From 8a7edce3e8ab94bdee85d569a66094b81fe3c490 Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Thu, 21 Sep 2017 13:20:21 +0300 Subject: [PATCH 01/33] Block-wise diagonalization Implementation of block-wise diagonalization with conserving number of particles by Hamiltonian --- pyed/SparseExactDiagonalization.py | 196 +++++++++++++++++------------ pyed/SparseMatrixFockStates.py | 81 +++++++----- pyed/TriqsExactDiagonalization.py | 53 ++++---- 3 files changed, 198 insertions(+), 132 deletions(-) diff --git a/pyed/SparseExactDiagonalization.py b/pyed/SparseExactDiagonalization.py index 6a5b4f0..6dd1f18 100644 --- a/pyed/SparseExactDiagonalization.py +++ b/pyed/SparseExactDiagonalization.py @@ -1,8 +1,8 @@ - """ General routines for exact diagonalization using sparse matrices Author: Hugo U. R. Strand (2017), hugo.strand@gmail.com + Yaroslav Zhumagulov (2017), yaroslav.zhumagulov@gmail.com """ # ---------------------------------------------------------------------- @@ -16,81 +16,83 @@ from scipy.sparse.linalg import eigs as eigs_sparse from scipy.sparse.linalg import eigsh as eigsh_sparse - +from scipy.sparse import vstack +from scipy.sparse import csr_matrix # ---------------------------------------------------------------------- - +import progressbar from CubeTetras import CubeTetras # ---------------------------------------------------------------------- class SparseExactDiagonalization(object): - """ Exact diagonalization and one- and two- particle Green's + """ Exact diagonalization and one- and two- particle Green's function calculator. """ # ------------------------------------------------------------------ - def __init__(self, H, beta, - nstates=None, hermitian=True, + def __init__(self, H,blocks, beta, + nstates, hermitian=True, v0=None, tol=0): self.v0 = v0 self.tol = tol - - self.nstates = nstates + self.nstates=nstates self.hermitian = hermitian - self.H = H + self.blocks=blocks self.beta = beta - self._diagonalize_hamiltonian() + self._number_of_states_reduction() self._calculate_partition_function() - self._calculate_density_matrix() - + # self._calculate_density_matrix() # ------------------------------------------------------------------ def _diagonalize_hamiltonian(self): - + self.full_U=csr_matrix(self.H.shape,dtype=np.float) + self.full_E=np.zeros(self.H.shape[0]) + print 'Hamiltonian diagonalization:' + bar = progressbar.ProgressBar() + for i in bar(range(len(self.blocks))): + block=self.blocks[i] + X,Y=np.meshgrid(block,block) + E,U=np.linalg.eigh(self.H[X,Y].todense()) + self.full_E[block]=E + self.full_U[Y,X]=U + self.full_E=np.array(self.full_E) + self.E0 = np.min(self.full_E) + self.full_E = self.full_E-self.E0 + # ------------------------------------------------------------------ + def _number_of_states_reduction(self): if self.nstates is None: - if self.hermitian: - self.E, self.U = np.linalg.eigh(self.H.todense()) - else: - self.E, self.U = np.linalg.eig(self.H.todense()) + self.E=self.full_E + self.U=self.full_U else: - if self.hermitian: - t = time.time() - self.E, self.U = eigsh_sparse( - self.H, k=self.nstates, which='SA', - v0=self.v0, tol=self.tol, ncv=self.nstates*8+1) - print 'ED:', time.time() - t, ' s' - else: - self.E, self.U = eigs_sparse( - self.H, k=self.nstates, which='SR', - v0=self.v0, tol=self.tol) - - self.U = np.mat(self.U) - self.E0 = np.min(self.E) - self.E = self.E - self.E0 + indexes=np.argsort(self.full_E)[:self.nstates] + self.E=self.full_E[indexes] + self.U=self.full_U[:,indexes] # ------------------------------------------------------------------ def _calculate_partition_function(self): - - exp_bE = np.exp(-self.beta * self.E) - self.Z = np.sum(exp_bE) + self.Z = np.sum(np.exp(-self.beta*self.E)) # ------------------------------------------------------------------ def _calculate_density_matrix(self): - - exp_bE = np.exp(-self.beta * self.E) / self.Z - self.rho = np.einsum('ij,j,jk->ik', self.U, exp_bE, self.U.H) + self.rho=csr_matrix(self.H.shape,dtype=np.float) + print 'Density matrix calculation:' + bar = progressbar.ProgressBar() + for i in bar(range(len(self.blocks))): + block=self.blocks[i] + X,Y=np.meshgrid(block,block) + exp_bE = np.exp(-self.beta * self.full_E[block]) / self.Z + self.rho[X,Y]= np.einsum('ij,j,jk->ik', self.full_U[X,Y].todense(), exp_bE, self.full_U[X,Y].H.todense()) # ------------------------------------------------------------------ def _operators_to_eigenbasis(self, op_vec): dop_vec = [] for op in op_vec: - dop = np.mat(self.U).H * op.todense() * np.mat(self.U) - dop_vec.append(dop) - + dop=self.U.getH()*op*self.U + dop_vec.append(dop.todense()) return dop_vec - + # ------------------------------------------------------------------ def get_expectation_value_sparse(self, operator): @@ -103,12 +105,13 @@ def get_expectation_value_sparse(self, operator): exp_val /= self.Z return exp_val - + # ------------------------------------------------------------------ def get_expectation_value_dense(self, operator): - if not hasattr(self, 'rho'): self._calculate_density_matrix() - return np.sum(np.diag(operator * self.rho)) + if not hasattr(self, 'rho'): self._calculate_density_matrix() + return np.sum((operator * self.rho).diagonal()) + # ------------------------------------------------------------------ def get_expectation_value(self, operator): @@ -117,7 +120,7 @@ def get_expectation_value(self, operator): return self.get_expectation_value_dense(operator) else: return self.get_expectation_value_sparse(operator) - + # ------------------------------------------------------------------ def get_free_energy(self): @@ -128,10 +131,10 @@ def get_free_energy(self): Z = e^{-\beta E_0} x \sum_n e^{-\beta (E_n - E_0)} = e^{-beta E_0} Z' \Omega = -1/\beta ( \ln Z' - \beta E_0 ) """ - + Omega = -1./self.beta * (np.log(self.Z) - self.beta * self.E0) return Omega - + # ------------------------------------------------------------------ def get_partition_function(self): return self.Z @@ -151,13 +154,42 @@ def get_eigen_vectors(self): # ------------------------------------------------------------------ def get_ground_state_energy(self): return self.E0 - + + def get_grand_potential(self): + return self.E0-np.log(np.sum(np.exp(-self.beta*self.E)))/self.beta + + def get_real_frequency_greens_function_component(self, w, op1, op2,eta): + r""" + Returns: + G^{(2)}(i\omega_n) = -1/Z < O_1(i\omega_n) O_2(-i\omega_n) > + """ + + # -- Components of the Lehman expression + dE = - self.E[:, None] + self.E[None, :] + exp_bE = np.exp(-self.beta * self.E) + M = exp_bE[:, None] + exp_bE[None, :] + + inv_freq = w[:, None, None] - dE[None, :, :] + 1j*eta + nonzero_idx = np.nonzero(inv_freq) + # -- Only eval for non-zero values + freq = np.zeros_like(inv_freq,dtype=np.complex128) + freq[nonzero_idx] = (inv_freq[nonzero_idx]) ** (-1) + + op1_eig, op2_eig = self._operators_to_eigenbasis([op1, op2]) + + # -- Compute Lehman sum for all operator combinations + G = np.zeros((len(w)), dtype=np.complex) + G = np.einsum('nm,mn,nm,znm->z', op1_eig, op2_eig, M, freq) + G /= self.Z + + return G + # ------------------------------------------------------------------ def get_g2_dissconnected_tau_tetra(self, tau, tau_g, g): g = np.squeeze(g) # fix for now throwing orb idx g = g.real - + N = len(tau) G4 = np.zeros((N, N, N), dtype=np.complex) @@ -181,7 +213,7 @@ def get_g2_dissconnected_tau(self, tau, tau_g, g): g = np.squeeze(g) # fix for now throwing orb idx g = g.real - + N = len(tau) G4 = np.zeros((N, N, N), dtype=np.complex) @@ -195,12 +227,12 @@ def gint(t_in): t1, t2, t3 = np.meshgrid(tau, tau, tau, indexing='ij') G4 = gint(t1-t2)*gint(t3) - gint(t1)*gint(t3-t2) - + return G4 - + # ------------------------------------------------------------------ def get_g2_tau(self, tau, ops): - + N = len(tau) G4 = np.zeros((N, N, N), dtype=np.complex) ops = np.array(ops) @@ -209,16 +241,16 @@ def get_g2_tau(self, tau, ops): idx, taus, perm, perm_sign = tetra print 'Tetra:', tidx - + # do not permute the last operator ops_perm = ops[perm + [3]] taus_perm = taus[perm] # permute the times - + G4[idx] = self.get_timeordered_three_tau_greens_function( taus_perm, ops_perm) * perm_sign - + return G4 - + # ------------------------------------------------------------------ def get_timeordered_two_tau_greens_function(self, taus, ops): @@ -227,8 +259,8 @@ def get_timeordered_two_tau_greens_function(self, taus, ops): ops = [O1, O2, O3] Returns: - G^{(4)}(t1, t2) = -1/Z < O1(t1) O2(t2) O3(0) > - + G^{(4)}(t1, t2) = -1/Z < O1(t1) O2(t2) O3(0) > + """ Nop = 3 @@ -256,7 +288,7 @@ def get_timeordered_two_tau_greens_function(self, taus, ops): G = np.einsum('ta,tb,tc,ab,bc,ca->t', et_a, et_b, et_c, op1, op2, op3) - G /= self.Z + G /= self.Z return G # ------------------------------------------------------------------ @@ -267,8 +299,8 @@ def get_timeordered_three_tau_greens_function(self, taus, ops): ops = [O1, O2, O3, O4] Returns: - G^{(4)}(t1, t2, t3) = -1/Z < O1(t1) O2(t2) O3(t3) O4(0) > - + G^{(4)}(t1, t2, t3) = -1/Z < O1(t1) O2(t2) O3(t3) O4(0) > + """ assert( taus.shape[0] == 3 ) @@ -305,7 +337,7 @@ def get_timeordered_three_tau_greens_function(self, taus, ops): 'ta,tb,tc,td,ab,bc,cd,da->t', et_a, et_b, et_c, et_d, op1, op2, op3, op4) - G /= self.Z + G /= self.Z return G # ------------------------------------------------------------------ @@ -319,18 +351,18 @@ def get_tau_greens_function_component(self, tau, op1, op2): G = np.zeros((len(tau)), dtype=np.complex) op1_eig, op2_eig = self._operators_to_eigenbasis([op1, op2]) - + et_p = np.exp((-self.beta + tau[:,None])*self.E[None,:]) et_m = np.exp(-tau[:,None]*self.E[None,:]) - + G = -np.einsum('tn,tm,nm,mn->t', et_p, et_m, op1_eig, op2_eig) - G /= self.Z + G /= self.Z return G # ------------------------------------------------------------------ def get_frequency_greens_function_component(self, iwn, op1, op2, xi): - + r""" Returns: G^{(2)}(i\omega_n) = -1/Z < O_1(i\omega_n) O_2(-i\omega_n) > @@ -354,18 +386,18 @@ def get_frequency_greens_function_component(self, iwn, op1, op2, xi): G = np.einsum('nm,mn,nm,znm->z', op1_eig, op2_eig, M, freq) G /= self.Z - return G - + return G + # ------------------------------------------------------------------ def get_high_frequency_tail_coeff_component( self, op1, op2, xi, Norder=3): - - r""" The high frequency tail corrections can be derived + + r""" The high frequency tail corrections can be derived directly from the imaginary time expression for the Green's function - + G(t) = -1/Z Tr[e^{-\beta H} e^{tH} b e^{-tH} b^+] - and the observation that the high frequency components of the + and the observation that the high frequency components of the Matsubara Green's function G(i\omega_n) can be obtained by partial integration in @@ -383,39 +415,39 @@ def get_high_frequency_tail_coeff_component( Using this the high frequency coefficients c_k takes the form - c_k = (-1)^(k-1) (\xi G^{(k-1)}(\beta^-) - G^{(k-1)}(0^+)) + c_k = (-1)^(k-1) (\xi G^{(k-1)}(\beta^-) - G^{(k-1)}(0^+)) = (-1)^k < [ [[ H , b ]]^{(k-1)} , b^+ ]_{-\xi} > """ def xi_commutator(A, B, xi): return A * B - xi * B * A - + def commutator(A, B): return A * B - B * A H = self.H - + Gc = np.zeros((Norder), dtype=np.complex) ba, bc = op1, op2 Hba = ba for order in xrange(Norder): - tail_op = xi_commutator(Hba, bc, xi) + tail_op = xi_commutator(Hba, bc, xi) Gc[order] = (-1.)**(order) * \ self.get_expectation_value(tail_op) Hba = commutator(H, Hba) - - return Gc + + return Gc # ------------------------------------------------------------------ def get_high_frequency_tail(self, iwn, Gc, start_order=-1): - """ from the high frequency coefficients Gc calculate the + """ from the high frequency coefficients Gc calculate the Matsubara Green's function tail G(i\omega_n) = \sum_k Gc[k] / (i\omega_n)^k """ - + Nop = Gc.shape[-1] Nw = len(iwn) G = np.zeros((Nw, Nop, Nop), dtype=np.complex) @@ -424,7 +456,7 @@ def get_high_frequency_tail(self, iwn, Gc, start_order=-1): G[iwn_idx, :, :] += \ iwn[iwn_idx, None, None]**(-idx+start_order) * gc[None, :, :] - return G + return G # ------------------------------------------------------------------ diff --git a/pyed/SparseMatrixFockStates.py b/pyed/SparseMatrixFockStates.py index 6b69f3e..b3e11db 100644 --- a/pyed/SparseMatrixFockStates.py +++ b/pyed/SparseMatrixFockStates.py @@ -4,6 +4,7 @@ and annihilation operators for a finite Fock space. Author: Hugo U. R. Strand (2017), hugo.strand@gmail.com + Yaroslav Zhumagulov (2017), yaroslav.zhumagulov@gmail.com """ # ---------------------------------------------------------------------- @@ -15,10 +16,10 @@ # ---------------------------------------------------------------------- class SparseMatrixRepresentation(object): - """ Generator for sparse matrix representations of - Triqs operator expressions, given a set of fundamental + """ Generator for sparse matrix representations of + Triqs operator expressions, given a set of fundamental creation operators. """ - + # ------------------------------------------------------------------ def __init__(self, fundamental_operators): @@ -43,52 +44,78 @@ def __init__(self, fundamental_operators): assert len(operator_labels_set) == len(self.operator_labels), \ "ERROR: Repeated operators in fundamental_operators!" - + self.operator_labels = [ (dag, list(idx)) for dag, idx in self.operator_labels ] self.nfermions = len(self.operator_labels) self.sparse_operators = \ SparseMatrixCreationOperators(self.nfermions) - + self.indexes_blocks=self.sparse_operators.indexes_blocks # ------------------------------------------------------------------ def sparse_matrix(self, triqs_operator_expression): """ Convert a general Triqs operator expression to a sparse matrix representation. """ - + matrix_rep = 0.0 * self.sparse_operators.I - + for term, coef in triqs_operator_expression: product = coef * self.sparse_operators.I - + for fact in term: dagger, idx = fact oidx = self.operator_labels.index((False, idx)) - op = self.sparse_operators.c_dag[oidx] - if not dagger: op = op.getH() + if not dagger: op = op.getH() product = product * op matrix_rep = matrix_rep + product - + return matrix_rep - + # ---------------------------------------------------------------------- class SparseMatrixCreationOperators: - """ Generator of sparse matrix representation of fermionic + """ Generator of sparse matrix representation of fermionic creation operators, for finite number of fermions. """ - + # ------------------------------------------------------------------ def __init__(self, nfermions): self.nfermions = nfermions self.nstates = 2**nfermions + # -- Make python based fock states + self.numbers = np.arange(self.nstates, dtype=np.uint32) + tmp = self.numbers.flatten().view(np.uint8).reshape((self.nstates, 4)) + tmp = np.fliplr(tmp) + self.states = np.unpackbits(tmp, axis=1) + + + + raw_states=self.states[:,-self.nfermions:] + states_up=raw_states[:,::2] + states_down=raw_states[:,1::2] + indexes_const=[] + + for n_up in range(self.nfermions/2+1): + for n_down in range(self.nfermions/2+1): + indexes=np.where((np.sum(states_up,axis=1)==n_up)&(np.sum(states_down,axis=1)==n_down))[0] + indexes_const.append(indexes) + self.permutation=np.zeros(self.nstates) + self.permutation[np.concatenate(indexes_const,axis=0)]=np.arange(self.nstates) + self.permutation=np.array(self.permutation,dtype=np.int) + self.indexes_blocks=[] + for n_up in range(self.nfermions/2+1): + for n_down in range(self.nfermions/2+1): + indexes=np.where((np.sum(states_up,axis=1)==n_up)&(np.sum(states_down,axis=1)==n_down))[0].flatten() + self.indexes_blocks.append(self.permutation[indexes]) + + self.c_dag = [] for fidx in xrange(nfermions): c_dag_fidx = self._build_creation_operator(fidx) @@ -96,27 +123,22 @@ def __init__(self, nfermions): self.I = sparse.eye( self.nstates, self.nstates, dtype=np.float, format='csr') - + + + # ------------------------------------------------------------------ def _build_creation_operator(self, orbidx): nstates = self.nstates - - # -- Make python based fock states - numbers = np.arange(nstates, dtype=np.uint32) - tmp = numbers.flatten().view(np.uint8).reshape((nstates, 4)) - tmp = np.fliplr(tmp) - states = np.unpackbits(tmp, axis=1) - # -- Apply creation operator - orbocc = states[:, -1 - orbidx] - rightstates = states[:, -1 - orbidx:] + orbocc = self.states[:, -1 - orbidx] + rightstates = self.states[:, -1 - orbidx:] - states_new = np.copy(states) + states_new = np.copy(self.states) states_new[:, -1 - orbidx] = 1 # -- collect sign sign = 1 - 2*np.array( - np.mod(np.sum(rightstates[:, 1:], axis=1), 2), + np.mod(np.sum(rightstates[:, 1:], axis=1), 2), dtype=np.float64) # -- Transform back to uint16 @@ -126,8 +148,10 @@ def _build_creation_operator(self, orbidx): # -- Collect non-zero elements idx = orbocc == 0 - I = numbers_new[idx] - J = numbers[idx] + I = self.permutation[numbers_new[idx]] + J = self.permutation[self.numbers[idx]] + # I=numbers_new[idx] + # J=self.numbers[idx] D = sign[idx] # -- Build sparse matrix repr. @@ -137,4 +161,3 @@ def _build_creation_operator(self, orbidx): return cdagger # ---------------------------------------------------------------------- - diff --git a/pyed/TriqsExactDiagonalization.py b/pyed/TriqsExactDiagonalization.py index 1965e93..4301e1a 100644 --- a/pyed/TriqsExactDiagonalization.py +++ b/pyed/TriqsExactDiagonalization.py @@ -1,8 +1,9 @@ -""" +""" Exact diagonalization and single- and two-particle Green's function calculator for Triqs operator expressions. Author: Hugo U. R. Strand (2017), hugo.strand@gmail.com + Yaroslav Zhumagulov, yaroslav.zhumagulov@gmail.com """ # ---------------------------------------------------------------------- @@ -23,16 +24,15 @@ # ---------------------------------------------------------------------- class TriqsExactDiagonalization(object): - + """ Exact diagonalization for Triqs operator expressions. """ # ------------------------------------------------------------------ - def __init__(self, H, fundamental_operators, beta): + def __init__(self, H, fundamental_operators, beta,nstates=None): self.beta = beta self.rep = SparseMatrixRepresentation(fundamental_operators) - self.ed = SparseExactDiagonalization( - self.rep.sparse_matrix(H), beta) + self.ed = SparseExactDiagonalization(self.rep.sparse_matrix(H),self.rep.indexes_blocks, beta,nstates=nstates) # ------------------------------------------------------------------ def get_expectation_value(self, op): @@ -47,36 +47,47 @@ def get_density_matrix(self): return self.ed.get_density_matrix() def get_ground_state_energy(self): return self.ed.get_ground_state_energy() - + + + def set_g2_w(self, g_w, op1, op2,eta=0.1): + + op1_mat = self.rep.sparse_matrix(op1) + op2_mat = self.rep.sparse_matrix(op2) + + w = np.array([w for w in g_w.mesh]) + + g_w.data[:, 0, 0] = \ + self.ed.get_real_frequency_greens_function_component( + w, op1_mat, op2_mat, eta) # ------------------------------------------------------------------ def set_g2_tau(self, g_tau, op1, op2): assert( type(g_tau.mesh) == MeshImTime ) assert( self.beta == g_tau.mesh.beta ) assert( g_tau.target_shape == (1, 1) ) - + op1_mat = self.rep.sparse_matrix(op1) - op2_mat = self.rep.sparse_matrix(op2) + op2_mat = self.rep.sparse_matrix(op2) tau = np.array([tau for tau in g_tau.mesh]) - + g_tau.data[:, 0, 0] = \ self.ed.get_tau_greens_function_component( tau, op1_mat, op2_mat) self.set_tail(g_tau, op1_mat, op2_mat) - + # ------------------------------------------------------------------ def set_g2_iwn(self, g_iwn, op1, op2): assert( self.beta == g_iwn.mesh.beta ) assert( g_iwn.target_shape == (1, 1) ) - + op1_mat = self.rep.sparse_matrix(op1) - op2_mat = self.rep.sparse_matrix(op2) + op2_mat = self.rep.sparse_matrix(op2) iwn = np.array([iwn for iwn in g_iwn.mesh]) - + g_iwn.data[:, 0, 0] = \ self.ed.get_frequency_greens_function_component( iwn, op1_mat, op2_mat, self.xi(g_iwn.mesh)) @@ -92,7 +103,7 @@ def set_tail(self, g, op1_mat, op2_mat): self.ed.get_high_frequency_tail_coeff_component( op1_mat, op2_mat, self.xi(g.mesh), Norder=tail.order_max) - + # ------------------------------------------------------------------ def xi(self, mesh): if mesh.statistic == 'Fermion': return -1.0 @@ -101,11 +112,11 @@ def xi(self, mesh): # ------------------------------------------------------------------ def set_g3_tau(self, g3_tau, op1, op2, op3): - + assert( g3_tau.target_shape == (1,1,1,1) ) op1_mat = self.rep.sparse_matrix(op1) - op2_mat = self.rep.sparse_matrix(op2) + op2_mat = self.rep.sparse_matrix(op2) op3_mat = self.rep.sparse_matrix(op3) ops_mat = np.array([op1_mat, op2_mat, op3_mat]) @@ -130,16 +141,16 @@ def set_g40_tau(self, g40_tau, g_tau): for (i1, i2, i3), (t1, t2, t3) in enumerate_tau3(g40_tau): g40_tau[[i1, i2, i3]][:] = \ g_tau(t1-t2)*g_tau(t3) - g_tau(t1)*g_tau(t3-t2) - + # ------------------------------------------------------------------ def set_g4_tau(self, g4_tau, op1, op2, op3, op4): - + assert( g4_tau.target_shape == (1,1,1,1) ) op1_mat = self.rep.sparse_matrix(op1) - op2_mat = self.rep.sparse_matrix(op2) + op2_mat = self.rep.sparse_matrix(op2) op3_mat = self.rep.sparse_matrix(op3) - op4_mat = self.rep.sparse_matrix(op4) + op4_mat = self.rep.sparse_matrix(op4) ops_mat = np.array([op1_mat, op2_mat, op3_mat, op4_mat]) @@ -155,5 +166,5 @@ def set_g4_tau(self, g4_tau, op1, op2, op3, op4): g4_tau[list(idx)][:] = perm_sign * d # ------------------------------------------------------------------ - + # ---------------------------------------------------------------------- From cfaf2fc9e179dd726c92017f82efa2f9313fd908 Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Thu, 21 Sep 2017 13:21:20 +0300 Subject: [PATCH 02/33] Cluster pertrubation theory Cluster pertrubation theory module for 1D and 2D square models --- pyed/ClusterPertrubationTheory.py | 135 ++++++++++++++++++++++++++++++ 1 file changed, 135 insertions(+) create mode 100644 pyed/ClusterPertrubationTheory.py diff --git a/pyed/ClusterPertrubationTheory.py b/pyed/ClusterPertrubationTheory.py new file mode 100644 index 0000000..10b4e36 --- /dev/null +++ b/pyed/ClusterPertrubationTheory.py @@ -0,0 +1,135 @@ + +""" +General routines for cluster pertrubation theory + +Author: Yaroslav Zhumagulov (2017), yaroslav.zhumagulov@gmail.com + +New classes will be soon +""" + +import numpy as np +from pytriqs.gf import * +from itertools import product +import progressbar +from pytriqs.operators import c, c_dag,n +import matplotlib.pyplot as plt + + +# ------------------------------------------------------------------ +class ClusterPertrubationTheory_2D_Square(object): + + """ Cluster Pertrubation Theory calculator of band structure and Fermi surface of two-dimensional square systems. + Parameters: + ed - TriqsExactDiagonalization object + k_mesh - tuple of kx and ky meshgrid, kx range (-pi,0,pi),ky range (-pi,0,pi) + V - pertrubation matrix: shape = (N,N,L,L), where L - number of sites of the system, (N,N) - size of kx or ky meshgrid + omega - frequency meshgrid + shape - shape of square cluster""" +# ------------------------------------------------------------------ + def __init__(self,ed,k_mesh,V,omega,shape): + self.ed=ed + self.kx,self.ky=k_mesh + self.V=V + self.N=self.V.shape[0];self.L=self.V.shape[2] + self.omega=omega + self.shape=shape + self._get_green_of_the_system() + self._coupling_system() + self._reduce_mix_representation() +# ------------------------------------------------------------------ + def _get_green_of_the_system(self): + self.G_I=np.zeros((self.L,self.L,self.omega.size),dtype=np.complex) + print "Calculation green function of full system" + index_combinations=[(i,j) for i,j in product(range(self.L),range(self.L))] + bar = progressbar.ProgressBar() + for k in bar(range(len(index_combinations))): + i,j=index_combinations[k] + g_w=GfReFreq(indices = [0], window = (np.min(self.omega), np.max(self.omega)), n_points = self.omega.size) + self.ed.set_g2_w(g_w, c('up',i), c_dag('up',j)) + self.G_I[i,j]=g_w.data.flatten() +# ------------------------------------------------------------------ + def _coupling_system(self): + self.G_Q=np.zeros((self.N,self.N,self.L,self.L,self.omega.size),dtype=np.complex) + index_combinations=[(i,j,k) for i,j,k in product(range(self.N),range(self.N),range(self.omega.size))] + print "Coupling system" + bar = progressbar.ProgressBar() + for l in bar(range(len(index_combinations))): + i,j,k=index_combinations[l] + self.G_Q[i,j,:,:,k]=np.dot(self.G_I[:,:,k],np.linalg.inv(np.eye(self.L)-np.dot(self.V[i,j],self.G_I[:,:,k]))) +# ------------------------------------------------------------------ + def _reduce_mix_representation(self): + self.G=np.zeros((self.N,self.N,self.omega.size),dtype=np.complex) + index_combinations=[(i,j,a,b) for i,j,a,b in product(range(self.N),range(self.N),range(self.L),range(self.L))] + print "Reduce mixed representation" + bar = progressbar.ProgressBar() + for k in bar(range(len(index_combinations))): + i,j,a,b=index_combinations[k] + x = a % self.shape[0] - b % self.shape[1] + y = a //self.shape[0] - b //self.shape[1] + self.G[i,j]+=np.exp(-1j*self.kx[i,j]*x)*np.exp(-1j*self.ky[i,j]*y)*self.G_Q[i,j,a,b] +# ------------------------------------------------------------------ + def calculation_bandstructure(self): + bandstructure=[] + for i in range(self.N/2,self.N,1):bandstructure.append(-self.G[i,i,:].imag/np.pi) + for i in range(self.N-1,self.N/2,-1):bandstructure.append(-self.G[self.N-1,i,:].imag/np.pi) + for i in range(self.N-1,self.N/2,-1):bandstructure.append(-self.G[i,self.N/2,:].imag/np.pi) + self.bandstructure=np.array(bandstructure).T + +# ------------------------------------------------------------------ + def calculation_Fermi_surface(self): + self.FS=-self.G[:,:,np.argmin(abs(self.omega))].imag/np.pi + + +class ClusterPertrubationTheory_1D(object): + """ Cluster Pertrubation Theory calculator of band structure of one-dimensional systems. + Parameters: + ed - TriqsExactDiagonalization object + k_mesh - kx meshgrid, kx range (-pi,0,pi) + V - pertrubation matrix: shape = (N,L,L), where L - number of sites of the system, N - size of kx meshgrid + omega - frequency meshgrid + shape - len of cluster""" +# ------------------------------------------------------------------ + def __init__(self,ed,k_mesh,V,omega,shape): + self.ed=ed + self.kx,self.ky=k_mesh + self.V=V + self.N=self.V.shape[0];self.L=self.V.shape[2] + self.omega=omega + self.shape=shape + self._get_green_of_the_system() + self._coupling_system() + self._reduce_mix_representation() +# ------------------------------------------------------------------ + def _get_green_of_the_system(self): + self.G_I=np.zeros((self.L,self.L,self.omega.size),dtype=np.complex) + print "Calculation green function of full system" + index_combinations=[(i,j) for i,j in product(range(self.L),range(self.L))] + bar = progressbar.ProgressBar() + for k in bar(range(len(index_combinations))): + i,j=index_combinations[k] + g_w=GfReFreq(indices = [0], window = (np.min(self.omega), np.max(self.omega)), n_points = self.omega.size) + self.ed.set_g2_w(g_w, c('up',i), c_dag('up',j)) + self.G_I[i,j]=g_w.data.flatten() +# ------------------------------------------------------------------ + def _coupling_system(self): + self.G_Q=np.zeros((self.N,self.L,self.L,self.omega.size),dtype=np.complex) + index_combinations=[(i,j) for i,j in product(range(self.N),range(self.omega.size))] + print "Coupling system" + bar = progressbar.ProgressBar() + for k in bar(range(len(index_combinations))): + i,j=index_combinations[l] + self.G_Q[i,:,:,j]=np.dot(self.G_I[:,:,j],np.linalg.inv(np.eye(self.L)-np.dot(self.V[i],self.G_I[:,:,j]))) +# ------------------------------------------------------------------ + def _reduce_mix_representation(self): + self.G=np.zeros((self.N,self.omega.size),dtype=np.complex) + index_combinations=[(i,a,b) for i,j,a,b in product(range(self.N),range(self.L),range(self.L))] + print "Reduce mixed representation" + bar = progressbar.ProgressBar() + for k in bar(range(len(index_combinations))): + i,a,b=index_combinations[k] + self.G[i]+=np.exp(-1j*k[i,j]*(a-b))*self.G_Q[i,a,b] +# ------------------------------------------------------------------ + def calculation_bandstructure(self): + bandstructure=[] + for i in self.range(L): bandstructure.append(-self.G[i].imag/np.pi) + self.bandstructure=np.array(bandstructure).T From 3b00f43627374db6e7c040502097af7625c65bff Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Thu, 21 Sep 2017 13:23:19 +0300 Subject: [PATCH 03/33] Cluster pertrubation theory example Documentation how to perform cluster pertrubation theory using pyed for 2D Hubbard model with nearest-neighbor hopping --- doc/Documentation_CPT_2D.ipynb | 270 +++++++++++++++++++++++++++++++++ 1 file changed, 270 insertions(+) create mode 100644 doc/Documentation_CPT_2D.ipynb diff --git a/doc/Documentation_CPT_2D.ipynb b/doc/Documentation_CPT_2D.ipynb new file mode 100644 index 0000000..9ba1b97 --- /dev/null +++ b/doc/Documentation_CPT_2D.ipynb @@ -0,0 +1,270 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **PYED+CPT **:Cluster pertrubation theory with exact diagonalization for finite quantum systems\n", + "\n", + "Copyright (C) 2017, H. U.R. Strand, Ya.V. Zhumagulov\n", + "\n", + "The python module `pyed` implements exact diagonalization for finite fermionic many-body quantum systems, together with calculations of several response functions in imagianary time.\n", + "\n", + "The many-body system is defined using `pytriqs` second-quantized operators and the response functions are stored in `pytriqs` Green's function containters.\n", + "\n", + "Cluster pertrubation theory addition to `pyed` allow calculate bandstructure and Fermi surface of several models. \n", + "\n", + "## Hamiltonians\n", + "\n", + " As an example let us solve the Hubbard model with Hamiltonian including only nearest-neighbor hoppings $H = U\\sum_{i}\\hat{n}_{i,\\uparrow} \\hat{n}_{i,\\downarrow} - \\mu\\sum_{i}( \\hat{n}_{i,\\uparrow} + \\hat{n}_{i,\\downarrow}) + t \\sum_{,\\sigma}c^\\dagger_{i,\\sigma} c_{j\\sigma}$, where $\\hat{n}_\\sigma = c^\\dagger_\\sigma c_\\sigma$. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "from pytriqs.operators import c, c_dag,n\n", + "from pyed.TriqsExactDiagonalization import TriqsExactDiagonalization\n", + "from pyed.ClusterPertrubationTheory import ClusterPertrubationTheory_2D_Square\n", + "import numpy as np\n", + "import progressbar\n", + "from itertools import product\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Parameters of model: U=8, t=-1, $\\mu$=U/2 and size of exact diagonaliztion cluster will be 2x2" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "t =-1;U=8;mu=U/2\n", + "Lx,Ly=2,2;L=Lx*Ly" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "T=np.zeros((L,L))\n", + "for i in range(L):\n", + " for j in range(L):\n", + " x = i % Lx - j % Ly\n", + " y = i // Lx - j // Ly\n", + " if (x**2+y**2)==1: T[i,j]=t \n", + "H_int = sum(-mu*(n('up', site) + n('dn', site)) + U * n('up', site) * n('dn', site) for site in range(L))\n", + "H_kin = sum(T[st1][st2]*c_dag(sn,st1)*c(sn,st2) for sn, st1,st2 in product((\"dn\", \"up\"), range(L),range(L)) )\n", + "H = H_int +H_kin" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Parameter $\\beta$ will be 200" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hamiltonian diagonalization:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0% | |\r", + " 4% |## |\r", + " 8% |##### |\r", + " 12% |######## |\r", + " 16% |########### |\r", + " 20% |############## |\r", + " 24% |################# |\r", + " 28% |#################### |\r", + " 32% |####################### |\r", + " 36% |######################### |\r", + " 40% |############################ |\r", + " 44% |############################### |\r", + " 48% |################################## |\r", + " 52% |##################################### |\r", + " 56% |######################################## |\r", + " 60% |########################################### |\r", + " 64% |############################################## |\r", + " 68% |################################################ |\r", + " 72% |################################################### |\r", + " 76% |###################################################### |\r", + " 80% |######################################################### |\r", + " 84% |############################################################ |\r", + " 88% |############################################################### |\r", + " 92% |################################################################## |\r", + " 96% |##################################################################### |\r", + "100% |########################################################################|\r\n" + ] + } + ], + "source": [ + "fundamental_operators = np.array([[c('up',i), c('dn',i)] for i in range(L)]).flatten()\n", + "H=H_int+H_kin\n", + "beta=200;nstates=200\n", + "ed = TriqsExactDiagonalization(H,fundamental_operators, beta,nstates=nstates)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we construct, frequency meshgrid, momentum meshgrid and pertrubation matrix V\n", + "\n", + "Further explation you can find in:\n", + "\n", + "https://www.physique.usherbrooke.ca/pages/sites/default/files/senechal/publis/Senechal2011vn.pdf" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "N=60\n", + "kx = np.linspace(-np.pi, np.pi, N+1);kx = np.delete(kx, 0)\n", + "ky = np.linspace(-np.pi, np.pi,N+1);ky = np.delete(ky, 0)\n", + "kx, ky = np.meshgrid(kx, ky)\n", + "V=np.zeros((N,N,L,L),dtype=np.complex)\n", + "for a,b in product(range(L),range(L)):\n", + " x=a % Lx - b % Ly ; y=a// Lx - b// Ly\n", + " if (y==(Ly-1))&(x==0):V[:,:,a,b]=t*np.exp(1j*Ly*ky);V[:,:,b,a]=t*np.exp(-1j*Ly*ky)\n", + " if (x==(Lx-1))&(y==0):V[:,:,a,b]=t*np.exp(1j*Lx*kx);V[:,:,b,a]=t*np.exp(-1j*Lx*kx)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To apply Cluster Pertrubation Theory to our exact diagonalization we use `ClusterPertrubationTheory_2D_Square` class for 2D Square models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculation green function of full system\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% |########################################################################|\n", + " 0% | |\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coupling system\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 41% |############################# |\r" + ] + } + ], + "source": [ + "omega=np.linspace(-10,10,200);\n", + "CPT=ClusterPertrubationTheory_2D_Square(ed,(kx,ky),V,omega,(Lx,Ly))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can calculate Fermi surface" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(6,6))\n", + "CPT.calculation_Fermi_surface()\n", + "plt.imshow(CPT.FS,interpolation='lanczos')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And band structure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12,6))\n", + "CPT.calculation_bandstructure()\n", + "plt.imshow(CPT.bandstructure,cmap=plt.cm.jet,interpolation='lanczos', aspect='auto')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 29380d466ef61abcc399a70756a1eda62f2807e7 Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Thu, 21 Sep 2017 13:26:51 +0300 Subject: [PATCH 04/33] Update Readme --- Readme.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/Readme.md b/Readme.md index 8353eed..a6767b0 100644 --- a/Readme.md +++ b/Readme.md @@ -1,6 +1,6 @@ # **PYED**: Exact diagonalization for finite quantum systems -Copyright (C) 2017, H. U.R. Strand +Copyright (C) 2017, H. U.R. Strand, Ya.V. Zhumagulov The python module `pyed` implements exact diagonalization for finite fermionic many-body quantum systems, together with calculations of several response functions in imagianary time. @@ -8,6 +8,10 @@ The many-body system is defined using `pytriqs` second-quantized operators and t The original purpose of `pyed` is to provide exact solutions to small finite systems, to be used as benchmarks and tests for stochastic many-body solvers. +Cluster pertrubation theory [1] addition to pyed allow calculate bandstructure and Fermi surface of several models. + +[1] https://www.physique.usherbrooke.ca/pages/sites/default/files/senechal/publis/Senechal2011vn.pdf + ## Installation To do: Add `setup_utils` install script @@ -23,6 +27,7 @@ in your `.bashrc`, `.bash_profile`, or `.profile` file. ## Documentation For documentation and usage examples please see the hands on [jupyter notebook](doc/Documentation.ipynb) +For documentation and usage of CPT addition examples please see the hands on [jupyter notebook](doc/Documentation_CPT_2D.ipynb) ## License From f8f5104bf5e78065fe9e5df88215e4201d619fb7 Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Thu, 21 Sep 2017 13:29:53 +0300 Subject: [PATCH 05/33] Cleaning Up --- Readme.md | 1 + doc/Documentation_CPT_2D.ipynb | 85 ++++++++++++++++++++++++++++------ 2 files changed, 72 insertions(+), 14 deletions(-) diff --git a/Readme.md b/Readme.md index a6767b0..f0c6e42 100644 --- a/Readme.md +++ b/Readme.md @@ -27,6 +27,7 @@ in your `.bashrc`, `.bash_profile`, or `.profile` file. ## Documentation For documentation and usage examples please see the hands on [jupyter notebook](doc/Documentation.ipynb) + For documentation and usage of CPT addition examples please see the hands on [jupyter notebook](doc/Documentation_CPT_2D.ipynb) ## License diff --git a/doc/Documentation_CPT_2D.ipynb b/doc/Documentation_CPT_2D.ipynb index 9ba1b97..26229cd 100644 --- a/doc/Documentation_CPT_2D.ipynb +++ b/doc/Documentation_CPT_2D.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -97,6 +97,8 @@ "output_type": "stream", "text": [ " 0% | |\r", + "/usr/local/lib/python2.7/dist-packages/scipy/sparse/compressed.py:774: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n", + " SparseEfficiencyWarning)\n", " 4% |## |\r", " 8% |##### |\r", " 12% |######## |\r", @@ -145,10 +147,8 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": true - }, + "execution_count": 5, + "metadata": {}, "outputs": [], "source": [ "N=60\n", @@ -171,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -200,7 +200,22 @@ "name": "stderr", "output_type": "stream", "text": [ - " 41% |############################# |\r" + "100% |########################################################################|\n", + " 27% |################### |\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reduce mixed representation\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% |########################################################################|\n" ] } ], @@ -218,9 +233,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWsAAAFpCAYAAABajglzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niBusb3GLW9ziHcQN1re4xS1u8Q7i/wc41oi5BXFnLwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(6,6))\n", "CPT.calculation_Fermi_surface()\n", @@ -236,9 +272,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAFpCAYAAABuwbWeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXuQHdd93/lpzgzmAcxghAExIB7igAQEECQkUIQFLEVJ\ntEWHst6uUsX27ia2sxUl5fVWsuWqjZNNre3sbpU3m91UbaUqWcXrslObteRS1rKkKKZNxVqJoUkb\nFCGRBAkJJIfCEAIgABpghvPAzLD3j+5z77lnzunHvX3v3Dv4fqq6bt/u092nX+d8z+/8zq+jOI4R\nQgghhBBC1LljozMghBBCCCFEtyGRLIQQQgghhINEshBCCCGEEA4SyUIIIYQQQjhIJAshhBBCCOEg\nkSyEEEIIIYSDRLIQQgghhBAObRPJURR9JIqic1EUnY+i6NfbdRwhhBBCCCGqJmrHx0SiKOoDvgf8\nNDAD/BXwC3Ecn638YEIIIYQQQlRMuyzJ7wPOx3H8WhzHt4DPA59q07GEEEIIIYSolP427XcvcMH6\nPwOctBNEUfRZ4LPJv4GHYGebsiKEEEIIIYThh1fjOL4zL1W7RHIucRx/DvgcQBTtiWt6WQghhBBC\niLbxW28USdUud4s3gf3W/33pMiGEEEIIIbqedonkvwIORVF0IIqiLcDPA19u07GEEEIIIYSolLa4\nW8RxvBpF0a8CTwB9wO/GcfxSO44lhBBCCCFE1bTNJzmO468BX2vX/oUQQgghhGgX+uKeEEIIIYQQ\nDhLJQgghhBBCOEgkCyGEEEII4SCRLIQQQgghhINEshBCCCGEEA4SyUIIIYQQQjhIJAshhBBCCOEg\nkSyEEEIIIYSDRLIQQgghhBAOEslCCCGEEEI4SCQLIYQQQgjhIJEshBBCCCGEg0SyEEIIIYQQDhLJ\nQgghhBBCOEgkCyGEEEII4SCRLIQQQgghhINEshBCCCGEEA4SyUIIIYQQQjhIJAshhBBCCOEgkSyE\nEEIIIYSDRLIQQgghhBAOEslCCCGEEEI4SCQLIYQQQgjhIJEshBBCCCGEg0SyEEIIIYQQDhLJQggh\nhBBCOEgkCyGEEEII4SCRLIQQQgghhINEshBCCCGEEA4SyUIIIYQQQjhIJAshhBBCCOHQtEiOomh/\nFEV/HkXR2SiKXoqi6O+ly38ziqI3oyg6k04frS67QgghhBBCtJ/+FrZdBX4tjuNvR1E0CjwXRdGf\npev+eRzH/6z17AkhhBBCCNF5mhbJcRz/EPhhOj8XRdHLwN6qMiaEEEIIIcRGUYlPchRFU8CDwLPp\nol+Noui7URT9bhRF76jiGEIIIYQQQnSKlkVyFEXbgH8H/P04jm8C/xK4FzhOYmn+3wLbfTaKotNR\nFJ2GhVazIYQQQgghRGW04pNMFEUDJAL538Zx/P8CxHF82Vr/r4Gv+raN4/hzwOeSdHviVvIhhBBC\nCNFZBjY6Az3EykZnoCmaFslRFEXA/wW8HMfx/24tvyv1Vwb4WeDF1rJYhM3yoPbmQySEEEJ0L5tF\nI/Qy7bgH7ddMrViS3w/8DeCFKIrOpMv+EfALURQdB2JgGvg7+buK0EMMzV8DiWshhBCbAWkBUZT2\nPyutRLd4ikTdunyt+eyI5ijyoEhICyGE6AQSuutpybu1IKsdOMbtRSfumugKJKSFEEIUZTMI3dtN\n4mzE+W5uYX67PUFsjlNu10OZVyhKRAshRHcgEZvPZrhG3UKo/q/qHnan2N4MitFDu06r6heuWdFZ\n5Pza8cBlnb8EtBBiMyGB1Ryt1r9lr3srx+vUPW71mrRLQLaz3i6777xrtDEiukdFctlsF3kRmrkU\nrQ60K3vMog/JShv3HUICWgixEUjMts5GCc0qJEjR42cda6M0QrN1Y5H6usi+zTkV2V/Zcwldr2bz\nXub6Vyeou1wkVymGW3lBqr5MzbaYTD6rEp32ftppnZaAFkL4kMAtR6eq7KqtuaH9+bbzpXXTuWny\n1uelL3rcULq8bcqQV8/66kzfNm46N427vmx6d73vvH35GvDsy92m1cZFde9Jl4jkiOJZaeVBDh2j\nzAuclb4Z8h4WH8M52+VtH7Jkl01fZNs8JKCF2Dz0sujtkuqwNM1c83b1nObVxf3OMve/b7lvm9D2\n9vHdfYbWF827S5Fr6AsAViW+77DZdfGKZ/mK9X8lsMxOW2Q+tMzNj1mXpT3ca55lVc7SHdXohx4p\nFcq2LLNeRN9LWfSF9O07lJ8QWa2z0AO2Elgfesh9/+1l/Z58NEtR15FmjqeBhEJ0lm4Uue2sprrx\nfPNol4tCs3Vq1vKsurXIfn3rQvvwrQul8dFuQdsG+n15Ts9/1ZovjRHfIT3hCmKfJgkJ7pB+CR3L\nvneunikjtu3titOFIjnvAS/6Mg44877f4Zx0ngew3/l154uy6pkP6siYxgfO/vUty3s4ywhqX4bt\n9VktuqLuHLJCC9F+ukEQVlXlNHsu3TrIq+qquEjvaJZYzTIe9Wcsy7L2ZuXLkFXv2OsWM7bJq7t8\nabLStpMyvdWexsCqNe9Nl3UPzDKf0I6ctAP+LECBS+bTL4vkaxdX50CjjvE9G1kaxqwv9x53iUh2\nv7gXsua6v67YdedLCmD38O581rKyFBHZtfttrs/A+gcy8wG1W4N5D2jooc0T1FkFUlNvVICy21XT\nihSiO9gogbvRotam1byUyUOzPrZFty+6n7z9ZllcQ4K1iNtClqXWd+wsFp15n5ApWs9kdeeHuvhX\nAvO+tD7aVWc0+wxl+VuXNSi6/+15z/rVLPcWCOuqfivtKjBG8R5yV5PgWeYaDfHMY21bnC4SyaEX\n1iz3CeFh57+9zL7hRbpRLN+eVevCrkI5C2sRQgVd6AEM5L+fxmdvnfCO6l0uq1YXzLosx9QLryIi\nGtY/mLD+IXZ9p+1WnGtlbsYSXYULh0Sz2Eg20rrbKdHZiuAs6idaJF1et3xZF4TQcYvuq+h2WfvJ\nswab5VnuDLlWGvxl/6Lzv4z/qs+f1Xdcd10ofz6qLNvL1jVF7vVifpJ1tPrONfPuZD1jvmVZWs5O\nl9VAG07nQ42hZq3Q7j7z6RKRfAf1ixKyHtsC2GBOdg7/zQuR1QoNCeJWX9ysS12mJWi36tJ1qwP+\n9eu2dfcfWb8jTp5CVuhVYMH6vxhI42vVYf238+ETz263iO96Z7l6FMX3vEg4i2bpVdFbheAt0sXv\nS1dU9NnzZaxjWf/tZXnHdrfzrfelcana97Xo4C3bQrLopAv5hpadL2LxdZe761x6sTxuV559wrqq\nMqeZxmwzDTbf/9CyIvieNbfMWGl6/10ikrNwX6y8B6JK/6Q8qvZfaraLLqvAdxsbvmUh1xT3GGaZ\n3cpzBbFrhV70pHG7v2yxa+ffZ0kue82rsDbb9GKBLcrTbrHbbNFb1tJYdPuylti8hn0oTdXLihzT\nXZ63zpcmRCcEr0tIgLo9enld0751EBa6RQxFZcrbsmVplfVtN5XjrZY1zRjmfHnIu76rnn3a19EW\n8GXOqajVu8g766O1e90lItkuGHwtWkPR7FbRKq1aAFdFP8Vbk0X8k1zrc0hAu0Ia678RzeAvqF1X\njqIW56LuGi7tcNMwhF7Obip0RXmqFsXNNniL7qdqq629Lsty61tWpCs/az+h/768GYq8w64vrKGM\nuGumq79ddUyWwSerdzRrXZH95OXDpdW683YrS6s8X/sdKnMffGl9ZU+RvBYR3HnHgeZFd7V0iUgO\ntcbzrMJlaWcrt1OErOm+BkWowDNCeyD9dUWvSZMlloeptyzd5f2E76lpEBmxbCzPi860gt832lwD\nn/XZPj+bVguLIpTt4RAbSzsL3SoEcjPWlbz17bLEGrJ66dolnPKsm2W6+H3H2Ah/2CrqqSL7aJcl\nt+qyrlsNVu2iFVlW1bUvK3Sb2b+hu+9vF4nkPP/TLFpt1Vd9k1p9UPMq8LIWdlswm4ffLHP/u8ex\nBbXZl09I+wZPDtMglvuhHtdxJD2NMed0bMHsmw9Zoe3fftr34rVLRIOEdFVspF8w+LslfYR86Mw+\nDL5xGAOBdO4+s6yBvn3kPYPt6ObciPK7ne/aRlT6VV2nThqihJ8qrmGr0q7oc1DEYNcqRQR7+6Rs\nF4nkspVKEbLSt9rV1U5CxypaQfkqXHN93S64RU96myxLlWuBdgW0E45vtR9WR1gvqu1dDiSTLZ6D\nIvomfgu0SWM2Dn2hsIg4cJe3YqXOo+j9vV3E9EaL3WZotkh1xa9NXi9RaB95vorNjK6vimbfm25/\n9rtVJEr83l50SlS6z1XRMtvkr4p8NDMGo5q9d5CIul9rkagG7nKf5bCT3VDtpuyNLtNtCEl0EJcy\nI12LCmnbr3nYP62mk2uBtve9mopoJtNlJlh5yHUj5MLhDg508+47N/f58e3DpR0ta9F+qigey96r\ndg7oM3Ra8HSiPG33OXVbnVCG21HgZt0vlZ/FfZDLEnIHbZZWBhqH0pWjS0RyH+tDkJmH3PU9DQlA\nN/pBaIBXO7viq6LsQ9ZO/7i8Y/qsWq512h5s6BvkE4quYQnndcI6qu+mP0qtzwP1rJmpFgN6jiR0\n3RyNonk1XW42dH0oh51lRURxnvXZpdufx81A1UVdu8Rvp0S1j065LVT1vPeicL3d3/VuuGftykOv\ni+8qrbtF6NSg5NbOp0tEcj+ww/rvRkgIDdgqYsHzieU8oVxECFVJO7vZmw0PU0X3nOvikZUfWP+Q\n24MBbZFsC+iRVER7BgzWfKBHqDXCauLZ5CvPdcMe2JhViNjn5XPvyHLtKPIa3u6Vq6HKIqvIe9dK\nV14red2ICrdI5BhofBaz8hnaTysuS1WVy3qfWqMbxG63UaU/f6/SSplaxaDkrAg9WccO0yUieQg4\nhn9QVtnwYT4B7BPL/c56l3YK5dCD1MlBhp2oJPL8gGH9oCTbn9JYoG/ifwnMvO3nbKYxWB2zrNAk\nj9mQfcjUdWNpDFZt140sVw37WQxF3vBhP09lRYLdOBQJ7bS4NlvQl7Vs+AryMtaQKtywQo230HJf\ntIrQsqwy1jVa+NK4VFEeSxxnI/HbPrLGHXQL7XL3asY9Iq/MdMdEmWW+8VG+8rUY3VHzbrkDdo3U\nrXxLwLxZaQSJz9JnTt6IZSOQBwL/syzL4Pc3tfMRouyI7077sLaTopZhQ5lzt7f3xUw0690vLvZT\nF86WBXrJ9YG2XHyMeLYtz/bzCDQKaN/z6Iu4YZ+XL560e87NVlK9XLk1U2k0Y+EtIzTLiGBX7LrL\nfQW2b1lWoZ5XyJselNDHKPLEsCt0V5xlRefd/fmW+QbThspm33ofRZ//POPIZqeXy4nNwGYSyM32\npDVrEXaXhcrUYWfedeMsdw+6RCQDUzT6khpxsjSQTPNjllhZoVGk2POmsPZZlLOswz6/ZnfbZsgq\nlDZ7IV3k/LJcGIq2vO19GDF93VrvviQj1IXyaPK7NMY64dyfTtvMgihx72AEViec0wtZoEODBkO+\n9qFXcuNC4LSXqgVykf2HhGeWRSL0GyqAHdefouR58uQSOuYA2V9zsw0K5r+JhW5YcX5DArtIGp/Q\nLpLGtz5E3vo8t7uqkUAV3WZNbqbeyMp7EWGcJYrdZbYgtstZ1wXTnbd24U6GqxmnEcjthjF0eIE9\n33qJBYaZf2uU+avjcGkAZoBpkl8zXQKuDsD8BHWxbAq7vHi6VYY8ChWuGymK21kIt/ulzhpt656X\nr1L2bedz3RjAb3k2gscXaSN13zD768eyPJtdpRbo1ZG0cWdnz23U+Vw1XItbESFiL+92C1mRoiav\n+82Xrojrgmvd9d37LNHrEby2246vAA551Ni/7nyDv7whpvEZCM3bv/aB3OXuunbhWnvyelF8z3uZ\n98H3fuS9E3luTFVcp40Sxu67JIHeXdiuhRtBlVbjZnvdQj1uecYJWxQPkBi6orpBazydtjm/Q9YE\n8JsZp2QRxXGRb8W3lxPbo/j0x4D9wH3AKXjtyG6e5v08zcM8zcN859WT8FQEp4EXScTzVSy3DFOZ\n2KLDtjLPWfN2AWwTqmTsdWXFcTsqpF4o8NpdABTtbi/bxe6zOpsX03XVCLRcbQFtlvkEkdeNww1j\nZzf+sqxuOMvLUMRKbVu/q6CoP1pI8OZZd4ed/85hXGHrE7i+oQ3uvCt2vcRWgqxGkSsQiwpD9+C+\nZ6CqMiPrvS7jupJHmXI2z8pcxOKdtbxIvrJoV3ndzjK2F+qYzUCnhXKrvXBZ+8kzXvjKd7PcNVKl\nPbyMWcvcQfnURe84sBPYl05T6XQQtk9dYs/gRXZxhXF+zCjz9LHK70e/8lwcxycyThLoEpE8FUXx\n/wRMAHcDh7bDwBGSsXzHgRPwo5PbOMNxTnOC53iI53mQ1166PxHNp4FXgPMk4rkW6ssVxSFLzALr\nxYhNs4KkSoG8GQutdhUQzQjoMi+zPW+3au0X27I82y3YUHe6Ec1ei6KvAWj3kGT5iUJYCFRF2QIz\nZEEwy7KsvgF3Alfw+gSwTxBD2Mq7rjED62Nyr1IvP/J6B5q1/PrS+GhHA6lVir6LvrR567MoK4TL\nWN6rENCtlucbZYXcjPXQRtHJe1iFOC76fmYtN4YnO0qV60884KxPy3xjId6dTlPpdAR4YIl79r7K\nYc5xlLMc5hzv4nsc5Dx7L16H7wMXgMsksnAJon9K74jkE++J4tNfBK4Ar5Oc0PeB14ALcPMarKzC\nju0Q3QOcBD4M1392iK/wSf6IT/OVy5/k7S9uha8CzwCzC8Cb1Cszt4I2FZ3r11xUJEsgt492FB5F\nfKWy0hbpJnJfdNsC7bhy2BZOu1Vsz4ey4hNxwe56aOyyh+a73/Ouob2+STGbJW59ZLktFHJjyGt4\n+BogoUZJmcaJ750uch9aLQs64WqRRVWWrLL7KkteT6MvTSfvaZEyssj16WWXkl6mUwK53eLYZ1AK\nWZHtOnCMxKjk6ZG1LcTbSCzEu4GD1Iym99z/Eg/yPO/jWU7wHCfWTjN2egXOAC+T6MeLwJVEP15b\nbuybBThFD4nk6OiJmP/wF+y++wJHOcsJnuMkz/IwT7P72RuJ6H2R5KTfArYCe4AHgFPw5skd/Dk/\nybf4AN/iA7z80nvhKZILZqzLM6QWoZvANRotzSFrMxnLcdK4VFUZqQBaT9UFTFEBXcTy7K73uQn4\nRLNdeFgWU1c8FxGO3UpI8IfwWXjNb1D82iJ3MfA/JI6h8T3Ps8AXeTerFEkSNOspWhZU+cKUqQey\n0hfZNo92NyS6yb1kM9Bt4rhVYeym6afRRcKu68y2bq/hQLLNEHVXiQeoCeK773+FE5zmfTzLg5zh\nIU6z4/RS4j3wfeAHJJLuLWANGCTRiBPArnTak/7fAWxPDhu9s5dE8vYTMR883dhq2AcchDsOvsW9\nk+c5zPc4ytnadHjtHGMvryQX6Ea6o0ngELy0/x6e5mGe5DGeXHuM61/cC18CvgFcukbS1DBWZqhb\n/OywYXZXqvFpditUqN/wrIKhlcpNBU5xqiqAihQwWRbosi1t2yfLXmf71dpdU26XlGW19flDl7HM\nlmXVMx8Ste58Dd/gtCzLrpsOzzw0Cl9fhstYesuK5E52vTfDRluUXXqx1Rei7LWt8v6X8SMtUl42\n2+vk2/Z2phPiuJ3C2F5vi1t3uWv8MdZiu/5Kt/NZiU/Bvvd/Px2J9jQf4Fu898LLdaPn90k8DlZJ\n6rpdwDuBQ8ADsHQczm69jxc4xjkOc5b7eJWDTL81xfz0nWngBxKD6S9FPSSSh0/E7Dtdj49sogNA\nciF2Ur+IH4F9j3+fj/I1PsGXefzGf2TgT0ku4BWSVsQh4EPwneOH+Aqf5Av8HC8+8RPweRJ3jKtG\nKF+n7orhDsJaYb07RjMWZYOE8sbRCfFcxH+ySOGUZYF2/4d+8Wzj7rMq8gSoT7hm+em2y5UhlNZH\nVa5UVb233SZoRWt0ujxvVhRlUXScw+1ed7VbHLfqTlGkF9Rd57MSZ7lYWK4VUyQ+xKmVeODRm5yY\nOM1DnOYkf8mDPM/9V15LPAdeI/EeuJnubiuJJfgAcB+8eWgHL3CM73KMF3g3Z7mP7711mPkX70ys\nzNMkHgRXgVnq2tI8rq/0kki+40RM3+k08yskV8WIUkhuwiRsG0jM8CeAR2HbR37Ew1ufrvmlHOUs\n+9+aYegGsAYrY3Bl+w7Oc5Dv8S6+yzGe4wSnr51g5ZmxeuvkRZKLyeskFubrNIrikGXQrLdDzpnl\nZfuWs7jdC5p2UWUBlldYFR2kFHLpyDtGM77VVdCKoGxFzBZ1cerkILiyxxCbg14rn8s01t3lNkV7\nZbLotWtXhG4Rxq1YjAcIG2883xlYNxjPGmxnLMWpe+y2x37EB7YmzrE/yZ9z8vp3iJ4iEbY/AJap\nu9QeAR6EV/bfXQvacJoTvLB8jBtndtcDNsyQWIlnWS+IjRxze1VnekkkR/fH8AfWEtfJ222tWP6a\ntpX5Udj+8Us8PvgEj/Ekf40nuPv0jxIhfDHd9B7g/fDMgffwR/wsX+DneOP3j8DvAd+IgSeBF6g3\nXyBpvuyg7mtj3DFCHzOxz8NG1uTup1PCuaiozdumleNVTZlntGprbCcr5s0qflXG3L60Mv7CkNWj\n04lG6UbRTa4UUL4R5K639ZbtgupzEzQabQIYS/TYFIkgTg2a209d4vjgGU5wmuOc4Rjf5eiN7zNg\nBtddJ3k8tpO4T9wHL+26h+d5kGd5X92w+dRYPZLZNHULsW0d9o5RscOqmmUA9xQSyV3iCLZE4v5g\nt1JGafTVNCd6k8Sv4jKs3oRLq3BpFJ46BP/i3dz4+G7+8L/8RU7/3Ale5wCfPPFlTk18J7m4V9JD\nXYH9By5wkPMc5hxvHD+S3NDzEczclx7L+CzbA6rMvMH9OInvwTOCmvR8mq1g7f2I9hG6xs0UhL57\nbV4533HcXou8/bmvr9mnbx8bLew2UkC3epxuQO++aCfuOBt3sIFdboXct9zxOb76LvTOZZV73fjs\n94IwLjv4zqy3o0/Yv6512TCSuMVaxso7Pv4Wj00+yeM8waN8g/e+/nISgOF1kjFkQ9SCL9z82ADP\n9p2sfRPj9NoJrv9/exPjphHExpd4Pp1qj5EbpQhr3hXI7kfmitGyJTmKommSUW1rwGocxyeiKNoB\nfIGkTTEN/PU4jn8c3sc9MfwT1pv0fWZ/+0TNCztG0gSZgvGRxB3jBPAIDJy6ybGJ73KY7zHFNDu5\nyjALrNHPHKNcYRcX2cN57k38WU7fmdwc44bxCjC/kJ7GFeqRMVyBHHKvKBIdowzdWGDczrRaWLaj\nndqpEdRl6MRz2+2iV++u6FV8AquIixj4LcrNumdk7buddLJMbXbguG/7kHh2B4H7BuJhrTOx/3fA\n0EAtsEItCsUjKzxw95nUtzhxfz1+40UGXqQujFdJDM6H4OaJAZ7ue7gWlewvXv1J+GpUd4GdBlZj\nEq1l8mRrQaOp5lj/XYyiY8Y+1Rl3i1Qkn4jj+Kq17J8C1+M4/u0oin4deEccx/8gvI8DMfxG+i/P\nJ9MdQTlKeHBT2vqZoiaa+XjMI/c+yaP8OR/kW0k4kVeWkpu4Fd46dAfPDp7kSR7jS3yal//9exNX\njC9Ccge/TdI/YJM+PLWRnFBvvYRcMexzUpidzUO3+KNtBiR6hege8rrxs6L32OQN4m2GTkUIaQft\nHHznS2v0k2st9gUtMPdkFJiCnSPwKPBpuPO/+AE/yx/xCb7Mx67/R6IngOeou7aaML3vh+8cOsTX\neYxv8gG+tfZBrj+5t/HrybaluJYHOx+haEb2rxvyM4+f31B3i0+RXEqA3ycJvhYUyfA2/nBNxlpr\nunts8WtaQ6tW2pskAtZMi0m66b0wfRK+cQjORzz1qz/NxOGrHOUsg8u34HskoUWWYes73+anPvQX\n9O1f4xoTXHh0P/Nn7kxu6PQkje4WBtslwy4YVqw8+mJf2V7kZQsIuV90J3kuE63S7cKxV9G7JESY\nrPrGlHmmnnb/G3wxKF0h3ayluRt7z7KoevCdu41tLbaNi+72qyS6aYW6tXgS+gfqMYtNNIpH4ND9\n3+Fhnub9qXPE/Rdeg+dJ9NPNdPPUuvza8d18ncf493yUr137GCtfHIM/IbE1Xl0hGW1nNJ7t0mrn\n04jfmzRai8Ff1xYd0F2cKizJrwM/JnEO+T/jOP5cFEWzcRyPp+sj4Mfmv38fd8fw6xlHsS9GKEg1\n1C+mbb0dIGkFHQKOwb4JeAz4CGz7eBId4xgvcC/nGWcWgGvs5AL7eYFjPM9xLv2ne5Ib+wz1rgAu\nU//Goe0CkvUZbBt3ZHC3tKJFNfRaob0Z0XshRHspOpjYZ2XOi5pRpaW5G+jE4DuTxmikEeoRKOz0\n7mC2NIIYhxJR/BjwaTj04e/wOE/wOE/wwbVvJV+1e41E9mwF9kN8DE7veIBnOZlO7+P7L70nMSwa\nvTRNYik2MYrX9bTbUcLMelhvMXYH39mEoq34WAV+pWPuFnvjOH4ziqJdwJ8B/w3wZVsUR1H04ziO\n3+Fs91ngs8m/dzwEv1ngaPaLlhfLz6xzw5Wkn2Hpj+p+NUeotX62H7/E4cFzTDHNHi4yzix9rLHA\nCBe5i1c5mIQf+cbuJBDGkyQPAs+RRMV4k/WDIEZZP+jPvunuw9GMwJIg6B7s+9fL3YDdgp5tIbqP\nUHnk1n8GVyBn1eGQHU/dl67bqFoUu2nta2inNdfX1T528IHULXSIxjBtqcV494de44PpN4wf5mke\nvP4y0QvUPU33w1vH7uDpweSjbU/wON959lTyHQqjiZYWqH+LAhrDxJlgDLZh07YUFw3dmWV8zGIF\n+HudDwEXRdFvkniV/G3g0TiOfxhF0V3AN+I4Phzebn8M/62z1O4OyBqlaY/od/1t7LBx0OjkbVok\nO4D7gHcnraefhx2/9Caf6Psyn+QryaexX7mRjNfbDjfvG+CbfR/ga3yML6z9HNf/1V74HeDMZZI2\nwnnPGboh5IxANg+G7XfjO8eiSExsHHkDVrqBdovsbjtfIcTGUZUPs03eh4g2krLeq2WsxW7kItta\nbLsp2K5ZCizdAAAgAElEQVSbdq+2ST8JHIR9Ue17EzwGD9z/V5zkWY5zJvneBBeY4Cr9a2ss9w0y\nyzgX2M80U5zjMC9wjLMc5Y1zR+q+xS+SyJ9pYCkmCXRg9+qHBnDagRiyghwUuc9l6qBiIrkln+Qo\nirYCd8RxPJfO/zWSMBVfBn4R+O3094/L790NQWMwPk++NOZ0hqn7AxsWSQTydZKbZ/Zh3CYW4cmT\nsBOuT+3lzIcfTB+Ua4wcep6xvpXa7keZZ5LLTPW9zvXje+EUMDMJV9+fHvMNGrsx7K4O9wMk9nn5\n5t3zzEJ+yhuH3QPQrfegW/MlhNg85DXG3aC2PoNYyNJsb2MP5Aodw93eXdcKzcinsgPvQn7cRvQa\nP2I7iIHBFqYDwCQMjSXi+CPAZ+CnDn+VT/IVPsrXOHR6JukQ/0G6+z3AQ/DaiQme4HG+wif40zcf\nhy8O1a3FM5DoKqNnXB1jNJgRwXM0uk1grYfwPc2zIPuo5j63ZEmOouge4I/Sv/3A/xPH8f8cRdEE\n8IckX9V+gyQEnBsSwtrPvjjx0rBpxuplD+6zW1bQGDLEtGzMNjtIPNTfDeOTSRSMdNpx6k0O951j\nDxfZloYjmWeUy0wyzRQzrx6E01FjyLjzkLSgLlP/ep/ru5z1gpv8QmutZIkiIYQQnaZI3GNf9Cqz\nPDSft083AoK9zl7mO27VFA3TFtrW/oKdCYlr456LEcqTwL7EhaIWog0GHql/AvrB1GJ8L69y58X5\nhg96vLXnDs4NJtZiE7v4xe/8RDLo7hskluOrdlhc2yBoay9odCm1pyLax1AmbRl+rZe+uOcTySHK\nDhSwl9n76Hd+3bByY8Be2BY1xgM8AUPHr3Nw+6tMMc04swyyzAIjXGYXr3KQN146kjxMT5KO5Jwh\nCR13nsYv+Zm8mOO5LeZQ+JMyDQgJZSGEEBtNWcOXzw3DV3fbuF32eRbKKikrivPGVdlBCnzuo7bo\nJE0zBUOpoe/TMPDzN/nkxJf5BF/hcZ5g97M3kqFT10kG3h0ATsBTu97LEzzO13msHrP4Geoh2uYh\nic3gGvoWCItg30A6XyPGpag/ch559/vXe+mLe2UInbjtzmD/2sLSFsG2EzkkFmbjemEZvecH4MwR\nOPMzcH4EtsHOD13jMZ7k03yJD17/y8Sh/QYwmQTJfvL+x/ji/Z/hDz79n8O/GoLf2QeXLuP3Vzbd\n9OZFMMvsUZytvNxywaiOdg3IE0KIzU7ZQel2Xe66ZLi/Nv3WOuN6ac+7+3fzmEfRc8iLX+xzKXEb\nBG7oNtvFYgC2kViMzcC7IySfgn4k+RR04mf8PO/mBUaX5xIXil2wsgOmt+/jDMf5Jh/gG/wkLz77\nE8nAuz8hsRbzOvUvD5u8uUEI7PC27nchzPo8C3grgzHbXw/3oEgO4VpZ7daK8WM2L48JDddvpV2g\nPpDO3e+bwDRMH4UZuHh5Dxcn93CRu7i8Yzu7J28ku9oKy32DAGxhmW3jc8yPDyXfM7+0D9jLekuy\n+7lrO0/mfMxD6OueKhpDUqKudXQNhRBiY1ixfm3f5JCl2Q4ba28fsjRDORFvjhkiz7JsRKcx2Nkf\nRnOjX6XffOBmmmai9gloPgK7H0+iUZzkWY7xQq2Xu49V5hjlAvs5PfgZzu1P3CjOcJyZlw7V3UTN\ngLtL6URM3Whnu6het/LmNjhC1zQvNFv3CGIfXeJusTeGXymQslVNHwqybd9c80CYG2JGhE4B98G2\nycTx/RTJ7wNw5+EfsJOrbOEWa/QxxyiXb+xi6fyOxEd53cjPBRKLtRn9afsrZ8UBNLTiryyhJ4QQ\nYiOoOsJOyAXD7iX29QC6oi4rxFwRsgbkhdYZHWK+eGeHZ3Mt52m6bSTW4keAx2Dfx77Ph3mSx3mC\nn+Qb7D5zI/mwx1vAduA+eO1I/aMeT9x4nKXP70gsxs+Q+ha/Sd145147qAtko43Mx0dC16wKUVy1\nTvEd8x/3kk9yUZHso5lYhKFtfC+bz+/JtADTAX/bRpJW3SngEbjj1Fscm3yBg5xPomOwwByjXGRP\nEjbluSOJv7JxhOc5Ep/lNz15KuKP5PpbFS2IJJiFEEJ0gnaEoMzyW4ZwXe4Tzln+zHnH9+H6FYeC\nCdiRroxFeQr6J5KxUKeAR2HgsWTgnbEWv4tzHGCa8eVZRt56m+VBmN26nYvs4VXu5SxHeZ7jPMcJ\nZp491PhBtPOQGOpmaAyJa87JvT6uZTsUps2kt8kTxVXokGYG8BUTyZvA3SIr1IuL8YnyOfHbXTP2\nZI5hwseZz10b9sL8e+H0Q4lP0DicnHyWT/AVHuNJjt94kYELSdbeOnAHzw6e5GsPfZQvPvQZ3th9\nJPn6zDNTJN70ofOz/ajNedjrfT4+RQokuWHUCV0vXR8hhChPO0Wxiy10bR9aN+KVqU9hveEJGsVg\nSCzb9Wuetdj9oIcRzmY/9v6HgYkkTNtxal+9e+9DT/HJdODdqYvfSfyFL6Sb7gGOw0uH7ql91OM/\nvPlR+FIapu00aZg202PtagPz/QbXn9h8BCTkP5xnec8Sra3UqVWF7yvOJhDJNqELaJ9mluO+67sc\nCmNi8yYwBktTcHoCDsJfHHyY8b2pP9D2UfZvv0Afq7XPXc8xSh9ria/yQeDFCZh/X3rMN6w8Gou1\nYcHKty2OfbfRbq1m0e2xfTcKXQ8hhChOuz9WVBZ3XJI9PmmApD41rg1GSJtp2NmXa2G26+iQtdon\nwM26Hcmxd5N4ch4kEcYPwNCp67x7+wvcx1mOcpaDvFr7uMelPdtZ+2Rf7eMe5zjMaU4kn4J+7j2J\nMP4GicV4ttUwba7W8V1b9/yKpi1C5wWxj03gblEGN8Sauy4UYxka/XJc14Zd1L7at3ss6R5JfZbv\neOAt9kwmn7cGWGCEa8sT3Di/u9Ff2cRXXlohafrZVmu3VVu0K6isz7KEoRBCiBDdIISLxBjO28bn\nv2xbfe3PJ7u6wbZWu/sbtv7D+kGGY8BEIo7T+MU8BodOfqc28O44Z5KBdzfmAZjdvo1ppjjLUc5w\nnGc5yelrJ1h5Zqz+tbtpkgF3s6Th2qAens39sq/5sJrbo25bh/MiUFRtKd4IQXzb+CSXxSeU7WXu\nS+HithKNoDa+w8ZXeRIYqbcSj9i/Mdt2X2V86yx9rLHACLPXxlk5P5b4C50hefhPQ+KF/zLJW+B+\nj8X2efJZvn2i2j6HLCSYhRDi9qEbBHCIop3eZcOz+T7a4bpc2mOC8vRBVJ/dZk07gX0keuAIcBzu\nfOgHPMjzHOMFjnOGw5yrieOBZXhr+x1cGEysxc9ykqd5mG9d/gBv/8nWxGL8DJZvcfrV4Nq5udfB\nFci+sUzQ/KC7snqhG6zEt41Pcll8N8eO5ReKSxxywjfb3CQRsraleQCmD8L0++DFA/AZ4BS8795v\n8mG+zsP8Jw7zPUaZY2FimFcnDvL0yYd5gsf5i+d+Cn4H+PwhmF1M9+2ywnp/JyOKB6j7IDXDZnHD\nUINACHG70M1CtwzNSJOiH/NwYxWb8t8IxwUaDV8rwARJj3EE4yTCdyid+p3fcepuFEeA40vct/cs\nx3iBY7zAUc4yxTR7uJjELgYWBke4wi6e4yHObT/MOZJQbWfXjnL99N66xfh8Os0Aqysk4tj2Jfb1\nHrsDE7M+9mFTpbW4G0Rxc3SJSI4JX/ROvfRuwHKD66Tvtijt7hfXFWOFxAo8BpcOJN0hS9DHGuP8\nmD38kKkbMwz8EBi8wciBReYY5XWmOPvAUW4c3J28aGcmk32ssyRDY9gYqPtamXl3YF/Z67lZxLIQ\nQmw0m0XEVkkrMiTvevoG1vlcLwxZLor9wEQy2N62EO8kEcWWtfiOI29x7+R5jnKWo7zMMb7LYb7H\nFK+z48JSEqJtEJZ2wbmthzjDg4mlmA/w8nPvTSJfPUXSq3wJEjF8jUad4n5kxGBEs88txJA18K4K\na3HvimKXLnG32BPDZ0ts0c6CJvSBDtd/yX4o3a/N2IwBdwPvhaEDiQ9SOg2duM6B7dNMcI0tLLPI\nCJeZZPryFG+f2Vp3uThDakieIRkoeJ26TxH4Yz/C+tYiTnoC67PoNbGc9az02rkIIbobieBsOiGI\nQ9uFxLHvIyS+EHKmx3YCGEssxlPU3SgfSKY77/8BB3idu7jIJFcYZ5aR1HC1zCBzbOMKk1xkD9NM\nMfPGFJwZaPyewnkS/+KaODYGODeal1vXG12QFaatqH9xL1qLy+T5tzazu0XIHaIKinYx+Fpo9qel\nXWvzC7B0Hp4cgydHgUmWmODl3Tsa/ZXT+aFT1xl9fJ4+VllcHuHGzCSc3wdn9tXF83RMEl/5ZRo/\nH2nnx+caYotpV2jnXUdfq3ujUVxoIUSnkBAOU4WkKHJ9fccp80EP1yfZrSt94eFM+tQlc34gGSQ3\nT2JdTqfFt4b58dZxAK4wyTJbuMZOLl7bk4w7sgfrT1MfcEeM31fYGOTs4AH2wDufbzFku1P0srW4\ns/V4j4pkH6G4x+06ju94vpGxUI87aEaZGl/iSbh0H1w6mTxvqf/ST+19kod5mnfzAndxEQbh2r07\neeHeYzz9+MNJHMTPD8H/HcGZQyTNTp9oN77JY1Z+7HAvcxT/tLWPjXbFCBV8hm5o2QohehOJ4fVU\nLRmaFcS+bfMsxfZ8nhXZHrQ3kLhWjFN3rTDTPmt+J9wx/hYAP3xrDxdW97M0PwJXh5JO4GnqVuJp\nkmWzkFiK7QgU4I9HnBXD2U6LtU1onS9NETpdp268YWsTiWSXstEc8gj5LEP2ZTQDAW2BDMmDPp3O\n74LpA+mLM8Ts3nHmGWWZQQBGWOQWc0xwlV1cZt/eC8wcPJRYn18Zg6X70v2/6Rw79GlOO98hC3IZ\n8exu24kHO6+AFEKIotyugrgT5WbZiBNF9pHlV2ynMaJ3hMZPPxuLsf1BLbsuHEumndRdKU4k0+73\nvMZhvsceLjLB1bR+3sIco1xmkgvsZ3p5ivlX7kysxcZiPENiNa5ZjqHuomnHX7YHEIJfDGcN0MOz\nzqVoHd1JUbzxgtjHbaIsqhLMoQfGF0qun3qrMCso903gFZgZg89PwAx8+9FH+PapR9h+/BL7By/U\nYiwnL+EuLr16IDnkQZKv8px5N8y8m3ooGBNf2X7JbrLeZxkaW9U25qMqzbwkVYvmIvcsL5/d+QIK\nITpNNwriXq2Ky17LMkLYTZ/lMmH7DWdZkG2LsWUpHqJuEZ4iqVsPAg/AjuNvcrDvPPemH/XYQ/Ld\ngy0ss0Y/s4xzkT1cZpKL3MW5G+9i6Zkd9XCuxnJ8FZI62bhL2OLYzNsD7kKuFGUH3XWrtbg36uQe\nHbhXFVUWlllf2gkd2+7WmaAeX3myPijAnkzXzk5gKH3AlgaSluk0yctoO//zejrzJo1xFCHb9yrP\n8b8K8l6QIgHhDVUUDEKIzclGiuLbRfzaNPOxD992vogUvmVubONRGr+k55LWfduoh2k7AZyCHY+8\nybG+FzjGd2tfuhslDdPGCNeY4AL7Oc9Bvse7OLd8mBsv7q7XudPULcZX0wmofxbajlfsfs8A1rtV\nwOaxFndbnbypB+5VRdUuGVn+ynZL1xWmiyQfDbHcMWaH4czexEo8dCixGH8EOLXEf7b36fSrPK/z\nDmZZPjzIxQ/t4Sz38TTv50d//E74EvDVA+lL6gpkc0zXhxrqAtkmFO2jlZcr5M+c1a1W1OLdbS+j\nEKIzdFIQd2P12e7IT63kIW+wXUgI25ZfNwRr1vkOAKPQHzVaiGuRKFa45+7vcZC6lXiSyw1W4jlG\nmWaq9gnosxzljXNHkg952JbiGUisw6YX1xeH2GcpXvSkMxQZkOfSTcJ4c9TD3fiWbxBFozuEMD7L\noYev35ncaBNzJC1NgxnstwpLk4k7Rvq5yVHm2MNFjnKWSa4AcJldjPNj1ujnyUe3sXRpR9Kq/cYB\n6t9ut32WzVeEbP8scw3sfNoi1H1cQj7aZci63ln+1Pb2rbiGCCF6l06I4qqqyW5x82j2fIqU1Vnp\nQwYPu5x3w6+Z9XbPpztF9WRD1OMX76Yuio/D9lOXODqYfNTjXs5zgCT86ggLLDOYuk3cxdM8XBPE\nr716FM5EdUvxDPUBd7OkVY4Ru66l2HWZ8IVqDcVlLhqmtdtcKTaHMLaRSF5HK2I56wG0LbRuOrPc\nFqpQDyc3nViUl4BXhvjTU5/iT49/im0P/IhdW68wwgJr9CU+y9cmWXllLLmzD5D8vnISZk6SvMS2\nz7J5id2WrZ1HOx60m3dbVIfOuQhlHsOyoW6EEJuDbhbE7XRPaBdl8lxVhImQv7Dr/mfcJdxjWPvp\nZ/3nnh8AjsO+w9/nXl7lIOfZzwV2pRbiQW6xRh8L6TcJnudBLrA/tRS/i0vn7qmHZ3uFujC+RFrN\nGCHsfqfAFr4+azH466l2f9Qjb19VsLnr3NvcJ7korRbOeREm7OP4BiPYrhpj1H2Xx9YFMucBGJq6\nzs7t19iSFgizy+PcOJ/6TZ0m6So6DSzNkMRYniaxNtsNBPtT13b4OHegQdnunaxCNG/bIgWLEGJz\n0A2uAzZVRGkou69mKHNurbpG2Mt9VmBYX6fZ60N1XCqCx63J/rqdEcZTsP3IJaYGp5ni9VTuXmCS\nK4wy1+A2cYVdvM4Ur3IwcZt49XDdSmwiUBg/4nnSaiamLnyNOHatxb7PQa8E5m2K+Be3UsdJGGcj\nn+QKqcoVw9e14voquzGWV6i7Ytgh5NIv+Z0/CfP7koJjHPa95/t8gG9xnOc5wDTDLDA3OMr0/Qc4\nc/9xvvlzH+DSv7sn9VneB7PGreOKc76mZW+EshHJeYMJBgLL8ywMbqFrXytfmJ7N8JIKIRK6RTRC\nsbyUjdJQdNtm8lKUVq3BrkEjJHR9kSSg8Wu1jouEcZMw8YjHqYvhNNLE0JHrHNz+KlMkgngPP2SS\ny6kYvlXrTb3GBC9wLAnFxhSvc4AfnXtn3TI8zfrBdbWQbD4R7FqKXRcKX9SoIiHa3PU23eRbfHsj\nkVyKFdrjs2x8ag12OvOiulZbI1gvw6V9yQCCcZjpP8TXHh3lwuB+9lMPHzfLOBfYz621wXrsx1mS\nj5HMHKRuTV4k+2U2vsqmwFtlvUuG75xs8gpl13fLnRdC9C7dIohbEbNFou8UFZ95xyp6/Kx1WQOg\nfevyevx86WyfYmP4SdMN0Sh+jZuEEcLp/NDu60xuTz7lPMFV3sEs48wyyhzbmGOQW2xhGYA1+nmd\nKWYZ5xo7a24Tb7xxL7wy4HeZqIlhNyaxK3htkWxiFuf1aGb5FRNIk5U2RDcI49DA+82H3C1aoorC\nPqsrysa11hqhartf7IX+kYauqNq8KZi2AUMxrEZJl9IsSQFyPp1MoTK/QCKc36RxxK6bB9dyHHKF\n8J1fqIsqb1CDj83/sgrRe3SDIG5GDDcrcIsszxLHzV6vZgfOhazCIctwagEe8kzbPJPtJlH7Wt0K\n23dfY2LwGpNcZoJrDYJ4mAVG0h5L4z9s3CUusicRw8v7ufHK7vVfsDNCeJb656KB+iefbdHrWozd\nUGyQbSUmY7lvvU2vieI8erH+lbtFB2jVDcPdl9mfPZAP1rtkuO4Yb5KUEqvJ+zQ9CtOTwCHon4RT\nwCPJNHTkOke3n+UA0zUr81UmmOYAZ68dZeWpMXgSeGoEzhxNj+GGhTP5MPEp7XPwhb9xC16D3YJ3\n/bpcoWzju94hNw8hRGfoBkEMrUdhcNPlCUqzLGTwCFlfqxbLRfbnDpiLGpPZ7g8+ATxOPXqE6ye8\nO2Zo54/Zuf0aE1xjnFnG+THjzDLCIoMss4VbtUPdYgur9LHICAuMcJ6DLDDMLOPMMcos7+Da5Qne\nvrS1/qW6GWuy3SXmodECHHKFCA2y8xl4fAafPGtxVYPtekEY27jP6+apgyWSK6EKN4zQfg22X24/\njb7BrqC0Yi+u9sOZiZpnxNLSDr594iTX7t7JHi4yyhx9rDHOLIcnznHhI/u5MbU7EdUvAuePJpMp\nlIipf2M+VLjY5+aei/1loQUaCyqfKLYbA0Wvsa8rKLRtu17mss/D5ilUxGannWIYNm7gWZawLSN2\nQ766dg/gQON2Q9Q92cwUOg086+3thpxfd5kRva4Idn9r8ysMbFtkcGiZ4a2J2B1mgUFuMcICwzWZ\nu8CWdNkWbtHHKv2sAbCaWoSvsjO1F49zjQkur01yfWYXzAzUrcGmrjEi2FiGG6zCWT7Cvq/XGesx\nFB8IXiRqUhXh2TopiFXPlEXuFpVTZXeZu0/XvcHd1h0gYb5XP0bijrED+gfqPmBTzrQbBnbfZGTb\nAlsGb3FreQsL8yOsXBpLCrBp6l1b09QLsdo77nuWTMveDDy8Tv3rQ7bYLkKvta47gQo90U66RRC3\nYh3OcofIsgpn/Q6TK3p91tiQEHUnWyj7TtVe74rhhimGoWUGhm4xOLTMlqFbjPTVxawrbM3vIMv0\nsZZOjWL3Vs0mPNggkecYTSzAa+PMzY6yMjsKV6O6yLUFrz3NW79mqhX1tnXYGFWyBtLhzGdZiO1l\n7nJ3XShNXvoQnajLVDdkI3eLDaJZq3IoRJq9z1XWv1yuScH+et4KiSi9TqJqgdVhmB6G6R3w1CQw\n0TCCeOXgGDemxmrRMmoF8G7qgy9M+hnq3V1XgfnIKuBWaBTD9q8vdE4RzHl1o1jOepXy8tuO17Db\nCsh2i60ydNu12Ug6dV865S4RcokIiWA8824az8cr7CgMts+t7/84NWvsyLYFhgcTK2wfaw1iFGgQ\npHn0ZaTLWrdG37r/s4wDiQheo9+Sx5YoXhtkYX6YpfkRmB9aL3avEhbDRgivC6vmWoFhvYj1CWJ3\nna8nMssS3KogDm0TQqK4V5FIbgutul8Y7NsT8n92C4xF1hf0Zn6YutCeq28/MwozI0n8ZFPI1wp3\nGq0b5pBLrNf1tWXXqH+0xBbGPl9l10fZPV8fvfTYOhamBoqcR56PW6jhYLvnbDTNDJwqQ9kKqFVh\n2C3XNcRGNkjaJYaLuEqExLLPR9gnfsfq+zBl3zjrY/W6vzuB8ZiB8TlGx+cY7ZtL3RCSAWhbWK6J\nYtcq6wpS+78rZs02hmUGAWpplxlsmL+1vIW11T6WlwZZWdoCS4OwFNUHtPl+7cmXxp5f9fzWyHOJ\nyLIIw3rR2+zguSJi2JfOpRt9iru9HNoc9JLa6CGqqqR8/so+sWzS+V4aV7DbflsmRnJaSSwNwKXh\nZHI/+WmLZHPImmHb+Cm7lmMjjn0jhM052Hnut9L4/Io7QSv3rmzF7pLV9VdkEIm7vlso4htuU6RR\nYZ972UGbrVZg3WQV30jaaR22t8nyBXb9fYu6SqQDj/txBp9RD0tW+00GpI1vT0KRmUFoxlprW1wX\nUnk8tzZat7ouDSa9bEZkutOqM99QtnpY9fza8779LHnms35r/r/QKHjdkGlY/+1M+cqpUBr3f146\nnOXucbLShNIV2S6LTvVudmP5vrmRSG6gGys/n1CG9WLZNuv6Cg8z2M/XDTlCYwXi6aJcci2WedYA\nkwdjve7HX4C6+IRzCF/B1O5HusggnzyKViA+C0lRy0i34hPMbu9CqNcEJ51vvRuz2/cutNoA60Z3\nn6pp5j1q1U0iZAn2DaLzpXGtw2PJZNzETBjM3da86xYx3jhIrY9VBtOIDH2scYtBFhmpWWrnZkd5\ne3Zro9vZrDNvXA1ci6wtjNeRNVYoTwzm9cjllSEh8VlE5Ibm3WVZefblwZcmlC4rfZnts5Aovl2Q\nSO55XEuxLaptsWFbm81/w03qlUyWuwbW/7yC2BYi9j7s/7ZgsfcTshiGjtEMedYrN11ogI8vja/y\nKDLIJMunzmazFJzuebjPpevPYz/r/Z7/sP7+uSEK7X0PB5bn5bdosdltYrqK4r4Z15nQO2WvC/kF\nu4Pjhj3/TThK6oPhjPjdh+ejFUmc3tHBOYZZoJ+1mqvDAsMsLo9w4+o489N3JkLXjrJg+9vaA858\nbgg18lwPDEXdzVyKiMeyVtis9VmiOms/zQrhUNoi25XZRyv7rorNUrZvHiSSG/BV2htNlbcoSyjb\nuMLb3cb1JTZWYlNpFel6c5c323WWt41LVgXt65rFmXfzklX5uW4mPoHs7svldio0i7pK2NZmV0i7\n/+17GLI+28fOspKVHTjqO1a3388iZV7RwXP2cvu9spe7whjWv4e2ZXgHENX9gXd7Jts6nA6Y6+tP\n7tnaaj9rq33cuDTBjfnddaHrCuDQILR5qH+YYo66C0JWmWdT1spbljI+t0UtyGXTlM1H3jZFti2z\nnyqOURXdXh6IphVYFEWHgS9Yi+4B/geSoulvAz9Kl/+jOI6/1nQON5SyornTbY6yIt7Nn6+7EhrD\nG/msN24l5iNLNPrWm2WGotY937kV8WUs8t/GzqOJUe2Lx7lCo9+enf+iFYwv7e1MyLXInTcuRSat\n7WKU1wOQ5aaR5faS9+y667qh4W3IK698ec0SyCGf/NAgOtcabITwKLUIEu7guawIEuPAtiUGhm7R\n17/K2mo/K0tbWLk6xoqxCNsfpXBj8c6bfId6frLKMAosd+c70SguK/qKHr8b9tvqtdqInh6V671G\n06oujuNzwHGAKIr6SD779kfALwP/PI7jf1ZJDruKPNHsvnRFu/RbIWuwmL0+1K0Z6tI0FdYwtUqr\nn/WB6M0y+1CrzuQbnJLbHWl2ZMgqXLLOrZ91X5Xy4oYlWvTM+wRxyFrsy7dEcWuErlFIPJsH0ueL\nb8/7GosjFG802QIKz7ydp2ao4tlo1U3Cdy18VuGQ24RrGTZf60zLFvfTxfZvzSrM+gHEpky5ShLl\ncnaIldmhRBRnhSNbhcZ323ym2NczZNIayojfZq3FnSoPmnkmN8JtoRcFMahc732qMn1+GHg1juM3\noqiIINkslBHN9qW2twtVXkV8++x0Wd2drhXNrbBsa7GpvIbr27jB792g+L4g+G72V61fWzivAqtR\nErNALskAACAASURBVFljdWC9wHYnPPO+/YfmG8pK021qxK+pKN3JtQ67Faihla5O0Tyue5Ahy+IM\nfrHs+131pMty3TB5so/rCi5XZLt5tJ+PqorprPyWbWz7rlMgpFo/jZ80dsOqbWN92eJ+UGOVxOq7\nSj1Eme0a4U61zxT7ouxg7dQm5BrVTO+BzUY2jqqkHUKzHee4UYIYuu+eiVapqvT9eeAPrP+/GkXR\n3wROA78Wx/GPKzpOl+N7QXxWrizs9D4XAgLLs1wHQj62w868LQAsjOXFiNt5/F+SyhLOtgXIde0M\nCWJf2CLXMm1bqN2p4ctNrgC+Sb5luIifqk3ePVYB2j7yrMw2PnG0yPp3LEtE50WECbUSs7rn3bxV\n6aseEr32upAfvq+McL4yZ/cy2ZMriF1RbIob824bATxD9pfazCA5YL1F2H6HXX9hX2OlSM9PMxbh\nTr7vGykMDRtRvm30eatM3+y0/FnqKIq2ABeB++M4vhxF0SRJsRYD/yNwVxzHf8uz3WepfYt6+0Pw\n91vKR+9QVbdnkeVZrhZFupzd9T5/ZCuWMqwXxq512XXNcAWzmc+aXJFc29ZUeiFLsLEm+T5vijPv\nZgrCBWKooFYB2l0UdXPKesd874XPVz8QSnHdewTr31ebPNHcjFgOuUFY77H9/hZtEIcaw0YE21m3\n32E7OoTvM8WrUG/k2lZht2FrCF2fPOu+Syvv9UaLtzJ0UznVC9etm66XaJ7OfZb6Z4Bvx3F8GcD8\nAkRR9K+Br/o2iuP4c8DnknR7WlPqPUWeq4WvkLCtyvb2rvXLEKrk3XXu+tA6X1eruzydXxpILTye\ndV5h4MsjrK/cfGI29JtlocurTLMESDdZjkR5snp6bGz3Ct9zaFPEdcNYnV2ff7PO3r6MH30gK/2B\nZe58kcnX4HUbvvbxfD08tnuEOw/U3SJc1yazLs/yHnKJyHvnbfLe3SrE2+1YPvSC6C3C7XjvBFQj\nkn8By9UiiqK74jj+Yfr3Z4EXKzjGJiXPp9nGV9i4lbi935Cbh3vLs4S2HSkgtI8iVu4sIZ6HT7SW\ncYMo0pXqW5+VLm8b0TtkvYN5DVZ7+6I+z9AoorOsz66F2nZ3cKy+RSY3rZ1Vd96c0qzz3+65cXtz\nXIFcw+3hyfLx94nbvAZsmYZt1WL4dnn/N4vYzeN2uZ+iKC2J5CiKtgI/Dfwda/E/jaLoOIm7xbSz\nTmRSZECfTZYADlnNihZ2PqHtWrPd/dnHt8X3YiCNTRkLT1bFV1QUNyOIQ9uJzUORhqv7Pvi2s9MZ\nyrpxhNybnGWr6aDXmsgGr5gO4XN5WocvAo3t7uBzfQj5Wtv43CHs9EUbwO46l24Xv7eLCN1IVHaL\n8rQkkuM4fguYcJb9jZZyJFLKWJkNoYLW56rhO5Z7jCJWaJsieSzyyDVjvS07uKlMpaTC9fYly0Uj\n9Ay51ua8NO7Awbyemqx0eftw15HzKhR5r/J8gO00zVqAfWny0odox/sskdsdqKwW1VKFu4XoCM2I\nZkPIVSPvGO6xNtovL+v46kYVnaJIj4+vgek+U3ZvjbuNeT+LNER973IoX50q8suK61CavPQhqnx/\nJYC7B5XLorNIJPcsrYhmyLc6Zx2rk5Q9drMVmgpf0QxlBwP6lofeudB+3PS+tD6XEJO2KlpptOZt\n3+w+yyDx2x2o7BXdi0TypiHkMlGWolbnrDx0ilYrORXOoh20OrYAwoNyQ/vPexc20oJcFIngzYHK\nVbF5kEjedLRqYfaxURVw1ZWcCm/RaYpaml2KvnNFn+l2lAt5x2gXEr/VoPJQiDwkkjc9nagcu7HS\nUgUgupU83/8iFH3nQkV8t70f3ViGtEq3XWMhRFkkkm87ynYF9xKqlEQvsxG9QHlkVRGbUdhmofJF\niNsNieTbmma7gjcaVVbidqAKi3OrbHYhrLJECBFGIlk4dJtwViUmRCNZ70QvNHI7gcoNIUTrSCSL\nArSrUlZFJkS15L1TvSyiVV4IITqLRLJoEVVcQvQOel+FEKIod2x0BoQQQgghhOg2JJKFEEIIIYRw\nkEgWQgghhBDCQSJZCCGEEEIIB4lkIYQQQgghHCSShRBCCCGEcJBIFkIIIYQQwkEiWQghhBBCCAeJ\nZCGEEEIIIRwkkoUQQgghhHCQSBZCCCGEEMJBIlkIIYQQQggHiWQhhBBCCCEcJJKFEEIIIYRwkEgW\nQgghhBDCQSJZCCGEEEIIB4lkIYQQQgghHCSShRBCCCGEcJBIFkIIIYQQwkEiWQghhBBCCAeJZCGE\nEEIIIRwkkoUQQgghhHAoJJKjKPrdKIquRFH0orVsRxRFfxZF0ffT33eky6Moiv6PKIrOR1H03SiK\n3tuuzAshhBBCCNEOilqSfw/4iLPs14Gvx3F8CPh6+h/gZ4BD6fRZ4F+2nk0hhBBCCCE6RyGRHMfx\nN4HrzuJPAb+fzv8+8Glr+b+JE54BxqMouquKzAohhBBCCNEJWvFJnozj+Ifp/CVgMp3fC1yw0s2k\ny4QQQgghhOgJKhm4F8dxDMRltomi6LNRFJ2Ooug0LFSRDSGEEEIIISqhFZF82bhRpL9X0uVvAvut\ndPvSZQ3Ecfy5OI5PxHF8AkZayIYQQgghhBDV0opI/jLwi+n8LwJ/bC3/m2mUi1PADcstQwghhBBC\niK6nv0iiKIr+AHgU2BlF0QzwG8BvA38YRdF/BbwB/PU0+deAjwLnSfwofrniPAshhBBCCNFWConk\nOI5/IbDqw560MfBft5IpIYQQQgghNhJ9cU8IIYQQQggHiWQhhBBCCCEcJJKFEEIIIYRwkEgWQggh\nhBDCQSJZCCGEEEIIB4lkIYQQQgghHCSShRBCCCGEcJBIFkIIIYQQwkEiWQghhBBCCAeJZCGEEEII\nIRwkkoUQQgghhHCQSBZCCCGEEMJBIlkIIYQQQggHiWQhhBBCCCEcJJKFEEIIIYRwkEgWQgghhBDC\nQSJZCCGEEEIIB4lkIYQQQgghHPo3OgNCCCGEEJ1lwLNspeO5EN2NRPJtja+QKIMKFCGEEO2k1Xpq\nI4+lOrLXkUje1LS7cMnavwoHIYQQLp0UvRtN3rmqnux2JJI3Bd1Y6KgrSwghbi+6sS7qZkLXS3Vl\ntyCR3HP0ciEk4SyEEL1DL9c3ZSkqh1bbmosE9dJ2CxLJXc9mL6Ts89PLL4QQ7WUz1imdlDKtHKsK\nge3eP9Wb7UQiueuougBr1y1uR2taL78QYjOxGQVpJ+ikNGnHPQrVXe2wVquHtp1IJG84rbygG3n7\n8o5ddYtZL70QomokYjtPlfVWt9af7r6L1oemnmu1fpVwrgqJ5A2h2Re77O3qZAvZxZfXVoSzBLMQ\nIg+J3o1no0VwkeOH9ttuSRSqA33i2Jc2S0SXFc6qR4sgkdwRyrzoebcka1/ddDt9L2Aof2XFs152\nIW4fJHy7g40Sv1nHdffjS+s7VqfqSrduM3lx66z+QFpfOt9+Q+lD+bDzYqO61KWbVNUmo4pCoExr\ntwrxXIWLhNlHmdA2rVqdZWUWoneRCN5Y2iUDit7XZgxD7jYDnnW+ZfbyLIFdhbB267AVz/KVAutW\nnf+hdCaPvnVF3T9Ul7pIJFdGKwVC6GXOWpfXgm6mOymreyeUpux6X4vZl7ZZq7Nax0J0HxLC7aXT\nVXk7e0d99ZwrfH1COK/Os7cdCPyOOP+HgSic9TzviHXEacIVz689v1gw3aqVCV8GQvVtyOq86qRx\nuf3qUonkpilSSBSx+JZpEZcpKJq5tb4W6DDZLVj3BXVfvqLdSCFC3U1uXrJQ61iIzrCZxfDtUF2W\nvX9lXCHc9KG6sIgFuN+Zt0WtLXiHWSd2+9NpyPn1TXh+XVYzflfd/xGsDqQT66caMeuF8oLzf5Fi\nIhrPvEtRn+jbz9Ux962Pouh3gY8DV+I4fiBd9r8CnwBuAa8CvxzH8WwURVPAy8C5dPNn4jj+u23I\n9wZSxq2hiBW4iBAe8PzP+rW3LxKU3O7WCc2bdO6L5v7P6iKyl7vi215nbxdqJdsCPbQPH7ffSy5E\ne+hVQXw7iF2bVu9Ts+NkilqEXfGbl9a1/A4Do0CULBoHtqWTPb+N9cLYxharSzm/7vw6MexZZv+G\nltWI0nML4bNIL1q/RmAvOuvsZVjb+urZZqzNm68+LVJa/B7wL4B/Yy37M+AfxnG8GkXR/wL8Q+Af\npOtejeP4eKW53FBa9QvOKhTM/LD1a7d8h8lsDftawqFWcSjL7oudVTgs2Rv6Wrp5rVj3xbTXh8R1\nyB8rJLJDAyPsfdnIyixEPt0siLtN9HbztYLi16to3Ve0pzPPaGRbgoetaSxZNkSj8PUJYLf+sw8V\nErFLnmne+bWndVZft/7yzRehjF91aFujGXx1cejXtVDbv3ZPcJ7hys2vobfr1dy3JY7jb6YWYnvZ\nn1p/nwE+U222NpJWLcW+AsIVw77CYMyaT/dhXnq7ALALgtBvnmAOFSBZhce8Ox/B/Ii/AGkQ024r\n1n4hfSIba97+P8x64ex2DdkCOeSLZeNzA3HzLsTtRreJvE6J4G47b0PV5593nnkGoDwLcZY7hK/+\nG6BmCYb1Qngc2On82uLYrsNM/TQPzHoms9wVwkDdOuuzvobqKDMPjfWFr+ezKEXdVIoY4CC5xi4+\nK7R97m79jGc+ZJDKqld7r06t4u37W8AXrP8Hoih6HrgJ/OM4jr9VwTHaSNkBbiFhbAth1x/KFcC2\nxThq3J1PxLri0+cr5c5nTSEx7e7L/Dci3bUq24XMvPUfPELZCGT3ZfRZosFfCEF2QeSud1u8RSvB\nUPree8GFyKZbhGE7hHDV59ZNFutmzq2IJdJOlyWAfa59vuO4hqBUDO/EP/kEsGsLMfXPLDCDXxTP\nO/M1IWzqoJv43RLMQULugzjLCazPSlsFZdxXfPO+/Zl75evxzbJMDzvLylqaoZvr1pbe+iiK/nuS\nK/Fv00U/BN4Zx/G1KIoeAr4URdH9cRzf9Gz7WeCzyb/trWSjCYpai7Oswu6vLYBtd4kBZ94Rxja1\nbhzTooWw+0IeeXm38mBbon1dVu5TkmVpruXPLoTsKfTSmR3j+R865zxLcR6hlrBbKrvpbbr35RZi\nPRspiqsSma2cQzN5aPZ47RbVrViEQ72ieet99Z5tFEpFsO0aYVuA3XlbFNvGGruOMWL3ajpdcn6v\nkopgt94x1lCzwyyXiDIGmWbqolbqCd99Llr3+e6jTxOERLQrnl0XySKulRC+lvZxurMubfotjqLo\nl0gG9H04juMYII7jZWA5nX8uiqJXgXcBp93t4zj+HPC5ZF974mbzUYyyvlVmXUgM+wRxVkvtZjpB\nts9tEX9ce3kWPrFvljsF3lL6O2+/MHba0GPi67Jxu2nsed/LZJ9fVkGUZzVuFyHBbJC1WXQzGyGK\nqxCHVVtKm91/O/bZzL7LHDNU9ofK9ZAAzrIgp+NjTE+jsQTvTqd91vxuYDzmjm0LbBlaBuDW0iBv\nL22B+YH1IthMPneJWdJi2AjikEXYJ+awlhnKit8i5Xurxpt27M/cy0VrWZY1OpTGXmYLaMgec2Qv\nG8Bf53enlbmptzSKoo8A/x3woTiOF6zldwLX4zhei6LoHuAQ8FolOW2KrAIky4/H/fUts9Pbnvyh\nrgp7WZ5AzCLvluUVmm6aMr5mof25BY3v5Vhx0vjmQzQjkKsuqIoi0Sw2kk6L4laEXpm8FjlO0R7C\nIttlWWFDadz1ZV35imxbZF9ZZbWbJmQESiMr2BZf1y/YtQgbq/DQCncM3aKvf42+/lXWVvtZWdrC\n2/MjLM1srQti2yJ8CY9leAGYY711uIgxKUuw2du46W2K1CHdXr6b++w7l7zzy3K78f13j2vEcD/+\ncUVFrfehHt/OkPu2RlH0B8CjwM4oimaA3yCJZjEI/FkURVAP9fZB4J9EUbQCvA383TiOr7cp7w5F\nW9RZrhOwXhi729utVgj70UJ1L2KzlC3ofdfIXIdFa37FSWteAt/+7X27hVi/Nb9Zsa/HZj5PsTF0\nUhQ3K4irsNqWEZxlDQV568uK47LrQ+l8ZPUQmmW+us3jYucODA8NmHOmO8bfYnznLCN9C2zhFoMs\ns0ofa/QzxyhzN7axdGkHb1+ClRkSv2EzGSHcYBWOScSwG8LMiFufmx6sN8ZAuP4Nlb3tdJMosv92\n4x6/zDts6vnQOWS55LjL3P+uvrL9mW0tUeT6t7dejVJPiQ0lcbf4bBNbhgqfoi2frJaQjzzf2Xbe\nrKIPaoiiVnXzP8uKntVdZ+fVdcVw3TKKtPLLNjpCaavCbiA0u70QzdLtoris0Cu6fRUNfnd5qNzz\nbZ93PN/6onl0KSqyo/WL3ShI9iA4V/g2iOCYgfE5RrYtsGUwEb59rNV2vUYfq/Rxa22QhflhluZH\nYH6oPkDOdpNwfYYvpWmIgeusd5Hw1QVQLyuLWI+z6uB2COGNFr/tpqoGcZHn3SXP1TLPv7no/fyt\n5+I4PpGXqgqnqA5RpEAp42MD9Qts++nYy5vxBc6iHQIptM8ylal9XnbXiKk0xoAd1Ecnp11xvoDs\nJgIGUI+l7Bu85xaSi9axTQvSzJvzdEV4VkvTbpFWQVkXESGqohPCuB2CuIzFNEsYZrl8DZBf7pdx\nKSvgh2vvyhejvpmQnHjmQ7/uPn0Dr9eJ5RiGlhnatsDItkW29C0zwiKDLNeswYZV+lhkhAVGmFsb\nZfbqOG9f3Vq3BLu/tg9xg1XYF38XzzLfQO4qB3GXLa83uwAuQlXXwL72rs4q6ubk1uU+TQCNuiCU\nh/L0gEhupussq+WxmJHOpZfFkJ1302XiXjfXbcKuDMaoi+PJZN7tfnNHJRvX7FpYuAhmR5LJFKKr\nUB9wcZ26WB6gsVA1+Xbvga9LxnfOsP7lapZefg5E79GNorgqQVxEDLvrbKuvK2SzerkKuh74hKzP\nHcHnnrDNk8a3bCiG/lXuGLrFlqFl+vvX2DJ0i76+xEbbzxp96QTQl5Zb/bX/Zn2yfJBbDcvMf3vb\ntfQ63WJLTfgusyVxiWCUa8sTzM2OJgLYWINtq3Bo4Ny6kGpz1EOrZVmHQ64SWf9xlhNYH0qXx0YK\n4m6uV5otg8pcT9c4B61dE9eIZmitPO0Bkezr3jYXIe/CNuuHZO+72e07Sd5tXHF+obGyMaF7jDB2\n5yN/eJ4hErFsRjKbLryhNJ3pipsBzgPTwCvA9BhcTb+ixGXqDRdTqdnBz93C1ha+vpfBJ5QNzd63\nor5RQjRDr4niIq4Pbros1zhb0EK28MVJB2ExnKZz3Q3cwWi+kGRpNIaRbQsMb220uNritN8StkDD\nfDOs0Vdo/Rp9LLOlJoRNjpYZ5BZbkt/lLSzMj7AyP1yPIuEKYTeahPldgnpP4Bx+NzkI+wX7/Ifd\nbe3twV/GVmEh7mR9vVnqiVbOo5nyrFP3qPx59YBIho158LpNCGfhNhrysC3Gw8AEiRg2LhXm4ydm\nf3E9HvLV1AJjC+Qp4GD6O5UsG9p9nZFtifhdW+1LLBaX0i67aTNNJpPptmOBxLpsJvvrP3bh6utG\n9VmcXVqxLEsoi6roNlHcqiAOiWGfqA35BvuEsCmjTMPZTNaX2WwBnPWltob5JbaNzzG+dTa1qc4x\nzCKjzDFCfTDaFm7VhLDBDFAz1tlbqShdSK20txhkgeH0d6RBsC4vbamHPlsaqPe6reZMeJblfUo5\n67PK9tdRgboYzhoz4o4rca3DRXyEN4MgVh2Qz+a6Rj0ikkM0ezPKVlLtuumhfLgW36KYAsytqHy+\ndXbFY6wuprvMDUjiqQSX+mFmIJmeSf2U+6nFyFw6soOlI8AR4IGY3fe+zp7Ji4y/Z5YRFrnFFmYZ\n5wfs59JL9ySRtJ8ZSaYz+4DXSczPJj/GimzOYTRw7nbkER8SyqLTdGqwXZlGctl9FHWL8C0bYX25\nY7/LI6wXyZYYtiMvuPF4p0ga6vtg29SP2LX1Cju5ygTXLAG8ULP41gagWbbhBYZZZIQ5RrnCrprg\nTdwTBll8a5jlpcG6RdYVpK5IDQlSWxS74piMeTzL3f01pHc/rxzy+S3r+lDGXcJehmedb30oXQgJ\nYtF+elwkN0u3PPxF8mH75xbdl28bu3KyhbFJb1wejF+wWe6ryMy6Veo+aSvJ35lhmJmEZ+6DoX1w\nCng04tKj93DrkUEO951jJ9eY5DJ3cZEJrjF9/ywvbzsG/QP1wv7FA1Y+jICH9aLfnKv9ZSXfNbFp\nVShn7Vv0Bp0Sru2kCmtx3viOIlZi8y4OO//t8sJnKTZjHwbqPVPGbcu4cO2z5ncvsX3nLOODs4yT\nWIHHmWWEBUaZcyzAa5YLQt0Pd45Rkq3HucpOrl2eSHxy7QForj/uPI3W2aBF1mAiRvmEKGQLSV+a\nIut9+/cJ2bz5MoPmfHlvxsVxo0WxynIRpsdDwN2uFKngQxVUaOS3r3C0jxdycXB9zAZIrLyTwF5g\nCnZGiUX5IHW3DFMZGv9lY4m5Sj2m5rQ1XYJksN8McAW4Rt1XzpcHF5/PXBWogO19ekUwt0MUu2mz\nRHHWQLphZwq4SNgD3oxV2HbZOghDB6+zf/sF9nOBPfyQnVytieIt3AISH9yF1PprRO9ldnGNncnv\ntZ2szIz5xa8ZK+G6JrjW4HWWWZ8ltoxl1V5OYL2PMgIzJGiLuD1kGRiK5juvXG2mvKxSHKu8FoZN\nFwJO1PEJQp9lx15ub1umoB7wpDeY44zRaG2GxLJ7HnglqZyeGoanjHjel1SIp9LpEbj7Pa9wmHPc\nxUVGmWONfq4xwXnu5czlB3n7G1vhyTF48ihMTwF/SeKGccXKvxHotrXcPmfjThI6FzLONYQsy71P\n0d6ajaAdLhRZg+jc9G5Pkk8M+xrjqTDeRt0ifJCksXwEeGCFfXdPM8U0e9IeJWMVNoPfjBBeZJgL\n7G+wBF9lglnewY/e3AWXhtZ/sMINTTZP+mrb/rfGHQH8YjGvrCwihMv43hYtQ4rss4zbQyhPvSqE\nXVQ2i+aRSN5U+AbAmOV5o5FD+wsV+PYxTPwku8JZpDHMm2EMOAjn3w9MJJakEzDCAvu5wHHOsIeL\nbGGZa+zkPPcyOXmFP//IoyzN70jjc47A6g7q7iP2gD7X19osN3GYfdEu7GXNumJILPc+zY4FqJJW\nRXFRS3HWuIVQZAnbZWuEpEFqiWHbX9i2Dh+BO+//AQd4nSmmUwtxIoyNKF6jjzlGucwkV9jF60xx\ngf1Mrx3g+it7k/b2efxfbbOtwZl+udDom5sXrcH3350vIy5D6ZuhVQt03j6K5LPbxLCLymLROnK3\n6GmyfARDFOlqayZKhusnbFtubYuNib18EHgv7BxJrMkn0ulIzI6pi4z2zQGwwEjiN3h+axI+7gzJ\n7yskFSbXSMLIXXeO5esezTrnvHVlUQHd27RTJDdrmyhqLW7GUmw3Lsfwu0700+A2MU598NwR4Dhw\nIuaee89ylLMc5HzNUmwGzy0zWHONSBwq9jPNAS6s7U/E8PT/3965B9d13Pf9swLAN0AGkAiJBCyQ\nBEQKIiVIRESaelEmG8qK/FDHE8t9JW1SJ5N08hh3OkmmM0mbyXQyk6btTDppM4nrZNrITuVIcRSN\n5VCWZLG0qIASZNIUKZISaD4EUiTD90MAtP1jz+IuFnte956Ley/4+8xc4LzP3r17dr/nt7/9LSWL\nsDtrWzA8WWjSCkg2BCT56UJ8HZDHIhx3fBq1cCvIes9af580pL4V8pLN3UJEckORZYS5Ja3SyFqB\npTXoSWmy++OsUrbxteHnOqF5QanRXVv6LOo1I9eNK4axOp04s4yxvW1RZIzoc+wK8CbG7OTGYLZp\nsdYvmw5fTKdFxygHqcAbg0YRxknW4qTIE/OZHmkiNKDOFc3RwDrfZSJ6LrtWH2QVh+nhfbo5Sien\n6OD05OQWNorNSTo5SjeHWMVhejlyeDUMK9hLyUps3SQmRXDcC64dMJxkDfb9hqs9+CxELZ77cuuu\nStI6E2JY6lChaMQneZYRN+AuRJEW0SR/ZP9eoQgUrh+jFcZXMK4YF6P/zmQi47fDoftgtN904W6E\nrnsOsoXtbGAXq3mXJZzjI+bwfkcPw4/cy/ZHtvDmrgfh68DXF8BoJ/AO0/2PbVp9dwzXV3nc++9/\nZ3HFmF1U260ibxVbibU4NLDOd6FwLcN2innvebDh1lwr8VpgUNO36gf0s49+3qGXQ3RzlCWco4kJ\nrjCfU3QyQg/7oqP2XF7HpaFbjBjeT0kM+24SQPI09iHBHDd4DueYpO0u1fCzzXrtmaCIumemvofU\nk0L9ICK5YRkjPoqDJam7sFJx4Prz+pXnuLc8zvTGzfosu0J2DDgOtMGlLhhugy44dnMfOx+4wkfM\n5RSdLOUkAKfo5ATLuEirScYSjLVrtC+69nymDuzzxXFo0I4l7tGQ8HGNz0z4G1czPFtSj421Fvux\niP37uOMJ2owgnnSZADYat4l1/IBeDtPDCB2cppWLNDMxGVliiMHIZaKHQ6zi4PHVsHfeVFE8gmMh\ndkJGJg6W81224sRwkpXYv3aIot0SiqDW9cNMi/paf19BiEfcLRqCrF2u5YyarpZgCLlYuIRGmFsL\n11KMCasPmjtLbhc2jFwXUfi46LxLLSX/xRHvcwy4pDHi2/ouWyu2e+80Qj6PRSANRPWZqUF41YhE\n4R4f90zFWYpdH+Noenk7+5w7uC6yFLcPHqe/aR+rOcAqDkfuEyeZzxUmaOYcSzhKNwe4gz3czfD1\nAc7vuNW4OdmxAscwVuJJdwmY7gbhW4ovEG8VjhPGLpVEi6jkWW7UZ7eWlu1GzTNh9iHuFg1Mlka9\nqEEjSZEtKiEpjJKlGeOLHGr0I0E73gLDrTAc+SzT4XT/tsAA3DRwmZ67RriZ08znKhM0mfBxZ3oZ\nG26DHQpe6YIdXTB+EPgB0/2VYaqo8KNg2GOLruT9uNNCMdSbtRiyWYzjBt3Zfb4QdgfVhfyJk1dO\nBQAAIABJREFU24wo7sE8N4PAACza+CH9C/dxB+/SyyGWcpJWLtHEOB9Fg+v20c8LPB5ZiXv58O1P\nlAbN2kgTNtrEJTDl1/YOJfkKu8uh4+x+y0xHk2jE57AeXDp8GjEfBWEqIpLrjrSBcHkbiXIp0j0j\nFJt4fvTf91m2Dae1+PoD73ph/wPGHaMLWALrOvfwKC+znt308D4LuMo5lnC4YxW7tmxg+5atHBlY\nY6xnz/TB+AWMG0ZcuuwHJz1+mDufcuMsu4g7RnnMZLi2asctjotEYddDlmLXWryg5HrkT9QRTRHf\nterQpKX4Zk4zThOn6OQwvZymgxFWcIA7GH07mi5+iNIAu1EwLhNxA1zHnM9V4iNQWEJRZ9ztxOxP\nOjaJmXq26lG0VhOps4TZiYjkhmemKqdyRHOcNciK4aRBiLaR9bcdAtrhWNekRevEhmWcpJMzdHAz\np2mOJiNoYmJy6tojrljYfydGhOP8j3wzJ61yIfy40SEq8Vl27+MiDVCJWsQvLtq/OMl9yrcMu9bh\n0PFjzvELjDDuoTSzpf3fBS03X6B1yUWuM5c9E+vYdel+rp1rhdMtpcgSx5z/bgi2KZZi+/KaFl0i\nNNAOZx3neGL2uVQzXFkSN5rgTUPqI+HGQXyS646sIqAeKqq8gsUdbBgXFi40YMelHTPd9d1AX+R2\nEX3WAr3XWHzzOebM/YiJiSbOnV7Cx8cWlsJM2e7iEUzjz0lMrOWzGKuXH1oKwhYwlyT/yaKph999\npqiFIIbqDrqLu34oXnFodjv3Ws5AvUWUQrT1MunDf1PvZZZ2nqSVi8zlI64zx8xUd+Q2ONRSGmBn\nn4ljwLgbgximD3B1BbL7nPrHh0Rx1p6wLM9QI0VsaERupLpGuPEQn+QGIWsot3rEtw6n4R7jCsur\nMee3Mt2S1hwd/6b57G2GvW3wtXaMyayD82tvNaP0N2L8Lwc+pGf9CB2cYS7XucICTrLUjMQf6oRX\nOmEHpluZ3ZjwcceZnv+2S9v1n3Z9K/08cSnCwgzpEU0ajVoJYZdq+BbHHefHKHancvePd33hWzDl\nr2Oqtdj656+B9jXH6W4qzWZnhPF1xmma9DM+wTIz5fu8Dj6O/R5+9JnQrHW+tTjkV+xej5h97v44\nKi3fIoSTaeT6QxCqi4jkmlAPwqBo8gpmmB4qzlqaQxa1Bc59LlCKVOFzH+z9DHQpuBX6HnibL/BN\ntvEig9eHWPj+xwBcXnETu5Zv4MXl2/jG577IkT9bA38C7OjBiORQw2HTZ6fEhqkxll3rs281t/+r\n0WDH5Xe9NH71WN6rYS12j80aps11rXBxn4VowGoPpsfkQWArrL3r79nETgYZYh17TJi28+dpnoAr\nC2/i9NwOjtLNYXo5wGpOspQzdHDi/G1mBsvJAXeUIl4AjLbAtc4obWeYGpEiyY0ii7/xbI9HXI/U\nSz0gCI2HiOS6pZErNjekW95z3HPdGMtuV68rTH3OAsdhxPgsHzy+mj3L19HJSZrmjtO95ijNTHCa\nDk6wjHMsMactwoiE5g4Yvzu6/nHnuu50vTBVAIReEKoRZzkv9ShOa0W1JvUIHZ8Uqg1vm11vBTqA\nNjPlc2CGu5sGL9PfuY917KGfUqi2Tk7SOnGRpvEJzixezEmWTg6+28Pd7GEd+870m9kprVuFndDj\nGqUJPeykHouIHru2aEOcL7G/nORbXFQ0nizXa1Qauc4XhNmJ+CQLVSavj7Xvs+xb2lyf5QuB63Ri\nlMXdsKiz5K88AKyFeT1nWbL4HM1McJ25nDuzhLFjbVN9lu3yKEy1Wrvdz6Eu6CTiQlwVERVDmE41\nRXGSZdkKYPtC5U//7JcZK5Aja7H1sd8MbL3GJ5fvZD1D3Mswd3CAZZyglUuTU7OfZCmH6eUQq3jH\nzoV3vB+G502fzOM0pZnuMuHOgOfGFrefpKmhIVuZzioM6/35EIErCI2F+CQLdUFWN4xyfG3bnXNd\ngXIBeBMuzYdXWo3PMcthieJabzuja9udCUrGuPWe9/ixe84xh4+YoInTdDB6pBuGW2CoDV5vMxMm\nXDoG7KE0MYmLP8jKEvJZDjX4IpYro5yqrKgwbe72kLuQK5hd0axKVuMeTHncCGw24ngTO9nALgYY\npuf8MVpOmdOuLYXDC1fyFveyk028wmbe2X0fbMf41g9jBuBNmd3OT2/Inzj0Hdzvb19Y3U+oR8fe\nL61MZ3mxrAdEAAvCjYqI5Koz2wZaVUIeN4xxSnGJ/cbSja3sig573kXgCFMtzW1wrheGNsDpTjP4\naSvcf/tOtvASG9hFL4dYxEWusoBDt69i5+0P8NLntvDG7kfga8AzXTB6gUiBBNJrB1e5MZ+tQLZu\nI2mIWM5GkaI4zX3CPzYUgcKWQVd0Xow+Z6NjlgMdxo3C+hZvhpXrf8ggQwwwTD/76OYoHZxmQTQx\nzjmWsHPx/Yws7uEAq9nDOvawjiNvrzEvbzaO8X6i2e5OUur1iHtJdQXyVaZbgUO9OPb7jzvb7HHj\nTI2F7FqY3XypV+o5bYIg1AoRyVVFfELDuIPakohzZfD9Od3wWFYc+K4YkXUZYOSzTgg4WMI/0M1R\n+s++hzphLr+s7wTNTRNcZT4n13dyZP8a03X97X5KE5G4AwfjLMkwdTKSEKFJRLJOJDPbqaSKyutX\n7J4TF9fYFcn2hSgu4ol90WsDlpvBpA8Cj8Hip0b5zNy/4XH+lm28SPur14zQPQMsBPpAPwgvt3+S\nF3icF3icd169D54HXsEce20MUwavOOm0L2duGXSnfrbHxvkPh15I7bXd727X3XI93/neVyiJY9fa\nXA9itB7SIAhCIyAiuapIZRxPOYP77PHugL4LTM1nN0pGKP+vAu/D9hUAvHHoEd54bBNrbx9mVfsh\nOttPMYfrXKSVD1jGIXo5cmCNGdTUhfFt3rsZxtdh3C7OMt1i5/toWuERN6jP3Z9UZuLOmy1UWh2l\nlaW8vsWhST0srqX0KkYoR1OnNy8wg+3WMBmKcNHGDxlYODw56M5ajDsnTtJ6foyxAbj4yDyO0s27\nrJ50pdh5ZhNj324ruVIcAngfU/asK4Vv2ba4USncCUD8MuaXu7hyFRfbPJSPdpt9UfCfhTh//mqU\naamHBUEoDxm4J9QJecVyqBs46dohP1E7YGo5LFKlSAJO7NnFvaMsnWsmYwC4SCtnJm7m7N7lxvdz\niFKXN+9TCh9wllLjHDcphN8tXUSXdD0L56LfyfOKYv+cLGHaWjFlJW7wqDv4rg9ubTHW4ieMtXjr\n3O1s40U2sZO7Trxnish1c8mxPhhevJadbOI1HuLliUc5++3lxlI8jClKo9GtJgfRuS4SvgB2o76E\nhGfShB7+OWnERfMgsOzf23fxGCc9beUiAlkQhBAycG+GSbMCCsmUGzYu1D1sxXArJbFjrVlXMD6b\n7zClG/jSfBi+G4Y/DVuBNdB319s8yXN8hm+x4fybtBzECJxlcPCeLl68ZxvP/vTn+e6uJ+APga+v\ngPEjTBXIUJosJeSzfCFK20Xnu1jrmyXkjhFH2iNdtAiZqSoka7nIOqGHb9X3191JPux++2IVxexe\nhPFtdwfeDcK8jWcZXLx7ctBdP/u4jRPmcn1wduk8DtPLEOvZyQO8wmaOvdhnXCm2Y8QxBzHl1JbR\nOP/7uNnvqh1yzV23A/hcv2ybx35ZtviDAl1f51D6yim3Uh8LglAZIpILwR3EIhVzZeT1V7bnuLQE\njglZ4FyuYgbknTQD+04bq/FHzGGCZpongMvRZy50rDhNJyfp5BQ39Vzm456Fxh1j5HaMJXnES08r\nJdE+39tnhUSawMsjluNohEc+y++f5lPsHucLurjIE+41rzJ14F0Lxp2iB+YtMFEotgJPwD33vM5D\nfI9N7ORu9tA9cZS2U+Y3utDRwtGmbl7jYfYt7WeYAd5igIOH74YhVRpwdwhT/E6DeZGzaWvx0uLH\nE/fdF5KiTVjyCs648uY/Z7YcuzNoxr2E+OXdtyYX0bMi9bEgCJUh7hZCHZPVeuiPyrfd5a5fqcUK\n5ItMb0A7gTuB+6FngYlVuxnYPMadt0czmnGGJsa5ygJO08EIK3jv+CoYmme6yG03+SFgfIzpMZZD\nUQD87wDhl4A0cdNIgiBPj0FWQewf7/sRu9v8wXehKBX2WlFvhI1KsRn4wjU+vdwOqftbVg6Nwi6M\nOwXAbebY0UcW8zd8hm/xWZ4/8nn43y3GYvw6TL6UBa3FFtedIsmvGOLLkrsvjmqVHVcch/yZQ+nw\nRbL/rOShkZ4JQRBmjmzuFiKShTqnnAghSSIpdO1QWK/26NMF81pK/soD5nPT4GVWdR5iRSScW7k4\nGWP5KN3sO9/PtdfbjX/pK0Si6CDGzcMO+HP9WdNiLMdZCbNQC6FQSWSXLL9Z2rHuS5I/oQdMt1Ta\n36EL6CxNAb0R2Ay3r9/PBnaxnqFotrt3WXb9BAvPfGxccBYaq/H7TT0cppe3GGA3g+ya2MDZ7ctL\nZWAIuHQF8xZlX57c7+a/1Nnf/QqlF7wsFtai3S2yEhcdA9L9lkP41uXQcl5EOAuCID7JBVNEV7eQ\nn3J8ld0Yy/5ECRAeSOf6CJ/ETBpywWy/1gp7+2DvA7C3Da7Bx2vnsJp32caLPMrLJnzcKWAhjHYv\nZtfiDby4bRvPbvs8o3+60tx6Rw9GHLkROawIbnM+Np2uW4gNI5ckQuIEQz2GIkyreuJ8if1j4vxd\n3d/X9SX2w7TZ32EBsBRuVpOuFDc9dZltnS/yOC+wle2s2XvEFItTmElAukFvhO8tu5/tbOEFHufN\n3Q9ODdN2GkoWYJdOzEuYP5vdSZJdDZJEYRED8tLukZXQ7+Lmt82PkKXZ3e5OA++GmPOfhTxpFjcM\nQRCykSqSlVJfBZ4ATmmt10bbfhv418CH0WG/qbV+Idr3G8DPAhPAL2utX0xPhqJ+RWjIL7Te0ngj\nkFcsx/ks+5YtP4SVnQTCWvhsLNpdZnn/Z401cLCFkdt7OMlSLtJqDr0EnIdbOc/q7gOc4DaO0s3z\nW7vhWAtcaoHhrRjBNhLdB0oRFHwroptOG/LOlj8/H8ZizvXzYqbI+v6d9HvGxSq2+0JhyEJxfV3R\nGYnlRZgBd10Yq3Ev0cC7a9y5fB/r2MMAw6zmAKs5wFJOopeBWlryM95HP7vYwGs8xBuHH4bnlRl4\n9zpw2rUWX3XS4k/Q4YaRu+IsZxG89SSKi7hmaMCf+9zbZbdcxA34q5UlXRCE2USqu4VS6mFM8//n\nnki+pLX+fe/YfuBp4H5gGabJuENrPZF8j+UafrHc7+BRZAXoNsxSsdYP5VhGrbD0xYrv3uCG1XJp\nA24H7obmPhPmayuwGW594D3upuSzPJfrXGcuZ+jgBMs4zCoOnellbLjN+CzvxeinEZzpg88y1Xc5\nS1i4tJiz9UJWIRw63rX8utvcaBOupdj+nv4gzbFoXyc0d5QG3n0e7rtnB9v4Do/yMhsmdtG2ZwxO\nRJfpAN0HP2jvYzeD7GQTu9jA3sODsMMZeHcME67ttE3qBedzNZCWOJ/zrL+5T57fvd7CBCaVgbiB\nfz7+sxByx4gb4EvCMYIg1AdF94j++2LcLbTW31NK9WS86+eAr2utrwPvK6UOYQTz9zOeXwDV6FqW\nirO+yBoBwyXuheci0x8DfxY19/8hGB+BV9rgFRNneZSVjK5ZWZpq+EETPm6Q3QwyxJM8S2vHRca3\nNHFqSyeHWMVuBvn+8U3w/Dx4fgFsXxDNoDaC8Vm+4KTFFYP+oC7fVzXO0lwr8vgX+8fbl5kkK7Fr\nmbURE+ZjeqcwFuMlGKuxtRY/CLc+8h6P8gqP8jJb2M7KvaPGAvw+JoLJ4ujYgdKsd8/yJO/99V3w\nHOb1/5hrLR5z0hOaztn2BGQJ01a0tbjeRLFPKH2+i5QfSi7JnzlLSDlBEGpDvbRN2ajEJ/nfKKX+\nBabz+Sta638AlhMNUYo4Fm1rcKSirT/KFYJ+N27IQulHPnC7xs9gnFI9S/P+O2H/p+FQm/FXvQv6\n2ceTPMtdQ+/B7ujUSHwdeeQWnlv+JH/x8/+EN3oeMec8syK69kkvbc2U/JWtAAtNdX3VOb5eSBto\nF+dmZfPdfUFwJ/WwItO+JNgpwqPBlj1MTgG96PMfsmWhmdTjYV7jrqPvwbeADzAzKS4EPgFsg4Pd\nXQyxnjfYwC42MHRmkLFvtplazkYuGYGS77rr5mJ9bv1eCD/8YJbwZpWK43oXxlnxLcF+eDl33W5z\n/7u+zHY9i5VZEIR0GkvwlkO5rekfAb+DmQbqd4D/DPyrPBdQSn2ZyZAWSypIiqXajYII5fqjnEF9\nLn6ZsZYnu3zVWfZFjk8UZ3mkH4bg4MA9bL/nA+ZwneuDL3HvyndQP4ou1WnOWMYJ7mcXF7e18s6S\n+4zlcscG2LvBdN1PCkC3YbcCy/0soCTsQ89BueGzspLXWmzPsS8kobBgvqXYulLY9chSPI/SpB5d\nTE7ocdPWy2zufJmtvMRWtvPj7++FVzFC9xTQBKwE1sOFn2zhb5se5zme5FvnP8O1r7ebwXc7gHNj\nGPOyG6bNprsdI9ytS0XIXcYnb5i2erYYF1UfZnl+4wbpuULZvvy6ZcgVzePesvuylSXChiDcCDSC\n8J05Q1CmEHCRu8Xz1ic5bl80aA+t9X+K9r0I/LbWOtHdQqluDb+WkopqVFSVNipSedYXRTzcoXiu\nLqHoEtbq3I7pOOkxURLs9NYDwCC0DxxnddMBehihm6N0cHrKdNen6GSEHg6wmr1HBmBHi+mXeR0j\n7sbPUJry2p0SOS50mGttDVkuQ4P/4vZlIc2/2N/u5l1o2m4/TVG84kUYS3GUr2yGvnveZhM72cAu\nBtlN//V9LDz4sbHeX49utRTGuuHdxSvZRz9vcS+7Wc/QxCBndywvhWizPuPjdnZGG3ki9FLmWoWz\nzHpXpCvFTIrimajrKnl+Q+4XSQM/434HsTILs5F6Fr4zIXhD3/8r1QsBp5S6TWv9QbT6JKZZAdOJ\n+RdKqT/ADNzrA95Iv+JNTB1AlSepeRoKv7JLE0BpyMCP+qJSX1y32xymh+1yfU7dgWLWMnUVeBPY\nDqevwo5x2NGKUXQPcHZgOd9/ajlDP3eBL3Z8g03sZNvl7zDvVUqTUHSDfhBevP0Rnrn9C3zj81/k\n0v+4xewbasfEWbYi2QpMG06szUuLHThmo3XEuTpY0p5BnywD8vwYxf5/PwwfJQPgPEqWYt+veCPc\nedebbGInD/EaD/E9Vu4fNSL3Rxi/4oXACmAQfti3kpd5lO1s4ZXrj3L+27eaMG3DmPeOUYzrxSTW\neNARpcsN0+a+pPh1RhaRVYk4nilhXIt6LO2eSeVt3PvvR8bwfZpbvOWkgZJSzwv1QD0LXZdKRW+e\n71lEFKUK76CUehozx9TNSqljwG8Bm5VSA5iWZAT4eQCt9Q+VUn8J7MPULr+UFtnC4IrkuMonNMod\nki0EoWskWXdC2VGEcPbvI1SPSoSy25CGxA6UrJ9+t7n1RbVRKqzAvhAd2w5718NeGBtu490td3CY\nVRxd2EXf0mNwFGO0PA/qMjy28VWWrjjFHQsP8OJXtvHdp7bCt+fBjgfNK+kIcM5NnhV11gXDjdxh\nJ0fJ2/2fJR+TLHhWDLsvE86gOp9FTA3NNhmWDbruOchANKnzvQyzjj30nj2Geg8TieJydI024Cdg\nrA/2LL6TPaxjFxvYxf28eWQDvNJi3Cisf/GlMUzGh2IZw1SLvB0oaZeTrMKNOPiu3uunPD0dcfW8\n604Vqu9DL5Kh3/JGiXxUhDCbzfnj0igiNiuNLHaLs07XyYx7vRp+L2ZvXj+9vJadpMYt6z0q4Uap\nQGaSIqzJLm6DGoqo4B7nY629S5mczc2xhjII7ZuPM9BkxJ+NzdvDCLecumRcBZrgQqeZzW0f/ZOh\nyL5/5CHY3mIsokM4LgKhaAuuONVMjbTgu2Okhduy+K4ejjXYYi3CrmXYri9iqk/xrRij+1pYPDjK\n+rlDbOANHuZ7bJrYSdurY+Z7HsS4URBl650mL49suCWKV7GZV3iUI7vWlATxIcww4tNEFmPNVPeI\n0McOxINwiLG0uqPe3SlmY91TTiMa56LhkuaS4e9rBGabqBOyU20BHHf9tIhGWa6fpV3KwlONNC31\ngIaXnC1ugxSKe+k3VO7/pFHM5Vh38oroohq2Rqps65GihXLadX33AdfP1uKKUpic5Y27YU2LmbLn\nZ+BTdz3PF3iGJ3mOW7973vjK/ig65RPAIHz4E4t4gcfN5/LjXHrulqnuA9bK7IpTX89fY+r4vzzZ\n4O53/4c+rhC2A+yiF4XFa0fpn7uPfvYxEFmI1/ED2oevGc+S9zED7ayleDFwG0YYD8KbS+9kJ5si\nh4uHGH1pZWkK6GGiGe+spTjOEun7E1urclwEiryD7+KODSHCuBjiLL1Jz2/cMXG9mP61i+gxKAoR\nwLOToiykeXoJK7lG0nWSIh+Fwn762+NCg4aWQ+lY2UAiec6gpnPIrLiuZe7yNbw6yLWGjXn/xwPL\nIeFt10lYd7f520P7Q4jleeaptJHIIpT9xte6E9iPnQY5FLLMlgk72O8+WKPgMeAJ6NvyNlt5iUd5\nmU3sZPnBs8aCej665GJgBZztm8c++tnDOozU7OddVnPscC/sV8Z6ai2o5zDPkf24XzXN0ygkhN11\n10q8hKmi+FZo6brAso4TrOIQvRyeFMX3TrxF246xUoi1gxj3ievRd+wG7gUehsvbbmL73K3Gr5hH\n2fvDHy+9GLiW4ktu2l0BfIWSX7EfgSLuJdzPCPfYUEYlHReHuFLMHJV0Aaedm6VcVON3mPku6Noz\n05FcymUm87wo94Zyrb1pvTMhoeuv+8uhbXFjXAJufH5b5W4/pxpUJJeDL6ynieq0rlVXZIe6UtMs\nRuW4bxT9kEsjOJUiLSlZG0vfBcOvKHwfXTsA0EbGWGAEZhfG7WAN0QQl1/jk8p1sYiebeZmHJ14z\nrgevY6ytJzCTwLdhwpoNAA+YwWq72MAQgwwzwCF6+fBwN4yqkmC2SXMrEn8MlPsVpgymG2Pekoss\nWXyOTk6xjBN0c5QeRujlEKs4TA/v077/GryLsQofjdJ7BiP47XUXY4zqn4i+971wZM0t7GYw8ine\nwK7z93PtlfaSldjOdDeuMeI3zgXC9Sm+QDZrcSgDGi0yhdQJycS97MZRieCphpW52tbAIgRelnJ+\nI5TTahpt8tyj3OukWXn9ds93SXTbPmtMCojbkAHGX076Gmnejy7HGkkkq/V66hwk9pt6bwau1SrU\nreszTlg4TxnF7luhfeHsWqtDIjrJpQNvn48I6OpRTZGcdJ/mwDa7PeSK4VYuvl9wNDFGF2bo7GPQ\n/tRxPtP0LZ7kOR4//x1ansV4Kg3BlaNw8TLMnwttSzGCeQUmxkxftLzM+DdfbGrlCgv4iDmTKRyn\nKebbT9AUfeZwnbl8xIKJK7SeH0Odxwjesxjx+6PovxXDp+DCGbhw3flWi6GlG+MyMQA8Ah8+sIjX\nojgVO9nEG8c3wI55JTFsp3y+RMkSPsVNxA6cdKN5WOEc99Kb5oZVtMU46RpFciM+70VRbr2RVVAm\n/f55fre86ayGj2iRVtIixXS9WZmr7f8bd484X/usrgxJll3fqouzHBicHeeK5+9L+jrj3nLIOBq3\nHqShRPJqDf+T9B/SX44zvQcGD4XEtY8roieFtO2ijeuqTeqaDTW+cQ3vTLpsuMzGBrWWAjlpnxXF\nvjgOVUTujHNLYZ4y1uVenNjLmjtXvcV6dkfxgYfYcPZtlI33Owy8A2cOwsiEce09Q6m0pj05ca+F\n/jdagLGHtwOdTdDRiQkA2Y0R6lakr4Ejy27hML0c4I6p7iEH+owY3o9xnRjBWIlHMVZvoNQj5LpT\njXnrSQMSy+n9KUrUzFTDPRuf53qi2r6+1RTKIcoVz3F1X5owy3JvyOaqUqte2yzkFcblDIRLa3/i\nNFTSf9cK7IldVzvN87aFkuiLWvs/JGj94yfRTNVIcctpbrOhsjHYSCJ5hTYT96WR9GC6BcIVIa5/\naGCygjThnOq+Yf1MrdUqzhqdNCIawj9imstH6Lhq0AgNbzUar6IbkKRzrSB2XTB8a7NXadnYwT0Y\n0bwR5m0+y4bFb7CJnWzi/7GBN7hl1yUjmt2BcOeBy6Cn9KoYVDPoQJFSzcBczDMzF+Mi0Y4RxDYq\neh+M9ZUm7LCfA6zmwPk7uLa33YhgK4StCHb9pYPPnLUUW1cJ94XVDoTM84Ka9nLaKFZjSyM8o7OJ\naonlWvos+2St/7JYKdO2ha4bt80nSZ3FEZfPRbazWQ0sSfkRZ0hxl0NuDyGjYkzoTV/khkRvmuYP\nieBp2eUbOax28seB+HoptAzh+pyY/SH+eSOJ5Ns1/HrKUXnepOJ8Zuw2vyAlCGk3MoDv7zKtMXfF\nszs9bZLbBkz/8dO6gV1qZYFOopoNdrUtOXksx2nHx50b16gk9ZD4AwKt2walAXI2coSNMzz5GaO9\n6xQdTae5mTMs4RytXKSVi8znCgu4yhyu08zUkObjNDEROVt8NOloMWfSTeMixmXjIq2cYwnnri/h\n/OklMDrPuEZYFwn7Oed8Ljn/L9k7xo0dCFWg9n/I9QninykCx8Ydk3RsEjP9zIlArk+y1FUz9dvV\nqnfNPz5rPejuj7OK+scn3SsrRfwe5eRBnMuDu2yPDbgy2P8hN4ZQEmC6BdcXuNOqsVDfYtw2AssQ\nX1/PhMubf+4vVm/GveLRpH/J0OCKuAxL+1px3RCuU3n0uTYfriVMhjDFx0ZF11gA4x1eQbMCwHfd\nsGLatTq3ROtpjjnuMXEiIO0a1aDaQrYo8lb2IbLkZTPZyre9ll9xum4a7oC/aNa9061wOiqT80Kf\nFs7OW87ZRcs56MYm9o9zbx1XcV4L/E/6uMfa5Un8eM1ZemLSutXitrnbXYpyp0i7lnCYhU2cAAAU\n1UlEQVTjUQ8vL9Woi/OW8zRjTzOliXz8ttnflsWftpL11sB9k9IUuoZ/bIwFNytZVFpQ1MbhTjyV\nVfS6//G2EdjnJsyl2j1xxT9zdSKSPyY821UcWYWJj19hWM9Me72LgXOT/HXaYHy++dBWur7rtzN5\nKQXjC8xnUijEWZ5dq3Nc1A0rkNNeFEL7rQjPwmxo+Mux9iaRlHeha7h56AtmWwbTsG4FbsXtuhbZ\nl7o282JHK6BK5dDtEXHLZZw/WSjp7nrI6uCLZ2BqF1toxrqkShhvm7s9zl0iyQoR96VcyqlkZ8Mz\nIhRPoxgLimSc5Pq2nJ7PtLY8i7uHK7BDrgjusn/O1cD5Sd/T1RTNlERpXrEcnTc++Yd0v9yQJTeL\nddf97+7zt4f2uxTpviYiuQLKzQjfUgfTxXlSV0nogXJj4UaiZXw+jPuWP8cCPem+EbA8T4oL64Np\nxbO1PPvC2R5LYN3d5m/3BXZcniYVkXoTB1l7EELYSqZS/Gv49wyVwST/OLdCHvOW3X2RVZkLTHmZ\nG2+BSy1wyXfn8NMQqsDjXLHiRGtSpZ3HIgHZy3KoDOa1GMedk0a9lP88L71CsdyIQjgN3yiQRprR\nIfScufWoe/5Vso8JyWMZzmpNDu3zifN78MliAIhrw9P2V9rLFndOHPUheMuhTkSyO4KxCNK6t0P7\nXBeONLOa2z101js/5FvqdZNfs/EAHOHsWp/t9cY7zGeS0IBB33/Tbg+9RcZ9Z6K0lPOgFEG5bjM+\neRqsmXoA4yzFoYYkrcz6FgxrkY3z3Usa4OHe190Wum9S2tPcH/JYLcr1SyvHYpx2bqPh/n6z5TvV\nEhG/lTFT0iKL0SFNRIeo1BWvnO9fiRtL0feppzq1tsaIOhHJRVOOO0boLSzpenFvg1ZAp3X7eAOy\nprhtBFw3msEI6shlY4oPUtKAp1DILJgqnkMVTVoejpO9IUl7aLK4xlRCrUVDmkuF/2Jm0+t327kC\n07cw23VfNLv/YXpe583nLJVn3LOUVfhmef6S0pJ0TtbzG5m0HpMbDRG8JRqhyS/n98rqpgHZjEZJ\n9Ueovcx6j6zkEYZZ7jVTArzINNQHjfDEVIm0HyxLd7h/PVfIJL2xhoSzPzgrWp4Uz+52/7oKmiO3\nDZuUKSRF3fCtzzBVPM/3ttntlViuiih21eg+rxZZKv1QD4Yrrn3Lc0gw+/vsNlc4l5s+e680yunG\nK8LnLe28vNfJQtKYgHqlXMFYq+dptgncRm9yq/l7VDNv6mEAYxoz4fJV1HPcaPVe+dTJE5slukUa\nRT8EWSypSdZo/xqhrHaFtesbHee6YUWyddVwwoGNe/Gf3QFZ444FGuu+4UYW8P2cfQt0yPE/zefJ\n3+eT1wqYtXzUWhyXUw7zPoZZLM+QXv4seQbNplGpBbfcMpMX+ztVes2453q2MdvEajlU0lw2Wv7V\niTQAZqZOLZpKjTgz9bLfqHXVzLXztS5JBZI104qsrPwClubCEbIKhs7zrdBx/qUtTBfRdvCgY4me\nIrDtZRQ0O1Zpm6QpSfMjE4TEdMhtI80P1VqokwZpkbAt6cGuVldzkeWm6Ggbllq/IEB1/eSykNe6\nWw1f3qyhQoT6oNriN8v160VEFykJ8n6nIvKpiHEsWeuBLMacuF6zJNfGMbK/fJfzsl8v9VA9tFfp\nzCKRnJUsP0y5FVa5hc/3WfUHHlox6hMX+sYXzoE4u1MicNjr+BEObPQNzXRRmySYQwLafkdbAYzH\nrIe+o//dy63YalHc85SlaqYvzje4CIpId5YoKtV8wc1y/WpYnZOol8asnqm07GUtU9UYRFztsRhx\n9wiRJxpD6PikiBJp++Pq/bR7ZqUcl6+4HtK483zx69ZZaQOyfcEc9+LuX9slj1iutptYY4jfrNyA\nIjkLaT9y0SI6JIrTznXP8X1S4wKz+xEOQlZod0Chf4yL3Rdyw8gimu1yyKKctdJwqWf/0Go1VCGS\nKv9K3FggPn0h16Ok44smy0uUJWTJyXqPIr5PnvyW6jkf5f4+afmcRwD6401C2+IGdWdJSxqVWFrT\nxtBkOT5PPVCN5ylU34XaGL/t8tfjrhVqp/PUuZUOSq7XNm52CWMXqYXLIsvo2Dwkiee0e7rn+2I7\nS8xIX0Bb8RsnmkNuHlmsgK5Iziqg49w13OsS2JdGkV375ZC1MWqJWY7DzSe3TMT5KadV2EnuQ0ki\nIK3xj/sucS+H81OOscdlodyXjUq6Zd1rFvni0CgNU1HfuZznrQiLblKPnd3m14X+Nt/Y4K+nPTNF\nl+9yyDNeIKtwjDMOJQlVd3sl27Kmq9yBxkX4HdfafS1Eo9Q7xSAiuVCSCk85DUVWy7N/f/derpXP\ntzj7FYG1QrdgomDECWJfRPsC2m8wkmYaciesSPNp9t03kvanVbL+vkqIexHJa2FKI/Td3BeKrII4\nq+tFkouP34thy1MoL0IWtEqsaXG/Yx6feP9cf3uW7stK/J99yimLN2L1ncfa6Zcxf5svWEP743rh\nslqH414O/fSmzcaWpY6Im/wnRFYLZpZnJcm6Wum6uy3P8+vvC+2POy7LOXmukUYtrcM3luAthxux\nlq0RocJYhOU59BP6Fj97fEgghwYJhtw1Qo1AkrXE/e/OSmjFtW0U3MlUWqLLt0y9VUj3+f+nob0D\nksRz4oViyNLl6m7P8sLg4k6wM0Y43rX/cpB1qucsjUWSa0Xcua5gdpfjRHScJc7vsYDswjmuQU3K\nhzwNddx6XkuSf/z8wDFFN2BFNMZFNBnluASk9Yj5y1nEbUgIhyy8cWVQMV2YJpUp9xkOHeNuy0La\n75m11yWPFTjL/rzHh47Jc1zS8VnPzXOdNET0ziZEJNcUv0AXZW1Oc9PwxUqSdTnuuhBukOy6L3BC\nLhyOFXq8GTOQMIcVutlL0pTkWfHtW3qqSJwRN7Q+BSuIQwI3JJD9Y0LTlKcJ47TKNG2/zU//S9sy\nFDehTpx7D0wvG3E9Ey0Z1t37utjyFLK6JQkcd7kIAe5u87eH1v3jQ8xEA5n0HKWJ26wvkv62NCtt\n6BnP4t6Thvv8QTbrZtJzmKVMVLN7vVyLaS3dBrJev5r3d6mVABbxWytEJNcVcQ9C3kq+3Ac5q3XV\nFhvf4uzvT9qX1NBl9O8b9yw/4+4xMeK62fsfSnIaSZbtafgC2LUCh8Suv43AsTC9oXW3hRJUdCUb\n1zMSyhy/vNhj8faniepyei7iykSofFTyQhXXc+Fuc7dnFch5LNR5nvui6hrIZvkNHZdHQGfZFyKU\nl6GXntDzFbfNPc+9rn8/f19cuqpFNQRdNUV8Ufeq9L5FIwK3kRGR3BAkPWRFuGz4+H6nPnGW6NA1\n/Ikq0hrPrOIpq3XJWx9vdpKcpSH3SRKfWS2RIdeASrr+83b1V4tyuop91x/IbnEMWY/jusvTrNn+\n9dPKUyhcop8Oj6y1bT2067nJ4g+bVk7jLLVJFtdyXWji1vM+f7USwfVcSKr53evte4sAnu2ISG54\nsnaL56ESAV0OrugOuXikzQiXtws4y3l5yGvZq/T4ShvmIhuaLFVInNU5S1qyCJK4dOQR2/Z/Hl9W\nSBfZzn3G49ID2X3VK6VcMWvJ4gYSV17zitzQvqLEbVzaQ5RT19XKclor6k28ZqWR81yYCUQkz3qK\ntkKnVYZZrNChNGS9bohQqLss5xc5HbNPuT5+RfudVrvxyitcLUnfw+7zf9MkH/lQuYrr4Yhz+whd\nN3RM2v4kMZy19yKuPFejyq5UFIeOSxPOeY/P2ltS7gtkEa4qtaBRxWm1qKffRpgNiEi+oSnSL9GS\npdJOG1joEyd4/PuWK8rc+2Sl2gNR6k0Ml0MlLz6Q/oKXdP248uBfM8sLVhp5qtFyXubKvVYWiup9\nqPXgrjz3qcb96/H5a0RE5Ar1hYhkIUA1XDhcqtWglFPBpvlWV/v+WZmNjXDW75RF7IbI+hJYrjU8\ndJ+0ZyPP75h277w9ITNRhmZSaFb6vM3GZ2qmEDEr3BiISBbKoBoDCZPIa53OQz1V9tJoh6nUIu2T\n5zf3B6mmIb9hMjfygLas1FOdJAg3NiKShYKpthU6jtnQOArlUdRvH1cdimgplkZ/VqU8CMKNgohk\nYYYp2jdYEIpipsXbTFW/jS5Kq4EIXUEQ0hGRLNQhIqRnjrxiQfK9OES8VoYIXUEQqouIZKFBKbeB\nnA0ir5biYCbuPRt+I2EqImgFQWg8UkWyUuqrwBPAKa312mjbN4DV0SFLgHNa6wGlVA/wDnAg2ve6\n1voXik60IJSPNNb1T9G/kYju7MjzIQiCYMliSf4a8IfAn9sNWusv2mWl1H8GzjvHH9ZaDxSVQEEQ\nhMoQ4ScIgiDkJ1Uka62/F1mIp6GUUsBPAZ8qNlmCIAiCIAiCUDtuqvD8h4CTWuuDzrYVSqm3lFKv\nKqUeqvD6giAIgiAIgjDjVDpw70vA0876B8AntNZnlFLrgeeUUndprS/4Jyqlvgx82awtrjAZgiAI\ngiAIglAcZVuSlVLNwD8GvmG3aa2va63PRMu7gcPAHaHztdZ/rLUe1FoPwoJykyEIgiAIgiAIhVOJ\nu8VWYL/W+pjdoJS6RSnVFC2vBPqA9ypLoiAIgiAIgiDMLKkiWSn1NPB9YLVS6phS6mejXU8x1dUC\n4GHgB0qpYeAZ4Be01meLTLAgCIIgCIIgVJss0S2+FLP9ZwLbvgl8s/JkCYIgCIIgCELtqDS6hSAI\ngiAIgiDMOkQkC4IgCIIgCIKHiGRBEARBEARB8BCRLAiCIAiCIAgeIpIFQRAEQRAEwUNEsiAIgiAI\ngiB4iEgWBEEQBEEQBA8RyYIgCIIgCILgISJZEARBEARBEDxEJAuCIAiCIAiCh4hkQRAEQRAEQfAQ\nkSwIgiAIgiAIHiKSBUEQBEEQBMFDRLIgCIIgCIIgeIhIFgRBEARBEAQPEcmCIAiCIAiC4CEiWRAE\nQRAEQRA8RCQLgiAIgiAIgoeIZEEQBEEQBEHwEJEsCIIgCIIgCB4ikgVBEARBEATBQ0SyIAiCIAiC\nIHiISBYEQRAEQRAEDxHJgiAIgiAIguAhIlkQBEEQBEEQPEQkC4IgCIIgCIKHiGRBEARBEARB8BCR\nLAiCIAiCIAgeIpIFQRAEQRAEwUNEsiAIgiAIgiB4iEgWBEEQBEEQBA8RyYIgCIIgCILgISJZEARB\nEARBEDxEJAuCIAiCIAiCh4hkQRAEQRAEQfBQWutapwGl1IfAEWfTzcDpGiWnEZH8yofkVz4kv/Ih\n+ZUfybN8SH7lQ/IrHzdCft2utb4l7aC6EMk+SqkhrfVgrdPRKEh+5UPyKx+SX/mQ/MqP5Fk+JL/y\nIfmVD8mvEuJuIQiCIAiCIAgeIpIFQRAEQRAEwaNeRfIf1zoBDYbkVz4kv/Ih+ZUPya/8SJ7lQ/Ir\nH5Jf+ZD8iqhLn2RBEARBEARBqCX1akkWBEEQBEEQhJpRdyJZKfWYUuqAUuqQUurXa52eekMp9VWl\n1Cml1F5nW7tS6u+UUgej/z9WyzTWE0qpbqXUy0qpfUqpHyqlfiXaLnkWQCk1Tyn1hlLq7Si//kO0\nfYVSalf0XH5DKTWn1mmtJ5RSTUqpt5RSz0frkl8xKKVGlFJ7lFLDSqmhaJs8jzEopZYopZ5RSu1X\nSr2jlPqk5FcYpdTqqFzZzwWl1K9KfsWjlPq1qK7fq5R6OmoDpP6KqCuRrJRqAv478GmgH/iSUqq/\ntqmqO74GPOZt+3XgJa11H/BStC4YxoGvaK37gY3AL0VlSvIszHXgU1rre4AB4DGl1Ebg94D/orXu\nBf4B+NkaprEe+RXgHWdd8iuZR7XWA06YKXke4/lvwLe11muAezDlTPIrgNb6QFSuBoD1wBXgWSS/\ngiillgO/DAxqrdcCTcBTSP01SV2JZOB+4JDW+j2t9UfA14HP1ThNdYXW+nvAWW/z54A/i5b/DPj8\njCaqjtFaf6C1fjNavohpYJYjeRZEGy5Fqy3RRwOfAp6Jtkt+OSiluoCfBP4kWldIfuVFnscASqnF\nwMPAnwJorT/SWp9D8isLW4DDWusjSH4l0QzMV0o1AwuAD5D6a5J6E8nLgaPO+rFom5BMp9b6g2h5\nFOisZWLqFaVUD3AvsAvJs1gi14Fh4BTwd8Bh4JzWejw6RJ7LqfxX4N8BH0frHUh+JaGB7yildiul\nvhxtk+cxzArgQ+B/Re48f6KUWojkVxaeAp6OliW/AmitjwO/D/wII47PA7uR+muSehPJQoVoE65E\nQpZ4KKUWAd8EflVrfcHdJ3k2Fa31RNRd2YXp3VlT4yTVLUqpJ4BTWuvdtU5LA/Gg1vo+jFvdLyml\nHnZ3yvM4hWbgPuCPtNb3ApfxXAUkv6YT+dB+Fvi//j7JrxKRb/bnMC9jy4CFTHfnvKGpN5F8HOh2\n1ruibUIyJ5VStwFE/0/VOD11hVKqBSOQ/4/W+q+izZJnKUTdui8DnwSWRN1xIM+lywPAZ5VSIxj3\nsE9hfEglv2KIrFdorU9h/EXvR57HOI4Bx7TWu6L1ZzCiWfIrmU8Db2qtT0brkl9htgLva60/1FqP\nAX+FqdOk/oqoN5H890BfNLJyDqa75Fs1TlMj8C3gp6Plnwb+uoZpqSsi/9A/Bd7RWv+Bs0vyLIBS\n6hal1JJoeT7wjzB+3C8DX4gOk/yK0Fr/hta6S2vdg6mvvqu1/qdIfgVRSi1USrXaZeAngL3I8xhE\naz0KHFVKrY42bQH2IfmVxpcouVqA5FccPwI2KqUWRG2lLV9Sf0XU3WQiSqnHMT5+TcBXtda/W+Mk\n1RVKqaeBzcDNwEngt4DngL8EPgEcAX5Ka+0P7rshUUo9CLwG7KHkM/qbGL9kyTMPpdTdmIEaTZiX\n6L/UWv9HpdRKjKW0HXgL+Gda6+u1S2n9oZTaDPxbrfUTkl9honx5NlptBv5Ca/27SqkO5HkMopQa\nwAwKnQO8B/xLomcTya9pRC9fPwJWaq3PR9ukfMUQhfn8IiYS1FvAz2F8kKX+og5FsiAIgiAIgiDU\nmnpztxAEQRAEQRCEmiMiWRAEQRAEQRA8RCQLgiAIgiAIgoeIZEEQBEEQBEHwEJEsCIIgCIIgCB4i\nkgVBEARBEATBQ0SyIAiCIAiCIHiISBYEQRAEQRAEj/8PfRknm+6BM1gAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(12,6))\n", "CPT.calculation_bandstructure()\n", From 