s58JsG4d+pJ9QHSffQYDBviISfU4nfszF16o7Xf3xBPw/PMwfLiBCRPc1BsSgm71apgyBd3WrdrLvRUrYMyYcpe1b38bNlsWBw8+wZEjc/DzC6Vz58cabV+gLmU9hc+Xw4wbN479+/cTExPj0s+YMWMICgqqsj6r1cqOHTuYPHlyuc8nT55c7uiqstiyZUuF6y+77DK2b99e7uzk6uCJ3upQ19N7IiMjPZ6O5ImsNzq9gbf2Wix57NlzJSZTAv7+nRk6dBX+/m0A7YE0caI2AOLYtmLcODd15uRoe3/ExCDt2hH52mu4tcDTRzzrMha6dYPXXoukSxftvPVLL4XMilt4VK9z4kSIioJzztGm4owdC3/9BUCLFueVmzp46NCziEijit0zrY02hjpr0tdY2p838FSnzWZj06YPiIm5mJ07zyMz8y9ACA29iuHDNzFixCbatLmqwgCINzpB+/JQE+x27cXbe+9ps5JDQ7XViKNGwVlnCZdeCq++CmvXlq77LiubkKD1V0tO8nPfXhH46Sfk7LO1PHbllXDXXfDUUzB3rjZd/Zdf4KeftEXo33/vxh2oiXt5Ww2GILp2fZbzz0+iR4+5GAwtKCraTVzc1cTEjCUnZ12Vsp7qrCucSW20MdVbla4zxVebN28kPn4aBQXbMBpDGTw4zNnHBG1//Ace0PqbOp02AfbBBz2wNy1NW7MtQvqVV2KfNq2WWFXE6d6feewxbVsVVYVbbhH++mu7+/aGhLB17lzkwgshP1/7XlBynGtZdOnyOD16vAJAYuITpKf/r1H1BRpjG3UFLs8EWb9+vae2VIrMzEwURaH9Ka+S27dvT1paWqUyaWlplV5vt9vJzMykY8eOtaIXwGKxYLGUbjyWn58PaLv7ujoA44DjenflVFWlQ4cOKIri1v4s3sh6o9NTnt7oVVWV9u1DSUi4hfz8rRiNrRg48F8Mho4UFdl4+mk9X32ljRDedJPKV18pBAdrG3+6rDM3F8Pll6PfsQNp0wbzsmUUnDhR5zzrOhZsNhvt25sICzNz2WWBxMXpuPRSYeVKO61bu6Hz7LNh0yYMt9+Ofs0auOEGlDlzUGfPpkWLifTu/TWJifeQkjIfg6ENnTrNbDSxeya1UcepLJ6gIeRTaHztr+zv2tZptWZw4MA92O1ryMsDnc5I27a30bnzMwQHD6zRFnd0WiwQGalj9Woda9YYiI29mpAQCA0VWrcu/7t5c4iL07Fpk478/PJTXlu0EHQ6lbw8A2vXagMgAEajMHy40KsX7N+vY+9eUCx2hrGL4k69+Pbv5gwd6sY9SkzE8MQT6NetQwfY27RB3727NmWuVSuk5DetW6Nbswb9ypUwfTrqpk0oH36orTGkNvJMIJ07v0i7dg9w/Pj7pKYuID9/K7GxE2nZ8hK6d59LSMioRpVnzpQ26k0+Bd/l1CZfuaYzMPA7srOXoNMF0L//Ivz8ejjrstvhgQcM/PyzHr1e+PZbhWnTxL1+JoCiYJg2DX16OuqAAZjeeouzmvozVcITnh9/DLt3G4mJ0TFnzmAmTFAICXHP3na9emFdsgS/G29EHx6OTJ2K8vffyCWXlLu2U6dZWCwZpKZ+yr59d9G3bzM6dBjY4PsC3sh5I1sX+VQn7rLxEU6cOEHnzp2JjIxkTJmpQ/PmzePHH39k3759FWT69OnD9OnTmTVrlvOzzZs3c+GFF5KamlphD5AePXrw1FNP8dRTT3mlF+CVV17h1VdfrfD5L7/84vISoSbUBVSCgj7C338DIv4UFc1FUfqRmxvA22+PIiEhFJ1OuPPOvVx33UHcXaJmNJkY88ortD5wAEvz5mx+7TUKevSoFSYNGSkpzXj55QvIzQ2kV69cXn11M82auTfKq1MUBn73Hb2WLQPg+NixxMyYgRIQgL//PwQFfQuAyfQ4NtulPufQBO9gMpmYNm0aeXl5tGjRwi3ZpnzasGEw7CE4+AP0+hxE/LFap2CxXIWI6yfEVQcRSElpTkxMW3btakd8fCgWi8vvZJwIDrYxcGAmgwZlMWhQJj165KHTwbFjzdm7N9T5k5VVOiu1C8d4kC+5n6/pSBq5tOQdwwvI4xdy/iXZ1erT22z0/usv+vz5JwabDcXfn/0338zBa65BqtoMW1Xp88cf9Pv1V3Qi5PbqRfTzz1Ps5tJJT6DTZRMQ8Cf+/qvQ6bT8bLOdi9l8N6ratdb1N8F1eJNPoSmn1iX8/ZcRFPQ1ACbTTGy2C53/s9t1zJ8/gk2buqDXqzzzzA4uvPCER3r6/vor/X79FXtgIBvefZfCrk1ttjaQkRHEzJnjyc8P4JJLjvLkkzFufzcA0FssjH7rLdrHxKD4+xM1axYnhw8/5SqVoKAP8fffiEgARUWvoij9fMKjCaVwNZ/W2yCI1WolODiYP/74g+uuu875+YwZM9i1axcbNmyoIDNu3DiGDx/ORx995Pzs77//5uabb8ZkMpU7dxgqHwTxRC9UPsretWtX0tLSaF3Ta/BTYLPZWL16NZMmTapgc3Ww2+1ER0czevTocmcy16asNzo95empXhHh8OFnSUv7BDDQv/9ftG49lZ074cYbjaSk6GjZUli4UGHq1IphX6POggIMV1yBfutWpHVr7CtXwtChdc7TGzlvZE/lGR8PkyYZyczUcd55Kv/+q1BVrqlOp+7bbzE88QQ6mw0ZNgz7okXQtStHjszm+PH30FbtvcJ5581s8LF7JrXR7OxsOnTo4FGnvSHkU2jc7a82dIoopKS8zdGjc9E6a/0pLn6e8867xWueJ0/C2rU61qzRs3atjuPHy/cy27cXLr1UuOQSG4qygfPPv4j8fD+ysiArC7KzdWRlactXzj4bxo9XGTKk/N4fyvbtJC1bRq+uXTEoClitiMVK3kkrxw7ZCNq/iz77/0VXsmmoBASgK4nDFDqz+OrXue/XyrnqNm7E8Oij6PbvB0C9dBI77vsv7y/uRUJCIZMnhzBhgo6xY6XSPQt1q1djuOsudFlZSKtWKD/8gHXixDrJM2bzEY4dm0dGxo+ACvjTs+dHdOp0X63pLIumNlozvMmn4Luc2uSr6pGV9Q/79t0ECF27vka3bi84/2e1wh13GFi8WI+fn/DLLwrXXFO+r+mqvbrwcAxTpqATwf7dd9huuaWpP1MDvIndNWtUrrzSH1XV8dFHCo884sKazMrsNZsx3Hor+rAwxN8f5bffkCuuKCejqlYSEm4gN3cl0JzBg9fRooXre0Y1tdGa4XI+lXrE6NGj5ZFHHin3Wf/+/eXFF1+s9Prnn39e+vfvX+6zhx9+WM4///xKr+/evbt8+OGHXuutDHl5eQJITk6OyzIOWK1WWbx4sVitVrfkFEWRlJQUURTFbZ2eynqj01OenuhVVVUOHHhSwsOR8HDkxInvRUTk559FAgNFQKRvX5F9+zzUmZ8vcsEFWkWtWons3On8V13y9FbOG9nKeO7aJdK6tXZbLrxQpKDAQ50RESJt2mgVtW8vEhkpqqpKQsL0Ep8aJS3tf27ZWx+xeya10ZycHAEkLy/PbdlTUR/5VKTxtz9f6rRY0mTXrkudOTQh4R6xWvM95mkyKfLbbxnywguqjBihNe2yP4GBIpMni7z7rkhsrIiqeshTVUXWrROZMKGikqp+LrlE5PffRcxmUb77QbKbdxUBseAnm5/5vXzd0dEid97plFXbt5dNj/9PRo9SK63a319k3DiRV18V2bhRxGIpY+uRIyKjRmkX6nRif+klWbxoUZ3lmaKifbJr1+RyPrbbi2pVp0hTG3UFvsynIp7n1CZfVY28vGjZsCFIwsORHTtuF7vd7vyf2Sxy1VWlOWDZMi/sTU3V+kEgct99rstVgab+jGt6X345V0DEaNRyt6tyFey1WESuu07zn5+fyF9/VZCz2wtlx44xEh6ObN7cUUymwy7b2tRGa4ar+bReB0F+/fVX8fPzk2+++Ub27t0rTz31lISEhMiRI0dEROTFF1+UO++803n94cOHJTg4WJ5++mnZu3evfPPNN+Ln5yd//vmn8xqLxSIxMTESExMjHTt2lJkzZ0pMTIwkJia6rNcVOB4wnjywvHFsY0Jd8VRVRfbte8jZsUtJ+UzsdpHnnivtmF5xhUhurocKCgpELrpIq+iss0S2by/37zPdnzt2aLcFRC6+WKTItT51RSQliQwZUtqL+P57URSb7N17R4lv9ZKa+qPXPFzBme5TV+BNDvRlXU2+8h7Z2eGyeXMHCQ9HNmwIltTU792uQ1VF4uNFPvxQZOpUkeDgigMEQ4aIzJwpsmqViMlUeT0u81RV7ZvGmDGlCoxGbYDjyitFrr9e5NZbRe66S+T++0UefVTk5ZdF9u6tWJWpWHb0vlkExI5eDs9cIPLBByKDBpUjsGP0QzKgY7bzo4AAkXvvFfn6a5G77xbp0qUi5xYtRMp0UbRvS4884rzAGhwsygUXaPZ9/rnIli1Vjyb7AKqqyJEj8yQ8XC/h4Uh09GApKtpfa/pEmtqoK/BlPvWmviZfVQ6TKUk2bWov4eFIbOwUURRbmf+JXAlkPZQAAQAASURBVHZZ6eDuypVeGGa3azkMtPzjcYeqFE0+dQ2qKnLLLdqt79BB5Phxr4wprcxgEPn110ouyZLo6EESHo5s3dpbLJY0F6tu8mdNcDX/1esgiIjIggULpHv37uLv7y8jRoyQDRs2OP939913y/jx48tdv379ehk+fLj4+/tLjx495LPPPiv3/6SkJEE7v6/cz6n1VKfXFThucFZWlltyIp471mazydq1a8Vms9V8sY9kvdHpTQC7qldV7ZKQcE/Jl2SdpKR8LYsXb5DLLlOcndBZs7Tnikc6CwtFxo/XKmrZUnsreArqgqev5LyRrY5nVJRI8+babbr00opfcFzWWVAgcu21pd8gZs4Ua3GRrFs31enjEye+dsne+ojdM6mNZmVl+XwQpC7zqcjp0/481amqdklKmuv8QhwVNUAKC+NdtvXkSZH//U9k+nSRzp0rDgC0amWWadMUWbhQ5MQJH/G027VZHMOGlZ9W8vjjYjt0yGO/mIvM8k/baRVIWPQBsqHLbTIuYIvz4w4dRObOFUlPL3+PVFUkMVHkiy9Ebr5ZpG3b0j5whZeBCxeK2qJF1bNVevXSBnJeeUXk779FDh0SKXmT5ov4O3lytfNLXUREc0lP/90luYbaF/CVnDeyDSWfinieU5t8Vdm1ORIVNaBk0HCoFBdnO3UWFpZOQgsOFlm71kt758zRKgsJEUlIcF2uWvub+jOu6s3NtcngwZoLxow5ZSafu/babKWzCPV6kYULK5H9TSIje0h4OLJt2zCx2Wp+W9vURmuGq/m03gdBGivqazlMenq6x9ORPJH1Rqe3U7Zq0qsoNomPv63ky7FB0tJ+lrg4Rc4+2yYgEhRU6eCr6zqLikqfbi1aiGzdWqlcbfP0pZw3sjXx3LxZe26DyJQp2gtPj3QqivbG1jH9fMoUST+wX/bte6TMbJ8FLlRT97F7JrXR02U5zOnS/tzVWXH5y3Sx2wurlTObtZUnL74oMnKkiE5X/nt7QIDIpEnaEpedOxVJTfWSp6KIHD4s8s8/IvPmidx2m0jPnqUKmzXTpvylplbJ0517dPhgmixs+7QISBSj5GE+lbMonfkxfLjWj3U1t9ntInfcUTpJZcmSU7gWFcnajz4S2/ffizz/vPY6uWPHqgdGmjcXGTtW1Icekvy33xZl40ZtqaabPB32ms0nZOfOcc4YOHDgCVGUynv9Dbkv4Es5b2QbSj4Vqb/lMKebrxTFIjExE0qWLXSS4uJjTp25uYqMG1eaiiIivLR39erSpPrTT67L1YCm/ox7eg8eLJ3d/PDDXtprt2vTBUuWQMo331SQLSzcL5s2tZPwcGTnznFit1cxTbIETW20ZriaT93fjr0J5aDX6+tUV7t27epU1hud3qAmvapqZe/eaWRmLkKnMzJgwK+sX38D994LhYV6unWDJUtg2DAPdRYXwzXXwLp10KwZrFgB553nHamadNaBnLey1WHsWAgLgylTtNt1003w55/g7++mTr0eXnsNBg2Ce+5Bt2IF7a64krZ//omhSyApKR+SmPgYqmqha9enq6mm7mP3TGujjaHOmvSdLu3PHZ05OeEkJEzDak1Drw+mT59P6dDh7gpyOp2erKx2/O9/sGoVrF8PJlP5awYPhsmTtZ+LLoIg50EsesB9nrrNmxn66acY3nwT4uOhsLDiRa1awZNPaj9lNn301i89e7Wn5b4P+OSr18hXQuhphP8YwWiEESPggguocGpAdToNBvjuO+3IzF9/hRtvhL//Buc+eX5+FHTvjlx+OZTd+O3kSdi9W/uJjdV+x8dDQQFERqKLjKR5WUVnnw1Dhmg/Q4dqv88+W8ullfB02BsQ0JGhQ9eSlPQyx469zfHj/yU/P5qBA38nMLCbyzxrE2dSG21M9Val63TylYiwf/+D5Oauw2BoxuDB/xIY2AWAgIB2TJ0KW7ZAixZan6fMgZPu25uaCrffrg133n+/VnZFrhZxpvVnHHp79YJfftHy9Oefw7nnwn1V7CFdo70GA3z1FQQEwGefaRVZLPDII2Vk2zFkyEp27RpPXl4Ee/feysCBi9Drff8V/XRro9XpdOm6WrbjtIcn5xd7o2vlypUe6fRU1hud3qA6vYpiJj7+hpIBEH/69fuLN9+8gZtv1vrLgwdns2WLza0BkHI6Cwrg2mthzRoICXH96eYB6sMvtenTceNg6VIIDNR+33or2Gwe6rzlFti0CenaFRIT4fzz6bV1ON26aUdkHzr0DMnJb1YpXh+xe6a10cZQZ036Tqf2V5NOq9XMkSNziY29FKs1jeDgAYwcua3SAZBdu7QTWAYMgKee0gY4TSZo1w7uuAMWLoQTJ7Tv5++9pw2ClA6AuMlTRBthmTAB4yWX0GPVKvRRUVpC9/fXvtjffju89RYsWwbJyfDKK+UGQNzWWcU9at7cxuMvhDB7Njz/PDzzjDbWcuGFFQdAXNFpNMKPP2qDwjYbXH89rFxZgzFt28LEifD00/D997Bzp3Yv4uLgl19QZs7k5KhRSKdO2vWHD8PixTB3LtxwA5xzjvbNbMwYeOgh+PRT2LQJ8vIq2KvXG+nV6y0GDVqK0diKgoIotm8fTlbWcrd41hbOpDbamOqtStfp5Kvk5NdIT/8BMDBgwO80bz4MgIwMG6NH57FlC5x1Fqxd63oXsVJ7FQWmTYOMDG1U+eOPXZOrZZxp/ZmyeqdO1dIpwKOPQnS0a3KVQq+HBQtgxozSCj/6qJxs8+bDGDx4KXp9IFlZ/7B///2IuHZCjTs43dpodTpdQdMgiI+gKAqKolQo2+32cmVVLQ1qR7ns5zabrVxZSk4wttls6PV6zj33XFRVRbSlTE5Hly2rqlqubLfbMRgMjBw50lmf43OHvWXLZXkAjBo1qtzfVXFypXwqp7I8ypYNBoOTa1lOimIiLu5qsrKWodcH0qHDYq6//ko+/FC7pzNnKqxZo/Uhq+JUlZ8ARg0ZguGmm7TXniEh2JctQy15ulXnJweq41SZn0SEUaNGodPpnDaU9U1VfgIYMWIEBoPB7dgTEUaOHInBYHA59hw8HPLVcRo3zsbixeDvL/z9N9x5JyiKzmmvq7GnKAqMHIk9KgrrxRejKy5Gd+dddH8vhx5d/gNAUtJskpLmlLPdwclgMDB8+HB0Jd9cXI09BycHKmtPVZVPbWeuxp6iKBgMBkaMGOG0t7o2VLas1+sZNWpUhbzgao5w1O9q7DlsN5Q9m9THqIt8KiLo9XqGDx+OXq+vk3xa1s8O1FU+HTq0G/Hxl3PkyBxApV27uxk5MpqgoH7lfJ6WZufhh2HkSGHjRj3+/sLEiSpvvaWwaxekpCh8/73CnXdCu3Y15NNRoxCRqnkoCqxejXrRRXDJJRAejvj5cWTSJOw//4wtNhYpKEBiYrB99x3y/PPI5ZdjCwys1E+e5tOqnnW+yqciNn76SbjuOu0YzWuuEVavrjzeqow9gwF7375w223Im2/Cv/8ix46hpqejrF4NH36Ies89yIgR2lvHoiLYuhW+/BIee0ybpnPWWRj79GHc4sUY8vPLcTrrrCmMGLGd5s3PxW7PZs+eyzl8+CVsNnO95VMHanrW+TKfioiTa9m80JjzaVk9p5bdzT+10Uetqd3WVh/Vgco4paUtLMmVcM45C2jR4lJEhJMnhcsuM3DgQEtCQ4VVq2yce67rce3gWtYGdc4cbRA4JATl119R/P0rcK0qz7jqp7JcXe0n6HQ6hg8fXqHfVpt91Orara/6qJX5Saer2EedPVvL1VarNracmurF81xRUN9/XxtZB3jqKQwfflgup4aEjKF//18BA+npP3Do0EynvadyKuvXuuijggvPcx/3UWuKt5qe566gaRDETSxYsIABAwY4k9jevXsBSEhIICEhAYDdu3eTmJgIQExMDElJSQBER0dz7NgxZ13p6ekAREREkJmZCcC6devIzc0FYNWqVRQUFAAQFhaG1WqlRYsWrFixArvdjtlsJiwsDICCggJWrVoFQG5uLuvWrQMgMzOTiIgI9Ho9FouFrVu3AnDs2DGiS4Y2k5KSiImJASAxMZHdu3c7Oe3fv5/WrVsTFxdXI6fIyEhSU1OdnLKyspzlqjiZzWbsdjthYWHlOOn1evz8/FizZk0ZTv+yZ8+V5OSsRiQQVQ3jwgsnExmpo0UL+OKLDK67Lop27VqTnJxcJaeq/JR84ACtH3gA/cqVqEFB8O+/ROr15ThV5qfw8HCnT6vjVJmfwsPDad26NdnZ2URERACQmppKZGRktX46dOgQKSkp6PV6t2Nv69atWCwW9Hq9y7Hn4AS4FHuXXQbffpuH0ajy229w++1W4uIS0Ov1Lseeg1NcWhqHP/kE/u//ADB89jk9bl+Jv+kOAJKT57J58z2cOHGinJ/0ej0xMTHk5+e7FXsAhWWm4FfWnqryk16vJzc3l9jYWLdiLykpCb1eT2JiIsePHwcqtqeq/FRUVETr1q1ZsWKFW7Hn4OTQ5WrsOTj5Ypp1feZTs1n7crdlyxZUVa2TfJqQoLWBlJQUDh06VC0nX+bTvLwNHDx4CXl54ej1wXTtuoBjx27DYAhxcrLZ4PXX8znnHOGLL0BVdUyYkElioo7PPz/MhAk7GToUDh1yMZ8mJ9O6dWu2b99ekZMI8e++izJ6NEyejH7zZiQgAB57jHVffknsY48hN91EWFIS5pLOTW3m08TEROLi4mjdujX79++vlXxqNhfw668wenQqFouOa66BDz7Q9LsTe45Yd8TSseJiooKD4amnOPzSS2z//HMoLOTwsmUkvfkmzJpF/rhx2Nq3B0B35AhBn3+Ovm9fUl56iaQSftHR0Zw8aWD48E3AtQAcPfoGmzaNIS1tb73kU4Dk5GSOHDnizI+1nU8LCgrQ6/Vs2bIFq9XaqPIpeJ9THfcrOjra5fsF3vVRN23aROvWrUlPT3f5fjk4edpHdeipjNOJE/+yf//9AHTs+Czt2t1LWFgYJ07YmTBB2LVLT7t2sGxZERkZ1bfVU2Ng+/bttG5dpo+6ejW6N97QDPnySxJUtVI/xcbGkpubi16vr/Y54cs+an5+PjExMej1epfaqi/6qI7rPXmeu9NHPdVP5XKqsz8Hc+YcpkePYlJS4JprLOzY4cXzPC0N3nqLpDu0vqv+hRfIe+65cjk1IOAS+vX7FoCUlA9JTn6zUj+V9Wtd9FGrfZ7X4CdP+6gOHllZWW4/zx15r0ZUu2NIE6qEY9Op9PR0ERGx2+3OM8PLlm02W7myoijOzV7MJburOT4X0TaCKVtWVdVZtlgssnTpUikqKhJVVUVVVeeGMWXLDh2Oss1mE6vVKkuXLhVTybEdjs8d9pYtl+VRXFwsy5Ytk+Li4mo5VVY2m82yePFiMZlMVXIqy6Ns2WFvUcnxYBZLjmzfPta5k/0nn6wXvV7ba2jwYFUOHNA4mUwmp71VcarUT0VFYr/iCm0jzqAgUdasqZZfWT8VFRU5N++pjlNlfnLYazabnfaW9U1VfiouLpalS5eK1Wp1K/ZEREwmk1PW1dhTVVUsFossXrxYLBaLy7G3aJFdjEYpOTUmWcxmq8ux5yiX4/rPP6K2aqX5KTRUjq5+2Lmp3/79T4iqquXa2dKlS53tzNXY0+LN4vRpZe2pqrJDZ3FxseuxV+KnU+11JfYceWHZsmUV8oIrOcKRiyrLCzX5KTMz0+cbo9ZlPnXE9NKlS92KaW/yaVk/O2KkNvOpotjk4MH/c57+snVrfyksjK/AacUKuwwYULr/5tChIuvWeZFPbTbns6OsvTarVZS//9Z2VS1zsov9ySdFOXZMROonn1b1rKuNfFpQYJUrrlBLTpNQ5ZlntklhocXl2BMRMZvNTp1u5dOsLLH8/rvkdetWuvH04MEiJTv3l4231NSfZcOGkJINITvIyZNr6jyfirj2rPNlPnXYfWpeaEz5VMTznFpZnqntPmpN7bY2+qgmk6lcnnF8npu7SyIiWkp4OLJ7942iKHZRVVWSk63Sv79acjqUIp99Fi4Wi6XGtnpquVxOTU52HiGlPPBAtX4q2w6qe074so9aVZ6pzT5qde22NvqoruTUPXtsztMPn3xSKcfD0+e5MneuMwfbZs0SKeMPEZGkpHedfdujRz+r8P1o8eLFzu9HddFHrfR5Xst9VEcb9eR5np6e3nQ6TG3C8YDJzc11W9bTHW9VVZW8vDxnI6kLWW90esrzVL1Wa7Zs3z66ZADkLHnwwShn//muu8ofo+6RvVar81hWNTBQ1NWr3bLVVzzrQs4bWU95/vGHiMGgdR4eeEAVd02uYO/hwyIjRjh320756irnw2LfvodEVZXK5dxAUxutGbm5uT4fBKnLfCpyerc/iyVNYmImOtvG7t13iM1W/vSXQ4fKn0gdGiry2WelR4r7jKeiiPz5pza64lAWHCwyc6bzZBdPeVaps45kPZErLtYOgik9cleVl18WOXKkdm11ymZlifrf/4qUDCgLiFx3nRYQZVBYmCBRUQNLYkgv+/fPEUVx4bz5U9DURmuGL/OpiOc59Uz2lcWSJlu2aMeV7tgxVux27YvisWMi55yjNZMuXUT27/eBvVaryPjxWqVDhoiYqj8RpKk/UzNqM3b//rs0Vf74o2/sVd9+u7TS55+XUzvHhw7NcubejIw/nZ+fyW3UVbiaT5uWw3gJXWU7pdWirhYtWnik01NZb3R6A4demy2L2NiJFBREo9O1Zs6ctXz55Wj8/eGLL7Q944KDvbDXZtN271y8GAIC0C1Zgu7SS2uDUqWoD7/UtU9vvBEWLtSh18NXX+l48kkt67uKCvb27AmbN2s7qIvQ+YGl9F0xBNCRmvoF+/ffh4hSL7F7prXRxlBnTfpOx/aXkxPO9u3DyM1di14fTL9+PzB48I8YjSGAtsfmyy/DgAFa6jMY4Ikn4MABePhh7W9vbdXpdLQICUH322/aaSU33qiddNKsGcyaBUeOwLvvQocOPuPdWNpfYKB2SsysWQotW5pJS9Px+utaarvySm1T6eqWNXvNs3VrdI8/rm04/fjjmsP//hv694fZs7VTaICQkH6MHBlF+/Z3AionTrxKfPy12GzZbuv1FKdrG61MZ2OqtypdjdVXimJiz56rMJuPEBTUm0GDlmAwBJKcDOPHa02le3eIiIA+fXxg79y5sGGDlg//+KP8rtLVyTUCf3ojW9/fOarSe+218NJLWvnBB7WNw12Rq1bn88/D/PnaH++8o22EXaZz3LPnPDp2fABQ2bt3Gjk5a93WUUFnI26j7up0BU2DIF6i7CY1daFryZIlHu/O64msNzq9gc1m459/fmDXrospLIxBUdry8MPrCQ8fQffu2nfgBx+suFu/W/ba7dqJA3/9Bf7+2P/8kyXFxY3Cp/URC97gpptsPP74TnQ64ZNP4NlnXR8IqdTewEDt2LFvvoHAQDq+vZv+X4QCBtLSvich4U6s1uI653mmtdHGUGdN+k6n9ieinHL6y0BGjtxOaOhtLFmyBKvVxs8/Q79+MG+edlLfxIlah+7jjysctuK5rXY79u+/p6B7d7jtNu1415YttX19kpPhjTe03at9jMbU/oKC4NVXVb7+ehW//GJn4kQtJ/77L1x9NfTooZ1OULJFg090VpANDYX//lcbnLr0Um3X1jffhD594IcfQFUxGELo1+8HevX6DBE/srKWsX37CPLzt7mt2xOcbm20Op2Nqd6qdDVGX4koJCTcTkHBNozGUAYPDsPfvw2HD2sn3h0+rJ04vWGDNlDprb2Rr7yCzJunffDll1p7c0GuqT9Te3BF76uvaqfGFBfDdddBVpYP7lGPHiiffKJ98NFH2ibWJZuK6nQ6+vT5jDZtbkDESlzctV7n3cbaRj3R6RLcnmPSBBGpv+UwJpPJ4+lInsh6o9ObqUzFxcdky5Y+Eh6O/PtvR+nWba+AyJQpIpmZPrDXZhO55RZtGpqfn8iyZR5z9XZqWl37pT55fvml6pz998ILFWb/eWbvzp0iPXuKgKRPNMr6dQYJD0f27LlBCgtz63z66JnSRk+X5TCnS/s7dflLQsJ9YrcXOXVu3lwsF1xQ2v569BD566/q26DbtlqtIt9+K9KrV+meE61aicydK5KT4xOe1aGxt78DB7QVQqGhpbOkDQaRa64RWb5cW1Xkrc4qZVVVZMmScr6TUaNEIiOdcidPRsqWLb0kPBxZv95fUlIWuGRDUxutGafLcpjG6KvExKdKYjpAcnM3iYjI/v0inTtrzaBPH5GUFB/Zm5Iiask+IPLQQ67L1cNzvrHn09rQm51dmiInTRKx2Xx0j775RkSn0yq+777SNakioihm57N948ZQyc3dfca1UXfRtBzmNIXRaKxzWW90egKz+Si7dl2M2XyAnJyuPPBABMeO9efVV7W3ZKGh1cvXaK+iwN13w2+/gZ8fLFoEV1zhmmwtoD78Ul88778fPv1U+/vtt+GVV1yXrRLDh8OOHXDllbRba2fgSwo6RU9m5iIOHpyGqpq9tt0dnAlt9HTC6dD+cnLWnbL8ZSH9+n2NwRBMRgY88ABceGEAmzfrCA6G11+HhATtbVZNs0ZdstVq1WZm9ekD994Lhw4hbdqgvP46JCXBf/4DZ53lE641oTG3v3PO0VYIpaTAzz9rb6EVBZYs0d5A9uqlTaJJS6sFnjqdNgUlPl6bmt28OWzbBmPHauecHz9Oy5bnMnLkdtq0uQ4RK4mJj5GQMA27vcAL1h7aW4ty3sqeyWhsvjpx4hNSUuYD0L//Qlq2vICEBG0JzPHj2pLBDRugc2cf6LTbYdo0dCdPIkOHwocfuiXemPqn3sjWV9tzRW+rVtrKweBgWL1aWyLjk3t0772wcCHo9doM5+nTtXgB9PoABg36m+bNR2G3ZxEffzk63UnvddaRnLeytYmmQRAv4TiSqa50OY4VqitZb3R6guLiw8TEjMNsPkRaWg8eeSQCs7k3y5drM6lrOkWuRnsVBe65B375BYxGbS3mVVe5JlsLqA+/1DfPRx4pXQY5d672hcxV2SrRqpX2TWHePNpE6Rk0S0Vv1ZGTE8aePdegKCafcfHaVh/L1oc/HXobQ5016WvM7U9b/vKqc/lLSMggRo7cTocOd2KzaX3sPn3gm290iOi49VaV/fu1jltgoA9sNZu1Uc3evbX1iUeOQLt28O672A8cYNmgQdjLbtpUyzhd2l9gIEybpn35io+HGTO0MaQjRzTfdesGF1+cwapVimPmtO/sDQiA557TNoi5915tcOSnn6BvXw7ecw8U6xk4cBG9er2PTmckI+NXduwYRWFhnJesPbTXx3LeynqK2tLV1J+pGkZjFElJzwJw9tlv067dzezZow2ApKXB4MEQHl5x2yKPdIrAE0+gi4jAHhiI/ZdfatwHxGudXuJ0yae+1jt4sDZOAfDOOzpefjnGN/fojju07yYGA/z4o/Z3ybIOo7E5gweHERzcD6s1hZCQV7HZMr3XWcty3sp6Cpd1uT3HpAkiUjrVMCsrS0Tq5khHRVHEYrE4j39SVdePgFJVVcxmc6XHKFV3/JjjSKVTebhyBJS7RzoWFu6TzZs7S3g4snDhOdK27VEZNUqR5GTXj4ByHB9ls9kqcrLbRbnrLuccY/sff5Tj4eBa1ge1efxYWXvL+sCVI6AcR1apqur2EblWq1XMZrOoqupy7Kmqd8eP2e1253Q4x+fvvls66/rtt2s+nsthb02xZ1+xQtQ2bSR7OLJhubY0ICbmEikuzq4y9k71k6dHOqqqKsXFxc7rXT1+zG63O6cM1sTvVD857Ds1L7gae54e6Vgby2HqMp86YrGoqEgURamTfOrwc9kjZz3Np2ZzquzceYlz+Ut8/HSx2QpFVVVZtswm/fqVLn0ZMUKV8HAtRqriVFm5ynxaUCAyf76onTqVLnvp2FGUDz4QKSpqdPm0qmddbeZTVVWdXMsetVlV7BUWKvLNN3YZM6Y0b4JI796qvPWWXTIyXDty3N18atuyRdSxY0v93L27yB9/iNVikZycTc5n9oYNQZKa+oNP8+mp9tZFPi3rm7J5oTHlUxHPc6o3R+R62ketqd3WRh81IyNC1q0LkPBwZO/eB8Rut8vOnSKhoVreHD5cJDW18vzjaK+V8TjVdkVRxGa1ijz7rNZ+dDqx/fpr5X3UaspWq7Vcu62LnFpZv61Sfj7so1bXbuu6j1pT7D39tCIgEhKiSkSEe8/zyrg6Of31l6h+flq8XH+9WAsLnX4qKDgkmzd3lfBwJDp6tNjtRXXSR/X0ee5NH9WbI3KzsrKalsPUBhYsWMCAAQMYNWoUAPHx8QAkJCSQkJAAwO7du0lMTAQgJiaGpKQkAKKjozl27JizrvT0dAAiIiLIzNRG9NatW0dubi4Aq1atoqBkl/awsDDMZjMWi4Xly5djt9sxm82EhYUBUFBQwKpVqwDIzc1l3bp1AGRmZhIREQHAiRMn2LJlCwDHjh0jOjoagKSkJGJiYgBITExk9+7d5TjZ7Xb27NlTI6fIyEhSU1OdnLKyspzl6jjZ7XZWrPicnTvHY7Ue58iR/jz11AauvLINs2evpFu3qjmlpqYSGRnp5LRt2zbsdjtHjhwpz2nXLnjgAfQLFyIGA/z6K7t79SrH6ciRI9jtdrZt21Ylp8r8FB4e7vRpWU6Okc+a/GS326vlVJWf4uLiyvkJXIu9LVu2cOLECbdjzzGy6knspaWlERUVVY7TzJnwzDNajLzwArz8cmaF2APcjr0Nfn5krVrFWf7nMeQ5MBRBbm44mzddQG7ucZf8VFhY6PSpq7Hnanuqzk87duxwK/YcfrLb7Sxfvtzt2HPAwcNdTt6ivvOp3W5n9erVdZpPAeLi4rzKpykp/7B9+zDy8sLR60Po1+9HEhOvYd8+A1ddJVx5pZF9+3S0aSM8+uguoqJg4MBcVq9eXS2nGvNpURFZs2ej9ugBTz2F7sQJbO3bwyefEPvXXxyYOhWCgxtlPt2zZw92u71O86nDH67EXlFRJj16bCAyElatSueaa47TvLlw8KCOF1800KULXHutiW++OYhI1bHn9rPcZOLkX38hv/xCcZs26JKT4aabyB85EmOccO65Mdhsw1DVYvbtu5vw8CuwWgt9lk+PHDnCrpKjGOoqnwKsXr260eVT8D6nHi/ZiTc6Otqt++VNH3Xjxo3Y7XbS0tI8eqa620c1mQ4SF3c1Op2Fs866jKNHryU8vJAJEyArS8fIkQpr10JUVOX5x8HPledEamoqKQ89BO+/D8ChF1/EdvXVFfuobsR1dc8JX+fUDRs2VMqpNvuoUVFRpKWluR17vu6jVsaprJ/uumsvl10mFBXpuOIKHTEx7vtpw4YNFTlddx1Rzz+P+Puj++svMi+5BHtREWazmbVr9zBw4DJUtRlFRdHs3XsLOTmZtd5H9fR57k0f1cEjKyvLbU6OvFcjqh0iaUKVcIyyp6eni0jdvLl0jHIWFRW5PROkujcU1Y10FhcXy+LFi6W4uLjWZoLk58dIeHgbCQ9Hvv56iHTokCbffWdzcq2KU2Vlx8hh2TetdqtVlPvu00ZV9XpRfvmlUj85uFb1BqQ23lx6OtLpsNXx9sSdN5cOnY63J3Uxyu6IBYeOspz+8x/F+Vbz44+VCjzKcnVnlN1aWCiHLr9c8vojG//R3pZv3zJcrNbsWpsJ4mhnxcXFFXjU5Kfq8kJ1fqoqL9T2m8vMzEyfzwSpy3zqTUx7mk/L+tkRI+7l00Vy4MBLEh6uk/BwJCpqoBQW7pX8fJHnnrOLv7/2FtNoVGXGDFWys9VyfvY4n2ZkiH3ePJE2bUqnIHTvLspnn4m9pM7GnE+retbVZj51dyZIZfk0O9sqX3yhyMiRpbN+QKRfP5H331ckI6MiD4/zqdUqS3/7TWwvvSQSGOh8qy0PPiiWlKNy+PAcZ1xu2zZMiooSfTITxJVnnS/zqeP+n5oXGlM+FfE8p3ozE8TTPmpN7daXfdTi4hOyZcvZEh6OrFlztphMWbJhg01atNDa0JgxiuTmVj9jtLJ2W2Vcv/OOs2Eq8+dX3kd1Ia5Pbbd1kVOr6rfVZh+1Pp7n1XGt6Xmek2OV/v0zBbTH4+7d7uXUslwrcFq+XFRHrr3sMlGLipw+XLr0LVm/PlC0TdDvLTfLszb6qJ4+z73po3rzPE9PT3cpnzYNgngIxwPGkweWIwgdzj5d4QrPvLxtsmZNKwkPRz7/fKQMG5Ypu3f70AhF0XbgBhG9XqRkAMSXaPKnd1BVkVmzSjvwX37p0+pFfvpJ8gcHysbF2kDItvV9xGI5Wa1Ik09rhjc50Jd1nSm+Kiw8KqtXDxbH8pd9++4Xq7VIFi4U6dixtP1Mniyyd6+PlObni7z2mkjr1qUKevXSToCppft9pvhTxLdct28XeeABkZCQUlcFBorcdZfI5s2uncTlMpKTRW69tVRRy5Yi778vWen/yqZN2guNiIgWkpHxl4icOT5tKPnUm/pOZ1/ZbPmybdsICQ9Htmw5W5Ys+V7WrrVJs2ZaGI8bJ1JQ4EOFn31W2kbmzfNhxe7hdPZpWdQXz9xckXPP1dzcsaPIwYM+rHztWpHgYK3yCRNECgudPFNTF0l4uF7Cw5HDh//jQ6UNA3WRT5uWw3gJEalTXfn5+R7p9FTWG501IT19C1u3TsRgyCE+/nzWr1/Dhg2hDB7sI3tLNqLiiy+0zd1++AFuu8012TpCffilofHU6WDePHhW25+Mhx6C7793TdYlndOm0fyXbQyb3x2/bCiUA8SuG4rVkuYFoxp0niZttCa9jaHOmvQ1hvZXVBRPbOxYjMY9zuUv+flfMW5cMHfdBamp2skhS5bAihXQv78P7N21Cxk+XDvZJTtb22F14ULYt0/bud7Pz+c8vcWZ0v4q0zlyJHz5JZw4AZ99BkOHavvWLlwIF1yg/b1gAeTm+oBnt27wv/9BRASMGAF5efDss7S+6GnOzX+fFi0uQFHyiY+/noMHn0VVbT7lWpty3sp6itrSVd9xWZty7siqqpX4+OspLNyJn19bBgxYxq5dvbnqKgOFhTBxIoSFQbNmPtL544/w6KNaedYsmD3bLXs90uljnCn51Bu9IoJOl8/y5cKgQdqzeOJEKLNS1zudEyZoD/VmzWDdOu2YsJJlQKGhV9Gnz2cAJCe/xvHjn/lGpw/lvJX1FK7qahoE8RKONWh1pcuxdrKuZL3RWR327IkgJmYy/v757N59EVbrKn755SxatPCRvTabtqX+p59q37K//17badkV2Ubg0/qIBW9Qk06dTjsa8okntLGre+/VDiTwxt5ycoMG0WzJboaFTcI/E4oCTxCzvC+WnERvqVWts45k68OfDr2Noc6a9DX09pebG0FMzIVYrSkoSmc6d97C7Nl3MHo0bN0KISHw5pvaKSJXX135kbdu2/vNN3D++egOHcLUti32hQth717tqFQXj7priHmmNmQbGs8WLeDhhyEmBrZs0Q5DCwqCPXvg8ce1oz7vuy+D4mIf8LzoIoiO1uKlXTs4cICAy+9m2P81o0vwdABSUj4gLu5SdDr3TzKoiWttyHkr6ylqS1dDicvakHNVVkRl377p5OSsQa8PYfDgMNauPYd5887HZNIxZQosXarlUp/oXLRIa3iOF3Hz5rllr0c6awFnSj71Rq9DrmVLO6tXa8ecJydrAyFpNbxnc1nnRRfBqlVact+4EcMVV2AsKgKgU6cH6dHjFQASEx/j5Mm/fKPTR3LeynoKl3VVO0+kCVWiafp2zaiK55Ilq2XFiiAJD0c+/niirF9f6FvFqiry1FPaXBCdTpu6XYs40/3pS6iqyMMPl65e+u033yso+u+LEvmbtqRgy+/+UhwfXuGyJp/WjKblMLWP9PQ/ZP16/5J9FsbKgw9ukubNS/d/uPNOkePHfaiwqEhk+vTSKdxXXCFScrpEXeF09uepqCuu2dkiH30kMmBAqWvHjhVJSfGhkrw8keeeEyk51UCMRsl460qJ2NBCwsORtWtbSlbWFh8qbHhoKPnUm/pOx/aXmPishIcj69cb5eTJFfLaayI6nVqS4hQp2ZLBNwgLK20D996rLcmuZ5yOPq0MDYHn0aMi3btr7h80SCQz04eVb9sm0qqVCEh2795iPXpURLS9NPbte7AkxgMkJyfCh0rrD03LYRoBVFWtU13Z2dke6fRU1hudp8Jmg3feCSMw8EoCAoo5cGAq11+/lPHjKw6/e2yvolD8xBMwf772wVdfaVO3XZH1IVdXUR9+acg8dTptyvZ994GqwrRpsGiRD++RTkfw428yrP2vBKYbMLe1ErNnAsWLP/WElms6a1m2Pvzp0NsY6qxJX0Ntfykp/2Xv3psRsQLXce+9q/nyywsoKNBx7rkQGaktdejUyUc6ExNhzBj47jvQ6+GNN1AXLyYbz/zSkPOML2UbA89WreDJJyEuDn7/XaVFC5XISBg+HNas8ZHOFi3gnXe0KUlXXQV2O21fXMa5jxoIMXdGr88jLm4iWVn/usjSRb0+lvNW1lPUlq6GHJfeyrkie+zY+6SkaCez9OjxLQ89dBn/+Q+I6Jg6NYnfflMICPCRzvXr4frrtc7uLbdo69P0etdkPdVZizhT8qk3ek+V69pVy6kdO2r5dsoUyM/3kc5zz4V165A2bWh18CDGMWMgOhqdTsc55ywgNPQaRCzExV1NUVHlp6M0xDZaG3BVV9MgiJdQFKVOdW3bts0jnZ7KeqOzLFJT4bHHljB8+LX4+1tIS7uGu+/+m86dg3ynVwR5/nmCFizQ/v7iC+3btIvwFVd3UB9+aeg89Xqt73DXXaAocNttOubPT/bpPQoafwvDxkQRlBmEpb0QY3sM0yv3gZfT9RpzG3UXtaGvPjg0tPYnonLo0AscPPgkIOzf/ygTJ/7Bnj3BtGxp5quv7ERFaeMVPrP3r7+0Dtbu3dqyhtWrYdYsFJHTNs/4SrYx8dTp4NprFebP38TQocLJkzB5Mrz2mjbo7BOd55wD//wDK1fCgAEExecw/PrjNI8LQlVN7NlzNSdOfOmyzQ2xjdYGaktXY4jL2vJVevovHDo0E4CWLd/hyivv5K+/tO2MPv/czkMP7cbf322V5XSKCDabDfPWrZgffRRz+/aY77kH81dfYbbZMJvN5X6KiorYuXMnRSVHnrr646mc48doNLot443OxsTT1/Z26WJm1Sozw4aZOXnSzPTpZrKzS2UcsepR3A8bhn39egq6dEF3/DiMGwfff49eb2TAgP/RosVY7PZcdu+egtlccWOShtZGawuu6tKJ1PHuM6cJ8vPzadmyJdnZ2bRq1cp5ww0GQ7my3W5Hp9M5y3q9HkVRCAsLY8qUKQQEBDg/1+v12Gw2DAaDs2w0GtHpdM4yaGudypb9/PwQEWdZVVUURXGWVVXFaDRWWXYkcke5Mh41cdLr9RXKiqKwYsUKgoKm8Omni3n88TswGu1YLDcxadLPKAq+46SqyH33oSvZSEL95BPUhx7yOafK/GQymVi9ejWXX3454ENOdeQnV2PPZrOxfPlypk6dip+fX61zUhS45x4D//sf6PXCJ5/oeOAB33KyFB1l97oRmFpk45cNQ34eQfMF/2Jt3Zrly5dz+eWXO+1pLH5yJ/YAwsLCmDx5MkFBQW5xKioqomXLluTl5dHCsZmPh2jKpxonq9XEwYMPkJHxCwC//DKPr76aBei47z6FSy5ZyfXXX0JAQIBvOAHy4ovoPvgAALnwQpSffsLYvXu9xfSZkk8BiouLWb16NVOnTkWn09UpJ6vVwOOPq3z7rfYu7LLLVH78Edq29WHuKS7G75tvkDlzkPwcDjwLaVO1Nt+t22y6d5/rtLch+6mx5VPwPKc6+m2TJ0925pnGmFNzc9cQH381Ijbs9hncfPOH5OTo6NhRWLRIx7BhxaxatcqZZzzhZLFYOHHiBOa8PEhP11aaBQYibduCTodOp3Nu0FifZVVVMZvNBAUFOT8Dx6aeVZcbgu3VlSuzt7i4mKCgoGqvqSsbLRYhPV1XEhZCu3al975Lly40b97co/YEsOrPP7ns558x/qvNrFMefxzDBx9gUfPYtWscxcUJBAcPYMiQ9QQGtm2UOdVisbBq1SqmTJmCwWBwK0fk5+fTunXrGvNp00wQN7FgwQIGDBjAqFGjAIiLiwMgISGBhIQEAHbv3k1iorbhYkxMDElJSQBER0dzrMyWwenp6QBERESQmaltGrZu3Tpyc3MBWLVqFQUluwCHhYVhMplITU0lLCwMu92O2WwmLCwMgIKCAlatWgVAbm4u69atAyAzM5OIiAhUVWX//v1ERkYCcOzYMaKjowFISkoiJiYGgMTERHbv3u3ktHfvXjIyMlziFBkZSWpqahlOWfz9d2/ef/83nnhiGkajnfz8SZx//ufo9X6EhYVhNpux2+0VOKmqSnJycrWcAFJTU4lavRquuALdTz8hBgP5H37IocmTq+RUlZ8OHz5MRkZGDZwq+ik8PNzp0+o4VeWnjIwMMjIyynGqyU8HDhwgOjoaVVXdjr3IyEj279+Pqqoux56DE+B27AGcOHGCDRs2oKqqy7F34EACCxfCdddloqo6Hn0U7r//JIcPuxJ7maiqypo1a8jOzq6SkyGgE2l8SIitO7bWsPvOnRReMwjz6tVOn1YXe6f6SVVV9uzZw86dO92KvaSkJFRVZdOmTSQnJ7sce6tWrSI/P5+MjAyPYs8BBw9XYs/ByRdTG+szn5rNZqxWK2FhYVit1jrJpwkJCaiqSnR0NAcOHCjHyW7PZ8uW8WRk/IKiGHnrre/56qvZDByo46OPdjB3bjrNmtmIiIiosZ26kk83//47XHKJcwCEmTM58eOPRJbEn4NTRkYGhw8fPq3zqYNTRkYGe/furbN8urokz9RFPi3LadeuXQQEqDz88A7efjudoCBYuVLPsGEqUVGe59MKflq9Gp54gtytWzl+4Xj6vgM9vtN8e/ToG+zefSsREWur9dPhw4fZsmULqqrWST4tKChAVVVnn6sx5VPwPqceP37cWXb1foF3fdQNGzaQkZHBiRMnfNJH3b79f8TH34iIjWPHrmTKlPfJydExYEA+S5emMWZMqY9q4lRV/klPT+fgwYMUFxTQSYQeoaF07dSJnqNG0bVbN9q1a0fPnj3p0qUL7du3L1fu0qULnTt3pkOHDvTs2ZNOnTrRsWNHZ7lTp0707NmTjh07OssdOnSgY8eO9OjRw1lHz549y5XbtWtH165d6dmzJ23btqVbt27OcteuXenRowdt2rShe/fudO/evUK5Z8+edOvWzVnu2rUrbdu2pUePHnTt2rVaTj179qzAqX379vTo0aNaTp07dy7Ho0ePHrRr165GTm3atClXdvDo2rVrtZy6detG27ZtnfzKcmrXrh09evSoltOpfnL4ozJOXbu2Z9iwLrRt25Nmzc5Gp+tBt2498PPzIzk5GZvN5n5OLWlP9uBgVj70EMyZA4Dhk09g8mTyD2VQVPQf/P07YTLtZevWiShKsdd9VE+f5970UR3Pu6ysLLef5468VyNq2lykCZXDselKRkaGiIjY7Xax2+0VyjabrVxZURTnZi/mkt2YHJ+LaBvBlC2rquosW61WWbNmjZhMJlFVVVRVdW4YU7bs0OEo22w2sdlssmbNGikuLi73ucPesuWyPMxms6xdu1bMZnO1nE4tHz9uk6uvtsvll38ta9fqJDwc2bPnPrFYistxKsujbNlhr8lkqpKTiIhy7JioQ4aIgKghIWJZssRpb1WcqvKTg2txcXGlnKryU1FRkXPznuo4Vean4uJiWbt2rVgsllJOZflV4Sez2Sxr1qxx+sLV2BMRKS4udsq6GnuqqorFYpHFixeLxWJxK/ZERCwWi1Onq7HnKBcXm+Weew46N/S7/XZFLJaqfVNWx5o1a8RisVTJyWG7xZIp2zYOlPBwZOMSJK+/XuLvuEOsJlPVsVdJ2aHT0bZdjT3HfShrryux54i5tWvXVsgLruQIRy4q285qij2H7VlZWT7fGLUu86njfqxZs6bc31Xdq7L3xNN8WtbPZe01mY5JVNRQCQ9HwsJCZNSo5RIYqMq8eaWxbjabnb6qjpNL+XT1alHbtdPyZosWYv/jjwo8yuan0z2fVvWsq818qqqqk6vFYqmzfFr22eHgFBsrcs452kaRfn4i8+fbxW73PJ+e6ifHs8MWFibq2WfLialI+Bptc+qdWy4Qmy23Sj+58qzzZT5VVdUpWzYvNKZ8KuJ5Tq0sz9R2H7WmdutOH9VkOiibNrWT8HDkp58miJ+f2blHaWFhKQ+TyVQuz7iTU00mk4SHh0t8bKwUxsSIbNsmany8KGW4Ouo4tawoiuTl5YmiKFVeU1nZIef4zJXrHWW73S45OTmiKIrzc4ctZctlbfREpzf2lr3OcX+q41RZWVEUJ8+qOJ1ark17S/8vsn27Ktu2iRw6JFJYWCTx8fFSWFjoUU515JmioiLN9kWLRG3WTHued+smtqgoKSjYLRERLUX77nWt2O1Wr/qonj7PvemjOtpo2f6Hqzk1IyPDpXzaNAjiIZpOM6gav/8u0qaNyDXXLJDwcK2js3//o6KqPt4le+9ekW7dtG/G7duLbN/u2/pdxOnuTwfqm+e334oYDJq7L71UO4zAl7DZcmVH9GgJD0ciliK5gxB1+HCRmBjfKmpAaCinGZzJ+bSwMEHCw7tLeDiyaFE76dNnu0yeLHLwYPnrfMJTUUTmzdOOXgKRoUNFEhO9st/XaOz+dAcNiWtensiNN5aeHnPLLSL5+b6puxzPoiKRF1+UzPP1EvGv1j+IDuskxUVHfKOsHtFQ8qk39TWkmHQXFku6bN3au2QAZKgEB+eJ0SjyySfayXNl4S3P4qQk2bt6tRRv2yYSFyfSgO9X2cGB0xkNmWdOjvYVZds2kYMHiyU+fq/zJYq7qDR24+NFzjlHS96BgSI//SQ5Oetl/foACQ9H9u17yDnA0ljQdDpMI4Bax7vdHj9+3OPdeT2RdUfu5Em46Sa4+Wbhqqv+w1NPPQZAp04zOOecT9DpXA+3GvVu2gQXXABHj0KfPrBlC4wcWS/3yBvUhV98KespfGHv3Xer/PsvhIRou29fdBGcOOE7nUZjS4YMX0PLluNQmkHse5AZHAOjRsHLL4PZ7LKtDbWN+hK1oa8+ONRn+zt2LJJNmy4Akjl27BzmzNnCK6+MZMUK6NXL7aqr1XkiLg658kp46SVtF8x779XyZu/eLtnqic7GmGcaQ/urDZ4tWsDvv8OHH4LRCL/9pqW++HjvdZZDcDC8+SahX+5i2NeD8c+CoqAT7Fx9DoU7/3TZ3prQGGO3MdVbla768pXNls+ePVdQXHyQjIwePPnkcoKDW7BmDTz2mLYpsK+gpqVhu+subSN1f39tM2A/P5dkRQSr1ercO8JVeCrnDbzR2Zh4eqPXVbmzzoKePbVyTo72oyg+zDMDBkB0NEydqvVV77iDs15fyoC+CwEdqalfkJz8elM+PQVNgyBeoq6deujQIY+D0BNZV+X++ENrg4sXW5k16x7uuut1AMzmW+jR4x3nZkA+0btoEVx6qZZFxoyBzZud2aU+7pE3qG2/+FrWU/jK3ssug4gIaN9eO8ji/PNh717f6TQamzNkyHJatbocNQDiX4OUK+0wb552nuSWLS7b6i7qIxa8wekyCFIf7e/gwUMsWrSEhISJBARkk5AwmujozUREnM1tt/m2ww6gRkfTetIkdMuXQ2AgfPON9hNU+elcZW09E/NMXen0FLXFU6eDp56CDRugc2fYvx9Gj4affqoFnoMH0/zPXYzInkvwMT3WljZiUm8i551bwWRyyd7q0BhjtzHVW5Wu+smn+4iPv4mCgu3k5rbh2WdX0r17R3bsgPHj3a6yemRno5s8Gb+jR8FggB49cPeIGYvF4pFqT+W8gTc6GxNPb/S6Kte6tRYuAAUF8PHH+DbPnHUWLF0Ks2drf7//Pm3v/IpzOr0FwJEj/8eJE1815dOycHuOSRNE5Myevl0WGRkiN92kzcAKCcmVzz67tGQJjEGOHv3c9zznzxfR6TSF116rTa2tZ5xO/qwODYnn4cMifftqYXDWWSIbNvi2fovFJCtWTHYu5zr4TLCoOrTYmzFDpKDAtwrrCQ1l+vaZlE8TE0Wee+5zWbNGL+HhyMcfXykREYU1ynnEU1VFPv1UxN9fayy9ejX45V2NzZ/eoCFzzcgQmTSpdHnMQw+JeDh7u0ae1qPxsvOnNhIejqxfhaROayOyYoUX1tcPGko+9aa+hhyTlUFVFdmz5w7R9lMKln79omTatJq7hh7xzM8XGT1aBKT43HNlb2ysx0sa6hKnLhMZP368zJgxw606EhIS5LzzzpOAgAAZOnSoJCUlCSAx1TxPwsPDBZCcnBzPjXcDDXk5TFmkpBTL8uV7pXv3YnnjDfflXYrd338XCQ7WknfPnnJoy/0l/Vm9nDz5j+fG1yGalsM0AtT1yFZycrLHI3GeyFYn55j98ccf0L59Cr/+ehH9+q1Brw9h8OCldOhwr9t2VqlXVWHmTO01lQg8+ij8+ac2tdZFe93WWQeoDb/Upqyn8LW9PXtqE4DGjoXcXJg0SYtDX+nU6YyYzY/QrdtcAI5dZWLvNz1QjQIffQSDB2trclyw1VXURyx4g9NlJkhd3XOrFebNE95552Uuv/xhDAaV9PT7efDBv7noohB3Ta8ZRUVw551arrRaMV12Geq2bTBsmMtVnOl5prZ1eoq64Nm2LSxfrh0+oNPBF1/A6NEWDh3yPU+/rgMYelsKbW0XIX6w74FMkn+cgky7DTU19Yx5RjameqvSVde+io19kczMn1AUA3Pn/sn994/mp58qdA29h8kEV14J0dFIaCgZb76JlBzx6Q5EBIvF4tGyC0/kKsNff/3Fa6+95pbOOXPmEBISwv79+1m7di1du3YlNTWVQYMGVSlrtVrdts0TnsnJyQQEBJCfn1/tdZMnT8ZgMLB161af6PVULjQUWrXSyrNnw8cf10IbvekmbeZyz56QlETPiT/TwXwxoBIffyPZ2Wvdqu50zadNgyBeoq6d2hDWO588CTffrP1kZsLkyXv47bfzadZsD/7+HRg+PILQ0Klu21ilXosFbr8d3n9f++dbb8Enn2hTEX3E01tZT+FLv9SFrKeoDXtDQ7VxiOuu075g3nILzJ/vG50adHTt+iL9+i1EpzNysucRYlcMxtavCxw5oo283HefNgrjA531EQve4HQZBKmLe75pE4wYoZKZ+SjTps0DoFmz/+Pmm78kIMD9jnSN2LdPW8fw889gMKC88w67/vMf1ObN3aqmKc/Urk5PUVc8DQZ45RUIC4PQUGHPngDOPVfH0qUeGF0D9PoABly6nq7tZwCQdD8c6PgrDB6A9euvURXFrfoaY+w2pnqr0lWXbWjt2nnk5r4LwIIFXzN37lSefdb3ywmxWOCGG7S1uC1aoPz7L8dDPB+4ttlsdSp3Klq3bk1zF58FDp2HDh3iwgsvpHv37oSGhmIwGOjQoQPGagaCFDfb7Kk6XcWSJUu4+OKLadGiRZXXHD16lC1btvD444/zzTff+ESvN3ItWsCjj2rxPmOGnm+/9Uh19RgyBLZtg0mT0JmK6XPlekLTz0HESlzcNeTlVRwMqgqnbT51e45JE0SkdKpNdna2iNTdkY6qG8e1OcruHi1U3XFJf/wh0ratdpSewSDy3nurJCKihYSHI1u39peiokNOTt4c6ejklJEhcvHF2tFPRqPIjz/6nFNVfqqu7KsjHWvLT95yqsxP3hyRW5uc7HaRRx9VnNO2Z8xQxGbzrj05uDrkMzJWlMZ5ZB8pfv6u0nniHTuKsmhRg/GTO7HnzZGOtbEc5nTLpydP2uX++xXR6+3ywgt3S3g4sm6dTo4d+8xt/7ucT3/5RdSQEC1nduwotnXrfMqpKZ/6rp16ekRufXBKThYZPbo0zz73nF2sVs/yaU2cjh79WMLDdRIejsS+gdgCEbnuOrGnpjblUxfhaU715ojcumqrRUV2WbDgXedy1SeffFf273cvBlw+ItdqFfW667R8GhwsakSEWK1WKS4ulr179zqPKVVVVex2RQoLRQoKVCkoUOu0rKquH5E7fvx4efLJJ53XdO/eXV5//XWZPn26NGvWTLp27SpffPGF8zhZoNzP//3f/8nhw4cFkJ07dzr1LFu2TM455xwJDAyUiy++WL799ltnDDquKWvjqeXY2FjR6XSSkZEhqqpKdna26HQ6ufHGG52c5s2bJ+eff345fhMmTJD//ve/1R6RO2fOHLn11ltl79690rx5cyksLHTrKFxflk0mk8THx4vJVCxPP20XSlZa//ijzbMjcmtqTzabqM8+KwJi90Nivmkt4eHIxo1nSW7u9gabU705Ijc7O7tpOUxtYMGCBQwYMIBRo0YBsGfPHgASEhJISEgAYPfu3SQmJgIQExNDUlISANHR0Rw7dsxZV3p6OgARERFkZmYCsG7dOnJL3iyvWrWKgoICAMLCwigqKuLAgQOEhYVht9sxm82EhYUBUFBQwKpVqwDIzc1l3bp1AGRmZhIREYGiKMTExLB582YAjh07RnR0NABJSUnExMQAkJiYyO7du52c4uPjOXjwIOvXx3PllUXcdBOcPKmjb18Lmzb9yMiRU1GUfFq2HIfZ/C45OQFOTllZWc5yVZzMZjN2u70CJ0VROLB2LcWjRsH69UizZmyfOxfuuMPJCSA1NZXIyMhynA4ePMjhw4er5FSVnw4fPszBgwfL+SkyMpLU1NRq/RQeHu70aXWcqvLTwYMHSU9Pr5bTqX7av38/mzZtQlEUt2Nv8+bNxMTEoCiKy7Hn4AS4HXsAx48fZ926dSiK4nLsOTjFxsYSFRXljOFTORkMcPPNm5g9Ow+Ajz7Sc8MNVoqKFFatWkV2drZbsQdQWFjo9Glubi47d8Lw4ZswGjtSbDnAzmtWcXTJe5i6doXUVPQ33EDuZZehnDjBtm3b2Llzp1uxl5SUhKIorF+/nuTkZJdjb9WqVeTl5XHw4EGPYs8BR7y5EnsOTp6+5SmL+synZrMZi8VCWFgYFovFp/l0584Y/vc/6NdP+O47hZdeup0pU34ADPTrt5BDh/qzf//+ajmd6v+a8unyJUtQHn0U3bRp6IqKUC++GHNkJP/m56MoCvHx8TW20zM1nyYmJhIbG8vBgweJj4+vs3y6evVqgAaVT6vyU+fOCrNmreCBB4oBePddAxMnKqSluZ9Pa4o9Vb0ao3Eeen0g2WMg9kOwhv+NDBxI8kcf1Rh73uTTgoICFEVx9rkaUz4F73Pq8ePHnWVX7xd410fdsGEDBw8e5Pjx4zXer5Urk5k580MGDHgOgKioF7n//usoLIytsa2WjQGHnmo5LVuGcvfd6P7+G8VoRFm0CPO55xIWFuac3u+IbUVRSE8voFkzaN5cR/Pmujotm0zaPXb00xxx7Cg7UHbJSH5+vnMZx/vvv8+5557L9u3bmT59Oo888ghxcXHk5+dz4sQJBgwYwOOPP05qaiozZsygqKgI0GZBFBUVcezYMW644QYmTZrErl27uOuuu3jxxRcBLb+ZS07XKy4udpZNJpNzU9GioiL69OlDaGgoy5cvx2azERERQevWrZ05oqCggPXr1zN+/Hjy8/NRVZXc3Fw2btzIVVdd5eQiIoiIk6uiKHz77bfccccdnHPOOfTq1Yvff/8du93uvDc2m815P6xWq5OfxWLBVLJRs9lspri4uBwnEaGwsLBKTo4lQYWFhc4ZI0VFRaiqiqoqTJiwgnvuKUYE7rlHz2+/FTvbU3U5FUqflTXmVKORo088wYE5c9Abgxj8WDbNEwzY7bnExk4mJmYJUDvPc2/6qA4eWVlZbj/PHXmvRlQ7RNKEKuEYZc/IyBCRunlzabVaJSoqSkwmk9uj7DabTaKiopybOLn69shsNsubbyaWm/0xe7ZdEhPnOkfh4+JuEUUxe/7mspJRQdvOnWJp27b0TfuOHS69OSguLpbo6OhyI4euvhEzm80SHR0txcXFdfbm0mGvxWJxa6TTbDZLVFSUM77cGb0tLi52ytbVTBCLxSJbt2516nPnzWVZrjWNSP/8s4ifnxarF12kyqpV28RisbgVew57K3tzaTIlS1TUIAkPRyIimklGyt8iL74oqsGgvSlq3VoOvvKKmEvamTtvY202m2zdutVpr6uj7FarVaKjoyvkhdp+c5mVleXzmSB1mU8d92Pr1q3l/q7qXpW9J9Xl0wMH7DJ5svbG3M/PLPPnXy3h4cj69X6SlvaHU7asvV7PBElOFqVkwz4Bsb/wgqg2W2k+LdFZ1s9N+bR8TDvsNZvNdZJPvZkJUlf5tGy5bH767TeRZs20PNuhg8iaNTafzgRxlHNzI2XjxlAJD0e2/uYvpk4l01DuvlvsWVm1kk9VVXXKls0LjSmfinieU72ZCeJpH7Wmdmu328VqtcmXX4pcf/1nzr7n8uWzK223PpkJoqpif+AB5yxk219/OTmZTCaJjo6W+Pj4cjNB8vNLZ0rV9U9hoXczQW6//XYnD7vdLu3atZMFCxZIQUGBqKoqQ4cOlf/7v/9zXnPqTJBZs2ZJ//79nfdRURR55pln3JoJoqqqXH/99fLQQw+Joijy1FNPyTPPPCNt2rSR+Ph4sVgs0qxZM1m+fLnT9p9//llGjBjhnPlR2UyQlStXStu2bcVW8kz84IMP5IILLqhwvxxc3ZnZoaqqFBYWlpt14upMkIKCAtm6dasUF1vkzjs1P/r7q7J6tY9ngpQp2/bskcK+fcUagmz7QmtHmzd1EpPpcK08z73po3ozEyQjI8OlfNo0COIhzoTTDNLTS09+AZFBg0S2bbPJvn0POB9CBw8+L6pa+U7MHvNct06kRQtNaf/+IsnJPmBTe2gs/vQWjYXn2rWl4TNggGfhUx1Xmy1XYmImiuMUpBMnvhHZsUNk6NDSxjJ5ssjBg96TqWV449Om02FOtUPkzTdFAgO1EGjRokj++ks7YWjDhkDJzPzXy/qr4Ll8uUjr1prSVq1Eli3zSk99o6H4sy7QmLnu2ycycKAWdnq9yFtviShVHMrgDc+ion2yZUsPCQ9HNq0Ilvw+JTm2a1eRNWu8ZOFbNJR86k19DTEmMzNFrrtOZMqUb519z127nnN+qfQE1fJUVZFnnhHnOoVff61wiWM5TNnTYVRVG4yoj5/qbkXZwQGRiqfDdO/eXd55551yMkOGDJFXX33V+ffQoUNlzpw5zr9PPR3m2muvlenTp5erY/HixYKbp8N8/PHHMmjQIBERGT58uCxbtkyuv/56+fTTTyUyMlIMBoPk5+c7r7/55pvllVdeqZSnA7fccos8/vjjzr/T0tLEaDTKvn37XLbLl6gsdmw2keuv10IuOFhk48aq5b1uoxaLyKxZYjkLifpOa09bwjuJ2Xzcs/pqCXWRT5uWw3gJX01hdFXXvn37PNLpjqwI/PJL6ckvBoMwe7ZKVFQh/v5Xk5r6FaDnnHM+oVevt9HpfBhG//sfXHYZ5OdjGjkSJSICunVzWbyu7pGv4KnOM4WnJ7ITJsDGjdC5s7B3L4wZI8TGuq22ShiNLRkyJIz27e8EFPbvv4+kVv8g0dGoc+ei+vvDqlUwaBC8+Sa4uGlWfcSCN6gNffXBwRf3fMsWGDECZs0CsxmmTClg+fLLadVqFXp9MIMH/0to6OVe6azECO3ojssvh+xsGDkSdu6EK66oNZ51Jesp6sPeM5Vn374QFaUdQKSq8OKL2ibVOTlum1QtAgJ607z5D4SEDMMWYCLmiyCyr+4Ix47BpZfCk09qJ3fUYK87qC+fNqZ6q9Ll6zYUHg5Dh0JBwc8899x9AHTuPIMhQ95Gp9PVjq9efRU++EArf/21tuv6KfYePny4wokgOh2EhFT/Exws6PXFBAdLjde6I+ftZrB+fn6ncNHubXFxsUsnn5x6jZScmuIuxo8fT3x8PImJicTFxXHRRRcxfvx4NmzYwPr16xk5cqRzU1ebzcaKFSu45pprqqwvOzubxYsX8+mnn2I0GjEajXTu3Bm73c63ZXYjFRGXuZ7K0xM5KB/3RqP29WfqVC2dXXEFbN/udpWu6Tx8GOW11/BfsoGhH3Yi8DiYOUHsmqFYi1NrtNUjnQ00n9b7IMinn35Kz549CQwMZOTIkWzcuLHa6zds2MDIkSMJDAzk7LPP5vPPP69wzaJFixgwYAABAQEMGDCAv//+u9z/X3nlFXQ6XbmfDh06+JRXbcGxHq22ZI8fh2uu0Q5jycqCoUOFH3/cz8svp7J373iys5ej1wcxaNBfdO78mMe2VIAIvPceTJsGNhvqjTdy4JNPSs+RcgO1fY98DU91nik8PZEdMgQ2bVLp1auYEyd0XHQRrHXvRLBqodf706/fD3Tr9hIAycmvsv/wwygvPse+339HLrlE+zY8e7b27bjM+uPqUB+xcKbDm3uem6udQHvBBRAXp51YtHBhLq+/PhmrdQMGQwuGDl1Fq1YTfKLTiZMntd7S3Lla7nz4Ye0Imh49qrXXEzTlmdrV6SkaAs+QEPjhB+34XH9/+Oef0rE4X8Jqbc6QIeto1epSVIrZ88xJ0t66RPvnf/8Lw4drIzI12OsOmnKqZ/BVbNls2uNz4kTo3fsPZs26C71e6NTpEXr3/hBdmW/9PvXVu+9qgyAAH38M995b6WWOPSA8gSdfmL2R8wau6hwwYECFo2cd+zW4g0GDBhEaGsq8efMYOnQoLVq0KDcIMn78eOe14eHhnHXWWQyr5tj3n3/+mS5duhAbG8uuXbucP/Pnz+eHH35w7qEC9eOXsrHr7w+LFsHFF0N+vvY+OC7O46pr1jluHAERexm66Vr8T4IpJJPdf/XGdqDyBH5a5lO355j4EL/++qv4+fnJV199JXv37pUZM2ZISEiIJFcxf/3w4cMSHBwsM2bMkL1798pXX30lfn5+8ueffzqvcUyXeuONNyQhIUHeeOMNMRqNsnXrVuc1c+bMkYEDB0pqaqrzx7Fu0lWcDtO3y0JVRb75RqRlSylZyy7y2mvaFO/Cwr0SGdldm466qa3k5W2tsT4RN3ja7SJPPlm6lOCpp6qeV9sA0RD9WRtojDxzckTGjy+N6Z9+ck3OHa7Hj38h4eH6kim6l4nNlq81qIULRdq0KY3rhx/WDGpAaCjTtxtbPlVVkd9+0/ZDcLj3nntEUlNPyrZtw0Xbeb2V5OVt85lOB09bRIRI586l82Z//NFnOhoCGmOe8RSnE9ft20V69tTCMiBA5IsvSqfo+4qnolgkPn6ac0nEkVXTRe3UsXRNzksvaVO96wkNJZ96U19DiMnERJFRozS3XnDB37J2rUHCw5GEhPuqXH7tLirluWBBaUJ/441q5Stb0tBQ4cpymA8//LCczKnLX2paDpOcnCz+/v7y9NNPy759++Tnn3+WDh06lFsOk5KSIn379pWoqKhq7b3++uvFYDDIzJkzRUTbK6J169ZiMBjk339Ll5U+9thj5Za5VLYcZujQofLCCy9U0JGfny8BAQGyePHiam2pDdQUO/n5Iuedp4Vh+/YiBw6U/39ttNHC39+TTX9rJ3LtWKAX+9efVL/Gqg5w2i+H+eCDD7jvvvu4//776d+/P/Pnz6dr16589tlnlV7/+eef061bN+bPn0///v25//77uffee3nvvfec18yfP59JkyYxa9Ys+vXrx6xZs5g4cSLz588vV5fRaKRDhw7On7Zt23rEoa6n98TFxXk8Hakq2eRkbcTxvvsgLw9GjdLe5Lz8MuTnr2X79vOxWJIJCjqHESO20KLFeb6go6G4WJtq+PHH2t/vvw8ffogi4hHX2rpHtQVPdZ4pPL2RVRSFlJQ4wsIUbrlFe7N0xx3w1ltaL8dX6NTpQQYP/ge9PpicnJVs2TIaU/FRbZ74vn0wfbp24eefQ79+8NtvlRpQH7HgDU6X5TDu3ruiIrj+euGWWyAtDfr00aZsf/55GkePXkxhYQx+fu0YNmw9LVqc6xOdAIhw9tKlGCZM0Kbs9e0L0dFaUNeApjxTe7JNPDWMHAk7dsBVV4HFAg89BHffrbUXb1BWr17vT//+P9K160wAkvy+4+CKK5Hbb9XW5MybB+edByXXN7bYbUz1VqXLm9iy2xV++EGb2LNtG1x66b+89trN6PUK7dvfSd++X1RYfu0zXy1cCI+VzG6ePVtb21iNvQcOHPBoBoDUw7ILT+GOzm7durFo0SKWLl3K0KFD+fzzz3nllVfKXWOz2di/f7/ztJWqdF5wwQUoisLFF18MaEtzLrroIgAuvPBC57X//PNPtUthduzYQWxsLDfccEOF/zVv3pzJkyfzzTffuM31VHu9WQ5TWew2bw7Ll2vLwNLTtdlQJYdceY2qdIbc9CxDB4ZhLDaSP0AlLudxlJuugZITX07XfFpvgyBWq5UdO3YwefLkcp9Pnjy53NFVZbFly5YK11922WVs377defRQVdecWmdiYiKdOnWiZ8+e3HrrrRw+fNhbSo0OqgqffqptXbB6NQQGajMBIyNh4EAhJeW/7NlzGSL5NG9+PsOHRxIU1Mt3BmRnw6RJ2vwvf3/49Vd45hnf1d+EJgABAdoeN88+q/09axY8/ri2pYKvEBp6BcOGbcDPrx2Kso+YmPPIy4vU1kd8+632LblvX+2Jduut2oLPkiP8mtB4kJmpdUgWL9bh56fyn/+oxMbC+ecfY9eucZhM8fj7d2LYsA00azbEd4oLCzHcfjuDv/kGnd0ON9+sfUsYONB3OprQBC/RqhUsXgxvvw16Pfz4I5x/PpScBu0T6HR6evV6l169PgTgeNZX7J1lR/n9Jy3f7toFI0eie/dd3yb5JtQq8vP13HGHjnvugcJCuPfeVbz88g3odDbatr2Zvn2/Racz1I7yRYtKX1Y8+SS8/nrt6GkgWL9+fbkXw0eOHOGpp54qd82uXbvKDWKc+nePHj0QkXJLUa688koSExMxm81s2LCBu+66C1VVOeuss8rJOAY3qsIjjzyCqqpcUWZ/q8WLF2O322nRogUAO3fuJD8/v9zymFMxcuRIRMR5XPSp+Oeff/jnn3+qtaW+0KqVtrVcv37a9kcTJ0Jq5dt1+AzNzpnC4PPXo1f8yTkX9g5fijpsEKxcWbuK6xHG+lKcmZmJoii0b9++3Oft27cnLS2tUpm0tLRKr7fb7WRmZtKxY8cqrylb53nnncfChQvp06cP6enpvP7664wdO5b4+HhCQ0Mr1W2xWMpt8uM4f1pVVecAjKtwXO+uHEDfvn1LzpdWvZI9eBAeesjAxo3aONgFF6h88YVCnz6gKGYOHHiMjIwfAWjb9jZ69focnS7ILZur5XnkCMarrkK3fz/SsiXKn38i48eX20TSU66+ukeuwht/eqrTGzlPZeuLpzeyDjlQefNN6NRJz8yZej79VEdKisqPPyoEBVWU84RrUNBQhgzZRELCDZhMe9i162J69fqE9u2na5tGbN+O/p130L/9Nrrly5GBA1H/8x/UGTOgZFOyuo4Fb3zqiR8daCj5FFy/d0eOwBVXGElM1NG6tfD33ypjxghmcyJxcVOwWI4QENCdgQNX4O/fq1p73PLXgQMYb7oJfUICqtGI/a230D3xhLYTnhucm/JM7cnWR/tryDyffhpGjtRx++0G4uJ0jBlj5OGHOzFpku982qHDYxgMbUlMvJeTJ//E0iud/tvDCXjsRfRhYehnzWLA0qUoX3+N2ru3T3TWhPrKp+C7nFof7W/rVh133tmf5GQdBoPwwQfrGDr0GkQstG59Db17f4eiCIpSuU3e+EpZuhTjbbehU1XU6dNR3nkHyuwTURV69uzJsWPHPGpHAQEBiHY6Z53IOa4XkTqz1RtZV+SsVisfffQRBoPByckbnrVtb1moquq0sbrYbdVKmxEyYYKRQ4d0TJworF1rp2XL2mujwc1G03/IMhLiryLrAgv7zOn0v3wKPPo4fefNazR9AVf16KQu51aVwYkTJ+jcuTORkZGMGTPG+fm8efP48ccf2bdvXwWZPn36MH36dGaVmaa2efNmLrzwQlJTU+nQoQP+/v788MMP3Hbbbc5rfv75Z+67774qNzIqKiqiV69ePP/88zxTxUyEV155hVcdmyWVwS+//EJwcLDLvOsbigLLlvXi55/7YbUaCQy0c+ede5k6NQm9HnS6kwQHv43ReBARPWbzPVitVwFebj1dBi0PH+b8114jMCeH4tBQtsyZQ4EbJ8A0oQneIDKyIx9+OBKbzUDfvtm89FIULVpYfaihmODgj/Hz2wKAxXIFZvN0HGPOzVJSGPrZZ7SJjwcgr0cPYh99lJw+fXxoQ+3DZDIxbdo08vLynG9nXEVjy6dJSS2YO3cMOTmBtGljYs6cLXTtWohen0JIyBz0+iwUpSNFRXMR8WxpZWXoEBXFiI8+ws9kwtyqFdEvvEBOv34+q78JTahNZGcH8P775xIf3waAq646xN13x2M0+q7baTDsISTkTXQ6E4rSjaLC/9B1TSyDv/4ao9mMPSCAvXffTdKUKdr0lAYKb/IpNL6c6sCqVd358ssh2O162rcvYs6c7+nXbyY6nRmb7VxMphcAvxrr8QRt9uzh/Ndew2C1knLRRex46ikwuDbbxLGkvmvXrvj7+9eKfU04PWG1Wjl27BhpaWnlNmatCunpwcyefSFZWUGcfXYuc+duplmzmuW8gdG4neDgN9HpFDougz7vQ0HXrux64olG0Vd1NZ/W2yCI1WolODiYP/74g+uuu875+YwZM9i1axcbNmyoIDNu3DiGDx/ORx995Pzs77//5uabb8ZkMuHn50e3bt14+umnefrpp53XfPjhh8yfP5/kahZVTZo0id69e1e5H0llo+xdu3YlPT2dVm6eYGKz2Vi9ejWTJk2qcCxVdVAUhUOHDtGrVy8MLibqsrKrVqXw+utns22b1hG45BKVzz9X6NlTuyYvL4L9+2/DZjuJ0RhK376/0Lz5OI91VsZTt2YNhptvRldYiAwahP2ff6BLF59x9fYeeSLrqT+90Xmm8PRGtjq5TZt03HCDgZwcHeecIyxdaufss0v/720bPfvsnqSmvsPRo1qntGXLS+jb9xf8/Epmmomg+/FHDM8/jy47G9HpUB58kMR77qHnsGF1Fgve+DQnJ4f27dt71GlvCPkUXLt3GzZosZKfr2PgQGHZMjsdOijs37+cwsKHsdkyCArqz6BBK/D37+gTnSgK+rlzMbz5JgDqhRdi/uEHVu/Z05RnGpi99dH+GhNPu13bX+yDDzR+Y8eq/PyzQufOvtNbVLSb+PirsNlS8ffvwsCBSwlMC8J21100KzmhQr3kEpQvv4Tu3X2iszLUVz4F3+XUump/Nhs8+6yezz/Xrps0KY8vv4zl2LErUZQCzjprEv37L0KvD/SZznJyGzZgvOoqjGYz6pVXovz2m3NGZo2yikJiYiIGg4EePXoQGFi9jafCYrEQEBDglow3ciJCQUEBzZs3L3eqTm3q9Ea2Pnh6o9ddObPZzJEjR+jcuTMpKSkuxe7+/TBxopGMDB3nn68wY8Zyrr56Qq220czMP9m//w5ApfO/wfR+zwQ6Heqjj6LOnattXuJjnWVRJ/nU7S1XfYjRo0fLI488Uu6z/v37y4svvljp9c8//7z079+/3GcPP/ywnH/++c6/b775Zpk6dWq5a6ZMmSK33nprlXaYzWbp3LmzvPrqqy7b3phOM7BaRebNE/H313Ybbt5c5MsvSzf+VVVVjh37WNavN0p4OLJt2zAxmZJ8oPcUnj/8IGI0akZccolIbq7XOhoCGsJu6nWB043n3r0i3bpp4diunXa6gQO+4nry5GKJiGgm4eHIli09paBgd/kLMjJE7rqrdEf6du20YxVsNq/0uoqGcppBQ82nv/9emjfHjSs93Cc/f7ts3NjamS8tFvdOF6sWWVkil11WGhMzZohYradd+6sKZwpPkTOHq9VqlRdf3CotWqjONLdunW91FBcfkaiofiUnM50lOTkR2ilzH38sEhRU2vn55ptaO/WgoeRTb+qri5jMyNDyqSPFvf66SF7eDomIaCnh4UhMzCVit5tqTb/8+quoAQEiIMqll4p4cMJLYz4d5nRFY+HpaezExoq0aqW1mcGDMyQ7u/afGydOfOs8jevwB0NKG22XLiJLltSq7tP+dJhnnnmGr7/+mm+//ZaEhASefvppjh49ysMPPwzArFmzuOuuu5zXP/zwwyQnJ/PMM8+QkJDAt99+yzfffMPMmTOd18yYMYNVq1bx9ttvs2/fPt5++23WrFlTbtOfmTNnsmHDBpKSkoiKiuLGG28kPz+fu+++220Orkxl8hXsdjvbtm1zS2dUlLZr+0svgdUKU6eqxMfDAw9oS8oVxcy+fdM5ePBJROy0azeN4cM3ExTUw2OdFSACb7yhbRVvt8Ntt2kL3Vq29ClXb+31Cdc60nmm8PRGtia5/v1hyxZtB+6MDBg/XgtLb3CqzjZtrmH48C0EBp6N2ZzEzp1jOHny71KBtm3hhx9g7VqkTx/NkIcegmHDXN6Mqj786dDbGOqsSV9V9+6TT7SDq6xWuP56zR1nnQX5+VHs2jURuz2b5s1HM3ToOvz9XV8CU62/du2Cc8/VlAUFwU8/wfz5Lr+h9EhnLch5K+spGmKeqQ00Rp7nn5/G1q12hgzR0tyll2obqNY0F9lVvYGB3Rk+fDMtWozFbs8lNnYSW6Lexf7IIxAbC2PGQEGBdgzelVfCiRNe6/QlaktXQ4tLR4qLiNBeJC9ZAo8/vpOdOy9BUfJo2fJCBg9eisFQyWZdHup0wtEXvfVWdBYLqaNHo/z5p3YqgBuw2+3s2bPH49NhioqKPNrXwxM5b+CNzsbE0xu93tjrbp4ZMgRWrIBmzYQ9e9pyySVGUlJqV2fHjtPp3Vs7vTN5+G6OrnoQzj4bUlLgmmvghhuqzaWe6PQFXNVVr4Mgt9xyC/Pnz2fu3LkMGzaMiIgIwsLC6F4yXTE1NZWjR486r+/ZsydhYWGsX7+eYcOG8dprr/Hxxx+XO/5o7Nix/Prrr3z33XcMGTKE77//nt9++43zzis91jUlJYXbbruNvn37cv311+Pv78/WrVudet2BJ1OuPIVOp6NVq1Yu6SwogBkztOf+nj0QGiq8+246//wjdO2qXWM2H2PXrotIT/8B0NOr1/v07/8TBkPp+lF3dFZqs6Kgf+IJbRQG4PnntY59DVPHPNXrjb3ecvUETTxrT9YVuU6dtM7YpEnaUY5XXaUd5uIpKtPZrNkgRo6M5qyzJqKqRcTHX8+RI68iUmbjpgkTUHft4uTLLyOtW0N8PEyZov3ExXnNszZQG/rqg8Op905ES1dPPKGVH3kEfv9d6yfn5UUSGzsJRcnD3/9cBg1aiZ+fe8t3qvTXTz9pCTspSetkbNkCt9/uC5pNeaYWZZt4uo7evbWwvusu7XS6F1+E666DvDzf6PXza83QoWto0+ZaRCxYLLNIS/sSzjkHNm6Ed97RTqILC9OOxfvll0pHYerLp42p3qp0VXfffvsNxo6Fo0c1l0RFwaWX7icubiqQT/Pmoxk8+F8MhhCf6XTCatUGwEr6ospTTxH9wgvgwX4pOp3OoyVLDri7hM1bOW/gjc7GxNMbvZ7KeZJnRo+GFSsUWrY0Exur47zzYOfO2tXZpcsTdO8+F4DDfl+SFj5LS94GA/z1l/ZG8bPPtKTuI53ewmVdbs8xaYKINNzp2yIiS5eKdO1aOmvpzju16YdlkZOzQTZtalcydTRUsrPX+twOa26unBg1SjNCpxP57399rqMh4Eya0ny68rRYyq9K+c9/7PL3377lqig2OXBghnNq4Z4914vNVlDxwuxskWeeEfHz04zR60UefFAkLc1ntjjQUKZvN5R8arWKTJ9eGgevvVY6cz4nZ6NzaVNMzMVitxd6rU9EtOB74olSpVOnajFQwbbTt/2VxZnCU+TM4XoqT1XVVv05lpr17q1N9fYVVNUu+/Y9VDqN+/AcUR0NOS5OZOTI0vZ2/fUi6ek+0dtQ8qk39dVGTNrtIi++WHrLp0zRlhYWFx+RyMguJcsKh4vVmuMzneWQkyMyYULp83TBAq95Ni2HaXhoLDy9jR2r1SpffLFS+vfXlhcGB4ssXuxjI0+BqqqSmPhUSU41SGbmMi1pjx5d2rDHjtXyq49w2i+HOR1Q19N7IiMjq9SZlqZN377qKu1c6Z49tVnVCxdCq1aarM1mIyXlE2JjJ2KzZdCs2TBGjtxOq1YTPNJZJY4exTBpEh23bUMCA+HPP+Hxx33G1ddy3sp6iiaetSfrjpy/P3z/Pcyerf392msG5s8fQUGB72zV642cc858+vb9Fp3On8zMv4iJGUNx8eHyss2bw/vvQ0KCNtVQVeHLL7XXqG+8AcXFHvP0JU6X5TCOe1dUBNdeC999px0i8dVX2oaOOh3k5kawe/cUFKWQs86aSP/+S4iKivU+nlNTYcIE+O9/tX/+3//B/7N33eFRXNf3zOyqgRAdRO+YXgyYYlNtsI1b4hrbcdwTmzhuSVxiOzaOS+LEJbZx+cUtjnsF2wiQQELCiCKEKCqAAAECCYR62Toz9/fHaIZdacvszM6uhN75Pj5frefOvWfefefNvn0z76ef5L3xTOIZCT+jvnrR1nUmXGjvPDkO+O1vgc2b5feUHjwIzJol36uEIy7HWTB8+Bvg+TsAAEePLkdx8TIQicD48fJylGefBaxW+ZfMCRPk/5rAVSvOlsdhWl63ujrgyiuBv/9d/vuRR2SJS0gox65dF8LpPI6EhLFwOv8GjksMS0wvlJTIy0/S04HERODHH4Fly/TQ84qZl5en+zGRxsZGXY9d6PEzAiMx2xNPI3GN5GtEZ/r2tSMrS8CSJYDNJq+oe+UVbY8X6okpiiJOnboGvXvfBEBEQcF1qBvSAGRny/cviYmyPXUq8NRTgMdurG1ZT9kkiEHwEdxyjed5DBgwoFVMIuC99+QVSV99Ja9Q+vOf5cdgliw549uvX08cPHg3Dh78Q/P7P270ev9HKDED4ocfgClTwOfkwJWYCHHtWvmh+jBwNcvPqK9eMJ7m+Ybqx3HA88/Lq/p4npCZOQgzZ1qRmxvemP363Y4pUzYiNjYZTU35yM2dgZqa9Na+I0bIk4ebNgEzZgCNjfJS3nPOkZdxNy89jEZ7KnHbwzmDxRswYACqq3lceKG8Sj4+Hvj+e+Cuu+RjamoysGfPpZCkJnTvvhgTJ/6ImJhE4/W8ZQtw7rnyN8GkJFk3ly83ZRtPpjPm+TKe+jF9OpCbKz/1Z7fLrw275x6v+2fdcS0WC4YM+StGjnwTAIeysndQUHA9RNEhv2PnqaeA7dvlCZDTp+UJ55tvBqqro9am7em8/mJ5Xrd9++Tl+4qufvaZ/B4YSarC7t2L4XAcQnz8MEyatA4DB04Ifz1v3QrMnCn/oDBgAPDzz8DSpUZpgud59OnTR7d/qDtdGPUzAiMx2xNPI3H1+hnVma5dgdWrZc0kAv74R/nxXbc7/DF5nsfAgYNwzjkfoEePpZAkO/buvRyN9kL5B+7CQnm20+0GnntOfoFJRkZYeOqB1lhsEsQglNk/URQhimIrWxAEL1vyeGZKsT0/d7vdXrZyfrfbDY7jMHjwYIiiCCICESE/342FC+UXndbWyvfU27ZJeP55Nzp3lmMIggCX6wSqqn6tvv9j+PB/YdSo/8Ji6QRRFNVZs5Y8iAhDhgwBEQXlJNhsoIcekl+WU1MDafp0ZL78MtznneeXk8Kjpc3zvMpVuVbu5p6tcPJlS5Kk5uuPk792Urgq52nFL0A7KQjESakXTx6iKKrvovHHyZOHYhMRBg4cCJ7nQ649SZIwaNAg8DyvufYUHkrsQJx8tRMAVQT9cfLHw5Or39rzYfM8j/79+7dqm2DtdM89wPr1Anr1suHgQQ6zZxP++U8RkhS49gRBaB4oBqq5+OOUmHgepkzZhi5dZkAQqrF79xKUla3wylfldMEFEH7+GdL//gcMGiQv87r5ZmDWLAgbN4LjOAwZMsRLF7TUnmfdhlJ7Cg8zBrRI6ikRgeM4uFz9MW8eh23bgO7dCRs2AFdcIV+rmpoN2Lv3MkiSDT16XIJx474HUQx4nsegQYO8+pTWmuY5DoN++AHcokXAyZOg8eMhbtsGXHFF0Jr2xY/pqXE99TfWma2nvnicbXqqxFTQklPXrgJWrwaeeUYCxxHefReYO5dw8OCZsU7hqrX2FH0aMGAA+ve/F+PGfaWuvtu791I4HFUyv6lT4d6yBfTYY/Lk42efgSZMAL92Lfr37w+O49q1nip5arleih1qX/V3j/rTT4SZMwkHDsjD1s8/E6691g1BqMfu3ZfAZitAbGx/TJyYhpiYfgH7ra571K++AhYuBE6fBk2dCmzbBmH8eC9OCrT2VcUWRREDBgwAx3GqHxF52S3vlzzbQtlO1d8xvmwAiI2NVWsy2PGSJLWKq3y+YMECPPDAA+oxLXP3HFdjYmLAcRyKioowa9YsxMfHY8qUKSgpKQHHcV4rYlrmm52dDZ7nUVNTo5krx3GIjY1Vz+GPkz/b089f2/hqp1Cvr5Z8g9lGNFU5xmoF3nxTwj//KYLjgHffBZYulVBXZ+we1d94DlgwduyX6guo9+y5GHb7EQj9+kH67jvg229B/foBxcXyu+5uuw1cdbWue9RgOhpsPNcCNgkSIlasWIFx48ZhxowZAIC9e/cCAIqKilBUVAQA2LNnD4qLiwEAeXl5KCkpAQBs374dpaWl6rlOnToFAMjKykJlZSUAID09HbW1tQCA1NRUNDSvx09JSUFjY6P68libTcAzzwg491wemZlAp06EO+8sxLZtwLBhtUhPTwcAVFZWIivrFeTmTkdj4w4ASZg8ORVE1yInJwcAUFJSgry8PABAcXEx9uzZo3IqKChAVlYWdu/eHZhTSQls06aBe+01AMCxa67Bya+/hq1vX2RlZfnl5HA4IAgCUlJSIAgCHA6Ham/cuBGpqakAgNralpyyAMgvz83OzgYAlJaWYtu2bcjKysKhQ4f8cvLXTocOHUJWVha2bdumtlN2djbKy8sDtlNG82xnME4A0NDQ0IpTVlYWTp065ZfT9u3bW7XT/v37kZqaCkEQQq69zZs3Iz09HYIgaK49hRMATZxattOJEyewdu1aCILgl5O/dtq9ezfWr1+vLkH1xclXOwmCgDVr1qCqqiqk2gOAyZMb8OqrG3HVVRLcbg6PPGLBZZcBRUVVAdtJEARs2LABuc3LRwLV3rFjNkyZkgmL5VIAIg4evB8bNy7F4cNFrTn9/DMqlywB9u/HgdtuA3XuDOTkwLpwIdxXXYXtn3+uq/YUKDy01J7CKRxLG6Oppw6HAzk5TsycKWL/fg4DB0p49tl0zJkjX6sNG/6OvXsvhyTZIYozMH7896ipaUJWVhYEQUB6ejo2b96s6VopnPbl5UH6zW/A338/OEEAbrgBO1esQInV2opTy5pW6pjpafj1tLi4GLt370ZWVhYKCgoipqdpaWkAcNbraWNjo9qmvjjxPHDXXeV46aV89OgB7NjBYdo0wtq1wKFDh9SxTmvtlZSUQBAErFu3DkeOHEGfPtcC+Ad4PhG1tRuxZctMlJcXyO20eTNq/vxnIDsbjQMHgisvBy67DKcuvBBNhYXtSk8B45p64sQJ1Q7WV33do2ZmZuH224tx5ZVAfT2HceMqsWMHMGpUA1JTf5B/PW7cAaKumDx5PZqauiAzMxNZWVk4ceKE5uulcGp1j0qE8oceguWGGwCHA9Vz5qD0k0+AAQO86lqJE4yTv7rOycmBJElqbYuiqPoJgqDabrdbPcblcqGpqQkNDQ1wOp2w2WwA5P5vb37M1eFwwNG8FMput6t2U1MT6uvrQSTvRuJyuQDIfUv54tjQ0KDWUUNDg/oltsHjud76+np88803ePbZZ9XzERHq6+sByF8sFVsQBPWYv/71r4iLi8P+/fuxZs0adO/eHeXl5Rg9ejSampoAoBUn5XN/nGw2G5xOp8rP5XKBiFBXVxeUU319vfpFPj8/H/Hx8ahrfsOyJ6cjR46A53lYLBZwHIe4uDiMHDkSzz33HNxut1c71dXVgYjUdvLFqWU7KXECcWrZTk1NTeoXeL2aCpwZK+vqajFx4nqsXAkkJBDWr+cxZw6wbVuFoXtUf+N5WVkVJk78EcAwuFzl2LNnMbKyVqGyqgq4+mqkv/kmHHfcAXAc+P/+FzR2LA48+ijW/PBDSOO5Mt5VVQW+7wZaa4Sie0FBDLqgvHSlsrKSiIgEQSBBEFrZbrfbyxZFUX3Zi8Ph8PqcSH4RjKetvMzL5XKRIAhUWlpKGzc6afx4SX0XzcUXEx06JKkvj1FiiKKLDh58RH052ObN46mubr96jNvtVvP1tD15uFwuOn78uBrfJ6evvybq2pUIIKl7d6JVq8jtdpPD4aCVK1eSzWbzy0mSJJIkqZUtiiKVlpaq10jh1DL3lrbT6VTz9cfJXzspXJ1Op5qvZ9v4a6empib15T2BOBGRl+2Zr9vt9svJVzu5XC46duwYiaIYUu0RETmdTtVXa+1JkkROp5NWrlxJTqczICdf7eR2u+no0aMkiqLm2lNsT67+OPmyRVGko0ePel0zLbWnXCOZq4tWrBApPl7ub337SrRmjeC3nURRpGPHjpHT6dRcey6Xi44c+QdlZPCUkQHatm08NTYWBK69sjKi3/2OJJ6X+11MDAl3303SkSOaa0+pV6WPaq09Jfeampqwvxg1UnpaV+eiFSskSkqS23XCBImOHTtzfSorU2jjxjjKyADt3n05ORwNXtekZTtrqumDB0maMkVuL4uFhJdeIpIkzTXN9NQ8PfU31pmpp5IkqVydTmeH0FPlmgSqvSNHiGbMkNT3qT/5pEAlJUcDjnW+2qllvm63m+rqdtDPP/eljAzQli3DqKmp2Lud6upIevBBkjhO7qdxcSQ98gi5Tp9uV3pKpF9TfemM1rquqxPo8sub1HvTe+6RqLHR1RzTTnl5SygjA5SV1ZWqq7ep1yVYv9V8j2q3yy8RVxL4wx/I7XD45GGz2bx0JhRNdTgcdOTIESooKKCmpiYikutBOYdynC9b4SuKot9jfNmKn/KZluMVW6kvpR8rx7W0ffFwOBwkSRJNmzaNnnrqqZDyTU1NJQBUXV2tmatnzECcPO3XXnuNlixZ4vViVIVHSUkJAaDU1FQqLy+nkpIS+vjjjyk+Pp7+85//eMVR4mrh53mc5xinxddms6m1o0dTFZ1Ras9TU3NyROrfX9bP3r0lysoyfo/qbzxvbDxC2dlDKCMDtH37VHI6a9TcRVEk2ryZpHHj1P4oDRpE0uuvk9TUpGk8V/qow+EIeTyvrKzUpKdsEkQnorGbQXU10e9/L98YyAVO9OmnZ3Yv8ITNVkK5ubNImQDZv38ZCYIt5FwDwuEguu++MwPOrFlER46o/7ujvvn+bEVH4UnUmuvevUTjx58p9UcekXcSCSeqq9Np8+ZkysgAZWYmUFnZB+qA6Bd798qv2lcSi4mRbwI9+mEgtJXdDCKlp5WVRMuXy9qpXLK5c703Y6ms/Ik2bowleQefX5AoOkPOqRVSU4l69Dgj3OnpIZ+io/S/jsKTqONwDZWnw0F0771n+ujllxOFaW6AbLaDtGXLcMrIAP38c2+qr9/R+qCdO4kWLjyTQO/eRG+9RdR8k+0PbUVPjZxPD4cjR+TN/5QxMiZG3v1HgSi6ae/eq5vHts5UW7s5pJw0obaWaPFiOQGeJ/r3vwMebrTv+dzhQ5KIGhuj8y/AvYLn5AAR0fz58+mBBx5Q//+QIUPo+eefp9tvv50SExNp0KBB9K5HAwLw+vf000+rkwt5eXnqcatXr6ZRo0ZRfHw8LViwgD788EMCQDU1NZqu6Z49e4jjODp9+jQREVVXVxPHcXTttdeqx7zwwgs0a9YsL79FixbRm2++6XN3GF95Kj7Lli3TlFe4EY7dYQLV7vHjRFOnyl0hLo7oiy+MZBsYTU376eefe5O8a95CEoQWnJxOon/9i6hv3zN62qcP0QsvyH02ACKhp+xxGIMI1xJGf3A45JeWX3MNkJxMWLFCrqLbbpPf9XTTTfILHT1RUfENduyYgvr6rbBau2H8+G8xfPi/kZm5JeR8lWXfrfwOHpTfuP3mm/LfjzwCZGXJr3oPA/zGNcnPqK9eMJ7m+YaT54QJ8jv07rlH/vull4ALLgAOHw5fzC5d5sLheAPdul0ESbJj//47sG/frRCERv9OEyZA+PFH5L76KqRFi+SXUik7yfz2t8CRIyHnoRVm1I9ZNXnkCHD//cDgwcDTT8vvQRwyBHjlFRF/+UsGunSR41ZW/oD8/F+CyIVeva7BuHFfgedjW51PczsTyW8CvOQSoLoamD4dwrZtSCdqF/2P6Yy5MfXibOcZFwe89Za8W0x8POGnn4A5cwjNT21ogr98ExJG4Nxzs5GYOBVu92ns2rUA1dXrvX0nTkT6E09A/P57+UXUp0/LO4pMmiS/iVDj8+ahwKzrasZ5JQnIyZHfLTt5MjB0KPCHPwAFBUC3bi6kpgr47W/lY4kk7N9/ByorvwPHxWLixFXo2nVOqxyN1HP255+Dzj8fSEsDOnUCVq6UBd8kCIKArVu3tn7vgM0m75IRjX/Nj2zoxcsvv4zp06cjLy8Py5Ytw7333ouioiLU19ejrKwM48ePxx//+EeUl5fjT3/6Uyv/0tJSXH311Vi6dCl27dqFO++8E48++mhIOUyYMAE9e/bEmjVrQETIyspCz5491UchAGDjxo2YP3+++ndtbS02bdqEK6+8UnOcHTt2YOfOnZg5c6b6GTU/1tKqTYNArx9gnqYOGCB/HbvySsDpBH71K/ldpUTh1/9OnUZj0qQ1sFi6oLY2A0VFN4NIPHNAbCyEBx7Axg8/hPjGG7JYVFTIWzEOHiy/7L+iIgysW+erBWwSxCDMeJmVJMkv1b3rLiA5WZ4A+e47wOXiMG6cG6mpEj78EOjZ09tPFO04cOBeFBZeB1GsQ1LSbEyblofeva8Gz/OYMCH0t2/79PvqK/kNrDt3ykmsXi3f6Ifxjc5hzTcCvnrBeJrnG26enTrJO8d88w3QrZs8KTJlCvD55+GJKfvOw8SJazBs2HMAeJw69T/s3DkDjY17AvoNuukm+QZw0ybgoosAQZD3eB01ShaSlrM1YUB72B1m507gxhvlOaE33pDvE6dOlXcpOHgQeOABDueeOx48z+P06e9RUHANiNzo3ft6jBv3OXjet6ZpaueGBuD664HHHpNF/Y47gE2bwA8Z0m76H9MZc2PqRUfhecstQEYGoW9fEQUFHM47T5Y4LQiUb2xsX0yZshHdui2CKDZi796lqKj4ytt34kRwV14pb7P35ptAr17yL0+XXw4sXgzs3h0ummpMMxCu89rt8ta2v/0tMHCgvOvLc88Be/bI75SdNw946SUJmzbVYd48OSYRobj4Dzh16n8ALBg//mt0736hzxx113NuLmY+8AC4ggKgXz+5QK64wijdwDF5HqNGjTI1RqSxdOlSLFu2DCNHjsSjjz6KXr16YePGjUhISEBycjKsVisSExORnJyMxMTEVv5vv/02hg8fjldffRXnnHMObr75ZvzmN78JKQeO4zBv3jxs3boVgDzhceutt0KSJBQ2v58nOzsbCxYsUH1SUlIwceJEDBo0KOC558yZg8TERMTGxmLGjBm4/vrrW+WXkJAQUr5G/czU1MRE+Xvjww/Lfz/1lLz7ltsdfv3v0mUaJkxYqb58+sCBe70mhXiex7hp08AtWwYcOCDPbo8bB9TXAy+8IE+M3H8/cOyYEcqt8tV0XNgidlCEq3iJgF275K1tBw8GFi0C3n9f3mN94EB5ocXu3UBBQQwWL24ds6mpEDt3noeysncAcBg8+HFMmZKpbn+rbOmlp/BVP7td3n/phhvkG/wLLpCTDsOWYwHjRsDPqK9eMJ7m+ZrF85pr5L54wQVyN7jpJvn7bWNjeHhaLFYMGfJE8za6A2Cz7cPOnTNRVvZ/Pn9t8Ip5wQXyZMjmzfL+2IIgC8no0cCdd4Z1MqStToIQAamp8veUadOAL74ARFH+Oy1N3pLzxhsBq/XMtaus/A6FhddD2Tp87NhP/U6AKHkGbOcDB4BZs+QZs5gYefbsvfeA+Ph21f+YzpgbUy86Ck8AmDWLR26uBdOmAZWVwIUXypIWDMHytVqTMGlSCnr3vhZEbhQW/grHj7/Z2jcmBvj97+VZ00ceAWJjgQ0b5NnUO+4AysrCwrMtToKcOgV88AHwi1/Iv3ddcYU8t15eLn/JuvZa+ftMRQWQmQn8+c88JkzoDZ7nQUQ4fPhxlJW9BYDD2LH/Q69evn+t111bH30EfuFCWE6fBiZOBLZtk3+gMxk8z6Nnz57gWi7D7tRJvhGIxr9OnQxxmjRpkmpzHIfk5GScPn1a3R0mGJTdY5RjOY7DBRdcEHIeCxYsQFZWFjiOQ2ZmJhYuXIh58+YhMzMTOTk5sNvtOP/889XjV61apWkVyJdffoldu3Zh9+7d+PLLL7Fq1So89thjXpy1cvWEXj/AfE21WICXX5ZvPywW4H//Ay6+mIfFEn797959EcaN+wwAj/Ly/6Ck5EnfvjEx8uz23r3A998DM2bI3y3feAMYMQK4/XZ5b22DYJMgEYLndkV6cOSIPBE2YYI8pv7rX8CJE/IvzXffDWzcCBw9Ki+0GDvWjXXr1nnFJCKUl7+P3NzpaGrKR0xMX0yatA7Dh7/gdRPvdrf21cpv3bp1cOfnyzf177wj/4/HH5eXqwwcaIh/0Lh689XRLkZ89YLxNM/XTJ6DB8vl//TT8q9gH34of+Hevj18PLt1m4vp03ehR49LIUkOHDjwOxQV3QRBqA/oB0B+VG3dOiA7G7j4YnkW4IMP5MmQ22+Xb+gNwozrauScbre8wmPqVJny+vXywH/TTfKKkNRUeZGM572K2+1GWtpfUFj4KxAJ6Nv31xgz5mPwvDVonn7b+aef5IG9sFD+ZTIzU36Oqjlwe+p/TGfMjakXHYWnEjc/fx02bHDjuuvkfn7XXfIvnKIY2C9Yvjwfh3HjvkD//ssAEA4e/ANKSp6Cy+Vq7du1q3wjtm+f/EMQkSz8o0YBzzwDNO8mYYSnGQjlvETyIy3ffDMKc+da0K+fPHe+apX8PWXQIHk+aN06eULq66/l7zPKqmTPa37s2IsoLf0HAGD06HfRt++NAXMMqbbsdjmx228H7HacnjED7owMOcEIwO124+eff279owTHAZ07B/wnJSSgThAgJSQEPTYkPx1fwj0R02IlN8dxEEURdXV1Xlvm+kPLayFJkrrDSiiYN28eCgoKcODAAeTn52Pu3LmYP38+MjMzsXHjRkybNg1dunQBILfD2rVrcdVVVwU976BBgzBy5EiMHTsW119/PR588EG8/PLL6q4ukiRp5tqSpx4/Jf9IaOo99wApKUBSkrxQatw4O37+ObTHYbTk2rv3NRg9+m0AwLFjL6C09DX/vjwvz65u2yb/OrVokfyj3UcfyatErrsOaN7tRQ+0XlM2CWIQFoslZJ+qKmDNmqFYsMCCYcPkR6IKC+XnYK+9Vp4cO3lSfrR//ny5VpRYM2bMUGMKQj2Kim7C/v13QZLs6N59CWbM2I0ePRb7zNPTNxR+5x85AuusWfK6x969gbVr5Zkba+AvCkZgJF89fkZ99YLxNM/XbJ5Wq3zvm54uP4N54AAwd64VO3deAI4LD8/Y2F6YOPEnDB/+EgALKiq+QG7uNDQ05AX0UzF7ttxft2wBLr1U/tbw0UfAmDHy2sjmLdD0wIzrquecjY3Ajz8Ox9ixVtx8s7xKp1Mn4IEH5LmeTz+VJ0Z8oarqS8TE/AOAiL59b8WYMR8FnQBR8mx1zSVJLogrrpCXeZ5/vrzsZPbs4L4mg+mMeb6Mp7lQ4nbpYsGXX8pdDABefVV+MqV5V0y/fsHy5TgLRo16E0OHLgcAHD36HA4fXobp06f69h02TF5etmWL3LdtNmD5cmDUKHAffRR4ZiYITzMQynndbmDePCs++WQctm3jQQRMnw48+6y86PfoUfnJoCVL5PtVX7FmzJiBkyffQknJEwCAESNeQf/+dwfNUXNtFRfLP8h98AHAcZCWL4clJQWW7t018zQKi8WCCRMm6PLlOA6dO3fWteJAj59RaI05btw49TEWQM53t45HxiZOnIiePXvihRdewOTJk5GUlOQ1CeL5PpCMjAx069YNU6ZMCTmOxWKBIAjqFrbRaJdIauqSJfJvYiNGECoqErBwoQX/+pd826IFWnPt3/+3zY9zA4cOPYSTJz8J7Mtx8q9TGzbImnrllfJs7DffIGbmTMxavhzc5s2h0tV8TdkkiEEos5+iKKr7V3vagiB42e++K2HQICvefXcysrN5cBywcKGE996TcOoU8Nlnblx5pYS4OHkmSzm/2+0Gx3Ho3r178+zsduzYMRUVFV8AsGDYsBcxduwPiI3tC0mS1FkwqXkvap7n0a1bN3W2UvlcydfTVnnU1QF33YXEe+4B19QEmj8f2LULwoUXenFSzqnFbsmJmvfzbmnzPK9yVfJtycmXLUkSevToAfJ48aC/tmnZTkSEHj16qOcJxsPTVhCIk1IvnjxEUUSPHj3U8wfi59lORISuXbuC53nNtefZ9t26dQPP8wE5+WonJXYgTr7aCQCSkpLA87y22vOwPbn64+TL5nle/cUgECd/7aQgWO3Nnw/k5Um48koJLheHv/ylMy69FDh+XHvtiaIInueRlJSkxlV4cByP/v0fwpQpmYiLGwy7/SB27pyFEydWwOVygeM49OjRA6Io+uc0axZo9WoImzfLj6+JIvDxx7BOnIhzX30VQmmp5tpTcjdjCWeoerp1q4QRI6x4//2JOHaMQ+/ewPLlIo4ckfDaa8CAAf5rurz8A+zbdysACcnJt+Occ96HIEhqHoFqupWeVldDuvJK+csQAGnZMiA9HWKfPq148DyPrl27qrmEUtMtbaan4dFTpe8oXCOlp754dHQ9bWkTkQdXEU8+KeKrr4CEBMLatfI8xIED2vW0pa3kO3ToXzFixAoAPE6efB8nTtwJSbL55zRzJtwbNwJffQUaNgwoL4f1t7/Fgj/+EWJ2dki1Z5aeKtfPM05L27MGeF7AFVdImD79JN54w4XSUgk5OcDjjwuYOFECxwWua47j4HSuxKFDDwIAhgx5GsnJ96l5+KtrRWeUfPxer2++AU2bJv8g16cPxLVrQU88gR69enn1W611rUBrX1VsURTRrVs3cByn+hGRl93yfsmzLazNPyD6O8aXDchf7DiO03S8JEmt4iqfe+bb8hhPHnJN8OoX/EAx77nnHhw6dAgPPfQQ9u/fj88++wwff/yx1zEnTpzAmDFjsG3bNr/n4Xke8+bNwyeffIIFCxaAiDBx4kS4XC5s2LBBnQSRJEl9FMaTh4KWPE6fPo2TJ0+itLQUq1evxr///W8sXLjQS9NCub6KzXGc1xfuUHyNaKpyjHIttGjqOecIyM3lcN11EgSBw5//DFx+OeHUqfCO5/36/RkDBsgvJt6//3ZUV68Jfo+KZk395htg717QTTeBeB598/KAH37QNZ5rAZsECRErVqzAuHHjMGPGDADAnj3yCwuLiopQVFSkflbc/AtrXl4eSppfab59+3b063cKgsBh+PBaPPFEDUpLgSef3IgrrqhE165Aeno6amtrAQCpqaloaGgAIL/8p6GhAatX/4j163+LXbvOh8NxGJLUG1OnbkL37suQlia/4by2thbp6ekAgMrKSmRlZcHtdmP16tXY3DyjVlpaiu3btwMASkpKkNe87Ki4uFjmtHMnhClTwH/4IYjjUHb33di/YgXQv38rTqXNX6Cys7NRXl4OAMjKykJVVZVq++PkcDggCAJSUlIgCAIcDgdSUlLUfFNTU/1yAoDy8nJkN99wlJaWYtu2bVi9ejUOHjzYmlOQdjp48CBWr16Nbdu2+eVUWVkJwLudMjIy1PoIxAkAGhoaWnFavXo1Tp486ZeTr3bat28fVq9eDbfbrbn2FE6bN29Wff1x8tdOADRxatlOx48fV2MGrb0W7bRr1y7VV2vtVVZWwu12IyUlReWntfYAoLHxzI4sWmrPZivFI49sxeuvi4iNFbF+PY+JE4FXXz2F3bu1aYSS75HmHV1acnK5RmH69DyI4iwQuVBcfB+yshaisvIIVq9eran21tXUAKtXoy4tDRUzZ4KTJCRv347tzb/YaKk9pZ3CsYTTqJ5263YcFgvQr18jXnihGkePAhdckAki/zVNRFi//i7s338nAAku12IMGvQ6nE6X5pr20tOCAojTp4NfvRqIj8epf/wDubfdBsTG+qxpxXdf83OvTE+jr6fFxcWqzuTn50dMT9PS0gCA6WmAdjp48KBawwqn664DPvzwEPr0caOoCDjvPMKnn57waqdgeuqrnfbsGYJhw/4LjotDdfVP2LlzHmy24/5rLy0NuO461GzejP133QXq2hVdjxxB/q5dIdVeuPQUMK6pzz57FE8+uQ0TJ24BxwW+Xi1r4Nixj7Fvn7zqo3//B9G37yOa6jozMxOrV6/G8ePHfV+v/ftR9etfA9ddB66hAY1TpwJ5eSjq3x/5+flYvXo1du3aFbSvetaAEicYJ391vXHjRkiSpNa2KIqqnyAIqu12u9VjXC4XmpqaUFtbC6fTCVvzji4OhwN2u121lUc07Ha7ajc1NamPXTQ1NakrGBobG9XaaWhoUO/TGhoa1C+xSi4AUF9f72UrXxY9HwtRjlFsZZJBiel2u9VHXRR78ODB+Pzzz/Hjjz9i8uTJePvtt/Hkk096cXK73di/f796rW02G5xOp8rP5XJBkiTMnDkToihiwYIFKo+5c+cCAGY3r6ysr6/Hjz/+iKuuukrl0XKSwZPrkiVL0K9fPwwbNgy/+93vsHTpUnzyySfqtXG5XOr1VdoJQNB2Uh6HUa6fL04t26mpqUmduNCrqcCZsTIUTe3UyY1f/3o1HnvsMOLigDVrOEycKCIrK3zjeUZGBnr0eAp9+twMIgEFBddhzZp/aR/PJ0xAzRtvYNN776HkkktQ+etfhzyeK7oXFMSgC8oexFVVVUREJAgCCYLQyna73S1skfbulfc+djgc6ufKvtYul8vLlpr3/na5XGS3n6Tc3CWUkQHKyADt3XsN2WwVREQkSZK6l7Ioil622+0mSZKopqam1edKvqrtcpH4j3/IG70DJA0cSI2rV/vgccZW8m1pOxwOWrlyJdlsNr+cJElSc29p19bWktPp9MvJl+1yuaiuro7cbrcXP19t07Kd3G431dXVebWBP36exzQ1Nal7WQfi5K+d6urqvNrAHz/PY9xuN1VXV5MkSZprzzP3mpoakiRJc+1JkkROp5NWrlxJTqczKCdfuSv5+q29ADw889VSe6IokiRJVFVVpR6vtfaISOWqXBMttaf0s+3b62jGDFHdEv2660SqrAyuEZIkUXV1dVB+TqeTjh17lTZujKGMDNCWLcOorCxdbZdQas+1dSvlPPww2Ww2zbWn5F5bW6tpH3Yt0KunoijSnj0u+vZbbXoqigIVF/9R1dDi4j9TZWWlWi9aa1rRU/dnnxF17ixr5eDBRLm5QWtaaWfPvsz0NLp6qtgK10joqSRJKlen08n01I8daKw7dkygGTNknbVaJXr33dD11Fc71dRsoqysHs36OpRqa3dr09OyMtr5+99HXU+J9GuqL50Jdr0kSaJjx96ijRutlJEBKiy8IyRNDdhvjx4l6bzzSBlQxT/9iYRmrffXb7XUtc1m89KZUDTV6XTS6dOnqaCggJqamlR+yjmU43zZoiiq9envGF+259ij9XjFFgSBampqSBRF9XMlF0/bF49QYra09fh6+gXilJOTQ127dlXbTslb4RmIk792Cne+/mybzUYFBQVks9l0aarLJX+HVGov1HtU5bvg7t1E55wjEUDE80TLl4vkdIZvPBdFF+3adQllZIA2bkygY8fe8tKFYJqq9FGHwxHyeF5VVaVJT9kkiE4oA4yeAUspYKWxtaCycg1t3pxMGRmgzMx4OnHiHbWzhA1lZUSLF6uDDf3yl0TNA6ge6OHZHsF4nn0wytXtJnr2WSKrVe5KyclEKSnhzbGubjtt2TKseYCJoSNHXiRJEkI6hxGeRjQwnOfSykEUXVRY+BtSJkCOHv2n3nTlBn7kkTNaeeGFRKdP6z+fBnSU/tdReBJ1HK5m8rTZiH71qzNd8Q9/kLunUTQ1HaAtW0ZQRgZo06ZuVF2dEdSnreipkfOFykEQ7FRUdKeqq4WFt4Q8DvlFSgpRjx5yw3brRrRqVXjOS8Zr0m63U2FhIdnt9rDlZBY8JwfOFmzbto0+/vhjr8/aC0+jtRNOPW1oILrlFu9bmfJyw6dVIQiNtHv3Jao+5OdfT253rSbfSOgpexzGIMK1hNEfRNGO4uL7sXfvpXC5TkIUB2HSpM3o3/93Ib2Ix+12Y9WqVf7zXb0amDRJfktvQgLw7rvAt9/C3aVLYD+TEDTfMPsZ9dULxtM832jyJHLjqafk9zyNGSO/6HjpUvkt3R4rww3lm5Q0A9Om7UTPnleDyI2SkseRlzcfdvuhMDLyDzOuq1ltJYo25Of/EqdOfQzAgjFj/ovBg/+kr0bsdkiXXw689JL895//LL98tlcvTe7tqf8xnTE3pl50FJ5a4iYkyLtC/e1v8t9vvCFrbUWFsWuUllaISZOykJQ0G4JQiz17luDkyU+MUAkasz2dFwAcjmPYtWsuTp58HwCPIUNewIEDV6vvVtKKVm0sCPKOAUuXAtXV8tZrO3fKL00M5hsBuN1ubNiwQfN7BzwhSRJqa2t17UKix88IjMQ0m+d5552HW265JeS8jMYNlx/QNsaOxER5m+sPP5RfKL9hAzBliry7XjhytVg6Y8yYlbDbfwOOs+L06a+wY8dU1NdvDxMj39CcZ8jTKwxEdGaWqbZW24yWJ7TObjU07KJt28arM2gHDtxHjY1VulaASJJENputta/dLv90okwDTppEVFAQ3E8DjMzi6Y1rJF+9voyneTGN+Eajdn3FtNmIHnzwTBcbMYJo8+bw5SuKIh09+i5lZXVpXinWmU6ceFfTeYy0qRmPw5ihpy5XFeXmzmm+NglUWfmT+v9CvuZ2O9HFF8uPv3TqRNIXX4Scb3vqf0xnzI0ZTp0x27c93At88w1Rp06yzo4eLdHu3XbD10gQbJSff616H1ZS8je/52wrekqkX1O1cqiuTqeff+7VvFKmB1VVpYantsrLiRYsODNgLltG1Pz4S1DfEGC0JmtqanT9mq88jqHnGunxI9K/QsJIzPbE00hcPX7KShCbzdamxsiCAqIJE+Rux3FETz55ZlVdOMac2tps2rJlaPPqZSsdPfpPkiT/bRUJPWUrQdogiCQcO/Yv5OaeB5utADExfTFxYgpGjnwdsbFdgp/AD6wtt7QtLATOO0/+6QSQ95Tctk3eozmQX4SgN66RfKPBlfE0z7ct8ExIkLdzTE8HBg0CDh0C5s4FHn8caH5nll9fLeA4Dv363Y7p03eja9f5kKQmHDjwO+zdezmcznIjVNo9nM4TyMubh/r6bFit3TB58nr07HmZ1zGar7nTCVxzDbBuHahTJ4g//QRcf72uvNpCXZrtZ9Q3GjHbs85Ewret3wtccw3w88/AwIHAgQMc5s+Pw7p1xmJaLAkYN+5LDBr0ZwDAkSNPYf/+uyBJkV0R01ZARCgtfRm7d18Et7sSiYlTMW1aLnr0WAzAYG1lZsr7mW/cCHTuLC/xWbHC9368LX0jjEhvFc1wdqItjR3jxslfAe+6S56BfO454MILgRMnAvtpjZmUNAvTpuWhd+9rQSTg8OE/Y+/ey+ByVeg+r1GwSRCDUN7EHC44HMexe/diHD78ZxC50LPnlZgxYy969rzU6426evJUfYmAd96Rlxju3Qv07i0/DvPaa0B8vH+/CEJv3LBdowiB8TTPt63xXLhQ7m633irvzf73v8tzkHv3GstX8bNaB2LKlHSMGPFy8+4GKcjJmYCKiq/DQc1n3LZ8TpttP3bunAObrQCxsf0xZcomdO06p1U8Tdfc5QKuuw5ISQESEiCuWoXV9fVnff9jOmNuTL3oKDz1xJ06FcjJAWbOlFBby+Gyy4B//lO+7dEbk+N4jBjxEkaNUrbQ/QB7914GQagPfKIQYNZ1Ded5BaERhYW/wqFDfwIgoW/f32Dq1M1ISBiqxtJVWy4Xiu+8U/7GdfIkMH48sGMHcOONGnKKTv/LysrS9TgMNe9aEqqvXj8jMBKzPfE0EtdIvm1x7OjUCfjPf4BPP5UflcnKAiZPBn78UQzLmBMT0w3jxn2F0aPfAc/Ho7p6LXbsmIKamnSj1FrF1ISQ15gwEJE5y7dPnfqKNm3q3rxsu1OrJe2eb9QNFarv6dNEv/jFmaWGS5YEfAuOkZhGl2zpiRuWaxThpWmMpzm+0ahdrTG/+46oVy+5C8bGEr30EpHbHT6ejY35lJMzVV3CXVBwM7lc1a1828ry7XDraV3ddnWp9tato8luP+LTV1N7uVxEV10lN1Z8PNH69R2m/3UUnkZ827LOhNO3vd0L2O0S3XbbmV26brqJqHkzBUMxT5/+kTIzO1FGBmj79olkt5eq/6+t6ClR+B+HaWo6QNu3T1CXsh8/vqLV9dHVVo2NJHnek95yC1Fjo2b3aOlMfX09exzGBN+O9jhMWx4jDxwgmjLlTNd8+GGBHI7wjTkNDXto27axzfepHB0+/CSJ4pm3WrPHYdoBxOZ9uEVR9GkLguBlSx4vz1Fsh6MaRUW3orDweghCDRITp2P69Dz07n27eqzb7QYRwe12q7byNwAvW2ref1qxlRkxIS1NfuPNypWgmBiIL70ErFkDsXdv9RhfPBQOwThpsZXcPW0l95acFK6BOPmylfwCcfLXToq/Fh6edst28sepZTt5xgvEScnX01b2Ig+19gRBUPcuD8TJVzspHEKtPUmSvPah98fJXzt55htK7Sn7uAfi5K+dFIRSe4C833wwTldeKWL3bhFXXCEvMnjkEXmlSFGRM+TaIyIIguDFIzZ2NKZO3YLBg58AwKOi4lPk5EzE6dNrW3FSzh+Ik792CjfCoaenT6/Frl0Lm5dqT8PkyVmIjx/it/0dDof/mna7Qb/6FbBqFRAXB+n77yHMnw/Au51DrWmn08n0tI3pqa+xzmw99cWD6WlrTsHGOl/tFBcH/PvfTfj3vyVYLPKTFRdcQDh2TFs7tdQFxe7a9WJMnZqF2NhkNDXtxc6dM1Ffv7NN6qlyfi3XS7F9tXtFxSrk5s5AU1M+YmKSMWlSOgYMWAZBEFrVQEj3qMePg+bOBbdyJSg2FtI770B4/32gc+eQ6lrvPaoCrX3V0xZFEUSk+rW0W94vtbSVv7UeH6otSZLX5wqUz5V8Pe2WPIzkaJSf5zl8cfJne/r5axtf7WRGvlpsvZqqHKPwD/c9qiiKGD5cxJYtwL33yvm98ooF06cDO3YYv0d1u93o3HkCzj13O/r0uR0A4ejR57Br1wI4HKWaeIRDU9kkSIhYsWIFxo0bhxkzZgAA9javZS8qKkJRUREAYM+ePSguLgYA5OXloaSkBACwfft2lJaWquc6deoU6uqysWXLhOZdC3gIwq8wbNhP6NRpNFJTU9HQ0AAASElJQWNjI9LS0rBmzRoIggCHw4GUlBQAQENDA1JTUwEAtbW1SE+XlxZVVlZi04YNkB5/HPGXXw7uxAlg9Gic/P57bDv/fIDnUVJSgry8PABAcXEx9uzZo3IqKChAamoqdu/eHZRTdnY2ysvldxBkZWWhqqpKtWtrawGgFSeHw+G1VErhJAgC0tLSkJaW5pNTVlYWAKC8vBzZ2dkAgNLSUmzfvh2pqak4dOiQX07+2unQoUNITU0NyKmyshIAkJ6ernLKyMhQ2zQQJ3/tlJqailOnTgXkBMCrnfbv348NGzZAEISQay87Oxvr16+HIAh+OflrJwAh1Z7C6cSJE0hPT4cgCH45+Wun3bt3q/lqrb3KykoIgoD09HS1DrXWHgA0emzhEkrtCYKA9evXY+fOnQE57dmzB/X1xVi1CnjyyRJ07izh5585zJgRj0cfrYPLpa32UlNTUVtbi9TUVKxZs8aLkyTx6NfvCTQ2voiEhFFwuU6goOBSFBffj+rqMpWTcv201p7CyfMmUi/CracVFV8iP/8KSFITune/CFVVT8Bmi1GvVcv2dzgcSE9PV23Pmk5bswb49a/BffcdpJgY4PvvUXnuucjKylLbWeu18uQkCAI2bNiA/fv3B+TE9DRyelpcXIzdu3cjNTUVBQUFEdNTpT2Ynvpvp0OHDqljndbaKykpgSAIyMhIx+WXH8H69UDXri7k5XGYPh148809AdtJ4drY2OiTU5cu0zBqVBokaTBcrjLs2jUPGRkvqRyjpaeAcU090fwCgO3bt6Os7ARKSp5BYeEvIIp1SEqag8bGl0E03ut6AaHfo+54911g5kxweXlwdeuGTc8+i+OXXorsLVs0XS+Fk957VKVdAG191bMG1qxZg82bN4OI1NoWRVH1EwRBtd1ut3qMy+WCzWZDfX09nE4nbDYbALn/2+121VYmOO12u2rbbDY0NDSAiNDU1KROZDY2NqpfgJXaVWzli6CSCwD10Q0iamUD8hdLxVZ4KF9i/XFqamoCgFacFN9AnJQJToUTEaGhoUHl549TfX29+kXbkweAgJz8tZPL5VLzDcSpZTsp+Qbi1LKdmpqa1C/wejUVODNWmnWPWlxcjPh44Pbbc/H66yeQlOREfj6HWbM4PPUUkJm5xdA9qjwxYkFx8VUYO/Zz8HwX1Ndvxo4dk3H06Gcqj6qqqpDHc0X3goIYdEFZalhdLS8xFwSBBEFoZbvdbi9bFMXmJT7fUnHxXygjg6eMDFB29hCqqckil8ulLuXyXD6k2J7LihSbiLxsJQYRkbhnD0lTp5Kynkm8/XaihgYSRZHcza/9FQTBy/bFIxgnX7bD4aCVK1eSzWYLLyeP3P3ZZnHy5KHYTU1N6pKts4WTr3ZyOp20cuVKcjqdZw0nf+2kcFX8zeZUXCzQwoVqN6UxY4jS0oSwcRKERtq//1718ZitW0dTVdVmdbmhzWYLmZPWfdi1wLierqQjR16jjAyOlL3oRdGhX0/dbhJvuIEIICkmhtzffx/w+jA9ZXoaiJO/dlK4Op3Os4ZTW9BTX5wOHnTTlCkSAURWq0RvvimSJOnnJEkS2WwVlJe3qFlXLXTs2NttQk+J9GuqojP19WW0e/dl6pixf/8yEkVneO5Rv/6apIQEWV/Hjyd3cXHI1ysUTr5q2WazeelMqJyURxqamp+xkpofi1Bs5RzRskWPRzQEQVAfExFFkebPn0/333+/ekzL3H3ZhYWFNHPmTIqLi6PJkyfT4cOHCQDt3LnTby7p6elqDYabky9b9HgcRvk7IyODAFBVVZXfdvrwww+pa9euYW2DQOe02WxUUFBAdrtdV+0p9zxK7UVKU0+dIrrmmjOPGE6YIFFOTvjGiaamYsrJmaZqTmHhvbRy5VfkcDhC5lRdXc0eh4kEeF6+hBaLRX1btKdttVq9bJ7nYbcfROfOj+P48Rcgv1zq15gxYze6dZuLmJgY9ZwxMTHgOE61AXkm1Gq1guM4cBynfu5p8zyPGIsFeOUV8DNmgMvLA/XoAdtHH4F7/30gMRE8z3u8/dziZXvy4Hke9fX14Hk+ICetdktOnjw8bWqeXVXy4nnei5/n5562xWJR8/XHyV87KVwV3sF4eNoKAnHy1U5WqxX19fXgOM4vJ1/txPM8mpqaQEQh1Z5yTGNjI4goICdf7aRwCFh7PtqJ4zjYbDYQkebaU2xPrqHUHhHBbre34qG1nRRorT2r1ar+ShOs3lq208iRFqxfT/i//7OjTx/Cvn3A4sUW3Horj1OngtdefX19K13wtC2Wzhg9+i1MmrQWsbH9YbcfwJ4983Ds2HIAQki1p+ROLZafhgN69JSIEBf3OUpKHgRA6N//9xg37jPwfFzQmgag/rKjtr8ogrvjDvBffglYreC+/hrWX/zC65pQ8y+AnjWqtaaVGvGsV6an0dVTf2Od2XrqiwfT09a2wlVr7Sn9zGazqbFHjLBi82YON9wACAKH++7j8bvfAZLUup0Urp7t4YtTQkJvTJq0Bn37/gaAiEOH7kVc3P9gsfAh1Z5ZeqrE1XK9FJvnefD8URQUzEN19WrwfDzGjPkIo0evAM/HGrtHtVqBF18Ef9114Ox24JJLwGVnwzJiRMB+a9Y9qgKtfVWxrVaruhJC8eM4zstueb+k2MCZR5T8HePLBs489qHleJ7nW8VVPv/uu+/w3HPPqce0zF2xgTOrKp555hl07txZXYU8ePBglJeXY+LEiQHz1cpPsanF4zj+OHnapaWliI+PV1d7ePKYM2cOysvL0b1794DtFOr11ZJvsDY2oqnKMcq1MPseled5xMfX4+uvOXz1FdCrF5pXhfB46ilAFI3dowJAp04jce652Rg48I8AgFOn3kZi4qNwOg/qGs+1gE2CGIQQ4hLGioqvsGvXDFitB2CxdMXYsZ9j7Nj/wWrtqinWpk2bgsc8ehRYtAj44x/lbR0vvRRCXh4yevUKOV/NMcMMvXGN5BsNroyneb7tjacoCujfPx179wpYtgzgOOCTT4AxY4C33wb8PeIYSswePS7GjBn56NPnRgAiSkufR+fOj8LhOBJyvmZc11DPSSTi8OE/ID7+SwDA0KHLMWrUG+A4bdsXtrp2kiTvD/e//wEWC/Dll8BVVwX3CwHtqS47Ck8jvoynuQhnvp06AZ9/Lu/MxXHyLgiLFgHl5cF9/YHnYzFmzEcYOvQZAEB8/Lc4efKdkHJVYpqBUM97+vRXSEx8BA7HQcTFDcHUqZuRnHyr5lh+r5vTCdx+O/CXv8h//+EPwI8/AklJ7VJnduzYoWviSpmQDtVXr58v9OjRA126dAkp5qFDh3DBBRdgyJAh6NmzJywWC5KTk/1um+o5kRgK9PBctWoVFixYgKSkpFb/LzY2FsnJyV4TE/4Q6XZpT2OHp9911wEFBfKGeaIob6U7fTqQm2s8Js/HYuTIf2HixNWwWnvBYilBaenfQ8pViakJAdeJMPiF3qWLNTUbKSODo7S0CdTQcDC8SUkS0YcfEnXpIq9V6tSJ6J135M+jACNv9m1PYDzPPrQFrtu3E02bduYRmRkziHJzw3f+U6e+oE2butOGDb3IZjsdsr8Zj8OEeq6qqjTKyAClp3N09OibxpIQRaK77pIvtsVC9NVXxs4XZrSFmowEOgpPoo7DtS3yXLOGqGtXubv370+0bZvxcx4//h6lpU0kh6M+ZF+zHocJ5XxOZwVlZXWhjAzQzp0XktMZ+rjgE6dPE82de0Zb3zSo1WGA0ZpUHocJdXeYaMDzMREiovnz59MDDzyg/v8hQ4bQ888/T7fffjslJibSoEGD6N1331X/PwCvf08//TSVlJQQAMrLy1OPW716NY0aNYri4+NpwYIF9OGHHxIAqqmp0ZTnnj17iOM4On1arrvq6mriOI6uvfZa9ZgXXniBZs2a5eW3aNEievPNN33uDqM8DuOZw4cffkiDBg2ihIQE+sUvfkH/+te/qGvXrl7n/OGHH+jcc8+luLg4GjZsGD3zzDPq4xZERC+//DJNmDCBOnXqRAMHDqR7772XGhoavGK0PKcCo7XTlvT0q6+Ievc+07WfeILI4QjPuRsaSmjdugVks1WE7KtV/9hKEIPwXAalBd26zceECWloalqOuLjBIceqrq72HbOiArj6anmmvaEBmDMH2L0b+N3vAI4L7Ks3pomIRr7R4Mp4mufb3nnOmAFs2wa88QaQlATk5Mif3X8/UFdnPGafPjdgypQ82GyPaVqJ5ivfcCPUc/bocRGGDv07bLY/o1+/3+qKV11dDUkUgWXLgPfeA3heXgly3XXB/c7y/tdReBrxZTzNhVn5XnKJrKljxwJlZcC8ecB//2ssZp8+v0FT03LwfHxIfkpMMxDKeWNje2PUqPfgdP4S48f/iNjYXiHHanXd9u0DZs0CNm2SB7LVq4Hf/z64n5GYJkOSJNTW1vrckUQUmwL+E4RGOJ11EITGoMeG4tcyl1Dx8ssvY/r06cjLy8OyZctw7733qi/yLisrw/jx4/HHP/4R5eXl+NOf/tTKv7S0FFdffTWWLl2KXbt24c4778Rjjz0WUg4TJkxAz549kZ6eDiJCVlYWevbsqb4UEwA2btyI+c07tAHyS0E3bdqEK6+8UlOMbdu24Y477sCyZcuwa9cuLFy4EM899xwAefUAEWHdunX49a9/jfvvvx+FhYV499138dFHH+H5559Xz8PzPF5//XXs3bsXH3zwAdLT0/HII4+ExLc9jR3+/JRVIddfL68Kef751qtC9MaMixsAu/1BWK3dQvJTYmoBmwQxCK3b8Hiia9d5ALQt124ZKycnp3XMVauACROAlSuBmBjgxReBrCxg5Mjgvnpjmoxo5BsNroyneb5nA0+LBbjvPvk+8sYb5ac13nhDfkTmiy/kNSJGYsbF9Ycojgx+oJ98ww095xww4GEIwhzd8XK2bwfddx/w7rvyGvn//le+2MH8OkD/6yg8jfgynubCzHxHjQK2bgWuvFJ+WuO224CHHgKcTiNc9d1Wm3VdQz1vz56/hMNxKzjO92MOwWJ5Xbf16+UJkEOHgKFDgexs4OKLg/sZiRkBiKKI/Pz8Vp9Lkg2bNiUG/Pfzz12wZUs3/Pxzl6DHhuInSTZDnJYuXYply5Zh5MiRePTRR9GrVy9kZGSgqakJffv2hdVqRWJiIpKTk5GYmNjK/+2338bw4cPx6quv4pxzzsFNN92EG4OMoy3BcRzmzp2L9evXg4iwceNG3HrrrZAkCYWFhRAEAdnZ2ViwYIHqk5KSgokTJ2LQoEGaYvz73//GxRdfjMceewyjR4/G/fffj4uba1J5V9Lzzz+Pxx57DLfeeiuGDx+OxYsX429/+xveffdd9TwPPvggFi5ciKFDh+K8887D8uXL8dVXX4XEtz2NHYH8eveWnxz++mvZzs8HZs4EnnxS1tVo8dQCNgliEJ4vqYlErIsvvvhMzPp64I47gF/8Ajh9Wp4I2b4deOwx+dtTIF+9MSOEaOQbDa6Mp3m+ZxPPfv2Azz4D0tKA0aOBkyfl7+lLlgAlJdHro+3hnAHjWa24eM0aWN55R54A+fBD4Ne/Du7XQfpfR+FpxJfxNBdm55uUBHz/PfDXv8p/v/YacPnlMTj33Mi3aXs6r79Y6jV/9115uU1dnbwyeft2YPz44H5GYkYIMTExuOCCCzS9Z6K9YNKkSarNcRySk5NRWVmJrl27anrJZFFREWbNmuX14lLPyQqtWLhwIbZs2QKe55GZmYmFCxdi3rx5yMzMRE5ODux2O84//3z1+FWrVmleBaLkOXv2bK/PlL8Vrrm5uXj22WeRmJio/rv77rtRXl6ubpebkZGBxYsXY9CgQRg4cCBuu+02VFVVqVvrakF7Gju0+F17rbwq5IYbvFeF7N4dHZ5aEPpUL4MXlH2flVkni8XiZQuCAI7jVNtTTJTlOsrnPM/D7Xarb7Z1u93q23Tdbjd4nkdlZSW6deuG2C1bgNtuA3f0KMBxoD/9CcJf/4qYxERIkgRRFBETEwNJkiBJEnieR0VFBXr06IHY2Fj1c6vVClEU1TfFt+QhSRJqamrQvXt39e3bvjjxPN/K9uTnj5PyUiVBELxsi8WC06dPo1u3boiLi/PJyWq1trIFQUBtbS26d++uvsXbX9u0bCciQk1NDbp166a+Ddkfv5bt5FkP/jgpuzQotme+PXr0AACfnHy1k9vtRmVlJfr27asug9RSezzPw+Vyobq6Gn369FFrI1jtKW+VBs68LdwfJ1/tJAgCTp8+jb59+6q1H6z2FNvtdqOqqkrNV0vtKf3s1KlT6N27t3rNtNSewsmzn2qpPV/9TGvtKW/J9szXF6eLLuKRm+vGK69Y8MILPNavByZOJNx3nw1PPmlFt26xmmvPc6bc8/Ngtaf4mbGEM5J6arVYQA8/DP711wEA9H//B+GmmxADBK1pvXoKQG3nXr16ISYmhulpG9BTf2OdmXrqWe9EBLfbzfTUh61lrNOrp57ttHw5j/HjBdxxhwXp6RzOOUfE3//O4c47OUhS+9RTIHRN9aUzId2jnjqFnn//O6xvvCHzuukm8O+/D4qLg9Bc4y1rIFi/Nese1fMahaKpLpcLVVVVXjuDyLUZj7lzG9U6VXYA8bTlR2ZEWCwWr11C/B2v2Mo183wRqecxPN+p1fEtzw/AK1/lGOWaK59T8w4poijC5XKpXyhb3gN62p7/lPie+qZ8Hozr/Pnz8cADD6C4uBj5+fm44IILcPDgQWRmZqKmpgbTpk1Dly5d1LpZu3YtHn30UZ+PJvnj7Zm7JxSukiThmWeewTXXXNMqx7i4OBw9ehRLly7F7373Ozz77LPo0qULtm7dirvvvhsulwudOnXyysNfHUiShPLy8pA1VYHbT38y4x5V63jeuzeP//3PjWuuseD3v+eRnw/MmkX47W/teOIJC/r3136PqnBV7FDGc8/7iEBgK0FCxIoVKzBu3DjMmDEDAFBQUABAnl0sKioCAOzZswfFxcUAgLy8PJSUlAAAtm/fjtLSUvVcp06dAgBkZWWhsrISAJCeno7a2loAQGpqKhoaGgDIS75sNhsKd+5E6Q03AIsWgTt6FE19+wKZmWh48kmkZmYCkJ+RS09PBwBUVlYiKysLkiRh9+7d2LJlCwD5+b3t27cDAEpKSpCXlwcAKC4uxp49e7w45efnY+/evUE5ZWdno7z5NetZWVmoqqpSbX+cHA4HBEFASkoKBEGAw+FASkoKJEnCnj17sH79er+cAKC8vBzZ2dkqp5ycHOTn5wfl5K+d8vPzkZOT45eTr3bKyMhQ2zQQJ0B++3RqaqrKKSMjA/n5+Th9+rRfTv7aKS8vD5IkhVx7W7Zswe7duyFJkubaUzgBCMrJXzvt2LEDkiSFVHsAsHfvXpWr1tqrrKyEJEnIyclBTU1NSLUHQN3+Lhinlu0kSRJ27dqlq/YkSUJubi6OHTvmlxMAbN6cjvvuq0VBATBtWgVcLg6vvNIZI0YQ3nrLDYdDW+0pnJRY/jj5a6dw3LRHS0+3vPYapMWLwf/73wAA1xtvwHHzzZpr2oieKnXM9LRt6enevXuRn58fUT1NS0sDAKanQdpp586dkCTJFD31bKfOnddh/fomjB9PqKmx4He/43H++YQ339zcLvQUMK6pJ06cUO1g16vVPeqpU7BcfbU6AeL+61/x43XXAfHxAet606ZNyM/PD+l6eXLSc4+qxAnGyVddr127FsXFxepW6YA8ySRvm94ZRHGw2SRYLJ0hSbGw2wkWS2eIYgycTg4uF6/aFktnuN0WuFy8arvdFlgsneFy8artdHLq8Q4HIIoxsFg6w24nSFIsOI5DQ0ODep/W0NCgfpFV+ADw2ka2vr5e/XLucDgAyF84lWMkSVJXPhARXC6X3K5ut7riQbHHjRuHrVu3qsc7nU71GjscDvX8drtdtW02G5xOJwD5URSXy4UJEyagR48e+Nvf/obJkyeD4zicf/75yMzMxIYNGzB37lw19w0bNqBbt24YPny4z4kZT67KNRg7diw2b94MQL6XbWhowNatW9V8AGDKlCkoKCjAyJEjMWjQIPTv3x8jR47EwIED4XQ6sWPHDgiCgOeffx6zZs3CoEGD1Brz5ARAvWaNjY3qF/Ompib1C7xeTQXOjJWRukcNZTy/8MJaFBYC8+adgChyePvtThg50oJHHhFw6pS28VzhUVVVFfJ4ruheUAR8bSqDXyhvnq2uriYiIkEQSBCEVrbb7fayRVFU3+zraH6FrvI5kfzWX09bat7ZxeV0krR+PUnjx6vbRUh33kmuqioiIpIkSX1TsBJDsZU3GvuzBUHwsn3xCMbJl+1wOGjlypVks9l8c2q2ldw97bbKyVc7NTU1qW9qPls4+Wonp9NJK1euJKfTedZw8tdOClfFv61ycjpd9PXXEg0demYXmTFjJPr6azeJYvDaU7TIZrOFzMmM3WFM19O8PKIrrjijoTExJLz5Zrvop0xP2y8nf+2kcHU6nWcNp/asp0ruLhfRP/8pUGKiRAARz0v0+99LVF3dPvSUSL+m+tKZoDVQVUXud94hadw4WVfj40n64os23VdtNpuXzoRa18oOH01NTSo/5RzKcdG0RVFUbUEQ1F1TRFGk+fPn0/33368eM2TIEHrllVe8eEyePJn++te/qpyUv5VjDh8+TABo586dJEkSHT16lGJjY+nBBx+kffv20SeffELJyclqDUqSRMePH6dzzjmHtm7dGjD3q6++miwWC/3pT39S8+nRowdZLBb66aefVH7Lli2j++67T+UheuwOo/yt7A5T1fxdKTs7mziOo3/84x+0b98+ev3116lbt27UtWtXNf6aNWvIarXS008/TXv37qWCggL64osv6C9/+QtJkkR5eXkEgF599VU6dOgQ/fe//6UBAwZ4cVV2h/HFz2azUUFBAdntdl21p+iMUnttoT8F0ojVqyWvnQ67dJHo8ccFqqoKPJ4rfdThcITMqbq6mu0OEwkoS8ksFgssze/h8LStVquX7bl8W7E9P4+JifGyuepq4JVXEDNpEriLLgJXUADq2xf48Udw772HmOblgxzHqUvWeJ73spVlQ+Xl5eq5lc+VfD1tTx4cx+HEiRPq0sJAnLTYMTEx6jVTbCV3T1uSJJSVlalxfHHyZfM8r+brj5O/dlK4KucJxsPTVhCIk692slgs6i8v/jj5aieO43Dy5ElIkhRy7fE8j/LycnUZmd/a89FOSuxAnHy1EyD/Ui81L8fTUnuK7ck1lNqTJAkVFRWt2kZrOynQWntKPzt58qSai9bas1gskCRJXc0QiJ9nO8XGxuDqqwkbNpzAq69K6NkT2LePw3XXWTF3Lofs7ODtpJw/ED9f7RSuXy49YZqe7tuHmF//GtzUqcCPP8o7wNx+O2jfPpz8xS/UJataa1qvnirX7eTJk17LkJmeRldP/Y11ZuupLx5MT71t5bGWQGNduPRUyd1ikXDjjSdRVET41a8ASeKwYgWHMWM4fP55DIjah54C+jU16PWqq0PMZ5+Bu+wyoG9fWO+5B1xhIcTevUEZGeBuuEFzXSs64+/amXWPqkBrX1Vsi8WCU6dOgYhUP47jvOyW90uebaGsDvB3jC8bkFddKGNVsON5nm8VV/ncM9+Wx3jyAM7smBIs38GDB+Pbb7/FTz/9hMmTJ+Pdd9/Fs88+63WM2+3G/v37Ybfb/Z6HiDB37lyIoogFCxao+SgrQJT/8jyPH3/8EVdddZUXDwUteSj27Nmz8d577+GNN97A1KlTkZaWhieffNLr+l5yySX46aefkJaWhvPOOw+zZ8/GK6+8gqFDh4LjOEyZMgWvvPIKXnrpJUyYMAGffPIJXnjhBZ9tFuiaGdFU5RiFm9n3qEbG80suIaxceQLffy9hyhSgoYHDiy9aMGwYh+XLOTQ1+daIYDoabDzXAjYJYhCmDFxEwKZN8gv6BgwA/vhHYP9+UGIiyq66CmJeHnD55SHneejQoZDz1etnFNHINxpcGU/zfDsSz+PHD+G++yQcOgQ88QSQkCC/iP+CC+T3JjevcAwrzLiuYT/n4cPylg/jxwPKm9t/9SugsBD44ANIgwez/meCn1FfvWA6Y55vR7wXSE6W8Pnn8iYnY8YAFRXArbcC8+fLOyCEE2Zd17Cet7FRfkP3VVcBffrI2rpmDSAIwKRJEP/2N2x/911I06eHnGN70xnlMSs98HxcIhJ+LbFx40a89tpr6t9HjhzBgw8+6HXMrl278Mwzz6gxlb8VDB06FESEKVOmqJ9dfvnlKC4uhsPhQGZmJn71q19BkiR069bNy2dBkBem3nnnnZAkCZdddpn62cqVKyEIApKSkgAAO3fuRH19vdd2ub6wYMECEJGaAwDccccdKC0thc1mww8//IA//vGPqKmp8bq+F198MTZv3gybzYa6ujps27YNd999t/r/H3roIZSVlaGpqQnffvstbrnlFq84t912m/qIlT+0p7HDaB89fPgQLr9cQm4u8N13wMSJ8t4ey5cDw4YBf/ub/He4oDnPgOtEIoAVK1bQ0KFDKS4ujs4991zKysoKePzGjRvp3HPPpbi4OBo2bBi9/fbbrY755ptvaOzYsRQbG0tjx46l7777znDcljCydFFZyqQs+1FRXU30738TNS8pVP9NnUr07rtE9fUhx4om/PI8y8B4nn1o71xPnCD67W+JLBZqXs5NdNdd8ueeMMLTjMdhwqanx48T3XMPkdV6Rkevuopo927DuUYL7b0mtaKj8CTqOFzPBp5OJ9GLLxJ16iTLicVC9PDD3rdlbUVPjZyvFYemJqKvviK65hqi+Hjve9OxY4meeYaoqCgsOUcSRmtSeRzGbreHObPww/MxkbMF27Zto48//tjrs/bC02jtnA16KopEX39N5PGGB+renej5589oaiT0NKorQb788ks8+OCDeOKJJ5CXl4e5c+fi0ksv9TvDWlJSgqVLl2Lu3LnIy8vDX/7yF9x///349ttv1WO2bNmCG264Abfccgt2796NW265Bddffz22bdumO24gGJ7BIwKad3pB//7AAw/Iv1J26gTceae8rVhuLvDb30Lq3BlHjx7VPROnx1evn1FEI99ocGU8zfPtyDz795d3KMzPl1eCSBLw3nvAyJHySpG6upDT8xk33DB8zooK4OGHgREjgHfekX+hXLJE1tGVKwGPbQCVeKz/hd/PqK9etJX+ZzY6Ck8jccN9jWJjgccek1fV/fKX8haQr7wirxD58kv5Vs4I2tJKEN7tBvfDD8BNN8krPq6/Hvj2W8DhODOI7Nkj74f59NPyRUDbaSuzIUkSTpw40Wp3ES0gIjidzpB99foZgZGYZvM877zzcMstt4Scl9G44fID2tfYEe4+yvPylrp79gBffAGMHQvU1MjSMmwY8Pe/ywvP9EJrnlGdBHnllVdw55134q677sLYsWPx2muvYdCgQXj77bd9Hv/OO+9g8ODBeO211zB27FjcdddduOOOO/Cvf/1LPea1117D4sWL8fjjj2PMmDF4/PHHceGFF3ot/Qo1biDoLV5rUxP4t98GJk+W91D/73/lAWbSJGDFCqCsTP7WMmMG0PxskyK8eotQj6+RmEYQjXyjwZXxNM+X8ZTvTb//Hvj5Z1lm7HbghRfkOYLXXgOMrK5tS5MgMY2N4J96Chg+HHj1VZnY3LlAZiawbp2so37isf4Xfj+jvnrR1vqfWegoPI3ENesaDR4sL+dOSZF1tKxMfsJuyRJg//6QQ3nFNAMhndfhgOWOO3DJrbfCeu21wOefA01NwNChwCOPyD/IHTgAPPecvJ69xXP3ba2tzIIkeb/LIVRo3b4zXH5GYCRme+JpJK5ev/Y0dpjVR3keuOEGYO9e4NNPgdGjgaoq4PHHgdGjrfj++5Fo3ogo5JiaEPIakzDB6XSSxWJp9ajK/fffT/PmzfPpM3fuXLr//vu9Pvvuu+/IarWqy2UGDRqkvuVYwSuvvEKDBw/WHdcXdC9d3LOHxNtuI3dc3Jk1QPHxRLfeSrRlC1Hzm4HPBpwNS7a0gPE8+3A2cpUkopUricaMOSM9Q4dK9NBDO8jhaKePw9TXk/DMM+RS1qgDRDNmEK1bd1ZpKdHZWZO+0FF4EnUcrmcrT7udaPlyIuV2LiZGomuu2U81Ne33cRh1h5cBA4geeoho69azTkuJ2OMwZyPaC0/2OIx/uN1EH39MNHLkmVu6m24KvT216p810ASJmaisrIQoiujbt6/X53379sXJkyd9+pw8edLn8YIgoLKyEv369fN7jHJOPXEB+YVEni/NUfafdjgcSEhICML2DLitW2H96CPwAKSxY0F33w3p5puB7t3lA5r3+PYFURRx9OhRDBkyRH1rr1bo9TUSU5kd1TNLGo189foynubFNOIbjdpt6zyXLpV/sfz4Yw7PPmvBkSMcXnvtXNx8s6PlkyJB4XA4QnPwQNj0ND8f1meegQWANH48pOXLQVdcIf9CGUBLFbD+Z46fEV+mM+bGbE88jcSNxDWyWORfLG+4AXj4YQtSUnh8++1oLFjgxO9+FxpXI3oKhE9TxeefR05REab94Q+IiYuTP9SgpUDbbquWMFqTJSUlICJIkhTyr+MulwuxsbEhx9XrR82Paij5RiKmEd9o8DQSN1Q/SZLUx2jayxgZyT76q1/Jj8p8/LGEZ55xY9kyC9zu0KYrtOopRxTBB8w8UFZWhgEDBiA7OxuzZ89WP3/++efxv//9D/v27WvlM3r0aNx+++14/PHH1c82b96MCy64AOXl5UhOTkZsbCz++9//4sYbb1SP+fTTT3HnnXfC4XDoigsAzzzzDJYvX97q888++wydOnXSzNvicGDif/6DYxdeiOqxY1stJ2RgYGCIBJxOC378cTiqq+Px29/uDdnfZrPhpptuQl1dnfrGdq0Il54CwPgPPkDtqFE4cf758tpKBgYGhgiDCNi+PRkbNgzGo4/mwGIJ7dbaiJ4C4dVUhuCwWq1ITk7GwIEDEadMFjEwaIDL5UJpaSlOnjwJQeMEY0eFKMqTzaFCq55GbSVIr169YLFYWq2+qKioaLVKQ0FycrLP461WK3r27BnwGOWceuICwOOPP46HH35Y/bu+vh6DBg3CkiVLQh6w3FdcgV1paVi8eLHXns9nG9xuN9IYz7MGHYUn0HG4Xn65fp71BvYzC6ueLl6Mgg7QVh2lJjsKT6DjcO0oPJcscWPmzMjrKRA+Te0obWWUpyiKOHz4MHie1zVpFUkQERoaGtClSxdwZ/EPr+2FZ319PRISErBo0SJYraF/DWd9NDi06mnUJkFiY2Mxbdo0pKWl4Ze//KX6eVpaGq666iqfPrNnz8aPP/7o9VlqaiqmT5+uXqDZs2cjLS0NDz30kNcxc+bM0R0XAOLi4nzO9vI8r7sIY2JiQvIVRRHFxcUYNWqUruVIenyNxFQQKk8jcaNxjRQwnubEbE+121F4ArL26UVb0FOA9T+z/Iz6AkxnzIrZnngaidveateIngLh11TWVoFjHjp0CF27dkVlZSV4nkenTp00ffFWHoOIi4sL6Yu6Xj9AfvzC5XLB6XSGVGdGYrYnnpHMV5IkVFZWonPnzoiJicHBgwfbxRjZ3vqo1vaP2iQIADz88MO45ZZbMH36dMyePRv/93//h2PHjuGee+4BIM9snzhxAh9//DEA4J577sGbb76Jhx9+GHfffTe2bNmC999/H59//rl6zgceeADz5s3DP/7xD1x11VVYtWoV1q9fj59//llz3LYMu90ecV8jMY0gGvlGgyvjaZ4v48kQDKz/meNn1DcaMdtT/+soPI3EbW+1ezago7SV3W7HyJEjwfN8SDvFEBHcbjdiYmJC/rKtx0/xtdvtSEhIiGjM9sIz0vnyPI/BgweD47h2NXa0tz6qBVGdBLnhhhtQVVWFZ599FuXl5ZgwYQJSUlIwZMgQAEB5eTmOHTumHj9s2DCkpKTgoYcewooVK9C/f3+8/vrruOaaa9Rj5syZgy+++AJPPvkknnrqKYwYMQJffvklZs6cqTluKND7i4geWCwWTJ06NaK+RmIaQTTyjQZXxtM8X8bTXJihfZHUUyUe63/h9zPqqxcdpf91FJ5G4rbH2m1P5/UXq6O0lRKzX79+6NOnT9S2ddUCt9uNrKwszJs376x/fKI98IyNjVVXKrSXsaM99lEtiPpb5JYtW4YjR47A6XQiNzcX8+bNU//fRx99hI0bN3odP3/+fOzcuRNOpxMlJSU+V29ce+212LdvH1wuF4qKinD11VeHFDcUiKKoy09vrPz8fF0x9foaiWkE0cg3GlwZT/N8GU9zYUY8pjPmgPE0z5fxNBcdqXbb03n9xeoobeUZ02KxID4+XtM/5RGImJgYzT5G/JR/giCE7GMkZnviGel8lQmQtlC7ZvsZ9dULrbGiPgnCwMDAwMDAwMDAwMDAwMDAEAlE9XGYswGRXmo4YcKEiPoaiWkE0cg3GlwZT/N8GU9zcbY8DsP6X/j9jPrqRUfpfx2Fp5G47bF229N5/cXqKG3VEfpfR+FpJG5Hqd32yFML2CSIThDJe8BXVVUBgNeyOE9bEARwHKfaPM9DFEXYbDbU1tYiLi5O/ZznebjdblgsFtW2Wq3gOA5utxscxyE/Px9jxoxR3wIuCAJiYmJARKotSRJEUVRtSZLAcRz27t2LsWPHIi4uTv3carVCFEUQkWp78hBFEUVFRRg7diwsFotfTjzPt7IVntXV1YiLi/PJSdkeShAEL5vneezduxdjxoxBfHy8T05Wq7WV7Xa7sW/fPowdOxY8z/vk5K+dJElCUVERxowZg5iYGJ+cfLWTzWaDzWZTt2Tyx8lXOyn5jhs3DhzH+eTkq51cLhcKCgowadIktSa11B7P83A6nSgqKsLEiRNBRJpqz2q1wu12w2azoa6uDjExMZprT/HNz8/HpEmTwHGcptpTbJfLhcLCQjVfLbXH8zyICHv27MGECRMQExOjufZiYmLgcrnUNlXyCVZ7nv1s3LhxiI2N1Vx7ygu1PPPVUnuKLhQUFOCcc87x0oVgtafkoPTRhIQETbWn+Cn1rmihEURDTxUue/fuxcSJE9VzmqmnCvbs2YPx48cjNjaW6Wkb0FN/Y52ZegrIL4tTNJXjOKanPmwtY1049VSJv2fPHi9daE966nmeUDXVl86YfY8arN+acY+q9D3lugfrq5414HQ6sX//fowfPx5EFLCvhktTW/bbQH01XJrqT2fMvEdtqTOhjOfRuEcF9I3nRjQVkHWmqqoKnTp10lx7Ru5R9Y7nRu5RnU6nem9nsVhCGs9ra2u9dNAf2CRIiFixYgVWrFgBl8sFABg+fHiUM2JgYGCIHhoaGtC1a1ddvkxPGRgYGM7AiJ4CTFMZGBgYFATTU47CNe3cwSBJEkaPHo3c3NyQt2Kqr6/HoEGDUFpaiqSkpJB8Z8yYgZycnJB8jPrq9TPC00jcSF8jxtO8mEZ9o1G7HYUnEWHatGk4cOCA5j3Z/SFaegqw/meWn15fpjPmxjTiy+4FAqOt6CmgX1NZW5kX06gv05nAYLVrXkwjfnp9I6GnbCWITvA8j9jYWEMz9klJSSE3rMVi0VX0RnyNxAT08TQSNxrXCGA8zYzZnmq3o/AEvLd6M4Jo6SnA+p9ZfkZ9mc6YF7M98TQSt73Vbrj0FDCuqaytzIvZnvpfR+FpJG5Hqd32xlOLnrLdYQzg97//fbuKqdc3GjyNxI3GNTICxtM8X8bTXIQzbnvj0FH6X0fhacSX8TQXrHbbxvnMjNmR2qo99b+OwtNI3I5Su2cjT/Y4TBRQX1+Prl27oq6uztBsZ1sH43l2oaPwBDoO17OB59nAQQsYz7MPHYUr49l+cDZw0IKOwhPoOFwZz7MLkeDJVoJEAXFxcXj66afVt+SerWA8zy50FJ5Ax+F6NvA8GzhoAeN59qGjcGU82w/OBg5a0FF4Ah2HK+N5diESPNlKEAYGBgYGBgYGBgYGBgYGhg4BthKEgYGBgYGBgYGBgYGBgYGhQ4BNgjAwMDAwMDAwMDAwMDAwMHQIsEkQBgYGBgYGBgYGBgYGBgaGDgE2CcLAwMDAwMDAwMDAwMDAwNAhwCZBTMJbb72FYcOGIT4+HtOmTcOmTZsCHp+ZmYlp06YhPj4ew4cPxzvvvBOhTI0hFJ7fffcdFi9ejN69eyMpKQmzZ8/GunXrIpitfoTango2b94Mq9WKKVOmmJtgmBAqT6fTiSeeeAJDhgxBXFwcRowYgQ8++CBC2RpDqFw//fRTTJ48GZ06dUK/fv1w++23o6qqKkLZho6srCxcccUV6N+/PziOw8qVK4P6tFUdYnraGkxP2wc6iqae7XoKnD2ayvS0NZietg8wPfUNpqc6QQxhxxdffEExMTH0n//8hwoLC+mBBx6gzp0709GjR30ef/jwYerUqRM98MADVFhYSP/5z38oJiaGvvnmmwhnHhpC5fnAAw/QP/7xD9q+fTsdOHCAHn/8cYqJiaGdO3dGOPPQECpPBbW1tTR8+HBasmQJTZ48OTLJGoAenldeeSXNnDmT0tLSqKSkhLZt20abN2+OYNb6ECrXTZs2Ec/z9O9//5sOHz5MmzZtovHjx9MvfvGLCGeuHSkpKfTEE0/Qt99+SwDo+++/D3h8W9UhpqdMT4nan54SdRxN7Qh6SnR2aCrTU6anRExP2zKYnvqGGVrEJkFMwHnnnUf33HOP12djxoyhxx57zOfxjzzyCI0ZM8brs9/97nc0a9Ys03IMB0Ll6Qvjxo2j5cuXhzu1sEIvzxtuuIGefPJJevrpp9vFIBMqzzVr1lDXrl2pqqoqEumFFaFy/ec//0nDhw/3+uz111+ngQMHmpZjOKFlgGmrOsT0lOkpUfvTU6KOo6kdTU+J2q+mMj1lekrE9LQtg+mpb5ihRexxmDDD5XIhNzcXS5Ys8fp8yZIlyM7O9umzZcuWVsdffPHF2LFjB9xut2m5GoEeni0hSRIaGhrQo0cPM1IMC/Ty/PDDD3Ho0CE8/fTTZqcYFujh+cMPP2D69Ol46aWXMGDAAIwePRp/+tOfYLfbI5GybujhOmfOHBw/fhwpKSkgIpw6dQrffPMNLrvsskikHBG0RR1iesr0FGh/egp0HE1leuofbU2LmJ4yPQWYnjI9bZ8wQ4us4UiM4QwqKyshiiL69u3r9Xnfvn1x8uRJnz4nT570ebwgCKisrES/fv1My1cv9PBsiZdffhlNTU24/vrrzUgxLNDDs7i4GI899hg2bdoEq7V9dDE9PA8fPoyff/4Z8fHx+P7771FZWYlly5ahurq6TT9zqYfrnDlz8Omnn+KGG26Aw+GAIAi48sor8cYbb0Qi5YigLeoQ01Omp+1RT4GOo6lMT/2jrWkR01Omp0xPmZ62V5ihRWwliEngOM7rbyJq9Vmw43193tYQKk8Fn3/+OZ555hl8+eWX6NOnj1nphQ1aeYqiiJtuugnLly/H6NGjI5Ve2BBKe0qSBI7j8Omnn+K8887D0qVL8corr+Cjjz5q0zPtCkLhWlhYiPvvvx9//etfkZubi7Vr16KkpAT33HNPJFKNGNqqDjE9DQymp20XHUVTmZ76RlvUIqangcH0tO2C6WlrMD3Vr0XtZxqwnaBXr16wWCytZuwqKipazWApSE5O9nm81WpFz549TcvVCPTwVPDll1/izjvvxNdff42LLrrIzDQNI1SeDQ0N2LFjB/Ly8nDfffcBkIWYiGC1WpGamopFixZFJPdQoKc9+/XrhwEDBqBr167qZ2PHjgUR4fjx4xg1apSpOeuFHq4vvvgizj//fPz5z38GAEyaNAmdO3fG3Llz8dxzz7XJX8NCRVvUIaanTE/bo54CHUdTmZ76R1vTIqanTE+ZnjI9ba8wQ4vYSpAwIzY2FtOmTUNaWprX52lpaZgzZ45Pn9mzZ7c6PjU1FdOnT0dMTIxpuRqBHp6APMN+22234bPPPmsXz6uFyjMpKQl79+7Frl271H/33HMPzjnnHOzatQszZ86MVOohQU97nn/++SgrK0NjY6P62YEDB8DzPAYOHGhqvkagh6vNZgPPe8ulxWIBcGYmur2jLeoQ01Omp+1RT4GOo6lMT/2jrWkR01Omp0xPmZ62V5iiRbpfqcrgF8r2Ru+//z4VFhbSgw8+SJ07d6YjR44QEdFjjz1Gt9xyi3q8su3PQw89RIWFhfT++++3qy3ItPL87LPPyGq10ooVK6i8vFz9V1tbGy0KmhAqz5ZoL2/fDpVnQ0MDDRw4kK699loqKCigzMxMGjVqFN11113RoqAZoXL98MMPyWq10ltvvUWHDh2in3/+maZPn07nnXdetCgERUNDA+Xl5VFeXh4BoFdeeYXy8vLUbdbaiw4xPWV66on2oqdEHUdTO4KeEp0dmsr0lOmpJ5ietj0wPY2cnrJJEJOwYsUKGjJkCMXGxtK5555LmZmZ6v+79dZbaf78+V7Hb9y4kaZOnUqxsbE0dOhQevvttyOcsT6EwnP+/PkEoNW/W2+9NfKJh4hQ29MT7WmQCZVnUVERXXTRRZSQkEADBw6khx9+mGw2W4Sz1odQub7++us0btw4SkhIoH79+tHNN99Mx48fj3DW2pGRkRGwv7UnHWJ6yvRUQXvSU6KOo6lnu54SnT2ayvSU6akCpqdtE0xPI6NFHNFZtFaGgYGBgYGBgYGBgYGBgYGBwQ/YO0EYGBgYGBgYGBgYGBgYGBg6BNgkCAMDAwMDAwMDAwMDAwMDQ4cAmwRhYGBgYGBgYGBgYGBgYGDoEGCTIAwMDAwMDAwMDAwMDAwMDB0CbBKEgYGBgYGBgYGBgYGBgYGhQ4BNgjAwMDAwMDAwMDAwMDAwMHQIsEkQBgYGBgYGBgYGBgYGBgaGDgE2CcLAwMDAwMDAwMDAwMDAwNAhwCZBGBgYGBgYGBgYGBgYGBgYOgTYJAgDAwMDAwMDAwMDAwMDA0OHAJsEYWBgYGBgYGBgYGBgYGBg6BBgkyAMDAwMDAwMDAwMDAwMDAwdAmwShIGBgYGBgYGBgYGBgYGBoUOATYIwMDAwMDAwMDAwMDAwMDB0CLBJEAYGBgYGBgYGBgYGBgYGhg4BNgnCwMDAwMDAwMDAwMDAwMDQIWCNdgLtFZIkoaysDF26dAHHcdFOh4GBgSGiICI0NDSgf//+4Hlj8+lMTxkYGDoywqmnANNUBgaGjgutesomQXSirKwMgwYNinYaDAwMDFFFaWkpBg4caOgcTE8ZGBgYwqOnANNUBgYGhmB6yiZBdKJLly4AgCNHjqB79+4h+brdbqSmpmLJkiWIiYnR7CeKIvLz8zFhwgRYLJaQYur1NRJTL89o5avXl/Fsm/lGo3Y7Ck8AqKmpwdChQ1UtNIJo6CnA+p9ZfkZ8mc6YG7M98YxWvtGo3XDqKaBfU1lbtc18mc4EB6td82K2N55a9ZRNguiEsrwwKSkJSUlJIfm63W506tQJSUlJIQtS7969kZSUpKsI9fgaiamXZ7Ty1evLeLbNfKNRux2FpxIXQFiWWkdDTwHW/8zyM+LLdMbcmO2JZ7TyjVbtAuHRU8/zhKqprK3aZr5MZ4KD1a55MdsjTyC4nrJJEIMItRiMxhozZkxEfY3ENIJo5BsNroyneb6Mp7kwQ/siqadKPNb/wu9n1FcvOkr/6yg8jcRtj7Xbns7rL1ZHaauO0P86Ck8jcTtK7bZHnlrAdocxCEEQIhorJydHV0y9vkZiGkE08o0GV8bTPF/G01yYEY/pjDlgPM3zZTzNRUeq3fZ0Xn+xOkpbdYT+11F4GonbUWq3PfLUAjYJYhCRfOs2x3Ho3r27rph6fY3ENIJo5BsNroyneb6Mp7kwIx7TGXPAeJrny3iai45Uu+3pvP5idZS26gj9r6PwNBK3o9Rue+SpBexxmDBBef7IYrF42YIggOM41fbcqkeSJABQP+d5Hm63GxaLRbWtVis4jlPtESNGeJ1HEATExMSAiFRbkiSIoqjakiTBarVi+PDhakzPz0VRBBGpdkseI0eOhCiKEEXRLyee51vZnvwCcVKO8bRjYmJUrhaLxS+nljYRqfkq5/TXNr7aaeTIkWruvjj5aycFwTi1bCdJkjBy5EhIkqQe74+fZzsBwLBhwzRxUmwiUq9L//794Xa7YbfbNdee8l+73Q5BEEKqPUmSkJycDLfbDafTqbn2FLtfv35qvlpqT7H79u0Lp9MZcu25XC5YrVY4HA643W5NtafY/fr1g91uD7n2LBYLkpOT1Xy11p7VasXAgQNhs9lCqj0lB6vViqamJsTHx3vxsFgs4DguYDuZhUjpKc/zGDJkCHie97o+ZuqpxWLBsGHD1LyZnkZfT/2NdYFqT8lDr54CgMPhUDXV4XAwPfXTTgpXh8OhufaM6CnHcejbty9cLpfm2mvLegpo11RfOmP2PaqiM/76rVn3qAq09FXPGlB0hojgdruD9tVwaCoRqfd8WvpqODSV4zgMGTJEHTO09FUj96g8z8NisWDo0KHql9hQao+IAMjbo4Y6nnMch6FDh7biatZ4rnAdMmSIylWrpnq2ZSi1p9jDhg1Tr1UkxnOr1YqRI0fC7XZ76UIwTVW46h3PtYBNgoSIFStWYMWKFepF3rt3L+bNm4eioiIAwIQJE7Bnzx4kJCRgzJgxyMvLQ/fu3TFy5Ehs374dAwYMQP/+/QEAp06dwuDBg5GVlYUJEyagT58+SE9Px4wZM9CjRw+kpqZi7ty5SEpKQkpKChYtWoTdu3ejqqoKS5cuhSAISE1NxVVXXYWGhgZs2rQJl112GWpra5GTk4OLL74YlZWVyM/Px7x587Bp0ybwPI/58+ejtLQUJ06cwJw5c1BSUoKamhrMmDEDxcXFsNvtmDp1KoqKiiBJEhobGxEfH49OnTr55TRkyBBkZ2djxIgRGDBgALKystRnwLKysvxyWrJkCaxWK1JSUrw4XXbZZcjOzkZDQ4NfTosWLUJ5eTkOHTqEefPmobS0FMePHwfHcejVqxfq6up8cvLXTl27dkVlZSWICAMHDvTJyVc7ZWRkqPURiJO/durSpQuGDRuGoqIin5x8tdP+/ftRWlqKCy+8EPv27QtYeyNGjEBubi4SEhIQExOj3kjGxcXB6XQiJiYGFosFdrsdcXFx4Hney7bZbEhISAARITk5GaWlpeA4Dna7HZ06dYIkSXA6nUhISPCyRVGE2+1GfHw8BEFQbeVmJS4uDm63G5IkqTYRITY2Vr35VGxRFBEfHw+XywWe5xETEwOn0wmLxaLeXFutVtWOiYnx4tGSn8IJAOx2u5etcFK4EpFfToIgqLYoioiNjYXT6QTHcUE5cRyn8uB53it3f5z8tZPyxSIYp5btFB8fj+TkZJSUlLTipHxJOu+883xqxJAhQ9q1ni5ZsgQA1LePK7aZegoAY8aMwYYNGzBo0CCMGzeO6Wkb0NPi4mLYbDY4HA4kJiaC5/mgtZeYmGhIT5W+qeiMw+FgetpCTxUenp9HQk+V8S0+Pl6124OehkNTk5OTAQDbt2/HyJEjA/bVcN2j7t27FwkJCRg0aBBKSkqC9tVw3KNmZ2er1yzYOOFLf3r37o2xY8ciOzs7Ipqam5uLhoYGLFiwIOA4EU5NraqqwtatW3HppZeiurraLyej96ie7TRgwACsW7cOkyZNwqBBg0Iaz5Uv9op+aB3PFy1ahBMnTmDPnj24+OKLceLECdPH8wkTJqBHjx5Yu3YtZs2ahd69e2uuvaVLlwIAMjIyQqq9EydO4LzzzsPGjRvRpUsXzJw50/TxPDU1FXPmzEFRURFOnz4d8ngOAFVVVdi3b19I43lFRYUmveRIqRqGkFBfX68WRc+ePUOa6RRFESkpKbjkkksQFxeneaaT53mUlpaiX79+iI2NBaB9JQjP8zh27Bj69++P2NhYzTOdkiShrKwM/fv3V2cutc50iqKItWvXYsmSJepNhtaZTovFgmPHjqFfv36Ii4vTPNMpCALKy8vRv39/r19dfLVNy3YiIpSVlaFfv37qr8RaZjptNhvS0tJUYQrll0sl3wEDBqi+WmY63W43jh8/jiFDhqjC76/2KioqUFNTgz59+qBz585eOSjgOA6SJIHjOL82EaGxsRGJiYnq38ov6P5s5dyev5h4xmx5jC9buU6h+gJnZsr9cQLkXw08bZ7n1RsrX1y1xFTqRws/f/kGuh4teXj2K3+cfLUTAJWnZ74A0NTUhIqKCnTv3h19+vRppRENDQ3o3r076urqQt7RpSWioadK/zp69Ki6GsRsPVXa8OjRoxg4cCBiYmKYnrYBPfU31vmqPZ7nceTIEYiiqE7A6dFTQP6Fq6mpCYmJiQDA9NTEfEPRU09ff5zasp4C+jXVl86YfY8arN+acY9qt9u9vlCGshLE5XLh5MmTGDhwICRJCunXeL2a6nK5cOLECQwZMkQdg8zWVEEQcOzYMQwdOhQAQloJEso9asuVnUeOHMHgwYNhtVpDGs/dbjfWrFmDSy+9VM1f60oQf1zNGs99cQ1lJUhKSgoWL16sTshqXQnC8zyOHj2KAQMGIDY21vTxXNGF48ePIzk52UsXgo3nTqcTqampuOSSS9QJeq3jeW1trTrRGEhP2UoQg1AGSc+ljJ620sietlJcSifwPMZzoPdlKx205efKryDKeZVze9qevp6f+8vdYrGoy7U84YuTL1sRPqXDaOHnj6s/Ti3t2NjYVvkG4tcyd61cQ+HhabdsJ1/5+uPnmW9MTIzXEjx/nBQx6Nu3L3r27Nnq+FAgSRJcLhfi4+PVnEKB8ktaJKE3phGuRnjq9TWDZ0JCgjqJ1qdPH7X+lRrTUwPBEGk9tVgsGD58eKvPzdJTBZ59l+lp9PXU31jni5PyWEb//v3RqVMnGIEkSepKiEjqjF5EQ0+NINJa3Nb0FAhdU33pjNn3qMH6rVn3qFo5tbTj4uLUmJ7tZ6amxsbGquOGZ62YqalWq9VrfAzGT889qi/bM2YonJSJSmVSM5Tx3B9Xs8ZzX1y1tpPyeFMwTv5sz3wjNZ639NUynivnbGlrGc89zx8I7MWoBuH5fFYkYmVlZemKqdfXSEwjiEa+0eBqNk9FLD1v1okIDQ0NXr9SmQ0jMfX6Mp7GodSN5zPFCszoJ0xnzAHjGT5fZdJN+UWL6Yy5iEa+ZnGNtJ6aeV5/sZjOmOPLeJoLVrvm+Bn11QutsdgkiEFE8lcNnucxYsQIXTH1+hqJaQTRyDcaXCPFU5khVxAXFxdSvHDASEy9voynMbSsG0+Y0U+YzpgDxjP8vp59g+mMuYhGvmZwjbSemnlef7GYzpjjy3iaC1a75vgZ9dULrbHY4zAGEelGVZ6ZjJSvkZhGEI18o8E1Gjw5jlN/xYwUjMTU68t4mouzZRKE6Uz4/Yz66kU08mU6Yy6ikW80uJ4tkyBMZ8zxZTzNBatdc/yM+uqFVt1jK0EMItLLe9LT03UvR9LjaySmEUQj32hwjQZPIkJ9fX3El2+HGnPBggV48MEHQ/Ldt28fZs2ahfj4eEyZMgV79+4Fx3HYtWuXX5+NGzeC4zjU1tZqzs0fjFxbvb7RaE/g7HkchulM+P2M+upFNPJleuqNcOopEB1djEabni2PwzCdMceX8TQXrHbN8TPqqxdaY7GVIAYR6Vn2CRMm6F6OpMfXSEwjiEa+0eAarXZpDy/V++6779SXG2n1ffrpp9G5c2fs378fnTt3RmJiIsrKytC7d++Q89ULf7kePXoUo0ePxunTp/2+rTohIQEXX3wxNmzYgM2bN2PWrFmGYpqJs2UlCNOZ8PsZ9dWLaOXL9NRc+MrXTD31F9NMnC0rQZjOmOPLeJoLVrvm+Bn11Qu2EiRCUH4pEEVRfWGapy0IgpctSZLqq9ien7vdbi9bOb/b7QbHcejdu7e6FZCyTZ6Sh2Irb5xXbEGQty7q1auXem7lcyVfT9uTBxGp27kF46TFbsnJk4enzfO8ytUfJ1+2JElqvv44+WsnhatynmA8PG0FgTj5aidRFNGnTx/1/IH4ebYTEaFnz57geV5T7Xlu4QdA3YbL83NJkoLaCohI5R/IDhYzkN29e3d06dIlJN9Dhw7h/PPPx+DBg9GrVy/ExsYiOTlZ3U6rJSd/uQfiGii+suWdp69yzMqVK7FgwQIkJSX59S0rK8OWLVtw33334b333vMZqyUPAOr2Yp6cAvELxNVXTF8aYcaAFkk9Va559+7dvbYdVfIwQ0+V69azZ081F6an0ddTf2OdmXra8hxMT33b/vI1S08VX2W3gfasp0pczzgt7VD1x4x7VEVnlFjK52bfoyoINk601B9FZ5StTH1xCremSpKk3vPpaSdPrlo1FQC6d+8OnucDcgrHPaqSO8/z6NGjhxo/lPG8ZV/TOp4r6NGjRyuuZo7nPM+je/furdpGSzt5tqvW2lPuXXr27KnmEonxnOM49OnTp5UuBGunYPUWbDzXAjYJEiJWrFiBcePGYcaMGQCAPXv2AACKiopQVFSkflZcXAwAyMvLQ0lJCQBg+/btKC0tVc916tQpAEBWVhYqKysBAOnp6eoy0tTUVDQ0NACQ94RuaGjAunXrkJKSAkEQ4HA4kJKSAkDeYz41NRUAUFtbi/T0dABAZWUlsrKy4Ha7sXbtWmzevBkAUFpaiu3btwMASkpKkJeXBwAoLi724pSfn49169Zh165dQTllZ2ejvLxc5VRVVaXa/jg5HA4IgtCKk5JvIE4AUF5ejuzsbJXTtm3bsG7dOhw8eNAvJ3/tdPDgQaxbtw7btm3zy8lXO2VkZKhtGoiTv3Zat24dTp486ZeTr3bat28f1qxZA7fbHZDT8ePHAQA2mw0ulwsA0NjYiPr6ekiShIaGBlU4GhoaVNFT/r9i+xpg6uvrAchCpNiiKKrtKwiCartcLtTV1UGS5C0Em5qaAABOpxM2mw0A4HA4YLfbVXv+/Pl48MEHYbPZUFdXh6FDh2L58uW47bbb0KVLFwwePBhvvfWWyonneeTm5uJvf/sbeJ7H008/jfz8fPA8j127dqmcUlJSMHr0aHTq1AmLFi3Cvn37VF6enBS05LR161bwPI/y8nI0NjaipqYGFosFV199NSRJQl1dHZYvX47Zs2d7cVq5ciUuueQSAIDdbofD4VDbxul0QpIkvPPOO7j00ktx77334quvvlJrLFA7iaKIuro6r3ZqaQdqJ+X6AfKApdgul0ttm+PHj7fqT752OAgV0dRTpW1SUlLU9jBbT4uKiuB2u7FmzRq17pieRl9Pi4uLsWvXLqxbtw75+flBa8/lcqn1b0RPlbZjeupbTxVOTz/9dMT0VLnJV+z2pKeAcU09ceKEagfrq+G6R83MzMS6detw/PhxTX01HPeoSpxgnPzpz7p161BTUxMxTc3NzcXatWvhdrsDjhPh1NTKykp1/AjEycg9ast2crvdSElJUe9dQxnPlT4d6vcjQO6TCtdIjOeVlZUqV4Wf1tpToLSr1trbvn27ei+Qm5vrl5OvdtI7nqempqKmpkbVhVDGc4VHVVVVyOO5wikoiEEX6urqCABVVlYSEZEgCCQIQivb7XZ72aIoksvlopUrV5LD4fD6nIjI5XJ52ZIkqbYgCFRZWUkOh4MkSSJJksjlchERedlKDMVWzn/69GlyOp1enyv5etqePFwuF1VVVanx/XHyZTscDlq5ciXZbDa/nDx5eNqiKKpc/XHyZTudTjVff5z8tZPC1el0+uXkq52amppo5cqV5HK5AnLy1U5Kvm632y8nX+3kcrmooqKCRFEMyKmxsZEKCwvJZrOp110QRKqpcVFDg6T+a2wkqq8XA9r19SIdP17j9XdjIwW0lXM0NEhUU+MiUZTUa6JcD3/2/Pnz6YEHHlDbfsiQIdSjRw968803qbi4mF544QXieZ6KiopIkiQqKyuj8ePH08MPP0xlZWVUX19PBw4cIACUl5dHoijS0aNHKS4uju6//34qKiqi//3vf9S3b18CQNXV1WqbiqJINTU1JIoiSZKkfq7YvXr1oq+//pokSaKVK1dSr169qE+fPmr7Ll68mB599FGVU01NDcXExNDRo0f98hZFkYYMGUI//vgjERFNmzaN3n///VbHKzl52kptep7L0/bM3dP25OkrL5vNRoWFhdTY2NiqP9XU1BAAqqurI6OIhp5KkkSCINCpU6dIEISI6KkgCCSKIlVUVKjnZHoafT31N9b5qj273U4FBQVks9maj9Gnpw0NEtXVCaqmMj1traeK7+LFi+mRRx6JmJ62rLf2pqdE+jXVl86YfY8arN+acY9qs9m8dCYUTXU4HFRVVaXG98Up3JrqdDrp9OnTXmNQIH7h0FS3202nTp1Sa9uMe9SW7aSMj8p5QhnPnU4nrVy5kpxOZ0jjuRJHyTcS47kS59SpU17XTMt4rtzzNDU1hVR7vu5dzB7PlX5ZVVXVSheCaYTSRx0OR8jjeWVlpSY9Ze8EMQjlGVtl6WRL23NpvGKLzb86KMsfPY9RzufP7tmzp8/4HMepNs/z6rk97V69eql+np/7y91iscBisXgtTfPHyZdNzSsHrFarGisYP39c/XFqacfGxrbKNxC/lrlr5RoKD0+7ZTv5ytcfP898Y2JifD6X3ZKTMjPOcZy6PZ/DwaN7d1+LwPggNgegm8fnnMd//dmex8agsRHo3NnjU48tA/3Zntdg6dKl+P3vfw8AeOyxx/Daa69h48aNGDNmDPr16wer1YouXbqgX79+AKD+eq6c55133sHw4cPx2muvgeM4jBkzBvn5+fjHP/4BjuN8Lkn2vHaKPW/ePGRmZuLaa6/Fxo0bceutt+K///0vioqKMHr0aGzZsgUPP/yw6peSkoKJEydi8ODBfrmuX78eNptN/XXz17/+NT744APccccdra5HS9uzNv2d3xcPz19offkpf1ssFjWGUmNmLN+OtJ5aLBZ1Gbbn52bpqQLPvsv0NPp66m+s88VJWd5rXE8BwAJvTQWYnp7R08LCwqjoKeBdb+1VT4HQNdWXzph9jxqs35p1j6qVU0s7Li5O3ULZs/3M1NTY2Fh1zPGsFTM11Wq1eo2PwfjpuUf1ZfsbH7XkrvzXk4eW8dxqtXrFNXs8V+Dr/iOYrawaC8ZJy71LpMbzlr5axnPlnC1tLeO55/kDgT0OYxDhWsKoNdbq1at1xdTraySmEUQj32hwjQbPljdqkYSe2JIkqUvrJk2apH7OcRySk5NRUVHh109ZrqygqKgIs2bN8ro5nT17dsg5LViwABs3bgQAZGZmYuHChZg3bx4yMjKQnp4Ou92O888/Xz1+1apVuPLKKwOe87333sMvfvELVdRvvPFGbNu2Dfv37w/op1yfSLerGf2E6Yw5YDzN82V6ap6eZmZmYtu2bbDb7V7nNVNPgehoqln9pD30P6Yz5sbUi47C00jcjlK77ZGnFkR9EuStt97CsGHDEB8fj2nTpmHTpk0Bj8/MzMS0adMQHx+P4cOH45133vH6///5z38wd+5cdO/eHd27d8dFF12kPjNkJK4/tPwF1kxYrVbMnTtXV0y9vkZiGkE08o0G12jw7NyZQ12diIYGQmMjNP+rr5dw/Hgt6uulkPwaG4GGBkJdnYjOnbngCbYAx3HqC/1azu76+vXN8/919vyZFND8sqRgWLBgAQoKCnDw4EHk5+dj7ty5mD9/PjZt2oScnBxMmzZNzdnd/AzmVVdd5fd81dXVWLVqFd5//33ExsbCarViwIABEAQBH3zwQcBclOvj+UUkEjCjnzCdMQeMp3m+evXUiKZ2FD3NzMxEZmYmzj33XHUHGLP1FIiOpprVT9pD/2M6Y25MvegoPI3E7Si12x55ajrO5DwC4ssvv8SDDz6It956C+effz7effddXHrppSgsLFSXOXqipKQES5cuxd13341PPvkEmzdvxrJly9C7d29cc801AOQ96m+88UbMmTMH8fHxeOmll7BkyRIUFBRgwIABuuIGQiQHSY7j/G4FZ5avkZhGEI18o8E1Gjx5nkNSkiX4gS0gSYAoysuvQ1+5y0Fe+h06PN/Ub9Rv3LhxWLlypddnW7duDfncEyZMQM+ePfHcc89h8uTJSEpKwvz58/Hiiy+ipqYG8+fPV4/NyMhAt27dMGXKFL/n+/TTTzFw4MBWuW3YsAEvvvginn/+eb+irvf6GIUZ2hfpiRymM+b4GfXVi2jkq1dPASOa2rH0dMGCBao2mK2nQHQ01Sztaw/3qExnzI2pFx2Fp5G4HaV22yNPLYjqSpBXXnkFd955J+666y6MHTsWr732GgYNGoS3337b5/HvvPMOBg8ejNdeew1jx47FXXfdhTvuuAP/+te/1GM+/fRTLFu2DFOmTMGYMWPwn//8B5IkYcOGDbrjBkKkl/esWrVK93IkPb5GYhpBNPKNBtdo8IzGUl8jMT2Xb4fq13L59j333INDhw7h4Ycfxv79+/HZZ5/ho48+8jrmxIkTGDdunPr2bF9QnmP/5JNPsGDBAgDy0nKXy4UNGzZg3rx56rE//PBD0KXb77//Pq655hoMHDgQ48aNw4QJEzBhwgTccccdqK2txerVqwPyZI/D6I/HdCb8fkZ99SIa+TI9NV9Pp0+frnI1W08VruxxGH2xmM6Y48t4mgtWu+b4GfXVizb/OIzL5UJubi6WLFni9fmSJUu8tq7yxJYtW1odf/HFF2PHjh1+CdtsNrjdbvWlLHriBkKkl/csWbJE93IkPb5GYhpBNPKNBtdo8FRmZaPxC5GemEZmrhMTE70+Gzx4ML799lv8+OOPmDx5Mt555x288MILXse43W7s379f3YrRHxYuXAhRFNWbdo7jMHfuXABQ/wvIN+2Blm7n5uZi9+7duOaaa1pdoy5dumDJkiV4//33A/KMdHsCZ8/jMExnwu9n1FcvopEv01Pz9XTJkiUqV7P1VIkd6TY9Wx6HYTpjji/jaS5Y7ZrjZ9RXL9r84zCVlZUQRRF9+/b1+rxv3744efKkT5+TJ0/6PF4QBFRWVqpvMPfEY489hgEDBuCiiy7SHReQ96p3Op3q38ovIm63W9eMmud/tYKa96l3u90hD856fY3E1MvTSNxoXKO2zNPtdoOIIEmS169ayrPcoTzT7emj5xeyUGMq+54rXA8fPgzA+2WAO3fu9Pqs5d9DhgxRdw9RPlu6dCmWLl3qFevWW29Vjxk8eDAEQUBDQ0NArsuWLcOyZcu8zv3dd9+BiNRn63fu3In6+nrMnTvX73mmTp2q5ujrGilLugNdcz3t2dLP1/klSVLrrOXycCOz+m1BTwGmM2b5GfGNBs9QfH1pqln9T6uvFrRXPfXMOTc3NyJ66s83GKKlp0D4NJXpjHkxjfhG4168o/A0Erej1G5746nVJ6rvBAFaP7ejfIEI5XhfnwPASy+9hM8//xwbN25EfHy8obgvvvgili9f3urztLQ0dOrUya9fIKSlpenya29gPKMHq9WK5ORkNDY2wuVyheWcDQ0NYTlPe4BRrnV1dfj73/8Ou90e9JfQaMIfT5fLBbvdjqysLHW7ZQU2m013PKan+sF4RhdMU/WD6ak5egqEX1Pbav8LNzoKT6DjcGU8zy7o4alVT6M2CdKrVy9YLJZWqy8qKipardJQkJyc7PN4q9Xaam/yf/3rX3jhhRewfv16r23g9MQFgMcffxwPP/yw+nd9fT0GDRqExYsXo2vXroHJtoDb7UZaWhoWL16seS9jQJ6oEQQBVqtV10ycHl8jMfXyjFa+en3bMk+Hw4HS0lIkJiZ6TQQGm/TzF7OhoUH3W/P1xDTqa8RPL1fPmAsXLsTChQt1+eqNGapfIJ4OhwMJCQmYN29eq4nkurq6kOMpaAt6CjCdMcvPiG80eIbi60tTo60zeuK2JZ3RGjdSeqrXN1p6CoRPU5nOtM18o3Ev3lF4Rivf9lS77Y2nVj2N2iRIbGwspk2bhrS0NPzyl79UP09LS/P7vOfs2bPx448/en2WmpqK6dOne12gf/7zn3juueewbt06TJ8+3XBcAIiLi0NcXFyrz2NiYkJuHL2+RARRFBETE6OrCPX4GompQM81ika+Rrm2RZ6iKILjOPA8D755+wHP1VOhxFSW9yrnCwV6YxrxNRJTL9ezjSfP8+A4zmdt69U9oG3oKcB0xiw/o75AZHmG4ttSU5nOBEc0xo5oXKNo6SkQfk1lOmNOzPZ0L95ReBqJ21Fqt73x1Hp8VHeHefjhh/Hee+/hgw8+QFFRER566CEcO3YM99xzDwB5Zvs3v/mNevw999yDo0eP4uGHH0ZRURE++OADvP/++/jTn/6kHvPSSy/hySefxAcffIChQ4fi5MmTOHnyJBobGzXHDQUtlzSaCUEQkJqaqiumXl8jMY0gGvlGg2s0eBIR6uvr1Ru9SMBITL2+jKe5MKOfMJ0xB4yneb5MZ8xFNPKNBlez+kl76H9MZ8yNqRcdhaeRuB2ldtsjT02gKGPFihU0ZMgQio2NpXPPPZcyMzPV/3frrbfS/PnzvY7fuHEjTZ06lWJjY2no0KH09ttve/3/IUOGEIBW/55++mnNcbWgrq6OAFBdXV1IfkRELpeLVq5cSS6XK2Tf9gTGM/qw2+1UWFhIdrvd8LlEUaSamhoSRTEMmbVtdBSuwXgGqh8jGhjOc7Xl/hdOMJ5tA0xTQwfjKSNSemrkfG29/4ULHYUnUcfhynieXTDCU6v+Rf3FqJ5vBG+JlvvNA8D8+fPVN5b7wpEjRwzHDQUU4V9/jDw/rMfXSEwjiEa+0eAaLZ6SJKlLcyMBIzH1+jKe5sIM7YuknirxmM6E38+or15Ea4xkOmMeopFvtNq0PZ3XXyymM+b4Mp7mgtWuOX5GffVCq+5F9XGYswGRXt6zadMm3cuR9PgaiWkE0cg3GlyjwVMRpGjcHOmJqdeX8TQXZ8vjMExnwu9n1FcvopEv0xlzEY18o8H1bHkchumMOb6Mp7lgtWuOn1FfvdAaK+orQdo7jL7MKtRYl112WUR9jcQ0gmjkGw2u0eDJ8zy6deumy1cvjMTU68t4mgsztC+SeqrEYzoTfj+jvnoRjXyZzpiLaOQbDa5maV97uEdlOmNuTL3oKDyNxO0otdseeWoBWwliEG63G4D8xnhRFFvZgiB42crbw4EzbxL3/NztdnvZyi8RbrcboiiiqqoKTqcTRAQiUuN72pIkednK+SsrK+Fyubw+V/L1tD15uN1uVFdXq/EDcdJit+TkycPTliRJ5eqPky/b5XKp+frj5K+dFK4ul0sTD0/bsx78cfLVTkq+giD45eSrndxuN06fPg1JkjTVnpKH57VUPvP8PJitQFkuHMxW/FpeD8/Pg9n+8g1me8b0x0nJ19NuiUCc/MXUenygfANdj5a2UpuBOPmzW3L1FdOXRvjyN4pI6imR/MbyiooKiKIYET1Vrtvp06fVczI9jb6e+hvrzNRTz2OVz5VzMz0Nf76h6GnL69te9RTQr6la+6qn/ui9Rw3Wb826R/W8RqFoqtPpRHV1tRrfF6dwa6rL5UJlZaXXGBRKO3ly1aqpgiCgoqICkiQF5BSOe1Qld2V8VM4Tynjesq9pHc+VOEq+kRrPJUlCRUWF1zXTMp57tmUgTr7slvcukRjPRVFEdXV1K10I1k7B6i3YeK4FbBIkRKxYsQLjxo3DjBkzAAD5+fkAgKKiIhQVFQEA9uzZg+LiYgBAXl4eSkpKAADbt29HaWmpeq5Tp04BALKyslBZWQkASE9PR21tLQB5+9+GhgYAQEpKCpqampCTk4O1a9dCEAQ4HA6kpKQAABoaGpCamgoAqK2tRXp6OgCgsrISWVlZEEUR27dvx5YtWwAApaWl2L59OwCgpKQEeXl5AIDi4mLs2bNH5VRYWIicnBxNnLKzs1FeXq5yqqqqUm1/nBwOBwRBQEpKihcnJd/169f75QQA5eXlyM7OVjnl5OQgJycHhw8f9svJXzsdPnxY9ffHyVc7ZWRkqG0aiJOvdsrIyEBOTg4qKir8cvLVTgcOHMC2bdsgimJATsePHwcA2Gw2VfSamppgs9lAJC/5VYSjoaFBFb36+npVxJS35LccYOrr6wHIQqTYoiiq7SsIgmq7XC40NTWBiFQbAJxOJ2w2GwDA4XDAbrer9vz58/Hggw/CbrervjabTf0i19TUpHJqbGyE2+3Gvn37cN555yEhIQFTp05FUVEReJ7Hrl27/HJKSUkBz/Ooqanx4qTAHye3263uOuXJT7m+vjg5HA4AgN1uV22Fk+LbkpPSNv7aSRRFNDU1eXFqaQdqJyWWL04Kj+PHj7fqT0oORhBNPXU4HHA6ndiyZQucTmdE9LSoqAiiKGLbtm04cOBAQE5MTyOnpwqnnJwcFBYWBq09l8ul9k0jeqq0HdNT33rakmsk9FSZ8GhqalLt9qKngHFNPXHihGoH66vhukfdtGkTcnJyUFZWprmvKpz03qMqcYJx8qU/a9euRU5ODurq6gKOE0B4NVW55ws0ToRTU6urq7FlyxaIohiQk5F71JbtJIoitmzZgrKysoCc/LUTgJC/HwFAWVmZyjUS43llZaXKtbq6WnPtKZw821Vr7W3fvl3NN1LjeWpqKurq6lRdCGU8V3hUVVWFPJ4ruhcUxKALyptnq6uriYhIEAQSBKGV7Xa7vWxRFNU33jocDq/PieS34XrakiR52ZIktbKJyMtWYii22+0OaAuC4GX74hGMky/b4XDQypUryWaznTWcfLVTU1OT+gbjtsapsbGRCgsLyWazqdddyamlLYpiQFv0ePO98rdyDn92sJiB7MrKSqqvrw/J9/rrr6dFixZRSUkJVVZWksvlorKyMrXtWnKSJIk2bNig9mUl90Bc9XIqKSmh2NhYqqura3XM4cOHvXaziomJoREjRtCzzz6rHqu1nZR8PW1/7eTJ01fuNpuNCgsLqbGxsVXtmbE7DNNTpqdtWU8FQSC73U4FBQVks9nUHPToqSRJJAiC2v+YnjI9DZeeEunXVF86czZqqs1m89KZs4FTe9VUPZx8tZPT6aSVK1eS0+k8azj5aiflnqepqems4eSrnZQ+6nA4QuZUXV2tSU/ZShCDUN50a7FYYLFYWtlWq9XL5vkzl1yxPT+PiYnxspXzx8TEgIhw+vRpWCwWcBwHjuPU5548bZ7nvWyr1aougVLOrXyu5Otpe/LgOA4VFRXgOC4oJy12S06ePDxtqXmJmRLHFydfNs/zar7+OPlrJ4Wrcp5gPDxtBYE4+Woni8WCiooK9fyB+Hm2E8dxqKqqgiRJmmpPyUOBIAggIq/PPd+E789WwHGcyj+QHSxmILtnz57o0qVLSL6HDh3CBRdcgKFDh6JHjx4AgOTkZLXtWnLyl3sgroHiE5HXMlvPY3744QcsWLAASUlJfs+zdu1alJWVobi4GMuXL8cLL7yADz74IGA7AfIvjsoxSr6B+AXi6isvXxohmbB8O5J6qrRXTU2NWltm66ly3aqqqtRcmJ5GX0/9jXVm6mnLczA99W37y9csPVV0QWxemdGe9VSJ6xmnpR2q/phxj6rojBJL+dzse1QFwcaJlvqj6AwRBRwnfNl6NZXnefWeT087eXLVqqkAUFNTA0mSAnIKxz2qkrskSerKiGCcfLWTEjuU8VxBdXV1K65mjueSJKGmpqZV22hpJ8921Vp7yr1LVVWVmkskxnMiQkVFRStdCNZOweot2HiuBWwSxCDMGrj8xcrPz9cVU6+vkZhGEI18o8E1Wu2iLCmOJEKNuWDBAjz44IOq77Bhw/DCCy/gjjvuQJcuXTB48GD83//9n3o8x3HIzc3Fs88+C47j8Mwzz2Dfvn3geXn5toKUlBSMHj0aCQkJWLhwoeZttRXs3bsXPM+rS/9qamrA8zyuu+46NdcXX3wRs2fP9vJbtWoVrrzyyoDn7ty5M5KTkzFkyBDcfPPNmDNnTsAtwRVEoz3N6CdMZ8wB42muL9NT8/QUAJ577jnMmTPHy89MPQUi36Zm9ZP20P+YzpgbUy86Ck8jcTtK7bZHnlrAJkEMwnMWMRKxFi1apCumXl8jMY0gGvlGg2s0eHIAkiwWcDYb0NQUkX+czSbHDDlb+WY8KSkJAPDyyy9j+vTpyMvLw7Jly3Dvvfdi3759AORnBcePH48//vGPKC8vx5///Gf1l08FpaWluPrqq7F06VLs2rULd911Fx577LGQ8pkwYQJ69uyJzMxMAPKzkD179kRWVpaaa2ZmJubPn6/61NbWYtOmTUFv2hMTE9UZ7B07dmDnzp2YOXOmpuujdeY7XDCjnzCdMQeMp3m+TE/N01Ml361bt0ZMT5WYkdZUs/pJe+h/TGfMjakXHYWnkbgdpXbbI08tYJMgBhHpma0TJ07ononT42skphFEI99ocI0GT2pqAhITQ/7HJyWh28CB4JOSdPkjMVGOHWq+zS//A4ClS5di2bJlGDlyJB599FH06tULGzduBHBmmXZiYiKSk5PRuXNn1U/B22+/jeHDh+PVV1/FOeecg5tvvhm33XZbSPlwHId58+apcTdu3Ihbb70VkiShoKAANpsN2dnZWLBggeqTkpKCiRMnYtCgQQHPPWfOHCQmJiI2NhYzZszA9ddfj9/85jearg8RBTwu3DhbVoIwnQm/n1FfvYhGvnr1NByaerbraWFhIdxuN7Kzs70mQczUUyA6mnq2rARhOmOOL+NpLljtmuNn1Fcv2EqQCCHSjXro0CHdRajH10hMI4hGvtHg2t7aJVpQdjCYNGmS+hnHcUhOTlafJfaFljftRUVFmDVrltcvfC0fW9GCBQsWqDftmZmZWLhwIebNm4fMzExkZ2fDbrfj/PPPV4/XsnQbAD766CPk5eVh9+7d+PLLL7Fq1SpNv6wq1yeSOFsmQZjOhN/PqK9etLd8o4X2pKc5OTkR11Mg8pp6tkyCMJ0xx5fxNBesds3xM+qrF1pjRXa90VmISC/vmTdvXkR9jcQ0gmjkGw2u0eDJde4MNG/bFwokSd4SMCkpyedL4DTF7tQpdB+OU5dhe74USvl//sSO4zgkJiZ6fRauX/YWLFiABx54AAcPHkR+fj7mzp2LQ4cOISsrC3V1dZg2bZqas9vtxtq1a/H4448HPe/o0aMxatQoAMDYsWNx+PBhPPXUU3jmmWcQHx/v08fz+kQSZ8vjMExnwu9n1FcvopGvXj0FjGvq2a6nmZmZqK2txbRp09RHeMzWUyA6mnq2PA7DdMYcX8bTXLDaNcfPqK9esMdhIgRl33lRFNW3iXvagiB42Z43GIrt+bnb7faylZsMt9sNURRx5MgROJ1Odc96Jb6nLUmSl62c/8iRI+ovOcrnSr6eticPt9uNo0ePqvEDcdJit+TkycPTVvJVfo3xxcmX7XK51Hz9cfLXTgpXl8uliYen7VkP/jj5aiclX0EQ/HLy1U5utxslJSWQJElT7Sl5AIBEBKfVCurUSf2Hzp0hJSQEtZV/1KkTpISEoLbiR506yTFb5BLIViBJkloH/o7xdR4i8vo1T5IkjB07Flu3boUkSeoxW7ZsUf18fQHw/FzxUZ5jf+655zB58mR06dJF/eUyPT1dFXwiQnp6Orp164bJkycH5e25BJuIwPM8BEFQ+7zCo6XtdDq9OLW0ffHwx9VoIJhDAAEAAElEQVRXXr40woxZ/UjqqbIDxOHDhyGKYkT0VLluJSUl6jmZnkZfT/2NdWbqqeexyudMT1vraUZGBi644AL12EjoqcK1vespoF9TtfbVcNyjBuu3Zt2jel6jUDTV6XTi6NGjanxfnMKtqS6XS73n09NOnly1aqrw/+xdeXwU5fn/zh4kJCRccolcgigBREAQqHJ4gPKrWqtWa61Va61aK0qt1baerbWtrVqV1nrXq6c2Fg2QQAIBAoQjEHJAAgmQhNwh1+7O7s7M8/tjecfdZGd3dmZnl03m+/nkw5Nlnvd5v/M87/d9MzszryCgqqoKkiSF5BSNNSrruyRJqKqqktuJZD7vOdbUzuehuBo5n/fkqnY+989lKE7BbNZftnaJxXwuiiKOHz/eSxfC5SlcvYWbz9XAvAgSIdasWYOMjAzMmzcPAFBaWgrAd1toeXk5AKC4uBiVlZUAgKKiIlRXVwMACgsLUVNTI7fV2NgIwPciMPZm9NzcXLS3twMAsrOz0dXVBcD3DKzT6URtbS3Wr18PQRDA8zyysrIAAF1dXcjOzgbge2lYbm4uAKClpQX5+fly4bMFSk1NDQoLCwEA1dXVKCoqAgBUVlaiuLg4gFNdXR0OHjwYllNBQQHq6+tlTq2trbKtxInneQiCgKysrABOkiThxIkT2LhxoyInwPfytoKCApnT7t27UVdXF5aTUp7q6uqwe/duRU7B8pSXlyfnNBSnYHnKy8tDXV0dmpubFTkp5amiogKSJIXkVFtbCwBwOp2y6DkcDnkx29XVJQtHV1eXLHqdnZ2yiHV2dgadYDo7OwF89W0m4BMfll9BEGTb6/XKb9v3eDxwnH6W3e12w+l0AgB4npeP4Xleju9yueT+er1e2XY4HHKfuru7ZdHzeDwyJ/9Fe2dnJ+69914cPXoUP/7xj3Ho0CF8/PHHeP/992VenZ2dqKurQ0ZGBvbu3avIieM4XHrppfjoo4+wdOlSeDweTJ48GR6PB3l5eViwYIHM47///S+uu+468DwPnudlTsz2z01DQwNOnDiB2tpafPbZZ3j11VexbNkycBynmCcm+P556mmHyhM7f+z8Mtvj8ci5qa2t7TWeorFoj6ee8jwPj8eDgwcPwuPxxExPJUlCRUWFqadnmJ4ePHgQdXV1qmrP4/HIeqNHT1nuTD1V1tPc3NyY6qn/eWIXPxJFTwH9mlpXVyfb4cZqtNaoW7duRV1dneqxGo01KosTjlMw/Vm/fj3q6urQ2dkZcp4Aoqup7JGCUPNENDX11KlTKC0thSRJITnpWaP2zBM7PtLaY5wARPz3EePE5udYzOctLS2QJN975Ng2uWrncwaWV7W1V1hYCEnyPZoSq/k8OztbngfWr18f0XzOeLS2tkZce0z3woJMaEJHRwcBoLa2NiIiEgSBBEHoZXu93gBbFEXyeDyUmZlJPM8HfE5E5PF4AmxJkgJsSZJ62UQUYLMYzPZ6vSFtQRAC7GA8wnEKZvM8T5mZmeR0OvsMp2B5cjgclJmZSR6P54zj1N3dTWVlZeR0OuXzzvrU0xZFMaQtiiKdOnUq4HfWhpIdLmYoe8mSJbRq1Sr58wkTJtBLL70UcMysWbPo6aeflo+ZNWsWPfXUU/IxR48eJQBUVFQk93vt2rU0ZcoUSkpKossuu4zefvtteSyLokjV1dUEgNauXRuUK2v71VdfJQD0xRdfyJ9ff/31ZLVaqb29XT5+3LhxlJOTE5JrVVUVAZB/rFYrnXPOOXTPPfdQU1NTRHli/fW3lfLkn9Ng/XI6nVRWVkbd3d29ao9pYEdHB+mFqaemniaCngqCQC6Xi0pLS8npdMp90KKnkiSRIAjy+DP11NTTaOkpkXZNDaYzfVFTnU5ngM70BU6JqqlaOAXLk9vtpszMTHK73X2GU7A8sTWPw+HoM5yC5YmNUZ7nI+bU1tamSk/NiyAa0XOCiQSsgFmy1UIQBKqsrJSLMRa+emJq5aknbjzO0ZnM0+VyUVlZGblcLvkzSZLI5XLJwqQWPRd4kUBrTD2+emJq5doz5t69e2nw4MGqauNM5BmsfhjUTjJqEA89JTJ1xig/Pb7x4BmJb88xcSboTCx848GzZ9xY6Kke3zNFT4m0a6qpM8bF1OMbj7V4f+GpJ25/qd1E46lWT83HYXSC/J6jjUWsU6dOaYqp1VdPTD2IR3/jwTVeeRFP3/4bS+iJqdU33jwFQcBrr73W6wWEany1xowVjBgnps4YA5Onsb7x1plY+caDp3/cWOmpXl8tMGqcJML4M3XG2Jha0V946onbX2o3EXmqgbk7jE7E+s3b7DnPWPnqiakH8ehvPLjGgyfHcUhNTdXkqxV6Ymr1PRN4zp8/H/Pnz9fkqzVmrNBXdocxdSb6fnp9tSIe/T0TdCYWvvHSGf+4sdBTvb5a0Vd2hzF1xhhfk6exMGvXGD+9vlph7g4TI8Ty2wJRFHHo0CFNMbX66ompB/Hobzy4xoMnEcHlcsX8qqzWmFp9TZ7GwohxYuqMMTB5Gudr6oyxiEd/48HVqHGSCOPP1BljY2qF9r66UFZ2B/bufTxgFx4jY+qFWbvG+On11Qq1scw7QRIM7G3vsfTVE1MP4tHfeHCNB89YL2T1xtTqa/I0EQ6mzhjjp9c3HjG1+po6Yyzi0V9TU7XB1BnjfBOFZ0PD39DU9CEAoLqax5QpL4PjOENjRgNm7Rrjp9fXSJgXQXTCarXGNNbs2bNj6qsnph7Eo7/x4BoPnhzHISUlRZOvVuiJqdXX5GksjNC+WOopi2fqTPT99PpqRTz6a+qMsYhHf+PB1SjtS4Q1qqkzxsbUCi0xiQgnT/5F/r2u7k+QJCemTv0LOC58LZp/cxiL/sRTDczHYXSC7UcviqJ8+42/LQhCgC357QXPbP/PvV5vgM2+jfB6vRAEAQcPHgTP8/Ke9V6vFwACbEmSAmzWh4MHD8Ltdgd8zvrrb/vz8Hg8KCkpgcfjCctJjd2Tkz8Pf5v1l+d5RU7BbLfbLfdXiZNSnhhXt9utioe/zRCKU7A8sf6y/Ibi558nj8eD4uJimUO42mP9YG06nU75M//Pw9kMRCTzD2WHi6nG1upLRHA6nXJ/gnFi/fW3e0KJk5qYevob6nz0tF0uV1hOSnZPrsFiBtMII25tjKWeEhEEQUBxcTEEQYiJnrKf4uJimaupp/HXU6W5zkg99T+Wfc7aNvU0+v2NRE+Zb6LrKaBdU9WOVX/90bpGDTdujVqjMoSbJ3rqD8/zKCkpgSAIIeeJYLZWTfV4PDh48KB8LmKhqV6vV15nhuLkz+PUqW1wOIphsSQjJeWnACyor38LZWXfhSR5w9Yemx9ZHyKZz3uONbXzeSiuRs7nPbmqnc/9cxlJ7fmvXcLpQjTnc0EQUFJS0ksXwuUpXL2Fm8/VwLwIEiHWrFmDjIwM+SUvZWVlAIDy8nKUl5cDAIqLi1FZWQkAKCoqQnV1NQCgsLAQNTU1cluNjY0AgPz8fLS0tAAAcnNz0d7eDgDIzs5GV1cXACArK0suoA0bNkAQBPA8j6ysLABAV1cXsrOzAQDt7e3Izc0FALS0tCA/Px8A4HA4sHPnTgBATU0NCgsLAQDV1dUoKioCAFRWVqK4uFjmdOjQIQBASUlJWE4FBQWor6+XObW2tsp2KE6CICArK6sXJ6/Xi02bNoXkVF9fj4KCApnT3r17AQDHjx9X5KSUp+PHjwMA9u7dq8gpWJ7y8vLknIbj1DNPmzdvDsspWJ6OHDki9yUUp9raWgCA0+mURcHhcMgC1dXVJQtHV1eXLHqdnZ3yMZ2dnUEnmM7OTgA+IWK2KIpyfgVBCLCZuHk8HjgcDgCA2+2G0+kEAPA8L98yx/O8/Aeby+WS++V0OuU/PB0Oh8ypu7tbbt+fk//CSIlTT5txYlDi5PV60d3d3YuTKIoBPJQ4Mdufk9frlXkoceqZJ387HKdgeWKxgnFiuamtrQ06nvQi3noqCAKqq6tjpqeMU0tLC44cORKSk6mnsdPTyspKlJSUAAAOHToUtvY8Ho88NvXoKcudqafKeur/x2Is9JT11ePxyBc/EkVPAf2aWldXJ9vhxmq01qjbtm0DADQ0NKgaq9FYo7I4ajj11J8NGzYA8OU53DwRLU3dv38/Ojo6QnKKtqZ2dHTgxIkTYTn556ms7AUAwIABV6O7+2pccMHHAGxobv47SkpuwIEDhWHzdPLkSTQ0NITkpJQnAJrm84aGBpw8eTIoJyPmc8bpxIkTcl4jmc/986q29hinjo4O7N+/PySnaM3n2dnZsgZu2LAhovmc8WhtbY14Pme6Fw4c+X8VYUI1Ojs7MXjwYLS1tWHo0KHyJGq1WgNsQRDAcZxsWywWiKKIrKwsXH311UhKSpI/t1gs8Hq9sFqtsm2z2cBxnGwDvgHub9vtdhCRbLMrjMyWJAk2m03RFkURRCTbwXiE42SxWHrZoihi/fr1WL58OZKSkvoEp2B5cjqdyMnJwcqVKwHgjOLkdrtx4sQJTJw4EcnJyeA4Tr6Y0dOWJAkcxynabAGYnp4u/26xWOTFYTA7WJxY2uE4Ab4/QPxti8UiL3CDcU1UTsHyBEDmGYwfz/M4duwYxo8fj6SkpIDaczgcGDx4MDo6OpCeng49MPXU1NNE0FN2nquqqjBp0iQMHDhQ1ziVJAldXV3y+DH1NHE4ncl6yvqhRVOD6Uxf1FSXy4Xs7GxZZ/oCp3hrKs83Ydeu8SBy46KLCpCWNh9WqxXNzWtRXv4tSBKP9PTLMH16JpKShuniFCxPXq8X69atwzXXXAO73Z5weVJbe4DvIslVV12FlJSUPsEpWJ7cbjeys7Nx9dVXw2q1RsSps7MTw4YNC6un5p0gUYLVapWfQfK3bTZbgG2xfHXKme3/ud1uD7DZxMuKYv/+/bBYLPJEbbfbASDAtlgsATYrigMHDsjtsc9Zf/1tfx4A5Ktr4TipsXty8ufhb4uiKHNV4hTM5jhO7q8SJ6U8Ma4cx6ni4W8zhOIULE8WiwVFRUXy4A3Fzz9PAORb99TUHusHA3vzvf/n7PyFshn8z1EoO1xMNbZWX7boZAjGifXX3+4JJU5KMf1fABVpf9X49uQB+L79ZMcocVKye3INFjOYRhh1+7Z/jJ52NPWU/aFTUlIi/8FjtJ6y8+b/za+pp/HXU6W5zkg97dmGqafR768WPe3JtS/oqX+cnnak+mPEGpXpjNK4NWqNyhBunuipP0xnJEkKOU8Es7VqKsdx8ppPS578uarVVCJCSUkJRFEMyYnZzc0fgsiNQYPmYNCgeXJ/R4y4FhdemA2rNR2dnVtx8OCV8HiaguaJPa7BLiBGMp/3HGtq53PGlT1uFKv5XBRFlJSUyFzVzuf+uQzFKZjN+uuvRUbP55IkoaioqJcuhMtTuHoLN5+rgXkRJMEwcODAmPvqiakH8ehvPLjGg6f/AjNWiDTm0qVL8fDDD0fke+jQISxYsADJycmYPXs2Tpw4AYvFIt/6FwybN28Gx3Hy7Xt6oeXcbt68GRaLRb4tMhjef/99DBkyJGoxWZsTJkzQ5NtXYOqMMX56feMRU6uvqadfIdp6CkTOVa+eaonJ2jT11NQZo3zPZJ6S5MbJk28AAM4++z5wHBfgO2TIZbjoos2w20egu7sIRUWXgedrgrZl/s1hLPoLTzUwd4fRiUiuOEUj1gUXXBBTXz0x9SAe/Y0H13jw7Dk5xQJaYn722Wfy1WK1vk8//TRSU1Nx+PBhDBo0CEOGDEF9fT3OOussLd2OGKH6evz4cUydOhXNzc29bs9btGgR6uvrMWrUqIgX3/HIJ9B3docxdSb6fnp9tSIe/TX11Fgo9dcoPQ0V00gYpX2JsEY1dcbYmFqhNqbbXY/S0hvhch2B1ToYI0d+O6hvWtpszJ69DQcOXAmXqwJFRZdh1qyNSEmZEnHMaMOsXWP89PpqhVrdM+8E0Qn2fFasYu3evVtTTK2+emLqQTz6Gw+u8eBJRHA4HPIteLGAlpjDhg1DWlpaRL5Hjx7FpZdeigkTJmDYsGHgeR6jRo0KuPXVSITq6+eff46lS5cGfT5xwIABGDVqlLwjQbRiGgkjxompM8bA5Gmcr6mnxkKpv0bpaaiYRsKocZII48/UGWNjaoWamJ2du7F37zx0du6AzTYE06f/BzbbIEXflJSpmD17GwYOPA9u93Hs338ZurtLIoppBMzaNcZPr69WqI1lXgTRiVjeBstxHIYOHar5mw0tvnpi6kE8+hsPrvHKS6y/cdcS0//2bavVikmTJuE3v/kN7r77bqSlpWH8+PF488035eM5jsPevXvx3HPPgeM4PPPMM6itre11+3ZWVhamTp2KgQMHYtmyZTh27FhE/Tp48CAsFov8JuxTp07BYrHg5ptvlvv6wgsvYOHChQF+n3/+Oa677rqgbbLbt/13GHj//fcxfvx4pKSk4IYbbpB3B/HH2rVrcfHFF2P48OGYPHkynn322QDxf+mllzBz5kykpqZi3LhxeOCBB+Q3deuFEePE1BljYPI01tfUU+P0FPDp2KJFiwL8jNLTuXPnYuDAgZgxY0bC66mR7SrFMnXGGN8zkWdDw4coKroMHk8dUlKmYc6cQgwbdmVY3+Tk8Zg9eytSU2fC42nA/v1L0Nm5R1VMo2DWrjF+en21Qm0s8yKITsT6VsMpU6ZoiqnVV09MPYhHf+PBNV55sdtFSJITouiIyY8kOWG3a3vxG8dxSE5OBgD88Y9/xMUXX4yioiI88MADuP/+++Ut8urr6zF9+nT85Cc/QX19PX76058iKSkpoK2amhp885vfxMqVK7F//37cc889ePzxxyPqz4wZMzB8+HBs2bIFgG9rsOHDhyM/P1/u65YtW7BkyRLZp729HVu3blVctDOwHXx27dqFu+++Gw888AD279+PZcuW4de//nXAsRs2bMDtt9+Ohx56CGVlZfjrX/+K999/H88//7x8jMViwauvvoqSkhL87W9/Q25uLh577LGI+CqhrzwOY+pM9P30+mpFvPpr6qkxesr6u23btpjr6Ztvvom//e1vCa2nRrarFMvUGWN8zySekuRFZeXDOHToDhC5MXz4tZgzZydSUs5T3d8BA0bhoos2Iy1tPgShDQcOXI729q3m3xwGoz/xVAPznSA6wfajj2RrIQa2H73arYUA377Tc+bMkRcxareAAoBdu3Zh7ty5SE5OVr1dkiAI2Lt3L+bOnSu/9Vjtdkn+/JQ4KW2XxBYuc+bMwcCBA1VvAeXxeLBv3z7MnTtXfluw2i2gRFHE3r17MWfOHAwYMCAoJ6U8MUS6BRTr78UXXyy/6VjNFlButxt79uzBJZdcEvCW52CcAMhb+HEcB0Hoxvbt+rfg04JLL+2C1ZoKoPdWhcFswDdOnE4nAGDlypW4//77wXEcHnvsMbz88svYvHkzzj//fPk27dTUVIwaNQoAAr6VlCQJf/7zn3Huuefij3/8IywWC6ZOnYri4mL8/ve/BxFBkqReb/kPtk3l4sWLkZeXh29+85vYvHkz7rjjDnzwwQcoLS3F2LFjUVBQgEceeUQ+PisrCzNnzsQ555wjt+nPlf3e3d2N9PR0/OlPf8KKFSvws5/9DBzH4bzzzkNBQQHWr18v72Ty/PPP42c/+xm++93vwul0YuLEiXjuuefws5/9DE8++SQ4jsOqVavkvk+cOBHPPvssfvSjH2HNmjVBb/VW6hfTAv/xZMStjbHUU5vNBkEQsGvXLlxyySXy70bqKTvHu3btwsUXXyxv6WvqaXz1VGmuC1Z7LIdsbJh6apyelpWVYcqUKSgoKMDDDz8cEz294447AAAjRozAs88+i8cffzxh9RSIXFOD6YzRa9Rw49aoNSpDpFt68jyPoqIizJs3DwAU54loaqr/mg+AIqdoaqrH40FhYSEWLFgAi8VymmMTSku/hc7O7QCAceN+jvHjn4bNNiDiNSrHpWPGjA0oL78B7e2bUVy8AtOm/QcVFYMxb948DBgwIKL5nI0vNtbUzuc2mw0ejwe7d+/GJZdcInM1cj5nMXbu3In58+djwIABEW2Ry3IZ6ba/AAL6G6v5fPfu3Zg9e3aALoSbzxlXZkcynzPdCwfzTpAIsWbNGmRkZMjix741KS8vR3l5OQDf1qWVlZUAfFsKVVdXA/BNDjU1X70NubGxEYDvmw92K2hubq78VvXs7Gz5ds6srCx4PB6MGTMG2dnZEAQBPM8jKysLANDV1YXs7GwAvm9JcnNzAQAtLS3Iz8+HxWLBoEGDUFhYCMD3DQ6zq6ur5S27Kisr5e2eysvLUVFRgbFjx6KsrCwsp4KCAtTX18uc2O2m+fn5ipx4nocgCMjKygrgZLFYMHz4cJlHME6A79uqgoICmdO+ffswduxYnDhxQpGTUp5OnDiBsWPHYt++fYqcguUpLy9PzmkoTsHytGXLFowdOxZtbW2KnILlqaqqSl5ghuJUW1sLwLeFqsfjAQA4HA7EEx6PR+6D2+2WF+Q8z8tbGvI8Lwu2/7aMF1xwgSxuTqcTo0aNQlNTE7q7u2Wx9Xg8AeLJ0NnZifLycixYsABdXV3yRDlr1iwAvsmss7Ozl58oinLNCoKArq4uLF26FJs3b0Z3dze2bNmCyy67DIsWLcKWLVuwb98+uFwufO1rX5M5ff7557jmmmtkLi6XS7adTqfcd0mS4PV6UV5ejjlz5sifd3V1Yf78+bLNJqRf/epXSE9Px+jRo5Geno57770X9fX1aGhoABEhNzcXV1xxBcaOHYv09HTceeedaG1tRWdnZ8Ct4uyWbq/XK9sej0fOTW1tba/x1POPGy2Ip56yGmttbYUkSTHR0/LycnnxU1VVFZKTqaex09PKykqUlZVh7NixqKioCFt7Ho9HHpumnhqrpwUFBTHV07S0NKSlpWHMmDH44Q9/mFB6CujX1Lq6OtkON1ajtUbdvn07xo4di6amJlVjNRprVBYnHKdg+pOdnY2xY8fC6XSGnCeA6GnqgQMH5D8+Q80T0dTUzs5OdHZ2yo+r5ee/hj175py+AJKKGTMyYbPdi9279/TKk9o1ak1NC2bOzALHLYIkuVBW9g24XNloamqKqPYYJwAR/30EAE1NTXA6nbBYLDGZz1taWmCxWORzrLb2GCf/vKqtvcLCQlgsFgwYMAAHDhxQXXt65vPs7Gw4nU6MHTsW2dnZEc3njEdra2vE8znTvbAgE5rQ0dFBAKitrY2IiARBIEEQetlerzfAFkWRPB4PZWZmEs/zAZ8TEXk8ngBbkqQAW5KkXjYRBdgsBrO9Xm9IWxCEADsYj3Ccgtk8z1NmZiY5nc4+wylYnhwOB2VmZpLH4znjOHV3d1NZWRk5nU75vPva7CJB6Cavt0u2PZ7OkLbH00ktLbUBv7M2lOyecdj5YH0JZS9ZsoRWrVolfz5hwgR66aWXAo6ZNWsWPf300/Ixs2bNoqeeeko+5ujRowSAioqKSBRFuv766+muu+4iURRln88++0weyyynoijSqVOn5OPY58ynuLiYOI6jiooKstvt1N7eTq+88grdcsst9Pzzz9P8+fPl491uN6Wnp9O+ffsUuebm5sp9YDyeeeaZgGNefvllGjx4sNyn5ORk+u1vf0sVFRVUWVlJhw8fpoqKCqqoqCCv10vV1dWUnJxMq1atoh07dtChQ4forbfeCuD6zjvvUHp6ei9+zHY6nVRWVkbd3d29ao9pYEdHB+mFqaemniaCngqCQC6Xi0pLS8npdPq1Gbmeer1d5HZ3yJpq6qmpp9HSUyLtmhpMZ/qipjqdzgCd6QucjNJUSZLo2LHfU16elfLyQIWFM6mzsyyqnNxuB5WU3Ex5eaC8PAudPPleSE7B8uR2uykzM5PcbndC5klt7bE1j8Ph6DOcguWJjVGe5yPm1NbWpkpPzTtBdIJO335ltVrlZ5D8bXZ7HrP9r/Yz2/9zu90eYLPbyOx2O0RRxNatW8FxnPxjt9sBIMC2WCwBNrvNe9u2bQGx2a1I7Ja8YDyICPn5+SCisJzU2D05+fPwtwVBkLkqcQpmA5D7q8RJKU+Mq1JuQuWJIRSnYHniOA75+fnyLV2h+PnniYiwfft2+ZbVcLXH+sFsl4tgsaTAZhsEm20QrNZU2O1pYW32Y7MNgt2eFtb293O5KGhflGx/sG/TlI4J1g4Ryd+8sXOZkZGBnTt3yuee4zj5SjLHcUG/jfP/nPmw59iff/55zJo1C4MHD8bSpUuxZcsWbNq0CYsXL5aP37x5M4YMGYLZs2eH5c14ZmRkYNeuXQHH7Nq1K6Bu5syZg4qKCkyZMgWjRo3CeeedJ//YbDbs3bsXgiDgpZdewoIFC3D++eejoaEhLNdg/QqmEexbl2gilnrKcRxEUcSOHTsgimJM9JSdt+3bt8tcTT2Nv54qzXVG6mlPfTT1NLiebtmyBQsXLpQf5zRaT8877zxMnjwZY8aMwZQpUxJaTwHtmqp2rEZjjQog5Lg1ao3KEG6e6Kk/TGfYIwjBOEVbUwHIaz4tefLnqlZTJUlCQcFGlJTchOrqxwCIGDXqdsyZsxNpadNC5inSNeqAASnIyPg7Ro26E4CEw4fvQl3dnyOaz3uONbXz+VdcC+THOmIxnwuCgB07dsh3yqmdz/1zGYpTMJv1lyEW87koivI78yKZz8PN4eHmczUwL4LoRLRuYVQba/LkyZpiavXVE1MP4tHfeHCNV156vuAuFtATU6vvgAEDAn6/7777cPToUaxevRqHDx/GJ598gvfffz/gmLq6OmRkZGDv3r2K7XIch8WLF+Ojjz7C0qVLAQAXXnghPB4PtmzZIn8GAP/73//CvsCPgfF86KGHsH79evz+979HRUUFXn/9daxfvz7g2KeeegoffPABnnnmGRw9ehTl5eX45z//iV/+8pcAgMmTJ0MQBLz22muoqqrChx9+iDfeeENVP9TAiHFi6owxMHka62vqqXF6umnTJixbtkw+NhZ6WlpaiqqqqoTXUyPbVYpl6owxvvHg6XKVw2b7MVpbPwPH2XHeeWtwwQUfwGpNCeurpb8cZ8XUqW9hyJDvAwAqK3+EEyd+p7n/kcCsXWP89PpqhdpYcb8I8uc//xmTJk1CcnIy5s6di61bt4Y8fsuWLfLL6M4999xek1BpaSluvPFGTJw4ERzH4ZVXXunVxjPPPBNwRYrjOIwePVpT/2Od1LFjx2ouQi2+emLqQTz6Gw+u8eDJcRwGDBjQ69tBI6EnJvONht/48ePx6aefYu3atZg1axbeeOMN/OY3vwk4xuv14vDhw/Lz9EpYtmwZRFGUF+0cx+Gyyy4DAPlfwLdov/7661X1mZ2jBQsW4O2338Zrr72Giy66CNnZ2fJinGHFihX44osvsHHjRixatAgLFy7ESy+9hAkTJgAALrroIrz00kv43e9+hxkzZuDjjz/GCy+8oKofatBXLoKYOhN9P72+WhGP/pp6aryeLlu2TOZqtJ7m5ORg/vz5WLx4MV5++eWE1lMj21WKZeqMMb6x5tnQ8DcUFS2A13sUSUnn4KKL8jF27AOqNUdrf61WG2bNegsTJvjGZ1XV46iq+oXqb/a1wqxdY/z0+mqF6lghH5YxGP/4xz/IbrfTW2+9RWVlZbRq1SpKTU2l48ePBz2+qqqKUlJSaNWqVVRWVkZvvfUW2e12+s9//iMfU1hYSI8++ij9/e9/p9GjR9PLL7/cq52nn36apk+fTvX19fJPU1NTRH1nz1u2trZG5EdE8vNc7NkntfB6vbRp0yb5uadY+OqJqZWnnrjxOEdnMk+Xy0VlZWXkcrnkzyRJoo6ODvk5PbXwf647UmiNqcdXT0ytXHvG3Lt3Lw0ePFhVbZyJPIPVD0Nra2vU3wkSSz0lMnXGKD89vvHgGYlvzzFxJuhMLHzjwbNn3FjoqR7fM0VPibRrqqkzxsXU4xurtbggOKi8/O7T7+YA5ebOIafzZMQxo8Hz+PHfyf2oqHiIJCm0fpi1a1zMROOpVk/jukXuSy+9hO9///u45557AACvvPIKNmzYgL/85S9Br7C/8cYbGD9+vHx3x7Rp07Bnzx784Q9/wI033ggAmDdvnvxW7FD71dtsNs13f/gj1le2ZsyYoflKnBZfPTH1IB79jQfXeOVl4MCBmvz0QE9Mrb7x5slun/Z/jlOtr9aYsUJfuRPE1Jno++n11Yp49TfeOhMr33jw9I8bKz3V66sFfeVOEFNnjPGNBU+n8zBKS2+Cw1ECgMOECU8jJeVeJCWNiritaPAcP/4xWK1pqKx8AHV1r0IUu3D++W+B46zhG4pDf2MVUw/6E081iNtFEI/Hg7179/a6ULF8+fKArav8sWPHDixfvjzgsxUrVuCdd96R90tWi8rKSpx99tlISkrCJZdcgt/85jc499xzI+YR66SOHDkypr56YupBPPobD67x4Nnz5UqxgJ6YWn3PBJ7z58+Xt2KM1FdrzFihr1wEMXUm+n56fbUiHv09E3QmFr7x0hn/uLHQU72+WtFXLoKYOmOMr9E8Gxs/weHD90KSHLDbRyEj4xMMHXq5tsaIYPnoI4zMyAAi7HNPnmPH3g+rdRAOHboTDQ3vQRS7MW3aR7BYIn+kL5K4Rvvp9dWK/sRTDeJ2EaSlpQWiKGLUqMArjKNGjZLfuN0TDQ0NQY8XBAEtLS0YM2aMqtiXXHIJPvjgA0ydOhWNjY349a9/jUWLFqG0tBTDhw8P6uN2u+F2u+Xf2b7OTqcz4m8MvKf3qmf/RuKXn5+PxYsXRzxBa/XVG9P/30h949FfrTH9/41VTDV+Xq8XRARJkuQ3UBMRuru7MWjQoIieKafTz2Sy9iKB1ph6+6snJvs3Eq59jackSSAieL3egLfUAwjYKSJSnAl6ynxMnYm+n96Y/v/GImYkvj011dQZdb7s31jNHfE6R+zfWOopED1NNXXGuJh6fI1ai0sSj6qqn6Cx8S0AQHr6Epx//ocYMGA0nE6nppjcnj2wfe97kGw2eD/8EJbTd+qr7WvPmMOH34oLLkjC4cO3o7n53xBFF84//5+wWOy9fJV4aolrpJ/emP7/xipmIvFUq6cckcFvm1HAyZMnMXbsWBQUFGDhwoXy588//zw+/PBDHDp0qJfP1KlTcdddd+GJJ56QP9u+fTsuvfRS1NfX93q8ZeLEiXj44Yfx8MMPh+yLw+HA5MmT8dhjj2H16tVBj3nmmWfw7LPP9vr8k08+QUpK+DclmzARD7DHvsaNG6fpZXgm+jc8Hg9qamrQ0NDQawtHp9OJ2267DR0dHUhPT4+oXVNPTSQqTE01oRVG6SlgaqqJyMFxDUhN/T2s1ioQcXC7b4bbfQsAfY+bjNu0CXNeew0AIFksKFq1CrVLlujur81WhJSUF8BxHng8i+By/UR3X030TajV07jdCXLWWWfBarX2uuujqamp190eDKNHjw56vM1mU7yDQw1SU1Mxc+ZMVFZWKh7zxBNPBFwg6ezsxLhx47B8+fKIJyyv14ucnBxcddVVcbm9NFYwecYfbrcbJ06cQGpqqu5nnIkIXV1dSEtLi+lOCPFAf+EajqfL5cLAgQOxZMmSXttqsm8atcDU08hh8jwzYGpq5DB5+mCUngLR09QzffxFC/2FJxCc66lT61FR8QQE4RRstrMwderfMHToVVGJZ9m5EwBA6emwdHZiziuv4KKzz4b00EOArvG/EqdOzUV5+Y0YMKAAY8dOwnnnvQOO8z360F9yavIMD7V6GreLIAMGDMDcuXORk5ODG264Qf48JydHcfuzhQsXYu3atQGfZWdn4+KLL9ZVCG63G+Xl5QHbWvZEUlJSr0mLQWtsu90e8a1B2dnZWL58uabbkbT46onJEClPPXHjcY4YzkSeFosFHMeB53mkpqYC8N2S29nZifT09IieF2a393IcF/Fzxlpj6u2vnphA5Fz7Gk+e58FxHAYOHNjr9m09OBP0FDB1xig/vb5AbHlG6stxHARBgMViMXVGpS8Q27kjXucIiL2eAtHXVFNnjIkZ77W4zWbF8eO/xrFjzwAgpKXNx/TpnyI5+ZzoxayuBgAcuukmTE1KgvUvf4H1pz+FddMm4L33gBCbUoSLOXLkteC4f6K09GY0N38Mmy0FU6f+NeCio1m70Y+ZaDzVIq67w6xevRrf/e53cfHFF2PhwoV48803ceLECdx3330AfFe26+rq8MEHHwAA7rvvPrz++utYvXo1fvCDH2DHjh1455138Pe//11u0+PxoKysTLbr6uqwf/9+DBo0CFOmTAEAPProo7j22msxfvx4NDU14de//jU6Ozvxve99L2IONlvsTqHNZsNll12mKaZWXz0x9SAe/Y0HV6N5Wq1WDBkyBE1NTQAg3xZrt9vhdrsj+lZOkiR4PB7wPB/xopKINMXU46snplaufYUnEcHpdKKpqQlDhgwJumA3YpyYOmMMTJ7R87XZbEhJSUFzczPsdrv8Ek1TZ5QRj7kjHufoTNNTI9tVimXqjDG+0eApCKdQXn432tq+BACcffZ9mDLlFVgswS+eaY559CgAYMKVV8Jyyy3AtGnAY48B69cDM2cCb70FfOMbmmOOGHEDpk37COXl30F9/VuwWAZiypRXIuujhrjR9NPrqxX9iaeq4wzuR0jccsstaG1txXPPPYf6+nrMmDEDWVlZmDBhAgCgvr4eJ06ckI+fNGkSsrKy8Mgjj2DNmjU4++yz8eqrr8rb4wK+d43Mnj1b/v0Pf/gD/vCHP2DJkiXYvHkzAKC2thbf/va30dLSghEjRmDBggXYuXOnHDcSxPLWTo7jND0rqsdXT0w9iEd/48E1FjzZu3LYhRCtICL5dt6+fEsz0H+4huM5ZMgQxa3EjTgvsT7Xps4Y46fXVyti0V+O4zBmzBhUV1fj+PHjmmIxmDrTt3Cm6amR7SrFMnXGGF+9PC2WYzhwYDV4vgoWSzLOO+8vGDPmzqjFJAIKCoBRIwlTTr9aIOXCCwGLBfjxj4HLLwduvx3Yvx+44Qbg+98HXnoJ6NG+2pijRt0KIjcOHboTdXWvwmIZiHHjnlPV12Awa9cYP72+WqFW9+J6EQQAHnjgATzwwANB/+/999/v9dmSJUuwb98+xfYmTpyIcO96/cc//hFRH0NBy1tr9cTKysrCypUrNd2OpMVXT0w9iEd/48E1FjzZon3kyJHwer0J9XZoPb5mTH2+drs95C3bRmhfLPWUxTN1Jvp+en21Ilb9HTBgAM477zx4PJ4+NebPtJiJ1t8zTU+NbFcplqkzxvjqidnU9DEGDXoMPO9BcvJETJ/+GdLSZof1UxuzpgZ44AHgiy+A889qw6GODgDA2tJSXD11qs93+nRg507gqaeAF18E3nkHyM4G3n4bWL5cE8/Ro78HUXShsvJ+1NT8DsAAAHNVnROtXKPlp9dXK/oTT1UgE5rQ0dFBAKi9vT1iX4/HQ5mZmeTxeCLykySJnE4nSZIUcUytvnpiauWpJ248zpHJ07iYenzjUbv9hScRUXt7OwGgjo6OiH17Ih56SmSOP6P89PiaOmNszETiqSduotVuNPWUSLummrkyLqYeXy1+kiRRdfVzlJcHyssDFRUtJ4+nJWoxRZHo9deJBg0i8t0LQjQPu4gAksaOVfbdvJno3HO/cvrBD4hO170WnidOvCxzzMq6w6xdA2ImGk+1ehrZw5km4g49z1Rp9Y31c/p648bjHOmBydM4X5OniXAwx58xfnp94xEzkcZff+GpJ26i1W5fQH/J1Zk+/ogIVVU/w7FjTwEAeP4mZGR8Drs9sp00lWIWFwOXXQY8+CDQ3Q0sWgTccQcwBUd8B0yerNzfJUt8DTz4oO/3t94CZszw3RkSIqYSxo17GJMm/QYAMHDgBzh5ck1E/gxm7Rrjp9fXSJgXQXTC7XYDAERRhCiKvWxBEAJs9vZw4Ks3ift/7vV6A2w6/WgPe4whKysLLpcLRAQikm/58bclSQqwBUGAIAjIysoCz/MBn7P++tv+PNxuN7KysuB2u8NyUmP35OTPw99m/XW5XIqcgtk8z8v9VeKklCfGled5VTz8bYZQnILlifXX4/EocgqWJ9ZXlotIao/FZHWhpvYYD8YhktoDfC8pZjHV1h6z/blGUnushjweT0S1xzgxqK09/3EWTheC5alnf9XWnpIuqM0Taz8Uv2B5Yr9HE7HUU3Y+srKyAn4HjNNT/zwzrqaexl9PleY6o/U0GA9TTwNtNXNdNPWUiGRff11IRD0FtGtqJOeL2VrXqOHGrVFrVAa1Y5XZLpdLro9wYzVamtpz3IbKE5GEw4fvR03NiwCA8eN/B7f7dnCcNSJNDaYzTU3AvfcSZs8mFBQAgwYR/vQnEVu3Ar/4hShfBOkcOSn0uE1NhfDyyxA3bQLOPdf3TM2KFaAf/ADZ//lP0HEbKk/jxz+Oc875OQCguvoRnDz5dkw0Vc98rkdT2TGR1J6eNaqe+VzrGjWcjoabz9XAvAgSIdasWYOMjAzMmzcPAHD48GEAQHl5OcrLywEAxcXFqDz9YqCioiJUn94uqrCwEDU1NXJbjY2NAID8/Hy0tLQAAHJzc9He3g7At/1vV1cXAMiD86qrrkJOTg4EQZDFFAC6urqQffoqant7O3JzcwEALS0tyM/Ph81mw+zZs7F7924AQE1NDQoLCwEA1dXVKCoqAgBUVlaiuLhY5lRZWYmVK1fKdihOBQUFqK+vlzm1trbKthInnuflQenPib1NOC8vT5ET4Ht5bkFBgcypqKgIK1euRG1trSInpTzV1tZi5cqVKCoqUuQULE+sj+E4BctTfn4+Vq5cKdvBOAXLU3V1Nc455xzYbLaIa2/37t2YPXs2bDab6tpjnABEXHvMHjJkCGw2m+raY5zKy8sxZcoU2Gw21bXX0tICm82G5ORkdHd3R1R7AGSfUJyC5clms+GCCy6QeaitverqathsNpx11lkyDzW1l52dDZfLhZUrVyInJyei2mOcWCwlTkp5isaV/XjqKbuAwRALPS0vL4fNZsM555wj8zD1NP56WllZifLycqxcuVK2g3GKtp7m5OQAiKz2mN0f9BTwvch+9OjRsNlsMdHTrq4uWdv8/2hIBD0F9GtqXV2dbKs9X4C+NeqOHTuwcuVKtLS0qD5fjJPWNSqLE45TsLrOycnBypUr4XK5wo7VaGlqcXExLrjgAthstpBjtbm5AYcO3YmGhr8C4DB16lsoL58uc41EU7u7u5GcnAybzYaTJ1vw4IPVOO884K23OEgSh299C8jOrsPFF++CxQJYLNWYP+yQry91Y9WtUcePB4qLUX96gwvLO+/giocfRucXX0RUezzPY8yYn8Ptvh4AUFFxL3Jyfha29vRqqtb5XK+mAoh4PtezRtU6n+tZozIera2tEc/nTPfCIuTDMiYUwZ63bG1tJSIiQRBIEIRettfrDbBFUZSfc+J5PuBzIt8zUP42e4aKfe5wOMjtdpMkSSRJkvyslL/NYjDb6/WSJEnU3d3d63PWX3/bn4fX6yWn09mLR09OwWye5ykzM5OcTqciJ38ePW3GVYlTMNvj8cj9VeKklCfG1T8HSvz8j3E4HPJza6E4KeXJ6XQG5ECJn/8xXq+Xurq6SJKkiGqP9b27u5skSVJde5IkkdvtpszMTLn+1NYe61dnZydJkqS69vz77t9fNbUniiJJkkSdnZ3y8Wprj4hkruycqKm9YONMbe0JgtCrv2pqj9lOp7OXLqipPaZFTqdTde2xvhvxTpBY6imrxc7OTrlejNZTlueurq6AsWzqaXz1VGmuM1JPWU6Yppp6GtxWM9dFU09Z33vqQiLpKZF2TQ2mM0avUcONWyPWqE6nM0BnItFUt9stn59wYzVamqqkM/62291NBw/ecPr9GFaqr/+IiLRrqiAI1NHRSZmZEk2ZIsmv8JgzR6K8vOB5apiykAigH4/6B3V2RrhGzc0l6fS7QiSLhehXvyIvz0e4Rv0vlZXdK5+DpqZPDdVUrfO5Hk1lOuNwOCKqPT1rVK3zuZ41KhujPM9HPJ+3traa7wSJBej0rUtWq1V+87e/bbPZAmz/veOZ7f+53W4PsNk2P3a7HaIoIicnBxzHyT/sTbv+tsViCbBtNhsEQcDGjRsDYrNvHqxWa4Dtz4OIkJ2dDSIKy0mN3ZOTPw9/Wzj9LRk7NhinYDYAub9KnJTyxLgq5SZUnhhCcQqWJ9ZfSZIUOQXLExFh06ZNEARBU+1t3LgRgiCorj3Gg3GIpPYA321rubm5EARBde0xm4gC+qu29gRBQG5urnxbntraY5wY1NZesHGmtvasVmuv/qqtPVEUkZ2d3UsX1OaJtR+KX7A8CQbcvh1LPeU4DqIoIjc3F6IoxkRP2XnbtGmTzNXU0/jrqdJcZ7SeBuNh6mmgrWaui6aechwn+/rrQiLqKaBdUyM5X8zWukYFQo9bo9aoDGrHqj+v7OxsiKIYdqxGS1OB4DrDbFF04tChm9DS8l9w3ADMmPEZRo/+jszDn6taTS0ulrB4sQvf+AaHI0c4jBrl29Rl924OS5cGz9PIjqMAgG2N5+G11/ZHtkZdtgzCnj04sWwZOEkCnnwStv/7P1iamlTnCeAwefKrGD36TgAiyspuRVvbOsM0Vc98rkdT/fMaizWqnvlc6xo1nI6Gm89VIeQlEhOKYFfZtVy1Z1fx2BWvvgqTZ99Cf+FJ1H+46uGpRwOj2ZaZq76F/sKTqP9wNXmGRzT1VE97Zq4SD15vB+3bt5jy8kBbtqRQa2tOwP9HyrWtjejHPyayWn13fgwYQPT440SdnWEcOzvlHV/S0U7f+Y5GQkRE779PlJLia2/kSKLs7LAu/jwlSaCSklsoLw+0eXMStbVt1NGZMwt9qXZDIRZ6at4JohOk9mpTlGJ1dnZqiqnVV09MPYhHf+PB1eRpnK/J01gYEc/UGWNg8jTO1+RpLPpT7SZSu0qx+kuuzpTx5/W24cCBK9HRkQ+rNR2zZmVj2LArI+4XAIiib6OWqVOB117z/f71r3tRVkZ44QUgLS1MA0d9d4F4h5yFTgzGxx8Df/6zRp533AHs2QPMnAk0NQErVgC/+AWg8o4pjrNi2rQPMXz49SBy4+DB69DRsT18XLN2o+qn11cr1MYyL4LohFG3MCrF2rp1q6aYWn31xNSDePQ3HlxNnsb5mjyNhRHxTJ0xBiZP43xNnsaiP9VuIrWrFKu/5OpMGH8eTyP271+Krq7dsNmG46KL8jB48Nci7hMAFBQA8+cD994LtLQAGRnAunUC7rsvG+PHq+zrEd/OMPYLpuCXv/TtLPLgg8C//qW+HwE8p00Ddu0CfvhD3/0lv/kNsGwZUFurqi2LxY7p0/+JoUOXQ5KcKC6+Bp2du8PHjQD9pXYTkacqhLxPxIQizNu3w8Pk2bfQX3gS9R+uZ8rt26aehofJs++hv3A1eYaH+ThMbJHoPF2uE7Rz53mUlwfavn0MdXeXKB4bimtdHdHtt8tPsdDgwUSvvEKk6bT89re+Rm6/nSSJ6Ec/8v1qtxNlZWlozx9//ztRWtpXj8ds29brECWeguCgffuWUF4eaOvWodTVdUBnZ+KLRK9dtTAfh0kAsBfaxCpWW1ubpphaffXE1IN49DceXE2exvmaPI2FEfFMnTEGJk/jfE2exqI/1W4itasUq7/kKp7jz+k8gqKiy+ByVSIpaQIuuigfqanTwzfiB0EAXn4ZOP984KOPAI4D7rkHqKgAVq0C7HYNfT19JwgmTwaRhGeeacO3vkXweoFrrwXWrPFdalHLMwC33grs2wdceKHv8Zhly3zP7qiA1ZqCmTPXIj19AQThFA4cuAoOxyF1ccOgv9RuIvJUA/MiiE54PB4AgCiKEEWxly0IQoDtnxhm+3/u9XoDbDqtGF6vF4IgoLCwEDzPg4hARPB6vQAQYEuSFGCzPhQWFsLtdgd8zvrrb/vz8Hg82L17NzweT1hOauyenPx5+NusvzzPK3IKZrvdbrm/SpyU8sS4ut1uVTz8bYZQnILlifWX5TcUP/88eTweFBYWyhwiqT232y37qq09xoNxiKT2WBssptraY7Y/10hqj9UQ64/a2mOcGNTWnv84C6cLwfLUs79qa08QBOzevbuXLqjNE2s/FL9geWL9jyZiqadEJGuqIAgx0VP/PDOupp7GX0+V5jqj9TQYD1NPA201c1009ZSIZF9/XUhEPQW0a2ok54vZWteo4catUWtUBrVjldk8z2P37t0QBCHsWI2WprJx0NVVjKKiy+B2H8fAgVNx4YV5SE4+N2ye/Llu306YO5ewejXQ3Q0sWEAoKBDw1lvAiBEUMMaD6YySTZWVviBTpsDj8WDPnkK8+66A22+XIIq+R2Puu08CzyvnKdS49U6YAGnrVuDGGwGv1/fszoMPwut0hl2jEiVj5sx1GDRoNrzeJhw4cAUcjkrdmqpnPtejqf55jcUaVc98rnWNGq7ews3namBeBIkQa9asQUZGBubNmwcAOHz4MACgvLwc5eXlAIDi4mJUnhaDoqIiVFdXAwAKCwtRU1Mjt9XY2AgAyM/PR0tLCwAgNzcX7e3tAHzbaHV1dQEAsrKyIIoirrjiCuTk5EAQBPA8j6ysLABAV1eXvH1Re3s7cnNzAQAtLS3Iz8+H3W7HzJkzsXu373m4mpoaFBYWAgCqq6tRVFQEAKisrERxcbHM6ciRI1ixYgXKy8vDciooKEB9fb3MqbW1VbaVOPE8D0EQkJWVFcDJbrdj0aJFyMvLU+QEAPX19SgoKJA5FRUVYcWKFaitrVXkpJSn2tparFixAkVFRYqcguWJ9TEcp2B5ys/Px4oVK9DR0aHIKViejh07hlGjRsFut0dce7t378bMmTNht9tV1x7jBCDi2mN2SkoK7Ha76tpjnMrLyzFx4kTY7XbVtdfS0iJvweVwOCKqPQDo7u6Wc6q29goLC2G32zFlyhQcPHgwJKdgebLb7UhPT0dDQ4Mip2B54nkeK1asQE5OTkS1xzixWEqclPLkv1WbVsRTT3meB8dxcLvd4DguJnpaXl4Ou92OUaNG4dixYyE5mXoaOz2trKxEeXk5VqxYgSNHjsRMT3NycgDA1NMQeaqtrcXw4cNht9tjoqddXV2w2+1wu93ywjpR9BTQr6l1dXWyrfZ8AfrWqDt27MCKFSvQ0tKi+nwxTlrXqCxOOE7B6jonJwcrVqwAz/Nhx2q0NPXgwYMYP57HwYNXwuttgN1+AWbPzsfevTWqNbW1Ffi//6vDpZdyKC7mkJbmwRtvCNi4kUdj45e98uRwOORtS9WMVaGiwndCJ0+W16ipqXY89lgZVq9uAMcBb75pweLFbrS0BM+T3W5HSkqKzKMXJ0EA/v1vHL79dl+sNWvQMX8++JqasGtUu30Ixo//JyRpAjyek9i//wrk5/9HzpkWTdU6n+vVVP+8xmKNqnU+17NGZTxaW1sjns+Z7oVFyIdlTCiCPW/U0tJCRESCIJAgCL1sr9cbYIuiKD/nxPN8wOdEvmeg/G1JkmRbEARqaGggnudJkiSSJEl+VsrfZjGYzdqvr68nt9sd8Dnrr7/tz8Pj8VBjY6McX4lTMJvnecrMzCSn06nIyZ+Hvy2KosxViVMw2+12y/1V4qSUJ8bV7XYrcgqWJ4fDIT+3FopTsDyx/nq9XkVOwfLk8Xjo5MmTJIpiRLVHROR2u6m+vj6gNsLVniRJ5Ha7KTMzk9xud0S1x+Kz/qqtPWZ7PJ6A/qqpPRbn5MmTAedMTe2xc8Ryqrb2go0ztbUnCEKv/qqpPTYuGxsbe+mCGo1gWuR0OlXXHuv7qVOnov5OkFjqqSRJJAgC1dXVkSAIMdFT/zyzNk09jb+eKs11RuqpJEkyV7fbbeqpgq1mroumnkqSRKIo9tKFRNJTIu2aGkxnjF6jhhu3RqxRnU5ngM5Eoqk8z1NjY6McPxinaGtqc3MubdmSRnl5oN275xHPN4fk58+pq8tBDz64j4YPl+R3f9x1l0QnT4auayWdCWp3d5PEcb7Gm5qCjtu1a4nS0nx9mDiRaNeu3nkKNW575SkzU35PiDRhAnkKC1WtUbu7v3qfyo4dU4jnT2rWVK3zuR5NZTrjcDgiqj09a1St87meNSobozzPRzyft7S0GPdOEHabS7DPT5w4oaXJhAXHcQAAq9UKq9Xay7bZbAG2xfLVKWe2/+d2uz3AZu3b7XYQEUpLS2G1WsFxnHyFlvWD2RaLJcC22WyQJAllZWVy2+xz1l9/258Hx3EoKSkBx3FhOamxe3Ly5+FvS5Ikc1XiFMy2WCxyf5U4KeWJcWXthOPhbzOE4hQsT6y/rP1Q/PzzxHEcysvLIUlSxLVnsVhQVlYGSZJU1x7jwWJHUnsMrL9qa4/ZHMcF9Fdt7UmSJF/VDsVJKU8Mamsv2DhTW3tWq7VXf9XWHhGhpKSkly6ozRNrPxS/YHlSmgf0IJZ6ynEciAiHDh0CEcVET/3zzPpi6mn89VRprjNaT4PxMPU00FYz10VTTzmOgyRJvXQhEfWU9dM/Tk87Uv0xYo0abtwatUZlUDtWmW21WlFSUgIiCjtWo6GpbW05KCv7OiSpC+npi3HRRZuQlHSWqjxVVQErViTj9ddno7WVw4wZwNatwLvvchgzJnRdA8F1ppd9/DiweDE4ImD0aOCss4KO269/Hdixg8O55wLHjgGLF9vwzjtWECnrTMjau/56YOdOYMoUcMePw7Z0Kc7evj1s7aWmjsOsWZuQlDQBPH8EBw5cCa+3WZOm6pnP9WgqOyaS2tOzRtUzn2tdo4bT0XDzuSqEvETSAx0dHXTzzTdTcnIyjRw5kp566in5ShERUUNDA1kslkiaTFiYuxmEh8mzb6G/8CTqP1z18DR3h4ktTJ59D/2Fq8kzPMzdYWKLROHZ3JxJmzcPoLw80IEDV5MgOFT7fv65b7cXgCg52Uu/+51AUaf7738Tpaf7ggwfTrRxY1iXtjaia6/9akea736XqLtbRx/a2oiWL5cbFJ57juj0HRWh4HQepe3bzz59d81s8nhO6ehE7JAotasXsdDTiO4EefLJJ3HgwAF8+OGHeP755/G3v/0N119/fcALSCjcq3/7GIy6eq8Uq66uTlNMrb56YupBPPobD64mT+N8TZ7Gwoh4ps4YA5Oncb4mT2PRn2o3kdpVitVfchWL/jY2/h0lJTeCyIOzzvomhg5dA45LDusnCMDPfgZcfz3Q0QEsWCDhtddy8cgjEiJ59UzIvvI88OMfAzffDHR2Al/7GrB/P3DFFWF9hw4FMjOB3/0OsFiADz8ELrkEOHRI47kdOhT48kuIq1YBAKxPPQV8//tAmJdjDhx4LmbN2gS7fSS6u4uwd+8V8Hg61MdF/6ndROSpBhFdBMnMzMRf//pX3HTTTbjnnnuwd+9etLS04Nprr5Xfkq/6FpQ+glgn9ejRo5qLUIuvnph6EI/+xoOrydM4X5OnsegrF0HM8Rd9P72+WtFfxl9/4aknbiLWbiK1qxSrv+TK6P6ePPkWysu/A0DEqFF34PzzP0Z1dW1Yv/p633WI3//e9/vDDwMbN4oYMcIVvb4WFgJz5wKvv+77/fHHgbw84JxzwvuehsUCPPYYkJvre4KmtBSYNw/4xz9I27m12SC9+CIO3HsvyGIB3nsPuPpq4NSpkG6pqRdg1qwc2GzDwPP7UFJyHUTRqTpsf6ndROSpCpHcXpKSkkJVVVUBn3V2dtLChQvp8ssvp6qqKvNxGBUwb2XqWzB59j30F65nyu3bpp6Gh8mz76G/cDV5hof5OExscSbzPHHiZcrLA+XlgQ4fvp8kSVTll5dHNGqU76mQtDTfkypEUeTqchH97GdEFosvyKhRRF9+qa9NIqqvJ1q69KvHYx58kOj0ezsjAuPp/d//iAYN8jV2wQVER4+G9e3o2E35+emUlwfav385iSKvgUlscCbXbjRxxj0OM27cuICXuABAWloasrOz4XK5cMMNN0TSXJ9ArK9sHT9+XPOVOC2+emLqQTz6Gw+uJk/jfE2exqKv3Alijr/o++n11Yr+Mv76C089cROxdhOpXaVY/SVXRvSXiHDs2K9w9OgjAIBx4x7DeeetAcdZQvpJEvDb3/ruAGlsBGbOBPbsAW66KXJuin3dtQuYM8f3DIskAbfd5rt9Y+XKiHn2xOjRQE4O8MQTvt9ffx248kpCc7O2ftPVVwPbtvnuTDl0CFiwANixI6TPoEFzMHLke7BYUnHqVDZKS78FSfKGjdVfajcReapBRBdBli9fjvfee6/X54MGDcKGDRuQnBz+WbW+Bq/XN0hEUYQoir1sQRACbP/EMNv/c6/XG2DT6XeseL1eiKKI2tpauN1uEBGISI7vb0uSFGCz9mtra+X3t7DPWX/9bX8eXq8XdXV1cvxQnNTYPTn58/C3WX/ZY1bBOAWzPR6P3F8lTkp5Ylw9Ho8qHv62fz0ocQqWJ9ZfQRAUOQXLk9frRW2t79bISGvP4/HIvmprj/FgHCKpPRafxVRbe8z25xpJ7UmShBq/vePV1h7jxKC29vzHWThdCJYn5ut/ztTUniiKqKur66ULavPE2g/FL1iejJjQYqmnRARRFFFTUwNRFGOip/55Zm2aehp/PVWa64zW02A8TD0NtNXMddHUUyKSufrrQiLqKePU8xypqetIzhezta5Rw41bo9ao/ucoEk11u92oq6uT4wfjFKmmCoKAqqqf4dixpwAAEyf+ChMmPB/Qr2A609ws4LrrCE884bs28d3vEnbuBCZN0q+pgiCg9sgR4Gc/Ay1aBJSXA6NGQfr0Uwh/+xswfHjU1qiAgF//WkJmpojUVAFbt3KYNw/Yt0/jGvXCC+Hdts134aa5GbRsGfCvf4XU1JaWUcjI+C84Lgmtrf9DefntEASPYfO5Hk31z2ss1qh65nOta9RwOhpuPleDiC6CPPvss3jmmWeC/l9aWho2btyI3NzcSJpMOKxZswYZGRmYN28eAODQoUMAfNtIsbtkiouLUVlZCQAoKipCdXU1AKCwsBA1NTVyW42NjQCA/Px8tLS0AAByc3PR3t4OAMjOzkZXVxcAICsrC4IgYP78+cjOzoYgCOB5HllZWQCArq4uZGdnAwDa29vlPLS0tCA/Px82mw0TJ05EYWEhAKCmpka2q6urUVRUBACorKxEcXGxzKmyshKLFi1CWVlZWE4FBQWor6+XObW2tsq2Eiee5yEIgsyPcbLZbJg5c6bMIxgnAKivr0dBQYHMad++fVi0aBFqamoUOSnlqaamBosWLcK+ffsUOQXLU15enpzTUJyC5WnLli1YtGgR2tvbFTkFy1N1dTVSUlJgs9kirr3CwkJMnDgRNptNde0xTgAirj0AaG5uhiiKsNlsqmuPcSorK8Pw4cNhs9lU115LSwtsNhscDge6u7sjqj0Ask8oTsHyZLPZMGrUKJmH2tqrrq6WtyFjPNTUHrsLb9GiRcjOzo6o9vy1mvFQU3uMk/8Wg1oRTz3leR4AZJ2KhZ6Wl5fDZrMhJSVF5mHqafz1tLKyEmVlZVi0aBEqKytjpqc5OTkAIqs9oP/oKbMHDBgAm80WEz3t6uqCzWZDa2ur/Ed5ougpoF9T6+rqZFvt+QL0rVELCgqwaNEiNDc3qz5fjJPWNSqLE45TsLrOzs7GokWL4HK5wo5VNZpaUXEYhYW3oabmRQBAaupjmDjxlzh48KDMqbi4GKNGjYLNZpM57dkDXHihgC+/5JCUBKxeXY4XX2xGSkp0NNW1eTNm3XUXLH/4AzhJQsMVVwBlZai/5BLD1qjXX2/Fn/9chAkTBBw/7nvf6gcfdKrOE+C3Rt2/H8jPh3flSnBuN3DLLeCffhq5mzb1yhPT1LPOugojR74BwIbm5n9h375bsW/f3qC1p2c+16up/nmNxRpV63yuZ43KeLS2tkY8nzPdC4uQD8uYUAR73qi5uZmIiARBkLcL9re9Xm+ALYqi/JwTz/MBnxP5noHyt6XT2zx5PB7yer1UUVFBLpeLJEkiSZLkZ6X8bRaD2awPFRUVckz2Oeuvv+3Pw+12U2VlJbnd7pCcgtk8z1NmZiY5nU5FTv48/G3WX5fLpcgpmM3zvNxfJU5KeWJceZ5X5BQsTw6HQ35uLRSnYHli/WX5DcXPP09ut5sOHz4sc1Bbe0REPM9TRUWF3J6a2pMkidxuN2VmZpLb7Y6o9lgbrL9qa4/Z/lzV1p4oiiQIAh06dEjuj9raYzFZTtXWHuvb4cOHyX36gVa1tcd+P3z4sBxLTe2xuqmsrOylC2o0gmmR0+lUXXusr21tbVF/J0gs9VSSJPJ6vXTo0CHyer0x0VP/PLMaMfU0/nqqNNcZqaeSJMlc3W63qacKtpq5Lpp6KkmSzNVfFxJJT4m0a2ownTF6jRpu3BqxRnU6nQE6E4mmulwuqqysJK/XG3ashtNUUfRSaentp98BwlFt7V/DjluPx0t//rNIAwb4Xntx7rkS7dsXRU3t6CB69FGSTr/7Qxo9msTPPovJGpWN24YGD11xxVfvCfnVr4jcbo1rVK+XhIcekhsT77yT6PT5UdLU+vp/UV6elfLyQOXl98q6EK35XI+mMp1xOBwR1Z6eNarW+VzPGpWNUZ7nI57Pm5ubVemp6kvPr776Ku69914kJyfj1VdfDXnsQw89pLbZhIfF4ruZxmq1yp/52/5X95nNbjFivv7H2P32r+ppC4KA9vZ2TJo0Sd6Fhx3DcZxsWywWuW1m+/v2PEap71arFUSEU6dOYeLEifL/BeMUzKbTt3XZbDY5Vih+SlyVOAWzrVar3F/Wn1D8/PsrCILsGyw3avMUyu6Zp2D9VeLn31+LxYKOjg4QUUC/wtUeO6a9vb2Xr5q+s3/9eYSrPXYM66/ValVVe+G4hrMFQUBnZ2evsRIJ11CclMZZR0dHRPz8+9vR0SHHVlt7/nUbimuwPLFbBiOpPWaz8R1NxFJPGTo7fd8yRVLTWvUUQK8aMfU0/nqqNNcZqadKPEw97W2Hm+uiqaeAT9uC6UJPTme6nrK4SjH9+8rsYDpj9Bo13Lg1ao2qhlMw22azyTHDjdVQXC0WEWVlt6Gl5TMAVkyb9iFGjfp2UK5sHHR3Ex580IaPPvJ9fv31wPvvcxgyBACioKkFBbDffTdQUQEOQMs112DI++/DNnKk/OiAkWtUNm7PPZfDunXA6tW+d4Q8+SRQWmrHO+8AKSkRrlFtNlj/9Cdg6lTgoYdgef994MQJWD79FBbfieulqaNH3wyO86K8/HY0NLwJm20QJk/+gxxD73zOfLVoKtOZSGtPzxpVz3yudY3qrwWRair7PCxCXiLxw8SJE6mlpUW2lX4mTZqktsmEhrmbQXiYPPsW+gtPov7DVQ9Pc3eY2MLk2ffQX7iaPMPD3B0mtog3T0Fw0IEDV1NeHmjz5gHU3Px5WJ/ycqLp0303NFitRL//PdHpGwdCQhXX7m6ihx8m4jhfgLPPJlq7NgJGxuGvfyWy2XzdmjuXqLY2+HGqeH755Vc7x0ybRtRjx9OeOHnyHXmnnqqqJ3WwiB7iXbuxQiz0VPU7QaqrqzF8+HDZVvqpqqpS22SfgCi/1Cc2sQ4dOqQpplZfPTH1IB79jQdXk6dxviZPY2FEPFNnjIHJ0zhfk6ex6E+1m0jtKsXqL7nS09+ysj04cOBqtLWth8WSgpkzv8RZZ10X0u/vf5cwd66I0lLfbiq5ucBPfwr43WSlHVu2ALNmAa+84ntg5K67gNJSiNdcc0bozL33Ahs3AsOHA3v3ApdcAhw8GHGXfFi50rdzzNixvhe9XnIJsHOnYn/HjLkbU6a8BgA4fvxXOH78hajw1OurFf1pjKpBRC9GDQbyeyOvCePhcrli7qsnph7Eo7/x4GryNM7X5GkiHMzxZ4yfXt94xEyk8ddfeOqJm2i12xfQX3KlNabX24bW1jvR2bkVVms6Zs3KxrBhVyoe7/EADz0E3HabBU6nFUuWEIqKgMWLtfbcDw4H8OMfA0uXAkeP+raUXbcOePddnH6+5ozRmSVLgN27gWnTgLo64NJLgdPvN40cs2b5tvydPRtobgaWLQP+8x/F/p5zzoM499zfAQCqq3+O2to/he2vWiRS7SYaTzXQfBHknXfewYwZM5CcnIzk5GTMmDEDb7/9djT7lhDwfw4pFrFmz56tKaZWXz0x9SAe/Y0HV5Oncb4mT2NhRDxTZ4yBydM4X5OnsehPtZtI7SrF6i+50hLT42nCwYNXQBRLYbMNx0UX5WHw4K8pHn/ihO9ix2u+GxHwxBPAxo0cRo/W0/vTyM8HLrzQ98INAPjBD4CSEuDqq+VDzjSdmTQJ2L7dd046O4FrrgE+/DDirvkwdqzvHFx7LcDzsN56K2Zv2ACrwrskxo9/DBMmPA0AOHLkYZw8+WbY/oZDItVuIvJUA00XQZ588kmsWrUK1157Lf7973/j3//+N6699lo88sgj+OUvf6mlyYRFrG/vKSkp0Xw7khZfPTH1IB79jQdXk6dxviZPY9FXHocxx1/0/fT6akV/GX/9haeeuIlYu4nUrlKs/pKrSGMKQgeKi6+Gw3EQFssIXHhhLtLS5igev36970aFXbt8N2VkZoq47bYScJxOnk4n8PDDvrs/qqqAceOA7GzgzTeBwYMDDj0TdWboUF93b7kF8HqBO+4AXnjB9xRPxBg0CPjvf4FVq3y/P/EEpHvu8TUcBBMnPo1x434KAKiouA8NDR/1i9rV46fXVysMfRzmL3/5C9566y288MILuO6663DdddfhhRdewJtvvok33nhDS5MJC3aiRVEMaguCEGBLkiT7Mtv/c6/XG2CzR42YTUS9bAABtiRJAbZweu9sIpJt/89FUQywtXJSY0ebk5JtNKeeeWKIlFM4HqE4sfhaOLEcRFp7jIOWPLG2teTJv7+R5Mk/bqS1xxBp7RFR2HpTypN/f9XWnt7xxNoPxUkpT9FGPPRUkqSY66kkSaaenmF6qoeTVj0NxsPU096cws11RuhpT11IRD1l7feME82xmsj6w2A0J4+nGwcPXo/u7iLY7SMxePB7SE6+ICgnj0fEk09KWLkSaGsD5s4l7NsHXHONGFRnItFUbscO0KxZwJ/+BBBBuvtuUHEx6MorFTkF0xmj16hK45ZxSkoC/vY3Lx591Hc+fv5z4L77CF6vhjUqEfDKKxBfeQVkscDy7ruga66B0NLSi5MkSZgw4Tc4++wfASAcOvQ9NDd/qms+16qp/nmN5Ro1lhoRjfk8HDRdBBFFERdffHGvz+fOnRsgLH0Ra9asQUZGBubNmwcAKC8vl/9ldnFxMSorKwEARUVFqK6uBgAUFhaipqZGbquxsREAkJ+fj5bTAy43Nxft7e0AgOzsbHR1dQEAsrKy4PV6ccEFF2DDhg0QBAE8zyMrKwsA0NXVhezsbABAe3s7cnNzAQAtLS3Iz8+H1WrFsGHDsGvXLgBATU0NCgsLAfhedFtUVAQAqKysRHFxscypoqICM2bMQGlpaVhOBQUFqK+vlzm1trbKthInnuchCAKysrICOFmtVkyYMAGbTj/0F4wTANTX16OgoEDmtHfvXsyYMQMnTpxQ5KSUpxMnTmDGjBnYu3evIqdgecrLy5NzGopTsDxt3rwZM2bMwKlTpxQ5BctTVVUVRFGE1WqNuPZ27dqFYcOGwWq1qq49xglAxLUHAE1NTWhvb4fValVde4xTaWkp7HY7rFar6tpraWmB1WpFfX29zENt7QFAd3e3nFO1tVdYWAir1YqBAwfiwIEDITkFy5PVakV3dzdOnjypyClYnpxOJ2bMmIENGzZEVHuME4ulxEkpT9G4tTGeesrzPIgI1dXVIKKY6Gl5eTmsVitEUZRfIm7qafz1tLKyEqWlpZgxYwYqKipipqc5OTkAYOppiDydOHECbrcbVqs1Jnra1dUFq9WK6upqeL3ehNJTQL+m1tXVybba8wXoW6Nu374dM2bMQFNTk+rzxThpXaOyOOE4BavrDRs2YMaMGXA6nWHH6smTNSgouAYdHVvAcWkg+h1mzfq/oJra3AwsWeLCr39tARFw003NeOedCkyaBBw4cAADBw6E1WoNO1Z7ctqyfj0y3n8f1qVLwR05Ajr7bAj/+x/WXncdhNRUxTx1dXWhvr4eVqtV1ViNxhrVavVtOd7U1BSS08aN2XjyyS689hrAcYQ33+Rw001W8LxV0xq14aabUPqb3wCpqeA2bYJn3jzg9IYf/nk6ePAgzjvvVSQl3QBAwuHD3wHPb454PterqcBXc2Us1qha53M9a1TGo7W1NeL5nPEIi6B7xoTBgw8+SI888kivz3/yk5/QAw88oKXJhAPbfqe5uZmIiARBIEEQetlerzfAFkVR3vaH5/mAz4l8WwL529Lp/a88Hg95vV7au3cvuVwukiSJJEmStw7yt1kMZrM+7N27V47JPmf99bf9ebjdbtq3bx+53e6QnILZPM9TZmYmOZ1ORU7+PPxt1l+Xy6XIKZjN87zcXyVOSnliXHmeV+QULE8Oh0PexikUp2B5Yv1l+Q3Fzz9Pbreb9uzZI3NQW3tERDzP0969e+X21NSeJEnkdrspMzOT3G53RLXH2mD9VVt7zHa73QH9VVN7oiiSIAi0Z88euT9qa4/FZDlVW3v+48ztdkdUe+x3//6qqT1WN/v27eulC2o0gmmR0+lUXXusr21tbVHfIjeWeipJEnm9XtqzZw95vd6Y6Kl/nlmNmHoafz1VmuuM1FNJkmSubrfb1FMFW81cF009lSRJ9vXXhUTSUyLtmhpMZ4xeo4Ybt0asUZ1OZ4DORKKpLpeL9u3bR16vN+RYlSSJysvvPr0NbhK1tm5S1NQtWwQaO9a3c2tKikQffhh63KrW1IMHSTz/fF/DAIl33EFSW5uqulbSGSPXqKHGrVKe/vUvLyUnSwQQnXdeG9XW6lij7tlD0tln+87XyJEkbN8etPa8XjeVlNx6evvcAdTYuE517enVVKYzDocjJKdorlG1zud61qhsjPI8H/F83tzcrEpPbeoulfTGO++8g+zsbCxYsAAAsHPnTtTU1OCOO+7A6tWr5eNeeuklrSESAuzqvf9VfH/bZrP1stltOpbTL+DxP8ZutyvaoigiJSUFdrsd3Ol9sdgxHMfJtsVikdtmNvNlsfyPUeo7s9nVZ/Z7ME7BbDp9C5TNZpNjheKnxFWJUzDbZrP16m84fqyPoihi4MCBAf1V4qeWR0+7Z55Yfy0Wi9wfJX49+56amhqWk5KdkpISMSdWcxzHBfAIV3vMZv2NpPaYHay/4WxRFJGamhpx7flzDcdJaZxFWnvB+qu29ljdBtOFnpx65ondehhMF9TkKdqIpZ4CvnOSmpoacU3r0VOWZ1NPzyw9BXrPdUbqqRIPU097cwo310VTTxmC6UJPTme6nvq3r1ZTg+mM0WvUcOPWqDWqGk5K9sCBA8FxXMhcV1U9gYaGdwFYkJHxDwwbdrmsi6y/RMCrr1rx2GOAIADnnw98+imH6dMBQPu4tdvtwKefAt/7HiwOB/ihQ2F7913YvvGNkPx65imYzhi5Rg01bpXycfPNNowbB1x7LaGyciiWLiVkZ3OYNEmDpp53HrjCQuDrXwf274f1iit8b1+96aYefR+AadM+gCS50Nr6OQ4duhFJSTkYPHhRSH6huKqtPaYzkc7netaoeuZzrWtU/7EVqaaq1VVNF0FKSkowZ47vhT5Hjx4FAIwYMQIjRoxASUmJfJz/xNdXEeu33V5wwQUx9dUTUw/i0d94cDV5Gudr8jQWRmhfLPWUxTPHX/T99PpqRX8Zf/2Fp564iVi7idSuUqz+kqtwMWtqXsKJE78FAJx//psYMeIbvXw7OoC77wY++8znc8stwFtvAWlp2mLKkCTgqaeA55/3/Xr55ci7805c+X//p85fS8wo+Wr1W7AA2LxZwLJlXhw5koJFi3y7/V50kYa4Y8cCW7cCt94KfPklcPPNwG9/Czz2GBBwQdeO6dP/iYMHr8OpU9koLl6Jiy4K/cJbxZgxQn8ao2qg6Z0geXl5qn78n5FUwp///GdMmjQJycnJmDt3LrZu3Rry+C1btmDu3LlITk7Gueee2+tFrKWlpbjxxhsxceJEcByHV155JSpxlSDE8B0ogiBg9+7dmmJq9dUTUw/i0d94cDV5Gudr8jQWRsQzdcYYmDyN8zV5Gov+VLuJ1K5SrP6Sq1AxGxo+wNGjPwEAnHvubzFmzPd7+e7dK+Dii30XQOx23za4f/978AsgamLK6OgArrtOvgCC1ashfvEFPOnpUedphK+emFOnAr/73VbMnEloaACWLAE2b9YYd9Ag4PPPgYce8v3++OPAvff22jlGkqxwu3+J9PRLIYodOHBgORyOMm0xY4D+NEbVQNNFEPYCumBgL1hRg3/+8594+OGH8Ytf/AJFRUW47LLLcM011+DEiRNBj6+ursbKlStx2WWXoaioCD//+c/x0EMP4dNPP5WPcTqdOPfcc/Hb3/4WoxU20440bijE8m4XjuMwdOhQTTG1+uqJqQfx6G88uJo8jfM1eRoLI+KZOmMMTJ7G+Zo8jUV/qt1EalcpVn/JlVLMlpYvcOjQ3QCAc85ZjXHjHuvlm5s7HpdeasWRI74darduBR58MOAmg4hiyjh0CJg/33f3QnKy7zGOP/4RsGm66T8hdWbYMB65uQKWLAE6O4EVK4D//EdjXKvVt5POn/4EWCzA228DK1f6LjT5+Q0bNgbTp/8PaWkXQxBaceDAlXC5jmqLaTD60xhVhZBvDFHAiBEj6PPPP+/1+YsvvkjJycmq25k/fz7dd999AZ9dcMEF9Pjjjwc9/rHHHqMLLrgg4LMf/vCHtGDBgqDHT5gwgV5++WXdcYOBvXRKy0us2Ett2Atg+ipMnn0L/YUnUf/hqoenHg2MZltmrvoW+gtPov7D1eQZHtHUUz3tmbnSjlOnttKWLcmUlwcqK7uDJEkM+H+Xi+j735ffUUpXX03U0hKl4J9/TpSW5mt43DiiPXvk/+qPOXW5iG680Xc6OI7o9dd1Nr52LVFqqq/BjAyi6uog8VuosHAG5eWBCgomkMt1QmfQ4OiP+YwUavVP050gP/vZz3DLLbfgvvvug8vlQl1dHS6//HK8+OKL+Oc//6mqDY/Hg71792L58uUBny9fvjxg6yp/7Nixo9fxK1aswJ49ewL2To523FCI9e09BQUFmm9H0uKrJ6YexKO/8eBq8jTO1+RpLPrK4zDm+Iu+n15fregv46+/8NQTNxFrN5HaVYrVX3LVM2Z3dzEOHvw6JInH8OFfx/nnvw2O++pPrLY24KqrgHfe8W3p+swzIr78Ehg+XHtMAL73fzz3HHD99UBXF7B4MbBnDzB3riE8jfaNVj6Tk4F//hO4/37fJacHHwR++UufrSnu17/uu2Xn7LOBsjLgkkuAwsIAP7t9OC68MAcDB54Ht/s4Dhy4Eh6P8pMTZ0rtGumn11cr1MbSdI/UT37yE1x55ZW4/fbbceGFF6KtrQ0LFixAcXExRo0apaqNlpYWiKLY6/hRo0ahoaEhqE9DQ0PQ4wVBQEtLC8aMGWNIXABwu91wu93y752dnQB8b/dVewGGgR0fqZ8kSRg9ejREUZTf4G20r56YWnnqiRuPc2TyNC6mHt941G5/4Ql8tSuLFpwJegqY488oPz2+ps4YGzOReOqJm2i1q0dPgehpqpmryGPyfDWKi6+GKHYgPf1rOO+8jyCKgCj62j5+HLj2WhsOHeIweDDh1VcbcMstwyCKEtSmPSjPri5Y77oLlv/9DwAgPvAApBdf9L1kxI9Xf9aZV14BRo604NlnrXj+eeDkSQlr1oi9nhBSFXfGDGDbNthuuAHcgQOgJUtA772H0fPny34Wy3BMn74OBw9eDperAvv3X4kZMzbCbh8WNa7mGA0PtXrKUaRsTqOrqws/+MEP5PdxvP322/je976n2v/kyZMYO3YsCgoKsHDhQvnz559/Hh9++CEOHTrUy2fq1Km466678MQTT8ifbd++HZdeeinq6+t7vQNk4sSJePjhh/Hwww/rigsAzzzzDJ599tlen3/yySfytlUmTJgw0V/gdDpx2223oaOjA+kRvnTN1FMTJkyY+Ap69BQwNTVe4Lh2pKY+Dqu1AaI4Ad3dzwMYJP9/dXU6nntuIU6dSsbw4S489dQOTJjQpTtu6smTmP/CC0ivqYFos6H4/vtx4oordLfbV5GdPQFvvDELksRh/vx6/OQne5GUpO3Co83lwtw//hGj9+wBAJTecQeO3HBDj51j6pGa+nNYLKcgCOfB4XgOwMBoUDGhAmr1VNOdINu3b8ftt9+O4cOHo7i4GNu3b8ePf/xjfPnll/jrX/+KoUOHhm3jrLPOgtVq7XX3RVNTk+LdJKNHjw56vM1mw3CV95RpiQsATzzxBFavXi3/3tnZiXHjxuHyyy/HsGG9r/CFgtfrRU5ODq666qqAvZLDQRAEFBYWYv78+QF7MhvpqyemVp7x6q9WX5PnmdnfeNRuf+EJAG1tbREd748zQU8Bc/wZ5afH19QZY2MmEs949TcetatHT4HoaaqZK/Ux58w5H4cOXQ2HowFJSRNx4YVbMGDAV3ek5+ZyeOopK7q6OGRkENautWHMmIW6z5F940ZYn3gCXEcH6OyzQf/6F2bMn48ZUebal3Rm5Urg8stF3H67FYWFY/DKKyvx3/+KYEMj4rjXXw/x0Udh/fOfMf2DD3A+x4HWrAEGDJAPcToX4ODBKwFUYty4PyMjYy2s1q8uSCbSHJloY1Stnmq6CHL55ZfjkUcewa9+9SvY7XZMmzYNy5Ytw3e/+13MnDkTtbW1YdsYMGAA5s6di5ycHNxwww3y5zk5Obj++uuD+ixcuBBr164N+Cw7OxsXX3yx6hOkJS4AJCUlISkpKejnkSaHwW63R+RrtVoxZcoUJCUlwWKJ7HUuWn31xGSIlKeeuPE4RwwmT2NiJlLt9heeAILqYSS+8dZTwBx/Rvnp9QVMnTEqZiLx1BM30WpXj54y/2hqqpmr0DHPPfccHDlyKxyOA7DbR2LWrBykpIyXj/nkE+DOO31PpSxZAmRmchgyxA5J0nGOJk9G0iuvwPKLX/hebrFoEbhPP4VNYSdMvVz7ms7ceCMwahRw7bXAjh0WXH65BevX+3boiTiu3Q6sWQNp6lRwq1fD9re/+Z57+vRTsCsrgwdfhFmzNmD//svR2bkVFRW3YsaMTFgsSVHhao5RZajVU00Vlp2djd/+9rcBnZo8eTK2bduGH/7wh6rbWb16Nd5++228++67KC8vxyOPPIITJ07gvvvuA+C7sn3HHXfIx9933304fvw4Vq9ejfLycrz77rt455138Oijj8rHeDwe7N+/H/v374fH40FdXR3279+PI0eOqI4bCbQOUi2wWCwYO3aspphaffXE1IN49DceXE2exvmaPI2FEfFMnTEGJk/jfE2exqI/1W4itasUqz/kCpDQ0fEoOjq2wGpNx4UXrkdKyhQAvmsTL74IfOc7vgsg3/oWsGEDMGSIvv5aXC6MXb0alp//3Bfk3nuBvDxA5QUQLeiLOnPppb73m44d63u/6aJFvn8193fVKnBr1wJpacDmzcCCBUBFhfz/aWlzMXPml7BYUtDWth5lZbdBknwv7UykuSPRxqjaWBH1yOVy4YsvvsCSJUsAfHX7Hft5/PHH8dOf/lR1e7fccgteeeUVPPfcc7jooouQn5+PrKwsTJgwAQBQX1+PEydOyMdPmjQJWVlZ2Lx5My666CL86le/wquvvoobb7xRPubkyZOYPXs2Zs+ejfr6evzhD3/A7Nmzcc8996iOGwli/bbb3NxczW/n1eKrJ6YexKO/8eBq8jTO1+RpLIyIZ+qMMTB5Gudr8jQW/al2E6ldpVh9PVdEhMOH70VLSyY4LgkzZnyOtLTZAABRBFatAh57zHfsI48Af/874P+ltKb+VlWBFiwA/v1vkN0OvPEG8Ne/Bjx6YQT6qs7MmAEUFADTpgG1tb4LI/n5OvqbnAxhyxZg/HigstJ3IWTzZvmYIUMuxYwZn4PjBqCl5TMcPnw3iKSEmjsSaYyymGoQ0eMwH3zwAb744gt8/etfBwC8/vrrmD59OgYO9L3s5dChQxgzZgweeeQR1W0+8MADeOCBB4L+3/vvv9/rsyVLlmDfvn2K7U2cOFHV22dDxY0Esb6yNWPGDM1X4rT46ompB/Hobzy4mjyN8zV5Gou+cieIOf6i76fXVyv6y/jrLzz1xE3E2k2kdpVi9fVcVVf/HI2N7wGwYNq0TzB06FIAAM8D3/0u8J//+I774x8Bv1e0aO/vxo3ALbeAa2uDOGIEuE8/BXfZZdGgEhZ9WWfGj/fdEfL1rwM7dwIrVljx5ptztPf3rLOAwkLfVsW7dvn2Q/7rX4G77wYADBt2JaZP/zdKSr6JxsYPYbWmYvLk1xNm7kikMcpiqjoukkY//vhj3H06oQyffPIJ8vLykJeXhxdffBH/+te/Imky4RHrpI4cOVJzEWrx1RNTD+LR33hwNXka52vyNBZ95SKIOf6i76fXVyv6y/jrLzz1xE3E2k2kdpVi9eVc1dS8hBMnfgsAOP/8NzFy5DcBAKdOAcuX+y6A2O2+uz+CXQCJqL9EvispK1YAbW3A/PmwFhXBEqMLIEDf15nhw4FNm3wXQniew113DcF77+no76hRvkeUbr0VEATg+9/33RYkSQCAs866DtOmfQiAw8mTb+DYsccxYsQIc4waAEMuglRUVGDq1Kny78nJyQGB5s+fj7KyskiaTHho2b9YT6wNGzZoiqnVV09MPYhHf+PB1eRpnK/J01gYEc/UGWNg8jTO1+RpLPpT7SZSu0qx+mquGho+wNGjPwEATJjwPIqLz4HX68WJE1+9ZyI93ff+j1tv1dlfpxO4/Xbg0Ud9f0DfeSe8GzdiQ0lJQuRTj2+sx15KCvDf/wLf+54EUQTuuQd4/nnfNSg16NXfgQN9b8V9+mnf7y++6Hsjq8MBABg16ts4//y3AAA1NX/Apk3fS4icJsIY7RlTDSK6CNLR0RGwvU1zczMmTpwo/y5JEtxudyRN9hmIoghRFHvZgiAE2NLpK4IAZNv/c6/XG2CzR3u8Xi8sFgsuvvhiSJIEIgIRyYn2tyVJCrAFQYDVasXcuXPl9tjnrL/+tj8PAJg3b17A70qc1Ng9Ofnz8LetVqvMVYlTMJuI5P4qcVLKE+NKRKp4+NsMoTgFyxPrL8dxipyC5QkA5syZA6vVGnHtERHmzp0Lq9WquvYYD+YfSe0BAMdxcn/V1h6zAQT0V23tWa1WzJ49G9zpvdvV1h7jxKC29oKNM7W1J4oirFYr5syZI/dXbe1ZLBbMmzevly6ozRNrPxS/YHmyWq0wCrHQUyKCxWLB7NmzYbFYYqKn/nlmMPU0/nqqNNcZrafBeJh6GmgD4ee6aOopEclc/XUhkfXUP05PO1L9MWKNGm7cGrVGZQg3Vpub/4dDh3x3wZ9zziMYM2Y15s2bh5ISCxYuJJSVAWPHEvLyvFi2TKemVlWBLr0U+OQTkNUK6U9/At59F5SUFFRnjNRUjuMwe/bsXjpj5Bo11Lg1ao1qswFvvSVh1SonAOCXvwR+9CPfRRFN87kkAc88A/GDD0BJSUBmJujSSyHV1AAARoz4HiZPfhkAYLN9iJMnXwnJKVie/PMaizUqoH0+17pGDVdv4eZzNYjoIsg555yDkpISxf8vLi7GOeecE0mTCYc1a9YgIyNDFjF250t5eTnKy8sB+M5DZWUlAKCoqAjV1dUAgMLCQtScHgQA0NjYCADIz89HS0sLACA3Nxft7e0AfLvwdHV1AQCysrLg8XiQnp6O9evXQxAE8DyPrKwsAEBXVxeys7MBAO3t7cjNzQUAtLS0ID8/HxaLBW63Gzt37gQA1NTUoLCwEABQXV2NoqIiAEBlZSWKi4tlTocPH8awYcNQUlISllNBQQHq6+tlTq2trbKtxInneQiCgKysrABOFosFdrsdGzduVOQE+F6eW1BQIHPas2cPhg0bhuPHjytyUsrT8ePHMWzYMOzZs0eRU7A85eXlyTkNxSlYnvLy8jBs2DC0tbUpcgqWp6NHj6K2thYWiyXi2tu5cyfcbjcsFovq2mOcAERce4Cv1svLy2GxWFTXHuNUUlKClpYWWCwW1bXnf3xnZ2dEtQcA3d3dck7V1l5hYSEsFgva29tx4MCBkJyC5clisaCyshJ1dXWKnILlyeFwYNiwYVi/fn1Etcc4sVhKnJTyFI1bG+OppzzPQ5Ik7NixA5IkxURP2Riora3F0aNHQ3Iy9TR2elpZWYmSkhIMGzYMhw8fjpme5uTkAICppyHydPz4cRw7dkzWR6P1tKurCxaLBTt27IDH40koPQX0ayo7X4WFharPF6Bvjbpt2zYMGzYMjY2Nqs8X46R1jcrihOPU3JyL8vJbAIgYMeJ2nH32r7Bhwwbs3z8MS5dacPIkh+nTgXXrOtDcrFNTN28GXXwxuKIi4KyzcOSNN1CxfDnAcThw4ADa29thsVjCjtWenLRqamdnJ4qKimCxWFSN1WisUdnxWuZzPWvUpqZGfPObe/DqqwDHEf7yFwtuvRU4dEj7fL5r8mQ0fvIJMGIEuP37QfPmAXv3Ij8/H0lJt2HSpF+fPm+P4eTJNyPSVP+8xmKNqnU+17NGZTxaW1sjns9VP5VCEeChhx6ijIwMcrlcvf7P6XRSRkYGPfTQQ5E0mbDo6OggANTY2EhERIIgkCAIvWyv1xtgi6JIHo+HMjMzief5gM+JiDweT4AtSZJsu91uWrt2LTkcDpIkiSRJIo/HQ0QUYLMYzPZ6veTxeGjt2rXkdDoDPmf99bf9ebhcLvriiy/I5XKF5BTM5nmeMjMzyel0KnLy5+Fvs/46HA5FTsFsp9Mp91eJk1KeGFf//irx88+Tw+GgzMxMud9KnILlifWX53lFTsHy5HK5aO3ateTxeCKqPSLfWGW+amtPkiRyu92UmZlJbrc7otojIuJ5Xo6ptvaY7c9Vbe2xfqxdu1YeZ2prj4hkrqy/amrPf5wxjVRbe4Ig9OqvmtpjuvDFF1/00gU1GsG0KJguhMtTS0sLAaCOjg7Si3joKavptWvXRlzTWvXUP8+sRkw9jb+eKs11RuqpJEkyV7fbbeqpgq1mroumnrJ+99SFRNJTIu2aGkxnjF6jhhu3RqxRnU5ngM4E43Tq1B7Kzx9MeXmg/ftXkiD46uG993iy2UQCiC67TKS2Np2a6vWS+MorRFYrEUDS7NlEx4+HHLex0FQlnTFyjRpq3MZqjfr3v4tkt0sEEC1dKlFLi875vKqKpIwMIoAoJYWEf/+bRFEkt9tNGzbcSHl5oLw8jmpr31M9n2dmZkY8n+tZo2qdz/WsUdkY1TKfNzY2qtLTiC6CNDQ00OjRo2n8+PH0+9//njIzM+nzzz+n3/3udzRu3DgaM2YMNTQ0RNJkwoJNMO3t7RH7sgJmyVYLSZKoo6NDHiSx8NUTUytPPXHjcY5MnsbF1OMbj9rtLzyJiNrb26N+ESSWekpkjj+j/PT4mjpjbMxE4qknbqLVbjT1lEi7ppq58sHpPErbt4+mvDzQvn2XkiA4SJKI/vAH39+yANHNN0sU5DvhyPrrchHdeedXjX7nO0Sn/7iNBk8iU2e0xN20iSgtzZeSWbOITp7U2d/2dqKrr/4qz7/9LUmiSO3t7XT48AOnL4RYqanps7B9NcdoeKjV04juvxs1ahQKCgowbdo0PP7447jhhhvwjW98A0888QQyMjKwbds2jBo1KpImEx7subVYxUpPT9cUU6uvnph6EI/+xoOrydM4X5OnsTAinqkzxsDkaZyvydNY9KfaTaR2lWL1hVx5PI04cGA5PJ4GpKbOxIwZa8FxKXjkEd+7SgFg1SrgH//gkJyso7+1tcDixcD77wMWi283mA8/9L2tM5RfjNCfdebyy4EtW3wbvhw4ACxaBJx+OkRbfwcPBtauBR580Pf744+Du+ceDB44EOed9xpGj74TgIiyslvQ1rYhqtw09TdKfnp9tUJtrIgfQpw0aRLWr1+P5uZm7Ny5Ezt37kRzczPWr1+Pc889N+KOJjr8X1ITi1iff/655rfzavHVE1MP4tHfeHA1eRrna/I0FkbEM3XGGJg8jfM1eRqL/lS7idSuUqxEz5UgdKC4+Grw/FEkJ0/EhRdugCgOwa23An/6k++Y3/1OxNKln0MUtfdX2LIFuPhiYPduYNgw37Yyq1cDCn+8mTpjLILFnT0bKCgAJk8Gjh3zXQjZvVtHf2024LXXfD8WC/Dee2iZMwdCYxOmTn0LI0bcDCIvSkpuQHt7fnQJaulvFPz0+mqF6lgR32Nigoji9ziM0+nUfDuSFl89MfXeshXr/mr1NXkaF1OPbzxqt7/wJOo7j8OY4y/6fnp8TZ0xNmYi8dQTN9Fqt688DpPIuRIEF+3bt4Ty8kDbto0kh6OS2tqIFi/2PcFgtxN98on+/rpffZUku93X6IUXElVVGcaTyNQZvXEbG4nmzvWlKzWVaP36KPR33TqSTj9vI02ZQnToEImimw4c+D/KywPl56dRR0dhUNf+PEbVwpDHYUzEH/5bFMfKV09MPYhHf+PB1eRpnK/J00Q4mOPPGD+9vvGImUjjr7/w1BM30Wq3LyBRcyVJAsrLv42Oji2wWtNx4YXr0do6BZddBuTnA+npwPr1wLe/rSOmxwP88IcY8NBD4Lxe4Fvf8t1qMGmSKndTZ4yFUtyRI4G8POCqqwCHA/j614GPPw7vFxJXXw1s3w6aOBHckSPAggWwbN6G6dP/jSFDlkEUu1BcvALd3Qc1slFGoo5RI2BeBNEJtiVTrGKxbYVi5asnph7Eo7/x4GryNM7X5GksjIhn6owxMHka52vyNBb9qXYTqV2lWImYKyJCRcV9aGnJBMclYcaMz3Hs2GwsXAiUlgJnnw1s3ep7T4TmmF1dwP/9H7i33gJxHMTnnwf+8Q8gNVWVu6kzxiJc3LQ04IsvfBfBBAG4/XbfK1x0naMLLsD6Z56BtGAB0N4OrFgB63sfY8aMz5GevgCCcAoHDlwFp7NCJzu/mAk6RrXEVIWI7zExQURf3WrY2tpKRLHZ0pFtqcS2f5Ik9VtASZJEPM8H3UYp1PZjbEulnjzCbT/m1bmloyRJMlclTsFstn2U1+uNeEtHxtU/B2q2gNKzpSPz8c+Bmi2g2JZVkiRFvEWux+MhnudJkqSYbT8mCIJ8O1ykWzr6c1Vbe6IoyrfgsePV1h6R9i0dJUkil8slH6+29gRB6NVfNbXn37+euqC29rRu6WjE4zCx1FNWiw6HQ64Xo/WU5dl/e0RTT+Ovp0pznZF6Kknat8jtL3ras7+x0FP/3PjrQiLpKZF2TdWzRa7WNWq4cWvEGtV/i9yKikfJtzuHherr/02bNkmUnu57/CEjQ6JjxwJrgI3XYDx69l0URfLW1cnPU0ipqeT9/POINdXj8QSM21hoqpLOGLlGDTVuz4Q1qscj0MMPf7XJy09+IpHDEfl8HsC1u5vottv8GyVn10navfsiyssDbd8+jpzOapkH0xktW+RqXaNqnc/1rFH1bJHb2tpqPg5jBNasWYOMjAzMmzcPAFBaWgoAKC8vR3l5OQCguLgYladfI1xUVITq6moAQGFhIWpqauS2GhsbAQD5+floaWkBAOTm5qK9vR0AkJ2dja6uLgBAVlYWeJ6H2+3GunXrIAgCeJ5HVlYWAKCrqwvZ2dkAgPb2duTm5gIAWlpakJ/ve8HOyZMnsWPHDgBATU0NCgsLAQDV1dUoKioCAFRWVqK4uDiAkyAIOHjwYFhOBQUFqK+vlzm1trbKdihOgt9VQn9OHR0dyMnJCcmpvr4eBQUFMqfdu3dDEAQcO3YsJKdgeTp27BgEQcDu3bsVOQXLU15enpzTcJyC5UkQhJCclPJUUlISllOwPO3YsQMnT54MyUkpTwA01V5DQwN27doVllOwPGmpPcZpy5Ytmmqvu7tbzqna2ouEk1Ke9u7dG1HtMU6CIGDdunUR1x4D4xEpJ72It54KgoCcnJyY6ikAlJSUmHp6hunpwYMHIQhCTPWU5cPUU+U8HTt2DPv37w/JKdp6CgA5OTkJp6eAfk2tq6uT7UjOl5416tatWyEIAhoaGjTNqVrWqCxOXd3LqKv7AwDg/PPfxEsvnYtrrgE6O4GMjBZkZ/M4++zAGmD81IzVpl274J0/H9i7F+KwYSj+05/gveoqTZoayViNlqZu2bKlFyej16i7du1CQ0NDSE7xWqOWlBTjpZeA1at9/fvjHzncdJMDpaU6NJXngY8+wuHvfAenG0X7Fd/A1LEfY+DA8+Hx1ODAgSvR2Vktc/LPayzWqFrncz1rVMajtbU1Yk5M98Ii5CUSE4pgV9kbGxuJKDbfXLKrnA6HI+I7QUJ9QxHqKrvL5aLMzExyuVwxvRNE65VOduXQ/5tWtVc6GVelb0CM+OZS65VO1lf27Ukk31z6fwMSq6vsrBZYjEi+ufTnGslV9p7jLBbfXLKYLpcrotrzfcOgrAuh8qSkC0Z/c9nS0hL1O0Fiqad6alqrnvrnmdWIqafx11Oluc5IPdVzJ0h/0dOe/Y2FnrJ+99SFRNJTIu2aqudOEK1r1HDj1og1qtPppC+/fOj0HSCgY8deoD/+8asv42+6iaizM3hdBxu3Qet6/36SRo/2NThhAonl5Zo1tee4jYWmKumMkWvUeMznobiGqr333hPJapUIIFqyRKBTpyK7EySopn7yCVFSku+uoYsuIldVIRUUTKK8PNCuXRnkcJzUPJ/rWaNqnc/1rFH1zOeNjY2q9NS8CKIRbILRMmGxImTJ7qswefYt9BeeRP2Hqx6eejQwmm2Zuepb6C88ifoPV5NneERTT/W0119y1dDwGeXmWigvD1RRsZoeeUSSL4A89BDR6b//tGPzZpKfqZk5k6iuLir91oL+ktN48Fy3jmjQIPboFNGxY1FodMcOopEjfY2OGUPOXZm0fftYyssD7d49h5zOZjOfYaBW/8zHYXSCiGIaq7OzU1NMrb56YupBPPobD64mT+N8TZ7Gwoh4ps4YA5Oncb4mT2PRn2o3kdpVipUIuWpv34LDh28Dx0kYPvx2/PKXL+LllzkAwIsvAq+8AlitOmL+97/AihW+Z2rY9jJnn625v3r89MDUmfBYsYKwbl03zj6bUFYGLFgA7N2rM+aCBUBhITBjBlBfj4FLv41ZjY/Bbh+B7u59KCu7HgAfUT/DxjTIT6+vVqiNZV4E0Qn2DFqsYrFnJ2PlqyemHsSjv/HgavI0ztfkaSyMiGfqjDEweRrna/I0Fv2pdhOpXaVYZ3quurr24eDBayFJPFyu+Xjoobfxr39ZYLf7tj199FGA43TEfPNN4KabALcb+MY3gA0bgCFDNPdXr58emDqjzq+jYwu2bRNw4YVAQwOweLFvJxldMSdMALZvB665BnC5kPrNVbhwzy2w2Yagq6sAKSkvQJIivxCSCGM0GlAdK+R9IiYUYd6+HR4mz76F/sKTqP9wPVNu3zb1NDxMnn0P/YWryTM8zMdhjIfDcYi2bRtBeXmggoLFNHlyAwFEaWlEmzbpbFySiJ577quXivzgB0Sn31EQb/TlnPoj3jw7OoiuusqXfouFaM2aKDTq9RKtWiXXVfujK2nLllTKywPt338NiSIfhSBnJmKhpzajrsL0F0iSFNNY7e3tGDJkCCyWyG7i0eqrJ6YexKO/8eBq8jTO1+RpLIzQvljqKYtnjr/o++n11Yr+Mv76C089cROxdhOpXaVYZ2queL4GBw5cBa+3GRw3B9/5zv9QXT0YY8YQ1q3jMGuW9piixwPvM88An3zi+wb//vuBhx4CBMH308O3s7MT6enpEZ8jLX4A4PV6YbPZwPM8RFGMScxE4hmt/g4YYMGnnwLPPAN8+inw+98DLS2+u4v8mxwwYAAsFou62rXZfM9nnX8+8OMfY/AfsjCjYSYO3HUYp06tQ2nptzB9+r9hsQxQ3d8zdYxGE2p1z7wIohORDjS9sXbv3o3LL7884kLS6qsnph7Eo7/x4GryNM7X5GksjNC+WOopi2eOv+j76fXViv4y/voLTz1xE7F2E6ldpVhnYq48nmYUFy+H210DQTgft9++Ho2NgzF+fCc2bRqIKVPsmmIuW7YMrY2NaK+s9L37Y/FiYOhQID0dOHYsqC8Rged5tLW1gQv33E0U/Jjv6NGjUVNTE9OYicIz2v295x7g5puB07vEYs8e4KyzvnrMymKxYNKkSeA4Tn3d338/cO65wLe+hWEfHcT59UNQ+UugtfV/KCu7DRkZ/4DFEv5P+jN1jEYbanWPI4rx22f6CDo7OzF48GC0tbVh6NCh8gm3Wq0BtiAI4DhOti0WC0RRRFZWFq6++mokJSXJn1ssFni9XlitVtm22WzgOE62Ad+zTv623W4HEcm2JEkQRVG2JUmCzWZTtEVRBBHJdjAe4ThZLJZetiiKWL9+PZYvX46kpKQ+wSlYnpxOJ3JycrBy5UoA6BOcguXJ6/Vi3bp1uOaaa2C32/sEJ6U8eTwerFu3DitXrpT7kOicguUJALKysrB8+XIMHDgwIk4OhwODBw9GR0cH0tPT1UpnUJh6auppf9NTAHC5XMjJycE111wDjuP6BCdTT+Ovp4B2TQ2mM4msqTzfhpKS5eju3guePwff+952NDWNxzXXiLjjjvW48cblsl+knE5WV6OjthYjBw7EQI4Dd/bZ4IYOlV/KyHFc3GxJksBxHDiOgyiKcDgcGDRokPz/gO8PeH/bYrGAiBTtM4lTMJuI0N3djUGDBsm/x5PTqVOEkycBIg4DBxLGjwesVkJdXR3sdjsmTJgAQRAiq73iYthuuAFcdTVaL0tCybMiiBNw1lnfwvTpn4CI6zOa6na7kZ2djauvvhpWqzUiTp2dnRg2bFhYPTVfjBoh1qxZg4yMDMybNw8AUFJSAgAoLy9HeXk5AKC4uBiVlZUAgKKiIlRXVwMACgsLUVNTI7fV2NgIAMjPz0dLSwsAIDc3F+2nLx9mZ2ejq6sLgG9idTqdqK+vR1ZWFgRBAM/zyMrKAgB0dXUhOzsbANDe3o7c3FwAQEtLC/Lz8yFJEg4fPoyCggIAQE1NDQoLCwEA1dXVKCoqAgBUVlaiuLhY5lRWVoampiZVnAoKClBfXy9zam1tlW0lTjzPQxCEXpwkScLx48dDcgKA+vr6XpyamppQVVWlyEkpT1VVVWhqagrJKVie8vLy5JyG4qSUp6amJjQ1NYXk1DNPFRUVKCwshCRJEddeQUEBDh8+DEmSVNce4wQg4toDgJMnT2LLli2QJEl17flz2r9/PyRJUl17LS0tkCQJGzduRFtbW0S1BwDd3d1yTiOpPUmScPDgQezbty8sp555kiQJ27Ztw/HjxxU5BctTZ2cnmpqaNNUeA+OhpvYYp2jcZh1PPeV5Hh6PB1lZWfB4PDHR0/LyckiShMLCQlRUVITkZOpp7PSUcWpqakJZWVnM9DQnJwcATD0Nkaeqqirs2LEDkiTFRE+7urogSZK85kokPQX0a2pdXZ1sqz1fgL416pYtW9DU1ISTJ09GdY0qijx27boK3d174XKdhXvvzUFT03j85CfAww9vxsCBQlhOSnXdePAg2g8fxsiBAzHcYkHSuefCk5KC5ORk+Uuj5ORk+Q/H5ORk+aK91WqV/3hPTk4G4LsIwWwASE5Olv+gTk5OhiRJICIkJSVBFEVYLBYkJyfLf0QnJyfLf2Qy2263y/aAAQOQnJwMj8eDpKQkJCUl9bKTk5MxYMAA2WY8kpKSwnJKTk7uxUkURSQlJYXkxHEckpOTZU5JSUnyH9GhOHk8nqCcBgwYEJKT3W6XefTkxLiG4tQzTywfwTilpYmYNMkGqzUZLtdAVFcnw2JJwaBBg9Dd3Q2PxxO5ph47BmH7djTPnInhW92Y/gsBnGRBS8u/cOjQXWhubjRkjap1PtezRmU8WltbI57Pme6FRcg3hphQBHvpSlNTExERCYJAwumNxf1tr9cbYIuiKL/shef5gM+JfC+C8bclSZJtj8dDGzduJKfTSZIkkSRJ8gtj/G0Wg9ler5e8Xi9t3LiRXC5XwOesv/62Pw+e52nTpk3E83xITsFsnucpMzOTnE6nIid/Hv4266/T6VTkFMx2uVxyf5U4KeWJcXW5XIqcguXJ4XDIL+8JxSlYnlh/3W63IqdgeeJ5njZu3CjnQm3tERG5XC7ZV23tSZJEbrebMjMzye12R1R7RERut1uOqbb2mO3PVW3tsRgbN24kt9sdUe2x/rKcqq09/3HGxrba2mPnwb+/amqP1dymTZt66YIajWBa5D/OwtUe63tra2vUX4waSz1l52Pjxo0BvyudK/9zolVP/fPs319TT+Orp0pznZF6KkmSzNXtdpt6qmCrmeuiqaeSJMm+/rqQSHpKpF1Tg+mM0WvUcONWyxrV6+WpuPh6yssDrVuXRlOn7iGbTaI33/T13el0BuhMJJrqKi2ltosvprKsLHLs20fU3U2SJMltsOOC2aIoUkdHB4miqHhMMJv5sc/UHM9sQRDo1KlTJIqi/Dnri7/t30ctMfX01/84dn5CcQpmi6Io81TipJQnI/rLbKeT6MABiXbvJioqImpudlBpaSl1d3dr0lSPx0Off/opeX78YyKAmi4D5W3iKC8PVF5+N3k8bplTtNaoWudzPWtUNkb91x9qNbWpqUmVnpoXQTTC3M0gPEyefQv9hSdR/+Eai7dvG92Wmau+hf7Ck6j/cDV5hoe5O0z0IEkilZV9j/LyQBs2JNGsWXk0bBjR5s1fHaOZZ1ER0ahR5JowgcpycsjV3h7VvhsB/4sDfRlnMk+Ph6i0lGj3bqI9e1y0e3eZ/CVK5G351e7HHxMNHEiNy0B5m0B5eaDDh++TL6YkMmKhp+bjMDohRekWRrWx6urqNMXU6qsnph7Eo7/x4BppTFF0oLOzEHV1b+HgwV+go2O3fHuhUTGjgf5Su/2FJ4ubCG2Gi2fqTPT99PpqRX8Zf/2Fp564iVi7idSuUqx454qIcPToT9DY+DeIohXPPfdPuN1LsWsXsGRJxM0HYvNmXyONjfBOngwaNQrwe9xDDYgIHo8n4nWbVj890BMzkXjqiavWz273bfAyZIhvr9uWFuDttyXU1urUmdtuA7Zvx8iqCZj2AgAJOHnyDRw58nDQPp0JYzQWUBvLvAiiE7FO6tGjRzUXoRZfPTH1IB79jQdXNTE9nmbU1b2B/fuXYevWdOzbdwkqK+9Fa+tvUFQ0Hzt3TsKRI4+grW0jJMkdlZjRRn+p3f7Ck8VNhDbDxTN1Jvp+en21or+Mv/7CU0/cRKzdRGpXKVa8c3X8+POorX0FAPD737+L1NTrsWMHMGVKxE0H4tNPgRUrgM5O0OLFOPjYY4DVqqkptzv8Oi2afnqgJ2Yi8dQTV62f1QpMngwMG+b7/Q9/sODBBzl4PDrH6OzZwJ49GCVdjvN/7/uoru5VHD3yk14XQs6EMRoLmBdBYgT2dttYxVq8eLGmmFp99cTUg3j0Nx5cg8UkIjidh1FX9wYOHFiBgoIxqKy8H+3tmwFIsNtHYejQKzF8+PWwWFLgdh9Hbe0rKC6+Ctu2DcfBg9ejru4NOBxlIOotBGcKT6N9TZ7Gwoh4ps4YA5Oncb4mT2PRn2o3kdpVihXPXB0//mccO/YkAOD111/G1Kl34Msvfd++68Ibb/j2PPV4gG9+E9yGDZizZEnE27ACvl1D0tLSIvbV6hcMS5cuxcMPPxxRzEOHDmHBggVITk7GRRddhGPHjoHjOOzfv1/Rd+/evbBYLPJLMtUgmjwjQazywnHAmDG+nZQ5Dvj887Nx0002+L07WhvOOgvYsAFjMh7B1D/6PqqtexnV5YEXQuI9RmMFtbHMiyA6EesrW8ePH9d8JU6Lr56YehCP/saDK4vpcBxFff07KCu7HTt2nIPCwgtQWXk/Tp3KBiBi0KA5OPfc3+OSS6rwta81YObMDUhP/xMWLmzC9On/xejRd2PAgNGQJAdaW/+Hysr7sXv3dGzbNhT791+JqqpfoKXlf/B4GuPKs6/Xbn/hyeImQpvh4vUnnTF5Rt/X5Gks+lPtJlK7SrHilauKio9x9OiDAIAPP3wSl1/+MF57DdD1dxcR8OyzwP33++x77wX+9S9IAwagrq5O82Mibrdb02MXWvyC4bPPPsOvfvWriGI+/fTTSE1NxeHDh7Fp0yaMGzcO9fX1mDFjhqKvx+OJuG9aeB4/fhxJSUno7OwMedzy5cthtVqxc+fOqMTV6sdxQHo68MorEpKSJHz5pe8pq9MbrWiHzQa89BLOvvUjnLfGV/gnml7G8d0PyoeYehoI8yKITsQ6qeZzwMb46fWNBEQSHI5SnDz5Fg4duhPV1Quwe/cUHD58D5qaPobHcxIcNwCDBy/BpEnPY/78Clx88V6MH/9TDBw4KaCvHJeEESO+gQsueAcLF9Zh7tx9mDTp1xg8eDEsloEQxU60t2/CiRO/QUnJ9SgoGI1duybh2LG7cOLEizh1ajMEoctQvv797eu12194sriJ0Ga4eH1VZ6IRs7/w1ONr8jQW/al2E6ldpVjxyNXu3f/DiRN3wmIhfPnlj3Drrc/igQcibioQogj86EfAM8/4fn/ySd8dIVYrJElCU1OT5qa9Xm9M/Xpi2LBhSEtLiyjm0aNHcemll2LChAkYPnw4rFYrRo8eHfIbd7Z1bKSIlOfnn3+OpUuXIj09XfGYEydOYMeOHXjwwQfxzjvvRCWuXr8rrpDw6qslOOsswr59wIIFQGmppqYC8Z3vYOzPCzH5k6EAgGPOP+N41ncAmHraE+ZFEJ1gt0CJoigPeH9bEIQA2z8xzPb/3Ov1Btjs6iLbN3vhwoUg364+ICJ58PnbkiQF2IIgwGazYcGCBQGxBUGQ++tv+/PgOA6LFi0Cx3FhOamxe3Ly5+Fv22w2masSp2A2ALm/SpyU8sS4KuUmVJ4YgnHyejvR3Lwex449hwMHrsG2bcOwe/cMVFTci6amDwE0gONsSEtbiAkTfomZM3OwYEELZs/ejHHjHseAAZN65YnjOFxyySWw2WwyD46zICXlQpxzzuOYPXsLFi5sw+zZezB16hsYNepOpKRMB8DB7T4BojwcO/Y4DhxYhm3bBqOwMAOlpd9Fbe3r6OwshNvdFTRPrNYiqT0AsFgsmD9/Pmw2m+raY7Y/10hqz2azYd68ebBYLBHVHuPEoLb22Di75JJLZF+1tSeKImw2G+bPny/3V23tWa1WLFq0qJcuqM0Taz8Uv2B5MuLWxljqKRHBarVi3rx5sFqtMdFTdt4uueQSmaupp5Hpaag8EREWLVoEi8UScU0Hm+vU1t6CBQtkfVJbe0o8TD0NtDmOk7nGQk+JSObqrwuJqKeAdk2N5HwxW+saFUDIcRuqrrOztyMt7THYbAJ27rwNd9/9KpYtE1VxYujFieeBW28F/vIXEMeBXnsN3iefBOGr9c/s2bPBcZzsR0QQRQkOB9DdTejupqC2wwFw3KCA30Mdz2yHAwBS4XRyQY8h+qpv7Pz5r9kYJEnC0qVLsWrVKvmYiRMn4vnnn8fdd9+NtLQ0jB8/Hm+++SYAICUlBRaLBXv37sVzzz0HjuPw9NNPo7q6GhzHoaioSG7/yy+/xNSpUzFw4EBcfvnlaGhokOP3XD8Gs4uLi2G1WuFyuQAAp06dgsViwc033yz3/Te/+Q0WLlwYwO/zzz/HtddeG1D3RBSQm3fffRdf//rXcd999+Gf//wnHA5HQHwASE1NBcdxqvtLROA4DqmpqQFx1frabDbcddc0FBQQpk4FTpwAvvY1Qm5ueE1ltcrOSy9NnT0bY399CJM2ngsAqE75BMf/egVsp/WfIRbzudY1ajgdDTefq4F5ESRCrFmzBhkZGZg3bx4A4ODBgwCA8vJylJeXAwCKi4tRWVkJACgqKkJ1dTUAoLCwEDU1NXJbjY2NAID8/Hy0tLQAAHJzc+Xn57Kzs9HV5fuWPisrCw6HAxUVFcjKyoIgCOB5HllZWQCArq4uZGdnAwDa29uRm5sLAGhpaUF+fj5EUURRURG2b98OAKipqUFhYSEAoLq6GkVFRQCAyspKFBcXy5xKS0tx5MgRHDhwICyngoIC1J++nys/Px+tra2yrcSJ53kIgtCLkyiKKC0tDckJAOrr61FQUBDA6ciRI6iqqlLkpJSnqqoqHDlyJCSnYHnKy8uTc5qV9QVOndqPurp3kJv7dezePQvbtw9Faek1OHbsaZw6tR6i2AGLJQWpqZdCEG7FWWe9gylTytHR8WtMmvQr8Pw07NpVFDJPhw8fxrZt2yCKoiKn/fsPorl5MM4++4c4deoHGDXqS1x6aTuAlzBgwIMYPvybIBoJgOB0lqO5+SMcOfJj7Nt3CQoKhqGwcDYOH/4hcnIeQWvrLng8vokp0toDgLq6OuTm5kIURdW1xzgdOHAAu3btkmtYTe21tLScXiBlo62tLaLaA4Buvwc0I6k9URSxe/du7Nu3L6Laq66uhiiK2Lx5M44fP6669rKzs9HR0YEjR46E5aSUJxZLiZNSnrR+y+OPeOopz/Nwu93IysqC2+2OiZ6Wl5dDFEVs27YNhw8fDsnJ1NPw4zRYno4cOYLGxsaQnILl6cCBAzhy5AhKS0sjqr3t27ejqKgIoihGVHuCICAnJwcATD0Nkaeqqips3boVoijGRE+7urogiqK85kokPQX0a2pdXZ1sqz1fgL416pYtW3DkyBHU1dWpPl9EwLPP5kGSrkdSEo/y8uVYvPjnmDbNoqquWZyenHIzMyEuXw785z8QbTZ4PvwQwg9/2IsTu72f1bYoimhs7MKgQUBaGoe0NC6mttPpO8fsD0FWx8xm8H9kpLOzU/7D/I9//CMuvvhi7NmzB3fddRfuv/9+lJSUoLOzEydPnkRGRgYefPBB1NfXY9WqVXD4rsrA6/XC4XCgpqYGN954I6666irs378fd9xxBx5//HEAPn3jeR4A4HK5ZNvpdMovFXU4HJg6dSqGDx+OdevWwev1Ij8/H8OGDZM1oqurC5s3b8aSJUvQ2dkJSZLQ3t6OrVu34tprr+11kYFxFUUR7777Lm6//Xacd955mDx5Mv71r39BEAT53Hi9Xvl8eDwemZ/b7YbT6ZR5sAs0jBMRobu7W5ETeySou7tb/gPf4XBAkiRZU4cObUNBAZCR0YaODg4rVgCPPnowpKYCX82Viprq9eL45W9hYrnv4kX1+bmoeXom9uXmRrxG1Tqf61mjMh6tra0Rz+dM98KCTGgC24O4qamJiIgEQSBBEHrZXq83wBZFUd77mOf5gM+JfPsi+9vS6b2ePR4PeTwe2rVrFzmdTpIkiSRJkvdP9rdZDGZ7vV7yer20a9cueV9q9jnrr7/tz4PneSosLCSe50NyCmbzPE+ZmZnkdDoVOfnz8LdZf51OpyKnYLbL5ZL7q8RJKU+Mq8vlUuTUM09ebzvV1f2X1q27hYqKrqL8/MGUl4dePwUFE6m09NtUU/MqtbXtIlH0BvTX7XYrcgqWJ57nadeuXXIu1NYeEZHL5ZJ9vV4vuVwnqbl5LR058gvav/9q2rbtrKActmxJppyc8+nQoYeosfFf1N19XFXtERG53W7auXOn3A81tcdsf65qa4/F2LlzJ7nd7ohqj/WX7U+utvb8xxkb22prj50H//6Gqz1mezweKiws7KULajSCaZH/OAtXe6zvra2tqvZhV4N46Ck7Hzt37gz4Xelc+Z8TrXrK/s+/Rkw9DcyTw+GQx164cdozT1r1VGmu06KnamtPkiSZq9vtVl17RP1HT1l/GddY6KkkSbKvvy4kkp4SadfUYDpj9Bo13Ljteb7a2gS6++5y+u9/fWuVjz66hNrb2yJaozqdzgCdEUWRqL6epFmzfDdVpKWRd8OGoHXtdDqpsLCQSktLyeFwyPw6O0XC6XsyYv3T3U1y/9j5Y7YgCHTq1CkSRZFEUaQlS5bQQw89JB8zYcIE+s53viPzEASBRo4cSWvWrKGuri6SJIlmzZpFTz31lHxMVVUVAaB9+/aRJEn0xBNP0LRp0+TzK4oirV69mgBQW1ub3Bf/Pgazv/nNb9IPf/hDEkWRHn74YVq9ejWdddZZVFpaSm63mwYNGkTr1q2T+/7xxx/TnDlzSJIkEkVR5sl+JyLasGEDjRgxgrxeL0mSRC+99BJ97Wtf63W+GNdwffS3JUmi7u5uOZYaX6fTSaWlpdTV1RWgUZ2dHrrlFknO6dNPSySKvfWH6QyrPTWaejTzenktX33HYHJv2RJSF6I1n+tZo7Ix6r/+UKupTU1NqvQ0tq/57oNIOr1HuNVvqyx/2/8WR2azq7PsNk3/Y+x2e0h7/vz5AfHZ5xzHybbFYpHb9rf9ff0/V+q71WqVbxf3RzBOwWw6fVXWZrPJscLxU+KqxKmnnZyc3Ku/ofj595fdAqvE1Wq1wu0+gY6O7ejo2IaOju34f/bOOzyqKv3jn3tnJr2HEkpCk15C7wg2QHTtfdeKfVcsP8uqaxfrWlZXXV37qrt2RAQMNaEnQIAAAUIIkEAI6ZNkZjLlnt8fN/cyk2SSKYSieZ/nPnNyc9r3nPe899z3vud96+pyAEFYGGgOsGU5nOjoMcTEjG+4xhEa2pXmqLn+esPn3t/Q0NAmvNAcpubSYWFhHmWNxi6EhV1Ihw4XApqjp4PU1GzEbM6ipmYjNTUbcbmqMRp3U1y8m+Litxrq6klMzERiYycREzORqKihzfJhSEiIhwmeL7zXGtbW+FCWZY82/eE9d1M6X3mvuXXmK+9p5N5fb/ia629jHvKGqfHcaF8m3MesNd7T0m1hvn2i5anJZGqWR9pKnmrkuf5+n/LUn3nylm48T4HKU2/PukDkabA4fOG935M8DQ0N9Wi3NXzHQ542PtZ4uspT8F+mNidn2nqP2tq6de9vXp6B2bP3c9995xIXV4bNNpKrr/4VozHWK6bW0iaTCfLzYfp0pH37oFMnWLQI48iRzWINDw9n6NChFBQU6H2UJImoKCn4KB8BUkRE03WmUXP3JUnyyJOamqrf1/x9lJWVERUV1aQeSZKapHNzcxk/frzHvJ555pm8/vrrTfI31y8tPW3aND744ANkWSY9PZ3nnnuO/fv3k56eTnV1NVarlUmTJunt/PTTT1x00UX6MRb3+rQ6P/74Y66++mp9zq+77joefvhh9uzZQ//+/fX+umNtqY+N0+7HYXzJr/WtsZyJjjbx1VfQqxe89BI884zE/v3wwQcmtOImk0mXM609J9zTvS76EbHuZgrtn7H/5moM/5xG8pq5GB55BFp5fhyP53kge1R3OeqvTNXkXmvUfhwmSDpeJoy+trVr166A2gy0bDBtBkMno7+NywrhoqYmm6Kif7JjxzWsX5/C+vU9yc39I4cPv0dd3TZUBUgf7Paz6N37n4walc3kyWZGjEinT5+X6djxUq8KEH/6W1cH27fDunWqjti1YQMV11+Py1eTLz/alCSJsLAedOx4OX36vMTw4UuZPLmCkSN3YLHcT1LSnURFDQdkbLb9HD36FXl5f2bTphGsXh3H1q3nUVDwFBUVv+J0VvuFM5D+Hu9ywdDvBafW7ulQZ2vtnWw5cyKoHWfblW3H2bb0e+Ld06leb2215Vz9+CPMmFHM3XefS8eOh5CkgUyd+gt79xYHhzM7GyZOhH37oHdvWLMG3BQgzfV33759Hi/doEb9iIxs+YqIEMiylYgI0Wpef8oFG1HW/aVUxaL6RrJarU1wNkeN86gf0+r97sfUqVPZsWMHeXl5bN++nSlTpjB16lTS09NZuXIlo0aN0p26OhwOFi9ezMUXX+y1voqKCubNm8e7776rv8x369YNp9PJxx9/7NFfX7E2xhlIOWie72UZXnwR3n8fDAb47DM4//xjH1kDJUmS6D3hE7p3nANA/t2Cg9seg+nT4fDhgPrqK53K8vSkK0HeffddevXqRVhYGKNGjWLVqlUt5k9PT2fUqFGEhYXRu3dv/vWvfzXJ8/333zNo0CBCQ0MZNGgQP/74o8f/n376aV0Tp11JSUnHFVdbkXYe7USWDabNYOhE99flqqWmZiUHDjzH1q3TWb06nk2bRrJ37z2Uln5NfX1RgwPTMXTvfj+DB3/HhAnFjBqVi9V6L1263E509HBk2bcvOjYbFBbC5s2QlgZffQVvvQVPPgl3362GpZ84EZKSICoKhg5V/37rHwL5pptI+OIL5NGj4bnn1Pj1vlJ5Oc59+6C8XO2ED8JbkmTCw/vicEylT5+3GD06m8mTKxk2bAk9ez5DfPx0DIYYXK5aKiuXcuDAs2zbNpPVq+PJyhpGXt7dVFd/i9WaH9DD4nTi3d/TGv0t0MkY89OJL38vOIMp246zben3wru/BWqLuXK54PHH4aabynn66fPo1i0fk6kX48cvISSkY1BzJa1cqcYnPXoUUlNVBcgZZ7RaTvMBEQgFsgcKplww5GubgwYNahJ6VvPX4A8NGTKExMRE5s6dS2pqKjExMR5KkKlTp+p5V6xYQVxcHMOHD/da35dffkn37t3ZunUrW7Zs0a8333yTzz77zMNB7smYF2+8e/vt8PPPqoJr+XKYPFl1nBoMSZJEz/6vERpyOwD77oD93ZYhUofBggUB99UXOlXl6Uk9DvP1119z33338e677zJp0iTef/99zj//fHbu3ElKSkqT/AUFBcyaNYvbbruNL774gjVr1nD33XfTsWNHLr/8cgDWrVvH1VdfzXPPPcell17Kjz/+yFVXXcXq1as9zI4GDx7M0qVL9b/dTWn8oUDLBdrWiBEjTmjZYNoMhk5Ef+vrD3scbamt3QK4Grxwa/XFEBMzgdjYyQ1HPsZiMER61ONw89Zssaixvo8cUX+19NGjUFqq/mqX6o/JAAzwqb+RkapFyIrn13Bv+S4AJLtd1Zp8+SVceilMmwaTJqlaEyHUDmze7HEZCgvxiOxuMHh+UoiJUc1BO3f2+JUSEojRlCedO2M0xpCQcC4JCecCquVMXd1OqqvXYDavpbp6LTZbPnV1OQ3HhmDjxicxmToRGzuRmJhJxMZOJDp6FLLs3XTtdOLd39saPR3qbK29Ez3mpxNf/l5wBlO2HWfb0u+Jd0+ner21dbznqqICrrsOVq0y89prM+nVawcmUxdGjlxKaGg3gIDnqsvatRjefFP9iDRtGsybB7GxrZRS+zto0CDd+ao/JEkSERERJ6xcMORPm3feeSevvfYaDzzwAHfccQebNm3iyy+/9Mhz6NAhzjnnHD7//PNmj+TBsWM0X3zxBffffz8Aw4YNw263s2zZMu6991497/z587nooota7NdHH33EFVdcwZAhHrteevTowSOPPMIvv/zCxRdffFLmpbX1cv75sGoVXHCBGjp33Dj45Rf1g2igZDQamTDxfQ4c6EVBwaPsvxmUkHJ6/eEPSHPmwCuvQDNHSX6r8vSkKkFef/11Zs+eza233grAm2++ya+//sp7773Hiy++2CT/v/71L1JSUnjzzTcBGDhwIBs3buTvf/+7rgR58803Oe+883j00UcBePTRR0lPT+fNN9/kv//9r16X0Wg8LtYfJ9q8Jzc3l4EDB/r9YAu0bDBtBkPHu79CKNTV7cRsPqb0sNmaPsBkuQuJidOIi5tMbOxkIiMHI0lqPRYL7N2ramM9LwO7dp1Dba0RN8fbPpHJJIiPd5KUZKRDB4mOHaFDh2NXUpJqndmrl6qf6NMHLitSQ5dVXnopsZdfjnzffbB7t3qI8KWXwGhUpeThw9AQMaMxKSYTsqa8cbnAbKa1zhuBswAeeADCwyE5GVJS9F8pOZmolBSikqfRLeV6iIykvv4IZvM6qqpWU1KyDJcrF4fjKGVl8ygrmweAJIUQHT26QTEykdjYiYSEdNbbPZ149/e2Rk+HOltr70SP+enEl78XnMGUbcfZtvR74t3TqV5vbR3PudqyBS67DA4ftvDqq39gwICNGI2JDB++lPDw3kG1Kb/zDmNefRVJCLWRL7+EsDCf+7tnz56A/LgIIbDZbISFhfkcxjOYcsGQdtQjzIdxSUlJ4fvvv+f+++/n3XffZezYsTz99NPceeedeh6Hw8Hu3bv1aCve2pw0aRI//PAD06ZNA1RFw5QpU1iwYAGTJ0/W886fP9/jSEtj2rRpE1u3buXf//53k/9FR0czffp0PvroIy6++OKTMi8ul4vt27e3yLsjRsCGDTBrlnok/swz4csvA5//Y+vlIWQ5jPz8+zn4R1BCoM9bbyFlZMB//wsDBngp99uSpydNCWK329m0aZMeQkmj6dOne4Sucqd169Yxffp0j3szZszgo48+wuFwYDKZWLduna49dM+jKU40ysvLo2vXrrrTrRdeeIHevXsHD6ydTglyuazU1GzWLT3M5rU4nVWNcslERQ0jNnYy0dGTsFrHs26di+LinhQVGZooOxqiPzVDMnDMoVJ4OHTpoiowunRRDSmSklSjisZXZKTCrl27fRYOf72jkiuf+BaAssuuIOaaq1V18fz5kJ4OK1fC/v3qGVdQDxgOGqSeb224XEOHkltUxMC+fTHYbOhB6LWrqsrTZKWkBI4eRZSUUH/gAGHV1WC1wp496uWNEhIITU6mY0oKid27Ex16Fh1GzaEuxUZ1bBFmaQfVNetwOEoxm9diNh9b92FhfYiNVR2uRkWNQ4iTfnKvndqpndqpndqpnU4AffGFeiTA4bDz2mtXMHRoBgZDNKmpvxIZOSjwip1OuO8+DO+8A4DrttswvPeeahH7O6GVK1d6/L1///4mebZs2aK/4Gt/u1PPnj2bHAO58MILufDCYw72bTYbt99+u64caK5Mc3TXXXdx//33eygV5s2b55Fn8+bNmM1mj+MxjWnUqFEttjd//vxW+3IqUHIyrF4NV1wBS5fCZZcZuO22nsyaFWy99yHLoeTl3U3RlaDEhNH35S1Io0bB22/DzTcH72zmFKeTpgTR4s537tzZ437nzp05cuRIs2WOHDnSbH6n00lZWRldunTxmse9znHjxvH555/Tr18/SkpKeP7555k4cSI7duwgMTGx2bbr6+s9nPxo8acVRfE4DuELafn9LQfQv39/FEVBUZQTVjbQcsHg9LddIZyYzeuprFxEfX0G69ZtRojG7UbgdI6jsnISBw5MZMeOCezbF0NhoURRETgcrS/2qChBSgqkpAiSkwXJydC1q5Pi4iwuvHAUyclGoqN9lxtC+IfzJuNnhGNjK8PYbbqSnoqCEh0Nf/yjegHs34+0eTN0744YMkR1G96I+vfvjwIoERHq/zt2bLVth8PBkiVLOO/MMzGVlCAVFcHBg+pvYSFSURFSYaGaNptVO9aKCti6FRnQ7K5iGy4hy4guSVhTUzEPN1Hdtx5z51IsYSXYbPnYbPmUlPwHAJOpM7t3zyA+fiZxcediNMb5MrwnhXd/L2s0EHwanSryFE78mAdath1n27UZbNl2OdMytfNu6xSMPIXjJ1NP9lzV1ys88ojMP/9pQJZd/POff2TgwEXIcjgDB/5EWNiwJn3zuU2zGcMf/4j8668A7LjhBnq98QYmRQE/+9urVy8KCwsDwhoaGooQwm9fEoGW0/ILIU5YX4Mp60s5u93OP/7xDwwGg44pGJxt3V93UhRF76OvvBsRAT/9BHfdZeDzz2Xefz8VSXLw2msOGvm0bZXc2+zU6VaEMLJ37x0cnmHD1bkLA/6vGGn2bJTFi3G98w7ExTUp5y+dqvJUEifD0w5w+PBhunXrxtq1a5kwYYJ+f+7cufznP/9h165dTcr069ePm2++WT/qArBmzRomT55McXExSUlJhISE8Nlnn3Httdfqeb788ktmz57t1ZFRXV0dffr04eGHH+aBBx5oNs/TTz/NM8880+T+V199dcLP6bWTSpJUgdGYjdG4CZNpC5LkaWJnNndm9+7xZGdPYdOmqeTnp+JyeZcWsqyQmGijQwcrHTta6NjRSocO1oa/rXToYCEy0nnyFKNCMO2++4g9cIC/8DZpff/IK69knJKKWmNdHeFlZU2v0lI9bXBzSOVOjkioGQTVQ6B6iIR5EChhbmJKyLjs/bArY3E6R6IoPYBTcBB+42SxWLjuuuuorq4mJibGr7Lt8rSd2qmd2ukYBSNP4bchUysrQ3n11dHs3NkBSVJ4552rGDjwe4QwYrE8htPpPWJLaxR+9Cjjn3+emIMHcYaEsPn++yl2e/fwl7Qj9cnJyYSEhARcTzv9/shut1NYWMiRI0c8HLP6QkLAd9/148svBwIwdGgpDz20kZgYP4IjNEMmUzrh4f9AkhRC83sy9s4DGJyCuk6d2PTAA1QO8M134alCvsrTk2YJ0qFDBwwGQxOrj6NHjzax5NAoKSmp2fxGo1G34PCWx1udoMZ4Hjp0KHl5eV7zPProox4KErPZTHJyMueccw7x8fFeyzVH+hf1885rEpaqJXK5XOTn59OnT5+AzmQFUjaYNgPF6a1dIZzU1GRSUbGYkpLFOJ1bPMpUVyeSmTmTjRuns23bFI4c6Yn7y3F8vKB7d9WCQ7XkoCGt/nbq5OLAgYKGNn3fhBxvnN5IWrcO44EDiNAwvuc6juTFU1o6i5tu8qvJgOf0eODs1NCmoigopaXHLEc0a5LCQgyFhcQXFRG/sRhJCBQTVA+F8nFQMQ4sPRQMobsIZxfwOSGWSBKsw4iPmU5Mv6sxdukDDeHdTjTv/p7WaGVlpV/53elUkKdwcsb8ZK6/dpzHv2y7nGmd2nm3dQpGnsLxk6kna66+//4wjz3Wi+Jiiehoha+/vo/w8O8BmQEDvqBDh8sCblPasAHD7bcjHT2K6NIFfviBIcOGURwEzry8PAwGA1FRUT75zHCn+vp6QptxPtlW5YQQ1NTUEB0d7bffikDbDKbsycAZTLv+lrPZbISHhzN58mSKior8Xi/TpztISdnA22+PJSenI08+OZPvv3f65DDV+3qZRVnZWPbsuZ76PvvZuXQag27dT+Te/Ux5/HGcTz7JnksuoU+/fr8peXrSlCAhISGMGjWKJUuWcOmll+r3lyxZ4jXm84QJE/j555897qWlpTF69Gh9gCZMmMCSJUs8/IKkpaUxceJEr32pr68nNzeXKVOmeM0TGhraLJOHhYX5PTkamUwmv8qaTCYGDx4ccFuBlA2mTfc6/B0jrV2LpYScnMUcPboIkymNkBBPxs7NHUNm5vls2DCL/PzRDBpkYMgQmD2bhmMrx3x3RkdrQtGbcAwOazA4vZKiqIcAP/pI9VwOSFdfxU1dE3jpJbj9dhNWK8yZcxzb9KH8ccHZvbt6efsaY7fD4cPIBw4Qv2sX8Tt3wpe5WMtyqEg5Qvk4qBoB9og6jkSs4wjrkPY8Q+w8I4mHupHgHM2gbuORBg6CgQOhRw/VR0obYv09rVF/N37udCrIU63MiR7zU2b9tWG5YMtq5U8UzmDKnoz193vBGUy7pxvvBiNP4fjL1BM5V59/buKuu3rjcKiP6s8/f4ba2rcB6N//I7p0uTrwNr/+Gm68EerrITUVacECjN27IxpM7APF2bdvXwoKCpBlGdnPfUV4eLhf+YMtpx0NkCTphPU1mLInA2cw7fpbTpZlJEkiNDQ0YDkzbtwRrrzSyRVXmNi3T+LMM018/rnq47clamm9dOlyDSZTJDt2XEG5WMnOr2cy+PWxyF9+g+mppxi8ciV89pn6QuUHncry9KR6G3zggQf48MMP+fjjj8nNzeX+++/n4MGDujfhRx99lBtuuEHPf+edd3LgwAEeeOABcnNz+fjjj/noo4948MEH9Tz33nsvaWlpvPzyy+zatYuXX36ZpUuXct999+l5HnzwQdLT0ykoKGDDhg1cccUVmM1mbrzxRr8x+GvKFAw5nU6ysrICajPQssG06S/ZbJCV5eLzz9fxz38+weefjyQzM4m6upuIjPyakJBKqqsTWLbsWl599XOee66EnJxMJk16hi++GIfZbCAry8mcOVk8/bST22+HmTNVv6DR0acW1hbbrKuDtDT461/V0DAzZsA336hKgTFj4NlneeYZJ9dco1o83XsvPPYY+Hps7pTB2RqFhEDPnjgnTSJr5Eicr70GaWmEby6m2+fVDDtnA5OOfsDQjVfSbXMvwo6aECaoSnWSP+sAWRd9z4auD7En7QLKr+uNKzFSdQ77xz/C3Lnwww+Qm+v7wLUVziDLnoz51No9Hepsrb0TPeanzfoLolywZQOlltpUFDsOR3lAZQNts63o9yZnfi+8ezrV660tf8ZNUdRtzq23qo/hSy9VmD//NWprnwXgjDPeokuXmwJrUwh4/nm45hpVAXLhhap3ye7dA4HWpM2cnJyA/GQIIairqwvIr0cg5YKhYNo8nXAG024w/Q1WzgwZAllZcO656mvD5ZfD00+37N6mtTY7dPgDQ4b8hCyHUW5eTM5D1bg+fR8RGQkrViCGDlUjKfmB91SWpyc1RO7VV19NeXk5zz77LMXFxQwZMoSFCxfSo0cPAIqLizl48KCev1evXixcuJD777+fd955h65du/LWW2/p4XEBJk6cyP/+9z/+9re/8cQTT9CnTx++/vprxo0bp+cpKiri2muvpaysjI4dOzJ+/HjWr1+vt+sPnahQVVpb8fHxAbUZaNlg2myJamth61bYvBl27izF4VhM166LGDXqV1JSKjzy5uWN4vDh85GkWfTqNZZrrjHw1FNqJNjG5HKd+DEKhiRJIiE8HGn5csjIgBUrIDNT9WCuUVyc+uI+e7YaLwswuFw8+2wtgwcrPPGEzIsvqt7U779f3VC0pPQ5WTiP67zExMDYsRjGjiWR20gE+gKWyu1U5P2H8srFVJl2YOvi4vAlcPgSkOw24rZkk7ghm4R3IeJwQ11GI/Ttq36C0q6+fZECUI78ltaoL+2eDnW21t6JHvPTaf2dzjgVxUlt7WaqqlZQWbmC6upVKIqFmJgJdO58PZ06XY3JlNBif10uC1VVK6msXI4kSYSH9ycioh8REf0xmTohSdJJx3miyp5MOfN74d3TqV5vbfk6blYr3HST+n0H4C9/qeCRR75l7171o2avXs/Tvfs9gbVZXw+33Qb/UR2rc//98Oqrxy0CjCRJxMTEBBx+ONAwoScyNPXxaPN0whlMu4GWOx5yJiEBFi2Chx6CN9+EZ56BbdtUg43m3gN8aTMxcSZDh/5CTs4fqKz8lZxUO4M2rES5bjZh27bBn/6kWqb/61/gJZjI8cbpL/na1klzjHq6k9lsJjY2NiAnVg6Hg4ULFzJr1qyATb9PB9JwTpgwi+3bTWzerEZuzc52ARsZO3YR48YtpH//jcjyMTa0WuOoqppOZOQs+vefQd++Sf6eYjih5Pd8lpbCunXqtWaNGgTc3sipUUoKnHWWaspy8cVq3F0v9Pnn8MgjoLnCiYuDO++EPn3AYlE3GxaLZzoxEYYOVa+BA8EXy7HTjW9drjoqK5dTUbGI8vJfqK8/6PH/8NJQEtYqJK52ELsVDI10Hs6QEORJk5CnTYOpU2HcON8G6jSiYOY0GBl4POs63fgyUGrH6Z2EENjtalQps3kDVVUrqKrKwOUyey0jSSYSEy8gOno0ISFJmEydEMKBy1WH3X6YysqlVFWtQoj6ZssbDLFERPQjPLwfISFJKIqt4bJ6/AqhEB7em4iIAUREDCQiYgBhYb2QZWP7nP7G6FSRp8HUdyLmqrRU3dasWwcmE3z4IcyY8T9yc68DBMnJD9G798uBvTSVlcGll6pWHwYDvPMO3HFHk2zB4rTZbBQUFNCrV6+gjzK1NSmKgtlsJiYmJqBjIqcLnS44g+Udb7z76acqq9vtqpXITz+pxuSBUlXVanJyZuFy1RATM4lhg+ZjfPUdePZZ9UNtUpJ6VD/YWL1e6ETI05NqCfJboBNt3pOZmcnYsWMxNmcG0QZlfSmnKFBSAgcOwMGD6u+BA1BQYGDTpnMpKTERE1PGmDFpjBu3kCuv/JW4uDKPOlyuESQmnk/PnrOIiRmHokBmZiZ9+nTwSwFyMsaoRXK5YMcO9Wm/dq3624wDXtG1K9JZZ6mKj7POgl69Woyz697XG24wctVVqiXIq6/Cnj3w0ku+d9FgUA0hNKVIaiqMHg1duwYCuPX+nqh5ESKUPXsSGTv2Lfr2fQeLJZfy8oVUVCykunoV1o71HLoYDl0Msggl3tyHhD0JJKyqJ2z1XoyVlaplzooVaoUhIaoiZOpU9ZowASIjTzrONuFbH9s9Hepsrb0TPeYnY75OP5wKLlctQthxuWpxueoarlpcrhrq6wuxWvdhsxU0/O5DUaxNajEa44iNnUp8/FnExZ2NyZTI0aP/o6TkP9TWbqGsbB5lZfNa7EloaA8SEqYjy+FYLLuxWvdgs+3H5aqmpiaLmpqsVtFUV6d7/C1JJsLD+xIW1pewMDsHDqwnNLQDRmM8RmM8JlOCnjYa4zEYIj1eCH9vcub04t3A6LdyHKa1cdu9W31n2rdP/WDz448waNB8du78EyDo2vVOvxQgHm3u3asee8nPh9hY+PZbOO+84wfQrc3s7GxiY2P9Lqsdn4iMjPRLyRNouWAomDZPJ5zBtBtMf51OJ2vXrj1ucuamm2DAAFUHuH27enr+22/h7LM92/RVtsXFTSY1dSnbts3AbF7Dmg1TGPfQSsLOPx+uvx527YILLoDbb4fXXoOoKK84T1V52q4ECZJOpLZRlmW6desWUJuBlpVlmQ4dulFQIFNYeEzB4a7sKCxsasgAgr59tzBjxgLGjl3EwIEbPKw9ZDmWxMTzSEiYRULCTEJDuzQqrwTc3xM9Rh506JBq2bFhg3qsZeNG9exPYxo4ECZORBk3juIzzqDLmWci+WFS17ivYWHqMZibb4b581UrUKdTjS0eEaEakmi/YWEKe/bUsn9/NDk5EhUVqizbtUsVmBp16aIqQ7QrNTXwYTlZvKuVkySJyMhBREYOIiXlQZxOM5WVy6ioWEh5+ULs9sOUx+6kfAwwBsIfH4DlwBhGVY8ketk+pPQM1dRm1Sr1ev559RjN6NHHlCKTJiFHRZ1UnCeS2qK9k4HhtJQzJ6jNtsAphILdXkJ9fSH19YXYbIXU1x90SxcSE1PM+vX+GqnKhIYmI8u9SUo6n4SEc4iKSkWSPOVqcvIDJCc/QG1tDmVlP2CzHcRuP4LDUYrdLoiIiG9QnkwiPn4GERH9m2xuXS4bNls+Fstu6up2UVl5gNjYThgM4chyOLIc1nCFAwpW614sll36pShWLJadWCw7CQ2FoqJFLSKTJBNGYxxGYwImUzwGQxwmUyj79nVrUJgkNChP4j2UJyZTArIc7tH/01HOnCq825bUVm2dSnImPV19QausVL/zLFwIXbqsZ+vWawAXHTteS9++7/j1Mqm3uWIFXHUVVFWplS9YoDqEawOSZZlOnTpRX9+8lVhrFIzz7+NB06ZNY/jw4bz55ps+t7lr1y5uuukmtmzZwoABA5g3bx69evUiOzub4cOHN1t27dq1zJgxg8rKSuLi4nzu38myFjvR89IWcmb8ePW149JLVX8h06fDG2/AX/6iflf1t82YmLGkpi5j69bpOJ072b59FqmpSzBt3qw6I3zzTfjgA1i2TDVLbyYIyaksT9uVIEGSdppIOxtoMBg80k6nE0mS9LT7xGiejLX7sizjcDgwGAx62mg0IkmSnk5JSdHr1MqaTCaEEHpaURRcLpeeVhQFo9FIcnIyiqIgy7LHfZfLhRBCTxcWwrp1BlatUlizRiInp0erPnBkGbp1Uxg3bisTJ35Lv37fEBmZ75EnMnIYcXEz6NDhAmJjJ+JyoWsFNXzumDSsWn+bw9RcukePHrhcLpxOp46publpbp569OihaxBlWfaYmybzZLUiZ2cjVq9mzC+/YPzzn1UlSGMeiYpCjB2LPGkSYvx4nKNHY+rUScfUrQGT1l9v+NznSQhB9+7dkWXZAwe4uOgiuPTS5nlPxaEgy1HIsoTD4eTIEZkdO2S2bHGxY4fM1q0SO3YIioslfv4ZjgVkMtGhw3lMnmxg3DjBqFEuxo0zEh3dOu8BuhBsifeamxt3rN4xNZ/u6mbO4r6ehAinQ4dL6NDhEhwOB/X1OxuOzSzEbF6H1boLqdMuNndKIyJ1IIlzbyOpfiLhqw9CejryqlWq9m/9evV6+WWELCOPHEnytGkof/oTcmqqX7xnMBjo1q2b3t8Wea+RjOjRowcOh8NDLjReT83JCPe2Wlpbzc1TWzzQTrQ8lSSJrl27NvCEaBN52hyO7t2761h95Wl3fC1h8jb/J0KeCiEQogaLpYD6+kKMxsPs23cAu71IV3LY7UUI0bK/Hfd3IFmOxGCIxGCIwmCIRJYjCQnpQkTEGYSF9cJk6kFUVD9CQrojhMEDkyQZvOILDx9E9+4DW8WkKEoT3hPCSHj4QCIjBxMf7yQlxbd1KkkSdns9LtcRLJZdVFdvZ8+eTHr2TMTlqsLlqsbprMThKMfprMLprEQIB0I4cDhKcThKsboZvNTVtb6mJCnETSmi/oaH96WsbCIREaMJC0vBaDQGJU9b4z33U9f+8J72rNOeHSdKnmpY3eXC6ShPwX+Z2pycOR571P/8RzB7NjgcEuPHC777zkl8fAGbN1+IolhJSJhJ//6f4HIpGI3+ydQeaWmIu+9Wv/xMmoTz22+ROnXC0MK8u38pbg2ThkNLu1wuunXrRkFBgT5equwTyLKsj7nGP43ToaGhen5veRqnQY2q6Wt+Vf5JHvdBXX/ff/+9zp9a3e5919KgvuBLksRTTz1FZGQku3fvJjIykri4OIqLi0lMTGwWh1bWfWx87XtISEgT/m2MyT1dUFDAgAEDKCkp8SgnhODgwYP06tVLv689C2+66SYee+wxvQ5/x9eX/raU1sY4EJnqnr+5PUrnzgoZGUZuu03wxRcSc+ZAdrbg7bddREYa6d69u7538UWmhocPY9iwpeTknEdt7Ua2bj2P1NQlSK++inzBBci33AL5+YgpU5AeeQTH449jCA8Peo+qYfX3/UhL+0Kn7qGpU5TeeecdBg0axJgxYwDIyckBIDc3l9zcXAC2bdtGXsORh+zsbAoKCgD1eEdhYaFel7ZgMzIyKCtTj4csX76cqqoqQA3tW1NTA8DChQupra0lIyODhQsX4nQ6sdlsLFy4EICamhrS0tIAqKqqYvny5QCUlZWRkZGB0+lk+fLlrFmzBoDCwkIyMzNRFFi8+BCPP17EH/8IyckKvXoZuO46eO89mW3bJISA0FCFXr3qmT4dLr64lPvuK+fzz+Gdd3LIyFjC7t1/4/PPe/DnP49kxIgXiYzMR5LCcDjG43Dcy4AB2xgzZit79kzFYBiBLJtYuHAhNpsNp9PZBJPT6WTlypUtYgLVee7atWt1TBs2bCAjI4P8/Hyys7MByMvLY9u2ba3OU35+PhkZGWzYsEGfp7Vr11JcXAwOB5s/+oja11+H2bOx9euHFB8P06YR8re/0XXdOqRDhxCyjDJ0KMrs2WT/+c84Nm/GVlzMz3PmwLPPUjN5MmlZWR6YMjIyKCkp8YopMzMTgIKCAh3T7t27SUtLw+l0+s17a9asYfny5TidTlatyiA0tIyZM2HEiKX84x+VbNsG33zzK2lpdfzjHzBtWiEDBihIkqCsLIJ582QefVRi+nQjcXEwcKDCzJklvPMOLF9ew+LFK5rM06FDh1i8eDFOp9MrJm/ztHXrVpYuXaqboDaHSZ8nt/XkdDpZtGgR5eXlza4njfcWLVpEWNhgOnW6j8LCB5k0qZTk5A9wOMYhSaFYLLkUFj5H1tHz2TD0LVbODse6eyVH1q1j96OPws0340hJQVIU2LgR+e9/xzh8OEycyNGXX2b7hg2t8l5BQQFOp5Nff/2V/fv3e8XUnIyoqqrS5YK39dSSjNDa8pX3tHk6HmbWJ1Oe2mw2fXzc0y2NVWvy1NtYuWNyOp2kpaWxe/fuFjE1nn+NjzMyMlrE1Jby1OWykJubxubN/6a4+BM2bZpDZuZVbN06gzVr+rJmTSxr1iSQnT2KnTsvIS/vboqKXubo0S+prs6gvr6gQQEiI0QHwsNH0bHjVTidl9O16wsMHvwDVusbmM0fM358JdXVPzBmTCljxxZRUvI6w4dvYtCg5ezbdyN9+rxCdPQfycoSREScgdlsCVie5uXlsXXrVjIyMtixY0fA8tQX3lu0aDHQiZiYs8nJOYP6+uvo2vUl9u69mmHDfuGMMxZTVvY6kyaVMGRIMfX18xg/vpDevVegKK8zePCPdOz4d+rrZ5Oc/BjR0dcjy+eRkDCTkJBUJCkFk6kDoFrACGHH4SjBat2F2byOioqFHDr0D3buvJqNG/uwdm13tm+/nHXr7iUv7wdcLmtQ8rQ52VPrZg3pK+9p85Sfn68/63x9lgcjT2tqanQctbW1p5U8heBl6qGGDzmZmZk+jxd436P+8stCnnkGbrhBwuGQuOIK+O67SrZv/45t22bidJYjRD8qK++luPioz+MFkLt9OxWzZ8PttyM5nVRfeCEsXUp2UVGrMlVrpzVM3vg6KysLRVF03na5XHo5p9Oppx0Oh57HbrdTV1dHTU0N9fX1WCwWQPURYW3QbmrPIgCr1aqn6+rqMJvN+vELe4P5dW1tLY4GJ+4a72pp7UVW6wuovhLi4+OJiorS6xNCYDar/pM0vxoaDi3P3r17GTNmDD169CAmJgar1UpSUhKKolDXoI1tjEm77w2TxWLRrWk0TEIIqqurW8VkNpt1BdQ333zD1KlTiW7wBNoYE8C8efMoLi4mNzeXRx55hLlz5/Lhhx96zFN1dXWDfym7V0yN50lrpyVMjeeprq5Of7EPVKYCrGg4qt2cTA0LgxdfPMzdd+cjy/DJJxLjx1soKnKybNkyNm3aBPj+fnTkSARO5ytAHLW1G9m2bTpr1vxK8cCBsG0bxeedp+6BX3wR67Bh1Kxbp2MKdI+qPRvKy8v9fp5rcq9VEu0UEFVXVwtAlJWVCSGEcDqdwul0Nkk7HA6PtMvlEna7XcybN0/YbDaP+0IIYbfbPdKKouhpp9MpCgsLhc1mE4qiCEVRhN1uF0IIj7TWhpbW6j948KCoqqoX6elCPP+8S8yc6RKxsUKosY6OXQaDIkaPFmLOHJf4738dIivrsKivt3vgqK7eKvbte1KsXz9ArFiBfqWnh4mcnMvE4cNfirq6cjFv3jxhsVi8YnLH4Z52uVw6Vm+YmkvX19eLoqIiYbfb9fve5qbxPNntdlFUVCTqbTbh2rNHiK++Eq45c4QycaIQYWFNBwqESE4WjksuEdtvuEE4li4V9ooKr5iamyetvw6Hwysmdxxa2m63i4MHDwqXy+UX7wkhRH19vV7WV95TFEWUldWL559fJV580SGuvFIRPXoozQ6JyaSIMWOEuOsuRXz0kVPs2CFEfb1DHDhwQLhcLq+YvOFwx+oNU3Npl8slDhw44DFmvvCeNkYq75aKw4c/EVu2nC9WrjR68PrGjWPE/v2vCKu1oW8FBcL1n/+IuvPPF4rRqA+IEhMjxF13CWdWVovz1Li/3vA1nien0ymKioqayAVfZIQmiywWi8+8p/W9srJSAKK6uloESydDniqKIpxOpzhw4IBwOp0BydP6+nqfxso9rZXV6vSVp2022wmTpw6HWZSVLRZ5eX8TmZnTRWZmqli1KtGD/1u6Vq1KFJmZqSIzc7rIzb1T7N//ojhy5CtRVrZC1NXtEy6X3es81dXViXnz5un8eSLkqdPp1OW/tqZ84T0hApeniqLoWOvr633mPa391uSpyjOVwlq7T1QVrxZl+d+LozvfFweznhabMy4XWeuHixUrDE3mbuVKo8jKGil27/6zOHLkC2E272pWPgUiT7Ux8eVZ7t6OhtXXZ3kw8lRRFL2su1w4neSpEIHL1ObkTKB7VJtNEX/6k0vfG/zf/zmFyyVEfX2VyMwcIVasQKxb11vU1Bxscd02K1PLy4Vy3nn6c9b55JPC6TbXrclUi8XiIWf8kak2m03s379f7NixQ9TV1QkhhM43Wlqro3Fak1Mul8trnubSWjntni/5tbTGX5q8mDp1qpgzZ46ep0ePHuL5558XN998s4iKihLJycni/fff17ECHteTTz4p9u3bJwCxefNmvZ0FCxaIvn37irCwMDFt2jTx73//WwCiomFf3BrWrVu3CkmSRFFRkVAURVRUVAhJksQVV1yhY5o7d64YP368B76zzz5bvP3228Llcuk4tbEuKCgQgNi0aZPHHJx99tnirrvu8mjffQ/l69woiqLPp69zabFYdN4JRKZqckbjvdZk6q+/ChEXp+7Xu3VTxPz5R/S9i7/vRxUVG/W9QFbWaFFfX35sbX3zjRCJieq+NyREiFdfFXarNeA9qrZGbTab38/zsrIyn+Rp+3GYIEkz93IPkeSedncC425KB8fMH93zuJ8tay7dvVGMc+2+JEl6WjP3A7BaZVavllm+HFatSmbjRjUeu7sRUGSk6uNx8mT1GjdOavBvIzdcqr+OurpcSku/4ejRb7BYdurlJSmUxMTz6djxKhITL8RoVLWxmtZTMy/1BZ83rO6YWkqHhIR4mMCC97nR06WlGLOyIDOTbpmZqi+PBs2sh6lUbCyMHXvsGjMGunRBOBzsXbiQfmee2SqmxvPUXH+94XPvu8lkIjk5mcbUGu+BavKnlXU3wW2t7zExEkOGlDNrlsBkUk3ajh5Vzx1mZh77LS+XyMqCrCwJ7WtkdLSRUaNS3IZOJjnZh7lphNW9v97wuadTUlJ8xuee1kz2jMZYunS5iS5dbsLhKKe09EeOHv0fVVUrdKeIBQUPExMziU6drqHjlVcQ8ac/qf5DPv0UPvwQKT8f3nsPw3vvwahRcPvtGK69Vo9f5q2/3jA119/GPOQL72mmiu71+8J7WrotzLdPtDw1GAzN8og3eeqedl9/rY1V47R7WV/4WDsWoKWPtzy12w9RXr4Gs3kN1dVrqK3dCig0R7IcSVhYCqGhyYSGJjeT7o7BENFsWc96jqX9weGebk2eulxQUSFTUiJTUgIul8zEiTJRUU3npvHxCe2+Rn7L0/p61S/B+vWYIiJUL5CxsZji4tR0XBymiAiiDx5E3rwZo8MBViuS1YrJagWrFbnhapw2Wq2kNKRxu29wu2dsuHA4aBx/QOM+V6SBmpGxmFNNmPu7MPe0YI+qp7Z2M7W1mzl8+B0VixJLjGEIMeGjqLFPIrrLWZgiO/o1Txr5+ix353H3Ndrc3Hibp0DlqSRJrT47Tgd56t5fX2Vqc3ImkD1qZSVcdhmsXClhMMC778LttxtQFAe5uVdTV5eNydSBYcMWExGR3MSnYovjtW0bXHIJUkGB6ujsk08wXH21V0ytpf2VP6GhoXTu3JmCggK9j5IkIQHU1eHu0aRxWgJCQNuIN5unubTcTDmPPBERTdaZnqeZ+5IkeeR5/fXXee6553jsscf47rvvuOuuuzjzzDMZMGAAxcXFnHvuucycOZMHH3yQqKgo3TJIO05SWFjI5Zdfzp133sldd93Fxo0b+b//+z+PPI370jg9dOhQEhMTWb9+PZdffjkZGRkkJibqVgCyLJOens7UqVP1vldVVbFq1So+/fRTz3Fxa7Mx7k2bNrF582ZuvPFGj36FhobSmFrqr0bux2F8ya/1TZblgPaompxpbY+ipadPh8xMiYsvhtxcicsv78zLL8N99/m2X3E/0g7dGD58OVu2nE1t7UZycs5n2LBfMZni4MorYcoUuO02pAUL4KGHMP38M3z6Kd3cjiR5w9dYprrLUX9lqq9+WtqVIEHS8TJh9LWtjIwMzjzzTK8edu121SfnsmWwfLnqrqCRzCQpSeXTyZNh0iTV4aU3h721tXvIzHyJqKgsLJbt+n1JCiEhYQYdO15Fhw4XYTQGH9LNnXzBGlA5iwU2b1bf1rWrwWzSg0JCYPhwT6VH376eO/fjQG2Gs43KNqZOnVTn0BdcoP4tBOzff2xos7Jg0yaoqYGVK9VLo86dVT2SNryjRzcfcvxkjFFzZDIl0rXrrXTteit2ewmlpd9z9Oj/qK5ehdmsvkDu3XsvQqTSr989JD10D4aHH1ajy/z73/DDD+pg3HEHPPAAXHMN3HabCl6SThmc/rR7OtTZWnun8/oLpE2DQaK2NgezeQ3l5auprl6Dy1XYpExtbU8OHpzIzp3DsFoHUV3dg6qqZKzWOITQzpGj+4tytwcDUBRBTU0NkZHRDeegvef1LGekru4s5s41EBsLMTHHruho9VIUsNmOXVar9qtQUFCF3R7P0aMSpaVqXoDOHCGZQg6Y+jLy7DguvFCVW9re7LjwgsWiKj6+/RYWL1Y71QKZgLNbzHGcKSQEERaGsFqRHQ4MdS7iVlUTt0r9twDqO4F5MJgHgnkQ1PQFR0g15WIN5ZY1YHkLDkDkAYmYgnBiDscRU5FEhNINKT4B4uMhoeG3IS1FRxNVVKT6zEpMVL+8+Pgs/T2t0dOpXm9tZWRk0L37mVx8sZFdu9T1+u23MGOGekxh9+7bqKz8FVmOYOjQX4iI6OvfeH/9Ndxyi7qX69UL57ffklFdzZluPgZOBM7169fTqVMnz39YLF4jZLQ51dY2iVbnD82aNYu7774bgEceeYQ33niDFStW0LVrVzp37ozRaCQqKoqkpCQAXQmi0XvvvUfv3r154403kCSJfv36sXHjRp+cr2okSRJnnnkmaWlpXHbZZaxcuZIbb7yRzz77jJ07d9KvXz/Wrl3L/fffr5dZuHAhQ4cO1X10eaOJEyc2KPztOBwObr/9dm644Qb9/0Koz6vo6Gi/o8MEUg7Qj9WeCDnTt6/6PnjzzQo//CDzwAPqe+Knn0KHDr71VVujUVHDdEVITU0m27bNIDU1DaMxVn3BnD9fDZ17//2QkYEYNoy9119Pr2eewdixY+uNHQfyVe61K0GCpLbS3ntra8iQIR5tulywZYuq8Fi2TA1c0XB8TaeUFDj7bMHIkTWcf34UffrILUVfxeWqo7T0O4qLP6G6Oh1ZVuuUJCPx8dPp1OlqEhMvUjV/bUTNYfW7nMsFO3ceeyPfsEGNG+XmwEynAQMQY8ZQO2gQkWedhTx8ODSjFT7edFxwnqA2fSFJUl8qevUC7cOM0wk7digsX15Lbm40GzdKbNumhlVesEC9NOrT55hiZMwYGDkSwsJO/Bi1RiEhnenW7W66dbsbm62I0tJvOXr0a2pqNiBJ2eTl3cK+fffSqdPVJI2+mZiz/4tUVqZ6z/73v9U4gR99pF5Dh8LttyNfe+0ph7O1dk+HOltr77ey/oSA6mooLfW8ystrcbnWYTSmc+DAS3Tpsp6wsBqPsi6Xgb17h7N9+yT9Kivr5qUlX0kCAlGOq+UKm+plfCAZSAAE/djNBaxmMquZKq+mt7JXzeKAol+7sePXwcy7ZxD2iHhiO4US1zWMxHP7Ica7jn0VsNvVkOa7d6sKjdBQVUEeEqKnZaORUbt2YXjzTUhLA/eIEX37qvFAQY1aUVWlTlJDWlRVYVcUQmJjkcLD1a/aPlxKWBh1LheRHTogR0aq4cFaKxcWBgYDQlEoKy2lQ2QkclWV+sm+shIqKpAqKwmrrCSsooJOlZWwphJlYRk1EYcwJx7F3N2Mua+d+s5Q11tQ19tCMRbgMEbzZmJyIWYnxKyB6FwwNbgCMQLngBqiQCNNo6Vpt9y1XG735JgYhvfpg+xyef9a0xwnnIJrtLU2T6d6vbVlsw1n8mQDpaXQvTv88gsMG6b+v6DgCUpKPgMMDB78DTExY/VyrY6306lGo3j1VfXv6dPhv/9FjotjSFnZCcfZt29fqqurT1ibbU3DtElCVUYkJSVx9OhRwsPDfSqfm5vL+PHjPRQBkydP9ksJAjB16lQ++OADANLT03nuuecoKCggPT2d6upqrFYrkyZN0vP/9NNPXHTRRa3W+/XXXzNw4EAcDgc5OTnMmTOH+Ph4XnrpJT2Pr1gbU6DlTrSciYmBb76B116r4ckno/jlF4nUVPjqKzXAoT99VRUhy9iy5RxqajLZunX6MUWIJKnhKs8+G268EWn1avq+9x7i44/hiivU/02dSosvokGSr2PargQJkk604O3YsRO7dh1Teqxcqe5f3KljR5X3zjlH/e3dWzPF8r4hFUJQXb2GI0c+obT0G1wuzZGZRHz8OXTqdB0dOlyCyRTfVvA8SJblplr21qiqCnnlSjqtWaMqPTZtat59fpcuMG7cMROEUaMgLg4JiD4uvfedAsIZRLlgywZCRiOkpsqkph7jP6tVVd65H6PJy4P8fPX63//UfAYDDBkiM3FiJ/24VjOW0c3SicIZFtad5OT7SU6+H6u1gJKSLzhy5FNstn0UF39IcfGHhIf3p0uXm+n8l+sJfeABWL1aVYZ8+y3k5MA99yA/9BCdpk1TTbNSU9WdY//+rW78T/R8urd7OtTZWnsne/0Job43aycZLJbm02azxPr1Pdm8Waaioqmyo6xMfU/o0KGIoUPXMGSIeo0evQWDwfMLWV1dNDt3TiAnZzK7dk2ivHwsMTFRdOwIXbuqrNexo3rFx6sf7bX9iiQdu9z/bul//uR1Op2sW7eBIUPGYbEYqakBs1m9amrUy2BQ3+nDwiDKYKVL3V66mHfTsXI33Q5lkZS/hhCz25dKRW1AdOyIdPQo3TlEdw4xgzSwAPsbrrVQ91wkRd1GEld/hNjyfchKM0pz9/kEYt3+Fv37U37WlWSfcSVrzEM5dFhi4EAYczmMGOH5odjpcLB44UJmzZrlV5hFmcCfVbIs06lzZ/WPqCj1TbWVtmJxw6go1FfswVySjrl6Leb6bGrYjTPGTsU4qBh3rGxEaQQx+8KIzpUIz7YQu6ceg62BF7XJPHy41fYTQP3KPXGiasZ65pnqs7uFl4/T6RmptXk61dsc/fCDzPXXJ2Czqby+YIEqTwAOHXqPgwfnAtCv379ITLzAo48tjnd5uWo5uXSp+vdf/6qGqTcYkOGkzFViYqKH400AIiJUi4yTQRGtH0FsiRrLHy2Kia9ySTtG5V4+EOuGs846i/vuu4/8/Hy2b9/OlClTyM/PJz09naqqKkaNGqU7QHU4HCxevJhHH3201XqTk5M544wzABg4cCD79u3jiSee4OmnnyYsLMzjKIY/FGg5ODlyxmCQefjhaGbOVD9U7tqlvic+8YR6uZ0qabWvUVGp3hUhoL58rlwJH34I776LtG0bfPmlep1xhqoMufFG1XrkOFO7EuQEkaPxWZM2ooMH4Y03XHz+uYOKCs8TvjExqlJNU3wMGdJUweZwOFi+fDlnn322x4K12YooKfmMI0c+xWrdq98PC+tDUtJNdOhwLWvW7GXQoLMDXuiBkLf+epDdDuvWqQ/GpUvVN+nG5nBRUZ5nLsaOhW7dmtVA+tTmcaZA2wymr6cCzvBw1Q/NhAnH8lRWqvHNNcXIhg2qa42tW9XrvffUfMnJx/zXTJ4Mgwc3L7hPBk6jsTu7d4/lrLMexmJZT3HxJ5SWfovVupt9+/7Kvn2PkZAwk6T+N9Ph038j/+Mf8MUXqkIkJ0c1oV+8+FiFoaEqwGHDPJUjbueGTgZOrd3Toc7W2vNl7FwuVdFw+DAUF0NRkZONG/Pp3v0MnE4D9fXol82Gx9+N79lsgqoqG4oShtUqYbUeOwrSMhmBVI87suyiV6/tjB+/miFD1jB48BqSkg42KWmx9KCkZCiRkdOJijqTxMQhXH65gTvuUD+4e/sgczLkjMMhsFjKVN9DRqEKhpKSY9eRI7B3r2qdsWeP+nBsbgBDQ1VltyYoJkxAiotTLTF27oQdO3Dk7KLqUC3m0npqCyvptn8NHUQZvYpW6dWYiWYXAzATQwh2Qqkn3GAnKsROpKGeUNmO2RBBVter+MpxFQv2Dca+u/kBlWUYOFA9+jdmDAwfLmE2m3yc/+DHNpiy7uVCOwygY4cBdOQOABTFQV1dDmbzOszm9ZjN67Fa92LpaMHS0cKRccBNAAYiwgYQGdKPKPoQ6Uwh0pJEWG0kkrlBKaJpuxp+leJiXKtWYaqthSVL1AtUa5yxY48pRSZOVDdDJ3GMgqG2kn1tLVNra9UTKh98oD67AS64QOF//5N1hV9Z2U/k5alWQD17Pk3Xrrc26aPX8d6yBS69VD1nGxkJn3yi+h7wpWwbkcPhYPXq1R7hTQFVkLZyJEVRFP34hD8KqkDLBUOiIVKLpnRoiQYNGsS8efP0vxVFIT093e82Bw0aREJCAs899xypqanExMQwdepUXnzxRSorK5nqZrKwYsUK4uLiGD58uN/taCGh7XY7YWFhJ2VeHA4H6enpJ20vvnGjiXvuUZfUM8+o+oovv1RfkVoq597XY4qQsxsUITNITf31mCLEYMBxyy0s79GDc2JjMX76qWp6snevqsx8/HH4wx9UhcjMmd61MAHg9IXalSBBkuE4TZg32rZNtf773//A6TQABsLCBJMmSbrSY9So1q1EDQYDY8aMaYj/bKOsbB5HjnxCZeUS1JPAqsO7Tp2uJCnpZmJjp+ixuMeMiW9znC31Vych1BdFTemRnt7k7I/o35/6CRMImTIFefx49Uu6j31vts02pkDbDKavpyrO+Hg47zz1AnW6Dx2CdesUVqyws3FjKJs3SxQWwn//q16g+qudOPHYu86YMeoHwpOJ02g0ERc3lbi4qfTt+zalpd9QXPwJZvMaKioWUlGxEKMxgc6d/0jSTbcQ/Ze/oGzahDU9nYi8PFVjnpOj7jA3b1Yvd+rWTVeMGIYOZXzPnhjsdjiBSpC2GNcTLWcUxUDXrmPZtMlASYmq4NAuTeFRXKy+e3vqV41A/wBblYDmv2AbDOoHPe0Eg5aOiICwMAW7PZ8pUwro1Ws9nTqtITJyPbLc6GskMlFRw4mNnURs7CRiYiYREtKVqqoq4uLi/NqotbmcEUId3L17VVOwvDwMe/YwdcsWjH/+s+p52ZfNTFwc9O+P6NsX6xlnEHbuucijRzd/pDE2VtfAmoCODZeiKJQUV7DoiyIKf97G0dBkDkUPoCosCUVIFBer3SwuBlyAtVG9bhaZkZGq7nLIEPVL+PbtqmL30CHYsUO9PvsMVD6axe23C7p2VfN263bscv+7a1fV8sXnsfVCbfHMkWUT0dEjiY4eSbdufwbAbi/FbN6A2bye6uq1VFZuRJZrsNh2YbHtotS97thoIrsOJTJyKFFRw4iMHE9k5FD1yK2iUFNRQdyhQ8irV6tnfjMy1IlYvVq9XnxR1TCNGKErRQwTJ552z8jTqd5Nm1TFx1dfHTN+MJkEt99u4403QvVHUXX1OnbuvAZQ6NLlVnr0eLLZPjY73l99pb4cWa3qWdl589RF5UvZNiSDwcCQIUOoqKjwu6wkSURGRvrtPyLQcsGSr23eeeedvPbaazzwwAPccccdbNy4kf9qm7QGOnToEOeccw6ff/45Y8eObbYeWZY588wz+fLLL3XfH8OGDcNut7Ns2TLuvfdePe/8+fN9OgoDapjVI0eO4HQ6ycnJ4R//+AdnnXUWMQ2K05MxLyd7Lx4ZCR9/rL5H3nmn+kqVmqo+mzQ/f770VVWEaD5CNjRRhBgMBsaMHYscF6c+e197TbWC/ve/1Q/Z8+apV7duqr+fW26Bnj2DxukLtStBgiTNBEyLUKAqGY6lnU4nkiTpafcNqObER7uveReXZQPp6TIvv6yQlnYs/9lnC+67D6ZNcxIVZdTLGo0mhBA4nU5MJhOKouByufS0oigN3u/z2LPnE8rKvsbprNLrjYmZQufON9C58zVIkro5lyRJx5GQkIDL5cLlcnnFJMtyk7Q7PoPBoOMzGo1IkqSnj+E4ljaZTMTHx+Pcvx955UrEkiWwfDlSSYnn+HfqhDj7bOTp01HOOgule3fCjEZcLhdOITA2mo/W5ikhIUF3qNMcJvd5csekUWuYmpunhIQEPWa40WjU56xx2uVyIYTQPbjHxsYiy7JfvCfLMoqi6C9DLWFqPE8ar4uGGOwtYXLnPW0MYmJi9Pabw9Qcju7dDVx2meCyy0wYDBLV1U4yMyXWrTOwapXC+vUS1dUSixbBokXqHJhMgtGjYfJkmQkTYpg8WTXr94f33E07vWHylo6NjUVRFH1uJCmCLl1m06nTTVgseygt/Q9HjnyO3X6IQ4fe5tCht4mMHE6XLjeTePf1iNAOSLKM025HPnAAeft2lOxspG3bkHJyYN8+9W3q0CFYtMjDHF907Qq9eyN69kTq0wd69cLVowfGfv0QnTvjVBSPedLIff5a473G0ViOJ7WFPG3M0xUVEt995+SHHwwsXy7hdPp2xE+WoVMnQZcu0LWrRHy8IDRUEB4uExIiCAnR0gohIYKICAMmk0JoKISHy5hMLkJDISLCQEiIi/BwiI42YDI5iYyUiIoyIEnH+l5fX4PVmkNdXTZm80ZqajZTV7cdSfK0djMYoomJmUBU1Hji46cQEzMOIcI8eFqWZVWeNqT94WlN/mvrxG956nBASQnyvn24du1Czs9H2rsX0XD+Tarx9E8iA3GN+SIuTvXCnJQEnTujpKRgGDQI0bcvzj59MHXpgiIELpeLiAZMTkXB2MAXvvJ0l24d6PRgPDw41A2T4oHJajWwe7eTfftk9u2T2b1bwW6XGDxYYuBAF0OHSvTqJeNyNeW9I0ck1q93kp1tYONG9UXy6FEJm01i3z51abdECQmCbt0kunaV6NIljuRkmS5dFLp2FSQnG0hKctGhgyAkxPs8uT87fH2Wa2n3L8KtyVOTqQOxsTPo0OFC7HY7ixYt5JxzRmCz7aCmZhs22w5qa3OwWHbictVgNq/FbF7rgTc0NLlBMTIWKWUyEbf+EeOdd2KQZVx79iCtWoW8Zg0iIwNp3z51QDdtgjffRAbik5NVvunQASU+HqljR6SOHXE1pOVOnXDExmLo3Bm5Y0ccDSb8GlbtSICvz3KNThV5Cv7L1Ob2bRoP1NTI/Oc/Lj7+WCY7+9hLX9++cMstLm6+WaZTpzCcTidCSFgsu8nJuRBFsZGQcAG9er2lf1xrLH889kGAePhhpDfeUDHMnInr888xduzYLI5A96gatbb30cbDPSJZXFwclZWV+nhp+yJZlvUx1/incVrbTwkhvOZpnNYw+5pfURQ9Cknj/YzWXy2PO59o/dJIluUW82jplJQUvvvuOx544AHeffddxo4dy9y5c5k9e7aex+FwsHv3buoajql76/tZZ53Fjz/+yLRp0/T7U6ZMYcGCBbo/EEVRmD9/Ph9//LEHDo0a4zj33HP1MezSpQuzZs3i+eef1+fA3/F1T7u/cPtaVuMVf2SqlnbP39K+29c96jXXwNixBq65RrB5s8SFF8K99yq8+KIgPNy396OwsMEMG7aEbdvO81CECBGB0WgkISEBh8OhjnVkJM4//QnjTTfBjh0oH36I4Ysv1D3tc8/B888jzjkHceONyA1WOv68H2lpn6jFALrt1IT++c9/ioEDB4p+/foJQKxcuVIIIUROTo7IyckRQgixefNmkZubK4QQIjMzU+Tl5QkhhFizZo3Yv3+/HuP5wIEDQgghli1bJkpKSoTDIcSjj24RqakO3Xe+LCvi6quF+PvfV4jq6mrx888/67HNtRjKQqgx4RcsWCCEEKK8vFwsXrxYCCFEcXGBWLbsfrFhwxCxYgX6lZHRVaSn3yjq6vJEXl6eyMzMFEIIkZubKzZv3qxj2rJli1iwYIHYuHFji5iEECI9PV0UFRXpmA4dOiTmzZsnFi9eLMrL1VjSCxYs0OM2a7HotfGw2+3CUlws1j/6qHDefbeo6dbNPYiAeoWHC9vZZ4s9d9whxNatoujgQZGeni6EEGL//v1i9erVYsGCBWLXrl1eMXmbp127dokFCxaI1atXe8VUUlIihBBNMGn9bxZTK/O0YMECcejQIbFs2TIhhBBFRUUemNasWSOEEB7ztH37dvHTTz8Ju93uF+8JIcTKlSvF/Pnzhd1ubxFT43mqq6vTY5P7wnslJSU6pgMHDujj4Q2Tt3nauHGjjrUxpr1794tNm4S45548ceGFdSIpqSnLgBCDBgkxc+Z+8f77FrFvnxA//tjyPJWXl+v3vWFqbp7sdruYP3++WL9+fYuYNm3KElu2fCC2b79KrFhh0tfl8uUhIivrWlFbm+uV95Z8/72oXrRIiHffFftnzhT2MWOEPTy8eeBulxIWJszJyUJccIGw3nabyL39duH47jux7M03xbJff/WZ9zRMvsZhb4naUp4KcYyny8qEmDNnqzj7bIcwGDyHxmBwiW7dXGLUKJcYO/awuPNOIR57zCr+8pet4uefhVi+vEp8+eVy4XQem39tnrX++sPTdrtd/PTTT2L79u06pt27t4mqqjUiI+M+kZV1hcjMHCpWrDB4yOxjPNJJZGdfLoqK/ikWLnxLVFVVCCG8yNMGnrbb7eLnn39udZ02nn+f5amiiG1Ll4r9X34pxKefikM33yyqzz9fVPXpIxwRES3zpiSJus6dhX3aNCHuukvsuPVWsf6xx4Rj7Vqx+N//FpbKyjaXp7m5uWLjxo1iwYIFYsuWLSdEnmqYvv12vti61SxeeCFDfP21EC+8YBWXXpovrr1WiIkT7aJLlzoRFtbq8tYvo9ElUlKESE21iHPOqRBz5ghx333F4oUXDorly4X4/vsc8d1385uVpy0997T+avhafZb7LE9/FbW120Vu7j/FihV/FFu3XiAyMro1y/srVsgiPX2A2L37zyIr6yWxZctioSiK2Lx5s8hbsUKIr74SJZddJmx9+/o+YG6XPSJCuHr2FK7Ro8XRIUOE/bzzhOvii0XhlCnCddNNwnH77SLv4ouFePxxYf3b38SOW24R4p13RM1bb4ktjzwiHN98I9Y++aRY+f33fvHe8ZKnQgQvU/Pz88W8efPEypUrRVFRkVAUId5+O0tcc41FuC/lkBAhpk4tEgsW1ApFUXnAfY9aW3tArF3bQ6xYgdi4cayoqDjsVf4sXbpULFiwQBw4cECs/eknIc4++1hDjz8u8lqQP4HuUZcuXarzpF971Aa+XrRokdi+fbu+HhwOh6iqqhJCCGG32/U66uvrhdlsFkIIYbPZRE1NjaisrBRWq1XU1tYKIYS+v9LSFotFCCH0vZYQQi/ncrlETU2NsNlsQgghzGazqK+vF0Ko8tButwshhKiqqhIOh0MIIURlZaVeVvttLi2EEE6nU0/b7XY9T2uYhBBNMGllvWGqra0VVqtVx2ez2fS+aPi8YVq5cqWIjY316KPWd6fT6RWTt3my2Wx6PS1hajxPWjstYWo8T6WlpSInJ0eYzeaAZKp2z9/neWt71E2btos//alUX3pDhljE3r3+vR+Zzdli+fJosWIFYtOm8WLBgq9FeXm5/p7k9Tlhs4m6jz8WR4cP95DJtuhoUfXMM34/z1euXOmTPJWE8Pc0ajsBmM1mYmNjKS8v17XQ4NuXS5fLxcKFC5k5cyahoaFUVzv57DOZN96Q2b9frT88HG6+2cUDD8j06SPpmuqamhrCw8P1M1nevkpYLAWUlHxAcfG/cTpVO11JCiMx8WK6dp1NbOw0hJBa/RovhMBisRDREIPcHy27y+Vi8eLFTJ8+ndDQ0Oa17IoC69ejpKUhr1gBmZlIbl9UhCwjjRmDOOccXGedhXHKFJQGjM1pBV0uF1arlYgGJ1H+fLkEsFgshIeHN/kC0pLVhMViYcmSJcxqiALgryWI1WolMjJSnwNfNJ1Op5Oamhri4uJ0zb6vliAOh4O6ujpiY2NxuVw+W4I4HA4WLVrE+eefj8lk8ssSxOVyYTabiYuL079A+GIJovW9trZW729LvCdJMnl5TtaulVmzRiIjQ2HPnqZmcV27Cj1E9PjxTkaONGIwHJsn9cvlImbNmqX3xxctu8FgoLq6msjISEwmk0+8Z7Uepazsa0pKPqa2dovex/j4mXTvfh8JCdNbnCeDwUBtTQ1hFgumwkLYtw9l717k/fvV8M8FBUgHDzYfFamBrMXFhCcl+fXlsra2lri4OKqrq3WT0kDpeMpT7X5lpcz33zv5/nvN4uNYe6mpgquugssuE3ToUEVCQhxSQ5hiX3i68Tz7Y90khIXi4tUIsYu6ui3U1GzCYslF9d7pSSZTZ6KjRxEZOZyIiOFkZZk577zrvMtTL7LHaDRiNpsJDw8nJCTE5y9HHvJUCIxVVbh27ULKz0fOz0fZswdp716kvXtVHw5eSEgSUkoKyhlnIPXti9S3L67evZH69UPu0weHLJ9UeertWdeW8hTAarWyZMkSzj//fN3BXnOYjEYTFRUKhYUKJSVGCgsVCgrqKS8P49Ah9ejW4cMSJSUCLXxxSxQSIkhNhZEjVau5sWNl+vVzEhLi3RJEkiQqKyuJjY3FYDD4ZVkXiDy12yuw2XZhNm+komI1VutG6usPNIOlC9HRE4iNnUhc3GTCwoZiMIQiV1RQs2ULkVYrhqoqXCUlyBUVSOXlKKWlSOXlSGVliLIyKC9HUpquv0CpfvFiQmfMOGnyFAKXqdq+bcyY6Xz3XSj//rfM9u3H6h0wQHD77YIbbpCJifHkAW2PajI52LnzPGprtxAWdgYjR67FZOrgVabq63b3buQrrkA6eBARFYXy8ccYrryyTfaoVquVtLQ0Xc74YwnicDgwm80cPXqUnj17EhER4fF1X3ut8mYBoOXzlqe5tPuzRyNfLUFcLhc1NTUeRz9AtVpwT2t9d09r1jW+tNk4HUhZQC/XEqb169ezZ88ebrjhBv2+EAKz2UxMTIz+d3OYvM3T8e6vt7TVaqWgoIBevXphtVr9lqmghgY+77zziIiI8MsSxJc96i+/GLj5ZkFFhUR0NLz3nourrxY+vx9VVmayY8f5OJ0VREePY+jQxdhsMmFhYR7vrl6f54WFiI8+gk8+QSouxvHGG0h/+Ytfz/Pq6moSExNblaftSpAASXvABPLAcjgcLFy4kNGjZ/H++ybeeQe0o4UdOsA998Ddd/sWu9mdhBBUV6+mqOgflJX9iLapDgvrRbdu95CUdNMJi+4Cx3B6eL4XQj0QvWTJMb8ejSO49O2rOoY491yYNk11FnEKU7M4f4N0uuIsK4M1a44dH9+0qambgeho9aii5ldk5EgHK1eeWKzq+l1FUdEblJX9hOarJzJyCN2730enTn/EYAhruRJv5HBAg4LE/VL27cNy+DChBw74jTMYGXg863Lny5oaE/PmqWHgli3DQ/ExfLjqT+/KK1UR09YkhAurNZ/a2m3U1eXol9Wajza37hQSkkRU1Ciio49dISFd9U3qCV1/ZrO6UDIzVceEeXmqQ4yWQkJKkuq1uG9f9TrjjGPpXr2OObRohU5XORMIHW+sDofqN/bw4WMn5jQliZYuKmo+aFp4uLpGRo8+5ri1X7/j46fueOGsrz9EdbV6bKa6ei21tZsRwumRR5bDiI4eTUzMROLjzyYu7hxkuZWT34qihi0uKzt2aWGZbLbWfxvSitWKuaSEyK+/xjR6tF/Yjqc8DbQ+IWDlSifPPFPMhg3dsdlU2RMWpkaSuO021f9WS+4PFMVOTs4FVFYuxWTqxMiRawkP79N64198oTZgs6kyY948GDTIp34HQsHypM1m019kw3yUbSeLFEXRlQMnMlrQiabTBWewvHMinpGFhXDddeqeGWD2bHjrLd+DENXUZLN16zk4nZXExIxn2LBfMRr93NtZrWyaO5dRc+Zg8jOKjq/yr90nSJAUiOftvXvhX/8axsqVRmw29V7v3vDgg2q0IG9M5o3xXS4bR4/+j0OH3qK2Nlu/Hxd3Nt2730ti4gU4nUpAi+a4LLaiItWRmab4aOTXg44dVc88552n/vbocazdqCj8aTWY/p6MzXegbf5ecAZT1r3cxRebuPhi9b7Fojop1JQia9eq73xpaeoF6tfn/v0nkZcnc+mlqk+2tsbpdDpJT69k1qxv6N37AIcOvUVx8cfU1W1n9+5b2bfvUbp2vYtu3e4mJKSzf22aTKqQ6d3b47bL4WDZwoXM8qunx9o93hRInRUVsHRpCu+9Z2ii+EhNhauu8q74OF7rz24vobZWU3Rs030dKEpj75kqKUoCiYkTiI0doys+QkO7+As94P56kN2uOuHNzDx25eY2H3FFU3S4Kzi0dO/eHooOvc0zzvjdyJmTjdNkUqcnOdl7WbvdwSefrCQ6+iy2bDHqvknMZtVH3bp1x/JGRcHIkapSZPhwJ7W1K7nllqmEhp7YKFQa1tDQbnTqdCWdOqnRQVwuKzU1G6muXqMrRpzOcqqrV1NdvZrCwlcwmZJUB9RJNxIVNbT5RmQZEhLUq1+/Y21edplfc+pyOEhfuJBZqakB4WwL8qdeqxUuucSA2awy0LBhcPvt8Mc/qr6HWyO73c7KlTMICVmJLEcydOgvrStAXC5cDz2EocH/BxdcoCpEfGmQk7f+li1bRq9evfwuG+iL+sl4wQ+mzdMJZzDtBtNfh8PBr7/+eko8OxpTcjKsWAHPPqtGo/7oI0hLM/P99+GMGdN6X6OjR5CauoytW8/BbF5Penoqo0b9QFzcCN87azRSMmZMQB/CfZV77ZYgAZKmZaqqqiI2Ntbncu+8A/fcc8xkdcwYeOghuOyy1r+4CCGw2Wx6TOv6+sMcPvwehw+/j8Oh+lqX5TA6d76ebt3mEBU1xGtZX8nvctXV6oZ62zZc2dlYFi8muqjIM094uBrS7txz1WvYMHUTEky7QZYLpmwwD+F2nG1X1tdyLpcavUFTiqxapX4xdadBg+Cii9Rr7Fjva/V443Q4qigu/pBDh96mvl4NfSpJIXTufB3du99HVFRqUG0GM6fV1dXH/TiMv/I0KwsmThQ4ncdwDxt2TPHRr1/L5QMZO4ejnIqKNCor12Kz7aSuLkeXv41JlsOJjBxMZORQIiOHERU1lIiIIShKzMlZf6GhSPn5ngqP7Gw1dm9j6tEDMXYszmHDMA4ZgqQpOsKbj2zjtc12OeOVAsV6vHEqivpxJitLDVO+caMakKpR8DUAYmMFo0ZJusXI6NGqI/+WunGi5lQIgdWaR3X1WqqrV1FWNh+ns0z/f1TUSJKSbqRTp2sJCel4XNp0p1NFnkLgMvX//s/Ftm1FPPNMNyZMMLY4r+4khGDfvkcpLHwZMDB06M8kJp7fciGLBf70J/jxR7WOv/0N6ZlnmuwHW2v3ZMiZ6upqiouL/f6arx3H0I51tHU5CPxFPZg2TyecJ7q/miVIz4YIKKf6M3L5cvjjHwVHjkiYTIJnn5V46CHfrARVi5DzcDrLkSQTPXs+Q3LyQ61b53Fi5Gm7JcgJpjPPVH9HjTrCSy914JxzfH/IAA1nuzdw6NBblJZ+q5uChoYm063bn+nS5VZMpkSvZQOhZss5naqJ9LZtntfBg3oWAxBNw3nw0aOPHXGZMMEn0+jj2t8TUPZEt/l7wRlMWV/KGQyqxUBqKvz5z+oH8D17HPz977vYt28wGRkyO3fCzp3w0ktqsIELL1QVIueeq4bDPB59ba6syRRHSsqDdO9+H2VlP1BU9AZm83qOHPmUI0c+JS7ubLp1u5fY2BkBt3k6U2qq+rU6Lq6am26K4pprDPT3M3Jta/MlhAuzOYuKisVUVCympiaTpsdZJMLDz2hQdmhhPocSHt4bSTI0qk94eHhvUyopURUdGzYQmpmpvt1WVjbNFx+vave0a8wY6NxZXQxOpxqD3c8NKbTLmbak44lTllWFYb9+6ld/UJXDu3YdU4ps3CjYsgWqqyWWL1c3xholJBw7QqMpRrp1C4hlfOqvN5IkiYiIfkRE9CMp6UZ69bJgNi+hpORzyssXUFu7mb17N5Of/38kJFxAUtJNJCbOQpZDAm7zt0YvvaSwcOEWxozp6vP82WwHyMu7h/LynwHo3/+D1hUgR46oD9GsLERoKK6PPsJw3XWnjZw50eHc2+m3SafDs+Pss2HrVrjtNoX582UefRQWLIDPP29iXNyEoqNHMHr0Nnbvvp3Kyl8oKHiMsrIfGTDgUyIj2+64m6906h6aOk3I383s0KGwe7eTJ57YwNSpwi8te0nJD6SnDyY7ewJHj/4XIZzExk5m0KBvGTduHykpj3hVgDidThYuXOh3f51OJ0u/+grXr7/C66/DTTep9rFRUern8WuugRdeUFeEpgDp3h1mzcL10ENkPvwwzuJidSM+dy6cdZZPCpBg+htIuWDLBkrtONuubKDlJEkV7BdeuI/Fi12UlsJ//wvXXguxsXD0qBpb/ZJLVL89f/iDGu68uLjtcMqykU6drmLkyHWMGLGOjh2vAgxUVS1nx46LWbWqN4cPf44Qx8+5ny/9Pdl1hoTA9u1O3nxzJY89pvitAPE25vX1Rzhy5DN27ryWNWs6kZ09gQMHnqGmZgMgiIgYQn39hZxxxgeMHJnJlCk1jBu3hyFDvqdXr6fp2PEyIiL6NlGAtNRm0FRbq/pYevVV1QymRw81pOxFFyHNnYu8ZAlSZSWEhqqK6HvvhS+/VJXZ5eWweLFq+3rhhaoCJMi+tsuZtqMTgdNggMGD1SO6b78NGRlOvvjiZ7KyHHz4Idx5p6rsMJnUI2lpaeoj/tJLVVPqLl1UVnr6afjlF4mqqtATitXpdLJ48VLi4i5gyJAfmDDhMGec8RZRUaMQwkl5+U/s2HEpa9d2JS9vDjU1m3TnhSdrTk+negEUxcHBg6+QmTmI8vKfkSQTVustdOhwfcsFd+yA8eNV06PERFy//sovUVGnlZzJyMjwcI7pK2nOO/0tG2i5YCiYNk8nnMG0G0x/T6dnR3y8k5tv/pkPP3QSHa362UtNVY/JtAbdYOjI/v230rfvxxiNcdTUZLFx4wgOHnwFIbw77A+GfMXXfhwmQArU1BD8N/ExmzeSn/8g1dXpgGoO36nTtXTvPofo6JE+tal9fdQ8D7dI9fWqD49FixCLFiHt2tV8vshIVaszbJjnb8P5rWBNtnzu73EoF0zZdpynZn+DadMbVrtdPS4zf756adGcNBo7VvCHPyjcdptM585ti9NmO8ihQ29z+PC/cblUh5VRUSPo3ftlEhLO86nNU8V8+0TKU3fSxlyWBTU163VrD3ffSgAGQywJCdNJSJhJQsIMQkK6ntz1B+oZLvdjLTt3quca3EmSYNAgxJgxKKNHI48fjzR0qKo9asO+BlO2Xc60bZvHG2d9vXoC9pjFiMqazQWjSkkRjBsnMW4cjBunfk9pzdFeW8xLbe12Sko+o6TkC+z2I/r9iIjBJCXdRKdO12EwdDyhvHuqHIfxFUNV1Sr27LkLi2UHALGxZ9K377uEhvZredyWLYPLL1ePTvftCwsXIvr0Oe3kTG1tLUVFRe3HYY5z2d/bcRij0XhaPCPdyx04IHHDDepeGI59COzcufWydvthdu++nYqKhQDExIynf/9PiIwccFxxth+HOUGkhRTyJ6SjRlpo08bh9rTwQ7W1+zh48CmOHv0C0Px9/IVu3eYQGdkdUJnE15COdrsdoPmQjvv2YVyyBLFwIaxYgdRwGFhCPc5C374wdCjSsGG4Bg+GYcMw9OmDU1GahkhqwOSOz5/wY1ra4XAghPA7pKM25uB/iFzt158Que4OeFoLFdjcPGn1auGdfA0BVV9f32poWW9h4ux2O1qoXV9DOmr6Uk3ot4SpuXmy2WxERUX5FU5US7v31xum5kI62mw2IiIi/A7p6K4bdsdkNCpMnapwzjlGXn9dYds2hYULjcyfL8jMlBouA88/L7j+epgzx8WgQa3zniRJyLLs0d/WeC8sLIWUlBdITv4bBw++QXHx69TWZrNt23Ti4s6ld++XiY4e0eI8aeR+39eQjm1BbSlPG8+/EDWUln5LaelCampW4nJ5hniNihpFYuL5xMVNJzJyNCEh4fqYAC3L0xZ4WpZl6uvrkSTJN56uq8OZkwM7dzJk/nzkl15CbNmCpHnVdiORnAxjx6KMGoU8fjyMGoUzPByj0Ui91YrRaGyXpy2EyNWwavPRlvJUwweqTPX1WX6qydPQUInUVAejRhm54w51bhwOI1u3QmamQna2gcxMhV27JA4elDh4EL79lob+CIYMgfHjJUaPVhgzRmHIECOS5ImptWedv/I0LGwAvXq9TK9eL1JWtojS0i8oK/sJi2UH+/Y9xL59jxAbO5Pk5L80hChXfOI9jU4VeQr+y9Tm9m3uY2exFHPgwOOUlHwCgMnUgT59/k5CwrUYjUZsNhtCCI9QmNp4uT76CONdd4HTiTJ5MvK8eSjx8biczhbXbWshchuvW1/5WiNf1qq7/HE4HHrb2nhp+yJfQuRqpL08t5S/cVlf87uHk228n9HkuVaflm4cQjaQPmrpQMs216/mMDWXdqfG8+Et3Zb99YUPAtmjaqQ9M/wNkeu+dwnkeZ6SYmDFCpm//93Fk0/K/PyzxNChgn/9S3DZZU2ffVod6vtcV4YM+ZnDhz+hoOABzOb1bNw4nN6959Kt2724XELHobWnpf15nvsqV9uPw/hJ77zzDoMGDWLMmDEA5OTkAJCbm0tubi4A27ZtIy8vD4Ds7GwKCgoAyMzMpLCwUK+rpCFKSkZGBmVlqvOu5cuXU15eyL59j5OVNVBXgNjtUxk0aBO7dk1m2bLNOJ1ObDYbCxeq2rSamhrSGkJbVFVVsbzhwG5ZWRkZGRk4nU6WLl3K2rVrASjMy2PP22/Dfffh6tsXY79+8Oc/I/3yi6oA6dqVyksvZf+rr7LwP/9h81dfsfv55+HJJ9mckkKBJIEse2Bau3YtxcXFOqby8nI9XVVVBUBaWho1NTWAGufaZrN5mGdpmJxOJ0uWLGHJkiVeMQEUFxcfw1RYSGZmJmlpaeTn55OdrX7NzcvLY9u2ba3OU35+PmlpaS1icp8nDdOKFSv0OW0Jk7d5SktLo6SkpEVMAAUFBTqm3bt3s2zZMpxOp9+8t3btWpYuXYrT6fSKyds8AX7zHsChQ4dYvnw5TqfTKyZv87R161a9vy1hajxPTqezYT2V+8V7ALW1tfqcesN05EgxNTVrefxx+Oabg/z0Uxbvvuukb99K6uslPvwQhg0zcOaZdSxZAjt3tjxPWn8PHDjgM++lpaVRW+tix44RVFb+k86d70aSTFRVLWXz5lFs334dixZ90uI8aW35ynvaPLk/jAOlEyFP3cfKbDZjNm9gxYqZrFvXjby8O6mqmo/LZcZo7IDdPpUBA/7D0KF7KSl5hl69nkOIwaxYoc65V3nqB087nU6WLVvG7t27PTFVVJD74YdUvvIK3H8/5kmTUJKTISYG46RJmG67jT4//4xh/XpVARIbS+nw4dQ/+CD89BOLP/4Y2+7dOP/7XxYMGoRzUODtmAABAABJREFU8mRsJlO7PPWRp7du3UpaWho7duw4YfJUm4/fmjw1mZwMH26jZ88FfPoprF5dxZdfLiQtzckTT1iYOLGErl3B5ZLYulXi/ffhtttkhg83Eh8PkyfbufnmI8ybB+vW7defdb7ynq/yVJaNZGeH0KXLe0yceIT6+j8TGTkWUKiuXsj27bPIzOzP0qW3Y7WW+DRPWlv+8N7xkqcQvEw91OAVPDMz02O8SkuPUlz8CRs2DNAVIA7HDAYOzCIp6UYWLVpEbW0tS5YsYdGiRZ5rVQjsDz+M8bbbwOmk/oorWPrww5CYqPN1Wloahw4d8lum7tixg7S0NLZu3erTWtUwae2Ab2vVXf4sWrSINWvW6BYhoCqZtHJOp1NPOxwOPY/dbsdisWA2m6mvr8fS8LHRZrNhtVr1tK1BwW21WvW0xWKhpqYGIQR1dXX6i2xtba2uTK2pqdH5qKamRn8R1PoC6Ec3hBBN0nDMmsIdh6Zo8oapriHmdmNMWtmWMNU3OOXWMAkhqKmp0fF5w2Q2m3UFlDsOoEVM3ubJbrfr/W0JU+N50vrbEqbG81RXV6e/2Ae6R4Vjz0p/nufa3mXz5s1A4M9zgwEmTFjDggVHGTYMSkslLr9cZvZs+PnndI/1VFVVRVpaGosWLcJms+Fyudi0qQMjRmwhNvZchKgnP/9BNm2azJIln+iYNBzl5eV+P881udcqiXYKiKqrqwUgKioqhBBCOJ1O4XQ6m6QdDodH2uVyCbvdLubNmydsNpvHfZfLIQ4c+KdYvbqTWLECsWIFYvPmM0V1dZaw2+1CURShKEqTtBDCI621oaUdDoea3rNHON98U4hZs4QSHi6EepRLCBCKwSDEmWcK19y5wrlpkxCK4hem5tI2m03MmzdPWCwW/b7Wd/d0UJi8pJ1Op0e6ORyBYHLHoaXr6urEvHnzhN1u/81gam6e6uvrxbx580R9ff1vBpO3edKwauX9weRwOMXKlQ5x2WVCSJKiL7OhQxXx0UcuYbO1Laa6ur0iJ+dqXYasXBki8vLuE/X1R5vMkyaLLBaL3/OkycDq6moRLLWFPHUfK4fDLA4efEdkZQ3Xx2XFCkRm5jCRn/+UqK7OFC6Xs+152uEQ4sgR4VyyRLjefluIP/9ZuKZNE0rnzh7yuPGldO4sXFOnivxZs0T9hx8K186dQjT0q12envqYvM2ThrW+vv43g8kfeXrggEt8/bVTPPywEFOnKiIyUml2CYwdq4h//lOII0dODKba2p1i166/iIyMGDc5Gip27rxRlJevaYLvVJOnQgQuU5vbt1VVbRGbNk1xk5tDRVXVat94wGwW4tprj03mE08Il9N50vnaYrF4yBl/+dpqtYqdO3eKuro6IYS6brU6tHwnM+1yufS00+kUlZWVDe8ZLjF16lQxZ84cPU/jvjeX3rlzpxg3bpwIDQ0VqampYt++fQIQmzdv9tqX5cuX6zx4vDE1l3a5XDpO7e8VK1YIQJSXl3udp08++UTExsYe1zloqU6LxSJ27NghrFZrQLynyRmN9072c8JmE+LBB136frdnT0WsXOkbJpfLJQ4e/JfIyIgWK1Yg0tPDxMGDbwin06GvUZvN5jemiooKn+Rp+3GYIEkzGXP3FO2edvfCq6U1TaZ72YqKReTnP4TFshOA8PC+9OnzKomJF3mYWdXU1BAdHa3f00wOJUnS05q5H04n8tq1yPPnI37+GXnPHr0vEkDXrnD++XD++UjnnguxsR6mQQaDQdemurfZHKbm0qJBK2s0GnWs7ue6Wkq7Y/XA1EpakqQm/fU2N43nyR+svuJonG48T+791er3hs+9v7Is6+PjD+9pebSy/mDSxkQzQWyR9xqlJUnCYrEQHR3tFZO3tDtWX3kPtHCJVp2H/J0n9/Z94T3NvNJiqePMM6OZOhXy8yXeekt1HpWTIzF7tsRjj8Ff/mLgzjtVx6ru/dXGqCV8za0Vd76NiOjDkCH/o6bmIfLzH6GqahlFRW9SXPwxKSl/pXv3ezEYIjyOHvjDe1paW9/Hk46HPHXPU1+/k8OH/0VJyRe4XLUN+cLo2PFquna9k+josdTW1upjJ8tqW63xtGj4AticfDIYDOp2v7AQgxZWKDdXTefmQkUF7u5SPcwxk5NVh9MDB6q/DWkpIQGnw0HOwoUkz5qF3NA3k9txoHZ5Grg89fasa0t56g3H70mepqTIpKSoIa1BwuVSl8uGDeqVmQnbt2tHDeG++wzMmgU33AAXXmggNLQpvmDlKUBExAC6dJlLnz4vcvTofzl8+F1qa7c0+BH5jKioUXTrdhedOl0LmDyOQLnXf7Llqdautzbd+6ql3fdtQljJz3+OoqLXEMKJLEfQs+czdO9+L7LseUa/sZyRJAnKyjBdeqkae95ohA8+gJtvRgbkhj74sm5b4utg9qjuffcnbTQaqa6u9hhfd58Q7vzeOC2EwOVy6bhby+9OSsPxc1/yux8VbXz/hx9+wGQyNZunsW8L0WBV8fTTTxMZGcnu3buJiooiLi6O4uJiOnTo0CwOrb/+jI3WnoazJUzu6cLCQvr166dbg7q3OXHiRIqLi4lv8FXorS9af30dX1/66/5347IaLwQiUzU509pzwtvepa6uTm/zeDzPQ0Ph1VdlLrpIlc3790ucdZYaRvfZZ01A07Luz4bk5Dvo2PF8du+eTWXlUvLz76es7Af69Hlf77u/MtWdP1qi9uMwQVKwJoy1tVvZtm06OTkXYLHsxGhM5Iwz3mLMmB106HCxx8JxOp2sWrWq5Tarq+Gbb+D661UvNVOnwmuvIe3Zg2IwoJx5phrnc+tWKCqCDz9UnVR5cZzlU5ttQIG2G0x/TwbWdpxtV/ZUwNmnD/zjH+pSe+UVNXBSSQk88YT6vnvnnWoYymD6661cdPQoUlOXMGzYr0RGpuJymSkoeIwNG/py+PCHKEpw49IW43o86nS5rBw58hmbN09g48bhHD78L1yuWsLD+9OnzxtMmHCIgQM/JTZ2PC6XK7gxr6+HvXvh55/h5ZfVUBpjx0JMDKSkwMyZ8MADqtewNWugogIhSdQlJaHMmgUPPwyffqq+8VVXqxG2Fi+GN96A226DSZPU+KNBUrucabuy7TiPHxkMqm/1W29Vl8zWrXDggJPbbtvBiBECp1N1SH3FFWrgozvuUN+vm9MfHI8xEiKUrl1vY9SozYwYsY7Ona9HkkKord3E7t23sm5dN/buvR+LZU/rlfrQZltQoPVWVPxCZuZgCgtfRggnHTpcwtixuaSkPNhEAeLelj7meXlqFKrVq9X95a+/ws03t17OTzpZ62/jxo0BKa40RZG/ZQMt1xwlJCToL8G+tpmfn8/kyZPp0aMHiYmJGAwGkpKSPJRJjctqR0f8oUBw/vTTT0ybNq1ZB5ghISEkJSU1UU40Ryd6Xk6nZ4cv5aZMUWX2LbeoMvmVV9Tt0ObNrZcNC0th2LA0+vX7FwZDFNXVq9iyZRQhIQsIJOqhz/hatBNpJ68UjOmi3W4XP/30sdix4yaxYoWkm6zv3fugsNsr/e9MQYEQb70lxLnnCmEyedqRJiQIcf31QnzzjRBVVf7XHQRpJluamdZvldpx/vaorbDa7UJ89ZUQo0Z5LtMLLhBi2TIhGqwGjzspiksUF/9HrF3bQzdp3rBhoCgu/l7Mm/djQDjb4jhMoPJ03rx5oqpqm8jLu0+sWhXnZsJuEtu3Xy0qKlboJpkBk8MhRHa2EO+9J8QNNwgxbJgQoaHej7EYjUIMHCjEZZcJ8be/qROfnS1Eg6l8oDh/6+vv94JTiN8P1uOFc8cOIf76VyG6d/dcar16CfHEE0Ls2XOcOtwC1deXigMHXhHr1vXyOFq3Zcu5orj4azFv3vcnXZ4GU19NTb5ISxuv41q7NkWUlv7kX+OrVql7T9U2Xp24U4yC5UntOIzVaj3OPTv+5H5MRAghpk6dKu699179/z169BBz584VN998s4iKihLJycni/fff1/8PeFxPPfWUKCgoEIDIzs7W8/3yyy+ib9++IiwsTEybNk188sknAhCVlZU+9XPbtm1CkiRRWloqhBCioqJCSJIkrrjiCj3PCy+8IMaPH+9R7uyzzxb//Oc/m+AUQujHYdz78Mknn4jk5GQRHh4uLrnkEvH3v/9dxMbGetQ5f/58MXLkSBEaGip69eolnn76af24hRBCvPbaa2LIkCEiIiJCdO/eXdx1112ipqbGo43GdWoULO+cDs+NefOE6NhRFQEhIUI8/rgQBw74VtZiKRDZ2WfpMmjHjpv8bt9X+dduCRIkaeZevpLLVcfBg88RHX0XR49+Cgg6dryKsWNz6dPnVUymuBbbqqioQHE6Yf16ePxxNSxtr14wZw4sXQoOB/TvDw89pIa5LSmBzz9HufxyKlwuv/urt+lnuWAp0HaD6e/JwNqOs+3Knoo4TSa49lrIyoL0dLj4YjWK6S+/wDnnQPfuLq68UvDGG+oSb/CvFVSbAJIkk5T0J8aN202fPq9jNCZgseSya9flREY+hs22PyCsx5v8n2M7paXfEBn5N7Kzh1FU9CZOZxVhYT3p1etFJkwoZPDg/xEfP63ZL0Etjl1ZGSxYoMrZs8+GuDgYMQLuugs+/xy2bVMnKCwMUlPViX3uOfjuO9Wm32JRf7//Xr1/7bUwfDhKaOgpx5fHu1ywZQOldjnTdmVPhb3AoEHw4otw4IAaafWmmyAqCgoK1CXWr59qfPDuu1Ba2jZjFBLSgZSUhxg3bi9Dhy4kMfFCQKKycim7dl1NdPTtVFYuDqjNtiB/6nU4KsjOHoHJtB5JMpKc/Ahjx+6kQ4eLfG6r9t//RpxzDlRUqJ+B169Xj/W1Uu50kzNVVVVNvv4LIXC56lq8nM5a6uurcTprW83rT7nGffGXXnvtNUaPHk12djZ33303d911l+7I+/DhwwwePJj/+7//o7i4mAcffLBJ+cLCQi677DJmzZrFli1bmD17Nn/961/96sOQIUNITExk+fLlCCHIyMggMTFRd4oJsHLlSqZOnar/XVVVxapVq7joIt94dMOGDdxyyy3cfffdbNmyhbPOOovnn38eQI9g8uuvv/KnP/2JOXPmsHPnTt5//30+/fRT5s6dq9cjyzJvvfUWOTk5fPzxxyxfvpyHH37YL7yn07PD33IXX6yGSr/oIrDbYe5c6NlTMGsW/PCD+qrqjcLDe5KaupTevd9CiHA6d27egqy1/vpC7UqQIMnXMDwaFRa+TmHhc0hSPdHR4xgxYi2DB39NeHjvVssqq1Zhue46pO7d1Sf9Cy9ATo5qQzp1Kvz977B7t2pf/8orqm2S27n5rKwsv/sbaLlg6WT092RgbcfZdmVPZZySBGeeCfPmqUv2z3+GiAjB4cMGvvtO4oEH1CUeG6ueiHjwQfVd+vDhwNsEkOVQkpPvZ9y4fFJS/ooshyHLxZhMHQLCerzJ3zprajazZ8+fMBq3AzKJiRcxdOhCxo3bS48efyUkxEvgerf2srKycNXXw5Yt8K9/qUda+vWDjh3hD39Q5eyKFVBXB9HRcO65uB57jM1PP40jNxdqa9WyX30Ff/uberxw4EBV49VSm6cgXx6vcsGWDZTa5UzblT2V9gKyrOolP/lE/c7z1VfqyTNZVt+5//xn6NZN4rzzFB59VPDtt+rpDF/3/b5glSSZxMTzGTr0Z8aN20dKyqOYTB2R5XJCQ1MCwtkW5E+9JlMCnTpdj9M5kNTUTPr0eQmDIdK3wvX1iGefJer225Hsdrj0UlVudm5ZBmt9PN3kzPbt25vcVxQLq1ZFtXitXh3NunVxrF4d3Wpef8opiiUoTLNmzeLuu+/mjDPO4JFHHqFDhw6sWLGCuro6OnfujNFoJCoqiqSkJKKiopqUf++99+jduzdvvPEG/fv357rrruPaa6/1qw+SJDFlyhSWLl2KEIKVK1dy4403oigKO3fuxOl0snbtWqZNm6aXWbhwIUOHDiU5OdmnNv7xj38wY8YM/vrXv9KvXz/mzJnDjBkzADVqixCCuXPn8te//pUbb7yR3r17c9555/Hcc8/x/vvv6/Xcd999nHXWWfTs2ZOxY8fyzDPP8M033/iF93R6dgRSrlMndY/73/86SU0tRwiJRYvULVL37vDII6pcbo4kSaZLlzsxm/9NTMwEv/qq9dcXaneMGiSZvGx0vVH37vdSVraAo0fPZOLEuYSEhLRcQAj1k/Gzz2JcsYLu2v2YGPWpf9FFqnPTVs6Nm0wmfaH7Q4GWC5ZORn9PBtZ2nG1X9nTB2bcv/POf8PLLqvO/deuOXeXlsHatemmUkqIqSLRr+HD/2zSZ4ujd+0U6dbqdlSu/xGBouqlpvQ7/ZF9b1BkTM474+PMpLo5h8uQXiIpqXZmsU24upq++YsaaNepTuSEkngcNGOA52AMHgsGAARjpV0+P0enCl8GUC7ZsoNQuZ9qu7Km6F4iIUI2srr0WjhyB//4X/vMfyM6W2Ly5Aw2RIAHVaiQ1VTXoGj5c/R08GN3Bqq9tNqbw8J707v0C3bo9xtKlfyciomXLh+aoLeRpIPX27Pkiu3YtJTJyiG8Ftm1TPX9/8QWGigr13v/9n+ojyc1ZYWt9PN3kzOTJk/UwvL8FGjZsmJ6WJImkpCTKysqI9eIvsDHl5uYyfvx43eJSlmWmTZvGm2++6Vc/zjrrLD744ANkWSY9PZ3nnnuOgoIC0tPTqa6uxmq1MmnSJD3/Tz/95LMViNbPSy+91OPehAkTWLx4sY5106ZNZGVleVh+uFwubDYbFouFiIgIVqxYwQsvvMDOnTsxm816CNu6ujoiI31THJ5Oz45Ay0kSXHONkWuuSWTvXlVUfPqpKqtfeUW9pk5V/UBdfjmEhzeuwf+9qdZfX6hdCRIkaV56Na2TwWDwSDudTiRJ0tOyHEVq6moWLlyom6+p9495FzcYDMiShPPXXzG88ALSqlUACJMJ25VXYvjjHzGdcw6EhOB0OnWv3FpaURRcLpee1jwXHz16lISEBEJCQvT7RqMRl8uFEEJPu+NQFIXKykri4+ORZdkLJrXvjdOaOZLT6dS99TocDoxGI5Ik6Wktj3vaYDBQWlpKXFwcoaGhzWIyGo1N0k6nk6qqKuLj45EkqVlM3uZJCEFlZSVxcXF6RBtv+PR5cvMIr/GDN0zNzZPW34QGJVZzmJqbJ4fDQVlZGZ07d9b5qHXeU/tut9upqKigU6dOOm80h6nxPGntiAZv4b7yntFoxOl0UlpaSueGr0K+8J6WdjgclJeX6/31hfc0z9AlJSV07NhRHzNfeE/DpJGvvNfcOvOV9yRJIjxcYsCAEqZM0frrZN8+mQ0bZNasUdiwQSInR+LgQdV/5tdfq/0LCxMMHeogNdXA4MEygwZB375OevQwIkneec/lchEa2h2Xa6jH/dZ4T+t7W5hw+i9PZQYN+on9+xdiMnXT59ArT0sS0i+/oLz9NvKyZR5ti5gYxNixyBMnIsaNwzlqFKbOnZvOf0P9gchTUDeYJSUldOjQAZPJ1C5PTwF56u1Z15by1J3fhRA4HI52edpM2pdnnZZOSjJwzz1O5syR2LlT4pdfasjPj2brVpmcHEFtrcSaNaqPYo2MRsGgQTBihMTQoS5GjZIZNkxgsx2hc+fO+hi0xntqxI9QnM4Rp4w8Bf9lKoQAsoecabJWa2uRv/kG5cMPkTdu1NsS3btjfuABwu6+mxBZBh/3Ca2t27bao7qPkT8y1W63U15erkcGAS26TxhTptTqfKpFAHFPi4boMAaDwSNKiLf8WlobM3dHpO55ZDmiSf7G9QMe/dXyaGOu3RdCIEkSLpcLu92uv1A23gO6p90vrX13+abdbw3r1KlTuffee8nLy2P79u1MnjyZvXv3kp6eTmVlJaNGjSI6Olrnm8WLF/PII480ezTJG273vruThlVRFJ5++mkuv/zyJn0MDQ3lwIEDzJo1izvuuINnn32W6Oho1q9fz2233YbdbiciIsKjH974QFEUiouL/ZapGmnPjBOxRw3meS7LMuXl5SQnx/LCCyE88wz8/LOLTz4xsGgRpKdLpKfDPfcIrrtO4Y47DAwZouhYtbn253nuvo9oidqPw/hJ77zzDoMGDWLMmDEA7NixA1C1i7m5uQBs27aNvAYbn+zsbF1bnJmZSWFhoV6XFs4pIyODsrIyAJYvW0bd99/DpEkYzz9fVYCEhFBw/vnUbdnC2ltuYZHDgVOWsdlsLFy4EFC9GqelpQHqGbnly5cDUFZWRkZGBoqisHXrVtatWweo5/cyMzMBKCgoIDs7G4C8vDy2bdvmgWn79u3k5OS0imnt2rUUFxfrmMrLy/V0VVUVAGlpadTU1ACqGZvNZsPpdLJw4UJdk7pw4UIURWHbtm0sXbrUKyaA4uJi1jZ8Ji8sLCQrK4vt27e3isnbPG3fvp2srCyvmPR5Wr5cx7RixQp9TlvC1Nw8rVixgu3bt1NaWuoVk7d5ys7ORlEUv3lv3bp1bN26FUVRvGLyNk9Aq5i8zdPGjRtRFMUv3gPIycnRsfrKe2VlZSiKQlZWFpWVlX7xHkBtba0+p77yXmZmJoqisGXLloB4T1EUNm3axMGDBxvmaS1RUcXccANcffVK0tJKqaqCl17K4rHHLMyaBdHRdmw2iaysED780MD990vMmCHRu7eJuDgYO1Ywc2Yxr7wC335r5bPPVuNyeWLSxs8f3tu2bdtx2bS3uTzVeLq8nL23347o0wcuuQR52TKELOO68EK23H03tqwsbIcO8fNf/gLPPEPN5MmkNWzuj7c81fi4XZ6eWvI0JyeH7du3n1B5umTJEoB2edrKPG3evBlFUfzivYEDFYYMWcWjj+4nMxN++WUVy5aV8J//wJVXHmTyZDvx8eB0SmzbJvHZZ/DggwbOOksiMVFmwIAOjBgBF14o+MMfinj2WYUPP3Tw4ouZ7N4NR440P0/a+PnDe8dLnkLwMvXQoUN62oMHSkshI4Ojs2YhdesGd96JvHEjwmSCyy9n3RNPUJuTw8Zhw1i8eLFf+4RVq1axfft2v8bLHVMge9S1buaVvqxVd75evHgxeXl5CCF03na5XNTW1mIwRCJEKBaLgsEQiaKEYLUKDIZIXC4T9fUSdruspw2GSBwOA3a7rKcdDgMGQyR2u6yn6+slPb/NBi6XCYMhEqtVoCghSJJETU2Nvk+rqanRX2Q1PABms9kjrb2c22w2QH3h1PIoioLFoh6zEUJgt9sB9eW7rsFyUksPGjSI9evX6/nr6+v1MbbZbHr9VqtVT1ssFuobHJ/V1dVht9sZMmQICQkJPPfcc6SmpiJJEpMmTSI9PZ1ly5YxZcoUve/Lli0jLi6O3r17N6uYcceqjcHAgQNZ06AFdTqd1NTUsH79er0/AMOHD2fHjh2cccYZJCcn07VrV8444wy6d+9OfX09GzduxOl0MnfuXMaPH09ycrLOY+6YAH3Mamtr9Rfzuro6/QU+UJkKx56VJ2qPGsjzPC0tDbPZzPbt21m8eDE2mw1JcmI0/sKPPzrZtcvGtdfm0qMHVFVJvPuugdRUGD1a4dFH92G1GikvL/f7ea7JvVapRbep7eSVNM+zFRUVQgghnE6ncDqdTdIOh8Mj7XK5dM++Npvt2H2nU4iffxau0aN1l+dKWJhQ7rlHiKIiYbfbhaIoQlGUJmkhhEdaa0NLax6NvaWdTqdHujkcrWFqLm2z2cS8efOExWLR72t9d0+fTpjccWjpuro63VPzbwVTc/NUX18v5s2bJ+rr638zmLzNk4ZVK3+qYqqvt4vcXEV88YUQf/2rU1x6qSIGDFCEwaB4DVoSFiZEaqoirr7aJZ56yikefniDKC+3+I2pLaLDHDd5qo3Vhg1CuflmFbRbxCzngw8KpaDgtFqn7fL09MXkbZ40rPX19b8ZTKeTPFUUIfbutYsff3SJp58W4g9/cIkePbzLzsZXfLwihg4VYuZMRdx6q1M89ZRT3HPPZrF378mVp0IELlObyJniYuF84QWh9O3rCX7gQOF8+WWhHDkihDj95I/FYvGQM/7ytRbho66uTsen1aHlO5lplb8VfWy0qCkul0tMnTpVzJkzR8/To0cP8frrr3vgSE1NFU8++aSOSftby7Nv3z4BiM2bNwtFUcSBAwdESEiIuO+++8SuXbvEF198IZKSknQeVBRFFBUVif79+4v169e32PfLLrtMGAwG8eCDD+r9SUhIEAaDQSxYsEDHd/fdd4u//OUvOg6XW3QY7W8tOkx5ebkQQoi1a9cKSZLEyy+/LHbt2iXeeustERcXJ2JjY/X2Fy1aJIxGo3jqqadETk6O2LFjh/jf//4nHnvsMaEoisjOzhaAeOONN0R+fr747LPPRLdu3TywatFhmsNnsVjEjh07hNVqDYj3tD2PxnunwnoK9jnhcgmxeLEiLr/c5RHkNCzMIZ57zuE3poqKivboMCeCNFMyg8GAoeH8o3vaaDR6pDWzUlDPzKEoGBcsQB4zBv7wB9W0MDwcHngAad8+pLfegm7ddHPCw4cPe5jRaWZq7mlZlj3SmtlQcXGx3r52X+uve9odhyRJHDp0SDctbAmTL2mTyaSPmZbW+u6eVhRFx+oNU3NpWZb1/nrD5G2eNKxaPa3hcE9r1BKm5ubJYDDoX168YWpuniRJ4siRIyiK4jfvybJMcXGxbkbmDVNz86S17Q/vaVRSUqKb4/nCe1raHas/vKcoCkePHm0yN77Ok0a+8p62zo4cOaL3xVfeMxgMKIqiWzO0hM99nkJCTPTrJ5g27RBz50r88INEbq6ExSKxYwd8+y08+yxcc416Fj40FGw22LpV4uuvZZ55xsArr4xFOxnpC+9pfT9eXy7dKWh5ChiFQP7mG5g8GdO4cUiffKKCHjECPv4YioowvPoqUs+eCCE4evSobrLa1vJUG7cjR454mCG3y9OTK0+9PevaWp42h6NdnnqmteNjLT3rApWnkgR9+pi45BKZp56C+fNl9u+XqKhQWLq0hAULFD74AJ58Em65Bc47T3ULpPmFrKyUyMmBxYslPvzQwDPPGHj77RHs3dsyvhMlT7Xx82W8PMbF5cK0aBHypZdC9+4YHnsMKS8PIiNh9mzVSdWOHRgefhip4UhWMHtUTc5offBlvLR0MHtUjXxdq1raYDBQUlKCEEIvJ0mSR7rxfsl9LjTrAG95mkuDanWhPatay6/yt2e72n33/jbO444DjkVMaa2/KSkpfP/99yxYsIDU1FTef/99nn32WY88DoeD3bt3Y7VavdYjhGDKlCm4XC6mTZum90ezANF+ZVnm559/5uKLL/bAoVFjHFp6woQJfPjhh7z99tuMGDGCJUuW8Le//c1jfGfOnMmCBQtYsmQJY8eOZcKECbz++uv07NkTSZIYPnw4r7/+Oq+88gpDhgzhiy++4IUXXmh2zloas2BkqpZHw9bWe9RgnudCCA4dOtRELrinZRlmzJD47juZoiI1zke/fgKbzUhiYmDPc1+o3SdIkBTwg0tRkL7/Xo331mCKRGSk6tb8//5PdavbTFv5+fn6OVV/+xlI2WDaDIZORn9PBtZ2nG1X9veMMyREjUrYODKhywX796uRW3NzYft2hV27SoiO9j86TFts2oOq88gRVcnx/vvQYKKJ0QhXXgl/+Yvq2LTRg7F9/bVNuWDLBkqnyvpra/q94Aym3WD6Gx2tYDLtZurURIzG5stWV0NR0bGrsBAOHlTYsqWUXr1adlTvrb9tQX7Va7UiP/UU0z/8EGODiT6gys7Zs+Gqq9QIWS209XuRMwcPHiQxMTGg8vX19QE5wg20XGNauXKlx9/79+9vkmfLli36cR+TycSWLVs8/t+z4SOCO1144YVceOGFAHrZW2+9VX8hba5MczR79mzuvfdejxfZefPmeeTZvHkzZrPZI1xuczRt2rQmbd5yyy3ccsstHvceeOABHSvAjBkzWnQEev/993P//ffrOKOiorjhhhv0/990003cdNNNLfbtdHp2nMg12qmT+hp8zz1OXnttPVdfPd6v9rQ2faIW7UROAL3zzjuiZ8+eIjQ0VIwcOVJkZGS0mH/lypVi5MiRIjQ0VPTq1Uu89957TfJ89913YuDAgSIkJEQMHDhQ/PDDD0G325gCNl10OoXj889FdXLyMXuf6GghHn9ciNJS/+o6xUkz2dLMtH6r1I7zt0e/F6zB4GyL4zB+16UowpGRIQqnTBGKuw1lUpIQTz8txOHDQfftVKF2nvzt0e8FazvO1qmtjsP4VZ/LJZTevdXj2B06CPF//yfEjh3HpT+nGgXLk9pxGKvVepx7dvzJ/ZjIb4U2bNggPv/8c497pwvOYHmnXZ62Tr7Kv5N6HObrr7/mvvvu4/HHHyc7O5spU6Zw/vnn644BG1NBQQGzZs1iypQpZGdn89hjjzFnzhy+//57Pc+6deu4+uqruf7669m6dSvXX389V111FRs2bAi43ZbIb+39Sy9hvOEGYgoLEbGxqp3l/v3w/PPQoeWvsYqicODAgYC+GARaNpg2g6GT0d+TgbUdZ9uVbcfZtnRKWIKsXo3xzDPpvmoVksMBEyeqMTIPHICnnoIuXVptr339Hf9ywZYNlH4v6+/3gjOYdk9H3j3p9coyruefJ/Phh3Hu36/apDc2J2ylrd/LXB06dMgnq4bGJISgvr7e77KBlguGgmmzrXGOHTuW66+/3u9+Bdvu8SoHp9ez43Rco77QSVWCvP7668yePZtbb72VgQMH8uabb5KcnMx7773XbP5//etfpKSk8OabbzJw4EBuvfVWbrnlFv7+97/red58803OO+88Hn30UQYMGMCjjz7KOeec4xGr2t92WyK/J/WWWxBdu5J77bU48/LgmWcgwTfTSU3wBsqEgZQNps1g6GT092RgbcfZdmXbcbYtnRJKkEmTEMOGceCcc3Bs2KDGvrzmGvU8kI/tta+/418u2LKB0u9l/f1ecAbT7unIu6dCveKKKyieONFnGdq4rd/LXLn7cvCXfA3febzKBUPBtHk64Qym3UDLnU7PjtNxjfpCJ00JYrfb2bRpE9OnT/e4P336dI/QVe60bt26JvlnzJjBxo0bdSb0lkerM5B2WyJ350o+UZcuOPfuZc/VV0NcnN9tTZw40f82gyj7/+y9e3wV1bk+/sy+5MIlyEUBuSNSRLwgF4EqoFWw9Fs9rT3tt+1pbas9x9JTUXrT32nr5Xg59rRqbVFbpdVW7em3F2PxpJBoIEGDBEMwhASIECAJgZBAkp3s21zW74+4lrN3Zvaey5492Zn1fD58eDPMO+/7zHrXsxYra2bsxLQDN/J1gyvn6Zwv5+ksnIhn+po+H6Tdu7HvO98ZePGphXi8/2Xez66vVXil/3mFp524uVi7uXRdvVheaauFCxcafvmiGoIgYNSoUaZ9rfrZgZ2YucTTTlw7+ebS2JGLfdTQeQ7noYvOzk7IsoyJH75ZmmLixIk4deqUps+pU6c0z5ckCZ2dnZg8ebLuOfSaVuICAy8kUn/7mX5/OhqNorCwMA3bRIgfbpsyu3ooyzKOHz+OGTNmsLf2Ou1rJyblZ2WV1I18rfpyns7FtOPrRu16hScwoH1WkVE9/XDFn/e/zMf0Ck87vlxn0oPXbnrY0VMgc5rK28pYzObmZhBCoCiK6d9wx+Nx5FnYaWPVjz6qQfPNRkw7vm7wtBPXrJ+iKOwxmlwZI3OtjxrVU4Fk8wEzFU6ePIkpU6agqqoKy5cvZ8cfeeQR/OEPf8DBgwcH+cydOxdf//rXcd9997Fj77zzDq655hq0t7dj0qRJyMvLw0svvYQvfvGL7JxXXnkFt99+O6LRqKW4APDAAw/gwQcfHHT81VdfxYgRIyzdAw4ODo5cRTgcxpe+9CX09PSgqKjIlC/XUw4ODo6PYEdPAa6p2UYgEMCkSZMwdepU5Ofnu50ORw4hFouhtbUVp06dgiRJbqczLGFUT13bCTJhwgT4/f5Buy86OjoG7dKgmDRpkub5gUCAfapK7xx6TStxAeC+++7Dxo0b2c+9vb2YNm0a1qxZY3rAEkURZWVluPHGGzPyuauhCs5zeMErPAHvcLXDk/6m0Qq4npoH5zn84BWunGd62NFTIHOaytvKGBRFQXNzM3p7e3H++ecjGAxm/REOoyCEoL+/HyNHjhyyOWYCucCTEILe3l6MGDECn/jEJ0zvqgB4HzUCo3rq2iJIXl4eFi1ahLKyMnzmM59hx8vKynDLLbdo+ixfvhxbtmxJOFZaWorFixezG7R8+XKUlZXhnnvuSThnxYoVluMCQH5+vuZqr8/ns1yEwWDQlK8sy2hqasLFF19saTuSFV87MSnM8rQT1417RMF5OhMzl2rXKzwB2Pqu/VDQU4D3P6f87PoCXGeciplLPO3EzbXataOnQOY1lbdV6phHjhzBjBkz0NHRgfb2dsO+hBBIkoRAIGDqP+pW/ahvJBJBYWFhVmPmCs9s5ysIAqZNm4ZgMJgzY2Su9VGjeuraIggAbNy4EV/5ylewePFiLF++HL/5zW9w4sQJ3HnnnQAGVrbb2trw+9//HgBw55134le/+hU2btyIb37zm9i1axc2b96MP/7xj+yaGzZswMqVK/H444/jlltuweuvv44333wTb7/9tuG4QxmRSCTrvnZi2oEb+brBlfN0zpfz5EgH3v+c8bPr60bMXOp/XuFpJ26u1e5wgFfaKhKJIC8vD9OnT4ckSZBl2ZCfLMs4dOgQLrroItP/CbXiBwz8Rr2yshIrV640vdhqNWYu8cx2vsFgEH6/H7Is59TYkWt91BCIy9i0aROZMWMGycvLI1dddRWpqKhg/3bbbbeRVatWJZy/Y8cOsnDhQpKXl0dmzpxJnn322UHX/POf/0w+9rGPkWAwSObNm0f++te/moprBD09PQQA6enpMeVHCCHxeJwUFxeTeDxu2jeXwHkOL3iFJyHe4WqHpx0NzOS1eFsNL3iFJyHe4cp5pkcm9dTO9XhbDT94hSvnObyQDT117RO5FOvXr8exY8cQi8VQU1ODlStXsn978cUXsWPHjoTzV61ahb179yIWi6G5uVlz98bnPvc5HDx4EPF4HI2NjfjsZz9rKq4ZGF39zQRkWUZ9fb2lmFZ97cS0AzfydYMr5+mcL+fpLJyIx3XGGXCezvlyns7CS7WbS9fVi+WVtvJC//MKTztxvVK7ucjTCFxfBOHg4ODg4ODg4ODg4ODg4ODIBlx9J8hwgNUXhFmNtWDBgqz62olpB27k6wZXztM5X87TWTihfdnUUxqP97/M+9n1tQqv9D+v8LQTNxdrN5euqxfLK23lhf7nFZ524nqldnORpxHwRRCLIIQAALq6ugB8tPWGvuyG2pIkQRAEZvt8PsiyjHA4jO7ubuTn57PjPp8PoijC7/czm75xWBRFCIKA+vp6zJs3j70FXJIkBINB9obiYDAIRVEgyzKzFUWBIAjYv38/LrnkEuTn57PjgUAAsiyDEMJsNQ9ZltHY2IhLLrkEfr9fl5PP5xtkU55nz55Ffn6+JqdAIMB4qG2fz4f9+/dj3rx5KCgo0OQUCAQG2aIo4uDBg7jkkkvg8/k0Oem1k6IoaGxsxLx58xAMBjU5abVTOBxGOBxmn2TS46TVTjTf+fPnQxAETU5a7RSPx3HgwAFcfvnlrCaN1J7P50MsFkNjYyMuu+wyEEIM1V4gEIAoigiHw+jp6UEwGDRce9S3vr4el19+OQRBMFR71I7H42hoaGD5Gqk9n88HQgjq6uqwYMECBINBw7UXDAYRj8dZm9J80tWeup/Nnz8feXl5hmuPvlVcna+R2qO6cODAAXzsYx9L0IV0tUdzoH20sLDQUO1RP1rvVAvtwA09pVz279+Pyy67jF3TST2lqKurw6WXXoq8vDyup0NAT/XGOif1FBh4WRzVVEEQuJ5q2EbGukzqKY1fV1eXoAu5pKfq65jVVC2dcXqOmq7fOjFHpX2P3vd0fVVdA7FYDIcOHcKll14KQkjKvpopTU3ut6n6aqY0VU9nnJyjJuuMmfHcjTkqYG08t6OpwIDOdHV1YcSIEYZrz84c1ep4bmeOGovF2NzO7/ebGs+7u7sTdFAPfBHEJDZt2oRNmzYhHo8DAGbPnu1yRhwcHBzuIRQKYcyYMZZ8uZ5ycHBwfAQ7egpwTeXg4OCgSKenAsnUsrPHoCgK5s6di5qaGtPfo+7t7cW0adPQ0tKCoqIiU75LlizBnj17TPnY9bXqZ4ennbjZvkecp3Mx7fq6Ubte4UkIwaJFi3D48GHD32TXg1t6CvD+55SfVV+uM87GtOPL5wKpMVT0FLCuqbytnItp15frTGrw2nUuph0/q77Z0FO+E8QifD4f8vLybK3YFxUVmW5Yv99vqejt+NqJCVjjaSeuG/cI4DydjJlLtesVngCQl5eXkQm7W3oK8P7nlJ9dX64zzsXMJZ524uZa7WZKTwH7msrbyrmYudT/vMLTTlyv1G6u8TSip/zrMDbw7W9/O6diWvV1g6eduG7cIzvgPJ3z5TydRSbj5hoHr/Q/r/C048t5Ogteu0Pjek7G9FJb5VL/8wpPO3G9UrvDkSd/HMYF9Pb2YsyYMejp6bG12jnUwXkOL3iFJ+AdrsOB53DgYASc5/CDV7hynrmD4cDBCLzCE/AOV85zeCEbPPlOEBeQn5+P+++/n70ld7iC8xxe8ApPwDtchwPP4cDBCDjP4QevcOU8cwfDgYMReIUn4B2unOfwQjZ48p0gHBwcHBwcHBwcHBwcHBwcngDfCcLBwcHBwcHBwcHBwcHBweEJ8EUQDg4ODg4ODg4ODg4ODg4OT4AvgnBwcHBwcHBwcHBwcHBwcHgCfBGEg4ODg4ODg4ODg4ODg4PDE+CLIA7hmWeewaxZs1BQUIBFixZh586dKc+vqKjAokWLUFBQgNmzZ+O5557LUqb2YIbn3/72N9x44404//zzUVRUhOXLl2Pbtm1ZzNY6zLYnxTvvvINAIIArr7zS2QQzBLM8Y7EY/uM//gMzZsxAfn4+LrroIvz2t7/NUrb2YJbrK6+8giuuuAIjRozA5MmT8fWvfx1dXV1ZytY8Kisr8elPfxoXXnghBEFAcXFxWp+hqkNcTweD62luwCuaOtz1FBg+msr1dDC4nuYGuJ5qg+upRRCOjON//ud/SDAYJM8//zxpaGggGzZsICNHjiTHjx/XPP/o0aNkxIgRZMOGDaShoYE8//zzJBgMkr/85S9ZztwczPLcsGEDefzxx0l1dTU5fPgwue+++0gwGCR79+7NcubmYJYnRXd3N5k9ezZZs2YNueKKK7KTrA1Y4XnzzTeTq6++mpSVlZHm5maye/du8s4772Qxa2swy3Xnzp3E5/ORX/ziF+To0aNk586d5NJLLyX/9E//lOXMjaOkpIT8x3/8B/nrX/9KAJDXXnst5flDVYe4nnI9JST39JQQ72iqF/SUkOGhqVxPuZ4SwvV0KIPrqTac0CK+COIAli5dSu68886EY/PmzSP33nuv5vk/+MEPyLx58xKO/du//RtZtmyZYzlmAmZ5amH+/PnkwQcfzHRqGYVVnl/4whfIj370I3L//ffnxCBjluc//vEPMmbMGNLV1ZWN9DIKs1z/+7//m8yePTvh2NNPP02mTp3qWI6ZhJEBZqjqENdTrqeE5J6eEuIdTfWanhKSu5rK9ZTrKSFcT4cyuJ5qwwkt4o/DZBjxeBw1NTVYs2ZNwvE1a9agqqpK02fXrl2Dzl+7di3ee+89iKLoWK52YIVnMhRFQSgUwrhx45xIMSOwyvN3v/sdjhw5gvvvv9/pFDMCKzz//ve/Y/HixfjpT3+KKVOmYO7cufje976HSCSSjZQtwwrXFStWoLW1FSUlJSCE4PTp0/jLX/6CT33qU9lIOSsYijrE9ZTrKZB7egp4R1O5nupjqGkR11OupwDXU66nuQkntCiQicQ4PkJnZydkWcbEiRMTjk+cOBGnTp3S9Dl16pTm+ZIkobOzE5MnT3YsX6uwwjMZP//5z9Hf34/Pf/7zTqSYEVjh2dTUhHvvvRc7d+5EIJAbXcwKz6NHj+Ltt99GQUEBXnvtNXR2dmL9+vU4e/bskH7m0grXFStW4JVXXsEXvvAFRKNRSJKEm2++Gb/85S+zkXJWMBR1iOsp19Nc1FPAO5rK9VQfQ02LuJ5yPeV6yvU0V+GEFvGdIA5BEISEnwkhg46lO1/r+FCDWZ4Uf/zjH/HAAw/gT3/6Ey644AKn0ssYjPKUZRlf+tKX8OCDD2Lu3LnZSi9jMNOeiqJAEAS88sorWLp0KdatW4cnnngCL7744pBeaacww7WhoQF33XUXfvKTn6CmpgZbt25Fc3Mz7rzzzmykmjUMVR3iepoaXE+HLryiqVxPtTEUtYjraWpwPR264Ho6GFxPrWtR7iwD5ggmTJgAv98/aMWuo6Nj0AoWxaRJkzTPDwQCGD9+vGO52oEVnhR/+tOfcPvtt+PPf/4zbrjhBifTtA2zPEOhEN577z3U1tbi3//93wEMCDEhBIFAAKWlpbj++uuzkrsZWGnPyZMnY8qUKRgzZgw7dskll4AQgtbWVlx88cWO5mwVVrg+9thj+PjHP47vf//7AIDLL78cI0eOxLXXXouHH354SP42zCyGog5xPeV6mot6CnhHU7me6mOoaRHXU66nXE+5nuYqnNAivhMkw8jLy8OiRYtQVlaWcLysrAwrVqzQ9Fm+fPmg80tLS7F48WIEg0HHcrUDKzyBgRX2r33ta3j11Vdz4nk1szyLioqwf/9+7Nu3j/2588478bGPfQz79u3D1Vdfna3UTcFKe3784x/HyZMn0dfXx44dPnwYPp8PU6dOdTRfO7DCNRwOw+dLlEu/3w/go5XoXMdQ1CGup1xPc1FPAe9oKtdTfQw1LeJ6yvWU6ynX01yFI1pk+ZWqHLqgnzfavHkzaWhoIHfffTcZOXIkOXbsGCGEkHvvvZd85StfYefTz/7cc889pKGhgWzevDmnPkFmlOerr75KAoEA2bRpE2lvb2d/uru73aJgCGZ5JiNX3r5tlmcoFCJTp04ln/vc58iBAwdIRUUFufjii8kdd9zhFgXDMMv1d7/7HQkEAuSZZ54hR44cIW+//TZZvHgxWbp0qVsU0iIUCpHa2lpSW1tLAJAnnniC1NbWss+s5YoOcT3leqpGrugpId7RVC/oKSHDQ1O5nnI9VYPr6dAD19Ps6SlfBHEImzZtIjNmzCB5eXnkqquuIhUVFezfbrvtNrJq1aqE83fs2EEWLlxI8vLyyMyZM8mzzz6b5YytwQzPVatWEQCD/tx2223ZT9wkzLanGrk0yJjl2djYSG644QZSWFhIpk6dSjZu3EjC4XCWs7YGs1yffvppMn/+fFJYWEgmT55MvvzlL5PW1tYsZ20c27dvT9nfckmHuJ5yPaXIJT0lxDuaOtz1lJDho6lcT7meUnA9HZrgepodLRIIGUZ7ZTg4ODg4ODg4ODg4ODg4ODh0wN8JwsHBwcHBwcHBwcHBwcHB4QnwRRAODg4ODg4ODg4ODg4ODg5PgC+CcHBwcHBwcHBwcHBwcHBweAJ8EYSDg4ODg4ODg4ODg4ODg8MT4IsgHBwcHBwcHBwcHBwcHBwcngBfBOHg4ODg4ODg4ODg4ODg4PAE+CIIBwcHBwcHBwcHBwcHBweHJ8AXQTg4ODg4ODg4ODg4ODg4ODwBvgjCwcHBwcHBwcHBwcHBwcHhCfBFEA4ODg4ODg4ODg4ODg4ODk+AL4JwcHBwcHBwcHBwcHBwcHB4AnwRhIODg4ODg4ODg4ODg4ODwxPgiyAcHBwcHBwcHBwcHBwcHByeAF8E4eDg4ODg4ODg4ODg4ODg8AT4IggHBwcHBwcHBwcHBwcHB4cnwBdBODg4ODg4ODg4ODg4ODg4PIGA2wnkKhRFwcmTJzF69GgIguB2OhwcHBxZBSEEoVAIF154IXw+e+vpXE85ODi8jEzqKcA1lYODw7swqqd8EcQiTp48iWnTprmdBgcHB4eraGlpwdSpU21dg+spBwcHR2b0FOCaysHBwZFOT/kiiEWMHj0aAHDs2DGMHTvWlK8oiigtLcWaNWsQDAYN+8myjPr6eixYsAB+v99UTKu+dmJa5elWvlZ9Oc+hma8btesVngBw7tw5zJw5k2mhHbihpwDvf0752fHlOuNszFzi6Va+btRuJvUUsK6pvK2GZr5cZ9KD165zMXONp1E95YsgFkG3FxYVFaGoqMiUryiKGDFiBIqKikwL0vnnn4+ioiJLRWjF105MqzzdyteqL+c5NPN1o3a9wpPGBZCRrdZu6CnA+59TfnZ8uc44GzOXeLqVr1u1C2RGT9XXMaupvK2GZr5cZ9KD165zMXORJ5BeT/kiiE2YLQa7sebNm5dVXzsx7cCNfN3gynk658t5OgsntC+bekrj8f6XeT+7vlbhlf7nFZ524uZi7ebSdfVieaWtvND/vMLTTlyv1G4u8jQC/nUYm5AkKaux9uzZYymmVV87Me3AjXzd4Mp5OufLeToLJ+JxnXEGnKdzvpyns/BS7ebSdfVieaWtvND/vMLTTlyv1G4u8jQCvghiE9l867YgCBg7dqylmFZ97cS0AzfydYMr5+mcL+fpLJyIx3XGGXCezvlyns7CS7WbS9fVi+WVtvJC//MKTztxvVK7ucjTCPjjMBkCff7I7/cn2JIkQRAEZqs/1aMoCgCw4z6fD6Iowu/3MzsQCEAQBGZfdNFFCdeRJAnBYBCEEGYrigJZlpmtKAoCgQBmz57NYqqPy7IMQgizk3nMmTMHsixDlmVdTj6fb5Ct5peKEz1HbQeDQcbV7/frckq2CSEsX3pNvbbRaqc5c+aw3LU46bUTRTpOye2kKArmzJkDRVHY+Xr81O0EALNmzTLEidqEEHZfLrzwQoiiiEgkYrj26N+RSASSJJmqPUVRMGnSJIiiiFgsZrj2qD158mSWr5Hao/bEiRMRi8VM1148HkcgEEA0GoUoioZqj9qTJ09GJBIxXXt+vx+TJk1i+RqtvUAggKlTpyIcDpuqPZpDIBBAf38/CgoKEnj4/X4IgpCynZxCtvTU5/NhxowZ8Pl8CffHST31+/2YNWsWy5vrqft6qjfWpao9modVPQWAaDTKNDUajXI91WknyjUajRquPTt6KggCJk6ciHg8brj2hrKeAsY1VUtnnJ6jUp3R67dOzVEpjPRVdQ1QnSGEQBTFtH01E5pKCGFzPiN9NROaKggCZsyYwcYMI301E3NUvX47VOeokydPRjQaNVV7djQVGNCZvr4+FBYWGq49u3PUqVOnJoxxTs9R6bgRiUQQi8US+lBBQQEEQUjZTkbAF0FMYtOmTdi0aRO7yfv378fKlSvR2NgIAFiwYAHq6upQWFiIefPmoba2FmPHjsWcOXNQXV2NKVOm4MILLwQAnD59GtOnT0dlZSUWLFiACy64AOXl5ViyZAnGjRuH0tJSXHvttSgqKkJJSQmuv/56vP/+++jq6sK6desgSRJKS0txyy23IBQKYefOnfjUpz6F7u5u7NmzB2vXrkVnZyfq6+uxcuVK7Ny5Ez6fD6tWrUJLSwva2tqwYsUKNDc349y5c1iyZAmampoQiUSwcOFCNDY2QlEU9PX1oaCgACNGjNDlNGPGDFRVVeGiiy7ClClTUFlZyZ4Bq6ys1OW0Zs0aBAIBlJSUJHD61Kc+haqqKoRCIV1O119/Pdrb23HkyBGsXLkSLS0taG1thSAImDBhAnp6ejQ56bXTmDFj0NnZCUIIpk6dqslJq522b9/O6iMVJ712Gj16NGbNmoXGxkZNTlrtdOjQIbS0tOATn/gEDh48mLL2LrroItTU1KCwsBDBYJBNJPPz8xGLxRAMBuH3+xGJRJCfnw+fz5dgh8NhFBYWghCCSZMmoaWlBYIgIBKJYMSIEVAUBbFYjIkxtWVZhiiKKCgogCRJzKaTlfz8fIiiCEVRmE0IQV5eHpt8UluWZRQUFCAej8Pn8yEYDCIWi8Hv97PJdSAQYHYwGEzgkcyPcgKASCSSYFNOlCshRJeTJEnMlmUZeXl5iMViEAQhLSdBEBgPn8+XkLseJ712ov+xSMcpuZ0KCgowadIkNDc3D+JE/5O0dOlSTY2YMWNGTuvpmjVrAIC9fZzaTuopAMybNw9vvfUWpk2bhvnz53M9HQJ62tTUhHA4jGg0ilGjRsHn86WtvVGjRtnSU9o3qc5Eo1Gup0l6Snmoj2dDT+n4RifauaKnmdDUSZMmAQCqq6sxZ86clH01U3PU/fv3o7CwENOmTUNzc3PavpqJOWpVVRW7Z+nGCS39Of/883HJJZegqqoqK5paU1ODUCiE1atXpxwnMqmpXV1dePfdd/HJT34SZ8+e1eWUyTmqVr8dynNUOgYEAoEE/XFSUwsLC5nOpOOUyTkqXSBNxSnTc9RJkybhxIkTmpyAga/AaGlER0eHIb0UCCHEuLxyUPT29rJJ3vjx40395lKWZZSUlOCmm25Cfn6+4ZVOn8+HlpYWTJ48GXl5eQCM7wTx+Xw4ceIELrzwQuTl5RleZVcUBSdPnsSFF14In89naqVTlmVs3boVa9asYR3C6Eqn3+/HiRMnMHnyZOTn5xte6ZQkCe3t7bjwwgsTfuui1TbJ7UQIwcmTJzF58mT2W2IjK53hcBhlZWVYt24dAJha6aT5TpkyhfkaWWUXRRGtra2YMWMGaBfWq72Ojg6cO3cOF1xwAUaOHJmQAwVdURUEQdcmhKCvrw+jRo1iP9PfoOvZ9Nrq35ioYyafo2XT+2TWFwCLqccJAAghCTYVez2uRmLS+jHCTy/fVPcjmYe6X+lx0monAIynOl8A6O/vR0dHB8aOHYsLLrhgkEaEQiGMHTsWPT09pr/okgw39JT2r+PHj7PdIE7rKW3D48ePY+rUqQgGg1xPh4Ce6o11WrXn8/lw7NgxyLLMFuCs6Ckw8Bvl/v5+jBo1CgC4njqYrxk9VfvqcRrKegpY11QtnXF6jpqu3zoxR41EIigtLWU6Y2YnSDwex6lTpzB16lQoimJqJ4hVTY3H42hra8OMGTPYGOS0pkqShBMnTmDmzJkAYGoniNU56ogRIxJ2oqTrq27PUYHMzfmMaiowoDMjR45ku4ScnqPSuqB9I1tzVHV7qnOJRCLo6OhAUVERpkyZMkgjuru72UJjKj3lO0FsgnYY9VZGtU0FRm1TEfD5fIPOUXdALZuKUfJxumJHr0uvrbbVvurjern7/X74/f5Bv6HQ4qRl005BO5sRfnpc9Tgl23l5eYPyTcUvOXejXM3wUNvJ7aSVrx4/db7BYDBhS70eJyoGEydOxPjx4wedbwZ0e1pBQQHLyQzoqm82YTWmHa52eFr1dYJnYWEhm6BccMEFrP5pjVmpgXTItp76/X7Mnj170HGn9JRC3Xe5nrqvp3pjnRYn+ljGhRdeiBEjRsAOFEVhv+HKps5YhRt6agfZ1uKhpqeAeU3V0hmn56jp+q1Tc1SjnJLt/Px8FlPdfk5qal5eHhs31LXipKYGAoGE8TEdP6/OUd3SmcLCwmE9F0/VnnTs7ejowKRJkwb1G3VtpwJ/MapNqJ8rzEasyspKSzGt+tqJaQdu5OsGV6d50mdB1ZN1QghCoVDCyrDTsBPTqi/naR+0btTPFFM40U+4zjgDzjNzvnTRjf6mm+uMs3AjX6e4ZltPnbyuXiyuM874DleeyXNUrjPOYjjxzISe8kUQm8jmbzV8Ph8uuugiSzGt+tqJaQdu5OsG12zxpNvOKPLz803FywTsxLTqy3naQ3LdqOFEP+E64ww4z8z7qvsG1xln4Ua+TnDNtp46eV29WFxnnPEd7jzd1lM7cYeazjgVc6jxzISe8sdhbCLbgkSfmcyWr52YduBGvm5wdYOnIAjst5jZgp2YVn05T2cxXBZBuM5k3s+ur1W4kS/XGWfhRr5ucB0uiyBcZ5zx9QpPrjPOwis8jeoe3wliE9nemlZeXm55a5oVXzsx7cCNfN3g6gZPQgh6e3uzvgXPbMzVq1fj7rvvNuV78OBBLFu2DAUFBbjyyiuxf/9+CIKAffv26frs2LEDgiCgu7vbcG56sHNvrfq60Z7A8HkchutM5v3s+lqFG/lyPU1EJvUUcEcX3WjT4fI4DNcZZ3y9wtOt+YzZuJnQ0+bmZgiCgNraWl0frqfWYLTu+E4Qm8j2KvuCBQssb02z4msnph24ka8bXN1ql1x4MdLf/vY39nIjo773338/Ro4ciUOHDmHkyJEYNWoUTp48ifPPP990vlahl+vx48cxd+5cnDlzRvdt1YWFhVi7di3eeustvPPOO1i2bJmtmE5iuOwE4TqTeT+7vlbhVr5cT52FVr5O6qleTCcxXHaCcJ1xxtcrPAF39NRsXLt6OmrUKIwZMwYnTpxgn6XOFrieqs5zOI9hD7qyRb8rnWzTT45Rm35nGQCz1cfpN6mpTa8viiIEQcD555/PPgVEP+lE86A2feM8telnsyZMmMCuTY/TfNW2mgchhH3OLR0nI3YyJzUPte3z+RhXPU5atqIoLF89TnrtRLnS66TjobYpUnHSaidZlnHBBRew66fip24nQgjGjx8Pn89nqPZoHhT0U1Xq44qipLUpCCGMfyo7XcxU9tixYzF69GhTvkeOHMHHP/5xTJ8+HRMmTEBeXh4mTZrEPuWWzEkv91RcU8Wnn7xT+9JziouLsXr1ahQVFen6njx5Ert27cK///u/44UXXtCMlcwDAPu0nZpTKn6puGrF1NIIJyZj2dRTes/Hjh2b8Ik8mocTekrv2/jx41kuXE/d11O9sc5JPU2+BtdTbVsvX6f0lPrSrw3ksp7SuOo4ybZZ/XFijkp1hsaix52eo1KkGyeS9YfqDP30pxanTGuqoihszmelndRcjWoqAIwdOxY+ny8lp0zOUVPNoYbSHHXs2LHs0616+SbbR44cwTXXXIPp06dj3LhxCAQCmDJlCvPX01T1tdQ6o/43u3PU119/HatXr2ZjRLIvALS3t2PXrl349re/jc2bN+vGytQcNbk9U/HTGs+NgC+CmMSmTZswf/58LFmyBABQV1cHAGhsbERjYyM71tTUBACora1Fc3MzAKC6uhotLS3sWqdPnwYAVFZWorOzEwBQXl7Otj2VlpYiFAoBAEpKShAKhbBt2zaUlJRAkiREo1GUlJQAGPjGfGlpKQCgu7sb5eXlAIDOzk5UVlZCFEVs3boV77zzDgCgpaUF1dXVAIDm5ma2HaupqSmBU319PbZt24Z9+/al5VRVVYX29nbGqauri9l6nKLRKCRJGsSJ5puKEzDQKauqqhin3bt3Y9u2bfjggw90Oem10wcffIBt27Zh9+7dupy02mn79u2sTVNx0munbdu24dSpU7qctNrp4MGD+Mc//gFRFFNyam1tBQCEw2HE43EAA98X7+3thaIoCIVCTDhCoRAbkOi/U1tLeHp7ewEMiBy1ZVlm7StJErPj8Th6enrYJ6/6+/sBALFYDOFwGAAQjUYRiUSYvWrVKtx9990Ih8Po6enBzJkz8eCDD+JrX/saRo8ejenTp+OZZ55hnHw+H2pqavCf//mf8Pl8uP/++1FfXw+fz4d9+/YxTiUlJZg7dy5GjBiB66+/HgcPHmS81Jwokjm9++678Pl8aG9vR19fH86dOwe/34/PfvazUBQFPT09ePDBB7F8+fIETsXFxbjpppsAAJFIBNFolLVNLBaDoih47rnn8MlPfhLf+ta38P/+3/9jNZaqnWRZRk9PT0I7Jdup2oneP2BggkTteDzO2qa1tXVQf9J6I7dZuKmntG1KSkpYezitp42NjRBFEf/4xz9Y3XE9dV9Pm5qasG/fPmzbtg319fVpay8ej7P6t6OntO24nmrrKeV0//33Z01P6X9UqZ1LegrY19S2tjZmp+urmZqjVlRUYNu2bWhtbTXUVzMxR6Vx0nHS059t27bh3LlzWdPUmpoabN26FaIophwnMqmpnZ2dbPxIxSmTc1Q6h6Lz1aE6R121ahW+853vsHxnzpyJRx99FLfddhuKioowffp0/PKXv2Q8qJ4+9NBD8Pl8+MlPfoKjR4/C7/fjvffeYzzeeOMNzJ07FyNHjsT111/P6jiZE70fyZxqa2vh9/vR2dkJURTR0tICn8+Hz33uc+jv74eiKLj//vtx9dVXJ3B6/fXXcdNNNzEd1dLUZ599FjfddBPuuOMO/OlPf8Lp06eZbjk5R1W3k1pTRVFk91dPI9KCcFhCT08PAUA6OzsJIYRIkkQkSRpki6KYYMuyTOLxOCkuLibRaDThOCGExOPxBFtRFGZLkkQ6OztJNBoliqIQRVFIPB4nhJAEm8agNr3+mTNnSCwWSzhO81Xbah7xeJx0dXWx+HqctOxoNEqKi4tJOBzW5aTmobZlWWZc9Thp2bFYjOWrx0mvnSjXWCymy0mrnfr7+0lxcTGJx+MpOWm1E81XFEVdTlrtFI/HSUdHB5FlOSWnvr4+0tDQQMLhMLvvkiSTc+fiJBRS2J++PkJ6e+WUdm+vTFpbzyX83NdHUtr0GqGQQs6dixNZVtg9ofdDz161ahXZsGEDa/sZM2aQcePGkV/96lekqamJPProo8Tn85HGxkaiKAo5efIkufTSS8nGjRvJyZMnSW9vLzl8+DABQGpra4ksy+T48eMkPz+f3HXXXaSxsZH84Q9/IBMnTiQAyNmzZ1mbyrJMzp07R2RZJoqisOPUnjBhAvnzn/9MFEUhxcXFZMKECeSCCy5g7XvjjTeSH/7wh4zTuXPnSDAYJMePH9flLcsymTFjBtmyZQshhJBFixaRzZs3Dzqf5qS2aW2qr6W21bmrbTVPrbzC4TBpaGggfX19g/rTuXPnCADS09ND7MINPVUUhUiSRE6fPk0kScqKnkqSRGRZJh0dHeyaXE/d11O9sU6r9iKRCDlw4AAJh8MfnmNNT0MhhfT0SExTuZ4O1lPqe+ONN5If/OAHWdPT5HrLNT0lxLqmaumM03PUdP3WiTlqOBxO0BkzmhqNRklXVxeLr8Up05oai8XImTNnEsagVPwyoamiKJLTp0+z2s7GHFWWFaanybrn9Bw1FvvofqTT1FWrVpG77rqL3S+1nh4+fJg89thjxOfzkYaGBkIIYXr63e9+l+np0aNHCQCyd+9eQgghx44dI/n5+WTDhg2koaFhkJ4m6wy9j2otonr6l7/8hSiKQl577bWE+amiKExPqe/Zs2eZnurxliSJzJgxg/z9738niqIwPdU6P5NzVPW4kRwnHA6TAwcOkEgkMkgjOjs7DekpfyeITdBnwujWyWRbve2I2nSFjG5/VJ9Dr6dnjx8/XjO+IAjM9vl87Npqe8KECcxPfVwvd7/fD7/fj3HjxiXE1OKkZZMPV2UDgQCLlY6fHlc9Tsl2Xl7eoHxT8UvO3ShXMzzUdnI7aeWrx0+dbzAY1HwuO5kTXZkVBIF9Tioa9WHsWK1NYL40tgDgPNVxQfW3nq0+N4i+PmDkSNVR1Seu9Gz1PVi3bh2+/e1vAwDuvfdePPXUU9ixYwfmzZuHyZMnIxAIYPTo0Zg8eTIAsN+e0+s899xzmD17Np566ikIgoB58+ahvr4ejz/+OARB0NySrL531F65ciUqKirwuc99Djt27MBtt92Gl156CY2NjZg7dy527dqFjRs3Mr+SkhJcdtllmD59ui7XN998E+FwmP1281/+5V/w29/+Ft/4xjcG3Y9kW12betfX4qEkbeFO9qM/+/1+FoPWmBPbt7Otp36/n23DVh93Sk8p1H2X66n7eqo31mlxotv+7espAPiRqKkA19OP9LShocEVPQUS6y1X9RQwr6laOuP0HDVdv3VqjmqUU7Kdn5/PPvmpbj8nNTUvL4+NOepacVJTA4FAwviYjl8m5qjhMDB2bFDlodY9Z+eofX1BJH/ERE9H1TnT+6XW0x/+8Id48sknUVFRgUsuuYTp6ahRowbpKb3ur3/9a8yePRtPPvkkBEHAJZdckqCnyTqjpUU+nw8rV67Ejh07cOutt6KiokJ3fkp9//GPfyToqRbv8vJyhMNhfPKTn4QgCAl6mnx+puaoRPVIi/p48v0HBvcbdW2nAn8cxiYytYXRaKz//d//tRTTqq+dmHbgRr5ucHWDZ/JELZuwEltRFLat8/LLL2fHBUHApEmT0NHRoeun3joIDGw1XbZsWYKQLl++3HROq1evxo4dOwAAFRUVuO6667By5Ups374d5eXliEQi+PjHP87Of/3113HzzTenvOYLL7yAf/qnf2KDxhe/+EXs3r0bhw4dSulH70+229WJfsJ1xhlwns75cj11Tk8rKiqwe/duRCKRhOs6qaeAO5rqVD/Jhf7HdcbZmFbhNT210uet6in1Vf/N9TRzMFp3ri+CPPPMM5g1axYKCgqwaNEi7Ny5M+X5FRUVWLRoEQoKCjB79mw899xzCf/+/PPP49prr8XYsWMxduxY3HDDDex5NTtx9ZC8uuUkAoEArr32WksxrfraiWkHbuTrBlc3eI4cKaCnR0YoRNDXB8N/ensVtLZ2o7dXMeXX1weEQgQ9PTJGjhTSJ5gEQRDYy5qSV3e1fvum/reR6l+TYvDLlqxi9erVOHDgAD744APU19fj2muvxapVq7Bz507s2bMHixYtYjmLH76P4ZZbbtG93tmzZ/H6669j8+bNyMvLYy/MkiQJv/3tb1PmQu9P8m8pnIYT/YTrjDPgPJ3ztaqndjTVK3paUVGBiooKXHXVVeyLBU7rKeCOpjrVT3Kh/3GdcTamVXhFT6mmTpxovs9b1VP67+q/uZ5mDkbrLrszziT86U9/wt13341nnnkGH//4x/HrX/8an/zkJ9HQ0JCwLYeiubkZ69atwze/+U28/PLLeOedd7B+/Xqcf/75uPXWWwEMfFP5i1/8IlasWIGCggL89Kc/xZo1a3DgwAFMmTLFUtxUyGajCoKg++kip3ztxLQDN/J1g6sbPH0+AUVF/vQnJkFRAFke2H5tfueugIGt3+ahflO/Xb/58+ejuLg44di7775r+toLFizA+PHj8fDDD+OKK65AUVERVq1ahcceewznzp3DqlWr2Lnbt2/HeeedhyuvvFL3eq+88gqmTp06KLe33noLjz32GB555BFdUbd6f+zCCe3L9kIO1xln/Oz6WoUb+VrVU8COpnpLT1evXs20wWk9BdzRVKe0LxfmqFxnnI1pFd7RU8CqptrRiuRFEK6nmYNR3XN1J8gTTzyB22+/HXfccQcuueQSPPXUU5g2bRqeffZZzfOfe+45TJ8+HU899RQuueQS3HHHHfjGN76Bn/3sZ+ycV155BevXr8eVV16JefPm4fnnn4eiKHjrrbcsx02FbG9Ne/311y1vTbPiayemHbiRrxtc3eDpxtY0OzHV2w3N+iVv377zzjtx5MgRbNy4EYcOHcKrr76KF198MeGctrY2zJ8/HzU1NbrXps+xv/zyy1i9ejWAga2Q8Xgcb731FlauXMnO/fvf/552q+HmzZtx6623YurUqZg/fz4WLFiABQsW4Bvf+Aa6u7vxv//7vyl58sdhrMfjOpN5P7u+VuFGvlxPndfTxYsXM65O6ynlOlS3bw+V6+rF4jrjjK9XeLo1n7Ea16qeUl/131xPM4ch/zhMPB5HTU0N1qxZk3B8zZo1CZ+uUmPXrl2Dzl+7di3ee+89XcLhcBiiKLIXEVmJmwrZ3pq2Zs0ay1vTrPjaiWkHbuTrBlc3eNIVejd+Q2Qlpp3fnIwaNSrh2PTp0/HXv/4VW7ZswRVXXIHnnnsOjz76aMI5oiji0KFD7FNoerjuuusgyzIbZARBwLXXXgsA7G9gYJBJtdWwpqYG77//Pm699dZB92j06NFYs2YNNm/enJJnttsTGD6Pw3CdybyfXV+rcCNfrqfO6+maNWsYV6f1lMbOdpsOl8dhuM444+sVnm7NZ6zGtbtbRv0319PMYcg/DtPZ2QlZljFx4sSE4xMnTsSpU6c0fU6dOqV5viRJ6OzsZG/cVePee+/FlClTcMMNN1iOCwx8KzoWi7Gf6W9ERFG0tKKr/tsoyIffVKZvp8+Gr52YVnnaievGPRrKPEVRBCEEiqIkrMLSZw/NPIOo9rGyoms2Znl5OTufEIKjR48CSHxx1t69exOOJf88Y8YM9vUQemzdunVYt25dQqzbbruNnTN9+nT2XfJUXNevX4/169cnXPtvf/sbCCHsWdC9e/eit7cX1157re51Fi5cyHLUukd0C2Kqe26lPZP9tK6vKAqrs+TtjHZ+IzUU9BTgOuOUnx1fN3ia8dXSVKf6n1FfI8hVPVXnXFNTkxU91fNNB7f0FMicpnKdcS6mHV835uJe0lOzcTOlp5IkJbw7hOupto9TeurqO0GAwc/t0P9AmDlf6zgA/PSnP8Uf//hH7NixAwUFBbbiPvbYY3jwwQcHHS8rK8OIESN0/VKhrKzMkl+ugfN0D4FAAJMmTUJfXx/i8XhGrhkKhTJynVyAXa49PT34r//6L0QikbQr925Cj2c8HkckEkFlZSX7lB1FOBy2HI/rqXVwnu6Ca6p1cD11Rk+BzGvqUO1/mYZXeAJDkyvXU+vgempfTwWSqdfRmkQ8HseIESPw5z//GZ/5zGfY8Q0bNmDfvn2oqKgY5LNy5UosXLgQv/jFL9ix1157DZ///OcRDocT3sz7s5/9DA8//DDefPNNLF682FZcQHuVfdq0aThz5gzGjBljirsoiigrK8ONN95o+FvGwMBCjSRJCAQCllZlrfjaiWmVp1v5WvUdyjyj0ShaWlowc+bMhIXAdIt+ejFDoZDltzxbiWnX146fVa7DiWc0GsWxY8cwbdq0QQvJPT09OP/889HT02N6O+hQ0FOA64xTfnZ83eBpxldLU7nOpPdzY+zI9j1yS0+BzGkq15mhma8bc3Ev6anduENJZ5yIacfPqm829NS1nSB5eXlYtGgRysrKEhYjysrKdJ9PWr58ObZs2ZJwrLS0FIsXL07o2P/93/+Nhx9+GNu2bUtYALEaFwDy8/ORn58/6HgwGDQtoFZ9CSGQZRnBYNBSMVnxtROTwso9ciNfu1yHIk9ZliEIAnw+H/u+t3r3lJmYdDsavZ4ZWI1px9dOTKtchxtPn88HQRA0a9uq7gFDQ08BrjNO+dn1BbLL04xvsqZynUkPN8YON+6RW3oKZF5Tuc44EzOX5uJe0VOar5W4Q1FnnIg5FHlmQk9d/TrMxo0b8cILL+C3v/0tGhsbcc899+DEiRO48847AQD33XcfvvrVr7Lz77zzThw/fhwbN25EY2Mjfvvb32Lz5s343ve+x8756U9/ih/96Ef47W9/i5kzZ+LUqVM4deoU+vr6DMc1g+QtOE5CkiSUlpZaimnV105MO3AjXze4usGTEILe3l7Tz1vagZ2YVn05T2fhRD/hOuMMOE/nfLnOOAs38nWDq1P9JBf6H9cZZ2NahVf01E7cXNMZr/A0XHfEZWzatInMmDGD5OXlkauuuopUVFSwf7vtttvIqlWrEs7fsWMHWbhwIcnLyyMzZ84kzz77bMK/z5gxgwAY9Of+++83HNcIenp6CADS09Njyo8QQuLxOCkuLibxeNy0by6B83QfkUiENDQ0kEgkYvtasiyTc+fOEVmWM5DZ0IZXuKbjmap+7GhgJq81lPtfJsF5Dg1wTTUPznMA2dJTO9cb6v0vU/AKT0KGNleup+bBeQ4gE3rq+otR1W+wTUby95EBYNWqVewNu1o4duyY7bhmQLK8gmfn+WErvnZi2oEb+brB1S2eiqKwrWTZgJ2YVn05T2fhhPZlU09pPK4zmfez62sVbo2RXGecgxv5utWmuXRdvVhcZ5zx9RJPrjPOwUs8jcDVx2GGA7K9NW3nzp2Wt6ZZ8bUT0w7cyNcNrm7wpIOTG5MjKzGt+nKezmK4PA7DdSbzfnZ9rcKNfLnOOAs38nWD63B5HIbrjDO+XuHJdcZZeIWn0bpzfSdIrsPuy6zMxvrUpz6VVV87Me3AjXzd4OoGT5/Ph/POO8+Sr1XYiWnVl/N0Fk5oXzb1lMbjOpN5P7u+VuFGvlxnnIUb+brB1Snty4U5KtcZZ2NahVf01E7cXNMZr/DMiRejDgeIoghg4A3HsiwPsiVJSrDp226Bj958qz4uimKCTVfORFGELMvo6upCLBYDIQSEEBZfbSuKkmDT63d2drLvcNPjNF+1reYhiiLOnj3L4qfiZMRO5qTmobYVRWFc9Thp2fF4nOWrx0mvnSjXeDxuiIfaVteDHietdqL5SpKky0mrnURRxJkzZ6AoiqHao3mo7yU9pj6ezqag29vS2dQv+X6oj6ez9fJNZ6tj6nGi+artZKTipBfT6Pmp8k11P5JtWpupOOnZyVy1YmpphJa/XWRTTwkZeHt9R0cHZFnOip7S+3bmzBl2Ta6n7uup3ljnpJ6qz6XH6bW5nmY+XzN6mnx/c1VPAeuaarSvqvXH6hw1Xb91ao6qvkdmNDUWi+Hs2bMsvhanTGtqPB5HZ2dnwhhkpp3UXI1qqiRJ6OjogKIoKTllco6aqt8OxTmqeow1cn4qrmY1VX09p+eo6rqzoqlW5qjJ7ZkqptZ4bgR8EcQkNm3ahPnz52PJkiUAgPr6egBAY2MjGhsbAQB1dXVoamoCANTW1qK5uRkAUF1djZaWFnat06dPAwAqKyvR2dkJACgvL0d3dzeAgc//hkIhAEBJSQn6+/uxZ88ebN26FZIkIRqNoqSkBAAQCoVQWloKAOju7kZ5eTkAoLOzE5WVlZBlGdXV1di1axcAoKWlBdXV1QCA5uZm1NbWAgCamppQV1fHODU0NGDPnj2GOFVVVaG9vZ1x6urqYrYep2g0CkmSUFJSksCJ5vvmm2/qcgKA9vZ2VFVVMU579uzBnj17cPToUV1Oeu109OhR5q/HSaudtm/fzto0FSetdtq+fTv27NmDjo4OXU5a7XT48GHs3r0bsiyn5NTa2goACIfD7D9s/f39CIfDIGRgixoVjlAoxAak3t5eJkb0rc7JwtPb2wtgQOSoLcsya19Jkpgdj8fR398PQgizASAWiyEcDgMY+OZ3JBJh9qpVq3D33XcjEokw33A4zP4j19/fzzj19fVBFEUcPHgQS5cuRWFhIRYuXIjGxkb4fD7s27dPl1NJSQl8Ph/OnTuXwIlCj5MoiuyrU2p+9P5qcYpGowCASCTCbMqJ+iZzom2j106yLKO/vz+BU7Kdqp1oLC1OlEdra+ug/kRzsAM39TQajSIWi2HXrl2IxWJZ0dPGxkbIsozdu3fj8OHDKTlxPc2enlJOe/bsQUNDQ9rai8fjrG/a0VPadlxPtfU0mWs29JROzvv7+5mdK3oK2NfUtrY2Zqfrq5mao+7cuRN79uzByZMnDfdVysnqHJXGScdJS3+2bt2KPXv2oKenJ+U4AWRWU+mcL9U4kUlNPXv2LHbt2gVZllNyyuQcNbnfDtU56qpVq/Cd73yH5Ut1NJWm7t+/H8uWLUNhYSGuvPJKNDc3Iy8vD++9954up+3bt8Pv9+PcuXMJnOj9SMUpk3NU6q/+v0S25qh6nERRZLkkawTVvbQgHJZA3zx79uxZQgghkiQRSZIG2aIoJtiyLLM3NUej0YTjhAy8xVltK4qSYCuKMsgmhCTYNAa1RVFMaUuSlGBr8UjHScuORqOkuLiYhMPhYcNJq536+/vZm7eHGqe+vj7S0NBAwuEwu+80p2RbluWUtqx6UzP9mV5Dz04XM5Xd2dlJent7Tfl+/vOfJ9dffz1pbm4mnZ2dJB6Pk5MnT7K2S+akKAp56623WF+muafiapVTc3MzycvLIz09PYPOOXr0KAE++ppVMBgkF110EXnooYfYuUbbieartvXaSc1TK/dwOEwaGhpIX1/foNpz4uswXE+5ng5lPZUkiUQiEXLgwAESDodZDlb0VFEUIkkS639cT7meZkpPCbGuqVo6Mxw1NRwOJ+jMcOCUi5qaq3PUzs5O1leN+qr19MyZM0SSJNLW1pZwj5M5lZeXs36crDP0PmaC07Fjx0heXh7p7u4edE4qPdWKm0lNVbdncpxwOEwOHDhAIpHIoNo7e/asIT3l7wSxCfqmW7/fz46p7UAgMMiWP1wh8/l8g85RP8eUbCsfbsGeMGECi0vPEQSB2T6fj12b2mrf5HP0cvf7/VAUBR0dHZgwYYJmvqls8uGqbCAQYL6p+Olx1eOkZQNg+VIuqfip8zXD1SiPZDu5ndT50uvr8VPnKwgCuz+pONGVWUEQEt7KLEkSAoFAwjEaR8+m7Zl8vVS2GloxU9njx49ncY36HjlyBJ/61Kcwc+ZM5jdp0qRBuWjxEwQh4bge11Tx1bkmn/P3v/8dq1evRlFRke71t27dissvvxzxeBxvv/027rjjDlx44YW4/fbbdXMnH25vVN8frdy02klJ2oKd7KfWN8qJ1liybyaQTT0FBjicO3eO9Xmn9ZTG7OrqYr5cT93XU72xTqv2RFHMiJ7S/JOPcz01xtUpPaX3SJZlFjNX9VSdg1FN1dIZp+eoQOp+69Qc1QgnLdvv97OYqcaJdFzNaKrP52P31glOWu0EIGF8TMcvE3PUVHOooTRH1dLTVOcLgpCgp9T3/PPPT6j5ZE7Jeat1Jh0nM3PU119/HatXr8aYMWN0fVPpaap2sjpHTW5PLT89fTP6FRr+OIxNODVw6cWqr6+3FNOqr52YduBGvm5wdatd6Pa3bMJszNWrV+Puu+9mvrNmzcKjjz6Kb3zjGxg9ejSmT5+O3/zmN+x8QRBQU1ODhx56CIIg4IEHHsDBgwfZ9m2KkpISzJ07F4WFhbjuuusMf1abYv/+/WxCAgxMEnw+H/75n/+Z5frYY49h+fLlCX6vv/46br755pTXHjlyJCZNmoQZM2bgy1/+MlasWJHyk+AUbrSnE/2E64wz4Dyd9eV66pyeAsDDDz+MFStWJPg5qadA9tvUqX6SC/2P64yzMa3CS3pqNm4m9PTYsWPIy8vjeuoAjNYdXwSxCfXKazZiXX/99ZZiWvW1E9MO3MjXDa5u8BQAFPn9EMJhoL8/K3+EcHggpulsBwYP+tu+n//851i8eDFqa2uxfv16fOtb38LBgwcBDDyneumll+K73/0u2tvb8f3vfx+jR49OuFZLSws++9nPYt26ddi3bx/uuOMO3HvvvabyWbBgAcaPH4+KigoAA8/hjh8/HpWVlSzXiooKrFq1ivl0d3dj586daQeZUaNGsRXs9957D3v37sXVV19t6P4YXfnOFJzoJ1xnnAHn6Zwv11Pn9JTm++6772ZNT2nMbGuqU/0kF/of1xlnY1qFV/SUaero0ab7vFU9/d73vjdodwPX08zBaN3xRRCbyPaqbFtbm+VVWSu+dmLagRv5usHVDZ6kvx8YNcr0H19REc6bOhW+oiJL/hg1aiC22Xw/fFEVAKxbtw7r16/HnDlz8MMf/hATJkzAjh07AACTJk1CIBDAqFGjMGnSJIwcOZL5UTz77LOYPXs2nnzySXzsYx/Dl7/8ZXzta18zlY8gCFi5ciWLu2PHDtx2221QFAUHDhxAOBxGVVUVVq9ezXxKSkpw2WWXYdq0aSmvvWLFCowaNQp5eXlYsmQJPv/5z+OrX/2qofuj3jqYDQyXnSBcZzLvZ9fXKtzI16qeZkJTh7ueNjQ0QBRFVFVVJUzandRTwB1NHS47QbjOOOPrFZ5u6mm8u9t0n7eqp6NGjUp4kSvA9TST4DtBsoRsC9KRI0csC5IVXzsx7cCNfN3gmmvt4hbo27Yvv/xydkwQBEyaNAkdHR26fsmT9sbGRixbtixhRTr5sRUjWL16NRtkKioqcN1112HlypWoqKhAVVUVIpEIPv7xj7PzjWw1BIAXX3wRtbW1eP/99/GnP/0Jr7/+uqHfBND7k00Ml0UQrjOZ97PraxW5lq9byCU93bNnT9b1FMi+pg6XRRCuM874eoWnm7Da563qaTK4nmYORuuOvxjVJrK9NW3lypVZ9bUT0w7cyNcNrm7wFEaOBD78xJQZKMrA56uKioo0X3hnKPaIEeZ9BIFtw1a/sIv+m57YCYKAUaNGJRzL1Er06tWrsWHDBnzwwQeor6/HtddeiyNHjqCyshI9PT1YtGgRy1kURWzduhX33Xdf2uvOnTsXF198MQDgkksuwdGjR/HjH/8YDzzwAAoKCjR91Pcnmxguj8Nwncm8n11fq3AjX6t6CtjX1OGupxUVFeju7saiRYvYlnOn9RRwR1OHy+MwXGec8fUKTzf1dPSIEYCFx2Gs6Cn9d/XfXE8zB/44TJZAv5EsyzL7SoHaliQpwVZ3CGqrj4uimGDTTiGKImRZxrFjxxCLxdj3lWl8ta0oSoJNr3/s2DH2mxx6nOarttU8RFHE8ePHWfxUnIzYyZzUPNQ2zZeuHmpx0rLj8TjLV4+TXjtRrvF43BAPta2uBz1OWu1E85UkSZeTVjuJoojm5mYoimKo9mgeAKAQglggADJiBPuDkSOhFBamtekfMmIElMLCtDb1IyNGDMRMyiWVTaEoCqsDvXO0rkMISVh9VhQFl1xyCd59910oisLO2bVrF/PTGrDUx6kPfe7y4YcfxhVXXIHRo0ezlfby8nI2+BNCUF5ejvPOOw9XXHFFWt7qLYOEEPh8PkiSxPo85ZFsx2KxBE7JthYPPa5aeWlphBO/4cmmntIvQBw9ehSyLGdFT+l9a25uZtfkeuq+nuqNdU7qqfpcepzr6WA93b59O6655hp2bjb0lHLNdT0FrGuq0b6aiTlqun7r1BxVfY/MaGosFsPx48dZfC1OmdbUeDzO5nxW2knN1aimSpKEo0ePQlGUlJwyOUclANPTZN1zfI76oV4Y0VT1z2pd1DpH/XcC16R/o3pK72uynibrjNpfrUWXXnppgp4WFRUxPd2xYweuueYadj7V0yuvvDLt+EHHcvoz1dO46r6pc1fbVuaoaqRrD63x3Aj4IohJbNq0CfPnz8eSJUsAAAcOHAAwsI2psbERAFBXV4empiYAQG1tLZqbmwEA1dXVaGlpYdc6ffo0gIEX19A3+ZaXl6O7uxsAUFpailAoBGDgma1wOIzW1lZs3boVkiQhGo2ipKQEABAKhVBaWgpg4CU35eXlAIDOzk5UVlayiTftUC0tLaiurgYANDc3o7a2FgDQ1NSEurq6BE5tbW3Yv39/Wk5VVVVob29nnLq6upitxykajUKSJJSUlCRwUhQFJ06cwJtvvqnLCRh42VBVVRXjtGfPHrS1taXlpNdObW1t2LNnjy4nrXbavn07a9NUnLTaafv27Whra8OZM2d0Oem10+HDh6EoSkpOra2tAIBwOMz+w9bf389EOxQKMeEIhUJsQOrt7WVi1Nvbqyk8vb29AD5afQcGxIe2ryRJzBZFkb0dOh6Po//DZ9ljsRjC4TAAIBqNsnOi0SiLH4lEWL6iKDK7v7+f5dTX18dELx6PM07qwam3txf/+q//iiNHjuA73/kODh48iFdeeQUvvvgi49Xb24u2tjbMnz8fNTU1upwEQcA111yDl19+GatXr0Y8HsdFF12EeDyO7du3Y9myZYzHa6+9hptvvhnRaBTRaJRxora6bU6dOoUTJ06gtbUVf/vb3/D000/juuuugyAIuu1EBV/dTsl2qnai94/eX2rH43HWNq2trYP6UyYm7W7qaTQaRTwex/79+xGPx7Omp4qi4PDhw1xPh5ie7t+/H21tbYZqLx6PM72xo6e07bie6utpeXl5VvVUfZ/oRD1X9BSwr6ltbW3MTtdXMzVH3blzJ9ra2gz31UzMUWmcdJy09Gfr1q1oa2tDb29vynECyKym0sdLUo0TmdTUc+fO4cCBA1AUJSWnTM9RI5EI05+hOkelizv0OI0dDocTftlAz+nr62PnqDnRPADgS1/6Eo4cOYKNGzeipqYGr7zyCl566aUETgcPHmR6SnNP5tTX14eVK1fi5ZdfZl+BmTdvHmKxGN566y0sX76ccXrttdewbt06xkNLUynnjo4ONDc34+jRo/jHP/6BX/ziF1j94WfKU7WT3Tlqcjup9ZXWTHLtUd1LC8JhCT09PQQAOXv2LCGEEEmSiCRJg2xRFBNsWZZJPB4nxcXFJBqNJhwnhJB4PJ5gK4qSYCuKMsgmhCTYNAa1RVFMaUuSlGBr8UjHScuORqOkuLiYhMPhYcNJq536+/tJcXExicfjQ45TX18faWhoIOFwmN13mlOyLctySluWZXLu3LmEn+k19Ox0MVPZq1atIhs2bGDHZ8yYQZ544omEc6644gpy//33s3OuuOIK8pOf/ISdc+TIEQKA1NbWsry3bNlC5syZQ/Lz88m1115LXnjhBdaXZVkmzc3NBADZsmWLJld67aeffpoAIG+88QY7fssttxC/30+6u7vZ+dOmTSNlZWUpuR49epQAYH/8fj+ZOnUqueOOO0hHR4epdqL5qm29dlK3qVZe4XCYNDQ0kL6+vkG1RzWwp6eH2AXXU66nuaCnkiSRSCRCDhw4QMLhMMvBip4qikIkSWL9j+sp19NM6Skh1jVVS2eGo6aGw+EEnRkOnHJRU3N1jrpq1Spy1113seMzZswgTz75ZMI5VD/pOcn6SrVx7969jMff//73BD3dvHkz68eKojAN3rJlC7uPWpx++ctfsvPocbWe0hynTZtGSktLU3JNpaenT5823U5mNFXdnslxwuEwOXDgAIlEIoNq7+zZs4b0lC+CWETyAGMGdNJOxcUoJEkiTU1NrPCz4WsnplWeduK6cY+GMs9IJEIaGhpIJBJhxxRFIZFIhAmJUSRP8MzAakw7vnZiWuWaHLOmpoaMGTPGUG0MRZ5a9UNhdJAxAjf0lBCuM0752fF1g6cZ3+Q+MRR0Jhu+bvBMjpsNPbXjO1T0lBDrmsp1xrmYdnzdmIt7RU/txB2KOmM0JtdTQvjjMDZBkp5bcjrWuXPnLMW06msnph24ka8bXN1qF/nDrWrZhJ2YVn3d5ilJEn75y18OemGWEV+rMbMFJ/oJ1xlnwHk66+u2zmTL1w2e6rjZ0lO7vlbgVD/Jhf7HdcbZmFbhJT21EzeXdEYdk+sp/zqMbWT7zdv0Oc9s+dqJaQdu5OsGVzd4CoKAkSNHWvK1CjsxrfoOBZ5Lly7F0qVLLflajZktDJevw3CdybyfXV+rcCPfoaAz2fB1S2fUcbOhp3Z9rWK4fB2G64wzvl7hORR0Jht+dn2tgutpIvhOEJvI5uqWLMs4ePCgpZhWfe3EtAM38nWDqxs8CSGIRCJZ/42C1ZhWfTlPZ+FEP+E64ww4T+d8uc44CzfydYOrU/0kF/of1xlnY1qFV/TUTtxc0xmv8DRad3wRJMdA39KbTV87Me3AjXzd4OoGz2wPMHZjWvXlPDnSgeuMM352fd2IadWX64yzcCNfrqnWwHXGOV+v8OQ64yy8wtMI+OMwNuH3+7Maa+HChVn1tRPTDtzI1w2ubvAUBAEjRoyw5GsVdmJa9eU8nYUT2pdNPaXxuM5k3s+ur1W4kS/XGWfhRr5ucHVK+3Jhjsp1xtmYVuEVPbUTN9d0xis8jeoe3wliE/QbxbIss+03aluSpARbUX0Lntrq46IoJth09UwURUiShP3797NvUhNC2Le01baiKAk2zWH//v0J36+WPvymsyzLCbaaRzweR319PeLxeFpORuxkTmoeapvmS79TrcVJy47FYixfPU567US5xmIxQzzUNkUqTlrtRPOl7ZuKn7qd4vE46urqGId0tUfzoNcMh8PsmPp4OpuCEML4p7LTxTRiW/UlhCAcDrN8tDjRfNV2MvQ4GYlpJ99U9yPZjkQiaTnp2clctWJqaYQT23KzqaeEEEiShLq6OkiSlBU9pX/q6uoYV66n7uup3ljnpJ6qz6XH6bW5nmY+XzN6Sn1zXU8B65pqtK+q9cfqHDVdv3VqjkqRbpxI1p9oNIr6+npIkpRynNCyrWpqPB7H/v372b3IhqaKosjmmak4ZXKOmqrfDsU5qlGdcUJT1ddzeo6qKErCPDPV+Zmaoya3Z6qYWuO5EfBFEJPYtGkT5s+fz17409DQAABobGxEY2MjAKCurg5NTU0AgNraWjQ3NwMAqqur0dLSwq51+vRpAEBlZSU6OzsBAOXl5eju7gYAlJaWIhQKAQBKSkrYwLJt2zZIkoRoNIqSkhIAQCgUQmlpKQCgu7sb5eXlAIDOzk5UVlYCAPr7+/Huu+8CAFpaWlBdXQ0AaG5uRm1tLQCgqakJdXV1jNPBgwcBAPX19Wk5VVVVob29nXHq6upidipOkiShpKRkECdRFPHWW2+l5NTe3o6qqirGqaamBgBw/PhxXU567XT8+HEAQE1NjS4nrXbavn07a9N0nJLbaceOHWk5abXTBx98wHJJxam1tRUAEA6HmSj09/czoQmFQkw4QqEQG5B6e3vZOb29vZrC09vbC2BA5KgtyzJrX0mSEmw6sMbjcfT39wMAYrEYwuEwACAajbLtk9FolP2HLRKJsLzC4TD7j2d/fz/j1NfXx66v5qSeGOlxSrYpJwo9TqIooq+vbxAnWZYTeOhxoraakyiKjIcep+R2UtvpOGm1E42lxYm2TWtrq2Z/sgu39VSSJDQ3N2dNTymnzs5OfPDBByk5cT3Nnp42NTWhvr4eAHDw4MG0tRePx1nftKOntO24nurrqfo/i9nQU5prPB5nE/Vc0VPAvqa2tbUxO11fzdQc9e233wYAnDp1ylBfzcQclcYxwilZf7Zt2wZgoJ3TjROZ0tR9+/ahp6cnJadMa2pPTw9OnDiRllOm56jqxbBcmKNKksQ0J1uaSq+TilMm56iUh9rO1hxVj5MoiiyXZI2gupcOAklebuEwhN7eXowZMwZnz57F2LFjWSP7/f4EW5IkCILAbJ/PB1mWUVJSgptuugn5+fnsuM/ngyiK8Pv9zA4EAhAEgdnAQEGo7WAwCEIIsxVFgSzLzFYUBYFAQNeWZRmEEGZr8UjHyefzDbJlWcbWrVuxZs0a5OfnDwtOWu0UDodRVlaGdevWAcCQ4hSLxXDixAnMnDkTBQUFEASBDRTJtqIoEARB16ZiVVRUxH72+XxMyLRsrTjZtNNxAgYGTLXt8/mYGGtxzVVOWu0EgPHU4heNRnHs2DFMnz4d+fn5CbXX39+PMWPGoKenB0VFRbADrqdcT3NBT+l9Pnr0KGbNmoXCwkJb/VRRFIRCIdZ/uJ7mDqehrKc0DyuaqqUzw1FTI5EISktLmc4MB065qKl8jmpef2j/Hj16NPx+/7DgpNdO6vZMjhOJRNDc3IzZs2ez2qW119vbi3HjxqXVU74TJEPw+/3sGSS1HQgEEmyf76NbTm318WAwmGDTIqEitG/fPvh8PlYQ9PvOatvn8yXYtCjef/99dj16nOarttU8ALDVtXScjNjJnNQ81LYsy4yrHictWxAElq8eJ712olwFQTDEQ21TpOKk1U4+nw+1tbWs86bip24nAGyroZHao3lQ0Dc1q4/T+5fKplDfo1R2uphGbKu+dNJJocWJ5qu2k6HHSS+m+mVgZvM14pvMAxhYnafn6HHSs5O5asXU0gintm+rYyTbmdRTOijX19ezwdlpPaX3Tf2bX66n7uup3ljnpJ4mX4PraebztaKnyVyHg56q4yTbZvXHiTkq1Rm9fuvUHJUi3TiRrD9UZxRFSTlOaNlWNVUQBDbns9JOaq5GNZUQgvr6esiynJJTJueoqfrtUJyj2p3z2dFU9fWcnqMCYDti0p2fqTlqcnumiqk1nhsBXwTJMRQWFmbd105MO3AjXze4usEzWWCyAbMxV69ejbvvvtuU78GDB7Fs2TIUFBRg4cKFOHHiBHw+H/bt26frs2PHDgiCwLaO2oWVe7tjxw74fD623VULL774Is4777yMxaTXnDFjhiXf4QKuM8742fV1I6ZVX66nHyHTegqY52pXT63EpNfkesp1xilfr/B0Q0/NxrWrp1deeSWOHTuGkSNHcj1NcU2n9ZR/HcYmzKw4ZSLWvHnzsuprJ6YduJGvG1zd4CkIQtYHUysx//a3v7HfVBj1vf/++zFy5EgcOnQIo0aNwnnnnYf29nZMmDDBStqmkSrX48ePY+7cuThz5syg7XkrVqxAe3s7Jk6caHqwcKM9geHzdRiuM5n3s+trFW7ky/XUWejl65SeporpJJzSvlyYo3KdcTamVXhFT63E5XpqP6aTMKp7fCeITajfMJ2NWHv27LEU06qvnZh24Ea+bnB1gychBP39/ezZumzASsxx48Zh9OjRpnyPHDmCa665BjNmzMC4ceMQjUYxceLEhC2aTiJVrq+//jpWr16t+XxiXl4eJk6cyN5OnqmYTsKJfsJ1xhlwns75cj11Fnr5OqWnqWI6Caf6SS70P64zzsa0Cq/oqZW4dvV0/Pjx8Pl87L0e2QLX00TwRRCbyOa2LUEQMHbsWMsrcVZ87cS0AzfydYOrW+2S7d+4W4mp3m7o9/sxa9YsPProo/jGN76B0aNHY/r06fjNb37DzhcEATU1NXjooYcgCAIeeOABtLa2Dtq+XVJSgrlz56KwsBDXXXcdjh07Ziqv/fv3w+fzsTefnzt3Dj6fD//8z//Mcn3sscewfPnyBL/XX38dN998s+Y16XZD9RcGXnzxRUyfPh0jRozAZz7zGfZ1EDW2bNmCxYsXY/z48bjooovw4IMPJoj/E088gcsuuwwjR47EtGnTsH79evZmbbtwop9wnXEGnKezvlxPndNTYEDHVqxYkeDnlJ4uWrQIhYWFWLBgQc7rqZPX1YvFdcYZX6/wBNzRU7NxM6Gnx44dw6hRo7ieuqinfBHEJrK91XDOnDmWYlr1tRPTDtzI1w2ubrVLMChDUcKQ5f6s/FGUMIJBay9+EwQBBQUFAICf//znWLx4MWpra7F+/Xp861vfYp/Ia29vx6WXXorvfve7aG9vx/e//33k5+cnXKulpQWf/exnsW7dOuzbtw933HEH7r33XlP5LFiwAOPHj0dFRQWAgc/SjR8/HpWVlSzXiooKrFq1ivl0d3dj586duoMMBX07+u7du/GNb3wD69evx759+3Ddddfh4YcfTjh327Zt+Jd/+RfcddddaGhowK9//Wu8+OKLeOSRR9g5Pp8PTz/9NOrr6/HSSy+hvLwcP/jBD0zx1cNweRyG60zm/ez6WoVb+XI9dUZPab5vv/121vX0N7/5DV566aWc1lMnr6sXi+uMM75e4QlkX0+ppubn51tanLKip9/73vcGvXiU62n29ZS/E8Qm6PegzXzWioJ+D9ro58eAge+DX3XVVazTGf2sFQDs3r0bixYtQkFBgeHPWkmShJqaGixatIi9ydnop7rU/PQ46X2qi3a0q666CoWFhYY/1RWPx7F3714sWrSIvS3Y6Ke6ZFlGTU0NrrrqKuTl5Wly0msnCrOfVKP5Ll68mL1l28jnx2KxGN577z1cffXVCW9H1uIEfPTddEEQIEl9eOcd+5/gs4JrrgnB7x8JYECM031uCxjoJ/St1OvWrcO3vvUtCIKAH/zgB3jyySexY8cOfOxjH2PbtEeOHImJEycCQMIquqIoeOaZZzB79mz8/Oc/h8/nw9y5c1FXV4ef/vSnIIRAUZRBb/nX+vzYypUrsX37dnz2s5/Fjh078NWvfhW///3vceDAAUyZMgVVVVW455572PklJSW47LLLMHXqVHZNNVf6c19fH4qKivCLX/wCa9euxQ9/+EMIgoCLL74YVVVV2Lp1K/uSySOPPIIf/vCH+MpXvoJwOIyZM2fioYcewg9/+EP8+Mc/hiAI2LBhA8t95syZePDBB/Htb38bmzZt0tyaqJcX1QJ1f3JiW2429TQQCECSJOzevRtXX301+9lJPaX3ePfu3Vi8eDH7pC/XU3f1VG+s06o92oa0b3A9dU5PGxoaMGfOHFRVVeHuu+/Oip5+9atfBQCcf/75ePDBB3HvvffmrJ4C5jVVS2ecnqOm67dOzVEpzH72NxqNora2FkuWLAEA3XEik5qqnvMB0OWUSU2Nx+Oorq7GsmXL4PP5TH0i1+ocVZb78fbbo21UvHUsXHgKRUUXsJ9TaSrwUc79/f0APtJTAPjhD3+IJ598Etu3b8e8efOYno4aNYrp6ZkzZwB8NHehevrkk0+CEDJIT9UaQuOr86Bau3LlSuzYsQOf/exnsX37dtx222146aWXcODAAcydOxdVVVXYsGED8/3f//1fpqf0/wlavLX0FAC75tatWxmfRx55BPfeey++8pWvAAAuuOACpqc/+clPQAhJyGHWrFl46KGHsH79ejzzzDOD9FQvL7WmqjWC6l468J0gJrFp0ybMnz+fiR9d5WtsbERjYyOAgc9CNTU1ARj4dFdzczOAgcGhpaWFXev06dMABlbq6Nal8vJy9hbg0tJStv2opKQE8XgckydPRmlpKSRJQjQaRUlJCQAgFAqhtLQUwMCqXnl5OQCgs7MTlZWV8Pl8GDVqFKqrqwEMrDhSu7m5mX2yq6mpiX2+sbGxEYcPH8aUKVPQ0NCQllNVVRXa29sZJ7o9qrKyUpdTNBqFJEkoKSlJ4OTz+TB+/HjGQ4sTMLC6WlVVxTjt3bsXU6ZMwYkTJ3Q56bXTiRMnMGXKFOzdu1eXk1Y7bd++nbVpKk5a7VRRUYEpU6bg7Nmzupy02uno0aNsgpmKU2trK4CBz1PF43EAYILtFuLxOMshFouxCXk0GmWf7opGo2xgUH9CbN68eUzcwuEwJk6ciI6ODvT19bGBPh6PswkOvQYw8F31xsZGLFu2DKFQiAnoFVdcAWBAZHt7ewf5ybLMalaSJIRCIaxevRo7duxAX18fKioqcO2112LFihWoqKjA3r17EYlE8PGPf5xxev311/HJT36ScYlEIswOh8Msd0VRIIoiGhsbcdVVV7HjoVAIS5cuZTb9D+Z//ud/oqioCJMmTUJRURH+9V//Fe3t7Th16hQIISgvL8cnPvEJTJkyBUVFRfja176Grq4u9Pb2JmxtpFsQRVFkdjweZ23T2to6qD9pfRLSLNzUU1pjXV1dUBQlK3ra2NjIJpNHjx5NyYnrafb0tKmpCQ0NDZgyZQoOHz6ctvbi8Tjrm1xPndXTqqqqrOrp6NGjMXr0aEyePBn/9m//llN6CtjX1La2Nman66uZmqO+8847mDJlCjo6Ogz11UzMUWmcdJy09Ke0tBRTpkxBOBxOOU4AmdPU999/ny0mpxonMqmpvb296O3tZY9XZGuO6haoThnRVLroBXy0CHH55ZcjHA4jFotBEARccMEFrG36+vrYf9hDoVDCIhy9zv79+9miUW9vLwghWLZsWUIMqqn0OtRfranLli3Djh07IIoiduzYgeuuuw7XXHMNysrKsGfPHkQiEVx11VWM02uvvYabb74Z0WhUU1Pp38FgEOFwGA0NDVi+fHnCOLFw4cKEvOijP0VFRWyOSvW0v78fvb29KC8vx4033oipU6di9OjRuO2229DV1YX+/n7Isszi0nECSNRUURRZzSRrBNW9tCAcltDT00MAkLNnzxJCCJEkiUiSNMgWRTHBlmWZxONxUlxcTKLRaMJxQgiJx+MJtqIoCbaiKINsQkiCTWNQWxTFlLYkSQm2Fo90nLTsaDRKiouLSTgcHjactNqpv7+fFBcXk3g8PuQ49fX1kYaGBhIOh9l9H7hmiEhSHxHFELPj8d6UdjzeSzo7WxN+ptfQs5Pj0PtBc0llr1q1imzYsIEdnzFjBnniiScSzrniiivI/fffz8654ooryE9+8hN2zpEjRwgAUltbS2RZJrfccgv5+te/TmRZZj5/+9vfWF+mbSrLMjl37hw7jx6nPnV1dUQQBHL48GESDAZJd3c3eeqpp8gXvvAF8sgjj5ClS5ey82OxGCkqKiJ79+7V5VpeXs5yoDweeOCBhHOefPJJMmbMGJZTQUEB+a//+i9y+PBh0tTURA4dOkQOHz5MDh8+TERRJM3NzaSgoIBs2LCB7Nq1ixw8eJA8//zzCVw3b95MioqKBvGjdjgcJg0NDaSvr29Q7VEN7OnpIXbB9ZTraS7oqSRJJBKJkAMHDpBwOKy6pnk9FcUQicV6mKZyPeV6mik9JcS6pmrpzHDU1HA4nKAzw4FTLmpq8hxVURRNrcvGHFWv32rZq1atInfddRc7PmPGDPLkk08mnEP1k56TrK/Nzc0EANm7dy8hhJCbb76ZfO1rX2P3VVEU8tprryVomSzLTE/pfUzW1Pfffz9BT3t6epiePvroo2Tp0qUsR6qntbW1ulypnp47d47xePDBBxPOoXpKcy8oKCCPP/4401G1pkqSRI4ePcr09J133iGHDh0iL7zwQkIctZ5q5RUOh8mBAwdIJBIZVHtnz541pKd8J4hNkA9X9vx+P3sGSW3T7XnUVq/2U1t9PBgMJth021UwGIQsy9i5cycEQWB/gsEgACTYPp8vwabbvN9+++2E2HQbHN2Sp8WDEILKykoQQtJyMmInc1LzUNuSJDGuepy0bAAsXz1Oeu1Eueq1Tap2okjFSaudBEFAZWUl206Yip+6nQgheOedd9iW1XS1R/OgdiRC4PONQCAwCoHAKPj9IxEMjk5r0z+BwCgEg6PT2mq/SIRo5qJnq0FXfvXO0boOIYSt4NN7OX/+fLz77rvs3guCwH6LIQiC5m/j1MepD33u8pFHHsEVV1yBMWPGYPXq1aioqMBbb72FlStXsvN37NiB8847DwsXLkzLm/KcP38+du/enXDO7t27E+rmqquuwuHDhzFnzhxMnDgRF198MfsTCARQU1MDSZLwxBNPYNmyZfjYxz6GU6dOpeWqlZeWRqh/k5EpZFNPBUGALMvYtWsXZFnOip7S+/bOO+8wrlxP3ddTvbHOST1N1keup9p6WlFRgeXLl7Pt0E7r6cUXX4yLLroIkydPxpw5c3JaTwHrmmq0r2ZijgogZb91ao5KkW6cSNYfqjP08RctTpnWVABszmelndRcjWqqoijYtWsXe8wmW3PUSIQkzDWzNUelXzAxoqn0Z0JIwss8tc5R/60+h/ZNiksvvXSQLtGfqY9aZ9TXVmvqZZddlqCnRUVFTE937NiR8NJ+qqdXXnll2vGD7saguq8+h+ZJc7/qqqtw6NAhzJ07d9Ac1e/3Y+/evUxPV6xYgblz57JdM8n3K5mrEU1Nvrd64IsgNpGpLYxGY1100UWWYlr1tRPTDtzI1w2ubrVL8gvusgE7Ma365uXlJfx855134siRI9i4cSMOHTqEV199FS+++GLCOW1tbZg/fz5qamp0rysIAlauXImXX34Zq1evBgBcfvnliMfjqKioYMcA4O9//3vaF05RUJ533XUXtm7dip/+9Kc4fPgwfvWrX7HnLSl+8pOf4Pe//z0eeOABHDlyBI2NjfjTn/6EH/3oRwCAiy66CJIk4Ze//CWOHj2KP/zhD3juuecM5WEETvQTrjPOgPN01pfrqXN6+tZbb+G6665j52ZDTw8cOICjR4/mvJ46eV29WFxnnPH1Ck/AHT21EzdT+XI9zb6eur4I8swzz2DWrFkoKCjAokWLsHPnzpTnV1RUsJfRzZ49e9BNO3DgAG699VbMnDkTgiDgqaeeGnSNBx54IGFFVxAETJo0yVL+2RakKVOmWBYkK752YtqBG/m6wdUNnoIgIC8vb9BKq5OwE5P6ZsJv+vTp+Otf/4otW7bgiiuuwHPPPYdHH3004RxRFHHo0CH27KcerrvuOsiyzAYZQRBw7bXXAgD7GxgYZG655RZDOdN7tGzZMrzwwgv45S9/iSuvvBKlpaVs8KBYu3Yt3njjDbz55ptYsWIFli9fjieeeAIzZswAAFx55ZV44okn8Pjjj2PBggV45ZVX8NhjjxnKwwiGyyII15nM+9n1tQo38uV66ryeXnfddYyr03paVlaGpUuXYuXKlXjyySdzWk+dvK5eLK4zzvh6hacbemonrlU9pb7qv7meuqCnKR+WcRj/8z//Q4LBIHn++edJQ0MD2bBhAxk5ciQ5fvy45vlHjx4lI0aMIBs2bCANDQ3k+eefJ8FgkPzlL39h51RXV5Pvfe975I9//COZNGkSefLJJwdd5/777yeXXnopaW9vZ386OjpM5U6ft+zq6jLlRwhhz7DTZ+2MQhRF8tZbb7HnnrLhayemVZ524rpxj4Yyz0gkQhoaGkgkEmHHFEUhPT097Lk6o1A/120WVmPa8bUT0yrX5Jg1NTVkzJgxhmpjKPLUqh+Krq6ujL8TJJt6SgjXGaf87Pi6wdOMb3KfGAo6kw1fN3gmx82GntrxHSp6Soh1TeU641xMO75uzMW9oqd24g5FnTEak+spIa5+IveJJ57A7bffjjvuuAMA8NRTT2Hbtm149tlnNVeEnnvuOUyfPp3t7rjkkkvw3nvv4Wc/+xluvfVWAMCSJUvYW7FTfV85EAhY3v2hRrZXZRcsWGB5VdaKr52YduBGvm5wdatdCgsLLfnZgZ2YVn3d5km3+6mfyTXqazVmtjBcdoJwncm8n11fq3ArX7d1Jlu+bvBUx82Wntr1tYLhshOE64wzvl7hCbivM9nys+trNybXU8C1RZB4PI6amppBCxVr1qxJ+HSVGrt27cKaNWsSjq1duxabN2+GKIqGGxIY+ITOhRdeiPz8fFx99dV49NFHMXv2bNM8si1IF1xwQfoTM+hrJ6YduJGvG1zd4Kl+8VW2YCemVd+hwHPp0qXsU4xmfa3GzBaGyyII15nM+9n1tQo38h0KOpMNX7d0Rh03G3pq19cqhssiCNcZZ3y9wnMo6Ew2/Oz6WgXX00S4tgjS2dkJWZYxceLEhOMTJ05kb9xOxqlTpzTPlyQJnZ2dmDx5sqHYV199NX7/+99j7ty5OH36NB5++GGsWLECBw4cwPjx4zV9YrEYYrEY+5l+qzkcDpte4RI//K4y/duMX2VlJVauXGm6oKz62o2p/tusrxv5Wo2p/jtbMY34iaIIQggURWHfQCcfvs161KhRpp5/JB++bZlezwysxrSbr52Y9G8zXIcbT0VRQAiBKIoJb6kHkPClCLMYCnpKfbjOZN7Pbkz139mIacY3WVO5zhjzpX9na+xw6x7Rv7Opp0DmNJXrjHMx7fi6MRf3ip5Sn+GiM07FHGo8M6GnAqFRsoyTJ09iypQpqKqqSvhczyOPPII//OEPOHjw4CCfuXPn4utf/zruu+8+duydd97BNddcg/b29kGPt8ycORN333037r777pS59Pf346KLLsIPfvADbNy4UfOcBx54AA8++OCg46+++ipGjBiR8vocHG6BPvY1bdo0yy9v4vAu4vE4WlpacOrUqUGfcAyHw/jSl76Enp4eFBUVmbou11OOXAXXVA6rcEpPAa6pHLkJrqccVpEJPXVtJ8iECRPg9/sH7fro6OgYtNuDYtKkSZrnBwIB3R0cRjBy5EhcdtllaGpq0j3nvvvuS1gg6e3txbRp07BmzRrTA5YoiigrK8ONN97oyravbIHzdB+xWAwnTpzAyJEjbT+TRwhBKBTC6NGjs/7m7mzDK1zT8YxEIigsLMSqVasGfQaO/qbRCriemgfnOTTANdU8OM8BOKWnQOY0daj3v0zBKzyBoc2V66l5cJ4DyISeurYIkpeXh0WLFqGsrAyf+cxn2PGysjLdz/UsX74cW7ZsSThWWlqKxYsX2+rYsVgMjY2NCZ+1TEZ+fr7ut6Ctxg4Gg6a3w5WWlmLNmjWWtqZZ8bUTk8IsTztx3bhHFEORp8/ngyAIiEajGDlyJICBLWS9vb0oKioy9bww3Y4mCILp54ytxrSbr52YgHmuw41nNBqFIAgoLCwctN3QDoaCngJcZ5zys+sLZJenWV9BECBJEnw+H9cZg75AdscOt+4RkH09BTKvqVxnnImZS3PxbPBMnqNynTHmB3CemdBTV78Os3HjRnzlK1/B4sWLsXz5cvzmN7/BiRMncOeddwIYWNlua2vD73//ewDAnXfeiV/96lfYuHEjvvnNb2LXrl3YvHkz/vjHP7JrxuNxNDQ0MLutrQ379u3DqFGjMGfOHADA9773PXz605/G9OnT0dHRgYcffhi9vb247bbbTHMIBLJ3CwOBAK699lpLMa362olpB27k6wZXp3n6/X6cd9556OjoAAC2LTYYDCIWi5laRVYUBfF4HNFo1LQIEkIsxbTjayemVa7DhSchBOFwGB0dHTjvvPM0Bxgn+gnXGWfAeWbONxAIYMSIEThz5gyCwSB76RvXGX24MXa4cY+Gmp46eV29WFxnnPEdrjyT56iFhYVcZ9LA6zwzqaeuLoJ84QtfQFdXFx566CG0t7djwYIFKCkpwYwZMwAA7e3tOHHiBDt/1qxZKCkpwT333INNmzbhwgsvxNNPP80+jwsMvGtk4cKF7Oef/exn+NnPfoZVq1Zhx44dAIDW1lZ88YtfRGdnJ84//3wsW7YM7777LotrBtnciiQIgqVnRe342olpB27k6wbXbPCk78qhg4xVEELY9rPhvAUP8A7XdDzPO+883U+JO3Ffsn2vuc4442fX1yqyka8gCJg8eTKam5tx/PhxS7EouM4MLww1PXXyunqxuM444zucefI5qjlwngPIhJ66uggCAOvXr8f69es1/+3FF18cdGzVqlXYu3ev7vVmzpzJ3iirh//5n/8xlWMqWHmztJ1YJSUlWLdunaWtaVZ87cS0AzfydYNrNnjSSfsFF1wAURRz6g3udnx5THu+wWAw5RZDJ7Qvm3pK43GdybyfXV+ryFa+eXl5uPjiixGPx4dVnx9qMXMt36Gmp05eVy8W1xlnfIczT/UcNRKJoKKigusMj5kdPSUcltDT00MAkO7ubtO+8XicFBcXk3g8bspPURQSDoeJoiimY1r1tRPTKk87cd24R5ynczHt+LpRu17hSQgh3d3dBADp6ekx7ZsMN/SUEN7/nPKz48t1xtmYucTTTtxcq91M6ikh1jWVt5VzMe34cp1JD167zsXMNZ5G9dTcw0QcrsPO84BWfbP9nL7duG7cIzvgPJ3z5Tw50oH3P2f87Pq6ETOX+p9XeNqJm2u1OxzglbbySv/zCk87cb1Su7nG0wj4IohNxGIxAIAsy5BleZAtSVKCTd92C3z05lv1cVEUE2zy4aM99DGGkpISRCIREEJACGFbftS2oigJtiRJkCQJJSUliEajCcdpvmpbzSMWi6GkpASxWCwtJyN2Mic1D7VN841EIrqctOxoNMry1eOk106UazQaNcRDbVOk4qTVTjTfeDyuy0mrnWiutC3M1B6NSevCSO1RHpSDmdoDBl5STGMarT1qq7maqT1aQ/F43FTtUU4URmtP3c/S6YJWOyXna7T29HTBaDvR66fip9VOyd9lzwSyqaf0fpSUlCT8DDinp+p2ply5nrqvp3pjndN6qsWD62mibWSsy6SeEkKYr1oXclFPAeuaauZ+UdvqHDVdv3VqjkphtK9SOxKJsPpI11czpanJ/TYbmqqnM07OUVP126E4R7UzntvRVHW7ZmOOamc8tzpHTVdv6cZzI+CLICaxadMmzJ8/H0uWLAEAHDp0CADQ2NiIxsZGAEBdXR2ampoAALW1tWhubgYAVFdXo6WlhV3r9OnTAIDKykp0dnYCAMrLy9Hd3Q1g4PO/oVAIAFjnvPHGG1FWVgZJkpiYAkAoFEJpaSkAoLu7G+Xl5QCAzs5OVFZWIhAIYOHChdizZw8AoKWlBdXV1QCA5uZm1NbWAgCamppQV1fHODU1NWHdunXMTsWpqqoK7e3tjFNXVxez9ThFo1HWKdWc6Jult2/frssJGHh5blVVFeNUW1uLdevWobW1VZeTXju1trZi3bp1qK2t1eWk1U40x3SctNqpsrIS69atY7YWJ612am5uxtSpUxEIBEzX3p49e7Bw4UIEAgHDtUc5ATBde9Q+77zzEAgEDNce5dTY2Ig5c+YgEAgYrr3Ozk4EAgEUFBSgr6/PVO0BYD6pOGm1UyAQwLx58xgPo7XX3NyMQCCACRMmMB5Gaq+0tBSRSATr1q1DWVmZqdqjnGgsPU567ZSJlX039ZQuYFBkQ08bGxsRCAQwdepUxoPrqft62tTUhMbGRqxbt47ZWpwyradlZWUAzNUetb2gp8DAi+wnTZqEQCCQFT0NhUJM29T/acgFPQXsa2pbWxuzjd4vwN4cddeuXVi3bh06OzsN3y/KyeoclcZJx0mrrsvKyrBu3TpEIpG0fTVTmlpXV4d58+YhEAik7avJnKxqal9fHwoKChAIBAz11UzMUQOBAM477zzGY6jPUa2O53Y1Vd2u2ZijWh3P7cxRKY+uri7T4znVvbRI+bAMhy7o85ZdXV2EEEIkSSKSJA2yRVFMsGVZZs85RaPRhOOEDDwDpbbpM1T0eH9/P4nFYkRRFKIoCntWSm3TGNQWRZEoikL6+voGHaf5qm01D1EUSTgcHsQjmZOWHY1GSXFxMQmHw7qc1DySbcpVj5OWHY/HWb56nPTaiXJVt4EeP/U5/f397Lm1VJz02ikcDie0gR4/9TmiKJJQKEQURTFVezT3vr4+oiiK4dpTFIXEYjFSXFzM6s9o7dG8ent7iaIohmtPnbs6XyO1J8syURSF9Pb2svON1h4hhHGl98RI7Wn1M6O1J0nSoHyN1B61w+HwIF0wUntUi8LhsOHao7k78U6QbOoprcXe3l5WL07rKW3nUCiU0Je5nrqrp3pjnZN6StuEairXU23byFiXST2luSfrQi7pKSHWNVVLZ5yeo6brt07MUcPhcILOmNHUWCzG7k+6vpopTdXTGSc1VU9nnJyjpuq3Q3GOanU8t6OpVGf6+/tN1Z6dOarV8dzOHJX20Wg0ano87+rq4u8EyQbIh1uX/H4/e1Ot2g4EAgm2+lvH1FYfDwaDCTb9zE8wGIQsyygrK4MgCOwPfWOu2vb5fAl2IBCAJEl48803E2LT3zz4/f4EW82DEILS0lIQQtJyMmInc1LzUNvSh78lo+dqcdKyAbB89TjptRPlqtc2qdqJIhUnrXai+SqKostJq50IIXjrrbcgSZKl2nvzzTchSZLh2qM8KAcztQcMbFsrLy+HJEmGa4/ahJCEfI3WniRJKC8vZ9vyjNYe5URhtPa0+pnR2vP7/YPyNVp7siyjtLR0kC4YbSd6/VT8tNpJcmD7djb1VBAEyLKM8vJyyLKcFT2l9+2tt95iXLmeuq+nemOd03qqxYPraaJtZKzLpJ4KgsB81bqQi3oKWNdUM/eL2lbnqEDqfuvUHJXCaF9V8yotLYUsy2n7aqY0FdDWGSc1VU9nnJyjpuq3Q3GOamc8t6Op6nbNxhzVznhudY6art7SjedGIBCjZ3IkoLe3F2PGjEFPT4/p72iLLnzqyg1wnsMLXuEJeIerHZ52NDCT1+JtNbzgFZ6Ad7hynumRST21cz3eVsMPXuHKeQ4vZENP+U4Qm8jmGhIhBL29vZZiWvW1E9MO3MjXDa6cp3O+nKezcCIe1xlnwHk658t5Ogsv1W4uXVcvllfaygv9zys87cT1Su3mIk8j4IsgNuHUFka9WDt37rQU06qvnZh24Ea+bnDlPJ3z5TydhRPxuM44A87TOV/O01l4qXZz6bp6sbzSVl7of17haSeuV2o3F3kaAX8cxiL49u304DyHF7zCE/AO16GyfZvraXpwnsMPXuHKeaYHfxwmu/AKT8A7XDnP4QX+OEwOgL7QJluxzp49aymmVV87Me3AjXzd4Mp5OufLeToLJ+JxnXEGnKdzvpyns/BS7ebSdfVieaWtvND/vMLTTlyv1G4u8jQCvghiE/F4HAAgyzJkWR5kS5KUYKsbhtrq46IoJth0o44oipAkCdXV1YhGoyCEgBACURQBIMFWFCXBpjlUV1cjFoslHKf5qm01j3g8jj179iAej6flZMRO5qTmobZpvtFoVJeTlh2LxVi+epz02olyjcVihniobYpUnLTaieZL2zcVP3U7xeNxVFdXMw5mai8WizFfo7VHeVAOZmqPXoPGNFp71FZzNVN7tIZoPkZrj3KiMFp76n6WThe02ik5X6O1J0kS9uzZM0gXjLYTvX4qflrtRPPPJLKpp4QQpqmSJGVFT9XtTLlyPXVfT/XGOqf1VIsH19NE28hYl0k9JYQwX7Uu5KKeAtY11cz9orbVOWq6fuvUHJXCaF+ldjQaxZ49eyBJUtq+milNTe632dBUPZ1xco6aqt8OxTmqnfHcjqaq2zUbc1Q747nVOWq6eks3nhsBXwQxiU2bNmH+/PlYsmQJAODQoUMAgMbGRjQ2NgIA6urq0NTUBACora1Fc3MzAKC6uhotLS3sWqdPnwYAVFZWorOzEwBQXl6O7u5uAAOf0QqFQgCAkpISyLKMT3ziEygrK4MkSYhGoygpKQEAhEIh9vmi7u5ulJeXAwA6OztRWVmJYDCIyy67DHv27AEAtLS0oLq6GgDQ3NyM2tpaAEBTUxPq6uoYpw8++ABr165FY2NjWk5VVVVob29nnLq6upitxykajUKSJJSUlCRwCgaDWLFiBbZv367LCQDa29tRVVXFONXW1mLt2rVobW3V5aTXTq2trVi7di1qa2t1OWm1E80xHSetdqqsrMTatWvR09Ojy0mrnY4dO4aJEyciGAyarr09e/bgsssuQzAYNFx7lBMA07VH7REjRiAYDBquPcqpsbERM2fORDAYNFx7nZ2d7BNc/f39pmoPAPr6+libGq296upqBINBzJkzB/v370/JSaudgsEgioqKcOrUKV1OWu0UjUaxdu1alJWVmao9yonG0uOk106Z2Ibppp5Go1EIgoBYLAZBELKip42NjQgGg5g4cSKOHTuWkhPX0+zpaVNTExobG7F27Vp88MEHWdPTsrIyAOB6mqKdWltbMX78eASDwazoaSgUQjAYRCwWYxPrXNFTwL6mtrW1Mdvo/QLszVF37dqFtWvXorOz0/D9opyszlFpnHSctOq6rKwMa9euRTQaTdtXM6Wp+/fvx5w5cxAMBtP21WROVjW1v7+ffbbUSF/NxBw1GAxixIgRjMdQn6NaHc/taqq6XbMxR7U6ntuZo1IeXV1dpsdzqntpQTgsoaenhwAgnZ2dhBBCJEkikiQNskVRTLBlWSbxeJwUFxeTaDSacJwQQuLxeIKtKAqzJUkip06dItFolCiKQhRFIfF4nBBCEmwag9r0+u3t7SQWiyUcp/mqbTWPeDxOTp8+zeLrcdKyo9EoKS4uJuFwWJeTmofalmWZcdXjpGXHYjGWrx4nvXaiXGOxmC4nrXbq7+8nxcXFJB6Pp+Sk1U40X1EUdTlptVM8HicnT54ksiybqj1CCInFYqS9vT2hNtLVnqIoJBaLkeLiYhKLxUzVHo1P8zVae9SOx+MJ+RqpPRrn5MmTCffMSO3Re0Tb1GjtafUzo7UnSdKgfI3UHu2Xp0+fHqQLRjSCalE4HDZcezT3c+fOEQCkp6eH2IUbeqooCpEkibS1tRFJkrKip+p2ptfkeuq+nuqNdU7qqaIojGssFuN6qmMbGesyqaeKohBZlgfpQi7pKSHWNVVLZ5yeo6brt07MUcPhcILOmNHUaDRKTp8+zeJrccq0purpjJOaqqczTs5RU/XboThHtTqe29FUqjP9/f2mas/OHNXqeG5njkr7aDQaNT2ed3Z2GtJTSztB6DYXreMnTpywcsmchSAIAAC/3w+/3z/IDgQCCbbP99Etp7b6eDAYTLDp9YPBIAghOHDgAPx+PwRBYCu0NA9q+3y+BDsQCEBRFDQ0NLBr0+M0X7Wt5iEIAurr6yEIQlpORuxkTmoealtRFMZVj5OW7fP5WL56nPTaiXKl10nHQ21TpOKk1U40X3r9VPzU7SQIAhobG6Eoiuna8/l8aGhogKIohmuP8qCxzdQeBc3XaO1RWxCEhHyN1p6iKGxVOxUnvXaiMFp7Wv3MaO35/f5B+RqtPUII6uvrB+mC0Xai10/FT6ud9MYBO8imngqCAEIIDh48CEJIVvRU3c40F66n7uup3ljntJ5q8eB6mmgbGesyqaeCIEBRlEG6kIt6SvNUx0m2zeqPE3PUdP3WqTkqhdG+Sm2/34/6+noQQtL21Uxpqp7OOKmpgLbOODlHTdVvh+Ic1c54bkdT1e2ajTmqnfHc6hw1Xb2lG88NIeUSSRJ6enrIP//zP5OCggJywQUXkJ/85CdspYgQQk6dOkV8Pp+ZS+Ys6Cq7lVV7uopHV7yGKzjP4QWv8CTEO1zt8LSjgZm8Fm+r4QWv8CTEO1w5z/TIpJ7auR5vq+EHr3DlPIcXsqGnpnaC/PjHP8b777+PP/zhD3jkkUfw0ksv4ZZbbkl4AQnx2Bd3nVq914vV1tZmKaZVXzsx7cCNfN3gynk658t5Ogsn4nGdcQacp3O+nKez8FLt5tJ19WJ5pa280P+8wtNOXK/Ubi7yNAJTiyDFxcX49a9/jc997nO44447UFNTg87OTnz6059mb8k3vAVlmCDbjXrkyBHLRWjF105MO3AjXze4cp7O+XKezmK4LILw/pd5P7u+VuGV/ucVnnbi5mLt5tJ19WJ5pa280P+8wtNOXK/Ubi7yNIJA+lM+QmdnJ2bMmMF+Hj9+PHtL8rp16/DCCy+Yy3IYQP08WTZirVy5Mqu+dmLagRv5usGV83TOl/N0Fk5oXzb1lMbj/S/zfnZ9rcIr/c8rPO3EzcXazaXr6sXySlt5of95haeduF6p3VzkaQSmdoJMmzYt4SUuADB69GiUlpYiEongM5/5jJnLDQtke2Xr+PHjllfirPjaiWkHbuTrBlfO0zlfztNZDJedILz/Zd7Prq9VeKX/eYWnnbi5WLu5dF29WF5pKy/0P6/wtBPXK7WbizyNwNQiyJo1a/C73/1u0PFRo0Zh27ZtKCgoMHO5YQFRFAEAsixDluVBtiRJCba6YaitPi6KYoJN37EiiiJkWUZraytisRgIISCEsPhqW1GUBJtev7W1lb2/hR6n+aptNQ9RFNHW1sbip+JkxE7mpOahtmm+9DErLU5adjweZ/nqcdJrJ8o1Ho8b4qG21fWgx0mrnWi+kiTpctJqJ1EU0draCkVRTNdePB5nvkZrj/KgHMzUHo1PYxqtPWqruZqpPUVR0NLSknDPjNQe5URhtPbU/SydLmi1E/VV3zMjtSfLMtra2gbpgtF2otdPxU+rnZwY0LKpp4QQyLKMlpYWyLKcFT1VtzO9JtdT9/VUb6xzWk+1eHA9TbSNjHWZ1FNCCOOq1oVc1FPKKfkeGalrM/eL2lbnqOn6rVNzVPU9MqOpsVgMbW1tLL4Wp0xranK/zYamSpKElpaWQTrj5Bw1Vb8dinNUO+O5HU1Vt2s25qh2xnOrc9R09ZZuPDcCU4sgDz74IB544AHNfxs9ejTefPNNlJeXm7lkzmHTpk2YP38+lixZAgA4ePAggIFPK9FdMnV1dWhqagIA1NbWorm5GQBQXV2NlpYWdq3Tp08DACorK9HZ2QkAKC8vR3d3NwCgtLQUoVAIAFBSUgJJkrB06VKUlpZCkiREo1GUlJQAAEKhEEpLSwEA3d3drB06OztRWVmJQCCAmTNnorq6GgDQ0tLC7ObmZtTW1gIAmpqaUFdXxzg1NTVhxYoVaGhoSMupqqoK7e3tjFNXVxez9ThFo1FIksT4UU6BQACXXXYZ46HFCQDa29tRVVXFOO3duxcrVqxAS0uLLie9dmppacGKFSuwd+9eXU5a7bR9+3bWpqk4abVTRUUFVqxYge7ubl1OWu3U3NyMESNGIBAImK696upqzJw5E4FAwHDtUU4ATNceAJw5cwayLCMQCBiuPcqpoaEB48ePRyAQMFx7nZ2dCAQC6O/vR19fn6naA8B8UnHSaqdAIICJEycyHkZrr7m5mX2GjPIwUnt0F96KFStQWlpqqvbUWk15GKk9yikT26zd1NNoNAoATKeyoaeNjY0IBAIYMWIE48H11H09bWpqQkNDA1asWIGmpqas6WlZWRkAc7UHeEdPqZ2Xl4dAIJAVPQ2FQggEAujq6mL/Kc8VPQXsa2pbWxuzjd4vwN4ctaqqCitWrMCZM2cM3y/KyeoclcZJx0mrrktLS7FixQpEIpG0fTVTmlpXV4eJEyciEAik7avJnKxqal9fH/r7+xEIBAz11UzMUQOBAGRZxpkzZ1JyGipzVKvjuV1NVbdrNuaoVsdzO3NUyqOrq8v0eE51Ly1SfjuGQxf08ztnzpwhhBAiSRL7XLDaFkUxwZZlmX32JxqNJhwnZOCTQGpbURRmi6JIDh8+TCKRCFEUhSiKwj4dpLZpDGrTHA4fPsxi0uM0X7Wt5hGLxUhTUxOJxWIpOWnZ0WiUFBcXk3A4rMtJzUNt03wjkYguJy07Go2yfPU46bUT5RqNRnU5abVTf38/+4xTKk5a7UTzpe2bip+6nWKxGDl06BDjYLT2CCEkGo2Sw4cPs+sZqT1FUUgsFiPFxcUkFouZqj16DZqv0dqjtpqr0dqTZZlIkkQOHjzI8jFaezQmbVOjtUdzO3ToEInFYqZqj/586NAhFstI7dG6aWpqGqQLRjSCalE4HDZcezTXs2fPZvwTudnUU0VRiCiK5ODBg0QUxazoqbqdaY1wPXVfT/XGOif1VFEUxjUWi3E91bGNjHWZ1FNFURhXtS7kkp4SYl1TtXTG6Tlqun7rxBw1HA4n6IwZTY1EIqSpqYmIopi2r2ZKU5P7bTY0NR6Pk4MHDw7SGSfnqKn67VCco1odz+1oKtWZ/v5+U7VnZ45qdTy3M0elfTQajZoez8+cOWNITw0vPT/99NP413/9VxQUFODpp59Oee5dd91l9LI5D59vYDON3+9nx9S2enWf2nSLEfVVnxMMBnVtSZLQ3d2NWbNmsa/w0HMEQWC2z+dj16a22jf5HL3c/X4/CCE4d+4cZs6cyf5Ni5OWTT7c1hUIBFisVPz0uOpx0rL9fj/Ll+aTip86X0mSmK9W2xhtp1R2cjtp5avHT52vz+dDT08PCCEJeaWrPXpOd3f3IF8judO/1TzS1R49h+br9/sN1V46rulsSZLQ29s7qK+Y4ZqKk14/6+npMcVPnW9PTw+LbbT21HWbiqtWO9Etg2Zqj9q0f2cS2dRTit7eXgAwVdNW9RTAoBrheuq+nuqNdU7qqR4PrqeD7XRjXSb1FBjQNi1dSOY01PWUxtWLqc6V2lo64/QcNV2/dWqOaoSTlh0IBFjMdH01U5pqtt9mQlMFQUBvb+8gnXFyjpqq3w7FOarV8Zz6WtFUqjNma8/OHNXOeG51jqrWArOaSo+ng+FFkCeffBJf/vKXUVBQgCeffFL3PEEQPLUIku03b9MtjtnytRPTDtzI1w2unKdzvpyns3BC+7KppzQe73+Z97PraxVe6X9e4Wknbi7Wbi5dVy+WV9rKC/3PKzztxPVK7eYiTyMw/E6Q5uZmjB8/ntl6f44ePWot4xwF/S1ktmIdPHjQUkyrvnZi2oEb+brBlfN0zpfzdBZOxOM64ww4T+d8OU9n4aXazaXr6sXySlt5of95haeduF6p3VzkaQSmXoyqBaJ6Iy+H84hEIln3tRPTDtzI1w2unKdzvpwnRzrw/ueMn11fN2LmUv/zCk87cXOtdocDvNJWXul/XuFpJ65XajfXeBqB5UWQzZs3Y8GCBSgoKEBBQQEWLFiAF154IZO55QTUzyFlI9bChQstxbTqayemHbiRrxtcOU/nfDlPZ+FEPK4zzoDzdM6X83QWXqrdXLquXiyvtJUX+p9XeNqJ65XazUWeRmBpEeTHP/4xNmzYgE9/+tP485//jD//+c/49Kc/jXvuuQc/+tGPrFwyZ5Ht7T319fWWtyNZ8bUT0w7cyNcNrpync76cp7MYLo/D8P6XeT+7vlbhlf7nFZ524uZi7ebSdfVieaWtvND/vMLTTlyv1G4u8jQCS4sgzz77LJ5//nk89thjuPnmm3HzzTfjsccew29+8xs899xzVi6Zs6A3WpZlTVuSpARbURTmS231cVEUE2z6qBG1CSGDbAAJtqIoCbb04bezCSHMVh+XZTnBtsrJiJ1pTnq205yS24nCLKd0PFJxovGtcKJtYLb2KAcr7USvbaWd1PmaaSd1XLO1R2G29gghaetNr53U+RqtPbv9iV4/FSe9dso03NBTRVGyrqeKonA9HWJ6aoeTVT3V4sH1dDCndGOdE3qarAu5qKf0+slxMtlXc1l/KHKBU3K/zZamaumM03NUvX47VOeodsZzq5qqbtdszlGzqRGZGM/TwdIiiCzLWLx48aDjixYtShCW4YhNmzZh/vz57E23jY2N7G9q19XVoampCQBQW1uL5uZmAEB1dTVaWlrYtU6fPg0AqKysRGdnJwCgvLwc3d3dAIDS0lKEQiEAQElJCURRxLx587Bt2zZIkoRoNIqSkhIAQCgUQmlpKQCgu7sb5eXlAIDOzk5UVlbC7/dj3Lhx2L17NwCgpaUF1dXVAAZedFtbWwsAaGpqQl1dHeN0+PBhLFiwAAcOHEjLqaqqCu3t7YxTV1cXs/U4RaNRSJKEkpKSBE5+vx8zZszAW2+9pcsJANrb21FVVcU41dTUYMGCBThx4oQuJ712OnHiBBYsWICamhpdTlrttH37dtamqThptdOOHTuwYMECnDt3TpeTVjsdPXoUsizD7/ebrr3du3dj3Lhx8Pv9hmuPcgJguvYAoKOjA93d3fD7/YZrj3I6cOAAgsEg/H6/4drr7OyE3+9He3s742G09gCgr6+PtanR2quurobf70dhYSHef//9lJy02snv96Ovrw8nT57U5aTVTuFwGAsWLMC2bdtM1R7lRGPpcdJrp0xsbXRTT6PRKAghaG5uBiEkK3ra2NgIv98PWZbZS8S5nrqvp01NTThw4AAWLFiAw4cPZ01Py8rKAIDraYp2OnHiBGKxGPx+f1b0NBQKwe/3o7m5GaIo5pSeAvY1ta2tjdlG7xdgb476zjvvYMGCBejo6DB8vygnq3NUGicdJ6263rZtGxYsWIBwOJy2r2ZKU99//30UFhbC7/en7avJnKxqaigUQnt7O/x+v6G+mok5qt8/8Mnxjo6OlJyGyhzV6nhuV1PV7ZqNOarV8dzOHJXy6OrqMj2eUx5pQSzg3//938k999wz6Ph3v/tdsn79eiuXzDn09PQQAOTMmTOEEEIkSSKSJA2yRVFMsGVZJvF4nBQXF5NoNJpwnBBC4vF4gq0oCrNFUSQ1NTUkEokQRVGIoigkHo8TQkiCTWNQm+ZQU1PDYtLjNF+1reYRi8XI3r17SSwWS8lJy45Go6S4uJiEw2FdTmoeapvmG4lEdDlp2dFolOWrx0mvnSjXaDSqy0mrnfr7+0lxcTGJx+MpOWm1E82Xtm8qfup2isVi5L333mMcjNYeIYREo1FSU1PDrmek9hRFIbFYjBQXF5NYLGaq9ug1aL5Ga4/asVgsIV8jtSfLMpEkibz33nssH6O1R2PSNjVae+p+FovFTNUe/Vmdr5Hao3Wzd+/eQbpgRCOoFoXDYcO1R3M9e/YsAUB6enqIXbihp4qiEFEUyXvvvUdEUcyKnqrbmdYI11P39VRvrHNSTxVFYVxjsRjXUx3byFiXST1VFIX5qnUhl/SUEOuaqqUzTs9R0/VbJ+ao4XA4QWfMaGokEiF79+4loiim7auZ0tTkfpsNTdXTGSfnqKn67VCco1odz+1oKtWZ/v5+U7VnZ45qdTy3M0elfTQajZoez8+cOWNITy1/QHzz5s0oLS3FsmXLAADvvvsuWlpa8NWvfhUbN25k5z3xxBNWQ+QE6Oq9ehVfbau/VUxtuk3H5/MNOicYDOrasixjxIgRCAaDEAQh4RxBEJjt8/nYtalNfWks9Tl6uVObrj7Tn7U4adnkwy1QgUCAxUrFT4+rHictOxAIDMo3HT+aoyzLKCwsTMhXj59RHsl2cjvRfH0+H8tHj19y7iNHjkzLSc8eMWKEaU605gRBSOCRrvaoTfM1U3vU1so3nS3LMkaOHGm69tRc03HS62dma08rX6O1R+tWSxeSOSW3k/jh1kMtXTDSTplGNvUUGLgnI0eONF3TdvSUtjPX06Glp8Dgsc5JPdXjwfV0MKd0Y10m9ZRCSxeSOQ11PVVf36imaumM03PUdP3WqTmqEU56dmFhIQRBSNvWmdJUs/02U5qqpTNOzlFT9duhOEe1Op5rcTXaTlRnzI7nduaodsZzq3NUdd8yq6lGddXSIkh9fT2uuuoqAMCRI0cAAOeffz7OP/981NfXs/PUA99wRbbfdjtv3rys+tqJaQdu5OsGV87TOV/O01k4oX3Z1FMaj/e/zPvZ9bUKr/Q/r/C0EzcXazeXrqsXyytt5YX+5xWeduJ6pXZzkacRWHonyPbt2w39UT8jqYdnnnkGs2bNQkFBARYtWoSdO3emPL+iogKLFi1CQUEBZs+ePehFrAcOHMCtt96KmTNnQhAEPPXUUxmJqwcpi+9AkSQJe/bssRTTqq+dmHbgRr5ucOU8nfPlPJ2FE/G4zjgDztM5X87TWXipdnPpunqxvNJWXuh/XuFpJ65XajcXeRqBpUWQF198EZFIxIprAv70pz/h7rvvxn/8x3+gtrYW1157LT75yU/ixIkTmuc3Nzdj3bp1uPbaa1FbW4v/7//7/3DXXXfhr3/9KzsnHA5j9uzZ+K//+i9MmjQpI3FTIZu7XQRBwNixYy3FtOprJ6YduJGvG1w5T+d8OU9n4UQ8rjPOgPN0zpfzdBZeqt1cuq5eLK+0lRf6n1d42onrldrNRZ5GYGkR5L777sPEiRNx++23J7xh2SyeeOIJ3H777bjjjjtwySWX4KmnnsK0adPw7LPPap7/3HPPYfr06XjqqadwySWX4I477sA3vvEN/OxnP2PnLFmyBP/93/+N//t//y/y8/MzEjcVsr3VcM6cOZZiWvW1E9MO3MjXDa6cp3O+nKezGC6Pw/D+l3k/u75W4ZX+5xWeduLmYu3m0nX1YnmlrbzQ/7zC005cr9RuLvI0AkuLIK2trXj55Zdx7tw5XHfddZg3bx4ef/xxnDp1yvA14vE4ampqsGbNmoTja9as0V1Y2bVr16Dz165di/feey/h28mZjpsK2d7eU1VVZXk7khVfOzHtwI183eDKeTrny3k6i+HyOAzvf5n3s+trFV7pf17haSduLtZuLl1XL5ZX2soL/c8rPO3E9Urt5iJPI7D0YlS/34+bb74ZN998Mzo6OvDyyy/jxRdfxI9//GPcdNNNuP322/HpT3+avbVVC52dnZBlGRMnTkw4PnHiRN3FlFOnTmmeL0kSOjs7MXny5LS5W4kLALFYDLFYjP3c29sLYODtvkYXYCjo+Wb9FEXBpEmTIMsye4O30752YlrlaSeuG/eI83Quph1fN2rXKzyBj77KYgVDQU8B3v+c8rPjy3XG2Zi5xNNO3FyrXTt6CmROU3lbORfTji/XmfTgtetczFzjaVRPBWKWjQZ2796N3/72t3jppZcwefJkdHd347zzzsPvfvc7rF69WtPn5MmTmDJlCqqqqrB8+XJ2/JFHHsEf/vAHHDx4cJDP3Llz8fWvfx333XcfO/bOO+/gmmuuQXt7+6B3gMycORN333037r77bltxAeCBBx7Agw8+OOj4q6++yj5bxcHBweEVhMNhfOlLX0JPTw+KiopM+XI95eDg4PgIdvQU4JrKwcHBQWFUTy3tBAGA06dP4w9/+AN+97vf4ejRo/inf/onvPHGG7jhhhsQiUTwox/9CLfddhuOHz+u6T9hwgT4/f5Buy86OjoG7dKgmDRpkub5gUAA48ePN5S3lbjAwHtQNm7cyH7u7e3FtGnTcP3112PcuHGGYlOIooiysjLceOONCd9KTgdJklBdXY2lS5cmfJPZSV87Ma3ydCtfq76c59DM143a9QpPADh79qyp89UYCnoK8P7nlJ8dX64zzsbMJZ5u5etG7drRUyBzmsrbamjmy3UmPXjtOhcz13ga1VNLiyCf/vSnsW3bNsydOxff/OY38dWvfjVBZAsLC/Hd734XTz75pO418vLysGjRIpSVleEzn/kMO15WVoZbbrlF02f58uXYsmVLwrHS0lIsXrzY8A2yEhcA8vPzNV+0mp+fb7pxKILBoClf+nKZ/Pz8lI8aZdLXTkwKszztxHXjHlFwns7EzKXa9QpPALovnjbq67aeArz/OeVn1xfgOuNUzFziaSdurtWuHT2l/pnUVN5WzsTMpf7nFZ524nqldnONp1E9tbQIcsEFF6CioiLhcZJkTJ48Gc3NzSmvs3HjRnzlK1/B4sWLsXz5cvzmN7/BiRMncOeddwIYWNlua2vD73//ewDAnXfeiV/96lfYuHEjvvnNb2LXrl3YvHkz/vjHP7JrxuNxNDQ0MLutrQ379u3DqFGjMGfOHENxzcBqJ7UCn8+HKVOmZNXXTkw7cCNfN7hyns75cp7Owgnty6ae0ni8/2Xez66vVXil/3mFp524uVi7uXRdvVheaSsv9D+v8LQT1yu1m4s8DZ1n5qKRSARvvPEGNm/ejOXLl7Ptd/TP97//fUSjUQAD3+idMWNGyut94QtfwFNPPYWHHnoIV155JSorK1FSUsL82tvbceLECXb+rFmzUFJSgh07duDKK6/Ef/7nf+Lpp5/Grbfeys45efIkFi5ciIULF6K9vR0/+9nPsHDhQtxxxx2G45pBtt92W15ebvntvFZ87cS0AzfydYMr5+mcL+fpLJyIx3XGGXCezvlyns7CS7WbS9fVi+WVtvJC//MKTztxvVK7ucjTCEztBPn973+PN954A//n//wfAMCvfvUrXHrppSgsLAQAHDx4EBdeeCHuuecew9dcv3491q9fr/lvL7744qBjq1atwt69e3WvN3PmTENvn00V1wyyvcq+YMECSzGt+tqJaQdu5OsGV87TOV/O01kMl50gvP9l3s+ur1V4pf95haeduLlYu7l0Xb1YXmkrL/Q/r/C0E9crtZuLPI3A1CLIK6+8MmiB49VXX8Xs2bMBAC+//DI2bdpkahEk15HtRr3ggguy6msnph24ka8bXDlP53w5T2cxXBZBeP/LvJ9dX6vwSv/zCk87cXOxdnPpunqxvNJWXuh/XuFpJ65XajcXeRo6z8xFDx8+jLlz57KfCwoKEgItXbqUvY/DK7Dy/WI7sbZt22YpplVfOzHtwI183eDKeTrny3k6CyficZ1xBpync76cp7PwUu3m0nX1YnmlrbzQ/7zC005cr9RuLvI0AlOLID09PQmftzlz5gxmzpzJflYUBbFYzMwlhw1kWYYsy4NsSZISbEVRmA+11cdFUUyw6aM9oijC5/Nh8eLFUBQFhBAQQlhDq21FURJsSZLg9/uxaNEidj16nOarttU8AGDJkiUJP+txMmInc1LzUNt+v59x1eOkZRNCWL56nPTaiXIlhBjiobYpUnHSaiearyAIupy02gkArrrqKvj9ftO1RwjBokWL4Pf7Ddce5UH9zdQeMPCOIJqv0dqjNoCEfI3Wnt/vx8KFCyEIQkpOeu1EYbT2tPqZ0dqTZRl+vx9XXXUVy9do7fl8PixZsmSQLhhtJ3r9VPy02snv98MpZENPCSHw+XxYuHAhfD5fVvRU3c4UXE/d11O9sc5pPdXiwfU00QbSj3WZ1FNCCOOq1oVc1lN1nGTbrP44MUdN12+dmqNSGO2r1FYUBUuWLIHP50vbVzOlqXo646SmCoKAhQsXDtIZJ+eoqfrtUJyj2hnP7Wiqul2zMUcFrI/nVueo6eot3XhuBKYWQaZOnYr6+nrdf6+rq8PUqVPNXDLnsGnTJsyfP5+JGN350tjYiMbGRgAD96GpqQkAUFtby76SU11djZaWFnat06dPAwAqKyvR2dkJACgvL0d3dzeAgc//hkIhAEBJSQni8TiKioqwdetWSJKEaDSKkpISAEAoFEJpaSkAoLu7G+Xl5QCAzs5OVFZWwufzIRaL4d133wUAtLS0oLq6GgDQ3NyM2tpaAEBTUxPq6uoYp0OHDmHcuHGor69Py6mqqgrt7e2MU1dXF7P1OEWjUUiShJKSkgROPp8PwWAQb775pi4nYODluVVVVYzTe++9h3HjxuH48eO6nPTa6fjx4xg3bhzee+89XU5a7bR9+3bWpqk4abXT9u3bMW7cOJw9e1aXk1Y7HTlyBK2trfD5fKZr791330UsFoPP5zNce5QTANO1BwzUemNjI3w+n+Hao5zq6+vR2dkJn89nuPbU5/f29pqqPQDo6+tjbWq09qqrq+Hz+dDd3Y33338/JSetdvL5fGhqakJbW5suJ6126u/vx7hx47B161ZTtUc50Vh6nPTaKRPbrN3U02g0CkVRsGvXLiiKkhU9pX2gtbUVR44cScmJ62n29LSpqQn19fUYN24cDh06lDU9LSsrAwCupyna6fjx4zh27BjTR6f1NBQKwefzYdeuXYjH4zmlp4B9TaX3q7q62vD9AuzNUd9++22MGzcOp0+fNny/KCerc1QaJx0nrbreunUrxo0bh/7+/rR9NVOa+v7776O7uxs+ny9tX03mZFVTe3t7UVtbC5/PZ6ivZmKOSs+3Mp67MUe1Op7b1VR1u2Zjjmp1PLczR6U8urq6TI/nhp9KISZw1113kfnz55NIJDLo38LhMJk/fz656667zFwyZ9HT00MAkNOnTxNCCJEkiUiSNMgWRTHBlmWZxONxUlxcTKLRaMJxQgiJx+MJtqIozI7FYmTLli2kv7+fKIpCFEUh8XicEEISbBqD2qIokng8TrZs2ULC4XDCcZqv2lbziEQi5I033iCRSCQlJy07Go2S4uJiEg6HdTmpeahtmm9/f78uJy07HA6zfPU46bUT5arOV4+fup36+/tJcXExy1uPk1Y70Xyj0aguJ612ikQiZMuWLSQej5uqPUIG+ir1NVp7iqKQWCxGiouLSSwWM1V7hBASjUZZTKO1R201V6O1R/PYsmUL62dGa48QwrjSfI3UnrqfUY00WnuSJA3K10jtUV144403BumCEY2gWqSlC+naqbOzkwAgPT09xC7c0FNa01u2bDFd01b1VN3OtEa4nrqvp3pjnZN6qigK4xqLxbie6thGxrpM6inNO1kXcklPCbGuqVo64/QcNV2/dWKOGg6HE3TGjKb29/eTN954I6HfOq2pyf02G5qqpzNOzlFT9duhOEe1Op7b0VSqM2bHcztzVKvjuZ05Ku2jVsbz06dPG9JTU4sgp06dIpMmTSLTp08nP/3pT0lxcTF5/fXXyeOPP06mTZtGJk+eTE6dOmXmkjkLOsB0d3eb9qUFTBvbKBRFIT09PayTZMPXTkyrPO3EdeMecZ7OxbTj60bteoUnIYR0d3dnfBEkm3pKCO9/TvnZ8eU642zMXOJpJ26u1W4m9ZQQ65rK28q5mHZ8uc6kB69d52LmGk+jemrq6zATJ05EVVUVvvWtb+Hee+9lzxQJgoAbb7wRzzzzDCZOnGjmkjkP+ixXtmIVFRVl1ddOTDtwI183uHKezvlyns7CCe3Lpp7SeLz/Zd7Prq9VeKX/eYWnnbi5WLu5dF29WF5pKy/0P6/wtBPXK7WbizyNwPRDiLNmzcLWrVtx5swZvPvuu3j33Xdx5swZbN26lX0q10tQv6QmG7Fef/11y2/nteJrJ6YduJGvG1w5T+d8OU9n4UQ8rjPOgPN0zpfzdBZeqt1cuq5eLK+0lRf6n1d42onrldrNRZ5GYGoniBrjxo3D0qVLrboPG6i/lpONWGvWrLEU06qvnZh24Ea+bnDlPJ3z5TydhRPxuM44A87TOV/O01l4qXZz6bp6sbzSVl7of17haSeuV2o3F3kaQWZeR82RNdgpIqu+2RYju3HduEd2wHk658t5cqQD73/O+Nn1dSNmLvU/r/C0EzfXanc4wCtt5ZX+5xWeduJ6pXZzjacR8EUQm6CfZMpWLPpZoWz52olpB27k6wZXztM5X87TWTgRj+uMM+A8nfPlPJ2Fl2o3l66rF8srbeWF/ucVnnbieqV2c5GnEfBFEJugL1+RZRmyLA+yJUlKsBVFYb7UVh8XRTHBpi+fFUURfr8fn/zkJ0EGvuoDQgh77kltK4qSYEuShEAggJtuuikhNi0SWZYTbDUPQRCwbt06CIKQlpMRO5mTmofaDgQCjKseJy0bAMtXj5NeO1Guem2Tqp0oUnHSaiear8/n0+Wk1U6CIGDt2rUIBAKWau+mm25CIBAwXHuUB+VgpvYAwOfzse1wRmuP2mquZmqPbsHz+XwpOem1E4XR2qP9bO3atczXaO3JsjwoX6O15/f7sW7dukG6YLSd6PVT8dNqJydW9rOpp4QQ+P1+3HjjjfD7/VnRU3rf1q5dy7hyPXVfT/XGOqf1VIsH19NE28hYl0k9JYQgEAgM0oVc1FPAuqaauV/UtjpHBVL3W6fmqBRG+6qa17p16+D3+9P21UxpKoCEfpsNTdXTGSfnqKn67VCco9oZz+1oqrpdszFHtTOeW52jpqu3dOO5EfBFEJPYtGkT5s+fjyVLlgAADhw4AABobGxEY2MjAKCurg5NTU0AgNraWjQ3NwMAqqur0dLSwq51+vRpAEBlZSU6OzsBAOXl5eju7gYAlJaWIhQKAQBKSkoQjUYRi8Xwj3/8A5IkIRqNoqSkBAAQCoVQWloKAOju7kZ5eTkAoLOzE5WVlQCAkydPYteuXQCAlpYWVFdXAwCam5tRW1sLAGhqakJdXV0CJ0mSsH///rScqqqq0N7ezjh1dXUxOxUnSfpolVDNqaenB2VlZSk5tbe3o6qqinHas2cPJEnCsWPHUnLSaqdjx45BkiTs2bNHl5NWO23fvp21aTpOWu0kSVJKTnrtVF9fn5aTVjvt2rULJ0+eTMlJr50AWKq9U6dOYffu3Wk5abWTldqjnCoqKizVXl9fH2tTo7VnhpNeO9XU1JiqPcpJkiT84x//MF17FJSHWU524baeSpKEsrKyrOopANTX13M9HWJ6un//fkiSlFU9pe3B9VS/nY4dO4Z9+/al5JRpPQWAsrKynNNTwL6mtrW1MdvM/bIzR925cyckScKpU6csjalW5qg0jhFOyTVA+Rnpq5nUVDN9NVOaWlFRkZZTpueou3fvxqlTp1JyGkpzVCvjuV1NVbdrNuaoVsdzO3NUyqOrq8s0J6p7aZHyA7ocuqDfYD99+jQhhBBJkogkSYNsURQTbFmW2bePo9FownFCBr6LrLbpd5Xj8TiJxWKkuLiY9Pf3E0VRiKIo7PvJapvGoLYoiixmOBxOOE7zVdtqHpFIhBQXF5NIJJKSk5YdjUZZTD1Oah5qm+bb39+vy0nLDofDLF89TnrtRLmq89Xjp26n/v5+9i3rVJy02onmG41GdTlptRPNNR6Pm6o9QgiLGY/HDdeeoiis/mKxmKnaI4SwWqAxjNQetdVcjdaeVj8zWnuEEMaV5muk9tT9LBKJmKo9SZJS6kKqdtLTBSMakUoX0rVTZ2enoe+wG4Ebemqnpq3qqbqdaY1wPXVfT/XGOif1VFEUxjUWi3E91bGNjHWZ1FOad7Iu5JKeEmJdU7V0xuk5arp+68QcVd1vjfTVdP3WaU1N7rfZ0FQ9nXFyjurGeJ6Kq1PjuR1NtTqe25mjWh3P7cxR7Yznp0+fNqSnAiGqfYochtHb24sxY8agp6fH9PePRVFESUkJ1q1bh2Aw6FCG7oPzHF7wCk/AO1zt8LSjgZm8Fm+r4QWv8AS8w5XzTI9M6qmd6/G2Gn7wClfOc3ghG3rKH4exiWyuIRFC0NvbaymmVV87Me3AjXzd4Mp5OufLeToLJ+JxnXEGnKdzvpyns/BS7ebSdfVieaWtvND/vMLTTlyv1G4u8jQCvghiE/QZtGzFos9OZsvXTkw7cCNfN7hyns75cp7Owol4XGecAefpnC/n6Sy8VLu5dF29WF5pKy/0P6/wtBPXK7WbizyNgD8OYxF8+3Z6cJ7DC17hCXiH61DZvs31ND04z+EHr3DlPNODPw6TXXiFJ+Adrpzn8AJ/HCYHQD8DlK1YZ8+etRTTqq+dmHbgRr5ucOU8nfPlPJ2FE/G4zjgDztM5X87TWXipdnPpunqxvNJWXuh/XuFpJ65XajcXeRoBXwSxCVn1De9sxNqzZ4+lmFZ97cS0AzfydYMr5+mcL+fpLJyIx3XGGXCezvlyns7CS7WbS9fVi+WVtvJC//MKTztxvVK7ucjTCPgiiE34fAO3UJZldtPVtiRJCbZ6dYra6uOiKCbY9GklURQRCASwZs0aAAMvfSGEQBRF9jO1FUVJsCVJQjAYxI033ghBEBKO03zVtpqHz+fD2rVr4fP50nIyYidzUvNQ28FgkHHV46RlC4LA8tXjpNdOlKsgCIZ4qG2KVJy02gkA1q5dC7/fr8tJq518Ph9uuOEGBINB07UnCAJuvPFGBINBw7VHeVAOZmoPAPx+Pz7xiU8gGAwarj1qq7maqb1gMIjrr78efr8/JSe9dqIwWnu0n91www2snxmtPVmWEQwG8YlPfILla7T2AoEA1q5dO6htjLYTvX4qflrt5MQ2zGzqKSEEgUAA119/PQKBQFb0lN63G264gXHleuq+nuqNdU7rqRYPrqeJts/nY1z/f/bePDyO6zrz/lV19YIdIMEN4CKSEkVRtCSaoihSFrVZlETPRHGssceZ8TixnWcUZWLZ+rLYk0VR4sTjxKMoTigr48hxFi9KYpuOI0gCJUrcxX0nSIEkSIIgCBB7A73Vcr8/GlXsBrqBXlBogn3f56mnThfq1Llv3XPfurh1q2oy9FQI4XBN1IWpqKeQu6Zmc75sO9c+qq0z6dqtW31UG5m21cR+0GOPPYamaeO21YnSVEVRktrtZGiqx+Ph4YcfHqUzbvZRx2q312MfNZ/reT6amlivk9FHzed6nmsfdbx8G+96ngnkIEiW2LhxI8uWLWPVqlUAHD9+HICmpiaampoAOHr0KM3NzQAcOnSIlpYWAPbu3Utra6tzrI6ODgC2bdtGV1cXAFu2bKGvrw+AxsZGgsEgAA0NDYRCIdrb22loaMAwDCKRCA0NDQAEg0EaGxsB6OvrY8uWLQB0dXWxbds2LMvi9OnT7Nq1C4DW1lb27t0LQEtLC4cOHQKgubmZo0ePOpxOnjxJZ2dnRpx27dpFe3u7w6m7u9ux03GKRCIYhjGKk2VZXLhwYUxOAO3t7aM4dXZ2cu7cubSc0tXTuXPn6OzsHJNTqnp69913nTodi1O6eurs7KSzs3NMTiPr6YMPPmDv3r1YlpV17u3atYvTp09jWVbGuWdzArLOPYDLly+zdetWLMvKOPcSOR0+fBjLsjLOva6uLizL4u2336anpyer3AMYHBx06jSb3LMsi2PHjnHw4MFxOY2sJ8uy2LFjBxcuXEjLKVU9DQwM0NnZmVPu2bB5ZJJ7NqfEAYhcUUg9jUQixGIxGhoaiMVik6KnTU1NWJbF3r17+eCDD8bkJPV08vTU5tTZ2cnJkycnTU83b94MIPV0jHo6d+4cu3fvxrKsSdHTYDCIZVlOn2sq6Snkr6ltbW2Onen5gvz6qFu3bqWzs5PLly9PWh/VjjMep3R53dnZycDAwKRp6sGDBzl27Jhz/ZgMTe3p6eHtt9/GsqyM2upE9FEty2Lr1q1cvnx5TE7XSx811+t5vpqaWK+T0UfN9XqeTx/V5tHd3Z319dzWvXEhJHJCf3+/AERnZ6cQQgjDMIRhGKNsXdeTbNM0RSwWE5s2bRKRSCRpuxBCxGKxJNuyLMeOxWLi7bffFqFQSFiWJSzLErFYTAghkmw7hm3rui50XRdvv/22CIfDSdvt8ibaiTwikYh45513RCQSGZNTKjsSiYhNmzaJUCiUllMij0TbLm8oFErLKZUdDoed8qbjlK6ebK7hcDgtp1T1NDQ0JDZt2uTUUTpOqerJLm80Gk3LKVU9RSIR8fbbbzt1kWnuCSFEOBx2fDPNPcuyRDQaFZs2bRLRaDSr3BNCiGg06sTMNPdsO5Frprlnx3j77bdFNBrNKvfs8tp1mmnuJbYzu21nmnv2eUgsbya5Z+fcO++8M0oXMtEIW4sS29l4uWeXvbu7WwCiv79f5ItC6Kl9Pt5+++2k3+nOVeI5yVVPE+s5sbxSTwurp+mudW7qqWVZDtdoNCr1NI2dybVuIvXUsizHN1EXppKeCpG7pqbSGbf7qOO1Wzf6qKFQKElnstHUUCgk3nnnHcc3FaeJ1tSR7XYyNDWdzrjZRx2r3V6PfdRcr+f5aKqtM0NDQ1nlXj591Fyv5/n0Ue02mthWMtXUzs7OjPRUfh0mR8ivGYwPyfPGQrHwhOLhmg9P+XWYyYXkeeOhWLhKnuNDfh1mclEsPKF4uEqeNxYmQ0/l4zB5YqKmMGYaq62tLaeYufrmEzMfFKK8heAqebrnK3m6CzfiSZ1xB5Kne76Sp7soptydSsdNF6tY6qoY2l+x8MwnbrHk7lTkmQnkIEiemOxKPXv2bM5JmItvPjHzQSHKWwiukqd7vpKnu7hRBkFk+5t4v3x9c0WxtL9i4ZlP3KmYu1PpuOliFUtdFUP7Kxae+cQtltydijwzgeZyOW54aNrknUJN01i3bt2k+uYTMx8UoryF4Cp5uucreboLN7RvMvXUjifb38T75eubK4ql/RULz3ziTsXcnUrHTRerWOqqGNpfsfDMJ26x5O5U5JkJ5EyQPDHZI1sXLlzIeSQuF998YuaDQpS3EFwlT/d8JU93caPMBJHtb+L98vXNFcXS/oqFZz5xp2LuTqXjpotVLHVVDO2vWHjmE7dYcncq8swEchAkT0x2pcrn89zxy9c3V0ie7vlKnu7iRhkEke1v4v3y9c0VxdL+ioVnPnGnYu5OpeOmi1UsdVUM7a9YeOYTt1hydyryzARyECRPKIoCgGmamKY5yjYMI8lOrBjbTtyu63qSbX+8R9d1PB4Pa9asQQjhLLquAyTZlmUl2YZhoGka9957b1Js+5vapmkm2Yk8FEVh7dq1KIoyLqdM7JGcEnkk2pqmOVzTcUplA05503FKV08213R1M1Y92RiLU6p6EkKwdu1aVFVNyylVPSmKwurVq9E0Lafcu/fee9E0LePcs3nYHLLJPQBVVbnnnnvQNC3j3LPtRK7Z5J6maaxatQpVVcfklK6ebGSae3Y7W716teObae6Zpommadxzzz1OeTPNPY/Hw9q1a0fpQqb1ZB9/LH6p6smNadaTqadCCDweD6tWrcLj8UyKntrnbfXq1Q5XqaeF19N01zq39TQVD6mnybaiKA7XydBTIYTDNVEXpqKeQu6ams35su1c+6jAmO3WrT6qjUzbaiKvtWvX4vF4xm2rE6WpQFK7nQxNVVWVVatWjdIZN/uoY7Xb67GPms/1PB9NTazXyeij5nM9z7WPOl6+jXc9zwRyECRLbNy4kWXLlrFq1SoAjh07BkBTUxNNTU0AHD16lObmZgAOHTpES0sLAHv37qW1tdU5VkdHBwDbtm2jq6sLgC1bttDX1wdAY2MjwWAQgIaGBoaGhvjggw9oaGjAMAwikQgNDQ0ABINBGhsbAejr62PLli0AdHV1sW3bNkzT5NChQ+zcuROA1tZW9u7dC0BLSwuHDh0CoLm5maNHjzqcTpw4wZkzZzhy5Mi4nHbt2kV7e7vDqbu727HTcYpEIhiGMYqTaZqcOHFiTE4A7e3t7Nq1K4nTmTNnOHfuXFpO6erp3LlznDlzZkxOqerp3Xffdep0LE7p6unMmTN0dHSMyWlkPZ0+fZodO3ZgmmbWubdz504OHTqEaZoZ557NCcg69wDa2trYsmULpmlmnHs2pyNHjrBnzx4nhzPJva6uLkzTpLGxkZ6enqxyD2BwcNCp02xyzzRN9u3bx8GDB8fklKqeTNPkvffe48KFC2k5paqn/v5+zpw5k1Pu2bB5ZJJ7NqfEzlmuKKSeRiIRotEoDQ0NRKPRSdHTpqYmTNNkx44dnD59ekxOUk8nT0+bm5s5cuQIZ86c4cSJE5Omp5s3bwaQejpGPZ07d47t27djmuak6GkwGMQ0TafPNZX0FPLX1La2NsfO9HxBfn3UrVu3cubMGdra2jI+XzanXPuodpzxOKXL6zNnztDf3z9pmnrw4EH27duHaZqTpqk9PT00NjZimmZGbXUi+qimabJlyxYnD6/3Pmqu1/N8NTWxXiejj5rr9TyfPqrNo7u7O+vrua1740JI5IT+/n4BiM7OTiGEEIZhCMMwRtm6rifZpmmKWCwmNm3aJCKRSNJ2IYSIxWJJtmVZjh2LxcSePXtEKBQSlmUJy7JELBYTQogk245h27quC13XxZ49e0Q4HE7abpc30U7kEYlExN69e0UkEhmTUyo7EomITZs2iVAolJZTIo9E2y5vKBRKyymVHQ6HnfKm45Sunmyu4XA4LadU9TQ0NCQ2bdrk1FE6TqnqyS5vNBpNyylVPUUiEbFnzx6nLjLNPSGECIfDjm+muWdZlohGo2LTpk0iGo1mlXtCCBGNRsX777/vxMsk92w7kWumuWfHeP/990U0Gs0q9+zy2nWaae4ltjO7bWeae/Z5SCxvJrln59zevXtH6UImGmFrUWI7Gy/37LJ3d3cLQPT394t8UQg9tc/H+++/n/Q73blKPCe56qn9t8QckXpaeD1Nd61zU08ty3K4RqNRqadp7Egk4nCdDD21LMvxTdSFqaSnQuSuqal0xu0+6njt1o0+aigUStKZbDQ1FAqJvXv3Or6pOE20po5st5Ohqel0xs0+6ljt9nrso+Z6Pc9HU22dGRoayir38umj5no9z6eParfRxLaSqaZ2dnZmpKeKEAnzFCUyxsDAAFVVVfT391NZWZmVr67rNDQ0sGHDBrxer0slLDwkzxsLxcITiodrPjzz0cCJPJasqxsLxcITioer5Dk+JlJP8zmerKsbD8XCVfK8sTAZeiofh8kTEzWFMdNYp06dyilmrr75xMwHhShvIbhKnu75Sp7uwo14UmfcgeTpnq/k6S6KKXen0nHTxSqWuiqG9lcsPPOJWyy5OxV5ZoKCD4K8/PLLLFy4kEAgwMqVK9m+ffuY+2/dupWVK1cSCARYtGgRr7zyyqh9fvzjH7Ns2TL8fj/Lli3jpz/9adLf/+iP/ghFUZKW2bNnTygvtxAOhyfdN5+Y+aAQ5S0EV8nTPV/JU2I8yPbnjl++voWIOZXaX7HwzCfuVMvdGwHFUlfF0v6KhWc+cYsld6caz0xQ0EGQ1157jS996Uv83u/9HocOHeL+++/niSee4OLFiyn3b2lpYcOGDdx///0cOnSI//2//zdf/OIX+fGPf+zss3v3bj71qU/xmc98hiNHjvCZz3yGT37yk+zZsyfpWLfffjvt7e3OkvFLVEbA4/Hk5JdrrBUrVuQUM1fffGLmg0KUtxBcJU/3fCVPd+FGPKkz7kDydM9X8nQXxZS7U+m46WIVS10VQ/srFp75xC2W3J2KPDNBQQdBXnzxRT7/+c/zhS98gdtuu42XXnqJefPm8e1vfzvl/q+88grz58/npZde4rbbbuMLX/gCn/vc5/jmN7/p7PPSSy/x6KOP8tWvfpWlS5fy1a9+lUceeYSXXnop6ViapjF79mxnmTFjRk4cJnt6z/Hjx3OejpSLbz4x80EhylsIrpKne76Sp7u4UR6Hke1v4v3y9c0VxdL+ioVnPnGnYu5OpeOmi1UsdVUM7a9YeOYTt1hydyryzAQFGwSJxWIcOHCA9evXJ21fv3590qerErF79+5R+z/22GPs37/f+bZwun1GHrO5uZm6ujoWLlzIf/2v/5Vz587lS0lCQkJCQkJCQkJCQkJCQuI6hlaowPY3kmfNmpW0fdasWVy5ciWlz5UrV1LubxgGXV1dzJkzJ+0+icdcvXo1//iP/8iSJUvo6Ojga1/7GmvXruXEiRNMnz49ZexoNEo0GnV+DwwMAGBZljMAkyns/bP1A7j11luxLAvLsibNN1e/fHjmE3eyz5Hk6V7MfH0LkbvFwjMXfjauFz0F2f7c8svVV+qMuzHz8ZV9gbFRKD2FidNUWVfuxczXV+rM2JC5617MfPxy9Z0MPS3YIIgNRVGSfgshRm0bb/+R28c75hNPPOHYH/rQh1izZg2LFy/mH/7hH3juuedSxv3617/OCy+8MGp7Y2MjpaWlacs7FjZv3pyT31SD5HljoVh4QvFwzYVnKBTKOZ7U09whed54KBaukmd65KOnMPGaKuvqxkOxcJU8byy4qacFGwSpra3F4/GMmvXR2dk5aiaHjdmzZ6fcX9M0ZwZHun3SHROgrKyMD33oQzQ3N6fd56tf/WrSAMnAwADz5s3jkUceoaamJq1fKui6zubNm3n00Uez+vaxaZqcPXuWxYsXZ/2CmVx984mZK89ClTdXX8nz+ixvIXK3WHgC9Pb2ZrV/Iq4HPQXZ/tzyy8dX6oy7MacSz0KVtxC5m4+ewsRpqqyr67O8UmfGh8xd92JONZ6Z6mnBBkF8Ph8rV65k8+bNfPzjH3e2b968mSeffDKlz5o1a/j5z3+etK2xsZG7777bOUFr1qxh8+bNfPnLX07aZ+3atWnLEo1GaWpq4v7770+7j9/vx+/3j9oeCASyrhwbXq83K1+v18vtt9+ec6xcfPOJmXiMbM9RIcqbL1fJ052YUyl3i4UnxLUvV1wPemr7yPY38X75+tr+UmcmPuZU4plP3KmWu/noKUy8psq6cifmVGp/xcIzn7jFkrtTjWemelrQr8M899xz/N3f/R3f/e53aWpq4stf/jIXL17k6aefBuIj2//jf/wPZ/+nn36aCxcu8Nxzz9HU1MR3v/tdXn31VX7rt37L2efZZ5+lsbGRb3zjG5w6dYpvfOMbvP3223zpS19y9vmt3/ottm7dSktLC3v27OGpp55iYGCAz372s1lzMAwj9xOQQ6x9+/blFDNX33xi5oNClLcQXCVP93wlT3fhRjypM+5A8nTPV/J0F8WUu1PpuOliFUtdFUP7Kxae+cQtltydijwzQUHfCfKpT32K7u5u/viP/5j29naWL19OQ0MDCxYsAKC9vZ2LFy86+y9cuJCGhga+/OUvs3HjRurq6vjWt77FJz7xCWeftWvX8qMf/Yjf//3f5w/+4A9YvHgxr732GqtXr3b2uXTpEp/+9Kfp6upixowZ3Hvvvbz//vtO3Gww1vtLJhqKolBTU5NTzFx984mZDwpR3kJwlTzd85U83YUb8aTOuAPJ0z1fydNdFFPuTqXjpotVLHVVDO2vWHjmE7dYcncq8swEBX8x6jPPPMMzzzyT8m/f+973Rm174IEHOHjw4JjHfOqpp3jqqafS/v1HP/pRVmUcC9k+G5VvrJtvvnlSffOJmQ8KUd5CcJU83fOVPN2FG9o3mXpqx5Ptb+L98vXNFcXS/oqFZz5xp2LuTqXjpotVLHVVDO2vWHjmE7dYcncq8swEBX0c5kbAZE/v2bVrV87TkXLxzSdmPihEeQvBVfJ0z1fydBc3yuMwsv1NvF++vrmiWNpfsfDMJ+5UzN2pdNx0sYqlroqh/RULz3ziFkvuTkWemUAOguQJVZ28U6iqKvX19TnFzNU3n5j5oBDlLQRXydM9X8nTXbgRT+qMO5A83fOVPN1FMeXuVDpuuljFUlfF0P6KhWc+cYsld6ciz4z2c7kcNzyEEED8E0CmaY6yDcNIsi3LcnxtO3G7rutJtn18XddRFIX58+djmiZCCIQQ6LrulMO2LctKsg3DQFVV5s2b5xzb3m6XN9FO5CGEYMGCBQghxuWUiT2SUyKPRFtVVYdrOk6pbMuynPKm45Sunmyu9nHG45Fo2xiLU6p6Mk3TeRdNOk6p6kkIwdy5c1FVNevcsyyLefPmoapqxrln87BjZ5N7NmwRzDT3bDuRaza5p6oqdXV1o+om03qykWnu2e1s7ty5TlkyzT3TNJ0LhY1Mc09RFBYsWDBKFzKtJ/v4Y/FLVU9uXNAmU0+FECiKQl1dHYqiTIqe2udt7ty5TlmknhZeT9Nd69zW01Q8pJ4m20IIh+tk6KkQwuGaqAtTUU/tcibGGWlnqz9u9FFtnbFj2dvd7qPayLSt2ratM4qijNtWJ0pTLctKareToakAdXV1o3TGzT7qWO32euyj5nM9z0dTE+t1Mvqo+VzPc+2jjpdv413PM4EcBMkSGzduZNmyZaxatQqAY8eOAdDU1ERTUxMAR48epbm5GYBDhw7R0tICwN69e2ltbXWO1dHRAcC2bdvo6uoCYMuWLfT19QHxT/sGg0EAGhoaGBwcZNu2bTQ0NGAYBpFIhIaGBgCCwSCNjY0A9PX1sWXLFgC6urrYtm0bhmGwZcsWdu7cCUBrayt79+4FoKWlhUOHDgHQ3NzM0aNHHU4nTpxg27ZtHDlyZFxOu3btor293eHU3d3t2Ok4RSIRDMMYxckwDN57770xOUH85bm7du1yOO3Zs4dt27Zx9uzZtJzS1dPZs2fZtm0be/bsScspVT29++67Tp2OxSldPW3bto2Ojo60nFLV0+nTp2lsbMQwjKxzb+fOnWzZsgXDMDLOPZsTkHXuAbS1tfHmm29iGEbGuWdzOnLkCG+//TaGYWSce11dXRiGwRtvvOHkYaa5BzA4OOjUaaa5t3fvXgzD4J133uHAgQNjckpVT4Zh8NZbb3H+/Pm0nFLVU19fn6ML2eaeDZtHJrlnc0rsPOSKQuppJBJxzk+iPda5yldPm5qaMAyDxsZGTp8+PSYnqaeTp6fNzc0cOXKEbdu2ceLEiUnT082bNwNIPR2jns6ePetc6yZDT4PBoMNjcHBwSukp5K+pbW1tjp3p+YL8+qhbt25l27ZttLW1ZXy+bE659lHtOONxSpfXtg5PlqYeOHCAd955B8Mwxm2rIznlqqnd3d288cYbGIaRUVudiD6qYRi8+eabTh5e733UXK/n+WpqYr1ORh811+t5Pn1Um0d3d3fW13Nb98aFkMgJ/f39AhBdXV1CCCEMwxCGYYyydV1Psk3TFLFYTGzatElEIpGk7UIIEYvFkmzLshzbMAzR2toqIpGIsCxLWJYlYrGYEEIk2XYM27aPf/HiRRGNRpO22+VNtBN5xGIxcenSJSd+Ok6p7EgkIjZt2iRCoVBaTok8Em3TNB2u6TilsqPRqFPedJzS1ZPNNRqNpuWUqp6GhobEpk2bRCwWG5NTqnqyy6vrelpOqeopFouJixcvCtM0s8o9IYSIRqOOb6a5Z1mWiEajYtOmTSIajWaVe3b8CxcuCNM0M849207kmmnu2XEuXLiQdM4yyT37HNl1mmnupWpnmeaeYRijyptJ7tnt8tKlS6N0IRONsLUoFAplnHt22Xt7ewUg+vv7Rb4ohJ5aliUMwxAXLlwQhmFMip7a9Xzx4kXnmFJPC6+n6a51buqpZVkO12g0KvU0jR2LxRyuk6GnlmU5vom6MJX0VIjcNTWVzrjdRx2v3brRRw2FQkk6k42mRiIRcenSJSd+Kk4TranpdMZNTU2nM272Ucdqt9djHzXX63k+mmrrzNDQUFa5l08fNdfreT59VLuNRiKRrK/nXV1dGelpwb8OM9Xh9XqB5DfRJtqapo2yzeEpRvb0x8R97OOls+fOnZsyvqIojq2qqnPsRHvevHmOX+L2dGX3eDx4PJ6kqWnpOKWyxfB0JE3TnFjj8UvHNR2nkbbP5xtV3rH4jSx7plyz4ZFoj6ynVOVNxy+xvF6vN6k+x+I00vb5fI5v4hTcTMpurxN5ZJJ7mqYxf/78JI4jy5vOTuSaWN5M8jAxZrb1lFjWTHIvVTvLJvdGljeb3BuZQ5nknqqqztRD+/iZ5J5tuzF9e7L11OPxpMwRt/TURqKv1NPC62m6a52bepqOh9TTZNvr9SbFHY/fROip/VjHePtc73qaWN5MNTWVzrjdRx2v3brVR82U00jb7/c7MRPrz01NTaczbmpqOp1xu4+art1er33UXK7nqbhmWk+2zozHaaL7qPlcz3PpoybqaLaamnj8sSAfh8kTxgRNYcw0lj3tdrJ884mZDwpR3kJwlTzd85U83YUb8aTOuAPJ0z1fydNdFFPuTqXjpotVLHVVDO2vWHjmE7dYcncq8swEchAkT7g1ep8u1vLly3OKmatvPjHzQSHKWwiukqd7vpKnu3AjntQZdyB5uucrebqLYsrdqXTcdLGKpa6Kof0VC8984hZL7k5FnplAPg6TJya7UmfOnDmpvvnEzAeFKG8huEqe7vlKnu7iRhkEke1v4v3y9c0VxdL+ioVnPnGnYu5OpeOmi1UsdVUM7a9YeOYTt1hydyryzGg/l8txw0NP+FzRZMR66623coqZq28+MfNBIcpbCK6Sp3u+kqe7cCOe1Bl3IHm65yt5uotiyt2pdNx0sYqlroqh/RULz3ziFkvuTkWemUAOguSJxJexTEasVatW5RQzV998YuaDQpS3EFwlT/d8JU934UY8qTPuQPJ0z1fydBfFlLtT6bjpYhVLXRVD+ysWnvnELZbcnYo8M4EcBMkT9pu0TdN0vlKQaBuGkWRbluX42nbidl3Xk2z7+LquoygKNTU1mKaJEAIhhDPalWhblpVkG4aBqqpUV1c7x7a32+VNtBN5CCGYNm0aQohxOWVij+SUyCPRVlXV4ZqOUyrbsiynvOk4pasnm6t9nPF4JNo2xuKUqp5M02TatGnO8cfil1hPQgiqqqpQVTXr3LMsi+rqalRVzTj3bB527Gxyz0ZlZSWqqmace7adyDWb3FNVlYqKilF1k2k92cg09+x2VlVV5ZQl09wzTRNVVamsrHTiZpp7iqIwbdq0UbqQaT3Zxx+LX6p6cmOa9WTqqRACRVGoqKhAUZRJ0VP7vFVVVTllkXpaeD1Nd61zW09T8ZB6mmwLIRyuk6GnQgiHa6IuTEU9tcuZGGekna3+uNFHtXXGjmVvd7uPaiPTtmrbts4oijJuW50oTbUsK6ndToamAlRUVIzSGTf7qGO12+uxj5rP9TwfTU2s18noo+ZzPc+1jzpevo13Pc8EchAkS2zcuJFly5axatUqAI4ePQpAU1MTTU1Nzrbm5mYADh06REtLCwB79+6ltbXVOVZHRwcA27Zto6urC4AtW7bQ19cHQGNjI8FgEICGhgaCwSCvv/46DQ0NGIZBJBKhoaEBgGAwSGNjIwB9fX1s2bIFgK6uLrZt24au67z++uvs3LkTgNbWVvbu3QtAS0sLhw4dAqC5uTmJ0/Hjx3n99dc5fPjwuJx27dpFe3u7w6m7u9ux03GKRCIYhjGKk13esTgBtLe3s2vXLofTnj17eP311zlz5kxaTunq6cyZM7z++uvs2bMnLadU9fTuu+86dToWp3T19Prrr3PlypW0nFLV06lTp3j99dfRdT3r3Nu5c6fjm2nu2ZyArHMP4NKlS07MTHPP5nT48GHHN9Pc6+rqQtd1GhoaHH6Z5h7A4OCgU6eZ5t7evXudmAcOHBiTU6p6sn3Pnz+fllOqeurt7XV0Idvcs2HzyCT3bE4TMbWxkHoaiUQIh8M0NDQQDocnRU+bmpoc31OnTo3JSerp5Olpc3OzozPHjx+fND3dvHkzgNTTMerpzJkzTg5Php4Gg0HHNxgMTik9hfw1ta2tzbEzPV+QXx9169atvP7661y6dCnj82VzyrWPascZj1O6vH799dfp7e2dNE09cOCA0w7Ga6sjOeWqqV1dXU7MTNrqRPRR7WvVpUuXxuR0vfRRc72e56upifU6GX3UXK/n+fRRbR7d3d1ZX89tTuNCSOSE/v5+AYju7m4hhBCGYQjDMEbZuq4n2aZpilgsJjZt2iQikUjSdiGEiMViSbZlWUnb+/r6RDQaFZZlCcuyRCwWE0KIJNuOYdu6rgvLskRvb++o7XZ5E+1EHrqui/7+/lE8RnJKZUciEbFp0yYRCoXSckrkMdK2uabjlMqOxWJOedNxSldPNtfEOkjHL3GfoaEhsWnTJhGLxcbklK6e+vv7k+ogHb/EfXRdFz09PcKyrKxyzy57b2+vsCwr49yzLEtEo1GxadMmJ/8yzT27XHZ5M829xLInljeT3DNNU1iWJbq7u539M809IYTD1T4nmeReqnaWae4ZhiEsyxI9PT3j8htZT6Zpiv7+/lG6kEnu2VoUCoUyzj277H19fQIQ/f39Il8UQk/tXOzu7nbyxW09TaznxLYs9bSwepruWuemnlqW5XCNRqNST9PYmVzrJlJP7bKP1IWppKdC5K6pqXTG7T7qeO3WjT5qKBRK0plsNDUajYr+/v6UPEaWfaI0NZ3OuKmphmGI7u7uUTrjZh91rHZ7PfZRc72e56Opts4MDQ1llXv59FFzvZ7n00e122gkEsn6et7d3Z2Rnsqvw+QJTYufwsTnjxJt+++Jtjk8xcie/pi4j9frHdOuqqpKim9vVxTFsVVVdY6daFdXVzt+idvTld22E6empeOUyhbD05E0TXNijccvHdd0nFLZicfKhF9i2TPlmg2PRDtVPY0sbzp+I8tbU1OTEadUZbdzIRtOiqI460QemeSex+NxyqsoSsa5Z5cxVXkzse0ptpnwS8V1LE6ZtLNscg9wztFYnFKVd2TeZpp7+vDdR/v4meSebSeeo4nCZOupPU0z1Xa39BQyq2epp5Onp+mudW7qaToeUk+T7UyudROtp5lwnQp6mliWTDU1lc5MRh91rHbrVh81U04jbZ/Ph8/nc8qZjlOmXDPJ63Q646amejwepx0k6ozbfdR07fZ67aPmcj23kYum2jozHqeJ7qPmcz3PpY+aqKPZampiecaCfBwmT+gTNIUx01g/+9nPcoqZq28+MfNBIcpbCK6Sp3u+kqe7cCOe1Bl3IHm65yt5uotiyt2pdNx0sYqlroqh/RULz3ziFkvuTkWemUAOguSJTEebJirW+vXrc4qZq28+MfNBIcpbCK6Sp3u+kqe7cCOe1Bl3IHm65yt5uotiyt2pdNx0sYqlroqh/RULz3ziFkvuTkWeGe3ncjkkJhj5JFGuvpMtRvnGLcQ5ygcjYwphYVkxhIgCoCh+VNU/arrsVOc5Gb6Sp8R4KFadcdsvX99CxJxK7a9YeOYTd6rl7o2AYqmrYml/xcIzn7jFkrtTjWcmkDNB8oSR8HmlyYhlv1F3snzziZkPClHe/LgKTHOISOQiweBBeno209HxI9raNnL+/J9w9uxvc/r0/+TkyV/m6NH/xKFDD7B//wr27LmF7dtns3PnDLZvr2DrVh9bt3rYvr2EHTuq2bGjmu3bS9i6VWXrVj/bt1eyc+cMdu+ex/bt9ezaNYcdO2rYtq2MrVsDbN3q4733PLz3nsq2bWXs3Dmb99+/mf37V3Do0P0cPbqB48f/C++++zGam3+L8+e/xqVLf8OVK/9MV9d/0Ne3ncHB40SjV7CsiZu6Viy5Wyw87bhT4ZjjxZtaOpMbJE/3fCVPd1FMuTuVjpsuVrHUVTG0v2LhmU/cYsndqcgzEyhCZPgxXYkkDAwMUFVVRV9f36gXQY0HffgTRRs2bBj1gqSxIIa/K65pWtYv0crVN5+YufIsVHlT+QphoetdxGLtRKOXk9aJdiTSjqJM7jOMkwGPpxKvtxavtxZNm05HR4SFC1cQCMzF55uDzzcbvz++9ngq057zYsndYuEJ0N/fT3V1Nf39/aNeepUtCqGncP3oTCaQPK/P8kqdGR8yd8fHROop5K6psq6uz/JKnRkfMnfdiznVeGaqp9fn/JQpBPvLBPba4/Ek2YZhoCiKY9tvsgWwLAvA2W6/Xdzj8Ti2nTT2dl3XEUI4CWEYBl6v10kyr9eLZVmYpunYlmXh8XiIxWIASds1TcM0TYQQjp3Iwz6u/bbldJxUVR1lJ/JLx8meImU3kETb5urz+VJy0jRtlG2aplM+ICUnAFWFSOQKsdhlDKOTcLiVWOwKkUgbhnEFXe8YHui4ghDjjyja7VpRfHi90/F6p+PxTMfnq8XrrUFRKvH5qvF4ylGUcny+GlS1DEUpQwgfPl/ZsG8Z4AW8eL2lw+c/jKoaGEYI0wyjqha6PkQkEqKsrAbwoqo+PB4flgWKoqKqGroexLIGESJELNaPZQ1iWUPEYn3EYr0oyhC63odp9mOa/Y6t6z0YRg/x2S0DmOYAkcg5AHw+aGt7N+U5UNUSfL7ZeL3XBkY0bRYlJfVo2gwsq4aqqsV4vbUIoY6be4l2LBZD07SMc09VVRRFIRKJUFpa6rSdTHLPbk+J7TST3EvVzsbiZNs2J1VVk8qbjt9IjbD3HakLqTiN1AgbidtT8UtVT25gMvVU0+JfQohEIpSVlY17riZCTyH+RvNoNIqiKFnl9PWsp6nqyY5tb0vXTlPVk41IZABdbyEcPs3g4Cmi0TOEw2ewLAOPJ4Ci+FEU37DtG17K8PtnoGnT8PtnomnT8HhqCARmoSjlw75gGFFMM0ws1sLg4FHC4WYsS6BpZXg85ZSV3UZp6e2UlCzF6y0Zs54S9WnsdtrD4OAZDOMSkch5hobO4/dfoa3tNF7vDAKBOXi9tahqDSUldSiKP209RSIRysvLs8q9qaanNo9oNJpV7uWjp3bZR+rCVNRTyF5TU+mM231UW2dUVXXOy2T0UW2Md52wedi2ruuYpukcP911YqI1NZXOZKupmbTVRE6pdCbTvB6v3aaqp5E6k+31HOL/dCfyGO96no+m5no9z0dTE+sym9zLp4+aGDvb63mufVQ7nm1nk3uZ6qp8HCZLbNy4kWXLlrFq1SoAjh07BkBTUxNNTU0AHD16lObmZgAOHTpES0sLAHv37qW1tdU5VkdHBwDbtm2jq6sLgC1bttDX1wdAY2MjwWAQgIaGBgYHB9m8eTNvvPEGhmEQiURoaGgAIBgM0tjYCEBfXx9btmwBoKuri23btmEYBm+//Ta7du0CoLW1lb179wLQ0tLCoUOHAGhububo0aMOpxMnTtDY2MiRI0fG5bRr1y7a29sdTt3d3Y6djlMkEsEwrk2VsjkZhsHmzZvZvHlzWk4A7e3tSZz27NnF5s0/pKlpE/v2/RWXL/8tBw8+y/vv/xeOHfvP7N59Bzt3zmHrVh979szl0KF7OHbsP3HmzK9z8eILdHb+HT09/0EwuI9o9NLwAIiC1zsTIRZTXv4Is2d/DsP4NHV1f87tt/+EcPj/MjDwt9x7bw99fa+xYsVZ7rrrIK2tX+bWW3/I/PnfoqlpHTfd9AdUVX2eI0fqmDnzKVR1Nfv3h9m5s4PBwens29dGIDCX7m6DvXuP4/EEaGvr5ODBU/h8M7l82eDUqUHKym6nra2MPXv6CQSWceGCRUtLhEBgPqdPd3PhwiB+/2yamvro7Kygquo+mpuriURWM2fOr3L+/Ic5eXIlCxb8H9rb/xszZ77MnXduprv7a9xyy/t85CNdDA1t4o47WrjnnlMMDn6dJUv+hYULXyYS+QyzZv0G06Y9hWHcTknJElQ1PspqWWEikRaCwd10df2Ey5df5uLF5zl9+gucOPEkTU3reP/9erZvD7Bjxyz27buLvXsf5v33P865c/+bw4f/kAMH/i99fds5fvwtTp48AMCRI0d4++23MQwj49zr6urCMAy2bNni5GGmuQcwODjotNNMc2/v3r1OOzt48GDK9jSWRtjlvXDhQlpOqTSir6+PxsZG3njjjTE5pdMIO1Y6Tuk0IvFinCsKqaeRSIRIJMKWLVsc2209bWpqwjAM3nnnHU6fPj0mp+tBT/fu3UtjYyNnz55N4nTkyBFMM8KJE3s5cWI7kchFDh36OSdPvs7AwD727/97Tp78J7Zs+TPef//PaWr6G9rbv8vOnb/DyZN/zIULX2f79s9z4sSznDnzZbZt+zjHjn2Gkyd/mV27HqW09Hn277+F3burOXDgLk6e/BQXLz5PR8c/MTCwm8HBffT3b6ev7216exvo6voJV6/+iM7Of6Sj49tcvPjHnDv3JZqafpljxx7n8OHVvP/+TezeXcuePbPZtWs2e/cu4MCBpRw79gQtLb/LlSt/R2fnq1y+/C1aW/+MU6c+w8GDH2bnzkr27fsQu3atZs+eezl48CPs3LmCPXtWcuDAPezcuZzdu+/kwIF72b59Bfv338/hwx9l+/Y1HDr0BEeOPM727TezY0clu3bN4OjRNZw8+V84d+636ejYSCDwY86f/12amz/HsWNPcPDgKvbvv5nt20vZsaOSHTvmceDAPRw8+Bjbt3+Ms2d/mxMn/oDt23+Pzs5/58yZn7NnzyYMY5Bz586NmXswtfQU4OzZs7zzzjsYhjEpehoMBh3fwcHBKaWnkL+mtrW1OXam5wvy76M2NjbS1tY2aX1UO854nFLl9RtvvJF07U3HCTLX1PHy+uDBg067Ha+tjuT07rvXblyN11YT66m7u5stW7ZgGEZGbTWxnk6fPu2022yu53bbs/Mwm+t54j/r2eQeQFtbm8N1Mq7n+WpqYr1ORh/17NmzNDY2Zp17+fRRbR7d3d1Z5V5zc7Oje+NBPg6TI+yphj09PdTU1GQ10mmaJg0NDTz++OP4/f6sRjoh87sS2dxpyeTu0VicUo0KmqbJm2++yfr16/H7/XlzisX6Mc0OIpFWIpFLGMYVotFLRCJt6PplotFLxGIdgJVhLap4vTPx++uc2Qt+fz2aNpNAoB6/vx6PZwZ+/xw0LX09hUIhNm/ezIYNGwCmXD1lmnu6rvPGG2/wxBNP4PV6kzhFo/0I0UMk0kYkchnTvEo02k4sdhld73Rm1WRXP+DxlA/XTR1+f/2wXU8gMBePZxaBwFwCgbkIoeXEKV09xWIx3njjDTZs2OCc16lST9nkHsQvtuvXr6ekpCQrTkNDQ1RVVU3o4zBSTydeT2OxMKoawTAGiEZ7UJQIut6HrvcDIXR9AMPoR4ghDGMAwxjAsgYT7CEsK4JlRZ21/dLmyYCmVVNSciuBwC2UlS2ltPQWhPCiKCaWFcEwwiiKgWmGMc3w8My3q+h6N6bZg653o+td6Ho3QsSc4yqKF0XRCAQWUlp6O6Wlt6KqGoYxhGH0Ew6fZHDwGKbZP6F8vN45BAILCARuwuOZzYULZ6ivL0PXuzCMLnS9E13vQojsH69U1QBe74zhpRavdwY+30w0bTpe7wz8/ll4PNPweKrw+aoQwo+mVaBppZim6Zr2SD2dXD2F3DU1lc7ciJoaDodpbGx0+m03AifZR03fR52qnFLVE8R15tFHH6W0tPSG4JSqnqLRKI2NjTz++ON4PJ6sOA0MDDBt2jT5OIzbUNX4ZBqPx+NsS7TtSk60E6dRjdwn8bmnkbYQgmAwSEVFhTOVzt5HURTHthMx0RZCMDg4SEVFxah90pXd4/EghGBgYCApZipOqWx7fE3TNCdWKn7xr6F0MzjYRjRqL60MDl7AsjqJxeLbTHOAzODB55uF3183/N6KuuHHMxLtOrzeGajqtTKnOr+GAcGgvWiOPTjodey+PsB1eYwAAQAASURBVD9Hjixl2zYVXfcQiUA0CpGId3gNuu4lFgNdVxJsFcNQqKgwmT9fpb5eY+5cqK9Xqa9XmTsXZs5UqawcXU+qqjpldbaHQniOHIEDB+D4cbRYDDweUFU0VXVsj6IQ83jw1daiVVVBZSVUVOCtrIzb1dV4a2th+DnixByz14n5pigKgUA1UE1JyaKUNWKf2/LyUgyje3iA5ErC4EjyOhptx7KGMM1BTPMMkciZMWs8PvW9Dp+vfnhdh99fh2nWUF19M35/PT7frFHtJp2d+NxiqvaUzhZCMDQ05LSzsdrWtbJrzjkKhUKOb7q2lUoXRrbRdJxGaoQ9TdY+fjp+qcruxvj5ZOopxDmEw2Hn3Lmtp3bMxBxJV8/xjsgg0Wg3htFDJNKB17uD7u4OhBjCNIOYZhDDGEham+YAhhFfm2YQy4qkO90ThvgXrAKoavJaCA1NKxu1feQ60V8IL8ePN7N69cepqLgdr7c24+eIU2l44t+EiKEoGqCm/PtI3/gAbytDQ8cxzSEURR32vbYWQiEcDhEI+AATIYykBcDvn0sgcBN+/zw8noATU9d1Tp9u4NZbk595jncG+9H1q84SH9i5Zkci7VhWr7MtPkAVIRptJRptJTuoeDzleDxlSWtVTfxdhmF4KSmZhqaVp/j7tbUQ8bWmlQC566ltj7rW4Z6e2r6pdCFxn6mgp3bcdDETy2rbqfptbvdRFUVxrmGpzp1bfdRMOKWyNU1zYo51nRiPq13eTPLa4/E459YNTqnqSVEUwuGwkwvj8Ru3j8r41/Ox2m2m/bZUfdTx6klRFCeuG9fzdFxtncmEn23bOpNt7uXTR82mnU1UHzVRC7LVVHv7eJCDIHlioqYwZhpr+/btrF+/PimB3PTNJ2YcUcLhMwwNdSYMcLQND2xcGrbbM3rvBoDHUzH8D209fv9c/P56Z/H56lGUmbz55nFWrXqUSCQ+UHH1auJAhj2IMXrbwIDg8mWBolzbFsno/wcPcCsAKiaVDFBNH9X0UUMv1fRRRT9V9CfZ9hLFT//hKvqopp8qTlLNbuK/Q5QiUPD7FUrLFKorTFbMvcrtte14+44yr9yPNtCH1nqe0otNqNb4z8EpgD8TWpoGtbXxZcYMPNOmccfQEOru3TBtGlRXX1uqqpJ/+5MjJOaRzzcLn2/WuOENY5BYrJ1QqJX9+99i6dJaDKNjOHcuE4tdJhptw7LCGEb8PSZDQ8fHOKI6/CLXkYMl9UnbNK0mk7OTpsxTsY3mBje0bzL11I43GedcCAvD6CUW6yAcvsz+/Vu4/fZ5WFYfhmHPVujBMOJrXY8PfIycDVBaCmfGHg8cowze4fcSVaBplXg8FXg8lWhafJ243V5DKQcOnGD16gfw+crSDGD4Ug5S6LpOY2Nj1udW13UOHWqgsvK+lH6RSFyatBS9l7HqJd4xTq98qXzjA7zzCQTmj1nePXuy5zkW4p3BarzeauCWlDETz60Q8a+TpRs0SfwdH0DpRlUTZ/RY2O9/mmjEB0rKKC/3cfz4LQQC9Qk3JOI3Kuy1x1M6yr8QulgITXVL+6ZCH7UQdSWECQwNDyCCZcUQQkcIPcGOYVnxdXx73Nb1MMePH2LZsltRVcvZf7Tf6GOYZoyOji7mzJmPpsX1U1V9wzPTbHv0WgiVo0ebuOuuu/F6S9PuZ68VxZvgm92LLPM9t/n4FrI/M1VyNx8UE89MIB+HyRH2VMNcpi7m88bb6wXxr6ZcTRjUuJxikOMy8RdsZgIFTZuNx1OPEPUYRj2x2FxCoXqCwXr6+urp6amnt7eC/n4YGEhe7G3Dj9NNBEPKGGI63dTSxWytm7mBLup8XczSupmhdjGdbqaZXVQZ3ZSEO6mwBimJTXwnMhu0M5sDrOQQKxiiDBULFQsPZpJdQphKBpKWCoLUqANU00uZNTh+sLEQCIweGBn5O9U2+3dp6bW3zY4B+46pnW/XcvHyiG1XgAxflKQG8PnqGBz0M2vWbQQC9oyi+Mte4532OcN3qD3jH/A6Rj5alI8GTuSxCqWnQpjEvxzV4SzxFyrbdmeSnelA70goih+vdzqaVsPAgGDGjJvweivTDlyMHNhQ1UpMswJdj89Mi8XiM9XS2SO3xWIgRPIS5596yfdvhmHywQdnqalZTF+fh76rOnR04O++TElfO9P1dhQEwutHLYkvWpkfT6kfb7kfX20lZQtnMu3WGdQv9DFvHsydG5eVsSTFNOMvzFaU4R+hEFZwCL1vCDM4hGqZeL3xCXXOgRLXwzPt8HiSbb8fZs5MGbzQfQHLMoZn3Nmz7pLX4/9t9HbTHMKyhnIqj8dTNWpgxOerIxCYh98fX+Iz+q7P19ldL3qaz/EKnZOZwDQjGEYfhtE7vI6/3D0+G25g+FG+fsdO9bdcc3SqIv4C/qqE68O1RdOq0LQaNK0ar7dm2LZ/T8PrnYHHM3p2XaEhhEk0eplI5ALR6AWGhs7ywQfHuPnmJXg8XhTF4yzgyeC3msW+nmEdSrTtWYJKgm3POlQnYJ/4NvudGtdzG50ITIaeypkgecJ+k/Zkxerr66O6ujrjqT7Z+lpWdPiOUbwDH41eYWDgAqraQyx2OeEufHvGzyybZinhcD2Dg/X098cHM65erefKlbm0tdVz8WI9Fy/OxrImLh09HkFFhUJFBVSUC2aVBqkPdDPH28UsTxe1SjfTRRfVZjdVehflsW5KQ134B69SMtSDd6AbNZbw3LsBZDMuUFqa+p/7qqprS3U1Vnk5ob4+SnUddWAA+vquLf39WKEwpiGcJWaodJi1nBucydmBGfRr0+nXphMsnU146Qpm3FnHklsV1i6O/0NxbZbLNbu/X3DlSoyeHh8dHQodHdBjj1UNp7OfCLV0UUsXM7iatJ7p62NWoJ8ZWh81aj+Voo8Ko4+SWB++yACKEPHbtZEIDL+sMmtomnPuRFUVhqqilZSgaBp4vfG/e70oXi9eTcPr9VI24m9Cu5mIcROBigrweoiVxoiVDBEtGSLqDxLzDxL1DRDT+olqfUQ9vRhq/PGBSOQcmgbd3U1jFNKDzzczaXDE55uNYVRSVbUAv382Xu9M4s/nTxu3455r+85HF/KBG9o3mXpqxxvr3BlGkHD4LOFw/KskkUjcDoXOEItdJpv32wBoWg1e7ywUpZqSkvh7GzRtGvEvSk0j/u6GaUSj07l6dRpXrkzn0qUS2toUWltNjh+/Qk3NHHRdzWgAIxqNP9LnFlRM/EQJEMl5KUmx7SMMMpsr1HGZmXSikuJejT68jDHu3EcVnczkBDPp8cwkVlJJwApRYg4RsIYosYYotwaoFP1UMoAXHS/XTphKhrPmxkGkfDrm8rsovW8FyodXwIoVsGRJzsebuL6AhqpWoWnjf0I1m5jxR1zDmEYQUx8gOtjFzr2buWvlTZhm5/BAdftwnyL+qXnLCmGa/YRC/YRC6XVXUbzDs0DjgyLxAZL5CfY8NK0GIYTr/aWJhFvadz31UQ0jPgvXXgYGLIaGOhgaukAweB6vN4QQ/QjRC/Shqr0oSi8eTy8eTx+a1ovX24umTewjfqbpQdd9GIYXw7DXifb4f9N1H6bpTbJHHtM0vZimhqqaaFoMrzc2au3x6Hi9cdvnS140LYrPpzv7xxcdjyduezzXFlVNvumjKDFnVlguUBQ/Pt8Mp09j2z6f3ceJ22Vly/F4SvPuz1RVVWIYXc7j8Ik3WCORC0Qi54lGW0fdXAgE4NKlnChOKVRVeNizqxLNU4mmVaJ5a/D4aoYHtCrxeKqGbXvgK26rajmhkIfp0xeiaaNn3qXDZPz/OZHIVPfkIEiecPvzZono6DD5t387zf33301VlUpZWfx/7ZKS4btTKcsXxjB6iUQ62b9/Mx/60Fwsq4dYrDPhTqVtd2b1EjghFEKhWfT11dPZWU9bWx0dHfV0dSUudQwOVhMf0RwfXm98fKCyUqAoQerry6mqUrFfV1FVbjLL30etEp+hUW12U212Ux7tpjTSjT94lf5zp5jtVVF7eqCrC850g579S+aA+F282lqYPj3t2qiuZmdTE2s/9jG8M2bECfh8GR3e1HV2btnCww8/jJpipFMdXuy/lAE1wGJdxzPsl+0Iqa4bbNnyXpKvrkNnZ3zMor0dWlsDXLo0l9bWubS2woFL0NoqiEQUiBFfUkDBooKg8zhQraeP+VX9zC3vpVK0sbBGZ6a3n2q1nyqrjzKjj5JoH/5wP57BPpS+vvhdWMOI111XF0oC/2ygACUJv/3DS8UYPqYXYtMgNgOi04bt6RCrVYjN0ojVqkSrLfRyHRRz+B0m7aOOc/nyiA1CxSsq8VmVeM1KfEYZXr0cn16GN1qCL1KCJ+zjfJ/g1l/+NUoW3omqZphDpsm+ffviOTSJgyBuaN9k6amuw6FDcPiwxYULJ1mzxkNV1Xn8/rN4PGcQ4gzR6Bl0fbxBPAWvtxafbxZe7yznUa/475kJ9ixUdSaRiI/eXp2f/3w/dXWr6OjQuHQJ2tpIWg+mHHD1APU58fVgUEGQCoJM1waY5g1SowWp8QxQ7QlS7QlSqQxQqQSpJEi5GKBcBCkzg2jRXspUE68VwWdF8JrxxWdF0KwcdTVLWB4No3YOYk4dat1sLNWDGYpihqJY4SgiEl+IRmGgh8pIDx7LoJp+qulnCc3xiWBZTnCzUBiijCHKMNBQhgdjRq5HzrhLXPuIERjshvffiS/DMP0liGXLuVcx8fy//xcfvE2cSTJyZknCb6EoBC9fpmrBApzpKWPsn/hbAB1NTVQtXBjXWl2/NuXHtlNti8WwrlyB8vK4Po+xvxKL4dF1PMPTiEqAjwGishKlpib+OOW0aTBtNkxbhqipxpxRSrRWJVZjEauIES0JEfMNEKabrq6T+AMDxMxOhNCJRFqIRFrS1ptqaPiHyrCCPspnLMQ7+2a0slnO3e1rS/Jvj6cMRVEKoqluaZ9bx7UsuHIFWlqSlw8+0CkthcFBAXTh87VSUtJKefklampamTmzlRkz4uva2ja8Xp3y8nhaZRdfYXCwmmCwhqGhKoaGqgiFKhkaqhxeVyXYlSn+XkkoVIFhxB81GQv2DDFFsW2BEBaapg4/Zpdqn9Q2CEKhGIriIxZTnKYz0VBVM2FAJUogMERpaZCysgFKSoKUltrLAGVl/ZSX91FR0UtFRS/l5X2Ul8ftysoeAoEQQkSHH2Efe4TB55vNbbd9n/Ly+9O2ISHE8E3WVmeJRIbXofME+8+iaP0Ixh/BV0zw92gEOsDfZqANghjuOAtP3B75G2V4uyfhb6l+Z/q3hGM6a4b/riSsU+2T62Ri1cQQvRhGb/wmbQ7jgqrhQYsF8OoBND2AVy9BM0rxmqV4zTI0sxSvVY4mylGNEi639FBxy72o5ZXxfzztf0BH2vbvQACucz2VgyB5wq2pSLEYHD0K778P+/ZFOXGim56ebioro/zLv/yHI1i2UFVV9VFV1UtlZXxbWVkfJSW9eL3XZjP4fDD8FacxYRgaAwMz6e+fSV/fTHp6Zo4Y3Kijq6uenp7ZmOZo/tOnQ10d3HKLxfz5bdx2WyXV1R7sd3A6798MRJgmuqkyuqmIdVMW6cYX7I7/89vdfW252g2nhu3e3mtzp9OgLN0fSkrGHdBIWtfWZvRYhtB1+kwTbrkl3iHNAl6vl8ceeywrn3z80vl6vVBfH1/SIRYzeO21zdx556P09nrp7Lw2cGLbnZ0qnZ1VdHRUcXFgQfwfj57hBWCc9/VVVQpumjHEwpo+FlT3U1/Wx+ySfsp8MUo0nYDHIODR8Q+vfUrc9ik6PtWI38lV4mvV1OOddX2M9YhtHl2nxDAIRKOEm65QEgqhDA0Bgvht5zgsFfTq4QGS6SMGTKaBXgWxmvg+RiWgWOhKH7raF1fdMW4v72v9DrQOX6CoQAvUopXOHDFdNbkDf8891YTDh4nFyodfUFgx/OJC9yTeDe1z45iGMcDVq+c5evQ8589foLv7PEKcZ8aM88yadZ4lS+LJOfwExCgEg7V0d99MMHgzodDN6PrNwGJ0fQH9/TMYHNQIheK+Q0PJ60T7WkfXC6wZt9xVVQy/KBlunhVkqa8Z69L7LJtXQ5k5RIkRxK8HCUQH8MeCeKNBfOEBtEgQLRTEMzSAGgqiDgVRwuGEEzK8TDQ0Ld7pyXMxvV6OnjnDhx59FG3+fJgzB7W2Fl82nSch4rPphoUp2tpJf3Mn4Y6BuKaXlaFUlKGUleGpqUSbXoU2rRLh9WEqGqaioZaXopUH0LwKFVr8nz77/3x7sWfcRCIQDifXu730XI7Qs+043uOHWKYfZgWHuJMjlEVDeA7tY/y3I42GB1iQg5/te1sOfipQm2NMG4r9/OrwZ2ud7cRlUWOM6zdx3Y3VQnQmRGbE19GZEBleR2eAXgOWZhCu6ocqiHIVevdCbwblUzRHU2fMmMGpU98evtM9M816+oQ9EulWXzLX4woR73K1tMD589cGOS5ejNDd3cngYCdlZZ1UV1+lurqT6upOZs/uYPnyS8MDHZfw+8f/z8yyVILBOQwO1hGNTiMWq0HXazCMGkyzBsuqxrJqEKIGRakBalDVajyeSvx+Fa83fq+qvDzex/X54r9tO92iqjrvvPMWG55Yj08BxdBRLQPF0JPsUf2ETH6P9zd7RGR4hEQoKpaiYgkVU6iYlhJfCxXDUjGFgmnZ9vDaUjCGtzmLGd+mWyqGGd9HN1WiOlxs66Smtp6o6SdieIjotUSCswh1awzFvFwN+/kg5Kc3HKBnyE9v2E8UPxEC+PyRhHq+mlTn9u/a2k5mzTpPRcUVjhz5KAsW/B7r1n2OwcFthMPn4rMpB08R7j9J2LiIpY4x8qPFe1yY4OsBf1d88Q2vA53gvwKBK+DvAcWawAuaPWI1cgRrrG32YvtnuRYIUJXhARLl2gCLAqhKfNBEATzxtVAUwvogWonAVMIYPh2jDMxyMEqJ22VgDP82y+LbDHtbefxYlmYS04aIkeGjYR+C7tg/E+iAQAsE2qGkPV4Pgfb44h0Yccs7EMBbUsJj8UaX/rHRFH/zKAofGRhA6e6Gz30uq2rMVPfkIEiesN/Sm+mnhUAnEmlDVc/Q0/MWECQa7aSnp4f29h66u7sIh7uBbioqulm0qJvly/N7P4NpqgwOVtPfX0tf30x6e2cNr2eOWM+it3dm2pkbNTWCujqFujqLu++Of8Vk1iyT+nqF+nqVmTN06koHKO26hNnSgnXhAud27WKxqMbT0ofS04O4ehV6euJJneo/jgwhKitRpk9HTJ+OmDYNtbYWMW0aZk0N4ZISSubNQ5kxA8/MmZg1NTB9Op6KijHrSQhBb28v1dXVztuwM/0ElI1sPwFlGAZ9fX1MmzYNIONPQOm6TldXF7NmzXLe5p7pZ61isRg9PT3MnDkTy7LSchr9WStBZaXO0qUCr1ek5WR/qisUsujosOjp0WhvNzl7NkgkUklnp0JHh6CrS6WzUwwPoigYBvQPKBwZKOcI5cDcnPMjfi4FJSWC0lKF0lKFQEBQVgYlJQolJVZ8ezX4/YKyMoWSEggELMrLPfh8Bq2th3n88buomxZjpqeTWmUA0XUVcfUqnr4+vF1deLu6qOjpQXR1wQfd0NOD1d+P6vej+HwIrxcr4MGoUYlVQaxKYFRZxCotYhUGerlBrDSGXhZDL4mie0KYpfFpfJZmEqOPmNEHA7m9DVNR/MODIuVoWgWqWuas7S87BAKdxGIP4vVWZfVJNTemWWerp/F22YeqnqOraxOG0UYo1EJf3wX6+y8A5/H5+oB4x/jWW1PH7e+fQ0fHzVy6tJiWllu4dOlmLl9eTFvbYoaGqieUo6oKZsywmDtXYd48lTlzLObOMVlafpn5RjP10Ram959Haz2L0tKCcvBcfGB4AiB8PpTKSkRFBZSXo1RVIcrLERUVqCNsq6wMq7ycQaB8xgwIBNDKyzG9XggE8JSVXbNLSzETvnSQj55GQyEubt7M8scfRyfhc44jtHVcPa2oQKupwbrlFjyWxcxxctqyLHp7e6mpqUFV1ZS5V1qai54G0PU7gZWcOKGwc6fJd/YIOnY0U9ZynFJCeDCTZo9MxO+Rf/Ng4dNMfB4TS1ExVD+moqErPgzVh6F4h9c+DNWLqXoxVD+64sXyxP8WEyqWtwTL48dQPZiqH8vjw1A1LM0/7OPF0vzDPl6E5sdUPUQHLrDmVi/zy3uZ7e+hLtDPNLqpNLspDfdBTzeitxe1txfR0xPvJ/T0xEcnAeH3o5SW4veWEoiVUdlbCtFSlJ4yxIUSRFkZalkZepmX2DSDSLXOUPcZPJebMUIdTuffWWaWYEzzYZQKDE8IQfxrPvHPKXcRDmeiufGZYNcee5xBIBAiGJzPtGkrCq6nkL2m/uEfbqKr6xxvvrmXsrJr/+zW1XWybFknZWXZvnhtFpo2F59vHn5/PRUVCygtrcfrraesbCGaNgvLUpL7QYqCNTiINTSEFothBoOIUAgtNogZbIdIBE8kgjU4iAiFCF/qpkQIlEgENRLBGhqCcNixlUgEJRRChMMQCjn2J0Mh1El+BDMVhv+/xUNus17dhFBV9NIZDPpn02vOpKt3Nq1XZ3E+NIvmgTs5HqpmgEqCVBDxe/kfv/mHrPnYT7lw4WtcuPC11AdVAQt83eC/OjyocRX8nfHF2+VBCVZihGoIe2oY8lYx5KniqlLJgFJFL9Posqrp9tfQWTuNzlgVV6I1XA7XOB8TUBRQPQqKx4Oixm3Vo6KooHhUVI+K6gFFVRxbYOHzefB44mMUmqbg8Yjh/8sVVFUM/3+eaINhRCkp8eHzqXg8Fh4PeL0qimLh9SpomoKqWmiagtd7zdY0CIcHqaoqxev1oCgmPp+KpikoionXqw6/ANzE7/c4tqbBkSP7eOCBFcycGaC8RKfcM8h0v44aDmINDqLFYvH2MTSEJxqN2/1DKFfCDPb14isRCC1MjAEMdQjTGyamDGJoIQxvBN1e+yIYvgi6L4oeiCJ8gvA8CM9LXbWe0PCAyPDASMmVCIH2CKoOZgkYJfG1WQJm6Yh1IMEuGR7AKYEFl/+FeuN/ZPz/kWmaSf+XjQU5CJIlNm7cyMaNG52LyIkTJ1i3bh1NTfFnWJcvX87Ro0cpKSlh6dKlHDp0iJqaGm6++Wb27t1LZeUeurqeo6ICmkY89lpSEr/zlwpCePB4ajDNEkyzlBkzFqKqVbS2DrJgwV0MDZVy9mw/CxfeS2+vl1OnBrjppvu4csXDBx8MUFd3K83NzZSUeFi27BZKS7uorOxhw4YldHe3E40GWb58CZcvxz9L+6EP3cr5881omsXQUDc33VTCtHIPS8vLOdXYSM3gILOiUa68t4+qvj5Krl7FaGlBG77jaAv6yP85Rg6tCI8Hpk1j0O+nbMECRE0Nl8Jh5t11F2f7+giXlnLHQw8x4PVytK2Njzz5JJ2GwfEPPuDhhx/mclsbZ8+eZd26dVy8cIFLly4RjUaZO3cu/f39rFqxguZTpwifOcOKFSvGrKeqqiouXbqE3+9n7ty5LFiwgF27drF48WLq6+vZtm0by5cvZ+bMmWzZsoVVq1Yxbdo03n33XYdPQ0MD69evR9M054U+hmHQ2NjIk08+STAYZPv27XzsYx+jr6+Pffv24fV6ue2222hqauLhhx+mvb3d4dTa2kpbWxtr166lpaWF3t5eVq1aRXNzM+fPn2f9+vWcOnUqo9yrr69nwYIF7N69m0gkwiOPPJKWU2NjI/fffz+VlZUOJ3uwxTAMTNMck9Njjz3G4GAXLS3Hefjhh2ltvYwQh3niiSdoa2tzOJ05c5be3l7uvnsVe/d+wOXLBrW1y9i//yLd3RqaVsfJk1fp7Y1SXV3HlSsDmKYPKOXq1SEsy0cs5iUY1IlGPUQi1+4WG4ZCMKgkvCw3MfsS7yonbrfv6mnA3bz4om3fhNcLM2aYlJcPccstlZSXD+H1drNq1Xw8nqtABw8+uJRjx7ZSXV3NihUr+eCDZsLhCMuXf4iTJ08BCkuW3MqxY8fx+0tYtGgxTUeOUllZRV1dPSdONPLhutnM6zjHxU3fp7rjDGrbaYyAjlEOut2Bn+bFWDiD/goFrb6SQQYRIoTXG3/RoT2NVIgohhEl/lni1Nri98P77+/hkUfWZ5x74XCYBQtyvRd9Dfnq6YwZEdraHqGiYvQst8Qn0vr7p9PdPR+4iZqahYTDMR544AECgfns2HGO9eufBKCxsZEXXniS9vYBNm/ex8qV1Vy8OMD+/WdYvPjDtLYGaW7upLp6IefOtVJeDkuXLiAa7SEa7eVDH1rMwEA7hjHAhz98Kx1XzuGNdHH34ll0NO2nLNbPoqpSmt/ezKyhIab19hL5jyb8V66gjHPRNmtqCA6/R6i8rg7ftGlc6O1l9i234K+t5diFC9x6992oVVXsO3WKVY88glFSwvbDh3noySd578ABokLwsY99jN6eHqedXu3s5Pjx46P0tDWVnq5cGdfToSFWLFlC0/Hj1+rp0KEpraehUIi+vj5qa2tRVdUVPb3rrkouXPgPPve59Wjazfz4x+cYGqpkxYr72LZtH6tXP8TVq0Ps33+S5ctX0dk5yKlTF7nppmV0dQ3ScrGLGTNuorNzkPb2Pmpq6ujpiTIwEEPTqujrizI0BJblZ3DQJBxWsawEfXNrFlBGuIPX96T+S0kJzJxpUFk5xK23VlF+6wCBQC/3rp6PGGqmtfMqd668l0uX2gmFYixYsIiWllZME+bMmUdLywVU1c+MGbM5d+4C/mgpleFptPcf46nP1vCRm/2cfOUV5re0UP7mfjh5EggPL/G7ztZdSzm/pI6q//wQyqql7Duxk1sX1iCMLi5cOMyMGV5iRhfB8GW00ii6MgiKGPWeBb8fTm6+i498asWk6ynkr6krV/42tbUXxgqBEF40bSaxWICampsoKanj4sUBFi1cwaVzA+g9GmtuvR9twMvhLTtYc9tthK9c4cKRIyya3UWk4wS9588zPRBAv3oV/epVqkIhVF2PT6nSdedRYEh+YiDRtv8+8kkaNY2tjLDTzfEVqoqiaRiqiicQAK+XqGHgr6hAaBpDsRiK10ugooJgJELNjBnoQH8oRO3s2UQti/5QiJl1dYR0nYFwmNlz5xKMRAiGw5imSXlZGbFIhFm1tfT29mLGYtROm0ZvdzdYFjXV1fR0deFRVarKy+nu6iIWjTJrxgx6u7oo8fspLSmh++pVykpKCPh8dHd1UVFWhk/T6O7qorK8HK+q0tPdDaZJdUUFAz09VJaVoZgmg/39lAcCiFiMWDBIQAjE8EullOH+nmJZ+Ho7mNbbwTRgMbA6XWJEgW9Cx0E485vxf2IDV+KzBUra4ot1uZyW9ts5eekOrpj1dDKTTmbSwSzH7qdqjNrJEIIc9C7VbMORWZPKDiTY6bIvnZ34gHa6TNdS2Gv4kz+xt3mJPygPJSUzKCmJUVvrp6QkhsczxPz5NajqIIoS5JZbZnH58hmqqvzccssCenvbUZQYS+Yt4MqVC/hVwa0338SFCx9QUeJl6dKFXPjgKNPLy+ntPk91dS9z5sSoqgpz+vRWKiqCKEoH/f2nUZRuzFIYWhxfJgrBm+vYtm1bVtfzzs7OjI4tvw6TI+w3z/b09FBTU5PxKPu//Mub1NY+RX9/LQMD0+nvn87AwHSCwekEArXU1tYwb950liyZSX19FSUlM/Fa5bC/Ce39vXDmDJamoQYC4Pdjeb14SksRPp9jW14vlteLVlaG5fUifL749lgsPipojxCGQvERwuE5vKpth8Oo4TBieD6vEgwiWlvhyhVHGMfEjBmIefOw5s3jYjTK3DvvxDN7NmptLUZVFZ6ZM1Fqa9ErK9GmTYPhu4Lj3eWzZxikGwkc707LyLrJZNZEJncuQ6EQmzdvZsOGDQBkMxPkeuWUaiaIruu88cYbPPHEE3i93uuSk6KoDA4aRKMqkYjKwEB8cCQaVRkYMIjFPITDCsGgQTTqIRyGwUGLaFQdnr5uEYl4CAYtPvigB8OYTkcH9PVNzlvRvV7B6tXw0EMK69aZrF2rUKrGMN5/H3XnTtQdOxA7d6KM8xkkSxsxwj5iSRyNt3xQ92KQkvLyrOppaGhowr8Ok62eHj8epa+vfPiRvQV0dNzElSs3cfXqfMrLF7JgwTzuuOMmPvKRSqZP19FUFeXkScx33kHdvh0OHozfyhl+hlWUlKCWliJKSxGBAGp5OSIQwAoE8FRUYAUCCNs2DER3N56BAURPD6KnB7WvD9HdDX19KL29iN5elEyfyfd6ETfdBAsXoixejHnTTSiLFqHefDPGvHmY5eW8+eabrF+/Hr/fP8aMramtPcWipwDhcJjNmzfzxBNPEP8s7sRyEgIsy8PgoEkoBNGoh2DQHJ6R70HXDYRQHNt+C1WybWJ/pSAWi29XlLgdfxREcWzLin/dx7Z13cTj0YhGDfbsaaK6ehlXrqi0twuuXFG5fFnQ3+++ti5ZAo88YvHoo/DIIyqlg5dRd+xA3b4d8d57KMfH+rR6algqGFUQq44/gpO4nvHJH1Cx/tMF01PIXVPfeec36Os7w7x5t1BbO4eysll4PNPx+2fh989GiQTwtfXjOX8es7kZ9fx5lHPnEGfPwvnzKJGJfWkpJSWI4fcNKMPaTCCAUlaGKCmJa3ZZGVYgACUlqOXlmMOzhtSysmt2eTmmz4dSVoZaXk5EUdi6Zw8PP/44eL14AgFUny8++8znK25N1XW8gBUOY/b14e3pwWpvR1y5gufqVcSVK4grV1CvXkUMP+KmBIOIgQF0T4APgnVcErNpZw7too525vABSzjKHVxhNokDCKWlgqoqhepqMfy9AIWKCouyMoWyMgW/P9E2KS1VqKhQ8fsNKis9lJUp+Hw6VVUaPp/O1q3v8OCDj6CqXmIxA1X1YhiCaNTA4/Gi6xaxmOnYum6hKBqGYWEYFmDbAvAQi5nEJwt50HUT04xroa5bw5PUVGKxuC2ESjRqYlkKlqUO+yqYpr2/gmUpxGLW8HZl+JjqqO2xmIUQ8RnShiEwTWX4FU4C04TOzgEUpZJgUGVgYPh9fZMEr9f+8ISgshIqKhTKyy2qqyPMmXOR2tozTJ9+gaqq85SVnaOk5DyqaiJEOapagaJUAKXDs5IrUNVSfL4qNK0cVS0hEKjG6y0DfBw4cIz165+ktLQmq/Y0MDDAtGnT5Ndh3IaSMAXYRqJtC4xt19V9jIceCjN9eoR16/ysXavy0Y/Chz8cf1QZiD/HvGsX/NMm2LkTDhxIenNSqrFCJcFOHEHPdkxyrFFzIH6bY/789MvcuVBaigJYus7RhgbmbtjgvPQzMeESp/0lPr9lX0Ta29uZM2dOvFzDwj6eDdDW1sacOXOcekhXNyPrybIsx9c+1sj6S1feTG27o5uqvPbx0/FLLK+iKM75yTT3bKiq6vhmw8nO9fiLwJS0nFKVHaCjo8M5t6k4pbMTuabjlGhXVGhUVMSnFytK53BMSD2aDqlalK6bNDTsdD7NFY3G333S0RF/GdxI+9paMDCQfDHK9IVpliUYHFTYsQN27IiXxe+HtWsDPPTQgzz00IPc89vgU434C4O2bYPt2xHbt6NcTXjju8+HOjw13FtWhvMGZdtO2GYGApy6dAltnLaVqm7cmL6drZ4uXaoxc+YgluVl3TqNj3xE5dOfhlWrhvXUsuDECfjX9+C992DrVujuTvsuMiVhnWgnaisp7JH7jzwefj8MvxBS1NQQrq2l5PbbURYvhkWL4kt9PUoi74TjaMTfPWTztuslU+2Rejp+Tqcqr5t6mo6HG3o6bZqH4ScNsKxrepr8zqCx9TGeQx0Z6OloW9cFs2adY8OGpXi98YGTOBTC4fjLuNvb4y+UHml3dgoMQycQ8A5PT4+/fmbkeuQ2VRU0NcU4fNjHBx8ofPCByre/Hf/b6tV1PProJ3n0v36Se14Eb38XbN8e14f33kMcPYoiRHxGQKJ+lpY6i1paiq+0FF/CdtPv53RrK4GFd49Zf5Olp5C9pj780F/zzg9/yD3mIrS9F+DcOTj3Ppw9G7cT3vqdqFGJuic8HpSRX8ZL9dv+Sl5lJV2xGLXz56OWl197yWIgAKo65r14YVm0OfmsjipXWlvXiZw5AzNmJLfJhH3StVuPx+NoxXhtdaI0NZ3OuKKp9jRKTaNzcJA5d96JumKFs+9Y1zsfYB2F//0rgkuXLKZNU6muVliwAH7t1vgjqbfeCgsWxFPA600+UuK1SlWH3yp67Ywk2KP/m9B1hcrKGLNnK8PHtfkl2iP/Q1KH45Kgb+lijp6HlFze8fcf7XslDdf0/43FPx27dbh/Gn/Taix27UuQ/f3xdaqlv1/Q3R3C4yklFlOIRuPvtLK/KpdoJ/8WxGKKc57jTyyOnF1dCiwdXiYKq3n+eZM/+qP4ucxUUzP9nLMcBMkT2V641qxROHdO5+jRxngCe4BTp+D7O+MDHjt3wpkUz6LOnIm1di2t1dXMq6+PTxsc+U3EMRYRjRLWdUqmTUOxL+SJb/RNszb9fk5dusStjzyCtmgRzJgx7otCJwKWZXH27FlmzZo1QpDc8cvXN1dInu75TiTPxLG/sWAYJtu37+IjH1mL15udvOq6yb/+60EGBlaybZuHd9+ND668+258gXizvO8+jYce+jAPPfRhVv7Gl1DQ2dfQwKr770errAQt87iWrnOmoYElObw873r4RG4gAPv2+Th5soH/9J+G9fTECXj1vaRBjySUlcFHPgIPPoixejXHmpq44+ab8USj8anY9hstE+0Rv61QiL7eXqoXLUKdPt0Z4KCmJrVdcu07RaZhsH/XLtauXZvUaXUTUmfc85U880NJybVxwFQwDJNdu97Pur3E/fZw++1r2b5dY/Nm2LwZmpvj95h27YIXXoCKCnjooVoeffTjPPrrH2fJX4I5GGT33r2sWbcOLQtttHSd5oYGbrnppox9HF+XBkGyPa62eDGPj/eN0cpKsAdwE9bGvHm8f/489z78cHbnzTA4tWsXa2+5BTVLTZTtzz3kGvOOO2DvXpNdOVznCsEzn7jXyzXS54t/02H69LH3i+vigZz0dPv2Xdxxx1rCYY2Bgfigy8gl1faBAYvOziCBQCWGoTjvCLY/LJbqd+IE2lze7Zyp7hX8cZiXX36Zv/iLv6C9vZ3bb7+dl156ifvvvz/t/lu3buW5557jxIkT1NXV8Tu/8zs8/fTTSfv8+Mc/5g/+4A84e/Ysixcv5k//9E/5+Mc/nlfckbCnGmY9dTEcxti9mw/+/u9Z2tOD+v770NMzer/bb4f77ru2LFo0KYMPE4n4aGWDczf9RoXkeePheuAqRPw9F/YgyHvvQeKED4i/Ef/++2HNmvhsktQj+GON8gsGBoa4eNGfNc+cNXAij2VZ6IcP0/Ttb7O8qyv+iMsYgx48+CCsXJnbVbXAuB5ycjJQLDyheLheTzwvXMAZEHnnndFyMW8ePPJI/C71GF8LTmlHo4L+/hA/+5mfe+/N7p/5idTTfI4nPvxhOHwY5s69NlNt5HratCnXHx2J6ykn3UaxcJU8bxxYFoRCOv/xH2/xsY89RkWFO/3Tgs4Eee211/jSl77Eyy+/zH333cff/u3f8sQTT3Dy5Enmp7jl2tLSwoYNG/i1X/s1/vmf/5mdO3fyzDPPMGPGDD7xiU8AsHv3bj71qU/xJ3/yJ3z84x/npz/9KZ/85CfZsWMHq1evzinuWMh69P7VV9F+8zdZlritpARWr4a1a+MDHmvWxO8gpojV2trKvHnzchpxzMU3n5j5oBDlLQRXydM936nOU1Fg6dL48uu/Hh8UOXHi2qDI1q3x8dM33ogvuUEByjEMPetxgethJgiHD+NduZI7ErdlMegh2587fvn65gqpM+753ih9gQUL4AtfiC+WBYcOXRsU2bEDWlvhe9/LtbQKUMbgYPZvn71eZoIYmzbx5t69PP7kk1n/gyV1xj1fydNdyNx1xy9XX1WNz8AuKTEJBMbfP1XMTFDQQZAXX3yRz3/+83zhC18A4KWXXuKtt97i29/+Nl//+tdH7f/KK68wf/58XnrpJQBuu+029u/fzze/+U1nEOSll17i0Ucf5atf/SoAX/3qV9m6dSsvvfQSP/zhD3OKOxayvnDddx9izhwuL1zI7F/6JTzr1sFdd2V0Z9J+Zrm+vj6nJMzFN5+Y+aAQ5S0EV8nTPd8bjaeiwPLl8eU3fzPegT96FN5+22TLlm5mz66lpETF748/JuL3X1vS/fZ4DA4c2IWqrsmJ60Qj62PeeSeivp7OmTOp/cQn8DzySFYzPWT7c8cvX99cIXXGPd8bsS+gqnG5WLkSvvKV+NNu27fDtm0WFy9e5qab6vD7VXy++HRzr5dRduI2RTHYv38XK1ZcH3qa03HnzMHK8e6y1Bn3fCVPdyFz1x2/fH1zRca6JwqEaDQqPB6P+MlPfpK0/Ytf/KJYt25dSp/7779ffPGLX0za9pOf/ERomiZisZgQQoh58+aJF198MWmfF198UcyfPz/nuKnQ398vANHf35+xjxBCCMsSsWhUbNq0ySnzjYpYLCZ53kAoFp5CFA/XfHjmrIETfKxYJCLr6gZCsfAUoni4Sp7jYyL1NJ/jybq68VAsXCXPGwuToacFmwnS1dWFaZrMmjUrafusWbO4cuVKSp8rV66k3N8wDLq6upgzZ07afexj5hIXIBqNEo1Gnd8DAwMARCIRShJeepcJdCM+VVIfftt/pjBNkwsXLrBgwYKkN+G66ZtPTJtftjzziVuIcyR5uhczH99C5G6x8IS49uWKCdXT4TdoyfY38TGLhWc+vlJnxofM3fGRj57CxGmqrCv3YubjK3VmfMjcdS/mVOOZqZ4W7MWoly9fpr6+nl27drFmzbWpg3/6p3/KP/3TP3Hq1KlRPkuWLOFXf/VXnUddAHbu3MlHPvIR2tvbmT17Nj6fj3/4h3/g05/+tLPP97//fT7/+c8TiURyigvwR3/0R7zwwgujtv/gBz+g1Pm2rYSEhERxIBQK8cu//Ms5vchP6qmEhITENeSjpyA1VUJCQsJGpnpasJkgtbW1eDyeUbMvOjs7R83SsDF79uyU+2uaxvTh7wKl28c+Zi5xIf5ukeeee875PTAwwLx581i/fn3WFyxd19m8eTOPPvroDftmX5A8bzQUC08oHq758LTvNOYCqafZQ/K88VAsXCXP8ZGPnsLEaaqsqxsPxcJV8ryxMBl6WrBBEJ/Px8qVK9m8eXPS52s3b97Mk08+mdJnzZo1/PznP0/a1tjYyN133+2coDVr1rB582a+/OUvJ+2zdu3anOMC+P1+/H7/qO2qquachF6vNytf0zRpbm7mlltuyWk6Ui6++cS0kS3PfOIW4hzZkDzdiTmVcrdYeAJ5veDqetBTkO3PLb98fUHqjFsxpxLPfOJOtdzN94WBE62psq7ciTmV2l+x8MwnbrHk7lTjmameFvTrMM899xyf+cxnuPvuu1mzZg3/7//9Py5evMjTTz8NxEe229ra+Md//EcAnn76af7mb/6G5557jl/7tV9j9+7dvPrqq85XXwCeffZZ1q1bxze+8Q2efPJJfvazn/H222+zY8eOjONezwiHw5Pum0/MfFCI8haCq+Tpnq/kKTEeZPtzxy9f30LEnErtr1h45hN3quXujYBiqatiaX/FwjOfuMWSu1ONZyYo6CDIpz71Kbq7u/njP/5j2tvbWb58OQ0NDSxYsACA9vZ2Ll686Oy/cOFCGhoa+PKXv8zGjRupq6vjW9/6lvN5XIC1a9fyox/9iN///d/nD/7gD1i8eDGvvfYaq1evzjhuNsh1pDIXeDweVqxYMam++cTMB4UobyG4Sp7u+Uqe7sIN7ZtMPbXjyfY38X75+uaKYml/xcIzn7hTMXen0nHTxSqWuiqG9lcsPPOJWyy5OxV5ZoLJ+whzGjzzzDOcP3+eaDTKgQMHWLdunfO3733ve7z33ntJ+z/wwAMcPHiQaDRKS0tLytkbTz31FKdOnSIWi9HU1MQv/dIvZRU3G5jDXyaYDJimyfHjx3OKmatvPjHzQSHKWwiukqd7vpKnu3AjntQZdyB5uucrebqLYsrdqXTcdLGKpa6Kof0VC8984hZL7k5Fnpmg4IMgEhISEhISEhISEhISEhISEpOBgj4OcyNgsqcaLl++fFJ984mZDwpR3kJwlTzd85U83cWN8jiMbH8T75evb64olvZXLDzziTsVc3cqHTddrGKpq2Jof8XCM5+4xZK7U5FnJpCDIDlCCAFAd3c3cG3qjcfjSbINw0BRFMdWVRXTNAmFQvT19eH3+53tqqqi6zoej8exNU1DURR0XUdRFI4fP87SpUudt4AbhoHX60UI4diWZWGapmNbloWiKBw7dozbbrsNv9/vbNc0DdM0EUI4diIP0zRpamritttuw+PxpOWkquoo2+bZ09OD3+9PyUnTNIdHoq2qKseOHWPp0qUEAoGUnDRNG2Xrus6pU6e47bbbUFU1Jad09WRZFk1NTSxduhSv15uSU6p6CoVChEIh55NM6Tilqie7vMuWLUNRlJScUtVTLBbjxIkT3HHHHU5OZpJ7qqoSjUZpamriQx/6EEKIjHJP0zR0XScUCtHf34/X680492zf48ePc8cdd6AoSka5Z9uxWIyTJ0865c0k91RVRQjB0aNHWb58OV6vN+Pc83q9xGIxp07t8oyXe4ntbNmyZfh8voxzT1EUgKTyZpJ7ti6cOHGCW2+9NUkXxss9uwx2Gy0pKcko92w/O99tLcwHhdBTm8uxY8f40Ic+5BzTTT21cfToUW6//XZ8Pp/U0+tAT9Nd69zUU4i/LM7WVEVRpJ6msDO51k2kntrxjx49mqQLU0lPE4+Traam0hm3+6jjtVs3+qh227PP+3htNTEHotEop0+f5vbbb0cIMWZbnShNHdlux2qrE6Wp6XTGzT7qSJ3J5npeiD4q5HY9z0dTIa4z3d3dlJaWZpx7+fRRc72e59NHjUajTt/O4/FkdT3v6+tL0sF0kIMgWWLjxo1s3LiRWCwGwKJFiwpcIgkJCYnCIRgMUlVVlZOv1FMJCQmJa8hHT0FqqoSEhISN8fRUERM17FxksCyLJUuWcODAAeeuQ6YYGBhg3rx5tLa2UllZmZXvqlWr2LdvX1Y++frm6pcPz3ziTvY5kjzdi5mvbyFyt1h4CiFYuXIlH3zwQcbfZE+HQukpyPbnll+uvlJn3I2Zj6/sC4yN60VPIXdNlXXlXsx8faXOjA2Zu+7FzMcvV9/J0FM5EyRHqKqKz+fLa8S+srIy64r1eDw5JX0+vvnEhNx45hO3EOcIJE83Y06l3C0WngA+n29COuyF0lOQ7c8tv3x9pc64F3Mq8cwn7lTL3YnSU8hfU2VduRdzKrW/YuGZT9xiyd2pxjMTPZVfh8kDv/EbvzGlYubqWwie+cQtxDnKB5Kne76Sp7uYyLhTjUOxtL9i4ZmPr+TpLmTuXh/HczNmMdXVVGp/xcIzn7jFkrs3Ik/5OEwBMDAwQFVVFf39/XmNdl7vkDxvLBQLTygerjcCzxuBQyaQPG88FAtXyXPq4EbgkAmKhScUD1fJ88bCZPCUM0EKAL/fz/PPP++8JfdGheR5Y6FYeELxcL0ReN4IHDKB5HnjoVi4Sp5TBzcCh0xQLDyheLhKnjcWJoOnnAkiISEhISEhISEhISEhISFRFJAzQSQkJCQkJCQkJCQkJCQkJIoCchBEQkJCQkJCQkJCQkJCQkKiKCAHQSQkJCQkJCQkJCQkJCQkJIoCchBEQkJCQkJCQkJCQkJCQkKiKCAHQVzCyy+/zMKFCwkEAqxcuZLt27ePuf/WrVtZuXIlgUCARYsW8corr0xSSfNDNjx/8pOf8OijjzJjxgwqKytZs2YNb7311iSWNndkW582du7ciaZp3HXXXe4WcIKQLc9oNMrv/d7vsWDBAvx+P4sXL+a73/3uJJU2P2TL9fvf/z533nknpaWlzJkzh1/91V+lu7t7kkqbPbZt28Z//s//mbq6OhRFYdOmTeP6XK86JPV0NKSeTg0Ui6be6HoKN46mSj0dDamnUwNST1ND6mmOEBITjh/96EfC6/WK73znO+LkyZPi2WefFWVlZeLChQsp9z937pwoLS0Vzz77rDh58qT4zne+I7xer/i3f/u3SS55dsiW57PPPiu+8Y1viL1794oPPvhAfPWrXxVer1ccPHhwkkueHbLlaaOvr08sWrRIrF+/Xtx5552TU9g8kAvPX/iFXxCrV68WmzdvFi0tLWLPnj1i586dk1jq3JAt1+3btwtVVcVf/dVfiXPnzont27eL22+/XfziL/7iJJc8czQ0NIjf+73fEz/+8Y8FIH7605+Ouf/1qkNST6WeCjH19FSI4tHUYtBTIW4MTZV6KvVUCKmn1zOknqaGG1okB0FcwD333COefvrppG1Lly4VX/nKV1Lu/zu/8zti6dKlSdv+5//8n+Lee+91rYwTgWx5psKyZcvECy+8MNFFm1DkyvNTn/qU+P3f/33x/PPPT4mLTLY833jjDVFVVSW6u7sno3gTimy5/sVf/IVYtGhR0rZvfetbYu7cua6VcSKRyQXmetUhqadST4WYenoqRPFoarHpqRBTV1Olnko9FULq6fUMqaep4YYWycdhJhixWIwDBw6wfv36pO3r169n165dKX127949av/HHnuM/fv3o+u6a2XNB7nwHAnLsggGg0ybNs2NIk4IcuX593//95w9e5bnn3/e7SJOCHLh+e///u/cfffd/Pmf/zn19fUsWbKE3/qt3yIcDk9GkXNGLlzXrl3LpUuXaGhoQAhBR0cH//Zv/8bHPvaxySjypOB61CGpp1JPYerpKRSPpko9TY/rTYuknko9BamnUk+nJtzQIm0iCiZxDV1dXZimyaxZs5K2z5o1iytXrqT0uXLlSsr9DcOgq6uLOXPmuFbeXJELz5H4v//3/zI0NMQnP/lJN4o4IciFZ3NzM1/5ylfYvn07mjY1mlguPM+dO8eOHTsIBAL89Kc/pauri2eeeYaenp7r+pnLXLiuXbuW73//+3zqU58iEolgGAa/8Au/wF//9V9PRpEnBdejDkk9lXo6FfUUikdTpZ6mx/WmRVJPpZ5KPZV6OlXhhhbJmSAuQVGUpN9CiFHbxts/1fbrDdnytPHDH/6QP/qjP+K1115j5syZbhVvwpApT9M0+eVf/mVeeOEFlixZMlnFmzBkU5+WZaEoCt///ve555572LBhAy+++CLf+973ruuRdhvZcD158iRf/OIX+cM//EMOHDjAm2++SUtLC08//fRkFHXScL3qkNTTsSH19PpFsWiq1NPUuB61SOrp2JB6ev1C6uloSD3NXYumzjDgFEFtbS0ej2fUiF1nZ+eoESwbs2fPTrm/pmlMnz7dtbLmg1x42njttdf4/Oc/z7/+67/y0Y9+1M1i5o1seQaDQfbv38+hQ4f4X//rfwFxIRZCoGkajY2NPPzww5NS9myQS33OmTOH+vp6qqqqnG233XYbQgguXbrELbfc4mqZc0UuXL/+9a9z33338du//dsA3HHHHZSVlXH//ffzta997bq8G5Ytrkcdknoq9XQq6ikUj6ZKPU2P602LpJ5KPZV6KvV0qsINLZIzQSYYPp+PlStXsnnz5qTtmzdvZu3atSl91qxZM2r/xsZG7r77brxer2tlzQe58IT4CPuv/Mqv8IMf/GBKPK+WLc/KykqOHTvG4cOHneXpp5/m1ltv5fDhw6xevXqyip4VcqnP++67j8uXLzM4OOhs++CDD1BVlblz57pa3nyQC9dQKISqJsulx+MBro1ET3Vcjzok9VTq6VTUUygeTZV6mh7XmxZJPZV6KvVU6ulUhStalPMrVSXSwv680auvvipOnjwpvvSlL4mysjJx/vx5IYQQX/nKV8RnPvMZZ3/7sz9f/vKXxcmTJ8Wrr746pT5BlinPH/zgB0LTNLFx40bR3t7uLH19fYWikBGy5TkSU+Xt29nyDAaDYu7cueKpp54SJ06cEFu3bhW33HKL+MIXvlAoChkjW65///d/LzRNEy+//LI4e/as2LFjh7j77rvFPffcUygK4yIYDIpDhw6JQ4cOCUC8+OKL4tChQ85n1qaKDkk9lXqaiKmip0IUj6YWg54KcWNoqtRTqaeJkHp6/UHq6eTpqRwEcQkbN24UCxYsED6fT3z4wx8WW7dudf722c9+VjzwwANJ+7/33ntixYoVwufziZtuukl8+9vfnuQS54ZseD7wwAMCGLV89rOfnfyCZ4ls6zMRU+kiky3PpqYm8dGPflSUlJSIuXPniueee06EQqFJLnVuyJbrt771LbFs2TJRUlIi5syZI/7bf/tv4tKlS5Nc6szx7rvvjtneppIOST2VempjKumpEMWjqTe6ngpx42iq1FOppzaknl6fkHo6OVqkCHEDzZWRkJCQkJCQkJCQkJCQkJCQSAP5ThAJCQkJCQkJCQkJCQkJCYmigBwEkZCQkJCQkJCQkJCQkJCQKArIQRAJCQkJCQkJCQkJCQkJCYmigBwEkZCQkJCQkJCQkJCQkJCQKArIQRAJCQkJCQkJCQkJCQkJCYmigBwEkZCQkJCQkJCQkJCQkJCQKArIQRAJCQkJCQkJCQkJCQkJCYmigBwEkZCQkJCQkJCQkJCQkJCQKArIQRAJCQkJCQkJCQkJCQkJCYmigBwEkZCQkJCQkJCQkJCQkJCQKArIQRAJCQkJCQkJCQkJCQkJCYmigBwEkZCQkJCQkJCQkJCQkJCQKArIQRAJCQkJCQkJCQkJCQkJCYmigBwEkZCQkJCQkJCQkJCQkJCQKArIQRAJCQkJCQkJCQkJCQkJCYmigBwEkZCQkJCQkJCQkJCQkJCQKApohS7AVIVlWVy+fJmKigoURSl0cSQkJCQmFUIIgsEgdXV1qGp+4+lSTyUkJIoZE6mnIDVVQkKieJGpnspBkBxx+fJl5s2bV+hiSEhISBQUra2tzJ07N69jSD2VkJCQmBg9BampEhISEuPpqRwEyREVFRUAnD9/npqamqx8dV2nsbGR9evX4/V6M/YzTZPjx4+zfPlyPB5PVjFz9c0nZq48C1XeXH0lz+uzvIXI3WLhCdDb28tNN93kaGE+KISegmx/bvnl4yt1xt2YU4lnocpbiNydSD2F3DVV1tX1WV6pM+ND5q57Macaz0z1VA6C5Ah7emFlZSWVlZVZ+eq6TmlpKZWVlVkL0owZM6isrMwpCXPxzSdmrjwLVd5cfSXP67O8hcjdYuFpxwUmZKp1IfQUZPtzyy8fX6kz7sacSjwLVd5C5S5MjJ4mHidbTZV1dX2WV+rM+JC5617MqcgTxtdTOQiSJ7JNhnxjLV26dFJ984mZDwpR3kJwlTzd85U83YUb2jeZemrHk+1v4v3y9c0VxdL+ioVnPnGnYu5OpeOmi1UsdVUM7a9YeOYTt1hydyryzATy6zB5wjCMSY21b9++nGLm6ptPzHxQiPIWgqvk6Z6v5Oku3IgndcYdSJ7u+Uqe7qKYcncqHTddrGKpq2Jof8XCM5+4xZK7U5FnJpCDIHliMt+6rSgKNTU1OcXM1TefmPmgEOUtBFfJ0z1fydNduBFP6ow7kDzd85U83UUx5e5UOm66WMVSV8XQ/oqFZz5xiyV3pyLPTCAfh5kg2M8feTyeJNswDBRFcezET/VYlgXgbFdVFV3X8Xg8jq1pGoqiOPbixYuTjmMYBl6vFyGEY1uWhWmajm1ZFpqmsWjRIidm4nbTNBFCOPZIHjfffDOmaWKaZlpOqqqOshP5jcXJ3ifR9nq9DlePx5OW00hbCOGU1z5murpJVU8333yzU/ZUnNLVk43xOI2sJ8uyuPnmm7Esy9k/Hb/EegJYuHBhRpxsWwjhnJe6ujp0XSccDmece/Y6HA5jGEZWuWdZFrNnz0bXdaLRaMa5Z9tz5sxxyptJ7tn2rFmziEajWedeLBZD0zQikQi6rmeUe7Y9Z84cwuFw1rnn8XiYPXu2U95Mc0/TNObOnUsoFMoq9+wyaJrG0NAQgUAgiYfH40FRlDHryS1Mlp6qqsqCBQtQVTXp/Lippx6Ph4ULFzrllnpaeD1Nd60bK/fscuSqpwCRSMTR1EgkIvU0TT3ZXCORSMa5l4+eKorCrFmziMViGefe9aynkLmmptIZt/uots6ka7du9VFtZNJWE3PA1hkhBLquj9tWJ0JThRBOny+TtjoRmqooCgsWLHCuGZm01Ynoo6Zrt9drH3XOnDlEIpGsci8fTYW4zgwODlJSUpJx7uXbR507d27SNc7tPqp93QiHw0Sj0aQ2FAgEUBRlzHrKBHIQJEts3LiRjRs3Oif52LFjrFu3jqamJgCWL1/O0aNHKSkpYenSpRw6dIiamhpuvvlm9u7dS319PXV1dQB0dHQwf/58tm3bxvLly5k5cyZbtmxh1apVTJs2jcbGRu6//34qKytpaGjg4Ycf5siRI3R3d7NhwwYMw6CxsZEnn3ySYDDI9u3b+djHPkZfXx/79u3jscceo6uri+PHj7Nu3Tq2b9+Oqqo88MADtLa20tbWxtq1a2lpaaG3t5dVq1bR3NxMOBxmxYoVNDU1YVkWg4ODBAIBSktL03JasGABu3btYvHixdTX17Nt2zbnGbBt27al5bR+/Xo0TaOhoSGJ08c+9jF27dpFMBhMy+nhhx+mvb2ds2fPsm7dOlpbW7l06RKKolBbW0t/f39KTunqqaqqiq6uLoQQzJ07NyWnVPX07rvvOvkxFqd09VRRUcHChQtpampKySlVPZ0+fZrW1lYeeeQRTp06NWbuLV68mAMHDlBSUoLX63U6kn6/n2g0itfrxePxEA6H8fv9qKqaZIdCIUpKShBCMHv2bFpbW1EUhXA4TGlpKZZlEY1GHTG2bdM00XWdQCCAYRiObXdW/H4/uq5jWZZjCyHw+XxO59O2TdMkEAgQi8VQVRWv10s0GsXj8Tida03THNvr9SbxGMnP5gQQDoeTbJuTzVUIkZaTYRiObZomPp+PaDSKoijjclIUxeGhqmpS2dNxSldP9j8W43EaWU+BQIDZs2fT0tIyipP9T9I999yTUiMWLFgwpfV0/fr1AM7bx23bTT0FWLp0Ke+88w7z5s1j2bJlUk+vAz1tbm4mFAoRiUQoLy9HVdVxc6+8vDwvPbXbpq0zkUhE6ukIPbV5JG6fDD21r292R3uq6OlEaOrs2bMB2Lt3LzfffPOYbXWi+qjHjh2jpKSEefPm0dLSMm5bnYg+6q5du5xzNt51IpX+zJgxg9tuu41du3ZNiqYeOHCAYDDIgw8+OOZ1YiI1tbu7m/fff58nnniCnp6etJwmso+aqt1ez31U+xqgaVqS/ripqSUlJY7OjMdpIvuo9gDpWJwmuo86e/ZsLl68mJITxL8Ck0ojOjs7M9JLRQghMpdXCRsDAwNOJ2/69OlZ3bk0TZOGhgYef/xx/H5/xiOdqqrS2trKnDlz8Pl8QOYzQVRV5eLFi9TV1eHz+TIeZbcsi8uXL1NXV4eqqlmNdJqmyZtvvsn69eudBpHpSKfH4+HixYvMmTMHv9+f8UinYRi0t7dTV1eXdNclVd2MrCchBJcvX2bOnDnOXeJMRjpDoRCbN29mw4YNAFmNdNrlra+vd3wzGWXXdZ1Lly6xYMEC7CacLvc6Ozvp7e1l5syZlJWVJZXBhj2iqihKWlsIweDgIOXl5c5v+w56Ots+duIdk8SYI/dJZdvnKVtfwImZjhOAECLJtsU+HddMYtr5kwm/dOUd63yM5JHYrtJxSlVPgMMzsbwAQ0NDdHZ2UlNTw8yZM0dpRDAYpKamhv7+/qy/6DIShdBTu31duHDBmQ3itp7adXjhwgXmzp2L1+uVenod6Gm6a12q3FNVlfPnz2OapjMAl4ueQvyO8tDQEOXl5QBST10sbzZ6muibjtP1rKeQu6am0hm3+6jjtVs3+qjhcJjGxkZHZ7KZCRKLxbhy5Qpz587FsqysZoLkqqmxWIy2tjYWLFjgXIPc1lTDMLh48SI33XQTQFYzQXLto5aWlibNRBmvrRa6jwoT1+fLVFMhrjNlZWXOLCG3+6h2XthtY7L6qIn1mViWcDhMZ2cnlZWV1NfXj9KIvr4+Z6BxLD2VM0HyhN1gEqcyJtq2wCTatgioqjpqn8QGmMq2xWjkdnvEzj6ufexEO9E3cXu6sns8Hjwez6g7FKk4pbLtRmE3tkz4peOajtNI2+fzjSrvWPxGlj1TrtnwSLRH1lOq8qbjl1her9ebNKU+HSdbDGbNmsX06dNH7Z8N7OlpgUDAKVM2sEd9JxO5xsyHaz48c/V1g2dJSYnTQZk5c6aT/3aO5ZID42Gy9dTj8bBo0aJR293SUxuJbVfqaeH1NN21LhUn+7GMuro6SktLyQeWZTl3uCZTZ3JFIfQ0H0y2Fl9vegrZa2oqnXG7jzpeu3Wrj5opp5G23+93YibWn5ua6vP5nOtGYq64qamapiVdH8fjV6x91ELpTElJyQ3dFx+rPu1rb2dnJ7Nnzx7VbhJzeyzIF6PmicTnCicj1rZt23KKmatvPjHzQSHKWwiubvO0nwVN7KwLIQgGg0kjw24jn5i5+kqe+cPOm8Rnim240U6kzrgDyXPifO1BN/tOt9QZd1GI8rrFdbL11M3jposldcYd3xuV58g+qtQZd3Ej8ZwIPZWDIHliMu9qqKrK4sWLc4qZq28+MfNBIcpbCK6TxdOedmbD7/dnFW8ikE/MXH0lz/wwMm8S4UY7kTrjDiTPifdNbBtSZ9xFIcrrBtfJ1lM3j5sultQZd3xvdJ6F1tN84l5vOuNWzOuN50ToqXwcJk9MtiDZz0xOlm8+MfNBIcpbCK6F4KkoinMXc7KQT8xcfSVPd3GjDIJInZl4v3x9c0Uhyit1xl0UoryF4HqjDIJInXHHt1h4Sp1xF8XCM1PdkzNB8sRkT03bsmVLzlPTcvHNJ2Y+KER5C8G1EDyFEAwMDEz6FLxsYz744IN86Utfysr31KlT3HvvvQQCAe666y6OHTuGoigcPnw4rc97772Hoij09fVlXLZ0yOfc5upbiPqEG+dxGKkzE++Xr2+uKER5pZ4mYyL1FAqji4Wo0xvlcRipM+74FgvPQvVnso07EXra0tKCoigcOnQorY/U09yQad7JmSB5YrJH2ZcvX57z1LRcfPOJmQ8KUd5CcC1UvUyFFyP95Cc/cV5ulKnv888/T1lZGadPn6asrIzy8nIuX77MjBkzsi5vrkhX1gsXLrBkyRKuXr2a9m3VJSUlPPbYY7zzzjvs3LmTe++9N6+YbuJGmQkidWbi/fL1zRWFKq/UU3eRqrxu6mm6mG7iRpkJInXGHd9i4QmF0dNs4+arp+Xl5VRVVXHx4kXns9STBamnCfu5XI4bHvbIlv1d6ZG2/ckx27a/sww4duJ2+5vUtm0fX9d1FEVhxowZzqeA7E862eWwbfuN87ZtfzartrbWOba93S5vop3IQwjhfM5tPE6Z2CM5JfJItFVVdbim45TKtizLKW86TunqyeZqH2c8Hom2jbE4paon0zSZOXOmc/yx+CXWkxCC6dOno6pqRrlnl8OG/amqxO2WZY1r2xBCOPzHsseLOZZdU1NDRUVFVr5nz57lvvvuY/78+dTW1uLz+Zg9e7bzKbeRnNKVfSyuY8W3P3mX6Gvvs2nTJh588EEqKyvT+l6+fJndu3fzv/7X/+Lv/u7vUsYayQNwPm2XyGksfmNxTRUzlUa40RmbTD21z3lNTU3SJ/Lscrihp/Z5mz59ulMWqaeF19N01zo39XTkMaSeprbTldctPbV97a8NTGU9teMmxhlpZ6s/bvRRbZ2xY9nb3e6j2hjvOjFSf2ydsT/9mYrTRGuqZVlOny+XekrkmqmmAtTU1KCq6picJrKPOlYf6nrqo9bU1Difbk1X3pH22bNn+chHPsL8+fOZNm0amqZRX1/v+KfT1MRjJepM4t/y7aP+7Gc/48EHH3SuESN9Adrb29m9eze/8Ru/wauvvpo21kT1UUfW51j8Ul3PM4EcBMkSGzduZNmyZaxatQqAo0ePAtDU1ERTU5Ozrbm5GYBDhw7R0tICwN69e2ltbXWO1dHRAcC2bdvo6uoCYMuWLc60p8bGRoLBIAANDQ0Eg0HeeustGhoaMAyDSCRCQ0MDEP/GfGNjIwB9fX1s2bIFgK6uLrZt24au67z55pvs3LkTgNbWVvbu3QtAS0uLMx2rubk5idPx48d56623OHz48Licdu3aRXt7u8Opu7vbsdNxikQiGIYxipNd3rE4QbxR7tq1y+G0Z88e3nrrLc6cOZOWU7p6OnPmDG+99RZ79uxJyylVPb377rtOnY7FKV09vfXWW1y5ciUtp1T1dOrUKd544w10XR+T06VLlwAIhULEYjEg/n3xgYEBLMsiGAw6whEMBp0Lkv13204lPAMDA0Bc5GzbNE2nfg3DcOxYLEZ/f7/zyauhoSEAotEooVAIgEgkQjgcduwHHniAL33pS4RCIfr7+7npppt44YUX+JVf+RUqKiqYP38+L7/8ssNJVVUOHDjAn/zJn6CqKs8//zzHjx9HVVUOHz7scGpoaGDJkiWUlpby8MMPc+rUKYdXIicbIzm9//77qKpKe3s7g4OD9Pb24vF4+KVf+iUsy6K/v58XXniBNWvWJHHatGkTjz/+OADhcJhIJOLUTTQaxbIsXnnlFZ544gl+/dd/nX/5l39xcmysejJNk/7+/qR6GmmPVU/2+YN4B8m2Y7GYUzeXLl0a1Z5SvZE7WxRST+26aWhocOrDbT1tampC13XeeOMNJ++knhZeT5ubmzl8+DBvvfUWx48fHzf3YrGYk//56Kldd1JPU+upzen555+fND21/1G17amkp5C/pra1tTn2eG11ovqoW7du5a233uLSpUsZtdWJ6KPaccbjlE5/3nrrLXp7eydNUw8cOMCbb76JrutjXicmUlO7urqc68dYnCayj2r3oez+6vXaR33ggQf4zd/8Tae8N910E3/2Z3/GZz/7WSorK5k/fz5//dd/7fCw9fSP//iPUVWVP/zDP+TcuXN4PB7279/v8PiP//gPlixZQllZGQ8//LCTxyM52edjJKdDhw7h8Xjo6upC13VaW1tRVZWnnnqKoaEhLMvi+eefZ/Xq1Umcfvazn/H44487OppKU7/97W/z+OOP84UvfIHXXnuNjo4OR7fc7KMm1lOipuq67pzfdBoxLoRETujv7xeA6OrqEkIIYRiGMAxjlK3repJtmqaIxWJi06ZNIhKJJG0XQohYLJZkW5bl2IZhiK6uLhGJRIRlWcKyLBGLxYQQIsm2Y9i2ffyrV6+KaDSatN0ub6KdyCMWi4nu7m4nfjpOqexIJCI2bdokQqFQWk6JPBJt0zQdruk4pbKj0ahT3nSc0tWTzTUajabllKqehoaGxKZNm0QsFhuTU6p6ssur63paTqnqKRaLic7OTmGa5picBgcHxcmTJ0UoFHLOu2GYorc3JoJBy1kGB4UYGDDHtAcGTHHpUm/S78FBMaZtHyMYtERvb0yYpuWcE/t8pLMfeOAB8eyzzzp1v2DBAjFt2jTxN3/zN6K5uVn82Z/9mVBVVTQ1NQnLssTly5fF7bffLp577jlx+fJlMTAwID744AMBiEOHDgnTNMWFCxeE3+8XX/ziF0VTU5P4p3/6JzFr1iwBiJ6eHqdOTdMUvb29wjRNYVmWs922a2trxb/+678Ky7LEpk2bRG1trZg5c6ZTv48++qj43d/9XYdTb2+v8Hq94sKFC2l5m6YpFixYIH7+858LIYRYuXKlePXVV0ftb5cp0bZzM/FYiXZi2RPtRJ6pyhUKhcTJkyfF4ODgqPbU29srANHf3y/yRSH01LIsYRiG6OjoEIZhTIqeGoYhTNMUnZ2dzjGlnhZeT9Nd61LlXjgcFidOnBChUGh4n9z0NBi0RH+/4Wiq1NPRemr7Pvroo+J3fud3Jk1PR+bbVNNTIXLX1FQ643Yfdbx260YfNRQKJelMNpoaiUREd3e3Ez8Vp4nW1Gg0Kq5evZp0DRqL30Roqq7roqOjw8ntyeijmqbl6OlI3XO7jxqNXjsf42nqAw88IL74xS865ytRTz/44APx9a9/XaiqKk6ePCmEEI6e/n//3//n6Om5c+cEIA4ePCiEEOL8+fPC7/eLZ599Vpw8eXKUno7UGfs8JmqRraf/9m//JizLEj/96U+T+qeWZTl6avv29PQ4epqOt2EYYsGCBeLf//3fhWVZjp6m2n8i+6iJ142RcUKhkDhx4oQIh8OjNKKrqysjPZXvBMkT9jNh9tTJkXbitCPbtkfI7OmPifvYx0tnT58+PWV8RVEcW1VV59iJdm1treOXuD1d2T0eDx6Ph2nTpiXFTMUplS2GR2U1TXNijccvHdd0nEbaPp9vVHnH4jey7JlyzYZHoj2ynlKVNx2/xPJ6vd6Uz2WP5GSPzCqK4nxOKhJRqalJNQlMHcdWgOqE7UrCOp2duK+XwUEoK0vYmvCJq3R24jnYsGEDv/EbvwHAV77yFV566SXee+89li5dypw5c9A0jYqKCubMmQPg3D23j/PKK6+waNEiXnrpJRRFYenSpRw/fpxvfOMbKIqSckpy4rmz7XXr1rF161aeeuop3nvvPT772c/yD//wDzQ1NbFkyRJ2797Nc8895/g1NDTwoQ99iPnz56fl+vbbbxMKhZy7m//9v/93vvvd7/K5z31u1PkYaSfmZrrjp+JhjZjCPdLP/u3xeJwYdo65MX17svXU4/E407ATt7ulpzYS267U08LrabprXSpO9rT//PUUwEOypoLU02t6evLkyYLoKSTn21TVU8heU1PpjNt91PHarVt91Ew5jbT9fr/zyc/E+nNTU30+n3PNScwVNzVV07Sk6+N4/CaijxoKQU2NN8EjUffc7aMODnoZ+RGTdDqaWGb7fCXq6e/+7u/yl3/5l2zdupXbbrvN0dPy8vJRemof92//9m9ZtGgRf/mXf4miKNx2221JejpSZ1JpkaqqrFu3jvfee49PfOITbN26NW3/1PZ94403kvQ0Fe8tW7YQCoV44oknUBQlSU9H7j9RfVSR8EhL4vaR5x9Gt5vE3B4L8nGYPDFRUxgzjfX666/nFDNX33xi5oNClLcQXAvBc2RHbTKRS2zLspxpnXfccYezXVEUZs+eTWdnZ1q/xKmDEJ9qeu+99yYJ6Zo1a7Iu04MPPsh7770HwNatW3nooYdYt24d7777Llu2bCEcDnPfffc5+//sZz/jF37hF8Y85t/93d/xi7/4i85F49Of/jR79uzh9OnTY/rZ52ey69WNdiJ1xh1Inu75Sj11T0+3bt3Knj17CIfDScd1U0+hMJrqVjuZCu1P6oy7MXNFselpLm0+Vz21fRPXUk8nDpnmXcEHQV5++WUWLlxIIBBg5cqVbN++fcz9t27dysqVKwkEAixatIhXXnkl6e/f+c53uP/++6mpqaGmpoaPfvSjzvNq+cRNh5GjW25C0zTuv//+nGLm6ptPzHxQiPIWgmsheJaVKfT3mwSDgsFBMl4GBiwuXepjYMDKym9wEIJBQX+/SVmZMn4BR0BRFOdlTSNHd1PdfUv8W1nibVJGv2wpVzz44IOcOHGCM2fOcPz4ce6//34eeOABtm/fzr59+1i5cqVTZn34fQxPPvlk2uP19PTws5/9jFdffRWfz+e8MMswDL773e+OWRb7/Iy8S+E23GgnUmfcgeTpnm+uepqPphaLnm7dupWtW7fy4Q9/2Pligdt6CoXRVLfayVRof1Jn3I2ZK4pFT21NnTUr+zafq57af09cSz2dOGSad5Pb4xyB1157jS996Uu8/PLL3Hffffzt3/4tTzzxBCdPnkyalmOjpaWFDRs28Gu/9mv88z//Mzt37uSZZ55hxowZfOITnwDi31T+9Kc/zdq1awkEAvz5n/8569ev58SJE9TX1+cUdyxMZqUqipL200Vu+eYTMx8UoryF4FoInqqqUFnpGX/HEbAsMM349OvsZ+4qxKd+Z4/EN/Xn67ds2TI2bdqUtO3999/P+tjLly9n+vTpfO1rX+POO++ksrKSBx54gK9//ev09vbywAMPOPu+++67VFdXc9ddd6U93ve//33mzp07qmzvvPMOX//61/nTP/3TtKKe6/nJF25o32QP5EidcccvX99cUYjy5qqnkI+mFpeePvjgg442uK2nUBhNdUv7pkIfVeqMuzFzRfHoKeSqqfloxchBEKmnE4dMda+gM0FefPFFPv/5z/OFL3yB2267jZdeeol58+bx7W9/O+X+r7zyCvPnz+ell17itttu4wtf+AKf+9zn+OY3v+ns8/3vf59nnnmGu+66i6VLl/Kd73wHy7J45513co47FiZ7atrPfvaznKem5eKbT8x8UIjyFoJrIXgWYmpaPjETpxtm6zdy+vbTTz/N2bNnee655zh9+jQ/+MEP+N73vpe0T1tbG8uWLePAgQNpj20/x/7P//zPPPjgg0B8KmQsFuOdd95h3bp1zr7//u//Pu5Uw1dffZVPfOITzJ07l2XLlrF8+XKWL1/O5z73Ofr6+nj99dfH5Ckfh8k9ntSZiffL1zdXFKK8Uk/d19O7777b4eq2ntpcr9fp29fLcdPFkjrjjm+x8CxUfybXuLnqqe2buJZ6OnG47h+HicViHDhwgPXr1ydtX79+fdKnqxKxe/fuUfs/9thj7N+/Py3hUCiEruvOi4hyiTsWJntq2vr163OempaLbz4x80EhylsIroXgaY/QF+IOUS4x87lzUl5enrRt/vz5/PjHP+bnP/85d955J6+88gp/9md/lrSPruucPn3a+RRaOjz00EOYpulcZBRF4f777wdw1hC/yIw11fDAgQMcOXKET3ziE6POUUVFBevXr+fVV18dk+dk1yfcOI/DSJ2ZeL98fXNFIcor9dR9PV2/fr3D1W09tWNPdp3eKI/DSJ1xx7dYeBaqP5Nr3HxnyySupZ5OHK77x2G6urowTZNZs2YlbZ81axZXrlxJ6XPlypWU+xuGQVdXl/PG3UR85Stfob6+no9+9KM5x4X4t6Kj0ajz274jout6TiO6ietMIYa/qWy/nX4yfPOJmSvPfOIW4hxdzzx1XUcIgWVZSaOw9rOH2TyDmOiTy4hutjG3bNni7C+E4Ny5c0Dyi7MOHjyYtG3k7wULFjhfD7G3bdiwgQ0bNiTF+uxnP+vsM3/+fOe75GNxfeaZZ3jmmWeSjv2Tn/wEIYTzLOjBgwcZGBjg/vvvT3ucFStWOGVMdY7sKYhjnfNc6nOkX6rjW5bl5NnI6Yz53JG6HvQUpM645ZePbyF4ZuObSlPdan+Z+maCqaqniWU+cODApOhpOt/xUCg9hYnTVKkz7sXMx7cQffFi0tNs406UnhqGkfTuEKmnqX3c0tOCvhMERj+3Y/8Dkc3+qbYD/Pmf/zk//OEPee+99wgEAnnF/frXv84LL7wwavvmzZspLS1N6zcWNm/enJPfVIPkWThomsbs2bMZHBwkFotNyDGDweCEHGcqIF+u/f39/J//838Ih8PjjtwXEul4xmIxwuEw27Ztcz5lZyMUCuUcT+pp7pA8CwupqblD6qk7egoTr6nXa/ubaBQLT7g+uUo9zR1ST/PXU0VM1Otos0QsFqO0tJR//dd/5eMf/7iz/dlnn+Xw4cNs3bp1lM+6detYsWIFf/VXf+Vs++lPf8onP/lJQqFQ0pt5v/nNb/K1r32Nt99+m7vvvjuvuJB6lH3evHlcvXqVqqqqrLjrus7mzZt59NFHM/6WMcQHagzDQNO0nEZlc/HNJ2auPAtV3lx9r2eekcj/z967x0dR3vvj79kLd8LFCyhyE2oVsYoIAke5aIVKv7XHtkfbnqqttedr+f5OVY6n1tObWrU3b7XFeqz2q/b+7WmNRVdMICFRgiRCIJAsECBAEkLCBpJsdnd25/L8/gjP4+xmZncuOzvZzLxfr7z4ZJjPfD7v+Xye9/MwzIVHa2srZs2alXYhMNdFP62Y0WjU9FuezcS06mvFzyzX4cST53kcPXoU06dPH3Qhube3F+eddx56e3sN3w46FPQU8HTGLj8rvk7wNOKrpqmezuT2c2LuKPQ5ckpPgfxpqqczQzNfJ9bibtJTq3GHks7YEdOKn1nfQuipY3eCjBgxAgsXLkR5eXnaxYjy8nLN55OWLl2KjRs3pm0rKyvDNddckzawf/7zn+Pxxx/Hu+++m3YBxGxcABg5ciRGjhw5aHswGDQsoGZ9CSGQJAnBYNBUM5nxtRKTwsw5ciJfq1yHIk9JksBxHHw+H/u+t/LuKSMx6e1o9HhGYDamFV8rMc1yHW48fT4fOI5T7W2zugcMDT0FPJ2xy8+qL1BYnkZ8MzXV05nccGLucOIcOaWnQP411dMZe2IW01rcLXpK8zUTdyjqjB0xhyLPfOipo1+HWb9+PV5++WX89re/RTgcxgMPPIDjx4/j3nvvBQA8/PDDuPPOO9n+9957L44dO4b169cjHA7jt7/9LV555RU8+OCDbJ+f/exn+N73voff/va3mDVrFk6ePImTJ0+iv79fd1wjyLwFx06IooiysjJTMc36WolpBU7k6wRXJ3gSQtDX12f4eUsrsBLTrK/H017YMU48nbEHHk/7fD2dsRdO5OsEV7vGSTGMP09n7I1pFm7RUytxi01n3MJTd98Rh7FhwwYyc+ZMMmLECHL11VeTqqoq9nd33XUXWbFiRdr+W7duJQsWLCAjRowgs2bNIr/+9a/T/n7mzJkEwKCfH/7wh7rj6kFvby8BQHp7ew35EUJIKpUipaWlJJVKGfYtJng8nUcikSBNTU0kkUhYPpYkSeTMmTNEkqQ8ZDa04RauuXhm6x8rGpjPYw3l8ZdPeDyHBjxNNQ6P5wAKpadWjjfUx1++4BaehAxtrp6eGofHcwD50FPHX4yqfINtJjK/jwwAK1asYG/YVcPRo0ctxzUCUuAreFaeHzbjayWmFTiRrxNcneIpyzK7lawQsBLTrK/H017YoX2F1FMaz9OZ/PtZ9TULp+ZIT2fsgxP5OlXTYjquVixPZ+zxdRNPT2fsg5t46oGjj8MMBxT61rT33nvP9K1pZnytxLQCJ/J1gqsTPOnk5MTiyExMs74eT3sxXB6H8XQm/35Wfc3CiXw9nbEXTuTrBNfh8jiMpzP2+LqFp6cz9sItPPX2neN3ghQ7rL7MymisT3/60wX1tRLTCpzI1wmuTvD0+XyYOHGiKV+zsBLTrK/H017YoX2F1FMaz9OZ/PtZ9TULJ/L1dMZeOJGvE1zt0r5iWKN6OmNvTLNwi55aiVtsOuMWnkXxYtThAEEQAAy84ViSpEG2KIppNn3bLfDRm2+V2wVBSLPplTNBECBJErq7u5FMJkEIASGExVfasiyn2fT4kUiEfYebbqf5Km0lD0EQcPr0aRY/Gyc9diYnJQ+lLcsy46rFSc1OpVIsXy1OWnWiXFOplC4eSlvZD1qc1OpE8xVFUZOTWp0EQcCpU6cgy7Ku3qN5KM8l3abcnsumoLe35bKpX+b5UG7PZWvlm8tWxtTiRPNV2pnIxkkrpt79s+Wb7Xxk2rQ3s3HSsjO5qsVU0wg1f6sopJ4SMvD2+q6uLkiSVBA9peft1KlT7Jienjqvp1pznZ16qtyXbqfH9vQ0//ka0dPM81usegqY11S9Y1WpP2bXqLnGrV1rVOU5MqKpyWQSp0+fZvHVOOVbU1OpFCKRSNocZKROSq56NVUURXR1dUGW5ayc8rlGzTZuh+IaVTnH6tk/G1ejmqo8nt1rVGXfmdFUM2vUzHpmi6k2n+uBdxHEIDZs2IB58+Zh0aJFAIB9+/YBAMLhMMLhMACgoaEBzc3NAID6+nq0tLQAAGpra9Ha2sqO1dnZCQCorq5GJBIBAFRUVKCnpwfAwOd/o9EoACAUCiEWi6Gurg6bNm2CKIrgeR6hUAgAEI1GUVZWBgDo6elBRUUFACASiaC6uhqSJKG2thbbt28HALS2tqK2thYA0NLSgvr6egBAc3MzGhoaGKempibU1dXp4lRTU4OOjg7Gqbu7m9lanHiehyiKCIVCaZxovps3b9bkBAAdHR2oqalhnOrq6lBXV4cjR45octKq05EjR5i/Fie1OlVWVrKaZuOkVqfKykrU1dWhq6tLk5NanQ4ePIgdO3ZAkqSsnNra2gAA8Xic/YMtFoshHo+DkIFb1KhwRKNRNiH19fUxMaJvdc4Unr6+PgADIkdtSZJYfUVRZHYqlUIsFgMhhNkAkEwmEY/HAQx88zuRSDB7xYoVuP/++5FIJJhvPB5n/5CLxWKMU39/PwRBwP79+7F48WKMHj0aCxYsQDgchs/nw+7duzU5hUIh+Hw+nDlzJo0ThRYnQRDYV6eU/Oj5VePE8zwAIJFIMJtyor6ZnGhttOokSRJisVgap0w7W51oLDVOlEdbW9ug8URzsAIn9ZTneSSTSWzfvh3JZLIgehoOhyFJEnbs2IGDBw9m5eTpaeH0lHKqq6tDU1NTzt5LpVJsbFrRU1o7T0/V9TSTayH0lC7OY7EYs4tFTwHrmtre3s7sXGM1X2vU9957D3V1dThx4oTusUo5mV2j0ji5OKnpz6ZNm1BXV4fe3t6s8wSQX02la75s80Q+NfX06dPYvn07JEnKyimfa9TMcTtU16grVqzAv//7v7N8qY5m09S9e/diyZIlGD16NK666iq0tLRgxIgR+PDDDzU5VVZWwu/348yZM2mc6PnIximfa1Tqr/y3RKHWqFqcBEFguWRqBNW9nCAeTIG+efb06dOEEEJEUSSiKA6yBUFIsyVJYm9q5nk+bTshA29xVtqyLKfZsiwPsgkhaTaNQW1BELLaoiim2Wo8cnFSs3meJ6WlpSQejw8bTmp1isVi7M3bQ41Tf38/aWpqIvF4nJ13mlOmLUlSVltSvKmZ/k6PoWXnipnNjkQipK+vz5DvbbfdRm644QbS0tJCIpEISaVS5MSJE6x2mZxkWSZbtmxhY5nmno2rWU4tLS1kxIgRpLe3d9A+R44cIcBHX7MKBoNkzpw55LHHHmP76q0TzVdpa9VJyVMt93g8Tpqamkh/f/+g3rPj6zCennp6OpT1VBRFkkgkSGNjI4nH4ywHM3oqyzIRRZGNP09PPT3Nl54SYl5T1XRmOGpqPB5P05nhwKkYNbVY16iRSISNVb2+Sj09deoUEUWRtLe3p53jTE4VFRVsHGfqDD2P+eB09OhRMmLECNLT0zNon2x6qhY3n5qqrGdmnHg8ThobG0kikRjUe6dPn9alp947QSyCvunW7/ezbUo7EAgMsqWzV8h8Pt+gfZTPMWXa8tlbsM8991wWl+7DcRyzfT4fOza1lb6Z+2jl7vf7Icsyurq6cO6556rmm80mZ6/KBgIB5puNnxZXLU5qNgCWL+WSjZ8yXyNc9fLItDPrpMyXHl+LnzJfjuPY+cnGiV6Z5Tgu7a3MoigiEAikbaNxtGxaz8zjZbOVUIuZzT7nnHNYXL2+hw8fxqc//WnMmjWL+U2dOnVQLmr8OI5L267FNVt8Za6Z+/zjH//AypUrUVJSonn8TZs24ROf+ARSqRTef/993HPPPbjwwgvx9a9/XTN3cvb2RuX5UctNrU5yxi3YmX5KfaOcaI9l+uYDhdRTYIDDmTNn2Ji3W09pzO7ububr6anzeqo116n1niAIedFTmn/mdk9P9XG1S0/pOZIkicUsVj1V5qBXU9V0xu41KpB93Nq1RtXDSc32+/0sZrZ5IhdXI5rq8/nYubWDk1qdAKTNj7n45WONmm0NNZTWqGp6mm1/juPS9JT6nnfeeWk9n8kpM2+lzuTiZGSN+uabb2LlypWYMGGCpm82Pc1WJ7Nr1Mx6qvlp6Zver9B4j8NYhF0Tl1asffv2mYpp1tdKTCtwIl8nuDpVF3r7WyFhNObKlStx//33M9/Zs2fjySefxN13343x48djxowZeOmll9j+HMdh586deOyxx8BxHB555BHs37+f3b5NEQqFcMkll2D06NFYtWqV7s9qU+zdu5ctSICBRYLP58O//Mu/sFx//OMfY+nSpWl+b775Jm655Zasxx47diymTp2KmTNn4l//9V+xbNmyrJ8Ep3CinnaME09n7IHH015fT0/t01MAePzxx7Fs2bI0Pzv1FCh8Te0aJ8Uw/jydsTemWbhJT43GzYeeHj16FCNGjPD01Abo7TvvIohFKK+8FiLWDTfcYCqmWV8rMa3AiXyd4OoETw5Aid8PLh4HYrGC/HDx+EBMw9kOTB70f/uefvppXHPNNaivr8e6devwzW9+E/v37wcw8Jzq5Zdfjv/4j/9AR0cH/vM//xPjx49PO1Zrays+97nPYe3atdi9ezfuuecefOc73zGUz/z583HOOeegqqoKwMBzuOeccw6qq6tZrlVVVVixYgXz6enpwXvvvZdzkhk3bhy7gv3hhx9i165duPbaa3WdH71XvvMFO8aJpzP2wONpn6+np/bpKc33gw8+KJie0piF1lS7xkkxjD9PZ+yNaRZu0VOmqePHGx7zZvX0wQcfHHR3g6en+YPevvMuglhEoa/Ktre3m74qa8bXSkwrcCJfJ7g6wZPEYsC4cYZ/fCUlmHjRRfCVlJjyx7hxA7GN5nv2RVUAsHbtWqxbtw5z587FQw89hHPPPRdbt24FAEydOhWBQADjxo3D1KlTMXbsWOZH8etf/xoXX3wxnn32WXz84x/Hv/7rv+KrX/2qoXw4jsPy5ctZ3K1bt+Kuu+6CLMtobGxEPB5HTU0NVq5cyXxCoRCuuOIKTJ8+Peuxly1bhnHjxmHEiBFYtGgRbrvtNtx55526zo/y1sFCYLjcCeLpTP79rPqahRP5mtXTfGjqcNfTpqYmCIKAmpqatEW7nXoKOKOpw+VOEE9n7PF1C08n9TTV02N4zJvV03HjxqW9yBXw9DSf8O4EKRAKLUiHDx82LUhmfK3EtAIn8nWCa7HVxSnQt21/4hOfYNs4jsPUqVPR1dWl6Ze5aA+Hw1iyZEnaFenMx1b0YOXKlWySqaqqwqpVq7B8+XJUVVWhpqYGiUQC//RP/8T213OrIQC8+uqrqK+vx549e/CXv/wFb775pq7/CaDnp5AYLhdBPJ3Jv59VX7MotnydQjHpaV1dXcH1FCi8pg6XiyCeztjj6xaeTsLsmDerp5nw9DR/0Nt33otRLaLQt6YtX768oL5WYlqBE/k6wdUJntzYscDZT0wZgSwPfL6qpKRE9YV3umKPGWPch+PYbdjKF3bRv9MSO47jMG7cuLRt+boSvXLlStx33304dOgQ9u3bh+uvvx6HDx9GdXU1ent7sXDhQpazIAjYtGkTHn744ZzHveSSS/Cxj30MAHDZZZfhyJEj+P73v49HHnkEo0aNUvVRnp9CYrg8DuPpTP79rPqahRP5mtVTwLqmDnc9raqqQk9PDxYuXMhuObdbTwFnNHW4PA7j6Yw9vm7h6aSejh8zBjDxOIwZPaV/r/zT09P8wXscpkCg30iWJIl9pUBpi6KYZisHBLWV2wVBSLPpoBAEAZIk4ejRo0gmk+z7yjS+0pZlOc2mxz969Cj7nxy6neartJU8BEHAsWPHWPxsnPTYmZyUPJQ2zZdePVTjpGanUimWrxYnrTpRrqlUShcPpa3sBy1OanWi+YqiqMlJrU6CIKClpQWyLOvqPZoHAMiEIBkIgIwZw34wdizk0aNz2vSHjBkDefTonDb1I2PGDMTMyCWbTSHLMusDrX3UjkMISbv6LMsyLrvsMnzwwQeQZZnts337duanNmEpt1Mf+tzl448/jiuvvBLjx49nV9orKirY5E8IQUVFBSZOnIgrr7wyJ2/lLYOEEPh8PoiiyMY85ZFpJ5PJNE6ZthoPLa5qealphB3/w1NIPaVfgDhy5AgkSSqIntLz1tLSwo7p6anzeqo119mpp8p96XZPTwfraWVlJa677jq2byH0lHItdj0FzGuq3rGajzVqrnFr1xpVeY6MaGoymcSxY8dYfDVO+dbUVCrF1nxm6qTkqldTRVHEkSNHIMtyVk75XKMSgOlppu7ZvkY9qxd6NFX5u1IX1fZR/pnGNePvqJ7S85qpp5k6o/RXatHll1+epqclJSVMT7du3YrrrruO7U/19Kqrrso5f9C5nP5O9TSlOG/K3JW2mTWqErnqoTaf64F3EcQgNmzYgHnz5mHRokUAgMbGRgADtzGFw2EAQENDA5qbmwEA9fX1aGlpAQDU1taitbWVHauzsxPAwItr6Jt8Kyoq0NPTAwAoKytDNBoFMPDMVjweR1tbGzZt2gRRFMHzPEKhEAAgGo2irKwMwMBLbioqKgAAkUgE1dXVbOFNB1Rraytqa2sBAC0tLaivrwcANDc3o6GhIY1Te3s79u7dm5NTTU0NOjo6GKfu7m5ma3HieR6iKCIUCqVxkmUZx48fx+bNmzU5AQMvG6qpqWGc6urq0N7enpOTVp3a29tRV1enyUmtTpWVlaym2Tip1amyshLt7e04deqUJietOh08eBCyLGfl1NbWBgCIx+PsH2yxWIyJdjQaZcIRjUbZhNTX18fEqK+vT1V4+vr6AHx09R0YEB9aX1EUmS0IAns7dCqVQuzss+zJZBLxeBwAwPM824fneRY/kUiwfAVBYHYsFmM59ff3M9FLpVKMk3Jy6uvrw7/927/h8OHD+Pd//3fs378ff/jDH/Dqq68yXn19fWhvb8e8efOwc+dOTU4cx+G6667D73//e6xcuRKpVApz5sxBKpVCZWUllixZwni88cYbuOWWW8DzPHieZ5yorazNyZMncfz4cbS1teHvf/87nn/+eaxatQocx2nWiQq+sk6ZdrY60fNHzy+1U6kUq01bW9ug8ZSPRbuTesrzPFKpFPbu3YtUKlUwPZVlGQcPHvT0dIjp6d69e9He3q6r91KpFNMbK3pKa+fpqbaeVlRUFFRPleeJLtSLRU8B65ra3t7O7FxjNV9r1Pfeew/t7e26x2o+1qg0Ti5OavqzadMmtLe3o6+vL+s8AeRXU+njJdnmiXxq6pkzZ9DY2AhZlrNyyvcaNZFIMP0ZqmtUenGHbqex4/F42n820H36+/vZPkpONA8A+PKXv4zDhw9j/fr12LlzJ/7whz/gtddeS+O0f/9+pqc090xO/f39WL58OX7/+9+zr8BceumlSCaT2LJlC5YuXco4vfHGG1i7di3joaaplHNXVxdaWlpw5MgRvPPOO/jFL36BlWc/U56tTlbXqJl1Uuor7ZnM3qO6lxPEgyn09vYSAOT06dOEEEJEUSSiKA6yBUFIsyVJIqlUipSWlhKe59O2E0JIKpVKs2VZTrNlWR5kE0LSbBqD2oIgZLVFUUyz1Xjk4qRm8zxPSktLSTweHzac1OoUi8VIaWkpSaVSQ45Tf38/aWpqIvF4nJ13mlOmLUlSVluSJHLmzJm03+kxtOxcMbPZK1asIPfddx/bPnPmTPLMM8+k7XPllVeSH/7wh2yfK6+8kvzgBz9g+xw+fJgAIPX19SzvjRs3krlz55KRI0eS66+/nrz88stsLEuSRFpaWggAsnHjRlWu9NjPP/88AUDeeusttv2zn/0s8fv9pKenh+0/ffp0Ul5enpXrkSNHCAD24/f7yUUXXUTuuece0tXVZahONF+lrVUnZU3V8orH46SpqYn09/cP6j2qgb29vcQqPD319LQY9FQURZJIJEhjYyOJx+MsBzN6KssyEUWRjT9PTz09zZeeEmJeU9V0ZjhqajweT9OZ4cCpGDW1WNeoK1asIN/61rfY9pkzZ5Jnn302bR+qn3SfTH2l2rhr1y7G4x//+Eeanr7yyitsHMuyzDR448aN7DyqcfrlL3/J9qPblXpKc5w+fTopKyvLyjWbnnZ2dhqukxFNVdYzM048HieNjY0kkUgM6r3Tp0/r0lPvIohJZE4wRkAX7VRc9EIURdLc3MwavxC+VmKa5WklrhPnaCjzTCQSpKmpiSQSCbZNlmWSSCSYkOhF5gLPCMzGtOJrJaZZrpkxd+7cSSZMmKCrN4YiT7X+odA7yeiBE3pKiKczdvlZ8XWCpxHfzDExFHSmEL5O8MyMWwg9teI7VPSUEPOa6umMfTGt+DqxFneLnlqJOxR1Rm9MT08J8R6HsQiS8dyS3bHOnDljKqZZXysxrcCJfJ3g6lRdpLO3qhUSVmKa9XWapyiK+OUvfznohVl6fM3GLBTsGCeeztgDj6e9vk7rTKF8neCpjFsoPbXqawZ2jZNiGH+eztgb0yzcpKdW4haTzihjenrqfR3GMgr95m36nGehfK3EtAIn8nWCqxM8OY7D2LFjTfmahZWYZn2HAs/Fixdj8eLFpnzNxiwUhsvXYTydyb+fVV+zcCLfoaAzhfB1SmeUcQuhp1Z9zWK4fB3G0xl7fN3CcyjoTCH8rPqahaen6fDuBLGIQl7dkiQJ+/fvNxXTrK+VmFbgRL5OcHWCJyEEiUSi4P+jYDamWV+Pp72wY5x4OmMPPJ72+Xo6Yy+cyNcJrnaNk2IYf57O2BvTLNyip1biFpvOuIWn3r7zLoIUGehbegvpayWmFTiRrxNcneBZ6AnGakyzvh5PD7ng6Yw9flZ9nYhp1tfTGXvhRL6eppqDpzP2+bqFp6cz9sItPPXAexzGIvx+f0FjLViwoKC+VmJagRP5OsHVCZ4cx2HMmDGmfM3CSkyzvh5Pe2GH9hVST2k8T2fy72fV1yycyNfTGXvhRL5OcLVL+4phjerpjL0xzcItemolbrHpjFt46tU9704Qi6DfKJYkid1+o7RFUUyzZcW34Kmt3C4IQppNr54JggBRFLF37172TWpCCPuWttKWZTnNpjns3bs37fvV4tlvOkuSlGYreaRSKezbtw+pVConJz12JiclD6VN86XfqVbjpGYnk0mWrxYnrTpRrslkUhcPpU2RjZNanWi+tL7Z+CnrlEql0NDQwDjk6j2aBz1mPB5n25Tbc9kUhBDGP5udK6Ye26wvIQTxeJzlo8aJ5qu0M6HFSU9MK/lmOx+ZdiKRyMlJy87kqhZTTSPsuC23kHpKCIEoimhoaIAoigXRU/rT0NDAuHp66ryeas11duqpcl+6nR7b09P852tET6lvsespYF5T9Y5Vpf6YXaPmGrd2rVEpcs0TmfrD8zz27dsHURSzzhNqtllNTaVS2Lt3LzsXhdBUQRDYOjMbp3yuUbON26G4RtWrM3ZoqvJ4dq9RZVlOW2dm2z9fa9TMemaLqTaf64F3EcQgNmzYgHnz5rEX/jQ1NQEAwuEwwuEwAKChoQHNzc0AgPr6erS0tAAAamtr0drayo7V2dkJAKiurkYkEgEAVFRUoKenBwBQVlaGaDQKAAiFQmxieffddyGKInieRygUAgBEo1GUlZUBAHp6elBRUQEAiEQiqK6uBgDEYjF88MEHAIDW1lbU1tYCAFpaWlBfXw8AaG5uRkNDA+O0f/9+AMC+fftycqqpqUFHRwfj1N3dzexsnERRRCgUGsRJEARs2bIlK6eOjg7U1NQwTjt37gQAHDt2TJOTVp2OHTsGANi5c6cmJ7U6VVZWsprm4pRZp61bt+bkpFanQ4cOsVyycWprawMAxONxJgqxWIwJTTQaZcIRjUbZhNTX18f26evrUxWevr4+AAMiR21Jklh9RVFMs+nEmkqlEIvFAADJZBLxeBwAwPM8u32S53n2D7ZEIsHyisfj7B+esViMcerv72fHV3JSLoy0OGXalBOFFidBENDf3z+IkyRJaTy0OFFbyUkQBMZDi1NmnZR2Lk5qdaKx1DjR2rS1tamOJ6twWk9FUURLS0vB9JRyikQiOHToUFZOnp4WTk+bm5uxb98+AMD+/ftz9l4qlWJj04qe0tp5eqqtp8p/LBZCT2muqVSKLdSLRU8B65ra3t7O7FxjNV9r1Pfffx8AcPLkSV1jNR9rVBpHD6dM/Xn33XcBDNQ51zyRL03dvXs3ent7s3LKt6b29vbi+PHjOTnle42qvBhWDGtUURSZ5hRKU+lxsnHK5xqV8lDahVqjanESBIHlkqkRVPdygSOZl1s86EJfXx8mTJiA06dPY9KkSazIfr8/zRZFERzHMdvn80GSJIRCIXzqU5/CyJEj2XafzwdBEOD3+5kdCATAcRyzgYGGUNrBYBCEEGbLsgxJkpgtyzICgYCmLUkSCCHMVuORi5PP5xtkS5KETZs2YfXq1Rg5cuSw4KRWp3g8jvLycqxduxYAhhSnZDKJ48ePY9asWRg1ahQ4jmMTRaYtyzI4jtO0qViVlJSw330+HxMyNVstTiHtXJyAgQlTaft8PibGalyLlZNanQAwnmr8eJ7H0aNHMWPGDIwcOTKt92KxGCZMmIDe3l6UlJTACjw99fS0GPSUnucjR45g9uzZGD16tKVxKssyotEoGz+enhYPp6GspzQPM5qqpjPDUVMTiQTKysqYzgwHTsWoqd4a1bj+0PE9fvx4+P3+YcFJq07KembGSSQSaGlpwcUXX8x6l/ZeX18fJk+enFNPvTtB8gS/38+eQVLagUAgzfb5Pjrl1FZuDwaDaTZtEipCu3fvhs/nYw1Bv++stH0+X5pNm2LPnj3seHQ7zVdpK3kAYFfXcnHSY2dyUvJQ2pIkMa5anNRsjuNYvlqctOpEuXIcp4uH0qbIxkmtTj6fD/X19WzwZuOnrBMAdquhnt6jeVDQNzUrt9Pzl82mUJ6jbHaumHpss7500Umhxonmq7QzocVJK6byZWBG89Xjm8kDGLg6T/fR4qRlZ3JVi6mmEXbdvq2MkWnnU0/ppLxv3z42Odutp/S8Kf/n19NT5/VUa66zU08zj+Hpaf7zNaOnmVyHg54q42TaRvXHjjUq1RmtcWvXGpUi1zyRqT9UZ2RZzjpPqNlmNZXjOLbmM1MnJVe9mkoIwb59+yBJUlZO+VyjZhu3Q3GNanXNZ0VTlceze40KgN0Rk2v/fK1RM+uZLabafK4H3kWQIsPo0aML7mslphU4ka8TXJ3gmSkwhYDRmCtXrsT9999vyHf//v1YsmQJRo0ahQULFuD48ePw+XzYvXu3ps/WrVvBcRy7ddQqzJzbrVu3wufzsdtd1fDqq69i4sSJeYtJjzlz5kxTvsMFns7Y42fV14mYZn09Pf0I+dZTwDhXq3pqJiY9pqenns7Y5esWnk7oqdG4VvX0qquuwtGjRzF27FhPT7Mc02499b4OYxFGrjjlI9all15aUF8rMa3AiXyd4OoET47jCj6Zmon597//nf1PhV7fH/7whxg7diwOHDiAcePGYeLEiejo6MC5555rJm3DyJbrsWPHcMkll+DUqVODbs9btmwZOjo6MGXKFMOThRP1BIbP12E8ncm/n1Vfs3AiX09P7YVWvnbpabaYdsIu7SuGNaqnM/bGNAu36KmZuJ6eWo9pJ/TqnncniEUo3zBdiFh1dXWmYpr1tRLTCpzI1wmuTvAkhCAWi7Fn6woBMzEnT56M8ePHG/I9fPgwrrvuOsycOROTJ08Gz/OYMmVK2i2adiJbrm+++SZWrlyp+nziiBEjMGXKFPZ28nzFtBN2jBNPZ+yBx9M+X09P7YVWvnbpabaYdsKucVIM48/TGXtjmoVb9NRMXKt6es4558Dn87H3ehQKnp6mw7sIYhGFvG2L4zhMmjTJ9JU4M75WYlqBE/k6wdWpuhT6f9zNxFTebuj3+zF79mw8+eSTuPvuuzF+/HjMmDEDL730Etuf4zjs3LkTjz32GDiOwyOPPIK2trZBt2+HQiFccsklGD16NFatWoWjR48aymvv3r3w+XzszednzpyBz+fDv/zLv7Bcf/zjH2Pp0qVpfm+++SZuueUW1WPS2w2VXxh49dVXMWPGDIwZMwa33nor+zqIEhs3bsQ111yDc845B3PmzMGjjz6aJv7PPPMMrrjiCowdOxbTp0/HunXr2Ju1rcKOceLpjD3weNrr6+mpfXoKDOjYsmXL0vzs0tOFCxdi9OjRmD9/ftHrqZ3H1Yrl6Yw9vm7hCTijp0bj5kNPjx49inHjxnl66qCeehdBLKLQtxrOnTvXVEyzvlZiWoET+TrB1am6BIMSZDkOSYoV5EeW4wgGzb34jeM4jBo1CgDw9NNP45prrkF9fT3WrVuHb37zm+wTeR0dHbj88svxH//xH+jo6MB//ud/YuTIkWnHam1txec+9zmsXbsWu3fvxj333IPvfOc7hvKZP38+zjnnHFRVVQEY+CzdOeecg+rqapZrVVUVVqxYwXx6enrw3nvvaU4yFPTt6Dt27MDdd9+NdevWYffu3Vi1ahUef/zxtH3fffddfOUrX8G3vvUtNDU14b//+7/x6quv4oknnmD7+Hw+PP/889i3bx9ee+01VFRU4Nvf/rYhvloYLo/DeDqTfz+rvmbhVL6entqjpzTf999/v+B6+tJLL+G1114raj2187hasTydscfXLTyBwusp1dSRI0eaujhlRk8ffPDBQS8e9fS08HrqvRPEIuj3oI181oqCfg9a7+fHgIHvg1999dVs0On9rBUA7NixAwsXLsSoUaN0f9ZKFEXs3LkTCxcuZG9y1vupLiU/LU5an+qiA+3qq6/G6NGjdX+qK5VKYdeuXVi4cCF7W7DeT3VJkoSdO3fi6quvxogRI1Q5adWJwugn1Wi+11xzDXvLtp7PjyWTSXz44Ye49tpr096OrMYJ+Oi76RzHQRT7sW2b9U/wmcF110Xh948FMCDGuT63BQyME/pW6rVr1+Kb3/wmOI7Dt7/9bTz77LPYunUrPv7xj7PbtMeOHYspU6YAQNpVdFmW8cILL+Diiy/G008/DZ/Ph0suuQQNDQ342c9+BkIIZFke9JZ/tc+PLV++HJWVlfjc5z6HrVu34s4778Trr7+OxsZGTJs2DTU1NXjggQfY/qFQCFdccQUuuugidkwlV/p7f38/SkpK8Itf/AJr1qzBQw89BI7j8LGPfQw1NTXYtGkT+5LJE088gYceegh33HEH4vE4Zs2ahcceewwPPfQQvv/974PjONx3330s91mzZuHRRx/F//k//wcbNmxQvTVRKy+qBcrxZMdtuYXU00AgAFEUsWPHDlx77bXsdzv1lJ7jHTt24JprrmGf9PX01Fk91Zrr1HqP1pCODU9P7dPTpqYmzJ07FzU1Nbj//vsLoqd33nknAOC8887Do48+iu985ztFq6eAcU1V0xm716i5xq1da1QKo5/95Xke9fX1WLRoEQBozhP51FTlmg+AJqd8amoqlUJtbS2WLFkCn89n6BO5ZteokhTD+++Pt9Dx5rFgwUmUlJzPfs+mqcBHOcdiMQAf6SkAPPTQQ3j22WdRWVmJSy+9lOnpuHHjmJ6eOnUKwEdrF6qnzz77LAghg/RUqSE0vjIPqrXLly/H1q1b8bnPfQ6VlZW466678Nprr6GxsRGXXHIJampqcN999zHft99+m+kp/XeCGm81PQXAjrlp0ybG54knnsB3vvMd3HHHHQCA888/n+npD37wAxBC0nKYPXs2HnvsMaxbtw4vvPDCID3VykupqUqNoLqXC96dIAaxYcMGzJs3j4kfvcoXDocRDocBDHwWqrm5GcDAp7taWloADEwOra2t7FidnZ0ABq7U0VuXKioq2FuAy8rK2O1HoVAIqVQKF1xwAcrKyiCKInieRygUAgBEo1GUlZUBGLiqV1FRAQCIRCKorq6Gz+fDuHHjUFtbC2DgiiO1W1pa2Ce7mpub2ecbw+EwDh48iGnTpqGpqSknp5qaGnR0dDBO9Pao6upqTU48z0MURYRCoTROPp8P55xzDuOhxgkYuLpaU1PDOO3atQvTpk3D8ePHNTlp1en48eOYNm0adu3apclJrU6VlZWsptk4qdWpqqoK06ZNw+nTpzU5qdXpyJEjbIGZjVNbWxuAgc9TpVIpAGCC7RRSqRTLIZlMsgU5z/Ps0108z7OJQfkJsUsvvZSJWzwex5QpU9DV1YX+/n420adSKbbAoccABr6rHg6HsWTJEkSjUSagV155JYABke3r6xvkJ0kS61lRFBGNRrFy5Ups3boV/f39qKqqwvXXX49ly5ahqqoKu3btQiKRwD/90z8xTm+++SZuvvlmxiWRSDA7Ho+z3GVZhiAICIfDuPrqq9n2aDSKxYsXM5v+A/NHP/oRSkpKMHXqVJSUlODf/u3f0NHRgZMnT4IQgoqKCtx4442YNm0aSkpK8NWvfhXd3d3o6+tLu7WR3oIoCAKzU6kUq01bW9ug8aT2SUijcFJPaY91d3dDluWC6Gk4HGaLySNHjmTl5Olp4fS0ubkZTU1NmDZtGg4ePJiz91KpFBubnp7aq6c1NTUF1dPx48dj/PjxuOCCC/C///f/Lio9Baxrant7O7NzjdV8rVG3bduGadOmoaurS9dYzccalcbJxUlNf8rKyjBt2jTE4/Gs8wSQP03ds2cPu5icbZ7Ip6b29fWhr6+PPV5RqDWqU6A6pUdT6UUv4KOLEJ/4xCcQj8eRTCbBcRzOP/98Vpv+/n72D/ZoNJp2EY4eZ+/eveyiUV9fHwghWLJkSVoMqqn0ONRfqalLlizB1q1bIQgCtm7dilWrVuG6665DeXk56urqkEgkcPXVVzNOb7zxBm655RbwPK+qqfTPYDCIeDyOpqYmLF26NG2eWLBgQVpe9NGfkpIStkalehqLxdDX14eKigrcdNNNuOiiizB+/Hjcdddd6O7uRiwWgyRJLC6dJ4B0TRUEgfVMpkZQ3csJ4sEUent7CQBy+vRpQgghoigSURQH2YIgpNmSJJFUKkVKS0sJz/Np2wkhJJVKpdmyLKfZsiwPsgkhaTaNQW1BELLaoiim2Wo8cnFSs3meJ6WlpSQejw8bTmp1isVipLS0lKRSqSHHqb+/nzQ1NZF4PM7O+8Axo0QU+4kgRJmdSvVltVOpPhKJtKX9To+hZWfGoeeD5pLNXrFiBbnvvvvY9pkzZ5JnnnkmbZ8rr7yS/PCHP2T7XHnlleQHP/gB2+fw4cMEAKmvryeSJJHPfvaz5Gtf+xqRJIn5/P3vf2djmdZUkiRy5swZth/dTn0aGhoIx3Hk4MGDJBgMkp6eHvLcc8+R22+/nTzxxBNk8eLFbP9kMklKSkrIrl27NLlWVFSwHCiPRx55JG2fZ599lkyYMIHlNGrUKPKTn/yEHDx4kDQ3N5MDBw6QgwcPkoMHDxJBEEhLSwsZNWoUue+++8j27dvJ/v37yW9+85s0rq+88gopKSkZxI/a8XicNDU1kf7+/kG9RzWwt7eXWIWnp56eFoOeiqJIEokEaWxsJPF4XHFM43oqCFGSTPYyTfX01NPTfOkpIeY1VU1nhqOmxuPxNJ0ZDpyKUVMz16iyLKtqXSHWqFrjVs1esWIF+da3vsW2z5w5kzz77LNp+1D9pPtk6mtLSwsBQHbt2kUIIeSWW24hX/3qV9l5lWWZvPHGG2laJkkS01N6HjM1dc+ePWl62tvby/T0ySefJIsXL2Y5Uj2tr6/X5Er19MyZM4zHo48+mrYP1VOa+6hRo8hPf/pTpqNKTRVFkRw5coTp6bZt28iBAwfIyy+/nBZHqadqecXjcdLY2EgSicSg3jt9+rQuPfXuBLEIcvbKnt/vZ88gKW16ex61lVf7qa3cHgwG02x621UwGIQkSXjvvffAcRz7CQaDAJBm+3y+NJve5v3++++nxaa3wdFb8tR4EEJQXV0NQkhOTnrsTE5KHkpbFEXGVYuTmg2A5avFSatOlKtWbbLViSIbJ7U6cRyH6upqdjthNn7KOhFCsG3bNnbLaq7eo3lQO5Eg8PnGIBAYh0BgHPz+sQgGx+e06U8gMA7B4PicttIvkSCquWjZStArv1r7qB2HEMKu4NNzOW/ePHzwwQfs3HMcx/4Xg+M41f+NU26nPvS5yyeeeAJXXnklJkyYgJUrV6KqqgpbtmzB8uXL2f5bt27FxIkTsWDBgpy8Kc958+Zhx44dafvs2LEjrW+uvvpqHDx4EHPnzsWUKVPwsY99jP0EAgHs3LkToijimWeewZIlS/Dxj38cJ0+ezMlVLS81jVD+T0a+UEg95TgOkiRh+/btkCSpIHpKz9u2bdsYV09PnddTrbnOTj3N1EdPT9X1tKqqCkuXLmW3Q9utpx/72McwZ84cXHDBBZg7d25R6ylgXlP1jtV8rFEBZB23dq1RKXLNE5n6Q3WGPv6ixinfmgqArfnM1EnJVa+myrKM7du3s8dsCrVGTSRI2lqzUGtU+gUTPZpKfyeEpL3MU20f5Z/KfejYpLj88ssH6RL9nfoodUZ5bKWmXnHFFWl6WlJSwvR069ataS/tp3p61VVX5Zw/6N0YVPeV+9A8ae5XX301Dhw4gEsuuWTQGtXv92PXrl1MT5ctW4ZLLrmE3TWTeb4yuerR1MxzqwXvIohF5OsWRr2x5syZYyqmWV8rMa3AiXyd4OpUXTJfcFcIWIlp1nfEiBFpv9977704fPgw1q9fjwMHDuCPf/wjXn311bR92tvbMW/ePOzcuVPzuBzHYfny5fj973+PlStXAgA+8YlPIJVKoaqqim0DgH/84x85XzhFQXl+61vfwqZNm/Czn/0MBw8exK9+9Sv2vCXFD37wA7z++ut45JFHcPjwYYTDYfzlL3/B9773PQDAnDlzIIoifvnLX+LIkSP43e9+hxdffFFXHnpgxzjxdMYeeDzt9fX01D493bJlC1atWsX2LYSeNjY24siRI0Wvp3YeVyuWpzP2+LqFJ+CMnlqJm698PT0tvJ46fhHkhRdewOzZszFq1CgsXLgQ7733Xtb9q6qq2MvoLr744kEnrbGxEZ///Ocxa9YscByH5557btAxHnnkkbQruhzHYerUqabyL7QgTZs2zbQgmfG1EtMKnMjXCa5O8OQ4DiNGjBh0pdVOWIlJffPhN2PGDPztb3/Dxo0bceWVV+LFF1/Ek08+mbaPIAg4cOAAe/ZTC6tWrYIkSWyS4TgO119/PQCwP4GBSeazn/2srpzpOVqyZAlefvll/PKXv8RVV12FsrIyNnlQrFmzBm+99RY2b96MZcuWYenSpXjmmWcwc+ZMAMBVV12FZ555Bj/96U8xf/58/OEPf8CPf/xjXXnowXC5COLpTP79rPqahRP5enpqv56uWrWKcbVbT8vLy7F48WIsX74czz77bFHrqZ3H1Yrl6Yw9vm7h6YSeWolrVk+pr/JPT08d0NOsD8vYjD//+c8kGAyS3/zmN6SpqYncd999ZOzYseTYsWOq+x85coSMGTOG3HfffaSpqYn85je/IcFgkPzP//wP26e2tpY8+OCD5E9/+hOZOnUqefbZZwcd54c//CG5/PLLSUdHB/vp6uoylDt93rK7u9uQHyGEPcNOn7XTC0EQyJYtW9hzT4XwtRLTLE8rcZ04R0OZZyKRIE1NTSSRSLBtsiyT3t5e9lydXiif6zYKszGt+FqJaZZrZsydO3eSCRMm6OqNochTrX8ouru78/5OkELqKSGeztjlZ8XXCZ5GfDPHxFDQmUL4OsEzM24h9NSK71DRU0LMa6qnM/bFtOLrxFrcLXpqJe5Q1Bm9MT09JcTRT+Q+88wz+PrXv4577rkHAPDcc8/h3Xffxa9//WvVK0IvvvgiZsyYwe7uuOyyy/Dhhx/iqaeewuc//3kAwKJFi9hbsbN9XzkQCJi++0OJQl+VnT9/vumrsmZ8rcS0AifydYKrU3UZPXq0KT8rsBLTrK/TPOntfspncvX6mo1ZKAyXO0E8ncm/n1Vfs3AqX6d1plC+TvBUxi2Unlr1NYPhcieIpzP2+LqFJ+C8zhTKz6qv1ZiengKOXQRJpVLYuXPnoAsVq1evTvt0lRLbt2/H6tWr07atWbMGr7zyCgRB0F1IYOATOhdeeCFGjhyJa6+9Fk8++SQuvvhiwzwKLUjnn39+7h3z6GslphU4ka8TXJ3gqXzxVaFgJaZZ36HAc/HixexTjEZ9zcYsFIbLRRBPZ/LvZ9XXLJzIdyjoTCF8ndIZZdxC6KlVX7MYLhdBPJ2xx9ctPIeCzhTCz6qvWXh6mg7HLoJEIhFIkoQpU6akbZ8yZQp743YmTp48qbq/KIqIRCK44IILdMW+9tpr8frrr+OSSy5BZ2cnHn/8cSxbtgyNjY0455xzVH2SySSSyST7nX6rOR6PG77CJZz9rjL904hfdXU1li9fbrihzPpajan806ivE/majan8s1Ax9fgJggBCCGRZZt9AJ2ffZj1u3DhDzz+Ss29bpsczArMxreZrJSb90wjX4cZTlmUQQiAIQtpb6gGkfSnCKIaCnlIfT2fy72c1pvLPQsQ04pupqZ7O6POlfxZq7nDqHNE/C6mnQP401dMZ+2Ja8XViLe4WPaU+w0Vn7Io51HjmQ085QqMUGCdOnMC0adNQU1OT9rmeJ554Ar/73e+wf//+QT6XXHIJvva1r+Hhhx9m27Zt24brrrsOHR0dgx5vmTVrFu6//37cf//9WXOJxWKYM2cOvv3tb2P9+vWq+zzyyCN49NFHB23/4x//iDFjxmQ9vgcPToE+9jV9+nTTL2/y4F6kUim0trbi5MmTgz7hGI/H8eUvfxm9vb0oKSkxdFxPTz0UKzxN9WAWdukp4Gmqh+KEp6cezCIfeurYnSDnnnsu/H7/oLs+urq6Bt3tQTF16lTV/QOBgOYdHHowduxYXHHFFWhubtbc5+GHH067QNLX14fp06dj9erVhicsQRBQXl6Om266yZHbvgoFj6fzSCaTOH78OMaOHWv5mTxCCKLRKMaPH1/wN3cXGm7hmotnIpHA6NGjsWLFikGfgaP/02gGnp4ah8dzaMDTVOPweA7ALj0F8qepQ3385Qtu4QkMba6enhqHx3MA+dBTxy6CjBgxAgsXLkR5eTluvfVWtr28vFzzcz1Lly7Fxo0b07aVlZXhmmuusTSwk8kkwuFw2mctMzFy5EjNb0GbjR0MBg3fDldWVobVq1ebujXNjK+VmBRGeVqJ68Q5ohiKPH0+HziOA8/zGDt2LICBW8j6+vpQUlJi6Hlhejsax3GGnzM2G9NqvlZiAsa5DjeePM+D4ziMHj160O2GVjAU9BTwdMYuP6u+QGF5GvXlOA6iKMLn83k6o9MXKOzc4dQ5Agqvp0D+NdXTGXtiFtNavBA8M9eons7o8wM8nvnQU0e/DrN+/XrccccduOaaa7B06VK89NJLOH78OO69914AA1e229vb8frrrwMA7r33XvzqV7/C+vXr8Y1vfAPbt2/HK6+8gj/96U/smKlUCk1NTcxub2/H7t27MW7cOMydOxcA8OCDD+Izn/kMZsyYga6uLjz++OPo6+vDXXfdZZhDIFC4UxgIBHD99debimnW10pMK3AiXye42s3T7/dj4sSJ6OrqAgB2W2wwGEQymTR0FVmWZaRSKfA8b1gECSGmYlrxtRLTLNfhwpMQgng8jq6uLkycOFF1grFjnHg6Yw88nvnzDQQCGDNmDE6dOoVgMMhe+ubpjDacmDucOEdDTU/tPK5WLE9n7PEdrjwz16ijR4/2dCYH3M4zn3rq6EWQ22+/Hd3d3XjsscfQ0dGB+fPnIxQKYebMmQCAjo4OHD9+nO0/e/ZshEIhPPDAA9iwYQMuvPBCPP/88+zzuMDAu0YWLFjAfn/qqafw1FNPYcWKFdi6dSsAoK2tDV/60pcQiURw3nnnYcmSJfjggw9YXCMo5K1IHMeZelbUiq+VmFbgRL5OcC0ET/quHDrJmAUhhN1+NpxvwQPcwzUXz4kTJ2p+StyO81Loc+3pjD1+Vn3NohD5chyHCy64AC0tLTh27JipWBSezgwvDDU9tfO4WrE8nbHHdzjz9NaoxuDxHEA+9NTRiyAAsG7dOqxbt07171599dVB21asWIFdu3ZpHm/WrFnsjbJa+POf/2wox2ww82ZpK7FCoRDWrl1r6tY0M75WYlqBE/k6wbUQPOmi/fzzz4cgCEX1Bncrvl5Ma77BYDDrLYZ2aF8h9ZTG83Qm/35Wfc2iUPmOGDECH/vYx5BKpYbVmB9qMYst36Gmp3YeVyuWpzP2+A5nnso1aiKRQFVVlaczXszC6CnxYAq9vb0EAOnp6THsm0qlSGlpKUmlUob8ZFkm8XicyLJsOKZZXysxzfK0EteJc+TxtC+mFV8netctPAkhpKenhwAgvb29hn0z4YSeEuKNP7v8rPh6OmNvzGLiaSVusfVuPvWUEPOa6tXKvphWfD2dyQ2vd+2LWWw89eqpsYeJPDgOK88DmvUt9HP6VuM6cY6swONpn6/H00MueOPPHj+rvk7ELKbx5xaeVuIWW+8OB7ilVm4Zf27haSWuW3q32HjqgXcRxCKSySQAQJIkSJI0yBZFMc2mb7sFPnrzrXK7IAhpNjn7aA99jCEUCiGRSIAQAkIIu+VHacuynGaLoghRFBEKhcDzfNp2mq/SVvJIJpMIhUJIJpM5OemxMzkpeShtmm8ikdDkpGbzPM/y1eKkVSfKled5XTyUNkU2Tmp1ovmmUilNTmp1ornSWhjpPRqT9oWe3qM8KAcjvQcMvKSYxtTbe9RWcjXSe7SHUqmUod6jnCj09p5ynOXSBbU6Zeart/e0dEFvnejxs/FTq1Pmd9nzgULqKT0foVAo7XfAPj1V1ply9fTUeT3Vmuvs1lM1Hp6eptt65rp86ikhhPkqdaEY9RQwr6lGzhe1za5Rc41bu9aoFHrHKrUTiQTrj1xjNV+amjluC6GpWjpj5xo127gdimtUK/O5FU1V1rUQa1Qr87nZNWqufss1n+uBdxHEIDZs2IB58+Zh0aJFAIADBw4AAMLhMMLhMACgoaEBzc3NAID6+nq0tLQAAGpra9Ha2sqO1dnZCQCorq5GJBIBAFRUVKCnpwfAwOd/o9EoALDBedNNN6G8vByiKDIxBYBoNIqysjIAQE9PDyoqKgAAkUgE1dXVCAQCWLBgAerq6gAAra2tqK2tBQC0tLSgvr4eANDc3IyGhgbGqbm5GWvXrmV2Nk41NTXo6OhgnLq7u5mtxYnneTYolZzom6UrKys1OQEDL8+tqalhnOrr67F27Vq0tbVpctKqU1tbG9auXYv6+npNTmp1ojnm4qRWp+rqaqxdu5bZapzU6tTS0oKLLroIgUDAcO/V1dVhwYIFCAQCunuPcgJguPeoPXHiRAQCAd29RzmFw2HMnTsXgUBAd+9FIhEEAgGMGjUK/f39hnoPAPPJxkmtToFAAJdeeinjobf3WlpaEAgEcO655zIeenqvrKwMiUQCa9euRXl5uaHeo5xoLC1OWnXKx5V9J/WUXsCgKISehsNhBAIBXHTRRYyHp6fO62lzczPC4TDWrl3LbDVO+dbT8vJyAMZ6j9pu0FNg4EX2U6dORSAQKIieRqNRpm3KfzQUg54C1jW1vb2d2XrPF2Btjbp9+3asXbsWkUhE9/minMyuUWmcXJzU+rq8vBxr165FIpHIOVbzpakNDQ249NJLEQgEco7VTE5mNbW/vx+jRo1CIBDQNVbzsUYNBAKYOHEi4zHU16hm53OrmqqsayHWqGbncytrVMqju7vb8HxOdS8nsj4so4FVq1aRM2fODNre29tLVq1aZeaQRQf6vGV3dzchhBBRFIkoioNsQRDSbEmS2HNOPM+nbSdk4BkopU2foaLbY7EYSSaTRJZlIssye1ZKadMY1BYEgciyTPr7+wdtp/kqbSUPQRBIPB4fxCOTk5rN8zwpLS0l8Xhck5OSR6ZNuWpxUrNTqRTLV4uTVp0oV2UNtPgp94nFYuy5tWyctOoUj8fTaqDFT7mPIAgkGo0SWZYN9R7Nvb+/n8iyrLv3ZFkmyWSSlJaWsv7T23s0r76+PiLLsu7eU+auzFdP70mSRGRZJn19fWx/vb1HCGFc6TnR03tq40xv74miOChfPb1H7Xg8PkgX9PQe1aJ4PK6792judrwTpJB6Snuxr6+P9YvdekrrHI1G08ayp6fO6qnWXGenntKaUE319FTd1jPX5VNPae6ZulBMekqIeU1V0xm716i5xq0da9R4PJ6mM0Y0NZlMsvOTa6zmS1O1dMZOTdXSGTvXqNnG7VBco5qdz61oKtWZWCxmqPesrFHNzudW1qh0jPI8b3g+7+7utu+dIFu3bmW37yjB8zzee+89M4csWpCzty75/X72plqlHQgE0mzlt46prdweDAbTbPqZn2AwCEmSUF5eDo7j2A99Y67S9vl8aXYgEIAoiti8eXNabPo/D36/P81W8iCEoKysDISQnJz02JmclDyUtnj2f8novmqc1GwALF8tTlp1oly1apOtThTZOKnVieYry7ImJ7U6EUKwZcsWiKJoqvc2b94MURR19x7lQTkY6T1g4La1iooKiKKou/eoTQhJy1dv74miiIqKCnZbnt7eo5wo9Pae2jjT23t+v39Qvnp7T5IklJWVDdIFvXWix8/GT61Oog23bxdSTzmOgyRJqKiogCRJBdFTet62bNnCuHp66ryeas11duupGg9PT9NtPXNdPvWU4zjmq9SFYtRTwLymGjlf1Da7RgWyj1u71qgUeseqkldZWRkkSco5VvOlqYC6ztipqVo6Y+caNdu4HYprVCvzuRVNVda1EGtUK/O52TVqrn7LNZ/rAUf07gmwW2auuuoqVFRUYPLkyezvJEnCpk2b8N///d84evSo3kMWLfr6+jBhwgT09vYa/o624MCnrpyAx3N4wS08AfdwtcLTigbm81herYYX3MITcA9Xj2du5FNPrRzPq9Xwg1u4ejyHFwqhp4buBLnqqquwYMECcByHG264AVdddRX7WbhwIR5//HH84Ac/MJRoscPANaS8xOrr6zMV06yvlZhW4ES+TnD1eNrn6/G0F3bE83TGHng87fP1eNoLN/VuMR1XK5ZbauWG8ecWnlbiuqV3i5GnHhi6CNLS0oLDhw+DEILa2lq0tLSwn/b2dvT19eHuu+82lXCxwq5bGLVivffee6ZimvW1EtMKnMjXCa4eT/t8PZ72wo54ns7YA4+nfb4eT3vhpt4tpuNqxXJLrdww/tzC00pct/RuMfLUA0OPw3j4CN7t27nh8RxecAtPwD1ch8rt256e5obHc/jBLVw9nrnhPQ5TWLiFJ+Aerh7P4YVC6Knub3L94x//0B38lltu0b1vsYO+0KZQsXp6ejBx4sS0l7LZ6WslphU4ka8TXD2e9vl6PO2FHdpXSD2l8bzxl38/q75m4Zbx5xaeVuIWY+8W03G1YrmlVm4Yf27haSWuW3q3GHnqge5s/vmf/1nXz6233mo66WIE/UqOJEmQJGmQLYpimq0sDLWV2wVBSLPpjTqCIEAURdTW1oLneRBCQAiBIAgAkGbLspxm0xxqa2uRTCbTttN8lbaSRyqVQl1dHVKpVE5OeuxMTkoeSpvmy/O8Jic1O5lMsny1OGnViXJNJpO6eChtimyc1OpE86X1zcZPWadUKoXa2lrGwUjvJZNJ5qu39ygPysFI79Fj0Jh6e4/aSq5Geo/2EM1Hb+9RThR6e085znLpglqdMvPV23uiKKKurm6QLuitEz1+Nn5qdaL55xOF1FNCCNNUURQLoqfKOlOunp46r6dac53deqrGw9PTdFvPXJdPPSWEMF+lLhSjngLmNdXI+aK22TVqrnFr1xqVQu9YpTbP86irq4MoijnHar40NXPcFkJTtXTGzjVqtnE7FNeoVuZzK5qqrGsh1qhW5nOza9Rc/ZZrPtcD3RdBZFnW9WOXkA8VbNiwAfPmzcOiRYsAAAcOHAAAhMNhhMNhAANf0WlubgYA1NfXo6WlBQBQW1uL1tZWdqzOzk4AQHV1NSKRCACgoqICPT09AAY+oxWNRgEAoVAIkiThxhtvRHl5OURRBM/zCIVCAIBoNMo+X9TT04OKigoAQCQSQXV1NYLBIK644grU1dUBAFpbW1FbWwtg4F0v9fX1AIDm5mb2FaBwOIxDhw5hzZo1CIfDOTnV1NSgo6ODceru7ma2Fiee5yGKIkKhUBqnYDCIZcuWobKyUpMTAHR0dKCmpoZxqq+vx5o1a9DW1qbJSatObW1tWLNmDerr6zU5qdWJ5piLk1qdqqursWbNGvT29mpyUqvT0aNHMWXKFASDQcO9V1dXhyuuuALBYFB371FOAAz3HrXHjBmDYDCou/cop3A4jFmzZiEYDOruvUgkwj7BFYvFDPUeAPT397Oa6u292tpaBINBzJ07F3v37s3KSa1OwWAQJSUlOHnypCYntTrxPI81a9agvLzcUO9RTjSWFietOuXjNkwn9ZTneXAch2QyCY7jCqKn4XAYwWAQU6ZMYV9R8/TUeT1tbm5GOBzGmjVrcOjQoYLpaXl5OQB4epqlTm1tbTjnnHMQDAYLoqfRaBTBYBDJZJItrItFTwHrmtre3s5svecLsLZG3b59O9asWYNIJKL7fFFOZteoNE4uTmp9XV5ejjVr1oDn+ZxjNV+aunfvXsydOxfBYDDnWM3kZFZTY7EY+2ypnrGajzVqMBjEmDFjGI+hvkY1O59b1VRlXQuxRjU7n1tZo1Ie3d3dhudzqns5QTyYQm9vLwFAIpEIIYQQURSJKIqDbEEQ0mxJkkgqlSKlpaWE5/m07YQQkkql0mxZlpktiiI5efIk4XmeyLJMZFkmqVSKEELSbBqD2vT4HR0dJJlMpm2n+SptJY9UKkU6OztZfC1OajbP86S0tJTE43FNTkoeSluSJMZVi5OanUwmWb5anLTqRLkmk0lNTmp1isVipLS0lKRSqayc1OpE8xUEQZOTWp1SqRQ5ceIEkSTJUO8RQkgymSQdHR1pvZGr92RZJslkkpSWlpJkMmmo92h8mq/e3qN2KpVKy1dP79E4J06cSDtnenqPniNaU729pzbO9PaeKIqD8tXTe3RcdnZ2DtIFPRpBtSgej+vuPZr7mTNnCADS29tLrMIJPZVlmYiiSNrb24koigXRU2Wd6TE9PXVeT7XmOjv1VJZlxjWZTHp6qmHrmevyqaeyLBNJkgbpQjHpKSHmNVVNZ+xeo+Yat3asUePxeJrOGNFUnudJZ2cni6/GKd+aqqUzdmqqls7YuUbNNm6H4hrV7HxuRVOpzsRiMUO9Z2WNanY+t7JGpWOU53nD83kkEtGlp6Yeznnsscey/rgJHMcBAPx+P/x+/yA7EAik2crnoait3B4MBtNsevxgMAhCCBobG+H3+8FxHLtCS/Ogts/nS7MDgQBkWUZTUxM7Nt1O81XaSh4cx2Hfvn3gOC4nJz12JiclD6UtyzLjqsVJzfb5fCxfLU5adaJc6XFy8VDaFNk4qdWJ5kuPn42fsk4cxyEcDkOWZcO95/P50NTUBFmWdfce5UFjG+k9Cpqv3t6jNsdxafnq7T1ZltlV7WyctOpEobf31MaZ3t7z+/2D8tXbe4QQ7Nu3b5Au6K0TPX42fmp1orc75hOF1FOO40AIwf79+0EIKYieKutMc/H01Hk91Zrr7NZTNR6enqbbeua6fOopx3GQZXmQLhSjntI8lXEybaP6Y8caNde4tWuNSqF3rFLb7/dj3759IITkHKv50lQtnbFTUwF1nbFzjZpt3A7FNaqV+dyKpirrWog1qpX53OwaNVe/5ZrP9cDU12EWLFiQ9rsgCGhpaUEgEMCcOXOwa9cuo4csOnhfM8gNj+fwglt4Au7haoWn93WYwsLjOfzgFq4ez9zwvg5TWLiFJ+Aerh7P4YVC6KmpO0Hq6+vTfvbt24eOjg7ceOONeOCBB8wcsmhh19V7rVjt7e2mYpr1tRLTCpzI1wmuHk/7fD2e9sKOeJ7O2AOPp32+Hk974abeLabjasVyS63cMP7cwtNKXLf0bjHy1IO8faumpKQEjz32GL7//e/n65BFgUIX9fDhw6ab0IyvlZhW4ES+TnD1eNrn6/G0F8PlIog3/vLvZ9XXLNwy/tzC00rcYuzdYjquViy31MoN488tPK3EdUvvFiNPPQjk3kU/enp60Nvbm89DDnkonycrRKzly5cX1NdKTCtwIl8nuHo87fP1eNoLO7SvkHpK43njL/9+Vn3Nwi3jzy08rcQtxt4tpuNqxXJLrdww/tzC00pct/RuMfLUA1N3gjz//PNpP7/4xS/wne98B7fffjs+9alPmTlk0aLQV7aOHTtm+kqcGV8rMa3AiXyd4OrxtM/X42kvhsudIN74y7+fVV+zcMv4cwtPK3GLsXeL6bhasdxSKzeMP7fwtBLXLb1bjDz1wNRFkGeffTbt5/nnn8fWrVtx11134aWXXjJzyKKFIAgAAEmSIEnSIFsUxTRbWRhqK7cLgpBm0/fWCoIASZLQ1taGZDIJQggIISy+0pZlOc2mx29ra0MqlUrbTvNV2koegiCgvb2dxc/GSY+dyUnJQ2nTfJPJpCYnNTuVSrF8tThp1YlyTaVSungobWU/aHFSqxPNVxRFTU5qdRIEAW1tbZBl2XDvpVIp5qu39ygPysFI79H4NKbe3qO2kquR3pNlGa2trWnnTE/vUU4UentPOc5y6YJanaiv8pzp6T1JktDe3j5IF/TWiR4/Gz+1OtkxoRVSTwkhkCQJra2tkCSpIHqqrDM9pqenzuup1lxnt56q8fD0NN3WM9flU08JIYyrUheKUU8pp8xzpKevjZwvaptdo+Yat3atUZXnyIimJpNJtLe3s/hqnPKtqZnjthCaKooiWltbB+mMnWvUbON2KK5RrcznVjRVWddCrFGtzOdm16i5+i3XfK4Hpi6CtLS0pP0cPnwYH3zwAZ588kmMHz/ezCGLBhs2bMC8efOwaNEiAMD+/fsBDHxaiX7qqKGhAc3NzQAGXiLb0tICAKitrUVrays7VmdnJwCguroakUgEAFBRUYGenh4AQFlZGaLRKAAgFApBFEUsXrwYZWVlEEURPM8jFAoBAKLRKMrKygAMPJZUUVEBAIhEIqiurkYgEMCsWbNQW1sLAGhtbWV2S0sL6uvrAQDNzc1oaGhgnJqbm7Fs2TI0NTXl5FRTU4OOjg7Gqbu7m9lanHiehyiKjB/lFAgEcMUVVzAeapwAoKOjAzU1NYzTrl27sGzZMrS2tmpy0qpTa2srli1bhl27dmlyUqtTZWUlq2k2Tmp1qqqqwrJly9DT06PJSa1OLS0tGDNmDAKBgOHeq62txaxZsxAIBHT3HuUEwHDvAcCpU6cgSRICgYDu3qOcmpqacM455yAQCOjuvUgkgkAggFgshv7+fkO9B4D5ZOOkVqdAIIApU6YwHnp7j35di+M4xkNP75WVlSGRSGDZsmUoKysz1HuUE42lxUmrTvm4zdpJPeV5HgCYThVCT8PhMAKBAMaMGcN4eHrqvJ42NzejqakJy5YtQ3Nzc8H0tLy8HICx3gPco6fUHjFiBAKBQEH0NBqNIhAIoLu7m/2jvFj0FLCuqe3t7czWe74Aa2vUmpoaLFu2DKdOndJ9vigns2tUGicXJ7W+Lisrw7Jly5BIJHKO1XxpakNDA6ZMmYJAIJBzrGZyMqup/f39iMViCAQCusZqPtaogUAAkiTh1KlTWTkNlTWq2fncqqYq61qINarZ+dzKGpXy6O7uNjyfU93LCeLBFHp7ewkAcurUKUIIIaIoElEUB9mCIKTZkiSRVCpFSktLCc/zadsJISSVSqXZsiwzWxAEcvDgQZJIJIgsy0SWZZJKpQghJM2mMahNczh48CCLSbfTfJW2kkcymSTNzc0kmUxm5aRm8zxPSktLSTwe1+Sk5KG0ab6JREKTk5rN8zzLV4uTVp0oV57nNTmp1SkWi5HS0lKSSqWyclKrE82X1jcbP2WdkskkOXDgAOOgt/cIIYTneXLw4EF2PD29J8sySSaTpLS0lCSTSUO9R49B89Xbe9RWctXbe5IkEVEUyf79+1k+enuPxqQ11dt7NLcDBw6QZDJpqPfo7wcOHGCx9PQe7Zvm5uZBuqBHI6gWxeNx3b1Hcz19+jQBQHp7e4lVOKGnsiwTQRDI/v37iSAIBdFTZZ1pj3h66ryeas11duqpLMuMazKZ9PRUw9Yz1+VTT2VZZlyVulBMekqIeU1V0xm716i5xq0da9R4PJ6mM0Y0NZFIkObmZiIIQs6xmi9NzRy3hdDUVCpF9u/fP0hn7FyjZhu3Q3GNanY+t6KpVGdisZih3rOyRjU7n1tZo9IxyvO84fn81KlTuvTU9KXnuro6/PWvf8Xx48fZLcEUf//7380etujg8w3cTOP3+9k2pa28uk9teosR9VXuo/wWcqYtiiJ6enowe/ZscByXtg/Hccz2+Xzs2NRW+mbuo5W73+8HIQRnzpzBrFmz2N+pcVKzydnbugKBAIuVjZ8WVy1Oarbf72f50nyy8VPmK4oi81Wrjd46ZbMz66SWrxY/Zb4+nw+9vb0ghKTllav36D49PT2DfPXkTv9U8sjVe3Qfmq/f79fVe7m45rJFUURfX9+gsWKEazZOWuOst7fXED9lvr29vSy23t5T9m02rmp1orcMGuk9atPxnU8UUk8p+vr6AMBQT5vVUwCDesTTU+f1VGuus1NPtXh4ejrYzjXX5VNPgQFtU9OFTE5DXU9pXK2YylypraYzdq9Rc41bu9aoejip2YFAgMXMNVbzpalGx20+NJXjOPT19Q3SGTvXqNnG7VBco5qdz6mvGU2lOmO096ysUa3M52bXqEotMKqpdHsumLoI8uc//xl33nknVq9ejfLycqxevRrNzc04efIkbr31VjOHLFoU+s3b9BbHQvlaiWkFTuTrBFePp32+Hk97YYf2FVJPaTxv/OXfz6qvWbhl/LmFp5W4xdi7xXRcrVhuqZUbxp9beFqJ65beLUaeemDqnSBPPvkknn32Wbz11lsYMWIEfvGLXyAcDuO2227DjBkzzByyaEH/F7JQsfbv328qpllfKzGtwIl8neDq8bTP1+NpL+yI5+mMPfB42ufr8bQXburdYjquViy31MoN488tPK3EdUvvFiNPPTB1EeTw4cP49Kc/DQAYOXIkYrEYOI7DAw884LqvwxQaiUSi4L5WYlqBE/k6wdXjaZ+vx9NDLnjjzx4/q75OxCym8ecWnlbiFlvvDge4pVZuGX9u4Wklrlt6t9h46oGp++QmT57M3mA7bdo07Nu3D1dccQV6enoQj8fzmuBQh/I5pELEWrBgQUF9rcS0AifydYKrx9M+X4+nvbBD+wqppzSeN/7y72fV1yzcMv7cwtNK3GLs3WI6rlYst9TKDePPLTytxHVL7xYjTz0wdSfI9ddfzz7tdtttt+G+++7DN77xDXzpS1/CjTfeaOaQRYtC396zb98+07cjmfG1EtMKnMjXCa4eT/t8PZ72Yrg8DuONv/z7WfU1C7eMP7fwtBK3GHu3mI6rFcsttXLD+HMLTytx3dK7xchTD0xdBPnVr36FL37xiwCAhx9+GA8++CA6Ozvxuc99Dq+88oqZQxYt6ImWJEnVFkUxzZZlmflSW7ldEIQ0m76pm9qEkEE2gDRbluU0Wzz77WxCCLOV2yVJSrPNctJj55uTlm03p8w6URjllItHNk40vhlOtAZGe49yMFMnemwzdVLma6ROyrhGe4/CaO8RQnL2m1adlPnq7T2r44kePxsnrTrlG07oqSzLBddTWZY9PR1iemqFk1k9VePh6elgTrnmOjv0NFMXilFP6fEz4+RzrBaz/lAUA6fMcVsoTVXTGbvXqFrjdqiuUa3M52Y1VVnXQq5RC6kR+ZjPc0H3RZD169cjFosBAPbt24fzzz9/4AA+H7797W/jH//4B5555hlMmjRJ7yGLEhs2bMC8efPYm27D4TD7k9oNDQ1obm4GANTX16OlpQUAUFtbi9bWVnaszs5OAEB1dTUikQgAoKKiAj09PQCAsrIy9thRKBSCIAi49NJL8e6770IURfA8j1AoBACIRqMoKysDAPT09KCiogIAEIlEUF1dDb/fj8mTJ2PHjh0AgNbWVtTW1gIAWlpaUF9fDwBobm5GQ0MD43Tw4EHMnz8fjY2NOTnV1NSgo6ODceru7ma2Fiee5yGKIkKhUBonv9+PmTNnYsuWLZqcAKCjowM1NTWM086dOzF//nwcP35ck5NWnY4fP4758+dj586dmpzU6lRZWclqmo2TWp22bt2K+fPn48yZM5qc1Op05MgRSJIEv99vuPd27NiByZMnw+/36+49ygmA4d4DgK6uLvT09MDv9+vuPcqpsbERwWAQfr9fd+9FIhH4/X50dHQwHnp7DwD6+/tZTfX2Xm1tLfx+P0aPHo09e/Zk5aRWJ7/fj/7+fpw4cUKTk1qd4vE45s+fj3fffddQ71FONJYWJ6065eM2ayf1lOd5EELQ0tICQkhB9DQcDsPv90OSJBw5ciQrJ09PC6enzc3NaGxsxPz583Hw4MGC6Sm9o9bTU+06HT9+HMlkEn6/vyB6Go1G4ff70dLSAkEQikpPAeua2t7ezmy95wuwtkbdtm0b5s+fj66uLt3ni3Iyu0alcXJxUuvrd999F/Pnz0c8Hs85VvOlqXv27MHo0aPh9/tzjtVMTmY1NRqNoqOjA36/X9dYzccalX5yvKurKyunobJGNTufW9VUZV0LsUY1O59bWaNSHt3d3Ybnc8ojJ4hOBAIBcvLkSUIIIT6fj3R2dup1HZbo7e0lAMipU6cIIYSIokhEURxkC4KQZkuSRFKpFCktLSU8z6dtJ4SQVCqVZsuyzGxBEMjOnTtJIpEgsiwTWZZJKpUihJA0m8agNs1h586dLCbdTvNV2koeyWSS7Nq1iySTyayc1Gye50lpaSmJx+OanJQ8lDbNN5FIaHJSs3meZ/lqcdKqE+XK87wmJ7U6xWIxUlpaSlKpVFZOanWi+dL6ZuOnrFMymSQffvgh46C39wghhOd5snPnTnY8Pb0nyzJJJpOktLSUJJNJQ71Hj0Hz1dt71E4mk2n56uk9SZKIKIrkww8/ZPno7T0ak9ZUb+8px1kymTTUe/R3Zb56eo/2za5duwbpgh6NoFoUj8d19x7N9fTp0wQA6e3tJVbhhJ7KskwEQSAffvghEQShIHqqrDPtEU9PnddTrbnOTj2VZZlxTSaTnp5q2HrmunzqqSzLzFepC8Wkp4SY11Q1nbF7jZpr3NqxRo3H42k6Y0RTE4kE2bVrFxEEIedYzZemZo7bQmiqls7YuUbNNm6H4hrV7HxuRVOpzsRiMUO9Z2WNanY+t7JGpWOU53nD8/mpU6d06anuF6POmjULzz//PFavXg1CCLZv365518fy5cv1HrboQa/eK6/iK23lt4qpTW/T8fl8g/YJBoOatiRJGDNmDILBIDiOS9uH4zhm+3w+dmxqU18aS7mPVu7Uplef6e9qnNRscvYWqEAgwGJl46fFVYuTmh0IBAblm4sfzVGSJIwePTotXy1+enlk2pl1ovn6fD6Wjxa/zNzHjh2bk5OWPWbMGMOcaM9xHJfGI1fvUZvma6T3qK2Wby5bkiSMHTvWcO8puebipDXOjPaeWr56e4/2rZouZHLKrJNw9tZDNV3QU6d8o5B6Cgyck7FjxxruaSt6Suvs6enQ0lNg8Fxnp55q8fD0dDCnXHNdPvWUQk0XMjkNdT1VHl+vpqrpjN1r1Fzj1q41qh5OWvbo0aPBcVzOWudLU42O23xpqprO2LlGzTZuh+Ia1ex8rsZVb52ozhidz62sUa3M52bXqMqxZVRT9eqq7osgP//5z3Hvvffixz/+MTiOw6233qq6H8dxtj7fONRg9wSWGevSSy8tqK+VmFbgRL5OcPV42ufr8bQXdmhfIfWUxvPGX/79rPqahVvGn1t4WolbjL1bTMfViuWWWrlh/LmFp5W4bundYuSpB7rfCfLP//zPOHnyJPr6+kAIwYEDB3DmzJlBP6dPnzaU6AsvvIDZs2dj1KhRWLhwId57772s+1dVVWHhwoUYNWoULr74Yrz44otpf9/Y2IjPf/7zmDVrFjiOw3PPPZeXuFoQFS9XshuiKKKurs5UTLO+VmJagRP5OsHV42mfr8fTXtgRz9MZe+DxtM/X42kv3NS7xXRcrVhuqZUbxp9beFqJ65beLUaeemD46zDjxo1DZWUlZs+ejQkTJqj+6MVf/vIX3H///fjud7+L+vp6XH/99bj55ptx/Phx1f1bWlqwdu1aXH/99aivr8d//dd/4Vvf+hb+9re/sX3i8Tguvvhi/OQnP8HUqVPzEjcblLd52g2O4zBp0iRTMc36WolpBU7k6wRXj6d9vh5Pe2FHPE9n7IHH0z5fj6e9cFPvFtNxtWK5pVZuGH9u4Wklrlt6txh56oGpT+SuWLECx44dw/e+9z186UtfYm/x3bRpExobG3Uf55lnnsHXv/513HPPPbjsssvw3HPPYfr06fj1r3+tuv+LL76IGTNm4LnnnsNll12Ge+65B3fffTeeeuopts+iRYvw85//HF/84hcxcuTIvMTNhkLfajh37lxTMc36WolpBU7k6wRXj6d9vh5PezFcHofxxl/+/az6moVbxp9beFqJW4y9W0zH1Yrlllq5Yfy5haeVuG7p3WLkqQemLoJUVVXhiiuuwI4dO/D3v/+dff6soaEBP/zhD3UdI5VKYefOnVi9enXa9tWrV6d9ukqJ7du3D9p/zZo1+PDDD9O+nZzvuNlQ6Nt7ampqTN+OZMbXSkwrcCJfJ7h6PO3z9Xjai+HyOIw3/vLvZ9XXLNwy/tzC00rcYuzdYjquViy31MoN488tPK3EdUvvFiNPPdD9YlQlvvOd7+Dxxx/H+vXrMX78eLZ91apV+MUvfqHrGJFIBJIkYcqUKWnbp0yZgpMnT6r6nDx5UnV/URQRiURwwQUX2BIXAJLJJJLJJPu9r68PwMDbffVegKGg+xv1k2UZU6dOhSRJ7A3edvtaiWmWp5W4Tpwjj6d9Ma34OtG7buEJwNILsIeCngLe+LPLz4qvpzP2xiwmnlbiFlvvWv2gQL401auVfTGt+Ho6kxte79oXs9h46tVTjhhlg4H3guzduxezZ8/G+PHjsWfPHlx88cU4evQoLr30UvA8n/MYJ06cwLRp01BTU4OlS5ey7U888QR+97vfYf/+/YN8LrnkEnzta1/Dww8/zLZt27YN1113HTo6Oga9A2TWrFm4//77cf/991uKCwCPPPIIHn300UHb//jHP7LPVnnw4MGDWxCPx/HlL38Zvb29KCkpMeTr6akHDx48fAQregp4murBgwcPFHr11NSdIBMnTkRHRwdmz56dtr2+vh7Tpk3TdYxzzz0Xfr9/0N0XXV1dg+7SoJg6darq/oFAAOecc45tcQHg4Ycfxvr169nvfX19mD59Om644QZMnjxZV2wKQRBQXl6Om266Ke1bybkgiiJqa2uxePHitG8y2+lrJaZZnk7la9bX4zk083Wid93CE4DhL4EpMRT0FPDGn11+Vnw9nbE3ZjHxdCpfJ3rXip4C+dNUr1ZDM19PZ3LD6137YhYbT716auoiyJe//GU89NBD+Otf/wqO4yDLMrZt24YHH3wQd955p65jjBgxAgsXLkR5eTluvfVWtr28vByf/exnVX2WLl2KjRs3pm0rKyvDNddco/sEmYkLACNHjlR90erIkSMNF4ciGAwa8qUvlxk5ciR8PmOvczHrayUmhVGeVuI6cY4oPJ72xCym3nULTwCaL57W6+u0ngLe+LPLz6ov4OmMXTGLiaeVuMXWu1b0lPrnU1O9WtkTs5jGn1t4Wonrlt4tNp569dTURZAnnngCX/3qVzFt2jQQQjBv3jxIkoQvf/nL+N73vqf7OOvXr8cdd9yBa665BkuXLsVLL72E48eP49577wUwcGW7vb0dr7/+OgDg3nvvxa9+9SusX78e3/jGN7B9+3a88sor+NOf/sSOmUql0NTUxOz29nbs3r0b48aNw9y5c3XFNQKzg9QMfD6f7jtt8uVrJaYVOJGvE1w9nvb5ejzthR3aV0g9pfG88Zd/P6u+ZuGW8ecWnlbiFmPvFtNxtWK5pVZuGH9u4Wklrlt6txh56trPzMGDwSD+8Ic/4ODBg/h//+//4fe//z3279+P3/3ud4Y+gXP77bfjueeew2OPPYarrroK1dXVCIVCmDlzJgCgo6MDx48fZ/vPnj0boVAIW7duxVVXXYUf/ehHeP755/H5z3+e7XPixAksWLAACxYsQEdHB5566iksWLAA99xzj+64RlDot91WVFSYfjuvGV8rMa3AiXyd4OrxtM/X42kv7Ijn6Yw98Hja5+vxtBdu6t1iOq5WLLfUyg3jzy08rcR1S+8WI089MHUnCMWcOXMwZ84cK4fAunXrsG7dOtW/e/XVVwdtW7FiBXbt2qV5vFmzZul6+2y2uEZQ6Kvs8+fPNxXTrK+VmFbgRL5OcPV42ufr8bQXw+VOEG/85d/Pqq9ZuGX8uYWnlbjF2LvFdFytWG6plRvGn1t4Wonrlt4tRp56oPsiiPKFS7nwzDPP6N632FHoop5//vkF9bUS0wqcyNcJrh5P+3w9nvZiuFwE8cZf/v2s+pqFW8afW3haiVuMvVtMx9WK5ZZauWH8uYWnlbhu6d1i5KlrP70HrK+vT/t5+eWX8d///d/YunUrtm7dipdeegmvvPIKdu/ebTbnooSZ7xdbifXuu++aimnW10pMK3AiXye4ejzt8/V42gs74nk6Yw88nvb5ejzthZt6t5iOqxXLLbVyw/hzC08rcd3Su8XIUw90XwSprKxkP5/5zGewcuVKtLW1YdeuXdi1axdaW1uxatUqfPrTnzaddDFDkiRIkjTIFkUxzZZlmflQW7ldEIQ0mz7aIwgCfD4frrnmGsiyDEIICCGs0EpbluU0WxRF+P1+LFy4kB2Pbqf5Km0lDwBYtGhR2u9anPTYmZyUPJS23+9nXLU4qdmEEJavFietOlGuhBBdPJQ2RTZOanWi+XIcp8lJrU4AcPXVV8Pv9xvuPUIIFi5cCL/fr7v3KA/qb6T3AIDjOJav3t6jNoC0fPX2nt/vx4IFC8BxXFZOWnWi0Nt7auNMb+9JkgS/34+rr76a5au393w+HxYtWjRIF/TWiR4/Gz+1Ohl5/5NRFEJPCSHw+XxYsGABfD5fQfRUWWcKT0+d11Otuc5uPVXj4elpug3knuvyqaeEEMZVqQvFrKfKOJm2Uf2xY42aa9zatUal0DtWqS3LMhYtWgSfz5dzrOZLU7V0xk5N5TgOCxYsGKQzdq5Rs43bobhGtTKfW9FUZV0LsUYFzM/nZteoufot13yuB6buk3v66afx4x//GJMmTWLbJk2ahMcffxxPP/20mUMWDTZs2IB58+YxEaNfogmHwwiHwwCAhoYGNDc3Axi4g6alpQUAUFtbi9bWVnaszs5OAEB1dTUikQgAoKKiAj09PQAGPv8bjUYBAKFQCKlUCiUlJdi0aRNEUQTP8wiFQgCAaDSKsrIyAEBPTw8qKioAAJFIBNXV1fD5fEgmk/jggw8AAK2traitrQUAtLS0oL6+HgDQ3NyMhoYGxunAgQOYPHky9u3bl5NTTU0NOjo6GKfu7m5ma3HieR6iKCIUCqVx8vl8CAaD2Lx5syYnYODluTU1NYzThx9+iMmTJ+PYsWOanLTqdOzYMUyePBkffvihJie1OlVWVrKaZuOkVqfKykpMnjwZp0+f1uSkVqfDhw+jra0NPp/PcO998MEHSCaT8Pl8unuPcgJguPeAgV4Ph8Pw+Xy6e49y2rdvHyKRCHw+n+7eU+7f19dnqPcAoL+/n9VUb+/V1tbC5/Ohp6cHe/bsycpJrU4+nw/Nzc1ob2/X5KRWp1gshsmTJ2PTpk2Geo9yorG0OGnVKR+3WTuppzzPQ5ZlbN++HbIsF0RP6Rhoa2vD4cOHs3Ly9LRwetrc3Ix9+/Zh8uTJOHDgQMH0tLy8HAA8Pc1Sp2PHjuHo0aNMH+3W02g0Cp/Ph+3btyOVShWVngLWNZWer9raWt3nC7C2Rn3//fcxefJkdHZ26j5flJPZNSqNk4uTWl9v2rQJkydPRiwWyzlW86Wpe/bsQU9PD3w+X86xmsnJrKb29fWhvr4ePp9P11jNxxqV7m9mPndijWp2Preqqcq6FmKNanY+t7JGpTy6u7sNz+dU93KCmMC4cePIli1bBm3fsmULGTdunJlDFh16e3sJANLZ2UkIIUQURSKK4iBbEIQ0W5IkkkqlSGlpKeF5Pm07IYSkUqk0W5ZlZieTSbJx40YSi8WILMtElmWSSqUIISTNpjGoLQgCSaVSZOPGjSQej6dtp/kqbSWPRCJB3nrrLZJIJLJyUrN5nielpaUkHo9rclLyUNo031gspslJzY7H4yxfLU5adaJclflq8VPWKRaLkdLSUpa3Fie1OtF8eZ7X5KRWp0QiQTZu3EhSqZSh3iOEkHg8znz19p4syySZTJLS0lKSTCYN9R4hhPA8z2Lq7T1qK7nq7T2ax8aNG9k409t7hBDGlearp/eU4yyRSBjqPVEUB+Wrp/eoLrz11luDdEGPRlAtUtOFXHWKRCIEAOnt7SVW4YSe0p7euHGj4Z42q6fKOtMe8fTUeT3Vmuvs1FNZlhnXZDLp6amGrWeuy6ee0rwzdaGY9JQQ85qqpjN2r1FzjVs71qjxeDxNZ4xoaiwWI2+99VbauLVbUzPHbSE0VUtn7FyjZhu3Q3GNanY+t6KpVGeMzudW1qhm53Mra1Q6Rs3M552dnbr01NRFkDvuuIPMmDGD/PWvfyWtra2ktbWV/PWvfyWzZs0id955p5lDFh3oBNPT02PYlzYwLbZeyLJMent72SAphK+VmGZ5WonrxDnyeNoX04qvE73rFp6EENLT05P3iyCF1FNCvPFnl58VX09n7I1ZTDytxC223s2nnhJiXlO9WtkX04qvpzO54fWufTGLjadePTX1idwXX3wRDz74IL7yla+w53qCwSC+/vWv4+c//7mZQxYt6LNchYpVUlJSUF8rMa3AiXyd4OrxtM/X42kv7NC+QuopjeeNv/z7WfU1C7eMP7fwtBK3GHu3mI6rFcsttXLD+HMLTytx3dK7xchTD0w9hDhmzBi88MIL6O7uZl+LOX36NF544QWMHTvWzCGLFsqX1BQi1ptvvmn67bxmfK3EtAIn8nWCq8fTPl+Pp72wI56nM/bA42mfr8fTXripd4vpuFqx3FIrN4w/t/C0EtctvVuMPPXA0J0gd999t679fvvb3xo5bFEjEDB1M43pWKtXrzYV06yvlZhW4ES+TnD1eNrn6/G0F3bE83TGHng87fP1eNoLN/VuMR1XK5ZbauWG8ecWnlbiuqV3i5Gnrv2MHPTVV1/FzJkzsWDBgrRPnnkoHKw0kVnfQouR1bhOnCMr8Hja5+vx9JAL3vizx8+qrxMxi2n8uYWnlbjF1rvDAW6plVvGn1t4Wonrlt4tNp56YOhxmHvvvRe9vb04cuQIVq1ahVdeeQVvvPHGoB83gX6SqVCx6GeFCuVrJaYVOJGvE1w9nvb5ejzthR3xPJ2xBx5P+3w9nvbCTb1bTMfViuWWWrlh/LmFp5W4bundYuSpB4Yugrzwwgvo6OjAQw89hI0bN2L69Om47bbb8O6777r2zhD68hVJkiBJ0iBbFMU0W5Zl5ktt5XZBENJsel4FQYDf78fNN98MMvBVHxBC2HNPSluW5TRbFEUEAgF86lOfSotNm0SSpDRbyYPjOKxduxYcx+XkpMfO5KTkobQDgQDjqsVJzQbA8tXipFUnylWrNtnqRJGNk1qdaL4+n0+Tk1qdOI7DmjVrEAgETPXepz71KQQCAd29R3lQDkZ6DwB8Ph+7HU5v71FbydVI79Fb8Hw+X1ZOWnWi0Nt7dJytWbOG+ertPUmSBuWrt/f8fj/Wrl07SBf01okePxs/tTrZcWW/kHpKCIHf78dNN90Ev99fED2l523NmjWMq6enzuup1lxnt56q8fD0NN3WM9flU08JIQgEAoN0oRj1FDCvqUbOF7XNrlGB7OPWrjUqhd6xquS1du1a+P3+nGM1X5oKIG3cFkJTtXTGzjVqtnE7FNeoVuZzK5qqrGsh1qhW5nOza9Rc/ZZrPtcDwy9GHTlyJL70pS+hvLwcTU1NuPzyy7Fu3TrMnDkT/f39Rg9XdNiwYQPmzZuHRYsWAQAaGxsBAOFwGOFwGADQ0NCA5uZmAEB9fT1aWloAALW1tWhtbWXH6uzsBABUV1cjEokAACoqKtDT0wMAKCsrQzQaBQCEQiHwPI9kMol33nkHoiiC53mEQiEAQDQaRVlZGQCgp6cHFRUVAIBIJILq6moAwIkTJ7B9+3YAQGtrK2prawEALS0tqK+vBwA0NzejoaEhjZMoiti7d29OTjU1Nejo6GCcuru7mZ2Nkyh+dJVQyam3txfl5eVZOXV0dKCmpoZxqqurgyiKOHr0aFZOanU6evQoRFFEXV2dJie1OlVWVrKa5uKkVidRFLNy0qrTvn37cnJSq9P27dtx4sSJrJy06gTAVO+dPHkSO3bsyMlJrU5meo9yqqqqMtV7Si3T23tGOGnVaefOnYZ6j3ISRRHvvPOO4d6joDyMcrIKp/VUFEWUl5cXVE8BYN++fZ6eDjE93bt3L0RRLKie0np4eqpdp6NHj2L37t1ZOeVbTwGgvLy86PQUsK6p7e3tzDZyvqysUd977z2IooiTJ0+amlPNrFFpHD2cMnuA8tMzVvOpqUbGar40taqqKienfK9Rd+zYgZMnT2blNJTWqGbmc6uaqqxrIdaoZudzK2tUyqO7u9swJ6p7OZH1A7o5cOzYMfLoo4+S2bNnk2nTppFoNGrlcEUF+g32zs5OQgghoigSURQH2YIgpNmSJLFvH/M8n7adkIHvIitt+l3lVCpFkskkKS0tJbFYjMiyTGRZZt9PVto0BrUFQWAx4/F42naar9JW8kgkEqS0tJQkEomsnNRsnudZTC1OSh5Km+Ybi8U0OanZ8Xic5avFSatOlKsyXy1+yjrFYjH2LetsnNTqRPPleV6Tk1qdaK6pVMpQ7xFCWMxUKqW792RZZv2XTCYN9R4hhPUCjaGn96it5Kq399TGmd7eI4QwrjRfPb2nHGeJRMJQ74mimFUXstVJSxf0aEQ2XchVp0gkous77HrghJ5a6WmzeqqsM+0RT0+d11Otuc5OPZVlmXFNJpOenmrYeua6fOopzTtTF4pJTwkxr6lqOmP3GjXXuLVjjaoct3rGaq5xa7emZo7bQmiqls7YuUZ1Yj7PxtWu+dyKppqdz62sUc3O51bWqFbm887OTl16avgiCM/z5I9//CP55Cc/SUaNGkW+8IUvkLfffpsRdgvoBGNmwqJNSIs9XOHxHF5wC09C3MPVCk8rGpjPY3m1Gl5wC09C3MPV45kb+dRTK8fzajX84BauHs/hhULoqaHHYdatW4cLLrgAP/3pT/G//tf/QltbG/7617+yZ/ncCFLAd6EQQtDX12cqpllfKzGtwIl8neDq8bTP1+NpL+yI5+mMPfB42ufr8bQXburdYjquViy31MoN488tPK3EdUvvFiNPPTB05eLFF19ESUkJZs+ejaqqKnzjG9/A5z73uUE/bgJ9Bq1Qseizk4XytRLTCpzI1wmuHk/7fD2e9sKOeJ7O2AOPp32+Hk974abeLabjasVyS63cMP7cwtNKXLf0bjHy1AOOGLg089WvflXXG1f/7//9v3oPWbTo6+vDhAkT0Nvbi5KSEkO+giAgFAph7dq1CAaDNmXoPDyewwtu4Qm4h6sVnlY0MJ/H8mo1vOAWnoB7uHo8cyOfemrleF6thh/cwtXjObxQCD019E2uV1991VASbgD9DFChYvX09GDixImGHz8y62slphU4ka8TXD2e9vl6PO2FHdpXSD2l8bzxl38/q75m4Zbx5xaeVuIWY+8W03G1YrmlVm4Yf27haSWuW3q3GHnqgTtf5JFHSIpveBciVl1dnamYZn2txLQCJ/J1gqvH0z5fj6e9sCOepzP2wONpn6/H0164qXeL6bhasdxSKzeMP7fwtBLXLb1bjDz1wLsIYhH0qpYkSeykK21RFNNs5dUpaiu3C4KQZtOnlQRBQCAQwOrVqwEMvPSFEAJBENjv1JZlOc0WRRHBYBA33XQTe5yJbqf5Km0lD5/PhzVr1sDn8+XkpMfO5KTkobSDwSDjqsVJzeY4juWrxUmrTpQrx3G6eChtimyc1OoEAGvWrIHf79fkpFYnn8+HT37ykwgGg4Z7j+M43HTTTQgGg7p7j/KgHIz0HgD4/X7ceOONCAaDunuP2kquRnovGAzihhtugN/vz8pJq04UenuPjrNPfvKTbJzp7T1JkhAMBnHjjTeyfPX2XiAQwJo1awbVRm+d6PGz8VOrkx23YRZSTwkhCAQCuOGGGxAIBAqip/S8ffKTn2RcPT11Xk+15jq79VSNh6en6bbP52NcC6GnhBDGVakLxaingHlNNXK+qG12jUp1Rmvc2rVGpdA7VpXroDVr1iAQCOQcq/nSVI7j0sZtITTV7/fjhhtuGKQzdq5Rs43bobhGtTKfW9FUZV0LsUa1Mp+bXaPm6rdc87keeBdBDGLDhg2YN28eFi1aBADYt28fACAcDiMcDgMAGhoa0NzcDACor69HS0sLAKC2thatra3sWJ2dnQCA6upqRCIRAEBFRQV6enoAAGVlZYhGowCAUCiEeDyOjo4OhEIhiKIInucRCoUAANFoFGVlZQCAnp4eVFRUAAAikQiqq6shyzIOHDiAmpoaAEBraytqa2sBAC0tLaivrwcANDc3o6GhgXFqampCV1eXLk41NTXo6OhgnLq7u5mtxYnneYiiOIiTLMs4duxYVk4A0NHRMYhTV1cXjhw5oslJq05HjhxBV1dXVk5qdaqsrGQ1zcZJq05dXV3o6urKyimzTgcPHkRtbS1kWTbcezU1NThw4ABkWdbde5QTAMO9BwAnTpxAVVUVZFnW3XtKTrt374Ysy7p7LxKJQJZlbN68GadPnzbUewDQ39/Pamqk92RZxt69e7Fr166cnDLrJMsy3n//fRw7dkyTk1qd+vr60NXVZar3KCgPPb1HOSkvQJiFk3rK8zxSqRRCoRBSqVRB9DQcDkOWZdTW1uLgwYNZOXl6Wjg9pZy6urrQ1NRUMD0tLy8HAE9Ps9TpyJEj2L59O2RZLoieRqNRyLLM1lzFpKeAdU1tb29ntt7zBVhbo1ZVVaGrqwsnTpwo2BqVxsnFSauvu7q60NfXVzBN3bVrF/bu3cvmj0Jo6unTp7F582bIsqxrrOZjjSrLMqqqqnDixImsnIbKGtXsfG5VU5V1LcQa1ex8bmWNSnl0d3cbns+p7uUE8WAK9BvEXV1dhBBCRFEkoigOsgVBSLMlSWLfPuZ5Pm07IQPfRVbasiwzO5VKkc2bN5N4PE5kWSayLLPvJyttGoPagiAQQRDI5s2bSSKRSNtO81XaSh48z5MtW7YQnuezclKzeZ4npaWlJB6Pa3JS8lDaNN94PK7JSc1OJBIsXy1OWnWiXBOJhCYntTrFYjH2LetsnNTqRPNNJpOanNTqxPM82bx5M6uF3t4jhJBEIsF89faeLMskmUyS0tJSkkwmDfUeIYQkk0kWU2/vUVvJVW/v0RibN28myWTSUO/RfGlN9faecpzRsa239+h5UOarp/doz23ZsmWQLujRCKpFynGWq/do7t3d3bq+w64HTugpPR+bN29O+13rXCnPiVk9VdZZma+np87qqdZcZ6eeyrLMuCaTSU9PNWw9c10+9VSWZear1IVi0lNCzGuqms7YvUbNNW7tWKPG4/E0nTGiqfF4nGzZsoX5qnHKt6ZmjttCaKqWzti5Rs02bofiGtXsfG5FU6nOxGIxQ71nZY1qdj63skalY1Q5VvRqaldXly49NfR1GA8fwfuaQW54PIcX3MITcA9XKzy9r8MUFh7P4Qe3cPV45ob3dZjCwi08Afdw9XgOLxRCT73HYSwiX7cw6o3V3t5uKqZZXysxrcCJfJ3g6vG0z9fjaS/siOfpjD3weNrn6/G0F27q3WI6rlYst9TKDePPLTytxHVL7xYjTz3wLoJYRKGLevjwYdNNaMbXSkwrcCJfJ7h6PO3z9Xjai+FyEcQbf/n3s+prFm4Zf27haSVuMfZuMR1XK5ZbauWG8ecWnlbiuqV3i5GnHgRszmPYIxAo3CkMBAJYvnx5QX2txLQCJ/J1gqvH0z5fj6e9sEP7CqmnNJ43/vLvZ9XXLNwy/tzC00rcYuzdYjquViy31MoN488tPK3EdUvvFiNPPfDuBLGIQl/ZOnbsmOkrcWZ8rcS0AifydYKrx9M+X4+nvRgud4J44y//flZ9zcIt488tPK3ELcbeLabjasVyS63cMP7cwtNKXLf0bjHy1APvIohFFLqo3vN59vhZ9TULj6d9vh5PezFcLoJ44y//flZ9zcIt488tPK3ELcbeLabjasVyS63cMP7cwtNKXLf0bjHy1APvIohFcBwHAJAkCZIkDbJFUUyzlYWhtnK7IAhpNv14jyAI8Pv9WLp0KQgh7EcQBABIs2VZTrNFUUQgEMCSJUvSYtNvakuSlGYreXAch2XLloHjuJyc9NiZnJQ8lHYgEGBctTip2QBYvlqctOpEuWrVJludKLJxUqsTIQTLli2Dz+fT5KRWJ47jcO211yIQCJjqvSVLliAQCOjuPcqDcjDSewDg8/mwePFiBAIB3b1HbSVXI70XCASwaNEi+Hy+rJy06kSht/foOLv22muZr97ekyQJgUAAixcvZvnq7T2/349ly5YN0gW9daLHz8ZPrU523GZdSD0lhMDv92PRokXw+/0F0VN63q699lrG1dNT5/VUa66zW0/VeHh6mm5zHMe4FkJPCSGMq1IXilFPAfOaauR8UdvsGhVA1nFr1xqVQu9YVfJatmwZ/H5/zrGaL00FkDZuC6GpPp8PixYtGqQzdq5Rs43bobhGtTKfW9FUZV0LsUa1Mp+bXaPm6rdc87keeBdBDGLDhg2YN28eFi1aBADYu3cvACAcDiMcDgMAGhoa0NzcDACor69HS0sLAKC2thatra3sWJ2dnQCA6upqRCIRAEBFRQV6enoAAGVlZYhGowCAUCiEWCyGgwcPIhQKQRRF8DyPUCgEAIhGoygrKwMA9PT0oKKiAgAQiURQXV0NSZJQX1+Pbdu2AQBaW1tRW1sLAGhpaUF9fT0AoLm5GQ0NDYxTY2MjDh06hD179uTkVFNTg46ODsapu7ub2VqceJ6HKIqDOEmShMbGxqycAKCjowM1NTVpnA4dOoQjR45octKq05EjR3Do0KGsnNTqVFlZyWqajZNWnQ4dOoTOzs6snDLrdODAAbz//vuQJMlw723btg319fWQJEl371FOAAz3HgC0t7ejoqICkiTp7j3Kac+ePdixYwfrYT29F4lEIEkSysrKcPr0aUO9BwD9/f2spkZ6T5Ik1NXVYdeuXVk5qdVJkiRs3boVx44d0+SkVqfe3l4cOnTIVO9RUB56eo9yUi7OzMJJPeV5HslkEqFQCMlksiB6Gg6HIUkS3n//fRw4cCArJ09PC6enzc3N2LNnDw4dOoTGxsaC6Wl5eTkAeHqapU5HjhzBe++9B0mSCqKn0WgUkiSxNVcx6SlgXVPb29uZrfd8AdbWqFVVVTh06BDa29t1ny/KyewalcbJxUmrrw8dOoTe3t6CaequXbtQV1cHSZIKpqmnT59GWVkZJEnSNVbzsUaVJAkVFRWsD4f6GtXsfG5VU5V1LcQa1ex8bmWNSnl0d3cbns+p7uUE8WAKvb29BADp6uoihBAiiiIRRXGQLQhCmi1JEkmlUqS0tJTwPJ+2nRBCUqlUmi3LMrNTqRTZsWMHicfjRJZlIssySaVShBCSZtMY1BYEgQiCQHbs2EESiUTadpqv0lby4Hme1NbWEp7ns3JSs3meJ6WlpSQej2tyUvJQ2jTfeDyuyUnNTiQSLF8tTlp1olwTiYQmJ7U6xWIxUlpaymqkxUmtTjTfZDKpyUmtTjzPkx07drBa6O09QghJJBLMV2/vybJMkskkKS0tJclk0lDvEUJIMpkkH3zwAYunp/eoreSqt/dojA8++IAkk0lDvUfzpTXV23vKcUbHtt7eo+dBma+e3qM9V1tbO0gX9GgE1SLlOMvVezT37u5uAoD09vYSq3BCT+n5+OCDD9J+1zpXynNiVk/p3yl7xNNT5/VUa66zU09lWWZck8mkp6caNs/zjGsh9FSWZear1IVi0lNCzGuqms7YvUbNNW7tWKPG4/E0nTGiqfF4nNTW1jJfNU751tTMcVsITdXSGTvXqNnG7VBco5qdz61oKtWZWCxmqPesrFHNzudW1qh0jCrHil5N7erq0qWnHCGK+xQ96EZfXx8mTJiA3t5elJSUGPIVBAGhUAhr165FMBi0KUPn4fEcXnALT8A9XK3wtKKB+TyWV6vhBbfwBNzD1eOZG/nUUyvH82o1/OAWrh7P4YVC6Kn3OIxF5OsWRr2x9u/fbyqmWV8rMa3AiXyd4OrxtM/X42kv7Ijn6Yw98Hja5+vxtBdu6t1iOq5WLLfUyg3jzy08rcR1S+8WI089cPwiyAsvvIDZs2dj1KhRWLhwId57772s+1dVVWHhwoUYNWoULr74Yrz44ouD9vnb3/6GefPmYeTIkZg3bx7eeOONtL9/5JFHwHFc2s/UqVPzyssuJBKJgvtaiWkFTuTrBFePp32+Hk8PueCNP3v8rPo6EbOYxp9beFqJW2y9Oxzgllq5Zfy5haeVuG7p3WLjqQeOXgT5y1/+gvvvvx/f/e53UV9fj+uvvx4333wzjh8/rrp/S0sL1q5di+uvvx719fX4r//6L3zrW9/C3/72N7bP9u3bcfvtt+OOO+7Anj17cMcdd+C2227Djh070o51+eWXo6Ojg/3ofolKBvx+vyk/s7EWLFhgKqZZXysxrcCJfJ3g6vG0z9fjaS/siOfpjD3weNrn6/G0F27q3WI6rlYst9TKDePPLTytxHVL7xYjTz1w9CLIM888g69//eu45557cNlll+G5557D9OnT8etf/1p1/xdffBEzZszAc889h8suuwz33HMP7r77bjz11FNsn+eeew433XQTHn74YVx66aV4+OGHceONN+K5555LO1YgEMDUqVPZz3nnnWeKQ6Fv79m3b5/p25HM+FqJaQVO5OsEV4+nfb4eT3sxXB6H8cZf/v2s+pqFW8afW3haiVuMvVtMx9WK5ZZauWH8uYWnlbhu6d1i5KkHjl0ESaVS2LlzJ1avXp22ffXq1WmfrlJi+/btg/Zfs2YNPvzwQ/ZtYa19Mo/Z3NyMCy+8ELNnz8YXv/hFHDlyxColDx48ePDgwYMHDx48ePDgwcMQRsCpwPQbyVOmTEnbPmXKFJw8eVLV5+TJk6r7i6KISCSCCy64QHMf5TGvvfZavP7667jkkkvQ2dmJxx9/HMuWLUNjYyPOOecc1djJZBLJZJL93tfXBwCQZZldgNELur9RPwD4+Mc/DlmWIctywXzN+lnhaSVuoc+Rx9O+mFZ9nehdt/A0w49iqOgp4I0/u/zM+no6Y29MK77eWiA7nNJTIH+a6tXKvphWfT2dyQ6vd+2LacXPrG8h9NSxiyAUHMel/U4IGbQt1/6Z23Md8+abb2b2FVdcgaVLl2LOnDl47bXXsH79etW4P/7xj/Hoo48O2l5WVoYxY8Zo5psN5eXlpvyKDR7P4QW38ATcw9UMz3g8bjqep6fm4fEcfnALV4+nNqzoKZB/TfVqNfzgFq4ez+EFO/XUsYsg5557Lvx+/6C7Prq6ugbdyUExdepU1f0DgQC7g0NrH61jAsDYsWNxxRVXoLm5WXOfhx9+OO0CSV9fH6ZPn44bb7wRkyZN0vRTgyAIKC8vx0033WTo28eSJOHw4cOYM2eO4RfMmPW1EtMsT6fyNevr8Rya+TrRu27hCQBnzpwxtL8SQ0FPAW/82eVnxdfTGXtjFhNPp/J1onet6CmQP031ajU08/V0Jje83rUvZrHx1Kunjl0EGTFiBBYuXIjy8nLceuutbHt5eTk++9nPqvosXboUGzduTNtWVlaGa665hp2gpUuXory8HA888EDaPsuWLdPMJZlMIhwO4/rrr9fcZ+TIkRg5cuSg7aNGjTJcHIpgMGjINxgM4vLLLzcdy4yvlZjKYxg9R07ka5Wrx9OemMXUu27hCQxon1kMBT2lPt74y7+fVV/q7+lM/mMWE08rcYutd63oKZB/TfVqZU/MYhp/buFpJa5berfYeOrVU0e/DrN+/Xq8/PLL+O1vf4twOIwHHngAx48fx7333gtg4Mr2nXfeyfa/9957cezYMaxfvx7hcBi//e1v8corr+DBBx9k+9x3330oKyvDT3/6U+zfvx8//elPsXnzZtx///1snwcffBBVVVVoaWnBjh078IUvfAF9fX246667DHMQRdH8CTARq66uzlRMs75WYlqBE/k6wdXjaZ+vx9Ne2BHP0xl74PG0z9fjaS/c1LvFdFytWG6plRvGn1t4Wonrlt4tRp564Og7QW6//XZ0d3fjscceQ0dHB+bPn49QKISZM2cCADo6OnD8+HG2/+zZsxEKhfDAAw9gw4YNuPDCC/H888/j85//PNtn2bJl+POf/4zvfe97+P73v485c+bgL3/5C6699lq2T1tbG770pS8hEongvPPOw5IlS/DBBx+wuEaQ7f0l+QbHcZg0aZKpmGZ9rcS0AifydYKrx9M+X4+nvbAjnqcz9sDjaZ+vx9NeuKl3i+m4WrHcUis3jD+38LQS1y29W4w89cDxF6OuW7cO69atU/27V199ddC2FStWYNeuXVmP+YUvfAFf+MIXNP/+z3/+s6Ecs8Hos1FWY82dO7egvlZiWoET+TrB1eNpn6/H017YoX2F1FMazxt/+fez6msWbhl/buFpJW4x9m4xHVcrlltq5Ybx5xaeVuK6pXeLkaceOPo4zHBAoW/vqampMX07khlfKzGtwIl8neDq8bTP1+NpL4bL4zDe+Mu/n1Vfs3DL+HMLTytxi7F3i+m4WrHcUis3jD+38LQS1y29W4w89cC7CGIRPl/hTqHP58O0adNMxTTrayWmFTiRrxNcPZ72+Xo87YUd8TydsQceT/t8PZ72wk29W0zH1Yrlllq5Yfy5haeVuG7p3WLkqWs/m/MY9iCEABj4BJAkSYNsURTTbFmWmS+1ldsFQUiz6fEFQQDHcZgxYwYkSQIhBIQQCILA8qC2LMtptiiK8Pl8mD59Ojs23U7zVdpKHoQQzJw5E4SQnJz02JmclDyUts/nY1y1OKnZsiyzfLU4adWJcqXHycVDaVNk46RWJ0mS2LtotDip1YkQgosuugg+n89w78myjOnTp8Pn8+nuPcqDxjbSexRUBPX2HrWVXI30ns/nw4UXXjioNnrrRKG39+g4u+iii1guentPkiQ2UVDo7T2O4zBz5sxBuqC3TvT42fip1cmOCa2QekoIAcdxuPDCC8FxXEH0lJ63iy66iOXi6anzeqo119mtp2o8PD1NtwkhjGsh9JQQwrgqdaEY9ZTmqYyTaRvVHzvWqFRnaCy63e41KoXesUptqjMcx+Ucq/nSVFmW08ZtITQVAC688MJBOmPnGjXbuB2Ka1Qr87kVTVXWtRBrVCvzudk1aq5+yzWf64F3EcQgNmzYgHnz5mHRokUAgL179wIAwuEwwuEwAKChoQHNzc0AgPr6erS0tAAAamtr0drayo7V2dkJAKiurkYkEgEAVFRUoKenB8DAp32j0SgAIBQKob+/H9XV1QiFQhBFETzPIxQKAQCi0SjKysoAAD09PaioqAAARCIRVFdXQxRFVFRUYNu2bQCA1tZW1NbWAgBaWlpQX18PAGhubkZDQwPj1NjYiOrqauzZsycnp5qaGnR0dDBO3d3dzNbixPM8RFEcxEkURWzdujUrJ2Dg5bk1NTWM044dO1BdXY3Dhw9rctKq0+HDh1FdXY0dO3ZoclKrU2VlJatpNk5adaqurkZnZ6cmJ7U6HThwAGVlZRBF0XDvbdu2DRUVFRBFUXfvUU4ADPceALS3t2PTpk0QRVF371FOe/bswebNmyGKou7ei0QiEEUR77zzDutDvb0HAP39/aymenuvtrYWoihiy5Yt2LlzZ1ZOanUSRRHvvvsujh49qslJrU49PT1MF4z2HgXloaf3KCfl4sEsnNRTnufZ+VHa2c6VVT0Nh8MQRRFlZWU4cOBAVk6enhZOT5ubm7Fnzx5UV1ejsbGxYHpaXl4OAJ6eZqnT4cOH2VxXCD2NRqOMR39/f1HpKWBdU9vb25mt93wB1taoVVVVqK6uRnt7u+7zRTmZXaPSOLk4afU11eFCaerOnTuxZcsWiKKYc6xmcjKrqd3d3XjnnXcgiqKusZqPNaooiti0aRPrw6G+RjU7n1vVVGVdC7FGNTufW1mjUh7d3d2G53OqezlBPJhCb28vAUAikQghhBBRFIkoioNsQRDSbEmSSCqVIqWlpYTn+bTthBCSSqXSbFmWmS2KImltbSU8zxNZloksyySVShFCSJpNY1CbHv/48eMkmUymbaf5Km0lj1QqRdra2lh8LU5qNs/zpLS0lMTjcU1OSh5KW5IkxlWLk5qdTCZZvlqctOpEuSaTSU1OanWKxWKktLSUpFKprJzU6kTzFQRBk5NanVKpFDl+/DiRJMlQ7xFCSDKZZL56e0+WZZJMJklpaSlJJpOGeo/GP3bsGJEkSXfvUVvJVW/v0TjHjh1LO2d6eo+eI1pTvb2nNs709p4oioPy1dN7dFy2tbUN0gU9GkG1KB6P6+49mvuZM2cIANLb20uswgk9lWWZiKJIjh07RkRRLIie0jofP36cHdPTU+f1VGuus1NPZVlmXJPJpKenGnYqlWJcC6GnsiwzX6UuFJOeEmJeU9V0xu41aq5xa8caNR6Pp+mMEU3leZ60tbWx+Gqc8q2pWjpjp6Zq6Yyda9Rs43YorlHNzudWNJXqTCwWM9R7VtaoZudzK2tUOkZ5njc8n0ciEV166vjXYYodwWAQQPqbaJV2IBAYZEtnbzGitz8q96HH07Ivuugi1fgcxzHb5/OxYyvt6dOnMz/ldq3c/X4//H5/2q1pWpzUbHL2dqRAIMBi5eKnxVWLU6Y9YsSIQflm45eZu16uRngo7cw6qeWrxU+ZbzAYTKtnNk6Z9ogRI5iv8hZcPbnTP5U89PReIBDAjBkz0jhm5qtlK7kq89XTh8qYRuukzFVP76mNMyO9l5mvkd7L7CE9vefz+dith/T4enqP2nbcvl1oPfX7/ao9YpeeUih9PT11Xk+15jo79VSLh6en6XYwGEyLm4tfPvSUPtaRa5+hrqfKfPVqqprO2L1GzTVu7Vqj6uWUaY8cOZLFVNbPTk3V0hk7NVVLZ3L29ZkzCF57LaZ/4hPAq68C48apctKjM0Y4ObVGNTOfq3HVWyeqM7k45XuNamU+N7NGVeqoUU1VHj8bvMdhLELM0y2MemPR224L5WslphU4ka8TXD2e9vl6PO2FHfE8nbEHHk/7fD2e9sJNvVtMx9WK5ZZauWH8mY75wQdAczPwt7+BrFwJdHXZH9MivN61x8+qr1nojeVdBLEIu67ea8WaP3++qZhmfa3EtAIn8nWCq8fTPl+Pp72wI56nM/bA42mfr8fTXripd4vpuFqx3FIrN4w/0zHPvs8DALidO4Fly4BDh+yNaRFe79rjZ9XXLPTG8h6HsYhCF/X8888vqK+VmFbgRL5OcPV42ufr8bQXw+UiiDf+8u9n1dcs3DL+3MLTStxi7N1iOq5WLLfUyg3jz3RMehHkppsGLn4cPgwsWgS8/jrwmc/YE9MivN61x8+qr1no1T3vThCLEBSfKypErHfffddUTLO+VmJagRP5OsHV42mfr8fTXtgRz9MZe+DxtM/X42kv3NS7xXRcrVhuqZUbxp/pmG1tAIBDU6dCqKoCliwBenqAW24BHn4YyPKoQtHpzOnT2PL3vw/73i3GMaoH3kUQi1C+jKUQsRYtWmQqpllfKzGtwIl8neDq8bTP1+NpL+yI5+mMPfB42ufr8bQXburdYjquViy31MoN4890zLN3gky95hr4p00DqqqAb31r4O9+8hNg9Wrg7Cft8xbTIgzFJWSA01e+gsCFF+KG229H4KtfBc5+dtaWmHmCm8aoHngXQSyCvklbkiT2lQKlLYpimi3LMvOltnK7IAhpNj2+IAjgOA6TJk2CJEkghIAQwq52KW1ZltNsURTh8/kwceJEdmy6neartJU8CCGYPHkyCCE5OemxMzkpeShtn8/HuGpxUrNlWWb5anHSqhPlSo+Ti4fSpsjGSa1OkiRh8uTJ7PjZ+CnrRAjBhAkT4PP5DPeeLMuYOHEifD6f7t6jPGhsI71HUVJSAp/Pp7v3qK3kaqT3fD4fxo8fP6g2eutEobf36DibMGECy0Vv70mSBJ/Ph5KSEhZXb+9xHIfJkycP0gW9daLHz8ZPrU523GZdSD0lhIDjOIwfPx4cxxVET+l5mzBhAsvF01Pn9VRrrrNbT9V4eHqabhNCGNdC6CkhhHFV6kIx6inNUxkn0zaqP3asUanO0Fh0u91rVAq9Y5XaVGc4jss5VvOlqbIsp43bQmgqAIwfP36QzuTs67MXQUbPmTMwbv1+SM88A/z5zyDjxgGVlcCVV0L6/e8hnfVV6ozWuHV8jXryJOSf/Qy49FJg5UrgD38Al0yCE0Vwf/wjcM01IMuXQ3rjDeCsn12aqqxrIdaoVuZzs2vUXP2Waz7XA+8iiEFs2LAB8+bNw6JFiwAADQ0NAIBwOIxwOMy2NTc3AwDq6+vR0tICAKitrUVrays7VufZK6HV1dWIRCIAgIqKCvT09AAAysrKEI1GAQChUAjRaBRvv/02QqEQRFEEz/MIhUIAgGg0irKyMgBAT08PKioqAACRSATV1dUQBAFvv/02tm3bBgBobW1FbW0tAKClpQX19fUAgObm5jRO+/btw9tvv43du3fn5FRTU4OOjg7Gqbu7m9lanHiehyiKgzjRfLNxAoCOjg7U1NQwTjt27MDbb7+NQ4cOaXLSqtOhQ4fw9ttvY8eOHZqc1OpUWVnJapqNk1ad3n77bZw8eVKTk1qd9u/fj7fffhuCIBjuvW3btjFfvb1HOQEw3HsA0NbWxmLq7T3Kaffu3cxXb+9FIhEIgoBQKMT46e09AOjv72c11dt7tbW1LObOs/8joLf3WlpamO/Ro0c1OanV6cyZM0wXjPYeBeWhp/cop3zc2uiknvI8j0QigVAohEQiURA9DYfDzHf//v1ZOXl6Wjg9bW5uZjqzb9++gulpeXk5AHh6mqVOhw4dYj1cCD2NRqPMNxqNFpWeAtY1tf3sP2Bra2t1ny/A2hq1qqoKb7/9Ntra2nSfL8rJ7BqVxsnFSauv3377bZw5c6Zgmrpz5042DnKN1UxOZjU1EomwmHrGKqvT2R6qPnw4fY16++0Iv/YaknPnAp2d8N9xB5LLlwP79zNOdK5qO/tIjeNr1MOHcfjFF4HbboNvxgz4HnoIOHgQ0pgxOP35z0Pcvh1VTz+Nnk9/GggEwL33Hvyf+xzw8Y/j+Le/jbaz83y+NVVZ10KsUc3O51bWqJRHd3e34fmccsoJ4sEUent7CQDS3d1NCCFEFEUiiuIgWxCENFuSJJJKpUhpaSnheT5tOyGEpFKpNFuW5bTtPT09JJlMElmWiSzLJJVKEUJImk1jUFsQBCLLMjlz5syg7TRfpa3kIQgC6e3tHcQjk5OazfM8KS0tJfF4XJOTkkemTblqcVKzU6kUy1eLk1adKFdlDbT4KfeJxWKktLSUpFKprJy06tTb25tWAy1+yn0EQSCnT58msiwb6j2a+5kzZ4gsy7p7T5ZlkkwmSWlpKes/vb1H86L56u09Ze7KfPX0niRJRJZl0t3dzfbX23uEEMaVnhM9vac2zvT2niiKRJZlcvr06Zz8MuskSRLp7e0dpAt6eo9qUTwe1917NPeenh4CgPT29hKrcEJPaS92d3ezfrFbT5V1Vo5lT0+d1VOtuc5OPZVlmXFNJpOenmrYeua6fOopzT1TF4pJTwkxr6lqOmP3GjXXuLVjjRqPx9N0xoimJpNJ0tvbq8ojM/d8aaqWztipqaIoku7u7kE6k7Wvo1FCBh4YIWcOH1Yft7EYIT/6EZFHjRrYNxgk0ne+Q6RoNOu4Lega9fhxQp54gsizZzM+BCDyNdcQ8pvfEPHMmcHzeVsbkb79bSJPmvTR/hMnEvLtbxPhyJG8aSrVmVgsZqj3rKxRzc7nVtaodIzyPG94Pu/u7talp97XYSwiEBg4hcrnj5Q2/XulTW8xorc/KvcJBoNZ7QkTJqTFp9s5jmO2z+djx1baEydOZH7K7Vq5U1t5a5oWJzWbnL0dKRAIsFi5+Glx1eKkZiuPpYefMne9XI3wUNpqdcrMV4tfZr6TJk3SxUktd9oLRjhxHMf+VPLQ03t+v5/ly3Gc7t6jOarlq8emt9jq4afGNRsnPePMSO8BYOcoGye1fDP7Vm/vCWf/95EeX0/vUVt5jvKFQuspvU1Tbbtdegroq7Onp4XTU625zk491eLh6Wm6rWeuy7ee6uFaDHqqzEWvpqrpTCHWqNnGrV1rVL2cMu0RI0ZgxIgRLE8tTnq56ulrLZ2xU1P9fj8bB0qdydrXXV0DBxw1ChNnzwY4bjCnMWOA730P3Je/DPz7vwOhEHw/+Qnwpz8Bv/wlJim+IFPQNWpJCfDOO+B+8xv4Nm4EJAkcAEyYAHzlK8A3vgHuyisH9sdHYDozbRp8P/0p8IMfAK+9Bjz7LLhDh4Cf/QyBp58GbrsNeOABBM7epQWY01SqM7k45XuNamU+N7NGVeqoUU1V5pMN3uMwFiHk6RZGvbHefPNNUzHN+lqJaQVO5OsEV4+nfb4eT3thRzxPZ+yBx9M+X4+nvXBT7xbTcbViuaVWbhh/pmK2t0MOAOHvB7Dp3a8gmezX3vfii4G33gLeeAOYPh04dgy45RZ0LF4MccsWQPHOL1vR2grpBz9AfOpU4NOfBkpLAUkC/umfgFdfBU6cAH71K+DsBRAlVM/R2LHAunXAgQPAP/4x8P4QSRq4yLN4MXD99cAbb0Dg+eKoqQU/q75moTtW1vtEPGiC3mrY09Nj2JfeykRv+9ELWZZJPB5nt0sVwtdKTLM8rcR14hx5PO2LacXXid51C09CiC2PwxRSTwnxxp9dflZ8PZ2xN2Yx8bQSt9h6167HYYxqqlcr+2Ja8S0anfnTn0j3IpDKyoGf2torSF/frtx+/f2EPPQQkQOBjx4/mTmTkO9+l5D9+3WFNsRTEAgpLSXk058mxOf76PGVyZMJuf9+QhobdcXUfY527SLkjjsICQY/inXxxST51FNENjjmvTGaG3r11LsTpMig9xaffPpaiWkFTuTrBFePp32+Hk8PueCNP3v8rPo6EbNf75YoAABRYklEQVSYxp9beFqJW2y9Oxzgllq5ZfwZjtnejtjMj36NxfZi167FOHr0MciyqO03duzA53N374b8ta+BlJQM3BnyxBMDX2NZvHjgboyzL980jZYW4HvfA2bMAP75n4G33wZkGWTlSoivvQa0tQHPPgvMm6f7kLrO0YIFwOuvA0ePAv/1X8DkyeCOHMGIBx8cyOU//xM4ftw0LSNwyxjVA+8iiEXQtxEXKhZ9o26hfK3EtAIn8nWCq8fTPl+Pp72wI56nM/bA42mfr8fTXripd4vpuFqx3FIrN4w/UzHb2hA/exEkmfwUzjnncyBExNGjP8Tu3dcjHj+UPeYll2DjZz8LsbUV+POfBx5P8fuBurqB94dccAHwxS8CH36oP6dUCvif/wFWrwbmzBm4sNLRAZx33sDFhwMHIJaV4e0JEyDq/Md6ItGCo0cfxY4dl6C6ejqOHfs5RDGa2/HCCwfit7ZC+tWv0H/hheB6e4Gnnhp4POiLXwTOfvHEDrhpjOqC4XtMPBBCnHscRvn24EL4Wolp9ZatQudr1tfjaV9MK75O9K5beBIyfB6H8cZf/v2s+Ho6Y2/MYuJpJW6x9e5weRzGDbVyy/gzFfNf/oXs+sXAozDt7a8RWZbJyZN/INXVE0hlJUhV1VjS3v4bzWOqxuzsJOS55whZuDDtSy1k+XJC/vEPQhRfIknjeeAAIf/5n4Scd1663+rVhPz1r4Sc/VqaXq6i2E86Ol4j9fWr2OM+yp/33ptIDh/+LkkmO3WdKlmWSYrnibxxIyE33JCe47JlhPzP/xBy9gstSnhjNDe8x2EKBPplAkmSVG1RFNNsWfGiH2ortwuCkGaTs2/qprYgCMymvwNIs2VZTrPpFbFUKsVs5XZJktLsTB6UQy5Oemw1TjT3TE6UazZOajbNLxsnrTpRfz08lDZFLk6ZdVLGy8ZJrU7JZFIXJzUeqVQqJye1OlEORntPlmXwPJ+Tk1adlPka6T2e5031Ht0vFyc1WznOjPReZr56e48QAlEUDfeesm+N9h7NN98otJ4SQsDzfEH1FACSyaSnp0NMT9XmOrv1VI2Hp6eDOeWa6/Ktp9S32PWUHj8zjpX1nF1r1Gzj1q41KoXesaq0RVHUPVbzpalqOmO3pqrpTLY6kbY29jhMIDAHAHDuubfj6qvrMXHiSshyDAcPfgN7934GPT11qnUaNG7PPRe47z4I27dD3rkTuOMOkEAAqK4GbrkF5LLLQF58ESQehy+VAvenP4GsWgV8/OPAz38OnDoFcsEFwHe/C/nQIQhvvQV84QuQA4GcmiqKIrq7t2L//ntQU3MB9u+/Cz09lQA4TJz4SVx66e8xa9YGjB79cYhiD44ffwIffDATBw/+H/T3N+fW1FQK8s03A1u2QKirA7nrLiAYBGpqgC98AWTuXEhPPw3S25tWG2VdC7VGNTufp61RUymQzk4Ie/eCbNsG8tZbEP/v/wV+8QuQH/wA0v/3/wFf+QrIzTfD/0//hBvXrQN+8xvbNNW7CGIQGzZswLx587Do7CeO9u7dCwAIh8MIh8MAgIaGBjQ3NwMA6uvr0dLSAgCora1Fa2srO1ZnZycAoLq6GpGzz7lVVFSgp6cHAFBWVoZodOD2qlAohP7+fpSXl+Odd96BKIrgeR6hUAgAEI1GUVZWBgDo6elBRUUFACASiaC6uhqiKGLz5s2oqakBALS2tqL27C1XLS0tqK+vBwA0NzejoaGBcWpsbERZWRn27NmTk1NNTQ06OjoYp+7ubmZrceJ5HqL40a1SlJMoiigvL0d5ebkmJwDo6OgYxKmsrAyHDx/W5KRVp8OHD6OsrCwrJ7U6VVZWsppm46RVp7KyMnR2dmbllFmnAwcOYMuWLRBF0XDv1dTUYPPmzRBFUXfvUU4ADPceALS3t6OiogKiKOruPcppz549LF+9vReJRCCKIioqKlgf6u09AOjv/+iN5kZ6j46zXbt2ZeWkViea77FjxzQ5qdWpp6cHZWVleOeddwz3HgXloaf3KCflItIsnNRTnufB8zwqKiqYbbeehsNhiKKILVu24MCBA1k5eXpaOD1tbm7Gnj17UFZWhsbGxoLpKa2Hp6fadTp8+DCb6wqhp9FolPn29/cXlZ4C1jW1vb2d2XrPF2B9jVpWVob29vaCrVFpnFyc1Pr6nXfeSZt7tTgB+dPUXbt2sXFbKE3t7u5mOqNnrLa0tCDWfRjiBIAQDj/8YQCnTw+sUVtaYrjyyi0YNep+AEGcPv02du9ejNraVejpqcKOHTvQ2trKxh7tw0GcZs0CXn8dFS+/jOR99wETJoA7eBDcN7+J4Ny5WPP1ryNw113gtm4F8fmAT38a8T/+Ee+8+CLw+OPomTRJl6bu2LERx449iQ8++Bj27l2FkydfgSRF4fNdhFmzfoRJk95FIPAsBOE2VFZejLFj/4rLL/87fL7LIcs8Tpx4AR9+eBl27vwc+vsb9GlqZyeizz8PHDuGA//yLyDnnAPu6FH4z743RLz/flS++iqrJa1rIdaouufzeBy1L7+M2GuvAY8/js41ayAvWgQycyZ8EyaAGzkS3NSpCH7iE+Cuuw7cZz6DwN13A/ffD+5HP4J/wwbgD38At2kTfHV1GHfiBPjDhw3P51T3coEjykv0HnSjr68PEyZMwOnTpzFp0iR21cnv96fZoiiCO/udbFEU4fP5IEkSQqEQPvWpT2HkyJFsO/3OvN/vZ3YgEADHccwGBv4RqrSDwSC70hYMBiHLMiRJYrYsywgEApq2JEkghDBbjUcuTj6fb5AtSRI2bdqE1atXY+TIkcOCk1qd4vE4ysvLsXbtWgAYFpzU6iQIAt555x3cfPPNCAaDw4KTVp1SqRTeeecdrF27luVQ7JzU6gQMLIpWr16N0aNHG+IUi8UwYcIE9Pb2DvoGvFF4eurpqdv0FAASiQTKy8tx8803g+O4YcHJ01Pn9RQwr6lqOjMcNTWRSKCsrIzpzHDg5LimCgL6lozGnqclnOq4CLd9uRXjxwNf+5qMBx4gmDVrgFM8HkZr60/Q1fVnAAMcx49fghkzvoNzz/0MJEnWP1b7+yG99BJ8v/wluLMXPMn06cDdd0O8804EL75YNydBiKGn522cPPlbnDmzGcDAP499vrE4//zbcP75d6Kk5DrEYgH89a8y/vAHDlVVHACC224j+N73fPj4xwX09VWjre3nOHPmXTYeJ036FKZPfwiTJq2AJEn6xlMqBfz+98Czz4I7+58mxOcDufVW1F52Ga5asQKjxo+H7PdD8vsRHDMGst8PORBAYPTowfbZ2lvuPQC+EycgNTWBa26G7+BByPv3D1yM0vFyV8JxwIQJwOTJwOTJIJMmwXf2T3niRPjPOw/yhAkQxo9H7aFDWPylL8E/fbqh8dTX14fJkyfn1NOh+brWIoLPN3Azjd/vZ9uUtvKNuIFAACdO/P/tvXeYHMWd8P/pMGFnc9YqIgFCyRghgpGNsAUIkG2cuON97fNhv8ZnHvvOGM72a94Dc9g++3A6jE84/TA+n8HmbIzD3QISSQiEQYAICqAcVto4m3dmZzrU74+enrA7szthZ4O2Ps9TT1dXd3XVt7vq29XfrgBbttgcPVrPggUq8+dDXZ1O7DJ4PJ74+SP9QggGBgYoLy9HUZSUc9wGlJsnN1+uXwjB4OAg5eXlo87JlHdN0xBC0N/fn5LmSJky+V37mq7r8bTGki+TrJlkSudXFGVUfseSb2R+s5U1WzlG+kc+p+T8utfPJF9yflVVjd+fbMte8jlu3Fxkcu+JoigpcoxX9txzQqEQ5eXlWZe9dLJmW/bA6dYZDofjZSjX55ScfjZlz61nQ0ND8TSzLXtuft17NJZ86erKyHKbTdlzX7rJ18+m7Ln+YtjPc9WnlgW//KXNkSON1NVpzJnj6FP3nTfec04uI7mU6Xz1qZtmchmR+nTq9Wmmd10x9WkmOaQ+He0f7103kfp0pKzJz2OkTNNdn7rpZkozOa+uP52eGXm/hIDeXggGPXR1QTDo+gWhUISSEh+6rqCqoKoeNA1UVUnyO/fC9SuKgmWFmD+/hPp6nbo6qK5W0fXitlGTZcrFr+t6PM3x6upE6dRMeqaYOlVRFMLhcLwsjCef1tNDeJ7z8Xz46CpKSwUDAwp3361y771wxx1w440a5eWrWLHiVyxe/DWOH/8ura0/Z2DgL+ze/UFKS1dRV/d5Fiz4KKpaOr5M5eVo//iPcOONmI88wouvvML5//f/4vH7cc8eS6c6z+NFWlt/TkfHb7CsvngalZWX0NT0SerqPoJtl/HYY45N4k9/guHh5IEUCg8+qPDgg/CRj3i49dZLOeecSxkY2Mnx49+mo+O/6Ol5lJ6eR6mocIw9tbXvH1+nejzwmc/Apz8Njz3mGEO2bEF56CHeAfCNbzhykBjWMaZfVRFeL6rXCzGnuX6PJ+FPDvd60b1ehNeLMTiIfvgwyv79EA6TKJ0jhpVUVztDkWJOLF3KUE0NpfPno9TWolRWOpPdxu9eYqslXU81DLqbm1Hnz89Zp7rh4yGNIAWSaxfGF1+ET3xCB9Zy++1OmKY5kxQ3Nqa6OXNS92tqTF55ZRtXXbUhpbJkm89t27axYUNucfONVyhTkd+pkFXKWby4p6ycpgkDA9DfD/39WD097H36adacdRZ6JAJDQ2O7wcG4Xx8a4oqBAejszEvWiSbXa/b0wPXX68A73PYAALoOdXXju+pqkz17dnDNNesoK5P1b6LiFRo3X6SeKV7cU74tIAT09UFnJ1ZbG3ueeYbzli9HNwwIhSAcHnerDQ2xrrUVFiyAc8/NWc5ikOt1H31U4fHHF7J3r0pvL0lGjoS/u9t5DY1GAfx55FIBAikhqur8KB5Ph1dVWbz22k7e/e61lJV58HrB5yO+1TRIssFNCLOl/uWc5okTDC10vEeOreC7332G+fPfyb/+q85zz8EXvwj/+Z/wk5/AhRdCSckSli69h0WLvkpLy79x8uSPGBraxdDQ33H8+Beord1Iff011NRsRNfLx05b1xFXXkmnbad8YGciEmmjvf0/aWv7BaHQnni4z7eIOXOuY86c6/D7l/DCC/D1r8ODDzpl32X5cvj4x+Gv/srgkUee5+mn38Xvf6/y0EPw0ENw9dVw222rOe+8X7N48Tdixp776O//C7t2fZBAYDlz5/4jr71Wy4YN7x37/qoqXHWV4954A/v732fwyScp93pRTNNZ/cYwEs7dH4ltowwPQ2zuk1xQgJQcejzOSjtJxo64q61NqXSmYbB182Y2nHbapJbdbJDDYfLE7WqYa9fFxx+Hb37T5uDBQYaGygkGc9fO5eVQUeG4ysr0/kzHKiuhqgoCgYl/MYzEMAyam5vZuHHjpDaaJhsp56lHUWQVwmkw9/Y6rqcn4XddzLBBf3/C0JFk8GBgwLnGBGMMDuIpLc0pTr46cCKv1doKn/iEzcGD/ZhmJV1dCkND+eWhosIxNjc0jO9qaiDLHw0Txmypf7NFTpg9sk6JnElGDTo7oaNjfH+6D4c8MLdsQb/sspziTKQ+LeR6p50mOHo0u8ZhaanzvVNX52xra51vI8sC23bceH53OzzsGFe6upxX4UShKKlGEXfr8QgikX7mzi2nokKlvJy0rqwsfbh7LIvv7Sln0urff/83r+19Pz3nw+9+9zP+/d+vB5zne999zmq0PT3OM/nEJ+C66+Bd70rcQ8Po5eTJTZw8+TMikaPxyyqKj5qaK6iv/wi1tVfj8VTlJadtRwgG/5vW1vvo7n4UdyiOqpZQX/8R5sz5JFVV7+bAAZX773d6fRw8mIg/Zw589KPwN38D55wz+htq925n9dvf/MZRP+DYLW67DS66CKLRdlpafsCJE5uwrH4AfL75zJ//jzQ1XY+ul2V1m7N6nkI4lSuTgSQazd2pKpxxhmPoWLzY+dtURAopt9nqP9kTpEDcGXCz5bLL4JJLLJqbn4qNz/PQ2Qnt7QnX1pa677quLoEQCgMDzrdQbN6gvNA0xyCS7KqqRoeVl9t4vUPMm1dKXZ1KdbXzAVBeXtyPANu26e3tpaqqKutuTYXEKzRuvkg5ixe3aHIODyd+hyW7YBARDBJpb8cXCqGMNHD09k5YIxtwWnIVFYjycqxAAK2yEqW01GmZuq6sLHV/xDHD5+OZl15iXR4vs1x1XzGu2dQE//3fFs3NW+MvynA48dfSdZ2dqfuJcEFnJ5imErczxeYCGxNVFdTVCebOVZgzR2HOHCcvyVvXXzaiXTOT6p/UM8VNM19mi5wp6fr9qO5XcrpKPWJfdHWh5KNvy8oQ9fWOTi0rQwkEoKTEca4/w9b0eHhpzx7WrFyZl5zFINfrXnyxoLa2g2XL6mloUEcZOZL9/hGdPiaqbBnGaB2eyXV2CoaGBNGoQjSqEFtIKI4Qzit79M9vBajkyJGcbs8oAoHxjSXJRpOyMhufb4iFC0upr1epq3OOjfdTckbomRMnCMV6gpxzzll0d3fH437qU/D+9yd6g9x3n+MaGuBDH4JrroF3v7uKBQtuoazsBnT9EMHg7+nsfIhweD/B4J8IBv+EoniorX0fc+d+hurqy1GUsfPlDF3dSVvbL2hvvx/T7I4fq6hYy5w5n6Sh4a8IBsv52c/CPPywwl/+kohfWgof/rBj+Fi/fvR3f/I9WrlS5YEH4Pbb4ZvfhPvvh0cecdxll8FttzWybt03WbjwK5w8+RNaWr5PJNLCwYM3cfTo15g37x+YN+8f8Hrrsn1EmVEUJ7MjMjyb2gLZII0gBVLo0mYeD8yd67jxCIdN/vSnZzn77HcRCnnijfa+PrLwC7q6TEIhHdtWsKzEt9vYqMDobmiq6hhNamqIG0aSt9XVUFmpcOjQHCoqlPjf05oa59ttPCzLYseOHaxfvz6nSpNvvELj5ouUs3hxx41n204lSdOyUtvbefvrr6Pdd59jvEg2dozREyOrzsBu5amqcipKzG+Xl3Osr48FK1eiVVendvly/clbrxdwuho++eSTrF+/Pve/PIbBYFtbXhbNQnVfsa5ZUgLz5ztuPAzD5IknnuTcc9fT0+Oho4MU197OqLCeHrBthY4OhY6O8dMoLU01jjQ2CoaGuli7toJ589S4waShoXg/VqSeKV5cKWee2LZTmdxKlvzHJ2lf6eigor0dNccecO43pSgrQ6mvdypYfb3jXH+6ML8/b50qDIP25mbHUpAjxdCn+Vz35z+3aG7+S8yoPDVly+NJGJLHwzDMlGclhDNUJxqFSCT9NhqFoSGTbdteZPnyCwiH9fjPRdcNDjIqLOEEpumUsFDIcbHFybJgdJta1xOGpZGGJne/stLmyJH9XH75uTQ1OT8ki90LJdfn2b7rEJGPOP4rrlg6Km5DA/zyl/B3fwf33gt//KNT1X/yE8fV1jqGkiVLjnDjjeewZMn5LF78TYaGdtHZ+RBdXQ8xNLSLrq6H6ep6GL//NJqaPs2cOZ/E52tKyUs02kl7+/20td3H0NDr8XCvdx5z5vwtc+Z8AkVZyp//7BhlHn1UYJpOb1hVhQ0bHMPHBz/ovMNzuUdnnQX/8R/w1a/Ct77l+B9/3HGXXAJf/WoF73nPl2hsvIFnnrmN8vL/YXj4AEePfo3jx79DU9P1LFjwj/j9i3J4Wtkxm9oC2SCHw+RJIV0Xp7ILrBDOdAB9fc63XV9fqssU1tPjuO7uvIaTpVBamjCI1NQ4ii95P1PYyL8O0wXZpXkaIITTasnm15HrgkGnu2A+uAOW0xVU1wroGjpGGDsoKyv+WLQsmYzuhsW+1mSXy2g00Yu+tdXpuee65P3WVnIamqMozjeY2/gf6erqTN566xk+/OGLaWz0zIhu2PkwrfXMBHNKyer2kHNdrHccwSBWZycnXn2V+R4PqmvgcLpg5ZaGpjlfhPX1qRNDjLVfUlIcedMwXfRpIdc7pcrkGBQipxCOQWVsQ0l6Y0p/f6JqdHXlP7pVUZzmRCbDSXJYRYXBzp1P8uEPr6e62lO05scL77ua8Bf/jNVfyqVXD457vmHAU0/B734HDz/s3A8Xv98xGFx5JVxxBSxb5sg8OLiL1taf0d7+S0yzN3YvdGprr6ah4f+wY8d2FizYRU9PM0KYseM+6uo+SFPTJ6msvIytWzV+9Ssn3dhqyACcd55j+Lj22uwMcNly5Ajceadj+HE7p61d6xhJNmwAsOjsfJhjx/6VwcGXY7E0Ghv/NwsW/F/KylaNuG+yjo6HHA4zSbizgeeyrJWL210n2+XHVFWlq6uLqqoqvO5f4CyXtVJVlY6ODmpqaigr8xII2DQ2jr9Ul23b9PT0UF1dHZvNW2NgwKS3V6G/X6Oz06S3V6WvT6Wry6K3V6W3V6Gry6anR3D4cB+2XUVPj0JPj4JtJ+ZnjC01nTWBgKCmRqGmRlBdLaitVeP+ujqVqiqbqioLr3eQ004ro65OobFRR1WzWwJKCEFPTw9VVVXx2bCzXVYvuTzksvyYaZr09vZSU1MDkPUSUIZh0NXVRWNjY3w292yXVItGo3R3d9PQ0BAvG+OVveRZ44UQKXJks/yYaZp0dnbS2NgYL/tjlr1QCDo70Xp6MNvaGDxyhArDcBrOwSBqMIjd2YkSDKJ0djpdn6PR3ApUDFFeHm8wi9pa1Pp6rKoq9gWDLH3HO1Dq6rCrqtAbGrCrqhx/dTV2khzp6pnX601f3hQl7XNSFIX29nbq6+vj9yybsqeqKsFgkMrKyhS9kO2Sjsnn5LKcXzG6b0+mPnVlbG9vp7GxMX6fxyvTqmrT1KSiaR2sXOk850z3qq/Poq0NOjo0Tp60aGtTaGtTOHw4TF+fn/Z2lbY2QXu727vEMa68/jpp0IH1/MM/gKIIamsVamtF7Ae2Qk2NRUODSn091NRYNDZq1Nc7k782NmoMDHRSVVWFz+fLeulDVz9VV1ejKEpOS+rNJH2a6V1XTH2aXN6FEBiGUTx9muQ3DINgMBjP7yiZhMDs6kLt6UHt6cHq6ECJ/QEZOnaM0nAYtacHu6sLpbsbpbsbEQyihEIZ67UGLMxwTFRXozQ0IBobEQ0NqI2N2A0NiPp6tKYmzJoaunWdumXLEBUVEMvveDoirk89npz0qfuua2trS9ELM1GfQu46NVmPuvel2G3U8eptrm3UbJeTTb5HuSyRG41G6e/vo7a2lspKe8y6Op5ONQydjg6LYBB6ejQ6O+2YX437u7tVurpsOjtteno0+voUhEj8nDxwYLxS4AGu4NOfdoZyuv9jKiqc/zNlZeD1CkpKVHw+gddrEwhoeDzOsrHl5W5XCIGqaghhAwJF0QiFnOFI4bDKR63DzgSaw/OzaqMqismllyps2KBx990mzz2n8tBDCn/4g01rq8ZjjzmLogAsXAiXX25z5ZUruPLKH7Bgwdfo6fkDra0/pb9/O11dv6er6/eUliZ6uJeVnUdT0/+hvv5adu0q5847PTzwgKClJXFnFi0S/M3fKFx7rUldXUKnmmZ2OtXVM3V1dXg8nrRl77TTVH74Q5NbblH57ndVfvpTwfbtCldeCeecE+Wf/1nj6quvoarqagYHn+H48Tvp6Xmc9vZf0d7+K6qrN7Jo0VeorHzXqHKbS9nLuo06ge/zSGSQSOQAweAhSkpUVDWKZYUwjEEggm2HMM1BhIhg20OY5hBCDGNZQ5hmiNLSNtraOmhq+mRO7/PkdsRYSCNIjmzatIlNmzbFC8ju3btZt24de/fuBWDVqlW8/vrrlJSUsGzZMnbu3El1dTVnnHEGL774IvPmzWNubOxLe3s7Cxcu5JlnnmHVqlU0NDTw5JNPcv7551NTU8PmzZu5+OKLqaiooLm5mfXr17Nr1y4GBwfZuHEjpmmyefNmPvCBDzAwMMC2bdt473vfS29vLzt27OCKK66gq6uLXbt2sW7dOl577TX8fj+XXHIJx48f58SJE6xdu5bDhw/T09PD+eefz/79+wmHw6xevZq9e/di23b8pRYIBFi2bBlvvunItHLlGWzf/iKrV89j0aJFPPPMc5x++unMmzePJ598mmXLlrFjxw78fj/nn38+VVU1/Pa3j3HWWWsxjHL+53+2s3jxefT1afzlL/uoq1tKMCjYt68LTWvkxIkQ/f06g4M+bBtCIYVQCFpaFFIXVXL97oJQ1SnPrLwcKitN5s7V8PlCVFebnH56NdFoOw0NCitXNtHVdYD58/2Ewy3U16ucdpoj0/bt2+MyZXpOTz31VDyt5uZmNmzYgK7rcQvmeM/J4/GwfPly9u7dy/r162ltbeXgwYOsW7duzOd05MgRNmzYwJtvvpl12Vu0aBHPP/88w8PDXHrppVmXvQ0bNsRfZKZpYllW1mVv/XveQ9ubb7LvueeoX7GC7rfeov/QIc6orKR//36stjbqhCDa0oIaDKL196Mlmed1oCpNXUzuVBf/seHzMVxRgdbYiNbYyMlolNqlSylZsIBdbW0sueACAgsX8tQbb/CO978fvbGR5ieeGPWc+rq72bdtG2ds3MjAwIAj05o1dHV0sOu115zndOLEqOd0wQUX8Oqrr1JdXc2FF144qj6N9ZxOO+00Xn75ZVasWMGSJUuyKnubN29m7dq17Nq1i4GBgZzL3vr16wHYvn07l156adZlLxwOs2hR4V01p1KfbnB+wbBjx464f6L1aWurc68uvng1u3btZeVKWLZsGZs3P81pp53GihUr2LHjJSoqqqmuPoNHH30VRWlCiEa2bz+EZTUwOFjG3r09DA2V0dYmGBz0IoQS69Ck8NZb7t1M7hqS/Gp3/qCUlNRSURFl0SJHHwrRyTnnzMPvDzE8fJx165YjRJDBwcO8970X0NNznBMnWohEIsyfP5++vr6074hMz6myspKWlhZ8Ph/z58+f9vo0FArR29tLXV0dqqoWXZ/qus6WLVsAGB4e5qmnnspOn8ZkevXVV7nqqqs4ceJE5no6MMDqhQs5tH07em8vi8vLaX35ZYZPnKChupre/fspCYUoHR7GOHkS78AA9PWhJ3UQTi5VyZ340+lfoWlEysrwzZmDXVNDh2XRtHIl4dJSDvX3c9a6dYTLy3mjo4O1H/wgHcCufftYv349J5P16dGjcZkO7dvH/v37uaKiggMHDhRdn1588cUEAoG4fvT7/TNGn0LhOnVO7Bf4iy++yBlnnJHV/Sq0jfrGG2+gKApnnnkmhw8fzup+jdVGzVRXk8vA9u3b4/csm7o6sgyUl5ezevVqtm/fPm5dTS4DLS2jdWo4vB9FCXPppavZtWtP/Dnt3Pla/Dm98MIOenp6uOyyy9i+/UVKSxcQCCzg8cdfJRBYANTywgsH8PmaCIVK2bu3HSFq6OvzcPJkhKEhD6apYttKhqHwmRYqTbcqy8ja78S9/vo2wkB99Rk5t1FfeeVFzjhjHnffPY8rr3yEQOA8Xn+9iQce6GbXrmqOHVO4915nqd2SElizZojPfOav+MhHruPxx+/mjDPepKvr1xgGzJ17HfX1H+ePf+zi6aev4D/+w2LXLi2e37Iyg49+1MP73tdDSclOLrtsPcePt/LSS1no1BH6x5XDfZ+PV/buvnseF1/8HE8+eS7/8R8lvPqqlw9+EFavhquuep0vfekC3v72Lfz5z99j0aLtdHc/TE9PMz09zZSVXUR7+3ouv/w2AJ566qmcyl4hbdTx3+dPceaZZfj9rbz22h+ore3HMPYxNPQWipK/gVfXobd3H/v3P5PT+7wjm7HKyOEweeN2tenu7qa6ujqnP5eWZdHc3MyVV16Jz+fL6c8lZP+XNxurYLZ/j7L5IzbSb1kWjz76KBs2bMDn8+Utk6Z56Ouz6eiwGBjw0NVlEwwK+vo0gkE7Npu4SjAoYt0MlbhfiPz6/VVWCurqnF4nTu9ahepqOzZ8WKWqyqS+XqWhQSUQCPHKK49z9dVXAcy455Rt2TMMg0ceeYSrLr0UT38/Zmsrnr4+REcHdns7Wk8PoqMD0dmJGgwiYpPVKV1duXd7xmlQK3V1iFgvDaW+Hju2Zp7a0IBVXY1SX4/a0IBZVYXa0IBaVoZpWQXXp2g0yiOPPMLGjRvj93WmPKdcyh4kPjRLSkpykmloaGjCh8NIfTq+Pn3PezYwNOQjGFRpbTXp7tYIBhXa2iyCQTU+WWBXlxKfK9Idv54LHk9C99XVORPB1ter1Nba1NVBQ4NKTY1FXR00NmpUV1t4vYWX6VAoxJYtW2ITh5+6+hQgHA6zZcsWrrrqKhRFGVsmy8Lu7UXv7cXu6EB0dKD19GDHxmap3d2I2ConSjCI6Ox0JmfOE1FejlJbi4gN+1Pq6rCqq1Hr6lBqazErK9EaGlDq6jAqKtAbG6GiAtOypD6dYn0K+evUdO22U1GnhsNhNm/eHNczp4JMY+nUzZu3sH79Rnp7YXBQp7cXgkGL/n4tNq+JjWlqhMMixR+J2Kiqhm07PYBVVU3x+/2CQEBQXq7yPsVLz1qDM8puoWn11ydMpuFhlW3bVB55xKK5WeXgwcT7rKwMrr7a5n/9L4X3vMfgz3/ewtDQFfzmNxpPPpn4BvB6Be97n8JHP2pzxRUWZWVT/5y6ulS+/W2LH/9YZWjIyeeqVYLbblO4+moDn08nFNrHsWPfoaPjPxHC6e0cCKwgGHwHK1ZciN9fjvPTw4PHE4j5dXQ9gKJ4EEJPCfd4SrFtFUXx4PH4cpbJMAwMo4VweA8DA68TCu0mFNrN0NBehBgxK3EMTavE55uHovjR9VJUNRDzl6FpJUAJHk8ZqloC+PF4ylFVP5bl4bXX3uRd7/rflJYuzek59ff3U1NTI4fDFBslNrhOSxqgnex3labrj0ROEAw+ja7vZ2ioDsuqR9erUNUqwJsy7mmk37ZtWltbaWpqiqfrnuM2oIB4RUv2J8cdeU6mvGuahm3bnDhxgqampvj5I2XK5Hfta27XqXQyZSOrqkJ1tUp1tWt9TrZCp1qknXgnaWpqQojEOvfJ00Ck8zv7CcNJX59CXx8pyjY1reSqEwCupqrKMZy4w5Hr6z3x4cn19Z7YVqGhwfEHAs713Pvr3r90z2/ks1EUJX5/si17cSlUNR5X13VnbFJnJx53mcBkf1dX3O/p7GRjayueWLdn94kl/zcY2UcnGbu0FKW+3pmkLnFjUsdwJ22VykpQVURKWUg8g0z/vV1Zbdumo6MjXuazLXvu/U2+X+meR6Z61tbWFk9zrLqVLr/t7e3xuJmeX7q6cvLkybR6wUlLwbKGsO0oth1heDgS9xvGEJr2Jrq+cUz50uW9GN23c9Wntm3S0fEAur6fwcFKDKMWXa9E1ytRlPJxn3NyGVFVtej61E0zuYzkqk9LSnQqKlSammDVquSSn1wjEuXX6TZts3t3B4rSQHe3SlJVjy+skbw/NASGodDa6sxtMrrHXbo0NSoq3CqsU1srKCsbYsGCAPX1Sjw8Uc11qqoyP5ts/COfE+SnTzO96/LSp9nKMTyMp6ODisOHUZ96Cr23N2409sQeitrVhZrsj3XxzfwGTONXFGeuotiNFzU1hAIBAgsWoKSbSCA2t5ESG87gXse2bdrc9oeqpulrFPMn6ehC9akb160vk6FP3bjp9MJImXRdw7KGESKCEBGi0SFUtRVFMYCSKdenbj4zpQnjt9tsO4pldROJBDGMbgwjiGlm8vdgGFG8XucDTFU9KErCZdoHnXDYoLJyPh5PDbpejcdTja67rgpdr0orRyFtVJdc9Y+mafE0x3pPpPNDQke5smRTrjPpmWxlUhSnB0VFRXJtTff+SLTobFvQ2tqWdG/T9cBWoH8AXn6ZF447+inQeEHebdR09baszFlq9qqrNH7wA9i501mG9sEH4dgxeOABZ1WWqioPodAVRKOJ6158sTPPxzXXKDijrdwe46nPBoinOxnvc4C6Opubbmrjy19u4u67FX74Q9i1S+Haa2HZMg+33grXXnsWy5f/fyxZ8nVaWu7i5MkfEQrtoaRkD4cP/5zCUGP10Ju09WYMGx7uwzQPYlkD6a+mllBaupLS0lWUlq4iEHD8Hk9T/B4l3+/xMAwD02wmEDgz5/d58jtnLKQRpEByfXH197/Ivn1/Q2kpvP76HSnHVDUQU/YJpe+8CKpihpIKjhzpRFFWoOsBVNWHovhQ1YTLtG/bGgcPHoyPcc1FvnziFUq+6SbH03U13q4766zx45qmxbZt21m+fC19fXpWxhNnvKazdHFvr0JvbzbjMx0CAccoEgiUs2SJu3JEqnMnRaysTJ1Lc8z7E4kkZm5M59rbqTh8GCUcds7LcmYuhaTGrqomZt4ax5hBfT1mZSXbd+5k7dq1KS+DbJiIsjBZZde2bQ4cOEB9fSWKYsbGOQ5n5QxjiCNH9hGJNAFGLK5jrHD8zn7CH0GIKJY1zNBQL8eP66OO2XYEGFtHlZWBEDflJetEk+s1TbOH/fs/QWkpvPHGv4w4qqBpFTGjSFXcOKJpiX1VLefo0Q4U5Ww8nko0rSytU1V/yku1kLI12eVSUaCiwkaIfVx0UR26Pn6armpoazN55pm9NDSsoLtbS7saaWyaHmw7sRrZwYPgaIwxptUHNG30XJY1NSo9Pcs4cECloWH0N3rMNpqWSX8uto3d20vL9u00Ll6M6s56mI0Lh/EA78kpl0AggKivZ9Dvp3TRItSGhtGTgSb7a2qcGx3DMk1e3r49Z108k9oCzhwrQxw48DKVlSuAMJY1mMENjAozzQH6+jppbfXF9Wiq7o3GDB+jeziWl8PQ0HL8/ktylrMY5HrdN9/8KGVlr/DSSzdimkEsa/wJLkeS5/Rc9PSMfVzTykYYRqrRtEo6OvoYHJyLquooiobzcacCWmyroiipftsWeL37OXnyALpektVHoBtm2yoHDuyiqupCPJ5AyjHHZf4Am2ntmXialuVM5HfoEBw6hDh0AOP4HiInXyMSbWG4AcJ/78QL1K8pmpyKAuee67g774S//MUxiPz2t9DaqgA6S5cK/vZvFT72MTjttIlJd6LjJcddu7aRb3xD5YtfhLvvhn/7N3jzTcd4c8cd8P/+H3zsY02cfvqdLFx4Cy0t93DgwB+pr6/GaStGk9qK0Zh+St464aP1lY0QESwrfQ+OTCiKh0BgWczYkTB6+P2LSbdkcW+vyV/+cpxzz22MGVaJO8siZT/ZRaMKb75Zzdlnw+LFOWUxa7035cNh7rnnHr7zne/Q2trKypUrueuuu7j44osznr9161Zuvvlmdu/ezdy5c/nyl7/MDTfckHLOQw89xG233cbBgwc5/fTT+Zd/+Rc+9KEPFZTuSPKdebun5wkOH/46PT1HCQQEptmLZfVlHb8QHEWdyVDiH8OY4k/jMoUnnGVpbN26nUsvvQqvtzweL1sL3UxheNjgv/7rcVavvozeXs+oDwX3D2tHR2KbayPB73eW2Jw/H96zupf31z3PKvNV/D2xX7XJSwvm0wXa50sYM0YaNVxDRlUVW/fsYd1HPoKnvj6lYT1dcSZxNbDtMJYVxrZDSf6Es6xQkj+MaQ6yb98uFi+eh6JER5w/POIawyOOFbh8UpFJ1G0viuIjHDZ517sO4vON/cE6kumwOkwk0saePR8jGDxGWRlYVh+m2RfvNjpxqBkMJOWxhnl50n7Cnylc00pR1dxmO5/OM8LbsdVOMxlJ0vkH0v9MGhdVdSb0q6xMuOR9d+n2TKuNxZdoF8Kx2LhLOLjLOAwOJpZwcJdx6O52BBy5dNrAgHOdPInPoTF3LoprzEhnTHb9tbWO9XyGkansCmFhWUMjjBAj9xNhtj0U8w/Fercl7w8mhYUYz/hbDJwPZI23ve1/qKtbn1Pc6bI6zAsvnEU4vG9EqBLrnVGLx1OLrtekbBP+GkBFCCPubNvIat+2w5hmb8z1YBg9mKbjMv15nq4kerq4hhHvCKOKnmQw0ZN6xSSHO/uJ3jN63uG2DS+99BJr1pyLpqk4H782YCGEjQj3Yfd0YPV1YA8EsYe6sUO92MN9WNF+TKsPUwthlIEZc1ZZetl1u5x3vqdv0tv5lgXbt5u89NI2Pve5d+H1Tq93ZC7098OmTfC97zmvH3CMALfcAtddB4qSX1tACDtmGHENJ0aSsWSsMMevKBqBwHJKSs7M2H7p6HB66rjulVey/zGcia9/3eLWW3P73pgRq8M8+OCDfOELX+Cee+7hne98Jz/5yU+46qqr2LNnDwsXjp5L/PDhw2zcuJFPf/rT/OpXv+K5557js5/9LPX19XzkI87i1M8//zzXXnstX//61/nQhz7Eww8/zF//9V/z7LPPcuGFF+aV7ljkamWvrr6UsrJ1NDc3s27dxti4QgvT7I8p/MRLwDR7Yy+C3pi/m8HBDnw+Jcm6l/jzO3J/5IeAEAaWZQC5W/bzpaICduxIDXNeCGMbVBTFy/CwoKysGk0riYWlM7z4RsXr7OxjzpwF8XjJxp3EdUYXfdu2OX78OAsWLMjJoqtpUFkZZdkyyKiP3DXVwmHEUIjBzjC9rWG6WwZpeeskalgn3BkiHBwi2j2I2TuE3T+IMjSINzpA2fAg5YcHaDrcyqptu1AZu+FtKRoDJQ0MlTYwXN5ApLIBs6YBu64BUV9Hv99HzVln4JnXiHduPSX1ZZQEFAIBR4Z07y9hGAz29zsN8RwNILZtc+zYEebNawCiI3pBhBmrh4RphujpaaO83BubNTq98SLVMBGK+/NtDPv97lCAQlDGNRYml91w2KSsrAZNcw2G3iTDZLLfMWKoqg/w0NXVR2Pj/FiZTxg4RvqdhlHi4bofJ6rqzVmy6dATxOebw6pVj9Lc3MwllyQaBJY1HDcwm2ZfTIcm/G64YfQwONiFz5fuY2wQ23ZXvLCxrH4sq3/CZFUUb5IxpXTEtgxVTQ1zxs0eoLOzLzZm1tFvzjMvie+rakk8zP0rk69uyzae2zEsudfdeHHdFVVHGkna2y1eeeUYZWWL6OlRU1ZbHRwExTaxekNEeocYJIRgiCghQoToZ4heBuihjy76qKKXSvrirope6pQgdUqQSrsHnTyXyB6B8HqdeTNGWl/cJbPTLaVdU4Pp9/NYbK6MXBqz+T7PbOLatjnCsOvoVcsK0dZ2lLq68tjfw5EG4HDaMFeHl5aeYOfOW2NGCrd+5bk+aA6oaiCj8TJzz68SuruHaGiYi6aVpOjbzH4vpmnS3NxMZWX2P9RcpktPkMWLv8eOHS9w0UVX4vc3xAwcVWn/8AJOu6a7G7q6sYNBOjo7aViwADUQcCyOfn/q1usd1cBwy+SSJZnLpGX1pRhG3DZyNNpNb28HFRVlgMD5wLdwP/ST/e5Hv6PPDVpajjF3bgOKYiX9Nc/uw9D5e24ixOhVKBIGnswrJk02paVO74JxKSf9XKgZ8Ihq/IFF+EqX4PcvpK7uQyiKUvR3zkg0Dd7xDkF3d3/a9mux0i2GLq6ocAwe//AP8OMfw3e+A4cPw9/9HXz96/ClL6nMmZN7byBFUVEULy0t7SxYsACPJ3c5a2udvArhDEVyDR2u0ePEifTxPR6BqoKqKrFtqtO00WGqKgiHQ1RW+nOWNVu9N6VGkO9///t86lOf4vrrrwfgrrvu4rHHHuNHP/oR3/rWt0ad/+Mf/5iFCxdy1113AbB8+XJeeuklvvvd78aNIHfddReXX345t9xyCwC33HILW7du5a677uLXv/51XumORc4vruPHUZ54gqa9e1FUFcrLUQIBPCUleAIBKJkDgSVQUTLqZWGaJi+++CKrVl2QVTdW5y94Yuz/K6+8wNlnL0dVrSTDyfAYhpRhTDPM8eOHmDu3jtEfr+lcovu/80c9FBsfK5LyFcWyoll9UIyx6t6YtLWNd3MUVKGjCg+qraPYGqqtYQ3bdL3qQ7U1FEtFNV2nOM5QUAxQDQXVADUKSsRmWc8gHY/7USMWathCHTZRwgZq2EAJGaiDERQTJ64Jmgn1JjQYsNwE1cI5boJijZ5PYyRH9DN41ryQYyyklSbaaaSdRjqop1OtY1AvRcNCjxhopoE+YKC3Gei6gaY5W48niq6fwOM5jK5HY/tRvN4opaURSkqilJRE8Puj+P0R/P5hbLufvXufwOuN4PUO4/G4LozHM4yuO07TUp2qDqOqBkeO5Pc8Ib8OLqkoSR+LJbHGcUnKR6PjDwA+jh1rY8mSZfEJm0Z/ZPpHXM+PEB5ee20va9asxestG7drbDJu/T7jjOzqd3K8fftyjweObc7K8ztwOhhBiEZR/vM/mfvWW06dqayE0lK00lK0QABKa6B0AZSXpLXsuff83HPT3zvnT3VolHEkGu3jrbd2snjxHCAU70Kf6Fqfuk0+5hqnhYhimt2Y5qhp+jMSCMC+fZuyPj9hcC7BMFTa2qpiBraSMcuxWx/Ay7Fjbej60tgQzNjfTFtFcXViVDh6LWqjGAI1YqNEBWIoSmj3W9jzT0M1bedjaXg4vvVHIswbHmbeiHA7HKartYW6QAA1HHYmKDGHwBNCeIfyXgo7jiD5dUQUD4OUMUQpA5THXTc1Ka6H6iRziuP6qcQIVGB7NSp9XkpRCAxDoBdKDQgMQKDTGYvvOr8/4fd4TPbunc/goGOA9notvN4IHo+rXyMxvRpB0xJbCHHkyG5UdT6a5g6dS37/pm6Tj1tWmP7+Ljo6tBFGaMdokW6IRzLjvlszoOtjvc8z9bQqQ1UDdHeHaWxcFOtZVZpiNHSMhaPDhPDx8su7uPDCi3LWi6ZpcujQiyxdmrtOzZfpYgSpPjmfyp2vUjF0Ar1/b6InVCaX9FBVYE42ifh8KYYRxe+nTginN1RFhePKy+NbtaICtbwcT1IYFWdBeTlmIEB722ssnHsuuqqm9qcXYnQf+1iYYUVo27eVsxrehUfTEueOt7WdZW7f3L2b5cuWoSoxo4dtYIuos8VA2FEEsZ4uGNgxw4hpDtN6/DCNNRUoVgQRDSOMYYQxjG0MI6xhhBlxnB1FmFHnulYU0xxGUQXYBkKxsXUQGgg95tTYVsM55h6P/bNSnJVtR21VA1Rbd94FnlJUbzmqrwKlpIK+iEL96efgnXMmet0iPN7alCH7mX6guHO1zJs3L2ejQj7xCmUq8jte3LIy+OIX4bOfhZ/+FL79bWdE0uc/r+HxbKS8XI1XpWTn9Y4Oc8M9Huju9jB/vhPm8Yx2uj46TFUFr70WYXAQXnvNMXiMXmHIaWadeaYzXGn1ase97W2OPr3ggtz0qWGYNDc/Hpu8OPcfr9kwZcNhotEogUCA3/72tylDVW688UZeffVVtm7dOirOunXrWL16NT/4wQ/iYW5Pj1AohMfjYeHChdx0003cdFNijPu//du/cdddd3H06NG80k1H3l0Xf/97iBlsxkVVnZZSIJDYxvvypmGsR+keEyLhct13XwTpXg5p/EIIzGgUTVVAsbA1G1t3ncD2gu3B2SY5kbyf5njG8KQw4UkKi/lzrENTimKpKEJFFToKGgoeVEVHUT0InxehitjfESM21s+IdVfLbm3sqcY0daJRfw6uhEjEdYG4P1N48rHh4QDRaAmG4WUs85KipFqghbDRNBVFUVAUUpx7/ljOPSefbXKeMu2PPJY8vnLkNtMxl0jEyLn76HQYDkNHhzN5TjYEAs7vsNLShD8QcN746X5HZON3H3Ymf5pjtmpjaQaWHsXSoo5fc/y269eNRHjMb2pRBiN9+Mo8CM3CVg1szcTSLGzVxNYtbM1CaJM/DGBcLMdgrCYZglO2hmMATtlaTiNdsVIddixcAJoHRdNB96BoHtA94PGi+Pzg9YHPD34/is+P8PoxNT/Dqp9hJUBI+Bmy/UQsnWgUolGBYSQ7m2jUcYbhrPxgWTa27fxNVlUr5hy/pplJzkjZ13Uj7lwjtOtPNnroeu6raBUT0/Rh235MswTLSjjb9se2rnP2hUgNE8IJsywfLS19NDScgW1XYlllmGZZfGvbPtxVHJKbHu42+Vs2nT/T8XS6L5OOHHmOZTmLmrnbkf70+wLThCeesHj3u3MznkyX4TBi4UKU48dzS0xVnd5NVVWOnksyahKJOE5SPFyraiCQ+r0Q89t+P0cHBli4ejVaY2NiiJ07MXJ9vfPFPcOZzkNGJ4LhYbj3XvjXfxW0tEz9tAK6DqtWJYwd554LZ5/t2CgngkKe57QfDtPV1YVlWTSOaMA2NjbSluFXQ1tbW9rzTdOkq6uLpqamjOe418wnXYBIJEIkSZH39zu9GIaHhykpKRlH2gRKRQXK+vX0trZS7fM5k1MODzvW9HAYQiEU14Jl285fsKGhrK8/nUiZSJPUWexzQWT6AEn3caIomEKglZSgJJkzhceD8GrYftep2F4F4VexfSq2T8HyQMgK46sMIHwqtkc4TgfhEY7hRrcRukgYdDQLSzHpGeimorYCNOeYUGxs1UIoBrZiIRQLgen8IRBmzHDhdK9MN1xDaDYCG5s0jeIMdo7MHQ6UEeNMnYm9PJ4Skif9Ai9CeLFtL0L4sG0Ptu3Dtr1Ylo9o1ENbWz/V1QsQIoBp+jFNH6ZZgmn6MQw/0agvtk24SMTP8LCPYNBG1+uIRPxEozqG4TQcDWOkU+J+03TmTzFNE9DTNnAzNXhtO7eXhNsrwjEOJK95c+oTjRo5dyEdHs5/7pOJ0qcYBurll9Nz8iTVXi+qq0djelNJzqOzDqAz5mIKUWOuWE00oTpGX8sLti/mYv54WNIxy5u67xqSraR9oTvGZHeb7BcesD2Ks9VB6AIxsmWhgV2STtMVihFz+Q2rKGW8qVunDttWMAw/huEjGnW2kUhiPxpNhI+1TdXJvth1HCOxo5+TDccJg3M06keIyfsTe2rgKNFIxMQwcvu/WIg+ddKcGJ2qnn46ISGc1YNiw7aEO6yrpgYR26b4KypAdZYrP3r0KIsWLUpZrQEhnBf5SOPI8DBKNIo1NETH0aM0BgKoQ0MoAwOJ+XoGBhL7sfl6lFg4/f0oaboXidQ/GiP/cMRd1DDw+nyjz8u0BWfFOkXBME08vtgcdyPOFeNcKywE/poalNJSKClBuIaLJOOFSN4vKcHy+Wjt66NpyRLUsrKEscPvzzxTdAzDMHh9yxYaL78888ekMbphmfF5ZkG+cQtJ04jJYKSRpVjpTuY90jRnSMzHP27wwAPPcf7578KydKJRhWg0YW90nWFAJKKkhA0PC4LBPkpKKmPvmNQ2d3LbO7l9Ho2CbQ+zZo2Xc8+Fc84RrFiR/r/8yNuf7z0q5Hlmq0+nfHWYkV3FhRBjdh9Pd/7I8GyumWu63/rWt7jjjjtGhT/xxBMEcp2o7POfz3xMCBTTRItE0KLR1G0kgmpm+FM03i9kknoAx/5KjruffA01NguFG560zeQX4LwMYi8NoSigaXF/yrER54nkl9J0xu1aXQrdebdhnLGqYAIWimKm8ZsoinuOCmgIoeFUYQ0hnG1iX0vaVxl/gE12VFc7W0VJdJMbzXDMTT2JP4UKQiij/LY9Vji49y3ROUrJ4B+9PzJ+8jZ9eJo6m6Ydnb7Dl4KqChRFxLeOTVAkhZNyTvL+44/nbgQJ5TtWjQnWp5/7XOZjto0WiaBHImjDw6P8WiSCYtuOixUW16/ErGpxf2yrjAhzH4hrvFZiYcrIY8n+2M0WmfR2UnjKOTHd6OrIuN5Mdu6xmFF41DFNizfSxYi4jLxGzNleL7bHg+XxpGxtp5+skzcr5iKQ0GlGkg5L70/VcQap+s5OurCdFBbrxx2bAwAEipIcljzmZaR/ZEFXRvhd5+jNhCFAHeHcY65zdK6je919PXaOlrQ/0u8BPAjhGqI9sTAtnjdNc5zf7xp6VUxTxTCUuN91hqGk7CecgmE4flfvOUVUiXUdDmHboZRwp0o4eUjWlcn7I89N3h95jaRiHNuOVmapxV3EwxRFxL4jRcq+q8/AbTIk74v4+Qm9mLrv6sKE39WNNqrqnO86d19V7THDBgaiNDfnZgQpRJ/CBOrUL3xh/HMMIzEZexrezGryiRHU1jrbkhKnh0K2WJaja0e0Q08p3C9YReG1w4fzvsyWLVvyipfX8ywwbiFp5itnIelO9j1qaoKWls2jwhXFeU/4c59GIydaW3OfMy/fe5TP88xWn06ZEaSurg5N00b1vujo6BjVS8Nlzpw5ac/XdZ3amALNdI57zXzSBWdukZtvvjm+39/fz4IFC9iwYUPOXRcNw2DLli1cPpZV9hRAynlqMVvkhNkjayFyun8a80Hq09yRcp56zBZZpZzjU4g+hYnTqfJZnXrMFlmlnKcWk6FPp8wI4vV6WbNmDVu2bEmZm2PLli184AMfSBvnoosu4s9//nNK2ObNmznvvPPiN+iiiy5iy5YtKXOCbN68mbVr1+adLoDP58OXpt+Pqqp5F0KPx5NTXMuy2L9/P2eeeWZe3a7yiVtImi65yllIulNxj1yknMVJcyaV3dkiJ1DQpGXTQZ+CrH/FildoXJB6plhpziQ5C0l3ppXdQieBnGidKp9VcdKcSfVvtshZSLqzpezONDmz1adTOhzm5ptv5uMf/zjnnXceF110ET/96U85duwYN9xwA+BYtk+cOMEvf/lLAG644Qb+/d//nZtvvplPf/rTPP/889x7773xVV/AmeB03bp13HnnnXzgAx/gj3/8I48//jjPPvts1ulOZ8Lh/JeUyzduIWkWwlTkdypklXIWL66UUzIesv4VJ16hcacizZlU/2aLnIWkO9PK7qnAbHlWs6X+zRY5C0l3tpTdmSZnNkypEeTaa68lGAzyta99jdbWVlatWkVzczOLFi0CoLW1lWPHjsXPX7x4Mc3Nzdx0001s2rSJuXPncvfdd8eXxwVYu3Ytv/nNb7j11lu57bbbOP3003nwwQe58MILs043F/K1VOaDpmmsXr16UuMWkmYhTEV+p0JWKWfx4ko5i0sxdN9k6lM3PVn/Jj5eoXHzZbbUv9kiZyHpzsSyO5Oumymt2fKsZkP9my1yFpLubCm7M1HObJjyqb8/+9nPcuTIESKRCC+//DLr1q2LH/vFL37B008/nXL+JZdcwiuvvEIkEuHw4cNpe29cc801vPnmm0SjUfbu3cuHP/zhnNLNBSt5nckiY1kWu3btyivNfOMWkmYhTEV+p0JWKWfx4ko5i0sx0pN6pjhIOYsXV8pZXGZT2Z1J182U1mx5VrOh/s0WOQtJd7aU3ZkoZzZMuRFEIpFIJBKJRCKRSCQSiWQymPIlcmc6k93VcNWqVZMat5A0C2Eq8jsVsko5ixdXyllcTpXhMLL+TXy8QuPmy2ypf7NFzkLSnYlldyZdN1Nas+VZzYb6N1vkLCTd2VJ2Z6Kc2SCNIHkihLMGfDAYBBJdbzRNS/GbpomiKHG/qqpYlkUoFKK3txefzxcPV1UVwzDQNC3u13UdRVEwDANFUdi1axfLli2LzwJumiYejwchRNxv2zaWZcX9tm2jKApvvPEGy5cvx+fzxcN1XceyLIQQcX+yHJZlsXfvXpYvX46maRllUlV1lN+Vs7u7G5/Pl1YmXdfjciT7VVXljTfeYNmyZfj9/rQy6bo+ym8YBm+++SbLly9HVdW0MmV6TrZts3fvXpYtW4bH40krU7rnFAqFCIVC8SWZMsmU7jm5+V2xYgWKoqSVKd1zikaj7N69m7PPPjteJrMpe6qqEolE2Lt3L29729sQQmRV9nRdxzAMQqEQfX19eDyerMueG3fXrl2cffbZKIqSVdlz/dFolD179sTzm03ZU1UVIQSvv/46q1atwuPxZF32PB4P0Wg0/kzd/IxX9pLr2YoVK/B6vVmXPUVRAFLym03Zc/XC7t27Oeuss1L0wnhlz82DW0dLSkqyKntuPLe8u7qwEKZCn7qyvPHGG7ztbW+LX7OY+tTl9ddfZ+XKlXi9XqlPp4E+zfSuK6Y+BWeyOFenKooi9WkafzbvuonUp276r7/+eopemEn6NPk6uerUdHqm2G3U8eptMdqobt1z7/t4dTW5DEQiEd566y1WrlyJEGLMujpROnVkvR2rrk6UTs2kZ4rZRh2pZ3J5n09FGxXye58XolPB0TPBYJBAIJB12SukjZrv+7yQNmokEom37TRNy+l93tvbm6IHMyGNIDmyadMmNm3aRDQaBWDJkiVTnCOJRCKZOgYGBqisrMwrrtSnEolEkqAQfQpSp0okEonLePpUERNldp5l2LbN0qVLefnll+N/HbKlv7+fBQsWcPz4cSoqKnKKe/7557Njx46c4hQaN994hchZSLqTfY+knMVLs9C4U1F2Z4ucQgjWrFnDvn37sl6TPRNTpU9B1r9ixcs3rtQzxU2zkLiyLTA200WfQv46VT6r4qVZaFypZ8ZGlt3ipVlIvHzjToY+lT1B8kRVVbxeb0EW+4qKipwfrKZpeRX6QuIWkibkJ2ch6U7FPQIpZzHTnElld7bICeD1eiekwT5V+hRk/StWvELjSj1TvDRnkpyFpDvTyu5E6VMoXKfKZ1W8NGdS/ZstchaS7mwpuzNNzmz0qVwdpgA+97nPzag08407FXIWku5U3KNCkHIWL66Us7hMZLozTYbZUv9mi5yFxJVyFhdZdqfH9YqZ5mx6VjOp/s0WOQtJd7aU3VNRTjkcZgro7++nsrKSvr6+gqyd0x0p56nFbJETZo+sp4Kcp4IM2SDlPPWYLbJKOWcOp4IM2TBb5ITZI6uU89RiMuSUPUGmAJ/Px+233x6fJfdURcp5ajFb5ITZI+upIOepIEM2SDlPPWaLrFLOmcOpIEM2zBY5YfbIKuU8tZgMOWVPEIlEIpFIJBKJRCKRSCSzAtkTRCKRSCQSiUQikUgkEsmsQBpBJBKJRCKRSCQSiUQikcwKpBFEIpFIJBKJRCKRSCQSyaxAGkEkEolEIpFIJBKJRCKRzAqkEaRI3HPPPSxevBi/38+aNWvYtm3bmOdv3bqVNWvW4Pf7WbJkCT/+8Y8nKaeFkYucv//977n88supr6+noqKCiy66iMcee2wSc5s/uT5Pl+eeew5d1znnnHOKm8EJIlc5I5EI//RP/8SiRYvw+Xycfvrp/PznP5+k3BZGrrLef//9vP3tbycQCNDU1MQnP/lJgsHgJOU2d5555hne//73M3fuXBRF4Q9/+MO4caarHpL6dDRSn84MZotOPdX1KZw6OlXq09FIfTozkPo0PVKf5omQTDi/+c1vhMfjET/72c/Enj17xI033ihKS0vF0aNH055/6NAhEQgExI033ij27NkjfvaznwmPxyN+97vfTXLOcyNXOW+88UZx5513ihdffFHs27dP3HLLLcLj8YhXXnllknOeG7nK6dLb2yuWLFkiNmzYIN7+9rdPTmYLIB85r776anHhhReKLVu2iMOHD4sXXnhBPPfcc5OY6/zIVdZt27YJVVXFD37wA3Ho0CGxbds2sXLlSvHBD35wknOePc3NzeKf/umfxEMPPSQA8fDDD495/nTVQ1KfSn0qxMzTp0LMHp06G/SpEKeGTpX6VOpTIaQ+nc5IfZqeYugiaQQpAhdccIG44YYbUsKWLVsmvvKVr6Q9/8tf/rJYtmxZSthnPvMZ8Y53vKNoeZwIcpUzHStWrBB33HHHRGdtQslXzmuvvVbceuut4vbbb58RL5lc5XzkkUdEZWWlCAaDk5G9CSVXWb/zne+IJUuWpITdfffdYv78+UXL40SSzQtmuuohqU+lPhVi5ulTIWaPTp1t+lSImatTpT6V+lQIqU+nM1KfpqcYukgOh5lgotEoL7/8Mhs2bEgJ37BhA9u3b08b5/nnnx91/hVXXMFLL72EYRhFy2sh5CPnSGzbZmBggJqammJkcULIV8777ruPgwcPcvvttxc7ixNCPnL+6U9/4rzzzuPb3/428+bNY+nSpXzxi18kHA5PRpbzJh9Z165dS0tLC83NzQghaG9v53e/+x3vfe97JyPLk8J01ENSn0p9CjNPn8Ls0alSn2ZmuukiqU+lPgWpT6U+nZkUQxfpE5ExSYKuri4sy6KxsTElvLGxkba2trRx2tra0p5vmiZdXV00NTUVLb/5ko+cI/ne977H0NAQf/3Xf12MLE4I+ci5f/9+vvKVr7Bt2zZ0fWZUsXzkPHToEM8++yx+v5+HH36Yrq4uPvvZz9Ld3T2tx1zmI+vatWu5//77ufbaaxkeHsY0Ta6++mp++MMfTkaWJ4XpqIekPpX6dCbqU5g9OlXq08xMN10k9anUp1KfSn06UymGLpI9QYqEoigp+0KIUWHjnZ8ufLqRq5wuv/71r/nnf/5nHnzwQRoaGoqVvQkjWzkty+KjH/0od9xxB0uXLp2s7E0YuTxP27ZRFIX777+fCy64gI0bN/L973+fX/ziF9Pa0u6Si6x79uzh85//PF/96ld5+eWXefTRRzl8+DA33HDDZGR10piuekjq07GR+nT6Mlt0qtSn6ZmOukjq07GR+nT6IvXpaKQ+zV8XzRwz4Ayhrq4OTdNGWew6OjpGWbBc5syZk/Z8Xdepra0tWl4LIR85XR588EE+9alP8dvf/pbLLrusmNksmFzlHBgY4KWXXmLnzp38/d//PeAoYiEEuq6zefNm1q9fPyl5z4V8nmdTUxPz5s2jsrIyHrZ8+XKEELS0tHDmmWcWNc/5ko+s3/rWt3jnO9/Jl770JQDOPvtsSktLufjii/nGN74xLf+G5cp01ENSn0p9OhP1KcwenSr1aWammy6S+lTqU6lPpT6dqRRDF8meIBOM1+tlzZo1bNmyJSV8y5YtrF27Nm2ciy66aNT5mzdv5rzzzsPj8RQtr4WQj5zgWNg/8YlP8MADD8yI8Wq5yllRUcEbb7zBq6++Gnc33HADZ511Fq+++ioXXnjhZGU9J/J5nu985zs5efIkg4OD8bB9+/ahqirz588van4LIR9ZQ6EQqpqqLjVNAxKW6JnOdNRDUp9KfToT9SnMHp0q9WlmppsukvpU6lOpT6U+nakURRflPaWqJCPu8kb33nuv2LNnj/jCF74gSktLxZEjR4QQQnzlK18RH//4x+Pnu8v+3HTTTWLPnj3i3nvvnVFLkGUr5wMPPCB0XRebNm0Sra2tcdfb2ztVImRFrnKOZKbMvp2rnAMDA2L+/PnimmuuEbt37xZbt24VZ555prj++uunSoSsyVXW++67T+i6Lu655x5x8OBB8eyzz4rzzjtPXHDBBVMlwrgMDAyInTt3ip07dwpAfP/73xc7d+6ML7M2U/SQ1KdSnyYzU/SpELNHp84GfSrEqaFTpT6V+jQZqU+nH1KfTp4+lUaQIrFp0yaxaNEi4fV6xbnnniu2bt0aP3bdddeJSy65JOX8p59+WqxevVp4vV5x2mmniR/96EeTnOP8yEXOSy65RACj3HXXXTf5Gc+RXJ9nMjPpJZOrnHv37hWXXXaZKCkpEfPnzxc333yzCIVCk5zr/MhV1rvvvlusWLFClJSUiKamJvGxj31MtLS0THKus+epp54as77NJD0k9anUpy4zSZ8KMXt06qmuT4U4dXSq1KdSn7pIfTo9kfp0cnSRIsQp1FdGIpFIJBKJRCKRSCQSiSQDck4QiUQikUgkEolEIpFIJLMCaQSRSCQSiUQikUgkEolEMiuQRhCJRCKRSCQSiUQikUgkswJpBJFIJBKJRCKRSCQSiUQyK5BGEIlEIpFIJBKJRCKRSCSzAmkEkUgkEolEIpFIJBKJRDIrkEYQiUQikUgkEolEIpFIJLMCaQSRSCQSiUQikUgkEolEMiuQRhCJRCKRSCQSiUQikUgkswJpBJFIJBKJRCKRSCQSiUQyK5BGEIlEIpFIJBKJRCKRSCSzAmkEkUgkEolEIpFIJBKJRDIr+P8B9E4FNb26QAcAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "row_lbls = [ str(ell) for ell in m_dga.preps.keys() ] + [ str(ell) for ell in m_dga.operations.keys() ] + [ str(ell) for ell in m_dga.povms.keys() ]\n", + "num_rows = len(row_lbls)\n", + "modelnames = ['argmin(LogL)', 'argmin(nTVD)', 'argmin(L10^10)']\n", + "modelnames_to_estnames = {'argmin(LogL)': 'logl', 'argmin(nTVD)': 'normalized tvd', 'argmin(L10^10)': \"('Lp^p', 10)\"}\n", + "num_cols = len(modelnames)\n", + "losscolors = ['b', 'r', 'y', 'k', 'd','.']\n", + "\n", + "fig, axs_grid = plt.subplots(num_rows, num_cols, figsize=(2*num_rows + 1, 4*num_cols + 1), sharey='all')\n", + "\n", + "def flexindelity(m1, m2, lbl):\n", + " if 'G' in lbl or lbl == '[]':\n", + " if lbl == '[]':\n", + " lbl = pygsti.baseobjs.Label(())\n", + " else:\n", + " lbl = lbl.split(':')\n", + " mm1 = m1[lbl]\n", + " mm2 = m2[lbl]\n", + " mm1d = mm1.to_dense()\n", + " mm2d = mm2.to_dense()\n", + " return 1 - entanglement_fidelity(mm1d, mm2d)\n", + " elif 'rho' in lbl:\n", + " mm1 = m1[lbl]\n", + " mm2 = m2[lbl]\n", + " mm1d = pygsti.tools.vec_to_stdmx(mm1.to_dense(), 'pp')\n", + " mm2d = pygsti.tools.vec_to_stdmx(mm2.to_dense(), 'pp')\n", + " return 1 - fidelity(mm1d, mm2d)\n", + " elif 'Mdefault' == lbl:\n", + " return 1 - povm_fidelity(m1, m2, lbl)\n", + " else:\n", + " raise ValueError()\n", "\n", - "fig, outer_axs = plt.subplots(4, 4, figsize=(12,12))\n", - "for i,metricname in enumerate(['LogL', 'nTVD', 'TVD', 'L10']):\n", - " axs = outer_axs[i,:]\n", - " for modelname, ax in zip(['argmin(LogL)', 'argmin(nTVD)', 'argmin(TVD)', 'argmin(L10^10)'], axs):\n", - " rows = []\n", - " datanames = ['(A)', '(mix)', '(B)'] \n", - " for df in dflist:\n", - " row = [ df[metricname + dataname][modelname] for dataname in datanames ]\n", - " rows.append(row)\n", - " y = np.array(rows)\n", - " x = mixture_weights.copy()\n", - " ax.plot(x,y)\n", - " if metricname == 'LogL':\n", - " ax.set_yscale('log')\n", - " ax.legend(datanames)\n", - " ax.set_title(modelname)\n", - " if modelname == 'argmin(LogL)':\n", - " ax.set_ylabel(metricname)\n", - "fig.supxlabel('proportion of mixed dataset taken from dataset A')\n", - "fig.tight_layout()\n" + "for membername, row_axs in zip(row_lbls, axs_grid):\n", + " row_axs[0].set_ylabel(membername.removesuffix(':0')) #, rotation=0)\n", + " for modelname, ax in zip(modelnames, row_axs):\n", + " ftoA_vs_p = np.zeros(num_mixtures)\n", + " ftoB_vs_p = np.zeros(num_mixtures)\n", + " ftoI_vs_p = np.zeros(num_mixtures)\n", + " for i, (res, _) in enumerate(reslist):\n", + " model_argmin = res.estimates[modelnames_to_estnames[modelname]].models['stdgaugeopt']\n", + " ftoA_vs_p[i] = flexindelity(model_argmin, m_dga_gopped, membername)\n", + " ftoB_vs_p[i] = flexindelity(model_argmin, m_dgb_gopped, membername)\n", + " ftoI_vs_p[i] = flexindelity(model_argmin, target, membername)\n", + " ax.plot(mixture_weights, ftoA_vs_p, losscolors[0])\n", + " ax.plot(mixture_weights, ftoB_vs_p, losscolors[1])\n", + " ax.plot(mixture_weights, ftoI_vs_p, losscolors[2])\n", + " modelname = modelname.removeprefix('argmin(').strip(')')\n", + " ax.legend(['infid. w/ A', 'infid. w/ B', 'infid. w/ ideal'])\n", + " ax.minorticks_on()\n", + " ax.grid(linestyle='dotted', which='minor')\n", + " ax.grid(which='major')\n", + " #ax.set_title('f = ' + modelname)\n", + " if membername == 'rho0':\n", + " ax.set_title('model = argmin( %s, data(p) )' % modelname)\n", + "\n", + "fig.set_tight_layout(True)\n" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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evfH19aVKlSoA+Pj4UL16dfz9/Zk2bRo3b95k2LBhBAYGSmeTEAVAeno6qampWscoMHKjjThw4ADp6enmn6Ojo2nZsiXvvvsuAFOnTmXmzJksXbqUypUrM2HCBFq2bMmpU6fMk+cGBQUREhJCcHAwxYoVY+jQofj6+hIZGWnO7+fnx6VLlwgNDQUy2gd/f39CQkKAjHOnbdu2FC9enF27dnHjxg26d++OUoo5c+Zk+37LuZq9cuNcFULkjDzXKdWrVy+io6NZvnw57u7urFixghYtWnD8+HFeeumlbGuYhBD5T0pKCufOncNkMmkdpUApUqQIbm5umk5UO2LECJKSkujbt6/5C4ctW7ZkelrHrFmzsLKyonPnzuYvHJYuXZrpur5y5UoGDhxofkpf+/btmTt3rvl1S0tLfv/9d/r27Uvjxo2xt7fHz8+P6dOn597OCiGynVKK+Pj4LCNfxPPL6TaiePHimX6eMmUKL7/8Mt7e3iilmD17NmPGjKFTp04ALFu2jBIlSrBq1Sr69OmDwWBg0aJFLF++3PwlxYoVKyhdujRbt26lVatWnDhxgtDQUCIiIsxPaF24cCFeXl6cOnWKKlWqsGXLFo4fP05MTIz5Ca0zZsygR48eTJw4Mdu+uJBzNefkhd9nhBBPT6eUUlqHuC8pKQknJyc2btxI27ZtzetfeeUVfH19+fLLL3F3dycoKIiRI0cCGaOiSpQowVdffWVumIoXL87y5ct57733ALh8+TKlS5dm06ZNtGrV6j9zJCYmotfrMRgM8s25EHmEUoqLFy+SmpqKu7s7FhZy9/HzUkpx7949rl69SpEiRShZsmSWGrkeZiXHRIi8Jy4ujlu3buHq6oqDg4P8UZoNtGgjUlJScHd3Z8iQIYwePZqzZ8/y8ssvc+jQIerUqWOu69ChA0WKFGHZsmX8+eefNG/enJs3b2Z6Qmvt2rXp2LEj48ePZ/HixQwZMiRLR1CRIkWYNWsWPXv25PPPP2fjxo38/fff5tcTEhIoWrQof/75J82aNXuiffivYyLnavZ7knNVCJH7nrSNyFMjpdLS0khPT8/ydCZ7e3t27drFuXPniI+PN38DDhlzfXh7e7Nnzx769OlDZGQkqampmWrc3d3x8PBgz549T9QpJURecP36da5fv07VqlW1jpInpKWlce/ePdzd3XFwcNA6ToFhb28PZEwY7urqKqNJc9j16xuxtS2Lk9MrWkcRosBIT083/5FfrFgxreMUKLndRmzYsIFbt27Ro0cP4P8egPHggyhKlCjBhQsXzDU2NjaZOqTu19x/f3x8PK6urlm25+rqmqnmwe04OztjY2OT5QEZ/8toNGI0Gs0/JyYmPrJWztWcI7/PCJEz4v7+m5Bdu+j9/+f5ywl5aqiBk5MTXl5efPnll1y+fJn09HRWrFjBvn37iIuLe2zD9L8Nyn81TA8yGo0kJiZmWoTQyvnz5+nXrx+lS5emWrVqjB8/njw0oFEz9+ebsLGx0ThJwXO/k0/mtshZBsNujh7tzKFD3ty69ZfWcYQoMO5fu+QLi5yRm23EokWLaNOmjfn2ufseHE2klPrPEUYP1jys/llqHjR58mTz5Ol6vZ7SpUs/slbO1Zwlv88Ikb1u3LhBS+/X6NO/P7O7ds2x7eSpTimA5cuXo5TipZdewtbWlm+++QY/P79Mvd3Z0TD9r6dpTITIKdHR0fj7+1OxYkXmzZtHcnIyAOPGjeP9998nKSlJ44R5gwxzz35yTHPHpUs1SIwuiVKJHD7ciuvXQ7SOJESBIteynJFbx/XChQts3bqVXr16mde5ubkBZPli+erVq+Yvqd3c3EhJSSEhIeGxNVeuXMmyzWvXrmWqeXA7CQkJpKamZvlC/H+NGjUKg8FgXmJiYv5zX+VczRlyXIXIPomJibRp04Zjhtu4FINGvs7//aZnlOc6pV5++WXCw8O5c+cOMTEx7N+/n9TUVMqXL59tDdODnqUxESK77N27l/bt21OzZk1WrFhBeno6LVu25M8//2TRokVYW1vz008/4e3tzeXLl7WOK4R4RkWij3Jo8AU2TQOdLpmjR94iLm651rGEECJPWLJkCa6urpnmlb3/+39YWJh5XUpKCuHh4TRq1AgAT09PrK2tM9XExcURHR1trvHy8sJgMLB//35zzb59+zAYDJlqoqOjiYuLM9ds2bIFW1tbPD09H5nb1taWwoULZ1qEECI/S0pKol27dhw4cAB9IZg2HSp5t8mx7eW5Tqn7HB0dKVmyJAkJCfzxxx906NAh2xqmB0ljInKbUorQ0FCaNm1Ko0aNCAkJQafT8c4773DgwAG2bNlCs2bN+PDDDwkLC6NYsWIcOHCA+vXrc+jQIa3jCyGewRHra3yRDtM3wd9fg84inZMnehAbu1HraEIIoSmTycSSJUvo3r07Vlb/N+WtTqcjKCiISZMmsX79eqKjo+nRowcODg74+fkBoNfrCQgIYOjQoWzbto3Dhw/TrVs3atasaX4aX7Vq1WjdujWBgYFEREQQERFBYGAgvr6+VKlSBQAfHx+qV6+Ov78/hw8fZtu2bQwbNozAwED520AI8UIZMWIEf/31F4Ud7PlqOpQrB2G7GubY9vJcp9Qff/xBaGgo586dIywsjGbNmlGlShV69uyZbQ2TEFpJT09nzZo1eHp60qZNG8LDw7G2tubDDz/kxIkT/Pzzz9StWzfTe7y9vdm3bx/VqlUjNjaWJk2asHbtWo32QDytHj160LFjxxz7/KZNmxIUFJRjny+yT+u33qJv374oYOQGHXELQGdh4uSxzly+vFfreEIIDUgbkWHr1q1cvHiRDz/8MMtrI0aMICgoiL59+1K3bl1iY2PZsmULTk5O5ppZs2bRsWNHOnfuTOPGjXFwcCAkJCTT9B8rV66kZs2a+Pj44OPjQ61atVi+/P9Gq1paWvL7779jZ2dH48aN6dy5Mx07dmT69Ok5u/P5hJyrQrw4fv31VwDmvNuAKlUg7Ypjjj6cIU89fQ/AYDAwatQoLl26RNGiRXn77beZOHEi1tbWQEbDlJSURN++fUlISKBBgwYPbZisrKzo3LkzSUlJNG/enKVLl8pTGIRmjEYjy5cvZ+rUqfzzzz9AxmSMvXv3ZujQoZQqVeqx73/55ZfZu3cvXbp0ITQ0lHfeeYcJEyYwevRouX9eiHxCp9PxzTffEBMTQ0hICP1+sWVpSSOF26VwLLIlSkXy0ktVtI4phBC5zsfH55EPddHpdIwbN45x48Y98v12dnbMmTOHOXPmPLKmaNGirFix4rE5ypQpw2+//fZEmYUQoiC6ePEiFy9exNLSkrou8VwFrv5bnjY+ObfNPDdSqnPnzpw5cwaj0UhcXBxz585Fr9ebX7/fMMXFxZGcnEx4eDgeHh6ZPuN+w3Tjxg3u3btHSEiITF4uNHH79m1mzJhBhQoVCAwM5J9//sHZ2ZmxY8dy8eJFZs2a9Z8dUvfp9XpCQkLM3yJ9+umndOvWzTwhush/wsPDqV+/Pra2tpQsWZJPPvmEtLQ08+u3b9+ma9eu5tuZZ82aJd8k5nOWlpasXr2aevXqkZBqpO/3hUgIB2unu0Tv9uL8+Uc/dlwI8WKRNkLkF3KuClFw7N69G4A6deqg7M4DcOlqPZxzbp7zvDdSSoiC4Pr163zzzTfMnTvXPOn+Sy+9xNChQwkMDKRQoULP9LlWVlbMmjWLqlWr0r9/f1atWsWZM2fYsGGD+UEALwql4N49bbbt4ADPO0AtNjaWN998kx49evDjjz9y8uRJAgMDsbOzM38bPGTIEHbv3s2vv/5KiRIl+Pzzzzl06BCvvPLKc++D0I6joyO//fYbr732GqdPn6b3V/bMskqiVOMEjm17lZTXTlG5stN/f5AQ4pGkjRD5hVbnanacpyDnqhAFzc6dOwFoUrs2SWUOAmCwbZmj25ROKSGyUUxMDDNmzGDhwoXc+/+/YVSqVImRI0fSrVs3bG1ts2U7ffr0oVKlSrzzzjvs27eP+vXr8+uvv75Qjfu9e/CMfXvP7c4dcHR8vs+YN28epUuXZu7cueh0OqpWrcrly5cZOXIkn3/+OXfv3mXZsmWsWrWK5s2bAxlPJnJ3d8+GPRBac3V1ZefOnbRq1YqoqCj6fWnLlM+NVGsUx8nfapP0xglqv5I91wshXkTSRoj8QqtzNTvOU5BzVYiCZteuXQA0ctZzt1zGuuLu9XN0m3nu9j0h8qOTJ0/y4Ycf8vLLL/P1119z79496tSpw5o1azhx4gQBAQHZ1iF13xtvvMG+ffuoUqUKMTExNG7cmA0bNmTrNkTOOXHiBF5eXpnmBGvcuDF37tzh0qVLnD17ltTUVOrX/79GQK/Xm58SJPI/V1dXduzYQZMmTUg0GhkyzooDO6Hwq+c490sddu8yaR1RCKERaSNEfiHnqhAFR0JCAtHR0QB4WsWjbCD9rjUeHuVzdLsyUkqI53Dw4EEmT57M+vXrzRN0Nm3alFGjRtGyZcscn4S8UqVKRERE0LlzZ8LCwujUqROTJ09mxIgRBX4CdAeHjG/5tNr281JKZfk3un8O6XS6TP/9sBpRMOj1ev744w/eeecdNm/ezJjxFowaZaJZixNcWe7JljuR+LSW74+EeFrSRoj8QqtzNTvOU5BzVYiCZO/evSilqFSpErZ3/wbgxpmytOiWs7+LSqeUEE9JKcWff/7J5MmT2bZtm3l9+/btGTVqFA0bNszVPEWKFGHTpk0EBQXx7bff8sknn3D8+HEWLFiQ7aOz8hKdLnuGnWulevXqrF27NtMvc3v27MHJyYmXXnqJIkWKYG1tzf79+80PakhMTOSff/7B29tby+gimzk4OLBhwwY++OADfvrpJ76cCHeTwPf9KBJme7HWsJu335PmWoinIW2EyC/kXBVC5BX3b91r0rgxd3WrAYiNf5WiRXN2u/JbrhBPyGQysXHjRiZPnsyBAweAjCdp+fn5MXLkSGrUqKFZNisrK+bOnUu1atUYNGgQP/74I2fOnGHdunW4urpqlktkMBgMREVFZVrXu3dvZs+ezYABA+jfvz+nTp1i7NixDBkyBAsLC5ycnOjevTvDhw+naNGiuLq6MnbsWCwsLLJ823jt2rUsn+/m5vbCTX6fn9nY2LBy5UqcnZ2ZP38+M2bA7dvw/sD93BrrzRLDDnr2ttY6phAiB0gbIfILOVeFKNjMnVKVK5NkbQTAYPlGzm9YiSwMBoMClMFg0DqKyANMJpNasWKFqlq1qgIUoOzs7FT//v3VuXPntI6XxZYtW1SRIkUUoMqWLauOHDmidaRskZSUpI4fP66SkpK0jvJUunfvbj5v/nfp3r272rFjh6pXr56ysbFRbm5uauTIkSo1NdX83sTEROXn56ccHByUm5ubmjlzpqpfv7765JNPzDXe3t4P/fyxY8c+ccbHHVu5HmaVk8fEZDKpUaNGmf8d/fxQOzajQjwaq1lTkrN9e0IUBPm1fVBK2oiC6HHHRM5V7c5VIcTjJScnK1tbWwWokzOmq13rUdu3o7755sAzf+aTthE6peSG3gclJiai1+sxGAwULlxY6zhCY/Pnz+fjjz8GMuZ/6devH4MGDcrTI5BOnTpFu3bt+OeffyhUqBCrV6/G19dX61jPJTk5mXPnzlG+fHns7Oy0jqOJu3fv8tJLLzFjxgwCAgKy7XMfd2zlephVbhyTqVOnMnLkSADatYOhPcEY1Ii9XcL4fIpDtjzGW4iCQtqHDNJG5A2POyZyrmbQ4lwVQjzenj17aNy4McWLF+dirw5E+PyASteRZrpLy5b2z/SZT9pGyOypQjzGyZMnGTJkCACDBw/m4sWLTJw4MU93SAFUqVKFiIgImjVrxp07d2jfvj3Tp0+XSSXzmcOHD7N69WrOnDnDoUOH6Nq1KwAdOnTQOJnIaSNGjGDBggXodDpCQmD8HLCbuAfvRW8wpFci6elaJxRCaE3aCJFfyLkqRN5nvnWvSRMSz2bMm3wrxp1XX322DqmnIZ1SQjxCSkoKfn5+JCUl0bJlS6ZPn56vvgUsWrQof/zxB71790YpxfDhwwkICCAlJUXraOIpTJ8+ndq1a9OiRQvu3r3Lzp07cXFx0TqWyAWBgYH89NNPWFtbs307jJgLTN2H3x9efPT2NeR/ZSGEtBEiv5BzVYi8zdwpVbUqSVbnADh7uSHFiuX8tmWicyEe4bPPPuPw4cMUK1aMpUuXYmGR//pwra2tmT9/PjVq1GDw4MEsWbKEf//9l3Xr1skvAvlAnTp1iIyM1DqG0NC7776Lk5MTnTq9xb59yfS7DV9NPs6AqZ582HI3328qna+f2iSEeHbSRoj8Qs5VIfI2k8nE7t27AWhiMHC7csb6JFODXNl+/vsrW4hc8OeffzJt2jQAfvjhB9zd3TVO9Ox0Oh0DBw7k999/p3DhwuzcuZP69etz7NgxraMJIZ5A69atCQvbirNzEY4fh49GwdEhMYyyqMOHTU5z86bWCYUQQgghRH518uRJbt68ib29PbUO7uJmvYz1hQt758r2pVNKiAfcvHmTDz74AKUUvXv3pmPHjlpHyhatW7dm7969VKhQgXPnzuHl5cXmzZu1jiWEeAKNGzdm9+49lC1bhthY6DcYwt+9wdiqr9K7/mHi4rROKIQQQggh8qOdO3cC0LB2bQyFjmCyh7jYctSrVy9Xti+dUkL8j/sdUbGxsVSuXJmZM2dqHSlbVa9enX379vH6669z+/ZtfH19mT17tkyALkQ+UK1aNSIi9vHqq69iMMCQIbCuxl3GtWtAUL0dnDmjdUIhhBBCCJHfmOeTcnDg6hsZ6yIPd6Vhw9x53LN0SgnxP5YuXcratWuxsrJi1apVOBbAyVpcXFwICwsjICAAk8nE4MGD6dOnj0yALkQ+4ObmRnh4OG+++SZGI3z+OSw0pfJJ/+Z88do6jhzROqEQQgghhMhP7ndKeV05z836GetKlOiCLnf6pKRTSoj7/v33XwYOHAjAl19+iaenp8aJco6NjQ0LFy5k5syZ6HQ6Fi5cSKtWrbhx44bW0YQQ/6FQoUJs3LiRwMBAlIJvvoGvokz0/fxt5r/5Hf9/nkohhBBCCCEe69KlS5w/fx4LCwsqup9FWcPFs1Xp2NEj1zJIp5QQQGpqKt26dePOnTt4e3szfPhwrSPlOJ1Ox+DBgwkJCaFQoULs2LGDBg0acPLkSa2jCSH+g5WVFd9//z0TJ04E4Kef4JOfoPPkvmzq+TkyXZwQQgghhPgv95+690rJktzzyVh3/LQ/lSrlXgbplBKCjJFR+/bto0iRIixfvhxLS0utI+Watm3bsnfvXsqVK8eZM2do2LAh4eHhWscSQvwHnU7H6NGjWb58OdbW1uzYAQOnQaPxX3Lss16sXq11QiGEEEIIkZeZb93TGUmok7GuQoX3cjWDdEqJF96uXbvMow2+//57SpcurXGi3Ofh4cG+ffto3LgxBoOBd955h8uXL2sdq0DbsWMHOp3uscuMGTOwtLTk4sWLD/2MqlWrmm85bdq0qfl9tra2vPTSS7Rr145169bl5m4JDXTr1o3Q0FAKF3bi6FH4eCSUGrSIpGVtmfetPMRAiPzoRWsjYmNj6datG8WKFcPBwYFXXnmFyMhI8+tKKcaNG4e7uzv29vY0bdqUY8eOZfoMo9HIgAEDcHFxwdHRkfbt23Pp0qVMNQkJCfj7+6PX69Hr9fj7+3Pr1q1MNRcvXqRdu3Y4Ojri4uLCwIEDZd7Nx3jRzlUhCpr7nVIeJa+DJfx7og5vvfVyrmaQTinxQjMYDHTr1g2TyUT37t3p3Lmz1pE04+rqytatW3nllVe4fv06/v7+pKenax2rwGrUqBFxcXHmpXPnzrRu3TrTut69e1OsWDGWLVuW5f27d+/m1KlTBAQEmNcFBgYSFxfHv//+y9q1a6levTpdunShd+/eublrQgNvvPEGu3fvoVSpl4iJgb6DIPWtTZQ4UJcJ49OQB2wKkb+8SG1EQkICjRs3xtrams2bN3P8+HFmzJhBkSJFzDVTp05l5syZzJ07lwMHDuDm5kbLli25ffu2uSYoKIj169cTHBzMrl27uHPnDr6+vpl+l/Hz8yMqKorQ0FBCQ0OJiorC39/f/Hp6ejpt27bl7t277Nq1i+DgYNauXcvQoUNz5VjkRy/SuSpEQWMwGDjy/5+SU75DxrqLcR9QvHguB1EiC4PBoABlMBi0jiJyWNeuXRWgypcvL//e/9/JkyeVg4ODAtSkSZO0jmOWlJSkjh8/rpKSkrSO8lS8vb3VgAED1PDhw5Wzs7MqUaKEGjt2bJa67t27qw4dOmRZP2TIEFWhQgVlMpkyrf/www+Vp6dnpu0MGjQoy/sXL16sABUWFvbIjI87tnI9zCovH5NLly6p2rVrK0DZ2aEmTUJt+qSsGjrgtkpP1zqdEDkjv7YPSkkboZRSI0eOVE2aNHnk6yaTSbm5uakpU6aY1yUnJyu9Xq/mz5+vlFLq1q1bytraWgUHB5trYmNjlYWFhQoNDVVKKXX8+HEFqIiICHPN3r17FaBOnjyplFJq06ZNysLCQsXGxpprVq9erWxtbZ94Hx93TORc1e5cFUJkFRoamvG3sL2N2r4d9ec2nfrpp9j/fuMTetI2QkZKiRfWqlWrWLlyJZaWlqxcuZLChQtrHSlPqFKlCnPnzgXgs88+Y+/evRonegSl4O5dbZanHHaybNkyHB0d2bdvH1OnTuWLL74gLCzsid4bEBDA2bNnM83zdffuXdasWZPpW8VH6d69O87OzjLs/QXx0ksv8ddff+Hj40NyMnz6KYQZL9CmXFn69bxMaqrWCYXIJdJG5Js24tdff6Vu3bq8++67uLq6UqdOHRYuXGh+/dy5c8THx+Pj42NeZ2tri7e3N3v27AEgMjKS1NTUTDXu7u54eHiYa/bu3Yter6dBgwbmmoYNG6LX6zPVeHh44O7ubq5p1aoVRqMx0+2E2Uqrc/UZhtC+6OeqEAXN/Vv3Xi2RcYvyqWgvfH3dH/eWHGGV61sUIg84f/48H3/8MZDR8eLl5aVxorylR48ebNmyheDgYN5//32ioqIyDaPPE+7dg0KFtNn2nTvg6PjE5bVq1WLs2LEAVKpUiblz57Jt2zZatmz5n++tXr06DRo0YMmSJTRt2hSANWvWkJ6ezvvvv/+f77ewsKBy5cqcP3/+ifOK/K1w4cL89ttv9OnThyVLljBrFlzxu0mvxpUIfPco362ugL291imFyGHSRuSbNuLs2bN89913DBkyhNGjR7N//34GDhyIra0tH3zwAfHx8QCUKFEi0/tKlCjBhQsXAIiPj8fGxgZnZ+csNfffHx8fj6ura5btu7q6Zqp5cDvOzs7Y2NiYax5kNBoxGo3mnxMTE59m97U7V5/yPAU5V4UoaO53SlV5LePnm3e64+CQ+zlkpJR44aSnp+Pv709iYiJeXl6MGTNG60h5jk6nY/78+ZQvX54LFy7Qp08flExK88xq1aqV6eeSJUty9erVJ35/QEAAv/zyi3nujMWLF9OpU6cn7ihUSqHT6Z54eyL/s7a2ZtGiRYwbNw6AVavgi5/v0c2nOgG+0RgM2uYTQvyfF72NMJlMvPrqq0yaNIk6derQp08fAgMD+e677zLVPZjxSXI/WPOw+mep+V+TJ082T5yu1+sL9ANzXvRzVYiCJCUlhYiICAAqNwdTmgX167+tSRbplBIvnClTprBr1y6cnJxYsWIFVlYyYPBh9Ho9q1evxsrKijVr1rBo0SKtI2Xm4JDxLZ8Wy1N+hWBtbZ3pZ51Oh8lkeuL3d+nSBZ1Ox08//cS///7Lrl27nmioO2R0wv7zzz+UL1/+qTKL/E+n0zF27FiWLFmClaUlO3bAgDlGOrerQ+9WEVy5onVCIXKQtBFP9N680EaULFmS6tWrZ1pXrVo185Pa3NzcALKMVLp69ap5VJObmxspKSkkJCQ8tubKQy58165dy1Tz4HYSEhJITU3NMoLqvlGjRmEwGMxLTEzME+23mVbn6jMMh3jRz1UhCpJDhw6RnJyMsy2UKQMnjjSjWbNimmSRv8bFC2X//v3mYcdz586lQoUKGifK2xo0aMCECRP45JNPGDhwII0bN6ZatWpax8qg0z31sPP8ysnJiXfffZclS5Zw9uxZKlSoYB76/l+WLVtGQkICb7+tzTcfQns9evSgXLlyvN2xPSdP3qb3pDSmDmzMQJ9QvtrYknLltE4oRA6QNuKJ3psX2ojGjRtz6tSpTOtOnz5N2bJlAShfvjxubm6EhYVRp04dIOMb/vDwcL766isAPD09sba2JiwszPwk5bi4OKKjo5k6dSoAXl5eGAwG9u/fT/369QHYt28fBoOBRo0amWsmTpxIXFwcJUuWBGDLli3Y2tri6en50Py2trbY2to++wGQc/WJ3psXzlUhCpL7t+7VrJFxGUrR9cDSUpss0iklXhh37tyha9eupKen895772V6BLB4tOHDh7Nt2zbCwsLo0qUL+/btw87OTutYL5yAgABee+01jh8/zrBhwx46fP3evXvEx8eTlpZGbGws69atY9asWXz88cc0a9ZMg9Qir2jatCn7Dh6ibavmnD57kY8nmhjXtxWfvfkzI9e8jYeH1gmFEM8jP7cRgwcPplGjRkyaNInOnTuzf/9+FixYwIIFC4CM0ThBQUFMmjSJSpUqUalSJSZNmoSDgwN+fn5AxujugIAAhg4dSrFixShatCjDhg2jZs2atGjRAsgYfdW6dWsCAwP5/vvvAejduze+vr5UqVIFAB8fH6pXr46/vz/Tpk3j5s2bDBs2jMDAQHkgTjbJz+eqEAXJ/U6p6vUh3WhN06YdNMuSp27fS0tL49NPP6V8+fLY29tToUIFvvjii0zDQpVSjBs3Dnd3d+zt7WnatCnHjh3L9DlGo5EBAwbg4uKCo6Mj7du359KlS7m9OyKPCQoK4t9//6V06dJ89913ck/6E7KwsODHH3+kePHiHDlyhGHDhmkd6YXUpEkTqlSpQmJiIt27d39ozcKFCylZsiQvv/wyb731FsePH+enn35i3rx5uZxW5EUVK1Yk4mAUzV5rQHIyjJql0L/6DrPe+oH/P6WAECKfys9tRL169Vi/fj2rV6/Gw8ODL7/8ktmzZ9O1a1dzzYgRIwgKCqJv377UrVuX2NhYtmzZgpOTk7lm1qxZdOzYkc6dO9O4cWMcHBwICQnB8n+++l+5ciU1a9bEx8cHHx8fatWqxfLly82vW1pa8vvvv2NnZ0fjxo3p3LkzHTt2ZPr06blzMF4A+flcFaKgUEqx66+/AKhZE05G+/DKK07/8a6co1N5aPbiiRMnMmvWLJYtW0aNGjU4ePAgPXv2ZMKECQwaNAiAr776iokTJ7J06VIqV67MhAkT+Ouvvzh16pS5Yfr4448JCQlh6dKlFCtWjKFDh3Lz5k0iIyMzNUyPkpiYiF6vx2AwyLciBcTatWt555130Ol0bN++HW9vb60j5TuhoaG0adMGgA0bNtChQ+72picnJ3Pu3DnKly8vI7Wy2eOOrVwPs8rvxyQ1NZW+vT/gh6XBAPi+AWUuTKHDvJH8z9PUhcg3pH3IWdJGPJ3HHRM5V3OWHF8hnszJkyepVq0aNtbw2+9w9uxa+vTplO3bedI2Ik+NlNq7dy8dOnSgbdu2lCtXjnfeeQcfHx8OHjwIZPTozZ49mzFjxtCpUyc8PDxYtmwZ9+7dY9WqVQAYDAYWLVrEjBkzaNGiBXXq1GHFihUcPXqUrVu3arl7QiOXLl0iMDAQgE8++UQ6pJ5R69atGTp0KAAffvihjD4UIp+ytrZmweJVTP9qHBY6+O1P2Gv/CaEfDWHNGq3TCSGEEEKInHT/1r1q1YEUW1q1aqNpnjzVKdWkSRO2bdvG6dOnAfj777/ZtWsXb775JgDnzp0jPj4en//5KtfW1hZvb2/27NkDQGRkJKmpqZlq3N3d8fDwMNc8yGg0kpiYmGkRBYPJZKJ79+4kJCRQt25d8+PRxbOZNGkSnp6e3Lx50zw/lxAi/9HpdAwdMZb161biaK/jcDT8bJzFgZHvM/+7PDOAWgghhBBCZLNdW7YAGbfunTnVjnLl7DXNk6c6pUaOHMn7779P1apVsba2pk6dOgQFBfH+++8D//co2AcfyVqiRAnza/Hx8djY2ODs7PzImgdNnjwZvV5vXkqXLp3duyY0MnPmTP78808cHBxYuXIlNjY2WkfK12xsbAgODqZQoUL89ddfTJw4UetIQojn0L6jH3/t2knJ4pZcugzzrwVzakozJn5pIu/c3C+EEEIIIbLLru1/AhmdUoXdAjROk8c6pX766SdWrFjBqlWrOHToEMuWLWP69OksW7YsU92DE1Qrpf5z0urH1YwaNQqDwWBeYmJinm9HRJ4QFRXF6NGjAZg9ezaVK1fWOFHBULFiRb777jsAxo8fz86dOzVOJIR4Hq++2piDUaepVdWWO3dhTmw4576vyfDBafzPc0aEEEIIIUQ+FxcXx5nrN9DpoFIpB9q0aa51pLzVKTV8+HA++eQTunTpQs2aNfH392fw4MFMnjwZADc3N4AsI56uXr1qHj3l5uZGSkoKCQkJj6x5kK2tLYULF860iPzt3r17+Pn5kZqaSseOHenVq5fWkQqUbt268cEHH2AymejatSs3b97UOpIQ4jm4u1dgz8FY2jQtTHo6LIo9zr+ryxDY7Q5paVqnE0IIIYQQ2WF3SAgAFSrA9SudcHa21jhRHuuUunfvHhYWmSNZWlpi+v9f1ZYvXx43NzfCwsLMr6ekpBAeHk6jRo0A8PT0xNraOlNNXFwc0dHR5hpR8A0fPpwTJ05QsmRJFi5c+J8j6cTTmzt3LhUrViQmJoZevXqRhx7kKYR4Bo6Oxfg1LI5+3d0B2Hg1juOb3XnvzViSkjQOJ4QQQgghnlv4qhVAxq17bhUDNU6TIU91SrVr146JEyfy+++/c/78edavX8/MmTN56623gIzb9oKCgpg0aRLr168nOjqaHj164ODggJ+fHwB6vZ6AgACGDh3Ktm3bOHz4MN26daNmzZq0aNFCy90TueS3335j3rx5ACxbtgwXFxeNExVMTk5OBAcHY21tzfr165k/f77WkYQQz8nKyoFvFp9l2udVsLGBiFu3+Xt3Bdo3jsRg0DqdEEIIIYR4HjuOHACgSjl7WrZsonGaDHmqU2rOnDm888479O3bl2rVqjFs2DD69OnDl19+aa4ZMWIEQUFB9O3bl7p16xIbG8uWLVtwcnIy18yaNYuOHTvSuXNnGjdujIODAyEhIVhaWmqxWyIXXblyhQ8//BCAwYMH07JlS40TFWyenp589dVXQMbxPnr0qMaJRF703XffUatWLfPt0V5eXmzevNn8ulKKcePG4e7ujr29PU2bNuXYsWOZPsNoNDJgwABcXFxwdHSkffv2XLp0KVNNQkIC/v7+5odW+Pv7c+vWrUw1Fy9epF27djg6OuLi4sLAgQNJSUnJsX3PjywsbBky9ijL5tSnaFE4cy+FQ9H1aVNnPVevap1OCCGEEEI8i9v//MPxW8kAuDp3xM4ub3QH5Y0U/5+TkxOzZ8/mwoULJCUlcebMGSZMmJDpiWk6nY5x48YRFxdHcnIy4eHheHh4ZPocOzs75syZw40bN7h37x4hISHyRL0XgFKKnj17cu3aNWrVqsWkSZO0jvRCGDRoEG3atMFoNNKlSxfu3bundSSRx5QqVYopU6Zw8OBBDh48yBtvvEGHDh3MHU9Tp05l5syZzJ07lwMHDuDm5kbLli25ffu2+TOCgoJYv349wcHB7Nq1izt37uDr60t6erq5xs/Pj6ioKEJDQwkNDSUqKgp/f3/z6+np6bRt25a7d++ya9cugoODWbt2LUOHDs29g5FPWFhY07nXbn5e4sPLL8PNVBORFzrhU2MG589rnU4IIYQQQjyt8FnTMSlwc4N6zYZoHef/KJGFwWBQgDIYDFpHEU9hzpw5ClB2dnYqOjpa6zgvlCtXrig3NzcFqN69e+fINpKSktTx48dVUlJSjnz+i+xxxzanrofOzs7qhx9+UCaTSbm5uakpU6aYX0tOTlZ6vV7Nnz9fKaXUrVu3lLW1tQoODjbXxMbGKgsLCxUaGqqUUur48eMKUBEREeaavXv3KkCdPHlSKaXUpk2blIWFhYqNjTXXrF69Wtna2j7V/r1IbYTJlK4i/3pbNWqEgoylokMvFR1t0jqaEGbSPuQsLdqI/Oxxx0TO1Zwlx1eIx+tXy0UBqlkTG5WWlvO/yz1pG5GnRkoJ8ayOHTvGsGHDAJg2bRo1atTQONGLxdXVlRUrVqDT6ViwYAG//PKL1pHyBJ1O99ilTZs2WFtbs2LFioe+v0+fPtSqVQuAcePGmd9nZWWFi4sLr7/+OrNnz8ZoNObmbj2X9PR0goODuXv3Ll5eXpw7d474+Hh8fHzMNba2tnh7e7Nnzx4AIiMjSU1NzVTj7u6Oh4eHuWbv3r3o9XoaNGhgrmnYsCF6vT5TjYeHB+7u7uaaVq1aYTQaiYyMzNH9zq90OgvqNFnDork9ee+9jHX/3vuBVq80JzxcZj8X4nlIGyHyCzlXhSgArlzhwJ3rAJQv3RhLy7zzIDDplBL5XnJyMn5+fhiNRt5880369eundaQXUvPmzRk5ciQAgYGBXLhwQeNE2ouLizMvs2fPpnDhwpnWBQcH07ZtW5YsWZLlvUlJSQQHBxMQEGBeV6NGDeLi4rh48SLbt2/n3XffZfLkyTRq1CjTrW550dGjRylUqBC2trZ89NFHrF+/nurVqxMfHw9AiRIlMtWXKFHC/Fp8fDw2NjY4Ozs/tsbV1TXLdl1dXTPVPLgdZ2dnbGxszDUPYzQaSUxMzLS8SHQ6C6q8soiJYwcyfDhYWkJs2nbeblabn3569HETQjyetBEiv5BzVYj87+qyZRyJzfhv33eDNM3yIOmUEvne6NGjOXLkCMWLF2fx4sXodHmn1/dF88UXX9CwYUNu3bqFn58faWlpWkfSlJubm3nR6/XodLos6wICAti+fTvnH5io55dffiE5OZlu3bqZ11lZWeHm5oa7uzs1a9ZkwIABhIeHEx0dbZ5wPq+qUqUKUVFRRERE8PHHH9O9e3eOHz9ufv3B/2+VUv/5//KDNQ+rf5aaB02ePNk8ebper38h5yjU6XRUqDqbjwM/Yfp0KFwYbqh/COhSg+nTj2gdT4h8SdoIkV/IuSpE/rd957ckG6GQo44OHXy1jpOJdEqJfG3Lli3MmjULgMWLF2cZBSFyl7W1NatWraJw4cLs2bOH8ePH59i2lFKkp9/VZFFKZdt+vPnmm7i5ubF06dJM6xcvXkzHjh0pVqzYY99ftWpV2rRpw7p167ItU06wsbGhYsWK1K1bl8mTJ1O7dm2+/vpr3NzcALKMVLp69ar5/2c3NzdSUlJISEh4bM2VK1eybPfatWuZah7cTkJCAqmpqY+9dowaNQqDwWBeYmJinnLvCwadTkf5SpN5q8MXzJsHpUvDXW4yeng9+vUL0TqeEJlIG5Ehv7QRLzKtztXsPE9BzlUh8rSrVzlgcRGAKi9XwMIib3UDWWkdQIhndf36dXr06AFA37598fXNWz2+L6ry5cuzYMECunTpwsSJE3njjTdo1qxZtm/HZLrHzp2Fsv1zn8Rrr93B0tIxWz7L0tKSDz74gKVLlzJ27Fh0Oh3nzp0jPDyc0NDQJ/qMqlWrsmXLlmzJk1uUUhiNRsqXL4+bmxthYWHUqVMHgJSUFMLDw83flnp6emJtbU1YWBidO3cGMm4liI6OZurUqQB4eXlhMBjYv38/9evXB2Dfvn0YDAYaNWpkrpk4cSJxcXGULFkSyOjYtrW1xdPT85FZbW1tsbW1zZkDkQ+VLf8ZFpb2fPvtcMaNg0OHUvhuXnvOn5tOyG9DsLCQ0apCe9JG/J/82Ea8SLQ6V7PzPAU5V4XIyy4v/IH9NzP+u0Wbt7UN8xB5q4tMiCeklKJXr17ExcVRrVo1pk2bpnUk8T/ee+89AgICUErRrVs3rl+/rnWkPC0gIIALFy7w559/AhnfKpYqVYoWLVo80fuf5FY3LY0ePZqdO3dy/vx5jh49ypgxY9ixYwddu3ZFp9MRFBTEpEmTWL9+PdHR0fTo0QMHBwf8/PwAzLcFDB06lG3btnH48GG6detGzZo1zceoWrVqtG7dmsDAQCIiIoiIiCAwMBBfX1+qVKkCgI+PD9WrV8ff35/Dhw+zbds2hg0bRmBgIIULF9bs+ORHpcsM49VX5/LVV9CuXcZj+TZtHkad2r1JSUnVOp4QBUpBbyNEwSHnqhB5UGoqiaemEX0s48d27dprm+chZKSUyJd++OEHNm7caL5dzMHBQetI4gFff/01u3fv5uTJk/Ts2ZNff/01W3/RsLBw4LXX7mTb5z3ttrNTpUqVeO2111iyZAnNmjVj2bJl9OzZ84mH1p44cYLy5ctna6bsdOXKFfz9/YmLi0Ov11OrVi1CQ0Np2bIlACNGjCApKYm+ffuSkJBAgwYN2LJlC05OTubPmDVrFlZWVnTu3JmkpCSaN2/O0qVLsbS0NNesXLmSgQMHmp/S1759e+bOnWt+3dLSkt9//52+ffvSuHFj7O3t8fPzY/r06bl0JAqWl17qh05ny+CgQMqUge/mwZHoHyhf9l8O/70WV9eiWkcULzBpI/5PXm8jXnRanavZfZ6CnKtC5EWJ3y8hqu4tEpaDtbUVdevW1TpSFtIpJfKdU6dOERQUBGRMQPzKK69omkc8nKOjI8HBwTRo0IDffvuNOXPmMHDgwGz7fJ1Ol63DzrUWEBDAxx9/TIcOHbh06RI9e/Z8ovedPHmS0NBQRo0alcMJn92iRYse+7pOp2PcuHGMGzfukTV2dnbMmTOHOXPmPLKmaNGij3wc9X1lypTht99+e2yNeHLu7r2wsLDlHV13SpVSTBgLl+N38HKF+uzZu4maNStrHVG8oKSNyJAf2ogXnZyrGeRcFSIHpKURFzGGgxlTuFK/foM8OSWF3L4n8pWUlBS6du3KvXv3aN68OYMHD9Y6kniM2rVrm0ehDB8+nMOHD2ucKO969913sba2pk+fPjRv3pxy5cplqUlLSyM+Pp7Lly9z9OhR5syZg7e3N6+88grDhw/P/dBCAG5u/lSvEYxXA0u++Q7c9XDn7hk8X23Ab79t1zqeEAWCtBEiv5BzVYi84/biH7ne/jobNmT83Lp1a03zPIp0Sol8ZeLEiURGRuLs7MyyZcvy3JMDRFb9+vWjffv2pKSk0KVLF+7c0eZ2irzOwcGBLl26kJCQwIcffvjQmmPHjlGyZEnKlClD06ZNWbNmDaNGjWLnzp0UKqTNhL5CALi6dqZGzbW8XMGKuUugdmlITbtF+3Y+zJz5g9bxhMj3pI0Q+YWcq0LkEenpXA4fzV9n4MwZKFSoEB9//LHWqR5OiSwMBoMClMFg0DqK+B/Xrl1Tjo6OClA//fST1nHEU7h+/bp66aWXFKB69uz5TJ+RlJSkjh8/rpKSkrI5nXjcsZXrYVZyTB7t+vVNKny7rfrjD9Sbr6DImANd9eo1VKWlpWkdTxRQ0j7krJxuI8aOHWu+VtxfSpQoYX7dZDKpsWPHqpIlSyo7Ozvl7e2toqOjM31GcnKy6t+/vypWrJhycHBQ7dq1UzExMZlqbt68qbp166YKFy6sChcurLp166YSEhIy1Vy4cEH5+voqBwcHVaxYMTVgwABlNBqfan8ed0zkXM1ZcnyF+D+Ji1eo3cGoatUyrqsjRozI9QxP2kbIMBORb8yaNYu7d+/y6quv8u6772odRzyFYsWKsXLlSiwsLFiyZAmrV6/WOpIQIgcUK9aGmrV/x87WnmEz4eM2Get/+GEGLVp0kpGSQoiHqlGjBnFxcebl6NGj5temTp3KzJkzmTt3LgcOHMDNzY2WLVty+/Ztc01QUBDr168nODiYXbt2cefOHXx9fUlPTzfX+Pn5ERUVRWhoKKGhoURFReHv729+PT09nbZt23L37l127dpFcHAwa9euZejQoblzEIQQIruYTMRuHcHeS3DiRMbcrEOGDNE61SNJp5TIFxISEswTHH/66afyuNh8yNvbm08//RSAPn36cPbsWY0TCSFygrNzc2rV/gMry0J0HgET+4AdsGPHr9Sp04SYmBitIwoh8hgrKyvc3NzMS/HixQFQSjF79mzGjBlDp06d8PDwYNmyZdy7d49Vq1YBYDAYWLRoETNmzKBFixbUqVOHFStWcPToUbZu3QpkPNUtNDSUH374AS8vL7y8vFi4cCG//fYbp06dAmDLli0cP36cFStWUKdOHVq0aMGMGTNYuHAhiYmJ2hwYIYR4BneC15LQ7jL3n//Tu3dvSpQooW2ox5BOKZEvzJkzh9u3b+Ph4UGHDh20jiOe0WeffUaTJk24ffs277//PqmpqVpHEkLkgCJFXuOVOjuxsS5Boy4wZwK4WsC///5N7dr1OXDggNYRhRB5yD///IO7uzvly5enS5cu5i+uzp07R3x8PD4+PuZaW1tbvL292bNnDwCRkZGkpqZmqnF3d8fDw8Ncs3fvXvR6PQ0aNDDXNGzYEL1en6nGw8MDd3d3c02rVq0wGo1ERkY+MrvRaCQxMTHTIoQQmlGKmN+HcfAaREWBtbV1nn+AgHRKiTzv9u3bzJ49G8gYJSWTm+dfVlZWrFy5EmdnZ/bv389nn32mdSQhRA5xcnqFOq9G4GBflYqN4dsFUN0OEhLiadz4dX7++WetIwoh8oAGDRrw448/8scff7Bw4ULi4+Np1KgRN27cID4+HiDLN/wlSpQwvxYfH4+NjQ3Ozs6PrXF1dc2ybVdX10w1D27H2dkZGxsbc83DTJ48Gb1eb15Kly79lEdACCGyz51ffsXge5GVKzN+7tGjB6VKldI21H+Qv+5Fnjdv3jwSEhKoUqUK77zzjtZxxHMqU6YMP/yQ8TSur776irCwMI0TCSFyir19Oeq8upvChRvh8jJMXw0tikJqajKdO3dmwoSJKKW0jimE0FCbNm14++23qVmzJi1atOD3338HYNmyZeaaB6dtUEr951QOD9Y8rP5Zah40atQoDAaDeZFblIUQmlGKmI2D+TsR9u0DS0tLPvnkE61T/SfplBJ52t27d5kxYwYAo0ePxtLSUuNEIjt06tSJjz76CAB/f3+uXLmicSIhRE6xti5K7dpbcXHpiH0R+CQYelXMeO2zzz7F3/8DjEajphmFEHmHo6MjNWvW5J9//sHNzQ0gy0ilq1evmkc1ubm5kZKSQkJCwmNrHva7xrVr1zLVPLidhIQEUlNTHzsXi62tLYULF860CCGEFu6F/IGhzTnzKCk/Pz8qVKigbagnIJ1SIk9buHAh165do3z58vj5+WkdR2SjmTNn4uHhwZUrV+jRowcmk0nrSEKIHGJpaU+NGr/g7t4XS2vouhC+fA0sgZUrV/DGG825du2a1jGFEHmA0WjkxIkTlCxZkvLly+Pm5pZpVHVKSgrh4eE0atQIAE9PT6ytrTPVxMXFER0dba7x8vLCYDCwf/9+c82+ffswGAyZaqKjo4mLizPXbNmyBVtbWzw9PXN0n4UQIjucWzOIEymwc2fGyM9Ro0ZpHemJSKeUyLOSk5OZOnUqkDE02srKSuNEIjvZ29sTHByMnZ0doaGhzJo1S+tIQogcpNNZUqnSXMqXnwxAky/gez8ojAV79uymfv0GHD9+XOOUQojcNmzYMMLDwzl37hz79u3jnXfeITExke7du6PT6QgKCmLSpEmsX7+e6OhoevTogYODg/nLSr1eT0BAAEOHDmXbtm0cPnyYbt26mW8HBKhWrRqtW7cmMDCQiIgIIiIiCAwMxNfXlypVqgDg4+ND9erV8ff35/Dhw2zbto1hw4YRGBgoo5+EEHle0h/buNPqtPmJe2+//TbVqlXTNtQTkk4pkWctXryYuLg4SpUqRffu3bWOI3JAjRo1zJPYjxo1imPHjmkbSAiRo3Q6HWXLfkLVqj+iw4qXA2HpCBPldBacP3+Ohg29+OOPP7SOKYTIRZcuXeL999+nSpUqdOrUCRsbGyIiIihbtiwAI0aMICgoiL59+1K3bl1iY2PZsmULTk5O5s+YNWsWHTt2pHPnzjRu3BgHBwdCQkIyTfuwcuVKatasiY+PDz4+PtSqVYvly5ebX7e0tOT333/Hzs6Oxo0b07lzZzp27Mj06dNz72AIIcQzOrtiIP/oYMeOjJ/HjBmjaZ6noVMyw2gWiYmJ6PV6DAaDfDOikZSUFCpVqsTFixeZM2cO/fv31zqSyCFKKTp06EBISAivvfYa4eHhD51QNDk5mXPnzlG+fHns7Ow0SFpwPe7YyvUwKzkm2ePmzS0ci+5EuukuqX/DuGEW7EkzYWFhwTfffEO/fv20jijyCWkfcpa0EU/nccdEztWcJcdXvKiSt+/k75jXGR8MmzeDr68vISEhWsd64jZCRkqJPGn58uVcvHgRNzc3AgICtI4jcpBOp2Pu3Lk4ODiwc+dOli5dqnWkbNWjRw86duz40NcWLFhA06ZNKVy4MDqdjlu3bmWpSUhIwN/f3/yoaX9//4fWCZHfFC3qwyt1dmJjVQLr2jB5iYmujmAymejfvz8DBgwgLS1N65hC5ChpI0R+IeeqEHnXv0v6c84GtmzJ+Dk/jZIC6ZQSeVBaWhqTJk0CYPjw4djb22ucSOS0MmXKMH78eCDj3/z69esaJ8od9+7do3Xr1owePfqRNX5+fkRFRREaGkpoaChRUVH4+/vnYkohco6TUx3qeO7F3q4SplLQZxWMcwMdGZ3Vvr6+GAwGrWMKoQlpI0R+IeeqENpJ3hnBvTeOEBwM6enQvHlzGjZsqHWspyIzR4s8Jzg4mLNnz+Li4kKfPn20jiNyyaBBg/jxxx85evQoI0eOZNGiRVpHynFBQUEA7Lh/8/cDTpw4QWhoKBERETRo0ADIeCKll5cXp06dMk/OKkR+Zm9fnjqv7iE6uj2J7KXpMig7WNHnuBV//PEHjRo14rfffqN8+fJaRxUiV0kbIfILOVeF0M6/P/Qnpj1s2pTx86effqptoGcgnVIiT0lPT2fixIkADBkyBEdHR40TidxibW3N999/T6NGjVi8eDE9evTgtddee2S9Uop79+7lYsL/4+Dg8NB5r7Lb3r170ev15l/gABo2bIher2fPnj3yS5woMGxsXKhdeyvHj/tx48ZGys2BDV+m0W2HHcePH6dBgwZs2LDB/Oh2If6LtBHSRuQXWp2ruXWegpyrQuQU4/7D3GsayZo1kJoKjRs3xtvbW+tYT006pUSesm7dOk6ePEmRIkVkktsXkJeXF71792bBggV89NFHHD58GBsbm4fW3rt3j0KFCuVywgx37tzJlQ7T+Ph4XF1ds6x3dXUlPj4+x7cvRG6ytHTAw2Mt//zTn8uX52M/Fn59KZkPVzpy+to1mjVrxuLFi+natavWUUU+IG2EtBH5hVbnam6dpyDnqhA55fR3fYnrCL9uzPj5008/zbXO5uyUp+aUKleuHDqdLstyv3NCKcW4ceNwd3fH3t6epk2bZnmEvNFoZMCAAbi4uODo6Ej79u25dOmSFrsjnpLJZGLChAlAxq1c8hSXF9PkyZMpXrw4x48fZ+bMmVrH0dzDGhalVL5scIT4LzqdJZUqzaN8+YwRs6m9YOWwu7TQFSIlJYVu3brx2WefYTKZNE4qRN4gbYTIL+RcFSJ7pRw+SnKTCH75BZKN4OnpSatWrbSO9Uzy1EipAwcOkJ6ebv45Ojqali1b8u677wIwdepUZs6cydKlS6lcuTITJkygZcuWnDp1CicnJyDjnuaQkBCCg4MpVqwYQ4cOxdfXl8jISCwtLTXZL/FkQkJCOHLkCE5OTgwcOFDrOEIjRYsWZcaMGXzwwQd88cUXvPfeew+dS8bBwYE7d+5okDBj27nBzc2NK1euZFl/7do1SpQokSsZhMhtOp2OsmVHY2PjzulTgdxpm8bU4nf4YYwD89LuMWHCBE6fPs3SpUvlQRjikaSNkDYiv9DqXM2t8xTkXBUiJ5ya05crnWD9uoyfx4wZk287efNUp1Tx4sUz/TxlyhRefvllvL29UUoxe/ZsxowZQ6dOnQBYtmwZJUqUYNWqVfTp0weDwcCiRYtYvnw5LVq0AGDFihWULl2arVu35tuewxeBUso8Sqpfv34ULVpU40RCS926dWPJkiVs376d/v3789tvv2Wp0el0BX7OMS8vLwwGA/v376d+/foA7Nu3D4PBIHPriAKvZMke2Ni4cSz6bQz179Hju3tUDbIl6F46a9as4fz582zYsIGSJUtqHVXkQdJGSBuRX8i5KueqEE8rJfoUyV672LAB7t6DGjVq0KFDB61jPbM81Sn1v1JSUlixYgVDhgxBp9Nx9uxZ4uPj8fHxMdfY2tri7e3Nnj176NOnD5GRkaSmpmaqcXd3x8PDgz179jyyU8poNGI0Gs0/JyYm5tyOiYf6448/OHjwIA4ODgwZMkTrOEJjOp2O7777jlq1arFp0ybWr1/Pm2++qXWsZ2YwGIiKisq0rmjRotjY2BAfH8+///4LwNGjR3FycqJMmTIULVqUatWq0bp1awIDA/n+++8B6N27N76+vjIpqHghFCvWmjqv/sWRv9twt+I16v5g5NeBlrx7oxD79++nQYMGhISEULt2ba2jCvHMpI0Q+YWcq0LkDSdnf8z1t+HnYRk/jxkzBguLPDUz01PJs51SGzZs4NatW/To0QPAPAneg0M8S5QowYULF8w1NjY2ODs7Z6l53CR6kydPZvz48dmYXjwNpRRffvklAB999FGWEXPixVSlShVGjhzJl19+ycCBA/PlkyTu27FjB3Xq1Mm0rnv37pQrVy7Ttef1118HYMmSJeZr38qVKxk4cKC5s719+/bMnTs3d4ILkQc4OXnyqmcER/5uRZLbv+gXprN7+B1anS1OTEwMjRs3ZvXq1bRr107rqEI8E2kjRH4h56oQ2ks9dQZj/e2EhEBiIlSqVInOnTtrHeu56JRSSusQD9OqVStsbGwICQkBYM+ePTRu3JjLly9nGqofGBhITEwMoaGhrFq1ip49e2Ya9QTQsmVLXn75ZebPn//QbT1spFTp0qUxGAwy2XYu2L59O2+88Qa2tracO3dObsUQZklJSdSsWZMzZ87w+eef06VLF8qXL4+dnZ3W0QqU5ORkzp0799Bjm5iYiF6vl+vh/5Bjoo2UlOscPdqW27f3Y2GEkuOg/YHSnEyPQafTMW3aNPPoavFiedw1TDw/aSOezuOOiZyrOUuOr3gRHOndiri3t+D3PtxMgEWLFvHhhx9qHeuhnrSNyJNjvC5cuMDWrVvp1auXeZ2bmxtAlhFPV69eNY+ecnNzIyUlhYSEhEfWPIytrS2FCxfOtIjcc3+UVK9evaRDSmRib2/Pt99+C8Dy5ctJSUnROJEQQis2Ni688sp2ihVrh8kWYifC5jdj8LF4GaUUw4YNo0+fPqSmpmodVQghhBAi26WdvUBKnTA2bcrokCpTpgzdunXTOtZzy5OdUkuWLMHV1ZW2bdua15UvXx43NzfCwsLM61JSUggPDzdPkOfp6Ym1tXWmmri4OKKjo2USvTxq9+7dbN++HWtra0aMGKF1HJEHtWrVivfeew+TycSNGzfIo4M7hRC5wNLSgRo11lGyZG+wgPNDYF7PM/TTvYyFhQULFy6kVatW3LhxQ+uoQgghhBDZ6vjUfiRUVPy0OuPnkSNHYmNjo22obJDnOqVMJhNLliyhe/fuWFn935RXOp2OoKAgJk2axPr164mOjqZHjx44ODjg5+cHgF6vJyAggKFDh7Jt2zYOHz5Mt27dqFmzpvlpfCJvuf/Eve7du1OmTBmN04i8atasWRQqVIiUlBRu3rypdRwhhIYsLKyoXHk+5cpljLKN6Qb9Rp7ha0phZ+vI9u3bqV+/PtHR0RonFUIIIYTIHmn/nCWl1ibCwuDKtYy7xPLqbXtPK891Sm3dupWLFy8+9ACPGDGCoKAg+vbtS926dYmNjWXLli04OTmZa2bNmkXHjh3p3LkzjRs3xsHBgZCQECwtLXNzN8QTOHDgAKGhoVhaWjJq1Cit44g8rGTJkgwePBiAK1euyO05QrzgdDod5cp9SpUqiwFLrrSC16ZcZB2FKK4vxdmzZ2nYsCHr16/XOqoQQgghxPNJT+f02NYYqihWrcxYNWzYsAIzd1qe65Ty8fFBKUXlypWzvKbT6Rg3bhxxcXEkJycTHh6Oh4dHpho7OzvmzJnDjRs3uHfvHiEhIZQuXTq34ounMHHiRAD8/PyoUKGCxmlEXtelSxdsbGwwmUzExMRoHadAkVsiRX5VsmRPatYMwUJnT0I9KP7NFcIxUMW1Dnfv3qVTp06MGzcOk8mkdVSRC+RaljPkuGY/OaY5Q46rKKjOjPiEG13/Yft2iL0MxYoVo0+fPlrHyjZ5rlNKvBiOHDnCxo0b0el0jB49Wus4Ih+wsbGhaNGiANy8eZPExESNExUc9+7dA8Da2lrjJEI8vWLF2vBKnb+wtizOncpg+O42u+z/5g3X5gCMHz+et99+m9u3b2ucVOSU+9eu+9cykb2kjcg+cq7mLDlXRUF0e8c+DGWnk2oPK5ZmzB81ePBgChUqpHGy7GP13yVCZL/7c0l17tyZqlWrapxG5AdWVlY4Oztz48YNTCYT58+fp2LFilhYSN/6s1JKce/ePa5evUqRIkXkNmeRbxUuXJdX6+7lSFQrkkqe4cQ8Ez9N2sbnKT4sureDDRs24OXlxcaNG3n55Ze1jiuymaWlJUWKFOHq1asAODg4oNPpNE6V/0kbkf3kXM0Zcq6KgkrducuFZb4kdoflP1hwITaFwoUL069fP62jZSvplBK57sSJE/zyyy8AjBkzRuM0Ir/Q6XSULFmSu3fvcunSJUwmE3fv3qVIkSJaR8v3ihQpgpubm9YxhHgu9vYvU8czguPHO3OL7URPgBGLtuDxS0PGFz/PsWPHqFevHj/99BMtW7bUOq7IZvevYff/2BfZR9qI7CXnas6Rc1UUNEd79yDhw+vs3QvLVmXcnvr1118XuL9/pFNK5LpJkyahlKJjx47UrFlT6zgiH7GxsaFq1aqcPn2aoKAgbGxs2LhxI+XLl9c6Wr5lbW0t3yiKAsPGxoVatf7gzJkhxMbO5XwAeL8cwU/TKjKw8iscPR1F69atmT59OkFBQTJCoQC5/8WFq6urPAwjG2nRRkyePJnRo0czaNAgZs+eDWSMhBk/fjwLFiwgISGBBg0a8O2331KjRg3z+4xGI8OGDWP16tUkJSXRvHlz5s2bR6lSpcw1CQkJDBw4kF9//RWA9u3bM2fOnEx/4F28eJF+/frx559/Ym9vj5+fH9OnT8+2x67LuZoz5PcZUdBcXLiO5Fa/EBMPE7+0RKl0+vbtS48ePbSOlu2kU0rkqjNnzrBq1SoAPv30U43TiPzIwsKCDh06sGDBAjZv3kz//v3ZunWr/HEphADAwsKaSpXm4OhYi9On+nKtaRqOpf5lw5giDPBsw6bIzQwZMoSoqCi+//77AvPkGpHB0tJS/jDNxw4cOMCCBQuoVatWpvVTp05l5syZLF26lMqVKzNhwgRatmzJqVOnzE/hDgoKIiQkhODgYIoVK8bQoUPx9fUlMjLSfE74+flx6dIlQkNDAejduzf+/v6EhIQAkJ6eTtu2bSlevDi7du3ixo0bdO/eHaUUc+bMydZ9lXNVCPEoxpir3DrejRs+8FkfHXeT0mncuDGzZs3SOlrOUCILg8GgAGUwGLSOUuAEBAQoQLVp00brKCKfO3PmjLKzs1OAWrFihdZxCiy5HmYlxyT/SEjYqXaGu6jt21G71qGu1LRQ/ev7KUtLSwWoevXqqUuXLmkdU4h8Kzuvh7dv31aVKlVSYWFhytvbWw0aNEgppZTJZFJubm5qypQp5trk5GSl1+vV/PnzlVJK3bp1S1lbW6vg4GBzTWxsrLKwsFChoaFKKaWOHz+uABUREWGu2bt3rwLUyZMnlVJKbdq0SVlYWKjY2FhzzerVq5Wtre0T76O0EUKI52IyqYMdGqg//0R5e6MAVbJkSXX58mWtkz21J70eygzBItdcuHCBZcuWAfDZZ59pnEbkdxUqVODzzz8HYMiQISQkJGicSAiR1xQp0oS69SNxtK9NqjOcmGnikxKrmF75bYoWLcqBAweoW7cue/fu1TqqEC+8fv360bZtW1q0aJFp/blz54iPj8fHx8e8ztbWFm9vb/bs2QNAZGQkqampmWrc3d3x8PAw1+zduxe9Xk+DBg3MNQ0bNkSv12eq8fDwwN3d3VzTqlUrjEYjkZGRD81tNBpJTEzMtAghxLOKGjaL5A/2ERwM4eEZt6b+8ssvlCxZUutoOUY6pUSumTp1Kmlpabzxxht4eXlpHUcUAEOHDqVatWpcvXqV0aNHax1HCJEH2dmV4dW6eyju0hllBf8MgTdbrWGRXW08qtcgPj6epk2bsnjxYq2jCvHCCg4O5tChQ0yePDnLa/Hx8QCUKFEi0/oSJUqYX4uPj8fGxgZnZ+fH1ri6umb5fFdX10w1D27H2dkZGxsbc82DJk+ejF6vNy+lS5d+kl0WQogsruw6DUVGsPcs/LAwY90333xDo0aNtA2Ww3K8U+rKlSt88cUXOb0ZkcddvnyZRYsWATJKSmQfGxsb5s+fD8D3339PRESExomEEHmRpaUD1WsEU778JJTScbkDlB2xnR9vptOueWtSUlIICAhgwIABMvGwELksJiaGQYMGsWLFisfO8fbg3JFKqf+cT/LBmofVP0vN/xo1ahQGg8G8xMTEPDaTEEI8jMmYSuys1pyslM6XX4BJwYcffkifPn20jpbjcrxTKj4+nvHjx+f0ZkQeN23aNIxGI02aNMHb21vrOKIAef311+nRowdKKfr06UNaWprWkYQQeZBOp6Ns2VHUrLkR0h0w1IbkWSeZeyGCEe8HAjB37lx8fHy4fv26xmmFeHFERkZy9epVPD09sbKywsrKivDwcL755husrKzMI5ceHKl09epV82tubm6kpKRkuZX/wZorV65k2f61a9cy1Ty4nYSEBFJTU7OMoLrP1taWwoULZ1qEEOJp7e42lOvdzvHZZ5B4G+rVq8e33377QjzMSW7fEznu6tWrfP/990DGE/dehP+xRO6aNm0aRYsW5ciRI3zzzTdaxxFC5GEuLu2o1/AAFqZyGN3g/De3GBS7hAVdgyhUqBA7duygXr16/P3331pHFeKF0Lx5c44ePUpUVJR5qVu3Ll27diUqKooKFSrg5uZGWFiY+T0pKSmEh4ebb2nx9PTE2to6U01cXBzR0dHmGi8vLwwGA/v37zfX7Nu3D4PBkKkmOjqauLg4c82WLVuwtbXF09MzR4+DEOLFFf3DTuybzmH6d/Dvv1C8eHHWrl37wjwhWDqlRI6bOXMmSUlJ1KtXL9MElEJkFxcXF6ZNmwbA559/LkPnhRCP5ehYHa/XDmFv/QYmezg9Po0WNrNZ83oXXn75Zc6fP0+jRo34+eeftY4qRIHn5OSEh4dHpsXR0ZFixYrh4eGBTqcjKCiISZMmsX79eqKjo+nRowcODg74+fkBoNfrCQgIYOjQoWzbto3Dhw/TrVs3atasaZ44vVq1arRu3ZrAwEAiIiKIiIggMDAQX19fqlSpAoCPjw/Vq1fH39+fw4cPs23bNoYNG0ZgYKCMgBJC5AjDpduk7e7AspMQFgaWlpasWbPmhZqfTjqlRI66efMm3377LSCjpETO6tGjB02aNOHu3bsMHDhQ6zhCiDzO2tqZel5/4FIsCIALH0Dphj+wpmhFWr7xBvfu3aNz5858+umnmEwmbcMK8YIbMWIEQUFB9O3bl7p16xIbG8uWLVtwcnIy18yaNYuOHTvSuXNnGjdujIODAyEhIVhaWpprVq5cSc2aNfHx8cHHx4datWqxfPly8+uWlpb8/vvv2NnZ0bhxYzp37kzHjh2ZPn16ru6vEOLFoBQc6tGV8FoJzJuXsW769Ok0bdpU01y5TaeUUs/zAUOGDHns69euXWPVqlWkp6c/z2ZyVWJiInq9HoPBIN+KPKexY8fyxRdfULt2bQ4fPiydUiJHRUdHU6dOHdLS0ti4cSPt27fXOlK+J9fDrOSYFDwxMcv451QvLKzScDwLRb+uxryGrzFzwQIA2rVrx4oVK+TfW4gHyPUwKzkmQogntX3wKu6U70r3cZCQAH5+fqxYsaLA/M38pNfD5+6Uatas2RPVbd++/Xk2k6ukMckeBoOBsmXLYjAY+Pnnn3nnnXe0jiReAJ988glfffUVZcqU4fjx4zg6OmodKV+T62FWckwKplu3Iji4xxcrhxtYGaDsFGf+9BlEn68mYzQaqVatGhs2bKBy5cpaRxUiz5DrYVZyTIQQT+LMrjhu/lKOD/5I4eRJqFXLg7179+Hg4KB1tGzzpNfD5759b/v27U+0iBfPt99+i8FgoFq1anTq1EnrOOIF8dlnn1G2bFkuXrzIF198oXUczWzevJlKlSpRrVo1GjRogI+PD7/88osmWSZPnky9evVwcnLC1dWVjh07curUqUw1SinGjRuHu7s79vb2NG3alGPHjmWqMRqNDBgwABcXFxwdHWnfvj2XLl3KVJOQkIC/vz96vR69Xo+/vz+3bt3KVHPx4kXatWuHo6MjLi4uDBw4kJSUlBzZd5F/FCnSkNea/03K7Vqk6eHMhAS8j04gbMRnvPTSS5w4cYL69esTGhqqdVQhhBBC5GPGZMXl8W0Z/29Gh5SzsxMbNvxaoDqknobMKSVyxJ07d5g5cyYAY8aMwcJCTjWROxwdHc3zmM2cOZOjR49qnEgbkydP5t9//+XkyZPs37+fsLAwYmNjNckSHh5Ov379iIiIICwsjLS0NHx8fLh79665ZurUqcycOZO5c+dy4MAB3NzcaNmyJbdv3zbXBAUFsX79eoKDg9m1axd37tzB19c30+3hfn5+REVFERoaSmhoKFFRUfj7+5tfT09Pp23btty9e5ddu3YRHBzM2rVrGTp0aO4cDJGn2dq+RPM3I0hLeg8s4fzANIonfcqf7d+hUaNGGAwG2rZty7Rp03jOgeZCCCGEeEH9/u4UQiod5vffwcJCR3DwL5QvX17rWJp57tv37nvU3FI6nQ47OzsqVqxIhw4dKFq0aHZsLkfJsNvnN336dIYPH07FihU5ceIEVlZWWkcSL5i3336bdevW0ahRI3bu3PlCdYzGx8fj7u6OUor169ej0+lITEykbt26VKtW7ak+Kyeuh9euXcPV1ZXw8HBef/11lFK4u7sTFBTEyJEjgYxRUSVKlOCrr76iT58+GAwGihcvzvLly3nvvfcAuHz5MqVLl2bTpk20atWKEydOUL16dSIiImjQoAEAEREReHl5cfLkSapUqcLmzZvx9fUlJiYGd3d3AIKDg+nRowdXr159on2UNqLgU0qxd/c0jMZP0FkqCkdDibC2jCtanB+WLgUyOkB/+OEH7O3ttQ0rhIbkepiVHBMhxOPsmH+c8397EPiDIi0NJk36klGjPtU6Vo7Itdv37jt8+DCLFi1iwYIFhIeHs2PHDhYuXMiiRYvYtm0bQ4YMoWLFihw/fjy7NinyqKSkJPNTSkaNGiUdUkITX3/9NYUKFWLPnj0sXrxY6zi5asOGDSilqFevHh07dqRDhw74+/s/dYdUTjEYDADmLynOnTtHfHw8Pj4+5hpbW1u8vb3Zs2cPAJGRkaSmpmaqcXd3x8PDw1yzd+9e9Hq9uUMKoGHDhuj1+kw1Hh4e5g4pgFatWmE0GomMjHxoXqPRSGJiYqZFFGw6nY5GTUbg6raJ1Lv2JHrAxa6/88WlfXw7cSJWVlasWrWKJk2aEBMTo3VcIYQQQuQDcRdSSPq1BcN/yeiQ6tChBZ98MkbrWJrLtk6pDh060KJFCy5fvkxkZCSHDh0iNjaWli1b8v777xMbG8vrr7/O4MGDs2uTIo/64YcfuHLlCmXKlMl024wQualUqVLmOaVGjBjBtWvXNE6Ue9auXQtkjBbLa5RSDBkyhCZNmuDh4QFkjOwCKFGiRKbaEiVKmF+Lj4/HxsYGZ2fnx9a4urpm2aarq2ummge34+zsjI2NjbnmQZMnTzbPUaXX6ylduvTT7rbIp2rUaE2NOodJjC+N0RX+GXaCdhHTCfv2W1xcXDh06BB169Zl165dWkcVQgghRB5mMsH2zh8z8mIc169DpQrFWb58XYF50t7zyLZOqWnTpvHll19mGpZVuHBhxo0bx9SpU3FwcODzzz9/5DfRomAwGo189dVXQMZT0KytrTVOJF5kAwYM4JVXXiEhIYHhw4drHSdX3Lhxw/xwibzYKdW/f3+OHDnC6tWrs7z2YKOslPrPhvrBmofVP0vN/xo1ahQGg8G8yMiYF4u7exWatT1K3L9NMdnCmSEJvHSoH/u/GEft2rW5evUqb7zxBt9//73WUYUQQgiRRwX338ZGh8UcPQaODhaEbPoLJycnrWPlCdnWKWUwGLh69WqW9deuXTPf6lCkSBF5wlEBt2zZMmJjY3F3d6dnz55axxEvOCsrK+bPn49Op2PZsmXs2LFD60g57tdffyU9PZ1atWpRsWJFreNkMmDAAH799Ve2b99OqVKlzOvd3NwAsoxUunr1qnlUk5ubGykpKSQkJDy25sqVK1m2e+3atUw1D24nISGB1NTULCOo7rO1taVw4cKZFvFicXLS07nnVs6dDAIgtksat2/158+OrejcuTOpqal89NFH9OnTR37PEUIIIUQmkX8a+PeYL2t2ZPy8fMViqlSpqmmmvCRbb9/78MMPWb9+PZcuXSI2Npb169cTEBBAx44dAdi/fz+VK1fOrk2KPCY1NZXJkycDGbdL2dnZaZxICGjQoAEfffQRAB9//DFGo1HjRDkrL966p5Sif//+rFu3jj///DPL00XKly+Pm5sbYWFh5nUpKSmEh4fTqFEjADw9PbG2ts5UExcXR3R0tLnGy8sLg8HA/v37zTX79u3DYDBkqomOjiYuLs5cs2XLFmxtbfH09Mz+nRcFhqWlJT0/msXFc8swGS256QX/lp/KQv09Jn3xBTqdjgULFtCsWbNM55cQQgghXlyJifDXwNZM2JsMwLBBb/HWW901TpXHqGxy+/Zt1atXL2VjY6MsLCyUhYWFsrGxUYGBgerOnTtKKaUOHz6sDh8+nF2bzDEGg0EBymAwaB0lX1m6dKkClKurq7p7967WcYQwS0hIUK6urgpQEyZM0DpOjjEYDMrGxkYBKjo6Ots+83mvhx9//LHS6/Vqx44dKi4uzrzcu3fPXDNlyhSl1+vVunXr1NGjR9X777+vSpYsqRITE801H330kSpVqpTaunWrOnTokHrjjTdU7dq1VVpamrmmdevWqlatWmrv3r1q7969qmbNmsrX19f8elpamvLw8FDNmzdXhw4dUlu3blWlSpVS/fv3z9VjIvK39esPqN/XFFHbt6N2/oq60qWK2rRihdLr9QpQ7u7uKiIiQuuYQuQ4uR5mJcdECHGfyaTU5CazVQkXFKBeb+iq0tPTtY6Va570ephtnVL33b59W/39998qKipK3b59O7s/PldIY/L00tLSVOXKlRWgvvrqK63jCJHFypUrFaDs7OzUv//+q3WcHHF/H6tUqaJMJlO2fGZ2XA+Bhy5Lliwx15hMJjV27Fjl5uambG1t1euvv66OHj2a6XOSkpJU//79VdGiRZW9vb3y9fVVFy9ezFRz48YN1bVrV+Xk5KScnJxU165dVUJCQqaaCxcuqLZt2yp7e3tVtGhR1b9/f5WcnPzE+yNthFBKqV27LquV31VX27ejtm9FnQ3Uq1Mb1qvq1asrQNnY2KhFixZpHVOIHCXXw6zkmAgh7guefk7VL6tTgCrlbqGuXbv4328qQJ70eqhTSqnsHn116dIldDodL730UnZ/dK5ITExEr9djMBhk7pAntHr1avz8/ChatCjnz5+XSdtEnqOUwsfHh61bt9K6dWs2bdpU4J528fbbb7Nu3TpGjx7NxIkTs+Uz5XqYlRwTcd+pU0Y2BXejjvcvADjvs6B06W8J+PkPNmzYAEC/fv2YNWuWPPhDFEhyPcxKjokQAuD0SROf+5Tgp5jr2NnB9q1LaNi4h9axctWTXg+zbU4pk8nEF198gV6vp2zZspQpU4YiRYrw5ZdfYjKZnvhzYmNj6datG8WKFcPBwYFXXnkl0xP7lFKMGzcOd3d37O3tadq0KceOHcv0GUajkQEDBuDi4oKjoyPt27fn0qVL2bWr4gEmk8n8B3BQUJB0SIk8SafTMW/ePGxtbQkNDeWXX37ROlK2unv3Lps3bwby1nxSQhRkVarY4vfRGjavnYlKsSChgYlTlh+z6DUXvhg/HoBvv/2WFi1aPPRhMEIIIYQoeFJSYLJPZ36KuQ7AtLE+L1yH1NPItk6pMWPGMHfuXKZMmcLhw4c5dOgQkyZNYs6cOXz22WdP9BkJCQk0btwYa2trNm/ezPHjx5kxYwZFihQx10ydOpWZM2cyd+5cDhw4gJubGy1btuT27dvmmqCgINavX09wcDC7du3izp07+Pr6kp6enl27K/7Hhg0bOHbsGIULF2bAgAFaxxHikSpVqsSoUaMAGDRoEAaDQeNE2Sc0NJSkpCTKlStHnTp1tI4jxAujRAkdX0wfzO+b92GMKYKxBByt9QMfWK1jw5qfcHJy4q+//sLT0zPTl2xCCCGEKJhGvbOG4PiMhw/5v12YviN+0zhRHpdd9wuWLFlSbdy4Mcv6DRs2KHd39yf6jJEjR6omTZo88nWTyaTc3NzUlClTzOuSk5OVXq9X8+fPV0opdevWLWVtba2Cg4PNNbGxscrCwkKFhoY+UQ65F/zJmUwmVadOHQWoMWPGaB1HiP+UlJSkKlWqpAA1YMAAreNkm/fff18BaujQodn6uXI9zEqOiXgYk0mp2bMT1dLP6mXMM7UdFTVXr478udE856Ktra1atmyZ1lGFyDZyPcxKjokQL7ZfVl5WLzlaKkDV80QlGo5pHUkzT3o9zLaRUjdv3qRq1apZ1letWpWbN28+0Wf8+uuv1K1bl3fffRdXV1fq1KnDwoULza+fO3eO+Ph4fHx8zOtsbW3x9vZmz549AERGRpKampqpxt3dHQ8PD3ONyD6bNm3i8OHDODo6EhQUpHUcIf6TnZ0d8+bNA2Du3Lns3r1b40TPz2g08ttvGd/AyK17QmhDp4NBg5xwa7yPP+YMRZcCCTUM3L7RibBv++Pr64vRaKR79+4MHjyYtLQ0rSMLIYQQIhvFxqYzvnc9Yu+m4+YGi+eOw6lwda1j5XnZ1ilVu3Zt5s6dm2X93LlzqVWr1hN9xtmzZ/nuu++oVKkSf/zxBx999BEDBw7kxx9/BCA+Ph6AEiVKZHpfiRIlzK/Fx8djY2ODs7PzI2seZDQaSUxMzLSI/6aU4ssvvwSgb9++uLi4aJxIiCfTokULunbtilKKli1bsmbNGq0jPZewsDBu376Nu7s7DRo00DqOEC+0Vq109Jg0ne8nhGAZY0OKSzpndQOZ06swn376KQCzZ8+mVatWXL9+XeO0QgghhMgOJhN0rt+No3djsbWF2Z/XoEaDz7WOlS9kW6fU1KlTWbx4MdWrVycgIIBevXpRvXp1li5dyvTp05/oM0wmE6+++iqTJk2iTp069OnTh8DAQL777rtMdQ8+MUsp9Z9P0XpczeTJk9Hr9ealdOnST5T3Rbdt2zb27duHnZ0dQ4YM0TqOEE9l3rx5tG7dmqSkJN577z0+/fTTp3ooQ16ydm3GPeudOnXCwiLbLutCiGdUpQp8u9GXOavOkratJFjCef0q3q39Ez8FL6ZQoUL8+eef1K1bl6ioKK3jCiGEEOI59em6nD2XgwEYPtCatwK2FbgnfeeUbPvrxdvbm9OnT/PWW29x69Ytbt68SadOnTh27BhLlix5os8oWbIk1atnHt5WrVo1Ll68CICbmxtAlhFPV69eNY+ecnNzIyUlhYSEhEfWPGjUqFEYDAbzEhMT80R5X3T3R0n17t3b/G8jRH5RuHBhfvvtN4YNGwbAxIkT6dSpU6aHJuQHqampbNy4EZBb94TIS5ydYVXIS2y+fZHzM1qiS4GbLv9Q2r4fYb/NpmLFily4cIFGjRqxevVqreMKIYQQ4hn9vOYEK9f0BODtt2HQoJXY2Dy870Fkla1fqbu7uzNx4kTWrl3LunXrmDBhAgkJCSxbtuyJ3t+4cWNOnTqVad3p06cpW7YsAOXLl8fNzY2wsDDz6ykpKYSHh9OoUSMAPD09sba2zlQTFxdHdHS0ueZBtra2FC5cONMiHu/777/nr7/+wsbGhuHDh2sdR4hnYmlpybRp0/jxxx+xtbVl48aNeHl5cebMGa2jPbHt27eTkJBA8eLFee2117SOI4T4H1ZWMGO2FarTFn4bMB67GDAWTiI5rRfrlrSjdetWJCUl4efnx4gRI+QpweKF9N1331GrVi3z7+BeXl5s3rzZ/LpSinHjxuHu7o69vT1Nmzbl2LFjmT7DaDQyYMAAXFxccHR0pH379ly6dClTTUJCAv7+/uY7I/z9/bl161ammosXL9KuXTscHR1xcXFh4MCBpKSk5Ni+CyHyv9jYRD7q1owkUzq1a8O4/h1xeeldrWPlK3nqPo/BgwcTERHBpEmT+Pfff1m1ahULFiygX79+QMZte0FBQUyaNIn169cTHR1Njx49cHBwwM/PDwC9Xk9AQABDhw5l27ZtHD58mG7dulGzZk1atGih5e4VGDt27KB///4AjB8/nlKlSmmcSIjn4+/vz19//UXJkiU5duwY9evXZ9u2bVrHeiL3b9176623sLS01DiNEOJhevaEt5Z8zlcjtlB4my1Ywo20WUwakcjw4YMAmDZtGm3atHnih8MIUVCUKlWKKVOmcPDgQQ4ePMgbb7xBhw4dzB1PU6dOZebMmcydO5cDBw7g5uZGy5YtM41sDgoKYv369QQHB7Nr1y7u3LmDr69vpo5ePz8/oqKiCA0NJTQ0lKioKPz9/c2vp6en07ZtW+7evcuuXbsIDg5m7dq1DB06NPcOhhAiXzGZTHjXe5ebqVcoXhwmDXem2msrtI6V/+T0YwCjoqKUhYXFE9eHhIQoDw8PZWtrq6pWraoWLFiQ6XWTyaTGjh2r3NzclK2trXr99dfV0aNHM9UkJSWp/v37q6JFiyp7e3vl6+urLl68+MQZ5FGuj3bmzBlVtGhRBSg/Pz9lMpm0jiREtomNjVX169dXgLK0tFTffPNNnj7H09LSlKurqwLUH3/8kSPbkOthVnJMxLO6cEGpNtXPqX2+L6kdf6C2b0ftCSuqliz5Qjk4OChAVahQQR05ckTrqEI8kZy6Hjo7O6sffvhBmUwm5ebmpqZMmWJ+LTk5Wen1ejV//nyllFK3bt1S1tbWKjg42FwTGxurLCwsVGhoqFJKqePHjytARUREmGv27t2rAHXy5EmllFKbNm1SFhYWKjY21lyzevVqZWtr+1T7J22EEC+OtzuNV4CytkbNm4sy3NyjdaQ85Umvh3lqpBSAr68vR48eJTk5mRMnThAYGJjpdZ1Ox7hx44iLiyM5OZnw8HA8PDwy1djZ2TFnzhxu3LjBvXv3CAkJkcnLs0FiYiLt2rXj5s2b1KtXjx9++EEmbxMFiru7O+Hh4XzwwQekp6czcOBAAgMDMRqNWkd7qF27dnH16lWcnZ1p1qyZ1nGEEP+hTBn4eX855lifxNC3KfYxYLS6SbkyY1n7SyDlypXj7NmzeHl58csvv2gdV4hcl56eTnBwMHfv3sXLy4tz584RHx+Pj4+PucbW1hZvb2/27NkDQGRkJKmpqZlq3N3d8fDwMNfs3bsXvV6f6Qm1DRs2RK/XZ6rx8PDA3d3dXNOqVSuMRiORkZGPzCxP8RbixbRo0WbWrRsLwKBB0KZREIWdvTROlT9ZPe8HdOrU6bGvP3ivtsif0tPT6dq1K8ePH8fd3Z0NGzZgb2+vdSwhsp2dnR1Lly6ldu3aDB8+nEWLFnHixAnWrVv3yIclaOX+rXvt27fH2tpa4zRCiCfh6Ag/ri3El+O3YdNnBO2HzuBqc4Wd/dcsW9yM8RPK8Oeff/Huu+8yevRovvjiC7k1VxR4R48excvLi+TkZAoVKsT69eupXr26ucPowfa3RIkSXLhwAch4AJKNjQ3Ozs5Zau4/HCk+Ph5XV9cs23V1dc1U8+B2nJ2dsbGxyfKQpf81efJkxo8f/5R7LITIz44f/5e+vd9DAe3bw/vNX6ZM7Wlax8q3nnuk1P3JAh+1lC1blg8++CA7sgoNjR49mt9++w07Ozs2bNiQ6VskIQoanU7HkCFD2LRpk/lb1Lp163Lo0CGto5mZTCbWrVsHyFP3hMhvdDr4fJwFlZZPZ/rU5ZSfYYUuBUy67Yz79DT9+78PwKRJk2jXrp18wScKvCpVqhAVFUVERAQff/wx3bt35/jx4+bXHxyZr5T6z9H6D9Y8rP5Zah4kT/EW4sVy8uQ/NKjXjBTTbWrUgIG9Lan2eigWFs893ueF9dxHbsmSJdmRQ+Rhy5cvZ+rUqQAsXryYevXqaZxIiNzRqlUr9u/fT/v27Tl16hRNmjRhyZIlvPfee1pHY//+/cTGxlKoUCFatmypdRwhxDN4+22ouL8bA1tVYnbftlwde4Ok0vG8/fYaKlbswiefbGDz5s3Ur1+fDRs2UL16da0jC5EjbGxsqFixIgB169blwIEDfP3114wcORLIGMVUsmRJc/3Vq1fNo5rc3NxISUkhISEh02ipq1evmp+87ebmxpUrV7Js99q1a5k+Z9++fZleT0hIIDU19bEjpW1tbbG1tX2W3RZC5DOHDkXTuFELko1XKF0Kxo+H6pVnYe9QUeto+Vqem1NK5C0RERH06tULgDFjxvD+++9rnEiI3FW5cmX27dtHmzZtSEpKokuXLowZMwaTyaRprvu37vn6+mJnZ6dpFiHEs6tdGxYeacCwYlFY9qmN6zaAdGrXDmbp0rqUKVOKf/75hwYNGrBhwwaN0wqRO5RSGI1Gypcvj5ubG2FhYebXUlJSCA8PN3c4eXp6Ym1tnakmLi6O6Ohoc42XlxcGg4H9+/eba/bt24fBYMhUEx0dTVxcnLlmy5Yt2Nra4unpmaP7K4TI+8LDD9KwgTfJxiu8XA6+/gaqODfBrWJ/raPle9IpJR4pJiaGjh07kpKSQseOHfniiy+0jiSEJvR6PSEhIYwYMQLIuKWmY8eOmk1mqpQyd0rJrXtC5H+urrB6ZymWdN7NsQlvU3kG6FKgRIldfDcvnSZN6nDnzh3eeustxo4dq3mnuBDZafTo0ezcuZPz589z9OhRxowZw44dO+jatSs6nY6goCAmTZrE+vXriY6OpkePHjg4OODn5wdktNEBAQEMHTqUbdu2cfjwYbp160bNmjVp0aIFANWqVaN169YEBgYSERFBREQEgYGB+Pr6UqVKFQB8fHyoXr06/v7+HD58mG3btjFs2DACAwMpXLiwZsdHCKG9337bRfM3mpGadpNqlWHm11BGX4Uqr/0qD/7KDjn+HMB8SB7lqtTdu3fVq6++qgBVs2ZNdfv2ba0jvXCMxmsqKem81jHEA1asWKHs7OwUoKpVq6b++eefXM9w6NAhBSh7e3t1586dHN2WXA+zkmMicorJpNQ3s9PVON04dftlVMSPqO3bUWFhFqpnz8YKUIBq3769nH8iT8iO6+GHH36oypYtq2xsbFTx4sVV8+bN1ZYtW8yvm0wmNXbsWOXm5qZsbW3V66+/ro4ePZrpM5KSklT//v1V0aJFlb29vfL19VUXL17MVHPjxg3VtWtX5eTkpJycnFTXrl1VQkJCppoLFy6otm3bKnt7e1W0aFHVv39/lZyc/FT7I22EEAXLypVblIUu43f/2rVQv/+OitpRT6Wmyt/H/+VJr4c6pZTSpjss70pMTESv12MwGF7Ib0aUUrz33nv8/PPPuLi4cODAAcqVK6d1rBfG7duHuXTpa65eXY1SKbi4dKRChWk4yL3KecaBAwfo2LEjly9fxtnZmTVr1pi/jc0Nn376KRMnTuStt94yT3aeU1706+HDyDEROW3rVljR4WfmqA+IGZrM1eYZ63furMXEiacwGo1UrVqVDRs2mEd5CKEFuR5mJcdEiIJj3ryN9O/XGUUK9evB+C+gdGEfqjX8FQsLmUvuvzzp9VBu3xNZfPnll/z8889YW1uzbt066ZDKBUqlc+3aOg4f9iYy8lWuXFmGUimAjuvXN3DgQHX+/XcIqakJWkcVQL169Th48CANGzYkISGBVq1a8fXXX5Nbffxy654QBVuLFjAm6l16lNiF0wR38+18r712hG+/1ePuXpyTJ09Sr149goODtY4rhBBCFDiTJq2mX7+3UaTw2mvw5QSoUMyP6l6bpEMqm0mnlMhk7dq1jB07FoDvvvuO1157TeNEBVtq6i1iYmawb19Fjh17G4PhL3Q6K1xd3+fVVyOoVy+aokXboFQqly7NYt++Sly6NBeTKVXr6C+8kiVLsn37dnr06IHJZCIoKIiAgACMRmOObvf48eOcPHkSa2trfH19c3RbQgjtVKoEi6M8GfXGQS79Vg/PvmB/CV5++Spz5tygXr1y3L59m/fff5+AgADu3r2rdWQhhBCiQBg+/AfGjOkKpNOyJYwdC5XchlCp7gp0Okut4xU40iklzKKiovjggw8AGDRoEAEBARonKrju3TvN6dP92bu3FGfODCM5+TxWVsUoU2Y0DRuep3r1VRQu3ABHx+rUqrWJWrVCcXCoQVraDf79dwAHD9bixo1NuTYyRzycnZ0dixcvZubMmVhYWLBkyRKaNWtGfHx8jm3z/iipli1botfrc2w7Qgjt6fWwbEtJNgSFs/GMH569wXUbFC1qYvLk8wQGvoxOp2Px4sXUrVuXv//+W+vIQgghRL4WEDCb6dMDAUX79vDJCKhSegrla82QSc1ziHRKCQCuXLlC+/btuXfvHj4+PkyfPl3rSAWOUoqbN7dw5Ehb9u+vwuXL32Iy3cXR0YPKlRfi5RVDhQoTsbV9Kct7ixZtRd26UVSq9B3W1i7cu3eSo0fbcuRIK+7cidZgb8R9Op2OwYMHs3nzZooUKcLevXvNt/flBLl1T4gXi6UlTJplT/rSFYxPmUi1CVB5BliZLPDzO8PXXxfDza0YJ0+epEGDBsydO1e+sBBCCCGeklKKt9+eyOLFgwF47z0YPABqVF5MqcojNU5XsMlE5w/xok1QaDQaeeONN9izZw+VK1dm3759FClS5P+xd9/hURVdAId/u+kJIZUkhNB7712aIEWqgKAUERQEQVCwgKgIogjSu3T4kCoqoIgGCL0ngKF3AiQklPSe3fn+uBAIiUhI2ZTzPs8+JLuzd8+9WebsPTt3xtRh5RkGQwzBwf/j1q3ZxMScfXivDheXDnh5jcDR8eV0Vd2TksK5ceNbbt2a9XDeKT2FCw+kZMkJWFq6Zck+iOdz6dIlOnXqxPnz57G2tmbp0qXJS1ZnhitXrlCmTBnMzMwIDg7GxcUl07b9b/Jbf/g85JgIUzl8GBa1+5XZYX2hdDSnv7EgrnAiYWE65swpx65dFwDo3LkzS5cuzZY+QuRv0h+mJsdEiNxHKUXLlmPw8ZkMQP/+0O8NPVWrb8bZXabLeFEy0bl4LkopBg8ezMGDB3FwcGDLli1SkMokcXE3uXJlNIcOFeXixcHExJzFzKwARYoMp169i1StugUnp5bpHgZqbu5A6dJTqFfvHK6u3QAjQUE/cuRIWQICpmA0Zu2cRuLflS1blsOHD9O+fXvi4uLo3bs3o0ePxmAwZMr2H42Sat68uZxsCpEPNWgAE/xfY2DFA9y/Uow6AxIp5K3H0VHxxRcX+OSTclhaWrJ582Zq1KjB3r17TR2yEEIIkaMZDEbq1BmWXJAaMgQG9LSiRt29UpDKJlKUyudmzJjBihUr0Ov1bNiwQZaWziClFOHhBzlzpieHD5fk5s3JJCU9wNq6JKVLz6Bhw1uULTsLW9syGX4tG5tSVKnyMzVq7KVAgdoYDBFcvfoZR49WJCTkZ7l8w0QcHBzYvHkzo0ePBmDy5Ml07tyZ8PDwDG9bLt0TQnh5wdLj1fmuqy9/xnWg0ndGyn8PZolmvPrqRRYsKEDp0kW4desWLVq04OuvvyYpKcnUYQshhBA5Tnx8EpUqDcDPbz46HYwcCX062VOzsS8OTo1NHV6+IZfvpSG/DLv9888/6dChA0ajkZkzZzJixAhTh5RrGY0J3L27kVu3ZhEZeSz5fkfHFnh5jcDFpUOWrtSglJHg4NVcvTqGhIRAABwcXqJ06RkULFgny15XPNvatWsZMGAAcXFxVKhQgS1btlC2bNnnfn5sbCzXrl3jypUrnD9/nk8//RSdTkdgYCAeHh5ZGPlj+aU/TA85JiInUApWLFecGzKbiQmfkFg8kdMTLIgtlkhMDCxbVo1Nm/4BoEmTJvz0008ULVrUxFGLvEb6w9TkmAiRO0RFJVCxQi9u3d6EmR4+Gw2dXnKleovjWFsXN3V4ecLz9odSlEpDfkgm586do0GDBkRERPDuu++yaNEiWU3gBSQk3CUw8EcCA+eTkBAEgE5nhbt7b7y8hlOgQPVsjcdgiCYg4Adu3pyC0RgLgLt7X0qW/A5ra69sjUVojh8/TpcuXbh9+zaOjo6sX7+e1q1bA48mv3/AlStX0rwFBgam2l7Tpk3Zs2dPtsWfH/rD9JJjInKSixdhfGdfvj7/BqUsL3NpGNzpqD22f395Jk++RVRUNM7OzixbtozOnTubNmCRp0h/mJocEyFyvrt3Y6lcsSt372/Hwhy+/AperVuCqi8fxdKykKnDyzOkKJUBeT2ZPHjwgHr16nHlyhWaNGnCjh07sLS0NHVYuUpU1D/cujWL4OCfUEqbw8nS0gNPz6F4er5n8s4sPv42V69+TnDwKgD0ehuKFv2UYsU+wczMzqSx5Ud37tyha9euHDp0CL1ez6uvvkpgYCBXrlz5z8v6ChYsSOnSpSldujRlypTh3XffpXTp0tkUed7vD1+EHBOR08THw4RPIqkw5336sprgFnDhUzOM1gbu3HHk++9dOXXqMgBDhw5l6tSpWFtbmzhqkRdIf5iaHBMhcrYbNyKpUbUDYZF7sbKCCROgdc2qVGlxAHNze1OHl6dIUSoD8nIySUxMpF27duzcuZPixYtz7NgxChWSavDzUMrA/fu/c+vWLMLCfJLvt7evg5fXhxQq9Dp6fc4q7kVEHOfKlY8ID98PgKWlJ6VKfYe7e190OplSLjvFx8czZMgQli9fnuoxT0/P5MJTqVKlkn8uXbo0Li4uJh3FmJf7wxclx0TkVNu3wx89VvJ95PvoPGM4Pd6MmDIGEhNh/fo6LF16HIBq1aqxbt06KlasaOKIRW4n/WFqckyEyLnOnAmlQZ3WRMUdx9YWvvsOWlVtQsXm3uj1VqYOL8+RolQG5OVkMmzYMObNm4ednR0HDx6kWrVqpg4px0tICCY4eA23b88lLu7qw3vNKFSoK15eH1KwYMMcfemjUop7937hypVPiIu7BkCBArUpU2YGjo5NTBxd/qKUYuvWrVy5ciW56FSyZElsbW1NHdq/ysv94YuSYyJysuBgGNv9AsP296SaxSmuvAe3H66N8M8/5Zk48R53797H1taW2bNnM2DAgBydw0TOJv1hanJMhMiZjhy5y8tNWhCTeAZ7e5gyBVpUfo1yL23M0rl/8zMpSmXAiyYTo9HI4sWLuXr1KpMnT87CCF/MwoULGTJkCAC//vorXbp0MW1AOVhSUiT37v1GcPBPhIbuAAwAmJs7UbjwIIoUeR9r62KmDTKdjMZ4bt2azY0bEzEYIgBwde1G6dJTsLEpZeLoRE4lH65Tk2MicjqjEeZOjUM/5lOGGedwtzGcH2OGwc5AaKg9M2aUYN8+fwB69uzJjz/+iIODg4mjFrmR9IepyTERIuf5++/bdH61KXGGqzg5wQ8/wMtVB1Oi7nz5YiYLSVEqA140mRw7dox69eqh0+k4dOgQ9evXz8Io02f37t288sorJCUl8e233/L555+bOqQcx2hM4MGDvwgO/on797ckTxQOYG9fFw+PAXh49M31czIlJIRw/fo4AgMXAUZ0Oku8vIZTvPgXmJvLSYlIST5cpybHROQWfn6wpONmJgb2x9Y9lNNf6YmqZMRohG3bGjJz5lEMBgMlS5Zk7dq1Oepzi8gdpD9MTY6JEDnL+vXX6PfmS8SrQAoVgmnToEX1r/CqNt7UoeV5z9sfyqQymahu3br069cPpRRDhgwhKSnJ1CEBcPXqVbp160ZSUhJvvvkmY8aMMXVIOYZSRsLC9nHhwmAOHizM6dOduHt3PUZjLDY2ZSlR4mvq1btI7dpHKVJkcK4vSAFYWrpRrtwC6tQ5hZNTa5RK4ObNqRw5UobbtxdgNOaM960QQoiMqVULplzozLevn+JY8EvUGm6k6FrQ66FDh0MsXlya4sW9uHbtGi+99BKTJ0/GaDSaOmwhhBAiUyxYcJ5+b9YjXgXi6QmzZ0Dr+vOlIJXDyEipNGTkG46QkBAqVKhAaGgos2bNYvjw4VkU5fOJiIigYcOGnD17ljp16rB3715sbGxMGlNOEBXlT3DwT4SErCU+PiD5fktLD9zc3sDNrTf29rXz/HBOpRQPHmznypVRxMScA8DWthIVKqykYME6Jo5O5ATyjW9qckxEbrRhTRLX+k/gk4SJhNZTnBtrRlJBAzExdvz4YxW2bDkCwCuvvMKqVavw8PAwccQiN5D+MDU5JkLkDN9+e4pvvmxKvIqgeHGYPklHs8brcSn2uqlDyzfk8r0MyGgy+fHHHxk8eDD29vZcuHCBwoULZ0GU/81gMNClSxd+//13ChcuzLFjxyhSpIhJYskJ4uJuEBy8lpCQNURH+yffb2ZmT6FC3XBz642TU4t8OdGd0ZhIUNAirl0bR1LSfczNXahd+wg2NqVNHZowMflwnZocE5FbXb8OU9v78PnZ3ri4BnHmCx0R1RVKwf79LzFpki+xsbG4ubmxatUq2rRpY+qQRQ4n/WFqckyEML2PPjrC/FktSFCxlC0L0yeY06TF3zi4tzB1aPmKXL5nQu+++y716tUjMjKSIUOGEB8fb5I4xo4dy++//46VlRW//fZbvixIJSbe5/bthZw40YTDh0tw7doYoqP90ekscXXtQqVKG2nUKJgKFZbj7NwqXxakAPR6C4oUGUr9+pewt69LUtJ9/P07kJgYZurQhBBCZJISJWDmqRasGnmKXffaUWOkovhK0AFNmuxn2TJPKlcuS0hICG3btuWTTz4hISHB1GELIYQQz0UpeOut3cyb1ZQEFUvlyjDnG2tatD0mBakcTIpSWcDMzIwFCxag1+vZvHkzdevW5Z9//snWGP73v/8lrwC4bNky6tWrl62vb0oGQwzBwevw9+/IwYMeXLo0hPDw/YAOR8fmlCu3mEaN7lClyq+4uXXHzEwuZ3zEwsKJKlU2Y2VVlJiY85w9+zpGY6KpwxJCCJFJzM1h9LRC2O76nYn20yiywoLqo8A81AwPjyvMmHGTfv20D+5Tp07lpZde4sqVKyaOWgghhHg2oxE6dtjG2tWtSFQJ1KoFc8cVpGnHM9g51jB1eOIZpCiVRWrVqsXmzZspVKgQ/v7+1K1bl6lTp2bLBKKHDx9m4MCBAIwZM4ZevXpl+WuamtGYxP372zl3ri8HDrhx7tyb3L//O0olUaBADUqV+oEGDQKoUcMHT893sbBwMnXIOZaVVWGqVt2KXm9HaOgOLl36ALnKVwgh8pZmLfQMuzqSL5of4MGJUtR7x4DjcbCwiOPtt32YNaspTk6OHDt2jJo1a7JmzRpThyzyqEmTJlG3bl3s7e1xc3OjS5cuXLhwIUUbpRRff/01np6e2NjY0Lx5c86cOZOiTXx8PB988AGurq7Y2dnRqVMnbt26laJNaGgoffv2xcHBAQcHB/r27UtYWFiKNgEBAXTs2BE7OztcXV0ZPny4jBgUIodLSIBmTTay/c8OJCkDjRrB7DHuNOp0ERvbUqYOT/yHHFWU+vrrr9HpdCluT060mVkJKbt06NABf39/OnToQEJCAp988gktW7bkxo0bWfaat27dokuXLsTHx9O5c2cmTpyYZa9lakopwsMPc+nSBxw65Im/fzuCg1djNEZjbV2SYsXGUrfuGerUOUGxYh9jbe1l6pBzjQIFqlOp0lpAR1DQj9y+PdvUIQkhhMhkzs4weVdd9sw4wW/hb1D9Uyi5CDDoqFZtL8uXO9KwYU0iIyPp3bs3/fv3JyoqytRhizxmz549DB06lMOHD+Pt7U1SUhKtW7cmOjo6uc2UKVOYPn06c+fO5dixY3h4ePDKK68QGRmZ3ObDDz/k119/Zd26dezfv5+oqCg6dOiAwWBIbtOrVy9OnjzJ9u3b2b59OydPnqRv377JjxsMBtq3b090dDT79+9n3bp1bNq0iVGjRmXPwRBCpFt0NNSrvYIDB3tgUIoWLWDmx2Wo3+Uillbupg5PPA+Vg4wbN05VrlxZBQUFJd9CQkKSH//++++Vvb292rRpk/L391c9e/ZUhQsXVhEREcltBg8erIoUKaK8vb2Vn5+fatGihapevbpKSkp67jjCw8MVoMLDw19sR56IRymljEajWrx4sbKzs1OAKliwoFq1apUyGo0vtv1/ER0drWrVqqUAVaVKlRTHJS+Jijqnrl79Uh06VFr5+JB827/fVV24MFSFhR3M9GObXwUETHt4fPXq3r3fTR2OMIEM94d5kBwTkRed9jeqL4ssVdHYqLAqqP0b9MrHB7Vjh6X68MN2Sq/XK0CVK1dO+fr6mjpckUNkRX8YEhKiALVnzx6llPY52sPDQ33//ffJbeLi4pSDg4NauHChUkqpsLAwZWFhodatW5fc5vbt20qv16vt27crpZQ6e/asAtThw4eT2xw6dEgB6vz580oppbZt26b0er26fft2cpu1a9cqKyur595HyRFCZJ+LF42qVLEZClCAatcOdXJzLZWUFGvq0IR6/v4wR42UAjA3N8fDwyP5VqhQIUAbFTNz5kzGjh1L165dqVKlCitXriQmJiZ5SHl4eDhLly5l2rRptGrVipo1a7J69Wr8/f3ZsWNH1gd/5QqJ7ZsR1re6NsvaQzqdjnfffZeTJ0/SoEEDIiIieOutt+jZsyf379/PlJdWStG/f3/8/PxwdXVly5Yt2NvbZ8q2c4r4+ED8/btw7FhFbtz4hri4K+j1dri59aZq1W00bBhIuXJzcXBoiE6nM3W4eYKX10cULjwQMHL27BtERfn/53OEEELkPpWr6Pj88gCm9/Llxumq1HvXiMsBMDNLoHPnP1my5CWKFPHk4sWLNGjQgMmTJ6cYgSJEZgkPDwfA2dkZgGvXrnHnzh1at26d3MbKyopmzZpx8OBBAHx9fUlMTEzRxtPTkypVqiS3OXToEA4ODtSvXz+5TYMGDXBwcEjRpkqVKnh6eia3adOmDfHx8fj6+mbRHgsh0stggPHjg6lVoQ1XAz4CoGtXmDb8Fap1PIqZmbWJIxTpkeOKUpcuXcLT05OSJUvyxhtvcPXqVSDzElJa4uPjiYiISHF7EVG2QRz6YC+n+1/D8PfWVI+XKVOGffv28c0332Bubs7GjRupWrUqf//99wu93pMmTpzIhg0bMDc3Z9OmTZQsWTLD28wplFLcubOSY8cqc//+ZsAMZ+f2VKy4hsaNg6lUaTUuLu3Q6y1MHWr6xcVBUBCcPQsHD8Iff8Dq1TBnDnzzDSxcqF0kbSI6nY6yZefh6PgyBkMU/v4diI+/Y7J4xAv64guoWhWWLzd1JEKIHMzaGr74qSIBG47wv/ghVPkCSs8FknSULLmXJUvM6NChOYmJiYwePZoWLVpw/fp1U4ct8hClFCNHjuSll16iSpUqANy5o33ucHdPeRmOu7t78mN37tzB0tISJyenZ7Zxc3NL9Zpubm4p2jz9Ok5OTlhaWia3eVpmnUcIIZ7P2bOKSuVXM3lCCaKM3piZwbvvwpRhvajQ5q98u5p6bmZu6gCeVL9+fVatWkW5cuUIDg5m4sSJNGrUiDNnzjwzIT2ao+l5ElJaJk2axPjx4zMcv51HQyz9ChLnEEHIH59TuE2nVG3Mzc354osvaNu2LX369OHChQu0adOGYcOGMXnyZGxtbVM9JyEhgZCQEIKDg9O83blzBx8fHwDmz59P06ZNM7wvOUVc3C0uXhzEgwd/AmBvX4fy5ZdToEAVE0f2kFIQGQlhYRAa+vj25O/Peiw+/r9fY9062LgRHo4azG56vQWVK/+Mn18DYmMvcvp0F2rU8JFVC3OTkyfh9GmtCCqEEP+hw+s23G40n/FtWzJi07vU8g/jzDg9eN5k1KhAmjbtxoQJf7Fv3z6qV6/OvHnz6N27t4xSFhk2bNgw/vnnH/bv35/qsaffX0qp/3zPPd0mrfYv0uZJmXUeIYR4tqQk+PrrEBZO6sF94x4AypSBz0fqadNgAp41x5o4QvGiclRRql27dsk/V61alYYNG1K6dGlWrlxJgwYNgMxJSE8bM2YMI0eOTP49IiKCokWLpjt+nc4MzyLvczXse26XO4OHvz+6qlXTbFunTh38/Pz49NNPmTdvHnPnzmXHjh288sorqYpOoaGhz/X6I0aMSF51L7fTRkct4/LlkRgMEeh0lpQoMZ6iRT9Gr8/Gt21SEuzbB7//DrdupS4uhYVp40czQq8HR0ft5uT0+GZvrxWj9uyBunVhyxaoVi3j+/QCLCycqFr1d/z8GhAZeYTz5/tTqdJaOQHJLc6d0/6tWNG0cQghco0iReDLk9348fM61JzyJnUHHeLCx3C3uYG6dTexenVVJk3Sc+TIKfr27cvvv//OggULUn0xKMTz+uCDD9iyZQt79+7Fy+vx4jSPFj26c+cOhQsXTr4/JCQk+ctqDw8PEhISCA0NTfEeDAkJoVGjRsltgoODU73u3bt3U2znyJEjKR4PDQ0lMTEx1Rfjj2TWeYQQ4t/5+0OPjiu5eWsQ0cYEzMygTx8Y/lpVKrX+HWubYqYOUWREVk5slRlatWqlBg8erK5cuaIA5efnl+LxTp06qbfeeksppdTOnTsVoB48eJCiTbVq1dRXX3313K+ZkQkKAwLuqd1/a5ODhn366nM9588//1SFCxdOnqAtrZuZmZkqXLiwqlGjhmrTpo1666231CeffKKmTp2qVq1apQ4cOJBnJveOjb2hTp5snTyB+fHj9VVU1NnsCyAhQant25UaOFCpQoWU0sZDPftmaamUu7tSFSoo1aCBUu3aKdWrl1JDhyo1dqxSU6cqtXSpUps2KbVrl1InTih1/bpSYWFKGQz/HsuZM0qVLq29hp2d9nwTCg3drXbvtlA+PqirV8eZNBbxnGJilNLptPdQUFC6n55ZE7bu2bNHdejQIbmv+/XXX1M8bjQa1bhx41ThwoWVtbW1atasmTp9+nSKNnFxcWrYsGHKxcVF2draqo4dO6qbN2+maPPgwQPVp08fVbBgQVWwYEHVp08fFRoamqLNjRs3VIcOHZStra1ycXFRH3zwgYqPj3/ufZFJbEV+c2R/gprnMEYlgQpqjdqz9dEk6BZq5MhWyszMTAGqSJEiaseOHaYOV2SjzOgPjUajGjp0qPL09FQXL15M83EPDw81efLk5Pvi4+PTnOh8/fr1yW0CAwPTnOj8yJEjyW0OHz6c5kTngYGByW3WrVsnE50LYSLx8Up98kmw8jKrn3xeXKoUavkcC3Xn4sI8c/6bVz1vf5iji1JxcXGqSJEiavz48ZmWkJ7HiyaTEyeUMjdXavrojsrHB3V6nP65TwLv3bunvv76azVmzBg1c+ZMtXbtWrVz5051+vRpdffuXWV4VuEijzAajer27YVq794CyscHtWePtQoImKqMxudfOfGFxcUptXWrUv36KeXklLLg5Oys1IABSs2apdSqVVq7ffuUOn1aqdu3tZP+rOwQ799XqmXLx/GMH//sQlYWCwxcllwwvHPnJ5PFIZ7TqVPa+8bR8YXep5n14Xrbtm1q7NixatOmTWkWpTJrddW2bduqKlWqqIMHD6qDBw+qKlWqqA4dOiQ/npSUpKpUqaJatGih/Pz8lLe3t/L09FTDhg177n2REw6RH4WHK/Xdy97qJkVUnAvq1HePV79dsaKiKlOmePIJw8iRI1VsrKx8lB9kRn84ZMgQ5eDgoHbv3p1iBe6YmJjkNt9//71ycHBQv/zyi/L391dvvvlmmjnCy8tL7dixQ/n5+amXX345zRxRrVo1dejQIXXo0CFVtWrVNHNEy5YtlZ+fn9qxY4fy8vKSHCGECfj6KlW1yEJV0Nz84SAN1FtvoU7++bJKSLhn6vDEc8iVRalRo0ap3bt3q6tXr6rDhw+rDh06KHt7e3X9+nWlVOYlpP/yosnEYFCqTBmlSpc+oXx8ULu9UXETRqRrG/lVTMxVdeLEy8kfcH19G6vo6AtZ+6LR0drIo169lLK3T1mIcnNTavBgpby9tZFTppaYqNTw4Y/j69ZNqagok4Vz+fKn2nt8t6UKCztgsjjEc1i3ThlBqYYNX+jpWfHh+umilCz3LUTu8dv/ItQim+HJo6b2btEpHx/Un39aqF696iUXpqpWrar++ecfU4crslhm9If/dpXA8uXLk9s8Gk3r4eGhrKysVNOmTZW/v3+K7cTGxqphw4YpZ2dnZWNjozp06KACAgJStLl//77q3bu3sre3V/b29qp3795pjqZt3769srGxUc7OzmrYsGEqLi7uufdHcoQQGRMXp9RHI+6oshZVU46OmmWn7t/+zdThiXTIlUWpR9+MW1hYKE9PT9W1a1d15syZ5MczKyH9l4wkk2nTtJrBzwsraJc4vWepDaESaTIaDerWrblqzx67h6OjbNTNmzOzbnRURIRSa9cq1b27Ura2KQtRRYpohZ89e5RKRxEzWy1ZopSFhRZv9eraJYAmYDQalL9/F+Xjg9q/v5CKiblmkjjEcxg3Tl0ajDq0tYC6fXthup+eHUWpzLo8e+nSpcrBwSHV6zk4OKhly5YppZT68ssvVbVq1VI8/uDBAwWoXbt2pRlvXFycCg8PT77dvHlTTjhEvnb/vlLj2x9RJ6iu4lxTjpqaPr2scnV1VoCytLRU06ZNyxejvfMrKcCkJsdEiBd3+LBSdd2mKkcL7bJwvR7Vtw/qzJ43VGJipKnDE+n0vP1hjprofN26dc98XKfT8fXXX/P111//axtra2vmzJnDnDlzMjm659O/v7b6+rx14/hq3Jvc6pCA0wfNcVx0SCYZfkps7BXOn3+H8HBt9QQHh6aUL78UW9symftCYWGwdSv8/DP89VfKFe9KlIBu3aB7d6hXT5t0PBsoBdHREBKi3e7effzzk/c5O8P332thAvDOO1ChAnTtCqdOaROgb9oETZpkS9yP6HR6KlZczYkTTYiKOoG/fwdq1TqIuXnBbI1DPIdz54ipCXEFokwdyb/KrNVVs2q5b1lZSYiUnJ3hq9/r4b3tGNt6z2TE51/h1iaOS8OgZs1LLFpkwbx5Fdi58zyjRo3ijz/+YOXKlSkmrxZCCCEeiY2F0R/eZO/KlzkZfxmAkiVh3EeuvNZnMwWdGpk4QpGVclRRKi9wcoLevWH58m6EBNXFrfAxTn0ZTvmvGuLxvS+ULm3qEE1OKSO3b8/h6tUxGI2x6PV2lC49GU/PIeh0mVQUuncPNm/WClE7d0Ji4uPHypbVilDdukGtWpBJK8jFxWmFpH8rMD39e2zs823X2xt++gnatn14R+PGcOwYdO4MJ09Cy5Ywbx5k88qLZmZ2VK26FV/fesTEnOHs2TeoUmVL9q6OKP7b+fPEdtR+tLEpZ9pY/kNOXe5bVlYSIm2vvGpBw5ufMHVYd+qvHEIL37+4MBJomMjYseepV684M2cGs2vXLqpWrcqPP/5Ijx49TB22EEKIHOTAARjbeRz+Ud/wIF6h10OvN+CLj0ZQttYU9HpLU4cospicPWaBoUNhyRILBgzcze6/exLF75wfGk7svNqUGHESXfESpg7RZGJiLnL+/AAiIg4A4Oj4MuXLL8HGpmTGN37nDvz6qzZyaPduMBgeP1a58uNCVJUqz12IUgqCg+HCBQgMTLu49OjniIj0h2xjA25uKW+FCmn/urpqtaZjx+DVV2HcOPjyy4eDuYoVg/37taF5GzfCoEHwzz8wfTpYWKQ/kBdkZVWEqlW3cOJEEx48+JMrV0ZStuzsbHt98R8MBoxXzhP7cAVtW9ucWZTK6ct9W1lZYWVllYE9FCLvKlAAxq0oycGBf/J5j3V88vkI3Nrc5dIwaN36BhUrWjB1ahH++ec2PXv2ZOvWrcydOxcHBwdThy6EEMKEoqNh7OALHP2lJYdibgPa1SHffFyc197ehp1dJdMGKLKNFKWyQI0a2mCWAwds+cN7M31fG0HAg7nc6BROzIaqVOh1CrMipUwdZrZSysCtWzO5du0LjMY4zMwKULr0VAoXHvSfIyGe6eZN+OUXrRC1f79WRXqkZk2tCNWtm3bJ2zMkJsKVK3D+fOpbePjzh2Nunrq4lFbB6dHNzu7Z23vjDfjoI1iwAL7+Gg4fhtWrwcUF7cnr10P16to1o3PnwtmzsGHDwwbZw96+NhUrrubMmW7cvj0HW9vyFCkyNNteXzzD9evEOieAGej1dlhaepo6ojSVLFkSDw8PvL29qVmzJgAJCQns2bOHyZMnA1C7dm0sLCzw9vZOHmkRFBTE6dOnmTJlCgANGzYkPDyco0ePUq9ePQCOHDlCeHh4cuGqYcOGfPvttwQFBSUXwP7++2+srKyoXbt2tu63EHlJo8Y6al15k2lj21B4xqf09l2aPGpq2rTbrF/vybJld1i9ejV79+7lf//7H02bNjV12EIIIUzAZ5diVrdh7I+bz/047Uv3N3voGf/Ft5Sq9GnmXT0jcgWdUk+exQvQLs1wcHAgPDycggVfbI6ctWuhVy9t3oXt28HLdhoXgz5GmYP9dWuqtDmOVeHKmRx5zhQdfY4LFwYQEXEYACenVyhffjHW1sVfbIPXrmlFqJ9/hqdGPFCv3uMRUaVSF/5CQ7VRT08Xnq5cgaSktF9Or9eq9sWK/XexydEx064GTOF//4P33tMu+SteXNv9FOfPv/0GffpoXzmUKgVbtmijw7LRjRvfc+3aGMCMatX+wNm5Tba+vkjD779zb1JHTn8LBQrUpE4dv3RvIjP6Q4CoqCguX9bmCKhZsybTp0+nRYsWODs7U6xYMSZPnsykSZNYvnw5ZcuW5bvvvmP37t1cuHABe3t7AIYMGcLvv//OihUrcHZ25uOPP+b+/fv4+vpiZmYGQLt27QgMDOTHH38EYNCgQRQvXpytW7cCYDAYqFGjBu7u7vzwww88ePCAt99+my5dujz3XISZdUyEyKtOnoS5PfYy6tJAnNpc5NIwMBSAM6fNmTzFnps3Q9HpdHz22WeMHz8eS0u5NCO3kv4wNTkmQvy7yEgY3+8oJ3a0Y1fkA0A7t/luTFW69vsda+tiJo5QZKbn7g+zfMr1XCgzVs2Ij1eqdm1tkTQbG6V++UWp0DNr1L6HSycf3GShIgP3ZWLUOY/BkKhu3Phe7d5tpXx8UHv3FlSBgUuU0WhM34ZiYpT66y+lRo5UqnLllCvm6XRKvfSSUjNmKHXjxsPXVeraNaX+/FO7+733lGrWTCl395RPffpmZ6f9zXr3Vuqbb5TauFEpf3+lYmMz+cC8oFOnlCpTRovV0lKpRYuUSnEo//lHqZIltQYFCii1eXO2xmc0GtXZs/2S/9ZRUWf++0kia02Zom700FbEWrmyp9qxI/2byKxVhHx8fNJc8rtfv35KKVnuW4i8JjFRqanfxqlvzMapcFcLdWqS1hf98QeqY0fX5D6gZs2aKVZaFrmL9IepyTERIm1/bY1X/Rx6KlcbklfW693TXF2/vCj954ciV3je/lBGSqUhs77hiIyEHj20kVI6HUydCu+9sp3T/h2I9TRgFq+nUuUNuHh1y8Toc4aoqNNcuDCAyMhjADg7t6NcuUVYWz/HyjtKwblz2kp5f/0Fe/Zos4g/otdD8+YkdOzGxcqvcfp+4RSjni5cSNn8aUWKaFfzPX0rUiRrRjllprAwePttbQ530KaUmjdPm5sK0CZ4f/11bU4tnQ4mToQxY7Jtx4zGBE6deoXw8L1YW5ekVq0jWFoWypbXFml45x0uOC0jqAOsXPkVVaqMZ9So9G1CvvFNTY6JEM/v4kX4pvd53jk+iApt9iWPmtq314zpM6wIC4vB2tqaH374gaFDh2bskn6R7aQ/TE2OiRAphYXB1B7b8TvenT9DowFtdNT345rQtfcmOVfIw563P5SiVBoyM5kkJcEHH8DChdrvQ4bAtHf2ceHgy4RVTQIjlCk5lSIlRuaJD2JGYyI3b07h+vXxKJWIubkjZcrMxN39rWfvX2go7NjxuBB161bKxz09iWzUBh/LNvwvqBXHrrrwcKX4NFlaaovsPV14Kl8eHl4JlGsZjfDDD/D559rP1atrl/MlL+yYmAgffgjz52u/9+wJy5aBrW22xJeQcA8/vwbExV2hYMHG1KixE71eJok2iUaNONHzEOHV4dtvV/PRR7159dX0bUI+XKcmx0SI9DEaYdFCI/4jlzPOfiR3RkXwoIH2Pcr0aQU5dFhbKaRt27YsW7YsxYIHImeT/jA1OSZCPLZ9fTh/vdeN1Uk7uRetjS3o9bo1309dSxGvLqYOT2QxKUplQGYnE6Vgxgz4+GPt53btYP2nB7n9d3PutE4EwNP9PcqUn4ten3vnno+K+ofz598mKuoEAC4uHSlXbiFWVmlMrmwwwNGjj4tQR49qn1ofsbKCpk15UK8NW+LaMH9PZY4dT13UcnaGihVTF59KlNAmHc/Ldu3SJkK/exccHLR5pzp2fKLBjz/CsGFaZbRWLW3eqWxaxj46+jx+fg0wGMJxd+9DhQqr8kTRNVdRCpydObgkjAQXGDz4KH/+WZeS6VzoUj5cpybHRIgXc/MmjO4fTPudH/Jym3VcGgZJdvDbL3p+XKwnPj4JFxcXFi9ezGuvvWbqcMVzkP4wNTkmQsD9e4pFnVZy5OogNgdr57vFisG07zrTpef/MDfP5aMExHORolQGZFUy+fVX6N1bm6y6WjX4+6u9xP/Wkqv9k0APTo6vUKnyeiwsnP57YzmI0ZhAQMAkbtyYiFJJmJs7U7bsHNzc3kxZiLh583ERascObSznkypWhDZtCKnZhnWBTVnzm22KecwfXrXHa69pKxxWqACurtmwgznY7dva1XqHDmm/f/45TJgAD+d8hr17tUnf790Dd3dtpcKHq5BltQcPdvDPP20BAyVLTqR48bHZ8rrioTt3SCpdmP1/aL926xZGSIjD4/fGc5IP16nJMRHixSmlLQbz2+DtfGs1kNhRt3jQAK5fh0nf2XDxUiwAAwYMYObMmckLHYicSfrD1OSYiPzuz0U3OTa6I3OTTnE38uHKet0LMm32b7i7tzB1eCIbSVEqA7IymRw7po1mCQ4GT0/Y84U3Dutf5dxnSRhtwMysAG5uvfD0HIS9fc5fnjwy8gTnz79NdPQ/ALi6dqVs2XlYWXlo1be9e7VJtf76S5sn6kmOjtCqFbRpQ2DVNqw/WJQNG+Dw4cdNdDpo1kybm6trV62uIlJKSNBG4T1aOKxVK1izRlsRENA+6XfuDP/8o13XuHChNhlVNrh9eyGXLg0BoFKlDbi5vZ4trysAHx8iBr+M34/w4IE7Eybc4eTJ9G9GPlynJsdEiIy7exc+HRpNxY1f07vNNK4OU8RZwfJlOtatB6UUpUqV4n//+x+NsunLFJF+0h+mJsdE5FcBV5PY0mUyu0K/4tdb2hUwRb1gxrR+vNb9R5nOIx+SolQGZHUyuXED2reHM2fAzg52jdxKxY2vce4zAzElHrcrUKA2np6DcHN7M0cMcVRKkZh4j9jYS8TGXiYy8hi3by8ADFhYuFK2zFwK3a2M7u+/tSLU3r2pJyivVw/atIE2bbjtWZeffzNnwwY4ePBxM50OmjbVRgB16wYeHtm+q7nS2rXw7rsQEwNeXvDzz1C//sMHo6KgXz9tpBTARx/BlCnZco3j5csfcevWTPR6a2rU2EvBgnWz/DUFsGABwZve59wXcOpUEw4d2su6denfjHy4Tk2OiRCZZ+tWmPPOSSaot7EddYoHDeDkSZj8vQV3ghPR6/WMHTuWL7/8EgsLC1OHK54i/WFqckxEfnPpdDy7By4l6OpY5saHcTdcO5/r1cOVGXP+pFChOqYOUZiIFKUyIDuSSXi4VnTx9tZqNb+/tYG2P/UmvFISgR3hbhNQllpbM2WDm3svPIsOyfLRU08Xnh79GxOj/WswhKd6TqG4hpTdUgLLLXu168meVLRochGKli0JjHVi0ybYsAH273/cTKeDl17SRkR16wYyx+mLOXNGG1F28SJYWMDMmdrk+jod2pxdEybA+PFa41degfXrwSlrLxdVyoC/f2cePPgDS0sPatU6grV1sSx9TQH07s01yzXc6Ae///4u9vaLGTcu/ZuRD9epyTERInOFh8PoTwxYLJ7LR20+49aweMKB2bN0eO/QPqbWrl2bRYsWUatWLdMGK1KQ/jA1OSYiv/A/EsPegbO5GvINK6NjuB+l3V/UC2bPHk7nLtPR6dI5b4TIU6QolQHZlUwSE+H992HJEu33LwYEMr7u7+j/2ErCUW+Cm8UT2AFinzh/LxBfHM/iw3Ar894Lj556kcLT4yeDVZQtNvcssbmtw+XPMFwPPvEWsrbWJn56VIiqUIGgOzo2bYKNG2HfPm0+iUcaN35ciCpS5IV2RzwlIgIGDNBW5APo00e7Ys/O7mGDn3/WRk3FxECZMrBlizafVxZKSorkxInGREf7Y2dXjZo19+eI0X951tmzULUqZz83EtISFi6cQteun9CjR/o3JR+uU5NjIkTW8PGBr/rf5JPodygxypsHDbRFPWbO0BMZZUSv1zNs2DC++eYb+b+XQ0h/mJocE5HXHd8VwZ73vuNc5AzWhiUQE6/d7+YG/fvV5NPRa3B2rmDaIEWOIEWpDMjOZKIU/PADfPaZ9nvVqjBoEPTpGoOj3y7U1i2EX/6FwIb3udv08egpfbwe99BaFC43koI133g4FObJ7T4sPIWfJvbeP8SGnyE25jIxhhvE6oMwmMX+e1BGsAoBm0CwuQU2t8H2tvazdSCYJT7VvkqVx0WoJk3A2prgYJJHRO3dm7IQ1bChVojq3l27zExkPqVg+nTtfWUwaH+iTZugXLmHDU6e1OaZCgiAggW1a/9efTVLY4qLu4Gvb30SE4NxcelIlSq/yrcnWaVrV/j1V46vdyTKLYwvvviN+fM7U61a+jclH65Tk2MiRNaJiYFxXymuTf+Fb1q/y71hYYTEw/x5sMtHa1O4cGFmzZpF9+7dZWVXE5P+MDU5JiIvUgr2b3nAvmFj8U1YwuZ7SRgeLpxeqiQMfq8Z7w35kYIFy5s2UJGjSFEqA0yRTH7+Gd5+G6Kjtd+trbXL+wYOhJcaK3Qn/EjYvp7g+2sJrHEr5eip6xa43auOwSyOWKt7xNhHEOsSi8H2GX/a/yo86Sy1mbJdXcHFRfv36ZuLC1SunDzEKSREm7JowwbYs0e7WuyR+vUfF6KKyZVb2WbvXujZE+7c0WpPK1ZoqxcC2h+se3dt+JpOB5MnazOmZ+EH/IiII5w40Qyl4vHyGkWZMlOz7LXyraNHoX59lF7Hvp02GImhX79zXLhQAWvr9G9OPlynJsdEiKx37BiMeDuct0JG0OijlTxopN03a4aO20Ha55u2bdsyd+5cSpcubeJo8y/pD1OTYyLyEqXAe/Ud9oz6kEPGjfjcf3yCV7OGjmFDO9Grz1ysrWWkgUhNilIZYKpkEhoKq1fD4sXg7//4/goVtAms+/XTakHq9m3CfWYQGLmOu2Vuo/5t3s8nC0/3bbCJKohtnCs2yhNri2KYOXmkXWxyddWu9XqO4sTdu1ohauNGbdj9k4WoevUeF6KKF8/YsREvLihI+zs8msPr00/h228fznGekAAffACLFmkP9umj/Wxjk2XxhISs5+zZNwAoV24Rnp4Ds+y18h2loGVL8PEhfkgPDvXYgMFgxpAhMVy8aPlCm5QP16nJMREieyQkaN+X/Dn+KFPqvIXF+xcI99BWmF2zRpsGwdramrFjx/LJJ59gZSUrO2U36Q9Tk2Mi8gKDAX5feJ3dXwzGh784Fabdr9dD05fMGPFhH15tPxVLS1eTxilyNilKZYCpk4lS2mCHxYth3brHo6csLLRRLgMHwssva51CQngAwYcnEBaxF0vlgo15MWxtymLjUAnrQtUwK+QFDg7pHv1iNGpFspAQ7RYc/PjnR78HBoKvr9ZpPVKnzuNCVMmSmXhQRIYkJsLo0dolfaBN+7VuHbi7o73hFiyA4cO1P2bdutrQvSwc0nb9+gSuXx+HTmdOtWrbcXJqmWWvla94e0Pr1mBpSeiplZy68ya3bpVh3bpL/P77i23S1P1hTiTHRIjsdeECfPKxwmH7Kj7tOpywtyK4/kBbzMPPT2tTvnx5FixYQIsWLUwaa34j/WFqckxEbpaYCD//cIZd37/LDnWY6w8nL7e0hFfbWPLhqGE0bvwV5uYOpg1U5ApSlMqAnJRMIiK04sHixXD8+OP7S5XSRk+9/fbzr1QXF6eNbHqywPRvP9+9C0lJz7fdWrW0QtTrr2txiZxr40ZtEvSoKO19s3GjNtk8oM0m+/rr8OCBdv3oqFHapFT2mT8huVKKc+f6EBKyBnNzR2rVOoytrVyDniFKaQVFX1/48EMCP6nAxYuDOXz4Ve7f/4Mffnixzeak/jCnkGMihGns3AljP4ym0+2v6DxwFiFtDezcCQvmw4NQrU2fPn2YOnUq7u7upg02n5D+MDU5JiI3iouD1V8eZPeC99huOM39OO1+e3vo1tmGESPHULXaKMzMbE0bqMhVpCiVATk1mZw4oRWnfvpJK1YBmJlBx47Qu7d2TvqsYtOj56SHo6O2koK7u/bvo5u7uzblVM2aIFM55C7nz2vzYJ87p13CN3WqNkhKpwOuXoX+/bXJqED7Y0+YAO+88/B6v8xjMMRx6tTLREQcwsamDLVqHcbCwiVTXyNf+flnrahYoABcvcr5+59x585yNm78kFq1ZvDOOy+22ZzaH5qSHBMhTMdg0OZHXDAmgM+cB1L6g78JLK6tZLxli/ZZyNHRke+//56BAwei1+tNHXKelln94d69e/nhhx/w9fUlKCiIX3/9lS5duiQ/rpRi/PjxLFq0iNDQUOrXr8+8efOoXLlycpv4+Hg+/vhj1q5dS2xsLC1btmT+/Pl4PbGqTmhoKMOHD2fLli0AdOrUiTlz5uDo6JjcJiAggKFDh7Jr1y5sbGzo1asXU6dOxdLy+S6DlxwhcpOoKFg84g/2rPsA74RrxDwclODuDm++XpD3h0+kdOlB6PVyebRIPylKZUBOTybR0doIl8WL4eDB9D3XwiJ1cSmtn93ctKKTTM+QN0VFaSPt1q/Xfu/ZU/tAX6AA2if6zZu1yacuXdIaVKqkLRPZrl2mToSekBCCn1994uKu4+DwEpUr/4KlZaFM236+kZSkLTpw8SKMG0fUx105frwmYOTDD32YP785jRq92KZzen9oCnJMhDC9yEiYMgX2/7Cf75q8hRp0jVP3YMaMx6mrfv36LFy4kBo1apg01rwss/rDP//8kwMHDlCrVi26deuWqig1efJkvv32W1asWEG5cuWYOHEie/fu5cKFC9g/HNE9ZMgQtm7dyooVK3BxcWHUqFE8ePAAX19fzMy01X7btWvHrVu3WPRwLs1BgwZRokQJtm7dCoDBYKBGjRoUKlSIadOmcf/+ffr160fXrl2ZM2dOth4TIbJSaCjMeWcle7d/xp74YJIezgtcujS89aYbA977AU/PXuj1mfultMhfnrs/VCKV8PBwBajw8HBTh/KfTp9W6sMPlapWTamXXlKqa1elhgxRatw4pebNU2rjRqX27lXq/HmlHjxQymg0dcQipzAalZo1Sylzc6VAqYoVlTp37okG8fFKzZ6tlIuL1gCUatVKqZMnMzWOyEh/tXevvfLxQe3f767u3fsjU7efLyxerP19XF2VMSxM+fk1UT4+qHHjuitQ6v79F990buoPs4scEyFyjoAApfr2NqgB1gvVqXft1I5tqGHDULa2KEDp9Xr10UcfqYiICFOHmidlRX8IqF9//TX5d6PRqDw8PNT333+ffF9cXJxycHBQCxcuVEopFRYWpiwsLNS6deuS29y+fVvp9Xq1fft2pZRSZ8+eVYA6fPhwcptDhw4pQJ0/f14ppdS2bduUXq9Xt2/fTm6zdu1aZWVl9dz7KDlC5GR3ggxqVIupqp6VgwKSb7VqoWZPLa6Cg39RRqPB1GGKPOJ5+0MZ05zLVa6sfSt46hTs2webNsH8+fD11/D++9qE402aQPny4OSUqYNcRC6n02mX7e3ZA56e2uV8detqo/AAbUbDDz6Ay5fh44+133fs0K7ZHDBAm+k+ExQoUIWaNfdha1uZxMRg/P3bc/HiEAyG6EzZfp4XG6v9hwcYO5aQuD8ID98H2LJgwTTc3MDZ2ZQBCiFE1ilaFFat1jN473t8fOYOJ98dwrvuelau1Bb1MBqNzJgxg4oVK7Jp0yaUXCCQ61y7do07d+7QunXr5PusrKxo1qwZBx9eMuDr60tiYmKKNp6enlSpUiW5zaFDh3BwcKB+/frJbRo0aICDg0OKNlWqVMHT0zO5TZs2bYiPj8fX1zdL91OIrHT9WhKDao+mRfECTPP5mKPx4ej10KIFrF5aCW/vvxg28hpubq+h00mJQGQveccJkc81aqTNV9aihXZZX48e2hRSISEPGzg6apfunT+vXeenFCxfDmXLwrhx2pMyqECB6tSufRwvrw8BCAxcyPHjtYiIOJrhbed58+bB7dtQtChJA3tz5crHAISHjyUkpBgVK5o4PiGEyAZ168JfBwpQYvZ8hi+6jNn3TZncDyZP1r54uX37Nt27d6dDhw5cu3bN1OGKdLhz5w5Aqsnr3d3dkx+7c+cOlpaWODk5PbONm5tbqu27ubmlaPP06zg5OWFpaZnc5mnx8fFERESkuAmRU5w+FcWb5QfSpLwti/0mcy4hFisr6NIFtmyszy+/7KP3gDM4O7dGJ6MXhIlIUUoIgZsb/P23ttgewLJlWs1pxgxtaVgASpbUloI8dEirZMXEaJOglysHS5dqs89mgJmZNWXKzKBaNW8sLYsQG3sRP79GXL8+AaPxOZeCzG/Cw2HSJO3n8eO5cecHEhKCsLEpw/79owCoUMGE8QkhRDbS6aBbN/j7UkmuvrmHCSN20uRoEVbOhr59tfU6tm3bRqVKFZk0aRIJCQmmDlmkw9MnzEqp/zyJfrpNWu1fpM2TJk2ahIODQ/KtaNGiz4xJiOzg432HjkW70bS2A+suLuFWYiIFC0K/t+Dv31uxapUv7bsextHxJVOHKoQUpYQQGnNz+P572L8fatXSVmscORKqV9cKVskaNNAabdwIpUpBUJA2a3rNmk81fDHOzq2oW/cfChXqCRi4fn0cJ082ISbmcoa3nedMnQoPHkDFikR3q8OtWzMAsLWdxdy52ioFLVqYMkAhhMh+VlYwahSsuPIya91v8Nu7Mxjpas3SxVqqiouL5/PPP6dGjers2bPH1OGK/+Dh4QGQaqRSSEhI8qgmDw8PEhISCA0NfWab4ODgVNu/e/duijZPv05oaCiJiYmpRlA9MmbMGMLDw5NvN2/efIG9FCLjrl+PYWjn6dS0KUObNoX5/dYvhBqMeHjA8A9g1/auzJt/hqatvLG3r2XqcIVIJkUpIUQKjRvD0aPa6o6FCmlzTbVpow3zvXr1YSOdTpuw7OxZmD5dm7DM319r2K4dnD6doRgsLJypXHkdFSv+hJmZAxERhzl+vAaBgYtlPpBHgoO1Yw+obydy+dpIlErC2bkjw4a9SlwctGypXY4phBD5kasrzJ5nxrBDH/Ld2WDCJ/Zj0Vvw+eda2jp37jzNmzfn7bff5u7du6YOV/yLkiVL4uHhgbe3d/J9CQkJ7Nmzh0YPl5atXbs2FhYWKdoEBQVx+vTp5DYNGzYkPDyco0cfTw1w5MgRwsPDU7Q5ffo0QUFByW3+/vtvrKysqF27dprxWVlZUbBgwRQ3IbJLTEwSEz75mQbO9ahcugDzt4ziZNwVEpV2McOXY3Xs+LMf30++Qu2Gm7Czq2TqkIVIJUcXpSZNmoROp+PDDz9Mvk8pxddff42npyc2NjY0b96cM2fOpHhefHw8H3zwAa6urtjZ2dGpUydu3bqVzdELkXuZmWmDny5ehA8/1H7fvBkqVYKxY5+YRsrKCj76SJsM/aOPwMICtm/XhlcNGgT/Mv/C83J370Xduv/g6NgcozGaixcHcfp0ZxISQv77yXndxInaJZT16nGvsZHQ0B3odFYcOTKTvXvB1lYrLMr0AEKI/K5CBdiwvSAVF65g3MJLNDxUl7U/QKdOWh+5cuVKypcrw+LFizEajaYON1+Kiori5MmTnDx5EtAmNz958iQBAQHJ5wLfffcdv/76K6dPn+btt9/G1taWXr16AeDg4MA777zDqFGj2LlzJydOnKBPnz5UrVqVVq1aAVCxYkXatm3LwIEDOXz4MIcPH2bgwIF06NCB8uXLA9C6dWsqVapE3759OXHiBDt37uTjjz9m4MCBUmwSOYbRqFi59CAtS3WgiKMt46a+zpHQY8QYFR4e0Ls3/G+JA9u2jmTslzepXGMFNjalTB22EP8uq5cBfFFHjx5VJUqUUNWqVVMjRoxIvv/7779X9vb2atOmTcrf31/17NlTFS5cOMVSv4MHD1ZFihRR3t7eys/PT7Vo0UJVr15dJSUlPddry1KuQqR05oxSrVoppc1yrlSRIkr99JNSRuNTDS9dUqpbt8cN7eyUmjBBqejoDL2+0WhQAQFT1e7dlsrHB7V/fyF19+6WDG0zV7t6VSkLC6VAJe38Qx08WFT5+KBOnPhKFSigHfrZszPnpaQ/TE2OiRC5V1KSUkuWKNXD/Q916G03tWAmqkyZx8uiN2hQV506dcrUYeYamdUf+vj4pFie/tGtX79+SimljEajGjdunPLw8FBWVlaqadOmyt/fP8U2YmNj1bBhw5Szs7OysbFRHTp0UAEBASna3L9/X/Xu3VvZ29sre3t71bt3bxUaGpqizY0bN1T79u2VjY2NcnZ2VsOGDVNxcXHPvS+SI0RW2b//ourcYKAqbGOX4v9JwYKozp1R86bbqgsn31FhYfuV0WgwdbhCPHd/mCOLUpGRkaps2bLK29tbNWvWLLkoZTQalYeHh/r++++T28bFxSkHBwe1cOFCpZRSYWFhysLCQq1bty65ze3bt5Ver1fbt29/rteXZCJEakajUr/+qlTJko9rTi+9pJSfXxqN9+9Xqn79xw09PZVavlw7G8iAyMhT6ujRqsrHB+Xjgzp/fqBKTIzM0DZzpb59teP6yivq6tUvlI8P6uDB4urVV6MVKNW4sVKGTPosIv1hanJMhMj9IiKU+vLzJPVJkYnqyJcWauhQlI2NdoJnZqZTI0d+pCIj82F+SSfpD1OTYyIy07VrQWrA61+rUo5uKQpR1taol19GfT/OUvnv6a4ePPBRRmPGPmcLkdmetz/MkZfvDR06lPbt2ycPt33k2rVr3Llzh9atWyffZ2VlRbNmzTh48CAAvr6+JCYmpmjj6elJlSpVktsIIdJPp9PmlTp7VrtyzNZWm++8dm147z1IMR1H48baKn3r1kGJEhAYCP37Q506sHPnC8dQoEA1atU6ipfXKEBHUNBifH1rEh5+OIN7l4v4+8Pq1QDETBxCQMAUAAICZrBtmy3W1trqifoc2bsLIUTOYG8PE74144NDY1l6IYSq+19n/Xho1gwMBsX06TMoX64E8+bNIyr5mnUhhMh64eERfDl6EVWLlKF0qcIs2/g1V8NC0Ouhfn34YpQZe35qycb12/nkq2iqNN2Ik1NzdDozU4cuxAvJcact69atw8/Pj0mPljl/wqPVMJ5e/cLd3T35sTt37mBpaYmTk9O/tnlafHw8ERERKW5CiLRZW2vzSl24AG++qQ2FWrRIm0xx9mxITHzYUKeDnj21mdJ/+AEcHODkSWjVCjp00O5/AWZm1pQpM5Xq1XdiZVWU2NjLnDjxEteujcNoTPzvDeR2Y8dqB/3117lsvRSlErC1bcN773UBYPx47W8hhBDivxUtCj+ud6TW4g2s23iG0Q5VmPIFFC4MgUH3GTZsGEWKuDNy5EiuJq/2IYQQmSshIYGlS36lQcW6uLk4MnHye5wOvIJRaXO6jnhPx9/z6rBt/UbGT4mlXtcdOLu2Qa83N3XoQmRYjipK3bx5kxEjRrB69Wqsra3/tZ3uqZl7lVKp7nvas9pMmjQJBweH5FvRokXTH7wQ+YyXF6xZA3v3Qo0aEBYGI0Zoy22nGAxlbQ0ff6xNhv7BB2BuDn/8AVWrwpAhEPJik5Y7ObWgTp1/cHPrDRi4cWMCJ040JibmYibsXQ518CBs3QpmZtwb+zIPHvyBTmfB/PmzCA/XUacOjBxp6iCFECL3qVsXVh+pRPRb/lxcu5E1XRwZMUwrWkVExDBjxgzKlClDly6d8fHxkZVghRAZZjQa+euvPbRp2g4XBzveHdiVI+ePk2BQFCsG7/SFXyaWZfdPC5k+P46Wg4/hXLw7er2FqUMXIlPlqKKUr68vISEh1K5dG3Nzc8zNzdmzZw+zZ8/G3Nw8eYTU0yOeQkJCkh/z8PAgISGB0NDQf23ztDFjxhAeHp58u3nzZhbsnRB5U5MmcPw4LFwILi5w5ow2GKpbN7h+/YmGrq7aUKozZ7TrAA0G7UllysCkSRAbm+7XtrBwpFKl1VSqtA5zc0ciI49x/HgNbt9ekPdOGJSC0aMBMLzTl8uxPwAQGTmSlSvLY2GhXbZnLl+YCSHEC9HptNw190x3fIvfxeyPGazt5MzkCVrRSinF5s1bePnll6levRpLliwh9gVylxAifzt58hS9e/bF3bkAbds25+9924mKS8LFBXq8BitGFmb/vK/5cWkUr429SKEa76HXW5o6bCGyTI4qSrVs2RJ/f//kJWFPnjxJnTp16N27NydPnqRUqVJ4eHjg7e2d/JyEhAT27NlDo0aNAKhduzYWFhYp2gQFBXH69OnkNk+zsrKiYMGCKW5CiOdnZqbNK3XpkjYYyswMfvkFKlaEr76CmJgnGpcrB7/+Crt3a3NMRUbC559DkSJasWr6dK3KlZT03K/v5taTOnX8cXRsidEYy6VL7+Pv34H4+LQv2c11EhLg009h3z6wsuLmUFfi4q5ibl6EIUO+ALSr+qpWNXGcQgiRB1hZwajPzJl45kPOVrvHzbVrmVy5OKvnQefO2gBgf//TDBw4kKJFvfj888+5deuWqcMWQuRgN27c4ONRH1Lc04maNWuwZsNq7oXHYmcHr7aGuQMdOPDtYNYsv0O/aYEU7TAOMws7U4ctRLbQqRw+nKB58+bUqFGDmTNnAjB58mQmTZrE8uXLKVu2LN999x27d+/mwoUL2NvbAzBkyBB+//13VqxYgbOzMx9//DH379/H19cXM7P/ngAuIiICBwcHwsPDpUAlxAs4fVq7lG/XLu33okW1aaV69NC+iU5mNMLatVpRKiAg5UYKFNAmTG/aVBuOVbeudibwDEoZuXVrNlevjkapeCwsXClXbjGFCnXJ1P3LVhcvQq9e4OsLQNy0TzlaZw5GYyw7dqzj2297UrWqVsezzIIv0aQ/TE2OiRD5i1Lw99/w17c+vOwxAotX/dl8VPt+5dHgfTMzM7p168aIESNo2LDhf04rkVdIf5iaHBMBEBcXx6FD+9m8eS3ef/7J2YtByY9ZWECDetChuCWtSnWg6lvjsShbxYTRCpE1nrc/zHVFKaUU48eP58cffyQ0NJT69eszb948qlR5/B85Li6OTz75hDVr1hAbG0vLli2ZP3/+c88VJclEiIxTSvvAPnIk3Lih3de0qXYFX/XqTzVOTAQ/P22Cqr17tWX9wsJStrGy0pYcadpUuzVsqBWu0hAVdZpz5/oQHX0KAA+PAZQpMxNzc/vM3cmspJR2Pd7w4dpQM2dnWLKE02VXc+/eLyQmNqd1612Ymek4ckRbBTErSH+YmhwTIfKvs2dh7TdnqBI5FK/ue/C5BZs2aet4PFK7dm1GjBhBjx49sLKyMlms2UH6w9TkmORPsbGx7N+/hz/+WIuP9y7OXbxNYtLj02ydDmpUgw4ldbzi0pAar3+Jfes2T31bK0TekmeKUqYgyUSIzBMbC1OnPp42Sq/XLvX75httDqo0GY3acKtHRaq9eyE4OGUbMzOtEvOoSPXSS/DEqptGYzzXrn3FzZs/AApr65JUrPg/HBwaZ9m+ZpoHD2DQIO1MB+Dll2HVKh7YnOGff9oAZnzyyUmOH6/C6NHasc0q0h+mJsdECHH3LqyeFoij73AqdfuN0xYGfvkFvL0fr0Lr7u7OkCFDGDx48L/Oa5rbSX+YmhyT/CE6Opr9+33Ytm0NPt67OX/pTooiFGjfJ9asCA0coZlVOaq2+BiPAb3B1tY0QQuRzaQolQGSTITIfAEB8MknsGGD9ruTk1aYeu+955icWyltwqoni1SPhl89otNpkyo9KlI1aQIeHoSF7eXcubeIj78B6ClWbDQlSozLuRNG7t4NffvCrVvagfn2W/j4Y4wkcuxYdWJjL3D27IcMHTqD8uW1b+f/46rGDJH+MDU5JkKIR+LjYdOKCCI3fUaN1su5UzaerVth82a4d09rY2lpSc+ePRkxYgS1s2pYq4lIf5iaHJO8KSoqir17/2b79vX4eO/h3KVgDIaUbVxdoWYFaFgQapsVoaRra4q16Y5Du5dA3gsiH5KiVAZIMhEi6+zZo12R9s8/2u9Vq2qX9DVvns4N3bihTfz9qEh14ULqNuXKQdOmJDWrw6UKfxMc9QsABQrUomLF1djZVczQvmSqxERtVvjJk7UiXLlysGYN1K5NZKQf58+/TXS0P0q507HjBWJiHNi3T5t2KytJf5iaHBMhxNOUgr07Ezk3+zuq1ZpOTKMI9u7XFv04c+Zxu8aNGzN8+HC6du2KeR5YLlX6w9TkmOQNERER7Nmznb//Xo+P917OXbqH0ZiyjZsb1CwHjQpCDV0xirm0w6vta7h2bPyvU0wIkZ9IUSoDJJkIkbWSkmDxYvjiC+1KNdBWNHr/fWjZUrsyL92Cg7W5qB4VqU6d0s4SnhDS1YWLAyNJsk5AjxWlyvyAp+cQ9HoTnxhcvqxNZn7smPb7u+/CzJkYbSy4ceNbAgK+Q6kkzM1d+fbbdWzf3pIRI+DhVHtZSvrD1OSYCCGe5fIlxZ5vllDG4yt45Q7nrmjFKZ9dkPRwZIWXlxdDhw5l4MCBuPzrtew5n/SHqckxyZ3Cw8Px8fkD77/XsXvnAc5ffpCqCOXuDrXLQcMCOqrrSuDh2J4ir3bB87WGckmeEGmQolQGSDIRIns8eKANDlqwgOTEX7QovP22ditVKgMbDwuDAwceF6mOH4ekJOJd4PxnEFpXa2YWZ4bDHTecosriZFYXO9e66LyKaYF4eDzHtYUZoBSsWAEffADR0do1jYsXQ7duREaefDg6SpusvVCh11myZB4zZxaiZEnw9we7bFgpWPrD1OSYCCGeR1gYbPtuK4ViR2H16iXuxsCWLbB1M4SGa22sra3p06cPw4cPp2rVqiaN90VIf5iaHJPcITQ0lF27tuDtvYE9Ow9y8WpYqiKUpyfUKg0N7XRUUaUp5NQJz3adKPZ6fXQ2WTh3ghB5hBSlMkCSiRDZ6+xZrTD1008QGvr4/hYtYMAA6NYNbGwy+CLR0XD4MOzdi9q7h9tuB7jeK4kkh5TNzMPB6QQ4ngCnU3psDIXReRXVilReXqn/LVz4xYZ2hYZqE2pt3Kj93rw5rFqFsYg7N258R0DAtyiVhIWFK0WKzGPhwh5Mnqw13bFDG1GWHaQ/TE2OiRAiPZKS4O8FR9GdHYp9m+PE2MKuXfDLBrh07XG7l19+meHDh9OhQwfMXmjIcPaT/jA1OSY5T0JCAufPn8XPz4dDB7azx+cIF6+GPz2gHi8vqF0KGtroqZhUFkfHLni070jpN+piZpND5yIVIgeTolQGSDIRwjTi4rTJYZct01YwetQ7OTjAm29qBao6dTJp9dz4eNQ/p4gK3EtY9H5Czf8h3PEmBsukFM0s7z5RpDoB1k8tAoiZmVaYelSkep7C1d690KcP3LypjcT65hv45BOiYk9z7ly/5NFRLi7dOHx4PmPHunH3rvbU7Lps7xHpD1OTYyKEeFHH/7hK8B+DcWy6gwR3xenTsGk97D8EhoejNLy8vHjjjTd48803qVmzJrocvGS89IepyTExHYPBwOXLlzlxYg8nT+7l9MkTnD17g4DA6FSTkoP2Ma1OSWhobUa5xIoUKNiFQq+2p2Kf2ljYWmT/DgiRx0hRKgMkmQhhegEB2pVty5fD9euP769SRStO9ekDhQpl7msajYlERh4nLGwXoQ92Eh5xEKXiU7SxDrfD6VIBHI8ZcNoViuW9ND7lPO3JwpWTE/z5p1ZxK1MG1qzBWLsGAQHfcePGxIdzR7kQGzuPjz7qwZkz2slI+fIwdSq0b59JRbnnJP1hanJMhBAZFXDmAWcWDcOh5kYSSiQRHAybf4Hft5oRGfs4r5QrV45evXrx5ptvUq5cORNGnDbpD1OTY5L1lFLcvHmTEycOcPKED//4HuXM2WtcuxlJQmLap7Z2dlCqOFRwhXqWZpSOr4p1wddwbf8qVfrUwMY+9y88IEROI0WpDJBkIkTOYTTC7t3a6KlNm7TRVAAWFtCxo1agatMma6Z+MhhiiYg4RGjoLsLCdhERcRRIWYSytSyPk7E6jqElcLzhiMWNB9oIqFu3tH8DA7VrN542YADMmkUUVzl//m2iok4AYGnZlWnT5vPLL+4AODvD11/D4MHaPmc36Q9Tk2MihMgskffjODJ9NDZFFpFYKZaEBDh6FHZt1XPAT5GQ9Phjeq1atXjzzTd544038PLyMmHUj0l/mJock8wVEhLCqVNH8PPbyT9HD3L6zGWuBIQTHWtMs72VFZQoDmUKQUUbKK4K4J5YGmvqg0cjHF+qTvU+VSnolDsukRUiN5OiVAZIMhEiZwoLg3XrtALVo4XqQJuIsl8/6N8fypbNutdPSookPHxfcpEqKuok8GQXqqNAgVo4Ob2Mo+PLODi8hLnORlsZ8FGR6tYtqFIFY4umBAR8z40b36BUImZmzuzbN48vv+yJ0ajDwkKb//yLL7TBVaYi/WFqckyEEJktKcHA0blTMRqmYqh+D2UJMTHaorK7/oJjJx8vCKLT6WjSpAm9evWie/fuJl29T/rD1OSYvJjw8HD++cePE37enDy8l9P+F7kU8ICwyLRHpJuZQbFiUMYdyttBcZ09HoYSFDCvj75QUxwaVKNYy3IUr2CDXp/NOyOEAKQolSGSTITI+fz9teLU//4H9+8/vr9JE20Q0uuvZ/3qdImJ9wkL2/OwSLWTmJjzKR7X6cyxt6+Pk9PLODm1pGDBBuj1VkRF+T8cHeUHwIMHXfjwwwXcvOkBQNeuMHmydnWfqUl/mJocEyFEVgo5e43zm6aCbiu66jcx2EN4OOzZAzu94Z/Tj9uam5vTunVr3nzzTTp37oy9vX22xir9YWpyTNJmMBi4d+8egYGB3L59iYDrZzh9bD///HOWywH3CX6QmObzdDrty8fSnlDOHoqbFcBDlcDJsh7mhZrjULcWxVuVpXhZSyk+CZHDSFEqAySZCJF7JCTA1q1agWr79sffJBcoAG+8oRWoGjTInnmY4uMDCQvzITR0F6GhO4mPv5Hicb3eGnv7ukREHEapRIxGJxYtmsv69W8COmrXhunToWnTrI/1eUl/mJocEyFEdol+EMmplXNICF2DeeVzJLkbCQkBHx/YuQMuXX7c1sbGhvr161O3bt3kW/HixbN0onTpD1PLb8ckJiaGoKAggoJuczPgHLcvnybgyiVu37pF8N173A2L4n5EHGGRhuTJ/P9NoUJQqiiUc4Di5nZ46IpTyLoeVm4v41CnHiVeLkWx0hZSfBIil5CiVAbkt2QiRF5x+zasWqUVqC4/8UG9QgWtONW3L3h4ZF88sbHXtEnTH17ul5BwJ/mxs2c788UXCwkN9aBIEZg0CXr3Jsd90JL+MDU5JkIIU0hKMHBy7VrCLy7CqsQxksrGERAAu3Zpt5s3Uz+nUKFCKYpUdevWxc3NLdNikv4wtbxwTIxGI/fv3ycoKIjA2wHcvODH9YvnuHn9OsF3gwkJDeV+RAz3IxOJiXv+U0mdDhwdtfkyXQpC8YJQ3NIOTzMv3GzqYuveCqfajSj5cimKljDLcZ+JhBDpI0WpDMgLyUSI/Ewp2LdPK05t3KjNywHa/AMdOmgFqnbt0j9xuFIQHw/R0RAVpf376PbfvyusrM7j6LiXgweLcfRoW2xtdXz2GYwalfWXGr4o6Q9Tk2MihDA1peDcX/sJ2j8DS1cfDJVDuXYLzp2DCxe0f69eBUMa0/EU9fSkXsOG1KhRgwIFCmBpaYmlpSVNmjShfPny6Yojr/aH8+fP54cffiAoKIjKlSszc+ZMmjRp8lzPzQnHJCkpiaioKCIjI7V/IyIIvxdExN0gHoQE8+DuXSIehBIRHkpEZAQRkeGEhN7jfkQU96PiCI3671FNT7KyAhcXbQ7MQvbgYq3DxdICB70NDmYO2Ju5U9C6KAUKVMDCoRyWbhWxL12UonXcKVpcL8UnIfIoKUplQE5IJkKIzBERARs2aAWqQ4ce3+/url3e5+CQdiHp34pMxnR8SPs3Oh28/TZMnKjNk5CT5dX+MLefcAghxJNu/nONS1vnYow9hnnBy5gVuUesSyKXrz4uUl24oI2m+rdP/t8M7M0Xi1an63XzYn+4fv16+vbty/z582ncuDE//vgjS5Ys4ezZsxQrVuw/n/+ix+SUz26+GdMXg9GI0agwGBVJRiNJRgNJBgOJBqP2u8FIotFIkkGRZND+TTQqkgyK2ARFbJwiIY1Ff19E8qgmJ3CxBRdrM5wsrHGwKICDuQsO1kVwsC9NAaeKWLuVp0DxijiVK4y7lwUFC2bP1AlCiJxLilIZkBcTrBACzp6F5cu1S/xCQjK2LSsrbXTTo1uBAmn/nNbvNWtCpUqZs09ZLS/2h6Y64RBCiOwUcvMB1/ZuJ/L6HpThJBaO14h1vs/5u0bOn4eAAG1exqQk7da1fBtGLduertfIi/1h/fr1qVWrFgsWLEi+r2LFinTp0oVJkyb95/Nf9JhsmTeXzsM+eKGY/425Odjago0NWFs//NkKbC3B1kqHjbkOGws9tuZmOFra4mjpiKOVO04OJXB2rYCta3nsi1XGuVw53ItaYWubqeEJIfK45+0PzbMxJiGEMKlKleCHH+C772DbNu1mZvb8xaQnfzeX3jPXmj59Ou+88w7vvvsuADNnzuSvv/5iwYIFz3XCIYQQuYFbUWfcevcCeiXfpxRUvnyTmy7biS51HENCOIakSCAKj0r9TRZrTpGQkICvry+jR49OcX/r1q05ePBgms+Jj48nPj4++feIiIgXeu1i5SrRu4krej2Y6XXo9TrM9XrM9WaY6c2x0JtjbmaOmd4MC70F5mYPb3pLLMwtMNNbYWtdEFs7F+wdXCng6IaNgxuW9k5YO7li7eSCrasrBRytsLHJeXNYCiHyLzmtEkLkOxYW0LmzdhP5iylPOIQQwtR0OihWtijFyg4EBpo6nBzn3r17GAwG3N3dU9zv7u7OnTt30nzOpEmTGD9+fIZfu8YrL7P6lbsZ3o4QQuQ2UiMXQgiRb7zoCYeDg0PyrWjRotkRqhBCCBPRPTUZklIq1X2PjBkzhvDw8OTbzbSWQRRCCPGvpCglhBAi35ETDiGEEE9zdXXFzMws1ZcUISEhqb7MeMTKyoqCBQumuAkhhHh+UpQSQgiRb8gJhxBCiH9jaWlJ7dq18fb2TnG/t7c3jRo1MlFUQgiRt0lRSgghRL4hJxxCCCGeZeTIkSxZsoRly5Zx7tw5PvroIwICAhg8eLCpQxNCiDxJJjoXQgiRr4wcOZK+fftSp04dGjZsyKJFi+SEQwghBAA9e/bk/v37TJgwgaCgIKpUqcK2bdsoXry4qUMTQog8SYpSQggh8hU54RBCCPEs77//Pu+//76pwxBCiHxBilJCCCHyHTnhEEIIIYQQwvSkKJUGpRQAERERJo5ECCFM61E/+KhfFJIjhBDiEckRqUmOEEIIzfPmCClKpSEyMhKAokWLmjgSIYTIGSIjI3FwcDB1GDmC5AghhEhJcsRjkiOEECKl/8oROiVfbaRiNBoJDAzE3t4enU6XrudGRERQtGhRbt68meeXDZd9zZtkX/OejOynUorIyEg8PT3R62XBVpAc8bxkX/Om/LKv+WU/QXJEZpMc8XxkX/Om/LKv+WU/IXtyhIyUSoNer8fLyytD2yhYsGCef4M+IvuaN8m+5j0vup/y7XdKkiPSR/Y1b8ov+5pf9hMkR2QWyRHpI/uaN+WXfc0v+wlZmyPkKw0hhBBCCCGEEEIIke2kKCWEEEIIIYQQQgghsp0UpTKZlZUV48aNw8rKytShZDnZ17xJ9jXvyS/7mRvkp7+F7GvelF/2Nb/sJ+Svfc3p8tPfQvY1b8ov+5pf9hOyZ19lonMhhBBCCCGEEEIIke1kpJQQQgghhBBCCCGEyHZSlBJCCCGEEEIIIYQQ2U6KUkIIIYQQQgghhBAi20lRSgghhBBCCCGEEEJkOylKvYD58+dTsmRJrK2tqV27Nvv27Xtm+z179lC7dm2sra0pVaoUCxcuzKZIMy49+/rLL7/wyiuvUKhQIQoWLEjDhg3566+/sjHajEnv3/WRAwcOYG5uTo0aNbI2wEyS3v2Mj49n7NixFC9eHCsrK0qXLs2yZcuyKdqMSe++/vTTT1SvXh1bW1sKFy5M//79uX//fjZF++L27t1Lx44d8fT0RKfT8dtvv/3nc3Jzv5TTSY5Im+SIGlkbYCaRHPHvJEeIzCA5Im25OUfkl/wAkiOeRXJEBiiRLuvWrVMWFhZq8eLF6uzZs2rEiBHKzs5O3bhxI832V69eVba2tmrEiBHq7NmzavHixcrCwkL9/PPP2Rx5+qV3X0eMGKEmT56sjh49qi5evKjGjBmjLCwslJ+fXzZHnn7p3ddHwsLCVKlSpVTr1q1V9erVsyfYDHiR/ezUqZOqX7++8vb2VteuXVNHjhxRBw4cyMaoX0x693Xfvn1Kr9erWbNmqatXr6p9+/apypUrqy5dumRz5Om3bds2NXbsWLVp0yYFqF9//fWZ7XNzv5TTSY6QHPEkyRE5l+SIf5eb+6WcTnJE3ssR+SU/KCU5QnKEJiv6JSlKpVO9evXU4MGDU9xXoUIFNXr06DTbf/rpp6pChQop7nvvvfdUgwYNsizGzJLefU1LpUqV1Pjx4zM7tEz3ovvas2dP9cUXX6hx48blioSS3v38888/lYODg7p//352hJep0ruvP/zwgypVqlSK+2bPnq28vLyyLMas8DzJJDf3Szmd5AjJEU+SHJFzSY74d7m5X8rpJEfkvRyRX/KDUpIjJEdosqJfksv30iEhIQFfX19at26d4v7WrVtz8ODBNJ9z6NChVO3btGnD8ePHSUxMzLJYM+pF9vVpRqORyMhInJ2dsyLETPOi+7p8+XKuXLnCuHHjsjrETPEi+7llyxbq1KnDlClTKFKkCOXKlePjjz8mNjY2O0J+YS+yr40aNeLWrVts27YNpRTBwcH8/PPPtG/fPjtCzla5tV/K6SRHSI54kuSInEtyxLPl1n4pp5MckfdyRH7JDyA5AiRHPJIV/ZJ5ZgSWX9y7dw+DwYC7u3uK+93d3blz506az7lz506a7ZOSkrh37x6FCxfOsngz4kX29WnTpk0jOjqaHj16ZEWImeZF9vXSpUuMHj2affv2YW6eO/4bvch+Xr16lf3792Ntbc2vv/7KvXv3eP/993nw4EGOvh78Rfa1UaNG/PTTT/Ts2ZO4uDiSkpLo1KkTc+bMyY6Qs1Vu7ZdyOskRkiMekRwhOSI3y639Uk4nOSLv5Yj8kh9AcgRIjngkK/olGSn1AnQ6XYrflVKp7vuv9mndnxOld18fWbt2LV9//TXr16/Hzc0tq8LLVM+7rwaDgV69ejF+/HjKlSuXXeFlmvT8TY1GIzqdjp9++ol69erx6quvMn36dFasWJHjv+WA9O3r2bNnGT58OF999RW+vr5s376da9euMXjw4OwINdvl5n4pp5McITlCcoTkiNwuN/dLOZ3kiLyXI/JLfgDJEZIjNJndL+We8mwO4OrqipmZWaoKaUhISKpq4SMeHh5ptjc3N8fFxSXLYs2oF9nXR9avX88777zDxo0badWqVVaGmSnSu6+RkZEcP36cEydOMGzYMEDrdJVSmJub8/fff/Pyyy9nS+zp8SJ/08KFC1OkSBEcHByS76tYsSJKKW7dukXZsmWzNOYX9SL7OmnSJBo3bswnn3wCQLVq1bCzs6NJkyZMnDgxx34b+SJya7+U00mOkBwBkiMkR+R+ubVfyukkR+S9HJFf8gNIjgDJEY9kRb8kI6XSwdLSktq1a+Pt7Z3ifm9vbxo1apTmcxo2bJiq/d9//02dOnWwsLDIslgz6kX2FbRvNt5++23WrFmTa66hTe++FixYEH9/f06ePJl8Gzx4MOXLl+fkyZPUr18/u0JPlxf5mzZu3JjAwECioqKS77t48SJ6vR4vL68sjTcjXmRfY2Ji0OtTdolmZmbA4+p/XpFb+6WcTnKE5AiQHCE5IvfLrf1STic5Iu/liPySH0ByBEiOeCRL+qUXniI9n3q0POTSpUvV2bNn1Ycffqjs7OzU9evXlVJKjR49WvXt2ze5/aMlEz/66CN19uxZtXTp0ly3lOvz7uuaNWuUubm5mjdvngoKCkq+hYWFmWoXnlt69/VpuWXljPTuZ2RkpPLy8lLdu3dXZ86cUXv27FFly5ZV7777rql24bmld1+XL1+uzM3N1fz589WVK1fU/v37VZ06dVS9evVMtQvPLTIyUp04cUKdOHFCAWr69OnqxIkTycvW5qV+KaeTHCE5Ii2SI3IeyRGSI0xBckTeyxH5JT8oJTlCcoQmK/olKUq9gHnz5qnixYsrS0tLVatWLbVnz57kx/r166eaNWuWov3u3btVzZo1laWlpSpRooRasGBBNkf84tKzr82aNVNAqlu/fv2yP/AXkN6/65NyU0JJ736eO3dOtWrVStnY2CgvLy81cuRIFRMTk81Rv5j07uvs2bNVpUqVlI2NjSpcuLDq3bu3unXrVjZHnX4+Pj7P/L+X1/qlnE5yhEZyxGOSI3ImyRH9lFJ5r1/K6SRHaPJSjsgv+UEpyRGPSI7I3H5Jp1QeG08mhBBCCCGEEEIIIXI8mVNKCCGEEEIIIYQQQmQ7KUoJIYQQQgghhBBCiGwnRSkhhBBCCCGEEEIIke2kKCWEEEIIIYQQQgghsp0UpYQQQgghhBBCCCFEtpOilBBCCCGEEEIIIYTIdlKUEkIIIYQQQgghhBDZTopSQgghhBBCCCGEECLbSVFKCCGEEEIIIYQQQmQ7KUoJIYQQQgghhBBCiGwnRSkhhBBCCCGEEEIIke2kKCWEEEIIIYQQQgghsp0UpYQQQgghhBBCCCFEtpOilBBCCCGEEEIIIYTIdlKUEkIIIYQQQgghhBDZTopSQgghhBBCCCGEECLbSVFKCCGEEEIIIYQQQmQ7KUoJkQUmTJhApUqVMBqNmbK95s2b07x583Q/7+LFi1haWuLn55cpcQghhMge/5ZH7t27h5WVFTqdjuPHj6f53L59+9KlS5dsiFIIIYQpPJ0jdDpdipudnR0VK1Zk/PjxREdHp3iu5AiR0+iUUsrUQQiRlwQGBlKuXDlWrFhB9+7dM2WbZ8+eBaBSpUrpfm7//v25evUqe/bsyZRYhBBCZK1n5ZEZM2YwcuRIAAYPHsyCBQtSPf/KlStUqFCBv/76i5dffjlbYhZCCJE90soROp2O7t27M2rUKACioqLYs2cP3333HV26dGHTpk3Jz5ccIXIaKUoJkck+++wzfvrpJwICAtDrTT8Y0dfXlzp16nDgwAEaNWpk6nCEEEL8h2flkapVqxISEkLx4sW5ePEiQUFB2NjYpNpGx44diY+P5++//86usIUQQmSDtHKETqdj6NChzJ07N0Xbt956i59++ono6Gisra2T75ccIXIS058xC5GHJCQksHTpUnr16pWcJK5fv45Op+OHH35g8uTJlChRAhsbG5o3b87FixdJTExk9OjReHp64uDgwGuvvUZISEiK7T59+d7333+PXq9n69atKdq9/fbb2Nra4u/vn3xf7dq1qVixIgsXLsy6HRdCCJEp0sojjxw5coTTp0/Tt29fBg4cSHh4eIpvv5/Ut29fduzYwZUrV7IjbCGEENngWTkiLQ4ODuh0OszMzFLcLzlC5CRSlBIiEx05coT79+/TokWLVI/NmzePAwcOMG/ePJYsWcL58+fp2LEj77zzDnfv3mXZsmVMmTKFHTt28O677z7zdT777DPatWtHv379uHHjBgDLly9n5cqVzJkzh6pVq6Zo37x5c/78809kYKQQQuRsz8ojS5cuBWDAgAG88cYb2NraJt/3tObNm6OUYtu2bVkarxBCiOzzrByhlCIpKYmkpCTCwsLYvHkzK1eu5I033sDCwiJFW8kRIieRopQQmejQoUMA1KpVK9Vjjo6O/Pbbb7Rv357evXszZswYLl68SHh4OEuXLqVt27a89957vPvuu2zdupWIiIh/fR2dTseqVauws7OjR48enDhxgmHDhtGnTx/eeeedVO1r1arFvXv3uHDhQubtrBBCiEz3b3kkJiaG9evX06BBAypVqoS9vT2vv/46e/bsSfObbjc3N4oUKcKBAweyJW4hhBBZ71nnGvPnz8fCwgILCwucnJzo0qULL730Ej/++GOqtpIjRE4iRSkhMlFgYCA6nQ5XV9dUj7366qsphtlWrFgRgPbt26do9+j+gICAZ76Wi4sL69evx8/Pj0aNGlGsWLF/vUTPzc0NgNu3bz//zgghhMh2/5ZHNmzYQEREBAMGDEi+b8CAASilWL58eZrbcnNzk35fCCHykGeda/To0YNjx45x7Ngx9u7dy+zZszl+/Dht27YlPj4+VXvJESKnkKKUEJkoNjYWCwuLVNdtAzg7O6f43dLS8pn3x8XF/efr1a9fn8qVKxMXF8eQIUOws7NLs92jiQ1jY2P/eyeEEEKYzL/lkaVLl2JtbU3btm0JCwsjLCyMatWqUaJECVasWIHBYEi1LWtra+n3hRAiD3nWuUahQoWoU6cOderUoUmTJnzwwQfMnj2b/fv3s2LFilTtJUeInEKKUkJkIldXVxISEoiOjs6W1xs3bhz+/v7Url2br776iqtXr6bZ7sGDB8nxCSGEyLnSyiMXL15k//79xMXFUaxYMZycnJJv169f5/bt2/z111+ptvXgwQPp94UQIg9J77lGtWrVADh16lSqxyRHiJxCilJCZKIKFSoAZMtKFt7e3kyaNIkvvvgCb29vHBwc6NmzJwkJCanaXr16Fb1eT/ny5bM8LiGEEC8urTzyaDLzxYsX4+Pjk+K2bds2LCwsWLZsWYrtJCUlcfPmTSpVqpR9wQshhMhS6T3XOHnyJPB4Ko9HJEeInMTc1AEIkZc0b94cgMOHDyd/M5EVgoKC6NOnD82aNWPcuHHo9XrWr19P06ZN+fTTT5k5c2aK9ocPH6ZGjRo4OTllWUxCCCEy7uk8kpSUxKpVq6hYseK/rszasWNHtmzZwt27dylUqBAA//zzDzExMWmu0CSEECJ3eta5RnBwMIcPHwa0aUBOnjzJxIkTcXR0pH///inaSo4QOYmMlBIiExUtWpQmTZqwefPmLHsNg8HAm2++iU6nY82aNcmTpzdo0IDvvvuOWbNm8dtvvyW3j4qKYufOnfTu3TvLYhJCCJE5ns4jf/zxB3fu3OG999771+cMGjSIxMRE/ve//yXf99tvv+Hq6krr1q2zPGYhhBDZ41nnGj///DMNGzakYcOGtG7dmjlz5tCpUyeOHz9O8eLFU7SVHCFyEp1SSpk6CCHykk2bNtGzZ09u3LhBkSJFTB0OS5cuZcSIEdy8eVNGSgkhRC6Q0TxiMBgoU6YMvXr14ttvv82CCIUQQpiK5AiR10hRSohMppSiUaNG1K5dm7lz55o0lqSkJCpVqkS/fv0YO3asSWMRQgjxfDKaR1auXMnHH3/MpUuXcHR0zPwAhRBCmIzkCJHXyOV7QmQynU7H4sWL8fT0xGg0mjSWmzdv0qdPH0aNGmXSOIQQQjy/jOYRo9HITz/9JCcbQgiRB0mOEHmNjJQSQgghhBBCCCGEENlORkoJIYQQQgghhBBCiGwnRSkhhBBCCCGEEEIIke2kKCWEEEIIIYQQQgghsp25qQPIiYxGI4GBgdjb26PT6UwdjhBCmIxSisjISDw9PdHr5XsMkBwhhBCPSI5ITXKEEEJonjdHSFEqDYGBgRQtWtTUYQghRI5x8+ZNvLy8TB1GjiA5QgghUpIc8ZjkCCGESOm/coQUpdJgb28PaAevYMGCJo5GCCFMJyIigqJFiyb3i0JyhBBCPCI5IjXJEUIIoXneHCFFqTQ8GmpbsGBBSSZCCAFyCcITJEcIIURKkiMekxwhhBAp/VeOkIu/hRBCCCGEEEIIIUS2k6KUEEIIIYQQQgghhMh2UpQSQgghhBBCCCGEENlO5pQSQuQqRqORhIQEU4eRZ1hYWGBmZmbqMIQQIlMYDAYSExNNHUaeITki68h7NXPJe1WI3EuKUkKIXCMhIYFr165hNBpNHUqe4ujoiIeHh0xUK4TItZRS3Llzh7CwMFOHkudIjshc8l7NOvJeFSJ3kqKUECJXUEoRFBSEmZkZRYsWRa+Xq48zSilFTEwMISEhABQuXNjEEQkhxIt5dJLv5uaGra2tnJRmAskRWUPeq5lP3qtC5G5SlMpEYWFhrFmzhkuXLjFjxgxThyNEnpKUlERMTAyenp7Y2tqaOpw8w8bGBoCQkBDc3Nxk6HsWmzx5A8WL16Vnz5LIeYgQmcNgMCSf5Lu4uJg6nDxFckTmkvdq1pH3qhBZZP9+mD8fli0Da+sseQkZapCJoqOjGTp0KLNmzSI4ONjU4QiRpxgMBgAsLS1NHEne86jIJ3NbZK1duy5Qs+ZbODhU4auvZnDtmsHUIQmRJzzqu+QLi6whOSLzyHs1a8l7VYhM5ucH7dvD2rUwaVKWvYwUpTJRkSJFqF27Nkop/vjjD1OHI0SeJMPcM58c0+xRt64FsbENsbGJoWXLkfz9d0MWLTqNUqaOTIi8QfqyrCHHNfPJMc0aclyFyETnzkGbNhhjI7jYvgrq08+y7KWkKJXJOnXqBMCWLVtMHIkQQoicxN6+FJ0sPsYldhyxsQ6UL38ML6+GDB++B5nvVgghhBBC5AjXrkGrViTF3uPgFHtufnSWJWt2ZdnLSVEqkz0qSnl7exMbG2viaIQQQuQYBw+i69qNqr1m0dx+FVFRLbC1jaJDh7b07fsXx4+bOkAhhBBCCJGvHToEDRsSnxDIoVmWnLGJZPFiM2rXzrrVz6UolcmqV69O0aJFiYmJYdeurKsmCiFyh7fffpsuXbpk2fabN2/Ohx9+mGXbz0n27t1Lx44d8fT0RKfT8dtvvyU/lpiYyGeffUbVqlWxs7PD09OTt956i8DAwBTbiI+P54MPPsDV1RU7Ozs6derErVu3smcHKlZkb5kynAwLw6bNm7SzGoWZWXusrOL44INOfPzxZubNQy7nEyIfkRwhcgt5rwqR96kVK7jarBk/GYPpXVfP62MTGDwY1q9P5Nq1rJurTYpSmUyn09GxY0dALuETQojMFB0dTfXq1Zk7d26qx2JiYvDz8+PLL7/Ez8+PX375hYsXLyaPXn3kww8/5Ndff2XdunXs37+fqKgoOnTokDyRflY6cPYsba9e5RULC87FxGDW/jUaB/fGwaE7lpYJfPllNzZtWkfPnhARkeXhCCFEnpLrv7gQQohslJCQwMmTJ1mxYgUjhg+nWdGiOPbvT+nERPrchU1/GgkO1la27N69Ox4eHlkWixSlssCjk6CtW7diNGbdMDchRO62Z88e6tWrh5WVFYULF2b06NEkJSUlPx4ZGUnv3r2xs7OjcOHCzJgxI19/k9iuXTsmTpxI165dUz3m4OCAt7c3PXr0oHz58jRo0IA5c+bg6+tLQEAAAOHh4SxdupRp06bRqlUratasyerVq/H392fHjh1ZHn+VKlWoWLEi9xITaWltzZXERPSv96L6n9Vwd+uLmZmBL77oRVTUMmrXhpMnszwkIUQOJjkifXL7Fxe5mbxXhcjZlFIcOnSI2bNnM2DAAGrWrEmBAgWoWbMm/fv3Z/acOey9dYsIwMICypaFl14qzPz5/+P+/fts3LiRxo0bZ1l85lm25XysefPmFChQgKCgIHx9falbt66pQxIiz1EKYmJM89q2tpDRBV5u377Nq6++yttvv82qVas4f/48AwcOxNramq+//hqAkSNHcuDAAbZs2YK7uztfffUVfn5+1KhRI8P7kB+Eh4ej0+lwdHQEwNfXl8TERFq3bp3cxtPTkypVqnDw4EHatGmT5nbi4+OJj49P/j3iBYcxOTg48Ndff9G8eXPOnDlDS3t79kVGUnTsV1Q4/Qb6L94hKGQpn376DrNmxdCgwTBmzYJBgzL+fhMiv5Eckf+0a9eOdu3apfnYoy8unjRnzhzq1atHQEAAxYoVS/7i4n//+x+tWrUCYPXq1RQtWpQdO3b8a47IKFO9VzPjfQryXhUip7t69SoDBw5Mc2ohx4IFqZGURLWkGFxf0+H5iqJ4cbh8eSR9+kymQIHsKRdJUSoLWFlZ0bZtW37++We2bt0qRSkhskBMDBQoYJrXjooCO7uMbWP+/PkULVqUuXPnotPpqFChAoGBgXz22Wd89dVXREdHs3LlStasWUPLli0BWL58OZ6enpmwB3lfXFwco0ePplevXhQsWBCAO3fuYGlpiZOTU4q27u7u3Llz51+3NWnSJMaPH58pcbm6urJjxw6aNGnC5cuXaeTkxLLwcF5Zu45yF2thtmQgt8IWM2LEB1hbxzB48Kfs3Qs//mi697sQuZHkCPFfcsoXF6Z6r2bG+xTkvSpETmUwGJgzZw5jx44lJiYGGxsbXnnlFWrUqEHNmjWpERVFsQ8+ILpwDKe+MSOpsIH4eBvi4pYyePCb2RqrXL6XRR7NK/XLL7/IJXxCiFTOnTtHw4YN0T3xNWXjxo2Jiori1q1bXL16lcTEROrVq5f8uIODA+XLlzdFuLlKYmIib7zxBkajkfnz5/9ne6VUir/D08aMGUN4eHjy7ebNmxmKz8PDg507d1K2bFluhYbS2mjkfSsron39KN1uC8XNBwDw3nuf0b//16xZo6hTB/z9M/SyQohcRHJE1srsLy4cHBySb0WLFs3S2HMaea8KkfOcO3eOl156iY8++oiYmBhatGiBv78/mzdvZvzXX9MlIIDib7/N3Tph+M7Vk1TYwIMHpShb9jAdOmRvQQpkpFSWad++PTY2Npw5c4Zvv/2WL7/80tQhCZGn2Npq3/KZ6rUzKq1CiHq47JpOp0vxc1ptRNoSExPp0aMH165dY9euXcknG6AVgxISEggNDU1x0hESEkKjRo3+dZtWVlZYWVllapzFihXjxIkTjB49mrlz57IgPp6/LCxYfieYpq1+Qr++B9ccNvDWW+NxcYlm6tQp1K+vY9486N8/U0MRIk+SHCH+TVZ8cTFy5Mjk3yMiItJVmDLVezUz3qcg71UhcpLExESmTJnChAkTSEhIwN7enqlTpzJw4EDt/2BCAgwbhnHZYq6+B7deBzASFtaGV19dg7W1s0nilpFSWcTFxYV58+YBMG7cOP766y8TRyRE3qLTacPOTXHLjDkYKlWqxMGDB1N8KDt48CD29vYUKVKE0qVLY2FhwdGjR5Mfj4iI4NKlSxl/8TzqUUHq0qVL7NixAxcXlxSP165dGwsLixTzigQFBXH69OlnFqWyip2dHXPmzGHHjh0UK1aMq4mJNAc+io+nUJcNlDmrXebQvv1Upk0bSlyckQEDYMAA082VI0RuITlCpOXJLy68vb3/9YuLJ4WEhODu7v6v27SysqJgwYIpbulhqvdqZs1VKO9VIXKGEydOUK9ePb744gsSEhJo3749Z8+eZdCgQVpB6u5djC1bkbBxMaemPipIgYXF53Tu/IfJClIgI6WyVP/+/Tl8+DCLFi2iV69e+Pr6UqJECVOHJYTIZuHh4Zx8aim1QYMGMXPmTD744AOGDRvGhQsXGDduHCNHjkSv12Nvb0+/fv345JNPcHZ2xs3NjXHjxqHX61N923j37t1U2/fw8MjSpVtNISoqisuXLyf/fu3aNU6ePImzszOenp50794dPz8/fv/9dwwGQ/LlFs7OzlhaWuLg4MA777zDqFGjcHFxwdnZmY8//piqVasmT2prCi1btsTf359Ro0axZMkSZgLbgJVDd1L+s6pcaHuaWrUWsHZtLH36LGH5cjN8feHnn7XVUYQQuZvkiOzx5BcXPj4+z/ziokePHsDjLy6mTJliipBzHHmvCpHzxMXFMWHCBKZMmYLBYMDFxYVZs2bRq1evx//HDh4ksUcvYgvcwP9HHYluisTEAlSosIqiRV8z7Q4AKJFKeHi4AlR4eHiGtxUbG6vq1KmjAFW7dm0VGxubCREKkf/Exsaqs2fP5rr/Q/369VNAqlu/fv3U7t27Vd26dZWlpaXy8PBQn332mUpMTEx+bkREhOrVq5eytbVVHh4eavr06apevXpq9OjRyW2aNWuW5vbHjRv33DE+69hmZn+YUT4+Pv96LK9du5bmY4Dy8fFJ3kZsbKwaNmyYcnZ2VjY2NqpDhw4qICAgXXFk5THZtm2b8vT0VIDSg/oM1I3ensrHx0z5+KB27WqomjTZp0Ape3ulNm7M9BCEyHVya35QSnJEZoqMjFQnTpxQJ06cUICaPn26OnHihLpx44ZKTExUnTp1Ul5eXurkyZMqKCgo+RYfH5+8jcGDBysvLy+1Y8cO5efnp15++WVVvXp1lZSU9NxxPOuYyHvVdO9VIfKi/fv3q/Llyyf/f+nRo4cKDg5+3CA0VBnfG6wUqMB2KJ+/dMrHB+XtXV5FRZ3N8vieN0dIUSoNmZ1gr1+/rpydnRWgBg4cmCnbFCK/kQ8aSkVFRSkHBwe1ZMmSTN1ubjnhyCmy+pg8ePBA9e3bN/kDRmVQ3g1t1J5d1srHB+Xjg1q4sL0qVeqUAqVGjFDqiXMqIfIdyQ+a/J4jcsMXF/Je1ZjivSpEXhIZGamGDx+udDqdApSHh4f69ddfHzcwGpXasEEZ3D1UkiXq/CiSP0MeOdJZJSZmT5/9vDlC5pTKBsWLF2ft2rXodDoWL17MsmXLTB2SECIXOHHiBGvXruXKlSv4+fnRu3dvADp37mziyERWcnJyYtWqVfz222+4ubpyBmh7KJa/OsThcqcBYEb58n+wZEkNxozpy/r112jWDAICTB25ECI7SY5IqXnz5ijtC/cUtxUrVlCiRIk0H1NK0bx58+RtWFtbM2fOHO7fv09MTAxbt27Nd6vpZQV5rwqReXbs2EHVqlWZPXs2Sin69+/P2bNn6dKli9bgxg3o2BF69CDO7A6H5loR1AGU0lG8+ATq1v0Fc/P0zX2X1aQolU1at27NhAkTAHj//ffx8/MzcURCiNxg6tSpVK9enVatWhEdHc2+fftwdXU1dVgiG3Tu3Jkz587xeteuGIDvYqDvm4exmFWPQk5d0ekUrVuvZtWq8tStO5zmzYPZvt3UUQshspPkCJFbyHtViIyJiori3Xff5ZVXXuH69esUL16cv/76i2XLlmmrSiclwbRpqEqV4I8/CGxqzsEfrUgqGw8Uonr1vylZ8kt0upxXApKJzrPR559/zpEjR/j999/p1q0bvr6+ODubbpZ7IUTOVrNmTXx9fU0dhjAhV1dXNmzaxIb163n/nXc4FR1Ns98O8eleZz76YxWBNqsJDf2brl3n0K7dMjZsGMnhwx/z5ZcFMTMzdfRCiKwkOULkFvJeFSJjTp06RY8ePbh48SI6nY5hw4bx3XffUaBAAa3B8eMwaBCcOIEyhz1Di0D325iTv4zt9wABAABJREFURIECL1G16jqsrIqYdieeIeeVyfIwvV7PqlWrKFWqFNevX6dv374YjUZThyWEECKH69GzJ2evXqVHy5YYgEkPHtC0UT9ifq5H9eo7sbOri41NNG+99Q01a5biyy9nEBQUZ+qwhRBCCCHEC1JKsWDBAurXr8/Fixfx8vJi9+7dzJ49WytIRUbChx+i6teHEye44+bAplmloPttAIoW/ZRatXbl6IIUSFEq2zk5ObFp0yasra3Ztm0bEydONHVIQgghcgE3NzfW79jBLytW4GFpyXmlaDxxIhOajqBiiT+oXHkTiYnlcXC4T+vWI9m/vzx79qxAKYOpQxdCCCGEEOkQFhZGjx49eP/994mPj6dDhw6cPHmSpk2bag22bIFKlWDWLHRGIz/Xb8axRXoKVbqKXu9IlSqbKV16Mnq9hWl35DlIUcoEatSowcKFCwH4+uuv2S6TgAghhHhOr/Xrx9nAQN6uWRMFzDx9mupeXvj/FUPLlqcpWHAJoaFFKFQoAKX68+ef1bh79zeUUqYOXQghhBBC/IejR49Ss2ZNfv75ZywsLJg+fTpbtmzBxcUFbt+Gbt2gc2e4dYvrFiX59t2euH6/B3uHUAoUqEPduidwde1k6t14blKUMpF+/frx3nvvoZSiV69eXL9+3dQhCSGEyCWcXFxY7ufH9u++o6hez9WEBFr27cuQtq9SunR3WrW6xJEjPxAR4YSt7VnOnHmNU6f6YDBEmzp0IYQQQgiRBqUU06dPp3Hjxly/fp2SJUty4MABPvroI3QA8+dDxYrwyy8Y9OZMcx7G5slFadx7PQBFigyjVq392NiUMOVupJsUpUxo1qxZ1K1bl9DQULp160ZcnMz/IYQQ4vm1GTOG02fOMMTDA4BF3t5U8fJin483n376McHBV1m3bgwGgxlhYWs4cKABMTGXTBy1EEIIIYR40r179+jYsSOjRo0iKSmJ119/nRMnTlC3bl2IjYU+fWDoUIiMxL9AA16vtpCSizdSveZe9PoCVKq0jrJl56DXW5l6V9JNilImZGVlxc8//4yLiwt+fn4MGzbM1CEJIYTIZQpWqMD8gAB29+hBaeBWVBTtO3em3+vd6drVwKBB3zFlyi7u3/fAaDzN4cN1uHv3N1OHLYQQQgghgH379lGjRg3++OMPrKysWLhwIevXr8fBwQGCgqB5c1izBqOZOZ9ZTWNK51cZOnUQzs7B2NlVpU4dX9zcepp6N16YFKVMrFixYqxduxadTsfSpUtZsmSJqUMSQgiR21hY0Gz9ev5Zu5ZRlpbogf9t2kSlMmW4dm0T69Y1ZcMGP/755yX0+gjOnHmNCxdGYzQmmTpyIYQQQoh8yWAwMHHiRJo3b87t27cpX748R44c4b333kOn04GvL9StC0ePEmnpTAe7jTiP9+add7/CzMyIh8cAatU6jK1tOVPvSoZIUSoHeOWVV/jmm28AGDZsGMePHzdxREKIrLZ79250Ot0zb9OmTcPMzIyAgIA0t1GhQgWGDx8OQPPmzZOfZ2VlRZEiRejYsSO//PJLdu6WMDHbN95g6tmzHCxblkpASFgY3bt3Z+C73Zg5U0909C5+/vlDAIKCJnPkSBsSEkJMGrMQIjXJESK3kPeqEC/mzp07tGnThi+//BKj0chbb73F8ePHqV69utZgwwZo0gRu3+ayRUVeLz+Ptxd/QP3629HrbShffjkVKizFzMzWtDuSCaQolUOMGTOGTp06ER8fT9euXbl7966pQxJCZKFGjRoRFBSUfOvRowdt27ZNcd+gQYNwcXFh5cqVqZ5/4MABLly4wDvvvJN838CBAwkKCuLy5cts2rSJSpUq8cYbbzBo0KDs3DVhaqVLU//UKfwGDOALwBzY9MsvVKlckaJF19O583RmzFhHbKwd8fG72L+/FuHhh00dtRDiCZIjRG4h71Uh0m/Hjh1Ur16dnTt3Ymtry4oVK1i5ciUFChQAoxHGjfs/e3cdFlX2BnD8O8PQkioCYneLii2ia67d3WsXdq66duvajbXWmmsnYhd2FxaCiSAdc35/3JX9ucaiApc4n+eZB5i58c71es/Me895DzRrBmFh7DGoydQW9Rk8uxV2ds8wNc1NsWJncXBor/bbiDcyKZVEaLVaVq9eTa5cuXj69CktWrQgOloOq5Ck5M7NzY0+ffowePBgbG1tsbe3Z8yYMRgZGWFvbx/7MDU1xdjY+KPnLCwsaNOmDStXrkQI8dF2V6xYQfHixf+5mwKYmZlhb29PpkyZKF26NFOmTGHx4sUsXbqUQ4cOJfZbl9Rkaorx8uWMW7WK88bGOANvAwJo06YNv/9ehxEjyrFo0TmePMmDVuuLt7crT5/O/+Q8kyQpYck2Qkou5LkqST8uOjqakSNHUq1aNV6+fEmhQoW4cOEC7dq1UxYICYGmTWHsWAB+T9eFu9NDaNlhElqtngwZ2lG8+AXSpCmk4ruIfzIplYRYWVmxdetWzM3NOXz4MCNHjlQ7JElKuoRQLtxqPL7xi/uqVaswNzfn7NmzTJ06lbFjx3Lw4ME4rdupUycePnyIl5dX7HMhISFs2rTpo7uKX9KuXTtsbGxkt/fUqm1bil64wNncuZkAGAG7d++matUC1Kx5ikuXzuLl1QitNooHD3px+XJbYmJC1I5akn6cbCNkG5FcqHWufsdNCHmuStL3e/r0KZUqVWLChAkIIejatStnz54lX758HxaA8uVhyxYiMWRU2d5kW76ZokWPYWCQhrx515Av30p0Ogt130gC0KkdgPSxggULsmLFCpo1a8aUKVNwcXGhUaNGaoclSUlPaCikSaPOvoODwdw8zosXLlyY0aNHA5ArVy7mzZvH4cOHqVq16n+umz9/fkqVKoWHhwdubm4AbNq0iZiYGFq0aPGf62u1WnLnzs2jR4/iHK+UwhQsiOGFCwzv2pX669fTETgbFET37p2pUmUjdeosYdmyMnToMITAwLWcOHEFF5ctmJnlUjtySfp+so2QbURyoda5+o3nKchzVZK+186dO2nfvj1v377F0tKSpUuX0rRp038WOH0a0aABmhcv8DdMh0e3ilRuOBeANGmKkz//+hT9uUz2lEqCmjZtyoABAwBo3749t27dUjkiSZJ+ROHChT/628HBgZcv415culOnTmzevJn3798DSlf3hg0bYm1tHaf1hRDKDB5S6mVhAX/8Qf6FCzlpaMgMwESj4dChQwwfXogsWYyZNesgb99mQIhrnD5dglevdqgdtSSlCrKNkJILea5K0reJjIykf//+1K1bl7dv31K8eHEuXrz4cUJqzRqEmxuaFy+4lCk3++fbUabhFgCcnPpTrNipFJ2QgmSYlJo0aRIuLi5YWFhgZ2dH/fr1uXPnzkfLCCEYM2YMjo6OmJqa4ubmxo0bN1SK+PtMnjwZNzc3goODadCgAUFBQWqHJElJi5mZcpdPjYfZt81yYWho+NHfGo0GvV4f5/WbN2+ORqNh48aN3L9/nxMnTsSpqzsoU83eu3ePbNmyfVPMUgqk0UC3bhicOUP/bNm4KgSuGg0hISFMnNibgIDRrF27mWvXymFgEMSNG/W5fXsYer2sbyglQ7KNiNO6so1IAtQ6V7/xPAV5rkrSt3j48CHly5dn1qxZALi7u3Py5Ely5MihLKDXw9Ch0LYtREayp0ZRXix5RpZcNzE0TE+hQnvImXMGWq2Riu8icSS74XteXl707NkTFxcXoqOjGTFiBNWqVePmzZuY/90FderUqcycOZOVK1eSO3duxo8fT9WqVblz5w4WFsljDKZOp2Pjxo0UL16cO3fu0L59e7Zs2SLvDkjSBxrNN3c7T64sLCxo0qQJHh4ePHz4kOzZs8d2ff8vq1atIiAgQA4Dlv5RrBhcvEiuDh3w3L6dRcBgnY5Tp45jYlKVmJgx3LlTjMaN5+LvP5m3b89RosR6jIzs1I5ckuJOthFxWle2EUmAPFfjtK48V6Xk5M8//+SXX34hKCgIGxsbVq5cSd26df9Z4P179C1aod29k2gz2NavAOmrXAbA2von8uVbg7GxgzrBqyDZJaX27dv30d8eHh7Y2dnh7e2Nq6srQghmz57NiBEjaNiwIaBcxDJkyMC6devo2rWrGmF/Fzs7OzZv3oyrqyvbtm1jypQpDB06VO2wJElSQadOnahQoQI3b95k4MCBn01Qh4aG4u/vT3R0NL6+vmzdupVZs2bRvXt3KlWqpELUUpJlbQ1bt6KdPZsegwfzc3Q0XczMOBgayl9/DSVfvpLcuTOV/v1/A45w/HhxXFw2Y2lZSu3IJUn6DNlGSMmFPFellCwsLIz+/fuzaNEiAMqVK8e6devInDnzPwv5+BBRoy7Gd6/zOo8hp361IX3GG4AB2bKNI3PmwWg0Buq8AZUku+F7/xYYGAiAra0tAD4+Pvj7+1OtWrXYZYyNjalYsSKnTp367DYiIiIICgr66PE9hIC7d2H79u9a/bNKlSrF3LlKkbMRI0bEeYYLSZJSlvLly5MnTx6CgoL+mTb2X5YuXYqDgwM5cuSgQYMG3Lx5k40bN7JgwYJEjlZKFjQa6NcPjh0jq5MT+0NDWa7TYWVqyq1b5/DyGsHgwR15+DA3BgbPuHChAk+eLPhkOm9JktQn2wgpuZDnqpRS3b59m9KlS7No0SI0Gg3Dhw/n6NGjHyekjh0jvEhJjO5d50ZTC67M02OZ8SXGxllwdj5OlizDUl1CCkAjkvGnSyEE9erVIyAggOPHjwNw6tQpypUrh6+vL46OjrHLdunShcePH7N///5PtjNmzBh+++23T54PDAzE0tIyzvHcugX584OpKbx5o/yMD0IIOnfuzPLly0mbNi3e3t5kyZIlfjYuSclEeHg4Pj4+ZMuWDRMTE7XDSVG+dmyDgoKwsrL65uthSpYij8nr10pNg7178QW6OTmx69kzADJkKESDBulp1uwIABYWrSladDEGBt9ej0SSEoJsHxKWbCO+zdeOiTxXE5Y8vpJaVq9eTY8ePQgJCcHOzo41a9Z81EkGIGLBcgx6d0dvFcXZoZbElFQ6wqRP34TcuZdgaGitQuQJK65tRLLuKdWrVy+uXr3K+vXrP3nt311BvzZbw7BhwwgMDIx9PH369LviyZsXMmaEsDA4duy7NvFZGo2GefPmUaJECd68eUPDhg0JCwuLvx1IkiRJqVu6dLBrF0yYQEatlr+ePeMPJydsrax48eIaS5Z40afPT4SFaXn/fi3HjpUhNPS+2lFLkiRJkiSpJjg4mPbt29OuXTtCQkKoXLkyV65c+TghFR3N6zb9MO75C0HOURxfZkJMySC0WhNy515M/vwbU2RC6lsk26RU7969+euvv/D09MTJySn2eXt7ewD8/f0/Wv7ly5dkyJDhs9syNjbG0tLyo8f30GigZk3l93+VvvphJiYmbNmyhXTp0nHx4kV69uwph1BIkiRJ8UerheHD4fBhNPb2tHz2jJtRUTQpVQq9PoZr1w7TrFlmzpyxQaO5yqlTJXj16i+1o5YkSZIkSUp0V69excXFhVWrVqHVahk3bhwHDhyIzUcAiIB3PC1SG9v1s3nQGa5OBwPbcMzNC1K8+AUcHbvIicxIhkkpIQS9evVi69atHDly5JNpQbNly4a9vf1HtZciIyPx8vKibNmyCR5fjRrKz71743/bmTNnZsOGDWi1Wjw8PFiyZEn870SSJElK3dzc4NIlqFSJDKGhbDp7ls3VqmFnZ8f7948YPjyQceMciY4O5MaNety6NQIhYtSOWpIkSZIkKVGsWrWKUqVKcfv2bRwdHfH09GTkyJEYGPxTDyrI+x7Ps5Qm3dv9eM/R8LSl8ryjYzeKFTuHuXkBlaJPepJdUqpnz56sXbuWdevWYWFhgb+/P/7+/rHD2TQaDe7u7kycOJFt27Zx/fp12rdvj5mZGS1btkzw+H76CQwM4M4d8PFJiO3/xKRJkwClt9iZM2fifyeSJElS6mZvDwcPwsiRADQ6cICb9va0qV8fIfQcOfKc5s2tuHIFXryYyMmTNYiMfKVy0JIkSZIkSQlHr9czdOhQ2rdvT3h4OD///DNXrlzB1dX1o+VuzjmEvmQpDEvc4fxSDSH5BTqdNQUKbCZ37oUYGMRT8ekUItklpRYuXEhgYCBubm44ODjEPjZu3Bi7zODBg3F3d6dHjx6UKFECX19fDhw4gIWFRYLHZ20NHzpkxfcQvg8GDRpEo0aNiIqKonHjxrx48SJhdiRJkiSlXgYGMG6c0vU3bVrSXr3Kak9Pdg0fTsaMGQkMDMTdHWbM0PH+/SGOHStOUNA5taOWJEmSJEmKd6GhoTRp0oQpU6YAMHLkSHbu3Em6dOlil9Hr4WD9+eQYUh3//gHcHAX6NAJLy3KUKHGZ9OkbqRV+kpbsklJCiM8+2rdvH7uMRqNhzJgx+Pn5ER4ejpeXFwULFky0GD8M4UuopJRGo8HDw4O8efPi6+tLs2bNiI6OTpidSZIkSalbjRrKcL6yZSEwkFoTJ3Kjfn06d+oEwK5d0bRtq+PKlaecP1+ex48XyZqHkiRJkiSlGM+fP8fV1ZWtW7diZGTEmjVrGDduHFrtP+mUF8+i2J2lB2Wu9eLKIj3+tQA0ZMkykqJFj2JikkW1+JO6ZJeUSg4+FDs/fBgiIxNmHxYWFmzbtg0LCwu8vLwYMmRIwuxIkiQpiTh27Bh16tTB0dERjUbD9u3bP3pdCMGYMWNwdHTE1NQUNzc3bty48dEyERER9O7dm3Tp0mFubk7dunV59uxZIr6LZCpTJjh6FAYMAMBq/nyW3LzJwT/+IGvWrLx+Hc3AgTBzZhTXrnXnwoX2xMSEqhuzJEmSJEnSD7p8+TKlSpXC29ubdOnScfjwYVq3bv3RMl5b33A3RzWKlViI90IIzQJGRo4UKXKYbNnGodXqVIo+eZBJqQRQpAhkyAAhIXDiRMLtJ2/evKxatQqAmTNnMmfOnITbmSRJkspCQkIoUqQI8+bN++zrU6dOZebMmcybN4/z589jb29P1apVef/+fewy7u7ubNu2jQ0bNnDixAmCg4OpXbs2MTGyUPd/MjSE6dNh+3awsoLTp6nSpw/XZsygV69eAOzZA+3bw6FDq/HyKkNY2ANVQ5YkSZIkSfpeO3fupHz58jx79oy8efNy9uxZypcvH/t6dDTM7noLh/YlsBl5lHt9QRiBrW0tSpS4jI1NJRWjTz5kUioBaLUJP4TvgwYNGjBixAgA+vbtS//+/eWXK0mSUqSaNWsyfvx4GjZs+MlrQghmz57NiBEjaNiwIQULFmTVqlWEhoaybt06AAIDA1m+fDkzZsygSpUqODs7s3btWq5du8ahQ4cS++0kX/XqwcWLULw4vHlDmkaNmGtpyTFPT7JmzcmbNzB8OEyadJU9e5x5+XKX2hFLkpQKyN60kiTFFyEEM2fOpF69eoSEhFClShVOnz5N9uzZY5d58gSGFN5Lk9MleLPsEa8rgAZDcuacTaFCOzEySq/iO0heZFIqgXxISu3cCQmdIxo3bhwTJkwAYNasWTRp0oTQUDlsQpLUptFovvqoWbMmhoaGrF279rPrd+3alcKFCwMwZsyY2PV0Oh3p0qXD1dWV2bNnExERkZhvK0ny8fHB39+fatWqxT5nbGxMxYoVOXXqFADe3t5ERUV9tIyjoyMFCxaMXUaKo+zZla7APXoof0+cSIXffuPGoX306zcQjUbLoUPQufN7Vqyow82boxFCr27MkpTEyDYifsnetAlHnqtSahIVFUW3bt0YMGAAQgi6du3Knj17sLa2jl1m+zbBonwz6OlSi3uzQomwA1Oj7BQrfgYnp75oNBr13kAyJJNSCaRaNTAzg9u3YdCghN2XRqNh+PDhrFu3DiMjI7Zt20alSpV4+fJlwu5YkqSv8vPzi33Mnj0bS0vLj57bsGEDtWrVwsPD45N1w8LC2LBhA53+LiYNUKBAAfz8/Hjy5Amenp40adKESZMmUbZs2Y8+VKdG/v7+AGTIkOGj5zNkyBD7mr+/P0ZGRtjY2Hxxmc+JiIggKCjoo4cEmJjA/Pmwfj2kSQNHj2JWrhwz6/zMmTOncXTMS0AADBsGw4aN5eDBGkRFvVU7aklKMmQbEb9kb9qEI89VKbUICAigZs2aLFmyBI1Gw8yZM1m4cCGGhoYARETA4O7vCevSiCYTB/KkgwADyJC+NcVLXsbCopjK7yB5kkmpBGJrCx+uy7NmwaJFCb/PFi1acOjQIWxsbDh37hylS5fm9u3bCb9jSZI+y97ePvZhZWWFRqP55LlOnTrh6enJo0ePPlp38+bNhIeHf1RIUafTYW9vj6OjI4UKFaJ37954eXlx/fr12OlpU7t/35kSQvzn3ar/WmbSpElYWVnFPjJlyhQvsaYYzZvDhQtQqBC8eAFVqlDywAHu371A69buAPz1F3TseJCFCwvy/v1lVcOVpKRCthGJR/am/THyXJVSgwcPHlC2bFkOHz6Mubk5O3bsoF+/frGfER88gF+KXqDN1dxkWraNwCJgEGNM3ryryVdgDTqdhcrvIPmSSakE8OFOcNOmMH688lyvXnDgQMLvu0KFCrHjXX18fChTpgxeXl4Jv2NJkr7Lzz//jL29PStXrvzo+RUrVlC/fn3Spk371fXz5s1LzZo12bp1awJGmfTZ29sDfNLj6eXLl7G9p+zt7YmMjCQgIOCLy3zOsGHDCAwMjH08ffo0nqNPAfLkgTNnoEMH0Ovh118xbdSINTOHs337IczNM+DrC/36+dG9ewkePvz0brokSZ+SbUT8kL1pE548V6Xk7Pjx45QqVYrbt2/j5OTEiRMnqFOnTuzrmzboWVF0CiOrlOLNBH+irSANuSle5hr29m1UjDxlkEmpeBQaehdvbxe8vV1ia2cMHw5t2yp1pZo0gX/VU0wQefLk4cyZM5QuXZp3795RtWrVL47xlqTkSghBTEyIKg8hRLy9DwMDA9q2bcvKlStjt+vj44OXl9dHXd2/Jm/evJ/cmUxtsmXLhr29PQcPHox9LjIyEi8vL8qWLQtA8eLFMTQ0/GgZPz8/rl+/HrvM5xgbG2NpafnRQ/oMMzNYsUJ5mJrC/v1QrBj1Mpjz5MltnJ0bo9fDH3/EULNmR7ZsaYleH6l21FIKJduIf8g24h9JsTetWudqfJ6nIM9VKflas2YNVapU4c2bN5QoUYJz585RtGhRAMLDYXD7l1gOcqPBjKH4NVK+4zul604x16uYmeVSMfKUQ6d2ACmJsbEToaF3iIl5z7t3x7CxcUOjgSVLwMcHjh+H2rXh7Fmws0vYWNKnT8+RI0do06YNW7ZsoU2bNvj4+DBy5EhZeE1KEfT6UI4fT6PKvitUCMbAwDzettepUyemTJnCkSNH+Omnn1ixYgVOTk5UqVIlTuvH5UN1ShAcHMz9+/dj//bx8eHy5cvY2tqSOXNm3N3dmThxIrly5SJXrlxMnDgRMzMzWrZsCRA7vGDAgAGkTZsWW1tbBg4cSKFCheJ8rKU46NABSpSAxo3h7l1wdcV2xgwuem9i+IgNzJzZkbt3w2nZcj1dupxm6tRjmJrKIZFS/JJtxD9SSxvxNf/fm9bBwSH2+S/1pv3/3lIvX7786o2LYcOG0b9//9i/g4KCvikxpda5Gt/nKchzVUpe9Ho9o0aNip0wrFGjRqxevRozMzNA+QgzveZh+udsxMslgQSbgi46DfmKridtutpqhp7iyJ5S8cjAwAw7uxYA+PuviH3e2Bi2boUcOeDRI6hfX8m6JjRTU1M2bdrEoL8rrY8aNYpOnToRFRWV8DuXJCnOcuXKRYUKFfDw8ECv17Nq1So6dOiAVhu3S/StW7fIli1bAkepvgsXLuDs7IyzszMA/fv3x9nZmVGjRgEwePBg3N3d6dGjByVKlMDX15cDBw5gYfHPGP9Zs2ZRv359mjZtSrly5TAzM2Pnzp0YGBio8p5SrEKF4Px5JTEVFQV9+kDLlkwcXgcvr/s4ODgTGQnz5j2ifPmcXL++Re2IJSnJkm3Ej5O9aROHPFel5CIsLIzmzZvHJqSGDRvGpk2bYhNS61ZFsdNlAO7NquA/LBC9KVgbuuBS4bZMSCUA2VMqnjk4dMTPbwmvXm0mV6656HRWAKRLB7t3Q+nScPq0ciN53TpI6JsBWq2WqVOnkjVrVnr37o2HhwdPnjxh8+bNH01rKUnJjVZrRoUKwartO7516tSJ7t27U69ePZ49e0aHDh3itN7t27fZt28fw4YNi/eYkho3N7evDjfQaDSMGTOGMWPGfHEZExMT5s6dy9y5cxMgQukjlpawaRP8/rsyDe2GDXDlCqW2bOHBA29q1fqNkyfHcvFiJGXKNGbcuLb07btS3iWX4oVsIxSpqY1Irr1p1TpXE+I8BXmuSkmfv78/9erV49y5cxgaGrJkyRLat28PQGgo/NbhEXUv1SXH/Gu8dAL0GrJm+pUsOUeh0cibmAlCSJ8IDAwUgAgMDPzmdfV6vTh7Nr/w9ET4+i7+5PUjR4TQ6YQAIUaNio9o427Xrl3C3NxcAKJAgQLi0aNHiRuAJP2AsLAwcfPmTREWFqZ2KN/Fw8NDWFlZffH1kJAQYWlpKWxsbESVKlU+eX306NGiQIECws/PT/j6+oqrV6+KOXPmCDs7O+Hi4iLev3//3bF97dj+yPUwpZLH5DucOCGEo6PS+JmbC7FhgxBCiPHjvYWDg60ABCCqVs0sXr58onKwUnKT3NsHIWQbER88PT1jryX//2jXrp0QQvmMPnr0aGFvby+MjY2Fq6uruHbt2kfbCAsLE7169RK2trbC1NRU1K5dWzx58m3XpK8dE3muqneuSpIQQly5ckVkzpxZAMLW1lYcPXo09rUbN4To57RR3G1kIo4eQHh6Ik4dSisCAo6rGHHyFtc2Qg7fi2cajQYHh44A+Pmt+OT1SpVg0SLl97Fj4Y8/Ei+2WrVqcfz4cRwcHLhx4walS5fG29s78QKQJOmLzMzMaN68OQEBAXTs2PGzy9y4cQMHBwcyZ86Mm5sbmzZtYtiwYRw/fpw0adSpnSJJcVKuHFy8qDSCISHQvDn07cuIQQXZutUPZ+caaLVw8OAT8ufPzq5dK9WOWJKSFNlG/LcPvWn//fgwG9yH3rR+fn6Eh4fj5eVFwYIFP9rGh960b968ITQ0lJ07d35z4fLUTp6rUlK1Z88eypUrx5MnT8idOzdnzpyhYsWKCAGrF4VyrnxbOnRvhm+vcIQhpDOrRgnXu1hbl1c79BRPI0Q8T72QAgQFBWFlZUVgYOB3jQuPjHzB6dNOCBGNi8t1zM0LfLLMkCEwdSoYGcGRI8rn9cTy9OlTatWqxbVr1zAzM2PDhg0fTXkpSUlReHg4Pj4+ZMuWDRMTE7XDSVG+dmx/9HqYEslj8gOio2HUKJg0Sfm7TBnYtIk3pk506LCYCxd64ucXA0CnTrWZO3cTpqamKgYsJQeyfUhYso34Nl87JvJcTVjy+EqfI4Rg7ty59OvXD71eT6VKldi8eTO2trYEB8OEFtdp+rQ2YSMeE5keNDEG5Mw1E8dMvWVJgR8U1zZC9pRKAEZGGUibVimA5ufn8dllJk2CBg0gMlIpfP7wYeLFlylTJk6cOEHVqlUJDQ2lfv36zJ8/P/ECkCRJklInnQ4mToS//gIrK6XIorMzaS8dYtu2rvzyyx3KlVNmyVq+fBcFC2bmwoVzKgctSZIkSVJyFB0dTa9evejbty96vZ5OnTqxb98+bG1tuXpFMD33fDqmK0rgTCUhZSoyUbyUNxkz95EJqUQkk1IJxN5e6a7q77+SqKi3n7yu1cKaNVC8OLx+DbVqwbt3iRefpaUlu3fvplOnTuj1enr16sWAAQPQ6/WJF4QkSZKUOtWpowznc3ZWGsFq1TCYPIGxY7IxdOgTGjSoi60tPHz4mtKlSzN27Aiio6PVjlqSJEmSpGQiMDCQWrVqsWDBAjQaDVOnTmXp0qUYGhrhMTOAh9V/pt6QXvi2iwEt2Fs3p4TrLdKkKaJ26KmOTEolEFvbmpiZ5SM6+g337/f97DLm5srNYicnuH37n5mzE4uhoSFLly6NnQpz5syZNGnShNDQ0MQLQpIkSUqdsmeHkyehUycQAkaOhLp1qV32PdOm7aB69YWUK6clJkYwevREypYt9tHMWpIkSZIkSZ/j4+ND2bJlOXDgAGZmZmzdupVBgwbx/r2G0VVOUmprbtIt3kdgEdBGG5Mv7xryFl2PgYG52qGnSjIplUC0Wh158qwAtLx4sZbXr3d+djlHR9i5U0lQHT4M3bsrn80Ti0ajYfjw4axbtw4jIyO2bt1KpUqVePnyZeIFIUmSJKVOpqawbBmsWAEmJrB7NxQvTo533ixb1g1n54t062aHuTmcP3+NwoULsGTJEmQ5TEmSJEmSPufUqVOUKlWKmzdv4ujoyPHjx6lfvz4Xz8ewPPdoWhWowMvxr4m2gjTafJQoe50M9q3VDjtVk0mpBGRlVZpMmfoDcPduV6KiAj67XNGisH69MqRv+XIYPToRg/xbixYtOHToEDY2Npw7d47SpUtz+/btxA9EkiRJSn06dFDqS2XPDo8eQdmymK1dwpzfC1OkyB169qxM0aIQFhZJ165dqV27Fv7+/mpHLUmSJElSErJu3ToqV67Mq1evcHZ25ty5czg7F8NjvC+BjctRfvxY/BoqN7ac7HpRrPxlzMxyqhy1JJNSCSxr1rGYmuYmMtKP+/f7fXG5OnVgwQLl93HjYOHCRArw/1SoUIHTp0+TPXv22C6PXl5eiR+IJEmSlPoULQre3lC3rjILSNeuaDp2oFtbI+rXP0jZsqPp1g0MDWHPnr0ULJifrVu3qh21JEmSJEkqE0IwZswYWrVqRUREBPXr1+f48eOYm2dkUrmdlD+RB4NFZwnJCbpoCwoV2kPO/HPRao3UDl1CJqUSnIGBKXnzegAaXrxYxZs3u7+4bNeu//SS6tkT1PisnSdPHs6cOUPp0qUJCAigatWq/PHHH4kfiCRJkpT6WFvDtm0webLSfXjVKihThjLpHjB69Biio/cwc6YFOXLAmzcBNGrUiA4dOhAUFKR25JIkSZIkqSA8PJxWrVrx22+/ATBo0CC2bNnCzUs6tuXpQf1KdfEdGoLeFKyNSuNS4TZp09ZUOWrp/8mkVCKwsiqLk5PSS+rOnS5ERb374rKjR0OXLkpdqZYtQY2OSunTp+fIkSM0atSIqKgoWrduzfjx42UND0mSJCnhabUwZIhSaNHODq5ehRIlsDu5jYULa/Lq1WX69y9Mixag0cDKlSspXLgwx44dUztySZIkSZIS0evXr6lcuTLr169Hp9OxdOlSpkyZysoR99F0KkL+GQt5WRXQa8iWaQxFypzA2NhR7bClf5FJqUSSLds4TE1zERn5nAcPBnxxOY1GGcZXvz5EREC9enDtWuLF+YGpqSmbNm1i0KBBAPz666906tSJqMScHlCSJElKvdzc4NIlKFcOgoKgYUN0wwbx26+ZyZDhNDlytGX2bLC3h8ePH+Pm5sbgwYOJiIhQO3JJkiRJkhLYo0ePKF++PKdPn8ba2pr9+/fToMEvzCq2Ctf7hQiZd4cwJzCKSY9z8eNkyTEajcZA7bClz5BJqURiYGD292x8Gvz9V/Dmzb6vLAvr1kH58hAYCDVqwOPHiRfrB1qtlqlTpzJ//ny0Wi0eHh78/PPPBAYGJn4wkiRJUurj6AientBfmTSE6dPhp59oVDaQli1XcvPmfBYu1FGzplJPYtq0abi4uHD9+nV145YkSZIkKcFcuXKFMmXKcOfOHTJlysSpU6cwi3HhWMEmVGnanmc9IxGGkM68Bi6ud7CyKqd2yNJXyKRUIrK2Lo+TU18A7t7tTHT0l5M7pqbw119QoAA8fw7Vq8ObN4kV6cd69OjBX3/9hbm5OYcOHaJcuXI8ViNLJknJUPv27alfv/5nX1uyZAlubm5YWlqi0Wh49+7dJ8sEBATQpk0brKyssLKyok2bNp9dTpJSLENDmDED/vwTLCzg2DEoVox8r44zbVoPDh8+RocOjowbB1ZWGq5du4aLiwvz5s2Tw86lJE+2EVJyIc9VKanw9PTE1dUVf39/ChYsyMmTpzmzIJQ0g/OSYe5m3pYBTYyOXDnmUaDEHgwNbdQOWfoPMimVyLJlm4CJSQ4iIp7x4MHAry5rYwP79kGmTHDnDtSuDSEhiRTov9SqVYvjx4/j4ODAjRs3KF26NN7e3uoEI0kpRGhoKDVq1GD48OFfXKZly5ZcvnyZffv2sW/fPi5fvkybNm0SMUpJSiIaN4YLF6BgQfD3h8qVsVg8nTm/l8bPzxsLC1dWrBCUKqUUPe3duzd169bl1atXakcuSd9FthFSciHPVSmxbNq0iRo1ahAUFISrqyvbtxxlX/U/qBBSktcznhOZDkxEFoqX8iZjpp5oNBq1Q5biQKd2ACnK27fg4QE+PjBv3mcXMTAwI2/eFVy+XBE/v2WkT98EW9tqX9ykkxPs36+U1DhzBpo1UyYmMjRMqDfxZc7Ozpw9e5ZatWpx7do1XF1dWbhwIc2bN8fISE6nKUnfyt3dHYCjR49+9vVbt26xb98+zpw5Q6lSpQBYunRpbHflPHnyJFKkkpRE5M6tNIbdusHatTBoEJpTp+jj4cHRS4fYvXswkybNZutWWLxYy65duyhcuDCrVq2iWrUvt7WSlBTJNkJKLuS5KiWGOXPm4O7ujhCCRo0aMaT6AAIrV6LE4Gs8K6wsY2/TmlwFF2FgYK5usNI3kT2l4lN0NAwcCPPnw+vXX1zM2tqVjBl7A3DnTmeio78+lXW+fLBrF5iYwO7d0LWrMjufGjJlysSJEyeoWrUqoaGhtGvXDkdHR/r27cvly5fVCUqSUqjTp09jZWUV+wEOoHTp0lhZWXHq1CkVI5MkFZmbw+rVsHAhGBkpd2pKlMDN9hb9+s1ix46F1K9vwMKFejJlMsPf35/q1aszYMAAWQRdSlFkGyElF/JclX6EXq9nyJAh9O3bFyEEPTp3ZuirjDitL0fIomsEFgZNtAn58q0jb5E1MiGVDMmeUvHJzk4pAnXjBnh5QaNGX1w0e/ZJvHmzm/Dwhzx4MJg8eRZ9ddNly8LGjdCggdIZy8EBJkxQXtPrI4mIeI6padZ4fDNfZmlpye7du5k4cSJLlizh+fPnzJkzhzlz5lC0aFE6dOhAy5YtSZcuXaLEI6VOQghCQ0NV2beZmVmidAf29/fHzs7uk+ft7Ozw9/dP8P1LUpKl0Si9pYoXV4b13b8PpUvjuHAhU6d2Y+rUbDg7N2Hp0vfMnm3Fvn2BzJw5kyNHjrBu3Try5cun9juQEphsI2QbkVyoda4m1nkK8lyVvl9UVBSdOnVizZo1AIxp2pqum3cR0M2PO393gDY3LE7BUpswNc2uYqTSj5BJqfhWqZKSlPL0/GpSysDAnDx5lnPlSiX8/BaTPn1jbG2rfHXTdevC4sXQrVsU27dfoFAhT/Ll8yQw8CR6fRiZMw8le/ZJ8f2OPsvQ0JDRo0czYsQIDh48iIeHBzt27ODy5cv07duXgQMHUqdOHTp06ECNGjXQ6eSpJsWv0NBQ0qRJo8q+g4ODMTdPnLswn/vAKISQY+QlCcDFBS5ehNatlSKM7dtjdPIkI+fMYcP2U2i1tRgy5AnFi1swb56Wy5cvU7x4cWbNmkWXLl3k/6MUTLYR8txOLtQ6VxPzPAV5rkrfLjg4mMaNG7N//34MDAyYk6swra6t5dZcCM8IQmjJmnUkWbL8ilYrv2smZ3L4XnyrVEn5+YUx1f/PxsYNR8eeANy58wvR0e8/u5xeH01Q0DmePJlCyZI12b/fhvnzy2JvP4KAgEPo9WEAPHkymcePEycp9YFOp6NmzZps2rSJ58+fM3fuXIoVK0ZUVBRbt26lTp06ZMqUicGDB3Pr1q1EjU2Skjt7e3tevHjxyfOvXr0iQ4YMKkQkSUlQ2rTK2PaxY5UeVEuXQunSNC9uTK5cZ3n40IUqVd6zaFEohQoVJCwsjG7dutGwYUPeqDWtrSTFA9lGSMmFPFelb/Xy5UsqVarE/v37MTMyYouBjp9dLnHp74SUhsw4O3uRLdtvMiGVAsh/wfjm6qr8vHEDXr5UhvR9Rfbsk3n7djfh4Y94+HAIuXMvQIgYgoMv8+7dUQICPAkMPE5MzD91pwwMICIiLWfOuHH9uhs9elQiR469PHw4CB+f4eh0lmTM2DMh3+VnpU2bll69etGrVy+uXr2Kh4cHa9euxd/fn2nTpjFt2jRKlSpFhw4daN68OVZWVokeo5RymJmZERwcrNq+E0OZMmUIDAzk3LlzlCxZEoCzZ88SGBhI2bJlEyUGSUoWtFr49VcoXRpatYIrV6BYMZyXLiVzvaNs2dKG3Lm3Mnv2debOrcbu3Z5s376dc+fOsWbNGipXrqz2O5DimWwjZBuRXKh1ribWeQryXJW+zYMHD6hevToPHjwgnYGOrWkiSTMaHv1dzNzKqgUFCy7A0NBa1Til+COTUvEtXTooXBiuXlXqSjVp8tXFdbo0fw/j+4nnzxcSFvaQ9+/PEh397l/LWWNlVREbm0pYW1fCxKQgS5dq2bJFmZ3v2LECZMkSyOPH47l3rxcGBlbY27dOwDf6dYULF2bWrFlMmTKF3bt34+HhwZ49ezh79ixnz57F3d2dhg0b0qFDBypXroxWKzvtSd9Go9EkarfzHxEYGPjJRAC2trYYGRnh7+/P/fv3Abh27RoWFhZkzpwZW1tb8uXLR40aNejcuTOLFy8GoEuXLtSuXVvOVCNJn1O1Kly+DC1bKm1wixak7daNTtPXsG5rDjJlmkbfvgfIlq0mf/31gAcP7lKlShUGDx7M2LFj5UyyKYhsI2QbkVzIc1Weq9I/vL29+blmTV6+ekVW4I8S0USPhMA0EB1tQYECC7C3byWHfaY0QvpEYGCgAERgYOD3baBPHyFAiO7d47zKnTvdhKcnsY9jxyzF1au1xZMnM0RQ0EWh10d/sk5YmBAVKyq7srcX4t49vbh7t/ff2zAQL19u+774E4ifn5+YPn26KFCggABiH5kzZxa//vqrePDggdohSklYWFiYuHnzpggLC1M7lG/Srl27j873D4927dqJ0aNHf/Y1Dw+P2PXfvHkjWrVqJSwsLISFhYVo1aqVCAgIiNcYv3Zsf/h6mALJY5IMREUJMXKkEBqN0kgWKSLEnTti374l4tAhA+HpiZg1q6yoXbtt7P+74sWLizt37qgdufQdkmv7IIRsI1Kirx0Tea6qd65KSd/+ffuEuYmJAEQRDcKr9z/fjY8cKS1CQ+V3xeQmrm2ERgghEirhlRCOHTvGtGnT8Pb2xs/Pj23btlG/fv3Y14ODgxk6dCjbt2/nzZs3ZM2alT59+tC9e/c47yMoKAgrKysCAwOxtLT89iC3b1emycubF+JYRykmJoTHjyeg01ljbV2JNGmc4zQ+9t07ZcTgtWtgbw8HDugxNOyEv/9KNBojChXa/Z8F1BObEIILFy7g4eHB+vXreffuXexrFStWpEOHDjRu3DjR7xqFhITw+PHjjx6PHj3i8ePH+Pr6ki1bNmrWrEnNmjUpVKiQzNAnsvDwcHx8fMiWLRsmJiZqh5OifO3Y/vD1MAWSxyQZOXBAKYL+6hWkSQNLlnCjcDqePGmMqWkQz57l4ujRvmzbNoq3b99ibm7OnDlz6NChg7zGJyOyfUhYso34Nl87JvJcTVjy+CZfa2fPpkP//kQLQUUzGDrbAJNcMej1WtKnH0mBArKYeXIU1zYi2SWl9u7dy8mTJylWrBiNGjX6JCnVuXNnPD09WbZsGVmzZuXAgQP06NGDLVu2UK9evTjt44cb2LdvlWF8QoCfn5ItSkD+/lCtmpKYsrGB3bujsbBoxuvXW9FqzShS5BBWVmUSNIbvFR4ezvbt2/Hw8ODgwYN8OB3TpElD06ZN6dChA+XKlfvhLwdCCAICAj5JNv3/41sK3jo6OlKjRg1q1qxJlSpVsLa2/qH4pP8mP2gkHPmF49vIY5LMPH/+z3A+gK5def1rZ05fbISFxWMCA23ZsWMxDx4s4NgxTwCaNGnC4sWLsbGxUTFwKa5k+5CwZBvxbWRSSj3y+CY/IiqKGQ0bMmjXLgDqZIVeC8HIBMLCslCy5FrSpi2vbpDSd0uxSan/p9FoPklKFSxYkGbNmvHrr7/GPle8eHF+/vlnxo0bF6ftxksD6+ys1LVYvx6aN/++bXyDgACoVQtOnwZzc9i+PYL06esSEHAAnc6aokWPkiZNkQSP40c8ffqU1atXs3Llytgx6QA5c+akffv2tGvXDicnp8+uq9frefHixVeTTnEpImltbU2WLFk+eTg6OnLp0iX27dvHkSNHCA0NjV3HwMCAMmXKULNmTWrUqEHRokVljawEID9oJBz5hePbyGOSDEVHw2+/wYQJyg2jIkWIWLeAI4/7YWp6jqgoQ9asWUb69H7MmTOS6OhoMmXKxNq1a3H9MIGJlGTJ9iFhyTbi28iklHrk8U1e9GfPMqBOHWa/egVA00pGdB0ZiVYLUVEtcHOTxcyTu1SblOrWrRve3t5s374dR0dHjh49St26ddm7dy/ly38+yxoREUFERETs30FBQWTKlOnHGtj+/WHWLOjSBf4u6JfQQkKUUYMHD4KREaxfH0LWrNUJCjqJoaEdzs7HMTPLnSix/AghBCdOnMDDw4NNmzYREhICKP/eVatWpW7durx58+ajhNOTJ0+IjIz8z21nyJDhs0mnrFmzkiVLljj9e4eHh3P8+HH27dvH3r17ufWvIZoZMmSgRo0a1KhRg2rVqmFra/t9B0L6iPygkXDkF45vI49JMnbwoDI739/D+WKWzOVkxl3o9VsAWL9+NDly/MzSpa24f/8+Wq2W4cOHM2rUKAwNDVUOXvoS2T4kLNlGfBuZlFKPPL7JRGAgEcOG0W7hQjb+/VTnjjpatokmPNyCjBkXULCgLGaeEqTapFRkZCSdO3dm9erV6HQ6tFoty5Yto02bNl/czpgxY/jtt98+ef6HGti//oJ69SBrVrhxAxJp2tWICKV8xubNygzZK1a8o0CBSgQHX8bYOBPOzicwMcmcKLHEh+DgYDZv3oyHhwfHjh376rJarRYnJ6cvJp0yZcqEqalpvMf46NEj9u/fz969ezl8+PBHPbK0Wi2lSpWKHepXvHhx2YvqO8kPGglHfuH4NvKYJHP/Gs4nunTmeldL3gTNAODQoZaEh//O69eDWbXKA4BSpUqxbt06smfPrlrY0pfJ9iFhyTbi28iklHrk8U3ihIAtWwjq3ZsG/v4cAXQGMHiIMnnumzelqVLlD6ysZFubUqTapNT06dNZunQp06dPJ0uWLBw7doxhw4axbds2qlT5fMHvBOkpFRgIWbIoP8uXh127wMrq+7b1jWJioGtXWL5c+XvOnJeULOlKWNgdTE1z4+x8DCOjDIkSS3x68OABK1eu5OLFizg4OHySeMqYMaPqd7IjIyM5efIke/fuZe/evVy/fv2j19OlS0f16tWpWbMm1apVI3369CpFmvzIDxoJJyV94YiOjmbMmDH88ccf+Pv74+DgQPv27Rk5cmRsQlgIwW+//caSJUsICAigVKlSzJ8/nwIFCsRpH8ntmEifER0NY8fC+PHKh+TChXm2oil3A8eg1UZz7Vo59u/fTv36Rxg4sAuBgYFYWFiwYMECWrdurXb00r/I9iFhpaQ2IjHIpJR65PFNwh49gl698Nu9m5rAFcDERMO4cQJnZy2hoSOpVUsWM09p4txGJMzkf4kDENu2bYv9OzQ0VBgaGopdu3Z9tFynTp1E9erV47zdeJve9sQJIayslOmoixUT4uXLH9veN9DrhRg4UNk1CDFu3BNx6lRm4emJOHeuiIiMfJtosaRmT58+FUuXLhUNGzYUlpaWH02Tq9FohIuLixg1apQ4ffq0iI6OVjvcJO3DNL+hoaFqh5LihIaGppjpvsePHy/Spk0rdu3aJXx8fMSff/4p0qRJI2bPnh27zOTJk4WFhYXYsmWLuHbtmmjWrJlwcHAQQUFBcdpHcjsm0lccOCBE+vRKQ5kmjXi7ebg4fNhKeHoi1q7NIUqUuC127HgsKlSoEHvtbtmypXj37p3akUv/R7YPCSsltRGJ4WvHRJ6rCetr56qkkshIIaZOFcLMTNwGkfXvttTaGrF4MWLz5izi8uXjakcpJZC4thEpahxRVFQUUVFRnwyPMjAwQK/XJ35A5crB0aOQPj1cvAiurvDsWaLsWqOBqVOVmq4Av/6aiR07DmFomIGQkCtcu1aL6Oj/Lvwt/RgnJyd++eUXtmzZwuvXr/Hy8mLo0KEULVoUIQTnz59n7NixlClTBjs7O1q0aMHq1at58eKF2qEnOQYGBgBxqh0mfSwmJiZ2ZsvP+VC4X+2ehvHh9OnT1KtXj1q1apE1a1YaN25MtWrVuHDhAqD0kpo9ezYjRoygYcOGFCxYkFWrVhEaGsq6detUjl5KdFWrKpOSVKwIwcHYNJ5Iqb3V0GqykDHjA379tQzjxj2kZUtPxo4dh4GBAevWraNo0aKcOnVK7eilv324dv3/JCRS/ElJbYTa5LmasOS5msScOQMlSsDgwZwNDaWsoY5HQMaMMH8+aLUtqFLlMkWKyNn1Urtk1z8uODj4o5nZfHx8uHz5Mra2tmTOnJmKFSsyaNAgTE1NyZIlC15eXqxevZqZM2eqE3DRonD8OFSpArdvK0P5Dh2CnDkTfNcaDQwfDjY20LMnTJmSi5CQgzRrVpGgoNPcuNGAQoV2odUaJ3gsktJAurq64urqyqRJk/Dz82Pfvn3s27ePAwcO8PbtWzZs2MCGDRsAKFasGA0bNmTQoEEYGRmpHL36dDodZmZmvHr1CkNDQ1mb6xv4+/vz9u1b7OzsSJcuXezzQghCQ0N5+fIl1tbWsYm/5Kx8+fIsWrSIu3fvkjt3bq5cucKJEyeYPXs2oLQZ/v7+VKtWLXYdY2NjKlasyKlTp+jatesn2/zcEG8pBXF0VNrlv4fzmU//k9Kn8nNlUhGwvMLEidWYPn0ZTk4jOXy4Ch06tMTHxwdXV1dGjRrF8OHD0emS3cepFMXAwABra2tevnwJgJmZmSyQGw9SYhuRGEO8v0aeqwkjJZ6ryVpgoPIldOFCEILdadLQOCKc8Kho8uaFkSPN0WgW0alTa+TpL0EyrCl19OhRKlWq9Mnz7dq1Y+XKlfj7+zNs2LDYL/lZsmShS5cu9OvXL84X/QQZH//4sZKYun8f7O2VGYAKFoyfbcfB+vXQtq1SRqNz5zO0bl0FvT6EdOkakD//Jjl+V2XR0dGcPXs2dkY/b2/v2Nfc3NzYunUrNjY2KkaYNERGRuLj46NOz8dkzN/fn4iICNKmTUuaNGk+ed3a2hp7e/vPXiOTW70QIQTDhw9nypQpGBgYEBMTw4QJExg2bBgAp06doly5cvj6+uLo6Bi7XpcuXXj8+DH79+//ZJsJMhmGlDT93+x8Mbbm3F5biFemZwBYvXokV66MZeXK90yb1os1a9YAUK5cOf744w+yZMmiZuSpnhACf39/3r17p3YoKU5KaiMmTJjArFmzWLVqFQUKFODChQt06NCB8ePH07dvXwCmTJnChAkTWLlyJblz52b8+PEcO3aMO3fuYGFh8Z/7+K9jIs/VhPO1c1VKBEIos2316QP+/gAsrVCSbifPo9cLSpaENm1cKF16AyVKyGLmqUGqKHSeUBKsgfX3h+rV4epVpfvSvn1QsmT8bf8/7N4NjRtDeDi0b3+E9u1/RogIMmRoQ968K9FoZM+TpOLly5f89ddf9O/fn/fv35MvXz727NlD1qxZ1Q5NdXq9Xg7h+waRkZG4uLgQERHB3r17yZYt20evGxoafvWOYnL7wrFhwwYGDRrEtGnTKFCgAJcvX8bd3Z2ZM2fSrl272KTU8+fPcXBwiF2vc+fOPH36lH379n2yzQSZDENKup4/VxJTR48iNOAztyhPClwG4PDh5ixd6sHq1Sa8evUH3bt35/3791hZWeHh4UGDBg3UjV0iJiaGqKgotcNIMVJaG1G7dm0yZMjA8g+zAQGNGjXCzMyMNWvWIITA0dERd3d3hgwZAihtQIYMGZgyZcpne9P+W1yPiTxX49d/natSAgsNVWbaWrsWAJErF8PL5GHy6l2AMlq+YsURdO8+Bltb2RkitUgVhc4TSoIWbXz7VojSpWOLqoojR+J/H1/h5SWEpaWy+zZtdghPTwPh6Ym4e7eX0Ov1iRqL9N+uXLkinJycBCDs7OzEuXPn1A5JSmbOnj0rAGFra/td/8eTWxFbJycnMW/evI+eGzdunMiTJ48QQogHDx4IQFy8ePGjZerWrSvatm0bp30kt2MifYfoaCFGjRJCoxECxPPOTsLziE54eiLmzi0rrK1fivHjhbh//6EoU6ZM7OQVM2fOVDtySUpUye16OGnSJJElSxZx584dIYQQly9fFnZ2dmLdunVCiO9rI8LDw0VgYGDs4+nTp8nqmEjSD3v4UIiiRZUvmAYGImrkcNG4YYnYCULq1bMUCxYcE/KrZuqTKgudJws2NsrwgJ9+guBgqFkTdu5MtN27uoKnp1J7fc2auqxYsQrQ4Os7Dx+fXxMtDiluChcuzJkzZyhSpAgvX77Ezc2Nv/76S+2wpGTkQzHmsmXLporu7KGhoV+d7CJbtmzY29tz8ODB2NcjIyPx8vKibNmyiRqrlIQZGMBvv8GBA2Bnh8PSZxQZqcMgxoyCBU8xb15pFi++Tf/+2di58xg9e/ZECEH//v1xd3cnJiZG7XcgSdJnDBkyhBYtWpA3b14MDQ1xdnbG3d2dFi1aAMpwd4AMGTJ8tF6GDBliX/u3SZMmYWVlFfvIlClTwr4JSUpKDhxQiplfvgzp0/NuzwrcPBezeesFNBqoVas4/fs/pnv3CrJ+lPRFMimlhjRpYNcuqFcPIiKgYUOl6FMiKVZMqb2eKROsWdOKlSsXAPDkyQSePJmWaHFIcZMxY0aOHz9OjRo1CA0NpX79+sydO1ftsKRk4vTp0wCpJuFSp04dJkyYwO7du3n06BHbtm1j5syZscOqNBoN7u7uTJw4kW3btnH9+nXat2+PmZkZLVu2VDl6KcmpUkX5oO3mhs2pcIp1CMUk2IKMGR8yf34ZnjzxpHRpHV27zmXaNKX9/P3332natClhYWHqxi5J0ic2btzI2rVrWbduHRcvXmTVqlVMnz6dVatWfbTcv2/iCCG+eGNn2LBhBAYGxj6ePn2aYPFLUpIhBEyerHSwePsWSpbk+pYRVBjQkZMn36DTQc2avVix4gKurtZqRysldYnSb+tvhw8fFj179hS1atUStWvXFr179xZeXl6JGUKcJFpX5MhIIVq3Vro6ajRCLFqUsPv7l8ePhcidW9n9L79MFp6eCE9PhK9v4sYhxU1UVJTo0qVLbFdYd3d3ER0drXZYUhL3Yfinp6fnd62f3IZmBAUFib59+4rMmTMLExMTkT17djFixAgRERERu4xerxejR48W9vb2wtjYWLi6uopr167FeR/J7ZhI8eD/hvNFWCG8V5gJT0/EwYM6Ub26hzAzE2LNGiE2bNggjIyMBCDKli0rXr16pXbkkpSgktv1UA7xlqR4EBQkRKNGypdIEPpOHcW831uLdOmU7ygmJgaiU6c/hPyaIiW54XvdunWjSpUqrF+/njdv3vDq1Sv++OMPKlWqRO/evRMrjKTF0BBWrYIePZT/0t26QSL2gMmcWekx5ewMy5YNYfNmZXaqu3e78+JF4vXckuJGp9OxaNEiJk+eDMDs2bNp0qQJoaGhKkcmJVVPnz7l2bNnGBgY4OLionY4icLCwoLZs2fz+PFjwsLCePDgAePHj8fIyCh2GY1Gw5gxY/Dz8yM8PBwvLy8KJuJsqFIy9H/D+YyM7SjSNZT0xwzQ6aIZOrQDLVqMoG1bPbt2NWP79oNYW1tz6tQpypYty8OHD9WOXpKkv8kh3pL0g+7ehdKlYcsWMDTk/Zzp1Ht5nF591/L6NdjaWjNz5jmWLWuJrDsvxVWiJKW2bduGh4cHK1as4PXr15w+fZozZ87w6tUrli5dypIlS1JvnRytFubNg7+nK8fdXSn6lEjs7JTdlS8P8+dPYNeuHoDg9u22vH69K9HikOJGo9EwZMgQNmzYgJGREdu2baNSpUq8fPlS7dCkJOhDPamiRYtibm6ucjSSlAL8PZzPoJwb+cfEkHmN8nTr1hOZOrUmXl536NnTlQULTpI5c2bu3btHmTJlOH/+vLpxS5IEyCHekvRDdu4EFxe4eRMcHTkyaxL5poxk5857ABQt6sa5c750715M5UCl5CZRklIeHh7079+f9u3bfzQeW6vV0rFjR9zd3T+amjXV0WhgwgRo3x70emjeHHx9E233Vlawfz/UrKlh5sy5HDrUGiGiuXGjMQEBiZcgk+KuWbNmHD58GFtbW86dO0fp0qW5c+eO2mFJScyHelJlypRRORJJSkEcHODQITS/jiK7h4a8k0ETraFEiQOsWFGQ6tUH0rWrE82bn8HZ2Tl2koqdiTipiSRJnzd37lwaN25Mjx49yJcvHwMHDqRr166MGzcudpnBgwfj7u5Ojx49KFGiBL6+vhw4cAALCwsVI5ckFen1MHo01K0LQUHElCtH9yo/Ua3vQHx9w7Gx0dCjxzS8vT3JkcNM7WilZEgjhBAJvRMnJye2bt1KyZIlP/v6uXPnaNiwIc+ePUvoUOIkKCgIKysrAgMDsbS0TLwdh4VBmTJw5Yry8+hR+L8hJwktMhLatYNNm6L57bfGlC+/AwODNBQpchhLy8//20nqunv3LjVr1uThw4fY2Niwfft2XF1d1Q5LSiJKlizJ+fPnWbduXezMQt9KtethEiaPiRTr0CFo1YpQw5c86K3lTSllCNDbtxlYunQS7983RKdrjpfXPrRaLfPmzaN79+4qBy1J8UdeDz8lj4mUorx7B61bw+7dADxo05Yap05y/8EDAEqUsGLcOE9q1HBWMUgpqYrr9TBRekq9fv2ajBkzfvH1jBkz8ubNm8QIJWkzNVXG51pZwenTMHBgou7eyAjWroUuXXSMHbsBb++fiIkJ5urVGgQHX0/UWKS4yZ07N2fOnKF06dIEBARQtWpV1q1bp3ZYUhIQFhbGpUuXgNQz854kJbq/h/OZ5atCoaF6Cg0B07fm2Nq+YMiQjrRqVY3g4JFUrtwJvV5Pjx49GDZsWGz9GkmSJElKsq5fV4br7d6NMDZmbvMO5N+4nvsPHmBmBp06lWD//mcyISX9sERJSkVGRn5UZPbfdDodkZGRiRFK0pcjh5IZAqXo+frELThuYAALFsDAgSaMHLmdGzdKEx0dwNWrVQkNvZ+osUhxkz59eo4cOUKjRo2IjIykVatWTJw4kUToBCklYRcuXCA6OhoHBwcyZ86sdjiSlHI5OChj4GfPJu0VY1yahZBjtTnaGFPy5TvH9OnlKVEiipIlBwEwefJk2rRpQ0REhMqBS5IkSdIX/PmnUtD8/n1eZ8yIa24X+mzwIDIyikKFYNYsd5YuPYutbRq1I5VSAF1i7ejXX3/FzOzzY0zl7GH/Urs2jBih1Jn65RcoXBgKFEi03Ws0MHEi2NikYejQPcye7UaOHFe5cqUKzs4nMDFxSrRYpLgxNTVl06ZNDB48mBkzZjBixAh8fHxYsGABhoaGaocnqeDEiROA0kvq/2v5SZKUALRa6NsXfvoJbatWZPK4it12eDgtNy9y3KVmzdW4ulowa1Yzjh7dwrp163j+/Dnbtm3D2tpa7eglSZIkSREdrXwPnToVgJ2Fi9Di/lNCfE+g00Hbtob06vUHzs5NVA5USkkSpaaUm5tbnL4UeSbirHNfkyTGgsfEQM2acPAg5M4N58+DCrEsWwZDh75g9uzyODndx9Q0L87OxzAySp/osUhxM3/+fPr06YNer6datWr8+eefsqZBKhMVFUWePHnw8fFh/vz59OjR47u3lSSuh0mMPCbSV0VEwMiRMGMGCEFQVScu97NEb3oTgL17MzJnzlvCw8MoUKAAe/bskb0ZpWRLXg8/JY+JlGy9fg0tWsChQ4QCvfIWxeP2ZQCyZIEBAxxo3XovNjZFVA1TSj7iej1MlKRUcpNkGpPXr6FYMXj6FBo2hM2blW5MiWzzZnB3f8yMGRXIkOEpZmbOFCvmiU5nleixSHGza9cumjVrRmhoKIULF2b37t04OckebqnF8uXL+eWXX7Czs+Phw4eYm5t/97aSzPUwCZHHRIoTT09o2xaePUPotDyZWZcb2U9jZv6C+/dh0CBj3r2LwMHBgT179lC0aFG1I5akbyavh5+Sx0RKli5eVL5vPn7MORMTGhnb8CzQD4BGjaBTp3L89NM22TFB+iZJqtC5u7s716/LQtnfLF06ZTyvoSFs3arcdVVB48awfHkWfv31IAEB6QkNvcSlS7WJiZHDLpOq2rVrc+zYMezt7bl69SqlSpXi8uXLaoclJYLIyEjGjx8PwJAhQ34oISVJ0g+oVAmuXoUWLdBE68nSZztVp2UixLcTWbPqWLw4gixZNPj5+VGhQgUOHDigdsSSJElSarR6NZQrR/Tjx/xmY0vpiCieBfqRLh1MmwYjR3ahevUjMiElJZhESUrt27ePIkWKULJkSZYsWUJQUFBi7DZlKFUKfv9d+X3oUPDyUiWM6tVh5co8jB17gOBgK0JCTnDxYkP0elmgPqkqXrw4Z86cIX/+/Dx//pwKFSqwb98+tcOSEtjq1at59OgRGTJkoFu3bmqHI0mpm40NrFsHf/wBVlYYel2gVtcN5Lo6mjdvajBvnsDZGYKDg6lV62c8PDzUjliSJElKLaKioE8faNeO++HhlEljzZiAtwgRg5sbLFumpXnz+RQpsgit9suTlknSj0qUpNTt27c5duwYhQoVYuDAgTg6OtK2bVuOHTuWGLtP/rp1gzZtlDpTzZrB8+eqhFG2LKxaVZSpU/cQFmZGSMh+LlxogV4fpUo80n/LkiULJ0+epHLlygQHB1O7dm2WLl2qdlhSAvl3L6kvTS4hSVIia9lS6TVVsSKEhJBlwK903meMz+W19OmTnSpVIDo6ho4dOzJ8eFc5e6okSZKUsPz94aefEHPnshQopDXkQvA7zMw0DB8Oo0bZ4Op6ECenHnLCHCnBJUpSCqBcuXIsX74cf39/5s6dy6NHj3BzcyNXrlxMnjyZ5yolWpIFjQYWLVJm4XvxApo2VTLbKihcGFasKMu8eduJjDQiNHQrZ882Qa+XU1snVdbW1uzdu5e2bdsSExNDly5dGD58OHq9Xu3QpHi2cuVKHj9+jL29vewlJUlJTebMcPiwMqORoSG6v3bQe94ACj2fRbZsE2naVJkpddKkJTRunJeQEF+VA5YkSZJSpDNnoHhxXh4/Tl0DHV2AcH0UhQtrWLFCUKdOAUqXvoCNTWW1I5VSiURLSn1gZmZGhw4dOHbsGPfu3aNp06ZMnTqVrFmzJnYoyYuZGWzZoszAd/IkDB6sWig5c8KSJVVZsmQHkZHGRETs4NSphsTEhKsWk/R1RkZGrFy5kjFjxgAwadIkWrVqRXi4/DdLKSIjI5kwYQIAQ4cOxdTUVOWIJEn6hIEBDBoE585B/vzw4gXFR9Tj9xhfTMQtatQoiVYLW7fepXLlrNy6NVn2RpYkSZLiz5Il4OrKzufPKaA1YFdMNFqtAd27w6xZgvz561KixGlMTbOrHamUiiR6UuqDkJAQvLy88PLy4t27d+TIkUOtUJKPnDmVQnQAs2fDpk2qhZIxIyxaVAMPj12Eh5sSHb2HEyfqyuLnSZhGo2H06NGsXLkSnU7Hhg0bqFq1Km/evFE7NCkeeHh48OTJExwcHOjSpYva4UiS9DVFi8KFC9C3LwCmK+az8lpt2hdZSJYsUzEy0nLuXDR16w5j7978vH17UN14JUmSpOQtIgI6dya4a1e6REVRF3itj8He3oIlS2Jo2hSyZh1BwYLb0Oks1I5WSmUSPSl17NgxOnTogL29PX379iV37twcP36cW7duJXYoyVO9ekrBc4COHeHmTdVCSZcOFi+uwrp1ewkLM0eIg3h51SI6Oli1mKT/1q5dO/bv34+VlRUnTpygbNmyPHjwQO2wpB8QEREhe0lJUnJjaqrcYNq/Hxwc0Ny+TbMZpThVN5osTscxMrLg/n3o1Ok+O3ZU4/r1BoSFPVQ7akmSJCm5efYMXF05s2wZzoBSXVZDjRq2rFr1nly5TMiXbz3Zs49Ho1Gtz4qUiiXKWffs2TMmTJhArly5cHNz4/bt28yaNQs/Pz9WrFhBuXLlEiOMlGPcOKhcGUJCoFEjeP9etVAsLWHRoops3bqfkBALtNqjeHrWIDpazrCYlFWuXJmTJ0+SOXNm7t69S+nSpTlz5ozaYUnfacWKFTx9+lT2kpKk5KhaNbh2DRo2hOho7H8fzi37IXSrvQuNJicvXkDv3nDo0HbOncvPw4cjiYkJUTtqSZIkKTnw8iKqWDFGnTtHOeA+YGiYngkTzBgy5C1p0mTE2fkEGTI0VztSKRVLlKRU1qxZmTNnDvXq1ePGjRucPn2aX375hTRp0iTG7lMenQ7Wr1fG0N2+rfSYUnGmHjMzmD+/HPv2HSI42ApDw5McOlSNqKh3qsUk/bcCBQpw5swZihUrxuvXr6lUqRJbt25VOyzpG0VERDBx4kQAhg0bhomJicoRSZL0zdKmhc2bwcMD0qTB4NQJfj9Ym8Nd+6HTlSUkBAYO1HDoUARPnkzg7Nk8vHixQc7SJ0mSJH2eEPD779ypXJlyr14xDtADmTIVZ/Pm15QtG4KlZWmKF7+AhUVxtaOVUrlESUpNnTqVBw8eMH36dPLly5cYu0z57OyUD7CGhsrP2bNVDcfICGbPLsmxY0cIDLTFxOQsBw9WISrqrapxSV/n4OCAl5cXtWvXJjw8nMaNGzNr1iz5RScZWb58Oc+ePSNjxox07txZ7XAkSfpeGg20bw9XrkDZsvD+PZUW9eRFNXsypq9DTIxg/Hjw8LAhIsKXW7dacPlyJSIi/NWOXJIkSUpKQkIQrVuz0N0dZ72e8wBYUa9eRVav9sbSUmBv356iRY9ibGyvcrCSlEhJqUGDBhEaKgtgx7vSpWHmTOX3QYOUuhQqMjCAKVOKcfmyJwEB6TEz82b//kpERLxSNS7p69KkScO2bdvo0aMHQgj69+9Pnz59iImJUTs06T+Eh4fLXlKSlNJkzw5eXjB+POh02O7ZymNDb2oVagTA6tUB9O5dkuhoUwIDvbh0qQwhIbIupyRJkgTcvUtIyZI0WLeOHkAYoNOVZ+rUHLi7ewFacuSYSZ48K9BqjVUOVpIUiZKUkr0uElDPntCqFcTEQN26sG2bquFoNDBqVGEePjzK27cZSJPmKvv3u8k7uUmcTqdj3rx5TJ8+HY1Gw7x582jcuLFMJidxy5cvx9fXl4wZM9KpUye1w5EkKb7odDBiBJw6BblzY/D8ObuubWFs8YqAhhs3zlGnTlnevMlOePgjLl0qy7t3XmpHLUmSJKlp2zbelihBlZs32QFoMCJdugFs3eqDi8tFDAysKFx4D5ky9UOj0agdrSTFSrTy+vLETyAaDaxYAY0bQ2Sk8nPVKtVDGjAgP69eefHqlSOWljfZt8+N0FBfVeOSvk6j0TBgwAA2btyIsbEx27dv56effuLVK9nTLSn6/15Sw4cPl72kJCklcnGBixehWzcAfvX2YpOTEzqtEeHhh2na1IpLl4oRHf2OK1eq8eLFOpUDliRJkhJddDQMGcKzhg2p8P49ZwAN1pQuM5JNmxZgYeGLqWkeihc/h61tdbWjlaRPaEQidGPSarXUrFkTY+OvdxFMKkWWg4KCsLKyIjAwEEtLS7XDiZuYGOjSRUlQAcyZo0zXo7I//niATleZDBmeEBiYg8qVj2BhkVntsKT/cOLECerVq8fbt2/JmTMne/fuJWfOnGqHJf2fuXPn0qdPH5ycnLh///5/Xl+/V7K8HiYweUwkVezerUxs8vIlJ3U6ammNCIwMBWxo2zYvHTqcBiBbtklkzjxE3gyUEoW8Hn5KHhMpUfn7Q4sW3D56lGrAUwAc6dixJm3aLAfA1rYG+fKtx9DQWr04pVQprtfDROspZWFhgZWV1Vcf0g8wMIClS8HdXfm7Tx+lHoXKQydbtcqBsbEXfn7ZsLJ6wOHDFQkI8FE1Jum/lS9fnlOnTpE1a1bu379PmTJlOHPmjNphSX8LDw9n0qRJgNJLKqESUpIkJSG1asG1a1CnDuWio7kYGUpxc0sggNWrT9OnT1EiI8HHZxh373ZDr49WO2JJkiQpIZ08CcWKcfboUcqjJKR0umzMnZs2NiGVKdMgChXaJRNSUpKWaD2l/P39sbOzS+hdxYtkfYdDCBg3DkaPVv4eMACmTVPG1KnowIGnvHr1Exkz3uPdu0yULXsEOzvZ8yap8/f3p3bt2nh7e2Nqasr69eupV6+e2mGlenPmzKFv375kypSJe/fuJWhSKllfDxOIPCaSqoRQbkL160dkaCjDLCyZ+T4IAGtrJ+bMeUamTGBrW5P8+Tei01moHLCUksnr4afkMZESnBDw++8waBD7o6NpgIYwBDY2mVm27AW2thHodDbkzr0IO7umakcrpWJJqqeU7EKeiJRK4zB7tvL3jBnKsD6VZ1KrVi0TWbIc5enTvFhbP+X0aVeePbutakzSf7O3t+fo0aP8/PPPhIWF0aBBA+bNm6d2WMmWXq9nyZIlzJgxg2vXrn3XJBBhYWGxvaRGjBgRp4SUjw88eKB6x0lJkuKDRqO06xcuYJQzJzPeB7HT2BgLYwvevXtG+/Ym7NtnyNu3e7l8uSIREc/VjliSJEmKL+/fQ/Pm0K8f66OjqfV3QipPHlv++OMJtrYR2NhUx8XlukxIScmGnH0vperbV6kvpdXCsmXKDH2RkaqGVL68IwUKHOXJk4JYWfnh7e3GgwfXVY1J+m9p0qRhx44ddOnSBSEEvXv3ZtCgQej1erVDS1aCg4Np3LgxXbt2ZeDAgRQuXBgnJyc6duzIxo0befv2bZy2s2TJEvz9/cmcOTMdOnSI0zozZkDOnDB8+I+8A0mSkpR8+eDcOahWjdoREdyMeE/JDJnR68OZMiWK8eNNePXqEhcvliY4WLa1kiRJyd6tW1CqFGzaxO8aLS2BGAQVKxoyd+5bzM3NyJVrAYUL78XY2FHtaCUpzhJl+J6XlxflypVDp9Nx+PBhDh8+zMuXLz/5UrviQ5FulaWobrebN0PLlhAVBT//DH/+CWZmqoZ069Zrzp2rSpYslwkKSkfOnIfIn7+IqjFJ/00IwaRJkxgxYgQAzZo1Y+XKlXLWtzh4+PAh9erV4/r16xgZGeHq6srJkycJCwuLXUar1eLi4kKNGjWoXr06JUuWxMDA4KPthIWFkT17dvz9/Vm8eDFdunSJ0/5dXODCBdiwAZo1+7bYU9T1MJ7IYyIlKX/PusTMmUQDo3LmZdL9O4DA0dGI8eMjyZnTkoIFt2FjU1ntaKUURl4PPyWPiZQgNm2CTp0QwcEMNk7D9IhgABo2hJ49wcqqFPnyrcHMLJfKgUrSP5LU8L2KFSui0+n47bffqFatGocPH+b169cEBAR89JASQOPGsHMnmJrCnj1QsyYEBakaUr586XB1PcyjRyWwtHzNw4eVuHTJW9WYpP+m0WgYPnw4a9aswdDQkI0bN1K9evU49/BJrY4cOYKLiwvXr1+PHQ558OBB3r59y8GDBxkwYAAFChRAr9dz9uxZfvvtN8qWLUv69Olp2rQpK1aswNfXF4BFixbh7+9PlixZaN++fZz2Hx4OV64ov5cqlUBvMonx9fWldevWpE2bFjMzM4oWLYq39z/XGCEEY8aMwdHREVNTU9zc3Lhx44aKEUvSD9DplO6QK1eiMzJi4v3bHMqalTSmdjx/HknXrlp27AjiypXq+PuvUTtaSZIk6VtERUG/ftCsGdHBwTQ1dYhNSHXqBL17G5A9+zicnU/IhJSUfIlEZG9vL1avXp2Yu/wugYGBAhCBgYFqhxJ/jh8XwtJSCBCieHEhXr1SOyLh6/tOrFhRWnh6InbtshKnTp1WOyQpjg4fPiwsLS0FIPLlyyd8fHzUDinJ0ev1Ys6cOcLAwEAAwsXFRTx79uyLyz99+lQsW7ZMNGnSRFhbWwvgo0fBggWFra2tAMTSpUvjHMepU8p/ezs7IfT6b38fye16+PbtW5ElSxbRvn17cfbsWeHj4yMOHTok7t+/H7vM5MmThYWFhdiyZYu4du2aaNasmXBwcBBBQUFx2kdyOyZSKnL6tBD29kKAeGFjI0pkd4m9hlSqhNi1C+HjM1bov+diIEmfIa+Hn5LHRIo3vr5ClCsnBIgwEMXNcghAaLWIAQMQZ8/mE0FBF9SOUpK+KK7Xw0RNStna2n70xeB7eHl5idq1awsHBwcBiG3btn2yzM2bN0WdOnWEpaWlSJMmjShVqpR4/PhxnPeRYhsTb28h0qVTvqHmyyfEV74gJ5ZXr4LEihUVhKcnYvfuNOLw4eNqhyTF0dWrV4WTk5MAhL29vfD29lY7pCQjPDxcdOzYMfbLYOvWrUVoaGic14+KihKnTp0So0aNEqVKlRIajSZ2W1mzZhWRkZFx3tasWcp/+Tp1vuONiOR3PRwyZIgoX778F1/X6/XC3t5eTJ48Ofa58PBwYWVlJRYtWhSnfSS3YyKlMs+eCVGihBAgYrRaMbxSPQE6AQhHR8SiRYhbtzqKmJi4X0ck6Uvk9fBT8phI8cLTU7mjCOK1mYXIZJFNAMLQEDF2LOLePXcRHR33z5aSpIa4Xg8TZfjeB7/88gvr1q37oW2EhIRQpEiRL84A9uDBA8qXL0/evHk5evQoV65c4ddff5V1bwCKFYPjx8HJSSmUV768MiWXitKls6Bhw708elQJM7NgIiOrs3v3UVVjkuKmUKFCnD59msKFC+Pv74+rqyt79+5VOyzV+fn5UalSJVasWIFWq2X69OmsXr0aU1PTOG9Dp9NRpkwZfvvtN86cOcOrV6/YsGED7u7ubNy4EUNDwzhv6+xZ5WdqGbr3119/UaJECZo0aYKdnR3Ozs4sXbo09nUfHx/8/f2pVq1a7HPGxsZUrFiRU6dOfXabERERBAUFffSQpCQrY0Y4dgxatUKr1zPBcwdeP/+MmWkWnj+HXr1g3rwVXL78M9HR8lyWJElKUoSAadOgShV4+ZKbmfKQ39SIp+99MDeHGTPS06vXYXLmnIWBQdw/W0pSkpZISTIhhBB9+vQR1tbWwtXVVfTq1Uv069fvo8e34jM9pZo1ayZat279Q3Gm+Dscjx4JkTOn0n3CwUGIa9fUjkgEB4eIlSurCU9PxN69puLPPw+oHZIUR+/evRNVqlQRgDAwMPimoWUpzblz50TGjBkFIKytrcW+ffvUDklky6b8Vz948PvWT27XQ2NjY2FsbCyGDRsmLl68KBYtWiRMTEzEqlWrhBBCnDx5UgDC19f3o/U6d+4sqlWr9tltjh49+pPhlMnpmEiplF4vxJQpQmg0QoB4U7q0KJynVuz5W7YsYs+e/CIs7KnakUrJWHJrI4QQ4tmzZ6JVq1bC1tZWmJqaiiJFiogLF/4ZAqXX68Xo0aOFg4ODMDExERUrVhTXr1+P8/aT4zGRkoh374Ro0ED54AZiS9lKIn06IwEIGxvExo21RGRkgNpRSlKcJcmeUlevXqVo0aJotVquX7/OpUuXYh+XL1/+4e3r9Xp2795N7ty5qV69OnZ2dpQqVYrt27d/db1Udxc8Sxalx1ShQuDnBxUrKtNKq8jc3IwWLXbw5EktTEzCsLSsw+rVe1SNSW1btsDw4Up9w6TMysqK3bt307ZtW2JiYujcuTOjRo1CJPzEnknK2rVrqVChAr6+vuTLl49z585RvXp1VWN69Qp8fECjUWbgSw30ej3FihVj4sSJODs707VrVzp37szChQs/Wk6j0Xz0txDik+c+GDZsGIGBgbGPp0+fJlj8khRvNBoYPBh27QJLS2zPnOFy6FUGNBuGRmPIqVPQrt1NFi92Jjj4qtrRSlKiCAgIoFy5chgaGrJ3715u3rzJjBkzsLa2jl1m6tSpzJw5k3nz5nH+/Hns7e2pWrUq79+/Vy9wKeW7fl35sLZtG3pjQyY1/4lONz159ToSBwcte/fOo2nTXRgaWqsdqSTFv0RJkSUQ/tVTys/PTwDCzMxMzJw5U1y6dElMmjRJaDQacfTo0S9uJ9XeBX/zRohSpZRsfJo0ythllUVFRYi1a+sLT0/EgQOGYtGi7WqHpIq5c2NvkoglS9SOJm70er349ddfY///tG3bVkRERKgdVoKLjo4WAwcOjH3ftWvXTjLXjp07/ykh972S2x3fzJkzi06dOn303IIFC4Sjo6MQQogHDx4IQFy8ePGjZerWrSvatm0bp30kt2MiSeLWLSFy5VIuCKam4vDQqcLMLHtswdz27Y2Ev/8etaOUkqHkdj2UdQelJGntWiHMzIQAEVTIQQx3zyXMzJTPldmzW4pHjy6rHaEkfZck2VMqoen1egDq1atHv379KFq0KEOHDqV27dosWrToi+ul2rvgtrZw6BBUrgzBwVCjhnJHVUU6nRHNm2/C378phoZR5MjRmFmz/iQ1dbqZMQN69/7n72nTICZGvXjiSqPRMHbsWJYuXYqBgQGrV6+mVq1aBAYGqh1aggkICKBWrVpMnz4dgOHDh7Njxw4sLS1VjkyR2upJAZQrV447d+589Nzdu3fJkiULANmyZcPe3p6DBw/Gvh4ZGYmXlxdly5ZN1FglKdHkzatcEKpXh7AwKk8ejG+3xuTJ3RS9HlaujKRmzVqcPj1L7UglKUElRN1BSfpuERFKob/WrRGhodzrUoDJdQKYNv8eoaFQrFhuvL0fkyVLEbUjlaQElaKSUunSpUOn05E/f/6Pns+XLx9Pnjz54nrGxsZYWlp+9Eg10qSB3buhbl3lwtigAaxfr2pIBgaGNG36B2/etEKni6Zw4eZMnryOv3OOKdqECTBwoPL7gAFgYwP37sGOHerG9S1++eUXdu7cibm5OYcOHcLV1RVfX1+1w4p3t27dolSpUuzfvx9TU1M2bNjAhAkT0GqTzmU1NSal+vXrx5kzZ5g4cSL3799n3bp1LFmyhJ49ewJK8tTd3Z2JEyeybds2rl+/Tvv27TEzM6Nly5YqRy9JCcjGRrnxNGAAANYzp3I7XwSd28zF0NCAS5cEtWr15/ffW6W64ddS6vHw4UMWLlxIrly52L9/P926daNPnz6sXr0aAH9/fwAyZMjw0XoZMmSIfe3fUl0ZECl+PH2qlFCZP59IKzi+KC+L0txg8uRwoqKgRo3qnDx55aOhpZKUUiWdb0/xwMjICBcXl6/eJZc+w8QENm+GVq0gOlr5uXixqiFptToaNlxFYGB7DAz0lCrVmrFjVyX5+krfSwj49VcYOVL5e+xYmDo1kr59AwCYMoVk1VusZs2aHDt2DHt7e65evUrp0qW5du2a2mHFm127dlGqVCnu3btH5syZOXnyJM2aNVM7rI/o9f+UinN29uDmzRa8fXtA3aASgYuLC9u2bWP9+vUULFiQcePGMXv2bFq1ahW7zODBg3F3d6dHjx6UKFECX19fDhw4gIWFhYqRS1Ii0Olg+nRYtQqMjGDHDpZcXMSfs/aQLp0dAQHg7r6Opk0LERERona0khTvEqLu4KRJk7Cysop9ZMqUKcHil1KIQ4eUWdHPnuV1FXOOrrFi1bnbzJypfH7r1Kkzu3btlrPHS6lHogwmjEfv378Xly5dEpcuXRJAbO2ox48fCyGE2Lp1qzA0NBRLliwR9+7dE3PnzhUGBgbi+PHjcd5Hqh0LHhMjRI8e/xQzmjJF7YiEXh8jdu3qKjw9EYcPa8Tw4UtEWJjaUcUvvV6IQYM+PuwxMVHi4sXy4uhRM5Ejxy0BQnh5qR3pt/Px8RH58uUTgLC0tBSHDx9WO6QfotfrxcSJE4VGoxGAqFChgnjx4oXaYX3WrVux5WPE5cs1hacn4vHjad+8nVR7PfwKeUykFOH0aSHs7ZULha2t8Fu/RxQt6hZbHy93bitx69YVtaOUkrjkdj1MiLqD4eHhIjAwMPbx9OnTZHVMpEQUEyPE+PFCaDQiyhRxY0JacfgwokGDf2oajxgxUuj1erUjlaR4kWJrSl24cAFnZ2ecnZ0B6N+/P87OzowaNQqABg0asGjRIqZOnUqhQoVYtmwZW7ZsoXz58mqGnTxotTBvHgwbpvw9ZIgy/ZuKXXQ0Gi0//7yQyMjeaLWCqlW7MGrUfN69Uy2keCUE9O2r1I0C+P13ZbKkp0+nExh4AiFCGTJkPqD0lkpusmbNysmTJ3F1dSUoKIgaNWqwZs0atcP6LqGhobRo0YLhw4cjhKBbt24cOnQIOzs7tUP7rA9D90qWDCcw8CgAtrbqzgYoSVISUro0XLigzPb09i32revg3aER7doNx8wM7t4NpEQJZ5YsWfrf25KkZCIh6g6m6jIgUtwFBED9+jByJO8KCM6ut8DX5Q3jx8O2bUrvvDlz5jB+/Lgv9sqTpBQrcXJkyUtyu+uTICZP/qfrTo8eSmZfRXq9Xnh6DhCenghPT0TTppvEv25iJTsxMUJ06fLPYf4wqUtw8A1x9KhR7Hs9etRSmJkFCxDi6lV1Y/5eYWFhomnTprF3gSZMmJCs7gI9fvxYODs7C0DodDqxcOFCtUP6qpgYISpVUs6rSZMOCE9PxMmTjt91zOX18FPymEgpSmioEK1b/9MY/fKLWLFkm8iZ0zD2mt2gQXMRHh6udqRSEpTcrofnzp0TOp1OTJgwQdy7d0/88ccfwszMTKxduzZ2mcmTJwsrKyuxdetWce3aNdGiRQvh4OAggoKC4rSP5HZMpERw6ZIQ2bOLGEPE/e4GwvOIRuzZgyhUyEQAwtDQUKxfv17tKCUp3qXYnlJSIhkyBBYuBI0GFiyAdu1Qs6CTRqOhYsVpGBm5A9CqVVdq1/ZlyZLkVWvpg5gY6NQJlixRDrGHB3TtCkLEcPt2R4SIxNa2JiYmORAiiIEDNwBKKZDkyMTEhPXr1zPw7yruI0aMoFu3bkRHR6sc2X87fvw4JUqU4NKlS6RPn57Dhw/TrVs3tcP6qpkzwdMTzMygSpV9gNJLSt55kyTpE6amsHo1TJ2qNEjLltFh9Qy2Lz9BrVrpANi2bQO5chXg7t17KgcrST9G1h2UEpUQyof9MmUIFg/xXmbI06YxvAsUdOyYjmvXwjE3N2fXrl00b95c7WglSTUaIZLjV/qEFRQUhJWVFYGBgbIL7rp10LatkkWpVw82bFAKo6tEr4/i/PkyhIV5c+5cdYYM2UubNhoWLgRzc9XC+ibR0cohXb8eDAyU7wIfJv168mQ6Dx8OwsDAEheXG7x8uZ6HDwej1ZagYsXz6HTw4AFkzqzue/gR8+bNo0+fPggh+Pnnn9m4cSNp0qRRO6zPWrx4Mb169SI6OpqiRYuyffv2JD9pwsWLyqicqCjlc1CRIgUJDb1B/vwbsbNr+s3bk9fDT8ljIqVYe/ZAixYQFASZMvF+/VrGbu/L0qWXCQwEIyNj5s9fzi+/tPrvbUmpgrwefkoeEwmAd++gSxfE5j952hR8ftEidHru3rXB3d2UsLDnpE2blj179lCyZEm1o5WkBBHX66HsKSV9XcuWykBnY2PYsQNq1YLgYNXC0WoNKVhwDVqtCSVL7qdBg4WsWaNMeX/7tmphxVlkJDRvriSkdDolx/chIRUaegcfH2X6vZw5Z2Fi4oS9fXs0GiP0+gu0anWB6GiYPVu9+ONDr1692Lp1KyYmJuzZswc3N7cvTrOslsjISLp37x7bm6tp06acOHEiySekQkOV8ykqCho0gNatnxIaegPQYmNTRe3wJElK6n7+WSlIlzs3PH2KRdUaTC4xiOnTm1CkCERGRtC5c2tatepFTEyM2tFKkiQlTWfOgLMz0Tv/5Po4DQ+7gdDp2brVje7djQkLe07mzJk5ceKETEhJEjIpJcVFnTqwdy+kSQNHjkCVKvD2rWrhmJvnI3t2pep3nz4DKVbsDjduQIkSSrInqQoPh0aNYMsWZSburVuhcWPltX+G7UVgY1Mde/sOABgZpSd9emWhDh0WA0rvl4AAVd5CvKlfvz6enp6kS5cOb29vypQpw82bN9UOC4CXL19StWpVFi1ahEajYeLEiWzYsAHzZNAVr39/uHMHHB1h6VIICNgPgKVlSQwNbVWOTpKkZCFvXiUxVb06hIVh0LwVnR7lYvasUbRrp4zwW7duPsWK1SQ4OETtaCVJkpIOvV6ZmahCBcJDH3FpsRFvygkiI40ZPnwI8+dfQa/3p0CBApw8eZK8efOqHbEkJQkyKSXFTaVKcPgw2NgoH1bd3EDF3i0ZM/bC2vonIIz589vw009RhIQovUR69oSICNVC+6zQUGX0465dyujHHTuUXN8Hz57NISjoFAYGFuTJs+Sj2j+Ojkr9IkPDdZQsGUhIiFLuK7krXbo0p0+fJkeOHDx69IgCBQrg4OCAm5sbXbt2ZebMmezZs4cHDx4k2h35y5cv4+LiwrFjx7CwsGDHjh0MGzYsWdRi2r4dFi9WvjCuXg1p0/6TlLK1raFucJIkJS/W1rB7NwwYAIBmwkTcZl9meL+F/PqrDiMjuHr1ILlyFeL69aTV01WSJEkVL15AjRowdCiBuaPx9jAhJHMkb9/a8csv4zl7dg56fQBly5bl2LFjODk5qR2xJCUZsqbUZ8ix4F9x/TpUraokpHLmhEOHQKUhTeHhz7hwoRDR0e/InHkMK1aMZsIE5bUSJeDPPyFrVlVC+0hwMNSt+0/h6Z07oXLlf14PDb3HhQuF0evDyZ17MY6OXT5aXwjB+fMFCQ29ybt382jQoCd2dvDokVKfNrl79eoVzZs358iRI19cxsjIiJw5c5InTx7y5s1Lnjx5Yh82NjbxEsemTZto3749YWFh5MyZk7/++ot8+fLFy7YT2vPnULgwvHkDgwYp9Yr1+mhOnkxHTEwgzs6nsbIq/V3bltfDT8ljIqUqq1dDly7K3Z78+Xn/5wyW7O3H2LG3CQoCGxszJk/eS5curmpHKqlAXg8/JY9JKnTggFIw9sULXtQ05PZAgdBGc/duYQYMqENIyGSEiKFWrVps2rQJMzMztSOWpEQR1+uhTEp9hmxM/sODB8oQvkePwM4O1qyBatVUCeXFi3XcutUKMKBYsdOcPOlC69bK6EIbG+WzdO3aqoQGKLVif/4ZTp4ECwulhmz58v+8LoSey5crEhh4AhubKhQufOCzvXKePZvL/ft9MDMrSNOmV3n8WMOiRcqMfSlFYGAgd+7c+ehx+/Zt7t27R8RXur6lT5/+k0RVnjx5yJ49Ozqd7j/3q9frGTVqFBP+zmhWq1aNDRs2xFuyK6Hp9coom0OHwNlZKWNgZASBgae4dKkcOp0N5cq9QqMx+K7ty+vhp+QxkVKds2eVQnV+fmBjg37JQraIM7i7z+b5czA319C69a/MmjUGU9Ok37NUij/yevgpeUxSkago+PVXmDIFoQGfIel5Uv0VAPv2VWPmzBiiog4D0LZtW5YtW4ahoaGaEUtSopJJqR8gG5M48PVVsi1Xryp/DxkC48ZBIl9ohRDcvNmCV682YmqahxIlLuLra0bTpspn6A+hjR+vFBZPTAEBSi/ec+fAygr271cKsv+/Z8/mcP9+X7Rac1xcrmNqmvWz24qKesfp047o9WHcv3+Szp3LkjOnUtzd4PtyDclGTEwMT548+ShR9eH358+ff3E9Q0NDcuTI8UmyKm/evKRNmxZQ/q+3bt2anTt3AjBw4EAmTZoUp2RWUjFjBgwcqPSau3hRKQcDcPdud54/X0T69E0p4FVRuYvXsaPSbe8byOvhp+QxkVKl58+VxNS5c8rfbm7cGdyauu59uHs3FENDaNasJAMH7qJIkfTqxiolGnk9/JQ8JqmEj48yW+nZs8SYwK0l2XidyQeAKVOacPCgFzExLzE1NWX27Nl07tw5WZSDkKT4JJNSP0A2JnEUFqbUm/hQ4Kh0aaXSeCKPmYuKesv584WIjHxOxoy9yJVrLpGRyjCmOXOUZVxdlZnuHBwSJ6Y3b5RRjpcuga0tHDwIxYp9vExY2APOny+MXh9KrlwLyJix+1e3eft2R/z9PUibtg1ubqt5+xY2b1aKp6dW79+/5+7dux8lqu7cucPdu3cJCwv74npp06YlT548vHjxggcPHmBsbMzSpUtp06ZNIkb/4y5dUhKdUVFKPakuf4/89Pdfw+3bbQEoVGgPadvNV+rDzJoF7u7ftA95PfyUPCZSqhUeDhMnwrRpyu9aLSGd2lHzxkmOn7oLQJs25lSqtJL27Rsjv3+lfPJ6+Cl5TFKBP/+EX36BoCDCc1hydUFaQo18CAkxpGfPKjx+vBeAQoUKsWHDBvLnz69ywJKkDpmU+gGyMflGW7ZAp04QGKh0CVq+PNEzJW/f7ufqVaWYc+HC+7G1VYYT/vmnEtr795Ahg5Izq1QpYWN58UJJSF27BunTK/XhCxX6eBll2F5lAgO9sLZ2o0iRw2g0X593ICjoHBcvlkKjMcbLy5fRo9Pi4qL0CJMf/D+m1+t5+vTpZ4cDPnv27KNlHR0d2b59Oy4uLipF+31CQ6F4caW3XP36ymyOGg0EBZ3l0qWKCBFB5szDyZ51nFL1/N07OH9eKbj2DeT18FPymEip3uPHMHgwbNoEgN7Kih7OuVh89AKgTORRtWpTGjZcQMaMadWMVEpg8nr4KXlMUrDQUOjXT5kKGwhqUpBL3V4gtK+4ccOGQYPsCAu7A0DPnj2ZNm0apimhAKwkfSeZlPoBsjH5Do8eKV1Yz5xR/u7WDWbOTNRK3Hfv9uL58/kYGTni4nINQ0Pbv5+Hxo2VJJFWq4wyHDpU+T2+PX8OP/2kJAocHJSE1OdqZfv6LuDevZ5otWa4uFzD1DT7f25bCIG3d3GCgy+RIcMMihXrT3i4UkDdzS3+30tKFRISwt27d7lz5w4BAQE0bNiQDBkyqB3WN+veHRYtUs6zq1chXTql+P/Fiy5ERvqTNm09ChbciubGTSUram6uJKa+cWiivB5+Sh4TSfrbsWPQty9cvgzAbAc7+vu/RAilF2ffvumxtl5KzZr11I1TSjDyevgpeUxSqBs3oFkz5adGw4vZdblRcD9abThr12bEw+Mden0Itra2rFixgnr15HVPkuJ6PUyAr+VSqpQ1q/LhdOhQpbvGokVQsiTcvJloIeTIMRVT09xERj7n3r2esc/nzq3kyjp0UIpCjxih3MV98yZ+9//kiTJM8PZtcHICL6/PJ6TCwnx48GAwANmzT4lTQgpAo9Hg6NgNgKCgRXTsqOSTp06Nn/hTC3Nzc5ydnWnevDndu3dPlgmpHTuU/2KgFPNPlw5iYkK5fr0+kZH+mJsXJF++NUrvuxMnlAVLl078wmqSJKVsrq5w4YLSayBdOtz9XrJVgImBlrNnYfToV4SG1mflyrYEBQWoHa0kSdK3E0K5xpUoATduIBwycG9fa24V3kFYWDj9+mVk+XJf9PoQKlasyJUrV2RCSpK+kUxKSfHH0BAmTVIqetvZwfXrygV8xQrlgp7ADAzMyJdvLWDAy5cbePFifexrZmZKGMuXg4mJMgtesWL/FEP/UT4+ULGiMjHhh/xcrlyfLieE4M6dX9DrQ7CyciVjxh7ftB87uxYYGFgQFnaPnj090Wph795/6s1LKd/z58qQVFAKnFep8uG86kRwsDeGhukoWPAvdDoLZaEPSan/n/ZRkiQpvhgYQOfOcO8e9O9PfZ2OozF60qM81aMHCLGGI0cKcv78brWjlSRJirt375TeUV27Qng4MbWrcm5DKXyN1nD7NrRsac3ly74YGBgwbtw4Dh8+jJOTk9pRS1KyI5NSUvyrWhWuXFF+hoUp36BbtYKgoATftaWlC1mz/grAvXs9CA//uH5Qx45Kr6mcOZWeTRUqwNy5P5Yzu3tXuVn86JGy3WPHIFu2zy/r57eEd++OoNWakifP8v+sI/VvOp0FGTK0BkCIRTRpojw/bdr3xy8lH3o9tG+v9PJzdlZmlQR48mQiL19uQKPRUaDAZkxN/+8ElEkpSZISg7W1Mh3o9euUqlmTM0Bu4OVL6N1Lg4/Pc0JCarN5c0ciIwNVDlaSJOk/nDmjfNj680/Q6YiYPZIjfd4SEv0Xf/yhpUcPA4KC3pE5c2a8vLwYOXIkBil9SmxJSiAyKSUlDHt72LdP6TllYKBUGHd2Vrr5J7DMmYdjYeFCdPQ77tzpgBD6j14vUgS8vZU6U1FR0KePchPke3JmN28qPaSePVOG6h07BpkyfX7Z8PDHPHgwEIBs2SZiZpbz23cIODp2BeD1620MGOAPKIf38ePv2pyUjMyerczkaGoK69aBsTG8erUdH5+RAOTKNR9r64r/rPDsmXJiGBgoBV4kSZISWp48sGcP2Xfv5lSOHJQHQkIFgwcqHanTpfNg376C3Lt3QO1IJUmSPqXXw5Qpyp3rR48ge3YCPJdxJPcK3r/3pn9/Q5Yt0yNEDE2aNOHKlSuUK1dO7aglKVmTSSkp4Wi1So2p48chSxZ4+BDKllUKoOv1/73+d+/WkHz51qDVmhIQcAhf3/mfLGNpqUwa9PvvSpmdP/8EFxelGHpcXb2qFBj391fqSB89qhSd/hxleFVnYmKCsbQsh5NT7+96bwBp0hTB0rIMQkRjZ7eCn36CmBglYSGlTELAypUwbJjy96xZkDcvBAdf5dYtpedcxoy9cHTs8vGKJ08qP4sWBQuLRItXkiSJn38m7c2bHJwyhaY6HdF6mDwZVs03wsLiGb6+1dm/vyvR0e/VjlSSJEnx4gXUqKF8f4mOhmbNuP3nKM6Hdufq1ee0b2/AlStRmJqasnTpUjZu3Ii1tbXaUUtSsieTUlLCK1NGmZmnUSOla9KAAUql8VevEmyXZmZ5yJFDGdP28OFgQkJufbKMRqP0kjp+XOnddPeu0plk1ar/3r63N1SqpLyFYsWUGfDs7L68vJ/fcgICDqLVmpA37wo0mh/r3vuh4Lmf31IGD44BYOlSePv2hzYrJUG3binJzw4dIDISGjaELl0gMvIV167VRa8Pwdr6J3LkmPXpynLoniRJajIywmTwYNY/fcqgwoUBWLk5kvm9NERHg7HxEvbtK4Sf3xGVA5UkKdU7cEAZTvF3l3SxbCmHf8nPk9ftWbYsjKFD4f37GAoXLoy3tze//PILGo1G7aglKUWQSSkpcVhbK92RFi5Uxhzt2aNc+D09E2yXjo49sLGpjl4fzq1bbdDroz67XOnScPEiVK+ulMBq3x5++UX5/XPOnIGfflISQKVKweHDkDbtl+MID3/KgwcDAMiWbTxmZrl/8J1B+vRN0OlsCA9/RLFiByhaFEJCYMGCH960lESEhiozRRYpogwLNTNTZlrcsAGEiOTGjcZERDzGxCQHBQpsQqv9zMx6MiklSVISoLW3Z+qVKywYNgwtsOWmYGIDiHigJU2ax9y58xMnT/YiOjpY7VAlSUptoqKUnlHVqys9pQoVIursCf60OMxzv9H06qV8hQHo3bs3Z8+eJd/npteWJOm7yaSUlHg0GujWDc6dUwow+fkp2Z3Ro5UusvG+Ow15865Ap7MhONibx4/HfXHZdOmUPNm4ccqow+XLlQ5e9+59vNzx40r99sBA5Xv+gQNKvu1LhBDcvduFmJggLC1L4+TkHi/vzcDAFHv7dgD4+S1i8GDl+TlzvpxMk5KPffugYEGYOFH5rFSnjlK/bNAg0OkE9+71JjDwGAYGFhQq9BeGhrafbiQw8J9pGWWtA0mSkoDuEyey46+/MDM2xjMYBnXWY/CH8lpU1HwOHSrC27fH1A1SkqTUw8dHqR01ZYryd/fu+O3Yzqar3bh4cQNduijfBdKmTctff/3FnDlzMDExUTdmSUqBZFJKSnyFC8P588qsfELA2LFQubJSlDmeGRs7kjv3IgAeP55IYOCZLy6r1cLIkUqvXTs7ZQLB4sVhyxbl9cOHlWHmwcFKuPv2KbWpvsbffxVv3+5DozEmT54fH7b3/xwclILnb97som7dp2TNqgwnXLky3nYhJbLnz6FpU6hZU/mc5OQE27bBX38pZdkAfH3n4+e3BNCQP/8GzM3zf35jZ84otdty5PhysTNJkqREVrtOHbxOnCCDnR3XBLRcrkHXA4z9wcTkIVcuu3HpkjsxMaFqhypJUkr2559Kzc2zZ5U7zFu2cLpNZw6dqICHx3mmTIHwcHBzc+PKlSvUqVNH7YglKcWSSSlJHebmsGyZMoWYhYXSBalIEeXbdzyzs2uKnV1LIIbbt9sQExPy1eUrV4ZLl5QbJ+/fK7P0tWwJtWsrQ6pq1IBdu5S38DUREb7cv+8OQLZsYzE3j9+uvubmebG2dgP0vHy5jAHKCEGmT1cKn0vJR0wMzJ2rFC//809lsrz+/ZV6UvXr/7NcQMDh2HMqe/YppE3785c3+iGbKofuSZKUxJQoUYIzfw+BeSYEP9/TEdgeHHaBRisIDPydo55FCAw8pXaokiSlNKGh0LWrchcwKAjKlkVcusyq93D2bBmGDn3O4cNgYGDAhAkTOHToEBkzZlQ7aklK0WRSSlJXixZKQafixZUiTfXqQd++EBERr7vJlWsexsZOhIXd58GDQf+5vKMjHDlC7LC49euVuyV16sD27WBq+vX1ldn2uhITE4iFhQtOTv1//E18Ns5/Cp63axdF2rTKJIdbtybI7qQEcOGCUpusTx8lCVqqlPLcjBmQJs0/ywUFnefGjSZADBkytCFTpoFf3ujKlUrle4DmzRMyfEmSpO+SNWtWTp48ScWKFXkfHU2daAO8NmSh0GAwegUGuvtcvFieW7cGERMTrna4kiSlBDduQMmSsGSJUlZkxAiCdh5l8vo1nDrViIEDI3j+HDJlysTx48cZPnw4BgbxN8pBkqTPk0kpSX05c8KpU0rXEFAKI32uoNMPMDS0IW/elQA8f76QN2/2/uc6Op0yxHzHDmXoVPv2sHmzUqf9v7x4sZa3b3ej0RiRN6/H54tQx4N06RpgaJieyEg/wsN30auX8vyUKcrISCnpCgyE3r2Vz0be3mBlpcwDcPKk0pv8AyEEvr4LuHSpPNHRAVhYlCJ37iVfnvHl5EnlDiAo41Fr1Ejw9yJJkvQ9bGxs2L9/Py1btiQ6Joauvo/53b4ueXumw34vaDSCFy+mc9KzAEFB59QOV5Kk5EoI5Wadi4uSmLK3h4MHud58JFNmNGfLll9ZskTpud6kSROuXr1KmTJl1I5aklINmZSSkgYjI6VryK5dylR2ly5BsWKwdm287cLG5icyZuwLwJ07HYmKehOn9erWhUePwMNDCfO/RET4cf9+HwCyZh2NuXmB7wvYz0+pvfUVWq0RDg6dAHj+fBG9eim9uLy9E3RiQ+kHCKEM0cuXD+bNU/5u2RJu31bmAfj/G3LR0cHcutWae/d6IkQk6dI1oEiR/RgYfKHI5qNH0KABREZCo0bw22+J8p4kSZK+l7GxMWvWrGH48OEAjNv5F/2rVsPw0QDyDjPA6A3odQ+5eKE0928NkL2mJEn6NkFBygetLl2U2YCqV4crV1j/siALFxZl/vyteHuDiYkRy5YtY+PGjVh/bRYjSZLinUxKSUlLrVpKhfGKFZWK4m3aKF2UguNnmujs2SdhZpaPyEh/7t7thojn7kTKbHvdiI5+R5o0xcmUafD3bWjPHsiTR+lGs3v3Vxd1cOgMaAgIOIC5+QM6KTkqpk79vl1LCScoSBmh2rSpknPMmVMprP/HH8pNu/8XEnKLixdL8vLlOsCAHDlmUKDAFnQ6q89v/P17JYP66hU4O8OqVUr1fkmSpCROq9UyYcIEFi9ejIGBAavWraPrm8tETjqH4bDq2B0EtIJnL2ZyxjMvgYGn1Q5ZkqTk4MIF5Sb3hg3KEIipUwnfuodB0x6zZk1OFiy4Q2Ag5MuXnUuXrtCpU6f/sXfXcVXdbwDHP5cUkBQlDOzAFhu7fjpnzO6u2R3TiW7Ork2dzo7p7Fo4dU6xZiK2ooINYiBIxz2/P85EGaigwOXC83697mty7rnnPt/L2Xkuz/nGu3uiCyHSjPzFIjKe3LnVpe6mTlX/qF63Tp1zytv7kw9taGhGiRIb0GiMePp0O0+ebPz0eN8SGPgLz5/vRaMx/rhhe4oCc+aos6q/eqVuGz0aYmLe+RIzs4LY2f0PgMePVzBypPqx7d+v1vdExhAQAHXqwK+/qj3uPDzg8mVo0CDxvk+e/ML585UID7+OiYkT5codIW/eke/+oqTVQpcu6gEdHNQxpx+aiT8LmDFjBhqNhuHDh8dvUxSFKVOm4OzsjJmZGXXq1OHq1au6C1IIEa9fv378+uuvZM+enUOHDtF0SHcc/lpBQNTvOE2yw+Q5xBjd44JXdW5fH05cXISuQxZCZESKAgsXQvXqcOeOOg/HsWPcbTuG9h2XsXNnVfbtU294f/llN7y8rlK8eHHdxixEFiZFKZExGRrC5MnqGLTcucHHB6pWhSVLPnmyJEtLN1xcPAC4dWsQkZH3UyNioqOfcOvWEABcXL4me/bSKTtAZCR0767Orq4o0KsX2Nur47peT1r9Dq8nPA8IWI2LSxTt2qnb58xJcTNEGrhzB9zd1VGpOXOq0z5NmQLZ/jMKT6uNwsdnMNevd0KrDcPGph4VK17AxuYDK+h99ZW6cqWpqVqQyps3zdqiL86ePcvy5cspU6ZMgu2zZ89m/vz5LF68mLNnz+Lo6EjDhg159boILITQqSZNmnD06FGcnJy4cuUK1apVxa6LM9l/ukPohI447Ac08PDJ95w6Upzg4BO6DlkIkZE8f64uXTxihHpTt1UruHCBXf6V6Nq1NQcODMLXV4uVlTG7dv3Cjz+uI9t/v5AJIdKVFKVExlarltrdp1kzdUW+wYPV5PI8efNBvUu+fOOxsqpKXFwIN270QFG0n3Q8ddjeQGJjX5A9ezny5RufsgP4+6vdaDZsUAtyixfDypVq5QLUbjXBwe98uZ1dU0xMchMT85SnT3fFrxq4ebM6zZDQHS8v9Uadry8UKKAWpCpWTLxfZOQ9LlyoxePHSwDIl28iZcsewMTE4f1vsH69OrM9wOrV6vJ9WVxoaCidO3dmxYoV2Nraxm9XFIWFCxcyceJEWrVqRalSpVi3bh3h4eFs2rRJhxELId5Wvnx5Tp06RcmSJXn8+DE1a9bk8oNTNLy5iXuvfiX3BFtMnkKM4X0ueNXg1o2hxMWF6zpsIYSuHT+urhazd6/aLX3JEqI3bWfgxMd89ZUzx4/vJDISKlXKzdWrt2jZUlYoFiIjkKKUyPhy5FB7f3z/vZpgdu+G0qVh34dX0HsXAwMjihffgIGBOS9fHubhw+8/KcSnT7fx7NlONBojihVbg4GBcfJffO6cuhrI6dNga6uOuxs0SF2qtl8/dW6pZ89gxoz3tsfJqQ+gTnhevjw0bKiuIrJgwSc1TXyCQ4fU6dECA9XvSCdPQpEiifd7/nwf585V4NWrMxgZ2VK69G8ULDgNjeYDyxCfPAl9+6r//uordSJPwaBBg2jatCkN/jM20s/Pj4CAABo1ahS/zdTUlNq1a3Py5Mn0DlMI8R758uXj+PHj1KtXj9DQUJo2bcqMGdNp9MNnmPx4m9Cx7XD8A9DAo4BFnPIsxsuXR3UdthBCF+Li4Lvv1Bu8Dx9C0aJw+jR+TQZSq/Y0Nmwow40bgRgbw4QJrfjnn3vkyeOi66iFEP+SopTQDxoNDB0K//yjLlvm7w+ffQZffglhYR91SHPzwhQuPB8AX98JhIV93Lwy0dFPuXVrEKD2brG0LJf8F//yC9SsCY8eqe06exbq13/zvLHxmzF4Cxe+t9uTWpQyIDjYk7Cw6/G9pVau/OSOZeIjbNkCTZqoc/TXrQuenoknM1eUOPz8vuby5c+IjX2BpWVF3Ny8yJGj6Yff4N69NyvtffEFfPtt2jREz2zevBkvLy9mJFHEDQgIAMDBIWHvMwcHh/jnkhIVFUVISEiChxAi7dnY2LBv3z569+6NVqtl4sSJNGvWjOz5FBrc2cKt57vIO9Ya00CIMXiIt3dtfG4MJDY2dRZHEULogYAAdUW9SZPU4lTXrnDuHOsv5aFevfKcPj2Z0FAthQubcvz4dqZP34Gh4Qdu+gkh0pUUpYR+qVABzp+HYcPUn5ctU1caO336ow7n5NQPO7vPUJQorl/vglYbneJj3Lo1mJiYZ1hYlMHF5avkvUirhYkT1Z4tkZHqqoOnTkGhQon3/fxztaoRFQUTJrzzkNmy5SFHjmYAPH78E/Xrqx9NeLg6FZdIP4sWQceO6lQGbdqonfqsrBLuEx0dyMWL/+PevWkAODsPpHz545iZ5f/wG4SGqivtBQZC2bLqED5ZaY8HDx4wbNgwfv755/fOD/HfCeMVRXnvajszZszA2to6/pFX5uwSIt2YmKjLtK9atYps2bLxxx9/UKFCBc6dO0OzVS1RfrhN6OgvcPpN3f9xwFJOHytOUNBh3QYuMjxZDCMTOHhQ/R506BCYm8PatUSvXE+r7r8zaFAe7t71xsAA+vQpycWLj6hcubWuIxZCJEH+ihH6x8xM7TV08CDkyQO3bqmzSE+e/N5V6pKi0WgoVmwlRkY5CA315u7dqSl6fWDgdp4+3QoY/rvansmHX/TqldqzZfp09eexY9Xhif+tWrwJEubNU/+7efN7C3CvJzx/8mQdWm0448ap2xctUotTIm0pilprHDpU/fegQeqvzNQ04X7BwSc4d64CL18ewsDAnBIlNlK06BIMDEyTPvDbtFr1LuClS+pKe3v3QvbsadMgPXP+/HkCAwNxc3PDyMgIIyMjPD09+eGHHzAyMorvIfXfXlGBgYGJek+9bcKECQQHB8c/Hjx4kKbtEEIk1qtXL06dOkXhwoW5f/8+NWrUYPHixRSqkoO6vju55r8Vl9FWmAZAjOYRFy/Ww+fGAGJjZREDkZgshqHnYmPVaQv+9z/1Bl3p0nDuHBfLfUHRos3ZtasjoaFR5MsHu3ePY/nyy5ib59B11EKId5CilNBfDRqof5h36qR21/32W6hWTV2tLgVMTZ0oVuwnAO7fn0lwcPLmlomOfsatWwMBdeJ0S8sKH36Rn5866/XrldI2bFAnqf5QN+Ly5dWV+QBGjnznCoR2do3Ili0/sbEvCQzcSuvW6uTaz57B2rXJapb4SLGx0KfPm1rjt9+qxcC3f7WKovDgwUK8vesQHf0Ic/PiuLmdxcEhBXNBTZqkzqtmYgK7dkG+fKnaDn1Wv359Ll++jLe3d/yjYsWKdO7cGW9vbwoWLIijoyMHDx6Mf010dDSenp5Ur179ncc1NTXFysoqwUMIkf7Kli3LuXPnaNWqFTExMQwZMoROnToREfGKLza2JWLuLcKHN8N5j7r/44CfOH28OC9e/KXbwEWGIoth6Ln799UJO2fMUL8PDxgAp08zdas/7u75uXfvVzQaaNPGmrNnT9Ks2cz39oYWQuieFKWEfrO1hY0b1e4otrbq0L7y5dVqgDb5K+rlzNkaB4dugJbr17smaz6K27eHEhPzFHPzkuTP//WH3+TIEXVC8ytXwMkJjh6FLl2SHSPTpqldk0+ehB07ktxFozHAyak/oE54bmQEo0apz82bpxZOROoLD1cXhVy9Wh1Ft2KFWjt6+zuQVhuDj08/7twZgaLEkitXBypUOIuFhWvy3+jnn99MeL9qlVqEFfEsLS0pVapUgoeFhQU5cuSgVKlS8cM0pk+fzq5du7hy5Qo9evTA3NycTjJJvBB6wdramu3btzN//nyMjIzYvHkzlSpV4urVq7jWyUXNe3u4eH8TBUdmJ9tjiOExly415Ob1PsTGynxwQhbD0Gt79rxZOcbKCrZu5eWM+bjVGMmUKfUJCwvCyQmWLq3NL7/cI1cu+Z4khD7Qu6LU0aNHadasGc7Ozmg0Gnbv3v3Offv3749Go2HhwoXpFp/Qkfbt4fJlaNRInaNp6FBo3FidQDyZihT5AVPTfERG+nLnzqj37vv06W4CA38BDP4dtveBYVfLlqnL4T1/DhUrqhOaV66c7NgAyJ0bRo9W/z1unDrHVBKcnHqi0Rjz6tVpXr3ypmdPsLcHX9931rLEJ3jxQj3tfv0VsmWDnTvVHlNvi4kJ4tKlxvj7rwQMKFRoASVKbMLIKAXD7g4denPg8eNTVtAU8caOHcvw4cMZOHAgFStW5NGjRxw4cABLS0tdhyaESCaNRsOIESPw9PQkd+7c3Lx5k8qVK7NhwwZMTDW03dGRoOk+hA9rQu6d6mv8n6zi9IlivHixX7fBC52SxTD0VFSU+t2+ZUsIClJv8l64wE7DfOTNWwIvr2UANG1qwF9/zaFfv8MYGVnrNmYhRLLpXVEqLCyMsmXLsnjx4vfut3v3bk6fPo2zs3M6RSZ0Lndu+PNPWLxYnXfq4EEoVUrtRZUMRkbWFC++DtDg77+cZ89+S3K/mJgX+PioczflyzcWK6tK7z5oTAwMHKiuEhgbqw41PHpUjfVjjBmjLuHm6/vO2ctNTBywt28FgL//T5ibw5Ah6nOzZ79z5J/4CA8fqosnnjgBNjZw4AC0aJFwn4gIXy5cqM7Ll39jYGBBqVJ7yJt3eMq6kv/6qzoZflSU+oXsu+9SsxmZ2pEjRxLcmNBoNEyZMgV/f38iIyPx9PSkVKlSugtQCPHRqlevzoULF2jYsCHh4eF069aNAQMGEBkZSdnGTlS//zvnb6+j8HALsj2CGCWAS5cac+N6L2JiXuo6fJHOZDEMPXXrljr1xaJF6s+jRhH999+0GbuKNm2qERp6jxw5YPp0BzZtOo2r62gZrieEntG7olSTJk2YNm0arVq1euc+jx49YvDgwWzcuBFjY+N0jE7onEajzi594YJ6F+XlS3UZtI4d1S4tH2BrW4c8eUYAcPNmH6Kjnyba5/btYcTEPMHcvAQuLh7vPtjz5+oEjEuXqnHNmKEOvzIz+8jGoU5oPU1drY1vv1XfIwlvJjz/mdjYVwwapI788/KCv//++LcXb1y/rn5HunYNnJ3h2DG1QPW24OCTeHlVITz8BqameShf/jj29p+n7I02b1bHBkZFqRPkb94sK+0JIcS/cubMyb59+/Dw8ECj0fDTTz/h7u6Or68v2cw0dPijG/5TbhI5pCG5twNaCHiyhjMni/P8+e+6Dl+kI1kMQw9t2qSuvO3lBTlywG+/ca5DN3IXqMaOHdNRFIUGDWDz5qaMHn0DK6uKuo5YCPERMt1fNlqtlq5duzJmzBhKliyp63CErhQrpnZfmTJFnWl682Z1ZY63Jjh+lwIFvsPcvCQxMU/w8emP8lbXomfPfuXJk58BA4oVW42h4TvutF29qhbFDh9WC0l79qhDrlLjzk2PHlCmjFpw+/bbJHexsamNmVkx4uJCCQzcRI4c0Lu3+tysWZ8eQlb3zz9QowY8eKCeaidPqp3y3vbkyS94e9cjJuYZ2bNXoEKF01halkvZG61cqfaui41Vh+tt3Zp4KT8hhMjiDA0NmTJlCn/++Sc5cuTAy8uLChUqsHfvXgAqtcxNpfv7OXN9FUWHmWH2AGKUJ1y+/DnXr3UjJiZIxy0Q6UEWw9AjYWHqF9fOnSE0FGrVIvbcOcYcuUzlym48e3YFKyuYPNmQlSsXUr/+rxgb2+g6aiHER8p0RalZs2ZhZGTE0KFDk/0aGQueSRkbg4eHWjEoWhQeP1Yn/xk6VJ2Z+h0MDbNRosTPaDTGPHu2iydP1gPqvEA+Puok4nnzjsLaumrSB/j1V6haVV1pr2BBOHUKmjVLvXYZGsLcueq/lyxRuzX/h0ajwdlZjfXRo6UoisLIkepLDx5UO5KJj/P771C/vtrxrkoVOH4cXFzePK8oCnfvfsP1651QlChy5GhB+fJHMTVN4VDihQuhb983K8usWwdGRqnaFiGEyEwaNWrEhQsXqFatGsHBwbRo0YJx48YRGxuLRXYNnQ/1wnfCDSKH1CPPFkALTwI3cOaf4jx7tlfX4Ys0Joth6IkrV9R5V1evVm/mTp6Mz9KluDbqyNy5E1CUWKpXh1Wr8jBq1ElcXIbJcD0h9FymKkqdP3+e77//nrVr16bo4iRjwTO5ypXVKsygQerPixapXYHPnXvnSywty5E//zcA3Lo1hIiIu9y5M5LoaH/MzIqSP//UxC9SFJg5U51UKDQU6taFM2cgLXrsNWwIn32m9qAZNy7JXRwdu6PRmBIWdpFXr86QP786HzzAnDmpH1JWsHat+uuNiIAmTdS5x+3t3zyv1UZx40Y37t5Vh3XmyTOKUqV2YGhokfw3URS1B9wIdRgpo0fDjz/KkD0hhEiGvHnzcuTIEYYPHw7A7NmzqV+/Pv7+/gDU6JSP8vf+4uTlZRQfkg2z+xCjDeTKlRZcu9aZmJikh8WLrEEWw9AhRVGXL65USZ0bwckJ7cGDzLe2p2TZity6dQoLC/Vr7/z5n9OixUWsrFK4aJAQImNS9Big7Nq1K/7nBQsWKBqNRjE0NIx/AIqBgYHi4uLyzuNERkYqwcHB8Y8HDx4ogBIcHJz2jRDp688/FcXJSVFAUYyMFGXqVEWJiUlyV602Vjl/3l05fBjl1KnCyuHDKIcPa5SXL08k3jk8XFE6dlSPC4oycKCiREenbVuuXlUUQ0P1/Tw9k9zl2rVuyuHDKNev91AURVEuXFB3NzRUFF/ftA0vM9FqFWXmzDe/3m7dEv96o6KeKl5eNf49TwyVR49++rg3GjPmzRt98426TYeCg4Plevgf8pkIoR+2bdumWFpaKoDi4OCg/P333wmeP7TSVzmavZZyux/K4b9QDh9GOe5prwQG7tRRxPpHroeJyWfyEYKDFaV9+zfff/73P+Xe+fNKtWr1FEABlAoVUDZtMlT8/OYqWh1/NxJCJE9yr4eZ6tZ7165duXTpUoKx4s7OzowZM4b9+9+9BLCMBc9C/vc/uHwZ2rZVexl5eKiTA/n4JNpVozGkRIn1GBpmJyLiNgB58gzH2vo/8wo8egS1asEvv6jDq5YuVYfVpfUk+66u6vAugJEjQatNtMvrCc8DAzcTExNEuXLqCMa4OJg/P23Dyyy0WvXjHT9e/XnMGLXH1Nu/3vDwm3h5VSU4+DiGhlaUKbMPZ+d+KX+jgQPfdGNbsAC+/jp15iETQogsqE2bNpw7d44yZcrw5MkTGjRowPTp09H+my/r9S5ASb/DHDu/iBKDTTG/CzHaZ1y92oqrVzskudiJECKVnTsH5cvDli1gZIQyaxZr2raleI26/PPP35iaqjNvzJ6dh88+O0b+/KNkuJ4QmYzeFaVCQ0PjC04Afn5+eHt7c//+/fgx4W8/jI2NcXR0pFixYroNXGQcOXKoie/nn8HaGk6fVpPh0qXq/Zm3mJkVpHDhhf/+uwgFCkxLeKwzZ9RuxufOqcc9eFCd/ye9TJ0KlpZw/ry6Qsl/WFlVxcKiDFptZPzcWK9H+61aBc+epV+o+ig6Wp1ffOFC9ed582D27IR1oqCgw3h5VSUy8g7ZsuWnQoV/sLNrmLI3io2F7t1h2TL14CtWwL9DT4QQQny8okWL8s8//9CjRw+0Wi0TJ06kWbNmvPh3RV47ewN6nBuM18ArxA2sTr6fgTh4+nQLZ0+V4MmTTQkWPBFCpBJFUb9gVa8Ovr7g4kLA7t00O3qcXn36EBERQsmS6povnTt/Rq1a3lhbV9N11EKINKB3Ralz585Rvnx5ypcvD8DIkSMpX748kydP1nFkQq9oNOqKHpcvQ7166sTnAwdC06bw77wTrzk59aZcuSOUK3cUQ0PzN0/8/LPaQ8rfX1167cwZqFMnfduRKxdMmKD++6uv1MmO3qJOeK4WyR4/XoaiKNStC25u6q5LlqRvuPrk1Sv4/PM3HeB+/lntMfU2f/81XLrUiNjYl1hZVaVChVNYWLim7I2iotSeez//rL7Rpk3Qp0/qNUQIIbI4c3Nz1qxZw6pVq8iWLRt//PEHFSpU4OzZs/H7NBlSmIJ3jnH49AJKDTTBwhditM+5fr0zly42Ijw88aIiQoiPFBCgTtI5YgTExECrVmzz8MC1a3d+//1XjIw09OsHCxYYUKPGLCpV+hVj4xy6jloIkUb0rihVp04dFEVJ9Fi7dm2S+9+9ezd+skshEsmbV+3dtHAhmJrCvn1qgWnbtgS72djUxtTUUf0hLk7tbtS1q1pQaN5cXeGvYMH0jx/UHjX58sGDB+qQr/9wcOiMgYEF4eE3CA4+hkYDY8eqzy1a9N6FCLOswEC1VnnwIFhYwG+/qTXM1xRFi6/vV9y82QtFiSVnzvaULfs3JiYOKXujsDB1Zcbdu9Xzb+dO6NAhVdsihBBC1atXL/755x8KFSrEvXv3cHd358cff4zvCeXgZECvS8M50ecycYOqkH8VaKIh6OVfnD1bmrt3v0GrjdJxK4TQY4qirqpXooS6WrWJCS9mz6aTqSntevUiKOg5BQtq+OknhQ4dclOpkif5849Fo9G7P1mFECkg/4cLYWAAw4aBl5c6jO/FC2jXTi06vXyZcN+QEPXOzuzZ6s8TJ8KuXeoQOl0xM4Pp09V/z5gBT54keNrIyAoHB7Wi8vjxMgBatVJraM+fq98NxBt+fuDuro7ItLeHv/9WpyJ7LS4ugmvX2nP//gwAXFwm4eq6CUNDs5S9UXAwNG6sVr7MzeH339UClRBCiDRTrlw5zp8/T6tWrYiJiWHQoEF06tSJ0NBQQO1I3WJMUfLePME/Xt9SoacBtmdBUaK4e9eDs2fLEBR0WMetEEIP3b4N9etD797q92s3N/6YN4+S8xfwyy+/oNFo6NoVli1TKFXqf7i7X8DGpoauoxZCpAMpSgnxmqsrnDqlFpoMDNThVGXKqFUJUJNp1apq8SBbNnVc17Rp6r661rEjVKwIoaHq5O3/4ezcH4CnT7cTHR2IkRGMHq0+N2+eOqWRAG9vdWqD27fBxQVOnIDKb602HB39BG/vOjx9uh2NxpjixddSoMC3Kb+D9+yZ+sXs+HF1XrODB9WfhRBCpDlra2u2b9/O/PnzMTIyYvPmzVSqVImrV6/G75M7nyEdr07i6ujj2H6VH9dvwPg5RET4cPFiPa5f70Z0dKAOWyGEnoiJgVmzoHRpOHwYzMx4PnUqfcuWpemQIQQE+OPkZMKSJQo9expQuPB3VKr0ByYmOXUduRAinWSAv6aFyEBMTNRC07FjUKiQOiSufn3o2VOtTly/Drlzq89npGFWBgZvltNbsQLe+mINYGlZAUvLSihKDAEBawHo0QNy5oS7d2H79nSNNkM6cgRq11anOShTRh2RWbTom+dDQ69w/nwVXr06g5GRLWXLHsTRsXvK38jfX32j8+fVrliHD6uVMCGEEOlGo9EwYsQIjhw5Qu7cublx4waVK1dm48aNCfarOqIaDo8v8iSoC5W7g/NuQAtPnmzg9OniPH68AkVJvPqtEAL1u07lyuoSxpGRRNWvz7zhwym8YAEr/+2q37KlEWvWRFO8uBPly/9NgQJfyXA9IbIY+T9eiKRUr652m+mv9jBi7VoICoIqVeDsWbVXUkZTs6Y6Lk+rhTFjEj39ZsLzn1AULWZmMGSI+tysWYkWHsxStm9Xh+iFhKhz13t6grPzm+dfvNjPhQvuREXdw8ysCBUqnMLGpnbK3kSrhc2b1XPo2jX1DY4eVYeMCiGE0Al3d3cuXLhAw4YNCQ8Pp0uXLnz55ZdERkbG72Oa04rSFzbw8tsN5FliSYVBYHbLgLi4IHx8+nHhQk1CQy/rsBVCZDBhYWqX/MqVwdsbxdaW7YMH4+rnx+gZM3j58iU5c9qycCEMGxaLrW2Df4frpfC7lRAiU5CilBDvkj07LFumznJdsqRaoDpyBJycdB3Zu82aBcbG6oTtBw8meCpXrvYYGloTGelLUNBfAAwapE5n5O0Nf/2lg3gzgKVL1SnEoqPVmt7+/WBj8+b5R4+WculSU+LiQrC2rkWFCv9gbl70ncdLkqenOvSzY0e1913BgmpvuxIlUrUtQgghUi5nzpzs27cPDw8PNBoNy5Ytw93dHT8/v4T7jeiC+U1v4qhKpQFaCi0BbYQRISEnOXeuAnfujCMuLkxHrRAigzh4UB2qN28eaLWcbtiQmkWK0HbxYnx9fTExsaN/f1t++SWIMmU05Ms3lUqV/kz5YjFCiExDilJCfEjTpnDlilqgypZN19G8X+HCaqUJYNQodaXAfxkaWuDo2A14M+G5nR307as+/3ru9qxCUdTptwYOVP/dvz9s3frmV6wocdy+PZJbtwYCcTg4dKNs2QMpW5L42jV18vI6ddQedtmzwzffwKVLulutUQghRCKGhoZMmTKFffv2kSNHDry8vKhQoQKrV69Gq31reF7BgtheOop23Nfk3mFA9e6xZDtqAcTy4MFsTp8uybNnv+msHULozPPn0L07NGoEfn7cc3KiU+3aVD14kBNnzmBoaIa7exl2735Bhw5BGBkVoFy5vylYcDIajaGuoxdC6JAUpYTIbL7+Gmxt4fJlddjhW15PeP7s2V6ioh4BMGIEGBqqPaW8vNI72PQXHAxLlqjzRn3zjbptyhS1x5Thv9+JYmNDuXKlFQ8fLgCgQIFpFC++FgMD0+S9ib8/9Oun3in87Tf1wAMHqjOof/01WFikfsOEEEJ8sv/9739cuHCBqlWr8vLlS3r37k3lypU5efLkm52MjTGa/g0GRw5jZJSXqh5hlPjKgIgAG6Kj73HlSjOuXGlNZORD3TVEiPSiKOriPyVKwPr1hAAT3Nwo9uIFv3h6otFoyJOnNsuWmTFt2iWyZTPAyWkkNWpcxta2jo6DF0JkBFKUEiKzsbNTCx8AkyapK/L9y8KiJNbWNYE4/P1XAeoqc6/nbM/MvaW8vNQ6Ue7cMHiw2vnNzEztAOfhoS4DDhAV9Qhv71o8f74XjcYUV9fNuLhMRPN6h/cJDVUrXEWKqBPOa7XQsqU68fySJeAgXdOFECKjy5s3L0ePHmXu3LlYWVlx/vx53N3d6dKlC48ePXqzY61aGF29CG3b4vCPlkY9X6JszktcnCHPnu3k9OkSPHiwEK1WlrgVmdT9+/D559CpE7FPn7LUyYnCtrbMPH+eqKgocuaszpdf1mLDBk8KF36BoWFJ3NxOUqzYPAwN5QadEEIlRSkhMqNBg9TVAwMCElWaXk947u+/Iv6L8tix6nPbtoGvb7pGmqbCw2HNGnWeTTc3tU4UFgaurvDDD/D48Zu57AFevbrA+fOVCQ29gLFxTsqVO0yuXO0//EaxsWp1q3BhmDpVfZOqVdV5o3btgmLF0q6RQgghUp2xsTGjRo3Cx8eH3r17o9Fo2LhxI0WLFuW77757MxG6rS1s2QKrV2NgaEHdnx5QvK8Fj68UQ1FCuXNnBOfOVSYk5KxuGyREaoqLg0WLwNUV5Y8/+MPIiDI5czLQ35+nQUE4OBSlevWBrFp1mbZtPdFqjcmXbwru7l5YWVXRdfRCiAxGilJCZEYmJuqk5wBz58LDN0MIcuZsjbGxPVFRD3nxYh+gDmVr3Fjt2DN/vi4CTl3Xr8OwYeoCd716qdM5GRur84x7eqq9pIYMSTih+bNne7lwoQbR0Y8xNy9BhQqnsbau9v43UhTYs0cdpvfll/DkiVoM3LYNTp6EGjXStJ1CCCHSloODAytXruTs2bNUr16d8PBwJk2ahKurK7t27UJRFLWrbc+eaLy8wM2N3H4hdBx6k4C5NQl9ZUN4+AXOn6+Cj89gYmODdd0kIT7NlSvg7g5Dh3IpLIxGNjY0jY3l+tOn2NrmoFy5rxkzxoHvvvsRS8tXGBtXoUqVCxQs6IGBgYmuoxdCZEBSlBIis2rVSi2KRESow/j+ZWBgiqNjT+DNhOfwprfU6tXw9Gm6RpoqoqJg82Z1TvHXPaGCg9X5xGfNUutymzZBrVpvhuoBKIrCgwcLuHKlJVptOLa2DShf/iRmZgXe/4anT0Pt2urwvBs3wN5efdNr16BNm4RvIoQQQq+5ublx/PhxNm7cSO7cufHz86NVq1Y0aNCAK1euqDsVLarekBg7FtDQ4fdjFOllz7kDn6HRKDx+vISTJ4sTGLhFLWYJoU+iomDyZKhQAf/Tp+ljbEw5jYa/Xr7ExMSEevVG0rDhUGbOnIOb2zHi4swpWHAB1aufwMKipK6jF0JkYBpFsmIiISEhWFtbExwcjJWVla7DEeLjnTkDVaqoBZLz56F8eQDCw29z5kwRQEOVKr6YmeVHUdRdz56FsmXVnlPVq6sPe3vdNuN9/Pxg+XJYtepNMc3AAJo3hwEDoGFD9eekaLWx3L49lMePlwLg5NSPIkUWY2Bg/O43vHMHvvpKXaoP1OX6RoyAcePA2joVW5YxyPUwMflMhMjawsLCmDlzJnPmzCEqKgpDQ0O+/PJLpk6dip2dnbrToUPQtSv4+xNraMLc8r3IPfQQefPeAsDa+n8UL74EM7NCOmzJp5PrYWKZ8jM5fhz69iX8xg3mAbMMDQn7d4Xnxo3boijd+eILD4oVOw+AqWkDypVb/uEbfEKITC2510MpSiUhUyYTkXV17qx2EapbV/2S/G8PnosXGxIU9Bf58n1FwYLfAbB/PzRrBjExCQ9RrJjaU/v1o2hR3XYEio2FP/5QV8zbv18dRQfqcL2+faFPH8iT5/3HiI5+yvXrXQkK2g9oKFRoDnnyjHz3hObPnsG0afDjj+oHpNGoSx9/++2H30yPyfUwMflMhBAAfn5+jB49mp07dwJgZ2fHt99+S79+/TAyMlLzRp8+6jBv4KxjfdY2Kk+Lzj9gYhKNomSjQIFJ5Ms3Rm+HNcn1MLFM9ZkEB8OECWiXLuVn4CsDAx5ptQBUqVKVihWnExNziLZtZ2FkFEtcnA0lSszHyalH8haIEUJkalKU+gSZKpkIce+eWlWKioK9e9WqE/D06Q6uXm2DsbED1ardj/9CfPcuHDkCJ06oj+vXEx/S3l7tQeXurv63YkW1w1Bae/wYVq5UJyx/a5osGjVSe0U1awZGRu8/RnT0E+7fn8Pjx0vRasMxMDCnRImN5MzZMukXRESow/JmzFC/nAH873/qBPJlyqRKuzIyuR4mJp+JEOJtf//9N8OGDYsfxle6dGm+//576tatq941+eknGDkSIiKItMrJWJdpuHTfipvbIQCMjEpQqtRSbGxq67IZH0Wuh4llms9kzx4YNIgjjx4xCvD6d7OLiwuDB8/i1KncNGvWFxeXGwBYWLSmTJnFmJo66ixkIUTGIkWpT5BpkokQr02YADNnqsWpy5fB2BitNoZTp/IRHR2Aq+tWcuVqm+RLnz+Hf/55U6Q6exZeLzr0momJurrd655U1atDrlypE7pWq3bwWrZM/X70b29xcuRQJzHv109d9O5DoqICePBgNo8fL0OrjQAge3Y3ihX7CUtLt6Tf+Oef1fm4HjxQt5UrpxajGjZMncbpAbkeJiafiRDiv2JjY/npp5/4+uuvCQoKAqB169bMnTuX/Pnzq/MNduwIly4BcKLCIGbYu9Gr/3js7AIBsLfvQdGiczAxycBj5v9DroeJ6f1n4u8PQ4dyc/t2xgJ7/91sZWXF+PETiY7uSVDQNzRvvgQDA4XYWEfKlFlCrlytdBm1ECIDkqLUJ9D7ZCLEf4WEqJWbp09h8WIYNAgAP7+vuXdvGjY29ShX7lCyDhUdDV5e6lyurwtVT54k3q9IkTcFKnd3KF783XM7JeXZM1i7Vr3BfPv2m+01aqgL3bVuDaamHz5OVNRj7t+fjb//T2i1ajXN0rIy+fN7YGfXJOnu5QcPwpgxcPGi+nPevPDdd+pQyJQ0IhOQ62Fi8pkIId7l+fPneHh4sHTpUrRaLaampowZM4bx48djYWio3iRauBCAyCKlGJl7GdnKb6B5858AUBQ7ihefjaNjTzSajJ9v5HqYmN5+JooCq1bxfORIpr56xVIgFjA0NKR///60ajWFlSvP0bJlfxwc1Jt1Vla9KF16LsbGtrqMXAiRQUlR6hPobTIR4n2WLoWBA9Wxd7dvg7U1kZH3OXWqAKClcuWbmJsXTfFhFQV8fd8UqE6cgKtXE+9na/umQOXuDpUqgZlZ4mOdPKmGum2bWgADsLKCbt2gf38oVSp5cUVGPuTBg1k8frwCRYn69zhVcXHxwM7uf0kXoy5eVFdNOnBA/dnaWp3UfMiQxMFmEXI9TEw+EyHEh1y+fJnhw4fz999/A5AnTx5mz55Nhw4d0Ozfr85JGBiIYmrKhc7zGHG7PN26fUmhQmpPKmPjSpQuvQgrqyq6bMYHyfUwMb38TG7dIqpPHxYfPco04OW/m5s2bcp3381h9+5cREePoGHDDQDExhagQoXl2Nk10FXEQgg9IEWpT6CXyUSID4mNVedAun5dLbzMmgXA5cvNeP78N/LkGUXhwnNT5a2CghIO+TtzRp2a6W1GRlChwpsiVUCAOkTv9craoD7/5ZfQoQNkz568946MvM/9+zPx91+FoqhVLSsrd/Ln98DWtkHSxagHD+Drr2H9erUyZmys9iabNEkdJ5iFyfUwMflMhBDJoSgKu3btYtSoUdy9excAd3d3fvjhByrkzg09e8K+fQDENv6c6YV/4vqzX+jSZSoWFq8AsLXtQYkSMzExcdBVM95LroeJ6dVnEhmJMmcOO775hnGxsfj+u7lsmTLMmz8fC4t6LFmylVathmBr+xSt1gB7++GULPkNhoYWOg1dCJHxSVHqE+hVMhEiJX7/HT7/XJ0E6sYNKFCA589/5/LlzzEysqNatUcYGqb+jOUxMeDtnbA3lb9/0vuamanTbnz5pTqBenJFRt7j3r0ZBASsRlHU5QOtrWuRP78HNjZ1ky5GBQerc20tXPhmoqz27dWheoX0e5nu1CLXw8TkMxFCpERkZCTz5s1j+vTphIeHo9Fo6N27N99Nm0auLVvU4eLR0eDoyP1p6xi9vwwFCkygSZO1AMTFWVGokAf58g3BwMBYt435D7keJqYXn4miELlzJ5u//JKFT5/y72QFOOXKxXczZ9KmTTdmzQrAzGwg7u7qrFKxsSWpXHlVhu+9J4TIOJJ7Pcz4g9WFEKnns8+gfn31y++ECQDY2TXG1DQfsbEvePp0e5q8rbGxOlxv+HB1WN6jR+qQvw0b1FXzypRRe0X98IO6wt6qVckvSEVE+HHzZl9Ony6Mv/9PKEoMNjZ1KFv2MOXLe2JrWy9xQcrXFyZOVAtPM2eqBamaNeH0adi8WQpSemzGjBlUqlQJS0tLcuXKRcuWLbl582aCfRRFYcqUKTg7O2NmZkadOnW4mtSYUyGESAXZsmVj4sSJ3Lx5k06dOqEoCitXrqRI0aLMj40l+uRJcHWFgADy9fkfW+2/oWnZb5k37x9u3KiIoWEId++O4siRMjx/flDXzRF6LuDkSTyKFsWlTRt6/luQMjc1xWPyZHzu3KFgwe4MG7aKqlVdcXffS1ycMblyTaFePS8pSAkh0oT0lEqCXtzhEOJjXbwI5curw9T++QeqVuXu3Wncvfs1VlbuVKhwXNcRJktExB3u3ZvOkyfrUZRYAGxs6pM//2RsbGolfkFUFOzeDStXwl9/vdlevLg6lLFZM0iqN1UWp2/Xw8aNG9OhQwcqVapEbGwsEydO5PLly1y7dg0LC3WowaxZs/juu+9Yu3YtRYsWZdq0aRw9epSbN29iaWn5wffQt89ECJGxnDhxgmHDhnH+/HkAihUrxsJZs2h84AD8+KO6k7Ex2q7d2ZJ/DLuvHKNDhwnY2j7996kvqFBhHmZmBXTVhHhyPUwso34mF06cYOGAAfxy5Qox/27LY2nJkDFj6DNoEMbGdnz33S0cHftSrpwnAHFxVahadRUWFiV1F7gQQm/J8L1PkFGTiRCppndvWL0aqlWDEyeIig7gn3/yAnFUrHiJ7NlL6zrCdwoPv8W9e9/x5MnPQBwAtrYNyZ/fA2tr98QvuH5dLUStX68u6Qdq8alhQ+jTB774Qp3gSiRJ36+HT58+JVeuXHh6elKrVi0URcHZ2Znhw4czbtw4AKKionBwcGDWrFn079//g8fU989ECKF7Wq2WtWvXMmHCBAIDAwF1UukF7dpRZPVq8FSLAhgYEN26A8vsBnHHdCvNmy/G0DCOuDhTHBzGUqLEeAwNzXXWDrkeJpaRPpO4uDj27tnDwokTOXrjRvz2ajY2DPfw4ItBgzA2NubgwVh27lxAy5aTMTWNJDbWHBeX7yhceAgajaEOWyCE0GcyfE8I8W7ffgvm5mpPqe3bMTV1wt6+JQCPH/+k29jeITz8Jtevd+XMmeI8ebIOiMPOrjHly5+kbNkDCQtS4eGwbh3UqKEOiZg/Xy1I5c6tTmju6wv790PbtlKQyuSCg4MBsLOzA8DPz4+AgAAaNWoUv4+pqSm1a9fm5MmTSR4jKiqKkJCQBA8hhPgUBgYG9OrVCx8fH0aNGoWRkRG///47Jfv0oV/Rovy1YAEx//sfaLWYbNvE0J/cmX79Lv+s+Bkvr3oYGkbx7Nm3/PVXcR492obcYxZvCw4OZsGCBRTOl49WrVtz9MYNjICOFhacmj2bky9e0HbYcE6dMmTo0L/x9a1K+/ZjMTWNRKttgLv7FYoUGS4FKSFEupCeUknISHc4hEgzU6fClClQoABcv86LsGNcutQQQ0NLqlV7jJFRMpe7S2NhYde5d28agYGbAS0AdnZNyZ9/MlZWlRPufOGC2itq40Z1EnMAQ0No2hT69oXGjaUIlUL6fD1UFIUWLVoQFBTEsWPHADh58iTu7u48evQIZ2fn+H379evHvXv32L9/f6LjTJkyhalTpybaro+fiRAiY7p58yYjR47kjz/+iN9mZ2dHc3d3Wj1/TsOTJ3m9DMmLSg1YUqgmRVqswtHxPgDR0XWpXv0Hsmcvla5x63OOSCu6/Exu377NDz/8wJo1awgNDQXADhhgZMTA4cPJ/e23RBtkY+dOPy5dWkfp0utwcroLQHS0DUWLzsfFpUfSi8MIIUQKyfC9TyAJVmQJYWFQtKg6s/jcuSgjR3DmTDEiIm6TM2dbLC0rY2LimOBhbGyHRpM+HSzDwq5y9+63PH26FVAvUzlyNMPFZTJWVm/Ngh4SAr/8AitWwL/zcwBqsa1PH+jRA94qPoiU0efr4aBBg/j99985fvw4efLkAd4UpR4/foyTk1P8vn379uXBgwf8+eefiY4TFRVFVFRU/M8hISHkzZtXLz8TIUTGdvjwYX755Rd2797N06dP47dnNzenaa5ctLp3jyaKgiUQULIqv9QqiGuLnZiaRhIXZ4iidKdKlXFYWhZNl3j1OUeklbT4TKKionjx4gVBQUEEBQUl+W8fHx8OHDgQ32vOFRgOdG7RAvPvv+eZhT27du0gJmYNrq5H4o8dHW2FlVUnypf3wNTUMVXiFUIIkKLUJ5EEK7KMNWugVy+wsYHbt3kQvpY7d0a/c3eNxhgTE4dExaqEDydMTBw/eo6L0NDL3Lv37b8rAaqXJ3v7lri4TMbSsry6k6LAqVNqIWrLFnW4HqjL/LVqpRaj6tUDAxmh/Kn09Xo4ZMgQdu/ezdGjRylQ4M1kwL6+vhQqVAgvLy/Kly8fv71FixbY2Niwbt26Dx5bXz8TIYT+iIuL4/jx4+zcuZOdO3fy8OHD+OdMDQ1ppCi01mppBoSWcuVQe0sK1DgNgFar4fnz1pQrN4EiRSqkaZz6dj2cMWMGO3fu5MaNG5iZmVG9enVmzZpFsWLF4vdRFIWpU6eyfPlygoKCqFKlCkuWLKFkyeRN9v2xn4mvry9Dhw4lLCws/vHy5UuCgoKIiIhI9nGaAsOABsWKweJFXM5lxrlza3B03Iq5udp7SqvVEBxcn1KlelKw4BcYGpol+/hCCJFcUpT6BPqWYIX4aHFx4Oamrsg3dCjaBXPw919JRIQv0dEBCR6xsc9TdGhDQ8t3FqwS9r7KiYGBEaGhF7l79xuePdsZfwx7+1a4uHyNpWU5dcPz57BhgzpE7+rVN29WvLg6PK9bN7C3T4UPRrymb9dDRVEYMmQIu3bt4siRIxQpUiTR887OzowYMYKxY8cCEB0dTa5cuWSicyFEhqQoCmfPnmXnzp3s2LGD27dvxz9nCNQFWgPly+fmWnMrzIpfJzQUQkPh9u1yaDSNyJs3F69eqQWO14WOyMhInJ2dcXFxwcXFhdq1a1O0aMp6WOnb9TAjr9B6/fp1XF1d3/m8gYEBNjY22NraYmdnh62trfrviAhsr10jx+3bNAWKWVoS8e0wTpYwJiRsA7a2b86Xp08LYWzcg3r1umFllS/ZsQkhxMeQotQn0LcEK8QnOXQIGjRQ51q6elUd0pcErTaa6OhAoqP9ExWsEj780WqTf0cPNBgb2xMT8zT+55w52+Di8rW6CqBWC0eOqIWoHTsgOlrdzcwM2rVTi1HVq6sr6olUp2/Xw4EDB7Jp0yb27NmT4M63tbU1ZmbqneBZs2YxY8YM1qxZQ5EiRZg+fTpHjhxJ8z84hBDiUymKwpUrV+J7UF26dCnVjr106VIGDBiQotfo+/UwI63QGhISwrZt27CwsCB79uxYWFhgbW0dX4SytLTE4HUP8JgY2LpVXcjFywuAOFMIHFODixUNMbE8ioGB+ideRIQFd+60o1SpntSqVQMDA/m+JIRIH8m9HsqMv0JkdfXrqxOB//47jBsHu3YluZuBgQnZsuUhW7Y87z2coijExb36YOFK/W8goP23IKUhV672uLhMwsKiJAQEwOKZajHqzp03b1CunFqI6tRJHXYoxFuWLl0KQJ06dRJsX7NmDT169ABg7NixREREMHDgwPihGQcOHEhWQUoIIXRJo9FQunRpSpcujYeHB7dv31YLVNu2cfrcOQBMAVvAxghMc2nIlkchuyVYWkJsbE5evGhEiRK1qVbNjGfPHnHv3j3u3btHqVLpO0l6RpDSFVqTKkolNe/gx4iIsGL//t5YWRH/MDFRn9NoQKPRYhF3g1K+SyjovwmN5Qui3CGitQHB+W2JzhmBofHx+AnxL1+uTWRkT5o3b02TJhlj8RohhEiK9JRKgr7f9REixa5fh9Kl1eF8R45A7drp8raKEkdMzDOiovwxNs5BNmNn2L9fnSvq11/VeED9Jt2pk1qMcnNLl9iESq6HiclnIoTIiF69eoWxsTHZbt2C9evh558hIIAoO3jYBh62MEAxV1exffSoILt3j8bfvxmWlnnIkwe6doVatVL2nvp8PcxoK7ReuvScceP2YmYWiplZKObmr7Cz88fB4T65He6QM+dDDEzi3nuMgIB8/PNPDwoV6kbXroWwtU322wshRKqTnlJCiOQrUQL694cff4SRI+Hs2Y+fJFxR1G7lsbHqf99+/GebJiYGk9hYTKKj4chuWL0aHjx4c6xq1dRCVNu2kF3u8gkhhBDvEt/bs3RpmDMHZsyAv/7CdP16Cq3fRb5NkTxqCY9aQ+7cvgwaNBAYiL9/fi5frsmjR72B9LkplREMHjyYS5cucfz48UTPaf4zJYCiKIm2vTZhwgRGjhwZ//PrFVpTytbWn3Hjer1/pzgwemHMqzAXAmLdCAkrQGioC6Gh+YiIcKFatRLMm2eAkfyFJ4TQI3LJEkKopkxR76p6eUHNmmBqmuziUoJtce+/i/dBdnbqhOW9e0MWHEoghBBCpAojI2jcWH0EB2O8bRv5168nb8dj+DeFgEYQWgicnO7i5HQX+4AcZJWi1JAhQ9i7dy9Hjx4lT5430xI4OjoCEBAQgJOTU/z2wMBAHBwckjyWqakppqamnxxTrsgwgnxzYRipwTBCwTAkBpM7QZg+gWyBkK1oTUz6jMOgzWcyj6YQIlORopQQQpUzJ0yaBGPHwsmTqXtsjQaMjd88jIwS/+ziAj16wBdfQLZsHzykEEIIIZLJ2hr69IE+fTD09SXPhg3kWbaH2NsXCSmu5WVpcKr3/jkjM4P/rtBaoECBBM8XKFAAR0dHDh48SPny5QF1hVZPT09mzZqVprGZxlpRpndgwo3GxtC5M8wfqfaAE0KITEiKUkKIN0aNgkKFICzs/QWklP5saKjrlgkhhBACoGBB8PAADw+MIiKw8/bG7swZqPmFriNLc4MGDYpfodXS0pKAgADgzQqtGo2G4cOHM336dIoUKRK/Qqu5uTmdOnVK2+By51bn1DQze/OoUAHe0UNLCCEyC70rSh09epQ5c+Zw/vx5/P392bVrFy1btgQgJiaGSZMm8ccff+Dr64u1tTUNGjRg5syZCSYrFEK8g4EBtGql6yiEEEIIkR7MzNT5G6tV03Uk6SJDr9BqZaX2ZhNCiCzmI2cy1p2wsDDKli3L4sWLEz0XHh6Ol5cXX3/9NV5eXuzcuRMfHx+aN2+ug0iFEEIIIYQQGYWiKEk+XhekQJ3kfMqUKfj7+xMZGYmnpyelZI5LIYRIM3rXU6pJkyY0adIkyeesra05ePBggm2LFi2icuXK3L9/n3z58qVHiEIIIYQQQgghhBDiA/Sup1RKBQcHo9FosLGx0XUoQgghhBBCCCGEEOJfetdTKiUiIyMZP348nTp1wsrK6p37RUVFERUVFf9zSEhIeoQnhBBCCCGEEEIIkWVl2p5SMTExdOjQAa1Wy48//vjefWfMmIG1tXX8I2/evOkUpRBCCCGEEEIIIUTWlCmLUjExMbRr1w4/Pz8OHjz43l5SABMmTCA4ODj+8eDBg3SKVAghhBBCCCGEECJrynTD914XpG7dusXhw4fJkSPHB19jamqKqalpOkQnhBBCCCGEEEIIIUAPi1KhoaHcvn07/mc/Pz+8vb2xs7PD2dmZNm3a4OXlxW+//UZcXBwBAQEA2NnZYWJikqz3UBQFkLmlhBDi9XXw9XVRSI4QQojXJEckJjlCCCFUyc0RGkXPssiRI0eoW7duou3du3dnypQpFChQIMnXHT58mDp16iTrPR4+fCjzSgkhxFsePHhAnjx5dB1GhiA5QgghEpIc8YbkCCGESOhDOULvilLpQavV8vjxYywtLdFoNCl6bUhICHnz5uXBgwcfnMtK30lbMydpa+bzKe1UFIVXr17h7OyMgUGmnIYwxSRHJI+0NXPKKm3NKu0EyRGpTXJE8khbM6es0tas0k5Inxyhd8P30oOBgcEn3+2xsrLK9Cfoa9LWzEnamvl8bDutra3TIBr9JTkiZaStmVNWaWtWaSdIjkgtkiNSRtqaOWWVtmaVdkLa5gi5pSGEEEIIIYQQQggh0p0UpYQQQgghhBBCCCFEupOiVCozNTXFw8MDU1NTXYeS5qStmZO0NfPJKu3UB1npdyFtzZyySluzSjsha7U1o8tKvwtpa+aUVdqaVdoJ6dNWmehcCCGEEEIIIYQQQqQ76SklhBBCCCGEEEIIIdKdFKWEEEIIIYQQQgghRLqTopQQQgghhBBCCCGESHdSlBJCCCGEEEIIIYQQ6U6KUh/hxx9/pECBAmTLlg03NzeOHTv23v09PT1xc3MjW7ZsFCxYkGXLlqVTpJ8uJW3duXMnDRs2JGfOnFhZWVGtWjX279+fjtF+mpT+Xl87ceIERkZGlCtXLm0DTCUpbWdUVBQTJ07ExcUFU1NTChUqxOrVq9Mp2k+T0rZu3LiRsmXLYm5ujpOTEz179uT58+fpFO3HO3r0KM2aNcPZ2RmNRsPu3bs/+Bp9vi5ldJIjkiY5olzaBphKJEe8m+QIkRokRyRNn3NEVskPIDnifSRHfAJFpMjmzZsVY2NjZcWKFcq1a9eUYcOGKRYWFsq9e/eS3N/X11cxNzdXhg0bply7dk1ZsWKFYmxsrGzfvj2dI0+5lLZ12LBhyqxZs5QzZ84oPj4+yoQJExRjY2PFy8srnSNPuZS29bWXL18qBQsWVBo1aqSULVs2fYL9BB/TzubNmytVqlRRDh48qPj5+SmnT59WTpw4kY5Rf5yUtvXYsWOKgYGB8v333yu+vr7KsWPHlJIlSyotW7ZM58hT7o8//lAmTpyo7NixQwGUXbt2vXd/fb4uZXSSIyRHvE1yRMYlOeLd9Pm6lNFJjsh8OSKr5AdFkRwhOUKVFtclKUqlUOXKlZUBAwYk2Fa8eHFl/PjxSe4/duxYpXjx4gm29e/fX6latWqaxZhaUtrWpLi6uipTp05N7dBS3ce2tX379sqkSZMUDw8PvUgoKW3nvn37FGtra+X58+fpEV6qSmlb58yZoxQsWDDBth9++EHJkydPmsWYFpKTTPT5upTRSY6QHPE2yREZl+SId9Pn61JGJzki8+WIrJIfFEVyhOQIVVpcl2T4XgpER0dz/vx5GjVqlGB7o0aNOHnyZJKv+eeffxLt/7///Y9z584RExOTZrF+qo9p639ptVpevXqFnZ1dWoSYaj62rWvWrOHOnTt4eHikdYip4mPauXfvXipWrMjs2bPJnTs3RYsWZfTo0URERKRHyB/tY9pavXp1Hj58yB9//IGiKDx58oTt27fTtGnT9Ag5XenrdSmjkxwhOeJtkiMyLskR76ev16WMTnJE5ssRWSU/gOQIkBzxWlpcl4xSI7Cs4tmzZ8TFxeHg4JBgu4ODAwEBAUm+JiAgIMn9Y2NjefbsGU5OTmkW76f4mLb+17x58wgLC6Ndu3ZpEWKq+Zi23rp1i/Hjx3Ps2DGMjPTjf6OPaaevry/Hjx8nW7Zs7Nq1i2fPnjFw4EBevHiRoceDf0xbq1evzsaNG2nfvj2RkZHExsbSvHlzFi1alB4hpyt9vS5ldJIjJEe8JjlCcoQ+09frUkYnOSLz5Yiskh9AcgRIjngtLa5L0lPqI2g0mgQ/K4qSaNuH9k9qe0aU0ra+9ssvvzBlyhS2bNlCrly50iq8VJXctsbFxdGpUyemTp1K0aJF0yu8VJOS36lWq0Wj0bBx40YqV67MZ599xvz581m7dm2Gv8sBKWvrtWvXGDp0KJMnT+b8+fP8+eef+Pn5MWDAgPQINd3p83Upo5McITlCcoTkCH2nz9eljE5yRObLEVklP4DkCMkRqtS+LulPeTYDsLe3x9DQMFGFNDAwMFG18DVHR8ck9zcyMiJHjhxpFuun+pi2vrZlyxZ69+7Ntm3baNCgQVqGmSpS2tZXr15x7tw5Lly4wODBgwH1oqsoCkZGRhw4cIB69eqlS+wp8TG/UycnJ3Lnzo21tXX8thIlSqAoCg8fPqRIkSJpGvPH+pi2zpgxA3d3d8aMGQNAmTJlsLCwoGbNmkybNi3D3o38GPp6XcroJEdIjgDJEZIj9J++XpcyOskRmS9HZJX8AJIjQHLEa2lxXZKeUilgYmKCm5sbBw8eTLD94MGDVK9ePcnXVKtWLdH+Bw4coGLFihgbG6dZrJ/qY9oK6p2NHj16sGnTJr0ZQ5vStlpZWXH58mW8vb3jHwMGDKBYsWJ4e3tTpUqV9Ao9RT7md+ru7s7jx48JDQ2N3+bj44OBgQF58uRJ03g/xce0NTw8HAODhJdEQ0ND4E31P7PQ1+tSRic5QnIESI6QHKH/9PW6lNFJjsh8OSKr5AeQHAGSI15Lk+vSR0+RnkW9Xh5y1apVyrVr15Thw4crFhYWyt27dxVFUZTx48crXbt2jd//9ZKJI0aMUK5du6asWrVK75ZyTW5bN23apBgZGSlLlixR/P394x8vX77UVROSLaVt/S99WTkjpe189eqVkidPHqVNmzbK1atXFU9PT6VIkSJKnz59dNWEZEtpW9esWaMYGRkpP/74o3Lnzh3l+PHjSsWKFZXKlSvrqgnJ9urVK+XChQvKhQsXFECZP3++cuHChfhlazPTdSmjkxwhOSIpkiMyHskRkiN0QXJE5ssRWSU/KIrkCMkRqrS4LklR6iMsWbJEcXFxUUxMTJQKFSoonp6e8c91795dqV27doL9jxw5opQvX14xMTFR8ufPryxdujSdI/54KWlr7dq1FSDRo3v37ukf+EdI6e/1bfqUUFLazuvXrysNGjRQzMzMlDx58igjR45UwsPD0znqj5PStv7www+Kq6urYmZmpjg5OSmdO3dWHj58mM5Rp9zhw4ff+/9eZrsuZXSSI1SSI96QHJExSY7orihK5rsuZXSSI1SZKUdklfygKJIjXpMckbrXJY2iZLL+ZEIIIYQQQgghhBAiw5M5pYQQQgghhBBCCCFEupOilBBCCCGEEEIIIYRId1KUEkIIIYQQQgghhBDpTopSQgghhBBCCCGEECLdSVFKCCGEEEIIIYQQQqQ7KUoJIYQQQgghhBBCiHQnRSkhhBBCCCGEEEIIke6kKCWEEEIIIYQQQggh0p0UpYQQQgghhBBCCCFEupOilBBCCCGEEEIIIYRId1KUEkIIIYQQQgghhBDpTopSQgghhBBCCCGEECLdSVFKCCGEEEIIIYQQQqQ7KUoJIYQQQgghhBBCiHQnRSkhhBBCCCGEEEIIke6kKCWEEEIIIYQQQggh0p0UpYQQQgghhBBCCCFEupOilBBp4JtvvsHV1RWtVpsqx6tTpw516tRJ8et8fHwwMTHBy8srVeIQQgiRPt6VR549e4apqSkajYZz584l+dquXbvSsmXLdIhSCCGELvw3R2g0mgQPCwsLSpQowdSpUwkLC0vwWskRIqPRKIqi6DoIITKTx48fU7RoUdauXUubNm1S5ZjXrl0DwNXVNcWv7dmzJ76+vnh6eqZKLEIIIdLW+/LIggULGDlyJAADBgxg6dKliV5/584dihcvzv79+6lXr166xCyEECJ9JJUjNBoNbdq0YdSoUQCEhobi6enJ9OnTadmyJTt27Ih/veQIkdFIUUqIVDZu3Dg2btzI/fv3MTDQfWfE8+fPU7FiRU6cOEH16tV1HY4QQogPeF8eKV26NIGBgbi4uODj44O/vz9mZmaJjtGsWTOioqI4cOBAeoUthBAiHSSVIzQaDYMGDWLx4sUJ9u3WrRsbN24kLCyMbNmyxW+XHCEyEt3/xSxEJhIdHc2qVavo1KlTfJK4e/cuGo2GOXPmMGvWLPLnz4+ZmRl16tTBx8eHmJgYxo8fj7OzM9bW1nzxxRcEBgYmOO5/h+/NnDkTAwMDfv311wT79ejRA3Nzcy5fvhy/zc3NjRIlSrBs2bK0a7gQQohUkVQeee306dNcuXKFrl270rdvX4KDgxPc/X5b165d+euvv7hz5056hC2EECIdvC9HJMXa2hqNRoOhoWGC7ZIjREYiRSkhUtHp06d5/vw5devWTfTckiVLOHHiBEuWLGHlypXcuHGDZs2a0bt3b54+fcrq1auZPXs2f/31F3369Hnv+4wbN44mTZrQvXt37t27B8CaNWtYt24dixYtonTp0gn2r1OnDvv27UM6RgohRMb2vjyyatUqAHr16kWHDh0wNzeP3/ZfderUQVEU/vjjjzSNVwghRPp5X45QFIXY2FhiY2N5+fIle/bsYd26dXTo0AFjY+ME+0qOEBmJFKWESEX//PMPABUqVEj0nI2NDbt376Zp06Z07tyZCRMm4OPjQ3BwMKtWraJx48b079+fPn368OuvvxISEvLO99FoNKxfvx4LCwvatWvHhQsXGDx4MF26dKF3796J9q9QoQLPnj3j5s2bqddYIYQQqe5deSQ8PJwtW7ZQtWpVXF1dsbS0pG3btnh6eiZ5pztXrlzkzp2bEydOpEvcQggh0t77/tb48ccfMTY2xtjYGFtbW1q2bEmNGjX46aefEu0rOUJkJFKUEiIVPX78GI1Gg729faLnPvvsswTdbEuUKAFA06ZNE+z3evv9+/ff+145cuRgy5YteHl5Ub16dfLly/fOIXq5cuUC4NGjR8lvjBBCiHT3rjyydetWQkJC6NWrV/y2Xr16oSgKa9asSfJYuXLlkuu+EEJkIu/7W6Ndu3acPXuWs2fPcvToUX744QfOnTtH48aNiYqKSrS/5AiRUUhRSohUFBERgbGxcaJx2wB2dnYJfjYxMXnv9sjIyA++X5UqVShZsiSRkZF8+eWXWFhYJLnf64kNIyIiPtwIIYQQOvOuPLJq1SqyZctG48aNefnyJS9fvqRMmTLkz5+ftWvXEhcXl+hY2bJlk+u+EEJkIu/7WyNnzpxUrFiRihUrUrNmTYYMGcIPP/zA8ePHWbt2baL9JUeIjEKKUkKkInt7e6KjowkLC0uX9/Pw8ODy5cu4ubkxefJkfH19k9zvxYsX8fEJIYTIuJLKIz4+Phw/fpzIyEjy5cuHra1t/OPu3bs8evSI/fv3JzrWixcv5LovhBCZSEr/1ihTpgwAFy9eTPSc5AiRUUhRSohUVLx4cYB0Wcni4MGDzJgxg0mTJnHw4EGsra1p37490dHRifb19fXFwMCAYsWKpXlcQgghPl5SeeT1ZOYrVqzg8OHDCR5//PEHxsbGrF69OsFxYmNjefDgAa6urukXvBBCiDSV0r81vL29gTdTebwmOUJkJEa6DkCIzKROnToAnDp1Kv7ORFrw9/enS5cu1K5dGw8PDwwMDNiyZQu1atVi7NixLFy4MMH+p06doly5ctja2qZZTEIIIT7df/NIbGws69evp0SJEu9cmbVZs2bs3buXp0+fkjNnTgAuXbpEeHh4kis0CSGE0E/v+1vjyZMnnDp1ClCnAfH29mbatGnY2NjQs2fPBPtKjhAZifSUEiIV5c2bl5o1a7Jnz540e4+4uDg6duyIRqNh06ZN8ZOnV61alenTp/P999+ze/fu+P1DQ0M5dOgQnTt3TrOYhBBCpI7/5pHff/+dgIAA+vfv/87X9OvXj5iYGDZs2BC/bffu3djb29OoUaM0j1kIIUT6eN/fGtu3b6datWpUq1aNRo0asWjRIpo3b865c+dwcXFJsK/kCJGRaBRFUXQdhBCZyY4dO2jfvj337t0jd+7cug6HVatWMWzYMB48eCA9pYQQQg98ah6Ji4ujcOHCdOrUie+++y4NIhRCCKErkiNEZiNFKSFSmaIoVK9eHTc3NxYvXqzTWGJjY3F1daV79+5MnDhRp7EIIYRInk/NI+vWrWP06NHcunULGxub1A9QCCGEzkiOEJmNDN8TIpVpNBpWrFiBs7MzWq1Wp7E8ePCALl26MGrUKJ3GIYQQIvk+NY9otVo2btwof2wIIUQmJDlCZDbSU0oIIYQQQgghhBBCpDvpKSWEEEIIIYQQQggh0p0UpYQQQgghhBBCCCFEupOilBBCCCGEEEIIIYRId0a6DiAj0mq1PH78GEtLSzQaja7DEUIInVEUhVevXuHs7IyBgdzHAMkRQgjxmuSIxCRHCCGEKrk5QopSSXj8+DF58+bVdRhCCJFhPHjwgDx58ug6jAxBcoQQQiQkOeINyRFCCJHQh3KEFKWSYGlpCagfnpWVlY6jEUII3QkJCSFv3rzx10UhOUIIIV6THJGY5AghhFAlN0dIUSoJr7vaWllZSTIRQgiQIQhvkRwhhBAJSY54Q3KEEEIk9KEcIYO/hRBCCCGEEEIIIUS6k6KUEEIIIYQQQgghhEh3UpQSQgghhBBCCCGEEOlO5pQSQugVrVZLdHS0rsPINIyNjTE0NNR1GEIIkSri4uKIiYnRdRiZhuSItCPnauqSc1UI/SVFKSGE3oiOjsbPzw+tVqvrUDIVGxsbHB0dM81EtY8ePWLcuHHs27ePiIgIihYtyqpVq3BzcwNAURSmTp3K8uXLCQoKokqVKixZsoSSJUvqOHIhxMdSFIWAgABevnyp61AyncyWI3RNztW0I+eqEPpJilJCCL2gKAr+/v4YGhqSN29eDAxk9PGnUhSF8PBwAgMDAXByctJxRJ8uKCgId3d36taty759+8iVKxd37tzBxsYmfp/Zs2czf/581q5dS9GiRZk2bRoNGzbk5s2bab6seVxcOIaG5mn6HkJkRa//yM+VKxfm5ubyR2kqyIw5AnR/40LO1dSXWc9VITKKsDCwsEi740tRSog0oigKUVFRhIaGJvl49erVO597/ShQoABz5szB1tZW183RudjYWMLDw3F2dsbcXP6oTy1mZmYABAYGkitXLr3v+j5r1izy5s3LmjVr4rflz58//t+KorBw4UImTpxIq1atAFi3bh0ODg5s2rSJ/v37p1lsWm0M+/Y1xMqqBO7uizA0NEuz9xIiK4mLi4v/Iz9Hjhy6DidTyWw5Qtc3LuRcTTuZ7VwVIqNYs3Azd2+OpfuYKxQsaJUm7yFFKSE+QKvVsm/fPvz8/D5YRPrvIy4u7pPf38/Pjz///BNjY+NUaI3+ev1ZmpiY6DiSzOd1kS8mJkbvv8Tt3buX//3vf7Rt2xZPT09y587NwIED6du3L6D+/xQQEECjRo3iX2Nqakrt2rU5efJkkkWpqKgooqKi4n8OCQn5qNjOnjnMqX9OkjffSZ4/PUOlKjvJk6fwRx1LCPHG63l55IZF2shMOULXNy7kXE1bmelcFUKXIiIi2LFjJ0vmzuTSrSuEh4O1thEjfzqVJu8nRSkh3uPZs2d0796dP/7445OOY2ZmRvbs2RM8LC0tE217+wEwYsQI/v77bwYPHsyyZcukizfIZ5AGMtNn6uvry9KlSxk5ciRfffUVZ86cYejQoZiamtKtWzcCAgIAcHBwSPA6BwcH7t27l+QxZ8yYwdSpUz85thd/32TWdDAyhYmTLpM9W2mOH1lP+85tyUS/AiF0JjNdyzKSzPS5ZpQbF5npM81I5HMV4tPdv3+fzz//nMuXL8dvs8+uwbloqzR7TylKCfEOx44do2PHjjx69AhTU1M+//xzrKys3ltISuphYWHx0XdrHB0dadGiBcuXL6d48eKMGDEilVspROai1WqpWLEi06dPB6B8+fJcvXqVpUuX0q1bt/j9/vvFVVGUd36ZnTBhAiNHjoz/OSQkhLx586Y4tur9OlJ/00/sv3qVyZNhwIBI2rZtxzKPXnzedwV588o8aUIIkZYy8o0LIYTQtfPnz9O06Wc8eRKIrS189hk01jrSceFFDHPlSrP3laKUEP+h1WqZMWMGkydPRqvVUqxYMbZs2ULZsmXTPZZmzZoxd+5cRo0axahRoyhcuDDNmjVL9zjEx+vRowcvX75k9+7daXL8OnXqUK5cORYuXJgmx9c3Tk5OuLq6JthWokQJduzYAaiFXlAnmn17ItTAwMBEf4S8Zmpqiqmp6SfHZm1vz2/e3gwdNIily5ezdCn4+cGQIas5+8txPB1O0LmbvfSaEiILkRyRvjLyjYuMTs5VITK3PXv20KFDeyIjoyhQAGZ+C4VP16bC3AOQxtOnyG1ZId7y5MkTGjduzKRJk9BqtXTt2pVz587ppCD12ogRI+jbty+KotCxY0cuXryos1iEyOjc3d25efNmgm0+Pj64uLgAUKBAARwdHTl48GD889HR0Xh6elK9evU0j8/IyIgly5axYMECNBoNf/4JvXuCn6EPeQzyMq7Tnzx5kuZhCCFElvSuGxf3798HEt64eNuHblxYWVkleAghhL5QFIUFC+bzxRctiYyMomJFWDEOSt8eR4XvD6d5QQqkKCVEvEOHDlG2bFkOHjyImZkZq1evZt26dfHzO+mKRqNhyZIl1KtXj7CwMJo1a5boy5LQT56enlSuXBlTU1OcnJwYP348sbGx8c+/evWKzp07Y2FhgZOTEwsWLKBOnToMHz5cd0FncCNGjODUqVNMnz6d27dvs2nTJpYvX86gQYMA9f+n4cOHM336dHbt2sWVK1fo0aMH5ubmdOrUKV1ifB3DoUOHyJ8/PwGBMHo0LNgeSb3WTVjbeRDbtirpEosQIuOSHJH6MvqNC30l56oQ+ik2NpZevXoycuQoFAWaNYNldU1xy/snJTxmkl7d92X4nsjy4uLi+Oabb/j2229RFIWSJUuydevWRHfSdMnY2Jjt27dTtWpVfHx8aNGiBUeOHIlf/jYrUhQID9fNe5ubf/o1+tGjR3z22Wf06NGD9evXc+PGDfr27Uu2bNmYMmUKACNHjuTEiRPs3bsXBwcHJk+ejJeXF+XKlfvkNmRWlSpVYteuXUyYMIFvvvmGAgUKsHDhQjp37hy/z9ixY4mIiGDgwIEEBQVRpUoVDhw48MlLfadU3bp1uXz5MmPHjmXp0qXs3QtnzsCYMT9iue8v+v/iyfSVjsiq4UKknOQIkZQRI0ZQvXp1pk+fTrt27Thz5gzLly9n+fLlQMIbF0WKFKFIkSJMnz49TW9c6OpcTY3zFORcFUJfvXr1igYNanPmzAU0GhjQA4aHFaDIYE80+dJ5CLIiEgkODlYAJTg4WNehiDT26NEjpXbt2gqgAEqfPn2UsLAwXYf1Tj4+Poqtra0CKO3atVPi4uJ0HVK6iYiIUK5du6ZEREQoiqIooaGKon6VS/9HaGjy4+7evbvSokWLRNu/+uorpVixYopWq43ftmTJEiV79uxKXFycEhISohgbGyvbtm2Lf/7ly5eKubm5MmzYsPhttWvXTvDzx/jvZ/s2uR4mlhafyaFDhxQXl3zx16IWLVA8vzZWejpvVvbuTbW3ESJTSuoaJjlCJTkisV9//VUpVaqUYmpqqhQvXlxZvnx5gue1Wq3i4eGhODo6KqampkqtWrWUy5cvJ/v47/tMMtK5mpLzVFH0/1wVQrzh43NTyZdX/ZvS1BRlTh8U/26tFSWV/99Jbo6Q4Xsiy/rzzz8pW7Ysnp6eZM+enY0bN7JixQrMzc11Hdo7FSlShJ07d2JkZMTWrVtltRc9dv36dapVq5Zg4lR3d3dCQ0N5+PAhvr6+xMTEULly5fjnra2tKVasmC7CFWmsXr16XL58hQEDBgCwZw+0XxFDxW4deDaoPV92DSU4WMdBCiHSjeSItPN6qfPIyEiuX79O3759Ezyv0WiYMmUK/v7+REZG4unpSalSpXQUbcYn56oQ+mXHji1UreLK/QdB2NrApv/BYNfvcVy7DbJl00lMMnxPZDkxMTF8/fXXzJo1C4By5cqxZcsWihYtquPIkqdOnTosW7aMPn368M0331C0aNEEQ5OyCnNzCA3V3Xt/KiWJlXwURZ1HSKPRJPh3UvuIzMfS0pKlS5fSpk0bevXswv0HAQyaCS2bbmWcjyddi+5h2KYq1K+v60iFyPgkRwh9oatzNbXuwcq5KoR+UBSFSZMGMH/+ciIjIX8e2OZgScVRv0GtWjqNTYpSIku5f/8+HTt25OTJkwAMHDiQefPmkU1HVeGP1bt3b27evMmcOXPo1asXBQoUyHITcGo0YGGh6yg+nqurKzt27EjwZe7kyZNYWlqSO3dubGxsMDY25syZM/FLS4eEhHDr1i1q166ty9BFGqtfvz5XrvowcuRgVq5cz+7f4bTTE+aWq8qxhpPZN2QS02YZ6+pmlhB6QXKE0Bdyrgoh0lpQ0DOGDq3Dpk1X0WqhaiHYbleK3Dv+gLzpPH9UEqQoJbKMvXv30qNHD4KCgrCysmLVqlW0adNG12F9tBkzZuDj48OePXto2bIlZ86cIX/+/LoOSyQhODgYb2/vBNv69evHwoULGTJkCIMHD+bmzZt4eHgwcuRIDAwMsLS0pHv37owZMwY7Ozty5cqFh4cHBgYGie42Pn36NNHxHR0d45e2FvrH0tKSFSvW0bZtR3r2aMtj/1A6+0OH6t/QZ/MuWu/bwfRtRShbVteRCiE+leQIoS/kXBVC/3h6HuabqZ/z92F1RYUOLrCmWheyLV8OGWXRrFSdySqT0MdJG8W7RUVFKcOHD4+fQLhSpUrKnTt3dB1WqggNDVXKly+vAErJkiUz9Tmrr5NXdu/ePf7ce/vRvXt35ciRI0qlSpUUExMTxdHRURk3bpwSExMT/9qQkBClU6dOirm5ueLo6KjMnz9fqVy5sjJ+/Pj4fd6eqP/th4eHR7JjzGyT2Ka19P5MQkJClK5d68T/bp0dUTbXNFQGGC1TZs/SKrGx6RKGEBmWvuYHRZEckRmldKJzfaHv56oQWU1cnFb57rsxSt486v9LBhqUqfYaRfv994ry1sIEaSm5OUKjKDKg979CQkKwtrYmODgYKysrXYcjPoGvry/t27fn3LlzgLoU8MyZMzExMdFxZKnn4cOHVK5cGX9/f5o0acLevXsxMsp8nSAjIyPx8/OjQIECejfcMrWEhYWRO3du5s2bR+/evVPtuO/7bOV6mJiuPpNff11Dv74DCHgSjUYDnWpDy1u1+DnfNn7YnIt8+dItFCEyFMkPKskRGcP7PhM5V1W6OFeFyEqePAlm2LA67NzpTUwMOFjCz+bWNNiyB9Jx2Gxyc4Ssvicyre3bt1O+fHnOnTuHra0te/bsYf78+ZmqIAWQJ08e9u7di5mZGfv27WPUqFG6DkmkkgsXLvDLL79w584dvLy84ie0b9GihY4jE7rQrFlPrt8IoH37aigKbDwCIzVHaZvdhbGl9rBxo7rQtxAia5AcIfSFnKtCpJ+9ez1p+pkzW7aoBakGjnC5rDsNvG+ka0EqJaQoJTKdyMhIBg4cSNu2bQkJCaF69ep4e3vTvHlzXYeWZipWrMiGDRsA+OGHH/jxxx91HJFILXPnzqVs2bI0aNCAsLAwjh07hr29va7DEjpiY2PL5s0n2bVrDTntTXjwELodisS0WktCJ7eie9twgoJ0HaUQIr1IjhD6Qs5VIdJWXByMGjWe3r3rcN4rHGMjmOUIB/p+Tc4jnpCB52bLfGN8RJbm4+NDu3btuHjxIgDjx4/nm2++wdjYWMeRpb3WrVszffp0vvrqK4YOHUrhwoVp1KiRrsMSn6B8+fKcP39e12GIDKhlyx7UqtWcAf2asW3HSdYfAM98u5ga40T30n8wbJ079evrOkohRFqSHCH0hZyrQqSte/de0bdvLf76yxtFgfw5YauxNZXWboOGDXUd3gdJTymRaWzatAk3NzcuXrxIzpw52bdvHzNmzMgSBanXxo8fT7du3YiLi6Nt27Zcu3ZN1yEJIdKInZ0dW7efYNu2teSwNebefej1Wwg5KtTg9JA+jBoeR2SkrqMUQgghhBBpZcMGTxo1dOTgQbUg1a4QXC5WnUpnr+lFQQqkKCUygfDwcPr06UPnzp0JDQ2lTp06eHt707hxY12Hlu40Gg3Lly+nRo0ahISE8Pnnn/P06VNdhyWESENt2nTn5i1/WjWrilYLa3+FVZGrKPHEkbaVr/Bvx1EhhBBCCJFJREdDjx7jGDiwDj63wjE3g+X5YUu7CWQ/7AnOzroOMdmkKCX02rVr16hcuTKrVq1Co9EwefJk/vrrL5z16H/C1GZqasquXbsoWLAgfn5+tGrViqioKF2HJYRIQzly5GDH3n/YvHkNtlZG+PrBgG3PcChams29RzJntoJWq+sohRBCCCHEp7p2LYQaNcqybt1sQkOhlAucc7Ck749/wPTpoGcrsUtRSuglRVFYs2YNFStW5OrVqzg6OvLXX38xdepUDA0NdR2eztnb2/Pbb79hbW3N8ePH6devH4osyyVEpte+fQ9u3HpEs0aViIuDVTtgR8gCst/ITacmd3nyRNcRCiGEEEKIj7Vw4SEaNnTk7NlLAPQrC+dyV6bE0avQpImOo/s4UpQSeic0NJTu3bvTq1cvIiIiaNiwId7e3tSrV0/XoWUoJUqUYOvWrRgaGrJ+/Xpmzpyp65CEEOkgV65c7PnzND9vWIVNdiNu3YKhG/yxsSnEuMarOXhQ1xEKIYQQQoiUCA9XaNZsOOPGNeDx4wjsbGCLK/zUaAymR45D3ry6DvGjSVFK6JWrV6/i5ubGhg0bMDAw4LvvvuPPP//EwcFB16FlSI0aNWLRokUAfPXVV+zYsUPHEQkh0oNGo6Fzl15cv/WAJjXLERsLP23Vcj66Nye++x8TJ8QRE6PrKIUQQgghxIf8808gpUuX5rffvic6Gqq6wjn77LSbuRdmzwY9X9hLilJCb/j7+9OoUSN8fHzInTs3R44c4auvvsLAQE7j9/nyyy8ZOnQoAF27duXcuXM6jkgIkV4cHR353dOLlUvmkt1Mw5VrMOv0AUK97Ghb6w737uk6QiGEEEII8S5jx+6mYcP8+PpexcgIxtSGo9ZuFPjrCjRrpuvwUoX8NS/0QlRUFK1ateLx48e4urri7e1NzZo1dR2W3pg3bx5NmjQhIiKC5s2b8/DhQ12HlOUdOXIEjUbz3se8efMwNDTk/v37SR6jePHi8QXHOnXqxL/O1NSU3Llz06xZM3bu3JmezRIZkEajoffAUVy+eptqpXISGQk/HAghILQwY+vNZdcuXUcohPgvyRFCX8i5KkTaePYsjNKlezJnzheEhUWQPz/8VhtmlR2C8ZGT4OKi6xBTjRSlRIanKApffvklp06dwsbGhj179mBvb6/rsPSKkZERmzdvpmTJkvj7+9OsWTNCQ0N1HVaWVr16dfz9/eMf7dq1o3Hjxgm29evXjxw5crBu3bpErz9x4gQ3b96kd+/e8dv69u2Lv78/t2/fZseOHbi6utKhQwf69euXnk0TGVT+AgU5fjGAGWM6Y2ICp6/AH4Fj+P3rigz5MobISF1HKIR4TXKE0BdyrgqR+rZvP4OLSymuXFkLQLuWcCy3If/rtg7N9z+AiYlO40tt+rVWoMiSFi9ezJo1azAwMGDLli0ULlxY1yHpJSsrK3777TcqV66Mt7c3Xbp0YefOnTL8MY3VqVOHMmXKkC1bNlauXImJiQkDBgxgypQpODo6xu9nZmZGVFRUgm2gDrlcu3YtkyZNQqPRxG9fvXo1bm5ulC1bNn6bubl5/Ovz5s1L1apVKV68OL169aJdu3Y0aNAgjVsrMjoDAwPGz/6Zz9r1pGP7JlzzjWHV1fPUjbaiudsRFu+qQtGiuo5SiKxDcoTQF3KuCpH2YmNj6dRpOtu2TQW02NvD5P7Q86+cZP/hN6hcWdchpgn5a1RkaIcOHWLEiBEAzJ07l0aNGuk4Iv2WP39+du/ejampKXv27GHChAm6DunjKQqEhenmoSgpCnXdunVYWFhw+vRpZs+ezTfffMPBZC6B1rt3b3x9ffH09IzfFhYWxtatWxPcVXyX7t27Y2trK93eRQJlKtbnwvUQhnd2xdAQDt+KxOthVQbXGMKGDbqOTohUIDlCcoS+0NW5msLzFORcFSItXb16mzx5arJtmwegpV492DYG+p9xI/uui5m2IAXSUypNaLVa6X2SCnx9fWnXrh1xcXF069aN4cOH6zqkTKF69eqsXr2azp07M3v2bIoVK0avXr10HVbKhYdD9uy6ee/QULCwSPbuZcqUwcPDA4AiRYqwePFiDh06RMOGDT/4WldXV6pUqcKaNWuoU6cOAFu3biUuLo6OHTt+8PUGBgYULVqUu3fvJjtekTWYmGRjwc9XadZ8Cr1GT+XeAzjIYqLG7OHgb178uMpeZ/+LCfHJJEdIjtAXujpXU3iegpyrQqQFRVH47ruVeHiMQKsNw8IChg+HTkCx290x/PUnMDXVdZhpSionqejBgwdUq1aN/Pnzo3zE3QfxRmhoKC1atODFixdUqlSJn376KUFXX/FpOnXqxOTJkwHo378/R44c0W1AmVyZMmUS/Ozk5ERgYGCyX9+7d2+2b9/Oq1evALWre6tWrbCxsUnW6xVFkf9/xDvVazeFC+eu0rmZBRoNHH3ygL/2OVC/2Aa8vXUdnRCZn+QIoS/kXBUidQUGBlKpUku+/rofWm0Y5crBqlXQ54GGEo4/YPjTmkxfkAIpSqWqXLlycf78eR48eMA9WWf7o2m1Wrp168aVK1dwcnJi165dZMuWTddhZTpTpkyhffv2xMbG0rp1a27duqXrkFLG3Fy9y6eLh7l5ikI1NjZO8LNGo0Gr1Sb79R06dECj0bBlyxZu377N8ePHk9XVHSAuLo5bt25RoECBFMUsshbbXK6s3x3Ehtk1cHAA/1dazvp3o0eN+ixcGPkxozyE0C3JEcl6reSIDEBX52oKz1OQc1WI1LRz52/kz1+a8+f3YmQEAwbAgulQd6Ml+Xv+jWbwEMgiRVgZvpeKTE1NKVOmDOfPn+fs2bPkz59f1yHppW+//ZZdu3ZhYmLCzp07yZ07t65DypQ0Gg1r1qzBz8+PM2fO8Pnnn3Pq1ClsbW11HVryaDQp7naurywtLWnbti1r1qzB19eXggULxnd9/5B169YRFBRE69at0zZIofcMDIzpPPoY1essZvDEofxxQOFi2N/M+8qeP3cd4Jfd1dGXy4MQkiPqJOu1kiMyADlXk/VaOVdFZhEWFkbv3iPZsmU5APnzGTBpspYSNlBqaRGsfjgAWayOID2lUlmlSpUAOHPmjI4j0U+7du1iypQpACxbtoyqVavqNqBMzszMjD179pA3b158fHxo06YNMTExug5LJKF3796cPHmSpUuX0qtXryS7r4eHhxMQEMDDhw85ffo048aNY8CAAXz55ZfUrVtXB1ELfVSg4mC277jBvIm22NrCw4gw/jrmTiWXwRw9Gqvr8IQQSZAcIfSFnKsiKzt9+jSFC5eLL0i1aaXhpxVaymqhwu+fY/XLhSxXkAIpSqW610Wps2fP6jgS/XP58mW6du0KwLBhw+jZs6eOI8oaHB0d+e2338iePTt///03gwcPljnRMqAaNWpQrFgxQkJC6N69e5L7rFixAicnJwoVKsQXX3zBtWvX2LJlCz/++GM6Ryv0nVn2ogyb+pg/VjWmZk2IU+DOqyW0q1eIUaNukoLRGkKIdCA5QugLOVdFVhQbG8ukSVOoVs2dgIDbZDfPzrx5MGiIgtMZKPfCg2yr9maZXpP/pVHkr89EQkJCsLa2Jjg4GCsrqxS99vLly5QpU4bs2bPz8uVLDA0N0yjKzOX58+dUqlQJPz8/6tWrx/79+zEyktGl6em3336jefPmKIrC999/z9ChQ3UdUgKRkZH4+flRoEABmWMslb3vs/2U62FmldU+kwCfpSxeNYiFSxTCwsBEY0CxwrPY7zkSJye5tyV0T/JD2pIckTLv+0zkXE1b8vmKjOjWrVu0bduVixdPA1ChlCNTpgdgaQl5d5hQsPF2NE2b6TjKtJHcHCHfJlOZq6srFhYWhIaGcvPmTV2HoxdiY2Np3749fn5+FCxYkK1bt0pBSgc+//xz5s6dC8Do0aO5cOGCjiMSQmQEjkW/ZMLXV9i8JCdubhCtaLl8awylXKqxadMDXYcnhBBCCJHhKIrC8uXLKV26HBcvnkajsWJY71zMWxSAVTYots6BQoMuZtqCVEpIUSqVGRoaUqFCBUDmlUqu0aNHc+jQISwsLNizZw85cuTQdUhZ1ogRI2jRogUxMTF06tSJ8PBwXYckhMgALLK70qTLXdbP+4yhQ9XViV/EnKFX56I0b7aBmBjpdC2EEEIIARAYGEizZi3o378/UVHhmGWryM8/xtGySyBGwVB2e1WcFtyA4sV1HWqGIEWpNCDzSiXfmjVr+P777wHYsGEDpUqV0nFEWZtGo2HlypU4OTlx48YNRo4cqeuQhEiRKVOmoNFoEjwcHR3jn1cUhSlTpuDs7IyZmRl16tTh6tWrOoxYfxgamuNa+3fGD13EqmUaSpSAKCL59bdu5HVowYULT3UdohBCCCGETv3666+UKFGK33//FTChaP5O/L7DC+fiYZjfA7dLA7D58TjY2Og61AxDilJpQIpSyXPq1CkGDBgAqH9IfvHFFzqOSADY29uzfv16AH766Sd2796t24CESKGSJUvi7+8f/7h8+XL8c7Nnz2b+/PksXryYs2fP4ujoSMOGDXn16pUOI9YvToUG06L1BVbMzUnv3mBoCE+CfqWqWzG++mqvrsMTQgghhEh3oaGh9O/fn+bNm/PixVOgND2adeCnVZvQmGux9TKgvNkqzL5eqn55EvGkKJUGXhelLl68SFRUlI6jyZgePXrEF198QXR0NF988QVff/21rkMSb2nQoAGjR48GoE+fPjx+/FjHEQmRfEZGRjg6OsY/cubMCai9pBYuXMjEiRNp1aoVpUqVYt26dYSHh7Np0yYdR61fsluWpUqj24zo2YilS6FAAYhWgpgxowUlS/QiMDBE1yEKIYQQQqSL06dPU65ceZYvXw5o0GiGs2pKDrqPXA8G4HwoO6XrnsK4XS9dh5oh6V1R6ujRozRr1gxnZ2c0Gk2iXhw7d+7kf//7H/b29mg0Gry9vdM9xoIFC2JnZ0d0dDSXLl1K9/fP6CIjI/niiy8ICAigVKlSrF+/HgMDvTsVM73vvvuO8uXL8/z5c7p164ZW1oAXeuLWrVs4OztToEABOnTogK+vLwB+fn4EBATQqFGj+H1NTU2pXbs2J0+e1FW4esvIyArX6n/yWf15LFuioX170Gjg2o01uOQpyc8/e+o6RCGESECGeAshUlNMTAxTpkzB3d2dO3duA3mxMN/OX6t3UbD2EYiDwn8UpuhQXwzKV9J1uBmW3lUCwsLCKFu2LIsXL37n8+7u7sycOTOdI3tDo9HIEL53UBSFfv36cfbsWezs7NizZw/Zs2fXdVgiCSYmJmzatAkzMzMOHTrE/PnzdR2SEB9UpUoV1q9fz/79+1mxYgUBAQFUr16d58+fExAQAICDg0OC1zg4OMQ/l5SoqChCQkISPIRKo9GQu+BIqrqfZngfexYuBCdHiIx5SNeudfnss1FERkbqOkwhhIgnQ7yFEKnh1q1b1KhRg6lTpxIXFwd0ws11PfvXd8Mg/z0Mw6D0yc/J8901+LfXvkia3hWlmjRpwrRp02jVqlWSz3ft2pXJkyfToEGDdI4sISlKJW3hwoVs2LABQ0NDtm7dSsGCBXUdkniP4sWLs3DhQgC++uorvLy8dBuQEB/QpEkTWrduTenSpWnQoAG///47AOvWrYvfR6PRJHiNoiiJtr1txowZWFtbxz/y5s2bNsHrMSurSrjV9qFupbqsXAWffw6gsG/ffPLkrsj589JrWAiRMcgQbyHEp1q/fj3lypXjzJkzgDWwieHtG7NgTn1icoSRLQAqPJ9Cjq9/BWNjXYeb4eldUSotpMVd8NdFKfVEFQAHDhyIn6do/vz51K9fX8cRieTo27cvX3zxBTExMXTq1ImwsDBdhyREsllYWFC6dGlu3boVP0Tjv72iAgMDE/WeetuECRMIDg6Ofzx48CBNY9ZXxsa2lKp6iFLFvmPUCA3Tp4OtFTx/cZXKlSoxefJcGQYshNC51B7iLb1phcg64uLiGD16NN27dyc8PByoC1xm89TTtOjbjThzLVY3jKmQ9zcsunnoOly9IUUp0uYueNWqVTEwMODatWts2bIlFaLUb7dv36Z9+/ZotVp69uzJkCFDdB2SSCaNRsOKFStwdnbm5s2bjBw5UtchCZFsUVFRXL9+HScnJwoUKICjoyMHDx6Mfz46OhpPT0+qV6/+zmOYmppiZWWV4CGSptFoyFfoK8pVOErtyrasXgc1qoJWiebbb8fg5laf+/fv6zpMIUQWlRZDvKU3rRBZw8uXL/n888+ZN2/ev1u+JofdPk4t64JDre/BEBzO5aBc0+uYuDfVaaz6RopSpM1d8Fy5cjFhwgQA+vXrh5+f3ycfU1+FhITQvHlzXr58SdWqVVm6dOl7h8qIjCdHjhysX78ejUbD8uXL2bVrl65D0gv/nUz1v48mTZpgbGzMzz//nOTr+/fvT5kyZYCEk7MaGRlhb29PrVq1WLhwoazy+ZbRo0fj6emJn58fp0+fpk2bNoSEhNC9e3c0Gg3Dhw9n+vTp7Nq1iytXrtCjRw/Mzc3p1KmTrkPPVGxsalCx5g3y56rKN9Nh9GgwNwZv7yMUK1aGjRtlKIwQkiPSX1oM8c4KvWnlXBVZnY+PD1WrVuXPP/8EzIAtNKw1iAPzCxFR7CgABc6Vp/igBxjkLaTTWPWRFKVIu7vgHh4eVKtWjZCQEDp27EhMTEyqHFefaLVaunbtyvXr13F2dmbnzp2YmprqOizxEerXr8+YMWMA6NOnD48ePdJxRBnf2xOpLly4ECsrqwTbNm/eTNOmTVmzZk2i10ZERLB582Z69+4dv+315Kz379/n8OHDtG3blhkzZlC9enWZhPVfDx8+pGPHjhQrVoxWrVphYmLCqVOncHFxAWDs2LEMHz6cgQMHUrFiRR49esSBAwewtLTUceSZj4lJLspWO46L02iaNoXla6B0AYiMDKZLl860bduRoKAgXYcphM5IjtC91BjinRV608q5KrKyAwcOULlyFW7evAnkAY7z3dC8TPuyACEujzCIhJJ+PXAZdR6NmZmuw9VLUpRKQ8bGxmzatAlra2tOnz6Nh0fWG1fq4eHB3r17MTU1ZdeuXTg5Oek6JPEJvv32W9zc3Hjx4gXdunWT+WE+4O2JVK2treOXnn57W+/evTl8+DB3795N8Nrt27cTGRlJly5d4re9npzV2dmZ0qVLM2TIEDw9Pbly5QqzZs1K59ZlTJs3b+bx48dER0fz6NEjduzYgaura/zzGo2GKVOm4O/vT2RkJJ6enpQqVUqHEWduGo0hBYvPoVSpvbg4mrFgBfTuCIbA9u2bcXUtw6FDh3QdphA6ITlC91JjiHdWIOeqyIoUReH777+nSZMmBAe/BKphbn6Ww3P2UauhO+GOEZg811DecDE5e64BGQn00fSuKBUaGoq3tzfe3t6AOimht7d3/BwVL168wNvbm2vXrgFw8+ZNvL293zsWPC3lz5+flStXAjBz5sws9eV727ZtTJs2DYDly5dTuXJlHUckPpWJiQkbN27E3Nycv//+m7lz5+osFkVRiIsL08lDUZRUa8dnn32Go6Mja9euTbB99erVtGzZkhw5crz39cWLF6dJkybs3Lkz1WISIrXZ2zfDrfpVrI2K0aUfLF4CBawgIOAhDRo0YOTIkURGRuo6TJGJSI5QSY5IKCMO8dbVuZqa5ynIuSoyl6ioKHr37sPw4cP/vQnfg1LF9nNuWgeoMInY7AqW98xxK3UMy4aDdB2u3jPSdQApde7cOerWrRv/8+tJl7t3787atWvZu3cvPXv2jH++Q4cOgNpjZ8qUKeka62tt2rShX79+LF++nC5dunDx4kVy5cqlk1jSy8WLF+nRoweg/o66deum24BEqilWrBjff/89ffv2ZeLEidSvXx83N7d0j0OrDefYsezp/r4ANWuGYmhokSrHMjQ0pFu3bqxduxYPDw80Gg1+fn54enr+O279w4oXL86BAwdSJR4h0oqZWQHK1/Dm9tUB4LqOJZthzTew7RQsWLCAgwcP8vPPP1O2bFldhyoyAckRb0iOeOP1EO9nz56RM2dOqlatmmiId0REBAMHDiQoKIgqVaqk+RBvXZ2rqXmegpyrIvN48uQJLVu25tSpE6h9eOYyvHUjetXOz5PSLwBw8i1BkbmgZUMAAQAASURBVA7/YGBurdNYMwu96ylVp04dFEVJ9Hhdle/Ro0eSz+uqIPXaggULcHV1JSAggB49emTqYU/Pnj2jRYsWhIeH07BhQ+mGmwn17t2bVq1aERsbS6dOnQgLC9N1SHqtd+/e3Lt3j7///htQ7yrmyZOHBg0aJOv1H5qEVYiMwtAwG8XKrKV4sbVYmBgzcAbMGQu2hoZcuXKFypUrM3fu3EydI4VIKckRqUeGeKctOVeFvvP29qZcuUr/FqSsMTT8gz9GZ6Nj8zI8L/0CTQwUfdWPYr2uSUEqFeldTyl9ZW5uzubNm6lcuTL79u1j4cKF8b28MpOYmBjatm3LvXv3KFy4MFu2bMHISE6zzEaj0bBixQpOnz6Nj48Pw4cPZ8WKFekag4GBOTVrhqbre7793qmpSJEi1KxZkzVr1lC3bl3WrVtHz549MTBI3n2D69evU6BAgVSNSYi05OjUneyWFbh6tgkVmzxiTcU4Fg014FBANGPGjOH3339n3bp15MuXT9ehCj0lOeINyREZm67O1dQ+T0HOVaHftm/fTufO3YmODgeKktdxO4d6TCLAfS/h2cEk2JiSRX/BukRrXYea6ehdTyl9Vrp0aebPnw/A+PHjOX/+vI4jSn0jRozgyJEjZM+enT179mBra6vrkEQasbOzY8OGDWg0GlauXMmOHTvS9f01Gg2GhhY6eaTFXbzevXuzc+dOduzYwcOHDxMMQ36fGzdu8Oeff9K6tSRIoV+yZy+NW81r2Fs0wTonTNyk5ZumYIIxR44coUyZMmzcuDHV5z0RWYPkCJXkiIxPV+dqWvVIknNV6ButVsukSVNo27btvwWp/9Gh+i8c7tGYR//bS1x2sH6WG7f6t6UglUakKJXOBgwYwBdffEFMTAwdOnTIVMuerlixgiVLlgCwcePGBN2hReZUt25dxo4dC0Dfvn15+PChjiPSX23btsXY2Jj+/ftTv3598ufPn2if2NhYAgICePz4MZcvX2bRokXUrl2bcuXKMWbMmPQPWohPZGRkRcmKv1Mo/2wMFANqjoZfZsXgapKd4OBgunTpQseOHQkKCtJ1qELolOQIoS/kXBX6JDQ0lM8+a8t33039d8tINnduz4QvqvDgf48ByB3VlLKt/DDNLr2304oUpdLZ614lefPm5fbt2wwalDlm6z9+/Hh8W7799luaN2+u44hEevnmm29wc3MjKCiIbt26ERcXp+uQ9JK5uTkdOnQgKCiIXr16JbnP1atXcXJyIl++fNSpU4etW7cyYcIEjh07RvbsupnQV4hPpdFoyJt/DOXcjmISZ4tdZVi0I5ShBbKhwZAtW7ZQunTpLLV6rRD/JTlC6As5V4W+uHfvHqVLu7N//07ABBuLn7jV4zEuTXrxomIsBjEaitvPp8j/fsPAwFjX4WZqGkX6xScSEhKCtbU1wcHBWFlZpcl7HDt2jDp16qDValm/fj1du3ZNk/dJDw8ePKBixYoEBgbStm1btmzZIpMUZjE+Pj6UL1+e8PBwZs6cybhx41L9PSIjI/Hz86NAgQJky5Yt1Y+flb3vs02P66G+kc8k7URHB3LtXHNeRp8G4NlS6L/dnhfaZ4A6RHz69OlyDRAJSH5IW5IjUuZ9n4mcq2lLPl+RXEeOHKdp01aEhz8FHKhT+Hs2VhvP7Y530ZqBabgVpaodxNK2sq5D1WvJzRHSU0pHatasiYeHBwADBw7k1q1bOo7o44SHh9OyZUsCAwMpU6YMa9askYJUFlS0aFF++OEHACZNmsS5c+d0HJEQQh+ZmOSibLUT5HNSh2/Yfwl75j7jf2Zql/kFCxZQqVIlLl68qMswhRBCCKGn5s9fSb169f4tSJVnXu0JrGjcFZ9eakHKNq4cbg3uSEEqHUlRSocmTpxI7dq1CQ0NpWPHjkRHR+s6pBRRFIW+ffvi5eVFjhw52LNnDxYWFroOS+hIr169aN26NbGxsXTq1InQUN2seiSE0G8ajSEFi82mdKlfMYo1J7Y8TPrlPouL2GFukoMrV65QuXJl5syZI8OFhRBCCJEssbGxtGkzlFGj+qIoMZgYtsa7cXHqNRvOw9YxAOS1/ZLSdc9iYmKv42izFilK6ZChoSE///wzdnZ2nD9/ngkTJug6pGRTFIWZM2eyadMmDA0N2b59e5ITGYqsQ6PRsHz5cvLkycOtW7cYPny4rkMSQuixHPaf4+Z+heyaYsRaQ8llLzj0xUsKm5YnOjqasWPHUr9+fe7du6frUIUQQgiRgT1//gJX1ybs2LEIgKK2Q3lU+wLh3X/hpRsYxBrjWvwXCpX9EQMDIx1Hm/VIUUrH8uTJw5o1awCYP38+e/fu1XFE7/fy5UsWLVpE6dKl+eqrrwD4/vvvqVOnjm4DExmCnZ0dGzZsQKPRsGrVKrZv367rkIQQeszMrADla3jjZNsdDCByQBzbvrlAD4tqmJpa4OnpSZkyZfj555+RKTKFEEII8V9nz17HxaUKt279BVjQr+gQjrgv49pYX6IcwUxxpkJVL3I5dtB1qFmWFKVS0fPnMG0a9OuXstc1b96cwYMHA9CqVStmz56NVqtNgwg/jqIonDlzht69e+Ps7MzQoUO5evUqZmZmeHh4MHDgQF2HKDKQOnXqxE903rdvXx48eKDjiIQQ+szQMBvFyq6leNG1GMQZ8bIy9Fv1DxvzOJA3VwVCQkLo2rUrHTp04MWLF7oOVwghhBAZxI8//kHVqlUIC7uNhnzsLFeXUY0XcXNUNFpTyGHRkAo1rpI9eyldh5qlSVEqFWk08PXXsGIFpPR78dy5c+nSpQtxcXGMGzeOFi1a6PzL9atXr1i+fDlubm5UqVKF1atXExERQcmSJVm0aBGPHz9mypQpMrG5SGTq1KlUrFiRly9f0rVrV5n3RQjxyRydu1Ohihdm2txEOYD9Ul/+rHqNGrm6YGRkxNatWylTpgx//fWXrkMVQgghhA4pikKHDnMYNOhztNpX2Bi7ccvVkPw9f+PxF+o+Lvm+plTFPzE2ttFprEKKUqnKLjaQn2zHs4mOnD2bsteampqyfv16li9fjqmpKb/99hsVKlTgzJkzaRPse1y8eJEvv/wSZ2dn+vfvz4ULFzA1NaVLly4cP36cy5cvM3jwYGxsbNI9NqEfTExM2LRpExYW6vCa2bNn6zokIUQmkD17adxqXcPevAmKMQSOiOSnfj8zzKIDefMW5dGjRzRs2JCRI0cSGRmp63CFEEIIkc5evoykWLHubNkyFlCoZluLmyUu8WSqH8FlwBALSpX6lQIFv0GjkXJIRiC/hdRkbEy/oFl0ZDNX/36S4pdrNBr69u3LqVOnKFy4MPfu3aNGjRr88MMPaT5XRnh4OGvXrqVq1aqUK1eOZcuWERoaStGiRZk3bx6PHj1iw4YNuLu7S88okSxFihRh0SJ1MsHJkydzNqWVWiGESIKRkRUlK/1OIZfZoDUgsD60W/gzP2JNnSo9AFiwYAGVK1fm8uXLug1WCCGEEOnm9Gl/cueuza1bGwBDJji7sdX9KNfnxhBtD+amxXCrfAF7+891Hap4ixSlUpOtLc8dSwIQfuifjz5MuXLlOHfuHK1btyYmJoZhw4bRrl07goODUyvSeNeuXWPYsGHkzp2bnj17cvr0aYyMjGjXrh1///03N27cYOTIkeTIkSPV31tkfj169KBt27bExsbSqVMnQkNDdR2SECIT0Gg05C0whvJuRzGOtSU8P1j9dJYfLfbQucIccubMxeXLl6lUqRILFy7MUPM0CiGEECL1LVhwjmrVKhIefgYjjRV7HXLRrfN5bo8CxRhy2remQqVzmJsX0XWo4j+kKJXKYqu4w//Zu+vwKK4ugMO/3bhDgBgkSAhuQYq7t0gLtBQoXlwaoEVLgSLBSinuUqRQnOIe3AnuFiQhSIj7zvfHFChfAg0lyUTO+zz7kMyOnL3d3tmcvfdcIOuVI3zM4CY7OzvWrFnDtGnTMDExYe3atZQtWxZfX9+PjjE6OpqVK1dSvXp1ihYtyrRp03j58iV58uTB29ubhw8fsnr1amrWrCmjosRH0el0zJ07F1dXV27dukXfvn21DkkIkYHY2VWmXLVrZDEpj8ECngwP4scqA+ll2o1atT4jOjqafv360aBBAx4/fqx1uEIIIYRIZnFx0LTpH/TvXxVFeYyDiRO+DpG4jPAn4FMAPfnyjadI0TUYG1trHa5IhCSlklnWxmpSyjPyCPfvf9y5dDodffr04dChQ7i5uXHr1i0qVKjA/Pnz/9N0vlu3bjFw4EBy5cpFmzZtOHjwIEZGRnz++efs2LGD27dvM3jwYBwdHT8ucCH+IWvWrCxbtgydTsfixYtZs2aN1iGlqg4dOvD5558n+ty8efOoUaMGtra26HQ6Xr58mWCfoKAg2rZti52dHXZ2drRt2zbR/YTIrExNHShZ6QhuTt8DEPCFwmc/jeb7WxH07/4LFhYW7N69m+LFi7N+/XqNoxXibXKPEOmFvFdFWuTvH0/+/EPZvLk1EEUVSydOFgzg5exYQguDsVFWSpTYgZvbIBlskYZJUioZxceHE/xJGA++hDKc4dSh5CmyWr58ec6dO0ejRo2Ijo6ma9eutGvXLklToWJjY1m3bh1169bFw8ODSZMm8ezZM3LlysWoUaO4f/8+GzZsoH79+uj18nYQKaN69eoMGTIEgK5du+Ln56dxRGlDREQEDRo0YOjQoe/cp3Xr1vj6+rJjxw527NiBr68vbdu2TcUohUj7dDoj8hWaRPFiWzCKtSKsAFjP2k/HG9783GIJpUuX5sWLFzRv3pzOnTsTGhqqdchC/Cu5R4j0Qt6rQgv79gWTL19T7t/3BqCfpS3LGgZwdyrEZgVr61KUKXsGe/u62gYq/pWx1gFkJHFxwVx+3gu6gfNfMTzZehraVkmWc9vb27Np0yYmT57M0KFDWb58OWfOnGHt2rUUKVIkwf73799n/vz5LFy4kICAAEAdedWwYUO6d+9Ow4YNMTaW//wi9YwcOZI9e/Zw8uRJ2rZty759+zAyMtI6LE15eXkBcODAgUSfv3r1Kjt27OD48eOUL18egPnz51OxYkWuX79OwYIFUylSIdKHbNk/o1zVa1w+0ZRQq7M8G/6MT7d9TfYbo7n8fT1++WUCixYtwsfHh+XLl1OhQgWtQxbineQeIdILea+K1KQoMHr0dUaMaApcxxgTFprFU7ZfCPfqqPs4On5DgQJzMTKy1DRWkTSSlUhGZmYumJm5ER3tR2hhMDpxBEiepBSAXq9n4MCBVKxYka+//pqrV69Srlw55s6dyzfffEN8fDzbtm1jzpw5bN++/fUUP0dHRzp37kyXLl3IkydPssUjxIcwMTFhxYoVeHp6cvDgQSZMmPDeb9T+jaIoREREJGOESWdpaZkqQ4CPHTuGnZ3d6w9wABUqVMDOzo6jR4/KhzghEmFungvPqie4f30Y9/0nEvipQpEiP+I2pRblF/7FgJE9uX37NlWqVGH48OEMGzZMvqTJgOQeIfeI9EKr92pqvU9B3qsi+URFQZMm29i9uxUQgoPenPX2UZiOh0B30GGMe/4p5MzZW6brpSPyKSyZ2dlVIjDQj+Ci4LbyCHFxkNyfdatWrcq5c+do06YNe/bsoW3btqxevZrz58/z4MGD1/vVqVOHbt260bRpU0xMTJI3CCH+g/z58zN9+nQ6duzIiBEjqFOnDp988sl/OldERATW1toUKwwLC8PKyirFrxMQEICDg0OC7Q4ODq9HQAohEtLrjclbeAJZHOtx8WQzIvKEYDxpHwVnnGdx39UsPLuIlStXMnLkSHbs2MHy5ctxd3fXOmyRjOQeIfeI9EKr92pqvU9B3qsiedy7p1ClygQePRoKKFQ0MmVByShejIRwGzAxcaBo0TVkyVJN61DFB5IiQsnM1rYSACFFobzhKJcufsQSfO/h4ODAjh07GDlyJDqdji1btvDgwQOyZcvG999/z40bN9i9ezctWrSQhJRIU9q3b89XX31FXFwcrVu3ltou/yKxb3kURZFvf4RIgqz2talQ6yZWcRUwmMHzAc/JEVyXb+NLsnzZcuzs7Dh+/DilSpVi8eLF/2kRESG0JPcIkV7Ie1V8jC1bIihYsBWPHg0BFLrqYFG7GAJ/gTgbsLEpT5kyZyQhlU7JSKlkZmenJqWCi0Bx3XP2/nWDUp4pMyTVyMiIESNGUKVKFRYvXsynn35Ks2bNMDc3T5HrCZEcdDodc+bM4dixY9y+fZs+ffqwZMmSDz6PpaVlkor9pwRLy9SZn+7k5MSTJ08SbH/69KmskilEEpmaOlC29hHuXBnDA/+RPK+lYFFwEPnm1eLE7kN0/b43Bw8epFOnTmzdupW5c+eSLVs2rcMWH0nuEXKPSC+0eq+m1vsU5L0q/jtFgcGD7zNx4ueAL0bomGqpUHksBJRS98mZsy/u7pPQ6001jFR8DElKJTMrqxLo9RbE20YS4QoRe47ATyk7T7p27drUrl07Ra8hRHLKmjUry5cvp2bNmixdupSGDRvSsmXLDzqHTqdLtWHnWqlYsSLBwcGcPHny9TTHEydOEBwcTKVKlTSOToj0Q6fT4170J7LnrMu5g42IyvkC3Y/7iP2lBssHbGNFw/0MHz6cdevWcezYMZYsWULdurJaT3om9wi5R6QX8l6V96pIXEgINGrkw6FDLYBnZEfHUncFu0kQnBWMjKwpWHARDg5fah2q+EiSlEpmer0JNjafEBzsQ0gxsDt6BOikdVhCpDnVqlVjyJAhjB07lm7dulGhQgVy586tdVgpIjg4GF9f37e22dvbY2pqSkBAALdu3QLg4sWL2NjY4Obmhr29PYULF6ZBgwZ06dKFuXPnAtC1a1caNWokRUGF+A/sslSkcsNbHN/ejHjbAzzr/oL4oxX5PMibeseP07pNG65fv069evXo168f48aNk9HHIsXJPUKkF/JeFanl8mWFWrVmExj4HRBHaWB6U4WYvhCrByurYhQtuhZLS3n/ZAiKSCA4OFgBlODg4P90/O3bQ5T9+1Gu/oByHQ9l5w5DMkcoRMYQExOjlC9fXgGUqlWrKrGxse/cNzIyUrly5YoSGRmZihF+vPbt2ytAgkf79u2VESNGJPrc4sWLXx///PlzpU2bNoqNjY1iY2OjtGnTRgkKCkrWGN/Xth/bH2ZE0ibpn8FgUC6emKTs26lX9u9HOfInysVmNZSwR4+VHj16vP5/sXjx4sqFCxe0Dlf8i/R6f1AUuUdkRO9rE3mvavdeFenHypVRirHxt6/fR18boxwfhbJ/v/q4erWDEhcXrnWYIgmSeo/QKYpU9fx/ISEh2NnZERwcjK2t7Qcf/+zZX1y61ASL+zrKd1Dob7+EUffaY2OTAsEKkc7dvn2bUqVKERYWxvfff8+kSZMS3S8qKoq7d++SN29eGbmQzN7Xth/bH2ZE0iYZx7On5zi3vz4mDk8hHrKvyop7h53sDXpCp06dePr0KWZmZowfP56+ffui18v6MGmR3B9SltwjPsz72kTeqylL2jd9i4uDvn0DmD27OXAUHTAqB9SeBjFOoNeb4+ExE2dnmYWUXiT1HiGfrlKArW1FACJzK8TawE8vvmN8n0caRyVE2uTu7s6iRYsAmDx5Mn/++afGEQkhMovsOTyp2ewOkX6fghE8axPEtRPlKXflEhcvXOCzzz4jOjqafv360aBBAx4/fqx1yEIIIUSGExgIFSqcYvbsssBR7IDfK0K1lTpinMDc3B1Pz2OSkMqgJCmVAkxNs2NhUQAA/3oeZCGYiku7cdBHBqUJkZgvv/ySH374AYBOnTpx6dIljSMSGYG3tzc6nQ4vL6/X2xRFYeTIkbi4uGBhYUGNGjW4fPmydkEKzRkbW9Ow3VZ04TNRIo0ILq1wy2MI4cNb8teKFcyaNQsLCwt2795N8eLFWb9+vdYhCyGEEBnGyZNQqNAyzp6pAjyisA7+6Am5xoFirJA9ezPKlj2DjU0prUMVKUSSUinEzk5dSSJ+YC1i9aY0YitbWi4jMlLjwIRIo8aNG0etWrUIDw+nWbNmvHz5UuuQRDp26tQp5s2bR4kSJd7aPnHiRKZMmcKMGTM4deoUTk5O1K1bl9DQUI0iFWlF9c96kiv/JSLuOxObFfzaHOTKyLx0LV+Ws2fPUrp0aV68eEHz5s3p3LmzvGeEEEKIj6AoMHt2HBUrDiAoqB0KMTS0gBlzdVh8CTqdMe7uUyhadC3GxnZahytSkCSlUsirKXzBpjeJHz4KgCFPvuOXATL0X4jEGBsbs2rVKtzc3Lh58ybt2rXDYDBoHZZIh8LCwmjTpg3z588na9asr7crisLUqVMZNmwYzZo1o1ixYixdupSIiAhWrlypYcQirfDwKES15nd4dKYZAE+bBuF7vDw5fdZx7OhRBg8ejE6nY9GiRXh6enL8+HGNIxZCCCHSn6goaNv2Bb171sdgmAJAv7wwcL0OvYeCqWlOSpXywdW1HzqdTuNoRUqTpFQKsbVVR0qFhBzFMKApL/OXJSsvKTW7G6dOyjQ+IRKTI0cO1q1bh5mZGX/99Rdjx47VOiSRDvXq1YvPPvuMOnXqvLX97t27BAQEUK9evdfbzMzMqF69OkePHn3n+aKjowkJCXnrITIua2tzWvdfx4Mri1HCjAktonDe5UeeDK2D99Ch7N+/Hzc3N27fvk2VKlUYOXIkcXFxWocthPhAMsVbCG3cvw9ly17izxUlMbAPS2BqY2iyCDBXyJq1HmXLnns980hkfJKUSiFWVkXJkqUGBkMUV663xXbD/L+n8W1hfbPlxMRoHaEQaVPZsmWZPXs2ACNGjGDbtm1vPS8Lhia/jNSmq1at4uzZs3h7eyd4LiAgAABHR8e3tjs6Or5+LjHe3t7Y2dm9fri6uiZv0CLN0emgbc8OKObXCbnmRpwN3G58mCvjXamSxZLz58/TunVr4uPjGTVqFFWrVuXOnTtah53pZaS+LC3JiO2q9RTvjNimaYG0a9q3Zw8UK7ae65fLEstDchvBglFGlOwPoCNPnpGUKLENU9McWocqUpEkpVKITqejUKFlGBvbExZ2hjvmy4gZMhKAgY/6Mm2Iv7YBCpGGdezYke7du6MoCm3atOH27dsYGRkBECMZ3WQXEREBgImJicaRfJwHDx7w3XffsXz58vcuBf3/w8AVRXnv0PAhQ4YQHBz8+vHgwYNki1mkbbXq5KN4/Vtc3NEGgMC6wZw9XR6TjTNYsXw5K1aswNbWluPHj1O6dGm2bNmiccSZ06u+61VfJpJXRrlHvKLlFG95r6asjPZezUgUBcaNM9Co7lDCwpoTRzRV7WD6CiOcq8VjYpKdEiV2kifPCHQ6I63DFanMWOsAMjJz81wUKrSYS5ea8vDhFLL23Uz0H2Wwv3OGgr9240K7TZQoKXNkhUjM1KlT8fX15fjx43zxxRccPXoUS0tLnj59iomJCXq95NQ/lqIoREREEBgYSJYsWV4n/tKrM2fOEBgYSJkyZV5vi4+P5+DBg8yYMYPr168D6ogpZ2fn1/sEBgYmGD31T2ZmZpiZmaVc4CJNy5vXhM4/LWfij42oXq094e4xnIkcTv6R22n9w04qV65Mq1atOHbsGI0bN2b48OGMGDEi3f//lJ4YGRmRJUsWAgMDAbC0tJQaJMkgo90jXvnnFO8xY8a83v5vU7y7dev20deW92rKyKjv1YwiJATatAnl0JYmRHMAgPbFoe2vYGQUj61tJYoUWY25eS5tAxWakaRUCsuevQk5c/bh0aPpXLvZiTLrVxNbuiGNDX8x/PMVFLn5DcbyX0GIBMzMzFi7di2lS5fm4sWLdOvWjUWLFnHv3j3u37+vdXgZSpYsWXByctI6jI9Wu3ZtLl68+Na2jh07UqhQIQYNGkS+fPlwcnJi9+7deHp6AurIOx8fHyZMmKBFyCKdsLSEEb98zYxpVXC5W51snne4UfMoL6flpEDj3Rw4cID+/fszc+ZMRo8ezalTp1ixYgX29vZah55pvOrDXv2xL5JPRrlHwJsp3qdOnUrw3PumeL/vc0d0dDTR0dGvf/+3uoPyXk05Gem9mlFcuQKNPrtF8L2qBBOAKTCkvTE1Oqi1GHPl6k++fOPR62V0W2Ym6ZBUkC/fRIKDDxEW5ss1ZSx5Bw7Hbvxw+t3ry5wRtek91vnfTyJEJpQzZ07WrFlDrVq1WLlyJZ988gl9+vSRKXzJyMTEJMN8o2hjY0OxYsXe2mZlZUW2bNleb/fy8mLcuHF4eHjg4eHBuHHjsLS0pHXr1lqELNIRnQ76fJeLPXtucHhFV6p8vYjASiGE+lag6PmRzJg+nfLly9OtWzd27NhBmTJlWLduHaVLl9Y69ExBp9Ph7OyMg4MDsbGxWoeTYWSke8SrKd67du1K1ine3t7ejBo1KslxyHs1ZWSk92pGsXYt9PhmHVHRrQgjFkcTGDXemIKl4zAysqVQocXkyNFM6zBFGqBTpCJcAiEhIdjZ2REcHIytrW2ynDMi4jqnT5fBYAgnb+7RWNXaSPZ7Z/hL34QClzdSsJAM3RXiXX777Te8vLwwMjJi7969VK9eXeuQMo2U6A9TU40aNShVqhRTp04F1D8uRo0axdy5cwkKCqJ8+fLMnDkzQTLrfdJ7m4iPd/cujOq3ifZtWqLLEY0uBtzPlSdnn71cuHmLZs2acefOHczNzZk9ezYdOnTQOmQhUkR66g83btzIF1988VbiIj4+Hp1Oh16v5/r16+TPn5+zZ8++Hk0L0LRpU7JkycLSpUsTPW9iI6VcXV3TRZsIkRLi4mDIEIU1k7/jAdMxAKWyw7BZkD0HWFuXokiRNVha5tc6VJHCknqPkKRUIlLqBuvvv4Tr1zsCRpSyWYxVuc6YKLGMKrCC4VdbIyVyhEicoih88803rFy5EgcHB86ePUvOnDm1DitTSE9/cKQWaRMBEBEBvXsF0qBANRwqqvXKsly3plCtHURmL0Lbtm3ZunUrAN26deO3336T2mQiw0lP/WFoaGiCaXj/nOJdtGhRXFxc6NevHwMHDgTUKd4ODg5MmDAhyTWl0lObCJHcAgPhqxbhPDtUlcucA6BpBSN6jorH1BScnb8lf/5pGBlZaBypSA1J7Q8lDZKKnJza4+DQGojnasxwggf+AEDvG31Y7P3u5ciFyOx0Ot3rpZsDAwNp0aLFW99KCiFEarO0hIWLHHhgeoXt0/qgi4KXBcM4daMK0afGsGnTJkaNGoVOp2Pu3LlUq1ZNVm4UQkOvpnj/8/HPKd46ne71FO8NGzZw6dIlOnToIFO8hUiiEyegZvFz+B9y4TLnMAL6dTXhu3HxmJtbUKjQEgoWnC8JKZGAJKVSkU6no0CB2ZibuxMdfZ+ANlcJdPUkGy9wGNGdu3dk0JoQ72JlZcX69evJkiULx48fx8vLS+uQhBCZnE4HAwboqddrGj8P8EF32Zp4K7hmOoWrG4oxeGBXtm7dStasWTl58iSlS5dm3759WocthHiHgQMH4uXlRc+ePSlbtiyPHj1i165d2NjYaB2aEGmWosCMGdC70nSeBpblBiFkMYVJk/U0aRWLpWUBSpc+gZNTe61DFWmUTN9LREoPuw0JOcW5c5VQlDjyW/yIY8UJmCixjCu2kiEXWiErwwrxbtu3b+ezzz5DURQWLlxIp06dtA4pQ5NpCAlJm4jE+PlB6+bh9ClQG6cOJ1BMwCTClAKF5xOqVKF58+b4+vqi1+vx9vbmhx9+kKXgRbon/WFC0iYiMwkLg67fGgha/SV7WU8sUMBZz8hfDDg7Q44cX1Gw4HyMjeX/hcwozUzfCw8P56effqJYsWJYW1tjY2NDiRIl+Pnnn4mIiEjpy6dJtrblyJvXG4A70ZMJHNYVgG6XerP8lydahiZEmtewYcPXq9z07NmT06dPaxyREEKAmxvsOWTFbovjbOjxMxa3dcRaxnD5fnui/fvi47ON9u3bYzAYGDRoEC1atPjXpeOFEEKItOrqVahVOpDg1e7s+DshVauSMVMXGnBxMSF//ukUKbJKElLiX6XoSKmYmBgqVarEpUuXaNiwIYUKFUJRFK5evcqOHTsoXbo0Bw8exMTEJKVC+E9S4xsORTFw8eJnvHixA0vLIrh+bYLz3fPs1tVFt2ULdT41TZHrCpERGAwGvvjiCzZv3oyrqytnzpwhR44cWoeVIck3vglJm4h/M38+LPruBDPb1Cfk62AwAtO4rBT0/IO1a+/Rp08fYmNjKViwIOvXr6dIkSJahyzEfyL9YULSJiIz+OMPmNJxH0bRn3GCKAA6d9LT5hsD5uZuFC36J7a25TWOUmgtTYyUmj17Ng8fPuT8+fNs2LABb29vxo8fz6ZNmzh//jx3795lzpw5H3TOgwcP0rhxY1xcXNDpdGzcuPGt5xVFYeTIkbi4uGBhYUGNGjW4fPlyMr6q5KHT6SlUaCmmpk5ERFwheLkHUUaW1FV287xJR04cM2gdohBpll6v5/fff8fDw4MHDx7QqlUr4uLitA5LCCEA6NIFpu4vT7utd4jqWwGLhxBjHMTFiw2oWe0sBw7sIleuXFy/fp1PPvmENWvWaB2yEEII8a+io6F3b1jVeiSB0bU5QRRWZjBmDHzT1kC2bJ9RtuxZSUiJD5KiSan169czfPhwChYsmOC5QoUKMWzYMNauXftB5wwPD6dkyZLMmDEj0ecnTpzIlClTmDFjBqdOncLJyYm6desSGhr6n15DSjI1daBQoWWAjoCYtQT91Y84nTEt41fiW7MfVy5LuS8h3sXOzo4NGzZgZWXF3r17GTZsmNYhCSHEa+XLwz5feybmOMLOLkPIuV7d/vjJPPRKB/bvn03NmjUJDw/nq6++4vvvv5fkuhBCiDTLzw/qVokkbmZNdjMKPyCXk44Zc6ByZSPy5ZtE8eKbMTHJpnWoIp1J0aTUlStXqFGjxjufr1mzJleuXPmgczZs2JAxY8bQrFmzBM8pisLUqVMZNmwYzZo1o1ixYixdupSIiAhWrlz5oeGnCnv7Ori5DQLgts1MwpdMBqBb9DS2VBzL/ftaRidE2la0aFEWL14MqAlpGW0ghEhLHBxg1x49Ib3G4TV9C/kGWGEWCFGx93n4sAmzZ5fhhx8GAPDLL79Qp04dnjyR2pJCCCHSlp074fMSN8l/Og9zOUAkUK6cjlnzFAoWdMXT8yBubt+j06V4yWqRAaXou+bly5dky/buTGm2bNkIDg5OtuvdvXuXgIAA6tWr93qbmZkZ1atX5+jRo8l2neSWJ8/P2NpWIC7uJTeL/E7IhIkADAwdzqJP5hAYqHGAQqRhX375JT/88AMAHTt2/OBEtxBCpCRjY5g8Gdqt+ozPrp7HrFMxHHcAKPj7T+bLL3eybNkkrK2t8fHxoXTp0hw7dkzrsIUQQgji42HECJjaYBU5gouwGPUP05YtwdtbIU+eRpQtew47u0oaRyrSsxRNShkMBoyMjN59cb2e+Pj4ZLteQEAAAI6Ojm9td3R0fP1cYqKjowkJCXnrkZr0ehMKF/4DE5MchIWd5W79Xbz0GgrAiMCe/FJhDbJAjxDvNm7cOGrVqkV4eDhffPFFsia7hRAiObRsCX+cdKeD0wlOTWhL0R/BJAjCwy/hmmsIGzd2onDhwjx+/Jjq1aszc+ZMUnAtGiGEEOK9nj2DRg3iCP25G3dpxS7iMDWBoUOhRw9jChSYTLFiMl1PfLwUTUopikLt2rUpXbp0oo+6deumyHV1Ol2COP5/2z95e3tjZ2f3+uHq6poicb2PhUUeihffhl5vRVDQHh53uUvQ193QozD6bht+rraHqKhUD0uIdMHY2JhVq1bh6urKjRs3Xi+7LoQQaUmxYnDojCXrGi/lpyOzKNnJmOwHQSEOI6NpzJljyRdfNCA2NpbevXvTrl07IiIitA5bCCFEJnP8ONQt8YRqe8qwiHlcB3LkgGnToVEjN0qVOoSr64D3/o0tRFLplBT8Gm7kyJFJeqOOGDHiP51fp9OxYcMGPv/8cwDu3LmDu7s7Z8+exdPT8/V+TZs2JUuWLCxdujTR80RHRxMdHf3695CQEFxdXTVZyvXFi11cvPgZihJHrpzfkbXnI7LtWUsYVoyqvh/vPeUwNk7VkIRIN06fPk2VKlWIjo5mzJgxUvw8GcjS1glJm4iPZTDAuHGwbfgx/qQ5xnX8ufkdxFuDTmfGnj0N8PbeQnx8PCVKlGD9+vW4u7trHbYQCUh/mJC0iUjPFAVmzIA1XoepavgUb0JRUL9UGTUKPDwaU6jQEkxM7LUOVaQDSe0PUzQpldL+PymlKAouLi7069ePgQMHAhATE4ODgwMTJkygW7duSTqv1jeTJ09WcPXqNwDkcxuH9Vf7sD+zh2dk49dmhxmzthCSlBYicYsWLaJz587odDq2bdtGgwYNtA4pXdO6P0yLpE1Ectm+HbxaPWFO8NdUzH6A6wMhqJz63K1bpRg27CGBgc+ws7Nj+fLlNGrUSNuAhfg/0h8mJG0i0quwMPi2s0K2PyfxnMGsRk0TNGoE331nRMGCE8mVq5+MjhJJltT+MEWn75UqVYoZM2YQFBSUbOcMCwvD19cXX19fQC1u7uvri5+fHzqdDi8vL8aNG8eGDRu4dOkSHTp0wNLSktatWydbDCnN0bEN7u7qKnx3/IYSs7YFQfnLkZ3ndFtfD++eDzSOUIi0q1OnTnTr1g1FUWjdujV37tzROiQhhEhUw4aw/awj35fYzfRn31NiIHj8CvoYPfnz+zJ7diRlyrgTHBxM48aNGTFiRLLW4hRCCCEArlyB6qVDqfZnY44xiNUoGBlBv34wdKgb5codxtW1vySkRIpI0aRU+fLl+fHHH3FxcaFVq1bs3bv3o895+vRpPD09X0/P69+/P56envz0008ADBw4EC8vL3r27EnZsmV59OgRu3btwsbG5qOvnZpcXQeQK5e6TPS1e70w7OrPS6eCuPGAz+fUZ9qI5xpHKETa9dtvv1G+fHmCgoJo1qyZ1GQRQqRZ+fLBoWPGnG8ziS9Zg+1ma8p2NGB7wxR7+3C8vW/z9de5Afj5559p1KgRL1680DhqIYQQGcUff0D7spfpd7MoI9nKOSBLFvjlF+jYscnfq+tV0DpMkYGl+PS9qKgo1qxZw+LFi/Hx8cHV1ZVOnTrRoUMH3NzcUvLS/1laGXarKAauXm1HYOAK9HpLSjmthE96Yxv8kBN8wrUZe2nfy1qz+IRIyx4+fEiZMmUIDAykTZs2LFu2TL7d+Q/SSn+YlkibiJTwqo7HvH5XWRP/BQX11/H7Ws+9znoUfRx79lgzZUoMkZEx5MmTh/Xr179VP1MILUh/mJC0iUgvoqOhf38ImrWS8nTge2KJA/LnhzFjjKhUaRK5cnnJ52fxn6WJ6XsA5ubmtG3bln379nHr1i3atm3LwoULyZcvH/Xr1+fPP/9M6RDSLZ1OT6FCi8iatR4GQwQXAjtjfGgu4eb2lOckzr2bs/HPGK3DFCJNypUrF3/++SdGRkasWLGCGTNmaB2SEEK8k04HffrA7AOFaexwknWG5uReaaDMt3FYP7enTp0wpk2LwdXVmnv37lGpUiWWLFmiddhCCCHSofv3oXaVaDxm9cCGNnj9nZCqWRPmzXOlYcMjuLpK/SiROlI8KfVPefPmZfTo0dy7d49Vq1Zx+vRpWrVqlZohpDt6vSlFi67DxqYscXHPOR/WE6N9S4kytqIeu4hp1Y79e6S+hBCJqV69Or/88gugTvU9dOiQxhEJIcT7VakCPuds+bXiGn5gIhZ39ZT++gVuuxzJn1/PzJlhVKpkRlRUFB07dqR3797ExMgXVEIIIZJmxw5oXNKPoacrslY/h3moX4x07QpTpzahSpXz2NqW1zpMkYmkalIKYP/+/bRv354OHToQHx9Ply5dUjuEdMfY2JrixbdiYZGf6Oj7XDQehm7zMmJ1JnxlWM3NT7/jzOl0u4iiECmqb9++tG7dmri4OL788kseP36sdUhCCPFeLi6w/4COyF4/UJfdPIvLQT7vJ5QaaIWDVS5Gj46mQwd135kzZ1K7dm2ePHmiacxCCCHStvh4GDECfm24i1+DS9DV+BxHDGBlBd7eRgwfPpXixTdiYpJV61BFJpMqSSk/Pz9+/vln8uXLR+3atbl//z6zZs3C39+fOXPmpEYI6Z6pqQMlSuzExMSR8PALXHH6jfilCzGgo2vsTPZU/5m/FyQUQvyDTqdj3rx5lChRgidPntCiRQsZVSCESPNMTdUaUx2W1qKS2VmOU54sp0Ip89lDXB+Xp317GDcOrKz0HD58mDJlynDixAmtwxZCCJEGPX0KnzYwoPz8M22pz2f6YB7FgasrLFyYkx49jpIr13cyXU9oIkWTUitXrqRu3brky5ePuXPn0rJlS27cuIGPjw/t2rXDwsIiJS+f4VhY5KNEiR0YGdkQHOzDLc9NRE2eBsCgiJFsLDeG+fMUUrZ0vRDpj5WVFevXrydLliwcO3YMLy8vrUMSQogkadcO1hzLRfvcPsyiB8ZR4NH2BCX+/IRqVXMwa5YBNzcdjx49olq1aixatEjrkIUQQqQhx45BrZLP6b3nMyJNRtAWiDZA+fKwevVnNGt2EVvbT7QOU2RiKZqU6tChA9bW1mzcuJEHDx7g7e1N/vz5U/KSGZ6NTSmKFduETmfKs2freNj4KuFDRgEwMm44SrdudPgmjrAwjQMVIo1xd3dn5cqV6HQ6Zs+ezeLFi7UOSQghksTTE46eMWNz/Vm0ZwmRmGM/+yRl+ppTPG9pZs1SqFIFYmJi6Ny5Mz179pQRoUIIkckpCkybBv2rnmKFfymmWe5gcqz6XJs2elav/pUKFf6S6XpCcymalKpTpw6///47jRo1Qq9P9fJVGVbWrDUpXHg5oOPx41k87arHMH0Gik5HV+bz1cqmVC8TxqVLWkcqRNrSsGFDRo1Sk7g9evTgzJkzGkckhBBJky0bbN0KrsPaU4mj3CEv5uceULL+JQpHN2DUKOjUSS1WO3v2bGrVqkVAQIDWYQshhNBAaCh83VLhyndzmB1fic+tHrInAszMYPToHMyadZzcub1kup5IE1I0U7Rz504iIyNT8hKZloPDl+TPr07du3dvOAHNzNBt2EC8mQWfsY25N2rQuFwAslq0EG8bNmwYjRs3Jjo6mmbNmvH06VOtQxJCiCQxMoIxY2DkRk9qWJ9hM43RR8aQr/kOSu6pQccOWRk7Vi1ae+TIEcqUKcPx48e1DlsIIUQqunEDqpWN4LM17alv1oMqZnHcDQdHR1i2rBaDBt3A1rac1mEK8VqKJqUUKW6UonLl6o2b21AAbtzoRkD5UIx89mPIlp2ynGFfVEXGd7xGx44QEaFxsEKkEXq9nmXLluHh4YGfnx81atTg/v37WoclhBBJ1rQp7D2blRElNjKQCcRhRDbvA5QZmp16VYszezbkzg2PHz+mevXqLFiwQOuQhRBCpIJt2+DrMjdZcqM8t7Ito3kMhEdDyZI6du4cS4sWezAxyaJ1mEK8JcXn1MmQwJSVN+8YnJw6AwauXWvLPccd6I4dRcmfn7zc4yiVuLXkEJ98Alevah2tEGmDnZ0dmzdvJmfOnFy5coWKFSviK8tXCiHSEQ8POHpcz8suA6nFPh7jjMXhm5T87DYV7OszaxZUrarWmerSpQs9evSQOlNCCJFBKQqMHw/zPtvEprDSDMt5idHP1e3Nmtmwd+9RSpYcKn+bizQpxZNSBQoUwN7e/r0P8d/pdDoKFpyHq+sgAO7dG8m1uJ9RDu+HChWwJ4jd1KXw5TWUKwfLl2scsBBpRKFChTh+/DjFihXD39+fqlWrsnPnTq3DEkKIJLOwgHnzoOuyalS2OMdeamEcEoFHo52U9a3H6NHWdO6s1pmaM2cONWvWxN/fX+uwhRBCJKPwcGj9VRzKkCGMsPucajnC2PoIjI1hxAhPVq3yI1u2ClqHKcQ76ZQUnGOn1+uZOnUqdnZ2792vffv2KRXCfxISEoKdnR3BwcHY2tpqHU6SPX48nxs3egDx2NlVpVi+FZh06AsbNwIwgMlMoT9duuj47Tf1w6wQmV1wcDDNmjVj3759GBkZMX/+fDp27Kh1WGlGeu0PU5K0iUiLrl6Fli3i+fLKSIYzBoDwBsW48mMcew9dY8wY9Q8XZ2dn1q1bR8WKFTWOWGQE0h8mJG0iUtO9e9Cx0VOGXW7FzYJ76X8PoqIhRw4dCxb0p3HjSTI6Smgmqf1hiielAgICcHBwSKlLpIj0fDN58WI3ly+3ID4+BAsLD4oX3Yzl0FkwfToA0+mDF79SvKQRf/4JBQpoHLAQacCrZdSX/z2UcMSIEYwYMUJu4qTv/jClSJuItCo8HHr3hoAl21nON2TjBbE57Li9/BNOB+7mxx/h/n0wMTFhxowZdO3aVeuQRTon/WFC0iYitezbB+O/OMGM2C8YWcifP86p2z/5xIbVq3eQJ08lbQMUmV5S+8MUnb4nf9ClPnv7upQufRQzs9xERt7krG9lXo5qAZMnA9CH6Ww2/ZLr5yMpWxb+/FPjgIVIA0xNTfn9998ZOlRdOGDUqFF07tyZ2NhYjSMTH2r27NmUKFECW1tbbG1tqVixItu3b3/9vKIojBw5EhcXFywsLKhRowaXL1/WMGIhko+VFSxeDF8takhFs3McpzwmT4MpVH83dV58ypw55lSrBrGxsXTr1o1u3boRHR2tddhCCCE+gKLAb1MV1taZw0SnSnzh8iYh1bt3ZQ4ffiIJKZGuyOp7GZCVVVHKlDmBjc0nxMW94PyFujz5xglWrwZTUz6L2cBpm1qYhT6lZUvo1QuiorSOWght6XQ6xo4dy9y5czEyMmLx4sU0btyY0NBQrUMTHyBXrlyMHz+e06dPc/r0aWrVqkXTpk1fJ54mTpzIlClTmDFjBqdOncLJyYm6devKf2eRoXTsCOtPu9GlwEGm8h0ATt9to/KCIkwYm49vv1XrTM2bN4+aNWvy+PFjjSMWQgiRFFFR0K1tBFm+b0f5uj2oHmDgym2wsdGzevVEpk8/jImJ1GgR6UuKTt9LrzLKsNv4+AiuXm3Hs2frAMiTZyS5/Wqi+/xzCAriWdb8VAjazm3yU7q0OmrK3V3bmIVIC7Zt28aXX35JREQEpUqVYuvWrbi4uGgd1n+2du1aTp48SfPmzSlfvvwHHZsR+kN7e3smTZpEp06dcHFxwcvLi0GD1MUhoqOjcXR0ZMKECXTr1i1J58sIbSIyh7Aw6N4dolasZRGdsCWUGNfs3FxajK2nDjB6tLqPk5Mj69atp1Il+WZdfBjpDxOSNhEp5dEj6PPpbYa+aMj8kjeZv00dNVW0aDY2btxP/vzFtQ5RiLekiel7QltGRpYULfonrq4Dgb9X5ssxH8Ph/ZA7N9mDbnHFtiJ1bU9w9iyULg3r12sctBBpwKeffoqPjw8ODg74+vpSsWLFdD3Fa9WqVUyaNIm9e/dqHUqqio+PZ9WqVYSHh1OxYkXu3r1LQEAA9erVe72PmZkZ1atX5+jRoxpGKkTKsLaGZcug/rwWVDI5zXlKYPrgGYXr+NDGvhFz5piQNy8EBDyhRo3qzJ07V+uQhRBCJOLoURhc7C8GOxell+NN5m1VE1Lt2tXl9OmHkpAS6ZokpTI4nU6Pu/sEChSYBxjx5Mlyzkf3JfbINihdGtOQZ+yIqcnAQpsICYHmzcHLC2JitI5cCG2VLVuW48ePU7BgQfz8/KhcuTIHDhzQOqwPpigKBw8eBKBatWoaR5M6Ll68iLW1NWZmZnTv3p0NGzZQpEgRAgICAHB0dHxrf0dHx9fPJSY6OpqQkJC3HkKkFzoddOkCy08WoE2+4yykE3qDQs4uW2i8pywL5uaienWIjY2je/fudOnSRepMCSFEGrJgbjxHmvSjQasmND4XzckzYG6mZ8GCqSxdugtzc3OtQxTio0hSKpNwcelCiRLbMTKyJTj4IGcffk7EzkXw6afooyIZf/0L1teeCcBvv0HVquoSo0JkZnnz5uXIkSNUrlyZ4OBg6tevz6pVq7QO64Ncv36dp0+fYm5uTrly5bQOJ1UULFgQX19fjh8/To8ePWjfvj1Xrlx5/fz/L8KhKMp7F+bw9vbGzs7u9cPV1TXFYhcipZQqBUfPWbDrq4V0ZBGRmGO75BiVeylMG1WFrl3VBNaCBQuoVq0Kjx490jpkIZKdLIYh0pOYGPih4zPclpYhoNlUOsyHwEDImyc7J06epXPn77QOUYhkIUmpTCTBynzXavPy9x+gSxd0isIXe3tz84uB2GcxcPIkeHrC5s1aRy2EtrJly8aePXto0aIFMTExtGrViokTJ6abhRxejZKqUKECZmZmGkeTOkxNTcmfPz9ly5bF29ubkiVL8ttvv+Hk5ASQYFRUYGBggtFT/zRkyBCCg4NfPx48eJCi8QuRUmxtYdUqKDezI1WMT3ADD8xuPqJo7WMMq96E8eP12NjAyZOnKVOmFEeOHNE6ZCGSlSyGIdKLJ0/Aq6oPTW1zM9HyPFPmQ1wcNGlSh3O+tyhRoqTWIQqRbCQplclYWRWldOnj2NiUIy7uOecv1efJz9VgzBgA8m+YhF/VNlQuG83Ll9C0KXz/PcTGahu3EFoyNzdn9erV9OvXD4BBgwbRu3dv4uPjNY7s32W2qXuJURSF6Oho8ubNi5OTE7t37379XExMDD4+Pu8t8GxmZvb6W/VXDyHSK50OevaEecdL0CL3af7kS4zi43Frs5kuD6uyYEEO8uWDJ0+eUaNGdWbPnp1ukvBC/JvGjRvz6aefUqBAAQoUKMDYsWOxtrbm+PHjKIrC1KlTGTZsGM2aNaNYsWIsXbqUiIgIVq5cqXXoIhM5c1phQeMhVGteg282RbB3LxgZ6Zg8eQIbN+7Czs5O6xCFSFaSlMqEzMycKFXqANmzN0NRYrh6rS332sSj/L4UjI2x+msVBy3qMaR7EAC//ALVq4MMDhCZmV6vZ8qUKUydOhWdTsesWbNo1qwZERERWof2Toqi4OPjA2SepNTQoUM5dOgQ9+7d4+LFiwwbNowDBw7Qpk0bdDodXl5ejBs3jg0bNnDp0iU6dOiApaUlrVu31jp0IVJVmTJw0NeWVZ+vpg/TiMGErNN9aDzOihVzylKjBsTFxdOzZ0++/bYTUVFRWocsRLKSxTBEWrRqcSgPZpQhtsF4Oo2E+/fBycGe/ft9GDBg4HvLDQiRXklSKpNSV+Zbg6vrDwDcuzeCa2X3YNixGWxt0R86yDifymybfR87Ozh2TJ3O949p90JkSt999x1r1qzB3NyczZs3U7NmTQIDA7UOK1H37t3j4cOHGBsbU6FCBa3DSRVPnjyhbdu2FCxYkNq1a3PixAl27NhB3bp1ARg4cCBeXl707NmTsmXL8ujRI3bt2oWNjY3GkQuR+rJkgXXrdbj/2oeaRofwwxWL8/co1/Qii3o3pmtX0Oth0aIlVK1aQepMiQxBFsMQaVFcHPzitR/HUEdWhJ9j1GiIjITq1atw7vxlqlatqnWIQqQYSUplYurKfBMpUGAu6sp8yzhvP57Yg1shZ064epWGoypw6fezlC4Nz5/Dp5/CkCFqxylEZtW8eXP27t2Lvb09J0+epGLFity8eVPrsBJ4NXWvXLlyWFlZaRxN6li4cCH37t0jOjqawMBA9uzZ8zohBWqR85EjR+Lv709UVBQ+Pj4UK1ZMw4iF0JZOp666O+VIeRrnPMc2GmIUGU3eFn8xLls9Jk+yxcYGTp8+j6dnMQ4dOqR1yEJ8FFkMQ6Q1z58rzO39HXnK1KLPvEjWrlW3Dxo0iD179r+uiSlERiVJKYGLS1dKlNj2ZmW+qE5EHPwDiheHgAByta7GsZ+207Onuv/48VCrFsgXpiIzq1SpEkePHiVv3rzcuXOHihUrcuzYMa3DeovUkxJCJFX58rD/QjbmfLaFoYwlHj3ZvHfRbZ0LyxcWJV8+ePr0JbVq1WDGjN+kzpRIt2QxDJGWnD/3hMPzixDlMY1v+8Lly2BnY82mTZsYP348xsbGWocoRIqTpJQAwN6+Hp6eR96szPf4C15unwi1a0N4OKbNGzPTcwGrVoGNDRw6pE7n27QJ5HOpyKwKFizIsWPHKFu2LM+fP6dWrVps2LBB67Bek6SUEOJD2NvDxs16sk4cSn39HgJwxPLoNRp+e5+Nkz6lVi2IizPQp48X7dp9JXWmRIYgi2EIrWxb/SfPbrvy161rDBwIL19CyRLFOXPOlyZNmmgdnhCpRpJS4jVr62Jvr8x3qylPlraDdu0gPh66dKHl5Z84fUqhRAl4+hQ+/xw++USSUyLzcnR05MCBAzRq1IioqCiaN2/O9OnTtQ6Lx48fc+vWLXQ6HZUrV9Y6HCFEOqHXww8/wKiDNfnU6Rw+VMMkJIzizbfxR61P6dXTHL0eli9fS8WKxfDz89M6ZCGSTBbDEGlBbGwE6+e2JN6yJT/+EsvChWAwQKdOnTh2/ATu7u5ahyhEqpKklHhLgpX5brbn3sh8KD8OU3cYPZoC3h05fjCGH34ACws4fVpNTpUqBWvWqPkrITITKysrNmzYQPfu3VEUhb59+/L9999jMBg0i+lV3ZdSpUrJ0sFCiA9WuTLsuujMxHp78WYwAA4/bmPCjYLMnJofW1vw9b1NqVIF2bNnp8bRCpE0shiG0FpAwFn2rs/PU/6ka1c4fhzMTE1ZsGABCxcuxMLCQusQhUh1OkWKAiQQEhKCnZ0dwcHBmXYIrqIYuHNnMA8eTALA0bEtBY9UQN+9r5p1qlMH1q0jMMqWX3+FGTMgLEw9tlAhGDYMvv4aZBq0yEwURWHChAkMGTIEgK+++oqlS5dibm6eqnH4+PjwzTff8PDhQ7y8vPj111//87mkP0xI2kRkJgYDTJgAx4ZtYYnSDnuCiLW15tiksnw7+QA3b4KREYwdO5iBA8fJcuWZjPSHCUmbiMQoSjxnT44jOOQntu2E6dMgJhby5cnD2vXr8fT01DpEIZJdUvtDGSklEpXoynxlVhP710qwsoI9e6BqVRxiHuLtDffvw08/gZ0dXLsGbduqyalFiyAmRutXI0Tq0Ol0DB48mOXLl2NiYsKff/5JvXr1ePHiRapcPy4ujp9++olatWrx8OFDChQoQP/+/VPl2kKIjEmvV1fdHbC/EQ1ynOUwlTEJCaNatwPsq/MJ9eubEh8PgwePp3nzykRERGgdshBCpCmRkfc4sLMiT4N+YuIv8MsvakKqcaNGnD57VhJSItOTpJR4rzcr89moK/PZ/0jE/uXg6AgXLkCxYjBuHPZm4YwapSanxo6FbNng9m3o3Bk8PGD2bJB6qCKzaNOmDTt37sTOzo5Dhw5RuXJl7t27l6LXvH//PtWrV2f06NEYDAY6derEmTNnZGlqIUSyqF4d/rqYh3H1fBjEeGIwIdfsk6y/ZcXAns7o9bBhwzFKl87FzZuXtA5XCCHShICA5Rw7VJTHz0/Ruwfs3Al6nR5vb282btpE1qxZtQ5RCM1JUkr8K3VlvqOYmbmpK/NFf8vLA9PV5feCg9W5eu7uMGMGdhYxDB0K9+7BpElq7srPD3r2VHeZOhXkS1SRGdSsWZPDhw/j6urKtWvXqFixImfPnk2Ra61Zs4aSJUty9OhRbG1t+eOPP1i4cCHW1tYpcj0hRObk6AhbthvhNGUQFY1Pc54SWN4OYvwcf1Z0z0/WrHD9ehBly5Zk06Y5WocrhBCaiYsL4bxvG65da8vRkxF0+xZu3wOHbNnZs3cPgwcPRq+XP8WFAElKiSRSV+Y7gY1NWXVlviff8GRrf1i+HPLlgydPoE8fdc7esmVYW8Tz/fdw9y5MmwY5c8Ljx9CvH+TNCxMnQmio1q9KiJRVrFgxjh07RokSJQgICKBatWps37492c4fHh5Oly5d+OqrrwgODqZChQr4+vry9ddfJ9s1hBDin/R69V6+4GQJvvE4iTeDMRj0fD3rFgeL2lOkkDEhIQaaNevB0KFfaLrggxBCaCE4+DjHDhUn8OlKZs6A4cMhPAoqV6zIuQvnqVmzptYhCpGmSFJKJJm6Mp8P2bN/oa7Md70tdypexXD5PMyaBU5OahaqXTsoWRI2bcLCXKFPH3Uq39y5kCcPBAbCoEHqz6NHw8uXGr8wIVJQzpw5OXToEHXr1iU8PJzGjRszYsQI9u3bx7Nnz/7zec+fP0/ZsmVZsGABOp2OoUOHcvDgQfLmzZuM0QshROI8PeH4OTPufOtNNQ5ym3wUO/yCY4/i+LyGNQYDeHtvpGHD3Lx8+UjrcIUQIsUpSjz37o3h7JnK+D3247vusHad+lz//v3Z7+ODi4uLtkEKkQbJ6nuJkFUz3k9dmW8QDx5MBsDSsigFCy7AzqQETJ8O48e/yTSVLw/e3vD3NwKxsbBiBYwbBzdvqrvY2kLfvuDlpdaiEiIjio2NpUuXLixduvSt7c7OzpQsWZISJUq8fhQsWBBTU9NEz6MoCjNmzOD7778nJiYGFxcXli1bRq1atVIkbukPE5I2EeJta9eC17dh/Bj8Pd2ZiwEYX92GEUdCiYuDfPlMWLv2Dzw9m2sdqkhm0h8mJG2SOUVF+XH5UhtCww6zbx9MmQDhMZDVNgtLl/9O48aNtQ5RiFSX1P5QklKJkJtJ0gQGrubmzT7Exj4FdOTM2Ye8ecdgHBavFpT6ZwGpunXVTFTZsgDEx8Off8KYMXDlirqLlZVae2rAALVuhRAZjaIoLFiwgB07dnDhwgVu3bqV6H4mJiYUKVLkrURViRIlMDIyolOnTmzZsgWAxo0bs2jRIrJnz55iMUt/mJC0iRAJ+fnBN9+A1aHtLKQzLvizK4+O1sHwPEjBygpmzOhM+/bz0el0Wocrkon0hwlJm2Q+gYFruHblWyKiQpg1Ff7aqW6vXLESf6xeJYvOiExLklIfQW4mSRcb+5xbtwbw5Ik6+sPMzI0CBWaTLdunEBCgZp3mzVOHSAE0b65uK1QIAIMBNmxQN/n6qrtYWEDXrvDDD2otKiEyqrCwMC5dusSFCxc4f/48Fy5c4MKFC4SEhCS6v7GxMXFxcZiZmTF58mR69eqV4n/cSX+YkLSJEImLj1e/f5o+8jnTDT1pyZ/4WUBTJyN878aj00GPHgX45ZdDmJs7aB2uSAbSHyYkbZJ5xMWFcevWdwQELOLePRg9FO74gw4dw34cxogRIzA2NtY6TCE0I0mpjyA3kw/34sVubtzoRlTUXQAcHFqRP/9vmJrmUOtMjRihFkVXFLVKavv2MHIkuLkB6uatW9UaUydPquc0NYVOnWDwYMidW6MXpqHAQHWgWZ48WkciUpOiKPj5+b2VpLpw4QI3btxAURQKFSrEqlWrKFmyZKrEI/1hQtImQrzf0aPQujVUuL+K2fTAkpd0zavj97vqR86qVc1ZvnwDbm4NNI5UfCzpDxOSNskcQkPPcOVyKyIib7JtK8yYClHxkCNrdv5Ys4ratWtrHaIQmpOk1EeQm8l/Ex8fzt27I3j48FfAgLFxNvLn/xVHx2/U0RyXLsGPP8KmTeoBpqbqfL2hQyFHDkBNTu3erSanDh9WdzM2VmunDxkC+fNr89pSQ1QUHDkCu3apD19fNX+3YgXIYmoiIiICPz8/3N3dMTExSbXrSn+YkLSJEP/u5Uvo0QMOrnrEQjrTgJ3MzAH9giA2DlxdYfHivtSqNQWdzkjrcMV/JP1hQtImGZuiGHjw4Bfu3hlGWHgsv3nD7iPqc3Vq1WH5yuU4Sh0SIYCk94ey+p5INkZGVuTPP5nSpU9gZVWSuLjnXLvWjgsX6hMZeReKFYONG+HYMbXweUyMWncqXz51JFVICDod1KsHhw7BgQNQuzbExcGiRVCwoFqv4vJlNXmV3imK+lp+/RUaNgR7e6hTByZOfDOV0WCAtm1hxw5NQxVpgKWlJYUKFUrVhJQQQvxXWbLAypUwdnFOWlhupzuz6fDUkgN6cLSBBw+gSZNpTJ3qSXS0v9bhCiHEv4qOfsz58/W4c2cg167H0qOtmpDS6/R4e3uzc/dOSUgJ8R/ISKlEyDccH89giOXBg1+4d28kihKNXm9J3ryjyZmzL3q9sZqR2bNHHf505ox6ULZs6u89e6qFpf527Jhac2rbtjfnd3RUa6aXKfPm3/SwwurTp+rLfjUa6vHjt593dlaTcvXqQa1a0L8//PGH2hy7d0PlytrELTIv6Q8TkjYR4sPcvAmtWsHLM7f4nXa4c4zPs8LxIPX59u0tmTRpDTlyfKptoOKDSX+YkLRJxvTs2WauXetEbOxzNqyCOfMhVgHnHC6s3biGSpUqaR2iEGmOTN/7CHIzST4RETe5caMrL18eAMDGpiwFCy7A2vrvejiKAuvXq9P6rl1Tt+XMqY6c6thRnbv3tzNn1OTUX3+pxVT/n7Pz24mqsmW1X8UvOlqtrfEqCXX27NvPm5tD9epvElFFi8I/61bHxkLTprB9u/qts48PlCiRqi9BZHLSHyYkbSLEh4uJUW/1UybF8QOTGM5P/GAdx6ww9fny5WH2bC9KlpyIXi8jQtML6Q8TkjbJWOLjI7l9+3seP55FcDBMGQEHz6vPfdawCb8vX4y9vb22QQqRRklS6iPIzSR5KYpCQMAibt0aQHx8MDqdMa6uP5A793CMjP4eERUXB7//rhY/f/BA3ebhoRaX+vJLtbjS3yIj4fx5OH1afZw5A1euqFPd/l/OnG8SVGXKqA+HFFzwR1HU3NqrJNSBA2qx8n8qWfJNEqpKFTUx9T4REeq+R46oSbbDhzN2bS2Rtkh/mJC0iRD/3Z496rR0xwBfluvaclZ/ia5AdLw64vnXX0vSpMkmzM0z4Qon6ZD0hwlJm2QcYWEXuHKlFRERV7h4EcYNgYBwMNYbM2Xqr/TunfKrIAuRnklS6iPIzSRlREf7c/NmH549WweAhYUHBQvOJ0uW6m92ioqCOXNg7Fh49kzd5ump/t6gwdvDiP4hPFytw3TmzJtE1dWrideecnN7ezRVmTLqzMH/6tkz2Lv3TSLq4cO3n3d0fJOEqlMHnJw+/BovX0KNGmoyLk8eNUGVHqYrivRP+sOEpE2E+DhPn6qr6+7aEs3P/ERtJtLMDB5Eq1/UDBliSY8eK8iR43OtQxX/QvrDhKRN0j9FUXj0aDq3bw8kLi6aPxfCwj8gHsjllJfN29bh6empdZhCpHmZPikVGhrK8OHD2bBhA4GBgXh6evLbb79Rrly5fz1WbiYp6+nTjdy82YuYGLWgkrNzF/Llm4iJSZY3O4WGqhXAJ09WfwaoWhW8vZNcWCksDM6de5OoOn0abtxIPFGVJ0/CRFXWrImfNyZGrXP1Kgl15szb5zQzg2rV3iSiihd/Zy7tgwQEqCOrbt9Wp/kdPKgWRxciJUl/mJC0iRAfT1Fg5kz4/nsoF32IafpvGGDsx/4Y9fmvv4affupFwYK/oNebaRuseCfpDxOSNknfYmICuXatIy9ebOPFC5g4EE7cVp/7omlrli6bg42NjbZBCpFOZPqkVMuWLbl06RKzZ8/GxcWF5cuX8+uvv3LlyhVy5sz53mPlZpLy4uKCuXNnMI8fzwHA1NQZD48Z5MjR7O0dnz2D8eNhxgy1QBNAo0bqtL5SpT74uiEhCRNVN28mvm++fG8SVCVLqgmtXbtg/351ZNY/FS/+JglVtepbddqT1d27amLq8WOoUEEtfm5tnTLXEgKkP0yMtIkQyefCBbUIut+VUCbSj9u6hfzy9yfTMmVg/PgSVKq0FktLD20DFYmS/jAhaZP06/nzHVy71oHY2CecPgYTRsGzaDA1MmPG7Dl8+217ma4nxAfI1EmpyMhIbGxs2LRpE5999tnr7aVKlaJRo0aMGTPmvcfLzST1vHx5iOvXuxAZeR2A7Nm/wMNjBmZm/zc37eFD+PlnWLToTZXz5s1h1Ch12NBHCA5WC5C/mvZ3+rQ6Gul9HBygbt03U/JScyrd5cvqSKwXL9QY/vpLHZ0lREqQ/jAhaRMhkldkJAwYALNnw6ds5XN9G7wIJsKgTnkfM8aCRo0W4OjYWutQxf+R/jAhaZP0x2CI5s6dITx8+CtxcbD8V1j696rfbs6F2bF3HYULF9Y2SCHSoUydlAoNDcXW1pY9e/ZQu3bt19srVqyImZkZBw4ceGv/6Ohool+NwkFtPFdXV7mZpJL4+Cj8/Mbi5zceRYnDyMgWd/dJODt/i06nf3vnGzfUYuirVqlj/3U6dYz/yJFQoECyxRQU9Hai6vx5yJUL6tdXE1ElSrxVez3VnTgBtWurI7ZatFCbw8hIu3hExiUfrhOSNhEiZWzapNaa0r14xjB9K6azh7sGMDVVp/l98007PDxmYGwsU2fSCukPE5I2SV/Cw69y5UorwsPPExAAk/rDWX/1uS+bd2XpsqlYpNQUCCEyuEydlAKoVKkSpqamrFy5EkdHR/744w/atWuHh4cH169ff2vfkSNHMmrUqATnkJtJ6goLu8j1698SGnoSADu7ahQsOA9Ly4IJd750SU1ErVOLpqPXq8v5/PSTOu8uE9izBz77TK1x9e23MG9e8tSuEuKf5MN1QtImQqScR4/U2/n+/QrNmE+Irhd7lDgAmjSB/v3z4um5ClvbTzSOVID0h4mRNkkfFEXB338et271w2CI5OhOmDgJguPBzMiSuQuX0L79l1qHKUS6ltT+UMOxHilr2bJlKIpCzpw5MTMzY9q0abRu3RqjRIaTDBkyhODg4NePBw8eaBCxsLYuTunSR8mffyp6vRXBwQc5daok9++Pw2CIfXvnYsVg7Vp1OFPjxmAwwNKlULAgdO0Kfn7avIhUVKcOrFyp5uMWLIAhQ7SOSAghhPg4OXOq9RK9vXVsNu7KFeUW7YzyArB5M3Tpcpctf1Xi/v1xKEq8xtEKIdKj2NjnXL7cnBs3uhMVFcncYTBsvJqQyu1SisvXL0pCSohUlGGTUu7u7vj4+BAWFsaDBw84efIksbGx5M2bN8G+ZmZm2NravvUQ2tDpjMiV6zvKlbuEvX0DFCWau3eHceZMGUJCTiY8wNNT/ZR64oQ6ty4uDubPBw8P6NMH/P1T/0WkoubN1RFSABMmwMSJ2sYjhBBCfCwjIxg8GI4cAfN8ufk9/jaf0Rt7nbo4Sddu8fz++zB8fWsTFSVfJAohki4oaB+nTpXk2bMNPLwHA1rCqqPqcy2/7M+Nuydwd88csy6ESCsybFLqFSsrK5ydnQkKCmLnzp00bdpU65BEElhY5KF48W0ULrwcY+NshIdf5OzZity61Z/4+PCEB3zyCezYAYcOQc2a6py2GTPUqXwDBkBgYOq/iFTSufObZNSgQeqoKSGEECK9++QTdcXcdu10bGU61spBSuhtCA9XZ+uPG+fDsSPFePp0ndahCiHSOIMhjjt3hnH+fB1iYh5x4A/o0QkuvQRzI1uWLt3Kqj9/wdTUVOtQhch0MmxSaufOnezYsYO7d++ye/duatasScGCBenYsaPWoYkk0ul0ODq24ZNPruLo+A1g4OHDXzl5siiPH8/HYIhOeFCVKrBvH+zdC5UqQVQUTJmiJqeGDFGXrMuAfvhB/VYZoFs3dWajEEIIkd7Z2qqz89euhfBsVblgeEpZXQ1A3da7bwj79rXg+rVvE//SSgiR6UVHP+L8+Vr4+Y0jMlJhem8YNQ/CFHBzrsTVW1do1+5TrcMUItPKsEmp4OBgevXqRaFChWjXrh1VqlRh165dmJiYaB2a+ECmpjkoXHgZJUrswMwsN9HR97lxoyvHj+fFz28ScXEhCQ+qVQsOH4bt26FsWXWZuvHjIU8etUB6cHBqv4wPYzDA5cvqV8RJNG6cWk7LYIDWrdWaHEIIIURG0Lw5XLwIn35qxmllPzmZho3OiCtXoGsX2LhpIadPlCQ09KzWoQoh0pAXL3Zx+rQnwcGHuHtFh1cLWH8ZdOho3uwn7jw4SJ48ObUOU4hMLcMmpb766itu375NdHQ0/v7+zJgxAzs7O63DEh/B3r4+n3xyGXf3XzEzy0VMjD937gzk2DE37twZSkzMk7cP0OmgQQM4eVJdZ7pkSQgNhVGjIG9eNYsTFqbNi/l/YWHqCK8xY6BhQ7C3V4u5ly4NP/8MSVgkU6eDWbPgyy8hNha++AKOH0+F2IVIQ7y9vSlXrhw2NjY4ODjw+eefJ1hxVVEURo4ciYuLCxYWFtSoUYPLly9rFLEQIqmcnWHLFpg7F15a9SFcuYKzzoWQUHW08PRZtzl5ohwPHvyCohi0DlcIoSFFiefu3eFcuNCA6OinbJ4DPXop3IgAC6NsLFq8j7XrRiW6CJYQInXpFCUJf+1mMrKUa9pnMMTw5MlKHjyYSETEVQB0OjOcnTvh6vo9FhaJFCg0GGD9ehgxAq5cUbdlz64WYurZEywtU+8F+PnB0aNqFdejR+H8eYj/v1WELCwgMlL9uUsXNeNkbPyvp46JURck3LULsmaFgwfV/JYQ/0V66w8bNGjA119/Tbly5YiLi2PYsGFcvHiRK1euYGVlBcCECRMYO3YsS5YsoUCBAowZM4aDBw9y/fp1bGxs/vUa6a1NhMiIbt2Cdu3g2LEostGe5/wJqN8/DR8O+XPXoFDxlZiZOWscacYm/WFC0ibai4725+rV1rx8eYCnT2Hq93D074W5XR3r4XNsOXnz5tA2SCEygaT2h5KUSoTcTNIPRTHw/Plf+PmNJyTk1bAgPQ4OX+HqOggbm1IJD4qPh9Wr1Wl8N2+q25yc1JpTXbuCuXnyBhkbqyadXiWgjh6Fhw8T7ufqCpUrq7WwKlVSP1nPnw+9e6sJtUaNYNUq+PsP6/cJD4c6ddSRUs7O6qUTWXhSiH+V3vvDp0+f4uDggI+PD9WqVUNRFFxcXPDy8mLQoEEAREdH4+joyIQJE+jWrdu/njO9t4kQGUVcnLrQx4gRoMQtw4jOxBCLfVb4cTh8UsqWgsWXkz17Y61DzbCkP0xI2kRbQUF7uXKlNbGxgRzcDb9OhJdxYIwJLdv8xu+/d0ev12kdphCZgiSlPoLcTNIfRVEIDj6En994XrzY/np71qz1cXMbTJYs1dHp/u8GFBcHy5er0/nu3VO35coFw4ZBp07wX1ffePECjh17k4A6eRIiIt7ex8gIPD3fJKAqVVKTUonZuBFatVKLtpcvD3/9BTn+/dudFy+genW4dAnc3dUSW05O/+0licwrvfeHt27dwsPDg4sXL1KsWDHu3LmDu7s7Z8+exdPT8/V+TZs2JUuWLCxdujTBOaKjo4mOfrOwQkhICK6urum2TYTIaM6dg2++gStXrmFBIyK5jV4HHTpCmzaQy+Fb3AtPw8jIQutQM5z0fo9ICdIm2lCUeO7dG839+z8THq4wexxsPao+Z2dahGVr19O4cUFtgxQik5Gk1EeQm0n6FhZ2Hj+/CQQGrgbUmhI2NuVxcxtE9uxN0en+r5RaTAwsXqzWc3o1gilPHnX8f7t2758ypyhw48abBNSRI3D1asL9smRRE0+vRkKVK5ekEU+vHT2qzsl78QLy54cdO9RM07/w91cvefculCgBBw6oU/pE5nLwoDpjtGLFJL1t3pKe+0NFUWjatClBQUEcOnQIgKNHj1K5cmUePXqEi4vL6327du3K/fv32blzZ4LzjBw5klGjRiXYnh7bRIiMKipK/U5pypQIjOhGPMsB9XY7dCi42OWhSLlNWFuX0DjSjCU93yNSirRJ6ouODuDq1Ta8fLmPixdhwo/wKEQtZl6q+A/s9RlN1qz/8ctmIcR/ltT+MMMWOheZl7V1SYoUWUn58jdxcemJXm9OaOgJLl9uxqlTRfH3X4zBEPPmAFNT6NZNnco3bZo6nOjePejcGQoXVkdTvar3FBkJhw7BhAnQpIk6YqlQIXVk1YIFbxJSBQpAx47q9LvLl+H5c9i6Vf1kXKPGhyWkQE1kHTkCuXOrhTQqVYLTp//1MGdndRU+Jye4cEGdARguK2ZnOgsXQtu26uzPzKR3795cuHCBP/74I8Fz/z9yUlGUhKMp/zZkyBCCg4NfPx48eJAi8Qoh/jtzc/jlF9i/35KcbsuARegx49Qp6NoZTly6x5kTpXl4ZxLyfawQGUdQ0H7OnPHk2bN9LJ4LXn3VhJSVzoHhPx3k7IUJkpASIo2TpJTIsCws8lGgwEwqVLiHm9tQjIzsiIi4xvXrnTh+PB8PHkwhLi70zQHm5tCnD9y+DZMnq0XQb91S/5ovVgwqVAA7O6hWTV3m56+/1GSTuTlUraoWTN+0CZ4+hevXYdEi+PZbKFIE9Mnwv1qhQuq0wFKlIDBQTW7t2PGvh7m7q0XPs2RRB1y1aKEODhOZx6vF5wpmolHrffr0YfPmzezfv59cuXK93u709xzWgICAt/YPDAzE0dEx0XOZmZlha2v71kMIkTbVqKF+CdO+fUcMnEJHfp6+gH5esHpdPDfvD+TiwcrExARqHarQgKzQmnG8mq53/nwdbt8OoG8X+H2VOkfCxa45B0/fYNSoKlqHKYRIAklKiQzP1NSRfPnGUrGiH/nyTcLU1JmYmEfcvj2A48dzc/fucGJinr45wNISBgxQ57yNG6fOd7t2DU6cUIuWOzlB8+YwZYpaSTw4WJ0fNX68Onoqe/aUezHOzuDjo1YxDw9Xhz4tWfKvhxUvrg7UsrRU81jt2iVc7E9kTIqSuZJSiqLQu3dv1q9fz759+8j7fxX+8+bNi5OTE7t37369LSYmBh8fHypVqpTa4QohUoCdnXprXL++OFntzwItiTfA7NkwfBjcDznGqX15eR6wWetQRSrz8fGhV69eHD9+nN27dxMXF0e9evUI/8cw8okTJzJlyhRmzJjBqVOncHJyom7duoSGhr7nzCI1xcQEcuFCQ+7e/YnNmw106wxX74E5FjSo9we3A9ZSurSd1mEKIZJIakolQuaCZ2wGQzRPnizHz28ikZE3ANDrLXB27kyuXAOwsMjz9gHBwepqfVZW6rS5PHngHdN8Uk1MjDq9cLlaM4MxY9Spgf8S186dammq2Fjo3h1mzdL+pYiUFRgI0x1H8xlbKb2wN6advvmg49Nbf9izZ09WrlzJpk2bKPiPLJydnR0WFmqR4wkTJuDt7c3ixYvx8PBg3LhxHDhwgOvXr2NjY/Ov10hvbSJEZhYQAN9+q7B162ygHxCDS3b4aYyaqM9l0op8FRej15tpHWq6lN77Q1mhNf15+dKHK1da8eSJP5PGw7GT6vZsRmXwnr2RLl1yvf8EQohUIzWlhHgHvd4MZ+fOfPLJFYoWXYuNTVkMhkgePZrBiRP5uXLlG8LCLr45wM4OunZVl/DJmzfZsziKohAXF0pk5G2Cg4/z7Nlf+Psv5P798X/Xv4pLeJCpKfz+uzqNEODHH6FHD3VFwfeoXx+WLVNfwpw5ai13kbFdvw7lOEUFTmAaGax1OClu9uzZBAcHU6NGDZydnV8/Vq9e/XqfgQMH4uXlRc+ePSlbtiyPHj1i165dSUpICSHSFycn+OsvHfPn98Tc/AiQh8fPoE8vdXHbBzF/cGZrbsJfntc6VKGB4GD1vmhvbw/A3bt3CQgIoF69eq/3MTMzo3r16hw9elSTGIVKUQzcvz8OX99a+Pj407mDmpAyRk+BXGM4eeOkJKSESKdkpFQi5BuOzEVRFF6+3I+f33iCgt5M6bG3/ww3t8FkyfJh89EVRSE+PpTY2KfExDwlNjbwHT+rv8fEPEVRot95vuzZv6Bw4ZUYGZknvsOMGdC3rzpPq0kT+OMPdZ7ee8ydq46UArUwbP/+H/QSRTqyYAFU7+KBB7dg716oVeuDjpf+MCFpEyHSp9u3oU2bIE6c6AhsAqB2Feg/BKyNdeTP+iPOZUe9c9EDkVB67g+Ta4XW6OhooqPffI4LCQnB1dU1XbZJWhUT85SrV9vi77+T2bNh898zb7PiQtP225g3vyQmJtrGKIRIKKn3iPesdS9E5qDT6ciatRZZs9YiNPQMfn4Tefp0LS9ebOXFi63Y2lbCzW0QlpZF/k4kPf07sRT4jp/fn2R6F73eEhOTHJiaOmBikgNj46w8fbqGZ882cPFiI4oV24CxcSIjOXr3BhcXaN1avUvXrq0WYX9Pbatu3eDFC3XG34ABatmsjh0/OGSRDty6HE3zwrd5lhVs3O2QCSpCiMzK3R2OHMnKxIkb+PHHXzEYBrH3cBy3OsBP4xUMpqN5vmEtBRvuw9TCSetwRQp7tULr4cOHEzz3ISu0ent7M2rUqBSJUcDLl4e5cuVrLl58xLgx4PdQ3e5i1o7Zf86lSZN3fGkrhEg3ZKRUItLztz4ieURE3OTBg8kEBCxBUf7bUnV6vRWmpjkwMcnxVrLp/39Wf8+BkZFVgnMEBe3j0qWmxMeHYWPzCSVKbMPEJFviFzx8WB0pFRQEBQqoFc3/r8jzPykKDByoLjSo18PatfDFF//ppYo0rFf1S/SpUJyAhpAnzyjy5Pnpg46X/jAhaRMh0j9fX2je/Ch37rQEHmJqpKOfl0KDRmAabELhPPPIWrKDxlGmfem1P+zTpw8bN27k4MGDby2IcefOHdzd3Tl79iyenp6vtzdt2pQsWbKwdOnSBOeSkVIpQ1EMPHgwiZs3h/LHHwaWLFEX6bHDivzFVrN552f8YzCbECINkpFSQnwES0sPChacS548I3n48Df8/edhMMT8nWRyeJ1Ievtn9fdXPxsZvX8KXVJkzVqLkiX3cuFCQ0JDT3LuXHVKltyFmVkid+EqVeDIEWjQAG7cgIoVYds2KF060XPrdDBxojpiatEi+Ppr2L79g2d3iTROf+MaEV+qP1taFtY2GCGESCNKlYLLlysxYMA5Zs36hpj4nUz4BS6dhN5DYzn/vCOuq38nb/Nt6I1lJEZGoSgKffr0YcOGDRw4cOC9K7S+Skq9WqF1woQJiZ7TzMwMMzMZh5ycYmOfc/VqOy5f3sa4cXDpkrrdhYp8M3AL48bZY2SkbYxCiOQjSSkh3sPMzBl39/Hky+etWY0JW9tP8PQ8xPnzdYmIuMy5c5UpWXIPFhbuCXcuXBiOHYOGDeHCBaheHdatg38U7PwnnU6tL/XyJaxfD02bwr59UK5cyr4mkTpiYsD+yRUicqu/W1oW0jYgIYRIQ8zNYebM7LRosY0vvhhHcPAIth4ycK29MT9NigO3/bxc7UDh8puxzF9D63BFMujVq9frFVptbGwICAgA3qzQqtPp8PLyYty4cXh4eLxeodXS0pLWrVtrHH3mEBx8lMsXv2TLtsf89htERIAlRuSwmcziTd9Rs6bUfBMio5HV94RIAq2LnlpZFcHT8wjm5u5ERd3j3LkqhIVdSHxnFxc4eFAd8hQWBp99pq7U9w7GxrBypVqKKixMzWedPZtCL0Skqjt3oHCWc8TZAIoOCwsPrUMSQog0p2ZNPX5+P1K//m7AkduBcXTtZMKBrRCaM5TTN2viv/ZbFINB61DFR5IVWtMuRVHw85vMQZ+qDBv+GG9vNSGVCzfKV7vGqdtekpASIoOSpJQQ6YSFRR48PQ9jZVWCmJgAfH2rExx8LPGd7ezUuXitWkFcHLRvD97eaiGpRJiZwYYN6gip58/Vfzt3hkePUvAFiRR3/Trkc1PHvJsbHN+9gqMQQmRytrawY0ctFi48h7FxdaLjYxk1GUb1M+ZhEFzPvpArC3IT++S21qGKj6AoSqKPDh06vN5Hp9MxcuRI/P39iYqKwsfHh2LFimkXdCYQG/uCSxebsGbND3TqbODAAXU6Tx5dF3p532bP/vzkyKF1lEKIlCJJKSHSETMzJ0qVOoCtbSXi4l5y/nwdXrzYlfjOpqawfDn88IP6+9Ch6kp98fGJ7m5jo+axvvwSDAa1zpSHB/z4I4SEpNALEinq+lUDDm73AZm6J4QQSdGpkzP37u3Bw2MYoOeAbxzt2+qYNwfu5XzI6YMFeLl9otZhCpFhBAcf58jB4owes4Xvv4dnz8AZa3I77OGPo/MYPNgYvfzFKkSGJv+LC5HOmJhkpWTJXWTNWh+DIYKLFxsRGLg28Z31erWa+dSpagGpWbPUrFNkZKK7Z8sGf/6plqWqXFndbexYyJ9fPTQ2NuVel0h+z876Eeum/kezyp54wXshhBBvy5nTmOvXxzB+vC8mJnWIMyj8sRratob1Jw2cMRrE3ellMIS+1DpUIdItRVF48OBX1q+tTOeuj1mzRt1eiOqUbeTPqWu1qVBB2xiFEKlDklJCpENGRlYUL76ZHDm+RFFiuXKlJf7+C999wHffwerV6uipDRugTh11nt47VKgAhw6pxc8LFICnT6FXLyhWTD38HbMARRoTf/kaEW7qz5bWRbQNRggh0hGdDgYNKo6//y7q1NkMeBAUApMnQ/desCn+LL6rnIg8/I4vhYQQ7xQT85QL55swYUJ/uvUwcOcOZMWIPEYz6D3jAJs2W5M1q9ZRCiFSiySlhEin9HpTihT5A2fnLoCB69e/xc9v8rsP+PJL2LULsmSBo0ehShW4f/+du+t08MUX6jK8M2dCjhxw4wY0awZVq8Lx48n+kkQys7h/9U1SSqbvCSHEB8uWTcfu3Y3ZufMS2bNPAey4dQv69QOvZdFsuvolT6Z9ri53KoT4V4GBa1i7Kj+dOm9h5kx1FH5RcuOQ+zbrT/WiVy/1M6gQIvOQpJQQ6ZhOZ0SBAnNxdR0IwJ07P3DnzlCUdw1lql4dDh+GXLng2jWoWBF8fd97DRMT6NkTbt1S60tZWMCRI+qhX36pbhdpz4sX4BZ/kWgn9XdJSgkhxH9Xr54pDx70Y8CAW+j1PQE9hw5Bu17gdWwTp8Y7EnfhhNZhCpFmxcQEcmj/Z3Tr9hVtO4Zw9iyY66AE3SnZ+i6nLubG01PrKIUQWpCklBDpnE6nw919AvnyjQfAz8+bmzd7oiiJFzSnaFG1aFSxYuDvD9Wqwd69/3odW1sYPRpu3oROndRvsdauhSJF1NmBz54l56sSH+v6dSiYyxcAk3hbTEyyaRuQEEKkc+bmMHlydi5cmEnJkueBOsTGwqpVUH/qS4ZPq0DQrL7qaiFCCECtHeXntwyvPrlp2HgbGzeq/4uU0TmR3eQgXotms3y5DhsbrSMVQmhFklJCZBBuboMoUGAuoOPx4zlcvfoNBsM7phPkyqUWjapRA0JDoWFDWLEiSdfJmRMWLoTz59XDYmNh2jRwd4fx499ZQ12ksuvXwSX3TQAsTd01jkYIITKOokXh7NlizJy5CwuLv7AwdSEoCMYvhHK/TOePdjlR7t3ROkwhNBcdHcBk7zJ88kk7Zs+LIjwcipgZU4ExWFbxZ5dvVTp2lOl6QmR2kpQSIgNxcelKkSJ/oNOZEBi4ikuXPic+PiLxnbNkgR07oGVLNbP0zTfqSn1JrGJevDhs2wZ79oCnJ4SEwJAhamH0pUvli2ItKQqc3PYMY7cwACztS2ockRBCZCx6PfTsqePWrUY0+OwupqZjsTQ34vYdaL0igDpN3bky5UdZGURkSoqi8NfmEXiWysnAYed48gScLKGVvi6hls/pumgYPj5QuLDWkQoh0gJJSgmRwTg4tKRYsc3o9Ra8eLGdCxcaEBcXnPjOZmawciX076/+PmiQOhcv/h1T/xJRuzacPg3LloGbGzx8CB06QOnSsHv3x78e8WEUBYYPhwtr/rHynm0JbYMSQogMysUF1q835c8/h2KbxZ+CbhXQ62HfBSg5aCy9y+ck+I6MmhKZx5Urx6lfy5EmTX/m6jUDFmbQNWt2nCIOY9ZuF2dv2MroKCHEWyQpJUQGlC1bA0qW3I2RkR3BwYfw9a1BTExg4jvr9fDLLzBlivr79OlQs6Y6Py+J9Hp1oNX16zBhAtjZqYfXqwcNGsCFC8nwokSS/PwzjB0LhZGV94QQIrU0bQrXr+egXtNjOObYRKkiZsTFwcxT/uQr7s6cPt2J/4AvfIRIb54/f073LvUpWbIiuw88Ra+Hr/Lr+CZ2OEcd/Zl6oDKLF0P27FpHKoRIayQpJUQGZWdXGU9PH0xMHAgL8+XcuapERfm9+4B+/dRqrRYWar2p0qWhR48PqmBubg4DB8Lt2+Dlpa7ct3MnlCoFHTuqo6hEyhkzBkaOBEvCGZ5/ORG51O2SlBJCiJRna6vWWNywqQmKSTAtG9TG1RVeRECPGXMp5ZyNfVu3ah2mEMkqJiaGyZNHkS+vI3MX7CIuDioUgd9cynDuwT1y//wzZ84bU7261pEKIdIqSUoJkYFZW5fE0/MwZma5iYy8wblzlQkPv/buA1q2hGvX1H8NBpgzRy0SNWMGxMUl+brZssGvv8LVq+qpFAWWLAEPDxg6VK0/JZLX+PHqtL1sPOOmay2yRxxEMQU9Zpib59Y6PCGEyDTKl4dTp8woVX0Pbs6b6N7BFGtruPQ0mNqNGvFFtWrcunVL6zCF+CiKorBu3ToKFcjFDz+MJCQ0nrx5YHZNc3JdX8WWIqfYftmNYcPA1FTraIUQaZkkpYTI4CwtPfD0PIylZWGiox/i61uV0NAz7z7AzU0dMXXgAJQoAUFB0KePWs18374Pura7u3qq48ehalWIigJvb3X7jBlqfXXx8SZPVovM5+Ye17NXxuXBSSKKWgNgYVUInU66eiGESE0mJjB4MCxZ0YSHzx4xom85vvhCne6+8dAhihQsyA/9+xMc/I6aj0KkYSdPnqRKlfK0aNGCu/efkjUrDG0DA5WvmHI5gBYrWrJ9hw53WfxXCJEE8peKEJmAuXkuSpU6iI1NWWJjn+HrW5OXL33ef1D16nDmDMyaBfb2cOmSWtW8RQu4d++Drl++PPj4wMaNULCgOiOwTx91We1162Rxoo8xdSr88AOU4DwXrSuS7dkNcHMjYlw3QKbuCSGElvLnh82bs+NQ8ARZzH5h/mw95cpBrMHA5F9/xSNfPubOnSv1pkS6cP/+fVq3bk358uU5evQUZmbQrjWsre3AgT8OcanRak7dsKNlSylkLoRIOklKCZFJmJpmp2TJvWTJUoP4+FAuXGjAs2db3n+QsbFaV+rmTejVS/2Kd906dQ3fESMgIiLJ19fp1EKwFy/C7Nng4KCetkULqFIF9u6VkVMfauZMtRRYdQ5wwqwaNmEBULw4ofvn4xe1BFCncAohhNCOTgft2umYOL0/5y9fYGA3N8aPVwcmP33xgu7du+Pp6cnevXu1DlWIRAUHBzN48GAKFizAH3/8gU4H9evD2p/A/Uh/Bt18yK/HqjBjhrrYjRBCfAhJSgmRiRgb21K8+HayZWuCwRDFpUuf8+TJin8/0N5enW937hzUqKHOw/v5ZyhUCP7884OGOpmYQPfucOsW/PQTWFrC0aNQp456mUaN1NE/ly7JCKr3mTMHeveGFqxhj1F9zKNDoFo1Qnb8xvnHXxMX9xwbm7LkzNlT61CFEEKgrjr2229FyZL7OnHBnVm4UO3HbS3h4sWL1KlTh6ZNm3Ljxg2tQxUCgNjYWGbNmkX+/PmZMGEC0dExeHrC3OkwIGsBhnrfwNbrF46dNuGTT7SOVgiRXukURf7s+38hISHY2dkRHByMra2t1uEIkewMhjiuX+/EkyfLAPDwmEHOnL2SdrCiqKOlBgwAv79X86teXV1yqESJD47l8WMYPRrWrk240J+TkzpjsE4d9V9X1w8+fYa0YAF06QK9mME0+qJHgWbNCJ7ThwvXmhIfH4KtbUVKlNiOsfHHfWUp/WFC0iZCiI8VEQGzZ2+haL7WROtD+X0RbPoL4hUwMjKiY8eODB8+HDc3N61DfS/pDxPKCG2iKApbtmxh4MCBXLumLpDj6qp+qVjTzoilEyfwtEx/fpumI1cujYMVQqRZSe0PJSmViIxwMxHi3yiKgVu3vHj0aDoAefKMJnfuYeiSWgQgIgImTVKXfYuKUqf2deumZpiyZfvgeAwGuHAB9uxRHwcPQmTk2/sULPgmSVWzJmTJ8sGXSfcWL4bOnRRG8yPDGKdu7NGDl2O+5OLlJsTHh2FnV43ixbdgbGzz0deT/jAhaRMhRHI5dy6AC8e/JHfhw9y/D4vGwcG/B0qZmprSvXt3hg4diqOjo7aBvoP0hwml9zY5d+4cAwYMYP/+/QDY2UL7DtC0Hjz/vTyjj21h3KzsNGqkbZxCiLRPklIfIb3fTIRIKkVRuHdvFPfvjwIgV65+uLtP/rDV2u7fVyttr1mj/p41K4wZA127qjWp/qPoaDh27E2S6tQpNXH1il4PZcuqCao6daBiRTA3/8+XSxeWLYNO7eKYS1c6sVjdOHo0QT0rcPFSUwyGCLJkqUXx4psxMrJKlmtKf5iQtIkQIjnFxRlYs3oKjg6D0ZvEc/Uo/D5Fx/Hn6kd0S0tL+vbtyw8//IC9vb3G0b5N+sOE0mubPHr0iGHDhvH777+jKAqmxtCsBbRpA1nuWTBl0gLyt2zNiBFglTwfMYQQGZwkpT5Cer2ZCPFfPXz4G7dueQHg5NSBAgXmo9d/YELpwAHo21etZA5QvLg6pa9GjWSJ8eVLdQW/V0mqv0eTv2ZhAVWrvhlJVaqUmrhKzxRFHT22ZQv89RdcPBHOalrSiK0oej26uXN50cyVS5c+x2CIwt6+AUWLrsfIyCLZYpD+MCFpEyFESrh16xxXzrXANscdFAWubIIFC03wDVNXAbGzs2PAgAF4eXlhY/PxI2GTg/SHCaW3NgkJCWHy5MlMnjyZyL+HqNeuBt/2AOescG1BQ1Y+XMf0uRb/pUqDECITk6TUR0hvNxMhkkNAwFKuXesMxGNu7o6zcyecnNpjZpYz6SeJi4N58+DHHyEoSN325ZfqNL/cuZM13ocP1RX7XiWpAgLefj5bNqhV681Iqnz5kvXyKSYqCvbvV5NQW7bAgwdgx0vqsIcfmER5TqKYm6NbvZpnlXRcvtwCRYkhW7YmFC36J3q9WbLGI/1hQtImQoiUYjDE4nNgOnFRP2JiGYmiwOU5MHObNdfCwgDInj07Q4YMoUePHlhYJN+XEP+F9IcJpZc2OX/+PLNnz2bFihWE/f3eKuWup0t/A0WKgHLRjvEz1tB8QF2+/Tb9f9EnhEh9kpT6COnlZiJEcnv2bBNXr7YjPj7k7y167O3r4+TUiezZGyc94fH8ubq03pw56pw7c3MYPFid5mdpmexxKwpcufImQXXgAPz9+eq1vHnfJKhq1VJXQUor/P1h61Y1CbV7N0RGGCiFLw3YwWf67VRQjmGkxKs7Z80KW7bw1COAK1daoihxZM/enCJFVqLXmyZ7bNIfJiRtIoRIaZGRgezdPhCrLEvR6YFwODdBz/SLWbn78jkALi4uDB8+nE6dOmFqmvz9f1JIf5hQWm6TqKgo/vzzT2bPns3x48dfb8/vYEG7byOpUgf00XBsQRuuWCxh0mRj0mg5MyFEOiBJqY+Qlm8mQqS0uLgwnj5dS0DAIoKDD73ebmycDUfHb3B27oi1dcmknezCBXVKn4+P+rubG/zyCzRvDkktqP4fxMaqNaheJamOHVMHcf1TqVLqzMIcOcDMLOHD3Dzx7YntZ2r6Yd8gKgqcO/dmWt7p02DPc+qym4Zsp6F+Jw6GJ28fVKgQNGgAvXvzxPoUV69+A8Tj4NCKQoV+//Dplkkk/WFC0iZCiNTy5MlZTvl8i7XDOQCMHsGh8bbMDjDn4bNAAPLmzcvIkSNp06YNRkZGqRqf9IcJpcU2uXnzJnPnzmXx4sW8ePECAGNjYz4tlp2GdZ9QsKGCTgdR5x2ZtmozA8Z8Qu3aGgcthEj3JCn1EdLizUQILURE3CQgYAkBAUuIiXn8eru1dRmcnTvi4NAaE5Os7z+JosDatTBggDoXDdRs0LRpat2pVBAWpq7m9ypJ9arsVXIyMfn35JWZmZrA8vUF/0fxlOEMDdlOA3bwCScx4h+V3K2t1QJZDRqojzx5AAgI+J1r1zoCBhwd21Oo0EJ0upT7IyS99YcHDx5k0qRJnDlzBn9/fzZs2MDnn3/++nlFURg1ahTz5s0jKCiI8uXLM3PmTIoWLZrka6S3NhFCpG+KonD58ioe3+qNaRY1oWB5CjYsKsDiZ0E8efYUgMKFCzNw4EBatmyZatP6pD9MKK20SVxcHJs3b2b27Nns2bPn9XY3Zye+KqWn2hePsfH4e2Oknl0Le2Ja7DcGD9Zn+IVjhBCpQ5JSHyGt3EyESCsMhjiCgnYTELCIZ882oShq0VWdzowcOb7AyakTWbPWfv+qfRERMHEiTJigFk7S66FHD/j5Z0jl1YSePIF9++D4cQgPV1f6e/WIinr79/c9PkQWgijMVYpymRocoD47yc7zt3cqXlxNQDVsCJUrqxmsf/D3X8j1610ABWfnLhQoMOfDVkr8D9Jbf7h9+3aOHDlC6dKlad68eYKk1IQJExg7dixLliyhQIECjBkzhoMHD3L9+vUkFw5Ob20ihMgY4uPDObr/Z+IMv6AzjYd4sN5gxPLj1fn9zjmC/q7lmDVrVjp06ED37t0pUKBAisYk/WFCWrfJo0ePmD9/PvPnz+fxY/ULRZ1OR72Knnxe5BEFmz5BZ/33zk9NObihFVfip/DzaHtS+O0ihMhkJCn1EbS+mQiRlsXEPCMwcAX+/gsJD38z5MjMzA0npw44OXXAwiLvu09w/z58/706egrUhNSYMdC1K6TytIOPoSgQE/N/iaoohTj/p+ivXcH45lVMb13B/O4VrO5fwTwoIOFJ7Oygbl01EVW/PuTKlei1QkJOcP/+OJ4/3wyAi0svPDympXhCSr12+u0PdTrdW0kpRVFwcXHBy8uLQYMGARAdHY2joyMTJkygW7duSTpvem4TIUT6FxFxjyPbumGSfRcAxiGgX5yNdfGNWH/Rh3v37r3et3bt2vTo0YMmTZpgYmKS7LFIf5iQFm0SHR3NgQMHmDNnDn/99Rfx8Wodyhw5cvBNreLUKXQOy6pB8PfHrPjbtmzZ2Bedx3B69zZNN4vBCCHSF0lKfQS5wQrx7xRFISzsLP7+iwgMXElc3MvXz2XJUgtn505kz94MI6N3TCHYv1+tN3Xpkvq7szPkz68mZlxd1X//+bODQ9pZ+kVR4PFjtbr6q8fVq+q/z5+/+7hcuaBIEShbVh0NVb68Ou8v0UsovHx5gPv3x/Ly5d6/t+pwdR1Ivnze6FKwJtc/pef+8P+TUnfu3MHd3Z2zZ8/i6en5er+mTZuSJUsWli5dmqTzpuc2EUJkHP7+B7h8uD3GOfwAsLwHhrVFOZ7lc3YHnWP79u28+pjv4uJCly5d6NKlCzlzfsCquv9C+sOEUqNNIiMjOX78OD4+Pvj4+HD8+HGioqJeP1+tahXalLOhqMcBYgtFvt4ecSInG/eNouinnejYUYf8JxNCpKSk9ocpUxlXY3FxcYwcOZIVK1YQEBCAs7MzHTp04Mcff0SfVv6oFSKd0+l02NiUwcamDO7uk3n2bCMBAYsICtrLy5f7ePlyH0ZGdjg6tsLJqRM2NmXfTqTUrKlW+547F4YPV5eg8/d/9wVNTCBnzncnrXLlAkfH5E1cGQzqyK5/Jp1e/RwSkvgxOp261F+RIlC4sPpvkSJqofIkfPpTFIUXL7Zx//5YQkKO/X1KYxwdv8HNbTCWlgWT7/VlMgEB6mg1x/9bSsjR0ZH79++/87jo6Gii/zFfM+Rd/+2FECIVOTvXwKnFHa6cnUag/1Ai8kTB95f55Ollqm+zp1WjAZx2Vfhj7TIeP37MqFGjGDNmDE2aNKFHjx7Url1bPhenE2FhYRw9evR1EurkyZPExsa+tY+DgwMtvmjMF/keYJV7H9GOccQCuhh4tqcou65NpWGnOsz+Pl0NTBdCZAIZMik1YcIE5syZw9KlSylatCinT5+mY8eO2NnZ8d1332kdnhAZjpGRBY6OrXB0bEVU1H0CApYSELCYqKh7PH48h8eP52BlVQwnp044On6DqWkO9UBjY+jVC9q1U1fqe/hQLYb+8OHbP/v7q0vq3bunPt7F2Dhpiav//zQWFwd37iQc+XT1KkRGJn4tIyN1ZNerpNOrBFTBgmBp+cFtqCjxPH26Hj+/cYSF+QJqzS5n5864uv6AhUWeDz6nSNz/jzJTFOW9I8+8vb0ZNWpUSoclhBAfTKczomiZfhSI7cCFQyMJCV9ATI4IYtq/wDV2MsV9TKhdphkXC1dm5+m1HDx4kA0bNrBhwwbc3NzIlSsXVlZWWFlZ0bNnT+rWrav1S8r0YmJiuHPnDteuXXudiDpz5szrKXmv5MyZk+rVq1O1ciXKOkZgHbyZpw6LiLdWiAaMgnXc+asq5+Nn07ZvEeZ6Jn49IYTQWoacvteoUSMcHR1ZuHDh623NmzfH0tKSZcuW/evxMhRZiI+nKAZevjyAv/8inj1bh8GgDivX6YzJlq0JTk4dsbdvgF6fhNx4bKyamHpX0upV4spg+PdzGRuDi4uaoMqWDe7ehRs31AJRiTE1hQIF3iSfXiWgPDzU5fQ+ksEQS2DgSvz8xhMRcQ0Avd6KnDl7kCtXf8zMnD/6Gh8jPfeHyTV9L7GRUq6urumyTYQQGZvBEM3DWyu4e24MiuPd19utb8DLnWW4at+Ty0ZnWbnyd0JDQ986dvHixXTo0OGDrpee7xEp5b+2ydmzZ/nqq6+4e/cuhkQ+z+TJk4fq1atTvVIlSlvEYBywjwijY0Tlf0K87Zs/54wfGHPhry8ILjCTjr1z4OSULC9LCCE+WKaevlelShXmzJnDjRs3KFCgAOfPn+fw4cNMnTpV69CEyDR0Oj1Zs9Yia9ZaxMbOIDBwFQEBiwgNPcWzZ+t59mw9pqbOODq2w9m54/unpZmYgJub+niX2FgICHh34urBAzVxFRcHfn7q458sLN6MdvrntLt8+dREVjKLj48iIGAxDx5MJCrqHgDGxlnImbMvuXL1xcQkW7JfM7PLmzcvTk5O7N69+3VSKiYmBh8fHyZMmPDO48zMzDBLhgSkEEKkNL3eDLcCnXAr0ImQl6e4umcokbb7CCtgwLjAGTyDO1N6hxPl6gyDeiXIkiOSiIhwwsPDqVixotbhZ2r29vbcvn0bAGtrazw8PChTpgwVipfF7amC7bO96G13YTBbSpAr4PrmWKMwiDnrwIWzrcn62Xg6/W6Gubk2r0MIIT5UhkxKDRo0iODgYAoVKoSRkRHx8fGMHTuWVq1aJbq/1AsRImWZmGQhZ87u5MzZnbCwiwQELObJk2XExPjz4MEEHjyYgIVFfoyMrNHrzdHpzNDrzf/vkYRt5uboC5ihL5QNvT4nen31t8+nGKN/Goz+YSD6RwHw9Cnkzq0mn3Ln/td6VOrAUvXxz5/f/G54z3Ovfo7lyZPlPHjwCzEx/n+3jwOurv1xcemBsbF80/wxwsLCuHXr1uvf7969i6+vL/b29ri5ueHl5cW4cePw8PDAw8ODcePGYWlpSevWrTWMWgghkp9tlnKUb7GbmJhn3D02lifPFxBnHwYtA3A3DMbqhBmBe4sRFloHc/fPia/wnpVzM4iDBw8yadIkzpw5g7+//1ujaUG9z48aNYp58+YRFBRE+fLlmTlzJkWLFk3x2EzCrBlZcyS54sFB/wwL43tkid6CUfwCQqpB5D/XRYmHuGv2vLhTjpe6ltiWaoVnS3NqDVdLWwohRHqSIZNSq1evZvny5axcuZKiRYvi6+uLl5cXLi4utG/fPsH+Ui9EiNRjbV2c/PmnkC/feJ4/34K//yJevNhOZOStfz84OeXQo3f8e/TLAwXF792JJPWR/MzMcuHqOhBn584YGX14HSqR0OnTp6lZs+br3/v37w9A+/btWbJkCQMHDiQyMpKePXu+/oNj165d2NjYaBWyEEKkKFPT7BSs/isFlMk8u72SWydGEZ3zNuEVo7GqeAYrzmAUNoEXaz6HUhu0DjdFhYeHU7JkSTp27Ejz5s0TPD9x4kSmTJnCkiVLKFCgAGPGjKFu3bpcv349xe8TwTf3UPu7kcTZvdkW9o/nlafWxPiXQZ+lBe6V25C3VlZJQAkhMoQMWVPK1dWVwYMH06tXr9fbxowZw/Lly7l27VqC/aVeiBDaio4OIDLyOgZDNAZD1N+Pf/787m2KkrT91H1j/z2YVGBhUQA3t4E4OrZFrzfVOpz3knohCUmbCCHSu/CXF/A/MYXnzw8TZX8PxTyebI9HUbz1Tx90nvTcH/5/3UFFUXBxccHLy4tBgwYB6t8Ijo6OTJgwgW7duiXpvP+1TfwvnuT68/LqL3F6dOE2GEXnJ3vuZrgV/xILi/zvXZRDCCHSmkxdUyoiIiLBErdGRkaJFg0EqRcihNbMzJwwM0v5SpyKYkiQ+IJXq7G9eaT870bywVIIIYRmrLKUIH/9JeQHDIY4wsPOY1Epv9Zhaeru3bsEBARQr16919vMzMyoXr06R48efWdSKrnKgDgW9cQm4iJmZs4YG9vL5wQhRKaRIZNSjRs3ZuzYsbi5uVG0aFHOnTvHlClT6NSpk9ahCSE0pNPpMTKywMjIQutQhBBCiDRBrzfGxraM1mFoLiAgAABHR8e3tjs6OnL//v13HpdcZUD0ehOsrYt99HmEECK9eX9V33Rq+vTptGjRgp49e1K4cGG+//57unXrxujRo7UOTQghhBBCCJFG/f8IJUVR3jtqaciQIQQHB79+PHjwIKVDFEKIDCVDjpSysbFh6tSpTJ06VetQhBBCCCGEEGmck5NaRiAgIABnZ+fX2wMDAxOMnvonKQMihBAfJ0OOlBJCCCGEEEKIpMqbNy9OTk7s3r379baYmBh8fHyoVKmShpEJIUTGliFHSgkhhBBCCCHEP4WFhXHr1q3Xv9+9exdfX1/s7e1xc3PDy8uLcePG4eHhgYeHB+PGjcPS0pLWrVtrGLUQQmRskpQSQgghhBBCZHinT5+mZs2ar3/v378/AO3bt2fJkiUMHDiQyMhIevbsSVBQEOXLl2fXrl3Y2NhoFbIQQmR4kpQSQgghhBBCZHg1atRAUZR3Pq/T6Rg5ciQjR45MvaCEECKTk5pSQgghhBBCCCGEECLVyUipRLz6BiUkJETjSIQQQluv+sH3fbOc2cg9QgghVHKPSEjuEUIIoUrqPUKSUokIDQ0FwNXVVeNIhBAibQgNDcXOzk7rMNIEuUcIIcTb5B7xhtwjhBDibf92j9Ap8tVGAgaDgcePH2NjY4NOp/ugY0NCQnB1deXBgwfY2tqmUIRpg7zWjElea8bzMa9TURRCQ0NxcXFBr5cZ3yD3iKSS15oxZZbXmlleJ8g9IrnJPSJp5LVmTJnltWaW1wmpc4+QkVKJ0Ov15MqV63/s3XdclXX7wPHPOYeNgBsQEbfiBHGbiZqmLa1cuU1TM1empmk5cqSmqZmV5s5VallPZlnhHimCmbgXKKA42Jvz/f1xJ/4INNFzOIzr/Xqd1yM397i+57m7L851vuOJzuHs7Fzob9B7pK2Fk7S18Hncdsq331lJjsgdaWvhVFTaWlTaCZIjTEVyRO5IWwunotLWotJOMG+OkK80hBBCCCGEEEIIIUSek6KUEEIIIYQQQgghhMhzUpQyMVtbW6ZMmYKtra2lQzE7aWvhJG0tfIpKOwuCovT/hbS1cCoqbS0q7YSi1db8rij9fyFtLZyKSluLSjshb9oqE50LIYQQQgghhBBCiDwnPaWEEEIIIYQQQgghRJ6TopQQQgghhBBCCCGEyHNSlBJCCCGEEEIIIYQQeU6KUo9h6dKlVKpUCTs7O/z8/Ni3b99D99+zZw9+fn7Y2dlRuXJlvvjiizyK9Mnlpq3btm2jXbt2lClTBmdnZ5o1a8Yvv/ySh9E+mdz+/3rPgQMHsLKywsfHx7wBmkhu25mSksKkSZPw8vLC1taWKlWqsHLlyjyK9snktq3r16+nfv36ODg44O7uzoABA7h9+3YeRfv49u7dy4svvki5cuXQ6XR8//33/3lMQX4u5XeSI3ImOcLHvAGaiOSIB5McIUxBckTOCnKOKCr5ASRHPIzkiCegRK5s2rRJWVtbq+XLl6uQkBA1atQo5ejoqK5evZrj/pcuXVIODg5q1KhRKiQkRC1fvlxZW1urLVu25HHkuZfbto4aNUrNmTNH/fnnn+rcuXNq4sSJytraWh0/fjyPI8+93Lb1nujoaFW5cmXVvn17Vb9+/bwJ9gk8Tjtfeukl1aRJE7Vr1y51+fJldeTIEXXgwIE8jPrx5Lat+/btU3q9Xi1atEhdunRJ7du3T9WuXVt17tw5jyPPvR07dqhJkyaprVu3KkB99913D92/ID+X8jvJEZIj/j/JEfmX5IgHK8jPpfxOckThyxFFJT8oJTlCcoTGHM8lKUrlUuPGjdXQoUOzbKtZs6aaMGFCjvuPHz9e1axZM8u2IUOGqKZNm5otRlPJbVtzUqtWLTVt2jRTh2Zyj9vW7t27q8mTJ6spU6YUiISS23b+/PPPysXFRd2+fTsvwjOp3LZ13rx5qnLlylm2LV68WJUvX95sMZrDoySTgvxcyu8kR0iO+P8kR+RfkiMerCA/l/I7yRGFL0cUlfyglOQIyREaczyXZPheLqSmphIYGEj79u2zbG/fvj0HDx7M8ZhDhw5l2//ZZ5/l2LFjpKWlmS3WJ/U4bf03o9FIXFwcJUuWNEeIJvO4bV21ahUXL15kypQp5g7RJB6nnT/88AMNGzZk7ty5eHh4UL16dcaOHUtSUlJehPzYHqetzZs359q1a+zYsQOlFDdu3GDLli08//zzeRFyniqoz6X8TnKE5Ij/T3JE/iU54uEK6nMpv5McUfhyRFHJDyA5AiRH3GOO55KVKQIrKm7dukVGRgaurq5Ztru6uhIZGZnjMZGRkTnun56ezq1bt3B3dzdbvE/icdr6b/PnzychIYFu3bqZI0STeZy2nj9/ngkTJrBv3z6srArGf0aP085Lly6xf/9+7Ozs+O6777h16xbDhg3jzp07+Xo8+OO0tXnz5qxfv57u3buTnJxMeno6L730Ep9++mlehJynCupzKb+THCE54h7JEZIjCrKC+lzK7yRHFL4cUVTyA0iOAMkR95jjuSQ9pR6DTqfL8rNSKtu2/9o/p+35UW7bes/GjRuZOnUqmzdvpmzZsuYKz6Qeta0ZGRn07NmTadOmUb169bwKz2Ry8/+p0WhEp9Oxfv16GjduzHPPPceCBQtYvXp1vv+WA3LX1pCQEEaOHMkHH3xAYGAgO3fu5PLlywwdOjQvQs1zBfm5lN9JjpAcITlCckRBV5CfS/md5IjClyOKSn4AyRGSIzSmfi4VnPJsPlC6dGkMBkO2CunNmzezVQvvcXNzy3F/KysrSpUqZbZYn9TjtPWezZs3M3DgQL799lueeeYZc4ZpErlta1xcHMeOHSMoKIjhw4cD2kNXKYWVlRW//vorbdq0yZPYc+Nx/j91d3fHw8MDFxeXzG3e3t4opbh27RrVqlUza8yP63HaOnv2bFq0aMG4ceMAqFevHo6OjrRs2ZIZM2bk228jH0dBfS7ld5IjJEeA5AjJEQVfQX0u5XeSIwpfjigq+QEkR4DkiHvM8VySnlK5YGNjg5+fH7t27cqyfdeuXTRv3jzHY5o1a5Zt/19//ZWGDRtibW1ttlif1OO0FbRvNvr378+GDRsKzBja3LbV2dmZkydPEhwcnPkaOnQoNWrUIDg4mCZNmuRV6LnyOP+ftmjRgvDwcOLj4zO3nTt3Dr1eT/ny5c0a75N4nLYmJiai12d9JBoMBuB+9b+wKKjPpfxOcoTkCJAcITmi4Cuoz6X8TnJE4csRRSU/gOQIkBxxj1meS489RXoRdW95yBUrVqiQkBA1evRo5ejoqK5cuaKUUmrChAmqT58+mfvfWzLx7bffViEhIWrFihUFbinXR23rhg0blJWVlfrss89URERE5is6OtpSTXhkuW3rvxWUlTNy2864uDhVvnx51aVLF3Xq1Cm1Z88eVa1aNTVo0CBLNeGR5batq1atUlZWVmrp0qXq4sWLav/+/aphw4aqcePGlmrCI4uLi1NBQUEqKChIAWrBggUqKCgoc9nawvRcyu8kR0iOyInkiPxHcoTkCEuQHFH4ckRRyQ9KSY6QHKExx3NJilKP4bPPPlNeXl7KxsZGNWjQQO3Zsyfzd/369VOtWrXKsv/u3buVr6+vsrGxURUrVlSff/55Hkf8+HLT1latWikg26tfv355H/hjyO3/r/9fQUoouW3n6dOn1TPPPKPs7e1V+fLl1ZgxY1RiYmIeR/14ctvWxYsXq1q1ail7e3vl7u6uevXqpa5du5bHUedeQEDAQ//bK2zPpfxOcoRGcsR9kiPyJ8kR/ZRShe+5lN9JjtAUphxRVPKDUpIj7pEcYdrnkk6pQtafTAghhBBCCCGEEELkezKnlBBCCCGEEEIIIYTIc1KUEkIIIYQQQgghhBB5TopSQgghhBBCCCGEECLPSVFKCCGEEEIIIYQQQuQ5KUoJIYQQQgghhBBCiDwnRSkhhBBCCCGEEEIIkeekKCWEEEIIIYQQQggh8pwUpYQQQgghhBBCCCFEnpOilBBCCCGEEEIIIYTIc1KUEkIIIYQQQgghhBB5TopSQgghhBBCCCGEECLPSVFKCCGEEEIIIYQQQuQ5KUoJIYQQQgghhBBCiDwnRSkhhBBCCCGEEEIIkeekKCWEEEIIIYQQQggh8pwUpYQQQgghhBBCCCFEnpOilBBCCCGEEEIIIYTIc1KUEkIIIYQQQgghhBB5TopSQpjB9OnTqVWrFkaj0STn8/f3x9/fP9fHnTt3DhsbG44fP26SOIQQQuSNB+WRW7duYWtri06n49ixYzke26dPHzp37pwHUQohhLCEf+cInU6X5eXo6Ii3tzfTpk0jISEhy7GSI0R+o1NKKUsHIURhEh4eTvXq1Vm9ejVdunQxyTlDQkIAqFWrVq6PHTBgAJcuXWLPnj0miUUIIYR5PSyPfPLJJ4wZMwaAoUOH8vnnn2c7/uLFi9SsWZNffvmFNm3a5EnMQggh8kZOOUKn09GlSxfeeecdAOLj49mzZw+zZs2ic+fObN26NfN4yREiv5GilBAm9u6777J+/XpCQ0PR6y3fGTEwMJCGDRty4MABmjdvbulwhBBC/IeH5ZG6dety8+ZNvLy8OHfuHBEREdjb22c7x4svvkhKSgq//vprXoUthBAiD+SUI3Q6HW+99RZLlizJsm/fvn1Zv349CQkJ2NnZZW6XHCHyE8t/YhaiEElNTWXFihX07NkzM0lcuXIFnU7HvHnzmDNnDhUrVsTe3h5/f3/OnTtHWloaEyZMoFy5cri4uPDyyy9z8+bNLOf99/C9jz76CL1ez48//phlv/79++Pg4MDJkyczt/n5+eHt7c0XX3xhvoYLIYQwiZzyyD1Hjhzh77//pk+fPrzxxhvExMRk+fb7/+vTpw+//fYbFy9ezIuwhRBC5IGH5YicuLi4oNPpMBgMWbZLjhD5iRSlhDChI0eOcPv2bVq3bp3td5999hkHDhzgs88+46uvvuLMmTO8+OKLDBw4kKioKFauXMncuXP57bffGDRo0EOv8+6779KxY0f69evH1atXAVi1ahVr1qzh008/pW7duln29/f35+eff0Y6RgohRP72sDyyYsUKAF5//XV69OiBg4ND5rZ/8/f3RynFjh07zBqvEEKIvPOwHKGUIj09nfT0dKKjo9m+fTtr1qyhR48eWFtbZ9lXcoTIT6QoJYQJHTp0CIAGDRpk+13x4sX5/vvvef755+nVqxcTJ07k3LlzxMTEsGLFCjp06MCQIUMYNGgQP/74I7GxsQ+8jk6nY+3atTg6OtKtWzeCgoIYPnw4vXv3ZuDAgdn2b9CgAbdu3eLs2bOma6wQQgiTe1AeSUxMZPPmzTRt2pRatWrh5ORE165d2bNnT47fdJctWxYPDw8OHDiQJ3ELIYQwv4d91li6dCnW1tZYW1tTokQJOnfuzFNPPcWXX36ZbV/JESI/kaKUECYUHh6OTqejdOnS2X733HPPZelm6+3tDcDzzz+fZb9720NDQx96rVKlSrF582aOHz9O8+bNqVChwgOH6JUtWxaA69evP3pjhBBC5LkH5ZFvvvmG2NhYXn/99cxtr7/+OkopVq1aleO5ypYtK899IYQoRB72WaNbt24cPXqUo0ePsnfvXhYvXsyxY8fo0KEDKSkp2faXHCHyCylKCWFCSUlJWFtbZxu3DVCyZMksP9vY2Dx0e3Jy8n9er0mTJtSuXZvk5GTefPNNHB0dc9zv3sSGSUlJ/90IIYQQFvOgPLJixQrs7Ozo0KED0dHRREdHU69ePSpWrMjq1avJyMjIdi47Ozt57gshRCHysM8aZcqUoWHDhjRs2JCWLVsyYsQIFi9ezP79+1m9enW2/SVHiPxCilJCmFDp0qVJTU0lISEhT643ZcoUTp48iZ+fHx988AGXLl3Kcb87d+5kxieEECL/yimPnDt3jv3795OcnEyFChUoUaJE5uvKlStcv36dX375Jdu57ty5I899IYQoRHL7WaNevXoAnDhxItvvJEeI/EKKUkKYUM2aNQHyZCWLXbt2MXv2bCZPnsyuXbtwcXGhe/fupKamZtv30qVL6PV6atSoYfa4hBBCPL6c8si9ycyXL19OQEBAlteOHTuwtrZm5cqVWc6Tnp5OWFgYtWrVyrvghRBCmFVuP2sEBwcD96fyuEdyhMhPrCwdgBCFib+/PwCHDx/O/GbCHCIiIujduzetWrViypQp6PV6Nm/ezNNPP8348eNZuHBhlv0PHz6Mj48PJUqUMFtMQgghnty/80h6ejpr167F29v7gSuzvvjii/zwww9ERUVRpkwZAP766y8SExNzXKFJCCFEwfSwzxo3btzg8OHDgDYNSHBwMDNmzKB48eIMGDAgy76SI0R+Ij2lhDAhT09PWrZsyfbt2812jYyMDF577TV0Oh0bNmzInDy9adOmzJo1i0WLFvH9999n7h8fH8/vv/9Or169zBaTEEII0/h3Hvnpp5+IjIxkyJAhDzxm8ODBpKWlsW7dusxt33//PaVLl6Z9+/Zmj1kIIUTeeNhnjS1bttCsWTOaNWtG+/bt+fTTT3nppZc4duwYXl5eWfaVHCHyE51SSlk6CCEKk61bt9K9e3euXr2Kh4eHpcNhxYoVjBo1irCwMOkpJYQQBcCT5pGMjAyqVq1Kz549mTlzphkiFEIIYSmSI0RhI0UpIUxMKUXz5s3x8/NjyZIlFo0lPT2dWrVq0a9fPyZNmmTRWIQQQjyaJ80ja9asYezYsZw/f57ixYubPkAhhBAWIzlCFDYyfE8IE9PpdCxfvpxy5cphNBotGktYWBi9e/fmnXfesWgcQgghHt2T5hGj0cj69evlw4YQQhRCkiNEYWPxnlJLly5l3rx5REREULt2bRYuXEjLli1z3DciIoJ33nmHwMBAzp8/z8iRI7NN6AwQHR3NpEmT2LZtG3fv3qVSpUrMnz+f5557zsytEUIIIYQQQgghhBCPwqI9pTZv3szo0aOZNGkSQUFBtGzZko4dOxIaGprj/ikpKZQpU4ZJkyZRv379HPdJTU2lXbt2XLlyhS1btnD27FmWL1+eL+b2EUIIIYQQQgghhBAai/aUatKkCQ0aNODzzz/P3Obt7U3nzp2ZPXv2Q4/19/fHx8cnW0+pL774gnnz5nHmzBmsra3NEbYQQgghhBBCCCGEeEIW6ymVmppKYGBgtmUo27dvz8GDBx/7vD/88APNmjXjrbfewtXVlTp16jBr1iwyMjKeNGQhhBBCCCGEEEIIYSJWlrrwrVu3yMjIwNXVNct2V1dXIiMjH/u8ly5d4o8//qBXr17s2LGD8+fP89Zbb5Gens4HH3yQ4zEpKSmkpKRk/mw0Grlz5w6lSpVCp9M9dixCCFHQKaWIi4ujXLly6PWyNgZoOSI8PBwnJyfJEUKIIk1yRHaSI4QQQvOoOcJiRal7/v2wVko90QPcaDRStmxZli1bhsFgwM/Pj/DwcObNm/fAotTs2bOZNm3aY19TCCEKu7CwMMqXL2/pMPKF8PBwPD09LR2GEELkG5Ij7pMcIYQQWf1XjrBYUap06dIYDIZsvaJu3ryZrfdUbri7u2NtbY3BYMjc5u3tTWRkJKmpqdjY2GQ7ZuLEiYwZMybz55iYGCpUqEBYWBjOzs6PHYsQQhR0sbGxeHp64uTkZOlQ8o1774XkCCFEUSc5IjvJEUIIoXnUHGGxopSNjQ1+fn7s2rWLl19+OXP7rl276NSp02Oft0WLFmzYsAGj0ZjZRezcuXO4u7vnWJACsLW1xdbWNtt2Z2dnSSZCCEH2Xq1F2b33QnKEEEJoJEfcJzlCCCGy+q8cYdHB32PGjOGrr75i5cqVnD59mrfffpvQ0FCGDh0KaD2Y+vbtm+WY4OBggoODiY+PJyoqiuDgYEJCQjJ//+abb3L79m1GjRrFuXPn+Omnn5g1axZvvfVWnrZNCCGEEEIIIYQQQjyYReeU6t69O7dv32b69OlERERQp04dduzYgZeXFwARERGEhoZmOcbX1zfz34GBgWzYsAEvLy+uXLkCgKenJ7/++itvv/029erVw8PDg1GjRvHuu+/mWbuEEEIIIYQQQgghxMPplFLK0kHkN7Gxsbi4uBATEyPdboUQRZo8D7OT90QIITTyPMxO3hMhhNA86vPQ4qvvCSFEbmRkZJCWlmbpMAoNg8GAlZWVzAcihCjwlFKkp6eTkZFh6VAKDckR5iH3qunJvSpEwSVFKSFEgREfH8+1a9eQDp6m5eDg8NDFIIQQIr9LTU0lIiKCxMRES4dS6EiOMC25V81H7lUhCiYpSgkhslBK5ctvmTIyMrh27RoODg6UKVMmX8ZY0CilSE1NJSoqisuXL1OtWrXMVUuFeSQmnsXGxh0rKxnSIYSpGI1GLl++jMFgoFy5ctjY2EiOMAHJEaYn96p5yL0qhHndunULJycnbG1tzXJ+KUoJUQgZjamkp98lLe0u6enRpKffzXxp2x78c0ZGEpUrz6RChfy1OEBaWhpKKcqUKYO9vb2lwyk07O3tsba25urVq6SmpmJnZ2fpkAqtlJTrnAh+BivrEtStuwM7u/KWDkmIQiE1NRWj0YinpycODg6WDqdQkRxhWnKvmo/cq0KYTmhoKEuWLOHEiROcPHmSiIgIdu/eTatWrcxyPSlKCZFPKaVIS7tJWtqdRy4o3fvZaHyyLuFXrkzHze11bGzKmKg1piPfKJqefJuYN1JTbpAWFUWKyzWOH29C3bo/4eTkY+mwhCg05FlmHvK+mp68p+Yh76sQTy4mJgZ/f38uX76cZfulS5ekKCVEUZKYeIHTp3sTF3fkic5jMLhgbV0CK6v7r5x/Lp75c0jIa8THBxIWNp8qVT4yUYuEEJe/uEmjBSmc/AgSK4YTFNSS2rW/oVSpjpYOTQghhBBCFHFKKd58800uX75MhQoVmDRpEp5lDXBtJc/262O260pRSoh8JjJyHefPDyMjIx7QZSkY/bugZGVV/CFFJxd0OkOur1+x4gf8/Xcnrl9fgqfnWGxsSpu+kUIUQVXe6sDXP6+j+/A3uDQ9megG8Zz863kqVpqOl9d76HTyDa8QQgghhLCMtWvXsnHjRgwGAxs3bsTm760k2H6CqqM4uGIAT72xzizXlaKUEPlEenoc588P48aNrwFwcXkab++vsbPzzNM4SpV6kWLFfIiPD+batYVUrjwjT69f2PTv35/o6Gi+//57s5zf398fHx8fFi5caJbzC9NxcFAcrhzAnw6DmTzhe+xHhhLxguLKlfeJjTmId62vsbYuaekwhRB5SHKEKCjkXhWicDt37hxvvfUWAJOGj0K3ozfxz2hD+OyP2WFt1cls15avZYXIB2Jjj3LsmO8/BSk9FStOx8fnjzwvSIE2Z5OX1wcAXL++mLS0O3kegxCF0c6dO1m5ciVf7VjMRz1f4s7KDtSYA/oUuHP3Z47sr09cXKClwxRCCCGEEEVIamoqr732GgkJCTSs4EWHmp+Q8k9BKu3X5tTseYMGo7uY7fpSlBLCgpQyEho6j6Cg5iQnX8TWtgK+vnupWPH9xxp6ZyqlS3fC0bEuGRlxXLu2yGJxFHZ79uyhcePG2Nra4u7uzoQJE0hPT8/8fVxcHL169cLR0RF3d3c++eQT/P39GT16tOWCFo+tY8eOzJs3D4Av1ixheWd3/nb+nBpv2WN3HdJ11wg80pTr15ahlLJwtEIIS5McIQoKuVeFKNjee+89jh8/joutnndnXyWlpoJYK6yuf0a7WQdwKeds1uvL8D0hLCQlJYIzZ/px9+4uAMqU6Ur16l9ibV3CwpGBTqfHy+sDQkK6cu3aIsqXfxtr6+KWDisLpSDxyRYZfGwODvCkiwBev36d5557jv79+7N27VrOnDnDG2+8gZ2dHVOnTgVgzJgxHDhwgB9++AFXV1c++OADjh8/jo+PzxO3QVjG2LFjKVWqFIMGDWLVqlVEvRDFrMWHsX5jOI7D9nG7RTrnLwwh4sJv+LZcg8Fgb+mQhSiQJEeIgsJS96op7lOQe1WIgu7nn35i/vz5AIydbKR0OUg9V4uGr/xCydLl8yQGKUoJYQG3b+/gzJn+pKVFodfbU7XqYtzdB6IzxV8HJlKmzCs4ONQiMTGE69cXU7HiB5YOKYvERChWzDLXjo8HR8cnO8fSpUvx9PRkyZIl6HQ6atasSXh4OO+++y4ffPABCQkJrFmzhg0bNtC2bVsAVq1aRbly5UzQAmFJAwYMoESJEvTo0YP//e9/nDx5kg2bNxD/aSAeIe9w/fU04g3fsve7wzTu8AeOxapaOmQhChzJEaKgsNS9aor7FOReFaIgC/52D737vARA587wVAMD9gkf0uqNCXn6uVSG7wmRh4zGFC5ceJuTJ58nLS0KR8d6+PkFUq7coHxVkIJ7vaXeB+DatU9IT4+1cESFy+nTp2nWrFmW/99btGhBfHw8165d49KlS6SlpdG4cePM37u4uFCjRg1LhJuvLV26lEqVKmFnZ4efnx/79u174L79+/dHp9Nle9WuXTsPI4bOnTtz8OBBqlSpwtWrV2nVuhVHG6VgM/AUTtPqYH0XdKXDOLrHm1N7VuZpbEIIy5McYToFMUcUJHKvClHwZCSnsa/reN6a5M+dFCOVKsEbL1ahQYtzNHl+Yp5/LpWeUkLkkcTEs4SE9CA+PhgAD4+RVK48B4PBzrKBPUTZsl25enUaiYlnuH79U7y8Jlk6pEwODtq3fJa69pNSSmV74N+bR0in02X5d077CM3mzZsZPXo0S5cupUWLFnz55Zd07NiRkJAQKlSokG3/RYsW8dFHH2X+nJ6eTv369enatWtehg1AgwYNOH78OIMHD2bz5s2MGzeO3c/vZtW3f3Du3SUUa/gh8XXSiVID+f2TbbQa/h1W1tZ5HqcQBZHkCAEFI0dY6l41xX0Kcq8KUdCc/yaIhFUvs7ncVQ6eBxsbmPv+EDr2X4pOZ5k+S9JTSggzU0oREbGKY8caEB8fjJVVKerU+YFq1Rbl64IUgE5nwMtrMgBhYQtIT4+zcET36XRat3NLvEzx5UGtWrU4ePBglj/KDh48iJOTEx4eHlSpUgVra2v+/PPPzN/HxsZy/vz5J794IbJgwQIGDhzIoEGD8Pb2ZuHChXh6evL555/nuL+Liwtubm6Zr2PHjnH37l0GDBiQx5FrnJ2d2bhxI19++SW2trb89NNPNGjYADWoHa7eJ7D/0RUAg+9PHPjcnXP7TlokTiEKGskRAgpGjrDUvWqqjhByrwpRMCTHpLC73RhSTvlxpPNVvlyrbZ875wO6DPjCYgUpkJ5SQphVenoM584N5ebNTQAUL94Gb+912NoWnHH0Zcv24MqV6SQlnSM8fCkVKrxr6ZAKnJiYGIKDg7NsGzx4MAsXLmTEiBEMHz6cs2fPMmXKFMaMGYNer8fJyYl+/foxbtw4SpYsSdmyZZkyZQp6vT7bt41RUVHZzn/vD+rCLDU1lcDAQCZMmJBle/v27Tl48OAjnWPFihU888wzeHl5PXCflJQUUlJSMn+OjTXtUFadTsfgwYNp2rQp3bp14+zZs/j7+/Phhx8yflYohz/oT4b/Roz1bnPzRn2uTJ5Cuw+nmOwDhRDCsiRHmEdhyRH5idyrQhRMQV/+idW6Lti8HUaYA8x4A9LT4eWXOzNy1FRLhwdKZBMTE6MAFRMTY+lQRAEWHX1QHTpUUQUEoAICDOrKldnKaEy3dFiPJSJijQoIQO3fX1qlpcVZJIakpCQVEhKikpKSLHL9x9WvXz8FZHv169dP7d69WzVq1EjZ2NgoNzc39e6776q0tLTMY2NjY1XPnj2Vg4ODcnNzUwsWLFCNGzdWEyZMyNynVatWOZ5/ypQpjxzjw97b/Pw8vH79ugLUgQMHsmyfOXOmql69+n8eHx4ergwGg9q8efND95syZUqO77E53pO4uDjVp0+fzGu0b99e3bhxQ136dbvat8ZOe578hvpteH0Vcea2ya8vREFUUPODUpIjzCk/5gi5Vy13rwpRFN25kap+aDZOhYzT/fOZFNWhrYMCVPny5dXt2+b9W/JRc4ROKRnQ+2+xsbG4uLgQExODs7OzpcMRBYxSGYSGfsTly1OADOzsKlGr1kacnZtYOrTHZjSm8+efNUlOvkjlyvOoUGFsnseQnJzM5cuXMycrLYoSEhLw8PBg/vz5DBw40GTnfdh7m5+fh+Hh4Xh4eHDw4EGaNWuWuX3mzJmsW7eOM2fOPPT42bNnM3/+fMLDw7GxsXngfjl9C+7p6Wm290QpxerVq3nrrbdISkrCzc2NDRs28FRjHw5/5U9G/b8AsAuyJSVlA60mvGLyGIQoSCQ/aCRHZJUfc4TcqxpL3KtCFCVKwc75pyjzXSdSR18ktQxghD8PteXdyb+j1+sJCAjg6aefNmscj5ojZE4pIUwoJeU6J0604/LlyUAGZcu+RsOGQQW6IAWg11tlTnIeFjaPjIxEC0dUNAQFBbFx40YuXrzI8ePH6dWrFwCdOnWycGT5Q+nSpTEYDERGRmbZfvPmTVxdXR96rFKKlStX0qdPn4d+2ACwtbXF2dk5y8ucdDodAwYM4NixY9SqVYvIyEieeeYZZn28mObDj1Pq9mR0STqSfVOwqvEqO7t2JTYq5b9PLIQoVCRHPFxhzREFkdyrQuSdq5cyWNXwQzxv1SN+plaQMiS6U7zkZmbM0eZ2e//9981ekMoNKUoJYSK3bv3I0aP1iY4OQK93pGbN1Xh7r8fKysXSoZmEq2tv7OwqkZZ2k/DwLy0dTpHx8ccfU79+fZ555hkSEhLYt28fpUuXtnRY+YKNjQ1+fn7s2rUry/Zdu3bRvHnzhx67Z88eLly4YNJvaE2tVq1aHD16lNdffx2j0cjUqVN56qmn0NXoTv16R9FdL0laCbB7cwtnJ3lybPVxS4cshMhjkiMerLDniIJG7lUhzCs9HVZMvszVPvWpOeEDbnUwghHcnQbTqM1phg5dQFxcHE899RSTJ0+2dLhZyETnQjyhjIxkLl0ax/XrSwAoVqwBtWptxMGhuoUjMy293poKFd7j3Lk3CAubS7lyQzEY7C0dVqHm6+tLYGCgpcPI18aMGUOfPn1o2LAhzZo1Y9myZYSGhjJ06FAAJk6cyPXr11m7dm2W41asWEGTJk2oU6eOJcJ+ZA4ODqxYsYLWrVszbNgwDh8+jK+vL+PHj2fixPOc+qkXSa47SegZhdPJhuzo9B5tN07D1sFg6dCFEGYmOeK/FfYcUVDIvSqEeQUdV+zss5jnO43lzsx0UgHrRFfqNN+Kg2Nj+vXrx5EjRyhevDjr16/Hyip/lYGkp5QQTyAhIYTjxxtnFqTKl3+HBg0OFrqC1D1ubn2xta1AamokERHLLR2OEHTv3p2FCxcyffp0fHx82Lt3Lzt27MhcKSkiIoLQ0NAsx8TExLB169YC9Q147969CQkJoXPnzqSnpzNr1ix8fZsQX3oslUp+CUlWxNVVOL8+k6DnfTj761VLhyyEEBZXVHKEEKJoSkiA6cMiiRzRmFbTR3OnfToYoXyxN2j67GWsbXzp3LkzGzduxMrKirVr11KhQgVLh52NTHSeg/w8aaPIH5RSREQs58KF0RiNSVhbl6VmzTWUKtXB0qGZ3fXrX3D+/JvY2JSjSZOLGAx5M5GkTF5pPgV1EltLsfR78t133zF8+HDCw8MB6NevHx9OH8blY90wltSKUWW+sSEs9nParxmA3qB72OmEKPAkP5iX5Ijcedh7Iveqecn7K4qSX36BncNW0bfHUGLapQJgm1CWWs234VKiBdHR0bz44ovs378fe3t7tm7dSseOHfM0RpnoXAgzSUu7w6lTXTh3bghGYxIlSjxLo0Z/FYmCFIC7+wBsbcuTmhpOZORKS4cjRJHz8ssvc/r0ad566y10Oh1r1qzBt8FzXI77gBL6fgBEdUulTIuB7K33HNeCb1k4YiGEEEIIYQo3b8LgrneJm9GKLvNe1wpSRvC0e53GHa7iUqIFN27coHXr1uzfvx8XFxd27dqV5wWp3JCilBC5EB29j2PHfLh1axs6nTVVqnxMvXo7sLF5+CouhYleb0uFChMACA2djdEoq34JkdecnZ1ZsmQJBw8epE6dOty+fZv+/Qcy9sPrONgtgWQ74rzBMGcnSaOq8cf4nUi/aCGEEEKIgkkpWL0a3vb9lsE1PSj94V7SSoJ9XGka1N9PlaYrMBjsuHr1Ki1btiQ4OJiyZcuyZ88eWrRoYenwH0qKUkI8AqMxnStXphEc7E9KShj29lVp0OAQnp7voNMVvf+M3NwGYmNTjpSUa0RGrrZ0OEIUWU2bNuX48ePMnj0bOzs7fvvtN1q1Hsu+oyMxJNchoxhcnxZN+ZSO/NpgNEmxaZYOWQghhBBC5ML58/C8fwL6VR0Y9mk34tsmQQZUMPSl4XNhOJfSik5nzpzhqaee4vz583h5ebF//37q169v4ej/W9H7NC1ELqWm3uLEiTZcuTIVMOLq2g8/v+M4OflZOjSLMRjsqFDhXQCuXp2F0Zhq4YiEKLqsra2ZMGECJ0+epG3btiQnJ/PBB3N5azxERfYAIPxlKD14ESH1WhB6ONzCEQshhBBCiP+SlgazZsFgvz1MaVKOCtN+Ia0kOESXpEHdvVRuuSZzft9jx47RsmVLrl27hre3NwcOHKBatWoWbsGjkaKUEA+RmhrFiRNtiInZh8HghLf313h7r8bKysnSoVmcu/sb2Ni4kZISSmTk2v8+QAhhVlWrVmXXrl2sXbuWUqVKcfLk33TvuZn1618gMdqROG9I/uQo6W/V4cic3ZYOVwghhBBCPMCRI9CkQRo2q4fw0RJ/kp6LBSNUyOhBw5fCcS7bMnPf3bt307p1a27dukWjRo3Yu3cvHh4eFow+dyxelFq6dGnmCgl+fn7s27fvgftGRETQs2dPatSogV6vZ/To0Q8996ZNm9DpdHTu3Nm0QYsiITX1JidOtCEh4SQ2Nm40aHAYV9delg4r3zAY7PH0HA9AaOgsjEYZFpQbu3fvRqfTPfQ1f/58DAZDtuWq76lZsyYjR44EwN/fP/M4W1tbPDw8ePHFF9m2bVteNktYmE6no0+fPpw5c4Z+/fqhlOKrr/7HG285cfRgedJKQOjcu7gFtWHXMx9hzJCJpoTIjyRHiIJC7lUhTCsuDkaOhP7NTvNpzSo0+mwZSRXAJt6e+lW3U7ntRvR628z9f/jhBzp06EB8fDxt2rTh999/p3Tp0hZsQe5ZtCi1efNmRo8ezaRJkwgKCqJly5Z07NjxgQ+slJQUypQpw6RJk/5zbOTVq1cZO3YsLVu2fOh+QuQkNfUmwcFtSEj4Gxsbd3x8duPoWMvSYeU75coNwdq6LMnJl7lx42tLh1OgNG/enIiIiMxXt27d6NChQ5ZtgwcPplSpUqxZsybb8QcOHODs2bMMHDgwc9sbb7xBREQEFy5cYOvWrdSqVYsePXowePDgvGyayAdKly7N6tWr+e2336hatSrh4ZGMn3SNj+dVJCYeLg9VuLecyOHKL3D3crSlwxVC/IvkCFFQyL0qhOn88APU8lYY1s1hw6w6pL0VhrKG0skNafRsGCUqvJRl/3Xr1vHKK6+QkpJC586d+emnn3ByKngjeixalFqwYAEDBw5k0KBBeHt7s3DhQjw9Pfn8889z3L9ixYosWrSIvn374uLi8sDzZmRk0KtXL6ZNm0blypXNFb4opFJTbxAc3JrExFPY2JTDx2c3Dg41LB1WvmQwOODpOQ6Aq1dnYjSmWzii/Mff35+RI0cyfvx4SpYsiZubG1OnTsXGxgY3N7fMl729Pba2tlm2OTk50adPH1avXo3619JpK1euxM/PL0uB3sHBATc3Nzw9PWnatClz5szhyy+/ZPny5fz222953XSRD7Rt25a//vqLcePGodfr+WnHFd54w5m9u/XcagVW03dwq21dzmwKtnSoQhRJkiNEQSH3qhDmExEBXbrA0E7hrHBvRJcVE4hpakSfpqdamVnUfvZPrK1LZTlm8eLF9O3bl4yMDPr168e3336LnZ2dhVrwZCxWlEpNTSUwMJD27dtn2d6+fXsOHjz4ROeePn06ZcqUyVJxF+JRpKRE/lOQCvl/Banqlg4rXytXbijW1qVJTr7IzZsb8+7CSkFCgmVeKndDntasWYOjoyNHjhxh7ty5TJ8+nV27dj3SsQMHDuTSpUvs2bMnc1tCQgLffPPNIz3j+vXrR4kSJaTbexFmb2/P3LlzOXToELVq1SIqKpYp04xMm2LLdWeIXHINp88bs//1lZYOVQjTkRwhOaKgsNS9msv7FOReFcLUjEb48kvw9gb99o38OqQKNvMCSSsJjknuNGgahEftieh0usxjlFJMmzaNUaNGATBq1ChWrlyJlZWVpZrxxCwW+a1bt8jIyMDV1TXLdldXVyIjIx/7vAcOHGDFihUEBwc/8jEpKSmkpKRk/hwbG/vY1xcFV0pKJCdOtCYx8Qw2Nh74+ATg4FAwViywJCurYpQv/w6XL0/k6tUZuLr2RKczmP/CiYlQrJj5r5OT+HhwdHzk3evVq8eUKVMAqFatGkuWLOH333+nXbt2/3lsrVq1aNKkCatWrcLf3x+Ab775hoyMDF577bX/PF6v11O9enWuXLnyyPGKwqlx48YcP36cDz/8kI8++ojde1MIOmHFiBHptJmaRoVNA9nnvY9GB5diV8Le0uEK8WQkR0iOKCgsda/m8j4FuVeFMKXTp2HwYPhrfwyfe/SjxkfbuVVT+105+x5UabkSgyHr32NGo5G3336bxYsXA1pnnMmTJ2cpWhVEFp/o/N9voFLqsd/UuLg4evfuzfLly3M1udfs2bNxcXHJfHl6ej7W9UXBlZISkVmQsrUt/08PKSlIPSoPj7ewsipJUtI5bt7cbOlw8p169epl+dnd3Z2bN28+8vEDBw5ky5YtxMXFAVpX91deeYXixYs/0vFP8lwVhYutrS0zZszg6NGj1K9fn5iYdGbMgPffh+D24PTGai55N+b6vkuWDlWIIkNyhCgo5F4V4smlpMDUqVC/Phj2B3CofWU8l20nriZYpdpTu8Y3VG+yMVtBKjU1lf79+2cWpD799FPef//9QvHfhMWKUqVLl8ZgMGTrFXXz5s1svace1cWLF7ly5QovvvgiVlZWWFlZsXbtWn744QesrKy4ePFijsdNnDiRmJiYzFdYWNhjXV8UTCkp4QQH+/9TkPL8pyBV1dJhFShWVk54eo4B4OrVD1Eqw/wXdXDQvuWzxMvBIVehWltbZ/lZp9NhNBof+fgePXqg0+nYvHkzFy5cYP/+/Y88PDkjI4Pz589TqVKlXMUsCjdfX1/+/PNPpk+fjrW1NQcOQP/+sOkmRC/8G93gegRN+8HSYQrx+CRHPNKxkiPyAUvdq7m8T0HuVSGe1L594OMDH01LZo71cD6b1IabE++Q4QAuOl8aPn2OMu5dsx0XERFB69atWbduHQaDga+//prhw4fnfQPMxGLD92xsbPDz82PXrl28/PLLmdt37dpFp06dHuucNWvW5OTJk1m2TZ48mbi4OBYtWvTAHlC2trbY2trm+DtRuKWkXCc4uDVJSeexta2Aj08A9vYyOf7j8PAYQVjYxyQmniEqagtly3Y37wV1ulx3Oy+onJyc6Nq1K6tWreLSpUtUrlw5s+v7f1mzZg13797l1VdfNW+QosCxsbHh/fffp3PnzgwYMIDAwEDmzIHdTeCdSQm02NSJg0+/S9PfZqC3KbjzFIgiSnLEIx0rOSIfkHv1kY6Ve1UUZNHR8O67sGwZ1CeYP2u/QuLEy0R5AEYdFctPxqvalBynQDl06BCvvvoqERERuLi4sGnTJjp06JDnbTAni/6VOWbMGPr06UPDhg1p1qwZy5YtIzQ0lKFDhwJaD6br16+zdu3azGPuzRUVHx9PVFQUwcHB2NjYUKtWLezs7KhTp06Wa9zrDvrv7UJkLUh5/VOQkm9fHpeVlTPly7/NlStTuHLlQ8qU6YpOZ/ERwoXGwIEDadmyJSEhIYwdOzbHrrqJiYlERkaSnp7O9evX2bZtG5988glvvvkmrVu3tkDUoiCoW7cuhw8f5uOPP2bq1KkcOZJC/5Pw5pswkDmc9DpIxT3f4lL98XoxCyHMT3KEKCjkXhVFiVKwdSuMGAE3IzMYr5vL0NcmEzrAiLICW2NZavl9h4tL8xyPX758OW+99RZpaWnUrl2b77//nqpVC9+IHot+YuzevTsLFy5k+vTp+Pj4sHfvXnbs2IGXlxegdVMLDQ3Ncoyvry++vr4EBgayYcMGfH19ee655ywRvijAkpOvERzs//8KUrulIGUCHh4jMRhcSEw8RVSUrI5iSk899RQ1atQgNjaWfv365bjP8uXLcXd3p0qVKrz88suEhISwefNmli5dmsfRioLGysqKCRMmEBQURNOmTUlMhPnzoXcAhA7fR5x/PS6s3m/pMIUQDyA5QhQUcq+KoiIsDDp3hq5dwS7yMoc8mvHavPe4+oZWkCrj3ImGLc/mWJBKTU1l6NChDB48mLS0NF599VUOHTpUKAtSADqlHmM90EIuNjYWFxcXYmJicHZ2tnQ4wsSSk8MIDm5NcvJF7Owq4uOzGzs7L0uHVWhcvjyFq1en4+hYj4YNg0zWWyo5OZnLly9TqVIl7OzsTHJOoXnYeyvPw+wK+3uSkZHB4sWLmTRpIklJKdjZwZt9YcJRHaE1P6bh+re14SZC5BOSH8xLckTuPOw9kXvVvOT9FflBRgYsXQrvvQfx8YpBhlV82PwtLo5JJq046I02VKu5FDf313PsKRgREUGXLl04ePAgOp2OGTNmMHHixAI5ofmj5ggZWyOKlOTkUIKD/f8pSFXCx2ePFKRMrHz5URgMTiQk/MWtW9stHY4QIpcMBgNvv/02f/31N0891ZjkZPhkGbTLUCTEvUNg1S6kRMVaOkwhhBBCiHzl5Elo0QJGjgT7+Jvsdn2J8cMGcma6VpAqZl0LvyYncC83MMci06FDh/Dz8+PgwYMUL16cn376iffee69AFqRyQ4pSoshITr76T0HqEnZ2lf/pIVXB0mEVOtbWJfHwGAnA1avTkc6YQhRMVatWZc+eQyxe/An29lb89Rc8uws2VdvGtRo+RO46+d8nEUIIIYQo5JKStJ5RDRrAkSPQzf5HTnt74zDzf1x/RdunfLmRNGh2HEfHmjmeY9myZbRq1YqIiAhq167N0aNH6dixYx62wnKkKCWKhKSkK/8UpC5jZ1dFClJm5un5Nnq9I/Hxwdy+/T9LhyOEeEx6vZ4RI0Zz6tQ5Wrb0JiUFPv4FOpe8zOX+foRMXGfpEIUQQgghLOaPP6BePZg9G2zT4/m14ht80uYlTi24Q0IVsNaVoG7dn6hafRF6vW2241NSUhgyZAhDhgwhLS2NLl26cPjw4UI7f1ROpCglCr37Bakr2NtX/acg5WnpsAo1a+tSeHgMB+DKlWnSW0qY1dKlSzPnj/Dz82Pfvn0P3T8lJYVJkybh5eWFra0tVapUYeXKlXkUbcFUqVIl9uw5xeLF7+HoqOPvi+AflcaGzX052mQwKinZ0iEKIUSOJEcIIczh9m0YMADatoULF+Cl0ge5XrkO5fp9xbmxYLSDEi5taNj0FKVK5bwwW0REBK1bt2bZsmXodDpmzZrFN998Q7FixfK4NZYlRSlRqCUlXSY4uBUpKVext6/2T0GqvKXDKhI8Pd9Br3cgPj6QO3d+tnQ4opDavHkzo0ePZtKkSQQFBdGyZUs6duyYbeXW/69bt278/vvvrFixgrNnz7Jx40Zq1sy5K7W4T6fTMWLETE6cOE6L5iVJTYOZl+H1S8v5ya0BsX9dsXSIQgiRheQIIYSpKQWbN4O3N6xeDTak8nODSaxxf4qQmVeJ8gcdBipXnks9n13Y2rrneJ5780cdOnQoc/6ogjqh+ZOSopQotJKSLhEc7E9KSij29tXx8QnA1tbD0mEVGTY2ZfDwGAbAlSsyt5QwjwULFjBw4EAGDRqEt7c3CxcuxNPTk88//zzH/Xfu3MmePXvYsWMHzzzzDBUrVqRx48Y0b559OV6RsypVfNizN5K5c5+jWDH4+xa8nHCamU1rcv7THywdnhBCZJIcIYQwpRs3oEsX6NEDoqLgxSoh3KrRhJp1ZhH8iSLFDexsKuHb4BAVKox74Crk/3/+qDp16hSp+aNyIkUpUSglJV38fwWpGlKQshBPz7Ho9fbExR3h7t1fLR2OKGRSU1MJDAykffv2Wba3b9+egwcP5njMDz/8QMOGDZk7dy4eHh5Ur16dsWPHkpSUlBchFxoGgzXjxv3EoUPLadHCQHoGzE1K4dVxnfim9SBtPWQhhLAgyRFCCFNRCjZtgtq1Yds2sDYY2fHsIjan+HLhzWCuDAAM4Orah4aNT+Ds3CjH8+Q0f9ShQ4eK1PxRObGydABCmFpi4gVOnGhNSso1HBxqUr/+Hw/sNinMy8bGlXLlhnLt2idcuTKNEiXaF8kuqcI8bt26RUZGBq6urlm2u7q6EhkZmeMxly5dYv/+/djZ2fHdd99x69Ythg0bxp07dx44Z0hKSgopKSmZP8fGxpquEQVcnTqD2LmzOQvmt2X+gkhOxkLPPSv4o+TvzAvah1NlGS4thLAMyRFCCFO4cQOGDdOKUQDtvcPY4jSA5KTfCVwK6U5g0DlSrcYXuLn1fuB5wsPDM4tQ9+aPevfdd+WzEdJTShQyiYkX/ukhdQ0HB2/q1w+QgpSFeXqOQ6+3Izb2EHfv/m7pcEQh9O9krpR6YII3Go3odDrWr19P48aNee6551iwYAGrV69+4Dfhs2fPxsXFJfPl6SkLJfx/xYrV4r1JZ9m+/TlatYIMBV/GXqFhtUr8ukAmBxZCWJbkCCHE41AKNm6EWrW0gpSVQbHt5XX8FFWb8Ba/c2qaVpBycmqEX6PghxakDh48mGX+qB07djBhwgQpSP1DilKi0EhMPE9wcCtSU6/j4FDrnyF7bpYOq8iztXXH3X0wAFevykp8wnRKly6NwWDI9o33zZs3s30zfo+7uzseHh64uLhkbvP29kYpxbVr13I8ZuLEicTExGS+wsLCTNeIQsLKyplWrf7H2rVzmDpVR4kScM6YzgvvDGTaS/3kv3shRJ6THCGEeFyRkfDqq9CzJ9y5A63rRBHl34V2QX05PjeOiJe0/Tw9x+Hrux8HhwcPv1u2bBn+/v5ERkZmzh/VoUOHPGpJwSBFKVEoJCae/acgFY6DQ218fP7AxibnPzhE3qtQYTw6nQ0xMfuJjt5j6XDyjE6ne+irY8eOWFtb8/XXX+d4/JAhQ6hXrx4AU6dOzTzOysqK0qVL8/TTT7Nw4cIswwaKEhsbG/z8/Ni1a1eW7bt27XrgpLQtWrQgPDyc+Pj4zG3nzp1Dr9dTvnzOQ81sbW1xdnbO8hLZ6XQ6KlQYz8iRu1izugRPPw1pwNQf1/JC5brExsRYOkQh8hXJEeYlOcJ05F4VRYVSsGGDNnfUd9+BlRVsfO0HfrtZmwSHbRz/AhIrgbW1K/Xq/UqVKnPR621yPFdKSgqDBw+W+aMegRSlRIGnFaRak5oagaNjHSlI5UO2th64u78BaL2lioqIiIjM18KFC3F2ds6ybdOmTTz//POsWrUq27FJSUls2rSJgQMHZm6rXbs2ERERhIaGEhAQQNeuXZk9ezbNmzcnLi4uL5uWb4wZM4avvvqKlStXcvr0ad5++21CQ0MZOnQooH2D3bdv38z9e/bsSalSpRgwYAAhISHs3buXcePG8frrr2Nvb2+pZhQqJUq0pV37YObOacjw4WAwwI4rp6jv7slfx45ZOjwh8g3JEeYnOcI05F4VRUFkJLzyCvTqpfWOeqpeLJEvDOTVHzsRMiyK82PAaAslSjxLo0YnKFmy3QPPFR4eTuvWrVm+fDk6nY6PPvqIb775hmLFiuVhiwoOmehcFGgJCWc4caI1qamRODrWpX7937GxKWPpsEQOKlR4l4iIZURH7yY6ei/Fiz9t6ZDMzs3t/vBRFxcXdDpdlm0AAwcOpFOnTly5coWKFStmbt+yZQvJycn07n1/fLqVlVXm8eXKlaNu3bq0a9eO+vXrM2fOHGbMmGHeBuVD3bt35/bt20yfPj1zWd0dO3bg5eUFkPlH7z3FihVj165djBgxgoYNG1KqVCm6detWJN87c7Kzq0CjxgdwKT4Ob+/FTJsGV27G0bhxYz5buIiBI0dYOkQhLE5yhPlJjjANuVdFYXZv7qgRI7RilLU1LO+9h75/9CPmwlWOfQUprqDTWVO58mzKl38bne7BfXsOHjzIq6++SmRkJMWLF2fTpk08++yzediigkeKUqLASkg4TXBwa9LSbuDoWI/69X8zX0EqNVV7QslkdI/Nzs4TN7fXiYj4kitXpuPj89sTnU8phdGYaKLockevdzDZxITPPfccbm5urF69mqlTp2ZuX7lyJZ07d6ZUqVIPPb5mzZp07NiRbdu2Fdk/4oYNG8awYcNy/N3q1auzbatZs2a24RzC9PR6G2rWXESJEi1xK9uXuR8nceSIYtCokezbvZulX6/DwcHB0mGKQkpyhEZyRP7PEZa6V015n4Lcq6JgioyEoUNh+3bt5yb1k/mx3iRKr13A1Z5wpT+gBzu7KtSqtQln54YPPd+XX37JiBEjSEtLo06dOnz//fdUqVLF7O0o6KQoJQqkhISQfwpSN3F0rP9PQaq0eS52+DC0bw/u7jBhAvTurRWoRK55eU0kMnIF0dG/ExNzABeXFo99LqMxkX37LNMFtmXLeAwGR5Ocy2Aw0LdvX1avXs2UKVPQ6XRcvnyZPXv2sHPnzkc6R82aNfn1119NEo8Qpubq2oUOz/ngYPU8W/53jlWrYM132wisE8zWnT9TvXp1S4coCiHJEfdJjsjfLHWvmvI+BblXRcFyb+6oESPg7l3to91nAwMZtKcvqT+HcGIeRPtq+5Yt24vq1ZdiZfXg+eJSUlIYMWIEy5cvB6Br166sXLlShus9IplTShQ4CQmnMgtSxYr54OPzu/kKUrGx2rILcXFw7hy8/jpUrQpLlsADlgYWD2Zn54WbW38Arlz50LLB5CMDBw7k6tWr/PHHH4D2rWL58uV55plnHun4hy1vLUR+4OBQlXYvBtPtxd58/DGUKAF/X76EX/36fPvtt5YOT4h8TXKEKCjkXhUFQUQEdO6s9TO4exca+aYT+saHvPFVU26XCOHoSh3RvqDXO1Kz5hpq1fr6oQWp8PBw/P39s8wftXnzZilI5YL0lBIFyv2CVBTFivlSv/5vWFuXNN8Fhw+Hy5ehYkWtb+fChRAaqpXVP/wQxoyBN9+EQrjSirlUqPAeERGruHv3F2Jjj+Ds3OSxzqPXO9CyZfx/72gGer1phxxVq1aNli1bsmrVKlq3bs2aNWsYMGAAev2jfW9w+vRpKlWqZNKYhDA1g8Ge9p3WYf+bP8uWDObDOUb++iuZbt26MWL4cD6ePx8bm5xXsBEityRH3Cc5In+z1L1q6vsU5F4V+ZtSsH49jBx5v3fUomFnGXKwL2r5n5x/E66/AqAoVsyXWrU24eDw8N7cMn+UaUhPKVFgKKU4c2bAPwWpBuYvSG3cCOvWgV4PX38N776rFaiWLgUvL7h5UxvO5+UFH3wAt2+bL5ZCxN6+Em5u2ko3V65Mf+zz6HQ6DAZHi7zM8S3ewIED2bZtG1u3buXatWsMGDDgkY47c+YMO3fu5NVXXzV5TEKYQ8tnBlKj9glmjy7Ja69p2z5dsoSWLVpw9epVywYnCg3JERrJEfmfpe5Vc/VIkntV5Ef3ekf16aMVpPx8jVx551PeXOZLUuSfHP9S/09BCsqXf5sGDQ79Z0Hqyy+/xN/fn8jISOrWrcuxY8ekIPWYpCglCoyYmH3ExR1Fr7ejXr0d5i1IXb2q9YACeP99aPHP3Ed2dtr28+dhzRqoWROio7VeU15e8M47EB5uvrgKiQoV3gMM3Lmzg9hYWSIetLHn1tbWDBkyhLZt22ZZueae9PR0IiMjCQ8P5+TJk3z66ae0atUKHx8fxo0bl/dBC/GY6tavQ712V+lUqwmzZoGTE/x57BgNfH3YsWOHpcMTIt+RHCEKCrlXRX6ilNa3oHZt+OGHf3pHjQ3jzxLtcf9oJBH+SQR+pSehkhFr69LUrfsTVasuQK+3feA5ExMTef311xk6dChpaWl069aNQ4cOyYTmT0CKUqLACAv7GAA3t/7Y2Lia70IZGdog45gYaNYMJk/Ovo+1NfTtC3//DVu2gK8vJCTAggVQqZI21O/SJfPFWMA5OFTF1bUXAFevPn5vqcLEwcGBHj16cPfuXV5//fUc9zl16hTu7u5UqFABf39/vvnmGyZOnMi+fftk3LoocMqXL0aHEYcoFvY2y5ZAjRpw5240zz//PO+99x7p6emWDlGIfENyhCgo5F4V+UVEBHTq9P96RzVQXJq2jpHL62I88junpxg4Ox6MNkaKF29Dw4YnKFXquYeeMyQkhMaNG7Nq1Sr0ej0fffQRmzZtwtHRdIsGFEU6pZSydBD5TWxsLC4uLsTExOAscwXlCwkJZzh61BvQ0bjxmf/sTvlEZszQekc5OUFwMFSu/N/HKAW//AIzZ8L+/do2gwFeew0mToRatcwXbwGVmHiOP//0Boz4+R3Hycn3ofsnJydz+fJlKlWqhJ2dXd4EWUQ87L2V52F28p6YVmoqzB+2lSYtuvPpDxl8/7223d/fn40bN+Lm5mbR+ET+J/nBvCRH5M7D3hO5V81L3l8B93tHjRypDWixtoa546IYGTIU/ffbiPWGkA9tSS6VAhioVOlDKlQYj05neOh516xZw7Bhw0hMTMTNzY2NGzfi7++fF00qsB41R0hPKVEgXLv2CQClS3cyb0HqyBGYOlX792efPVpBCkCngw4dYN8+2LsXnn1W63F1r7/oK6/AMRmm9v85OFSnbFltQhnpLSVE0WVjAxOWv8rRv0/ztk8J3n8f7O1h9+7d+Pr6sGfPHkuHKIQQQogCIDwcXnpJG9ASHQ1+fnB+wY+MXlEX3fZthPbSE7RET3KpFGxtvfD13YeX18SHFqQSEhIYMGAA/fv3JzExkXbt2hEcHCwFKROSopTI91JTbxIZuQaA8uXfMd+F4uKgZ0+tmPTaa9oQvsfRsiXs3KkVoV75Z8a8776DRo20YtXevVoJX+DlNQnQcevW98THn7B0OEIIC9Hp4N351Thb4hqN/vbhi0+1RU8jI2/Qpk0bZs+ejdFotHSYQgghhMiHlIK1a7W+AP/7n/aF18cfxPJnvUF4jXiJlNQb/LXUkUuDjCi9kTJlutGwYTAuLs0eet57w/VWr16NXq9nxowZ7Ny5E1dXM04lUwRJUUrke9evf4ZSKTg5NcHFpYX5LjRypDYPlJeXtsLek65K4ucHW7fCqVPaYGaDAX79FVq10gpXP/9c5ItTjo7elCnTDYCrV2dYOBohhKW9MdIB+gUSv7AvK6ZA+/ZgNBp57733eOmll7hz546lQxRCCCFEPnKvd1S/flrvqIYN4fQXe3hnbX30q1ZwuzEc22jP3ZoJ6PX21KjxFbVqbcLauvhDz7t69WoaNWpESEgI7u7u/PHHH0yaNAm9XkoopibvqMjXMjISCQ9fCoCn51izLV/LN9/A6tWg18O6dVC8uOnOXauWVro/d06bAN3GBg4cgOee0wpXW7ZovbOKqIoV3wcgKmoLCQmnLRyNEMLSXuykx++rNeyYuIg5T8PYsdp8ED/99BMNGvjy559/WjpEIYQQQuQD33wDderc7x01Z1oyR1qOpfLA1hivXeHCu86cnANptkk4OtbDzy8Qd/eBD/1MmZCQQP/+/RkwYECW4XqtWrXKw5YVLVKUEvlaZORa0tJuYWdXiTJlXjbPRUJDYcgQ7d/vvaf1YjKHypXh88/h8mV45x1wdISgIOjaVetrumYNpKWZ59r5mKNjbUqX1v6/vbfCohCiaGvSBIbsHcnHS3cx6JodSz+FcuXg6tVQnnqqBUuWLEHWaRFCCCGKpthYbd6o7t21lfUaNoSQr48zfrMf+k/mk+iuOL6pNNc6xALg4TGcBg2O4Ojo/dDznjp1ikaNGrFmzRr0ej0zZ85k586dlC1bNi+aVWRJUUrkW0oZuXZtAQDly7/9nysiPJaMDG1oXXS09inogw9Mf41/K1cOPv4Yrl7Vrle8OJw9C/37Q7Vq2tDBpCTzx5GPeHqOB+DGjXWkpIRbOBohRH5QrRosCHqGyX8H0XypG6tmad8ZpKWlM2LECHr06EFcXJylwxRCCCFEHtq/H+rX1wa36PUwZVI6h1+YQZWeTSAkhMhXnQn82p74UrewsipJnTrfU63apxgMD16RUSnFqlWraNSoEadPn6ZcuXIEBATw3nvvyXC9PCDvsMi3bt/+kaSk81hZlcDNbYB5LjJ3rjbxeLFisH69NkYkr5QqBdOmacWpOXOgbFnt32+9BZUqwbx52uTrRYCLS1NcXFqiVBrXri2ydDhCiHyibFlYfbgmM8r+hePohix+UXtEGgzwzTff0LChHydPnrR0mEIIIYQws7Q0mDxZm573yhXt49Kf688z9benMEx9n3TrdE5/XoEzw2PJ0CXh4vI0DRueoHTpTg89773heq+//jpJSUm0b9+eoKAgnn766bxpmLB8UWrp0qVUqlQJOzs7/Pz82Ldv3wP3jYiIoGfPntSoUQO9Xs/o0aOz7bN8+XJatmxJiRIlKFGiBM8884zMP1FA3RvKVa7cUKysipn+AkeP3u8ZtWQJVKli+ms8CmdnGD9ee7ouWQIVKsCNG9q2SpVg48YiMSH6vd5S4eFfkJ4eY+FohBD5haMjrP25DOtf3sfpCd0YGwcLP4EyZeDcufM0adKYNWvWWDpMIYQQQpjJuXPQvDnMnAlGI/Trqzg1ahl+A33gyBHiGhQjcLsbN2qGAnoqVpyGj88f2NmVf+h5//77bxo1asTatWszh+v9/PPPMlwvj1m0KLV582ZGjx7NpEmTCAoKomXLlnTs2JHQ0NAc909JSaFMmTJMmjSJ+vXr57jP7t27ee211wgICODQoUNUqFCB9u3bc/36dXM2RZhYbOwRYmL2o9NZ4+ExwvQXiI+Hnj0hPR26ddMGJVuavb3WBeD8eVi1CqpXh9u3tThffVUrVBVipUo9h4NDLTIyYgkPX2bpcIQQ+YiVFXy2wo4LH2xk3drJ9FwDq+ZBo0aQlJRM//79GTRoEElFbOizEEIIUZgpBcuWga8vHDsGJUrA9uU3WX23E/ajh6ASEwmbUJXj81NIso7E1tYTH5/dVKz4wUOnflFKsXLlSho3bizD9fIBi77jCxYsYODAgQwaNAhvb28WLlyIp6cnn3/+eY77V6xYkUWLFtG3b19cXFxy3Gf9+vUMGzYMHx8fatasyfLlyzEajfz+++/mbIowsbCw+QC4uvbC1tbd9BcYNQouXABPT/jiCzDXqn6Pw8ZGm1/q77+14X1WVvDdd9pk6N98Y+nozEan0+PpORaAa9cWYjSmWjgiIUR+otPB+1P0lF/5Ie8cX83TY634vCcMGKD9bsWKFTRr1pTz589bOlQhhBBCPKGbN6FTJ209qsREaNsWzi34Hy9Nqgs//khGMWtOb/Hl4rMXUKRRuvTLNGwYTPHiD1+0Kj4+nn79+jFw4ECSkpJ49tlnCQ4OluF6FmSxolRqaiqBgYG0b98+y/b27dtz8OBBk10nMTGRtLQ0SpYsabJzCvNKSrpEVNRWAMqXf8f0F9iyBVau1D7FfP21VnLPj6ytteGFR49qs/ndvq0tMdG1K0RFWTo6s3B17YmNTTlSU8O5cWODpcMxif79+9O5c+ccf7ds2TL8/f1xdnZGp9MRHR2dbZ+7d+/Sp08fXFxccHFxoU+fPjnuJ0RRMWAA9Pi5H93id1HxneJMstKm4CtRAk6c+As/vwZ8U4gL+KJwkRwhCgq5V0Ve2rED6mq1J2xsYNGsBHZVHkLpAS/CzZskt6xO0I6q3CwVhE5nRdWqi6ldeyvW1g//zH/y5EkaNWrEunXrMBgMzJ49mx07dlCmTJk8apnIicWKUrdu3SIjIwNXV9cs211dXYmMjDTZdSZMmICHhwfPPPPMA/dJSUkhNjY2y0tYzrVrCwEjJUt2oFixOqY9eVgYDB6s/XviRCgIFXEfH/jzT61AZTBoRbXatbX/LWT0elvKlx8NQFjYPJQyWjYgM0tMTKRDhw689957D9ynZ8+eBAcHs3PnTnbu3ElwcDB9+vTJwyiFyH+efRZmHvDnpRKHUcur0G8LrFwA9epBXFw83bt3580335ThfKJAkxwhCgq5V4WpJCZqs5k8/7zWU6pOHfh75Z+MXOWLbrk2vUf0jG4Ezo4mPuM01talqVdvF+XLj0D3kJEvSilWrFhB48aNOXPmDB4eHgQEBDBhwgQZrpcPWPz/gX/fPEqph95QuTF37lw2btzItm3bsLN78BKQs2fPzqzau7i44OnpaZLri9xLS7tDRMRKwAy9pDIytLmj7t7VJiKZOtW05zcnGxttKN+ff2pfG0RFaT2mevSAW7csHZ1JlSs3GIPBmcTEEG7f3mHpcMxq9OjRTJgwgaZNm+b4+9OnT7Nz506++uormjVrRrNmzVi+fDn/+9//OHv2bB5HK0T+0qABrD9Wg95VDnP68FN0mADL3oBevbSOsF988QVNmzaV/1ZEgSU5wjRys6jS7t270el02V5nzpzJw4gLHrlXhSkcPw5+frB0qfbzmJHpHO88jWr9mmtz7np4cP33UZx4ahtpaTcpVswHP79jlCjh/9DzxsfH07dvXwYNGkRycjIdOnTInM9a5A8WK0qVLl0ag8GQrVfUzZs3s/Weehwff/wxs2bN4tdff6VevXoP3XfixInExMRkvsLCwp74+uLxhId/idGYgKNjfUqUaGvak3/8MezerS3ltGGDNjyuoGnQQBvON2mS1mtq82at19R331k6MpOxsnKhXLmhAISFzX3gfkopEhISLPJSebQa4qFDh3BxcaFJkyaZ25o2bYqLi4tJhzkXdPKBo+iqWBF+OlKa95v+xtYbPWk0Bj4oDXPmQPHi8Ndff+Hn58fXX39t6VBFHpMcITkCcr+o0j1nz54lIiIi81WtWjWzxWipezWv7lOQe1U8XEaGlrebNoUzZ8DdHfatusD8I09hPWMqZGRg7NmVszue4bx+EUqlU6ZMd3x9D2Bn5/XQc588eZKGDRvy9ddfZw7X++mnn2S4Xj5jZakL29jY4Ofnx65du3j55Zczt+/atYtOnTo90bnnzZvHjBkz+OWXX2jYsOF/7m9ra4utre0TXVM8OaMxhevXFwPg6TnWZD3mAAgMhMmTtX9/+ilUrWq6c+c1W1uYMQM6d4Z+/SAkBF55RVulb/FiKFXK0hE+sfLlR3Ht2ifExOwjJuYQLi7Nsu2TmJhIsWLFLBCd9o2Lo6Oj2a8TGRmZ45K0ZcuWNekw54Ls3geOpUuX0qJFC7788ks6duxISEgIFSpUeOBxZ8+exdnZOfNn+eOk4CpVCn7+w5ZePb/m3PfVmbZoKn3aQdWlOj6cpwgKSqBPnz788ccffPrpp3ny366wPMkRkiMg66JKAAsXLuSXX37h888/Z/bs2Q88rmzZshQvXjxPYrTUvZpX9ynIvSoeLDRUG8iyZ4/28ysvK9a0/Ipiw9+GhARwcSH1i9n8XX09sXcOADoqV56Np+f4RxquN2LECJKTk/Hw8GDTpk089dRTedMwkSsWHb43ZswYvvrqK1auXMnp06d5++23CQ0NZehQrZfExIkT6du3b5ZjgoODCQ4OJj4+nqioKIKDgwkJCcn8/dy5c5k8eTIrV66kYsWKREZGEhkZSXx8fJ62TeTejRsbSU2NxMbGg7Jlu5vuxAkJWsEmPR26dNFWtisMGjbU+rlOnAh6vdb7q04d+OEHS0f2xGxty+Hqqs0zEBY2z8LRWFZOCdeUw5wLutyu4npP2bJlcXNzy3wZDA9eNljkf/b28O0WHXeGT6ET3+Owy4l2kxVLxhro318bzrdq1SoaN27MqVOnLB2uECYjOeLBnmRRJV9fX9zd3Wnbti0BAQHmDLPIkHtV/NuGDdpckHv2QLFisGHhTbZkdKbYmMHa5zd/f2KPfk2g1yxiYw9gMLhQt+7/qFDh3YfeN3FxcfTu3Zs33niD5ORkOnbsSHBwsBSk8jGL9ZQC6N69O7dv32b69OlERERQp04dduzYgZeX1g0vIiIiW/daX1/fzH8HBgayYcMGvLy8uHLlCqAN40hNTaVLly5ZjpsyZQpTC9IcQkWMUoqwsI8BrZeMXm/CoXVvvw3nzkH58vDll9qnk8LC1hZmzbrfa+rMGW3t1D59YNGi/Luy4CPw9BxLZORKbt36nsTEc+j1WXu9ODg4WKzY7ODgkCfXcXNz48aNG9m2R0VFmWSYc0F37wPHhAkTsmx/1A8cycnJ1KpVi8mTJ9O6desH7puSkkJKSkrmz7IYRv5kMGidRWe5daLx5D/5/lJnGg0+i9N7eurNNzJjBoSEhNCoUSOWLFnCgAED5MNQISY5QnLE4yyq5O7uzrJly/Dz8yMlJYV169bRtm1bdu/e/cDl4p80R1jqXs2r+xTkXhVZRUdrk5lv+Geh7aZNYdvAn3Cf9Lo2u7m1NcyaRWSvspw73xWjMRkHh5rUqbMdB4fqDz33X3/9RdeuXTl37hwGg4GZM2cybtw4mcw8n7NoUQpg2LBhDBs2LMffrV69Otu2/xr/fK84JQqWO3d+ITHxFAaDE+XKDTbdib/7DpYv1wpR69ZByYcvE1pgNW4MQUEwZYo2d9a6dfDbb7BsGbzwgqWjeyyOjt6UKvUSt2//QFjYfLy8FmX5vU6nK/TDcJo1a0ZMTAx//vknjRs3BuDIkSPExMTQvHlzC0dneXn1gWP27NlMmzbN5PEL09PptCn3ypSpSdOhf7I6oS+dJm/HuQdUXA6zZsOxY0kMHDiQP/74g88//xwnJydLhy3MQHKE5Ih7crOoUo0aNahRo0bmz82aNSMsLIyPP/7YbDlC7lW5V4uSPXu0787DwrQvk6a/m8CEW2PRv/GFtkPt2hi/XsOlYhu5dnYcAKVKvYC399dYWbk88LxKKZYvX86oUaMyh+tt3ryZFi1a5EWzxBOyeFFKCIBr1+YD4O4+6KEPnFy5fh3+mUOAd98Ff3/TnDe/srPTZgns3FkbonjuHLz4otaDauFCbdbfAsbTcxy3b/9AZOQa3N0nWzqcxxYTE0NwcHCWbSVLlsTGxobIyEguXLgAaJMxOjk5UaFCBUqWLIm3tzcdOnTgjTfe4MsvvwRg8ODBvPDCC1n+aC7qzP2BY+LEiYwZMybz59jYWFmlNZ8bPBhKlXKmx2vbGJc2k2kbp+B8VlF6ihXr/pfOypWwfv16jh49yubNm/Hx8bF0yKIIkxxhHqZaVKlp06YPXSyhKOUIuVfF40pNhQ8+gLlzQSmoUgW+e+8odT/qpa2sB/D226RNH0vIxf7cvbYLgAoVJlGp0nR0ugf3dIqLi2PIkCFs3LgRgOeee441a9ZQunRps7dLmIgS2cTExChAxcTEWDqUIiE2NkgFBKACAgwqKemKaU6akaFU27ZKgVJ+fkqlpJjmvAVFYqJS77yjlE6nvQceHkrt2GHpqHLNaDSqwMBmKiAAde7cLBUSEqKSkpIsHVau9OvXTwHZXv369VNTpkzJ8XerVq3KPP727duqV69eysnJSTk5OalevXqpu3fvmjTGpKSkB763+fl5mJKSogwGg9q2bVuW7SNHjlRPP/30I59nxowZqmbNmo+8f35+T0RWf/yhlJOTUs/zo4rVO6uk0qjAZdZq8WJUmTLaf2+2trZq6dKlymg0Wjpc8Zge9gzL7yRHmFfjxo3Vm2++mWWbt7e3mjBhwiOf49VXX1WtW7d+5P0f9p7IvWq5e1VYTkiIUr6+2kcSUOqNAWkqedJ0pQyG+59Tdu1S8fF/q0OHqqiAANSePQ7qxo1v/vPcR48eVdWqVVOAMhgMas6cOSojIyMPWiUexaPmCClK5SC/J9jCJiSkjwoIQJ069ZrpTjpvnvaQc3BQ6uxZ0523oNm/X6mqVe9ngddfVyo62tJR5crNm9+pgADU/v111alTf8sfGmYgHzhM94FD5D+BgUqVKaNUNc6qcza1VIYV6uxovfr+e1SzZvc/PHXt2lVFF7Dno9DIB1HzKsg5YtOmTcra2lqtWLFChYSEqNGjRytHR0d15Yr2JeiECRNUnz59Mvf/5JNP1HfffafOnTun/v77bzVhwgQFqK1btz7yNQtrUaogkPc3fzEalVqyRCk7O+1jSKlSSv2y9IJSTZve/2zSrZtSt2+rmze/U3v3FlMBAahDhyqquLjgh547PT1dzZo1S1lZWSlAlS9fXu3fvz+PWiYe1aPmCBm+JywqOfkaN29qXS09Pd8xzUmPH4f33tP+vWgRVH/4hHiFWosWcOKENsnKokWwciX8+iusWAH/Wo0mvypd+iXs7auTkhJLRoasoimyGjNmDH369KFhw4Y0a9aMZcuWZVvF9fr166xduxbQlgOvWLEitWvXJjU1la+//pqtW7eydetWSzZDmFGDBnDgALRvX50GVw6zybY/zy/chsvfMHuans3fGVm2DL799lsCAwPZvHkzDRs2tHTYQggTyO2iSqmpqYwdO5br169jb29P7dq1+emnn3juuecs1QQhCqQbN+D112HHDu3n9u0Um9uvoPi40drKes7O8NlnqJ6vceXqh1y9qs3LVrx4a2rV+gYbmwcPvQsNDaVPnz7s3bsXgFdffZVly5ZRsrDOHVwESFFKWNT164tRKp3ixf1xcvJ78hMmJkLPnpCWBq+8AgMHPvk5CzoHB/jkE+39GDAALl6EZ5+FN97QJkV3drZ0hA+l0+nx9BzHhQszyMiIRSmjpUMS+Yh84BCPolo1rTDVoYMTL5zcwjTb2bz/+2QcLxqxm2tHnTrJTJ8Oly5donnz5sybN4+RI0fK6nxCFAK5WVRp/PjxjB8/Pg+iEqLw+vFH7SNYVJS2UPinH0Qx6Mgb6MZt13Zo1QrWrCHdoyRnQrpw69b3AHh4jKJKlXkPXYV98+bNDBkyhJiYGBwdHfn000/p37+/5OsCTtZGFBaTnh5LeLg22WH58ibqJTVmDJw9Cx4e2spz8oC6r2VLrdfUiBHaz8uXQ9262ip9+Zyra2+srcugVAbp6TGWDkfkM8OGDePKlSukpKQQGBiYZcLy1atXs3v37syfx48fz4ULF0hKSuLOnTvs27dPClJFRLlysHcvPPWUjikp79HZ6idsbhbHr38yTyfYsny59phMS0tj9OjRvPzyy9y5c8fSYQshhBAFQkICDB0KL72kFaTq1YOzn+zgjcV10f2wHayttUWZfv+dxDJpHD/ejFu3vkens6FGjVVUq7bwgQWp2NhY+vXrR48ePYiJiaFx48YEBwczYMAAKUgVAlKUEhYTEbGCjIxYHBxqUqqUCT4Ubt8OX36pFaLWroVSpZ78nIWNoyMsXgwBAVCxIoSGQrt28OabEBdn6egeyGCwo2zZPgCkpd1CKWXhiIQQBVHx4toI5hdfhB/SOlIn6RhxxetQe3wK9TfpmTYVRo4Ea2sd27dvx9fXl0OHDlk6bCGEECJfO3cOmjbVPooBTByVyPGmw/Aa9rw2lq92bfjzTxg/njsxv3P8eCMSE09hY+OOr+9e3N37P/Dchw4dwtfXl7Vr16LX63n//ffZv38/VatWzZvGCbOTopSwCKMxnWvXFgJaL6mHLfP5SMLD7w/VGzsW2rR5svMVdv7+cPIk3OvO/sUX2tcZAQEWDethypbtAehQKoWMjFhLhyOEKKDs7WHbNujfH84bq+B5/RBn63WjwgYjPmOhW0c7lixReHjoCQ0NpWXLlsydOxejUYYOCyGEEP+2bRs0bAh//w2urnDks2PM+tkXw7LPtR1GjYKjR1H16xMWNp+//upIeno0zs5N8fM7hrNzkxzPm56ezvTp02nZsiWXLl3Cy8uLPXv2MH36dKytHzzETxQ8UpQSFhEVtYWUlFCsrcvi6tr7yU5mNGqfLm7f1ma0nTHDJDEWesWKwWefwe+/g5cXXLmiFfOGD4f4/DehuLW1MwZDMZSC1NRIS4dTqMiHbVHUWFlp6z6MGwcJFMP7r0389PQcip/Q07BPMg1tHPnySyOtW0NGRgbvvvsuL7zwAlFRUZYOXTyEPMvMQ95X05P31Dzkfc1b6ekwfjy8+qo24ML/qXTO95tB41HNtK5T5cpp3ZMXLiTDBs6c6cvFi2MBI25ur+Pjsxtb23I5nvvy5cu0atWKKVOmkJGRQc+ePTlx4gRPPfVU3jZS5AmZ6FzkOaUUYWHzAPDwGI7BYPdkJ1y4EHbt0r7+Xr8ebGyePMiipE0brdfUuHFan9vPPoNffoF167R+uPmEtbU1VlbOREcnAXEodQeDwcHSYRVoSilSU1OJiopCr9djI//tiCJEp4O5c6FMGRg/XscLe8cz9xkfxgb2wOeNuziNsef995Pw9YXPPtPz888/4+Pjw8aNG7PMWyYsz8bGBr1eT3h4OGXKlMHGxkbmGDEByRGmJ/eqeci9mvciI6F7d22uRoApw6L44HQP9HP/0DZ07aqNxChZkuTkMP7++2Xi4wMBA1WrLsTD460c732lFOvXr2fYsGHExcXh7OzM0qVL6dWrV941TuQ5nZLJWbKJjY3FxcWFmJgYnPP5ymQF0d27uzlxojV6vT1Nm4Y+dMnP/xQcDE2aQGqqVlAZPNhkcRZJu3Zp67deuwZ6PUyaBO+/r01MmA/Ex8dz4cJBlMrAYHDE2rqMpUMqFBwcHHB3d8/xjzh5HmYn70nhs2aNNgI8IwMGtr7EsqiX0f/9Fzfa6Tk7wYrzl1P58EMrrl5NR6/XM23aNCZOnIjBYLB06OIfqampREREkJiYaOlQCh3JEbnzX++J3Kvm87B7VZjOvn3QrZtWmHJygu8nHaXNZ69CWJg2f+3SpdCnD+h0REfv59SpV0lLu4mVVSlq1/6WEiVa53je6Ohohg0bxsaNGwFo0aIF69ato1KlSnnZPGFCj5ojpCiVA0mw5nXy5Ivcvv0/ypV7k+rVlz7+iRITtQHMp09Dp07w3Xey2p4pREdrQ/jWr9d+9vODr7+GmjUtGtY9sbF/ERT0Ijqdgbp1f8be3svSIRVoBoMBKyurB35TK8/D7OQ9KZz+9z/ti93kZGjbNIEdHm9gs3Uj8RXh1EJn7tjEsmiRjl9+0f5satu2LV9//TVubm6WDVxkUkqRnp5ORkaGpUMpNCRH5N6jvCdyr5ref92r4skppQ1QGTdO+xKndm34rcdXuH34ltZBoFo17fNY7doAhId/yfnzI1AqDUfH+tSp8z329hVzPPe+ffvo3bs3oaGhGAwGpkyZwsSJE7GykoFdBZkUpZ6AJFjzSUg4zdGjtQAdjRufxcGh2uOf7K23tEq8uzv89ReUfoIeVyK7zZu1dV2jo8HODubN097zfJDs//rree7c2fHkhU3xn+R5mJ28J4XX/v3aynzR0VCntmL/q5/gMmMc6XZGzswuzq160ezcCYsXW5GUlI6rqyvr16+nbdu2lg5dCIuQ52F28p6Iwig2VutRvGWL9nO/7skstx+B9eqvtA2dOmndjl1cMBpTuXBhFOHhXwBQpkw3atZcicHgmO28aWlpTJs2jdmzZ2M0GqlSpQrr16+nSZOcJz8XBcujPg9lonORp65dWwBA6dKdn6wg9eOPWkEKtAegFKRMr3t3bRmNdu20rgMjRkDHjtpKhxZWocJ4ACIjV5GaetPC0QghCounntLmx3B3h79P6ai/ZgxhK3dhZV+K2qOiqbzOgQ7P6lm6NJ0qVey4ceMG7dq144MPPiA9Pd3S4QshhBAmd+oUNG6sFaSsrWH1h2GsuvS0VpDS6WDmTG0JPhcXUlNvcOJE238KUjoqVZpFrVqbcixInT9/nhYtWjBz5kyMRiMDBgwgKChIClJFkBSlRJ5JTb1BZOQ6ADw933n8E0VEaPMeAbzzjlY0MaOICPj8c60OVuQ+c3h48E+3AK231C+/QN268O23Fg3LxeVpnJwaYzQmc/36EovGIoQoXOrWhYMHtVEIV6+C37g2nFwdiM7XlworE6k/Fqp5FGPJkmReeMEapRQffvgh/v7+XLp0ydLhCyGEECazcaNWkDp7FsqXh+AFf9BvUQN0R49CyZLw88/w3nug1xMbe4zAwEbExOzHYHCmbt0f8fKamG1IpVKKFStW4Ovry9GjRylevDjffPMNK1euxMnJyUItFZYkRSmRZ65f/wylUnB2boqzc/PHO4nRCP37w61b4OOjVebN5MgR6NULvLxg2DB46SXw9IQJE7QHc5Gh12u9pAIDoUEDuHNHm92wTx9tjIsF6HS6zN5S168vIT093iJxCCEKp4oVtaF8DRpAVBQ0f82L3TMPQO/elDhupOFr8ZSNKc0776QxeTIUK2bDgQMHqF+/PitXrkRmRhBCCFGQpaZqf/737KlN4/tMW8XpAXOpNaqd9jnM11f7bPDsswBERKwgKOgpUlLCsLevgZ/fn5Qq9Xy2896+fZsuXbowaNAgEhIS8Pf356+//qJr16553USRj0hRSuSJjIxErl/XhtuVL//O409CuHAh/Por2NvDhg1ga2u6IIGUFFi3TvtGoGlT7RJpadp86mXLaqtMzJmjzfn91FOwahXEF5V6SK1acOiQtiKfXq9Nfl6vHuzebZFwSpfujL19VdLT7xIZudIiMQghCq+yZSEgANq00Z7zz3a2Z8tLa2HRImyjDfj0voXH3tK0bQvLlqXi6+tCfHw8AwcO5OWXX+bmTRlaLIQQouC5dg1atYIl/wxGmDY2jl9dulLsw3fvdxA4cAAqVsRoTOHs2cGcPTsIpVIoVeol/PyO4OBQI9t5f//9d+rVq8e2bduwsrJizpw5/Pbbb3h6euZtA0W+I0UpkSciI9eQnn4bO7tKlCnz8uOd5PffYbzWO4b588Hb22TxhYfDBx9AhQrQty8cPQo2NtCvHxw7pv187Zq2oMQLL2g1mQMHtFGE7u4waJBWryn0X47b2MCMGdpasJUra0u/tmkDY8dq807lIZ3OgKfnWADCwhZgNBa1sZVCCHNzdoYdO6BLF+1b427ddXxhMxJ++w19iTJUm3IL70+K4eFmx7x5MQwb5oS1tRXbt2+nbt26/Pjjj5ZughBCCPHIfv9d6wR1+DAULw5/LD3DBz81Qbdtqzah1Oefw8qVYG9PcnIYQUEtiYhYjjZ/1Ezq1PkOKyuXLOdMSUlh/PjxtGvXjvDwcKpXr87hw4cZP348BoPBIu0U+YsUpYTZKZWROcF5+fJj0Oke4+Fz4YK2VndGhlYpGjrUBHFp84a89po2RO/DD+HmTW0apRkztHrL6tXg56ftb20NnTtrc0uFhcHs2dqcI/HxsGIFNG+urYD68cdw48YTh5e/NW8OwcFaNU4prUjYuLG2CmIecnXti7V1GVJSrhIVZdl5roQQhZOtLWzaBEOGaI+7N9+E6Xv9UccCoWFDXH+Ix29gCk5prnTtGsfSpUZq1HDj5s2bvPTSSwwePJj4ItOlVgghREFkNMKsWdC+/f3ReadnfUfrdxvD6dNQrpy2EsjQoaDTcffu7wQGNiAu7ihWViWpV+9nvLzeQ6fLWl44ffo0zZo1Y968eSilGDJkCMePH8fv3gcsIZCilMgDt279QFLSBaysSuDuPiD3J4iN1SZ0untXG1P3xRfaSg+PKTlZW7CvUSNo0UL7sJGerg3H27wZLl/WRqiVLfvgc5Qrd39uqb17tTqZg4P2zB43TpsI8F4Bq9BOju7kBMuXw/btUKYMnDypvanz5mnFwzxgMNjj4TESgLCwuTKPixDCLAwG7cvhDz7Qfp4yBUbM9cS4Zx/074/jZUWDl29Q9noNqlY1snhxJP36VUOn07F8+XLq16/PwYMHLdsIIYQQIgd372qfWyZN0opTgwZkcKTtRNyGvQJxcdpYvuPHoWlTlFKEhs7hxIn2pKXdolgxX/z8AilZ8tks51RK8cUXX+Dn50dQUBClSpXi+++/54svvsDRMftKfKJok6KUMLuwsPkAlCv3Zo7LgT5URoY2w97p01oXpm3btFXgHsP16zB5sjZEr39/bW4+W1sYMEB7zu7bp83fbW396OfU6aBlS61HVUQELFum1c3S07Vazf+fHP3cuccKO/976SX4+2/tf1NTtSGWbdrAlSt5cnkPj2Ho9Q7Exwdz9+5veXJNIUTRo9PBtGnw6afavz/7DHq+bkfqFythzhysksG791mq7fPD1taa/v3Ps2SJO+XLu3Hp0iVatmzJ5MmTSU1NtXRThBBCCEAb+NCwofZFuq0trPvkFsvDOmD98UfaDmPGwK5d4OpKenosp0514dKlCYARN7cB+PoewN6+YpZzRkVF0alTJ958802SkpJo3749J0+epFOnTnndPFFA6NRjdC04f/48Bw8eJDIyEp1Oh6urK82bN6datWrmiDHPxcbG4uLiQkxMDM7OzpYOp0CLiTlEUFBzdDobmja9gq2te+5OMGGCNrO4nZ1WNWrYMFeHK6XN/fTpp7B16/0OPOXLayvqvfEGlC6du5AexalT2iToa9dqKzfd89RTMHCgNj9JsWKmv65FKaWNYxw9GhIStJ5US5Zoq/Q9Qc+2R3H+/GiuX19EiRLPUL/+LrNeq6iR52F28p6ITZu0+QfT0qBdO+37kmLff619y5GeTmwvP069eYOUtGskJNiyerUfW7ZoPaUaNGjA119/jbcJ50UUwlLkeZidvCeioFi1Svs8lJwMlSrBTx8G4v3eKxAaqg0BWbECevQAICHhNKdOvUJi4hl0OmuqVfsUd/fB2Rav2rlzJ/379+fGjRvY2NgwZ84cRo4ciV4vfWGKokd+HqpciI6OVi+99JLS6XSqePHiqnr16qpatWqqePHiSq/Xq06dOqmYmJjcnDJfiomJUUChaIulnTz5qgoIQJ0+PSD3B3/9tVJaqUOpjRtzdWhSklIrVyrl63v/FKDU008r9e23SqWl5T6cx5GSotS2bUo9/7xSev39OIoVU2rgQKUOHlTKaMybWPLMhQtKNWt2v7GvvqpUVJRZL5mUdEUFBBhUQAAqNjbQrNcqauR5mJ28J0IppX75RSlHR+0x16jRP4+5X3/VHvCgUpvWUieOtFEBAaiAANTChW1UyZIlFaDs7OzUokWLVEZGhqWbIcQTkedhdvKeiPwuKUmpQYPu/6n+/PNKxS9eoZStrbahWjWlTp7M3P/GjW/V3r3FVEAA6sABDxUTczjbOePi4tRbb72lAAWo2rVrqxMnTuRls0Q+9KjPw1yVLEeMGMHly5c5dOgQd+/e5ezZs5w7d467d+9y8OBBLl++zIgRIx6zjiYKm6Ski9y69R0Anp7v5O7go0e1LkUA772XWaX/L2Fh2u6entrKeEFBWierQYO07ql79mi9lKyschfO47KxgZdfhv/9T4tt1iyoWrWQT45epYo20dbMmdobvXUr1K0LP/9stkva2XlRtqx2j4SFzTPbdYQQ4p727eGPP6BUKS1lNW8OFyq1055/bm5YHw6hbrfzVHQcCeioX/8Pvv7anWeeaUlycjKjRo3i2Wef5dq1a5ZuihBCiCLi8mVtTt2vvtIGMsyemsKP5YbgOHIgpKRo03EcPQp16mA0pnPx4nhCQrqSkRFP8eL+NGx4HGfnJlnOuW/fPurXr89nn30GwPDhwzl69Cj16tWzRBNFQZSbSpeLi4s6fDh7ZfSeQ4cOKRcXl9ycMl+SbzhM49y54SogAHXiRMfcHXj9ulLu7lql/sUXlfqPb5KNRqX27lWqSxelDIb7Vf8KFZSaM0epW7eeoBFmYDQqtWePUn37KmVvfz9eKyulOndW6scf864nl9kFBirl7X2/kcOGKRUfb5ZLxcUF/9MjQa8SEy+Z5RpFkTwPs5P3RPx/p08r5eWlPeJKlVJq3z6l1KVLSlWvrm0sUULd3vOJ2r+/tAoIQO3Z46zmzBms7O3tFaCKFy+uNm3aZOlmCPFY5HmYnbwnIr/66SelSpTQUlPp0krt+TpUqcaNtQ06nVIffpj5uSsl5YYKCmqd2dv3woWxKiMj6weUhIQENXr0aKXT6RSgKlSooHbt2mWJpol8yiw9pYBs40Yf9XeiaElLu0NExEogl72kkpK05R8iIrQuROvXwwPGICclab2NfH3h6adhyxZtzqjWrbX5PS5e1ObcLlXKBA0yIZ1Oi3fNGoiMhC+/hCZNtMnRv/8eXnzx/uToFy5YOton1KCBNqP8SG2FPJYu1bb9+afJL1WsWH1KlHgWMHLt2gKTn1/kX0uXLqVSpUrY2dnh5+fHvn37Hum4AwcOYGVlhY+Pj3kDFIVazZpw+LC2+Ojt29C2LWw6Ukmb0LBpU7h7l5LPTsQvahbOzs0xGmNp3HgZ27f3pFGjhkRHR9OjRw969erF3bt3Ld0cIQodyRGiqMvI0FaPff55baW9Jk3g1JIAnn7bT/ubvEQJ2LFDWxFKryc29giBgX5ERwdgMBSjVq1vqVJlHnr9/aEmhw4dwtfXl4ULF6KUYtCgQZw8eZJnnnnGgi0VBVZuKl29e/dW9erVU0ePHs32u6NHjyofHx/Vp0+fXFXP8iP5huPJXbkyUwUEoI4e9VHGR500yWhUqndvrVpfsqRSFy/muNvVq0q9+662y70OOPb2Sg0erNRff5mwEXns5EmlxozRvrm41y6DQamPPy4k8079+qtS5crdb9jUqSbvEnbnzu//9ESwVykp5p3HqqjI78/DTZs2KWtra7V8+XIVEhKiRo0apRwdHdXVq1cfelx0dLSqXLmyat++vapfv36urpnf3xNhGQkJWm/Xe8/vmTPPWlmlAAChJElEQVSVMsYnKPXSS5nfQmd8tlidP/925jfPR460UJMmjVEGg0EBqnz58uq3336zdFOEeGT5/XkoOUIUdVFRSrVrdz83vTXMqNJmz7s/vMTHR+vdq5QyGo3q+vUv1O7dNiogAHX4cA0VHx+S5XxJSUlq/PjxSq/XK0CVK1dO7dixwxJNEwXAoz4Pc1WUunv3rurQoYPS6XSqRIkSqkaNGqpmzZqqRIkSSq/Xq44dO6q7d+8+Sdz5giSTJ5ORkawOHHBTAQGoiIh1j37g3Ln3CxZ//JHjLrNmZZ0wvGJFpebNU+r2bRMFnw+kpCi1ZYtSzzxzv51duigVG2vpyEzg9m2lune/37DGjZU6e9Zkpzcajero0QYqIAB1+fI0k523KMvvz8PGjRuroUOHZtlWs2ZNNWHChIce1717dzV58mQ1ZcoU+cAhTCY9Xam3377/iBs4UKnUxDTtW5N7GydOVDdvfKv27nVSAQGo/ftd1a+/fqaqVauWOUHsqFGjVGJioqWbI8R/yu/PQ8kRoig7fFgpT08t9Tg4KLVpeaxSXbvez0d9+2rfqCil0tMT1enTAzK/NDl58mWVlpb1Hv7zzz+Vt7d3Zq7q27evunPnjiWaJgoIswzfK168OD///DMhISHMnz+ffv360bdvX+bPn8+pU6fYsWMHxYsXz1VPrdx0qY2IiKBnz57UqFEDvV7P6NGjc9xv69at1KpVC1tbW2rVqsV3332Xq5jEk7lxYwOpqZHY2HhQtmz3Rzvop5/g3Xe1fy9erI3B+5cdO7RJzI1GbXjE999rw9vGjoWSJU0Xv6XZ2MCrr8Kvv8KSJWBtrQ1NbNQIQkIsHd0TKlkSNm7UhmW6uGhdhn194YsvtPT4hHQ6HRUqjAfg+vVPychIfOJzivwrNTWVwMBA2rdvn2V7+/btOXjw4AOPW7VqFRcvXmTKlCnmDlEUMQYDLFigPbv1em2IeccXrYj+6AuYPl3bafZsyoz7Eb/6h3B0rEta2g2srUewfXtvhgwZAsCiRYvw8/Pj+PHjFmyNEAVbXuWIlJQUYmNjs7yEsLQvv4SWLbWFlqpXh6BNZ+n+SVP49lttIaLPPoPVq8HBgaSkKwQFPUVk5CpAT+XKH1G79lasrJwB7R6fPHkyzZo14/Tp07i6urJ9+3bWrFlDiRIlLNpOUUjkUZEsR7ntUnv58mU1cuRItWbNGuXj46NGjRqVbZ+DBw8qg8GgZs2apU6fPq1mzZqlrKysHjpB+7/JNxyPz2g0qiNHaqmAANTVq/Me7aCQEKWcnLSK/ZAhOY5VCw3VJpAFpYYPN3HQ+dyhQ0p5eGhtd3RUqtDMhxsaqlSbNve/rXnmGW0G+Cccq5iRkaYOHaqkAgJQ1659ZqJgi678/Dy8fv26AtSBAweybJ85c6aqXr16jsecO3dOlS1bVp39p4feo3wLnpycrGJiYjJfYWFh+fY9EfnH//6nPbNBqdq1lbpyRSm1YsX9IRPt26v06EgVEtI385vpv/56UW3fvlm5uroqQFlZWamZM2eq9PR0SzdHiBxJjtD24Z+eI///lR/fE1H4paUpNWLE/T+vX3lFqYT1393/rOXurtT/+2/i9u1f1L59Jf/puVta3b6ddaLy48ePq7p162be1z169FC38tsqUiLfMttE5w9z9+5d1q5d+8j7L1iwgIEDBzJo0CC8vb1ZuHAhnp6efP755znuX7FiRRYtWkTfvn1xcXHJcZ+FCxfSrl07Jk6cSM2aNZk4cSJt27Zl4cKFj9MkkUt37uwkMTEEg8GJcuXeeJQDtKVH4+KgVSutl9S/JsxPS4MePbQJZBs0gI8/NlPw+VTTpnD8OLRpAwkJ2nvx9tva+1KgeXrCrl3wySdgawu//abdA40ba72pHrOBer1V5uT6YWHzMRrTTRm1yIf+vciGUirHhTcyMjLo2bMn06ZNo3r16o98/tmzZ+Pi4pL58vT0fOKYReH3/POwbx+UKwenTmnP8mP1XocffwQHB/j1VwxtnqNm8Y+oXn0ZOp0tt2//SNmy73L48HpeeeUV0tPTmTRpEk8//TSXLl2ydJOEKJDMnSMmTpxITExM5issLOyJYxbiccTFQadO8Omn2s+zZ2SwpcYkHHq9rP2yZUvtQ0Xz5ihl5OrVmfz1VwfS0+/g5NQQP79ASpbUJipPS0tj2rRpNG7cmJMnT1K6dGm+/fZbNm7cSKn8toqUKPBMWpQKDQ1lwIABj7Tv43ap/S+HDh3Kds5nn332ic4pHl1Y2HwA3N3fwMoq58JhpvR06N5dG4NXsaLWndTGJttu778PBw+CszN8841WvyhqypaFX37RVuQDWLhQG+EYHm7RsJ6cXg+jR8PJkzB4MNjZwbFj0LMnVKmiVSBjYnJ9Wje3AVhZlSI5+RK3bm0zfdwiXyhdujQGg4HIyMgs22/evImrq2u2/ePi4jh27BjDhw/HysoKKysrpk+fzokTJ7CysuKPP/7I8TrygUM8Ll9fOHIE6tXTVlt9+mnYntoRdu+GMmXg+HF0LVpQLr4VDRocxM6uEsnJV7h69TkWL27P6tWrcXJy4uDBg9SvX58VK1agTDDUWYiiIK9yhK2tLc7OzlleQuS10FBo0UKb7sTeHn5YdZsJe59DN3uWtsPo0fD77+DmRnp6DH///QqXL08GFO7ug/Dx2YedXQUATp48SZMmTZg6dSrp6em88sornDp1ii5dulisfaJwy1VR6t/jpf/9iouLe+Rz3bp1i4yMjGxJwdXVNVvyyI3IyMhcn1PGgptGXFwQ0dG/AwbKlx/13we8847WO8bREbZv1/5A/5cdO2DOHO3fK1ZodYqiysoKZs/W5tJydtZWG2/QAPbssXRkJlCtmjb4PTQUpk3TqnBhYTBuHJQvr3UNu3LlkU9nMDhQvvwIAEJD58qHuELKxsYGPz8/du3alWX7rl27aN68ebb9nZ2dOXnyJMHBwZmvoUOHUqNGDYKDg2nSpEmO15EPHOJJlC+v9Zjq0AGSkuDll2HRwUbaty1VqsDly9CiBU4hafj5BVKq1Esolcr580Np3Ph3goIO8/TTTxMfH8+gQYPo3LkzN2/etHSzhMj38ipHCGFpR49qAw1OngQ3Nziy9iwvTm+kTVDr4AAbNmgjE6ytiY//m8DARty+vR2dzobq1ZdTo8ZyDAY70tPTmT17Nn5+fgQFBVGiRAk2bNjAli1bKFu2rKWbKQqxXE90XqJEiQe+nn766VwH8Khdas15ThmaYRr3ekmVLdsts9L+QF99pQ3VA1i3TvsaOdv5oG9f7d/Dh4MU5zWdOmmdierWhRs3tEnf5883yTzhllemDHzwAVy9qt0jtWpBfLzWNaxKFejWTet28AjKlXsLvd6e+PhAoqMDzBu3sJgxY8bw1VdfsXLlSk6fPs3bb79NaGgoQ4cOBbReTn3/eZDo9Xrq1KmT5VW2bFns7OyoU6cOjo6OlmyKKMScnbVRe0OGaM/q0aNh5OKqZOw7CA0bwq1b0Lo11r8coE6d76lceQ6g58aNddy5053//e9z5s2bh42NDT/88AN16tThhx9+sHSzhMj3JEeIwm7LFm32ixs3tM8GQUsOUHdIc+0Lj8qV4fBheO01AG7e3Mzx401ISjqPra0nvr77KVduEACnT5+mRYsWvPfee6SlpfHiiy9y6tQpXnvttSf+bC7Ef8lVUcrJyYnZs2fzxx9/5PhatmzZI58rt11qH5Wbm1uuzylDM55ccnIYUVGbATLn83mg/fth2DDt3x9+qH1t/C9FfR6p/1KtGhw6BL17Q0aGtgJht27acPFCwc4OBg6Ev/+Gn3+Gdu20ZRe//VabmKVFC9i2TWv8A9jYlMbdfSAAYWHz8ipykce6d+/OwoULmT59Oj4+Puzdu5cdO3bg5eUFaKu2hoaGWjhKIbTerp9/DnPnaj9/+il0HlyW+B8DoGNHrRtVp07oVqygQoXx+Pj8gbW1KwkJfxMc3JS+fb04evQodevWJSoqik6dOjFo0KBc9VIXoqiRHCEKK6W0ERRdu2rp4/nn4ci4Lbj1aqvN2dukifZhoW5djMY0LlwYQ0hID4zGRIoXb4ufXyDOzo3IyMjg448/xtfXlz///BMXFxfWrFnD9u3bcXd3t3QzRVGRm9nT/f391Zw5cx74++DgYKXT6R75fI0bN1Zvvvlmlm3e3t5qwoQJ/3lsq1atclx9r1u3bqpjx45ZtnXo0EH16NHjkePKzyuJ5FcXLoxVAQGooKDWD9/xyhWlypTRVn/o2vWBK629+662i7OzUhcumCHgQsJoVGrJEqWsrbX3q0YNpU6dsnRUZnLihFL9+99vLChVubJSixcrFReX4yGJiZdUQIBeBQSg4uJO5HHAhYM8D7OT90Q8qW+/VcrOTnuM+foqdf1KqlIDBtx/tk2dqpTRqJKTw9Xx409nrs537twolZAQo8aOHat0Op0CVKVKldTevXst3SRRRMnzMDt5T4S5paRofxLfSxmjRhpVxrz5Sul02oZOnZRKSFBKKZWcHJElj1y8OEEZjdqKrufOnVPNmzfPXFnv2WefVWFhYRZsmShszLL6Xs+ePbGzs3vg793c3JgyZcojny83XWrvuTfGOz4+nqioKIKDgwkJCcn8/ahRo/j111+ZM2cOZ86cYc6cOfz222+MHj06N00VuZCeHkt4uNZL7qG9pBIStLFnUVHa7K+rVmVbaQ/gp5/uzyO1cmXRnkfqv+h08NZbsHevNm/J2bPamPLNmy0dmRnUq6fdM1evwqRJULIkXLoEI0dqK/lNmADXrmU5xN6+EmXKdAWkt5QQIv/o0gUCArQRy0FB0OQpa06OXgGTJ2s7TJ0KgwdjayhD/fq/4+n5LgDXry/izJkOfPjhKAICAvDy8uLy5cu0atWKcePGkZycbLlGCSGEMLvbt6F9e1i9Wlsv6LPFGSxkNPpx72g1quHDYetWcHAgJuYggYF+xMTsxWBwonbtbVSuPBuldCxatIj69etz8OBBnJycWL58OT///DPly5e3dBNFUWTKSlhoaKgaMGBAro757LPPlJeXl7KxsVENGjRQe/bsyfxdv379VKtWrbLszz+V3P//8vLyyrLPt99+q2rUqKGsra1VzZo11datW3MVk3zDkTuhofNVQADqyBFvZTRm5LxTRoZSr76qVe/LllXq6tUHnEupkiW13UaMMGPQhdDNm0q1afP/vjUZpVRqqqWjMqOEBKWWLlWqWrX7jbayUqp3b6WOH8/cLTb22D/fDhlUUtIVCwZcMMnzMDt5T4SpXLyo9XAFpZyclNq5Uyn1xRdK6fXaxhdeUCo+XimlVFTUdrV3r4sKCEDt319a3b79q4qJiVGvv/565t9DtWvXVsf/3/NPCHOT52F28p4Iczl37v6fvU5OSv3yfaJSL798/+/gjz9WymhURqNRXbu2RO3ebZ35GS0+/rRSSqmLFy+qp59+OjNvtGnTRl25In8fC/N41OehSYtSwcHBSq/Xm/KUFiHJ5NFlZKSqgwc9VUAAKjz8qwfvOG2a9rC0tlbqwIEcd0lNVap5c203Pz+lkpPNFHQhlpam1IQJ93NTixZKXb9u6ajMLCNDqe3blWrV6n7DQanWrZX68UelMjJUUFBbFRCAOn9+tKWjLXDkeZidvCfClP6PvfsOi+J4Azj+PXoRsKGiIth7BXsUY4sltmjUGLtGjRo1Go0ltpjoz65JbLEn9lhjiR2wYUPsNTZQwU5H2s3vjw2nBDQ2OMr7eZ57hL29vXfWY+Z2duadx4+fV1+mpkotWKCU2rz5+fy+qlW1uw5KqcjI6+rEiYr/dLTr1M2b45VeH6+2bNmicuXKpQBlbm6ufvzxRxUbG2vUconMQerDpOSciJTg5fX8xr2Li1IXvR8oVa2atsHCQqm1a5VSSsXGhqoLF9obpuudP99GxcaGqvj4eDV37lxla2urAGVjY6PmzJmj4uNfMqBAiPcgRabvCfFvDx/+QXR0AObmuciV6/Pkd9qwARKmdc6fD8kswwvarIUjR7RVitatA0vLFAo6AzMz05Iebt6sncfDh7VE8d7exo4sBZmYQPPm4OWlrYnboQOYmmpzY5o1g1KlKHCuNAD37i0kNvaJceMVQogXZM+urdrdqZO2dkPv3vDtkRbo9+7Xnjx2TFvc4cYNrK0LUbHiEZycvgAUt26N5dy5pjRuXJPz58/TqlUrYmNjGTVqFLVq1eLatWvGLp4QQoh3tGyZtuZPQv7yk2v+pmSPGtrKetmywd690LYt4eHn8PV158GDNeh0ZhQuPI1SpdZx795TGjZsSN++fYmIiKB27dqcO3eOvn37YmIi3QHC+ORTKN6aUoqAgOkA5Mv3FaamyeQbO3MGEvKCDRoE3bsne6zt25+vSLRkibaCqXh7LVrAyZPa0rD370O9ejB9ujaEKENzd4eVK7VlcIcOBQcHuHKFbJ1/wvaWKXp9BPeuSm4pIUTaYmEBy5drqaRAaw/bz67Os/1HwMUFrl2D6tXB1xdTUyuKF/+VEiWWYWJixZMnOzl5shKWlrfYsGEDv/32G/b29hw9epQKFSowd+5cVIav/IUQIuPR62HkSOjWTVuZvG1b8Jp8lJzNqsPff4Orq3YHulYtAgOXcepUVaKirmJhkY8KFbzIn38wS5YsoUyZMuzbtw9ra2tmzZqFp6cnheRiS6Qh0ikl3lpwsBfh4acwMbEmX74vk+7w4IE2giUyUuven5p8Z0BAwPN+q6++gtatUzDoTKRoUW0l2I4dtbvv33yjNWaZYvVwZ2ftqi4gAGbNQufqSoEV8QDcufo/4nt1gQsXjBykEEI8p9Npg4p/+w3MzeGPP6Dul8V5vM0HKlTQ2lQPD9i1C4A8ebpQqdIxrK2LEB3tj5/fB9y7t4COHTty7tw56tWrR2RkJP369eOjjz7izr8WghBCCJF2RUZCu3baDAjQZpSsbrcZq0YfwqNH4OYGPj7EF3Ph8uUeXLnSDb0+imzZPsLd3Y/wcFeaNm1Kz549CQsLo3r16pw+fZqBAwfK6CiR5ujUG9w+++STT175fHBwMN7e3sTHx79zYMYUGhqKg4MDISEh2NvbGzucNOvs2aY8ebKDvHn7UqzYnMRPxsRA/fpw8KDWO3LsmDa89F9iY6FOHW3anpub1tkv0/beL6Vg3jxtoFpsLBQvDhs3QqlSxo4sFcXFod+8nmOmXYjOFkOxGZB3K9CoESxaBPnyGTvCNEvqw6TknIiU5uUFrVpBcLA2cnjnulCKDm+tTdEwM4PFiw13c+LiQrh8uSuPHm0GIHfujhQrNh+dzpo5c+YwbNgwnj17RtasWZkzZw6fffYZumRWvhXibUh9mJScE/GugoK0+/onTmg3KRYtgs4hP8PAgdoX+yZNYO1aIk3uceFCGyIizgEmuLqOp0CBEaxevYb+/fsTHByMpaUlEyZMYPDgwZiamhq7aCKTed368I26SR0cHF75cHFxoXPCkBeRoUVEXODJkx2Ajvz5v078ZMJypAcPaomN/vwz2Q4pkDxSqUGng7594cAByJ8frlyBKlVg7VpjR5aKzMwwadMe58raaL2A7llQZjrYuRMGDDBycEIIkVidOtpI10KF4MYNqNrAngPfbofPP4e4OOjSRbt9rhRmZg7/LPM9FTDl/v0VnDpVjWfP/uarr77Cz8+PypUrExwczOeff067du14/PixsYsohBAiGWfPanmjTpzQ0gru3a2n89lvtO+rSkGvXrBlCw8id+Dr60ZExDnMzXNRvvwebG1707ZtOzp27EhwcDDu7u6cOnWKoUOHSoeUSNPeaKRUZiF3OP7bpUuduH9/BTlztqZMmfWJn/zlF20enk6nJYtq3DjZY2zbpuWhBli/XqbtpYaHD6F9e9i/X/t94EBtVqW5uXHjSi3x8RH4+BQgLu4JpS0n41jjWy0p+q1bWo+dSELqw6TknIjU8vChdrf86FGtnl6ySE/HCyOeJ2Hs2xdmz9ZGTwHBwd5cuNCO2Nj7mJraUaLEMhwdPyEuLo6JEycyYcIE4uLiyJMnD4sWLaJp06ZGLJ3ICKQ+TErOiXhbO3ZoU/bCw6FYMdix8RmFx3fW5nMDTJyIftjXXL8xlLt3fwHAwaE2pUqtZteuk3zxxRc8ePAAMzMzxowZw/DhwzHPLF/yRZqUIiOlhACIirrJ/furAXBxGZH4yX37tHlioH1pfkmHlL+/dqMXtI5/6ZBKHY6OWjqS4cO132fPhg8/hHv3jBtXajE1tSVfvr4A+FtsQNXx0BJuLVhg5MiEECIpR0ftJkKbNtr0605dTBhvMxk1a7Z242fuXGjYUOu9ArJm9cDd3Q8Hh1rEx4dx4UJr/v77G0xMFGPGjOHo0aOULFmSoKAgPv74Y3r16kVYpkg0KIQQadvPP2s368PDte/mR3c8oXCfBlqHlLk5rFhB1KDP8Dtd29AhVaDAcAoW3MSXX35HixYtePDgAaVKleLYsWOMHj1aOqREuiGdUuKNBQRMBeLJlu0j7Ozcnj9x/Tp8+ql2kd+5MwwZkuzrY2O10TpPnmiLpSXc8BWpw8xMm/WxebM2bfLwYahUCby9jR1Z6siX7yt0OkvCwo4TMrCutvHXXyE62riBCSFEMqyttenWw4Zpv48bB11PDSB27UbIkgU8PbWkjL6+AFhaOlG+/D6cnb8B4M6d6Zw5U5fo6Hu4ubnh6+vL4MGD0el0LFy4kPLly3Pw4EEjlU4IITK3uDhtgsmAAdpqe927w855N8nWtAYcOqStJL1rF48+csDXtxJhYScwM8tG2bLbuH27IeXLV2Tp0qXodDq++eYbfH19qVSpkrGLJcQbkU4p8UaiowMJDFwC/GuUVGio1r3/9Kk2EXrBAu0ubjJGjdJyZTg4aF+0JY+UcbRooV3DlC0L9+9DvXowfbo2XT0js7DIhZNTNwD8Cx6DvHm1Va02bDByZEIIkTwTE5g8WWtaTU21Ffoazm1JyO5j2mIiAQFQsyYsW/bP/uYULjyV0qU3YGpqR0jIIU6erERwsDfW1tZMnz6d/fv3U6BAAW7evImHhwfDhg0jWjrnhRAi1YSGalO0f9EGPjF5MizqcxKL2tW0JLDOzugPenG9wE7On29GXNxT7OyqUKrUEb7/fjd169bF39+fggUL4u3tzdSpU7GysjJqmYR4G9IpJd7InTszUSoae/saODjU1jbGx0OHDnDpknaBv2kTvKRC3LZNy2EEsGSJlsRVGE+RIlquko4dtf/Gb77RBruFhho7spSVP/8QQMeTpzsI/7qltnHOnFe9RAghjK5XLy1Vo52dtkJf1W6luLbqhHZTKDoaunXTFhqJiQHA0fET3NxOYmtbltjY+5w+XQ9//ykopahTpw7nzp2jW7duKKWYOnUq7u7unD592qhlFEKIzOD2be1ewl9/aSNiN2yAYaW2oavjod0sLV+e6IObOBM3gIAAbVpJvnwDiI2dQY0aLfnpp58A6NWrF2fOnKFWrVpGLI0Q70Y6pcRri419yr178wBwcRn5fEnp777TviVbWWlzwpyckn39v/NIffJJKgQt/pONjXbXfc4cbcr6hg3QqBFERho7spRjY1MER0ctkdndesHanMYjR0AuxoQQadxHH2kzOhJWU3Wr68DGzpu1eX2gVeb16mlrigM2NsWoVOkouXN3BuK5ceNbzp9vRWxsMPb29ixZsoTNmzeTK1cuzp8/T5UqVZg0aRJxcXHGKqIQQmRox45pE0vOn4c8ebQVsj95uECbxhAZCQ0b8mTbeE7eaUxIyEFMTe0oUmQVv/1mzwcfeHDlyhWcnJzYsWMHCxYswM7OzthFEuKdSKeUeG137/5CfHw4trblyJ69ibZx5Ur43/+0nxcvhsqVk32t5JFK23Q6bRGnAwcgWzZtemXbttr/W0aVL98AAO6HbSHusxbaRhktJYRIB8qV05YLr10bwsKg9acmDI8aS/ymP7VkgYcOaXmmjh4FwNTUhhIlllGs2AJ0OgseP96Cr6874eFnAGjRogXnz5+nVatWxMbGMnLkSGrXrs21a9eMWUwhhMhw/vgD6tTRUmeULw/Hj+px3zAC+vQBvR7VvQu35lbj7N+tiI19iK1tOaysVtO8+VR++OEH4uPj+eyzzzh//jyNX7KglBDpjXRKidcSHx/BnTuzAShQYIQ2SurECejRQ9thxAhtCt9LSB6p9KFaNW2KpbW1NvitV6+Mm2PKweEDbGxKoNdH8KBnQW3jypVaXjQhhEjj8uSBvXth8GDt98mToeHPzXiy6wSULKktq+rhAQsXAqDT6cibtxcVKx7G0tKFZ8+uc+pUNQIDlwHg6OjIhg0bWL58Ofb29vj4+FChQgXmzZuHyqgNgRBCpBKlYOJE7abvs2fQtCkc3BuN88hOhhv8MROHcXZAILcCvgcUuXJ1Z//+dnzwwSf4+fmRPXt21q5dy6pVq8iePbtxCyTEeySdUuK13Lu3kLi4x1hbFyFXrk+1C/eWLbUcFs2awQ8/vPS1kkcqfalRA9at05LpLlum9TdmRDqdDienXgDcs9mvDT2IijIkChZCiLTO3FxboGLNGrC1hf37oULbYpycc0ybIx8To91d6N3bsMKovb077u6+ZM/eGL3+GVeudOPKlV7Exz9Dp9PRuXNnzp07R926dYmMjKRv3740btyYu3fvGrm0QgiRPiWk/Bs1Svt90CDYsjwYu08bwapVYGZG8KoRnPRYydOnuzExscbKagq9e19l+PBRxMTE0LRpU86fP0/btm2NWhYhUoJ0Son/pNdHExAwDQBn52HodKYwYYJ2F7ZYMVixQlsaKBmSRyp9+vhjWLRI+3nyZJg507jxpJQ8eTqj01kQHn6KsMEfaxvnzNHW5BVCiHSiXTstR0mxYv8sxNfIjl8brkf9OFGbn/3rr9p8kX86lszNc1C27DZcXb8HdAQGLsTPryZRUTcBKFCgAHv27GH27NlYWVmxa9cuypQpw+rVq2XUlBBCvIEnT6BhQ1i+XLvhO2cOzPzaH9PaNcHLC2WXBf+/unLaaQoxMXexti6On9831K8/nkOHDpElSxYWLVrE1q1bcXpJ3l4h0jvplBL/KSjod2Ji7mJhkZc8eTrDtWvP1y79+Wctf0UyYmO1L8pPnmippiSPVPrStevzdGGDB2sz2zIac/McODq2AeCee6A2v/T6ddi928iRCSHEmyldGo4f1wYxx8RA7z46el4fQfSmHVqywKNHtTxThw4BoNOZ4Oo6mnLldmFmloPw8FP4+lbi8ePtAJiYmDBgwAD8/Pxwd3cnODiYDh060L59ex4/fmzEkgohRPqQsMLegQPa5dL27dC3up+WL+PiRWKL5OH8X+7cMFsExKNUC8aOLcCgQROIiIjAw8ODc+fO0aNHj+cLTAmRAUmnlHglpeIJCJgMgLPzN5iYWMLQoVqPU5MmWtf/S4wcqX0HljxS6dewYdoQY9A6qXbtMmY0KSNvXm0K34MnfxDX85+8aJLwXAiRDjk4wMaNMGmSNoB5yRKo8X0j7mw+qU1Rvn8fPvxQq+P+GfGUPXsD3N1PYWdXlbi4YM6d+5gbN75DqXgASpQowZEjRxg/fjympqasW7eO8uXL4+XlZcSSCvH65s6dS8GCBbGyssLNzY2DBw++dN9Dhw5Rs2ZNcuTIgbW1NSVKlGBmRh0uLlLU2bNaSozLl7XVUg8fho/Ypa1QERhIWOPC+C4153GsF2DO+fPd+OQTb3bt2oOVlRUzZ85k//79uLq6GrkkQqQ86ZQSr/Tw4Xqiov7GzCw7Tk5fgKcnbNmijT+dNu2lr9u69fnTS5dCwYKpFLB4r3Q6LV9Jhw4QFwetW2t34jMSB4faWFsXIz4+nAef59U2bt8ON28aNzDx2uSCQ4jndDoYPly7iZAjB5w6BeVbFWLfhCPaMrhxcdC/v5bgJCoKACurAlSseIB8+foD4O//I2fOfERMzEMAzM3NGTNmDEePHqV48eLcvXuXunXrMmrUKGIz8jKtIt1bu3YtgwYNYtSoUfj5+VGrVi0aN26Mv79/svvb2trSv39/Dhw4wKVLl/juu+/47rvv+PXXX1M5cpGeeXlBrVpappPSpeHIEShzbDE0bYoKD+fu0GKc+jaAZ3EBPHtWgNmza/PVV0sJDg7G3d2dU6dOMWjQIExekh5FiAxHiSRCQkIUoEJCQowdilHp9Xp1/Hh55emJunlzvFJxcUpVqKAUKNWv30tfd/u2UtmyabsNHJh68YqUEx2tVMOG2v9pjhxKXb5s7IjeL3//acrTE3XypPvzgg4bZuyw0oS0Xh+uWbNGmZubq4ULF6qLFy+qgQMHKltbW3X79u1k9z916pRatWqVOn/+vLp586b6/ffflY2NjVqwYMFrv2daPydCJLh9Wyl3d61K0+mU+mGCXsVPm66UiYm20c1N2+kFQUGrlLe3jfL0RB0+nE8FBx9J9Hx4eLjq0aOHAhSgqlatqq5fv56axRJpSFqvD6tUqaL69OmTaFuJEiXU8OHDX/sYrVq1Uh07dnzt/dP6OREpa+1apSwstCq2Vi2lnjzWKzV6tFKgYq1RFxa7Kk9PlKcn6uefq6rcuXMpQJmZmanx48ermJgYYxdBiPfmdetD6ZRKhjQmmkePtitPT9SBA1lUTMxjpRYv1mpYBwelHj5M9jUxMUpVq6btVrmy1pkhMoawMO3/FJQqUECpO3eMHdH7Ex39UHl5WShPT1To1hnPe9+ioowdmtGl9fpQLjiEeLWoKKW++EKr1kCp5s2VCtuyT6mcObUNOXMqtX9/oteEh59XR48WV56eKC8vMxUQMFvp9fpE+6xbt045ODgoQNnZ2akVK1akZrFEGpGW68Po6GhlamqqNm7cmGj7gAEDVO3atV/rGKdOnVK5c+dWCxcufO33TcvnRKSsWbO0GwCgVOvWSkU9CNV+ABXmijq6LYfy9ERt326q2rWrYujcL1WqlDp58qSxwxfivXvd+lDGBIpkKaW4fftHAPLm7YP5M/Pn65iOGQM5cyb7un/nkbKwSK2IRUrLkkWb1Va0qLaqYqNGEBxs7KjeDwuLnDg6aktDBha5Ai4u8Pix9iEWaVZMTAy+vr40/Fduu4YNG3LkyJHXOoafnx9HjhzBw8PjpftER0cTGhqa6CFEemFlpS2+t2iRltvxzz+h4pC6XFl5EipVgkePoEEDmDHDkGfK1rY0bm4ncHRsi1Jx/P33QC5e/Iy4uHDDcT/99FPOnDlDzZo1CQsLo2PHjnTu3Fn+PkSa8ejRI+Lj48mdO3ei7blz5yYoKOiVr82fPz+Wlpa4u7vTr18/evbs+dJ9pY0Qer2WcnfQIK0a7dcP1o67hFXtKrBhA0FNTDm12Jwo28ecP5+TPn0cWbv2ODqdjiFDhuDr64ubm5uxiyGE0UinlEhWSMhBQkOPoNNZkD//YG3pvKAgKFxYq2mTIXmkMj5HR21hOicnOH8emjc3pCRJ95yctITn9x+uIq5vN21jwiqTIk1KrQuOSZMm4eDgYHg4Ozu/l/iFSE09emgL7xUoAH//DZVaubBuwCHo3Bni42HIEPj8c4iMBMDMzI5SpdZQpMgsdDozHj5cy6lTlYmIuGg4pouLC15eXowbNw4TExN+//13KlasyPGMlnxQpGv/XrVMKfWfK5kdPHiQkydPMn/+fGbNmsXq1atfuq+0EZlbTIxWjSZcA02aBD/X/gPT6lWIv3GZy2NtuDw0nqjYWBYtcuGrrx4REBBEwYIF8fLyYtq0aVhZWRm3EEIYmXRKiWT5+08CIE+ebljej31e006dmuwyev7+0KWL9vPAgdCqVWpFKlKbqyvs3KmNhjt4ED77TMubm95lzVoHa+sixMeH8bClgzbM7+TJjJfZPQNK6QuOESNGEBISYngEBAS8l7iFSG3u7uDrqw2MioyEdl2tGZR1GfEzfwIzM1i9GqpXhxs3AO1vK3/+gVSo4I2FRV4iIy/j61uFBw/WGY5pZmbG2LFjOXDgAAUKFODGjRvUrFmT//3vf+j1emMVVQhy5syJqalpkpsUDx48SHIz498KFixI2bJl+eKLL/j6668ZN27cS/eVNiLzCguDpk1h5UqtCv1tSRzDHw9F164tkVnDObXclqA6kVy+DP365WTlytsA9OrVizNnzlC7dm0jl0CItEE6pUQSYWGnePJkJ2BCgQLDYMQIePYMPDygZcsk+8fEQLt28PQpVK6sDaoSGVu5ctoUEEtLbTHGL780zPpIt3Q6nWG01L2w1dqHGrSl00WalFoXHJaWltjb2yd6CJFe5cwJf/2lTbcHmP2TjjobvuLxun2QK5e2jrm7uzYs9h8ODjVwd/cja9a66PURXLzYjuvXh6NUvGGfmjVrcubMGdq2bUtcXBwjRoygQYMG3L17N7WLKAQAFhYWuLm5sWfPnkTb9+zZQ40aNV77OEopoqOjX/q8tBGZU1CQdmm0dy/Y2sKu3+7T6bcGMG0aD+qA71JzQh0jWLXKlv79Tbh+/RFOTk5s376dBQsWYGdnZ+wiCJFmSKeUSCJhlFSuXJ9hffYhrFqlrTE9Y4b2778k5JHKmlXySGUmtWvDmjVgYqLlKhkzxtgRvbs8ebqg05kTFnacsL7/5Clau1bLuSLSnNS64BAiozE1hR9/hM2bwd5em9ZXpm9tjs/zhSpVtLtMjRrB//5nuONgYZGL8uV34+w8DICAgMmcPduU2NinhuNmzZqVNWvWsHjxYmxsbNi/fz/ly5dny5YtxiimEAwePJhFixaxZMkSLl26xNdff42/vz99+vQBtFFOnTt3Nuw/Z84ctm7dyrVr17h27RpLly5l2rRpdOzY0VhFEGnQ1atQowb4+WmpLU785EPdbyqhP+zFtcHmXBwLQU9jGT48KwsXRhAfr6ddu3acP3+eJk2aGDt8IdIc6ZQSiURGXuHhww0AFCjwLXz9tfZEly5aQtR/+fNPmD5d+1nySGU+LVvC/Pnazz/8AD//bNRw3pmFRS5y5tTmngZm99FGC0RHw+LFRo5MvIxccAjx9lq0gBMnoHRp7a5/zXb5mdPuAKpHT60zasQIaNtWm6MC6HSmFC48mZIlV2NiYs3Tp7vw9a1MRMQFwzF1Oh3du3fn1KlTVKpUicePH9OyZUv69etHVEZJQijSjXbt2jFr1iy+//57KlSowIEDB9ixYwcuLi4ABAYG4u/vb9hfr9czYsQIKlSogLu7Oz///DP/+9//+P77741VBJHGHDsGNWvCzZtQuJDifL+5lOzjQZT+Hn6/WnG3WSxHjkDv3tacPBmMra0ty5cvZ/Xq1WTPnt3Y4QuRNqX8QoDpT2ZeyvXSpW7K0xN19mxzpdas0dY0tbFR6u7dJPveuqVUtmzaLoMGGSFYkWZMmKB9DnQ67WOTnj15sld5eqIOHLBXccvmawVzcVEqLs7YoRlFeqgP58yZo1xcXJSFhYWqVKmS8vb2NjzXpUsX5eHhYfj9p59+UqVLl1Y2NjbK3t5eVaxYUc2dO1fFx8e/9vulh3MixJsIC1OqXTutugOlOnRQ6tlPC5QyN9c2lCql1NWriV4TGuqnjhxxUZ6eKG9vW/XgwYYkx3327JkaMmSIYdnz0qVLq7Nnz6ZWsUQqkPowKTknGde2bdplEShVs2KEivy0k1KgHtZAHfzLXO3ahWrTxtJQ51WsWFFduXLF2GELYTSvWx/qlErvmWDev9DQUBwcHAgJCclU88KfPfPn2LHCKBVHxdJeOFTuArdvw/jxSeZmxcVBrVratL3KlbWh/zJtL/NSCr76Sku/ZG4OO3ZA/frGjurtKKXn2LFiPHt2neKF5uNUcSQ8eaINC2zWzNjhpbrMWh++ipwTkREpBbNnwzffaIvxlS0LO0b7kH9gawgM1Fa3WLECPv7Y8JqYmEdcvNiW4GBPAFxcvsPVdTw6XeKB+Lt376Zz587cv38fS0tLpk2bRr9+/f5zQQKR9kl9mJSck4xp8WLo3VurH7vVvs7Cp63RXTzDzS8goJ226NPEiTZcuaKtYPr1118zadIkLJNZIEqIzOJ160OjT9+bO3cuBQsWxMrKCjc3Nw4ePPjK/b29vXFzc8PKyopChQoxP2Hu0AtmzZpF8eLFsba2xtnZma+//ppnz56lVBEyjICA6SgVR9asH+Kw2EfrkMqfX/uG+i/z52sdUg4OkkdKaKnGZs+GTz+F2Fht9UVfX2NH9XZ0OhPy5tUSngc+Wqatow7wyy/GC0oIIVKYTgeDBsH+/ZA7N5w7B2W+qM7uSb7aXJWQEK1j/vvv4Z9V9SwsclKu3G7y5x8EwO3bP3D+fAvi4kISHbthw4acPXuWJk2aEB0dzVdffUWLFi14JPn6hBBpnFIwYQL07Kl1SM2ot53FZ92JDTzD6Tnm+LfVFo/o08eMK1ciyZkzJ9u3b2fGjBnSISXEazJqp9TatWsZNGgQo0aNws/Pj1q1atG4ceNEc7tfdPPmTZo0aUKtWrXw8/Nj5MiRDBgwgA0bNhj2WblyJcOHD2fs2LFcunSJxYsXs3btWkaMGJFaxUqXYmIeEhi4EIAC9l/CxInaE5MmgY1Non0fP34+cGrSJMkjJTSmpvD771C3LoSHQ+PGcO2asaN6O3nydEWnMyc09CjhPepoV2u7d2uZLYUQIgOrXRtOndKS+IaEwEddnRhXez/6vv20HcaO1ZKg/7OqnomJGUWKzKREid8wMbHi8eNt+PpWJSLicqLj5sqVi23btjFr1iwsLCzYunUr5cqVY9++faldRCGEeC3x8doK02PGgAnx7PtgLF/v+5inRYLxXWrGvXyxTJxoxpQpEBUVR926dTlz5owkMxfiDRm1U2rGjBn06NGDnj17UrJkSWbNmoWzszPz5s1Ldv/58+dToEABZs2aRcmSJenZsyfdu3dn2rRphn18fHyoWbMmHTp0wNXVlYYNG/LZZ59x8uTJ1CpWunTnzmz0+ijs7CqT7X+7taSmlStDhw5J9h0zRluYp1w56NXLCMGKNMvSEjZtgooV4eFD+OgjLXlueqMlPG8JwD3dDkj4cvGSukkIITKSvHnB01Oblg0wfpIFTa7/QvhPS8DKCvbs0eb3rVtneE2ePJ2oWPEQlpbOREVd4dSpKjx6tDXRcXU6HQMHDuT48eOUKFGCwMBAGjRowPDhw4mJiUnNIgohxCtFRUHr1rBgAWTnCTdKfcyHR77nZjc4OwXO+MfRu7cFe/fGYWpqysSJE9m9ezd58+Y1duhCpDtG65SKiYnB19eXhg0bJtresGFDjhw5kuxrfHx8kuz/0UcfcfLkSWJjYwH44IMP8PX15fjx4wDcuHGDHTt20LRp0xQoRcYQFxfC3bva1KQC6jN0i5doT8yYASaJPyLnzj1fbW3WLG10jBAvsrfXhjEXLqytTNKokXa3Pb1xctJ6XO/f/534/j21jUuXQkSEEaMSQojUYWEBP/2kjYC1toZdu6DsjG5cWOGnrUz69Cm0awcdO0JwMAB2dm64uZ3EwaE28fFhnD/fnFu3vkcpfaJjly9fHl9fX3r16oVSismTJ1OzZk3+/vtvI5RUCCESe/JEy426ZQtUMffDP5cbeQJ3cma6CTc7wuo1MHCgCffuxeDq6sqhQ4cYMWIEpnJhJMRbMVqn1KNHj4iPjyd37tyJtufOnZuglwytCAoKSnb/uLg4Q16C9u3bM2HCBD744APMzc0pXLgwH374IcOHD39pLNHR0YSGhiZ6ZCZ3784jPj4EG5uS5By+TcsV8emn8MEHifZTCgYO1J5u3Ro+/NBIAYs0L3du7QImVy44cwZatoT0ltYtW7a6WFkVIj4+lAflnkCRIlrv2qpVxg5NCCFSTceOWg7JwoXh1i1w+7wEC7sdQX03WrtxtXKlNmpq/35AG2lavvxe8uXrD8CtW2O5cKE1cXFhiY5rY2PDggUL2LBhA9myZePkyZNUrFiR5cuXI2vwCCGM5fZtLY3ekSPwpfUyjuhqEOt0C98lptwooGfYMBN+/RXi4vS0bdsWPz8/qlWrZuywhUjXjJ7o/N8rryilXrkaS3L7v7jdy8uLH3/8kblz53Lq1Ck2btzItm3bmDBhwkuPOWnSJBwcHAwPZ2fnty1OuhMfH8WdOzMBKPCkEbp9+7Xbo5MnJ9l30yZtOL+lJUydmtqRivSmcGHYuRPs7MDLS7uwiY83dlSvT6czwcnpCwACgxZpSQVAW2JQLpiEEJlIuXJw4oS28F50NPTqZ06rc98TvP2w1mF/5w7UqweDB8OzZ5iYmFO06M8UL74Enc6CR482c+pUNSIjkyYa/OSTTzhz5gy1a9cmPDycrl278vnnnxOSHofYCiHStbNntXx6Ny5H85ttH+Y868adNs84PQMOXY2nZ09TfH31WFtbs2jRItasWUPWrFmNHbYQ6Z7ROqVy5syJqalpklFRDx48SDIaKkGePHmS3d/MzIwcOXIAMHr0aDp16kTPnj0pW7YsrVq1YuLEiUyaNAm9Xp/cYRkxYgQhISGGR0BAwHsoYfoQFLSE2NgHWFq6kGvwdm3joEFJspc/ewZDhmg/Dx0qyc3F66lYETZv1vo5N2yA/v3TV3+OlvDcjNBQH8LbV9XmsJw5A4cPGzs0IYRIVdmyaVNZpk0Dc3Pt51Ldq7F/up+2TjrAzJna1L7TpwFwcupGxYoHsLDIS2TkRXx9K/P48Y4kx3Z2dmb//v1MmDABU1NTVq9eTYUKFfDx8UnFEgohMjNPT6hVC0zuBXDCujbtTRdwbhJc7QrzFsC338LTp/GUK1cOX19fevTo8cqBFEKI12e0TikLCwvc3NzYs2dPou179uyhRo0ayb6mevXqSfbfvXs37u7umJubAxAZGYnJv/IgmZqaopR66XBwS0tL7O3tEz0yA70+Fn//KQAUuFEFk0tXwdERRo1Ksu+MGdqw/Xz54BUzIYVIom5dWLFCW8Bu/nxtNfH0wtIyDzlytAAgMGLt88T/c+YYMSohhDAOExPtBtXx41CyJAQGQr0WWRhiO5/YTdu0udsXLkCVKtqI6/h47O2r4ubmi719DeLjQzh37mNu356U5DuZqakp3333HQcPHsTV1ZVbt25Rq1YtfvjhB+LT0zBbIUS6s3atlgPVLXQ/58wr4eJ6nJOLdJzNB199pTOs6dC/f3+OHTtGyZIljRuwEBmMUafvDR48mEWLFrFkyRIuXbrE119/jb+/P3369AG0EUydO3c27N+nTx9u377N4MGDuXTpEkuWLGHx4sV88803hn2aNWvGvHnzWLNmDTdv3mTPnj2MHj2a5s2bS/K5f3nwYDXR0f6Ym+Uizzd7tY0TJmiZql9w9y5MnKj9PHky2NqmcqAi3fv0U/hFy6XPuHHPk+WnB3nzvpDwvG93beOGDelzWUEhhHgPKlSAkyefz2qeMQPcxzbl8h/ntCSCsbHaHaw6deDmTSwt81ChgidOTr0Bxc2bI7l4sS1xceFJjl29enVOnz7NZ599Rnx8PKNHj6ZevXqZahS7ECL1zJoF7dsrBsZMYQ/1CWv+iNM/w1Y/Re/eOq5cUWTPnp3Nmzfz888/Y2VlZeyQhch4lJHNmTNHubi4KAsLC1WpUiXl7e1teK5Lly7Kw8Mj0f5eXl6qYsWKysLCQrm6uqp58+Ylej42NlaNGzdOFS5cWFlZWSlnZ2fVt29f9fTp09eOKSQkRAEqJCTkXYqWpun18erYsRLK0xN1+5eaSoFSZcooFRubZN+OHbWna9RQSq83QrAiwxgzRvss6XRKrV9v7Ghej14fr3x8XJWnJyowcLlS1atrhfj+e2OHlioyQ334puScCPHcn38qlTOnVi1aWSn1y896pV+8RKksWbSNWbIotWSJ4QvE3bsLlJeXufL0RB0/XkZFRv6d7HH1er1avny5ypIliwJU9uzZ1bZt21KzaOI1SH2YlJyT9CE+XqlvvlHKjhC1gVYqxhZ1bjxq+3ZUgwYo0B61a9dWAQEBxg5XiHTpdetDnVLpKcNL6ggNDcXBwYGQkJAMO5Xv4cONXLjQGlOdHdWbR2IWGq8tl9awYaL9fHy0hH+gJTl1dzdCsCLDUAr69IFff9XyTO3apd1IT+tu357IzZujsLevSaXLfeHzz7W5rLdugZmZscNLUZmhPnxTck6ESCwoCLp21ep0gKZNYdnYm+Qc3BkOHdI2tmypVf6OjoSEHOHChdbExARhZpaNUqXWkD17w2SP/ffff9O+fXt8fX0BbRT9999/j1kGr3vTC6kPk5JzkvbFxED37nBq5UU20Yq8Ra9yYRycCdMmjdy9CyYmJowbN46RI0fKbBsh3tLr1odGX31PpD6lFP7+kwDIf9RJ65Bq0iRJh5ReDwMHaj936yYdUuLd6XQwdy60aqV9IWjRwpAPN03Lk6cbYEpo6GEiGpeEXLm0byxbthg7NCGEMLo8eWDHDpg9W1uhd/t2KP1xQf761gv+9z8tM/rmzVCmDGzbhoNDDdzcTmJnV5W4uKecPdsYf/9pyeb+LFKkCIcPH6Z///6AtmJy/fr1CQwMTN1CCiEyhLAwreM8ZuU6jlGZLM2ucvIn+O2QtiDP3bva4gve3t6MHj1aOqSESAXSKZUJPX26l7Cwk5goS/JNugqmptpyOv/y22/a6Cg7u+c5pYR4V6amsGoV1K4NoaHQuDHcuGHsqF7N0tKJnDmbA3Dv0XL44gvtCUl4LoQQgJYEfcAA7XtDmTLw4AE0aWbKgLvf8uzAcShdWtvYrBn07o1lrAMVK3qTJ093QM+NG0O5dOlz4uMjkxzb0tKSn3/+mTVr1pAlSxa8vb2pUKEC+/fvT/2CCiHSraAgqFc7lkZ7h7DKqh0BoyI53g1GjIN58yAuDlq1asXp06f54IMPjB2uEJmGdEplQv7+Wg+T0wE7LELQ5lP9axWJ0NDnq+yNHq3dBRXifbGy0gYZlSunfUH46CPtWiUtc3JKSHj+G/FfdNGuwDw9tZWmhBBCAFC2rNYxlTDS+uefwb1nBc4tPQmDB2sbf/0VKlTA5NgpihdfRNGiv6DTmfHgwWr8/GoSFXUr2WO3a9cOX19fypYty4MHD2jQoAETJkxAr9enTuGEEOnWhQvwceX7TD1dn96uMzg1H/7KBj17wrFjYGVlxbx589iwYQPZs2c3drhCZCrSKZXJhIT4EBzshU6Z4jznETg4aMuh/cvEiXD/PhQpot35FOJ9y5oV/voLXF3h77+1EVOhocaO6uWyZ2+ApaULcXFPeWh5TJt7CNp8RCGEEAZWVtqKVn/9BblzaxeDlWtZMbvAdPR79kH+/HD9OnzwAboxY8iXqxfly+/D3NyR8PDT+Pq68/Rp8qOgihUrxtGjR+nevTt6vZ4xY8bQuHFjHj58mLqFFEKkG3v2QL+qJ9l8x43iHx3g+M8waxcMHQpPnkCpUqU4ceIEffr0QafTGTtcITId6ZTKZBJySeX2ssDqITBmDOTMmWifv/+GmTO1n2fM0PJDCJES8ubVEuPmzAmnTmm5pqKjjR1V8nQ6U5ycegIQGPgr9OunPfHbb2m7N00IIYykUSM4exY+/lir2wcNgibT6hK055y2YIReDz/8ANWrkzUoF25uvmTJ4kZc3GPOnGlIQMCsZPNM2djYsHjxYpYuXYq1tTW7d++mYsWKHD58OPULKYRI0xYuhKUfrWFb/AeED72LZxfoPwxWr9YW4OnduzcnTpygTJkyxg5ViExLOqUykfDwczx+vBUUFFgSpQ2D+idx6IuGDNGSUH/0kfZFUoiUVKyYdjc9SxbYvx86doT4eGNHlTwnJy3heUjIQSKqOkGJEhAeDr//buzQhBAiTcqVC/78UxtUamWl3YgoWysrf7ZdAWvWQLZs4OsLFStitWgLFSscIHfuTkA8169/zeXLXYiPj0r22F27duXYsWMUL16cu3fv4uHhwfTp05PtyBJCZC56PXw7VM+DXqP41fUzLs6LZpWVlhb00iXImjUrf/zxB/Pnz8fGxsbY4QqRqUmnVCbi7/8/ABwPmGBzB5gyBSwsEu2ze7f25dHUVBstJSNYRWpwd9cWZrKwgPXrtUFIafGawtIyHzlyaD21gUGLno+WmjMnbQYshBBpgE4HX36pjYitUAEePdJmQH/p1Y6o4+egQQN49gy++grTpq0oYT+JIkVmAabcv/87fn61ePYsINljly1blhMnTtC+fXvi4+P55ptvaNWqFU+fPk3NIgoh0pDISOjUMowa01rStdlEjsyE79fDhAkQEQE1a9bk9OnTtGnTxtihCiGQTqlMIyrqOg8erAGgwAo9eHhAy5aJ9omNha+/1n7u3z9J7nMhUlS9erBihXbxsmBBsqnO0oS8ebWE50FBy4nv2FYb4nXpEnh5GTcwIYRI40qWhKNH4ZtvtN/nz4dKzfLhN2mnlhHdygp270ZXtiz5ffJSvvxuzMxyEB7ui69vJR492pbsce3s7Fi1ahVz587FwsKCLVu24Obmhq+vbyqWTqRVc+fOpWDBglhZWeHm5sbBgwdfuu/GjRtp0KABjo6O2NvbU716dXbt2pWK0Yp3FRQEHard4Nv9VSgydiu7W0HvQbB9O+h0OkaNGoWXlxcuLi7GDlUI8Q/plMok/P2nAHqyHwW76zotWdS/hkHNmwcXL2r5fcaONU6cInP79FNt0BHA99/DL78YN57kZM/+EZaWzsTFPeFR9F7o1El7IiFwkerkgkOI9MPSEqZO1RIP580Lly9D1eomTHvWH72vH7i5wdOn0LYt2QYsxa3ofrJkqUhs7CPOn2/GtWsDiI9/luS4Op2OL7/8ksOHD+Pq6srNmzepUaMGc+fOlel8mdjatWsZNGgQo0aNws/Pj1q1atG4cWP8/f2T3f/AgQM0aNCAHTt24Ovry4cffkizZs3w8/NL5cjF2zh/HgaW9+KnuIqEL7zMsghtlOatW5A7d2727NnDDz/8gJmZmbFDFUK8SIkkQkJCFKBCQkKMHcp78ezZXeXlZaE8PVFPy6BU165J9nn4UKmsWZUCpebPN0KQQrxg/Hjts6jTKbV6tbGjSermzfHK0xN16pSHUufPa8GamioVEGDs0N67tF4frlmzRpmbm6uFCxeqixcvqoEDBypbW1t1+/btZPcfOHCgmjx5sjp+/Li6evWqGjFihDI3N1enTp167fdM6+dEiPTi0SOlWrXSqlBQqm5dpe7cjFFq9GilTEy0jc7OKn7/TnXt2iDl6Yny9EQdP15WhYdfeOlxnzx5olq0aKEABaj27dur0NDQVCxZ5pHW68MqVaqoPn36JNpWokQJNXz48Nc+RqlSpdT48eNfe/+0fk4yqp07lRpk9Yu60d5Ebd+MqlsXQx3QsGFDFRQUZOwQhch0Xrc+lJFSmUBAwAyUisHhLGS9aQs//phknzFjIDgYypWDnj1TP0YhXjR69PO8Up07a7nO0pI8eboDJoSEeBNZ0EybDhsfr807FKlqxowZ9OjRg549e1KyZElmzZqFs7Mz8+bNS3b/WbNmMWzYMCpXrkzRokWZOHEiRYsWZevWrakcuRAiRw7YsEFbHcvGRlvsomwlczZW+B4OHYLChSEgAJO6jSgyB8oWWY+5uSMREefw9XXj7t35yY6CypYtG5s2bWLatGmYmpqyZs0aKleuzLlz54xQSmEsMTEx+Pr60rBhw0TbGzZsyJEjR17rGHq9nrCwMLJnz54SIYr35Nc5sfh/1pWu4/uzu46eL/pq9YmpqSmTJk3ir7/+Infu3MYOUwjxEtIplcHFxj7m3r35ABRYCXz7rTZe/gVnzjy/lv7pJy3JuRDGpNNpn8V27bRcZ598AsePGzuq56ys8pMjR1MA7t1b+Dzh+cKF2tKVIlXIBYcQ6Z9Op90M83th5l7r1tBzcXXCD52GXloeP2bNIkf1Qbjfn0i2bA3R659x7dqXXLjwCbGxj5M5ro4hQ4bg7e1Nvnz5uHLlClWrVmXZsmWpWj5hPI8ePSI+Pj5JZ0Tu3LkJCgp6rWNMnz6diIgI2rZt+9J9oqOjCQ0NTfQQqSM+Hsb0fUTRJZUp9etyFvpreXHv3YMCBQpw4MABhg8fjomJXPIKkZbJX2gGd/fuL+j1EWS5Btnv5YMhQxI9rxQMGqQtm/rpp9qADyHSAhMT+O03bVGmiAho0kTLPZJWODklJDxfRnzzRlpn7/372m1/kSrkgkOIjKNYMThyBEaM0DqqFi+GSrWzcKLnAtixAwoWhDt3sGz9BeVGmFA46wh0OnMePdrMiRPlefrUK9nj1qxZEz8/Pxo2bEhUVBTdunWjR48eREZGpm4BhdHo/pVDVSmVZFtyVq9ezbhx41i7di25cuV66X6TJk3CwcHB8HB2dn7nmMV/i4iAIR+dpmV0EcJGn2HYTC29Z1wctGzZEj8/P2rUqGHsMIUQr0E6pTKwuLgw7vjPAqDAKtBN+p82Pv4FGzdqi4ZZWcGUKakfoxCvYmGh9fFUrgyPH0PDhhCQ/KrgqS579kZYWuYnLu4xj4K3Qe/e2hOS8DzVyQWHEBmDhQVMnAienuDsDNeuQY0aMOl0Y+LPXtDmdltYoPtrJ87VZ1LJtwfWVkWJibnLmTN1uXFjFHp9bJLjOjo6smPHDr7//nt0Oh1LliyhWrVqXL161QilFKklZ86cmJqaJrlJ8eDBg/+cyrV27Vp69OjBunXrqF+//iv3HTFiBCEhIYZHQFr5opKBBQbCpA8X8XkLNw5XCOGL3lqntoWFBT///DMbN26UEdBCpCPSKZWBBQb+Spw+GOsAcIx0hw4dEj0fFfV84NTQoeDqmvoxCvFf7Oy0ZXyLF9c6pD76SOugMjYTEzPy5OkBaH9rfPEFmJnB4cPanFiR4uSCQ4iMycNDq0bbttVGPYwcCR82seZG1+/h7FmoVw+ePcNuyHzcvtCTR98IUPj7T8TPrxZRUTeSHNPU1JTRo0ezZ88ecuXKxblz53Bzc2PdunWpX0CRKiwsLHBzc2PPnj2Jtu/Zs+eVI2hWr15N165dWbVqFU2bNv3P97G0tMTe3j7RQ6Scs2cUW7t+TsORX7DgjJ6vB8GDB1CkSBGOHj1K//79X+vGlBAi7ZBOqQxKr48m4MZkAAqsBt2MWdp8qBdMnw63b0P+/FqqKSHSKkdHLdl5vnxw6RI0baoN2zY2J6cegAnBwV5EOoRpiVBARkulErngECLjypYN1qyBZcsgSxY4eBDKloVf9hRHv2sPrF4NefJgdv46JertpNSe6pia2BMWdoyTJytw//6qZI9br149/Pz8qF27NuHh4bRr146vvvqK6Ojo1C2gSBWDBw9m0aJFLFmyhEuXLvH111/j7+9Pnz59AO2mQ+fOnQ37r169ms6dOzN9+nSqVatGUFAQQUFBhISEGKsI4gW7Nz/h7tJi5Oy9isE/aNN84/Xw+eefc+rUKSpWrGjsEIUQb0E6pTKooMBlxKiHWD6A3PatoWbNRM/fuQOTJmk/T5kCtrZGCFKIN1CggNYxlS0bHDum9f8YO6e4lZUzOXI0ASAw8IWE5ytWaNl6RYqTCw4hMi6dDrp00QZHffghREbCV19Bvfo6blRpryUaHDAATEzINdGHyt3isY8oSHx8GJcufc6lS52JiwtLcty8efOyb98+hg8fDsAvv/xCrVq1uHXrViqXUKS0du3aMWvWLL7//nsqVKjAgQMH2LFjBy4uLgAEBgbi7+9v2H/BggXExcXRr18/nJycDI+BAwcaqwjiH6tmHMDqYX4u5P+bnj3B1xdsbGxYsmQJv//+O3Z2dsYOUQjxlnQqubV0M7nQ0FAcHBwICQlJl3fE9fo4jns588wkiCLzTMn/v2tagtAXdOwIK1dqfVUHD2pf/IRID3x8oH597eKkQwf4/fckgwBT1aNHWzl/vjnm5jmpXi0Ak4pV4Nw5mDEDvv7aeIG9J+mhPpw7dy5TpkwhMDCQMmXKMHPmTGrXrg1A165duXXrFl5eXgDUqVMHb2/vJMfo0qXLa6/KlR7OiRAZjV4P8+bBsGFa/W9rC5Mnw5dfgskZP+2HY8fQm4D/0DzcavQA0GNlVZhSpVZjb1852eNu376dTp068fTpU7Jmzcpvv/1Gs2bNUrdw6ZjUh0nJOXm/4uNh5fix5KvyPUtWa4MklYIyZcqwbt06SpYsaewQhRAv8br1oXRKJSO9Nyb37/3OpaudMQuB6se/xvTHGYmeP3JE64zS6eDECW0JZiHSk7/+gubNtVwjAwbArFnG61jV6+M4etSVmJi7lCq1hlybQ7Sk50WKwJUrxu0xew/Se32YEuScCGE8N25A9+6Q0Lf84YfaFJ6CLnpYtAiGD4enTwkuA5f+l4Vo23B0OjMKFvwBZ+eh6HRJ6+Tbt2/Ttm1bjh8/DsCwYcP44YcfMDc3T82ipUtSHyYl5+T9CQ0NZ++CRsQWOMyECXDhgra9d+/ezJw5E2tra+MGKIR4pdetD9P31ZJIQik9/me0BFH5/7LB9NtxiZ7X67WLeNC+1EmHlEiPGjfW8owA/PTT86moxmBiYvZPbim4d+9X+PxzcHCAv/+Gf+U6EkII8W4KFYL9++Hnn7UFhT09tVxT8xaYoO/ZS7sZ0LUrWc+De/twHI9YoFQcN24M58yZBkRH30tyTBcXFw4ePMiAf74gTZkyhbp163L37t3ULp4Q4h83/vbl2LYCXIw+zBdfaB1S9vb2rF27lvnz50uHlBAZiHRKZTCPb68hwjoQ0wjI5zYB/tUjuXy5Ngfbzg5+/NFIQQrxHnz+uTZCCmDUKFi40HixaJ1SOoKD9xOpuwddu2pPSMJzIYR470xMoH9/LddU7drawhd9+0KDBnArwhGWLoUDBzB3LUOpUTEUnwomMSYEB+/nxIlyPHq0NckxLSwsmD17Nn/88Qd2dnYcOnSIihUr4unpaYQSCpF5KaU47j2Fa5fdmbf+KaNHQ1gYuLu74+fnR9u2bY0dohDiPZNOqQxEKYX/qSEA5D2SE/NuAxI9HxoKI0ZoP48ZA/+xYroQad7AgdpS4QB9+sDGjcaJw8qqANmzNwYgMHCRdnUEsG0bSOJcIYRIEYULayOlZs8Ga2ttBFXZsjB/PqgPasGpU+imTsXJ2xb3nnqyXIO4uMecP9+cq1f7Ex8fleSYbdq0wdfXl/Lly/Pw4UMaNGjA7NmzkWwXQqS82NgneG9vyNWAb/lqMGzapG0fPHgwhw8fplChQsYNUAiRIqRTKgMJPr+C0OxB6GIgf51fwMws0fM//AD370PRos+n8AmR3v3wA/TsqU1N/ewz7QLFGPLm7QVAUNBS9EVctVv2SmlXR0IIIVKEiYn2nebsWahVC8LDtZznDRrA7Xvm8M03cOkSNpU/oVI/yL9Oe929e3M4daoK4eHnkxyzaNGi+Pj40LFjR+Lj4xk0aBDdunXj2bNnqVw6ITKPkJDDHNxdkr1H99KrF1y7BtntHdi2bRvTp0/HwsLC2CEKIVKIdEplIP6+2igppwsFsGzQLtFz1649n+o0cyZIvS4yCp1OW5GpVSuIiYEWLcDPL/XjyJ69KRYWTsTGPuTRoy3Qr5/2xKJFIBcyQgiRoooUAS8v7buOtTXs2wdlysCCBaDyO8OGDZhs3k6RXYUoOwzMn0BExHlOnXTn7t25SUZCWVtb89tvvzFjxgxMTExYvnw5tWvX5s6dO0YpnxAZlVLx3Lz5I4cP1mLanAf8+CNERUGtqtU4c+E8TZs2NXaIQogUJp1S79uDB3DoUKq/bej+eTx1fQjx4NxgcZLnBw+G2Fho1AiaNEn18IRIUWZmsGoVeHhoeQcaNdLyjKemFxOeBwb+Ch9/DAUKwOPHsHZt6gYjhBCZkImJNq37zBltleHwcG1q90cfgb8/2heg8+fJ0Wg0lb80J/tR0BPNtWv9OH+2OTExjxIdT6fT8fXXX7Nr1y6yZ8/OiRMncHNz45ARvucJkRFFRwdx6mQDvLy+o29/xV9/gQ4YPXIk+w8dJH/+/MYOUQiRCqRT6n3y84PixeGTTyA4OPXeNz4e/3Nasqjcd4pjXaZ+oqd37tRS25iZaaOkdLrUC02I1GJlBVu2QIUKWt9ww4YQGJi6MeTJoyU8f/p0L1Ext7Q5JCAJz4UQIhUVLQre3jBjhtY27NmjjZpauBCUlTV8/z0WB89Tdl89Cv8Cuhh4/HQbJw+X4OnT/UmOV79+fU6cOEHZsmV58OABdevWZcGCBUYomRAZx5MnuzjmU4bVaz3p0wdu34ac1nbs3buX73/8EbN/pSERQmRc0in1PpUuDXnywMOHWibx1BAfT8SU/jwqGwJAgXqJR0nFxsLXX2s/f/UVlCiROmEJYQwODlonbOHCcPOmdnc8NfuHra1dyZ79I+CfhOc9emhzZU+c0B5CCCFShamp9v3nzBmoUUMbRdurlzaS1t8fKFYM3e49OH+6hkpjc2LtDzG6x5w5XY8bZ/qj18cmOl6hQoXw8fHh008/JTY2lj59+tC7d29iYmKMU0Ah0im9Pobr17/lyJFGjPv+MdOna+kXPijhxoVbf1O3Xj1jhyiESGXSKfU+WVjAL79oP8+ZA6dPp+z73bpFXKPaXLGfDyaQM6QstvlrJtpl7ly4fBkcHVOvn0wIY8qdG3bv1vqHz52DZs203ASpxclJS3geGLgEfQ4HaPdPfjcZLSWEEKmuWDE4cACmT9dGTe3erY2aWrQIFDpo1w67XddxP9MXp22ADvyfzsHvryJEhV1JdCxbW1vWrl3LxIkT0el0/Prrr3z44YcEpvawXCHSqaio6/j5fcCWLVPo3VtbnMYMGNVrKN4XjpMrVy5jhyiEMAKjd0rNnTuXggULYmVlhZubGwcPHnzl/t7e3ri5uWFlZUWhQoWYn8zKVsHBwfTr1w8nJyesrKwoWbIkO3bsSKkiJFavnnYRqtdD//7av++bUrBsGfHuZTnX9AihpcEs3oaC9RPnrXn4EMaO1X7+8UfImvX9hyJEWlSoEOzapY2cOnRI+5OMi0ud986R42MsLPIQG/uAx4+3Pk94vmYNPHr06hcLIYR470xNtdyap09D9eraqKkvvoDGjSEgALC3x3T6HIp39KPUyqKYhUGYrT8nD5fi/tGJiY6l0+kYMWIE27Ztw8HBgSNHjuDu7s7x48eNUjYh0ov791exe1c5Row4wZAhcO8eOJlY89fG3fywYAomJka/LBVCGIlR//rXrl3LoEGDGDVqFH5+ftSqVYvGjRvj7++f7P43b96kSZMm1KpVCz8/P0aOHMmAAQPYsGGDYZ+YmBgaNGjArVu3WL9+PVeuXGHhwoXky5cvtYoF06aBrS0cPgy///5+j/3wIbRujb5XN85/G05IBTDVZaFcZW9sbUsm2nX0aAgJ0XLsdO/+fsMQIq0rVw62btXujG/dql2A/GtxpRRhYmJOnjzaH9y9e79ClSrg5gbR0bBkScoHIIQQIlnFi8PBgzB1KlhaajcvypSBxYv/aR8qVCDXgsu4P5qEwwUz4q30XHo2iqsrq6CPDk90rCZNmnD8+HFKlizJvXv3qF27NsuWLTNKuYRIy+Liwrl4sSvTpn1Op86R7NmjJTNv5vIB5+/fpX6rBsYOUQhhbMqIqlSpovr06ZNoW4kSJdTw4cOT3X/YsGGqRIkSibb17t1bVatWzfD7vHnzVKFChVRMTMxbxxUSEqIAFRIS8tbHUFOmKAVKWVkpNXKkUqGhb3+sBNu2KZU7t4o3RZ39Uac8PVHe3rYqOPhQkl39/JTS6bQQvL3f/a2FSK+2bFHK1FT7Wxg2LHXeMzLyhvL0RHl6oiIjbyi1ZIkWgIuLUnFxqRPEe/Je6sMMRs6JEOnfpUtKVaumVc2gVKNGSgUEPH9efz9QXZ9a0lCXn1xuo6LO7UlynJCQENW8eXMFKEANGDDgnb6DpjdSHyYl5+S50NBTatUKZ1W2LIa/kRI6K/X7uFXGDk0IkQpetz402kipmJgYfH19adiwYaLtDRs25MiRI8m+xsfHJ8n+H330ESdPniQ2VktI+eeff1K9enX69etH7ty5KVOmDBMnTiQ+Pj5lCvIyAwdq2TSfPYOJE6FIEViw4O3mEIWHQ+/e8PHH6B/e59IUex7XUJiYWFG27J84OCTOI6WU9vZKQdu2ULv2eyqTEOlQ8+baiksAU6ZoAxlTmrV1QbJl0+qqwMBF0L49ZM+uLS2TWlOJhRBCvFSJEtr07ilTtFFTO3dqo6aWLtW+P+ly5aHQkAuUfTIUszAdYQUiOXmzAY8Xf5EoNYO9vT2bNm1i7D/5En766ScaNmzIw4cPjVU0IYxOKcXVq1P4so87nboEcO4c2JpA91wfcujOIzqO/czYIQoh0hCjdUo9evSI+Ph4cufOnWh77ty5CQoKSvY1QUFBye4fFxfHo39ytdy4cYP169cTHx/Pjh07+O6775g+fTo//vjjS2OJjo4mNDQ00eOdWVhoF5+bNmlrEz94AH36QPny8Ndfrz+PyMdHm3/3668oHVxZVpKHlULR6cwpXXoj2bLVTfKS9eu1pJ5WVtoQdSEyu27dYPJk7eehQ2H58pR/z7x5tYTnQUFL0FuaaSvxgSQ8F0KINMLUVGsT/PygalUt5UH37vDxx3D3LqDTkeOTKbiX9cHunj1xdnCu8CJu/FAQ/c2/DccxMTFh3LhxbNq0iSxZsuDl5UXlypXx8/MzXuGEMJKYmIfMn1eFOnW+ZeUqPfHxUN/Mirl91rEoaD858toaO0QhRBpj9IxyOp0u0e9KqSTb/mv/F7fr9Xpy5crFr7/+ipubG+3bt2fUqFHMmzfvpcecNGkSDg4Ohoezs/PbFuffwULLlnD+PMyerY2UuHgRmjSBhg3h7NmXvzY2VksK9cEHcP06yjk/V/d9zH3nS4AppUqtJUeOxkleFhUF33yj/fztt1CgwPspihDp3dChMGSI9nOPHrBtW8q+X44czTE3z01MTJCW8PzLL7U6YdcuuHYtZd9cCCHEaytZUhs1NXny83uKpUvDsmXaPUQr16pUbHeffE/rAOBf25+zW0sQs2xGopuMLVu25OjRoxQpUoTbt29Ts2ZNVq9ebZxCCWEEly79QZNGzvTtd5LAQHCygpF2Hsw/FkTnOZ/yiks8IUQmZmasN86ZMyempqZJRkU9ePAgyWioBHny5El2fzMzM3LkyAGAk5MT5ubmmJqaGvYpWbIkQUFBxMTEYGFhkeS4I0aMYPDgwYbfQ0ND31/HFGjfcAYMgE6dtKl8P/0Ee/c+z0A+YQI4OT3f/9Il6NgRTp0CQHX8nL+/tSPw0XxAR8mSv+Po2CrZt5o6Ffz9wdkZhg17f0XIjB4/fsyhQ4c4cOAAPj4+hIaGopR644der3+r1ymlsLW1pVSpUpQtW5YyZcpQpkwZSpcuTZYsWYx9etIdnU6bpvHwIfz2G3z6KezZo/X7pgQTE3OcnLrh7/8/7t37FcfyO7UO6e3bYd48mDEjZd74JR4G+eKYxy1V31MIIdILMzPte9PHH0PXrnDihDbKduVK+OUXKF7ciqKtPLE/N5sr9wYTXC6ek4+GUOrL9WQdtxHy5AGgdOnSHD9+nA4dOrBz5046dOiAn58fkyZNSvTdVIiMJC4umkkTmzN5ym4iIsBEB93tLahScxFdNnUimcsvIYR47r1lsXoLVapUUV9++WWibSVLlnxlovOSJUsm2tanT59Eic5HjBihXFxcVHx8vGHbrFmzlJOT02vHleIJCq9fV6pt2+fZNW1tlRo/XqmwMKVmz9aSo4NS2bMrtW6dun59pCHR5r17S156WH9/payttZeuWZMyoWdkAQEBauXKlapPnz6qdOnShoSMafFRsGBB1bx5czVy5Ei1atUqde7cORUdHW3sU5guxMQo9fHH2t9J1qxKLV6s1OPHKfNekZF///O3q9MSnu/Yob2xTqfUJ58odeRIyrzxC+LiwtW+3xopT0+U366Fb/x6SdialJwTITK22FilJk1SytJSq7LNzZUaMUKpiAjt+fDQc+rYjtxa/b4Xdbu7jdL/sS7RMeLi4tS3335raLcbNmyoHqdUY2NEUh8mldnOydGj21WZ0jaGz3qZ3Kj5NpWV9+q7xg5NCGFkr1sf6pRKjUXSk7d27Vo6derE/PnzqV69Or/++isLFy7kwoULuLi4MGLECO7evctvv/0GwM2bNylTpgy9e/fmiy++wMfHhz59+rB69Wpat24NQEBAAKVKlaJr16589dVXXLt2je7duzNgwABGjRr1WnGFhobi4OBASEgI9vb2b1SmsLDTmJtnx8rqNebN+fjA4MFw9Kj2u7W1Nv8O4KOPYMkSbsUs4dat0QAULTqHfPn6vvRwHTrA6tXayI8DB5Ahsq+glOLatWscOHCAgwcPcuDAAW7dupVkv5IlS1KrVi1q1aqFk5MTOp3ulQ8TE5P/3OdN9nv69CkXLlzg3LlznD9/nnPnzr0055qZmRklSpQwjKhKGF3l6uqKiYnRZ+qmKZGR2gzaw4e1383MoH59aNNGm3H7z8DL9+LMmQY8fbqXAgVGUcj1e2105ItJrWrU0OYWNmumJTh5j0KCfTm5tzGmOR+CHky3uFFr9sk3Osa71IcZlZwTITKH69e1ge4J61O4uGjZGJo3B70+gqvH2nM/RpsLnuMwlPi7DeYzfoVs2QzHWLt2Ld26dSMqKorChQuzefNmypQpY4zipAipD5PKLOckIiKCYcM+Y8H8rcTrwcYaRuQzIbvFTD71/ArHXHIhIkRm99r1YWr0kL3KnDlzlIuLi7KwsFCVKlVS3t7ehue6dOmiPDw8Eu3v5eWlKlasqCwsLJSrq6uaN29ekmMeOXJEVa1aVVlaWqpChQqpH3/8UcW9wTLsb3uHIyrKXx0+nEcdOpRbhYQce70X6fVKrV2rVMGC2u04a2ul5s5VSq9X/v7TDCOk/P2nvfIwBw8+H4Bx6tQbhZ0pxMXFqVOnTqnZs2er1q1bq1y5ciUZfWRiYqLc3d3V119/rTZu3KgePHhg7LCT9fDhQ+Xp6al+/vln1bt3b1WjRg1lb2//0lFVtra2qkqVKqp79+5q5syZas+ePSooKEjp9XpjF8WoQkOV+v57pcqWfT5oEZQyNVWqQQOlFixQ6n18BO7fX6c8PVGHDzup+Ph/lgm/cEGpHj2UsrB4/sZFiyo1b97zW/FvSa+PV0+e7Fcnj3RQ+/eYaO+9DnX0k49UXHjUGx8vs93xfR1yToTIPPR6pTZtUqpAgefVddOm2qB3vV6v7t6eo7z2mSpPT5TPSlRojZxK7dyZ6BinT59Wrq6uhjZ5w4YNxilMCpD6MKnMcE42b/5D5XWyNXzXrFcBtc2xuPp91CWVyb9eCiFekC5GSqVVb3uH49mzAM6da0pExDlMTKwoUeJ3cuVq83ovjo6GLVvA3R0KFeLu3blcu9YPAFfXCbi6fvfSl8bHQ5UqWgqqL76AX3997ZAzrOjoaE6ePGkYCXX48OEkqypaWlpStWpVatWqRe3atalevTp2dnZGivjdKKUICAgwjKY6f/4858+f5+LFi8TExCT7mpw5cyYaUVW2bFlKly6doe/qvcyVK9qqlX/8AWfOPN9uYgJ16mj5p1q1gpeku3slvT4GH5/8xMY+pHTpTTg6tnz+ZFAQ/PyzlmPq6VNtW86c0K8f9O0LuXK99vtERFzkzqW5BAWtQmV5atie4wBE352I+/zhbzV8MrPc8X0Tck6EyHwiIuCHH2D6dG0tGisrGDlSG+gaG+vLhVPNeabuoYuBor+AU77e6KZOg39yQD569Ih27dqxf/9+AEaPHs24cePS/Ujm9FAfzp07l6lTpxIYGEjp0qWZNWsWtWrVSnbfwMBAhgwZgq+vL9euXWPAgAHMmjXrjd4vPZyTt3Xnzh369evKn3/uA7TvRT+Ug/izw6mxfTxl3SR5lBDiuXQzUiotepc7HH/8Eao8PZsaRjhdudJXPXy4WUVHB732Me7dW2p4/fXrI146oiUuTql9+5Rq1067c2dvr9T9+28ccoYQGhqqdu3apb777jvl4eGhrKyskowYsrOzU40aNVITJ05UBw8eVM+ePTN22CkuNjZWXbp0Sa1bt06NGTNGtWrVShUtWlTpdLqXjqwqUKCA6tq1q9q0aZOKeMdRO+nR1ataLpFKlRKPoDIxUapOHaV++UWpe/fe7Jh//z1MeXqizpxpnPwOYWFK/fTT8xGToOWW691bqStXXnrc6GdByv/ECHV4Q15DneHpiTqwFXV5COpClYLq6vw96v79+6p///4qKipjjpSaM2eOcnV1VZaWlqpSpUrqwIEDL9333r176rPPPlPFihVTOp1ODRw48I3fLz2cEyFEyrh0Sam6dZ9X1UWKaAOjYmKeqLN+z7//XRyBiitZUKlDhwyvjY2NVYMGDTK0t82aNUv39Uharw/XrFmjzM3N1cKFC9XFixfVwIEDla2trbp9+3ay+9+8eVMNGDBALV++XFWoUEHaiH/ExcWpWbNmKVtby39mF6A+b4E6Xi63mtLi0LsO8hZCZFAyUuodvO0dDj8/qF4dlIpn7dohZM06O9HzVlau2NtXMzyyZKmAiYllon3u31/DpUufA3ry5RtIkSIz0b0wukEpOHsWVqzQ8kfdvfv8tb/8og2wyAwePXrEoUOHDPmg/Pz8iI+PT7SPo6MjtWvXNoyEKleunKx884/IyEguXbqUaGTVuXPnuHfvXqL9rKysaNiwIS1atKBZs2Y4OjoaKWLjuHFDG0G1fr22ElMCnU7L3damDbRuDfnyvfo4kZHXOH68GKCjWrWbWFm5JL9jXBxs2qQto5nwhjqdlsDkm2+gZk3i4yJ5dGw6d/2XE5r7BvzzkdbFQfZjEOFZjDDzLpQY+inOdYty/PhxWrdu/c/dzX788ssvb3QO0vod34TchHPnzqVmzZosWLCARYsWcfHiRQoUSJrb79atW8ycORM3NzdmzpyJh4eH3AUXQrwRpWDtWi0taGCgtq1NG5gxQ6HUNG5cHw46PTY3ofQ4sG0/DL7/Hiy173y//fYbvXr1Ijo6mhIlSrB582aKFy9uvAK9g7ReH1atWpVKlSoxb948w7aSJUvSsmVLJk2a9MrX1qlThwoVKmT6NsLX15devXpw6pQ2lLxUKfi+HkSv7oDd7Pk065A+ZxkIIVLe69aH0imVjLdtTKKitOWD167Vfp88eQvNm28jLOwoEREX0G6MPafTWWBnV8nQSaXXP+Py5R5APE5OvSlWbJ6hQ8rfH1at0jqjLlx4foysWbWpRZ07p9zS9mnF1atXWb58OZs3b+bixYtJnnd1dTV0QNWqVYtixYol6tAT/+3JkyecOnWKrVu3smXLFm7fvm14TqfTUbNmTVq0aEGLFi0oWrSoESNNfbduwYYNWgdVwtoECWrU0P4OW7cGZ+fkX3/6dD2Cg/fj4jKaggW/f/WbKQWHDsHUqeh3bCXCFcJKQHDt7Dwq8xS9zfO6JMslHU/3lOReWHeKfvk51VrmIWE2yOLFi+nbty8xMTEUL16czZs3U6JEiTcqd1r/ci0XHEIIYwkNhbFjtVnY8fFga6v93q3bAa5ebktM3H1MoqD4VMj9qCz89htUqADAyZMnadWqFXfu3MHe3p5Vq1bRtGlT4xboLaTl+jAmJgYbGxv++OMPWrVqZdg+cOBATp8+jbe39ytf/7ptRHR0NNHR0YbfQ0NDcXZ2TpPn5E2EhYUxevRofv75Z/R6PVmyQK/u0OOODauurKDPzlbkz2/sKIUQaZl0Sr2Dd2lgldJuho0bp/1etizUrAmVKoVSpswJHB2PEhV1lNDQo8TGPkr2GLlzd6JEiWU8fWrC+vVaR9TBg8+ft7SEjz+Gjh2hcWPDjbcMKTQ0lHXr1rFs2TIOJyyV9o9SpUoZOqBq1aqF88t6A8RbUUpx9uxZtmzZwubNm/Hz80v0fKlSpQwdVJUrV073eTHehL8/bNyo5aA6ciTxc9WqaXfM27TRVmpK8ODBWi5ebI+FRV6qVbuNiYlZkuMqFU9k5BXCwk4QGnqSR49OEP3MD51J4rxgFoE6Hu8pycUbPcnXtgctOtmTkA7twYMHrF27lhUrVnD8+HEAWrZsyfLly9/qy7FccGTcCw4hxPtx5oyWBjChPShdGn75JYisWTsQHOwJQN5NUGSRGSajxsOwYWBmxv3792nTpg2HDh1Cp9MxYcIERo4cma5uqKXlNuLevXvky5ePw4cPU6NGDcP2iRMnsnz5cq5cufLK179uGzFu3DjGjx+fZHtaPCevQynFpk2bGDBgAHf/mZJRrx4MbgG6nz7gaps/6P9Dnve9YLAQIgOSTql38D4a2HXroEsXePYs8XZTUyhRAipWVFSpcoPSpY/i6HiUmJijhIefJkeO9ly+vJSVK83Yvl1LpgnaDJ46deDzz7XRGFmzvlMR0zS9Xo+XlxfLli1j/fr1REVFAWBiYkLjxo3p1KkT9erVI2fOnEaONHPx9/fnzz//ZMuWLXh5eREXF2d4zsnJiebNm9OiRQvq1q2LZUbuKf2Xu3efj6A6dEjrmE5QufLzDipX1+cJz8uU2UKOHM2IirpOWNgJwsJO8vjxSSIjT6HThSd5j/BwB25eKYfJFRtuXalHlpr96djDmsKFtecjIyP5888/+f3339m1a5dhKquZmRljx45l5MiRb91pKBccGe+CQwjx/un1sHy51t/06J97jp06xTF06FgeP54IgN0lKD0erApW03YuVoyYmBgGDRpkGO3Zpk0bli5dSpZ/EqSndemhjThy5AjVq1c3bP/xxx/5/fffuXz58itfnxlvXNy+fZuvvvqKrVu3ApA3LwwaBI0fmbJu5XSa/DGAatXTT6epEMK4pFPqHbyvBvbePThwAE6f1vJN+fnBw4fJ7+viAsWLK44d0xES8nx7uXLaiKjPPiPDD5G9efMmy5cvZ/ny5dy6dcuwvUSJEnTr1o1OnTrh5ORkvACFQXBwMDt27GDLli3s2LGD8PDnHSlZsmShcePGtGjRgiZNmpAtWzYjRpq6AgO1EVTr12t/+3r98+cqVYIhQ4aRN+9UzMxyExsbjU4XnOQYUVG2XL1aiStXKvP33+7odJUpVKgwlSvrcHeHMmW0VQHj4+Px9PRkxYoVbNiwIdH/QeXKlenYsSPt2rUj99ssGfgCueDIWBccQoiU9eQJjBoFCxZoNyns7WH27O0UKdKJuLinmIVByR8hx1lrmDJFG2JlYsLChQvp168fsbGxlC5dmvXr17/xdGtjSMttRGqNpv23tHxOXiYuLo7Zs2czZswYIiMjMTOD9u2hSyuwnlOYjTY7GLOiGA4Oxo5UCJGeSKfUO0ipxkQpraPqxU4qPz+4eTPxfs7O2oiozz/XLkAzsoiICDZs2MDSpUvx8vIybHdwcKB9+/Z069aNKlWqpKuh7JlNdHQ0np6ebNmyhT///DNRsnQzMzM8PDwM0/ySSzydUd2/r+Us/+MP8PLSOqjy57/K8uUlMDHRqt2YGEv+/rsCly9X5to1d2Jj3SlQoATu7qZUrqx1Sv970NmZM2dYsWIFq1atSnSuXV1d6dixIx07dnyvCXPT8pdrueAQQqRVJ07Al1+Cr6/2e926txkz5lOU0haxKLACXJeCyYf1YOlScHbmyJEjtG7dmqCgILJkycLChQtp3769EUvx39J6fVi1alXc3NyYO3euYVtC+gHJO6g5dOgQ/fv358wZLZF5uXJaEv/SkeA9aQC5xk2jYzdz5Ku4EOJNSafUO0jtxiQ4WMtHcOkSlCwJtWpBRk7Po5Ti8OHDLF26lHXr1hlGeOh0OurXr0+3bt1o2bIl1tbWRo5UvCm9Xs/JkyfZsmULW7Zs4cKLWfmBChUq0LJlS1q0aEH58uUzTWfjw4daB9X69RASshtHx9tERLiTN28Z3NzMqVwZKlaE5GZrhIeHc/DgQfbt28fOnTsTndNs2bLRrl07OnbsSI0aNVLkfKb1L9dywSGESKvi4+HXX2HkSO27nrl5NL/8MoRixeYAkPWMCSXH67HUO8BPP0GnTgTdv89nn31muFH35ZdfMnPmzDQ7LT6t14cJK7TOnz+f6tWr8+uvv7Jw4UIuXLiAi4sLI0aM4O7du/z222+G15w+fRqAnj17Urx4cYYOHYqFhQWlSpV6rfdM6+ckwZkzZxg5ciQ7duwAwN5OR+8+ikaNwH5NVuYe2cbIzTUpUsTIgQoh0q3Xrg+VSCIkJEQBKiQk5I1fGxsbmwIRZQz+/v7qhx9+UEWKFFFoSxEqQBUuXFhNmDBB3b5929ghivfs2rVratq0aapWrVrKRBseZHi4uLioAQMGqH379qmYmBhjh5pqgoOVevLk5c/HxMSoQ4cOqfHjx6tatWopc3PzROfNwsJCtW7dWm3atEk9e/YsxeN9l/owNaxZs0aZm5urxYsXq4sXL6pBgwYpW1tbdevWLaWUUsOHD1edOnVK9Bo/Pz/l5+en3NzcVIcOHZSfn5+6cOHCa79nWj8nQoi05f59pbp2VUobM69U8+ar1b59tsrTE3V4i7l6Wu6fJ1q1UurBAxUbG6tGjRplqPfd3NzUjRs3jF2MZKWH+nDOnDnKxcVFWVhYqEqVKilvb2/Dc126dFEeHh6J9n+xzX3xO8vrSuvn5Nq1a+qzzz4zlM3URKeaNUFt2oQ6tAH1m1tT9d3X4So62tiRCiHSu9etD2WkVDLe9g6HUorWrVuTN29epk6dKiN9gKioKDZv3szSpUvZu3cvCR83W1tb2rZtS7du3fjggw8yzYiZzOzhw4ds27aNLVu2sHv3bkMCe4CsWbPSsGFDatSoQfXq1alQoQIWFhZGjDb1KKU4f/48e/fuZd++fXh7eyfKDwXg4uJC/fr1qVevHo0aNUrVPF3p4Y7v3LlzmTJlCoGBgZQpU4aZM2dSu3ZtALp27cqtW7cSTQ9Orr5xcXFJlMvuVdLDORFCpD2HDmkppM6dgwIFLjFlShty574IyoRCi8B5lR6doyPMmAFt2/LXvn107NiRJ0+ekDVrVpYvX07z5s2NXYxEpD5MKq2ek3v37jFhwgQWLVpkWKym7ofQrbuWt9b2uAUL5y2i3aJO1Ktn5GCFEBmCTN97B2/bmPj4+BhWgCpdujSrVq2iXLlyKRVmmqWU4vjx4yxdupQ1a9YQ8kLmdg8PD7p160br1q3Tzcoy4v2LjIxkz549bNmyha1bt/IoYamif1hZWeHm5ka1atWoXr061apVI1++fEaK9v27desW+/btY+/evezfv58HDx4kej5HjhzUrVvX0BFVqFAho3XcptUv18Yk50QI8bZiY+GXX2DMGIiLi2DIkN7Ur78SgBxn7SgxKgzzcCBPHujVC/+mTWk3cCBHjx4FYOjQofz444+Ym5sbsRTPSX2YVFo7J0+fPmXy5Mn89NNsoqK0ZcGrVoUePaBoUbA9Y8rlLXXZbf4bM1fmQRa3FkK8L9Ip9Q7epTHZuXMnXbt25f79+1haWjJkyBAaNWpElSpV0mw+gPclMDCQ33//nWXLlnHp0iXDdhcXF7p06UKXLl0oVKiQESMUaVF8fDw+Pj54eXlx9OhRfHx8ePLkSZL9nJ2dDR1U1atXp2LFimn+b+rhw4dcunTJ8Lh48SIXL17k7t27ifazsbGhdu3a1KtXj/r161OuXDlM0khiubT25TotkHMihHhXd+/CkCGwdq2iWbNf+eqrAZibx2AVlY3i0yGr51N0esDUlJgWLfjWxIRZ69cD8MEHH7BmzZo0cbNG6sOk0so5iYiI4KeZM5n8vx8JidA6o0qXhi++gEpFIGq3Cxv//IrAYv3o3teK5s2RZOZCiPdKOqXewbs2Jg8ePKB79+5s377dsM3S0pJq1arh4eGBh4cH1apVw8bG5n2GbRTR0dFs3bqVpUuXsnPnTvR6PQDW1ta0bt2abt26UadOnTRzgS3SPqUU165dM3RQ+fj4cO7cOcNnK4GFhQWVKlVK1FHl7OxslHgDAgISdT4ldEA9fvw42deYmppStWpVw0ioatWqpdnpimnly3VaIudECPG+7N0L/fuDUr6MHfspefNqSzKbxWch+3lrcmx9SPYTYB4KG5yd6f7wIaHPnuHo6MjKlStp0KCBUeOX+jApY5+TmJgYFowZyYS5P/MwLAaAQoWgZ0+okdMSv63NWHV2Mo27FKJ7d3BxSfUQhRCZhHRKvYP30ZgopVi1ahVbtmzB29s7yfQcc3NzKleujIeHB7Vr16ZmzZrY2dm9j/BTRHR0NHfu3MHf39/wuH79Olu3bk00qqVGjRp07dqVtm3b4uDgYMSIRUYSHh7OiRMnEnVU/XvKH0C+fPkMHVTVq1enUqVKWFlZvZcY4uLiuHHjBhcvXkzU+XT58uUkOaAS6HQ6XF1dKVmyZKJH2bJl0/Tf+4uM/eU6LZJzIoR4n6KjYfp0mDXrKV26DKV27Q3Y2QU/30Gvw/6yjhxH9Dw9CF3vmHBGr0en0zF27Fi+++47TE1NjRK71IdJvfU5efQISpWCIkWeP3LmBGtrsLLS/n3x539ti4+IYNmYfoz78y/uBGs38pycoHtnKEdxVm0bib5QJ3r10tGoEZiZpdAJEEKIf0in1Dt43w2sUoqrV6/i7e3NgQMH8Pb25s6dO4n2MTU1pWLFiri7u1OmTBlKly5N6dKlcXR0fOf3f534Hjx4QEBAQKJOpxcf9+/ff+nr8+bNS+fOnenatSvFixdP8XiFUEpx48YNfHx8DB1VZ86cIT4+PtF+5ubmVKxYMdFoqgIFCrwyP1NUVBRXr141jHZK6Hy6du0aMTExyb7GzMyMokWLUqpUqUSdT8WLF0/3IyLlgiMpOSdCiJRw6xaMHg179sTh6HiUatW2U63aDgoXPptoP3UX5k2BP/7Z3KBcOVb89Re58uZN9ZilPkzqrc+Jjw/8k5s2gdJBvDXE2UFclueP2H/+DdDBqXA4/RS8bsP1IO112bJBx+YWOES0Z+vF/9G6vRPdumkJzYUQIrVIp9Q7SOkGVinFzZs3DR1UBw4c4MaNG8numytXrkSdVAk/Z82a9bXfLyIigoCAgJd2OgUEBBAdHf2fx7G2tqZAgQKGh7OzM9WqVaN+/fpGu0MnRIKIiAh8fX0NI6l8fHySjFAEcHJyMnRQlStXjnv37iWacnfz5k1eVi3a2NhQokQJSpYsmagDqnDhwmkm6ez7JhccSck5EUKkJKXg77/h8GE4cgQuXAgge/a/qFp1B25ue7G2jgBg926YOQOeRUNuGx1LO7akfuOW6GzswNoWna0dOls7sMmCztYeXZYsYG7+XhMHSX2Y1NueE/9rp7ke4Kb1ROl1Wk4x8zj4JwNGRARcuQKXLsHly9rj34PGbW2haY08PAyeQjbnz/niCxMaNAD5mi6EMAbplHoHxmhgAwICOHToEOfOnePChQucP3/+pR1VoE1TerGTqmDBgjx8+DDZTqeX5bV5kU6nw8nJKVGn04udTwUKFCBHjhxGWwFMiDellOLWrVuJpvydPn3asAzyq2TPnj3RiKeEDihnZ+dMlx9NLjiSknMihEhtT5/C0aNw5Eg0gYEHyJJlO9Xc/iQq7ibjxoG/P5iYaLmDbGy0zokk/1qD7T8PGyuwstT6O3QASkfe8O40G7vojeKS+jCptz0nx46d5PTpysTHg14PcXFw+7bW+XTxIgQEaB2WL9LpdGTP7kjWrK5ksSmKznQY7dqVo2tXbQFHIYQwJumUegdppYGNiIjg0qVLnD9/3tBRdeHCBQICAt74WHZ2dsl2OCU88ubNm2YTLQvxvkRFReHr62voqLp48SL58+dP0gHl6OgoHbD/SCv1YVoi50QIYWyxsXD2LJw4cY2gO3+w968pHD4V8k7H/LbVB/xv48E3eo3Uh0m97Tnx8vLjww8rvXKfHDlcKFy4KiVKVKFMmaqULl2R7NltsbbWOh4LFdI6J4UQIi143fpQUtylYba2tri7u+Pu7p5oe0hICBcvXkzUWeXv70+ePHkMo5r+/ZCk40JoU1A/+OADPvjgA2OHIoQQQrw1c3NwcwM3t6LASMZOGIGn5xkCAu4QFhZKaGgIYWHBhIeHEhYWQnhIMOGhTwkPCyEiLJTwiHCinkUByjD6psgHnxqzSJle/vx2ODs7Y2pqani4uLhQtWpVqlSpQpUqVcidO7exwxRCiPdOOqXSIQcHB8PqYkIIIYQQInPT6XTUrVsBqGDkSMTbKlKkCP7+/sYOQwghUp0M8BRCCCGEEEIIIYQQqU46pYQQQgghhBBCCCFEqpNOKSGEEEIIIYQQQgiR6qRTSgghhBBCCCGEEEKkOumUEkIIIYQQQgghhBCpTjqlhBBCCCGEEEIIIUSqk04pIYQQQgghhBBCCJHqzIwdQFqklAIgNDTUyJEIIYRxJdSDCfWikDZCCCESSBuRlLQRQgihed02QjqlkhEWFgaAs7OzkSMRQoi0ISwsDAcHB2OHkSZIGyGEEIlJG/GctBFCCJHYf7UROiW3NpLQ6/Xcu3cPOzs7dDrdG702NDQUZ2dnAgICsLe3T6EI0wYpa8YkZc143qWcSinCwsLImzcvJiYy4xukjXhdUtaMKbOUNbOUE6SNeN+kjXg9UtaMKbOUNbOUE1KnjZCRUskwMTEhf/7873QMe3v7DP8BTSBlzZikrBnP25ZT7n4nJm3Em5GyZkyZpayZpZwgbcT7Im3Em5GyZkyZpayZpZyQsm2E3NIQQgghhBBCCCGEEKlOOqWEEEIIIYQQQgghRKqTTqn3zNLSkrFjx2JpaWnsUFKclDVjkrJmPJmlnOlBZvq/kLJmTJmlrJmlnJC5yprWZab/CylrxpRZyppZygmpU1ZJdC6EEEIIIYQQQgghUp2MlBJCCCGEEEIIIYQQqU46pYQQQgghhBBCCCFEqpNOKSGEEEIIIYQQQgiR6qRTSgghhBBCCCGEEEKkOumUegtz586lYMGCWFlZ4ebmxsGDB1+5v7e3N25ublhZWVGoUCHmz5+fSpG+uzcp68aNG2nQoAGOjo7Y29tTvXp1du3alYrRvps3/X9NcPjwYczMzKhQoULKBvievGk5o6OjGTVqFC4uLlhaWlK4cGGWLFmSStG+mzct68qVKylfvjw2NjY4OTnRrVs3Hj9+nErRvr0DBw7QrFkz8ubNi06nY/Pmzf/5mvRcL6V10kYkT9qICikb4HsibcTLSRsh3gdpI5KXntuIzNI+gLQRryJtxDtQ4o2sWbNGmZubq4ULF6qLFy+qgQMHKltbW3X79u1k979x44aysbFRAwcOVBcvXlQLFy5U5ubmav369akc+Zt707IOHDhQTZ48WR0/flxdvXpVjRgxQpmbm6tTp06lcuRv7k3LmiA4OFgVKlRINWzYUJUvXz51gn0Hb1PO5s2bq6pVq6o9e/aomzdvqmPHjqnDhw+nYtRv503LevDgQWViYqJmz56tbty4oQ4ePKhKly6tWrZsmcqRv7kdO3aoUaNGqQ0bNihAbdq06ZX7p+d6Ka2TNkLaiBdJG5F2SRvxcum5XkrrpI3IeG1EZmkflJI2QtoITUrUS9Ip9YaqVKmi+vTpk2hbiRIl1PDhw5Pdf9iwYapEiRKJtvXu3VtVq1YtxWJ8X960rMkpVaqUGj9+/PsO7b1727K2a9dOfffdd2rs2LHpokF503L+9ddfysHBQT1+/Dg1wnuv3rSsU6dOVYUKFUq07aefflL58+dPsRhTwus0Jum5XkrrpI2QNuJF0kakXdJGvFx6rpfSOmkjMl4bkVnaB6WkjZA2QpMS9ZJM33sDMTEx+Pr60rBhw0TbGzZsyJEjR5J9jY+PT5L9P/roI06ePElsbGyKxfqu3qas/6bX6wkLCyN79uwpEeJ787ZlXbp0KdevX2fs2LEpHeJ78Tbl/PPPP3F3d2fKlCnky5ePYsWK8c033xAVFZUaIb+1tylrjRo1uHPnDjt27EApxf3791m/fj1NmzZNjZBTVXqtl9I6aSOkjXiRtBFpl7QRr5Ze66W0TtqIjNdGZJb2AaSNAGkjEqREvWT2PgLLLB49ekR8fDy5c+dOtD137twEBQUl+5qgoKBk94+Li+PRo0c4OTmlWLzv4m3K+m/Tp08nIiKCtm3bpkSI783blPXatWsMHz6cgwcPYmaWPv6M3qacN27c4NChQ1hZWbFp0yYePXpE3759efLkSZqeD/42Za1RowYrV66kXbt2PHv2jLi4OJo3b87PP/+cGiGnqvRaL6V10kZIG5FA2ghpI9Kz9FovpXXSRmS8NiKztA8gbQRIG5EgJeolGSn1FnQ6XaLflVJJtv3X/sltT4vetKwJVq9ezbhx41i7di25cuVKqfDeq9cta3x8PB06dGD8+PEUK1YstcJ7b97k/1Sv16PT6Vi5ciVVqlShSZMmzJgxg2XLlqX5uxzwZmW9ePEiAwYMYMyYMfj6+rJz505u3rxJnz59UiPUVJee66W0TtoIaSOkjZA2Ir1Lz/VSWidtRMZrIzJL+wDSRkgboXnf9VL66Z5NA3LmzImpqWmSHtIHDx4k6S1MkCdPnmT3NzMzI0eOHCkW67t6m7ImWLt2LT169OCPP/6gfv36KRnme/GmZQ0LC+PkyZP4+fnRv39/QKt0lVKYmZmxe/du6tatmyqxv4m3+T91cnIiX758ODg4GLaVLFkSpRR37tyhaNGiKRrz23qbsk6aNImaNWsydOhQAMqVK4etrS21atXihx9+SLN3I99Geq2X0jppI6SNAGkjpI1I/9JrvZTWSRuR8dqIzNI+gLQRIG1EgpSol2Sk1BuwsLDAzc2NPXv2JNq+Z88eatSokexrqlevnmT/3bt34+7ujrm5eYrF+q7epqyg3dno2rUrq1atSjdzaN+0rPb29pw7d47Tp08bHn369KF48eKcPn2aqlWrplbob+Rt/k9r1qzJvXv3CA8PN2y7evUqJiYm5M+fP0XjfRdvU9bIyEhMTBJXiaampsDz3v+MIr3WS2mdtBHSRoC0EdJGpH/ptV5K66SNyHhtRGZpH0DaCJA2IkGK1EtvnSI9k0pYHnLx4sXq4sWLatCgQcrW1lbdunVLKaXU8OHDVadOnQz7JyyZ+PXXX6uLFy+qxYsXp7ulXF+3rKtWrVJmZmZqzpw5KjAw0PAIDg42VhFe25uW9d/Sy8oZb1rOsLAwlT9/ftWmTRt14cIF5e3trYoWLap69uxprCK8tjct69KlS5WZmZmaO3euun79ujp06JByd3dXVapUMVYRXltYWJjy8/NTfn5+ClAzZsxQfn5+hmVrM1K9lNZJGyFtRHKkjUh7pI2QNsIYpI3IeG1EZmkflJI2QtoITUrUS9Ip9RbmzJmjXFxclIWFhapUqZLy9vY2PNelSxfl4eGRaH8vLy9VsWJFZWFhoVxdXdW8efNSOeK39yZl9fDwUECSR5cuXVI/8Lfwpv+vL0pPDcqblvPSpUuqfv36ytraWuXPn18NHjxYRUZGpnLUb+dNy/rTTz+pUqVKKWtra+Xk5KQ+//xzdefOnVSO+s15enq+8m8vo9VLaZ20ERppI56TNiJtkjaii1Iq49VLaZ20EZqM1EZklvZBKWkjEkgb8X7rJZ1SGWw8mRBCCCGEEEIIIYRI8ySnlBBCCCGEEEIIIYRIddIpJYQQQgghhBBCCCFSnXRKCSGEEEIIIYQQQohUJ51SQgghhBBCCCGEECLVSaeUEEIIIYQQQgghhEh10iklhBBCCCGEEEIIIVKddEoJIYQQQgghhBBCiFQnnVJCCCGEEEIIIYQQItVJp5QQQgghhBBCCCGESHXSKSWEEEIIIYQQQgghUp10SgkhhBBCCCGEEEKIVCedUkIIIYQQQgghhBAi1UmnlBBCCCGEEEIIIYRIddIpJYQQQgghhBBCCCFSnXRKCSGEEEIIIYQQQohUJ51SQgghhBBCCCGEECLVSaeUEEIIIYQQQgghhEh10iklhBBCCCGEEEIIIVKddEoJIYQQQgghhBBCiFQnnVJCCCGEEEIIIYQQItVJp5QQQohMYceOHYwbNy7Z51xdXenatWuqxvOm/Pz88PDwwMHBAZ1Ox6xZs1LsverUqUOdOnVS7PgvM27cOHQ63Vu//l3injhxIps3b37r936fIiMjGTduHF5eXq+1/7179xg3bhynT59+6/dctmwZOp2OkydPvvUx3qfU/LynFC8vL3Q63Wv/P77oyJEjjBs3juDg4Pce19t4Vf35Xz755BN0Oh39+/d/v0EJIYTIEKRTSgghRKawY8cOxo8fn+xzmzZtYvTo0akc0Zvp3r07gYGBrFmzBh8fH9q3b59i7zV37lzmzp2bYsdPi9Jap9T48ePfqFNq/Pjx79Qpldak5uc9LTpy5Ajjx49PU51SL6s/X+XBgwds27YNgJUrV/Ls2bP3HZoQQoh0TjqlhBBCvDdRUVHGDiGJyMjI/9ynYsWKFC5cOBWieXvnz5+nfv36NG7cmGrVqpEnT54Ue69SpUpRqlSpFDu+EP/ldT/vUVFRKKVSOTrxun777TdiY2Np2rQpwcHBbNy40dghCSGESGOkU0oIIYRBwvQpPz8/PvnkE+zt7XFwcKBjx448fPgw0b6urq58/PHHbNy4kYoVK2JlZWW4k37+/HlatGhBtmzZsLKyokKFCixfvjzR6xOmtqxYsYLBgweTJ08erK2t8fDwwM/PL0lsf/75J9WrV8fGxgY7OzsaNGiAj49PsvGfOnWKNm3akC1bNgoXLkzXrl2ZM2cOADqdzvC4deuWoSz/nr7n7+9Px44dyZUrF5aWlpQsWZLp06ej1+sN+9y6dQudTse0adOYMWMGBQsWJEuWLFSvXp2jR4++1jn/r3OVMK0qLi6OefPmGWJ/mYSYpk6dyuTJk3F1dcXa2po6depw9epVYmNjGT58OHnz5sXBwYFWrVrx4MGDRMf49zS4//3vf5iYmLB169ZE+3Xt2hUbGxvOnTtn2LZ3717q1auHvb09NjY21KxZk3379iWJc/v27VSoUAFLS0sKFizItGnTXut8ASilmDJlCi4uLlhZWVGpUiX++uuvJPs9e/aMIUOGUKFCBRwcHMiePTvVq1dny5YtifbT6XRERESwfPlyw/lNKP/Dhw/p27cvpUqVIkuWLOTKlYu6dety8ODBJO83b948ypcvT5YsWbCzs6NEiRKMHDky0T5BQUH07t2b/PnzY2FhQcGCBRk/fjxxcXGA9v/n6OgIwPjx4w3xvGx6qZeXF5UrVwagW7duhv0TplqdPHmS9u3bGz4Hrq6ufPbZZ9y+ffs/z3NgYCBubm4ULVqUa9euARAaGso333xDwYIFsbCwIF++fAwaNIiIiIgk57R///78/vvvlCxZEhsbG8qXL28YNfMyr/q8Jzy3e/duunfvjqOjIzY2NkRHR6PX65kyZQolSpTA0tKSXLly0blzZ+7cuZPo+HXq1KFMmTL4+PhQo0YNwzlZunQpoH0uK1WqhI2NDWXLlmXnzp3/eZ4ALl++TKNGjbCxsSFnzpz06dOHsLCwJPvt2bOHFi1akD9/fqysrChSpAi9e/fm0aNHhn3GjRvH0KFDAShYsKDhHCSMnFu7di0NGzbEyckJa2trSpYsyfDhw5P8H9y4cYP27duTN29eLC0tyZ07N/Xq1Usyom7t2rVUr14dW1tbsmTJwkcffZSoDv6v+vNVlixZQu7cuVm+fDnW1tYsWbLkdU6nEEKIzEQJIYQQ/xg7dqwClIuLixo6dKjatWuXmjFjhrK1tVUVK1ZUMTExhn1dXFyUk5OTKlSokFqyZIny9PRUx48fV5cvX1Z2dnaqcOHC6rffflPbt29Xn332mQLU5MmTDa/39PRUgHJ2dlYtWrRQW7duVStWrFBFihRR9vb26vr164Z9V65cqQDVsGFDtXnzZrV27Vrl5uamLCws1MGDB5ON/9tvv1V79uxRmzdvVn///bdq06aNApSPj4/h8ezZM0NZunTpYjjOgwcPVL58+ZSjo6OaP3++2rlzp+rfv78C1JdffmnY7+bNmwpQrq6uqlGjRmrz5s1q8+bNqmzZsipbtmwqODj4lef7dc7VgwcPlI+PjwJUmzZtDLG/TEJMLi4uqlmzZmrbtm1qxYoVKnfu3KpYsWKqU6dOqnv37uqvv/5S8+fPV1myZFHNmjVLdAwPDw/l4eFh+F2v16smTZqobNmyqVu3bimllFqyZIkC1KJFiwz7/f7770qn06mWLVuqjRs3qq1bt6qPP/5YmZqaqr179xr227t3rzI1NVUffPCB2rhxo/rjjz9U5cqVVYECBdTrfDVJ+H/u0aOH+uuvv9Svv/6q8uXLp/LkyZMo7uDgYNW1a1f1+++/q/3796udO3eqb775RpmYmKjly5cb9vPx8VHW1taqSZMmhvN74cIFw//Rl19+qdasWaO8vLzUtm3bVI8ePZSJiYny9PQ0HGP16tUKUF999ZXavXu32rt3r5o/f74aMGCAYZ/AwEDl7OysXFxc1IIFC9TevXvVhAkTlKWlperatatSSqlnz56pnTt3GsqXEM/ff/+d7LkICQlRS5cuVYD67rvvDPsHBAQopZT6448/1JgxY9SmTZuUt7e3WrNmjfLw8FCOjo7q4cOHhuMkHOPEiRNKKaXOnTunnJ2dVfXq1Q37RUREqAoVKqicOXOqGTNmqL1796rZs2crBwcHVbduXaXX6w3HS/i7qFKlilq3bp3asWOHqlOnjjIzM0v0t/1vr/q8J8SYL18+1atXL/XXX3+p9evXq7i4ONWrVy8FqP79+6udO3eq+fPnK0dHR+Xs7JyonB4eHipHjhyqePHiavHixWrXrl3q448/VoAaP368Klu2rFq9erXasWOHqlatmrK0tFR37959abxKKRUUFKRy5cql8uXLp5YuXap27NihPv/8c8Pn+cXPybx589SkSZPUn3/+qby9vdXy5ctV+fLlVfHixQ31a0BAgPrqq68UoDZu3Gg4ByEhIUoppSZMmKBmzpyptm/frry8vNT8+fNVwYIF1YcffpgoruLFi6siRYqo33//XXl7e6sNGzaoIUOGJIrnxx9/VDqdTnXv3l1t27ZNbdy4UVWvXl3Z2toa/gb+q/58mcOHDytADR06VCmlVMeOHZVOp1M3btx45euEEEJkLtIpJYQQwiDhYv/rr79OtD2hU2jFihWGbS4uLsrU1FRduXIl0b7t27dXlpaWyt/fP9H2xo0bKxsbG0NHTUKnVKVKlRJdzN66dUuZm5urnj17KqWUio+PV3nz5lVly5ZV8fHxhv3CwsJUrly5VI0aNZLEP2bMmCRl69ev30s7PP7dKTV8+HAFqGPHjiXa78svv1Q6nc5Q5oQOoLJly6q4uDjDfsePH1eAWr16dbLv96bnSintIr9fv36vPN6LMZUvXz7R+Zo1a5YCVPPmzRPtP2jQIAUYLniVStoppZRSjx49Uvnz51dVqlRRp06dUjY2Nqpjx46G5yMiIlT27NmTdHDFx8er8uXLqypVqhi2Va1aVeXNm1dFRUUZtoWGhqrs2bP/Z6fU06dPlZWVlWrVqlWi7QkXwP+O+0VxcXEqNjZW9ejRQ1WsWDHRc7a2tok+A/91jHr16iWKoX///ipr1qyvfG3v3r1VlixZ1O3btxNtnzZtmgIMnQAPHz5UgBo7dux/xqOUUidOnFCAWrp06WvFHx4ermxtbdXs2bMN21/slNqzZ4+yt7dXbdq0SfR/NGnSJGViYmLouEqwfv16BagdO3YYtgEqd+7cKjQ01LAtKChImZiYqEmTJv1nnMl93hNi7Ny5c6Ltly5dUoDq27dvou3Hjh1TgBo5cqRhm4eHhwLUyZMnDdseP36sTE1NlbW1daIOqNOnTytA/fTTT6+M9dtvv1U6nU6dPn060fYGDRok6ZR6kV6vV7Gxser27dsKUFu2bDE8N3XqVAWomzdvvvK9E47h7e2tAHXmzBmllPb3CqhZs2a99LX+/v7KzMxMffXVV4m2h4WFqTx58qi2bdsatr2q/nyZ7t27K0BdunRJKfW8zh89evQbHUcIIUTGJtP3hBBCJPH5558n+r1t27aYmZnh6emZaHu5cuUoVqxYom379++nXr16ODs7J9retWtXIiMjk0y569ChQ6LpaC4uLtSoUcPwXleuXOHevXt06tQJE5PnzVaWLFlo3bo1R48eTZI3qnXr1m9Y4sT2799PqVKlqFKlSpIyKKXYv39/ou1NmzbF1NTU8Hu5cuUA/nOK1JueqzfRpEmTROerZMmShlhflLDd39//lcfLkSMHa9eu5dSpU9SoUYMCBQowf/58w/NHjhzhyZMndOnShbi4OMNDr9fTqFEjTpw4QUREBBEREZw4cYJPPvkEKysrw+vt7Oxo1qzZf5bLx8eHZ8+eJfmM1qhRAxcXlyT7//HHH9SsWZMsWbJgZmaGubk5ixcv5tKlS//5Xgnmz59PpUqVsLKyMhxj3759iY5RpUoVgoOD+eyzz9iyZUui6VgJtm3bxocffkjevHkTnaPGjRsD4O3t/doxva7w8HC+/fZbihQpgpmZGWZmZmTJkoWIiIhkz8Hy5ctp0qQJPXv2ZN26dYn+j7Zt20aZMmWoUKFCovg/+uijZFeZ+/DDD7GzszP8njt3bnLlyvVaUwdf5d9/3wl1xb+nOFapUoWSJUsmmT7q5OSEm5ub4ffs2bOTK1cuKlSoQN68eQ3bE/42/iteT09PSpcuTfny5RNt79ChQ5J9Hzx4QJ8+fXB2djZ8lhI+t6/7mbxx4wYdOnQgT548mJqaYm5ujoeHR6JjZM+encKFCzN16lRmzJiBn59foqnHALt27SIuLo7OnTsn+v+0srLCw8PjrVYNTBAeHs66deuoUaMGJUqUAMDDw4PChQuzbNmyJLEIIYTIvKRTSgghRBL/TipsZmZGjhw5ePz4caLtTk5OSV77+PHjZLcnXOz9+xjJJTDOkyePYb+Ef192TL1ez9OnT/8zrjfxpmXIkSNHot8tLS2B/078/qbv8yayZ8+e6HcLC4tXbn+dVbGqVq1K6dKlefbsGV9++SW2traG5+7fvw9AmzZtMDc3T/SYPHkySimePHnC06dP0ev1L/1//y8J5+R1Xr9x40batm1Lvnz5WLFiBT4+Ppw4cYLu3bu/9ipgM2bM4Msvv6Rq1aps2LCBo0ePcuLECRo1apTo/7dTp04sWbKE27dv07p1a3LlykXVqlXZs2dPonO0devWJOendOnSAMl2ZL2rDh068Msvv9CzZ0927drF8ePHOXHiBI6Ojsl+PtesWYO1tTU9e/ZMkrvs/v37nD37//buPybq+o8D+POIO06O67wDZPywg1gerHGaZppM6GIE7M4/ih+tciMX9muNYDqmAzwEGr/+qMZWtrKVeTo0SSRaEzBsi5YuFwhYlk2l8SOmg4VbR8Dr+4e7+3LcIZiJfvs+Hxtj9+H9696f9/uz8d778351e7Vfq9VCRLzaP3teANfnxq0GRJg9Z+Z7RsyeR7PnAHB9HvzduXHlypUFjcfp6Wk88cQTaGxsRFFREdrb23Hq1Cn3+XML6Zfx8XFs3LgR3333HSorK9HR0YHTp0+7DxB3laFQKNDe3o60tDTU1tZi9erVCA0NRX5+vvusK9ecXbt2rdc9bWhouKXx2NDQgPHxceTk5GB0dBSjo6MYGxtDTk4O+vv7PeYFERH9f/O/0w0gIqK7z9DQECIjI92fJycnceXKFa9/Mn0duB0cHIzBwUGv6wMDAwCAkJAQr7p81e+qy/V7rjL9/Pyg1+vnbdfNuNnvcLfX80+x2+04e/Ys1qxZg127dsFms+H+++8H8N+21tfXY/369T7zh4WF4a+//oJCoZjzvs/HNR7myh8dHe3+vH//fsTExKChocFjTDidznnrmVnGY489hnfffdfjuq9DrLds2YItW7bg2rVr+Prrr2G322Gz2XD+/HkYjUaEhITAbDbjjTfe8FnXzF06/4SxsTF8/vnnsNvt2LFjh/u60+nE1atXfeZxOBwoLS1FcnIyjh8/jlWrVrn/FhIScsPDqhdrvM6e3zOfEVFRUR5/GxgYuO3tCg4OXtB47unpQVdXFz766CPk5ua6r//yyy8LruvEiRMYGBhAR0eHe3cUAIyOjnqlNRqN2Lt3LwDg/PnzOHToEMrKyjAxMYE9e/a4++XTTz/1ucvwVrjqLSgoQEFBgc+/p6Wl/aN1EhHR/ybulCIiIi8Oh8Pj86FDhzA5OekRkW0uKSkp7n+cZtq3bx8CAwO9FiwOHjzoEdL90qVL6OzsdNdlMpkQGRmJAwcOeKS7du0ajhw54o7IN5+F7l5yfYe+vj6cOXPG6zsoFApYLJZ5y1iIm+2rO6m1tRVVVVUoKSlBa2srdDodnn76aUxMTAAAEhMTsXTpUvT19eHhhx/2+aNSqaDRaPDII4+gsbHRYwfKH3/84RXdz5f169dDrVZ7jdHOzk6v16wUCgVUKpXHIsbQ0JBX9D1g7h08CoXCPXZcuru7b/hqpUajQUZGBoqLizExMYHe3l4AgM1mQ09PD2JjY332j2tR6mbG6o3SKxQKiIhX+z/44ANMTU35LMtgMKCtrQ3x8fGwWCweUSRtNhsuXLiA4OBgn+2fuSC4mB5//HEA1xcQZzp9+jTOnTuHlJSU21q/xWJBb28vurq6PK4fOHDA47NrHM6+H++9955XmTe6pwstY6YVK1agpKQECQkJ7udaWloa/P39ceHChTnn7Hzt8eXcuXP49ttvkZmZia+++srrJyUlBU1NTbe0E5SIiP49uFOKiIi8NDY2wt/fH6mpqejt7UVpaSlWrlyJnJycefPa7Xb32Tm7du2CwWCAw+FAS0sLamtrodPpPNL//vvvePLJJ7F161aMjY3BbrdDrVZj586dAAA/Pz/U1tbiueeeg81mw0svvQSn04m6ujqMjo6iurp6Qd8pISEBAFBTU4OMjAzcc889MJvN7ld0ZiosLMS+fftgtVpRXl4Oo9GIlpYWvPPOO3jllVe8ztH6u262r+6UwcFBbN68GcnJybDb7fDz80NDQwOSkpJQVFSEt956C0FBQaivr0dubi6uXr2KrKwsLFu2DCMjI+jq6sLIyIh7t1FFRQXS09ORmpqKbdu2YWpqCjU1NdBoNHPu4HHR6/XYvn07KisrkZeXh+zsbPT396OsrMzrdSmbzYbGxka8+uqryMrKQn9/PyoqKhAeHo6ff/7ZI21CQgI6OjrQ3NyM8PBwaLVamEwm2Gw2VFRUwG63Izk5GT/99BPKy8sRExODyclJd/6tW7diyZIlSExMRHh4OIaGhlBVVQWdToe1a9cCAMrLy9Ha2ooNGzYgPz8fJpMJf/75Jy5evIgvvvgCe/bsQVRUFLRaLYxGI5qampCSkgKDwYCQkJA5F31iY2OxZMkSOBwOxMfHIygoCBEREYiIiEBSUhLq6urc+U+ePIm9e/di6dKlc/axVqvFl19+iaeeegqpqak4duwYLBYLCgoKcOTIESQlJaGwsBBmsxnT09O4fPkyjh8/jm3btmHdunU3vH+3g8lkwosvvoj6+nr4+fkhIyMDFy9eRGlpKZYvX47CwsLbWn9BQQE+/PBDWK1WVFZWIiwsDA6HAz/++KNHuri4OMTGxmLHjh0QERgMBjQ3N/t8lc31vHr77beRm5sLpVIJk8mEDRs2QK/X4+WXX4bdbodSqYTD4fBaEOvu7sZrr72G7OxsPPDAA1CpVDhx4gS6u7vdu+aio6NRXl6O4uJi/Prrr0hPT4der8fw8DBOnToFjUaD3bt3e7RnIc9P1y6poqIir3P5gOsL0O3t7di/fz9ef/31m+1uIiL6t7mDh6wTEdFdxhW97vvvv5dNmzZJUFCQaLVaeeaZZ2R4eNgjrdFoFKvV6rOcs2fPyqZNm0Sn04lKpZKVK1d6RQZzRWL65JNPJD8/X0JDQyUgIEA2btzoERnL5ejRo7Ju3TpRq9Wi0WgkJSVFvvnmG5/tnxkC3sXpdEpeXp6EhoaKQqHwiGw1O/qeiMilS5fk2WefleDgYFEqlWIymaSurs4jop0r0l1dXZ1XfVhg9LSF9JWrvJuJvje7Ta7+Pnz4sMf1mVHXXGZG35ucnJTk5GQJCwuTwcFBj7yuCGGfffaZ+9rJkyfFarWKwWAQpVIpkZGRYrVaveo9duyYmM1mUalUct9990l1dbX7/s1nenpaqqqqZPny5aJSqcRsNktzc7PPqIHV1dUSHR0tAQEBEh8fL++//77Pen744QdJTEyUwMBAjyh+TqdTtm/fLpGRkaJWq2X16tVy9OhRyc3NFaPR6M7/8ccfi8VikbCwMFGpVBIRESE5OTnS3d3tUc/IyIjk5+dLTEyMKJVKMRgMsmbNGikuLpbx8XF3ura2NnnooYckICBAAMwbGfDgwYMSFxcnSqXSY+z99ttvkpmZKXq9XrRaraSnp0tPT4/XmPc1DpxOp2RmZoparZaWlhYRERkfH5eSkhIxmUyiUqlEp9NJQkKCFBYWytDQkDvvXOPV11zzxVd+X210mZqakpqaGlmxYoUolUoJCQmRzZs3S39/v0e65ORkefDBB322y9fzbKHzrq+vT1JTU0WtVovBYJAXXnhBmpqavKLvudJptVrR6/WSnZ0tly9f9vm82Llzp0RERIifn59HOZ2dnfLoo49KYGCghIaGSl5enpw5c8YjAuPw8LA8//zzEhcXJxqNRoKCgsRsNsubb77pESlU5Pqz1WKxyL333isBAQFiNBolKytL2tra3Glu9PycaWJiQpYtWyarVq2as68mJyclKipKEhIS5u1XIiL691OIzHgXgoiI/q+VlZVh9+7dGBkZue3nsHR0dMBiseDw4cPIysq6rXUREREREdHdh2dKERERERERERHRouOiFBERERERERERLTq+vkdERERERERERIuOO6WIiIiIiIiIiGjRcVGKiIiIiIiIiIgWHReliIiIiIiIiIho0XFRioiIiIiIiIiIFh0XpYiIiIiIiIiIaNFxUYqIiIiIiIiIiBYdF6WIiIiIiIiIiGjRcVGKiIiIiIiIiIgWHReliIiIiIiIiIho0f0HP/YzQlzyyh8AAAAASUVORK5CYII=", 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", 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" ] }, "metadata": {}, @@ -600,43 +1040,68 @@ } ], "source": [ - "datanames = [ '(mix)', '(A)', '(B)'] \n", - "modelnames = ['argmin(LogL)', 'argmin(nTVD)', 'argmin(TVD)', 'argmin(L10^10)']\n", - "lossnames = ['LogL', 'nTVD', 'TVD', 'L10']\n", - "losscolors = ['b', 'r', 'y', 'k']\n", - "fig, outer_axs = plt.subplots(len(lossnames), len(datanames), figsize=(12,12))\n", - "for i,metricname in enumerate(lossnames):\n", - " axs = outer_axs[i,:]\n", - " for dataname, ax in zip(datanames, axs):\n", - " rows = []\n", - " for df in dflist:\n", - " row = [ df[metricname + dataname][modelname] for modelname in modelnames ]\n", - " rows.append(row)\n", - " y = np.array(rows)\n", - " x = mixture_weights.copy()\n", - " for i,yi in enumerate(y.T):\n", - " ax.plot(x,yi,losscolors[i])\n", - " if metricname == 'LogL':\n", - " pass\n", - " #ax.set_yscale('log')\n", - " ax.legend(lossnames)\n", - " ax.set_title(dataname)\n", - " if dataname == '(mix)':\n", - " ax.set_ylabel(metricname)\n", - "fig.supxlabel('proportion of mixed dataset taken from dataset A')\n", - "fig.tight_layout()" + "fig, axs_grid = plt.subplots(num_rows, num_cols, figsize=(2*num_rows + 1, 4*num_cols + 1), sharey='all')\n", + "\n", + "def flexibeniusnorm(m1, m2, lbl):\n", + " if 'G' in lbl or lbl == '[]':\n", + " if lbl == '[]':\n", + " lbl = pygsti.baseobjs.Label(())\n", + " else:\n", + " lbl = lbl.split(':')\n", + " mm1 = m1[lbl]\n", + " mm2 = m2[lbl]\n", + " mm1d = mm1.to_dense()\n", + " mm2d = mm2.to_dense()\n", + " return la.norm(mm1d - mm2d)\n", + " elif 'rho' in lbl:\n", + " mm1 = m1[lbl]\n", + " mm2 = m2[lbl]\n", + " mm1d = pygsti.tools.vec_to_stdmx(mm1.to_dense(), 'pp')\n", + " mm2d = pygsti.tools.vec_to_stdmx(mm2.to_dense(), 'pp')\n", + " return la.norm(mm1d - mm2d)\n", + " elif 'Mdefault' == lbl:\n", + " mm1d = np.array([e.to_dense() for e in m1[lbl].values()])\n", + " mm2d = np.array([e.to_dense() for e in m2[lbl].values()])\n", + " return la.norm(mm1d - mm2d)\n", + " else:\n", + " raise ValueError()\n", + "\n", + "for membername, row_axs in zip(row_lbls, axs_grid):\n", + " row_axs[0].set_ylabel(membername.removesuffix(':0')) #, rotation=0)\n", + " for modelname, ax in zip(modelnames, row_axs):\n", + " ftoA_vs_p = np.zeros(num_mixtures)\n", + " ftoB_vs_p = np.zeros(num_mixtures)\n", + " ftoI_vs_p = np.zeros(num_mixtures)\n", + " # ax.set_title(modelname)\n", + " for i, (res, _) in enumerate(reslist):\n", + " model_argmin = res.estimates[modelnames_to_estnames[modelname]].models['stdgaugeopt']\n", + " ftoA_vs_p[i] = flexibeniusnorm(model_argmin, m_dga_gopped, membername)\n", + " ftoB_vs_p[i] = flexibeniusnorm(model_argmin, m_dgb_gopped, membername)\n", + " ftoI_vs_p[i] = flexibeniusnorm(model_argmin, target, membername)\n", + " ax.plot(mixture_weights, ftoA_vs_p, losscolors[0])\n", + " ax.plot(mixture_weights, ftoB_vs_p, losscolors[1])\n", + " ax.plot(mixture_weights, ftoI_vs_p, losscolors[2])\n", + " modelname = modelname.removeprefix('argmin(').strip(')')\n", + " ax.legend(['FroDist to A', 'FroDist to B', 'FroDist to ideal'])\n", + " ax.minorticks_on()\n", + " ax.grid(linestyle='dotted', which='minor')\n", + " ax.grid(which='major')\n", + " if membername == 'rho0':\n", + " ax.set_title('model = argmin( %s, data(p) )' % modelname)\n", + "\n", + "fig.set_tight_layout(True)" ] }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -644,29 +1109,87 @@ } ], "source": [ - "# datanames = [ '(mix)', '(A)', '(B)'] \n", - "modelnames = ['argmin(LogL)', 'argmin(nTVD)', 'argmin(TVD)', 'argmin(L10^10)']\n", - "lossnames = ['LogL', 'nTVD', 'TVD', 'L10']\n", - "losscolors = ['b', 'r', 'y', 'k', 'd','.']