From 4c0b719fd755f5ca19cf38d5e67a1493036f2fa6 Mon Sep 17 00:00:00 2001 From: Thomas Baumann Date: Fri, 19 Aug 2022 17:05:40 +0200 Subject: [PATCH 01/10] Added preconditioner based on Taylor expansion --- pySDC/core/Sweeper.py | 28 +++++++++++++++++++++++++++- 1 file changed, 27 insertions(+), 1 deletion(-) diff --git a/pySDC/core/Sweeper.py b/pySDC/core/Sweeper.py index fdc3f3f25b..2546e051a6 100644 --- a/pySDC/core/Sweeper.py +++ b/pySDC/core/Sweeper.py @@ -3,6 +3,7 @@ import numpy as np import scipy.linalg import scipy.optimize as opt +from scipy.special import factorial from pySDC.core.Errors import ParameterError from pySDC.core.Level import level @@ -200,8 +201,33 @@ def rho(x): 'implemented') QDmat[1:, 1:] = np.diag(x) self.parallelizable = True + elif qd_type == 'TAYLOR': + ''' + Do a Taylor expansion using u0 and f(u_m) backwards in time from tau_m and solve for u_m. + We take as many values as we have available, meaning the order of the Taylor expansion is m. + That means for the first node, we get backward Euler, but for later nodes we get higher order + solutions and in some cases we can get more than one order per sweep. + ''' + j = np.arange(len(self.coll.nodes) + 1) # index variable + inv_fac = 1. / factorial(j) # compute inverse factorials once to use in Taylor expansions + for i in range(1, len(self.coll.nodes) + 1): + t_expand = self.coll.nodes[i - 1] + h = np.append([0], self.coll.nodes[:i]) - t_expand # time difference to where we expand about + + # build a matrix containing the Taylor coefficients + A = np.zeros((i + 1, i + 1)) + A[:, 0] = h[0]**j[:i + 1] * inv_fac[:i + 1] + for k in range(1, i + 1): + A[1:, k] = h[k]**j[:i] * inv_fac[:i] + + # build a right hand side vector for solving the system + b = np.zeros(i + 1) + b[0] = 1. + + # solve the linear system + QDmat[i, 1: i + 1] = np.linalg.solve(A, b)[1:] else: - raise NotImplementedError('qd_type implicit not implemented') + raise NotImplementedError(f'qd_type implicit "{qd_type}" not implemented') # check if we got not more than a lower triangular matrix np.testing.assert_array_equal(np.triu(QDmat, k=1), np.zeros(QDmat.shape), err_msg='Lower triangular matrix expected!') From 6239b7905bc623349035fb9453ad281c6ea38f02 Mon Sep 17 00:00:00 2001 From: Thomas Baumann Date: Tue, 23 Aug 2022 17:19:26 +0200 Subject: [PATCH 02/10] Added general function for deriving linear multistep methods and made some of them available as preconditioners. --- pySDC/core/Sweeper.py | 45 ++++++++++++++------- pySDC/helpers/preconditioner_helper.py | 56 ++++++++++++++++++++++++++ 2 files changed, 86 insertions(+), 15 deletions(-) create mode 100644 pySDC/helpers/preconditioner_helper.py diff --git a/pySDC/core/Sweeper.py b/pySDC/core/Sweeper.py index 2546e051a6..d267263a75 100644 --- a/pySDC/core/Sweeper.py +++ b/pySDC/core/Sweeper.py @@ -3,7 +3,6 @@ import numpy as np import scipy.linalg import scipy.optimize as opt -from scipy.special import factorial from pySDC.core.Errors import ParameterError from pySDC.core.Level import level @@ -11,6 +10,7 @@ from pySDC.implementations.collocation_classes.equidistant_right import EquidistantNoLeft from pySDC.implementations.collocation_classes.gauss_lobatto import CollGaussLobatto from pySDC.implementations.collocation_classes.gauss_radau_right import CollGaussRadau_Right +from pySDC.helpers.preconditioner_helper import get_linear_multistep_method # short helper class to add params as attributes @@ -201,31 +201,46 @@ def rho(x): 'implemented') QDmat[1:, 1:] = np.diag(x) self.parallelizable = True - elif qd_type == 'TAYLOR': + elif qd_type == 'LMMEuler': ''' Do a Taylor expansion using u0 and f(u_m) backwards in time from tau_m and solve for u_m. - We take as many values as we have available, meaning the order of the Taylor expansion is m. + We take as many values as we have available except for the rhs. of the initial conditions, meaning the order + of the Taylor expansion is m. That means for the first node, we get backward Euler, but for later nodes we get higher order solutions and in some cases we can get more than one order per sweep. ''' - j = np.arange(len(self.coll.nodes) + 1) # index variable - inv_fac = 1. / factorial(j) # compute inverse factorials once to use in Taylor expansions for i in range(1, len(self.coll.nodes) + 1): t_expand = self.coll.nodes[i - 1] h = np.append([0], self.coll.nodes[:i]) - t_expand # time difference to where we expand about - # build a matrix containing the Taylor coefficients - A = np.zeros((i + 1, i + 1)) - A[:, 0] = h[0]**j[:i + 1] * inv_fac[:i + 1] - for k in range(1, i + 1): - A[1:, k] = h[k]**j[:i] * inv_fac[:i] + u_signature = np.zeros_like(h) + u_signature[0] = 1 - # build a right hand side vector for solving the system - b = np.zeros(i + 1) - b[0] = 1. + f_signature = np.ones_like(h) + f_signature[0] = 0 - # solve the linear system - QDmat[i, 1: i + 1] = np.linalg.solve(A, b)[1:] + u_coeff, f_coeff = get_linear_multistep_method(h, u_signature, f_signature) + + QDmat[i, 0: i + 1] = f_coeff + elif qd_type == 'LMM': + ''' + Do a Taylor expansion using u0 and f(u_m) backwards in time from tau_m and solve for u_m. + We take as many values as we have available, meaning the order of the Taylor expansion is m + 1. + That means for the first node, we get trapezoidal rule, but for later nodes we get higher order + solutions and in some cases we can get more than one order per sweep. + ''' + for i in range(1, len(self.coll.nodes) + 1): + t_expand = self.coll.nodes[i - 1] + h = np.append([0], self.coll.nodes[:i]) - t_expand # time difference to where we expand about + + u_signature = np.zeros_like(h) + u_signature[0] = 1 + + f_signature = np.ones_like(h) + + u_coeff, f_coeff = get_linear_multistep_method(h, u_signature, f_signature) + + QDmat[i, 0: i + 1] = f_coeff else: raise NotImplementedError(f'qd_type implicit "{qd_type}" not implemented') # check if we got not more than a lower triangular matrix diff --git a/pySDC/helpers/preconditioner_helper.py b/pySDC/helpers/preconditioner_helper.py new file mode 100644 index 0000000000..67dec8c9c5 --- /dev/null +++ b/pySDC/helpers/preconditioner_helper.py @@ -0,0 +1,56 @@ +import numpy as np +from scipy.special import factorial + + +def get_linear_multistep_method(steps, u_signature, f_signature): + ''' + Derive a general linear multistep method from step sizes and a signature. This function will provide a consistent + linear multistep method by cancelling terms in Taylor expansions, but note that this must not be convergent! + + The resulting coefficients must be multiplied with the corresponding value of u or f and then all must be summed to + get a numerical solution to the initial value problem. + + Since we can cancel as many terms in the Taylor expansion as we have entries in the signature, that will also be + the order of consistency of our method. + + Args: + steps (list): The step sizes between the multiple steps that are used + u_signature (list): A list containing which solutions at which steps should be used. Set to 1, for all times at + which you want to use the solution and to 0 at all other times + f_signature (list): Analogue to u_signature for the right hand side evaluations + + Returns: + list: Coefficients for u + list: Coefficients for f + ''' + n_u = np.sum(u_signature, dtype=int) + n_f = np.sum(f_signature, dtype=int) + n = n_u + n_f # number of available values + j = np.arange(n) # index variable + inv_fac = 1. / factorial(j) # compute inverse factorials once to use in Taylor expansions + + # build a matrix containing the Taylor coefficients + A = np.zeros((n, n)) + + # fill the entries for u + for i in range(n_u): + A[:, i] = steps[u_signature > 0][i]**j * inv_fac + + # fill the entries for f + for i in range(n_f): + A[1:, i + n_u] = steps[f_signature > 0][i]**j[:-1] * inv_fac[:-1] + + # build a right hand side vector for solving the system + b = np.zeros(n) + b[0] = 1. + + # solve the linear system + coeff = np.linalg.solve(A, b) + + # distribute the coefficients + u_coeff = np.zeros_like(u_signature) + u_coeff[u_signature > 0] = coeff[0: n_u] + f_coeff = np.zeros_like(f_signature) + f_coeff[f_signature > 0] = coeff[n_u:] + + return u_coeff, f_coeff From 79feebc33eee6748cfe8aa8113a8e9a743d1e48f Mon Sep 17 00:00:00 2001 From: Thomas Baumann Date: Tue, 23 Aug 2022 19:04:55 +0200 Subject: [PATCH 03/10] Found a jupyter notebook while rummaging in the Millenium Falcon --- .../AdvectionEquation_1D_FD.py | 3 +- .../HeatEquation_ND_FD_forced_periodic.py | 3 +- .../Preconditioners/LMM_preconditioner.ipynb | 231 ++++++++++++++++++ pySDC/playgrounds/Preconditioners/heat.py | 136 +++++++++++ pySDC/projects/Resilience/accuracy_check.py | 90 +++++-- 5 files changed, 440 insertions(+), 23 deletions(-) create mode 100644 pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb create mode 100644 pySDC/playgrounds/Preconditioners/heat.py diff --git a/pySDC/implementations/problem_classes/AdvectionEquation_1D_FD.py b/pySDC/implementations/problem_classes/AdvectionEquation_1D_FD.py index 7b34aa79f9..ba53e165b0 100644 --- a/pySDC/implementations/problem_classes/AdvectionEquation_1D_FD.py +++ b/pySDC/implementations/problem_classes/AdvectionEquation_1D_FD.py @@ -167,12 +167,13 @@ def solve_system(self, rhs, factor, u0, t): me[:] = L.solve(rhs) return me - def u_exact(self, t): + def u_exact(self, t, u_init=None, t_init=None): """ Routine to compute the exact solution at time t Args: t (float): current time + u_init, t_init: unused parameters for common interface reasons Returns: dtype_u: exact solution diff --git a/pySDC/implementations/problem_classes/HeatEquation_ND_FD_forced_periodic.py b/pySDC/implementations/problem_classes/HeatEquation_ND_FD_forced_periodic.py index fc28729cd6..eeaeedb5eb 100644 --- a/pySDC/implementations/problem_classes/HeatEquation_ND_FD_forced_periodic.py +++ b/pySDC/implementations/problem_classes/HeatEquation_ND_FD_forced_periodic.py @@ -169,12 +169,13 @@ def solve_system(self, rhs, factor, u0, t): tol=self.params.lintol, maxiter=self.params.liniter)[0].reshape(self.params.nvars) return me - def u_exact(self, t): + def u_exact(self, t, u_init=None, t_init=None): """ Routine to compute the exact solution at time t Args: t (float): current time + u_init, t_init: Dummy variables to provide unified interface Returns: dtype_u: exact solution diff --git a/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb b/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb new file mode 100644 index 0000000000..e7d3828c7b --- /dev/null +++ b/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb @@ -0,0 +1,231 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "de15cd5b", + "metadata": {}, + "source": [ + "# High Order Linear Multistep Preconditioners" + ] + }, + { + "cell_type": "markdown", + "id": "09b17076", + "metadata": {}, + "source": [ + "## Constructing linear multistep methods (LMMs)\n", + "LMMs are based on Taylor expanding the solution in time and then summing solutions and right hand side evaluations at different steps to cancel as many terms in the Taylor expansion.\n", + "The Taylor expansion of the solution at time $t$, which we want to solve for is\n", + "$$u(t-h) = \\sum^{p}_{i=0}\\frac{(-h)^i}{i!}\\partial_t^i u(t) = \\mathcal{O}(h^{p+1}).$$\n", + "Since we want to compute $u(t)$, we want to cancel all other terms in the Taylor expansion.\n", + "\n", + "The solutions at previous steps share the same expansion, but the right hand side evaluations are missing the solution and a power of the step size.\n", + "Their expansion is\n", + "$$f(t-h) = \\sum^p_{i=1}\\frac{(-h)^{(i-1)}}{(i-1)!}\\partial_t^{i}u(t) + \\mathcal{O}(h^{p+1}).$$\n", + "\n", + "The linear multistep method will then look like\n", + "$$u(t) = \\sum_{i=0}^p \\left(\\alpha_i u(t-h_i) + \\beta_i f\\left(u\\left(t-h_i\\right), t-h_i\\right)\\right),$$\n", + "and we need to choose $\\alpha$ and $\\beta$ in a way that suits us and that cancels the most possible terms in the Taylor expansion.\n", + "\n", + "What suits us best is mainly determined by what we have available, which is in the pySDC implementation the initial conditions and the right hand side evaluations at all the nodes.\n", + "That means with $h_i$ the time differences to where we want to solve for in descending order: $\\alpha_0=1$ and $\\beta_i \\neq 0$.\n", + "In particular, $\\beta_p\\neq 0$, to make the scheme implicit.\n", + "\n", + "In order to cancel as many terms as we can, we setup a linear system of equations that carries the coefficients of the Taylor expansion.\n", + "We construct a matrix $A$ with\n", + "$$A_{ij} = \\cases{(-h_i)^j / j!, & j = 0\\\\\n", + " (-h_i)^{(j-1)} / (j-1)!, & i,j > 0}.$$\n", + "We will then procees to solve a system $Ax=b$, which means we need a vector $b$ on the right hand side.\n", + "Since we want to solve for the solution at time $t$ itself and want to cancel all other terms, we need a one in the first entry, corresponding to $u(t)$ and zeros everywhere else:\n", + "$$b = \\delta_{i0}.$$\n", + "\n", + "Conveniently for us, $u(t-h_0)$ is the only term carrying $u(t)$ in its expansion, and since we want a one in the corresponding row of the solution, we will always get $\\alpha_0=1$, which is exactly what we wanted!\n", + "The remaining coefficients make up a single row of the preconditioner and are the $\\beta_i$ coefficients that will be multiplied to the right hand side evaluations in the sweeps.\n", + "\n", + "Crucially, we get increase the order of the LMM by one in each row of the preconditioner.\n", + "In the first row, we get the trapezoidal rule (or implicit Euler, if we set $\\beta_0=0$), and then we gain an order in the expansions with each row." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4f15eae9", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from pySDC.projects.Resilience.accuracy_check import plot_all_errors, plot_orders\n", + "from pySDC.projects.Resilience.vdp import run_vdp\n", + "from pySDC.projects.Resilience.advection import run_advection\n", + "from pySDC.projects.Resilience.piline import run_piline\n", + "from pySDC.playgrounds.Preconditioners.heat import run_heat" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e186936d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "IE_desc = {'sweeper_params': {'QI': 'IE'}}\n", + "Taylor_desc = {'sweeper_params': {'QI': 'LMM'}}\n", + "trap_desc = {'sweeper_params': {'QI': 'TRAP'}}\n", + "\n", + "\n", + "ks = [1, 2, 3]\n", + "\n", + "def compare_preconditioners(prob, dt_list, ks, Tend_fixed):\n", + " fig, axs = plt.subplots(1, 3, figsize=(16, 5), sharex=True, sharey=True)\n", + " descriptions = [IE_desc, Taylor_desc, trap_desc]\n", + " titles = ['Implicit Euler', 'LMM', 'Trapezoidal']\n", + " for i in range(len(descriptions)):\n", + " plot_orders(axs[i], ks, True, Tend_fixed=Tend_fixed, custom_description=descriptions[i], dt_list=dt_list, prob=prob)\n", + " axs[i].set_title(titles[i])\n", + " if i > 0:\n", + " axs[i].set_ylabel('')\n", + "\n", + " fig.tight_layout()\n", + "\n", + "\n", + "compare_preconditioners(run_piline, 0.1 * 2.**(-np.arange(5)), ks, 0.1)" + ] + }, + { + "cell_type": "markdown", + "id": "b37779e7", + "metadata": {}, + "source": [ + "What you see above is the order for the pi-line problem.\n", + "$k$ is the number of sweeps and $p$ is the order of the scheme.\n", + "\n", + "The left panel shows the expected $p=k+1$, but the other panels show something akin to $p=2k+1$ until we reach three sweeps at which point, the quadrature rule is not high order enough, since we use only three Gauss-Radau nodes.\n", + "\n", + "This looks nice! We did no extra work and yet we got a higher order method!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "50897e94", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Me2PMK8aY9wO2DTHGzDfGvGCMGeJcdSIiIoU0PQEueBa2LoQv/u50NSIiIiJSR1VLwGOMmWqM2WOMWVFo+zBjzBpjzHpjzEQAa+1Ga+24QoewQAoQBiRUR80iIiLlduJl0P9mWPIC/PqO09WIiIiISB1UXT14pgPDAjcYY9zAZOAcoCtwhTGmawnPn2+tPQe4G3ioCusUERE5OkP/AW0Gwce3wc5fna5GREREROqYagl4rLXfA/sLbT4JWO/vsZMJvA1cWMLzc/3fJgGhxe1jjLnBGLPMGLMsMTGxkioXEZGqUuvabXcIXDYdIhrCO2MgrfDHnohI8Kt1bbeISC3i5Bw8LYBtAfcTgBbGmIbGmBeAXsaYewCMMSOMMS8CrwGTijuYtfYla228tTY+Li6uqmsXEZFjVCvb7ag4uPw1OLQL3r8OcnOcrkhEpFLVyrZbRKSW8Dj42qaYbdZauw+YUGjjTGBmtVQlIiJyLFr2geH/hY/+BPP+AUM1slhEREREqp6TPXgSgFYB91sCOxyqRUREpPL0vhr6XAs/PA0rZztdjYiIiIjUAU4GPD8CHY0x7YwxXmAU8JGD9YiIiFSecx6Hln1h9s2wZ7XT1YiIiIhILVddy6S/BSwCOhljEowx46y12cCtwOfAauBda+3K6qhHRESkynlCffPxeCPh7Ssh/YDTFYmIiIhILVZdq2hdYa1tZq0Nsda2tNa+4t8+11p7vLW2g7X2keqoRUREpNrENIPLX4UDW2DWjZCbW/ZzRERERESOgpNDtERERGq/NgPg7Edh7Wfw/b+drkZEREREaikFPCIiIlXtpOuhxxXw7aOw5jOnqxERERGRWkgBj4iISFUzBs57Cpr1gJk3wL4NTlckIiIiIrWMAh4REZHqEBIOI18HlxveHg0ZKU5XJCIiIiK1iAIeERGR6hLbGi6dCnvXwoc3g7VOVyQiIiIitYQCHhERkerU4TQ480FY9SH88IzT1YiIiIhILaGAR0REpLoNvA26XQzzHoIN3zhdjYiIiIjUAgp4REREqpsxcMEkaNQJ3r8OkrY4XZGIiIiIBDkFPCIiIk4IjYJRb0BuDrwzBrLSna5IRERERIKYAh4RERGnNOwAI16CXb/BnL9o0mUREREROWoKeERERJzUaRgMuQd+fQuWvux0NSIiIiISpBTwiIiIOO2Uu+D4c+Dze2DLQqerEREREZEgpIBHRETEaS4XjHgRYtvAu2MheYfTFYmIiIhIkFHAIyIiUhOE1fNNupyZCu9eDdkZTlckIiIiIkFEAY+IiEhN0bgLXPQcJPwIn97tdDUiIiIiEkQU8IiIiNQk3S6Ck/8My6fBT686XY2IiIiIBAkFPCIiIjXNGf8H7U+DT+6AhOVOVyMiIiIiQUABj4iISE3jcsOlUyG6Kbx7FaQkOl2RiIiIiNRwCnhERERqoogGMPJ1SNsH710DOVlOVyQiIiIiNZgCHhERkZqqWQ84/3+wZQF8+X9OVyMiIiIiNZjH6QJERESkFD1Gwo6fYPFz0Lw3nHiZ0xWJiIiISA2kHjwiIiI13Vn/hDYnw0d/gp2/OV2NiIiIiNRANS7gMca0N8a8Yox5v7RtIiIidYY7BC6bDuH14Z0xkLbf6YpEREREpIaploDHGDPVGLPHGLOi0PZhxpg1xpj1xpiJANbajdbacYH7FbdNRESkTolqDCNfg0M74YNxkJvjdEUiIiIiUoNUVw+e6cCwwA3GGDcwGTgH6ApcYYzpWk31iIiIBJ+W8XDuf2DD1/D1P52uRkRERERqkGoJeKy13wOF+5OfBKz3987JBN4GLqyOekRERIJWn2ug91hY8CSs+sjpakRERESkhnByDp4WwLaA+wlAC2NMQ2PMC0AvY8w9AMVtK8wYc4MxZpkxZlliYmKVFy8iIsdG7fYxOPc/0CIeZt8Ee/5wuhoRqUPUdouI1FxOBjymmG3WWrvPWjvBWtvBWvuof2ORbcU88SVrbby1Nj4uLq5KCxcRkWOndvsYeELh8lchJBzeuRIOH3S6IhGpI9R2i4jUXE4GPAlAq4D7LYEdDtUiIiISXOq1gMtmwP5NMGsC5OY6XZGIiIiIOMjJgOdHoKMxpp0xxguMAjSZgIiISHm1PRnOfgTWzIX5TzhdjYiIiIg4qLqWSX8LWAR0MsYkGGPGWWuzgVuBz4HVwLvW2pXVUY+IiEit0W8CnHA5fPMvWPuF09WIiIiIiEM81fEi1torStg+F5hbHTWIiIjUSsbA+c9A4mqYOR6u/wYadnC6KhERERGpZk4O0RIREZHK4I2Aka+DccE7V0FmqtMViYiIiEg1U8AjIiJSG9RvC5e84uvJ8+GtYK3TFYmIiIhINVLAIyIiQW39nkNMmb8Rq0ADjjsDTr8fVs6ERZOcrkZEREREqpECHhERCWpvL93GPz9ZzX2zV5Cdo6XCGfQX6HIBfPl/sPE7p6sRERERkWqigEdERILaved24aYhHXhzyVbGv7qMlIxsp0tyljFw0XPQ6Hh4/1o4sM3pikRERESkGijgERGRoOZyGe4e1pl/XXwC89ftZeSLi9idfNjpspwVGg0j34CcLHhnDGSlO12RiIiIiFQxBTwiIlIrjO7Xmilj49m0N5WLJ//Aml2HnC7JWY2Og4tfhJ2/wCd3aNJlERERkVpOAY+IiNQap3VqzLs3DiA713Lp8wv5Yf1ep0tyVudz4dS74Zc34McpTlcjIiIiIlVIAY+IiNQq3VvUY9YtJ9M8NpyxU5fy/vIEp0ty1qkToePZ8NlE2LrY6WpEREREpIoo4BERkVqnRWw47900gP7tG/K3937lqS/X1t1l1F0uGPESxLaGd6+G5J1OVyQiIiIiVUABj4iI1EoxYSFMvaYvl/ZpyTPz1vG3934jM7uOLqMeHuubdDkjBd4bC9mZTlckIiIiIpVMAY+IiNRaXo+L/1x6In8583g++CmBa6Yt5WB6ltNlOaNJV7hwEmxb4huuJSIiIiK1igIeERGp1Ywx3H5mR/57WQ9+3Lyfy15YSEJSmtNlOaP7CBh4Gyx7BX5+3elqRERERKQSKeAREZE64ZI+LZlx7UnsPHiYi59byIrtB50uyRlnPADtToU5f4XtPzldjYiIiIhUEgU8IiJSZww8rhEf3DQQr9vF5S8u4us/djtdUvVze+DSaRDVBN65ClISna5IRERERCqBAh4REalTjm8SzaybB9I+LpLxM5bx2uItTpdU/SIbwsjXIG0vvH8t5GQ7XZGIiIiIHCMFPCIiUuc0jgnjnRsGMKRTY+6fvYJH564mN7eOLaPevCec9zRsng9fPeB0NSIiIiJyjBTwiIhInRQZ6uGlq/owpn9rXvx+I396+2cOZ+U4XVb16nkFnHQDLJoEv7/vdDUiIiIicgwU8IiISJ3lcbt4+MLu3HNOZz75bSdjpiwhKTXT6bKq11mPQKv+8OGtsGuF09WIiIiIyFFSwCMiInWaMYYbT+3ApNG9+G37QUY8v5At+1KdLqv6eLxw+asQVg/euRLS9jtdkYiIiIgcBQU8IiIiwHknNufN8f04kJbJxc8t5KetSU6XVH2im/gmXT64HWZeD7l1bKiaiIiISC2ggEdERMQvvm0DZt58MtFhHq54aTGfrdjpdEnVp9VJcM7jsP4r+OZfTlcjIiIiIhWkgEdERCRAu0aRzLxpIF2bx3DTGz8xZf5GrK0jK2zFXwe9xsD8J2D1HKerEREREZEKqPEBjzFmsDHmBWPMFGPMQqfrERGR2q9hVChvXd+fYd2a8s9PVvPQx6vIqQvLqBsD5/4XmveGWRMgca3TFYmIiIhIOTkS8Bhjphpj9hhjVhTaPswYs8YYs94YMxHAWjvfWjsBmAPMcKJeERGpe8JC3Ewe3Zvxg9oxfeFmbnxtOWmZ2U6XVfVCwnzz8XhCfZMuH052uiIRERERKQenevBMB4YFbjDGuIHJwDlAV+AKY0zXgF1GA29VV4EiIiIul+Hv53XloQu68fUfu7nipcUkHspwuqyqV68lXDYd9m2A2TdBbq7TFYmIiIhIGRwJeKy13wOF12E9CVhvrd1orc0E3gYuBDDGtAYOWmtLvIxojLnBGLPMGLMsMTGxqkoXEZFKEkzt9tiBbXnpqnjW7k7h4ud+YP2eQ06XVPXaDYazHoY/5sCCJ52uRkRqiGBqu0VE6pqaNAdPC2BbwP0E/zaAccC00p5srX3JWhtvrY2Pi4urohJFRKSyBFu7fWbXJrxzY38OZ+Uy4rmFLN64z+mSql7/m+GEy+Drf8K6r5yuRkRqgGBru0VE6pKaFPCYYrZZAGvtA9ZaTbAsIiKOOrFlLLNuHkjjmDCuemUJs3/e7nRJVcsYOP9/0KQbfDAO9m9yuiIRERERKUFNCngSgFYB91sCOxyqRUREpFitGkTwwYSB9G5dnz+/8wuTvl5Xu5dR90bAyNd9378zBjJTna1HRERERIpVkwKeH4GOxph2xhgvMAr4yOGaREREiqgXEcKr407iop7NeeKLtdwz83eycmrxRMQN2sElr8DulfDRbVCbAy0RERGRIOXUMulvAYuATsaYBGPMOGttNnAr8DmwGnjXWrvSifpERETKEupx89TInvzp9ON4+8dtjJuxjEOHs5wuq+p0PBNO/zuseB8WP+d0NSIiIiJSiMeJF7XWXlHC9rnA3GouR0RE5KgYY7jjrE60rB/OvbNWcNkLi5h2bV+a1Qt3urSqMfgO2PEzfHE/ND0B2p3idEUiIiIi4leThmiJiIgEpZF9WzPtmr4kJKVz8eSFrN6Z7HRJVcMYuOh5aNgB3rsWDiY4XZGIiIiI+CngERERqQSnHB/HuzcOAOCyFxbx/dpEhyuqImExMOpNyM7wTbqcddjpikREREQEBTwiIiKVpmvzGGbdMpCW9cO5dvqPvPvjNqdLqhqNOsKIF33DtebeoUmXRURERGoABTwiIiKVqFm9cN6bMICBHRpy1we/8cTna2rnMuqdh8Mpd8LPr8OyqU5XIyIiIlLnKeARERGpZNFhIUy9pi8j41sx6Zv1/OWdX8jIznG6rMo35B44bih8ejdsW+p0NSIiIiJ1mgIeERGRKhDidvHYJSfwt7OOZ/YvOxg7dSkH02rZMuouN1zyMtRrAe9cBYd2OV2RiIiISJ2lgEdERKSKGGO49fSOPD2yJz9tOcAlLyxk2/40p8uqXOH1YeQbkJEM746F7EynKxIRERGpkxTwiIiIVLGLerXg1XEnsSf5MBc/9wO/bjvgdEmVq2l3uOBZ2LYYPr/X6WpERERE6iQFPCIiItWgf/uGzLx5IGEhbka9tJgvV+12uqTKdcKlMOBW+PFl+OVNp6sRERERqXMU8IiIiFST4xpHM+vmk+nYJIobX1vGjIWbnS6pcp35ELQdDB//2beEuoiIiIhUGwU8IiIi1SguOpS3b+jP6Z2b8MBHK/nnnFXk5taSZdTdHrhsOkTG+SZdTt3rdEUiIiIidYYCHhERkWoW4fXw4lV9uGZgW6Ys2MQtb/7E4axasox6ZCMY+Rqk7IH3r4WcbKcrEhEREakTFPCIiIg4wO0yPHB+V/4+vAufrdzF6JcXsy8lw+myKkeL3nDek7Dpe5j3oNPViIiIiNQJZQY8xphFhe5HG2N6VV1JIiIidYMxhvGD2/Pc6N6s3JHMiOcXsmlvqtNlVY5eYyB+HCx8FlZ84HQ1IiIiIrVeeXrwhAIYY54EsNYeAp6ryqJERETqknNOaMab1/fn0OFsRjz3A8s273e6pMox7DFo1Q8+vBV2r3K6GhEREZFarTwBjzHGNAbGGGOMf1t4FdYkIiJS5/RpU5+ZNw0kNsLL6ClL+OS3nU6XdOw8XrhsBoRGwztXQvoBpysSERERqbXKE/DcAywA3gSeMsbcXM7niYiISAW0bRTJBzcN5MQW9bjlzZ948bsNWBvkK2zFNIPLX4UDW2Hm9ZCb63RFIiIiIrVSmUGNtfYza+3x1to/A+8AxwHjqrowERGRuqhBpJfXx/dj+AnNePTTP7j/wxVk5wR5KNK6v2+41rovYPYE9eQRERERqQKeiuxsrV0ELCpzRxERETlqYSFunr2iFy3rh/Pi9xvZceAwz17Ri8jQCn1s1yx9x0NqInz/H9/qWuc9DZ2GOV2ViIiISK2hoVYiIiI1kMtluOfcLjx8UXe+XbOHkS8tYk/yYafLOnrGwGn3wvivILw+vDUSZt4AabVkQmkRERERhyngERERqcGu6t+GKWPj2ZiYysXPLWTt7kNOl3RsWvSBG76DUyf6lk+ffBKs+tDpqkRERESCngIeERGRGu70zk1498YBZObkcsnzC1m4fq/TJR0bjxdOuwdu+Baim8G7V/tuKXucrkxEREQkaCngERERCQLdW9Rj9i0n06xeGGOnLeWD5QlOl3Tsmp4A138NZ/wfrPkUJveD396DYF85TERERMQBNT7gMcZ0Mca8YIx53xhzk9P1iIiIOKVFbDjvTRhI37YNuOO9X3nmq3XBv4y6OwQG3wETFkDDDjBzPLx1BSTvcLoyERERkaDiSMBjjJlqjNljjFlRaPswY8waY8x6Y8xEAGvtamvtBOByIN6JekVERGqKeuEhTL/2JEb0bsFTX63lzvd/IzM7yJdRB4jrBNd9Dmc9Ahu/gcn94afX1JtHREREpJyc6sEzHSiwNqoxxg1MBs4BugJXGGO6+h+7AFgAzKveMkVERGoer8fFfy/rwe1ndOT95QlcN/1Hkg9nOV3WsXO5YeCtcNNCaNodProVXh8BB7Y6XZmIiIhIjedIwGOt/R4ovC7qScB6a+1Ga20m8DZwoX//j6y1A4ErSzqmMeYGY8wyY8yyxMTEqipdREQqidrtY2OM4S9Dj+c/l57I4o37uOz5Rew4kO50WZWjYQcYOwfOfQK2LoHnBsCPUyC3FvRUEglyartFRGqumjQHTwtgW8D9BKCFMWaIMeZ/xpgXgbklPdla+5K1Nt5aGx8XF1fVtYqIyDFSu105LotvxfRrT2LHgXQumvwDK7YfdLqkyuFywUnXw82LoGU8fHIHzDgf9m1wujKROk1tt4hIzVWTAh5TzDZrrf3WWnubtfZGa+3kaq9KRESkhhvUsRHv3TQAj8sw8sVFfLOmFi03Xr8NXDUbLngWdv0Gz58MiyZDbo7TlYmIiIjUKDUp4EkAWgXcbwloCQ0REZFy6Nw0hlm3nEybhpGMn7GMN5fUonlrjIHeV8PNi6HdKfD5vTB1GCSudboyERERkRqjJgU8PwIdjTHtjDFeYBTwkcM1iYiIBI0mMWG8O2EAgzs24t5Zv/P4Z3+Qm1uLVqGq1wJGvwMjXoZ96+CFQTD/ScjJdroyEREREcc5tUz6W8AioJMxJsEYM85amw3cCnwOrAbetdaudKI+ERGRYBUV6mHK1fGM7tea57/dwO3v/EJGdi0azmQMnHg53LwEjj8L5j0EU86A3TplEBERkbrN48SLWmuvKGH7XEqZSFlERETK5nG7eOSi7rSqH8Hjn/3B7oOHeenqPsRGeJ0urfJEN4GRr8PK2b4JmF88FU75Gwz6K3hq0fsUERERKaeaNERLREREKokxhpuGdODZK3rxy7YDjHh+IVv3pTldVuXrdhHcstT39dtH4aUhsONnh4sSERERqX4KeERERGqx83s0543r+7E/NZOLn/uBn7cmOV1S5YtsCJdMgVFvQdo+ePkM+OohyDrsdGUiIiIi1UYBj4iISC3Xt20DPrhpIJGhHq54eTGfrdjldElVo/O5cMsS6HkFLHgSXhwM25Y6XZWIiIhItVDAIyIiUgd0iIti5s0D6dw0hpveWM4rCzY5XVLVCI+FCyfDmJmQlQ6vnAWf3QuZtXB4moiIiEgABTwiIiJ1RKOoUN66vj9ndW3Cw3NW8eBHK8mpTcuoBzruDLhpIcRfB4snw/MDYdN8p6sSERERqTIKeEREROqQcK+b567sw3Unt2P6ws3c9Ppy0jNr0TLqgcJi4LwnYewcwMKM83wrbmUccroyERERkUqngEdERKSOcbsM/3d+Vx44vytfrt7NqJcXszclw+myqk67wb7ePP1vhh9fgecGwPp5TlclIiIiUqkU8IiIiNRR157cjhfG9GHNrmQufu4HNiSmOF1S1fFGwrBH4brPwRMGr4+AD2+B9ANOVyYiIiJSKRTwiIiI1GFnd2vK2zcMID0zhxHPLWTppv1Ol1S1WveDCQtg0F/glzfhuf6w5jOnqxIRERE5Zgp4RERE6rierWKZedPJNIzyMmbKEj76dYfTJVWtkDA480EYPw/CG8BbI+GD6yGtlodbIiIiUqsp4BERERFaN4xg5k0D6dk6ltve+pnnvl2PtbV0ha08LXrDDd/CqRNh5UyYfBKs+tDpqkRERESOigIeERERASA2wstr407igh7N+fdna7h31gqyc3KdLqtqebxw2j2+oCemObx7te+WssfpykREREQqRAGPiIiI5Av1uHl6ZE9uHtKBt5ZuZdyMZaRkZDtdVtVregKM/xrOeADWfOrrzfPbu1DbezGJiIhIraGAR0RERApwuQx3DevMvy4+gQXr93L5C4vYnXzY6bKqntsDg//qm4S54XEw83p46wpIruVzEomIiEitoIBHREREijW6X2teGRvPln2pXDT5B/7Ylex0SdUjrpNvOfWz/wUbv4XJ/eGn19SbR0RERGo0BTwiIiJSoiGdGvPuhAHkWsulzy9i/rpEp0uqHi43DLgFbvoBmnaHj26F1y6GA1udrkxERESkWAp4REREpFTdmtdj1s0n07J+ONdO+5F3l21zuqTq07ADjJ0D5z4B25bCcwNg6cuQW8snnxYREZGgo4BHREREytQ8Npx3JwxgQIeG3PX+bzz5xZrav4x6HpcLTroebl4ELfvC3L/BjPNh3wanKxMRERHJp4BHREREyiUmLISp1/Tlsj4t+d/X67nj3V/JzK5DPVnqt4GrZsEFk2DX7/D8ybBoMuTmOF2ZiIiIiAIeERERKb8Qt4t/X3oidww9npk/b2fs1KUcTM9yuqzqYwz0vgpuWQztT4XP74WpwyBxrdOViYiISB2ngEdEREQqxBjDn87oyFMje7Bsy34ufX4hCUlpTpdVvWKawxVvw4iXYd86eGEQzH8ScrKdrkxERETqKAU8IiIiclQu7tWSGdedxK7kw1z83EJ+TzjodEnVyxg48XK4ZSkcfzbMewimnAG7VjhdmYiIiNRBNT7gMca0N8a8Yox53+laREREpKCBHRox86aBeN0uLn9xEfNW73a6pOoX1RhGvgaXzYDk7fDSqfDNo5Cd6XRlIiIiUodUacBjjJlqjNljjFlRaPswY8waY8x6Y8zE0o5hrd1orR1XlXWKiEjwWpB0iP9u2sXBLA2NcUrHJtHMumUgxzWO4vpXl/Ha4i1Ol+SMbhfBzUug2wj47jF4aQjs+NnpqkRERKSOqOoePNOBYYEbjDFuYDJwDtAVuMIY09UYc4IxZk6hW+Mqrk9ERILcD0kp/GfzLvosWsW/Nuxgb6aCHic0jg7j7Rv6c1qnxtw/ewX/mrua3Nw6sox6oMiGcMnLvvl50vfDy2fAVw9C1mGnKxMREZFarkoDHmvt98D+QptPAtb7e+ZkAm8DF1prf7fWnlfotqe8r2WMucEYs8wYsywxMbES34WIiFSFymq3727fjHl9O3F6wxie3bqHvotW8cC67ezKqEMrO9UQkaEeXryqD1f1b8NL32/kT2/9zOGsOrqEeKdz4ObF0PMKWPAUvDgYti11uiqRY6ZzbhGRmsuJOXhaANsC7if4txXLGNPQGPMC0MsYc09J+1lrX7LWxltr4+Pi4iqvWhERqRKV2W53iwrnpW5t+f6kzpzXuB5Ttidy0qJV3LVmG1vTMyqpYikPj9vFPy7sxr3nduaT33dy5ZQl7E+to3PRhMfChZNhzEzISodXzoLP7oXMOrbimNQqOucWEam5nAh4TDHbSuzDba3dZ62dYK3tYK19tArrEhGRINcxMoxnu7RhYb8ujGrWgLd27mfgktXcvnorG9I0RKa6GGO44ZQOTB7dm9+3H+SS5xeyeW+q02U557gz4OZF0HccLJ4Mzw+ETfOdrkpERERqGScCngSgVcD9lsAOB+oQEZFaqk14KP/u1Iol/btwbYtGfLgnicFL/mDCys2sTkl3urw6Y/iJzXjr+n4cSMtkxPMLWb4lyemSnBMaDcP/C2PnABZmnAdz/goZh5yuTERERGoJJwKeH4GOxph2xhgvMAr4yIE6RESklmse5uXhji35cUBXbm7dmC/3JXPaj2u49vdN/JKsYTLVoU+bBsy8+WRiwjxc8fJipszfSEpGHZ4Iu91guGkh9L8Flk2F5wbA+nlOVyUiIiK1QFUvk/4WsAjoZIxJMMaMs9ZmA7cCnwOrgXettSursg4REanb4rwh/L1Dc5YN6ModbZuw8EAKw5av5YpfN7DkQIrT5dV67RpFMvPmk+nXrgH//GQ1Ax6dx6NzV7PjQB3tTeWNhGH/gnFfQEg4vD4CPrwF0g84XZmIiIgEMWNt7VvCND4+3i5btszpMkRE6hxjzHJrbXxFn1fd7fah7Bymb9/LC9sS2ZeVzYDYSP7SpimD60dhTHFTxUll+XlrElMWbOLT33fiMobhJzbj+sHt6d6intOlOSPrMHz3GPzwP4hqDOc95VuBS6SaHG27DTrnFhFxQmntthNDtERERBwV7XHzpzZN+HFAVx4+rgWb0jK5/NcNDP9pHV/sPUhtvPhRU/RqXZ/Jo3vz3Z2nMXZgW+at3sN5zy5g5IuL+GrVbnJz69jPPiQMznwQxn8F4Q3grVHwwXhI2+90ZSIiIhJkFPCIiEidFeF2cX2rOJYM6MK/j29JYmY2V/++iTOXreGjPQfIUdBTZVo1iOD+87qy8J7Tue/cLmzbn8b4V5dx5pPf8friLaRn5jhdYvVq0Rtu+BZOnQgrZ8Hkk2DlbKerEhERkSCigEdEROq8UJeLq1s0YmG/LvyvS2syci03rNzMkKV/8N6u/WTXtV4l1SgmLITrT2nPd3edxv+u6EVUmIe/z17BwMfm8d8v1rDnUB1a3t7jhdPugRu+g5jm8N5YeOcqSNnjdGUiIiISBBTwiIiI+IW4DJc3bcB3J3XmxW5tCDGGP63eysAlq3ltx14ycnOdLrHWCnG7uKBHcz685WTevXEA8W0bMOmb9Qx67BvufO9X1uyqQ8uJN+0O47+GMx6AtZ/5evP89i6oR5mIiIiUQgGPiIhIIW5juLBxfeb17cSrJ7SjQYiHO9ck0H/xaqYkJJKWo6CnqhhjOKldA16+Op6v7xjCyL6t+Pi3HZz99Pdc9coSvl+bWDfmSHJ7YPBfYcICaHgczLzeNz9P8g6nKxMREZEaSgGPiIhICYwxnNWoHp/26cg7PTrQJszL39dt56RFq5i0ZTcp2XVsnphq1q5RJA9f1J1FE8/gzrM78ceuQ1w9dSnDnp7Pu8u2kVEXfv5xneC6z+Hsf8HG72ByP/jpVfXmERERkSIU8IiIiJTBGMOpDaKZ3bsjs3odR/eocP65cSfxi1bx3027OJCV7XSJtVr9SC+3nHYcC+4+jScu64ExcNf7v3HyY9/w7Lx1JKVmOl1i1XK5YcAtcNMP0PRE+OhP8NrFcGCr05WJiIhIDaKAR0REpAIGxEbxds8OfNrnePrHRvKfzbuIX7SKRzbsIDEzy+nyarVQj5tL+7Tk09sH8/q4fnRrHsN/v1zLgMfmcd+s39mYmOJ0iVWrYQcY+zEM/y8k/AjPDYClL4PmhhIREREU8IiIiByVXjERTD+hPV/37cQZDWOYtHUPJy1axf+t287OjFreo8RhxhgGdWzEjOtO4ou/nMKFPVrw3rIEznjyO8bP+JHFG/fV3nl6XC7oOx5uXgQt+8Lcv8GM82HfBqcrExEREYcp4BERETkGXaPCebFbW+b368z5jWN5ZXsi/Rat5q4129iSnuF0ebXe8U2iefzSE/lh4un86fSO/LT1AKNeWswFk37gw1+2k1VbJ8SObQ1XzYILJsGu3+H5k2HRZMitA/MSiYiISLEU8IiIiFSC4yLC+F+XNizs14VRzRrw9s79DFyymttWb2F92mGny6v14qJD+evQ41k48XT+dfEJpGZmc/vbv3DKv7/hxe82cDC9Fg6fMwZ6XwW3LIb2p8Ln98LUsyFxjdOViYiIiAMU8IiIiFSiNuGh/LtTK5YM6MK4FnF8vOcAg5f8wY0rN7MqJd3p8mq9sBA3o/u15qu/nMrUa+Jp2zCSRz/9g4GPzuOhj1eybX+a0yVWvpjmcMXbMOJl2LceXhgE8/8LOZr8W0REpC5RwCMiIlIFmoV6+UfHFiwd0JVbWzdm3r5kTv9xDWN/38hPyalOl1fruVyG0zs34a0b+jPnT4M4q1tTXlu0hVP/8w23vPETP21NcrrEymUMnHg53LIUOp0D8/4BU06HXSucrkxERESqiQIeERGRKhTnDeG+Ds1ZNqArd7ZtypIDqZy7fB2jftnA4gO1fNWnGqJ7i3o8NbIn8+8+jRtO6cD8dYmMeG4hlzy/kE9/30lObi2akDmqMVz+Klw2A5J3wEunwjePQrYm/hYREantFPCIiIhUg9gQD3e0a8qyAV35e/tmrEhJ56Kf13PRT+v4bv+h2rvqUw3SrF44E8/pzKJ7zuDB87uy59BhbnrjJ4Y88Q3TfthEakYtGtLU7SK4eQl0GwHfPQYvDYHtPzldlYiIiFQhBTwiIiLVKMrj5tY2TVg6oCv/7NiCLYczGfnrBs5dvo7P9x5U0FMNIkM9XHNyO77922k8f2VvGkeH8dDHqxjw6Dwe+/QPdh6sJXMlRTaES172zc+Tvh+mnAFfPgBZmvRbRESkNlLAIyIi4oAIt4vxLeNY3L8L/+nUkn1Z2Yz9fRNn/LiGD/ckkaOgp8q5XYZzTmjGBzcNZObNAxncMY6Xvt/A4Me/4S/v/MKK7QedLrFydDoHbl4MPa+EH572TcK8dYnTVYmIiEglU8AjIiLioFCXi6uaN2Jhvy4826U1WdZy48otnLr0D97dtZ+s2jQ/TA3Wu3V9Jl/Zm+/uPI2rB7Tli5W7OO/ZBVzx0mLmrd5NbrD/HsJj4cJJMGYmZB/2Laf+2T2QWQtXFRMREamjFPCIiIjUAB6X4bKmDfj2pM681K0tXmO4bfVWBi5Zzavb95KRm+t0iXVCqwYR/N/5XVl4zxncc05nNu9LZdyMZQx96jveXLKVw1k5Tpd4bI47A25eBH3HweLn4PmBsGm+01WJiIhIJVDAIyIiUoO4jeGCxrHM69uJV09oR6MQD3etTaD/4tW8vC2RtBwFPdWhXngIN57age/vOo1nRvUk3Ovm3lm/M/Cxr3nyy7UkHspwusSjFxoNw/8L13ziuz/jPJjzV8g45GxdIiIickwU8IiIiNRAxhjOalSPuX068m6PDrQN93L/+u30XbSKZ7fs5lB2kPckCRIhbhcX9mzBx7cO4u0b+tO7dX2e/XodJz/+NXe//xtrdwdxKNJ2ENy0EPrfAsumwnMDYP1XTlclIiIiR0kBj4iISA1mjOGUBtHM6tWRD3sdx4nR4TyycSfxi1bxn007ScqqRUt712DGGPq3b8iUsfHM++upXNanJR/+up2znvqesVOXsmDd3uBcAc0bAcP+BeO+gJBweP0S+PAWSD/gdGUiIiJSQSYoT0bKEB8fb5ctW+Z0GSIidY4xZrm1Nr6iz1O7XTG/JKfxzJbdfLr3IJFuF9e2aMSNreKI84Y4XVqdsj81kzcWb2HGoi3sTcmgc9Noxg9uz/k9mhHqcTtdXsVlHYbvHocfnoGIhtBjJHS9GFr0BmOcrk6qyNG226C2W0TECaW12zU+4DHGDAEeBlYCb1trvy3rOfqwERFxhgKe6rU6JZ1ntuzmwz0HCHMZxjRvyE2tGtM8zOt0aXVKRnYOH/6yg1fmb2LN7kM0jg5l7MC2XNmvNbERQfi72PEzfPMobPgacrMgtjV0vQi6XQzNeynsqWUU8IiIBJfS2u0qHaJljJlqjNljjFlRaPswY8waY8x6Y8zEMg5jgRQgDEioqlpFRESCTZeocF7o1pb5/TpzQeP6TN2+l/6LV3Pnmm1sSQ/iSYCDTKjHzeXxrfjsz4N59bqT6NQ0mv98voYBj37N/bNXsGlvqtMlVkzzXnDlu3DnOrjwOYjr7Ftx6+XT4Jke8OX/+UKgGn6RUEREpK6p0h48xphT8IUzr1pru/u3uYG1wFB8gc2PwBWAG3i00CGuA/Zaa3ONMU2AJ621V5b1urqaICLiDPXgcdbW9Awmb93DWzv3k4NlRJP63Na6CR0jw5wurc5Zs+sQU+Zv5MNfdpCVm8uZXZpw/eD29G1bHxOMPWDS9sOaubByFmz8FnKzoX7bIz17mvVQz54gpR48IiLBxdEhWsaYtsCcgIBnAPCgtfZs//17AKy1hcOdwsfxAm9aay8t4fEbgBsAWrdu3WfLli2V9h5ERKR8KvKHgtrtqrMrI4vnt+7h1R17OZxrOb9xLLe3aUK3qHCnS6tz9hw6zGuLtvD64i0kpWVxYst6jB/cnnO7N8XjDtK1LtL2wx+f+MKeTd/5w5520O0iX9jT9ESFPUGkogGP2m4REWfVtIDnUmCYtXa8//5VQD9r7a0lPH8EcDYQCzyvOXhERGou9eCpWfZmZvPStj1M3b6XlJxczmoYw5/bNqF3TKTTpdU56Zk5fPBTAlMXbGLj3lRaxIZzzcC2jDypFTFhQTw5dtp++GOOv2fPd2BzoEF7X9DT9SJoeoLCnhpOPXhERIJLae22p7qLAYr7lC8xZbLWzgRmVl05IiIitVMjr4d7OzTn5taNmbp9Ly9vS+Tc5es4tX40f27bhAGxUU6XWGeEe92M6d+G0Se15us/9vDy/I08Mnc1z8xbx8i+rbj25La0rB/hdJkVF9EAel/tu6XuOxL2LHga5v8XGnTwhT3dLoIm3RX2iIiIVCEnAp4EoFXA/ZbADgfqEBGpk6y1ZFvItLlk5VqyrCXT/zXW4yY2xImPBqlKsSEe/tq2KTe0jGPGjn08v3UPF/+8nv71Irm9TROGNIgOznlhgpDLZTizaxPO7NqE3xMOMmXBRqYv3Mz0hZsZ1r0p1w9uT89WsU6XeXQiG0Kfsb5b6j7442N/2PMkzH8CGh7nD3suhsZdFfaIiIhUMieGaHnwTbJ8BrAd3yTLo621KyvrNdVdVESckBMQlPi+5ubfz8q1ZOZ9zd8nt0C4UmAfa8nKzS10v2AYU+D5RR4r5fVLaff/2bEF41vGHfXPQEO0gkN6Ti5v7tzH5K172JGRRY/ocP7SpilnNYrBpT+6q92OA+nMWLiZN5du5dDhbPq2rc+4Qe0Z2rUJblct+H2kJPrDntmweT7YXGjYMSDs6aKwx0EaoiUiElwcm4PHGPMWMARoBOwGHrDWvmKMORd4Gt/KWVOttY9U5uvqw0ak9rC2pPCjaAhSNOCoWAhSXBiS4X9+8a9fsAdMbhX9DLzGEOIy+V9DjMHrMoQYl/+ryf8a4jIB21xFHyvw1VXk2D2jI45pxSUFPMElMzeX93Yl8b8tu9lyOJMukWHc3qYJ5zeOxa0/uKtdSkY27/64jak/bCIhKZ02DSO47uR2XNqnJZGhtaRnXUoirP7I17Nnyw++sKdRpyMTNDfu4nSFdY4CHhGR4OLoJMtO0IeNSOnyhuhkVTQgKSlgKSUgyQtBSn6NknuZ5O1fFdyGgMCkhKAk/6urhIDl6IKWwq/hdZX0+i48hqAaOqOAJzhl51pm70nimS27WZeWQYfwUG5r04QRTeoTUht6kASZ7Jxcvli1m5fnb+TnrQeoFx7C6H6tuWZgW5rE1KIl71P2+MOe2bB5AWAhrvORCZobd3a4wLpBAY+ISHBRwCNSTWrDEJ1jFVpMUJEXclS4J0rhgKUCPVEKBipFe7Kod0LVUMAT3HKt5ZPEgzy9ZRcrUw7TKszLra0bM6pZA0JdQbqkd5BbvmU/U+Zv4vOVu3C7DOef2Jxxg9vRrXk9p0urXId2Hwl7tvyAL+zpcmSC5rhODhdYeyngEREJLgp4JKjlHm2vkhKCkezyHqccvUtqwhCdEBfH3gOlggFJ0dfwPd8dZL1NpPIp4KkdrLV8uS+Zp7fs5qfkNJp6Q7i5dRxXNm9IpNvtdHl10tZ9aUz9YRPvLttGWmYOAzs05PrB7Tn1+Dhcta2X1aFdsNo/QfOWhYD1Tcqc17Mn7ninK6xVFPCIiAQXBTxSRE5ZvUEqEJpU6DglhCYFQxffPnmP5VTRP1GPKT4YKTxEpkBQUkKwcUzBSBmvH2xDdKRuU8BTu1hrWZCUwlNbdrPwQAoNQzzc2CqOa1s0ItqjoMcJB9OzeGvpVqb/sJldyYc5rnEU4wa14+JeLQgLqYW/k+SdR3r2bF2EL+zpdmSC5kbHOV1h0FPAIyISXBTwVJPyDM/JrsgQnBLmL6nJPU0qGpp4jmFek5ASApb84xUX0AS8llaKEal8Cnhqr6UHUnh6y26+3n+Ieh4341o24vqWcdQPqSWT/waZzOxc5v6+k5fnb2TljmQaRnoZ078NVw1oQ6OoUKfLqxrJO2DVR7Bqtj/sAZqcAN0uhK4Ke46WAh4RkeCigKcC7l6zjd2ZWeUIZqovNCl+zpGiQYjHHH1Pk8B5UsozFKfwcRSaiAgo4KkLfj2UxjObdzN370Ei3S6uadGICa3iiPOGOF1anWStZfHG/UyZv5F5f+zB63ExolcLxg9ux3GNo50ur+oc3H5kNa5tS3zbmp7gG8LV7WJo2MHR8oKJAh4RkeCigKcCrvh1A3sys456ThPPMQzPKRDQBBxXw3NEJFgo4Kk7Vqek878tu/lwzwG8LsOVzRpyc+vGtAjzOl1anbUhMYVXFmzig+UJZGTnMqRTHNcPbs/ADg1r97nEwQRfz56VsyBhqW9b0xN9kzN3vUhhTxkU8IiIlC4r15KWk0NqTm7+LS0nl1T/trScXNJyc0nNPrIttdC2/H38z/uhX5ejvjimgEdERKqFAp66Z2NaBs9u3c17u/ZjMIxs2oA/tWlMm/BaOkwoCOxLyeCNJVt5ddFm9qZk0qVZDOMHteP8Hs3xemr5amgHE2DVh/6w50fftmY9jkzQ3KCdo+XVRAp4RKS2yLHWF6TkHAlSUku4X/I+AYGM/35FViB2G4hyu4l0u4jw3yLdLiLd7oDvXdzRtulRD3NXwCMiUktl5eSSlplDWmY2aZk5pGfmkJaZQ2pmdv73eY+lZeaQlpFNWlbefgHb/fdvPe04LotvddT1KOCpu7YdzmTy1j28tXMf2dZyceP63NamCcdHhjldWp11OCuHj37ZwZQFG1m7O4XG0aGMHdiWK/u1JjaiDvS0OrDtSNiz3d++NOt5ZOn1+m0dLK7mUMAjItXNWktabuGg5UiwUrCnTMEAJrDnTGBvmrScHNJzy59tGMgPWyL8AUxkQCATuK2k+8U9z1sNI3AU8IiIOCg315KeVUroUt5wptB+aZnZZFVwmbnwEDcRXjcRoW4iQjyEe91EhroJD/EQ4XUzoncLhnRqfNTvVQGP7MrI4vlte3h1+z4O5+ZyXlwst7dpTPfoCKdLq7OstXy/bi9T5m9k/rq9hIe4uSy+Jded3I62jSKdLq96JG3xhT2rZsP25b5tzXsd6dlTv42T1TlKAY+IlMRaS0auPTLcKCeHtOyA4Ud5YUsx20rrJZOWk0tFzmDDXa5CQUvBYKW4bZHF9JyJCLgf7greqVAU8IiIlMFaS0b2kd4w6Zk5pBb6Pr2EsCXwseL2O5xVsSnYvR6XL4QJcfsDGE9AMOMhwv99uNdDpNe3T4TX4w9qfN/nBTeBIU6Yx43L5dwVhdKo3a599mZm83JCIlMTEjmUk8vQhjH8pU0TeterI4FCDbV6ZzKvLNjEh79sJzvXclbXJowf3J74NvWD9kS3wpI2+3v2zIYdP/m2Ne99pGdPbGsHi6t+CnhEaoesXFvuIUmFe8kUDWRyScv1Pbci1xJDXaZgkJIXzHgK3i+pl0xx28LdLi3kU4gCHhGpNfKGJKWX0tMlPTPbH84c+b64IUmFHyurV2f2wd3sef8hmo97DrfLlCOAOfLYF68+zdIvZpN26CAzl6wv8rzIUF8QExHixuM+tjky3nvvPR588EFWr17N0qVLiY8v/rz9s88+4/bbbycnJ4fx48czceJEAO68804+/vhjvF4vHTp0YNq0acTGxpbrtRXwSGEHs7KZun0vL21LJCk7h+MiQomPiSS+XiTx9SI4PiJMJ24O2JN8mFcXbeH1JVs4kJZFj1axjB/UjnO6Nz3mNqg6bN68mfPOO48VK1ZU+Ln33Xcfr776KklJSaRsW+ELelbNhh0/+3ZoEX9kgubYox+yWpxnnnmGl19+GWst119/PX/+85+L7JOUlMR1113Hhg0bCAsLY+rUqXTv3p01a9YwcuTI/P02btzIP/7xj2KPUREKeESqV948McUNNyo8cW/J+xQMatIqOE+Mx1DqcKPihi2V2UvG5cJzlBcSK61NT0k5qtcvr/KeI5d0jr1//35GjhzJ5s2badu2Le+++y7169evcB0KeESkWuXmWg5n55CacSSIKWmIUfE9YgL3CwxucsjMqVhvmPAQ/xAkr683S0SoP3wJ8fV4yfv+yLAlXw+YiEKPRYa62btrO9eOuoRffvsNr9tVoavdixcvpk2bNnTs2LHKP3xWr16Ny+Xixhtv5Iknnig24MnJyeH444/nyy+/pGXLlvTt25e33nqLrl278sUXX3D66afj8Xi4++67AXj88cfL9doKeKQkqdk5vLVrP9/vP8Sy5FT2Z+UAEONx0ScmMj/06R0TQbTH7XC1dUdaZjYf/LSdqQs2sWlvKi1iw7n25LaM7NuK6LCjW92jOhzLHwMltsf7N/mCnpWzYOevvm0t+/qXXr8I6rU8pppXrFjBqFGjWLp0KV6vl2HDhvH888/TsWPHAvvdeeedREVF8cADD/DHH39wyy23MG/evAL75OTk0KJFC5YsWUKbNsc2vEwBj0jxKjJPTHFDkorOJVPxeWJcUOaQpMDeMFGlDEkK7CXjddWsIL9K2vQqUJ5z5NLOse+66y4aNGjAxIkTeeyxx0hKSir3OXag0trto5u2WURqheyc3PwJd1MzioYtBb7PKDgEKTWzYC+a/K8ZOaT7/3Arr8AhSRGhHn+w4iYuOpRwb0R+j5i83jF5vV/y9gsMZAJDnMoekuRJC8PtMoR63GzcuJFLLrmEl156ib59+5b53P79+1fotaZPn86sWbPIyMhg06ZNjB49mgceeKBcz+3SpUuZ+yxdupTjjjuO9u3bAzBq1Cg+/PBDunbtyllnnVWg7vfff79CtYsUJ9LjZnzLOMa3jMNay8b0DJYdTGNZcirLDqbyxOZdWHyTHnaJDPP38PEFP+3CvXVn+FA1i/B6uKp/G648qTVfrd7NlAWb+Ocnq/nvF2vp3SaWPm0a0KdNfXq1jiWmhgY+ldYeN2gHg/7iu+3f6OvZs3IW0/99J7P++AsZnnpsOuRm9OgxPPDofytc5+rVq+nfvz8REb75qE499VRmzZrFXXfdVWC/VatWcc899wDQuXNnNm/ezO7du2nSpEn+PvPmzaNDhw7HHO44JicLUnZDTAvQ/205RpU9T0zgkKaKdIWIcLuODEnyBytRbjdNvCFlTNxbXHjjux8WxPPEHK2afI5dnnPk0s6xP/zwQ7799lsAxo4dy5AhQ44q4CmNAh6RGs5aS2ZOboG5XlIzCgcxBQOZwJ4zRYcy+ffJzCEzu/y9YYzBH64cmeslMtRDVKiHxtGhvpDF6w9Z8r4PGK4UGNzkhzPHMCTp/nUJrEhJg0x8t0oI7LtHhfNwx7Kv0K5Zs4ZRo0Yxbdo0evbsWaTbfKBvv/223MObClu6dCkrVqwgIiKCvn37Mnz4cOLj4xk8eDCHDh0qsv8TTzzBmWeeWa5jb9++nVatjgw9aNmyJUuWLCmy39SpU0t8byJHyxhDh4gwOkSEMbJZAwCSs3P4KTnVF/ocTGXW7iRe3bEPgAYhbuJjIunrD316REcQEQTDiIKJy2U4q1tTzurWlN8SDvD+8gSWbU5i0tfryLW+z4BOTaLp06Y+fdrUJ75NA1o1CMcY42+P0yu1Hsfb4wbtYfBffbfsf7N0/j9ZcU8HIpJW0/flpxnuWUD88GsY/NfpHErLKPL04trj7t27c99997Fv3z7Cw8OZO3dusT0se/TowcyZMxk0aBBLly5ly5YtJCQkFAh43n77ba644oryvZeaKPEPeGEQeKOgUUdo1Anijvd/7exb4cytP1Nqo7x5YkrqEVNWD5gC++YeCWUqMk9MmMsUOySpfoi3YNCSP3+Mf1tgeFNoW22aJ6ZWtumFVMY5dknnyKWdY+/evZtmzZoB0KxZM/bs2XNU9ZdGLadIJbHWcjgrt0Co4uv5UkKPmOICmowc0rKy/c85EsxkV6Arp9tl8oOWyLzJdr0eYiO8tKh/ZGhS3vYCgYy3YG+YwOeHhVRsSFJtlZiYyIUXXsgHH3xAt27dAOjUqRO//PJLpb/W0KFDadiwIQAjRoxgwYIFxMfHM3/+/GM+dnHDcwv/fh955BE8Hg9XXnnlMb+eSFliPG6GNIhhSIMYwDdHwdrUw/4ePr7Q54t9yYBv7oBuUeG+wMc/tKtFaIjaqEpyYstYTmwZC0BKRja/bD3A8i1JLNuyn49+2cEbS7YCEBcdSp/W9dndKpyUUENkqKdaO2NUW3sc1Zihwy+i4d9ehX0bGJEwgQV/bCDePZH5ZwGt+vtX47oAYpqXeJguXbpw9913M3ToUKKioujRowceT9FT8YkTJ3L77bfTs2dPTjjhBHr16lVgv8zMTD766CMeffTRyn2f1SmqCQz/LySuhb1rYNP38NvbRx53e6FBh4DQpxM0Ot4XBoWEO1d3HZI3T0xJqyCVNHlvwX2K9pKp6DwxUcX0bGkSGlJ0/pcShi0Vnk/mWOaJkaoVTOfYpZ0jl+ccuyop4JE6JyfXFlklqcCkuxk5pGUdGZJUUjiTmpHtW/o6w79qUlYOFZnSKm9Y0pEQxRe6NIkOI7xhwPbQYgKYwqFMXk+ZUHeF54YJVuW5ClAV6tWrR6tWrfjhhx/yP3yq6upC4d9j3v3K6MHTsmVLtm3bln8/ISGB5s2P/GEyY8YM5syZw7x58+rEvyepedzG0CUqnC5R4Vzl/6e5LzOb5f4hXcuS03hjx36mJOwFoKk3hPh6EfmhT/focEJr2BwDwSgq1MOgjo0Y1LER4PsMXbv7EMu3JOXftq7cBfg+13q0rEdvfw+f3q1jaRgVWmW1OdIeN+wAbQZgGp4HV57L4NPP4lDSAsj6CrgFvJEQVo8n/vMEnXoP5PzzzwdgwoQJTJgwgXHjxjFu3DgA7r33Xlq2LPpZFhMTw7Rp0wDfHwrt2rWjXbt2+Y9/+umn9O7du0CPnqAT1Rj6ji+47XAy7F3nC3wS18DetbBrBaz+GGxej2PjW+UsL/CJ63Sk9094xScqrQ0qMk9MWhmT9x7LPDHFBSsNQty0CvOWGLaUPrlvzZsnpq7QOXbp59hlnSOXdo7dpEkTdu7cSbNmzdi5cyeNGzc+qvpLo4BHaqy81ZLSiukJkxo4L0xWTn6Pl8I9YgKHNPnCmGwyKjAsCQhYHang/C4NIiMKBC2R3qLDlwr2kgnoHVMJKyWJM7xeL7Nnz+bss88mKiqK0aNHH9PVhVmzZrF06dJir8R++eWX7N+/n/DwcGbPns3UqVMBKqUHT9++fVm3bh2bNm2iRYsWvP3227z55puAb+b/xx9/nO+++y5/rgiRmqCh18NZjepxVqN6AGTnWlalpvPjwSOhz5zEg4BvqdYToyIKhD6NQ2vmHDLBxO0ydGkWQ5dmMYzp75v/Zc+hw/zkD3uWbUli6oJNvPjdRgDaN4qkd/6wrvp0iIuqtLnRHG+PG3Vk/m+bfDskrvVP0Dwb9qyEn2+E/QP55cWbfT17opsCsGfPHho3bszWrVuZOXMmixYtKvJaBw4cICIiAq/Xy5QpUzjllFOIiYnJf/ytt94K7uFZJQmLgZZ9fLdAWYdh/4YjoU/e143fQU7A8LjIxoWCH//X6GY1Yp6f0uaJKTDcqJzzxASGNBWdJyY/SHH5gpVot5umoSGEu470gCm8ulLRnjRHAp26OE+MVD7H23TKPscuzzlyaefYF1xwATNmzGDixInMmDGDCy+88KjeW2kU8Mgxy8rJzR9alJpR/LCjwqso5W0rOE9MwcCmIqsluQzF9nKJCfPQLCbsSEBTSk8YXzBTcPhSeEjlTtIrtUNkZCRz5sxh6NChREZGlqtxvuuuu3jzzTdJS0ujZcuWjB8/ngcffJANGzYUOHEPNGjQIK666irWr1/P6NGjS1zuvLBZs2bxpz/9icTERIYPH07Pnj35/NNP2bF5LeNvvIm5rz6D5/BBJt11NWcPGUhOdjbXDetJty0zIOJ8br31VjIyMhg6dCjgm0TuhRdeKP8PSKSaeFyGE6MjODE6gnEt4wDYnZHFsuTU/NDnlYS9PL8tEYDWYV7/xM0RxNeLpGtkuLrqV4LG0WEM696MYd198woczsrh9+0HfYHP5iS+/mMP7y9PAKBeeAi9W8f65/JpQI9W9YjwHv3paI1pj+OOh1Pv8t0S1+RP0Mynd8Knd0Gbk6HbRVzy51fYdyCZkJAQJk+enL88bl4bO2HCBFavXs3VV1+N2+2ma9euvPLKK/kvk5aWxpdffsmLL754dD+wYBQSBk26+W6BcnPgwJYjw7zyvv7+PmQcPLJfaEwx8/x08s3z4yp+xb68eWLKMySp7NWUcknLPfp5YgovSd3AP09MWUOSCm+LcLsId9WeeWKkdqoxbXoJSjpH3rFjB+PHj2fu3Ll4PB4mTZrE2WefTU5ODtddd11+j6SJEydy+eWX88orr9C6dWvee++9cv5kyk/LpNchmdm5AXPD+Hu4ZOSQXiSYOTJRb3HDk1IzsgMm/K14EBOZP8eLp+AQpdDApasLBjElDVPKm6w31FM3hiVJ7TNmzBieeuop4uLiCmyfPn06y5YtY9KkSUc2Zh2Gwwf9twO+r+kH/N8H3i9mn4zkgC7uJQitB2c+AH3HHfX70TLpUtNk5Oby+6F0lh1M5Uf/8K7dmdmA70p2r+iI/NCnT71IGoTo2ldls9ayaW9qgWFd6/b4Zsb3uAxdm8fQu3V94tv6evo0q+fMHCsVao/La88fR5ZeT/wDMNB2kG/Z9S4X+IYqOSyYl0nPmyemQNiSnUPWoV24967Fu28tEUnriU5aT+yBDUSnJ+Y/N8sVwo6oNmyObMuGyDasjWjDqrDWrA5twSGXt9w1hBhTdJ6XoxySFLiPW+e1IsekStr0GkLLpAeZzOzc/FWOCq+YVNyQo4KPFdwWOFdMVgUuGxSeqDdveFKDSC8t64f7Qpe8IUnegstW5z0WEVo0kFEQI3Vabo4vaAkIYl6/9zLY9imsPVAwlFn0K6zbBZOWHtmWfbj043vCICwWwupBeKxvEstGxxfcFlbvyP3AbaExJV7JFAlmoS5X/pLrE/CFDQkZWb4hXf7QZ9LW3flX1o+LCM2fuDm+XgTHR4TpivcxMsbQPi6K9nFRXBbvW1nkQFomP289wLIt+1m+JYm3f9zK9IWbAWgRG+6fx8cX+HRuGl0tw5pff/31yj9o487QeCIMmQh7Vvt79syET+6AuXf6e/Zc7A974so8XG2Rmp3DqtTDRZalDuwpk17GxL3pObllzBPTAjwtIO40iPPNE9M0N5VuhxPonL6VjmmbaZe6hU7JfzB45zxc/oFOucZFclRLDsZ2ILV+Rw43PI6shsdjGx1PWET9Ir1iNE+MSM1UJW16EFAPnqOUt3R1WqEJeVMzs4tsKyl8yd+W9xx/IHM0KyZFBqx8lB+wFJqEt0ggE9CTpvC2ujJRr0iFWAtZ6cX0oAm4X6BXjX97un97RjKUNlLeuI4EL0VCmcBt9YsGNWH1fN3YHaYePBKMUnNy+DU53b9iVyrLklPZn5UDQIzHRZ+YI6t19Y6JINqjMLSyZeXksnpncv48Pss3J7Er2RdqR3jd9GwVS3yb+vRuU59eretTLzyI51Oy1h/2zPLd9q3ztf9tBx0JeyIbVVs5TvTg+fVQGmcvW1vi4wV7tRxZ/SjSU3A1pCNLVh+ZTyY/fCm0LbS0eWKyDsO+9b5eVoHz/OxbDzmZR/aLalp0Za+4Tr4LKjpvFpFqUlq7rYCnkMnfrGdP8uGC4Uteb5hjWLrakxfEFDfxrn8FpMhierwcCWYCnhOwTUGMSAXlZPuClvSkEoKZMsKbwBO94oRElt1bpqTwxhsFQX4lUAGP1AbWWjamZ/iWZ/eHPn+kHsYCBugSGZbfKyg+JpJ24V59FleB7QfSfUO6Nu9n+dYkVu1IJtf6/o4+vnF0fi+f+Lb1ad0gIjh/B9bCnlUBYc96f9gz2B/2nF/lYY8TAU9Kdg7LklOLzC9T4+aJycn2z/OzpuA8P4lrITNgpZ3QegHBT8DX2DbqHSsilS6oAx5jTFfgQWAfMM9a+35ZzzmWPxTOfPI7Eg9lHBmeFOoptCJSwflffI8VP2Fv4DavJ7j/aBOpMayFzNSy55wp9v7BgidkxXF5SghhYksIamILDnPylH/cfm2kgEdqq+TsHH5KTvWFPgdTWZ6cyiH/HHQNQtzEx0T6VuuqF0mP6AgitFJipUvNyObXbQd8PXy2JPHT1iQOHfbNp9QoyltgHp/uLeoRGmw9rayF3SuPhD37N4BxQ7tTfHP2dD4fIhtW+ssG8xw8jrEWDu0surJX4hpI3XNkP08YNDzO39On85Hwp2EH8IQ6V7+IBDXHAh5jzFTgPGCPtbZ7wPZhwDOAG5hirX2slGPcASy11s43xnxkrb2grNetsx82IsEiOzMglDkIh5PKDmYCe9HkZpd+fG90Kb1lYkvvURMSoW7Wx0ABj9QVOdayNvWwv4ePL/TZkO5bstljoFtUeIHQp0VoSHD2MKnBcnMt6/ak5M/js3xLElv2pQHgdbs4oWW9/GFdfdrUp1FUEP1BbS3sXhEQ9mz0hT3tT/X17Ol8HkQ0qJSXUsBTydL2w951/p4+AcHPga3kD9M2LqjfrtCy7p18q32FFb/qj4hIHicDnlOAFODVvIDHGOMG1gJDgQTgR+AKfGFP4UXqr/N/fQBIAwZaa08u63X1YSNSxQrPRZM350xJXwuHN1lppR/f7S05mCl2TpqAfUJjwK35452igEfqsn2Z2SzPn8cnjZ+T00jP9fXyaeoNIb5ehC/wiYmke3Q4oUE+JLMmSjyUkd+7Z/mWJH5POJi/2mfbhhH0adOAPv5hXcfFReFyBUHoZi3s+u3I0utJm3y9TdvlhT3DjynsUcBTTTLTfPMt5Q/zypvnZwPkZh3ZL7p5McO9OkFknC5AiQjg8BAtY0xbYE5AwDMAeNBae7b//j0A1trC4U7h47iBmdbaC8t6TX3YiJSDtb6gpaxwpqSvZc1FExrjH75Ur+AwprxhTaWFNyHOLJFbls2bN3PeeeexYsWKCj0vLS2Nyy67jA0bNuB2uzn//PN57LESOy4es/fee48HH3yQ1atXs3TpUuLjiz9vf+aZZ3j55Zex1nL99dfz5z//GYAHH3yQl19+OX9ZyX/961+ce+655XptBTwiR2TnWlalpvPjwSOhz7bDvrYz1GU4MSqiQOjTODSIJw6uZuVtjw9n5bByx0GWbfYFPj+u28maN/9B1oFduN0uOsYP4YY7/k6ftvXp2SqWCG/VXSDYtm0bV199Nbt27cLlcnHDDTdw++23A3DnnXfy8ccf4/V66dChA9OmTSM2NrboQawlJ+En4k87jxahqcy5zEDjbty/8ww+/PBDXC4XjRs3Zvr06TRv3rxcdSngcVhOFiRtLjrPz951kJlyZL+w2KI9fuKOh3qtg37+PpFgOce+//77y9XWPvXUU0yZMgVjDCeccALTpk0jLCyMX3/9lQkTJpCSkkLbtm154403iImpeK+9mhbwXAoMs9aO99+/Cuhnrb21lOffC0QCz1trF5Sw3w3ADQCtW7fus2XLlkp+JyI1kLW+D/+jCmkOFrxiVITxdRPOD2cq8DWsXq2cVPBYPnyWLFnCaaedRmZmJmeccQb33nsv55xzTpXUuXr1alwuFzfeeCNPPPFEsQHPihUrGDVqFEuXLsXr9TJs2DCef/55OnbsyIMPPkhUVBR/+9vfKvzaFflDQe221EW7M7JYlpyaH/r8diidTP+5WOswr3/i5gji60XSNTIcTzD0MHHA0bbHqampfPTld4S0OpGl6/fw8j3X4O1zCeEd4nG7DF2bxdDHP6wrvk19msdW3gWHnTt3snPnTnr37s2hQ4fo06cPs2fPpmvXrnzxxRecfvrpeDwe7r77bgAef/zxYo/z5JNPsmzZMpKTk5nz0sOQnkRyXHz+Hwn/+9//WLVqFS+88EK56qpowKO2u5pYC8nbi5/nJ23vkf084dDouEIre3WGBu3r/LyAEjyC5Rw7OTm5zLZ2+/btDBo0iFWrVhEeHs7ll1/OueeeyzXXXEPfvn35+z/+xfE9+/HDJ++xZctmHn744QrXUVq77cQ4huLOVEpMmay1m/F/iJTGWvsS8BL4riYcbXEi1S431zfxb0XDmbyhT6XNR5O/7HbskfAltlXZ4Ux4rG9FCF0RKtHGjRu55JJLeOmll+jbt2+p+0ZERHDaaacB4PV66d27NwkJCaU+Z/r06cyaNYuMjAw2bdrE6NGjeeCBB8pVW5cuXcrcZ/Xq1fTv35+IiAgATj31VGbNmsVdd91VrteoDGq3pS5qEhrC8LhYhsfFApCRm8vvh9JZdjCVH5NT+SHpEDN3JwEQ4XbRKzoiP/TpUy+SBiEaglpYRdrjyMhIrrjI1yPx0j4tyVh4Gh2Ob8IJZ/blpy1JLNucxDs/bmP6ws0ANKsXRsTm+exf9QMRbsvendsq1B4HatasGc2aNQMgOjqaLl26sH37drp27cpZZ52Vv1///v15//3i1xRJSEjgk08+4b777uPJJ5+E5r0ACLz+m5qaWqXzPantribGQL2WvttxZxR8LG1/0R4/25bCioB/N8btC3kK9Po53ncLjare9yJSATX5HDuwt01eW5uVk8u+lEz2pmSQmJLB2g07OJB6mH/M/pnU3BB++GMHGxvsY1LCl/z020pu+zYD8918Zl19Ck8++d+jCnhK48RZQgLQKuB+S2CHA3WIVJ7cXMg4WIG5aApts7klH9u4C4Yw4fWhQbtSQpqAQMcbXWtDmoc+XsmqHcmVesyuzWN44PxuZe63Zs0aRo0axbRp0+jZsydr1qxh5MiRxe777bffFuhmf+DAAT7++OP8bvmlWbp0KStWrCAiIoK+ffsyfPhw4uPjGTx4MIcOFV0N7IknnuDMM88s87gA3bt357777mPfvn2Eh4czd+7cAj19Jk2axKuvvkp8fDz//e9/qV+/frmOKyIVE+py5S+5PgHfEu0JGVm+IV3+0GfS1t3k+P+MPi4ilPgY/xLt9SI4PiLM8SWlg709/uqr22nfvjGndWoMQHZOLqt3HmL5lv0s25LEZ8vT2PzzcpqNm0xE/wieeumv7G/QjYvOOoX7rhtBWmpKkdcqqz3evHkzP//8M/369Svy2NSpU0t8D3/+85/597//XexnwH333cerr75KvXr1+Oabb0p8bakFIhpAmwG+W6DMVN/Qrvzwx9/rZ+1nBS8IxrQsYZ6fRtX7PqRGCvY2vTLOsa2F7NxcsnMt2TmWUbfeS7OufdmbksnHU59k9fdzcHkjaDv2cTre92mR45sTz+fxsWfg9obStMtJ9Bp4Ko2iQtnboRMXNdjBWeeez+dzXmfbtm1l1lpRTgQ8PwIdjTHtgO3AKGC0A3WIFJSb4w9gksruOVMkpEmmlI5o4AopGMJENPItm1lcz5nCgY03SpPq1SCJiYlceOGFfPDBB3Tr5vug6tSpE7/88kuZz83OzuaKK67gtttuo3379mXuP3ToUBo29C2JO2LECBYsWEB8fDzz588/pvcAvl4+d999N0OHDiUqKooePXrg8fg+Em666Sbuv/9+jDHcf//93HHHHUydOvWYX1NEymaMoVWYl1ZhXi5u4gtWU3Ny+DU53b9iVypf7DvI27v2AxDjcdEnJjI/9OkdE0F0sC0PfpSqqj32+FfgOqFlPa45uR3TM39jjuccxl53Ksu3JPHmL4N548Mv+HB7OAy+h+ObRNGnTf38CZzbNowotQdNSkoKl1xyCU8//XSRuRceeeQRPB4PV155ZZHnzZkzh8aNG9OnTx++/fbbIo8/8sgjPPLIIzz66KNMmjSJhx56qMyfg9Qy3kho3tN3C5ST5VuJrXCvn59mFFz4IrxB8fP8xLSstRcMpeaoynNsay2pmTnsPZTBpr0pdOs7iE/XpbA3ZR/1uw7itqfeovmgTNwX/IPslExSMgqOkHgjAUhYS3SYh7hBV3HesPFs/eYNvJu/4frr/0qjaC+NokJpFBWKNzuNW5c8wfvbtxIbG8tll13Gqa4/GDNiDGO7vsVtt93G/Hdf5IILLsDrrfxhlFUa8Bhj3gKGAI2MMQnAA9baV4wxtwKf41s5a6q1dmVV1iF1SE5WCb1kDpTSu8Yf2mSUkVS7QwsGL1FNfWOcCw+BKu6rlt6udOW5ClAV6tWrR6tWrfjhhx/yP3zKe3XhhhtuoGPHjvmTGZel8B8IefcrowcPwLhx4xg3bhwA9957Ly1btgSgSZMm+ftcf/31nHfeeeU+pohUvki3m4H1oxhY3zeswlrLxvQM3/Ls/tDnic27sPjGwXeJDMvvFRQfE0m7cG+VDtmpC+1xZKiH83s05/wezcld1proerGcfFE/rrpoGMsOJjM/M4cc/1xKHpdhyNV/ZXD8iUx74GbCQ9zcdNMEJkyYQFZWFpdccglXXnklI0aMKPAaM2bMYM6cOcybN6/Y39cPP/zARx99xNy5czl8+DDJycmMGTOG119/vcB+o0ePZvjw4Qp45Ah3iC+wietUcHtuLiQnFF3Za/XHvvAn//mhENPMt8JXTHPf9zEtINr/NaaZ77xYq5jWCsHSpn/zzTe4QqNITMngz7fciKd+c2L7XsgTn69hb0oGe/3DpvJuh7N8IyZSft/A4a37WT/bN9/P4T2HqBcbQhu3i7Wv/JXczHQ8LoPH5cLjMrjdhoceeYyLhg8jLOTIBZQt5zRn+PDh3H7m8wXqeu+9rziuffv8xUpGjBjBwoULGTNmDJ07d+aLL74AYO3atXzyySeV+0OkigMea+0VJWyfC8ytyteWIJaTXUIQk1T2EKjMot2kC/CEFwxeYlpA427lmzy4hq7sJNXL6/Uye/Zszj77bKKiohg9enS5ri78/e9/5+DBg0yZMqXA9lmzZrF06VIefbToQoJffvkl+/fvJzw8nNmzZ+f3oqmMHjwAe/bsoXHjxmzdupWZM2eyaNEiwDcRaN48EbNmzaJ79+6V8noiUjmMMXSICKNDRBgjm/mWx07OzuGn5FRf6HMwlVm7k3h1xz4AGoS4iY+J9K3WVS+SHtERRLiD/2q80+1xfIdGbPjdt4JUbq5lfWIKy/3z+Py0NYkXlyfDBY+B28UXuTHsnbOSb196kA4djuevf/1rgeN/9tlnPP7443z+9TfkeEPZn5VNVq4ly9r8r2Pu+z8uv+d+sq1l8fff8eak/zH26cksOZBCg8SddOzYEYCPPvqIzp07H8NPVuoMlwtiW/tuHQtdIErde6THz/6NkLwTDu2E7ctg9U7IySi4v3FBZGN/AOS/BQZAeYGQN6L63p8EFa/XywczZzH0rLNIznYz4KwL2Zsexa3PziwS1uxLyaTfE4vJzMkl6fvXyN63jUYXTeT3j1fhMtAgMpScjYvJ2rWOoWP/QqOoIz1sfozdyNSn3+azCb1o3qgegwbew9QnpvqmKrjh51JrXLduXZltbevWrVm8eDFpaWmEh4czb968/GkQ8s69c3Nz+ec//8mECRMq/eeomFWqRpHhTiWEM+lJRXvcZBbtmVBASEShSYNbQ9MTyxfSeEIr/a1K3RMZGcmcOXMYOnQokZGRXHjhhaXun5CQwCOPPELnzp3p3bs3ALfeeivjx49nw4YNJS6POGjQIK666irWr1/P6NGjS1zuvLBZs2bxpz/9icTERIYPH07Pnj35/PPP2bFjB+PHj2fuXF++fskll7Bv3z5CQkKYPHly/jw7d911F7/88gvGGNq2bcuLL75Y3h+NiDgkxuNmSIMYhjTwtSc51rI29bC/h0+af2iXr6eqx0C3qPACoU+L0JAq7eVTVYprj3OsJbNQOJL3/baEbTzyyCO0Pb4TnXv0xAIXjb+eoVdezZxfV5DpCuH9XfvJtJbsXEumtczfl0yT3vGcfMnl7N2yiRPOv5iP6zXjg7UJZPtfK/9ruCWzUzRxx0cSmZ5FcmI6h/ams3LfYRa+M5fdH7/P93FtmTZrLsZtqD9qAqFDhrD9xuuxWVm0H3QKACFdTyDmL38nZ+8ekp/4B/Ufm1TgfWeu30FqchpX/b6JvjGReP81kTVr1uByuWjTpk25V9ASKVFkI9+t7clFH7PWN9HzoR2QHHDLu79vA2ya75ufsrCw2EIhUPOioVB4ffV8r0WycnLZn5pJ4qGM/J41+1IyCvSy2bplMxsSU+n5r+/I7nc79z18P/XmbSaiY38AQtyGRlGhNPQHNZ2bxtAoKhRX2j7ufvwd2rTvSPgn9+FxGf50663ccMP1PPHEL2R17Mw9I3sWqOfQ79EMOWUwd9xyfYXPsSdOLL6tDTzH7tevH5deeim9e/fG4/HQq1cvbrjBt2bUW2+9xeTJkwFfz55rr722kn7KR1T5MulOiI+Pt8uWLXO6jOCXF9JUpDdN3vdlDXcq3JMmcALhYsOZ+ke+15KPUouMGTOGp556Kr8bZ57p06ezbNkyJk2aVMIza6aKLrebR+22SNXbl5nNcv+QrmXJafycnEZ6rq/LelNvCPH1IvJDn+7R4bgxvqCjQIiRWyAwKfZrBbcdCVNyybaQmZtbNDixvv2yitmWF8TkbT/aM9uD/7qP6JvvwBXboMD29M8+ImvNKmJun4gBvC5DiPHfXIW+GoPHZfAW2OYixAXuXEg7kEHynjSSEtPYtyeNjMO+eR68XjdNG0fSslkUrZpF06ZpFJGhngLHzfve6zJ4jO81PC5DjMfNcRFhR/3v4mjbbVDbLWXISPH1+ikcACXv9C0Bf2gnpOyhyDyWnvBCw8CKCYSiGoOrbsw3VhNlZOf4wplDGQE9a3whzr7UgtuT0rKKPUZYiCu/V43v5j3yNfrI9rioUGLCPRW+CFHbzrED1bRl0qU65eb6wpYCPWYOlC+wKWvi4AJz0tT3NbaNu5YvsAk5+hMRkdqk8NwJIiJVpaHXw1mN6nFWo3oAZOdaVqWm8+PBI6HPnMRirrhXAbehSHDhCQwvXP5gxP94pMtFbDFhSnEBS6mPBzzmdbnwGPxfDd5Z7xcITrz+mt7b+BO/pOxm8pAeuCuxV4G1lq3703zDurYksXxzEouW7WChBZeBLs1iiG9Tn95t6hPftgHN64UFZS8rqcNCoyC0IzTqWPI+OVlwaJc/+NleMBBK3gHbFvsCodxCIYFxQ3TTgACoRdFQKLqZ/uaogNSMbPalZJIYMBRq76GCQ6PyetwcOpxd7DGiQj35QU2HuCj6tW/g73kTSlzAMKlG0aFEet1V2qbV1XNs9eAJBtZCxqGKhTN5YU5GculLcLu9ZfeYKSmw0Zw0IlKIevCIBLfdGVksS07lj5TDuPJCmGJDFFeRICQwnCnwtZheLU4v7V5THUzP4pdtB1i+eT/Ltybx89YDpGXmANA0Jow+bevTp3V9+rSpT9fmMYRUwlxK6sEjNV5uLqTtCwiAtvt7Ae0oOEysuLk4IxqWMDl0QK+g0JhaOSTMWkvy4Wx/UOMfGpXq+z6x0Jw2ew9lkp6VU+xx6oWHHOldE+3rUdOoUFjTMNJLXHRogUmIpeqoB09NYK2v0Sky/8yBcgQ2B8EW/x8OAJenYABT3BLcJQU2Wt1JRERE/JqEhjA8LpbhcWXvK5WvXngIpx4fx6nH+34B2Tm5/LHrEMu3JOXfPvltJ+Ab3tCjZSxDOjXmpiEdnCxbpGq5XBAV57vRs+T9DieXHgBtXw5pe4s+LySyhAAooFdQRCPHl4rPyM7hQFoWB9KySErL5EBaJkn532eRlJrpGx6VF+ikZpKZXfRCvzHQMNJLw8hQGkV76d26fsFhUvkhTigNIr14PcE/KX9dooCnIqyFrLQyJgw+UHxgc/gg5BbflQ3wdTMs3FOmQbvyzUnjjVRIIyIiIlLLeNwuureoR/cW9Rg7sC0AOw+mFwh8ftqa5GyRIjVFWIzvVng5+EDZGf4QKC8I2lEwFNo0H1J2Ff27zRXiD36aFZocOiAUim5WrrlC83rWFAxoMklKzSoa2gR8zevNV5xQj4v6Ed78SYiPaxyVH9I0ivYWmOumQaQXt0t/O9ZWCngK++EZOJhQcmBTePxnIOOCsHoFA5jY1uWbkyY0WiGNiIiIiJSqWb1wzjsxnPNObA74/lgUkXLyhEL9tr5bSXJzIDUxYHLowF5B22HX77D2c9+F/0IyQhuSGtqYgyFx7Hc3Yg8N2JnbgG3ZsWzKrMeGwzHsOOwhJ7f4/7fG+HryxYaHEBvhpXF0KJ2aRBMb4aV+RAixkb6v9SO8xPq/1o/wEu7V0KhjlpsLORmQfdgXBObfDhfz1f99Tmn7FD5WoW3XzvWtVFfJFPAU9tNrvv/QgQFMTIvyzUnjjXa8656IiIiI1B2aeFnk2OX1qjno7y2TlObiQFoTktLqk5R2vK9nTUYWB3IySSKTJFcmObkHiMlKpKnZ77uRRNPsfTRNS6KZ2Ux78zN9zKEir5URHklaaGMyI5qRE90ME9McT2wLwhu2IrxRK9z1WvjmDqpr/7dzsosPUbIPQ05mGSFKeUKZMo6Vk3ns78Ht9S1E5AkFT1jRr95I3+/W4wWq5vergKewW3+se/+ZRKRcNm/ezHnnnceKFSsq/Nxhw4axc+dOsrOzGTx4MJMnT8btrpqrLXfeeScff/wxXq+XDh06MG3aNGJjY4vs99RTTzFlyhSMMZxwwglMmzaNsDDfahPPPvsskyZNwuPxMHz4cP79739XSa0iIkcjWNrjQIcPH+aUU04hIyOD7OxsLr30Uh566CGg/O32gQMHGD9+PCtWrMAYw9SpUxkwYAC//vorEyZMICUlhbZt2/LGG28QExNT5e9JpCbKzM4tOvyp0Fw1SWl5w6F82w6kZ5XaqyYmLMTXgybCS1xUKMc3jiY2ohn1I04svVdNVnpALyBfj6DQ5B2E5s0NtHcRbN5VdFEcd6hvlbD8eYACh4XlLRXfFNyV9Oe8teUIUwK/LytwySjUG6a0r5lsTkzlvDdTWHFzVIVLH/Z6KjtTLNm5MLhtKJMvaoTbG14oXPF/H9Gg+ODFE1p6MFPgOEceO5CSwfjb7mTFqjUYlyu/TQ6UlJTEddddx4YNGwgLC2Pq1Kl0794dKP1c/GhpFS0RkXI6lj8okpOTiYmJwVrLpZdeymWXXcaoUaOqoEr44osvOP300/F4PNx9990APP744wX22b59O4MGDWLVqlWEh4dz+eWXc+6553LNNdfwzTff8Mgjj/DJJ58QGhrKnj17aNy4cbleW6toiUh1CJb2OJC1ltTUVKKiosjKymLQoEE888wz9O/fv1ztNsDYsWMZPHgw48ePJzMzk7S0NGJjY+nbty9PPPEEp556KlOnTmXTpk08/PDD5apLq2hJTWWt5VBGNgdS83rVHJmP5khAcySoyZvDJrUcc9XkBzGRIfnDn3zbvflBTt4+9cJDqnbOmpxsSN1TcHn4/Mmhdx5ZPSz7cKEnGohqUnAeIJe7jGFCJfVqyTj29+EKKTEIKfar25t/f/PedM77+5useP3eQvt6ywhcwkhOPUxMg8ZYl4dLL7us2tp0KLlNDnTnnXcSFRXFAw88wB9//MEtt9zCvHnzSj0XL4tW0RIRqWQbN27kkksu4aWXXqJv375l7p93JTU7O5vMzMwyu9RPnz6dWbNmkZGRwaZNmxg9ejQPPPBAuWo766yz8r/v378/77//frH7ZWdnk56eTkhICGlpaTRv7pvP4fnnn2fixImEhoYClDvcERFxQk1ujwMZY4iK8l2dzsrKIisrK/+1y9NuJycn8/333zN9+nQAvF4vXq9vQtc1a9ZwyimnADB06FDOPvvscgc8TtiQmMId7/5KiNvgcbnwuA0hbhcel/+rf3uI2+B2HXnM43YVes6R/Tz5+wRsK3TswOcXOHYxxwlxuzQRbSUq2qummICm0LYDaVlkV7hXTRDOVeP2HOmVUxJrffPDFgmA/Lf9G2HzAsCWEKaE+uaKLS1wKRLMVLCni+sYfrabN0PkF3DybQXb9B7laNMjfV+zs7KqtU0vrU0OtGrVKu655x4AOnfuzObNm9m9e7ev5hLOxY+FAh4RCU6fTvRNcleZmp4A5zxW5m5r1qxh1KhRTJs2jZ49e7JmzRpGjhxZ7L7ffvttfpJ/9tlns3TpUs455xwuvfTSMl9n6dKlrFixgoiICPr27cvw4cOJj49n8ODBHDpUdEz3E088wZlnnllg29SpU4utrUWLFvztb3+jdevWhIeHc9ZZZ+X/gbF27Vrmz5/PfffdR1hYGE888US5/mgSkTpK7XGR/YtrjwFycnLo06cP69ev55ZbbqFfv35F9imp3d64cSNxcXFce+21/Prrr/Tp04dnnnmGyMhIunfvzkcffcSFF17Ie++9x7Zt28p8T04yQHSYh+wcS3ZuLulZvq/ZOZasnFyyc22h7wO25eZSXQMQjIEQf0gUGDQdCYWKCYhcRx4rEkiVEDiVO8RyuXAXOE7pAVle4JX3mNtljnnOpuroVXN8k6givWpiw0MCettUQ6+amsYY3/CiiAbQtHvVvpba9CL7F9eml9YmB+rRowczZ85k0KBBLF26lC1btpCQkECfPn1KPBc/Fgp4REQqIDExkQsvvJAPPviAbt26AdCpUyd++eWXMp/7+eefc/jwYa688kq+/vprhg4dWur+Q4cOpWHDhgCMGDGCBQsWEB8fz/z588tV6yOPPILH4+HKK68s8lhSUhIffvghmzZtIjY2lssuu4zXX3+dMWPGkJ2dTVJSEosXL+bHH3/k8ssvZ+PGjZrIU0RqlGBqj/O43W5++eUXDhw4wMUXX8yKFSvy52KA0tvt7OxsfvrpJ5599ln69evH7bffzmOPPcbDDz/M1KlTue222/jHP/7BBRdcUOxV5JqkfVwUr40rGm6VV05uwfAnK+dIQFR4W1bOkYAoKyc3P1TKyrElHsf3nKLPPxJCFQ2kAo99OLtoQFXcc/L2L6GTSpUoKRRy54VBeQGV20WIf1tOrq17vWqk2gVbm15amxxo4sSJ3H777fTs2ZMTTjiBXr164fF4Sj0XPxYKeEQkOJXjKkBVqFevHq1ateKHH37I//Ap79UFgLCwMC644AI+/PDDMj98CgcqeffLc3VhxowZzJkzh3nz5hUbzHz11Ve0a9eOuLg4wPfhtnDhQsaMGUPLli0ZMWIExhhOOukkXC4Xe/fuzd9XRKQAtcdF9n/iiSfo1KkT559/PgATJkxgwoQJ+Y/HxsYyZMgQPvvss/yAp6x2u2XLlrRs2TK/18+ll17KY4/5fvadO3fmiy++AHy9MD/55JNS30+wc7sM7mMZDlLD5Ob6eiZl5xzppVRcQJRTYL9cskoLj/J6PuWU/Zz81yjwnCPfu12Gjo0L96opPIdNHexVU1upTS+yf3Ft+kUXXVRimxwoJiaGadOmAb4ecO3ataNdu3Z8/vnnJZ6LHwsFPCIiFeD1epk9ezZnn302UVFRjB49usyrCykpKRw6dIhmzZqRnZ3N3LlzGTx4MACzZs1i6dKlPProo0We9+WXX7J//37Cw8OZPXs2U6dOBSjz6sJnn33G448/znfffUdERESx+7Ru3ZrFixeTlpZGeHg48+bNIz7eN1fbRRddxNdff82QIUNYu3YtmZmZNGrUqDw/HhGRahMM7XFgLYmJiYSEhBAbG0t6ejpfffVV/oTK5Wm3mzZtSqtWrVizZg2dOnVi3rx5dO3aFSB/Mvzc3Fz++c9/FgiTpOZzuQyhLjeh+stM6rBga9OBEtvkQAcOHCAiIgKv18uUKVM45ZRTiImJKfVc/FioGRERqaDIyEjmzJnD0KFDiYyM5MILLyx1/9TUVC644AIyMjLIycnh9NNPzz/53rBhQ4lL2Q4aNIirrrqK9evXM3r06HI3+rfeeisZGRn5Vy/69+/PCy+8wI4dOxg/fjxz586lX79+XHrppfTu3RuPx0OvXr244YYbALjuuuu47rrr6N69O16vlxkzZmh4lojUSDW9PQ60c+dOxo4dS05ODrm5uVx++eWcd955QPnabYBnn32WK6+8kszMTNq3b59/Vfitt95i8uTJgO8q8LXXXlvh+kREnBZMbTqU3Ca/8MILgK+nz+rVq7n66qtxu9107dqVV155BaDUc/FjoWXSRUQcNGbMGJ566qkiw5+mT5/OsmXLmDRpkkOVHR0tky4iwaq2tcflpWXSRaQ2qs1tupZJFxGpoV5//XWnSxAREdQei4jUJnW1TVcPHhERqTTqwSMiElzUg0dEJLiU1m67qrsYERERERERERGpXAp4RERERERERESCnAIeEREREREREZEgp4BHRERERERERCTIKeAREREREREREQlyCnhERERERERERIKcAh4RERERERERkSBnrLVO11DpjDGJwBan66jF6gEHnS7CIcH23mtKvdVdR1W/XmUfv7KOVxnHaQTsPYbnt7HWxlX0SWq3q0VNaQ+qW7C975pSr9rt6jvmsR7HkXYb1HZXg5rSHlS3YHvfNaletd3Vc7wa227XyoBHqpYx5iVr7Q1O1+GEYHvvNaXe6q6jql+vso9fWcerjOMYY5ZZa+OPtRapeWpKe1Ddgu1915R61W5X3zGP9Thqt2uvmtIeVLdge981qV613dVzvJrcbmuIlhyNj50uwEHB9t5rSr3VXUdVv15lH7+yjldTft9SM9XVfx/B9r5rSr1qt6vvmDXldy41T139txFs77sm1au2u3qOV5N+5wWoB4+IiOTTlWARkeCidltEJLioB4+IiFSXl5wuQEREKkTttohIcKmydls9eEREREREREREgpx68IiIiIiIiIiIBDkFPCIiIiIiIiIiQU4BjzjCGNPFGPOCMeZ9Y8xNTtdTXYLtfQdbvZWlrr5vkdLU1f8Xwfa+g63eylSX37tIcery/4lge+/BVm9lqavvuyop4KkDjDGtjDHfGGNWG2NWGmNuP4ZjTTXG7DHGrCjmsWHGmDXGmPXGmImlHcdau9paOwG4HKiSGcSNMWHGmKXGmF/97/uhYzhWtb1vY4zbGPOzMWZOMNRbGYwxsf6G/Q//v9MBR3mcoHrfIiVRu612uyrrrSxqu0WOqKvttr8mtd3B87tSu13bWWt1q+U3oBnQ2/99NLAW6Fpon8ZAdKFtxxVzrFOA3sCKQtvdwAagPeAFfgW6AicAcwrdGvufcwGwEBhdRe/bAFH+70OAJUD/mv6+gb8CbwJzinmsxtVbSb+rGcB4//deILYuvO9gugEXAS8DHwJnOV1Pbb+hdhvUbtfY31NAbWq7a/gNtd3V+bOuk+22/zXUdgfP70rtdg2/cYzttuNvQLfqv/n/sQwttO0y4GsgzH//emBuCc9vW8x/5gHA5wH37wHuKWc9n1TDe44AfgL61eT3DbQE5gGnl/BhU6PqraTfTQywCf+qfiXsU+ved3XegKnAnmJ+LsOANcB6YGI5j1UfeMXp91TXbmq3a+77rovttv/Yarur+Ka2O7hvdbHd9r+O2u4a+rtSu10t//4db7c9SJ1ijGkL9MKXrOez1r5njGkHvG2MeQ+4DhhagUO3ALYF3E8A+pVSxxBgBBAKzK3A61SIMcYNLAeOAyZba2v6+34auAvflZ8iamC9laE9kAhMM8b0wPf7ut1am5q3Qy1939VpOjAJeDVvg///xmR8P8cE4EdjzEf4rrw8Wuj511lr9/i//7v/eVJN1G6r3a7keiuL2u6qNx213UGprrXb/tdS21219VYGtdtVbzoOt9sKeOoQY0wU8AHwZ2ttcuHHrbX/Nsa8DTwPdLDWplTk8MVssyXtbK39Fvi2Asc/KtbaHKCnMSYWmGWM6W6tXVFonxrxvo0x5wF7rLXL/Y1dSceoEfVWIg++Lp5/stYuMcY8A0wE7i9US21739XGWvu9/2Qz0EnAemvtRgD/z/ZCa+2jwHmFj2GMMcBjwKfW2p+quGTxU7utdrsy661karurmNru4FQX223/a6ntrqJ6K5Ha7SpWE9ptTbJcRxhjQvB92LxhrZ1Zwj6Dge7ALOCBCr5EAtAq4H5LYMdRlFolrLUH8DUewwo/VoPe98nABcaYzcDbwOnGmNcL71SD6q0sCUBCwJWe9/F9+BRQC9+304q7wtKilP3/BJwJXGqMmVCVhYmP2m2123419fekttsZartrsLreboPa7gA18XeldtsZ1dpuK+CpA/wp4CvAamvtkyXs0wvfZE4XAtcCDYwx/6zAy/wIdDTGtDPGeIFRwEfHVvmxMcbE+a8iYIwJx/cf5Y9C+9SY922tvcda29Ja29Z/nK+ttWNqar2VxVq7C9hmjOnk33QGsCpwn9r4vmuAil5h+Z+1to+1doK19oUqrEtQu+3/Xu12JddbmdR2O0Ztdw1VV9ttUNtd1fVWFrXbjqnWdlsBT91wMnAVvnT6F//t3EL7RACXWWs3WGtzgbHAlsIHMsa8BSwCOhljEowx4wCstdnArcDnwGrgXWvtyqp7S+XSDPjGGPMbvsbmS2vtnEL7BNv7DrZ6y+tPwBv+31VP4F+FHq+t79tJusJSs6ndVrtdk39PedR2Vz+13TVXXW23QW13Tai3vNRuV79qbbeNtSWGRyIiUksY33jgOdba7v77HnxLuJ4BbMd3QjZaH8IiIjWH2m4RkeDidLutHjwiIrVccVdZdIVFRKRmU9stIhJcakK7rR48IiIiIiIiIiJBTj14RERERERERESCnAIeEREREREREZEgp4BHRERERERERCTIKeAREREREREREQlyCnhERERERERERIKcAh4RERERERERkSCngEdEREREREREJMgp4BERERERERERCXIKeEQcZoy52BhjjTGdA7a1NMaMdLIuEREpmdpuEZHgonZb6gIFPCLOuwJYBowK2HYG0NuZckREpBzUdovI/7dzt6paRGEYhu9VxbYPYoOCiMFkNfsJgngCnptbi0UMBoNYbDaD2MToDwYxLMsXdthic1zOdaWZN73pGXhYa1iL3Oa/N+acW+8AuzXGuFy9r25Xj+ecp2OMW9XT6nP1rbo75/yw3ZYAnCe7AdYit9kLJ3hgW4fqxZzzbfV9jHFjzvmqelPdmXNe96EB+Occkt0AKzkkt9kBBQ9s60F1dnw+O75XnVbvNtkIgD+R3QBrkdvsgoIHNjLGOKluVs+Po0fV/eP8y5zz52bLAXAh2Q2wFrnNnih4YDv3qmdzzh9Vx2Ohn6or1cctFwPgt2Q3wFrkNrvhJ8uwkTHGy+pa9fXc+KR6Ul2tLlUP55yv//52AFxEdgOsRW6zJwoeAAAAgMW5ogUAAACwOAUPAAAAwOIUPAAAAACLU/AAAAAALE7BAwAAALA4BQ8AAADA4hQ8AAAAAIv7Bd5y0UQZqumyAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Tend = 1e-2\n", + "compare_preconditioners(run_heat, Tend * 2.**(-np.arange(4)), ks, Tend)\n", + "#compare_preconditioners(run_heat, [0.1, 0.05], [1, 2, 3], 0.2)" + ] + }, + { + "cell_type": "markdown", + "id": "72a2ef4f", + "metadata": {}, + "source": [ + "This is all messed up! We get one order too little with the Euler precondtitioner. Something is off!" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2ec57949", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Tend = 4e-1\n", + "compare_preconditioners(run_advection, Tend * 2.**(-np.arange(8)), ks, Tend)" + ] + }, + { + "cell_type": "markdown", + "id": "eb92cc70", + "metadata": {}, + "source": [ + "## Put this somewhere:\n", + "\n", + "A true RK scheme would expand $f$ in two variables to cancel the variations caused by the inaccurate $f$ evaluations. That means this is not a real Runge Kutta method! Maybe this is why the preconditioner is so bad for PDE's\n", + "\n", + "I actually derive linear multistep methods, but some of them are unstable.\n", + "I need to enforce stability somehow, which I think is entirely possible." + ] + }, + { + "cell_type": "markdown", + "id": "2ddbc735", + "metadata": {}, + "source": [ + "## Stability\n", + "### Consistency\n", + "Since the only coefficient for $u$ is one for the initial conditions (but on the right hand side), we get a first characteristic polynomial\n", + "$$\\rho(r) = r^p - 1,$$\n", + "which means that indeed 1 is a root and we satisfied a condition for consistency.\n", + "Actually, the roots are all distict with modulus equal to or smaller than 1, which is a condition for convergence.\n", + "\n", + "The second characteristic polynomial, on the other hand, collects quite a few coefficients and is equal to\n", + "$$\\sigma(r) = \\sum^{p}_{i=0}\\beta_ir^i.