86ecd4df72b942884d9be9be9d49a2e130bc39cb Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Thu, 21 Sep 2017 13:45:23 +0300 Subject: [PATCH 06/33] Update Readme --- Readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Readme.md b/Readme.md index f0c6e42..aab1404 100644 --- a/Readme.md +++ b/Readme.md @@ -1,4 +1,4 @@ -# **PYED**: Exact diagonalization for finite quantum systems +# **PYED+CPT**: Exact diagonalization for finite quantum systems with cluster pertrubation theory Copyright (C) 2017, H. U.R. Strand, Ya.V. Zhumagulov From 0ccbd88add5d4ec427f8a5c92faf7cfee3131c79 Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Sun, 1 Oct 2017 19:58:33 +0300 Subject: [PATCH 07/33] Cleaning up --- pyed/ClusterPertrubationTheory.py | 12 +- pyed/CubeTetras.py | 137 -------------- pyed/SparseExactDiagonalization.py | 291 ++++------------------------- pyed/SquareTriangles.py | 133 ------------- pyed/TriqsExactDiagonalization.py | 82 +------- 5 files changed, 43 insertions(+), 612 deletions(-) delete mode 100644 pyed/CubeTetras.py delete mode 100644 pyed/SquareTriangles.py diff --git a/pyed/ClusterPertrubationTheory.py b/pyed/ClusterPertrubationTheory.py index 10b4e36..362efae 100644 --- a/pyed/ClusterPertrubationTheory.py +++ b/pyed/ClusterPertrubationTheory.py @@ -91,9 +91,9 @@ class ClusterPertrubationTheory_1D(object): # ------------------------------------------------------------------ def __init__(self,ed,k_mesh,V,omega,shape): self.ed=ed - self.kx,self.ky=k_mesh + self.k=k_mesh self.V=V - self.N=self.V.shape[0];self.L=self.V.shape[2] + self.N=self.V.shape[0];self.L=self.V.shape[1] self.omega=omega self.shape=shape self._get_green_of_the_system() @@ -117,19 +117,19 @@ def _coupling_system(self): print "Coupling system" bar = progressbar.ProgressBar() for k in bar(range(len(index_combinations))): - i,j=index_combinations[l] + i,j=index_combinations[k] self.G_Q[i,:,:,j]=np.dot(self.G_I[:,:,j],np.linalg.inv(np.eye(self.L)-np.dot(self.V[i],self.G_I[:,:,j]))) # ------------------------------------------------------------------ def _reduce_mix_representation(self): self.G=np.zeros((self.N,self.omega.size),dtype=np.complex) - index_combinations=[(i,a,b) for i,j,a,b in product(range(self.N),range(self.L),range(self.L))] + index_combinations=[(i,a,b) for i,a,b in product(range(self.N),range(self.L),range(self.L))] print "Reduce mixed representation" bar = progressbar.ProgressBar() for k in bar(range(len(index_combinations))): i,a,b=index_combinations[k] - self.G[i]+=np.exp(-1j*k[i,j]*(a-b))*self.G_Q[i,a,b] + self.G[i]+=np.exp(-1j*self.k[i]*(a-b))*self.G_Q[i,a,b] # ------------------------------------------------------------------ def calculation_bandstructure(self): bandstructure=[] - for i in self.range(L): bandstructure.append(-self.G[i].imag/np.pi) + for i in range(self.N): bandstructure.append(-self.G[i].imag/np.pi) self.bandstructure=np.array(bandstructure).T diff --git a/pyed/CubeTetras.py b/pyed/CubeTetras.py deleted file mode 100644 index 36c6bcb..0000000 --- a/pyed/CubeTetras.py +++ /dev/null @@ -1,137 +0,0 @@ - -""" -Helper routines for the equal time imaginary time cube and -its sub tetrahedrons. - -Author: Hugo U. R. Strand (2017), hugo.strand@gmail.com -""" - -# ---------------------------------------------------------------------- - -import itertools -import numpy as np - -# ---------------------------------------------------------------------- -def zero_outer_planes_and_equal_times(g4_tau): - - beta = g4_tau.mesh.components[0].beta - - for idxs, (t1, t2, t3) in enumerate_tau3(g4_tau): - if t1 == t2 or t2 == t3 or t1 == t3 or \ - t1 == 0 or t1 == beta or \ - t2 == 0 or t2 == beta or \ - t3 == 0 or t3 == beta: - g4_tau[list(idxs)][:] = 0.0 - -# ---------------------------------------------------------------------- -def enumerate_tau3(g4_tau, make_real=True, beta=None): - - from pytriqs.gf import MeshImTime, MeshProduct - - assert( type(g4_tau.mesh) == MeshProduct ) - - for mesh in g4_tau.mesh.components: - assert( type(mesh) == MeshImTime ) - if beta is not None: assert( mesh.beta == beta ) - - for (i1, t1), (i2, t2), (i3, t3) in itertools.product(*[ - enumerate(mesh) for mesh in g4_tau.mesh.components]): - if make_real: - yield (i1, i2, i3), (t1.real, t2.real, t3.real) - else: - yield (i1, i2, i3), (t1, t2, t3) - -# ---------------------------------------------------------------------- -class CubeTetrasBase(object): - - """ Base class with definition of the equal time tetrahedrons - in three fermionic imaginary times. """ - - def get_tetra_list(self): - - tetra_list = [ - (lambda x,y,z : x >= y and y >= z, [0, 1, 2], +1), - (lambda x,y,z : y >= x and x >= z, [1, 0, 2], -1), - (lambda x,y,z : y >= z and z >= x, [1, 2, 0], +1), - (lambda x,y,z : z >= y and y >= x, [2, 1, 0], -1), - (lambda x,y,z : x >= z and z >= y, [0, 2, 1], -1), - (lambda x,y,z : z >= x and x >= y, [2, 0, 1], +1), - ] - - return tetra_list - -# ---------------------------------------------------------------------- -class CubeTetras(CubeTetrasBase): - - """ Helper class for two-particle Green's function. - - Looping over all tetrahedrons in the imaginary time cube. - \tau_1, \tau_2, \tau_3 \in [0, \beta) """ - - # ------------------------------------------------------------------ - def __init__(self, tau): - - self.tau = tau - self.ntau = len(tau) - self.tetra_list = self.get_tetra_list() - - # ------------------------------------------------------------------ - def __iter__(self): - - for tidx in xrange(6): - - func, perm, perm_sign = self.tetra_list[tidx] - - index = [] - for n1, n2, n3 in itertools.product( - range(self.ntau), repeat=3): - if func(n1, n2, n3): index.append((n1, n2, n3)) - - index = np.array(index).T - - i1, i2, i3 = index - t1, t2, t3 = self.tau[i1], self.tau[i2], self.tau[i3] - - taus = np.vstack([t1, t2, t3]) - - yield list(index), taus, perm, perm_sign - -# ---------------------------------------------------------------------- -class CubeTetrasMesh(CubeTetrasBase): - - """ Helper class for Triqs two-particle Green's function - in imaginary time. - - Looping over all tetrahedrons in the imaginary time cube. - \tau_1, \tau_2, \tau_3 \in [0, \beta) """ - - # ------------------------------------------------------------------ - def __init__(self, g4_tau): - - self.g4_tau = g4_tau - self.tetra_list = self.get_tetra_list() - - # ------------------------------------------------------------------ - def __iter__(self): - - """ for pytriqs three time greens functions """ - - tetra_idx = [ [] for n in xrange(6) ] - tetra_tau = [ [] for n in xrange(6) ] - - for idxs, taus in enumerate_tau3(self.g4_tau): - - for tidx, tetra in enumerate(self.tetra_list): - func, perm, perm_sign = tetra - - if func(*taus): - tetra_idx[tidx] += [ idxs ] - tetra_tau[tidx] += [ taus ] - break - - for tidx in xrange(6): - func, perm, perm_sign = self.tetra_list[tidx] - - yield tetra_idx[tidx], tetra_tau[tidx], perm, perm_sign - -# ---------------------------------------------------------------------- diff --git a/pyed/SparseExactDiagonalization.py b/pyed/SparseExactDiagonalization.py index 6dd1f18..98c58cb 100644 --- a/pyed/SparseExactDiagonalization.py +++ b/pyed/SparseExactDiagonalization.py @@ -9,18 +9,24 @@ import time import itertools +import progressbar import numpy as np -from scipy.linalg import expm - +import multiprocessing +from joblib import Parallel, delayed # ---------------------------------------------------------------------- +import scipy.sparse as sparse from scipy.sparse.linalg import eigs as eigs_sparse from scipy.sparse.linalg import eigsh as eigsh_sparse from scipy.sparse import vstack from scipy.sparse import csr_matrix +from scipy.linalg import expm # ---------------------------------------------------------------------- -import progressbar from CubeTetras import CubeTetras +# ---------------------------------------------------------------------- + +def gf(M,E,x): + return np.sum(M/(x-E)) # ---------------------------------------------------------------------- class SparseExactDiagonalization(object): @@ -46,29 +52,25 @@ def __init__(self, H,blocks, beta, # self._calculate_density_matrix() # ------------------------------------------------------------------ def _diagonalize_hamiltonian(self): - self.full_U=csr_matrix(self.H.shape,dtype=np.float) - self.full_E=np.zeros(self.H.shape[0]) + self.U=csr_matrix(self.H.shape,dtype=np.float) + self.E=np.zeros(self.H.shape[0]) print 'Hamiltonian diagonalization:' bar = progressbar.ProgressBar() for i in bar(range(len(self.blocks))): block=self.blocks[i] X,Y=np.meshgrid(block,block) E,U=np.linalg.eigh(self.H[X,Y].todense()) - self.full_E[block]=E - self.full_U[Y,X]=U - self.full_E=np.array(self.full_E) - self.E0 = np.min(self.full_E) - self.full_E = self.full_E-self.E0 - # ------------------------------------------------------------------ + self.E[block]=E + self.U[Y,X]=U + self.E=np.array(self.E) + self.E0 = np.min(self.E) + self.E = self.E-self.E0 + # ------------------------------------------------------------------ def _number_of_states_reduction(self): - if self.nstates is None: - self.E=self.full_E - self.U=self.full_U - else: - indexes=np.argsort(self.full_E)[:self.nstates] - self.E=self.full_E[indexes] - self.U=self.full_U[:,indexes] - + if self.nstates is not None: + indexes=np.argsort(self.E)[:self.nstates] + self.E=self.E[indexes] + self.U=self.U[:,indexes] # ------------------------------------------------------------------ def _calculate_partition_function(self): self.Z = np.sum(np.exp(-self.beta*self.E)) @@ -76,13 +78,9 @@ def _calculate_partition_function(self): # ------------------------------------------------------------------ def _calculate_density_matrix(self): self.rho=csr_matrix(self.H.shape,dtype=np.float) - print 'Density matrix calculation:' - bar = progressbar.ProgressBar() - for i in bar(range(len(self.blocks))): - block=self.blocks[i] - X,Y=np.meshgrid(block,block) - exp_bE = np.exp(-self.beta * self.full_E[block]) / self.Z - self.rho[X,Y]= np.einsum('ij,j,jk->ik', self.full_U[X,Y].todense(), exp_bE, self.full_U[X,Y].H.todense()) + exp_bE=csr_matrix(self.H.shape,dtype=np.float) + exp_bE[range(self.E.size),range(self.E.size)]=np.exp(-self.beta * self.E) / self.Z + self.rho=self.U.getH()*exp_bE*self.U # ------------------------------------------------------------------ def _operators_to_eigenbasis(self, op_vec): @@ -90,37 +88,16 @@ def _operators_to_eigenbasis(self, op_vec): dop_vec = [] for op in op_vec: dop=self.U.getH()*op*self.U - dop_vec.append(dop.todense()) + dop_vec.append(dop) return dop_vec # ------------------------------------------------------------------ - def get_expectation_value_sparse(self, operator): - - exp_val = 0.0 - for idx in xrange(self.E.size): - vec = self.U[:, idx] - dot_prod = np.dot(vec.H, operator * vec)[0,0] # - exp_val += np.exp(-self.beta * self.E[idx]) * dot_prod - - exp_val /= self.Z - - return exp_val - - # ------------------------------------------------------------------ - def get_expectation_value_dense(self, operator): + def get_expectation_value(self, operator): if not hasattr(self, 'rho'): self._calculate_density_matrix() return np.sum((operator * self.rho).diagonal()) - # ------------------------------------------------------------------ - def get_expectation_value(self, operator): - - if self.nstates is None: - return self.get_expectation_value_dense(operator) - else: - return self.get_expectation_value_sparse(operator) - # ------------------------------------------------------------------ def get_free_energy(self): @@ -158,205 +135,17 @@ def get_ground_state_energy(self): def get_grand_potential(self): return self.E0-np.log(np.sum(np.exp(-self.beta*self.E)))/self.beta - def get_real_frequency_greens_function_component(self, w, op1, op2,eta): + def get_real_frequency_greens_function_component(self, w, op1, op2,eta,xi): r""" Returns: G^{(2)}(i\omega_n) = -1/Z < O_1(i\omega_n) O_2(-i\omega_n) > """ - - # -- Components of the Lehman expression - dE = - self.E[:, None] + self.E[None, :] - exp_bE = np.exp(-self.beta * self.E) - M = exp_bE[:, None] + exp_bE[None, :] - - inv_freq = w[:, None, None] - dE[None, :, :] + 1j*eta - nonzero_idx = np.nonzero(inv_freq) - # -- Only eval for non-zero values - freq = np.zeros_like(inv_freq,dtype=np.complex128) - freq[nonzero_idx] = (inv_freq[nonzero_idx]) ** (-1) - op1_eig, op2_eig = self._operators_to_eigenbasis([op1, op2]) - - # -- Compute Lehman sum for all operator combinations + Q=(op1_eig.getH().multiply(op2_eig)).tocoo() + M=(np.exp(-self.beta*self.E[Q.row])-xi*np.exp(-self.beta*self.E[Q.col]))*Q.data + E=(self.E[Q.row]-self.E[Q.col]) G = np.zeros((len(w)), dtype=np.complex) - G = np.einsum('nm,mn,nm,znm->z', op1_eig, op2_eig, M, freq) - G /= self.Z - - return G - - # ------------------------------------------------------------------ - def get_g2_dissconnected_tau_tetra(self, tau, tau_g, g): - - g = np.squeeze(g) # fix for now throwing orb idx - g = g.real - - N = len(tau) - G4 = np.zeros((N, N, N), dtype=np.complex) - - def gint(t): - sign = 1.0 - if (t < 0).any(): - assert( (t <= 0).all() ) - t = self.beta + t - sign = -1.0 - - return sign * np.interp(t, tau_g, g) - - for idx, taus, perm, perm_sign in CubeTetras(tau): - t1, t2, t3 = taus - G4[idx] = gint(t1-t2)*gint(t3) - gint(t1)*gint(t3-t2) - - return G4 - - # ------------------------------------------------------------------ - def get_g2_dissconnected_tau(self, tau, tau_g, g): - - g = np.squeeze(g) # fix for now throwing orb idx - g = g.real - - N = len(tau) - G4 = np.zeros((N, N, N), dtype=np.complex) - - def gint(t_in): - t = np.copy(t_in) - sidx = (t < 0) - sign = np.ones_like(t) - sign[sidx] *= -1. - t[sidx] = self.beta + t[sidx] - return sign * np.interp(t, tau_g, g) - - t1, t2, t3 = np.meshgrid(tau, tau, tau, indexing='ij') - G4 = gint(t1-t2)*gint(t3) - gint(t1)*gint(t3-t2) - - return G4 - - # ------------------------------------------------------------------ - def get_g2_tau(self, tau, ops): - - N = len(tau) - G4 = np.zeros((N, N, N), dtype=np.complex) - ops = np.array(ops) - - for tidx, tetra in enumerate(CubeTetras(tau)): - idx, taus, perm, perm_sign = tetra - - print 'Tetra:', tidx - - # do not permute the last operator - ops_perm = ops[perm + [3]] - taus_perm = taus[perm] # permute the times - - G4[idx] = self.get_timeordered_three_tau_greens_function( - taus_perm, ops_perm) * perm_sign - - return G4 - - # ------------------------------------------------------------------ - def get_timeordered_two_tau_greens_function(self, taus, ops): - - r""" - taus = [t1, t2] (ordered beta>t1>t2>0) - ops = [O1, O2, O3] - - Returns: - G^{(4)}(t1, t2) = -1/Z < O1(t1) O2(t2) O3(0) > - - """ - - Nop = 3 - - assert( taus.shape[0] == 2 ) - assert( len(ops) == Nop ) - - G = np.zeros((taus.shape[-1]), dtype=np.complex) - - E = self.E[None, :] - - t1, t2 = taus - t1, t2 = t1[:, None], t2[:, None] - - assert( (t1 <= self.beta).all() ) - assert( (t1 >= t2).all() ) - assert( (t2 >= 0).all() ) - - et_a = np.exp((-self.beta + t1)*E) - et_b = np.exp((t2-t1)*E) - et_c = np.exp((-t2)*E) - - dops = self._operators_to_eigenbasis(ops) - op1, op2, op3 = dops - - G = np.einsum('ta,tb,tc,ab,bc,ca->t', et_a, et_b, et_c, op1, op2, op3) - - G /= self.Z - return G - - # ------------------------------------------------------------------ - def get_timeordered_three_tau_greens_function(self, taus, ops): - - r""" - taus = [t1, t2, t3] (ordered beta>t1>t2>t3>0) - ops = [O1, O2, O3, O4] - - Returns: - G^{(4)}(t1, t2, t3) = -1/Z < O1(t1) O2(t2) O3(t3) O4(0) > - - """ - - assert( taus.shape[0] == 3 ) - assert( len(ops) == 4 ) - - Nop = 4 - G = np.zeros((taus.shape[-1]), dtype=np.complex) - - E = self.E[None, :] - - t1, t2, t3 = taus - t1, t2, t3 = t1[:, None], t2[:, None], t3[:, None] - - assert( (t1 <= self.beta).all() ) - assert( (t1 >= t2).all() ) - assert( (t2 >= t3).all() ) - assert( (t3 >= 0).all() ) - - et_a = np.exp((-self.beta + t1)*E) - et_b = np.exp((t2-t1)*E) - et_c = np.exp((t3-t2)*E) - et_d = np.exp((-t3)*E) - - dops = self._operators_to_eigenbasis(ops) - op1, op2, op3, op4 = dops - - if True: - q_tac = np.einsum('tb,ab,bc->tac', et_b, op1, op2) - q_tca = np.einsum('td,cd,da->tca', et_d, op3, op4) - G = np.einsum('ta,tc,tac,tca->t', et_a, et_c, q_tac, q_tca) - else: - # Not efficient... - G = np.einsum( - 'ta,tb,tc,td,ab,bc,cd,da->t', - et_a, et_b, et_c, et_d, op1, op2, op3, op4) - - G /= self.Z - return G - - # ------------------------------------------------------------------ - def get_tau_greens_function_component(self, tau, op1, op2): - - r""" - Returns: - G^{(2)}(\tau) = -1/Z < O_1(\tau) O_2(0) > - """ - - G = np.zeros((len(tau)), dtype=np.complex) - - op1_eig, op2_eig = self._operators_to_eigenbasis([op1, op2]) - - et_p = np.exp((-self.beta + tau[:,None])*self.E[None,:]) - et_m = np.exp(-tau[:,None]*self.E[None,:]) - - G = -np.einsum('tn,tm,nm,mn->t', et_p, et_m, op1_eig, op2_eig) - + G = Parallel(n_jobs=4)(delayed(gf)(M,E-1j*eta,x) for x in w) G /= self.Z return G @@ -368,22 +157,12 @@ def get_frequency_greens_function_component(self, iwn, op1, op2, xi): G^{(2)}(i\omega_n) = -1/Z < O_1(i\omega_n) O_2(-i\omega_n) > """ - # -- Components of the Lehman expression - dE = - self.E[:, None] + self.E[None, :] - exp_bE = np.exp(-self.beta * self.E) - M = exp_bE[:, None] - xi * exp_bE[None, :] - - inv_freq = iwn[:, None, None] - dE[None, :, :] - nonzero_idx = np.nonzero(inv_freq) - # -- Only eval for non-zero values - freq = np.zeros_like(inv_freq) - freq[nonzero_idx] = inv_freq[nonzero_idx]**(-1) - op1_eig, op2_eig = self._operators_to_eigenbasis([op1, op2]) - - # -- Compute Lehman sum for all operator combinations + Q=(op1_eig.getH().multiply(op2_eig)).tocoo() + M=(np.exp(-self.beta*self.E[Q.row])-xi*np.exp(-self.beta*self.E[Q.col]))*Q.data + E=(self.E[Q.row]-self.E[Q.col]) G = np.zeros((len(iwn)), dtype=np.complex) - G = np.einsum('nm,mn,nm,znm->z', op1_eig, op2_eig, M, freq) + G = Parallel(n_jobs=4)(delayed(gf)(M,E,x) for x in iwn) G /= self.Z return G diff --git a/pyed/SquareTriangles.py b/pyed/SquareTriangles.py deleted file mode 100644 index b8f3b97..0000000 --- a/pyed/SquareTriangles.py +++ /dev/null @@ -1,133 +0,0 @@ - -""" -Helper routines for the equal time imaginary time square and -its sub triangles. - -Author: Hugo U. R. Strand (2017), hugo.strand@gmail.com -""" - -# ---------------------------------------------------------------------- - -import itertools -import numpy as np - -# ---------------------------------------------------------------------- -def zero_outer_planes_and_equal_times(g3_tau): - - beta = g3_tau.mesh.components[0].beta - - for idxs, (t1, t2) in enumerate_tau2(g3_tau): - if t1 == t2 or \ - t1 == 0 or t1 == beta or \ - t2 == 0 or t2 == beta: - g3_tau[list(idxs)][:] = 0.0 - -# ---------------------------------------------------------------------- -def enumerate_tau2(g3_tau, make_real=True, beta=None): - - from pytriqs.gf import MeshImTime, MeshProduct - - assert( type(g3_tau.mesh) == MeshProduct ) - - for mesh in g3_tau.mesh.components: - assert( type(mesh) == MeshImTime ) - if beta is not None: assert( mesh.beta == beta ) - - for (i1, t1), (i2, t2) in itertools.product(*[ - enumerate(mesh) for mesh in g3_tau.mesh.components]): - if make_real: - yield (i1, i2), (t1.real, t2.real) - else: - yield (i1, i2), (t1, t2) - -# ---------------------------------------------------------------------- -class SquareTrianglesBase(object): - - """ Base class with definition of the equal time tetrahedrons - in three fermionic imaginary times. """ - - def get_triangle_list(self): - - triangle_list = [ - (lambda x,y : x >= y, [0, 1], +1), - (lambda x,y : x < y, [1, 0], -1), - ] - - return triangle_list - -# ---------------------------------------------------------------------- -class SuqareTraingles(SquareTrianglesBase): - - """ Helper class for two imaginary time Green's functions. - - Looping of the triangles on the imaginary time square. - \tau_1, \tau_2 \in [0, \beta) """ - - # ------------------------------------------------------------------ - def __init__(self, tau): - - self.tau = tau - self.ntau = len(tau) - self.triangle_list = self.get_triangle_list() - self.N = len(self.triangle_list) - - # ------------------------------------------------------------------ - def __iter__(self): - - for tidx in xrange(self.N): - - func, perm, perm_sign = self.triangle_list[tidx] - - index = [] - for n1, n2 in itertools.product( - range(self.ntau), repeat=2): - if func(n1, n2): index.append((n1, n2)) - - index = np.array(index).T - - i1, i2 = index - t1, t2 = self.tau[i1], self.tau[i2] - - taus = np.vstack([t1, t2]) - - yield list(index), taus, perm, perm_sign - -# ---------------------------------------------------------------------- -class SquareTrianglesMesh(SquareTrianglesBase): - - """ Helper class for Triqs three imaginary time Green's functions. - - Looping of the triangles on the imaginary time square. - \tau_1, \tau_2 \in [0, \beta) """ - - # ------------------------------------------------------------------ - def __init__(self, g3_tau): - - self.g3_tau = g3_tau - self.triangle_list = self.get_triangle_list() - self.N = len(self.triangle_list) - - # ------------------------------------------------------------------ - def __iter__(self): - - """ for pytriqs three time greens functions """ - - triangle_idx = [ [] for n in xrange(self.N) ] - triangle_tau = [ [] for n in xrange(self.N) ] - - for idxs, taus in enumerate_tau2(self.g3_tau): - - for tidx, triangle in enumerate(self.triangle_list): - func, perm, perm_sign = triangle - - if func(*taus): - triangle_idx[tidx] += [ idxs ] - triangle_tau[tidx] += [ taus ] - break - - for tidx in xrange(self.N): - func, perm, perm_sign = self.triangle_list[tidx] - - yield triangle_idx[tidx], triangle_tau[tidx], perm, perm_sign - -# ---------------------------------------------------------------------- diff --git a/pyed/TriqsExactDiagonalization.py b/pyed/TriqsExactDiagonalization.py index 4301e1a..ee38bf7 100644 --- a/pyed/TriqsExactDiagonalization.py +++ b/pyed/TriqsExactDiagonalization.py @@ -17,8 +17,6 @@ # ---------------------------------------------------------------------- -from pyed.CubeTetras import CubeTetrasMesh, enumerate_tau3 -from pyed.SquareTriangles import SquareTrianglesMesh, enumerate_tau2 from pyed.SparseExactDiagonalization import SparseExactDiagonalization from pyed.SparseMatrixFockStates import SparseMatrixRepresentation @@ -49,7 +47,7 @@ def get_ground_state_energy(self): return self.ed.get_ground_state_energy() - def set_g2_w(self, g_w, op1, op2,eta=0.1): + def set_g2_w(self, g_w, op1, op2,eta=0.1,xi=-1): op1_mat = self.rep.sparse_matrix(op1) op2_mat = self.rep.sparse_matrix(op2) @@ -58,24 +56,7 @@ def set_g2_w(self, g_w, op1, op2,eta=0.1): g_w.data[:, 0, 0] = \ self.ed.get_real_frequency_greens_function_component( - w, op1_mat, op2_mat, eta) - # ------------------------------------------------------------------ - def set_g2_tau(self, g_tau, op1, op2): - - assert( type(g_tau.mesh) == MeshImTime ) - assert( self.beta == g_tau.mesh.beta ) - assert( g_tau.target_shape == (1, 1) ) - - op1_mat = self.rep.sparse_matrix(op1) - op2_mat = self.rep.sparse_matrix(op2) - - tau = np.array([tau for tau in g_tau.mesh]) - - g_tau.data[:, 0, 0] = \ - self.ed.get_tau_greens_function_component( - tau, op1_mat, op2_mat) - - self.set_tail(g_tau, op1_mat, op2_mat) + w, op1_mat, op2_mat, eta, xi) # ------------------------------------------------------------------ def set_g2_iwn(self, g_iwn, op1, op2): @@ -109,62 +90,3 @@ def xi(self, mesh): if mesh.statistic == 'Fermion': return -1.0 elif mesh.statistic == 'Boson': return +1.0 else: raise NotImplementedError - - # ------------------------------------------------------------------ - def set_g3_tau(self, g3_tau, op1, op2, op3): - - assert( g3_tau.target_shape == (1,1,1,1) ) - - op1_mat = self.rep.sparse_matrix(op1) - op2_mat = self.rep.sparse_matrix(op2) - op3_mat = self.rep.sparse_matrix(op3) - - ops_mat = np.array([op1_mat, op2_mat, op3_mat]) - - for idxs, taus, perm, perm_sign in SquareTrianglesMesh(g3_tau): - - ops_perm_mat = ops_mat[perm + [2]] - taus_perm = np.array(taus).T[perm] - - data = self.ed.get_timeordered_two_tau_greens_function( - taus_perm, ops_perm_mat) - - for idx, d in zip(idxs, data): - g3_tau[list(idx)][:] = perm_sign * d - - # ------------------------------------------------------------------ - def set_g40_tau(self, g40_tau, g_tau): - - assert( type(g_tau.mesh) == MeshImTime ) - #assert( g_tau.target_shape == g40_tau.target_shape ) - - for (i1, i2, i3), (t1, t2, t3) in enumerate_tau3(g40_tau): - g40_tau[[i1, i2, i3]][:] = \ - g_tau(t1-t2)*g_tau(t3) - g_tau(t1)*g_tau(t3-t2) - - # ------------------------------------------------------------------ - def set_g4_tau(self, g4_tau, op1, op2, op3, op4): - - assert( g4_tau.target_shape == (1,1,1,1) ) - - op1_mat = self.rep.sparse_matrix(op1) - op2_mat = self.rep.sparse_matrix(op2) - op3_mat = self.rep.sparse_matrix(op3) - op4_mat = self.rep.sparse_matrix(op4) - - ops_mat = np.array([op1_mat, op2_mat, op3_mat, op4_mat]) - - for idxs, taus, perm, perm_sign in CubeTetrasMesh(g4_tau): - - ops_perm_mat = ops_mat[perm + [3]] - taus_perm = np.array(taus).T[perm] - - data = self.ed.get_timeordered_three_tau_greens_function( - taus_perm, ops_perm_mat) - - for idx, d in zip(idxs, data): - g4_tau[list(idx)][:] = perm_sign * d - - # ------------------------------------------------------------------ - -# ---------------------------------------------------------------------- From 236ba05bd4ab36a618a5d512ac50596f80427850 Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Sun, 1 Oct 2017 19:59:01 +0300 Subject: [PATCH 08/33] Add DMFT Adding DMFT library --- pyed/DynamicalMeanFieldTheory.py | 68 ++++++++++++++++++++++++++++++++ 1 file changed, 68 insertions(+) create mode 100644 pyed/DynamicalMeanFieldTheory.py diff --git a/pyed/DynamicalMeanFieldTheory.py b/pyed/DynamicalMeanFieldTheory.py new file mode 100644 index 0000000..f804f81 --- /dev/null +++ b/pyed/DynamicalMeanFieldTheory.py @@ -0,0 +1,68 @@ +import numpy as np +from pytriqs.gf import * +from itertools import product +from scipy.optimize import fmin_l_bfgs_b +from pytriqs.operators import Operator, c, c_dag, n +from TriqsExactDiagonalization import TriqsExactDiagonalization + +class DynamicalMeanFieldTheory(object): + """docstring fs DynamicalMeanFieldTheory.""" + def __init__(self,Hloc,G0,beta,nbath): + self.norb=G0.data.shape[1] + self.nbath=nbath + assert(self.nbath%self.norb==0) + self.BPO=self.nbath/self.norb # baths per orb + self.beta=beta + self.G0=G0 + self.h=np.zeros((self.norb,self.nbath)) + self.ek=np.zeros(self.nbath) + self.em=np.zeros(self.norb) + self.nmatsubara=(np.array([iwn for iwn in self.G0.mesh])).size/2 + for i in range(self.norb): + parameters=self._bath_fit(i) + self.h[i,self.BPO*i:self.BPO*(i+1)]=parameters[0:self.BPO] + self.ek[self.BPO*i:self.BPO*(i+1)] =parameters[self.BPO:-1] + self.em[i]=parameters[-1] + + fundamental_operators = np.array([[c('up',i), c('dn',i)] for i in range(self.norb+self.nbath)]).flatten() + self.Hkin = sum(self.h[i][j]*c_dag(s,i)*c(s,j+self.norb) for s, i,j in product(['up','dn'], range(self.norb),range(self.nbath))) + self.Hkin+= sum(self.ek[i]*c_dag(s,i+self.norb)*c(s,i+self.norb) for s,i in product(['up','dn'],range(self.nbath))) + self.Hkin+= sum(self.em[i]*c_dag(s,i)*c(s,i) for s,i in product(['up','dn'],range(self.norb))) + self.Hloc=Hloc + self.H=self.Hkin+self.Hloc + + self.ed = TriqsExactDiagonalization(self.H,fundamental_operators, self.beta,nstates=None) +# ------------------------------------------------------------------ + def _molecular_GF(self,parameters): + iwn = np.array([iwn for iwn in self.G0.mesh]) + h = parameters[0:self.BPO] + ek = parameters[self.BPO:-1] + em = parameters[-1] + fitG0 = np.zeros(iwn.size, dtype=np.complex128) + for i in xrange(iwn.size): fitG0[i] = (iwn[i] - em - np.sum(h ** 2 / (iwn[i] - ek))) ** (-1) + return fitG0 +# ------------------------------------------------------------------ + def _bath_fit(self,i): + error=lambda parameters:np.sum(np.abs(np.conj(self.G0.data[:,i,i].flatten()-self._molecular_GF(parameters))*(self.G0.data[:,i,i].flatten() - self._molecular_GF(parameters)))) + return fmin_l_bfgs_b(error, x0=2*np.random.random(self.BPO*2+1)-1, approx_grad=True, disp=True)[0] +# ------------------------------------------------------------------ + def get_iwn_GF(self): + G = GfImFreq(indices = range(self.norb), beta = self.beta, n_points = self.nmatsubara) + index_combinations=[(i,j) for i,j in product(range(self.norb),range(self.norb))] + for k in range(len(index_combinations)): + i,j=index_combinations[k] + g_iwn=GfImFreq(indices = [0], beta = self.beta, n_points = self.nmatsubara) + self.ed.set_g2_iwn(g_iwn,c('up',i),c_dag('up',j)) + G.data[:,i,j]=g_iwn.data.flatten() + return G +# ------------------------------------------------------------------ + def get_w_GF(self,omega): + G = GfReFreq(indices = range(self.norb), window = (np.min(omega), np.max(omega)), n_points = omega.size) + index_combinations=[(i,j) for i,j in product(range(self.norb),range(self.norb))] + for k in range(len(index_combinations)): + i,j=index_combinations[k] + g_w=GfReFreq(indices = [0], window = (np.min(omega), np.max(omega)), n_points = omega.size) + self.ed.set_g2_w(g_w, c('up',i), c_dag('up',j)) + G.data[:,i,j]=g_w.data.flatten() + return G +# ------------------------------------------------------------------ From bfbb4e8e142105ac50b7c912afdb42c8cbe70701 Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Sun, 1 Oct 2017 20:01:25 +0300 Subject: [PATCH 09/33] Changing README --- LICENSE.txt | 5 ++--- Readme.md | 4 ++-- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/LICENSE.txt b/LICENSE.txt index 545c2d7..82f06f9 100644 --- a/LICENSE.txt +++ b/LICENSE.txt @@ -1,13 +1,13 @@ PYED: Exact diagonalization routines for finite quantum systems -Copyright (C) 2017 by H. U.R. Strand +Copyright (C) 2017 by H. U.R. Strand, Ya.V. Zhumagulov PYED is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. - + PYED is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. @@ -15,4 +15,3 @@ PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with TRIQS (in the file COPYING.txt in this directory). If not, see . - diff --git a/Readme.md b/Readme.md index aab1404..6db10c4 100644 --- a/Readme.md +++ b/Readme.md @@ -1,4 +1,4 @@ -# **PYED+CPT**: Exact diagonalization for finite quantum systems with cluster pertrubation theory +# **PYED**: Exact diagonalization for finite quantum systems Copyright (C) 2017, H. U.R. Strand, Ya.V. Zhumagulov @@ -8,7 +8,7 @@ The many-body system is defined using `pytriqs` second-quantized operators and t The original purpose of `pyed` is to provide exact solutions to small finite systems, to be used as benchmarks and tests for stochastic many-body solvers. -Cluster pertrubation theory [1] addition to pyed allow calculate bandstructure and Fermi surface of several models. +Cluster pertrubation theory [1] addition to pyed allow calculate bandstructure and Fermi surface of several models. [1] https://www.physique.usherbrooke.ca/pages/sites/default/files/senechal/publis/Senechal2011vn.pdf From 1bbabb5d95ef50656d1ec4aa782632c1c0599202 Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Sat, 7 Oct 2017 13:52:33 +0300 Subject: [PATCH 10/33] Merge --- .../Documentation-checkpoint.ipynb | 439 ++++++++++++++++++ Documentation.ipynb | 439 ++++++++++++++++++ .../Documentation_CPT_2D-checkpoint.ipynb | 327 +++++++++++++ figure_densdens_tau.png | Bin 0 -> 10298 bytes figure_g_iwn.png | Bin 0 -> 13506 bytes figure_g_tau.png | Bin 0 -> 11132 bytes pyed/SparseExactDiagonalization.py | 207 ++++++++- 7 files changed, 1411 insertions(+), 1 deletion(-) create mode 100644 .ipynb_checkpoints/Documentation-checkpoint.ipynb create mode 100644 Documentation.ipynb create mode 100644 doc/.ipynb_checkpoints/Documentation_CPT_2D-checkpoint.ipynb create mode 100644 figure_densdens_tau.png create mode 100644 figure_g_iwn.png create mode 100644 figure_g_tau.png diff --git a/.ipynb_checkpoints/Documentation-checkpoint.ipynb b/.ipynb_checkpoints/Documentation-checkpoint.ipynb new file mode 100644 index 0000000..fd2a1c4 --- /dev/null +++ b/.ipynb_checkpoints/Documentation-checkpoint.ipynb @@ -0,0 +1,439 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **PYED**: Exact diagonalization for finite quantum systems\n", + "\n", + "Copyright (C) 2017, H. U.R. Strand\n", + "\n", + "The python module `pyed` implements exact diagonalization for finite fermionic many-body quantum systems, together with calculations of several response functions in imagianary time.\n", + "\n", + "The many-body system is defined using `pytriqs` second-quantized operators and the response functions are stored in `pytriqs` Green's function containters.\n", + "\n", + "## Hamiltonians\n", + "\n", + "As an example let us solve the Hubbard atom with Hamiltonian $H = U\\hat{n}_{\\uparrow} \\hat{n}_{\\downarrow} - \\mu ( \\hat{n}_{\\uparrow} + \\hat{n}_{\\downarrow})$, where $\\hat{n}_\\sigma = c^\\dagger_\\sigma c_\\sigma$.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "H = -0.1*c_dag(0,0)*c(0,0) + -0.1*c_dag(1,0)*c(1,0) + 1*c_dag(0,0)*c_dag(1,0)*c(1,0)*c(0,0)\n" + ] + } + ], + "source": [ + "from pytriqs.operators import c, c_dag\n", + "%matplotlib inline\n", + "up, down = 0, 1\n", + "n_up = c_dag(up, 0) * c(up, 0)\n", + "n_down = c_dag(down, 0) * c(down, 0)\n", + "\n", + "U = 1.0\n", + "mu = 0.1\n", + "\n", + "H = U * n_up * n_down - mu * (n_up + n_down)\n", + "\n", + "print 'H =', H" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Thermal equilibrium solution\n", + "\n", + "To solve the thermal equilibrium of the system we can diagonalize $H$ and determine the partition function $\\mathcal{Z}$ (or alternatively the free energy $\\Omega = -\\frac{1}{\\beta} \\ln \\mathcal{Z}$) and the many-body density matrix $\\rho$ using the egenstates $|\\Gamma \\rangle$ and eigenvalues $E_\\Gamma$ of $H$. The partition function $\\mathcal{Z}$ is given by the sum of Boltzman weights\n", + "\n", + "$$\n", + "\\mathcal{Z} = \\sum_\\Gamma e^{-\\beta E_\\Gamma} \\, ,\n", + "$$\n", + "while the many-body density matrix is given by the ket-bra Boltzman weighted sum\n", + "\n", + "$$\n", + "\\rho = \\frac{1}{\\mathcal{Z}} \\sum_\\Gamma e^{-\\beta E_\\gamma} |\\Gamma \\rangle \\langle \\Gamma|\n", + "\\, .\n", + "$$\n", + "\n", + "To accomplish this we pass the Hamiltonian $H$ and a list of unique annihilation opeators used in $H$ together with the inverse temperature $\\beta$ to a `pyed.TriqsExactDiagonalization` class instance:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hamiltonian diagonalization:\n", + "Density matrix calculation:\n", + "Z = 2.9840296413\n", + "\\Omega = -0.646637307852\n", + "\\rho =\n", + " (0, 0)\t0.27437085133\n", + " (1, 1)\t0.335117314573\n", + " (2, 2)\t0.335117314573\n", + " (3, 3)\t0.0553945195228\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0% | |\r", + "/usr/local/lib/python2.7/dist-packages/scipy/sparse/compressed.py:774: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n", + " SparseEfficiencyWarning)\n", + " 25% |################## |\r", + " 50% |#################################### |\r", + " 75% |###################################################### |\r", + "100% |########################################################################|\r\n", + " 0% | |\r", + " 25% |################## |\r", + " 50% |#################################### |\r", + " 75% |###################################################### |\r", + "100% |########################################################################|\r\n" + ] + } + ], + "source": [ + "beta = 2.0 # inverse temperature\n", + "fundamental_operators = [c(up,0), c(down,0)]\n", + "\n", + "from pyed.TriqsExactDiagonalization import TriqsExactDiagonalization\n", + "ed = TriqsExactDiagonalization(H, fundamental_operators, beta)\n", + "\n", + "print r'Z =', ed.get_partition_function()\n", + "print r'\\Omega =', ed.get_free_energy()\n", + "print r'\\rho ='\n", + "print ed.ed.get_density_matrix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Thermal expectation values\n", + "\n", + "Using the many-body density matrix we can evaluate the expectation value of any operator $\\mathcal{O}$ by taking the trace\n", + "\n", + "$$\n", + "\\langle \\mathcal{O} \\rangle = \\textrm{Tr} [ \\rho \\mathcal{O} ]\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " = 0.390511834096\n", + " = 0.390511834096\n", + " = 0.0553945195228\n" + ] + } + ], + "source": [ + "print ' =', ed.get_expectation_value(n_up)\n", + "print ' =', ed.get_expectation_value(n_down)\n", + "print ' =', ed.get_expectation_value(n_up * n_down)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imaginary time single-particle Green's function\n", + "We can also calculate the dynamical fluctuations of the system by computing its response functions. The simples case is the single-particle Green's function, defined as the imaginary time ordered expectation value\n", + "\n", + "$$\n", + " G_{\\sigma \\sigma'}(\\tau) \\equiv\n", + " - \\langle \\mathcal{T} \\, c_{\\sigma}(\\tau) c_{\\sigma'}^\\dagger(0) \\rangle\n", + " =\n", + " - \\frac{1}{\\mathcal{Z}} \\text{Tr}\n", + " \\left[ e^{-\\beta H} c_{\\sigma}(\\tau_1) c_{\\sigma'}^\\dagger(0) \\right]\n", + "$$\n", + "where the imaginary time dependent operators are defined in the Heisenberg picture $c_{\\sigma}(\\tau) \\equiv e^{\\tau H} c_{\\sigma} e^{-\\tau H}$ and $c^\\dagger_{\\sigma}(\\tau) \\equiv e^{\\tau H} c^\\dagger_{\\sigma} e^{-\\tau H}$.\n", + "\n", + "To calculate $G(\\tau)$ we first create `pytriqs.GfImTime` instance to store the result and pass it to our ED solver instance:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEKCAYAAADTgGjXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pytriqs.gf import GfImTime\n", + "g_tau = GfImTime(name=r'$g$', beta=beta, statistic='Fermion', n_points=50, indices=[1]) \n", + "ed.set_g2_tau(g_tau, c(up,0), c_dag(up,0))\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from pytriqs.plot.mpl_interface import oplot\n", + "\n", + "plt.figure(); oplot(g_tau); plt.savefig('figure_g_tau.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "![Single-particle Green's function](figure_g_tau.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The two-operator response function calculator is more general and can be used to calculate any type of two operator response, e.g., the density-density response function: $\\chi_{\\sigma \\sigma'}(\\tau) \\equiv -\\langle \\hat{n}_\\sigma(\\tau) \\hat{n}_\\sigma' \\rangle$. However for the very simple single-Hubbard-atom system this response function is $\\tau$ independent as seen below:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEKCAYAAADenhiQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFzMzS52Ti5mZpc7JxczMUufkYmZmqfv/W/+27YrlTU4AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pytriqs.gf import GfImTime\n", + "densdens_tau = GfImTime(name=r'$\\langle n(\\tau) n(0) \\rangle$', beta=beta, statistic='Boson', n_points=50, indices=[1]) \n", + "ed.set_g2_tau(densdens_tau, n_up, n_down)\n", + "\n", + "plt.figure(); oplot(densdens_tau); plt.savefig('figure_densdens_tau.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Density density response function](figure_densdens_tau.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For fermionic two-operator response functions `pyed` can also directly calculate the fourier transformed response function\n", + "\n", + "$$\n", + "G(i \\omega_n) \\equiv \\int_0^\\beta d\\tau \\, e^{i\\omega_n \\tau} G(\\tau)\n", + "$$\n", + "defined on the (fermionic) Matsubara frequencies $i\\omega_n = \\frac{2\\pi}{\\beta}(2n + 1)$. \n", + "\n", + "NB! `pyed` currently lacks support for handling bosonic response functions in frequency." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEMCAYAAAAF2YvKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pytriqs.gf import GfImFreq\n", + "g_iwn = GfImFreq(name=r'$g$', beta=beta, statistic='Fermion', n_points=10, indices=[1])\n", + "ed.set_g2_iwn(g_iwn, c(up,0), c_dag(up,0))\n", + "\n", + "plt.figure(); oplot(g_iwn); plt.savefig('figure_g_iwn.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Single-particle Green's function in imaginary frequency](figure_g_iwn.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Four-operator response functions\n", + "\n", + "In `pyed` there is functionality to compute also higher-order response functions (involving more than two operators and more than one time). Currently two- and three- time ordered expectation values are supported solely in imaginary time.\n", + "\n", + "The two-particle Green's function $G^{(4)}(\\tau_1, \\tau_2, \\tau_3)$ is a prominent example\n", + "\n", + "$$\n", + "G^{(4)}_{\\alpha\\bar{\\beta}\\gamma\\bar{\\delta}}(\\tau_1, \\tau_2, \\tau_3) \\equiv\n", + "\\langle \\mathcal{T} \n", + "c_\\alpha(\\tau_1) c^\\dagger_{\\bar{\\beta}} (\\tau_2) \n", + "c_\\gamma(\\tau_3) c^\\dagger_{\\bar{\\delta}} (0) \\rangle\n", + "$$\n", + "\n", + "That easily can be calculated with `pyed` by passing a suitable `pytriqs` container to the ED solver:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from pytriqs.gf import Gf\n", + "from pytriqs.gf import MeshImTime, MeshProduct\n", + "\n", + "ntau = 10\n", + "imtime = MeshImTime(beta, 'Fermion', ntau)\n", + "prodmesh = MeshProduct(imtime, imtime, imtime)\n", + "\n", + "g4_tau = Gf(name=r'$G^{(4)}(\\tau_1,\\tau_2,\\tau_3)$', mesh=prodmesh, target_shape=[1, 1, 1, 1])\n", + "ed.set_g4_tau(g4_tau, c(up,0), c_dag(up,0), c(up,0), c_dag(up,0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To visualize this three dimensional scalar field one have to resort to some cut plane to represent it in a two dimensional plot. So instead of plotting $G^{(4)}$ we here show the special case of a two-time response function correspoding to $G^{(4)}(\\tau_1, 0^-, \\tau_2)$ namely the particle-particle equal time response function\n", + "\n", + "$$\n", + "G_{\\alpha \\beta \\gamma}^{(3)}(\\tau_1, \\tau_2) \\equiv \n", + "\\langle \\mathcal{T} c_{\\alpha}(\\tau_1) c^\\dagger_{\\bar{\\beta}}(\\tau_2) \\hat{n}_\\gamma(0)\\rangle \\equiv \n", + "- \\langle \\mathcal{T} c_{\\alpha}(\\tau_1) c^\\dagger_{\\bar{\\beta}}(\\tau_2) c_{\\gamma}(0^-) c^\\dagger_{\\bar{\\gamma}}(0) \\rangle \\equiv \n", + "- G^{(4)}(\\tau_1, \\tau_2, 0^+)\n", + "\\, ,\n", + "$$\n", + "that can be calculated separately as:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "prodmesh2 = MeshProduct(imtime, imtime)\n", + "g3pp_tau = Gf(name=r'$G^{(3)}(\\tau_1, \\tau_2)$', mesh=prodmesh2, target_shape=[1, 1, 1, 1])\n", + "ed.set_g3_tau(g3pp_tau, c(up,0), c_dag(up,0), c(up,0)*c_dag(up,0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "To visualize this we use `matplotlib` directly\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFgCAYAAAA2BUkTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAVNGRkZGRkYL5IApIyMjIyOjBf8fF1CMWrrMf/0AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from mpl_toolkits.mplot3d import axes3d\n", + "fig = plt.figure(figsize=(3.25*2, 2*2.5))\n", + "ax = fig.add_subplot(1,1,1, projection='3d')\n", + "\n", + "data = g3pp_tau.data[:, :, 0, 0, 0, 0]\n", + "tau = [tau.real for tau in g3pp_tau.mesh.components[0]]\n", + "t1, t2 = np.meshgrid(tau, tau)\n", + "ax.plot_wireframe(t1, t2, data.real)\n", + "ax.view_init(30, 60)\n", + "ax.set_xlabel(r'$\\tau_1$')\n", + "ax.set_ylabel(r'$\\tau_2$')\n", + "plt.tight_layout()\n", + "plt.savefig('figure_g3pp_tau.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Equal-time two-particle Green's function](figure_g3pp_tau.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Documentation.ipynb b/Documentation.ipynb new file mode 100644 index 0000000..fd2a1c4 --- /dev/null +++ b/Documentation.ipynb @@ -0,0 +1,439 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **PYED**: Exact diagonalization for finite quantum systems\n", + "\n", + "Copyright (C) 2017, H. U.R. Strand\n", + "\n", + "The python module `pyed` implements exact diagonalization for finite fermionic many-body quantum systems, together with calculations of several response functions in imagianary time.\n", + "\n", + "The many-body system is defined using `pytriqs` second-quantized operators and the response functions are stored in `pytriqs` Green's function containters.\n", + "\n", + "## Hamiltonians\n", + "\n", + "As an example let us solve the Hubbard atom with Hamiltonian $H = U\\hat{n}_{\\uparrow} \\hat{n}_{\\downarrow} - \\mu ( \\hat{n}_{\\uparrow} + \\hat{n}_{\\downarrow})$, where $\\hat{n}_\\sigma = c^\\dagger_\\sigma c_\\sigma$.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "H = -0.1*c_dag(0,0)*c(0,0) + -0.1*c_dag(1,0)*c(1,0) + 1*c_dag(0,0)*c_dag(1,0)*c(1,0)*c(0,0)\n" + ] + } + ], + "source": [ + "from pytriqs.operators import c, c_dag\n", + "%matplotlib inline\n", + "up, down = 0, 1\n", + "n_up = c_dag(up, 0) * c(up, 0)\n", + "n_down = c_dag(down, 0) * c(down, 0)\n", + "\n", + "U = 1.0\n", + "mu = 0.1\n", + "\n", + "H = U * n_up * n_down - mu * (n_up + n_down)\n", + "\n", + "print 'H =', H" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Thermal equilibrium solution\n", + "\n", + "To solve the thermal equilibrium of the system we can diagonalize $H$ and determine the partition function $\\mathcal{Z}$ (or alternatively the free energy $\\Omega = -\\frac{1}{\\beta} \\ln \\mathcal{Z}$) and the many-body density matrix $\\rho$ using the egenstates $|\\Gamma \\rangle$ and eigenvalues $E_\\Gamma$ of $H$. The partition function $\\mathcal{Z}$ is given by the sum of Boltzman weights\n", + "\n", + "$$\n", + "\\mathcal{Z} = \\sum_\\Gamma e^{-\\beta E_\\Gamma} \\, ,\n", + "$$\n", + "while the many-body density matrix is given by the ket-bra Boltzman weighted sum\n", + "\n", + "$$\n", + "\\rho = \\frac{1}{\\mathcal{Z}} \\sum_\\Gamma e^{-\\beta E_\\gamma} |\\Gamma \\rangle \\langle \\Gamma|\n", + "\\, .\n", + "$$\n", + "\n", + "To accomplish this we pass the Hamiltonian $H$ and a list of unique annihilation opeators used in $H$ together with the inverse temperature $\\beta$ to a `pyed.TriqsExactDiagonalization` class instance:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hamiltonian diagonalization:\n", + "Density matrix calculation:\n", + "Z = 2.9840296413\n", + "\\Omega = -0.646637307852\n", + "\\rho =\n", + " (0, 0)\t0.27437085133\n", + " (1, 1)\t0.335117314573\n", + " (2, 2)\t0.335117314573\n", + " (3, 3)\t0.0553945195228\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0% | |\r", + "/usr/local/lib/python2.7/dist-packages/scipy/sparse/compressed.py:774: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n", + " SparseEfficiencyWarning)\n", + " 25% |################## |\r", + " 50% |#################################### |\r", + " 75% |###################################################### |\r", + "100% |########################################################################|\r\n", + " 0% | |\r", + " 25% |################## |\r", + " 50% |#################################### |\r", + " 75% |###################################################### |\r", + "100% |########################################################################|\r\n" + ] + } + ], + "source": [ + "beta = 2.0 # inverse temperature\n", + "fundamental_operators = [c(up,0), c(down,0)]\n", + "\n", + "from pyed.TriqsExactDiagonalization import TriqsExactDiagonalization\n", + "ed = TriqsExactDiagonalization(H, fundamental_operators, beta)\n", + "\n", + "print r'Z =', ed.get_partition_function()\n", + "print r'\\Omega =', ed.get_free_energy()\n", + "print r'\\rho ='\n", + "print ed.ed.get_density_matrix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Thermal expectation values\n", + "\n", + "Using the many-body density matrix we can evaluate the expectation value of any operator $\\mathcal{O}$ by taking the trace\n", + "\n", + "$$\n", + "\\langle \\mathcal{O} \\rangle = \\textrm{Tr} [ \\rho \\mathcal{O} ]\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " = 0.390511834096\n", + " = 0.390511834096\n", + " = 0.0553945195228\n" + ] + } + ], + "source": [ + "print ' =', ed.get_expectation_value(n_up)\n", + "print ' =', ed.get_expectation_value(n_down)\n", + "print ' =', ed.get_expectation_value(n_up * n_down)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imaginary time single-particle Green's function\n", + "We can also calculate the dynamical fluctuations of the system by computing its response functions. The simples case is the single-particle Green's function, defined as the imaginary time ordered expectation value\n", + "\n", + "$$\n", + " G_{\\sigma \\sigma'}(\\tau) \\equiv\n", + " - \\langle \\mathcal{T} \\, c_{\\sigma}(\\tau) c_{\\sigma'}^\\dagger(0) \\rangle\n", + " =\n", + " - \\frac{1}{\\mathcal{Z}} \\text{Tr}\n", + " \\left[ e^{-\\beta H} c_{\\sigma}(\\tau_1) c_{\\sigma'}^\\dagger(0) \\right]\n", + "$$\n", + "where the imaginary time dependent operators are defined in the Heisenberg picture $c_{\\sigma}(\\tau) \\equiv e^{\\tau H} c_{\\sigma} e^{-\\tau H}$ and $c^\\dagger_{\\sigma}(\\tau) \\equiv e^{\\tau H} c^\\dagger_{\\sigma} e^{-\\tau H}$.\n", + "\n", + "To calculate $G(\\tau)$ we first create `pytriqs.GfImTime` instance to store the result and pass it to our ED solver instance:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEKCAYAAADTgGjXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pytriqs.gf import GfImTime\n", + "g_tau = GfImTime(name=r'$g$', beta=beta, statistic='Fermion', n_points=50, indices=[1]) \n", + "ed.set_g2_tau(g_tau, c(up,0), c_dag(up,0))\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from pytriqs.plot.mpl_interface import oplot\n", + "\n", + "plt.figure(); oplot(g_tau); plt.savefig('figure_g_tau.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "![Single-particle Green's function](figure_g_tau.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The two-operator response function calculator is more general and can be used to calculate any type of two operator response, e.g., the density-density response function: $\\chi_{\\sigma \\sigma'}(\\tau) \\equiv -\\langle \\hat{n}_\\sigma(\\tau) \\hat{n}_\\sigma' \\rangle$. However for the very simple single-Hubbard-atom system this response function is $\\tau$ independent as seen below:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEKCAYAAADenhiQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFzMzS52Ti5mZpc7JxczMUufkYmZmqfv/W/+27YrlTU4AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pytriqs.gf import GfImTime\n", + "densdens_tau = GfImTime(name=r'$\\langle n(\\tau) n(0) \\rangle$', beta=beta, statistic='Boson', n_points=50, indices=[1]) \n", + "ed.set_g2_tau(densdens_tau, n_up, n_down)\n", + "\n", + "plt.figure(); oplot(densdens_tau); plt.savefig('figure_densdens_tau.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Density density response function](figure_densdens_tau.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For fermionic two-operator response functions `pyed` can also directly calculate the fourier transformed response function\n", + "\n", + "$$\n", + "G(i \\omega_n) \\equiv \\int_0^\\beta d\\tau \\, e^{i\\omega_n \\tau} G(\\tau)\n", + "$$\n", + "defined on the (fermionic) Matsubara frequencies $i\\omega_n = \\frac{2\\pi}{\\beta}(2n + 1)$. \n", + "\n", + "NB! `pyed` currently lacks support for handling bosonic response functions in frequency." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEMCAYAAAAF2YvKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pytriqs.gf import GfImFreq\n", + "g_iwn = GfImFreq(name=r'$g$', beta=beta, statistic='Fermion', n_points=10, indices=[1])\n", + "ed.set_g2_iwn(g_iwn, c(up,0), c_dag(up,0))\n", + "\n", + "plt.figure(); oplot(g_iwn); plt.savefig('figure_g_iwn.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Single-particle Green's function in imaginary frequency](figure_g_iwn.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Four-operator response functions\n", + "\n", + "In `pyed` there is functionality to compute also higher-order response functions (involving more than two operators and more than one time). Currently two- and three- time ordered expectation values are supported solely in imaginary time.\n", + "\n", + "The two-particle Green's function $G^{(4)}(\\tau_1, \\tau_2, \\tau_3)$ is a prominent example\n", + "\n", + "$$\n", + "G^{(4)}_{\\alpha\\bar{\\beta}\\gamma\\bar{\\delta}}(\\tau_1, \\tau_2, \\tau_3) \\equiv\n", + "\\langle \\mathcal{T} \n", + "c_\\alpha(\\tau_1) c^\\dagger_{\\bar{\\beta}} (\\tau_2) \n", + "c_\\gamma(\\tau_3) c^\\dagger_{\\bar{\\delta}} (0) \\rangle\n", + "$$\n", + "\n", + "That easily can be calculated with `pyed` by passing a suitable `pytriqs` container to the ED solver:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from pytriqs.gf import Gf\n", + "from pytriqs.gf import MeshImTime, MeshProduct\n", + "\n", + "ntau = 10\n", + "imtime = MeshImTime(beta, 'Fermion', ntau)\n", + "prodmesh = MeshProduct(imtime, imtime, imtime)\n", + "\n", + "g4_tau = Gf(name=r'$G^{(4)}(\\tau_1,\\tau_2,\\tau_3)$', mesh=prodmesh, target_shape=[1, 1, 1, 1])\n", + "ed.set_g4_tau(g4_tau, c(up,0), c_dag(up,0), c(up,0), c_dag(up,0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To visualize this three dimensional scalar field one have to resort to some cut plane to represent it in a two dimensional plot. So instead of plotting $G^{(4)}$ we here show the special case of a two-time response function correspoding to $G^{(4)}(\\tau_1, 0^-, \\tau_2)$ namely the particle-particle equal time response function\n", + "\n", + "$$\n", + "G_{\\alpha \\beta \\gamma}^{(3)}(\\tau_1, \\tau_2) \\equiv \n", + "\\langle \\mathcal{T} c_{\\alpha}(\\tau_1) c^\\dagger_{\\bar{\\beta}}(\\tau_2) \\hat{n}_\\gamma(0)\\rangle \\equiv \n", + "- \\langle \\mathcal{T} c_{\\alpha}(\\tau_1) c^\\dagger_{\\bar{\\beta}}(\\tau_2) c_{\\gamma}(0^-) c^\\dagger_{\\bar{\\gamma}}(0) \\rangle \\equiv \n", + "- G^{(4)}(\\tau_1, \\tau_2, 0^+)\n", + "\\, ,\n", + "$$\n", + "that can be calculated separately as:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "prodmesh2 = MeshProduct(imtime, imtime)\n", + "g3pp_tau = Gf(name=r'$G^{(3)}(\\tau_1, \\tau_2)$', mesh=prodmesh2, target_shape=[1, 1, 1, 1])\n", + "ed.set_g3_tau(g3pp_tau, c(up,0), c_dag(up,0), c(up,0)*c_dag(up,0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "To visualize this we use `matplotlib` directly\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFgCAYAAAA2BUkTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAVNGRkZGRkYL5IApIyMjIyOjBf8fF1CMWrrMf/0AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from mpl_toolkits.mplot3d import axes3d\n", + "fig = plt.figure(figsize=(3.25*2, 2*2.5))\n", + "ax = fig.add_subplot(1,1,1, projection='3d')\n", + "\n", + "data = g3pp_tau.data[:, :, 0, 0, 0, 0]\n", + "tau = [tau.real for tau in g3pp_tau.mesh.components[0]]\n", + "t1, t2 = np.meshgrid(tau, tau)\n", + "ax.plot_wireframe(t1, t2, data.real)\n", + "ax.view_init(30, 60)\n", + "ax.set_xlabel(r'$\\tau_1$')\n", + "ax.set_ylabel(r'$\\tau_2$')\n", + "plt.tight_layout()\n", + "plt.savefig('figure_g3pp_tau.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Equal-time two-particle Green's function](figure_g3pp_tau.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/.ipynb_checkpoints/Documentation_CPT_2D-checkpoint.ipynb b/doc/.ipynb_checkpoints/Documentation_CPT_2D-checkpoint.ipynb new file mode 100644 index 0000000..26229cd --- /dev/null +++ b/doc/.ipynb_checkpoints/Documentation_CPT_2D-checkpoint.ipynb @@ -0,0 +1,327 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **PYED+CPT **:Cluster pertrubation theory with exact diagonalization for finite quantum systems\n", + "\n", + "Copyright (C) 2017, H. U.R. Strand, Ya.V. Zhumagulov\n", + "\n", + "The python module `pyed` implements exact diagonalization for finite fermionic many-body quantum systems, together with calculations of several response functions in imagianary time.\n", + "\n", + "The many-body system is defined using `pytriqs` second-quantized operators and the response functions are stored in `pytriqs` Green's function containters.\n", + "\n", + "Cluster pertrubation theory addition to `pyed` allow calculate bandstructure and Fermi surface of several models. \n", + "\n", + "## Hamiltonians\n", + "\n", + " As an example let us solve the Hubbard model with Hamiltonian including only nearest-neighbor hoppings $H = U\\sum_{i}\\hat{n}_{i,\\uparrow} \\hat{n}_{i,\\downarrow} - \\mu\\sum_{i}( \\hat{n}_{i,\\uparrow} + \\hat{n}_{i,\\downarrow}) + t \\sum_{,\\sigma}c^\\dagger_{i,\\sigma} c_{j\\sigma}$, where $\\hat{n}_\\sigma = c^\\dagger_\\sigma c_\\sigma$. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pytriqs.operators import c, c_dag,n\n", + "from pyed.TriqsExactDiagonalization import TriqsExactDiagonalization\n", + "from pyed.ClusterPertrubationTheory import ClusterPertrubationTheory_2D_Square\n", + "import numpy as np\n", + "import progressbar\n", + "from itertools import product\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Parameters of model: U=8, t=-1, $\\mu$=U/2 and size of exact diagonaliztion cluster will be 2x2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "t =-1;U=8;mu=U/2\n", + "Lx,Ly=2,2;L=Lx*Ly" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "T=np.zeros((L,L))\n", + "for i in range(L):\n", + " for j in range(L):\n", + " x = i % Lx - j % Ly\n", + " y = i // Lx - j // Ly\n", + " if (x**2+y**2)==1: T[i,j]=t \n", + "H_int = sum(-mu*(n('up', site) + n('dn', site)) + U * n('up', site) * n('dn', site) for site in range(L))\n", + "H_kin = sum(T[st1][st2]*c_dag(sn,st1)*c(sn,st2) for sn, st1,st2 in product((\"dn\", \"up\"), range(L),range(L)) )\n", + "H = H_int +H_kin" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Parameter $\\beta$ will be 200" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hamiltonian diagonalization:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0% | |\r", + "/usr/local/lib/python2.7/dist-packages/scipy/sparse/compressed.py:774: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n", + " SparseEfficiencyWarning)\n", + " 4% |## |\r", + " 8% |##### |\r", + " 12% |######## |\r", + " 16% |########### |\r", + " 20% |############## |\r", + " 24% |################# |\r", + " 28% |#################### |\r", + " 32% |####################### |\r", + " 36% |######################### |\r", + " 40% |############################ |\r", + " 44% |############################### |\r", + " 48% |################################## |\r", + " 52% |##################################### |\r", + " 56% |######################################## |\r", + " 60% |########################################### |\r", + " 64% |############################################## |\r", + " 68% |################################################ |\r", + " 72% |################################################### |\r", + " 76% |###################################################### |\r", + " 80% |######################################################### |\r", + " 84% |############################################################ |\r", + " 88% |############################################################### |\r", + " 92% |################################################################## |\r", + " 96% |##################################################################### |\r", + "100% |########################################################################|\r\n" + ] + } + ], + "source": [ + "fundamental_operators = np.array([[c('up',i), c('dn',i)] for i in range(L)]).flatten()\n", + "H=H_int+H_kin\n", + "beta=200;nstates=200\n", + "ed = TriqsExactDiagonalization(H,fundamental_operators, beta,nstates=nstates)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we construct, frequency meshgrid, momentum meshgrid and pertrubation matrix V\n", + "\n", + "Further explation you can find in:\n", + "\n", + "https://www.physique.usherbrooke.ca/pages/sites/default/files/senechal/publis/Senechal2011vn.pdf" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "N=60\n", + "kx = np.linspace(-np.pi, np.pi, N+1);kx = np.delete(kx, 0)\n", + "ky = np.linspace(-np.pi, np.pi,N+1);ky = np.delete(ky, 0)\n", + "kx, ky = np.meshgrid(kx, ky)\n", + "V=np.zeros((N,N,L,L),dtype=np.complex)\n", + "for a,b in product(range(L),range(L)):\n", + " x=a % Lx - b % Ly ; y=a// Lx - b// Ly\n", + " if (y==(Ly-1))&(x==0):V[:,:,a,b]=t*np.exp(1j*Ly*ky);V[:,:,b,a]=t*np.exp(-1j*Ly*ky)\n", + " if (x==(Lx-1))&(y==0):V[:,:,a,b]=t*np.exp(1j*Lx*kx);V[:,:,b,a]=t*np.exp(-1j*Lx*kx)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To apply Cluster Pertrubation Theory to our exact diagonalization we use `ClusterPertrubationTheory_2D_Square` class for 2D Square models" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculation green function of full system\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% |########################################################################|\n", + " 0% | |\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coupling system\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% |########################################################################|\n", + " 27% |################### |\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reduce mixed representation\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% |########################################################################|\n" + ] + } + ], + "source": [ + "omega=np.linspace(-10,10,200);\n", + "CPT=ClusterPertrubationTheory_2D_Square(ed,(kx,ky),V,omega,(Lx,Ly))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can calculate Fermi surface" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWsAAAFpCAYAAABajglzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvT+oPd333/Vee59znweDhUEJQQOxsLMMpg0EwUJIF0yl\nIKSys8ivs00rWP0K0TSadKYIiASCnSTaqShBIkZ+miaV8Dz3ntnLYq+19lpr7z1n7v38/cLdcO+Z\n2fPnzMyZec173mvtPcTM+Cyf5bN8ls/yc5fyozfgs3yWz/JZPsvz8gnrz/JZPstn+QMon7D+LJ/l\ns3yWP4DyCevP8lk+y2f5AyifsP4sn+WzfJY/gPIJ68/yWT7LZ/kDKJ+w/iyf5bN8lj+A8gnrz/JZ\nPstn+QMoXwRrIvp3iOh/I6J/TER/9LU26rN8ls/yWT5LLPTRFoxEVAH87wD+bQD/FMA/BPDXmPl/\n+Xqb91k+y2f5LJ8FAG5fsOy/BeAfM/P/AQBE9F8D+CsAtrB+oV/4V/yp52umacBV0WJeWi83TUvz\n+XXJMOt8Ng+k3i1rdW4dBDDyNMzT/HRXl6eDOH4fAQCPr5PpRL2OwH02qS86HYwi021+zPPruA6P\nQzDqbdqi0LJW9vVk2phnvYZ5WXoy/R1ful7lk2p+Ml2nPd+A3fK7Jf0x8lvBUsHuW1l+UdViOo1Z\nPkFpOH4ijAPgsS4wwX0R7ATWU9ZP4zSOuY7cvNPy4D4+zc9unjFOadwOztPhzXj+MZgXv09e7n3l\nN/x/eOXfz04nAF8G638VwP/lxv8pgL94tsCv+FP4i/SX+8gE3SIfFOtknHT+Uvqypbg6GvMSAbX2\naTJfr5NPInAtoZ5L6YZQKeBK9slF/m5FhtHrddoNaHXUt+o/+1+rNIZvUl/k8wa0yqPuJsM3Blfu\n21QZuDVQZZRbQykNtTJqbail4VYP3GX4Xhru9cC9HLiVhhsdeKkHbtR6nX0euNOBQow79fkLGJXa\n+KSGCg6fAFDR5JDHM1PrV+VYuG2NaTtP45Km0aVp83dcc/l033Kp6erL8/np87Tmps1X8dnxAvLx\noFDfuNh+63DjgoML3riigWz40QoOFDxaxRvHzwcXvB43PLjg7ah4bRVHK3hrBY+j4nGUvvzR/9pR\n0BqBDwI/CnAQ0Ah0EPAglAdAB4EaQAdADwId6PVaJ399XkY5ADSgHEB5cJ/egHKwLMMoD5Z5GHQw\nqDHo0T/RGHS0PnwwqLX+yQy0BhwN4D4PtK5xHz6ODl9mqdN5ZLjptDbg3/rvZssAfV4A3NzvzOn3\nPXEw/gf++6fngpZvHmAkor9ORP+IiP7RG343YMaZFpuxqiuruivrmhX0ctkrxSvpIXMvLPdkGi3m\noyFBaHHBP1t9hkQGygoiOp8H9LPyDDyr6bvv1u+/Oi0D9ep63jPPqpx970eOx9k8a9ivv7+6/alo\nYdlKvD0nKE3rT2z89LzzhfNJKMtO9cD8hAm5nk6uk7CefJmH63pTP833DvxduMbpjCdXGPGkfAms\n/28Af86N/2tSFwoz/zEz/wVm/gt3/DKvxcF1t7O0g61fR1ncBOYVnU5jVd4KYn9jIazhnk46sz9o\nnme13Hbahd+2EIdN8hdWyapQxgviRVnSMvUEhitVfQU8u/murucMjM/KDsZFnh6ufud7biBx2vUn\nkHkb9tse51v/1rtl6gTs83EPbvLnbRYW2IAZ77gu/Pw2TJt6xOuyYCvM2F8o8aKJfEmF9OnclvXD\nyoaLYnMlVN9RvgTW/xDAv0FE/zoRvQD49wD83XetYbVDvn53p/rIHXEH/I8cvN0iqZ5XJ6er49W0\nxTqDXU7j4rFZnCqaYLxRyBNELsLoI6C+On9Uk19HXev8+e98O87Xd1VVfwmoc9F1+XX4p5/pmCSF\nvVrfCtJ2Hp2p6uk851DPSDGZJ8v3p9R5eo77jO97fs3y6ho/W06tVFum7PkDbAE/2bjrhePfxfJh\nWDPzA8B/BOC/BfC/Avg7zPw/X15B2hHa3KWCV53rz5R4viPqdpfFD/fsKLiTxxR0ifXPSp4nBxif\n1W+DYDuVTA1ZQffH4PkizkrMe9V53u04teVfLnm5Mzskb9OXTP+ScvXGcf50sDgWm2O2A23+fVfr\n809HGb6rG7efrmXSEMoUZ8sh/2523o76DOxw/djTa1zHWGb1FOsV9kXLYbJJn4D7hCvLZRbcugTs\nD5QvCTCCmf8egL/3roUWG7+0P8JjiyzzzALZ3Q1XgM7jROMxqqdP9BNHvvrpY5k7+eykJKQTMJ6w\n+cReqhEa88XH0aF8/EWZvUeFtC9BndEAdl2ozpWqXi2/Kzr9cMG+ihYCaIXYgmh+WqG2DRKuplXw\nacDxSvlaN4VnTyBXjpses3y8/HgFA9QALuFbChiH25aD47RCDUWOubfIKJxH620j4p4xQjxoDkQV\n/B5lnQo7hjNRVPiFRrDOXW8gBomFSX66H0YaVkF3HNiWQnJ4iwUXIcNE1AONhXqwkYoFFqnQCDh6\n5uXA4zvKD23BSIXWd6GcAdJHZFo5VdXxC9xjxgTmPs5+nk0JgcQEXh2O37sY38zjs6B6PU+Pldhs\nooJZJ01KKittrKdncKwyQD4Cal/OFOM078YOueIjf1RhV/By2Y+o6jNQ7544ltvk5q1o0++wurGq\nurbl0cL8OchY5GlrG8dIfnUQvison9XlaStR466LreoG5uspFyWbu76f+tZqhfjhsM4SOBSnPVHY\no3L+u1h+CKyXkN6Bemfu63JJVS8tEP9jrdQ0BrRzcNFAHbYXcR0YJ1dW2vGxLy4fLQ9VKVrHmB47\n9YIhzOyffOrx2GvKyT0eewskp+v5Ej1SufATcAra9i+XK4/4Z+UqsN8D7d28V1P13gPqsP6T41ZO\nbor5JrzK3DmzQvp0XnrUxf2tnuTIzlN3zPK5n+pWFmAOFJ551lyGYArzlhRk9E/HOPGtV8NnpaQg\npBsO9Rtgn2aJvKN8X1jT4m7j7yw7UOeDc3Xn/bz5YL8nyLi423u/LYB6d+L64ROlkefXgKIfHhcV\n4gWmkE6Rf2+FBHW2UHkxv3qG+wrSZ2U1zxVgXw02nk1/Bu2z6e/JqR7110B95bjpfHn51W/it7G4\n39TAjKycG7x/vXraorQ/vpxlhChM2dUBszW4Xt4/xcb6Mf8CxGcbecYALaKYL6trW6+0DdkBewHt\n/Pee8uNskPwI8AzUvhGMLu9VtS6rB/HZXRXJAtn51Qt1MAUXd8f85MQ8DSQuTup+nq6zQFZ+9WSD\nYFZS/nF5pap1Pi0DAisAc/ib1nOiFt8L7PfaFQrl/Lcr78k2uZQznUA9T+ft8fPHLQNbh1fqWp+W\nvML2Voifz58L3rcG5JyDu3S8Reee/DjXY32Oz/Ebsunz0ykhP43Kzsi6yDbMnmLD07BbZvV0/R5Y\nenW9eNpfAhuYOZcKFcKWH3kTrm7r1yk0b3wZd74zUIf5lw1mFnvsfpiQBZLsEF4BPN/BLZhBWIH6\nLLjIaTicsEj1+QR3ILaj6KCts+8i/15Bef9yVl+xtWL2Rz2otZzBeTftRwH7anmaw/zObT4D9e7Y\nraYptD2wd41lvLrO2+bPkRrOmYZ8o9cgY/atQTnn2ll46fxd5VdngXPqT5O/Bhc3gGyF9IMVr99d\n3Eo/ncB7pq53cbRpmuNan/gxn9qXL8oG+XBJYI07uQb1ZH+cedU7Xyp/llg3e2vpDhzgPJZZ2yA0\nQTusQx4Zex27uuhRQzc/+dVeVZO3QJyCruFz9jBVecWMj+egPssuAebm5Tr/YdkHDU2zGU6yHp6V\nVfbHWQbJWbkSrPwoqFeQXhW/rGV7LI4dqGfXVDRYBgKhP/K5zBCNR4AaGhMaEQ4GfDZIh3RZn0sb\nz5qwOp+xEBqwc59yPdIw5nnDcOlfSnrdENIvr+sggFmySAgj64OAow9zAejQegAN2DYHt3kKUFpv\n3k40skPk+4xTvjk6MNjR0voDsJd7MpXvD2sH3CWk+4RQF0C9sD9CMWgPSNujj1vPCCjSqQWSbRAL\ndlC++8f588mq8+3miX8D0kQ+sLj3q0can1NIQV2zPQprxkCZ1PQMYQ+cuoFSLivoAB08V4F9NZ1v\nB2zgy/oG+RagfnaT2007UAK0rwC7yDFpQjq9kVduaESoIDTqdlC+0TMPKBcHaHLnZE+T46EegPm8\nxhAa3u4gNx+l66FPIzvnJ9DbSsc64zo4QZqBw208/DqAkNqnKXlyGNFGKl6fZwFsYKT0KbQlrU+L\npfd9YfmuNkj/wcn+xlY4NX0F1L6cZYDsVLUHtNStskC8DxYskAnO44TdWSCTDXIy3wRtDFD7v/zY\nGgONLahqVct20QY/c7Y/dqDWFLJx+M9bB+b586P9qJ9heDXgeBYkXG3Ts9aM3xrU+Zistil/5wjw\n6o14tkRWrRtzGt90jsCfO83ZH3srxD/1+awlnurXgcX5mthdV4jXJlx9GcvsskKCIIObho0t+swO\nAQKDTm1bx6Il8z5QfowNAmBOw1tAGliD+pn94YB+pqr7+t13Y1bVZxbIGHb1cBaI7VuGsZzIfnk9\nyRHHh5Kxe4ZdTDa8sUD67s2qetQ3C7atQJ1tjwHR69kZzSlmYK0SVwp7rOvjCnu3TWflap8gXwLq\nq9uUj6MtS2VS2GEZUdcHV1Rqp+r6Ict0S4SXf7NQGOehwnT/lCgbthAotBmfrBCtX1ghujG2LrAs\nQwjq2i5IqWMO1z0KEFoOjQPaFTaa2CGMSWE7RR0Uu2ecqO8VsK8i/PvCeqV+ffkoqMP6HZzPVDX8\nnRfjTp3KUMMULBCdtlXGGMPZBonzs6sfKsVbIKCoplcWiFdGPrB4RVUDuATq92RJKDizJRFa34kt\nsgJ2BPN1YPvvfm953iXqSoV/HVDvjuXqOJr1ATj7A8h2SBO/ugi8G8i86wJGYb2Js0A73vj3Vogq\nbJLzk8K576+FbHN8VStE4dkPOtD6cpQtEEi9tTbsy6vFH1s5Itoh3sv235mADSDaItlmOevP6KLi\n/v7KegPoPmkB6TCeQO3XkYFMNKtqfSRaWCHPLJCdbcFputWX+LmDOpcF6AvgLZBS1hZIIcZNAQ3G\nrWjfEH3anQakFcLVgdyrai0Z1FlNX21sksHpwfotga3f/R5gX8ksWdkfV0H9noyWp9umy7N7StkA\nu8gJ1kRtexCrur6XA+0g3MqB1iTgCNpaIY1cRkhhcCOMp0ICl94PtYKc+4b0/qad0JmvGWlWrnB2\n04I/LuvPoAe75aSptzU/z+paFLodSP+UrT/Pzr92Ys+CjsCssoEIbV8++HauHxBgnO8wE6SBCGoD\ncQL1yv54BmoB9EpVqwXCOl4pwDfkchYP7AFdLjQDO0B75eWl1KeNBVKJUQujlibDDUQCaYrq+V6O\npao2ZQ2WFxBE+yP70ytIX20u3YE7oJ3V4Rmw7bu+ANi2HRtwX28Qs/apbfo7QT0Fct95PO1Y6mqC\noh7jDXOw8Uxd6w2/HSM7ZGWFlNLQWu2nauEOSoM2DeglUSJCP4iarRWSLY/SVw+5xkhsDK7j+Ph1\nk3yfh7QpdGCINOn7Y/xIOAe2HEmYSldOtbENUjizTgORF5V0Lj/Es558m5P+QUxNS91TUCP51Asl\nnTNA/PyW6VG1LgVAPKRL9Ou8ig5AxqKO0E8kW3a2QEjUi14gWVXHYJGHdPKvnaqu1AzSu4AiEBtb\nADNQzlrgeYUMrKH9DNhXUvoysIF1Bsh7mp5fBfWqRadNO/H4z256z46pza/76FT2CtgFbbJDztS1\nDzzeSof6g4pZIbUwjoZ4fpK7BhSQej43CpAFcb/WGkRtD4gryLOFMgC+V9fqz1Bhs6K1oylT1wAg\nTwJ9mGY7ZPwQe2ADw8PWea0Ok9LWwtnD/kD5zp71RkUDa0in+uBRbxT1ZIec2B8+yuxV9SprY84M\nQYD3aRaIn6fIH6U6f8ITSxA65rZuA4vURF0/V9UD0jyBWlX1DtS7vj6moKDMt07N4y8CtlfXq/LR\nPGtdNuzbO0B9JRi7uvHtAJ2Pa5hPd99DewHsJj6at0PO1HWAttTdSrN3NTYm1NJztkthtDbERQdh\n3zgWSKq/y+rnGXjd5vOosx723HVE7lqKKlznle+x3Gpdvyhf1m3TDA8eqXzOAgnA9t42MHvYqqjV\nFsk97wED2vqTnT087U/nUL6zsqa1igZOId2rr4H61P7AgPOYjklVj3XCAosrEM8peHNkPEJb+k5A\nVNe5qS4llV3KOrBYSwuedQnDa1XtfepnoJ4bw+zB4otCxkP7KrBX676aITKmvw/Y6/S9c1CH5Teg\njus7B/Vaoc91B5fpZja6SUUA9oHZDrnrOpK69hkhNwUylXWg0ewQBphxEGFYIGoeew8ZQ3HLuFoW\nwRKRTYeDOKuSZnliFuns/e1wIygyC/RaZGTPeihtB2QgNpY5AbbOG2wRwKlsB22g79RpnvU1Wv+A\nAOMC0MAMaZk+2R46fBHUQUn7F+PqiaDLJbUbIUvmR3sw2/wlzz/+dhaJt0AU0igavBn3ELU/SmnB\nqzZIO1V9p5GjW9HsZbh3Gn9++jNQryD9zFI4QGH+DBet2wH7SwOO49TZ2yKr+Xx5T38fu1adft1n\nTygZyE8tG299AAj0SMB+oQdeccMdD4BvAB14A3Cn0X+zqus7NTQqAdq7nOtKjKZwJBdoVOtDhEm3\nKsid87BA486zhvnRTnkzpvm9p63XOKudUgE+VPGTHavJDjH1749vAufCEtHDHm0RYII28vQvK9/d\nBlkCGlgraT9PbkZ+Bmr/JvOd/VFLuNPrm8x3qtpbGWsY01zn5g+BRV2XB7YuV8SnLk2yQCDZHVhc\nOLNXrRkg3hbx9sedDgO5AvnFAo0DKrHRRQTI6tHdwJqyQBS0a/heB7aW9wC7zzO2VcF9lt88N1a5\n5lPneZ6B+uwGuLNGGob3v4T2Atia5uf9a2+HKLjNu2ZCoyLq+kArBGZCEw+bS8PRCKU0MAPMpQt5\nAXX3qjGuN7FC7JpSVS0gzp61vyZP1XUhAX96whU1HKAOgA4MSJdOYEbpzcMdkLl26pO+AR2YgQ3M\nKhsYUCYgtHx8xmp6Ml3Kj0vdm7oefAJprfNQlrpTUEugUAOGA8jyw9eYqjcA2yPNI4g4TrYzVe2V\ntYdwBDnbiWyBRYO0PHKWrqZVWWdVfSsNNzr6ZzmCV61AXtkf5lMvQJ3VtILhrF/qbHkAESqqtBXY\nNv0JsO20WPjXu/Ksb5FnjVCugDpMf+JTPwN16Hb15BgDCMdPlwAQob0Ctqlq4A3dvy6gKdh4pwNN\n7JGRztfw4CEMbqXhYEItvUm6qmsqDcQ1BBVDoFHVrcLXKfBZLaMr5pW61ulFxisASF416/eyXXcM\ncoFIHpB2/vUK2HCHMRxuvQml9DxjbaWRl+3VdIb3B8v396wXTcbPerJ6qqb9PBnUpqS9uo7jbLDv\n6/eWSIAvDdjOtsdQ1SFdz4CeVHUGvIO4nfy6iQBqWT2O+kYuPIBNh11w3v7ofw8UYrzQw6yPDGoP\nkmee6qp+grLA2MN9pZjn9Y5+RKZpG3Wdp72nXO2nY2d/fBTUV47xcppx3kF7A+yGPqwBR1vMrVJ9\n65x3fSOnrolQiSd13VqF9cInGRki5EEKb7U/FL5OUee0vghup65F5BDHzJAOatl5VfEFILjskFqA\noy1U9TuBjThPyKnGOLyTrji1Qq5J6x/SkdOUuufHczt7r6Z1PKvppLgN1KVM6poN2AhQ5jqg7Vsr\nsgLfbBCnsJPt4YFsEE6gt5zqwjFdT1W1BRWb+dREfKqqb9QGsEmAnewPH1BUUN/pMYHaQ+Q9XjWA\nLZR3LQvfa4fs0vm+FrB9Ocunvlo+CupLqYbZBtGoHUNuwAWvOt0fdoacAdEO0fOmcRkNZBAFwq00\nHK3Ik95I4yuFeyMUaYxiFoio4BA09IFG90cJ0MOPHrZIWNaNB8+7kuwkBdUuB8Yay8RGMheBbao6\nztPXsYa2X8+Xlu+furfKNVwBGhgK/Mz2eAZqU9fRvoDAOdsf54qaJlVsYFdwF4T1+ZvCEvDq69FI\n11OvmgCzQPQxdKWqC3V4ey96ZX/06Q8LJq5AvfOqn1kIjZ9bH8DsYWdgPytX7JC+7deB/a3sjzjv\nGtQ7SJ8db3+sI7QLKh39ODLwQjBgH6KqD/Rjn+2QOx1o5BV1w1GODnP1ro+RunfIecno/GriJZNY\nEez8ZyoQeLK7PhlkqVFyTTiveVLXwa8Wda1qmtnBXW8UbCJMD5NuC+t4BSzW+gTYgHjk9mPJ9+6g\nDYxGMEC/QWy7Yd3+1KH8gNS9dAEtLZATSPv6FairGzZoYwQUXTqfgVYbwBQEr1poOVsgCc6nqlrT\n9VRVy4m6UtU67FV1LUNVawuzrKpzUHFlf+ingtoHGDOoDSgBJGt4rBql7KwPoEwgz3bI1wg2rqbv\nypXMjytlZ3/44R2or7zbMZSglJMNIt+g41WGXwC8MvBCj7ThABos2Ni4Wx8HEe5EXX0XQuP+eQPh\naKWDmsm8a1XXTdW186nVEvHgNcXt1XWBZXRM6tqUevezbZwgvHDgdjYrtX7NMUOUP4YFgw2wWe8O\n3G2VHkkdKtuDV6HtLRibVveAbu8/735ggDHdTqY863dCugBraC9AraragdpsDw0qFv/pLREIhNP0\nZHmwgHpAeyh1U9P2x2LlswQWFdBRVUcbpEP6Xg7cxJ9WCKtSGql62nlTtD50ePKqbfj80fwAhXl6\noxVVemXME+wLnuveqa6/Vlmq5oX9ATz3qlelLI7fVmkvjrmfHku8ERxygnlVbcDGwywRBXZFm/zr\nJt7ygdLhTWWk8pFYI6DQMGbyrgWkpq69dy3w7ar7RF07WCsv7RAoYG2c3EQaYNabQHXQBkZ7IV2k\nDYWNJkFNDUCOH3EcbhKvnDlCWz8zuGWZuNH6w7l28j+lZ03oQcBQt1DW8rmENHAO6uqn7UFtd2Ry\nYK6ItsgGylN6XqrP83ZAL1R1SV51Gc3Kc151VUirmhZIe/vjXrpavpdHSNV78cp6A+oVpM8ey8Oj\nOCK4FdotAdh72FOdg/dOXWu5aoXovuyaql9Z9j3lTFUD10H9tGOpOXoVoA2SlD2+mcJWS+TFL+b8\n6zsdOLhEOwTa0rGN1o40WjX6zBAWkWHeNbMpYYOx2BXmNxdXp4BWpW0quwNQs0GyHQK4zBGXHcKm\ndGFPzVpPegMBzBIJaX2kNxqaVTYQoD3ZIxO4bYL8VIub70V98gOyQRZwdsO8UtjPIA3AGrw4aI9g\noAc54AOKU4DQA1ofv7Q+fNJQ0yVOD4HEldI2dS3AJizzqmtRQIu6Fvsj+tVn9scxAorOo96BOsPi\nzDed+1ruyyi0M7CxsD5W0D1T1x+xQvI836KsjtNKVQPXQX312ANw8O7SsUByyknT9iKwqxxJXw61\nQRBzr4/imqc7gD+oWGZIb3XeFXZQ1y7v2jJDRLiwT/FTUCe7ZEzv8Lf0PfS6kd5n2IX510WA3Wce\n/ZR4YBOGh82EWVXzrLK9ag6HP0EbiJaIlro6t39GZQ0sbRB2HtMppKV+aXvIuOZR70DdFfRQz6NO\n501KOtkf0/Qt7JOqVrXgVTVhmQFiWSCAZILMfX+oql7ZHzmg+KLzPQH1DJCN3+bK3JseL4ENRDiv\n1PXPWqZWhicWyDNV3ZebQb16irncAVWwnmSYZJwe0p81MDzs4VtrwPGOA40K7ji6HdKK2Ge8DDYe\nHPsMac1517zJu/ZK2tV1W2Gk9ek0n+1h0wBT4OE+r/MquI/UunEF7P7VYpHwUNWHw6fazqqynaI3\nP1ugzM4GoWc2yAfKd7dB+B02yGVIAxZINFC7xiwhLU9BLfnUofFLGcBeQTl40wsoj2kclbjVDWir\nwvCtFbNXffN/i6CiKWjNqRb7IwcUC7UlqK3xjIOHPcpfBPW6N70IbF3fCs4r71q35aMdMn2rctYr\n3q6By66XvZVHvez06SqwAfT+P7Slphq0xZR1P/xdbTed7kqThmreDmlEU7DxwV1AaKvGVhqOQihM\nrlWjqOucdy3zkKbOqSD2ihqIfrXaIUUm6jRV0h7SkHrbYYEnNsBWNS/ApQMzoIHxNEAbaCf/WrNQ\nBqTHwZ4V+LWf98co6xxcXGWBuM9seazBDfOndRzaOvEdoF7BOf4t7I/wx1PwkNM4CoNq6x61BRVF\nOa+86k1Q8Vaagbo6eBcNJMrni/OrDdhOTa8gvQu0aWkcQT3nSjs4X1DX36rs7I8DZamMv0XZwttZ\nH1M2SVDaT4CdJwu0KzMOit516MPEAN5/T7U/DoG2wrpR6UFIsUBCQ5miQceuqDVDhGtveMLS3zUq\nAEm3C74zD6idBRtlxwDwALmr2w+7T5Kdlu80b1tUPDfZzia2yMa77n64g/YqI0T2KbBONpqXVsjz\n8uNe65VaMS4BDQQgW32C9Mr2MBirjVE0rxoXQb0C9yL7ww/XrLQV1Ayuw/5QRV3cZ61zqt6NGl7q\nsQwqGpgxe9UvHwB1hvTzDoX6h4c2EBV2X89aXX+t4mGc/epnPvV7gd2Qm3zvy2ydnCtoP7y6YZ79\nHpXkRgm236PnVaucfaDosWCgEgF42Lj61YAMl0fPCCmEAwUHDskQ6XbICwiNi6XyabCRmXC0/gTJ\n3IONqCxuAQFVZfQCxhVTsNHbIQz9dMFLLSkrZAtsuzG4ZUnWe2hgFEMl76DNHNQ3gKHA9TdTeANz\nZkguu/pUvr+yPrFBgq2RVTRwCumspkGY8qhHsDCBmpAsC1oAeWOJkJ9nYX9UTkFFiPWBp/ZHyK1e\n2B9eTaul4TM/XuiQ4CJvgB0hHR/l93Bozv7w0AYGkIuAYqWu+/qjT/2jfOsM7N41aNz3g8sE31V5\nTw962f7Ygfrqb2JFLyfjlT7nd9ujQskHGLClHBDPun8ZWiMcVExha0dPD3DMFimE2gq4NNTSfeyn\nqXwF0EwPhehKTauKJsRP71+bHSGqu1/rM7Cp6csPeAT6ZJuoirLmblMwSPrCUncbXXR5Vd1/IKe2\nMWwSr67s3TjNAAAgAElEQVSzkv6gf/1DbBDOd5IFoIG1ujbxpP146DrV2vC2B10E9WrY5oezTRDt\njwB4TPYHbP6oqMfbX3gKKm5T9ZL9Ydkfyaf2mR/WUVMCtge1h7SHwarLT02f0/kU2h3ObCotK+ic\njfEs9e4jCvy9qjrPu1LYX9sq8XbHbhyYQf3sdwEQ1SKQBGYGNhmwNSvkQMGLg3cT39rDGgCOQrhx\nCcFGZsIhrRlrKTGVT4ONOZWveCUtnwL1pR3i91GXmfabrCMlahtgG1g7ROkQgMvxoENuJoy1yk6q\n2sAs9QZuYHT8BKe8MereW767DRL8mlXqnge01mdIZzWdbA/vT/s86l7vgUxbRb2yO5bAdgo62x8x\nqOiAXWNQ8ab2R2m412Pq/2OX/RH7qfa2R1TW3gpZgVphUE8eu3tDFmd3sDx2JzjPoH4/eJ/Nr6A/\n86O/pKzUdfx+2kMzlWVveicNX3agzt835bjTDK9KjAJClebhxUHa5iUA2ue1lOxfmx3SCu7U8KA2\ntWys3FyDGaAwodYWg40sT5qqliXYqGAmBXTwsjFY4KHNQ0n7IxOYuAL24dL6dFLr30etBwbNv+Yd\ntGVc7RFgtkMU3jLMZ+f0T2uDuObmQWFv1TUGoIE1pJ1fbfN7f5q6mo4ZHj49zytuD2A3X0X49LbG\nZH9UOSmrzCNWCFVGqT6oGEFdXUvFl/LASz3wUh6T/fFLeUywHj71HtSa1pfVdE2A8D+HYmUFBw9s\nACHPWXOrv/TN477sbBKvqjOozzJKvKodvnrOP5bGOReskKt2Sf+ePXxXv81qGXcljXmIx9MNu2vG\nmHVsgK37MEANdCWtdoh+4VDWPvdamqqz9HvNXWmXQhZsVH9E1bLG7Kxlo/erdcN8Qz8Ha/WvZc1j\nkkCcSXjaws73BQ9pOMNaF9dtKvsM2sBQ24B51RnefdK1G/uz8l1hbbaElqWylvEdoHXeheUBU8qq\nvB2EsxUSlDEtIL0AeB6uQKvcbwQO0uwgnrM/tKWiBhS9/XGv3Yd+EavjLPvj7oDtfepdUDGDWkEw\nwLAuub4HzDkAG8Cpur5SMog9ZH3rxayqd6C+kva3ehmBwnmnrn2Q8Uttkty4xX9fsD4WN9Gw3FQj\n8wuEClhaIIqaOAG2pu9pOdCzQUKGiNiPBxN2uddABydXzRDpMQk2u6CDmT1IvaLW7TJ4x9Kg8ccO\nbEVFdx70jeVJVRMEot36oAMdsk22hdBf6+VvGjtoAxO4gRne/TjM2+8LX7xUfmhHTmEjS7JBVDnr\nfC74uIP0CDw6te1eIBDskCugXkE6LMtOXWP41FWyP4LtIZkftaHWboFo9sfN2R+77I+gposPIsaA\nonXOtAH13Zo8K7Qh489/vUN2LwMbjKW6zuW9udMBvm74I6Ceumd1BMjQzsBeqWtvhfTWgm1Svrrd\nVzNIdLu89bG6mT79rXgMqNIe3Gqo3K+BqhE72YdD7DVfDpQZ1pLO14p7PRj38cYj95qrpPWJHdKt\nAckOqepfCCgtBU62v+qOrHeWOAJ7SfScGSKKnT3Mm1PZ3nppJ9CWG8sEbmCCd9/WZz/Yz2iDJGUd\nbBCvmuEADQT42nKyrgBpp6onFe2GfeBw5VFP1ocO06Iu+9Q1NX4RiFOBBBKj/XGvR8j+OG38sv0b\navqOY2l93Kkt1XQdh/i0NJl3BWwtzxT1M1Cvgo4rVT3NY/CeVeH2u9J26/LPgA1cU9fHCcB92U2v\nQWVrHaa6ZSFdd/+9TFU6bnVQN7zseYgm2SGNCo5CUzqf7zvkluwQ7ehJs0NKIWkyziANxInFwXD+\ntab3HZgCjrb5DtQZ2NnDDjtuN4dkiwjILfjIbrEltPuAqm3dJmAB71C5Osi8Pf65fHfPetm0HE5l\npwCjT8MbsMbI8hBI6zzmTa9sjw2kDd4rj9orau9ba0CxuGFN0zOV7Rq/GKifZX9s7I8NpKvzpa+C\negVpbw36Yr1HYg1s2PThXZ+Vp8FDkKi8a6p6Berdd+zewejtGz/PyhLZqWtbF5f+APleNb2wXGLr\nx/4ZFPaVlRurvMouprBfCVtgHyjSmvHRlytzOl/oO0TS+JgPs0NulcwOASDpd66xjPxxjf61V7ka\ncCQZb9U43p0NREukAIELGgy0n4Q9wDtDqC1U9hm05dgapG3dg/+6w1P2G9w63tFA5vsr6w2sA5x1\n3jCM2e5wkPZq2YZ13gDdfcOWfZAxj6/yqXVdYn/UYX/kxi83Z3/4xi8aTPylPPBLfeAlBRJ/LW/B\np9bMD7U9Ko23kz8DdYZ0XZxQB7NNV6HjgQ1bdv8KrlU5uHd/f4BseD1fVNXPQO0hfabi87ShpmlS\n2R7YoGGHZCCfWSFXy2rZshj2kF79bpOSo6yyNdkZeOGGVwIqk/wmDZUIDQ13PHBQOtZiiQQ7RBrL\n3LhYdojmazcms0O4MGoV76AyGhCCilwdBScLZIxrep3bvdnDnu6VfZ0dzDJOEAjDgK5eNvEe2gyy\nzJVgiwND2Tf3vc9M659VWVujFV9MMSc4AwHQfR6cQ5pULc9qOnrVEdIB2hnQpqYF1FlhV0SfWkFd\nm6XpVfGqKzFutXvU93rgpRziV3cF/Ut9hDS9X8qjQ3rpUycLhA68QOs115rxos2PE6Tzha7TLANE\nL1SBtge2/ZxPrI+rpcN4raozqK9C+tKbZ5yaBqBXstXvgK3bpr3Zh369BYRxvr7uZ3bTchvdbuxu\nrmG96RFcfzuvskENhQlvVPCiHT3bMhjj6Qc3WBf9Hd56vWSG+NKYcOeurG/uDjOy2wqgmSKQDVQl\n7YFtrOO4b1rbH7JHWrMqaeOEVKlXrdMaxL/uhGU3nVmGGab0e58mqvwRwS2b18dZ7xy2X18jI+Q7\nZ4MQmm/B6M8pB2cbfwLoMO4h7acnNT0D24F65VF7UAfrYw9qH1gspaHWoahvkgVyF8vjLop6UtaL\n9Lw7PZy6fjwF9QD2ALWH9A4cvr7J/B7Yfr6v0WRk51UrqG1bTkC9AvdYbr2nHcRj/gBulWY2jgBs\ncG/c44GtQLY+UxZquwnIR1M+/R79jvVFfeW3Wta5wFdFv7yajHce9r6qT4Ts6OAJJd7RC/Ab7jhw\n4Bf7HdbpfH61/QUqhMJstzNN7etf23vtmCyRvGEYIJ6L3ZnGkHKkifVB4yUC3cJnEcIO6gzLFjG1\njQhuxrBDzC7xmwsg51l/pJHMD/Cs3YgHMxDhDIyDi3NImy9twwrapKYJs+2R7RAfNNx41OpJG6h9\nQFFUtc+nrnV00nSrB+5FgX0sO2k6y6f2anoFam3wcgZq/xPUjfo8XBDSA9uWo2iFPCuNo+2RLRCv\nqjOoD5QJ1GeQ3sHZ9+6X5/PgjqqaBsQ3wM77qTbDs/dWfrTkm+3uN+xFfqSUrXAnoBiwexbPSmEf\nKJYh0qThjAYcV/71L+F3iP615l/3zRjSXW8eIeCowN5ZIrJ8tkR6pf45KIuSBpCsEPGy1QppsBsZ\nM42XHqiKthQ+l6ed7JAA734gFr9KvIFdKT8uG2QZXIyfGdChLinpWLdX0942uQzqEkFtilpbKAqk\noR61KOqRpieQriOgaJBOaXov5bHNp84BxSKpVi84nFfdlqB+cRe4v7jL4q7emG2e4/KpFMuA74Ds\ncr6N/THWcx3UMTf7/DtPy6Sq2wRs3bbBk9kO8fYHvtAKAaJXvQL16rcMFgKAkBtMCAr7jYpkibiU\nvFVwlyjmXxdR1M6/biC88KNbIe55zN7bqJ09cQOh2yFAsRaNXJPCPpX++2PWlxwq2wxuG3YBRlaQ\nK4B5gNiePpzals0gUddQjxuIwC7umO828kL5oal7Wgcg5ldjD+jxSWlcQeuyQBTeTllP/nQGtv9L\nHrUHNZv1AbM+SHOqaQQUV83JvU/9Ug4LJk5Kujzwq9kdsSm5Zn5U9KDiC9oIJgqo7zSrab24lxe2\nlEKE5k6ubHec2R8drBu1vlHVcZ6hqneg3log3jJ5r4++AniAtAO4wE1T+jKwDdAbdZ2tkJpym8+K\n96qv/pZ9u21HT4HdP/urvg5qoRm6Hu+DiuVf68mgHnZ4FVghvGgqn3Sleqsj8ChbCCA1mOlf1g81\nkynuJbCJPIrHPjk2BB+7uenNqezJCunfbUpboa0wLmJlqE8d0vh4