\n", - "dataname = '(mix)'\n", - "fig, outer_axs = plt.subplots(1, len(lossnames), figsize=(15,4))\n", - "for metricname,ax in zip(lossnames, outer_axs):\n", - " rows = []\n", - " for df in dflist:\n", - " row = [ df[metricname + dataname][modelname] for modelname in modelnames ]\n", - " rows.append(row)\n", - " y = np.array(rows)\n", - " x = mixture_weights.copy()\n", - " for i,yi in enumerate(y.T):\n", - " ax.plot(x,yi,losscolors[i])\n", - " ax.legend(modelnames)\n", - " # ax.set_title(dataname)\n", - " ax.set_title(metricname)\n", - " # if dataname == '(mix)':\n", - " # ax.set_ylabel(metricname)\n", - "fig.suptitle('Loss functions L(fit(data(p), L), data(p)), where...\\ndata(p) is the mixed dataset with weight p on dataset A, and\\nfit(ds, L) is the model from fitting loss L to data d')\n", - "fig.supxlabel('proportion of mixed dataset taken from dataset A')\n", - "fig.tight_layout()" + "fig, axs_grid = plt.subplots(num_rows, num_cols, figsize=(2*num_rows + 1, 4*num_cols + 1), sharey='all')\n", + "\n", + "def flexitracenorm(m1, m2, lbl):\n", + " if 'G' in lbl or lbl == '[]':\n", + " if lbl == '[]':\n", + " lbl = pygsti.baseobjs.Label(())\n", + " else:\n", + " lbl = lbl.split(':')\n", + " mm1 = m1[lbl]\n", + " mm2 = m2[lbl]\n", + " mm1d = mm1.to_dense()\n", + " mm2d = mm2.to_dense()\n", + " return pygsti.tools.optools.jtracedist(mm1d, mm2d, 'pp')\n", + " elif 'rho' in lbl:\n", + " mm1 = m1[lbl]\n", + " mm2 = m2[lbl]\n", + " mm1d = pygsti.tools.vec_to_stdmx(mm1.to_dense(), 'pp')\n", + " mm2d = pygsti.tools.vec_to_stdmx(mm2.to_dense(), 'pp')\n", + " return pygsti.tools.optools.tracedist(mm1d, mm2d)\n", + " elif 'Mdefault' == lbl:\n", + " mm1d = np.array([e.to_dense() for e in m1[lbl].values()])\n", + " mm2d = np.array([e.to_dense() for e in m2[lbl].values()])\n", + " return pygsti.tools.optools.povm_jtracedist(m1, m2, lbl)\n", + " else:\n", + " raise ValueError()\n", + "\n", + "for membername, row_axs in zip(row_lbls, axs_grid):\n", + " row_axs[0].set_ylabel(membername.removesuffix(':0')) #, rotation=0)\n", + " for modelname, ax in zip(modelnames, row_axs):\n", + " ftoA_vs_p = np.zeros(num_mixtures)\n", + " ftoB_vs_p = np.zeros(num_mixtures)\n", + " ftoI_vs_p = np.zeros(num_mixtures)\n", + " for i, (res, _) in enumerate(reslist):\n", + " model_argmin = res.estimates[modelnames_to_estnames[modelname]].models['stdgaugeopt']\n", + " ftoA_vs_p[i] = flexitracenorm(model_argmin, m_dga_gopped, membername)\n", + " ftoB_vs_p[i] = flexitracenorm(model_argmin, m_dgb_gopped, membername)\n", + " ftoI_vs_p[i] = flexitracenorm(model_argmin, target, membername)\n", + " ax.plot(mixture_weights, ftoA_vs_p, losscolors[0])\n", + " ax.plot(mixture_weights, ftoB_vs_p, losscolors[1])\n", + " ax.plot(mixture_weights, ftoI_vs_p, losscolors[2])\n", + " modelname = modelname.removeprefix('argmin(').strip(')')\n", + " ax.legend(['TrDist to A', 'TrDist to B', 'TrDist to ideal'])\n", + " ax.minorticks_on()\n", + " ax.grid(linestyle='dotted', which='minor')\n", + " ax.grid(which='major')\n", + " #ax.set_title('f = ' + modelname)\n", + " if membername == 'rho0':\n", + " ax.set_title('model = argmin( %s, data(p) )' % modelname)\n", + "fig.set_tight_layout(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "odict_keys([Label(()), Label(('Gxpi2', 0)), Label(('Gypi2', 0))])\n", + "['[]', 'Gxpi2:0', 'Gypi2:0']\n", + "odict_keys([Label('rho0')])\n", + "odict_keys([Label('Mdefault')])\n", + "Lindblad-parameterized POVM of length 2\n", + "\n", + "OrderedDict([('logl', ), ('normalized tvd', ), (\"('Lp^p', 10)\", )])\n", + "23\n", + "23\n" + ] + } + ], + "source": [ + "print(m_dga.operations.keys())\n", + "print([str(ell) for ell in m_dga.operations.keys()])\n", + "print(m_dga.preps.keys())\n", + "print(m_dga.povms.keys())\n", + "print(m_dga['Mdefault'])\n", + "print(results.estimates)\n", + "print(len(reslist))\n", + "print(num_mixtures)" ] } ], From 25ee2998cae575d9c4dfc9e70ae1cb6788902986 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Tue, 13 May 2025 12:40:48 -0700 Subject: [PATCH 45/71] check in (needed for notebook committed in last commit to actually run --- wip_notebook_sharing/README.md | 4 ++ wip_notebook_sharing/experiment_helpers.py | 76 ++++++++++++++++++++-- 2 files changed, 74 insertions(+), 6 deletions(-) create mode 100644 wip_notebook_sharing/README.md diff --git a/wip_notebook_sharing/README.md b/wip_notebook_sharing/README.md new file mode 100644 index 000000000..6a233cd35 --- /dev/null +++ b/wip_notebook_sharing/README.md @@ -0,0 +1,4 @@ +# This folder + +Contents of this folder should be deleted before merge. + diff --git a/wip_notebook_sharing/experiment_helpers.py b/wip_notebook_sharing/experiment_helpers.py index 33ec85228..82cb33b30 100644 --- a/wip_notebook_sharing/experiment_helpers.py +++ b/wip_notebook_sharing/experiment_helpers.py @@ -6,6 +6,7 @@ from pygsti.objectivefns import ObjectiveFunctionBuilder, ModelDatasetCircuitsStore import pygsti.objectivefns from pygsti.optimize import SimplerLMOptimizer +import scipy.linalg as la def make_depolarized_dataset(modelpack, depol_level=0.01, max_max_len=128): @@ -23,7 +24,7 @@ def make_depolarized_dataset(modelpack, depol_level=0.01, max_max_len=128): return ds, depol_model -def make_tweaked_dataset(modelpack, depol_level=0.01, rand_unitary_scale=0.001, max_max_len=128): +def make_tweaked_dataset(modelpack, depol_level=0.01, rand_unitary_scale=0.001, max_max_len=128, sample_error='multinomial', seed=0, shots_per_circuit=1000): ideal_model = modelpack.target_model() prep_fids = modelpack.prep_fiducials() meas_fids = modelpack.meas_fiducials() @@ -31,13 +32,76 @@ def make_tweaked_dataset(modelpack, depol_level=0.01, rand_unitary_scale=0.001, max_lens = [2**p for p in range(1+int(np.log2(max_max_len)))] lsgst_circuit_lists = pygsti.circuits.create_lsgst_circuit_lists(ideal_model, prep_fids, meas_fids, germs, max_lens) all_circuits = lsgst_circuit_lists[-1] - shots_per_circuit = 1000 - depol_model = ideal_model.depolarize(op_noise=depol_level, spam_noise=depol_level/2, seed=1997) - final_model = depol_model.randomize_with_unitary(scale=rand_unitary_scale, seed=250422) - rng_state = np.random.default_rng(0) - ds = pygsti.data.simulate_data(final_model, all_circuits, shots_per_circuit, rand_state=rng_state) + + depol_model = ideal_model.depolarize(op_noise=depol_level, spam_noise=depol_level/2, seed=seed+1997) + final_model = depol_model.randomize_with_unitary(scale=rand_unitary_scale, seed=seed+250422, transform_spam=True) + rng_state = np.random.default_rng(seed) + ds = pygsti.data.simulate_data(final_model, all_circuits, shots_per_circuit, sample_error=sample_error, rand_state=rng_state) + return ds, final_model +def make_tweaked_dataset_pairs(modelpack, depol_level=0.01, rand_unitary_scale=0.001, max_max_len=128, sample_error='multinomial', seed=0, shots_per_circuit=1000, gaugeopt=True): + ideal_model = modelpack.target_model() + prep_fids = modelpack.prep_fiducials() + meas_fids = modelpack.meas_fiducials() + germs = modelpack.germs() + max_lens = [2**p for p in range(1+int(np.log2(max_max_len)))] + lsgst_circuit_lists = pygsti.circuits.create_lsgst_circuit_lists(ideal_model, prep_fids, meas_fids, germs, max_lens) + all_circuits = lsgst_circuit_lists[-1] + + depol_model = ideal_model.depolarize(op_noise=depol_level, spam_noise=depol_level/2, seed=seed+1997) + + model_a = depol_model.copy() + model_b = depol_model.copy() + rndm = np.random.RandomState(seed+250422) + import pygsti.tools.optools as _ot + import pygsti.modelmembers.operations as _op + from pygsti.modelmembers.povms import create_from_dmvecs + from pygsti.modelmembers.states import FullState + unitary_dim = 2 + basis = depol_model.basis + + def rand_unitary_as_superop(): + rand_mat = rndm.randn(unitary_dim, unitary_dim) + 1j * rndm.randn(unitary_dim, unitary_dim) + rand_herm = rand_mat.T.conj() + rand_mat + rand_herm /= la.norm(rand_herm) + rand_herm *= rand_unitary_scale * np.sqrt(unitary_dim) + rand_unitary = la.expm(-1j * rand_herm) + rand_op = _ot.unitary_to_superop(rand_unitary, basis) + assert la.norm(rand_op @ rand_op.T - np.eye(unitary_dim**2)) < 1e-12 + return rand_op + + for opLabel, gate in depol_model.operations.items(): + rand_op = rand_unitary_as_superop() + model_a.operations[opLabel] = _op.FullArbitraryOp(rand_op @ gate) + model_b.operations[opLabel] = _op.FullArbitraryOp(rand_op.T @ gate) + + for preplbl, rho in depol_model.preps.items(): + rand_op = rand_unitary_as_superop() + model_a.preps[preplbl] = FullState(rand_op @ rho) + model_b.preps[preplbl] = FullState(rand_op.T @ rho) + + for povmlbl, M in depol_model.povms.items(): + rand_op = rand_unitary_as_superop() + dmvecs = {elbl: rand_op @ e.to_dense() for elbl, e in M.items()} + model_a.povms[povmlbl] = create_from_dmvecs(dmvecs, 'full') + dmvecs = {elbl: rand_op.T @ e.to_dense() for elbl, e in M.items()} + model_b.povms[povmlbl] = create_from_dmvecs(dmvecs, 'full') + + rng_state = np.random.default_rng(seed) + dsa = pygsti.data.simulate_data(model_a, all_circuits, shots_per_circuit, sample_error=sample_error, rand_state=rng_state) + dsb = pygsti.data.simulate_data(model_b, all_circuits, shots_per_circuit, sample_error=sample_error, rand_state=rng_state) + + if gaugeopt: + from pygsti.algorithms.gaugeopt import gaugeopt_to_target + for model in [model_a, model_b]: + model.convert_members_inplace('full') + model.default_gauge_group = 'unitary' + model_a = gaugeopt_to_target(model_a, ideal_model) + model_b = gaugeopt_to_target(model_b, ideal_model) + + return dsa, model_a, dsb, model_b + def corrupt_dataset(ds, prop_corrupt, rng=0): From 1599eb788e1bd9ce1cc76aa6924c6328f6a1e907 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Tue, 13 May 2025 13:06:23 -0700 Subject: [PATCH 46/71] remove temporary notebooks --- ...unch-of-data-fix-embarassing-oversight.txt | 226 --- wip_notebook_sharing/experiment_helpers.py | 251 ---- .../objectives-handling-mixtures.ipynb | 1217 ----------------- 3 files changed, 1694 deletions(-) delete mode 100644 wip_notebook_sharing/bunch-of-data-fix-embarassing-oversight.txt delete mode 100644 wip_notebook_sharing/experiment_helpers.py delete mode 100644 wip_notebook_sharing/objectives-handling-mixtures.ipynb diff --git a/wip_notebook_sharing/bunch-of-data-fix-embarassing-oversight.txt b/wip_notebook_sharing/bunch-of-data-fix-embarassing-oversight.txt deleted file mode 100644 index 37e908293..000000000 --- a/wip_notebook_sharing/bunch-of-data-fix-embarassing-oversight.txt +++ /dev/null @@ -1,226 +0,0 @@ - -I used the following function to generate noisy models and 1000-shot datasets for GST. - - def make_tweaked_dataset(modelpack, depol_level=0.01, rand_unitary_scale=0.001, max_max_len=128): - ideal_model = modelpack.target_model() - prep_fids = modelpack.prep_fiducials() - meas_fids = modelpack.meas_fiducials() - germs = modelpack.germs() - max_lens = [2**p for p in range(1+int(np.log2(max_max_len)))] - lsgst_circuit_lists = pygsti.circuits.create_lsgst_circuit_lists(ideal_model, prep_fids, meas_fids, germs, max_lens) - all_circuits = lsgst_circuit_lists[-1] - shots_per_circuit = 1000 - depol_model = ideal_model.depolarize(op_noise=depol_level, spam_noise=depol_level/2, seed=1997) - final_model = depol_model.randomize_with_unitary(scale=rand_unitary_scale, seed=250422) - rng_state = np.random.default_rng(0) - ds = pygsti.data.simulate_data(final_model, all_circuits, shots_per_circuit, rand_state=rng_state) - return ds, final_model - -Here is how I used it; note that Model A has more noise than Model B. - - mp = smq1Q_XYI - target = mp.target_model() - fids = (mp.prep_fiducials(), mp.meas_fiducials()) - germs = mp.germs() - maxmaxlen = 64 - dsa, m_dga = make_tweaked_dataset(mp, depol_level=0.001, rand_unitary_scale=0.001, max_max_len=maxmaxlen) - dsb, m_dgb = make_tweaked_dataset(mp, depol_level=0.010, rand_unitary_scale=0.020, max_max_len=maxmaxlen) - - -In the tables below, rows labeled "argmin(func)" show results for the model obtained by fitting "func" to the mixed dataset. - - ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The mixture dataset had weight 1.00 on dataset A and weight 0.00 on dataset B. - - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.058305 0.731190 0.058305 467.504133 68540.938167 467.504133 0.326392 3172.543057 0.326392 8.786676 60.511077 8.786676 16.335460 123.354424 16.335460 -argmin(nTVD) 0.058255 0.731205 0.058255 500.047586 69561.100943 500.047586 1.842914 3220.082562 1.842914 8.720995 60.394434 8.720995 15.342389 124.476252 15.342389 -argmin(TVD) 0.058421 0.731976 0.058421 484.225199 69215.050104 484.225199 1.105592 3203.956620 1.105592 8.657254 60.464797 8.657254 15.826993 123.250965 15.826993 -argmin(L10^10) 0.056057 0.740446 0.056057 638.122108 69640.974153 638.122108 8.277176 3223.804647 8.277176 9.578566 60.363776 9.578566 17.786360 122.383633 17.786360 -Model A 0.058701 0.731059 0.058701 479.867127 68138.580337 479.867127 0.878730 3152.059162 0.878730 8.853287 60.327248 8.853287 16.534238 123.431015 16.534238 -Model B 0.731329 0.059377 0.731329 38338.240487 505.249618 38338.240487 1764.118964 2.060910 1764.118964 60.134177 10.943588 60.134177 125.523234 21.227815 125.523234 - - ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The mixture dataset had weight 0.99 on dataset A and weight 0.01 on dataset B. - - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.058280 0.724711 0.057080 507.324913 63646.108882 418.094069 2.182037 2944.444398 -1.976113 8.942991 59.846071 8.432625 16.504940 121.838636 15.728875 -argmin(nTVD) 0.058199 0.725208 0.057029 523.844937 64807.005109 450.864053 2.951869 2998.542071 -0.449034 8.881305 59.807557 8.380632 15.663741 122.889098 14.786347 -argmin(TVD) 0.058479 0.724452 0.057338 509.232448 63988.729224 431.947107 2.270928 2960.410479 -1.330563 8.828511 59.859282 8.314256 16.191643 121.921252 15.361203 -argmin(L10^10) 0.056076 0.738725 0.054859 650.330393 68607.667786 638.572710 8.846081 3175.652652 8.298174 9.610355 60.149631 9.356175 17.971954 121.908982 17.208866 -Model A 0.058701 0.731059 0.057534 479.867127 68138.580337 468.637758 0.878730 3152.059162 0.355726 8.853287 60.327248 8.592865 16.534238 123.431015 16.151412 -Model B 0.731329 0.059377 0.723682 38338.240487 505.249618 36887.387442 1764.118964 2.060910 1696.546001 60.134177 10.943588 59.237540 125.523234 21.227815 123.602545 - - ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The mixture dataset had weight 0.95 on dataset A and weight 0.05 on dataset B. - - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.058553 0.699850 0.054809 873.908924 52790.312819 370.270628 19.264823 2438.565156 -4.204682 10.142951 57.797471 8.212986 18.901034 117.555414 14.999158 -argmin(nTVD) 0.059798 0.699797 0.055133 883.531715 53037.287180 391.807285 19.713245 2450.074142 -3.201075 10.156287 57.551119 8.186089 17.928632 117.965291 14.160187 -argmin(TVD) 0.058915 0.697846 0.055142 884.866988 52729.115781 379.257525 19.775468 2435.713379 -3.785893 10.053967 57.813796 8.103999 19.094600 117.175958 14.728985 -argmin(L10^10) 0.058181 0.685159 0.052683 1095.965264 51993.590764 541.985234 29.612631 2401.437971 3.797205 10.804595 57.729039 9.199417 17.573569 120.776182 16.172637 -Model A 0.058701 0.731059 0.056414 479.867127 68138.580337 829.861371 0.878730 3152.059162 17.179587 8.853287 60.327248 9.539133 16.534238 123.431015 17.309003 -Model B 0.731329 0.059377 0.694352 38338.240487 505.249618 33064.605213 1764.118964 2.060910 1518.501283 60.134177 10.943588 56.927442 125.523234 21.227815 118.767794 - - ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The mixture dataset had weight 0.90 on dataset A and weight 0.10 on dataset B. - - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.069575 0.669120 0.053253 1707.234071 42918.600355 348.222883 58.097710 1978.544133 -5.232105 12.207427 55.203937 8.132309 23.235284 112.218973 14.596372 -argmin(nTVD) 0.079953 0.666450 0.058557 1705.770360 43067.584239 363.791579 58.029501 1985.486770 -4.506605 12.338981 54.893533 8.109634 21.600853 113.270335 13.542902 -argmin(TVD) 0.072924 0.665629 0.053595 1713.855390 42873.967832 353.081662 58.406262 1976.464261 -5.005686 12.164062 55.122870 7.974263 23.594074 111.497258 14.229698 -argmin(L10^10) 0.094878 0.640004 0.050696 2031.709888 41913.699383 536.640612 73.218257 1931.715827 3.548147 12.693870 55.157799 9.248214 20.035872 115.901433 16.354479 -Model A 0.058701 0.731059 0.073987 479.867127 68138.580337 1868.850198 0.878730 3152.059162 65.570121 8.853287 60.327248 11.344055 16.534238 123.431015 20.830502 -Model B 0.731329 0.059377 0.657746 38338.240487 505.249618 28840.694551 1764.118964 2.060910 1321.774150 60.134177 10.943588 53.972022 125.523234 21.227815 112.825645 - - ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The mixture dataset had weight 0.80 on dataset A and weight 0.20 on dataset B. - - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.128069 0.607533 0.058235 4073.797769 29501.445680 344.775113 168.379391 1353.305782 -5.392771 16.805776 49.914257 8.147237 32.461797 101.228335 13.417375 -argmin(nTVD) 0.146037 0.598981 0.094634 4183.566412 29188.668078 372.506101 173.494602 1338.730370 -4.100509 17.372651 49.184236 8.021515 32.596331 100.884726 12.828388 -argmin(TVD) 0.146679 0.595218 0.091479 4232.062255 28995.323212 373.301298 175.754504 1329.720514 -4.063453 17.390356 49.195911 7.972158 33.085064 100.576929 13.119409 -argmin(L10^10) 0.172593 0.562341 0.048265 4352.138395 29632.311270 600.673234 181.350043 1359.404108 6.532062 16.844040 50.229908 9.723182 28.929435 104.827273 15.294466 -Model A 0.058701 0.731059 0.145681 479.867127 68138.580337 5324.814797 0.878730 3152.059162 226.530439 8.853287 60.327248 15.643033 16.534238 123.431015 29.515782 -Model B 0.731329 0.059377 0.584742 38338.240487 505.249618 21821.205206 1764.118964 2.060910 994.843938 60.134177 10.943588 48.146528 125.523234 21.227815 100.913491 - - ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The mixture dataset had weight 0.70 on dataset A and weight 0.30 on dataset B. - - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.192544 0.542873 0.074748 7091.943198 20493.271003 374.808710 309.024731 933.525551 -3.993207 21.834864 44.494855 8.290449 42.852141 90.101863 12.886797 -argmin(nTVD) 0.204957 0.532177 0.101387 7261.663790 20147.948696 391.445729 316.933697 917.433559 -3.217924 22.304388 43.941396 8.081331 42.897418 89.962380 12.315999 -argmin(TVD) 0.216290 0.524384 0.124270 7352.556204 20050.338197 428.017151 321.169276 912.884917 -1.513698 22.608459 43.610406 7.988594 43.227342 89.623360 12.475021 -argmin(L10^10) 0.245854 0.489594 0.051606 7656.645776 20355.374322 727.390771 335.339826 927.099577 12.437089 22.360088 44.437987 10.407362 40.644331 92.165539 15.493826 -Model A 0.058701 0.731059 0.218786 479.867127 68138.580337 10221.507329 0.878730 3152.059162 454.592147 8.853287 60.327248 20.452823 16.534238 123.431015 39.614498 -Model B 0.731329 0.059377 0.511604 38338.240487 505.249618 16131.152604 1764.118964 2.060910 729.831768 60.134177 10.943588 42.329793 125.523234 21.227815 89.111709 - - ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The mixture dataset had weight 0.60 on dataset A and weight 0.40 on dataset B. - - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.261164 0.474408 0.091391 10572.145717 14071.423474 420.571207 471.201894 634.267962 -1.860679 27.077488 38.959680 8.587280 53.755140 78.694803 13.186374 -argmin(nTVD) 0.272057 0.464147 0.111901 10640.631764 14000.784948 435.077184 474.393338 630.976212 -1.184701 27.306314 38.733142 8.396607 51.752982 80.636663 12.291792 -argmin(TVD) 0.293808 0.445746 0.154856 10930.017392 13774.770683 520.762005 487.878686 620.443965 2.808205 27.986584 38.230402 8.195175 53.533009 78.948949 12.597676 -argmin(L10^10) 0.315435 0.420561 0.056281 11423.650183 13836.536763 834.807801 510.881935 623.322260 17.442714 28.142159 38.727617 11.162597 54.015160 78.750509 16.638443 -Model A 0.058701 0.731059 0.291888 479.867127 68138.580337 16192.471084 0.878730 3152.059162 732.687654 8.853287 60.327248 25.670594 16.534238 123.431015 50.763009 -Model B 0.731329 0.059377 0.438699 38338.240487 505.249618 11570.277416 1764.118964 2.060910 517.410633 60.134177 10.943588 36.631982 125.523234 21.227815 77.203040 - - ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The mixture dataset had weight 0.50 on dataset A and weight 0.50 on dataset B. - - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.336724 0.399223 0.103135 14453.459856 9389.478776 481.317795 652.070827 416.089708 0.970108 32.566106 33.543358 9.169555 65.724820 67.846493 16.004581 -argmin(nTVD) 0.348744 0.387786 0.122024 14579.777698 9296.960102 499.272540 657.957229 411.778345 1.806797 32.851237 33.297344 8.877317 64.277568 68.784671 14.620929 -argmin(TVD) 0.368227 0.369535 0.153857 14910.668610 9096.655991 565.071618 673.376719 402.444189 4.873029 33.516252 32.955458 8.697918 66.899668 66.687181 15.806218 -argmin(L10^10) 0.381766 0.355110 0.058901 15341.892516 9342.669387 900.267841 693.471719 413.908394 20.493147 33.948918 33.010908 11.802510 65.332360 68.314439 17.769593 -Model A 0.058701 0.731059 0.364902 479.867127 68138.580337 23004.788336 0.878730 3152.059162 1049.968900 8.853287 60.327248 31.004211 16.534238 123.431015 61.974967 -Model B 0.731329 0.059377 0.366102 38338.240487 505.249618 7940.470086 1764.118964 2.060910 348.353651 60.134177 10.943588 31.177551 125.523234 21.227815 65.981165 - - ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The mixture dataset had weight 0.40 on dataset A and weight 0.60 on dataset B. - - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.419938 0.316692 0.108947 18867.574117 5752.944088 474.649704 857.768204 246.627478 0.659375 38.293271 27.854670 9.021935 76.723239 55.628956 14.741397 -argmin(nTVD) 0.434744 0.302557 0.129251 19030.009064 5674.293087 494.156212 865.337660 242.962347 1.568377 38.541429 27.808615 8.754129 75.034583 57.196161 13.549772 -argmin(TVD) 0.442236 0.295717 0.142902 18933.804562 5795.961973 530.979411 860.854538 248.632108 3.284335 38.486363 28.009825 8.653013 75.854167 56.356211 13.840286 -argmin(L10^10) 0.448831 0.288644 0.058838 19654.203801 5884.199204 863.322840 894.425086 252.743956 18.771513 39.638916 27.580884 11.777146 78.797379 54.682824 18.139113 -Model A 0.058701 0.731059 0.438333 479.867127 68138.580337 30831.323781 0.878730 3152.059162 1414.486997 8.853287 60.327248 36.680717 16.534238 123.431015 74.100404 -Model B 0.731329 0.059377 0.293411 38338.240487 505.249618 5056.599171 1764.118964 2.060910 214.038392 60.134177 10.943588 25.803049 125.523234 21.227815 54.630480 - - ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The mixture dataset had weight 0.30 on dataset A and weight 0.70 on dataset B. - - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.508091 0.229811 0.102495 23577.816968 3236.178060 462.412103 1077.265150 129.346379 0.089104 44.115671 22.393879 9.130840 88.726527 44.168714 15.734786 -argmin(nTVD) 0.511618 0.226578 0.108070 23516.635010 3269.965680 468.633753 1074.414076 130.920879 0.379032 44.009629 22.560514 9.030624 86.514322 46.236778 14.452820 -argmin(TVD) 0.517180 0.222034 0.127903 23312.347102 3401.875282 503.243620 1064.894275 137.067856 1.991849 43.854686 22.950017 8.905500 88.963584 43.867608 15.718299 -argmin(L10^10) 0.520573 0.217841 0.055216 24007.507335 3469.827760 753.052320 1097.288687 140.234437 13.632915 44.771370 22.378776 11.375579 89.778594 43.822841 17.980791 -Model A 0.058701 0.731059 0.511825 479.867127 68138.580337 39256.191000 0.878730 3152.059162 1806.872182 8.853287 60.327248 42.327993 16.534238 123.431015 86.193286 -Model B 0.731329 0.059377 0.221235 38338.240487 505.249618 2940.770627 1764.118964 2.060910 115.494432 60.134177 10.943588 20.712529 125.523234 21.227815 43.896415 - - ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The mixture dataset had weight 0.20 on dataset A and weight 0.80 on dataset B. - - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.593498 0.146681 0.082374 28465.212070 1616.355278 436.127541 1305.017377 53.862765 -1.135755 49.812296 17.179430 9.393956 100.692317 32.977846 16.957784 -argmin(nTVD) 0.594112 0.146755 0.089686 28305.058345 1668.395129 446.901333 1297.554226 56.287818 -0.633697 49.607226 17.440040 9.315584 98.327986 35.191299 15.409819 -argmin(TVD) 0.590347 0.150556 0.094477 28086.296649 1726.543671 451.447459 1287.359948 58.997535 -0.421848 49.446819 17.645242 9.251400 100.983947 32.606542 16.987316 -argmin(L10^10) 0.594091 0.146093 0.049514 28535.089216 1795.085966 592.176231 1308.273646 62.191601 6.136102 49.858494 17.422069 10.711664 100.412987 33.630635 18.473805 -Model A 0.058701 0.731059 0.584358 479.867127 68138.580337 48317.163314 0.878730 3152.059162 2228.883735 8.853287 60.327248 48.226359 16.534238 123.431015 98.409177 -Model B 0.731329 0.059377 0.151299 38338.240487 505.249618 1503.926489 1764.118964 2.060910 48.573929 60.134177 10.943588 16.043020 125.523234 21.227815 34.029001 - - ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The mixture dataset had weight 0.10 on dataset A and weight 0.90 on dataset B. - - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.668679 0.076564 0.056305 33419.625902 747.866994 429.312337 1535.892671 13.391279 -1.453343 55.170456 12.812219 9.801565 112.532181 23.162979 18.171220 -argmin(nTVD) 0.668440 0.078943 0.062971 33269.383067 776.580541 440.796012 1528.891367 14.729328 -0.918204 54.977003 13.018956 9.790191 109.947903 25.628907 16.632490 -argmin(TVD) 0.666052 0.079781 0.062700 33280.020316 774.007514 440.255326 1529.387062 14.609425 -0.943400 54.969167 12.971492 9.714384 112.902957 22.935576 18.372205 -argmin(L10^10) 0.657603 0.085642 0.047752 33053.604675 863.393466 497.358545 1518.836111 18.774804 1.717605 54.851380 13.490325 10.493062 110.463381 25.261768 19.602431 -Model A 0.058701 0.731059 0.657304 479.867127 68138.580337 58057.143232 0.878730 3152.059162 2682.519826 8.853287 60.327248 54.174145 16.534238 123.431015 110.783513 -Model B 0.731329 0.059377 0.084357 38338.240487 505.249618 695.274792 1764.118964 2.060910 10.911265 60.134177 10.943588 12.104857 125.523234 21.227815 25.858820 - - ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The mixture dataset had weight 0.05 on dataset A and weight 0.95 on dataset B. - - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.702262 0.057311 0.053609 35901.832160 551.198688 444.775466 1651.563288 4.226552 -0.732762 57.715396 11.268873 10.094609 118.373585 19.866242 18.893372 -argmin(nTVD) 0.701985 0.060941 0.057731 35768.647398 565.964380 452.541828 1645.356888 4.914632 -0.370850 57.528568 11.375743 10.084489 115.798353 21.208465 17.470876 -argmin(TVD) 0.701885 0.059799 0.054853 35612.554235 576.319495 455.902128 1638.082959 5.397179 -0.214260 57.334562 11.389517 10.013522 116.983272 20.113419 18.207249 -argmin(L10^10) 0.688055 0.059954 0.049035 35406.887349 628.231765 493.856172 1628.498898 7.816287 1.554395 57.298475 12.048697 10.697845 115.475494 21.996857 20.429503 -Model A 0.058701 0.731059 0.694001 479.867127 68138.580337 63158.419146 0.878730 3152.059162 2920.109931 8.853287 60.327248 57.151030 16.534238 123.431015 116.958974 -Model B 0.731329 0.059377 0.060244 38338.240487 505.249618 520.566267 1764.118964 2.060910 2.774278 60.134177 10.943588 10.952796 125.523234 21.227815 22.906553 - - ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The mixture dataset had weight 0.01 on dataset A and weight 0.99 on dataset B. - - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.727873 0.055933 0.054759 37904.100877 492.538014 471.712639 1744.868852 1.492969 0.522508 59.687372 10.723672 10.424930 122.997044 19.884425 19.506542 -argmin(nTVD) 0.728014 0.062239 0.061105 37834.963909 506.591477 485.253476 1741.647075 2.147859 1.153510 59.558941 10.707916 10.409266 120.845389 18.528914 17.960292 -argmin(TVD) 0.731742 0.057337 0.056173 38023.698465 502.273367 482.427318 1750.442090 1.946636 1.021811 59.667143 10.617281 10.308995 122.850117 19.403571 18.992379 -argmin(L10^10) 0.716759 0.051705 0.050511 37407.042940 541.441313 515.687253 1721.705991 3.771859 2.571722 59.240605 11.358666 10.993873 119.617487 21.591750 21.098547 -Model A 0.058701 0.731059 0.723362 479.867127 68138.580337 67346.000215 0.878730 3152.059162 3115.145026 8.853287 60.327248 59.503700 16.534238 123.431015 121.896791 -Model B 0.731329 0.059377 0.058335 38338.240487 505.249618 489.082140 1764.118964 2.060910 1.307916 60.134177 10.943588 10.690820 125.523234 21.227815 21.075044 - - ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - -The mixture dataset had weight 0.00 on dataset A and weight 1.00 on dataset B. - - L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) TVD(A) TVD(B) TVD(mix) nTVD(A) nTVD(B) nTVD(mix) -argmin(LogL) 0.734850 0.056020 0.056020 38320.137210 490.400273 490.400273 1764.256112 1.393350 1.393350 60.127224 10.679844 10.679844 123.975368 19.966883 19.966883 -argmin(nTVD) 0.735721 0.063619 0.063619 38182.176420 502.788631 502.788631 1757.827150 1.970647 1.970647 59.933777 10.644054 10.644054 122.274428 18.476274 18.476274 -argmin(TVD) 0.740118 0.057454 0.057454 38516.766492 500.884024 500.884024 1773.419021 1.881892 1.881892 60.222429 10.548472 10.548472 123.836231 19.332965 19.332965 -argmin(L10^10) 0.725658 0.051594 0.051594 38018.348946 528.518821 528.518821 1750.192803 3.169672 3.169672 59.861629 11.253837 11.253837 121.219802 21.526994 21.526994 -Model A 0.058701 0.731059 0.731059 479.867127 68138.580337 68138.580337 0.878730 3152.059162 3152.059162 8.853287 60.327248 60.327248 16.534238 123.431015 123.431015 -Model B 0.731329 0.059377 0.059377 38338.240487 505.249618 505.249618 1764.118964 2.060910 2.060910 60.134177 10.943588 10.943588 125.523234 21.227815 21.227815 - diff --git a/wip_notebook_sharing/experiment_helpers.py b/wip_notebook_sharing/experiment_helpers.py deleted file mode 100644 index 82cb33b30..000000000 --- a/wip_notebook_sharing/experiment_helpers.py +++ /dev/null @@ -1,251 +0,0 @@ -import pygsti -import numpy as np -from typing import Union, List -from pygsti.algorithms import run_gst_fit -from pygsti.drivers.longsequence import _get_optimizer, _get_badfit_options, _update_objfn_builders -from pygsti.objectivefns import ObjectiveFunctionBuilder, ModelDatasetCircuitsStore -import pygsti.objectivefns -from pygsti.optimize import SimplerLMOptimizer -import scipy.linalg as la - - -def make_depolarized_dataset(modelpack, depol_level=0.01, max_max_len=128): - ideal_model = modelpack.target_model() - prep_fids = modelpack.prep_fiducials() - meas_fids = modelpack.meas_fiducials() - germs = modelpack.germs() - max_lens = [2**p for p in range(1+int(np.log2(max_max_len)))] - lsgst_circuit_lists = pygsti.circuits.create_lsgst_circuit_lists(ideal_model, prep_fids, meas_fids, germs, max_lens) - all_circuits = lsgst_circuit_lists[-1] - shots_per_circuit = 1000 - rng_state = np.random.default_rng(0) - depol_model = ideal_model.depolarize(op_noise=depol_level) - ds = pygsti.data.simulate_data(depol_model, all_circuits, shots_per_circuit, rand_state=rng_state) - return ds, depol_model - - -def make_tweaked_dataset(modelpack, depol_level=0.01, rand_unitary_scale=0.001, max_max_len=128, sample_error='multinomial', seed=0, shots_per_circuit=1000): - ideal_model = modelpack.target_model() - prep_fids = modelpack.prep_fiducials() - meas_fids = modelpack.meas_fiducials() - germs = modelpack.germs() - max_lens = [2**p for p in range(1+int(np.log2(max_max_len)))] - lsgst_circuit_lists = pygsti.circuits.create_lsgst_circuit_lists(ideal_model, prep_fids, meas_fids, germs, max_lens) - all_circuits = lsgst_circuit_lists[-1] - - depol_model = ideal_model.depolarize(op_noise=depol_level, spam_noise=depol_level/2, seed=seed+1997) - final_model = depol_model.randomize_with_unitary(scale=rand_unitary_scale, seed=seed+250422, transform_spam=True) - rng_state = np.random.default_rng(seed) - ds = pygsti.data.simulate_data(final_model, all_circuits, shots_per_circuit, sample_error=sample_error, rand_state=rng_state) - - return ds, final_model - -def make_tweaked_dataset_pairs(modelpack, depol_level=0.01, rand_unitary_scale=0.001, max_max_len=128, sample_error='multinomial', seed=0, shots_per_circuit=1000, gaugeopt=True): - ideal_model = modelpack.target_model() - prep_fids = modelpack.prep_fiducials() - meas_fids = modelpack.meas_fiducials() - germs = modelpack.germs() - max_lens = [2**p for p in range(1+int(np.log2(max_max_len)))] - lsgst_circuit_lists = pygsti.circuits.create_lsgst_circuit_lists(ideal_model, prep_fids, meas_fids, germs, max_lens) - all_circuits = lsgst_circuit_lists[-1] - - depol_model = ideal_model.depolarize(op_noise=depol_level, spam_noise=depol_level/2, seed=seed+1997) - - model_a = depol_model.copy() - model_b = depol_model.copy() - rndm = np.random.RandomState(seed+250422) - import pygsti.tools.optools as _ot - import pygsti.modelmembers.operations as _op - from pygsti.modelmembers.povms import create_from_dmvecs - from pygsti.modelmembers.states import FullState - unitary_dim = 2 - basis = depol_model.basis - - def rand_unitary_as_superop(): - rand_mat = rndm.randn(unitary_dim, unitary_dim) + 1j * rndm.randn(unitary_dim, unitary_dim) - rand_herm = rand_mat.T.conj() + rand_mat - rand_herm /= la.norm(rand_herm) - rand_herm *= rand_unitary_scale * np.sqrt(unitary_dim) - rand_unitary = la.expm(-1j * rand_herm) - rand_op = _ot.unitary_to_superop(rand_unitary, basis) - assert la.norm(rand_op @ rand_op.T - np.eye(unitary_dim**2)) < 1e-12 - return rand_op - - for opLabel, gate in depol_model.operations.items(): - rand_op = rand_unitary_as_superop() - model_a.operations[opLabel] = _op.FullArbitraryOp(rand_op @ gate) - model_b.operations[opLabel] = _op.FullArbitraryOp(rand_op.T @ gate) - - for preplbl, rho in depol_model.preps.items(): - rand_op = rand_unitary_as_superop() - model_a.preps[preplbl] = FullState(rand_op @ rho) - model_b.preps[preplbl] = FullState(rand_op.T @ rho) - - for povmlbl, M in depol_model.povms.items(): - rand_op = rand_unitary_as_superop() - dmvecs = {elbl: rand_op @ e.to_dense() for elbl, e in M.items()} - model_a.povms[povmlbl] = create_from_dmvecs(dmvecs, 'full') - dmvecs = {elbl: rand_op.T @ e.to_dense() for elbl, e in M.items()} - model_b.povms[povmlbl] = create_from_dmvecs(dmvecs, 'full') - - rng_state = np.random.default_rng(seed) - dsa = pygsti.data.simulate_data(model_a, all_circuits, shots_per_circuit, sample_error=sample_error, rand_state=rng_state) - dsb = pygsti.data.simulate_data(model_b, all_circuits, shots_per_circuit, sample_error=sample_error, rand_state=rng_state) - - if gaugeopt: - from pygsti.algorithms.gaugeopt import gaugeopt_to_target - for model in [model_a, model_b]: - model.convert_members_inplace('full') - model.default_gauge_group = 'unitary' - model_a = gaugeopt_to_target(model_a, ideal_model) - model_b = gaugeopt_to_target(model_b, ideal_model) - - return dsa, model_a, dsb, model_b - - - -def corrupt_dataset(ds, prop_corrupt, rng=0): - dsc = ds.copy_nonstatic() - rng = np.random.default_rng(rng) - num_circs = len(dsc) - selected = rng.choice(np.arange(num_circs), size=int(num_circs*prop_corrupt), replace=False) - circuits = list(dsc.keys()) - selected = [circuits[i] for i in selected] - for c in selected: - num_shots = dsc[c].total - old_row = dsc[c].to_dict() - distn = rng.random(len(old_row)) - distn /= np.sum(distn) - new_row = {k: num_shots * distn[i] for i,k in enumerate(old_row.keys())} - dsc[c] = new_row - dsc.comment = 'corrupt' - return dsc, selected - - -def run_gst(ds, fids, germs, target_model, final_objectives: List[Union[str, tuple]], verbosity: int, mode='CPTPLND', - iteration_objective='chi2'): - """ - In the context of this notebook, `ds` is produced by either make_depolarized_dataset or corrupt_dataset. - final_objective can be anything accepted by `ObjectiveFunctionBuilder.create_from`. - - This function wraps up three steps of a GST pipeline. - - 1. Construct a StandardGSTDesign based on (target_model, ds, fids, germs). - * processor_spec is the value returned from target_model.create_processor_spec. - * max_lens list is all powers of two that are <= the depth of the longest circuit in ds. - * circuits in the design are filtered to only include circuits that appeared in ds. - - 2. Construct a StandardGST protocol object based on (final_objective, mode, verbosity). - * The gauge optimization suite is 'stdgaugeopt', minus the TPSpam optimization step. - * objfn_builders, optimizer, and badfit_options are all set so the final - iteration's objective function is based on final_objective. - - 3. Run GST with checkpointing turned off. - We dot NOT save the results to disk! The calling function is responsible for that. - """ - if isinstance(final_objectives, str): - final_objectives = [final_objectives] - assert isinstance(final_objectives, list) - - max_exp = int(np.log2(np.max([len(c) for c in ds.keys()]))) - max_lens = [2**p for p in range(1 + max_exp)] - prep_fids, meas_fids = fids - - target_model = target_model.copy() - target_model.default_gauge_group = 'unitary' - - gos = pygsti.protocols.gst.GSTGaugeOptSuite.cast('stdgaugeopt') - gop_params = gos.to_dictionary(target_model) - # ^ a dict with one key, 'stdgaugeopt', whose corresponding value is a list of dicts. - # The internal dicts will indicate Frobenius-based losses for gates and SPAM, - # along with varying weights. Additional elements can be added to any one of these - # internal dicts to be passed to gaugeopt_to_target. - gop_params['stdgaugeopt'] = gop_params['stdgaugeopt'][:-1] - # ^ drop the 1-dimensional TPSpam gauge optimization step. - - exp_design = pygsti.protocols.StandardGSTDesign( - target_model.create_processor_spec(), - prep_fids, meas_fids, germs, max_lens, - None, # germ_length_limits - None, 1, None, # fidPairs, keepFraction, keepSeed - True, True, # include_lgst, nested_circuit_lists - None, # string_manipulation_rules - None, # op_label_aliases - ds, 'drop', verbosity=verbosity - ) - data = pygsti.protocols.ProtocolData(exp_design, ds) - - # - # Run long-sequence GST where the final objective is the first entry - # in the final_objectives list. - # - final_objective = final_objectives[0] - builders = pygsti.protocols.GSTObjFnBuilders( - [ObjectiveFunctionBuilder.create_from(iteration_objective)], - [ObjectiveFunctionBuilder.create_from(final_objective)] - ) - _update_objfn_builders(builders.iteration_builders, dict()) - optim_iter = SimplerLMOptimizer.cast( - _get_optimizer(dict(), target_model) - ) - advanced_options = { - 'extra_lm_opts': {'tol': - {'relx': 1e-8, 'relf': 1e-6, 'f': -1.0, 'jac': -1, 'maxdx': 1.0}, - } - } - _update_objfn_builders(builders.final_builders, advanced_options) - optim_last = SimplerLMOptimizer.cast( - _get_optimizer(advanced_options, target_model) - ) - bfops = _get_badfit_options(advanced_options) - proto = pygsti.protocols.StandardGST( - (mode,), gop_params, target_model, None, - objfn_builders = builders, - optimizer = optim_iter, - badfit_options = bfops, - verbosity = verbosity - ) - modelest_results = proto.run(data, disable_checkpointing=True) - modelest_results.rename_estimate(mode, str(final_objective)) - - # - # Run one GST fit for each entry in final_objectives[1:]. - # Initialize the fit at the last model that fit with iteration_objective. - # - est = modelest_results.estimates[str(final_objective)] - seed_name = f'iteration {est.num_iterations - 1} estimate' - seed_model = est.models[seed_name] - seed_vec = seed_model.to_vector() - circuits = exp_design.all_circuits_needing_data - printer = pygsti.VerbosityPrinter.create_printer(verbosity, None) - import copy - for final_objective in final_objectives[1:]: - builder = ObjectiveFunctionBuilder.create_from(final_objective) - curr_seed_model = copy.deepcopy(seed_model) - # ^ A copy is needed because this will be used as the foundational of a ModelDatasetCircuitStore, - # which in turn will be the foundation for an MDCObjective. - curr_seed_model.from_vector(seed_vec) - array_types = optim_last.array_types + \ - builder.compute_array_types(optim_last.called_objective_methods, curr_seed_model.sim) - mdc_store = ModelDatasetCircuitsStore(curr_seed_model, data.dataset, circuits, None, array_types=array_types) - printer.log('') - _, outobjective = run_gst_fit(mdc_store, optim_last, builder, verbosity - 2) - - fobjstr = str(final_objective) - - curr_est = copy.deepcopy(est) - curr_est._final_mdc_store = outobjective - curr_est._final_objfn = outobjective - curr_est._final_objfn_cache = None - curr_est._final_objective_fn_cache = None - modelest_results.add_estimate(curr_est, fobjstr) - - curr_est = modelest_results.estimates[fobjstr] - curr_est.models['final iteration estimate'] = outobjective.model - curr_est.models.pop('stdgaugeopt') - curr_est.add_gaugeoptimized(gop_params['stdgaugeopt'], label='stdgaugeopt') - - pass - - return modelest_results - diff --git a/wip_notebook_sharing/objectives-handling-mixtures.ipynb b/wip_notebook_sharing/objectives-handling-mixtures.ipynb deleted file mode 100644 index ccb8ba32c..000000000 --- a/wip_notebook_sharing/objectives-handling-mixtures.ipynb +++ /dev/null @@ -1,1217 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import pygsti\n", - "from pygsti.modelpacks import smq1Q_XYI, smq2Q_XYCNOT\n", - "import numpy as np\n", - "from pprint import pprint\n", - "from experiment_helpers import make_depolarized_dataset, run_gst, corrupt_dataset, make_tweaked_dataset, make_tweaked_dataset_pairs\n", - "from scipy import linalg as la\n", - "import pandas as pd\n", - "from pygsti.data.datasetconstruction import mix_datasets\n", - "from pygsti.report.plothelpers import rated_n_sigma\n", - "from pprint import pprint\n", - "from pygsti.tools.optools import fidelity, povm_fidelity, entanglement_fidelity\n", - "\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mp = smq1Q_XYI\n", - "target = mp.target_model()\n", - "fids = (mp.prep_fiducials(), mp.meas_fiducials())\n", - "germs = mp.germs()\n", - "maxmaxlen = 32\n", - "sps = 10_000\n", - "dsa, m_dga, dsb, m_dgb = make_tweaked_dataset_pairs(\n", - " mp, depol_level=0.001, rand_unitary_scale=0.03, max_max_len=maxmaxlen,\n", - " sample_error='multinomial', seed=5000009, shots_per_circuit=sps, gaugeopt=False\n", - ")\n", - "\n", - "import pygsti.algorithms\n", - "\n", - "mods = []\n", - "for model in [m_dga, m_dgb]:\n", - " model = model.copy()\n", - " model.convert_members_inplace('full')\n", - " model.default_gauge_group = 'unitary'\n", - " model = pygsti.algorithms.gaugeopt.gaugeopt_to_target(model, target)\n", - " mods.append(model)\n", - "\n", - "m_dga_gopped, m_dgb_gopped = mods\n", - "\n", - "mixture_weights = np.array([1, 0.95, 0.9, 0.85, 0.8, 0.7, 0.6, 0.575, 0.55, 0.525, 0.51, 0.5, 0.49, 0.475, 0.45, 0.425, 0.4, 0.3, 0.2, 0.15, 0.1, 0.05, 0.0])\n", - "num_mixtures = mixture_weights.size\n", - "integer_counts_in_mixed = False # older runs effectively had this == True\n", - "\n", - "fit_mode = 'CPTPLND'\n", - "Lpnorm_spec = ('Lp^p', 10)\n", - "verb = 1\n", - "\n", - "m_dga.convert_members_inplace(fit_mode)\n", - "m_dgb.convert_members_inplace(fit_mode)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--- Circuit Creation ---\n", - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/objectivefns/objectivefns.py:4062: UserWarning: This derivative is discontinuous and does not return a full subgradient.\n", - " _warnings.warn('This derivative is discontinuous and does not return a full subgradient.')\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: 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Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n", - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [--------------------------------------------------] 0.0% (CPTPLND) --\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n", - "--- Circuit Creation ---\n", - "-- Std Practice: [##################################################] 100.0% (CPTPLND) --\n", - "\n", - "\n" - ] - } - ], - "source": [ - "reslist = []\n", - "for i,p in enumerate(mixture_weights):\n", - " ds = mix_datasets(dsa, dsb, p, integral=integer_counts_in_mixed)\n", - " results = run_gst(ds, fids, germs, target, ['logl', 'normalized tvd', Lpnorm_spec], verbosity=verb, mode=fit_mode)\n", - " reslist.append((results,ds))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "dflists = []\n", - "for results, dsc in reslist:\n", - " currdfs = []\n", - " for ds in [dsa, dsc, dsb]:\n", - " circuitlist = list(ds.cirIndex.keys())\n", - " pvecs = []\n", - " objectives = []\n", - " Nsigs = []\n", - " for estname, est in results.estimates.items():\n", - " model = est.models['stdgaugeopt']\n", - " try:\n", - " Nsig, _ = rated_n_sigma(ds, model, circuitlist, 'logl')\n", - " except Exception:\n", - " Nsig = np.NaN\n", - " Nsigs.append(Nsig)\n", - " objective = est.final_objective_fn()\n", - " objective.dataset = ds\n", - " objective.add_count_vectors(force=True)\n", - " objective.add_omitted_freqs(force=True)\n", - " objectives.append(objective)\n", - " pvecs.append(est.models['final iteration estimate'].to_vector())\n", - " Nsigs.append(rated_n_sigma(ds, m_dga, circuitlist, 'logl')[0])\n", - " Nsigs.append(rated_n_sigma(ds, m_dgb, circuitlist, 'logl')[0])\n", - " Nsigs = np.array(Nsigs).reshape((-1,1))\n", - "\n", - " objvals = np.zeros((len(pvecs)+2, len(objectives)))\n", - " for j,objective in enumerate(objectives):\n", - " for i,pvec in enumerate(pvecs):\n", - " val = objective.fn(pvec, stateless=True)\n", - " objvals[i,j] = val\n", - " objvals[i+1,j] = objective.fn_from_model(m_dga)\n", - " objvals[i+2,j] = objective.fn_from_model(m_dgb)\n", - " objvals = np.concatenate((objvals, Nsigs), axis=1)\n", - "\n", - " df = pd.DataFrame(\n", - " objvals,\n", - " index=['argmin(LogL)', 'argmin(nTVD)', 'argmin(L10^10)', 'Model A', 'Model B'],\n", - " columns=['LogL', 'nTVD', 'L10', 'N_sigma'],\n", - " )\n", - " df.rename_axis(index='model')\n", - " currdfs.append(df)\n", - " dflists.append(currdfs)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 1.00 on dataset A and weight 0.00 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.017095 1.143082 0.017095 356.490631 870722.190464 356.490631 -1.031219 44854.838049 -1.031219 3.915744 137.589484 3.915744\n", - "argmin(nTVD) 0.017287 1.142589 0.017287 360.248995 870045.936046 360.248995 -0.837525 44819.986056 -0.837525 3.704271 137.461658 3.704271\n", - "argmin(L10^10) 0.015880 1.141510 0.015880 548.027542 865450.799571 548.027542 8.839982 44583.167393 8.839982 4.292753 137.443425 4.292753\n", - "Model A 0.017573 1.143172 0.017573 374.434906 872019.805514 374.434906 -0.106428 44921.712986 -0.106428 4.037776 137.616702 4.037776\n", - "Model B 1.142056 0.019150 1.142056 873945.399412 411.929169 873945.399412 45020.951948 1.825906 45020.951948 137.853377 3.774255 137.853377\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.95 on dataset A and weight 0.05 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.046194 1.105109 0.035502 2819.767774 766652.039453 1226.076815 125.918220 39491.394741 43.784477 8.450762 133.934899 6.581532\n", - "argmin(nTVD) 0.061438 1.105614 0.059253 3185.092343 763352.874618 1408.176914 144.745882 39321.366319 53.169334 8.675401 133.224769 5.669143\n", - "argmin(L10^10) 0.055301 1.093434 0.023177 5100.881754 755038.940319 2812.480028 243.479552 38892.892690 125.542633 10.354776 134.852384 7.933155\n", - "Model A 0.017573 1.143172 0.057863 374.434906 872019.805514 4171.398954 -0.106428 44921.712986 195.576976 4.037776 137.616702 8.418903\n", - "Model B 1.142056 0.019150 1.084884 873945.399412 411.929169 790483.410656 45020.951948 1.825906 40719.587524 137.853377 3.774255 130.963634\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.90 on dataset A and weight 0.10 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.088179 1.071385 0.068067 8898.892335 691037.753082 3522.021369 439.216891 35594.475843 162.110128 14.948660 130.731460 10.957710\n", - "argmin(nTVD) 0.134362 1.065096 0.131374 12354.307495 669445.516092 4472.671155 617.297956 34481.680888 211.103583 17.952717 129.549450 9.711630\n", - "argmin(L10^10) 0.108957 1.041488 0.040869 14024.546660 672165.545097 6247.889390 703.376749 34621.862498 302.592663 16.774763 131.534909 12.274848\n", - "Model A 0.017573 1.143172 0.114482 374.434906 872019.805514 13948.215634 -0.106428 44921.712986 699.442892 4.037776 137.616702 14.612190\n", - "Model B 1.142056 0.019150 1.027713 873945.399412 411.929169 713001.270999 45020.951948 1.825906 36726.405431 137.853377 3.774255 124.078971\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.85 on dataset A and weight 0.15 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.129666 1.039715 0.102642 18086.814606 626537.235812 6902.941809 912.733069 32270.325120 336.351973 21.837154 127.613544 15.383077\n", - "argmin(nTVD) 0.207047 1.024162 0.202889 27096.400336 592385.654457 9438.352505 1377.058352 30510.261656 467.018945 26.981130 126.110105 13.618625\n", - "argmin(L10^10) 0.156970 0.995362 0.059257 28018.837079 608243.064487 12601.035167 1424.597797 31327.501939 630.013512 24.954749 129.572462 18.155908\n", - "Model A 0.017573 1.143172 0.171491 374.434906 872019.805514 28669.805304 -0.106428 44921.712986 1458.146621 4.037776 137.616702 21.280094\n", - "Model B 1.142056 0.019150 0.970542 873945.399412 411.929169 640463.962930 45020.951948 1.825906 32988.064170 137.853377 3.774255 117.194751\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.80 on dataset A and weight 0.20 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.171381 1.008515 0.138944 30344.751476 568691.363953 11141.617709 1544.467999 29289.133574 554.799796 29.147456 124.504348 19.811381\n", - "argmin(nTVD) 0.292415 0.972151 0.287453 47825.067226 523759.961906 16139.589932 2445.347661 26973.512569 812.379327 35.077023 121.829965 17.255425\n", - "argmin(L10^10) 0.203040 0.951608 0.077583 46358.458594 553271.933719 20868.697414 2369.763335 28494.465371 1056.102427 33.600519 127.437909 24.307347\n", - "Model A 0.017573 1.143172 0.228585 374.434906 872019.805514 47831.053085 -0.106428 44921.712986 2445.656153 4.037776 137.616702 27.991667\n", - "Model B 1.142056 0.019150 0.913371 873945.399412 411.929169 572366.257601 45020.951948 1.825906 29478.525860 137.853377 3.774255 110.311423\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.70 on dataset A and weight 0.30 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.258163 0.943361 0.214229 64828.989566 465280.154088 21418.368308 3321.675529 23959.649989 1084.430710 44.730951 117.662152 27.910551\n", - "argmin(nTVD) 0.408399 0.854345 0.399343 102098.612145 412311.588812 31616.533349 5242.432882 21229.819252 1610.011576 53.125580 112.177459 23.679532\n", - "argmin(L10^10) 0.291917 0.869233 0.113762 95658.312517 459932.703537 41394.658872 4910.520402 23684.059461 2113.944946 50.856084 121.829809 34.952054\n", - "Model A 0.017573 1.143172 0.342854 374.434906 872019.805514 98322.081572 -0.106428 44921.712986 5047.802554 4.037776 137.616702 41.533669\n", - "Model B 1.142056 0.019150 0.799030 873945.399412 411.929169 448339.370775 45020.951948 1.825906 23086.576105 137.853377 3.774255 96.555043\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.60 on dataset A and weight 0.40 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.359033 0.864997 0.291294 116853.651135 369164.727544 32673.987836 6002.860482 19006.167782 1664.509404 62.806959 108.603184 33.807731\n", - "argmin(nTVD) 0.495681 0.721576 0.472984 169481.141550 323578.543744 46016.010303 8715.113309 16656.801416 2352.114643 70.425665 100.552823 27.932039\n", - "argmin(L10^10) 0.374371 0.794443 0.149206 158763.759223 389876.384712 66104.715510 8162.773642 20073.580464 3387.422371 69.509902 116.623167 45.652330\n", - "Model A 0.017573 1.143172 0.457162 374.434906 872019.805514 163928.489876 -0.106428 44921.712986 8428.947370 4.037776 137.616702 55.164323\n", - "Model B 1.142056 0.019150 0.684692 873945.399412 411.929169 339427.932234 45020.951948 1.825906 17473.628295 137.853377 3.774255 82.807048\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.57 on dataset A and weight 0.43 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.390306 0.840704 0.311146 134223.116635 344441.396690 35340.592648 6898.027276 17732.006246 1801.937702 68.084280 105.338935 34.629188\n", - "argmin(nTVD) 0.513494 0.684431 0.480013 187868.127553 301789.312738 48059.337541 9662.719861 15533.854019 2457.421205 74.471237 97.559244 28.703496\n", - "argmin(L10^10) 0.395285 0.775506 0.157910 176215.496621 378364.633413 73903.586249 9062.180468 19480.301557 3789.351268 74.775241 115.906125 48.917220\n", - "Model A 0.017573 1.143172 0.485742 374.434906 872019.805514 182598.432178 -0.106428 44921.712986 9391.136587 4.037776 137.616702 58.589446\n", - "Model B 1.142056 0.019150 0.656108 873945.399412 411.929169 314468.367672 45020.951948 1.825906 16187.292028 137.853377 3.774255 79.370582\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.55 on dataset A and weight 0.45 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.426815 0.812684 0.331646 154505.580212 318389.229806 37799.967040 7943.320691 16389.360744 1928.686006 73.960071 101.352361 35.006462\n", - "argmin(nTVD) 0.539447 0.645818 0.492305 210353.569393 280080.149616 51277.275693 10821.547742 14415.033064 2623.263463 79.495787 93.217978 29.085771\n", - "argmin(L10^10) 0.415820 0.757163 0.166849 193983.025885 366917.672213 81350.361397 9977.862198 18890.361724 4173.134283 80.146052 114.870936 51.898359\n", - "Model A 0.017573 1.143172 0.514322 374.434906 872019.805514 202161.589751 -0.106428 44921.712986 10399.359267 4.037776 137.616702 62.015117\n", - "Model B 1.142056 0.019150 0.627524 873945.399412 411.929169 290402.095104 45020.951948 1.825906 14946.993179 137.853377 3.774255 75.934306\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.53 on dataset A and weight 0.47 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.472159 0.779173 0.353226 179224.550857 289634.864350 39877.511959 9217.257516 14907.452555 2035.756038 81.100340 95.811670 34.693401\n", - "argmin(nTVD) 0.557663 0.618013 0.490492 225286.910115 262052.835981 50958.785359 11591.164444 13485.962877 2606.849488 82.691929 90.368760 29.254841\n", - "argmin(L10^10) 0.435855 0.738712 0.175734 212313.530212 358592.508767 90004.106799 10922.557865 18461.309381 4619.120692 85.794787 114.737510 55.982184\n", - "Model A 0.017573 1.143172 0.542904 374.434906 872019.805514 222614.047342 -0.106428 44921.712986 11453.413631 4.037776 137.616702 65.441796\n", - "Model B 1.142056 0.019150 0.598940 873945.399412 411.929169 267225.050555 45020.951948 1.825906 13752.522302 137.853377 3.774255 72.498645\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.51 on dataset A and weight 0.49 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.503365 0.752448 0.367487 196444.716929 271235.366777 40922.799531 10104.729895 13959.201196 2089.626822 85.318313 92.114354 34.638214\n", - "argmin(nTVD) 0.578105 0.594765 0.497356 239971.288974 250199.949013 52813.996406 12347.950446 12875.102927 2702.461142 85.272729 87.884260 29.272573\n", - "argmin(L10^10) 0.451079 0.726472 0.181289 222806.063613 352695.013608 94282.313427 11463.309536 18157.371310 4839.605802 88.734141 113.549035 56.793083\n", - "Model A 0.017573 1.143172 0.560053 374.434906 872019.805514 235311.336294 -0.106428 44921.712986 12107.791363 4.037776 137.616702 67.498546\n", - "Model B 1.142056 0.019150 0.581790 873945.399412 411.929169 253744.656430 45020.951948 1.825906 13057.785829 137.853377 3.774255 70.437555\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.50 on dataset A and weight 0.50 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.525808 0.730174 0.378125 209376.504570 258051.435824 41470.278853 10771.192941 13279.743489 2117.842158 88.241723 89.238192 34.565610\n", - "argmin(nTVD) 0.587828 0.583887 0.498207 246991.525604 243288.824535 52896.483725 12709.751028 12518.925637 2706.712276 86.384073 86.606059 29.369697\n", - "argmin(L10^10) 0.459907 0.718566 0.184843 230385.564671 349647.368233 97772.775107 11853.932821 18000.305398 5019.493053 91.093991 113.339135 58.078814\n", - "Model A 0.017573 1.143172 0.571485 374.434906 872019.805514 243953.428865 -0.106428 44921.712986 12553.177222 4.037776 137.616702 68.869768\n", - "Model B 1.142056 0.019150 0.570357 873945.399412 411.929169 244934.972946 45020.951948 1.825906 12603.762870 137.853377 3.774255 69.063499\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.49 on dataset A and weight 0.51 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.731121 0.506288 0.378186 271618.467034 197098.739531 41437.127109 13978.944960 10138.436131 2116.133623 93.255994 84.550062 34.316787\n", - "argmin(nTVD) 0.599226 0.571129 0.495832 255120.033760 234443.285410 52398.612820 13128.668386 12063.054792 2681.053599 87.814232 85.151958 29.407567\n", - "argmin(L10^10) 0.728272 0.446160 0.181194 336564.979937 229597.292572 89835.180582 17326.080874 11813.307794 4610.414774 111.702385 90.372357 57.801162\n", - "Model A 0.017573 1.143172 0.582918 374.434906 872019.805514 252737.288429 -0.106428 44921.712986 13005.869299 4.037776 137.616702 70.241001\n", - "Model B 1.142056 0.019150 0.558923 873945.399412 411.929169 236267.057198 45020.951948 1.825906 12157.046167 137.853377 3.774255 67.689445\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.47 on dataset A and weight 0.53 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.762886 0.474621 0.362197 290881.822487 179131.754463 40403.743946 14971.716796 9212.475086 2062.876354 97.190539 80.213858 35.046510\n", - "argmin(nTVD) 0.613784 0.553874 0.487832 264649.030693 222741.122203 50838.085161 13619.762463 11459.962663 2600.628986 90.127751 82.920699 29.368744\n", - "argmin(L10^10) 0.739535 0.433916 0.175608 343518.528432 217472.313477 85534.972045 17684.444563 11188.425084 4388.795755 112.545038 86.906840 56.241001\n", - "Model A 0.017573 1.143172 0.600068 374.434906 872019.805514 266178.950531 -0.106428 44921.712986 13698.609647 4.037776 137.616702 72.297864\n", - "Model B 1.142056 0.019150 0.541774 873945.399412 411.929169 223531.033784 45020.951948 1.825906 11500.672184 137.853377 3.774255 65.629648\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.45 on dataset A and weight 0.55 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.800393 0.429306 0.339442 319702.562793 154300.301009 38243.368898 16457.045733 7932.741257 1951.537522 102.461053 73.529709 35.804160\n", - "argmin(nTVD) 0.648239 0.528095 0.488898 286300.646306 203776.843787 50424.606529 14735.617603 10482.604303 2579.319617 93.590802 78.858126 29.107831\n", - "argmin(L10^10) 0.757706 0.414236 0.166320 354884.230784 199168.101002 78752.409352 18270.196573 10245.084414 4039.244130 113.754067 81.384610 53.516701\n", - "Model A 0.017573 1.143172 0.628650 374.434906 872019.805514 289291.449866 -0.106428 44921.712986 14889.754069 4.037776 137.616702 75.726209\n", - "Model B 1.142056 0.019150 0.513191 873945.399412 411.929169 203014.033124 45020.951948 1.825906 10443.291477 137.853377 3.774255 62.199861\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.42 on dataset A and weight 0.57 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.830263 0.392070 0.318209 345943.430101 133872.611129 35725.412999 17809.416252 6879.963342 1821.770114 106.447432 67.621482 35.758309\n", - "argmin(nTVD) 0.678813 0.507580 0.484278 306083.895608 186322.628853 48943.869399 15755.183108 9583.069793 2503.007153 96.695675 75.174431 28.661775\n", - "argmin(L10^10) 0.775628 0.394558 0.157157 368138.331239 180920.217298 72210.619555 18953.270593 9304.646750 3702.101170 114.985070 75.934725 50.594172\n", - "Model A 0.017573 1.143172 0.657233 374.434906 872019.805514 313293.215949 -0.106428 44921.712986 16126.728460 4.037776 137.616702 79.157890\n", - "Model B 1.142056 0.019150 0.484609 873945.399412 411.929169 183386.365138 45020.951948 1.825906 9431.744136 137.853377 3.774255 58.770576\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.40 on dataset A and weight 0.60 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.855801 0.360114 0.297807 370730.448504 116456.778037 32992.899051 19086.860041 5982.406909 1680.945070 109.604491 62.296657 35.106070\n", - "argmin(nTVD) 0.713211 0.485344 0.472518 327255.914145 167571.984950 46272.210114 16846.321345 8616.721473 2365.318364 100.035958 71.023627 28.071218\n", - "argmin(L10^10) 0.793517 0.374744 0.148114 383041.334238 163095.121592 65900.260130 19721.323785 8385.998229 3376.885393 116.300822 70.624216 47.582292\n", - "Model A 0.017573 1.143172 0.685817 374.434906 872019.805514 338188.321863 -0.106428 44921.712986 17409.742734 4.037776 137.616702 82.593045\n", - "Model B 1.142056 0.019150 0.456027 873945.399412 411.929169 164651.972550 45020.951948 1.825906 8466.233357 137.853377 3.774255 55.342108\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.30 on dataset A and weight 0.70 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 0.937163 0.258018 0.219063 466431.832777 64637.806907 21493.254655 24019.003831 3311.822585 1088.290113 118.372641 44.406570 29.397749\n", - "argmin(nTVD) 0.848551 0.397227 0.398948 416091.319302 100522.705394 31510.528669 21424.614494 5161.215679 1604.548433 111.154663 54.754358 24.326325\n", - "argmin(L10^10) 0.867410 0.293123 0.112546 455613.399323 97904.546934 41534.441958 23461.456312 5026.284152 2121.148920 121.362614 50.457551 35.420632\n", - "Model A 0.017573 1.143172 0.800152 374.434906 872019.805514 446843.419194 -0.106428 44921.712986 23009.479537 4.037776 137.616702 96.340620\n", - "Model B 1.142056 0.019150 0.341709 873945.399412 411.929169 98789.215839 45020.951948 1.825906 5071.877163 137.853377 3.774255 41.638390\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.20 on dataset A and weight 0.80 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 1.004504 0.171110 0.142458 568859.462809 30466.157132 11076.047899 29297.796852 1550.724858 551.420537 124.901431 28.928334 21.006062\n", - "argmin(nTVD) 0.968066 0.289869 0.292726 523975.091866 48816.891215 16779.761102 26984.599681 2496.463103 845.371705 120.357413 37.390472 18.339919\n", - "argmin(L10^10) 0.948358 0.207571 0.076269 547841.062685 47906.618275 20824.736447 28214.575618 2449.550542 1053.836819 126.383526 33.201476 24.243971\n", - "Model A 0.017573 1.143172 0.914490 374.434906 872019.805514 570621.966009 -0.106428 44921.712986 29388.630640 4.037776 137.616702 110.094111\n", - "Model B 1.142056 0.019150 0.227420 873945.399412 411.929169 48049.854110 45020.951948 1.825906 2456.932460 137.853377 3.774255 28.025354\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.15 on dataset A and weight 0.85 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 1.036579 0.129418 0.105549 626389.074367 18283.709974 6831.379668 32262.689352 922.880428 332.663889 127.828492 21.635788 16.420418\n", - "argmin(nTVD) 1.018728 0.222629 0.224893 590476.853618 28553.406634 10173.788544 30411.888155 1452.147805 504.920971 124.826776 28.161849 14.657706\n", - "argmin(L10^10) 0.992331 0.160923 0.057869 603381.642140 29005.135822 12493.477071 31076.959753 1475.428485 624.470311 128.778043 24.851508 18.476589\n", - "Model A 0.017573 1.143172 0.971660 374.434906 872019.805514 638604.877952 -0.106428 44921.712986 32892.252866 4.037776 137.616702 116.971717\n", - "Model B 1.142056 0.019150 0.170307 873945.399412 411.929169 28773.815889 45020.951948 1.825906 1463.506994 137.853377 3.774255 21.283896\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.10 on dataset A and weight 0.90 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 1.069202 0.088090 0.069850 690832.458850 9104.593239 3466.121932 35583.895633 449.818059 159.229250 130.631553 14.746141 11.733843\n", - "argmin(nTVD) 1.063294 0.143161 0.144484 668764.889270 12913.902284 4687.742944 34446.603555 646.137684 222.187696 129.223049 18.425271 10.630982\n", - "argmin(L10^10) 1.039019 0.111528 0.039468 669006.429825 14479.521363 6120.954140 34459.051784 726.824693 296.050825 131.115027 16.941541 12.999624\n", - "Model A 0.017573 1.143172 1.028830 374.434906 872019.805514 711043.993203 -0.106428 44921.712986 36625.533583 4.037776 137.616702 123.849738\n", - "Model B 1.142056 0.019150 0.113260 873945.399412 411.929169 13954.019078 45020.951948 1.825906 699.741983 137.853377 3.774255 14.637279\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.05 on dataset A and weight 0.95 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 1.103517 0.046503 0.035750 767236.366039 2970.730490 1215.864059 39521.509068 133.698357 43.258144 134.203725 8.265166 7.070433\n", - "argmin(nTVD) 1.104745 0.064443 0.064720 764736.362288 3337.516587 1439.310732 39392.666856 152.601341 54.773872 133.247723 9.193885 6.425464\n", - "argmin(L10^10) 1.092544 0.055558 0.022420 755081.775260 5633.096483 3137.382090 38895.100266 270.908199 142.287048 135.402631 10.913877 9.100373\n", - "Model A 0.017573 1.143172 1.086001 374.434906 872019.805514 788469.374009 -0.106428 44921.712986 40615.790506 4.037776 137.616702 130.729944\n", - "Model B 1.142056 0.019150 0.056531 873945.399412 411.929169 4120.454663 45020.951948 1.825906 192.951470 137.853377 3.774255 8.316892\n", - "\n", - "\n", - "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "\n", - "The mixture dataset had weight 0.00 on dataset A and weight 1.00 on dataset B.\n", - "\n", - " L10(A) L10(B) L10(mix) LogL(A) LogL(B) LogL(mix) N_sigma(A) N_sigma(B) N_sigma(mix) nTVD(A) nTVD(B) nTVD(mix)\n", - "argmin(LogL) 1.143018 0.019721 0.019721 878354.400236 391.057008 391.057008 45248.177773 0.750222 0.750222 138.090204 3.689869 3.689869\n", - "argmin(nTVD) 1.143098 0.019853 0.019853 879427.393095 393.861521 393.861521 45303.476399 0.894757 0.894757 138.238434 3.572134 3.572134\n", - "argmin(L10^10) 1.138419 0.017489 0.017489 866835.723106 635.495770 635.495770 44654.541930 13.347815 13.347815 137.827680 4.678087 4.678087\n", - "Model A 0.017573 1.143172 1.143172 374.434906 872019.805514 872019.805514 -0.106428 44921.712986 44921.712986 4.037776 137.616702 137.616702\n", - "Model B 1.142056 0.019150 0.019150 873945.399412 411.929169 411.929169 45020.951948 1.825906 1.825906 137.853377 3.774255 3.774255\n", - "\n" - ] - } - ], - "source": [ - "dflist = []\n", - "for dfl in dflists:\n", - " dfa, df, dfb = dfl\n", - " temp = dfa.join(df, lsuffix='(A)', rsuffix='(mix)')\n", - " dfb = dfb.copy()\n", - " dfb.columns = dfb.columns.map(lambda x: str(x) + '(B)')\n", - " temp = temp.join(dfb)\n", - " temp = temp.sort_index(axis=1)\n", - " for colname in ['L10(A)', 'L10(B)', 'L10(mix)']:\n", - " temp[colname] = temp[colname]**0.1\n", - " dflist.append(temp)\n", - "\n", - "pd.set_option('display.max_columns', 100)\n", - "pd.set_option('display.width', 300)\n", - "for i,df in enumerate(dflist):\n", - " print( '\\n' + 250*'-')\n", - " p = mixture_weights[i]\n", - " print(f'\\nThe mixture dataset had weight {p:1.2f} on dataset A and weight {1-p:1.2f} on dataset B.\\n')\n", - " print(df)\n", - " print()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "datanames = [ '(mix)', '(A)', '(B)'] \n", - "modelnames = ['argmin(LogL)', 'argmin(nTVD)', 'argmin(L10^10)']\n", - "legendnames = [ 'f = ' + name.strip('argmin(').strip(')') for name in modelnames ]\n", - "lossnames = ['LogL', 'nTVD', 'L10', 'N_sigma']\n", - "losscolors = ['b', 'r', 'y', 'k', 'd','.']\n", - "dataname = '(mix)'\n", - "fig, outer_axs = plt.subplots(1, len(lossnames), figsize=(15,5))\n", - "for metricname,ax in zip(lossnames, outer_axs):\n", - " rows = []\n", - " for df in dflist:\n", - " row = [ df[metricname + dataname][modelname] for modelname in modelnames ]\n", - " rows.append(row)\n", - " y = np.array(rows)\n", - " if metricname in {'LogL', 'chi2', 'N_sigma'}:\n", - " ax.set_yscale('log')\n", - " for i,yi in enumerate(y.T):\n", - " x = mixture_weights\n", - " if metricname in {'LogL', 'chi2', 'N_sigma'}:\n", - " ind = yi > 0\n", - " x = x[ind]\n", - " yi = yi[ind]\n", - " ax.plot(x, yi,losscolors[i])\n", - " ax.legend(legendnames)\n", - " ax.set_title( 'g = ' + metricname)\n", - "fig.suptitle('For various loss functions g and f, plot\\n\\ng(argmin(f, data(p)), data(p)) over 0 <= p <= 1, where\\n\\n'\n", - "'data(p) is the dataset with weight p on dataset A, and\\nargmin(f, ds) is the model from fitting loss f to dataset ds')\n", - "fig.supxlabel('proportion of dataset taken from dataset A')\n", - "fig.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## View into what models look like\n", - "```\n", - "rho0\n", - "Mdefault = Lindblad-parameterized POVM of length 2\n", - "[] = Exponentiated operation map with dim = 4, num params = 12\n", - "Gxpi2:0 = Exponentiated operation map with dim = 4, num params = 12\n", - "Gypi2:0 = Exponentiated operation map with dim = 4, num params = 12\n", - "```\n", - "Could make a 5-by-3 figure, each of 15 panels plotting two measures of fidelity.\n", - " * Top row is SPAM fidelity, bottom row is POVM fidelity, middle rows are gate fidelity.\n", - " * First column is f = LogL, second column is f = nTVD, third column is f = L10^10.\n", - " \n", - "The two measures of fidelity are mixed-to-A and mixed-to-B." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "row_lbls = [ str(ell) for ell in m_dga.preps.keys() ] + [ str(ell) for ell in m_dga.operations.keys() ] + [ str(ell) for ell in m_dga.povms.keys() ]\n", - "num_rows = len(row_lbls)\n", - "modelnames = ['argmin(LogL)', 'argmin(nTVD)', 'argmin(L10^10)']\n", - "modelnames_to_estnames = {'argmin(LogL)': 'logl', 'argmin(nTVD)': 'normalized tvd', 'argmin(L10^10)': \"('Lp^p', 10)\"}\n", - "num_cols = len(modelnames)\n", - "losscolors = ['b', 'r', 'y', 'k', 'd','.']\n", - "\n", - "fig, axs_grid = plt.subplots(num_rows, num_cols, figsize=(2*num_rows + 1, 4*num_cols + 1), sharey='all')\n", - "\n", - "def flexindelity(m1, m2, lbl):\n", - " if 'G' in lbl or lbl == '[]':\n", - " if lbl == '[]':\n", - " lbl = pygsti.baseobjs.Label(())\n", - " else:\n", - " lbl = lbl.split(':')\n", - " mm1 = m1[lbl]\n", - " mm2 = m2[lbl]\n", - " mm1d = mm1.to_dense()\n", - " mm2d = mm2.to_dense()\n", - " return 1 - entanglement_fidelity(mm1d, mm2d)\n", - " elif 'rho' in lbl:\n", - " mm1 = m1[lbl]\n", - " mm2 = m2[lbl]\n", - " mm1d = pygsti.tools.vec_to_stdmx(mm1.to_dense(), 'pp')\n", - " mm2d = pygsti.tools.vec_to_stdmx(mm2.to_dense(), 'pp')\n", - " return 1 - fidelity(mm1d, mm2d)\n", - " elif 'Mdefault' == lbl:\n", - " return 1 - povm_fidelity(m1, m2, lbl)\n", - " else:\n", - " raise ValueError()\n", - "\n", - "for membername, row_axs in zip(row_lbls, axs_grid):\n", - " row_axs[0].set_ylabel(membername.removesuffix(':0')) #, rotation=0)\n", - " for modelname, ax in zip(modelnames, row_axs):\n", - " ftoA_vs_p = np.zeros(num_mixtures)\n", - " ftoB_vs_p = np.zeros(num_mixtures)\n", - " ftoI_vs_p = np.zeros(num_mixtures)\n", - " for i, (res, _) in enumerate(reslist):\n", - " model_argmin = res.estimates[modelnames_to_estnames[modelname]].models['stdgaugeopt']\n", - " ftoA_vs_p[i] = flexindelity(model_argmin, m_dga_gopped, membername)\n", - " ftoB_vs_p[i] = flexindelity(model_argmin, m_dgb_gopped, membername)\n", - " ftoI_vs_p[i] = flexindelity(model_argmin, target, membername)\n", - " ax.plot(mixture_weights, ftoA_vs_p, losscolors[0])\n", - " ax.plot(mixture_weights, ftoB_vs_p, losscolors[1])\n", - " ax.plot(mixture_weights, ftoI_vs_p, losscolors[2])\n", - " modelname = modelname.removeprefix('argmin(').strip(')')\n", - " ax.legend(['infid. w/ A', 'infid. w/ B', 'infid. w/ ideal'])\n", - " ax.minorticks_on()\n", - " ax.grid(linestyle='dotted', which='minor')\n", - " ax.grid(which='major')\n", - " #ax.set_title('f = ' + modelname)\n", - " if membername == 'rho0':\n", - " ax.set_title('model = argmin( %s, data(p) )' % modelname)\n", - "\n", - "fig.set_tight_layout(True)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs_grid = plt.subplots(num_rows, num_cols, figsize=(2*num_rows + 1, 4*num_cols + 1), sharey='all')\n", - "\n", - "def flexibeniusnorm(m1, m2, lbl):\n", - " if 'G' in lbl or lbl == '[]':\n", - " if lbl == '[]':\n", - " lbl = pygsti.baseobjs.Label(())\n", - " else:\n", - " lbl = lbl.split(':')\n", - " mm1 = m1[lbl]\n", - " mm2 = m2[lbl]\n", - " mm1d = mm1.to_dense()\n", - " mm2d = mm2.to_dense()\n", - " return la.norm(mm1d - mm2d)\n", - " elif 'rho' in lbl:\n", - " mm1 = m1[lbl]\n", - " mm2 = m2[lbl]\n", - " mm1d = pygsti.tools.vec_to_stdmx(mm1.to_dense(), 'pp')\n", - " mm2d = pygsti.tools.vec_to_stdmx(mm2.to_dense(), 'pp')\n", - " return la.norm(mm1d - mm2d)\n", - " elif 'Mdefault' == lbl:\n", - " mm1d = np.array([e.to_dense() for e in m1[lbl].values()])\n", - " mm2d = np.array([e.to_dense() for e in m2[lbl].values()])\n", - " return la.norm(mm1d - mm2d)\n", - " else:\n", - " raise ValueError()\n", - "\n", - "for membername, row_axs in zip(row_lbls, axs_grid):\n", - " row_axs[0].set_ylabel(membername.removesuffix(':0')) #, rotation=0)\n", - " for modelname, ax in zip(modelnames, row_axs):\n", - " ftoA_vs_p = np.zeros(num_mixtures)\n", - " ftoB_vs_p = np.zeros(num_mixtures)\n", - " ftoI_vs_p = np.zeros(num_mixtures)\n", - " # ax.set_title(modelname)\n", - " for i, (res, _) in enumerate(reslist):\n", - " model_argmin = res.estimates[modelnames_to_estnames[modelname]].models['stdgaugeopt']\n", - " ftoA_vs_p[i] = flexibeniusnorm(model_argmin, m_dga_gopped, membername)\n", - " ftoB_vs_p[i] = flexibeniusnorm(model_argmin, m_dgb_gopped, membername)\n", - " ftoI_vs_p[i] = flexibeniusnorm(model_argmin, target, membername)\n", - " ax.plot(mixture_weights, ftoA_vs_p, losscolors[0])\n", - " ax.plot(mixture_weights, ftoB_vs_p, losscolors[1])\n", - " ax.plot(mixture_weights, ftoI_vs_p, losscolors[2])\n", - " modelname = modelname.removeprefix('argmin(').strip(')')\n", - " ax.legend(['FroDist to A', 'FroDist to B', 'FroDist to ideal'])\n", - " ax.minorticks_on()\n", - " ax.grid(linestyle='dotted', which='minor')\n", - " ax.grid(which='major')\n", - " if membername == 'rho0':\n", - " ax.set_title('model = argmin( %s, data(p) )' % modelname)\n", - "\n", - "fig.set_tight_layout(True)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs_grid = plt.subplots(num_rows, num_cols, figsize=(2*num_rows + 1, 4*num_cols + 1), sharey='all')\n", - "\n", - "def flexitracenorm(m1, m2, lbl):\n", - " if 'G' in lbl or lbl == '[]':\n", - " if lbl == '[]':\n", - " lbl = pygsti.baseobjs.Label(())\n", - " else:\n", - " lbl = lbl.split(':')\n", - " mm1 = m1[lbl]\n", - " mm2 = m2[lbl]\n", - " mm1d = mm1.to_dense()\n", - " mm2d = mm2.to_dense()\n", - " return pygsti.tools.optools.jtracedist(mm1d, mm2d, 'pp')\n", - " elif 'rho' in lbl:\n", - " mm1 = m1[lbl]\n", - " mm2 = m2[lbl]\n", - " mm1d = pygsti.tools.vec_to_stdmx(mm1.to_dense(), 'pp')\n", - " mm2d = pygsti.tools.vec_to_stdmx(mm2.to_dense(), 'pp')\n", - " return pygsti.tools.optools.tracedist(mm1d, mm2d)\n", - " elif 'Mdefault' == lbl:\n", - " mm1d = np.array([e.to_dense() for e in m1[lbl].values()])\n", - " mm2d = np.array([e.to_dense() for e in m2[lbl].values()])\n", - " return pygsti.tools.optools.povm_jtracedist(m1, m2, lbl)\n", - " else:\n", - " raise ValueError()\n", - "\n", - "for membername, row_axs in zip(row_lbls, axs_grid):\n", - " row_axs[0].set_ylabel(membername.removesuffix(':0')) #, rotation=0)\n", - " for modelname, ax in zip(modelnames, row_axs):\n", - " ftoA_vs_p = np.zeros(num_mixtures)\n", - " ftoB_vs_p = np.zeros(num_mixtures)\n", - " ftoI_vs_p = np.zeros(num_mixtures)\n", - " for i, (res, _) in enumerate(reslist):\n", - " model_argmin = res.estimates[modelnames_to_estnames[modelname]].models['stdgaugeopt']\n", - " ftoA_vs_p[i] = flexitracenorm(model_argmin, m_dga_gopped, membername)\n", - " ftoB_vs_p[i] = flexitracenorm(model_argmin, m_dgb_gopped, membername)\n", - " ftoI_vs_p[i] = flexitracenorm(model_argmin, target, membername)\n", - " ax.plot(mixture_weights, ftoA_vs_p, losscolors[0])\n", - " ax.plot(mixture_weights, ftoB_vs_p, losscolors[1])\n", - " ax.plot(mixture_weights, ftoI_vs_p, losscolors[2])\n", - " modelname = modelname.removeprefix('argmin(').strip(')')\n", - " ax.legend(['TrDist to A', 'TrDist to B', 'TrDist to ideal'])\n", - " ax.minorticks_on()\n", - " ax.grid(linestyle='dotted', which='minor')\n", - " ax.grid(which='major')\n", - " #ax.set_title('f = ' + modelname)\n", - " if membername == 'rho0':\n", - " ax.set_title('model = argmin( %s, data(p) )' % modelname)\n", - "fig.set_tight_layout(True)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "odict_keys([Label(()), Label(('Gxpi2', 0)), Label(('Gypi2', 0))])\n", - "['[]', 'Gxpi2:0', 'Gypi2:0']\n", - "odict_keys([Label('rho0')])\n", - "odict_keys([Label('Mdefault')])\n", - "Lindblad-parameterized POVM of length 2\n", - "\n", - "OrderedDict([('logl', ), ('normalized tvd', ), (\"('Lp^p', 10)\", )])\n", - "23\n", - "23\n" - ] - } - ], - "source": [ - "print(m_dga.operations.keys())\n", - "print([str(ell) for ell in m_dga.operations.keys()])\n", - "print(m_dga.preps.keys())\n", - "print(m_dga.povms.keys())\n", - "print(m_dga['Mdefault'])\n", - "print(results.estimates)\n", - "print(len(reslist))\n", - "print(num_mixtures)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "rogst", - "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": "3.11.11" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 0ccaaad509b0823a7684952bb2e7d1eca036e3f4 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Tue, 13 May 2025 13:06:45 -0700 Subject: [PATCH 47/71] remove temporary notebook --- .../case0-gst-with-outliers-beyond-tvd.ipynb | 735 ------------------ 1 file changed, 735 deletions(-) delete mode 100644 wip_notebook_sharing/case0-gst-with-outliers-beyond-tvd.ipynb diff --git a/wip_notebook_sharing/case0-gst-with-outliers-beyond-tvd.ipynb b/wip_notebook_sharing/case0-gst-with-outliers-beyond-tvd.ipynb deleted file mode 100644 index 9db5e6cde..000000000 --- a/wip_notebook_sharing/case0-gst-with-outliers-beyond-tvd.ipynb +++ /dev/null @@ -1,735 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import pygsti\n", - "from pygsti.modelpacks import smq1Q_XYI, smq2Q_XYCNOT\n", - "import numpy as np\n", - "from pprint import pprint\n", - "from experiment_helpers import make_depolarized_dataset, run_gst, corrupt_dataset, make_tweaked_dataset\n", - "from scipy import linalg as la\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "mp = smq1Q_XYI\n", - "target = mp.target_model()\n", - "fids = (mp.prep_fiducials(), mp.meas_fiducials())\n", - "germs = mp.germs()\n", - "maxmaxlen = 64\n", - "ds, m_datagen = make_tweaked_dataset(mp, depol_level=0.001, rand_unitary_scale=0.001, max_max_len=maxmaxlen)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--- Circuit Creation ---\n", - " 952 circuits created\n", - " Dataset has 952 entries: 952 utilized, 0 requested circuits were missing\n", - "-- Std Practice: Iter 1 of 1 (CPTPLND) --: \n", - " Precomputing CircuitOutcomeProbabilityArray layouts for each iteration.\n", - " Layout for iteration 0\n", - " Num Param Processors (1,)\n", - " MapLayout: 1 processors divided into 1 x 1 (= 1) grid along circuit and parameter directions.\n", - " 1 atoms, parameter block size limits (None,)\n", - " *** Distributing 1 atoms to 1 atom-processing groups (1 cores) ***\n", - " More atom-processors than hosts: each host gets ~1 atom-processors\n", - " Atom-processors already occupy a single node, dividing atom-processor into 1 param-processors.\n", - " *** Divided 1-host atom-processor (~1 procs) into 1 param-processing groups ***\n", - " Layout for iteration 1\n", - " Num Param Processors (1,)\n", - " MapLayout: 1 processors divided into 1 x 1 (= 1) grid along circuit and parameter directions.\n", - " 1 atoms, parameter block size limits (None,)\n", - " *** Distributing 1 atoms to 1 atom-processing groups (1 cores) ***\n", - " More atom-processors than hosts: each host gets ~1 atom-processors\n", - " Atom-processors already occupy a single node, dividing atom-processor into 1 param-processors.\n", - " *** Divided 1-host atom-processor (~1 procs) into 1 param-processing groups ***\n", - " Layout for iteration 2\n", - " Num Param Processors (1,)\n", - " MapLayout: 1 processors divided into 1 x 1 (= 1) grid along circuit and parameter directions.\n", - " 1 atoms, parameter block size limits (None,)\n", - " *** Distributing 1 atoms to 1 atom-processing groups (1 cores) ***\n", - " More atom-processors than hosts: each host gets ~1 atom-processors\n", - " Atom-processors already occupy a single node, dividing atom-processor into 1 param-processors.\n", - " *** Divided 1-host atom-processor (~1 procs) into 1 param-processing groups ***\n", - " Layout for iteration 3\n", - " Num Param Processors (1,)\n", - " MapLayout: 1 processors divided into 1 x 1 (= 1) grid along circuit and parameter directions.\n", - " 1 atoms, parameter block size limits (None,)\n", - " *** Distributing 1 atoms to 1 atom-processing groups (1 cores) ***\n", - " More atom-processors than hosts: each host gets ~1 atom-processors\n", - " Atom-processors already occupy a single node, dividing atom-processor into 1 param-processors.\n", - " *** Divided 1-host atom-processor (~1 procs) into 1 param-processing groups ***\n", - " Layout for iteration 4\n", - " Num Param Processors (1,)\n", - " MapLayout: 1 processors divided into 1 x 1 (= 1) grid along circuit and parameter directions.\n", - " 1 atoms, parameter block size limits (None,)\n", - " *** Distributing 1 atoms to 1 atom-processing groups (1 cores) ***\n", - " More atom-processors than hosts: each host gets ~1 atom-processors\n", - " Atom-processors already occupy a single node, dividing atom-processor into 1 param-processors.\n", - " *** Divided 1-host atom-processor (~1 procs) into 1 param-processing groups ***\n", - " Layout for iteration 5\n", - " Num Param Processors (1,)\n", - " MapLayout: 1 processors divided into 1 x 1 (= 1) grid along circuit and parameter directions.\n", - " 1 atoms, parameter block size limits (None,)\n", - " *** Distributing 1 atoms to 1 atom-processing groups (1 cores) ***\n", - " More atom-processors than hosts: each host gets ~1 atom-processors\n", - " Atom-processors already occupy a single node, dividing atom-processor into 1 param-processors.\n", - " *** Divided 1-host atom-processor (~1 procs) into 1 param-processing groups ***\n", - " Layout for iteration 6\n", - " Num Param Processors (1,)\n", - " MapLayout: 1 processors divided into 1 x 1 (= 1) grid along circuit and parameter directions.\n", - " 1 atoms, parameter block size limits (None,)\n", - " *** Distributing 1 atoms to 1 atom-processing groups (1 cores) ***\n", - " More atom-processors than hosts: each host gets ~1 atom-processors\n", - " Atom-processors already occupy a single node, dividing atom-processor into 1 param-processors.\n", - " *** Divided 1-host atom-processor (~1 procs) into 1 param-processing groups ***\n", - " --- Iterative GST: Iter 1 of 7 92 circuits ---: \n", - " --- chi2 GST ---\n", - " --- Outer Iter 0: norm_f = 3483.29, mu=1, |x|=0, |J|=1553.28\n", - " --- Outer Iter 1: norm_f = 2632.76, mu=304.068, |x|=0.0132745, |J|=12846.5\n", - " --- Outer Iter 2: norm_f = 899.905, mu=810.848, |x|=0.138517, |J|=1492\n", - " --- Outer Iter 3: norm_f = 97.8796, mu=270.283, |x|=0.0435912, |J|=1755.73\n", - " --- Outer Iter 4: norm_f = 79.08, mu=540.565, |x|=0.0702357, |J|=1624.07\n", - " --- Outer Iter 5: norm_f = 63.3538, mu=469.671, |x|=0.0726337, |J|=1645.58\n", - " --- Outer Iter 6: norm_f = 61.0621, mu=978.633, |x|=0.0705162, |J|=1652.27\n", - " --- Outer Iter 7: norm_f = 58.1969, mu=880.157, |x|=0.0649962, |J|=1663.25\n", - " --- Outer Iter 8: norm_f = 58.0818, mu=1044.49, |x|=0.0659669, |J|=1663.6\n", - " --- Outer Iter 9: norm_f = 57.8849, mu=1024.43, |x|=0.0651468, |J|=1667.45\n", - " --- Outer Iter 10: norm_f = 57.8503, mu=1024.26, |x|=0.065333, |J|=1669.48\n", - " --- Outer Iter 11: norm_f = 57.8363, mu=1024.24, |x|=0.0652145, |J|=1669.37\n", - " --- Outer Iter 12: norm_f = 57.8335, mu=1049.24, |x|=0.0652728, |J|=1669.41\n", - " --- Outer Iter 13: norm_f = 57.832, mu=1066.25, |x|=0.0652502, |J|=1669.34\n", - " --- Outer Iter 14: norm_f = 57.8319, mu=1953.35, |x|=0.0652778, |J|=1669.33\n", - " --- Outer Iter 15: norm_f = 57.8306, mu=2112.42, |x|=0.0652695, |J|=1669.31\n", - " --- Outer Iter 16: norm_f = 57.8299, mu=2149.84, |x|=0.0652698, |J|=1669.3\n", - " --- Outer Iter 17: norm_f = 57.8294, mu=2148.38, |x|=0.0652685, |J|=1669.28\n", - " --- Outer Iter 18: norm_f = 57.8289, mu=1874.06, |x|=0.0652692, |J|=1669.27\n", - " --- Outer Iter 19: norm_f = 57.8285, mu=1058.62, |x|=0.0652703, |J|=1669.24\n", - " --- Outer Iter 20: norm_f = 57.8278, mu=707.388, |x|=0.065274, |J|=1669.18\n", - " --- Outer Iter 21: norm_f = 57.8274, mu=864.668, |x|=0.0652824, |J|=1669.09\n", - " --- Outer Iter 22: norm_f = 57.8267, mu=2194.64, |x|=0.0652825, |J|=1669.12\n", - " --- Outer Iter 23: norm_f = 57.8258, mu=2235.16, |x|=0.0652794, |J|=1669.1\n", - " --- Outer Iter 24: norm_f = 57.8253, mu=2231.91, |x|=0.0652778, |J|=1669.08\n", - " --- Outer Iter 25: norm_f = 57.8249, mu=1771.33, |x|=0.0652781, |J|=1669.06\n", - " --- Outer Iter 26: norm_f = 57.8244, mu=757.668, |x|=0.0652796, |J|=1669.03\n", - " --- Outer Iter 27: norm_f = 57.8234, mu=579.355, |x|=0.0652868, |J|=1668.92\n", - " --- Outer Iter 28: norm_f = 57.823, mu=1216.24, |x|=0.0652864, |J|=1668.92\n", - " --- Outer Iter 29: norm_f = 57.8223, mu=2432.66, |x|=0.0652855, |J|=1668.92\n", - " --- Outer Iter 30: norm_f = 57.8219, mu=1975.32, |x|=0.0652851, |J|=1668.9\n", - " --- Outer Iter 31: norm_f = 57.8214, mu=658.44, |x|=0.0652867, |J|=1668.87\n", - " --- Outer Iter 32: norm_f = 57.8203, mu=388.144, |x|=0.0652961, |J|=1668.74\n", - " --- Outer Iter 33: norm_f = 57.8198, mu=858.823, |x|=0.065298, |J|=1668.73\n", - " --- Outer Iter 34: norm_f = 57.8192, mu=2091.22, |x|=0.0652993, |J|=1668.74\n", - " --- Outer Iter 35: norm_f = 57.8183, mu=2134.93, |x|=0.0652974, |J|=1668.72\n", - " --- Outer Iter 36: norm_f = 57.8178, mu=2134.13, |x|=0.0652965, |J|=1668.7\n", - " --- Outer Iter 37: norm_f = 57.8174, mu=1858.38, |x|=0.0652969, |J|=1668.68\n", - " --- Outer Iter 38: norm_f = 57.8169, mu=1011.97, |x|=0.0652982, |J|=1668.66\n", - " --- Outer Iter 39: norm_f = 57.8161, mu=669.726, |x|=0.0653026, |J|=1668.6\n", - " --- Outer Iter 40: norm_f = 57.8158, mu=877.972, |x|=0.0653121, |J|=1668.53\n", - " --- Outer Iter 41: norm_f = 57.815, mu=2121.74, |x|=0.065311, |J|=1668.56\n", - " --- Outer Iter 42: norm_f = 57.8142, mu=2162.41, |x|=0.0653081, |J|=1668.55\n", - " --- Outer Iter 43: norm_f = 57.8137, mu=2160.74, |x|=0.0653069, |J|=1668.54\n", - " --- Outer Iter 44: norm_f = 57.8132, mu=1797.31, |x|=0.0653073, |J|=1668.52\n", - " --- Outer Iter 45: norm_f = 57.8128, mu=867.323, |x|=0.0653088, |J|=1668.5\n", - " --- Outer Iter 46: norm_f = 57.812, mu=615.008, |x|=0.0653142, |J|=1668.44\n", - " --- Outer Iter 47: norm_f = 57.8115, mu=1230.14, |x|=0.065314, |J|=1668.45\n", - " --- Outer Iter 48: norm_f = 57.8112, mu=1696.6, |x|=0.0653186, |J|=1668.43\n", - " --- Outer Iter 49: norm_f = 57.8107, mu=2034.52, |x|=0.0653204, |J|=1668.42\n", - " --- Outer Iter 50: norm_f = 57.81, mu=2075.77, |x|=0.0653187, |J|=1668.41\n", - " --- Outer Iter 51: norm_f = 57.8096, mu=2075.22, |x|=0.0653179, |J|=1668.41\n", - " --- Outer Iter 52: norm_f = 57.8092, mu=1850.93, |x|=0.0653182, |J|=1668.4\n", - " --- Outer Iter 53: norm_f = 57.8088, mu=1100.56, |x|=0.0653193, |J|=1668.39\n", - " --- Outer Iter 54: norm_f = 57.8082, mu=717.441, |x|=0.0653224, |J|=1668.36\n", - " --- Outer Iter 55: norm_f = 57.8078, mu=750.118, |x|=0.0653291, |J|=1668.31\n", - " --- Outer Iter 56: norm_f = 57.8075, mu=2190.32, |x|=0.0653298, |J|=1668.34\n", - " --- Outer Iter 57: norm_f = 57.8066, mu=2229.69, |x|=0.065328, |J|=1668.34\n", - " --- Outer Iter 58: norm_f = 57.8062, mu=2223.96, |x|=0.0653263, |J|=1668.33\n", - " --- Outer Iter 59: norm_f = 57.8059, mu=1621.18, |x|=0.0653268, |J|=1668.33\n", - " --- Outer Iter 60: norm_f = 57.8056, mu=588.951, |x|=0.0653284, |J|=1668.32\n", - " --- Outer Iter 61: norm_f = 57.8047, mu=512.066, |x|=0.0653372, |J|=1668.24\n", - " --- Outer Iter 62: norm_f = 57.8046, mu=1617.52, |x|=0.0653362, |J|=1668.29\n", - " --- Outer Iter 63: norm_f = 57.8042, mu=2225.15, |x|=0.0653402, |J|=1668.28\n", - " --- Outer Iter 64: norm_f = 57.8035, mu=2253.82, |x|=0.0653377, |J|=1668.28\n", - " --- Outer Iter 65: norm_f = 57.8032, mu=2236.55, |x|=0.0653367, |J|=1668.28\n", - " --- Outer Iter 66: norm_f = 57.8029, mu=1426, |x|=0.0653376, |J|=1668.27\n", - " --- Outer Iter 67: norm_f = 57.8026, mu=475.334, |x|=0.0653402, |J|=1668.26\n", - " --- Outer Iter 68: norm_f = 57.8018, mu=459.22, |x|=0.0653545, |J|=1668.16\n", - " --- Outer Iter 69: norm_f = 57.8013, mu=3337.35, |x|=0.065345, |J|=1668.24\n", - " --- Outer Iter 70: norm_f = 57.8012, mu=1112.45, |x|=0.0653479, |J|=1668.23\n", - " --- Outer Iter 71: norm_f = 57.8007, mu=370.816, |x|=0.0653548, |J|=1668.19\n", - " --- Outer Iter 72: norm_f = 57.7994, mu=210.304, |x|=0.0653842, |J|=1667.99\n", - " --- Outer Iter 73: norm_f = 57.7983, mu=213.387, |x|=0.0654752, |J|=1667.4\n", - " --- Outer Iter 74: norm_f = 57.7958, mu=1351.61, |x|=0.0654521, |J|=1667.75\n", - " --- Outer Iter 75: norm_f = 57.7952, mu=1351.68, |x|=0.065483, |J|=1667.6\n", - " --- Outer Iter 76: norm_f = 57.7947, mu=1404.86, |x|=0.065512, |J|=1667.48\n", - " --- Outer Iter 77: norm_f = 57.7941, mu=1635.13, |x|=0.0655415, |J|=1667.35\n", - " --- Outer Iter 78: norm_f = 57.7934, mu=1785.18, |x|=0.065567, |J|=1667.23\n", - " --- Outer Iter 79: norm_f = 57.7926, mu=1813.89, |x|=0.06559, |J|=1667.12\n", - " --- Outer Iter 80: norm_f = 57.7918, mu=1813.89, |x|=0.0656141, |J|=1667\n", - " --- Outer Iter 81: norm_f = 57.7912, mu=1783.83, |x|=0.06564, |J|=1666.88\n", - " --- Outer Iter 82: norm_f = 57.7905, mu=1576.36, |x|=0.0656675, |J|=1666.75\n", - " --- Outer Iter 83: norm_f = 57.7898, mu=1221.34, |x|=0.0656998, |J|=1666.6\n", - " --- Outer Iter 84: norm_f = 57.7889, mu=1082.51, |x|=0.065743, |J|=1666.4\n", - " --- Outer Iter 85: norm_f = 57.7882, mu=1084.46, |x|=0.0657927, |J|=1666.19\n", - " --- Outer Iter 86: norm_f = 57.7874, mu=2168.92, |x|=0.0658139, |J|=1666.1\n", - " --- Outer Iter 87: norm_f = 57.7868, mu=1820.32, |x|=0.0658361, |J|=1665.99\n", - " --- Outer Iter 88: norm_f = 57.7863, mu=744.391, |x|=0.0658646, |J|=1665.87\n", - " --- Outer Iter 89: norm_f = 57.785, mu=466.905, |x|=0.0659369, |J|=1665.53\n", - " --- Outer Iter 90: norm_f = 57.7844, mu=950.44, |x|=0.0659875, |J|=1665.35\n", - " --- Outer Iter 91: norm_f = 57.7837, mu=1937.22, |x|=0.06601, |J|=1665.26\n", - " --- Outer Iter 92: norm_f = 57.7831, mu=1937.14, |x|=0.0660316, |J|=1665.16\n", - " --- Outer Iter 93: norm_f = 57.7827, mu=1825.35, |x|=0.0660543, |J|=1665.07\n", - " --- Outer Iter 94: norm_f = 57.7822, mu=1328.02, |x|=0.0660781, |J|=1664.96\n", - " --- Outer Iter 95: norm_f = 57.7817, mu=890.704, |x|=0.0661107, |J|=1664.83\n", - " --- Outer Iter 96: norm_f = 57.7811, mu=883.836, |x|=0.0661582, |J|=1664.63\n", - " --- Outer Iter 97: norm_f = 57.7806, mu=1775.8, |x|=0.0661778, |J|=1664.56\n", - " --- Outer Iter 98: norm_f = 57.7803, mu=1775.79, |x|=0.0661981, |J|=1664.48\n", - " --- Outer Iter 99: norm_f = 57.7799, mu=1757.94, |x|=0.0662182, |J|=1664.4\n", - " Least squares message = Maximum iterations (100) exceeded\n", - " Sum of Chi^2 = 57.7796 (92 data params - 60 (approx) model params = expected mean of 32; p-value = 0.00346544)\n", - " Completed in 1.7s\n", - " Iteration 1 took 2.0s\n", - " \n", - " --- Iterative GST: Iter 2 of 7 168 circuits ---: \n", - " --- chi2 GST ---\n", - " --- Outer Iter 0: norm_f = 197.313, mu=1, |x|=0.0662378, |J|=2473.45\n", - " --- Outer Iter 1: norm_f = 146.159, mu=689.768, |x|=0.0570769, |J|=2304.6\n", - " --- Outer Iter 2: norm_f = 142.417, mu=479.065, |x|=0.0536076, |J|=2323.12\n", - " --- Outer Iter 3: norm_f = 142.19, mu=485.598, |x|=0.0542792, |J|=2321.46\n", - " --- Outer Iter 4: norm_f = 141.856, mu=485.482, |x|=0.0535151, |J|=2322.08\n", - " --- Outer Iter 5: norm_f = 141.562, mu=405.803, |x|=0.0528087, |J|=2326.02\n", - " --- Outer Iter 6: norm_f = 141.51, mu=811.601, |x|=0.0524609, |J|=2327.69\n", - " --- Outer Iter 7: norm_f = 141.499, mu=859.672, |x|=0.0524809, |J|=2328.14\n", - " --- Outer Iter 8: norm_f = 141.493, mu=895.439, |x|=0.0523328, |J|=2328.75\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "WARNING: Treating result as *converged* after maximum iterations (100) were exceeded.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " --- Outer Iter 9: norm_f = 141.491, mu=1057.57, |x|=0.0523946, |J|=2328.66\n", - " --- Outer Iter 10: norm_f = 141.49, mu=1166.05, |x|=0.0523309, |J|=2328.87\n", - " --- Outer Iter 11: norm_f = 141.49, mu=2050.66, |x|=0.0523643, |J|=2328.78\n", - " --- Outer Iter 12: norm_f = 141.489, mu=2153.65, |x|=0.052346, |J|=2328.83\n", - " --- Outer Iter 13: norm_f = 141.489, mu=2256.61, |x|=0.0523475, |J|=2328.83\n", - " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", - " Sum of Chi^2 = 141.489 (168 data params - 60 (approx) model params = expected mean of 108; p-value = 0.0168947)\n", - " Completed in 0.3s\n", - " Iteration 2 took 0.3s\n", - " \n", - " --- Iterative GST: Iter 3 of 7 285 circuits ---: \n", - " --- chi2 GST ---\n", - " --- Outer Iter 0: norm_f = 311.393, mu=1, |x|=0.0523475, |J|=3402.26\n", - " --- Outer Iter 1: norm_f = 308.229, mu=4856.95, |x|=0.061184, |J|=3305.72\n", - " --- Outer Iter 2: norm_f = 270.928, mu=1618.98, |x|=0.0532447, |J|=3357.59\n", - " --- Outer Iter 3: norm_f = 269.767, mu=1523.14, |x|=0.0540491, |J|=3354.45\n", - " --- Outer Iter 4: norm_f = 268.868, mu=598.973, |x|=0.0525103, |J|=3364.86\n", - " --- Outer Iter 5: norm_f = 268.841, mu=830.643, |x|=0.0533074, |J|=3360.17\n", - " --- Outer Iter 6: norm_f = 268.207, mu=1487.83, |x|=0.0532672, |J|=3360.65\n", - " --- Outer Iter 7: norm_f = 267.934, mu=1272.14, |x|=0.053316, |J|=3358.56\n", - " --- Outer Iter 8: norm_f = 267.799, mu=1176.36, |x|=0.0538154, |J|=3353.21\n", - " --- Outer Iter 9: norm_f = 267.733, mu=1180.63, |x|=0.0543916, |J|=3347.56\n", - " --- Outer Iter 10: norm_f = 267.664, mu=1185.17, |x|=0.0547029, |J|=3344.42\n", - " --- Outer Iter 11: norm_f = 267.578, mu=1087.75, |x|=0.0546495, |J|=3344.54\n", - " --- Outer Iter 12: norm_f = 267.551, mu=698.869, |x|=0.0545473, |J|=3345.29\n", - " --- Outer Iter 13: norm_f = 267.549, mu=698.864, |x|=0.0545321, |J|=3345.3\n", - " --- Outer Iter 14: norm_f = 267.548, mu=1570.68, |x|=0.0545212, |J|=3345.46\n", - " --- Outer Iter 15: norm_f = 267.547, mu=1708.77, |x|=0.0545212, |J|=3345.4\n", - " --- Outer Iter 16: norm_f = 267.547, mu=1770.1, |x|=0.0545189, |J|=3345.44\n", - " --- Outer Iter 17: norm_f = 267.547, mu=1772.85, |x|=0.0545185, |J|=3345.43\n", - " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", - " Sum of Chi^2 = 267.547 (285 data params - 60 (approx) model params = expected mean of 225; p-value = 0.0272684)\n", - " Completed in 0.5s\n", - " Iteration 3 took 0.5s\n", - " \n", - " --- Iterative GST: Iter 4 of 7 448 circuits ---: \n", - " --- chi2 GST ---\n", - " --- Outer Iter 0: norm_f = 452.138, mu=1, |x|=0.0545185, |J|=5274.29\n", - " --- Outer Iter 1: norm_f = 416.766, mu=3443.72, |x|=0.0559625, |J|=5234.27\n", - " --- Outer Iter 2: norm_f = 413.511, mu=3121.53, |x|=0.0551826, |J|=5241.1\n", - " --- Outer Iter 3: norm_f = 412.638, mu=3067.47, |x|=0.0547291, |J|=5244.38\n", - " --- Outer Iter 4: norm_f = 412.205, mu=2542.54, |x|=0.0542903, |J|=5248.18\n", - " --- Outer Iter 5: norm_f = 412.109, mu=1837.11, |x|=0.0540098, |J|=5250.06\n", - " --- Outer Iter 6: norm_f = 412.084, mu=1581.39, |x|=0.0538142, |J|=5250.54\n", - " --- Outer Iter 7: norm_f = 412.062, mu=1566.05, |x|=0.0536469, |J|=5250.84\n", - " --- Outer Iter 8: norm_f = 412.037, mu=1553.18, |x|=0.0535164, |J|=5251.27\n", - " --- Outer Iter 9: norm_f = 412.013, mu=1553.13, |x|=0.0534733, |J|=5251.44\n", - " --- Outer Iter 10: norm_f = 411.986, mu=1551.52, |x|=0.0535262, |J|=5251.45\n", - " --- Outer Iter 11: norm_f = 411.957, mu=1460.74, |x|=0.0536341, |J|=5251.56\n", - " --- Outer Iter 12: norm_f = 411.94, mu=1222.67, |x|=0.0537467, |J|=5251.67\n", - " --- Outer Iter 13: norm_f = 411.934, mu=1000.14, |x|=0.0538424, |J|=5251.66\n", - " --- Outer Iter 14: norm_f = 411.932, mu=770.797, |x|=0.0539027, |J|=5251.62\n", - " --- Outer Iter 15: norm_f = 411.931, mu=628.278, |x|=0.0539405, |J|=5251.56\n", - " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", - " Sum of Chi^2 = 411.931 (448 data params - 60 (approx) model params = expected mean of 388; p-value = 0.193286)\n", - " Completed in 0.7s\n", - " Iteration 4 took 0.7s\n", - " \n", - " --- Iterative GST: Iter 5 of 7 616 circuits ---: \n", - " --- chi2 GST ---\n", - " --- Outer Iter 0: norm_f = 585.139, mu=1, |x|=0.0539405, |J|=8813.75\n", - " --- Outer Iter 1: norm_f = 555.884, mu=5905.76, |x|=0.0561539, |J|=8796.71\n", - " --- Outer Iter 2: norm_f = 553.618, mu=3485.3, |x|=0.0562766, |J|=8802.55\n", - " --- Outer Iter 3: norm_f = 553.211, mu=3117.32, |x|=0.0562064, |J|=8805.34\n", - " --- Outer Iter 4: norm_f = 553.025, mu=2900.4, |x|=0.0560686, |J|=8806.35\n", - " --- Outer Iter 5: norm_f = 552.903, mu=2872.13, |x|=0.0559834, |J|=8805.33\n", - " --- Outer Iter 6: norm_f = 552.797, mu=2875.53, |x|=0.055949, |J|=8802.51\n", - " --- Outer Iter 7: norm_f = 552.514, mu=2505.7, |x|=0.0558017, |J|=8803.05\n", - " --- Outer Iter 8: norm_f = 552.375, mu=5019.47, |x|=0.0558568, |J|=8800.05\n", - " --- Outer Iter 9: norm_f = 552.086, mu=4824.25, |x|=0.0557519, |J|=8800.95\n", - " --- Outer Iter 10: norm_f = 551.9, mu=2071.66, |x|=0.0555706, |J|=8803.02\n", - " --- Outer Iter 11: norm_f = 551.853, mu=1609.26, |x|=0.0554713, |J|=8803.51\n", - " --- Outer Iter 12: norm_f = 551.832, mu=1128.16, |x|=0.055309, |J|=8804.73\n", - " --- Outer Iter 13: norm_f = 551.823, mu=1122.11, |x|=0.0551568, |J|=8805.32\n", - " --- Outer Iter 14: norm_f = 551.812, mu=848.588, |x|=0.0550168, |J|=8806.21\n", - " --- Outer Iter 15: norm_f = 551.807, mu=788.134, |x|=0.0549077, |J|=8806.76\n", - " --- Outer Iter 16: norm_f = 551.802, mu=550.146, |x|=0.0548286, |J|=8807.31\n", - " --- Outer Iter 17: norm_f = 551.8, mu=473.662, |x|=0.0547768, |J|=8807.6\n", - " --- Outer Iter 18: norm_f = 551.799, mu=278.631, |x|=0.0547472, |J|=8807.84\n", - " --- Outer Iter 19: norm_f = 551.799, mu=226.867, |x|=0.0547359, |J|=8807.91\n", - " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", - " Sum of Chi^2 = 551.799 (616 data params - 60 (approx) model params = expected mean of 556; p-value = 0.54233)\n", - " Completed in 0.7s\n", - " Iteration 5 took 0.7s\n", - " \n", - " --- Iterative GST: Iter 6 of 7 784 circuits ---: \n", - " --- chi2 GST ---\n", - " --- Outer Iter 0: norm_f = 783.551, mu=1, |x|=0.0547359, |J|=15826.7\n", - " --- Outer Iter 1: norm_f = 750.942, mu=15202.5, |x|=0.0547454, |J|=15777.2\n", - " --- Outer Iter 2: norm_f = 749.281, mu=14465.9, |x|=0.0537542, |J|=15776.8\n", - " --- Outer Iter 3: norm_f = 747.511, mu=9148.89, |x|=0.053007, |J|=15788.4\n", - " --- Outer Iter 4: norm_f = 746.793, mu=9065.67, |x|=0.0526032, |J|=15794.9\n", - " --- Outer Iter 5: norm_f = 746.121, mu=8116.74, |x|=0.0523044, |J|=15806\n", - " --- Outer Iter 6: norm_f = 745.85, mu=8115.52, |x|=0.0523011, |J|=15810.4\n", - " --- Outer Iter 7: norm_f = 745.547, mu=7630.24, |x|=0.0524001, |J|=15812.9\n", - " --- Outer Iter 8: norm_f = 745.348, mu=7254.76, |x|=0.0525984, |J|=15813.1\n", - " --- Outer Iter 9: norm_f = 745.191, mu=6816.28, |x|=0.0528335, |J|=15812.5\n", - " --- Outer Iter 10: norm_f = 745.066, mu=6448.2, |x|=0.0530809, |J|=15811.6\n", - " --- Outer Iter 11: norm_f = 744.963, mu=6047.94, |x|=0.0533136, |J|=15810.7\n", - " --- Outer Iter 12: norm_f = 744.879, mu=5656.39, |x|=0.0535221, |J|=15810.1\n", - " --- Outer Iter 13: norm_f = 744.814, mu=5237.96, |x|=0.0536972, |J|=15809.7\n", - " --- Outer Iter 14: norm_f = 744.765, mu=4798.89, |x|=0.0538356, |J|=15809.6\n", - " --- Outer Iter 15: norm_f = 744.731, mu=4253.1, |x|=0.0539351, |J|=15809.9\n", - " --- Outer Iter 16: norm_f = 744.711, mu=3424.31, |x|=0.0540046, |J|=15810.5\n", - " --- Outer Iter 17: norm_f = 744.702, mu=2640.2, |x|=0.0540601, |J|=15811\n", - " --- Outer Iter 18: norm_f = 744.698, mu=1868.55, |x|=0.0540998, |J|=15811.4\n", - " --- Outer Iter 19: norm_f = 744.696, mu=1164.1, |x|=0.054132, |J|=15811.7\n", - " --- Outer Iter 20: norm_f = 744.693, mu=891.762, |x|=0.0541716, |J|=15811.8\n", - " --- Outer Iter 21: norm_f = 744.689, mu=297.254, |x|=0.0542196, |J|=15811.8\n", - " --- Outer Iter 22: norm_f = 744.684, mu=792.677, |x|=0.0542247, |J|=15812.3\n", - " --- Outer Iter 23: norm_f = 744.676, mu=1475.78, |x|=0.0542658, |J|=15811.2\n", - " --- Outer Iter 24: norm_f = 744.669, mu=1653.86, |x|=0.0543124, |J|=15809.7\n", - " --- Outer Iter 25: norm_f = 744.633, mu=1164.12, |x|=0.0543028, |J|=15810.7\n", - " --- Outer Iter 26: norm_f = 744.62, mu=475.259, |x|=0.0543026, |J|=15811.5\n", - " --- Outer Iter 27: norm_f = 744.618, mu=462.416, |x|=0.0543497, |J|=15812\n", - " --- Outer Iter 28: norm_f = 744.616, mu=369.25, |x|=0.0543912, |J|=15812.7\n", - " --- Outer Iter 29: norm_f = 744.616, mu=369.187, |x|=0.0544509, |J|=15813.2\n", - " --- Outer Iter 30: norm_f = 744.615, mu=317.41, |x|=0.0545062, |J|=15813.7\n", - " --- Outer Iter 31: norm_f = 744.614, mu=314.481, |x|=0.0545751, |J|=15814.2\n", - " --- Outer Iter 32: norm_f = 744.613, mu=616.178, |x|=0.0546056, |J|=15814.6\n", - " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", - " Sum of Chi^2 = 744.613 (784 data params - 60 (approx) model params = expected mean of 724; p-value = 0.289745)\n", - " Completed in 1.2s\n", - " Iteration 6 took 1.2s\n", - " \n", - " --- Iterative GST: Iter 7 of 7 952 circuits ---: \n", - " --- chi2 GST ---\n", - " --- Outer Iter 0: norm_f = 951.549, mu=1, |x|=0.0546056, |J|=29521.4\n", - " --- Outer Iter 1: norm_f = 922.257, mu=52763.7, |x|=0.0547357, |J|=29496.4\n", - " --- Outer Iter 2: norm_f = 919.796, mu=17587.9, |x|=0.0547233, |J|=29502.2\n", - " --- Outer Iter 3: norm_f = 918.843, mu=17587.9, |x|=0.0545352, |J|=29481.9\n", - " --- Outer Iter 4: norm_f = 917.113, mu=12599.1, |x|=0.0539607, |J|=29504.8\n", - " --- Outer Iter 5: norm_f = 916.395, mu=9575.77, |x|=0.0535451, |J|=29514.9\n", - " --- Outer Iter 6: norm_f = 916.101, mu=3593.81, |x|=0.0532769, |J|=29524.3\n", - " --- Outer Iter 7: norm_f = 916.055, mu=2588.9, |x|=0.0531642, |J|=29527.4\n", - " --- Outer Iter 8: norm_f = 916.039, mu=2089.85, |x|=0.0531501, |J|=29528.9\n", - " --- Outer Iter 9: norm_f = 916.029, mu=1500.37, |x|=0.0531624, |J|=29529.5\n", - " --- Outer Iter 10: norm_f = 916.024, mu=725.547, |x|=0.0531951, |J|=29529.9\n", - " --- Outer Iter 11: norm_f = 916.022, mu=400.97, |x|=0.0532904, |J|=29530.2\n", - " --- Outer Iter 12: norm_f = 916.02, mu=358.522, |x|=0.0533858, |J|=29530.4\n", - " --- Outer Iter 13: norm_f = 916.019, mu=702.794, |x|=0.0535076, |J|=29530\n", - " --- Outer Iter 14: norm_f = 916.018, mu=1006.26, |x|=0.0536529, |J|=29529\n", - " --- Outer Iter 15: norm_f = 916.014, mu=1006.21, |x|=0.0537709, |J|=29528.5\n", - " --- Outer Iter 16: norm_f = 916.007, mu=853.296, |x|=0.0538329, |J|=29528.9\n", - " --- Outer Iter 17: norm_f = 916.004, mu=1701.12, |x|=0.0539266, |J|=29527.8\n", - " --- Outer Iter 18: norm_f = 915.999, mu=1701.12, |x|=0.054028, |J|=29526.6\n", - " --- Outer Iter 19: norm_f = 915.993, mu=1705.36, |x|=0.0541384, |J|=29525.1\n", - " --- Outer Iter 20: norm_f = 915.986, mu=1712.58, |x|=0.0542542, |J|=29523.6\n", - " --- Outer Iter 21: norm_f = 915.977, mu=1714.21, |x|=0.0543715, |J|=29522\n", - " --- Outer Iter 22: norm_f = 915.967, mu=1714.21, |x|=0.0544866, |J|=29520.4\n", - " --- Outer Iter 23: norm_f = 915.957, mu=1712.87, |x|=0.0545963, |J|=29518.9\n", - " --- Outer Iter 24: norm_f = 915.947, mu=1702.15, |x|=0.0546984, |J|=29517.5\n", - " --- Outer Iter 25: norm_f = 915.939, mu=1674.29, |x|=0.0547924, |J|=29516.2\n", - " --- Outer Iter 26: norm_f = 915.933, mu=1627.91, |x|=0.0548788, |J|=29515\n", - " --- Outer Iter 27: norm_f = 915.928, mu=1564.91, |x|=0.0549585, |J|=29513.9\n", - " --- Outer Iter 28: norm_f = 915.924, mu=1489.15, |x|=0.0550322, |J|=29512.9\n", - " --- Outer Iter 29: norm_f = 915.92, mu=1405.14, |x|=0.0551008, |J|=29511.9\n", - " --- Outer Iter 30: norm_f = 915.918, mu=1317.04, |x|=0.0551647, |J|=29511\n", - " --- Outer Iter 31: norm_f = 915.915, mu=1228.17, |x|=0.0552246, |J|=29510.2\n", - " --- Outer Iter 32: norm_f = 915.913, mu=1140.9, |x|=0.0552808, |J|=29509.5\n", - " --- Outer Iter 33: norm_f = 915.912, mu=1056.82, |x|=0.0553335, |J|=29508.8\n", - " --- Outer Iter 34: norm_f = 915.911, mu=976.884, |x|=0.0553832, |J|=29508.2\n", - " --- Outer Iter 35: norm_f = 915.91, mu=901.637, |x|=0.0554299, |J|=29507.6\n", - " --- Outer Iter 36: norm_f = 915.909, mu=831.234, |x|=0.0554739, |J|=29507.1\n", - " --- Outer Iter 37: norm_f = 915.908, mu=765.757, |x|=0.0555153, |J|=29506.6\n", - " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", - " Sum of Chi^2 = 915.908 (952 data params - 60 (approx) model params = expected mean of 892; p-value = 0.282035)\n", - " Completed in 1.6s\n", - " Iteration 7 took 1.6s\n", - " \n", - " Last iteration:\n", - " --- dlogl GST ---\n", - " --- Outer Iter 0: norm_f = 472.363, mu=1, |x|=0.0555153, |J|=20866.1\n", - " --- Outer Iter 1: norm_f = 468.633, mu=26136.5, |x|=0.0539846, |J|=20902.6\n", - " --- Outer Iter 2: norm_f = 467.799, mu=8712.18, |x|=0.0529566, |J|=20895.5\n", - " --- Outer Iter 3: norm_f = 467.539, mu=2904.06, |x|=0.052252, |J|=20890.8\n", - " --- Outer Iter 4: norm_f = 467.506, mu=968.02, |x|=0.051971, |J|=20889.3\n", - " --- Outer Iter 5: norm_f = 467.504, mu=322.673, |x|=0.0519071, |J|=20888.8\n", - " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", - " 2*Delta(log(L)) = 935.008 (952 data params - 60 (approx) model params = expected mean of 892; p-value = 0.154326)\n", - " Completed in 0.3s\n", - " Final optimization took 0.3s\n", - " \n", - " -- Performing 'stdgaugeopt' gauge optimization on CPTPLND estimate --\n", - "\n", - "--- normalized tvd GST ---\n", - " --- Outer Iter 0: norm_f = 16.3355, mu=1, |x|=0.0519071, |J|=3533.75\n", - " --- Outer Iter 1: norm_f = 15.8436, mu=882.058, |x|=0.0516419, |J|=4309.22\n", - " --- Outer Iter 2: norm_f = 15.6493, mu=294.019, |x|=0.0513698, |J|=5816.59\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/rjmurr/Documents/pygsti-tvd/pygsti/pygsti/objectivefns/objectivefns.py:4055: UserWarning: This derivative is discontinuous and does not return a full subgradient.