$$\n", + "The second condition for consistency is that $\\rho^\\prime(1) = \\sigma(1)$, which means in our case \n", + "$$p = \\sum^{p}_{i=0}\\beta_i$$" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pySDC/playgrounds/Preconditioners/heat.py b/pySDC/playgrounds/Preconditioners/heat.py new file mode 100644 index 0000000000..f58f465e18 --- /dev/null +++ b/pySDC/playgrounds/Preconditioners/heat.py @@ -0,0 +1,136 @@ +# script to run a simple advection problem +from pySDC.implementations.collocation_classes.gauss_radau_right import CollGaussRadau_Right +from pySDC.implementations.problem_classes.AdvectionEquation_1D_FD import advection1d +from pySDC.implementations.problem_classes.HeatEquation_ND_FD_forced_periodic import heatNd_periodic +from pySDC.implementations.sweeper_classes.imex_1st_order import imex_1st_order +from pySDC.implementations.controller_classes.controller_nonMPI import controller_nonMPI +from pySDC.core.Hooks import hooks +from pySDC.helpers.stats_helper import get_sorted +import numpy as np + +from pySDC.implementations.convergence_controller_classes.estimate_extrapolation_error import EstimateExtrapolationErrorNonMPI + + +def plot_embedded(stats, ax): + u = get_sorted(stats, type='u', recomputed=False) + uold = get_sorted(stats, type='uold', recomputed=False) + t = [get_sorted(stats, type='u', recomputed=False)[i][0] for i in range(len(u))] + e_em = np.array(get_sorted(stats, type='e_embedded', recomputed=False))[:, 1] + e_em_semi_glob = [abs(u[i][1] - uold[i][1]) for i in range(len(u))] + ax.plot(t, e_em_semi_glob, label=r'$\|u^{\left(k-1\right)}-u^{\left(k\right)}\|$') + ax.plot(t, e_em, linestyle='--', label=r'$\epsilon$') + ax.set_xlabel(r'$t$') + ax.legend(frameon=False) + + +class log_data(hooks): + + def post_iteration(self, step, level_number): + super(log_data, self).post_iteration(step, level_number) + if step.status.iter == step.params.maxiter - 1: + L = step.levels[level_number] + L.sweep.compute_end_point() + self.add_to_stats(process=step.status.slot, time=L.time + L.dt, level=L.level_index, iter=0, + sweep=L.status.sweep, type='uold', value=L.uold[-1]) + + def post_step(self, step, level_number): + + super(log_data, self).post_step(step, level_number) + + # some abbreviations + L = step.levels[level_number] + + L.sweep.compute_end_point() + + self.add_to_stats(process=step.status.slot, time=L.time + L.dt, level=L.level_index, iter=0, + sweep=L.status.sweep, type='u', value=L.uend) + self.add_to_stats(process=step.status.slot, time=L.time, level=L.level_index, iter=0, + sweep=L.status.sweep, type='dt', value=L.dt) + self.add_to_stats(process=step.status.slot, time=L.time + L.dt, level=L.level_index, iter=0, + sweep=L.status.sweep, type='e_embedded', value=L.status.error_embedded_estimate) + self.add_to_stats(process=step.status.slot, time=L.time + L.dt, level=L.level_index, iter=0, + sweep=L.status.sweep, type='e_extrapolated', value=L.status.error_extrapolation_estimate) + + +def run_heat(custom_description=None, num_procs=1, Tend=2e-1, hook_class=log_data, fault_stuff=None, + custom_controller_params=None, custom_problem_params=None): + + # initialize level parameters + level_params = dict() + level_params['dt'] = 0.05 + + # initialize sweeper parameters + sweeper_params = dict() + sweeper_params['collocation_class'] = CollGaussRadau_Right + sweeper_params['num_nodes'] = 4 + sweeper_params['QI'] = 'LMM' + + problem_params = { + 'freq': (2,), + 'nvars': (2**7,), + 'type': 'center', + 'order': 8, + 'nu': 1., + 'ndim': 1, + } + + if custom_problem_params is not None: + problem_params = {**problem_params, **custom_problem_params} + + # initialize step parameters + step_params = dict() + step_params['maxiter'] = 5 + + # initialize controller parameters + controller_params = dict() + controller_params['logger_level'] = 30 + controller_params['hook_class'] = hook_class + controller_params['mssdc_jac'] = False + + if custom_controller_params is not None: + controller_params = {**controller_params, **custom_controller_params} + + # fill description dictionary for easy step instantiation + description = dict() + description['problem_class'] = heatNd_periodic # pass problem class + description['problem_params'] = problem_params # pass problem parameters + description['sweeper_class'] = imex_1st_order # pass sweeper + description['sweeper_params'] = sweeper_params # pass sweeper parameters + description['level_params'] = level_params # pass level parameters + description['step_params'] = step_params + + if custom_description is not None: + for k in custom_description.keys(): + if k == 'sweeper_class': + description[k] = custom_description[k] + continue + description[k] = {**description.get(k, {}), **custom_description.get(k, {})} + + # set time parameters + t0 = 0.0 + + # instantiate controller + controller = controller_nonMPI(num_procs=num_procs, controller_params=controller_params, + description=description) + + error_est = controller.convergence_controllers[controller.convergence_controller_order[0]] + + # insert faults + if fault_stuff is not None: + controller.hooks.random_generator = fault_stuff['rng'] + controller.hooks.add_fault(rnd_args={'iteration': 5, **fault_stuff.get('rnd_params', {})}, + args={'time': 1e-1, 'target': 0, **fault_stuff.get('args', {})}) + + # get initial values on finest level + P = controller.MS[0].levels[0].prob + uinit = P.u_exact(t0) + + # call main function to get things done... + uend, stats = controller.run(u0=uinit, t0=t0, Tend=Tend) + return stats, controller, Tend + +if __name__ == '__main__': + #convergence_controllers = {EstimateExtrapolationErrorNonMPI: {}} + #custom_description = {'convergence_controllers': convergence_controllers} + custom_description = None + run_heat(custom_description=custom_description) diff --git a/pySDC/projects/Resilience/accuracy_check.py b/pySDC/projects/Resilience/accuracy_check.py index 8900c8cf45..3bffa39ecf 100644 --- a/pySDC/projects/Resilience/accuracy_check.py +++ b/pySDC/projects/Resilience/accuracy_check.py @@ -12,6 +12,9 @@ from pySDC.projects.Resilience.piline import run_piline +class do_nothing(hooks): + pass + class log_errors(hooks): def post_step(self, step, level_number): @@ -44,28 +47,40 @@ def setup_mpl(font_size=8): mpl.rcParams.update(style_options) -def get_results_from_stats(stats, var, val): - e_extrapolated = np.array(get_sorted(stats, type='e_extrapolated'))[:, 1] - - e_loc = np.array(get_sorted(stats, type='e_loc'))[:, 1] - +def get_results_from_stats(stats, var, val, hook_class=log_errors): results = { - 'e_embedded': get_sorted(stats, type='e_embedded')[-1][1], - 'e_extrapolated': e_extrapolated[e_extrapolated != [None]][-1], - 'e': max([e_loc[-1], np.finfo(float).eps]), + 'e_embedded': 0., + 'e_extrapolated': 0., + 'e': 0., var: val, } + if hook_class == log_errors: + e_extrapolated = np.array(get_sorted(stats, type='e_extrapolated'))[:, 1] + e_embedded = np.array(get_sorted(stats, type='e_embedded'))[:, 1] + e_loc = np.array(get_sorted(stats, type='e_loc'))[:, 1] + + if len(e_extrapolated[e_extrapolated != [None]]) > 0: + results['e_extrapolated'] = e_extrapolated[e_extrapolated != [None]][-1] + + if len(e_loc[e_loc != [None]]) > 0: + results['e'] = max([e_loc[e_loc != [None]][-1], np.finfo(float).eps]) + + if len(e_embedded[e_embedded != [None]]) > 0: + results['e_embedded'] = e_embedded[e_embedded != [None]][-1] + return results -def multiple_runs(ax, k=5, serial=True, Tend_fixed=None): +def multiple_runs(k=5, serial=True, Tend_fixed=None, custom_description=None, prob=run_piline, dt_list=None, hook_class=log_errors): """ A simple test program to compute the order of accuracy in time """ # assemble list of dt - if Tend_fixed: + if dt_list is not None: + pass + elif Tend_fixed: dt_list = 0.1 * 10.**-(np.arange(5) / 2) else: dt_list = 0.01 * 10.**-(np.arange(20) / 10.) @@ -82,11 +97,19 @@ def multiple_runs(ax, k=5, serial=True, Tend_fixed=None): EstimateExtrapolationErrorNonMPI: {'no_storage': not serial}, } } + if custom_description is not None: + desc = {**desc, **custom_description} Tend = Tend_fixed if Tend_fixed else 30 * dt_list[i] - stats, _, _ = run_piline(custom_description=desc, num_procs=num_procs, Tend=Tend, - hook_class=log_errors) + stats, controller, _ = prob(custom_description=desc, num_procs=num_procs, Tend=Tend, + hook_class=hook_class) - res_ = get_results_from_stats(stats, 'dt', dt_list[i]) + level = controller.MS[-1].levels[-1] + e_glob = abs(level.prob.u_exact(t=level.time + level.dt) - level.u[-1]) + e_loc = abs(level.prob.u_exact(t=level.time + level.dt, u_init=level.u[0], t_init=level.time) - level.u[-1]) + + res_ = get_results_from_stats(stats, 'dt', dt_list[i], hook_class) + res_['e_glob'] = e_glob + res_['e_loc'] = e_loc if i == 0: res = res_.copy() @@ -95,18 +118,30 @@ def multiple_runs(ax, k=5, serial=True, Tend_fixed=None): else: for key in res_.keys(): res[key].append(res_[key]) + return res + - # visualize results - plot(res, ax, k) +def plot_order(res, ax, k): + color = plt.rcParams['axes.prop_cycle'].by_key()['color'][k - 2] + key = 'e_loc' + order = get_accuracy_order(res, key=key, thresh=1e-11) + label = f'k={k}, p={np.mean(order):.2f}' + ax.loglog(res['dt'], res[key], color=color, ls='-', label=label) + #ax.loglog(res['dt'], res['e'], color=color, ls='-') + #ax.loglog(res['dt'], res['e_loc'], ls=':', color='black') + ax.set_xlabel(r'$\Delta t$') + ax.set_ylabel(r'$\epsilon$') + ax.legend(frameon=False, loc='lower right') + def plot(res, ax, k): keys = ['e_embedded', 'e_extrapolated', 'e'] ls = ['-', ':', '-.'] color = plt.rcParams['axes.prop_cycle'].by_key()['color'][k - 2] for i in range(len(keys)): - order = get_accuracy_order(res, key=keys[i], order=k) + order = get_accuracy_order(res, key=keys[i]) if keys[i] == 'e_embedded': label = rf'$k={{{np.mean(order):.2f}}}$' assert np.isclose(np.mean(order), k, atol=3e-1), f'Expected embedded error estimate to have order {k} \ @@ -123,12 +158,13 @@ def plot(res, ax, k): ax.legend(frameon=False, loc='lower right') -def get_accuracy_order(results, key='e_embedded', order=5): +def get_accuracy_order(results, key='e_embedded', thresh=1e-14): """ Routine to compute the order of accuracy in time Args: - results: the dictionary containing the errors + results (dict): the dictionary containing the errors + key (str): The key in the dictionary correspdoning to a specific error Returns: the list of orders @@ -144,7 +180,7 @@ def get_accuracy_order(results, key='e_embedded', order=5): # compute order as log(prev_error/this_error)/log(this_dt/old_dt) <-- depends on the sorting of the list! try: tmp = np.log(results[key][i] / results[key][i - 1]) / np.log(dt_list[i] / dt_list[i - 1]) - if results[key][i] > 1e-14 and results[key][i - 1] > 1e-14: + if results[key][i] > thresh and results[key][i - 1] > thresh: order.append(tmp) except TypeError: print('Type Warning', results[key]) @@ -152,10 +188,22 @@ def get_accuracy_order(results, key='e_embedded', order=5): return order -def plot_all_errors(ax, ks, serial, Tend_fixed=None): +def plot_orders(ax, ks, serial, Tend_fixed=None, custom_description=None, prob=run_piline, dt_list=None): for i in range(len(ks)): k = ks[i] - multiple_runs(k=k, ax=ax, serial=serial, Tend_fixed=Tend_fixed) + res = multiple_runs(k=k, serial=serial, Tend_fixed=Tend_fixed, custom_description=custom_description, + prob=prob, dt_list=dt_list, hook_class=do_nothing) + plot_order(res, ax, k) + +def plot_all_errors(ax, ks, serial, Tend_fixed=None, custom_description=None, prob=run_piline): + for i in range(len(ks)): + k = ks[i] + res = multiple_runs(k=k, serial=serial, Tend_fixed=Tend_fixed, custom_description=custom_description, + prob=prob) + + # visualize results + plot(res, ax, k) + ax.plot([None, None], color='black', label=r'$\epsilon_\mathrm{embedded}$', ls='-') ax.plot([None, None], color='black', label=r'$\epsilon_\mathrm{extrapolated}$', ls=':') ax.plot([None, None], color='black', label=r'$e$', ls='-.') From 5d8cbe9a56e325c8e5065c8d81db27f39f7a9c54 Mon Sep 17 00:00:00 2001 From: Thomas Baumann Date: Wed, 24 Aug 2022 10:27:14 +0200 Subject: [PATCH 04/10] Stopped garbage compactor 3263827 on the death star by verifying the root condition for convergence --- pySDC/helpers/preconditioner_helper.py | 47 ++++++++++++++++++++++++++ 1 file changed, 47 insertions(+) diff --git a/pySDC/helpers/preconditioner_helper.py b/pySDC/helpers/preconditioner_helper.py index 67dec8c9c5..7ee2834336 100644 --- a/pySDC/helpers/preconditioner_helper.py +++ b/pySDC/helpers/preconditioner_helper.py @@ -53,4 +53,51 @@ def get_linear_multistep_method(steps, u_signature, f_signature): f_coeff = np.zeros_like(f_signature) f_coeff[f_signature > 0] = coeff[n_u:] + # check if our method is convergent + verify_root_condition(first_characteristic_polynomial(u_coeff)) + return u_coeff, f_coeff + + +def first_characteristic_polynomial(u_coeff, r=1): + ''' + The first characteristic polynomial of a linear multistep method is equal to the coefficients multiplied with + powers of r. + + Args: + u_coeff: The alpha coefficients for u of the LMM in order of descending time difference to the solution we want + r: The variable of the polynomial + + Returns: + numpy.ndarray: List containing the polynomial in r. Set r=1 to get the coefficients. + ''' + j = np.arange(len(u_coeff)) + rho = np.zeros_like(u_coeff) + rho = -u_coeff * r**j + rho[-1] = r**len(u_coeff) + return rho[::-1] + + +def verify_root_condition(rho): + ''' + For a linear multistep method to be convergent, we require that all roots of the first characteristic polynomial + are distinct and have modulus smaller or equal to one. + + Args: + rho (numpy.ndarray): Coefficients of the first characteristic polynomial + + Returns: + bool: Whether the root condition is satisfied. + ''' + # compute the roots of the polynomial + roots = np.roots(rho) + + # check the conditions + roots_distinct = len(np.unique(roots)) == len(roots) + modulus_condition = all(abs(roots) <= 1. + 10. * np.finfo(float).eps) + + # raise errors if we violate one of the conditions + assert roots_distinct, "Not all roots of the first characteristic polynomial of the LMM are distinct!" + assert modulus_condition, "Some of the roots of the first characteristic polynomial of the LMM have modulus larger \ +one!" + return roots_distinct and modulus_condition From 0998a0e6f8987bba0f63a75519537c66bbb65c68 Mon Sep 17 00:00:00 2001 From: Thomas Baumann Date: Wed, 24 Aug 2022 12:00:44 +0200 Subject: [PATCH 05/10] Won a check for consistency at a pod race --- pySDC/helpers/preconditioner_helper.py | 63 +++++++++++++++++++++++++- 1 file changed, 61 insertions(+), 2 deletions(-) diff --git a/pySDC/helpers/preconditioner_helper.py b/pySDC/helpers/preconditioner_helper.py index 7ee2834336..8d26df520f 100644 --- a/pySDC/helpers/preconditioner_helper.py +++ b/pySDC/helpers/preconditioner_helper.py @@ -13,6 +13,8 @@ def get_linear_multistep_method(steps, u_signature, f_signature): Since we can cancel as many terms in the Taylor expansion as we have entries in the signature, that will also be the order of consistency of our method. + We check if our method is consistent and zero stable, which together means it is convergent. + Args: steps (list): The step sizes between the multiple steps that are used u_signature (list): A list containing which solutions at which steps should be used. Set to 1, for all times at @@ -53,7 +55,10 @@ def get_linear_multistep_method(steps, u_signature, f_signature): f_coeff = np.zeros_like(f_signature) f_coeff[f_signature > 0] = coeff[n_u:] - # check if our method is convergent + # check that the method is consistent + check_linear_difference_operator(u_coeff, f_coeff, steps) + + # check if our method is zero stable verify_root_condition(first_characteristic_polynomial(u_coeff)) return u_coeff, f_coeff @@ -75,13 +80,16 @@ def first_characteristic_polynomial(u_coeff, r=1): rho = np.zeros_like(u_coeff) rho = -u_coeff * r**j rho[-1] = r**len(u_coeff) - return rho[::-1] + return rho[::-1] # reverse the order to go along with usual definitions def verify_root_condition(rho): ''' For a linear multistep method to be convergent, we require that all roots of the first characteristic polynomial are distinct and have modulus smaller or equal to one. + This root condition implies that the method is zero stable and Dahlquist's theorem states that a zero stable and + consistent method is convergent. If we can also show that the method is consistent, we have thus shown it is + convergent. Args: rho (numpy.ndarray): Coefficients of the first characteristic polynomial @@ -94,6 +102,7 @@ def verify_root_condition(rho): # check the conditions roots_distinct = len(np.unique(roots)) == len(roots) + # give some leeway because we introduce some numerical error when computing the roots modulus_condition = all(abs(roots) <= 1. + 10. * np.finfo(float).eps) # raise errors if we violate one of the conditions @@ -101,3 +110,53 @@ def verify_root_condition(rho): assert modulus_condition, "Some of the roots of the first characteristic polynomial of the LMM have modulus larger \ one!" return roots_distinct and modulus_condition + + +def check_linear_difference_operator(u_coeff, f_coeff, steps): + ''' + Check if the linear multistep method is consistent by doing a Taylor expansion and testing if all terms cancel + except for the first, which should be one. + + Args: + u_coeff (numpy.ndarray): Coefficients for u in the LMM + f_coeff (numpy.ndarray): Coefficients for f in the LMM + steps (numpy.ndarray): Steps from point of expansion + + Returns: + None + ''' + order = len(steps) + taylor_coeffs = np.zeros((len(u_coeff) + len(f_coeff), order)) + + # fill in the coefficients + for i in range(order): + # get expansions of u + if u_coeff[i] != 0: + taylor_coeffs[i, :] = u_coeff[i] * taylor_expansion(steps[i], order) + + # get expansions of f + if f_coeff[i] != 0: + taylor_coeffs[order + i, 1:] = f_coeff[i] * taylor_expansion(steps[i], order - 1) + + # check that all is well + L = np.sum(taylor_coeffs, axis=0) + want = np.zeros_like(L) + want[0] = 1. + assert all(np.isclose(L, want)), "Some derivatives do not cancel in the Taylor expansion!" + + return None + + +def taylor_expansion(step, order): + ''' + Get coefficients of a Taylor expansion. + + Args: + step (float): Time difference from point around which we expand + order (int): The order up to which we want to expand + + Returns: + numpy.ndarray: List containing the coefficients of the derivatives of u in the Taylor expansion + ''' + j = np.arange(order) + return step**j / factorial(j) From 7f21a23f2c4646551f6e3d34125c1b48d3337c9b Mon Sep 17 00:00:00 2001 From: Thomas Baumann Date: Wed, 24 Aug 2022 15:52:12 +0200 Subject: [PATCH 06/10] Got myself frozen in carbonite again... --- pySDC/helpers/preconditioner_helper.py | 72 +++++++++- .../Preconditioners/LMM_preconditioner.ipynb | 136 +++++++----------- pySDC/projects/Resilience/advection.py | 5 +- 3 files changed, 124 insertions(+), 89 deletions(-) diff --git a/pySDC/helpers/preconditioner_helper.py b/pySDC/helpers/preconditioner_helper.py index 8d26df520f..5b3141393d 100644 --- a/pySDC/helpers/preconditioner_helper.py +++ b/pySDC/helpers/preconditioner_helper.py @@ -2,10 +2,10 @@ from scipy.special import factorial -def get_linear_multistep_method(steps, u_signature, f_signature): +def get_linear_multistep_method(steps, u_signature, f_signature, checks=False): ''' Derive a general linear multistep method from step sizes and a signature. This function will provide a consistent - linear multistep method by cancelling terms in Taylor expansions, but note that this must not be convergent! + linear multistep method by cancelling terms in Taylor expansions, but note that this must not be stable! The resulting coefficients must be multiplied with the corresponding value of u or f and then all must be summed to get a numerical solution to the initial value problem. @@ -14,12 +14,15 @@ def get_linear_multistep_method(steps, u_signature, f_signature): the order of consistency of our method. We check if our method is consistent and zero stable, which together means it is convergent. + However, some of the methods that we generate are not A stable. As it turns out, according to Dahlquist's second + barrier theorem, there are no A-stable LMMs of order greater than 2. Args: steps (list): The step sizes between the multiple steps that are used u_signature (list): A list containing which solutions at which steps should be used. Set to 1, for all times at which you want to use the solution and to 0 at all other times f_signature (list): Analogue to u_signature for the right hand side evaluations + checks (bool): Perform some checks on stability and convergence Returns: list: Coefficients for u @@ -55,18 +58,23 @@ def get_linear_multistep_method(steps, u_signature, f_signature): f_coeff = np.zeros_like(f_signature) f_coeff[f_signature > 0] = coeff[n_u:] - # check that the method is consistent - check_linear_difference_operator(u_coeff, f_coeff, steps) + if checks: + # check that the method is consistent + check_linear_difference_operator(u_coeff, f_coeff, steps) - # check if our method is zero stable - verify_root_condition(first_characteristic_polynomial(u_coeff)) + # check if our method is zero stable + verify_root_condition(first_characteristic_polynomial(u_coeff)) + + # Check if the method is stable for h*lambda=-1 + p = stability_polynomial(u_coeff, f_coeff, -1.) + strict_root_condition(p) return u_coeff, f_coeff def first_characteristic_polynomial(u_coeff, r=1): ''' - The first characteristic polynomial of a linear multistep method is equal to the coefficients multiplied with + The first characteristic polynomial of a linear multistep method is equal to the coefficients of umultiplied with powers of r. Args: @@ -83,6 +91,24 @@ def first_characteristic_polynomial(u_coeff, r=1): return rho[::-1] # reverse the order to go along with usual definitions +def second_characteristic_polynomial(f_coeff, r=1): + ''' + The second characteristic polynomial of a linear multistep method is equal to the coefficients multiplied with + powers of r. + + Args: + f_coeff: The alpha coefficients for f of the LMM in order of descending time difference to the solution we want + r: The variable of the polynomial + + Returns: + numpy.ndarray: List containing the polynomial in r. Set r=1 to get the coefficients. + ''' + j = np.arange(len(f_coeff)) + sigma = np.zeros_like(f_coeff) + sigma = f_coeff * r**j + return sigma[::-1] # reverse the order to go along with usual definitions + + def verify_root_condition(rho): ''' For a linear multistep method to be convergent, we require that all roots of the first characteristic polynomial @@ -160,3 +186,35 @@ def taylor_expansion(step, order): ''' j = np.arange(order) return step**j / factorial(j) + + +def stability_polynomial(u_coeff, f_coeff, h_hat): + ''' + Construct the stability polynomial for a value of h_hat = h * lambda. + + Args: + u_coeff (numpy.ndarray): Coefficients for u in the LMM + f_coeff (numpy.ndarray): Coefficients for f in the LMM + h_hat (float) Parameter where you want to check stability + + Returns: + numpy.ndarray: List containing the coefficients of the stability polynomial + ''' + rho = first_characteristic_polynomial(u_coeff) + sigma = second_characteristic_polynomial(f_coeff) + return rho - h_hat * sigma + + +def strict_root_condition(p): + ''' + Check whether the roots of the polynomial ae strictly smaller than one. + + Args: + p (numpy.ndarray): Coefficients for the polynomial + + Returns: + None + ''' + roots = np.roots(p) + assert all(abs(roots) < 1), "Polynomial does not satisfy strict root condition!" + return None diff --git a/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb b/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb index e7d3828c7b..fff91b9259 100644 --- a/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb +++ b/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "de15cd5b", + "id": "2f78d1a2", "metadata": {}, "source": [ "# High Order Linear Multistep Preconditioners" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "09b17076", + "id": "03c53290", "metadata": {}, "source": [ "## Constructing linear multistep methods (LMMs)\n", @@ -25,9 +25,9 @@ "\n", "The linear multistep method will then look like\n", "$$u(t) = \\sum_{i=0}^p \\left(\\alpha_i u(t-h_i) + \\beta_i f\\left(u\\left(t-h_i\\right), t-h_i\\right)\\right),$$\n", - "and we need to choose $\\alpha$ and $\\beta$ in a way that suits us and that cancels the most possible terms in the Taylor expansion.