HHL2G5U39H3lx6Xuka9DqMtp\neh7QWueB7JX0pLCDol71ppfmCb519KjV+phArY1epLGLKmrvU9/F/sjNyXM+tQYUf3He9K/lDb/S\n2wTqYYEMUL9Q64+4iKDOF/bZo/MBNmBX0KSuW5iXrCVbhrSqaq+GgxWSVPUO1B7OS2Utnx7QV9/R\naMVDNUAf1oOgzeeA3evWwK50LNV1hvN4KcOXBaFO7RCdpAR5Bmyd163SLCui4V+30X/IL6kXP6BD\n+SVtVw442ooqwTEvbAJDmSA1xgfp88R/xQRq/RMVrfvqBLuknUdoq9LW4CID+nqxEUyU4QRvIJ5S\npz/tz6ismQC+6bDbQg/mPO7T9KzOqeRJWY9hb3l4cE/A9go8Z3wowL31kUEtWR/PQN0/u5rWgOIv\n5ZCA4tukrAegXYCR3pagHkHFNagLUXxkTqo2v5PvWTl4r669+tVxr6TfC+qspj2kFdBnqnoHbu83\nN64DltkGQYK2fDaIcg6KGjZe4MGu6+3pcVbvni+6Fy4jHBvG7Mrq5rv6bW06AQX9RlxIU/oU1M+B\nLV8gP/5LUNcrf2fVmnXdwrXIza/sgU2yA9K/h+5Pb24ux+7AUMwB1K6OnZedbPEJ2uJNW1etDtw5\nuKget2603d+fZIIsD8eifPde91roG2QMLoOMhABwr6InBb2CNFEA8lZNO3g3AXG0Q1IwcQFqfT1X\nBvWtHtbvxwC1Zn0kUJcjqWrNrR6ZH1dBfXdw9qDOF7KWYiBtSzV9VlRVewvEq+o+z7A/dPp7QP0M\n0ktoP7kKGtfYGMUCjx3cBwToHvYGag9vLIHd33cYgV3psCyRboG0bY72oQt5YDKvc6ulrH7fWKdP\nA5hV9gVg21MNlR5wVHV9IeBoW7BpCDKYtgE2iYdNjq5aNB1PVbb3pZPK1l5Ne0BRcq9X0OYB4B24\n7ecxYHMMLjqlDTdfLNdo/RTWRPSfA/h3AfwzZv43pe5PA/jbAP48gH8C4K8y8z+/8oXD8hgbuFbU\ncXjtWy8g7RV3gvFyPIDZdcpEcC0T8RTUqxS9m1PU2kHTS310n1rg7Dto+sXlT3dVPUM6d3GqHvUK\n1F516QW7u9CPJ3f/5TKgqTGMh6rPAOnfMeyPPu/7QL2D9A7QV7pKPdjlQ3t4O3U9QTv413tgH3Yl\nD/+6w27YIYfQrsix02U/Yol4KO8aOen75atC7QmwGwiHtHbU5ueg4VsD64CjlsZletnuKkOE0VP6\n/N5MwCYgZ4n0cTjeyb4oQ/y9SeqoYSyjgUOFdrNVD5XN+ifzqidtCtsDm4a7FDaedevm8hWV9X8B\n4D8D8Ldc3R8B+PvM/DeJ6I9k/G88XRMh9Q0Sp1mdDUdA++mzZ72G9BLWOi2r51CXQF3lBM7BxATq\nIo1eVFFrit6qgyYNKlrudJkbuwwlPedS39GswcsK1F5Nn6mxs+IVtjxhThbITlUDMag42R8noH7j\nGrxpBfUK0rlFo6+7UhoGpBXeBu4E5sYVRRTxAPUa2AU5/3oMDziv1XUTcF/tN9uX3W9t9XZzawam\nHbDvaJOIBQOHNkEHrJMnuNVaC8cy+9i2f09LAvaBmCWiG68pd9DtdBDXTXYgNpWdAoywBjG9jhiW\n6j3U9FDbA9I8Kexp3O/v+39SABdgzcz/PRH9+VT9VwD8JRn+LwH8A1yANQNouUMDD2YZX1kffR6E\nx5oB3xNIq5ApCKBXEKta9v40DNRsGSC+Rz0fTCQH6iopet768P70WeaHDyiuMj/OQK1ZHxnUWU2v\nHpHVq8551HGetfLWwCKggB7WhiliZ394UL9yDaB+43pJTSuk5ybnFD79tLMyVPXo/8PgzQPa/UbV\n++9GVtkbYFf1YQXYrwBehC/aW2EHt3gJXHBYPcSKUZnX6y71ByJl93vrb23fewpsvVkh8CZk8qgV\ngrJW13Izh4Bbe+i7uhcGbEIHNgTY6l0kC0RT8aihe9tOYXc7BUN5e9tD5bdQ1itn/cvgDoFGPWTu\nWpla/i8usW/tWf8ZZv4TGf5/APyZS0u9W1kjwNnW4VR0UNcXIQ2vnHVcpwdAK8w5ZX3MwcSuqA+8\n3FIwcQPqX5yqVlD/Sm8h8+PX8mqZHx8B9Rmktehj8ZXiVbVX0aqqM2zf+Gag1uE3vgVF3RX0ue2R\nIb0C9JmqPmvB2Bz+rGtSNEk/I6uv0ODggHYDoXDPLS7QToTIgP2Kmyz3MGAf6GB55QFuAHLO9/UA\nvd63LNRtOwBPlq7g3/HUZOdCCKJ2gI9Omwew2wbYU+aPboNT1ygd4Pq6sCJKbedZn201E4OPYtAe\nNoZ81yHQdoHHzgYGNUmt0z+M+6u/19q9lTtfqGHyqjO4gQFvG7YNnOHM9s+Vbwzr8eXMTLR/ViOi\nvw7grwPAy5/6l6Ky9k8GDtKT5WGfC0AvQB2A7SFtw1FNG7w1n9r7064JubZMJFpnfdxy1scHQD0C\ni69Tip6Cur+u6f2gLu6At5NnsQOMxtw/0VX1EaaPdL0M6sMr6XeAWm2PZ5BeAXrUOWAvroDcS17j\nCGptaNZIukXlCG4PbbNEBNp3wK78N0D8XYX0rd8QE5w9sKtAvDo46u9V5Xf4iLoe+xd/+wBtOW53\nuSm8cXuqsKdeEZ269sD+tbxN23K1T3MiYe/B0uS9bxcfuu2ioklU9tE/6aCYT239hAg/ZFW6jlld\n9z+DuAYSM7gx5tXfNfvVSzi76e8pH4X1/0tEf5aZ/4SI/iyAf7abkZn/GMAfA8C/8K/8OeYzWEtd\nHE6AxgDvqarWacGvXqvppe1BgO+UCa5lor3p5Szrw3XO5P3pVfNxn6JngcUTUN8loHgG6h2kd+VZ\ngDGoah551fFPoMsFb6jRr3Ye9SvfDLaT/SGA1vVkKM/QHh42EHvqW0Fh0fI3NjUnFl9aLBEPbulH\no4Odhqct3+NVdm8HfRPfGlFBu+HDFLvaIEPq6UsCinwCrn+WpK6HMp/3Of/+Om7QppZskWLAPrif\na5Cb950aDia8SKAxHFsqELPnNKXvajxhCIS+ooZi+8k2nIWeHBMCrJWhV9nuacVUuY57aPNmPIEb\nwAzv1SfiuK/+1jbI3wXw7wP4m/L531xdkJMsmDNB8gF2n77eQXkLaa+alxZJegWXedhDVfve84oo\nat/V6RmofS61b/QyN3x5nqLXu0DtoK4XQX0GaX1s9xZIQztV1ef2R3GwHZ+vAmP1qHegfmu3UzUd\nhwegjwTtPuxV5PmV0MHbT8r+lpsBbw/uQuo9R6UdXv/lwP0GQDuueNPzllPQEQBwA+iBYr53H/f+\ntdohVW4az9T18SS9b+w7PQX2CxFeFdhoeOX+0lwA7iYhT1v6NplNSt+JG7cs8YG9r5ChHdO2wYoG\nAD3o2A+VCjy5oSWV3e9zQwT2YJrbH6+W0/gK3IBX1Gz3TyAp6A+oaV+upO79VwD+EoB/mYj+KYD/\nBB3Sf4eI/kMA/yeAv3rp22jhV02wHuNbz7rE6VtIE+AtD51nqaa97SHD9t5E7etDO2WSJuTe+uit\nEgegb9JKMedSrxu+rCA951IrqO/oAcGroK7aaxp7MF87c3wGyM7+eEUGcj0FtdoiZn84QHeA7yHt\nFfRIEaQA5WfqWsuq1z1AlCyARuw6c5Je4kRpe2jfceANNXjZd0DsEHbD+l3Dw+7l5pTdCDjqb6if\nPthYRO1eVde2j+lcGOfJDOwm35eBHYKNaotwAcor0F7Gsd/42P3742+2KoXyfvQnFw06Qllpylps\nEAK4Adyov1PRq2xV+94a8ZZHc4fTwfos2GjHAQgABxACjNvGMV9LWTPzX9tM+svXvsIVSsrabeQV\nz3qltIMlYuqZN+DGgLSBWUBNCN60pufljI9SYsvErKjVo76VYwtqVdUK6l9Lb5Vo42KLnIH6fgHU\nNZ3slUoAtpaDeauqV/bHmwUQh898BdRBScvwDtYrSO8A7et1mg2fXQlc4POZm1x52p9HBU/gVmg/\nUFGkiX8/5kNl2/soWeEiv4kCGZg87KKP9dkuocPsEGeijuchAXZW2of4zDtw+3PjYL0x6Pr7d3X7\no3/fM2A3DaCSS9UzH5smYDeJH+zyyYkY2e0mYhxH6S0Pj555wsTgJj62+dZ920g50Qis2SHOGumH\nk4cvTeEQdyDr7L7OVPQM7CnH2oNbDtjkV38tWH/NwnCwzqCWusnu2Eyb1LXzpPN0D2mF+eRNZzVd\nMPnTmvGh704MDV4WwcQ7tQBqDSCegdreBIPYOvE9oM6Q1pJB3dAM1DZPsj8U1K/mJUdQv/FtCWoN\nJnYV3SG8ArT3pmdVHSGtgPbWh3nX0Lr4eaWYjcEyLBD38K4Y9SWBuxHhTkewRlBgKlt/l76domzt\n099YZn+7Msurt/o09a+te1NIk/ETdW1ZKpuiN3EDNmCWzgrYBZp62JagqdSGwhZgV7Ctusp5+LsL\nIBSSboQP+ZT0yXLUHtAn4CGg7sAGcDCYSucvNbBP09Prvnt30OAiFwdtNx8x991pcmNREDt7JIDY\nK2z0z32OtftN4+ByfFd+QK97DrpS5z/3gUZM6nrAmMe49649wJ3VYZYHResjvIXcvdxW/elSWu/m\nVF4e4JuQ38q6B70VqH0HTRnULzimHvTuF0C9U9OrsrJAVFUDA9T6VNjzpUcg8QqoLdAoGR8Z1G/c\nbYO3Vg3OQ1XvIe3HgRF01GHbx4sZB1pUTRcDtNoNXV0/pE7BXZiC2gY6nDUIiYZJZaOMY3/HA6+4\n4YUeeA0/h7NEAID6OxIV2G/im/d5WCyItR2iMrGnZ3ZgH9yW54gHtvexe8vLQaQX+x655hyw7cbD\nMEvEd/gkB6mr7eRhl6kBRle9YVwUNUEz9PqTCxENlX2UwQf1sqkDmhXSBJifzW7bRLhp+h7kpmt+\n9grSGPBmNwxkC2TaPTfxZJorPw2st3UbQIOS1bFT2WaFJMvDq2kZ9ml5+nJb/youH0jUJuKhr48N\nqHPDl1/p1fzpDOo7HdaMPPSg94WgVlWtsPCqerI/0M/PNx4+9eFUtc+hvgrqJayTmn60egnSGdA5\nI0Trfcnj+QW15g9ztEI6oIfVESE9xlEwPG1njYz190cUtUV6XR8+A3YF20ttFdg+4DiCYAPYWUW3\nJz62lgzsXifH2m4g8l5Od7NozN2b970JbiwRGwamTBHfcAYACkWA6+/gA49EIx++EfUbzEEhiwwN\nkzXS86jlsVGhbSuVfWUXjFzAevjUYzgo6o2ynspPCWvgpAWjG9/Cmuc6jdNkSOt4hnRW067peCE2\n20NhnV/FpYHEEVDs1sfdped5WHuP2oPaZ3/sWicqqF+o9573pYrafoMzUDufWkH9Bs3MIAHvLShr\nP+5hPOqrZXvsYP3gOkH5rdVTQO+sDw9m5vWV0J+ME7CDqi6iqge8FdwraLeDcCvH0hrRG3AuHqpn\nwH4BDNiHbNudembGi3R0oQFH64vcqWvNDHmmroHZEhmgdxaNfFczYM/ZIf37ktVzAdg4bgbskpR1\naX2rHrLthRgPKv3diiK3LVskizynsu2hgdBBXXhshypvD+Sstk9gHYYxhq9aHWfl+3eRuggwBlDL\n56hbAHqltKegIptHNVkehOBN95cFzNkeRV4acHcZHzeK703UdybeylDSHtLZo/agHoHE56C+U/kw\nqL2qzq0VM6jf0EH9lkD9igzgNah9xocq6gHvCOihtstTSHtAR2j3YYVyBvhpkSvI9wuive2RV9TU\nIacpfKa2GbjRgHZrXVUrtIGhrvWzZ4ocUhe3Mbx0V6clYHcgHhOwY8ARDpxrO+SsTB62pSQOYA9Q\n6+ca2GPf9GBjCjpW9k8bjNJuqMT43eGpEOONqt1giRiPQz6p2Atxj0PUdiFwI/GmCXxAoCzjknPN\nAmptsahvgrH+P9zNxTeKMY9a4a377u0QuHrsx6+GV77/ywcqwo/poWzjGdoKZWANaK0zMJ8oaVnO\nZ3qoN13kbS7VvdlFX2qrtkd/X2LKoRZVHd5GHmyPGEz8GqDW8hFQe1X9DNQ+oJg96gHomPGRbQ8P\n7EcrwfJQMGdgP1qZAJ3hvPKtvZK+Amzt9wMYSjv41QnehcpQ19Qsla9QwY2aQftGDQcR7m4eVdko\nPdWtK+9if3exDax3Oy3UPfjeIhJLYANNMjXwFNh6XpydO8+AfXcKe6QRngMbkBtSCjqW1j8rmgG6\n8FDPhRivuIXA49sxPol6MLhDm9FaAbVuhbRCEngUeBcAjaGv8+rKeeweN4U027TwpnOMzwxvYABc\nZ/2uedZftRDQbhzG/We2QhhpXOAb0vWcil5CWh97ku2hlofaHSs1rb3mjddwNQsk+tQ8VdXZ8liB\neuVRn4G64v1ZH1qyT93rBqjfuL0b1Noy0YPa7A/nT7+1WwgiemWtlkcG9jNIK6AnaCf7I18TO2j3\nlokUTkWtV4ibwnbwXoH7ViQbRKDdQd0DX0WyeiApa/oSWq+y7ffJoLZ690QkkDnEAnkvsCFPZV8D\n2APUOAV28dvfK7rF1IAq6raU8csVriG1rxDjVQBNHtToDW9WKrsHers10kEsaX7W7NwpbdkGjd9G\nSAtLvOLO1gd8HUf7YwHpAPGfUll7G4RiPeCUs86r0F3ZHlLfbY8B61NIU7Q8ioP1Lanp28L20LeQ\n/2IvD+g51LfSNmr6POtj19WpB/WdyodAreUsoPgRUPeUvDKBegCbDNTZ+ni0agr6wSUAO0NaxzOg\nM5z1nJ/skAuqWpHo/WQSgBcaXcYrvJvzrJndOLGo6cMUdJGXx97L0dPtCmyeHmgcKntszwxvLTUH\nCLP7uygAACAASURBVGkEHRXYvctV/q7A9pbIMw+7r8+l9QECbZnf+deFR7ygj8u5Lz72ow07pLQS\nVPYDEB+bQPI7cBN4U2cCN1HaHtr98Mlu9hFWH9sAzW54Y4HA1flPV0LVzwhrBrQ17omq5li3AnRW\n0bqc96RLN5WyL02EKdOjOFD7bA/Nn1bbQ/uizjnU+ibyDGr1pq+C+k5fD9QHt1NQv4E/BGrf2CWD\nOvxJSt6jif3h1LQHdmMF8zVIK6C1DliB2p1zJ9BW28PegUij/kCHNFHvOfng/mJWhTM5UBf0c0ft\nDoU2CizwqNZIV9cFN+ki9Gi9S9GDygRvX5Y9IyZg97oWgN21kafIt1HYQ1njHNgM5JaO/eDLd2nL\nSlHdBm/JTFAb5FXtEjAeAmwA8rQDHPrWmtLzslVlw2e3KEds+2FpemaRsFPbHtACbgaG0oabDleX\nh1O56o58f2V946kOWFggGd5Wn1S0Dm8gDVpbHoU4eNMB1NRMYfum43dT1Qpnl5rn3kT+q8ulfg+o\n5SU1E6i1fG1Qa6OX94A6BxJ/4/vkT3vbw1sdDy5meQxQ1wnSOm0H6AjqAeQzv/rZU2fPiVaAkwCb\nUMhZIRjDCu5aminrDO3G/abvPezGHd47dd3rh489/O11J/4KbM0S6XUD2K/sc6P7/CMR48cB26f1\ndYvkZWSLlARtjABtJR7gpoZXuoHauIG+yW9E3r+Wz9Z6PyVmjajgayQZHzS2QQHc5EUHYoMQY3jU\nGd5ABDiQgL0RDj+jst7ZICtVvYSznydZHj5waHZHgnRX1dfVdLY9ciDRZ3x4y8N71bFDpmsedQa1\nZn58Kai9R32g51FnUL9xDX197EAdVXQc36nphwB7BWltxWjAbmUJaIWzV9XjOhknVVbUoYHC4uJo\nDsb+9FQIjOAjUEuzebWJuc53A5mv3QqhsQQdy7BGGqj3zketd9aPw71ZJVkhDXtQ2yz9Bnunw+Bw\niExcAfuNjr7d+IEKG90SqXzrNkgDKm5mi6g1otCu6Mfr93YLPnZP36t4bRXaulSDj1W97EYSfyAU\n6ViqtX6D5VZ69xSNzB5BoaGki8jsBvTA4bBB+jsYefjSTl2vgZ2E6lVJLeX7Z4OsAowyzLp3lKed\nAFqArKqagMnuKKVfPCtIe29ag4m34kCdAoi30qa0PGsi7rI8PKh9Xx+F+DKov6b1sQsmDiU9QB1U\n9SKQuLM9vJpWOHtV/WhlC+mjDVgfJ4BWOHs1rSBeKew+Ph+rDOzY0IJtenOgVjV9NLIm0IdT1yTf\nreqbmQakHbQbE45yoAZ4k7zLcNgiBxUcZXQ/e6C/pFb7DDdIp8wRDYb2U2EAu7rp38MSyVki2iLX\n0vg8wIv05d06mCsYtTB+a3eU0tP4euok41WO7++tmi1SqOFRqgUfH63gEC/7aAVH67/b0coS2mqP\nkAQiUbpiZvWri+yLU9KcYawAF6Czn5aHw8m3PdyhfGdlLe8ytPH0KfNYXbY6HKCh6lnBDVjgUBV0\nVtKqihTUanVUipaHZn1422PYHfk1XFlFr18c4JuQvxfUz8qz1olnoPa2x0dA/Vu7BzXduOD3dgsB\nRAO1ZoO0YnbHYdCO3vTRaAlohfMZrPtwOkj5EZTiDCR0HjYIbHwFbwV3KQ3MdQAaA+qtkCltD+0m\nL5D11shRaKmyvW+7LQpgNz6apvczoth8jO8BbAW1DzqOfrl7KdRQWVqCqicvFkjwrOXTBx01dfJV\nbJFCHIKP3ssm+x0LaonQ9uAmJssWYYGxqW09z1Z/0GF9NySeQpr8+fhTwhoYJ56/WIL1McM6A7p/\njgtJ58l2BwGTL03ApKZ97rS90DbZHvcA7MeU8fEtQf0lzcifKer3gFr96TPbw3vTXk0/nIJ+JDWt\nSrqPw4ZXgM5wHuqGIqyfnYduXs86U9gGaDJYj0+ywGPTdDLqSlBtkVoa2lFNad9APRNCskL0Td83\nogCkAGcZbo0mH3sqT4D9Qg1vk02BbwZs8KrhDBA6f1pYI6G4TJHKzf4Ayddud6A8Ori5oGAEIR/E\nwcvWjJHGFPzsg0h+IxEG8skqFoChtsX2CODW/cjj4XPeSX5G80X57p416rgYrC58DhWzArSpaA0i\n0qykFdJ6AXnLg9T6EG9ac2R9EFFV9a0kSKdAYs74WL3hJfeed8WjBq6BWsu3sD4UzGf+9O8y7G0P\n702rmvaWhz6eriDdNMiIPpwBbXD2qloPgtXp+JUTMizaTzmT1CN1bwiCDO4+rGqbefjbCm1GD4qp\nJcJMFoT0Ktt72dkWmeAswclulZATQPipgL32sPEU2N0qaSHwqL420L/qTcGs+dit2ivbisD5RsW8\n7AcxqpxrmiN/kGTttSK/x4A2i9rW882rbYAl0KiWCFn9OhuEl8D2v9OV8v1hfYspSBnMvc4PIwAa\nxGZ16IXhPWkFNNGAtCodb3mo1aHDXk1rtkf2p1eBxC8BdW6ZCIxgInCtdeJVUGunTNn6+I3vobFL\nh/QcSPxd7A7vT3e741xNP1oJvnQfH5BWu+MwKPcTP8Iappz7NeHA7CyRZ5H33inP7tzksJj17SNK\nOp+DK3CzigcAzM2grb52Uw+be8OZVjQ98QhetrdFgDf8hvvkY4ciGSR9+sO87AMFoDd5ryN+OLDV\nGgkdUOnvtgXWaw88AiP42Nga0Kg1MtQ1d8FA/fxTL7uKSPDWSC1NUicxxAINO64UPQ95eR6SwptH\nvGI89iXxcAbsnxXWJMo6dKKTwKxV3uJYKRpV0eodqidtwcQE6SKq2md6RBXdQjDRq2k/vgsk5oyP\nDuj5xQErUH+0ZWJuQv7GzZqQv0rDlzeO/VH7fj5WGR+/GZgHrH9zsFbb4/d226rpV7EA1JdWSB/i\nV3tIZxUdFI0DdIBzUNiIF8LJE+YkuMkNOOHA6luLOobAuX9iKSBag4kHU9YZ2iyvCxM/+2gFj1Lw\nwsdWZb/gEXzsJilmPvDY3324Li8agBQwV6WMwLN5qH5jDxvUz0ftgAoE86w1sFjQpkwRIAYeK/d5\nVDz0AGRU2d7LVmukcsNBBbX087IS45AbqQYh9Tw8mFAKu3OSJnADCPAGhvqGHWJ/V/p4+c6ede+G\n1PvV4VETCHDu9RHQOlxpPHKeQVpzXxXSA9jnajq+G9G/uHb4089S8/w7E7/0xQFavgWof2svT/3p\n35398WjVgogrNf121Cl4eIiyWUE6wNp5hRqJnx83AR+Vt3HgFNTbMllxbpXOFoEobBZLjp3q7iDq\niruJ8mMH7V6n6rrZk0YtDTd0NfeyU9miyIMtssjHzpkiYf94DDfN0AgBwG8L7AHm/sPoWdy9bZrV\ntR+XTBENPFpqn8YkT1S2etlqjTQuwc9uTFKvsYYhKMwiEZW9i588i53QUxvk2on6XWGtajjA2lsf\niHDO46qivdVRHLx9I4UbDX/aWx7qUa+8aZ/tsbM9Ktqoo96MvFD77qAedV8X1AZpb31sbI/fj9vk\nTR+t+4SHKOnGw+5ozgJpzcO6k68xRQUdovALOK9g7ct7Ye0rnBXXJbSD9wLcrUSrpCuwYY806t2q\nMjfxRIfiZu4Uez1gudleZWvRbBEAwRaZApO7fTRgu8yM5Cd/c2BjfkVYeGmBDPpMEV/Mx+4jKNzw\n1jQ/u8O6om1VduMmwcaKR3M30wRt/V3UInmWmTSykzjC+uz81J/jp7RBAJTawsb5Lg/7Z6zzgFYV\nTQ7KOn0FaW36q8AuxFs1XdGmbA9ve5z5093u+L6g9h71R0AdvemK3/hl6U//1u5b2+O11cmbfrSu\nrLPlYXnUZ5BuSUWHPhmeKGl7MNvYIWfFlLS35pwtovO4vwzuDuahtntvbE0CVgPa/ULXt6gDXMat\nVwOQ3stGBSB9gastAogaTVkjPvD4NGMkKewfCuxF4xnz2aUUsUB6YLH72JMtIn9vqFuV3bgEa6Tn\n/A9oP1rpfZWzBhx5QJr3Of8D1nDDHtpfZoEA311Z9/6iT2ENhKa9HtC+T4aaxleQzpbHrKrHm1nO\n/OlngcThTc/Nx78GqLW8F9S5L2oFdLY7VoFEnz/d6wpe2y3YHq/ttlTT3vLQFDzf3HcLaedVQ1qM\nBVgD8MAmnQ4sPi/Klb4iOfccoBXMEIjTYpoHtzRZNrVdAJL8a249c6mrLvRApHrXDANDbQVH6cAZ\ndkjBS30EWwRAeDuK5mOHwGNDyBTpL5IQy8T228E4NGL5NsAu6K0nrwC7N59HAPblIveGQrPKfqiN\ngjoUtYO2vjdTnwiLg/JZa1rf4+OqJe0Zq0P87qR8d2V9q+OxLvd25pv0jmFnd2A0MVUlrZ50MWgP\nSGc4V5nuvelK0aN+5k8XtCnjw7dK9KC+K6AXoH5Pap5mfbzHo87Nx3epeR3KtwBo7097QKvt8ers\njzeB9COAelgeGdKs+dPqR2vvZwrerKYBG6cA7zHdTqP86actyrimFipap4sPbfWmqnU+UdcFvYky\nCbSl200igIoo7SaNY6Q9AJfZGlEodCAcprJvdHRPW4B2CMhbeTMfOwceAZi3jYKgrPs8/fzt+/Yd\nPGwA4Nr3QdpzvxDhkO8rDtjWGMxbIynwuPKxfbZIAy1VduNi8A42CCpaaZZqeqP2xf3U+D5qvlRb\nf3dlfaujaazv5ayPD0Wd4ezrVUUXN65v8PCQrjJNLY+ooLvSPlPT3p+OSrqF1Dxt7PK1QX0lmPjG\no5+PDu09qC1NL8D5ht/4hrd2w+/cwfy7Cyqq7aEq+pmafhxrSKuSHnDGeJNHgLScFG0NZwpWyArU\n4+J4Jlh0ziDCdaEAYzhIC7ytTlQ3u2Wsf+QO7q6sHbS5obVq0NZMhFtFCEAy96bkmvLnVfYvHtzO\nxwYQgo6WKdJgqX3TQXBe9je1ROw7iqSx9hTTfvz896bWjtz7EXnDbam2u0Uy2yJvLMc4edmNi1kj\nDbSGtqjryj2971kPkACiwnYn1bNeIH9KZU0E3J2y7nVOQUvdCtC543eF8hmktdP3/pqlZt50DiIW\np66z7TEraQ0oxhzqbwnqUTcr6o+AWhW0qmsfSPy93Za2x+tRB6iTN/046tKXbgLmy5BWBZ3UtQFa\n4ezAvFPVH3l6lhNurMBsDzZmcwC1zF7GePaRmWEwMmirPcINzJLHK352a4yjEO51qOx7PczLBoCm\n3avKZ/CxMYKPgG7PCxpOOoP6TsDOb03vd+SyATbgG8+Ebhdke6KPHUuhNoKPui2lq/A3rl2ZE+NN\nbppvXFCYJTebDcYPrmGcmZbghvxWehg9tGuom8F89Vz9vrAWy6IP91IcrIEBZ62Lb+YY0M6Q1uBh\nBHZU03442h8PFOKlP9396HNQ2xvI8fWsD+CaR/0eUAfLI4FaA4m/t7ul5flsj9d2Cyl5jzZaI+7U\nNEuAkVuyOxomSAdAtw2gV8oaiOe/wvo9z5wLC8TgDCz8anaetfjsqrg5qW0BS4A2C7SF5szRzy4C\ngVvtKhuA5WX38yF9shHNgoyWIaL1qVTNc/b7/Z2A3evofcDmAlB8e3rd/MiFOpD9MdGcbPWyH9r/\ndQB1t1N8X+uF2aBtQUemvi80jn8TKCu8qwDeGlsBi1uKHO6fUlkDuJehrLP9YZ8J0DpNIX1zfrX2\nuHV32R7jpaWzmi7gYHt4q6MST4FEhbqm5mkwUXsQuwpqLV/Se97Ko36f9TGD2vvUPn/69+MWsj3e\njhpsjzdR002gfaqm7RNrSHf7csBYsztXyhpjGMhK251r74A1wVkhq8+pbqhrWCCs13FnTx/W7jUV\nhApt99ecNZJVNhC9z+yDNhZlHdL7HKgdsKc+RdoLUF5/CLC7JlZvum9k0y9/B7BfGXih/D0O1IDZ\nIlpXZDir7Id+tipplmtor143V+TObtaJ/Ea6Fdn6aGn8avnunvW9xtcWad+0k6J24PZvlPaQVjBH\nYLegovXTK+lsewyrQ6HdLOPD51B7UPsOma6A+ku7OX1vMDFnfvzW7ks1rf60Wh6/tzr502+tbm2P\nY6GmW4a02h2a4aF+tEBaVTWtoA0Y2ILlEcb7yT9ZItiM2wnpBv1TdrI/bN78Jwv2p3qntht5ls3Q\nZnnSsHeCdmsE3KLKluBj4/6eQm+LAAg+tp0/LvAIYAZ3ArY2Tw/HJAH7W2aJHNytkbtC+Qmwq/ey\nEd9NWanhNYC6BYukULdFVipbrY5KbL1Femg3sz+aA/UANzAUNuBusm5jdVpW2D9tnrU2+9ayUtVF\nI8ILQBeS/j4mYLctoL2aVlvD2x45kOh9ap9DrbB+r6L+Gv1RfxTUI4h4n9Lyuj99m1S196c12+NN\nYb1Q08exsDyOsoa0g7cBWaaTh/B2mCZoAxtQX1XXyQYxpU39VV7mR6OLChYoa151ByGNusICcBqA\nlPlYj0lhgzaqArqErJFae5ZI5ZExogH63rKxmX+toPCBR6AHGH8tbyO1z9kkBzbn5ALY3zJLRIFd\n9CnlBNhvVPASvGy9xiKYKxpetbMngW5X1KKmwUFl6yvoOrwramVp1NW7tdVX0WknW/2YR3B7da3F\ng7xuoEwXT9TvCutCjBefDQIFdYSzDcOPn0M6Wx4FvFTTK9tDAZ596h2oC94H6ivlSqdM7wH1b+0e\n0vRWalpBrar6VRS0+tPZ9ngcZVbTR5ktj4MipL3d0cgUtEFax/vO7wGdAQ43DlyH81nZ2CAB4App\nVd4eyKKsh5Idqpq551eb0tZsEUZPjaotWSNArf2zOZXNlXBLtsiLO8808AiMTBG41L6+ECYvO7yB\n/DtaIgrsGgIF14AdGs/YNumbcuT73I42zxznZRfu8bQI766uD7NBul1jEF+AW3+Pxpr9EtW1bYcD\n+k+rrG/iOe3UdXxlTwR0rx92hyW9O0Xt1fQe1tGfViWt0WUPan2zxSrro25AbfslP9LX6I/6ah61\nKupV8/FnoNb8aQW1b4n4OFRNl/4CUvWmW4kBRAN1grQbniCtEA7juyBjgjXWsH6PZx0sxAzrXBf+\nHLgVGpSWZQ+cHowkgQoXHejbzlxMZVsAEsPLBtBT2tLuthKv9hUc+sKIkF4AO5QTYBed9kFgx83a\nt3SEy8NeAfuV+jsoZ2APtHlwD6+837gUqgUsL9Xt2TQe3gXFoN24SHBxjPf1xoAj0JYKOxxbG/wJ\nlTVhtDDU4uHcP9vol3ZpfURI63BW08XU8HVQZ+sjBxMLgOpAvTsVy+kVMMp7XhzgQa2PWs+yPnIg\ncQVq39BFlbSCOmd7dEhH26MdGzWtloezPigr6wzpbRZIBPQ0jDE+1V8pCzDrPVctjzDfBG10xezV\ntgeIKlpms4C4jP3gMvZzUtkYGSOAAl5WmwKOoz7GhUIp6G9UScCuArGcJdKvOxowTh62B3ZFxdWX\n8D7PEIEB28wNAto7gG3XYVDcCmeF7GBNE4AX7sMHFYG35l/P0AYNtd2PfUFOpez1e/n8U8K6EPBS\nUoCR9CSJgNZpzyC9U9MD1sOfNjBTBPMzUGuDlyrXnIL6iv2xO1nf+4aXVV8fZ1kfK1B7WCuoNePj\ntdUQSOyfJfjTx1Gi7XE8UdMC5jNIB786WyD+qXwBcF9vw1reAet1cNF9OoAbvPN0s0JEbU8wk2Ng\nAUeI0nbWyEZlo3KwRWxbk4+tJSttIKntM4WdskSsIyVVvgtLxODIjEp0Gdi+FMwZIrpu88sB3N33\nKrirNKQJwUc77o8wXuw79LobrMkq26vrDu8IbQAR3ECoBxDtkA2wf0obhNC9Zi0rVQ3AAK11Z5A+\nU9PqT+v0OJ696Y2iJg6gfjkB9ZWikAa+DNTj7S1zg5eVR50buyioX9sNr0d/8cDbUS2QmP1pBXVQ\n0wZqRDV9JEg7EK/86pEFMkP5irreqmx/7rm65TVD48MDe1LYNOozuO1JnjDArL61B6L4+NDenFwQ\nZFLZtc9GNdkiVWbjuLPaTD2XCRRnHrZkiczH6BzYZ29NB/bCJTeYOdI+FQDmX9tmctgeA/VCYSuw\nO6hvAB7mY3tbJKtsD231tRW6B8oW3Lo+PWYrW+pwv8dPqawB4GbBRJ9y41X1CDbqo9kZpM/UdLY9\nqs17Dmrrc4Tkbcxk11IoNf0Iq6CiKgsPaaCDOjch372Kq/dPHEFtgcSF9bEDdc6hVlC/OX9aQf0w\nb3rYHs2r6aCok+XRVDXTEtI5Vc9gDZm+APRTRX2iqild/HYt5xvsMwvEQxpjmIqbV7/KqVcS26NH\nCqWO5ftZ6tXPFnCz2w97A0ltaJLip/VcNSMBFnjs51dKKSsLayQp7JKO09RwBoA2Be/5zLrP+Ski\netj9q2i6BnZlZ4cosJurMz/aFPZh21HBAdgK6IoiOdoPvOKGOx6i7KPK9tDWYwoMT9ur7b6PJcB5\nUteu3Nypd1FY/4A866WyHlAGYFDWujNIK6C9L/0RUI/1sHVzmjM/sv3R9+G5qt6p6T5tgLqJN717\nC7nv5jQHEldvHfcdMr1Jgv8O1G/On1ZQayCxBxEF1Ecxi8NALdDOlocp6hRUzJAOQG44VdVBPRus\nedQhqmgrvs5U9KhcBxnHY/WkpH09jzrPGfte0m2nEYxkiI8tX6513hqpMq1q3nWRNy2Jtlz42LY/\nChBi4KypuQd2Djg6S6R4cMv+XH1FmILwamaULzlD5NBNlDp7RRjGtRWBTeE3eKH+Tp0XwIB9QF4H\nht6lsSl9kubqwqdGvefCgsM8bYU24L3tDmhlWFuekAPmP20LRs0GAWJGSFbRWuchDWCrptX28P50\nFWhnUGvWR1xW1bt+97A/+vcOUH+0PAP1G/gSqDOkfV8fz4KJO1BrQ5cAavWnDxq50872oMPDeqOm\npW4GtwPws/FJVbMbRhzWcu38lwW92vZ1nNQ1RXWtyppjnYlABaDCRjhLAmQDtn5xVtkAuHI/5rK8\nHEYAxW5Su76SiXqfzb0MYPusK1kVSust/fRt4urtVr6JavUqd+yTB7a+BeYM2LLl258ie9c6rKU6\nO+SAqGfZljKBu7kf1m9zBLa/ybwB46mAC1Aek8qWlwkC3FCpK+0O6SOAG4DBW4tX2dbFxvZoxPLd\nPWtvbwBzCp+HdlXFfaKmPajVj/agzh61drM44N4sPa9PZ7NldvaH96pXxZ+UviOmPm14citQH5jf\nQq4vt80vDghpeW549E29B7X3qLeg1rS8owx/+lALg4BDQHxgq6Y9vK0+K+mF9RF97KGclwrbfb4b\n2O5K8fEpU8rwtoeD9wLceg0HNa3QdsBmAfaAs1vOqWxGv8lxlRWOw9DvAQUACvQlB+d5eADQ7cHf\n26KXCv80UABBmcF7BBr7BlRweGt6Q08vfSGZYQHsvuoZ2vkaATqUs3dd3UE9uNuTqri7lzzAfVgA\noaGpPWEQjsA+/IElXWI8DbzpBnBBoSP42UFpY7SELNJbooo9y0Kh2Y76KT1rAnBf5Fl7QAPYKmkL\nQCIGERXyHtQr60NBrXZJgLR997A/bPtO0vTOij8Js5oGMHnU2oOeB7XmUueX23q7Y5f5cVVRjxxq\nCqAOaXnZn9Yg4nGippsfl3Mg2SA764MaR1WNGehAmubGQ91J8fbHlBXiVLZ+mk+9A7daCd4S8NDm\nIdr6fpyobPDYQIG8bR+TeNgAoeDxiJki7y0Vrb/LMAM7WyNyHAoaXiR9rm9r737hFNjAEtq+ZCXd\nt40GjK1ubAsYYo+MG4pmiIDLADc9AL4ZsCsGvHWf+nSJDBOg/ZGoLdK4zNBGh3IxEabqWoKYAu9d\n+TmVNbHBGkjATiraD3tIr2wPD+oXZ40YvB2oO5zX9kfOp86q2nvVq6J3ZD+uZaWmrZvTjfWhitqy\nPTDemfgb30Mvel8K6qio12l5S9vjmC0OEpAHCJ9AOqvobINkOC+tDxt3ALjArghomuqZMAccVXVv\nwE3CWJes4DuYCxkfHYbiISSV3X3rvjIuAFVlOHUfG0UsAVGPQlazv5nCFZ6bQvvyu86YFHbFrYMv\nlSoWww7YFYg3LF0pF5feh+31clYKMKHP2yEHKKQcjkyRBGwJOr5Ct7NYnfrYWWXvoH244Uowxd33\naz7moan/RVp//3cwOnvD6pKK1uE+fwTrqLsIave58qkBINsfX1JWJ1xW02egPpiC9fGKuny57avl\nU9/g35no86jHK7jq+0GtgcSjE4mOje0R1LNX24iWhrc7EqSzis5qG5iVd6/jS/bH6mddMUsDPdG3\nhvVhrZbIKtgYwM3oFStLRLffq1Xbh4XKRrJFgJGTDVkf3OM+emqfLX2MnWfuSvCVnlz2RTxqVdVO\nXZuX7VTsCzc0IrzKHemux2+Va+5Utod2LtkC0VJBOMDBu44BSArg7t+oJxS60tYdSpaIAdv785PK\nbnLDOZbQrkAAd9/PXhcP8bjd/KQ2SFTWQFTUYXyC81DTvW62Plag1vG8fLY/tGRVfZbpoXddPbH8\nvP5kew+ol83InfWRX2771sZbXxTSD2ngok3IX9s1j5q1sYs2asmgNsXcgU08wBxT9hDUdB7XYQX1\nrK53nws4e9Ut43a+nVwD/lfNmSDk6wgx0LgBNxdR4AbmGdrS10/YzylzxHnZxjge5DFg61Q7JBHY\nRAVEcx4I0ejcyMvT3ln/eAWW8qxwv2Zq8q5X/YjcSd5UzrwGth34eN0A+2vnvSVmh5Dtm/evR0ri\n8LKrnpQXgF3pkP2U4KmDdt9HPVYD0h7eufyksEZQ1T4i7SENDMvDD/uUvgxqn/URlDQ8/FtYr60b\nI6d6Va761fkk85AG8DFQ8w0Hl5QB4tLz5FVc/WUA420vHtRvqZ8PBfWqsYv505aOF0FtFogHtbc9\nJu/aKWUPa293JNsjqO4nwcUxPI791dQ9PxqBTROwdZmVVw2CWR8WZNQLXVsrFrFHvGrOKpvHH7EE\npxly1ZOpRws8Osm6AvbuzC3EoKMCFfaW79d80ERdd1jztLorLy/wwLb2N7bJ0QZZAfqqLbIrylLl\nuQAAIABJREFUMTtE67w3BRyonRFBOeMc2PDzDkArtD2kEVgD2dePRMB+hA2yADRwDuk+PNsefR2a\nh60/fgS1DvuyU9UfKWcnVIZ0n/99oPbZH7kZeX4VV/8cbx/3b3XxLROX1kdbgNp51EsoHwvbo8W/\noKjbBtLO8sgqeqmyAYB5H2RErH9ayF2DKn2TwiYig/FQ1ezUNc2BxeLUsylMVc5uXq+yZbwznEZa\nnk4zXZ0UtlozF4BdjvU7S6zFriprrZM8bg2gdaV9i4AemybDM7AxBR4BXADy8eSHLOG7h5I/3F3Z\nq+thhyApalwDNtK8wKSqtQRw27ZEd2H/7B7L93+tV1LQgHu0cpDW8TPbo8/bgv3hS7Y/sqq+Wvwp\n35hRiOwEWuVde0jruEL6gAJ7BvUBWr6J3OdSe1CvWidqINF3c/p21HNQ+2CiB7WHsQQSSXZAvWub\nntX0BOu1ko7APgF0gvM2sJiu62c/d7iOArT70GixyM6rHjOOPq3ZWiWOnvhgF/rIBukS3JS4gjqp\n7J6yhzGTV+CQLlQ1L0wFpC7zBNirRhg9pc/hoHQ4/863ybsuaKPOAbqC8erG+3XQHEwHsEOXqNDl\n99fSbrxiDjaOaRy2L58K2Q65AuxKEhyUugKMlEAAw48/7Gbh+x1ZlZ/SBgHNQAbWkO7ja9tDSwb1\nyv5YlSkDZJGutyoH+gmmwNa6XDykAVwG9Rv3Ny+HN7xscql/C0HF0Tox90etnTKtQV3mYKIHdbI5\nhopWxT1UMR0Y0DUfW+v4JLgYIR0tEd4EGSOYDcjup8hNzHfFrjEfm6B+AZlYIiR4z+C2DBC1SUoE\ncgwyjj6sA6gx9r3VcUwBVeNun9yKmbvOjvevAWyiAexCjMey5fnJ8fKg5oacg637VqjhhWHAvlPD\nKxdrONMIAmn1qDn8ZjuJ2S7+ls/KSl3jncA+uEgmzEJlA1EdeECfqYaL0voprInozwH4WwD+DPqm\n/zEz/6dE9KcB/G0Afx7APwHwV5n5n5+uCzOQsaib8q5PrI8dqHeqeudL70pPvh9H0wN7u4yDtK5D\nQf0mj2crUM+tE6sEFUdz8l3mh0/Re6RuTt+0U6YE6nakYOIO1AHC5NT1/Bfro5rOtofPAtlBehlc\nlHmBjaLO6nrxU+VsEFIVNirGTB7eHtBw4C5SoVYJ6/wULI4YZGRwoXitliGigxhTaMvOdaY4YJNu\nP7lDIAE0CzoSHpL7a+84lbejvLYKe30eF5TW36air8YqhUOGyJTSR6OV4w7Ywf7wtgjw9JoC5uuq\n133bUhbABmB1/V2Lvl48eJDxzAKYm/I1bZAHgP+Ymf8nIvoXAfyPRPTfAfgPAPx9Zv6bRPRHAP4I\nwN84X9Uc3NPyDNS2TAL11yz5gbF7WvM0BTbCvGNbVpA+WD6fgDpnfrz6Pj9c5sd4R2INKXr+VVwa\nUGzWe5709cH99Vvw1gfTlNHhrY93g/pwWR5OTQe1rSBW1X0C6QnQUUYuA42hPhXP4lE5KpgcwClU\n9qChrkQtEXZqW8HNMq9CdLgW7qt0X8Z3k5wr64uYbKcysHsd23cxATjIQnl9FxkHEehw6X3EltLX\nm6JX8a5LyBDRjJCcIdLX3vCmOBFgH3KXOgP2AaeypVy9tq6UitF3x/CnvU10bod437tnmrjlVGED\nUWUDl1X1mT2Sy1NYM/OfAPgTGf7/2XubUH+eL7/rfarvvb+fDyNjgsRxBjTgQkQwgRCUbMKIIBrU\nRXChhFkMzFaRoMSVQgSzMbpSBmYxC2GMDzDizoWzcBOZkKjobDQgZFCzSfABMt97u46LqnPqnFOn\nqvtzv/f78P+Tgs+9/fzpT3fXq9/9Pqeq/x8i+h0APw/gXwDwx/tivw7gt3ABa6uspXh4r0FtfWpb\nHrE/3ltEXcuWCxb2h1kewBbUledm5BHUsXMmCSi+1dIUdh0WiAQUI6h960Ry1oe+MCCm52086gnU\nJxJgcxpgFMtj9qwTSIsqh0zvUEvg7C6LOkau4sczsLmBVmYCSlWxOEC9Pguk9UW5Q1lTV8xqj4hX\nbWyP0YJR6jVDgo9chvNgmR72fEym/kXil1t40wA39+wfaqe6q+uCV/QMkTrnYEvAcU7nY9dL3ySc\n9B5yOmAfcgDscVeVnf5YLbFmn4vpsUhWyBluAmKLxOm22ADkeHGBt0RsM/MxnqvqdB9uis6HPGsi\n+ocA/GEAfxHAH+ggB4D/E80muVVikC+DNIAU1LuAom5vYYHsimv1hKGkRV1bO2S1pQjpNq0NS3/U\nJ5qyjR0zCagzGyR2zmR96hhQjJkfJ5Om6LH0nhdewRXzqDOPWtR15kv7bBBGEahnalpV9FDXA95e\nRaeAtnDuYI7BxjbtXgUYopnGNtXi6BsVpQwAPStkAvcRoM3cA3CkvoZaIxjT2O5/B7bwTBgPYFyM\nbs95GndZIl1ZM0aiHNqkqUiGiCjrZpEc3suWdL7e6ZN93DyyWpEAW+4jlX1HaeJlyzFelZWiPu+d\n7lvlMPtpMzlESZ9Ml8AGMEG7bWPe0Y+0QdoGif5uAP8FgH+Nmf9vso+MzEyLfv6I6FcA/AoA/L3/\nwA+pkgYeA7Utoqo/p5xMTrWf3C6iDNjLbfT/EdKipgXUO+sjdnd6p3OmLKB4mfmhvectQO2A7EFd\nrLp2qtqo6ai+OSrtaIGsIe0AbeGsAO8DV6COl0h4+mwNX8hOGNBgHvAW3xpw4B6ZIB3IGmDkDmpS\nNZ2qbPkNZQDXFZ73uUF/A2yzEQfss22MiJtFRsBr97Bfyaf1FaoptGOGyLJ0YJ8dgA1o5mRzr19R\naW+KvWfVxfAjxaXvIVof3TYhyaFeA7v93nYwVtD+nHIL1kT0jAbq/4SZ/8s++f8iop9j5v+DiH4O\nwF/P1mXmXwXwqwDwD/5jP8PxDpx16GRBHee14flkfpR/rYBOgH21HjDu8BHUqx70ZlDbl98On1o+\nb3w4n1oCipJT3ewPMh61aZ1oQS1dnYrFYRV14lFvQX3OtkdRFc5BYSfqOlXWXkEvM0HYHHApd3zr\nGi2QIOhKALjAOwN3UNvN/+eWRqfr9xtQt1kmlW32VbNFrMm9LGKByDFIgC1+CrWbR6WWIXIG3/q1\nH6gCxlsPPk62SAw4AlO3qtkuStDxxbQEbM3Ge2M0Hj/3TrHfZFV1tBl2FgewT6srxteOyx7E2h/1\nQVUVeFTZMm3sz/saxAD3skEIwK8B+B1m/vfNrP8KwC8B+Pf6/9989MvvgNoqXv92mfnCeI9fbd8E\nIVZIBmz/PYl/lkBatm/VdPv/1OwQFJzcPlUaxNgXCQioQwtFtUC6sj4NqN/OQwOKTVU328VlfihU\nST1q2HkC8OBRX4HafwZ4Bc4pmM9ZSVPlAWj45YE+LCeAjdqGn+ZKFKBxHTNdN0E9IU4UMZllpG+K\nHbRBI/9arZS+ITJfJD55V9nDmlnwS8GM8T2O+ID41HSGlD71sNvbuIlIA46FuP8MY4nUewFHoGeI\nkPQX3/rgKN2zlZfaOmCbwwEw9D2+4UdrG4dwGCykPbz9BnYvq10VC+D221jBL0Bu+zaasM+NX3j6\n/swu+sg86z8G4E8B+J+I6K/0af8WGqT/AhH9MoD/HcC/dOsb4SENLPyuG/MzOK8skfbmhmrG20Ur\nilyskAzYY9t93T6eXSzR9rCBRAvqqKY/ce+/IwYUgwVisz5sC0V50a1tSt5aJpbxGi59qa00IxdA\nm74+Oqhh4WwBvbA+Iqh9cNGq62B5uE6cht0hp2rkYHtAT9kfVzaILRnLQ8dAZJSeTu5gZmqNUqza\nTqEtQUOBNBlCWcUeZKWMSfebE2oy9pD5VFHbIaWP0J6sqKJqCh804HhWbkHHWvBGrUm6vMBaU/lM\nwNFnhzBecbgMEd0v7pnZG2CfECvSQHvxe5eQDiIJYd5HF1HXEdgW5gCcyv6ccicb5L/D+gHln3r0\nC3eg3qnqsfzjdkcMHs7BxKauM2DrfmMOYlhAA3AXyqOgTjM/XCvF+f2JMZ962B80mpCL/ZEFFC1g\nReWeZGyQ8ImgzpYJALe2h6xj4TtB2tgjgIF0BPRkg9ywPnbFrtCV7mhqLsvAJy4onJFCm0GTygYk\n7zoo7AWwiYKHbdU/9WOsNwg2m2yUHC0qedghlbod0q+J7l+/nWXKv37josNZ/jUKRsARmN7jaHZF\nWywKsA9mVJg3NLlDYLaTbDJC2s+jdHoG7MwCsSoa8Cr7qjWiBXb7ztEYyX/v4/D+6n2D2JKBelVW\nBn1c7+Tipp0owTMaoLbqOgP22E+eNLydv4J0Gy4O0BVlCepVC8XoU9/Kp44NX8zLA6zNEfv3SD9x\nXrQ+tCVjVNrWCmE3zQUaNUskqGhrcUw2SLA/XOu+x2/oNmCucJCNG3iTUckK7hW024b7q7/Ye9lC\nfvfcb2DOI14yedh9XwowWjoCkP6W9dcTgU6bITLWFTOPUVC7f03ESf713I+I9NAnlaKYzJASlbVu\nzB5X9D6mT/uzAaB31M9GaQMprbGug3ae1EUpj+Q174qo56iuV8vF8p5+ib4ZrO9aG1cWSYTzrlgr\nxEJ7KGl57jXfH8A9vnd/cQioP6E1IY+gPkFBUc8BxcyndsFE41NP+dSx4YtA+hw+tbwbcUDXqGo7\n3dkgC1BXq6BnOKs3bdX06ZX0pKKNxTEBWuflz8R3U/ekOKHTVaIC3MK7kFom0iXqEtrid9txUdnR\nFhE9bAKPkvUX9baCGgPYopopNDCR/eE+TwKO0lS+dnATcQM2kOZfFzrmHvoKRg42Ri99yxKADW63\ni5OqU9nu5wIB3L5Yfzqri7oc8uFs3BYL4ai4V2X2u3NgP1q+Oqwz+Gawfc+LAOSdDjp+Aee2DKGQ\nDR4MaIvSzr9rD+k2XCZQVwylXVEcqCXIKAFFm089fOpD7Y83AXYlzae2PrX2pGd9alXXA+AuI8Ra\nIhcWxzR9B2pV2ddq2ippgbS3QMT6aP9oparvAFv6/LDG9Nk9bA0uNrBqymCHtlrSK2jrRICPdoxb\nFlhHsHmJwBR4BOvNwi3Vv4/h/6N/nX6vZojIcPSv2TWYqZVAVFDLSOcjYpxih9Q5/9o1mEEO6xFs\nZAChpz7qJ9F1hNRtyVj/N6fS+tMR1CtVvYOnW+cByK7UNfAxwP7q/Vl/reLhPKwQAbi7Q+rTbg7t\n1fbb9sZFcQfU3gJ5wol1UHFlf9jsj9NaH11Z+5cIRPsDHsod1D5LxPyvfnz68BrUoxGNBzWdA8Qt\n8wP3IR0BfaGwr8tQiXY7Q1UPC0TATWRAXZo33R3qZkJUAok1QGhgP4fl4YDtUdz6CqnGj7Z7SX09\nF5zEgHSdp43OpvoNh8S/JrMMg6nZIbX27JAO7zdibY4u6XyFGM8gvBr/+igVr9zUt20o8zohxgO7\nor0j8YUx/eAU2ovioexBvVLVDt437JFHgpRZdsjnlm/qWX9kcXA21kimri2w5dGmTe+KemF9SImP\nVwPcA9SvPbtjB2qB8si9Hp/am45n9sdbaE5eJ1UNb3+wKGdSOE82h/kg/T93yrRT1CmoF7ZHWyaB\ntAWxqHBgBnSmsO38qyLpEBBFDcyqutG59VvNDtrDHplVNh+kgOWOdSBkiyyAHU2QAeKgwi2Yq4jn\npqTJtlunYIcYmHMBuLbuCGxz9LPQyA4xdsgr9eBiSOd7RfOyj8IJqFspKOY+1ZR1CziOdxyOcxh+\n+6Zktkesm21amZYDvIr+aNACn6+uv3tYiyqOqXfAsD0cnI0VkqnrHbABOGiv9ym/EERNtxSiNahb\nXrUBdJJPPafpHa6VYm5/mCyQaH+YND1nf9ge9iyIQ+aHaxCTWiIe1D5Fj4fXrfnVvFfTRkmTtT52\n6joO64WygLbmNw8L5MoOod5b05i+hjaXdhwnW0QG0bNFEoWdDls4W89bITzWUP9aU5hI15FWlQJx\nJkI95Te3Rwht3VhDdkjwrVM7hJrKHoeZcaDgkzsNbwBJ3al40d/V4F3hkwpO9hlcWbmloFHmaZzD\nO5v/Lct3C+vYBFynY33SMnUt70prYF4DG7gXQIgXgb1bW1C/witrC+o0oFitsh6dMtkX3vpWitb6\nsL3pSf/U9+wPVdMrpd0hK9so501Q6/ew86ed7aE51heQNgp6ra4vgJ2eTLOcDcyVAVVrh0yAVqAj\nhXbbgPGy+7j931T0TWDHdfskq+jH70k+rUPpls53Dqir6q6UZoeMxjKzHRLT+dD7D5FSDLhf6M0B\nu6IC9AZ0kB8gtUWOBbSvSgbpqKjj9LYvM5Af8au/RvluYX1VBMYxqAgsFHcCbAC43UG4bDu5AOw0\nyfRwypo7YHkRUORDrRPxqU8mZ3+w+NPqU+dBxaam4ewPbOwP51NPsDaNWVQlj8/K+mgAXoM62h6i\nsi8hvQL0Tllfgdv0/6HjpxnGAC916nId0G6tHBvoNIsEBK6jWTqVboGcxhYp43u4dEV/LoBNff9a\n6oZCWnOwLZDR/6v1YQOLbZwq53ZIJe2dr1KzRNLGMiE7xLZuRIXmXwNzKt8LvbVgo56Wp6asgy2C\nbsUItNF/0h1weyW9B3WmqjMlbZ+i7fbc935hG+WrwnpVbVY2hs4PVkimrq2SbsHB2Q6xwAbgoC3z\nr0p28i2cM1A7Za3ZIN2b7sNif0jrx5j98datk8qk4NaAYhZU1OwPAXGwPwTgCtYVjP2w7/60Azfm\nTguQr0B91r2ajkrapO+lgUUzTHV1tcXSuzO124i9vol/vIH2sEeg4wyMPuuNLZICGxKUNMCWQa0O\nXRVXmAYxHIKF0PmNbhhZLDy2x/L0INtXWLfGMtSFQCnA2YON1g6pXFG7oHDZIcRNSdeWf/1an3CU\nlpb3ao+peZABAZ8YDtgnH9DXclke3ISgt0T2oM4gK9OuoLvKFsv36ScoGwSAA+XlsgsrZMz36joC\n2y5jgQ1gCe27++8skABq7fuDbbpecZkfqrLrof61QPo6+6N3e9lVtcupNqp6DWUDbgviAOWxDQzo\nToq6b8P41ALVFahd3x/MwMmpLz0r7ERdi13xHhukFzojoHnAjMx/WS6DtqrqobKpYgAbXdFmwO4Q\nbS2wBdQB3LCAhgGsBzRRtpxR1318Utf9+7U3wKCuJfeaAByl4q2Wpq65t3I00EaBQtv6189o1odC\nawHsyl1okVghNl71iCUy4HgF6p1X/RGq+icyz1rK1LLwnep6BWy7nfha+BW0r0p8vBJI2/EM1J9i\nU/Ia/etmf2TZH6KmXU61UdUwinpS1aqkKEA3qGkHdw9ia39M64uSjnnU5ztBfQXpFaDfa4O4Vovs\npg2AB3ADs9JGsEGYwS1VYjytHBtgSym9VaTxsLXPEjYZIpJ/be0QyQNX+6P/pD5Omr43zrWDuHxs\nU/QuBkigTVjmXlcuqFzxygWFG8yjfx1LA3KZgN3qbxm2CDAU9X0h207VxuaIoM6U9q7cVdUfAWrg\nG3vWEdjpMom6jpkhmX897pQdykF5A2vrY9X+PzupzgLh0eCl2Rke1O2CLmp9jJaK1qMe9kftPvcU\nVDRedWULauRWR7WPwmY5WCU9wD2r7ABw61MrXB8AtcDXgvkcoHOWR4D0BOjMDmkXSXY5heJBqdsJ\nNojjhcwPStvbIADVJnUlKYJOAMTgo+yBDdvv9fhuZ41UY4eg7y6Z/RZ1TbpJ90lzr3t2CFeZhxZs\nrM3SkdzrozThsAo2oo4GbYW55WKXtmx7M4tvvt6CjOZUWCukt/5VlQ0Me+Rm8U3SjdK+APWVqvYt\nJ9eq+qNADXwHAUYL7GUKnvbXse7nw22zH7DMw47zbBmtGvNHowhpmbYDdVPGowvUAe7eOx63LlSj\n/VElj1ryqndBRYWwUdW2peIEXgPnoLK1K1KrptkoRF5kfkgwkh8Eda2JVx1UdIR0pqQ/wwbpJ9LQ\njUdGiLFBRG07aAPqDQOJypZskQMgEHDWFNjtmI887GF/yP/+z9gedtqAcBt3QUdV24vcazteMVo2\nFtaWjaWwxkq2wcZjAPqVj+Fh63F6QwGhoDTLJEC61VdpCygXaRnQBuwdDMDcuduq3rZduKG0P8j+\n+EhQA98BrIH3AfvKDrHbssAG4KANjObuVzmW0fKwJ1dbLwZQj572mu0hHwk2vmlWyLA8mnftXyjA\n5qOBRcCr6gjZoKqnJuXRBmFM8wewh5J2wSoFNLRl4rBUPgPUBsqUwLudkERV70BdeyUvNyqR8CBZ\nND6Z6/Id4i24WLyP3V83RN2fYHRvGKOhEGpvAUloN13JACHuN+OhqMc0qOc8lm/jHM6TnKuxTr8R\nVa+uQQAKt+uKCMw8tWw8iDXY/VaL2iFFQR3sEKrtibI++WO6UNXasbeM25NhTnEmuoAZnBmk7XJX\noPbbpnmbN0H9OV2lfnVY21aCttwBdlx2B2xgY32YAxnBPe3v4k5s1XT7XSaYyPJC3OJAPSvrYX9U\n9aqbDcICbs3+GKqau5JWVW29alXY6JUR43F6UXlHeh675TX9Li7LHJYXyIritsCF96gfAPWkpleQ\ntoCu/lrRYpc5T0Sro10MyXVwAW136agtAlCta2D3/1zR0voqMF5yMFQ2E83nq7eG9JCGU9c22Jiq\n68JuHVXX5nu4Q78W6jAv2lSduV2Lqq65dc0g12sDNeOtK2uxQ1CAZzq7FWgyRPrvcB52BLYccHMy\nrupuO30Rol5N22UiqP16s/2h826A+qv0Z/2linSgZMsVsK1/vQM2gCnoKNMstGXbu7K6Czs1jaGo\nrWqOPrW/oEf2xwD2WCY2gBmKejSA0UplK3T0mxXUQVXLdGuHTErcWBsG2lE9O5/agpbh0/MeBXUM\nKq4gbQGdKGtOpqVVx0JcwD1lgphpEH7I28y9LULMc6aI7IpwqR93Fj+a+xOKvKLIZoVInyQ7cFcT\nbDTLeuBbOBt1LdtZqOsWWhjetVzLJxff0RMz3pi7sj5wlIqTGsALhn9d+l2i6sF4QmvZmAAbWEL7\nquzszDgtb1Sz9qmvQP0RkJbyTW2QTGV/BLDtupn1sXp0ykq8A9uTegfUPgtk7vtDgC1K26bqRVWt\n7GIayjrLAKmyDByEU6jbir9Y3tsmw6t2WSHWp5b1OMn6yEB9VgdgSqyQNG1PIG1gPIF5EWjUXp+L\nvw50LAM3MCtt8bOjyq7tz2SJnOg98GG40kWOV1uPS7M8JDvDpvMxmRaPhGGNWKXcIb5S19pq0oEZ\nPXUPTl1T6d0vBHVtvWt5E4oVGgrqY+RdIx6fIgfkCc8K6RnYPucaCbSvy9YW2Vgf3wuoge/Es44q\n+3OBDSBV2cC9Rye3bxsvK4JacqlFTYu3rR8MX1o86gHs4pR37Y+aK1XNdaGqE196UtXGq1YAB0jb\nnGoNKm7sj8mnZmOH2PS8FahXtkeE9A7QEcy7vpX1BPtrwV4aWtWs1y2quitqC/sU2IklAsnD5tbS\nER22fDLogFG8mNQxCVwF7DSmWQtE4Z+oa13equvKfV9Hn9cN4NQCkjyr69hQxqprBXX3sM+uuCt6\n/nYXO80GmYHd8rFbsPGQC1UOboT2A2XlM38kqFeQXvnYjHtQ/8otGMntsM3siCr7c4Ddpi164QsH\nLGuqHktU0/KdEdRWTZ89mBLtjyyo2LJCytQApvnWyFV1/5/mVbOH+NSCzSji6GmngcXN+gP2Vg3D\n2Ro2PW9lfaRBxAtQT5DO4JxYIL6EdQy8IxtQ6wA20KHNTjEugW0sER0mHgFH41+DxM7oXx6CjSzW\nBY9gI/N4wwzJ8e+ZK151C6SDui4AavOz7c0Xpd1QJO/aZobYuEoMjheXFdKUdgVNdkg7RO1CKiLv\nVVUjjMv5jyemleydrLv6DOS2R5t+D9R31PRPbZ61VdnvBTaApcoG/MsOVp716q5rT6IFtaToSZep\n4lPb7A+bU60pfRgXebzwJa/aZYJwaK3oPqa1IgSkXQUKXxL40vTfZCh0+K5UtfrUK/ujyjbWIN6C\n2loedyC9gnNU3bHHPaDBV7ZFRddxbMgySsIjfgps6n2FKKTNVxv/Ws5Da0ADtSWW6toAeuVPi+qO\n5ztbHhXLVo2irqu0bmS4YOMT9ZaNpavsIoHHJlDssbItG0s/YFWsjh2w5QDbEwP/9JyVXX8fwOxP\nt3U+D9Q/dal7WfPzR4ENADEPewVt2c69ffOPRjtQS+aHWiAB0lZ5WEhbVR0zQFrGm0nbkxaLXWmL\nqk4VtFXK8qlj+pjHvnKbSm7XTVW1+y4eKjqm6LUTZqwQA2ILy2y+UdNLSN/0qS+XcZdFgHZpoJpU\ntuRh74AtywAB0sYOAQaoK1o/IQLLK3UtwDbqGfKVEdDy04o5f31fmqqW7cJ9uINZP+ig7t61eNUu\nMwQ0bBBuudVih7zWA8/lxEjne9OA4xbY/TjtxNiuxFjV6u0xnwPqO5B+z7sgv0Hq3oCoLVFlPwLs\nNn3kYQPYQjuWVdeoq2hwBuroU2f2h+ZhG6/aqWqQKpXqKgXGG2C0ApFWNNcIplfeLWStCk6gHFX1\nWM530iQqGlaJmzQ9a3msQD0FE++AOlPSrlHMDa86FqOkARjw9m11e4QLemtFHsC2i2bAjjDP7BBJ\n59Pj2AKI9ri6jJEAUn3BgTvf3G/udppsD3B9W1ug233QjBDDcbZ9hpyTyFip62oCkdYOAdXgX6+B\nrXW8n6qsXtvX963KI5Bu0/Pl76rpj3hR7zdM3ZuhPYN2+Niu06Vl4NCrbL+tcbDmG8XetwKGmm7D\nM6jFp7bNyWNQ8UpVS2tFzQAx/VULNxXUopATBW1tkVR1C3wlOOg8ap6259P2GlAHtOHgnNofdvgR\nUFvbY6WmV371rebmphSzvgV3Au3JFrFpfgHYQ+Ji9q8TO0Q7WbJeM9BS+fTY2ibpTZlbO8RCPAJ6\nrGfmVWgXqejwbmqfzTXUgotcC7ic4z4ukJZh9B77bqhrCTaCy+RfbxW2nCNAoa2ncSNcUtCuAAAg\nAElEQVTK8i4kvhyoPwLQtnxzG2QF7Y9Q2bI8EG2W/UHMTqBP2/Ogtj61zf54j6pmGOsDg11Th03c\nA4sGyBa8UUnHaUtLgwd8o6p260tR2MLBNs3+MNke7wb1DtIB0Fl+dVZIAoVSigFCt0AaCbunXYu3\nRVaWiAGv+bb2+2EhTa2xjEBRskOMZw0JBvbzLapZIUz9JhBu0EMlt6+mCdhjf8Y1FN4mU8ZyDPSu\nU4clclZC6a0ah6pu/2sPLEpDmUxdg9oy8pIQfYHuCtj2vNM4uDZmtSsRqrGhy6OgzhvBfCyogW/Q\nn/WqP4+pc6YPADaACdp3S7zL7kCtdkmwP3aqevQZMkfVefGJalkPqplHSBR2Ni0dH3bGgDjr91hI\na2DRjesBUxJNXZ9agO6sDzwAagPaJaBtw5nQUlHW0bfCKKC9BSIAvgS2/X2A968le0TS/8QOsceu\n983RtgE4ocgzlIEB5mhlqBWSLBMVeP7pkJbrq/9XUHfBIKmmJVPXJNd6TdU1UJwdAgNse6bE/vAN\n3uan6bvlPZAG9qBeQXpnydyTFN9IWceAn5SosjNb5A6wASyhfblvm8DCCtRZ9se0rLmgM1W9DSwy\n9DEzq1hXFgiQLc+IlV2Wi5U5WiBWxVtPta3vVbVMc6rbZn/IfFvEo16BOlHTc2OYjW+dzSvFbWM0\nkqOh7Cxod8AGcv/6EEvEqGuxSaK6nm6o43yy7B91EPdtWiU9Q9sc5uT6Yfc9rGBOrRC5JvWUz4Kj\nhOwmEJbeNbiiEuHAqGsHnf30DnWtdijvLdCrkjUZ/1Kgvtv47k75xql7a2ivVPYO2AAuoX2nxBM3\nXjhA4VHIduDUl+kXqSw/q+riLmKnprG2QKx69uP5h3iGuAN6L0sLBBgBRR0foI/Bw8mrRqKqgdz+\nkPnd/rgF6gzSGYSvrBBV033dDltmHvbIDWC7fbDAPmalPanrQ9SyTGPAWiE9qBitkAy2MajoQAwD\nccBsw7wo2Gx3FgbDCnHppON0jGua7NMjD+UNmtQ1QrDxQGdDZocAsGl8K2jfLVPfIR/kT38kpKV8\nc88ayKH9CLDbuntoA3nSvC15E9QZ1PYtMQLq26r6nRbIgDZMxYsQz+2OlVr2anq2QKZl76rqdjA9\nkDMLJPGpHXzvgjpC+grQq2WJHLTVHlGVvQa28GTqICqodVXXXRmrdy3gJtNQZjp3NpDIsIFGCgDe\nATy7HvZWiF1mKPiWFUKgY3SfWg6fHeKfHkVVe3UNIA02IvGvmy9NAJVtssGuXDWUAe6D+j1qel7n\nHti/MqyHv5u9+DL62ZktkmWKxHmAt0Z0ezc8rezOmoHaAdum60XVHFW1vTh5BBavLJBVhXIKWnZ8\ngrpfPp2WAFxLk1J9vQUIg+1BDrICvQTawAxbAfMjoH7EBonFZnMAKbSHyr4B7JUdYvKt3evCxNKw\n02IJ51M3oyA2WSF9+cwKuQfmLuW5CQK//PCv5RxoTAVDWR99ervGm19dsVDXOHGiq2s6NdjYfmCr\ntUNpe2ADI0Ns1Up5Vx7p3+O9avqjgo0fH7K8WSQvORbJrIjLjvn7g3kGdSyfq2KXs9vZgbqGbVvL\nRFT1mDeDXLYvqU87C4Q5XAibypZZILa4rlAj1CHTcwtkrM8KZWudRGiP/TXzM7Vt7Y/oST8C6lof\nA3W2TmKtpHne0Xu36y8smd2bbtzLgTWPHXBC0bX2xOKG7PcpfUoKZc7yQYe2HR/TXOBbf15m/5lp\nQbDYZe0y57RMtB5H3QRsO4j31Xed9sGgXjHuveWb2yDrRjKzys4Udpu37ghKp908aKvHH6uk3f4H\nVS3r6YVkL0ZV3uKL+nVkWqwEbQbg/GqZNi0ThgPA03FYULOD9qS2OzycBZKp7ghnOy2WJfAsNG+C\n2m1i8X2hUOZbW4XblbIqbAC+RV0/KKKudRnZVwwVLtNNvyLtnY33A40MmSbr9d/BXlVbr1rnJ8ta\nNU1umtmGGZd1xQoZtkcfJ/bgtk+cxj6sxF01N1V9UKunpQcXtc4aO2Q0eCmqsCXwGJ+s75ZHm43f\nBfVHl2+mrGO54/3cVdgy/5FUnqim7V17vrPnqlrW1e0EC0S3gXjh5oB+1K9u0+Eq6NgY3Pq2pOl5\ncbmrRiYrC0S+I4LXetVArqoBN74EdVDGHL3vizItf3UjSG4iN79ofeOS2FlmE4XiUijl6We66W7U\ndrhhTzfjeNOffsYQDfqQhVl8RCEixcdw8noT1bWua1NlE4X93jov5XNBfUdNi3sgn7tX0FeFNSO3\nOaS8B9hRCa+gnZ3EbN7q0ecMwAXmi2rKFHHKfLY/AKjt0aat/WrI/1CZrF89VTqEyogx7Y5f7Tpt\nggQhB2hTC0TKSlnfgWgEd+U9qHW1BNLSNH71cV97AWy7TNbsnevYV7kRWdunF2eFxE6spmNgrJBw\nXqebr7GsphIAHUt6k3fXk3mqC8tZoRGBPRS2fbnGLFxk+QZxL4Km9gwbYMu0Vb3f8eAjQL0qFs7v\nLd9MWa92/FFgt/n7u6Nd7u7JyjI/ZF8yVZ0pChk/3bx82dSv7kXVdSizxwiIRZIpJpdfvdqWwKD6\neVfpq1MWCDDDxw4bmDlVncFWh/egnta726FTaFRzdWOYMlbuquvV8YB9umEEBy/fZ7fd9Y02Vc52\nOELfXkd63dC0vrVAxk9KgJ20JRjTbR0L8E3UdRRMGbCzur+r86v1PgrUnwtoW765DfIlgb2C9vx9\nflkLavt92euBYrBkZYHER7rMr56K86eDsskqYC8TxBfDNlWv71Rfn/0ydv2FX23X7z9q7E9Uklcl\nqFbeQG6av4N0e2PD+juzbW5uEOn3ZOrab3geXnj24+bK5knG0tLbG0sLKzmPE8SxekrLMlNG1knm\nW8smeXF92zTW+PSZLS/q2k0z3ncbz5+Od2UHd7/c46D+SEhL+eawBt4P7Hl+rpZXJ2V3R7WPXHG/\nMj/tKvdSvw+z8gbGBT81MQdSJZxOX6ioWDF3KnlX4ZfFqfCo+h6Hs5uuwzG9L1G5k+Ks/pNNj9+X\nAfju68NWvzVaIcsbXPu3TI+EzA/jYpEgOc83Rf/l01ZU4JuSXd/xqdItj6Qe3vCs53mzJXq33mfb\naMu+D9RfonwXsAbeF1G96kVrXn5Onbuz7ioTBJgtkDF9VhNuHN7nWxVXb7VCGt8wKm1shk0Zqprn\n5dzwTTV8pRgnPzZrBHOhmqVkaXEZqO+UzXK3+hlZ3WguCk37u1HZ+l1m/WyxCOwwz71UIivJdZht\ny1+Tc5BRd3dxfa/S8uw6UmyDsyt13abtBdtOdX/PoAa+I1gD94B974A9/rMyZXz1XdmdP/Ors8dA\nKazTxvwU3pvKeTlNpq9U9aqSRwUXHsn7zo4FLLN2oN6VHfxWwb5puQfhead71eQGsf3+pRWTHIv0\n+8z8xLIAYuplRtV5HTePw1OXmT7dvBkO5poRsii7a76Nz7nUvo3CHnoz5PfAvip3O2T63CLZIvZz\n9+nn68KaP+YgfDSwdxbGdLMwd3q/nLnwFhCPwcW4ro2eu0yQlW/Yi3iMWdbHNsq/KXPDinkl2oFC\nlnlvmtvKArlS1e958UC23qqDqK3y9VbIQ741sLgB3nmqmbftWqra5XZPWrvt6rRr5Rgbx0jJ7A4A\nqW/dpmcqeQ74Z+Vu/b+b7ve5qvojGsh8E2V9leLyyPK7cnUSdifqzgXR9o2cIhjTr/f5biBES6J6\nbleyZJlV68VYrjJB2no3HuOX6+aA3eVKp/NW26mcfu6un5YJwBfrXh2XR47b1aLvPIfvAfYqzsIb\n8ZIJlVVx2SAP2qJ36v+97XyetfFT0dz8a2xvl2P9xfdpkwkSy863bgvMk25B9B0lpu29u9yxP7Kg\n29W2svVXq23m7+b5rxa74l4myWZD+fB7SvKdd89VGjjEZlpW+PN+QswEkWn2v5RVsHFlV7p1l2Ls\n/XX9a3vVUr6pZ73LTbwqjx6czzk5wOIELR7THttuBu6HNhFWXs9a2RWPAPnWso9C8AuVuzAOK338\njjxSVl8fk+9l2o2ybARzt7DJtX5neURBf3Txjdv2tsfXEpHvKd9VgHFXPsbr3ifHj+W+3IVzddFq\n/fvMyhHLzuYYX75YR0rNl3tXSbM8smlfFp5boN+A/YfccL7wTevd5W6ge7eJd17H7xVXjz5h/ySV\nn+y9D+VL3xVvdwb1zgt0VQ++17r87vIlAPytFfG3Ku94knp0O4+W1T3u4RjNT0j5GhYI8FMG6++l\n3H2F2N2SdW/8t8vfLleFH7lw/vY19u6SvVP2S5RLWBPRj0T03xPR/0BE/zMR/Tt9+h8kor9IRP8r\nEf2nRPTyRXc0eVnBe5b5nO+MLzP46EL4cmBWUbPb/t3v/tx9LA9ohLvL0rfRHfSt76Rf8uvfKTrK\nYp/KA9uLXRzf/+6f3iesO1f47wH4RWb+xwH8IQD/DBH9EwD+HIA/z8z/MIC/AeCXH/7yr3hgD1T3\n2S/75e6Udy5YksTpr1R2XZO48pE8JJpBt6rlX7DQ7ju/1v7cAf5n3BQ+wn1gTdx+bD3q611d9+UL\n1rlH6v73XC6rH7fy//bR5/5hAL8I4D/v038dwL/4UTt1B5aPADU7Qdm03c0jm5cp7bhcth6ZC1cu\nYnoHnNNKuKlMDz0WL77jVsXPIPcNFOgWxOuV5kmy7yuV/5FQ39XIeAzD+CNQZsL9p6wvcOoK8dIu\nzMD+kTDfQfujn+A/UpDe0kpEdBDRXwHw1wH8NwD+NwB/k5nf+iJ/DcDPP/TFX9iykLK7k969y+5u\nDPbCituTC2x1oT3yWPjhFYbWFTbGUW9BIIPxHUAXaoC8WnY134IyA22hFNqr6bdLBPcdG8b+hgvw\nXn//Zvl9g1d/Ps01oNfDHVBT22UrMlbDO9CWRLjEIqIoiiOp81I/U0H1hev/nWU+inW3YM3MJzP/\nIQC/AOCPAvhH7n4BEf0KEf02Ef32//c3PqFQ3e589qMn7zgs8x5Qr5a5cwHc+Z5ZYcd9vr5ISf+Y\nCfpp62g/TkkF0+4bYoXbVebPvSlEtfcZQEz94A7JpVe8gKbAeQtpu+579nv1RFHKGtTvucndOJ/L\n9T7iph+uVyJWOGdPjbYU4q1ImerJzboey13L49En7Ef3473Lp9t4ZGFm/psA/lsA/ySAnyUieYfj\nLwD43cU6v8rMf4SZ/8jf9fv2Mcg7oL5THvWm7i5fqOKgigJO7vLXSqLdqMKFHrZB+rELLS7uzKLY\nKCFdZlG8+oqP2PM494iozrNXUwakuwCk4pe9CjLG7b4n2LiCfLRAzO9KbxhEuv+PBh/dMS59W4X2\nSlfPK7n9k2vh0u7Q704m2hu4CIRbMZcZ2IV4WS8O4lTcrAL6B3grqh71pe8A+yOsWRGq7wX3nWyQ\nv4+IfrYP/x0A/mkAv4MG7T/ZF/slAL/5rj14sFyp6o8IIMQLIfteABO0o2oQX26lqv1FfbFT7nH1\nnf5dfBSmXslXSq3DeKyjz74X37N7RKehNrN54UBQCv1EXX8OsOOyVyejfIYCv7qJLY4dE1xtTTu9\n292QF09objv65oGLbaFfu/2jl4VMhxcvMR5TOmwLOIVsZifezcR6b/1/D7Bz2+Ve3XTgvnkJ3Xm7\n+c8B+HUiOtAul7/AzP81Ef0vAH6DiP4sgL8M4NfufWVePsL+WAcN1gfQJuofqNrKqVBF5dK+sw+v\nSiHGGb7iIHaNAwTkhWcf2z5CxmESBRuLq2DkKpi3S1qFs5uI43Z76bxC1635iABmMNFojNGnTcNm\nHUL3yGvZN2wpJW1MQ0TrVoT69vHFdjOgG3DuVPW0nTvQNstM9tADKnwK+t68L/HqWgL21lhY7mpX\nnZp2gmR+sgR8XYhPqF4M1VRADXGVATe/NrIGOrb+3y0lYcMB/iINZS5hzcz/I4A/nEz/q2j+9WeV\n1Z3oI0B9J4Any8jJ250wATeAVkOoovKh61Uc7T8RKh/t4mRq+2pOaCHGiXExn0wd0m0f7LAWUT12\n+qKCZZcJSyXbrUMEbcoWl81UXKG2eGHgDPMLASeHbZrhq0IFKEm/0AL9DN4CwGmdmxUwA/W0zMYz\nFwvEjotffcezlntLX55pDEtZ2lNRCYtizs75pKjt9kIMJFHbsg7F3SfWQ2gXzXzq+MRpl9lZIG1+\n3YL6PfVe9yvU/wjjDMRfC9jftAXjtwZ1XF7Wke1ZO2TlM9m7/86fa498YzuUqA8i1gqgudZGbbeF\n2FecoKKBURF3lTX6mb6SWziESl0WXUZYqOyCjIXGD8wyKqIVYr3fqHBX4HzUmij+O729svGqJZMl\n2d46AJqA+irg2KdzXC7AdhckXl4Lur21reLG5bp0u+avUft0aDOi9OnSZlBRboVIkfoVVTXwflD7\nbSQ3kkXSwZh/L7Z2gD+0zcYdG+TDCuGeCW/Lo6Bepv9svCx/J2VUJr3D2rumKmSjrk+zHriparFE\nDmJUaiesEquotBftCRGp851Y6iP3YVfhwriuKbZILLo8gZAAH16Vc2kTCCPrZKxLZnvcwSQ/DkNl\ny34w++HwI0m+L1PELDe3YHcEhS1w1GUisOO2F0DfgnrlVesNaOHDa6Cw76MZlu24m10ILk6ZPs7j\nRl8fsABnc45ikDFeR/mNfzxhTTESvd9y+mk/yytlW6xfLTA/MCySVRBf1vUBxjWo79Z5u659wrbL\nvVdhy7JtWw+KiFC+q75BPhrU91ss1vSkW4W9CjZmF1a78Ma6qi4E0kZtOBVCbZmoVEYkPuy4WUYe\nXaO6mhR2qPzZ8nMwiszyiwuujOXio/s0XMJ895tydT2Gg80QphEtVK2o56Cil+vdUfE7Vb0Losbh\n28dXlolPRXb9JBPEnOPJJkFy3aSfEVBsy43goq5Gs3oe06q/9mk0ilmBParquR7Waf331nn9zg3w\n36uw7fLZ5y7CvwtYZ+ksj4B6erTanKyjp9/JJ27TXgDxe+wd3abxZfsRrZChJvxFaS/ulVJx9Vcr\nGIeKBFcpU+VktuErtalxSJRX21EzbJaJ4I0ZGpmatMt2T5eSTBBnM2R2iP2+AMYltJNlJkhfgTrs\n11JVu99hfn9mGZmb3eRXBxiPYRqhkORGbctItfTzJ28a2bzxEUVtd306rfqzOb3ObcvFBuChknPx\n460Su86uURqwr+/6nTdEn/1uP+8xYH9O+ao2SCzrxiyPgXo1T6ft7nZmnnSBOiyQYYmA4LJDwEW/\n6ejjZ19XrJB2UZFaIdLcs4G7BxW5WyBMCmzA1FFVNOSh7CQNJvUU6yubyjYVsvMJQM/qcBZI32ab\nnWyjwYCYG3i0pvd9t1khQLdLkg0VapkhqGadxA6xAUZriQAu8Hg713nyzzegtvsa9t2p6viEAePf\nm5vTZIEkha1nbf1qIAW1BXNmg0wglunZfFv6Naiz+zUrwUUZjn71OETeAskyRyywyxbaiaC6AGVW\n3+02hu3Bl5lidr/aumN52c+f+JcP7BLDPwrUu7tpQUXZ3IVlW85Hw7zPsZHMIY96xLpucWrcPCYi\nKGqzXV8RxiMmRcVrKxUwVzzCpJKHYsO+kqP5yDYPW9a1is+lg0UrpP1o+TF5ulpU13G+rj/S45zC\nztLqZPplY5pkOQfOBahtqp6o6lVQ0arqTIpmFkj0q8UCIbsPSM+hPb+TX51cPxPQCUYUMFwHTvYn\nL54CLXyvLBBbL1QpB7Bb+8P61HdBvarrsnz2dD2+f6+w76rsj1LaX1lZr7Mqrh4ndo8qcd7UunCV\nf51Mryi6/smlq+riFLYGGU2wsfKBAnbqWgKOB2ioia6g20ksDtpHqThrV/QCalHbqqwpVD52QEVf\nxy7nVLWpnOQqalf6YZ5T8gU9WIhmhUjcUFL4JM+6q/MJTjbQ2E5Ae/uMDRQCPu9avrcybDqfCyiK\nyrbb1kDjTT2S+dW4Ceq4nZ2qdlZKv/FpQBHILBCmcVMcH/I3U3vTDPCd4A2EbQEZwFO/2ozrLgJD\nUSOIkvBRnxpB1JARPC7D6hrULhf7wbpu149P1sDgzCq1907gUZZbl+xRdS7f1AYB7jaGuQfqO5Be\n9fQlb3eRdQTad4FdJO+6k6ygPUYVag1hCo2skKiqZbwydWATpEGMWCGklYrhgUumAhHWsJ2hnY0T\n0Ct+317h9jNl2X68WPYNcrPoAC3cFi4Mrr2BTKEG5Q4YPU2LBjMkXuwO2IBCmzML5K79YUsGaeAa\n1Cv7Y6Wqk2mTqtbvDvs43azb4GR7wExHAmS7vtyksQe4igeY4Y0FUqb/xgKh8eRqVfUuVS8ur0Cf\nxNq+jo9DO+q6bM8CG8htkTvAbuu+4xrclG8G6/s51vdtD13u5slbLXMyjW0onQDXIq4DO/rWRVs8\nkqrrN/gGMqqu+4XMPOepiloRaGvkneiGEporm7JH5wUlDb+ewh4CZutfJ60Ur9R19K5lXVHXwFDI\nNv1uBWyufX6isqXceX1YorxTNe3GN6D2G9LPMl0vCSy24cRXjhYIPFijxZXZH2trTMbZ/3frd796\nY4E4EWItELU+fBcMUVVn2R8Z2DNQX9XzWMelFNTbKnsHbABfFNrfVZ71XVDfVdPZybtK6TlRdD2B\ntlojDA08gtv0mHtdIa0X2ahrH2iUnGuxQup0kbffyzAVA1ZZ01TZoqcYLRNV2/DrpVaILG+WvbRC\n2Ch9AXNU171Fo1PX7dsMyIwdoqp8AWxgUtmQ9WRbN8uyYc0dUPsNYVLHdnqmtIHZ73+PBRLgm03b\nLTPf8K39IaAGdhaIhXY7RImqtt61yQAZOdfXoC46vBZwWTkVyqOOt8O/V9krYNtttvl5a8bx/e8H\n9ze3QYDcz3kU1DtIP9oDH+ChLWC2wNb9TuyQqK4LxAoZ6vqJ+jzr55W2zkmMUirqeZi6bfoJUdXT\n7AoqACopoDOP2sJ5hrQfHwo+t0LGc3BYr6Cpa2l+HtW1AZsCW4FslPodYAOzyu7TVhkgAvF1U3Iz\nPesudQXqC/tjmQGiHrWAuX+XnJcYWIyquW92KOv5qSufZse9rXZpgSiocwtEVHIhxhOdC1U9ACww\nt6r6UVC/t37bbVhoPwLsOK/NX/cllInVu/j+7lL33uNPr0B91Wx0Var41FJM6hg6f3QawQG7im/N\nTYE/9+Z8Emi06nr29Aa0uavzShJgHEGdAWTjU3dQUoVaC5MVIkqYcytEKmUekJRlaamuGQTxrlWZ\ni7puJwD+lFhFjRFsBK6BLdOAnuonJzmcY9Ny8Vaz9BWk7bxHQR2VdAS1BhizTBuaICqqmgl9+4ln\nnUB3Z4EwgMkC0RsFtxt2MZAuvm3ALqiYpes9laqq+jDzpaFIBLW1PTJIP5JxEet39jQN+LiVfN8j\ngUf5ro8qXx3W+xY+7wf1CtKrNvvbYtapJsAIwAAaKbALCM9mU69oWSLPdMoOo56kFshTOVFrCyzW\nOpT25AcWBphRSw+oVYEmTAUjcLHzGMQDkLusEC4CcWoVkzsIhMQHG8jLh/z2YLxrgbS1Q9TG6PtT\nyNsh1r8GHLDRD/EAs2zEQtvYI3KurvoJ2XWPmkC6TTbwvQtqM981dpkyQPaqOnrSs2eNAfTS7ZJy\nwwIpfphLv6uXoaRFMJQyAH2UJi4Okv9eVT9RVVX9TBVP5cQznThQ8VxOF1QUSGegtvU89t0j5ape\niwURQWpBexBPKnvKDsM68GjnxX38XHB/FzbIIy2IrgILK1CvTmTeKMYGwQQKMm4BHccLape3Njsk\n2iEC6CdqqX1PdOp88aelIjAzuHCLlVl1XbiJfQWur8ROZYnlEBRXtD50WZ6XuaWuMRS/ywwBwjDW\n/rVV2MYSAcY9EcAMbVlXSgT3rqy6Kw0qe1LTMhxBDexBHeyPMX9sb6WqrbXBuk4OcTdesumJBVKC\nqjbLjd1bBxZXqvq5nFv745nafAF5BHVU07aTNVtiDMs3fPHesYV2tD8/0hYZl9B99Z+Vbw7rLwHq\nHaTvtmYE5E4qXmr1KnsB7NJrRjXZIXphZnnXVJz98QQCc2s7WJlCxeidHmkFoqGKGApvmioaDZvE\nqqcKWCtkhrKt3Nk06D40G0RuJKQ3N7VD7HDIt74N7P77ZR0AA9pij9htPFLsuokV8gioZ8vDQhke\n2hBQCqSxVdVs5k32CK1VtAW2BfhYnvV7tSGMqGsCqDCo1K6q5dSHwKJYHEFVP4mCluvc2B8C6WGB\n5KBeQfqRFouAF2RWaUeVnSUZyPbuAhuYO4z6nPINU/fmg/xeUN9R03cS56fSj71C26rsDbBf0aGN\nnh3yGepahwuj1vBCArE8ABdoVCukV8phU0Ch3ayKVhfZjGuGnYEFdQWtPfEt1LV8p6prEHxGCFI7\nRPbr0hIBNLVPMz8mpY0Z3HfKwg6hCGD7PsUFqJ2qNsOZ/WFVtcQRtqq60HJavOEu/WnKFXSmuklA\nrbvbrsGjVLVA5nQ9r6pXQcXMp16BegfpO/U5S82zqXVRZUdb5C6w2za+DLS/cuoep5AG1v408Dio\n70J6d1c+uYzl5dgLGXpWyArYFQWgE6/A8K8NfERdP1HFSeTUtYuq9+wQ5va/dFuEOIIZQ30J9OTa\nYHiPWzjmKqhX15KVMCBNEG+Y6kZdF3atGrkOO4RRQLWGYVwDG5ihbfv+CKemLf+OihGskBTSMq7T\nDHwF0naZCOrE/uBjHr+lqgO0rUr2yxn1LAesGGjDrsMD4rIN41nHwKKo6qNUPFHFU6lLVf1MJ55K\nU9LyyXzqFagjpD0L9rB2dRlZK+VZZb8X2G0bXmXLMm6f3gHvb26D7NQ08H5Qr7NFbgYlnEJvJ6ZQ\nP1lccNDZLigUnGAd/oQnPOMN4CcFtqTziaKQYONJhGeiZpkUoPLZA4iEWgj1PFzFYPGwuYL4CMHE\nppaa+0IDcL3SkgGjVdcC9klds1muV15ioL0Yp3XyNHo06Qv1G4He02ROxeRfM/PLQ1wAACAASURB\nVEpvWFPXwCa5ScAD+zigWSPGzzYnef2qr0VJ3/UIeEjLf9vAxYI68agdqEtpw4dM7/ZH8fYHlw5x\nDRAS+DCAtl51UNWaKVIW0BZwF/vhtqwBdMv+6B9q/yWwaFX1U/8IsAXULT3VADvYH5lPLQDPQB05\ncKceC4BjXCpvpexV9pcAtt9fKzjuXavfOHXv80Gd2R5Xd9/85bfhzhuXMRaIhXZU2eJpFzTPuoI1\nnU/yGsTueKaKSgXP5UQ9CU+lonJFLTR516KuRzN0E2gU0PJQzw6O4mcLfI26HkBuCpg6d0Vxj2k+\n73plh/BBoHNsg8oANh8A6dsa8gwRQGDfVfw4ge07oohKetpraz9QomWSdQ4lAJbhle1xBWpV1wAf\nZRxrgfPC/vAwhoOxBbFTxWE5VdWFh6oWUFtVrct2Vd29aquqj1Inr7p0dS2ZHw3YI2AY7Y9HQB0h\nvavDCtOwzAlyijrP9uAPBTYwvzrsveUbpO7ld5EvBeoVpHc+V5zn4O18a8zAVlUNb4OYIsq6NYpp\nDWXEw7YdPBE1v+/M1HVlULcaRsSne5HdW4bYH1LxKrlKrMpaKrhV011dO6ALw+S3R6CITdKVets/\njMYyfbf00s0sEVHTzElLx7EP0+mzahu419RctxkeSScLhObp6pk/COpS1GYyRrD61Cv7Y9ggNIAd\nrRGjtB3MdV6iqg3EJ1WtgW0MG4RalpLkSltV/VRs5ocAmyf747m8DStEAoodyC90ItoeBXWC9KP1\n164r0LbAlu/6aGC3y+djoP3NbRBgDWo/bf2ocAXqFaSvvC4t7qujb40J2BVjngQbC2iyQ+SCRsEI\nNhaa1LV417WORjJUjHfNQV07gPbdrh7EEjzyAccGSqe6QWpFEFNXYKQboW5S84HxglzZds8OYRAg\nqvoA+EyAra11eG2LJKdhWWcjvHclKusdpPt/TqyQu4oaZazPhRw0m4dtwCv2h/GZU5Udpg94j+ly\nM49e9SpdTxrBWFVdisC1BxeNqi7Woy7e4nAvAYC8JaWt80JvE6ijmo6Qvlt3o+0BjEwQC2yZfwfY\nUh4BdrucxvX4HnB/c1jvQL1r8LLyqFegfpdvLcUur4sOQLcLrOCTzKexbIWRkuFxv/ZKLKAewcam\nrg+F92iCXpgu1XVTtuzVdYc6aU3tFdn4zJOSVr+6Qdl52MYOAbpqLw3Q8oPpFNh2f1syRDJgVzLj\n/jhxt0DIKm/5L/YIMIObzLzluQ2VJm0UEyAt0xbBxiWoW6cvA9RyropYIAPSCJCVYz4ATR7Msh0L\nbAP4ySbpXrV7IiPvVYO8wraNYFy6qQYX56BioQZup6zpxDO9OVCPjBF2anoF6Tsvos1sD2A8KVtg\ny3dUA9kVsB/Nw87KymHYlW8K6zugzpZ/L6iXlsjFXbrAtD6yNghaoPFEA+ALASdYgaa/gdsFIv41\n0CD+TCcqt7Q9FPT0vqGuG3RbsPFgUu+a0RncIRrVdROppOOalVbNY7CZJ9l3rHKLfaDQ2iOqqgHA\nZIeIzwzoBsW/VmAfBTjrADZj9rDtQQe1xi0duA7atlhwy/ePmdtzO20nDlu7Q8azYKOFdAptpIra\n+dQHDQhTUNWFDJh9ANHOjx8LZ/GnJXVPVbVbToCNrqTXqvoQSJcTT9RaJD5R1ZaKYn9En1pU+E5R\na6ZIYoEA13VW3+qk5zPxqg2wZR7goRtLBuxV+ehc6+8qz1rKVeZHG38fqFcn/LIzGBFQ6BcCnTgh\nJ9XaIH1562En5RkN0CdKS4jg3vQcPSfbNEMv3C6OWmoLOvZWjS2Vr2eGMA813FP6yAxr8LGra3Vu\nnA0iw+07BMuNfValdjXNGNkhlYdtIktVzoHNrJDmo+2j2CDMZlwyQRbQ1psF4FX0HUXtzi2l4w7Q\n8X+wQVJQm6yPLahLALXYHwLxDMTkxy1w82XY+dR2Pe6AzjNAKkr3pbVpeUjVk6Ci2B+uSXniUz/T\nmYL6md4cpIFggdysr61nTJMwwDOY3RMyTFN0E3TM7JCsrNT1R5dvAuvsQN/1oLZ9i9wE9eqk7x+t\nzPcqkI0VgnZj+ZQAu/a78XMA98mlqWtqAUZUoFLFqdDu84wdwqW2ltRMrWe+evSK1b6Pq7EnRPGK\nilblPYDtII2xnHIvqGrJvW7QkR87wJkFHCdgd2WNCk3r86rajAt4xRoJNghbGyTCG7iGdgC1e/VY\nElC8BWk3jktQu4Bi9KmtlbGyPxTqRk0bO2SGvIDb5lWz2iK2tWIpoqRHHyCroGJT2AbSpWV4xIDi\ngTmYGEEdIb0Sa7GcILesgNsq7Wh/6DZD0PHKv75jh7R93Vsid8s36M96D+o72R/bFL2boF5Benkz\ncCe2jh/EsxUyA7sNfsIT2jvhWsDxmU61Q4CRHXJQSYONZy2Q/q4PCuq6GcZdRcOra27KVEHC4YMB\ncglMSbARBugKbi0GnB3cLv+6vTV4BjZDs0QYGIFLq6p1HAPaehfpSjv610CAt5ykm2Vjg8S0vfGO\nyTAtgFrS8yaP2oDaWh3Rp/aqekAZFsyJ/eGDi4wJ3n09612rqu4/oZTaA4zVddZUwF1Nj6Ci2B/P\nRmkPQI+AovjVAuoXaa+QgNrW1at3swKz9eHATX6ZSUknQcf6AYCV8hHA/uYBxs8tmR99F9SZ0pay\nzLMGDLHahXVKbekqu3nXFXp4uV04L+SVdaUy7JAqKrv1K3IStT6vu8o+axktGru6Ll1dMzdroRZq\nrQhFXfcbiHYx6uyO0CNfgHgDc1PNJA6LtUNUPesBaiv2/GtVqBmw0QOjfTNcGaqrM5UN8tBWewSm\n8Y+B9AfZIFk2yCWkZTltmYitop4atQSfeqmSdZwmeGfetQKajKqWIFe0QBaq2qbqCbAV0l1ZW0hL\nkFEAra0WezAxgvqZ3paQvuphzzYZBzAr6Q5Lp7Kjksb8/sRH1PVV+Vxgf7ewvn5Fzw3A4j6od+vO\n392UtPjXAu0iJ4KBJkXfYA/xJ4YDdjX9UZxUuoc9fOtmZzc75K2UZUOZ1qrRpPIdJthYunLtgHYN\nZSzEK1RFi4Al2G14O0QlObJh8bDHdwEYwBZlLZ65wLUuVDazBiTFChmvFev/RXEDQ3XbEuEd5wMD\nzna++NcJpHV6Znt0X3s0LtqAWuYlAUWnqicA+2lRbc9BRfmwzsfRhmkKKlYNKsYuUKegYrA/pHXi\nc3nr6Xtvfroo615vnuGbnmeQnjpkmxS0H/d9fXAKbGC2PlbT7pQ73vXnAPu7hXUsW6huVLVfd38R\nzI9aZnx176DqoN3sWMYJ6SYVA+LwwJ5gjWaFVCKcvcm4ZIe8cMscwdGWb5AmnDVphh6U9NRQRuEN\n3Z7+HHEa9PdaGLslzfQFsLmrYru6KHeMeZMtIhkiCuiRzy07KGpbwW096keyQlaqGgmg+/xcXWNA\n2toeRmGrjSGBR1XC90GNZNo68MgD4KKmj76Ng72iPkZQ8Thq+5SK52OA2gYVXxTSw/74obyNNL0Q\nUMxAvVLWgPjU3C+Ddb1sL7HOK6dXwh7Yst3oX2fq+iPLT6WylnI3a8Stk4I4g/d8McxN4EXBxe9m\nvVBOkJGJ/Xnd2CKNK+P7PzG6CmnQPoNv3T7FKOtmh7yA8OlsKXtHLeBS8XQMi6LZId0WOcQe6Tsv\n4LMfa4cYJT5d+ywWh4zLjAtgn2LLYKjWyiPvW9h7DltEbyB1iOZUVXe1reDGmA/AA/xGYbus1KdM\nXdt5O0gHNS2xgJj1kVkfd0CNaZpsP8DbwLkNS1BxwBrG/jiOurU/FNjldPZH9KnvgnoEGf1Lcttp\nF3CvVXWsl7ZOztbFAHYLe3hwLnOmv2CGxyPlq8J6b2x8XlndXW0kOVPR2QVx2xKRC4X7HZkYBYSD\nGSdVtGbnNqVvPtwtS6T51qgNxCcKTiyyQ0pX1KWqHVKduuYOM8x2SFTXfd9HAxoYiGOAift6hYcd\nYg6C650vArv2oGO/abSp5vsqQEdT1taC8dYIElVtFLUo6A7vtsumFq/u97H+JQFGp6JlugAZWIJa\nes9TUEvLxDugFrCvcqcX//38TU61+YhPneVU7+wPafzyREmaXg8qSoreo6COdXKXpTXbHzyp7QzY\ndv0voaQzuP/EBRhPhPcbfmDJskLieNafSLwoyubiSIuBdhvvKpvevI8dgF0F1DLeA44nldG6UZR1\nyA45ul9dS8VZaGrZqK//CoFF17LR2iH2A2hQUeDcxn3+Ncz88bMXlojNw+6T6ey+dj82YoeAu78t\n3jAzIEFIGbetyWuipq0FEqye+fz5ypoCuv/XTBlZLlPTwfYQ7zrmUftsD9s6cQHqHcAJzqcewzPE\nY0tFBXbhWznV0f6QNL3YQtFmf0imxwrUAnig1cdH62K1vrU+xFE//XmudOZd78oV2Hf+9k9c6p6U\nCOyTyxQwPJlapPUC7rv87BhBtvbHFaivApwAlEkFjGogBGC2RWT5DuwT5EANDA9bYQ2of+0ay4jK\n7q0an4+qgcepZaOxQxp8aWuHCKDroWJfY5OEwUD5qZohgpZj3Q5xALa2Y+8HoacS4uj/mTT/mgr3\ntEOzmlXagFfbF3bI7WLr0mR/yDLGr95BOqhpl7ZHw58emRt0C8jNQkmmkx3nAGgBt8mpdn41esOX\n7lVf5FQLpH8ob6OV4uRTvzlgix8dQf2C06npDNJ36mHrWIn7qbf2R7vorSUCSF2fvWspWWofgHcr\n8J/4FowrYLecijyHMkuxi6Cf+/1Yp/xcgXrfSAb9ZjLshALu3Z+25uFiiwxIww/H32hhrQFGGul8\nkx1yKqSfelCNmXActcO1QdrZIYegtP/tJGqXb9tf6co0ArsV6oK8bUG4SCQvvw2qmtCVb79bSOCR\nqK8M5TfVcXxUZfdN2oY6EdwA9Hf0ke15S4sLLPYBq6DlJ1mAryAtatqk5TnbIwYXFx619Z2n6Yk3\nPQ8n9odkfxyMcrADtbU/nkvF89H85pdy4uV4w0tpCvqHo4H6hxBEjKB+MdNejMK2KXoR1PJmGSlX\ndbAFCNsyAu2awPm9oL3KBsnS9qxC/6l4rRcAc2fzQBVgi7r266yjv6uyS/uxoM4gfX2oB+ClYQyA\nIC5PHF2aHipxk011MkomCIoco1dnh+BoN69amjoXO4R7Ol8DdsNvRWnWh1gBTOBjNJbho9sU/adY\n/1rdHMC5Cg3SLZ9aXBQ/1xyErqbZwrzKDcwOt9/LoppjKmEdm53AjbCD6Ir9kWLS9hyczXdYQOty\nGaRFTQuQKdgeRlEPdX0T1McG1D2IqNs/2jTIf1HTR3VNyltQseLpOPF8+GDiy3G6NL2X8qY+tf38\nUF4dsK9ALbZHBulRJ/elmmUF2hbY7bT6lwDsmo1LuQP2CGFV3xeg/pxuUr86rGVnbUBPVHZmh9j5\nH1ns95cU3qMcu+PLYaQDuTA5lf2JgBdG+y/rhe225udv7WIpQO1ZE38Lz3jB27iL1ye8HG8K8OfD\n+N4MHN3qkMYyOLh3rUFNWZ0CacD51+rtbuS/s0FmD9vOVemtXkYfFkPcBh8l4Gg3ZsdpCOYIbiCw\nmnFx0pJiF4/2h/GwLciXkI5qegKzaYlop90BdeJDTwHFDufMp7ZedTn22R/Px7n3qdPP+0Ed697u\nFJ48lhNoW2ADs6LOFPYteJv5dln5nkx9TzD/DEhL+WbKOkI7AjtT19EKySyTFfCvWikeDt4yDdO0\nqdhzIFaITDd8euE6AbtywYnWvWp1qXzt26KHXYtcHKS99bnXgDHh6M3Pp8Yy4uuy7FeDI6s0bhCf\nAo59ViKhUw/bzbUEZbOEgFkozGjWiEDb+tl2efkcAdyA+/LJBclaMyaetqtPIcjoVLT8vAWkJzWd\n2R4O1gsvOknbs0FDKMBZQS7+tPOpS7th4+j51ManluyPpyPJ/gg+tU3Tiz71gYoXesPR0/fGq7ty\nUD/TeJku0AUO7tW5apYTaFtgt3WHugZyUD9S7Lora0OgbufvIC3L8c39+sqpezSp5Mo05VGmAccb\nQcmV370rmaoGxsUQT8s2sUCOeQfRsEZKCuzYsZOUJayDf/0CUjvEtm4UO0SK+tc297qDmg5zsQgM\nS1/JXkRncjD0Rxti20/3ql3gkdCDisBIx4PaIa4hjT2eddgz4xiPZfQSqgG6dtlp18OC5vdFK8Sp\naGAL6VRNy7IFBtZ5C8SRj+0VNUqLI6hlZECtCvu4kaanoLY96jVQPx/Nl7b2hwYTo4qeAoqndsj0\nTG+tZWIHte0jxIJaIA20OueeaJNTZi9DgbYFdtuObwK+ex/iSlVnmSE7VX0F6p/obBApB+r4UR1O\norCbGzCra5jEeaAfqDDtkZKpajscL5rDVnK2sG8XU7NC2kWEbv4WJrxSwQtXnJoXl+9PhW/hKAFH\nyb+2/vXLEfobYcLTcWrfIVx4+Nd8gA52R2k4DcZvmOYuCo2BBuT5+DuvmtBh2nvHYzPfQlsku1XU\nVroLuGGmyf/kCWD7G5JFZlDLtk32h0zfQVrV88L20GGENLs9qIdvzSGwyPpffWpV1LUFFYtvpfhU\nqqrqZnv4ND0bTJSA4o/Gn/6RXg2wh+3xjHMMGzVtbY/nfqIspG1dO8LN9GTW+QJtC+y2/uN51Kv5\nFWWCtoPxAtRXkL6yXVblG3jWY0cL2fSZqirbWiIW2KChrq1iXanpKtExtEjZVYflUuyj2O7iSaf1\nC0qgrY/7BIBrS8njgt6jUcoRPcH9ajwLqX89/0abzjf8aw6Gn7RulKOlPOtAXwKbkp0kBLh1folS\nVoBhvOORCKhGZVPz1fXlBZxAW5V230+GywIkA28GJfbHfLzSEn6LFoEzcAnoycMW5ezmDxAPaHtI\nI04zME5BrQHFBNRTB03Dp7agbmq6ZX08Hy2I+HJIMLG6rA8ZjgHFH8vrEtQvqOpPD2DPkM7qly0y\n/zR1zCpqO/xoObn1h3mCEkBTtywFznQJaq+wP19VA9+gBaNNp5EfIdC+C2wwJu+61d6eQtCHH1Ha\nU/8Ddp65iC4Pu8lOONCSDCqAoonKwLOmWeTAtnd6ybdWcBtL5CyEHxLaVyY8hcc+do8HHdgScETv\nqYPRodeU6zgiYSdNjWCKYnZIYOrzZXmnovWOIerZQFssGuYBcetri9/eNyXduTqAj4MxHR9XXBaI\nmT4FGTGALLuwgjQZdV3g4a0WCHrA8h6oU49aQC1ZIBbUZDxqVdXrfOrn41zmU0/9fjhV3dT0Faif\nUZ3tIaC2kL6DtNqXt8AGvLr+iCKqWkAtJYL6LqR36v7ubn/DPOsZ2sOjlOTZGdhtXQPnRUDxPSVe\nLNH+GCpgpwDEvx3QbuNtuL2goCiwT6p4mVhoTrJYIWh+tfTOB/T0vST/+uTRSMa/CoxC73zVOIba\ntVJX5a3F4BLYGKDWNij66bYIsSrr4Vu3FdU2EZir5dEpzGaaVdt9V6TXPXWhemWZgN1Bnpap5aKd\nN/5ngG7L50raT0MIMGIazvv/eADUVlGXAWpN0+ugPo6RT/109FS9RT71D+VNLRAJKP5Ab6GDJp/5\nkVkfGaifadStgyj41HndOvuVOKyPBmw7LV+vv8MUZBRysxR1OKjqVXaH9cF3oBaeRUCvFfb+iULK\nV4Y1zTtsWhVKtodV2RHY4DrnXus82d7HwTze9e3FVLLHNg4DtgFH38cXqgbY0KCjbNpFnmlcCApr\n9MYy5XX6+hZw9C0j9VVghz0OpYnOkCEyHO0E2BJ0FHbV0XAm4lDWFhWtelvhjNkasYBOoC1qG/IE\n4FL4zG1l5VmzH80WATycgQ7LPh4B3eYnkE7UdQ7sjwC1rMt55of2+zHyqW3mR5ZPfRVQfAmgPsCp\nR21B/bxQ01d1qjLrMmcH/pfpsGIUq6otqIfiHqDeQToDdIT4d6msrQ0CRFUtz8qJyk6APfrTvLY7\nTjm9PRApO2Ohf3ac7B7F5IJJId2LzrPQJuoBx2GFrICt/YXIV4j90WEt41cBx2rS+Z66j13Z9B8i\nb5fprU+cjcFojWNkZ+3vtR42QVs6emWN4WNXo7I7qLkzfAQb23bFk27/adgnLI14MOSvldAW3uaw\n3y4W1MVPm1L1dDrgcqozSAdAOzUtrR8zMFt7xIBYbZEI6oOHR236p9YWisGnPgprHrX41NLt6Q/H\nG17Km1ogP5S3KaD4Y9oA5m0J6mfwZHtYSO/qk8yvi6cja4U8Wlaq+r2gFrZd2R8TwG9eq19XWbPf\n0QrjPe+gLXDu28iADQW5bONaXdum4rs+CFYA39oherORC4pVZYuPDXhgn6H5ugy2rJBPMGwd/5NS\nmfCSXiRm94jw9iaWiwE2yVcTWMAcAA0JmEolk0MbQd0/VEWRjpuVgzZheNUMkykiPCYDcjYNYdr3\n67hcMivbY1NcF6kWyHJMdDgHdFTdE6QV1OS86hTYBtRsQD2WNR61WCCTos5BrQFFA2oJKNqGLz6A\n2AKKI/PD+9U2de8RUAuk97ZiV9Md2AdILZG7ZWWBxHIX1FeQ3inrLCXwu82zfuXD6bjRCKYHGvlo\nStlCW+AMGEDjNrDbm11mdf1w73pAeoGVlJqyv/rjAQNsoHl3QDWAbq/20tJ/p1ofeJsyRGwPffrN\ni5xSnyFSXJN0BXY/fmpZAJ28fWcMmFn6PhEwC4Azld1tjzacQ5vltEumCA+1PQKKJisEwBRYZH/x\n77oJmQ4ThXlJkDECGsDSq7YWiLU8nMJO1LQDuOZNw+dRW486gLr0Fwk8mvnxg6bovTpVHTM/pjQ9\nOvGCNaifkavpqzpk+4Bflaiqm0JuHZ+JX+3nr71qWd+C2qbvWVDXsLwMAwbaTpjeA/Ku3IY1ER0A\nfhvA7zLznyCiPwjgNwD8fgB/CcCfYuZPu20wxAuCV7qJj+2g3Q9qoRoAjS2wjw4+m+Znw3fNEx87\nd6unvVByUNvpBtod2CUCWykFp6h1vHwC6otpLEPyJaNUPJwhMjYSgK2R1R50JAFgh6wBdAWDyAPb\nlmGH6CaHNdIDjFQxfPN+aBq8jdq2HjZggE0G5PaLWQ/frWKUda6oA6DTDBC4G5T1pR24MxU9wXvY\nHLrOg6CWzI8I6l3mxw/l1bdQDEpaUvQiqNty16C2YmdVd6TIi7Wu1HRFywTZBxmLU7wr+yMDtUC6\nbWvAPoP0CtC77lfvXqOPKOt/FcDvAPh7+vifA/Dnmfk3iOg/BvDLAP6jq41U/WFGYUfrQwp5JexU\n9gWwW6BSrvI2fUC6bTN63eJbO2D2PpKzQ20vtixH9GT20CagoPtvAmlcA7uy6TMEeYaI9n/N6x76\nsqJ9iERg6xImS8Te2aq8SMDv8JgGY4HAw1yn0wgwCog10Bg/Fso8A3oCNt2rBVFNh+lXgHYQD8pa\nfemotHfDCajHG14MqE16HgqWijqm6B1FOmRaZ36oJ51kfkR/+lFQq7Lu9WKVW32+w8qSYlW11hn4\nDBCdFnzq9t0zqKPtEe0PgXHVbdxT1ScXfKgNQkS/AOCfA/DvAvjXiYgA/CKAf7kv8usA/m1cwJrN\n4wYAVdgC7jgOCDrIqew7wD6sZw3JLvHqWnvJ68fq/S0g84Ntk/iLjWEL0RJgN3+O8cJV90svCgG/\nWCEoPp2vIs0QkVJB6vNKkV9MtAB2V9IO2AQ4Hxt9nNAUqgGzZnqQUdkMZ400SJubpIG0Dzr2aaBg\njYjcNr/nwVOZg5omaKdK26pmMlA3II4K2qlvp6w5ybdmhfgE6sM0ellYHzZF77mEjplM5odv/PKK\nH6kp7Zj58dwBLaCWZuR3QH0F6feWimGBSLFeNYDZ/uA5oPiJjxTUE5wF2Amkd4r6a/S69x8A+DcA\n/Ewf//0A/iYzS1vnvwbg5682Ip41AG2JWPnojWACqAO0xRqRPpelH5Da75gv9OaA/QnAi0Kxor1i\n601hfwQlq/0JUJtw9XKRrCwf66YnB5PLFoGNFmyUpumgvMnqGa2QTcBRilwoZEimQTqSjSUeNjBg\nC4FS/wE2zY0ApsQWIX1A8cMJtDOvOoIbiMP9NwSVHYv2o52VTGGv4BzGbQZIVNcO5MabXqppB2/b\nQRPPwcTCvl/qxPqIoBZ/2mZ+xKbkw6NuKXquSXnPpbagfpaOnm6A2jcwmy9Y8altHrUtJ7jbHo0Z\nYoGM9b1XbYOK0ad+FNQrSKfD7qaxr5gfpqyJ6E8A+OvM/JeI6I/f2qpf/1cA/AoA/Mzf/3firQbf\nFeOhe7ReHHcq6T2rcM/osNaIUdmf8NRvAG8O2AdKn+aBfQaLpaiEEnBjVExHl+tUo1j0onTQ9sA+\nAHxiXgA7UctGXUdg6zscs30hRrG3oieATjZdShlgUwXO/naUs/1mJoDOlinCRKCzK2rxoQta7RGg\nC/etZ91/szusciqs5bECN6DzAQ9wKZmy3lWKKwtksj10WlDRCagn5T3ZHwJiq7Z5VtUFDtKtX2pM\nWR9H0ujlCtQxmCgpehbUP9JryP6oJqjI2thFQP1MJVXTO69aJJgtO79avGpR1bERjID6lQ8H6ld+\nUlC34QHvVz5uQzoDdNZHyE5R33V87ijrPwbgnyeifxbAj2ie9X8I4GeJ6Kmr618A8Lv5jvCvAvhV\nAPgD/+jv49G+vs23TcybecF9WlPcAmUBeOmebFPXQ2U/K6Sf2sk2qtpaIkVBb5V2xdFlY7NjBpgF\npFmRjlyzUgIY2r72gIn0D2L3E4wXohTYBzNebO61bFNS+vDigP3jwgppF0ze09+099R+XQUgbpFs\nX+wRDTwSGh2JpmwRVc1WaVu7Q6Btpnk4L8ANwILb1eegrFNwx7pzAWtnccgyGZxXqlrmOSskvoJr\nKGxne+hbyDFsD2mZmFgfR+HPBnXsozqC2vagJ4paQP28UNO2rmT1w5aoqmu3CKOqBnwGyNxasShk\nI6gFygLqT/x0qaYjpDNAr1T1Mkvro5Q1M/8ZAH8GALqy/tPM/K8Q0X8GKuCiuwAAIABJREFU4E+i\nZYT8EoDfvNwWCG91oK9Qi/WWrmQF3JVIpwnIj6iogaCynxpaFH7tJL4gWiLyX0Dbbwa9SXMVD5Xa\nHmvSfVDXDeL5QY4XokwTYLt9XwC7oDWcsZ66HZYXFKD2lD7ANZqx9xD7/sZW7gHbWiHC2PHTmrom\nCIwI6lsrwPvhitYIY4a2zbtmMw05uAEsA426hzxNmkqmqHW6gprWCtsMC6DjuAO4BbKFeVfRA9zG\n9nCBxP6Wl9jXR+xB7wHrY876kJ71Rope7O9jZX1cgTqrG+8pMQNE7A8L6k84vL0RFPUK1K98PATp\nYbnQQlVnds+Yf1NYf1ae9b8J4DeI6M8C+MsAfu1qBQbwyvJIxKjdvy699llIHz2vur1thY36Hq/t\nsSq7dYpU8ApzIAj4xFgDuy/TQndyUUkN9XbIzsM+mW8FTGTbCu0FsI++X6+8Bra05pTMkP4FzRrZ\n9NI3Sgc85qCjFJI849po4lR2V8ncl2NR1v0mZ/vxGMobgL0ZZtA2oEaHt8YPjeKWTqfUz5Z9ToKN\nl8VBmvw0ga0djqraTitj+TzAyKquHdTVnw62h0nPE0hLg5dD+/y4B+rsbS+xhWLs7nTO/ngfqO9C\n2logFbWp6URVt/m5/TGp4o318cpPzp/OQP3Kx2R3yDoZoDWg2X9z9p7G95SHYM3MvwXgt/rwXwXw\nRx9bfyjrap5Nx1vHaQnuHbSfgQ5p00iDoYFHC2zxsI+upFuWCTowzqHU4VP5CiFV1zBWSGaLHAam\nJw9/fAts/a41sGv35U8KKtkq603QUV74ULkdx5ZG7+e/UQER4zxlI0WhzGeZLAJScFH3rcmBt8GJ\nXUAxQltUsj4wqYqGg/k0XYqSMv/daYmK2kzb+tYLaE9Kmjp8p2kYVkhPz1NQE9SfhoO0z6EuZc6j\n1vS8oKilu9PY6MW3UBy51DIuENce9LpXHYOJd0Cd1YdYVml7MaiY2R/Vwjj+D9aHgNoCWoeDmn7t\n3IqQbsM5nCt7iK/KSizF8tX7BhFlLcq1DZcG3j7e3nrcKvN4Yzgvod3eXXgOlQ1BqNwE5H9B86nb\nzz46MA4D+PZyWxkfUS+9rAywx3c14MoFWs08W+RCPbma+QPYBcCr7TbVALs9ibDeSHbFNpoplSdg\nF2L8nrGjPtGTTi/nASLG24n2nwo0vnhyu0kQ2n70oKJIbrE7NPhY+3OqZHoQQOo8cf/ZNNwsq7TZ\nTAuQdpZHsD9iPb9K4UttEArzLJzD+BLeFtLFT0Of7rxpq6Yz26MHEu2LA1rveSeOwq7Bi6TnRetD\nmpGvQC3WRwT1j/SqirpgdMpkQW2DiW2atz0spFflUlVjgPrVgPrVgHYF6k8K5BFsFHA7WNenyfJo\n82cVXXWZAWdV16b+R2UdbZHvtrm59azfMBRe6erxoPayWZkn4C68hzYwApAoHpjaEKdfDOOieJst\nEbE9BIxG4j2rsm3AlsYt2iNYX8eq65PrdKHO0B5qXltVBmBLo5ki8xE6xaIK1Jc2IqraDju3ZCZY\nIQadXl2TNr4ZaxJVMLXGOK3vkJ7eRzzgXfpDCgmMDbSNN90Orfy+rrRlfyOkMcAdh8UW0cJ+cAXs\nVZDRqeugsK8sEQWxhXPpuxQDiITZmzbWB5kc6uhPS8aHvOBW+vo4elPyp3JOHrXYHnlA8e0WqF+o\nfjiobXDxZF7aH9an3oFaPGr3n+1486cHzIeatpZHW+aYVHQGaKuqXf9H1rteQPnuQ+DXhTUTPtUj\nfbN4kR/fAQ00i6K9Jfwa2gDUGkGFU9kOyNP4DGwBtWaI6H82VgR3awSTHdLGcnVty0HFAXvYKLkl\nIv9FYbv0PAZcs/QA7EsP+8RkzMt5IvP/PIsq9yrtyIlGEK5Pak4HAcxzANJGLGlAe2SF0ARr95FV\nM4WNeXhZGW7AOirvJaD7DcsFFS2kO5inAGIANApAfTg2Hbf+tGR8yIsDRu95refFJ6ppMHEF6sz6\nkP4+WrrnAHXBxypqoCvpDahPDFB/EmviJqijPy1w1vWMmo5wHqp6QHoHaDv9fured6msocravull\nqOt2UVhbpHR/zKrt53Km0H6mM1XZbVxUYp9gAO1S/fo8fQs5V7xSAaRFoQH2Ib/KAFuUeDNhHgf2\nzsOuRmGDC15kGXOAnYd9obBjKeGtKqXnxBfpSKqPE1Fzgk4Co7T9WgGMMQcg9ebjoa0qWnxtUdsJ\nrOOwuxevwJ0Vc/y2SnsHaKeq+3/A+c8jyBgsD8B70/rxtofAOgYSRUlLp0xPMl7OJai3b3pZNCO3\noH75AFCLX13BiHnVUiyoX0VRY3TSlFkfO1BH28PaISvL460eKaTteNtX2xAng/X6WHyfyhqEN/Ws\ny1DY3BS2ALzZH60GFip93gB3PSmFNjCUulXZYou0+ePC8H3NPm2B7VL6OmzORcDRAhvIrRBb7gC7\nmu/d9iMyGdS4DeyCOchIxJCs7d4uBqfAGi0bR6wQDT4mTc5BXTAXwL0UN0Lb7qdrEGPALb/7prLe\nlgzWmbLeAToZtkpaIS1qW4dnNZ3ZHqW06/8wgURR1E9UfTenNN5G/kOZWyfGYOKLSdOTtL3PBfWV\nmragHtNmVW0DigLqoabLBGpR1nbcAjnaHg7WwfK4A2lNiTVwjsHGqKgzhf19KmsGXrsvaps8D2Vd\nBqQ7vAXcxajrp3I6aJeeSiTqWjJJnql3ZlTb9BPUOvZHO2jSHemJ3qgG6DbDUN8j2Nj3MyrsAGzb\nH4j0S1JAnw3s59QSyYHttos6AVuCjgcqfg8SXKz4FJ5wPtVj3CjPA68d3nS2RkpiixAxaiUwFXAB\n6gkPMWN3dNdkQLsAkCAo92nc1qX+22yDGPWnmZx3rcdBvi6D9OY4ZaDW6X1jS0D346rL9t8n2R6Y\ngN0h3YddSh6NrA/J9rD+tE3NO6i/4YVG5sdztz+egj8990vtX3L7CKgP0LtBLUVAvbI/Xg2oXzkH\ndcyjFlBbBf2pj0fbQ9T0az06nEsK6TcuS0BHOK+U9QrGAvTvVll/UhtkgOHscBYARHhHcNdKCpGm\nrlvA67mckFeDCbSf6QQKTMZI6/zIgvolNBI5XVR6gHtpiThgy8VdviiwRyphDuzpje+le+4VOEr7\nX4q5YUrOOxi/Vy3An1oQsJ+r134Ozsp4O4sBdlPZXJuE5koNvJVaV+RdZSu0xc3RNMMO6dItkGqU\ndFTQ3Ec6yAEDZx4V4CoTxCweNmJgDORwDkp7qGkzzapoMy2zPIgYVMTu4Cnbw77YNgskNiV9OlU9\n9/VxTlkf7wH1M5V3g/rk+jCoM4/6E46hjCF2hwBaVLQo6sMBe6Wo3/jAyZQCOwI6wtmC+WHPeqUg\nQvnqyvqsPsMgBrGskrbwjuB+Ki06bdW1ZIYItJ/KqY81orKfMYJyZxwX6JNdxkCPJPiILbCbFVO/\nOLCdhw2kytFliQAd2n15Y4lIfrvt01vOxSd66qA+5DD013Q1lU0Aamk30VqLetlqjRQDbfnwgBvV\n9rqxNp0GaRXOHtz6M1MbhJ3CfsQGcWpaf6jZ1grQMt2A2UE6KGrq42p5dDVtvemD2v9nUdTB9oiB\nRJtD/VTqBGrXGVOS9ZGBuietfCiopTyiqO+A2qnqxPaIatqDuuCVy6Skd5CWT4RzVNN3gP2RfYN8\nWGEQXus4qU1Vk9aXQgymAGm0dwZWhXbvD+QsqrafSvXq+qQG6kp4orNnh3iVfVLBcxmK2toitkgu\nthZtOIMlsF/1NWR5hshXtUQYmiXiinx9B7bAupgrp3BRH3v8bw1oSi3uRltrmbxsrq3rAGLMfjbD\nQ5sxskcYTm2DA7jldwX7w/3mbDgrGazteLRDIqCtYpabUwbpDmenqAFV09GbFjX9VCT7o7r86RhI\nlBxq2x91BupV1kcG6me6B+o7JQYUH7E+XrngExpMBdSZTy22h6TlWVWdqWlR0K9VppUU0m8m6GgB\nPSlqA2h72V351N+lsgYDb8azPuGV9dmVsYW3BLms4pbxp9JT+fp6T6UGUFecPSf4qfeLcVajnCX4\nyCUFdVskZIrABEZuAjvLEPnawLZZIs0iGZ0/HVyHyrZqux6qtOU8vZn/NlvkpKaQrZfdQE0KbQsx\n1iAjJmg3X7pnkERlLcrZwTrcUO/COtaRSVlzgDUmZT0paVlP7A6BNM2WBxFcSp6oaQG1ZHs8lTr5\n0zaQaBu7yBteIqhXDV52oO6vfdRuTt+bnncH1LbRyx1QZ4HETwHQ1vaIajpaHgJsAbVM18yQ6lW1\nVc4WznGeXoa7IOL3qqzfTkn/ivYHdNwCXOCdgVsgLRZJ5aY8xNM+y4nnDuuK0UFUpTZcywg+xtL6\nE/CBRzmopzT/1ulrYFdQa0yjrSGBbxV09JaI6a2vA1r6Y1GVDW7pj2jgbv+LNqCx2SLNFimqsuV/\nFVDXbo1YUMuwZn/0T20vO5CgIzHGa78svOV3cngPo0y3xVaWzMzOLJD+f4K3QtuOexXtlDQ1SIOG\n5SGQztT0yvZ4Mso6BhKfzf/n8oYfu90xgouPg/q5Nx+3ilrKo9bHHUUt6XnWo/4E39JwVtK5Py22\nh3xsANEq6B2k32pxKvqsZVLPqrSdwoYO6+X3k+hZv1UPa2DUC6n8Mr+QAXYC7jcq6l0TMWohvDHj\nqedbN0BX9bPPQq3Zdof1iWaH6KeM/m+fcXa7hFRpnmjdLD6HhjTNC69oL0xolfXk1oMeTNBRmqT3\n1wN/FWAfxOMNON0SOfip+dYVOPCkPnbhdix/T5rjo6JwU9cSK/hETyi1jb91m6QFHAveiNsxrCNj\npBQMaNfma6PfKLkSSIHdPW1GAx6Teths1DR1+a3wlt/FdAHshXwx9cRBWeY5aHOYllge1N5ZqXYH\nyY1shjQRp950pqblXYnWn35SFe1T80RZxxcH2J7zfqRX94LbO8FEoF21HxVMfMVIz7OgfkWD4qcI\naIiV0UD8t/jZ+dPR9hA1HS2PHaTfOpBPY4WwgBsezhHMUVHr5Xllg3yfyhqmUyCrrtt4tbDGgPcO\n3Myk4zJcC6nSbsPNQ63w1siJc1LZki3SAo1dVVdMgUfdSY5Ku0Kapn8KwNanAYHuFwB2zBKZPEWr\nuEsPmNYG5gOsmSIowCsOFOahtOvwr4uxQaia89KVt2SMnGqNUP8UMHPPtW4XfUuw4fZ4xWjwZnRl\nzc4G0Qvb/BeAa2xxUtWLA0v74QnOQFDXQ02TGXeQNnYHEU8BRJvpId70E1WX7aGgDpkeNpBorY9n\nOi9BrZ0yvSPr4075HFDbYKIFdeZPX6lpsTysL/3Wgb2DtKjoNm0GNHNQ1BgAbvPM5RdgfTW+Kl/Z\ns6YtrMnCmga8d+AW9hQDaQvtyu3xsXYwiDUij953VPa2kM/FbtNWwO47a3zsL54l4uyXlo53cHhZ\ncDG+dR+X1D4H677OQYxiug0odOBN1HYHNun5HJbISYTSs0YctNkEIu2Tglz0qw8wlDfstHCKMmUT\nrZAluNmp6whrVdEW1PLbu5IWSMs1+/+39z4xtnTdedezdvW9X1AIBOfPJ8sOGEQk5AExUmQlwgPH\niMhAhBkgCwSSB5E8YRAkEJhMEJEiJZOEDBhgQYQHBGIBJhYDFMsYwSgkIYkSSBAhcgSfHH8EEuEM\n3tvdtReDvdbaa629d5063X27b785S7q369+pU1Wn6ldPPWvtXTPLw6tphbNWe6gv7UHt/enRo47v\nTMwvDpj19ZFB/VyP+gyoHxA7ZVJQ5+bjK1B/qh9C8vAT3wU1rcnBT/tdUNUK5kcB+gzSe4AzYa8U\nAK1w7oA+D2td/tp4dRukTmANAzQFWNvj4wLcW+m+NYlqvSutccxdqeBdFDOk0yd947eo7Eotww5I\nSaE/B1NLv2aBPELf5eZbCvpabPO1JXP2VsDGCtheWedwtdgK8JB81MWkO4DuZzMKNjwSL1R2T0Ay\nk1WNNGs/Qpu5dQbVIK2qegFuTIaB6E8DHeZH4eE9szpselLRzo8mwJR0V9EpQX5BTTdQ76Haw15s\nG3zpHd+gR3sD+aiinw7qlUcNvA6ofWneV/xh8KfP2h6qor2abnAuNryC9K5+tUB4FxtEAa1wnsM6\nnodTWPvhL9EGAdA8SwkySd2tDw9whfcK3HsFtlJNbXOp2Ln50nst7eSX5IB52GKNqMpWL/uDwGxl\ni6wEdq7VVmB/oIp7AemXDuyCUWmXpLQ1+Rh87IktcqSyt9JOeqqt0mPnEdp2IcjFYWp7Bm7dl0FR\n8wDsgdf5+vE+dfjr4CzTTUW781KXmylpD2lT1gs17W0PX5Z3F1R0TyTOQG1vecH4hpdZp0xnPGo9\n947iCNQt2Y4B1LNWiUeJRJ33Fd9dtD3Um1Ywe/tjr1JTvYC0KmnvW3tA92E9vxy4ZdxOvbO23IV4\ndRuEH4ud+AzIRdGg3Ma1VhddrbiLgeyiaAqmiuXRAE3YCoMF4Hrw9aKoTCEJ+RGERzDuuOCRKu64\nNEhPbBEAAm6SadQB7i0QGd9ZbgAMdO3d3wLzWsD2HrbvXnWlsFvD3V7aZ4lHaRLufeyedOy2yExl\nb6W2i0OqRBqYKUBb7RG7GKT80tQ2HLjBMkz9SsiqZgbsVThV7c89PT4ezqtzUuFs47Lf+gQ4g3QB\nD2ra2x5a7THzp8P7EcvEm56AWt/wov1RX+NRfy5Qzxq7eFA3ZT3602aBJDX9qd4NCcT7/W4KaU0a\nGrD1yc+p6HBOekC78zKLB1PSQT4fnItfpmcN8O6eMd2FwSC7GBTi1K7VpsDTRVLrFi6SKgBn7tDe\n5cKwjK5AbqMq6lpaOTprBMA0+YiK4GPvKJZ4bPB+hL4+qDVh3/s+srQMdGq3eqh+JmB3ULe/+nYc\nkHQ/i2qJxYJ2XDa+E0V9j03eGO8Tj97HVpUNND/7AwgPtAm8m8quXPGo5X4yfRNAB6UtvjWjVY8w\nA6UcXyTKWFPeerjt5OfLKsZdJ/2pTsf7l6xEQwR1O0c9pLfS1OzmAO4tjwIOTcY34qHaQ22P7E9r\nInH0qmNpnlV9OFCf7Y/62hrqNq0NP3C1Gup7bn9nNdSXEolf1Q/ItsdXAmuvpj/td4Plob70TEnv\nVcelYsn71LWfe1k0eEsugNkLBg/gS8r6S7VBrOs2wF0oDcZMOgzY27IBa/1G+tclcWrVi0U8cYG2\nWiRNpXV7hEVd69/BGmFy3nZX2SiY2CIfg48dwqttAnwttu9B73MC29SzfJd/RdgDFXzUbdLQ5UVR\nb9y6n5352BtXPGDr04nbq4+0QY2o7GZBRWukMqHIxeGVtvmDsikzf9BbJd4TDA82buSSH0h+/x2Y\n27wOZx3Plpy3OnS6V9IEmC/doN0tj5Lsjw80TyZ2YPfxWSJxBuoO6PGdiS8J6j7t+aD+qn64aHt8\nkmHvSauq9paH+tIKbG937NI9grVOrCOoIfMGQJtw6OdhTnzbNDsZj8/FS/HqyhqV5vOc4NZhs0lE\nWetfrc9Vxd1gxGAuzgPVCz7aIwyYHaJ+tlaNKKQhy+ylwRl4wFf40C0RqyRZe9m2HwtgR5tC9/0L\nAnYObbiSpwHRFnHJx4J2wbR53RppTzkd2lrSqI+gRLvBeXdA1nyHB3cf1+2nbrMxRRhPwtf790R3\nn5crlQKsEZPdwf5AgzQRLy2PIio6JxG97ZFBvUokrhT1U0F9KTKovfWxAvWssYsH9Vf14+BPqw3y\nUO9CtUcDc/Sm7+u2VNOPAupTkK5dRWsZ6RTQCucjWPtYwfrLVNYEeqQAAyuhsquiLzu2GEMHN6tF\nIv0rF24sLBW1biiFg9Jm5va4w4TqrJGZygYQKkYAmC2i0IrQpj4OVx2i++OAbe9RdH7y57ZEXhLY\n5mGLjz2zRTT5qH8fuaQEZAExY3fT9amG9OlHPETS3wkAbSyPnS4R6cYBGMDb8GXoAB3YHuy5jHQG\naF9GqspZ52dfWsGsCUS1P7Kantke3p++BtSrt5CfAfW5dyZ+HlB/xR+X/rQq6k/1bqj0UG96pqbV\n8tirq6FeQVrPLeubZgHoDGsAHtiUp6/PwIvHGngLZR1sEAap7QEEdR3eLOLnKbhdk+Wgtrl52Vyl\n7lqg3brebK3o9iKeNhO2WrCX9ljf7RDXY5/8/YbaIgr25GMDEIiTa/Eo9ZvuxO/9itSgsF/bElFd\nBEZ8sYL/rahXiuhLh+37nI9t00pdquxqYG4quzVEcC8+dtaI1rEWKtOGCEBv7utrW4Foe7wErHXS\npQZaWxpXX1qHS7A/eKmmN9SpP72hGpx9P9QNyq9nfQDrZOIRqHOrRC3JyypaE4m+hlr9aQ/o+3pn\nqvp+34Kavq/bIaQV0PovWB1VzpvqAC0taY+UNOn8NL2Nj+chhfkXDzmAN4C1dnQX/GkZtOke4kFV\n63Kirgukr2Rq0JauNokAKgRwhHYptSltJjB3P9usEW5QNM+aSSpEpBa7aMvHgm/gMfjYKO6VYS7x\niHJvP8YO6YfElHY1Zf25gL1J9YZ/a/qGdjF9kO+559IrV0iSj6S+dDxFGrzvml/dJnQPm7elyq5c\nzRrpLTkjtO+oWtaeJcegHiPQu9f18O7jpKdYP91OwHrV7YHO833XHHV9oGDWrg+yL+0tj6yqN7SE\nYiGe+tM+kXhU8eGbj2/E+CDJxA0N1B/QGru8BKhXycRZPx+zGmqt9JglEr0/HZS0qevmVd/LX1XT\nDwLpbHk87tsU0loealZHJRmmbtlOlDR5cMPNB6Yvw7Dpq/giYQ2ARFkT3A3HZeHbdAdqdLvDwF2c\nKY0IaqExuLbGCrVQe8tIVtrc+rDYCmErUt5nKs4nHUWDmrruVSMAhtK+Du37tkD9GN+LaDuvw59X\nYVfU1h8Jk/veVvPtgW1PB6yvMuvHf/yOnnjMtoiHOCoMYA/cqme2TeFNE2gXeSMM2Xix34NQtr1D\nWRW42CDXdp4Tfg4HZwBP6lRsI/dWI+r903jLYyMe1LQB+oLtsRFfDertM4G6j78MqFU5z/zpbHto\nEtGr6YfaVfWDU9JVE4rWN01U0h3O6C/LCJDuT8wB0DKP/DgyqPu5N4D60vgiXhXWJHatjUO3M8IZ\noq77MMwSYTJ2ybia17DKBGZZnju0SaHNFczSAKOwJSHVz/YqmwXYAJy67sAAMPrYQKgU0dh8/yFx\n518F2MNb0zOw2f04F4A9vIWmz2hApl4t8sBbry6hVjFSSoN3YTY/+7GWVmtt0CYbDyVVOkytZt1U\nuAzrQWX2B1h+Kxktk33qoM7g7oC2cQdl70l7SHf7g5dqWpOIG9VDUKs/3ZY7D2q1Pl4a1Cvr41pQ\n+5I8BbXaH1/VD3isRca77aFJxPt6Z970w76Zmn7ct8GXVlhzTZBWq2MGaVXRSV17QJObDsxVdTjV\nFlA+JynewAah3BspLf4mGyQmG0kYphUhSW0rtCvalcntBQZUAOINXBm8VSkDa59VawRASEDaOxtl\nw3zSERBYI0K7jadKkfoxWCK2rwtgv0qVCNhekXsNsLeDEpitcAM0ENS19rYYQd2U+WPdUDbuHnaG\ntssXeEBXsUr0Jqv74tV33O4xvA1ioHbjWVnnPtVnkPY+9YcpsJOqdraH96BfAtQFOAT1mTjrUb8E\nqH399Kf6wZT0p/0uJBEf9s3+em/6cZ+o6b1ET9rBeoC0V9HiVSuE7bKfgRt+nju/gi+3OMBforIG\n0KpBANuLaIfAWSF9vM3z8JajI30hD2q7oIOOYfWR5mkXgFGCNbJt1RJWvHUvG1Cl3RT1R340tQ00\nHxvo0DZgu0oRY5tYIutqkTesEkEH9i4AAAM7sbtpyD4vlPWGicet8C6txK/52hWP+rdu2LauuEPi\nMSlrg7TeILkD3CceFeCnXsKIDmk/7C2QFaABLCG9DcCu0yRiAQ/+dEsmxoqPjeqTQJ2Tibaf8oM+\ntWXiLJl4BGqfUOzedE8iKqjV9lAl/alupqLVp9YkYgR196b3vURfOkNak4YK7LZzDdIyPYA4Qdvg\nvLRA3DSNk5bcUbyhsib7Q260Jxg7sLUlow3rhwqAKg1iBIpcuD26CMS70k6edmFg42CNlELgranq\nrRDutgaDD94WYcJH+Ws+NhdUyZzuKPhV5cEqRQAYvJc9+E2A/SYNZ4BJ0rG0H80Bu3AZ1HZJoC7U\nfOyHetdtEWmSri0aZ6D2w5WrA/X4/ruNYP42AAO3DvvIKpsSyD2Y2/4soE29mf0K0tGj7graK2lv\nfRQ3/NFAXJO6zpUfvYvTFajzW8ghZ81LNCE/qvo4C2pveagvrYlEbY14X+9wv2+D7aHe9MNezJve\nZXiwPPaJkpZhszkUyBXd4sjQdjAe7RBnhcgy4S+w1g5forImztUgOsP9VXIHSHNcRhR0E31k49A+\nkcUCIWGSKW3vaXNbfLRGaFTZTJZ8/AABtk8ylm6V2JvSpVKkQZq62gbG1o66XwtL5KWADfnEJWAX\ntDrsFbA3jGrbWyOFuhXibZGssrciFSRijVQuAdrWipQJlZtarmg3itnbpRXeejzr7DinKO5Kyb61\nV9AzQLdpPIW0DYs3rWq6QBOKCl+nlKkDWsH8HFD7bk71t7/2VVzACGrt6+MaUPtWiQpqD2kP65k/\nnW2Ph31rlofW6KuaFssj+NI1KekEabJpiPO4X3IzOGc1/SRQX5jn4/VtEDkHgv1hM0cLpI2TVYM0\ny4Mba3Q5BYd51iyqmxpn5KAGaG9yYTs/m1GmKvtO5JrZIilDNXv1PBATjhs4AFt7uQvJugNgP9fD\nbt9FaO+AnAPbq3ltOOOBvVPFRxYvWw+I7PLuzkwPbl890PvDbip7pyKJWTZrRN8s7QEOEiAntd2m\nl2CL+L8rlQ1E6yOPK4jbcF0o6whp70urUtblPYi97eEBfBbUm36XgHoTUBeMoN7czeopoJ69ius5\noNbGLpdA7f3pme2hScTHKqp6d2q6ltGX3inaHQLvKaRrtDiC0l7fpumUAAAgAElEQVQmGNuqA7xd\nPFdRa7xpglEttOBZ+786PPwTKPuko8Kb03w5yMzOHinypZt7PZSqbZHe1pS59COaYdCny06VR+s2\ntI3DEo7dt3bDsyBRcVrh4jxsf2MKuUvwRWB7O6QuzpICQKtoqnxXBHZPOt5Tg0IHdj+V7hn4KOWK\nD2iKD2jgQ5Wbm6hr/as3vIKCStITH3ODM8gg7tV2O/bch/WoLFS1LpdB3b63w9kvM6rrEdLely7E\nQU0PsHaWh1odHtQfqfWcnkHtm443QOu4gBoYQJ0Timfi0jsTK7RkL7ZMrDz2nKfWRwa179/Dw1pB\nfe/qqB8E0o+1t0h83KVf6koO1B3YUzXt4Iza7A7y/rRC2qnoqVc9G5bw4xcTi5fmT+IN6qz9yPyv\n2R86LQ+TCkPqPfOJ2iaCqWnuHYRAy/uYW0vGBmHqfvbGHdLmZbe6YEBX02ySuy2WtGSlHeIA2Noq\nMJf1bfr8lfoS2XRLGNLIZcO1b03vm7WuEAERdndz0N76CjE+ch2rRC4AWxtQgDegPOKh3pnKbPMp\nqOxWe92hXbmpQj/ee0iM4LbfZJbQWfxMswTjDNAABkhnyyOrabU9MqwvgVpbjnZg7wOwCzqoNwU0\njT5124/LqvoSqP2LA7RTJt+E/FLVh/eoj0Ct/rSCOvvTj05NB9tDvWlV0t7yUDCbik5KWiEtf4MV\nsgA0+Wnoy9g8uOn+NLxSUWt8EaV7JH+BboGQHyZMqkPQfgBT1STs4Q4PhbYoan0RK7uDbtaIDm/i\nZW8AmcoGNrVCVHFromtriUdAHsk3gYmHRcH4xpUwHMv67EUAAuyNWlkUEF9gUM1P7neDM8D26nrT\nu1sCtj0fuJuFvgz4CNhexak14hOrRVR1laboQANuVtmXoA3ApqlNkhX1FNiz45GSijptBug2zyvr\nrp5VVQ+wdv50hHVs7OJBHZV1r/woAuxW6dFBvfq1z6jqo06ZVm94mfeeNwd1TiYqqH0NtQf1/b6F\nRKL3p3evqHdX6bEXB2rAyvBq/uuGg4Ju0z2wgxWCBOcJtPMwEIdXkP4iPWufYPQqZ6WivU3S+7Zu\n81ghDRGIOl7lBayEBmeRHx7aVBhcxM+W+d0OcSpbKkYAWL8UVV4ldufcDq1I0IYzPkJzaedh56Tj\ntKWjHhNudoR6hWffmg4cK6mmyF3iUYC9MwvE+02jd0BVArDt/ZOAU9iPaTx+JyDWjbNFCjh42UfQ\nBmDgBnWbRKOys0MOgD361h3ObVuPAQ1gsDyymu5wblAuxPhIjx3MVKegLlTnoCYWfzqC+oyqnsVT\nQP0wAbX50wtQ54qPTwbtbnkoqL2iflRQu2qPfVdvOlV6mKK+AOm2o90K4ai4LwH6ENpYjy/jS4Q1\nANDubzX6l2xcVfYA8InCtqd4ncbxL8ld10NbgdysEcD8bKbgX/PW/lagJRxNUY9H9uhYV7S630+W\n9JQZknQsaX1qiRRvjZjiHV8R1tSgHgjAJx2BmNX3kdX17rZj0y90wC6yp5ZIJHkzeirr0+FufUAU\nt94UFAo8qOwH3lqSzkA9hzaAObj1mDsI7wew3jKsoWCOgNZ53pPW5XRck4ubQXW0Pa4B9Ud0YFtL\nSVf54W/BGdR9f8Yb9ex8eC6o1Z9+CqjV8rivd/j02BOKHtRT22OnrqYrGqiZWncWzvIgP6yqOUF6\nAPUM0Pp5uGn+r5sOAOSv62cqao3X78hJxKe/hrRkKgLaATzbIDpN4evHV9BWOAdgA+pnqzXS35fe\nlmMmYOv6s7VyjBfBpcdt/3g9eNhpdRvfCQi9LeH2JwH7wdUoFhTxmvtKL7VSW6lrtUP0WWGTafa2\nGfiLvF0FVXzwZk08wk4vBkDyPXJ4HwBoSeCD2x57ubFYI2to67YTNtoTuGW7mKbJxLD/wa/uIFMV\nrdO9kvaQ9mraA1o7wzoD6vZ5r6hrALVPKALRp549N3lVfSmOQL0LqHccg1qTiPcO3Plltt6jfuAy\ngPo+leZlUO8CZqv22Et761TwownYF2o6wDp714jwXUDbTo9BabNNBw4U9iq+RGVNECHmnpw9S3xi\nkYhHeGdw82J8Bm0PZ68Ck8rW6Wzzm35VrUgTwvJCvS5DPOxS5a0iVgNdbTj61m28vbWmbe8HsStw\nAGxNPLZ1xwt4VRGSw9shu26XbFMRcO+utA9cpIWj/B0skj7c4H0H0G62yEO9649MetOx/dO1trf3\nFNLGSfICAwE30BT1Ud533M+uoIFYFTJT0hnS9vckqP2r1D5gd5/VLmn7P9smyE0Tx2V6Z8JDuh2v\nEdS+YyZ9C3l7bR0FUOsbXmadMvlWiT6Z+OhqqL31sQK1+tNVAM37wvbYaa6md/TEYVLTAcqWYMSh\n7UHCDA/mwfZwl9gRsM/+cm+SYPQ3/qFxjAN09KzZqWuKMJ7YIAO07e5Iosi5F+J6kMPZImKFtE0X\nY3sTBemAfQZ7hWos6wMmlSIfbbW5/rqoKgUwNpohqX8lKeXyChsB2qvIVggQ7ZDmY+vOMnaQgXuT\n7/I12K0apP1dA7vgAx4F2ALo8mjqeKqyIf1iS8neRtIkHv0JR+GtMSvjK+l4eFWdVbRO8+MzUGdV\n7Ss+GpgZ3YtuTcg3+az2Ge59am9/ZFU9i1UycfXbH4G6InbMtDOFWurZiwOs/+nUKdOsjvqSR51B\nrWV5EdId0KjU2JISiKSg9jXVXml7Je3hfgnQfh7cOPq0o4qQEF+isgYDRTxrFhKT/ecUt1PYM89a\nwd0Siz2h6AWZFThYXbWbzjBrhH0CUlX25myRTQjDaPBGkfI+AriGSpGjKAkogMDAd+BfgA13DX4p\ntmBPoCnnBOy2keqrtruVJh7XF/L4VLBJ8sCXwvnqkMpzOwRULeGoNdgrYBdT1Q3YvvzQbBH5MVsN\nsYO2QbqIJbLbNKDDW4/3UT2Eh7pX0P7vJUhHQM9BHZOGHdS5lnplf8xU9VPDQ1rHV4q6Yt7oJYP6\nK27edO6UqfvUI6g/PfZOmR5qwf3jJg1eYsVHrd2jNn96Znvs3YPuUKY03o5BWO5QVfNgiwBx/Exi\nkSbXtMUXCWvASvd83wzd+tBxmlaCLMFdAZSktlVdsxt2ZXrtn1PUedrGGGwRWRSb8vX8JVN2OUvK\nI7DfoYDxSQ9/Lu0bbfHBFmnDo8K2jSVgTDzq12lFxpX2jX0e7XvI+cIB3N2/zsDeBeLN1lBVrVYH\noD622iKVW38Qeqct1F6vtnNJlki/GSq8gQ64w/0JvnUHtI5v9oQSId3mz22PbepFi4r2NsgAef3+\nbn/ofmRVrRbIKvJN2v/eqqYBTEEtbFv3oGfWh/rV3as+A+rW30e2PuQNL3ur+LCGLo+uLG8XhSzD\nBt292xv9X5xm6nk1bMBOCnoC65m6XnrXbtpz4k37s24T5Rr19ge65dHmucYvE3A3ZSyfsZZ/6H/9\nsJ674fHGJSB1HpT0YouA1AGwRa4Bdkh0lUeUOjn0Ba3fZ7SOj4Drm6XPgK22iCaeZpDOFojGrDJE\n1XXbLlVnJPsZ/WtNOGqVy+Ygfgxs71trCz1p6ajQlh8/wznDexU5+ZiVdYa0H16p6Qxqb33YNHS/\nWj/Xvp9NVbfv76r6qZF/66ym2zEaQX3UjNz70751Yn4D+QO3FwSsrA999dajr6PeJ6V5antkUDvb\nw5TzPlfTxp48HOwQjuB2QDZuBUXdmTGAHH15jZVvfbYq5NWVdXGlex3QNAG2hzKP4J6V7Cm0CyK0\nFdLu4E9Vth7ZDc2w475yhgBb5+E8sIkYJTmNG3F7zRW7V2HJG1d8SV9u5dhet8WtPw1QaJZ+qLCB\nrrIRKwZWoD6KXsqnkPbquts2O29yoxFww0FYgN2rRObA3mh3b7LpAPfQ3ghNgQNtHbafso/uN8rv\nk7Ttt890MOt4GL4C1JYwtGn9b1DRTsVkVX1t6O+pv3H+fb3t0Y7NCGptnVgXScVY/SH+tLzhRV8c\noJDWdyVqUtE3ePEedbM+MqhLB7WqZQX13pOIJBtPwbtGUNDr8RHSWU1nQM9Utg0DXdj4ac+M108w\nZoFIMgNONcuICeWgptk+GNS2K+NrQrBDu/nRcN40RpWtW6TyubhpYIAdsB2bZ8CmZqVJ95oFD2gw\noMootKHUboFskizz/LAX0bpE43FJn047AjbclpYpoJ9ii0TvOqlraPVIg/EOAXeAsVgjdl+cABtx\neAO6NaLzgD7fg1uiYGywFPcjAlqnZUgDuArUVlWyAPRKVfftjhbIUVletj1mkAai7VFluQzqWYne\nziWV540levqKrQZvfbntZp0yXUomcga1JRQRE4kTf7rYchM1vbQ/OPjUM0gbzCeAbn+5T0NaxscK\n2l+msubYKAYIPnUc92oaIKLUkpHdOPUn9Rm0VTm76VOVbdUf8sgfIO4UNlM4vhnY++R6KvuYhfS9\nuzULpKnrr+oHs0SktmJZ0teHLwBblttAF6G8nz17JvszU9feDumKuk/f4AGNAOwCuM85QGOEto0D\nziY5sd0J0H7azJtu33UO1Kqq7TOyXN+HqKr1u30FyDVx9Ntm2+MaUN+nVoq58uNyo5crqj4WoKYw\n3BW1WSGmrsd/o/2xgLRX105F63ygA3quqHHYIGawgZGWP4g3aG7eN4ydSrCGMWJvqOXRh9mBOoIb\npR24AG1vj+jdL1kjRyrbfGydpiuUkj7PSl3EgL37Fcb987FqsDGrEJklHS8Be2d5T6FAL6ps9aNj\n+Avaj4ftC9/drZC4bwJXTTZCnxIisHfeBOoLYNt3tc8FeNt+OCW9mr4ID84ptJ0aHofPgdoD2pKL\nTlW3be0VINdEZUYhst8p/6b+9/O/6QzU2oPeDNQ+oejfRJ67PNVGL48cXxyw17GvjymoH0v0qB2o\nG4ydzeH96b0DtSygrcnDrLI9pLOKzgp6mVjUYf1J3c+4hPGV9+JTsCaiXwTwK2hP94/M/FuJ6DsA\n/HEA3wPgFwH8KDP/rcMVyZ3Qqj/AAR7BuCbRseZNR3CjuGk8gbZaHnoNM3qpnnxHYtewrcHH7hso\ns2kKbCZu719MdM0NNAox7nEnNcMFpW5NUaE2i0QqRIr3X+VH98nGBgN5pJgpbOhwUtm6Oy8Q3gpp\nAI4rNnCruvafdTbJFNjw+1Swg9yNAKNUyXCeSRndrnS1BHAnBT0OXwdqVdV9v8cKkGtjRyvRVGDr\ntBz5xutBbXXUiD3oVS7YUQZQ+8qPnFAc+qR2Lw7QFwn49yROFXXo5yOCOlR8KKidPx2g7P/tDsIT\nO8QnGL1nvQK0r/qIituJO/8XOn/xQ578+a9R1r+Dmf+mG/8JAD/PzH+AiH5Cxv/tSyuhyoHJbaLA\nkxLAzQMZwd2gLGq7OGgXACTQLmQqmou7G0r3qEFZuzsl4EG+AHZS+c2kRnt8Q2w4Q8R43Et/h18t\neKD2iqv7ZI+012H1hKMyf1UhYq/Sov08sAG7yI/CX9w+jk2Uvn2z5WbqOiccPbCtssP72ECHsAd4\n3n7X9Hy5rdmCoHSML4D62pjZHteEr+rxcfRbziDdhjuo1frY0VqE5hK9nFBs/nRLKLZ3JbbXcT2K\n3eETivaCW+nmVKs+rKELa4OXufUxgNrZHgZjdsMDsEc1HRU1p3GsIe3V+ATOswQjcADqK+I5NsiP\nAPhBGf4pAP89LsGaYZ51BDYDxVWEeHjrXjpwo3BQ22AHbYao7jZdJa1X2fqdqo6Ptrd9eKWwxyoR\n1uUJTUtRxS6gpr3Yp7VCpMG7DbcOn1Rts8HZV4g0QMsrsxRwtlk7NiY8OOBVZnmvYjseOzRJ1T94\n+Ois055SMeKsELU8zsQW4NwrOdQDr1ywif3iVbaHtpULnt1O/W6379n20G3woL5GVZ/dnlW0hG0L\nfW5b2R/AeLM9ArWv+PAlemNScUwofpLm4zrsE4pa+aEvtq3Wzakq6oJq5Xno5XmuhSJSvbQB/FpQ\nOyWt8yKgk7r207KC9ssAGJKMtZ9HF0H9wsqaAfxJauT8D5n5JwF8k5l/Seb/DQDfvLQSghwgN26+\ntavhncHbg7tdnzSFdrM/yLpM1ZcIsPoQ7iAzp+SjO2jEQN2QXvCbgE1NrRqwZXJX14Aq630vBulH\n7eeCGCTKukG7WSH3/tctSH2ItIQj0CtE8mZ95BqADZbeTUi9a1XZ8YLPkdHi6yn2kyeYj2B5HKjr\nUGbnFHd7IW51TwtJVfu78ZVgzJD2086C+jlxya9WOANdXYdpM/vDLd+WiaBWj7or6wjqOaQnwHad\nMzVF3dS0+tS5vw8r0WPpFXPV4EWrPrzVYdM9kP18D2Ye5ndlnROM2QKZqOgJoIlhYM7VIG1a+l0m\nP/FZN/IsrH+Amb9FRL8RwM8R0V8J38/MNMugASCiHwfw4wDwjW/8/SBNMOrTLLgD2zxpageAdE8Y\nVCiCWx5dWt21Nhvv9ohB2xKHDCv342x/yDw5ifVvQVLj3sMmSNlHUti6KDqwaS+tnM/9KkSMh13f\n9t38aw3rE0JUdCkc/OtNli0hAecP+gTYYO/tWPKxK+15jOVf8+GXiuLgDG520gzYzR45UNVX+sAz\nde0h3cbd04iAOq/jSFX7xKL3qy9t686+XlwTpx3Y689FSOu6tFOmc6DexKvuPvWqheJj3SyhqD61\nJhT1nYnmU9fSuzl1fX0sQe096gzqPQ9P1LQpap5C+xDSzhoBYICeVoB49e3j6Id6yWoQZv6W/P02\nEf0MgO8H8MtE9J3M/EtE9J0Avr347E8C+EkA+Pt+zXexKeuKlGiUkeIA7uGt/qvzrU1tiwSnys2O\nUE9b3xrDFBvMTO9lDbc+NTg/hgeWiG6L/NN9qBQrRAoxHvf298F51toV56e6NVDUDzZvoxr8ayAm\nHDfi1qTbbZYC214cIMBugA4/0mxHLXKFslfV0ze1n4iVd53XV1w1iVaNZGB7OGf/Oic0h+1ICvwS\nqDWhaMs7++NzhFfP3frowD4K/d2y7aGgVvvDg3q3aXdWmnfPd71TpkkLRbVA7quOd586vzNR+6RW\nj9p3zHTU4GWaTJyOL0Cd1HSwQdgNu+ndCuE1oDOc9TQ42ygmlAgfx0VYE9GvBlCY+Vdk+HcC+H0A\nfhbAjwH4A/L3T1z8NvfI0Nbtv6hvu80rbp7aHVX86a1PC9CGs0eU9koD5932L4T9KDUBO/CM0C0R\ncmAGQA7Ymnj0u8zEYGr+NRHhkYop6+5fM+5J/GpszQopj9bC8UH8XvWvAQwJx48E7GgNIzZmefWW\nWiGlVQ/o47Y7BDn09pEhvVLVRx38T9dP9bCkzqtrXbf/TCGeArtte4e2revMNk1tkBHUtgz6iwn8\nOp7qS+tryfx2WM+G6OraA7t/ry6f1ol+Yz0CtSYVdy6WUNxBuBd4rxq+KKS1VC+3UNyZLKFobyHX\nV3H5yg97gUCG6wTUh/YHT1svlt2DeKKwuX1O7ZAlpGeATnCeJhgXMD5bX61xRll/E8DPUCPrHYA/\nxsz/LRH9aQA/TUS/G8BfB/CjZ76Qdncyu3q23JkTaAJuN8/+ZqUNRq+nZpdg5O5bmy1AdkNQ3TsD\ndmglWfVr+5tWWDdS/BOrEJEVcCVUatMrUQD2XgilFjxSa+H4SJp03KxxRGHGQ212yEO9ay0cgSHh\nGA+0ewu5KWs4SLs7umNtBgGQweyG3QfPNkDRxOA187O61gqRwRIBltA++r7wXRdAne2PadP1K5OJ\nHso2jcluBgZoB2zA+9U98m+lkNZ1zkCda6kPE4rJp1ZI28sDXOdMlkjk/hZy9an728fRKz+sTw9K\nCtgBmTFCOynqQU0HeHt1Parpttwa0kFBM49wzkobCAL1OXER1sz81wD8lsn0/wfAP3XVtzH3BCNR\nvPJ1rwWoCsg2Dz0npq0NdR3ZHmGnrH3LGA8ou0lEecnQ70zATtc7yzaRAtqBGbtX3aK0d6kxlsQi\nyb5VZ4eU2gB0LxZIrr8eeuiDquzxJzSYkL7Utk7flWjNwf25tGCb/6lWqvqplojGTHFvxIO6XgEb\nQFDZwAjjVUTP+jp1vFLvZ757Bmrtx9vP98AGIrTD+tLTuFfTTUHTAOpWY10Gr3qXaQ8ThT2U6an9\noW8frwV7JffuRHJNyYurpRb7I3dzOuvrYwVwBfKguiPAoQ1iFNoTNU2V15CWdQBdeY82SFLY6J9Z\nxpfYghGAu+o5tMgjqwrRCTyCW6DIJD9A4d6xk3JXPOlePeK87GyLIA/7iMCehm6Tn0BsmUR7RRhx\ne+yj1iczdhrsEI0CxmPZQjnfrMMnQGEdt24jUdpONVsf08xoXY+2viEK2B0PdzgWESC9UNUKg2vD\nq2fbFw9h9GTjGWC3bTn2q/2y/jv7vFFV23ITC2S1X6toL0mI86P10dS1B3Zb57oax0Na13EJ1Arp\nBmRt/DKvp1b749N+F8r0HmsJPent0qTc11OHhi/6zkSDNNz7E9u/kitBjhT1BVDHxCLbNJ88bP44\nLyE9BfRQCcLxr/9Rnhlv0EWq23KnymxQHvHJKkLIgbvDeaq2i0ys8ryoFNekorNFFNK+hK+9pbxv\nq1oiy2KJXdar0AvKWjuaEng7hc0o2HdYGZ92+EQAtlKsnE/D2yGz+muNlmSUz+pDhR9W0z3dq3aQ\ng8/xXd5DAOigzoB+rsoOPrRT10fhga3r0PDgXivhy6A+skCea334d0aquvbAbt/Jy2vfH/MZpM2b\ndoCuKIvKj9Gn9i0VuwXSy/Tyi24rU6in5upAbZUfTk0HCDtwZ+vDKkJ4AXOvoBOcuS/jbRHYZ44h\nPQA6w1kfaoNnfXxNHSYgXbydsvblFkSu9KWraV2GpM8P86QLBbXNDNCGbkVoow8p52s5No62SPsW\nRBKzKXxG97BtGWfLBOuD9EYis6WkT0Gv/jULzJu10k5kIuCxbeZQzqeNZh5Sc/QHbKH+GpDKktzo\n5ASwDRp+uQuxAnXuO3qmbM9AfOVrZ/Wd1bWfNnz2AkhnoF4ue/bqAqBvwvSxo4TvCyB2iUYPbP3e\no+M3+12uAbWq68o0wtrbH6F1or5LcVvaH0M9tfeqvU9t0I0ler6vj17hcdmj9onEbHuYLbL70j2+\nCOkB0BnOXxdl3Q6A7IQyw7emU4ArvJn6/CIgJQJXbmW4QiACgXfuKltbgLiGNgwCFbUoOrC5cLuL\no9srtrk6TmpnwNYHB+E2q9ktEeJoJyn5z7T11CIJFUk47pVAVATUrZn0o3jZjzTaIajo9ddUA6gb\nsBhAajQjwxWMnVq/2PYCXNvpcz/lCtQKBo0ZXC51rnQpZt72GWAv1/dSV9OJyNaHHw8WiFkfbZ88\ntJfrnvwmM1CrR63AbssUp67v5mV6Wv0x6Z+62x8usajNyd2/dl1Gn3pooZj+ZVCvl+U47JV16h/E\ng7p71gzs5yG9BPRMXfv5Ob5Yz9rbIF5RSyfQ7FUxe0AnaDOg9khQ2ZCmKQpIQLIxqnQV1COwNblo\nH/RwTgp7SIBCwS7/KgHE4OpelhAg3m5EWh2yix2yFfH7pDrkjtpjZq4O0bd5P/AWO3tCxT28B15s\ndzuYK2Jf0fLY7fzRMzEDtf3MDqbXgDOv56lxLbBX63hO5GN5NO6H1Q6J1sflY5IhrdO0r49LytoD\nu2pCUYDc1HY7FytTsD8enU+t1R9WU80e1HCATqBlnUZh2hGUg0XirQ/GHNRObQ+2x85BTff2IDLs\nQNw+c0Jda1xZoreKV3/5ALR0Lyhq9TIE3gO4O7ShvfbNoK1tLGQVvFE/6AZWD+q5wrbpAdZqwyDc\nHExJA6kSpH0X7dwVuKyTBea1NHVBVFALo9ZyujrEt258qNJLH3hexodHB2zdrwRs3W1/rDAmzjJI\nM6hXqlpBYeMJpHn8JeIMsDOQZxbIzK+exX7QH0me560QVdfh2NqlEa2PDHsf+elGj/mOslTWOaHY\n3wgzsT9m1R/Or1b7Q0Gt1R/w/1iBPCvT6yDvEF6D+lIy8SKos+3hFbZX015Je1AfqusrgH2S5a/+\n8oFhp3wJn7ViJBkdAW3Trf8PB20kkAKuYY1T7AHUTkkv/GyFNJFLOPp/GiQnXC7nq7oPHKbxThg6\ne5LHSP2+M9UhQPsu719vaI1g+onQgd0WL2KFEPTltP5n0lDF7cN70zM4ezBnWO5Bcb+sgvZWyGx+\nnv45w8M5+9Yz68MDWz1787AlMrhnv0P+DRTU3vLwJXo707zhSwL1p70nFbX6I9ofLbFYXSdNofFL\nKNPDqLIvdXW6qKUu+0lQqy1iDWR4rqYvQdqr6KCuJ6r6tML+Qm0Q3yiGVVED6DXTAm+DK3XFrR62\nQhvNTrDxHQAxeGvlIaayAfOxWZzuU8DW0j8V8CTzteMoimrbPLWgupvHDmpKwpKN7b1f4AJpNNMS\njjuAvXIDtdghWy3oPfQlO0SO3+Z8a2+LBM/aA5tUWQOqsisWibjJuXSkoPv0Mkxr3+aW4fnwS8ZL\nWCLAPFl4zXL6VhuFM0i3bQ3svg/jjejMjVJrpDOoW4tFmtged8H+UDVdQaHLU7U/fFJxtD+wsD/m\n1R8z++NSQjEq8ANQZ39aQZ3VtFoeE0gHFe0V9FFyEehP98+M17dB3I6QQbnNZGeHDOB2LRFHVe3G\nS2lWi3rZvhrEAVsJ2w6kA7YOMuQuqhYM7K4KKc2zlo2E7iZUYGiOLgC3ZCPaDYYF5LW1kEGtY7Jx\n4+5hqzWS7ZCtOFCnZCP066Cvz2rjDRaPEdjAAO2jFoczSGdF7adfA+MzNdJvFb51oU1beP09UdgB\nHr3qsgS2xuo3WB1/b3tkULdqj1yuF4GtSntH65WvMoWkYmXq9oecn3ttSlqTiuDUnBzy1GnJRAr+\nsl1v2f5I0+Gmh65L+cD6YFwEtU47BPUM0gnQAcynE4zzyTle3wbRBKM1gunAJqeoA7gBA6dBG0lV\n6zgqqJReMVKVvxSArUeIi6j2XewLm+csEIMsDNqDby3q20F9ZCgAAB1RSURBVKpDrD68fR8XFn9a\n7RACRFWL54FaCUUSjFtpSkpBvXGrO67y95Hn1SHaHL2CUUVRNki0n/pjUNZ3BuxqTbY7tNv+HivJ\nqKTXoLblsZ6Wp5+JM4r5uap65UXnEry87OxzwR4xMI/A1hi8bBf52PvjvgJ1h3B7E8xu1SDNq9ak\ndUsi5r4/kkdtoD5Q1YyuqpNiXiltGIAVzLGnPLMxbH0e2u6zutzOa1DvNdgeh9BuBzcCOkP6SUnG\nc7R+g2oQ3bAO3gBlCfLM0PkkP7pyJKvsUjqwqXVXSkU+XhOwWU8k8aFdWZ/18sckoG5wDaV6Ml/H\nQ3WINNJhU+dOXRP6DaNKIx8B80pdl1qwU1bXCu92AWqdtbdDYjxiQ8E9RmDvkL4sTGUDA7QPIj6O\nz0E986ovWSC6rl4/fLwt2SY43uYRtE+NbF8A0Qo5Utce2LI2AO5tOwfHPx/3/BSTQd1L9CYNX5z9\n0eusYwdNVStB5K8lFYOqdo1f5JzWpGJIMEJB2+0R6LQE9ay0h3EP7eBts8AVzwf1BNJLQHswv5AF\nAryxDRITixyqQqw6RNR2gLaSMatsg3Rp8Na7LeD6tve9TiOW9Ymi72JIFHa2OlRJq6quXWX7Er3Y\nd0gb1vWbuqaurhkFtbKp66FlY2UUmiUbKx5rMTukSMKwgFBQxC4puOf+6oIAbLNHqlPUegzOAc2r\n1xWoZ171DMCXLJBr7JRrVbVvRXgE9N6y8Jy69sAGI07j3kw+Q/vsvnk13T49gtonFCuT3eyz/aFJ\nxcrF7I9gfYiqthaKvgIEGEr1rEl5UtWXPeiomv20Pt5L8OLyjFD1sQJ1rQOYA7Tbwe3jGdJHVSDv\n3gbZpTVMcRfRKrE4sUjCdeqskQjp5lOwewcSAT0ZOAO2HPfWwE8gDTgYd3B3GKvid9MS0CPApZ8S\nAbk+ERisCXbSawvHnYCtVOxVG8z414H1ZCOAYIf4JxCNj/QYgL2hoCUdO7BbX9cd2t0euRxTSyRB\neWqJTOwSW8+BWo6wmnz2AqivUdcK3VXy8EhdZ2Dr+gDYCbOG9jpmN0Vr5KLDzvrwCcXco162P3an\noBuoe3JxlVRcleqZmlbLgxXMbv5ETev0S/ZHbvRiDV6uBfXOczV9CdKXKkHqpXPsHK1f3wbRnWid\nY/TpJZ2cllhEhLYAmqVKxFsjVKsBG2jg5U0VNQZLRJOJ+l7IRltVvCz0FKsi2yF6Ikmlh00TaCu8\nFdCWuBRo24sRzLMGuDC4NnXdGsoQCqN52FKWVpks6aPD1n1qskMqF/Gum399j7sAbJBPOlbs2HrT\ncw/o5KFqXGpOvgL13PKgsM4Mqwz2S3FWUc+Afa26ns+LcPfA1rvoOB2DZw2sO6aa3RAzqNX6UFBn\nn3pnAbqzP7TyY9VSkYHQAMYsEIiqFsCGUj0916fqeZxmYF4up2B2/xycmz3S4XsI6om6noL6EqRN\nXftr56RsPhGvboOwrwbx8zy8Pbh1v3WS2iMVo8o+A2zI5wy2aOC3puKquj2A2W4KIeGoVoiDtK47\nJDp0WwXU8MpXkqQsJzMXtkQjc1fXe6nmVw/qukj/IckOyfEBjw1iBGgNtloilUUxelsEuAgSYFTE\nHqgrUB/BN3vVs7ikqq+Jswp7pa7180Pp3QLYtt2T6f5Y9/2bJ2BnN8MMarU+NKE49amt9WK3PrIg\nsGFtqcgIqtqqPwzKsp1eVcslpqo6+9BDYpDTNJ9oTNNMcbvPG8BtHa6G+qmgXiUagQ5pxzi+BGvd\nzhPxhglGgH2SEQ7eutNFpXBXzz7OAluBSOxqnhmtP5ENckL0RzJVBgbpyTQbZ3R17aBtVSOSzAzv\naJMbRNt+7uraLBFnhRD1C4PnF1ADNeNRlPWOIi8m4Kl/bSeH3GiaJcJd/ZM72P5EumCHzCANXAZ1\nVtU5Ppeqzp/xwL6krjtoo3c9s0PG73I3PwdsDX+DnH7+4InFg1prqVtrxbvBp67o55FvUp5L9XID\nGO9VsypruX7YWR+WOJyMr6s33PhMUTsQz+yPWUIRorDz+DSRuNcIaSCCemaFJEgHQB8lGK09xDla\nv0ELxnTB136SjrktB20gWiMSZ4BNzKGsD8ytpE9qsJvXLAdazO1215WkoFkZztvO6rrCOpKyx0C3\nXK+x1u1DSEbqia6VIeQugloL9sqhGXpQ18xSylftwttKbaCXi1btkAf072tKOiUXA7BlY93PdylW\nZXhnQX2mAuRIVT+nTO+Mwj5qUn60fIbykHREb85+6aa0grQOW38g1v9HA/XM/vCetiUVnVedVXVT\n1tmrdsravOp2DncFLRUg3IYDfPU6cTCeKe0V1IPSNpXdgdurQmSaQvvAoz5U0x7SR4DOrNN4ojXy\ndp61jTv7Q8A9hXa2RjywNfm4lTDfgI3ay/qo3Y2xObW8AyxJRvWvh+oQVcoKdq+u1c+W+tLcb0ir\nRKGo9Ek6eVK4G9RlWu2dPFUp5dtKV0EFjFqqZembBSIVIuJbz55GPgAO2HdoCcYFsIGkps8q2xHS\n7We5DtRhnRMv+yyoV4p9ljiNfXbM1fUI4GN17ZcHDrzqK49v8Kq5hONpzcsFvMECSb3pjUnF8Z2K\nvq5aRQT7f3Wuqg2q1U3LsHXQNuUdlHVMKpqlofOCVy0Adl2fwrVMnKrrmaKeQRyQ8a6k2YMciIDO\nrFup7JPsfv3SvdkG2zkqOzqDdlbZCiKXfIwZ2DbfVDW1Zuasr4YW/5hqe+N598eiHWJN2Jnkh2yW\nxaCu3R3e2yYG8+LUtUtQsu1H/36WpKZ/xGS5UJqSJkkC9WbolQsqtW40H+vWKkhoboe0RjBNVW+6\nYTNg62+G+aP5UadCwHWgnsWR/XEG1JdKAH31RV7XJWD7bTwLbACHKtvH0TskZ08pveOmbn141ax+\ndLY/FOSaVAxVIBNVzZzrqvt52uuqRVXnuuoVkBXYtUMY6XrK6tksSYYDKgL8zf6wZTCq60uK+pKa\nzpA+skBWSvtkvIGyThtMpe/UAbSnKruil/gBBl741Sik5St0mAS+DOrQVEVp1RvNDmF26lohvlDX\nreWlgzYwPfFYKk0gqlsTlSz7xNyUCpeoYFbedVDXW/etK2iwQ0ANF+BqCccpsPWA6T5I5GSYjwzW\nWW31vOn03P6IjWnm8B0qR0562375a4E9b5142b+eJSk9tPv3n7/5rUCtlR8K6Wx/tHkd0naOyLTK\nNKjqXao+avXnJeDL9DJcw7RQuge7dgYou2l9eQ6qOgBcp+fEoMLWe9MO2qdBvVLTGdKhznoB50Gw\n5vF5vEFz87Rhxe2Q+tcTaA8qOwMbkAOOCGw7iKMNYZbtANOkrhXEqq41mSKfCX/1+9M62dbLaXqf\n1p8W9BzRJugNBEWnyYVzRzXB+5y6hkEbPeE4AzYwQDsnw1ax7KzJQeglfOrngtp/7qie3APbvntR\nHaLbdQbYwHmvGpg/oaxAreeE+tRHqlqTkV5Va9LRq+r4T07z6oFN/VxOEA/WBxYwH8b79TJbfq2q\n+3cEyyNA+wpQr9T0Ja/6QgtG1oN4It62dI/IqWrqO+mhTTK9FlGcvAB2/5oAbFWvaodsMswYq0MW\n6no4kdS7dqqbKQLb7A45f80KcesIiUYHc0002rlYC4r0d70TWstGubA2rq0KhNnUtUJbfWufbGx9\nXz8OdsgM2Ee+q4/Zy241ZqCe9cz3FJ/6pUCdP9+7L51XiJzxr/3nc+dMM1skx+qYzmwkD2r9Xp9Q\nNGWdkor69hfvVWc4x2FYYlHFR/epqQPSQTuX662h3MGcVbnZGA76am8sVbUHrrc/vKXh4ezHrwV1\nhnQC9MXyvZPxpm+KYZc07MUHCdpeZWdb5Ayw9cdKdgjMxxZADieRf6xjtBroPk6iLLJHbf2V+JMR\n6Hd918WrqWpNKoLtezXR2NbF7sJBuIjUxw7qmhs0Cgp2gbaqa1CDR0sy7maHrIAdfpwEQ99fs4+s\nEC91oepBPSrHp4P6TKdQs/K62N/0vELkqcDW7RpaMgJDDbZ91+R4+qcSPX5eUc/sj9xSsdtoUVU/\n6nyIR53grU98lR2oTUmjw3Y2zU/PUDbfWsc5ADvXTx+qap9UvMb+uARqb3ssID3A+WILxnPxdtUg\nRFNwD9AO1ki0RS4BG5b5c99rNdfswI1T6noo3wuQRoJ2H/d+d/elu9r207oVEhONaofYBQMZNs+6\nAdnXXVeFuFygGkVAoeV8owXixoEB2jPQzCLD8lpQx8++PKj9cuObxufAvjbh6JeLzcnH7z06niv7\naGYdhS5Sk/2hLRVtmXCTzw1fmhXSkoo9sWjJRO9Zs0ssJsEz2BcYoR1UdgB2/OxZVT0kFc/YH8Bl\nUHs1fQTpDOgXUNevD2sNv/EK7lJshzu0BRpiZ3hb5BDYrtIDard4O4QYvrHMkbpmOSEY8TFOp3sl\n7QE+g/hsmfjPbwu5J7ULicaklLK6zslGCIzUDlkBe5Uw83FUuQDMIQ1cBvUsoXgJ1Nd2seo/9xxg\nh65PD4A9rnfc3lX/1Ucev1fVut6Z/aHbH86VUPXRPWttWm5+NZDOR4TrxJ/DvoY6L5MThx24Mg9x\nOdg8dt+hAJXxpKqVL1ZvPQHy1KdOcTWoLzU1n6nskyB/nsl3begdS//56X4e/M6nA5N2jCd3Qr/O\n6Z1T/oY3P1j9Jvojnfzw8S7f79o5g92H47xghejJzHKjcY+SpPAy5aJQV2jPrRDOF9rkQtSIF21X\nYDVf7Kyt4dpjsO/6VP/ZOkFLuLwEqHOZnl+/j6eC2n/+zNNA3qZ9sv01AXX8fBm2329H3oazoJ53\nlSo3aPl8VtW9EmRdrjf8AxagjsDVv9MEIdxywzrYlhk+46YvVXU7ODB1DR0eObCyP54Mas8bz7tn\n2iGvqqwb41onRQDixoem5V1lWxIyK2z1sEu6QPXH0evAv3XGq2u1SCzpCJiiBdnJYss5VQ14Za2f\no2MrJJ2Mo4rv04LKBruLRI/haIWQv9CIRFWTqevK7Q3qBXu/eFXhccFGux1CrxZbxUhTiWqLeIV4\nJrIafiqoZ92w9nnngO5jVf2RVfYZhW2fXShsAPaUAjzvGIZk4gTUXlXbMu5G3rbN38x9l6mu+Tj0\nXEOCNhAtEGC0QPq/eTJxsozEWM0hUE4WSNtAd5AY0eJYiLQg4nzMQO0jgfpQTScwHyUZ13NivK6y\nlmA5GFOPJ92Rpgpbl+PaD5pX1+kOOu8xqw2GH9SvGzp/PGGGkxBZWffhUVWfmJ7gzbq7B1aIH2+H\nq4QLspdy0aCugVgCphc+0BV2G47q7EwXpBkyrwXqI+V6ZplrFXbcz1Fht+lxf840jffL6fHLoNbl\n4lNSVNUd5k5Nu79BVWOsBOnnIBAT6PIUCD/e/hL0WqDlOT+rAolA5whmHVYLpB1kpHtmV9VIXjX6\n9EFVz0JV9SVQL9yBgXO67f7fyXjTjpy0FA+AKGjZcfWfZRqXEhW2V9TZv/ahB0nVtX6/qmv7sdXD\nxjLRSNJQBYjKnNyJS+439162KWwgnJTxhGeZHitJumLPkEYENsVqkIqurttuiw8p7zrL3nV/O0P0\nr6cKGwhe7JmYQbpNf1lQP6V8b92S8agzpslLByYVIkBX2G369cdvdYPr1lRMPoZqkeRVe5tk7lk7\nVY1RIPQEI7CyQLJypsn01TLBAhmW6crlUmLRLE4H7amqPmN/nAG1G5/C+QXiTZS1hbuzhDvQUVeD\neuBWd8OFunYr6n+Tuu53ef/j57sx+kmEcTj/nXp0ALxv3T9P03VkIaAXkbdC2qHJ3mNXZDNP2qtr\nr8jsUE4Udk0q71Ks1LSu04bfCNSXPr8qO4yfm2/vSmG3eZeP39GTiAe1V9X+u1eq2p8bPrEIJAGw\nsEA6xBFhjTS++peWmz6VIg3XPi1cb7PEovOoKXBjcjH5mIHah4fuGVBn5dw6UBn/nTRCXhnWnDZS\nwu3QFNirO5bOz3ZIng+Md9M0/ygimNkNj9ODYkifnykMpHm6PvvLmmicK5wjKwSYqye70OViHpqI\nc7Ivkl2RH+2P/oX1LkC2iiPV+RRQn0lAXgPsMzeYDOwVtI+OXbxJxu/sNki/2V5S1d4C8duuINdh\n5sXxMtuDMNoggBcd+Zwe1XW/UEZQ6zXF4VoJFsgqErSngm3WnDyHF4cQBl0C9QrSz4y3VdZ+J46A\nPQzHAziEV9dthXFY/sZqEP3LHcTupudh6p+W56VGiwz2bNrRPL/ZthsxAdQPSQRK9ibzsl5h62fy\nBR9LwUZgn+0lLi87NvK4rvLjGlBbl6BWDUHDtKPvurwt1wEbGFX24XdN1uN/l9n2qqqebUtfb7+J\n9/GeVGQ4RQ13CXlF7YI8tOGeGP3TosZsPIsbHodl42T6CQtEl8/5K7eesBlHqlrnp+2YgtqWGSHN\nlYd/J4X1a5fu9Y2N0w+A3UZkfsXUD3Lqegg3bfpDnbnhcRpmd7fHJLkx+fywTAZ1Wr9XLJwuBNt0\ngXfM8I8qCYhWCCCJK+ddHindDNBLavFo3llQr+yPs6A+gvGZ5a4pC3wqsFfHbjZv1qJzllSs4bdJ\n50OyQML0tJzG1K8GME0aYv6EODxN4oRfjXjNXHW9tQ0foZ2Hk1c9hBOFRwJyCWob5Dn3row3U9bD\nxq+AvfKvc3WIn5d/gKNHIJl25FuH5d2JGB/V/DQd5vEEhZ8/96vJwzH42K6RDDqo/XCGtK8KaeOj\nTeKXnalr/dxZ+Kwg9FxQ51hZFk+pt74W2E8ruSvTm+LRcfOfWf0WeXm9+WYLpG97fOLKfrUus7ZB\n3N8FgFfn/DTHM4nwxHqtX52v90tWaNg3l1TMcWTN+qbnwqVDQM/s4Avx+rCePBYM84b6xoUdYtMY\n0zsjMN5F/TREzyxH95p5vMv7ZXic3r6nz1sCGv2R0R4bJ5/3FghPLqT4WDxm9oEM5miF1JPKMZaS\nzeEzfH6y3Nk+P+JnLiTkXqBRTI6zwD6ycsY685Ogn6x/VfkxU9X6OQ/l3Q2vnsR6/sNZITxJLmok\nQIdpSNPTvJl1uMoRhc9fihm0Z8NHddVZVafPThW1fXSyricA2sfbKOu0wUfAXh0oWybUXi8SjTMr\nJK9v4lu3z/ZFllaGH85Ann0OF9TFcOLPIZT9a6+GAkiSilop5D3VHQ8AOHjEnwFoOf2s132F/fFc\nUB+t56y6vxbY05vYZPrw2QvHzyvq2fbkG7Mft9aJLsLldJRctOn+w31wvC4OqBuuTTdNrt9Dv3px\n7Y/rPUN9Xc8FwQgERT2b/tx4+wSjDV44cOlgnep28Kid/jW+9QS80yTjQSxL+BCnUz7RJ1CfPZ7O\nEnMz39rP81UhPjKk27QRADlCx/ZLLzlXnryM/fGS8VIK/hKwbfrBMVvdAHKpnq5nAHGC9MyvzsM6\nPoA7g1mCltMxnT7Mm6lqTK6FK2LqV18C9ayuOsw/UNVPBfUVN4y3hTUwB/bKDgGuuxvm5c/+aNPP\n4nB4/giHOaBnYPbrXdxXOOxCzN5r5L5AgBFuM+AeWSHrJN51p88RqI8/9zx4+v5MZn2bnImnqOvn\nxqWWkvrd05vrhW1aqu6TvwmAJUCtEmS1zKVL+AKkDxX5tbGyQIBj3/rUuhfA9/+uiDfwrBd3tMOP\nXNipo0YyR5EskSHJmLc3/XjLk+bMZiRlMZ8eH09nEXfh+FG3JiBnNQXkOt7U0GKirk+3YLz06P4E\na+TMsk9V6J9TXZ9tZr5e54XSQoxgn9VX++AFsGdvh2kz8gok/7ICeBYuEyU9LG/DvJgu68nJRV9f\nrcvWyXV8AryhyCHHkarOTHsCnHO8kWe93uhDdT3rHOXKpMDsuy/dqeePVBinHQWPnl2eP/Wmh8oQ\nmv7ufnT1aOvjsLJj8oh9pM6fAuIjBXfkVY/rvu5p4KnLXbPsc2G8egKJid6J7TVt4HR0nNc5iVkS\nu89M05dPiscA15iV7S2Xs409XmffhsVj6rUxs0CWy05A/QLxdjbIc3fgzOPIWYvjcwVfuBEcgTuM\nLxKM8vfSY+yZR9ujapAzsVKMzwXXa8TZzpyeso427/IN82jaU77z0vevLa8rbZCVzbEC+GLadH5W\n0dfEU+3PlV+9/J6XSR6eibf3rDWea4VcihfqTAXA8iR6TkLkyZtyzcUlca1fO7NC1suW8G/+/S9T\nubGKz52A1HiJCpTQSOnKJ5CwnisU9VO/4y1i2ovtK11bQxwIxHU99ctt7BtXgzz/keQpsSzfe04c\nJFSmquAznHDPvejO1v++VXyucr23jueo6Vly8dp1XIogCI4Sh0fTJa5WyK8ZRyV/OV5S/J2ML/vq\nfIlHjOe8nWGR5LgU15yQ6wTL80A0Jg7PP4o/9TteIl5LFX/u731q5cvnimtsLk7edft7zOFzTcBP\nb8L7jc9oi3zZsP67MT7zCX2mW9NbfHnxuX395+YsLsYTvOujz/Z1vNST8et5z0+NLxLWz+3w5EXi\nS9iGLyDeSune4unxpdtZLxYvBerFtf7sPNkLFzH8XfKrftlxe4S8xS1ucSno2XePa76M6P8G8Ndl\n9NcD+Juv9uWvF7f9en/xdd232369j/iHmPk3XFroVWEdvpjozzDzb32TL/+Mcduv9xdf13277dfX\nK242yC1ucYtbvIO4wfoWt7jFLd5BvCWsf/INv/tzxm2/3l98Xffttl9fo3gzz/oWt7jFLW5xPm42\nyC1ucYtbvIN4dVgT0Q8T0f9GRH+ViH7itb//JYOI/igRfZuI/pKb9h1E9HNE9L/L33/gLbfxKUFE\nv4mIfoGI/lci+l+I6PfI9He9b0T0q4jofyKivyD79e/J9H+YiP6UnJN/nIg+vvW2PiWIaCOiP0dE\n/42Mf1326xeJ6C8S0Z8noj8j0971ufiUeFVYE9EG4D8A8M8A+F4A/zIRfe9rbsMLx38C4IfTtJ8A\n8PPM/JsB/LyMv7d4BPBvMPP3AvhtAP41+Z3e+759AvBDzPxbAHwfgB8mot8G4A8C+MPM/I8C+FsA\nfvcbbuNz4vcA+Mtu/OuyXwDwO5j5+1zJ3ns/F6+O11bW3w/grzLzX2PmewD/OYAfeeVteLFg5v8B\nwP+bJv8IgJ+S4Z8C8C+86ka9QDDzLzHz/yzDv4IGgO/CO983bvF3ZPSD/GMAPwTgv5Dp726/AICI\nvhvAPwfgP5Jxwtdgvw7iXZ+LT4nXhvV3Afg/3fj/JdO+TvFNZv4lGf4bAL75lhvz3CCi7wHwTwD4\nU/ga7JtYBX8ewLcB/ByA/wPA32bmR1nkvZ6T/z6Afwv9HSq/Dl+P/QLaDfVPEtGfJaIfl2nv/ly8\nNu7eegO+zsHMTPRF9+B7GET09wL4LwH868z8/zWx1uK97hsz7wC+j4h+LYCfAfCPvfEmPTuI6HcB\n+DYz/1ki+sG33p7PED/AzN8iot8I4OeI6K/4me/1XLw2XltZfwvAb3Lj3y3Tvk7xy0T0nQAgf7/9\nxtvzpCCiD2ig/k+Z+b+SyV+LfQMAZv7bAH4BwG8H8GuJSIXLezwn/0kA/zwR/SKatfhDAP4I3v9+\nAQCY+Vvy99toN9jvx9foXDwbrw3rPw3gN0uW+iOAfwnAz77yNnzu+FkAPybDPwbgT7zhtjwpxO/8\njwH8ZWb+Q27Wu943IvoNoqhBRH8PgH8azY//BQD/oiz27vaLmf8dZv5uZv4etGvqv2PmfwXvfL8A\ngIh+NRH9Gh0G8DsB/CW883PxKfHqjWKI6J9F89c2AH+UmX//q27ACwYR/WcAfhCtF7BfBvDvAviv\nAfw0gH8QrYfBH2XmnIT8ooOIfgDA/wjgL6J7oL8Xzbd+t/tGRP84WjJqQxMqP83Mv4+I/hE0Rfod\nAP4cgH+VmT+93ZY+PcQG+TeZ+Xd9HfZL9uFnZPQOwB9j5t9PRL8O7/hcfErcWjDe4ha3uMU7iFsL\nxlvc4ha3eAdxg/UtbnGLW7yDuMH6Fre4xS3eQdxgfYtb3OIW7yBusL7FLW5xi3cQN1jf4ha3uMU7\niBusb3GLW9ziHcQN1re4xS1u8Q7i/wc41oi5BXFnLwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6,6))\n", + "CPT.calculation_Fermi_surface()\n", + "plt.imshow(CPT.FS,interpolation='lanczos')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And band structure" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAFpCAYAAABuwbWeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12,6))\n", + "CPT.calculation_bandstructure()\n", + "plt.imshow(CPT.bandstructure,cmap=plt.cm.jet,interpolation='lanczos', aspect='auto')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/figure_densdens_tau.png b/figure_densdens_tau.png new file mode 100644 index 0000000000000000000000000000000000000000..9d791185e0efb7c5cfdee3b60f13a32231c974e5 GIT binary patch literal 10298 zcmdUVXH-<{w&f;BkXRyuL;(>*G6;xdil8D{K$M_JkR&Qef>j7gArcf2lqiUZfFzL| z6c9<0L^6^kN6D$zKIio9cf0$(bKj31<28d&RimiAzi+KI*PL^GLAu%+GzVD@A_zir zP7|w-AVfLvbBuxvUg_jNaTETK-c>ngNC96y6gLR)oYF<}@?8X>I)wfZwGZ6iL6Bp} zIjpjwXWZNXPWP~Fg5u&jlAL!v=95~nA7l6uIW_44>ewvLA(>E0)+$ahulk1<#xw`U z?WKPVIu%`t2oHJs-Hdl4Fq$pZCQXXJBwGH2^I^(E7n@ETx=Kl`j!