\n", - " _warnings.warn('This derivative is discontinuous and does not return a full subgradient.')\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " --- Outer Iter 3: norm_f = 15.5993, mu=98.0064, |x|=0.0515442, |J|=8849.06\n", - " --- Outer Iter 4: norm_f = 15.4789, mu=32.6688, |x|=0.0511876, |J|=10740.7\n", - " --- Outer Iter 5: norm_f = 15.4365, mu=10.8896, |x|=0.0508289, |J|=7197.58\n", - " --- Outer Iter 6: norm_f = 15.409, mu=7.25974, |x|=0.050839, |J|=18949.8\n", - " --- Outer Iter 7: norm_f = 15.3957, mu=7433.97, |x|=0.0505789, |J|=13428.1\n", - " --- Outer Iter 8: norm_f = 15.3693, mu=2477.99, |x|=0.0505844, |J|=13077.8\n", - " --- Outer Iter 9: norm_f = 15.3649, mu=47577.4, |x|=0.0505742, |J|=17215.9\n", - " --- Outer Iter 10: norm_f = 15.3634, mu=114186, |x|=0.0505784, |J|=81869.3\n", - " --- Outer Iter 11: norm_f = 15.3587, mu=114154, |x|=0.0505777, |J|=70532\n", - " --- Outer Iter 12: norm_f = 15.3539, mu=273971, |x|=0.0505777, |J|=107728\n", - " --- Outer Iter 13: norm_f = 15.3525, mu=164382, |x|=0.050581, |J|=83229.6\n", - " --- Outer Iter 14: norm_f = 15.3507, mu=394518, |x|=0.05058, |J|=71635.8\n", - " --- Outer Iter 15: norm_f = 15.3489, mu=236711, |x|=0.0505776, |J|=64967.4\n", - " --- Outer Iter 16: norm_f = 15.3467, mu=568106, |x|=0.0505762, |J|=55143.7\n", - " --- Outer Iter 17: norm_f = 15.3444, mu=1.36345e+06, |x|=0.0505755, |J|=93355\n", - " --- Outer Iter 18: norm_f = 15.3434, mu=409036, |x|=0.0505737, |J|=113623\n", - " --- Outer Iter 19: norm_f = 15.3431, mu=7.85349e+06, |x|=0.0505735, |J|=234545\n", - " --- Outer Iter 20: norm_f = 15.3426, mu=2.35605e+06, |x|=0.0505729, |J|=191090\n", - " --- Outer Iter 21: norm_f = 15.3426, mu=4.52361e+07, |x|=0.0505729, |J|=288643\n", - " --- Outer Iter 22: norm_f = 15.3426, mu=1.35708e+07, |x|=0.0505728, |J|=224014\n", - " --- Outer Iter 23: norm_f = 15.3426, mu=3.257e+07, |x|=0.0505728, |J|=321322\n", - " --- Outer Iter 24: norm_f = 15.3426, mu=9.771e+06, |x|=0.0505728, |J|=237417\n", - " --- Outer Iter 25: norm_f = 15.3426, mu=2.34504e+07, |x|=0.0505727, |J|=690149\n", - " --- Outer Iter 26: norm_f = 15.3425, mu=7.03512e+06, |x|=0.0505726, |J|=321702\n", - " --- Outer Iter 27: norm_f = 15.3424, mu=1.35074e+08, |x|=0.0505726, |J|=457133\n", - " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", - "Total Variational Distance (TVD), normalized by circuit depth = -1 (952 data params - 60 (approx) model params = expected mean of 892; p-value = 1)\n", - "Completed in 2.8s\n", - "\n", - "--- tvd GST ---\n", - " --- Outer Iter 0: norm_f = 8.78668, mu=1, |x|=0.0519071, |J|=3533.75\n", - " --- Outer Iter 1: norm_f = 8.71865, mu=882.058, |x|=0.0517526, |J|=4843.52\n", - " --- Outer Iter 2: norm_f = 8.70297, mu=294.019, |x|=0.0516355, |J|=5380\n", - " --- Outer Iter 3: norm_f = 8.69, mu=98.0064, |x|=0.0513986, |J|=7211.6\n", - " --- Outer Iter 4: norm_f = 8.68104, mu=32.6688, |x|=0.0510276, |J|=9671.75\n", - " --- Outer Iter 5: norm_f = 8.6741, mu=10.8896, |x|=0.0508288, |J|=13581.2\n", - " --- Outer Iter 6: norm_f = 8.67139, mu=21.7785, |x|=0.0507586, |J|=16988.3\n", - " --- Outer Iter 7: norm_f = 8.66352, mu=7.2595, |x|=0.0505225, |J|=25195.7\n", - " --- Outer Iter 8: norm_f = 8.66152, mu=2.41983, |x|=0.0504138, |J|=26204.5\n", - " --- Outer Iter 9: norm_f = 8.66128, mu=5.28878, |x|=0.0505487, |J|=45952.7\n", - " --- Outer Iter 10: norm_f = 8.66046, mu=5.10978, |x|=0.0506781, |J|=77666.6\n", - " --- Outer Iter 11: norm_f = 8.65947, mu=1.70326, |x|=0.0507142, |J|=93340.2\n", - " --- Outer Iter 12: norm_f = 8.65902, mu=1.17013, |x|=0.0507437, |J|=176298\n", - " --- Outer Iter 13: norm_f = 8.65873, mu=0.77144, |x|=0.050751, |J|=290538\n", - " --- Outer Iter 14: norm_f = 8.65847, mu=0.43221, |x|=0.0507501, |J|=161232\n", - " --- Outer Iter 15: norm_f = 8.65827, mu=0.14407, |x|=0.0507653, |J|=168779\n", - " --- Outer Iter 16: norm_f = 8.65809, mu=0.0480233, |x|=0.0507796, |J|=147152\n", - " --- Outer Iter 17: norm_f = 8.65802, mu=0.0404085, |x|=0.0508331, |J|=166650\n", - " --- Outer Iter 18: norm_f = 8.65795, mu=0.0382692, |x|=0.0509005, |J|=169199\n", - " --- Outer Iter 19: norm_f = 8.6579, mu=0.0324028, |x|=0.0509892, |J|=177459\n", - " --- Outer Iter 20: norm_f = 8.65783, mu=0.0281327, |x|=0.0510943, |J|=203554\n", - " --- Outer Iter 21: norm_f = 8.65778, mu=0.0249417, |x|=0.0512223, |J|=198388\n", - " --- Outer Iter 22: norm_f = 8.65771, mu=0.0193391, |x|=0.0513644, |J|=141927\n", - " --- Outer Iter 23: norm_f = 8.65766, mu=0.0185955, |x|=0.0515618, |J|=208600\n", - " --- Outer Iter 24: norm_f = 8.65758, mu=0.0148754, |x|=0.0517749, |J|=126175\n", - " --- Outer Iter 25: norm_f = 8.65755, mu=0.0148696, |x|=0.0520519, |J|=115782\n", - " --- Outer Iter 26: norm_f = 8.65747, mu=0.0129986, |x|=0.0523393, |J|=122712\n", - " --- Outer Iter 27: norm_f = 8.65746, mu=0.0137916, |x|=0.0526814, |J|=103789\n", - " --- Outer Iter 28: norm_f = 8.6574, mu=0.0136512, |x|=0.0530078, |J|=114060\n", - " --- Outer Iter 29: norm_f = 8.65739, mu=0.0168338, |x|=0.0533236, |J|=134665\n", - " --- Outer Iter 30: norm_f = 8.65736, mu=0.016854, |x|=0.0535883, |J|=167954\n", - " --- Outer Iter 31: norm_f = 8.65735, mu=0.0252614, |x|=0.0538599, |J|=162223\n", - " --- Outer Iter 32: norm_f = 8.65733, mu=0.025474, |x|=0.0540612, |J|=248203\n", - " --- Outer Iter 33: norm_f = 8.65732, mu=0.0256207, |x|=0.0542709, |J|=217091\n", - " --- Outer Iter 34: norm_f = 8.65731, mu=0.028703, |x|=0.054487, |J|=228723\n", - " --- Outer Iter 35: norm_f = 8.6573, mu=0.0300427, |x|=0.0546843, |J|=246173\n", - " --- Outer Iter 36: norm_f = 8.65729, mu=0.0324868, |x|=0.0548768, |J|=246665\n", - " --- Outer Iter 37: norm_f = 8.65729, mu=0.0352858, |x|=0.0550583, |J|=266147\n", - " --- Outer Iter 38: norm_f = 8.65728, mu=0.0470822, |x|=0.0552287, |J|=277589\n", - " --- Outer Iter 39: norm_f = 8.65728, mu=0.0512434, |x|=0.0553594, |J|=376186\n", - " --- Outer Iter 40: norm_f = 8.65727, mu=0.0548577, |x|=0.0554804, |J|=393093\n", - " --- Outer Iter 41: norm_f = 8.65727, mu=0.0855496, |x|=0.0555958, |J|=425220\n", - " --- Outer Iter 42: norm_f = 8.65727, mu=0.0997164, |x|=0.05567, |J|=670533\n", - " --- Outer Iter 43: norm_f = 8.65726, mu=0.112366, |x|=0.0557348, |J|=735990\n", - " --- Outer Iter 44: norm_f = 8.65726, mu=0.143338, |x|=0.0557923, |J|=846488\n", - " --- Outer Iter 45: norm_f = 8.65726, mu=0.177549, |x|=0.0558381, |J|=1.07287e+06\n", - " --- Outer Iter 46: norm_f = 8.65726, mu=0.0532647, |x|=0.055875, |J|=1.28598e+06\n", - " --- Outer Iter 47: norm_f = 8.65726, mu=0.157908, |x|=0.0559378, |J|=783920\n", - " --- Outer Iter 48: norm_f = 8.65725, mu=0.223373, |x|=0.0559802, |J|=1.21034e+06\n", - " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", - "Total Variational Distance (TVD) = -1 (952 data params - 60 (approx) model params = expected mean of 892; p-value = 1)\n", - "Completed in 2.2s\n", - "\n", - "--- Lp^p GST ---\n", - " --- Outer Iter 0: norm_f = 4.54032e-13, mu=1, |x|=0.0519071, |J|=0.000774409\n", - " --- Outer Iter 1: norm_f = 4.22989e-13, mu=1.76429e-07, |x|=0.0519089, |J|=0.00139413\n", - " --- Outer Iter 2: norm_f = 4.08546e-13, mu=1.19564e-07, |x|=0.05191, |J|=0.00114735\n", - " --- Outer Iter 3: norm_f = 4.01246e-13, mu=4.66244e-08, |x|=0.0519112, |J|=0.00115444\n", - " --- Outer Iter 4: norm_f = 3.94983e-13, mu=3.80545e-08, |x|=0.0519141, |J|=0.00123205\n", - " --- Outer Iter 5: norm_f = 3.90037e-13, mu=1.40786e-08, |x|=0.0519143, |J|=0.00118648\n", - " --- Outer Iter 6: norm_f = 3.81598e-13, mu=5.1136e-09, |x|=0.0519138, |J|=0.00121366\n", - " --- Outer Iter 7: norm_f = 3.70547e-13, mu=2.40967e-09, |x|=0.0518977, |J|=0.00117361\n", - " --- Outer Iter 8: norm_f = 3.60039e-13, mu=9.50923e-10, |x|=0.0518498, |J|=0.00116702\n", - " --- Outer Iter 9: norm_f = 3.4658e-13, mu=3.73005e-10, |x|=0.0518059, |J|=0.00111234\n", - " --- Outer Iter 10: norm_f = 3.29304e-13, mu=1.54748e-10, |x|=0.0520676, |J|=0.00111376\n", - " --- Outer Iter 11: norm_f = 3.22645e-13, mu=1.58458e-10, |x|=0.0535845, |J|=0.00139206\n", - " --- Outer Iter 12: norm_f = 3.12353e-13, mu=1.19186e-10, |x|=0.0543886, |J|=0.00105821\n", - " --- Outer Iter 13: norm_f = 3.1022e-13, mu=8.55255e-11, |x|=0.0555549, |J|=0.00107934\n", - " --- Outer Iter 14: norm_f = 3.09153e-13, mu=5.58402e-11, |x|=0.0563069, |J|=0.00102567\n", - " --- Outer Iter 15: norm_f = 3.08472e-13, mu=3.52505e-11, |x|=0.0565967, |J|=0.00104656\n", - " --- Outer Iter 16: norm_f = 3.08031e-13, mu=2.48036e-11, |x|=0.0566524, |J|=0.00102207\n", - " --- Outer Iter 17: norm_f = 3.07789e-13, mu=1.71581e-11, |x|=0.056319, |J|=0.00104161\n", - " --- Outer Iter 18: norm_f = 3.07604e-13, mu=1.01037e-11, |x|=0.0558281, |J|=0.00102815\n", - " --- Outer Iter 19: norm_f = 3.07431e-13, mu=5.7762e-12, |x|=0.0552678, |J|=0.00104054\n", - " --- Outer Iter 20: norm_f = 3.07246e-13, mu=2.10893e-12, |x|=0.0548908, |J|=0.00103169\n", - " --- Outer Iter 21: norm_f = 3.07089e-13, mu=4.21163e-12, |x|=0.0547622, |J|=0.00103832\n", - " --- Outer Iter 22: norm_f = 3.06708e-13, mu=3.69084e-12, |x|=0.0547342, |J|=0.00103226\n", - " --- Outer Iter 23: norm_f = 3.06586e-13, mu=3.24647e-12, |x|=0.0543116, |J|=0.00103641\n", - " --- Outer Iter 24: norm_f = 3.06514e-13, mu=3.07967e-12, |x|=0.0537928, |J|=0.00103397\n", - " --- Outer Iter 25: norm_f = 3.06474e-13, mu=2.97911e-12, |x|=0.0533374, |J|=0.00103714\n", - " --- Outer Iter 26: norm_f = 3.06448e-13, mu=2.89797e-12, |x|=0.0529933, |J|=0.00103571\n", - " --- Outer Iter 27: norm_f = 3.0643e-13, mu=2.82251e-12, |x|=0.0527225, |J|=0.00103758\n", - " --- Outer Iter 28: norm_f = 3.06419e-13, mu=2.74997e-12, |x|=0.0525218, |J|=0.00103664\n", - " --- Outer Iter 29: norm_f = 3.06411e-13, mu=2.6733e-12, |x|=0.0523582, |J|=0.0010377\n", - " --- Outer Iter 30: norm_f = 3.06406e-13, mu=2.59234e-12, |x|=0.0522307, |J|=0.00103711\n", - " --- Outer Iter 31: norm_f = 3.06403e-13, mu=2.49734e-12, |x|=0.0521202, |J|=0.00103771\n", - " --- Outer Iter 32: norm_f = 3.064e-13, mu=2.38453e-12, |x|=0.0520269, |J|=0.00103737\n", - " --- Outer Iter 33: norm_f = 3.06399e-13, mu=2.23697e-12, |x|=0.0519411, |J|=0.0010377\n", - " --- Outer Iter 34: norm_f = 3.06397e-13, mu=2.04443e-12, |x|=0.0518621, |J|=0.00103751\n", - " --- Outer Iter 35: norm_f = 3.06396e-13, mu=1.78318e-12, |x|=0.0517846, |J|=0.00103769\n", - " --- Outer Iter 36: norm_f = 3.06395e-13, mu=1.45314e-12, |x|=0.0517052, |J|=0.00103759\n", - " --- Outer Iter 37: norm_f = 3.06395e-13, mu=1.06591e-12, |x|=0.0516184, |J|=0.00103769\n", - " --- Outer Iter 38: norm_f = 3.06394e-13, mu=6.97481e-13, |x|=0.0515132, |J|=0.00103766\n", - " --- Outer Iter 39: norm_f = 3.06393e-13, mu=4.18659e-13, |x|=0.051379, |J|=0.0010377\n", - " --- Outer Iter 40: norm_f = 3.06392e-13, mu=2.69945e-13, |x|=0.0512023, |J|=0.00103773\n", - " --- Outer Iter 41: norm_f = 3.06391e-13, mu=2.15137e-13, |x|=0.0510285, |J|=0.00103772\n", - " --- Outer Iter 42: norm_f = 3.0639e-13, mu=2.13101e-13, |x|=0.0509237, |J|=0.00103783\n", - " --- Outer Iter 43: norm_f = 3.0639e-13, mu=7.19687e-13, |x|=0.0509016, |J|=0.00103765\n", - " --- Outer Iter 44: norm_f = 3.0639e-13, mu=9.95405e-13, |x|=0.0508962, |J|=0.00103785\n", - " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", - "L_10 norm to the power 10. = -1 (952 data params - 60 (approx) model params = expected mean of 892; p-value = 1)\n", - "Completed in 1.9s\n" - ] - } - ], - "source": [ - "fit_mode = 'CPTPLND'\n", - "\n", - "Lpnorm_spec = ('Lp^p', 10)\n", - "verb = 4\n", - "\n", - "results = run_gst(ds, fids, germs, target, ['logl', 'normalized tvd', 'tvd', Lpnorm_spec], verbosity=verb, mode=fit_mode)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# GST model, fit using logl as the final iteration\n", - "logl_est = results.estimates['logl']\n", - "final_logl_model = logl_est.models['stdgaugeopt']\n", - "# GST model, fit using Lp^p for the final iteration\n", - "Lp_est_name = str(Lpnorm_spec)\n", - "Lp_est = results.estimates[Lp_est_name]\n", - "final_LpP_model = Lp_est.models['stdgaugeopt']\n", - "# data generating model.\n", - "results.estimates['datagen'] = logl_est.copy()\n", - "to_replace = [k for k in results.estimates['datagen'].models.keys() if k != 'target' ]\n", - "m_datagen.convert_members_inplace(fit_mode)\n", - "for k in to_replace:\n", - " results.estimates['datagen'].models[k] = m_datagen\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "repdata = ds.repData\n", - "cirindex = ds.cirIndex\n", - "empiri_probs = []\n", - "mprobs_logl = []\n", - "mprobs_LpP = []\n", - "for circ, indices in cirindex.items():\n", - " counts = repdata[indices]\n", - " empirical_probs = counts / np.sum(counts)\n", - " empiri_probs.append(empirical_probs)\n", - " model_probs_LpP = np.array(list(final_LpP_model.probabilities(circ).values()))\n", - " mprobs_LpP.append(model_probs_LpP)\n", - " model_probs_logl = np.array(list(final_logl_model.probabilities(circ).values()))\n", - " mprobs_logl.append(model_probs_logl)\n", - "empiri_probs = np.array(empiri_probs)\n", - "mprobs_logl = np.array(mprobs_logl)\n", - "mprobs_LpP = np.array(mprobs_LpP)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "bins = 100\n", - "x = np.array([empiri_probs.ravel(), mprobs_logl.ravel(), mprobs_LpP.ravel()]).T\n", - "bins = 50\n", - "x2 = x[:,1:3].copy()\n", - "x2[:,0] = np.abs(x2[:,0] - x[:,0])\n", - "x2[:,1] = np.abs(x2[:,1] - x[:,0])\n", - "plt.hist(x2, bins=bins)\n", - "plt.legend(['logl', Lp_est_name ])\n", - "plt.yscale('log')\n", - "plt.title('Histogram of abs(model_probs - empirical_probs)\\nwhere model_probs uses one of two final objectives')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " LogL(m) nTVD(m) TVD(m) L_{10}(m) N_{\\sigma}\n", - "m_logl 4.675041e+02 16.335460 8.786676 0.058305 0.326392\n", - "m_{ntvd} 5.000476e+02 15.342389 8.720995 0.058255 1.842914\n", - "m_tvd 4.842252e+02 15.826993 8.657254 0.058421 1.105592\n", - "m_{L_{10}^10} 6.381221e+02 17.786360 9.578566 0.056057 8.277176\n", - "m_datagen 1.833490e+06 835.190192 467.173129 126.554588 0.878730\n" - ] - } - ], - "source": [ - "Nsigs = []\n", - "from pygsti.report.plothelpers import rated_n_sigma\n", - "circuitlist = list(ds.cirIndex.keys())\n", - "pvecs = []\n", - "objectives = []\n", - "for estname, est in results.estimates.items():\n", - " model = est.models['stdgaugeopt']\n", - " Nsig, _ = rated_n_sigma(ds, model, circuitlist, 'logl')\n", - " Nsigs.append(Nsig)\n", - " objective = est.final_objective_fn()\n", - " objectives.append(objective)\n", - " pvecs.append(est.models['final iteration estimate'].to_vector())\n", - "\n", - "\n", - "objvals = np.zeros((len(pvecs),len(objectives)-1))\n", - "for i,pvec in enumerate(pvecs):\n", - " for j,objective in enumerate(objectives[:-1]):\n", - " val = objective.fn(pvec, stateless=True)\n", - " if val < 1e-8:\n", - " val = val ** 0.1\n", - " objvals[i,j] = val\n", - "objvals = np.concatenate((objvals, np.array(Nsigs).reshape(-1,1)), axis=1)\n", - "\n", - "df = pd.DataFrame(\n", - " objvals,\n", - " index=['m_logl', 'm_{ntvd}', 'm_tvd', 'm_{L_{10}^10}', 'm_datagen'],\n", - " columns=['LogL(m)', 'nTVD(m)', 'TVD(m)', 'L_{10}(m)', 'N_{\\sigma}']\n", - ")\n", - "\n", - "print(df)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "rogst", - "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": "3.11.11" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From b5ed48e550ffe30330121e3ca03de68bb63665f3 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Tue, 13 May 2025 15:01:34 -0700 Subject: [PATCH 48/71] remove uninformative warning --- pygsti/objectivefns/objectivefns.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index 7bf68cf5d..9d5be156c 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -4059,7 +4059,6 @@ def dterms(self, probs, counts, total_counts, freqs, intermediates=None): numpy.ndarray A 1D array of length equal to that of each array argument. """ - _warnings.warn('This derivative is discontinuous and does not return a full subgradient.') t = probs - freqs d = 0.5*_np.ones_like(t) d[t < 0] *= -1 From e77f99c433197da90ac6a43beb6209d96cc4dffa Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Thu, 15 May 2025 15:33:41 -0700 Subject: [PATCH 49/71] remove the MyClass.builder(...) method for MyClass subclasses of MDCObjectiveFunction. In-line equivalent code of the form builder = ObjectiveFunctionBuilder(MyClass) --- pygsti/objectivefns/objectivefns.py | 82 +++---------------- pygsti/protocols/estimate.py | 2 +- pygsti/protocols/gst.py | 2 +- .../mpi_2D_scaling/run_me_with_mpirun.py | 12 ++- test/test_packages/drivers/test_timedep.py | 8 +- test/test_packages/objects/test_hessian.py | 2 +- test/unit/algorithms/test_core.py | 24 +++--- test/unit/objects/fixtures.py | 6 +- test/unit/objects/test_objectivefns.py | 6 -- test/unit/protocols/test_gst.py | 4 +- test/unit/tools/fixtures.py | 6 +- 11 files changed, 50 insertions(+), 104 deletions(-) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index 9d5be156c..52eb0215b 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -178,7 +178,7 @@ def cast(obj): # This was a classmethod, but I made it static because this class has no derived classes. @staticmethod - def create_from(objective='logl', freq_weighted_chi2=False): + def create_from(objective='logl', freq_weighted_chi2=False, penalty_callable=None): """ Creates common :class:`ObjectiveFunctionBuilder` from a few arguments. @@ -193,37 +193,23 @@ def create_from(objective='logl', freq_weighted_chi2=False): Returns ------- ObjectiveFunctionBuilder - - Notes - ----- - This function's implementation calls relies on various "builder" classmethods of other classes. - There is a default implementation of `.builder` that's triggered in most codepaths. That - default is to just return - - ObjectiveFunctionBuilder(cls, name, description, regularization, penalties, **kwargs). - - In these cases, the kwargs that we pass to `.builder` functions get seen as **kwargs - for a call to the ObjectiveFunctionBuilder constructor. Those kwargs are stored in the - `.additional_args` member of the returned ObjectiveFunctionBuilder object. That member - is passed as **kwargs to the constructor for the input `cls` when we call `.build()` on - the ObjectiveFunctionBuilder instance. """ if objective == "chi2": if freq_weighted_chi2: - builder = FreqWeightedChi2Function.builder( + builder = ObjectiveFunctionBuilder(FreqWeightedChi2Function, name='fwchi2', description="Freq-weighted sum of Chi^2", regularization={'min_freq_clip_for_weighting': 1e-4} ) else: - builder = Chi2Function.builder( + builder = ObjectiveFunctionBuilder(Chi2Function, name='chi2', description="Sum of Chi^2", regularization={'min_prob_clip_for_weighting': 1e-4} ) elif objective == "logl": - builder = PoissonPicDeltaLogLFunction.builder( + builder = ObjectiveFunctionBuilder(PoissonPicDeltaLogLFunction, name='dlogl', description="2*Delta(log(L))", regularization={'min_prob_clip': 1e-4, @@ -239,15 +225,19 @@ def create_from(objective='logl', freq_weighted_chi2=False): assert objective == 'normalized tvd' else: assert objective == 'tvd' - builder = TVDFunction.builder(name=objective, description=descr) + builder = ObjectiveFunctionBuilder(TVDFunction, name=objective, description=descr) elif isinstance(objective, tuple) and objective[0] == 'Lp^p': power = objective[1] - builder = LpNormToPowerP.builder(name='Lp^p', description=f"L_{power} norm to the power {power}.", power=objective[1]) + builder = ObjectiveFunctionBuilder(LpNormToPowerP, + name='Lp^p', + description=f"L_{power} norm to the power {power}.", + power=objective[1] + ) else: raise ValueError("Invalid objective: %s" % objective) - assert(isinstance(builder, ObjectiveFunctionBuilder)), "This function should always return an ObjectiveFunctionBuilder!" + return builder def __init__(self, cls_to_build, name=None, description=None, regularization=None, penalties=None, **kwargs): @@ -4259,31 +4249,6 @@ class TimeIndependentMDCObjectiveFunction(MDCObjectiveFunction): %s """ - @classmethod - def builder(cls, name=None, description=None, regularization=None, penalties=None, **kwargs): - """ - Create an :class:`ObjectiveFunctionBuilder` that builds an objective function of this type. - - Parameters - ---------- - name : str, optional - A name for the built objective function (can be anything). - - description : str, optional - A description for the built objective function (can be anything) - - regularization : dict, optional - Regularization values. - - penalties : dict, optional - Penalty values. - - Returns - ------- - ObjectiveFunctionBuilder - """ - return ObjectiveFunctionBuilder(cls, name, description, regularization, penalties, **kwargs) - @classmethod def _create_mdc_store(cls, model, dataset, circuits, resource_alloc, method_names=('fn',), array_types=(), verbosity=0): @@ -5173,31 +5138,6 @@ class TimeDependentMDCObjectiveFunction(MDCObjectiveFunction): Level of detail to print to stdout. """ - @classmethod - def builder(cls, name=None, description=None, regularization=None, penalties=None, **kwargs): - """ - Create an :class:`ObjectiveFunctionBuilder` that builds an objective function of this type. - - Parameters - ---------- - name : str, optional - A name for the built objective function (can be anything). - - description : str, optional - A description for the built objective function (can be anything) - - regularization : dict, optional - Regularization values. - - penalties : dict, optional - Penalty values. - - Returns - ------- - ObjectiveFunctionBuilder - """ - return ObjectiveFunctionBuilder(cls, name, description, regularization, penalties, **kwargs) - #This objective function can handle time-dependent circuits - that is, circuits are treated as # potentially time-dependent and mdl as well. For now, we don't allow any regularization or penalization # in this case. diff --git a/pygsti/protocols/estimate.py b/pygsti/protocols/estimate.py index 897549ae1..cfb459966 100644 --- a/pygsti/protocols/estimate.py +++ b/pygsti/protocols/estimate.py @@ -163,7 +163,7 @@ def __init__(self, parent, models=None, parameters=None, extra_parameters=None): self.profiler = parameters.get('profiler', None) self._final_mdc_store = parameters.get('final_mdc_store', None) self._final_objfn_cache = parameters.get('final_objfn_cache', None) - self.final_objfn_builder = parameters.get('final_objfn_builder', _objfns.PoissonPicDeltaLogLFunction.builder()) + self.final_objfn_builder = parameters.get('final_objfn_builder', _objfns.ObjectiveFunctionBuilder(_objfns.PoissonPicDeltaLogLFunction)) self._final_objfn = parameters.get('final_objfn', None) self.extra_parameters = extra_parameters if (extra_parameters is not None) else {} diff --git a/pygsti/protocols/gst.py b/pygsti/protocols/gst.py index 6c4bc392b..58033d682 100644 --- a/pygsti/protocols/gst.py +++ b/pygsti/protocols/gst.py @@ -1654,7 +1654,7 @@ def run(self, data, memlimit=None, comm=None): parameters['protocol'] = self # Estimates can hold sub-Protocols <=> sub-results parameters['profiler'] = profiler parameters['final_mdc_store'] = final_store - parameters['final_objfn_builder'] = _objfns.PoissonPicDeltaLogLFunction.builder() + parameters['final_objfn_builder'] = _objfns.ObjectiveFunctionBuilder(_objfns.PoissonPicDeltaLogLFunction) # just set final objective function as default logl objective (for ease of later comparison) ret = ModelEstimateResults(data, self) diff --git a/test/performance/mpi_2D_scaling/run_me_with_mpirun.py b/test/performance/mpi_2D_scaling/run_me_with_mpirun.py index b4b966c52..9f1514550 100644 --- a/test/performance/mpi_2D_scaling/run_me_with_mpirun.py +++ b/test/performance/mpi_2D_scaling/run_me_with_mpirun.py @@ -31,10 +31,14 @@ ds = ds_ref MINCLIP = 1e-4 -chi2_builder = pygsti.objectivefns.Chi2Function.builder( - 'chi2', regularization={'min_prob_clip_for_weighting': MINCLIP}, penalties={'cptp_penalty_factor': 0.0}) -mle_builder = pygsti.objectivefns.PoissonPicDeltaLogLFunction.builder( - 'logl', regularization={'min_prob_clip': MINCLIP, 'radius': MINCLIP}) +chi2_builder = pygsti.objectivefns.ObjectiveFunctionBuilder( + pygsti.objectivefns.Chi2Function, + 'chi2', regularization={'min_prob_clip_for_weighting': MINCLIP}, penalties={'cptp_penalty_factor': 0.0} +) +mle_builder = pygsti.objectivefns.ObjectiveFunctionBuilder( + pygsti.objectivefns.PoissonPicDeltaLogLFunction, + 'logl', regularization={'min_prob_clip': MINCLIP, 'radius': MINCLIP} +) iteration_builders = [chi2_builder]; final_builders = [mle_builder] builders = pygsti.protocols.GSTObjFnBuilders(iteration_builders, final_builders) diff --git a/test/test_packages/drivers/test_timedep.py b/test/test_packages/drivers/test_timedep.py index 7e7830cb9..8c726a6b1 100644 --- a/test/test_packages/drivers/test_timedep.py +++ b/test/test_packages/drivers/test_timedep.py @@ -1,4 +1,6 @@ import logging + +import pygsti.objectivefns mpl_logger = logging.getLogger('matplotlib') mpl_logger.setLevel(logging.WARNING) @@ -109,7 +111,8 @@ def test_time_dependent_gst_staticdata(self): target_model.sim = pygsti.forwardsims.MapForwardSimulator(max_cache_size=0) # No caching allowed for time-dependent calcs self.assertEqual(ds.degrees_of_freedom(aggregate_times=False), 57) - builders = pygsti.protocols.GSTObjFnBuilders([pygsti.objectivefns.TimeDependentPoissonPicLogLFunction.builder()], []) + builder = pygsti.objectivefns.ObjectiveFunctionBuilder(pygsti.objectivefns.TimeDependentPoissonPicLogLFunction) + builders = pygsti.protocols.GSTObjFnBuilders([builder], []) gst = pygsti.protocols.GateSetTomography(target_model, gaugeopt_suite=None, objfn_builders=builders, optimizer={'maxiter':2,'tol': 1e-4}) @@ -162,7 +165,8 @@ def test_time_dependent_gst(self): target_model.operations['Gi',0] = MyTimeDependentIdle(0) # start assuming no time dependent decay target_model.sim = pygsti.forwardsims.MapForwardSimulator(max_cache_size=0) # No caching allowed for time-dependent calcs - builders = pygsti.protocols.GSTObjFnBuilders([pygsti.objectivefns.TimeDependentPoissonPicLogLFunction.builder()], []) + builder = pygsti.objectivefns.ObjectiveFunctionBuilder(pygsti.objectivefns.TimeDependentPoissonPicLogLFunction) + builders = pygsti.protocols.GSTObjFnBuilders([builder], []) gst = pygsti.protocols.GateSetTomography(target_model, gaugeopt_suite=None, objfn_builders=builders, optimizer={'maxiter':10,'tol': 1e-4}) data = pygsti.protocols.ProtocolData(edesign, ds) diff --git a/test/test_packages/objects/test_hessian.py b/test/test_packages/objects/test_hessian.py index 2a1f3bcfc..67c793a1a 100644 --- a/test/test_packages/objects/test_hessian.py +++ b/test/test_packages/objects/test_hessian.py @@ -130,7 +130,7 @@ def test_confidenceRegion(self): res = proto.ModelEstimateResults(data, proto.StandardGST(modes="full TP")) #Add estimate for hessian-based CI -------------------------------------------------- - builder = pygsti.objectivefns.PoissonPicDeltaLogLFunction.builder() + builder = pygsti.objectivefns.ObjectiveFunctionBuilder(pygsti.objectivefns.PoissonPicDeltaLogLFunction) res.add_estimate( proto.estimate.Estimate.create_gst_estimate( res, smq1Q_XY.target_model(), smq1Q_XY.target_model(), diff --git a/test/unit/algorithms/test_core.py b/test/unit/algorithms/test_core.py index 737a5f7f8..af7c06765 100644 --- a/test/unit/algorithms/test_core.py +++ b/test/unit/algorithms/test_core.py @@ -6,7 +6,7 @@ from pygsti.baseobjs import Label from pygsti.circuits import Circuit, CircuitList from pygsti.objectivefns import Chi2Function, FreqWeightedChi2Function, \ - PoissonPicDeltaLogLFunction + PoissonPicDeltaLogLFunction, ObjectiveFunctionBuilder from . import fixtures from ..util import BaseCase @@ -117,7 +117,7 @@ def test_do_mc2gst(self): # TODO assert correctness def test_do_mc2gst_regularize_factor(self): - obj_builder = Chi2Function.builder( + obj_builder = ObjectiveFunctionBuilder(Chi2Function, name='chi2', description="Sum of chi^2", regularization={'min_prob_clip_for_weighting': 1e-4}, @@ -130,7 +130,7 @@ def test_do_mc2gst_regularize_factor(self): # TODO assert correctness def test_do_mc2gst_CPTP_penalty_factor(self): - obj_builder = Chi2Function.builder( + obj_builder = ObjectiveFunctionBuilder(Chi2Function, name='chi2', description="Sum of chi^2", regularization={'min_prob_clip_for_weighting': 1e-4}, @@ -143,7 +143,7 @@ def test_do_mc2gst_CPTP_penalty_factor(self): # TODO assert correctness def test_do_mc2gst_SPAM_penalty_factor(self): - obj_builder = Chi2Function.builder( + obj_builder = ObjectiveFunctionBuilder(Chi2Function, name='chi2', description="Sum of chi^2", regularization={'min_prob_clip_for_weighting': 1e-4}, @@ -156,7 +156,7 @@ def test_do_mc2gst_SPAM_penalty_factor(self): # TODO assert correctness def test_do_mc2gst_CPTP_SPAM_penalty_factor(self): - obj_builder = Chi2Function.builder( + obj_builder = ObjectiveFunctionBuilder(Chi2Function, name='chi2', description="Sum of chi^2", regularization={'min_prob_clip_for_weighting': 1e-4}, @@ -202,7 +202,7 @@ def test_do_iterative_mc2gst(self): # TODO assert correctness def test_do_iterative_mc2gst_regularize_factor(self): - obj_builder = Chi2Function.builder( + obj_builder = ObjectiveFunctionBuilder(Chi2Function, name='chi2', description="Sum of chi^2", regularization={'min_prob_clip_for_weighting': 1e-4}, @@ -218,7 +218,7 @@ def test_do_iterative_mc2gst_regularize_factor(self): # TODO assert correctness def test_do_iterative_mc2gst_use_freq_weighted_chi2(self): - obj_builder = FreqWeightedChi2Function.builder( + obj_builder = ObjectiveFunctionBuilder(FreqWeightedChi2Function, name='freq-weighted-chi2', description="Sum of chi^2", regularization={'min_freq_clip_for_weighting': 1e-4} @@ -269,7 +269,7 @@ def test_do_mlgst(self): # TODO assert correctness def test_do_mlgst_CPTP_penalty_factor(self): - obj_builder = PoissonPicDeltaLogLFunction.builder( + obj_builder = ObjectiveFunctionBuilder(PoissonPicDeltaLogLFunction, name='logl', description="2*DeltaLogL", regularization={'min_prob_clip': 1e-4}, @@ -283,7 +283,7 @@ def test_do_mlgst_CPTP_penalty_factor(self): # TODO assert correctness def test_do_mlgst_SPAM_penalty_factor(self): - obj_builder = PoissonPicDeltaLogLFunction.builder( + obj_builder = ObjectiveFunctionBuilder(PoissonPicDeltaLogLFunction, name='logl', description="2*DeltaLogL", regularization={'min_prob_clip': 1e-4}, @@ -302,7 +302,7 @@ def test_do_mlgst_CPTP_SPAM_penalty_factor(self): # FUTURE: see what we can do in custom LM about scaling large # jacobians... #self.skipTest("Ignore for now.") - obj_builder = PoissonPicDeltaLogLFunction.builder( + obj_builder = ObjectiveFunctionBuilder(PoissonPicDeltaLogLFunction, name='logl', description="2*DeltaLogL", regularization={'min_prob_clip': 1e-4}, @@ -356,7 +356,7 @@ def test_do_iterative_mlgst(self): # ) def test_do_iterative_mlgst_use_freq_weighted_chi2(self): - obj_builder = FreqWeightedChi2Function.builder( + obj_builder = ObjectiveFunctionBuilder(FreqWeightedChi2Function, name='freq-weighted-chi2', description="Sum of chi^2", regularization={'min_freq_clip_for_weighting': 1e-4} @@ -404,7 +404,7 @@ def test_do_mlgst_raises_on_out_of_memory(self): # XXX if this function needs explicit coverage, it should be public! def test_do_mlgst_base_forcefn_grad(self): forcefn_grad = np.ones((1, self.mdl_clgst.num_params), 'd') - obj_builder = PoissonPicDeltaLogLFunction.builder( + obj_builder = ObjectiveFunctionBuilder(PoissonPicDeltaLogLFunction, name='logl', description="2*DeltaLogL", regularization={'min_prob_clip': 1e-4}, diff --git a/test/unit/objects/fixtures.py b/test/unit/objects/fixtures.py index 8b11bf9fb..9dc2fea29 100644 --- a/test/unit/objects/fixtures.py +++ b/test/unit/objects/fixtures.py @@ -65,9 +65,11 @@ def lsgstStructs(self): @ns.memo def mdl_lsgst(self): - chi2_builder = pygsti.objectivefns.Chi2Function.builder( + from pygsti.objectivefns.objectivefns import ObjectiveFunctionBuilder, Chi2Function + chi2_builder = ObjectiveFunctionBuilder(Chi2Function, regularization={'min_prob_clip_for_weighting': 1e-6}, - penalties={'prob_clip_interval': (-1e6, 1e6)}) + penalties={'prob_clip_interval': (-1e6, 1e6)} + ) models, _, _ = pygsti.algorithms.core.run_iterative_gst( self.dataset, self.mdl_clgst, self.lsgstStrings, optimizer=None, diff --git a/test/unit/objects/test_objectivefns.py b/test/unit/objects/test_objectivefns.py index df5873705..d1e3650a1 100644 --- a/test/unit/objects/test_objectivefns.py +++ b/test/unit/objects/test_objectivefns.py @@ -270,12 +270,6 @@ def setUp(self): super().setUp() self.objfns = self.build_objfns() - def test_builder(self): - #All objective function should be of the same type - cls = self.objfns[0].__class__ - builder = cls.builder("test_name", "test_description") - self.assertTrue(isinstance(builder, _objfns.ObjectiveFunctionBuilder)) - def test_value(self): for objfn in self.objfns: terms = objfn.terms().copy() diff --git a/test/unit/protocols/test_gst.py b/test/unit/protocols/test_gst.py index 2b712f868..9365c61dd 100644 --- a/test/unit/protocols/test_gst.py +++ b/test/unit/protocols/test_gst.py @@ -2,7 +2,7 @@ from pygsti.forwardsims.mapforwardsim import MapForwardSimulator from pygsti.modelpacks import smq1Q_XYI from pygsti.modelpacks.legacy import std1Q_XYI, std2Q_XYICNOT -from pygsti.objectivefns.objectivefns import PoissonPicDeltaLogLFunction +from pygsti.objectivefns.objectivefns import PoissonPicDeltaLogLFunction, ObjectiveFunctionBuilder from pygsti.models.gaugegroup import TrivialGaugeGroup from pygsti.objectivefns import FreqWeightedChi2Function from pygsti.optimize.simplerlm import SimplerLMOptimizer @@ -64,7 +64,7 @@ def test_gaugeopt_suite_raises_on_bad_suite(self): GSTGaugeOptSuite("foobar").to_dictionary(model_1Q, verbosity=1) def test_add_badfit_estimates(self): - builder = PoissonPicDeltaLogLFunction.builder() + builder = ObjectiveFunctionBuilder(PoissonPicDeltaLogLFunction) opt = SimplerLMOptimizer() badfit_opts = gst.GSTBadFitOptions(threshold=-10, actions=("robust", "Robust", "robust+", "Robust+", "wildcard", "do nothing")) diff --git a/test/unit/tools/fixtures.py b/test/unit/tools/fixtures.py index 0d84edc48..95dd6492e 100644 --- a/test/unit/tools/fixtures.py +++ b/test/unit/tools/fixtures.py @@ -58,9 +58,11 @@ def lsgstStrings(self): @ns.memo def mdl_lsgst(self): - chi2_builder = pygsti.objectivefns.Chi2Function.builder( + from pygsti.objectivefns.objectivefns import ObjectiveFunctionBuilder, Chi2Function + chi2_builder = ObjectiveFunctionBuilder(Chi2Function, regularization={'min_prob_clip_for_weighting': 1e-6}, - penalties={'prob_clip_interval': (-1e6, 1e6)}) + penalties={'prob_clip_interval': (-1e6, 1e6)} + ) models, _, _ = pygsti.algorithms.core.run_iterative_gst( self.dataset, self.mdl_clgst, self.lsgstStrings, optimizer=None, From 41cc795213b9631d7dd1f1f494ae990087e4a57f Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Fri, 16 May 2025 11:23:18 -0700 Subject: [PATCH 50/71] remove unnecessary warning --- pygsti/tools/optools.py | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/pygsti/tools/optools.py b/pygsti/tools/optools.py index b8d8995dd..b6e622beb 100644 --- a/pygsti/tools/optools.py +++ b/pygsti/tools/optools.py @@ -87,6 +87,18 @@ def assert_hermitian(mat): assert_hermitian(a) assert_hermitian(b) + def check_unit_trace(mat): + tr = _np.trace(mat) + if abs(tr - 1) > __VECTOR_TOL__: + message = f""" + The input matrix is trace {tr}, which deviates from 1 by more than {__VECTOR_TOL__}. + Beware result! + """ + _warnings.warn(message) + + check_unit_trace(a) + check_unit_trace(b) + def check_rank_one_density(mat): """ mat is Hermitian of order n. This function uses an O(n^2) time randomized algorithm to @@ -159,17 +171,11 @@ def psd_square_root(mat): if _np.min(evals) < -__SCALAR_TOL__: message = f""" Input matrix is not PSD up to tolerance {__SCALAR_TOL__}. + The negative eigenvalues are {evals[evals < 0]}. We'll project out the bad eigenspaces to only work with the PSD part. """ _warnings.warn(message) evals[evals < 0] = 0.0 - tr = _np.sum(evals) - if abs(tr - 1) > __VECTOR_TOL__: - message = f""" - The PSD part of the input matrix is not trace-1 up to tolerance {__VECTOR_TOL__}. - Beware result! - """ - _warnings.warn(message) sqrt_mat = U @ (_np.sqrt(evals).reshape((-1, 1)) * U.T.conj()) return sqrt_mat From fb6ddc6c656384640defbd2397a47f0e8f705084 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Fri, 16 May 2025 11:25:03 -0700 Subject: [PATCH 51/71] add ExplicitOpModel.members() function that returns a dict keyed by modelmember labels whose values are the corresponding modelmembers. Allow abstract penalty functions in ObjectiveBuilder.create_from(...) (this required changing create_from signatures in something like 7 classes). --- pygsti/models/explicitmodel.py | 8 ++ pygsti/objectivefns/objectivefns.py | 128 +++++++++++++++++++--------- 2 files changed, 95 insertions(+), 41 deletions(-) diff --git a/pygsti/models/explicitmodel.py b/pygsti/models/explicitmodel.py index 50e218d3d..24dac8c61 100644 --- a/pygsti/models/explicitmodel.py +++ b/pygsti/models/explicitmodel.py @@ -927,6 +927,14 @@ def all_objects(self): self.factories.items()): yield (lbl, obj) + def members(self, include=('preps', 'operations', 'povms')): + out = dict() + for member_type in include: + member_dict = self.__getattribute__(member_type) + for k,v in member_dict.items(): + out[k] = v + return out + #TODO: how to handle these given possibility of different parameterizations... # -- maybe only allow these methods to be called when using a "full" parameterization? # -- or perhaps better to *move* them to the parameterization class diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index 52eb0215b..818a5f5ab 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -15,6 +15,7 @@ import time as _time import pathlib as _pathlib import warnings as _warnings +from typing import Callable import numpy as _np @@ -178,7 +179,7 @@ def cast(obj): # This was a classmethod, but I made it static because this class has no derived classes. @staticmethod - def create_from(objective='logl', freq_weighted_chi2=False, penalty_callable=None): + def create_from(objective='logl', freq_weighted_chi2=False, callable_penalty=None): """ Creates common :class:`ObjectiveFunctionBuilder` from a few arguments. @@ -194,18 +195,26 @@ def create_from(objective='logl', freq_weighted_chi2=False, penalty_callable=Non ------- ObjectiveFunctionBuilder """ + if callable_penalty is None and isinstance(objective, tuple) and len(objective) >= 2 and isinstance(objective[1], Callable): + callable_penalty = objective[1] + # callable_penalty_scale = 1.0 if len(objective) == 2 else objective[2] + objective = objective[0] + + if objective == "chi2": if freq_weighted_chi2: builder = ObjectiveFunctionBuilder(FreqWeightedChi2Function, name='fwchi2', description="Freq-weighted sum of Chi^2", - regularization={'min_freq_clip_for_weighting': 1e-4} + regularization={'min_freq_clip_for_weighting': 1e-4}, + callable_penalty=callable_penalty ) else: builder = ObjectiveFunctionBuilder(Chi2Function, name='chi2', description="Sum of Chi^2", - regularization={'min_prob_clip_for_weighting': 1e-4} + regularization={'min_prob_clip_for_weighting': 1e-4}, + callable_penalty=callable_penalty ) elif objective == "logl": @@ -215,7 +224,8 @@ def create_from(objective='logl', freq_weighted_chi2=False, penalty_callable=Non regularization={'min_prob_clip': 1e-4, 'radius': 1e-4}, penalties={'cptp_penalty_factor': 0, - 'spam_penalty_factor': 0} + 'spam_penalty_factor': 0}, + callable_penalty=callable_penalty ) elif 'tvd' in objective: @@ -225,14 +235,18 @@ def create_from(objective='logl', freq_weighted_chi2=False, penalty_callable=Non assert objective == 'normalized tvd' else: assert objective == 'tvd' - builder = ObjectiveFunctionBuilder(TVDFunction, name=objective, description=descr) + builder = ObjectiveFunctionBuilder( + TVDFunction, name=objective, description=descr, + callable_penalty=callable_penalty + ) elif isinstance(objective, tuple) and objective[0] == 'Lp^p': power = objective[1] builder = ObjectiveFunctionBuilder(LpNormToPowerP, name='Lp^p', description=f"L_{power} norm to the power {power}.", - power=objective[1] + power=objective[1], + callable_penalty=callable_penalty ) else: @@ -1124,9 +1138,9 @@ class MDCObjectiveFunction(ObjectiveFunction, EvaluatedModelDatasetCircuitsStore """ @classmethod - def create_from(cls, raw_objfn, model, dataset, circuits, resource_alloc=None, verbosity=0, array_types=()): + def create_from(cls, raw_objfn, model, dataset, circuits, resource_alloc=None, verbosity=0, array_types=(), **kwargs): mdc_store = ModelDatasetCircuitsStore(model, dataset, circuits, resource_alloc, array_types) - return cls(raw_objfn, mdc_store, verbosity) + return cls(raw_objfn, mdc_store, verbosity, **kwargs) @classmethod def _array_types_for_method(cls, method_name, fsim): @@ -1138,7 +1152,7 @@ def _array_types_for_method(cls, method_name, fsim): if method_name == 'dpercircuit': return cls._array_types_for_method('dterms', fsim) + ('cp',) return () - def __init__(self, raw_objfn, mdc_store, verbosity=0): + def __init__(self, raw_objfn, mdc_store, verbosity=0, **kwargs): """ Create a new MDCObjectiveFunction. @@ -1147,6 +1161,8 @@ def __init__(self, raw_objfn, mdc_store, verbosity=0): """ EvaluatedModelDatasetCircuitsStore.__init__(self, mdc_store, verbosity) self.raw_objfn = raw_objfn + self.callable_penalty = kwargs.get('callable_penalty', None) + self.callable_penalty_factor = 0.0 if self.callable_penalty is None else 1.0 @property def name(self): @@ -4174,16 +4190,16 @@ def chi2k_distributed_qty(self, objective_function_value): return -1 def terms(self, probs, counts, total_counts, freqs, intermediates=None): - return 0.5 * _np.abs(probs - freqs) ** self.power + return _np.abs(probs - freqs) ** self.power def dterms(self, probs, counts, total_counts, freqs, intermediates=None): t = probs - freqs - d = (0.5 * self.power) * _np.abs(t) ** (self.power - 1) + d = self.power * _np.abs(t) ** (self.power - 1) d[t < 0] *= -1 return d def zero_freq_terms(self, total_counts, probs): - return 0.5 * _np.abs(probs) ** self.power + return _np.abs(probs) ** self.power ###################################################### @@ -4266,9 +4282,9 @@ def _create_mdc_store(cls, model, dataset, circuits, resource_alloc, @classmethod def create_from(cls, model, dataset, circuits, regularization=None, penalties=None, resource_alloc=None, - name=None, description=None, verbosity=0, method_names=('fn',), array_types=()): + name=None, description=None, verbosity=0, method_names=('fn',), array_types=(), **kwargs): mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, array_types, verbosity) - return cls(mdc_store, regularization, penalties, name, description, verbosity) + return cls(mdc_store, regularization, penalties, name, description, verbosity, **kwargs) @classmethod def _array_types_for_method(cls, method_name, fsim): @@ -4300,9 +4316,9 @@ def compute_array_types(cls, method_names, fsim): return array_types @set_docstring(DOCSTR_TEMPLATE % TEMPLATE_FIELDS) - def __init__(self, raw_objfn, mdc_store, penalties=None, verbosity=0): + def __init__(self, raw_objfn, mdc_store, penalties=None, verbosity=0, **kwargs): - super().__init__(raw_objfn, mdc_store, verbosity) + super().__init__(raw_objfn, mdc_store, verbosity, **kwargs) if penalties is None: penalties = {} self.ex = self.set_penalties(**penalties) @@ -4420,7 +4436,11 @@ def set_penalties(self, regularize_factor=0, cptp_penalty_factor=0, spam_penalty if self.cptp_penalty_factor != 0: ex += _cptp_penalty_size(self.model) if self.spam_penalty_factor != 0: ex += _spam_penalty_size(self.model) if self.errorgen_penalty_factor != 0: ex += _errorgen_penalty_size(self.model) - + if self.callable_penalty is not None: ex += 1 + # ^ We want to be able to change the penalty factor without having to rebuild + # this objective function. So we allocate space for the callable penalty + # if it exists at all, rather than requiring self.callable_penalty_factor > 0. + # return ex def terms(self, paramvec=None, oob_check=False, profiler_str="TERMS OBJECTIVE"): @@ -4572,6 +4592,27 @@ def _dterms_fill_penalty(self, paramvec, terms_jac): terms_jac[off:off+n, :] *= 2*errorgen_penalty[:, None] off += n + if self.callable_penalty is not None: + # + # Ideally we'd have put in the effort to do this earlier when doing finite-difference to get + # the Jacobian of terms. But we start with a simple implementation. + # + terms_jac[off:off+1, :] = 0.0 + if self.callable_penalty_factor: + vec0 = self.model.to_vector() + val0 = self.callable_penalty(self.model) + eps = 1e-7 + for i in range(vec0.size): + vec0[i] += eps + self.model.from_vector(vec0) + val = self.callable_penalty(self.model) + dval = (val - val0) / eps + terms_jac[off, i] = dval + vec0[i] -= eps + self.model.from_vector(vec0) + off += 1 + pass + assert(off == self.local_ex) return @@ -4600,6 +4641,11 @@ def _terms_penalty(self, paramvec): errorgen_penalty = _errorgen_penalty(self.model, self.errorgen_penalty_factor) ** 2 blocks.append(errorgen_penalty) + if self.callable_penalty is not None: + f = self.callable_penalty_factor + val = f * self.callable_penalty(self.model) if f > 0 else 0.0 + blocks.append(_np.array([val])) + return _np.concatenate(blocks) def _clip_probs(self): @@ -4816,9 +4862,9 @@ class Chi2Function(TimeIndependentMDCObjectiveFunction): ) @set_docstring(TimeIndependentMDCObjectiveFunction.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) - def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0): + def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0, **kwargs): raw_objfn = RawChi2Function(regularization, mdc_store.resource_alloc, name, description, verbosity) - super().__init__(raw_objfn, mdc_store, penalties, verbosity) + super().__init__(raw_objfn, mdc_store, penalties, verbosity, **kwargs) class ChiAlphaFunction(TimeIndependentMDCObjectiveFunction): @@ -4837,15 +4883,15 @@ class ChiAlphaFunction(TimeIndependentMDCObjectiveFunction): @classmethod def create_from(cls, model, dataset, circuits, regularization=None, penalties=None, resource_alloc=None, name=None, description=None, verbosity=0, - method_names=('fn',), array_types=(), alpha=1): + method_names=('fn',), array_types=(), alpha=1, **kwargs): mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, array_types, verbosity) - return cls(mdc_store, regularization, penalties, name, description, verbosity, alpha) + return cls(mdc_store, regularization, penalties, name, description, verbosity, alpha, **kwargs) @set_docstring(TimeIndependentMDCObjectiveFunction.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0, - alpha=1): + alpha=1, **kwargs): raw_objfn = RawChiAlphaFunction(regularization, mdc_store.resource_alloc, name, description, verbosity, alpha) - super().__init__(raw_objfn, mdc_store, penalties, verbosity) + super().__init__(raw_objfn, mdc_store, penalties, verbosity, **kwargs) class FreqWeightedChi2Function(TimeIndependentMDCObjectiveFunction): @@ -4857,9 +4903,9 @@ class FreqWeightedChi2Function(TimeIndependentMDCObjectiveFunction): ) @set_docstring(TimeIndependentMDCObjectiveFunction.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) - def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0): + def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0, **kwargs): raw_objfn = RawFreqWeightedChi2Function(regularization, mdc_store.resource_alloc, name, description, verbosity) - super().__init__(raw_objfn, mdc_store, penalties, verbosity) + super().__init__(raw_objfn, mdc_store, penalties, verbosity, **kwargs) class CustomWeightedChi2Function(TimeIndependentMDCObjectiveFunction): @@ -4879,9 +4925,9 @@ class CustomWeightedChi2Function(TimeIndependentMDCObjectiveFunction): @classmethod def create_from(cls, model, dataset, circuits, regularization=None, penalties=None, resource_alloc=None, name=None, description=None, verbosity=0, - method_names=('fn',), array_types=(), custom_weights=None): + method_names=('fn',), array_types=(), custom_weights=None, **kwargs): mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, array_types, verbosity) - return cls(mdc_store, regularization, penalties, name, description, verbosity, custom_weights) + return cls(mdc_store, regularization, penalties, name, description, verbosity, custom_weights, **kwargs) @set_docstring(TimeIndependentMDCObjectiveFunction.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0, @@ -4900,10 +4946,10 @@ class PoissonPicDeltaLogLFunction(TimeIndependentMDCObjectiveFunction): ) @set_docstring(TimeIndependentMDCObjectiveFunction.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) - def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0): + def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0, **kwargs): raw_objfn = RawPoissonPicDeltaLogLFunction(regularization, mdc_store.resource_alloc, name, description, verbosity) - super().__init__(raw_objfn, mdc_store, penalties, verbosity) + super().__init__(raw_objfn, mdc_store, penalties, verbosity, **kwargs) class DeltaLogLFunction(TimeIndependentMDCObjectiveFunction): @@ -4915,9 +4961,9 @@ class DeltaLogLFunction(TimeIndependentMDCObjectiveFunction): ) @set_docstring(TimeIndependentMDCObjectiveFunction.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) - def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0): + def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0, **kwargs): raw_objfn = RawDeltaLogLFunction(regularization, mdc_store.resource_alloc, name, description, verbosity) - super().__init__(raw_objfn, mdc_store, penalties, verbosity) + super().__init__(raw_objfn, mdc_store, penalties, verbosity, **kwargs) class MaxLogLFunction(TimeIndependentMDCObjectiveFunction): @@ -4931,16 +4977,16 @@ class MaxLogLFunction(TimeIndependentMDCObjectiveFunction): @classmethod def create_from(cls, model, dataset, circuits, regularization=None, penalties=None, resource_alloc=None, name=None, description=None, verbosity=0, - method_names=('fn',), array_types=(), poisson_picture=True): + method_names=('fn',), array_types=(), poisson_picture=True, **kwargs): mdc_store = cls._create_mdc_store(model, dataset, circuits, resource_alloc, method_names, array_types, verbosity) - return cls(mdc_store, regularization, penalties, name, description, verbosity, poisson_picture) + return cls(mdc_store, regularization, penalties, name, description, verbosity, poisson_picture, **kwargs) @set_docstring(TimeIndependentMDCObjectiveFunction.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0, - poisson_picture=True): + poisson_picture=True, **kwargs): raw_objfn = RawMaxLogLFunction(regularization, mdc_store.resource_alloc, name, description, verbosity, poisson_picture) - super().__init__(raw_objfn, mdc_store, penalties, verbosity) + super().__init__(raw_objfn, mdc_store, penalties, verbosity, **kwargs) class TermWeighted(TimeIndependentMDCObjectiveFunction): @@ -5055,9 +5101,9 @@ class TVDFunction(TermWeighted): ) @set_docstring(TermWeighted.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) - def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0): + def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, verbosity=0, **kwargs): raw_objfn = RawTVDFunction(regularization, mdc_store.resource_alloc, name, description, verbosity) - super().__init__(raw_objfn, mdc_store, penalties, verbosity) + super().__init__(raw_objfn, mdc_store, penalties, verbosity, **kwargs) self.normalized = name == 'normalized tvd' self._terms_weights = None self._update_terms_weights() @@ -5096,9 +5142,9 @@ class LpNormToPowerP(TermWeighted): @set_docstring(TermWeighted.DOCSTR_TEMPLATE % TEMPLATE_FIELDS) def __init__(self, mdc_store, regularization=None, penalties=None, name=None, description=None, - verbosity=0, power=2): + verbosity=0, power=2, **kwargs): raw_objfn = RawAbsPower(power, regularization, mdc_store.resource_alloc, name, description, verbosity) - super().__init__(raw_objfn, mdc_store, penalties, verbosity) + super().__init__(raw_objfn, mdc_store, penalties, verbosity, **kwargs) self.power = raw_objfn.power self._update_terms_weights() @@ -5145,7 +5191,7 @@ class TimeDependentMDCObjectiveFunction(MDCObjectiveFunction): @classmethod def create_from(cls, model, dataset, circuits, regularization=None, penalties=None, resource_alloc=None, name=None, description=None, verbosity=0, - method_names=('fn',), array_types=()): + method_names=('fn',), array_types=(), **kwargs): #Array types are used to construct memory estimates (as a function of element number, etc) for layout creation. # They account for memory used in: # 1) an optimization method (if present), @@ -5153,7 +5199,7 @@ def create_from(cls, model, dataset, circuits, regularization=None, penalties=No # 2b) intermediate memory allocated by methods of the created object (possibly an objective function) array_types += cls.compute_array_types(method_names, model.sim) mdc_store = ModelDatasetCircuitsStore(model, dataset, circuits, resource_alloc, array_types) - return cls(mdc_store, regularization, penalties, name, description, verbosity) + return cls(mdc_store, regularization, penalties, name, description, verbosity, **kwargs) @classmethod def compute_array_types(cls, method_names, fsim): From b958304c3b018d848d701c568ad53b4bb671a917 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Tue, 27 May 2025 08:05:53 -0700 Subject: [PATCH 52/71] change datasetconstruction.mix_dataset. Leave comments about efficiency elsewhere. --- pygsti/data/datasetconstruction.py | 19 ++++++++++++++++--- pygsti/objectivefns/objectivefns.py | 4 ++++ pygsti/tools/rbtheory.py | 8 ++++++++ 3 files changed, 28 insertions(+), 3 deletions(-) diff --git a/pygsti/data/datasetconstruction.py b/pygsti/data/datasetconstruction.py index 878c23a08..22c8d10a6 100644 --- a/pygsti/data/datasetconstruction.py +++ b/pygsti/data/datasetconstruction.py @@ -209,14 +209,27 @@ def simulate_data(model_or_dataset, circuit_list, num_samples, return dataset -def mix_datasets(dsa, dsb, p, integral=True): +def mix_datasets(dsa, dsb, p, integral=True, choose_machine=False, seed=None): dsc = dsa.copy_nonstatic() # arr = _np.array(dsc.repData).ravel() # print((arr, arr.size)) # print((dsb.repData, dsb.repData.size)) + num_circuits = len(dsb) + if choose_machine: + if seed is None: + _warnings.warn('Set the random seed! Using 42.') + seed = 42 + rngstate = _np.random.default_rng(seed) + interp_weights = rngstate.uniform(low=0, high=1, size=num_circuits) + interp_weights[interp_weights < p] = 0.0 + interp_weights[interp_weights > 0] = 1.0 + else: + interp_weights = p * _np.ones(num_circuits) + for i, (_, dsrow) in enumerate(dsb.items()): - interpolated = p*dsc.repData[i] + (1-p)*dsrow.reps - if integral: + p_i = interp_weights[i] + interpolated = p_i * dsc.repData[i] + (1-p_i) * dsrow.reps + if integral and (not choose_machine): assert interpolated.size == 2 total = int(_np.ceil((_np.sum(interpolated)))) j = _np.argmin(interpolated) diff --git a/pygsti/objectivefns/objectivefns.py b/pygsti/objectivefns/objectivefns.py index 818a5f5ab..f9dc894e6 100644 --- a/pygsti/objectivefns/objectivefns.py +++ b/pygsti/objectivefns/objectivefns.py @@ -4597,6 +4597,10 @@ def _dterms_fill_penalty(self, paramvec, terms_jac): # Ideally we'd have put in the effort to do this earlier when doing finite-difference to get # the Jacobian of terms. But we start with a simple implementation. # + # --> TODO: change so that if callable_penalty has a grad method then we'll call it instead + # of relying on finite-differences over all model parameters. This would help when + # the penalty is constant w.r.t. some params (like those that only appear in SPAM). + # terms_jac[off:off+1, :] = 0.0 if self.callable_penalty_factor: vec0 = self.model.to_vector() diff --git a/pygsti/tools/rbtheory.py b/pygsti/tools/rbtheory.py index 7191bbb03..2f97b3107 100644 --- a/pygsti/tools/rbtheory.py +++ b/pygsti/tools/rbtheory.py @@ -323,6 +323,14 @@ def L_matrix(model, target_model, weights=None): # noqa N802 weights[key] = 1. normalizer = _np.sum(_np.array([weights[key] for key in list(target_model.operations.keys())])) + # TODO: improve efficiency + # + # 1. Accumuate the summands in this list comprehension in-place. (Might already happen but that's non-obvious) + # 2. Use the fact that target gates are unitary and so their superoperator representation inverses should + # be their transposes. + # 3. Have the option to return this matrix as an implicit abstract linear operator, so that anyone who wants + # eigenvalue info can try to get it from an iterative method instead of a full eigendecomposition. + # L_matrix = (1 / normalizer) * _np.sum( weights[key] * _np.kron( model.operations[key].to_dense(on_space='HilbertSchmidt').T, From 4dc24d057de6991d974c84475f49fb6e3c34f615 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 3 Sep 2025 10:24:37 -0700 Subject: [PATCH 53/71] make model.copy() more robust --- pygsti/models/model.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pygsti/models/model.py b/pygsti/models/model.py index 6dd0016d8..4d01e028e 100644 --- a/pygsti/models/model.py +++ b/pygsti/models/model.py @@ -2343,7 +2343,8 @@ def copy(self): """ self._clean_paramvec() # ensure _paramvec is rebuilt if needed if OpModel._pcheck: self._check_paramvec() - ret = Model.copy(self) + state = self.to_nice_serialization() + ret = type(self).from_nice_serialization(state) if self._param_bounds is not None and self.parameter_labels is not None: ret._clean_paramvec() # will *always* rebuild paramvec; do now so we can preserve param bounds assert _np.all(self.parameter_labels == ret.parameter_labels) # ensure ordering is the same From 4cf4a3e791c43c33e49964f2f45956793c3c54a4 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 3 Sep 2025 11:18:46 -0700 Subject: [PATCH 54/71] make ExplicitOpModel.randomize_with_unitary parameterization-preserving. --- pygsti/models/explicitmodel.py | 37 ++++++++++++++++++++-------------- 1 file changed, 22 insertions(+), 15 deletions(-) diff --git a/pygsti/models/explicitmodel.py b/pygsti/models/explicitmodel.py index 24dac8c61..24cd48c94 100644 --- a/pygsti/models/explicitmodel.py +++ b/pygsti/models/explicitmodel.py @@ -1156,26 +1156,33 @@ def rand_unitary_as_superop(): rand_herm /= _scipy.linalg.norm(rand_herm) rand_herm *= scale * _np.sqrt(unitary_dim) rand_unitary = _scipy.linalg.expm(-1j * rand_herm) - rand_op = _ot.unitary_to_superop(rand_unitary, self.basis) + rand_op = _op.StaticUnitaryOp(rand_unitary, self.basis) return rand_op for opLabel, gate in self.operations.items(): rand_op = rand_unitary_as_superop() - mdl_randomized.operations[opLabel] = _op.FullArbitraryOp(rand_op @ gate) - - if transform_spam: - from pygsti.modelmembers.states import FullState - for preplbl, rho in self.preps.items(): - rand_op = rand_unitary_as_superop() - mdl_randomized.preps[preplbl] = FullState(rand_op @ rho) - from pygsti.modelmembers.povms import create_from_dmvecs - for povmlbl, M in self.povms.items(): - rand_op = rand_unitary_as_superop() - dmvecs = {elbl: rand_op @ e.to_dense() for elbl, e in M.items()} - mdl_randomized.povms[povmlbl] = create_from_dmvecs(dmvecs, 'full') - + ops_to_compose = [] + if hasattr(gate, 'factorops'): + ops_to_compose.extend(gate.factorops) + else: + ops_to_compose.append(gate) + ops_to_compose.append(rand_op) + mdl_randomized.operations[opLabel] = _op.ComposedOp(ops_to_compose) + + if not transform_spam: + return mdl_randomized + + from pygsti.modelmembers.states import ComposedState + for preplbl, rho in self.preps.items(): + rand_op = rand_unitary_as_superop() + mdl_randomized.preps[preplbl] = ComposedState(rho, rand_op) - #Note: this function does NOT randomize instruments + from pygsti.modelmembers.povms import ComposedPOVM + for povmlbl, M in self.povms.items(): + rand_op = rand_unitary_as_superop() + mdl_randomized.povms[povmlbl] = ComposedPOVM(rand_op, M) + + # Note: this function does NOT randomize instruments return mdl_randomized From ca892e55bdb448f6c10fd758e0d24f186cf2a650 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 3 Sep 2025 11:19:40 -0700 Subject: [PATCH 55/71] changes to ComposedPOVM and ComposedState so the previous commit works --- pygsti/modelmembers/povms/composedpovm.py | 75 +++++++-------------- pygsti/modelmembers/states/composedstate.py | 40 ++++++----- 2 files changed, 48 insertions(+), 67 deletions(-) diff --git a/pygsti/modelmembers/povms/composedpovm.py b/pygsti/modelmembers/povms/composedpovm.py index b23722007..25c9ddaa2 100644 --- a/pygsti/modelmembers/povms/composedpovm.py +++ b/pygsti/modelmembers/povms/composedpovm.py @@ -22,8 +22,8 @@ class ComposedPOVM(_POVM): """ - TODO: update docstring - A POVM that is effectively a *single* Lindblad-parameterized gate followed by a computational-basis POVM. + A parameterized POVM that is effectively a *single* parameterized gate followed by + a static (usually, computational basis) POVM. Parameters ---------- @@ -37,7 +37,7 @@ class ComposedPOVM(_POVM): povm : POVM, optional A sub-POVM which supplies the set of "reference" effect vectors that `errormap` acts on to produce the final effect vectors of - this LindbladPOVM. This POVM must be *static* + this ComposedPOVM. This POVM must be *static* (have zero parameters) and its evolution type must match that of `errormap`. If None, then a :class:`ComputationalBasisPOVM` is used on the number of qubits appropriate to `errormap`'s dimension. @@ -50,59 +50,34 @@ class ComposedPOVM(_POVM): """ def __init__(self, errormap, povm=None, mx_basis=None): - """ - Creates a new LindbladPOVM object. - - Parameters - ---------- - errormap : MapOperator - The error generator action and parameterization, encapsulated in - a gate object. Usually a :class:`LindbladOp` - or :class:`ComposedOp` object. (This argument is *not* copied, - to allow ComposedPOVMEffects to share error generator - parameters with other gates and spam vectors.) - - povm : POVM, optional - A sub-POVM which supplies the set of "reference" effect vectors - that `errormap` acts on to produce the final effect vectors of - this LindbladPOVM. This POVM must be *static* - (have zero parameters) and its evolution type must match that of - `errormap`. If None, then a :class:`ComputationalBasisPOVM` is - used on the number of qubits appropriate to `errormap`'s dimension. - - mx_basis : {'std', 'gm', 'pp', 'qt'} or Basis object - The basis for this spam vector. Allowed values are Matrix-unit (std), - Gell-Mann (gm), Pauli-product (pp), and Qutrit (qt) (or a custom - basis object). If None, then this is extracted (if possible) from - `errormap`. - """ self.error_map = errormap - state_space = self.error_map.state_space + state_space = errormap.state_space + evotype = errormap._evotype + + if povm is None: + povm = _ComputationalBasisPOVM(state_space.num_qubits, evotype) + elif isinstance(povm, ComposedPOVM): + from pygsti.modelmembers.operations import ComposedOp + errormap = ComposedOp([povm.errormap, errormap]) + povm = povm.base_povm + + assert(povm.evotype == evotype), \ + ("Evolution type of `povm` (%s) must match that of " + "`errormap` (%s)!") % (povm.evotype, evotype) + assert(povm.num_params == 0), \ + "Given `povm` must be static (have 0 parameters)!" + + self.base_povm = povm if mx_basis is None: - if (isinstance(errormap, (_op.ExpErrorgenOp, _op.IdentityPlusErrorgenOp)) - and isinstance(errormap.errorgen, _op.LindbladErrorgen)): - mx_basis = errormap.errorgen.matrix_basis + if hasattr(errormap, 'errorgen') and isinstance(errormap.errorgen, _op.LindbladErrorgen): # type: ignore + mx_basis = errormap.errorgen.matrix_basis # type: ignore else: - raise ValueError("Cannot extract a matrix-basis from `errormap` (type %s)" - % str(type(errormap))) + raise ValueError(f"Cannot extract a matrix-basis from `errormap` (type {type(errormap)})") self.matrix_basis = _Basis.cast(mx_basis, state_space) - evotype = self.error_map._evotype - - if povm is None: - assert(state_space.num_qubits >= 0), \ - ("A default computational-basis POVM can only be used with an" - " integral number of qubits!") - povm = _ComputationalBasisPOVM(state_space.num_qubits, evotype) - else: - assert(povm.evotype == evotype), \ - ("Evolution type of `povm` (%s) must match that of " - "`errormap` (%s)!") % (povm.evotype, evotype) - assert(povm.num_params == 0), \ - "Given `povm` must be static (have 0 parameters)!" - self.base_povm = povm + self.errormap = errormap items = [] # init as empty (lazy creation of members) try: rep = evotype.create_composed_povm_rep(self.error_map._rep, self.base_povm._rep, state_space) @@ -188,7 +163,7 @@ def __getitem__(self, key): assert(self.parent is None or num_new == 0) # ensure effect inds are already allocated to current model _collections.OrderedDict.__setitem__(self, key, effect) return effect - else: raise KeyError("%s is not an outcome label of this LindbladPOVM" % key) + else: raise KeyError("%s is not an outcome label of this ComposedPOVM" % key) def __reduce__(self): """ Needed for OrderedDict-derived classes (to set dict items) """ diff --git a/pygsti/modelmembers/states/composedstate.py b/pygsti/modelmembers/states/composedstate.py index 9826db229..2a2ddcc73 100644 --- a/pygsti/modelmembers/states/composedstate.py +++ b/pygsti/modelmembers/states/composedstate.py @@ -19,21 +19,25 @@ from pygsti.modelmembers.errorgencontainer import ErrorMapContainer as _ErrorMapContainer -class ComposedState(_State): # , _ErrorMapContainer +class ComposedState(_State): """ - TODO: update docstring - A Lindblad-parameterized State (that is also expandable into terms). + A representation of the superket `errormap @ state_vec` induced by a superoperator + `errormap` and a superket `state_vec`, where `state_vec.num_params == 0`. + Parameters ---------- - pure_vec : numpy array or State + static_state : numpy array or StaticState or ComposedState An array or State in the *full* density-matrix space (this - vector will have dimension 4 in the case of a single qubit) which - represents a pure-state preparation or projection. This is used as - the "base" preparation or projection that is followed or preceded - by, respectively, the parameterized Lindblad-form error generator. - (This argument is *not* copied if it is a State. A numpy array - is converted to a new StaticState.) + vector will have dimension 4 in the case of a single qubit). + + This argument is *not* copied if it is a State. If it's an + ndarray then we convert it to a new StaticState. If it's a + ComposedState, then we use a ComposedOp to pack `errormap` + and `static_state.errormap` into a single superoperator, and + we replace `static_state = static_state.state_vec`. + + After possible conversions, we set `self.state_vec = static_state`. errormap : MapOperator The error generator action and parameterization, encapsulated in @@ -43,21 +47,23 @@ class ComposedState(_State): # , _ErrorMapContainer parameters with other gates and spam vectors.) """ + STATIC_STATE_ERROR_MSG = "`static_state` 'reference' must have *zero* parameters!" + def __init__(self, static_state, errormap): evotype = errormap._evotype - #from .operation import LindbladOp as _LPGMap - #assert(evotype in ("densitymx", "svterm", "cterm")), \ - # "Invalid evotype: %s for %s" % (evotype, self.__class__.__name__) - if not isinstance(static_state, _State): # UNSPECIFIED BASIS - change None to static_state.basis once we have a std attribute static_state = _StaticState(static_state, None, evotype) # assume spamvec is just a vector + if isinstance(static_state, ComposedState): + from pygsti.modelmembers.operations import ComposedOp + errormap = ComposedOp([static_state.error_map, errormap]) + static_state = static_state.state_vec + assert(static_state._evotype == evotype), \ "`static_state` evotype must match `errormap` ('%s' != '%s')" % (static_state._evotype, evotype) - assert(static_state.num_params == 0), "`static_state` 'reference' must have *zero* parameters!" + assert(static_state.num_params == 0), ComposedState.STATIC_STATE_ERROR_MSG - #d2 = static_state.dim self.state_vec = static_state self.error_map = errormap self.terms = {} @@ -67,7 +73,7 @@ def __init__(self, static_state, errormap): rep = evotype.create_composed_state_rep(self.state_vec._rep, self.error_map._rep, static_state.state_space) _State.__init__(self, rep, evotype) - _ErrorMapContainer.__init__(self, self.error_map) + _ErrorMapContainer.__init__(self, self.error_map) # type: ignore self.init_gpindices() # initialize our gpindices based on sub-members @classmethod From 12d1ffb3f58e9997364e70df12f7f2609501527f Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 3 Sep 2025 12:44:46 -0700 Subject: [PATCH 56/71] propagate change to DenseState._from_memoized_dict from master branch --- pygsti/modelmembers/states/densestate.py | 30 +++++++++++++++++++++--- 1 file changed, 27 insertions(+), 3 deletions(-) diff --git a/pygsti/modelmembers/states/densestate.py b/pygsti/modelmembers/states/densestate.py index 9dbb36596..17ffe92f7 100644 --- a/pygsti/modelmembers/states/densestate.py +++ b/pygsti/modelmembers/states/densestate.py @@ -242,9 +242,33 @@ def to_memoized_dict(self, mmg_memo): @classmethod def _from_memoized_dict(cls, mm_dict, serial_memo): vec = cls._decodemx(mm_dict['dense_superket_vector']) - state_space = _statespace.StateSpace.from_nice_serialization(mm_dict['state_space']) - basis = _Basis.from_nice_serialization(mm_dict['basis']) if (mm_dict['basis'] is not None) else None - return cls(vec, basis, mm_dict['evotype'], state_space) + try: + state_space = _statespace.StateSpace.from_nice_serialization(mm_dict['state_space']) + basis = _Basis.from_nice_serialization(mm_dict['basis']) if (mm_dict['basis'] is not None) else None + return cls(vec, basis, mm_dict['evotype'], state_space) + except AssertionError as e: + """ + This codepath can get hit when deserializing TPPOVM or UnconstrainedPOVM objects. + + More specifically, it can get hit when objects other than POVMEffect vectors were + passed to the constructors of these classes. When that happens, their base class + constructor (for BasePOVM) constructs the effects as FullPOVMEffect objects (which in + turn rely on FullState and then DenseState) with None passed for the basis argument. + Somewhere downstream that None gets cast to a Basis object where `.dim == 0`, which + is inconsistent with the array representation of the effect having length > 0. + + In an ideal world we'd have POVMs enforce not only non-None bases but also *consistent* + bases for all constituent effects. For now, our fix is to just try and recover the basis + from serial_memo. (Perhaps not-coincidentally, this function wasn't using the serial_memo + argument before this code path was added.) + """ + se = str(e) + if 'Basis object has unexpected dimension' in se and len(serial_memo) > 0: + member = list(serial_memo.values())[0] + basis = member.parent.basis + state_space = basis.state_space + return cls(vec, basis, mm_dict['evotype'], state_space) + raise e def _is_similar(self, other, rtol, atol): """ From 5876e950c8f80afd0c42c3c451493e922fa85bb8 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 3 Sep 2025 14:41:02 -0700 Subject: [PATCH 57/71] smarter unittest decorators --- test/unit/util.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/test/unit/util.py b/test/unit/util.py index 54c930632..6bf0ef5d8 100644 --- a/test/unit/util.py +++ b/test/unit/util.py @@ -6,18 +6,25 @@ from contextlib import contextmanager from pathlib import Path from tempfile import TemporaryDirectory +import importlib.util import numpy as np +__cvxpy_not_importable__ = importlib.util.find_spec('cvxpy') is None + +__deap_not_importable__ = importlib.util.find_spec('deap') is None + def needs_cvxpy(fn): """Shortcut decorator for skipping tests that require CVXPY""" - return unittest.skipIf('SKIP_CVXPY' in os.environ, "skipping cvxpy tests")(fn) + cond = __cvxpy_not_importable__ or ('SKIP_CVXPY' in os.environ) + return unittest.skipIf(cond, "skipping cvxpy tests")(fn) def needs_deap(fn): """Shortcut decorator for skipping tests that require deap""" - return unittest.skipIf('SKIP_DEAP' in os.environ, "skipping deap tests")(fn) + cond = __deap_not_importable__ or ('SKIP_DEAP' in os.environ) + return unittest.skipIf(cond, "skipping deap tests")(fn) def needs_matplotlib(fn): From c268571b84aeb48bac16e53dad196092ddebc9f3 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 3 Sep 2025 14:42:42 -0700 Subject: [PATCH 58/71] make it possible to control __SCALAR_TOL__ in the fidelty function by setting a module-level variable --- pygsti/tools/optools.py | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/pygsti/tools/optools.py b/pygsti/tools/optools.py index b6e622beb..1a883e8cf 100644 --- a/pygsti/tools/optools.py +++ b/pygsti/tools/optools.py @@ -28,7 +28,18 @@ from pygsti.baseobjs.errorgenlabel import LocalElementaryErrorgenLabel as _LocalElementaryErrorgenLabel from pygsti.tools.legacytools import deprecate as _deprecated_fn -IMAG_TOL = 1e-7 # tolerance for imaginary part being considered zero + +__SCALAR_TOL_EXPONENT__ = 0.75 +"""^ +__SCALAR_TOL_EXPONENT__ is used when checking properties of matrices and vectors. +It's intended only when we can check the property without incurring rounding +errors from some reduction (like a sum or a matrix-vector product). If we're +working with a matrix whose dtype is `d`, then we set + + __SCALAR_TOL__ = d.eps ** __SCALAR_TOL_EXPONENT__, + +or a modest multiple thereof. +""" def _flat_mut_blks(i, j, block_dims): @@ -70,7 +81,7 @@ def fidelity(a, b): float The resulting fidelity. """ - __SCALAR_TOL__ = _np.finfo(a.dtype).eps ** 0.75 + __SCALAR_TOL__ = _np.finfo(a.dtype).eps ** __SCALAR_TOL_EXPONENT__ # ^ use for checks that have no dimensional dependence; about 1e-12 for double precision. __VECTOR_TOL__ = (a.shape[0] ** 0.5) * __SCALAR_TOL__ # ^ use for checks that do have dimensional dependence (will naturally increase for larger matrices) From e12280d225bdf5fe22c931e1e4657633e71b7cd1 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 3 Sep 2025 14:44:59 -0700 Subject: [PATCH 59/71] try-catch for more robust model copying --- pygsti/models/model.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/pygsti/models/model.py b/pygsti/models/model.py index 4d01e028e..7d9efb74b 100644 --- a/pygsti/models/model.py +++ b/pygsti/models/model.py @@ -2343,8 +2343,15 @@ def copy(self): """ self._clean_paramvec() # ensure _paramvec is rebuilt if needed if OpModel._pcheck: self._check_paramvec() - state = self.to_nice_serialization() - ret = type(self).from_nice_serialization(state) + try: + # This only works if all modelmembers are NicelySerializable. + # It does a better job than `Model.copy` for resolving ambiguities + # in how we should set the `parent` field of Modelmembers in the + # copied model. + state = self.to_nice_serialization() + ret = type(self).from_nice_serialization(state) + except: + ret = Model.copy(self) if self._param_bounds is not None and self.parameter_labels is not None: ret._clean_paramvec() # will *always* rebuild paramvec; do now so we can preserve param bounds assert _np.all(self.parameter_labels == ret.parameter_labels) # ensure ordering is the same From 87f91dee8afa2c4449ed3ed042eb56c67089d22b Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 3 Sep 2025 14:45:35 -0700 Subject: [PATCH 60/71] guard to prevent silent errors during deserialization --- pygsti/modelmembers/modelmembergraph.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/pygsti/modelmembers/modelmembergraph.py b/pygsti/modelmembers/modelmembergraph.py index 839c7f642..a42b777f0 100644 --- a/pygsti/modelmembers/modelmembergraph.py +++ b/pygsti/modelmembers/modelmembergraph.py @@ -45,6 +45,10 @@ def load_modelmembers_from_serialization_dict(cls, sdict, parent_model): if 'memberdict_types' in mm_node_dict and 'memberdict_labels' in mm_node_dict: for mm_type, lbl_str in zip(mm_node_dict['memberdict_types'], mm_node_dict['memberdict_labels']): lbl = _parse_label(lbl_str) + if isinstance(lbl, str): + # This is a sanity check that we can deserialize correctly. Without this check + # it's possible to silently return incorrect results. + assert lbl_str in lbl if mm_type not in mm_nodes: mm_nodes[mm_type] = {} mm_nodes[mm_type][lbl] = mm_serial[mm_node_serialized_id] From f6b1683d359c98cb6e2d1f69e9d780c2212c564b Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 3 Sep 2025 14:46:51 -0700 Subject: [PATCH 61/71] revise ExplicitOpModel.randomize_with_unitary so that users have the option of preserving parameterization. I set the default to False, since this seems to be needed for existing germ selection tests. --- pygsti/models/explicitmodel.py | 64 +++++++++++++++++++++++----------- 1 file changed, 43 insertions(+), 21 deletions(-) diff --git a/pygsti/models/explicitmodel.py b/pygsti/models/explicitmodel.py index 24cd48c94..234513ce1 100644 --- a/pygsti/models/explicitmodel.py +++ b/pygsti/models/explicitmodel.py @@ -1109,7 +1109,7 @@ def rotate(self, rotate=None, max_rotate=None, seed=None): newModel._clean_paramvec() # rotate may leave dirty members return newModel - def randomize_with_unitary(self, scale, seed=None, rand_state=None, transform_spam=False): + def randomize_with_unitary(self, scale, seed=None, rand_state=None, transform_spam=False, param_preserving=False): """ Create a new model with random unitary perturbations. @@ -1133,6 +1133,12 @@ def randomize_with_unitary(self, scale, seed=None, rand_state=None, transform_sp across multiple random function calls but you don't want to bother with manually incrementing seeds between those calls. + param_preserving : bool + If False, then all members of the returned model will have "full" parameterization. + + If True, then we use ComposedOp (and ComposedState and ComposedPOVM) to define a + model with unitarily-transformed members while retaining the parameterization of `self`. + Returns ------- Model @@ -1158,30 +1164,46 @@ def rand_unitary_as_superop(): rand_unitary = _scipy.linalg.expm(-1j * rand_herm) rand_op = _op.StaticUnitaryOp(rand_unitary, self.basis) return rand_op - + + if param_preserving: + from pygsti.modelmembers.states import ComposedState + from pygsti.modelmembers.povms import ComposedPOVM + def transformed_gate(rand_op, gate): # type: ignore + ops_to_compose = gate.factorops if hasattr(gate, 'factorops') else [gate] + ops_to_compose.append(rand_op) + return _op.ComposedOp(ops_to_compose) + def transformed_stateprep(rand_op, rho): # type: ignore + return ComposedState(rho, rand_op) + def transformed_povm(rand_op, M): # type: ignore + return ComposedPOVM(rand_op, M) + else: + from pygsti.modelmembers.states import FullState + from pygsti.modelmembers.povms import create_from_dmvecs + def transformed_gate(rand_op, gate): + rand_op = rand_op.to_dense() + return _op.FullArbitraryOp(rand_op @ gate) + def transformed_stateprep(rand_op, rho): + rand_op = rand_op.to_dense() + return FullState(rand_op @ rho) + def transformed_povm(rand_op, M): + rand_op = rand_op.to_dense() + dmvecs = {elbl: rand_op @ e.to_dense() for elbl, e in M.items()} + return create_from_dmvecs(dmvecs, 'full') + for opLabel, gate in self.operations.items(): rand_op = rand_unitary_as_superop() - ops_to_compose = [] - if hasattr(gate, 'factorops'): - ops_to_compose.extend(gate.factorops) - else: - ops_to_compose.append(gate) - ops_to_compose.append(rand_op) - mdl_randomized.operations[opLabel] = _op.ComposedOp(ops_to_compose) + mdl_randomized.operations[opLabel] = transformed_gate(rand_op, gate) - if not transform_spam: - return mdl_randomized + if transform_spam: - from pygsti.modelmembers.states import ComposedState - for preplbl, rho in self.preps.items(): - rand_op = rand_unitary_as_superop() - mdl_randomized.preps[preplbl] = ComposedState(rho, rand_op) - - from pygsti.modelmembers.povms import ComposedPOVM - for povmlbl, M in self.povms.items(): - rand_op = rand_unitary_as_superop() - mdl_randomized.povms[povmlbl] = ComposedPOVM(rand_op, M) - + for preplbl, rho in self.preps.items(): + rand_op = rand_unitary_as_superop() + mdl_randomized.preps[preplbl] = transformed_stateprep(rand_op, rho) + + for povmlbl, M in self.povms.items(): + rand_op = rand_unitary_as_superop() + mdl_randomized.povms[povmlbl] = transformed_povm(rand_op, M) + # Note: this function does NOT randomize instruments return mdl_randomized From 3c2580e4698e46100ddbbc5cab455ba10d86700d Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 3 Sep 2025 14:47:59 -0700 Subject: [PATCH 62/71] add a guard in _create_explicit_model_from_expressions to ensure that if the model is serialized then it can be deserialized correctly. --- pygsti/models/modelconstruction.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/pygsti/models/modelconstruction.py b/pygsti/models/modelconstruction.py index 3bfff16b0..f1f740d03 100644 --- a/pygsti/models/modelconstruction.py +++ b/pygsti/models/modelconstruction.py @@ -566,7 +566,20 @@ def _create_explicit_model_from_expressions(state_space, basis, if len(effect_vecs) > 0: # don't add POVMs with 0 effects ret.povms[povmLbl] = _povm.create_from_dmvecs(effect_vecs, povm_type, basis, evotype, state_space) + from pygsti.circuits.circuitparser import parse_label + from pygsti.baseobjs.label import LabelStr for (opLabel, opExpr) in zip(op_labels, op_expressions): + # Before calling create_operation(...) we check that we'll be able + # to deserialize the model correctly, should serialization and + # deserialization be attempted. + # + # It's good to perform this check here because it's much easier + # to diagnose the source of the error than if we made the check during + # deserialization. + # + parsed = parse_label(opLabel) + if isinstance(parsed, LabelStr): + assert opLabel in parsed ret.operations[opLabel] = create_operation(opExpr, state_space, basis, gate_type, evotype) if gate_type == "full": From d1a9f5871ac1852752c374213bf1d1a1ef0b1daf Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 3 Sep 2025 16:03:12 -0700 Subject: [PATCH 63/71] change error checking so we only raise an error if we actually observe a collision that puts correctness at risk --- pygsti/modelmembers/modelmembergraph.py | 41 ++++++++++++++++++------- pygsti/models/modelconstruction.py | 33 +++++++++++--------- 2 files changed, 49 insertions(+), 25 deletions(-) diff --git a/pygsti/modelmembers/modelmembergraph.py b/pygsti/modelmembers/modelmembergraph.py index a42b777f0..58439a3af 100644 --- a/pygsti/modelmembers/modelmembergraph.py +++ b/pygsti/modelmembers/modelmembergraph.py @@ -40,18 +40,37 @@ def load_modelmembers_from_serialization_dict(cls, sdict, parent_model): for mm_node_serialized_id_str, mm_node_dict in sdict.items(): mm_node_serialized_id = int(mm_node_serialized_id_str) # convert string keys back to integers - mm_class = ModelMember._state_class(mm_node_dict) # checks this is a ModelMember-derived class + mm_class = ModelMember._state_class(mm_node_dict) # checks this is a ModelMember-derived class mm_serial[mm_node_serialized_id] = mm_class.from_memoized_dict(mm_node_dict, mm_serial, parent_model) - if 'memberdict_types' in mm_node_dict and 'memberdict_labels' in mm_node_dict: - for mm_type, lbl_str in zip(mm_node_dict['memberdict_types'], mm_node_dict['memberdict_labels']): - lbl = _parse_label(lbl_str) - if isinstance(lbl, str): - # This is a sanity check that we can deserialize correctly. Without this check - # it's possible to silently return incorrect results. - assert lbl_str in lbl - if mm_type not in mm_nodes: - mm_nodes[mm_type] = {} - mm_nodes[mm_type][lbl] = mm_serial[mm_node_serialized_id] + + md_types = mm_node_dict.get('memberdict_types', dict()) + md_labels = mm_node_dict.get('memberdict_labels', dict()) + + assert len(md_types) == len(md_labels) + + for mm_type, lbl_str in zip(md_types, md_labels): + lbl = _parse_label(lbl_str) + if mm_type not in mm_nodes: + mm_nodes[mm_type] = {} + d = mm_nodes[mm_type] + if lbl in d: + msg = f""" + We've encountered a collision during deserialization. The + string-valued {mm_type} label "{lbl_str}" was mapped to the + Label object `{lbl}`, but data has already been recorded for + a(n) {mm_type} with this Label. + + This can happen when a string-valued modelmember label doesn't + follow formatting rules required by the parse_labels function + in pygsti.circuits.circuitparser. + + We're raising an error because we can't gaurantee correctness + of the result if we proceeded. + """ + raise RuntimeError(msg) + # ^ Another approach would be to raise an error when `lbl_str not in lbl` + # if `lbl = parse_label(lbl_str)` is a LabelStr. + d[lbl] = mm_serial[mm_node_serialized_id] return mm_nodes diff --git a/pygsti/models/modelconstruction.py b/pygsti/models/modelconstruction.py index f1f740d03..d38a36ee7 100644 --- a/pygsti/models/modelconstruction.py +++ b/pygsti/models/modelconstruction.py @@ -567,20 +567,25 @@ def _create_explicit_model_from_expressions(state_space, basis, ret.povms[povmLbl] = _povm.create_from_dmvecs(effect_vecs, povm_type, basis, evotype, state_space) from pygsti.circuits.circuitparser import parse_label - from pygsti.baseobjs.label import LabelStr - for (opLabel, opExpr) in zip(op_labels, op_expressions): - # Before calling create_operation(...) we check that we'll be able - # to deserialize the model correctly, should serialization and - # deserialization be attempted. - # - # It's good to perform this check here because it's much easier - # to diagnose the source of the error than if we made the check during - # deserialization. - # - parsed = parse_label(opLabel) - if isinstance(parsed, LabelStr): - assert opLabel in parsed - ret.operations[opLabel] = create_operation(opExpr, state_space, basis, gate_type, evotype) + + parsed_op_labels = [parse_label(opLabel) for opLabel in op_labels] + if len(set(parsed_op_labels)) != len(op_labels): + msg = f""" + There are fewer unique Label objects after parsing op_labels than + there are elements in op_labels. If we proceeded with the current + op_labels then we would not be able to return a model that could + be serialized and subequently deserialized (specifically, + deserialization would fail). Since all pyGSTi Model objects implement + the NicelySerializable API, we're raising an error. + + The initial op_labels are {op_labels}. + + The parsed op_labels are {parsed_op_labels}. + """ + raise ValueError(msg) + + for (lbl, opExpr) in zip(parsed_op_labels, op_expressions): + ret.operations[lbl] = create_operation(opExpr, state_space, basis, gate_type, evotype) if gate_type == "full": ret.default_gauge_group = _gg.FullGaugeGroup(ret.state_space, basis, evotype) From ac8971224c36af96b4ee8ec85bb37c533c3c86e8 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 3 Sep 2025 17:10:03 -0700 Subject: [PATCH 64/71] introduce private tolerance parameter in fidelity(...) that needs to be near zero for correctness. Tests now pass even when optools.py.__SCALAR_TOL_EXPONENT__ = -1, which is reasonable to set when trying to suppress warnings or certain errors --- pygsti/tools/optools.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/pygsti/tools/optools.py b/pygsti/tools/optools.py index 1a883e8cf..91206e8c8 100644 --- a/pygsti/tools/optools.py +++ b/pygsti/tools/optools.py @@ -10,7 +10,6 @@ # http://www.apache.org/licenses/LICENSE-2.0 or in the LICENSE file in the root pyGSTi directory. #*************************************************************************************************** -import collections as _collections import warnings as _warnings import numpy as _np @@ -131,7 +130,11 @@ def check_rank_one_density(mat): """ n = mat.shape[0] - if _np.linalg.norm(mat) < __VECTOR_TOL__: + ZERO_THRESHOLD = n * _np.finfo(mat.dtype).eps**0.75 + # ^ This value affects correctness, not just when we raise an error or a warning. + # Don't change from the value given here. + + if _np.linalg.norm(mat) < ZERO_THRESHOLD: # We prefer to return the zero vector instead of None to simplify how we handle # this function's output. return 0, _np.zeros(n, dtype=complex) @@ -145,7 +148,7 @@ def check_rank_one_density(mat): alpha = _np.real(candidate_v.conj() @ mat @ candidate_v) reconstruction = alpha * _np.outer(candidate_v, candidate_v.conj()) - if _np.linalg.norm(mat - reconstruction) > __VECTOR_TOL__: + if _np.linalg.norm(mat - reconstruction) > ZERO_THRESHOLD: # We can't certify that mat is rank-1. return 2, None @@ -155,9 +158,9 @@ def check_rank_one_density(mat): return 0, _np.zeros(n) if abs(alpha - 1) > __SCALAR_TOL__: - message = f"The input matrix is not trace-1 up to tolerance {__SCALAR_TOL__}. Beware result!" + message = f"The input matrix is not trace-1 up to tolerance {__SCALAR_TOL__}." _warnings.warn(message) - candidate_v *= _np.sqrt(alpha) + candidate_v *= _np.sqrt(alpha) return 1, candidate_v From db5e406b2e37508e2d64b1c276900c43472f6e4c Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Thu, 4 Sep 2025 16:22:58 -0700 Subject: [PATCH 65/71] bugfix in circuit parsing from string. Discovered the underlying bug when (de)serializing a model with gate labels like `GCNOT` and `GIX`" --- pygsti/circuits/circuitparser/fastcircuitparser.pyx | 11 +++++++++++ pygsti/circuits/circuitparser/slowcircuitparser.py | 13 ++++++++++++- pygsti/models/modelconstruction.py | 2 +- 3 files changed, 24 insertions(+), 2 deletions(-) diff --git a/pygsti/circuits/circuitparser/fastcircuitparser.pyx b/pygsti/circuits/circuitparser/fastcircuitparser.pyx index 73aad90a8..e77c62bcf 100644 --- a/pygsti/circuits/circuitparser/fastcircuitparser.pyx +++ b/pygsti/circuits/circuitparser/fastcircuitparser.pyx @@ -236,6 +236,11 @@ cdef get_next_simple_lbl(unicode s, INT start, INT end, bool integerize_sslbls, else: while i < end: c = s[i] + if c != 'G': + # We need to convert to lowercase in case there are strings like + # "GXI" "GXX", "GIGY", or "GCNOT". We don't convert 'G' because + # that's a special character for our purposes. + c = c.lower() if u'a' <= c <= u'z' or u'0' <= c <= u'9' or c == u'_': i += 1 else: @@ -248,6 +253,9 @@ cdef get_next_simple_lbl(unicode s, INT start, INT end, bool integerize_sslbls, last = i; is_float = True while i < end: c = s[i] + if c != 'G': + # We convert to lowercase here for the same reason as above. + c = c.lower() if u'a' <= c <= u'z' or c == u'_' or c == u'Q' or c == u'/': i += 1; is_float = False elif u'0' <= c <= u'9' or c == u'.' or c == u'-': @@ -262,6 +270,9 @@ cdef get_next_simple_lbl(unicode s, INT start, INT end, bool integerize_sslbls, last = i; is_int = True while i < end: c = s[i] + if c != 'G': + # We convert to lowercase here for the same reason as above. + c = c.lower() if u'0' <= c <= u'9': i += 1 elif u'a' <= c <= u'z' or c == u'_' or c == u'Q': diff --git a/pygsti/circuits/circuitparser/slowcircuitparser.py b/pygsti/circuits/circuitparser/slowcircuitparser.py index 5ad293b98..971bfe8ee 100644 --- a/pygsti/circuits/circuitparser/slowcircuitparser.py +++ b/pygsti/circuits/circuitparser/slowcircuitparser.py @@ -65,7 +65,7 @@ def parse_circuit(code, create_subcircuits=True, integerize_sslbls=True): return tuple(result), labels, occurrence_id, compilable_indices -def parse_label(code, integerize_sslbls=True): +def parse_label(code: str, integerize_sslbls=True) -> _lbl.Label: create_subcircuits = False segment = 0 # segment for gates/instruments vs. preps vs. povms: 0 = *any* interlayer_marker = u'' # matches nothing - no interlayer markerg @@ -171,6 +171,11 @@ def _get_next_simple_lbl(s, start, end, integerize_sslbls, segment): else: while i < end: c = s[i] + if c != 'G': + # We need to convert to lowercase in case there are strings like + # "GXI" "GXX", "GIGY", or "GCNOT". We don't convert 'G' because + # that's a special character for our purposes. + c = c.lower() if 'a' <= c <= 'z' or '0' <= c <= '9' or c == '_': i += 1 else: @@ -183,6 +188,9 @@ def _get_next_simple_lbl(s, start, end, integerize_sslbls, segment): last = i while i < end: c = s[i] + if c != 'G': + # We convert to lowercase here for the same reason as above. + c = c.lower() if 'a' <= c <= 'z' or '0' <= c <= '9' or c == '_' or c == 'Q' or c == '.' or c == '/' or c == '-': i += 1 else: @@ -199,6 +207,9 @@ def _get_next_simple_lbl(s, start, end, integerize_sslbls, segment): last = i while i < end: c = s[i] + if c != 'G': + # We convert to lowercase here for the same reason as above. + c = c.lower() if 'a' <= c <= 'z' or '0' <= c <= '9' or c == '_' or c == 'Q': i += 1 else: diff --git a/pygsti/models/modelconstruction.py b/pygsti/models/modelconstruction.py index d38a36ee7..e8f0846e2 100644 --- a/pygsti/models/modelconstruction.py +++ b/pygsti/models/modelconstruction.py @@ -568,7 +568,7 @@ def _create_explicit_model_from_expressions(state_space, basis, from pygsti.circuits.circuitparser import parse_label - parsed_op_labels = [parse_label(opLabel) for opLabel in op_labels] + parsed_op_labels = [parse_label(str(opLabel)) for opLabel in op_labels] if len(set(parsed_op_labels)) != len(op_labels): msg = f""" There are fewer unique Label objects after parsing op_labels than From 509b94f8cf2a1d3cf7e18e3028cce45841dc5289 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Tue, 9 Sep 2025 11:27:15 -0700 Subject: [PATCH 66/71] add some circuitlist generation utilities for model locking --- pygsti/protocols/locking.py | 130 ++++++++++++++++++++++++++++++++++++ 1 file changed, 130 insertions(+) create mode 100644 pygsti/protocols/locking.py diff --git a/pygsti/protocols/locking.py b/pygsti/protocols/locking.py new file mode 100644 index 000000000..9f9bf764e --- /dev/null +++ b/pygsti/protocols/locking.py @@ -0,0 +1,130 @@ +# *************************************************************************************************** +# Copyright 2025 National Technology & Engineering Solutions of Sandia, LLC (NTESS). +# Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights +# in this software. +# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except +# in compliance with the License. You may obtain a copy of the License at +# http://www.apache.org/licenses/LICENSE-2.0 or in the LICENSE file in the root pyGSTi directory. +# *************************************************************************************************** +import copy as _copy +import numpy as _np +import itertools as _itertools +import pathlib as _pathlib +import warnings as _warnings + +from pygsti.protocols.treenode import TreeNode as _TreeNode +from pygsti import io as _io +from collections.abc import Container as _Container +from pygsti.circuits import ( + Circuit as _Circuit, + CircuitList as _CircuitList, + CircuitPlaquette as _CircuitPlaquette +) +from pygsti import data as _data +from pygsti.tools import NamedDict as _NamedDict +from pygsti.tools import listtools as _lt +from pygsti.tools.dataframetools import _process_dataframe +from pygsti.baseobjs.label import ( + LabelStr as _LabelStr +) +from pygsti.baseobjs.mongoserializable import MongoSerializable as _MongoSerializable +from pygsti.baseobjs.nicelyserializable import NicelySerializable as _NicelySerializable +from pygsti.protocols.protocol import Protocol as _Protocol, CircuitListsDesign as _CircuitListsDesign + +from typing import Union, Literal + +BinningStrategy = Union[ + int, Literal['auto-int'], Literal['auto'], Literal['fd'], Literal['doane'], Literal['scott'], Literal['stone'], Literal['sturges'] +] +LengthTransformer = Union[ + Literal['log'], Union[Literal['none'], None], _np.ufunc +] + +def histonested_circuitlists( + circuits: Union[_CircuitList, list[_Circuit]], + bins: BinningStrategy = 'auto-int', + trans: LengthTransformer = 'log' + ) -> list[_Circuit]: + """ + This is a helper function for building CircuitListsDesign objects with certain + nested structures. If `clists` is the output of this function, then the induced + design is canonically + + d = CircuitListsDesign(clists nested=True, remove_duplicates=True). + + If `circuits` contained no duplicates, then we'll have + + set(circuits) == set(d.all_circuits_needing_data). + """ + assert len(circuits) > 0 + lengths = _np.array([len(c) + 1 for c in circuits]) + if isinstance(bins, str) and 'auto' in bins and 'int' in bins: # type: ignore + bins = int(_np.log2(_np.max(lengths))) + if isinstance(trans, _np.ufunc): + lengths = trans(lengths) + elif trans == 'log': + lengths = _np.log2(lengths) + elif (trans != 'none') and (trans is not None): + raise ValueError(f'Argument `trans` had unsupported value, {trans}.') + # get bin edges from numpy, then drop empty bins. + counts, edges = _np.histogram(lengths, bins) + edges = _np.concatenate([[edges[0]], edges[1:][counts > 0]]) + assignments = _np.digitize(lengths, edges) + assignments -= 1 + # edges[ assignments[j] ] <= lengths[j] < edges[ assignments[j]+1 ] + num_bins = edges.size - 1 + circuit_lists = [list() for _ in range(num_bins)] + for j, c in zip(assignments, circuits): + for i in range(j, num_bins): + circuit_lists[i].append(c) + """ + # The following approch to building circuit_lists is (in theory) less efficient than + # the approach above, but it has the advantage of avoiding the nested for-loop. + circuit_lists = [] + last_size = 0 + edges = _np.histogram_bin_edges(lengths, bins) + for upperbound in edges[1:]: + cur_inds = _np.where(lengths < upperbound)[0] + if cur_inds.size == last_size: + continue # empty bin + last_size = cur_inds.size + circuit_lists.append([circuits[j] for j in cur_inds]) + """ + return circuit_lists # type: ignore + + +def logspaced_prefix_circuits( + c: _Circuit, + povms_to_keep: _Container[_LabelStr] = (_LabelStr('Mdefault'),), + base: Union[int, float]=2, + editable = False + ) -> list[_Circuit]: + + if len(c) > 0 and c[-1] in povms_to_keep: + M = c[-1] + if not isinstance(M, _LabelStr): + M = _LabelStr(M) + c = c[:-1] + circuits = logspaced_prefix_circuits(c, tuple(), base, editable=True) + for c in circuits: + c._labels.append(M) # type: ignore + c.done_editing() + return circuits + + if editable and not c._static: + c = c.copy(editable=True) # type: ignore + + circuits = [c] + assert base > 1 + next_len = int(len(c) // base) + while next_len > 0: + c = c[:next_len] + circuits.append(c) + next_len = int(len(c) // base) + + if not editable: + for c in circuits: + c.done_editing() + + return circuits + From 4cdcf78b564290886e99acba78bf78a45bb25799 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Wed, 10 Sep 2025 10:43:30 -0700 Subject: [PATCH 67/71] less invasive change to implementation of ExplicitOpModel.copy(). Changes needed for test_gaugeopt_correctness. --- pygsti/algorithms/gaugeopt.py | 19 ++++++++++++++----- pygsti/models/model.py | 11 +++++------ pygsti/tools/optools.py | 2 +- .../algorithms/test_gaugeopt_correctness.py | 18 ++++++++++-------- 4 files changed, 30 insertions(+), 20 deletions(-) diff --git a/pygsti/algorithms/gaugeopt.py b/pygsti/algorithms/gaugeopt.py index c332553fa..a63d73822 100644 --- a/pygsti/algorithms/gaugeopt.py +++ b/pygsti/algorithms/gaugeopt.py @@ -335,12 +335,21 @@ def _call_jacobian_fn(gauge_group_el_vec): if bToStdout and (comm is None or comm.Get_rank() == 0): print_obj_func = _opt.create_objfn_printer(_call_objective_fn) # only ever prints to stdout! # print_obj_func(x0) #print initial point (can be a large vector though) - else: print_obj_func = None + else: + print_obj_func = None + + minimize_kwargs = {'method': method, 'maxiter': maxiter, 'maxfev': maxfev, 'tol': tol} + minimize_kwargs['jac'] = '3-point' if _call_jacobian_fn is None else _call_jacobian_fn - minSol = _opt.minimize(_call_objective_fn, x0, - method=method, maxiter=maxiter, maxfev=maxfev, - tol=tol, jac=_call_jacobian_fn, - callback=print_obj_func) + minSol = _opt.minimize(_call_objective_fn, x0, callback=print_obj_func, **minimize_kwargs) + if not minSol.success: + msg = f"""\n + Gauge optimization failed to converge! Algorithm parameters were + {minimize_kwargs}. + + The optimizer returned with message "{minSol.message}". + """ + _warnings.warn(msg) solnX = minSol.x solnF = minSol.fun diff --git a/pygsti/models/model.py b/pygsti/models/model.py index ba7b6ca52..cea0bcbf8 100644 --- a/pygsti/models/model.py +++ b/pygsti/models/model.py @@ -2321,14 +2321,13 @@ def copy(self): self._clean_paramvec() # ensure _paramvec is rebuilt if needed if OpModel._pcheck: self._check_paramvec() try: - # This only works if all modelmembers are NicelySerializable. - # It does a better job than `Model.copy` for resolving ambiguities - # in how we should set the `parent` field of Modelmembers in the - # copied model. + ret = Model.copy(self) + except: + # This can do a better job than `Model.copy` at resolving ambiguities + # in setting the `parent` field of Modelmembers in the copied model. state = self.to_nice_serialization() ret = type(self).from_nice_serialization(state) - except: - ret = Model.copy(self) + if self._param_bounds is not None and self.parameter_labels is not None: ret._clean_paramvec() # will *always* rebuild paramvec; do now so we can preserve param bounds assert _np.all(self.parameter_labels == ret.parameter_labels) # ensure ordering is the same diff --git a/pygsti/tools/optools.py b/pygsti/tools/optools.py index a52690882..a3dd4c00c 100644 --- a/pygsti/tools/optools.py +++ b/pygsti/tools/optools.py @@ -143,7 +143,7 @@ def check_rank_one_density(mat): """ n = mat.shape[0] - ZERO_THRESHOLD = n * _np.finfo(mat.dtype).eps**0.75 + ZERO_THRESHOLD = n**0.5 * _np.finfo(mat.dtype).eps**0.75 # ^ This value affects correctness, not just when we raise an error or a warning. # Don't change from the value given here. diff --git a/test/unit/algorithms/test_gaugeopt_correctness.py b/test/unit/algorithms/test_gaugeopt_correctness.py index af4b39c70..3012bc7a7 100644 --- a/test/unit/algorithms/test_gaugeopt_correctness.py +++ b/test/unit/algorithms/test_gaugeopt_correctness.py @@ -41,15 +41,15 @@ def check_gate_metrics_are_nontrivial(metrics, tol): for lbl, val in metrics['infids'].items(): if lbl == idle_label: continue - assert val > tol, f"{val} is at most {tol}, failure for gate {lbl} w.r.t. infidelity." + assert val > tol, f"{val} is <= {tol}, failure for gate {lbl} w.r.t. infidelity." for lbl, val in metrics['frodists'].items(): if lbl == idle_label: continue - assert val > tol, f"{val} is at most {tol}, failure for gate {lbl} w.r.t. frobenius distance." + assert val > tol, f"{val} is <= {tol}, failure for gate {lbl} w.r.t. frobenius distance." for lbl, val in metrics['tracedists'].items(): if lbl == idle_label: continue - assert val > tol, f"{val} is at most {tol}, failure for gate {lbl} w.r.r. trace distance." + assert val > tol, f"{val} is <= {tol}, failure for gate {lbl} w.r.r. trace distance." return @@ -111,18 +111,20 @@ def _gauge_transform_model(self, seed): self.model = self.target.copy() self.model.default_gauge_group = self.default_gauge_group np.random.seed(seed) - self.U = la.expm(np.random.randn()/2 * -1j * (pgbc.sigmax + pgbc.sigmaz)/np.sqrt(2)) + strength = (np.random.rand() + 1)/100 + self.U = la.expm(strength * -1j * (pgbc.sigmax + pgbc.sigmaz)/np.sqrt(2)) self.gauge_grp_el = self.gauge_grp_el_class(pgo.unitary_to_pauligate(self.U)) self.model.transform_inplace(self.gauge_grp_el) self.metrics_before = gate_metrics_dict(self.model, self.target) - check_gate_metrics_are_nontrivial(self.metrics_before, tol=1e-2) + check_gate_metrics_are_nontrivial(self.metrics_before, tol=1e-4) return def _main_tester(self, method, seed, test_tol, alg_tol, gop_objective): assert gop_objective in {'frobenius', 'frobenius_squared', 'tracedist', 'fidelity'} self._gauge_transform_model(seed) tic = time.time() - newmodel = gop.gaugeopt_to_target(self.model, self.target, method=method, tol=alg_tol, spam_metric=gop_objective, gates_metric=gop_objective) + verbosity = 0 + newmodel = gop.gaugeopt_to_target(self.model, self.target, method=method, tol=alg_tol, spam_metric=gop_objective, gates_metric=gop_objective, verbosity=verbosity) toc = time.time() dt = toc - tic metrics_after = gate_metrics_dict(newmodel, self.target) @@ -133,7 +135,7 @@ def test_lbfgs_frodist(self): self.setUp() times = [] for seed in range(self.N_REPS): - dt = self._main_tester('L-BFGS-B', seed, test_tol=1e-6, alg_tol=1e-8, gop_objective='frobenius') + dt = self._main_tester('L-BFGS-B', seed, test_tol=1e-5, alg_tol=1e-7, gop_objective='frobenius') times.append(dt) print(f'GaugeOpt over {self.gauge_space_name} w.r.t. Frobenius dist, using L-BFGS: {times}.') return @@ -142,7 +144,7 @@ def test_lbfgs_tracedist(self): self.setUp() times = [] for seed in range(self.N_REPS): - dt = self._main_tester('L-BFGS-B', seed, test_tol=1e-6, alg_tol=1e-8, gop_objective='tracedist') + dt = self._main_tester('L-BFGS-B', seed, test_tol=1e-5, alg_tol=1e-7, gop_objective='tracedist') times.append(dt) print(f'GaugeOpt over {self.gauge_space_name} w.r.t. trace dist, using L-BFGS: {times}.') return From 5e08e32ebf61888cf95df5b9f205aced184019c7 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Thu, 11 Sep 2025 11:07:14 -0700 Subject: [PATCH 68/71] type annotation and comment fix --- pygsti/drivers/longsequence.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/pygsti/drivers/longsequence.py b/pygsti/drivers/longsequence.py index 547194184..f97240b9f 100644 --- a/pygsti/drivers/longsequence.py +++ b/pygsti/drivers/longsequence.py @@ -25,7 +25,7 @@ from pygsti.models.modelconstruction import _create_explicit_model, create_explicit_model from pygsti.protocols.gst import _load_pspec_or_model from pygsti.forwardsims import ForwardSimulator -from typing import Optional +from typing import Optional, Any ROBUST_SUFFIX_LIST = [".robust", ".Robust", ".robust+", ".Robust+"] DEFAULT_BAD_FIT_THRESHOLD = 2.0 @@ -315,7 +315,7 @@ def run_linear_gst(data_filename_or_set, target_model_filename_or_object, def run_long_sequence_gst(data_filename_or_set, target_model_filename_or_object, prep_fiducial_list_or_filename, meas_fiducial_list_or_filename, germs_list_or_filename, max_lengths, gauge_opt_params=None, - advanced_options=None, comm=None, mem_limit=None, + advanced_options: Optional[dict[str,Any]]=None, comm=None, mem_limit=None, output_pkl=None, verbosity=2, checkpoint=None, checkpoint_path=None, disable_checkpointing=False, simulator: Optional[ForwardSimulator.Castable]=None, @@ -388,9 +388,7 @@ def run_long_sequence_gst(data_filename_or_set, target_model_filename_or_object, and values include: HISTORICAL NOTE: "XX" indicates that we've at least _intended_ for the - keyword argument to be removed. I've removed documentation for options - that we never reference in the code (truncScheme, estimate_label, and - missingDataAction). + keyword argument to be removed. - objective = typically, a string in {'chi2', 'logl', 'tvd'}. But this can be anything accepted by the `ObjectiveFunctionBuilder.create_from` function. From 9c64c9d29a4117feba1bc1bb1644b4f26a712c2a Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Thu, 11 Sep 2025 11:39:55 -0700 Subject: [PATCH 69/71] revert seemingly unnecessary change to copalayout.py (in the context of a TypeError from CircuitOutcomeProbabilityArrayLayout.indices_and_outcomes_for_index) --- pygsti/layouts/copalayout.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/pygsti/layouts/copalayout.py b/pygsti/layouts/copalayout.py index 265490d0e..97a2b6efc 100644 --- a/pygsti/layouts/copalayout.py +++ b/pygsti/layouts/copalayout.py @@ -203,7 +203,6 @@ def __init__(self, circuits, unique_circuits, to_unique, elindex_outcome_tuples, self._outcomes = dict() self._element_indices = dict() - self._element_inditer = dict() ind_array = _np.arange(self._size) sort_idx_func = lambda x: x[0] for i_unique, tuples in elindex_outcome_tuples.items(): @@ -212,7 +211,6 @@ def __init__(self, circuits, unique_circuits, to_unique, elindex_outcome_tuples, self._outcomes[i_unique] = tuple(outcomes) s = _slct.list_to_slice(elindices, array_ok=True) self._element_indices[i_unique] = s - self._element_inditer[i_unique] = ind_array[s] # def hotswap_circuits(self, circuits, unique_complete_circuits=None): # self.circuits = circuits if isinstance(circuits, _CircuitList) else _CircuitList(circuits) @@ -744,10 +742,7 @@ def indices_and_outcomes_for_index(self, index): def __iter__(self): for circuit, i in self._unique_circuit_index.items(): - try: - iterator = zip(self._element_indices[i], self._outcomes[i]) - except TypeError: - iterator = zip(self._element_inditer[i], self._outcomes[i]) + iterator = zip(self._element_indices[i], self._outcomes[i]) for element_index, outcome in iterator: yield element_index, circuit, outcome From 09d70466c150b668cf5e7c6e8fba61c1176605f1 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Thu, 11 Sep 2025 11:42:02 -0700 Subject: [PATCH 70/71] remove unused variable --- pygsti/layouts/copalayout.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pygsti/layouts/copalayout.py b/pygsti/layouts/copalayout.py index 97a2b6efc..affa46653 100644 --- a/pygsti/layouts/copalayout.py +++ b/pygsti/layouts/copalayout.py @@ -203,7 +203,6 @@ def __init__(self, circuits, unique_circuits, to_unique, elindex_outcome_tuples, self._outcomes = dict() self._element_indices = dict() - ind_array = _np.arange(self._size) sort_idx_func = lambda x: x[0] for i_unique, tuples in elindex_outcome_tuples.items(): sorted_tuples = sorted(tuples, key=sort_idx_func) # sort by element index From cc687dd141c49168de4e6b30e5ca8ec4b8146721 Mon Sep 17 00:00:00 2001 From: Riley Murray Date: Thu, 11 Sep 2025 13:38:17 -0700 Subject: [PATCH 71/71] add type annotation and fix now-obvious bug in CircuitOutcomeProbabilityArrayLayout.__iter__ (needed to convert slice to iterable) --- pygsti/layouts/copalayout.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/pygsti/layouts/copalayout.py b/pygsti/layouts/copalayout.py index affa46653..d1bda42dd 100644 --- a/pygsti/layouts/copalayout.py +++ b/pygsti/layouts/copalayout.py @@ -202,14 +202,14 @@ def __init__(self, circuits, unique_circuits, to_unique, elindex_outcome_tuples, "Inconsistency: %d distinct indices but max index + 1 is %d!" % (len(indices), self._size) self._outcomes = dict() - self._element_indices = dict() + self._element_indices : dict[tuple, slice] = dict() sort_idx_func = lambda x: x[0] for i_unique, tuples in elindex_outcome_tuples.items(): sorted_tuples = sorted(tuples, key=sort_idx_func) # sort by element index elindices, outcomes = zip(*sorted_tuples) # sorted by elindex so we make slices whenever possible self._outcomes[i_unique] = tuple(outcomes) s = _slct.list_to_slice(elindices, array_ok=True) - self._element_indices[i_unique] = s + self._element_indices[i_unique] = s # type: ignore # def hotswap_circuits(self, circuits, unique_complete_circuits=None): # self.circuits = circuits if isinstance(circuits, _CircuitList) else _CircuitList(circuits) @@ -741,7 +741,8 @@ def indices_and_outcomes_for_index(self, index): def __iter__(self): for circuit, i in self._unique_circuit_index.items(): - iterator = zip(self._element_indices[i], self._outcomes[i]) + indices = _slct.to_array(self._element_indices[i]) + iterator = zip(indices, self._outcomes[i]) for element_index, outcome in iterator: yield element_index, circuit, outcome