\n", + "and we need to choose $\\alpha_i$ and $\\beta_i$ in a way that suits us and that cancels the most possible terms in the Taylor expansion.\n", "\n", - "What suits us best is mainly determined by what we have available, which is in the pySDC implementation the initial conditions and the right hand side evaluations at all the nodes.\n", + "What suits us best is mainly determined by what we have available, which is, in the pySDC implementation, the initial conditions and the right hand side evaluations at all the nodes.\n", "That means with $h_i$ the time differences to where we want to solve for in descending order: $\\alpha_0=1$ and $\\beta_i \\neq 0$.\n", "In particular, $\\beta_p\\neq 0$, to make the scheme implicit.\n", "\n", @@ -35,21 +35,24 @@ "We construct a matrix $A$ with\n", "$$A_{ij} = \\cases{(-h_i)^j / j!, & j = 0\\\\\n", " (-h_i)^{(j-1)} / (j-1)!, & i,j > 0}.$$\n", - "We will then procees to solve a system $Ax=b$, which means we need a vector $b$ on the right hand side.\n", + "We will then proceed to solve a system $Ax=b$, which means we need a vector $b$ on the right hand side.\n", "Since we want to solve for the solution at time $t$ itself and want to cancel all other terms, we need a one in the first entry, corresponding to $u(t)$ and zeros everywhere else:\n", "$$b = \\delta_{i0}.$$\n", "\n", - "Conveniently for us, $u(t-h_0)$ is the only term carrying $u(t)$ in its expansion, and since we want a one in the corresponding row of the solution, we will always get $\\alpha_0=1$, which is exactly what we wanted!\n", + "Conveniently for us, $u(t-h_0)$ is the only term carrying $u(t)$ in its expansion, and since we want a one in the corresponding row of the solution, we will always get $\\alpha_0=1$, which means we don't have to modify the implementaion of pySDC in the sweeps.\n", "The remaining coefficients make up a single row of the preconditioner and are the $\\beta_i$ coefficients that will be multiplied to the right hand side evaluations in the sweeps.\n", "\n", - "Crucially, we get increase the order of the LMM by one in each row of the preconditioner.\n", - "In the first row, we get the trapezoidal rule (or implicit Euler, if we set $\\beta_0=0$), and then we gain an order in the expansions with each row." + "Crucially, we increase the order of the LMM by one in each row of the preconditioner.\n", + "In the first row, we get the trapezoidal rule (or implicit Euler, if we set $\\beta_0=0$), and then we gain an order in the expansions with each row.\n", + "\n", + "Let's look at this in practice:\n", + "## Numerical experiments" ] }, { "cell_type": "code", "execution_count": 1, - "id": "4f15eae9", + "id": "0a28db56", "metadata": {}, "outputs": [], "source": [ @@ -65,12 +68,12 @@ { "cell_type": "code", "execution_count": 2, - "id": "e186936d", + "id": "896c94ff", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -82,11 +85,10 @@ } ], "source": [ - "IE_desc = {'sweeper_params': {'QI': 'IE'}}\n", - "Taylor_desc = {'sweeper_params': {'QI': 'LMM'}}\n", - "trap_desc = {'sweeper_params': {'QI': 'TRAP'}}\n", - "\n", - "\n", + "num_nodes = 3\n", + "IE_desc = {'sweeper_params': {'QI': 'IE', 'num_nodes': num_nodes}}\n", + "Taylor_desc = {'sweeper_params': {'QI': 'LMM', 'num_nodes': num_nodes}}\n", + "trap_desc = {'sweeper_params': {'QI': 'TRAP', 'num_nodes': num_nodes}}\n", "ks = [1, 2, 3]\n", "\n", "def compare_preconditioners(prob, dt_list, ks, Tend_fixed):\n", @@ -102,64 +104,37 @@ " fig.tight_layout()\n", "\n", "\n", - "compare_preconditioners(run_piline, 0.1 * 2.**(-np.arange(5)), ks, 0.1)" + "Tend = 0.1\n", + "compare_preconditioners(run_piline, Tend * 2.**(-np.arange(7)), ks, Tend)" ] }, { "cell_type": "markdown", - "id": "b37779e7", + "id": "6bcafa4d", "metadata": {}, "source": [ - "What you see above is the order for the pi-line problem.\n", + "What you see above is the order for the pi-line problem, a non-linear ordinary differential equation, which we integrate with an IMEX scheme such that we only need to solve linear systems in each step.\n", "$k$ is the number of sweeps and $p$ is the order of the scheme.\n", "\n", - "The left panel shows the expected $p=k+1$, but the other panels show something akin to $p=2k+1$ until we reach three sweeps at which point, the quadrature rule is not high order enough, since we use only three Gauss-Radau nodes.\n", + "Each of the panels shows a different time marching scheme for the preconditioner and we see the expected $p=k+1$ for implicit Euler.\n", + "For the trapezoidal rule, we increase the order of accuracy by two with each sweep, which is what we expected, but at the third sweep we stay at order 5 instead of the maximal possible order 6, for some reason.\n", + "The LMM, on the other hand, gives us a thrid order accurate method in the first sweep, but then the order rises only by one with each sweep.\n", + "This behaviour is slightly odd, but maybe this is just down to numerics.\n", "\n", - "This looks nice! We did no extra work and yet we got a higher order method!" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "50897e94", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "Tend = 1e-2\n", - "compare_preconditioners(run_heat, Tend * 2.**(-np.arange(4)), ks, Tend)\n", - "#compare_preconditioners(run_heat, [0.1, 0.05], [1, 2, 3], 0.2)" - ] - }, - { - "cell_type": "markdown", - "id": "72a2ef4f", - "metadata": {}, - "source": [ - "This is all messed up! We get one order too little with the Euler precondtitioner. Something is off!" + "This looks nice! We did no extra work and yet we got a higher order method!\n", + "\n", + "Now let's try a partial differential equation:" ] }, { "cell_type": "code", "execution_count": 4, - "id": "2ec57949", + "id": "d28091be", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -171,39 +146,38 @@ } ], "source": [ - "Tend = 4e-1\n", - "compare_preconditioners(run_advection, Tend * 2.**(-np.arange(8)), ks, Tend)" + "Tend = 2e-2\n", + "ks = [1, 2]\n", + "compare_preconditioners(run_advection, Tend * 2.**(-np.arange(8)), ks, None)" ] }, { "cell_type": "markdown", - "id": "eb92cc70", + "id": "0303cce6", "metadata": {}, "source": [ - "## Put this somewhere:\n", - "\n", - "A true RK scheme would expand $f$ in two variables to cancel the variations caused by the inaccurate $f$ evaluations. That means this is not a real Runge Kutta method! Maybe this is why the preconditioner is so bad for PDE's\n", + "Cowabanga! Looks like we need to talk stability...\n", "\n", - "I actually derive linear multistep methods, but some of them are unstable.\n", - "I need to enforce stability somehow, which I think is entirely possible." - ] - }, - { - "cell_type": "markdown", - "id": "2ddbc735", - "metadata": {}, - "source": [ - "## Stability\n", - "### Consistency\n", + "## Stability of the preconditioners\n", + "The standart tool for analysing LMMs are the characteristic polynomials.\n", "Since the only coefficient for $u$ is one for the initial conditions (but on the right hand side), we get a first characteristic polynomial\n", - "$$\\rho(r) = r^p - 1,$$\n", - "which means that indeed 1 is a root and we satisfied a condition for consistency.\n", - "Actually, the roots are all distict with modulus equal to or smaller than 1, which is a condition for convergence.\n", - "\n", - "The second characteristic polynomial, on the other hand, collects quite a few coefficients and is equal to\n", - "$$\\sigma(r) = \\sum^{p}_{i=0}\\beta_ir^i.$$\n", - "The second condition for consistency is that $\\rho^\\prime(1) = \\sigma(1)$, which means in our case \n", - "$$p = \\sum^{p}_{i=0}\\beta_i$$" + "$$\\rho(r) = r^p - 1.$$\n", + "\n", + "Now, first of all, we need to make sure that our method is convergent, by showing that it is consistent and zero stable.\n", + "Showing that a scheme is consistent usually works by computing the linear difference operator and Taylor expanding all the terms to make sure everything cancels up to the order of consistency.\n", + "Since that is exactly how we derived the scheme, it is consistent to the order of the number of collocation nodes by default.\n", + "\n", + "To show zero stability, we need to make sure that the roots of the first characteristic polynomial are all smaller or equal to one and that they are distinct.\n", + "In that case, the solution will stay bounded when applied to the test problem $\\partial_t u = 0$.\n", + "Since our first characteristic polynomial is particularly simple, we can immediately see that the roots $r = 1^{1/p}$ are all bounded by one and destinct.\n", + "\n", + "So we have a consistent and zero stable method, which is hence convergent by Dahlquists theorem.\n", + "But is it A-stable?\n", + "Dahlquist, who was very helpful with his theorems up to now, will crush our dreams at this point with his second barrier theorem, which states that there is no A-stable LMM with order greater than 2.\n", + "Wanting to improve on the trapezoidal rule, it is at this point that we realize that we can't.\n", + "\n", + "Now, we can of course find problems that fit within the stability region of the LMMs in the preconditioners, but they depend on the nodes that we use, which impact the second characteristic polynomial and hence the stability polynomial.\n", + "So nothing stops us from checking if this might be a good preconditioner for our problem, but it is difficult to make general statements about where this may be applicable." ] } ], diff --git a/pySDC/projects/Resilience/advection.py b/pySDC/projects/Resilience/advection.py index dcbba1021e..3f25cecae4 100644 --- a/pySDC/projects/Resilience/advection.py +++ b/pySDC/projects/Resilience/advection.py @@ -67,7 +67,7 @@ def run_advection(custom_description=None, num_procs=1, Tend=2e-1, hook_class=lo 'nvars': 2**9, 'c': 1., 'type': 'upwind', - 'order': 5 + 'order': 5, } if custom_problem_params is not None: @@ -122,3 +122,6 @@ def run_advection(custom_description=None, num_procs=1, Tend=2e-1, hook_class=lo # call main function to get things done... uend, stats = controller.run(u0=uinit, t0=t0, Tend=Tend) return stats, controller, Tend + +if __name__ == '__main__': + run_advection() From 3e49e604120038e6b457a81bbe1b4700833739b2 Mon Sep 17 00:00:00 2001 From: Thomas Baumann Date: Wed, 24 Aug 2022 15:56:37 +0200 Subject: [PATCH 07/10] released the flaky kraken. --- pySDC/projects/Resilience/accuracy_check.py | 11 ++++++----- pySDC/projects/Resilience/advection.py | 1 + 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/pySDC/projects/Resilience/accuracy_check.py b/pySDC/projects/Resilience/accuracy_check.py index 3bffa39ecf..c6de943f3b 100644 --- a/pySDC/projects/Resilience/accuracy_check.py +++ b/pySDC/projects/Resilience/accuracy_check.py @@ -15,6 +15,7 @@ class do_nothing(hooks): pass + class log_errors(hooks): def post_step(self, step, level_number): @@ -72,7 +73,8 @@ def get_results_from_stats(stats, var, val, hook_class=log_errors): return results -def multiple_runs(k=5, serial=True, Tend_fixed=None, custom_description=None, prob=run_piline, dt_list=None, hook_class=log_errors): +def multiple_runs(k=5, serial=True, Tend_fixed=None, custom_description=None, prob=run_piline, dt_list=None, + hook_class=log_errors): """ A simple test program to compute the order of accuracy in time """ @@ -101,7 +103,7 @@ def multiple_runs(k=5, serial=True, Tend_fixed=None, custom_description=None, pr desc = {**desc, **custom_description} Tend = Tend_fixed if Tend_fixed else 30 * dt_list[i] stats, controller, _ = prob(custom_description=desc, num_procs=num_procs, Tend=Tend, - hook_class=hook_class) + hook_class=hook_class) level = controller.MS[-1].levels[-1] e_glob = abs(level.prob.u_exact(t=level.time + level.dt) - level.u[-1]) @@ -128,13 +130,11 @@ def plot_order(res, ax, k): order = get_accuracy_order(res, key=key, thresh=1e-11) label = f'k={k}, p={np.mean(order):.2f}' ax.loglog(res['dt'], res[key], color=color, ls='-', label=label) - #ax.loglog(res['dt'], res['e'], color=color, ls='-') - #ax.loglog(res['dt'], res['e_loc'], ls=':', color='black') ax.set_xlabel(r'$\Delta t$') ax.set_ylabel(r'$\epsilon$') ax.legend(frameon=False, loc='lower right') - + def plot(res, ax, k): keys = ['e_embedded', 'e_extrapolated', 'e'] ls = ['-', ':', '-.'] @@ -195,6 +195,7 @@ def plot_orders(ax, ks, serial, Tend_fixed=None, custom_description=None, prob=r prob=prob, dt_list=dt_list, hook_class=do_nothing) plot_order(res, ax, k) + def plot_all_errors(ax, ks, serial, Tend_fixed=None, custom_description=None, prob=run_piline): for i in range(len(ks)): k = ks[i] diff --git a/pySDC/projects/Resilience/advection.py b/pySDC/projects/Resilience/advection.py index 3f25cecae4..fccb0d59e7 100644 --- a/pySDC/projects/Resilience/advection.py +++ b/pySDC/projects/Resilience/advection.py @@ -123,5 +123,6 @@ def run_advection(custom_description=None, num_procs=1, Tend=2e-1, hook_class=lo uend, stats = controller.run(u0=uinit, t0=t0, Tend=Tend) return stats, controller, Tend + if __name__ == '__main__': run_advection() From 35535b6b1bcdd4038d77047897f7ea8ff5b2f682 Mon Sep 17 00:00:00 2001 From: Thomas Baumann Date: Wed, 24 Aug 2022 18:54:05 +0200 Subject: [PATCH 08/10] Added plots of the region of absolut stability for various conditions --- .../Preconditioners/LMM_preconditioner.ipynb | 173 ++++++++++++++++-- .../playgrounds/Preconditioners/dahlquist.py | 138 ++++++++++++++ 2 files changed, 298 insertions(+), 13 deletions(-) create mode 100644 pySDC/playgrounds/Preconditioners/dahlquist.py diff --git a/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb b/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb index fff91b9259..711ddfd702 100644 --- a/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb +++ b/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "2f78d1a2", + "id": "63acb33f", "metadata": {}, "source": [ "# High Order Linear Multistep Preconditioners" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "03c53290", + "id": "287d6ed6", "metadata": {}, "source": [ "## Constructing linear multistep methods (LMMs)\n", @@ -52,7 +52,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "0a28db56", + "id": "925e4c85", "metadata": {}, "outputs": [], "source": [ @@ -62,13 +62,14 @@ "from pySDC.projects.Resilience.vdp import run_vdp\n", "from pySDC.projects.Resilience.advection import run_advection\n", "from pySDC.projects.Resilience.piline import run_piline\n", - "from pySDC.playgrounds.Preconditioners.heat import run_heat" + "from pySDC.playgrounds.Preconditioners.heat import run_heat\n", + "from pySDC.playgrounds.Preconditioners.dahlquist import run_dahlquist, plot_stability" ] }, { "cell_type": "code", "execution_count": 2, - "id": "896c94ff", + "id": "e9955fe6", "metadata": {}, "outputs": [ { @@ -110,7 +111,7 @@ }, { "cell_type": "markdown", - "id": "6bcafa4d", + "id": "f0d03d0f", "metadata": {}, "source": [ "What you see above is the order for the pi-line problem, a non-linear ordinary differential equation, which we integrate with an IMEX scheme such that we only need to solve linear systems in each step.\n", @@ -128,8 +129,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "d28091be", + "execution_count": 3, + "id": "6e821b7c", "metadata": {}, "outputs": [ { @@ -147,13 +148,12 @@ ], "source": [ "Tend = 2e-2\n", - "ks = [1, 2]\n", - "compare_preconditioners(run_advection, Tend * 2.**(-np.arange(8)), ks, None)" + "compare_preconditioners(run_advection, Tend * 2.**(-np.arange(8)), [1, 2], None)" ] }, { "cell_type": "markdown", - "id": "0303cce6", + "id": "3e4b3e97", "metadata": {}, "source": [ "Cowabanga! Looks like we need to talk stability...\n", @@ -176,8 +176,155 @@ "Dahlquist, who was very helpful with his theorems up to now, will crush our dreams at this point with his second barrier theorem, which states that there is no A-stable LMM with order greater than 2.\n", "Wanting to improve on the trapezoidal rule, it is at this point that we realize that we can't.\n", "\n", - "Now, we can of course find problems that fit within the stability region of the LMMs in the preconditioners, but they depend on the nodes that we use, which impact the second characteristic polynomial and hence the stability polynomial.\n", - "So nothing stops us from checking if this might be a good preconditioner for our problem, but it is difficult to make general statements about where this may be applicable." + "We can numerically determine the region of absolute stability of a preconditioner by just running many Dahlquist problems and checking if the solution grows or not." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "192885eb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/miniconda/base/lib/python3.9/site-packages/numpy/ma/core.py:2825: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " _data = np.array(data, dtype=dtype, copy=copy,\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(16, 5), sharex=True, sharey=True)\n", + "descriptions = [IE_desc, Taylor_desc, trap_desc]\n", + "titles = ['Implicit Euler', 'LMM', 'Trapezoidal']\n", + "for i in range(len(descriptions)):\n", + " axs[i].set_title(titles[i])\n", + " stats, _, _ = run_dahlquist(custom_description=descriptions[i])\n", + " plot_stability(stats, ax=axs[i], iter=ks)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "412007eb", + "metadata": {}, + "source": [ + "The above experiments shows why implicit Euler is such a popular preconditioner for SDC.