_#ABuFrXWPHv3 zwYTViW4EQJj~V{7o?)LcJD--g*DBGs@cVbifB;tjg26n!Q6^^wf7=Eu@$f8?*%FIA z&P&FO!JG>sQpaNTumAt?vX7iumCFmsn}gf$ni4zE~pM{4HO&!__QRF)G&nvFa>Z*Di9K6(Mv3F=-VBl**gNn9xX!Y_;SN3Rn zTEgey>Oc%LGlBiAJ3on-lw&tu=*q{czCPAlmAg_}CnC;X;486pb$3tB&Be^k%_Rsw zeAHHSdzAjrp$B4CUpz}9OD8@eGxPIb?zYGW>L~6W=HlYg)Y8J5nzAG(Cr3(M`RM3p zn)RhVf=|!HgurC(Np#kIR>{dp=pYr9o`He?#DtBYNe;rN9fvP0EG%t+t4T^peg65A z^V+p*y6J`q!hw(c=!uOo7(+rrDCp?cUC($lJ1KHKkB;UM7e9o-U=~+bYacV7vdlGM z>opNPa>K@k7vpTKeE%) zl`SnT6aDhtW{dfA;b9y(Y?>V^tTbQ0VP|lP1re>&YiCC@AQz@I7nq;1IuM zkkOgb<4pVTAb8c-<-@qW(p-VWeGDVSO zh^zw^QT}xEsRqZVAO=2N3yZMR4nGjY{N6a@xRzso(d~_D&OLktpH6aF85^W{Ly5zW zOl4nR`A|;9&_K@n-SO58Ps!apJgk0J_-bCbP@QX42b)OivpF99HZwB-DoK^1-+q05 zK=b0oOK-wB=#*cc*V4pBEWG8&o5 zu6z_7&Gg$#zG8DCmT2{t{bFzPv5N9aSny#+#xuL@9@EuPejlTI-m=svV^vRi7}MCt ziqPFm)Wb<&MC4fpY)c)w-g5~H(?Z3d0eYg&Gw8h_&!*tR9269EJ~k=oSeje5c{v1y zcKl^eXNK|ZsdnB>^KzD|kk|qeVqz5JqPsfZWn^I3=S)%x3#B1bma6F#0hzp&c8l*g z;P&?RV#KV-U`GbC-KNL@-9t2`v;EA5_7Gm(|Y3lJLUAcUf7racAzvO;}XkXCM6%QeF7-eqZ+9 z77N}zUlEbHJMpA3_VM;v!klwo;}hs3<fT2<** zi@)jKUA^4sIy-h0qGWpM(k0(Paw*$yY_DIx{w>SKS#+D{uDUwe8=b_#Us4Ni6bnp4 zS`_!oS}N+Fo~PiFGpljY)YMc`Q+t@!ckyFO2Q03`!i!QRg1N*jRK9|=8tYk%$5 zw$Ja5vU@A@?c2)V z!^=jxx=CeU#sCXr4;GiO(Y@FZjA#_A(BI_>nUflOD3_@CadeQ|;J(rS~7p`0~ zHa=TU+75&Wf>z`@)sEl3J8pwh+*u^q+1&*SM9rpnzszE$okorX_Q45=Mnh$JSy|*w z5fPDSe}SEU01tw)9;l!whpSKl4Sf-HVVq%WYwl?ZP)<=&g(dfQWwqmE+7|Xcmd>OU(+ljt(TINkk-5NB4T0~ zH#avK-z`bFDsZw~r#`8`y)RFWk_>%uD~;XlpZ+V#5~AEJZqDTiuHJZbPvXpkt`^ag z({Z?({JbH4t@GU_8J8iv_(Fb*f~vj!G=6JkY3WnxLIqVseZb{>n`@iP;|)3y(%Yps zyWX1#lg$3{sc(;|r*CX{>O?CYPwnkB)rl584uQei+S(@0(Q+yXCipDZEA$DOmzlsO z`Se&zKzer;~91!k#hZXVj-f0cKV!}oWa z`9cvNcd~cH72>ArxyK|V7?AK6FQ%5ASIWKD0l`&L+LW@4ssYNOhalmRkpV+PmL8Gk zvDjxW7w`oiKMD{@S~nd2@bRN;L2(y{p9&W1FL6U!Tm+c)ua-oQmzDQ-y#jAWoj(wD z{sPV`+&kUJ_QBt$_= ztA61E)q@8Q;(F~0OlN;qTD)*}bj04dvy8U~ma6XVE`?|1xk<1a`VzuQp3(WZs!Him zH$QIxzSG{#bBpk#f0;l1!w1QjW}C6Gu_{NY(z3q(8{)&#m75{rE-9~Hld?)Vx@g?P zx(1^qEhV*V{6xfNV}1Q60IXc=#v@wsvW#av=1CDlL&Krn^`CVU z&9SfA>~fq3WRIUXL5!IDEUDQ^vLIF2xLpUwzK=A<+2(4RniP3?d5g=9BBd#=$==_9 zI9ydQd~Wx8IHU=SeH{)hL*v|kTcubpL}ZiSV=2i3SqHzb7515Zl!$F^opOb5(b#4L zpJANMefl(`gzYyhAl#ihcl!0jF_;h+ZKAcV$~{JqeX1o-%gW>+2fufAezFujCL+?3 z9@1>1>g+5`%Pt$RynJVDVnRT9J$rvEdt<(I!DZ|#24$1o`L-?LKQyq|<)!JVsZW6F z;V)l42L9F*cZT89r%!~(k3U!L_Eio8{`(ISYOgB;`-DXR%t@>2*N?BU~B5pEP)rR8Y{Fe$l{yK}A(HYUp}^z#z`#KJm9sPr>l&$qTAb1caLsMyq>Q+`fu*YI|C$yfhpFM&sitI0ofR|7* zD#dW<5V#Oxlf6NNd0uh$vr$OL%<2Ygjjowl2%tJ}+G;3w@EVp~Lp;>p(D@PU(2|?F zyTS=Q6*UX4Ur*+n;o|0=W*TSd_9G#eyG#!54c>FN(t>_?Vd1AkN~{xsKu|xJ9T|DJ zY~|Zo8Sk~s>Ia}6K;v>rNEjSd27)K4zj2s}$!g|DmPzp)UR1w===o4sm{MBGit^o+ z7R9ihd|U4lZEkLE@&T#Af}p+g zw+J5R=4Pq)L8_b!M@PdH)%El+(5C$s7hPVbrSZti4~$zS!p+Gw+txqh&FPy1$yEyy zDikE~=xk4Zj)|b>ay?<ifvB6BTam&6?ch~jJ=^~~)uEMJ-Z>-X~ z=7wFjSzlOFi@+s!`kkzO<$!0srJ)VFF!ORg@_PXVIqdw{>efuQ6<8(oDwgy8#W80+gahc& zomyUAmNVmo61*S~ugzy~Zy)z9PgYizo}E1e`W9W3l#F;h83QKdFa#HvO<8My|LljP z)YJp!=H|ji5VmEx#1K~L&z7P}X&qpm97bFqv*P6L9Rf1=KCqF8oBK|)ZCkRM%3?kTJ^@$y)zo&ow`V~Rs*1D}!OE4hZ>Ns(1 zz_cl-sH%aAy)w#(?|sp1Q?@mo@khOJqb(ZsSU!CCfO;`>^1tb&oO)}x_ORG>nQ=YV zm5Ju+*jp7rSA>ITUi?SoE%N!2{J-NaunH2yeZD^kJhvl2%fKxH{l6HyF3;}H)KYkq7J3COyq@IFd2_XaB01s+wYY{wn3&coC zNl9y4+b8dK0%3BWyjz3QH-Ldpd+7I6hv#^dF|e4$)h-LVdUew)S1@dB)JTlD^+9+X z-wWjWWLO?C9#D0>{up8@{(>GvEe?kh1W!+M8aDRcX1_uTXa-=MAg4KXHNa=8+Fbb+ zKap)wIe5)9RezxK==Z^LZ&0_Pd;P5 zm=3HPTU($^NFq*LRI#@gNbp^|j8#`(>(RS;^JaGA37v#Lu$7!V1(Hx%Sy|f&xa-=& z&uo-2bDufW^Qsxe!h{6c;Mx)Ks=WOCr}6PLLto0N0VKTcrM`ZhR8heJtOLE-Hb7-S zhoPZQ{$SmFrV=T`(<$z_=(3)>aKV5>!}?z%Z_%Ccg8=nNe-YVQJzt{bMAHlZcYzSG zwCTx7`ohYlWp}7lZILoI3SdQG6rpesBkLvrp{5K`_kaBOVA=lOc6Zr53B(dm%X6-v z3qc^dpE`6DLw@`)+S&P~ShqUU)z;DBj{+aKF^fyLw^lp+v^*mID#$2}bMgST8!L(X zvr}G=o@j9r|A}7)M;~?%4$gmA3>K+yaS(%o+D+Y|_1M|CEoffG3~R19XL*ht zL%?R91Vb5m*V4{Pi`Cj}Pr~~M0^zIQ9Ucjn(I8eP5%ZdjrBMt${ZM@g*~K@pOK59& z5ehBUibcZa$#O3P3bR_2IdG}NhYufv&-5zi$j!}#B{QFO&*YmyA?X}tj%~s{GFEA4 z5FC7o<~ zC9Wp{OSdK;!OF_Zz#-$n?c8?!S-~DHV(|r*Ck7tJ#`bm!u${XTO-rsJu$$UGYv6Q1 zPX#Uh`pui<{Coz1sDog40RgmunMNC7j0Dls)BA&!?Qd6(_7*rWgFH=}f%`1sNejM$ zc9v&E8n*QRJS6xRW0AjOp4yEa?d^cLfu&dBWARsr1w};Y$oz>wQG&nmwXyN8y;Xis zcx-I2Y9Q?^@G^^wi)oeD>lF7e3@5Z)G>U;p5fzfg{2R0psfs#fy!eIn7HY-L^G6d1 z$A_LH4o*&LE-s>oWkcjiJ0~YM`;I@|0c+Xg$4S9_MDT7iKZcDfw-`Z(_v;D61gI)) z;lysfcxW@5o*9rRhbC@Cq~pX+r0z5-ws5#r?J zv^z+K!PMJ+CC&uQ@3IDS_77R)@88zm{s1*JrNGzUUSBXqef_6#aZj=To%K~tvZGQ5n9ZEI~sIq~(a-&4ZX!34s*kTYdYTpXLv)>Ink0e&GN zHEnHLBq=$W`10k;2m(L@d%y)cAM85MrCKIXOZ|EruoIfR0rUyN*JX|$KMwEy++j_R zIj8o|*(>=n9X_m-(K!Ofe--fjrR|^l96Iu=kp`up;m3ac zB0+k4d&xe~KnXU6A29hpdt~kSX~8U`;N9)@Qynlx)6~{BdQOC|tW?~aU)ZAsqfeMb z5Hb>`P6$3AAV7Hsom90KkMq@lY_~Ri)#Iq{wq-+T3=6uiuPlmLXf0?D0Xg0Op%HWZb=rJ z_ZF43a0&3RA$rQNV9xy|&VfHeARu;L1N;9lQCaesOhbXmzOV9~0yRzkvz!jqD@b$!+*QU-@@u%7!Ww!&BUks$ZXrpg(fN-FW6>LBTM< z;8N=mNm7%ps+{+J*UU(y^&MEX%z$>*~BqNTdJhW7FMec+E+m?VViW!$P98%QIO* z=S3$d+&nx_qoaR)*a0Z?+*zu_96cID&f$r`eKh2O7(WJMRtcM?D&TPgrd_PJ%KYBG zh#4#1$H7LqK3TTUW*pF6>6wEXenmGOe#Z6l^X}(_JGg_SMH39z+3u_ zeLF9~{0dEynwp_c0+6y`7UmcIO5a{bG_bx;)2O9qp>RVhEB1xYCf0iLVJzaZ+d~1`=&8%<>2BvD~&p#HI zkK4{DF1`#iIulqJu+@j!X?M3u%cLO*NfO7QppSZB+ z&!1l#Fy2j{ffNWZ%_1ABSTMp(Pd8guM5~%bmsVUsPzweOCImr!dLUDvXU=?B`tH$W zFrSV%*xRev*}WBKKXhp1_kp5;Zy?*KHBJbILi}N$es7*IWLr3yZ5th9lZaSy` zYSZAXg*_B=pEcQ8$L&jtOGxM$8BIKQTGxOnBAkvOgQB%g;9Kv~T!VS{HCU{S=VFrh z0;r%?`+_2uVPasUHs9ic?yXD|rzXHYqO~3)dX3n9VGx~lf{$2i-M5R{Z0P9euFRFY zb$cbCh@K0ZJq9=6I!tm@dkY=s(K*0@b>*2-WYLczkM;Y&9VNel&f8^-< z-OlL2X^wBcofa@s0XymeFiA_xB-LgFv}yoYJ9IrQ6T4!!N3iJF7g_wu6P5&|8Cn(_ zW}B*L#lryt1?(TaX;h$IcrJL`RN+~rRrTi%rs4kd{CtCez`$S_;hto|Wn^Tq_oBxR zX#Hh4yl2I@0_Z`k!;{a|)q#%m8o22#IAjBr&s_t`n{A@cQ3{2F0Q%&ttc##uX?qUL z?S1d;@6X)X4#VxFcn5R=(D3-uNrz$WNdVjSzxrudtw-dl* zZ*O7Wd}leja<(9ZuhJ^PMdv^ay|nt&q^m5bA}({^)l0B+5UdPGg4F;kz<;dCvu><` zQy!?3_clA*_=hh{pw+-Rg)vM?H{j5f8F6N6Zhn4EH0?*gpJfVD*m_Gp@`XaY+*|~p z^`Ut-Q>^qYcL%dGXU=d72%Luw^B7X#_H4J9^#Ghn`eT9UBD1a(mJ|qc1$a)SM%RE` zAIceYna_qpJ_Im?Dd1!X9IB~>sX(pwLOCC5n{d^HkhG$6NMOqpF#WET{^^sFc{XnQ zU3Q64mMJf|Mv)6fK0amiDuk^qFX%R>P^>pJjLb_MxYTuap)AFxki&C`C=l=eb z%;o%~!WWX2{RiyWYAurm1;Tyeg!*Qt!sb{6W{G{}utJbOoXK+4F@u(y+~)sp9XfM% z*<7}6N|0v-ZN9j&qI&fz8(3nk9UY@n?S$90tS^H4-vS83xh>B2E*$+ph9{Z&m^x%2 zAv6?73O0q9S#rd^Cp*8qhs>p iya|4PH~C22Cz)9c$|pI=O96-fkaKF<*!L=y5B>*kEhOpy literal 0 HcmV?d00001 diff --git a/figure_g_iwn.png b/figure_g_iwn.png new file mode 100644 index 0000000000000000000000000000000000000000..d0a4d9f385ea300874da6614c610f6c9283ed000 GIT binary patch literal 13506 zcmdUWbySpJ+wRavBP}JMprAA;Ee%R4T|-EBOCv~$fQZs1AxL)&F*Ha@cXxNkoISty z{m%QHbVm9}51y=OCx;41wU@M}AO-W_@oUkcSX?DRH$A$-A>&E^4a{h{N-h z{zrN&=$zt`*wvr({2lUtyk=5S&BVqbi`KApP|qJFU%k0%&6CB+p9s&%&9QWqCq$i! zH7Oy#LMymm7$A-+k&Z6^RjP@oQce$V6&Z9@~B_z0GL}I)RtWEoRQ_c4;?H*tD z`ks}i+4A7EzLJt^8$j36_YVz~(|yte{^EH0|M@qDw!VM0l#I-MYHDj&Dn>@%?A+W? z5*8&beVP0VDSz9Un&?hCFT9-LKY@gzT^Z8hi4`8(h1DO{63j~`ZL+emN<2@u*z9=t z`D15h^z45;V;p*#ot>?%FOjcJXQ-4D6GHDBub9M7OhVE+QEKw1tLq^H!_P)VadDJ4 zaqO#^QKA{{`*Ys6r{m-r&lnjSn~t0fr>pH*L`73;YF8{tUiZ_*E%c)jAmbCmy~cC8yjO25LCgw^Op-ZWn^XwIIYOtZZ9nvgJbH-u&^+r zn~OtqEG!<2zYl{6XkUY2nsi3mjv9tg3I^S#S=rAsyEMV^} zrVJL>)~0H2Z^wJU+qLC1)qBL=R%+7uRz>BNkr9Ksx_W_rUF?@HUu10eYL*G+{1846 zrR0~*VAP@E;pOQ%#D`=Q?0QX$4o~W*n#{@<9bVqtZHyj4aB*;q+dq>jB=JWOGs!rd z&$`FN$9D}Yi}hWdZllEW+dd!&3HJ`E$kbkMf<X`S#8FN%~z) zTV4{!%jGyZgkOGGW3%4=NrOS{A>Ub;D-#Dtl!({a-yCIn1JBLJQFd^}H>T3&v?AS8$1hi)B;&W<9SaY~9TmfFHj$4iTJDnC0ecSI*9b_ZbKItZrN zjZaqF)5kC?G71PJtoFo_(9_Rv+>R-n{Y^c)Q$S%}dKx4Umzu^=UPMs(X?XK}Pweye z@86ez5zcuZD~euiFdZEoVdLSgM7)@@C{qHaLGG`bho(5_={({v*-W(6`xNfQGu~RfRH`kvGy-`H9nXC7haq4;g=<#F6 z<%k!+Gflsb{TUo1@-jtf503D?Pm)BqlW)5}Kx)XCsuGXpvy^U*_eoY_6JA&(CnzT^ zEuA^OUw^E0dpucg4t1Ob+qTMf8nX!)cmkJ6@U+`-jDHMRtWWo6t$!^26>=F3dq#3it4pnyAE$NffWKL*JrMy>ad8ZPmH9amp3 zoZnOO9Y!QQmG8hNMG&}m+dF;+gS>l@5&8l|l&My2e5kL}?`xu7b}4^nSiE*y5*X~R z>JgP%8c)c;xaCC96Gnqf2#IA>OX1qG>wJAh!5+4Pekh|sHnK6J@Gr-`7V0loINSG7 z#1oZXw}DqP+N%4YMB3QT_+T5BZ|ZPH6QqM{^Oux=uC-(@9^+3kE#CZzESAs0RrL?6 zP*)|GKwW^@^ZIbi{A8CR=6Hu}G$O#0`)JpY*k~T5Mqx}rOia9ZIxtb=$kZB)&jft7 zX-}+2K_MJ@t@NlzIqsnTIYaz<6PP->1s6e&i^Y)15+k(B%gX}o^5C1B8@K9RJY>%t zS4bSxThr}p{ltXtvnO-2T>Vfg29ap7HRQNq-zVy)WC*wWo*uvbT<_^Pbl@w4!pO^a~d^L&<`asm`!sV!M;_y8TXWq58 zvWgdSXXj|VV61cBU;F!0@(YbXnS9;1Z}%|?XtWT5%*?^yxSCH-ak~Fi%;o0_X;lI} zo(B?10tnR?)Sa~?M8uHX zPuNBei6V7Bs z4nw=HqDvy75p|lUmpCD<$WR37>gh2V%~KaX9eJlX6+tT$8xVksgNqv$8ELjVRas!r zFty8H{O@;JD+{t4)W_$fdVU7`6E~04GTnj=J-Dff%{##gSocy#y~lA^U*F@WPs22! zK0hWVw3e2Z5-a|X5pBaQ3Awx_H`u*k47Sr%d{)Pk=4p;AUBq^^tK_j9(7D0KpLsQE z$c#J09je!+UA9d`x3CL_^K)|{z3feb#UN9-J)W4Dkl7!-hl`t!(5?2qbTaFWAKWol zNE1!j9L}lEOU<|3-hU>BY8}?Ix)jEex}XWaQV+K+FDsLK^M>j9^N69LA%a{bSaP@1 zOW1YyYzX9|IMLV}S;L`%`J1&DkC>UiB__UBRV5Cm;HZKaVUr96CB@3ZI_k*Gfcg{-Bxz^v15*LG2P-wFUK|HFj z&+mhP*RB9(>w$qHqkcT9LRKY?^TS7|MV5lP0lJIP3wa-La~gCtDa~(otoaFwfdOR}6coI>Vc4?>Hd4{hSUV??yl4~PBu;ZbqfCAY$y}@S zO@Un!4WvLi4fjVzgWB8LRP^);tVO4vJbU&fKAtka$ASVQUe20yECpfJehPJM*=9Z& zC60gb??f%XhY0sKu4)$F=PC&;<&CtNE7z?$BG>2fp)Wv^uan7%Rdmgaj$Y-P5)3@- zk|XG3Y_cHgYLMo*4>pl;h+!S@a(*?)s=!*zHNi>o_{gsN@2z_UGn+RIgkmU&;t z&a2iT-98 zLx+>N7}>Zkg`!3iA|5l<7~#;@!LalkJYtR1*HF}87nqWzjOV{D%Xxz3+4gGRO@UI^ z9sO5yC?j1Cgv7CPH6VkqZ?Cl~6@glE&P6{8_9YlSvHR=ugjwI+O;L30Z44Cg2W<>X z7|3Rv>clA92LIZ~J`EviAsg7bKX(26+W`qm^QB$D#-^5wfwpHp?a#>=(HCyKCdErkbD?!D{t zi0ka9>B7xLRGZTE3|(8OMICwS`M^W2g2K$<+V?-Di`#q4r2$a{?#;gBLTHE=0#(FB zxX#Cw;q!-}RawKq!9f}Ur>r|yM@KGjtT<&mSD*0sO>Bwpt*3;fidHReW;ubi}qmyCfnAlhu85vAm3ihmwjDWg57_Pr{aYO9s_PDNI z?M2}h?g2dEYZ^X@FZR2*SN?JEo*>ygU@WaR!A6uC6pQ$lmX<4PYdZSD7m3|YTF!*t zmmEAbjfZlb-h27)7@m->!H2ohScYWoPO68Gm_SzKyoFuQg{wptkWef!R1kS>I3z%Y>3zaj z;6;QP8M1HhpQ>2QvBQZ>+?K(~%bMhp#itV|ypQ_*J+{+zIjpVA*ubZ3HxlEkIQNWA%Icl_ij5wG? zG&L(4*7Vk~%wN!RF7acR>gxaWVsOK*+NOyQ#d_M*Cib1qKgBOV-+EE+sS7=1-g`!P z$qBCt1{?cbN3I|xEP=w@xNVHFa|=D58H=_hB0`shqh;;~Hg6Jbm?OM*36NIxHSHGT ztJ1(~z&E{%9c8l*>gxU`*a{v5ZyRxmeZ7TJ4&-P*hJE;os!-rs)QeZJr1#{G^tEVU zcuwE3beMa~hR0e^7k|>|kE%40`${w=Fq~*5Nfc(37s>l|R)DEE>&m{d1)# z1<){+{E4e2EZnWPPkHNF@ob0>Lp87_YMs}yadDYAIsYb|%9$HcuI);`c8mY0o{Zb+ z7Fgcx4Zntj$_H}I^Uz~uNmzXPLFNUmLCYHucYTcRUJiRe3?H}guhnA*HBb)-zs)&m zzOOATo=|WYJmTj5rfXLlgn>&=1WM27%Fi550*fxC9*>+S zZdjB>PDy#pdKc!r_IFE++ZjhG7DkMC(z~rC^Y%P0jhtgo1wS(UWg!Sw3f$mY8&p?A zMaR_DgcoDmJ8+eQ2t5lP-N-0g$Ctkprna8|83kI9&OI*^#Lu2h(+~LZ7 zNk9f|P56c>s}{6o{a~h(HB0CujL%+?`$l7f2iud7J21bKk0{;Fta^vwSan!F?1NM} z${Kt9)R4@xx6X%eAM)Yld^W+4^|57luf1fvPiKnxZs!FxmRg$l8vRl~Cu>ROs4`3T zM;gXd2lY&%_1=Vc^g3vmcnfjxn{3U`-!PN;))a}@dUO%ajd<+IPUT!llM!Tt12>yO zG_do!6Tu96mwkItw=)zn4eVkeR8Wz8Xi>+QXbU6p*eUIVJYgH=SR|vuR7(p9H=jkQ zsW-Lr;K!hruMXVXzBlU$z1nmw^w^FJy<3xp&sx(nT_r7MJFAY} z+s3GRg*4bMbqp;xH-1nI%{a8;|aDzf~YGX!8a&pl>@xqE6C+@VJX5gZ_ke@MS#g6t?4P&r|Qx_{VR;2I&L> zi&&d93U%6x^y{>$?P#IihZuert@QQkajs5KK6~l?SbT|=K5fP=Gm2ubxx({oQ-(!+ ziQjbs8$TDf|AtPLY!Qh6q=k7Ib1AYsUgCO=IRSvT?z`LT(tj{KhXIjuhvU8%?ozg6 zlp^%O3Js)E&9e_*&fw*wbK)C(JsbRT!bmr~)G$VJ|Grj@14ER^86~JwX1w+r3baaF zGNo+*PH5B`OiWKdP-fbbSnqeZByBd3s_5-4yxbX;lbhR>nKmD4QX+7~?#X*XYnJ*B z$30&B*TJ_l98gGRgoad58WM_}5WV}QU<69@o0A-|$1h&Q%_Hv4>DZb!U5*Y~3CoRI zF^`Uqwad-$K>-;tRe!aWf3i25ge>0xIB^Wn2ala&X~FORBvf=mTh4}v+j&G^b08KH zu@Lb*InYA+54PAulb4o~q6i>HpQN1~n^ccADu`KMVmnimaDWlzgp1UpyAC?bNKV(W zi}~ALDQ|^*zrbJ0Y5l(xlaOnGIHX4`mZH9X{hIQngW>Hq$TI=TFlLhk;+W^zjyy=r zqobqg@-zD*TUS^7Tw#YTJpn`dHU_ELRhWej8plf}e~kV1vdXu&uDdA&a2)-tx7n;` z)s(5LZSOk1b;&3P8g|KOV^zX*$*3J$x*PTBKA#rH!*s{GCI~eO|8b|cUDdq;eR85# zf#>XnB$S3sFFF0cHbGEP1t;Ua!#1;6dl)i`D=R-}MGQ+<4ySi-wybhHP+=;yHgPs> zay!7R(x_br`p)&)d#ft9Yplk(AaOlSP4%qpoa`d*qhiI@)+6ljk;tj4kw$aT%tn#Z zfj(D@ivDc=h*-V}g1F-Gm9T5u8q2W_k-!f8;tB)j2s_H5xQQ+Y_vb=C$kBF+>h_ju z*HcXZtR;NVgyxO7o;M%KRh{wNE~HhRSO3%9&B(@v>kq0Xep{1wcaC@0fgHlxu@!+- zW^t&`A3gD3X!3v4V+9RC$0VX5eI=J3@ps|X)r6#j)Tf0|?8n?Q-a90867Qw3MOp$1 zab>*dj}LF|^`Q1{az7D+N^47#nUYZmI2L`5A)|OIq}d+!nN=yJI3uq^`p2-edPMlv zlwqD5Oj~2RXbQ~C8Ara>Vz!qITN7lhFR629vYZPqkjtb)1(>;5X)%kGtgQ1&SM=35 z0BON-oh>q11Mpvta$3it1Mj4UnHh6JLINZ!JG=6CD!q?TDwaK^8UnUiP5%O(ZF^zn zx@r%wCw}}mz8lO1Ab!c@HQk@=}t8 zq3yqq_tv-Ya7qSQgtPenO7x}U7JK0>>V%|(ITGx1cAtKj1mu7%?$q_Ywtm=T0qoQFGlo8*j)Vlug+sDB$ zFmvmQX%AC&zEXB>FR~HM3Oa4^7x$Gd$Ez5o+cht&bjJZH{V4IwabBBoLy-sPdas6e z`Me|Q`0YFuW(tQBQy;kF+9>Ms>WDE`Si2}!*TjLs7m2l6kF&e&P6hR>8Usf_$7Z-Q zQA$cms-mSO2Y?If@gh@z)GJzP)jB-}G{p*s-zDz+-C1>m+Z~%QZ|DL0kr=!@-wpPp zSS0VINB<3zZ^l{f^84MRGG~g*qn_hrVNL1w_QWe!`!}`0+3w69cZA~l6d4qlPNZ>Z z>x-j8ldda6yv8DR7|Ws94BW!sn|+ulU*w+U^0weP$X;ZxH|#K=yH&C4ZjL10?s@|n z1WAQlY-C0O42l^*Hs8OK4p0!0wtXgJL*naz0lD6XrvWfA6;#*2*1Nj87CvIp83K?K zz*PW>qz75T_Mu?b@jU{(hk3G;sv<2qqjwUOF=fu`H~Z%8H=e^NXR*m^*|nKeTush_ zu@7cRdIR~_>DO8O=7M@m52h!7dz+L5&Mh*aV|f-;OSO$p;I-WdFyhN6Y6RiT#9fbgUJ83GVtM|(tw>wf23@_&fJKqB%bhd*k_bqi%O{yi!bn(9 z55{!uQP$eq0Ku_(JRoL1U1cju55P+xKZ;XlVbGRB0K=5fY` zB?2MYSTG{Ep!%Pvv7Pswi%x5bHIZqv+yNzqeG!}I$)k$v+O{WJo4%=Iu<_CL#iXd# zN|J&(l49$#>cgY$0#Le8iFop6%0}5$MaY>CReL>Q+o$jRd`@sQ{TypT-zhO}J<`kq zzPCGF(r{bucyl}tJ#I_#P!q!UJ)c#biFH!Ew)%FK|C^g5s>UI&Ipy)pjBQ3<#$}zF zo-l5IxaN5+N^|9LNiZ}qs`9Gd{cNz4m)bCfMcHhvH(|#2dRNbDxAL?nKQHeBgeq-d zo#5P>*Zj4C?8sB&FyOMIW_drAmNs~}*wRt4iLP7Ie7 zhX`1_tvf!N8EiWpZI~9$+qzMo@!KECULRf8nR8Ro+C`ryj@x@RxSWVd;y2D-oW@&k zC#rF6+Veo+$y|_q^oY$rl-;<2SH!;Wc^#6Gm}~HQr>zYzE90$^-!~VXVrIYz(`x63 znl=*C|Cy)1W{xVF{G*Jh54R2J{;+oz?SgA58J9APlG9kfH5!@H*pzAQR@SibXr9*n zxfuMpxPEZqs#5~=0jt%%lJD7vUH$GTsN4Ecai1;&9}DM7+4VmDCzL-C_8z2TJv+=r zgugMx^k3T4|C|)3!B^MRbY5z^2Xoz)DK=;j%qVg@&LN3j5=&+eq6sH*9K{{0r4)FvS9u5L8nMnt6>n2xIU?g|CZw;B^QmmW4(GWIws1s2?&^+es_ zR;?R=q7&4_2`=A+{S;xJJ*eEnELrsFWUo(R3 zIx?aLQ2yFI*tTE>KXDoeqsw8xTjj!cTbJz<^*?LUK5^9#4IW&gmyAL;GhZm+kJ9PE z?Mu(Y&4+a+;>Jx)q4pAe-Y~yAQO?89_5Is0pS?<~YUZANQAmzv(;R>SRsb8oqX1Yy zBFKT5_{JSysGb4jbHR}I{+n?gOAF|n&j}`S(Dnbo|-e=R91>>8Weu%k$aZIpRPn}>5Wb{g74WWk@hlFx!6av z%X`*29kvAv1)Tth?8@ruDBO4BXUK=$%JGfCOe|&LLv;A{?0&sYdT8<1JkOU=F-oXd zF5`Zer2))bttXIm2V@K>LW4H`x7!Q%==76h+uN-SYS^-U0%g3_M+GjdSV-3;3r*MkxfZ}+xtR6B zXgiHC46gbx1c8uhA+-RfgdNp>6O9}WwF_+@r_!%R@bZg~IcU{S>Cj8ZC0!D!w4x8q z{KNFb!%@zz=Mkx(tQ$*1T%E%a5wmt!y$q*}s4*e3hv%~P3(=!#ha*rixbgHO-Fy3= zT8iiHD3bK|iZ5%O)p!veopOkjxbln&{sJqs?few8xTmiyltZb|T76NuDnF9#UFtH) zua7U;cOERZPB!`o0sP(sFoM9tAMcb8IPJ{{05>sA%=h^s$lB9&xetS6(y=DlDkp!^ z?I+pbSA1oq&+qRR92MvGiOCh56qFH3kJ5K=K~;WM#3PG}I)VBV%J^tjom#_kAry;n zZcqYNq=BD#E_=sIss@YCb3O>SUif9+`_%77d(>m1nojhZ{^iY20aOPELA*qCE)!7F zdo&kb-~)1YGv}6s(f8S-85-&JY_9;16r*_k03igHC?b54Z-RwjC@uKAS9&KTpY1z4 zsMn2y+!R<$6OLJo&UMWkXy;9W9a?rdJ?Zq^8)PtEhk&|Dt9kDr3gTRwe&ACc?tqwY z(#9D75}@mgFJE5tfi@|$8ZcS~hVB<`6idIEzMf<5c%v!#S@!S0Si4>u)IdDS&nH0v z$le%s)Tc7k{CtbN9o&m+iHjwXCjT1h#b0Ty2f7S3uI^Rjn@<49$Tq>Wm>lKGK)&9x zRoCp$T%G!9r1r1waq8#&x1cW!Ge&b_d9b16#ThHa9K`RgQDAbnVY`WIV(YV%PFDUs z=>nA(^cfs@D_uVLV{>S#t&1NOaX1dC|9P}^j#8P^!w{n;uQv4a(s%1VmjQ9sXcdY|V8WN3#jU>6sD4Nc2Si+{gb&5ipGd?`IN8CN&p|u+#Ta9a zV>G)2IXnw_w$&@ZHLJYS=v3dqhH&N#%4z1?>D!0|O{5Bl#(I)}H!#%^DbsvXcW26I`81_pE>F?z(s6}__Z9?4qcvXUJJL?#kCBI-(x_?_oku}G zGtJ7$c?V<$o}Pk$>$V>1u&X}K5xWg*@H#JZT$b^@S*Ca18hMJFM|M~e86SN&thw0d z#lR{){y{#+#M0l?+?>a+(c`|y-OXkF?b)>A6execGe`zQ9QydiiO9&L0sIy+_H>DC zZbyO_Z{jHJV2^}ItbK?7%f@!f!2QzD?DeKdda-_gl(oM7tMmPV!{yH63E%6pT{U%e zt`8-R(yv*F=r&WtY5J|S5Kne=@H5wwszvXOY}##l*WVRi{YtW10>To-SPt#7tU>Ff zzTjP)@eCRf&+i_`Yo$)BuTfEF=wF(dnnwTlfsvlcd+6harIw0n9m1CKrQ$S%$VSmUX6?S^M-T+I85uD59Lopt=Fuq;WmL_VdM)Hv=C{L23(n z1JNkb`?Fg;4;D&YoeT-oG+ytSj1_3p>Su(We;V)WSZuayYbhie9M};g-CTKwUm_h& zUTQha9E?ZR0!kC@3JYRLZEdaa#X=yY{&Y;&W)}8i9O{?yuSoW%r>DHkhIruM0+W*RA5!PK}N2phsNDLiQTY4ZvqEl3lUwTG;2!4-yO&M;c7U zBLJv-C)?#P8j~px$U!Scy=hzm&8t|J02Iln@jLDBxTLIMknT@eyc4>Wgx#=>i}=>D#lI{&MnIDE-&l*^nVqlVnUGTi#k0^is7FMrY-#(8jb$DEp;E68Zp}vCIu=W{8D)cJ zPKI|C>u&Y$t0rmJ+Pg+6bl`wgnLu5Hh0W!^Y3uyg<<`xgofZ;RiKb?S_`)twz}$=i z?5(tNHk5*`m*E74h;u=bH~IRzE^nc}s6`L-o1W>XqAIjmo$7}c3Zt@3;h;2gIs}{} zUlxlw0YJtCZc`k|U_2w>W4_15y_i1hP2g#3Ydh#-igGawVx&zk!Y!>t8UusbvriKZ znlYjDCA>|s&vj*hKLH{u;9SaqlQEk{a=r2~BkLR~Dq0-QQbyL1P3+0RnRO#RPXgLe zVpCH3ZZ3~e;>G+#?cujo({4g;I|~qDDk}4c)^=?*J#1?}n3^q27qy&|$b7EKlTk?t1!+qP=F6!A7pcb6)|$VrN%0Y8qP6U-fLFgvi&Ei17+$ ze?0VIgBbsK%+#QA!9_qU^U3q>kALa@ie)bYb3)o3^zRa}7J=S5K6Zu6LP@AH;1w|EEIl=yx2 zUG62{NX&*X#{JWpPK3R?*@#>Eq|u@P{z+@hh?ZJp}tq1&o=|nRF zSJ0RNEO&9o4p?qI#|f>SKA4Z!$mi`403Av@&zf~Br|iRyIf&if50;@(u)(~gZ`*q- zj~QZpP|Tz9u2r(AkYiF6OHs4+Cj)${6(l577uiI-$4S2p;Dpsz=hh(YfF(+K1Lhla z%Nnh~?xCzvtjNW;1Gx()YPA&g z^Ph6KKy|b9W+&S0!$)>Qa?6jP6LBMb9fRhaCp8^YI>5&xTPA?}+Y%+UjQ;1eKJKqQ z$!Kz9-~PCcC&@ws;kK@i8AS(%AVMBTkC5;zurZ`c?dZs`%09E!wY4f@@c5OLl@jN5rJ{y29e`Z-Z5PA)(FleAX4(G7A9z|20?jXlhe#6; zBF*f0-Cmt8gK`RwlG7gm4=|BxG5`t!oui|pBV@+0lh*s_4~rezvwvwnApraluE1hs zTnc}-E79B4Ww_e?15%(@`wa}TK|TrSJ)J;>e#Mk`KIawn>C-2I!go?q-ci{oRThJc z{I*kVNYzul2TwTpbJT|b9LJ*fCh~7;dS&oo5d)~s^%0ngHX4i%>;=Cgdb z$jHbXL5)tRxk8)5-!#>o*633qb-k2S!OwVq$m}$ef7}-{6$!0ND#OQ2l{C1!*!s)Pvz~L@9i= zkqN4#nB?SM^E4kb$L#02fL~}0A!GrUQt_BMAgAOU=onuEDglVhmO<(g5~N<$54bLK zpqsjfjcvi5*FArRVYu88fk(~%<>KPP%$|NFA1M{g$jG1+aC(1RHa0grFb;U{Pavqp z06~?pF(XpVwUBo0yx4*P1j4efU*iDz?#<1*7*={o$ybnu0&VC3z=40~<(1XE<=|`s zBG{KcS1z~56j1L$trEkJV15tx>h>SZU-rA>#r2pX0k@7C$K?Xn($TxaC_l>4(NRD` zcf=@*S_)sT#2}9W8WREX@?~I`9YECf1&Ew8fBov{eyjUEEzN#s9zK6DR^4doeYKf$ z+H>m&1-eb-HOt`~jhrBeS^(^h8X#+xumX+ig+RhDKqyKH`hD6%&aP+iOdmD^dZ`6- zvAl)zLC;4up;RD}f+o)XWC0UUCbUh|xfetCqIy=j`%^`%K>z@0HWipT9?`oClVt$e zbAw#e1hhZXDrx!v;$q>mVKx_ZjeL*=pAHP92x*G(_yV#ENQvqBlOpbsFoP9(8c8Q& zrJhj84V0V#0lglHEDG_jfWE+RuP0f+85yZ zhmiS?*jpoR)Cy>HGnCUrBVsGpQhggOn*33|(1|?o-b>Kw9Ac?pAA1XAl)Ep znwszQ^&|SsmO(a)%%%MiJ3XUs{rL{c({8^2W!g~(wfzCmEupBqecRIX5p8=KBr)LF zSUpS=AR-3KYf1K)H-69uN1Z;0or$_biC$Niu#5~wx4oG^APu>7^GjA@`ThGhkezdx zc9UWf(kTG=Y75@w7VKxe=kgm;2NDaUrNyMd?3+PuvDg*O)SDs{N5-b1rMLT|vVy~K zPgzBU2&jcCONL@A&7f`PeD2C(Pz_bpW{^}}?%MlJ=TczaSo@)(p@Aemv!*6~ce)y> zN6de-MPvY{kLRczYWVXY5x__QHC_k$w#R~ki9k|40@wVPlCS!;O=axaDiBDn+xuNp z>sHzPMm#sTvV?q#R;O;eG3Q(xLOA)d^~zl8qR&~Gf*MkYvsIHiBaAF8e1QVz~U*O zzJcGYa%jVSMcnEdd_90<1Az6bOYPyL)SB zwyVR<&0WBrudm|_CgeROCZX^Fgh}Sh0?^py0$aU)2kI-ap_;O?Z$O^N1y&y^SBKsm zQ8f9Tt>$Tqd~Va6@mNohF*eQs0?PMDf=R~Fu@unu*lF~42O%5@wGBt489YEO`0Y}{KlWR;00tkl(sQM$mekEc88kTrL zSF2mEyDE-gqev@S?)Ra@6cpydh?`@LIA?VL?wFkH&(CAy;BX`F5d+)51AyUQz&cFJ zczf4Jr>5F{|M5dx-z*=*8A^Z9Pc?+s5%3;Yt9Lt7l|29(Snvy zkQFP-WsL3z!iXhlvFpi!4`-9+)X%AfKX8K5yZpaSJ+5=xZCP~!lP5<&FWSpiT#u^a z2WKX>qzCRl4#0|BX1$aU5S!G*j9fsJ1}gUN-@kKu9KH8FPMYWRIq;!-)w;m&{94tuIuU|)h zB?(L2_YIKrNpJ7_lZ`8DgfHr>%3iaJD29z+j%Z3X0 z9|>{9-K`Kzm>8Zj{W~-?6#DkRMX!Iwu>by@jUa>;Lh<+$wz$AQAwlG&m86O!3}66mSM$18L3&wHLWxjmB*i_CpJvq0&)sPI<8%FDfxN<_+$hVcjlXhqhQ4Y(yY(#?FIfB*{J{H0THsXx0lcDe@vNvr z>12K(?aGy{Z^Snej>ja8K(Gkh)(`=&E}YaR;N4RqUKlL)J`Ob$+CzpFfkc{zVaOp6 z73~ma7)_-p`v;^hXai%%FfPKm}@O_q=Nm^gPK!yHJ|J*M1Q%t{KK29JX!0=?BwKhuClwc z(|LZp>3Hy4kv(3i;dI_xLMF)atpIIgeo0Aai^+#N3SB2>=O>ny?5SyKId&@viVP;K zarkPF&sc6VNFWCX)w6SRVa&`}<>loGDQV*FtqTi`iQI;*pC!G^-i)xKha_`TL1+|$ z@oH*BTHD$()Y-G`c+-Ajv@$zxk@aqj)DT-*RNk6wvCOA;o;-c{KYCL zc<1}~@0M@g1ZouA-P4~v>N)c^W2X{y6KRziMb_4eN?aX{p!V8GFd%foHqvHh z%$b>)dzXg;%p!^UiU@Q6)qyY3U^OAlC5H75GqyEMj~>xPOI?d9vyP-6eoRPs+42@! zL0P#SObMl+7_&cjP<5eG`JM{)5eiA*(hq&rn{Z5bda-abe=ytEN9=#rYiu(|2^}g+ zR%S)-BYN65ZywBh?@FeMI%4+s_j@dbvF-JX@0whlA78DmZ*OxRVpVm6a7K zIeF00kxRqLw6&<`0gJ}-=UhU8fq_`7zBgB=F&{q$>sDGEa|sB$4T{m;1;Ai=$L=oH z=GInw?ER;EKR8Q4Y~ADJBpi5ru|?AFz50`eg^f)=bl|gOc+S(rlR}#QFuSaRL;(OS zJ@XFLAB+0h+OYi3N4}Ifttj~%4aw_NSyA}yha2DH1K{LIXhTUP*9N8)1yV7F|ERjF`pZRNL){_JfrU& zN5?0)CkmS@%1oW>lF1qx8mOQI#Ig=p{+BObX4{!*PS4CNuB>1~CL4Xl_4M@0UiDIb z;_@$ZWzXAH?NQ5OgQesfc`RbLcXXiRfP#Wzvca=*l&goporw&pXnwfbS94b{GBR?| zQV^??QOw11P@E!C9u2!R8XWQL4<5wS5i9;@T>38oc!+(rHZenJd!dO@z~ZO0rsk-M ze~ceXl~Wg3m<=Hd)4R_G zuB}<#p`aiZGX`rw`A{dIy&Yk*&`1y92g~aH`}d?26xdaI#8gyNMTYg1*x1-Xjq?gt ze;by%)@7Z{@3_!-v3|F)&3(c59r&i|ie~I`9*}%@b6$EwtG)nEyX)roVh5FRd$Pc5 zbt9uF6bgmmDkLP7Bc*f}}d;yJa^AD8q#`8DUhKz}48uOZIB z3+83n`&U7TJNoF+qs5g(<4$m(O!$-c*0Q2u+}w796L%H2J<>_xcm3 zh#yi@Bi_F!u$gbr-AFK(A2vjS4HtF#jR!#={x@&lOjg^@1c*h-nH7N3NFr=M@6($x zHaUq0#x%Lic=8S;BKesV8wFYuv9#M9@dOakyLXAwRM}pM1Cjy$`AkCt=1Lb^=@r0b z)Ija(>WbBAKUY_^PzKKkfM-yTRU8}~d`L{hh2)o(GLBgTI^qkirJ$s|{fLX59Z#>A z5)7(W>^4fZ|LXN?W^r*k>4iWA7>u@wMKX*TWZ$#1Gf2SYrB9JgIsAtkATn|+#AT_e zckfDgkPZzEfuKjIs@{gA*Vaa!hG;De zFt?W|5?3t6s$_j+*ei`-c3=5H;oFH@wD0Ij^CACr9lkGMv zHV04vO>OPN;9~vXtE-{lUBL`B1sWJ*D=QBJig8nPJVd#Z8Aiyur4xv<3@4)f@cdFu|gg~6g4MTeS`aZ%2P-F+C$8}=rE;?5m(xIjL{$A^$SMwv+W9j&9MBZegL zzaqfI!g^+GY(Mfi4B37C&w+9pbSx_?ySBbAV{V?!H4R9q_^SCf_QQV_EC;TC>2Wc5 z{)Ybk^QW!FbZ@q=xA%#W5d*}5GZJZ;`mf@K5~RGK@v7?Tmmo<@*12-7_I(ohz4`Z0 zf_#XL<*(}m_f?`;N67adqv@rz3ZNql9=|Y(jGGvelas6MVvxwKkJ%UradAOGxFj$! zG1`!jknWxy3<%(%X3L$Ckbv*rr%Q(eQeFD?-$MZKfTWp~osIZW4d%Gb9!v_~U@c<0 ze;>P}qeG)c2EoGVbiVUDP4vHke-D>o6=h{%vBveJd)=sql!1 z+J7bsWM~wS($R(IefA;%%(mjy7X$)vMMPslNX6a0(wn%yRXLDZQgZvnix;(S+q6-P zQc<3VtEMBlDj>Bda(RRBs8GjaYBwQ|kYffc+TUSyaZ~|h{{Qg&cby2g1VZrf>=DcDpcY<} z*>bb4PV>cY-@bt)46r%~#IK#*P5^DZQV35Y3KPtT+9)jj@aa=HIK6zjk3o%)IMPSJ zZBQG4%isZVbaX^UN6V|LlM36-B6|~gqP-UXGY;xE(SoGdd$_Y>m(;iVj3*I^{Joot zK?#KhE?l3~h74y8yhG z`ChmwC-c8jPEbQ24qQhd&W?^UR#qH(6K@SK#9G1{yvib+7gx%p^o%f|fmYpgZ#xKNNrOPSP` zY8~Ca*VLST5Fl~Hxh{KUZhlWniV^bR%a>>hPA#JRf|3$BZEY$@dO-m`I{Qb^2*_0# z0Ela1Z1`}NlW5dBR;Yauxx2pJve@=6CO-ZZ{ullD|HH8v9@hAoCQhZOs91ZkQ@_7y zd_#+mkDsN-4R&hb0~J3V5l+L|>L-ssY3_hf-1hhPcX4%Xu@PPdgs_ENO(GDm*{Qk( z)3}ii4*+9u)rGVtIFqL|k#d59g3WVt^nQMR)oa$HzmHf+p;Ls9eBI}dSfT(EVqsyq z_uv73Pfw46s;ZohP7;?Fz#!UTmEHDz)N(Yls3^5k4%x-Y_7ekxyP%fYI~q}IvlN`p zl!w7GG3@HFtF0%vKYskE6Y75u_vKfg$U<;*G>IQz{7YaNDy)LQi3$4lCgAM+9KCs< zY`6shps>6)lqKqWE@(0E+4dMG42i^X9)Wl-M~al$&58Bymx2S<3<$OK!h>TlfJW^) zUm4(1nV?F1n#kQzWjz5{S_t3<)b#Z9I`wYFTwW~Tu1$3EhI(UCRk82GDyRUmG^|sC zx<5wp|9zRip4-%IqXKxn^>~qttSmMl-sSGQMyQJm61|z}>Gyz|6&ZXZ<7v9Ah!h8R zLtLqwK4J-f?am|St-Q9j9?}BdMB^JuvbxYTomvXEmWn*%fT-a|tp;r%b(X> z-+;*%Y7;g`^oVaf8lXC>pDwGv!!bPSL|aD2pr1=r_nchc=7p2PuOxr1Y~Yb-vw?rL zE_+4R_eL%@{$b$u#aQt(n>{Xba&-dCsH{(^LLe3=Ni{D>DF+jB3m5lE-Ux9%-={v* zSgBu#VZB>MS=rz?7g>xlO$4i;+mZCbd{AeU-I}=9Lw}VtNy`mh_`+1sYCpr8c!cH7 zmdq|m_J>cESU$6oW6uj?QmuJ_$6;rT$u;KlL!1oEgJ6vb(|K#C;b= zBRKx@7^2_c!MCxoffKP%=W5+#8Up8AyOC`;-ze0nwxNMPd17Bz)X*UDdu0U!vaz{o zUv37g&<49YTH~74I5&9eaaEd;g-dKZXSuZ|^Tb<(cDin8NB~tL%7%-5DPh3dGeYCX zz^#t!VXCUL;OrmWC@*!o8O_kYcjyor{Ye1qjO}G<&Zly(fU7~R=K!Dq1JLB406zh% zz#aA3@1PQO+oCq|J$~_5O-)u_9l)xp$o6cNYa82jjz}o%hm=+R7(R9p=0K zmk%Tj7rPCe$z%V`pbj2a#}gps-F`5`ocW0qp8jnFxsl5~ zD(G(HzXk6*?wVfZ79=K9#W3Q=J8^xI-z2WldIbeLZer|y%b27{no$H*>-aRxFA0w4 z80Y-@_k;WQLtZ~5i^K>T3NAcXHL%@kf8opaHAU=M&Mw^bxv@#r&4`^O36*>>>Kk+W z0{!#LS-y@5V(^l2;M#RZN(4O1zDF#59J=z*H$j*)>JiIn3cD;Rnc;HKiK!1whH?0^ zaU9bRR=J%Py1Kiyk1FmYZR`emTHS|V%|XF$>i+Iv@V0@rWPMe0aw6VL{8kgejEZvd zmE&!8`!#7#UtA7aarL3Wz|N@8epM}x?UIChu-s+UFeMh(f81Ou&ZF>IE1Yd{n8D}NzuLBi)Cn65R%IwpjPvCC%%$;3b`w5b z4uXOKo(1~)KlhpQJjJE00n)2ms|{uC*WGqcTh9K8>ZSTREg$WPP_U~{BNGeBb8$UQ z6%Ba7i}B52tGT^4Lt-ZWI}ACNz#SvRX`Xev#i!^KtS5D-kZWH8VNyJEuk&rA1 z6AQd;;A5Vo%edYl;>g?t6KPrSrhq%^n3~o~Eqv2bBg9pkZyhu=>Mj=oCVHtk-^3K^M4~DhpvKy3T=iJKb7?WOkMY40Q#iM>GCgj`Em4(V{?I+o+iVF3N3tOU z%hcaz-$oqdH6a$aJe?Eo z4T%E%N%z~hMe`8hv8Yo9Ll=)wdQLB12&Mwjeuvs_RQucYI6mtX##DMwCGjB-pgGlB z@DqO26k6;K@$zw7r0pO73loC*-Oy3ZhLlqION4kPCO~u9CDpL*acUEDcYpvse&r1B zlu8#X2`2_;-vkF?CXug3=Oz4XZlwuCKJz>jr5w729@wF(oIE5)?C!bhyV4)WYeIx5 zZ&9F}d>*Sx9vf4+e zHD#_7-$L2vdCj#=j0L+62CS!-iL{j?AO3>MVVoUdkh zYq(Jny7sD|eKj$?xbZu{dpRiP(QA%Xc@9r`qD*{r)gv+eI1oh0m&7qMwV@YxIlhM) zRW_&c`b%7xU?#AA^)LY3gci#N^Q+xPyYc(N{lq1X(Q4}SBbSy?app2HR}L7Px8fWF z@G5wlS@Gg@e^u_5m`-TNi+Yvsk_FXgvY8$FmV=?IeoR_8%0gzisC6;0XcCdU@{ zSGbBZ6~Kgjd%`UASb*zh+eIVS68M@snV4=h)@63h7;7yxPl+9+VDCf_G_PK~Ob^3< zTghBg4J24Dij)v?2Y2SfKOj4~aFUeqnf6>|$=w)ux>^9SyF>6WjIcG@K3dwU z_U6NBJPVALhuVbSOfY$Wy+&_K!Veql#;-q%5npEM!~Bz}LLd0;Xw(3aRDgO*Ts zy|i?xn4BWtmQR}gR~Q%Pa=DcoyZ`HOBr6L}Qd*%kpUIznr-qBM7cNIhyvD`tH-x26YK)$abV`pIazbbxF1hfmrB*RARyLF0Y)Jjl>K>d3uilJ?U6${`&~tzxsK!~C zm>=AVY6uNST55tT?gcmRKPC3qX~ss=iN6#XJa#fzij+UMa&f~l`9M^e<&_98l3eio z3O|qo%+PG+>n#aU@sC}#K%t<DaGz^@+ZINKU$qy?b1x3CaX=u^b2*(LO9nUPg=hOhp(=M`8IpSbmvAa6f*M9Q z1)zHY{wAUp>o@sHe)igU06+m<#sghac;|@G%d2nF^O9g^{+j0CF;ACo7>Ccvrr@QU zE)ggqxFdCS)5x8LuTK56R_?gmaoYHk#=z4brG8jNC7HS>%agB&};9N7<_vROgUx| z5vr)DYVk))E-wVG53`&B{}Qmk@n*gmnkl;{1|h&MT>%=;`!Ot={iTll!a_P;zs>RD zt56Dpj`TpZ%L^2x#nFQ2Ny4_Yz@Z7MnfD?DUMIEsr^Li}piG2F`}$txdzV`C{UX6_ zY`a((z1Q4JF{5?=o^!vZQLr+?9Jh$Ux$YlZSU;VE6xLXJ^gGq3$+_W6(elMPvGw}0@ zl#aIlfpi2C7L1Jz7ou)p5D`fyf(f}xPrpCwJOC^!v|k0slQ@|))7mVk{zmwCr+@Nl zQljC6$C+29#~98EtB@yP3jR=sZ>^xHDC6k3kNmSKlnnXIyWb0q;e8jY;+E??a zG=#xCf7J;6Q&+?P&OKZ{fv*Mx1n6H6^`4)5DSwvmw4tNL+OjsEoainqjj5cNmmARr zdJQctZD%d>CUp&lFzt!geX$E#niC=ZE$iP~RU+Qo)&Q_Di>ax#WlR12j;y8?pu?l$q7J?1p9!Hm)H06oYC^VTB2$yI}-FgcR`(<>E`NM z`%0d#WdSK0;p20eJ`zHTni}v6&Ch2CQ!IV5Z!A}?T&=6qH3rrDZ8CE5m=7NUX6&2j z^VGgnD-)8C$iv~7Am(CXV_%LmM%{gcX>4rV>wS#2_4&6gcAJDQrW<|hbMjCq4^c5O zc}5xji5lvt0jda=jz?VLA_7N6fEk#lM2MbJ2*iy9}^P^tMs_MyuEG5CxDzPD=+^J zT&r#DJ2sq13it5xic(~(8J++gF0c20$En>;swppj24q8IXQy1o#HK6)5j?ngEWEjS zaeb6073P06;a^3hZ|zGj>7|(};i&;^R${TMIJL)6ATCl^RO+H-m8I$#J94;=yCHF2 zDLI>}PQvMW-WR=dGB~oVj2HMhQWoDMvP(gGfbv7*MxSO>OyiB&+{CFbYVqC6#nzAn zQ|ppaAcdp*YnXybi;IhaUEz-C}_J#*D73GT6zlv%}gL>8>`63 zU{q98Kp?b#T_ zB&QogoqjcAWas6X4QDIvou0~jd5M9}A09>HdiUMVHWDcb*4}F-Ldu8D*@|(YsYdGR z0mqvYPcqa^<{LaM?d(Fe3ad8iXjP4C7W~97udXmAcPfQa8$Vi z$xxem`XojkY z!lEJs==k~3+golq1VhW?{QSeLSF`>%R~}$*(HkK$?|}sh5>X#inM#7>UGI}E84C+m zfPP%d*#^ewgZ;P-gtp@)%CWb0xhtBPh2_>niQyBl9~S+oVc3L}li6Q@A)f_&9U#}C zfTUgyx<)?6$4{A0we1hdGcHDn@2u5SS%e>t&Cf@@DAZbv_P=EI@$uvt+XjNF zK&P4%l3rIw%f!U=Wc=vlBr_)mTPep$=TNVb2ZHW(7%$e}Uwp^dIyP3da0vmC7L$+= z`aDl{s)#2??H3+@I?frh z;@X`9@N+*`HI3t^#bMckzxDC`VA8(6KF|Ya41`!PgST&TpT$1tj6kA0@1Be+oYBO! z1{3fyo%auiMMY79hq3{A7r2AKl9iQ_2?UQ-Qo;>vtp}i~PS(omYpu^&#jAPH^@9h{ zFIVjg_v5Rh=buh;hAa^#Cg}=FN@Tz;TDrd2rGg)&$xt#f@&FK*_3>$#EH`_na`B_5 z2MSvAV0?VfvU766fWy?=Anv|%CnhEa{d8zMNqGAy9@A&fRhn|p=;3z=R;AdmUJGdL zgrIc}N=62A+nQuD^4=EOpSCW=XF6{T`0TNS4@d}l78LLyxeoQ54d;qViRpy#q@<*J zUt9O5J_3asw{hb`^v%3{nTd>ytSWk~e|$Xrlt=fmzVn~{ZK|@M?>E&OPmzt4_7g?A zBleH2OG^QD!iT_^m|6~ud=Glq-1ceW2zJ|LKo^BzMpPhZI6$@hU$ty@>>>>>> Stashed changes # ---------------------------------------------------------------------- class SparseExactDiagonalization(object): @@ -49,7 +54,7 @@ def __init__(self, H,blocks, beta, self._diagonalize_hamiltonian() self._number_of_states_reduction() self._calculate_partition_function() - # self._calculate_density_matrix() + self._calculate_density_matrix() # ------------------------------------------------------------------ def _diagonalize_hamiltonian(self): self.U=csr_matrix(self.H.shape,dtype=np.float) @@ -58,10 +63,17 @@ def _diagonalize_hamiltonian(self): bar = progressbar.ProgressBar() for i in bar(range(len(self.blocks))): block=self.blocks[i] +<<<<<<< Updated upstream X,Y=np.meshgrid(block,block) E,U=np.linalg.eigh(self.H[X,Y].todense()) self.E[block]=E self.U[Y,X]=U +======= + E,U=np.linalg.eigh(self.H[block][:,block].todense()) + self.E[block]=E + for i,n in enumerate(block): + self.U[n,block]=U[i] +>>>>>>> Stashed changes self.E=np.array(self.E) self.E0 = np.min(self.E) self.E = self.E-self.E0 @@ -78,9 +90,19 @@ def _calculate_partition_function(self): # ------------------------------------------------------------------ def _calculate_density_matrix(self): self.rho=csr_matrix(self.H.shape,dtype=np.float) +<<<<<<< Updated upstream exp_bE=csr_matrix(self.H.shape,dtype=np.float) exp_bE[range(self.E.size),range(self.E.size)]=np.exp(-self.beta * self.E) / self.Z self.rho=self.U.getH()*exp_bE*self.U +======= + print 'Density matrix calculation:' + bar = progressbar.ProgressBar() + for i in bar(range(len(self.blocks))): + block=self.blocks[i] + X,Y=np.meshgrid(block,block) + exp_bE = np.exp(-self.beta * self.E[block]) / self.Z + self.rho[X,Y]= np.einsum('ij,j,jk->ik', self.U[X,Y].todense(), exp_bE, self.U[X,Y].H.todense()) +>>>>>>> Stashed changes # ------------------------------------------------------------------ def _operators_to_eigenbasis(self, op_vec): @@ -142,10 +164,193 @@ def get_real_frequency_greens_function_component(self, w, op1, op2,eta,xi): """ op1_eig, op2_eig = self._operators_to_eigenbasis([op1, op2]) Q=(op1_eig.getH().multiply(op2_eig)).tocoo() +<<<<<<< Updated upstream M=(np.exp(-self.beta*self.E[Q.row])-xi*np.exp(-self.beta*self.E[Q.col]))*Q.data E=(self.E[Q.row]-self.E[Q.col]) G = np.zeros((len(w)), dtype=np.complex) G = Parallel(n_jobs=4)(delayed(gf)(M,E-1j*eta,x) for x in w) +======= + M=(np.exp(-self.beta*self.E[Q.row])+np.exp(-self.beta*self.E[Q.col]))*Q.data + E=(self.E[Q.row]-self.E[Q.col]) + G = np.zeros((len(w)), dtype=np.complex) + G = Parallel(n_jobs=12)(delayed(gf)(M,E,eta,x) for x in w) + G /= self.Z + return G + + # ------------------------------------------------------------------ + def get_g2_dissconnected_tau_tetra(self, tau, tau_g, g): + + g = np.squeeze(g) # fix for now throwing orb idx + g = g.real + + N = len(tau) + G4 = np.zeros((N, N, N), dtype=np.complex) + + def gint(t): + sign = 1.0 + if (t < 0).any(): + assert( (t <= 0).all() ) + t = self.beta + t + sign = -1.0 + + return sign * np.interp(t, tau_g, g) + + for idx, taus, perm, perm_sign in CubeTetras(tau): + t1, t2, t3 = taus + G4[idx] = gint(t1-t2)*gint(t3) - gint(t1)*gint(t3-t2) + + return G4 + + # ------------------------------------------------------------------ + def get_g2_dissconnected_tau(self, tau, tau_g, g): + + g = np.squeeze(g) # fix for now throwing orb idx + g = g.real + + N = len(tau) + G4 = np.zeros((N, N, N), dtype=np.complex) + + def gint(t_in): + t = np.copy(t_in) + sidx = (t < 0) + sign = np.ones_like(t) + sign[sidx] *= -1. + t[sidx] = self.beta + t[sidx] + return sign * np.interp(t, tau_g, g) + + t1, t2, t3 = np.meshgrid(tau, tau, tau, indexing='ij') + G4 = gint(t1-t2)*gint(t3) - gint(t1)*gint(t3-t2) + + return G4 + + # ------------------------------------------------------------------ + def get_g2_tau(self, tau, ops): + + N = len(tau) + G4 = np.zeros((N, N, N), dtype=np.complex) + ops = np.array(ops) + + for tidx, tetra in enumerate(CubeTetras(tau)): + idx, taus, perm, perm_sign = tetra + + print 'Tetra:', tidx + + # do not permute the last operator + ops_perm = ops[perm + [3]] + taus_perm = taus[perm] # permute the times + + G4[idx] = self.get_timeordered_three_tau_greens_function( + taus_perm, ops_perm) * perm_sign + + return G4 + + # ------------------------------------------------------------------ + def get_timeordered_two_tau_greens_function(self, taus, ops): + + r""" + taus = [t1, t2] (ordered beta>t1>t2>0) + ops = [O1, O2, O3] + + Returns: + G^{(4)}(t1, t2) = -1/Z < O1(t1) O2(t2) O3(0) > + + """ + + Nop = 3 + + assert( taus.shape[0] == 2 ) + assert( len(ops) == Nop ) + + G = np.zeros((taus.shape[-1]), dtype=np.complex) + + E = self.E[None, :] + + t1, t2 = taus + t1, t2 = t1[:, None], t2[:, None] + + assert( (t1 <= self.beta).all() ) + assert( (t1 >= t2).all() ) + assert( (t2 >= 0).all() ) + + et_a = np.exp((-self.beta + t1)*E) + et_b = np.exp((t2-t1)*E) + et_c = np.exp((-t2)*E) + + dops = self._operators_to_eigenbasis(ops) + op1, op2, op3 = dops + + G = np.einsum('ta,tb,tc,ab,bc,ca->t', et_a, et_b, et_c, op1, op2, op3) + + G /= self.Z + return G + + # ------------------------------------------------------------------ + def get_timeordered_three_tau_greens_function(self, taus, ops): + + r""" + taus = [t1, t2, t3] (ordered beta>t1>t2>t3>0) + ops = [O1, O2, O3, O4] + + Returns: + G^{(4)}(t1, t2, t3) = -1/Z < O1(t1) O2(t2) O3(t3) O4(0) > + + """ + + assert( taus.shape[0] == 3 ) + assert( len(ops) == 4 ) + + Nop = 4 + G = np.zeros((taus.shape[-1]), dtype=np.complex) + + E = self.E[None, :] + + t1, t2, t3 = taus + t1, t2, t3 = t1[:, None], t2[:, None], t3[:, None] + + assert( (t1 <= self.beta).all() ) + assert( (t1 >= t2).all() ) + assert( (t2 >= t3).all() ) + assert( (t3 >= 0).all() ) + + et_a = np.exp((-self.beta + t1)*E) + et_b = np.exp((t2-t1)*E) + et_c = np.exp((t3-t2)*E) + et_d = np.exp((-t3)*E) + + dops = self._operators_to_eigenbasis(ops) + op1, op2, op3, op4 = dops + + if True: + q_tac = np.einsum('tb,ab,bc->tac', et_b, op1, op2) + q_tca = np.einsum('td,cd,da->tca', et_d, op3, op4) + G = np.einsum('ta,tc,tac,tca->t', et_a, et_c, q_tac, q_tca) + else: + # Not efficient... + G = np.einsum( + 'ta,tb,tc,td,ab,bc,cd,da->t', + et_a, et_b, et_c, et_d, op1, op2, op3, op4) + + G /= self.Z + return G + + # ------------------------------------------------------------------ + def get_tau_greens_function_component(self, tau, op1, op2): + + r""" + Returns: + G^{(2)}(\tau) = -1/Z < O_1(\tau) O_2(0) > + """ + + G = np.zeros((len(tau)), dtype=np.complex) + + op1_eig, op2_eig = self._operators_to_eigenbasis([op1, op2]) + + et_p = np.exp((-self.beta + tau[:,None])*self.E[None,:]) + et_m = np.exp(-tau[:,None]*self.E[None,:]) + + G = -np.einsum('tn,tm,nm,mn->t', et_p, et_m, op1_eig, op2_eig) + +>>>>>>> Stashed changes G /= self.Z return G From 24e00f8dd739fd75058b75b91050925343307219 Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Sat, 7 Oct 2017 13:54:16 +0300 Subject: [PATCH 11/33] Clean up --- Documentation.ipynb | 439 ---------------------------------------- figure_densdens_tau.png | Bin 10298 -> 0 bytes figure_g_iwn.png | Bin 13506 -> 0 bytes figure_g_tau.png | Bin 11132 -> 0 bytes 4 files changed, 439 deletions(-) delete mode 100644 Documentation.ipynb delete mode 100644 figure_densdens_tau.png delete mode 100644 figure_g_iwn.png delete mode 100644 figure_g_tau.png diff --git a/Documentation.ipynb b/Documentation.ipynb deleted file mode 100644 index fd2a1c4..0000000 --- a/Documentation.ipynb +++ /dev/null @@ -1,439 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# **PYED**: Exact diagonalization for finite quantum systems\n", - "\n", - "Copyright (C) 2017, H. U.R. Strand\n", - "\n", - "The python module `pyed` implements exact diagonalization for finite fermionic many-body quantum systems, together with calculations of several response functions in imagianary time.\n", - "\n", - "The many-body system is defined using `pytriqs` second-quantized operators and the response functions are stored in `pytriqs` Green's function containters.\n", - "\n", - "## Hamiltonians\n", - "\n", - "As an example let us solve the Hubbard atom with Hamiltonian $H = U\\hat{n}_{\\uparrow} \\hat{n}_{\\downarrow} - \\mu ( \\hat{n}_{\\uparrow} + \\hat{n}_{\\downarrow})$, where $\\hat{n}_\\sigma = c^\\dagger_\\sigma c_\\sigma$.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "H = -0.1*c_dag(0,0)*c(0,0) + -0.1*c_dag(1,0)*c(1,0) + 1*c_dag(0,0)*c_dag(1,0)*c(1,0)*c(0,0)\n" - ] - } - ], - "source": [ - "from pytriqs.operators import c, c_dag\n", - "%matplotlib inline\n", - "up, down = 0, 1\n", - "n_up = c_dag(up, 0) * c(up, 0)\n", - "n_down = c_dag(down, 0) * c(down, 0)\n", - "\n", - "U = 1.0\n", - "mu = 0.1\n", - "\n", - "H = U * n_up * n_down - mu * (n_up + n_down)\n", - "\n", - "print 'H =', H" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Thermal equilibrium solution\n", - "\n", - "To solve the thermal equilibrium of the system we can diagonalize $H$ and determine the partition function $\\mathcal{Z}$ (or alternatively the free energy $\\Omega = -\\frac{1}{\\beta} \\ln \\mathcal{Z}$) and the many-body density matrix $\\rho$ using the egenstates $|\\Gamma \\rangle$ and eigenvalues $E_\\Gamma$ of $H$. The partition function $\\mathcal{Z}$ is given by the sum of Boltzman weights\n", - "\n", - "$$\n", - "\\mathcal{Z} = \\sum_\\Gamma e^{-\\beta E_\\Gamma} \\, ,\n", - "$$\n", - "while the many-body density matrix is given by the ket-bra Boltzman weighted sum\n", - "\n", - "$$\n", - "\\rho = \\frac{1}{\\mathcal{Z}} \\sum_\\Gamma e^{-\\beta E_\\gamma} |\\Gamma \\rangle \\langle \\Gamma|\n", - "\\, .\n", - "$$\n", - "\n", - "To accomplish this we pass the Hamiltonian $H$ and a list of unique annihilation opeators used in $H$ together with the inverse temperature $\\beta$ to a `pyed.TriqsExactDiagonalization` class instance:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hamiltonian diagonalization:\n", - "Density matrix calculation:\n", - "Z = 2.9840296413\n", - "\\Omega = -0.646637307852\n", - "\\rho =\n", - " (0, 0)\t0.27437085133\n", - " (1, 1)\t0.335117314573\n", - " (2, 2)\t0.335117314573\n", - " (3, 3)\t0.0553945195228\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0% | |\r", - "/usr/local/lib/python2.7/dist-packages/scipy/sparse/compressed.py:774: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n", - " SparseEfficiencyWarning)\n", - " 25% |################## |\r", - " 50% |#################################### |\r", - " 75% |###################################################### |\r", - "100% |########################################################################|\r\n", - " 0% | |\r", - " 25% |################## |\r", - " 50% |#################################### |\r", - " 75% |###################################################### |\r", - "100% |########################################################################|\r\n" - ] - } - ], - "source": [ - "beta = 2.0 # inverse temperature\n", - "fundamental_operators = [c(up,0), c(down,0)]\n", - "\n", - "from pyed.TriqsExactDiagonalization import TriqsExactDiagonalization\n", - "ed = TriqsExactDiagonalization(H, fundamental_operators, beta)\n", - "\n", - "print r'Z =', ed.get_partition_function()\n", - "print r'\\Omega =', ed.get_free_energy()\n", - "print r'\\rho ='\n", - "print ed.ed.get_density_matrix()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Thermal expectation values\n", - "\n", - "Using the many-body density matrix we can evaluate the expectation value of any operator $\\mathcal{O}$ by taking the trace\n", - "\n", - "$$\n", - "\\langle \\mathcal{O} \\rangle = \\textrm{Tr} [ \\rho \\mathcal{O} ]\n", - "$$\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " = 0.390511834096\n", - " = 0.390511834096\n", - " = 0.0553945195228\n" - ] - } - ], - "source": [ - "print ' =', ed.get_expectation_value(n_up)\n", - "print ' =', ed.get_expectation_value(n_down)\n", - "print ' =', ed.get_expectation_value(n_up * n_down)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imaginary time single-particle Green's function\n", - "We can also calculate the dynamical fluctuations of the system by computing its response functions. The simples case is the single-particle Green's function, defined as the imaginary time ordered expectation value\n", - "\n", - "$$\n", - " G_{\\sigma \\sigma'}(\\tau) \\equiv\n", - " - \\langle \\mathcal{T} \\, c_{\\sigma}(\\tau) c_{\\sigma'}^\\dagger(0) \\rangle\n", - " =\n", - " - \\frac{1}{\\mathcal{Z}} \\text{Tr}\n", - " \\left[ e^{-\\beta H} c_{\\sigma}(\\tau_1) c_{\\sigma'}^\\dagger(0) \\right]\n", - "$$\n", - "where the imaginary time dependent operators are defined in the Heisenberg picture $c_{\\sigma}(\\tau) \\equiv e^{\\tau H} c_{\\sigma} e^{-\\tau H}$ and $c^\\dagger_{\\sigma}(\\tau) \\equiv e^{\\tau H} c^\\dagger_{\\sigma} e^{-\\tau H}$.\n", - "\n", - "To calculate $G(\\tau)$ we first create `pytriqs.GfImTime` instance to store the result and pass it to our ED solver instance:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEKCAYAAADTgGjXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmYXHWd7/H3t/ctS69JZ+l0QhKyL1hEFoHIIkFEnNGL\nKGpQvFFxfGYe586QuVznzjiMZgYd1Cs6k8G5Fy8qKKOAV1BJABERQkdDEpKQPaQ7nXSnk3Sn9+17\n/6iT0OlUJ92nu6uqO5/X89RTZ/nVOd+cOl2fnPOrU8fcHRERkTBSEl2AiIiMXAoREREJTSEiIiKh\nKURERCQ0hYiIiISmEBERkdAUIiIiEppCREREQlOIiIhIaGmJLmC4FRUVeXl5eaLLEBEZUTZu3HjU\n3YvP127Uh0h5eTkVFRWJLkNEZEQxswP9aafTWSIiEppCREREQlOIiIhIaKO+T0RERreOjg4qKytp\nbW1NdCkjUlZWFlOmTCE9PT3U6xUiIjKiVVZWMmbMGMrLyzGzRJczorg7dXV1VFZWMn369FDLSKrT\nWWa2wszeNLPdZrY6xvxMM3ssmP+qmZXHv0oRSSatra0UFhYqQEIwMwoLCwd1FJc0IWJmqcCDwE3A\nPOAjZjavV7O7gOPuPhN4APin+FYpIslIARLeYLddMp3OWgbsdve9AGb2KHArsK1Hm1uBvwuGHwe+\nbWbmw3WP32dWw+Etw7JoERkiC/4ajibTR1kSSc+GcVOGdRVJcyQCTAYO9hivDKbFbOPunUA9UNh7\nQWa2yswqzKyitrZ2mMoVEZFRGd/uvhZYCxCJRMIfpdy0ZqhKEpHhsn07FM1KdBUXrGQ6EqkCpvYY\nnxJMi9nGzNKAcUBdXKoTETmH1NRUlixZwoIFC7jllls4ceLEkK/jl7/8JRdffDEzZ85kzZrz/yd3\noO3DSKYQeQ2YZWbTzSwDuB14qlebp4CVwfCHgOeGrT9ERGQAsrOz2bRpE1u3bqWgoIAHH3xwSJff\n1dXF5z//eZ555hm2bdvGj370I7Zt2zZk7cNKmhAJ+jj+DPgVsB34sbu/YWZfNrP3B82+BxSa2W7g\ni8BZXwMWEUm0yy+/nKqq6ImURx55hGXLlrFkyRI+85nP0NXVFfM127dv5+qrr2bRokXcf//9zJw5\n84z5GzZsYObMmcyYMYOMjAxuv/12nnzyyT5rGGj7sJKqT8Tdnwae7jXtb3sMtwL/Jd51icjI8Pc/\nf4NthxqGdJnzJo3lf94yv9/tu7q6WL9+PXfddRfbt2/nscce43e/+x3p6encfffd/OAHP+ATn/jE\nGa/p7Ozkjjvu4Hvf+x5Lly7lc5/7HAsWLDijTVVVFVOnvn3Gf8qUKbz66qt91jHQ9mElVYiIiIxU\nLS0tLFmyhKqqKubOncsNN9zAd7/7XTZu3Mill156uk1JSclZr/3pT3/K4sWLWbp0KQDz5s2L2S4Z\nKUREZNQYyBHDUDvVJ9Lc3MyNN97Igw8+iJmxcuVKvvrVr57ztZs3b2bJkiWnx7du3cqKFSvOaDN5\n8mQOHnz7KojKykomT+59FUT49mElTZ+IiMhokJOTw7e+9S2+/vWvc8011/D4449TU1MDwLFjxzhw\n4Ox7PRUWFrJz504ANm3axCOPPMLixYvPaHPppZeya9cu9u3bR3t7O48++ijvf//7z1pW2PZh6UhE\nRGSILV26lEWLFvH6669z33338Z73vIfu7m7S09N58MEHmTZt2hntP/7xj3PzzTezcOFCli9fTnl5\nOTNmzDijTVpaGt/+9re58cYb6erq4lOf+hTz5/d95DXQ9mHZaP+GbCQScd0eV2T02r59O3Pnzk10\nGYPS2NhIXl4eAPfffz/19fXcd999cVt/rG1oZhvdPXK+1+p0lohIgj3wwAPMnz+fJUuWsH//fr70\npS8luqR+0+ksEZEE+9KXvhQ6OOrq6rjuuuvOmr5+/XoKC8/6acEhpxARERnBCgsL2bRpU8LWr9NZ\nIiISmkJERERCU4iIiEhoChEREQlNISIiIqEpREREJDSFiIiIhKYQEREZAqd+tmQ4DeR2t/G4NS4o\nRERERoSB3O42XrfGBYWIiMiQ2b9/P3PmzOHOO+9k9uzZ3HHHHaxbt44rr7ySWbNmsWHDhpivO9+t\ncWFgt7uN161xQSEiIjKkdu/ezV/+5V+yY8cOduzYwQ9/+ENeeuklvva1r/GVr3zlrPanbo37zW9+\nk82bN7N3796zbo0LsW93e+o+7oNpO1j67SwRGT2eWQ2HtwztMicuhJv636cwffp0Fi5cCMD8+fO5\n7rrrMDMWLlzI/v37z2o/km+NCzoSEREZUpmZmaeHU1JSTo+npKTQ2dl5VvtYt8btOX7KQG53G69b\n44KORERkNBnAEUOyiHVr3Hvuueesdj1vdzt58mQeffRRfvjDH8Zc5kDaDpZCREQkgfpza1wY2O1u\n43VrXEiS2+OaWQHwGFAO7Aduc/fjMdr9ErgMeMnd39efZev2uCKj20i/PW6ib40Lo+P2uKuB9e4+\nC1gfjMdyP/DxuFUlIjLMRvKtcSF5TmfdCiwPhh8GXgDOOino7uvNbHnv6SIiI9VIvjUuJE+ITHD3\n6mD4MDAhkcWIiIwEib41LsQxRMxsHTAxxqx7e464u5vZoDpqzGwVsAqgrKxsMIsSEZFziFuIuPv1\nfc0zsyNmVuru1WZWCtQMcl1rgbUQ7VgfzLJERKRvydKx/hSwMhheCQzPj7yIiMiQSpYQWQPcYGa7\ngOuDccwsYmYPnWpkZr8FfgJcZ2aVZnZjQqoVkaSSDJcqjFSD3XZJ0bHu7nXAWV8xcPcK4NM9xq+K\nZ10ikvyysrKoq6ujsLAQM0t0OSOKu1NXV0dWVlboZSRFiIiIhDVlyhQqKyupra1NdCkjUlZWFlOm\nTAn9eoWIiIxo6enpTJ8+PdFlXLCSpU9ERERGIIWIiIiEphAREZHQFCIiIhKaQkREREJTiIiISGgK\nERERCU0hIiIioSlEREQkNIWIiIiEphAREZHQFCIiIhKaQkREREJTiIiISGgKERERCU0hIiIioSlE\nREQkNIWIiIiEphAREZHQFCIiIhKaQkREREJLihAxswIze9bMdgXP+THaLDGz35vZG2a22cw+nIha\nRUTkbUkRIsBqYL27zwLWB+O9NQOfcPf5wArgG2Y2Po41iohIL8kSIrcCDwfDDwMf6N3A3Xe6+65g\n+BBQAxTHrUIRETlLsoTIBHevDoYPAxPO1djMlgEZwJ7hLkxERPqWFq8Vmdk6YGKMWff2HHF3NzM/\nx3JKgf8LrHT37j7arAJWAZSVlYWuWUREzi1uIeLu1/c1z8yOmFmpu1cHIVHTR7uxwC+Ae939lXOs\nay2wFiASifQZSCIiMjjJcjrrKWBlMLwSeLJ3AzPLAH4GfN/dH49jbSIi0odkCZE1wA1mtgu4PhjH\nzCJm9lDQ5jbgauBOM9sUPJYkplwREQEw99F9ticSiXhFRUWiyxARGVHMbKO7R87XLlmOREREZARS\niIiISGgKERERCU0hIiIioSlEREQkNIWIiIiEphAREZHQFCIiIhKaQkREREJTiIiISGgKERERCU0h\nIiIioSlEREQkNIWIiIiEphAREZHQFCIiIhKaQkREREJTiIiISGgKERERCU0hIiIioQ04RMws18xS\nh6MYEREZWc4bImaWYmYfNbNfmFkNsAOoNrNtZna/mc0c/jJFRCQZ9edI5HngIuBvgInuPtXdS4B3\nAa8A/2RmHxvGGkVEJEml9aPN9e7e0Xuiux8D/hP4TzNLH/LKREQk6Z33SORUgJjZy+drE5aZFZjZ\ns2a2K3jOj9Fmmpn9wcw2mdkbZvbZwaxTREQGbyAd61m9J5jZVUNUx2pgvbvPAtYH471VA5e7+xLg\nncBqM5s0ROsXEZEQ+nM665SLzexnwBvAVuAI8BDR/pLBuhVYHgw/DLwA3NOzgbu39xjNRF9PFhFJ\nuIGEyD7gK8AC4B3AJODvh6iOCe5eHQwfBibEamRmU4FfADOBv3L3Q0O0fhERCWEgIdLu7q8Br4VZ\nkZmtAybGmHVvzxF3dzPzWMtw94PAouA01hNm9ri7H4mxrlXAKoCysrIw5YqISD8MJESuGcyK3P36\nvuaZ2REzK3X3ajMrBWrOs6xDZrYVuAp4PMb8tcBagEgkEjOQRERk8PpzsaEBuPvJ87UZhKeAlcHw\nSuDJGOuYYmbZwXA+0etU3hzkekVEZBD6dbGhmX3BzM44L2RmGWZ2rZk9zNsBENYa4AYz2wVcH4xj\nZhEzeyhoMxd41cxeB34DfM3dtwxyvSIiMgjmfu6zPWaWBXwKuAOYARwHsokG0K+B77j7H4e5ztAi\nkYhXVFQkugwRkRHFzDa6e+R87c7bJ+LurcB3gO8EV6YXAS3ufmLwZYqIyEjW74714FTTFuB1YJOZ\nbXL3A8NWmYiIJL2BXLD3b0Sv4agDbgLeMLMtZvZl/XaWiMiFaSBf8f1Y8JMjAJjZvxLtK2kA/gX4\nwhDXJiIiSW4gIVJvZovcfTOAu28ys2vcfbGZ/WGY6hMRkSQ2kBD5DPADM9sEbAIuBpqDeRlDXZiI\niCS/fveJuPsOYBnwS6AE2A28z8xygUeHpzwREUlmAzkSwd27gJ8Ej57uG7KKRERkxNDPqYuISGgK\nERERCU0hIiIioSlEREQkNIWIiIiEphAREZHQFCIiIhKaQkREREJTiIiISGgKERERCU0hIiIioSlE\nREQkNIWIiIiEphAREZHQFCIiIhLagO4nMlzMrAB4DCgH9gO3ufvxPtqOBbYBT7j7n8WrRhGR4eTu\ntHR00dTWRXN7J01tXbR0dJ4x3tzRRWt7F83tXbR0dNHS3klzexetnd20tHfR1tlFa0d0XmtHN7Mn\n5PGdO94xrHUnRYgAq4H17r7GzFYH4/f00fYfgBfjVpmIyDm4O60d3TS0dnCytYP6lk5OtnbQ0Bp9\nPtnaSWNrJ41tndHhtg4a296e1tTWRVNbJ03tnXR7/9ebkZpCdkYq2empZGekkpWeSlZ6CtnpqYzJ\nSic7PZWLSvKG7x8eSJYQuRVYHgw/DLxAjBAxs3cAE4jeojcSp9pE5ALQ3e3Ut3RwvLmd480d1Le0\nc6K5I/po6aC+uZ0TLdHxhtYO6ls6aGjpoKGlk/au7nMuO8UgLzONMVnp5GWmkZeVxricDCbnZ5Ob\nkUZuZhp5mdHn3MzUYFoq2Rlp5GakkpORRk5GKjkZqaeDIy01OXojkiVEJrh7dTB8mGhQnMHMUoCv\nAx8Dro9jbSIyArV3dnOsqZ2jjW0cbWzjeHM7dY3tHGuKPuqa2jne1M6x5lNh0d7nkYAZjMtOP+Mx\naXw2Y7PeHh+bHQ2JsVnR53HZb4dGTkYqZhbfDRAncQsRM1sHTIwx696eI+7uZhbrrbwbeNrdK8/3\nZpjZKmAVQFlZWbiCRSTpdHZ1U9fUTu3JtrcfjWcOH21so66xnfqWjpjLSEsx8nMzKMzNoCA3g7ml\nY8nPSacgJ4PxOdFp43LSyc/JYHx29HlMVhopKaMzBAYrbiHi7n0ePZjZETMrdfdqMysFamI0uxy4\nyszuBvKADDNrdPfVMda1FlgLEIlEBnCWUUQSwd053tzB4fpWjjS0Uh0815xs5UhD2+nno41teIy/\n6LFZaRSNyaQ4L5O5E8dSmJdBUV4mhXkZFOZmUpQXDYfC3EzGZqeN2qOCREiW01lPASuBNcHzk70b\nuPsdp4bN7E4gEitARCS5uDsNLZ1UnWihur6FQ/WtVJ9o4dCJ6PDh+lYON7TS3nlmv4IZFOZmUjIm\nkwljM5lfOo4JYzMpHptFyZhMioPQKB6TSVZ6aoL+dZIsIbIG+LGZ3QUcAG4DMLMI8Fl3/3QiixOR\nvrk7RxvbqTzezMHjLVQdb6HqRHPwHB1vau864zVpKcaEsVlMGp/F4qnjWTEuiwljsyjt8Vw8JpP0\nJOk8lr6Zxzo2HEUikYhXVFQkugyREa2lvYuDx5s5UNfMgbomDh6LBsbBY81UHm+hpePMkBifk86k\ncdlMzs9m8vhspuRnM2l8NqXjspg0PpuivExS1ceQ1Mxso7uf91uwyXIkIiIJ1tjWyf6jTeyva+JA\nXTP7jjZxIBiuOdl2Rtu8zDSmFuQwvSiXq2cXMyU/m6n5OUwtyGFyfjZ5mfpouVDonRa5gHR0dfPW\nsWb21jaxt7aRvbVN7DvaxN6jTRxtPDMoJozNZFphLtfMLqasIIeywhymFeZSVpBDfk66OqcFUIiI\njEqNbZ3sqWlkd00ju2ujz3tqGjlwrJmuHhdDFOZmMKM4l2vnFFNelMv0wlymFeZSXpRDToY+HuT8\ntJeIjGCNbZ3sOnKSXUca2XnkJDtrGtl15CTV9a2n26SlGOVFucyeMIabFk5kRlEe04tzuagoj3E5\n6QmsXkYDhYjICNDR1c2+o01sr27gzcMn2XH4JG8ePknViZbTbTLTUphZksdlMwqZWZLHRcV5zCzJ\nY1phjr7lJMNGISKSZE40t7OtuoFthxrYVt3A9uqT7KlpPP37TGkpxoziXC6Zls9Hlk1l1oQxzJ4w\nhrKCHH3jSeJOISKSIO7O4YZWtlTWs/VQNDS2VzeccXRRMiaTuaVjuXp2EXMmjmHOxLHMKM4lM00X\n10lyUIiIxIG7c6i+lS2VJ9hSVc/Wqga2VtVT19QORH/ldUZxHpHyfD5ROo25pWOZWzqW4jGZCa5c\n5NwUIiLDoK6xjc2V9bxeeYLNlfVsrjzB0cZoYKSlGLMmjOG6uSUsmDyO+ZPGMbd0jL4NJSOS9lqR\nQWrr7GLboQb++NYJNh08wR8PHufgsegpKTOYWZzHNbNLWDx1HIumjGfOxDH6rScZNRQiIgNU09BK\nxYHjbAwe2w41nO70Lh2XxZKp4/nYO6exeOp4Fkwep6u3ZVTT3i1yDl3dzpuHT7LxwLHTwVF5PHqU\nkZmWwqIp4/jkleUsmTqeJWXjKR2XneCKReJLISLSQ3tnN1uqTrBh33E27Kuj4sBxTrZ2AtFvSkXK\n87nzinIi5QXMKx1LRpquv5ALm0JELmhtnV1seusEr+w9xit76/jDW8dpC+5rcVFxLu9bVMql5QVc\nWl7AlPxs/V6USC8KEbmgtHd2s+ngCX6/p+6M0DCDeaVjueOd01g2PZ9IeQFFefp6rcj5KERkVOvu\ndrZVN/DynqO8tLuO1/Ydo6Wj63RofOyyaVw2o5Bl5QX6HSmREBQiMupUnWjhxZ21vLTrKC/vOcrx\n5g4AZpbkcVtkClfMLOKy6YUKDZEhoBCREa+prZNX99Xx4s6jvLirlr21TQBMHJvFtXMmcOXMQq64\nqIiJ47ISXKnI6KMQkRHH3dlT28jzO2p5YWcNG/Ydo6PLyUpP4Z3TC/nosjKumV3MzJI8dYSLDDOF\niIwILe1d/H7vUZ7fUcvzb9acvlZj9oQ8PnnldK6eVUykPF9XgovEmUJEktbh+lbW7zjC+u01/G73\nUdo6u8lOT+XKmUV8bvlFLL+4hMnjdXGfSCIpRCRpuDtvHGpg3fZocGypqgdgakE2H1lWxnVzS1g2\nvUA/gy6SRBQiklCdXd1UHDjOr944zK/fOELViRbMYOnU8fzVjRdzw7wJzFLfhkjSUohI3LV2dPHS\nrqP86o3DrN9Rw7GmdjLSUrhqZhF/ft0srp1bogv9REaIpAgRMysAHgPKgf3Abe5+PEa7LmBLMPqW\nu78/XjXK4LR2dPGbnbU8vaWa9dtraGzrZExmGtfOLeE98yZyzcXF+rVbkREoWf5qVwPr3X2Nma0O\nxu+J0a7F3ZfEtzQJq7Wji+d31PD01sM8t/0ITe1djM9J5+aFpaxYOJErLyrSDxiKjHDJEiK3AsuD\n4YeBF4gdIpLk2ju7eWl3LU9tOsSz26LBUZCbwfuXTOa9Cydy2YxC0lMVHCKjRbKEyAR3rw6GDwMT\n+miXZWYVQCewxt2fiNXIzFYBqwDKysqGulbppavbeWVvHT9//RDPbD1MfUsH47LTuWXxJG5ZPIl3\nTi8gTcEhMirFLUTMbB0wMcase3uOuLubmfexmGnuXmVmM4DnzGyLu+/p3cjd1wJrASKRSF/LkkFw\nj/6w4RN/rOLJTYeoOdlGbkYq75k/kVsWl/KumcU6VSVyAYhbiLj79X3NM7MjZlbq7tVmVgrU9LGM\nquB5r5m9ACwFzgoRGT6HTrTwxKYqnvhjFTuPNJKeaiy/uIQPLJnMtXNKyM7QNRwiF5JkOZ31FLAS\nWBM8P9m7gZnlA83u3mZmRcCVwD/HtcoLVHN7J89sOczjGyt5ZV8d7hCZls99H1jAzQtLyc/NSHSJ\nIpIgyRIia4Afm9ldwAHgNgAziwCfdfdPA3OBfzOzbiCFaJ/ItkQVPNq5Oxv2HePxjZU8vaWapvYu\nphXm8BfXzeZPlk6mrDAn0SWKSBJIihBx9zrguhjTK4BPB8MvAwvjXNoFp7q+hZ9UVPL4xkreOtZM\nbkYq71s0iQ9FphCZlq8rx0XkDEkRIpJYHV3dPLejhsdeO8gLb9bQ7XD5jEL+4vpZrFgwkZwM7SYi\nEps+HS5gB+qaeOy1g/xkYyW1J9soGZPJ3ctncltkqk5XiUi/KEQuMJ1d3azbXsMPXj3Ab3cdJcXg\n2jklfPjSMt59cbGu5xCRAVGIXCAO17fy6Gtv8eiGgxxuaKV0XBZfvGE2t0Wm6raxIhKaQmQUc3d+\nv6eO7//+AM9uP0K3O1fPKubLt87n2jklOuoQkUFTiIxCze2d/OyPVTz88n52HmkkPyedT181nTuW\nTVNfh4gMKYXIKHLwWDPf//1+HnvtIA2tncyfNJb7P7SIWxZP0r3HRWRYKERGOHfntf3Heei3e3l2\n+xFSzFixYCKfvKKcd+i6DhEZZgqREaqjq5tnth7mod/uZXNlPeNz0rl7+UV87LJplI7LTnR5InKB\nUIiMMA2tHTy64S3+z+/2c6i+lRlFudz3gQV88JIp+vFDEYk7hcgIcaShlf94aR8/ePUtGts6uXxG\nIf/wgQW8++ISUlJ0ykpEEkMhkuT21Day9jd7+dkfq+js7ubmRZP4zNUzWDB5XKJLExFRiCSrTQdP\n8N0XdvPrbUfISE3hw5dO5b9eNUNf0RWRpKIQSTKv7K3j28/t5qXdRxmblcbnl8/kzivLKcrLTHRp\nIiJnUYgkAXfnt7uO8u3ndrNh/zGK8jL57++dw0ffOY28TL1FIpK89AmVQO7O+u01/K/nd/P6wROU\njsvi726Zx+3LynRxoIiMCAqRBDgVHt9Yv5OtVQ1MLcjmq3+6kD+9ZDKZaQoPERk5FCJx5O688GYt\nD6zbyebKesoKcrj/Q4v4wNLJpOvHEEVkBFKIxIG78+Kuozzw7E42HTzBlPxs/vmDi/iTSxQeIjKy\nKUSGWcX+Y/zzL99kw/5jTB4fPW31wUumkJGm8BCRkU8hMkzeOFTP1371Js+/WUvxmEy+fOt8Pnzp\nVPV5iMioohAZYvuPNvH1Z3fy89cPMTYrjXtWzGHlFdPIydCmFpHRR59sQ6T2ZBvfXL+TRzccJD01\nhc+/+yJWXX0R47LTE12aiMiwSYoQMbMC4DGgHNgP3Obux2O0KwMeAqYCDrzX3ffHrdAYmts7+fcX\n97H2xT20dXbzkWVlfOG6mZSM0X3LRWT0S4oQAVYD6919jZmtDsbvidHu+8A/uvuzZpYHdMezyJ66\nup2fVBzkX57dSc3JNlbMn8hfr7iYGcV5iSpJRCTukiVEbgWWB8MPAy/QK0TMbB6Q5u7PArh7Yxzr\nO83deWFnLV99ejs7jzRySdl4vnPHJUTKCxJRjohIQiVLiExw9+pg+DAwIUab2cAJM/spMB1YB6x2\n96441ciuIyf5h19s58WdtZQX5vDdOy5hxYKJugWtiFyw4hYiZrYOmBhj1r09R9zdzcxjtEsDrgKW\nAm8R7UO5E/hejHWtAlYBlJWVDapugONN7Xxj3U4eefUtcjJS+R83z+UTl5frWg8RueDFLUTc/fq+\n5pnZETMrdfdqMysFamI0qwQ2ufve4DVPAJcRI0TcfS2wFiASicQKpH7p6OrmkVcO8I11uzjZ2sFH\n31nGF2+4mILcjLCLFBEZVZLldNZTwEpgTfD8ZIw2rwHjzazY3WuBa4GK4Sro4LFm7vzfG9hT28S7\nZhbxpffN4+KJY4ZrdSIiI1KyhMga4MdmdhdwALgNwMwiwGfd/dPu3mVm/w1Yb9FOiI3Avw9XQRPH\nZTGtMJfVN83l+rkl6vcQEYnB3EOf7RkRIpGIV1QM2wGLiMioZGYb3T1yvnbqGRYRkdAUIiIiEppC\nREREQlOIiIhIaAoREREJTSEiIiKhKURERCQ0hYiIiIQ26i82NLNaolfBh1UEHB2icoaS6hoY1TUw\nqmtgRmNd09y9+HyNRn2IDJaZVfTnqs14U10Do7oGRnUNzIVcl05niYhIaAoREREJTSFyfmsTXUAf\nVNfAqK6BUV0Dc8HWpT4REREJTUciIiIS2gUbIma2wszeNLPdZrY6xvxMM3ssmP+qmZX3mPc3wfQ3\nzezGONf1RTPbZmabzWy9mU3rMa/LzDYFj6fiXNedZlbbY/2f7jFvpZntCh4r41zXAz1q2mlmJ3rM\nG87t9R9mVmNmW/uYb2b2raDuzWZ2SY95w7m9zlfXHUE9W8zsZTNb3GPe/mD6JjMb0pv09KOu5WZW\n3+P9+tse8865DwxzXX/Vo6atwT5VEMwbzu011cyeDz4L3jCzP4/RJj77mLtfcA8gFdgDzAAygNeB\neb3a3A38azB8O/BYMDwvaJ8JTA+WkxrHut4N5ATDnztVVzDemMDtdSfw7RivLQD2Bs/5wXB+vOrq\n1f4LwH8M9/YKln01cAmwtY/57wWeAQy4DHh1uLdXP+u64tT6gJtO1RWM7weKErS9lgP/b7D7wFDX\n1avtLcBzcdpepcAlwfAYYGeMv8m47GMX6pHIMmC3u+9193bgUeDWXm1uBR4Ohh8HrjMzC6Y/6u5t\n7r4P2B0sLy51ufvz7t4cjL4CTBmidQ+qrnO4EXjW3Y+5+3HgWWBFgur6CPCjIVr3Obn7i8CxczS5\nFfi+R73DP7/GAAAD7UlEQVQCjDezUoZ3e523Lnd/OVgvxG//6s/26stg9s2hriue+1e1u/8hGD4J\nbAcm92oWl33sQg2RycDBHuOVnP0GnG7j7p1APVDYz9cOZ1093UX0fxqnZJlZhZm9YmYfGKKaBlLX\nB4PD5sfNbOoAXzucdRGc9psOPNdj8nBtr/7oq/bh3F4D1Xv/cuDXZrbRzFYloJ7Lzex1M3vGzOYH\n05Jie5lZDtEP4v/sMTku28uip9qXAq/2mhWXfSwt7AslsczsY0AEuKbH5GnuXmVmM4DnzGyLu++J\nU0k/B37k7m1m9hmiR3HXxmnd/XE78Li7d/WYlsjtldTM7N1EQ+RdPSa/K9heJcCzZrYj+J96PPyB\n6PvVaGbvBZ4AZsVp3f1xC/A7d+951DLs28vM8ogG11+4e8NQLru/LtQjkSpgao/xKcG0mG3MLA0Y\nB9T187XDWRdmdj1wL/B+d287Nd3dq4LnvcALRP93Epe63L2uRy0PAe/o72uHs64ebqfXqYZh3F79\n0Vftw7m9+sXMFhF9D29197pT03tsrxrgZwzdadzzcvcGd28Mhp8G0s2siCTYXoFz7V/Dsr3MLJ1o\ngPzA3X8ao0l89rHh6PRJ9gfRI7C9RE9vnOqMm9+rzec5s2P9x8HwfM7sWN/L0HWs96eupUQ7Emf1\nmp4PZAbDRcAuhqiDsZ91lfYY/hPgFX+7E29fUF9+MFwQr7qCdnOIdnJaPLZXj3WU03dH8c2c2em5\nYbi3Vz/rKiPaz3dFr+m5wJgewy8DK+JY18RT7x/RD+O3gm3Xr31guOoK5o8j2m+SG6/tFfzbvw98\n4xxt4rKPDdmGHmkPot9c2En0A/neYNqXif7vHiAL+EnwB7UBmNHjtfcGr3sTuCnOda0DjgCbgsdT\nwfQrgC3BH9EW4K441/VV4I1g/c8Dc3q89lPBdtwNfDKedQXjfwes6fW64d5ePwKqgQ6i55zvAj4L\nfDaYb8CDQd1bgEicttf56noION5j/6oIps8IttXrwft8b5zr+rMe+9cr9Ai5WPtAvOoK2txJ9Ms2\nPV833NvrXUT7XDb3eK/em4h9TFesi4hIaBdqn4iIiAwBhYiIiISmEBERkdAUIiIiEppCREREQlOI\niIhIaAoREREJTb+dJRJnZjYW+A3RK6ynE71QrpXoBXTdiaxNZKB0saFIgpjZMqJXMg/ZT5eLxJtO\nZ4kkzgKiP4khMmIpREQSZx4Q87arIiOFQkQkcSYBhxNdhMhgKEREEudXwPfM7JrzthRJUupYFxGR\n0HQkIiIioSlEREQkNIWIiIiEphAREZHQFCIiIhKaQkREREJTiIiISGgKERERCe3/A7YC7ZmMyN7S\nAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pytriqs.gf import GfImTime\n", - "g_tau = GfImTime(name=r'$g$', beta=beta, statistic='Fermion', n_points=50, indices=[1]) \n", - "ed.set_g2_tau(g_tau, c(up,0), c_dag(up,0))\n", - "\n", - "import matplotlib.pyplot as plt\n", - "from pytriqs.plot.mpl_interface import oplot\n", - "\n", - "plt.figure(); oplot(g_tau); plt.savefig('figure_g_tau.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "![Single-particle Green's function](figure_g_tau.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The two-operator response function calculator is more general and can be used to calculate any type of two operator response, e.g., the density-density response function: $\\chi_{\\sigma \\sigma'}(\\tau) \\equiv -\\langle \\hat{n}_\\sigma(\\tau) \\hat{n}_\\sigma' \\rangle$. However for the very simple single-Hubbard-atom system this response function is $\\tau$ independent as seen below:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEKCAYAAADenhiQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFzMzS52Ti5mZpc7JxczMUufkYmZmqfv/W/+27YrlTU4AAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pytriqs.gf import GfImTime\n", - "densdens_tau = GfImTime(name=r'$\\langle n(\\tau) n(0) \\rangle$', beta=beta, statistic='Boson', n_points=50, indices=[1]) \n", - "ed.set_g2_tau(densdens_tau, n_up, n_down)\n", - "\n", - "plt.figure(); oplot(densdens_tau); plt.savefig('figure_densdens_tau.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Density density response function](figure_densdens_tau.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For fermionic two-operator response functions `pyed` can also directly calculate the fourier transformed response function\n", - "\n", - "$$\n", - "G(i \\omega_n) \\equiv \\int_0^\\beta d\\tau \\, e^{i\\omega_n \\tau} G(\\tau)\n", - "$$\n", - "defined on the (fermionic) Matsubara frequencies $i\\omega_n = \\frac{2\\pi}{\\beta}(2n + 1)$. \n", - "\n", - "NB! `pyed` currently lacks support for handling bosonic response functions in frequency." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEMCAYAAAAF2YvKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pytriqs.gf import GfImFreq\n", - "g_iwn = GfImFreq(name=r'$g$', beta=beta, statistic='Fermion', n_points=10, indices=[1])\n", - "ed.set_g2_iwn(g_iwn, c(up,0), c_dag(up,0))\n", - "\n", - "plt.figure(); oplot(g_iwn); plt.savefig('figure_g_iwn.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Single-particle Green's function in imaginary frequency](figure_g_iwn.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Four-operator response functions\n", - "\n", - "In `pyed` there is functionality to compute also higher-order response functions (involving more than two operators and more than one time). Currently two- and three- time ordered expectation values are supported solely in imaginary time.\n", - "\n", - "The two-particle Green's function $G^{(4)}(\\tau_1, \\tau_2, \\tau_3)$ is a prominent example\n", - "\n", - "$$\n", - "G^{(4)}_{\\alpha\\bar{\\beta}\\gamma\\bar{\\delta}}(\\tau_1, \\tau_2, \\tau_3) \\equiv\n", - "\\langle \\mathcal{T} \n", - "c_\\alpha(\\tau_1) c^\\dagger_{\\bar{\\beta}} (\\tau_2) \n", - "c_\\gamma(\\tau_3) c^\\dagger_{\\bar{\\delta}} (0) \\rangle\n", - "$$\n", - "\n", - "That easily can be calculated with `pyed` by passing a suitable `pytriqs` container to the ED solver:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from pytriqs.gf import Gf\n", - "from pytriqs.gf import MeshImTime, MeshProduct\n", - "\n", - "ntau = 10\n", - "imtime = MeshImTime(beta, 'Fermion', ntau)\n", - "prodmesh = MeshProduct(imtime, imtime, imtime)\n", - "\n", - "g4_tau = Gf(name=r'$G^{(4)}(\\tau_1,\\tau_2,\\tau_3)$', mesh=prodmesh, target_shape=[1, 1, 1, 1])\n", - "ed.set_g4_tau(g4_tau, c(up,0), c_dag(up,0), c(up,0), c_dag(up,0))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To visualize this three dimensional scalar field one have to resort to some cut plane to represent it in a two dimensional plot. So instead of plotting $G^{(4)}$ we here show the special case of a two-time response function correspoding to $G^{(4)}(\\tau_1, 0^-, \\tau_2)$ namely the particle-particle equal time response function\n", - "\n", - "$$\n", - "G_{\\alpha \\beta \\gamma}^{(3)}(\\tau_1, \\tau_2) \\equiv \n", - "\\langle \\mathcal{T} c_{\\alpha}(\\tau_1) c^\\dagger_{\\bar{\\beta}}(\\tau_2) \\hat{n}_\\gamma(0)\\rangle \\equiv \n", - "- \\langle \\mathcal{T} c_{\\alpha}(\\tau_1) c^\\dagger_{\\bar{\\beta}}(\\tau_2) c_{\\gamma}(0^-) c^\\dagger_{\\bar{\\gamma}}(0) \\rangle \\equiv \n", - "- G^{(4)}(\\tau_1, \\tau_2, 0^+)\n", - "\\, ,\n", - "$$\n", - "that can be calculated separately as:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "prodmesh2 = MeshProduct(imtime, imtime)\n", - "g3pp_tau = Gf(name=r'$G^{(3)}(\\tau_1, \\tau_2)$', mesh=prodmesh2, target_shape=[1, 1, 1, 1])\n", - "ed.set_g3_tau(g3pp_tau, c(up,0), c_dag(up,0), c(up,0)*c_dag(up,0))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "To visualize this we use `matplotlib` directly\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFgCAYAAAA2BUkTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAVNGRkZGRkYL5IApIyMjIyOjBf8fF1CMWrrMf/0AAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "from mpl_toolkits.mplot3d import axes3d\n", - "fig = plt.figure(figsize=(3.25*2, 2*2.5))\n", - "ax = fig.add_subplot(1,1,1, projection='3d')\n", - "\n", - "data = g3pp_tau.data[:, :, 0, 0, 0, 0]\n", - "tau = [tau.real for tau in g3pp_tau.mesh.components[0]]\n", - "t1, t2 = np.meshgrid(tau, tau)\n", - "ax.plot_wireframe(t1, t2, data.real)\n", - "ax.view_init(30, 60)\n", - "ax.set_xlabel(r'$\\tau_1$')\n", - "ax.set_ylabel(r'$\\tau_2$')\n", - "plt.tight_layout()\n", - "plt.savefig('figure_g3pp_tau.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Equal-time two-particle Green's function](figure_g3pp_tau.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/figure_densdens_tau.png b/figure_densdens_tau.png deleted file mode 100644 index 9d791185e0efb7c5cfdee3b60f13a32231c974e5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 10298 zcmdUVXH-<{w&f;BkXRyuL;(>*G6;xdil8D{K$M_JkR&Qef>j7gArcf2lqiUZfFzL| z6c9<0L^6^kN6D$zKIio9cf0$(bKj31<28d&RimiAzi+KI*PL^GLAu%+GzVD@A_zir zP7|w-AVfLvbBuxvUg_jNaTETK-c>ngNC96y6gLR)oYF<}@?8X>I)wfZwGZ6iL6Bp} zIjpjwXWZNXPWP~Fg5u&jlAL!v=95~nA7l6uIW_44>ewvLA(>E0)+$ahulk1<#xw`U z?WKPVIu%`t2oHJs-Hdl4Fq$pZCQXXJBwGH2^I^(E7n@ETx=Kl`j!_#ABuFrXWPHv3 zwYTViW4EQJj~V{7o?)LcJD--g*DBGs@cVbifB;tjg26n!Q6^^wf7=Eu@$f8?*%FIA z&P&FO!JG>sQpaNTumAt?vX7iumCFmsn}gf$ni4zE~pM{4HO&!__QRF)G&nvFa>Z*Di9K6(Mv3F=-VBl**gNn9xX!Y_;SN3Rn zTEgey>Oc%LGlBiAJ3on-lw&tu=*q{czCPAlmAg_}CnC;X;486pb$3tB&Be^k%_Rsw zeAHHSdzAjrp$B4CUpz}9OD8@eGxPIb?zYGW>L~6W=HlYg)Y8J5nzAG(Cr3(M`RM3p zn)RhVf=|!HgurC(Np#kIR>{dp=pYr9o`He?#DtBYNe;rN9fvP0EG%t+t4T^peg65A z^V+p*y6J`q!hw(c=!uOo7(+rrDCp?cUC($lJ1KHKkB;UM7e9o-U=~+bYacV7vdlGM z>opNPa>K@k7vpTKeE%) zl`SnT6aDhtW{dfA;b9y(Y?>V^tTbQ0VP|lP1re>&YiCC@AQz@I7nq;1IuM zkkOgb<4pVTAb8c-<-@qW(p-VWeGDVSO zh^zw^QT}xEsRqZVAO=2N3yZMR4nGjY{N6a@xRzso(d~_D&OLktpH6aF85^W{Ly5zW zOl4nR`A|;9&_K@n-SO58Ps!apJgk0J_-bCbP@QX42b)OivpF99HZwB-DoK^1-+q05 zK=b0oOK-wB=#*cc*V4pBEWG8&o5 zu6z_7&Gg$#zG8DCmT2{t{bFzPv5N9aSny#+#xuL@9@EuPejlTI-m=svV^vRi7}MCt ziqPFm)Wb<&MC4fpY)c)w-g5~H(?Z3d0eYg&Gw8h_&!*tR9269EJ~k=oSeje5c{v1y zcKl^eXNK|ZsdnB>^KzD|kk|qeVqz5JqPsfZWn^I3=S)%x3#B1bma6F#0hzp&c8l*g z;P&?RV#KV-U`GbC-KNL@-9t2`v;EA5_7Gm(|Y3lJLUAcUf7racAzvO;}XkXCM6%QeF7-eqZ+9 z77N}zUlEbHJMpA3_VM;v!klwo;}hs3<fT2<** zi@)jKUA^4sIy-h0qGWpM(k0(Paw*$yY_DIx{w>SKS#+D{uDUwe8=b_#Us4Ni6bnp4 zS`_!oS}N+Fo~PiFGpljY)YMc`Q+t@!ckyFO2Q03`!i!QRg1N*jRK9|=8tYk%$5 zw$Ja5vU@A@?c2)V z!^=jxx=CeU#sCXr4;GiO(Y@FZjA#_A(BI_>nUflOD3_@CadeQ|;J(rS~7p`0~ zHa=TU+75&Wf>z`@)sEl3J8pwh+*u^q+1&*SM9rpnzszE$okorX_Q45=Mnh$JSy|*w z5fPDSe}SEU01tw)9;l!whpSKl4Sf-HVVq%WYwl?ZP)<=&g(dfQWwqmE+7|Xcmd>OU(+ljt(TINkk-5NB4T0~ zH#avK-z`bFDsZw~r#`8`y)RFWk_>%uD~;XlpZ+V#5~AEJZqDTiuHJZbPvXpkt`^ag z({Z?({JbH4t@GU_8J8iv_(Fb*f~vj!G=6JkY3WnxLIqVseZb{>n`@iP;|)3y(%Yps zyWX1#lg$3{sc(;|r*CX{>O?CYPwnkB)rl584uQei+S(@0(Q+yXCipDZEA$DOmzlsO z`Se&zKzer;~91!k#hZXVj-f0cKV!}oWa z`9cvNcd~cH72>ArxyK|V7?AK6FQ%5ASIWKD0l`&L+LW@4ssYNOhalmRkpV+PmL8Gk zvDjxW7w`oiKMD{@S~nd2@bRN;L2(y{p9&W1FL6U!Tm+c)ua-oQmzDQ-y#jAWoj(wD z{sPV`+&kUJ_QBt$_= ztA61E)q@8Q;(F~0OlN;qTD)*}bj04dvy8U~ma6XVE`?|1xk<1a`VzuQp3(WZs!Him zH$QIxzSG{#bBpk#f0;l1!w1QjW}C6Gu_{NY(z3q(8{)&#m75{rE-9~Hld?)Vx@g?P zx(1^qEhV*V{6xfNV}1Q60IXc=#v@wsvW#av=1CDlL&Krn^`CVU z&9SfA>~fq3WRIUXL5!IDEUDQ^vLIF2xLpUwzK=A<+2(4RniP3?d5g=9BBd#=$==_9 zI9ydQd~Wx8IHU=SeH{)hL*v|kTcubpL}ZiSV=2i3SqHzb7515Zl!$F^opOb5(b#4L zpJANMefl(`gzYyhAl#ihcl!0jF_;h+ZKAcV$~{JqeX1o-%gW>+2fufAezFujCL+?3 z9@1>1>g+5`%Pt$RynJVDVnRT9J$rvEdt<(I!DZ|#24$1o`L-?LKQyq|<)!JVsZW6F z;V)l42L9F*cZT89r%!~(k3U!L_Eio8{`(ISYOgB;`-DXR%t@>2*N?BU~B5pEP)rR8Y{Fe$l{yK}A(HYUp}^z#z`#KJm9sPr>l&$qTAb1caLsMyq>Q+`fu*YI|C$yfhpFM&sitI0ofR|7* zD#dW<5V#Oxlf6NNd0uh$vr$OL%<2Ygjjowl2%tJ}+G;3w@EVp~Lp;>p(D@PU(2|?F zyTS=Q6*UX4Ur*+n;o|0=W*TSd_9G#eyG#!54c>FN(t>_?Vd1AkN~{xsKu|xJ9T|DJ zY~|Zo8Sk~s>Ia}6K;v>rNEjSd27)K4zj2s}$!g|DmPzp)UR1w===o4sm{MBGit^o+ z7R9ihd|U4lZEkLE@&T#Af}p+g zw+J5R=4Pq)L8_b!M@PdH)%El+(5C$s7hPVbrSZti4~$zS!p+Gw+txqh&FPy1$yEyy zDikE~=xk4Zj)|b>ay?<ifvB6BTam&6?ch~jJ=^~~)uEMJ-Z>-X~ z=7wFjSzlOFi@+s!`kkzO<$!0srJ)VFF!ORg@_PXVIqdw{>efuQ6<8(oDwgy8#W80+gahc& zomyUAmNVmo61*S~ugzy~Zy)z9PgYizo}E1e`W9W3l#F;h83QKdFa#HvO<8My|LljP z)YJp!=H|ji5VmEx#1K~L&z7P}X&qpm97bFqv*P6L9Rf1=KCqF8oBK|)ZCkRM%3?kTJ^@$y)zo&ow`V~Rs*1D}!OE4hZ>Ns(1 zz_cl-sH%aAy)w#(?|sp1Q?@mo@khOJqb(ZsSU!CCfO;`>^1tb&oO)}x_ORG>nQ=YV zm5Ju+*jp7rSA>ITUi?SoE%N!2{J-NaunH2yeZD^kJhvl2%fKxH{l6HyF3;}H)KYkq7J3COyq@IFd2_XaB01s+wYY{wn3&coC zNl9y4+b8dK0%3BWyjz3QH-Ldpd+7I6hv#^dF|e4$)h-LVdUew)S1@dB)JTlD^+9+X z-wWjWWLO?C9#D0>{up8@{(>GvEe?kh1W!+M8aDRcX1_uTXa-=MAg4KXHNa=8+Fbb+ zKap)wIe5)9RezxK==Z^LZ&0_Pd;P5 zm=3HPTU($^NFq*LRI#@gNbp^|j8#`(>(RS;^JaGA37v#Lu$7!V1(Hx%Sy|f&xa-=& z&uo-2bDufW^Qsxe!h{6c;Mx)Ks=WOCr}6PLLto0N0VKTcrM`ZhR8heJtOLE-Hb7-S zhoPZQ{$SmFrV=T`(<$z_=(3)>aKV5>!}?z%Z_%Ccg8=nNe-YVQJzt{bMAHlZcYzSG zwCTx7`ohYlWp}7lZILoI3SdQG6rpesBkLvrp{5K`_kaBOVA=lOc6Zr53B(dm%X6-v z3qc^dpE`6DLw@`)+S&P~ShqUU)z;DBj{+aKF^fyLw^lp+v^*mID#$2}bMgST8!L(X zvr}G=o@j9r|A}7)M;~?%4$gmA3>K+yaS(%o+D+Y|_1M|CEoffG3~R19XL*ht zL%?R91Vb5m*V4{Pi`Cj}Pr~~M0^zIQ9Ucjn(I8eP5%ZdjrBMt${ZM@g*~K@pOK59& z5ehBUibcZa$#O3P3bR_2IdG}NhYufv&-5zi$j!}#B{QFO&*YmyA?X}tj%~s{GFEA4 z5FC7o<~ zC9Wp{OSdK;!OF_Zz#-$n?c8?!S-~DHV(|r*Ck7tJ#`bm!u${XTO-rsJu$$UGYv6Q1 zPX#Uh`pui<{Coz1sDog40RgmunMNC7j0Dls)BA&!?Qd6(_7*rWgFH=}f%`1sNejM$ zc9v&E8n*QRJS6xRW0AjOp4yEa?d^cLfu&dBWARsr1w};Y$oz>wQG&nmwXyN8y;Xis zcx-I2Y9Q?^@G^^wi)oeD>lF7e3@5Z)G>U;p5fzfg{2R0psfs#fy!eIn7HY-L^G6d1 z$A_LH4o*&LE-s>oWkcjiJ0~YM`;I@|0c+Xg$4S9_MDT7iKZcDfw-`Z(_v;D61gI)) z;lysfcxW@5o*9rRhbC@Cq~pX+r0z5-ws5#r?J zv^z+K!PMJ+CC&uQ@3IDS_77R)@88zm{s1*JrNGzUUSBXqef_6#aZj=To%K~tvZGQ5n9ZEI~sIq~(a-&4ZX!34s*kTYdYTpXLv)>Ink0e&GN zHEnHLBq=$W`10k;2m(L@d%y)cAM85MrCKIXOZ|EruoIfR0rUyN*JX|$KMwEy++j_R zIj8o|*(>=n9X_m-(K!Ofe--fjrR|^l96Iu=kp`up;m3ac zB0+k4d&xe~KnXU6A29hpdt~kSX~8U`;N9)@Qynlx)6~{BdQOC|tW?~aU)ZAsqfeMb z5Hb>`P6$3AAV7Hsom90KkMq@lY_~Ri)#Iq{wq-+T3=6uiuPlmLXf0?D0Xg0Op%HWZb=rJ z_ZF43a0&3RA$rQNV9xy|&VfHeARu;L1N;9lQCaesOhbXmzOV9~0yRzkvz!jqD@b$!+*QU-@@u%7!Ww!&BUks$ZXrpg(fN-FW6>LBTM< z;8N=mNm7%ps+{+J*UU(y^&MEX%z$>*~BqNTdJhW7FMec+E+m?VViW!$P98%QIO* z=S3$d+&nx_qoaR)*a0Z?+*zu_96cID&f$r`eKh2O7(WJMRtcM?D&TPgrd_PJ%KYBG zh#4#1$H7LqK3TTUW*pF6>6wEXenmGOe#Z6l^X}(_JGg_SMH39z+3u_ zeLF9~{0dEynwp_c0+6y`7UmcIO5a{bG_bx;)2O9qp>RVhEB1xYCf0iLVJzaZ+d~1`=&8%<>2BvD~&p#HI zkK4{DF1`#iIulqJu+@j!X?M3u%cLO*NfO7QppSZB+ z&!1l#Fy2j{ffNWZ%_1ABSTMp(Pd8guM5~%bmsVUsPzweOCImr!dLUDvXU=?B`tH$W zFrSV%*xRev*}WBKKXhp1_kp5;Zy?*KHBJbILi}N$es7*IWLr3yZ5th9lZaSy` zYSZAXg*_B=pEcQ8$L&jtOGxM$8BIKQTGxOnBAkvOgQB%g;9Kv~T!VS{HCU{S=VFrh z0;r%?`+_2uVPasUHs9ic?yXD|rzXHYqO~3)dX3n9VGx~lf{$2i-M5R{Z0P9euFRFY zb$cbCh@K0ZJq9=6I!tm@dkY=s(K*0@b>*2-WYLczkM;Y&9VNel&f8^-< z-OlL2X^wBcofa@s0XymeFiA_xB-LgFv}yoYJ9IrQ6T4!!N3iJF7g_wu6P5&|8Cn(_ zW}B*L#lryt1?(TaX;h$IcrJL`RN+~rRrTi%rs4kd{CtCez`$S_;hto|Wn^Tq_oBxR zX#Hh4yl2I@0_Z`k!;{a|)q#%m8o22#IAjBr&s_t`n{A@cQ3{2F0Q%&ttc##uX?qUL z?S1d;@6X)X4#VxFcn5R=(D3-uNrz$WNdVjSzxrudtw-dl* zZ*O7Wd}leja<(9ZuhJ^PMdv^ay|nt&q^m5bA}({^)l0B+5UdPGg4F;kz<;dCvu><` zQy!?3_clA*_=hh{pw+-Rg)vM?H{j5f8F6N6Zhn4EH0?*gpJfVD*m_Gp@`XaY+*|~p z^`Ut-Q>^qYcL%dGXU=d72%Luw^B7X#_H4J9^#Ghn`eT9UBD1a(mJ|qc1$a)SM%RE` zAIceYna_qpJ_Im?Dd1!X9IB~>sX(pwLOCC5n{d^HkhG$6NMOqpF#WET{^^sFc{XnQ zU3Q64mMJf|Mv)6fK0amiDuk^qFX%R>P^>pJjLb_MxYTuap)AFxki&C`C=l=eb z%;o%~!WWX2{RiyWYAurm1;Tyeg!*Qt!sb{6W{G{}utJbOoXK+4F@u(y+~)sp9XfM% z*<7}6N|0v-ZN9j&qI&fz8(3nk9UY@n?S$90tS^H4-vS83xh>B2E*$+ph9{Z&m^x%2 zAv6?73O0q9S#rd^Cp*8qhs>p iya|4PH~C22Cz)9c$|pI=O96-fkaKF<*!L=y5B>*kEhOpy diff --git a/figure_g_iwn.png b/figure_g_iwn.png deleted file mode 100644 index d0a4d9f385ea300874da6614c610f6c9283ed000..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 13506 zcmdUWbySpJ+wRavBP}JMprAA;Ee%R4T|-EBOCv~$fQZs1AxL)&F*Ha@cXxNkoISty z{m%QHbVm9}51y=OCx;41wU@M}AO-W_@oUkcSX?DRH$A$-A>&E^4a{h{N-h z{zrN&=$zt`*wvr({2lUtyk=5S&BVqbi`KApP|qJFU%k0%&6CB+p9s&%&9QWqCq$i! zH7Oy#LMymm7$A-+k&Z6^RjP@oQce$V6&Z9@~B_z0GL}I)RtWEoRQ_c4;?H*tD z`ks}i+4A7EzLJt^8$j36_YVz~(|yte{^EH0|M@qDw!VM0l#I-MYHDj&Dn>@%?A+W? z5*8&beVP0VDSz9Un&?hCFT9-LKY@gzT^Z8hi4`8(h1DO{63j~`ZL+emN<2@u*z9=t z`D15h^z45;V;p*#ot>?%FOjcJXQ-4D6GHDBub9M7OhVE+QEKw1tLq^H!_P)VadDJ4 zaqO#^QKA{{`*Ys6r{m-r&lnjSn~t0fr>pH*L`73;YF8{tUiZ_*E%c)jAmbCmy~cC8yjO25LCgw^Op-ZWn^XwIIYOtZZ9nvgJbH-u&^+r zn~OtqEG!<2zYl{6XkUY2nsi3mjv9tg3I^S#S=rAsyEMV^} zrVJL>)~0H2Z^wJU+qLC1)qBL=R%+7uRz>BNkr9Ksx_W_rUF?@HUu10eYL*G+{1846 zrR0~*VAP@E;pOQ%#D`=Q?0QX$4o~W*n#{@<9bVqtZHyj4aB*;q+dq>jB=JWOGs!rd z&$`FN$9D}Yi}hWdZllEW+dd!&3HJ`E$kbkMf<X`S#8FN%~z) zTV4{!%jGyZgkOGGW3%4=NrOS{A>Ub;D-#Dtl!({a-yCIn1JBLJQFd^}H>T3&v?AS8$1hi)B;&W<9SaY~9TmfFHj$4iTJDnC0ecSI*9b_ZbKItZrN zjZaqF)5kC?G71PJtoFo_(9_Rv+>R-n{Y^c)Q$S%}dKx4Umzu^=UPMs(X?XK}Pweye z@86ez5zcuZD~euiFdZEoVdLSgM7)@@C{qHaLGG`bho(5_={({v*-W(6`xNfQGu~RfRH`kvGy-`H9nXC7haq4;g=<#F6 z<%k!+Gflsb{TUo1@-jtf503D?Pm)BqlW)5}Kx)XCsuGXpvy^U*_eoY_6JA&(CnzT^ zEuA^OUw^E0dpucg4t1Ob+qTMf8nX!)cmkJ6@U+`-jDHMRtWWo6t$!^26>=F3dq#3it4pnyAE$NffWKL*JrMy>ad8ZPmH9amp3 zoZnOO9Y!QQmG8hNMG&}m+dF;+gS>l@5&8l|l&My2e5kL}?`xu7b}4^nSiE*y5*X~R z>JgP%8c)c;xaCC96Gnqf2#IA>OX1qG>wJAh!5+4Pekh|sHnK6J@Gr-`7V0loINSG7 z#1oZXw}DqP+N%4YMB3QT_+T5BZ|ZPH6QqM{^Oux=uC-(@9^+3kE#CZzESAs0RrL?6 zP*)|GKwW^@^ZIbi{A8CR=6Hu}G$O#0`)JpY*k~T5Mqx}rOia9ZIxtb=$kZB)&jft7 zX-}+2K_MJ@t@NlzIqsnTIYaz<6PP->1s6e&i^Y)15+k(B%gX}o^5C1B8@K9RJY>%t zS4bSxThr}p{ltXtvnO-2T>Vfg29ap7HRQNq-zVy)WC*wWo*uvbT<_^Pbl@w4!pO^a~d^L&<`asm`!sV!M;_y8TXWq58 zvWgdSXXj|VV61cBU;F!0@(YbXnS9;1Z}%|?XtWT5%*?^yxSCH-ak~Fi%;o0_X;lI} zo(B?10tnR?)Sa~?M8uHX zPuNBei6V7Bs z4nw=HqDvy75p|lUmpCD<$WR37>gh2V%~KaX9eJlX6+tT$8xVksgNqv$8ELjVRas!r zFty8H{O@;JD+{t4)W_$fdVU7`6E~04GTnj=J-Dff%{##gSocy#y~lA^U*F@WPs22! zK0hWVw3e2Z5-a|X5pBaQ3Awx_H`u*k47Sr%d{)Pk=4p;AUBq^^tK_j9(7D0KpLsQE z$c#J09je!+UA9d`x3CL_^K)|{z3feb#UN9-J)W4Dkl7!-hl`t!(5?2qbTaFWAKWol zNE1!j9L}lEOU<|3-hU>BY8}?Ix)jEex}XWaQV+K+FDsLK^M>j9^N69LA%a{bSaP@1 zOW1YyYzX9|IMLV}S;L`%`J1&DkC>UiB__UBRV5Cm;HZKaVUr96CB@3ZI_k*Gfcg{-Bxz^v15*LG2P-wFUK|HFj z&+mhP*RB9(>w$qHqkcT9LRKY?^TS7|MV5lP0lJIP3wa-La~gCtDa~(otoaFwfdOR}6coI>Vc4?>Hd4{hSUV??yl4~PBu;ZbqfCAY$y}@S zO@Un!4WvLi4fjVzgWB8LRP^);tVO4vJbU&fKAtka$ASVQUe20yECpfJehPJM*=9Z& zC60gb??f%XhY0sKu4)$F=PC&;<&CtNE7z?$BG>2fp)Wv^uan7%Rdmgaj$Y-P5)3@- zk|XG3Y_cHgYLMo*4>pl;h+!S@a(*?)s=!*zHNi>o_{gsN@2z_UGn+RIgkmU&;t z&a2iT-98 zLx+>N7}>Zkg`!3iA|5l<7~#;@!LalkJYtR1*HF}87nqWzjOV{D%Xxz3+4gGRO@UI^ z9sO5yC?j1Cgv7CPH6VkqZ?Cl~6@glE&P6{8_9YlSvHR=ugjwI+O;L30Z44Cg2W<>X z7|3Rv>clA92LIZ~J`EviAsg7bKX(26+W`qm^QB$D#-^5wfwpHp?a#>=(HCyKCdErkbD?!D{t zi0ka9>B7xLRGZTE3|(8OMICwS`M^W2g2K$<+V?-Di`#q4r2$a{?#;gBLTHE=0#(FB zxX#Cw;q!-}RawKq!9f}Ur>r|yM@KGjtT<&mSD*0sO>Bwpt*3;fidHReW;ubi}qmyCfnAlhu85vAm3ihmwjDWg57_Pr{aYO9s_PDNI z?M2}h?g2dEYZ^X@FZR2*SN?JEo*>ygU@WaR!A6uC6pQ$lmX<4PYdZSD7m3|YTF!*t zmmEAbjfZlb-h27)7@m->!H2ohScYWoPO68Gm_SzKyoFuQg{wptkWef!R1kS>I3z%Y>3zaj z;6;QP8M1HhpQ>2QvBQZ>+?K(~%bMhp#itV|ypQ_*J+{+zIjpVA*ubZ3HxlEkIQNWA%Icl_ij5wG? zG&L(4*7Vk~%wN!RF7acR>gxaWVsOK*+NOyQ#d_M*Cib1qKgBOV-+EE+sS7=1-g`!P z$qBCt1{?cbN3I|xEP=w@xNVHFa|=D58H=_hB0`shqh;;~Hg6Jbm?OM*36NIxHSHGT ztJ1(~z&E{%9c8l*>gxU`*a{v5ZyRxmeZ7TJ4&-P*hJE;os!-rs)QeZJr1#{G^tEVU zcuwE3beMa~hR0e^7k|>|kE%40`${w=Fq~*5Nfc(37s>l|R)DEE>&m{d1)# z1<){+{E4e2EZnWPPkHNF@ob0>Lp87_YMs}yadDYAIsYb|%9$HcuI);`c8mY0o{Zb+ z7Fgcx4Zntj$_H}I^Uz~uNmzXPLFNUmLCYHucYTcRUJiRe3?H}guhnA*HBb)-zs)&m zzOOATo=|WYJmTj5rfXLlgn>&=1WM27%Fi550*fxC9*>+S zZdjB>PDy#pdKc!r_IFE++ZjhG7DkMC(z~rC^Y%P0jhtgo1wS(UWg!Sw3f$mY8&p?A zMaR_DgcoDmJ8+eQ2t5lP-N-0g$Ctkprna8|83kI9&OI*^#Lu2h(+~LZ7 zNk9f|P56c>s}{6o{a~h(HB0CujL%+?`$l7f2iud7J21bKk0{;Fta^vwSan!F?1NM} z${Kt9)R4@xx6X%eAM)Yld^W+4^|57luf1fvPiKnxZs!FxmRg$l8vRl~Cu>ROs4`3T zM;gXd2lY&%_1=Vc^g3vmcnfjxn{3U`-!PN;))a}@dUO%ajd<+IPUT!llM!Tt12>yO zG_do!6Tu96mwkItw=)zn4eVkeR8Wz8Xi>+QXbU6p*eUIVJYgH=SR|vuR7(p9H=jkQ zsW-Lr;K!hruMXVXzBlU$z1nmw^w^FJy<3xp&sx(nT_r7MJFAY} z+s3GRg*4bMbqp;xH-1nI%{a8;|aDzf~YGX!8a&pl>@xqE6C+@VJX5gZ_ke@MS#g6t?4P&r|Qx_{VR;2I&L> zi&&d93U%6x^y{>$?P#IihZuert@QQkajs5KK6~l?SbT|=K5fP=Gm2ubxx({oQ-(!+ ziQjbs8$TDf|AtPLY!Qh6q=k7Ib1AYsUgCO=IRSvT?z`LT(tj{KhXIjuhvU8%?ozg6 zlp^%O3Js)E&9e_*&fw*wbK)C(JsbRT!bmr~)G$VJ|Grj@14ER^86~JwX1w+r3baaF zGNo+*PH5B`OiWKdP-fbbSnqeZByBd3s_5-4yxbX;lbhR>nKmD4QX+7~?#X*XYnJ*B z$30&B*TJ_l98gGRgoad58WM_}5WV}QU<69@o0A-|$1h&Q%_Hv4>DZb!U5*Y~3CoRI zF^`Uqwad-$K>-;tRe!aWf3i25ge>0xIB^Wn2ala&X~FORBvf=mTh4}v+j&G^b08KH zu@Lb*InYA+54PAulb4o~q6i>HpQN1~n^ccADu`KMVmnimaDWlzgp1UpyAC?bNKV(W zi}~ALDQ|^*zrbJ0Y5l(xlaOnGIHX4`mZH9X{hIQngW>Hq$TI=TFlLhk;+W^zjyy=r zqobqg@-zD*TUS^7Tw#YTJpn`dHU_ELRhWej8plf}e~kV1vdXu&uDdA&a2)-tx7n;` z)s(5LZSOk1b;&3P8g|KOV^zX*$*3J$x*PTBKA#rH!*s{GCI~eO|8b|cUDdq;eR85# zf#>XnB$S3sFFF0cHbGEP1t;Ua!#1;6dl)i`D=R-}MGQ+<4ySi-wybhHP+=;yHgPs> zay!7R(x_br`p)&)d#ft9Yplk(AaOlSP4%qpoa`d*qhiI@)+6ljk;tj4kw$aT%tn#Z zfj(D@ivDc=h*-V}g1F-Gm9T5u8q2W_k-!f8;tB)j2s_H5xQQ+Y_vb=C$kBF+>h_ju z*HcXZtR;NVgyxO7o;M%KRh{wNE~HhRSO3%9&B(@v>kq0Xep{1wcaC@0fgHlxu@!+- zW^t&`A3gD3X!3v4V+9RC$0VX5eI=J3@ps|X)r6#j)Tf0|?8n?Q-a90867Qw3MOp$1 zab>*dj}LF|^`Q1{az7D+N^47#nUYZmI2L`5A)|OIq}d+!nN=yJI3uq^`p2-edPMlv zlwqD5Oj~2RXbQ~C8Ara>Vz!qITN7lhFR629vYZPqkjtb)1(>;5X)%kGtgQ1&SM=35 z0BON-oh>q11Mpvta$3it1Mj4UnHh6JLINZ!JG=6CD!q?TDwaK^8UnUiP5%O(ZF^zn zx@r%wCw}}mz8lO1Ab!c@HQk@=}t8 zq3yqq_tv-Ya7qSQgtPenO7x}U7JK0>>V%|(ITGx1cAtKj1mu7%?$q_Ywtm=T0qoQFGlo8*j)Vlug+sDB$ zFmvmQX%AC&zEXB>FR~HM3Oa4^7x$Gd$Ez5o+cht&bjJZH{V4IwabBBoLy-sPdas6e z`Me|Q`0YFuW(tQBQy;kF+9>Ms>WDE`Si2}!*TjLs7m2l6kF&e&P6hR>8Usf_$7Z-Q zQA$cms-mSO2Y?If@gh@z)GJzP)jB-}G{p*s-zDz+-C1>m+Z~%QZ|DL0kr=!@-wpPp zSS0VINB<3zZ^l{f^84MRGG~g*qn_hrVNL1w_QWe!`!}`0+3w69cZA~l6d4qlPNZ>Z z>x-j8ldda6yv8DR7|Ws94BW!sn|+ulU*w+U^0weP$X;ZxH|#K=yH&C4ZjL10?s@|n z1WAQlY-C0O42l^*Hs8OK4p0!0wtXgJL*naz0lD6XrvWfA6;#*2*1Nj87CvIp83K?K zz*PW>qz75T_Mu?b@jU{(hk3G;sv<2qqjwUOF=fu`H~Z%8H=e^NXR*m^*|nKeTush_ zu@7cRdIR~_>DO8O=7M@m52h!7dz+L5&Mh*aV|f-;OSO$p;I-WdFyhN6Y6RiT#9fbgUJ83GVtM|(tw>wf23@_&fJKqB%bhd*k_bqi%O{yi!bn(9 z55{!uQP$eq0Ku_(JRoL1U1cju55P+xKZ;XlVbGRB0K=5fY` zB?2MYSTG{Ep!%Pvv7Pswi%x5bHIZqv+yNzqeG!}I$)k$v+O{WJo4%=Iu<_CL#iXd# zN|J&(l49$#>cgY$0#Le8iFop6%0}5$MaY>CReL>Q+o$jRd`@sQ{TypT-zhO}J<`kq zzPCGF(r{bucyl}tJ#I_#P!q!UJ)c#biFH!Ew)%FK|C^g5s>UI&Ipy)pjBQ3<#$}zF zo-l5IxaN5+N^|9LNiZ}qs`9Gd{cNz4m)bCfMcHhvH(|#2dRNbDxAL?nKQHeBgeq-d zo#5P>*Zj4C?8sB&FyOMIW_drAmNs~}*wRt4iLP7Ie7 zhX`1_tvf!N8EiWpZI~9$+qzMo@!KECULRf8nR8Ro+C`ryj@x@RxSWVd;y2D-oW@&k zC#rF6+Veo+$y|_q^oY$rl-;<2SH!;Wc^#6Gm}~HQr>zYzE90$^-!~VXVrIYz(`x63 znl=*C|Cy)1W{xVF{G*Jh54R2J{;+oz?SgA58J9APlG9kfH5!@H*pzAQR@SibXr9*n zxfuMpxPEZqs#5~=0jt%%lJD7vUH$GTsN4Ecai1;&9}DM7+4VmDCzL-C_8z2TJv+=r zgugMx^k3T4|C|)3!B^MRbY5z^2Xoz)DK=;j%qVg@&LN3j5=&+eq6sH*9K{{0r4)FvS9u5L8nMnt6>n2xIU?g|CZw;B^QmmW4(GWIws1s2?&^+es_ zR;?R=q7&4_2`=A+{S;xJJ*eEnELrsFWUo(R3 zIx?aLQ2yFI*tTE>KXDoeqsw8xTjj!cTbJz<^*?LUK5^9#4IW&gmyAL;GhZm+kJ9PE z?Mu(Y&4+a+;>Jx)q4pAe-Y~yAQO?89_5Is0pS?<~YUZANQAmzv(;R>SRsb8oqX1Yy zBFKT5_{JSysGb4jbHR}I{+n?gOAF|n&j}`S(Dnbo|-e=R91>>8Weu%k$aZIpRPn}>5Wb{g74WWk@hlFx!6av z%X`*29kvAv1)Tth?8@ruDBO4BXUK=$%JGfCOe|&LLv;A{?0&sYdT8<1JkOU=F-oXd zF5`Zer2))bttXIm2V@K>LW4H`x7!Q%==76h+uN-SYS^-U0%g3_M+GjdSV-3;3r*MkxfZ}+xtR6B zXgiHC46gbx1c8uhA+-RfgdNp>6O9}WwF_+@r_!%R@bZg~IcU{S>Cj8ZC0!D!w4x8q z{KNFb!%@zz=Mkx(tQ$*1T%E%a5wmt!y$q*}s4*e3hv%~P3(=!#ha*rixbgHO-Fy3= zT8iiHD3bK|iZ5%O)p!veopOkjxbln&{sJqs?few8xTmiyltZb|T76NuDnF9#UFtH) zua7U;cOERZPB!`o0sP(sFoM9tAMcb8IPJ{{05>sA%=h^s$lB9&xetS6(y=DlDkp!^ z?I+pbSA1oq&+qRR92MvGiOCh56qFH3kJ5K=K~;WM#3PG}I)VBV%J^tjom#_kAry;n zZcqYNq=BD#E_=sIss@YCb3O>SUif9+`_%77d(>m1nojhZ{^iY20aOPELA*qCE)!7F zdo&kb-~)1YGv}6s(f8S-85-&JY_9;16r*_k03igHC?b54Z-RwjC@uKAS9&KTpY1z4 zsMn2y+!R<$6OLJo&UMWkXy;9W9a?rdJ?Zq^8)PtEhk&|Dt9kDr3gTRwe&ACc?tqwY z(#9D75}@mgFJE5tfi@|$8ZcS~hVB<`6idIEzMf<5c%v!#S@!S0Si4>u)IdDS&nH0v z$le%s)Tc7k{CtbN9o&m+iHjwXCjT1h#b0Ty2f7S3uI^Rjn@<49$Tq>Wm>lKGK)&9x zRoCp$T%G!9r1r1waq8#&x1cW!Ge&b_d9b16#ThHa9K`RgQDAbnVY`WIV(YV%PFDUs z=>nA(^cfs@D_uVLV{>S#t&1NOaX1dC|9P}^j#8P^!w{n;uQv4a(s%1VmjQ9sXcdY|V8WN3#jU>6sD4Nc2Si+{gb&5ipGd?`IN8CN&p|u+#Ta9a zV>G)2IXnw_w$&@ZHLJYS=v3dqhH&N#%4z1?>D!0|O{5Bl#(I)}H!#%^DbsvXcW26I`81_pE>F?z(s6}__Z9?4qcvXUJJL?#kCBI-(x_?_oku}G zGtJ7$c?V<$o}Pk$>$V>1u&X}K5xWg*@H#JZT$b^@S*Ca18hMJFM|M~e86SN&thw0d z#lR{){y{#+#M0l?+?>a+(c`|y-OXkF?b)>A6execGe`zQ9QydiiO9&L0sIy+_H>DC zZbyO_Z{jHJV2^}ItbK?7%f@!f!2QzD?DeKdda-_gl(oM7tMmPV!{yH63E%6pT{U%e zt`8-R(yv*F=r&WtY5J|S5Kne=@H5wwszvXOY}##l*WVRi{YtW10>To-SPt#7tU>Ff zzTjP)@eCRf&+i_`Yo$)BuTfEF=wF(dnnwTlfsvlcd+6harIw0n9m1CKrQ$S%$VSmUX6?S^M-T+I85uD59Lopt=Fuq;WmL_VdM)Hv=C{L23(n z1JNkb`?Fg;4;D&YoeT-oG+ytSj1_3p>Su(We;V)WSZuayYbhie9M};g-CTKwUm_h& zUTQha9E?ZR0!kC@3JYRLZEdaa#X=yY{&Y;&W)}8i9O{?yuSoW%r>DHkhIruM0+W*RA5!PK}N2phsNDLiQTY4ZvqEl3lUwTG;2!4-yO&M;c7U zBLJv-C)?#P8j~px$U!Scy=hzm&8t|J02Iln@jLDBxTLIMknT@eyc4>Wgx#=>i}=>D#lI{&MnIDE-&l*^nVqlVnUGTi#k0^is7FMrY-#(8jb$DEp;E68Zp}vCIu=W{8D)cJ zPKI|C>u&Y$t0rmJ+Pg+6bl`wgnLu5Hh0W!^Y3uyg<<`xgofZ;RiKb?S_`)twz}$=i z?5(tNHk5*`m*E74h;u=bH~IRzE^nc}s6`L-o1W>XqAIjmo$7}c3Zt@3;h;2gIs}{} zUlxlw0YJtCZc`k|U_2w>W4_15y_i1hP2g#3Ydh#-igGawVx&zk!Y!>t8UusbvriKZ znlYjDCA>|s&vj*hKLH{u;9SaqlQEk{a=r2~BkLR~Dq0-QQbyL1P3+0RnRO#RPXgLe zVpCH3ZZ3~e;>G+#?cujo({4g;I|~qDDk}4c)^=?*J#1?}n3^q27qy&|$b7EKlTk?t1!+qP=F6!A7pcb6)|$VrN%0Y8qP6U-fLFgvi&Ei17+$ ze?0VIgBbsK%+#QA!9_qU^U3q>kALa@ie)bYb3)o3^zRa}7J=S5K6Zu6LP@AH;1w|EEIl=yx2 zUG62{NX&*X#{JWpPK3R?*@#>Eq|u@P{z+@hh?ZJp}tq1&o=|nRF zSJ0RNEO&9o4p?qI#|f>SKA4Z!$mi`403Av@&zf~Br|iRyIf&if50;@(u)(~gZ`*q- zj~QZpP|Tz9u2r(AkYiF6OHs4+Cj)${6(l577uiI-$4S2p;Dpsz=hh(YfF(+K1Lhla z%Nnh~?xCzvtjNW;1Gx()YPA&g z^Ph6KKy|b9W+&S0!$)>Qa?6jP6LBMb9fRhaCp8^YI>5&xTPA?}+Y%+UjQ;1eKJKqQ z$!Kz9-~PCcC&@ws;kK@i8AS(%AVMBTkC5;zurZ`c?dZs`%09E!wY4f@@c5OLl@jN5rJ{y29e`Z-Z5PA)(FleAX4(G7A9z|20?jXlhe#6; zBF*f0-Cmt8gK`RwlG7gm4=|BxG5`t!oui|pBV@+0lh*s_4~rezvwvwnApraluE1hs zTnc}-E79B4Ww_e?15%(@`wa}TK|TrSJ)J;>e#Mk`KIawn>C-2I!go?q-ci{oRThJc z{I*kVNYzul2TwTpbJT|b9LJ*fCh~7;dS&oo5d)~s^%0ngHX4i%>;=Cgdb z$jHbXL5)tRxk8)5-!#>o*633qb-k2S!OwVq$m}$ef7}-{6$!0ND#OQ2l{C1!*!s)Pvz~L@9i= zkqN4#nB?SM^E4kb$L#02fL~}0A!GrUQt_BMAgAOU=onuEDglVhmO<(g5~N<$54bLK zpqsjfjcvi5*FArRVYu88fk(~%<>KPP%$|NFA1M{g$jG1+aC(1RHa0grFb;U{Pavqp z06~?pF(XpVwUBo0yx4*P1j4efU*iDz?#<1*7*={o$ybnu0&VC3z=40~<(1XE<=|`s zBG{KcS1z~56j1L$trEkJV15tx>h>SZU-rA>#r2pX0k@7C$K?Xn($TxaC_l>4(NRD` zcf=@*S_)sT#2}9W8WREX@?~I`9YECf1&Ew8fBov{eyjUEEzN#s9zK6DR^4doeYKf$ z+H>m&1-eb-HOt`~jhrBeS^(^h8X#+xumX+ig+RhDKqyKH`hD6%&aP+iOdmD^dZ`6- zvAl)zLC;4up;RD}f+o)XWC0UUCbUh|xfetCqIy=j`%^`%K>z@0HWipT9?`oClVt$e zbAw#e1hhZXDrx!v;$q>mVKx_ZjeL*=pAHP92x*G(_yV#ENQvqBlOpbsFoP9(8c8Q& zrJhj84V0V#0lglHEDG_jfWE+RuP0f+85yZ zhmiS?*jpoR)Cy>HGnCUrBVsGpQhggOn*33|(1|?o-b>Kw9Ac?pAA1XAl)Ep znwszQ^&|SsmO(a)%%%MiJ3XUs{rL{c({8^2W!g~(wfzCmEupBqecRIX5p8=KBr)LF zSUpS=AR-3KYf1K)H-69uN1Z;0or$_biC$Niu#5~wx4oG^APu>7^GjA@`ThGhkezdx zc9UWf(kTG=Y75@w7VKxe=kgm;2NDaUrNyMd?3+PuvDg*O)SDs{N5-b1rMLT|vVy~K zPgzBU2&jcCONL@A&7f`PeD2C(Pz_bpW{^}}?%MlJ=TczaSo@)(p@Aemv!*6~ce)y> zN6de-MPvY{kLRczYWVXY5x__QHC_k$w#R~ki9k|40@wVPlCS!;O=axaDiBDn+xuNp z>sHzPMm#sTvV?q#R;O;eG3Q(xLOA)d^~zl8qR&~Gf*MkYvsIHiBaAF8e1QVz~U*O zzJcGYa%jVSMcnEdd_90<1Az6bOYPyL)SB zwyVR<&0WBrudm|_CgeROCZX^Fgh}Sh0?^py0$aU)2kI-ap_;O?Z$O^N1y&y^SBKsm zQ8f9Tt>$Tqd~Va6@mNohF*eQs0?PMDf=R~Fu@unu*lF~42O%5@wGBt489YEO`0Y}{KlWR;00tkl(sQM$mekEc88kTrL zSF2mEyDE-gqev@S?)Ra@6cpydh?`@LIA?VL?wFkH&(CAy;BX`F5d+)51AyUQz&cFJ zczf4Jr>5F{|M5dx-z*=*8A^Z9Pc?+s5%3;Yt9Lt7l|29(Snvy zkQFP-WsL3z!iXhlvFpi!4`-9+)X%AfKX8K5yZpaSJ+5=xZCP~!lP5<&FWSpiT#u^a z2WKX>qzCRl4#0|BX1$aU5S!G*j9fsJ1}gUN-@kKu9KH8FPMYWRIq;!-)w;m&{94tuIuU|)h zB?(L2_YIKrNpJ7_lZ`8DgfHr>%3iaJD29z+j%Z3X0 z9|>{9-K`Kzm>8Zj{W~-?6#DkRMX!Iwu>by@jUa>;Lh<+$wz$AQAwlG&m86O!3}66mSM$18L3&wHLWxjmB*i_CpJvq0&)sPI<8%FDfxN<_+$hVcjlXhqhQ4Y(yY(#?FIfB*{J{H0THsXx0lcDe@vNvr z>12K(?aGy{Z^Snej>ja8K(Gkh)(`=&E}YaR;N4RqUKlL)J`Ob$+CzpFfkc{zVaOp6 z73~ma7)_-p`v;^hXai%%FfPKm}@O_q=Nm^gPK!yHJ|J*M1Q%t{KK29JX!0=?BwKhuClwc z(|LZp>3Hy4kv(3i;dI_xLMF)atpIIgeo0Aai^+#N3SB2>=O>ny?5SyKId&@viVP;K zarkPF&sc6VNFWCX)w6SRVa&`}<>loGDQV*FtqTi`iQI;*pC!G^-i)xKha_`TL1+|$ z@oH*BTHD$()Y-G`c+-Ajv@$zxk@aqj)DT-*RNk6wvCOA;o;-c{KYCL zc<1}~@0M@g1ZouA-P4~v>N)c^W2X{y6KRziMb_4eN?aX{p!V8GFd%foHqvHh z%$b>)dzXg;%p!^UiU@Q6)qyY3U^OAlC5H75GqyEMj~>xPOI?d9vyP-6eoRPs+42@! zL0P#SObMl+7_&cjP<5eG`JM{)5eiA*(hq&rn{Z5bda-abe=ytEN9=#rYiu(|2^}g+ zR%S)-BYN65ZywBh?@FeMI%4+s_j@dbvF-JX@0whlA78DmZ*OxRVpVm6a7K zIeF00kxRqLw6&<`0gJ}-=UhU8fq_`7zBgB=F&{q$>sDGEa|sB$4T{m;1;Ai=$L=oH z=GInw?ER;EKR8Q4Y~ADJBpi5ru|?AFz50`eg^f)=bl|gOc+S(rlR}#QFuSaRL;(OS zJ@XFLAB+0h+OYi3N4}Ifttj~%4aw_NSyA}yha2DH1K{LIXhTUP*9N8)1yV7F|ERjF`pZRNL){_JfrU& zN5?0)CkmS@%1oW>lF1qx8mOQI#Ig=p{+BObX4{!*PS4CNuB>1~CL4Xl_4M@0UiDIb z;_@$ZWzXAH?NQ5OgQesfc`RbLcXXiRfP#Wzvca=*l&goporw&pXnwfbS94b{GBR?| zQV^??QOw11P@E!C9u2!R8XWQL4<5wS5i9;@T>38oc!+(rHZenJd!dO@z~ZO0rsk-M ze~ceXl~Wg3m<=Hd)4R_G zuB}<#p`aiZGX`rw`A{dIy&Yk*&`1y92g~aH`}d?26xdaI#8gyNMTYg1*x1-Xjq?gt ze;by%)@7Z{@3_!-v3|F)&3(c59r&i|ie~I`9*}%@b6$EwtG)nEyX)roVh5FRd$Pc5 zbt9uF6bgmmDkLP7Bc*f}}d;yJa^AD8q#`8DUhKz}48uOZIB z3+83n`&U7TJNoF+qs5g(<4$m(O!$-c*0Q2u+}w796L%H2J<>_xcm3 zh#yi@Bi_F!u$gbr-AFK(A2vjS4HtF#jR!#={x@&lOjg^@1c*h-nH7N3NFr=M@6($x zHaUq0#x%Lic=8S;BKesV8wFYuv9#M9@dOakyLXAwRM}pM1Cjy$`AkCt=1Lb^=@r0b z)Ija(>WbBAKUY_^PzKKkfM-yTRU8}~d`L{hh2)o(GLBgTI^qkirJ$s|{fLX59Z#>A z5)7(W>^4fZ|LXN?W^r*k>4iWA7>u@wMKX*TWZ$#1Gf2SYrB9JgIsAtkATn|+#AT_e zckfDgkPZzEfuKjIs@{gA*Vaa!hG;De zFt?W|5?3t6s$_j+*ei`-c3=5H;oFH@wD0Ij^CACr9lkGMv zHV04vO>OPN;9~vXtE-{lUBL`B1sWJ*D=QBJig8nPJVd#Z8Aiyur4xv<3@4)f@cdFu|gg~6g4MTeS`aZ%2P-F+C$8}=rE;?5m(xIjL{$A^$SMwv+W9j&9MBZegL zzaqfI!g^+GY(Mfi4B37C&w+9pbSx_?ySBbAV{V?!H4R9q_^SCf_QQV_EC;TC>2Wc5 z{)Ybk^QW!FbZ@q=xA%#W5d*}5GZJZ;`mf@K5~RGK@v7?Tmmo<@*12-7_I(ohz4`Z0 zf_#XL<*(}m_f?`;N67adqv@rz3ZNql9=|Y(jGGvelas6MVvxwKkJ%UradAOGxFj$! zG1`!jknWxy3<%(%X3L$Ckbv*rr%Q(eQeFD?-$MZKfTWp~osIZW4d%Gb9!v_~U@c<0 ze;>P}qeG)c2EoGVbiVUDP4vHke-D>o6=h{%vBveJd)=sql!1 z+J7bsWM~wS($R(IefA;%%(mjy7X$)vMMPslNX6a0(wn%yRXLDZQgZvnix;(S+q6-P zQc<3VtEMBlDj>Bda(RRBs8GjaYBwQ|kYffc+TUSyaZ~|h{{Qg&cby2g1VZrf>=DcDpcY<} z*>bb4PV>cY-@bt)46r%~#IK#*P5^DZQV35Y3KPtT+9)jj@aa=HIK6zjk3o%)IMPSJ zZBQG4%isZVbaX^UN6V|LlM36-B6|~gqP-UXGY;xE(SoGdd$_Y>m(;iVj3*I^{Joot zK?#KhE?l3~h74y8yhG z`ChmwC-c8jPEbQ24qQhd&W?^UR#qH(6K@SK#9G1{yvib+7gx%p^o%f|fmYpgZ#xKNNrOPSP` zY8~Ca*VLST5Fl~Hxh{KUZhlWniV^bR%a>>hPA#JRf|3$BZEY$@dO-m`I{Qb^2*_0# z0Ela1Z1`}NlW5dBR;Yauxx2pJve@=6CO-ZZ{ullD|HH8v9@hAoCQhZOs91ZkQ@_7y zd_#+mkDsN-4R&hb0~J3V5l+L|>L-ssY3_hf-1hhPcX4%Xu@PPdgs_ENO(GDm*{Qk( z)3}ii4*+9u)rGVtIFqL|k#d59g3WVt^nQMR)oa$HzmHf+p;Ls9eBI}dSfT(EVqsyq z_uv73Pfw46s;ZohP7;?Fz#!UTmEHDz)N(Yls3^5k4%x-Y_7ekxyP%fYI~q}IvlN`p zl!w7GG3@HFtF0%vKYskE6Y75u_vKfg$U<;*G>IQz{7YaNDy)LQi3$4lCgAM+9KCs< zY`6shps>6)lqKqWE@(0E+4dMG42i^X9)Wl-M~al$&58Bymx2S<3<$OK!h>TlfJW^) zUm4(1nV?F1n#kQzWjz5{S_t3<)b#Z9I`wYFTwW~Tu1$3EhI(UCRk82GDyRUmG^|sC zx<5wp|9zRip4-%IqXKxn^>~qttSmMl-sSGQMyQJm61|z}>Gyz|6&ZXZ<7v9Ah!h8R zLtLqwK4J-f?am|St-Q9j9?}BdMB^JuvbxYTomvXEmWn*%fT-a|tp;r%b(X> z-+;*%Y7;g`^oVaf8lXC>pDwGv!!bPSL|aD2pr1=r_nchc=7p2PuOxr1Y~Yb-vw?rL zE_+4R_eL%@{$b$u#aQt(n>{Xba&-dCsH{(^LLe3=Ni{D>DF+jB3m5lE-Ux9%-={v* zSgBu#VZB>MS=rz?7g>xlO$4i;+mZCbd{AeU-I}=9Lw}VtNy`mh_`+1sYCpr8c!cH7 zmdq|m_J>cESU$6oW6uj?QmuJ_$6;rT$u;KlL!1oEgJ6vb(|K#C;b= zBRKx@7^2_c!MCxoffKP%=W5+#8Up8AyOC`;-ze0nwxNMPd17Bz)X*UDdu0U!vaz{o zUv37g&<49YTH~74I5&9eaaEd;g-dKZXSuZ|^Tb<(cDin8NB~tL%7%-5DPh3dGeYCX zz^#t!VXCUL;OrmWC@*!o8O_kYcjyor{Ye1qjO}G<&Zly(fU7~R=K!Dq1JLB406zh% zz#aA3@1PQO+oCq|J$~_5O-)u_9l)xp$o6cNYa82jjz}o%hm=+R7(R9p=0K zmk%Tj7rPCe$z%V`pbj2a#}gps-F`5`ocW0qp8jnFxsl5~ zD(G(HzXk6*?wVfZ79=K9#W3Q=J8^xI-z2WldIbeLZer|y%b27{no$H*>-aRxFA0w4 z80Y-@_k;WQLtZ~5i^K>T3NAcXHL%@kf8opaHAU=M&Mw^bxv@#r&4`^O36*>>>Kk+W z0{!#LS-y@5V(^l2;M#RZN(4O1zDF#59J=z*H$j*)>JiIn3cD;Rnc;HKiK!1whH?0^ zaU9bRR=J%Py1Kiyk1FmYZR`emTHS|V%|XF$>i+Iv@V0@rWPMe0aw6VL{8kgejEZvd zmE&!8`!#7#UtA7aarL3Wz|N@8epM}x?UIChu-s+UFeMh(f81Ou&ZF>IE1Yd{n8D}NzuLBi)Cn65R%IwpjPvCC%%$;3b`w5b z4uXOKo(1~)KlhpQJjJE00n)2ms|{uC*WGqcTh9K8>ZSTREg$WPP_U~{BNGeBb8$UQ z6%Ba7i}B52tGT^4Lt-ZWI}ACNz#SvRX`Xev#i!^KtS5D-kZWH8VNyJEuk&rA1 z6AQd;;A5Vo%edYl;>g?t6KPrSrhq%^n3~o~Eqv2bBg9pkZyhu=>Mj=oCVHtk-^3K^M4~DhpvKy3T=iJKb7?WOkMY40Q#iM>GCgj`Em4(V{?I+o+iVF3N3tOU z%hcaz-$oqdH6a$aJe?Eo z4T%E%N%z~hMe`8hv8Yo9Ll=)wdQLB12&Mwjeuvs_RQucYI6mtX##DMwCGjB-pgGlB z@DqO26k6;K@$zw7r0pO73loC*-Oy3ZhLlqION4kPCO~u9CDpL*acUEDcYpvse&r1B zlu8#X2`2_;-vkF?CXug3=Oz4XZlwuCKJz>jr5w729@wF(oIE5)?C!bhyV4)WYeIx5 zZ&9F}d>*Sx9vf4+e zHD#_7-$L2vdCj#=j0L+62CS!-iL{j?AO3>MVVoUdkh zYq(Jny7sD|eKj$?xbZu{dpRiP(QA%Xc@9r`qD*{r)gv+eI1oh0m&7qMwV@YxIlhM) zRW_&c`b%7xU?#AA^)LY3gci#N^Q+xPyYc(N{lq1X(Q4}SBbSy?app2HR}L7Px8fWF z@G5wlS@Gg@e^u_5m`-TNi+Yvsk_FXgvY8$FmV=?IeoR_8%0gzisC6;0XcCdU@{ zSGbBZ6~Kgjd%`UASb*zh+eIVS68M@snV4=h)@63h7;7yxPl+9+VDCf_G_PK~Ob^3< zTghBg4J24Dij)v?2Y2SfKOj4~aFUeqnf6>|$=w)ux>^9SyF>6WjIcG@K3dwU z_U6NBJPVALhuVbSOfY$Wy+&_K!Veql#;-q%5npEM!~Bz}LLd0;Xw(3aRDgO*Ts zy|i?xn4BWtmQR}gR~Q%Pa=DcoyZ`HOBr6L}Qd*%kpUIznr-qBM7cNIhyvD`tH-x26YK)$abV`pIazbbxF1hfmrB*RARyLF0Y)Jjl>K>d3uilJ?U6${`&~tzxsK!~C zm>=AVY6uNST55tT?gcmRKPC3qX~ss=iN6#XJa#fzij+UMa&f~l`9M^e<&_98l3eio z3O|qo%+PG+>n#aU@sC}#K%t<DaGz^@+ZINKU$qy?b1x3CaX=u^b2*(LO9nUPg=hOhp(=M`8IpSbmvAa6f*M9Q z1)zHY{wAUp>o@sHe)igU06+m<#sghac;|@G%d2nF^O9g^{+j0CF;ACo7>Ccvrr@QU zE)ggqxFdCS)5x8LuTK56R_?gmaoYHk#=z4brG8jNC7HS>%agB&};9N7<_vROgUx| z5vr)DYVk))E-wVG53`&B{}Qmk@n*gmnkl;{1|h&MT>%=;`!Ot={iTll!a_P;zs>RD zt56Dpj`TpZ%L^2x#nFQ2Ny4_Yz@Z7MnfD?DUMIEsr^Li}piG2F`}$txdzV`C{UX6_ zY`a((z1Q4JF{5?=o^!vZQLr+?9Jh$Ux$YlZSU;VE6xLXJ^gGq3$+_W6(elMPvGw}0@ zl#aIlfpi2C7L1Jz7ou)p5D`fyf(f}xPrpCwJOC^!v|k0slQ@|))7mVk{zmwCr+@Nl zQljC6$C+29#~98EtB@yP3jR=sZ>^xHDC6k3kNmSKlnnXIyWb0q;e8jY;+E??a zG=#xCf7J;6Q&+?P&OKZ{fv*Mx1n6H6^`4)5DSwvmw4tNL+OjsEoainqjj5cNmmARr zdJQctZD%d>CUp&lFzt!geX$E#niC=ZE$iP~RU+Qo)&Q_Di>ax#WlR12j;y8?pu?l$q7J?1p9!Hm)H06oYC^VTB2$yI}-FgcR`(<>E`NM z`%0d#WdSK0;p20eJ`zHTni}v6&Ch2CQ!IV5Z!A}?T&=6qH3rrDZ8CE5m=7NUX6&2j z^VGgnD-)8C$iv~7Am(CXV_%LmM%{gcX>4rV>wS#2_4&6gcAJDQrW<|hbMjCq4^c5O zc}5xji5lvt0jda=jz?VLA_7N6fEk#lM2MbJ2*iy9}^P^tMs_MyuEG5CxDzPD=+^J zT&r#DJ2sq13it5xic(~(8J++gF0c20$En>;swppj24q8IXQy1o#HK6)5j?ngEWEjS zaeb6073P06;a^3hZ|zGj>7|(};i&;^R${TMIJL)6ATCl^RO+H-m8I$#J94;=yCHF2 zDLI>}PQvMW-WR=dGB~oVj2HMhQWoDMvP(gGfbv7*MxSO>OyiB&+{CFbYVqC6#nzAn zQ|ppaAcdp*YnXybi;IhaUEz-C}_J#*D73GT6zlv%}gL>8>`63 zU{q98Kp?b#T_ zB&QogoqjcAWas6X4QDIvou0~jd5M9}A09>HdiUMVHWDcb*4}F-Ldu8D*@|(YsYdGR z0mqvYPcqa^<{LaM?d(Fe3ad8iXjP4C7W~97udXmAcPfQa8$Vi z$xxem`XojkY z!lEJs==k~3+golq1VhW?{QSeLSF`>%R~}$*(HkK$?|}sh5>X#inM#7>UGI}E84C+m zfPP%d*#^ewgZ;P-gtp@)%CWb0xhtBPh2_>niQyBl9~S+oVc3L}li6Q@A)f_&9U#}C zfTUgyx<)?6$4{A0we1hdGcHDn@2u5SS%e>t&Cf@@DAZbv_P=EI@$uvt+XjNF zK&P4%l3rIw%f!U=Wc=vlBr_)mTPep$=TNVb2ZHW(7%$e}Uwp^dIyP3da0vmC7L$+= z`aDl{s)#2??H3+@I?frh z;@X`9@N+*`HI3t^#bMckzxDC`VA8(6KF|Ya41`!PgST&TpT$1tj6kA0@1Be+oYBO! z1{3fyo%auiMMY79hq3{A7r2AKl9iQ_2?UQ-Qo;>vtp}i~PS(omYpu^&#jAPH^@9h{ zFIVjg_v5Rh=buh;hAa^#Cg}=FN@Tz;TDrd2rGg)&$xt#f@&FK*_3>$#EH`_na`B_5 z2MSvAV0?VfvU766fWy?=Anv|%CnhEa{d8zMNqGAy9@A&fRhn|p=;3z=R;AdmUJGdL zgrIc}N=62A+nQuD^4=EOpSCW=XF6{T`0TNS4@d}l78LLyxeoQ54d;qViRpy#q@<*J zUt9O5J_3asw{hb`^v%3{nTd>ytSWk~e|$Xrlt=fmzVn~{ZK|@M?>E&OPmzt4_7g?A zBleH2OG^QD!iT_^m|6~ud=Glq-1ceW2zJ|LKo^BzMpPhZI6$@hU$ty@ Date: Sat, 7 Oct 2017 13:55:38 +0300 Subject: [PATCH 12/33] Clean up --- doc/figure_densdens_tau.png | Bin 10000 -> 0 bytes doc/figure_g3pp_tau.png | Bin 88726 -> 0 bytes doc/figure_g_iwn.png | Bin 13295 -> 0 bytes doc/figure_g_tau.png | Bin 11001 -> 0 bytes 4 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 doc/figure_densdens_tau.png delete mode 100644 doc/figure_g3pp_tau.png delete mode 100644 doc/figure_g_iwn.png delete mode 100644 doc/figure_g_tau.png diff --git a/doc/figure_densdens_tau.png b/doc/figure_densdens_tau.png deleted file mode 100644 index 9b6606199087b7e8a4092beec9de6c78593ac398..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 10000 zcmdUVc|4T=yZ4QN5m)ZsKFs^jI^l%;Qz^|YA@*ZEUR5$eXL<6Oy5A~QXN;ZFO%urz zC7vPHRQrp#rB$XT8bv=Acy8MDFhVn=ZJ6~BEJAVU*Y1I!(BCVbE$gxVQBf|U0j z2Y%aW{ub5Cn>iXdv0;_Osv3sFU6cxGM#JwpGl^FCPm57PQ1BreRWuIwtOF4e6g(mM z|HsV~&FCljqkf-aaM96g*h3hMK|x7LSAUL1u;pFtZPze0b@iuJRf8luR+Of&q+~xy zxGp-M_1-_9@n@oo&DPeI-}Zcd|A!BEqoWyb*x6ytLiT?7D{jx_3^n+QtE#H5^lR{+ zpprByHoH^pLTa0s;P+_W5-~6|Y%*2o$Iec7MwNYZmG@mvFV)WGlkl88=U8pFwn*wD zSC{r0pk@twa83X=$nLIfh>za+MwRY-fU#j?l_o#h&?mUX@lx zPOja)QsCst9?}fFV}`2Oi4*5tUB#Z}2Km)cC4yYM z&UDtD7xjJPPK4TZo_&l&t8z{2%~u^R6S@Ks?TyyoT1$rOD$q#ETYET=bB2b5>PK1_ z4BLuXm{YYGuX2z{YRyMiS{4@HVf8}d)9UJjuj2S4&5C`TERol5-eBzQMZ(-iqkZ+> zRTpavl76in@XUEfy6X34fmm4X*e__EJoO|yo8tFYU#REr-HjbQ`@G*0cgjnfb82e) zU%YtXIn}oR^OrAeH>9x6wo<7!=nE@ooBU7Pf!izRBX+aaCvMljK=6`59c<Bx~PcV;7e~Tx={yQc}|NudN@drKG6U)m3!ul5U_w zi*4$j&#ERb=4i}_M~mg5KYqQW;h(G-alfFdN}1_5O;=T2ea6zE{Vr3=S>F=v-Ik5< zz!Yf*-Q?aFbs<^z(K#a(H8nLZId@*fX0-0+Q2niZLNlJ*a;fLk#W5#wX=*N%W_EEg z(^;RdVcj>{^i*~u$n6BrIW8(1-qND-q1b%3ES8n>m|kW_*|qjW{|4W0T?9Rqm_Fql zM)Mlim)hb@&%|6@T`@K`N1!su+o6*pL9sFB%fH<6J0x8xkRzapud}lgz42qQ@BI1m z@?M{55$^>C$ z{3*u9#&+4#GKTJeNUNjFbcXNv%kiebYZ6?Evf0_$x+KmPJX8KINBYW&r%t*&ibvM< z@}p`NJV4Cm=BK!fSPR#0eMuq6co=ve!VdtmB!+Gt!r z_4#n>?4lyZ)2C0@c}`K1>xow2L1t!bMTJyiQqok*?_Kdnr_8^Um1yO*JrKB{!1OCc z(O-(0PZ4+Tz_IDg@1MA293NDC1$0BgP8jAP$im{{;cdlRU!r?=kz|cSnaaW3i05<% z-KaO=5_2%cIV&qZ`@YPjG9_aVA^eA*QlC&K4J{?Nx%b(AKiS^znuMx``xTuw9vaiG&rK*ItLqJJb{? zm#33D;N<-y`$A-*&ta(RdSc(_?u74Sz7td9f#1|LHR1hma$T z&@wW1e5`axI?p#VGt+}Ix_p_{+1WW!C^1$;A9lazL{IGSgP8(r2@hvEN}IZroZ!Fz9IsvULU0AVSlBv;%4?!Ihf^r2Y! z#w$Y{27~63_p~Yfx){*)KVS&kjl@FZ9ef1(w2aJTWuNj7>>-2eVJQ79BSXXRvNDO} z5#OLgcD{APdL|}ZFA58lD&8(K0S^hNe)8mrdvS~Z4||xD$s?JH{sY(c_I7vl4Gg9~ z$d2roHV2&nR4MbC=|S&kX=$}(Dh1-dimRyb3u(k+VD5Q&d6m?;`40EQQmA8sKg<*I zB^}>C(G^emsudjE)XtMIGQS=Ww7X7%#<*?1>pIbVSe5oosx%F!xK#(qI&`V36cFk# zY_;x=i;GJ;w7~aHPV33mKd4MvLX?|s{{8zB?xXTZvUOMet=V_z zodM_S=f%a?xzcCP_LY~H1Cpu(w#PquL|sT+@E%#NT4LCg2@u5N#Vf*ue$ReX4wa^^ zd$n$8LBqn=ahHk5Mm7y_WMwLne;VrNCCr+Y+Lm1=e}DgsVFm%u)z6xYJhD2ewSgP% zumV(fclR8rl>g__NTjg#BLv~*;X!e6(IU@Zyl5k{k)9qcM;UHlGA#Tdn*tw-OvCpx zaMX?Jz4|Nmh4pH9F9lbpNjosVv6s#aTJ6?V(QtD+B`7GE=u~VpGBdC4JN$D$RM|t- zD_7XNq7P9axTvTLT3Ym1P9B7R2{T7%1u*$eNm~0681b(wIKZ%n`}LZ*xW3j5lTYckx~gn1ho?*4&+ z>8gy*>woCxhr`i%UH{AP_0mu^37DOgQusbSG{y8 z#w%jr`*C{YepD3HiNaeKmQ%(%smj3$j~_oyG>k4NC}0k*_9(Qi!?LrohSiP-Fu+Y* zr=8iq^kv1>)z!JzE**&A{52yUiYhcJW>cjtcP5_v{*pgW*wsMwEPfKF;gzZ3#g?a0px2_X8EHm=@xba!9& z@>f&4;|CMBVJxn)*(!8*z3}JAf%M&2yX}$rBq51H;{CNXZ{%D+0KVN(02!)r zy#uG)ds0+1o3wbi2!2-=S@vbYq#eZ6gg~0YCXPrnLr93BFtv-_0--(i1 zbwm=RUcA3vF0W*vd+Cze`STQJW@hgP25!Tbu83|h8;BP;#4Pdb&ehz=3T(V;A#oaz^SyJmNsVi&T#tz0Q!5?e zbjzRxDz*a$^v%o`7Nu~{)`4YBeK7Y}BE$*eGwB3Ye)Or>4nEh<_|m&po1iy+L@=M4 znHL9FFQ2ODqWhnUMmtm~womn_V1AX^DOXt@jkTZpR5Z-q+jA#Vq1#^+xR%IpUZPDP0~uc&{-UxB;z(>N=bI#k^=3GbvTmnov zPJl|QuCC4)1`K5c?V%1sw~s>ygK4sA-CkeRx3FMDft6Ue1Ym(Xs> zINWD;R(}3C8a_V+5Ph!dNks*ledQtBSFxQynEi>dH69b=GZjE121%p~IsH$XoB4n} zGrK{x_$&?Q>WsL`9zAwUl#rU5`hMPf^ArE?p&1`&<@nT8I^?2`PNVZ%4CbhKi==HA z(5n5+4A-w;Ul}yi6A%=n!Gl=jRY4QW#01!ut%Wmi!AEdK$5k>zQjLx5O&(-gLfex*fUvLv;xphu=aa+yuvNXn*Z-wAvIUOAsXwhvvqo82H>$O^Gm|*y_-C_N!SCgckhr@bmSE<6lJ~#v_2nl=s z{CGa81DZYHfC>T*LdP@BgmhW?pv^L41{m31cP4=KW}xMLzH-4h2dHbbU_+t$iTfM( zwig{;T|0oKydNH>4-E}vGDziUaFlS z8w@~zx3aweCf*?q(g_bA!m~cFHCe*zHN8AYb5lNWi4Fx1m@MZs zgGKurp0lvv-3kVOVVbelhE2UX55@a8^{@@$({J zUQCrVEE{lRG&FX~6ybWzQi4)rWoM57jg>BclM;kw>${Az@d*i^7Kh1eWe0aa;Vr0D zG+CbJc)@KJ2g>YvImkN%E>XT-7J1P34-Q87|M;?nmlYIziryatE(G~v=4vOia&X-3 zPB><}y*A%m0?x|Zeac5@%YaU5U>WKdOhj@Il%p5Z#B1m^gK3^rvU?RIB#lC8c28Yv zhCKzQwyJgtt^t|>2|FliGznavOVLll{$2V#(0O83jYFhr*e7f^3(^(C6S*-F$`8;Y zk6mC_kLH@{kAYbY2r=ia06(Fy<=q_0O`Z_u;Q%8faf|Fkbj2Qys;iS1GpnL?b93V> zj)Ug>M1vF;7jG^%d?QmUFqo#Acb0$5sRQ$V@bKa7`}gU|N+Vb&Y0O>2$43@ADE#3= zwi^x(qUSfhzvBM{t=|gt4hQPVb*SuR&N}Q!&@W`NSG=VIk6rYf2;P{=j^t}I_h_2w zk1;7WdX}G0g#fsLy)paVex$}tf;;ANA(57u`EI`>CL}}|v3dPK0726%-C^K-Vo;!H z#*adEA48BYZ_{YN!qQ9-nfm&%^0>J8#+*7gH}{i%N8r2vKI@%bT+WV36ce5G&CLsS z^7HbZ0{`9^y~Vtb%Cahx)(Yf4(gti+7pYP0>dc(9osA#~lg5FYKDtq%r z=Z3vKW$WoX5^&N&N0gQMSQJ+fYDTX2PUr3HgrS+qn|StHW2HHdWU5Y?i5N`x+nv{6 z!X-I8{46hT8b*pSwL3dIyAAQhRdRXgW-8LfsE2>}VB`u^)-X?J-be`mQ{}$^%!-PN z%O)nY8M={Rb%KvdfoN%K$H&K0Ah1&=6*uS@sj1+A3a76&iy`Fewcn6BUrMt5I#dql z*aku3kM64rq)E5LH3^wq}y`uUy%O;O^h|3b+J5_j9@LRAH}QU(|Ul)9n-v z#4E7+2t40-CFTvBp>T72Dw$BoNgY7NCvK|SlkM@pbRa!~Zi(Nz!)etD@}Ug7XcX5% ze(Y-kW!UfE>h|F;!>X!etVv{uyGqeIBPT_54vHVBucTcM`>!dM&=sd^aHsKf68XhZ z{*MWAQhH!8fj*NDSbM&TKL7wResmI45SP>qT7>+^P~A--C@Y$T?tR5Zi=S5EtkM}a zfq8i@44$ySd(&NrWL7gXB+OO78=QX2aU3csBKyA6YpvI&F!O60O3pB~s` zD9w8yj4@<2{(rx@kG)4E>g>Nh@s(;(j?6upwc_ zltn<^p+OMnrT;o-L_l2icr zH&a@?X0(z&aH`6`F)X+O=3x8FaGLMii={bu{Pz9ezdO+h+9Se(f@l22TK_e+kyBU4 z6}YjicH_oTGjsF)p&{Jo&!2VOM(phE1*N4AAP-VfhL*DKL0yWMrmM=N>hwHUjq1D& zL-%Zz=6|5&l;p15X!)%K1Cs0(LgGMOJL(lhSb-W@j54Ma6BT6uoB(ojTucmOWW<3y zsj6Z>bm$O*KzD(jZ?%zt!)AUU7us+9IKcMNXKe4#%Ig&O?%g|n>eTw5p-KF_$FZ!A z^Z#8n{LgCVfAP^!A!F{5{f-eJbOGtLwWA0=98vqBDB<9UQcKNBPmhil(hvlX9Apy= z*0sO!;eVP&M~)qPl^ydR=aDZxjGGT+pA^IopwRl?p8PM6=?R`0XrKt_6ecF7_l`0M z5~KcUjos$&4oDO*XuIw5SA1lT+bUpQm{uU5*%7dcNU2Edk3J;r`S~>}a!b^>#M>#@ zA^Vv@0b-#X42;Z^Y>I*>)Co4N2RVvjUV3XU4M2`ODJba9SOq2^1kT4$iItm63v2&W5_XtSuUxE|lh@@mW zj|vmee;=zecO0cs6ZP-F)va{s^Kndg{P_Ou+mvLwo142IzAi$Ey10nO@+r|Hb$;Ik zYg|9x-rV%_NW26bEaiV%k@BH?ia@GCM3brRRC}cDRNI~L<7v@hK^|fdK=^tirJMk$)bgwI4p1FRsdDf)@U3IJ?(NcJ3Di19Bil z*u?M4H%Sis^VkV+acinZ>rI0x1!GH3fDHh?qyg#e>w5(%j2t+nr}IY6%=Tryy1=3o zF*IaC&KQV9Vo$}@LL+hn2+XFV#%-k0jO0?P^6F}^hg2oKYH}&5$H9M zgziFY)9<~Vy7|1c#HCWxN_A8Ff8dVrd~o=42EBVv)!Kl0(o>N+Y~8Xm_KlUB`#w}n ze(xPtzlDN)RvsP(AVA+*ep{J-NZ46$#)9F`3^5BFzN0s3LN=#p>`Vbs!7U7rh}d^j zL?ocP#InKX8e|GR-<-ErXL>3MclQD-N{p?8uNIsD^0T+MS2nVQ1o(s=Ru1AK7zGvU!Gm|8 zlE9?!D=u*&OrqD+)Few$aJ>S(t?La)hkZx?n4Gjh#&_K74UCQPKbqFOEyuUrDZsCI36hxt zksI}-BIm8c!^66NZgbM&$yQ+ZzzecrJ-%zawzld)7kW(|`grpw*3YKF0XgXuvm4Yki>YH|BnMH+TH2gtf zfDA$FMB|=DDC1DFT@J3YMDVsZ%n@oyNlB{$7x3u)Hm4#bA<-|gYT^bX2x3x)(vQ}V zHIZX~aA=bJm8x4xx;Q6J`33sm^m zUd3r#y2J!VA(`=#Qxb@*n(RZ}AjgB4W5K)XOUy17$mpAlvz#7{->L619vW~m_4pm@ zKa9oh%Og4`3GG85y@UmStGGlSAp9!K)n-crcqLO#gq)VZ`0IeIg8VrIyO6+Mo*ui9gk7AUXs~GALiD)@F(iFR%-co7d{EOwxQR9-~mQWQbDJh$3 zGZY)!(>ETaLNYS!Aaz?_>83SY2VDq+lM;E8e&zr<-~mVr?A)U)`8Bpsu#5dZtwvsW zoZ>1ipWh@!5dlUKZueCtz;# zhyE%j9Ek;AC5~y@Gq;3{VqhOcS%+-p5`r4W(7@b}MX)|CDA4Ta>I(RJyG9fX+kGNS zWp~X`^5!%~pkpSg3nWsTdo2Sz9K2RFzzEU;n<_3Vbk8)JOHvg$zGf;ZVMm)*05WNO z<7SIp?;~B#Atvf@)~5~|N$`q;gSeCwE)Y3`PJ0E0`Q(;P?5mv!Y1o{O5^ppzhR|!Z zVWiRlC?JkC8ExscwJ~7`NYw$QiShI-1HG3hRhqv+0!yO9BXr07`x-&d_hVP`grs#{ z38fCMH^;`--3P?-l>w%&SR&YcV6 zQVyykzn0%@hHnQ5UZIG(Uo>;j#q0p}g38KPL69x<;Fao2ON>aL;#AO(n&k!6is+;q zfqjBe0Am1_)H&CmleGJ_7{B*DF7)_BFEocp!{GWtx)J)u;7YF{N zKdWs2oQ(xpGRLoh3;tRg@^MN8IN-)jVN5U;RrKp9=EDll8cox1_(%%%&uS28N^mhY sqr;!;O%fnwzNn>0`QLux>o<$~#iILXb6F3-N7@igHC;@Js?F_x0~pMks{jB1 diff --git a/doc/figure_g3pp_tau.png b/doc/figure_g3pp_tau.png deleted file mode 100644 index 6d324d4e6df1449c879ec3d2421984975bd6863d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 88726 zcmeFZWm}a|*DkDrfP^$it278mH%POP2I=nZW&r{M(%lWx-3=nr-QC?KUGL<+pZy%~ zxBUk;2TKuw#Wk)u$2e=QKv`)q6hwT)7cX9*K*WXRU%YrZ0Ujm@aNsjX42XK*FIam) zh#~@bgCZFG1pj_xBd%)y;sp}M^W){%qUZgK7w=y{gas5`QVvp`p_+~lPwCr7iOJy2 zwMu!OWmf`T{g(w5RnU7H8Y>NLZ1`KDzsJh5ScKm{{#9?&~nw< ztJ%hHHGC*#H(u;Mo(oM27bBLpAFgNLPMdZOAKs_Cf%1{O^8WH34FSh*%^>~z^GC3G zFoLqLpFfN6|33J?6rnKMn9ASNvM;7e&@5g}31XLv}kt0HH;%1z)744`xFrQBkM zkU_jjUib-m%NHOc6r(A%@A{)DdVl#$YS*U}#)=Mxt8c5G}62l`1B>cK}s zACDB??W>|7|>gE4w_L7xVPwGpkW! zp??!c4pxcJcnanux8ciJ(qS9X0Ijn4(NQ^nfB$#H#9g}%QBhGoi&p6lgg0$>cXyxw zA>rY{w}m>YA7Q_}=a5QJJ0=8Qw||kPR7BZAkFiEgV(sF>@je4oD0}p0ufZRqs?6zq zQwxh+=OZuYMnelohu;!iH3nK#3D3>>Fn!-lOYlm-l;w`1pTz9kS#L zY?>DHC(S@TKfgmhFLX^BGztm|+BrHhT5k3D`}c1|Vj}0EqXI$vhQ2>{BM!nJKX4OQ zus$lrmrU=Y+*|Qo46VRrHxN2II5~R$_!fX>2eM zN}c=A^X{2zbSW_g1r_#FiNoQ2+rNX_n$<)lB?XCQtByQ+UAPg2W3iu4o{wE0%u;Hp zkwP%u2KfH?0lVaRXBiY6+&eHp#>eN`HKf;I3n@@Uq!ntXe!h(Ne8S zH6;%W41AB4txxv5y}f<4#>vF=3N7EctGhcoHr7u9yH&mY`*^4t^{KcmGl@q0&q0o+ z&$O`;;BpDrEHWA!ll=YR&d$z$hJ~g5Yg{pzTt8iL;5xm53X6yYvXrmEfwWz- zwtWpx(q>L6Q+DOTEsyo&(~p9SA#d-OF%r*JN*FYfmzV#HABrBnlXTnFQ@&epNWA`7 zhhKkKnW9=3IX$pcs{$9Neyx@Xzgr`S~*H>f%#U2uIOBaqK?t zUL0|b{nT^bqnVkRUonT**BoXw_*JtZvld-eb>rjX&%35#s$s@HAuyMZa$kYYLNa(O zaIr)~IY!tj#dhl9HBw8jfnJ9{%D;dA%DtXEv$C^$`}&gN{Nw7P$si7}NYY%-i%gYc z_Ck7=Pyk1!Za!42tlNM|w?ai+T3WPdQkvV!+S+<%NWWCFzNH#9EE=O^T|YU>^+<=( zxlOI(xQu~gb(={6CU($Bzdw$yx~4`>O-({x9>dhs^yMjvrwzK2zfW}N^WFLhHnnR> z&Y)2;7P?5*5?)Fr5gs1sPP(7py^=E)742fl_Qyo0W=u3~ql1x#ehK;(E>gDjbvxH~ zx}Vje>}b;`_LTW(c>iLkUIr2+E+O%ek1q|}HK(Hmh>;P+n>TO9KhEpWcATEtf&!|@ z|HwdGsv4+S;$&oO|NZ-gt*xyt_vPQeupJ#84$jWw^YdR#O{t^EUdQE=QBvx&;NyN% z=f(5=m2{i(;+Kl!3ymheNXo0~vvXy0PRi*pD>&WcehiWv`RC^X%X4qf44;4?FfcH2 zN*24BMAwi?MdJAxF=540!9r(Z&av23ToXLGfpVk{L7vOoZo!tVRK%Dlxv{;Sjw7dN zzx-|`T=wOJ#OpeZh~7b&*mDC@q;wwUe@sa?#fg(9EX8UyL6MQ@m&eQD!`r0v^l!6m z?NXfz*Q3gru!QkHV$&&W)5D%w-_Iu7M_PpD+5bUaC_4M?96TZ~3u|dllcUAjI z{Y9Y-vqjB&(1nN2L)r=1*g*LFx4NT74I6rq?k#vWgu;+f6EbpgztvUUx5PZ+ii%jX zv$Ny)@jqfSbqdgYXjE!pr@2$5w|~7=zwt60)sU=`%|`%Rx?Bl zOiUp)wYlOgg#HVEK83$a3+$qH)mCG7QDlMYwMM6z0arFp$&U9cmCX@%>OIC_WPMH^ zELN%TxxBpm?yCU4x7xwAyxhsb;XMT7^z`&kpDY?1C~DkmX2;pSiOET@EPsZEqPSO6 z5+Ja!u`%)R4D|H$kTGA2Cy=J}K~H;qo7LAhg4ZvgdiIELgSM+?d-hTI+3B@X(kYRs zWinf*V@9RRJMa7XIC%;B>}=?J%w5PKAHl{QsaMp{kQ5aa?P-4zMaIa)dlrsQ-Hl&d6&n*d~2GM;KYB4b} zO<0n?v@rAW5w~a!pPYP_l9EDa6jzb#7`G5MYwM6^f1wuZQ>+5h=28+S7My(3b4%8> zs3o73Czk($Q4~d+iy=C_MWb_mk~byVLt_JnCZAcP7C+QKmR6aGi%Y`Lko@A}Vq#2GQH&fZiQe0YHd{Mr%lzYbzJS1*S#)KHm%Q= zT%q!bgYa#&nq|*nXXpioR(iN?T|5;^NTUbXU(ijDU7qq5Z}=sv7a6(kKi<@_Z~RIO zx0YV*T^wAv&?7g7+A`B@8u)X0#J@dTPenAhKh zvwi3qAom5o68D3nT(A8Fk334^#DbDt?PLh76)#U3suXU1C&5nit$XQ*O0?H8^sV;G z5U&*QmOve%VN^7JaK#@ME72yiS%RkO%jEeZvS zW6R4VIG8SpD{jWd#+3B&!BbO8-~s>)__T$9<|?2mt*5!njQCxO;S@Zn>(&=;ey6F%L{(JF(@6ofom2)n~m=qiW%@ zV4cAH0Kou_@NZ{lh5MBi4Gj%|2cWwJSZE}f8B$Fv`;{Xa0o0-+J)m?PV{LH;8*7u? z&J7wA^}G(VOLVWWg~ccQP`H2&^xAc4-T-m5-DLavEA7$U@M}zD#4HDzRtGCCe%FE| zb{cn+JrdI1tj&YscSC*EmD#o7cgtog0Z#>MnM6_xP%XImDY7hin(HO<2Ixt<=Dm|U z(Q*R^`vLeRGi9m3#y2o97y(V#)|Qczlk;y^m$bWOd2Hqm0?q&cn(ZvPQ{0;L$}7uf zC=~Tmc+|`;syqO6a;4Ibe2R&QVHrejt*LQLveciO5KijXV z?MQwS{mF`t;_B)e5*jLHroogv7_CLn{8U;>4?4puL*Gp|ON5WUXcJc)?Q(g|Y}RHI z)K5R$Q*$ifNHoro*?j9}Eq>+Y4fi5z0eIll)ZymIl;!W1lB+-E!|3LPubC5ZJ@q;f zx@#RUQh=>d;a0g^K?X^Q*I!>ac(+uzyUlJrW!ul+ce8lu#@EW}_e8w6aui=w?_n-4 zoD^mB@p@IZY`V&55FZW>j>B#P!L20?palo6#pw7is;X^`ZNczT4@B`=-<7hr^_)IlRcxHhT`2fwP(oYfULZMLk zJ|#Bacw@!foE(4w{-_0DWa0A7!KVllOzcdIlUTWlQoQ&%CfO=ls+leh@!$_o3&^!3 zHU9WtsHKraGAu&7er@dv-{#ihpN5Xh@6nP7Uq?Q8r-6%~IaHlW1_Ap3oq*u0y*)EI zIr*>L+$c~kl_YX&_8QyXLnrvXB0-hqWCQ`tEim3TSrqfp4eSe?X=3~ErtO9t4hq23 zM%E%0ii?Oq`{`nsJ}Lg5dr3IxDk#IPMT@@G3zw_L2h26wZ@N&O+5Vj$0gUmuv4VnS zZ|U@or!FhXYKfBPuoHG^j6d=aqdGb|zV!8@MZ~B#_(piutTagC#Bz3Z_R=$g`{_JX zm^oT7^F1qT?5|&BX6EM9^z;b%d^0YEqFJdIyZc zKG!Vmp1J_opCCyAI#EGEK~+*a1KTUU(FTTT=>_qi3+Ql5ju&AWswidi{f-|E@!V*S z@vy{Z;h*a-4->`bd2G)c%x%V_8uLZyt0Rx>G;Ri|RHN~H`GR-v-mQ#e3aNa;sjRFV zF=2t;Woekr-?^@R;h#?9l(f44cah1AzI&t-D1i=}`5kdJK2%-9 z>m?C{J?b9Gi=ntU91Aw}FV#5QB8D`T8I6la5IN&=yyjj5HSlNilTE2etZFIsGWdlB zhi=5XPh$@ITu3GSu+XNY-nM17Kz5(IzAro~920srBCpZ|Y&sXIpSxCB_EN$60NHu< zgL;V)oAr{Ux;jBlP7WA30D@odslJJ+2{~!;BH6M|r$*$U8tl@Oyx2aU-FM(ZL`39( zUVkPcA_CMbDmvO%i+|QgP6e;E4&BEsDlwQm{ZopsudlF}SirXkz#w4$%#M!>IXgS+ zc;@vQAQs^@r^I3Vxb4YL?y_4^iGQ{!_xPdChBup(v6ZNX`$T{I`lRfsCPB5$NlK^b za+O}b*v)DtVL-vqa^b;@PR6S%NP^KdB>BD#0(oAda@yK5pbIJ$#U|JNJ&R`NcG*}I z<#U$4O&M~go-XrwYT4e{0Ep=B@{p8*!XNM))6|_meJ3q}XcOdQ9DB$(!#dIiy?Kf> zxXn}YY`&<{vti-k*`p?}XvLyvF94H-oLGp7XX;moKVh0 z5wX=)0B-rZ8~w4NF4UZTV8P^w8nEl|{}ngtLJ+^7E#$B%~vcgQ$XMBGm0hW$9B=umu8SXM#dtR+rm0Tec|21g92hd~_vbiSBmo>OZdG5@I0d+f|&H=amc{Wip2#GC)2ft4W+r2yw> zdFgk~jctM>W$WOM0~c5pcmxFNzW?OsUMxRMe@MF@poj^=^y2%ftNLzWf?hkILqJea z@cr0OU*AW1dissl&3y$GytG!(PRQCp=a3V}8C_wJ;MhyegoS8Dy!q^hMB1~HxRD+dWnpJuw`+K1rNGQ9`_cjBF1}L*1%D|%+fX6N559KV{$`Kk znUxenm~6)-0*Ng@qFPw5h%qR>9N}aMo8K48iKsU;rqV5k7la^va+(i<3Y*Nc;MmSUS;=)iiyclVR^?K>J6@t(&a~L%vEZTPIO{4 zJ3t;ItI)0L+@oBE{95I3Y5aa%&g|4YcgI7LK9`e2I5qftAAEgT8M`mHi}NTvCr+eK zbIa{VT`&2Zm1vZ){@NSr@_5Zo{t1{SkJ3ZoC1t(#=uu4$Pp$GHl3M3cni9&r)Ev@^ z4hD)2KQ@z{YY72tLUE+RnPW-MyUlS!$t5H;wPXD)Hgp1?E|=R+0_y5T{Pl-lgK*EFs) zD=sM^r=c+$(&IY1WZF8@8zpI@D;EE3dy(Wqyu8>~5yGJ4i_vS}+)g#OviuZY*0XuK zSaRRC{ZOz1kfqDb&%Z5Y)3(E`Mba(@I=V4rf!wtv8)tV;XLW8UiKmAwEG*@Eok6C2 zO=tV@K@Gxxjj9q+2-rpz7Y837?kdzuUh4nU5ndnL4M3ZqMn*<1$c525&QZ=^90n7C5IYHQiR%mGL+*fX6u^$AEu!j~wKq2EcVrzm|_ zGI!1#fhI_qbGFSr?Z~!)z@o38NwNG5wWrT-D%0m3chV#ZemlG739941Z41=3ZPh10!lW#Oy`u;?c zih<#@v9U40fy_5HK-o$7U(KkdCf4KbQp#Q}Dv_r6PoK@rM_5Nhn%E%rWoY!V*VEm3 zYl5LbYJnCt!3aER+of9z(8a#|h>VM4(-+QEq#-312*TeRTR*>fm0H^_^lBq>2TAkF zJ!{>su{Jq;Qv6Wi-sRAU&e%}RwWSjN1Zi)j=kzf7E|sIW`yqXJxaPDq<=s;L!Rq7T z1v1AX^zqaedKRKss+BA*z2LC!FT3qv=8Ox`y@lVE?>Ml@1u+)fjnAxZp={@R6B^ zho_fNpmu{9KlHp2LT@xXpn=9Z(ZgI5XaN+!iV8+GHMLi>KRc1%B!nK{-ys0BwS{Dy zcXle(`>v+DtZmD)ErUW-D*SP*__3E3Lr3;2@@o%%pfK48%dm5}cxipw-kz(!y5M?L zSr`B`VP%yF1uW8vlFyN}h--o2XUFU@qdsJi1=~#t`Zh1hWv{Kz;fwX&Kx8{`;zmJ1 z0T;piIoi1Z)|gRBc64;qz}Q$pK>^c5qf)J8T#K?8Ii5jKHt)T5wf#d>A8yM6nWCH= zdcI!|^}J0Y=_h9a&7pl+T%Y;Fv?*`8ySY9o13)6P#lkD?Hv7mq^B(AgT$=fwJFNCs z_3OVz@YA^Dx?_bV2j|ILWU9GHX7eMk_&I-nba&InJ``6F9xGYyI@^hMIq1G<kL z=R~^f4U^TRpJr(i1u}Ti`UDVPqtdol650$hzESrCv!Cv>@4Hsl1Bxq=n`;jH{oVS% zC?WgwXJJNgb=~>6*7n%X^W`CNn$EyxCwfV8t<>i_t(3u1f6t)(Ti3PRP?n3((|d2H zG~SV}t8`8uwU@@mZ%;Er`Gy9=M6$UaIXz4~Fc{*TPtKK#IN90RMi`4H%|`bxp7j+V zF*|T+_HgDZs7eYq$qJ;-sRKr@(&owA($Yf6<6;w4XZyMRNDqLD3FFx}d%+B?J=3k1 zte}Ua-wYUEA33*iAF|5uUSQQNdvZsSRR9h`>~(+YN@8qb5z^SmRjg#bLIKd|tBawX z9TR}RC|JdSUL*2PPpbXtu5Nv;f&KcZ^L)V{<}{ zM2Z!|8#%SK`w1@3D`}W3YwItbIaC7g#J) zks&L7?=nwQ>Y_vWbUaIXe~P(J(NdP5PxY*t9~}HDE)D_Ba%_CuSTdQxK`?Yc5aokl zs*I%M0GahV0O#@w3Y0BV>!%r`CKQ7@*hKAl6#Wviyu_%Wdoh({p^JoERWWh={K@HYo5 zE;_HIf-e5befW+2`n1^wbUf{{)q5UpQ#P0=GP#}dli3TLR%cuKfV*ZM=ZsY2tGd&= zMLIIE{rbxH-eBWhU;1(0pWI~g)a3aA3QcjS9z_PaAk4+&+L|5{GqaFUaNHcSUafxL zQWsG30RNQ#kH!WEMz44j)@Q#6ZCWlL8O^xZjH9`l;DZATCgz7B+G$N^qG6u%0WtxP zoy>sxo2_J+&c}x9v1dF&ndai-xIcB8!{8Bkd+SCFlm%A?8My+*`o$wxOJU5(FC$oVbgk(G_SWe|JrCtegT*vubUSh@ z2(qu#halQY{;%%$Oq^zBdV;WN%T+#kPk)-|c4mYPUX!GX?fcy@{a;bg%F3!|$H`2r zyqt-LRYl2XZfs0YLPBC?8LT#7b3w`qii=@aimQhHte@K49oE_RlE0pQf0s>JdkDu55WSrFdZGcTGngSi97RVrH{t;VNsg^jiI+N4rJQ|LJg zr#bfHH@#mcn^h;j7N1_rm!)g9M3t_-A%^C82RO5w=0)MW;NdIBnG zLEA4O7{m7Y2b66s+W~sDM%el^fSxw92-@-e@^*o#O14sG?H}PF{no*|?FE|;S( z+$b%NugkT)_Fl*=D6*{Evf!iK+}t3epvcL|d5dO$U}wiom3HnE$yXQ!nz%+o6wt(J zXld~X36V`rUUrwc1KMC#UJT1R5o){_I&tWv0QOw6bt?qW{^tq=yd_H7DB>glzzWo0 zR-5B>8Xg{ z1z!bg`AueQ;gV>GI8GxhKU~Sta6Ra0$S)78_TuwZj}54YVjfuHlkwYH!L)B)xeYdw zX>m-n4{H}}HK(W$Dm}a*@Y9o02v>L~xw`p|JiZxv?&N~)Qr(K^%YX&zq zHd4{j0->LN1Ajz({nTa4BNQ)GbgD$X`)IMLpsXwp@Zf`k1JgTTs^q=x-(v)HYh+>q z>)IJyD&UgiTK1FQlrxozdV6}_Gcbe~78be^Z7X6=h_f#VtDVH@{hjOSK>&9ftT{O) zB~dUESy@?u)}>pc);m0GJf16sg@Xg?D_~)P%wuNsf3g4v5X|wM+}3G^d^)W%@-;tW>^C3s}@a>e>SL)yYXX}N{==h+mufJt?F7Wqzv@@Fdp0A2FT;y zCD|)+#B!ME4RS(4{~-UO(F*%Cww;K;tE(2(?$M>$GD=9)&){IB5b1f)8A0FsI)~kw zkgt*>FQ=mN6WB|`X}mxq|Is`Q+tkpoXq6%jv{ZA|;$c2bup=66*G0kQ<`)&|>*@JW zrbDAF=WC_h1}BxZ2MMg3Uw(0RW{-`H^-kK?2n-6+v$3&R+Q!|*H~SU%Z`^{zJRf++ z!2Fn-n*&tB06Y^A2IQ5MyMg7#mK^Hi^NP)KPFO(TC4hf`G;rR{rM;EJrk;I6?V>u+ zFw1Bm9iCfXiW>XMA~SB_U$`!J9LD2C%}q))dZ&GC2avg6e(yN!sOLF%mz4;cvhVlf zJjmbOJT@#jSL<72hpGrP4*rI@OL0IR8l+dql#NJB8`_&JvTe{f0DbZC@sTtAT(5Mo z6QOW=UnEC9G9u!49GyCVH(0p1<6~n?`cu+4Q%OnqT-@9+aPNG2DvM#i<;dGEdtBL` zcH&mqZAz53T&kU&pEn%V&bwUB8o#W+UJI_6wRi?U=fnKzO~%hMR)}*o5 zD%MA$O#0W6Sd)ID&$R&-kD9tVuq<`1jusC9F9dwz#nZ#}8nh z0aJs;UL)%GI|pKdK^%RK#|H5zuZg)I-SFan-{pB3NqI<1b`!=TMh`69J{lag@uV>` zJq$|tO0$!=TaW$gAb zNaRE%B)kLH1hgmJr^g4diU%7P^@MHdr6BgRN}g0R)ux!he19&{;v$h9a8uq95uvcq zhlYnE>4tvpFZ4&WnKg{J1rMp)r=$Ayu-{%^qyF+9s``b&?X7!s{DV&y+>|YWkbH8{v9xt_c&$csb zst1QM;gWVK)O{}fV1T{wO37a#Io_wq%a_|nWL@3>%{d260sYW&v^=nT}} zwn4VO{p%$2aI*8vT)WPeFw#7HvJ#KCh4PHE9k?s21c(i*qaI{x)xWxTTpNo++u~}Y z^d@apJqhDCyCd<;5nP7~7~vkbeJqZk6;V&byQ@MX<8;?Xf40wUtqRkK{bIpnDd%HN zLQ)#w85K1ute-t}vS1?^92^9qKiC)209&d|m(f!Q@{Zb~08ePDIJ2#d|8poM&`Vcv zSRGMl3AI1wD$@AH>L#H3;QJ_WZqnvS#V7ZG*ai^s-vN`oxj6+?hzyc6nRKl9Q680P zRieFyh@dT3N#Y>?a5FYrYe>TREcSe|KAfndIC<%2mX9M03I|rM6Z@q?%-(TO{B-GU z{D8@;ZCVHh_;l6d!@I7xO=(aaI_1T9@Ad+D$ZC()i05!Oqqqe@AZCtkDy*Vt23@pUg zV2zVePyp;H1cAH(_C)l+zKqfj#46)lU_Q;(+v@+OqoJn{11`U*An~&q{m%18hQCfG z^>R-Ad&AMNUm5h5{S1JY@n>ge17Pu;S`ZM)i>`Y^47M?rp~VOrZOXbeIbn=-+aSD(VK3s z@N`PeJY6ic0%pSfT8*00JlT|5ib1JAYMu%8zJHVx>Q$@UFvD_w9u-{@fR6Pp-*`s5 zvzCTSN6&rh#I8?~utGd&32SMk$%wP@>K=$uXJCc`Le@AB#bv7T=@buGVmc!JR&Hu#z*!2v83C z_ak)N9FzyF9JN>MLtBsQD4akFIp5Z#yWf_jRL1!`KEdy=M$Ne65N*ky78Xkun7hPy zcEGi_zmJEHzXsErePDTZGCqx8zhB7$oMNhx{f3j7!6sLolesJ@a z4L`J4yDcp(jrchsFlf|aRhJ$Ua|{Iv-LIUS$e5VF^VV%rV8;K~UQ4rqD=KHw;1QG+ zDjz{`q?V<^LT3+8D6lmtWYsP&E19)Oo1A8gn({?!ppsv@Di}JcC-7D4#T|yM-}n;o z)LKWlGhCCl5BBKEzR z#*cAZe3TFzIwD`pcQM33Y~0z~(=%`gsTCkGCMy`-b$GTa-D=YKglPFaa{R~W18FfS zrlpwmq@YiG7trzDjmh14G;@px}!SjdzkpDrYI7773bVt|+_pL#Dn%N-kfjKRzczRjcp*$y}$VZHy z6K8QYht30=?VG2%Ye)(4~S6% z`B)gET-dzXRtFSx%PwB{>8{Nw%gu_?j`L`uIk+ObDk&P)#$1_XyOhW~X_6qo(ox34&!-z1)?OKF4MU;TZ@-Aa7jm+cy#5JAmZb zxmsA=2m&kNw7*taUU_*0$TI<*1gIk~3Azekk<5W_ZB~<%kdUCzShuPu2(}nb^jWCG zxNN;btAd9mJUskto0pf4q$ZX2yx*A?+j2Nd$wQ^7Z`;t#XqLgF{6f#eaqa2j!q+nsjxb-~Tm z!WbABCg$eZ*&WuopB?)`RmH2Qs9=^J z1uki8D<4QGfQ(;`e4tth&gkAJ1OXIpr6SFf72{btYI&c=gqULusHK>g7y#!##Z#Hb zru-vuS`<`e)#2`6;aXpc{lJg)3CB*l=^XNWY$yx86nMHQj$UA4Wo2Yx5l(VXAhDD` z*_?jRY<_V?toR%isJ}qOoaU*9ubU1Q0bq88e!2F;6KetnZ%Z~C^VDzLVX1Xx>xuKT zNP2oT&U1MjR|i#1O`^0wTjU2z9yd+mIv(xeBY!?VMTxk~!Z zKYc1FrlP2(s%elcJd&Js-09-2aj&KR!(o!uJd&PtGFZNJYk_5%hW2iq&*fH&TyE~} zU=8qtC=JluhRnz+(HN5o3QRHq(O03E0DKipE^2CO4#F_g?#S`hGX&02bRKR&S()F0 zX*BVF-h0t!ZJgf#h?Yv0W>v?H=Bier1O{H7>gmP5f5Hd%@rC~Q1s>^rca{ei>^S7A zVP*UaI`KVK1vi`ftlpy*A*h*(b1?le{$0EwZI#inr=nc`6elm2Z?5FV^#2u0e74PS z{2BSi3!iN!uH3T6Pn24hJ)qRy)9x!G1Ig~zlUAXTf*Q+|>Rx6PS!8^CHy}!4;l!H? zv{;Icg|53cluy?S6p~!nmfb%bhbz+gC;qL$Sb3dGDUm0^pdZ{SA{iUaUZ1%p-fs=6 z1<99asK*d;ObNn=$b?z|Mlw@r`1MP|^8iO2*HbyToy5Fg{`KouL_&f%xFwyPoj`BZ zubYRN?@4XTvNb&di>FKk?nx7%mlyx&uM8kp)w!gw_(S|eq7Po{agXd+AD0p*6EMn(WPgoU>OoH8lXz!E%{(2-JT@V>-#jmbcXk7PE8(NjXfGF zL0y^RNA3-L$OG7}RNpm@i9FTYLxj2arzt?#7APcS4D@05r0X$tH`?ruM1>X$ zpN}r1Oh&UJ$mrEfxBp;QK%Zt2)3_%fPYL{j56iek^-xEiSvS-YA^I^3N_wP?&gA;t z0W5$FBEO!m*XwL9Y$MGdI$1Rjh~_zygdgm#wNHni++3BVI6SrP#=&H=AazvX{`meHBe4K ze(euPI)b!liPBdPcP;^aFzu}oQ;e(e2x;+cNyC9BRo zf{in}BAAPZ0yq@V^jO~_nG@phc_`rn(Y-NE?y;pTRLq&}th4*-e1>v5ePm`;ctGUm zGur=e0t{pU^eR+<-JEt+VSu9`q$%!T&Xx=l`V~0j*{!W06q6D$OQ&ndk$QE+o&Gwy zxB3^{4T!5hK6)l4B|TgJKy^J>Y>Efb1Tx4+PR>N2DZUZPH=Z3Aba}1>I^(IzTnQ65XT9!@l4{mHf>bPQ#je4q#7`nh*r+(t+G22P zd%D=jtN+FFqcNvdIOQjhs{%64Y_lsf(9OWk`pn2!kpVv`o(DDl$i{{f-TMPXfu~C~ zC#I(Zc)U2(V!t8-k>z+7ulS>2s<*gwifz+k^j<1hPE=SyznNkIYNn4>7_%cAqh7Z8 zfGUXkcnA03_nk3@SqZ~JaWd! zQ5fakTf;4zJUc=0^S&BqS|JUTy@KODXu)J(NhOt6Zy z{cCK7y9;SE^u~fVNK(#Hd#9CfhHq}?5Cu{b6)LD9N40U|mv$WqJaXM2p1r(0oGX^VV|+keq1%nME2Y=` zc%QaY4e0c?yENSKTiubE#vA>|{VSn;#GWSy=xKLjai-=%#`>Whf9PT~U~?vmlobH-QEB6f z&oQN>6^xA}djnEHN=i8YlZpm`g8qI|E-tQ(eR2pmq+oZlA^_Z#C56zhg0jY7h%s|; zaFkh6P*A*L9|qzU=ItDPjJP@~4OUS3vEUptPqMm==kG%a`p|x za9s<}(GweKeCHMop}Q4WXD=Oc_Vnbg=Wt{xoLBtuQ;W)A-mv&-Fxls=voY?yk4wcL zn5VvLWpCh?P1`BOVf&Gda($K-h~n?7dM%P)0<-So5urCZ&delG+}jtbP6)lP@Mu<@2+(-s!-(K;QHz3uJwW+q3%<+3*#e2MPOy}09xEA4fo;@{=8)$W~L zm*cTQtEm#pcr=HWuWIeJq>-)47|`h8+_%|Ck{$7Jb97x6dKw`6MYvUg7u0X$d4myCm+;0;WkQwUe3MEjjv(o&1ZeJ0(Bbh?eg^ol%wy!~2 zVVyo4(7m1sPI!RS&E?VJ^XY^CRo|cW)8{C0M#lGl{~SPm{|`kjkVJ{gw2Q&KeL8+yC{OyXiVR^UOEQxE=uncpus zI4Ki|^<#8_gz}6@5sT7`m1*-a5Y@cu(%|dTplp3>2@Eb=^3xg0sk1)iI-7pK*}sYR zRnl#j|2_;gx^q7hBVvy_?<7xD%%j;SCpkCPw#M4;prLuzwh)m#wz+F)5UST&yv(St zPbe)d#kiVGRzy&kO;lMc~3dyc`wq znOis(E|PyQLmVGoqD(tVMJ}qr_dF)S4@enz7q8s89+=&QZXGzJagTJ-J$(`@lDEru zjEH}GNB8=8>72r^SFZT$X%kV&*T6IfX{H;99P|k|-CmDWG6(ys>JZ5Hr{na*L|Yq=jk-D2{;5Yc-mlrJFT<|YW}U@T~*VOOVP z6Pz^LW-I2MsI})Tz1Kk_A(0Uhg5h>PB4cL8^b=&HTO=ebfm6rx-)}!x)KNv;E7fcQ z8-s#*{K>HOsBzzAE73Uj%^q~N(UJDQX(d@%*)QN^9{>F+>UIq(y1-EDlR}QIxE4Pp zQXbDYvAl6~Y;3%oNx+1Yp9ZMsC*x`jp~^7o1qqb5l!c5 zz~8M$M#51pw{Set9`rpB$3>41$95v}qA}fCi*-_>94r}E+UolwJO|fx(^)jR;N(iI zuKf3Oqli2NAA`#LU^~*8;QE34u}$Bo*!!Yj^f4U^X_w7wYr8SaV2NX*hs4RzW~k4A z&6x0*aB9~9IAw5CK58XX5Y6$*--fe}3tkWSd=Gg{t@j(Ak0+-Wl!TJ4BW_z7wb8?_ zIy&Dw0#Nmt5_@`kRnHvrne6(UN~vNcfJ+Ww>*C@fv-CPhLjgL>L`O#lm?7-rp$!KW zHFe@;qu2d9iN$hjD#)iCwOlPh=6|f!Olu+CUm-Cx|E4W#W;(V<%Bv8fiW5AEGl%6# z&XC5I$s5Oehm3}ejg2?{P}6`-02JB6sxfBGtb;;5f|OJ0B0N89jtyANc+dysEg?;^0?xR};XKW7{; zI*-EV9taS3>ZSA3VGu!a1Pxif?r>)2omvUo`!pmxh;t=uP3v@eZD_il@YCc~&a=qU z6|QMDg+|?9oB0NGVNg%LayD;uhM_GmR(bBNK%%G($;GRGg{ zT=uek#|j`{z18K^o7{B(X`zVx{QL-_!94Zu<4P<=z~(_xY-Jf(G-O0Y**&m%1blb1 z!)K|-P-CrbmHY~!UzCNyN#c1iq@x@x;4I$A2-?HLgM*_Z$UTRPX21D#uif+cUqQ+M zL+gPRi=*DxERk;Vq5~fH5pCZTvpsOk(v&DcM>9tIlRmN;3(f24;;$QptZz~-&WS=A znjKJNMp${;rMzZ`JBtp0RR0bnW6JkT_6R!i<+2p+tOukXy0Z*}m z$Z9=}GB(29_4HWBwBr(imP6iI*sVJmtq0y~^n$~3vv1Kr*5D%p14BgQ^`C~_>VM&< z^M{Tc#aw*Ve6Dk@pA=~lU&Nw$v)2gpUw|nJ3JbHqTm#%C4SEK7&R#*vOwG+n85z;P8>f53#nNuG9SRyT z3U$RZ=eeOwZrVu&^Guqt8dZS=0p`1}xK=Mi4EnAr&fc`PK4`Xj=u*U&g{3R@!PVSK`{G=r4Z0=OCnvz{@%acbkR0GKrTMs@^zH#dD;KT0-W-=m%}tcgtkXu z`5&!W&sLLfyLR5CBvkX&0PYJRMRy#CzT(C;s;C0i#$#R-|8hM}j-F@wo&SVM>*b^5 zk!wj@+mYFpezJ^4Tx1}l*-XN(&AknS$K_g+BhQ8);Q!e?h{Br_$$`AvYflg-$TX*N zX|4ECT8xxj8jMwxwm?PzsL{}HmBKvlAIo84@(&W-v$N6W-@jsK!hzJp2*|u?wIsE- z3xpB#2DR(Fr=&EV^3RUO%Y>WO`Tw8xb93VYh0a?%OEjd;2LV)o6v9FB?Ok|xM;`x! z>HqlUR5Ubt^R<@ux6^G~v&QMbe@6!=ytBjkxXScDfiN{9lu!HblwFzx^|DTUL3?{U zL=+D&aNui5sqN2$9arJE9*H#}#H$6D%IA5aLEh%k6e(kAYjcqVTTS!SA$G->z9dUj zW+#CI&cVgl+lK{z>Hf)&TO9KxJBFeGvp4tJ*htm&QU{)w6qCp0!Hq`?oHFEnPUB9G zDUtmyRye)Sd<)|CqZ7wu?JoDV==u{UEy0!7mu=zCfu@D}PR8Rauk|ehE&iDvO@?ok zUAmd?U9HenPMlYg)vHW*ozH|#nbnyPulsJI$7`kI0|4fc^3bFFF^2Mw#EIbcyy^ZL{^BEl^P3?iHQlE zWy}RA8N5gUpnq}I`g~)c`M$0{sO*1!P&{}!FW8E$v>i8}OCL3#54}EqQVf$dlna^| zi;fBDq{QqIcd zlj3O}!E{u*$<|<@7V(422kpB%-4uG%W%t}yB@*6(J`+^r>qqP9ouDB#%P#eLSWnt} z0&{YVMVV{`X&?DM-zBAt-y_1y!xK}k>XWW_@2fmn@r4WTZ`G(=H<$eVke_1}Y@7A( z_9pti%&Cl*Yz;7nbF+qqg;nnVmYWOE%>VRe;$PVLeY=y@?YI_ki>-%F*q&bQ#HZ?L z$Gt0!{{-qP<`2D17-(r}f3+RyWes#D38v(KRmmO&xp72bX-m^G`WadrpqVPtO#1kd zkFj5hPjQl_`T$-Y-!G!AO%fU!8WSI1Wj;+0j6xY@Wn7TW4GBSJvs&o6z1RmwPd@-S zmOaX0zJa$(&Kaq>1pBa(D0dv)=)y;|;|5^)|M$nST25zj34rZO_ z+Z4{V=5_^wu=KV+Ew(CFeO4f6;01}pe+RofqZu4cgxqOJePs28Z8uQ z;I0G_<0?O#A}l{nqXU!N=+F8(eEdd+yu`wzAo-F$UJQssg9TMkTKfDN2EfxsLrfnQ zi&5%rA)(Lk_}yDFOqe#lY-<)Lnx}O3`@;Muv-!;$=A8-G)Br01oUiwlrRuQAzP-3p z-I+J?^*-gaK3FLe>3tk?t@V;MAAcU55SGCEdf&_t`(- zn4RaH6O6C)Q)Nk*E-E7&HjMswd?(8lC!=}nOXsX$r5O%NT7N*9#iXvN-WI)$@BaFf zkgO8ze?g67wQwM_ZPm|eqie;&y?=XsyNr6?s(rMvG;Ll_MHw{a|I+hf!OH1yP6V9c z1)83>V+L3T2lI8PdG=>OPHc0e9);SQvy~C&#o z$~X5f$pc4>J3!WGWY>Y^+rhSRJl9B3?83DQ9ySruuo4~H9QUZRz~!`NgB>|YR{?DB z`Q(N^_Y+nx0&Bu}iNBj$OY-NB418J${hDC*5dsa?+uPfeS(BTK3&fY6ZR(U1Nh2ep zFFaBd3Nx*Wk;Kb5Zv-q!2;uLgg|)f)iNc4w!JdP8tjWbK`Bx?|QH1^f(R7wkRjyyO zM@3TUknS$&21)6b?rzw0a}<#7ZYco~>5`IeH{IP0n@+h8|9kHk9zSt(_|~)5Tyy@G zP*ZWT{0ZAu{SH&f2|>@sB@lFCbQzw%dcc=PK!}wAr&|JvW$T5o&=s)z?}fY%wFN{$jx77yZ{|J*+fl{& zUS}>$lJe>@$~a2x>i~UW0!{)Ivr-boA^me7RB>%OSvRGp^Zx$M+dK7jIMTnW1*ybY z8F3#{;4A|f*!+n-dREq6Yc4PB#j&0dw%5JSxxw|| zKf)ovtmNqR%TYSvD+TyF0Rx1djqT%+r?Y$&PWn66KvSaa_@->V^Kccbh&XmHkX`_K z1DGd{9Qb8B1q;kv+{DLF)qlN{BG*vPItu3=>pVK|T{EuQ26H$90%YE|(NR(4Sm9s{ zjCkorLQJeb>UJ4#`F@#{I9zs;>Ah5`wwC24nVw){W2cN^K3An^&lHk(9J-!SI-Ew>a*+Ye9KNq zpc|4zq$INwd%|Nvb{RsHqd|%OY*s{|dAW5=Vb3GxyxL-R*0gJmOKS9=awbcQ{>;ez z+gD(gWf}0K#EGoSKcwIjq5d|BHNphq!px-A3oc0~czQbvm?%tf!X+Qp7DBSFzrD}B z8?0mco(wOf4X>F)I&#u(&>w3!-@|6O(nSW%Hmo#Z42DNhQx6rZ&0KMWIBO+`CYTE9 zz-q7=wCUsT!jWtU`+9oDrl-FyITizr3bboFh=@j$B7t+xNh;_s%Qc5iPfw#-@cPU| zE@#q>^Qb1njYQg4$Y`O!fa+Y6qFE*d0`gphR>U@>;i{YBzm|l^CvU|yt!MmLlC?bD zYW|6i2-SKpWb@#2RE5#kCE)}mEGR*@efx@F=0)knKZgHPK_7`y$3}v0T!R-oT!Uun z?t0$Ijt)BeJYZt}5XJao?+!6lVh-{^cL_&|)-M@iQ^d%*mYsm(N9kxAJ*lL?>swE- zj*}-gC)#*V5RG{r=TaJm?P|u)&YLphad8^Sdj+w#s(uNi~72w2O6n!(NGXGp50rjVjbZjJ~fo4Pg>*h zW+ibSKvjw-U?bRCacPsimu%e5u!3YE?@#)#nd%~RQc2Xn_Th`Jf?_jntG=Y7;#)AB^6%bsry^_mnIUcD$(cX3XjZ%pKs&e zz8%(Pe#dO_eZN zfgC-W`Gb=a8$c{*)S;oA46yfqTay2&`M26)C{C`3OEdkm$uGX%?yg*0gTrdlJtKz-S_ixn^@3kM!Fl&Pyd?l;;3M3S=-k!F4LQ50d%@zLD*_ zVGquos^Y>(+Y%+HIuhAKvr#K2eQVB=!#)TvUjSD+$kl=UAEHa(PerO}De2n7PW!L5IrgK7L)RW= zOjlXmmy!o(BRZ-d*1$JXZA~-U4eNQ`viv*MtCjgdCv;o4LYOr=wce9+_jL9A78(9! zt)4*=3kL>LBf6|PV|s8iRBt%rE%)M`tuPU*0jU_HOKI9xDMCsy;mwulD2~hw@9+Y8fgw%DBEv%>JdNmw`M;~!UNH&m}cauo?H?M#5%yKHNLz| zYJ&HEswz`CSRnK3_X38M3XJU^DHkih^1!77h_GPET<8vJq7_KY24_!c9FT=aOMEm{ zS6ks?lv(MIq~aBmkf;#8x!Hsc+PlYpOPt_c?>HegU0D{UZX#Y(rP%r>@LEssqfIsP z{euEkRLN!*URjZ^7d&R1y~_LD(>P(JpoXsXZk%#))?}5m{H=mVyyNA`PDMHkq`Nj% z#+RSR2n>)oHzps}qlg3k^@kIbb3o z)?$~-^4K=a+mNjy*Xc7$O>w@(=6)zr8YaV%vY)FC0c{Ke(SO@=QK%+Mw0My^wbqzG zYB^kK&%L_x0MiHB%vDET>fXb%|5)5OkLsAcmD;ynH-jvpW%4hDl^SE@^#cxzIb&Kvc{tOs+>ET z%4vbuMOVx(O{HL}+Szp$2r4(C6K?FHg#1r3yJQ3wqatEuGy9Y*Y15at6oyPY$-hZmu!Cu{Caq~2)7dp~Y1F*` z489_(SBobdsnSUl<|vk10y4g<_|MU|#Q3`=13CnYR~2||IhlW)G;v2Qh_+gxwxhH~ z6DE*x6ZbI_h?@JKFaBI-i`*rJCN2h5{eKpG@0+Z% zZ*LDTS(|pYa#xGMj4aDFt1&A*IH2izN4CaqQ~z6LOjedlW^Q3)v$)tY!Cd9+YFUKz zPFn^!*TX5fGWQYfcc`@N*ARjy0L8rn*&w2W#s6OPmMrt)wc5*uT*>Ep}#EpRjtPPao^#Bf+EEgh4DF&k4si_U* zj@L#>R(tXFSM~!wfw{sD%`=D%?NfhR$!cBpP_TFK>Bb0>p44TLSLmam99(V)aq@qT zg8Y?8W|Hy0&e##TVST{xg9}qtj>D^M(k7*o{-rTKda$YWWV=~pC)ad0%a!wMf3uO9 zWWa~!se}trHjqg^p{}td;Emi`7Gb3?y}~qP3seR$D9L}uF3Y3b%^TlR5Ux_XV3 z=)%`pp$pgxltlNRtlS*?mu6&_>&~b;L9ljoe&MJ_p;=F`s#vj;QF?rb z63Z@{9?-pq9u0z)V8<_!m#p=8E&&1tZ@_A2!s!v)$7xkm#Ads>?cDvpZw7$EY;SMt zIMF4`SxqJ3P(oogdcZ!WRw`LB6Us6mEG?NgH|wQ)-b>`$MA-$^6a-SbnsPj6(tN|tPSEF>8E4->uO zXcuACiLCYvG6DArjaM!`t-6+e+)SeuO`!CIKy3JM;xj+8uA``6?t0@GV z{#!azftZ`z8Jz4=JNl#mOA{q?{=cV8a}(g0kfm9a5- zXMs1Zb{OG)2RUA(th_uG14DSM=s!N@79jj_mbu4FtHheh4HthEFbF0@f(Mqx=-qnv z<|Q!B0ayVbK!DmH==Xc#0oPw2T?Retcabt6x$RtEtqu@oe>bPwRX=9gOrBo75Y2T* ziuHY<%vY~rmy+_x^_bVU{*q!RP?RL90wZmW^5p{7*(F*A9>r_ zh;go0v4PV{CJ7(g6h}!<3S~9w2tn6!c|t?%O|xxN ztumLcNygBD>T8*FZk(StN55UXJ99i1sKD5JV_QNQj})TtMVyv`Hq8R6JYF?vz4{|? z+Ih@uYO%qCfQi!-OuF)li$m+{>z!YX-Uw`UoUULs;79gY*Xg35qS{znum1V`3ZTVE zT!gIaD;_Q6uRB#)R)S31TuUg<4i63rnwru zUjb(YnvIPcpLYtWlHF6oj*5gdoP&eIb7bnyBTwI1KO%T*z}9f-B=1{E@JW{TphRl2 ztDgrKCFbm+^I!i@i+B6Wl}NtiT;Ef=y$fd7i zrVJsZob*aYDw~jxlI~?GHb+5`JZXRfMHI&>(jXc_!2((tHs)ziB4^Wm%S$+=p;)v2 zt)dB@h(3Jdz}z5F2NIKyJI=G3jy!#*F*WA{VV}-7Qa#2*IS2zPYML7NlhjDt3g4x} zPiqOxKFslCWM*2q87kUi{60+M3h=Y#aXQ*xtDm`2klXtDKGB+;$*`+ND>?C}mdmAR zLk+|eojmItIGF$isNQMiFMvi^xpm!1O{x@&mmvHYvj%zsEelIDxRijO%3CnJpv5o# z+);JdQ%!;x%-#LHjgwQKWesqH_bj*i0+We!@dR+dew31W4Tz3ZoScb`jeIx}=?%xB zr&35CiNoFhvL*ZkZ5SvX05ufwUY3!l;L@Mrc?gbG7~wcuRrwPsOj z5cTkvkwZgYA35|%$$*<7eVURrxYY1qe`CDqc9Yq#>|pzf_i8=&i-y&FX(l+OCzCCh zWyf64Q3bufy9{&Ff#eO~|!2u%VIp+e_mG;1mG>1UW!=)oXApYHfY| zcK`S(Mx{NY0h2QrP(SO3W{x~Q7CK}r(V>LA1jq=(3h@2;4^)D$`E6p%#f7no5GB&T zK8pq(Ci%o08oIqH-}EAt7-)3MH?nAuA6)Ga@l&W1?Ntyy#l7rilv;F=qtznI9CTgy z?e$}Dctp_`u9E+?IJi)Yel7^2KHh8s)DB?bK@&%eLd3DqU_Hi61do(2rW z7Z(n2dOa84i;Ig42c4W0=eOEJB!Btxk=-|XqZi#K6mR@Qezo1%2HZ{LD&4l=8~MdQ zTiif4LPuvVY6e}Z1XxloE-yo#06Qjrz})0aK~7G*G^&Lj1oMqMl+r@7unl%E(XW$E zZjSdyN7U$FgW6tJK|!d5zW2uctG=N*7UsCIz*R5y>$SUf|ItAbtF3V|>hiej1~HnA z=Ds4x9tu_aQe&=`=f$XrJG-&xxvc@O6>hJ(`6R3XT^Dxun3YBJ(V-86penwuJcfJ@ z%{SN#Q*JLpl2TzzZkPxE>Hb7I{}YF4UpyqVrw2CI5l?WhEztPgpi))-Z3OkAO8)jt zF8^+I^x15Wl-Hv;W8*KgFp_ME!}sVlrD9e7REBJ?Moe4!vo{t*;%`ks*#z<}&V3GnfzKZ{PLIQPF5a-aSMPd@%o5+qvHp5r zwN~SyG!TjUWWW?Dl$ykW9BCTj21$~O5QS^^Q-9ZVD;+>AI4n*;+4F7lvG+y96t;Ss zl7cGZSLqO_vmg8d?>a4=p~Tb+qCNH>&cA2&U}H|6p>;lpN2K7S@kvg)G7iolj56eW zW9ldZ!93utJ2OvCmuFPKp|)X=8LvhVd%wJXS~(#kneCkhZeB}OL8evNNXIp`S-PyU zSVD|f_7|h4zTu!9R@1Sln4kZ_a?g|dzC(=vbby+)nqG6nGeSKK3_L5tP^HumsWjqc zx>;8GdpA%|Z>${psZGIAp*x;ZX5KX!y z`axK84Omw)B}>{Lw%Be5lmj}!0LrTNxc%QHTfM`AH1KKcpQ<7189exwx{zB z@xF?DbrEsqb&k$Ts5v zc?w1B49t`%Aun_=o8-tMB`b?e)Jex2`3jx){_z7lTw8_x4Dn%>w$SgDm|cDH-q@RH zlq)-m@i)R(*;}%^Nouz94kihQ@3iz@Q;0{a4q-$}*6L;LCXH8SsFPzDJJ?_?2z+hY z5oN&vvXpGNf{t{Vg`Y58O2rP6BrTS_HG#9*j7dR#m$X4>H2O6G_(r|qSs6f995jqfX63oI1NcPOvUz6rBn~xCS_%4 z2dKby=@8@OyBGcBrn9DEK$m}j!p%G&w{h*n>vEY`_gr@UhU|pMP*Z~JQgCpvvjDfK z=u^9NNzEJ&z{cS+X^sLb3>XK2*VaoYhX8XwRrSMD7>x#iecRgF-aG*=WVv30w2&2O zlxrIrT>tX#45z8g0Q|sj_k;hcgr4-zA5ks)&T;?07eI$jyC3ON=J@O_z&J|{U(O$S zP-7wcv;r8d=z&7lA@V2klJNX1P|@1O8g-K`+n_ut3ij|piXD=uTfYb|QAw6r5a*I~ z!RFW}J>I}F19__}X8x{-;!H+fT?=$_Pl<&h7Wc(6=!Vxp84rVe6iPbwV2Om~ri$tD zuE!PmO-7Zm2S^|Dew@0R1vTxWMKUSWQiS!G2KXznmXVdNeXKa#3MrGLlrg2GHpOfT z*LA_J`^kz+P4Lyg&?C)3sJiL1^1|WaN!`SxAeK@{C^>GV3=h3*xSHvk1p8{GzhONe zbyc9+w3z3qs1e%tK*<(r2-^9@O2=e~jTeGc^!XFx6vYms6eVUmA18V&+n-0Wdh*0D zJ~{~wD`CTM$A zU)oMEp?DW94d#g;N#9{v1Kj+8FO)WMKsLZy+9Tw&F>kXt^@Bv$PBL4PXXK38G(3*?h z<^V&atEwu^7>BUD^BuKJjYE>>!F}gF5oTUYjl+;KA?GC85S{{CQVI$)prLx4xeLcIx5cf*9*^@LSTigvkdBF5bhU_EO+$kVXmofHgNdzTG{g=1zZc&U z(kZ)*!|or!)&-#3sg)*y(G*lTn=2mUuWw1luRPU&hiHYrItlNlcivLq3)$t8Y?~k?gkJa+{aF-6EKqT@qQITY%Oq{Dy0^ zQs<2L4>4U5=eYVUvSM0(l6DXYh-iFZ{BukJE-sX9xGP5&Lv#zG z<2dfRAxrS!t&Ykz=>`$o8&+uOqryOP9*K}dknVX{%a$HDoyy9>SC-DQ9NdZZ@gv?P z+fJNswO*fiztAh=>Dz~a7KO~n101gLpGfs7ct6cVU43U>4I#V{%x%+1@Vb3 z6{9RUF}K8`t6nUF#V`hXy>@vEyLsj1|HAJd9BDgQ1>1i!WJsdhs3OPCP4DUjRZo6& z9h`$wy+3L~Wree|3EkeJtB8hKIa>fFdBFTRHue7fA3#VM0RB|4BS4zB=sa4*icy3e zx7(B*v@cyC7Yd9IKx5a@(MjAsi1oN7l?GKqaa9!{%Hc&xQ$a=UnUzH!-EJU39UlS% z18<%HWQg)$rXm+N{R=s%FhIB2i9hB`>*3|2RmEL7)O2{s_Npv#nj^f=PE@k8+WA79 z7|yT)=J<7BwZQ*EX``6rKM837f+}( z5^MHeCcl@Sru>!o4^IOfj=-hG!9K;HLg~`Z6Lsgj_z7XIh#4|+cl|+6Q-+PRWLieO z^5+3qO8SL~cXxW{$u$*bWYa+ljsC+dkE1sPU)exsaD8(?w*?L*J-F7IhAJFIPL7u8 z?ssdz^6XsCQN>ImUcq{AVofj+dqeEiFT)m@IQq#qXwjofPGZ!FpA}*zShy_tnU#ng zjc$x&4ozIoF)}klX&?D#-Pif(khQ1KdjyE>f!-_96vwlj<0c$V7S4 zmEMZ>bZA4WavLUiH`da(lag>iMjT{)sld?{r@G=t7__|+B0vg=bkAT5fENZ}3-Bfa zmTv%qT`>i5RWM_+T+{WkFaP7k1X$Og5A5NlHkPZF+3LytH^_fR_2V>z*8Ttp7Bo z(yQ30JLmhC-L{*xdhlv!Q<@SdfkNHjaM4Sqn^ZSi9&*)61_~r)H8NH>Jn-vm?O;<{ zC+l}U9;84zx{~$p-ALH<)s^xC_H_pLwq?bfqi&U|;^~vKh;kP?L>p?)`)PVgwy-iX zKu%p395Y=9wY9aQfNbmNcwBhmy<78Zt}d0%gMV()+QMBHr6DILNSslO5sv~-Iq)x# zk^y}mphB}RX1^_sC_+y*r?svsEBgM%DviWgw-J(AFyf%3uo^^6*;)Wd%iy<3GID*FOzidHIH65J+07e2F8k=`#qc}`OSVYvn z%dr*(83>Tk<%T|A_1F3&%!xCMvaLlN>!VGHQ|qH=2AVd?5rjlU!JkAM^g*1}>A#<9?$kfvS{)OeX-ea7Bz z80QN$98G7lQY|VI0~O0Z(H#R%{rTSU_gHV*H`b=PZ_)Dj`TTbx{As9ih^XqQn`j#V z@SX$z2bUU}n&V?*><-8l6psqUU2Z2@jL8Yv*@_^&4ccIES_Uwvw|(Tw3%~?F{N^p- z2@s>?5$=ndP*ANxeH}o?1#E?BZOvY4_5^umz~e7yZzlt+GY}60s7wIkd7e!8`8`nQ zMRmQ%L)Eg1GmjYv%LjGF3Qt1+5rmv*Q%l9w{z03 zOvIH(8Nho5n&_eLnVK%PZ5ZJoOOP3vTk^`-YqBrfiF*{={w{o~-=+bjbTAm1q!bto zB74O3^z?4N%5X1sgoFr)@U0f|$9({rn0zWL4baoT!~;C+av(SX+SiT^f`iR$nV4Hy zfqyMN48F~ww=ntUZJO6!mL`;ed<}8s<#>)>j)(<0r8NS1j>Z7Auc$ae?XZfWy6EGB zCMv+4$?JLLyu>+y{ZHXq{aN_nzkS%x@)NNmHNWgiu7!8%{Z3V^qICo?alo5naxh&E zlByDU87F*WP&TZIM8UefMj(3+S9-7g=DGghDo+M;zE(H+ zzFAwp*lm`YYz*;gTwP-nS!SbGBsmn8D^de~VV!DoWZ;88oUNjw{GG$JtrwUb9xV|^%Bk$5bp=cQgc5}^%!Z2wH(DS*N&l6A!FaV*Ryns@q_!P*H42dIvR2^Lc|$+ z(W!}W_!dQ^{mmG5z#aVHoGYdx9-%rEHo@2*ga6xxthq2uJDNGG*Uwj(4VX9B7o_>m zJ9Lk|Dk41vX=?d!Hd%DQvH?=yB?Vrb(-0#{JXomHRzZU7%@~s}#K&)K1OE>E!^FY+ z10r}J0JrFPozx8(h|ri$&h)xSd?iGtp@U*l|JJ)qKKkM8r%-){!Q!pbLG3RN@~o%y z^mI%cxYbhOYAYG)4k zNMIIJV?W0+I5ecw;>8Y{<#%tsGPI`|Nk(6t_j-xHEk&W(56(D7SNhw%1%s{jiO<*D zE%tC57O#F8$60Qy&uWG;eW*x+ZL@YvJB+SL(_j}QM>cNHYKS;ipXx-0C}lF~Q)SOi za+l4m|wzHU;JW%cdA_5 zzZ^PKk?lz;BH*Lo%^|h=FSNnU73zU*)O@erc4^Gr?qx;>bviJ2x<*YqO0C z)U=#RBi=)a-fiC_-M6F#LZvYy|B$`&jge$tul?c$>g5ez=pis=MXNgfH{*Hr-ah{9 zk<-k}Q4jxM#qRsW{_{`iB4$zVQpQ5S+o4GzR*_`t?$aMbhK(Q-izxsx`gO3D_C{<| zM6xk26E0{{9>k1D9wq3z13V6A#pH8i;mf1GwCG2qGI&que=PS73*?SnzqsS)XG;Of z9o%ddso}hSv|eWC-@hgzi=yvh8k5!7BNOYG6~}5OOUx8)IG~OP`IN#DPAs`X%P++f zpBdApReXoWSY)C+=Zwi=x99ssi8yiFK4VXoF(`F^|Gdg|`xY0`be?$|Dx?KwV1Th5 zCYgUK^7Z#;E%WZvP23{=N2uh^1byMdW6w3M6=TGJd{wyCr471k}Ok%6wNo;zoT| zX1t+VM**P9eJnv0E7_PiAi+~>-f;JM+Bt~ya@9hAx(+~9t3s+%cu7gQpPT0{-5PL) z#eH~4Od+Z|^!*BxM_5B5UKM(UMnRNds$vZB=~s+u!SI+5cB_=3j*z*1nO0s9_99ml zBPInBJvuu2;j%5j+|*geN9Em{S%1?rwR{E^mem}R1s$BSH|Xad%U+uV-HTvdd{r;# zm9~7tNFZ-YqoIMMcJ=uiQjgO4dqIKFIUCaLbX!hkdt65mptLr$YC@+qi6Rr-3nh_t zTNza4F63-(wi&^P0t^)a4~ji=1oP^73=4{|oiCwfH_PgELV=36S)yj`D^Tvfj1SCP zpJR37<$E3;h8HD%fenXX3DxuR4nU*@vG>mVMm`aIlIlUVhvU z_UTGIq#)QwhIknJ(wI+QSl(ZR3|P7`2^+wZ!aR{0AOBgm{QK|UJ{%&2>5J5jWCR@wHW9TLXL_nklEp=&9emm8&&HCkPK1-?)5Nkf4I7-*PR>9myG8$fg^t&D@JqzTQ?h z^n5M#pX^Bj1?(JOz5r(^P}oL_&DYNEydP?KaMajEQGO39gkrEOO(LM_bQksg@ZSF6 z^6Q_~+Zmyz?8_CMPJ)nd*O*yx-Zxfwv|#_x9VX!QGGWvZ^D@X z1hd|8Ne;w=fIACS+WI4?cp)mm!6cTEY?R`{lUa5pDOhp~F;P*$(N&i|!r+Jiryg6& z#<7fB4RZN`GpZy)VzxP}p2_;Hg8}d~gsaMrDRTzR3knTJxOE_~%F#%0Gj6AtaojA! zB5yk6eMS;pioWf+kBNVHqa9ePllca2eyVn&7=)vLIXKl-9Mvq+i*+{_lWnJ#QS!JCPu ziGvHJUa@{FL#V`EZ5zkBWi$ zi1Hl-do03oe7&}?$5xh|p5N1(1FD(X;R@`WE0-C%xT- z1HBZH(xAYu$3{CHz*PezXmAgTi>s?_ROH40)6nzVwD{1ebnND%olm&Hih*S8d0)vV zq)kG~jK-^Jwd=W7JjL+fSk=5&!(aJih0O|`_1xO2II=K;B8iG{TO}-7AYseugJ+8) zT}DB{9I!vvc6}Jv9&ZCY!Tzx*bJSSr5+haOzXoju1!hZy;5h+l*tMg*>-#Pa|K|#& zZk}L;h0Q$LHIHQxFF~a_G}w$aF^tw=6ev|Qq`AFLJjYg(L}_X?rB+@zfpA=6{=nN2 zrP_gm5F{tvlTZHxZ5D$ri&V5LsFf={B*R+CmSamkB&^^h z2+GIsDOS43TuK@T)UjJ@(BTK4X`1=XrbBv-Uzx?d^l*rxy)v)uCGTp0I;Y@tgWuS|33()t}@xr|+iw+T=kgGqL_N zW=36gH`UUo#xu^}vcH*?jhg~O?!S0eTx-E?9@I?w=bXgx1aUU|}Xghd}t>TMF zqSe02v6_^sesneBx^{HT^R-UmM8$^PJa|0<1w_;__ZzgiVyPdplh_q_1?oEQNH5LYeL7L}uqm)Ui!o5iu~6x

In| ziLp`%a)*N}Xk9mo{Lki`{AqUl@0ABppkXVQcn4$fUg0YX4uaROUq=D9Bap?xA|zg3 zUU5zLwz~~Zl1zR%a5q5-#*g_!gV8{xs4z}g2ZSTQj1KJa02^$9D;KQCtYp8^XD3_u zEwScYABxRobM3ml4zHO?tvtT|*8TLWn+F{wjyGDY&zK@CE6zGdpORQBNKVe^R2%W- zi+O}Y$ujDjhZkR^ANA9==s1@@Ed3WYQvT)Nv7Mq}JvvG|RXhb&xby!kPA0b>K$ord zvf^leXY=8Ow6s4a6(?NPK15EY<0y+%ter=b%1n9YeG{_nrW$g)udgTwC2(v^y8bq~ zy*k6uMIaVBHDS_$5&q)y7*-PEN-fcp8IT=KNW>^rCqjaUchCNA$pxbo;vjjtUKXkM z6u8mVd^4nokVV=ahv0eNc|u{Haa?=zCHA%|*5{OqEUnQOu0?xdAd)+KqCH&rE#^}) ze4Vca7A8mdyKtnJ!jJS@Up#;I?G!`Tq^PeTejdbCx=kNqGFFxAK73PUBtS@J7WWth z9BD8oJ==;;2(e;QTo2CTbM$pU-w~-yUJ3MNFUFIcFC>Y;sncZv8S9IyD==AA1opzn zFQu2a`!}SDx1>E{>{{ZFLR1={O3#BFxHNUzl!34+STgpr=N>%-gPldPZ+R{2?D71b z<>F1#ZTm>M(1k3~^SA+MrO38onLJ!(0@>0|^j|I!jg7m7_aLruaT55@|LovXhkAg_ z18B%x?!eV1fJqW#`X&!MwCIR9-RdY7-K#_*Q)InyzB{e3Z^%ZI`o$ zJPQ%8IaD(GU9NWT$qJsdmblFlMqaP=rbYwuS{1`#5aXfiPiZAEG-ppATXZuO*b!Ch zVqrU3OJ|DM@v&~&D`eKaBYA*jOXIyJu~;3&l(+Wa77;P5+?SQcmj`3 zCGO|O_KxS-qm`#KXrBUH=Cx70Xx+h>P!g1m;Dd`v*vy!0YZ!d<>EujUAk+s5Wa9aw z+lTA;0ZQ&G&C>tNgaO<`w`tWkzzPRGt(F#{L(X^j_}5K|Hr7x02^VNu;_lvlNo*18 zJ!SHIb>ula_3n@>|4(4=m-Llh%NmlqX^je!!G}O3&+X%4m?J&`e-LI724;Cm!6zBj zeroju2FVC<;CgZv@1zgB(?nDB_s|s8ANch*eWaEwB2BH}laT8tg4ACMTe*4*I``Jx zM;$+V>}3>z?-&SQKt%)alS80k7_Q`|OP731PH4;5?nf7KiwAvXD8Qwa0qr1G0Pno>WCrLTbrY!^*JP#wD(P8%Hu!w|w!o zG$_@!IX>(=UTY|O@jJNG8R%WZekY0=$O9~6lEHKdGIsgi*mj(#v8EPipk%ZybyU5} zR3|qr)$Xo%T9P5h_|~;gDa|6ml4|Vk6&JQTXr|`!;ua+YxGpaL_{FTx;Lgd zIo!)uPXY>;PQ5(=pxV7bLmM9*eP5g~r6s)Oo1|j(pNhX-n;kfDMyIBF+WhZJe*Ho- zGM6y$0w1`fu#_63GF`a!|LW69z(6(c|U%t0z_p^0^1xP=C_zx^P(M(E(EOGG5uUkAEP6=~TmIa9cNkEj& z+~t1G+um_^Cxfnmb3MbC4OHQlTZ75wz4SLWRcVWSxV)n6iE7v6f&bqN(B8V@rc(5M zE=f;`MP@bGPAiVIZ2Rkuhx19X@J*WDNswGor#T1M-vp>JcG^zf-}&T9o~!D%-5UN< zagv7rZIkbEtNL0iTn6hfxArjiF$IDYUW=^_20WJ0QX$QP4=S9d^Ofk9rZW7{g}$w)lA@(`V2(Ab|BVFya*goE2o1);4XawZ}x z!JQ^*?Za-i{}He%O+TAxX6N|3gX+*M@54j#b&J$wrEJ&(ZW~^0U(BzO@U3mTS@i4K)>v~HkxK#DF_yL zRig1D3^O?#_?MseD}qS`m?Au5CaP3rCZqsN%WY2$e88U-BOrGJ+pRbLF}l`TSo*4S zc^=wpD$G_j@*gUic{MeOV0`maq6?_SFht`i(^r8{N@zWird}59nJ2AteL+*20zV6O zgCx^2i?{P#sGJUWO^jl{xi4^X$wi2Lp4v;F6caMMS>C_EC{Lik3%{9~Ct6-0-v0rMRZjP`*sKL^0*&ICLa?VXWQ-XZ6sUfc;b* z0(weSnI3z)n!NZ(y7)GBxq+=n3m`yjJ7wzaxMfM)NQ=E}@T1BsH!wVYa^C;Du=gIq z%EiqePuG6W%~9}L9p>p-OJaVIzLr%a``o&NPy9cXJ(QWs9TdR1lhQ$MH%NxQZsJ&8 zUK;vs6MVUoP@fD8mL8U*qJknRe<^rZ0*slQPSOp!McRgVHEIljiuk-?YrYP(!Pli= zNi}LsUaW+O*O3~m=m1M^{+))3D)>BnMkF=f8!uuuYRlS)5sU#Fg@HxJVVmB9<+OqA&d3!34M{a9*j`P)GILCLSsptD zKvk$kk{O}Iu$808V#cs!9L*)Endg68+XUaqNl$71x@Lo0Upqqqt`l=)wedN>AzA1s z@r&1|38@0A+%OWtAlIX;JVYz3DRmN-2_j^ocVLriJ0Df1QC9p2F#Eh|Y60m>zfr!h zrK6qU`yV$jCUE51Aui0@gQjv2i$-5NVc1Mh;KuqX&G=E@`Emvi(xN)}NJg@HNn#MX zOdeLc|4Wtd^;3%_%^o|Wqj%kBeBPPiOgN?f=#4dZsafe)K$)~npjpxfa4TrGUF=Rw zBDT#j$#x53LnTO2zsyo@u!rPFU6xhLjv}MTiw4-tMA>zyxBgNpE&wZ_z`o2=&vhBj zhMRonZn|cmx6Bv?3ZJyyvjUG4paE!>>*A(pztV1SB$+YvWr3?`|0nPazXB-!4T>2L zK4K670OP#py;(ybWd+D&U~w@T$h?63TcfNsWAJ1Y-WL^oUE_bwIk>{_ED0ZdT9?CA zsR2u;S)wtHx_4K*y89i)KQa(6aE>N9ficR0j^P;Wb3~O-Y;=}zXw2I!t~lDts3?Mr z%#1hQi;lFk7Nh@1-Ab8umW`1&FD*Md5HH&e^v8q?v&$iD7gMhvKN1_RuOJh$1en#q zc+bWvvUoIv(E%wp^k#4nts&Iucjo?OS$+6uJG)|4%%ZI)EF$<7B#%+$Lnf zGEr-$WE&ik;LJEufX&$wG-00ET3#|N#<@`5qHz$~qy%PFaOeRl9Bn%MpTj{@qv@m* zOyv?_X9UBu#B_#5hYFpVug|yii^}C)Rbszm1KC;EAwbpxcaKbwT0~?dfc5J8!FsZM zkDF>38`<1+Jt(S16=hLreAqu-99-Xn;S_i`O={*SFCzO^-g#%TobItIAhRd3T2jVa z>x#ea*uo_hts{>KJURx}_$@H9>9JP9C`vffNM79|C`$%M4sDU2VEHd3HD(z?Y(6C9 zGxS;EhsPjIdnLB9j-x)k-yPa!@rD%NCur^QWB%uu zR?x5uNX~x^6Z*lN0`6O2_sH=t6AyQ^bJ#K(&j}gQ3SSn)2)D|XU;u$;0O7T)PtUA! zYXWfP6_&8)PKATgZ<$*Pi~_dO7$^q zWIN2R(|+~(ejqC4{Nj_8$%}{k8CEkX>EWztqSzuENXx&+jmF~vhlj&UgneC~rmaIt zU4*RJ7E)pol9!OLxP@64ifK+MVN%q4xP|YMGzvdbLFTj*;>D%EkeVe$YzGLo`TXu3)SmRBz9TKVTKE(|t_N6$@c|kq7oYfVfoG6K}eL z{nBsu@$-NGuG_(ywQI1Id#=`ILTpAsL1A+vRum2D8Sr$!LdVVynO?F}E4>J#nGCU) z)7{;j-*N$vcM^(@k}+#A>jv?PTHT|hv60h~4;bnU#4p|m^j&!~nuLz_M`Q@9{jNP`Cw261j;t@GoEdR62pDBcLGhJFceA`N@H8oW;FB zT`^%C%osGiDpG_14mu^a7vvxc`>8A$K3mB2yUXaYSTber0lK8yO|Q*XRvh*aGJjQ4 znGXXnFm>&(56E#UYkB5>k{e^7IXtO3hfEl2pYXv0?-#W`&4ACERfL#qf|wXIESEAo z?b%W=LiGu_L|CAPpJF^BGE*?nxSKOTprPA>281Pe0?xyu@3nTSfL}_Vb>=7AL!OU= z&(DnF3g(T$K*ybZI=kDU{UTsQ5Iyn$9aJFse=H?XL2FM`f!3H&=<10FdO$pUw0LKY zV7XB9d6#UTlJl-A>@tfF1mvSsJD;Le<*?i%kiPu<_3L|yHFFp6BtJeKW_$bThZ-EU zdtUjE@gzv!w|O;KH0#OfS(oH7_&XSlzho&yy$Gppol*0`Ag?V7QKJuL9c7^|WMCN$ zIaN!jHMcVxiZlnV5}ky)lkvm*VgJCzCN0i!nT&X$n2miuk$>9bOWVuoO*{YUU2kG_ z5d-gT$a5b%kf8bo>*`MhnP1hg?cysg0H1XeB$Aj}SjJXXlz|f&xC6Zh(3byt&0*XA z;-#O#KWe)_-gy3R>?7Hh8QJ|ibm5E9ulE|I8ak%P4L?2vu)|zrZ!Lew3_Jl}nT3T# zQcBAB>}=X~P#haGFU^)=AOof8cqwvE0{=|B6iGsU*wQz#a;%&57?*(Gtx>Mnh9VCq zsp;t5w_PW)SRS$H!|JBfCb9=ze{YMv7dQ(^am5eHAuD@RYG}JZoa8#Lhic*L$U=t( zhyMGyt68C%r`5G-ic_xpZIRMHXX|diIY#5Witg9iTRJ33Z0!re^#PnVnV0T&_v)E> zCz;4PMP<`Ao7X4y9hRe~Av=BEeI1O;t}O_vI4~)!VXuPWyZqss{9(lP;k&}x&4GiV z;d?V`&R*$%ry)O@BWHBi}NK zKJ#p~bZ^pRQPDG9FFO(GX7j(~BD z!>{(U_gZVOMM23x0VR@&O~^21FdW8R-}ieNi+FhW$L@;At&pK=i{T%7!`@G{*n~Ej zPfzZ5yk4Ka%J7PgY_14*x~#7{O!SAKCnO};?$59QLt<{XOM3ipN+vjLG46;V1SG|P zzl>X^qM`zBL`&9xLD3IH_dND}LA0(yF_FREqCM*>Lpl7FIY=}cD0XQOpNx->17U^( z*MR#fgg~2FhA*x(_Hyvl|TB(<3Da(YP z%L2Y6mc?LERJ@Q?3t>*{x%kGy>B|l|YuM`HVLOCxT{TuB5PuGllN@6!Qd~lsbo9mY z?;kY_AO9|sO-d+vYV=?gB-@(%MP5RKZZ|4e3%#7UIXgW%7sKn~90@g7RA*izH&JBk>2ycgyl}Ox{?FutDEsJ0ldGw*x%r@57_=%n zC1Y7rLbk>C)Ac3Azw;A8PisT($hKU`XyFB!ITeru2jJmZ^UYKE001F@PAOX1^iHCE zAYBgF`+`MJeS3TCy7{4Rkso`2KU;FmFzCI2%ia_If7k2%BRBd~5p3PqzY~$0@O&q_ zi|sbYxyeKH6E0GhkHoM=at~>|yr-9GxIk*=JLO~_IGl`av=$He%~B;b?=&R=UI)fX8+xHDx$g&%IaD)fU)Y;?V@?`oXLiIaNx8IJluibYN(NDJvCH}4TksRJGgx;Tw zI9cmQnu&G1_WAG#g*iBI0O22C=0FS`mw9O;(McthQn+ZRFS@{QkgqDinzXUxcJzpz z-W|1N0JnT=5F>iB;){Mdp(vXcoPJ=eJ?zF|rj zBATskGVjb^sy5P~#R&StQ&-z136Cja33I%(e(lz9oVU<4?2F>=YAzwOh9t9Bg8r!n zScr)l}U75d0n@_-1ipf%PeU0hKaN1 z($fV|4F|AfO8sWHbixr;glrXx-NsQQ<}hGjrhS)_dP-_LJPa(+{_dWEAuRByc8 z4HYltAlRAQY*aUU5&hD$XXs_CL%6Qn2EEsx>~6!vr8Y2-;NapWrKjfsd$L#)j{{2v zf#YPvXQK$Z?uWe4wB+TcDh^m4e$x0KHKShB8p{TzcU!%Q&dX#T=dw4~r%c2-O>?5G zNe1@zasK`<0EKPMo3>Z0siC27!y@Rm=gNB5)O2+cITCr2lwZaI4?b>iI?P{a{A(;5 zAVGFoceK4RTJ`FF2tA0L!1;0t28+jU>THHy8{2uB`R+sd!6j}OD-R5<6g$a=YU1a;~RHBArhdi=S)Eb+A0f+gW3#J zC((jFROCrq2@GjSVVBm9jy!hKg=j-|t{>YQo&A7)BnJ`hvvT4Z=7@u<$ zz=UvJ#Wi)@6e)WwONQ8bAL{nUL@A`IufHD!*rNie1rS2lO6mO@CP)SygZoAAIyk~T z*Bzw$D{DH?ZhqD*bY(R64C`KsI$uo>A9w5W|B7~{0`9>8Lbka%HGGZen^i{ttqQy7 z1Csj+2P%6)()Dqh&IZwga|l!O-V>tuDT|h#!EdG<_>XT3+SJ@lnw9oSWctNPQ<=jX zUbh4wwQeq5RWYVzJG;1gzZyECy=6BNcG{tQZ9mCcX4%JtrE`~T$eNH=zQK|bOC>ut zH6<$1H_Vr9Ww?OTfz}-`X-a{Cgx$P26!o%s5}6|&vU+uwl$@-vO9yf&6veB-hBwFv zw_W-56sUhe^y$2Oh%ptdRI9jqXF$%*YuqHQJy2VPv1p3SfrPB9k#VK48r`%;JCcjA zw^%V-xlEu!)-Aif`1bsDk+37NyY$Cp_hmhSf18f*dR=GBRIAh^!OtNHfYqB_yAb-5C;#)67377#KeP-hnX@qciR1lW{IJe^W!TU^KG7CUmVc>-R~f` z@)=QK$)2o*GF1Z1qU5iUP_~NAvMk5R%$+N<@k$H&)yb+?OMT&#HRFb<@t84tb!}~> z*Mlc;WZBu?($ z?8?g11m{K8`{<1Tf&5AfO;UKydWv{r0iwn;~ zx8jUD9r)(OTW=`5=`sy%D`Xuc{lxK-Nw{rt-i)zwP0k^ ztv6!IaB=msx)@puCBN>vDxiREDXcbyJ)j+fM`EeHHvbb0NH% zClZ*&yd0>gHGBAN;FZ_m+She+AR-GT5xDD=#~+Zo-%l?ph6CCO9L3BZKMrkdklWeW z)oYce-i;L~yWd*41$EustUus&<&4YK3?V5~7GhOJc-_W#-p_ryI%!&N{`?_kg(z_G zS5ZL8^d2zjeDa^2Efbwy>)CVuQ&`~5cHK+o;~N@AuH@^K%Q*diX-u{>f`!V@h+VbW z0(S}TGrU@UGYL$LJhwXBcsq#?6jfqi`4MD`(Lr8!$Ar-%z8n59v??M&A$e$ugJ8`U z#-5mD%q&{}&5LeP`DQxLaAW+Bl7>d3WWlGM`gpR56pp!=k>4RYWg~l(s^&ZG7SnRs z66OR!ykJ(^R>IowIp9^BO2DbVRFkA*t0Q&U%;!QA;yGm4 z71NQF9b1IT=`y()VeesrzT?Fr48Pdg&QNfkv%Dw%u~qe{Ibx_l&soP6={i2}ZnW^l z9Nl0Ty}z|42R*`i^;|BRbAt_%2VNpWM>SXfl~_mfRt>@YOUoC67|ttO^FPjouea-% z6m?BA?S`Z^Fc20jxqz;9aA=4IQ?Ha-EMNYl&gp3VZ)EEF)jyB#%lrmFM3WXBH~DjQ zpv^rB-D92K>w0kjAq&~_BL1S^7Q|qH^0s|+WCUWNt7cQDxfA5`H_Mh=aT8CTp{J#9 zEqU<{EHAZ0Vx5KWIC?1C(JcSF_;x?c#dPZR?}GG-(B`b256$-WlsYo)mZeJXI*@J) zPyID5y6p#!iph9969z6j&^j`}agqn5*%Ojkc5~eN`eLbEx=F5Tb*5z-9SHWZOm*s& zx8y==yyUTiMYBOQDAj7n3M>9yI1f~JKxR-&Kx*0jPE{V8>)mz@YNhN}VZTsjO#7|T67iReh#bZt z#OuJV-kTL77y_AYzPrTKPvx^&;Ega`c6riXc6BmzMXb#Gl`T`Da0d99NJ>hk@;OBS zB{WbRP80Qle|+Oaw4zE3?qu4<=wfS!+1zOc*Kj-<$L@stVcOlzahvu=>@sl&uEcRl znVG`W8-%BUo!*_RU!l+dpg?PGG*_nec5|aGU=DJ>?4~&Ur@*A^JpD9zU?|114pXRt zTrr&)ym^XrdM1}M#kqr7>lNdQ{En^=bye82y2(zRy4X76qP&I$Dai(ZjeMJNahLot z?;EUL8G=GfL+}9J1I~7yHfQ;DXbnNdgBBSbSeZ9ql>m}%KzVYV_noO!TfOR4Bv)7q zkd|OuqLz6+7@$BPTA*x#`%aBGQ^t)9fExu}m_8;mtH-1s;w`*&o}h6DU2-s}pR(n~Af9nmV5&ogZ7)X=&E{ zeO$wX7}CWk*v69zt0>n-}9fY2#IeKL*3zvfu~sGf#E5k_V{lzT<&z} zZwK5DljQHC5xkD8EFh>X@en36N3=}m1D z14a*9Iy#fh0v?1B6iJs5p{rOrJ=e0X>z)ml;LjiR5`1UJpYevH)OCIa!$XST7pM66 z1e8~A1F&t52oH-udQMBofMIS$1wRwkJWb}I>CGXQsUY<^)Rr#K{Y|i6md)PT4N_ny zWIVXo0XDrC+`Fz-Z%wkLwIpLhHzjAtG!afZ@lMwd2lixxa?!*p&NSt{t_J*fdW8%G zJLyF92H;LuG?-rONzw&+J8W6bX#s2d60ug)2?jiPstNx368aLD*|dEiNbXzc)64jx z$B-Ln73SNV>NT@SqSc4iV=|4RR1}&Lgq-t?{j1x(vch%oG+$@d?#YG^NB6rt6hbYn z)JdKX|D4KxIhB3oGA*dyrKvL>rRuc5=`F0@v6;13ntNSe+=%*ug_o}6Kj$w&WZD*b zt6h?8TM6HuTii_*khYeTpaSJBIM2cPv-f_Rkdooyt~|X>0G;@HOW?ihNG5}}($_Of z?Hb6+Hzx;uHskC5$et51EcbhcG~}#h3bh_ zCdP(_{>ndaPLu_Lv>|)6J`LX1=e{0V2{s*xnI*a-s#xZ~Ea15~xf&o= zlFj%>1!;UofxVZ<&c!d1aEytjpBfadcfFvUhK^@bjy|y&4dSA$Y^w?IeVIF9zJH5^ z7>?q_PBL3NI}Ic6j+tF&*Qh~Bl#UEiGIG<_q`As4Uhgl?sE9|ZYNfVot!6Ca>Zqm~ z9{)Byt^GVu-glr#_Kn4@7i@!nTtjb_OLw|!`Jc7l6bf6LXW)i@)##U^NmKatXf}Y) zsoBEdaG21!W6Q7UB|FUOCd~iU0tD&a_nR!;9rL`une41qQky!UdPAVz+J}MQ4S+ZB zst+vRfBo^9y};dqkKtv%VPQVSKAlJN?&zeWv$w=57g1{f;YG z4t^u$GO96lA%bqV2vqf#$rdds$$Z#A;=Mp(2)|GZa}{lo=>LZN z?;e-;ajM%;ea}0P=(+RZE$7DyKLbV#$-D>I2Z6ekZsHJDyyT(Vre+=f^{eL`j_LSX zD_y>ydWs~l#0OVHwK@obWAC!}I*U~0Ov5(a`wOYj zr+j*`Oy9L`>!b$+K^|ESYnFlw>EO7;bV zyk-3si|m4efNRTdU1IYu%jP``j;{^0Ifmx)A{WEwNvn0LHGeQu)S9bxusEp*JLArY zG^Nr`LKdTs0A!V+iAD)?#F1MY*9T7|)qk zgDx?jg2Vj$>Ixv@u?}I*3gpQ+jU(i9a_qyuFKqI*a7>I%zWgx`?oo6mv#AC$-tai0 zKY0(+h2sxqO2zaHKa~YN!U-0W4S8$IGj}e#Lvg_}>HOHo+^VWK86&;Fem#-Nx0{DZ zbdxI0@S|$cXNT7~^DO40bTejd1|RSa|4ysF@su-6n;$HlAB>Kl(=eIQ0nr_RP$wo{ zu8_Nw^wwlZYffsou2yBeqVynBOF12q+PNjQ*%u8bZ2&ezc=ZCl(l5zWUnZT2b2W9k z&6!ZwVM^y5d)~S1LUOO{MG7lS<2_XQREQH)gkXNWJHcNkN@Gfzn{yYsa;Ss~1%G3m z=%V$o7%NG+kP()S9EJ^r$Z`ESVL-IaMSdapIUf_?Jv@rXVg!;dhYeeQD52CfiDMHJ zakx*zVfR3~W{N_nJu5Zf#9+>uvWCtJY;R=p2j)pA&z$*4CZw3Q!awAD!%Gbl2mD2| zXD@B6oYs%sE5Pxi-{jg_xjqP$-A>v7CK+!N;{iYj02a=L+8+=JCROvg0NRvLOMwL*9nB>1BGU{!KOQ{S`H6&VA*R|KyaH63O zJ|kc+Dt#PeVycmt<8uoL}Q z4?em%erXROuxXaUhV(YVDPAkard?b!ME}?8n4Gw?Fito#x$t{y@@Lr%Hz`XMWJ6aeL zqSJzT$y;UBKhR!pyHG>3ocooqL_?%xyne^cF54>P#&l%U2OLM4nNNX)8MH4y07To; z;_=1K|KPnD6~Z(K`=Q5C%z1id6GQ{;=fVYQb`a7aoGq`SqQdQTsMU^R<8Oa`nCI>! zZ{4qI$qfb{fK0s!7OoUk0mtwlvGAN-@In2K{gHYJ>xs@Opn@ zfssx1y^j_nZZun>iMm){F>32WqDmLg9jwP2y2dov0 zcU&)7Ywb1;iMN;Lh!EvWzg;!{OFP@|AXgi*+1S1pLVa#s3{f7kDk8XpV%8Ck>fkCYv$6%ZM`9Ap-u*b*LdQA$#5>n=VESd|& zlOR0gIsVYBR)ZQQ|Ad4huH+^R73tk6}-elCLuE zH>jo=9tMpBz5r`2;JvhtYUTX=+qZ8#mz6B6WD~-q?Wd=#(`C9__cv#$ybd9Bs-+mO z5dov|7-0+m)Un0I@bR6luguu~aUq4=0I%`x7K?j&wi6dnl~(8wLScO#2Y;BF2Urwv%kNgm~pGr9{z<% zRjB2ywqC7|R6($?5Q$9LbP9Yj8Ro`|kuvj{!o-1`5)b!no8mjRcU_y|;(iG{`MhZ| z^RzU}az20G)eO;C`8|ij?zE6gunu#me3Z*nNavhcDR!P1QInLEl=VMoUYHKfO$L2d z5jqvYA}r`j&p#Kz3gOxuAc3bGaheScW{++s6b=vsmjuaYk5X5`5*f8SKzj1nm=J5y zLM`=xjA>GeManE{DjjMndgecJYMMnHKVA+F5etiwQ#TOHKqYHgoO}vr@=D@r*^$;f zyt2!jM_d_?q zvsxR=l?2{uxWziv+8bZ)Ia3%U>t^&THsDRkulOWztwcg>@#}#x2uLpj&X%um{AZ+Z zT3<)eb=Qp&T!Ck4eS2<_?y{N0aFucsSFasw15i+5ol7xjU%*Bcpsn2072U8;NzF;c=01bZp=LV5TuI6V^lX2_^;nZX- znLpwj;}ZuAwDojqM9=(GOJ?($G4Ul;70YckRAg=%r!m$c)S|)a8dFN9&xHCbhvK3( zwreJehj=*Sf`vYrWl>{=c5@Q8Q$zidkp(fB@x!ZYYJg95J3!KbeF)xbt~zKl=Ekft z2&MupdZrvw(_;FhQark&Ez|F-RZ3NSH9*88pHaQ5dyzRcF#$5nGw%g}CxYGQo5K9` zwRPeL?h*bosrKl+Mawn{>7LNpV9k9bjtqR$ytSz5y`kjAn*W$XKph~K`HXbagmxer zxjDKz=PPeo6Nrnv6tz}&C{XU$n~C|o%C}|{FJKSsrX?c>!-?9 zrr}$XBQa_cQ|)@Cq*WtN1|S1KQF?fIxQvvxV*i$U9vKm# zpsISM(3X=(2(J0^{G1sO58$k{tV`*A?-mAPh}q53?6!ta%-Sx^_<`BjJS5*-)%6(a zoP{GfaSP3NuQMeyH8uvh+E%V-^TXjbY#6S`aY&)9QiH@H91gVEdo>xsS#NV1|mQye@am*gNDJPuyhe21n6#k{V z1G>3ZIZ{|D6M=N0=9?3`huT|(ACur?lNB5{mP;pA9`RaTutejRN!%sPv^bHdbiiMY z%h$A@Nhu)ziuM#*W0(cRb7&aLSa%hu17sL}Vj=X?&&p>>?K5Z^FZ7S*FCVEAMzAiI zfv&~@-4URC|HV9kw}e)Mt$$L|J5Zg$i#|`{i(trAHV zM%^25*)Oc3)Gdv=7gs=?(~d| zfK>tcuYFB61ySGL`Y1CgT{O1c0ZhW&Rb+2Ep+bfqSifK>@;bMeL8aoO--%4yw*H0mcf-SP7C51JFF#{2Z6)$y^B5KO-n8Sm#5DhzFdV)%7uEnE4KP`qCExA6o;Xb@w`Q0uzNa~ zvpAwFH7&}sc31h`fT{@5XUGm`T(P5}ObI4b&X1UtbRwc(nN|I5PvT&wDPSJH|iCZ3Ak-WeprAfzSH9K+nL_CO?fQwd3-dgS-Y6 zS7i!?Q@JoJ;dKmbSyyYms1~_tkpfn|JjGVpr(wyHXBsJE@d~ZUVFzS0;rR4BH!8Af zSXzGy5CAs;s4&Z>r-DHcGPX=^{P^!8WzexVnLVwZ^rEMNsERcK?C?A?-3Xp>1J*tt z-H^P=OhcJ84bbvFrqwbrQN^T+~!z=(o@ zk2=TyA7(T2FCs&9| zt^s^~F>mSzB=zWg_fCEwA^>O$7@HHOrlx!pYHd-}(q%!~6R>*&<04p~ef|3N*BT8I z3yZ~LyP8;6-As5gXdHzyF>o2Reij$gTd|#`2^ZX8wS7PW4ohpc4WMB*FgG`!N0IEt zsZ%XfnmM=x%1A~P;7$5=^<47>SB%^=Cz=cYdkRRyC@97_mrMw)1qO??m(9>-d0Gos zC7lZ4)TEe)TTSV3^X{ATM3QCW`K-z!Mj(5BZ?@lfl48CAp(D>Rv*DNQwmE`3gCpVx#{H8nM0 z6tmkIqX2=7Q{f@|z5gUjTq2HBl-LSLAgJ0PX=ZrLU=P? zu4}?q8)hD_|3kj}D}Sl?-Pb;alcxF=GYNYXu@cX`KMRI`(J8Ad2>((SV)9FLbB%2Y zsZ8ok78l`&V1YaZL(^2F1w|YV^!NT6DwQ!c2hIF02Q6esga7nYE;WxemX%e}aC{X! zFV~hqdKd8=VHFi}m(BzT)l0MqT9d{j>9C)QuexGLCpn2u>L(__BE8yqev>mOGol_U z=PnlVbGmuYWk{l$WTh$J03jh*(V!`l_-78Txx~y?qbX8K@j1wz(J6bJBKr%(!FSJ? z2rE!XX(+#Rqf1E|86L*z9(1RQ8m|R*^1vhyI#gyh z7i_+d{vq!qh%nl|7OjK|iO3sJV1J2BU{fu9zLM0~qc!F|1npccdeI!05a0P5+u~3G z{#PPbfgX_*ubNiBj~rY#2r#a#Wvsd30&%j&i z+#w-l8vPj(`DEc{_kC`xlcj=d)T>_dwbE*Xe9Mok2>3N9*(y0jT|OU>0Q9nE&Fy@6 zBJjlC)O+OLSHD&Xrmg&Yzj4tGMpy)&2A2%*2bZUC*t>9*po|6BIU(9h@bLED4}(R= zcR(%d=$J4(JnVz8&6cfva|T4BvrSG+kDLXM9b(gBYi9Lgcn@@gAtkA-xVU(HaglN7 zh@7J_0sDpEFSxC|hDOm~siULgW}KQ7Io6B46eCLjNMbpb2|X>>D4BOmrGc(T@vC`d z69k5obSRk%4dMP1$J)c+<5b`O*`G&65pvyuT`fnN>C#2&Fap4K9Bm-{&xtu&vA7++a^l)g-vm}S zxy_7$M7{A22>}FPfqDS=Od!TyIBl1MO^$)h=&>;@Fsy)WHf;WpBsz`w3#XAZzFtt& z0QM!~#NB5k+`D6Mdw+f60NtDqzINobjNNOAHc~YV7D*YZEe*Ye1_>=3?&yHh-~*0o zAabI1Zu+0Q72XrQ_RQ1XR~a1-o{v%5)=|kWvOG@OmiPT@`n%K zG4Cmbp=whSqE+F=Ug~O_=I2%=uUyHFF3L%OZX{DqJes0E-alp$0mNtQw-(ndFh&3M9y3o<>F89?%+PHL!!&TsUxkW`FSnkQzNJcN* z77p*JT>QrqQ!m! zo9y4-Hg2DXOBT$V5Tf`CF%49kv-CnyNcN0LMZgishMO_`gCz}J{FA0VO!#97JK9dL zFe#{iqeUzF%j!=`naFL_*+X{!%pw#2iELWvE7paet7=q-to_;WnIKfsA7fb$w=HfD zmdRJN+*}Gef6sKC9rwfjejv$&QRa@DYsb)8uO@Ul?Dxoljm^)VVend9P>3G>JADUu zF3+NpFvP%QaB9k=@%sN~dt0w=-UjS8PERjeuQ~wW!Oe{itZjp!uOb)>q$4k?RMT=X z^pSCb?hv9hGdWqa!#yvvfl~Jk>Upvz0yKveYVv7Sz6m5LR`iZrJ zDQ2k5*PjpcV6u+?$?j@*ydnsuidMV)LMAran z%53^|i8#B*L>TZvL6qE|;Ol{^IP%EW79m(RRe?~{WKOjNkH~<9Ox%w0QM^r4>ilx>J2!e90y`c1g`*M`yQh~ubwPXK? z>%!rNAdL`cND)`bCEvfKu z6&PqgySL5&7X8`9g$H<&?jZrSDGiNrgV<+FpBQb+Cx%@;=A3rt+w$Oz;lkFL35L%* zlGZ8F`i~kLfJAUGM{Qa_J*!?ENIe47`q5Q=(0d=tWCVT12}G_vlhnj!%hk zb57!!P{qrDD0m=KgRku$5L8dhT+fBR0ph?gMt|qMcG^Q@+v5w|va{K(eCG!;kN%su z6eT6qCNN>iy${L?rr8&@7#>MkwA}g~P4@T>fu0|LNulpc=FB-G2zfd#_NI3Lt&*AP z4HgxIGw{O^13NP(Y`9E)z)nO-Sy?_<1DC`QT5_cFYfL-5m$C&ETIY`arXjR-{3QOs zoX(844xn-T{Ha&_ML^ZuaDcWtk6K5RDF9HV`zfoxIDG~&S1m^#?&sOWLmL0~t42=e z+y2f!((;c`R9)NO1DB(D%et*FIe~LU#&IX= zG`Fd(p5avku);=o&~Jh(r7ABkHr0{gC!sUa8i17mT^fRY^HM4Q;58|-X{tiipN^Qg zr4j}XIai%1-+Wu>KRBgVg)5<`T1G_La*R1A%)~yL}yZ>t;)L|JNrzQSG$! zeh8JZ55Rcq(ot;APM(~Vt2GRj7x z8GyF|VhE53HOsV!gm$o#(@jzMo&ix`ICi?AdIxkg#X0aBbHs4+Eu(U*;QRs_7TWMH zt>Reu5+IFG!CmQNCbzDqURbLH@raHlX*$-UUKSG}`16sAf6CvT&CC&gjzP9Bl1j6$ zjg_7O9qO0LR4~qf02lS+!tz}T1^0D2bUn5bQG~XA)ibf>rf0tvb$L6FS}OeOh~g&P zaGog?HH_6d{2jKevuYYndS#pOUF$CH!VOMIJ^NJ)`?1_N*uqhOyQ*g6P_f8nkTjIa z8mv_&GU-BrwVshF5Anjb(g_s0c|7Kl4CO@atAgDKPm}{5zRSYl?FQA@|IKNbg6bXh zFg~`wPygw<9i7|dlpNsc`UVDmWgZ7+%?B1%bOoT5DEiDKhTRl^A1sUn?AkUz0I6OL z1eGVrGb2$}_W1xegDX6Mzk&Y7S4ATyXo6TGOZgT1`YY!r$t1o=k?;KbVB`PL*!=6cm8Frre2A7=+D$kN z5dC1^2+W~c&CZyK12u+=vZ4TfgJ#1SFT&5=rdxGEiF};Pz)A-&-wYTMAs|t$1}d!h zO_;a$(`pTi_DX-gB&x+htQ3}#=|=sQ5}X<|>F+c;3Y7IYjQ$6>M!0@oLl>%^^V@J+ZOT|k24DLa{5B-aEq|dN{L8_&(J}w#A zvz`3XlZ270$`J;UMQKBSnpd;twG7#lS|||C6ls(9tVvQYJS+_wnhOhQT;1Hj#G4H= z+kZ}{Nn&IHw+BE@A|fJkd)%^v@HTZ;@083Rh5Ekjzkn*gYU3#!$&HV?$G9I_1!Fyx z=4jKZGVx!jC7saXXh>@fF7x`k=kj+?H>2i32ze9Aj`miPxmitr19UK@L|px00x8MK zKT=b30Pt_dC?6qUI2N}WNy%)iq$Y(3cNoe?o;-*WHy*|n$;P^#xC-1(Ik(Z%hB+bms1n-6^bdh5I2!kdZ%n4v_gvTQ;9pXunjAofb1pt!)+9QA$ydW<7@jF$! z1;`d-V`J6lx|^n~VD%+PHF))B;Sbd5*nhNzb^9858CDo%XR}5$?@Nq*rm23^Uq=v|)8k{Feuj zLmsW6y5S+~!_b!d*%ZEGzcsaU(}XWJ{=W_B^ELfiR=J1bl?es31^{Asc6J7MJUZ6E zs#YYg^A^EjY3Fwm`K~X10uE4jsE4CmE2wuJayzwt@*#wDU|@iro*sCxK%AVMIGkSTP` zIH%Z;Iz&oVmMlbn2_P~et!$0J{}6b^lvh>?d3m+59@eFtYIlSB>H7cJh;{uEnK^0< zeyqJ#X6+B`*jWh4l?`L_c2ZJ_ z0`dc$oLfV;=VV>GUwRQ}PfVuh1Pa=28Y!1->XRcq?^ucNZ*R_B#LEhMq8C4JNQf2C z$cG567a(bxxg4HidtL5F*4I;8Tw8fm0n8Y*R4!*axG&8P_6uwE#w-u^CW)6n4Xu;s2GN<*48C{!SB0TeR(R{rY3K?>x z+s|{?h5q^f5m_xQEzK7tR=^6t|H$LW?f!)Lf#v!0=Ptw`mIgRxyu$Zn%=_Kk{1EO} zVKvbj>J6?!IQ>R6!`4D7g5S_UGy{yF;8!05yJ&!Xh`9}?Qq)R&HO94*Yo&k{k%)Y_ zsu*jptkJM#%>j~Ja>nxVTf4g2E{1+8Bik#h2M72Jy70dqDg8Pg0-Ct@U|pafUO`vv z(xH;wE=`VK#G^sE!5bk<0;r7xc%1*?Ou$SBT(WBs* zpt0)=6@*`v-5ZC!{C5^Ydih7mRG7g}BIr0?cW$7fXN^H59At1sHr7 zi2}afw01BY$<>_=p`X)xk4diJ!vO$GKu?coZUogxS^pMkq}PURNAxE))~9OpI1%qZ z_5;rZQd5w&SQXKtEwm_6qz04$TR(P88fSgGWJLee##RooYk95u<30+U@L`@YHkz2U zzk=UT0ftR@YWcnM%2W7Odis07px3o{sWTK}2r(!Xx5zbXnNI;ja$B-1uq@iC2f93z z5COyAStpOALH(lTAI<9?yTl)1yY+{-ct1|wL`ZHa=uLvjScWtyYtC|o?nvOf4%wno ztA}U8rozj%lMq(kV$dioU_E+C99>{8xzxwNk(#q|W5~qjo^D^sO7_w_>9Z%V1R4c1 zj9`Q}0wa--nv~8Q_+Wiv@@psDG|}#+h^b?6n}f!dThBZ2+!i?z^PZneyT*^=eg_*- z8T0E3Jr?GOsCNQ@v=k)gaf^T3dmAJgacj7SDI#z3ey;zX57&UiSzTCs>M!JpX|pb5D2metajxmI%!m=~>D%9ZvrU2&y;feoAlR zNhhqQYF~L%KRBr4SB>V#?(NV?@_QHtql89$3zq@vJW!X&o}jmE{alMi!3d)i4H^uMnRJ92G{`wwf9lA2qvz=f`S=Lc?+uUq=9jJI<_;d`M0iv z2cDE0M}-S&6eFczEob8ls5cJvi7b_cJPOGsYDSF&YGk8Y2mM7eKD=|ilD*xQ&9}Ov zYJr@qn@&}>^$hc&sZ_M}$T-!+8v{d?l@AUSX#GQ`fIdHQ=BIy|<<~437Df)BDC1)I zFH*6q>OWJP3cHC%yEfnc6y2Or1$n$W3m9Y7>zIG#iB%_<>uZ{s&@QVFjum6i}p#ZT^<+G{M+A5j#QQ` z$PNdsY}v9IBVyFpv;~(mPH#=w@xgNdo^(Rlqvqo2z?8z7q+sIX9hCY?tKX z;r0$Z_Vq||XVu6Q^ybE=HIT+lP`~A<{eYNycM=9j`WHYVTb1KyEtFG|8Q^h8&JuNE zCi&f90Fin}qe*^1`;HbI!fJ?+B+UpIt$m^j@SK=E`M4T?*X_=QX{U%Taosuand zxUWQe>eB#(2;3i=f~nyq*9GTJRluo1=Nai!vc4Q=CFre=n0RU^072FO)VL@&= zmstpBxit@YL9#f&U(M+>hKfse?Rn7%xqsc?^Mh^+91OsR*F%xCxP){_3%ZFGxK0r& zXNTD^X_^ayw?VH9fVqFJj0wN<==!aP51*e@yWbBT5ZV#cKLoSHkkS`BH}J9x2agw~4y^s+w@#XZh^hPq zr}Lv46*#y6HL7-^4;>=V*8&3Ie@HGcdB;)H0yb>NE3y}8sM^gr>9o5*&6|(@Ilglb zjES6_Q!ZLAKh0Gdnz?GGpVY?ANJt^N#n=8}hPX(2r$4bU$z;fmlc&yQh!cACvHDL3 z&*zu<#d!$E-U^(^e)RLf>n-{NZjf4o+2kYQ)Bi)$S3q@{Hg6k%l)%zRBi-GhAR!h2QhsbI)9J#j)m5h;ffVe)#_O zQsmoBqT%fM(^Jw@Z&h%&kdy1nLcB5iNq}mK54aBaHNJcIt`li9^f^DxT9WA+EIT34 zbD6b>t;w~F$0l#TNH$md-_YLe1N8@%pO7c)O=%Hoa#m3bdKe|rlDxG`)gcn-@bK_I zlMF2F2E3aT_gO@Le-iVGgN$;YpA_0#NVFD2b_;Y^K@K?FUS-sND%A-p)e-2+PE(3; zyW2!q<)@t08BK&DifUmFrwW*^?^zQ|tzyJKc!-NXfQA$ElpKkIHb{NgChZw(JQg{Z zBu>&#C4b*PCTY(oV-Lbrx!8$Eg)e-FA2IV%e@AScMf25VY%7Ue`oNIAAE^`QzO{_O zDn^s>kZppBw31!02=8I4^Gvu3ZQJ6}m)LqBDw{cRQ;;bajKvTZXL^nfL@SG7YFxN2 zWFa^;d|#qA-YQ-)W4cAgb_G61P9T`Kv3a6!@$x1>a`VXOXeVfWC7X@@mw}m`nYr+} zo&^P~a~;v|7ToBAEJPep#AT?l!ORVM>^zvvn9;uFQpCusvUBYNj+>Wf`ulo&wci$T zZ!4H043p_TkKu-sAmF)FKJyO$1QH#rpNrbI>^R!lVa3bhsZ=E;d9zZdvg1TBbnJXg z%iggj%X0tTHfg-@CwsL6x3jo@@vpq>h*QbSG3@MQpBMhjbec@+|C4P%z9)E{6C}xZ zOD%i|SFoK|?nx^jKQK|Er~dw~=4V z_9n5cxp@H{EiDq@_i|?BjD_Th1n9D2C@3gEzzsr+U#>~Jr~6Gbi4Wt{@i)$8-7Z&g zo%I`3(x-l8mTeDYW!l@P=jQHG<{6lob(Q)lRL-t_c3eDz?$@y+n*gE5#{YSt%sx*YE(@|Y$U37vr)FlDQN{ZSz9(>wJ=w&Hi$am5#s+{J+(nV6`*nSL zmbJKNX;ODwOn$l|9vfB?Z8>0K_&+7#(AbB)cGk$|+JI?c%(qua43EIUVd3@SUBVsi~=d*`!gtEM9t>A1)gjK7oPg zCENN>>{0$6@mEHH#Ke3RLCH7uXT9FWJ(44%`)}KKvyI#yL=FXdZE;mbH2yAJJWw!x*-gzwp2Zmd&EjO+zG?^b3n zo+It}QKY3+*afSCX>?N3O9(kFaj?!ohJZNjQt$;aG&Fp1I}BmfQTsqbGnx8uu8T|- zS5#h3;92FU5U6PH;O~jTDwz-V%D>G!K@}Vylaayph7eAI4LtJt)W%%UPW;R#@5OQGe(|<0xZ^ z@zKvY7Yt{_2RExjukVfI&IyUdb_B?a%%2@UAN&!z+-PPRx?U+DvO?2&FrdnHzD3S- zUg!L|T(~)7KaE5>Z2qO^J_V|nX28P^1qh%1?=lTWxW5{&ec3Ad_=v^1LY4C$8GOvJ zRKg+8UTQ7Z?K{3~IO`H(sS_5>!R{12`i+Hy^ArQq|7ky#bsMd)oTF9+ENl>-ZIq&> z)GjM~=(YNYkdl(_OxH$!`0yYrD@&X<*jqG%|D~`#gEswrG(Q!rFiiYU$lp?h63W;X z#re&sP1hbIWCwa=8Lq|#VTF>)YAf^*QEH<3snQ3Mv0S3>c@B16#>wVFZq*khdVZ%3 ztn>5pEOX!a{ro;bN`*Of=Rf8f&Os)>np%Q{ zjJZp)(6C-yPr(=R&h{mxn3z8Gfe;DC@W@DF&Vw33wLbk;<-w1))@z%Jf^OvozC-wQ z6`r#EH`}*3Q8%hGG;Ur|rLhO(bh&ojTj=TMo$Ydu8tN=npTv@}Nnr3XG;H33{6V{h zW_tS(H);w(hu@4u?_9%<2Q&ohHotOvg_x^6mi4fX&FPW!N5|~y?p{hLJaE^2(f54u z^xnv+pzvzI(?nTwQo92O6U_L8@1|WmG|`_6!b^1N;J0^mDMQC^_#0oh3wrY`r}_S> z3quNo10x6{?8OJ8H%E^L@2Uv!p;kYOk8j z%>B;GH_a)!`f2xL$jQBD(b^c>c<~9@9@MBjkP;Jv<#YLVJCGDJusy31B6=u~GYg z!d|FS_MzXu@lf8wH|3G!P1-{r_nk9iR5qh|@17g%H?tJAci~Z=6QXy8; zg%=4od}dsfc4_H>N7V)H*}^^dBvTCMKS3%o94{X67b?D~PY~PRUGl3OgjHP*BE!4UZ9Cm@Sk}82usllQ*5tY%3A;L)j!IWW*LRC;}*_d9*^C|Z_!#;%B{x;#>iC>Zka z`l&Y1LY??8irV;@#3D3Wv<6AbN9~8^i6_T6`z&y;ULu>N&qb z*eQJAknMzvi~C6!6QVG|F(@=Pt8ai9Hw^vLy;`Mj4ZlpE{dm?fzz_E6U`&+_^At#D zqEk}jbajn0c6E>Zfo=tzBj5mc+XVP!SSVER(aAC3k(Xi$CMMNa*b9B#O?&Rk$&lnw*rHWHdM5oTMctKFqJB*}9zUpWg#1)g26nqOxAP*ke*oY1xmLJqn z1!;2>leBrC_VZJXO-(b$3#;7Jb#deI`7*v~39L_KJn$j)n|e6jG^JEee{n69}rpR()@B|dl}Kj$=SB?DUgXb&%xA|L_IrM z--k7QZ11PGxBQHfmJG<>0;YjV3V9@ytr>Rgsx7~k4})6r;J`u}^IHXigd~2wT#dH= z8PjUzH*Y|XsOwbkP9yQS>nB~M%%|)=@G7a8-kr4Og=Yl_77&Spsul0IC9U^=FPqrb zf<`LiHObn*mTkk#Z<4Ji*_HE@PfY17)Jk})&LhL`-xwRHFw36*wFeG=M@I+ckzJc; z61*@_JbB-J8mhr?>|t`AIrAXkOE>L)f5r~ZQ-GRCvUolG*Xt`6)+H6+CG{$jG?CA4 z_O~q2a+z;OALd}+>ZtBh2aC7t1`=p;lb z<{YUP!ofHRQ=Ka}MY~IZzfqfkkx7Le_87@Z(vzcxKg#lAE z<1GyT-3$~vT9}(-y>SHqJ2J^>UKb007Q7*VtlQh)A6DR1kM*IDzimF89VZH+nZZR7 z&>$)TCjrW6nFoIcksgAG816Btm0hBK6H-VEc5sM+oB_zrhUM(56kdPhPscXGk*;j2 z`6HF~%Tl6O2OW|rNoJ<^`K2d@#Zx1gn^y-f@ za@3m9vWxvq{Gw3U3$^58ct`90Y&vmrzeQUlu&nu0v9kEhn0U(mjeu*5N{__8vz>wu ziAGT=5a{4Nt9$FRa(Mng-6~Y~<9+Bg4&pvt`&M>)bVDj*4699gqxoSPlUk zjwMm|@@equ+i;b^NVXsA{lYl&@9Wit-&fgs+|Ni&MK}Kufq=ANJzGls#2J#u08NJK zA-zgKh6dK)yw;P?jEu5Bmj+DZ5Kve%``>#a-}O>fHu<;_9vQBCagQ9Qa#Sxd1Z_fI z1F(ApN*8rmHG(Q6*%Q_5no(|E$>pk`qM~+&u~SllZTsPEKz`w`OCX>;p6(YWTucbn zkZrkbKkE^@X$$9@3!2zowbH+Q;yPn5{o~G__rqVqv^CVWY0_WfvUBcXfBe-ps9e-2E!kIf_Sx6qUm_pEgJ?5{R3Uwv z1(-wCyDa#*CZGxh)nOq&WCYj;N`vGf+N&otie4s<-%r{sip^W)=4te%<xRbC#q*}P~_(#915)B>^0sl39w4NOtPCbC6(;`qE+oXghy9JtJbp%kJnq8l$;0S zs5CarE!E}9r&T8KE=Lox-WAn_Z>C=0+CEI52 zE|HtFWB*1d92tyY(T2wiJ3S@6=55&3^SSVUVC!BV@h&x{P5byOa`S>Zm$7Sf2BG$_ z$Wyk6V%}tTLo=a!6E_B8^P0u9Ki6LXt}wA}@AiOXluKcbx@E|=HdNgL5{LU*G*QM4 zU|R?EZK1&Acap4Zdt9wvd1O$?QyE$$G%n`a?_vn%hHX=p&?qi1FaP&g0x@(KX1?OF zDmYcY8SQLL+!hgSTd!U8AZ-9Ka(Z@l07B5FnmzfXBS;p3o3$3Fs<8NP`W82e&IJSa#i^Go9`17pr!e<9v6olXZ7&@%$UyDCiK~IdMQ9KdNe_V|yFJEw0&bE5+ zU57=uzBujFih0JPDtL$5Nb@1%qBvu;9gN`6be!Mx@yuM+y0Gdbb`G9eZlo&>Tc7;q zB$RzqZ*CLAFC3R8)F9K4iMLH$eRO?-s`vV*dyT2@^=`-2S(7|(Vm!>Q5L5o8r3Gl0 zB7MH@tYHdnL1~QZ+C8)Uv!Ve#vsZryq!?L7QsOT}A8{0YH*|h!fjyCy9IQoNx0iD3 z@VVEP_>pCbpYMtRJA&BQ-0zoz{g`(5K0b`dEha$0B4^39u=_AQ@9K|yAvBABIP>5W zWja`M0wGfoBG5EX8(%}$3ce_SB<8oaX25Wciz+E|?vfW1Gi9y!Re|{~6kIBRAEjhu zQ?tf$tcqpHr&XUM;K)rFd?{3wmHm2+sesFwWl&Jx`43SG-uxFp_xf#Fvt;VR@Z7#~ zYFA!BxvjtH%NGMZu45THs?D?ZR)c2{d(S2yAYg3vC}*+dYL)GHtGGM>VN^rkJKl4@ znZQ?$tvT-9`^#yxe=jaCF6u6}il>^~xz^J?Sz*+=+A$Nu8MG)eJtJagc{#Fx6byiI2o)WHqkD5xi*Gcv^9z3*8( zT5$3NDA{gAAEWwI<{8iC{+PFUT$QiX{vT==lAYCR$-}E^*6+^;!Y88eN4LMeatj)f zq6`j}qTFr!GwsJwAZ(GwN$%=?{e#X17L0V%3qQC8b7JoE8h6=`Xid!=%0Bybd)-aB zCn82(J0Z6IJsyxNI=V+G1Kr>dkTBXBfdEZ)f4@Aq7Ejh#5jfP7giGCxe^oKV;&*eR zF+!x|ws(xoesO7=XH(}J;*SVCi=ZNCTdiUt_dW;qya{TrPcvcF3*2ENvAyU|4dWpfdIuO z-LLST%z13H9L_`51PKgxjz^*&{c%i9GXRb6?C!n`PW19>qCZ`8@|-*heRGDwpSVKP zUE%x++bW;AZy5cH)(lu>USE%79qN_4j#FV>U04lrDz_A6>|fU;Y^Mu#PUQWyMii8< zJGi`dnnw8fio&sMza~&g18&OyGRWsjHl=f;>?Pe9X*Jl24V@j4R1IP@HAi8`(|JGM zpEK2Y(?hSt_Mw=2bF_Zzd^a6Shp?Odf4zb{#Twnt$BVSnet;ZF-gpY`JTf>nkb z798(4J9uak@;x|cCS4zr+^wv(QnKyF|Y=9f~vtnJ_;O!pacQQP!qwMUHb%dAo@Cbk$i)tcF%oE{j)q58zg=CgYaf$jcB(l! zZ(!8N*ulKb`zG$ed7S(rfjZsMN|}qO#bX4!>ADn1TUl#SHp~&dxV(g)8|X-}$;ly{ zNQOHu-MX>3Ib8$VO&9qH-2JLr`@Y^Wubc1CB>)x8x89+ts)N$jJXKFwxsrY>@SXvo z?3s@*;=_oxw^*x`$45udt%M4`qsFQl%TCHiOW|4cf!9=t9v4s^2sXzbtxZFN`3@Kj zm7_k~Mt=2JJF85{;&9K6m+DA^^#hz&fWfgEJ6Ah@=Q;Vy$~ZVwnVY63Cem5PmKRX_ zFTqs{NifX#p=bMpg9<7tDr;WhQ`Y#GTG2b`w-*zwN2Tw)L#J&U!vo1Y=nW&%p|(IK z7YG`D9~$*I)7c;Y>zMhg@>rvEe?5zY=3k|9bOv7rk2-Zwu*%_vTFTk+pah|sEN^n38E5+VH@exT*3XIL<^AYT$PanN3~?Y=O8 zls-E#K!=1leMI8q;(|?1uBfX^0#Su>yvYeN)Lwb)_X}UNKi&TZrPWgD2X&T`Kc)8b zvgh3l44k6XtbtYNIF|ll5^u71_$Nc%EswrU^9F0PKAysrca&cRG?3i__v(NvsU$q{ z@Q1XB?%zg{vwQyfgWu~S9nqF1zcT#N1}z=1Ifr@7B^gn!`c>AVNO#gu3*M3JEeEGb zzlgAl6aSeUr|I+?)mGF~O*6xvJzA)`M}XYa<2};5@ulVE4pe^e`GL3WN{~qW{{8z- z2cJ+sibA@{-Ud~9$t;}`T%wk&!XqQ4siKu&7#07rkcZ92#?5#( z;&;Eull$(-sD$-MF+cZxLACgE1eIYRXt{frXXe0=XUhI}ov@x@sb7~aWvr6$Bj;6f z^@Er=@3>zsZC3g{F)8)v^q(->5~WrB?}aK5jAaVN)t2hIFjNK z3Ei+5a&OVd>Z4_lVc$85|N7<2G9D?&5nWlhqe>{dRrx9^3-j?FuNeg~`QC0;rQ^Lc zVJvFC+^zFtHOZMOM)4$nY zAw>G=)Bju7o=-2{_qmXI%3&$c+LRL%%bD21&hp}opYOk2fEMkfY-gHA_?Bo@STR{=_lhdf*eyu?Cq4W^o&hMDy3$vM$6^9cU~LCrXCh6M#+9(4=hhh z9b|bvWn63X0WD)PbZEV2`(*LZ4>JmDBQ`O)rpYan7)9yuSFsM2C6_A2l>)&|y?rjukDdM>z45gl=sr z$jh^Ha$-~2Dr;&6sTWI9MK2t>nOjHz-{K}=qRYX8B!p@vkQ(P zmCrk1K>{lhR>J+$s`yByRI9AF#Y^*?nPl(&o}AdP#%omB&I;}|Y=d!^yUeW=mPb^t zF?<{L$|(ZE*h)7Rn3M7X3v0VPUoj(()3~b6E*OZ=EJu(!_D>y+f4nrw5C5o|+&u6Q zgM$5UTn6D&q)g80h%}zvtD|hP6;7g4ea#;Nh}A6VYg^KYp$pq57uh%F!lLA@mj}p- zJJxg~xB$M%mv}SsH=eqb@vRoGiB3XdK@wjmF_G(GM=Q^P zUIzuB0}i+Th@CwJF9x?VL@idQ1Tlx+3lP^&57xZjHhYuxK}X>g_WpYMM9S2(X#4d1 zto+oF=t@y+|BT1p-}VvCg$1Ao&2>?!*KOy9>u9$~*xF`UCg{Ky}~{>c~$rFm|- zeibfB-9t?1Qpdl#7#XbAH|028Xy9FYY{1GO{zZS|&<&=dq%Kcg1sH8X+ymS4vdjCN@21?%;5Ws);t3p9uOjb516dEuQ!r^Y@=wnMouB~n`Y=Y0|eo& z>^eAb5)lzq|Lgd+$7#tiNfKM4{w(50T zb{<;YYE)n=MgCW!&cruk_Yg?hhVyIEVi(b8%b2c{-SC2_s!C{UKM_=YAa>#T@ZIQN zN6U!lM{JC!heUYL^b5@J$gaLd(jF6FL`5|pbKtN`$lKHu!F103Vij~Z!9C3uEu&Xl zO{lK26YLiM`4Pma=)7{9(z_PKi~lq%iHKZ}$BvAzj2`~28{-alv%sj*VUh_+Le2DJ z=5~iNT@M6V_rqs=m^al#;VRu}X8FoFr{FtqZnaCuR_o@z<|qgNyufkoqw~EaEwAHI z`NQi8vG+l9l`qqqydLM@ZsF|R=(6fF@I7SbT{Q5NjsHEk-vUIL?p0v{mF6eHr#q_(+P}yq+h?r1!@IVKKx_I zsS!INK65$RbG$r6{VN|aFa`jo@*t`%5RwxT@W;#E5FehnI5|;7%ZPveZ!xR9922b9 zi`0t6`};o@V>8`%M*QzCZdH!89-&@W0R|RWLSTxX`930Z^BqXxW83-+a!jyK%u!*0 z;nBK$YZ(?(>=tptS6x)tHI~2ktABE)G*AW6LYHaW#j?{t_14hv!<^r9GWYy=S7trV z8Wmfit`~j+7-W{qBJS$q94HQA5;Z>HtDvveQj5dHef2V7obLanqS z8twWsNp?5wnr~L0=!$47Ca-mMtrspcdRI3^M6N6p>E55KpuTG6ZMpSJ%g}%QW1MGX zBwpjD<$=F^d$^hDVf?&Cl}&6CN@OI^EV@M>wPQ*bCm3b?Jva_UJ-k?4bk(j9n6;jc z>wf=!=Kgm~vBaL$)xu|Qqhal`Jd$vK?{>5|!TKSvpBomg`<-G?5*l%S)-IaM)z;Q> zv9niX=IvAh@{*sO{c&ziuQG>d$3$?1sOK#^N$mQrn`v>IAt_uhpmcx&3c~@U8A_rQ z_QR^#uMbkah!LOH*yNR1@WTiok*hNBWCr~aFd$%UtZbt2W0(a$)ZBFM_#9eRy0E!@ z*=KL4;LETkG8uo*tEq`+Dy#dz=5rY81@T_GuX*8phVzcSn-MYTHPdv>KKl(N&oD~n z4MW;b(TTy#vi`uN*Qsx9)L@ouJb5U5_M8v9FfPNEWu;mYMOtJ{m#{)Hq(kC<+a-sv zDnW)th@nkU0(@+)+mAT9=+E1TdOP_a@@hxRzd@6gZCh1<)k+;58#bAHZM@m3Q%qte zC2W4Zchy~d_Hl2Noj>X5SuOuAO-j{C_MhI){xURpGNkb|zWvj5-^Po~dFCL|xZF)b zSNHR4TeSQ&!c9V#qqW+HT5CV*LOpNBzc)S}X+LdkgmbnYpdx$IP#aEuvy4oDyWw^q zXF?ellC*mAmW~Ksqa4!K3VJ{&$;a-mYC@wiJOMM6=QC3dMn+j?*Da?8lif-dX~l_E zhu|^4w;s`Uin6&epa}tCp)x)0%IYf1mQV_lj{mb;6?!%a3LVf5I2`t3gTw9JwrKF_Xt`2*;$P99@kmR5^ z1`QL{Z?f@t{o!rzmKTZC)efV)*g#XR_*^?+iW3<43DKj>;>TLLfvrbjvU*D7ZLO%k z&|sgIoq6~6@gs!&j{$)QUwm?z->S5*vS6Lb4QPiYZ*G@uk}Z3(P5csATG`g=xBflF z{sjFvf!6F##VZAckdY#Bce@sfL%=Luo3#VSHoLJ(7qWE})`mPA&!%)LVamn?jw_Yo31*{^+r}<_NQ~hxX(sV1d9Xx(_>SjL%(L-c-7=Se zm$Rz5)-JT}XD637S3Q=G&GKJz5Tb{Lhx^+(CTi`d}75CWqBaXEG{y}bONl~)SiJH1{djS45Y?CwI24RIm`K}(c%$3VKa-% zIbN>)Q@2IU1=|D|gw{uhK1xKkO#D<3TX8 zg>J3q)q?&r{;8r0N-R#Lzz#}50wVMPNgF#bYz5s1d}z>n*-Z1cT+Z%0OqnFISRP_s zC_$jqU09sh#**jzQiI9GrN;a`2alrH{o@Io{Tu{(Q`4Q^`%X)Ta+(=sMs71K|F95I zgML-NNz8ky)_Qg`5m98j=UI(XkK3Z2MWwU*Eco^P51;tjg@En-)3}6#SiUE2#V)?l z_I!NlNnOs*AHKRbv{wnZ^uWfb${^dXWAQQeJbILQ-C|RTgy4)FlEr8FA5U%n$j7MP zg~@=#)|@-pNS06F32X%6<8l*Duh^OTcwd_l*nZZpSB}#FaXm4D38(+<)(_OdJN_w*x|W&e%~sus9?7~@4fphp`Dgg zL;duvO?;|lXdNzrH1-H?Iw`W7*036@iKCm#ea8Ww382N3f$YRD=tLdSgb)}?5K0S0 zX!X%{?v+MOoW9Smmue9ao@_sQ3f^2~QOjVcYd(-rj8Eh37P;r+QmgecNbM+Gb(mp@ zy`sB>MylqlM`mFSHPo?cdf=hihFghYsy?Zm!}-N)(PCGyWok#wiM zE|}rt2_P@8w2;PgH_!GXrx{+2+3)sl>YiVscGETspDPQ^i*kj2{!FNjSLRnMX5v(l z$jW5=+4Ozc_~E@o7vmY0A_|mi3m_g#=M0bU^|^3?7%Q$eXIX*q;O~0s zzqZs0U&qDA8&qoE`3k{)J=7NO#RF(Y?CmQHX5wRGCA_?R1@UzEaL;1IyPjfi_SIuC zAG`-k-sSO;lTV>j5&xeH;3f>3Ehj-zaH)jh4*mcSJ!(2SwqikLaq$o4k0&N3=q@m% zKO`Ob+tw08@VbHm2E4{Q^Q{?x_5zd>aiPU3DQd@RaE(LCl+kVf0i@EG|8|98nW<~n zhIj>mzZqlw87O|T6bK}3+(I-o{;)$G#t9d$HNjK#wqC5noHoDC=(t)>l>HGL$y|UT zq`TB>+SSX^R8j0l`WR!*hH&M>*vtPAN9ys-{8Auo=+duoKMnp+iTtl*zD8t)i{z+~lW-UWV&F+vVqF?W zM@9KUqIp9@GFSrvt8gQQ$^2Z(P#>SwNifY^T0>dczTdOq14SnkeELk-6H1EA12O}X zNutPSRIDqLz<>Y<+zOdwkWp&FmK|nI423Nftm^7G%+1X+^YhmW{#nVPguq41t1cS8 zj0~Q7U^VOgv#+oI@334wB&PGPr#g1F4oQY%;jbsDSQe|7WkCo3=UgAJV8D@UuG6zk2=%nczF|rrzr1Sr3Sl+ijFSIHy$0?_Nya69D{= zKA(9O-SHsA;Lq__u;vX3K?SOLqDf^&JwKYdJA@>_F*7h&w6}j2-D|wEwq{tPE#P*r zkhQykkF6vlD@(=3_HLZZj5mcNNgiA?eosu)TH`x$L3Rk>WB(U!_7eX4RALlALu&q& zGcQs5EKNHjRQ&10USr@NK)Q`Q2y&sF&8i-gCy7LNmh`Yg?8ESD1sC|AP&|RqYd!sq z#dwl7Rpk$4qyi?lvcA5oW?px)s{OAg6A?Ca^Ry&`jFSwqcY^efSjQ8)zaS3Dka@0tdDdGMmDL&)oTQ2Nxn`_P6>Bf zjd@Sf<>zAKWaY-0C23~-N58m6ZQiaP^TgGC>fku?Y;I~w7Z&|ox5qep=U!y`b5XL9 zHXnra3XSv|@qDKTW!Jx)3f8UW5FTaa*ZadjrTZ^mFe{ET0$rf6Vf$~?@oBh%!$7qf z(EVa_)HvAJ+P6JK@rFRR~PGy>3H&9>~wcWYx<`jaJ{=|9T%A$qn+ntCBs2 zK22=r78c&g;X^_~@MmO@Xu!4Qt<&hb+A4DFxX%}jhszX28WyW+Sgw1KW*m^$h zzc5$yUFYtO`T8};dn<|M6Bs{yV$u1=yp`;_KR3`FPE6p%>LohQv50$H{FWw@%?0b#GH*S*u5m(_r@(wH`qf%4*bBD;=a+H^jFWq-;OV^(D{H<|) zr5teY2!}g0p1h~QNyLg^%FzWxOA`}Mqh)>!4PkCqrwz3*lQc*la3ClLERT)gvBMh* zOza;B4(ukfKMLS)C2!UZ5pYe#qmDu&B7odsrq!1$PHYVB4tE}V0RiwM!|f1Cb0)*} zqaJ}0;UcZq7tSDGU z6;cK%q`f^y<8)7*$)$m?sMs`b?j7OW$skCyKk%*PCkkgeI~t~jZuf(1Jz8&U01wC# z z<0Pwae$ZcWXSuD^J#1=s=2VaGG_9nYL}+&C+H-^-v(*)$RJ(iQQzEQ*?3cBse&ni< z;IQi5eogQcd-9wicH!iaTOIo=BJISyJgKlM{pX8HlGyV17BBVS{t5Z**(1I=x2G^H-1%zE*!Pf9z6bux zO0oVm<0A}`@5Y>Wag!k`I*`nI?~bkj3ITmsrlh*gxreqyz^<;W=}5M$ZV9!lel1y& z%?oaAXv9%_YjxqzjtM-_*owt=Y`eBLac5^|*|g>HNRgOjjfb*mc{|DTiZus$`LHw60qI`I zYbFtJ_ymd+R5Ub#H$b9qp`5ELAKbPWP|oF^m_p9U zIsocsK>h*~X{XR)twbRhFlsrOGy^Bn2q%4hwBYIG z)wWi(L$4G1UO_yl>v^u+6k?OS+;3V~6e93Gnpq^P=Y+p_0MqC3Wux0c#sCo5XS}~9 zeLl4PeXC0N?LU08#6FsT;~1D7r+PMPx#gv1(rRqwR`F90~GaAh7 z;tVmxez&Nny?cbQe98Cfxb?g6a%mkL9lOG#`j)f*nvr9d##P=%j8L_-)?-=gn^sTR z)$78AS6S=P#9U>~OAKRFC>aShR-x|)fU+TJBV(x6-2PLSo>|>gkx~r)Uhnc@=9r}f zuR!#7z%(^90^8nF2nk(&HYvvXyj)ORoLy2PpYu}Ep&zIld;I2fnj{f}qnBsk-eN!5nN9vFYTYKW<5cb|2zx?(_$SSfH%nnKe?q z?oj?m1}=%H2$VJAKfBRIwuesuhM|9n^8SEIt{4K{(00Bj62YsAJ5VG6l&LP36&jtAOr6 z)!*3KG91Z!MY+EVNqt8=FR8IxZcfUpVf9efa$#xPe4@_Ua<&viAlp__2OR8X&(oOL z)P4yw;`8&t=PlPNj}vjt&&C-naPe|qG=0Q%D~?x{;1Bfdw)1>=S%q$dyN*vAWutUh z`w>4`%IgJ_iLPFH1NHr&w+N4j%g27&Sa}Q27cA6&?08Kl?RZT4a2SPnb91x$XABHq zsB&)`P9jKafUDYpd3BVAKOC-{nqXxJH-iS|3FqR!VT-i4My9@jXb1T=Cc=lx-$&G* z6^VxY{p)Ke54&sFZ1S)8P0)1))IJ+fdxFck1(?2jz8c(xcbW=VzJQyGKj};Ys=<9S z!0>@K2pmpq92^Nft`TKgP32AVkw&!VN&_JL0*aH^4rOVsc&WOolF~ESo5D6t{EJfN zYeqnT2*ahSs;d)Cd|!f|EIRr*s9f$&4gNVn-h0Flu>OSKsidwhrLRv8^V3ss4P!=; zjqJf0KUa)1puSJ%^el}@AmjXA|7o(_TqEus^8t=Qq|NlVN|<_mE=Do}YCQj*dr`Kp zUFZOHGQrp;WeWGp!xW6x^S86qMEE<;i6)e0})g|fJ%$GUybS4+`Y4%H*+gTp0 z9yuft4*C=K!9<)!&r`M4=%P+k%pvL1fEJQZLk&gF-=^lOK*D&Elo1J8tWrtJ*rPPA z>cflctBEpSp6S6g%JJOo&V}*G$shOSp3yM86gmwQ%J5DCUc#7TyEmj-5&?d0XeX4O zl+tT#*b(p;ni+6bmVgbtOQSdb>{BkTtJ9zthE<%aZe2w96zR;h6k4K~ zx9_xQY=_2Ag1yE8=4^PF%9E{`nc${)VHUa^tS(!>KR~kDH&5=L?!ZP!$j1 z(fs;lW@gsMuJ}gnXI`KSnpx_Lkb?gci8Z>xER+YvS1YuWuaFj;j{k) zbr~#P;01>^n{6}w+NYJDMud}-O5r6jIua=0g2TffyW8SmV?%b#e;=m}Npz_EAz{aw z7nt{SAxrCPYlD|p2pXav`gYp8U*)O25%ehxbsCsE;;B1`QDrRnIEl$MfER0 zsQ&dV&gkc1o*|>B=%hf%h{&$amYw?@;#3;DBom8K-H`JMtgZJvN+V`VpI+@9@#tQy zh?3QRbxT;^W@w#}w0B?@(g`>}9^3AWDfiV^e04wHJl^a0D>Io}F-p8{x3>-&Ki5#_ z^}Er-89&i7R^KCpmooY`_1BcwK4L(k1lJG}E~DMIvJr1HSrCZ*_JF4@&Y#-DRM7rc z1mOF?-F#37h|9Nq^lU)-NNG)p{iU`@r7*!6hYJU8RoL5r{UOyBn`g70{W5I{j`*qs zyKTlcKUe7@tXrT`LSP77Noys15_^^TV zq6>H(49W1IPAd#LkiOZ@{;ZkM>r>Nsa|ba2L!Lm-?Y{5Qk|As34kt{6@Rfvb&X)VC z++Z1dbClS6aM(k>eByCuAJu6TD z1`IbCe=FfsF&+1JOJK!&89;KRI(;j&w-@(k17O2Kw&!^_FK1!H(E`wZatj0<=T+PEZHQ?{o#Dy6mbZ< zOg9lBAq6GnVjX`Gpm2byfk6$-c|lbFqS2EP+4|ca+@+Q@H8r_22tXx+g)3k~;Nml% z^@$Jv;bJ=C!~XLnHgOOv#y=@5rQUH_ z>v4an;>=#u(YCjX)a*gCqx#`kcr~e%myg)MH3r9J$wtL*8#ILP^!{-?g@1f~+m%NH zk-BN?uj2(pZHO0vys8^bx9XQBQIMT=ezo-iK=5G9cT?CZN6zvd^zZHHR*5crH##9; z1Onbzri;%rP2yrzS9Ddg&}EpqEx}Y;8CYH)5e^=ziSd)>>M}BpFGca(duxf*=mZbu?K$pyGhi`tfUXz>uE93{QM_n>Ct%J8yAl9{fLQ&b zuTN-gDyeT3yKhImm?uYZ*QL?o-8(uVp%gHQpwa0-#N;4_QKO|r6kcB#2c4Xq7hM`9 z6>fCF8*2&SF!)2f?*$t&bnyRnr|)uC++i^We)l?m?IC9wv?Q=A_+@+SF!Ha%DO}zT zf+=;Y1vr^lzIZVm9DEoUFx$d8)!Fsh?~=v%>wb6g!DS%OWU}}EbAevYpOeO!uEZsx zUs>Ns`xv5!up=^3uzUUeOGkROcFhZ0o`cIERS$aEP@~JX=Zrpf+A`mFzP+*=%x+|O z8QitzRK5JCrn(b}tjx~7!ZqeyTJqpyGvg6XcCsJoLe^fxg8fk);kt~Azh=$k^#dn} z#Z3g$b1UBNnS(S8KB1*{66Q?1ubiGa@S}o18?1nz+1nphnQnZbe^F7~58xM=W9$%+ zVIm|NCMK5x>JoIVxaf)fv(~W}8&&sB`9x!mXFAxZM$N+JOcGogl<4uD`n6FCm^87Z zRnT7P!rTY>BYArTdx-g1%$ z#`#@_ZLom#$@6F?1LgnDRQC5o5%u)^w$Z}q=>LHEW?7?smHx>Sn>QTlImjkS5|66y zJe2?ygJ#{pq`0l2r9I&FXGV;cd^>i2ei@K(12e_o_uI9`biixiy@C@D@klV+t$W|X zro62QSz2xL`gGoGxjP()Wyg*4*AaJQ2AFC8L!PupiH+(36St4sWsMlYFWSp}PKqV_ z+Uj*#p@akl2UEh^zK;3#;Fnm+%h4jYmC&vdg|Pr75y;BqKcGPPb$(&_;X=voRyA{u z-b7{iM7F^iN8V#mC*S0#bcD0RB_bFXns`<68^XvT#S+;70rl=u6BX#GTpC4`cpZz* z4i%WRYr?9Jm+P;aNElhNUppa`wKiC3^KyVG#EP2Kc>cC#VjkT9mfk_e=g*n2l!Ilr zIb!NLmasJrvnw4!3IK}W>=~g`uILL0BOLq6%E}-S#q}p4A)RX7i*`ynI*rs_Uto}G z`?Am~K!U;Hi?n@#diphP`>&gD)>*gLxZUDhN5A!*OCk`nK(7d0sbj*BwiYcsgG27s<9kYqByuf5S`u{)(f*>ptGidrvbm0=4Gb+x74Fs^0x%rK-YQdQiu< zF)+%>bv*Hki6yZq-bC+RBg;RV+SP<)3saZrjl5c`(BxW&hj<9~2LCB3$4O!ZNd8L_ zXcYtE&?FJk%;53i0YLNpT5tLHkKNhIHI^T_USFJ8|M?mmPR#f1d~_2314R@pfh;T- z78e&uhKd(9ZIa^Dt#WDl^U7mv zVShieww7<6X1($#k@=pk4GVp#<+Ds>UXK2e^e^6|=NAWvPrg35^)xi*V(5vW8qZ^W z(=cl4T`ki2r_%&>iLzJ00MPmlaeeJs7B%va-N z+uPeix=eUjBwu`YbEOHn-l>A_wOk#^!M01Y+VoZEG@Dix0wyM=dWq(JZWmlkGGRg( zDXND=`@)F0T={a*fvG-AJv&?DVEGib(Y!J)UlZQiMC|G~_trk2 z?)s9IbO%qc>Z5%!hca6NTV$|J<_I4j#m=1%kOVBC3BWws>o&_>l#~||)DNDdeaO+v zuc+Wk*Cv$$u6%WE&83gBA}NPu@7}#|l23Y2@alQsN!YPs_0qlj_n8cv=zkr!di5$A zhO={X80ne7o;uSuItGUR~>kM!|t@Y=FtI0 z$y-Orc2Jy{lA>Z3e>~)~6qMkOco3*q*=Edx#d%Mr&PPmo1dF`0i z4xwh*IE#F<`C$g`lc%fV0usGso^R{Uo(eSYSodGUt_a=p>-(HGZk!_N(q)RHdd06` zm3;kSikR2j^hIv#7z;0BCF<(G%)M_e8>L0|{?T-miaKi|VG9iz=Fy&(JY2nPqmo(I z(6C!VqCg;1;FA!KhHwsN<~L|$L49z@%EtZs`^UuAR^``VnFEH>iqbW;z)E5!{}?Cd z1B_9gJ$LSj`0FnzNsM12Y8`8Ot?^bv8aS|;^8hXFc?inDN)(oqjLppS*}b!As2pf* zrBYKmEzWC&5!V8ho3Wi8MxMl%!*6Epc>OEKvo!wY5B(0V zv~&f|jkMFEodr2$Zt<_|t#r9F-;SsK3chus;;zPKYpIuR)ZMp751yu-l({{bBHd&o z_9Vig=*gHw-C|?gMiGhGo%p?4ANz|8Qbwu=o}?>R@FH!z68rs|J*5FpE z_$DtZ&CpSR7uKGtsJ5GpL|a=MssC89VPq@p#)zx(V%m%heEC43nUE-qzhe(5agG8YxJ!vVje#BAtg+jeW`=O5mG9@+;kl- z+8s^-^z`)KBYVPP^t(r=rTUv#_wD3$O=fV~I$>X~)NC^GlxDS9bVtbEX{OEjIOpJr z%h$6=GD)tfxEaSc-m3ZWHNer_UixGGX6v1m%3%S+#4EhCx+KH~4SjvWy4t|d@OEkW zRUMt$hG(2pJ=urj7|qM+cr z@-AM#RS0*f&%Pq|;lo|>A*@fpuMu1eUai?wdy;&%9YDxM6_rmdEtK|gvm82@Y+&bx49`iA< zmWJhDm(%y~E+@ggPOfIO^WXniZHTo8`EX7=bG+a#_eL(mq?YqR19zIdRr2t&l8qA? ze4^?bYo|U$)m2>j@w4jH#?Vrzs-f;B*K6w%Cmih5Z&LGeb00l0Gem5Y!GOn^uv-e{G5sqOCVN`e{gs=KOw0R6n|w?i*eO3#k%OJrgaY?jp9*k)X3w)R z*q*MZyf%P4l^r0B*pClzEho`4|rx?{uXmm671o(x9Qa^jE zbD`){a*NTZ|J|sFtn~C_Y;5N|Lxon1<$Ut?92_X}E<3if)9%fwqYojwUBn*8k-0fa zQP&-U28GnV*mCXHV|1I+myTEGg#BQtKy1*FU3bvVd8?Nfh?hr0Lu0&DUQ;9uQA^(S zhJ{Qk7O88kWg12&oovoTQ;8n4^)L&R`0;-FK5v@7!{!G+-u#Rx-s{S~yjqv-&aX?4 z{`i`+_E}}8qsO0uj`M$^ zE6~=XZ1wbyV;R48PY52p?-cOb#i&EuLYJHtppsMV75S0l$KNQ}JjVL~kM&xc2T(Pk zw6uRX`Q}?bV+HCGhmypr=}AN3TuP&}vybHykMi7Zm)QnoF$a#mK%nm{Ayp(a_S?q{Mmmrg<6sswd5ho_t7MGu{%733+RAPc1z? zCFD2+NiNzwc-4xks^W@<*xL5QV{tskvhCQZ#+? zqO9QX3R5k0_IK_#WmymX>KU=?+qWfIUAt;f8vAzFqVw>-tLf%_%0^zHd#Cm2gICO^ z&TsJ^YPFa&v2O@`G`EY~%C@_Jw1@+&mF)LVXoL~MK{133Tj zV+t$zqRRy-={(^23BP3YT;n0ZF|NKZWY;eV^S8%3mc_5a$gCyW!m@_Dw@rf$X?k_) z_7zxYkOVOZ!te4v+9=WA`{5UHHP@0xTwEL{uW@ocMkj;VFulv~jHM;lZM8HHZ~d{~ z>p=zUG$Vngj)Trg9PvjMN6peaChOOP19_MguFhsYr5AiOA(bncNCZPg@20J}p5_!|bt5&i3VX8o zp>B}}{8sqK1!+%iI&HCCZy7Txs|PZ*C|L|IyfrH+E^e>z@p*P}^^v37=#^m1IoKhGUf97ghLuO6JO?<;i|Zj#mV(L3UmYwhJ^3s)x~%Nf(49p}ILeLW_t}}C zL$&m4sG{Sqkc5OnlBUDvh6mLl>3fkGyxEW6k0-uqI{B}|>ZguW&PD{Or1V#C&e`(kBWa_2tPEz0M3s7W6_#5J0= z{0QLZpb1&8xQ9S?+jX0?H*b^kFO1!aJ#x|I+i7ugT6%g-ba5Jzgw0p0@IMeNIQScGHR^+FL)_(3F$#{x(mF~astme`ZM(UA&q(MeE4ZIn3iQ;x9xGKcAz^@5k%vfkQOHol&}{ff$mRrjV+(XAZv`4pcc z%M>!s5vCEMw~F87x80_joaqm0+Y~_2GBP{c44?j_u1jjyf&YC;$$-#-U8iE_qFktks9{rk^is|HRiwgBvuqkf~8 zaHR*^KEymKKfHT~aefVaIxt2B1_e?4Uj&~#99>0KP00J6J!5lGk6!-@V|n68V=x(m z$PdpZT<=WPD24D2?CS#Hp|J5sa`V<0t9Fy2i}lMtDO31`6Kq2=)H`Hb*L$ld$Q)g+32fBP=WoDh_gAcn>^{iRsVOxdveTQX5p-lDI&s*HVL zck;H5AD$JgY5W)?VR4Vuc>f_;aF0Mx=Vuq{qwrvsKY2zM+@$c1e}7Rj&QhiR=Yu>x!)R=9_CLwC&c^>*-62 zG%ce?F8UOxNAK;$;-hQh?S1onN{AO~4xEtRSXJ2jEs=j|LT@=*^L$zfw8M%^Wt=nm zuaEc(xjK(3smShae_CK=LtLv@uY#XEQ3>I_)Ya4TEG(?;^+cL?8Teu?Lql6lH^w$) zgOm<2{meanl{hPpf`ZuafzWTn9tlPnS3bnH>)-3cINAgwgw9i?3g21o(na+nV%~hP ze_%lB_6ncVc(XBH=E?QRMEQNW5*P}=D1BxoKPf5chWqHT?9HbEhJx(BS}9DT;;eK; zRO%cTE;Tl2?%i-Ne%0S7D?2!v+0@<0BE!o*)Onyq}`_Mm`>ah}W_{Z#sMNt!hyi)pRt>1W@gayPx zweA zszC(6-qzBuy)2(D}Vj z&o;+1W%7Ww_fBK_ngfD!m~D{`m|Pdwwe6bs8vEA9ge;8C%H;kPe0V6a8Xtee^PAK{ zqu`5;r+PJCNf-uS(cFKgGLs?sk6AZOPeoOcvPg_RCQUum$Uto*eC@LSC8vhclZ`!( zZVBG@yc+e)V`P6*{u{^q*Yk;XN!llz-Z2Oc(s#~Y+1eNrWgd0(ux=+|Wty^CS`K`u zbnb?(trppMviFN*gSLk%=P+b7JUq;LDIk!IFGVdbxwx1KTQ14WlCqQz6>3q@bUAntxd#v zU@ONeB;YEyZoN|s=I?O$?(6yF%eSmyjYRnz#~(@@Dlf^M)y~jKVQ(K`)sc`>D+fVe zWVcCFBeH91X*oDLzV_ML-1vKu)PD2jlZ;IE!_AsP^(jIsoYNr@;o%Nx+AUMOLWvl3d@bEGD|KD>7RAhnqOLa zV?R_LyV)-uYuja=cPPSu)}|3$9yvL=^SZIT@<0%&sF)ZXE|22*qr$@PyEM-dteWM% zCQ${d)Sg}X`8SOxT4Qha7t;6&R%|>)kItM&i3I=2q~;-wLE$aA^>grOruhGpznPqPC8_5$Kfl;hpP6&yQsxg+ z4gNe`3gy2=SAfxvKN|ik?Piz6)|(|a#nQraN9luf(8HDW&QLE`TDNxhEy zlG(h$o}g6G&zWCYNxp_!f`f|WiLe94W1d#}=<1fMEZhxz>NbMXoP`^jMRxgcb{UaEiU@8Lw!Yp&Arx#QW#Me zw&~{cPZ>nV7L8>|r*Vn{oT{dX6BGweRGLmqOitEUZ2hx8y*f;mtP)*|e||5E39wI3 zO+C`dU_X7D6*RFC%V1E;l=&U+T))rRH%*dL**mLWZ81!-XPyNi%TE&CWBF#QM6c>S zzg0hTk~sB}V5S{OWmDRsVZDmNsT!*O!Lr*T*DFb2lMlf4CM5--`339TMHFZ30Gk~t zYHoTzQl}_VQyFGPNBt@)d`cudaX|o-plC*3OjC%y-`vu|el!qiadc*e*Q!}uRPiw6gmL}v+@g2?}vSt8gX&MQLB zf5)H|8FSHTl3H9l zm>jr{tZLmi7bpI+B!;hH91f>;g6;sc&vJUHIMrFmv&)a;6SMbN=Dsbl9kI0~r{z z4Vhi{IkeQFA@ce&P=~cG9zTmF-bCO1TG;lI%cDac{SXCL*Zl9MPxY>}fPi_fBlf%h zImu5(Yw4M{_WLI0ePrIB(5=j&6N=)d-3SeBVfHaNQ~5aD?cTeENM&+TDp{G<;j(o* zb`V6E;?8|7l$`hhE95Z@ka2@1Iqv=(QA*0p%u=Khmc?m{`bnBD(H7>H&Mw?|_66W! zHwA@guX@QTf__nrxlWKIb#>@ppF?GykdUyk&?KlFcRF?=&S&uWs}$iLDrA=XmTJ4U z4HHI0NTmgXo|im_;P`i?)LjVg9Y5vOodrdDzAInmCNkPAui#9;0{Zxg6QNjTwE&+u zy!m5~D4j(!-LZ=oRmHBod(UOeeiD&2rFJWRZbdfWv9@yLCVxq}=aq^rzQaFTtNPjk zxA+gSmw6Y*KSy=$H~YR;FSDeKz2cP6$1`LFSy`=>*p??eaB$Gw-Tj~#nKj?*)dy*;T<3dOG+53$x6pKjFCC^(Hteyrv>g7LW1>lkk(Njh zt!X=$Xlaihx|Z1X&bo^S?6}I{%n>J-Fm%vT0AnVmQsUs>v>U7E`uqEV&WS>DM9^m~ zsO@hitep^p!ReNkmaT~jM2g*Z`t6iA;(v97yx4_bZ2jk!>ik;5;`zVQIUa-5&VFZH zHR!-FvPf%v+|)|yw5l4Cs|sSvObUD0dh+1GAg&>c>Gjuz@4Mr|X+O=!#>BisF`J$~ zoepwZeh{p7tfNC)L9B?pp<=%R`UgctMX{_7=uC0Kl6+4*P&vZ+l|AujI)#?pv_JdZ zm#4!DFNOFvjcOJwl;E&0ggN~Ou9(|WwfdNo+D=Vs&AZ5nXpyg*0$Zb zc_)5Si8Bk@5Nc+OX@zS39JN%v^cNNcF(~uF;wEaQ^|I`qqB>9WxU%vLVaq@Jmw$Id zP`=aa(9)cJ+eRM-`TL*Cxcd(5F1kkCz9<<*+<#xUdi;rYFt_K}+@lsqQH0!n8lVzI z593G4ukvaPdRVgj`wM46z`lvZkkdDCL3Ky=c+yM78`U+($;QR`Y%;>#5lKGU>l!tA zgn(fq1Sp^yjTgsTC=N-}{-4xooEm7vOA&ktxLjg3-Oh@<+Se@0<=+h--cp?ST_kKy zIsbi)`|i?v%Zj^+#YRmzdw(3ChzN7nQE{{jcr!9%Y$Jc~fmqu=!-LGTiq|4#wF+-s zU;O?1gtd1Lue12Osn5RYUDM_tp2iwp4KnXNM0ZDm9nwMM1EPy|=8SLE9_?#ljbC57 z=z}#w%Zi|Vh-~#GUXJDT9el$;%W!$L+TPm{7nC8VP-A|2bJZjm9U5TWvo~+@$!`26 z8}j;d2)bc#9MQ0cTUcBy`VpZI3Z;02IUDfk&wdc+Kml@=!S1}uKwZxSlQ!YnDMLQ?G49oAp_*NSo zDTTvsZr!s|P4^3($V>X>tN7@=v+lR>nTb)dG?eW>h*Mc2_!Y@3;$MueD|9}l|}DG(MLJJ5U~>4Qjotvc#!*n*(QJSE=C)_5Qy zB0>|IV;2`O0w7Lx)O}yL$FBjKyBjx-5hhJweX+giR-bJF4YbSf{t1zIVz}4&{7rKl zkJ5!kUi7y1!?N$egPrhG)eL!1n7QdHA-wRl*`>MAVNCM&%FT?Sp?q6rr-@c{k5AmF z*@ewLxc=862}7z=eD5DEYPFwenmrmy2L-aZu|~ltd~a=W;=|2v!avLZ+9le_1FxOG zZ~@i(UA@fl`S~h}xtV&AEtQEaua`Ai1yq&Ynu_g3!c=kmKa?BjD6eg&xTPU%6EDKH zbIJU?uMAZ;eSiK6b&{AEc}anNCWq%8qf+-UBp+4=gI@zB5-Te!Xes$EPJA{oGlN$C zLW;$iCspn3Pq9JNY2@Q};(JTe=5XV%O3UX>PTon$bWb&f?bNA5nL2&^RZ9y$ny_!= zozIpxSRAyKD3Qz`AC`S(@yY{Cr8w~$uWtX8DlIEh@tmCrK$X9~GRNyRH#T+-nK+{l z1vpqZjQbshc^M6x{J~NQLBVVb)eRn0X3S9$n@2!y>Uj-3C*F(f=Bhv3&K};oM{eZv z&m~cZ@LTx(`<8$~;TcqR8Y3fjDz0-im8<;-kNZIKPSpR)V8ltU~!rh}*NNRHKoOc29J zUh7Ljh51{5E4J8<9)&xSTFVZW2J9&S34gLCAK>C*fptW>c! zg*o~89KyncS90(nnQrz=;$C|1EoQnh7;v;(YL4RE5lue?x_$e`m_JAdMX}famnBWr zPNzC`aB6QuMi+LLJ?Rvi`|+bIp*QQr3q~M37}nwMldtgp_j_sS<{vLmEE*bB$lY(~ zxGEpgutl9P-bUC(3J3_yd5@!MGWatmYkRK^i|5wAKi>)+*zgUV_F8{u)xoj8Y{Tb~ z`$MyfCWq0188t`Rt6!s|?c513nRiRY4_4-`B&^KO8{=y1$gZoYac+`|dhuc&Dm6lV z{`Tqz_pv7uQIZZfqJPosT)1$+R;UHO>A`4kqGW;g=F0Nc4k7jr_ZYmJhksH&@UW5R z5|V3?IX@C;kuLurFW~9}rk8rf!NbGXK^PrhJIR^S$}N>=D#|zfa9zw@3@ry)k~*f|^x8oKU!vXT;_6Dgaj-Jb<($8G@uCQu zJq{1(C$ROn6xZ$ST2nO=SDUVFeryt~c>L^H2W8ROcI&*8D_>1^JUIuTE@8>SBy741 z-J`ExzrMq0$Sd!4tMb1;W#Z?J=fPm}|CEuIeu?iCnCJJ(3J11C*_W@?UXPyYNLl>E zaP5TS_lx;AzkNi%0EnGHT?EyP5;k^Y;|>NV7{za1`Sb5c77ao*nFvU-?zdN~F$*?fCfkj>UVUJSr^p z_A1kM$RIH?ZbW*mrDCa%xX{Me_p~uFhx3YLVz?tyia}ACP-!cLh-@P z!}GDL>#}CBpKNp@QiH_;hXDT2$LeYIXSkNV_!IrW`9B zPV_M$<6#!|6=xR`3iI_P0fS9Sj6p@Gy-|reA-BGT1H1~CTj^Z;|BgdMo%DErTpSRC zqQ)F>w32M!$unopkl=8m^xGD%nN|or69GQcwBKXl2y=Ng!#yMFJ0v8Gm5S%(w7&}s z&Adea3Pd3*&cr?FD|mIY-XtVw^#6l%F0z zZjPtJ%7aVY3ie3+mDMU)`;phbFe3Nyzq$c+<;jyQ`uh3=kkGd;>q^pANBj*oY+b<-Sb?1)#J;f|VB;23t6z zWK}LDD?3z~G!2+5QIuBOf;gRh0L7vj?gJ8>d|$Gmiomtz7XmCqw4qh1j)aUm^I@t10 zmuQQXg@uBV5i3+k<>(l_ZdAW`@nPHDr2Kpxy{j`a%@0X)^PFQ3c7F0OG_g*CR53EL zj`*-4A;9h1QH$0FAL8BqrJGe1v7%SGWN6aaREKsL4c0Me{w<)Y%48~&T^;sUjuwA% z^@F*~FU6tCdeUWS$UpC+UnZu-b(8m#b?rGaVyWm%Qx3;hz>%0PKnb%u?q#0 z=q?gjSy>!PqFtD#{q*_s&b*-Fvp`TVh8}{@YGW~OYxZgQi_`^li2TAc$+GP}f3F*3 zVEpl``HbMs?z-5qG(|FZOVc{mi+a|<`*q{M$L z#EB-u2EykLXci}P6P0f_Dxrl9bV`dEAlKQiRS?}Bqjdbcwibp@{<_s#mxlZ2l*R`Z62?ZE!=M^b+ zC}>OWp`wboC*rvWWj$s%&=rGJ(CTtew|j7^zs;Vbe}EbkjnhCYw$FcB+;6s_8Y6i( zGBWb{$0xzThi1k9;RxgP3@?qpJclW~Mt6ySCFCXXTeiX2mVPUC5 z%?VSau8QY}vC~^yR~PY7^0H==Ne(Ah`dgEpF}B~1@9i{h4rt3Iqq@({6$Nc0ovSA* zhUQevNe}dw7=Vl5#UcD6^v>;Few993f3Fb$4#Wm4Rw7+gQ>%j{Z~xQ7($1CzeVt&~ zkBjx`o%x5#AJ4WA*?H&n`O!}>GLJg{O}RF=-ym*!CKMA&=#socz(-bta*`oQQxSOt zo$&$jzGciQ3As#bPuT>Vt;#GI3WV)&Po6B**NWnADZm22 z*E$(FA3m61j%GmmJ=Sj${LX)sBVkx0i`MEY>Pq+DRXf*^YV20&EN?rXuiZzui4wcl zz@!swAy9E2Tcf#fvK)9Ysd~+`EQ}AoqAwBfs;Zhc~n2A6JMF1*gjWlh;bhVkpE^FYU zQ0sVLF7oZ$2~`IKz{Ey)1cVewJ&(eFgB zN-wi^2ttQ6(^yrD6GM8sx}1jk>gv=e2KfiC)9u+_US2LKCFL+$v-^&h7nt+erIsrh zUHFqc+rI#+9bjT=L`~<8#@Oz?du`@lqH~5}*J_o_2fXbx+^7Fl6kG_RC*bPEUvu1M zA}_Lz_8@5h#pC#5J~AvscJu3Gm%P4MC&*(C)tLH<&1FIPCrs>RMi`b=O{pFne3hQg zgC;`_oc3#;b89^=e--SA5)2@x8X@^g{>>N8W8Idb?W=+1ow1VBZJL33@$tSWE@@a< z2ThJG9{c|)*lk-=^N&*>ogS7(GGyPM-DZh{c|N5B0?XdL^f zh4)fXi7OuH$h&wuwy-IJ-vrFc851d@8OD@geTkufZJ-d&4VvQPKqkX;1w-SQ=SQ!8 zKODp%0&qQ?uh~a#I-JDk5$Sj7 zF+4N}P!Vn<2B-IruegY>0<5iScvxcNfe6P109KyC)D%d!`{-_SvogH|^4XtcQ ziV3oo(g+&CGw0tG6x-0V?GT+JHKUj{El}fg^N)RdQ-vi? zJ5nx7Crs@bF)tj!0wV_$Tfcvn{8Hj>hun(LKLDp5f!LJZ$;k;jf}uC%u>WfH2>xpR zbX=m+sjzQ@jdV-AoPY7<`R-f z0dcCJ{xunn(bm_$q^i1)g#ppl9((Z@g~Rxv>b5J7$<-Jgoy+Y%X1jl z^waLMvTb`|*Na(DBzLpWf%=vfu`izvN#2UbgFib;Jh%r!@YVOGyYb0vU7~YL>J}D1 z%QBaC)5OV}Ab^kI+cO2o>38k31twQ~pFPb-MMXt0R>+$`odDdiSqy>u1Mr04iP3%m zbXod25RFqMuGRRu#{%3ERtTkfn!&*cX^05#(CFgzt2jH0B8*4&U(nl2K`3z>pL0ui zod^ha84V^G-yf2 za_U%3mZ!V=-G;n2z@qVIosjkm2-psFCfb}qh@?PKz5V6*wy`|~MHC?+0|H0x9vc~v z!^5EPKo5`*k0z|YO-f>iTU>2>`}={hy?$rR%-Fa74sE%w{fK-4cB){&l^J9hCraH+ z{Q@Aq8qk9uu@ON=l*8%N_70F$W8>rUAk2e?7|8Du9ug?Ei2K%h#j>-Sl7fOD@*ymJ z{q6QLW!}up45}Zq^`)tn{3ZmXOdSyvu~T9WF}s<5aAFmWnM!{&wMPI?hZz{9dYoJ6 zAVvq=hQgoaI zX=EhB(W6H>rhG4R(Gses@84_M5*1)KWA^dhZm^<2mISR0DFj}?>n>|Xp&R7?BlD{2 z>RK+|6KI%F_P7CM1IQAN{|qUTvwD1|!4M1=Kfl?J#_%1Ug+v{HYhvnQg1D#>DJ;Ot zcE~Q`Zju@HBTwt#n>^|mnXO!sD^{Y1-7G*EAoOmcD~sacDY!}QIC(9&e0U(S5SNl! z!mb9R^{M~xcsTLkUeB1s&rls*%MXr=i~EeiB}6mr4VKg3z9V~FfVjn^F8EVJ!&LI_ zc>cETiJY`n+grCz-~xtiHh2~a4?HS88$PukN4b5=zGP0E)oV$E7$i#be9ldX9Pl9s zI~FM1h`1tb-W=sR#JOu58qAuMx#{V9(7XeChXoXb+4j44@9a88>B#T4w`(Gb5IT~h zM@dnSqx!gnJAy-Y2lEn+{8|3F$+vlDMjDsM9nav$16X4?0|SW{m6ef%My96&(K6Bw zS@>V9MsnY0i|NiZi1r*nItyV0BZBX0`%UkH_wO~COlShWUpTCRveLgRF7=ZQkBqn+ zb)oar)8nrR=P8Uo5ehxT)Ug(cp&CqG_Zlgi_~1z-meX?+bGy_2*P~b+@N!{4d^pa0 zT8Zdx7Wp?W5A0vjK}=zRMowL!KntGV$oD@IIi|M|(z#zuDg_HAN$()i~%`xE}(7=hcMau`Q(4XRG3 z>oK_;M2f!uBjiBpy#<#X(~bN44%>D1&n}-;i~3q{`P@HD?qg1ilDN5mxWuhQrF(Us zZn8qCF4781#pXIM_`N&pQz>vjg_x(Y)MJr=g6Jj-S;zgi9jd9VT?6<8g!cH53_afX ztr0)kGg_LOh|T*j`)KmBzvvDIa0jLXJTnO}*!t>J-}Ikl6$bgo2djYJr1Q2`ggHic zg++N0bU!K5`3QI`G?JKnBaJ*2sgi{`6pRuYqheOtz2UDnit=*@)29_^<}hAd3s(KO zRYe2_1Wl{_wnH=dyH)1bLH=c&PuyG6hNtW}RFGf?;-V9ev#~mljP|xK&pq3vS5Izw z5hvPl;k#nmfl?Guk}@)G8IQ^yE4v-rsm=JT!>Cs+F3x-X5h0NUwM;hY-uw9=-u7Xy zB^q>gkOX$i%2rU7O#rVDFalCVLY4)N5)2a|r9)|f4nIY4ti7xBes#h+4Xq;x%3{8g zy^M>qzkZ{_b9vfsu|GU)it79G?8gmOf^#XKQIhwWeDK8^CT2E3?K}J``-5ge_6cxt z{x8!(uk-Q-;fG<52$qV)ZADl=8k-n1reqqLXtEEe<_w zY7YGkGtj*v7Ok`()&g*scHsh)6NLi6m2;nz0Y9tfw^fA_fX6jSNf8EjJ7ZTHu|@3eIQQ8O%;dAm-_Hw``_#M2!x~H)D~(_ zT9nhq%|9CI4aA5JBfs#UejtZsUay_ZVBSYaA_#S$IN3Ci>ENu(6M44Pzkm1Q-q24^Pv1j__9#Dp z%k^m7L|u5vQ^QXZjeULyTQng4izQ&595uNy4w)aq675^~S!|c1zK77h7fF@q`eVe7 zqjaU$hNIm29I|V%%a*VNr(>h;cshusKE8pzXI8Qx5rJ__%!Yb|V-*`_67AhdF5cg{ z{xT0EX139PzZNgLf+l#61E_R+7Aj zOU3{9Du1!8UdA9DQ)%~rmF$aPhs=YlQG1ZF&^__3_4p1(dm9;9(O9qyq<^|=v!-^( ziTlwkCV!iw7h&KA1QqB)N1?X^5+2hO+nFZZ!Nk;~(~@+g{VHt8lDbmMN8}VJ3$ri7 zYWS9`t9(Y+wqfXRi0k$9ZSG@bg)js<;huwYFDzx3JGD;YpUSw82iJu?OWc0hX&oH% z6g_!jJ4VM4GL)^;7ykUQfB*wRLBk&+&x$lc!=IXF?FQ-h8FDgk%{5qGNszKg+e|mv zc5Opz(G2}gg186$f~&Qs664=hHrZu6DnX4#!9Y6Xnm_kc>Ybq&DJP;&0x9CQVqGgG zdHG~peB5TSyP9d=!R+F&`J=~^0~83XMw$OD$7Cmicq887_zmP~G!+QQ z(-_WcAJ}^Zbp<+y-x1;>jI_9D`#one0ao8**doK-wP)vo=h@ol^`w`_UsiT>XzAJZ z_VuBL?Y(*2q%YsLIaY=N(F^=Hu^|}gx)E8OTl|L&deoYagc@&*CiELI1q1}D!L9NdoV0`Z+5r#>sV z1_bXhK@3}>00<~bE`*Q>kX3?cbwx`R=8Et9_G~c>S|JO-F$|Wx)=XRdUA-^b7Oz+B zle@oMnfc#1^5f)UXn>HZY1XOl0@q&Hz}sdA(J94yI1R!0x7oj$EpqFt ztAwqwe~-zH8^u3ZKI#2JwVDPC9H3ZyOvd_OQ$3zwDBWR+fX+>*br~N0zY{#5Ogu%5 zNv|F}CqFLzBRvgT(aD64se4#daOO&}vsFeP0l7b-J|~Y=Vd6%?0A$CXHa*D~9OMn~@z&1$v-a?7_LQbjic_2!+;wFwdwoy1hZ{zHc- zE5R6;eo5^xtj{xo7TpUG)k)<90p%19VxNvpPnPZcZM1kT&|n^Lu;zEn?C~@+HT7gI z;#QPTe&(FiefyO4NF#1A1#WP?^ENr^EAOt85^A1*zkKW+{?l*D_W8FLyemqH|J^zw zwyx`C%4h0)u;~%tPq%mM%a diff --git a/doc/figure_g_iwn.png b/doc/figure_g_iwn.png deleted file mode 100644 index 3947684cb11e1826c066af4d93940aaf71f31e57..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 13295 zcmd6ObySpZ*X|%CT_PbNh=77HigZbXv_T3(2uOFwAV?`8A|a`Spu`Z;-AYS$Gjt3? z4{`4Kz3&%mednxm{yeTV3+I_9?%4awWx2odF{Ey+e{PTf*D2Vbir4n(Ix zLH%)$73Z$@Z`*z=DKtZvDLnx@JG&6gYj+&*SMnQ#hKkDYSzttX_=9^*^73s7H%!>s z%~Xk@R8%E*@sq;C$3It$E`S7->#+_B->= zXniBaL_GOG`-##J-`#q;%BkM2cpm+7o0qjhh`N2^7>WJsa&e{Qdj)Z62NvpFe*_3MnWkNSrRm z)g90JlCnyrW+}xO=#_+r<6Y}Uyj@vcjr;sr-pq{M)YO#Mup%<*mT-kNTKWntJ?(!y z1I}j(OiH5Xc}hza3o-K!j89CYVP%~=7v7K3ktv=uH{vzosWLROh zzWGQ&o})=`HslSxVyy*vSXS?9Xehz^_wT({(>%F(c$VJt6wRairLD$`P3~?~$FXoZJ_v>9sgc^~}7y@W{wWq3zPLvOj%&1Y7QG&*XiIz{$N3nN?LuH8o;FcH>WUbaYaLR1<6=SbnIS9~9M=uj<7Z(B3; zjoJgV_BI+W_vU&`y=t$IP-N%kZc0JdeQQTsI%*fVGFppI`leJ7+Y@DBf8w|djY1!I zV;rq==h6}~-bNho!0ZKJn#{i{$k$z)o14`#q|)v^|6cs&FS|yn$imQtBd{%%IL_G5 zpW8M@^8H6g`+)N_n9*x}vTBevkNM%%<_Uakyxvzh1AZxbM=^4~dNmzA(U8LF=e1c_ z$<53A`r_>H#?6~YHH9<4`}}eiRYhjJFdf$T<4-&$ED{Ch`fEJ44J><;+Yi=3Lsidx z&~!IgBvyv}zeh5(Aa3S(Ozvz=lwp_P(p?s+0PE%d(DLUbzToc zLP8|=f8WtbG`~d*jhz)cCrTB^rzv_2PSb)DZ1g=z8k(GJyJ_r)Qh*dSUY_weKG)a( zQ|FDId@u)=Ht-9N4i4v_q53iW?km#Pr%z#noRhs$MGzOR%! z;Gmc2I^VJ*hPvN*e-d|1En9f{aPN@t!AzKm2ivvOQDu25IEW($il?1g@JS}Axomg3 z%icpy4%Cr?9ZT_=+CYhO;=M}pT3@f14gB+heebTGf(a@1$IW9)Y!tnn?t7W?T)8~`YR@k@%Djp3@f$5P=Iht5$bK0ana7WR?LOoC5AT)@ z2#^Ak^iDZ+c6R=pkRSx-lzvL3iL~~0!*{f&S8%tucHX9y&&r;6cuF6yj$b^{Ra2XpFfixTT?@J>(J!Vm#CPj zg8tvXzk)!vxMEJ6~cK**iSbf&mSiI(OT3TBlXx#8sqU5#k}ne8kA!F3Xny?o z!MuY2JJ7Q)0Cl8el0M&S6|(o(o>Z{5zBgv%$qcsg1uT(!PzU7^o`V04in%g^-n3zZ z)UGM-#}6EA2zN9dJ;=5QeE061&}Q-r9i9EPtQeP#;d?@kvs>ITA93ANJ$~n)X&D zm&I>S2H>O`FmaFtg^#R^zhA)_oxtw|+bnf;?uzV7BWYS_4g1s8Wj6F(9mLGa30)5l zp^1qJ$o6Q&N!wfaHN{S0Xyr|YLILRdi=jmHCZe&yaO$Ow&LhX!I*;y3`>D|GmHkqM zD|7EacaLW0wSjL;;-mWyJa(q5B@Tz56LXg+qCBog1Xl5#UktfDTU7cl#NlOa`}-Ha zGmJGLNj*hK-zZ(#u-zNF^Xxjqu=mvOHzMGfRPd5>Qqnq@li$H#rTPkgkOG~3WvAlT zU=7ZKHGK}N8J!hvN#)m;KW_oMKsCkvM*t?C3@oy*!`8%@;C_ViE`W*}02M8nCOrp5 z2nz+O12aZOI?vKk}>A_DNv9SB|lLWmd174U%VZG(j8V$&sz;+ycfc^Gwhmzjs z;jx!~DF5UYE)9{_NZ7)-agwv)SfzC`1?u3Tllp4l zG}aD`a8qy|Iz;eIUyX2-TFb)H-kv^x^M;#-Q?0m$v?Q>GQlTdEwfH-VkiXAs8`NFt zGI#K4P=o*nb#JUTpNLZ4m_D>O@ML=*-VY`r=9+oq=lj|*|Lc^A;S)hEA=`5qnTn2TxGRq$OT*PAS`Ri4sKzCZzO!P7qe> zi|cBe9Bcfq)9(EVW^;zt(b2RErMmykUlD8J{Z2A1`g+< zdOJ(m6he3=46@LAn_E6uME?8<_Atr=U7jlp6?PV3v|7>vp#cGws_tH2@B$H(x8WEw z_^yFH0MtIB8J%Q=9tRF5eztv78?rZ0xcEuGPs!oLsdN;ts4Wti9lzJe2B=<~4p zy2}xHqws%S2~%N_hv5Y)VJzXXf1N6br^y;7GQuG^)FvQZHyzAY0SHmzbGYHL3CB#$ zfxy!;{Nwo-VMi81a;D<1PZ=nu51r;S>RJ8A4Tk)}tzs|^8SG~Mfz>k(I}I3zS)a`Z z#3Us8jRBG%#@kNratU;Uyv?!WxTwZDE zjO=UO9gz$}MKd6VdM`$BjsLQy2__)huR&~qyn~pS zo15EY`FCtUzWy0eYaslR<%;x{fcB-(2hw3z$Gaa(@f`e_*+Vg_R`_AbacB2I zD&Dov*Y@!vWxD^7KDNl1+D1l;@7xv?jK|{A!GT+8X=&BfB?iF#LdPvfJd^1vXK?F3 zi(Ty%8@3Dl6IxYNmRFQFC2(W{F8Syf3i;%zSY7F7`D71q*Vos>ky*-V{~`{pkV!J(oHH95q{EvW+=|CBpCwn?nHIL z*Rgq^WOO8JJzE~@cNa08WR8;8t109*4xOepDIfMsCZ}RE^P$`1>#)A`Ky~R*$&~q0 zRvD!EWn*VPPqO9GK)ngOR2GB=BS6YMfm3@qRwr7$z{KlE&4>9m5?>xZ3|z7SfG$SN zozeplY-IcG=eQqf*TD+Z6~9fRtfrDn z5~$m-X^JQP64UZ)SN3xhYc35o3jXu7at+<&w=Z=d7OXO-olZO1-u2vv1XasIXvJmn z^g(-`zBeKjV;lG7&&a^?riHpXouE6u+K?9hG;>Y6bKUHjY1bj8^es*pVK9NAAp#+4 zWNGptT$8)0O`5dT&rkVOL!h{ecz!Q;zDQtBfzjzXcGdeKP1mL0zv)f3~v_6yzYR8Usw@2<9FC3~@Zh|m|DbOm>@_&#j zVnXoZrMa?TJwnW+uXL6}gjA8EMS;Y0OAWnspA$bwt}tI(OTMD!i#`ZPw#K+ySus-V z{QNOJJ!%b@ncWJ*M#niR7Z;Z#A$vw$Blq~Z4>zPAk%}1NkA!_gULR%(dA?HEm-oK6 z|Cdkn%W)I>SQDw_<;6{tHavQ%X3`=y^V{6xKeLGJG!jpLV$HI}HZUYC3_X-_`KvXG zQzP}ag?t{CQp-wguW`O zLE%)#VcKu~3%$keku6iA@DVr(2??V)Q8Y9({sxg=3l31$Ag_v_oAACDvfD)b;;ubcP(&E>}}PwIFoV3&Hj7M=GOsoofT z3znsrvXEn|Jxat%5N(1rOXbRIyny%^7|;S))jGnbK-ayAsaVUigK7VjX9sC!%@ES| zVj|(V7hz=(sx&6ZhsYP=O5Rq}%JXg<`q~VA6YhdBCvgB|K2=JCn6zR*rkT7u>mb8C9 zb)M`8CEO;C3LH=tNR@37^pf#W5*Yk&qLzzu)aw`!SM9p`DK%B?^=l4SWRL0j2}W&UJ=wC4^}nJZX@n*T;8n2HT;1WQA>d<511@|(^quCCqW>NrRC9q6uYfI1C@8EDgCSm7xj4RU*z@Yp1~vW;p4{O zmXR!y$$;*P1oKNIWbtas#sB=@xIqf(^Ws(^Ha0d=X0gwJGRVxz@;4x9>cf2rsG13n z1@E!?IwO4XtHGqKm-(Zc2Ew%iLh6ppz4pwj2%Fi4(~<6eyUD)4RRuqyP8;0GqLCI5 zndpvwa#z;q4u5>Xoh)yo{OzEi$qEv37s_SmRnl&+P;*pwP|%z5B8!yy+l1emW!~Wv zQr#HtFA^0}=W?$g&Qvro{178pB{^J6wlU2j!GarOIxj>@iaX7MTbYZh|9dd|Y8Pcv zVL#n5R27BeB|$)dG`bi1cYk*1yx~XT->SMC<|upXS5^&K)snrt*65I`oWewl!lTyH zsdC>Xe^%qA-GLU_pzCDhHu|F{ zqP!yE10Qd<1Tm8mlUcqKAk}?AUz8^!uau0q!`>PmO>D00mFM2P#$?)%E^4VCSS?BL z(rP2G;73*|lBrW;%()K96rZ~(qVv2nLco1k&@0c?H_vuYYmQ#+(1E|3acZPi(rfr0 zH8j0;g&Lo}De0lP*HV;l9s*%C*Wk~qU)q+74;b11PQrTG(}284P7aq#GyQd2IMw!1 zd|H}1sKOv1eUSXQ+BwEivj3i_H)9HpLG2@c5zZCxo!j^9O@2Qt9i6*fZ|^wZgGq4w zr9MSp*chLFTy3qt=~$hvGxypM-hKFZr|M96>AT}VVWYNhA7<-l0BKm$36&Q1e~viz z%5#J=!nJ)w#?DpCRty7-HZ8~qtolcvkIjFpnC(RgxTlINS-j`H&GEZA{C9E3lUuI| zj9w4f(l5Qz;8S(;zW6k3+owZwb|zyK9OikfM(aON6-|E| z6jHXc2*@G{quhKxmz0!9Q523o`cG!M#JscAw3Ps)oMYeBb#!jpjutGGcJnQjj~Oo| z+ZLhv`RQ)EXkCxRU4{q1b$5@nM!a;K&wrh&p6e~?ccMC0;&b~^hhtVE0X5Odt`Ej? zZmQpEjT6p3y`+-?To)^W%jcJA3p?($C)vL&7)!oUeXGiB36ExKYm3$HNgnXhVYDEq z;wfFZfLhTvUlexhoCzYX#2l+v5|ES=b>0FaBO|XZEKn0x8QO-1-6OiN?b>UyvR+3M zR{B*=Y@j_MzEc%EJp6(s;KX!rXb9Boi+$p|%N0`&fQgKtx}#VOW|wi{x85il930L- ztq-nW3?Frkies%iH1fu39wnVr`~@G)+%f1#lKCL_kod`Ce^G zE;=fLFH|X%ig7x}*?_%b8Cv(s#OBSL+Z=5@6;|oq1c}}40MeaBn6UVjo3Zoe`}?NY z^mGjwnYSYr;p+ayjh}o+cNdo@d;-$qyLuT=4WgT#W3A?Yp~rO4%Hzu5;abUav<*ODi-K)kUwo7x61RDb$sD)&{X0vE%Z=tnrzl#U(%b3DV4UUgJ8g3}0 zq^{Fkcdh>6nbk5K>ndt9cb;_9BGJq#Pc$rtqK1+pU8Fl^Y}ah{?R|$7$#htN@1uop z$+w1%rVqDQ_3hC{yOLA=Wl%XL6L07Z;8ZN@z{s5Q~J{V zb4Ej5ice+pZlidx!cd1>L+pvJT$;D9{C0$nX2Rb7uZWuD=!|F1MU2y{HOH>OvbR=E zqwK!KlwU@|qaQZ?A}N*$CWuW*QFV701g#S`cJ|B}MJ|oh`FLGpw!3!~KsIjR^fB0X zl`Ae5n(8aG#ko1bEYZLbl_Rj^(=cSLwl+0?v>Y4=mm_+(Y|8KF zj+$_Kqi5!d|HXRWF*Eh7NVDgQZW`{;NNYiq=h8k>r8NaEt7$cWl zLw2>}q7Va(7l z_RmJ$TT$xsbve~WYb!Gok&Ex1`l2Ys%eU{uw1j<`Y3pSh+31Im--53d%fS!U{+=(= zHO^z^uB1R?j|WlrNz@g&cCY7=NjoX|YEFEax6h_=z*P+5-+fm#4|8a?WGmV*+7A zhlT(vC$ibx6)Zi%9>rky>Zgl_JuMV!yF(BhKNKk4|nsGKWOe4?(6eCD!O#ghk z3u4N-WPYL9GyiT7ExkJrn zZM=jbLau&8ZT!4-Ta2t2^j6|lx)X7+d^<>bs}@3UDXXZUe4W|f+8qoyjrv~=B4uc6 zRW6!WaWXgV|NtOX2N^Bi>6DZD%>w(ZzLl6Sa z2ZSCqDX-4Lg1P$kv)ozHs4*_SalYP?jNahOw0L)E^L(rCV?x3`^xfvFWZZFN5C}~O zRtxZb+|#%(`F%&R4QWAeZ4ck`Kp8nUhwltJ&QWh^*D*?WRQku)J#*Es+{5wGAGWf4 zofdVEdw+fJQ<>Z95p2`ZqUbf>^+rc6b;RM~=8?JC619<9OvU$JtW)8S5IoOfhGD#nNa_+M zQryafBjU`GJxAosfE+l0=8n94DxCvJQCrhkB)6EfBu&pQY3mmC^kL)PgstV+e7Jnl zF;uMUnc+OL`Y0^V_a^%QnzmC6aPJ4jxL zzr++{nx(rtJ77TC7-Rox>A!ZT-F~?f+&DvlnI#}qP9;IZ@#$cM-;8`q%avh()zoWd zAj`!qASo$;jLzVN5*GPDzVnAIEEN_#&6dLsM!u$JRcRzc&`P9ovT-&@x6w+&!*_FY zKKYHi0(;uv%sY0SH^~$aDG6L|oKp!+UwcT`npREBUjoW`Fv_UsT>7FPTcPvYT=R(I zFT$*dll;?gDykGL|45sRyc9Rr4y@iZMb{&hTNx@_+#84PD7Se4ZdB(DnBVNNH0Bjq zos0O^6oGDGP<6f=O(|A;(MDXDK{T59Gp4EL*23Ou@ft%?b*3Y{A)EqGngx_nJ*hwH z`D-a-{PAra1BYn$W)7YHwlrWS{-G^PWVN*B`Z~0eDK;I1tUg>-HYWGaQFLP^{Su=T(DHKyUFTTP2m)NC_vuRFgS;1Q6lXS4ZTxfZ1V8k6lkA?u ziTT%sdC7`L@^u9qXC;6FAqkM0`ML;)F+ddT=Sp8h@Hd2JrbSyL16xn!cnZb_JHE3%BI$mHj1c8FK zqs3mRMGyOpjg6|yv(4&@qp50C$1O~aJk?c%U2;@w*i3l)6r0WG33u+netkxhe4Rpe zVd2E(<<~B*u0Zxe3jzJPf>+hlfx5=tA=^`xXwX1>=XLR-H ztsD-g*g+&9E`>>tgA}n zc`~b2bx8|GjAOykMkCQ%z{THwtnywTt`an|u?OxRfhPjSIO5~NuLpX>UvPp6Fu9ym zx3s|2de*hq2kMPZpr#SG!wkq#IFGz!e8hX9bpmyjH`VQH zve9h$raA1-9n_9f;k=}%Q=o^_^q!UnTTItgOjWybDMd4xHV5JR>!&NvT_=8&K{6Vz z9Zq;j6sjZ){b1r8$Smse4j=`@%-mem=ip7P5KBy0#>JKuP!2V&Z|=*VY3Sz0-Jd0A zTd<_d{0*U7++~HrFa5|_rKTNDX*4DN=4=Bkk2hAKvADD(ZlCgBO^z9!7i~|C(xS3N zC>E_`-c()LQWH4^P!ty$R(e!94CiSxiMfS?XJZjUO--#-4h_%DFisSTz?39nuksab8Y?