\n", + "Yes, it's only first order, but also yes, it is stable.\n", + "The trapezoidal rule, which is also A-stable, turns out to become only conditionally stable when we perform multiple iterations.\n", + "The LMM, actually shows rather good stability properties for a single sweep, but the region of stability quickly diminishes when we perform more iterations.\n", + "\n", + "We can see what happens we take a different amount of nodes:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "603195fd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(16, 5), sharex=True, sharey=True)\n", + "nodes = [1, 2, 4]\n", + "for i in range(len(nodes)):\n", + " desc = {'sweeper_params': {'QI': 'LMM', 'num_nodes': nodes[i]}}\n", + " axs[i].set_title(f'{nodes[i]} nodes')\n", + " stats, _, _ = run_dahlquist(custom_description=desc)\n", + " plot_stability(stats, ax=axs[i], iter=ks)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "5da3555b", + "metadata": {}, + "source": [ + "When we use only one node, we have an A-stable method!\n", + "But recall that we get just the trapezoidal rule here and the quadrature rule is very low order so we don't get very far with this!\n", + "\n", + "Two nodes actually shows very restricted stability at the first sweep, so that's not helpful.\n", + "Four nodes is a bit better than 2, but also very severe restrictions on stability.\n", + "So it seems that we found a sweet spot with three nodes.\n", + "\n", + "What about different quadrature rules?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "640133b0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sweeper - WARNING: we need to do a collocation update here, since the right end point is not a node. Changing this!\n" + ] + }, + { + "data": { + "image/png": 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iCi3bbQfZLMycCYMHF6/dLTBokNzffTeccQb4/aXtj8FgqD9mXDcYDIaWR7mP7UbgMRgMhhz1Ve0LQWVlJT179mTSpEkMHDiQuXPnMn78+M0+t6Wf9Ho6ewgfFSb2RIzMlxn8uxZR1ejXT+6XLSsLgefYY+Hpp+X+kUdE5DEYDPXHjOsGg8HQ8jBj+5YxAo/BYDCUAT6fj+eee45DDjmESCTChAkTyuo0oNh4+3uxOlqkPksVV+DZeWfw+eC112D06OK1WwdHHy3pWVOmGIHHYGhOmHHdYDAYWh7lPrYbgcdgMBjKhHA4zEsvvcSoUaMIh8OMHTu2zudffvnlPPLII8TjcXr06MHZZ5/NVVddVZzOFhhlKbx9vaSmp4rbcEUFtGsHS5YUt906WLcOYjGIRkvdE4PB0FDMuG4wlBdurYuOadyEi45rdHKTWyp3S2t0Rm5kkKqeDmhHSxGIDTcNGqn4qVCgkAIRFihbgS3FI/CIvyBeuVf+TW6BjTcrZKFCChXO/cxUVyhbynlsNwKPwWAwlJg+ffowa9YsANq0aVMvN36A6667juuuu66QXSspyqe+X1gpT5EWOevXS8mq7t2L0149iMfFFshkbxgMzQczrhsMxUWnNe56F7faxa3K3ddsvGWXZMGFqhuqNn8BCxFVfAp8sgZRXhFevhdqbOTfVk7M+fHSRIN2cyKQs4kwlM0JRTGNm3ZFQEppSNfxhmxQYYUVtrCiclNRhVVpbbxVWNIXQ9FoDmO7EXgMBoPBUJbYXWzQkPkqg29HX3EabdtWSqXffDP85S9loar06AHbbgvvvQe//W2pe2MwGAwGQ2nQaY2zxsFd48r9WhdnnYO7XqJyfoACFVFYUQu7rS1RMZYieGgQK5yLlAkqrKAlIo6XokfMaDcXMZTU6IRGxzVuXCKL3JiLrpV7d70IVDrx0/doVVpYbeVmt7ex2uXu2xrxp7ViBB6DwWAwlCV2exssiD0dw6q08HQvwpSllJje3HQT/PWvcP31hW+zHlRXl1VQkcFgMBgMBUM7GmeVg7PCkfuVDu5qiczZlA3ihnc7L1YbC7uN/X10i4qoHwgc9m02AIHdAkV9L3WhLBGZCAJtt/58ndEbI5SqRPhx1jm461wyczKkk5uEBNmI2NPRxu6Uu3WW349J/WrZGIHHYDAYDGWJ3cmm4vwKqu+sJjUtVRyBB+C660Tg+fjj4rRXD3bYAd5/v9S9MBgMBoMhv+isxlnhkF2WxfnOwVkuog4btBwb7A42np4erKEWdgf7+0iVoqVvlwnKq7Dby/vfHG7claimNfI7dFe7OMscMnMyG68RUNidbeyuNp4uHuzuuWgfI/q0GIzAYzAYDIayxWprSf56Wm/9yfmipkbuhw8vXptbYdttYfbsUvfCYDAYDIam4VZLulF2SZbs0izO8o1ijgop7K42/n5+PJ09EnHS3qQa1RcrZGGFLDw9frjF16mNEVHZ5fI7T01NkcpKIQsVUNjdbTw9PHLr7kH5ze+8uWIEHoPBYDCULUopvP28ZGZnyA7PFieKp1072G8/+Oc/oVMnuPzywre5FeJxWL0aHn0UTjyx1L0xGAwGg2HraK1x17lkF2fl9k12Y5qVFzzdPPiH+/F09+Dp5kFVmMpRhUD51ffijR8/sEka3DKJnsp+myX5bjL3AvFB9PT24OnlwdPbgxWwSvgODA3BCDwGg8FgKGvsrjaZeRnir8WJnhkt/OJPKbjxRhg6FK66qiwEnhNOEHHnkUeMwGMwGAyG8sWNu2QXZMksyJBdtFHQUWGFp1dO0Okl0TkmMqd0KFvh6eLB08WDf5ec6JPUZJeKEJddkiU1JUXqo5QIPl1tvH28eLbx4OnpaXXpcc0JI/AYDAZDiVm0aBFjxoz5vuxifYjH4xx//PF8/fXX2LbNEUccwbXXXlvAXpYObz8v6dlpySOfm8E3oAgVtbp0kftEQmqUe0o7XfbuLfdvvgmxGITDJe2OwWDYCmZcN7QWtNYyP8/LkPk6g7PMASTtx9PHg39PP94+XqwOxuel3FEBiZr29vMC4o+UXZoluyhLdmGW5EdJ+BDwgrePF29/L95tvViVrSe6pzmM7UbgMRgMhmbKL3/5S/bff3/S6TQHHnggr776KqNHjy51t/KOp4eHivMqqLqxitS0VPEEnn//Gy65BFatgq5dC99mHVi5tVMqJZqTEXgMhpZJaxnXDc0bndVkF2ZJf5kmMy8jJcoV2N1tAvsF8PbzYnc1ETrNHeVReHt78fb2wn7i5ZNZlJEIrfkZMvPEvNnubOPdzot3ey92F9sIeZuhmGN765HbDAaDoRmwYMEChg4dypQpU+p8XigUYv/99wfA5/Oxyy678O233xajiyVBWQrfAB/ZRVm0W0TDZZCQmRLz+edyHw5DmzYl7YrBYGggZlw3tAR0RpP+Ik3tM7Ws/+d6ah+rJT0njae3h9BRISp/XknFGRUE9w2KSa8Rd1ocyq/wbe8jNDpExUUVVJxXQfDAIMqnSH6QpObuGqpvrib+Vpzs0ixaF3m9VmTKdWzPWwSPUsoGpgJLtdZjlFLtgMeBPsAiYJzWel2+2jMYDIa8c9llMH16fq85ZIj4udSDuXPncsIJJ3DfffcRDAYZMmTIZp83ceJE2myyy1+/fj0vvvgil156aZO7W854+nhIfZoi80UG38AiRPEceqjcH3YYfPVV4durgxNPFM/nadPgb3+DK68saXcMhuaDGdcNhkajXU12QZb0zDTpr9KQlkpXvoE+fAN8ePoYL5bWilIKu6ON3dEmMCKAG3PJfJUh/WWa1McpUpNTWG0s+azs5MPutPnS7o3GjO1bJJ8pWpcCXwAVuf//Bnhba32tUuo3uf//Oo/tGQwGQ4th1apVjB07lqeffpqBAwcCML0eE1c2m+XEE0/kkksuYZtttilwL0uLd1vJCU/PTePd0Vv4EOD+/eHii+E//4EnnoBx4wrbXh28/76IO7DRHshgMJQ3Zlw3NFeyy7OkZ6RJz0qj4xoVyIk6O+ZEHROdY/gRVtjCP9SPf6gfN+mS+TJDek6a5IdJkpOS2J1tfINF7LHCzTuJqNzH9rwIPEqpHsDhwN+An+ceHguMzP37v8BEjMBjMBjKmXqq9oWgsrKSnj17MmnSJAYOHMjcuXMZP378Zp+76WnAueeeS//+/bnsssuK19kSoXyKwF4BkpOSZAdlvxd8Csr118Njj8F//1tSgWeffTaWSF++vGTdMBiaH2ZcNxjqhZt0JVJnWhpnhQM2ePt78Q3y4e3nNZE6hnpjBSz8Q/z4h/hxYy7p2WnSn6dJvJEg8VYCb38v/qF+PP2aIBaasX2L5CuC50bgciC6yWOdtdbfAWitv1NKddrcC5VS5wLnAvTq1StP3TEYDIbmhc/n47nnnuOQQw4hEokwYcKErZ4G/OEPf6Cqqoq77767OJ1sAIUa2wP7BUjPSZP4X0IWBoWO4vH5JGR3yZLCtrMVLAvGjBEPnq+/LmlXDAZDPTHjuqE5kF2WJTU1RXp2GrJgd7EJHhqUSItg8460MJQeK2wR2D1AYPcAzkqH1IwU6c/TZOZmsCotfEN9+If6sSLN57NW7mN7k3+TSqkxwEqt9aeNeb3W+k6t9TCt9bCOHTs2tTsGg8HQbAmHw7z00kvccMMNPP/883U+99tvv+Vvf/sbc+bMYZdddmHIkCFltSEo1NiubEVgnwDOcuf76g0FZ7fdYOZMqaZVQiIR2Gkn+O67knbDYDA0ADOuG8oR7WjSM9NU31tNzT01pOek8Q3yET07SsU5FQR2Cxhxx5B37E42oVEhKi+rJHxsGKudRXJikqqbqog9GyO7NFvqLtabch7b8xHBsxdwpFLqMCAAVCilHgJWKKW65qJ3ugIr89CWwWAwtDj69OnDrFmzAGjTps1W3fgBevTo0eKrE2wJ304+4i/Fcb51YLsiNFiRs5aLxaDEm5pAoOQ6k8FgqAdmXDeUI27SJf1pmuSUJLpGY7WzCB4SxD/Yj/KbFCxDcVC2wrejeDo5qx1Sn6ZITU+RnpXG7mkTGB7Au723LL2emsPY3mRpVmv9W611D611H+AE4B2t9cnAC8BpuaedBtQtbRkMBoPBUA+UrbA72WTmFymCJxKR+//9rzjt1UH37jB7NqxeXeqeGAwGg6G54Na4xN+KU3VTFYl3EtgdbCInRKi4oILA7gEj7hhKht3BJnRIiDaXtSF4SBBdo4k9FaP6tmpS01Jox4jeDaWQsXfXAqOUUvOAUbn/10k2KzeDwWAwGOrCu70XZ4WDm3AL39g558j9t98Wvq2tsPfeMk8mk6XuicFgMBjKHbfaJf5qnKr/VJH6KIW3v5foOVGiJ0fx9i9CNUqDoZ4ovyKwe4CKCysIHxNG+RTxl+JU3VwlEWdZI/TUl3yWSUdrPRGploXWeg1wYENeP2sW3H47XHRRPntlMBgMhpaGd1svyXeTxJ6METk5UtgwXq9XonjKwPymulruo9G6n2cwGAyG1otb45KclCT1WQo0+Ab7CIwIYLezS901g6FOlKXwDfTh3dFLdkGWxPsJEq8lSE5KEtwniG+ID2UbYbIuyso9KxSCP/7RhJ4bDAaDoW483TwEDwmSXZwlu7jAoZ9KwYABMG9eYdupB4GA3M+ZU9p+GAwGg6H8cJMuiXcSVN1cRerTFL5BPomIGBM24o6hWaGUwtvPS/S0KJGTI1iVFvFX4lTfVk16Ztr4ldVBWQk8PXvK6eQf/lDqnhgMBoOh3PEP8YOC7IIi5Pbuthu89RbcfHPh26qDPfaQ+zLQmgwGg8FQJmhHk/wkSfXN1SQnJfEN8FFxfk7YaWOEHUPzRSmFt6+X6OlRIidEUD5F7LkYNXfXkFlUJC/GZkZZCTzBIFx8Mdx5J3zaqKLrBoPBYGgtKJ/C7maTXVIEgeeaa0Tk+fWvC99WHey8s9ybCB6DwWAwAGTmZ6i+o5rE6wnsLjbRs6OEjzYRO4aWhVLqew+p0NgQOqGpfbCW2idqcdY5pe5eWVFWAg/AVVdBp05wwQXgFsE702AwGErNokWL2GmnnRr8ukMPPZTBgwczcOBAzjvvPByn9U1wVsTCqXLQqQKH6lZWwtixEI/D118Xtq062FCIQGtohX9ug6HZYMZ1Q6Fx1jvUPl5L7aO1oCE8PkzkpAiernm1WDUYygqlFP5BfirOryAwMkBmQYbq26pJvJsoihFzcxjby07gqayEf/wDPvkE7rmn1L0xGAyG8uWJJ55gxowZzJo1i1WrVvHkk0+WuktFx7ezD12jqXm0Bu0WeGI/5RS5f/nlwrZTBxvMla+7Dk49tWTdMBgMBcKM64atoR1N8sMk1bdVk1mYIXhgkIrzKvBt5zNVsQytBuVVBPcJUnlBJd7tvSTfS1J9h3wnypFiju1lJ/AAnHwy7Lsv/OY3sGpVqXtjMBgMxWPBggUMHTqUKVOmbPW5FRUVAGSzWdLpdKtc2Pl28BE6IoSzxCH5foFrh/fqBX4/LFxY2HbqYNM/8VtvlawbBoOhAZhx3ZAvst9lqbmnhsTbCbzbeKk8v5LAiICpKmRotVgVFpFjI0ROigBQ+1AtsRdiuInCpwKV69heljF8SsGtt8KQIXD55XDffaXukcFgaA3EX4/jrMhvyKTd2SZ0SKhez507dy4nnHAC9913H8FgkCFDhmz2eRMnTqRNmzYAHHLIIXzyySeMHj2a4447Lk+9bl74BvnIzM+Q/DCJf5gfK1zAs4u+feHGG2HgQDj77MK1UwfDh8PHH4Nt7BUMhq1ixnVDS0A7muQHSZIfJFEhRfi4ML4dfKXulsFQMjY3tquwQmUV6Rlp0rPSWO0trGD914QtZWwvS4EHZO38y1/CtdfC6afDfvuVukcGg8FQOFatWsXYsWN5+umnGThwIADTp0/f6utef/11kskkJ510Eu+88w6jRo0qcE/LD6UU/mF+MnMypD9P49/DX7iTkX/8A444Ap57rmQCz+uvw5gxMHt2SZo3GAz1xIzrhnzgrHaIPRfD+c7Bt7OP4CHBBm1aDYbWgrIUdlsbHdI4axzclS46orHaWigrf+vCch/by1bgAbjiCnj8cfjZz2DGDImMNxgMhkJRX9W+EFRWVtKzZ08mTZrEwIEDmTt3LuPHj9/sczc9DQAIBAIceeSRPP/88612I+Dp5sHqYJF4K4EKKimhXgj23hv69IH33oN0GnzFP0GtrJSCXtOmFb1pg6HZYcZ1Q3NFa016Rpr4a3GUx0TtGAybsrWxXWc1iYkJUpNTaL8mdEwIT5f8SB/lPraXtcATCkmq1ujRcPXV8Kc/lbpHBoPBUBh8Ph/PPfcchxxyCJFIhAkTJtR5GlBbW0tNTQ1du3Ylm83yyiuvsM8++xSvw2WG8ioqzq2g+tZqkh8l8Q3y5fW05nvatIG//x3GjxeFZfjw/LdRD4JBSKWgthYikZJ0wWAwbAUzrhsai05r4q/ESc9M4+njIXxUGCtqonYMhvqiPIrQQSG8/bzEnotRc28NoUND+IY23Yy83Mf2shZ4AA49FE46Ca65BsaNk9Qtg8FgaImEw2FeeuklRo0aRTgcZuzYsVt8biwW48gjjySVSuE4DgcccADnnXdeEXtbfihbEdg3QPyFOM4yB0+PAk1xgwfL/Ztvlkzg2WUXKZk+fboEFRkMhvLEjOuGhuKscah9shZ3lUtgvwCBvQOFObAwGFoB3r5eKs6tIPZcjPjLcbJLsoQOC6G8TftOlfPYrrQufL34+jJs2DA9derUnzy+ahXssAP07w8ffGCMJQ3lw8iRIwEJvzMYmgtKqU+11sOK1d6WxvZC4CZcqm6owjvAS+SYAoW2uC4ceCC8/z4sWCDVtYrMqlXQqRNMmAAPP1z05ls0Zlw3NFeKObYXc1xvTWTmZ4g9EwMLwseE8W7jLXWXWgxmbG/daFeTfD9J8r0kdlebyLgIVkX5R8U1Zlwv/3cFdOwIN90EH30E//lPqXtjMBgMhnLFClr4h/jJzM7gxgpUItOy4JJLwHGgRBuceFzuSxRAZDAYDIY8k/wkSe1jtViVFtGzo0bcMRjyiLIUwf2ChMeFcdY4VN9TTXZZttTdKgjNQuABOaU87DD43e/g669L3RuDwWAwlCuePpKaVX13NTpVoCjVYFDuJ08uzPW3Qja3JgmVzj/WYDAYDHlAa038jTiJ1xN4+3uJnhHFbmPSFQyGQuDb3kfFGRUoW1HzQA3pr9Kl7lLeaTYCj1Jwxx3g9cJZZ0mEvMFgMBgMP8a3o4/QkSF0tSb9ZYEm7gMOgF13hX//WypqFZm+fcVc+fXXi960wWAwGPKEdjSxZ2OkPk7h391P+Pgwymf8dgyGQmJ3someGcXuYBN7IkZqeqrUXcorzUbgAejRA264Ad59F265pdS9MRgMBkO54hvkw2prkZycRGcKEMXj88Frr0Hv3lACE1TLgn33haeegrvvLnrzBoPBYGgiOqOpfaKWzOwMwQOCBA8OGjNlg6FIWBGL6KlRPH09xF+Mk/w4Weou5Y2yr6L1Y844Qxa0v/mNVNjq37/UPTIYDAZDuaGUIrBXgPhLcbJLs3j7FMDLoEMHOPdc+NWvYPly6NIl/23UwZ13ysHHtdfC2WcXtWmDwWAwNAGd0dQ+Xkt2YZbQ4SH8u/hL3aXWRSIBK1fKbc0aWLsW1q2DqiqoroaaGjG7i8chmYRUCjIZyY92XdhQpEgpqf7j8cjBTyAgt1AIwmGIRqGiAtq0gbZtoX17WTt06iT3nma3FW9RKJ8iMj5C7NkYiTcS4EBgRKDU3Woyze5TpRTcdRfstBOcdpoUMTFVtQwGg8HwYzYYVGYXFkjgARgxQu4nTYJjjy1MG1uge3fYbz/48ENYsQI6dy5q8waDwWBoBDorkTvZhVlCR4bwDzbiTl5xHPj2WzFtXbgQFi2Cb76BGTNEqGnTRoScLeH1ijATDotQEwiIeOPziSDj9UoYLYjY4zgiGFVVyfUTCRGGYjERirbkK6KUVBLq2lUm9J49JSq4Tx/Jw+7XT0QgZaK6ConyKMLHhkXkeTsBFgT2aN4iT7MTeEC+AzffDCefDP/4h0TzGAwGg8GwKValhXcHL8kPknh6ePD2L4DIM2yYLPomTy66wAPw97/DHnvABx+UpHmDwWAwNADtamLPxMguyBI6wog7TSKVgi++gFmzYM4c+ffcuSLspDfx37MsEVEcR0SbE0+UiNvOnUVg6dAB2rWTCJvKShF08oXWIvRUVUmU0Nq1sGqVRA6tWCHRv8uWiSD18ccSTbQplZWSrjJgAOywAwwcKFEOfftuFJkMTUZZivDRYWI6RuLNBMqnmnVUXbMUeECqaj33HPzxj5KqNWRIqXtkMBgMhnIjPDZM9cpq4m/GqehdkX/zSp9PFJYHHoCjj4a99srv9bdCz55yv3RpUZs1GAwGQwPRWhN/JU5mbobgIUH8Q5rvBrLoxOPw2WcwZYrcT5sGX34pog1IZM2224oQcsQR8u9ttpFbjx4SdTNypDz33/8uXr+VkooIkYhEKGyNWEwijhYsEKFq/nwRrSZOhIce2vi8SAQGDYJddpHbbruJAGTSWhrNBpGnNlNL/OU4KqTwDfCVuluNotkKPErB7bdLVPxJJ8HUqRur1hoMhsah0xo35qJjGjfuohN64y2l0encfUZDRnLIdVaDA7iAltOpH6BA2Qqs3L1HwiHxgvIqlH+TW1ChAgorZKHCChVSWBFLnm8wNALlVQQPCBJ7Mkb17dVUXCilMfPK+efLieBBB8Hq1XJCWCS6dpXb/ffDJZcUrVmDwWAwNJDkpCTpaWkCewcI7N68U0AKzrffig/HpEmSh/z55xvFnK5dRdQYO1ZEjp12gu22ExGnuRMOS5TOwIE//Vl1tUQpzZwp6WbTp8vkf/PN8vNoFHbfHfbcE/beW1LIo9Fi9r7Zo2xF5LgINQ/WEHs2hnWqhad785NLml+PN6F9e/lcH3II/PrXxRVkDYbmhnY07npXblW5++rcrcbFrXWhjorSyq/AL4ZkyqdEnAkpLI8Fdk68UUhtvg37Z527udI+Tu4+Azqhcavc70Uj6qhQqAIKFVVYFZbcKuVmt7Wx2ubEIJOjbNgCvgE+MjtlSM9Ko2MaVZHnz8oJJ8gi9Pbbi26YqJREcEciRW3WYDAYDA0g/UWa5P+S+HbyERhpxJ2fsHIlvPUWvP22RKssWCCPRyIwfLj4cQwfLmnRXbuWtKslo6JCfgfDh298zHXhq6/gk08kxWvyZLjmGhHDbFuEsAMOkAOovffOb/pZC0V5xXi55t4aah+vpeLsCqyK5pUO16wFHoCDD4bLLoMbb5RUrcMOK3WPDIbS4sZdnFUO7moXZ7WDs8bBXSuCDpsG1yiwKixUhcLuYuONerHCFiqySQRNMHfzF15A0W4uOiih0fFcBFFM49a66FotIlS1S2Z5Bh37UZSQD+x2NnYHG6u9hd3Rxu6UE39MyVED4B/uJz0rTdVtVVScVYHdIc9hzIMGSXWNr76CnXfO77W3QpcuUFtb1CYNBoPBUE+cVQ6x52PY3W1CR4TMgRSIAPHxx/Dyy/Dqq5JyBWKAPHIkXHwx7LuvzK2m0tSWsSxJSxswAE49VR6rrRWh5913RSz75z/FsC8QkN/t6NFw+OFi4mzYLFbYInJChOp7q6l9spboadFmlU3QIr4x11wD77wDp58uEWutVdg1tC601rhrXZzvHLIrsjgrHJwVDrp2E/HDC3Z7G7urjW+gD6udhdXGwm5jo6KqrMQPZYmYRBBoV/dzdUaif9x1Ls46EbCcNQ7ZJVncWZtUK/CC3cnG7mzj6erB7irCT95TdAxlj6ebh+hpUWr+W0N6Zprg/nnO6T3iCFmEPvSQLKSKyOjREsV6111wzjlFbdpgMBgMdaDTmtqnaqUc83GRZrVJzDvJJLz5JjzzDLz0kqQ027akEv3tbzBqlEScGB+ZphGJyO9y1Cj5f22tiD1vvili2qWXym3gQElzO/ZYGDrUVOv6EXZHm/DYMLEnxXg5NDpU6i7VmxYh8AQC8OijErV36qnw+uvGWNzQ8nDjLtlvszhLHbJLszjLHEltArBkIPJu4xVBo6PcVEXLTF1SXoXdQSJ2vPww51qntUQurXS+F70yszOkP8vln3kQsaeHjaenB09PD1bIDBitAU8vqaSV/CSJf7g/v3/3Ll0kcufJJ+EXv4BOnfJ37a1w8cUi8Lz/vhF4DAaDoZyIvx7HXe0SOTnS7NI88kI6DW+8AY89Bi+8IGXD27SRlIsjjxSfjTZtSt3Llk0kIhE7hx8uKS9ffw0vvih/j7//Ha6+Wsyox40TP8FBg0rd47LBN8BHdo8sqY9SePp6mo3pcosQeAB23BFuugnOPVc+q7/9bal7ZDA0DbfGJbsoS+abDNlvsrirc5EpSqJSvAO9eLp5sLuYqJRNUT6Fp5sHT7eNw5vWWqJ9ljlkl2XJfpsl9XGK1GQx/rE6Wnj7ePH08eDt40UFzO+ypRLYL0Dm3gzxF+KEx4fzK4Bec40soK68Em67LX/X3QrBIBx1lByK3nuviWY3GAyGciA9N016eprAXgG8fVuAAXB90Vqq39x/vwg7a9dKGfLx4+G448QTpiUYIjdX+vUTf5PLLpMoquefl8Opf/wDrr1WDqtOPVWqGJm0GIIHBMkuzhJ/KY6nhwcrUv5CbYtaBp59tnhzXXGFpG0WuVqtwdAkdFqTWZQhuyBLZkEGd01O0PGDp6cH384+iTjp6sl/qecWjlJKPHra2fh2EvVdZ7WIPYuzZL/JkpqWIjUlJQJaDxtvPy/ebb3YXewWGQXVWvF09RA8IEjirQTZ+Vm8/fO4yDzkEDj+eMmVOuKIoprCde4s1VWrq2UdbTAYDIbS4SZc4i/HsTvbBPZrJca269bBgw/KHDhrlqRYjB0LJ58s86MRdcqPDh3grLPktmqVCD0PPgi/+pUYWx9+uERPHHpoq02dU7YiPDZM9V3VxF+JExlX/lUtWpTAoxTceaeIxiecIH5dHTqUulcGw5Zx1jhk5mXIzM+QXZyVUuMe8PT24B/ix9PXg93ZLiuvnJaC8ii8vbx4e8mCQzua7LdZEdi+zpCcmCQ5MYmqUPj6+/AO8OLp7TGRUi0A/+5+EhMTZBZk8ivwgExCs2bJJLRyZdEqVuy1F9xxB0yYAK+9VpQmDQaDwbAFEm8n0HFNaEKo5a8bpk+H//wHHnlEfHZ2202qSp5wgpR5NDQPOnaECy6Q29y5EhL83/9KKlfv3nD++ZIH3gpPkeyONsH9giTeSZD+Ml32qVrlH2PUQCoq4IknZF19yilSPc5gKBe01mS/y5J4J0HVbVVU31pN4s0Ebo2Lf7ifyMkR2vyqDdEJUQIjAhKtY8SdoqBshbe3l+D+QSrOrqDy/yoJHRHC09VD6vMUtQ/XUnVDFbGXYmQWZtCu3vpFDWWJshWeHh4yczM46x20zuPfMhqVRVBNDUyZkr/rboVDDpH7Ilr/GAwGg2EzZJdmSU9L49/Dj6dLizpL34jrilHy/vuLQe9jj0laz7RpUrL7Zz8z4k5zZvvtxfNkyRLZWPftKxE9PXrIGmfevFL3sOj49/Bjd7JJvJFAZ8p7D9DiBB4QA/abbpJTzKuvLnVvDK0drTXZ5Vnib8WpvrmamrtrSH6YxIpYBA8JUnFRBZXnVxI6KIS3r7d1V1goI6yIhX+In8i4CG1+0Ybw8WG8/bykZ6epfaiWqpuqiL8Zx1nplLqrhkbgG+jDrXKp/o+E3OaVU06R8NF//CO/162DTp3g6KPh2WdFWzIYDAZD8dFaE38jjooogvvkuVpjOZDNSrXIQYMkFfnrr2WuW7pUwkiHDCl1Dw35xOuV1PP//Q8+/1zChO+9VwSgceOkfHUrQdmK4KFB3CqX5EfJUnenTlqkwAMiHJ90Evzxj1IVzmAoNm6VS+KDBNW3V1NzVw2pj1NY7S1CR4So/Hkl0VOiBHYPYLdtnTmtzQnlVfgG+AgfHabNz9sQPjaMp5uH1Ccpqu+opvqealLTUuh0eSv6ho34d/ETPTuK3ckmMy+T34tHoyLyvP46ZPJ87To47zyphjp1atGaNBgMBsMmZOZmcL51CI4Movwt6MAum4UHHoAddpD5TSnxavn6a/jlL00lrNbAzjvD3XfD4sVSuvP110XQO+ooEX9aAd7eXrzbe0l+mMSNl2+aUIsVeJQSIXnHHaXi2zfflLpHhtaAzmrSs9PUPFxD1b+rSP4viQoqQoeFqPy/SqIToviH5Lk8s6GoKK/Ct6OPyPgIlZdVEjw4iM5o4i/FWX/jeuKvx3HWmKie5oCnq5iX6xpNem46vxfv1EnKw06fnt/r1sE228j9V18VrUmDwWAw5NBak3w3idXOwje4vD066o3WEhq6885w2mlScvvZZyVy4+STjXFya6RLF6kaungxXHUVTJwoQs+ECbBgQYk7V3iCBwQhA8kPyzeKp0XvMsNhKRubycCxx4rvl8FQCJx1DvG341TdVEXsmRjuGpfAPgEqLqqg4vQK/LsaUaclYoUtAsMDVPysgujpUXz9faSmpqi+tZrax2rJLM7k19/FkHf8u/ix2lrEX4ijnTz+rU48UUpbnX560czg+vWTtffMmUVpzmAwGAybkJmXwVnpENgn0DL8Ez/5BPbeG445Rv7/1FPw2WcSsWGZNW2rp00buPJKWLhQ/Hmefx4GDIBf/ALWry917wqG3cHGN1DW+26iPKN4Wvy3c7vtJKJw6lQxBTd7LUO+0FqjkxpnpUP1zdWkJqfw9PQQOTFCxcUVBEcGTfpVK0Ephaenh/DRYSovrSSwb4Ds0iy1D9RSc38NmXlG6ClXVEAR3D+ITmoSbybyJ/L07g3XXgtz5sDHH+fnmltBKQkcevbZovo7GwwGgwFIfZTCqrTwDWzm0TsrVsAZZ8Dw4ZKCdeedcnJw7LEy0RgMm9K2rZjezpsnRts33CAb8HvvbbHVjvwj/JCB9Gd5jv7OEy1e4AEYOxauuALuuw9uuaXUvTE0d7SrSc1MUXNXDc4KB53SBPYOUHlJJZFxEbzbelFmAmy1WBGL4H5BKi+pJDg6iK7R1D5WS809NWTmG6GnHPFu68XTz0NqSor09DxO1sccA6GQCD1FWuT8+98Qj8NllxWlOYPBYDAAzkqH7OIs/mH+5lsW3XVFzBkwAB5+GC6/XDbt55wDnhZaDcyQP7p1E4+eTz8Vgeess2DffWH27FL3LO94Onvw9PGQ+jRVllV1W4XAA5IieMQRsuidOLHEnTE0S3RWk/o0RfUt1cSfk3QOq72Fp4eH4P5BrIpW83Uy1APlVQSGBai4sILQESF0QlP7aC21D9SSXZotdfcMm6D8isiJEezuNon/JcgszJMxckWFTD4vvADXX5+fa26Fww+XrLDp01vswZnBYDCUHanpKbDAN6SZRu/Mny8lz3/2M/FTmTlTymRHo6XumaG5MXQovPeeRPB8+aX8/69/LWrRiWLg38WPW+WSXVB+a/pWsyO1LKnqt912cNxxrcIDypAndFaTnJqk6uYq4q/EUWFFeFyYivMqsCIWNNODGkNxULbCP8RPxQUVhEaHcNY41NxbQ+0ztbhVZgdeLiilCO4XRKc1tQ/X4qzNk1H2L38JY8ZIScfly/Nzza2wzTYSxbNqVVGaMxgMhlaNdjXpWWm823mbn9+i1nDbbTB4sBgn33MPvPOOlME2GBqLZUma3xdfSGrfFVfAiBEi+LQQvNt7UQFFemb5pWk1s1GoaVRUyEGq68KRR0J1dal7ZChntKtJzRDD3MSrCaw2FpGTIkTPiOLb3mfSsAwNQtkK/zA/lRdVEtgnQGZuhqpbq0h8kEffF0OT8PbzUvGzCtCQ+TpPJ01KSRRPKgXvvpufa26FbbeV+7lzi9KcwWAwtGqy32TRMd38vHdWr5YN0QUXiJny7Nlw5pnGZ8eQPzp2hEcfhSefFDPmXXYREbEF2BUoj8K7g5f0V2l0trzeT6sSeEAWvk8+KQLiiSeCY6oZGzZD5usMNXfVEH8hjgopIhMiRE+L4t3G+OsYmobyKYIjg1ScX4G3n5fk/5JU31VN9tvyC/FsjVjtLKwOFslJSZx1eZogdtoJfD746KP8XG8r7Lyz3LfAtHeDwWAoOzJfZcAWP7dmw4cfSirWG2/ATTfBa69B9+6l7pWhpXLccfD55xLFc/bZcNppEmrczPEN8EEasovKaw3f6gQegAMPhJtvhldekUpuBsMGnDUOtY/VUvtILTqjCR8TJnpWFG8/I+wY8ovdxiYyLkJ4fBid0tTcV0P8rXjZnQK0NpRShI8JoxOa+Avx/JTA9PvFBO6224pSw7xbN2jfHp57zhxiGAwGQ6HJLMjg6e1B+ZrJOvH222G//SAQkIOHSy4xUTuGwtOtG7z+OvzpT+KbMmIELF5c6l41CU9vD3hkDCgnykvg0bpoIVvnnQeXXiqi9a23FqVJQxmjM5rE/xJU31FNZnGG4IFBKs6rwDfQpGIZCotvOx+V51Xi28VHanKKmntqcFaaXXkp8XT2EDosRPbbLIk3Evm56K23Sp5wEU4VLAuuvFIOZovk7WwwGAytEjfu4q5y8fZpBtE72SxceCGcfz4cfDBMmSIGuAZDsbBt8SR8+WVYtAh2261o0c2FQHkVnh4eE8FTJ/PmiXt7ujhmRf/8p3hfXnyxRPMYWieZhRmq76gm+UES3w4+Ki+oJDAigPIYYcdQHJRfET48TOSECG7MpfqeaqnIYSgZ/sF+fDv7SM9Jk3g30fTIqk6dYMIEeP99WLYsP52sg4svhoMOkrLpRZpSDQaDodXhLJUDGbuHXeKebIVYDI46Sg4bfvlLMSVt27bUvTK0VkaPFmGnokKqtz3/fKl71Gg8PT04Kx10unwi8MtL4AmH4a675A/93XcFb862xfdpyBAYNw4++6zgTRrKCJ3SxF6OUftQLSiInBwhfHQYK1peXwtD68Hb30vFuRV4enqIvxgn9lLMGDCXkOABQTxdPSTfS5KclGz6BS++WMLgTzyxKOVCL7xQtKQPPyx4UwaDwdAqyS6Xk3tPF0+Je1IH69bBqFHw6quSKvyPf8gmyGAoJQMGwOTJMGgQHHMMPPBAqXvUKOyuNmhwVpRP9H157WS7d4fHH4fp02HXXeWPXmAiEXjpJfErOPxwiRYztHwy30jUTnpaGv+efirOrcDbtxmE1xpaPFbEIjIhQmCvAOlpaWofrMWNm3LqpcCKWERPj+LdwSumy+ubOHn36wd33gnvvSchpAVmv/3A6xXvTIPBYDDkH2eVg1VpofxlGvW9dq2Yj06dKlVmzjuv1D0yGDbSsSO8/bYEd5x+ulTYambYnUQsdVaXj8BTfnLzuHGi6B19tKxO//1vSdsqoA9K164iau+1Fxx6KEyaJIKPoeWhXU3y3STJD5JYbS2ip0Xx9Cy/r0HRSaWguhpqaiSMN5GAZFJyOzIZcN2N/lhKgccjO0efT0z6wmG5RaMSbmlOhpqEshTBA4LYnWxiL8Soua+GyIQIdlvzey0FoVEhqr6sIjU1ReigUNMudvLJckp11VUSqXrDDWKaUwDatoU99xQvnmuvLUgTBoPB0Kpx17lY7crrvPx7qqokcmfOHEmBGT261D0yGH7KhmiLo4+Gc86RvcUpp5S6V/XGqrTAAndt+RzGlufOdtAgUZpPOkmMwD7+WHJGg8GCNbnjjpKOOmqU+PK8/TaEmriON5QXbo1L7JkY2W+y+Ab7CB0aaj4VDxpDNgtLl4pD/bffyu2772D5clixAlavhjVr5HQnmYf0k02pqBCVtEMHUee7dpVbt27Qowf07g19+4ogZNgivp18WJUWtY/XUnN/DdGTot+fFBiKh1Vp4RvoI/VJCrujjX+wv2kXvO02GD9eDjDOOEPyhAvEfvvBX/4C06YZL02DwWDIN26Ni7djGUaAJxJSvXHmTCmpaMQdQzkTCMCzz0o6zRlnQLt28u9mgLIUVoWFW20Enq3Tti28+CL8+c9ymzYNnnoKtt22YE3usw888ggcf7zcnntOghQMzZ/skiy1T9ai05rQ2BD+QU3coJULWotYM3s2fPEFzJ0rZuXz54uwk/2Rq3skAl26iOHrNtvA7rvLd61NG6isFMElFJKb3y83r1ciDDZEGbiuXDeTkcifZBLicYn8qa6WE6N160Q4WrVKRKVp02Dlyp/WbO7QQb7T220H228vSuvOO4v4U6CohuaGp6eH6KlRah6uoebBGqKnGJGnFARHBXFrXOIvxEHRtDGkXz+4914YPFi+uwUUePbZR+732gsWLoTOnQvWlMFgMLQqtNbomEaFy+yw0HUlAuKDD8Rs9LDDSt0jg2HrBAKy+d5/f8no+eCDZnMypcKqrOwUylfgAUnz+NOfYPhwCWvfdVf473/FBb5AHHOMHK7+7Gdw5pnSnNlnNm9S01LEX4ljtbGIntyMN8euK+LN1KniCD59Onz+uUTibCAaFbFk111lcOzbF3r1kluPHqWNmHEcEaO++UbEp4ULYcECEaPefvuH5mrhsETy7bILDBsmQtSAAa32y2h3skXkebCGmodqiJ4WxW7fTD/HzZQN3ki1j9YSfz6O3dZuWnrnDjuI79yTT0q0aoEYNUr01V13lWwwk6plMBgMecIBXMrPf+fKK+Hpp8Xrbfz4UvfGYKg/0aika+2+Oxx5pOx5msHJlPIrdLJ8iqKUt8CzgcMOkw3t8cdLft7Pfy6r1AKF15x7rgQe/OEPEiF2440FtQAyFAitNcl3kiQ/TOLZxkP4mDBWsBkJBLGYlBD84AMpg/PJJ7B+vfwsEJBIl6OOkvuBA2XD2LVr+X5YbVtStLp1gz32+OnPq6slCmnmTBGupk8XhfWWW+TnlZUwYgTsvbfkney2m+TpthLs9jbRk6PU/LeG2kdqiZ4RxYo0o89zC0B5FJFxEapuq6L2iVoRjDs3UmjzeuWz/Pbb8r1u0yafXf0BQ4aIv9wzzxiBx2AwGPJGLkhaecpo3fXCC/DXv8op9f/9X6l7YzA0nC5dxDNqxAipOvrmm2Xv7ak8qqyq3jYPgQegTx/Z6P7iF/Cvf8mG97HHxMujAPzud5Jh8q9/yb7yz38uSDOGAqEdTfzFOOmZaXy75vx2rDKagDdHKiWV4956C955B6ZMkVQopUTEGTdOFO3ddpNUJk/z+frWi4oKidYbPnzjY64LX34p4tbkyeKA/vvfy8/CYRF6Ro0SEXi77UrT7yJid7CJnBCh5oEaERhOjZbXwrIVoPyK6ClRau6vIf5GnMjJEVRjRdVLLpHU47FjJYKtQPMZyNfq1VelUmSfPgVrxmAwGAylYskSOO00iX6+5ZbyPfAzGLbG0KHiv3vmmXIytWHtX86Uj75TZmXSt4bfDzffLKXUZ8+WP/7zzxekKaXg+uvlc/WXv8A//lGQZgwFQGc1sSdjpGemCYwMEBpdxuLO0qVwxx2ywWvfXvJOr71WvHV++UvZka1bBzNmyPPOOktSl1qauLMlLEvErNNPl/c/a5aE1z39tCxi5s+XE6rtt4f+/UUAnjRJhKEWiqe7h/BRYZylDvHX4qXuTqvEbm8T2DdAdlGW9Mx04y80YoSUTZ86VYTKNWvy18kfceyxMqy89VbBmjAYDIbWRW4pprNlsLNzXVkrZbOyTwoESt0jg6FpnH66pBhedZVE9Zcx2tVQRkFGzUvg2cC4cZKy1bevpKhceqlEP+QZpWTtPX48XH75xkwRQ/mis5raJ2rJzMsQGh0iuE+w8afrhWL+fLjmGonG6dEDzjtPBJxTTxVzsTVrJFrlmmskr6KystQ9Li86dBCzrFtuEVPpBQtE+N12W/jPfyTtpWdPEX4++WRjefcWhG8HH4G9AqSnpZsmMBgajX9XP3Y3m/gLceJvxdGN/ZydeSa8/rr4a91xR347uQk77ihT5s03S3SqwWAwGJqIDVigU2Wwzrj7bon+/te/ClqQxmAoGkpJFE/79nD22T8t1FJG6JQuKy+uJgs8SqmeSqn/KaW+UErNVkpdmnu8nVLqTaXUvNx926Z3dxO23VbStC69VErN7rGHbPbyjG3Dgw+Kz9NFF8E99+S9CUOe0I6m9slasl9nCR0Rwj+sjCplLV0qIWG77CKRJr/7nTz+t79JVMrChTKIjR1rBJ2G0rcvXHihRDutWgUPPyxpbLfeKnkpO+wgUVHffVfqnuaVwMgAdk+b+KvxsirN2FpQliJyUgTfzj5Sk1OkZzRBaNt7b0nPuvlmGQsKgFLwm9+IlnzuuQVpwmAwGFoVSilUWKFjJRZ4Vq2CX/9aosDPPru0fTEY8km7dnDTTfDppxJ1UabomEYFW5DAg1iM/UJrvQOwB3ChUmpH4DfA21rr/sDbuf/nF79fHJBffFHyTnfZRRTsPJ/Ye73wxBMSTHHOOeL7aigvtNbEno+RnZ8ldFgI/5AyEHdSKfGJOvhgiSj51a8kteqf/5QqUp98IkLPwIEmTzpfVFbChAkSCbViBdx1F3TsCL/9rVQSO+44mDixRUT1KEsRPjKMdjXxV0yqVimwAhahI0J4enmIvxgn9WkTIkn//ncJrTnvvPx18Eece64EwL73HqRN4JfBYDA0GStqlf6Q5Y9/hNpa47tjaJmMGyeem3/8oxRkKTO01rg1LlZF+SRGNbknWuvvtNaf5f5dA3wBdAfGAhukkP8CRzW1rS0yZoxU3dlzT1Fgjj8+714Gfr9UIDnwQDjjDHjoobxe3tBEEm8nyMzOEDwgiH/XEos7X38t/jndu4v7+9y5UpLtq69E1Pn5z0VsMBSWNm3kJOv99+VvcNllIu7sv7+UFXrgAchkStvHJmK3swnuFyQzL0NmXvN+L80VZSkiEyJ4tvUQfy1O5utG/h3Gj5ex4a23IJHIbyc3Ydw4Oex9/fWCNWEwGAytBquthbuuhALP/PlymPWzn0nEssHQ0lBKzHBXr5asnTJD12rIyFhQLuS1J0qpPsBQ4GOgs9b6OxARCOi0hdecq5SaqpSaumrVqsY33q0bvPEGXHedlAgcNCjvbpLBoHg677+/2KU8+GBeL29oJKnpKVKTU/iH+fGPKJG4o7WUOx4zRlKwbrpJPihvvCEpF3/+szxuKA3bbSeTw5IlkmfpumLSvO22kspVAA+vYuHf3Y/VzhIfGLd8IpPyNrY3A5RXET46jN3BFg+wRY0UeXbdVT6bY8eKUFwA9txT7qdMKcjlDQZDC6Y1jev1xe5o4653S+fDc/XVkmrwhz+Upn2DoRjstpvssW68EWKxUvfmBzirxRvI7lA+Lst5E3iUUhHgaeAyrXW946e01ndqrYdprYd17NixaZ2wLEmD+fhjKbk8apSc2ufxNDQUkoywAw6Q/aFJ1yot2aVZ4q/E8WzjIXhICQyVtYaVK2VjdtBBsmu64gr45ht48kn5DFrlo+i2eoJBMbX9/HN45RUxub7wQhHf7rlHqk80M5StCO4fxF3tkp5dPnk3eR3bmwFWwCJycgSrjUXs2RhushEnukcdJWUbP/xQfOUKIDx26yZZo/fdBzU1eb+8wWBowbS2cb0+eLpIKa3s8hKsH5Yvl5SCs86CLl2K377BUEwuv1wydB5+uNQ9+QHOdzmBp3MLE3iUUl5E3HlYa/1M7uEVSqmuuZ93BVbmo616MXSomDFdfLFEUuy6q/w/T2wQeQ46SNK17rorb5c2NAA34RJ7OoYVtQgfEy5uKXTHkUl1yhT44guIx8X/afFi+NOfoGvX4vXF0HCUgtGj4YMP4M03Zdd79tkweHCzzF3x7uDF6miR+jDV+GpOhiZjhS1Ch4fQtZqqf1WRmd/ASB7bllPY//5XQpEff1zu88xvfyue46efLgFDBoPBYGgcdnfZ1GWXlEDguftuSTW/9NLit20wFJu995Z1egErjjaG7LdZrLYWVqh8DvTzUUVLAfcAX2it/7XJj14ATsv9+zTg+aa21SBCIcnTe+MNMWTaYw/ZeOfJcyMYlEyw0aPFuPI//8nLZQ31RGsxlnVrXMLHhrGCRfpSaS0GvoMGwSmnSHTOjjvC7NlyghIIFKcfhvyglCi1kyfD00+L8+yhh0okxTfflLp39UYpRWB4AGelg7OkfMtItga8vbxEJkSwKixiL8VwaxqhoIwaJWLPaafJOJNnRo6UilrPPANXXZX3yxsMBkOrwQpZWB0tsouKLPBoDfffL3YAxgKgaDgOVFXBsmWwaBHMmyc2j199JZnVS5aIz1083iLqeZQXSklkxWefyeF6GaBdTXZxFk8vT6m78gPysSveCzgFOEApNT13Owy4FhillJoHjMr9v/iMGgUzZ4qB5VVXidAzc2ZeLh0IyAL5qKPgkkukErOhOGTmZMjMyRDYL4CnW5G+VFOmwD77wNFHy7H3E0/AsGFSpckun7A8QyNQCo45RkrWX3utRPXsuKMot80kxME30Ac+SH3efP2EWgrefl7CR4XRCU31HdUND92vqJDxBeC116Q6Sp75y19kWvzHP2SBajAYDIbG4e3nJftNtrg+PJ9+KorCyScXr80WjNZSfPX990U3u+oqCew+7DBJBOndGyIRKYbbpo3UUenbVyweBwyA7bcXW8devaBTJwiHxRqpQwf52d57SyHXSy+VYrpPPw3Tp5ednUz5c/zxcv/ss6XtRw5nmYNOarzbeEvdlR/Q5J2x1voDYEu5MQc25FpulUv8lTiB/QJY4TxGZLRtK+k0xxwjJWh33RWuvFJy+bxN+4P4/bIOP+00CXuvroa//c1UKSwkbsIl/nocu6tNYEQRImZWrZLj7nvvlVH7jjvEx8XjkZKUhpaD3w+//rXsfM8/X5TbZ56Rils9e5a6d3WifArfdj4yX2bQh+nipiwafoKnh4eKcyqoebiG2gdrCR8dxrttA+abY46RQgEHHSQrztGj89o/paQy+wsvwC9+IfcGg8FgaDje7bykPkqRmZ+Rw5Zi8OKLEkV+5JHFaa8F4TgSeD9ligSDzJgh/1+/fuNzlILOnSWDv0sX2Hln2U62aQPRqCSK+P2yjbQsEYgcRwLBk0kRbqqrYd06sY1ZtUraeOONn/rf9ewJAwdKgddddhE/4d69zV5ys3TrJvv4V16B3/2u1L0hPTcNCjz9yiuCp7x6oyD1WYrUrBTBvYL4h/tRnjx+uo85RiIwLrpIfA6eeUY27YMHN+myXq9U1IpG4ZprZIC4+WbjrVsoku8m0XFNaEKosJtYrcWJ9Fe/klH6l78UA+WKisK1aSgP+vSRyePee8WoffBgEXnGjCl1z+rEu52X9Kw0zjIHT4/yGt5bI3YHm+hJUWqfqCX2bIzKyypR3gaMWXvsIULyE09I6mCeV3u9e8OBB0qQ0OTJGytsGQwGg6H+eHp6UBFFena6eALPW2+JEtChQ3Haa8Y4DkydKpn369eLSLMhMDYalSXeCSdI4Hb//tCvn8yPvgL9KdevlwK78+dLeteXX0pyyVtvbaz10bkz7LUX7LuvZOHtvLMRfL7nwAPhhhukiFIwWLJuaK3JzMng2cZTPKuQelJWOwCrwqLiZxUk3k6QeCdB6tMUgf0D+Hby5a86UseOYlw5bhxccIGk2Pz2t/D734sU20hsG26/XdTdv/8d1q6V/WChBofWirPaITU1hX8X//eVCwrCN9+Ip85bb0lc5R13yMhvaD0oJZ+B/faT8eKII0Tgu+qqslVvPX1z1TwWZ43AUybYHWxCh4WofbCW2idqiRwfQfnqOZ+Fw1Ll7aabJOH/4Yfzvpi/9VYYMUICWt9/P6+XNhgMhlaBshS+nXykPknhxtz8ZiFsjlRKFAtjrrxFamvh1VfFNvO112RfBhJ5c9ppMu/tvrukVRV7SdemjdQDGjr0h4+nUiL0TJkihy7vvy+xCCBRRIceKueMhx4qy4NWyx57iKfujBny7xKR/SaLu94lsF/5+a+W3S7F7mgTOSFC5OQIKqiIPxen5u4aMgvzY478PcceC3PmwIknihnB0KFSmrYJKCX2HdddJxrSmDEFsU5o1STeTYCXwn6ZHn5YpPLJk2X38+67RtxpzWy7rYwNZ5yx0bgkkSh1rzaLFbKw2llklzW/cu8tGW8fL6EjQmQXZqn5bw1ubQN8nf71L0nYf/NN+PnP8963nj0lE/GDD8SCymAwGAwNxz/EDy6kp6cL39js2ZILNGxY4dtqRmSzkrk2frw4KowbJylRY8bAI4+IqLPbbrK0P/lk8c8pp/M6v1/+pOefL0ECCxdKcd5775WzxueeEx+fDh1kG/vkk5IO1urYkHlT4kVL6tMU+ME3oPyiOcroY/1DvH29RM+OEhobQic0tQ/VUvNITcPNKuuifXv5Br3yiiRL7r23RPVUVzfpsr/6lWT2vPOOVCtZWbwC8S0aZ7Ujxsq75dmjaQOxmMj6J58sAs/nn8soW06jv6E0BAJwzz0bnfEOPljKKJQhdkcbZ7WppFVu+If4iYyP4Kx2iD0bQzv1NOO0LBF2fv97yQU+66yNMdx54sQT5f722yWU3WAwGAwNw+5o4+njITk1Wf/xvbFsqCA0cGBh22kmfPutBFj37CmWRO+8I2dyEyfC8uXw3//KPNdE29WS0KuXvJfHHhMfn3feEfPnDz8UAatLF9m6zphR6p4Wkd69JX19wYKSdcGtccl8kcE/2F//qOwiUtY7V6UU/kF+Ki6oIHhQEGeZQ81dNcSejeGsy+MqdPRoUcMvuURScXbYQTZxTahvd/rporTOmSO+BqZKSdNJfpwED/iHNz6VbovMnw/Dh8sG6o9/lKidbbbJfzuG5otSstF+9FH46COp0FeGIo/VxsKtah6Vv1ob3v5eggcEyS7Ksv7q9cReiaHrO89ceaW4Id97r4xReaRrV6kGcsstYlFnMBgMhoYT2COArtakZxU4imfxYrnv27ew7ZQ5s2bBKafIr+FvfxPv3eeek4zmW26RqJeWVOTW4xE/nv/8R0StN9+U6KT77hOD5r33hqeeagUHNbYtC5dly0rWheTHSdDg360Ae9I8UNYCzwaURxHYM0DFRRUERgRIf5mm+tZq4q/GcWvytJGJRODGGyUtp2NHiYE78khYtKjRlxwzRpTW6moJC/zoo/x0tTWiU5r0TDGvy3v0zttvS8zmd99Jou6f/tSyZgRDfhk/XpKip0+X+pnxeKl79ANUSEEGdLaI5VoN9SYwPEBodAiA9Kdpst/UMxrH44Hrr5eJ5cEHJf88T9i2nP4dc4ycdKZSebu0wWAwtBo823qwO9skPyhwFM/KlRtLObVC5s0TU+Sdd5Zq2RddJBXjX3oJxo5tnpE6DcW2pcjmQw+JzvGvf8k25vjjJU7hvvvyHuxbXrRpU7JDVjfmkpqawrujF7tdee4Xm4XAswErYBE8MEjlRZX4hvpIfZqi6pYqEm8ncBN5Enp2312My66/Hv73P/FeufZayXVtBHvsIWF0lZWium4wyzI0jPQXaciAf5c8K6X33y9uZd27y9/94IPze31Dy+SIIyShe/Jkifsto+OSDVWadMYIPOWKf5ifyl9UokKq4RuBc8+Vo7vTT4fVq/PWp2hUzjUSiSbb0RkMBkOrRClFYGQAd61bWC+emppWWdG1qgr+7/9ka/bSS1Ile/FiKajUmoOZ2raV38tXX4kvTzQKZ54pQs+TTzYpIaV88ftLdhqVnJSELAT3LV0Fr63RrASeDVhRi/BhYSouqMC3vY/kh0mq/1NN4r0EOpWHT7HHI6Hwc+bAIYdIla3BgyUcpxH07y/7wCFDZAF9/fUt9MtWQNKz01htLezueVRK//EPSWwdOVJ2NK15djA0nOOOk+pGL7yQ95QZQ8vHCll4B3jJLshSfXs1mfkZtFuPiWHMGDm6fOQREaTzOJkcdhh06ybmjRsyAAwGg8FQf7z9vXh6eki8m0AnC7TYz2Zlr9KKeP55ESxuukmW7l9/LWlZ7duXumflg23L0nTqVPl9BQLi07PPPjBtWql7l2ccpyTZFs5ah9SUFL7BPuwO5Rm9A81U4NmA3c4mfHSYinMrxNjs3SRV/6kiOSmJTudhUO3VS2L/XnpJVMIDD5T0jG+/bfClOnYUfejYY8WE+bzz8hph36LRKU12URbvAC9K5cnI6i9/kbrA48fDyy+3ypMQQx646CIxvb36anj99VL3BuD7sW9DJI+hfAkdGiJ8XBid1tQ+WkvN3fWosKWUVPr7/e9lxTZ3bt76U1kpuf3r1kGfPvDll3m7tMFgMLQKlFIEDwmiY5rEewWquOn1tppNRCwm0ShHHSWVsT7+GO68Ezp3LnXPyhelxGVk+nS4+26J7Bk2TPafZVoEtuHU1ha9VrzWmsQbCfBAcGT5Ru9AMxd4NmB3tomMixA9K4rdzSbxTkKEnsnJ/KQpHH64mDBfdZWc1m+/vWzoGlibLhiU8um/+Y0MTqNHy0LaUDeZxRlwwbttnpJqr71WIi5OO002Sr7yK2/XWLJZWLtWNMh582DmTNkDTp0qt88+k+JgX34pJ/SrVxu/jSahlOyId9pJjpTK4AutYxr84l1mKG+UrfDt4KPywkrCR4VxVjskJtZj9WVZckoQCMjR3C23gJufNOXRo8VL3LLkJLCEHoYGg8HQLPF09eDf1U/qkxTZZQUwQolGm1zxtzmwof7J/ffLmcYnn4hlpqF+2LacQc6dK/fXXw9Dh8peoNmzYoUofkUk82WGzLwMwX2DWNHyllBaVHyfp5uH6IQo2W+zJCYmSLyVIDk5SWDPAP5dm1jGLBiUKiannbaxZO2Gssljx8pGrx5YFlxzjWhEP/uZDFwvvij/N2weZ4kDFni65+HjetddknI3YYL8/ZqJmXImI4LMggXi+71kCSxdKoZqK1aIULN2rZx0NAa/H9q1gw4dZLzs2lVSNXr2lJP8bbaRWyCQz3fVQggGxZl2990lIfy220raHXe9i13ZPD7XBkH5FL6dfWS+yZCekcY3wIent6fuKKwePeD99yWd+KKLZHI5//wm9yUYlGltxAgJcDzySGkmWN6HVQaDwVBWBA4IkP4qTeyFGBVnV+T30KVTJ4lgiMdbrNHyRx9JRrLWEiA9alTx+6AdjVvjoms1bsxFxzU6qdEpLQEEWSS1WgNKorfwyAGb8itUIHcLK6yohVVhlSS6um1bCSw4/ng5i9xjD6krdP759d6+lhfr1onA2atX0Zp04y7x1+LYne3CVHPOMy1K4NmAp4eH6MlRskuyJN7dROjZI4B/WBOFnj59xCn5rbfgssvg6KPhgAPE4WvQoHpf5vTTxZvn6KNF5HnsMfH6NfyU7PIsdke76YPi66/Lqffo0XIcUIbijtYi4EybJlVtZs6EL76QU4xN3fAtC7p0ga5dNf16aA7eVdOtrUvHqKbSr4n4NAFb47M0HsBGo3LXd13Iasg4imQWYmlFdUKxplbx3XrF4hUWc6cpnn3KIpbc+DtXSj7+O+wgASuDB8Muu8B220l/WjW77AIXXyzJ4RdcIKUdSoSz0smvV5WhaAT3lRLqtY/WosKK0OgQnh6eLZ8UDRsGEydK+vCvfiU1UvP02Tv2WNHDzzxT/v3KK3m5rMFgMLQKrIBF+PAwtY/VkvhfgtCoPAoxvXvL/YIFsiBrYbz/vizVu3SR4rbbblvY9tyki7PCwVnp4KxycNe4OGsddE1OvNkc3lyktAWywJabzmrIsMXXqZAST9F2NlZ7C7uzjaeLBxVV+bOh2AKjRsne4tRT4cILJYXrlluaYdWxOXPkfsCAojSntSb+Shwd14RODKGs8lfFWqTAswFPz5zQ802WxPsJEm8nSH6YxD/cT2C3ACrQhD/QQQfJN+P22yWyZ+hQiX/7y1/qnRi6114wZYoEAB1+uET2/OpXzVRNLSDOKgdvnyaOPvPny3H0TjvBE0+UzWiWTEo+8fvvixH3xx/DmjXyM8uSSW3gQM1JR2mG9HLoU+nQ0eMSzDjoKhe3yoUfR/+mcjfYOAHZbEzI1IALOLmJSAOB3K0DsMlEqoOKpNdinWuxpNpm9jKbD7+wuO0/NjUJ+aBGIhIyu9deki0yYoQ81uq44gqpS3nVVfD00yXpglsjnwn/buV/umD4KVbUouLsCtIz08RfjxN7Kobd2abi3Do8wpSSsum77y7Hc59/nre00zPOkGKSzz8vwnCrF3INBoOhAXj7eyVV66MU3r7e/FkN7LCD3M+Z0+IEnunTZU/Us6d4l3btmv82nDUO2cVZst9kyS7N4q7dmOKsAgqrvYW3txerjUTdWFELFVGooMIKWuCjTjFG61x0T1Kj4xo37sr6rNrFXe/irnPJLM6gZ25UgVRY4enmwdPLg6e3B7urXRAhoX17yRy54gpxG1myRJaszSoQbMoUud9ll6I0l56RJvNFhsD+ATxdmod00jx62UQ8vTxET5LUreQHSZITk6Qmp/Dv5sc/3I8VauSq1eOR0PgJE+DPfxYZ9NFH4de/ljSuenxbeveGSZNkIf3rX0vkxt13F903qmzRjkbXaKy2TdhZJJNiJmHbslMpofqgtdg5vfIKvPEGfPDBRg+cHXcUsW+vXV2G987S0+ugVmVxljvouIbVyM0HtLOxO9p4t/XK5BORyccKWaighIbirXsC+r5Pbi7cNDcR6biEpLq1Mhl517uE1zl0szMM7wFn9gAOhkzU4rushxnLbV79zMN119r89a8Kj0fCPw85RKLSdtmllWwM27WT8eDqq0VQLPSR02bILBTTRU+fVjG0t0iUX+Ef5sfuahN/NY7znZwo2h3riMrq3l2ix44/XlTWN94Qx+Q8cPjhoh+NGAG33lq09ZTBYDC0CIKjgmSXZIk9FyN6dhS7TR4ibHfcUQ4qp06VMkkthFWrJC24TRtJlMiXuKOzmsyCDM4aB53UVN8q/kUqrPB09+Ab7MPTxYPd2RYhp4kn7Url1uBeBVGw2fzfXKc0zkqH7PIszjKH7NIsmXk582wfeLfx4u0vNyucv4W0ZUkFsj59xC7k8MOl3kyzEXnee086361bwZvKrsgSfzWOp4+HwIjm41PRqnYBnh4eIidEyH6XE3o+SJL8OIl/Fz+BPQJYFY388rRrJ8mMF14oKs0VV4gPx5//LLlYW0kFCofFfHmXXcTCY84cyQLr169x3WlJ6Lio21akCQPbb38rMYkvvSQDQpFxXanC/tRT8NxzG8sP77ST5L+O2k+zZ48M/lVZMgszuGtcWAOOAruTjbe/F7uzjd1JRB0Vzm8Yp7LkVIIg0HbLz9NZjbvWxVklYaye5Vl6L8vQqyLNESPh1oNhfdDD9FVeHpnk4Y9/tLniCkW3biJcHXcc7LdfWWbG5Y8LLhAT77vvlvsik/kqgwor7C4t+ZfcOvB09xAZF6Hqpipq7qvBu4OX4Mg6jP2OO04OGCZMkBXybbfJJqCJjBsnpuxXXSXe9C+91ORLGgwGQ6tBeRXh48JU31NN7MkY0dOiTbOKADFOHDZMwr9bCFrD2WeLr+TkyXJu0aTrOZrsgiypmSkyX2UgI0UoVDCX/tzHg9XeKnhaVF0ov8LT04On58btuFvrkv0mS2ZBhsz8DJkvM6DA09uDbycfvh18TctA2YRzzpE96CmnSCr2Cy+UTYLDlkmn4e23iyJsugmX2JMxVEARPjrcLFKzNtCqBJ4NeLp6iBwfwVnlkJyUJPVJSmraD/IR2DPQ+Lr2/fuLMvP++5JrdfbZ4s1zzTXiFFbHIKKUVNcaOhROPFHG7YceElW1NaNTuZLPjZ0M339fTrYvvLDov8wvvxTv3YcflhBIv1/yX3//ezh8X4d26zNkvsqQnZWFmZDyygDuH+zH7mHj6bYVk9UiozxKRKZONgyUx7TWuFUuzrcOmW8ytFuUZaQvwcj94Y4xim+Ul2dm+Pj3gx5uu03RpYt8vs84o6Q2NYWjWzcJXXrsMfneF3HhoJOazLwM/sH+ki5YDPnDqrAIjAyQXZQlPSNNdkmW0OgQ3r5bWIGdcIIYd119NRx2mIQLNjEcVCnJQk6l4O9/lwOIPOhGBoPB0Gqw29tEjo5Q+1gtsedihI8PN32ePvBAGevXrpWD5mbOM8+IwHD99U2LFHXWO6Q/S5OakULXiqDj29knxQve8oAC/7DyTWO3Iha+HX34dvShtcZZ4ZD5IkN6Tpr4S3Hir8Xx7eDDv5sfu5vd5M/RhAlSoOXcc+GSS0peJ2TrvP22GCwfeWRBm9GOJvZUDLfaJXpqtGmBBiVAaZ2HMuJ5YtiwYXrq1KlFb9dZ75CanCI1PQVZ8G7vJTAigKdHE/QvrSWp8Xe/k3rVe+0lG7599tnqSxcuFCV12jT4wx/k5LRFRz3UgbPSofqOasLHhvHt2EBfiUwGhgyRKgOzZhUk723kyJEATJw4ERBh+emnZYB8/335ux18MJx0Eow50MX/TZr0rDTOUgcAq6OFr78Pz7YePD08KLv5b8zdaldOHuZlyCzIQBoIKL71e3loio8bH/eQzih2310CXsaPb2HVue66S2bKWbNg4MCiNZuamiL+apzomdGtVpxTSn2qtR5WpK6VbGxvSWQWZ4g9G0PXaCKnRvD2ruOY7f33Yd99xUnx3nvzMoEsXSpRh9msiNYFXluVlB+P641FazHbdKtyvgtxF2elg85o3PXu96eBOqklVbZWo0JShcUKWHIEp5G0W1t8Iayohd3FxmpT2pNnQ3lSzLHdjOsNJ/lxksQbCfy7+wkeHGzad/iTTzbWED/ttLz1sRQ4jtgK+f3iwdOYKSu7PEtyUpLMF5Li5N3Wi2+oD++23u/X1vka20uB1hpnmUNqRor0zDSkwe5uE9grgHc7b5Png1//Gq67rhl8nCZMEOft776TD0wB0FoTfzFOekaa0JEh/INLKwg2ZlxvlRE8P8ZuYxMaHSKwb0CieaamqJlbg6eXB/+efrz9G/HFUUpC5seOlQX2n/4kC+5DD5XExzrk6b59xZfnoovgr3+VUoEPPyxVEVsdGwRTpxGvve02OW5+/vmCmxpVV0tz//43LFsm6XV//zucdqqmXTxL6tMUmXsyJFywO9sEDwji3cGL3a7lKXdWhYV/iB//EL/kPX+dIfNFhh5fpvnNdml+fb3FtKSfPzzi4/TTLX71K/msX3ihmL81ew48UO7ff79oAo92NclPkthdbexuLe8zZQBvby+V51dSfVc1tY/WEjosJKHam4vy22cfCbv505/ksOGBB5rcfvfucugwbpyIsl9+ubGQi2GTaMalDpl5GQmzX5oVgXtTbEnZsNpaaEdLad2ACDh2Txs34YIjB0+4gBIBiMzGiFaQx632FnYnG6vSwtM9d0iQB/8Ig8FQGPy7+3HXu6Q+SaHCiuDewcZfbLfdZBB+5JEy35FvnZdekrPwJ59suLjjrHRI/C8haVh+8O8hhXSsyuYVcbE1lBK/IE93D6EDQ6Q+T5H6KEXsCSnGEBgZaNx+NcfVV0uhl4suEjuFEjhabJ2VK+UU/Wc/K5i4A5D8X5L0jDSBfQIlF3caixF4NsEKWwT3DxLYK0BqWorUxylij8ew2lsEhgfwDdrCYrouvF75IJ5yCtx8s+z6d90VjjlGFt9bcL8PBuGee8TY8qKLJHXrscfqFQDUotiQZ+qm3K0880fU1EhFswMOgCOOKEDPBMeBb7+VOXb9eimudtddcMhBmuysNMlnktSucVFBhX93P/5BfuzOrWcDrjwK3/Y+fNv7CKVDpL9Mk56WZpeqBK+MS7DmPB//eDvAlVfaXHedVBr/5S+budDTty+0bSvHUEUi84V4N4WPyUPYt6FsUX5F9NQo1fdVE38+Tnp6msjJkc3nhV91lRiA/eUvos785jdNNl7u00fWVttsI4vB225rJQbqm8GN58ScbzLfl9fVNbmU4pASoXtnv0TaRBR2O1uqs7SzGpXHr3XOAL/KxfnOkfuVDs53Dpm5GVKOuPVbHS28/bzY7W18O+bPq8FgMDQdpRTBg4PouCb5vyTKpwjs3sgQZqUkSvOvfxVzx2asuD/yiBxiH3VU/V/jxl0S/0uQ/iwNfgjsF8C/u1+iH1s4yq8I7BbAv6uf9Kw0yfeSxB6P4enrIXRIqO6iDFvAtsVWYuBA2XeWpd/erbdKqsQFFxSsieSHSZKTkvh28RHYr/mmF5gUrTrQriYzJ0NychJnuYMKKfy7+vEP8zc+F6+qSnx5/vUvqK2V49Arr9xY8nAzzJghxVEWLBDf5t/8pvUsqrWrWX/1egJ7BQju34CTjuuuk3jDjz6SENY84zjio3vxxSPJZODIIydyxRWw62BN6tMUyclJdK3G7mLjH+6XhbbHLLQ34KxySE1Jkfo8BRlIdPVyw6QA//yvh2hUfLEvu6wZp27tvruUgXjjjYI3pR1N9e3VYEPFuRX12jyaFK3mjbPSIfFOgsy8DFZHS+alXf0//dtnMnDWWVIGq1s3mDhRvOKayIknyoHDsGFSRr2EhQkLwpbC+N2ES3pmWgwwv8yABiwxw7c72OKd1sWD3b0w5W23hHY0zndSgSU9O42z3JGoVxvsjjaBvQN4t/FKdUVDi8akaDUPtKOJPR0jMzdD8JBg40Web76RQ6Vf/ELWvc0QraFDB0n7ve+++jxfk5mVIf5GHJ3Q+HfzE9gnUK+KyM05RasutJPbe7ybRKc1gb0CBPYJNMr24frrxUb2rbc2BqSXBVVV8lnfe28xayoAyY+SJN5M4B3oJXxU+ZgqN2ZcNwJPPdBak/0mS2pySsrX2eAb6MM/3I+nSyODoNaulW/Rf/4j7lbjxkn1rS2kdNTUiK3HY4+JUe8DD0CXLk14U82IqpursLvaRI6t5y4inZaj5oED4c03896fqVPlbzFtGlRWjqRfP5g65X+kZ6RJvJtA12gpp7d3AE8fj4moqAM37orQ83EKndLEu3v54ytB7n3apk8fSXkrYABW4TjySHHWnjat4E0lJydJvJUgckIEb//6lT8wAk/zR2tNanJKPL1WOPiG+ggdGtq8kDx5spgu77YbvP56k82/V6zYWCjypJPgzjsl6rSlsOkmwE24ZL4Sg8vswiw4oCIK30Af3u29eLp6ml4RJ89s8GpIz0pLutg6FzxIIYndAmKUb2iRGIGn+fADkecAyR5oFOPHiyfJ4sVysNTMWLZMgkxvvllS9evCTbrEX46TmZPB7m4TPjzcoKj4lirwbMCNuSTeTJCemcbuahM+OozdvmHjfTIp50DbbAPvvlugjjaGP/xBLE4+/bRpLtxbIDkpSeKdBN4dvFIxq4w8URszrreSOJCmoZTC29tL5IQIFRdU4B/qJ/1Fmpq7aqj5bw3pL9Jot4FCWbt2EuO+cKFEmrz8spQVOu64zW4Ko1EJYbzzTrH2GDy4KMEBZYHdwcZZ0QATnuefF/Ot//u/vPYjnZYKWMOHw/LlIrYNGQIRn6bm7hriL8WxKi0ip0SInhLF27fppmctHStkEdwvSOUllQT2CRBameH6XaqZc3ecdlHNkUeK9rlyZal72kCCQUgkCt6Ms94h8W4Cb38vnm1Nxm1rQilFYESAinMrCIwIkJ6WpuaeGvFu+TF77ikpW2++Cf/8p6RuNYHOneVyv/+9VHscMkREn5aETmuq766m6voq4i/EcVe7+HfzEz03Spv/a0Po4BDe3t6yE3dgo1dD6JAQFedVEDk5gm9nH+kZaarvqKb63moyizOU0wGfwdDaULYifGwY70AviXcSxN+KN+47+dvfihHkDTfkv5NFYMP6rmvXup/nrHSoubuGzJcZAvsHiJ4ebVWWB/XBCluEjwoTPj6Mu96l+u5q0l/92AiubgIBiaB/7z34/PPC9LPBLFoka5cTT8y7uKO1JvFOQsSdgeUn7jQWI/A0ELu9GDJXXlZJ8KAgbpVL7KkY1TdXk5yUxI03cOHcoYNU11q0SFbLb74pH94xY+DDD3/wVKXgnHNgyhTo2FGqMf/qVyI8tGTs7jbuGrf+v9t774WePeUXlCe++UY8sq++Wrzs5syBcUdr3LUuznIHNy7+J9HTo3j71C+KwrARFVAERwapvLAS30AfXZalePusKv77lwzPPy9WVS+/XOpeNoB0Wvy3CsgGl38UBA9tYjUOQ7MmeGCQyAkR3CqXmjtrSH6YFLPeTbn4YhnEfvUrOPNMiYtvIn/5iwQELV4sl8xmm3zJkuNWuzirxNvGjbkE9gsQPSNKxUUVhEaF8HRuXkKq8ii8fb2Ex4SpvLSS4MFB3BqX2gdqqbmrRoygDQZDSVC2InxUGP8wP6nJKeLPxdHZBo7NQ4bI4fA//ymnj82MDZYTdZ07ZBZkqL6vGp3RRE+NEtw7WDbpM+WIb4CPinMqsNvZxB6PkZySbNDrTz9dlrAPPVSY/jUIrcUUyLLExzafl3Y08Zfi33vuhI9qGeIOGJPlRmMFLAJ7BvAP95P5KkNqSkoUwHcT+Hby4R/mx9OtAb/e9u1ltfyLX8Att8CNN0pp9X33lQif0aO/D6vfaScReX7+c8nyeucdie7ZfvvCvNdS4+3rJTkxSXZhFt/ArZRKX7VKRLLLL89bbfnJk6UYWiolDv/HHQfZb7NUPxTDrXGxohaVF1SW5Uluc8OKWoTHhvEP9RN7OcYRupav7/Zx3A0hxoxRXH65RGh6yn3kWrtWjJYLSHJSkuyiLKExIew25hSrtePt7yV6VpT4a3ESbydIfZYiPDaMp2fuy2JZEvZ5xRXwj39IXPwjj8ghQxM4+GA5OL7gAhg5UhaEZVl9YytkFmckDfvrDDqhsSotKs6pqJevQ3PBCkvBCP9QP+mZaRLvJ6i5twbv9l5Co0NY0ZbzXg2G5oKylBzSRBTJiUncapfwcWGscAO+j1dfLdHrv/mN1LluRmyI3Pnmm83/PP1VmthTMez2NpETI1gVRRqnqqrE/PSbbyQrYNUqWLdOoqUSCTnI01rmVp9PIrejUcnQ6NBB3ljPnlJWt4kFDhqDVWkRPT1K7JkYidcSkIXAnvVLA2zfXubzl14qA2unBx+UE94bbpDfZ57QSU3t07VkF2QJ7BMgsF+gRR2Ulvs2qexRlsI3wIdvgA9npUNqqhjHpmeksbvZ+Hf14xvYgOpbbdpIJM9ll4mL7/XXw+GHi6pz+eWSa+vzEQyKB8Ihh4iH5i67yGf/nHOabK9QdtjdbFRIkf4yvXWB56WXxAH5uOPy0vYbb4irf7ducuntt9ckP06ReCuBVWFhd7FRflVYcUdrmWi++07yINaskZJd1dUQj0vCrOPI8YdSon74/RAKyWTTtq2M1p07y4TTpk3Zf0g8vTxUnFNBYmICJqd47aws//oiwl+us5kxA554AioqSt3LOli4UMTZApFZmCE5MYl3Ry++IVv5ThhaDXZ7m+hJUTLfZIg9FaPm/tzm/ZCQlIz1++UErGdPKVc3Zgw8/niTq6+cf76sX88/Xw6Tn3tOFofNATfmknwvSWpqChVV+HfzY79qozyqRYk7m6J86vu1SXJKkuQHSapvryZ4YBDfUF+LWuQaDM0BpRTBfYLYbW1iL8SoubeG8PHh+vt89u8vB8TXXgtnnCF1rpsJHTvKlDRpkhxcb0pmkcxldmebyIQIVrAAY7LWIuRMniz+LjNmwOzZm/cGCIdl8RkMiqhj27L+TqdlPV5TI76qP6ZzZymms/POUkl5zz3lb1bgsVZ5FeHjwsSei5F4K4HyK/y71K/s9wEHSPbfmjUlrGy7YIFE7+y9t0Qh5wlnrUPt47W4a11CY0L4hzbPUuh1YUyWC4BOalIzU6SmpnBXu6iAwjfYh38XP3aHBp60p9Pw6KNy4jp7tjiRXXKJuPzmzNSWLZO0obfeEm/Xu+6ScoMtifircVLTU1ReVln3AH/88TJIL1nS5IHzvffkdHrAABF6OrbTxF+Ok/48LZumI0MccOgBQJ4M2+JxmDVLkl6//BK++koGt0WLNj9hbEApmWQsSyaqbLbu9ItgEHr1Ege17baDHXcUQ+qddirJKcPWyCzMEHsuhk5pPgyHOernPnbcUVJDytJofN26jR5bv/1t3i/vrHWoubcGFVZUnFnRqMo4xmS55aPTmuQnsnkHCOyWO6HaYML8zDMSh23bol7vtVeT21y4UDSjefNkyrr00iZfsmBorcl8mSH2fAwy4B/uJzgyiPKpwhhxxmKy+F+0SAT6tWtFjPd4RJwPBES079NHBraePQue5rkBZ41D/OU42cVZ/Lv5CR5iUj6bK8ZkufmTXZql9sladEITOjyEf1A9N5/xuAgISolIEQ4XtqN55MILxV1h6VJZPkFurXN3DSqqiJ4ezYu48/3Y/vzz8MorYk791luykQJZHw8aJGvi7beX6JteveSUt2NHEXW2Rjot0T7ffSf5y19/LWv6L76AmTM3ruc7dJCTkNGj5bY1E6ImoB1N7RO1ZL/OEjk5Ui8riTfekCCCiRNLpBcmkyLszJ8P06fnLTQ4Mz9D7NkYKAgfF24WthqmilaZobUmuzhL6tOUlFR1wdPbg3+oH+8O3oaVzdZaBqINOVnhsKj0l1wC/fvjunDTTbKfrKyU4J9mWX1oC2S/y1Jzdw3BUUECe2whxNB1ZQCub63FOliwQArOdOokptbto5raJ2vJLswS2DdAYF8J5WvSRmDRIhk5P/gAPvlEBLwNSciBAGy7rUwuffrIBNO1q5wCtG8vUTnRqHwOvN4fillaS3nkDacJ69eLBL98uUw43367cdKZN++H4lH//uIiveeeMrDutNPGBOkS4la71D5Zi7PM4ZveQfa6IEC3blKiuXv3UvfuR7z4onwG//e/vIcxuDGXmvtr0AlN9MwodrvGpWYZgaf14KxzSE5Mkp4lVTWCBwXx9M5V91uwQBaWixZJRcdzz21ye2vXwskny+Lwk08KUuyiybhVLrWP1eKsdLC72ISPCmN33PhdatK4XlMjG4cvvoCPPoLVq2XMbagLdSAgY3737htLmuy7r2w+ClApR2tN4q0EqY9S+Ib4CB0eMh4XzRAj8LQM3FqX2NMxst9k8e3iI3RwqH6ZAO+9J+uOc86BO+4oeD/zxcyZMrT9/vfw17+Czmpq7q3BrXaJnhXFbpuHNPRUipG77ALLlzOxpkbWye3bSy3w/feXde/AgYX1AHBdEXsmTZK1/9tvi6oFMGKEmAiPHy97mTyjU5rqe6rRKU3Fz7aefvzVV6JxPfAAnHJK3rtTN1rLAdQDD0hI8NixTb+kq0m+lyT5fhK7s034+HB+PldFwAg8ZYwbc0lPT5OalsJd56KCCt8gH/4h/oaXLJ0+XfKxHntMBqjRo0XoGTWKmbMtTjlFxPuzz4Z//Ut0gJZAzQM1OGsdKi+q3Lw4tmGGuP9+CWlqJI4j2saXX0pJ9G16aGoeqcFZ6hA6IoR/8MbTlAZtBJJJEXReekk2AAsXyuNt24qoMmyY7IYGDYK+fYsjrLiu5BfPni2fq6lTJQJqw2akY0eZ/A49VFIFm+jX0RR0RhN7VkqKruobYNfzg/TsmRPgShU+ujl+9jPxNlm9WlJi8oSbdKl9sBZntUP0lCieHo1fhBiBp/WR/iJN/PU4ukbjH+YnNDokP1izRlZvr74q6VrjxjW5rWXLJDgwFpNs2XvvLY95SLsaZ6lD/PU4zmqH0MEhfDv5fpJi26BxfcUK+PhjOXj5+GOJwIzHRXTfeWcRaDp1klDQaFTS4TaI9Om0TDi2LZ4O4bCMxytWSDTnihXy//nzRaDf4Pew555ypDpmjMwdeZortNYkJ0rUl38PP6FRobxc11A8jMDTctCuJvG/BKkPU1gdLSJHR+pXNerXvxbjlMceE7GgmXDSSfDUU7IM3XZNguT7ScLjw/i2a2Ia+vLlcoBx552MXL0afD4mXnSRTE677543v85GobVs2F56SebfWbPk0PbYYyUlacSIvDaX/S5Lzb01+Hb2ET6y7givlSvlTPk//5EsqaLyt79JWfSrroIrr2zy5dxql9jzMbKLsvgG+QgdVk/BtEwwAk8zQGtNdmGW1GcpMnMlqsfubuMf6se3o69h6RbffScK/e23y0Jwu+3gggtInXAaV97Qhuuuk7Xk/fc3q3TcLZJZlKH2wVqCBwYJjNhMFM/tt4sJxPz5EvnSSO6+Ww4/HnwQThqvqX2kluySLOFjw/h2+OFEs9WNgOtK+Ocjj8Czz0oIfigEBx0kt5Ej5cSgDKJkvkdrOdF/7z0xrH77bZkgLUuUr/HjJRWuACcMW+2aK4736RlplvcOMPTcILvtJr/iPGopjSedlg3dAQfIZJ0n3KRL7cO1OMsdIuMjeLdtWkipEXhaJzqTi9KYmsLubuPb2Yd/Zz/Kzso4tHSpmLudemqT25oxQ8bQG24QvfqFFyQbtFS41S61z9TiLHHAB+GxYXwDNr9x2Oq4nkrB00/LmP7MMzLO+/0itgweLCLZ4MH5VbXWrBEBafJkieadNk3EoY4dJVz3lFPyFjEYeyVG+tM00bOiDSsWYSg5RuBpeWS+llRSndQE9w/iH+6vO7ouk5FF/8yZMmaUcuBtACtXyrC5bSeH58dV49/RR/joJqSZrVkjVYpvuUXG7LFjGfn119C2LRPffTd/Hc8ns2aJz8YDD0j0/YgRInIcdFDe/Hrib8VJTU4RPTuKp+uWx/flyyWI9JZbpIhC0bj3XjGXPekkWUQ08X2nv0wTf0mq04UODeEfUg6bhYZhBJ5mhhtzSc/MRfWsdsELvh18+Ib48PTy1D8HPpUS2fvmmyUkPBiECROYsed5HHvNMBYsEM/mv/1NftScqX2slsziDJXnVYpp6KacdposfJcvb/SA4Lqik7VvD5Mna+JPScRI+JjwZg2et7gRWLdOBKc77xSxpKICjjlGhJEDDpDw+wagtUbHNG6Ni45rdEKjkxqd1lJS09nkyZaUxsWLGEAHxSxURRRW1Gp4CUCt4bPPJEzy2Wcl2se24bDDJEzssMOKWtZKa038BfFCmt0pxD7n+fnZz+TXXXIee0xCbF95RSLr8oAbc6l9RNJJwseF8W3fdFNlI/C0XrSjSU5OkpmTwVnhoPyKwAEB/OHFqHPPhg8/lO/5kUfmpb0PPpChb+1aGaLvuKP4VfCcNQ41D0lqY+jgEN4B3jrD07c4rldVyQnALbdIBGbbtnIacNRREq0TiRTsPfyE6mpRzV55RSqMVFfLpu7SS6U/TVgUu0mX6turQUH0tKip0teMMAJPy8SNucRfipP5KoPd0yZ8RBi7fR3fy6VLxcw3EhGRp6zCnLfMRx/BlOtiHD8ojTuhkm7bNeLwU2sRCS6/XESSU06B3/0Ottuu0em32tW4VS5utYuu1rhxV9bgGQ1ZQAMKsMXYWPkUKqSwwhYqqrDb2g07wI/F5D1cf71EcR5wgFRX3nnnBvV7c7hJl+p/V+Pp5yFy7JbnrC++EG3w4YdhwoQmN1s/Hn1UhJ1Ro8TuoD6eR1vATbokXk+Q/lzS08NHhRvug1smGIGnmaK1hI2npqdIz05DGqy2Fr5BPnyDfA1bXH32mZzAPvIIxOM4g4fyRMU5nPf+BLoNqOS//5WIxOaKs86h+o5qPD08RE6K/FAE22AY/Nxzjb7+J5/IIex//wvH90iQnJQkeHCQwPDNCzI/mSxWrZIqBnfcIQP0/vtLys7YsfUSdXRa46x05LbKwVnt4K5zcatccLfyYoVMMlt7WoVMNlZ7C7uDjd3ZxtPFgwrUc