_&zPlhHhKH7hI#whv%V2q+9oEPEMs3Juzm z1#R`}yqs%=`0Edzp)qtYzgWP!k&quzmgE$JnhqziT?T z-Z0U<(ThEJLr`Aq?39G(JyHge%KF*L@;`xQpkzyqkmH~fQA$EElP%|o64SzdVzjbp zLCf#<(EUVsX~2y?5eA1gty%l)#wiB^WdvT3Mt{@3$+__D@xiyP z>^Pvvp}x+ZT=bUNv2o|MDAQc-j$AAnIPx_aWqMM^je3$2j()$9^DuUM>_LU>ZeKbx zXBK(VFzx4O4V@x?+w9t19?^V(RLzK1R|O}ax# zQv?ZXot%`v_VJn;ht}h{o@o(QvlXd4scbM*@_$B!c6%0uqjO$#`V)tV-5C2QFi<~x z#^2gt`kjHcneJd?q1Y!n2@&WGJ&9hFD8p!tHOkik$iXAF%C|L9I!>}OOP2a4Ux|G} zb(09#sgqzYJdemvK`h%!ARLc4EDL2^{8Yr4w6&vo52d4fm%7-EfRkD|_0If(HuuE0 zzhUfY0}NmzA|gW(((h_)+*#kd$pW3y+cpOKP&(k?u^QOnstnmN-QjHc_;7TToq8U) z{M$AFdM}T^Dd%FpWUm`Tu~$9?Tcg|ob4d4Buj|-&Dt|3K2gl?5#2FMoSPcviNde6j zV!Pq;%=RX)UeofQI29F@Z$QT3NJ6{LbB8~OHigZs3h_S(|&8YL}l$k6)MkQmTS z?&&d_0Ayej61o}quRLM0s22z<8HDWUg{Pfx1uT2!FEP8F+aAa6Kft|B&ZTHN&ri{! zekV?~E1D80`NRrGz;}T5g;A}?Lx*V*hlW#2tg33K4&CQr@A_Xnqi4v6aOEdx-$47B zn(|inEXAlrkXE#HbhxYyup!1OfQ%Hde*_T`5fE&R1_Y=Oyj&9y?&8RzdD!xiS3X75w5bRm3^ddnQg(O6A!zYM@qK+=XA&hP?FX z1zoJCHa7Qx>S$Z><;1ogq{42ZC0iv<`RUV*J(SGb4QxY~lQR@ZIv?|IZU|glQwQ}k z5MT)cSt2K(lYs_{v3M0VJUaTwc9i$KdWs^rUE|pZ0>aJ5ha6Pp$6{!TW5XAqZvDV> zh${ycG!k4Z)CfOY&yB*e(NNM>FQD*?atuJHgmx}HoP}Tcn~xUg>%@;+z^^cKQD)`e;_D>f`zaF&t=3M?+OF*DGEq^c~rMwn3{e=tt7(!>6fAYDzOUk zS1y%2e*Bmom{UiCKPJ+3wT}U469pSi7D@8EH%dL{{Kv+tU2{Q7KUud0R!%tGZ(jxb zls{YVJo=krw>@$R?LOmm%fP7}koD)X$6cT)+wTq)VpI zaA`OJ`_J#%`h-t%laku3Z%6_tM!UMYAZWdW2ZkMAWrCA{+89{%^6ILgzD+KtW_l0U ztjPyphTzcu&{G>2=_DF?uf#{vKg{ecEs_}Gr)&pK_UCRQ1nRYC0xnnc_O5;q)4AXT zkO2RQzrW<+;h_i<`D_5j9!MyN$5-`gRB=YTK$A-qgmUlooJ236Cc@$7=jW1=YN-CV z1On`oJrIhro&pgB{`i%iLD^EtcnPx4ZRJbA+ajR<1AyQRuAMycnsyK#%GKloCI#v$ zYzGohugQ(SkHQ*lXG!xQDbjGM=ik+=k8{@z_dqHk1FWq(k$)9XyZAk}Cabn$;$_dQ zI1bdck0qK+v(EqL^6S-#Ju;yNL`(5?zGSIWM(&D>^ohDLr`$=1W*3c;)P^34)2n!HXIHjjd80VD74u&+N6h*j~qQ*!JuLvJ}iw)`a^t)XP*K*i+; z6sW-Uf`Dbp%F3R#&j~sb3^R;}+@PfuY@7qi4UmF7G?6OX(uAMU0NiONWQDq|4IueK zpOjm4-@F8B_&6Z%T5uCfNwfla3}C5&l32;v`61A5^dt**w>UAJ38*|f1jk;PgVrI# z*elm4ZGC-f_b*1D5)-W;iUtM-=*BqVIlrNVPBidbdSGY5K#v#+i2o(9f#o1SD*^5F zJ9qB1CILJw0ZqoNii*+2r~aA(&Oo*J;0KBJ|NBcEtjrQ#A7iA>@_I`_iToS}bJSK~ zzX$`hM2yt=o*alnBK~KOF-P0D^FQ;8*twOj4&;4Quind_=dItXs;;i?jVS{(Qf6b#)S0*q6FIF&`~5vUUIR&l@JFEU=?k z!BncKYm%VtO+@_>BeugQC6$g^>ZD<0oJ2<8un)Q?_vp4=yCr020_5%>42wrH3jRF( rF#&!7wGnuG1vJV3Z*R4%UlGc6KbX&UZia(5b|5MW8uCT5CPDuT*9!~P diff --git a/doc/figure_g_tau.png b/doc/figure_g_tau.png deleted file mode 100644 index 6a8741e3acab908ae303a6c86065415f3e2e9873..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 11001 zcmd6NcTkgG*KGm{C^evfAT1zLMUmcu5NXmudKCnuBfW$4j-d436zL!xLJ38rcaUC0 z=^)bEJ$Zlk&b;%^+_~TVeInOzJuf6tKJ4{(on(#K&Z3qNHC@X_hg+QR? z;D;U$2fQ-Q!u$q2-EfqURmTJWyzopz!SA>1Wwae35PVY14|HVC=K=zuhsYwIsk^0Z z&w1*pJ6|F8Chc2#=GvZ>V$0CeKX8+M`Zh0u+%itbThk{M>Ak7qE%@Y-7bckkZi*#?BlXngPO9iEzE zai%umeuK@y!h#nR6r@`F49Udq;NYNI``An*Q06~wRJ%j4934VzGFqVUdv{mb(vodt zbTp)IcYVDrn2_3jV~o|!-F*p7j97&b!m%Kf1UTw1Up_0Fd@&bZEFd7zIygvNQ&Xc_ z`*xIxxcutMN925~>RV5be`Y4#^2*Bl4<7=*eUmZiFV$~cTw3z)P2@6JKzUC!xjz-V z*ps}~$P9m>^73WSo6!OWu-w~05*se2&+z;U3YfuV7z6~gHe75|RzpbeZ{H@NqPktJ zLntgPTx{4vJ3T!uX<1oS^*$~R3nHVa*fpFh%@7!yqYtlq?9ohXU|>K{=Ag`SXYbFS zxJ!egRZpYw@$tRmV}7yYg9CI={6p!Iii!&V)Kuz}l$6EoZP9%~?*yW;tH=9gFOUVW zLo1dPrfZfej+$t{)CHvk`QX;Xi!!m1j4_1WRK*xX+{}!b`{`3CgpiPsY>WX0(}wq8 z_kqp$`0-=cua_@h?j0V|RCIwCyuH0GY;1x)efm{CDfi-qq>c`ipKcxHu76~zZggm# ziXMSmi0s_W;*t^=4NVjZC3XWBccuAwKA^GjJh1sH3a62POHM}@6BkGN z;p4~b+FFY9qiso5)sfhgygVk;*RP?Ft3Qb?M(ZQ_@vn{(Lz#)CrKRuOy}PtEUA6c3 zZ-=g3Gp})(w|DuBd8T*p!LLD+-T7t#->VDG$?xf_P%{&gfbE&u71a5TBocXJqEtU( zy2^ZozvYxr?CQh>(%Kb8Jv=rRI5ec(bal4*?c1UODNMUG2%jRSp@9lgZazrcmJ z_S9{o@YB%30%B*9y4R>SweX{G4>1E)>z|*!6<()*7)E`L8yD{Ry4u^f`eWnv&5^@~ z>kf~PSAGxYPSrUuhDSu~FNM&-5ePaES8i*`=6hG?yC&#hLUck=VPP94kal-tR=Q(M zehs8|xtrYEa(P<))*J>>mbbkS;MzcX zL`Mg*>0m%uz8ti~(Frlmz1UMy26vwa*|ce1dvlQuG1jD4MwnhK+( zC4zkG?L|8dit?LY#o#vfrwNjQQ%2*`c|Be}>sDiTjU!&Bho@NEwp_aOB|pVAA4qFU z;MBF>Z9axGGvkDXg$-F&qc?JMbC-Vq4vi5z!|RTwk+iV*9ebKf=tAA&X8BW*fq7jt zd3kyHBq#x?Chp+y1P+Ji*{&pvjgLPoRL?0XAx!17Y2TS^?5~`EUz46DChU&)K2+va zD!*OV_mAujR~PP07k_$HYZ<9A$QY)-u%grKahR0&#?tbgRPH^#OK_AR?NUBZa8Lnw zl?iTYYEo8LXL6>FTc#)azs1s=TZ_xfuPP_SyiOjsqtP%bDxm_qJUY8(Jq$*3X!-gg zGQ5wpy^f~MK{SgT?aW1h;8xOVPQ(12Sq7ZDZR0jyUO@qzjqP>};!5DHqhp1|Fg!3Q zC_5|5XtqpSS=kSS_2+TRsp)Cs%d^980|Oc59)c#I5-4P0APaz_fa4m$BfUC5VzCoQ zcMM&(RatZJ^;y|9l|yn$3f0BcaZ5MYviB(|LZ$}J&b$|wmr;Y47Z=L<`l1_W-MU=n zP03s^1=`Gf>}rJkmeoKg4lU)md*0%r5mH(j8)7z?K^P^49VFrZ`?tl1#Kf4ozELGa zl&I%XR&z7W>FKHDTGZcnOR_-QAs=mv`g!>(@zwj?72fGa=xnjm9Ra?V8gZh{1tM3=a?6J2`bo(|HG) zbVoDr@Hl<*qNb*{UO>?t&$-fpfGT&{*6GU-_59~ktQ|7A^8Lt9qsDU$&MaJ9(#pyt zN4pEe#KbYi&c_SB2-N8sHHSeHrSJL7Lf79PpjxqTaHRcC2>aH4hk^o>Wr+N@KE-Oo{q=CbyLCj02sJtJuk@>y~t+Afw z9-EjTN<}$uPTVro{P%isb%sQm3d++O)z{bO4jk<7`)d>h$>qNTkE(@D`Of+I`3#(# zgsqEVGH`fG6BA7brj8aDb3nv3G{`kd^;lmE17gA$hoj};A@ug`TM}~e;Iy>+d7nOE zLvG{ahs+Xhl{W6QGscL#%ax942USQq`u_Vbui=&Aqy~gcy79)TvK}-^tUc>GIPeB{ z%t`avP=PdM-WI)PP9eckP*A`ilfC_3toz%ut<{peP??Df5Y8`OcKYkCtS&A>5C}x! zOx*(j0dc&#x--U}Fdp7LgV6sr!o>8(Z{10ViJ2MB!9f6k(8AW1Rzpo)JrI@42Ad7q^VhgULz-F9)|Q&k20mz9-Oz-@1_JVm?8i~(E@0ul>9Uo|W^7^mt;7Ku!d z9)T1U6$QxUV?jQvvyDwoUYmM@g!J(AgprcIiarVr{q}A;$>U~k0;i;n&35^^ZmliN z( zv6Yn-I{Yqs`RiVC4y|&KsW1NF84&tMk9H=g9>U=f%&w9#Fn%2EzmXj3rJ}8c-6)?c}@$muW<&RfaSLsDXX&}m4TA^cO>X6o}%k%wl zO+(}3oqrWoYio}1@bEnARDi^O5_k-Gw(WO7X#sBsBor`B_@_a|`LKZhW{S<1+y8rW z6AkEw{c^`Gh`71=LqNBdR#yX)k|;%vrcCVZ?H}vrB9TZ0-zz`w^X*yzmn}+CGP3>O zxiLhkzCXX;1;_AeAWg9De5X-io(TpUMo{mQYaxMwi>JRb zLt0u8;Hd39ehQ@%fl$Q5$CrHd3Jywq$hf+?y3x*TJzylAfN@j+_)ImR19gU$5l73( z>FfS+H?61jSkSm7@7C6qt;f%M_wEf@@UXD5`b*_LlalHxr}`)tRMv7u_5AsB0rx*l z+}zyCYHE(7n@sSWo~7Ub0Ro(W9*qnDZ67||`4_Yi(TWHZcMS{>9UUD({4z3V2?z+1 ziUCN+#i-yq=xM5{1%Nmk8yjn#p1!xgzb_*rlN9@L60m`MRVd}`=$EoGx{A`uN>X-> z;?|$NiTfKxW$lxbt`ZYUNaP=9Gz9e?CsgF{`?6R@~mha&cFbr;4--AdF^v`kZIBxeizXGtDc6sI)OXnTJvH1WFKcyx&0$U~EuzUlM2`hqV--(ftcVLUa(any0AN}7Z2Zl?n4QARk zt!-(xoQ=n-#37M9yip!_6*V>FzF^J6Lqm{e1LA--2*_B_JkitBLkbHE?ZER%OxpSLCyYfZ^1y`_637@$y2vo*~C1)e>LRT+W z3jyGtO{Z(Ap%D>iG9?lJBF(atTm&4R1Dku?3QGUd#zr{U`K2iS`PK+7R4D(v8}@F? zHA?Ud6jrKX!ZPd`K)h*vFB~8N=y@6%n*BXAF(Wtk$Z9_f28&t?h8Cz6#)60n55LXB z&(8>={qXP*1Aw9E`LNK?zNKKOi@uQ&Jr*`L6e5>Tx{Eq5?MVSFpaJH?`1JpImpziy z@_zb`lMu~BiEao8pBp!C8ed;s?(OYSR@4AG3vL1fb2WAgwrh%Ep?xMs;#s{zF{K$| zPKQUiRkg$(l86S6^WnLf7+Z7v2!RqrPJ0nK^0vyVP9S?^`f@z#zAR7i_lI7bw_FR)d+v&YE@%u?k@xcJgqIc@TS29{6bk90L(WH3zA23!!?SPv zN{c8s^_z#BsfI?zBT$(#47j@NbwX3^{SJHP!A00+r}6Bie_Ae^k$2#-_J}uiG@6+#qOA!oVq_*R2Th&IuhWwg@uG2w zf1#5^p*m5KAi#`h8sXsD1s`gVN58kXgCZjE0YnFn7yQHNAS4tN{0`T6aBkM-Y`W?a zkFlC{l}SEIMQ0n2F+*Q#YJx9E87Sjk9ae~vxv-ny47#_I$nj!!>E|n4I|@%Xp&9*D zbMs;c=da0?A+yZXvcwF`4<0Z8Oj}=H|MvZRJLu3t#+$CM&bt;|cju$p+uLF2h)@nS zS3AVllDW@shI0$5seh1*TFx|N+l*677Uf%=WMO+!v!B!nwRVrw!q_x9l3x?&7l{@$ zPMMs2^}WC>A|+(4fJqjc18@PW$;djqAI>~{v{6i^`z4KxQ z=_OAP$uf?K`6}z9N70PrVa$H>Lor1SkMwLd+F$BZu%$LC7j85e{E$Q0vkj~cB*flUJbO zm-!7(VaC?cThT5e9nuLgP$?!!2|ON`zd3oOG>IEO94bn&6E|lZ>QN@&vTlL&36ytj z+lJC=Ifl@Uji$r6=We#m57^fLBFkjwg-fPg=h6%XpHnHe>F)gDqE~DsEvtVXoAuW5 zYbOUC#Vj2!Hp8es$(Q>V`_7K5b95FXKmpYb^@sX{txub*S@Ke#el%9IBw-``C8aTn z_t6nw($AH5=VpVg9GPz>ee@oCr-%-|82*5UFvf{186xC1gzRe?#woQrfAFnr#}t>` zQ3f|Ou|N|ufG=J*$P+l2go7ZK#<@Gp3z`SAQR*|WH1u=|Us{oEC-Hbmo%$q^=QCgv8+wsd10k!dW5!KsW zjcJX-({PUhYQ9GrjFtuLIbL%li|ZW+>PUWnsFYKDAjwe8hQpa9emGky=4oK!tArgT z1S0QP{wy++Izum~>pp@X+3$pwccsN*N?{>*obzOa`pluG;qE84l>NDJvpX+qD0fwj z%}s(>3^hggxJVo?=15k4MT_rf!;Y)U*!CR#|?+_Q0i`khJ`O0c8f+2@2N zi+J~he+b+4hDYm6r@?Y`SSVCVEsg|BlS*h`;SCvz)uz4nr_johIKQ-R2d?|_u=`aK z_3yCH$))ABOQGWM7idT>D-cvix)jsuMw~dmI$3N{5^&IIdiD_?NqCh%T)XKGmtSvp z)I@T(LULt`K(3x9QtuyrWL`D2MS7nju&f+DQ;>hg^~GvV6X1@O38pmSdGmFh@Z{_* zS+t>T>ttf8vd8xQgv2Tu@?bY(mCyX-+~uLnggBHv8rOcU$zRC5oC{&ya#Z#Wl{YW3 zu?Acwk|?&qy4P@xz_=k<31hfO}LCZYN zy9ZSTD@PF5DIG@{;bGHm2xG(qf?ipDIIH4~k`g!*J zo>NVHof@2*ABR#q4FG_lzGdt1vWJ&L1udsOjuxw5y+5tn63Tv-tP~k&Ni}E!Q*a=7 zT#2Wd+7PR&7m`b{VPB~pf`UScAL?=tYA$EYe9$80qp?*4YPT$sJP;}sM zgCg=pXhz^yZ$tc)^YQuS??%3ciPbT-Vo%)ndWOt3@_Rr1ml*F(!lwPI9u0Uq`OE$} zWfs2^aixpya6FEPy=SqtRbw`waLP~_fu3P=qC^r1IY8Gh_r3OUb8{mos;a4xa&qE< zU^pY4??pwuZhq?6B*HKLwb1>uXAz9QEQhCt@rA9}IfxFr9A8LghwUwS-wgX*0KV~` z9zsS*NgB`>P>udkI-;YaS=iX(vLqYs1z$nE)P8{I*vQ)43d$|vO^{;Q7at>F3S?h; zHJI)TZ{Usd2{-*uDiX->Zm%PK9w z1$0Oz@^4$f6E==yUK*kr0!}zLVLa|sL_+#0BGBfh85!w6(P{r}2-NBUoGjBsfJy z#q!!(X%iDh(2MME8eZ$Xv;9O1I6{4=^GRzn{ELpmqWS}|M_-<$qKMh}aq~D;Ob)l7KIt`283TF%6mkc&_0D7kfVYGIx>-0A z8wJ?Z#bG1c#ZUFSavQ=%b|yi%IcD_kr3jG!`_Yt0wLpw!T;qLvlaA!%OCI$6cImQ( zgXkH~jWryP#g8Gm6>zD}10AI4Fr|^2@=HewqB|N(;>Hgwbq@F5)}XsO+q9^6V`A4I zsn!A|j+vmGHOE(cF#`W^%CQp5(15C`V-XZuxAis|3%Hk&IhxCl3GQy}r||Z0+hgmx zhSU?EO=WI`@F$O_h^rFIMAtcb5G}g_G#6VcY->_diAB?7qFrw_e@1hwVSf5@fiY?> zQA(&nAhC5xeeHAPpKeGhvcdD*q5LzIwqfE5D15W`ZT4Gs2--~!u(Q_cIpHa$lt%I5 z`K9=tsKQp19Xr4(@xbqQeYMB}^0W-+BVt3DIdPKYZ!uud+=Yhg`M{|VXyRu1XK=4K z2aEtvi)Sx7>mqsTpR}5G4I%s_5f&AjZubV%tJ~*#$dwjo-uY^i;2F9~7tS!kxv9Z- zeRZMUTdLKp5N^58TjKG#Ilbcb%R3XZOWnKE@Eq1KsM(UG?%|Yk(;d3BrtpDz9}acT zrQ@O;d!;&3jizQYn1V6EV|?d&6wUQ#w@J5e*%5k*8)qv2+408!mlMK8ot(2&6{DY5&o@EG<$ZMaxJAoWuF63`gWnK2p2 z@Ypc>EOL@3(rPruUw=C@g2PGj<~RCpwqgDDPTR>LHQ*K~OUZ3A|PE z$(5+t7Xq52!Sq}YFEX7SV|mP(i`@w+V8fioe#w3$SlwLw%hik&``$0vu|(4MelR+g zvrQy=g6|*;gz~j18rpQx5gB%bjWA;fXASSL+X)dOV+CllMg--z-WHb9l+o6QrX4tA z!q<*AvSS4w%g7F`5`;t+v_cx6k8{qsUG66CO^&{_uc&I{A~RnK>uj@Mat2pmez5+j zSn(6O!Ff~Jz#uv+i+*dNg^re%7QInw&>Rj#H+2mSiEQP?p=?RK+qb!W(TI^BHmx)6 ziTk!bf{;aUfG3lUl8Z~$58oIfk!nGC56u9?XAN5@tEyr}Nd-hkM#45R5+8+u8?>{t zlZA!FPqmQ8@zeFt48Na#cdz|%ks&Pxoyz<9LaR|KMTQEFGE8*O#e0d zNr@5ENl-@7z?7+QSm`pZPEJn#*4K9f!o$NetFuCR!QF$nU+>rAqe%$*Dr;S`sMY$b zCcNa(Z&*npt-vCZPXD#Cf?cA44`lul4dLZ_PM~luf^J{K)za{DgxcR|I!u#&*bki~1`!JSF^TOO%5(=pELoTHS!<42YNqnuoxab z#08xn#1CLIu(+@wK-k>>VO91FXxc!hk-!rQq_Oc*UE<~@ou5SZqWdFwN}TOcmD=ql{SL%Zze`x?s(V>vRKyHGkp7>e7{!J@|8j2HN&C}tDiA4haFfce64%Gdr3!r=!>(&xwXJ;oNe8s@^qYez*f8NFXT}0$Q zFi4(Q83JKd2)r9cMurVi2k2cZB0#F^=<0&=@sZ2Q%9?Fk0p&YM%)7ziQrLBeXQ21{ zcL^;mO3-FUE;QMcqI~uv3@JTRc&*51=jNgg#e3U~ze;y@Zi02QEm=-Jn z<3<3*$UM#byNz=$YTozSyQ8Cn9(cH}^X?B(*H@k^O#llpR0dob_+e(->_7&xwYBYP z8Q=9e7G7zhzPwbJo-8UuS#GfcXLDj=0{F=g`Nn1-`8f=V9@E3&SXJhOU3Awc-NwAC z&D~6l1+iehAX}rT^CP<^Qb{QWtO5qi4rUbbJh4}T^ahO2#23Sd$C>>4z#HwT1w14BY^AqUO1 zT_ckgwm-cEhrN?Gn{Q$;)OFSY7h?3|>zmNfP;qDH!&s`qc@x!SRj7u(KB2F#?}{S= z(PC`pM>jb&HPzbRPYC=x6P<|<-j`EAy4h>RCZ^2XJoM=3>@;qBcN3%J2L9#AK#86G zX2e7xkm$h}%L?WLQ1R!_Z+`#&y&UlB6&^L4GU|IJc51do`)9w76C|=*!JtY+z2h(V z)2E|bmuUhHSdMFbZP>&jevZEeppUmbtwb6c8thN@jEl9ax&-?5W{R4c?tfCs?7BET z2$4@jds@S%W#;?@!(cdiYVNH0X z9}Wz1AcQ96mxa)J;DHeb7T|(o6sWwsyhRY1wMpC6b_-%m4y6=Vlkx zO=0Neuixmhbh5wF^#(ZVn6LEq76mqs!;DR>aZlU>v-Mxm!aJBd*3;9o|2@P|tW$ma z!Gj0nRn20jt7IHHUoF1PrwG<)N=xHT6l(`yg!IzVQpq*#Gf(Rrg+kHCphT?DVnM*{ zQX6nc#!^7KeXXf!A02g+?@g)Xf_&@m?*uK&{(d(ddVYRh;L@W+r$kCZ0!uMd%%%nf z9N5Ta3}S6dua3BhF1d&eLMqmT44BT>F3YaX+F)w zzK&Ro65diTb08B$1QVMG)!Ie&?2Ys0NY<}=qMt#_Q!YW?~A$w*2K~YO9 z3829%V9SELG4NP_01$yQkR+$EQ5OVML~JYxjE;`0RImP(o(twpB^nXH0uGZ5Axf_1 zijrddAeW!?)I1vV7%N zwVn&;+~7=1ND$bei0jiqvu&_3K42IkydMlzX%Bpuu>t_}sw?tddsmlKUz(sAMsLLo zGA*aO&ch@Kd?2C+eNQp#`TUt>f4g>pDv|eRO@oRdHNWkhot>So3eJ zt_++{&;`Dk9a!7gRPsHG-*4sxvtSHR;gN*}J?~pj1kPN5xGtIS^zcZ?Wh)6npOc## z}9 zkWj6a`q#YW)50%b+JV=M$!n3zzoV+Pl1!dpc+r&EbsI0 z6@DwS;Sys%Sn_Zf*+UX=M8JKH$J&(w4rOJBC|KhEVSe+{m;1M8yGje&7?@p!$Uav@ JmP#1C`yZ>FadrRz From 575103c06aa62ac414ad7f3e366059efcd3f8945 Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Sat, 7 Oct 2017 13:57:34 +0300 Subject: [PATCH 13/33] Clean up --- pyed/SparseExactDiagonalization.py | 68 ++++++++++++++----------- pyed/TriqsExactDiagonalization.py | 82 +++++++++++++++++++++++++++++- 2 files changed, 117 insertions(+), 33 deletions(-) diff --git a/pyed/SparseExactDiagonalization.py b/pyed/SparseExactDiagonalization.py index 326554f..1c3567c 100644 --- a/pyed/SparseExactDiagonalization.py +++ b/pyed/SparseExactDiagonalization.py @@ -25,13 +25,8 @@ from CubeTetras import CubeTetras # ---------------------------------------------------------------------- -<<<<<<< Updated upstream -def gf(M,E,x): - return np.sum(M/(x-E)) -======= def gf(M,E,eta,x): return np.sum(M/(x+1j*eta-E)) ->>>>>>> Stashed changes # ---------------------------------------------------------------------- class SparseExactDiagonalization(object): @@ -63,17 +58,10 @@ def _diagonalize_hamiltonian(self): bar = progressbar.ProgressBar() for i in bar(range(len(self.blocks))): block=self.blocks[i] -<<<<<<< Updated upstream - X,Y=np.meshgrid(block,block) - E,U=np.linalg.eigh(self.H[X,Y].todense()) - self.E[block]=E - self.U[Y,X]=U -======= E,U=np.linalg.eigh(self.H[block][:,block].todense()) self.E[block]=E for i,n in enumerate(block): self.U[n,block]=U[i] ->>>>>>> Stashed changes self.E=np.array(self.E) self.E0 = np.min(self.E) self.E = self.E-self.E0 @@ -90,11 +78,6 @@ def _calculate_partition_function(self): # ------------------------------------------------------------------ def _calculate_density_matrix(self): self.rho=csr_matrix(self.H.shape,dtype=np.float) -<<<<<<< Updated upstream - exp_bE=csr_matrix(self.H.shape,dtype=np.float) - exp_bE[range(self.E.size),range(self.E.size)]=np.exp(-self.beta * self.E) / self.Z - self.rho=self.U.getH()*exp_bE*self.U -======= print 'Density matrix calculation:' bar = progressbar.ProgressBar() for i in bar(range(len(self.blocks))): @@ -102,7 +85,6 @@ def _calculate_density_matrix(self): X,Y=np.meshgrid(block,block) exp_bE = np.exp(-self.beta * self.E[block]) / self.Z self.rho[X,Y]= np.einsum('ij,j,jk->ik', self.U[X,Y].todense(), exp_bE, self.U[X,Y].H.todense()) ->>>>>>> Stashed changes # ------------------------------------------------------------------ def _operators_to_eigenbasis(self, op_vec): @@ -114,12 +96,33 @@ def _operators_to_eigenbasis(self, op_vec): return dop_vec # ------------------------------------------------------------------ - def get_expectation_value(self, operator): + def get_expectation_value_sparse(self, operator): + + exp_val = 0.0 + for idx in xrange(self.E.size): + vec = self.U[:, idx] + dot_prod = np.dot(vec.H, operator * vec)[0,0] # + exp_val += np.exp(-self.beta * self.E[idx]) * dot_prod + + exp_val /= self.Z + + return exp_val + + # ------------------------------------------------------------------ + def get_expectation_value_dense(self, operator): if not hasattr(self, 'rho'): self._calculate_density_matrix() return np.sum((operator * self.rho).diagonal()) + # ------------------------------------------------------------------ + def get_expectation_value(self, operator): + + if self.nstates is None: + return self.get_expectation_value_dense(operator) + else: + return self.get_expectation_value_sparse(operator) + # ------------------------------------------------------------------ def get_free_energy(self): @@ -157,19 +160,13 @@ def get_ground_state_energy(self): def get_grand_potential(self): return self.E0-np.log(np.sum(np.exp(-self.beta*self.E)))/self.beta - def get_real_frequency_greens_function_component(self, w, op1, op2,eta,xi): + def get_real_frequency_greens_function_component(self, w, op1, op2,eta): r""" Returns: G^{(2)}(i\omega_n) = -1/Z < O_1(i\omega_n) O_2(-i\omega_n) > """ op1_eig, op2_eig = self._operators_to_eigenbasis([op1, op2]) Q=(op1_eig.getH().multiply(op2_eig)).tocoo() -<<<<<<< Updated upstream - M=(np.exp(-self.beta*self.E[Q.row])-xi*np.exp(-self.beta*self.E[Q.col]))*Q.data - E=(self.E[Q.row]-self.E[Q.col]) - G = np.zeros((len(w)), dtype=np.complex) - G = Parallel(n_jobs=4)(delayed(gf)(M,E-1j*eta,x) for x in w) -======= M=(np.exp(-self.beta*self.E[Q.row])+np.exp(-self.beta*self.E[Q.col]))*Q.data E=(self.E[Q.row]-self.E[Q.col]) G = np.zeros((len(w)), dtype=np.complex) @@ -350,7 +347,6 @@ def get_tau_greens_function_component(self, tau, op1, op2): G = -np.einsum('tn,tm,nm,mn->t', et_p, et_m, op1_eig, op2_eig) ->>>>>>> Stashed changes G /= self.Z return G @@ -362,12 +358,22 @@ def get_frequency_greens_function_component(self, iwn, op1, op2, xi): G^{(2)}(i\omega_n) = -1/Z < O_1(i\omega_n) O_2(-i\omega_n) > """ + # -- Components of the Lehman expression + dE = - self.E[:, None] + self.E[None, :] + exp_bE = np.exp(-self.beta * self.E) + M = exp_bE[:, None] - xi * exp_bE[None, :] + + inv_freq = iwn[:, None, None] - dE[None, :, :] + nonzero_idx = np.nonzero(inv_freq) + # -- Only eval for non-zero values + freq = np.zeros_like(inv_freq) + freq[nonzero_idx] = inv_freq[nonzero_idx]**(-1) + op1_eig, op2_eig = self._operators_to_eigenbasis([op1, op2]) - Q=(op1_eig.getH().multiply(op2_eig)).tocoo() - M=(np.exp(-self.beta*self.E[Q.row])-xi*np.exp(-self.beta*self.E[Q.col]))*Q.data - E=(self.E[Q.row]-self.E[Q.col]) + + # -- Compute Lehman sum for all operator combinations G = np.zeros((len(iwn)), dtype=np.complex) - G = Parallel(n_jobs=4)(delayed(gf)(M,E,x) for x in iwn) + G = np.einsum('nm,mn,nm,znm->z', op1_eig, op2_eig, M, freq) G /= self.Z return G diff --git a/pyed/TriqsExactDiagonalization.py b/pyed/TriqsExactDiagonalization.py index ee38bf7..4301e1a 100644 --- a/pyed/TriqsExactDiagonalization.py +++ b/pyed/TriqsExactDiagonalization.py @@ -17,6 +17,8 @@ # ---------------------------------------------------------------------- +from pyed.CubeTetras import CubeTetrasMesh, enumerate_tau3 +from pyed.SquareTriangles import SquareTrianglesMesh, enumerate_tau2 from pyed.SparseExactDiagonalization import SparseExactDiagonalization from pyed.SparseMatrixFockStates import SparseMatrixRepresentation @@ -47,7 +49,7 @@ def get_ground_state_energy(self): return self.ed.get_ground_state_energy() - def set_g2_w(self, g_w, op1, op2,eta=0.1,xi=-1): + def set_g2_w(self, g_w, op1, op2,eta=0.1): op1_mat = self.rep.sparse_matrix(op1) op2_mat = self.rep.sparse_matrix(op2) @@ -56,7 +58,24 @@ def set_g2_w(self, g_w, op1, op2,eta=0.1,xi=-1): g_w.data[:, 0, 0] = \ self.ed.get_real_frequency_greens_function_component( - w, op1_mat, op2_mat, eta, xi) + w, op1_mat, op2_mat, eta) + # ------------------------------------------------------------------ + def set_g2_tau(self, g_tau, op1, op2): + + assert( type(g_tau.mesh) == MeshImTime ) + assert( self.beta == g_tau.mesh.beta ) + assert( g_tau.target_shape == (1, 1) ) + + op1_mat = self.rep.sparse_matrix(op1) + op2_mat = self.rep.sparse_matrix(op2) + + tau = np.array([tau for tau in g_tau.mesh]) + + g_tau.data[:, 0, 0] = \ + self.ed.get_tau_greens_function_component( + tau, op1_mat, op2_mat) + + self.set_tail(g_tau, op1_mat, op2_mat) # ------------------------------------------------------------------ def set_g2_iwn(self, g_iwn, op1, op2): @@ -90,3 +109,62 @@ def xi(self, mesh): if mesh.statistic == 'Fermion': return -1.0 elif mesh.statistic == 'Boson': return +1.0 else: raise NotImplementedError + + # ------------------------------------------------------------------ + def set_g3_tau(self, g3_tau, op1, op2, op3): + + assert( g3_tau.target_shape == (1,1,1,1) ) + + op1_mat = self.rep.sparse_matrix(op1) + op2_mat = self.rep.sparse_matrix(op2) + op3_mat = self.rep.sparse_matrix(op3) + + ops_mat = np.array([op1_mat, op2_mat, op3_mat]) + + for idxs, taus, perm, perm_sign in SquareTrianglesMesh(g3_tau): + + ops_perm_mat = ops_mat[perm + [2]] + taus_perm = np.array(taus).T[perm] + + data = self.ed.get_timeordered_two_tau_greens_function( + taus_perm, ops_perm_mat) + + for idx, d in zip(idxs, data): + g3_tau[list(idx)][:] = perm_sign * d + + # ------------------------------------------------------------------ + def set_g40_tau(self, g40_tau, g_tau): + + assert( type(g_tau.mesh) == MeshImTime ) + #assert( g_tau.target_shape == g40_tau.target_shape ) + + for (i1, i2, i3), (t1, t2, t3) in enumerate_tau3(g40_tau): + g40_tau[[i1, i2, i3]][:] = \ + g_tau(t1-t2)*g_tau(t3) - g_tau(t1)*g_tau(t3-t2) + + # ------------------------------------------------------------------ + def set_g4_tau(self, g4_tau, op1, op2, op3, op4): + + assert( g4_tau.target_shape == (1,1,1,1) ) + + op1_mat = self.rep.sparse_matrix(op1) + op2_mat = self.rep.sparse_matrix(op2) + op3_mat = self.rep.sparse_matrix(op3) + op4_mat = self.rep.sparse_matrix(op4) + + ops_mat = np.array([op1_mat, op2_mat, op3_mat, op4_mat]) + + for idxs, taus, perm, perm_sign in CubeTetrasMesh(g4_tau): + + ops_perm_mat = ops_mat[perm + [3]] + taus_perm = np.array(taus).T[perm] + + data = self.ed.get_timeordered_three_tau_greens_function( + taus_perm, ops_perm_mat) + + for idx, d in zip(idxs, data): + g4_tau[list(idx)][:] = perm_sign * d + + # ------------------------------------------------------------------ + +# ---------------------------------------------------------------------- From 9313a40bf9ee3cdfbd95945cf6646c977f462e8c Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Sat, 7 Oct 2017 13:59:53 +0300 Subject: [PATCH 14/33] Clean up --- pyed/SparseExactDiagonalization.py | 295 +---------------------------- pyed/TriqsExactDiagonalization.py | 116 +----------- 2 files changed, 2 insertions(+), 409 deletions(-) diff --git a/pyed/SparseExactDiagonalization.py b/pyed/SparseExactDiagonalization.py index 1c3567c..18dfd24 100644 --- a/pyed/SparseExactDiagonalization.py +++ b/pyed/SparseExactDiagonalization.py @@ -22,8 +22,6 @@ from scipy.sparse import csr_matrix from scipy.linalg import expm # ---------------------------------------------------------------------- -from CubeTetras import CubeTetras -# ---------------------------------------------------------------------- def gf(M,E,eta,x): return np.sum(M/(x+1j*eta-E)) @@ -96,7 +94,7 @@ def _operators_to_eigenbasis(self, op_vec): return dop_vec # ------------------------------------------------------------------ - def get_expectation_value_sparse(self, operator): + def get_expectation_value(self, operator): exp_val = 0.0 for idx in xrange(self.E.size): @@ -108,20 +106,6 @@ def get_expectation_value_sparse(self, operator): return exp_val - # ------------------------------------------------------------------ - def get_expectation_value_dense(self, operator): - - if not hasattr(self, 'rho'): self._calculate_density_matrix() - return np.sum((operator * self.rho).diagonal()) - - - # ------------------------------------------------------------------ - def get_expectation_value(self, operator): - - if self.nstates is None: - return self.get_expectation_value_dense(operator) - else: - return self.get_expectation_value_sparse(operator) # ------------------------------------------------------------------ def get_free_energy(self): @@ -174,280 +158,3 @@ def get_real_frequency_greens_function_component(self, w, op1, op2,eta): G /= self.Z return G - # ------------------------------------------------------------------ - def get_g2_dissconnected_tau_tetra(self, tau, tau_g, g): - - g = np.squeeze(g) # fix for now throwing orb idx - g = g.real - - N = len(tau) - G4 = np.zeros((N, N, N), dtype=np.complex) - - def gint(t): - sign = 1.0 - if (t < 0).any(): - assert( (t <= 0).all() ) - t = self.beta + t - sign = -1.0 - - return sign * np.interp(t, tau_g, g) - - for idx, taus, perm, perm_sign in CubeTetras(tau): - t1, t2, t3 = taus - G4[idx] = gint(t1-t2)*gint(t3) - gint(t1)*gint(t3-t2) - - return G4 - - # ------------------------------------------------------------------ - def get_g2_dissconnected_tau(self, tau, tau_g, g): - - g = np.squeeze(g) # fix for now throwing orb idx - g = g.real - - N = len(tau) - G4 = np.zeros((N, N, N), dtype=np.complex) - - def gint(t_in): - t = np.copy(t_in) - sidx = (t < 0) - sign = np.ones_like(t) - sign[sidx] *= -1. - t[sidx] = self.beta + t[sidx] - return sign * np.interp(t, tau_g, g) - - t1, t2, t3 = np.meshgrid(tau, tau, tau, indexing='ij') - G4 = gint(t1-t2)*gint(t3) - gint(t1)*gint(t3-t2) - - return G4 - - # ------------------------------------------------------------------ - def get_g2_tau(self, tau, ops): - - N = len(tau) - G4 = np.zeros((N, N, N), dtype=np.complex) - ops = np.array(ops) - - for tidx, tetra in enumerate(CubeTetras(tau)): - idx, taus, perm, perm_sign = tetra - - print 'Tetra:', tidx - - # do not permute the last operator - ops_perm = ops[perm + [3]] - taus_perm = taus[perm] # permute the times - - G4[idx] = self.get_timeordered_three_tau_greens_function( - taus_perm, ops_perm) * perm_sign - - return G4 - - # ------------------------------------------------------------------ - def get_timeordered_two_tau_greens_function(self, taus, ops): - - r""" - taus = [t1, t2] (ordered beta>t1>t2>0) - ops = [O1, O2, O3] - - Returns: - G^{(4)}(t1, t2) = -1/Z < O1(t1) O2(t2) O3(0) > - - """ - - Nop = 3 - - assert( taus.shape[0] == 2 ) - assert( len(ops) == Nop ) - - G = np.zeros((taus.shape[-1]), dtype=np.complex) - - E = self.E[None, :] - - t1, t2 = taus - t1, t2 = t1[:, None], t2[:, None] - - assert( (t1 <= self.beta).all() ) - assert( (t1 >= t2).all() ) - assert( (t2 >= 0).all() ) - - et_a = np.exp((-self.beta + t1)*E) - et_b = np.exp((t2-t1)*E) - et_c = np.exp((-t2)*E) - - dops = self._operators_to_eigenbasis(ops) - op1, op2, op3 = dops - - G = np.einsum('ta,tb,tc,ab,bc,ca->t', et_a, et_b, et_c, op1, op2, op3) - - G /= self.Z - return G - - # ------------------------------------------------------------------ - def get_timeordered_three_tau_greens_function(self, taus, ops): - - r""" - taus = [t1, t2, t3] (ordered beta>t1>t2>t3>0) - ops = [O1, O2, O3, O4] - - Returns: - G^{(4)}(t1, t2, t3) = -1/Z < O1(t1) O2(t2) O3(t3) O4(0) > - - """ - - assert( taus.shape[0] == 3 ) - assert( len(ops) == 4 ) - - Nop = 4 - G = np.zeros((taus.shape[-1]), dtype=np.complex) - - E = self.E[None, :] - - t1, t2, t3 = taus - t1, t2, t3 = t1[:, None], t2[:, None], t3[:, None] - - assert( (t1 <= self.beta).all() ) - assert( (t1 >= t2).all() ) - assert( (t2 >= t3).all() ) - assert( (t3 >= 0).all() ) - - et_a = np.exp((-self.beta + t1)*E) - et_b = np.exp((t2-t1)*E) - et_c = np.exp((t3-t2)*E) - et_d = np.exp((-t3)*E) - - dops = self._operators_to_eigenbasis(ops) - op1, op2, op3, op4 = dops - - if True: - q_tac = np.einsum('tb,ab,bc->tac', et_b, op1, op2) - q_tca = np.einsum('td,cd,da->tca', et_d, op3, op4) - G = np.einsum('ta,tc,tac,tca->t', et_a, et_c, q_tac, q_tca) - else: - # Not efficient... - G = np.einsum( - 'ta,tb,tc,td,ab,bc,cd,da->t', - et_a, et_b, et_c, et_d, op1, op2, op3, op4) - - G /= self.Z - return G - - # ------------------------------------------------------------------ - def get_tau_greens_function_component(self, tau, op1, op2): - - r""" - Returns: - G^{(2)}(\tau) = -1/Z < O_1(\tau) O_2(0) > - """ - - G = np.zeros((len(tau)), dtype=np.complex) - - op1_eig, op2_eig = self._operators_to_eigenbasis([op1, op2]) - - et_p = np.exp((-self.beta + tau[:,None])*self.E[None,:]) - et_m = np.exp(-tau[:,None]*self.E[None,:]) - - G = -np.einsum('tn,tm,nm,mn->t', et_p, et_m, op1_eig, op2_eig) - - G /= self.Z - return G - - # ------------------------------------------------------------------ - def get_frequency_greens_function_component(self, iwn, op1, op2, xi): - - r""" - Returns: - G^{(2)}(i\omega_n) = -1/Z < O_1(i\omega_n) O_2(-i\omega_n) > - """ - - # -- Components of the Lehman expression - dE = - self.E[:, None] + self.E[None, :] - exp_bE = np.exp(-self.beta * self.E) - M = exp_bE[:, None] - xi * exp_bE[None, :] - - inv_freq = iwn[:, None, None] - dE[None, :, :] - nonzero_idx = np.nonzero(inv_freq) - # -- Only eval for non-zero values - freq = np.zeros_like(inv_freq) - freq[nonzero_idx] = inv_freq[nonzero_idx]**(-1) - - op1_eig, op2_eig = self._operators_to_eigenbasis([op1, op2]) - - # -- Compute Lehman sum for all operator combinations - G = np.zeros((len(iwn)), dtype=np.complex) - G = np.einsum('nm,mn,nm,znm->z', op1_eig, op2_eig, M, freq) - G /= self.Z - - return G - - # ------------------------------------------------------------------ - def get_high_frequency_tail_coeff_component( - self, op1, op2, xi, Norder=3): - - r""" The high frequency tail corrections can be derived - directly from the imaginary time expression for the Green's function - - G(t) = -1/Z Tr[e^{-\beta H} e^{tH} b e^{-tH} b^+] - - and the observation that the high frequency components of the - Matsubara Green's function G(i\omega_n) can be obtained by partial - integration in - - G(i\omega_n) = \int_0^\beta dt e^{i\omega_n t} G(t) - = \sum_k=0^\infty (-1)^k - (\xi G^{(k)}(\beta^-) - G^{(k)}(0^+))/(i\omega_n)^(k+1) - = \sum_{k=1} c_k / (i\omega_n)^{k} - - where the n:th order derivative G^{(n)}(t) can be expressed as - - G^{(k)}(t) = - < [[ H , b(t) ]]^{(k)} b^+ > - - where [[H, b]]^{(k)} = [H, [H, [H, ... [H, b] ... ]]] is the k:th order - left side commutator of H with b. - - Using this the high frequency coefficients c_k takes the form - - c_k = (-1)^(k-1) (\xi G^{(k-1)}(\beta^-) - G^{(k-1)}(0^+)) - = (-1)^k < [ [[ H , b ]]^{(k-1)} , b^+ ]_{-\xi} > - - """ - - def xi_commutator(A, B, xi): - return A * B - xi * B * A - - def commutator(A, B): - return A * B - B * A - - H = self.H - - Gc = np.zeros((Norder), dtype=np.complex) - ba, bc = op1, op2 - - Hba = ba - for order in xrange(Norder): - tail_op = xi_commutator(Hba, bc, xi) - Gc[order] = (-1.)**(order) * \ - self.get_expectation_value(tail_op) - Hba = commutator(H, Hba) - - return Gc - - # ------------------------------------------------------------------ - def get_high_frequency_tail(self, iwn, Gc, start_order=-1): - - """ from the high frequency coefficients Gc calculate the - Matsubara Green's function tail - - G(i\omega_n) = \sum_k Gc[k] / (i\omega_n)^k """ - - Nop = Gc.shape[-1] - Nw = len(iwn) - G = np.zeros((Nw, Nop, Nop), dtype=np.complex) - iwn_idx = np.nonzero(iwn)[0] # -- Only eval for non-zero freq. - for idx, gc in enumerate(Gc): - G[iwn_idx, :, :] += \ - iwn[iwn_idx, None, None]**(-idx+start_order) * gc[None, :, :] - - return G - - # ------------------------------------------------------------------ - -# ---------------------------------------------------------------------- diff --git a/pyed/TriqsExactDiagonalization.py b/pyed/TriqsExactDiagonalization.py index 4301e1a..6105227 100644 --- a/pyed/TriqsExactDiagonalization.py +++ b/pyed/TriqsExactDiagonalization.py @@ -13,12 +13,6 @@ # ---------------------------------------------------------------------- -from pytriqs.gf import MeshImTime, MeshProduct - -# ---------------------------------------------------------------------- - -from pyed.CubeTetras import CubeTetrasMesh, enumerate_tau3 -from pyed.SquareTriangles import SquareTrianglesMesh, enumerate_tau2 from pyed.SparseExactDiagonalization import SparseExactDiagonalization from pyed.SparseMatrixFockStates import SparseMatrixRepresentation @@ -59,112 +53,4 @@ def set_g2_w(self, g_w, op1, op2,eta=0.1): g_w.data[:, 0, 0] = \ self.ed.get_real_frequency_greens_function_component( w, op1_mat, op2_mat, eta) - # ------------------------------------------------------------------ - def set_g2_tau(self, g_tau, op1, op2): - - assert( type(g_tau.mesh) == MeshImTime ) - assert( self.beta == g_tau.mesh.beta ) - assert( g_tau.target_shape == (1, 1) ) - - op1_mat = self.rep.sparse_matrix(op1) - op2_mat = self.rep.sparse_matrix(op2) - - tau = np.array([tau for tau in g_tau.mesh]) - - g_tau.data[:, 0, 0] = \ - self.ed.get_tau_greens_function_component( - tau, op1_mat, op2_mat) - - self.set_tail(g_tau, op1_mat, op2_mat) - - # ------------------------------------------------------------------ - def set_g2_iwn(self, g_iwn, op1, op2): - - assert( self.beta == g_iwn.mesh.beta ) - assert( g_iwn.target_shape == (1, 1) ) - - op1_mat = self.rep.sparse_matrix(op1) - op2_mat = self.rep.sparse_matrix(op2) - - iwn = np.array([iwn for iwn in g_iwn.mesh]) - - g_iwn.data[:, 0, 0] = \ - self.ed.get_frequency_greens_function_component( - iwn, op1_mat, op2_mat, self.xi(g_iwn.mesh)) - - self.set_tail(g_iwn, op1_mat, op2_mat) - - # ------------------------------------------------------------------ - def set_tail(self, g, op1_mat, op2_mat): - - tail = g.tail - - tail.data[:tail.order_max, 0, 0] = \ - self.ed.get_high_frequency_tail_coeff_component( - op1_mat, op2_mat, - self.xi(g.mesh), Norder=tail.order_max) - - # ------------------------------------------------------------------ - def xi(self, mesh): - if mesh.statistic == 'Fermion': return -1.0 - elif mesh.statistic == 'Boson': return +1.0 - else: raise NotImplementedError - - # ------------------------------------------------------------------ - def set_g3_tau(self, g3_tau, op1, op2, op3): - - assert( g3_tau.target_shape == (1,1,1,1) ) - - op1_mat = self.rep.sparse_matrix(op1) - op2_mat = self.rep.sparse_matrix(op2) - op3_mat = self.rep.sparse_matrix(op3) - - ops_mat = np.array([op1_mat, op2_mat, op3_mat]) - - for idxs, taus, perm, perm_sign in SquareTrianglesMesh(g3_tau): - - ops_perm_mat = ops_mat[perm + [2]] - taus_perm = np.array(taus).T[perm] - - data = self.ed.get_timeordered_two_tau_greens_function( - taus_perm, ops_perm_mat) - - for idx, d in zip(idxs, data): - g3_tau[list(idx)][:] = perm_sign * d - - # ------------------------------------------------------------------ - def set_g40_tau(self, g40_tau, g_tau): - - assert( type(g_tau.mesh) == MeshImTime ) - #assert( g_tau.target_shape == g40_tau.target_shape ) - - for (i1, i2, i3), (t1, t2, t3) in enumerate_tau3(g40_tau): - g40_tau[[i1, i2, i3]][:] = \ - g_tau(t1-t2)*g_tau(t3) - g_tau(t1)*g_tau(t3-t2) - - # ------------------------------------------------------------------ - def set_g4_tau(self, g4_tau, op1, op2, op3, op4): - - assert( g4_tau.target_shape == (1,1,1,1) ) - - op1_mat = self.rep.sparse_matrix(op1) - op2_mat = self.rep.sparse_matrix(op2) - op3_mat = self.rep.sparse_matrix(op3) - op4_mat = self.rep.sparse_matrix(op4) - - ops_mat = np.array([op1_mat, op2_mat, op3_mat, op4_mat]) - - for idxs, taus, perm, perm_sign in CubeTetrasMesh(g4_tau): - - ops_perm_mat = ops_mat[perm + [3]] - taus_perm = np.array(taus).T[perm] - - data = self.ed.get_timeordered_three_tau_greens_function( - taus_perm, ops_perm_mat) - - for idx, d in zip(idxs, data): - g4_tau[list(idx)][:] = perm_sign * d - - # ------------------------------------------------------------------ - -# ---------------------------------------------------------------------- + From 05541f3bd40c58f52800dfccdfedfbb29ef4e2e9 Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Tue, 17 Oct 2017 14:38:42 +0300 Subject: [PATCH 15/33] Full sparse PYED --- pyed/CubeTetras.py | 137 +++++++++++++ pyed/SparseExactDiagonalization.py | 305 +++++++++++++++++++++++++---- pyed/SparseMatrixFockStates.py | 6 +- pyed/SquareTriangles.py | 133 +++++++++++++ pyed/TriqsExactDiagonalization.py | 122 +++++++++++- pyed/tests/test_G_tau_and_G_iw.py | 87 ++++++++ 6 files changed, 741 insertions(+), 49 deletions(-) create mode 100644 pyed/CubeTetras.py create mode 100644 pyed/SquareTriangles.py create mode 100644 pyed/tests/test_G_tau_and_G_iw.py diff --git a/pyed/CubeTetras.py b/pyed/CubeTetras.py new file mode 100644 index 0000000..36c6bcb --- /dev/null +++ b/pyed/CubeTetras.py @@ -0,0 +1,137 @@ + +""" +Helper routines for the equal time imaginary time cube and +its sub tetrahedrons. + +Author: Hugo U. R. Strand (2017), hugo.strand@gmail.com +""" + +# ---------------------------------------------------------------------- + +import itertools +import numpy as np + +# ---------------------------------------------------------------------- +def zero_outer_planes_and_equal_times(g4_tau): + + beta = g4_tau.mesh.components[0].beta + + for idxs, (t1, t2, t3) in enumerate_tau3(g4_tau): + if t1 == t2 or t2 == t3 or t1 == t3 or \ + t1 == 0 or t1 == beta or \ + t2 == 0 or t2 == beta or \ + t3 == 0 or t3 == beta: + g4_tau[list(idxs)][:] = 0.0 + +# ---------------------------------------------------------------------- +def enumerate_tau3(g4_tau, make_real=True, beta=None): + + from pytriqs.gf import MeshImTime, MeshProduct + + assert( type(g4_tau.mesh) == MeshProduct ) + + for mesh in g4_tau.mesh.components: + assert( type(mesh) == MeshImTime ) + if beta is not None: assert( mesh.beta == beta ) + + for (i1, t1), (i2, t2), (i3, t3) in itertools.product(*[ + enumerate(mesh) for mesh in g4_tau.mesh.components]): + if make_real: + yield (i1, i2, i3), (t1.real, t2.real, t3.real) + else: + yield (i1, i2, i3), (t1, t2, t3) + +# ---------------------------------------------------------------------- +class CubeTetrasBase(object): + + """ Base class with definition of the equal time tetrahedrons + in three fermionic imaginary times. """ + + def get_tetra_list(self): + + tetra_list = [ + (lambda x,y,z : x >= y and y >= z, [0, 1, 2], +1), + (lambda x,y,z : y >= x and x >= z, [1, 0, 2], -1), + (lambda x,y,z : y >= z and z >= x, [1, 2, 0], +1), + (lambda x,y,z : z >= y and y >= x, [2, 1, 0], -1), + (lambda x,y,z : x >= z and z >= y, [0, 2, 1], -1), + (lambda x,y,z : z >= x and x >= y, [2, 0, 1], +1), + ] + + return tetra_list + +# ---------------------------------------------------------------------- +class CubeTetras(CubeTetrasBase): + + """ Helper class for two-particle Green's function. + + Looping over all tetrahedrons in the imaginary time cube. + \tau_1, \tau_2, \tau_3 \in [0, \beta) """ + + # ------------------------------------------------------------------ + def __init__(self, tau): + + self.tau = tau + self.ntau = len(tau) + self.tetra_list = self.get_tetra_list() + + # ------------------------------------------------------------------ + def __iter__(self): + + for tidx in xrange(6): + + func, perm, perm_sign = self.tetra_list[tidx] + + index = [] + for n1, n2, n3 in itertools.product( + range(self.ntau), repeat=3): + if func(n1, n2, n3): index.append((n1, n2, n3)) + + index = np.array(index).T + + i1, i2, i3 = index + t1, t2, t3 = self.tau[i1], self.tau[i2], self.tau[i3] + + taus = np.vstack([t1, t2, t3]) + + yield list(index), taus, perm, perm_sign + +# ---------------------------------------------------------------------- +class CubeTetrasMesh(CubeTetrasBase): + + """ Helper class for Triqs two-particle Green's function + in imaginary time. + + Looping over all tetrahedrons in the imaginary time cube. + \tau_1, \tau_2, \tau_3 \in [0, \beta) """ + + # ------------------------------------------------------------------ + def __init__(self, g4_tau): + + self.g4_tau = g4_tau + self.tetra_list = self.get_tetra_list() + + # ------------------------------------------------------------------ + def __iter__(self): + + """ for pytriqs three time greens functions """ + + tetra_idx = [ [] for n in xrange(6) ] + tetra_tau = [ [] for n in xrange(6) ] + + for idxs, taus in enumerate_tau3(self.g4_tau): + + for tidx, tetra in enumerate(self.tetra_list): + func, perm, perm_sign = tetra + + if func(*taus): + tetra_idx[tidx] += [ idxs ] + tetra_tau[tidx] += [ taus ] + break + + for tidx in xrange(6): + func, perm, perm_sign = self.tetra_list[tidx] + + yield tetra_idx[tidx], tetra_tau[tidx], perm, perm_sign + +# ---------------------------------------------------------------------- diff --git a/pyed/SparseExactDiagonalization.py b/pyed/SparseExactDiagonalization.py index 18dfd24..e451142 100644 --- a/pyed/SparseExactDiagonalization.py +++ b/pyed/SparseExactDiagonalization.py @@ -1,8 +1,8 @@ + """ General routines for exact diagonalization using sparse matrices Author: Hugo U. R. Strand (2017), hugo.strand@gmail.com - Yaroslav Zhumagulov (2017), yaroslav.zhumagulov@gmail.com """ # ---------------------------------------------------------------------- @@ -11,20 +11,17 @@ import itertools import progressbar import numpy as np -import multiprocessing -from joblib import Parallel, delayed +from scipy.linalg import expm + # ---------------------------------------------------------------------- -import scipy.sparse as sparse from scipy.sparse.linalg import eigs as eigs_sparse from scipy.sparse.linalg import eigsh as eigsh_sparse -from scipy.sparse import vstack from scipy.sparse import csr_matrix -from scipy.linalg import expm +from scipy.sparse import diags # ---------------------------------------------------------------------- -def gf(M,E,eta,x): - return np.sum(M/(x+1j*eta-E)) +from CubeTetras import CubeTetras # ---------------------------------------------------------------------- class SparseExactDiagonalization(object): @@ -34,20 +31,23 @@ class SparseExactDiagonalization(object): # ------------------------------------------------------------------ def __init__(self, H,blocks, beta, - nstates, hermitian=True, + nstates=None, hermitian=True, v0=None, tol=0): self.v0 = v0 self.tol = tol - self.nstates=nstates + + self.nstates = nstates self.hermitian = hermitian + self.H = H self.blocks=blocks self.beta = beta + self._diagonalize_hamiltonian() - self._number_of_states_reduction() self._calculate_partition_function() self._calculate_density_matrix() + # ------------------------------------------------------------------ def _diagonalize_hamiltonian(self): self.U=csr_matrix(self.H.shape,dtype=np.float) @@ -63,34 +63,27 @@ def _diagonalize_hamiltonian(self): self.E=np.array(self.E) self.E0 = np.min(self.E) self.E = self.E-self.E0 - # ------------------------------------------------------------------ - def _number_of_states_reduction(self): - if self.nstates is not None: - indexes=np.argsort(self.E)[:self.nstates] - self.E=self.E[indexes] - self.U=self.U[:,indexes] + # ------------------------------------------------------------------ def _calculate_partition_function(self): - self.Z = np.sum(np.exp(-self.beta*self.E)) + + exp_bE = np.exp(-self.beta * self.E) + self.Z = np.sum(exp_bE) # ------------------------------------------------------------------ def _calculate_density_matrix(self): - self.rho=csr_matrix(self.H.shape,dtype=np.float) - print 'Density matrix calculation:' - bar = progressbar.ProgressBar() - for i in bar(range(len(self.blocks))): - block=self.blocks[i] - X,Y=np.meshgrid(block,block) - exp_bE = np.exp(-self.beta * self.E[block]) / self.Z - self.rho[X,Y]= np.einsum('ij,j,jk->ik', self.U[X,Y].todense(), exp_bE, self.U[X,Y].H.todense()) + + exp_bE = (np.exp(-self.beta * self.E) / self.Z)[:,None] + self.rho=self.U.getH().multiply(exp_bE)*self.U # ------------------------------------------------------------------ def _operators_to_eigenbasis(self, op_vec): dop_vec = [] for op in op_vec: - dop=self.U.getH()*op*self.U + dop = self.U.getH() * op * self.U dop_vec.append(dop) + return dop_vec # ------------------------------------------------------------------ @@ -105,8 +98,6 @@ def get_expectation_value(self, operator): exp_val /= self.Z return exp_val - - # ------------------------------------------------------------------ def get_free_energy(self): @@ -141,20 +132,260 @@ def get_eigen_vectors(self): def get_ground_state_energy(self): return self.E0 - def get_grand_potential(self): - return self.E0-np.log(np.sum(np.exp(-self.beta*self.E)))/self.beta + # ------------------------------------------------------------------ + def get_g2_dissconnected_tau_tetra(self, tau, tau_g, g): + + g = np.squeeze(g) # fix for now throwing orb idx + g = g.real + + N = len(tau) + G4 = np.zeros((N, N, N), dtype=np.complex) + + def gint(t): + sign = 1.0 + if (t < 0).any(): + assert( (t <= 0).all() ) + t = self.beta + t + sign = -1.0 + + return sign * np.interp(t, tau_g, g) + + for idx, taus, perm, perm_sign in CubeTetras(tau): + t1, t2, t3 = taus + G4[idx] = gint(t1-t2)*gint(t3) - gint(t1)*gint(t3-t2) + + return G4 + + # ------------------------------------------------------------------ + def get_g2_dissconnected_tau(self, tau, tau_g, g): + + g = np.squeeze(g) # fix for now throwing orb idx + g = g.real + + N = len(tau) + G4 = np.zeros((N, N, N), dtype=np.complex) + + def gint(t_in): + t = np.copy(t_in) + sidx = (t < 0) + sign = np.ones_like(t) + sign[sidx] *= -1. + t[sidx] = self.beta + t[sidx] + return sign * np.interp(t, tau_g, g) + + t1, t2, t3 = np.meshgrid(tau, tau, tau, indexing='ij') + G4 = gint(t1-t2)*gint(t3) - gint(t1)*gint(t3-t2) + + return G4 + + # ------------------------------------------------------------------ + def get_g2_tau(self, tau, ops): + + N = len(tau) + G4 = np.zeros((N, N, N), dtype=np.complex) + ops = np.array(ops) + + for tidx, tetra in enumerate(CubeTetras(tau)): + idx, taus, perm, perm_sign = tetra + + print 'Tetra:', tidx + + # do not permute the last operator + ops_perm = ops[perm + [3]] + taus_perm = taus[perm] # permute the times + + G4[idx] = self.get_timeordered_three_tau_greens_function( + taus_perm, ops_perm) * perm_sign + + return G4 + + # ------------------------------------------------------------------ + def get_timeordered_two_tau_greens_function(self, taus, ops): + + r""" + taus = [t1, t2] (ordered beta>t1>t2>0) + ops = [O1, O2, O3] + + Returns: + G^{(4)}(t1, t2) = -1/Z < O1(t1) O2(t2) O3(0) > + + """ + + Nop = 3 + + assert( taus.shape[0] == 2 ) + assert( len(ops) == Nop ) + + G = np.zeros((taus.shape[-1]), dtype=np.complex) + + E = self.E[None, :] + + t1, t2 = taus + t1, t2 = t1[:, None], t2[:, None] + + assert( (t1 <= self.beta).all() ) + assert( (t1 >= t2).all() ) + assert( (t2 >= 0).all() ) + + + dops = self._operators_to_eigenbasis(ops) + op1, op2, op3 = dops + + for i in range(len(G)): + et_a = np.exp((-self.beta + t1[i])*E).flatten()[:,None] + et_b = np.exp((t2[i]-t1[i])*E).flatten()[:,None] + et_c = np.exp((-t2[i])*E).flatten()[:,None] + G[i] = (op1.multiply(et_a)*op2.multiply(et_b)*op3.multiply(et_c)).diagonal().sum() + G /= self.Z + return G + # ------------------------------------------------------------------ + def get_timeordered_three_tau_greens_function(self, taus, ops): + + r""" + taus = [t1, t2, t3] (ordered beta>t1>t2>t3>0) + ops = [O1, O2, O3, O4] + + Returns: + G^{(4)}(t1, t2, t3) = -1/Z < O1(t1) O2(t2) O3(t3) O4(0) > + + """ + + assert( taus.shape[0] == 3 ) + assert( len(ops) == 4 ) + + Nop = 4 + G = np.zeros((taus.shape[-1]), dtype=np.complex) + + E = self.E[None, :] + + t1, t2, t3 = taus + t1, t2, t3 = t1[:, None], t2[:, None], t3[:, None] + + assert( (t1 <= self.beta).all() ) + assert( (t1 >= t2).all() ) + assert( (t2 >= t3).all() ) + assert( (t3 >= 0).all() ) + + dops = self._operators_to_eigenbasis(ops) + op1, op2, op3, op4 = dops + for i in range(len(G)): + et_a = np.exp((-self.beta + t1[i])*E).flatten()[:,None] + et_b = np.exp((t2[i]-t1[i])*E).flatten()[:,None] + et_c = np.exp((t3[i]-t2[i])*E).flatten()[:,None] + et_d = np.exp((-t3[i])*E).flatten()[:,None] + G[i]=(op1.multiply(et_a)*op2.multiply(et_b)*op3.multiply(et_c)*op4.multiply(et_d)).sum() + + G /= self.Z + return G + # ------------------------------------------------------------------ + def get_tau_greens_function_component(self, tau, op1, op2): + + r""" + Returns: + G^{(2)}(\tau) = -1/Z < O_1(\tau) O_2(0) > + """ + + G = np.zeros((len(tau)), dtype=np.complex) + op1_eig, op2_eig = self._operators_to_eigenbasis([op1, op2]) + bar = progressbar.ProgressBar() + for i in bar(range(len(tau))): + et_p = np.exp((-self.beta + tau[i])*self.E)[:,None] + et_m = np.exp(-tau[i]*self.E)[:,None] + G[i] = - (op1_eig.multiply(et_p)*op2_eig.multiply(et_m)).diagonal().sum() + G /= self.Z + return G + + # ------------------------------------------------------------------ + def get_frequency_greens_function_component(self, iwn, op1, op2, xi): - def get_real_frequency_greens_function_component(self, w, op1, op2,eta): r""" Returns: G^{(2)}(i\omega_n) = -1/Z < O_1(i\omega_n) O_2(-i\omega_n) > """ + op1_eig, op2_eig = self._operators_to_eigenbasis([op1, op2]) - Q=(op1_eig.getH().multiply(op2_eig)).tocoo() - M=(np.exp(-self.beta*self.E[Q.row])+np.exp(-self.beta*self.E[Q.col]))*Q.data - E=(self.E[Q.row]-self.E[Q.col]) - G = np.zeros((len(w)), dtype=np.complex) - G = Parallel(n_jobs=12)(delayed(gf)(M,E,eta,x) for x in w) + + # -- Compute Lehman sum for all operator combinations + G = np.zeros((len(iwn)), dtype=np.complex) + op=(op1_eig.getH().multiply(op2_eig)).tocoo() + M=(np.exp(-self.beta*self.E[op.row])+np.exp(-self.beta*self.E[op.col]))*op.data + E=(self.E[op.row]-self.E[op.col]) + for i in range(len(iwn)): + G[i]=np.sum(M/(iwn[i]-E)) G /= self.Z + return G + # ------------------------------------------------------------------ + def get_high_frequency_tail_coeff_component( + self, op1, op2, xi, Norder=3): + + r""" The high frequency tail corrections can be derived + directly from the imaginary time expression for the Green's function + + G(t) = -1/Z Tr[e^{-\beta H} e^{tH} b e^{-tH} b^+] + + and the observation that the high frequency components of the + Matsubara Green's function G(i\omega_n) can be obtained by partial + integration in + + G(i\omega_n) = \int_0^\beta dt e^{i\omega_n t} G(t) + = \sum_k=0^\infty (-1)^k + (\xi G^{(k)}(\beta^-) - G^{(k)}(0^+))/(i\omega_n)^(k+1) + = \sum_{k=1} c_k / (i\omega_n)^{k} + + where the n:th order derivative G^{(n)}(t) can be expressed as + + G^{(k)}(t) = - < [[ H , b(t) ]]^{(k)} b^+ > + + where [[H, b]]^{(k)} = [H, [H, [H, ... [H, b] ... ]]] is the k:th order + left side commutator of H with b. + + Using this the high frequency coefficients c_k takes the form + + c_k = (-1)^(k-1) (\xi G^{(k-1)}(\beta^-) - G^{(k-1)}(0^+)) + = (-1)^k < [ [[ H , b ]]^{(k-1)} , b^+ ]_{-\xi} > + + """ + + def xi_commutator(A, B, xi): + return A * B - xi * B * A + + def commutator(A, B): + return A * B - B * A + + H = self.H + + Gc = np.zeros((Norder), dtype=np.complex) + ba, bc = op1, op2 + + Hba = ba + for order in xrange(Norder): + tail_op = xi_commutator(Hba, bc, xi) + Gc[order] = (-1.)**(order) * \ + self.get_expectation_value(tail_op) + Hba = commutator(H, Hba) + + return Gc + + # ------------------------------------------------------------------ + def get_high_frequency_tail(self, iwn, Gc, start_order=-1): + + """ from the high frequency coefficients Gc calculate the + Matsubara Green's function tail + + G(i\omega_n) = \sum_k Gc[k] / (i\omega_n)^k """ + + Nop = Gc.shape[-1] + Nw = len(iwn) + G = np.zeros((Nw, Nop, Nop), dtype=np.complex) + iwn_idx = np.nonzero(iwn)[0] # -- Only eval for non-zero freq. + for idx, gc in enumerate(Gc): + G[iwn_idx, :, :] += \ + iwn[iwn_idx, None, None]**(-idx+start_order) * gc[None, :, :] + + return G + + # ------------------------------------------------------------------ + +# ---------------------------------------------------------------------- diff --git a/pyed/SparseMatrixFockStates.py b/pyed/SparseMatrixFockStates.py index b3e11db..222d2c7 100644 --- a/pyed/SparseMatrixFockStates.py +++ b/pyed/SparseMatrixFockStates.py @@ -51,7 +51,7 @@ def __init__(self, fundamental_operators): self.nfermions = len(self.operator_labels) self.sparse_operators = \ SparseMatrixCreationOperators(self.nfermions) - self.indexes_blocks=self.sparse_operators.indexes_blocks + self.blocks=self.sparse_operators.blocks # ------------------------------------------------------------------ def sparse_matrix(self, triqs_operator_expression): @@ -109,11 +109,11 @@ def __init__(self, nfermions): self.permutation=np.zeros(self.nstates) self.permutation[np.concatenate(indexes_const,axis=0)]=np.arange(self.nstates) self.permutation=np.array(self.permutation,dtype=np.int) - self.indexes_blocks=[] + self.blocks=[] for n_up in range(self.nfermions/2+1): for n_down in range(self.nfermions/2+1): indexes=np.where((np.sum(states_up,axis=1)==n_up)&(np.sum(states_down,axis=1)==n_down))[0].flatten() - self.indexes_blocks.append(self.permutation[indexes]) + self.blocks.append(self.permutation[indexes]) self.c_dag = [] diff --git a/pyed/SquareTriangles.py b/pyed/SquareTriangles.py new file mode 100644 index 0000000..b8f3b97 --- /dev/null +++ b/pyed/SquareTriangles.py @@ -0,0 +1,133 @@ + +""" +Helper routines for the equal time imaginary time square and +its sub triangles. + +Author: Hugo U. R. Strand (2017), hugo.strand@gmail.com +""" + +# ---------------------------------------------------------------------- + +import itertools +import numpy as np + +# ---------------------------------------------------------------------- +def zero_outer_planes_and_equal_times(g3_tau): + + beta = g3_tau.mesh.components[0].beta + + for idxs, (t1, t2) in enumerate_tau2(g3_tau): + if t1 == t2 or \ + t1 == 0 or t1 == beta or \ + t2 == 0 or t2 == beta: + g3_tau[list(idxs)][:] = 0.0 + +# ---------------------------------------------------------------------- +def enumerate_tau2(g3_tau, make_real=True, beta=None): + + from pytriqs.gf import MeshImTime, MeshProduct + + assert( type(g3_tau.mesh) == MeshProduct ) + + for mesh in g3_tau.mesh.components: + assert( type(mesh) == MeshImTime ) + if beta is not None: assert( mesh.beta == beta ) + + for (i1, t1), (i2, t2) in itertools.product(*[ + enumerate(mesh) for mesh in g3_tau.mesh.components]): + if make_real: + yield (i1, i2), (t1.real, t2.real) + else: + yield (i1, i2), (t1, t2) + +# ---------------------------------------------------------------------- +class SquareTrianglesBase(object): + + """ Base class with definition of the equal time tetrahedrons + in three fermionic imaginary times. """ + + def get_triangle_list(self): + + triangle_list = [ + (lambda x,y : x >= y, [0, 1], +1), + (lambda x,y : x < y, [1, 0], -1), + ] + + return triangle_list + +# ---------------------------------------------------------------------- +class SuqareTraingles(SquareTrianglesBase): + + """ Helper class for two imaginary time Green's functions. + + Looping of the triangles on the imaginary time square. + \tau_1, \tau_2 \in [0, \beta) """ + + # ------------------------------------------------------------------ + def __init__(self, tau): + + self.tau = tau + self.ntau = len(tau) + self.triangle_list = self.get_triangle_list() + self.N = len(self.triangle_list) + + # ------------------------------------------------------------------ + def __iter__(self): + + for tidx in xrange(self.N): + + func, perm, perm_sign = self.triangle_list[tidx] + + index = [] + for n1, n2 in itertools.product( + range(self.ntau), repeat=2): + if func(n1, n2): index.append((n1, n2)) + + index = np.array(index).T + + i1, i2 = index + t1, t2 = self.tau[i1], self.tau[i2] + + taus = np.vstack([t1, t2]) + + yield list(index), taus, perm, perm_sign + +# ---------------------------------------------------------------------- +class SquareTrianglesMesh(SquareTrianglesBase): + + """ Helper class for Triqs three imaginary time Green's functions. + + Looping of the triangles on the imaginary time square. + \tau_1, \tau_2 \in [0, \beta) """ + + # ------------------------------------------------------------------ + def __init__(self, g3_tau): + + self.g3_tau = g3_tau + self.triangle_list = self.get_triangle_list() + self.N = len(self.triangle_list) + + # ------------------------------------------------------------------ + def __iter__(self): + + """ for pytriqs three time greens functions """ + + triangle_idx = [ [] for n in xrange(self.N) ] + triangle_tau = [ [] for n in xrange(self.N) ] + + for idxs, taus in enumerate_tau2(self.g3_tau): + + for tidx, triangle in enumerate(self.triangle_list): + func, perm, perm_sign = triangle + + if func(*taus): + triangle_idx[tidx] += [ idxs ] + triangle_tau[tidx] += [ taus ] + break + + for tidx in xrange(self.N): + func, perm, perm_sign = self.triangle_list[tidx] + + yield triangle_idx[tidx], triangle_tau[tidx], perm, perm_sign + +# ---------------------------------------------------------------------- diff --git a/pyed/TriqsExactDiagonalization.py b/pyed/TriqsExactDiagonalization.py index 6105227..4ee7180 100644 --- a/pyed/TriqsExactDiagonalization.py +++ b/pyed/TriqsExactDiagonalization.py @@ -3,7 +3,6 @@ Exact diagonalization and single- and two-particle Green's function calculator for Triqs operator expressions. Author: Hugo U. R. Strand (2017), hugo.strand@gmail.com - Yaroslav Zhumagulov, yaroslav.zhumagulov@gmail.com """ # ---------------------------------------------------------------------- @@ -13,6 +12,12 @@ # ---------------------------------------------------------------------- +from pytriqs.gf import MeshImTime, MeshProduct + +# ---------------------------------------------------------------------- + +from pyed.CubeTetras import CubeTetrasMesh, enumerate_tau3 +from pyed.SquareTriangles import SquareTrianglesMesh, enumerate_tau2 from pyed.SparseExactDiagonalization import SparseExactDiagonalization from pyed.SparseMatrixFockStates import SparseMatrixRepresentation @@ -22,11 +27,12 @@ class TriqsExactDiagonalization(object): """ Exact diagonalization for Triqs operator expressions. """ # ------------------------------------------------------------------ - def __init__(self, H, fundamental_operators, beta,nstates=None): + def __init__(self, H, fundamental_operators, beta): self.beta = beta self.rep = SparseMatrixRepresentation(fundamental_operators) - self.ed = SparseExactDiagonalization(self.rep.sparse_matrix(H),self.rep.indexes_blocks, beta,nstates=nstates) + self.ed = SparseExactDiagonalization( + self.rep.sparse_matrix(H),self.rep.blocks, beta) # ------------------------------------------------------------------ def get_expectation_value(self, op): @@ -42,15 +48,113 @@ def get_density_matrix(self): def get_ground_state_energy(self): return self.ed.get_ground_state_energy() + # ------------------------------------------------------------------ + def set_g2_tau(self, g_tau, op1, op2): + + assert( type(g_tau.mesh) == MeshImTime ) + assert( self.beta == g_tau.mesh.beta ) + assert( g_tau.target_shape == (1, 1) ) + + op1_mat = self.rep.sparse_matrix(op1) + op2_mat = self.rep.sparse_matrix(op2) + + tau = np.array([tau for tau in g_tau.mesh]) + + g_tau.data[:, 0, 0] = \ + self.ed.get_tau_greens_function_component( + tau, op1_mat, op2_mat) + + self.set_tail(g_tau, op1_mat, op2_mat) + + # ------------------------------------------------------------------ + def set_g2_iwn(self, g_iwn, op1, op2): - def set_g2_w(self, g_w, op1, op2,eta=0.1): + assert( self.beta == g_iwn.mesh.beta ) + assert( g_iwn.target_shape == (1, 1) ) op1_mat = self.rep.sparse_matrix(op1) op2_mat = self.rep.sparse_matrix(op2) - w = np.array([w for w in g_w.mesh]) + iwn = np.array([iwn for iwn in g_iwn.mesh]) - g_w.data[:, 0, 0] = \ - self.ed.get_real_frequency_greens_function_component( - w, op1_mat, op2_mat, eta) - + g_iwn.data[:, 0, 0] = \ + self.ed.get_frequency_greens_function_component( + iwn, op1_mat, op2_mat, self.xi(g_iwn.mesh)) + + self.set_tail(g_iwn, op1_mat, op2_mat) + + # ------------------------------------------------------------------ + def set_tail(self, g, op1_mat, op2_mat): + + tail = g.tail + + raw_tail = self.ed.get_high_frequency_tail_coeff_component( + op1_mat, op2_mat, self.xi(g.mesh), Norder=tail.order_max) + + for idx in xrange(tail.order_max): + tail[idx+1][:] = raw_tail[idx] + + # ------------------------------------------------------------------ + def xi(self, mesh): + if mesh.statistic == 'Fermion': return -1.0 + elif mesh.statistic == 'Boson': return +1.0 + else: raise NotImplementedError + + # ------------------------------------------------------------------ + def set_g3_tau(self, g3_tau, op1, op2, op3): + + assert( g3_tau.target_shape == (1,1,1,1) ) + + op1_mat = self.rep.sparse_matrix(op1) + op2_mat = self.rep.sparse_matrix(op2) + op3_mat = self.rep.sparse_matrix(op3) + + ops_mat = np.array([op1_mat, op2_mat, op3_mat]) + + for idxs, taus, perm, perm_sign in SquareTrianglesMesh(g3_tau): + + ops_perm_mat = ops_mat[perm + [2]] + taus_perm = np.array(taus).T[perm] + + data = self.ed.get_timeordered_two_tau_greens_function( + taus_perm, ops_perm_mat) + + for idx, d in zip(idxs, data): + g3_tau[list(idx)][:] = perm_sign * d + + # ------------------------------------------------------------------ + def set_g40_tau(self, g40_tau, g_tau): + + assert( type(g_tau.mesh) == MeshImTime ) + #assert( g_tau.target_shape == g40_tau.target_shape ) + + for (i1, i2, i3), (t1, t2, t3) in enumerate_tau3(g40_tau): + g40_tau[[i1, i2, i3]][:] = \ + g_tau(t1-t2)*g_tau(t3) - g_tau(t1)*g_tau(t3-t2) + + # ------------------------------------------------------------------ + def set_g4_tau(self, g4_tau, op1, op2, op3, op4): + + assert( g4_tau.target_shape == (1,1,1,1) ) + + op1_mat = self.rep.sparse_matrix(op1) + op2_mat = self.rep.sparse_matrix(op2) + op3_mat = self.rep.sparse_matrix(op3) + op4_mat = self.rep.sparse_matrix(op4) + + ops_mat = np.array([op1_mat, op2_mat, op3_mat, op4_mat]) + + for idxs, taus, perm, perm_sign in CubeTetrasMesh(g4_tau): + + ops_perm_mat = ops_mat[perm + [3]] + taus_perm = np.array(taus).T[perm] + + data = self.ed.get_timeordered_three_tau_greens_function( + taus_perm, ops_perm_mat) + + for idx, d in zip(idxs, data): + g4_tau[list(idx)][:] = perm_sign * d + + # ------------------------------------------------------------------ + +# ---------------------------------------------------------------------- diff --git a/pyed/tests/test_G_tau_and_G_iw.py b/pyed/tests/test_G_tau_and_G_iw.py new file mode 100644 index 0000000..774167c --- /dev/null +++ b/pyed/tests/test_G_tau_and_G_iw.py @@ -0,0 +1,87 @@ + +""" Test calculation for Hubbard atom with two bath sites. + +Author: Hugo U.R. Strand (2017) hugo.strand@gmail.com + + """ + +# ---------------------------------------------------------------------- + +import numpy as np + +# ---------------------------------------------------------------------- + +from pytriqs.gf import Gf +from pytriqs.gf import MeshImTime, MeshImFreq + +from pytriqs.gf import GfImTime, GfImFreq, TailGf +from pytriqs.operators import c, c_dag + +from pytriqs.gf import inverse, iOmega_n, InverseFourier + +# ---------------------------------------------------------------------- + +from pyed.TriqsExactDiagonalization import TriqsExactDiagonalization + +# ---------------------------------------------------------------------- +def test_cf_G_tau_and_G_iw_nonint(verbose=False): + + beta = 3.22 + eps = 1.234 + + niw = 64 + ntau = 2 * niw + 1 + + H = eps * c_dag(0,0) * c(0,0) + + fundamental_operators = [c(0,0)] + + ed = TriqsExactDiagonalization(H, fundamental_operators, beta) + + # ------------------------------------------------------------------ + # -- Single-particle Green's functions + + G_tau = GfImTime(beta=beta, statistic='Fermion', n_points=ntau, indices=[1]) + G_iw = GfImFreq(beta=beta, statistic='Fermion', n_points=niw, indices=[1]) + + G_iw << inverse( iOmega_n - eps ) + G_tau << InverseFourier(G_iw) + + G_tau_ed = GfImTime(beta=beta, statistic='Fermion', n_points=ntau, indices=[1]) + G_iw_ed = GfImFreq(beta=beta, statistic='Fermion', n_points=niw, indices=[1]) + + ed.set_g2_tau(G_tau_ed, c(0,0), c_dag(0,0)) + ed.set_g2_iwn(G_iw_ed, c(0,0), c_dag(0,0)) + + # ------------------------------------------------------------------ + # -- Compare gfs + + from pytriqs.utility.comparison_tests import assert_gfs_are_close + + assert_gfs_are_close(G_tau, G_tau_ed) + assert_gfs_are_close(G_iw, G_iw_ed) + + # ------------------------------------------------------------------ + # -- Plotting + + if verbose: + from pytriqs.plot.mpl_interface import oplot, plt + subp = [3, 1, 1] + plt.subplot(*subp); subp[-1] += 1 + oplot(G_tau.real) + oplot(G_tau_ed.real) + + plt.subplot(*subp); subp[-1] += 1 + diff = G_tau - G_tau_ed + oplot(diff.real) + + plt.subplot(*subp); subp[-1] += 1 + oplot(G_iw) + oplot(G_iw_ed) + + plt.show() + +# ---------------------------------------------------------------------- +if __name__ == '__main__': + + test_cf_G_tau_and_G_iw_nonint(verbose=True) From dc07d715e6c6a01387b892e5cdb5262525cd66c6 Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Tue, 17 Oct 2017 14:58:47 +0300 Subject: [PATCH 16/33] Clean up add Anderson impurity model example --- Readme.md | 5 - doc/Anderson.ipynb | 356 +++++++++++++++++++++++++++++ doc/Documentation_CPT_2D.ipynb | 327 -------------------------- pyed/ClusterPertrubationTheory.py | 135 ----------- pyed/DynamicalMeanFieldTheory.py | 68 ------ pyed/SparseExactDiagonalization.py | 3 +- 6 files changed, 358 insertions(+), 536 deletions(-) create mode 100644 doc/Anderson.ipynb delete mode 100644 doc/Documentation_CPT_2D.ipynb delete mode 100644 pyed/ClusterPertrubationTheory.py delete mode 100644 pyed/DynamicalMeanFieldTheory.py diff --git a/Readme.md b/Readme.md index 6db10c4..050b7f6 100644 --- a/Readme.md +++ b/Readme.md @@ -8,9 +8,6 @@ The many-body system is defined using `pytriqs` second-quantized operators and t The original purpose of `pyed` is to provide exact solutions to small finite systems, to be used as benchmarks and tests for stochastic many-body solvers. -Cluster pertrubation theory [1] addition to pyed allow calculate bandstructure and Fermi surface of several models. - -[1] https://www.physique.usherbrooke.ca/pages/sites/default/files/senechal/publis/Senechal2011vn.pdf ## Installation @@ -28,8 +25,6 @@ in your `.bashrc`, `.bash_profile`, or `.profile` file. For documentation and usage examples please see the hands on [jupyter notebook](doc/Documentation.ipynb) -For documentation and usage of CPT addition examples please see the hands on [jupyter notebook](doc/Documentation_CPT_2D.ipynb) - ## License This application is free software: you can redistribute it and/or modify it diff --git a/doc/Anderson.ipynb b/doc/Anderson.ipynb new file mode 100644 index 0000000..115aa00 --- /dev/null +++ b/doc/Anderson.ipynb @@ -0,0 +1,356 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Another example: Anderson impurity model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The calculation takes about an 10 minutes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hamiltonian\n", + "\n", + "As an example let us solve the Anderson impurity model local with Hamiltonian $H = U\\hat{n}_{\\uparrow} \\hat{n}_{\\downarrow} - \\mu ( \\hat{n}_{\\uparrow} + \\hat{n}_{\\downarrow})$," + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "H_loc = -0.1*c_dag('dn',0)*c('dn',0) + -0.1*c_dag('up',0)*c('up',0) + 1*c_dag('dn',0)*c_dag('up',0)*c('up',0)*c('dn',0)\n" + ] + } + ], + "source": [ + "from pytriqs.operators import c, c_dag\n", + "up, down = 'up', 'dn'\n", + "n_up = c_dag(up, 0) * c(up, 0)\n", + "n_down = c_dag(down, 0) * c(down, 0)\n", + "\n", + "U = 1.0\n", + "mu = 0.1\n", + "\n", + "H_loc = U * n_up * n_down - mu * (n_up + n_down)\n", + "\n", + "print 'H_loc =', H_loc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "with 5 bath sites. Parameters of bath sites in ek and V arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from itertools import product\n", + "ek = [-2.0,-1.0, 0, 1.0,2.0]\n", + "V = [0.5,0.5, 0.5, 0.5,0.5]\n", + "H_hyb=sum(V[i]*(c_dag(s,i+1)*c(s,0)+c_dag(s,0)*c(s,i+1)) for s, i in product(['up','dn'], range(len(ek))))\n", + "H_hyb+=sum(ek[i]*(c_dag(s,i+1)*c(s,i+1)) for s, i in product(['up','dn'], range(len(ek))))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5*c_dag('dn',0)*c('dn',5) + 0.5*c_dag('dn',0)*c('dn',4) + 0.5*c_dag('dn',0)*c('dn',3) + 0.5*c_dag('dn',0)*c('dn',2) + 0.5*c_dag('dn',0)*c('dn',1) + -2*c_dag('dn',1)*c('dn',1) + 0.5*c_dag('dn',1)*c('dn',0) + -1*c_dag('dn',2)*c('dn',2) + 0.5*c_dag('dn',2)*c('dn',0) + 0.5*c_dag('dn',3)*c('dn',0) + 1*c_dag('dn',4)*c('dn',4) + 0.5*c_dag('dn',4)*c('dn',0) + 2*c_dag('dn',5)*c('dn',5) + 0.5*c_dag('dn',5)*c('dn',0) + 0.5*c_dag('up',0)*c('up',5) + 0.5*c_dag('up',0)*c('up',4) + 0.5*c_dag('up',0)*c('up',3) + 0.5*c_dag('up',0)*c('up',2) + 0.5*c_dag('up',0)*c('up',1) + -2*c_dag('up',1)*c('up',1) + 0.5*c_dag('up',1)*c('up',0) + -1*c_dag('up',2)*c('up',2) + 0.5*c_dag('up',2)*c('up',0) + 0.5*c_dag('up',3)*c('up',0) + 1*c_dag('up',4)*c('up',4) + 0.5*c_dag('up',4)*c('up',0) + 2*c_dag('up',5)*c('up',5) + 0.5*c_dag('up',5)*c('up',0)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "H_hyb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Thermal equilibrium solution" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hamiltonian diagonalization:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/dist-packages/scipy/sparse/compressed.py:774: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n", + " SparseEfficiencyWarning)\n", + "100% |########################################################################|\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Z = 1.00455892128\n", + "\\Omega = -7.409527089\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "beta = 20.0 # inverse temperature\n", + "fundamental_operators = np.array([[c(up,i), c(down,i)] for i in range(len(ek)+1)]).flatten()\n", + "\n", + "from pyed.TriqsExactDiagonalization import TriqsExactDiagonalization\n", + "ed = TriqsExactDiagonalization(H_loc+H_hyb, fundamental_operators, beta)\n", + "\n", + "print r'Z =', ed.get_partition_function()\n", + "print r'\\Omega =', ed.get_free_energy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Thermal expectation values" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " = 0.39987118753\n", + " = 0.39987118753\n", + " = 0.125252462256\n" + ] + } + ], + "source": [ + "print ' =', ed.get_expectation_value(n_up)\n", + "print ' =', ed.get_expectation_value(n_down)\n", + "print ' =', ed.get_expectation_value(n_up * n_down)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imaginary time single-particle Green's function" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% |########################################################################|\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEKCAYAAADTgGjXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEVMSERGRiP3/VwduLPgoG8kAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pytriqs.gf import GfImTime\n", + "g_tau = GfImTime(name=r'$g$', beta=beta, statistic='Fermion', n_points=50, indices=[1]) \n", + "ed.set_g2_tau(g_tau, c(up,0), c_dag(up,0))\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from pytriqs.plot.mpl_interface import oplot\n", + "%matplotlib inline\n", + "plt.figure(); oplot(g_tau); plt.savefig('figure_g_tau.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% |########################################################################|\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEKCAYAAADenhiQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIROyUVEREKn5CIiIqFTchERkdD9f816EfNsQSwSAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pytriqs.gf import GfImTime\n", + "densdens_tau = GfImTime(name=r'$\\langle n(\\tau) n(0) \\rangle$', beta=beta, statistic='Boson', n_points=50, indices=[1]) \n", + "ed.set_g2_tau(densdens_tau, n_up, n_down)\n", + "\n", + "plt.figure(); oplot(densdens_tau); plt.savefig('figure_densdens_tau.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% |########################################################################|\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEMCAYAAAAF2YvKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pytriqs.gf import GfImFreq\n", + "g_iwn = GfImFreq(name=r'$g$', beta=beta, statistic='Fermion', n_points=100, indices=[1])\n", + "ed.set_g2_iwn(g_iwn, c(up,0), c_dag(up,0))\n", + "\n", + "plt.figure(); oplot(g_iwn); plt.savefig('figure_g_iwn.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Four-operator response functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from pytriqs.gf import Gf\n", + "from pytriqs.gf import MeshImTime, MeshProduct\n", + "\n", + "ntau = 10\n", + "imtime = MeshImTime(beta, 'Fermion', ntau)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "prodmesh2 = MeshProduct(imtime, imtime)\n", + "g3pp_tau = Gf(name=r'$G^{(3)}(\\tau_1, \\tau_2)$', mesh=prodmesh2, target_shape=[1, 1, 1, 1])\n", + "ed.set_g3_tau(g3pp_tau, c(up,0), c_dag(up,0), c(up,0)*c_dag(up,0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from mpl_toolkits.mplot3d import axes3d\n", + "fig = plt.figure(figsize=(3.25*2, 2*2.5))\n", + "ax = fig.add_subplot(1,1,1, projection='3d')\n", + "\n", + "data = g3pp_tau.data[:, :, 0, 0, 0, 0]\n", + "tau = [tau.real for tau in g3pp_tau.mesh.components[0]]\n", + "t1, t2 = np.meshgrid(tau, tau)\n", + "ax.plot_wireframe(t1, t2, data.real)\n", + "ax.view_init(30, 60)\n", + "ax.set_xlabel(r'$\\tau_1$')\n", + "ax.set_ylabel(r'$\\tau_2$')\n", + "plt.tight_layout()\n", + "plt.savefig('figure_g3pp_tau.png')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/Documentation_CPT_2D.ipynb b/doc/Documentation_CPT_2D.ipynb deleted file mode 100644 index 26229cd..0000000 --- a/doc/Documentation_CPT_2D.ipynb +++ /dev/null @@ -1,327 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# **PYED+CPT **:Cluster pertrubation theory with exact diagonalization for finite quantum systems\n", - "\n", - "Copyright (C) 2017, H. U.R. Strand, Ya.V. Zhumagulov\n", - "\n", - "The python module `pyed` implements exact diagonalization for finite fermionic many-body quantum systems, together with calculations of several response functions in imagianary time.\n", - "\n", - "The many-body system is defined using `pytriqs` second-quantized operators and the response functions are stored in `pytriqs` Green's function containters.\n", - "\n", - "Cluster pertrubation theory addition to `pyed` allow calculate bandstructure and Fermi surface of several models. \n", - "\n", - "## Hamiltonians\n", - "\n", - " As an example let us solve the Hubbard model with Hamiltonian including only nearest-neighbor hoppings $H = U\\sum_{i}\\hat{n}_{i,\\uparrow} \\hat{n}_{i,\\downarrow} - \\mu\\sum_{i}( \\hat{n}_{i,\\uparrow} + \\hat{n}_{i,\\downarrow}) + t \\sum_{,\\sigma}c^\\dagger_{i,\\sigma} c_{j\\sigma}$, where $\\hat{n}_\\sigma = c^\\dagger_\\sigma c_\\sigma$. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pytriqs.operators import c, c_dag,n\n", - "from pyed.TriqsExactDiagonalization import TriqsExactDiagonalization\n", - "from pyed.ClusterPertrubationTheory import ClusterPertrubationTheory_2D_Square\n", - "import numpy as np\n", - "import progressbar\n", - "from itertools import product\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Parameters of model: U=8, t=-1, $\\mu$=U/2 and size of exact diagonaliztion cluster will be 2x2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "t =-1;U=8;mu=U/2\n", - "Lx,Ly=2,2;L=Lx*Ly" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "T=np.zeros((L,L))\n", - "for i in range(L):\n", - " for j in range(L):\n", - " x = i % Lx - j % Ly\n", - " y = i // Lx - j // Ly\n", - " if (x**2+y**2)==1: T[i,j]=t \n", - "H_int = sum(-mu*(n('up', site) + n('dn', site)) + U * n('up', site) * n('dn', site) for site in range(L))\n", - "H_kin = sum(T[st1][st2]*c_dag(sn,st1)*c(sn,st2) for sn, st1,st2 in product((\"dn\", \"up\"), range(L),range(L)) )\n", - "H = H_int +H_kin" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Parameter $\\beta$ will be 200" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hamiltonian diagonalization:\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0% | |\r", - "/usr/local/lib/python2.7/dist-packages/scipy/sparse/compressed.py:774: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n", - " SparseEfficiencyWarning)\n", - " 4% |## |\r", - " 8% |##### |\r", - " 12% |######## |\r", - " 16% |########### |\r", - " 20% |############## |\r", - " 24% |################# |\r", - " 28% |#################### |\r", - " 32% |####################### |\r", - " 36% |######################### |\r", - " 40% |############################ |\r", - " 44% |############################### |\r", - " 48% |################################## |\r", - " 52% |##################################### |\r", - " 56% |######################################## |\r", - " 60% |########################################### |\r", - " 64% |############################################## |\r", - " 68% |################################################ |\r", - " 72% |################################################### |\r", - " 76% |###################################################### |\r", - " 80% |######################################################### |\r", - " 84% |############################################################ |\r", - " 88% |############################################################### |\r", - " 92% |################################################################## |\r", - " 96% |##################################################################### |\r", - "100% |########################################################################|\r\n" - ] - } - ], - "source": [ - "fundamental_operators = np.array([[c('up',i), c('dn',i)] for i in range(L)]).flatten()\n", - "H=H_int+H_kin\n", - "beta=200;nstates=200\n", - "ed = TriqsExactDiagonalization(H,fundamental_operators, beta,nstates=nstates)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we construct, frequency meshgrid, momentum meshgrid and pertrubation matrix V\n", - "\n", - "Further explation you can find in:\n", - "\n", - "https://www.physique.usherbrooke.ca/pages/sites/default/files/senechal/publis/Senechal2011vn.pdf" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "N=60\n", - "kx = np.linspace(-np.pi, np.pi, N+1);kx = np.delete(kx, 0)\n", - "ky = np.linspace(-np.pi, np.pi,N+1);ky = np.delete(ky, 0)\n", - "kx, ky = np.meshgrid(kx, ky)\n", - "V=np.zeros((N,N,L,L),dtype=np.complex)\n", - "for a,b in product(range(L),range(L)):\n", - " x=a % Lx - b % Ly ; y=a// Lx - b// Ly\n", - " if (y==(Ly-1))&(x==0):V[:,:,a,b]=t*np.exp(1j*Ly*ky);V[:,:,b,a]=t*np.exp(-1j*Ly*ky)\n", - " if (x==(Lx-1))&(y==0):V[:,:,a,b]=t*np.exp(1j*Lx*kx);V[:,:,b,a]=t*np.exp(-1j*Lx*kx)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To apply Cluster Pertrubation Theory to our exact diagonalization we use `ClusterPertrubationTheory_2D_Square` class for 2D Square models" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculation green function of full system\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100% |########################################################################|\n", - " 0% | |\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Coupling system\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100% |########################################################################|\n", - " 27% |################### |\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reduce mixed representation\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100% |########################################################################|\n" - ] - } - ], - "source": [ - "omega=np.linspace(-10,10,200);\n", - "CPT=ClusterPertrubationTheory_2D_Square(ed,(kx,ky),V,omega,(Lx,Ly))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can calculate Fermi surface" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWsAAAFpCAYAAABajglzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niBusb3GLW9ziHcQN1re4xS1u8Q7i/wc41oi5BXFnLwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(6,6))\n", - "CPT.calculation_Fermi_surface()\n", - "plt.imshow(CPT.FS,interpolation='lanczos')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And band structure" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAFpCAYAAABuwbWeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12,6))\n", - "CPT.calculation_bandstructure()\n", - "plt.imshow(CPT.bandstructure,cmap=plt.cm.jet,interpolation='lanczos', aspect='auto')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/pyed/ClusterPertrubationTheory.py b/pyed/ClusterPertrubationTheory.py deleted file mode 100644 index 362efae..0000000 --- a/pyed/ClusterPertrubationTheory.py +++ /dev/null @@ -1,135 +0,0 @@ - -""" -General routines for cluster pertrubation theory - -Author: Yaroslav Zhumagulov (2017), yaroslav.zhumagulov@gmail.com - -New classes will be soon -""" - -import numpy as np -from pytriqs.gf import * -from itertools import product -import progressbar -from pytriqs.operators import c, c_dag,n -import matplotlib.pyplot as plt - - -# ------------------------------------------------------------------ -class ClusterPertrubationTheory_2D_Square(object): - - """ Cluster Pertrubation Theory calculator of band structure and Fermi surface of two-dimensional square systems. - Parameters: - ed - TriqsExactDiagonalization object - k_mesh - tuple of kx and ky meshgrid, kx range (-pi,0,pi),ky range (-pi,0,pi) - V - pertrubation matrix: shape = (N,N,L,L), where L - number of sites of the system, (N,N) - size of kx or ky meshgrid - omega - frequency meshgrid - shape - shape of square cluster""" -# ------------------------------------------------------------------ - def __init__(self,ed,k_mesh,V,omega,shape): - self.ed=ed - self.kx,self.ky=k_mesh - self.V=V - self.N=self.V.shape[0];self.L=self.V.shape[2] - self.omega=omega - self.shape=shape - self._get_green_of_the_system() - self._coupling_system() - self._reduce_mix_representation() -# ------------------------------------------------------------------ - def _get_green_of_the_system(self): - self.G_I=np.zeros((self.L,self.L,self.omega.size),dtype=np.complex) - print "Calculation green function of full system" - index_combinations=[(i,j) for i,j in product(range(self.L),range(self.L))] - bar = progressbar.ProgressBar() - for k in bar(range(len(index_combinations))): - i,j=index_combinations[k] - g_w=GfReFreq(indices = [0], window = (np.min(self.omega), np.max(self.omega)), n_points = self.omega.size) - self.ed.set_g2_w(g_w, c('up',i), c_dag('up',j)) - self.G_I[i,j]=g_w.data.flatten() -# ------------------------------------------------------------------ - def _coupling_system(self): - self.G_Q=np.zeros((self.N,self.N,self.L,self.L,self.omega.size),dtype=np.complex) - index_combinations=[(i,j,k) for i,j,k in product(range(self.N),range(self.N),range(self.omega.size))] - print "Coupling system" - bar = progressbar.ProgressBar() - for l in bar(range(len(index_combinations))): - i,j,k=index_combinations[l] - self.G_Q[i,j,:,:,k]=np.dot(self.G_I[:,:,k],np.linalg.inv(np.eye(self.L)-np.dot(self.V[i,j],self.G_I[:,:,k]))) -# ------------------------------------------------------------------ - def _reduce_mix_representation(self): - self.G=np.zeros((self.N,self.N,self.omega.size),dtype=np.complex) - index_combinations=[(i,j,a,b) for i,j,a,b in product(range(self.N),range(self.N),range(self.L),range(self.L))] - print "Reduce mixed representation" - bar = progressbar.ProgressBar() - for k in bar(range(len(index_combinations))): - i,j,a,b=index_combinations[k] - x = a % self.shape[0] - b % self.shape[1] - y = a //self.shape[0] - b //self.shape[1] - self.G[i,j]+=np.exp(-1j*self.kx[i,j]*x)*np.exp(-1j*self.ky[i,j]*y)*self.G_Q[i,j,a,b] -# ------------------------------------------------------------------ - def calculation_bandstructure(self): - bandstructure=[] - for i in range(self.N/2,self.N,1):bandstructure.append(-self.G[i,i,:].imag/np.pi) - for i in range(self.N-1,self.N/2,-1):bandstructure.append(-self.G[self.N-1,i,:].imag/np.pi) - for i in range(self.N-1,self.N/2,-1):bandstructure.append(-self.G[i,self.N/2,:].imag/np.pi) - self.bandstructure=np.array(bandstructure).T - -# ------------------------------------------------------------------ - def calculation_Fermi_surface(self): - self.FS=-self.G[:,:,np.argmin(abs(self.omega))].imag/np.pi - - -class ClusterPertrubationTheory_1D(object): - """ Cluster Pertrubation Theory calculator of band structure of one-dimensional systems. - Parameters: - ed - TriqsExactDiagonalization object - k_mesh - kx meshgrid, kx range (-pi,0,pi) - V - pertrubation matrix: shape = (N,L,L), where L - number of sites of the system, N - size of kx meshgrid - omega - frequency meshgrid - shape - len of cluster""" -# ------------------------------------------------------------------ - def __init__(self,ed,k_mesh,V,omega,shape): - self.ed=ed - self.k=k_mesh - self.V=V - self.N=self.V.shape[0];self.L=self.V.shape[1] - self.omega=omega - self.shape=shape - self._get_green_of_the_system() - self._coupling_system() - self._reduce_mix_representation() -# ------------------------------------------------------------------ - def _get_green_of_the_system(self): - self.G_I=np.zeros((self.L,self.L,self.omega.size),dtype=np.complex) - print "Calculation green function of full system" - index_combinations=[(i,j) for i,j in product(range(self.L),range(self.L))] - bar = progressbar.ProgressBar() - for k in bar(range(len(index_combinations))): - i,j=index_combinations[k] - g_w=GfReFreq(indices = [0], window = (np.min(self.omega), np.max(self.omega)), n_points = self.omega.size) - self.ed.set_g2_w(g_w, c('up',i), c_dag('up',j)) - self.G_I[i,j]=g_w.data.flatten() -# ------------------------------------------------------------------ - def _coupling_system(self): - self.G_Q=np.zeros((self.N,self.L,self.L,self.omega.size),dtype=np.complex) - index_combinations=[(i,j) for i,j in product(range(self.N),range(self.omega.size))] - print "Coupling system" - bar = progressbar.ProgressBar() - for k in bar(range(len(index_combinations))): - i,j=index_combinations[k] - self.G_Q[i,:,:,j]=np.dot(self.G_I[:,:,j],np.linalg.inv(np.eye(self.L)-np.dot(self.V[i],self.G_I[:,:,j]))) -# ------------------------------------------------------------------ - def _reduce_mix_representation(self): - self.G=np.zeros((self.N,self.omega.size),dtype=np.complex) - index_combinations=[(i,a,b) for i,a,b in product(range(self.N),range(self.L),range(self.L))] - print "Reduce mixed representation" - bar = progressbar.ProgressBar() - for k in bar(range(len(index_combinations))): - i,a,b=index_combinations[k] - self.G[i]+=np.exp(-1j*self.k[i]*(a-b))*self.G_Q[i,a,b] -# ------------------------------------------------------------------ - def calculation_bandstructure(self): - bandstructure=[] - for i in range(self.N): bandstructure.append(-self.G[i].imag/np.pi) - self.bandstructure=np.array(bandstructure).T diff --git a/pyed/DynamicalMeanFieldTheory.py b/pyed/DynamicalMeanFieldTheory.py deleted file mode 100644 index f804f81..0000000 --- a/pyed/DynamicalMeanFieldTheory.py +++ /dev/null @@ -1,68 +0,0 @@ -import numpy as np -from pytriqs.gf import * -from itertools import product -from scipy.optimize import fmin_l_bfgs_b -from pytriqs.operators import Operator, c, c_dag, n -from TriqsExactDiagonalization import TriqsExactDiagonalization - -class DynamicalMeanFieldTheory(object): - """docstring fs DynamicalMeanFieldTheory.""" - def __init__(self,Hloc,G0,beta,nbath): - self.norb=G0.data.shape[1] - self.nbath=nbath - assert(self.nbath%self.norb==0) - self.BPO=self.nbath/self.norb # baths per orb - self.beta=beta - self.G0=G0 - self.h=np.zeros((self.norb,self.nbath)) - self.ek=np.zeros(self.nbath) - self.em=np.zeros(self.norb) - self.nmatsubara=(np.array([iwn for iwn in self.G0.mesh])).size/2 - for i in range(self.norb): - parameters=self._bath_fit(i) - self.h[i,self.BPO*i:self.BPO*(i+1)]=parameters[0:self.BPO] - self.ek[self.BPO*i:self.BPO*(i+1)] =parameters[self.BPO:-1] - self.em[i]=parameters[-1] - - fundamental_operators = np.array([[c('up',i), c('dn',i)] for i in range(self.norb+self.nbath)]).flatten() - self.Hkin = sum(self.h[i][j]*c_dag(s,i)*c(s,j+self.norb) for s, i,j in product(['up','dn'], range(self.norb),range(self.nbath))) - self.Hkin+= sum(self.ek[i]*c_dag(s,i+self.norb)*c(s,i+self.norb) for s,i in product(['up','dn'],range(self.nbath))) - self.Hkin+= sum(self.em[i]*c_dag(s,i)*c(s,i) for s,i in product(['up','dn'],range(self.norb))) - self.Hloc=Hloc - self.H=self.Hkin+self.Hloc - - self.ed = TriqsExactDiagonalization(self.H,fundamental_operators, self.beta,nstates=None) -# ------------------------------------------------------------------ - def _molecular_GF(self,parameters): - iwn = np.array([iwn for iwn in self.G0.mesh]) - h = parameters[0:self.BPO] - ek = parameters[self.BPO:-1] - em = parameters[-1] - fitG0 = np.zeros(iwn.size, dtype=np.complex128) - for i in xrange(iwn.size): fitG0[i] = (iwn[i] - em - np.sum(h ** 2 / (iwn[i] - ek))) ** (-1) - return fitG0 -# ------------------------------------------------------------------ - def _bath_fit(self,i): - error=lambda parameters:np.sum(np.abs(np.conj(self.G0.data[:,i,i].flatten()-self._molecular_GF(parameters))*(self.G0.data[:,i,i].flatten() - self._molecular_GF(parameters)))) - return fmin_l_bfgs_b(error, x0=2*np.random.random(self.BPO*2+1)-1, approx_grad=True, disp=True)[0] -# ------------------------------------------------------------------ - def get_iwn_GF(self): - G = GfImFreq(indices = range(self.norb), beta = self.beta, n_points = self.nmatsubara) - index_combinations=[(i,j) for i,j in product(range(self.norb),range(self.norb))] - for k in range(len(index_combinations)): - i,j=index_combinations[k] - g_iwn=GfImFreq(indices = [0], beta = self.beta, n_points = self.nmatsubara) - self.ed.set_g2_iwn(g_iwn,c('up',i),c_dag('up',j)) - G.data[:,i,j]=g_iwn.data.flatten() - return G -# ------------------------------------------------------------------ - def get_w_GF(self,omega): - G = GfReFreq(indices = range(self.norb), window = (np.min(omega), np.max(omega)), n_points = omega.size) - index_combinations=[(i,j) for i,j in product(range(self.norb),range(self.norb))] - for k in range(len(index_combinations)): - i,j=index_combinations[k] - g_w=GfReFreq(indices = [0], window = (np.min(omega), np.max(omega)), n_points = omega.size) - self.ed.set_g2_w(g_w, c('up',i), c_dag('up',j)) - G.data[:,i,j]=g_w.data.flatten() - return G -# ------------------------------------------------------------------ diff --git a/pyed/SparseExactDiagonalization.py b/pyed/SparseExactDiagonalization.py index e451142..8a18c79 100644 --- a/pyed/SparseExactDiagonalization.py +++ b/pyed/SparseExactDiagonalization.py @@ -310,7 +310,8 @@ def get_frequency_greens_function_component(self, iwn, op1, op2, xi): op=(op1_eig.getH().multiply(op2_eig)).tocoo() M=(np.exp(-self.beta*self.E[op.row])+np.exp(-self.beta*self.E[op.col]))*op.data E=(self.E[op.row]-self.E[op.col]) - for i in range(len(iwn)): + bar = progressbar.ProgressBar() + for i in bar(range(len(iwn))): G[i]=np.sum(M/(iwn[i]-E)) G /= self.Z From 07e244a1c175a1a27d7df4566456ed0d110c3575 Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Tue, 17 Oct 2017 15:00:30 +0300 Subject: [PATCH 17/33] Reload example --- doc/Anderson.ipynb | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/doc/Anderson.ipynb b/doc/Anderson.ipynb index 115aa00..46d305f 100644 --- a/doc/Anderson.ipynb +++ b/doc/Anderson.ipynb @@ -283,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": true }, @@ -298,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -311,9 +311,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFgCAYAAAA2BUkTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQQX+P9W3qJ67o8APAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "from mpl_toolkits.mplot3d import axes3d\n", From 5d5a6755ef8ff0e9361beb778aeb9a096819f813 Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Tue, 17 Oct 2017 17:33:26 +0300 Subject: [PATCH 18/33] Impove eigenvectors collection --- pyed/SparseExactDiagonalization.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pyed/SparseExactDiagonalization.py b/pyed/SparseExactDiagonalization.py index 8a18c79..467c43b 100644 --- a/pyed/SparseExactDiagonalization.py +++ b/pyed/SparseExactDiagonalization.py @@ -56,10 +56,10 @@ def _diagonalize_hamiltonian(self): bar = progressbar.ProgressBar() for i in bar(range(len(self.blocks))): block=self.blocks[i] - E,U=np.linalg.eigh(self.H[block][:,block].todense()) + X,Y=np.meshgrid(block,block) + E,U=np.linalg.eigh(self.H[X,Y].todense()) self.E[block]=E - for i,n in enumerate(block): - self.U[n,block]=U[i] + self.U[Y,X]=U self.E=np.array(self.E) self.E0 = np.min(self.E) self.E = self.E-self.E0 @@ -310,7 +310,7 @@ def get_frequency_greens_function_component(self, iwn, op1, op2, xi): op=(op1_eig.getH().multiply(op2_eig)).tocoo() M=(np.exp(-self.beta*self.E[op.row])+np.exp(-self.beta*self.E[op.col]))*op.data E=(self.E[op.row]-self.E[op.col]) - bar = progressbar.ProgressBar() + bar = progressbar.ProgressBar() for i in bar(range(len(iwn))): G[i]=np.sum(M/(iwn[i]-E)) G /= self.Z From 30865bc29e2e630fa79fcc8b709707e8391372aa Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Wed, 18 Oct 2017 13:59:48 +0300 Subject: [PATCH 19/33] Improve eigenvectors calculation Cleaning up diagonalization --- doc/Anderson.ipynb | 190 ++++++++++++++++++++++------- pyed/SparseExactDiagonalization.py | 1 - pyed/TriqsExactDiagonalization.py | 10 ++ 3 files changed, 153 insertions(+), 48 deletions(-) diff --git a/doc/Anderson.ipynb b/doc/Anderson.ipynb index 46d305f..f9167c2 100644 --- a/doc/Anderson.ipynb +++ b/doc/Anderson.ipynb @@ -25,14 +25,16 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, + "execution_count": 2, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "H_loc = -0.1*c_dag('dn',0)*c('dn',0) + -0.1*c_dag('up',0)*c('up',0) + 1*c_dag('dn',0)*c_dag('up',0)*c('up',0)*c('dn',0)\n" + "H_loc = -0.5*c_dag('dn',0)*c('dn',0) + -0.5*c_dag('up',0)*c('up',0) + 1*c_dag('dn',0)*c_dag('up',0)*c('up',0)*c('dn',0)\n" ] } ], @@ -42,8 +44,8 @@ "n_up = c_dag(up, 0) * c(up, 0)\n", "n_down = c_dag(down, 0) * c(down, 0)\n", "\n", - "U = 1.0\n", - "mu = 0.1\n", + "U = 1\n", + "mu = U/2.\n", "\n", "H_loc = U * n_up * n_down - mu * (n_up + n_down)\n", "\n", @@ -59,31 +61,33 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from itertools import product\n", - "ek = [-2.0,-1.0, 0, 1.0,2.0]\n", - "V = [0.5,0.5, 0.5, 0.5,0.5]\n", + "ek = [-1.0,-0.5, 0, 0.5,1.0]\n", + "V = [0.25,0.5, 1, 0.5,0.25]\n", "H_hyb=sum(V[i]*(c_dag(s,i+1)*c(s,0)+c_dag(s,0)*c(s,i+1)) for s, i in product(['up','dn'], range(len(ek))))\n", "H_hyb+=sum(ek[i]*(c_dag(s,i+1)*c(s,i+1)) for s, i in product(['up','dn'], range(len(ek))))" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "execution_count": 4, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { "text/plain": [ - "0.5*c_dag('dn',0)*c('dn',5) + 0.5*c_dag('dn',0)*c('dn',4) + 0.5*c_dag('dn',0)*c('dn',3) + 0.5*c_dag('dn',0)*c('dn',2) + 0.5*c_dag('dn',0)*c('dn',1) + -2*c_dag('dn',1)*c('dn',1) + 0.5*c_dag('dn',1)*c('dn',0) + -1*c_dag('dn',2)*c('dn',2) + 0.5*c_dag('dn',2)*c('dn',0) + 0.5*c_dag('dn',3)*c('dn',0) + 1*c_dag('dn',4)*c('dn',4) + 0.5*c_dag('dn',4)*c('dn',0) + 2*c_dag('dn',5)*c('dn',5) + 0.5*c_dag('dn',5)*c('dn',0) + 0.5*c_dag('up',0)*c('up',5) + 0.5*c_dag('up',0)*c('up',4) + 0.5*c_dag('up',0)*c('up',3) + 0.5*c_dag('up',0)*c('up',2) + 0.5*c_dag('up',0)*c('up',1) + -2*c_dag('up',1)*c('up',1) + 0.5*c_dag('up',1)*c('up',0) + -1*c_dag('up',2)*c('up',2) + 0.5*c_dag('up',2)*c('up',0) + 0.5*c_dag('up',3)*c('up',0) + 1*c_dag('up',4)*c('up',4) + 0.5*c_dag('up',4)*c('up',0) + 2*c_dag('up',5)*c('up',5) + 0.5*c_dag('up',5)*c('up',0)" + "0.25*c_dag('dn',0)*c('dn',5) + 0.5*c_dag('dn',0)*c('dn',4) + 1*c_dag('dn',0)*c('dn',3) + 0.5*c_dag('dn',0)*c('dn',2) + 0.25*c_dag('dn',0)*c('dn',1) + -1*c_dag('dn',1)*c('dn',1) + 0.25*c_dag('dn',1)*c('dn',0) + -0.5*c_dag('dn',2)*c('dn',2) + 0.5*c_dag('dn',2)*c('dn',0) + 1*c_dag('dn',3)*c('dn',0) + 0.5*c_dag('dn',4)*c('dn',4) + 0.5*c_dag('dn',4)*c('dn',0) + 1*c_dag('dn',5)*c('dn',5) + 0.25*c_dag('dn',5)*c('dn',0) + 0.25*c_dag('up',0)*c('up',5) + 0.5*c_dag('up',0)*c('up',4) + 1*c_dag('up',0)*c('up',3) + 0.5*c_dag('up',0)*c('up',2) + 0.25*c_dag('up',0)*c('up',1) + -1*c_dag('up',1)*c('up',1) + 0.25*c_dag('up',1)*c('up',0) + -0.5*c_dag('up',2)*c('up',2) + 0.5*c_dag('up',2)*c('up',0) + 1*c_dag('up',3)*c('up',0) + 0.5*c_dag('up',4)*c('up',4) + 0.5*c_dag('up',4)*c('up',0) + 1*c_dag('up',5)*c('up',5) + 0.25*c_dag('up',5)*c('up',0)" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -101,8 +105,10 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 9, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -115,17 +121,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python2.7/dist-packages/scipy/sparse/compressed.py:774: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n", - " SparseEfficiencyWarning)\n", - "100% |########################################################################|\n" + " 87% |############################################################### |\r" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Z = 1.00455892128\n", - "\\Omega = -7.409527089\n" + "Z = 1.00151450695\n", + "Omega= -5.63647224313\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 89% |################################################################ |\r", + " 91% |################################################################## |\r", + " 93% |################################################################### |\r", + " 95% |##################################################################### |\r", + " 97% |###################################################################### |\r", + "100% |########################################################################|\r\n" ] } ], @@ -138,7 +154,7 @@ "ed = TriqsExactDiagonalization(H_loc+H_hyb, fundamental_operators, beta)\n", "\n", "print r'Z =', ed.get_partition_function()\n", - "print r'\\Omega =', ed.get_free_energy()" + "print 'Omega=', ed.get_free_energy()" ] }, { @@ -150,16 +166,18 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 54, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " = 0.39987118753\n", - " = 0.39987118753\n", - " = 0.125252462256\n" + " = 0.5\n", + " = 0.5\n", + " = 0.224729977459\n" ] } ], @@ -178,8 +196,10 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 55, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stderr", @@ -190,9 +210,9 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEKCAYAAADTgGjXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEVMSERGRiP3/VwduLPgoG8kAAAAASUVORK5CYII=\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEKCAYAAADTgGjXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3XmcXGWd7/HPr7be92zdCSGBhJCw\nBW1ARGPusLpcYK5z5zIihkFf6Og4zoxzR+7lpTN6nRGHGZnxggujXlFUUNQBZwSFKOiAIEGTAGlC\nAiSmO52t9/RW23P/qFOdTqfX6q46p9Lf98uyT516qs+PU5Xz62c5z2POOURERHIR8jsAEREpXkoi\nIiKSMyURERHJmZKIiIjkTElERERypiQiIiI5UxIREZGcKYmIiEjOlERERCRnEb8DyLcFCxa4FStW\n+B2GiEhRee6554445xZOVe6kTyIrVqxgy5YtfochIlJUzGzvdMqpOUtERHKmJCIiIjlTEhERkZyd\n9H0iInJySyQStLa2MjQ05HcoRam0tJRly5YRjUZzer+SiIgUtdbWVqqqqlixYgVm5nc4RcU5R0dH\nB62traxcuTKn3xGo5iwzu8rMdprZbjO7ZZzXS8zsfu/1Z8xsReGjFJEgGRoaoqGhQQkkB2ZGQ0PD\nrGpxgUkiZhYG7gLeCqwD/sjM1o0p9l6gyzm3CrgD+GxhoxSRIFICyd1sz12QmrMuBHY7514FMLP7\ngGuAHaPKXAP8rbf9AHCnmZnL1xq/D98CB57Py68WkTly9l/DkSBdygIkWgY1y/J6iCCd+aXAvlHP\nW4GLJirjnEuaWQ/QABwZXcjMbgZuBli+fHm+4pU54HA4B2nnSDlHOp3ZzjwgnXa47LY7Vjbtjn+v\nc2QeeK85h8Pb520z6j0jr2V2Zp6PKoP3OiP7s3uPlRv1g2PvOm4ns/3rxsZ5YmP/344vaxje/zLP\n7cT9Zsd+g43sG72dKW/m7c/uMyM0qmwou2/kZ3Z/Zl/I2zeyHcpsh0OWiUeKXpCSyJxxzt0N3A3Q\n3Nyc+7/jt942VyGdtJxzHB1O0tWfoKN/mM7+ON0DCXqHEvQMJugdTNIzmKBvKMHR4ST9w0mOeo/+\n4RSDiRSpdO4fkRnEwiFi4RCRsBEZvR0youEQ4VBmfyRkmW3vZzhkhO3Ydsh7HjK8i92oC54de+3Y\nxTb7/Ph92bjGXoyBKS+b2eSW2Z4k4WUTqZdE4fjk65wjlT6WiDNJOrOdSmcSdip94iOZTpNKOxKp\nzHYy5Uhkf3r7Esk0iZQjnkrn/LlB5nMri4WpLIlQWRKhoiRMRUmEqtII1aVRasqiVJdFqS6NUF0W\npb4iRl15jIbKGPUVMUoi4cwvammBBatnFYvkLkhJpA04ZdTzZd6+8cq0mlkEqAE6ChPe/OOco7M/\nzt7OAfZ1DrC/e4iDvUMc6BniYN8QB3uGOHI0PunFpLIkQk1ZlKrSzIWitjzGsrrykQtGeSxMeSxC\naTRMeSxMWTRMaTRESTRMacTbjoQpiWaSQ0k0REk4TCwSIhbJJAjxh3OOZNoRT6Yzj1Sa4USa4WSK\n4WTm51Di2M/BeOaPhsF4igFveyCeHPnjon84Rd9Qkv3dg/QNZf74GE5O/N2qiIVZVF3KJzfUUtE5\nQDRsREMhot53IxYu7PcjHA5zzjnnkEwmWblyJd/85jepra2d02M88sgjfOQjHyGVSvG+972PW245\nYfzRrMrnIkhJ5FlgtZmtJJMsrgPeNabMQ8Am4FfAHwA/y1t/yDzinKOte5CW9j5a2nvZeaCP1470\n87vOAY4OJ48rW1USYXFNKYurS3jD6Q0srCyhviLzl2HmL8QSasuiI4kjEg7M2A2ZY2aWuXCHQ1SU\n5OcYQ4kUvUMJegcTdPYn6OyPe49hOvrjHOobBmAgniSRyjR9jhYJhygJhyiJhCiNhr1HKC/fy7Ky\nMrZu3QrApk2buOuuu7j11lvn7PenUik+9KEP8eijj7Js2TIuuOACrr76atatGzv+KLfyuQpMEvH6\nOP4U+AkQBr7mnHvRzD4FbHHOPQR8Ffimme0GOskkGpmhgXiS5/Z28fSrHTy7p4uW9l76hjLJwgyW\n15ezckEFF66sZ3l9Oac2lLO8vpym2jIqSgLzlZF5IHvhX1RVOmGZlpYWzlxSPdKEF0+lj9WOkmmG\nU2l6h5J0DsRH3hMNhyiLZmrDlSWZY8zlCK+LL76Y7du3A3Dvvffy+c9/nng8zkUXXcQXvvAFwuHw\nuP8d73//++nu7uaGG27gy1/+Mrt37x55/de//jWrVq3itNNOA+C6667jwQcfnDApzLR8rgJ1RXDO\n/Rj48Zh9nxi1PQT890LHVeycc/x2XzebWw7y9KudbNvXTTLtiISMs5bWcPV5TaxtrGZtYzVnLqlS\nopCiZGb83Y93sGN/77ivO471D43uK8q8N9Pxn+kzCzE6n6xrquZv/utZ044jlUqxefNm3vve99LS\n0sL999/Pk08+STQa5YMf/CDf+ta3eM973nPce5LJJNdffz1f/epXOf/88/mTP/kTzj777OPKtLW1\nccopx1r8ly1bxjPPPDNhHDMtnytdLU5iOw/08dC2Nn60rZ3fdQ4QCRnnLqvh5g2n8YbTGnj9qXVK\nGDJvGJlBEuFRwxucY2SQQTrbv0P62ACMGTR7DQ4Osn79etra2li7di2XX345X/ziF3nuuee44IIL\nRsosWrTohPf+4Ac/4LzzzuP8888HYN26deOWCyJdQU4yQ4kU3/n177jv1/vYebCPcMh44+kN/Nml\nq7nirMVUl+Y2P45IMZhJjWE8w8kUPQMJugcTDCVSGEZlaYTeoQRVJZFJm7yyfSIDAwNceeWV3HXX\nXZgZmzZt4jOf+cykx92+fTvr168fef7CCy9w1VVXHVdm6dKl7Nt37C6I1tZWli5dOuHvnGn5XCmJ\nnCQG4ym+9cxevvTEqxw5Osz6U2r51DVn8bZzGllQmadeT5GTTEkkzKLqzKivwUSKnoE4XQMJ9hzp\npzwWYVFVCVWlkyeT8vJyPv/5z3Pttdfyox/9iHe+8538xV/8BYsWLaKzs5O+vj5OPfXU497T0NDA\nyy+/DMDWrVu59957+djHPnZcmQsuuIBdu3bx2muvsXTpUu677z6+/e1vTxjHTMvnSkmkyPUPJ7n3\n6b386y9f5cjROG88vYE733U+bzitwe/QRIpaWTRMWU0Zi6pL6RqIc7h3mD0d/ZRFM0mmepJkcv75\n53Puueeybds2Pv3pT3PFFVeQTqeJRqPcddddJySRG264gbe//e2cc845bNy4kRUrVox0iGdFIhHu\nvPNOrrzySlKpFDfddBNnnTVxzWum5XNlJ/sI2ebmZneyLo/7q1c6+Oh3t7K/Z4g3r17An126mgtW\n1PsdlkhBtbS0sHbt2rwfJ+0c3QMJDvUNEU+mqSyJcEp9OdE5GC589OhRKisrAbj99tvp6enh05/+\n9Kx/73SNdw7N7DnnXPNU71VNpAjFk2nueOxlvvTEK6xoqOB7H7hYyUMkz0Jm3l3zUTr747T3DLHr\nYB9L68qpKZtdX+Mdd9zBfffdRzQa5ZJLLuFzn/vcHEWdf0oiRebVw0f58/u3sr21h+suOIWPv2Od\nRliJFJCZ0VBZQkVJhH2dA+zt6Ke+IkZjTVnOd8h//OMf5+Mf/3hO7+3o6ODSSy89Yf/mzZtpaMh/\ns7auPkXke1v28YkHX6QkGuJL734dV53d6HdIIvNWaTTM6YsqOdg7xOG+YfqHUyyvL6csduKNhPnU\n0NAwcqe8HzQnRZG456k9/M8HtrP+lFoe+cgGJRCRAAiZ0VhTxmkLKkg7x6tHjjIYT/kdVkEpiRSB\n7z67j7956EUuX7eYb7z3QpbUTDwFhIgUXmVplNMXVhAy47Uj/Qwl5k8iURIJuAe3tvGxH2xnwxkL\nufNd58/JSBARmXuxSJjTFlSAwWtH+hlOzo9EoitSgP3kxQP85Xe3ceGKer787tcfWz9BRAKpJJpJ\nJM45XjvcT3ySqexPFkoiAfX4zkN8+Nu/5dxlNXz1xgsK3lknIrkpjYZZuaCClHO8duQoiVku3hV0\nSiIBtPtQHx+49zlWLark6398IZUawitSVMpiEVY0VJBIOfYc6R+ZLfhkpCQSMOm042Pff57SaJiv\n33TBrG9iEhF/VHh3tA8mUhzxFs86GSmJBMw3n97Lc3u7+MQ71k26EI+IBEt22pLRarxVPg/2Dc/J\niK1HHnmENWvWsGrVKm677bY5KzsbSiIB0to1wD888hIbzljI758/91M2i0jhNdWWETJo6xo8Yfne\nmcgud/vwww+zY8cOvvOd77Bjx45Zl50tJZGAcM5x6w9fwAF///tnz+lSnSJSGHv27OHMM8/kxhtv\n5IwzzuD666/niZ//jJve+VZ+76LzeOyJJ8d9X0tLCxs2bODcc8/l9ttvZ9WqVSeUGb3cbSwWG1nu\ndjwzKTtbSiIB8W9b23ji5cP89ZVrWFZX7nc4IpKj3bt389GPfpSXXnqJl156iW9/+9v86sn/5H9/\n8u+47bbPnDDsN7s07r/8y7+wfft2Xn311ROWxoXxl7tta2sbN4aZlJ0tDfsJgCNHh/nUj3bwuuW1\n3HDxCr/DESleD98CB56f29+55Bx46/T7FFauXMk555wDwFlnncWll15KKBTiLW94PXd89u/Y3z3I\nqQ3lI60Nxbw0LqgmEgif/NEO+odTfPad5+Y8C6iIBENJybGVREOh0Mjz0lgUc2l6hxL0DCZGyoy3\nNO7o51kzWe62UEvjgmoivnvqlSP8aNt+/vLyM1i9uMrvcESK2wxqDH4Ih4zyWIT93UNUl0YJhWxa\nS+PCzJa7LdTSuKAk4ruv/edrNFTEuHnDaVMXFpGit6SmlFcPH6V7ME59Rcm0lsaFmS13W6ilcUHL\n4/pqb0c/G//xcT78X1bxl1es8TsckaJUqOVx54pzjl2HjgKwelEl/f39vi6NC7NbHld9Ij76xq/2\nEjbj+jec6ncoIlIgZsaCyhKGEin6h1PccccdnHXWWaxfv549e/bkvMKhX9Sc5ZP+4STffXYfbzun\nkcXVujNdZD6pLYtyoMfo6B8u6qVxQUnENz/4TSt9w0luvGSF36GISIGFQkZ9RYzDfcPEkyliOS7z\n4PfSuKDmLF+k047/99QezltWw/mn1Podjoj4oL6iBDA6+uN+hzIrSiI++OXuI7x6uJ8bL1mh6U1E\n5qlYJER1WYTO/jipdPEOcFIS8cHXn3yNhVUlvP2cJr9DEREfLagsIZV2dA8Ub20kEEnEzOrN7FEz\n2+X9rJug3CNm1m1m/17oGOfKa0f6+fnOw1x/0XJikUCcfpGiV6y3KpTHwpRFw3Qcjfv23zDb4wbl\nKnYLsNk5txrY7D0fz+3ADQWLKg/ueWoP0bDxrouW+x2KyEmhtLSUjo6OokwkZkZDZQlDyRT9w8mC\nH985R0dHB6WluY8QDcrorGuAjd72PcDjwAn3/TvnNpvZxrH7i0XfUIIHnmvlHec2acEpkTmybNky\nWltbOXz4sN+h5MQ5x5GeIXrbQzRUlkz9hjlWWlrKsmXLcn5/UJLIYudcu7d9AFjsZzD58tC2/Rwd\nTrLpjSv8DkXkpBGNRlm5cqXfYczKwz/dyZ0/382TH/s9mmrL/A5nRgrWnGVmj5nZC+M8rhldzmXq\npLOql5rZzWa2xcy2BOmvk80thzi1oZz1GtYrIqNcfV4TzsHjO4NzvZqugiUR59xlzrmzx3k8CBw0\ns0YA7+ehWR7rbudcs3OueeHChXMR/qwNJVI89coRNp4RjHhEJDhWLapkaW0Zj++c1aXPF0HpWH8I\n2ORtbwLys46jj57d08lQIs1b1iiJiMjxzIwNZyzkqVc6Tlj5MOiCkkRuAy43s13AZd5zzKzZzL6S\nLWRmvwS+B1xqZq1mdqUv0ebg8Z2HiUVCXHzaAr9DEZEA2rhmIUeHkzy3t8vvUGYkEB3rzrkO4IRZ\nxJxzW4D3jXr+5kLGNZeeePkwF62spyyW2xw5InJyu2TVAiIh44mXD3Px6YWZPHEuBKUmclJr7Rpg\n96GjvEX9ISIygcqSCM0r6oquX0RJpACeeDkz4mLjmkU+RyIiQbZxzSJeOtDHwd4hv0OZNiWRAnh8\n52GW1pZx+sIKv0MRkQDLtlY8UURDfZVE8iyeTPPU7iNsXLNQM/aKyKTOXFLF4uqSkdaLYqAkkmdb\n9nbSH0+pP0REpmRmvOWMhfxy12GSqeIY6qskkmdP7DxMNGy8cZWG9orI1DauWUTvUJLf7uv2O5Rp\nURLJsydePkzzqfVUlgRiNLWIBNwlqxYQDlnR9IsoieRRe88gLx3oY6PuUheRaaopi/K65bU8/nJx\nDPVVEsmjX2hor4jkYOOaRbzQ1svhvmG/Q5mSkkgePb7zMEuqSzljcaXfoYhIEckOxPlFEYzSUhLJ\nk0QqzX/u0tBeEZm5dY3VLKgsjqG+SiJ58tvfddM3nNTQXhGZsVDI2HDGAn6x6zCpdLCX/VUSyZMn\nXj5EOGRcslpDe0Vk5jauWUT3QILtrcEe6qskkifb9vWwtrGK6tKo36GISBG6aGU9ANsCfr+Ikkge\nOOdoae9lXWO136GISJFaVFVCfUWMlvY+v0OZlJJIHhzqG6ajP85aJRERyZGZsbaxih3tvX6HMikl\nkTzIfuiqiYjIbKxrrGbnwb5Az6OlJJIHLV4SOVNJRERmYW1jNfFkmteO9PsdyoSURPJgx/5eltaW\nUVOmTnURyV22STzITVpKInnQ0t7LuibVQkRkdk5fWEksHFISmU8G4yleO9KvTnURmbVYJMSqRZWB\nHqGlJDLHdh7sI+1gXWOV36GIyElgbWM1O/arJjJvtIyMzKrxORIRORmsa6rmyNHhwM7oqyQyx1ra\ne6ksibCsrszvUETkJLDWa9VoCWi/iJLIHGtp7+XMJVWEQpq5V0RmL3u/mZLIPJBOO1ra+9SpLiJz\nprY8RmNNaWBHaCmJzKHWrkGODic1vFdE5tS6xmrVROaD7F8KqomIyFxa21jNK4f7GUqk/A7lBEoi\nc2hHey8hgzWLNbxXRObO2sZqUmnHroNH/Q7lBEoic6ilvZeVCyooi4X9DkVETiLZJvIgNmkpicyh\nlvZeNWWJyJw7tb6c8lg4kJ3rgUgiZlZvZo+a2S7vZ904Zdab2a/M7EUz225m/8OPWCfSM5igtWtQ\nSURE5lwoZKxZEsy1RQKRRIBbgM3OudXAZu/5WAPAe5xzZwFXAf9sZrUFjHFSL2XvVNfILBHJg+wI\nLeec36EcJyhJ5BrgHm/7HuDasQWccy8753Z52/uBQ8DCgkU4hRYtRCUiebS2sZq+oSRt3YN+h3Kc\noCSRxc65dm/7ALB4ssJmdiEQA16Z4PWbzWyLmW05fPjw3EY6gR3tvdRXxFhUVVKQ44nI/DKytkjA\nJmMsWBIxs8fM7IVxHteMLucydbUJ62tm1gh8E/hj59y4a0Y65+52zjU755oXLixMZaWlvY91jdWY\naboTEZl7Zy6pwozATQsfKdSBnHOXTfSamR00s0bnXLuXJA5NUK4a+A/gVufc03kKdcaSqTQ7D/ax\n6eJT/Q5FRE5SFSURVjRUBG6Yb1Casx4CNnnbm4AHxxYwsxjwQ+AbzrkHChjblF490k88mdbILBHJ\nq7WNwRuhFZQkchtwuZntAi7znmNmzWb2Fa/MHwIbgBvNbKv3WO9PuMdr0XQnIlIAa5dU87vOAfqG\nEn6HMqJgzVmTcc51AJeOs38L8D5v+17g3gKHNi072nuJhUOcvrDS71BE5CSWvYVg54E+mlfU+xxN\nRlBqIkWtpb2PVYsqiUV0OkUkf9YGcG0RXfXmwL7OAVYurPA7DBE5yTXWlFIaDbG3Y8DvUEYoicxS\nOu1o6x5kWa2WwxWR/DIzmmrL2N8TnBsOlURmqaM/TjyZpklJREQKYGltGW3dQ36HMUJJZJayUxAo\niYhIITTVlNHWVcQ1ETOrMDMtmOHZ7yWRpUoiIlIAS+vKOHJ0ODCrHE6ZRMwsZGbvMrP/MLNDwEtA\nu5ntMLPbzWxV/sMMruxfBEoiIlII2VaP9p5gNGlNpybyc+B04H8BS5xzpzjnFgFvAp4GPmtm785j\njIHW1j1IZUmE6rJA3HIjIie57B+s+wMym+90rnyXOedOuD3SOdcJfB/4vplF5zyyItHWPUhTbakm\nXhSRgsgmkaD0i0xZE8kmEDN7aqoy89H+7kE1ZYlIwSypKcWMwKwrMpOO9dKxO8zszXMYS1Ha3z2o\nkVkiUjCxSIhFVSWBSSIzachfY2Y/BF4EXgAOAl8h018yLw3Ek3QNJFhapyQiIoWztLasqPpEsl4D\n/h44G3g90AR8Mh9BFQsN7xURPzTVlvF8W4/fYQAzSyJx59yzwLP5CqbYtHbpRkMRKbyltWX89MWD\npNOOUMjfQT0z6RN5S96iKFL7vakHVBMRkUJaWldGPJXmSP+w36FM62ZDA3DOTbiwr83T8a1t3QOE\nQ8aiqhK/QxGReaSpJjjDfKd1s6GZfdjMlo/eaWYxM/s9M7uHY0vbziv7u4dYUl1KJKwpyESkcLKD\nefYHYCLG6fSJXAXcBHzHzE4DuoAyMgnop8A/O+d+m78Qg6tN94iIiA+y/bBt3f6vKzJlEnHODQFf\nAL7g3Zm+ABh0znXnO7iga+sa5MKVwViiUkTmj5qyKFUlkaKpiQBgZruA54FtwFYz2+qc25u3yAIu\nlXYc6B2iqfaEezBFRPKuqbZsZISon2bSmP9l4ADQAbwVeNHMnjezT83HubMO9g6RSjuW1pb7HYqI\nzENL64Jxw+FM7hN5t3NuffaJmX2JTF9JL/A54MNzHFug7R9ZjEo1EREpvKbaUp7b2+V3GDOqifSY\n2bnZJ865rcBbnHP/CFwy55EFXJvuVhcRHzXVltEzmODocNLXOGZSE3k/8C0z2wpsBdYA2aEBsbkO\nLOi0LK6I+Gn0uiJnLK7yLY5p10Sccy8BFwKPAIuA3cA7zKwCuC8/4QXX/u5BasujVJRoMSoRKbyR\ndUV87heZ0RXQOZcCvuc9Rvv0nEVUJNq6dI+IiPjn2A2H/iYR3Wqdo/3dQ2rKEhHfLKoqJRIy36c+\nURLJgXNOd6uLiK/CIWNJTalqIsWodyjJ0eGkkoiI+Kqptsz3PhElkRxkq49a0VBE/LSstsz3qU8C\nkUTMrN7MHjWzXd7PunHKnGpmvzGzrWb2opl9wI9YYfSNhkoiIuKfptoyDvQOkUylfYshEEkEuAXY\n7JxbDWz2no/VDlzs3TV/EXCLmTUVMMYR+3t0t7qI+K+ptoxU2nGwz7/FqYKSRK4B7vG27wGuHVvA\nORd3zmXPVAk+xt7WNUgsEmJBhRajEhH/ZJvU/RyhFZQkstg51+5tHwAWj1fIzE4xs+3APuCzzrn9\nhQpwtLbuQZpqSn1f21hE5relXmuInyO0Cna7tZk9BiwZ56VbRz9xzjkzc+P9DufcPuBcrxnr38zs\nAefcwXGOdTNwM8Dy5cvHvjxrbd2D6lQXEd81BeCu9YIlEefcZRO9ZmYHzazROdduZo3AoSl+134z\newF4M/DAOK/fDdwN0NzcPG5Cmo393YNsWL1wrn+tiMiMlMci1JVHfU0iQWnOeohj67RvAh4cW8DM\nlplZmbddB7wJ2FmwCD3xZJpDfcOqiYhIIPi9rkhQkshtwOXe6omXec8xs2Yz+4pXZi3wjJltA54A\n/tE593yhAz3QM4RzGt4rIsHQVFPma8d6IKagdc51AJeOs38L8D5v+1Hg3LFlCi1bbVymJCIiAbC0\nrowndx/BOYdZ4Qf7BKUmUjS0joiIBMnS2jL64yl6BhO+HF9JZIaybY9LanSjoYj4z+8RWkoiM9TW\nNcjCqhJKo2G/QxERObY4lU/9IkoiM7S/Z1BNWSISGE21/i5OpSQyQ21dg+pUF5HAWFAZIxYJqTmr\nWBzsHWJRtebMEpFgMDOWVJdysNefSRiVRGYgnkzTH0/RUBHzOxQRkRF1FTG6BuK+HFtJZAa6vQ+p\ntlxJRESCo648SveAhvgGXqeXROqUREQkQOrKY3T2qyYSeF39mUxfVx71ORIRkWNqy6MjLSWFpiQy\nA9kPqU59IiISIPXlMfrjKeLJwi+TqyQyA10D2ZqIkoiIBEet94etH7URJZEZ6BrpWFdzlogER7aJ\nvVNJJNi6+uOURcOa8kREAiXbOpLtty0kJZEZ6BpIqFNdRAIn2zqi5qyA6x6Iq1NdRAKn3rsudflw\nr4iSyAx0DsTVqS4igTPSnKWaSLB1DyTUqS4igVMaDVMaDdHlww2HSiIz0KWaiIgEVF15TM1ZQZZK\nO3oGE+oTEZFAqiuPqWM9yHoHEzinKU9EJJjqKqLqEwkyTb4oIkFWWx7zZSZfJZFp6tbd6iISYHXl\nUd2xHmTHZvBVTUREgqeuPEbPYIJU2hX0uEoi05Rta6xXx7qIBFBdeQznMv23haQkMk2afFFEgqyu\nInNtKnTnupLINHUNJIiEjMqSiN+hiIicoNanu9aVRKapeyBObXkMM/M7FBGRE/g1k6+SyDR19Seo\nr1BTlogEU71qIsHW6dVERESCqLYiOx28aiKB1D0Q193qIhJYVSURIiGbnzURM6s3s0fNbJf3s26S\nstVm1mpmdxYyxsyCVKqJiEgwmRm15YWf+iQQSQS4BdjsnFsNbPaeT+T/AL8oSFQe55wWpBKRwKsr\nj83bjvVrgHu87XuAa8crZGavBxYDPy1QXAD0x1MkUk7NWSISaJnp4OdnTWSxc67d2z5AJlEcx8xC\nwD8BfzXVLzOzm81si5ltOXz48KyDyy70oo51EQmy2vJowTvWC3bnnJk9BiwZ56VbRz9xzjkzG2/y\nlw8CP3bOtU51r4Zz7m7gboDm5uZZTyTTpRl8RaQI1JXH2Lqvu6DHLFgScc5dNtFrZnbQzBqdc+1m\n1ggcGqfYxcCbzeyDQCUQM7OjzrnJ+k/mRHa1MDVniUiQ1XprijjnCnZjdFCasx4CNnnbm4AHxxZw\nzl3vnFvunFtBpknrG4VIIHBsGnh1rItIkNWXx0ikHP3xVMGOGZQkchtwuZntAi7znmNmzWb2FV8j\nAzr71ZwlIsF3bOqTwnWuB2InqPWqAAAJnElEQVQ2QedcB3DpOPu3AO8bZ//Xga/nPTBP10ACM6gp\nU3OWiARXdpbx7oEEp9QX5phBqYkEWvdAnOrSKOGQJl8UkeDKNrkXcoVDJZFp6BpIaDEqEQm8bHNW\nt5JIsGSmgVdTlogEW3YEaSH7RJREpqGzP65OdREJvGy/bVcBbzhUEpmG7oGEaiIiEniRcIjq0oia\ns4Kma0A1EREpDnUVMTpVEwmOoUSKgXhKHesiUhTqymOqiQRJdjIzNWeJSDGoK/CaIkoiU9DkiyJS\nTAq9poiSyBSySUQ1EREpBrVqzgqWbHOW+kREpBjUV0Tpj6cYThZmEkYlkSlo8kURKSa1I3etF6ZJ\nS0lkCt1qzhKRIjIyk2+BmrSURKbQNZCgPBamJBL2OxQRkSkdm/pENZFA0I2GIlJMalUTCZbugQR1\nFWrKEpHikB0EpCQSEJp8UUSKyeiFqQpBSWQKmWnglUREpDiURsOURcMFmw5eSWQKXQOJkY4qEZFi\nkJn6RDUR3yVTaXqHEmrOEpGiUlcRU59IEPQMJnAO1UREpKjUlSuJBEK2OlinKU9EpIjUlkfVsR4E\nx+5WVxIRkeKhmkhAjNRE1JwlIkWkrjxKz2CCVNrl/VhKIpPQWiIiUozqKmI4l+nXzTclkUlkx1mr\nT0REikkhJ2FUEplE10CCaNioiGnyRREpHsfuWlcS8VX2bnUz8zsUEZFpG6mJFGAmXyWRSXQNxKlX\nf4iIFJnsJIydqon4q6s/ocWoRKTozLvmLDOrN7NHzWyX97NugnIpM9vqPR7Kd1xaS0REilFlSYRI\nyAoyf1YgkghwC7DZObca2Ow9H8+gc26997g630F1aS0RESlCZkZteWz+1ESAa4B7vO17gGt9jAUA\n55ymgReRolVXHp1XHeuLnXPt3vYBYPEE5UrNbIuZPW1meU00fcNJkmmnjnURKUp1FbGCdKxH8n4E\nj5k9BiwZ56VbRz9xzjkzm+he/VOdc21mdhrwMzN73jn3yjjHuhm4GWD58uU5xZtKOd5xbiNrllTl\n9H4RET9tWL2AgXgq78cx5/I/t8qUQZjtBDY659rNrBF43Dm3Zor3fB34d+fcA5OVa25udlu2bJm7\nYEVE5gEze8451zxVuaA0Zz0EbPK2NwEPji1gZnVmVuJtLwAuAXYULEIRETlBUJLIbcDlZrYLuMx7\njpk1m9lXvDJrgS1mtg34OXCbc05JRETERwXrE5mMc64DuHSc/VuA93nbTwHnFDg0ERGZRFBqIiIi\nUoSUREREJGdKIiIikjMlERERyZmSiIiI5CwQNxvmk5kdBvbO4lcsAI7MUThzSXHNjOKaGcU1Mydj\nXKc65xZOVeikTyKzZWZbpnPXZqEprplRXDOjuGZmPsel5iwREcmZkoiIiORMSWRqd/sdwAQU18wo\nrplRXDMzb+NSn4iIiORMNREREcmZkghgZleZ2U4z221mJ6zvbmYlZna/9/ozZraiADGdYmY/N7Md\nZvaimX1knDIbzazHzLZ6j0/kO65Rx95jZs97xz1hwRbL+Lx3zrab2esKENOaUediq5n1mtmfjylT\nkHNmZl8zs0Nm9sKoffVm9qiZ7fJ+1k3w3k1emV1mtmm8MnMc1+1m9pL3Of3QzGoneO+kn3ke4vpb\nM2sb9Vm9bYL3TvrvNw9x3T8qpj1mtnWC9+bzfI17ffDlO+acm9cPIAy8ApwGxIBtwLoxZT4IfMnb\nvg64vwBxNQKv87argJfHiWsjmYW5/Dhve4AFk7z+NuBhwIA3AM/48LkeIDPWveDnDNgAvA54YdS+\nfwBu8bZvAT47zvvqgVe9n3Xedl2e47oCiHjbnx0vrul85nmI62+Bv5rG5zzpv9+5jmvM6/8EfMKH\n8zXu9cGP75hqInAhsNs596pzLg7cB1wzpsw1wD3e9gPApWZm+QzKOdfunPuNt90HtABL83nMOXYN\n8A2X8TRQ661aWSiXAq8452Zzo2nOnHO/ADrH7B79PboHuHact14JPOqc63TOdQGPAlflMy7n3E+d\nc0nv6dPAsrk63mzimqbp/PvNS1zeNeAPge/M1fGma5LrQ8G/Y0oimRO/b9TzVk68WI+U8f6x9QAN\nBYkO8JrPzgeeGefli81sm5k9bGZnFSomwAE/NbPnLLOm/VjTOa/5dB0T/+P265wtds61e9sHgMXj\nlPH7vN1EpgY5nqk+83z4U6+Z7WsTNM34eb7eDBx0zu2a4PWCnK8x14eCf8eURALOzCqB7wN/7pzr\nHfPyb8g015wH/F/g3woY2pucc68D3gp8yMw2FPDYkzKzGHA18L1xXvbznI1wmXaFQA2NNLNbgSTw\nrQmKFPoz/yJwOrAeaCfTdBQkf8TktZC8n6/Jrg+F+o4piUAbcMqo58u8feOWMbMIUAN05DswM4uS\n+YJ8yzn3g7GvO+d6nXNHve0fA1HLrD+fd865Nu/nIeCHZJoVRpvOec2XtwK/cc4dHPuCn+cMOJht\n0vN+HhqnjC/nzcxuBN4BXO9dfE4wjc98TjnnDjrnUs65NPCvExzPr/MVAf4bcP9EZfJ9via4PhT8\nO6YkAs8Cq81spfcX7HXAQ2PKPARkRzD8AfCzif6hzRWvvfWrQItz7nMTlFmS7ZsxswvJfJ6FSG4V\nZlaV3SbTMfvCmGIPAe+xjDcAPaOq2fk24V+Ifp0zz+jv0SbgwXHK/AS4wszqvOabK7x9eWNmVwF/\nDVztnBuYoMx0PvO5jmt0H9rvT3C86fz7zYfLgJecc63jvZjv8zXJ9aHw37F8jBwotgeZkUQvkxnl\ncau371Nk/lEBlJJpGtkN/Bo4rQAxvYlMVXQ7sNV7vA34APABr8yfAi+SGZHyNPDGAp2v07xjbvOO\nnz1no2Mz4C7vnD4PNBcotgoySaFm1L6CnzMySawdSJBpc34vmX60zcAu4DGg3ivbDHxl1Htv8r5r\nu4E/LkBcu8m0kWe/Z9mRiE3Ajyf7zPMc1ze97852MhfHxrFxec9P+Pebz7i8/V/PfqdGlS3k+Zro\n+lDw75juWBcRkZypOUtERHKmJCIiIjlTEhERkZwpiYiISM6UREREJGdKIiIikjMlERERyVnE7wBE\n5hszqwaeIDN1+UoyN8oNkbnxMe1nbCIzpZsNRXziTbtyq3NuzqYuFyk0NWeJ+OdsMlNiiBQtJRER\n/6wjz5MYiuSbkoiIf5rILBwkUrSURET88xPgq2b2Fr8DEcmVOtZFRCRnqomIiEjOlERERCRnSiIi\nIpIzJREREcmZkoiIiORMSURERHKmJCIiIjlTEhERkZz9f/p2veE7TbwlAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -201,7 +221,7 @@ ], "source": [ "from pytriqs.gf import GfImTime\n", - "g_tau = GfImTime(name=r'$g$', beta=beta, statistic='Fermion', n_points=50, indices=[1]) \n", + "g_tau = GfImTime(name=r'$g$', beta=beta, statistic='Fermion', n_points=60, indices=[1]) \n", "ed.set_g2_tau(g_tau, c(up,0), c_dag(up,0))\n", "\n", "import matplotlib.pyplot as plt\n", @@ -212,8 +232,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 56, "metadata": { + "collapsed": false, "scrolled": true }, "outputs": [ @@ -226,9 +247,9 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEKCAYAAADenhiQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIROyUVEREKn5CIiIqFTchERkdD9f816EfNsQSwSAAAAAElFTkSuQmCC\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEKCAYAAADenhiQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nO+S2H8MfAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -237,7 +258,7 @@ ], "source": [ "from pytriqs.gf import GfImTime\n", - "densdens_tau = GfImTime(name=r'$\\langle n(\\tau) n(0) \\rangle$', beta=beta, statistic='Boson', n_points=50, indices=[1]) \n", + "densdens_tau = GfImTime(name=r'$\\langle n(\\tau) n(0) \\rangle$', beta=beta, statistic='Boson', n_points=60, indices=[1]) \n", "ed.set_g2_tau(densdens_tau, n_up, n_down)\n", "\n", "plt.figure(); oplot(densdens_tau); plt.savefig('figure_densdens_tau.png')" @@ -245,8 +266,10 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 57, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stderr", @@ -257,9 +280,9 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEMCAYAAAAF2YvKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEMCAYAAAAF2YvKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+cbqTlBKwIAAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -268,12 +291,44 @@ ], "source": [ "from pytriqs.gf import GfImFreq\n", - "g_iwn = GfImFreq(name=r'$g$', beta=beta, statistic='Fermion', n_points=100, indices=[1])\n", + "g_iwn = GfImFreq(name=r'$g$', beta=beta, statistic='Fermion', n_points=120, indices=[1])\n", "ed.set_g2_iwn(g_iwn, c(up,0), c_dag(up,0))\n", "\n", "plt.figure(); oplot(g_iwn); plt.savefig('figure_g_iwn.png')" ] }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% |########################################################################|\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBYQxxpiqLCCMMcZU9f8BceRLmVx/1QMAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pytriqs.gf import GfReFreq\n", + "g_w = GfReFreq(indices = [1], window = (-4, 4), n_points = 200)\n", + "ed.set_g2_w(g_w,c(up,0),c_dag(up,0))\n", + "plt.figure(); oplot(g_w,mode='S'); plt.savefig('figure_g_w.png')" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -283,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 59, "metadata": { "collapsed": true }, @@ -292,13 +347,13 @@ "from pytriqs.gf import Gf\n", "from pytriqs.gf import MeshImTime, MeshProduct\n", "\n", - "ntau = 10\n", + "ntau = 21\n", "imtime = MeshImTime(beta, 'Fermion', ntau)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 60, "metadata": { "collapsed": true }, @@ -311,14 +366,16 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": 61, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFgCAYAAAA2BUkTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQQX+P9W3qJ67o8APAAAAAElFTkSuQmCC\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFgCAYAAAA2BUkTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngAMmY4wxJgEHTMYYY0wCDpiMMcaYBP8DBL3AiD3DucsAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -341,6 +398,45 @@ "plt.tight_layout()\n", "plt.savefig('figure_g3pp_tau.png')" ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(54.01,0.5,u'$\\\\tau_2$')" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdoAAAHICAYAAADp+is/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJ7A5kgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,8))\n", + "plt.imshow(data.real)\n", + "plt.xticks(range(21))\n", + "plt.yticks(range(21))\n", + "plt.savefig('im_g3pp_tau.png')\n", + "plt.colorbar()\n", + "plt.xlabel(r'$\\tau_1$')\n", + "plt.ylabel(r'$\\tau_2$')" + ] } ], "metadata": { @@ -359,7 +455,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.12" + "version": "2.7.13" } }, "nbformat": 4, diff --git a/pyed/SparseExactDiagonalization.py b/pyed/SparseExactDiagonalization.py index 467c43b..adea6ef 100644 --- a/pyed/SparseExactDiagonalization.py +++ b/pyed/SparseExactDiagonalization.py @@ -387,6 +387,5 @@ def get_high_frequency_tail(self, iwn, Gc, start_order=-1): return G - # ------------------------------------------------------------------ # ---------------------------------------------------------------------- diff --git a/pyed/TriqsExactDiagonalization.py b/pyed/TriqsExactDiagonalization.py index 4ee7180..66f76a6 100644 --- a/pyed/TriqsExactDiagonalization.py +++ b/pyed/TriqsExactDiagonalization.py @@ -156,5 +156,15 @@ def set_g4_tau(self, g4_tau, op1, op2, op3, op4): g4_tau[list(idx)][:] = perm_sign * d # ------------------------------------------------------------------ + def set_g2_w(self, g_w, op1, op2,eta=0.05): + + assert( g_w.target_shape == (1, 1) ) + + op1_mat = self.rep.sparse_matrix(op1) + op2_mat = self.rep.sparse_matrix(op2) + + w = np.array([w for w in g_w.mesh]) + + g_w.data[:, 0, 0] = self.ed.get_real_frequency_greens_function_component(w, op1_mat, op2_mat,eta) # ---------------------------------------------------------------------- From 9b3252775ab6bf30f6d2e7102233f221666a7b62 Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Wed, 18 Oct 2017 14:02:08 +0300 Subject: [PATCH 20/33] Clean Up --- pyed/SparseExactDiagonalization.py | 36 +++++++++++++++++++++--------- 1 file changed, 25 insertions(+), 11 deletions(-) diff --git a/pyed/SparseExactDiagonalization.py b/pyed/SparseExactDiagonalization.py index adea6ef..e56f293 100644 --- a/pyed/SparseExactDiagonalization.py +++ b/pyed/SparseExactDiagonalization.py @@ -46,7 +46,7 @@ def __init__(self, H,blocks, beta, self._diagonalize_hamiltonian() self._calculate_partition_function() - self._calculate_density_matrix() + #self._calculate_density_matrix() # ------------------------------------------------------------------ def _diagonalize_hamiltonian(self): @@ -60,6 +60,7 @@ def _diagonalize_hamiltonian(self): E,U=np.linalg.eigh(self.H[X,Y].todense()) self.E[block]=E self.U[Y,X]=U + del X,Y self.E=np.array(self.E) self.E0 = np.min(self.E) self.E = self.E-self.E0 @@ -88,16 +89,8 @@ def _operators_to_eigenbasis(self, op_vec): # ------------------------------------------------------------------ def get_expectation_value(self, operator): - - exp_val = 0.0 - for idx in xrange(self.E.size): - vec = self.U[:, idx] - dot_prod = np.dot(vec.H, operator * vec)[0,0] # - exp_val += np.exp(-self.beta * self.E[idx]) * dot_prod - - exp_val /= self.Z - - return exp_val + op=self._operators_to_eigenbasis([operator])[0] + return (op.diagonal()*np.exp(-self.beta * self.E)).sum()/self.Z # ------------------------------------------------------------------ def get_free_energy(self): @@ -387,5 +380,26 @@ def get_high_frequency_tail(self, iwn, Gc, start_order=-1): return G + # ------------------------------------------------------------------ + def get_real_frequency_greens_function_component(self, w, op1, op2, eta): + + r""" + Returns: + G^{(2)}(i\omega_n) = -1/Z < O_1(i\omega_n) O_2(-i\omega_n) > + """ + + op1_eig, op2_eig = self._operators_to_eigenbasis([op1, op2]) + + # -- Compute Lehman sum for all operator combinations + G = np.zeros((len(w)), dtype=np.complex) + op=(op1_eig.getH().multiply(op2_eig)).tocoo() + M=(np.exp(-self.beta*self.E[op.row])+np.exp(-self.beta*self.E[op.col]))*op.data + E=(self.E[op.row]-self.E[op.col]) + bar = progressbar.ProgressBar() + for i in bar(range(len(w))): + G[i]=np.sum(M/(w[i]+1j*eta-E)) + G /= self.Z + + return G # ---------------------------------------------------------------------- From 121a67de671f09de30ee82c0ef7e8fe893a69de7 Mon Sep 17 00:00:00 2001 From: Yaroslav Zhumagulov Date: Wed, 19 Dec 2018 17:57:14 +0300 Subject: [PATCH 21/33] Update Compatible version with TRIQS 2.0 --- LICENSE.txt | 2 +- MANIFEST | 8 ++ Readme.md | 2 +- dist/.DS_Store | Bin 0 -> 6148 bytes dist/pyed-0.0.0.tar.gz | Bin 0 -> 8474 bytes doc/Anderson.ipynb | 185 ++++++++++------------------- doc/Documentation.ipynb | 178 +++++++++++++++++---------- pyed/CubeTetras.py | 42 ++++--- pyed/SparseExactDiagonalization.py | 6 +- pyed/TriqsExactDiagonalization.py | 120 +++++++++++++------ setup.py | 8 ++ 11 files changed, 308 insertions(+), 243 deletions(-) create mode 100644 MANIFEST create mode 100644 dist/.DS_Store create mode 100644 dist/pyed-0.0.0.tar.gz create mode 100644 setup.py diff --git a/LICENSE.txt b/LICENSE.txt index 82f06f9..ea03edd 100644 --- a/LICENSE.txt +++ b/LICENSE.txt @@ -1,7 +1,7 @@ PYED: Exact diagonalization routines for finite quantum systems -Copyright (C) 2017 by H. U.R. Strand, Ya.V. Zhumagulov +Copyright (C) 2018 by H. U.R. Strand, Ya.V. Zhumagulov PYED is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software diff --git a/MANIFEST b/MANIFEST new file mode 100644 index 0000000..886e2d6 --- /dev/null +++ b/MANIFEST @@ -0,0 +1,8 @@ +# file GENERATED by distutils, do NOT edit +setup.py +pyed/CubeTetras.py +pyed/SparseExactDiagonalization.py +pyed/SparseMatrixFockStates.py +pyed/SquareTriangles.py +pyed/TriqsExactDiagonalization.py +pyed/__init__.py diff --git a/Readme.md b/Readme.md index 050b7f6..3493362 100644 --- a/Readme.md +++ b/Readme.md @@ -1,6 +1,6 @@ # **PYED**: Exact diagonalization for finite quantum systems -Copyright (C) 2017, H. U.R. Strand, Ya.V. Zhumagulov +Copyright (C) 2018, H. U.R. Strand, Ya.V. Zhumagulov The python module `pyed` implements exact diagonalization for finite fermionic many-body quantum systems, together with calculations of several response functions in imagianary time. diff --git a/dist/.DS_Store b/dist/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0@xT0eRByIwN3Fxd!=u`x8vJNA zAF;zn5AZC)$O;&Hw6p?0bgk<$H~<@u*EA9v694^!JoB{wU;pD}_4Mq;`BwV>sMRX# z|HD@OFw_67!`9&=R@?Re)}L2)WR0xIs{YdsLdW;otZvpSXV%PaGvtj*l1@xgd9#?! ztYFz@Z_oa5cK)xk%85U-t8;5&r%BHi(bNyJzpM7la@;iaXJ=@8A!Mt(es}y+n;qX; z!-$O>YvOyB>wK~zsFnr(B62)CWMe;IW5;tMoBg`5yl63F;WCWuSy-t&_vg#NnM@<5 zJ=a;IR{t9tEZK?4-kKLCdlLngH!|2e%lxOwewi+2)@0%O*A=RmIZ$?B+bs0Q(Tx=V zLQ8+ah8EOqkDM?HoWTMrbs`3(8~_$Gf8>mpn6U6hcEF;kU5V^q7BYW~KiJE&x9p|u z*@5M<*NcJc44G&wvqCn;C zY&0uEgahW!F_R8hE}3fqLKTtEOdFcEXT%(j%9{Ff8(yFkXwZ%0x@=&xMQD!~uE7A3 z{pwX7thWv-~AUrLZ8A{``YH^IJ3FyK(WvWAa}GxTwfhuJU;>W zXFs0)e0uo~(S31xd3OBf4SR8Z!JbuKKfAa*eg5|6XBX`C+l$xdZ;qk6Y+fRQ_5Q|sD>z8l)kHQ-~hA#lfCy2kar3AL+od2#wbZ0=wm`2gOeQ!MpFpvg}SkT2G4u0Z>0#snwvJimyph`=72?N{P3lm7-L=X7YgRvQwAnQZlIY3U3 z62aVuT7U>K=|dnEn+N{2GqMHsL*HF+9N<$pvtaZJ;!6Y;1A2tQXJC87DG2Z@(I7jF zxM&6Zhf^zzl-`2kuZwX_ULHSYryk6WF7LI1R{3n?x7oANhYOJ>c0rAJ(heti87qB+ksjj{M0VE^us@^yCXDXC#9VRTm-5{m70e8K4hb+X8xr zLWoZR1V*yQy8m9lxPWmEQqmsy{#8$dvJR9gO=rIJ26`cGLP{6039yJYhskHik^24f zc~WLFud^?m*OMaS`JHWIeosoJc|OUnA=Ot;K6*jVpGF%{4OpTYB;{^KMTkq=n5vMp z|20q##Yw7?KKk=L()y32vpHWr^!l&XIBcftzXp7-AJz|#cI&@~c*cQ0qp?r3vN^<| z0^<~CDdLAph5l$XY&=*NcJ`2E_6$nE85hKV*y)PY9#0nnrvn1S)AoKoeSUlf;}vx0 zJ>3j!YozG{4pi>`DAk5Vh6?}zeQq|A!1X`l=zP%ePx{TbVgGBjYOS69?`!n`B`#fP z8TymDvipLGVYU3PA2lWUUu!lV)f$aD_Ww@)Z~v)$_uY4uVzUEk_qaZG?J8d^-uTtI z6~Gb^R!;e~tysF_`XBxyK1!C)xbT66Gis@5tceYTCyR;C-kL12O)#xdtADQ>Yzkk@ zkkbBpf_6ignP!A*e8SHd)tL90K5hClznoR6K1;TmU0o_`F%;wG(+F*~&=Zk@T zX+sYs*rX}g3|QR&9T8!>A6bhgwI`Gcc=PK5){EKURWF0LbS6qso6vO}Mdafv_+YC`}>*MNdsd;mgza&7A ziE;&?5C&eFc?(3aT(=La^#x!v(I#=rC+vqG2+#DhhCcxlcVw`M0qfz}U}UjdgDqK` z-LfZasUkhuAhJW(3PV;pVQT*1-}W$ykq6ne!o0geTr!e+WCqy)=2Q@px!*@aVZX11 zwmUZ1iGiYgWRWq(?I)rJ7W%MojvIjw!kd@~OoM;a55QlLUOr;9!di5-5POuAQApVY zTCUa%DMNoowQ482k$xJuFsMU=nz992PVA@;T?V~P`1P+XcVP>{`g0&&jH}>6(MYpA zUqOSQpl#uf_Od?OPbN#MeKp21w62sx+5xpBRtxS>TtOza)DbKtVMdGTu)f4h2DP+F zfCjjJh!XzpZ7#~Sjot{ct_IF>f_*0Vj$Bm7e;V>Y1nC2UUt`g7ZfgW*&S19GS*If@ z6->`%kVbHar5Sk=v?|E}wIUH=FqZOUU6-I6>!2HYS~ye-pvg4TMKovtkU+$*jwfN) zld|$u+_vtvjha#G#dV6_g0IkHprD2cPvN9G_dSq1s`L<;7;#&TxEWG)kE!gC3U*vA z?UJGlTVe;9`z5a66LSGma1z1N2I&z_t+|bz9WCart#w`7OEiPtenV$cW0ClpgWOgJ z392~_cmoLNi*?#bPAE&vOz3p6Sf|m$vJIiUYEXE#XSp_#Q3CLT1Q^ffn7lp0J2zfJ zad=-n4l~0F;J?POf@+mvg=nP13cu2@im_9N6{eOCD-is@E^KeOywBi@$)=1IF*V;h zT8fFjYQzz?9w=hz$+lWTwWXm^fofF;UHN$QJ-Oc<%-G z2sCm%uE3ll<20})su~UgXIt~Iq*w(Oa%6e5(Rjlxz zCbF0;rbFn9t^(1MzY4LO%Gz?xo%q3@BRfFGUY=3VQ^Q)ozpx~l@qhcFGa+eRU=qcJk)o^|Gl63llVB~}ZS;B>u3K<;xfPhGo0pWSrxuS>Lsr!S6_c3S9`&Oezex2e zRJeG(hJvxK)+Q(eatn#@$(d`MPKZTj_>dqYigeM+gn&8@q?C;NY_u)5YSm=E;=hE7 z_Pf;eJL+;8u%-BaZaRq|H3E)}nl#&UoTxgLZsJQfg-5DUeOgqnMlcQS$3L4~jnG*& z0f-IaWX0C;LLWA9Qyl^qo_6C+Q z8djfLgW+h;P`cj8_PoBf=&Dlm$T9}`qD5hb!ZX`r8f9!2$b9^#wO;TO*jJJMs##s* zjku@aZ*E!ePthdw6-9sRYl;3MiL`!g-n#qZymg1A0=^-GHtDSeZ!KCryjKXdYBABu z@>9B+-~&idm@g;bKd8=W<(3tQL;Aa=EHhfAEBjW-(!Ryl_8YU;@0>mEu>YUYH8g1l6NZP2k2mdut*&h^~cKJm3@ zA@K?N1yr0UWZ`1KqJ(Mwq&xgLEqt;AzYk=DRsP(yfR=q|V4vtnQ)ZZ1xrvQ5p2+Qm ziBTf#)_P{L#a2=E1&!-nBd-rC%zI!IjBYo)rg@o z3MpTI@hxdUQSWuzI1yvi*k);5*j$6ntScL)70c~h1`C^STz1ZYk}<9C=YWwCU0q5v z^J@u_Bc7?r=NOVnfiLXXcJVT0^#TQYk^067(TSd6%i2l))Y2Sxp@a;n3{j#o2(HQd z4eWQ79!=c{IAZJD)yo)@;vP{sW$D=Nh$H2Y_5zCnghI^dMOQ6F>oQY|6rfFS@@Hnn zAFa?X*xtuv#xqdd2t^su)@JP3FUivgRr;wt3Ve@;agozK1}>y=Q>x4rt^f&J#ygs^ z+$EJR-pfo)rSex?)+mJ15|0V)F!8j*mImwA3|0s2?nzzv>6U^2zz7_Apa$U8EO;*; zY#@|71*?twWV~7YO5J6V_gbhGbf4UBV)o;CY3jT2v-4fn-%SJ(&_6%>{v6}TL?jxko{cW)KzywJA_vERdcK0^DKQb#y(*C)+jw-m4?D2G5rI6aL z=E*P{CFXfFlj}^BmoGx!j^hi?&i&j-p`KT5_8h&;ic?*w0zc%J?VSR0bR)9n=SpzA zk$sB;vzJ=}d%zi9qgYnw7QuW2B*7Z{NiW?k5&{~r*LW>@n`2^H^SSMfv^Y7lGLNxM ztOk^zo0sJz;-VVz5U6U0AlaRei&mIDL;BUo7$VSoz-zw-g?xCW3G-90AZjd~Ex1lX zh?W>yDqEg0$aHgGF~pt1_@wZs5=Z&X4K7HU!iK)ThI$wgglvF4FO*c#_tZ_? zvL;0xCvJhtERXUFWC=(UE^M_Lf{$+B1&0BosySg zWdbAsQ8D8Gy2Tb^i(I-g8cYNX$3T zC}=@mPHsob-r3>48T((MC;XS00oU07nzh4P#{O4t?(BaL82`gNo$-APR2e0QZB!%D z73mNp76m?_DbMmeXX=PU7}DSr6k}F=4h8ZM3+L9*{sOCDEO6e5K9bHZNpi;hXMRe) zZQZV@m=Vdk6b_wv5_`k<0XMH&mOIzyZ4x{zK90%?S9CNUb3ob3V-Y|zb1<0S!rd=o zjMSN*vc~L@a>E0KFBC@=WEIFlD3~}4(=&PF^uB8iY&TRFYVxA|BH;!^QjG@Z!A!;BKb5S$&Wy(tB+-`$0uVw zW_-&yR}yt~vbxC@V`TP+zdOFTIKODKiyXG4Z2Xt+)(;ZZen?CdZxQI+q?|C8;}w_E z%hckD`6y&7nx|$FE*bKmS6eR&)#pNroyX9cT5YCm9$eHZtVev@g8PBuVxCWyLdD;!037f0*iul*9CSJQ%2#Y`?fC%+t&QC=rBY z5ie^}#$Q2L#h@z6d55~<`oV#Nh1Z? zs+yA)%9oE?2Phq5) zsaj>PtgGaNzk!8^!;MK%r>vRshzenB^OY>j9NJ(b+`R0-*M;LnO=&AGnkB3)N-@VU z?MEQ9b@SS>Z#0qVZ+OlNIYMcfvRit#ILI<~ow*xm@H;E!HeQCU99Ra5T% zSelRfgN5Uc`mvx-rdADs^t{U>l@Ozy7N=*>JjL>9sxj@QT@a6+$gdJfId**vE27gZ zqt4zi4E7SqwAq=Zi~u4!Js4o~QZlBqhm5&8c|4orp_#eKGhNgK1AjP7l$u!jv1*-F zMU83q(qXk~rG+`#@+yFHr)S@AQI9A5YEo9c05XdJ{P)Y3C3T>`_0$R#jK!~6`&I8K-KBY93t z>5xo#X|vOLuP{eClVX})Fs#MIEwTteK6&TneHHiTf}Ec3RZ)O5TO1vn&VV#fV|IaM zLV6};%nOQOPf?-BxRif>{^%$^|F6}6_gWpeptym)u$Z%>Ms82F;6Xn11`T%#j^eN<7fRdcwB+#w&p zT&d{e{m{`1O8DWL_Qb&rt(CzI#eoeNP?`^JXr>611UK;Tgs7PYH1PLL1wG`UnkiJX zBs4;ePYm@3h>M6RwfVS+f|!Wb{bC|o-(*aL2%{8i_hr!$tqny-V5ntLgao2_zfg&^ zq-1n$++?3B4_`GxLXD2VQc6N2l25DRB8*Q-WCR_9yTsQ|B1i&O^lRcIC_Cl3rR=wH zb_|-}*{gTT{)w`GqLO&_1`oSfTl=RB@|e9r$k*;t`)#In5g^jwF(TDF)h@Mt$!jkQ zG3n(NvuQVNkzqBJch`l7lplm_R#YeKYsiY%+{oMW?-d7Q5gxX?-P=NnSN(HMog-^ z24lp;+E^sU(Y=B&R?ZG00z++*k;QqN(rcan|7I@$S#AGo)?3Zg{&#qY{{M}}?*88g zwEx9-rzr-&;`=@MjhXTm*U%2XHG3b*-cs8iC|VPAA{bW) z8jz8FPr3((Wwo|SJSSu>!+Egl8^jd{Wal#SbDY>SCc7UDMl_&Aj^%NaU>^LsCLkl< z?bLGG*>w$Oy6aj-EAuIQz;`QU4vAioe_?_P03`33B2M7jNug(zHQIfeSApLVR> zpIS4=jeHNP%Z3H_;|{dTuO}!1&fPq)g^n+4Lgo?!LhWrQ9pYSZJ2A5-b;W%)^f+=Sn`^$#ruy|Akj0~>i`gq>s^V1KksQ> z&*)*4+>s!XvR%ZefY6dx;c>PjYJ3SibG#COSmQjdiVWLe zxs{_KyBvafvOhTHc*DC^O~<5g%g;9A7Y>ZhQ#`Z2-9{8&D3Wi0wxR{>K;W)-+Xjlt zUgjR0sn416t?{VF8>LYTaSJJ+Ag0jUEHoAo+n?ZP-e*a`JoxoIoK%uj@#nnfO3B!bKlBtg zXVDJLpPR8H)b#xoJfsuL>j#{X9_(tL6k$|&@Rn-E0fchT@4Dfxlkt~u>Wro64;-=Y z0r|ei><_g?bZ*%Bn}KLLE!C{5V+?BR$I{sCj6d}krReP-}8VNcnY+& z;5OKF=ZOC4PYgDZ<<3OR_rES|`;)CrFg$qd+;W4ae}e{8|Avg)!2peXO2!Fod#8n+ z740~^Z1F0%4y&CR4F9i74*0k$@7l6sKeY>VS}kkRj6J}m3oL}$Qj6FRtfrfmx776V z_-q905S$A$LP6+tV#X#3^MvJN3*#aUEx^I$w-ID@ALO2T^qoYleF}*0I4M}TMx*ue{@dmJ6^Vnq=*RmoMC0! zY!G}~#O09JN53!Exw5q=57G^(kN1fe)tbep3@h{0FW^~)>E!iX+-u?=+WCXN=@Yt(M)X!KTX%ta4miavY{Ix2CCe3@9UG1EwTD^6di> zp~{g6;=Jl1Lq0{{*;OD71d;R?#d45@?Uk;VN#IC#tEIQ9KvpcFk~aXR#=n)Le zrRF7hW&H$6yll8n+_y~Jm@gVjv$MY2M{EslJNJ55`LLbkvNH(Dw^j+5QhCx`BTrhgG~r^Z^)S+x;{J%J=eL={%i5ul+FG~aK@5Gjrz1boh1}^3-_g`k zs+laC8nHx|%!nmXFLr8Q9cXmHbgz1>7mtRA8V>iq-Z|d+ONT{rpK~*}KZYkhVht>* zf!{to^n<__UcE!$!GR4311Vouq$xLmX6trLvC|U;3;XfC>es6K{wqa zJNbyqy*`b;D#bmxt? zC$Bn?+wsQHl4;et{^6<^nlbJDtwSR33a4hQ??V+WgmF^s!xLRUkg)rpT|KF|*_OI9hlXL!d^*L=Zoy$c1;I*SEKSq=@5>V&bCImcOu_SaAOap|qq=j>gDCZH7 z(cLaPVFnvuO0UPiB3sC78D4p}djfC?3@oVNcl@7env8#^*IuuOv6hJMRf2C)o@5!>;eD%J z!M{>R!moT+QX+L`MeQ-p9!R%@a)NA_1%CqRdVLskx6bF%^&r1oy`Q#TduFg7#l%}+ z0)NK#*^hu5;EXT(?3r3trl8e@Wb&WH^XrMkB}3JCOx?F~_8VBx^dT8oxww=n7?78p zrFx7I1E+5DqKf=&_kCLQs>C|swGqA4iD%fQa(cX{OnrZ!TL;jJ`o3&Xr)Jx{_lH11 zt%kQqM##Xlf<1xZ!_8%~D)$m-gZ}DKpHElSdM*$po@!Q&C}4xc8|tni9#JV5)Ff=N zo345pX?Im*l6^T$EgI>k8?oPksz-^&c6NhP^_>1$$+u93h^&FHp6_9PDi{qkq(h$L zfz&o|gV@azhg2cS%>0JwWT@_pyd36rBVMJ!+Guydkdr)TlFAgraT5(KQh=YUkoH9C zT)I|$7~64FQ;w=pZJrHr = 0.5\n", - " = 0.5\n", - " = 0.224729977459\n" + " = 0.5000000000000032\n", + " = 0.49999999999999495\n", + " = 0.18296952428699315\n" ] } ], @@ -196,10 +178,8 @@ }, { "cell_type": "code", - "execution_count": 55, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -210,12 +190,14 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEKCAYAAADTgGjXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "image/png": "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\n", "text/plain": [ - "" + "

" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -232,9 +214,8 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 7, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -247,12 +228,14 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEKCAYAAADenhiQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3XmcVPWZ7/HP0ysgrTTNKoig4sYy\nqK3RxIgREKOJjcY4ZozCyLyM12TujZPMYK55ZXE00cliJpHkhnFNRCXRQXCPMESiibIoQtOsKkQ6\nCE2zC3RD93P/OKewaKq6q7pPVfXyfb9eRZ/ld37n4VSdeuqc3znnZ+6OiIhIlPJyHYCIiHQ+Si4i\nIhI5JRcREYmckouIiEROyUVERCKn5CIiIpFTchERkcgpuYiISOSUXEREJHIFuQ4gV/r06eNDhw7N\ndRgiIh3K0qVLt7l735bKddnkMnToUJYsWZLrMEREOhQz25hKOZ0WExGRyCm5iIhI5JRcREQkcl22\nzUVEWufgwYNs2rSJAwcO5DoUyaBu3boxePBgCgsLW7W8kouIpGXTpk2UlJQwdOhQzCzX4UgGuDu1\ntbVs2rSJYcOGtaqOdnNazMwuM7M1ZrbezG5PML/YzGaF8980s6Fx874VTl9jZhOzGbdIV3PgwAHK\nysqUWDoxM6OsrKxNR6ftIrmYWT4wHfgscCbwJTM7s0mxqcAOdz8FuA+4N1z2TOA6YARwGfDLsD4R\nyRAlls6vre9xezktdh6w3t3fAzCzJ4EKoCquTAXwvXD4KeB+C/73FcCT7l4HvG9m68P6/pKRSF+8\nHT5ckZGqRTqEkf8G29rLV4ekrbA7HDc446tpF0cuwCDgg7jxTeG0hGXc/RCwCyhLcVkAzOxmM1ti\nZktqamoiCl1ERJrqUj8/3H0GMAOgvLzcW1XJZ++JMiSRjmfVKugzPNdRSDvXXo5cqoET4sYHh9MS\nljGzAuA4oDbFZUWkk8nPz2fMmDGMHDmSz3/+8+zcuTPtOi655BIOHTrUbJn9+/czduxYGhoakpap\nr6/noosuarGuRPW99NJLnHbaaZxyyincc889CevbsWMHV111VdI6E9XRnHTLt0Z7SS6LgeFmNszM\nigga6Oc2KTMXmBwOXwP8j7t7OP268GqyYcBwYFGW4haRHOnevTvLli2jsrKS3r17M3369LSWX7ly\nJWVlZRQUNH8C56GHHuLqq68mPz/5dUJFRUWMGzeOWbNmtbje+PoaGhr46le/yosvvkhVVRVPPPEE\nVVVVR9VXWlrK9u3bqa2tPaq+ZHUkk2751moXySVsQ/ka8DKwCvidu680szvN7Mqw2INAWdhg/y/A\n7eGyK4HfETT+vwR81d2T/8QQkU7nggsuoLo6OGHx2GOPcd555zFmzBi+8pWvJD3imDNnDpMmTTo8\nfvXVV/Ptb3+biy66iCFDhjBv3jwAZs6cSUVFBQC7d+/mrLPOYsSIEfTo0YMxY8Zw/vnn09jYyKRJ\nk5g5c2Za9S1atIhTTjmFk046iaKiIq677jrmzJkDcFR9V1xxBc8+++xR/4/m6kgk3fKt1W7aXNz9\nBeCFJtO+Ezd8APhikmXvBu7OaIAicpTvP7uSqr/tjrTOM48/lu9+fkTK5RsaGpg/fz5Tp05l1apV\nzJo1i9dff53CwkJuvfVWZs6cyY033njUci+88ALPPffc4fEVK1bwyU9+koULFzJ79mxmzpzJRRdd\nxHvvvUese45jjz2Wt99+m0WLFnH33Xcf8aU8cuRIFi9enFZ91dXVnHDCx2f1Bw8ezJtvvpmwvoqK\nCqZNm8aUKVOO+H80V0ci6ZZvrXaTXERE0rF//37GjBlDdXU1Z5xxBhMmTOBXv/oVS5cu5dxzzz1c\npl+/fkctu2/fPurr6+nVq9fh8V27dnHbbbcBwSNuevXqxbZt2w6XiVdZWcmIEUcmwPz8fIqKitiz\nZw/5+flp1ZdIfH0lJSWcdtpprFmzJvUNlGNKLiLSaukcYUQt1uayb98+Jk6cyPTp0zEzJk+ezA9/\n+MNml+3Rowdmxt69e+nZsydVVVWcc845h9tVli9fzsiRI+nevXvCu9Srqqo4++yzj5peV1dHt27d\neOedd1Kqb9CgQXzwwcd3UmzatIlBgwYdVR/Axo0bEz6KpaU62lq+tdpFm4uISGv16NGDn//85/zk\nJz9h7NixPPXUU2zduhWA7du3s3Fj4r6tJk6cyEsvvQQEp7DGjBlzeN7y5csZPXo0paWlNDQ0HJVg\n/va3vzFgwIAjptXW1tKnTx8KCwtTru/cc89l3bp1vP/++9TX1/Pkk09y5ZVXHlUfBG1EsbaaeM3V\nkUi65VtLyUVEOryzzjqL0aNH884773DXXXdx6aWXMnr0aCZMmMDmzZsTLlNRUcEzzzwDHJ1cKisr\nGTlyJACXXnopr7322hHLTpw4kalTp/Lqq68enrZgwQKuuOKKtOorKCjg/vvvZ+LEiZxxxhlce+21\nh0+3xdcH8OyzzyZMLs3VkUi65VvN3bvk65xzznERSV9VVVWuQ4jMqFGj/ODBg82WWbp0qX/5y19u\nsa6rrrrK16xZ02K51tS3fft2//SnP93iMlFL9F4DSzyF71gduYhIl7V8+fIW73M5++yz+cxnPtPi\nTZSTJk3i1FNPbXGdramvtLSUhQsXtlh3e2JBIup6ysvLfcmSJbkOQ6TDWbVqFWeccUauw5AW1NbW\nMm7cuKOmz58/n7KyspTqSPRem9lSdy9vaVldLSYi0gmVlZWxbNmynK1fp8VERCRySi4iIhI5JRcR\nEYmckouIiEROyUVERCKn5CIiIpFTchERkcgpuYhIh9SzZ882LZ9KF8fQ+bo5zkYXx6DkIiJdUKpd\nHEPn6uY4W10cg5KLiHRgGzZs4PTTT2fKlCmceuqpXH/99cybN49PfepTDB8+nEWLFiVcLtUujqF1\n3RynWl+2uznOVhfHoOQiIh3c+vXr+cY3vsHq1atZvXo1jz/+OK+99ho//vGP+cEPfpBwmRdeeOGI\nx9mvWLGCXr16sXDhQv7zP//z8Jd6fX19wm6OH374YSZMmMCyZct44403yMvLO6Jb4lTrS9TlcHV1\nNZC4m+NYFwHxmqujLWXbSs8WE5HWe/F2+HBFtHUOGAWfTb0tYNiwYYwaNQqAESNGMG7cOMyMUaNG\nsWHDhqPKp9rFMdCqbo537dqVdn2JdPRujnXkIiIdWnFx8eHhvLy8w+N5eXkJG9jjuzgGknZxDDTb\nzXGsTLy6ujrWrl2bcn3Z7uY4W10cg45cRKQt0jjCaE9iXRxfc801CbskjrWJxHdLHPuSh6Cb48sv\nv/yIOmPdEldWVqZcX3yXw4MGDeLJJ5/k8ccfP6K+dLo5blpHW8q2lY5cRKTLSbWLY0i/m+N06st2\nN8dZ6+IY1M2xiKSns3RznEoXx+7q5rgp1M2xiEhyqXRxDOrmuLXUzbGIpEXdHLd/UXRxDOrmWERE\n4uS6i2NQg76IiGSAkouIiEROyUVERCKX8+RiZr3N7BUzWxf+LU1SbnJYZp2ZTY6b/kczW2Nmy8JX\nv+xFL9I1ddULgbqStr7HOU8uwO3AfHcfDswPx49gZr2B7wKfAM4DvtskCV3v7mPC19ZsBC3SVXXr\n1o3a2lolmE7M3amtrT3iqQTpag9Xi1UAF4fDjwJ/BKY1KTMReMXdtwOY2SvAZcAT2QlRRGIGDx7M\npk2bqKmpyXUokkHdunVj8ODBrV6+PSSX/u6+ORz+EOifoMwg4IO48U3htJiHzawBeBq4y/WTSiRj\nCgsLEz5AUSReVpKLmc0DBiSYdUf8iLu7maWbGK5392ozKyFILjcAv0kSx83AzQBDhgxJczUiIpKq\nrCQXdx+fbJ6ZbTGzge6+2cwGAonaTKr5+NQZwGCC02e4e3X4d4+ZPU7QJpMwubj7DGAGBHfop/8/\nERGRVLSHBv25QOzqr8lAoj43XwYuNbPSsCH/UuBlMyswsz4AZlYIfA6ozELMIiLSjPaQXO4BJpjZ\nOmB8OI6ZlZvZAwBhQ/6/A4vD153htGKCJLMcWEZwhPNf2f8viIhIPD24UkREUpbqgyvbw5GLiIh0\nMkouIiISOSUXERGJnJKLiIhETslFREQip+QiIiKRU3IREZHIKbmIiEjklFxERCRySi4iIhI5JRcR\nEYmckouIiEROyUVERCKn5CIiIpFTchERkcgpuYiISOSUXEREJHJKLiIiEjklFxERiZySi4iIRE7J\nRUREIqfkIiIikVNyERGRyCm5iIhI5NJOLmZ2jJnlZyIYERHpHFpMLmaWZ2b/YGbPm9lWYDWw2cyq\nzOxHZnZK5sMUEZGOJJUjlwXAycC3gAHufoK79wMuBN4A7jWzL2cwRhER6WAKUigz3t0PNp3o7tuB\np4Gnzaww8shERKTDavHIJZZYzOzPLZURERGB9Br0uzWdYGafjjAWERHpJFI5LRZzmpnNBlYClcAW\n4AGC9hgREZHD0jlyeR/4AfAucA7wT8D32xqAmfU2s1fMbF34tzRJuZfMbKeZPddk+jAze9PM1pvZ\nLDMramtMIiLSNukkl3p3X+zuD7v7v7r79e7+mwhiuB2Y7+7DgfnheCI/Am5IMP1e4D53PwXYAUyN\nICYREWmDdJLL2AzFUAE8Gg4/CkxKVMjd5wN74qeZmQGXAE+1tLyIiGRPKjdRGoC772mpTCv1d/fN\n4fCHQP80li0Ddrr7oXB8EzAoWWEzu9nMlpjZkpqamtZFKyIiLUrpJkoz+2czGxI/0cyKzOwSM3sU\nmNxcBWY2z8wqE7wq4su5uwOe9v8iRe4+w93L3b28b9++mVqNiEiXl8rVYpcBNwFPmNkwYCfBZcn5\nwB+An7n7281V4O7jk80zsy1mNtDdN5vZQGBrytFDLdDLzArCo5fBQHUay4uISAa0mFzc/QDwS+CX\n4Z34fYD97r4zohjmEhz53BP+nZPqgu7uZrYAuAZ4Mt3lRUQkM9J6KrK7H3T3zREmFgiSygQzWweM\nD8cxs3IzeyBWyMz+BPweGGdmm8xsYjhrGvAvZraeoA3mwQhjExGRVkjpJsrw3pMrCa7EOpXgnpc5\nwBx3T+c01lHcvRYYl2D6EoJ7aWLjCZ8G4O7vAee1JQYREYlWi8nFzP4bKAWeB6a5+9qwcb8CeMzM\nitz94syGKSIiHUkqRy43NT0N5u5/BX4B/MLMemUkMhER6bBSeSryEYmlaU+UEbe/iIhIJ6CeKEVE\nJHLqiVJERCKnnihFRCRyqdxEedDMTie4Oiz23K5qYK67r4qVyVyIIiLS0aTS5jKN4O53AxaFLyN4\nHEyyx+OLiEgXlsppsanAiKZHJ2b2U4JeKe/JRGAiItJxpdKg3wgcn2D6wHCeiIjIEVI5cvk6MD98\n9tcH4bQhwCnA1zIVmIiIdFypNOi/ZGanEjy/K75Bf7G7N2QyOBER6ZhSebaYuXsjwT0tzZXJWCdf\nIiLSsWSlJ0oREela0u2J8iRgB9CdIDGl1BOliIh0Le2hJ0oREelkUuosDMDMLgGuB3YClWa2HKh0\n97pMBSciIh1TyskFeIjgsuRCYDRBr5QjCC5JFhEROSyd5LLR3Z8Jh3+fiWBERKRzSOVqsZiFZnab\nmVnGohERkU4hnSOXM4FRwDQzWwosA5a5u45iRETkCCknF3f/AoCZdefjRHM+OkUmIiJNpHPkEpNH\ncMSyNOpgRESkc0ilP5c8M/sHM3vezLYCa4DNZlZlZj8yM10tJiIiR0jp8S/AycC3gAHuPtjd+wEX\nEjxv7F4z+3IGYxQRkQ4mldNi4xN1Y+zu24GngafDO/dFRESAFI5cYonFzP7cUhkRERFI7z6Xbk0n\nmNmnI4xFREQ6iXSuFjvNzGYDK4FKYAvwAEF7jIiIyGHpJJf3gR8AI4FzgOOB72ciKBER6djSSS71\n7r4YWBxlAGbWG5gFDAU2ANe6+44E5V4iuGnzNXf/XNz0R4CxwK5w0hR3XxZljCIikp502lzGZiiG\n24H57j4cmB+OJ/Ij4IYk8/7V3ceELyUWEZEcS+UmSgNw9z0tlWmlCuDRcPhRgkf5H8Xd5wNJYxAR\nkfYjpZsozeyfzWxI/EQzKzKzS8zsUWByG2Lo7+6bw+EPgf6tqONuM1tuZveZWXEbYhERkQik0uZy\nGXAT8ISZDSPoibIbkA/8AfiZu7/dXAVmNg8YkGDWHfEj7u5m5qkEHudbBEmpCJgBTAPuTBLHzcDN\nAEOGDElUREREItBicnH3A8AvgV+Gd+L3Afa7+85UV+Lu45PNM7MtZjbQ3Teb2UBga6r1hnXHjnrq\nzOxh4JvNlJ1BkIAoLy9PN4mJiEiK0mnQx90PuvvmdBJLCuby8Wm1ycCcdBYOE1Ks3WcSwT04IiKS\nQylfimxmlwDXE5wWqwSWA5XuXtfGGO4BfmdmU4GNwLXh+sqBW9z9n8LxPwGnAz3NbBMw1d1fBmaa\nWV/ACDowu6WN8YiISBuZe2pnh8xsA/B1oBAYHb5GuHuHfOR+eXm5L1myJNdhiIh0KGa21N3LWyqX\nzk2UG939mXBYvU+KiEhS6bS5LDSz29p4T4uIiHQB6Ry5nAmMAqaZ2VKC9o1l7q6jGBEROUKLycXM\n8ty90d2/EI535+NE8wkze9rdGzMcp4iIdCCpnBZ7xcxmmdmXzOxYd98PrCJ4FEt/4K2MRigiIh1O\nKjdRjjOzMwmeAfZ8eCOlAy8D97m7kouIiBwhpTYXd68CqoAfmln38OhFREQkobTu0AdQYhERkZak\nnVxERERaouQiIiKRU3IREZHIKbmIiEjklFxERCRySi4iIhI5JRcREYmckouIiEROyUVERCKn5CIi\nIpFTchERkcgpuYiISOSUXEREJHJKLiIiEjklFxERiZySi4iIRE7JRUREIqfkIiIikVNyERGRyCm5\niIhI5JRcREQkckouIiISOSUXERGJXM6Ti5n1NrNXzGxd+Lc0QZkxZvYXM1tpZsvN7O/j5g0zszfN\nbL2ZzTKzouz+D0REpKmcJxfgdmC+uw8H5ofjTe0DbnT3EcBlwM/MrFc4717gPnc/BdgBTM1CzCIi\n0oz2kFwqgEfD4UeBSU0LuPtad18XDv8N2Ar0NTMDLgGeam55ERHJrvaQXPq7++Zw+EOgf3OFzew8\noAh4FygDdrr7oXD2JmBQM8vebGZLzGxJTU1N2yMXEZGECrKxEjObBwxIMOuO+BF3dzPzZuoZCPwW\nmOzujcGBS+rcfQYwA6C8vDzpekREpG2yklzcfXyyeWa2xcwGuvvmMHlsTVLuWOB54A53fyOcXAv0\nMrOC8OhlMFAdcfgiIpKm9nBabC4wORyeDMxpWiC8Amw28Bt3j7Wv4O4OLACuaW55ERHJrvaQXO4B\nJpjZOmB8OI6ZlZvZA2GZa4GLgClmtix8jQnnTQP+xczWE7TBPJjd8EVEpCkLfvx3PeXl5b5kyZJc\nhyEi0qGY2VJ3L2+pXHs4chERkU5GyUVERCKn5CIiIpFTchERkcgpuYiISOSUXEREJHJKLiIiEjkl\nFxERiZySi4iIRE7JRUREIqfkIiIikVNyERGRyCm5pOlQQyNd9WGfItLxHWpozMp6lFzSdNfzq/jn\nJ97mo7pDLRcWEWlHXqr8kPE/fZXNu/ZnfF1KLmlwdwYc140XVmymYvrrrN+6N9chiYi06FBDIz98\ncRW3PLaU47oXko2TL0ouaTAzbhl7Mo9N/QQ7Pqqn4v7XeGHF5lyHJSKSVM2eOm54cBG/fvU9rv/E\nEH53ywUc36t7xter5NIKnzylD8/97ws5dUAJt858i7ueq+Jgls5jioikaunG7XzuF3/irb/u4Cdf\n/DvuvmoUxQX5WVm3kksrDTyuO7NuvoDJF5zIA6+9z40PLmJ/fUOuwxIRAeCZt6v5+1+/QXFBPrNv\n/RRfOGdwVtev5NIGRQV5fL9iJD/+4t/xxvu1fO3xt7J2JYaISDIL1mzlG79/h/KhpTz7tQs58/hj\nsx6DkksErjlnMHdWjGT+6q3839krdKmyiOTM23/dwa2PvcXpA0r4rxvLOa5HYU7iKMjJWjuhG84/\nkZo9dfx8/jr6lXTjmxNPy3VIItLFvFuzl5seWUzfkmIe+cfzKOmWm8QCSi6Rum38cGr21HH/gvX0\n6VnElE8Ny3VIItJFbNl9gBsfXER+nvGbm86jb0lxTuNRcomQmfHvFSPYtreO7z9XRZ+SYj43+vhc\nhyUindyu/QeZ/NAidu6rZ9ZXLmBon2NyHZLaXKJWkJ/HL750FuUnlnLbrGX8bvEHuQ5JRDqxv+3c\nz/UPvMG7NXv59Q3ljBx0XK5DApRcMqJbYT4PTD6X808q49+eXs735q7UVWQiErmlG7dz5f2vs2Hb\nPn59wzlcOLxPrkM6TMklQ47rXsjDU87lpk8N45E/b2Dyw4vY8VF9rsMSkU5i1uK/ct2MN+hZnM/s\nWz/JJaf3z3VIR1ByyaCC/Dy+8/kz+dE1o1n8/g4qpr/O2i17ch2WiHRgBxsa+d7clUx7egXnn1TG\nnK9eyPD+JbkO6yhKLlnwxfITePIr57P/YANXTX+d6QvWs1dPVRaRNP3l3Vqu/fVfeOTPG5h64TAe\nnnJuzu5jaYl11Rv+ysvLfcmSJVld54e7DnDH7BXMX72V0h6FfGXsydx4wYn0KNJFeyKS3KL3t3Pf\nK2v5y3u19D+2mG999gwmnTUoJ7GY2VJ3L2+xnJJL9i37YCf3vbKWV9fWUHZMEbeMPZkvlg+mV4+i\nnMQjIu1PQ6OzeMN2pi9Yz5/WbaNPz2Juvfhk/uETQ+hWmJ2HTybSYZKLmfUGZgFDgQ3Ate6+o0mZ\nMcCvgGOBBuBud58VznsEGAvsCotPcfdlLa03l8klZunGHfxs3lr+tG4beQZnDynlM6f345LT+3H6\ngBLMLKfxiUh27dp/kIVra1iweit/XFvD9o/qKTumiP918clc/4kT6V6Uu6QS05GSy38A2939HjO7\nHSh192lNypwKuLuvM7PjgaXAGe6+M0wuz7n7U+mstz0kl5jK6l38oWoL/7N6C5XVuwE4/rhunH1i\nKcP7lXBq/54M79+TE8uOoTBfzWQiHZ27s21vPeu27mH91r2s27KXVZt38/YHO2lodEp7FHLxaf34\nzOn9GH9Gv3Z16rwjJZc1wMXuvtnMBgJ/dPdmH8xlZu8A14TJ5hE6eHKJt2X3Af64ZisLVtewcvMu\nNu3Yf7jXuII84/he3enTs4g+PYvpU1JM357F9D6miGOKC+hZnE+PogKOKS7gmOJ8ivLzKCoIXsX5\n+RQV5FGQb+SbkZenoyKRdLk7DY3OoUbnYEMj9YcaqY/9PdTIgYON7K07xEd1h/io/hAf1TWw+8BB\ntu2pY9veOmr21rFtTz0f7j7Arv0HD9dbUlzA8P49ueDkMi45vR9jTiglv53uox0puex0917hsAE7\nYuNJyp8HPAqMcPfGMLlcANQB84Hb3b2upfW21+TS1L76Q7xX8xHrtu5h3Za9VO/cz7bwA1qzt44d\n++pb1WVpnkFBXh75eUaeQV6ekWfBcPChNsyCckYwPf40nVnwIigZNxybf/SOkXBXSTAxnV0ql6cO\n2+eu37xc7u2pftckLJVk0UST3f3w9NgqHcedI/aVWLlGD+eF0xo9mNbY+PFwQ+PHSaU1uhfm06ck\n+FHYt2cxfUuKOblvcEbi1P4l9Csp7jCnwVNNLlk51jKzecCABLPuiB9xdzezpO9eeGTzW2Cyu8du\nef8W8CFQBMwApgF3Jln+ZuBmgCFDhqT5v8iNHkUFjBx0XNJHOhxqaGTX/oN8VNcQ/lI6xN66Q+yr\nbzj8a6ou7pdVQ2Mjh+J2lEMNjYd3IPegEbEhHAansTHYMWP7VLAT+uG9OrZDxoZjZZpK9iWQSrmk\ncvhN6Tn9mm4by2VaTHHViYol+/JNXPboHzoW/hP7/8f/eLLwx1NsWr4ZZnE/uPKNgjwjPy8v/GtH\nnBmIDXcrzOeY4nx6FodnEIoKKOkWDHc1Wfkfu/v4ZPPMbIuZDYw7LbY1SbljgeeBO9z9jbi6Y53Y\n15nZw8A3m4ljBkECory8vON+O8QpyM+jrGcxZT1zHYmIyMfaQ+vwXGByODwZmNO0gJkVAbOB3zRt\nWwkTUuyU2iSgMqPRiohIi9pDcrkHmGBm64Dx4ThmVm5mD4RlrgUuAqaY2bLwNSacN9PMVgArgD7A\nXdkNX0REmsp5g36udJQGfRGR9iTVBv32cOQiIiKdjJKLiIhETslFREQip+QiIiKRU3IREZHIddmr\nxcysBtjYysX7ANsiDCcqiis9iis9iis9nTWuE929b0uFumxyaQszW5LKpXjZprjSo7jSo7jS09Xj\n0mkxERGJnJKLiIhETsmldWbkOoAkFFd6FFd6FFd6unRcanMREZHI6chFREQip+TSDDO7zMzWmNl6\nM7s9wfxiM5sVzn/TzIZmIaYTzGyBmVWZ2Uoz+z8JylxsZrviniD9nUzHFa53g5mtCNd51FNBLfDz\ncHstN7OzsxDTaXHbYZmZ7Tazrzcpk5XtZWYPmdlWM6uMm9bbzF4xs3Xh39Iky04Oy6wzs8mJykQc\n14/MbHX4Ps02s4S9w7b0nmcgru+ZWXXce3V5kmWb3XczENesuJg2mNmyJMtmcnsl/G7I2WfM3fVK\n8ALygXeBkwh6uXwHOLNJmVuB/xcOXwfMykJcA4Gzw+ESYG2CuC4GnsvBNtsA9Glm/uXAiwSdAp4P\nvJmD9/RDguv0s769CLqNOBuojJv2HwRdcwPcDtybYLnewHvh39JwuDTDcV0KFITD9yaKK5X3PANx\nfQ/4Zgrvc7P7btRxNZn/E+A7OdheCb8bcvUZ05FLcucB6939PXevB54EKpqUqQAeDYefAsaFnZZl\njLtvdve3wuE9wCpgUCbXGaEKgg7f3IPeRHvFOnvLknHAu+7e2ptn28TdFwLbm0yO/ww9StDhXVMT\ngVfcfbu77wBeAS7LZFzu/gd3PxSOvgEMjmp9bYkrRansuxmJK9z/rwWeiGp9qWrmuyEnnzEll+QG\nAR/EjW/i6C/xw2XCHXEXUJaV6IDwNNxZwJsJZl9gZu+Y2YtmNiJLITnwBzNbamY3J5ifyjbNpOtI\nvtPnYnsB9PePu+r+EOifoExO+2WjAAADPElEQVSut9tNBEecibT0nmfC18LTdQ8lOcWTy+31aWCL\nu69LMj8r26vJd0NOPmNKLh2UmfUEnga+7u67m8x+i+DUz98BvwCeyVJYF7r72cBnga+a2UVZWm+L\nLOgq+0rg9wlm52p7HcGD8xPt6vJNM7sDOATMTFIk2+/5r4CTgTHAZoJTUO3Jl2j+qCXj26u574Zs\nfsaUXJKrBk6IGx8cTktYxswKgOOA2kwHZmaFBB+eme7+303nu/tud98bDr8AFJpZn0zH5e7V4d+t\nwGyC0xPxUtmmmfJZ4C1339J0Rq62V2hL7NRg+HdrgjI52W5mNgX4HHB9+KV0lBTe80i5+xZ3b3D3\nRuC/kqwvV9urALgamJWsTKa3V5Lvhpx8xpRcklsMDDezYeGv3uuAuU3KzAViV1VcA/xPsp0wKuE5\n3QeBVe7+0yRlBsTafszsPIL3OaNJz8yOMbOS2DBBg3Blk2JzgRstcD6wK+5wPdOS/qLMxfaKE/8Z\nmgzMSVDmZeBSMysNTwNdGk7LGDO7DPg34Ep335ekTCrvedRxxbfRXZVkfansu5kwHljt7psSzcz0\n9mrmuyE3n7FMXLXQWV4EVzetJbjy5I5w2p0EOxxAN4LTLOuBRcBJWYjpQoLD2uXAsvB1OXALcEtY\n5mvASoKrZN4APpmFuE4K1/dOuO7Y9oqPy4Dp4fZcAZRn6X08hiBZHBc3LevbiyC5bQYOEpzTnkrQ\nRjcfWAfMA3qHZcuBB+KWvSn8nK0H/jELca0nOAcf+4zFroo8Hnihufc8w3H9NvzsLCf40hzYNK5w\n/Kh9N5NxhdMfiX2m4spmc3sl+27IyWdMd+iLiEjkdFpMREQip+QiIiKRU3IREZHIKbmIiEjklFxE\nRCRySi4iIhI5JRcREYlcQa4DEBEws2OBVwkeET+M4AbAAwQ3dDbmMjaR1tBNlCLtSPj4mTvcPbJH\nxIvkgk6LibQvIwkeDSLSoSm5iLQvZ5Lhhz+KZIOSi0j7cjxBh04iHZqSi0j78jLwoJmNzXUgIm2h\nBn0REYmcjlxERCRySi4iIhI5JRcREYmckouIiEROyUVERCKn5CIiIpFTchERkcgpuYiISOT+P1ZA\nO+S2H8MfAAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -266,10 +249,8 @@ }, { "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -280,12 +261,14 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEMCAYAAAAF2YvKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+cbqTlBKwIAAAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -297,38 +280,6 @@ "plt.figure(); oplot(g_iwn); plt.savefig('figure_g_iwn.png')" ] }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100% |########################################################################|\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBYQxxpiqLCCMMcZU9f8BceRLmVx/1QMAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pytriqs.gf import GfReFreq\n", - "g_w = GfReFreq(indices = [1], window = (-4, 4), n_points = 200)\n", - "ed.set_g2_w(g_w,c(up,0),c_dag(up,0))\n", - "plt.figure(); oplot(g_w,mode='S'); plt.savefig('figure_g_w.png')" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -338,10 +289,8 @@ }, { "cell_type": "code", - "execution_count": 59, - "metadata": { - "collapsed": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "from pytriqs.gf import Gf\n", @@ -353,10 +302,8 @@ }, { "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": true - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "prodmesh2 = MeshProduct(imtime, imtime)\n", @@ -366,19 +313,19 @@ }, { "cell_type": "code", - "execution_count": 61, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFgCAYAAAA2BUkTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngAMmY4wxJgEHTMYYY0wCDpiMMcaYBP8DBL3AiD3DucsAAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -401,29 +348,29 @@ }, { "cell_type": "code", - "execution_count": 62, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Text(54.01,0.5,u'$\\\\tau_2$')" + "Text(54.01,0.5,'$\\\\tau_2$')" ] }, - "execution_count": 62, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdoAAAHICAYAAADp+is/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJ7A5kgAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -455,7 +402,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.13" + "version": "2.7.15" } }, "nbformat": 4, diff --git a/doc/Documentation.ipynb b/doc/Documentation.ipynb index 66543f4..d1ae542 100644 --- a/doc/Documentation.ipynb +++ b/doc/Documentation.ipynb @@ -21,12 +21,22 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "H = -0.1*C^+(0,0)C(0,0) + -0.1*C^+(1,0)C(1,0) + 1*C^+(0,0)C^+(1,0)C(1,0)C(0,0)\n" + "H = -0.1*c_dag(0,0)*c(0,0) + -0.1*c_dag(1,0)*c(1,0) + 1*c_dag(0,0)*c_dag(1,0)*c(1,0)*c(0,0)\n" ] } ], @@ -67,20 +77,36 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Starting on 1 Nodes at : 2018-12-19 17:48:02.624435\n", + "100% |########################################################################|\r" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Z = 2.9840296413\n", - "\\Omega = -0.646637307852\n", + "Hamiltonian diagonalization:\n", + "Z = 2.984029641299568\n", + "\\Omega = -0.6466373078517809\n", "\\rho =\n", - "[[ 0.27437085 0. 0. 0. ]\n", - " [ 0. 0.33511731 0. 0. ]\n", - " [ 0. 0. 0.33511731 0. ]\n", - " [ 0. 0. 0. 0.05539452]]\n" + " (0, 0)\t0.27437085133022276\n", + " (1, 1)\t0.33511731457348803\n", + " (2, 2)\t0.33511731457348803\n", + " (3, 3)\t0.05539451952280125\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] } ], @@ -112,16 +138,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " = 0.390511834096\n", - " = 0.390511834096\n", - " = 0.0553945195228\n" + " = 0.39051183409628926\n", + " = 0.39051183409628926\n", + " = 0.05539451952280125\n" ] } ], @@ -152,11 +178,17 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% |########################################################################|\n" + ] + } + ], "source": [ "from pytriqs.gf import GfImTime\n", "g_tau = GfImTime(name=r'$g$', beta=beta, statistic='Fermion', n_points=50, indices=[1]) \n", @@ -168,14 +200,6 @@ "plt.figure(); oplot(g_tau); plt.savefig('figure_g_tau.png')" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "![Single-particle Green's function](figure_g_tau.png)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -185,11 +209,29 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% |########################################################################|\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "from pytriqs.gf import GfImTime\n", "densdens_tau = GfImTime(name=r'$\\langle n(\\tau) n(0) \\rangle$', beta=beta, statistic='Boson', n_points=50, indices=[1]) \n", @@ -198,13 +240,6 @@ "plt.figure(); oplot(densdens_tau); plt.savefig('figure_densdens_tau.png')" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Density density response function](figure_densdens_tau.png)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -221,11 +256,29 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100% |########################################################################|\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "from pytriqs.gf import GfImFreq\n", "g_iwn = GfImFreq(name=r'$g$', beta=beta, statistic='Fermion', n_points=10, indices=[1])\n", @@ -234,13 +287,6 @@ "plt.figure(); oplot(g_iwn); plt.savefig('figure_g_iwn.png')" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Single-particle Green's function in imaginary frequency](figure_g_iwn.png)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -263,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -296,10 +342,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "prodmesh2 = MeshProduct(imtime, imtime)\n", @@ -317,9 +361,22 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "from mpl_toolkits.mplot3d import axes3d\n", @@ -336,13 +393,6 @@ "plt.tight_layout()\n", "plt.savefig('figure_g3pp_tau.png')" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Equal-time two-particle Green's function](figure_g3pp_tau.png)" - ] } ], "metadata": { @@ -361,7 +411,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.13" + "version": "2.7.15" } }, "nbformat": 4, diff --git a/pyed/CubeTetras.py b/pyed/CubeTetras.py index 36c6bcb..b80796d 100644 --- a/pyed/CubeTetras.py +++ b/pyed/CubeTetras.py @@ -1,5 +1,5 @@ -""" +""" Helper routines for the equal time imaginary time cube and its sub tetrahedrons. @@ -11,23 +11,29 @@ import itertools import numpy as np +# ---------------------------------------------------------------------- +def Idxs(integer_index_list): + from pytriqs.gf import Idx + return tuple( Idx(i) for i in integer_index_list ) + # ---------------------------------------------------------------------- def zero_outer_planes_and_equal_times(g4_tau): + from pytriqs.gf import Idx beta = g4_tau.mesh.components[0].beta - + for idxs, (t1, t2, t3) in enumerate_tau3(g4_tau): if t1 == t2 or t2 == t3 or t1 == t3 or \ t1 == 0 or t1 == beta or \ t2 == 0 or t2 == beta or \ t3 == 0 or t3 == beta: - g4_tau[list(idxs)][:] = 0.0 + g4_tau[Idxs(idxs)] = 0.0 # ---------------------------------------------------------------------- def enumerate_tau3(g4_tau, make_real=True, beta=None): from pytriqs.gf import MeshImTime, MeshProduct - + assert( type(g4_tau.mesh) == MeshProduct ) for mesh in g4_tau.mesh.components: @@ -40,13 +46,13 @@ def enumerate_tau3(g4_tau, make_real=True, beta=None): yield (i1, i2, i3), (t1.real, t2.real, t3.real) else: yield (i1, i2, i3), (t1, t2, t3) - + # ---------------------------------------------------------------------- class CubeTetrasBase(object): """ Base class with definition of the equal time tetrahedrons in three fermionic imaginary times. """ - + def get_tetra_list(self): tetra_list = [ @@ -57,17 +63,17 @@ def get_tetra_list(self): (lambda x,y,z : x >= z and z >= y, [0, 2, 1], -1), (lambda x,y,z : z >= x and x >= y, [2, 0, 1], +1), ] - + return tetra_list # ---------------------------------------------------------------------- class CubeTetras(CubeTetrasBase): """ Helper class for two-particle Green's function. - + Looping over all tetrahedrons in the imaginary time cube. \tau_1, \tau_2, \tau_3 \in [0, \beta) """ - + # ------------------------------------------------------------------ def __init__(self, tau): @@ -79,21 +85,21 @@ def __init__(self, tau): def __iter__(self): for tidx in xrange(6): - + func, perm, perm_sign = self.tetra_list[tidx] - + index = [] for n1, n2, n3 in itertools.product( range(self.ntau), repeat=3): if func(n1, n2, n3): index.append((n1, n2, n3)) index = np.array(index).T - + i1, i2, i3 = index t1, t2, t3 = self.tau[i1], self.tau[i2], self.tau[i3] taus = np.vstack([t1, t2, t3]) - + yield list(index), taus, perm, perm_sign # ---------------------------------------------------------------------- @@ -101,16 +107,16 @@ class CubeTetrasMesh(CubeTetrasBase): """ Helper class for Triqs two-particle Green's function in imaginary time. - + Looping over all tetrahedrons in the imaginary time cube. \tau_1, \tau_2, \tau_3 \in [0, \beta) """ - + # ------------------------------------------------------------------ def __init__(self, g4_tau): self.g4_tau = g4_tau self.tetra_list = self.get_tetra_list() - + # ------------------------------------------------------------------ def __iter__(self): @@ -120,7 +126,7 @@ def __iter__(self): tetra_tau = [ [] for n in xrange(6) ] for idxs, taus in enumerate_tau3(self.g4_tau): - + for tidx, tetra in enumerate(self.tetra_list): func, perm, perm_sign = tetra @@ -133,5 +139,5 @@ def __iter__(self): func, perm, perm_sign = self.tetra_list[tidx] yield tetra_idx[tidx], tetra_tau[tidx], perm, perm_sign - + # ---------------------------------------------------------------------- diff --git a/pyed/SparseExactDiagonalization.py b/pyed/SparseExactDiagonalization.py index e56f293..f738334 100644 --- a/pyed/SparseExactDiagonalization.py +++ b/pyed/SparseExactDiagonalization.py @@ -46,7 +46,7 @@ def __init__(self, H,blocks, beta, self._diagonalize_hamiltonian() self._calculate_partition_function() - #self._calculate_density_matrix() + self._calculate_density_matrix() # ------------------------------------------------------------------ def _diagonalize_hamiltonian(self): @@ -282,8 +282,8 @@ def get_tau_greens_function_component(self, tau, op1, op2): op1_eig, op2_eig = self._operators_to_eigenbasis([op1, op2]) bar = progressbar.ProgressBar() for i in bar(range(len(tau))): - et_p = np.exp((-self.beta + tau[i])*self.E)[:,None] - et_m = np.exp(-tau[i]*self.E)[:,None] + et_p = np.exp((-self.beta + tau[i])*self.E)[:,None] + et_m = np.exp(- tau[i]*self.E)[:,None] G[i] = - (op1_eig.multiply(et_p)*op2_eig.multiply(et_m)).diagonal().sum() G /= self.Z return G diff --git a/pyed/TriqsExactDiagonalization.py b/pyed/TriqsExactDiagonalization.py index 66f76a6..cf7e88c 100644 --- a/pyed/TriqsExactDiagonalization.py +++ b/pyed/TriqsExactDiagonalization.py @@ -12,15 +12,32 @@ # ---------------------------------------------------------------------- -from pytriqs.gf import MeshImTime, MeshProduct +from pytriqs.gf import MeshImTime, MeshProduct, Idx +from pytriqs.operators import dagger +from pytriqs.utility import mpi # ---------------------------------------------------------------------- -from pyed.CubeTetras import CubeTetrasMesh, enumerate_tau3 +from pyed.CubeTetras import CubeTetrasMesh, enumerate_tau3, Idxs from pyed.SquareTriangles import SquareTrianglesMesh, enumerate_tau2 from pyed.SparseExactDiagonalization import SparseExactDiagonalization from pyed.SparseMatrixFockStates import SparseMatrixRepresentation +# ---------------------------------------------------------------------- +def mpi_op_comb(op_list, repeat=2): + + work_list = list(itertools.product(enumerate(op_list), repeat=repeat)) + work_list = mpi.slice_array(np.array(work_list)) + + return work_list + +# ---------------------------------------------------------------------- +def mpi_all_reduce_g(g): + + g << mpi.all_reduce(mpi.world, g, lambda x, y : x + y) + + return g + # ---------------------------------------------------------------------- class TriqsExactDiagonalization(object): @@ -32,7 +49,7 @@ def __init__(self, H, fundamental_operators, beta): self.beta = beta self.rep = SparseMatrixRepresentation(fundamental_operators) self.ed = SparseExactDiagonalization( - self.rep.sparse_matrix(H),self.rep.blocks, beta) + self.rep.sparse_matrix(H), self.rep.blocks,beta) # ------------------------------------------------------------------ def get_expectation_value(self, op): @@ -53,35 +70,57 @@ def set_g2_tau(self, g_tau, op1, op2): assert( type(g_tau.mesh) == MeshImTime ) assert( self.beta == g_tau.mesh.beta ) - assert( g_tau.target_shape == (1, 1) ) + op1_mat = self.rep.sparse_matrix(op1) op2_mat = self.rep.sparse_matrix(op2) - tau = np.array([tau for tau in g_tau.mesh]) + tau = np.array([tau.value for tau in g_tau.mesh]) - g_tau.data[:, 0, 0] = \ - self.ed.get_tau_greens_function_component( + g_tau.data[:,0,0] = self.ed.get_tau_greens_function_component( tau, op1_mat, op2_mat) - self.set_tail(g_tau, op1_mat, op2_mat) + #self.set_tail(g_tau, op1_mat, op2_mat) + + # ------------------------------------------------------------------ + def set_g2_tau_matrix(self, g_tau, op_list): + + assert( g_tau.target_shape == tuple([len(op_list)]*2) ) + + for (i1, o1), (i2, o2) in mpi_op_comb(op_list, repeat=2): + self.set_g2_tau(g_tau[i1, i2], o1, dagger(o2)) + + g_tau = mpi_all_reduce_g(g_tau) + + return g_tau # ------------------------------------------------------------------ def set_g2_iwn(self, g_iwn, op1, op2): assert( self.beta == g_iwn.mesh.beta ) - assert( g_iwn.target_shape == (1, 1) ) + op1_mat = self.rep.sparse_matrix(op1) op2_mat = self.rep.sparse_matrix(op2) - iwn = np.array([iwn for iwn in g_iwn.mesh]) + iwn = np.array([iwn.value for iwn in g_iwn.mesh]) - g_iwn.data[:, 0, 0] = \ - self.ed.get_frequency_greens_function_component( + g_iwn.data[:,0,0] = self.ed.get_frequency_greens_function_component( iwn, op1_mat, op2_mat, self.xi(g_iwn.mesh)) - self.set_tail(g_iwn, op1_mat, op2_mat) + #self.set_tail(g_iwn, op1_mat, op2_mat) + + # ------------------------------------------------------------------ + def set_g2_iwn_matrix(self, g_iwn, op_list): + + assert( g_iwn.target_shape == tuple([len(op_list)]*2) ) + + for (i1, o1), (i2, o2) in mpi_op_comb(op_list, repeat=2): + self.set_g2_iwn(g_iwn[i1, i2], o1, dagger(o2)) + + g_iw = mpi_all_reduce_g(g_iwn) + + return g_iwn # ------------------------------------------------------------------ def set_tail(self, g, op1_mat, op2_mat): @@ -92,7 +131,7 @@ def set_tail(self, g, op1_mat, op2_mat): op1_mat, op2_mat, self.xi(g.mesh), Norder=tail.order_max) for idx in xrange(tail.order_max): - tail[idx+1][:] = raw_tail[idx] + tail[idx+1] = raw_tail[idx] # ------------------------------------------------------------------ def xi(self, mesh): @@ -103,13 +142,10 @@ def xi(self, mesh): # ------------------------------------------------------------------ def set_g3_tau(self, g3_tau, op1, op2, op3): - assert( g3_tau.target_shape == (1,1,1,1) ) - op1_mat = self.rep.sparse_matrix(op1) - op2_mat = self.rep.sparse_matrix(op2) - op3_mat = self.rep.sparse_matrix(op3) - ops_mat = np.array([op1_mat, op2_mat, op3_mat]) + ops = [op1, op2, op3] + ops_mat = np.array([self.rep.sparse_matrix(op) for op in ops]) for idxs, taus, perm, perm_sign in SquareTrianglesMesh(g3_tau): @@ -120,29 +156,37 @@ def set_g3_tau(self, g3_tau, op1, op2, op3): taus_perm, ops_perm_mat) for idx, d in zip(idxs, data): - g3_tau[list(idx)][:] = perm_sign * d + g3_tau[Idxs(idx)] = perm_sign * d # ------------------------------------------------------------------ def set_g40_tau(self, g40_tau, g_tau): assert( type(g_tau.mesh) == MeshImTime ) - #assert( g_tau.target_shape == g40_tau.target_shape ) - for (i1, i2, i3), (t1, t2, t3) in enumerate_tau3(g40_tau): - g40_tau[[i1, i2, i3]][:] = \ - g_tau(t1-t2)*g_tau(t3) - g_tau(t1)*g_tau(t3-t2) + assert( g_tau.target_shape == (1, 1, 1, 1) ) + + for t1, t2, t3 in g40_tau.mesh: + g40_tau[t1, t2, t3] = g_tau(t1-t2) * g_tau(t3.value) - g_tau(t1.value) * g_tau(t3-t2) + + # ------------------------------------------------------------------ + def set_g40_tau_matrix(self, g40_tau, g_tau): + + assert( type(g_tau.mesh) == MeshImTime ) + assert( g_tau.target_shape == g40_tau.target_shape[:2] ) + assert( g_tau.target_shape == g40_tau.target_shape[2:] ) + + for t1, t2, t3 in g40_tau.mesh: + g40_tau[t1, t2, t3] *= 0. + g40_tau[t1, t2, t3] += np.einsum('ba,dc->abcd', g_tau(t1-t2), g_tau(t3.value)) + g40_tau[t1, t2, t3] -= np.einsum('da,bc->abcd', g_tau(t1.value), g_tau(t3-t2)) # ------------------------------------------------------------------ def set_g4_tau(self, g4_tau, op1, op2, op3, op4): - assert( g4_tau.target_shape == (1,1,1,1) ) - op1_mat = self.rep.sparse_matrix(op1) - op2_mat = self.rep.sparse_matrix(op2) - op3_mat = self.rep.sparse_matrix(op3) - op4_mat = self.rep.sparse_matrix(op4) - ops_mat = np.array([op1_mat, op2_mat, op3_mat, op4_mat]) + ops = [op1, op2, op3, op4] + ops_mat = np.array([self.rep.sparse_matrix(op) for op in ops]) for idxs, taus, perm, perm_sign in CubeTetrasMesh(g4_tau): @@ -153,18 +197,20 @@ def set_g4_tau(self, g4_tau, op1, op2, op3, op4): taus_perm, ops_perm_mat) for idx, d in zip(idxs, data): - g4_tau[list(idx)][:] = perm_sign * d + g4_tau[Idxs(idx)] = perm_sign * d # ------------------------------------------------------------------ - def set_g2_w(self, g_w, op1, op2,eta=0.05): + def set_g4_tau_matrix(self, g4_tau, op_list): - assert( g_w.target_shape == (1, 1) ) + assert( g4_tau.target_shape == tuple([len(op_list)]*4) ) - op1_mat = self.rep.sparse_matrix(op1) - op2_mat = self.rep.sparse_matrix(op2) + for (i1, o1), (i2, o2), (i3, o3), (i4, o4) in \ + mpi_op_comb(op_list, repeat=4): + + self.set_g4_tau(g4_tau[i1, i2, i3, i4], o1, dagger(o2), o3, dagger(o4)) - w = np.array([w for w in g_w.mesh]) + g4_tau = mpi_all_reduce_g(g4_tau) - g_w.data[:, 0, 0] = self.ed.get_real_frequency_greens_function_component(w, op1_mat, op2_mat,eta) + return g4_tau # ---------------------------------------------------------------------- diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..d599605 --- /dev/null +++ b/setup.py @@ -0,0 +1,8 @@ +from distutils.core import setup + +setup( + name='pyed', + packages=['pyed',], + license=open('LICENSE.txt').read(), + long_description=open('Readme.md').read(), +) From 8e7b9f6b791055369952a2ad4b48c21f1d2f1b02 Mon Sep 17 00:00:00 2001 From: Yaroslav Zhumagulov Date: Wed, 19 Dec 2018 17:59:22 +0300 Subject: [PATCH 22/33] Ignore --- .gitignore | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..ae6324e --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ + +*.pyc +*checkpoint.ipynb +doc/.DS_Store +.DS_Store From 3370d8f0afa98d4f7e1f7e6a93819e6a0c0740d8 Mon Sep 17 00:00:00 2001 From: Yaroslav Zhumagulov Date: Wed, 19 Dec 2018 18:01:01 +0300 Subject: [PATCH 23/33] Update Readme --- Readme.md | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/Readme.md b/Readme.md index 3493362..6be86bc 100644 --- a/Readme.md +++ b/Readme.md @@ -11,15 +11,10 @@ The original purpose of `pyed` is to provide exact solutions to small finite sys ## Installation -To do: Add `setup_utils` install script - -There is currently no formal installation scripts packed with `pyed`. To use and develop the module simply ammend your `PYTHON_PATH` with the `./pyed/` folder, e.g., add the follwing - ``` -export PYTHON_PATH=${HOME}/path/to/pyed:$PYTHON_PATH +pip install git+https://github.com/yaros72/pyed ``` -in your `.bashrc`, `.bash_profile`, or `.profile` file. ## Documentation From a704a9ad18025083ec88509c1b637a8ccec63653 Mon Sep 17 00:00:00 2001 From: Yaroslav Zhumagulov Date: Wed, 19 Dec 2018 18:05:04 +0300 Subject: [PATCH 24/33] Update --- .gitignore | 1 + dist/.DS_Store | Bin 6148 -> 0 bytes dist/pyed-0.0.0.tar.gz | Bin 8474 -> 8319 bytes 3 files changed, 1 insertion(+) delete mode 100644 dist/.DS_Store diff --git a/.gitignore b/.gitignore index ae6324e..30a9827 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,4 @@ *checkpoint.ipynb doc/.DS_Store .DS_Store +dist/.DS_Store diff --git a/dist/.DS_Store b/dist/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0q~2`Rj#|&2*Xl=4YVe~~ zf5M(Ud4y*XgqF|PlcnW*fn(hi!2#HKyrz-Zkm&E9yde%C6!fLzm-}>_-53P|ET9tosKd?Qw&Fa-!>D-#}HbdGd#py&OrMHXO z%<`9Q_U`VId0B|ELMch$=(dmH+eJ1VpHR`p+1_S1ARvnC72yD3q{%!aak&RO7%!&}P- zgqGfd4K1jfkL)1y?ZE;nwL=D_90C?IZ)A^`n6Pk1+-KpGmqPB(0_KhJ2Rk``$4M+AS3wZE0MmtK2NDz&}LxDDTZb|Z#>HKPh-X=Mi3 z7*>b~`^=kTCKIq+GRFdhN-|$HX=u`(5wl$?YwFE8yg(_?pj+E<*nqP|z{d-x%m9-8 z`|RrU;@uT{dH%li@0XXCFVC;u{}({Qn8H_n!$mpvZ0^`lEHnbTJ6s~JKOSGcItBQb z-<|z%cJ&_7eSLOye*E?=dwp@qUY6dxyu3Pl_3nq4m+Z~E%QqKqk72ktU)PIc8U>%T z5m2w~1kl3wFlGT@iLsHN7ND= zz5ryu=fTjoX}sF(Dcj%2@!8)mngc6HpUEeF$xemx`iYP!L5=;9gBWM$xumTCOUEc7Ovh#Wf}W}P!!I8+kNrU1wbhsx){ z+@q0SxI^(32EhjWZOa9A#6o+UEN zL3QZO4rv*F-Z-SSC$EfL6u$R{3#7^d99}~MM=A(4ei6W`g&m1B4Sd<*7Ib(Z+0PU} zfCgRG{bd0o3KR8-wmB3?{r;6GE;C+{ z*q5$Iagou=#5S=q#ih~;73Vjw*H^PmGlvS#!VN5HEMC;&ayPTMMWtE%5wQ7x>{pV|p{}qa^ zq=tO2OZH!|Fs$bP^`oZ3|7*?0lUk!uueEmk|1qA@H{X0y%2zv4Mn?U};T54Q+0;u6YRY2Wlo_D0J~zv33SX)L zrTzB=?MA2^5U!=Ctg?}(RFdl)s7rq6c}@@|xQp3*iGr~^-(+>P)Jo;BIvGdH?2kN{ zp3Sa6fi2Vf8y__8VOVBoqdPrMqKwL{BWp6@z7B-zxD67j1ewk4Ewq7;s;?FUe#K#g zVr-HWYzF8E(2s~P?}yf+N$m-g0^Wj_?Q@_L3rQ?4C6%00R6{KU2r}S#{3BT^1Mga4-dn*g8F4)_CTsw6DhRu|-$z4HzYnUnGcL2! zGP3fKMJ5fWAF~=zmqGoE8i5bOS1}QY2LI?EfW9ETc*YpHwU~M#plNqjOk5*|R;#TB zl%YSPT8&e)oO~KMFsXwwHDwEZIpJX+h73lV@ax}L&VtL1_2$rd5w4O7Wh1M_`EnWr z1#Js=v^w?C3Ncwy?W-|Xp-rtEk`AaPkyvn#;&L+SrH(+AgcU97hxH|05L@TYP$+{JteD1 z#cj)OyId>RdQqKnwBRd@7zn6AOjEe1&OI0Sj?O(KCgrHD<)|4-bx*16fC{!9BN>vs z3|k@xmHHK`5DRk-QHU-A)du?`m|Am=gB>pB4mY|EcVo$*x8E?C(pc<#!$xYW0|zzi z2D||TjKw=a{^`j<;{!xX0mr z^*l^XD}etR(+aB9rWK-*Oe_3K(<;JFpH`S!JgtE7|GMDraQTqQ6?L07SLD)sXB!D7 z=BgQ&TZ*~WfltpkiNcB*hb{R-%s3=lLg!vH&c4l`aF9Q5!a?$u`klsFc6ZKbx7D@7 zb^z%~6<-t_t=qo*q|)uwo=g?uMF{9qJ{}j*?Fw=gq*`X@o3WgTI}QR zwIdp+po>tHhdcSXzViY#&w-u?{eFDQ4mfmVcosPo@X@jFd;YejbyJnAnz~?x|1^=r zR5l&JSadarp88d?<%HK3OYTGu9`1f)#$J|CaHrl{z`vjp&BVX`z@8Ad&M}D?0)KT* zL`YFqftkdyi%FmhcN@K4is}{uUTp;?n$;`H)YFT{r6Db9frv>&%}4$4*2@#Uas@6? zZ&}0G)N7MH1AGfR;gdZ#L_Z-Gsp&(EjC!PtRwe{AaUvz%xX(u0BCA$Sr7Q7EifF$} zT)(F*X8>8s|7Vty5K=>+*sw{fy}*g8)B8<+=_c^lYgC^R)+-?_L;KOsrVt}cR*3O(cX+ivu}tbj(lx5=T~@32~REz|)`c5b`DVrJ|Otnz4B zd0`EPqrI{=^yT!(n`?`%Xhn~#@*rEZEUc{2%=U;zo0~a2AOC5sXZ!^ARk*)yRyRZ= z9?1BcTW0)IGzoo0*5CSCtUr$!?-AL@P~C zi8di7knCZhPQZUqo!!c)6^KLfyQQfatwPPdRiN6pgl@kvd;QMY;~x9}DOqzP?qF;7 z|7(rf5$*pspB*)y*P2ftp?1{R?f*a4{y!_7atHXO9&CZ`iJ&rD4mno~XY1J4ngv8B z=oe73!+-^g0SjZI`HSxGiJtgM7-gUu2~;D3N(rPw z{}Nl$fTG#!ws9fGsG-gFac*%4!7#Mv=zG4$u{yVawWa{>0K84WWVzi40*ZGDxb)`VH)N zwGmC+2n1s5+to`MlcEvPDP?JMXC#oaNqPaL0HF{wdNFlN(YnmkA`NKMoBXL&@jENv zIotb~%tQu?W}zq(+S-iV;}w1yqDVjGBj0mH7!SFwqx*jnH>2}h=?V~&WwfIi@m&(> zqP@(-RH}YOwMOeGBlegO4ihgrY*}XATA9^Bx;v;#KiwkmZyABZ2-E<)k_PW(UG}AL zr(v~mpNzMQUun23@?Hy!oZ(Z)O|E_-FD-qyUV6Pt`@2bZeER1H&zoZ$ksRcB9mMDn z5j~Kzbc*~RP=A2#qrVOI0f+!Q{{wj{=)=8D?~jy5Nz%U%*HHx5l3kIGS_+BndY%-q zQDT-xQ@PG%dD$Y&?Kr;R>fF!F6dHNmW-ri7X`H&I3iOaw+dDbk(GAI(pDDq1NBj;a zW-p@xyFeLkBcE4ilwh#|5@U_yWESogNd}GB8=@AoEikEC^Er1%MwFb=%p+`Ls{tjL z)vMwz6092X5U6g4Al@B!7ipNivhu5uGDM*HK-7K%0{QUTkmjdePV7~(S_qkh5G^pY zl(xKLkm=^3Y)CkTiACWr#g4L@8-kHEr42#6Y@79H1ob#92+06PUJ9vl>}i;UWlf1X zjokvZRUY{l@DkuAf`eq4nX)R!xkR_F$yH#q%uzs;(OStnCBrSph-hWy4N8`a6*1r_ zU#XDq?UTW?oy$no{uGQG&E{QdHtVgk0p5X23P}zh>=OEoJ|!H+S~GM~wdwgU-Z028xV|!#0W$iAFjE zjzxhFXv(u(*Phyr1dD@Hkd0ZUESOtE{smUSh~d1KW2Bs25$BA?ulgzewspIr zW=16IQZTgVaqNxQ2i&}9S?pY&wMmHJ@+c}RSTWFeSO8@!jzs{;%*K#-3wOVWFcN2e z${LYJLJf};zK|bPkQN|wpG()*4z;7*_`Y+`>`0wf)V3wdtY?W`O`>nwSt ziEZLcVOc>Xvi*r%oerxrvk-_BcBsTG0;G1K-~>S3T2^2gS(7po;w6C=7GS)#96SO- z>}TSBtO;Z9xz19Yl2X7oGUerUd!(JxFV$fbhQQ#zxCXg{iEI7K-PrVDO`|4l{c)szG48%tEIP(4jX@gvac>SNjK@kv>anb_13FQ^_Ape7ZJ3fSDPwZ1Q&I3>yaO~;C`U_m`E!gGVXp3>qx7Z zuRQk#>BeFcdG<&l&&TtFtY{^RT?z8mkE*Vb3Yfl#2Lthv?dOj~^(>_T#f*?F;zdnL z`721P7*s_$?@?D&-xx#kuqLRtUCQHn6YfWh4}mivp7@r_uE^+QL>!T5KAV3Y<<8(c z>_h!sFE$*e)L7gG5(u_MDc&4S9;b$xZAj8X(c3u}nbDL)RPJQ*jA1Zb;bPG&~t zN;uwlrGTSJvIXKwSv|lE#Jd6am28hC%_N+ry@K_{?PUQV4W%m{x;1# z2~ultB+1Fd)Ig%aX(6U9VuoH7fkqj{@Lhn18ioMnq7_lARUle3d!2k@UX6y>@koCu z$Mg@BoG%-sRE0nP*e3HhUu+lcj>b4ML=v%{8Wl%CwBTiRtFdksRr#Y@HRbM)mG!tk zSlG^}A2IrPX*CeY%(^^M2@%>!ab^n5Gc2E$nkrxNoOtw@e-&`bvEyM_5gy`Zg`K@& z;P1tpX|pR!Q3uHAbbo-&OS&;xJygu~#pC6C4!T@s@l2E&f8Y&=u}~9nKUS%;imWji zUOKE)uQWGDTh#&tb-MgkuzEb<)ljmUIgn`tAZIb1 zmoz(B_i}S2D=DU#In!D$+%k&<6pMFe-Pdt{CA-u0+zK*q#^uqm$qGmlH6j;K6Oxl1 zBU;cHdx8o@#wA4bk34Wy?nuEuWoMmqY0AfcbTWD4uis-;KoebkXN6k)9nQ5J88nH> z7z%McVIM=!i-#)+)0adcZl1tnaj}X6HP;dmgy|6KNuw zJO^~$`Jd7GfARiL{iyZ)`ELK`Ym9#qZL~QGSm*!${OBk;|F6|J!t)=23Yxp|f3*F- zhr9m|flPGgf5n~kuk8OXV$j@>!}8lFHAi(^DeyqYlm1laSlT$jRKpe~a+`brGo_-B z_hUydXyJ!z+7ksgv{nW;-G774(pWY9>(4g3t&( zJ~1#KAub~7sm;eljbzI4F6Po8O%Xo0v`sx+`Ri-RxDDhiu7_@Qbc! zKtlFhMAvNObf33u9aWAbw93aSGo~b3LmR|LWY!DfBP>WMFxJ zYRzmX^jxSe9Twb=29OtDPml+kxp`m<17FmH)FlRl+Ph9N#f9K@S|x9SvhH}2YSD4Z z=T2to)|4>&xo^86+dEA#NY9$~UJMMpRyZCzvAFccF+L&@~P# zH_RM=vEwe4*XtZ-zrE|eFaK|^^G;wh;tpbz)6t4o;cBWQJ{! z+{)RIR)=7n><^9w-tcZzk~t~e^3#p@NdTksl*nvuw-NaliewvLtY`r{l(?JSb{Sb^ zFLe*j)DuMcPI}~5XA38^=gu-}KH;X}2>BaxIB z(`(AB>$4}I8(wuHs~ zZ(;)Wx2=hc^H5CTU=;?(+pQm%Nfa1ZfWW&gA@INpthOQ%OvF7SBRdF&p6fzq;3?47 zLfBxFog@0EKPj_`%6BGmz5jK=`6q5nFg$o{-wA`JcZ&v8@0N_){s4`9TE+=&dnbjR z=j}M1v_us`hBeO14F7K`4*0k$?{L+zpV|d7t&uiqMjqhG1s2k5X@u-sRx_)XyEM$= z_-q905S#-mLQd#)Y{n)IbHK7Oh7t$5c|-dc4YfGvTEw7me{O_I*Jq5v_d0Fd$e!M# zZR=nW+Sij-S31^ovP18qbC2Moj(|h&Yu0t zUaEuts05iJL2s@_e10jx==T}KnkLqg-Rj8Q>q&8g6s@h`B)OYC(qupdw)e_Ztq$=N zv;cDC5^)@yb}=0*2(v*(>a7Rj#)Es^c7X5Vh6AWft%dxDN(Brp*<;+;tp}(sDO)>_ zMRfPMuP3I+AVZRVbOxZZMIRl(2%3*5m@IEE!upG_@uHR{!|nw@U%-66PLLJ0;YUo?%H$zEuivC0G@CAFDzDGXp9+qBR-u#t0* zi#SyyAm>9YNtrY!+8k6$tJHg|d3R#4=?Vj@8rK#dhpDZtNopMf%E;J&De;eN`=EoDv$<(DDsPDIf%pd3iV}TILh5>$*n4o6-y{(4S7)c=~{C%uMTqcyqPQvC&jspL+?$1?(uWvDD z!LJvsRSc$(pETF-la|U&1eqz+GHdA<6J2m2)&y?-HFWkW+B(u|HUNl`8`Gof5 zCdqLj#mBPj6-pZ3L5s~uo$T(Rq)cd5)%R|D=D{k6q385Wq({1tJ6Yj7hF(e~ zm1R>imii?%V~N$vo!TD{4Z2{uS2;HGXTxJnhX-Hp9PRui(;~jlxtZA?!xJB|1{T*K zZl4}{zR#ss?+|EkzyTrOzTu-p4zUr!S&9I=9EWV>n7t_EHsb`IbgdD_Q}D{~CVOP3 zpK)I-gg*n?AfvaB2da#QUQ1B8Hvrk`Mz}PEQ!AvzRO6CxH5rnXkhB#B3m_6JgCyXl zOn=!M-T?Z^Me%(pyrnEhIr=K(6JH==Yi-on%?E(%*FK77jzVl*{GElLsY`O*d86&g z>kj0$-Ep{NMx}0kyv~Pa%=mC;6U)2Cr5Wq{SV0S6lvMk8V48;tb{~!FgZk>qf4lC~ zKeJ)~njJDV=Wo}alNQstOf(K|JDiGRL;~SUf@G+jA zGvY`^bPkI+mG4wU`wgl|)$eGlIh@MEGDHZ|*B;tLhS-ClbjL9 zR~bn7l^se-q`@rdBPPfL`z`J{*=?8we?r&w`Y`A2?9YYzptxMUpS0e1S!UnKg*V3p z{*vvp?*KQz8DI9hopmZ|qyEwto?(~D>4}l5n*000I)GNx4@HwYG23RnKO_oz zHKIi-LS;AIKVcGr0(*^^;n(MUes%KZ)$JqjeY^IKf1=k(7?x`i@iWDR_EJs0y+!Dyf%8S(-T zq~<^kayL&NQiWYs&2E@ZhWgIP$uKJ$i7Lyijdm9dImuEciA)ikFwu~b0`y#gv;$>u z>00$+WXI7>Il4r(c{0R}%Lk|#x2iP7>5Ar^mZ51Gw>uppN5{Bbr(xW}&x86e`+)XC z)QsJ!XS-+j?4I4Tdv?$6**&{w_w1hCvwL>W?%6%NXZP%$-Lrdk&+gehyXP@xT0eRByIwN3Fxd!=u`x8vJNA zAF;zn5AZC)$O;&Hw6p?0bgk<$H~<@u*EA9v694^!JoB{wU;pD}_4Mq;`BwV>sMRX# z|HD@OFw_67!`9&=R@?Re)}L2)WR0xIs{YdsLdW;otZvpSXV%PaGvtj*l1@xgd9#?! ztYFz@Z_oa5cK)xk%85U-t8;5&r%BHi(bNyJzpM7la@;iaXJ=@8A!Mt(es}y+n;qX; z!-$O>YvOyB>wK~zsFnr(B62)CWMe;IW5;tMoBg`5yl63F;WCWuSy-t&_vg#NnM@<5 zJ=a;IR{t9tEZK?4-kKLCdlLngH!|2e%lxOwewi+2)@0%O*A=RmIZ$?B+bs0Q(Tx=V zLQ8+ah8EOqkDM?HoWTMrbs`3(8~_$Gf8>mpn6U6hcEF;kU5V^q7BYW~KiJE&x9p|u z*@5M<*NcJc44G&wvqCn;C zY&0uEgahW!F_R8hE}3fqLKTtEOdFcEXT%(j%9{Ff8(yFkXwZ%0x@=&xMQD!~uE7A3 z{pwX7thWv-~AUrLZ8A{``YH^IJ3FyK(WvWAa}GxTwfhuJU;>W zXFs0)e0uo~(S31xd3OBf4SR8Z!JbuKKfAa*eg5|6XBX`C+l$xdZ;qk6Y+fRQ_5Q|sD>z8l)kHQ-~hA#lfCy2kar3AL+od2#wbZ0=wm`2gOeQ!MpFpvg}SkT2G4u0Z>0#snwvJimyph`=72?N{P3lm7-L=X7YgRvQwAnQZlIY3U3 z62aVuT7U>K=|dnEn+N{2GqMHsL*HF+9N<$pvtaZJ;!6Y;1A2tQXJC87DG2Z@(I7jF zxM&6Zhf^zzl-`2kuZwX_ULHSYryk6WF7LI1R{3n?x7oANhYOJ>c0rAJ(heti87qB+ksjj{M0VE^us@^yCXDXC#9VRTm-5{m70e8K4hb+X8xr zLWoZR1V*yQy8m9lxPWmEQqmsy{#8$dvJR9gO=rIJ26`cGLP{6039yJYhskHik^24f zc~WLFud^?m*OMaS`JHWIeosoJc|OUnA=Ot;K6*jVpGF%{4OpTYB;{^KMTkq=n5vMp z|20q##Yw7?KKk=L()y32vpHWr^!l&XIBcftzXp7-AJz|#cI&@~c*cQ0qp?r3vN^<| z0^<~CDdLAph5l$XY&=*NcJ`2E_6$nE85hKV*y)PY9#0nnrvn1S)AoKoeSUlf;}vx0 zJ>3j!YozG{4pi>`DAk5Vh6?}zeQq|A!1X`l=zP%ePx{TbVgGBjYOS69?`!n`B`#fP z8TymDvipLGVYU3PA2lWUUu!lV)f$aD_Ww@)Z~v)$_uY4uVzUEk_qaZG?J8d^-uTtI z6~Gb^R!;e~tysF_`XBxyK1!C)xbT66Gis@5tceYTCyR;C-kL12O)#xdtADQ>Yzkk@ zkkbBpf_6ignP!A*e8SHd)tL90K5hClznoR6K1;TmU0o_`F%;wG(+F*~&=Zk@T zX+sYs*rX}g3|QR&9T8!>A6bhgwI`Gcc=PK5){EKURWF0LbS6qso6vO}Mdafv_+YC`}>*MNdsd;mgza&7A ziE;&?5C&eFc?(3aT(=La^#x!v(I#=rC+vqG2+#DhhCcxlcVw`M0qfz}U}UjdgDqK` z-LfZasUkhuAhJW(3PV;pVQT*1-}W$ykq6ne!o0geTr!e+WCqy)=2Q@px!*@aVZX11 zwmUZ1iGiYgWRWq(?I)rJ7W%MojvIjw!kd@~OoM;a55QlLUOr;9!di5-5POuAQApVY zTCUa%DMNoowQ482k$xJuFsMU=nz992PVA@;T?V~P`1P+XcVP>{`g0&&jH}>6(MYpA zUqOSQpl#uf_Od?OPbN#MeKp21w62sx+5xpBRtxS>TtOza)DbKtVMdGTu)f4h2DP+F zfCjjJh!XzpZ7#~Sjot{ct_IF>f_*0Vj$Bm7e;V>Y1nC2UUt`g7ZfgW*&S19GS*If@ z6->`%kVbHar5Sk=v?|E}wIUH=FqZOUU6-I6>!2HYS~ye-pvg4TMKovtkU+$*jwfN) zld|$u+_vtvjha#G#dV6_g0IkHprD2cPvN9G_dSq1s`L<;7;#&TxEWG)kE!gC3U*vA z?UJGlTVe;9`z5a66LSGma1z1N2I&z_t+|bz9WCart#w`7OEiPtenV$cW0ClpgWOgJ z392~_cmoLNi*?#bPAE&vOz3p6Sf|m$vJIiUYEXE#XSp_#Q3CLT1Q^ffn7lp0J2zfJ zad=-n4l~0F;J?POf@+mvg=nP13cu2@im_9N6{eOCD-is@E^KeOywBi@$)=1IF*V;h zT8fFjYQzz?9w=hz$+lWTwWXm^fofF;UHN$QJ-Oc<%-G z2sCm%uE3ll<20})su~UgXIt~Iq*w(Oa%6e5(Rjlxz zCbF0;rbFn9t^(1MzY4LO%Gz?xo%q3@BRfFGUY=3VQ^Q)ozpx~l@qhcFGa+eRU=qcJk)o^|Gl63llVB~}ZS;B>u3K<;xfPhGo0pWSrxuS>Lsr!S6_c3S9`&Oezex2e zRJeG(hJvxK)+Q(eatn#@$(d`MPKZTj_>dqYigeM+gn&8@q?C;NY_u)5YSm=E;=hE7 z_Pf;eJL+;8u%-BaZaRq|H3E)}nl#&UoTxgLZsJQfg-5DUeOgqnMlcQS$3L4~jnG*& z0f-IaWX0C;LLWA9Qyl^qo_6C+Q z8djfLgW+h;P`cj8_PoBf=&Dlm$T9}`qD5hb!ZX`r8f9!2$b9^#wO;TO*jJJMs##s* zjku@aZ*E!ePthdw6-9sRYl;3MiL`!g-n#qZymg1A0=^-GHtDSeZ!KCryjKXdYBABu z@>9B+-~&idm@g;bKd8=W<(3tQL;Aa=EHhfAEBjW-(!Ryl_8YU;@0>mEu>YUYH8g1l6NZP2k2mdut*&h^~cKJm3@ zA@K?N1yr0UWZ`1KqJ(Mwq&xgLEqt;AzYk=DRsP(yfR=q|V4vtnQ)ZZ1xrvQ5p2+Qm ziBTf#)_P{L#a2=E1&!-nBd-rC%zI!IjBYo)rg@o z3MpTI@hxdUQSWuzI1yvi*k);5*j$6ntScL)70c~h1`C^STz1ZYk}<9C=YWwCU0q5v z^J@u_Bc7?r=NOVnfiLXXcJVT0^#TQYk^067(TSd6%i2l))Y2Sxp@a;n3{j#o2(HQd z4eWQ79!=c{IAZJD)yo)@;vP{sW$D=Nh$H2Y_5zCnghI^dMOQ6F>oQY|6rfFS@@Hnn zAFa?X*xtuv#xqdd2t^su)@JP3FUivgRr;wt3Ve@;agozK1}>y=Q>x4rt^f&J#ygs^ z+$EJR-pfo)rSex?)+mJ15|0V)F!8j*mImwA3|0s2?nzzv>6U^2zz7_Apa$U8EO;*; zY#@|71*?twWV~7YO5J6V_gbhGbf4UBV)o;CY3jT2v-4fn-%SJ(&_6%>{v6}TL?jxko{cW)KzywJA_vERdcK0^DKQb#y(*C)+jw-m4?D2G5rI6aL z=E*P{CFXfFlj}^BmoGx!j^hi?&i&j-p`KT5_8h&;ic?*w0zc%J?VSR0bR)9n=SpzA zk$sB;vzJ=}d%zi9qgYnw7QuW2B*7Z{NiW?k5&{~r*LW>@n`2^H^SSMfv^Y7lGLNxM ztOk^zo0sJz;-VVz5U6U0AlaRei&mIDL;BUo7$VSoz-zw-g?xCW3G-90AZjd~Ex1lX zh?W>yDqEg0$aHgGF~pt1_@wZs5=Z&X4K7HU!iK)ThI$wgglvF4FO*c#_tZ_? zvL;0xCvJhtERXUFWC=(UE^M_Lf{$+B1&0BosySg zWdbAsQ8D8Gy2Tb^i(I-g8cYNX$3T zC}=@mPHsob-r3>48T((MC;XS00oU07nzh4P#{O4t?(BaL82`gNo$-APR2e0QZB!%D z73mNp76m?_DbMmeXX=PU7}DSr6k}F=4h8ZM3+L9*{sOCDEO6e5K9bHZNpi;hXMRe) zZQZV@m=Vdk6b_wv5_`k<0XMH&mOIzyZ4x{zK90%?S9CNUb3ob3V-Y|zb1<0S!rd=o zjMSN*vc~L@a>E0KFBC@=WEIFlD3~}4(=&PF^uB8iY&TRFYVxA|BH;!^QjG@Z!A!;BKb5S$&Wy(tB+-`$0uVw zW_-&yR}yt~vbxC@V`TP+zdOFTIKODKiyXG4Z2Xt+)(;ZZen?CdZxQI+q?|C8;}w_E z%hckD`6y&7nx|$FE*bKmS6eR&)#pNroyX9cT5YCm9$eHZtVev@g8PBuVxCWyLdD;!037f0*iul*9CSJQ%2#Y`?fC%+t&QC=rBY z5ie^}#$Q2L#h@z6d55~<`oV#Nh1Z? zs+yA)%9oE?2Phq5) zsaj>PtgGaNzk!8^!;MK%r>vRshzenB^OY>j9NJ(b+`R0-*M;LnO=&AGnkB3)N-@VU z?MEQ9b@SS>Z#0qVZ+OlNIYMcfvRit#ILI<~ow*xm@H;E!HeQCU99Ra5T% zSelRfgN5Uc`mvx-rdADs^t{U>l@Ozy7N=*>JjL>9sxj@QT@a6+$gdJfId**vE27gZ zqt4zi4E7SqwAq=Zi~u4!Js4o~QZlBqhm5&8c|4orp_#eKGhNgK1AjP7l$u!jv1*-F zMU83q(qXk~rG+`#@+yFHr)S@AQI9A5YEo9c05XdJ{P)Y3C3T>`_0$R#jK!~6`&I8K-KBY93t z>5xo#X|vOLuP{eClVX})Fs#MIEwTteK6&TneHHiTf}Ec3RZ)O5TO1vn&VV#fV|IaM zLV6};%nOQOPf?-BxRif>{^%$^|F6}6_gWpeptym)u$Z%>Ms82F;6Xn11`T%#j^eN<7fRdcwB+#w&p zT&d{e{m{`1O8DWL_Qb&rt(CzI#eoeNP?`^JXr>611UK;Tgs7PYH1PLL1wG`UnkiJX zBs4;ePYm@3h>M6RwfVS+f|!Wb{bC|o-(*aL2%{8i_hr!$tqny-V5ntLgao2_zfg&^ zq-1n$++?3B4_`GxLXD2VQc6N2l25DRB8*Q-WCR_9yTsQ|B1i&O^lRcIC_Cl3rR=wH zb_|-}*{gTT{)w`GqLO&_1`oSfTl=RB@|e9r$k*;t`)#In5g^jwF(TDF)h@Mt$!jkQ zG3n(NvuQVNkzqBJch`l7lplm_R#YeKYsiY%+{oMW?-d7Q5gxX?-P=NnSN(HMog-^ z24lp;+E^sU(Y=B&R?ZG00z++*k;QqN(rcan|7I@$S#AGo)?3Zg{&#qY{{M}}?*88g zwEx9-rzr-&;`=@MjhXTm*U%2XHG3b*-cs8iC|VPAA{bW) z8jz8FPr3((Wwo|SJSSu>!+Egl8^jd{Wal#SbDY>SCc7UDMl_&Aj^%NaU>^LsCLkl< z?bLGG*>w$Oy6aj-EAuIQz;`QU4vAioe_?_P03`33B2M7jNug(zHQIfeSApLVR> zpIS4=jeHNP%Z3H_;|{dTuO}!1&fPq)g^n+4Lgo?!LhWrQ9pYSZJ2A5-b;W%)^f+=Sn`^$#ruy|Akj0~>i`gq>s^V1KksQ> z&*)*4+>s!XvR%ZefY6dx;c>PjYJ3SibG#COSmQjdiVWLe zxs{_KyBvafvOhTHc*DC^O~<5g%g;9A7Y>ZhQ#`Z2-9{8&D3Wi0wxR{>K;W)-+Xjlt zUgjR0sn416t?{VF8>LYTaSJJ+Ag0jUEHoAo+n?ZP-e*a`JoxoIoK%uj@#nnfO3B!bKlBtg zXVDJLpPR8H)b#xoJfsuL>j#{X9_(tL6k$|&@Rn-E0fchT@4Dfxlkt~u>Wro64;-=Y z0r|ei><_g?bZ*%Bn}KLLE!C{5V+?BR$I{sCj6d}krReP-}8VNcnY+& z;5OKF=ZOC4PYgDZ<<3OR_rES|`;)CrFg$qd+;W4ae}e{8|Avg)!2peXO2!Fod#8n+ z740~^Z1F0%4y&CR4F9i74*0k$@7l6sKeY>VS}kkRj6J}m3oL}$Qj6FRtfrfmx776V z_-q905S$A$LP6+tV#X#3^MvJN3*#aUEx^I$w-ID@ALO2T^qoYleF}*0I4M}TMx*ue{@dmJ6^Vnq=*RmoMC0! zY!G}~#O09JN53!Exw5q=57G^(kN1fe)tbep3@h{0FW^~)>E!iX+-u?=+WCXN=@Yt(M)X!KTX%ta4miavY{Ix2CCe3@9UG1EwTD^6di> zp~{g6;=Jl1Lq0{{*;OD71d;R?#d45@?Uk;VN#IC#tEIQ9KvpcFk~aXR#=n)Le zrRF7hW&H$6yll8n+_y~Jm@gVjv$MY2M{EslJNJ55`LLbkvNH(Dw^j+5QhCx`BTrhgG~r^Z^)S+x;{J%J=eL={%i5ul+FG~aK@5Gjrz1boh1}^3-_g`k zs+laC8nHx|%!nmXFLr8Q9cXmHbgz1>7mtRA8V>iq-Z|d+ONT{rpK~*}KZYkhVht>* zf!{to^n<__UcE!$!GR4311Vouq$xLmX6trLvC|U;3;XfC>es6K{wqa zJNbyqy*`b;D#bmxt? zC$Bn?+wsQHl4;et{^6<^nlbJDtwSR33a4hQ??V+WgmF^s!xLRUkg)rpT|KF|*_OI9hlXL!d^*L=Zoy$c1;I*SEKSq=@5>V&bCImcOu_SaAOap|qq=j>gDCZH7 z(cLaPVFnvuO0UPiB3sC78D4p}djfC?3@oVNcl@7env8#^*IuuOv6hJMRf2C)o@5!>;eD%J z!M{>R!moT+QX+L`MeQ-p9!R%@a)NA_1%CqRdVLskx6bF%^&r1oy`Q#TduFg7#l%}+ z0)NK#*^hu5;EXT(?3r3trl8e@Wb&WH^XrMkB}3JCOx?F~_8VBx^dT8oxww=n7?78p zrFx7I1E+5DqKf=&_kCLQs>C|swGqA4iD%fQa(cX{OnrZ!TL;jJ`o3&Xr)Jx{_lH11 zt%kQqM##Xlf<1xZ!_8%~D)$m-gZ}DKpHElSdM*$po@!Q&C}4xc8|tni9#JV5)Ff=N zo345pX?Im*l6^T$EgI>k8?oPksz-^&c6NhP^_>1$$+u93h^&FHp6_9PDi{qkq(h$L zfz&o|gV@azhg2cS%>0JwWT@_pyd36rBVMJ!+Guydkdr)TlFAgraT5(KQh=YUkoH9C zT)I|$7~64FQ;w=pZJrHr Date: Mon, 14 Jan 2019 16:21:53 +0300 Subject: [PATCH 25/33] Add symmetry Symmetry selection: SU(2),Sz,U(1) --- pyed/SparseExactDiagonalization.py | 45 +++++++++++++++---------- pyed/SparseMatrixFockStates.py | 54 ++++++++++++++++++++---------- pyed/TriqsExactDiagonalization.py | 9 +++-- 3 files changed, 68 insertions(+), 40 deletions(-) diff --git a/pyed/SparseExactDiagonalization.py b/pyed/SparseExactDiagonalization.py index f738334..9024865 100644 --- a/pyed/SparseExactDiagonalization.py +++ b/pyed/SparseExactDiagonalization.py @@ -31,7 +31,7 @@ class SparseExactDiagonalization(object): # ------------------------------------------------------------------ def __init__(self, H,blocks, beta, - nstates=None, hermitian=True, + nstates=None, hermitian=True,symmetry='SU(2)', v0=None, tol=0): self.v0 = v0 @@ -39,6 +39,7 @@ def __init__(self, H,blocks, beta, self.nstates = nstates self.hermitian = hermitian + self.symmetry=symmetry self.H = H self.blocks=blocks @@ -50,20 +51,30 @@ def __init__(self, H,blocks, beta, # ------------------------------------------------------------------ def _diagonalize_hamiltonian(self): - self.U=csr_matrix(self.H.shape,dtype=np.float) - self.E=np.zeros(self.H.shape[0]) - print 'Hamiltonian diagonalization:' - bar = progressbar.ProgressBar() - for i in bar(range(len(self.blocks))): - block=self.blocks[i] - X,Y=np.meshgrid(block,block) - E,U=np.linalg.eigh(self.H[X,Y].todense()) - self.E[block]=E - self.U[Y,X]=U - del X,Y - self.E=np.array(self.E) - self.E0 = np.min(self.E) - self.E = self.E-self.E0 + + if self.symmetry=='U(1)': + + self.E, self.U = np.linalg.eigh(self.H.todense()) + self.U = csr_matrix(self.U) + self.E0 = np.min(self.E) + self.E = self.E - self.E0 + + else: + + self.U=csr_matrix(self.H.shape,dtype=np.float) + self.E=np.zeros(self.H.shape[0]) + print 'Hamiltonian diagonalization:' + bar = progressbar.ProgressBar() + for i in bar(range(len(self.blocks))): + block=self.blocks[i] + X,Y=np.meshgrid(block,block) + E,U=np.linalg.eigh(self.H[X,Y].todense()) + self.E[block]=E + self.U[Y,X]=U + del X,Y + self.E=np.array(self.E) + self.E0 = np.min(self.E) + self.E = self.E-self.E0 # ------------------------------------------------------------------ def _calculate_partition_function(self): @@ -92,9 +103,9 @@ def get_expectation_value(self, operator): op=self._operators_to_eigenbasis([operator])[0] return (op.diagonal()*np.exp(-self.beta * self.E)).sum()/self.Z # ------------------------------------------------------------------ - def get_free_energy(self): + def get_U(1)_energy(self): - r""" Free energy using ground state energy shift + r""" U(1) energy using ground state energy shift Z = \sum_n e^{-\beta E_n} \Omega = -1/\beta \ln Z diff --git a/pyed/SparseMatrixFockStates.py b/pyed/SparseMatrixFockStates.py index 222d2c7..3554bff 100644 --- a/pyed/SparseMatrixFockStates.py +++ b/pyed/SparseMatrixFockStates.py @@ -21,7 +21,7 @@ class SparseMatrixRepresentation(object): creation operators. """ # ------------------------------------------------------------------ - def __init__(self, fundamental_operators): + def __init__(self, fundamental_operators,symmetry='SU(2)'): self.fundamental_operators = fundamental_operators @@ -49,8 +49,9 @@ def __init__(self, fundamental_operators): (dag, list(idx)) for dag, idx in self.operator_labels ] self.nfermions = len(self.operator_labels) + self.symmetry=symmetry self.sparse_operators = \ - SparseMatrixCreationOperators(self.nfermions) + SparseMatrixCreationOperators(self.nfermions,self.symmetry) self.blocks=self.sparse_operators.blocks # ------------------------------------------------------------------ def sparse_matrix(self, triqs_operator_expression): @@ -84,10 +85,11 @@ class SparseMatrixCreationOperators: creation operators, for finite number of fermions. """ # ------------------------------------------------------------------ - def __init__(self, nfermions): + def __init__(self, nfermions,symmetry): self.nfermions = nfermions self.nstates = 2**nfermions + self.symmetry=symmetry # -- Make python based fock states self.numbers = np.arange(self.nstates, dtype=np.uint32) @@ -101,20 +103,33 @@ def __init__(self, nfermions): states_up=raw_states[:,::2] states_down=raw_states[:,1::2] indexes_const=[] + self.blocks=[] - for n_up in range(self.nfermions/2+1): - for n_down in range(self.nfermions/2+1): - indexes=np.where((np.sum(states_up,axis=1)==n_up)&(np.sum(states_down,axis=1)==n_down))[0] + if self.symmetry== 'SU(2)': + for n_up in range(self.nfermions/2+1): + for n_down in range(self.nfermions/2+1): + indexes=np.where((np.sum(states_up,axis=1)==n_up)&(np.sum(states_down,axis=1)==n_down))[0] + indexes_const.append(indexes) + self.permutation=np.zeros(self.nstates) + self.permutation[np.concatenate(indexes_const,axis=0)]=np.arange(self.nstates) + self.permutation=np.array(self.permutation,dtype=np.int) + for n_up in range(self.nfermions/2+1): + for n_down in range(self.nfermions/2+1): + indexes=np.where((np.sum(states_up,axis=1)==n_up)&(np.sum(states_down,axis=1)==n_down))[0].flatten() + self.blocks.append(self.permutation[indexes]) + + elif self.symmetry=='Sz': + + for n in range(self.nfermions+1): + indexes=np.where(np.sum(states_up+states_down,axis=1)==n)[0] indexes_const.append(indexes) - self.permutation=np.zeros(self.nstates) - self.permutation[np.concatenate(indexes_const,axis=0)]=np.arange(self.nstates) - self.permutation=np.array(self.permutation,dtype=np.int) - self.blocks=[] - for n_up in range(self.nfermions/2+1): - for n_down in range(self.nfermions/2+1): - indexes=np.where((np.sum(states_up,axis=1)==n_up)&(np.sum(states_down,axis=1)==n_down))[0].flatten() - self.blocks.append(self.permutation[indexes]) + self.permutation=np.zeros(self.nstates) + self.permutation[np.concatenate(indexes_const,axis=0)]=np.arange(self.nstates) + self.permutation=np.array(self.permutation,dtype=np.int) + for n in range(self.nfermions+1): + indexes=np.where(np.sum(states_up+states_down,axis=1)==n)[0].flatten() + self.blocks.append(self.permutation[indexes]) self.c_dag = [] for fidx in xrange(nfermions): @@ -148,10 +163,13 @@ def _build_creation_operator(self, orbidx): # -- Collect non-zero elements idx = orbocc == 0 - I = self.permutation[numbers_new[idx]] - J = self.permutation[self.numbers[idx]] - # I=numbers_new[idx] - # J=self.numbers[idx] + if self.symmetry is 'Free': + I=numbers_new[idx] + J=self.numbers[idx] + else: + I = self.permutation[numbers_new[idx]] + J = self.permutation[self.numbers[idx]] + D = sign[idx] # -- Build sparse matrix repr. diff --git a/pyed/TriqsExactDiagonalization.py b/pyed/TriqsExactDiagonalization.py index cf7e88c..fdf4650 100644 --- a/pyed/TriqsExactDiagonalization.py +++ b/pyed/TriqsExactDiagonalization.py @@ -44,12 +44,11 @@ class TriqsExactDiagonalization(object): """ Exact diagonalization for Triqs operator expressions. """ # ------------------------------------------------------------------ - def __init__(self, H, fundamental_operators, beta): - + def __init__(self, H, fundamental_operators, beta,symmetry='SU(2)'): + self.symmetry=symmetry self.beta = beta - self.rep = SparseMatrixRepresentation(fundamental_operators) - self.ed = SparseExactDiagonalization( - self.rep.sparse_matrix(H), self.rep.blocks,beta) + self.rep = SparseMatrixRepresentation(fundamental_operators,symmetry=symmetry) + self.ed = SparseExactDiagonalization(self.rep.sparse_matrix(H), self.rep.blocks,beta,symmetry=symmetry) # ------------------------------------------------------------------ def get_expectation_value(self, op): From 068a9898e654a779e75e42777ad92a8a370ea41a Mon Sep 17 00:00:00 2001 From: Yaroslav Zhumagulov Date: Mon, 14 Jan 2019 16:26:56 +0300 Subject: [PATCH 26/33] clear --- pyed/SparseMatrixFockStates.py | 2 +- pyed/TriqsExactDiagonalization.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyed/SparseMatrixFockStates.py b/pyed/SparseMatrixFockStates.py index 3554bff..90cadef 100644 --- a/pyed/SparseMatrixFockStates.py +++ b/pyed/SparseMatrixFockStates.py @@ -163,7 +163,7 @@ def _build_creation_operator(self, orbidx): # -- Collect non-zero elements idx = orbocc == 0 - if self.symmetry is 'Free': + if self.symmetry is 'U(1)': I=numbers_new[idx] J=self.numbers[idx] else: diff --git a/pyed/TriqsExactDiagonalization.py b/pyed/TriqsExactDiagonalization.py index fdf4650..fbb5e94 100644 --- a/pyed/TriqsExactDiagonalization.py +++ b/pyed/TriqsExactDiagonalization.py @@ -55,8 +55,8 @@ def get_expectation_value(self, op): return self.ed.get_expectation_value(self.rep.sparse_matrix(op)) # ------------------------------------------------------------------ - def get_free_energy(self): - return self.ed.get_free_energy() + def get_U(1)_energy(self): + return self.ed.get_U(1)_energy() def get_partition_function(self): return self.ed.get_partition_function() def get_density_matrix(self): From 978d7eb6ad1c36be479cc21478028349e8e42fdd Mon Sep 17 00:00:00 2001 From: yaros72 Date: Sat, 30 Mar 2019 11:49:37 +0300 Subject: [PATCH 27/33] update --- pyed/SparseExactDiagonalization.py | 68 +++++++++++++++++------------- 1 file changed, 39 insertions(+), 29 deletions(-) diff --git a/pyed/SparseExactDiagonalization.py b/pyed/SparseExactDiagonalization.py index 9024865..5cca306 100644 --- a/pyed/SparseExactDiagonalization.py +++ b/pyed/SparseExactDiagonalization.py @@ -30,8 +30,8 @@ class SparseExactDiagonalization(object): function calculator. """ # ------------------------------------------------------------------ - def __init__(self, H,blocks, beta, - nstates=None, hermitian=True,symmetry='SU(2)', + def __init__(self, H,blocks, beta,occupation, + symmetry='SU(2)',nstates=None, hermitian=True, v0=None, tol=0): self.v0 = v0 @@ -42,39 +42,49 @@ def __init__(self, H,blocks, beta, self.symmetry=symmetry self.H = H + self.occupation = occupation self.blocks=blocks self.beta = beta - self._diagonalize_hamiltonian() - self._calculate_partition_function() - self._calculate_density_matrix() + if beta==np.inf: + self._diagonalize_hamiltonian() + else: + self._diagonalize_hamiltonian() + self._calculate_partition_function() + self._calculate_density_matrix() # ------------------------------------------------------------------ def _diagonalize_hamiltonian(self): - - if self.symmetry=='U(1)': - - self.E, self.U = np.linalg.eigh(self.H.todense()) - self.U = csr_matrix(self.U) - self.E0 = np.min(self.E) - self.E = self.E - self.E0 - + if self.beta==np.inf: + self.H_blocks={};self.numbers=np.zeros(self.H.shape[0]) + for i in range(self.nfermions+1): + self.H_blocks[i]=self.H[self.blocks[i]][:,self.blocks[i]] + self.numbers[self.blocks[i]]=i + self.E0,self.U0=eigsh_sparse(self.H,k=1,which='SA') + self.U0=csr_matrix(self.U0) + self.N=int((self.U0.getH()*self.occupation*self.U0).data[0]+0.1) + self.U0=np.matrix(self.U0[self.blocks[self.N]]) else: - - self.U=csr_matrix(self.H.shape,dtype=np.float) - self.E=np.zeros(self.H.shape[0]) - print 'Hamiltonian diagonalization:' - bar = progressbar.ProgressBar() - for i in bar(range(len(self.blocks))): - block=self.blocks[i] - X,Y=np.meshgrid(block,block) - E,U=np.linalg.eigh(self.H[X,Y].todense()) - self.E[block]=E - self.U[Y,X]=U - del X,Y - self.E=np.array(self.E) - self.E0 = np.min(self.E) - self.E = self.E-self.E0 + if self.symmetry=='U(1)': + self.E, self.U = np.linalg.eigh(self.H.todense()) + self.U = csr_matrix(self.U) + self.E0 = np.min(self.E) + self.E = self.E - self.E0 + else: + self.U=csr_matrix(self.H.shape,dtype=np.float) + self.E=np.zeros(self.H.shape[0]) + print 'Hamiltonian diagonalization:' + bar = progressbar.ProgressBar() + for i in bar(range(len(self.blocks))): + block=self.blocks[i] + X,Y=np.meshgrid(block,block) + E,U=np.linalg.eigh(self.H[X,Y].todense()) + self.E[block]=E + self.U[Y,X]=U + del X,Y + self.E=np.array(self.E) + self.E0 = np.min(self.E) + self.E = self.E-self.E0 # ------------------------------------------------------------------ def _calculate_partition_function(self): @@ -103,7 +113,7 @@ def get_expectation_value(self, operator): op=self._operators_to_eigenbasis([operator])[0] return (op.diagonal()*np.exp(-self.beta * self.E)).sum()/self.Z # ------------------------------------------------------------------ - def get_U(1)_energy(self): + def get_free_energy(self): r""" U(1) energy using ground state energy shift From df0c0cb0bab56452f8d6df7a12fff6c049a256e6 Mon Sep 17 00:00:00 2001 From: yaros72 Date: Sat, 30 Mar 2019 11:58:56 +0300 Subject: [PATCH 28/33] xi update --- pyed/SparseExactDiagonalization.py | 77 ++++++++++-------------------- 1 file changed, 26 insertions(+), 51 deletions(-) diff --git a/pyed/SparseExactDiagonalization.py b/pyed/SparseExactDiagonalization.py index 5cca306..34839a9 100644 --- a/pyed/SparseExactDiagonalization.py +++ b/pyed/SparseExactDiagonalization.py @@ -30,61 +30,31 @@ class SparseExactDiagonalization(object): function calculator. """ # ------------------------------------------------------------------ - def __init__(self, H,blocks, beta,occupation, - symmetry='SU(2)',nstates=None, hermitian=True, - v0=None, tol=0): - - self.v0 = v0 - self.tol = tol - - self.nstates = nstates - self.hermitian = hermitian - self.symmetry=symmetry - + def __init__(self, H,blocks, beta): self.H = H - self.occupation = occupation self.blocks=blocks self.beta = beta - if beta==np.inf: - self._diagonalize_hamiltonian() - else: - self._diagonalize_hamiltonian() - self._calculate_partition_function() - self._calculate_density_matrix() + self._diagonalize_hamiltonian() + self._calculate_partition_function() + # self._calculate_density_matrix() # ------------------------------------------------------------------ def _diagonalize_hamiltonian(self): - if self.beta==np.inf: - self.H_blocks={};self.numbers=np.zeros(self.H.shape[0]) - for i in range(self.nfermions+1): - self.H_blocks[i]=self.H[self.blocks[i]][:,self.blocks[i]] - self.numbers[self.blocks[i]]=i - self.E0,self.U0=eigsh_sparse(self.H,k=1,which='SA') - self.U0=csr_matrix(self.U0) - self.N=int((self.U0.getH()*self.occupation*self.U0).data[0]+0.1) - self.U0=np.matrix(self.U0[self.blocks[self.N]]) - else: - if self.symmetry=='U(1)': - self.E, self.U = np.linalg.eigh(self.H.todense()) - self.U = csr_matrix(self.U) - self.E0 = np.min(self.E) - self.E = self.E - self.E0 - else: - self.U=csr_matrix(self.H.shape,dtype=np.float) - self.E=np.zeros(self.H.shape[0]) - print 'Hamiltonian diagonalization:' - bar = progressbar.ProgressBar() - for i in bar(range(len(self.blocks))): - block=self.blocks[i] - X,Y=np.meshgrid(block,block) - E,U=np.linalg.eigh(self.H[X,Y].todense()) - self.E[block]=E - self.U[Y,X]=U - del X,Y - self.E=np.array(self.E) - self.E0 = np.min(self.E) - self.E = self.E-self.E0 + self.U=csr_matrix(self.H.shape,dtype=np.float) + self.E=np.zeros(self.H.shape[0]) + print 'Hamiltonian diagonalization:' + bar = progressbar.ProgressBar() + for i in bar(range(len(self.blocks))): + block=self.blocks[i] + X,Y=np.meshgrid(block,block) + E,U=np.linalg.eigh(self.H[X,Y].todense()) + self.E[block]=E + self.U[Y,X]=U + del X,Y + self.E=np.array(self.E) + self.E0 = np.min(self.E) + self.E = self.E-self.E0 # ------------------------------------------------------------------ def _calculate_partition_function(self): @@ -132,7 +102,12 @@ def get_partition_function(self): # ------------------------------------------------------------------ def get_density_matrix(self): - return self.rho + try: + return self.rho + except: + self._calculate_density_matrix() + return self.rho + # ------------------------------------------------------------------ def get_eigen_values(self): @@ -287,7 +262,7 @@ def get_timeordered_three_tau_greens_function(self, taus, ops): et_b = np.exp((t2[i]-t1[i])*E).flatten()[:,None] et_c = np.exp((t3[i]-t2[i])*E).flatten()[:,None] et_d = np.exp((-t3[i])*E).flatten()[:,None] - G[i]=(op1.multiply(et_a)*op2.multiply(et_b)*op3.multiply(et_c)*op4.multiply(et_d)).sum() + G[i]=(op1.multiply(et_a)*op2.multiply(et_b)*op3.multiply(et_c)*op4.multiply(et_d)).diagonal().sum() G /= self.Z return G @@ -322,7 +297,7 @@ def get_frequency_greens_function_component(self, iwn, op1, op2, xi): # -- Compute Lehman sum for all operator combinations G = np.zeros((len(iwn)), dtype=np.complex) op=(op1_eig.getH().multiply(op2_eig)).tocoo() - M=(np.exp(-self.beta*self.E[op.row])+np.exp(-self.beta*self.E[op.col]))*op.data + M=(np.exp(-self.beta*self.E[op.row])-xi*np.exp(-self.beta*self.E[op.col]))*op.data E=(self.E[op.row]-self.E[op.col]) bar = progressbar.ProgressBar() for i in bar(range(len(iwn))): From e0f84209f3a7821103da01d37b5add52824651f4 Mon Sep 17 00:00:00 2001 From: yaros72 Date: Sat, 30 Mar 2019 12:00:57 +0300 Subject: [PATCH 29/33] update --- pyed/SparseMatrixFockStates.py | 49 ++++++++++--------------------- pyed/TriqsExactDiagonalization.py | 13 ++++---- 2 files changed, 21 insertions(+), 41 deletions(-) diff --git a/pyed/SparseMatrixFockStates.py b/pyed/SparseMatrixFockStates.py index 90cadef..24cc0dc 100644 --- a/pyed/SparseMatrixFockStates.py +++ b/pyed/SparseMatrixFockStates.py @@ -21,7 +21,7 @@ class SparseMatrixRepresentation(object): creation operators. """ # ------------------------------------------------------------------ - def __init__(self, fundamental_operators,symmetry='SU(2)'): + def __init__(self, fundamental_operators): self.fundamental_operators = fundamental_operators @@ -49,9 +49,8 @@ def __init__(self, fundamental_operators,symmetry='SU(2)'): (dag, list(idx)) for dag, idx in self.operator_labels ] self.nfermions = len(self.operator_labels) - self.symmetry=symmetry self.sparse_operators = \ - SparseMatrixCreationOperators(self.nfermions,self.symmetry) + SparseMatrixCreationOperators(self.nfermions) self.blocks=self.sparse_operators.blocks # ------------------------------------------------------------------ def sparse_matrix(self, triqs_operator_expression): @@ -85,11 +84,10 @@ class SparseMatrixCreationOperators: creation operators, for finite number of fermions. """ # ------------------------------------------------------------------ - def __init__(self, nfermions,symmetry): + def __init__(self, nfermions): self.nfermions = nfermions self.nstates = 2**nfermions - self.symmetry=symmetry # -- Make python based fock states self.numbers = np.arange(self.nstates, dtype=np.uint32) @@ -105,30 +103,16 @@ def __init__(self, nfermions,symmetry): indexes_const=[] self.blocks=[] - if self.symmetry== 'SU(2)': - for n_up in range(self.nfermions/2+1): - for n_down in range(self.nfermions/2+1): - indexes=np.where((np.sum(states_up,axis=1)==n_up)&(np.sum(states_down,axis=1)==n_down))[0] - indexes_const.append(indexes) - self.permutation=np.zeros(self.nstates) - self.permutation[np.concatenate(indexes_const,axis=0)]=np.arange(self.nstates) - self.permutation=np.array(self.permutation,dtype=np.int) - for n_up in range(self.nfermions/2+1): - for n_down in range(self.nfermions/2+1): - indexes=np.where((np.sum(states_up,axis=1)==n_up)&(np.sum(states_down,axis=1)==n_down))[0].flatten() - self.blocks.append(self.permutation[indexes]) - - elif self.symmetry=='Sz': - - for n in range(self.nfermions+1): - indexes=np.where(np.sum(states_up+states_down,axis=1)==n)[0] + for n_up in range(self.nfermions/2+1): + for n_down in range(self.nfermions/2+1): + indexes=np.where((np.sum(states_up,axis=1)==n_up)&(np.sum(states_down,axis=1)==n_down))[0] indexes_const.append(indexes) - self.permutation=np.zeros(self.nstates) - self.permutation[np.concatenate(indexes_const,axis=0)]=np.arange(self.nstates) - self.permutation=np.array(self.permutation,dtype=np.int) - - for n in range(self.nfermions+1): - indexes=np.where(np.sum(states_up+states_down,axis=1)==n)[0].flatten() + self.permutation=np.zeros(self.nstates) + self.permutation[np.concatenate(indexes_const,axis=0)]=np.arange(self.nstates) + self.permutation=np.array(self.permutation,dtype=np.int) + for n_up in range(self.nfermions/2+1): + for n_down in range(self.nfermions/2+1): + indexes=np.where((np.sum(states_up,axis=1)==n_up)&(np.sum(states_down,axis=1)==n_down))[0].flatten() self.blocks.append(self.permutation[indexes]) self.c_dag = [] @@ -163,12 +147,9 @@ def _build_creation_operator(self, orbidx): # -- Collect non-zero elements idx = orbocc == 0 - if self.symmetry is 'U(1)': - I=numbers_new[idx] - J=self.numbers[idx] - else: - I = self.permutation[numbers_new[idx]] - J = self.permutation[self.numbers[idx]] + + I = self.permutation[numbers_new[idx]] + J = self.permutation[self.numbers[idx]] D = sign[idx] diff --git a/pyed/TriqsExactDiagonalization.py b/pyed/TriqsExactDiagonalization.py index fbb5e94..03b1eaf 100644 --- a/pyed/TriqsExactDiagonalization.py +++ b/pyed/TriqsExactDiagonalization.py @@ -15,7 +15,7 @@ from pytriqs.gf import MeshImTime, MeshProduct, Idx from pytriqs.operators import dagger from pytriqs.utility import mpi - +from pytriqs.operators import c, c_dag,dagger # ---------------------------------------------------------------------- from pyed.CubeTetras import CubeTetrasMesh, enumerate_tau3, Idxs @@ -44,19 +44,18 @@ class TriqsExactDiagonalization(object): """ Exact diagonalization for Triqs operator expressions. """ # ------------------------------------------------------------------ - def __init__(self, H, fundamental_operators, beta,symmetry='SU(2)'): - self.symmetry=symmetry + def __init__(self, H, fundamental_operators, beta): self.beta = beta - self.rep = SparseMatrixRepresentation(fundamental_operators,symmetry=symmetry) - self.ed = SparseExactDiagonalization(self.rep.sparse_matrix(H), self.rep.blocks,beta,symmetry=symmetry) + self.rep = SparseMatrixRepresentation(fundamental_operators) + self.ed = SparseExactDiagonalization(self.rep.sparse_matrix(H), self.rep.blocks,beta) # ------------------------------------------------------------------ def get_expectation_value(self, op): return self.ed.get_expectation_value(self.rep.sparse_matrix(op)) # ------------------------------------------------------------------ - def get_U(1)_energy(self): - return self.ed.get_U(1)_energy() + def get_free_energy(self): + return self.ed.get_free_energy() def get_partition_function(self): return self.ed.get_partition_function() def get_density_matrix(self): From 2ac73c4363b90204a4c194edb50ca9a48b7e6ccc Mon Sep 17 00:00:00 2001 From: yaros72 Date: Sat, 30 Mar 2019 17:12:14 +0300 Subject: [PATCH 30/33] update --- doc/Anderson.ipynb | 108 ++++++++++++++++++----------- doc/Documentation.ipynb | 87 ++++++++++++++--------- pyed/SparseExactDiagonalization.py | 21 +++--- 3 files changed, 132 insertions(+), 84 deletions(-) diff --git a/doc/Anderson.ipynb b/doc/Anderson.ipynb index 836c006..0f902eb 100644 --- a/doc/Anderson.ipynb +++ b/doc/Anderson.ipynb @@ -25,14 +25,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "H_loc = -0.5*c_dag('dn',0)*c('dn',0) + -0.5*c_dag('up',0)*c('up',0) + 1*c_dag('dn',0)*c_dag('up',0)*c('up',0)*c('dn',0)\n" + "H_loc = -2*c_dag('dn',0)*c('dn',0) + -2*c_dag('up',0)*c('up',0) + 4*c_dag('dn',0)*c_dag('up',0)*c('up',0)*c('dn',0)\n" ] } ], @@ -42,7 +42,7 @@ "n_up = c_dag(up, 0) * c(up, 0)\n", "n_down = c_dag(down, 0) * c(down, 0)\n", "\n", - "U = 1\n", + "U = 4\n", "mu = U/2.\n", "\n", "H_loc = U * n_up * n_down - mu * (n_up + n_down)\n", @@ -59,29 +59,29 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "from itertools import product\n", - "ek = [-1.0,-0.5, 0.5,1.0]\n", - "V = [0.25,0.5, 0.5,0.25]\n", + "ek = [0]\n", + "V = [-1]\n", "H_hyb=sum(V[i]*(c_dag(s,i+1)*c(s,0)+c_dag(s,0)*c(s,i+1)) for s, i in product(['up','dn'], range(len(ek))))\n", "H_hyb+=sum(ek[i]*(c_dag(s,i+1)*c(s,i+1)) for s, i in product(['up','dn'], range(len(ek))))" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.25*c_dag('dn',0)*c('dn',4) + 0.5*c_dag('dn',0)*c('dn',3) + 0.5*c_dag('dn',0)*c('dn',2) + 0.25*c_dag('dn',0)*c('dn',1) + -1*c_dag('dn',1)*c('dn',1) + 0.25*c_dag('dn',1)*c('dn',0) + -0.5*c_dag('dn',2)*c('dn',2) + 0.5*c_dag('dn',2)*c('dn',0) + 0.5*c_dag('dn',3)*c('dn',3) + 0.5*c_dag('dn',3)*c('dn',0) + 1*c_dag('dn',4)*c('dn',4) + 0.25*c_dag('dn',4)*c('dn',0) + 0.25*c_dag('up',0)*c('up',4) + 0.5*c_dag('up',0)*c('up',3) + 0.5*c_dag('up',0)*c('up',2) + 0.25*c_dag('up',0)*c('up',1) + -1*c_dag('up',1)*c('up',1) + 0.25*c_dag('up',1)*c('up',0) + -0.5*c_dag('up',2)*c('up',2) + 0.5*c_dag('up',2)*c('up',0) + 0.5*c_dag('up',3)*c('up',3) + 0.5*c_dag('up',3)*c('up',0) + 1*c_dag('up',4)*c('up',4) + 0.25*c_dag('up',4)*c('up',0)" + "-1*c_dag('dn',0)*c('dn',1) + -1*c_dag('dn',1)*c('dn',0) + -1*c_dag('up',0)*c('up',1) + -1*c_dag('up',1)*c('up',0)" ] }, - "execution_count": 3, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -99,16 +99,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Starting on 1 Nodes at : 2018-12-19 17:52:11.596385\n", - "/usr/local/lib/python2.7/site-packages/scipy/sparse/compressed.py:746: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n", - " SparseEfficiencyWarning)\n", "100% |########################################################################|\r" ] }, @@ -117,8 +114,8 @@ "output_type": "stream", "text": [ "Hamiltonian diagonalization:\n", - "Z = 2.7189992931258677\n", - "Omega= -4.170619031319008\n" + "Z = 1.0\n", + "Omega= -3.236067977499788\n" ] }, { @@ -131,7 +128,7 @@ ], "source": [ "import numpy as np\n", - "beta = 20.0 # inverse temperature\n", + "beta = 50.0 # inverse temperature\n", "fundamental_operators = np.array([[c(up,i), c(down,i)] for i in range(len(ek)+1)]).flatten()\n", "\n", "from pyed.TriqsExactDiagonalization import TriqsExactDiagonalization\n", @@ -150,16 +147,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " = 0.5000000000000032\n", - " = 0.49999999999999495\n", - " = 0.18296952428699315\n" + " = 0.4999999999999998\n", + " = 0.4999999999999995\n", + " = 0.13819660112501048\n" ] } ], @@ -178,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -190,7 +187,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -208,13 +205,12 @@ "\n", "import matplotlib.pyplot as plt\n", "from pytriqs.plot.mpl_interface import oplot\n", - "%matplotlib inline\n", "plt.figure(); oplot(g_tau); plt.savefig('figure_g_tau.png')" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 30, "metadata": { "scrolled": true }, @@ -228,7 +224,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -241,15 +237,15 @@ ], "source": [ "from pytriqs.gf import GfImTime\n", - "densdens_tau = GfImTime(name=r'$\\langle n(\\tau) n(0) \\rangle$', beta=beta, statistic='Boson', n_points=60, indices=[1]) \n", - "ed.set_g2_tau(densdens_tau, n_up, n_down)\n", + "densdens_tau = GfImTime(name=r'$\\langle n(\\tau) n(0) \\rangle$', beta=beta, statistic='Boson', n_points=1001, indices=[1]) \n", + "ed.set_g2_tau(densdens_tau, n_up, n_up)\n", "\n", "plt.figure(); oplot(densdens_tau); plt.savefig('figure_densdens_tau.png')" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -261,7 +257,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -289,20 +285,20 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "from pytriqs.gf import Gf\n", "from pytriqs.gf import MeshImTime, MeshProduct\n", "\n", - "ntau = 21\n", + "ntau = 10\n", "imtime = MeshImTime(beta, 'Fermion', ntau)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -313,12 +309,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -348,22 +344,22 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Text(54.01,0.5,'$\\\\tau_2$')" + "Text(60.385,0.5,'$\\\\tau_2$')" ] }, - "execution_count": 12, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -377,13 +373,45 @@ "source": [ "plt.figure(figsize=(8,8))\n", "plt.imshow(data.real)\n", - "plt.xticks(range(21))\n", - "plt.yticks(range(21))\n", + "plt.xticks(range(10))\n", + "plt.yticks(range(10))\n", "plt.savefig('im_g3pp_tau.png')\n", "plt.colorbar()\n", "plt.xlabel(r'$\\tau_1$')\n", "plt.ylabel(r'$\\tau_2$')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pytriqs.applications.impurity_solvers.cthyb import Solver\n", + "from pytriqs.gf import *\n", + "\n", + "\n", + "# Construct the impurity solver with the inverse temperature\n", + "# and the structure of the Green's functions\n", + "S = Solver(beta = beta, gf_struct = [ ['up',[0]], ['down',[0]] ], n_l = 100)\n", + "\n", + "# Initialize the non-interacting Green's function S.G0_iw\n", + "for name, g0 in S.G0_iw: g0 << inverse(iOmega_n+inverse(iOmega_n))\n", + "\n", + "# Run the solver. The results will be in S.G_tau, S.G_iw and S.G_l\n", + "S.solve(h_int = U * n_up * n_down, # Local Hamiltonian\n", + " n_cycles = 500000, # Number of QMC cycles\n", + " length_cycle = 200, # Length of one cycle\n", + " n_warmup_cycles = 10000, # Warmup cycles\n", + " measure_G_l = True) # Measure G_l" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/doc/Documentation.ipynb b/doc/Documentation.ipynb index d1ae542..8f3e67b 100644 --- a/doc/Documentation.ipynb +++ b/doc/Documentation.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -29,27 +29,27 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "H = -0.1*c_dag(0,0)*c(0,0) + -0.1*c_dag(1,0)*c(1,0) + 1*c_dag(0,0)*c_dag(1,0)*c(1,0)*c(0,0)\n" + "H = -0.2*c_dag('down',0)*c('down',0) + -0.2*c_dag('up',0)*c('up',0) + 1*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n" ] } ], "source": [ "from pytriqs.operators import c, c_dag\n", - "up, down = 0, 1\n", + "up, down = 'up', 'down'\n", "n_up = c_dag(up, 0) * c(up, 0)\n", "n_down = c_dag(down, 0) * c(down, 0)\n", "\n", - "U = 1.0\n", - "mu = 0.1\n", + "U = 1\n", + "mu =-0.2*U\n", "\n", - "H = U * n_up * n_down - mu * (n_up + n_down)\n", + "H = U * n_up * n_down + mu * (n_up + n_down)\n", "\n", "print 'H =', H" ] @@ -77,14 +77,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Starting on 1 Nodes at : 2018-12-19 17:48:02.624435\n", "100% |########################################################################|\r" ] }, @@ -93,13 +92,13 @@ "output_type": "stream", "text": [ "Hamiltonian diagonalization:\n", - "Z = 2.984029641299568\n", - "\\Omega = -0.6466373078517809\n", + "Z = 2.4900911680955877\n", + "\\Omega = -0.42807983087483964\n", "\\rho =\n", - " (0, 0)\t0.27437085133022276\n", - " (1, 1)\t0.33511731457348803\n", - " (2, 2)\t0.33511731457348803\n", - " (3, 3)\t0.05539451952280125\n" + " (0, 0)\t0.1804467924204023\n", + " (1, 1)\t0.40159172194678966\n", + " (2, 2)\t0.40159172194678966\n", + " (3, 3)\t0.016369763686018366\n" ] }, { @@ -111,7 +110,7 @@ } ], "source": [ - "beta = 2.0 # inverse temperature\n", + "beta = 4.0 # inverse temperature\n", "fundamental_operators = [c(up,0), c(down,0)]\n", "\n", "from pyed.TriqsExactDiagonalization import TriqsExactDiagonalization\n", @@ -138,16 +137,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " = 0.39051183409628926\n", - " = 0.39051183409628926\n", - " = 0.05539451952280125\n" + " = 0.41796148563280805\n", + " = 0.41796148563280805\n", + " = 0.016369763686018366\n" ] } ], @@ -178,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -187,15 +186,28 @@ "text": [ "100% |########################################################################|\n" ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ "from pytriqs.gf import GfImTime\n", - "g_tau = GfImTime(name=r'$g$', beta=beta, statistic='Fermion', n_points=50, indices=[1]) \n", + "g_tau = GfImTime(name=r'$g$', beta=beta, statistic='Fermion', n_points=500, indices=[1]) \n", "ed.set_g2_tau(g_tau, c(up,0), c_dag(up,0))\n", "\n", "import matplotlib.pyplot as plt\n", "from pytriqs.plot.mpl_interface import oplot\n", + "%matplotlib inline\n", "\n", "plt.figure(); oplot(g_tau); plt.savefig('figure_g_tau.png')" ] @@ -209,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -221,7 +233,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEKCAYAAADenhiQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHaRJREFUeJzt3XuQVeW55/Hvj5tI1NCAF6QlYMQLt4PaYmISb1zV0UaTY5mYCZ6QIjm51MSkZsQyExKjhsxJJpejSYrxmCIVFBI9CiYqAaJhTEaxSRCaRsSjcuwOinbjrRQw+MwfezXubnZ379299qWb36dqV6/1rne9++m1N/3wrnet9SoiMDMzS1O/cgdgZmZ9j5OLmZmlzsnFzMxS5+RiZmapc3IxM7PUObmYmVnqnFzMzCx1Ti5mZpY6JxczM0vdgHIHUC4jRoyIMWPGlDsMM7NeZcOGDa9ExNFd1Ttkk8uYMWOoq6srdxhmZr2KpB351PNpMTMzS52Ti5mZpc7JxczMUnfIjrmYWfe88847NDY2smfPnnKHYkU0ePBgqqurGThwYLf2d3Ixs4I0NjZy5JFHMmbMGCSVOxwrgoigubmZxsZGxo4d2602Kua0mKTZkrZJekbSghzbD5O0PNn+uKQxWduuT8q3SZpVyrjNDjV79uxh+PDhTix9mCSGDx/eo95pRSQXSf2B24CLgPHAJyWNb1dtHrA7Ik4Cfgh8L9l3PHAVMAGYDfw0ac/MisSJpe/r6WdcKafFpgLPRMSzAJKWAbVAQ1adWuBbyfLdwK3K/Pa1wLKI2As8J+mZpL3/V5RIH1wAL24uStNmvcLE/wGvVMqfDivYwMPh/dVFf5uK6LkAo4AXstYbk7KcdSLi78BrwPA89wVA0nxJdZLqXn755ZRCNzOz9g6p/35ExGJgMUBNTU10q5GLFqUZklnvs3UrjBhX7iiswlVKz6UJOCFrvTopy1lH0gDg/UBznvuaWR/Tv39/pkyZwsSJE7n00kt59dVXC27jwgsv5O9//3undd5++23OO+889u/f32Gdffv2ce6553bZVq72HnroIU455RROOukkFi1alLO93bt3c/nll3fYZq42OlNo/e6olOTyBDBO0lhJg8gM0K9sV2clMDdZ/gTwh4iIpPyq5GqyscA4YH2J4jazMjn88MPZuHEj9fX1DBs2jNtuu62g/bds2cLw4cMZMKDzEzh33HEHV1xxBf37d3yd0KBBg5g2bRrLly/v8n2z29u/fz9f+tKXePDBB2loaOCuu+6ioaHhoPaqqqpoaWmhubn5oPY6aqMjhdbvropILskYypeBVcBW4NcRsUXSjZIuS6r9GzA8GbD/GrAg2XcL8Gsyg/8PAV+KiI7/i2Fmfc6HP/xhmpoyJyx+9atfMXXqVKZMmcLnP//5DnscK1asYM6cOQfWr7jiCr7xjW9w7rnnMnr0aNasWQPA0qVLqa2tBeD111/n9NNPZ8KECQwZMoQpU6bwoQ99iHfffZc5c+awdOnSgtpbv349J510EieeeCKDBg3iqquuYsWKFQAHtXfJJZdw//33H/R7dNZGLoXW766KGXOJiAeAB9qVfTNreQ/wjx3sezNwc1EDNLODfPv+LTT87fVU2xx//FEsvHRC3vX379/P2rVrmTdvHlu3bmX58uX86U9/YuDAgXzxi19k6dKlfOYznzlovwceeIDf/va3B9Y3b97MOeecw7p167j33ntZunQp5557Ls8++yyt03McddRR/PWvf2X9+vXcfPPNbf4oT5w4kSeeeKKg9pqamjjhhPfO6ldXV/P444/nbK+2tpbrrruOa665ps3v0VkbuRRav7sqJrmYmRXi7bffZsqUKTQ1NXHaaacxY8YMfvazn7FhwwbOOuusA3WOOeaYg/Z966232LdvH0OHDj2w/tprr3HttdcCmUfcDB06lFdeeeVAnWz19fVMmNA2Afbv359Bgwbxxhtv0L9//4LayyW7vSOPPJJTTjmFbdu25X+AyszJxcy6rZAeRtpax1zeeustZs2axW233YYk5s6dy3e/+91O9x0yZAiSePPNNzniiCNoaGjgzDPPPDCusmnTJiZOnMjhhx+e8y71hoYGzjjjjIPK9+7dy+DBg3nyySfzam/UqFG88MJ7d1I0NjYyatSog9oD2LFjR85HsXTVRk/rd1dFjLmYmXXXkCFD+MlPfsIPfvADzjvvPO6++2527doFQEtLCzt25J7batasWTz00ENA5hTWlClTDmzbtGkTkydPpqqqiv379x+UYP72t79x3HHHtSlrbm5mxIgRDBw4MO/2zjrrLLZv385zzz3Hvn37WLZsGZdddtlB7UFmjKh1rCZbZ23kUmj97nJyMbNe7/TTT2fy5Mk8+eST3HTTTcycOZPJkyczY8YMdu7cmXOf2tpa7rvvPuDg5FJfX8/EiRMBmDlzJo8++mibfWfNmsW8efP44x//eKDs4Ycf5pJLLimovQEDBnDrrbcya9YsTjvtNK688soDp9uy2wO4//77cyaXztrIpdD63RYRh+TrzDPPDDMrXENDQ7lDSM2kSZPinXfe6bTOhg0b4tOf/nSXbV1++eWxbdu2Lut1p72Wlpb42Mc+1uU+acv1WQN1kcffWPdczOyQtWnTpi7vcznjjDO44IILuryJcs6cOZx88sldvmd32quqqmLdunVdtl1JlElEh56ampqoq6srdxhmvc7WrVs57bTTyh2GdaG5uZlp06YdVL527VqGDx+eVxu5PmtJGyKipqt9fbWYmVkfNHz4cDZu3Fi29/dpMTMzS52Ti5mZpc7JxczMUufkYmZmqXNyMTOz1Dm5mJlZ6pxczMwsdU4uZtYrHXHEET3aP58pjqHvTXNciimOwcnFzA5B+U5xDH1rmuNSTXEMTi5m1os9//zznHrqqVxzzTWcfPLJXH311axZs4aPfOQjjBs3jvXr1+fcL98pjqF70xzn216ppzku1RTH4ORiZr3cM888w9e//nWeeuopnnrqKe68804effRRvv/973PLLbfk3OeBBx5o8zj7zZs3M3ToUNatW8ePf/zjA3/U9+3bl3Oa41/84hfMmDGDjRs38thjj9GvX7820xLn216uKYebmpqA3NMct04RkK2zNnpSt6f8bDEz674HF8CLm9Nt87hJcFH+YwFjx45l0qRJAEyYMIFp06YhiUmTJvH8888fVD/fKY6Bbk1z/NprrxXcXi69fZpj91zMrFc77LDDDiz369fvwHq/fv1yDrBnT3EMdDjFMdDpNMetdbLt3buXp59+Ou/2Sj3NcammOAb3XMysJwroYVSS1imOP/GJT+Sckrh1TCR7WuLWP/KQmeb44osvbtNm67TE9fX1ebeXPeXwqFGjWLZsGXfeeWeb9gqZ5rh9Gz2p21PuuZjZISffKY6h8GmOC2mv1NMcl2yKY/A0x2ZWmL4yzXE+UxxHeJrj9vA0x2ZmHctnimPwNMfd5WmOzawgnua48qUxxTH08mmOJQ0DlgNjgOeBKyNid456c4FvJKs3RcSSpPxm4DNAVUT07HkQZmZ9QLmnOIbKGNBfAKyNiHHA2mS9jSQBLQTOBqYCCyVVJZvvT8rMzKxCVEJyqQWWJMtLgDk56swCVkdES9KrWQ3MBoiIxyJiZ0kiNTOzvFRCcjk2Kzm8CBybo84o4IWs9cakzMzMKlBJxlwkrQGOy7HphuyViAhJRbvCQNJ8YD7A6NGji/U2Zn1eRCCp3GFYEfX0Yq+SJJeImN7RNkkvSRoZETsljQR25ajWBJyftV4NPNKNOBYDiyFztVih+5sZDB48mObmZoYPH+4E00dFBM3NzW2eSlCosl8tBqwE5gKLkp+5nv+8CrglaxB/JnB9acIzs2zV1dU0Njby8ssvlzsUK6LBgwdTXV3d7f0rIbksAn4taR6wA7gSQFIN8IWI+FxEtEj6DtD6/OkbI6Ilqfe/gE8BQyQ1ArdHxLdK/UuYHSoGDhyY8wGKZtl8E6WZmeUt35soK+FqMTMz62OcXMzMLHVOLmZmljonFzMzS52Ti5mZpc7JxczMUufkYmZmqXNyMTOz1Dm5mJlZ6pxczMwsdU4uZmaWOicXMzNLnZOLmZmlzsnFzMxS5+RiZmapc3IxM7PUObmYmVnqnFzMzCx1Ti5mZpY6JxczM0udk4uZmaXOycXMzFLn5GJmZqlzcjEzs9Q5uZiZWeqcXMzMLHVOLmZmlrqyJxdJwyStlrQ9+VnVQb25SZ3tkuYmZUMk/U7SU5K2SFpU2ujNzCyXgpOLpPdJ6p9iDAuAtRExDlibrLd/z2HAQuBsYCqwMCsJfT8iTgVOBz4i6aIUYzMzs27oMrlI6ifpU0kPYRfwFLBTUoOkf5F0Ug9jqAWWJMtLgDk56swCVkdES0TsBlYDsyPirYh4GCAi9gF/Aap7GI+ZmfVQPj2Xh4EPAtcDx0XECRFxDPBR4DHge5I+3YMYjo2Incnyi8CxOeqMAl7IWm9Myg6QNBS4lEzvx8zMymhAHnWmR8Q77QsjogW4B7hH0sDOGpC0Bjgux6Yb2rUZkiKPmNq3PwC4C/hJRDzbSb35wHyA0aNHF/o2ZmaWpy6TS2tikfTniDinszqdtDG9o22SXpI0MiJ2ShoJ7MpRrQk4P2u9Gngka30xsD0iftRFHIuTutTU1BScxMzMLD+FDOgPbl8g6WMpxLASmJsszwVW5KizCpgpqSoZyJ+ZlCHpJuD9wFdTiMXMzFKQz2mxVqdIuhfYAtQDLwG3kxmP6YlFwK8lzQN2AFcCSKoBvhARn4uIFknfAZ5I9rkxKasmc2rtKeAvkgBujYjbexiTmZn1gCLyOzskqR74J2AiMB44HlgVEb8sXnjFU1NTE3V1deUOw8ysV5G0ISJquqpXSM9lX0Q8wXu9BzMzs5wKGXM5r2hRmJlZn5LPTZQCiIg3uqpjZmYGed5EKekrktrcGCJpkKQLJS3hvau9zMzM8hpzmQ18FrhL0ljgVTKXJfcHfg/8KCL+WrwQzcyst8nnJso9wE+BnyZ34o8A3o6IV4sdnJmZ9U6FXC3Weif+zi4rmpnZIS2v5JLcFX8ZmScWnww8R+ZO+hURketxLWZmdgjrMrlI+negCvgdcF1EPJ0M7tcCv5I0KCLOL26YZmbWm+TTc/ls+/GViPhP4F+Bf00edW9mZnZAl5cit08s7Wei9MC+mZm1VwkzUZqZWR9TCTNRmplZH1OSmSjNzOzQktdMlJJOJXN1WOu89U3AyojY2lqneCGamVlvk8+Yy3XAMkDA+uQlMo+DWVDc8MzMrDfK57TYPGBC+96JpP9NZlbKRcUIzMzMeq98BvTfJTPrZHsjk21mZmZt5NNz+SqwVtJ24IWkbDRwEvDlYgVmZma9Vz4D+g9JOhmYStsB/SciYn8xgzMzs94pn2eLKSLeJXNPS2d1ItXIzMys1/JMlGZmlrpCZ6I8EdgNHE4mMXkmSjMzO4hnojQzs9TlPROlpAuBq4FXgXpJm4D6iNhbrODMzKx3KmSa4zvIXJY8EJhMZlbKCWQuSTYzMzugkOSyIyLuS5Z/U4xgzMysb8jnarFW6yRdK0lpBiBpmKTVkrYnP6s6qDc3qbNd0tys8ockPSlpi6SfZ09kZmZm5VFIchkP/DOZicJ+J+lmSf+YQgwLgLURMQ5Ym6y3IWkYsBA4m8zNnAuzktCVEfEPwETgaCCNmMzMrAfyTi4R8fGIOBkYC3wT2A58KIUYaoElyfISMmM57c0CVkdES0TsBlaTuUSaiHg9qTMAGAT4Zk4zszIrZMylVT9gY0RsSCmGYyNiZ7L8InBsjjqjeO+5ZgCNvPcoGiStItOjeRC4u6M3kjQfmA8wevTojqqZmVkP5TOfSz9Jn0pOhe0CtpE5NdYg6V8kdXm1mKQ1kupzvGqz6yWPkCm45xERs8g8pfkw4MJO6i2OiJqIqDn66KMLfRszM8tTPj2Xh4E1wPVk7mt5Fw6Mg1wAfE/SvRHxq44aiIjpHW2T9JKkkRGxU9JIYFeOak3A+Vnr1cAj7d5jj6QVZE6zrc7j9zIzsyLJJ7lMzzWNcUS0APcA9yR37nfXSjLPJluU/FyRo84q4JasQfyZwPWSjgCOTBLTAOAS4P/2IBYzM0tBl6fFWhOLpD93VaebFgEzkvlipifrSKqRdHvSfgvwHeCJ5HVjUvY+YGXytICNZHo9P+9BLGZmloJCBvQHty+Q9LGI6FFPISKagWk5yuuAz2Wt30HmKQHZdV4CzurJ+5uZWfoKSS6nSLoX2ALUAy8BtwMfLEZgZmbWexWSXJ4DbiFzs+KZwPHAt4sRlJmZ9W6FJJd9EdE65mFmZtahQh7/cl7RojAzsz4ln5soBRARb3RVx8zMDPLruTws6SuS2jwvRdIgSRdKWkLm/hQzMzMgvzGX2cBngbskjSUzE+VgoD/we+BHEfHX4oVoZma9TZfJJSL2AD8FfprciT8CeDsiXi12cGZm1jsV9FTk5E78nV1WNDOzQ1reyUXShcDVZE6L1QObyDzIcm+RYjMzs16qkJ7LHcBXgYHAZDKTek0AunzkvpmZHVoKSS47IuK+ZPk3xQjGzMz6hkJuolwn6Vrf02JmZl0ppOcyHpgEXCdpA5lH3G+MCPdizMysjS6Ti6R+EfFuRHw8WT+c9xLN2ZLuaZ2d0szMDPI7LbZa0nJJn5R0VES8DWwF3gCOBf5S1AjNzKzXyecmymmSxpOZm/53yY2UQWbq4R9GhJOLmZm1kdeYS0Q0AA3AdyUdnvRezMzMcirkajEAnFjMzKwrBScXMzOzrji5mJlZ6pxczMwsdU4uZmaWOicXMzNLnZOLmZmlzsnFzMxSV/bkImmYpNWStic/qzqoNzeps13S3BzbV0qqL37EZmbWlbInF2ABsDYixgFrk/U2JA0DFgJnA1OBhdlJSNIVwJulCdfMzLpSCcmlFliSLC8hM8Nle7OA1RHREhG7gdXAbABJRwBfA24qQaxmZpaHSkgux0bEzmT5RTJPWm5vFPBC1npjUgbwHeAHwFtFi9DMzApSyGRh3SZpDXBcjk03ZK9EREiKAtqdAnwwIq6VNCaP+vOB+QCjR4/O923MzKxAJUkuETG9o22SXpI0MiJ2ShoJ7MpRrQk4P2u9GngE+DBQI+l5Mr/LMZIeiYjzySEiFgOLAWpqavJOYmZmVphKOC22Emi9+msusCJHnVXATElVyUD+TGBVRPwsIo6PiDHAR4GnO0osZmZWOpWQXBYBMyRtB6Yn60iqkXQ7QES0kBlbeSJ53ZiUmZlZBVLEoXl2qKamJurq6sodhplZryJpQ0TUdFWvEnouZmbWxzi5mJlZ6pxczMwsdU4uZmaWOicXMzNLnZOLmZmlzsnFzMxS5+RiZmapc3IxM7PUObmYmVnqnFzMzCx1Ti5mZpY6JxczM0udk4uZmaXOycXMzFLn5GJmZqlzcjEzs9Q5uZiZWeqcXMzMLHVOLmZmljonFzMzS52Ti5mZpc7JxczMUufkYmZmqXNyMTOz1Dm5mJlZ6pxczMwsdWVPLpKGSVotaXvys6qDenOTOtslzc0qf0TSNkkbk9cxpYvezMxyKXtyARYAayNiHLA2WW9D0jBgIXA2MBVY2C4JXR0RU5LXrlIEbWZmHauE5FILLEmWlwBzctSZBayOiJaI2A2sBmaXKD4zMytQJSSXYyNiZ7L8InBsjjqjgBey1huTsla/SE6J/U9JKlKcZmaWpwGleBNJa4Djcmy6IXslIkJSFNj81RHRJOlI4B7gvwK/7CCO+cB8gNGjRxf4NmZmlq+SJJeImN7RNkkvSRoZETsljQRyjZk0AednrVcDjyRtNyU/35B0J5kxmZzJJSIWA4sBampqCk1iZmaWp0o4LbYSaL36ay6wIkedVcBMSVXJQP5MYJWkAZJGAEgaCPwXoL4EMZuZWScqIbksAmZI2g5MT9aRVCPpdoCIaAG+AzyRvG5Myg4jk2Q2ARvJ9HD+T+l/BTMzy6aIQ/PsUE1NTdTV1ZU7DDOzXkXShoio6apeJfRczMysj3FyMTOz1Dm5mJlZ6pxczMwsdU4uZmaWOicXMzNLnZOLmZmlzsnFzMxS5+RiZmapc3IxM7PUObmYmVnqnFzMzCx1Ti5mZpa6kkwW1pd8+/4tNPzt9XKHYWbWLeOPP4qFl04o+vu452JmZqlzz6VApcj4Zma9nXsuZmaWOicXMzNLnZOLmZmlzsnFzMxS5+RiZmapc3IxM7PUObmYmVnqnFzMzCx1iohyx1AWkl4GdnRz9xHAKymGkxbHVRjHVRjHVZi+GtcHIuLoriodssmlJyTVRURNueNoz3EVxnEVxnEV5lCPy6fFzMwsdU4uZmaWOieX7llc7gA64LgK47gK47gKc0jH5TEXMzNLnXsuZmaWOieXdiTNlrRN0jOSFuTYfpik5cn2xyWNydp2fVK+TdKsEsb0NUkNkjZJWivpA1nb9kvamLxWphVTAbFdI+nlrBg+l7VtrqTtyWtuieP6YVZMT0t6NWtbUY6ZpDsk7ZJU38F2SfpJEvMmSWdkbSvmseoqrquTeDZL+rOkf8ja9nxSvlFSXYnjOl/Sa1mf1TeztnX6+Rc5rv+eFVN98n0almwr5vE6QdLDyd+CLZL+W446pfuORYRfyQvoD/wHcCIwCHgSGN+uzheBnyfLVwHLk+XxSf3DgLFJO/1LFNMFwJBk+Z9bY0rW3yzz8boGuDXHvsOAZ5OfVclyVanialf/K8AdxT5mwLnAGUB9B9svBh4EBHwIeLzYxyrPuM5pfT/gota4kvXngRFlOl7nA7/t6eefdlzt6l4K/KFEx2skcEayfCTwdI5/jyX7jrnn0tZU4JmIeDYi9gHLgNp2dWqBJcny3cA0SUrKl0XE3oh4Dngmaa/oMUXEwxHxVrL6GFCdwvumElsnZgGrI6IlInYDq4HZZYrrk8BdKb13hyJiHdDSSZVa4JeR8RgwVNJIinusuowrIv6cvC+U8PuVx/HqSE++l2nHVZLvFkBE7IyIvyTLbwBbgVHtqpXsO+bk0tYo4IWs9UYO/nAO1ImIvwOvAcPz3LdYMWWbR+Z/Jq0GS6qT9JikOSnE053YPp50we+WdEKB+xYzLpJTiGOBP2QVF/OYdaajuIt5rArV/vsVwO8lbZA0vwzxfFjSk5IelNQ6B3lFHC9JQ8j8gb4nq7gkx0uZ0/WnA4+321Sy79iAnuxslUXSp4Ea4Lys4g9ERJOkE4E/SNocEf9RwrDuB+6KiL2SPk+m13dhCd+/K1cBd0fE/qyych+ziiTpAjLJ5aNZxR9NjtUxwGpJTyX/sy+Fv5D5rN6UdDFwHzCuRO+dj0uBP0VEdi+n6MdL0hFkEtpXI+L1NNsuhHsubTUBJ2StVydlOetIGgC8H2jOc99ixYSk6cANwGURsbe1PCKakp/PAo+Q+d9MWrqMLSKas+K5HTgz332LGVeWq2h32qLIx6wzHcVdzGOVF0mTyXx+tRHR3Fqedax2AfeSzqngvETE6xHxZrL8ADBQ0ggq4HglOvtuFeV4SRpIJrEsjYh/z1GldN+xYgws9dYXmZ7cs2ROk7QOBE5oV+dLtB3Q/3WyPIG2A/rPks6Afj4xnU5mAHNcu/Iq4LBkeQSwnXQHNvOJbWTW8uXAY8nyMOC5JMaqZHlYqeJK6p1KZoBVJTxmY+h4gPoS2g62ri/2scozrtFkxhDPaVf+PuDIrOU/A7NLGNdxrZ8dmT/S/5kcu7w+/2LFlWx/P5lxmfeV6nglv/svgR91Uqdk37HUDnZfeZG5muJpMn+sb0jKbiTTIwAYDPwm+ce2Hjgxa98bkv22AReVMKY1wEvAxuS1Mik/B9ic/OPaDMwrw/H6LrAlieFh4NSsfT+bHMdngH8qZVzJ+reARe32K9oxI/O/2J3AO2TOac8DvgB8Idku4LYk5s1ATYmOVVdx3Q7szvp+1SXlJybH6cnkM76hxHF9Oeu79RhZyS/X51+quJI615C5wCd7v2Ifr4+SGdPZlPVZXVyu75jv0Dczs9R5zMXMzFLn5GJmZqlzcjEzs9Q5uZiZWeqcXMzMLHVOLmZmljonFzMzS52fLWZWASQdBfyRzB3lY8ncALiHzI2B75YzNrPu8E2UZhVE0lQyd26n9oh4s3LwaTGzyjKRzKNBzHo1JxezyjIeyDl9rllv4uRiVlmOB14sdxBmPeXkYlZZVgH/Jum8LmuaVTAP6JuZWercczEzs9Q5uZiZWeqcXMzMLHVOLmZmljonFzMzS52Ti5mZpc7JxczMUufkYmZmqfv/W/+27YrlTU4AAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -234,7 +246,7 @@ ], "source": [ "from pytriqs.gf import GfImTime\n", - "densdens_tau = GfImTime(name=r'$\\langle n(\\tau) n(0) \\rangle$', beta=beta, statistic='Boson', n_points=50, indices=[1]) \n", + "densdens_tau = GfImTime(name=r'$\\langle n(\\tau) n(0) \\rangle$', beta=beta, statistic='Boson', n_points=1000, indices=[1]) \n", "ed.set_g2_tau(densdens_tau, n_up, n_down)\n", "\n", "plt.figure(); oplot(densdens_tau); plt.savefig('figure_densdens_tau.png')" @@ -256,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -268,7 +280,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -281,8 +293,8 @@ ], "source": [ "from pytriqs.gf import GfImFreq\n", - "g_iwn = GfImFreq(name=r'$g$', beta=beta, statistic='Fermion', n_points=10, indices=[1])\n", - "ed.set_g2_iwn(g_iwn, c(up,0), c_dag(up,0))\n", + "g_iwn = GfImFreq(name=r'$g$', beta=beta, statistic='Fermion', n_points=100, indices=[1])\n", + "ed.set_g2_iwn(g_iwn, n_up, n_down)\n", "\n", "plt.figure(); oplot(g_iwn); plt.savefig('figure_g_iwn.png')" ] @@ -309,14 +321,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "from pytriqs.gf import Gf\n", "from pytriqs.gf import MeshImTime, MeshProduct\n", "\n", - "ntau = 10\n", + "ntau = 20\n", "imtime = MeshImTime(beta, 'Fermion', ntau)\n", "prodmesh = MeshProduct(imtime, imtime, imtime)\n", "\n", @@ -342,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -361,12 +373,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -393,6 +405,13 @@ "plt.tight_layout()\n", "plt.savefig('figure_g3pp_tau.png')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/pyed/SparseExactDiagonalization.py b/pyed/SparseExactDiagonalization.py index 34839a9..ad14be4 100644 --- a/pyed/SparseExactDiagonalization.py +++ b/pyed/SparseExactDiagonalization.py @@ -19,6 +19,7 @@ from scipy.sparse.linalg import eigsh as eigsh_sparse from scipy.sparse import csr_matrix from scipy.sparse import diags +from tqdm import tqdm # ---------------------------------------------------------------------- from CubeTetras import CubeTetras @@ -44,8 +45,8 @@ def _diagonalize_hamiltonian(self): self.U=csr_matrix(self.H.shape,dtype=np.float) self.E=np.zeros(self.H.shape[0]) print 'Hamiltonian diagonalization:' - bar = progressbar.ProgressBar() - for i in bar(range(len(self.blocks))): + # bar = progressbar.ProgressBar() + for i in tqdm(range(len(self.blocks))): block=self.blocks[i] X,Y=np.meshgrid(block,block) E,U=np.linalg.eigh(self.H[X,Y].todense()) @@ -220,7 +221,7 @@ def get_timeordered_two_tau_greens_function(self, taus, ops): dops = self._operators_to_eigenbasis(ops) op1, op2, op3 = dops - for i in range(len(G)): + for i in tqdm(range(len(G))): et_a = np.exp((-self.beta + t1[i])*E).flatten()[:,None] et_b = np.exp((t2[i]-t1[i])*E).flatten()[:,None] et_c = np.exp((-t2[i])*E).flatten()[:,None] @@ -257,7 +258,7 @@ def get_timeordered_three_tau_greens_function(self, taus, ops): dops = self._operators_to_eigenbasis(ops) op1, op2, op3, op4 = dops - for i in range(len(G)): + for i in tqdm(range(len(G))): et_a = np.exp((-self.beta + t1[i])*E).flatten()[:,None] et_b = np.exp((t2[i]-t1[i])*E).flatten()[:,None] et_c = np.exp((t3[i]-t2[i])*E).flatten()[:,None] @@ -276,8 +277,8 @@ def get_tau_greens_function_component(self, tau, op1, op2): G = np.zeros((len(tau)), dtype=np.complex) op1_eig, op2_eig = self._operators_to_eigenbasis([op1, op2]) - bar = progressbar.ProgressBar() - for i in bar(range(len(tau))): + # bar = progressbar.ProgressBar() + for i in tqdm(range(len(tau))): et_p = np.exp((-self.beta + tau[i])*self.E)[:,None] et_m = np.exp(- tau[i]*self.E)[:,None] G[i] = - (op1_eig.multiply(et_p)*op2_eig.multiply(et_m)).diagonal().sum() @@ -299,8 +300,8 @@ def get_frequency_greens_function_component(self, iwn, op1, op2, xi): op=(op1_eig.getH().multiply(op2_eig)).tocoo() M=(np.exp(-self.beta*self.E[op.row])-xi*np.exp(-self.beta*self.E[op.col]))*op.data E=(self.E[op.row]-self.E[op.col]) - bar = progressbar.ProgressBar() - for i in bar(range(len(iwn))): + # bar = progressbar.ProgressBar() + for i in tqdm(range(len(iwn))): G[i]=np.sum(M/(iwn[i]-E)) G /= self.Z @@ -391,8 +392,8 @@ def get_real_frequency_greens_function_component(self, w, op1, op2, eta): op=(op1_eig.getH().multiply(op2_eig)).tocoo() M=(np.exp(-self.beta*self.E[op.row])+np.exp(-self.beta*self.E[op.col]))*op.data E=(self.E[op.row]-self.E[op.col]) - bar = progressbar.ProgressBar() - for i in bar(range(len(w))): + # bar = progressbar.ProgressBar() + for i in tqdm(range(len(w))): G[i]=np.sum(M/(w[i]+1j*eta-E)) G /= self.Z From bb3ef918ed1af2f72c4dc8c3224c65db53ceb37e Mon Sep 17 00:00:00 2001 From: yaros72 Date: Sat, 30 Mar 2019 17:12:20 +0300 Subject: [PATCH 31/33] update --- Anderson.ipynb | 422 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 422 insertions(+) create mode 100644 Anderson.ipynb diff --git a/Anderson.ipynb b/Anderson.ipynb new file mode 100644 index 0000000..c3e64c7 --- /dev/null +++ b/Anderson.ipynb @@ -0,0 +1,422 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Another example: Anderson impurity model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The calculation takes about an 10 minutes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hamiltonian\n", + "\n", + "As an example let us solve the Anderson impurity model local with Hamiltonian $H = U\\hat{n}_{\\uparrow} \\hat{n}_{\\downarrow} - \\mu ( \\hat{n}_{\\uparrow} + \\hat{n}_{\\downarrow})$," + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "H_loc = -0.5*c_dag('dn',0)*c('dn',0) + -0.5*c_dag('up',0)*c('up',0) + 1*c_dag('dn',0)*c_dag('up',0)*c('up',0)*c('dn',0)\n" + ] + } + ], + "source": [ + "from pytriqs.operators import c, c_dag\n", + "up, down = 'up', 'dn'\n", + "n_up = c_dag(up, 0) * c(up, 0)\n", + "n_down = c_dag(down, 0) * c(down, 0)\n", + "\n", + "U = 1\n", + "mu = U/2.\n", + "\n", + "H_loc = U * n_up * n_down - mu * (n_up + n_down)\n", + "\n", + "print 'H_loc =', H_loc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "with 5 bath sites. Parameters of bath sites in ek and V arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "from itertools import product\n", + "ek = [-2,-1,1,2]\n", + "V = [-1,-2,-2,-1]\n", + "H_hyb=sum(V[i]*(c_dag(s,i+1)*c(s,0)+c_dag(s,0)*c(s,i+1)) for s, i in product(['up','dn'], range(len(ek))))\n", + "H_hyb+=sum(ek[i]*(c_dag(s,i+1)*c(s,i+1)) for s, i in product(['up','dn'], range(len(ek))))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-1*c_dag('dn',0)*c('dn',4) + -2*c_dag('dn',0)*c('dn',3) + -2*c_dag('dn',0)*c('dn',2) + -1*c_dag('dn',0)*c('dn',1) + -2*c_dag('dn',1)*c('dn',1) + -1*c_dag('dn',1)*c('dn',0) + -1*c_dag('dn',2)*c('dn',2) + -2*c_dag('dn',2)*c('dn',0) + 1*c_dag('dn',3)*c('dn',3) + -2*c_dag('dn',3)*c('dn',0) + 2*c_dag('dn',4)*c('dn',4) + -1*c_dag('dn',4)*c('dn',0) + -1*c_dag('up',0)*c('up',4) + -2*c_dag('up',0)*c('up',3) + -2*c_dag('up',0)*c('up',2) + -1*c_dag('up',0)*c('up',1) + -2*c_dag('up',1)*c('up',1) + -1*c_dag('up',1)*c('up',0) + -1*c_dag('up',2)*c('up',2) + -2*c_dag('up',2)*c('up',0) + 1*c_dag('up',3)*c('up',3) + -2*c_dag('up',3)*c('up',0) + 2*c_dag('up',4)*c('up',4) + -1*c_dag('up',4)*c('up',0)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "H_hyb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Thermal equilibrium solution" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 36/36 [00:00<00:00, 285.18it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hamiltonian diagonalization:\n", + "Z = 3.94725339567755\n", + "Omega= -10.988298359655117\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "beta = 5.0 # inverse temperature\n", + "fundamental_operators = np.array([[c(up,i), c(down,i)] for i in range(len(ek)+1)]).flatten()\n", + "\n", + "from pyed.TriqsExactDiagonalization import TriqsExactDiagonalization\n", + "ed = TriqsExactDiagonalization(H_loc+H_hyb, fundamental_operators, beta)\n", + "\n", + "print r'Z =', ed.get_partition_function()\n", + "print 'Omega=', ed.get_free_energy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Thermal expectation values" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " = 0.4999999999999989\n", + " = 0.5\n", + " = 0.23891890902080304\n" + ] + } + ], + "source": [ + "print ' =', ed.get_expectation_value(n_up)\n", + "print ' =', ed.get_expectation_value(n_down)\n", + "print ' =', ed.get_expectation_value(n_up * n_down)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imaginary time single-particle Green's function" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 60/60 [00:00<00:00, 101.34it/s]\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from pytriqs.gf import GfImTime\n", + "g_tau = GfImTime(name=r'$g$', beta=beta, statistic='Fermion', n_points=60, indices=[1]) \n", + "ed.set_g2_tau(g_tau, c(up,0), c_dag(up,0))\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from pytriqs.plot.mpl_interface import oplot\n", + "plt.figure(); oplot(g_tau); plt.savefig('figure_g_tau.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1001/1001 [00:10<00:00, 91.83it/s]\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEKCAYAAAA8QgPpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3Xl0XPV99/H3V5sleZFkeZMlG9vYGOMFYQSBELYY4wQCNjQJ5MnipKSkSdqnSdOnoU9y2ibNQk+SNkmTtuFJIKSYJU1K2CHYIThAwMjgVbaRF7zKlizZ8iLJkkbf548ZGdkaSaORZu6M9HmdM2funfnN737HOtZXv9/v3vs1d0dERKS/MoIOQERE0pMSiIiIxEUJRERE4qIEIiIicVECERGRuCiBiIhIXJRAREQkLkogIiISFyUQERGJS1bQASTSuHHjfNq0aUGHISKSVtauXXvY3cf31W5IJ5Bp06ZRWVkZdBgiImnFzHbH0k5TWCIiEhclEBERiYsSiIiIxGVIr4GISHza2trYt28fLS0tQYciCZSbm0tZWRnZ2dlxfV4JRES62bdvH6NHj2batGmYWdDhSAK4O/X19ezbt4/p06fH1UfKTGGZ2fvMbJuZbTezu6K8P8LMHom8/5qZTUt+lCLDQ0tLC8XFxUoeQ5iZUVxcPKBRZkokEDPLBH4MvB+4APiImV1wVrM7gCPuPhP4V+CfkxulyPCi5DH0DfRnnCpTWJcC2919J4CZPQwsBaq6tFkK/GNk+1fAj8zMPFE1eZ+5Cw5uTEjXIilv3t/C4VT59SBxyc6DgrKEHiIlRiBAKbC3y/6+yGtR27h7O9AIFJ/dkZndaWaVZlZZV1eXoHBFRGTI/Ynh7vcA9wBUVFTEPzp5/92DFZJI+tmyBcbNCjoKSXGpMgLZD0zpsl8WeS1qGzPLAgqA+qREJyKByMzMpLy8nHnz5nHTTTdx9OjRfvfx3ve+l/b29l7bNDc3c/XVVxMKhXps09raylVXXdVnX9H6e/bZZ5k9ezYzZ87k7rvvjtrfkSNHuOWWW3rsM1ofvelv+3ikSgJ5HZhlZtPNLAe4HXj8rDaPA8sj2x8Efpew9Q8RSQl5eXmsW7eOTZs2MXbsWH784x/36/ObN2+muLiYrKzeJ1vuvfdebr31VjIzM3tsk5OTw6JFi3jkkUf6PG7X/kKhEJ///Od55plnqKqq4qGHHqKqqqpbf0VFRTQ0NFBf3/3v4p766El/28crJRJIZE3jL4DngC3AL919s5l93cxujjT7GVBsZtuBvwa6neorIkPX5Zdfzv794YmJBx54gEsvvZTy8nI+85nP9DhyeOyxx1i2bNnp/VtvvZWvfvWrXHXVVUydOpWVK1cCsGLFCpYuXQrAsWPHuOiii5g7dy75+fmUl5dz2WWX0dHRwbJly1ixYkW/+luzZg0zZ85kxowZ5OTkcPvtt/PYY48BdOvvxhtv5Iknnuj2PXrrI5r+to9XyqyBuPvTwNNnvfb3XbZbgA8lOy6R4e5rT2ym6sCxQe3zgslj+Ieb5sbcPhQKsWrVKu644w62bNnCI488wssvv0x2djaf+9znWLFiBZ/4xCe6fe7pp5/mySefPL2/ceNG3v3ud7N69WoeffRRVqxYwVVXXcXOnTvpLP0wZswY3nzzTdasWcM3v/nNM37xzps3j9dff71f/e3fv58pU96ZoS8rK+O1116L2t/SpUv58pe/zCc/+ckzvkdvfUTT3/bxSpkEIiJytubmZsrLy9m/fz9z5sxh8eLF/Md//Adr167lkksuOd1mwoQJ3T7b1NREa2srhYWFp/cbGxv54he/CIRv11JYWMjhw4dPt+lq06ZNzJ17ZpLLzMwkJyeH48ePk5mZ2a/+ouna3+jRo5k9ezbbtm2L/R8oYEogItKr/owUBlvnGkhTUxNLlizhxz/+MWbG8uXL+fa3v93rZ/Pz8zEzTpw4wahRo6iqquLiiy8+vc6xYcMG5s2bR15eXtSrsauqqli4cGG310+dOkVubi7r16+Pqb/S0lL27n3nKoV9+/ZRWlrarT+A3bt3R72tSF99DLR9vFJiDUREpDf5+fn88Ic/5Hvf+x5XX301v/rVr6itrQWgoaGB3buj1z9asmQJzz77LBCebiovLz/93oYNG1iwYAFFRUWEQqFuSeTAgQNMmjTpjNfq6+sZN24c2dnZMfd3ySWXUF1dza5du2htbeXhhx/m5ptv7tYfhNdsOtdOuuqtj2j62z5eSiAikhYuuugiFixYwPr16/nGN77B9ddfz4IFC1i8eDE1NTVRP7N06VJ+85vfAN0TyKZNm5g3bx4A119/PS+99NIZn12yZAl33HEHL7744unXXnjhBW688cZ+9ZeVlcWPfvQjlixZwpw5c/jwhz98emqsa38ATzzxRNQE0lsf0fS3fdzcfcg+Lr74YheR/quqqgo6hEEzf/58b2tr67XN2rVr/WMf+1iffd1yyy2+bdu2PtvF019DQ4NfeeWVfX5msEX7WQOVHsPvWI1ARGRI27BhQ5/XgSxcuJBrr722zwsJly1bxnnnndfnMePpr6ioiNWrV/fZdyoxH8LX4lVUVHhlZWXQYYiknS1btjBnzpygw5A+1NfXs2jRom6vr1q1iuLibrcKjCraz9rM1rp7RV+f1VlYIiJpqri4mHXr1gV2fE1hiYhIXJRAREQkLkogIiISFyUQERGJixKIiIjERQlERETiogQiIiJxUQIRkZQ1atSoAX0+lnK2MPRK2iajnC0ogYjIEBVrOVsYWiVtk1XOFpRARCTFvf3225x//vl88pOf5LzzzuOjH/0oK1eu5IorrmDWrFmsWbMm6udiLWcL8ZW0jbW/ZJe0TVY5W1ACEZE0sH37dr70pS+xdetWtm7dyoMPPshLL73Ed7/7Xb71rW9F/czTTz99xq3SN27cSGFhIatXr+YHP/jB6V/cra2tUUva3nfffSxevJh169bx6quvkpGRcUYJ2lj7i1ZetrO2e7SStp23n++qtz4G0nagdC8sEendM3fBwY2D2+ek+fD+2Ofmp0+fzvz58wGYO3cuixYtwsyYP38+b7/9drf2sZazBeIqadvY2Njv/qJJ95K2GoGISMobMWLE6e2MjIzT+xkZGVEXtbuWswV6LGcL9FrStrNNV6dOneKtt96Kub9kl7RNVjlb0AhERPrSj5FCKuksZ/vBD34wavnZzjWKriVoO3+RQ7ik7Q033HBGn50laDdt2hRzf13Ly5aWlvLwww/z4IMPntFff0rant3HQNoOlEYgIjIkxVrOFvpf0rY//SW7pG3SytmCStqKSHdDpaRtLOVs3VXS9myopK2IDHexlLMFlbSNl0raikg3Kmmb+gajnC2opK2IyLATdDlb0CK6iIjESQlERETiogQiIiJxCTyBmNlYM3vezKojz0U9tHvWzI6a2ZPJjlFkOBrKJ9hI2EB/xoEnEOAuYJW7zwJWRfaj+Q7w8aRFJTKM5ebmUl9fryQyhLk79fX1Z1x931+pcBbWUuCayPb9wO+BL5/dyN1Xmdk1Z78uIoOvrKyMffv2UVdXF3QokkC5ubmUlZXF/flUSCAT3b0msn0QmBhkMCIC2dnZUW/qJ9JVUhKIma0EJkV56ytdd9zdzWxAY2YzuxO4E2Dq1KkD6UpERHqRlATi7tf19J6ZHTKzEnevMbMSoHaAx7oHuAfCV6IPpC8REelZKiyiPw4sj2wvBxJTe1FERAZVKiSQu4HFZlYNXBfZx8wqzOynnY3M7A/AfwOLzGyfmS0JJFoREQFSYBHd3euBbncEc/dK4NNd9q9MZlwiItK7VBiBiIhIGlICERGRuCiBiIhIXJRAREQkLkogIiISFyUQERGJixKIiIjERQlERETiogQiIiJxUQIREZG4KIGIiEhclEBERCQuSiAiIhIXJRAREYmLEoiIiMRFCUREROKiBCIiInFRAhERkbgogYiISFyUQEREJC5KICIiEhclEBERiYsSiIiIxEUJRERE4tLvBGJmI80sMxHBiIhI+ugzgZhZhpn9LzN7ysxqga1AjZlVmdl3zGxm4sMUEZFUE8sI5AXgXODvgEnuPsXdJwDvAV4F/tnMPpbAGEVEJAVlxdDmOndvO/tFd28Afg382syyBz0yERFJaX2OQDqTh5m90lcbEREZPvqziJ579gtmduUgxiIiImkklimsTrPN7FFgM7AJOAT8lPD6iIiIDDP9GYHsAr4F7AAuBj4NfG2gAZjZWDN73syqI89FUdqUm9kfzWyzmW0ws9sGelwRERmY/oxAWt39deD1QY7hLmCVu99tZndF9r98Vpsm4BPuXm1mk4G1Zvacux8d5FhERCRG/RmBXJ2gGJYC90e27weWnd3A3d9y9+rI9gGgFhifoHhERCQGsVxIaADufryvNnGa6O41ke2DwMQ+4rkUyCE8lRbt/TvNrNLMKuvq6gYQloiI9CamCwnN7C/NbGrXF80sx8zea2b3A8t768DMVprZpiiPpV3bubsD3ks/JcB/AZ9y945obdz9HnevcPeK8eM1SBERSZRY1kDeB/wp8JCZTQeOEj6lNxP4LfB9d3+ztw7c/bqe3jOzQ2ZW4u41kQRR20O7McBTwFfc/dUY4hYRkQTqM4G4ewvw78C/R644Hwc0D+IC9uOERzB3R54fO7uBmeUAjwK/cPdfDdJxRURkAPp1N153b3P3mkE+++luYLGZVQPXRfYxswoz+2mkzYeBq4BPmtm6yKN8EGMQEZF+svCyQx+Nwtdm3Ez4DKnzCF8T8hjwmLtHnXJKBRUVFV5ZWRl0GCIiacXM1rp7RV/t+pzCMrP/AYoIrz982d3fiiyoLwUeMLMcd79moAGLiEh6iWUR/U/PnrJy9z3AvwH/ZmaFCYlMRERSWix34z0jeZxdkVBXg4uIDE+qSCgiInFRRUIREYmLKhKKiEhcYrmQsM3Mzid81lVp5OX9wOPuvqWzTeJCFBGRVBTLGsiXgYcBA9ZEHkb41iZ3JTY8ERFJVbFMYd0BzD17lGFm/0K4OuHdiQhMRERSWyyL6B3A5Civl0TeExGRYSiWEcgXgFWRe1Xtjbw2FZgJ/EWiAhMRkdQWyyL6s2Z2HnApZy6iv+7uoUQGJyIiqSuWe2FZpHhTjzU4Im36viujiIgMGUmpSCgiIkNPfysSzgCOAHmEk09MFQll6Al1OK3tHbS2d3AqFDq93RZy2js66OiAkDuhDqej87nDae9wQh7efuc9zngt5I674x6ubxx+fmcf93de77pNeJ8on+m6T7d277yfCIYNbn+D2x0ZBmaGWTjW8D5kRA6UEXmv89ks/I3eeT38uc73ev087xyrcz8z08jOyCAr08jONLIyMsjMMLIzI69F3svKMLKivGaD/Q8iMUuFioSSBO7O8VPtHDnZypGmtshzKw0nWznW0k7TqXZOtoZoam3n5KkQJ0+1h7dbQzS3hjjV3kFre4jWUDhRdGjCUlJEbnYGedmZ4UdO5JGdSW6X1/JzMhmTl01BXjaFeTnh5/zwfkFeNuNHjyA3O7Pvg8kZYhmBAGBm7wU+Srgm+iYz2wBscvdTiQpOYufuHD7RyvbaE+w8fIL9R5qpaWzhwNHw88HGFlpDPZ91PTInk/wRWeHnnCxGjciiaGQOZUVZ5GZnMiI7g5zMDHKyujxnZZAd2R6R+c5+ZoZFHuG/MjMzjMzO5wwjo8v+6fcjbbMyMsJ/nWZ0/rXa5a9bgLP2zc5sh9Hje51/qHbbP6vt4P5cBrm/we3u9Aiuo3PEFxmJdURGdx0ePmjH2e14p/3p9yJ/VXS26YgyUjzjvchzKDIybQt10B4ZwbaF/PT2ma91RNqGt9tCHbS0d9DcGqK5LfxoiWyfONVO3fFTtLSFONkaorG5jdb2nv8PFORlM2lMLhMLcpk0ZgSTC/OYPm4k544fxfRxIxk5IuZfl8NGf/5F7iV8Sm82sIBwdcK5hE/nlSRyd/Y2NPPm3iO8uecomw80Ul17gqNN71zrmZlhTBqTS0lBLuVTCimZl8u4USMoGpnD2JHZFObnMDY/h6KROYwekUVGhqYBEiH1Z1dSPsBB1dIW4mhTG43NbRxtauVocxuNTW3UnTjFwcYWDh5r4dCxFrbWHKPuxKkz/gAoKchlTskYFpQVcGFZIRdOKWTsyJzgvkwK6E8C2e3uv4ls/3cigpGeHTnZyurqOl7cVsfq6sMcPhEe+OVlZzKvdAw3zC9h5vhRzJwwinMnjGLSmFwylRREzpCbncmkgkwmFeT22balLcTb9SfZWXeSnXUn2FF3kk37G3lhW+3pxDKnZAxXzRrHlbPGc8n0IkZkDa9psJhqogOY2T8BDYQXzdNiBjzda6I3t4b4bdVBHn1zP3+oPkyowynKz+bKWeO5dPpYLppayOyJo8nKjOVkOhEZDCdOtbNpfyNrdx/hperDVO5uoC3kFORlc8P8Sdx8YSmXzRib1ov7sdZE708C+TUwHxgDrAXWAevcPWVHI+maQGoam/n5K2/z4Gt7ON7STmlhHjeXT+b6CyayoKxQIwuRFHLyVDt/3FHPUxtreG7zQZpaQ8ycMIpPXTGNWy8qIy8n/UYlg55AunScB1xAOJnMd/cvxRdi4qVbAqk/cYofrqpmxWt76HDn/fNK+Pjl53DptLFaoxBJA02t7Tyz8SD3vbKLTfuPMX70CP5q0Sxuu2QK2Wk0U5DIBDISaEmH25ikSwIJdTj3vbyLH6yspqktxG2XTOGzV5/LlLH5QYcmInFwd9bsauC7v93G628fYca4kXzzlvlcfm5x0KHFZNASiJllALcTPoX3EqAVyAEOA08BP3H37QOOOAHSIYHsOnySv/nv9azdfYRrZ4/nKzfOYeaE0UGHJSKDwN1ZtaWWf3qqit31TXz8snP4vzfMSflprVgTSCxnYb0ArCRcE31T5L5YmNlY4FrCNdEfdfcHBhLwcPR81SG+8PCbZGYY37+tnKXlk9N64U1EzmRmXHfBRK6YOY7v/nYb9768i8rdR7jn4xcPiRmGWEYg2X2VrI2lTRBSdQTi7vz773fw3d9uY35pAT/5+MWUFOQFHZaIJNjvt9Xyvx96k4wM4ycfu5h3zUjNKa1YRyB9rup0JgYze6WvNtI3d+fuZ7bynee2cdOCyfzyM5creYgME9fMnsATf/keikfmsPy+Nbz4Vl3QIQ1If04L6HbljZldOYixDAvffmYrP1m9k49fdg7fv61c998RGWbOKR7JLz9zOTPGjeLP7q/kD9Xpm0T6k0Bmm9mjZvYNM7vdzK4Ffp6guIak+17exT2rd7L88nP4+tK5OjVXZJgqHjWCh/7sMmaMH8lnH3iDzQcagw4pLv1JILuAbwE7gIuBTwNfS0RQQ9HKqkN8/ckq3jd3Ev9w01wtlosMcwX52fz8U5cyOjeLT933OrXHW4IOqd/6k0Ba3f11d7/P3f+Pu3/U3X8x0ADMbKyZPW9m1ZHnoihtzjGzN8xsnZltNrM/H+hxk2nfkSa++Mt1zC8t4F9vK9fIQ0QAmFSQy32fuoRjLW184eF1hNKsTkJ/EsjVCYrhLmCVu88CVkX2z1YDXO7u5cC7gLvMbHKC4hlU7aEOvvDwOtzhRx9ZmPLnf4tIcp0/aQxfv3ker+yo50e/S8lL6nrUZwKxyFyLux/vq02clgL3R7bvJ3yb+DO4e2uXuiMj6F/iC9R/vriDyt1H+OYt85hanP7nfYvI4PtQRRnLyifzw99Vp9V6SCrURJ/o7jWR7YPAxGiNzGxKpIjVXuCf3f3AAI6ZFHvqm/i3323nhvmTWFpeGnQ4IpKizIx/vHkuRfnZ/N3/bEybqaxYEsj7gBDhmugHzKzKzHYC1cBHCN/e/ee9dWBmK81sU5TH0q7tIreJj/ov5+573X0B4QJWy82sp0Rzp5lVmlllXV1wp8e5O//w+CayMoy//8DcwOIQkfRQmJ/DP9w0lw37Gnng1d1BhxOTpNREd/frenrPzA6ZWYm715hZCVDbR18HzGwTcCXwqyjv3wPcA+Er0WONcbD9bmstL2yr46s3zompeI2IyAcWlPDQmj18f+Vb3LKwlDG52UGH1Kt+rSW4e5u71/QnecTgcd6ZAlsOPHZ2AzMri9xGnshZWu8Btg1iDIOqo8P5znPbmFacz/J3Tws6HBFJE2bG/71hDkea2vjP3+8IOpw+xZxAIusdPzOz75nZp8zsYjMbMQgx3A0sNrNq4LrIPmZWYWY/jbSZA7xmZuuBF4HvuvvGQTh2Qjy1sYatB4/zxcXnpVUNABEJ3rzSApaVT+ZnL+06Xbo6VfXnt9u9wBPAq8AM4O+BzQMNwN3r3X2Ru89y9+vcvSHyeqW7fzqy/by7L3D3CyPP9wz0uInS0eF8f+VbzJ44mpsWpMWZxiKSYv5y0SxaQx3c+9KuoEPpVSy3c++0291/E9lO2TK2QXthWy076k7yg9t1waCIxOfc8aO4YV4J//XH3fz5Neem7FpIf0Ygq83siwO85mPI+39/2ElJQS43zC8JOhQRSWOfveZcjp9q58HX9gQdSo/6k0AuAD4L1JjZU2b2TTP7UILiSkub9jfy6s4GPnXFNK19iMiAzCst4F3Tx7Litd10pOh1IbFciZ4B4O5/4u7nAdMJr39UA+/qfF9gxWt7yM3O4LZLpvbdWESkDx+77Bz2NjTzYore8j2WX/7Pm9kjZvYRMxvj7s3AFuA44avG30hohGmiqbWdJ9Yf4Ib5JRTkpeZ8pYiklyVzJzFuVA4rXk3NaaxYLiRcZGYXEL5n1VORiwkdeA74V3dXAgGe3niQE6faua1iStChiMgQkZOVwYcqpvCTF3dQe7yFCaNT66LkmKaf3L3K3b/t7lcC17r75e7+j0oe7/hl5V6mFedz6fSxQYciIkPInywspcPhyfU1fTdOsn6vX0SmsKSLmsZm1uxq4NaFZSoUJSKDauaE0cydPIbH1qfe/WO1AD4Intl4EIAbF+jUXREZfEvLJ7N+71HePnwy6FDOoAQyCJ7eWMP5k0Zz7vhRQYciIkPQTRdOxgyeSLFRiBLIAB1sbKFy9xFdOCgiCVNSkEf5lEKe33Io6FDOoAQyQM9tDk9fKYGISCJdN2ciG/Y1cuhYS9ChnKYEMkAvbKtlWnE+Mydo+kpEEue6OeEaequ29FoyKamUQAagpS3EH3fUc83sCUGHIiJD3HkTRzFlbB4rU2gaSwlkAF7dWc+p9g6umT0+6FBEZIgzMxadP5GXtx+muTUUdDiAEsiA/H5bHSOyMrhsRnHQoYjIMHD17PGcau9g7e4jQYcCKIEMyOq36rj83GJyszODDkVEhoFLp40lK8N4ecfhoEMBlEDidrCxhZ2HT/KemeOCDkVEhomRI7Ion1LIKzvqgw4FUAKJ22u7wj9ATV+JSDK9e+Y4Nu47SmNzW9ChKIHE67VdDYwekcWckjFBhyIiw8i7zy2mw2HNroagQ1ECiddrO+upmFZEpuqei0gSXTS1kNzsDF5JgXUQJZA41B0/xY66k7xL01cikmQjsjJZUFbIG3uOBh2KEkg8Xn87PHR8l2p/iEgAFk4tYvP+Rlragr0eRAkkDmt3HyE3O4N5pQVBhyIiw9DCqYW0dzgb9zcGGocSSBzW7z3KvMkFZGfqn09Ekm/hOUUAvBHwBYX6DdhPbaEONh1o5MIphUGHIiLD1LhRIzinOJ839iiBpJW3Dh2npa2DBWWavhKR4CycWsQbe47i7oHFoATSTxv2heccyzUCEZEAXTS1kLrjpzjQGFx9ECWQflq/9yiF+dlMHZsfdCgiMozNnRyeBdkc4EK6Ekg/rdt7lAVlhZjpAkIRCc6cktGYweYDxwKLQQmkH1raQrx16DgXav1DRAKWn5PFjHEjh3cCMbOxZva8mVVHnot6aTvGzPaZ2Y+SGWOn6kMn6HC4QPe/EpEUMHdyAVUHhvcU1l3AKnefBayK7Pfkn4DVSYkqii0Hw5n+fCUQEUkBcyeP4UBjC0dOtgZy/FRIIEuB+yPb9wPLojUys4uBicBvkxRXN1trjpOXnakFdBFJCacX0gOaxkqFBDLR3Wsi2wcJJ4kzmFkG8D3gb/rqzMzuNLNKM6usq6sb1EC3HjzGeZNG6w68IpIS5k4Oz4ZsDmgaKysZBzGzlcCkKG99peuOu7uZRbsq5nPA0+6+r6+zn9z9HuAegIqKikG7wsbd2VJzjCVzo30NEZHkKxqZQ0lBLlU1wYxAkpJA3P26nt4zs0NmVuLuNWZWAtRGaXY5cKWZfQ4YBeSY2Ql37229ZFDVHj/FkaY2zp80OlmHFBHp06yJo9leeyKQY6fCFNbjwPLI9nLgsbMbuPtH3X2qu08jPI31i2QmD4AtNVpAF5HUM2vCKLbXniDUkfxbmqRCArkbWGxm1cB1kX3MrMLMfhpoZF1sPXgcQCMQEUkpsyaM4lR7B/uPNCf92EmZwuqNu9cDi6K8Xgl8OsrrPwd+nvDAzrK99gQTRo+gMD8n2YcWEenRrImjAKiuPc7U4uSeIZoKI5C0sKPuBDPGjww6DBGRM8wcH54VCWIdRAkkBu7OzrqTnDt+VNChiIicoSA/mwmjR1CtBJKaGk620tjcxgwlEBFJQTMnjFICSVU76k4CaApLRFLSrAmj2H7oeNKLSymBxGBnXTizz9QIRERS0MyJoznZGqImycWllEBisPPwSXKyMphcmBd0KCIi3cwYF54debv+ZFKPqwQSgx21J5gxbqTugSUiKemcyOm7u+ubknpcJZAY7Dx8UusfIpKySgryyM40jUBSTWt7B3sampgxTusfIpKaMjOMKWPz2X1YI5CUcuBoM6EOPz1EFBFJRdOKR7K7QQkkpeyJ/EBUREpEUtk5xfnsrj+Z1FN5lUD6sPdIJIFoBCIiKWxa8UiaWkPUnTiVtGMqgfRhT0MTOZkZTBydG3QoIiI9CuJMLCWQPuxtaKKsKI8MncIrIilsWnHkWpDDyTsTSwmkD3sbmpmi9Q8RSXGlRXlkZphGIKlkT0MTU8bqCnQRSW3ZmRmUFuYl9UwsJZBeNDa30djcpjOwRCQtlBbmsf+IEkhK2KtTeEUkjZQW5XHgaPJuqKiAWBaVAAAGNUlEQVQE0ovOBFJWpAQiIqmvtDCPQ8dbaG3vSMrxlEB6cfoiQl0DIiJpoLQwD3c4mKTbuiuB9GLfkWbG5GYxJjc76FBERPpUWhQ+4Wf/0eakHE8JpBc1jc2qASIiaaO0UAkkZRw42qIEIiJpY1JB+I4Z+48ogQTu4LEWSgp0CxMRSQ+52ZmMHz2CAxqBBKulLUTDyVYlEBFJK6WFeZrCClpncfqSAk1hiUj6KC1SAglcTeQHUFKoEYiIpI/OEUgy6oIogfSgcwQyWSMQEUkjpYV5tLZ3cPhEa8KPpQTSg5rG8AhkktZARCSNdJ45moyFdCWQHhxobGHsyBxyszODDkVEJGaTxoT/6D14LPFXoweeQMxsrJk9b2bVkeeiHtqFzGxd5PF4ouOqOdqsM7BEJO10zpocGg4JBLgLWOXus4BVkf1omt29PPK4OdFB1TS26AwsEUk7xSNzyM600+u4iZQKCWQpcH9k+35gWYCxnFbT2MJknYElImkmI8OYMDqXQ8MkgUx095rI9kFgYg/tcs2s0sxeNbOEJpmm1nYam9u0gC4iaWlSQW5SRiBZCT8CYGYrgUlR3vpK1x13dzPr6eTlc9x9v5nNAH5nZhvdfUeUY90J3AkwderUuOJtaevgpgsnM7+0IK7Pi4gE6drZ42luCyX8OJaMi016DcBsG3CNu9eYWQnwe3ef3cdnfg486e6/6q1dRUWFV1ZWDl6wIiLDgJmtdfeKvtqlwhTW48DyyPZy4LGzG5hZkZmNiGyPA64AqpIWoYiIdJMKCeRuYLGZVQPXRfYxswoz+2mkzRyg0szWAy8Ad7u7EoiISICSsgbSG3evBxZFeb0S+HRk+xVgfpJDExGRXqTCCERERNKQEoiIiMRFCUREROKiBCIiInFRAhERkbgEfiFhIplZHbB7AF2MAw4PUjjpYrh95+H2fUHfebgYyHc+x93H99VoSCeQgTKzyliuxhxKhtt3Hm7fF/Sdh4tkfGdNYYmISFyUQEREJC5KIL27J+gAAjDcvvNw+76g7zxcJPw7aw1ERETiohGIiIjERQkkCjN7n5ltM7PtZtZTjfYhw8zuNbNaM9sUdCzJYmZTzOwFM6sys81m9ldBx5RoZpZrZmvMbH3kO38t6JiSwcwyzexNM3sy6FiSxczeNrONZrbOzBJWFElTWGcxs0zgLWAxsA94HfjIUL59vJldBZwAfuHu84KOJxkixctK3P0NMxsNrAWWDfGfswEj3f2EmWUDLwF/5e6vBhxaQpnZXwMVwBh3/0DQ8SSDmb0NVLh7Qq990Qiku0uB7e6+091bgYeBpQHHlFDuvhpoCDqOZHL3Gnd/I7J9HNgClAYbVWJ52InIbnbkMaT/gjSzMuBG4Kd9tZX+UwLprhTY22V/H0P8F8twZ2bTgIuA14KNJPEi0znrgFrgeXcf6t/5+8DfAh1BB5JkDvzWzNaa2Z2JOogSiAxrZjYK+DXwBXc/FnQ8iebuIXcvB8qAS81syE5ZmtkHgFp3Xxt0LAF4j7svBN4PfD4yTT3olEC62w9M6bJfFnlNhpjIOsCvgRXu/j9Bx5NM7n6UcHno9wUdSwJdAdwcWQ94GHivmT0QbEjJ4e77I8+1wKOEp+YHnRJId68Ds8xsupnlALcDjwcckwyyyILyz4At7v4vQceTDGY23swKI9t5hE8U2RpsVInj7n/n7mXuPo3w/+PfufvHAg4r4cxsZOTEEMxsJHA9kJAzLJVAzuLu7cBfAM8RXlj9pbtvDjaqxDKzh4A/ArPNbJ+Z3RF0TElwBfBxwn+Vros8bgg6qAQrAV4wsw2E/1B63t2Hzamtw8hE4CUzWw+sAZ5y92cTcSCdxisiInHRCEREROKiBCIiInFRAhERkbgogYiISFyUQEREJC5KICIiEhclEBERiUtW0AGIDCdmNgZ4EcgBphMuHdACvNvdh9sN/yTN6UJCkQCY2aXAV9x9SJcKkKFNU1giwZgHDOlb5MjQpwQiEowLSNAN7kSSRQlEJBiTgYNBByEyEEogIsF4DviZmV0ddCAi8dIiuoiIxEUjEBERiYsSiIiIxEUJRERE4qIEIiIicVECERGRuCiBiIhIXJRAREQkLkogIiISl/8PLx4rSn0rfrwAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from pytriqs.gf import GfImTime\n", + "densdens_tau = GfImTime(name=r'$\\langle n(\\tau) n(0) \\rangle$', beta=beta, statistic='Boson', n_points=1001, indices=[1]) \n", + "ed.set_g2_tau(densdens_tau, n_up, n_up)\n", + "\n", + "plt.figure(); oplot(densdens_tau); plt.savefig('figure_densdens_tau.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 239/239 [00:00<00:00, 1912.81it/s]\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from pytriqs.gf import GfImFreq\n", + "g_iwn = GfImFreq(name=r'$g$', beta=beta, statistic='Boson', n_points=120, indices=[1])\n", + "ed.set_g2_iwn(g_iwn, n_up, n_down)\n", + "\n", + "plt.figure(); oplot(g_iwn); plt.savefig('figure_g_iwn.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Four-operator response functions" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "from pytriqs.gf import Gf\n", + "from pytriqs.gf import MeshImTime, MeshProduct\n", + "\n", + "ntau = 100\n", + "imtime = MeshImTime(beta, 'Fermion', ntau)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5050/5050 [01:34<00:00, 53.32it/s]\n", + "100%|██████████| 4950/4950 [01:32<00:00, 53.81it/s]\n" + ] + } + ], + "source": [ + "prodmesh2 = MeshProduct(imtime, imtime)\n", + "g3pp_tau = Gf(name=r'$G^{(3)}(\\tau_1, \\tau_2)$', mesh=prodmesh2, target_shape=[1, 1, 1, 1])\n", + "ed.set_g3_tau(g3pp_tau, c(up,0), c_dag(up,0), c(up,0)*c_dag(up,0))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from mpl_toolkits.mplot3d import axes3d\n", + "fig = plt.figure(figsize=(3.25*2, 2*2.5))\n", + "ax = fig.add_subplot(1,1,1, projection='3d')\n", + "\n", + "data = g3pp_tau.data[:, :, 0, 0, 0, 0]\n", + "tau = [tau.real for tau in g3pp_tau.mesh.components[0]]\n", + "t1, t2 = np.meshgrid(tau, tau)\n", + "ax.plot_wireframe(t1, t2, data.real)\n", + "ax.view_init(30, 60)\n", + "ax.set_xlabel(r'$\\tau_1$')\n", + "ax.set_ylabel(r'$\\tau_2$')\n", + "plt.tight_layout()\n", + "plt.savefig('figure_g3pp_tau.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(54.135,0.5,'$\\\\tau_2$')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,8))\n", + "plt.imshow(data.real)\n", + "# plt.xticks(range(10))\n", + "# plt.yticks(range(10))\n", + "plt.savefig('im_g3pp_tau.png')\n", + "plt.colorbar()\n", + "plt.xlabel(r'$\\tau_1$')\n", + "plt.ylabel(r'$\\tau_2$')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From f4ae751ae64338a7f7c08bb7fa0fd87ccfd6ad54 Mon Sep 17 00:00:00 2001 From: yaros72 Date: Sat, 30 Mar 2019 17:12:26 +0300 Subject: [PATCH 32/33] update --- Documentation.ipynb | 467 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 467 insertions(+) create mode 100644 Documentation.ipynb diff --git a/Documentation.ipynb b/Documentation.ipynb new file mode 100644 index 0000000..1b9f060 --- /dev/null +++ b/Documentation.ipynb @@ -0,0 +1,467 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# **PYED**: Exact diagonalization for finite quantum systems\n", + "\n", + "Copyright (C) 2017, H. U.R. Strand\n", + "\n", + "The python module `pyed` implements exact diagonalization for finite fermionic many-body quantum systems, together with calculations of several response functions in imagianary time.\n", + "\n", + "The many-body system is defined using `pytriqs` second-quantized operators and the response functions are stored in `pytriqs` Green's function containters.\n", + "\n", + "## Hamiltonians\n", + "\n", + "As an example let us solve the Hubbard atom with Hamiltonian $H = U\\hat{n}_{\\uparrow} \\hat{n}_{\\downarrow} - \\mu ( \\hat{n}_{\\uparrow} + \\hat{n}_{\\downarrow})$, where $\\hat{n}_\\sigma = c^\\dagger_\\sigma c_\\sigma$.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "H = -0.5*c_dag('down',0)*c('down',0) + -0.5*c_dag('up',0)*c('up',0) + 1*c_dag('down',0)*c_dag('up',0)*c('up',0)*c('down',0)\n" + ] + } + ], + "source": [ + "from pytriqs.operators import c, c_dag\n", + "up, down = 'up', 'down'\n", + "n_up = c_dag(up, 0) * c(up, 0)\n", + "n_down = c_dag(down, 0) * c(down, 0)\n", + "\n", + "U = 1\n", + "mu =-0.5*U\n", + "\n", + "H = U * n_up * n_down + mu * (n_up + n_down)\n", + "\n", + "print 'H =', H" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Thermal equilibrium solution\n", + "\n", + "To solve the thermal equilibrium of the system we can diagonalize $H$ and determine the partition function $\\mathcal{Z}$ (or alternatively the free energy $\\Omega = -\\frac{1}{\\beta} \\ln \\mathcal{Z}$) and the many-body density matrix $\\rho$ using the egenstates $|\\Gamma \\rangle$ and eigenvalues $E_\\Gamma$ of $H$. The partition function $\\mathcal{Z}$ is given by the sum of Boltzman weights\n", + "\n", + "$$\n", + "\\mathcal{Z} = \\sum_\\Gamma e^{-\\beta E_\\Gamma} \\, ,\n", + "$$\n", + "while the many-body density matrix is given by the ket-bra Boltzman weighted sum\n", + "\n", + "$$\n", + "\\rho = \\frac{1}{\\mathcal{Z}} \\sum_\\Gamma e^{-\\beta E_\\gamma} |\\Gamma \\rangle \\langle \\Gamma|\n", + "\\, .\n", + "$$\n", + "\n", + "To accomplish this we pass the Hamiltonian $H$ and a list of unique annihilation opeators used in $H$ together with the inverse temperature $\\beta$ to a `pyed.TriqsExactDiagonalization` class instance:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:00<00:00, 470.75it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hamiltonian diagonalization:\n", + "Z = 2.2706705664732256\n", + "\\Omega = -0.7050187979007294\n", + "\\rho =\n", + " (0, 0)\t0.05960146101105877\n", + " (1, 1)\t0.44039853898894116\n", + " (2, 2)\t0.44039853898894116\n", + " (3, 3)\t0.05960146101105877\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "beta = 4.0 # inverse temperature\n", + "fundamental_operators = [c(up,0), c(down,0)]\n", + "\n", + "from pyed.TriqsExactDiagonalization import TriqsExactDiagonalization\n", + "ed = TriqsExactDiagonalization(H, fundamental_operators, beta)\n", + "\n", + "print r'Z =', ed.get_partition_function()\n", + "print r'\\Omega =', ed.get_free_energy()\n", + "print r'\\rho ='\n", + "print ed.ed.get_density_matrix()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Thermal expectation values\n", + "\n", + "Using the many-body density matrix we can evaluate the expectation value of any operator $\\mathcal{O}$ by taking the trace\n", + "\n", + "$$\n", + "\\langle \\mathcal{O} \\rangle = \\textrm{Tr} [ \\rho \\mathcal{O} ]\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " = 0.5\n", + " = 0.5\n", + " = 0.05960146101105877\n" + ] + } + ], + "source": [ + "print ' =', ed.get_expectation_value(n_up)\n", + "print ' =', ed.get_expectation_value(n_down)\n", + "print ' =', ed.get_expectation_value(n_up * n_down)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imaginary time single-particle Green's function\n", + "We can also calculate the dynamical fluctuations of the system by computing its response functions. The simples case is the single-particle Green's function, defined as the imaginary time ordered expectation value\n", + "\n", + "$$\n", + " G_{\\sigma \\sigma'}(\\tau) \\equiv\n", + " - \\langle \\mathcal{T} \\, c_{\\sigma}(\\tau) c_{\\sigma'}^\\dagger(0) \\rangle\n", + " =\n", + " - \\frac{1}{\\mathcal{Z}} \\text{Tr}\n", + " \\left[ e^{-\\beta H} c_{\\sigma}(\\tau_1) c_{\\sigma'}^\\dagger(0) \\right]\n", + "$$\n", + "where the imaginary time dependent operators are defined in the Heisenberg picture $c_{\\sigma}(\\tau) \\equiv e^{\\tau H} c_{\\sigma} e^{-\\tau H}$ and $c^\\dagger_{\\sigma}(\\tau) \\equiv e^{\\tau H} c^\\dagger_{\\sigma} e^{-\\tau H}$.\n", + "\n", + "To calculate $G(\\tau)$ we first create `pytriqs.GfImTime` instance to store the result and pass it to our ED solver instance:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 500/500 [00:00<00:00, 1714.22it/s]\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from pytriqs.gf import GfImTime\n", + "g_tau = GfImTime(name=r'$g$', beta=beta, statistic='Fermion', n_points=500, indices=[1]) \n", + "ed.set_g2_tau(g_tau, c(up,0), c_dag(up,0))\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from pytriqs.plot.mpl_interface import oplot\n", + "%matplotlib inline\n", + "\n", + "plt.figure(); oplot(g_tau); plt.savefig('figure_g_tau.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The two-operator response function calculator is more general and can be used to calculate any type of two operator response, e.g., the density-density response function: $\\chi_{\\sigma \\sigma'}(\\tau) \\equiv -\\langle \\hat{n}_\\sigma(\\tau) \\hat{n}_\\sigma' \\rangle$. However for the very simple single-Hubbard-atom system this response function is $\\tau$ independent as seen below:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1000/1000 [00:00<00:00, 1987.30it/s]\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from pytriqs.gf import GfImTime\n", + "densdens_tau = GfImTime(name=r'$\\langle n(\\tau) n(0) \\rangle$', beta=beta, statistic='Boson', n_points=1000, indices=[1]) \n", + "ed.set_g2_tau(densdens_tau, n_up, n_down)\n", + "\n", + "plt.figure(); oplot(densdens_tau); plt.savefig('figure_densdens_tau.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For fermionic two-operator response functions `pyed` can also directly calculate the fourier transformed response function\n", + "\n", + "$$\n", + "G(i \\omega_n) \\equiv \\int_0^\\beta d\\tau \\, e^{i\\omega_n \\tau} G(\\tau)\n", + "$$\n", + "defined on the (fermionic) Matsubara frequencies $i\\omega_n = \\frac{2\\pi}{\\beta}(2n + 1)$. \n", + "\n", + "NB! `pyed` currently lacks support for handling bosonic response functions in frequency." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 200/200 [00:00<00:00, 22229.13it/s]\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from pytriqs.gf import GfImFreq\n", + "g_iwn = GfImFreq(name=r'$g$', beta=beta, statistic='Fermion', n_points=100, indices=[1])\n", + "ed.set_g2_iwn(g_iwn, c(up,0), c_dag(up,0))\n", + "\n", + "plt.figure(); oplot(g_iwn); plt.savefig('figure_g_iwn.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Four-operator response functions\n", + "\n", + "In `pyed` there is functionality to compute also higher-order response functions (involving more than two operators and more than one time). Currently two- and three- time ordered expectation values are supported solely in imaginary time.\n", + "\n", + "The two-particle Green's function $G^{(4)}(\\tau_1, \\tau_2, \\tau_3)$ is a prominent example\n", + "\n", + "$$\n", + "G^{(4)}_{\\alpha\\bar{\\beta}\\gamma\\bar{\\delta}}(\\tau_1, \\tau_2, \\tau_3) \\equiv\n", + "\\langle \\mathcal{T} \n", + "c_\\alpha(\\tau_1) c^\\dagger_{\\bar{\\beta}} (\\tau_2) \n", + "c_\\gamma(\\tau_3) c^\\dagger_{\\bar{\\delta}} (0) \\rangle\n", + "$$\n", + "\n", + "That easily can be calculated with `pyed` by passing a suitable `pytriqs` container to the ED solver:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1540/1540 [00:01<00:00, 945.61it/s]\n", + "100%|██████████| 1330/1330 [00:01<00:00, 966.69it/s]\n", + "100%|██████████| 1330/1330 [00:01<00:00, 977.98it/s]\n", + "100%|██████████| 1330/1330 [00:01<00:00, 965.98it/s]\n", + "100%|██████████| 1330/1330 [00:01<00:00, 976.40it/s]\n", + "100%|██████████| 1140/1140 [00:01<00:00, 942.44it/s]\n" + ] + } + ], + "source": [ + "from pytriqs.gf import Gf\n", + "from pytriqs.gf import MeshImTime, MeshProduct\n", + "\n", + "ntau = 20\n", + "imtime = MeshImTime(beta, 'Fermion', ntau)\n", + "prodmesh = MeshProduct(imtime, imtime, imtime)\n", + "\n", + "g4_tau = Gf(name=r'$G^{(4)}(\\tau_1,\\tau_2,\\tau_3)$', mesh=prodmesh, target_shape=[1, 1, 1, 1])\n", + "ed.set_g4_tau(g4_tau, c(up,0), c_dag(up,0), c(up,0), c_dag(up,0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To visualize this three dimensional scalar field one have to resort to some cut plane to represent it in a two dimensional plot. So instead of plotting $G^{(4)}$ we here show the special case of a two-time response function correspoding to $G^{(4)}(\\tau_1, 0^-, \\tau_2)$ namely the particle-particle equal time response function\n", + "\n", + "$$\n", + "G_{\\alpha \\beta \\gamma}^{(3)}(\\tau_1, \\tau_2) \\equiv \n", + "\\langle \\mathcal{T} c_{\\alpha}(\\tau_1) c^\\dagger_{\\bar{\\beta}}(\\tau_2) \\hat{n}_\\gamma(0)\\rangle \\equiv \n", + "- \\langle \\mathcal{T} c_{\\alpha}(\\tau_1) c^\\dagger_{\\bar{\\beta}}(\\tau_2) c_{\\gamma}(0^-) c^\\dagger_{\\bar{\\gamma}}(0) \\rangle \\equiv \n", + "- G^{(4)}(\\tau_1, \\tau_2, 0^+)\n", + "\\, ,\n", + "$$\n", + "that can be calculated separately as:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 210/210 [00:00<00:00, 1107.79it/s]\n", + "100%|██████████| 190/190 [00:00<00:00, 999.15it/s] \n" + ] + } + ], + "source": [ + "prodmesh2 = MeshProduct(imtime, imtime)\n", + "g3pp_tau = Gf(name=r'$G^{(3)}(\\tau_1, \\tau_2)$', mesh=prodmesh2, target_shape=[1, 1, 1, 1])\n", + "ed.set_g3_tau(g3pp_tau, c(up,0), c_dag(up,0), c(up,0)*c_dag(up,0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "To visualize this we use `matplotlib` directly\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from mpl_toolkits.mplot3d import axes3d\n", + "fig = plt.figure(figsize=(3.25*2, 2*2.5))\n", + "ax = fig.add_subplot(1,1,1, projection='3d')\n", + "\n", + "data = g3pp_tau.data[:, :, 0, 0, 0, 0]\n", + "tau = [tau.real for tau in g3pp_tau.mesh.components[0]]\n", + "t1, t2 = np.meshgrid(tau, tau)\n", + "ax.plot_wireframe(t1, t2, data.real)\n", + "ax.view_init(30, 60)\n", + "ax.set_xlabel(r'$\\tau_1$')\n", + "ax.set_ylabel(r'$\\tau_2$')\n", + "plt.tight_layout()\n", + "plt.savefig('figure_g3pp_tau.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From fad1b09807ccf933e094bf82ac241ed4e3d5bbfa Mon Sep 17 00:00:00 2001 From: Yaroslav Date: Sun, 3 Nov 2024 19:53:57 +0100 Subject: [PATCH 33/33] Update Readme.md --- Readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Readme.md b/Readme.md index 6be86bc..e9164f4 100644 --- a/Readme.md +++ b/Readme.md @@ -1,6 +1,6 @@ # **PYED**: Exact diagonalization for finite quantum systems -Copyright (C) 2018, H. U.R. Strand, Ya.V. Zhumagulov +Copyright (C) 2018, H. U.R. Strand The python module `pyed` implements exact diagonalization for finite fermionic many-body quantum systems, together with calculations of several response functions in imagianary time.