/L5/HMZ8R94QMS0nj3FIe/ccwteGnwD3xvGLcjycG2Ei6/1fl/CvmRoLWl2NTWS35eHqCxnnUPtI7W41S6R45seubMBI/AYtNY43zkk3k6QXZTF7mwTGgn2SQeipk6VRdadd0rEYRNZsQL+9CeZmk48USKvi3WonF2WpfbhWlAQOTlSr6o0PxnXHQfuuUdKEK9bJ4bUv/udOFCWMrx/A8kk3HqrxNIvWiQpXFddJYvkRgo92eVZau6vwbfj1kP5DeWDEXhaLlpr0p+nSbyRQGe0+EHuGdjyod3kybIG3X13MUVr4MFiKXATLuv+VcWjn/n424dhHnlE3kK9WbdOIlBfekk8y26++QeiSH0EHp3WZJdlcZY6cr9K1uGbXYN7cgequfW3djRkNn9dFVHYnW3sTjaerh48vT1Yka2sE5NJiei58koR8X/1KxnbmxiyHn8zTuqTXOGa8Ob78Mor4szwwQd5qcGwdR56SE6B9tlHGm/C2iP9VZr4K3F0rSawV843taGH22WEEXhaADqtSX+RJv15muyiLCDGzL5BPnw7NCCFq6pKNuF33gkzZuD4gzxrH8+tiTPY89f78serrPJIaWkEqc9SxF+OE9g3QHC/XEjSd9+Jy/1118kA2EhuuUVyTb97L41/Ygzf0JzZ5BYWyd9PFm+8IS7Xf/6z+C+cdJL0Yytqu1vjkl2UJbMkg7NEhJ3v8YLdwcZqa2G1sbAqLKyI3FRQoQK58uwefhCuq12J6NFpjU7lon3iGrdWTh/c9S7OWgd3jYtObPz+W+0tPD1k0vH29WJVbGXiyWRkEr35ZvGfiEREzLr88qKUcdNJTfV91ei4ZvyzFXw21+KLL0poE/TssxKqcO+9YoDeRLLfZql9ohYciIyP4OmVv7AHI/AYNqC1JvNFhvircXRc4+2dJnzr8aiP34ejjxajwzxF85x7rqxVKysl+3O33fJy2S2Smp4i8aaUho2cEqm3oeIPNgGffSYnwHPmyIbhhhvK0zkapILif/8LV1whc+JZZ0mKQiPTaWPPxcjMz1B52RZ87wxlhxF4Wj5ujUv8tTiZLzNYHS1Co0N4e2/h8Ofxx+GEE8TT5fHHy0OQroMN6/uV+0c58iwP8+bJsvIvf6nH2m7BAhHdFy+WMo4XX/yTg7YtCTzOSofMvAyZ+RmyS7LfH5ZabS0RZdrbWO1y6/CohQrn1uCbSZXTWkNaxCpdq2Xdvc7FWb3x8HZD1L3V3sLbz4t3gBdPT8+WU+/WroVf/lIKyAwZAk88Ieb7jWRD4ZrQkT/0Fd2UP/9ZdKX164tQYPfWW2XzNXKkRO40sgKcW+sSfz1OZo58N8JHhltEmrEReFoYznqH9EwRe9y1LnjAu70X/85+PP3qGAg2RWtxLLvnHvSjj6Kqq1lAX17veAr73HkKOx21beHfSJ7RWhN/UXxYQkfk8ikffFBU+6lTJSy1kVxzDTxwg8OHP6/G7mwTPSVa58J25MiREI8z0XFkIzBmjEwsAwZsse/OcofMnAyZryVFAgAfIq5092B3FYXfamMVvFStW+virHDILs3iLHPIfpv9XvSx2lt4t/fi296H3d2uuy8zZmw09fP7JU3g178uSLWXTXFWOVTfXU2yvYdeF0Y480xVmsIRyaRUHPP5JMKpiTkoqRmyyLEqLCInRPIeVmoEHsOP0SlN8qMkyfeSYIOdWkH45sOx1y6Af/9bFst5YO5cOQ1cs0YsvW67Tb46+SY1I0X8hTieXh5CY0J1pzP8iO83AeedJ75ukYgIO8cemzcfhIKSSsnK/LrrRNx5/32JcG0gma8z1D5SS/j4LfsVGcoLI/C0HtJz0yReT+BWufh28hE8MLj5g7kbboCf/1wE3zvvLC/Pxx9R+7iko1dcVEE8rvjDHyQwMRiUQPELL5Sg8Z/wzTcyocRiIhBswZx4U4HHjUvxm/TM9PeHq3ZnG28/L55eHuzu9lYrTTUG7WhZdy/KklmUIbs4C1lQISW+eHUV33nxRTl0cV3xgTvggMb1QWuq/lWFt793ixGa++0nsQLTpzeqifrhuhLSe801cpD02GON8hLRriY1NUViYgKyENg7QGCv5h21sylG4GmhbEjhSn+eJj0njU5oGQgG+vDt7MPutpXN9wbicXj2WVZffz/tpr+NhWZJ9z3o+suT8EwYV5Soi3yhHU3tY5KiEzoihP9PJ0ie8bffNmnyeux+l37TaujbXdPuvIqtRrGMHDwYZs9mYrt2kpZ19NGbfZ6z3pGJZFZaQj0t8PT0yESyjQe7s10WJWm13mTimZ+beFxJ6/Lt5MM/yP+DcsI/Ye5cycV49FHJ+f7LX+TYvoCnRslPkiReT/BsTYhz/uln9uwt6muF4w9/EJOrN98UM7xGorOaxBsJUp+m8PTxED42XJAFhhF4DFsi+11WvOE+S0EG7Pg3BF78A74/nS5mvk3Iid/Ad99JoMk//ymng717SyHIiy9uun6iXU3qoxSJtxN4+niITIg0eJE3cuRIqK1l4qefSlXD55+HPn2a1rFSMGOGjEder2wMGnj4oVOa9detJ7BfgOC+zdzAr5VgBJ7Whc5okh8kSU5OggWBPXNpWz+qDMgf/yjrsfPPl1D1MhSqtyQ6zJkj0SRPPin/P/hg8bE//PDctiWTkVODuXPh3XclwmULjBw5Ep3RvPLrV0jPSoMjhW58O0mWhBUtvvil05rMvAzpOWkyX0nxHc82HgJ7BvD03Ywf68KFIoZ89ZX8UhoZYVvzUI2UTD+r4ic/W75c6oX8/vfyuy8ItbUiVj39tOwTbrmlUYejmUUZEq8ncFY6eLbxEDq0YQc6zQEj8LQCtKPJzM+QnpkbCJycX89AH76BvnqXXF8/eymvn/owO3z2EIOYibZt1AEHSCjnUUdBu3aFfSN5QGc2+rD4J/2H4MBq1D+vb/z1Upq199eQXuZw54ooV96+lYHmo48YOWIEhEJM/OKLzR4rZJdkSX6YlL8VMmj7Bvrwbu/FCpbvKcoG3KQrE8+sNNkFIvZ4ennw7+rHu4N3yxunadPg//5PJttddxXxqwmRVXWhtabmvhqya112+FslhxyheOCBgjS1eSZPlpOjU06RknWNxFntEHs2hrPcwb+nn+ABwYKJfkbgMWwNZ03uUGFWHHe9hbV6PkH/DHzXnZ23NpYuldPZjz+GiRMlA+r882Uaagxaa2LPxsjMzuAd4CV8VLhRpVBHjhwJn33GRL9f/LR+ZFAaj0sp+PXrZSFcXS0Hx8mkHEiCaNqOI2Vm27SRW9eusmguqsn07NmyE6qqkgjXBlaYrLq5CrubTeSYIhpIGxqNEXhaJ8468VPLfJFBRRTBfYP4hvg2rtG0Fg+x666TnKdbby27SB631qXqhiqChwQJ7P5Tv6BFi8Tj/qGHJAsLxAXhDxX/ZtykS5l39ZN0uuC4LaYTuTUu+43YD12refHcF/EP9uMfVke0TAlwYxJVlPwkia7VePp4CB4U/Gm1q3Xr4NBDJbzm9dcbVUEx9nyM7OIslZf89Bd29dUi7nz5JWy/fePeS53MmycRsbNnS9bD//1fg0VHZ03uMz83g1VpERwVxDvAW/DMh1JgBJ5Whk5q0nMlKiS7UHJGrY6biD3ttj5ovfAC3HDmTA5e+xg/a/MY7dYtkNXngQeKn8jYsbJCLVO0o4n/7nXSkeHYHbKEjmmLp3PDV8/OeofYkzGclQ7PxMKcfY2v7qyETAZ22IGR334Lu+7KxEmTfni91Q6JtxJk5mVQQYV/mB/fkAYaZpcZbswlPSNX9W2ti6pQBPYI4B/q/+lpEciC4vHHZeBetUryh//0p4LUM88uzVJzbw3vxAKc+K8g335bpIC0NWvEj8OyZKJtRKKy1pr0Z2nib8ZRHkXoyBC+7QqbDmEEHkN90VlN4sklZKatxY32xs4sxX9gR3z7dMqbAOm6MjTcdZdE94wcCXvsIVE9XbvW8xoJl9TkFMlJSQL7BQjsE2j0Qm/k7rvDlCk8+IsbmTj4Ur78Ug6H58+HJUvEDqEpRKOw7bZSEvjAAyVFbeedCxjouHChCOz9+kmVNG/9zdqr76rGikqqqKH8MQJP6yb7bZbEWwmyS7JYbS0C+wTw7eyTsVprMYe/9lqxNLjnnuKXNKyDDeu48PhwnWsgreUM8dVX4d2Jmtve2Y6lblf24z1Azqd79ZK5o3NnaN9eM6JNmr10nGPuPYK01+K6GyYSaGcRiUgGbjQqxW7zEKCaF3RWk/pU5jMd0/iH+wnuH/zhgcXatXK4uHy5WEQ0MMo09lKMzLwMbf6vzQ8eTyRgm21kTnrjjaa/l5/wxBNSfdLjkYJAhxzSoJe7tS7J95KkpqXAA4ERuai1FuwTZwSeVoxb64o586w0zre5XNKuNr4dfXh39NYpLKxdK0LGI49oThrwGTeOeIIO7z4NX38tiuoee4jQM2aMlD4pJ3X0xRfhyCNJ/+Zu4u2PRSc1voE+/Hv4f6p4bwad0aQ+S5F4NwEaIsdE0L29jBsnUfnHHCOZNz9J+XnvPdhvP0buuCN07PgDw7bU9BTxV+NgQ3DvIP5hWxBAmilaSzhp6qMU2cVZVEgR2DuAf1f/5gfY9etF3LnnHpkxHnmkIKYbtY/XklqYpc+VlfzpGsXPf573Jn5IJiMT06RJchvW8DW1s94h/nKc7IIsnm08hI8Ib93cOg8YgcfQUNwFS0le8zZZbx+crjuDrbHa2IQODeHdJj/V3WIxsYmYM0duti1Dxe9/L2Px5tBak56eJv5aHLLgG+QjdOSWjfE3x6pV8Omnsmn49FOY8sL29M18xTLmMo/tsG1Z8G63nWwcevSQW5s20KWL6LqhkNxUbh/lOGBbmhVLoXatpmo9LF8B3y6HVWsUX8xXfPihRP+ABAkNGiTlaI8+ugBVjZ9+WsoMXn89/OIX9X5Z1W1V2O1sIuONwNMcMAKPQWtNdn6WxMQEznIHq51FYK8NQg+yqL3iiiZ5nhSCzLwMtY/VEj0jiqdHPYWnXIGVVb+/kUnDLuWrr0TPXrJEflS9RvOHPWMcuWOG9xd6GP/gGJJZBUzc7OUCARGI2rUT+7LOnWWM79ZNxv4+fUScL1bVeTfhkvxfktSnKawOFpHjf+TJ+PXXkpK2++7w1lsN2p/VPlWLs8Kh8sIfHkxeey389rcSgL/vvnl6IyAVZi+7TAqRDB8uB8C9e9f75W7cJTk5SeqTFLjg38VPYJ/A1iuRtQCMwGMAwK1ySc8Rvx5nWU7s6ZYTewZ4t1hJ5Omn4bzz5Dv4t79qLjtwJvaLz0mYz6efypN69YLRo2Vju//+BTfRrZM335R0su23hw8/xNU+kh/IQEgmZxLc34unqwerrfV9BTKd0DhrxGcm/VUaUpI6FTos9P3vxnEkkvWvf5Vw/H32gcMOk+qzAwdC+49fQY05nJE77wzt2n0v8Hxv7NnXQ3hsGCtqobV4XiYScp9KiTbgurIRABGyfT6ZXMJhmW/LSUfbEtklsojILpLTouABQUnd2lznX34ZzjxTdjQ33SQKfh7f5IbTn3/PCPLKkgAff5y3S/8UrWUnet99YiZy6qkNe/kGQ7j/ibAYPCgoAlmR/uhG4DE0Fv3BJDIX3Uhmx4PIDjoCNxNEVSipBLKtFxVSeLp7mmxuOH++ZHa++qpEcXftKhrqoYfCDjuI6N4x4BB/PoazxMHT20Nw/yB2j8170mktKWHLl4sP+ldfwZQp8u/Vqzc+r18/UCu2p3vtV1x59WS6HLUH/fpt/WRXpzTZb7Jkl0hZXWdNHaV1QSohVlrEvDbf1FpM/MLDk+95+GyWwuORvdf550s0U94O2Q87TMTohQvrlYatM5r1164nsE+A4Mjy2AQa6sYIPIYNaK3JzM2QfD+Js9zZGHU9xI+6+1Y52R0xQtb4ZWDLkP4iTeypGNFzo/WPxF+8WFSX22+X1LNN0GlNzSM1OEscvPsGiW3v58ix++M4cMMNE6mt5f/ZO+s4ucrzi3/fq2MrcXd3EiA4BIeiRYpDsRYo3l9bKJRCCy0ttLRQQQrFXQOkOIEQCBCDBGKEJMR9d8evvb8/no0S2d1sDOZ8PvPZZGfm3juzM6+c5zznrL6l07I0raqS7qcVK4T4X7xYiKJsdt3TNmsm9e5+/WDwYOEr+vXbeoIof6ZP9sUsOtSkTk5hd1mrqHL33TJZPP00nHxynY9Zc18NKqEoO6Ns9e/mzJHXcfDBUuRuNIwcKev/2bOlVfCmm+qsJI2yEYUxBYpji+CB098hdkCsTl0q3xWUCJ4SvoVwpSQ2eVM8woVrKXv6ONh97G99QRYvljHypZfEQf2hh2oJ1nnzYMQIWW2/9ZaMiIYhObfDhgnNu/fe24bwyWalQfTWW2UkevPNddrIonyE/4WPN9Uj+CZYHUe4PlRcYfewcQe7G42gXrpUQgeeflo2AqvQIpZmkt+LH0YrmBPrRbt+E9EoHju0isVZg/NeLqM6o8jlhCCq79fMsqBJpaZN0yLtW3q0bx3QoQN07abo0tOm16AYTVvuONJaf6ZP/q1ak7MuFokjN2JytnixeNW8+Saceabs4BKJRruOmvtqWLwQet1YzpIlWykyXWtRJP31r5JUc+ON9Xp6MD8gNyJHuCjE6lZLLG7j1r0SwVPCFuHvf4crr0RbMbybn8XvfgD+DB/EagyVULhDXdyBLqpsw1GydUUQwN/+JvYxI0fKENIqFXHa4CJX7l/ANOCFpQkmBw6t2ypiMVk3LlokY+/ixTJVffaZtP2vgmWJoHDIEFmoDx4st8pKGLbffjB6NCMPOEBMLDcxkPizfIoTivjTfAgAA4ymhsTqNjMw4oYoOE0gEum99tdE50bLI6LqWhZIQb7CZNRCh+sfcvhqgUHXrlJoOOWURrDM+Owzqfb+61+yIdgM/Fk+mUczm22ZKGHHQYngKWF9rFL0FD4sEHwToGIKZ7BDbMlbGOeeDF26yPq+a9ftep3eZI/sC1nKLyrfdJjH2ogi8b/s0kWU9bWDpNaa7DNZ/Ok+yROSOH1l/NpYTPo6WL4cpkyRCaNWDuQtyuF5lURGU4i3gERzSDZHxyuJnBTadNAoNBp0iGEF2GVgNHUwO1Vg9miO0cLEqGh4Om5UHZF+PE1UFZE6LYXduZYgCUORfxqGbFTqcHxd1FTdXkVsrxjxg+Kr38rDDxdbycmTGylXoLoafvlL2Uh17y5F0X33rdNTo+paxc6EIgRg97WJ7x+v+2fjO4QSwVPCJhGuDPGn1JI9q5Q9LU3s3ra4x7eQgUdr8Yq9/HKRyP/733DaaWsdyPNgzBghet5+W8qgvi+DSr9+QmUPHSo9//36iSylMTBvnjBO//iHrN5//GOJ7y0r2+hTdCBqnag6giKgQMUURhMDo2n9BtolS0TING2ayD9TU8fy5mv74UQFHo93Y2LzQxhy1m+pLni8OnUunuvgxAySbkDSKpJSWZJhDXG/mkRhJW5+JW52JU52JXZmJVa2CqtYwLISWPEKjGRzovK26LLWRKmW6EQTdKwS7Zah7TjadMAw0YAixDBClB2ikgqjRRyzc1OM5g5mC9lobO2ULh3V9gy/W0AHmtj+tX2x61fyo0h2LDfeKDusF1+UybkRUPi0QP61PHvdVc4f7zE3FmrWcGgNv/iFxP9cdplsdOv4GYrSEfl383ifeagyReKQBHa/7WMIVyJ4Sthi5HLihvzyy/DTn6Kvv5GQ5tIuPNETwgch0s1WJnZPG6uDJYaWBvUaj7Qn6X7+HJ/sjAA1P8DQMNcz+eXbSaYuMMnlhJBfhXhc1JDNmsmQ062bqH/atxfvmy5dNu55M2zYMFi4kJHTp4t054orZBLs23e1h5j2NNnhWTE1jStRyPaxsdpb9TZ21gVNMD8g+CbAn+ETLg7BgMVJm5tfjvHYOxaDB8v0N2BAvQ693om0mJMdf7wYHm0Gmeczqz0avkttxt9llAieEjaFYH5A4aMC/lQZn+1m1bj3Xo41/2PUiy9sNF58W6D4eZHcSznKLymvXwrSf/8r6pCTTxbX/lat8KZ6ZJ/JEj80TmzPNXuQdQieYlFchCdMkNvkyTB5MnrJEqIWvfB7HEzQaS+Cjruhk2t5kUZFDJ3GIIfSBfxikVxNET/rE+Z9HCKScRMrWUFU0R6dWKOOUqqA1Q7sgU2xuln1Lu5F2Yj0w2l0RlN2Xtma9+n+++GCCySxYOjQzR7Hm+SRfTFL6uwUdichim65RQJh77lHQq22CFrDY49JO/CyZdKa9bvfyaS8GQQLA4pjinhfeKDAGeAQ2zu2bmva9wwlgqeEOiOqFs8ef6pPMDcApOro9K5t42prMmuW4qyzxJPx7LMlwS61oTb8XE4Inw8+EOr3449F4wiygu7WTYienj2lQtCpkzS0tmgBTZp823TX82RAmDtXdPTjx0sz6IQJcv8hhwg5sM8+W+39qSuG7b8/LFnCyG7dYPRoip0PJ/fDf0JQwJ45EnPhJIzqeahCDUQ+GJaQM7EKdFkLdLMORJXticraECVaou1vxxUqiigjj6KI73sU80Xy6QKFdBEvXcAKiiRNjzInJBZ30amWROXt0Mm1moTNEKujg9XJlra11ltP/ROlI3Kv5fCn+phtTZLHJjfMuP/vf7JpMk2pkh900Jafuyai+u/V3Px2DHfvOH/4wxYfcg3CEH72M5n9fvYzWUjUpVLiaQof1caYhohZ3n7x1S2D2wMlgqeERkEUCdH+6KPSQ/W//0klEVF/hMtCwoW1tyVrpJSqXK2uPto9bWljsgAPUIAh3+VwaUi4KCRcEcp91Lbe9hTl5fqbgDCU6aNYFF+chnKnqzcB//iHKEUff1wWrPG4FC7Ky8kNuZKisxux5jOJ9S2gKsukktykyRa3noaLQ4qfF/EmeuiCZknc4pz7EoyfZa7mlhuMfv2krfn55zf5sGBeQPq/aWJ7x4gfXGrP2llQInhKqAui6ojC2IKMMTmNkZ6HM/Zh3B/vhXH2RgzPtjKK44rkRuSouLKifnHlWou32LXXinzzqKPI7PprAtpSsedUFFr6rxYvZtgtt0Aux8jmzUWhE8q8pMubEuz7I7x+x+CXD0JrUZYbTRRWOxuzjSm35iYqoTZZmFu4UETqbz+zgrlvTGEXaxo/6DiPXVumcZp1xu82DF3eFgCzZYgzOIU7yK3zmjBcEZJ+II3RxKDs3DIplqxcKUrT66+X9qfNIP1ommhFRPll5SilePFF8X47/XSZzrdoCvvkE7j6amkH3n13aSEbMmSTT9GRxp/qU/y0KJ0XTq3HztAYRsV332NncygRPCU0CFE6wp/m401bk8alyhVOLwejh82fHrH43e8V3bvDs8/WoYKoNXz9tRAyn30mBgpTpogZmO9/+/G2vYbk8X1Zna+NWExUQYcfLgx99+6N8robA+tUA7SGJUsIpy6hMC1GUJUkKm56YaziCqPMQJUrjHJj3VuFIfdtphq8eLHwX6+9Bm+/WiC1ZCa7u5M4vddE9qqYi5NTBG2HEHTZm6i55B0alQZOfwd3V3ermfp6X3rkRuTQviZ+cBx39w14zHz1lRh4T58ufRiXXLLFm6Oa+2oY97nib9+UNV4PcS4HZ5whaqNrrpEWwc1cpw7EwLvwgaQg2H1s4gfHN+qBtS1RInhKaFSMHy/jc02NfD9++9t1+om01kQrI4K5AdHyiOCbgKgmQhc1urCJNYgDVisLlVQ4Ax2sdtY2MVT8lox//nyR/48ZAxMmoGsyVB37CsbKbyj/x16sMxIYhhQvWrYUz7ru3UUu1Lu3ECudOtV5jNOepvhpkcKHBSJf89+ZCf7vAZebbxbj6XojCISAOvNMkeZuBFE+Iv2fNADlF5ajYiX1zs6CEsFTQn2gAy0BLZ9kCRYoiCIsvsE5rg9On7qTDo2B3Fs5ih8Xqby2smGK8+nTRVU9fDjpYX8hataV8jv3QOk1RmjDAOJxRh5+OLrvIPwuB+C7vfAWxkXlb4Pd3cbuamN1rb/CZn1ks2J5cf/98M470MOezQ2DX+aH7igM1R5/wImErfqCFeIOSRDbN4aR3Pwct6qdLfGDBO6utfunQYNEovrqq5t8bjA3IP1gmvjBcWJ7xxg1Cg47TJ7+7rtb4Lk9a5ZMTE88IfPfLbeIsmoTvcVROqI4oUhxQhFdozEqDdzdXEnnLc07q1EieErYYkT5CH+6jz/Vx/9aPAVUQrG8zOa6B2xe+8LmH/9WnHlmAw4ehtJmtcrefulSYZ0zGSF1tBY5fColTHS7dtCjhyyQ6xHrui2xuX5eHWiitGxkanupULZCuUpMNrfQiHR9RBGMGiUM/FNPiXHcoXtnuePIN+j31UtEr7+P335/vN1PJWgzFJTC6ecQ22/ryB+jdETu1Rz+DF9Soo5NfrsyU10tvjwvvywS03/8Y4ui1HNv5Mh8VOTw5yuZ8FkjvL9z50pLw4QJQkJdfvkmH64DTXFiLbGT1lidxfy1zqkQ2wAlgqeERsfixSLDfvJJcaO//nox9d0EtK8liS+lIEQ2E1qqeUa5gYptulK6tVAXn4bsyxm8iT52qxxuk3lYLETN/VrcOZcskfdj9mwpbKzt0FleLmY/e+8tqsX99tvseBelI7IvZQlmBbyxPMapf4/zn/+Iz3u9MGmSKKwefVQI6w1AR5rs01n8r33Kzq5Hmk0JOwRKBE8JDUW4uID355fwdC+iJp3ArCU7+tg4PZytuuHWWpO+Nw02lJ/3bSV7feGNXkr2HQurPIPTeoWoPSsrOfji09E+vPp/r0r3QiDzjt3LxunrYHWuf4ttXTFjhvBPDzwgW55fnDSL65v9m9jIsRT7noI38ESwDOIHJHD32EgqbS201qQfTKPTmvJLy4UQO/54IVk++2zjz4vkeVF1RMXPKvjwU8WRR0pTxahRUpuoN+bOhT/+Udp+bVvWAddcI3Pdhq4h1PgzfbwJta3cGqxuFu4QF7unvdXtJHZGlAieEhoV2qv9Ek7xCL4K0EVNLoDXpthEXR3O/a2NFf9+fxHrZNi2nVBdLa3Jf/qTWBadcALc9ccMbT94Gm6/nXBRjuLR11DsdTJoA3dPl/gB8Uaf3LTWeOM8cm/mULYi8YPEasO71QhDie384x9l4/PMMzLjNADeFx7Z57Mc82gZH0zbws3JO++Iz0g+L1WJo4/e6EO1V6vYGSPEjtnBJH5AXBYMO1gkWongKWGrQGv4z3+k775pU1FybrEz8LZHXcZ1HWmKHxfJv5cXc2lDPO1WGyyXrVJjKgx/BWrmFDFwmzhRzNzGjZNxr7xcIrNOOgmOOmqjMSw60uRG5PAmeDw4M85vno0xaVI97cv++Ef49a/Fd6JXr2+fI9TkXsnhfe4RPyJObPdG8s8rYZuhRPCUsEXQGv2XvxLe+RTewRfj9T8OnVNggNXRwu4p6hajecPNgjeEVe1ZiWMSuLs0vMC3ClprvAke+ffz6PSafe4xDxwDwIhrR2B1srB7iTdcXYutOtJEyyLCJbVJiTUROqvRnkaHWt4TW0gjI2mgyhRmUxOzlbna93PRIknpvesuaVC4/foqLvT/ib7vGfL7/RK/15EYLQySxyc3aaewKnUsdXoKu5stTvyffSbj+0ZQGF0g/06e5PFJ3p7ncOKJ0ln87rsNWHLPmgW33SbypCiSZNzrrpPi/AYQLArwJnl4kzx0VqOSCneQizPY+V4lYjUEJYKnhK0GHWqCWQGFLz2qJ/gkDY0XQayXTXyAg93D/l6aMG5Pgkdrjc7XtjjUelgYMQNc1pl483kJfLrlFpFePvooHHl4JITFL35BlNXkr3kJL+iB2cokdUpqq/S8hstDsi9kCReG2H1tEkcmMBLrneeZZ+Dcc8U4+8knJcqtvudZFlLz7xoufi7Bo2PdhsVWhqG8YTfdJN5RL7wgLRYbQJSJKH5SpDi+iM5rrE4Wsf1iOySxswolgqeErYpHHxVV3mGHyUp20KDtfUX1Qn3Gde1pgrkBwZyAYFFAtCIiqopEsbkWVEJhVAj5Y7Y0MZt6WNM/QL34jKgXly8Xef3ll8Oll25QJ68jTfbZLN50nyP/W0bHIRbPPFPHF7VkiShi9913gxJ+7WmyL0jqTOyAGPH9S747OyNKBE8JjYLhw+G009DNmhM+8iae3x5/hk+0TNqdVJn4qFkdLaz2lhA+DVBeaE9T+LBAYVQBq5tF6rRUo66bdKSJlkZEabnuQ84+BGUrRr43sl7H8L/yJaRmqrfaDw7kfTCShqhPDdYkJeY1OrduC7JKKMw2JnY3UQvNXmpw8cXwxhsSRvzYXxbR9uZL8KYUyJ30T3SykuRxSZw+G04w1L6m6s9rJWEdcoh0RIwZs8HHB98EpB9OY/e2eXRpkksvUwwcKNZ5awURbx4TJgix88wzUsA55xwhdjp1+tZDw6oQb7KH/4UvPnyGqMKcXRzs7najdzF8V1EieErYJtCR5tk7A2a/7XPCQI+mMQ0W2D1snP61ZM/35Eu7rQmeYH6AN9kjmBcQLg1XxxKvAxvMZiZWBwurk4XV2cKIG0ybJmKUzz8XZc/ZZyMtcuedBy++iP+b+8mmTgAHys8t3yokj440hdEymStbET80jjPIWXdCnzxZqtkzZoiXx69/vdGq9gbPEWpW/KGKv70f4+fPxKmsrOdFzpkjm9NRo8Rx7u67N5jUFiwMKH5SxJvsgQa7l01s7xhWux2/paFE8JSwVRFFayKqUin5TjVtuunn7EDY0nFdhxqd1UTVkZhFr5Qkx6gqIlwWomtq112K2oQxE3vBaMwHbkO99Zastu+4Qwbs9TY7UT6i5p81fFllcfBfUqxcWYegSq3hxBOFSJo06VtkdbgkJPNshmhFRPzwknJnZ0aJ4Cmh0TB+vKiWczkZO/bbj7AqJPg6wJ/lE8wO0LnascwBq7W1OrXVbGpKm21SLAkwkfbbopYxcUlIMCfAn+qjCxpngEPiqMRWa49ahfqO7f7XPvm38oSLQ5QLdts8VvUUrG/GYXzzOWrpIiFVwlDmvGRSLCY6dIDu3dFdexG270eYryBYEKz2oQMw25i4u7k8Odbh0qsUsRi8+IJmny/vI/r1H8ic+Thh894kj0/h9N8wyVN9dzVmE5PUj5KinDnkEHj44W89Llwakn4oDTHFbyeXcec9BkcdJXXeTQQRr/VG+PIZ+NvfZG1cViaKnZ///FvSn3BFKL6uX3iEC2sTmzuYOP0cnH7Otwu7JWwWDRnXd/ydSAk7HJShOPlKm+c62PQ7Pc7pBwb89SKfYKYnkbExhd3Pxt3FxWxj7rAqhp0JUTYi+4J4MGCB1VrhdspgkEYZHirmoMuaouMVRGlNuDSkOLFI8dMimOD0d+g21OWDDyyOP16Cb9q3h4MOaiLO2RddhP3780k90pb0/D3IPJuh7LyyRv/bKUMR3y+O08shOyJL7uUcxXFF4ofEV0c10r8/fPqpGC7/9rdS3njwwTqbaytTkTMMOlWGFAr1uLhV7SVXXSWbqoceEqJnrfdA+xrvS4/i+CLhvBBscHd1cXf/dqJPCSV8b2EYYko8apRU9k44QcaZ5s2395VtEyhTrTbO3xCivBhOhwtC/Fk+hfc9CuyOeepzuBfOxrn9J6jTT5cF9d13r+NlYMQNnEEOvcYUMULN9OlqVXDZhqE1/PKXokL8y1/WIXd0sKbNTLmK1Bkp7C47pt9dCSWUsI0xZIgk4x5+uKgxX3gB84gjMIeYuENcMc9fEUnBcUFIsDjA+8LbtHH+2nDB6eHg7uZiddjxtqPedIlaN5KaZOFl7P/+HjVrJhrQTdoS9hiCbt2PqHUzsOIQaShkUcsXoz6biLHsRYyVc7CiAKtHD9xDDoGDDiI8+kj8eSbFiUVyL+c4Lpln2MMxjrrW5cCDFM888xOOe6YHZSeeSPrMJ8m+NACjonyD75GRMNB5LQbTCxduMOY+XBKSfixNEME5T6YYMdrgmmvg5pvX1GE2im++kYrwvffCggWi0rntNvHLrK2eaq0JF4bi3zrNX52YabYxiR8cx+5j7xDhIt837HjfqBJ2Gpx4IjjPKk44wWa2Z/O/EXGMebU9lp95eOM8zJbCUDsDnO9lC1djIft8lmB2QLx8Mu4zv4Ux76OdcrSTRGkNXgZVrEFVVkrM2QEHoE87kzDVVeJ2J3t4n3u4Q1xeeDrOrnsqzj9fLCEcx5RElbFjsX77MxLPf0FueB5/iv9tr5xGgtnSpOycMrxJHvl38mQezmB1s4jvF5dJrKxMqhBHHCGR5IMHS8vUpZfWydMjbxm0LtcbDG3bIGbOhJ/+FN5+W7SyDz64jtw0XBJSHC/vo85rjKYG8cPjuANLTv8llLBB7Lcf9O0r36VRo+DAA2WzkEpt7yvb7jDiBk5PB3pCfFicKB3hTfUoflIkt6ADhfNeJ3HMi9g3nifBBCNGrPO+We0tDIp0bRZiGJtYxoWhZKr/+99CmF911eq7/K99cq/liJZH2L1sEkcl6pTeUkIJJXyP0KkTq2OWjjtOSOfDDgPECsBsZkpxq7YLV2tpTYpWinoxytaGjERIyIirMFK1PmUtGtbWta1QGF1AKZ+yW3YlirWiePjlBD0OJNBt0X4dx0ojwtRVGEunYk18HeexKzDVBZjHHot76WUErXah8F6B8sl53r+4yLWvJzjpJJsXXzyQo154ktTxp5C+5D0yzxuUX1j+LfWLDrWYMT/8sKyNjzlmnfuDeQGZpzPkcnD0fWV8kzV55RWxe9v4Cy9IQeChhyTvXWtRBt19Nxx5JFgWuqjxp3jSujbTF58jBWZ7k/hhceyeJVJne6NE8JSwRTjmGHGEP/tsuPrnin/+08bubpMoJPC+8FYbp+XfyUv03R5uSZ7XENQWRLwpPsW9/0Z0WBtQ6399Qwx/BdbiSdgvPob1l72x+nfD+uMfiV8xjMLIAsVPi5jzA+66PcXhxxoMHy7dUFiWLP7POQeH6eSTHfFnbD2CB2Rx4A50cfo4EgU8pkD6wTRme5PYnjFx0z/jDNh/f5GCXnEFPPaYVBI24+nhGYrmiZDNdqAWClLVvvlmSbL517/gootAKaJchD/FpzihKDJTU9qw3F1drE47rr9OCSXsMGjWTCqLr7wiCR/DhomyJ5HY3le2Q8EoM4jtHsPdzcWf4pN/N0+GY3H/Ppb4lUNRl10mVdRVqB16TCVJ7BvEggWSlDVypCh4br0VDYRzA/Lv5gnmBBhNDFKnpbC7l1Q7JZRQwkbQooUUvw46SMbx99+H3TbcLaKUQiXVd4IsNhNFwtCm5qIP0I6oKI24gd21thWtwsAoq017tBQosSHAgygXEVVHhItDohUuQVkz/OZ7kz/kJgx/Gc4nD+McfiJ2v47Yf/oT/t5Dyb2e49a9MuzT2uGMUxO8894BDPnT70j+/kzSP3mdwugCiUPXnTuj6gi7gxL1+Q9+sI7BcfHzItmXcyzKGPzwPym67W7y6v3Qps0GXqzvw3vviX/e889LBG+nTnDttXDhhej2HUVx+qGPPzNHOD8ELYSd1aXWfLuHXdrf7UAoETwlbDHOOkt8XW6/Xcjdo48GFVO4u7o4QxzCeSGFjwoUPihQ+LRAfJ847p7u98anZxV0FFGcV012dhp/WZYw7UE+QAURBhrDMLBcE7vMxm2bxOrfBqNSvBASR8UpXvQvwlhrVLmFYYaoygRGqwpU83KwFTqviVbG8Mtb4rU/CIhwv3gG99RLMA8ZQuLuu7E6J8k+m2WvJjni8SQffaSE4AFo2RIAVVONiim0t238uZStiO0tm5vixCLFj4tkn82Kw/5gF2dAW8z//U8mnquvFtnwz34mqVsbyXQsKkVlXG9c7KO1VMWvvBK++kpYrjvuQLdqJ0Z6X3j4032IxCMjfmgcZ4DznVi0lFDCNsfRR8PTT4vs8+GHhUT9DkEXPaI5S4kWVBMuykJVQJRRRAULHbpEOoZWzhpS3tAYTohRqTA7VWC0iWF3sjEqDJy+DnY3m9xrOYqfd4br3yB+00Gos88WFRTgfRMQRtC6t/ntJNogEBL8mmvk3w8+iD7zbPwvfQqfFAjnhaik+J+5u206ireEEkooARAPtddfhz33FCXPxIkNzNTe8aFDTVQV4SQXoyd9jE40RSUdtOGgVyQI5qXwrQSYDtqwAIUiAhWilI9h+xhlGqNlArNLc+xeLsk2SaKqCH+Wjz+jNQX7agr7XIX91WvET7gQe9gAyv96J/nPyziGIl3ODTnv1BRvjLqQli+8gPP50xTVj8QOoFKUMeHKUBJbvxgpJvq//KVcf6Cp+V+eaGKRcfNMLhuR4ld/Mjj33PUs3QoFeOstUWU984x4cpaXw8kno085naDnfgTzQoLRAcE3Vas9P822pvhNdrWwOlo7tArr+4wSwVNCo+CWW+C116SD5tBDRQwBwuZbHSxSHVKES0Ly7+TJv5On+HmR1AkpzFbfPQnfknFLWPLidIx5aRKRSTxRhp1qgko2A9PCoAyXjbuaBUAwDXg3T2HOx7w+cR4vtTqZ3mXHcE7mdVqVHSpy1xW1N4oQFjDVCsz2CeKHtsGoNPE+9yiap1DsexLxEb8mdvzxOG+8QbhfjML7BQa0CQmCtYaAZ54B28av6E+0PGqUqMr6QDmK2FAheoKZAYWxBTFk/qCA2d7E6XMyzoSjMP5wPfzzn+IO9+tfy4fOXrcC7aNIuXrD/cVTpsD//Z8QPN27o//3GkG3g/DGefjTq9EFjUoo3KEuTn8Hs3XJR6qEErYYP/wh9OkDTz21UxI8fjFk1jMzyH48H3dlnoRhk0iksMuaQrI5mAlgreqqn8eomovKzEblq1DFDEZQQOkIrBhRqjlh8x74C+Ng1JpupjI4uzfH3SNG4tgEylUUP90Fa7dTcR59FA48EF3UZMYUeWe6zdXXrDUuaS2V15tuEiPlYcMI/3I/xRWt8e6qRqc1RoVB/Ig47iC31DJdQgkl1A+tWknrzp57wsUXi6/adwArx8xj+cszsZdHxGJNsSvbokwLaAcDTgCkQOtnqvAy1fjLlhHkM6iggKU9bCPAVT62AabloFMt8Zt0Qq9IwXQf8CHyseI1mP2bkzhS1v/FcUWK5pH43Q7FHXMv8T33InH/3ZjHHkC/l3M8cHSa/7swxYN3/ZPYnofiDTwF73NvdcJhMCsAwP7v76WdYr/9CBYFzHsoR4UXcv8nLpMr47w3Vq1JyVqxQhLSRowQf8vqakgmiX54OsGhZxO0GEi4RBGMC+DjLABGMwN3F1GuW52skkpnJ0GJ4CmhUeA40uly+OEitDj//G8/xmxpkjo1hT/DJ/tKlpoHakj9KIXdbeeXh6/8cgUL/voJzZ1KnDa9aOf0g64QVS2gULOMzOI5eMzCtxWBYxLFbELHIrRtQtMkRBGGmiAfQK6Am83TIgxo3a4Xxx3dl6Peu4W7PmjG5ZVn8NSQtc67Mo1RPZuUFWDqAD/fFW9BHnSI3SpP8ketKIwqkD/qT/DmTcRuuw3nwl9SeL9Al7KQXXe1ZGNw223oBx7A/9W9ZP9XO6Dvum0JnlVQhsLuIXLPKBOJn9MXHvk38+RRmLvfinPY/2Hfdx3m1VfDXXfBH/8IJ5+82p/H14qkA76hWd3PsHgx3HAD3H8/uklzgj8+itfzWPxJIfrTjJiD97BxBjnSglWqSpRQQuNBKRg4UAieO++UOPCdAFP/MQHrk8U0bd2VFmWtaNGyFTQLCGsWU0yvpGbpPApLviFvmGQsmxo7wRKVYlFUwWJvAEvScZYtVyxcKH6VeD69ymvoYC+jT+ob9i17i13cr6msbEPUaR/ymRT5d2pwd3GIH1GJP9OncOBVOG/8BB1qpv8tQ0sF08tjnH4YIqV/+mkZw6dPJ9zjKPx//xeP7kSvRmAUsbvZuEe5WN1LraUllFDCFmCXXWQddd110tJzwAHb+4oahPTsaube+jHN7abYLbvRLDYQKpaRXziTqpmT8LLLaLric1pWTSNz1tMoJ4FT3hSnvCnQBYBiCNlAsayoqMkrlmcU6QLklnp4s6px81Po5Uyni7OYJokEumVfitkUxXEaI54jcXxz3L3LKbxVoKguwR9wPMkfn47746Mwzr2B9k/l+VWvNHc/0YlLjtkfa/YovNS+xPaLoZTCm+JheMswFn6Bfv1p5jyeo+yrIl5WcdMXSU78lcP/DS3CuHFw1wjJQ58wgSjRjLDvfgTn/ZOwy54EYTPx0JkDzAsxW5urrQisDiVCZ2fFVo9JV0odAfwdCcn7j9b61o09thS5uHNDa/H3bdpUWnQ3hSgTkXk8Q7gipPy8csyWO6eSZ9iwYWS/WskbZ70IiUq8BdNY7lVhDGpJ+xN6kGpfl/zBjSPKR+T+8zV+VTOMlXNwx9yNXwYrexxFId6DZLw5CXvNe7dkWYYyK0+8vAkYFuiQ+IAa/JoWBLMyVDxxNDUPf4h+I8fFI1I8+OeJuDdci7/IoXjMbwjKemO2MkmenNzhDNLCpSHel5649C8Wl34jlsMZ+wT2mGcxK/Ko3/8Ojj2Wd24rMLhYQF9USVNzJdx+O9E9D+N32Af/qEvwUwMgUELq9LJX9w9/X9oGSzHpJWwXTJ4Mp50Gs2ZJ5XCzER7bB8OGDcPP+Dwx8HeUdRkCxQw186eSbWJRtk872h3VBSNWv8JE4ZMC3mceweIQtYFlV00BFi9cQtvCJMqatiVq1QfDymD1bYL3uU/qtR/z9aGP0NIMeXaWxdlHv4/z+nCiER8RNO9PMOQo/K4HoyMh5q1O4ovg9HcwUqUF+vcFpZj0ErY6CgXxZ9lrL3jxxe19NfXCsGHDyH5dzeunPY1KNcebP4VFhWrYowMdT+pFkxZrjetao59/gZqxvYgSbbFmvI25fDqqMgFNm6ArW6PjFWg7iTZjRLj4vkPkW5iRgbEWmR5FMH9ZgXDJl3QxF0LrAegmHXG6hyRPa4433SP3YhZdCIm/dTNOfizhtX9m6ed98DzF7LJF7PXcreSOu4Pyi8tRjqL671XE3r+DaGCSr+OX0N4NGTnLxG05lSNafYh6eyTh5PlE5Z0IW/Yk6HsgYfM+aJVcfV1GUwOrrYXZysTqYEnycaltd4dDQ8b1rUrwKKVMYDpwKDAP+BQ4TWv95YYeX5osdn5cc40oedJpiMU2/dgoHVFzbw1mG5Oy07eMCNle2L3dIFynjKcP/QP5/VvQ7cw+jX4OrTX+DJ/8KyuIsrJ4V/kqzIWfoWoWyYPiKfxUC4KKzpjJFt+q0iaOMMm9FmJPGUF1t8MwwhBn+jPEIhO/x0HoVCtUDGL7x3GHuCh7xx7gwyUSL+x94YnZG6CyS7G/ehdTL2ZK52PpXNkJd9kDMDsgbNqLoMu+oExwwBng4PSoVep8D1sVSgRPCdsN//0vnHeemC/36LG9r2aD2H+f/QlnZ3nl/JdZOP9zOv5uX1LtGp7+pQuaqtuq5D+Rj1nzDRTToAyw40RuOVGsOcrYPOGVmT+FpotGo2MtCToORZe1ljtshKjuZmN3Fj+fEr5/KBE8JWwTXH453HefEPXO1gvjaGzs3mkwrkrw3JF/YeXABL0vHrjZ50SZiPzLS/C+soHacTUoYNQsRGWXobwsysuCX0AFRVSQhygkKm+DTjUnKmtBFG8BsYp1jmssnETUZgCpU1OiWK+JyL4kibmqUIU99XV0s5b4HQ5kSRrafXkX/h6XkTwpCQZkn87ivH87K/e4mqRr4KeXkJw+HKwKwubdCdsMkEIvgNIYzczVZI7Z0sRsbZbUOTsJdkSCZy/gRq314bX/vxZAa/3HDT2+rKxM77rrrlvtekrY+li8GKZOhaFDIR7f/ONXRSlaHa3VnTQ7EyaMGQ86Ypc9dt0mLT060OiCRhciKEboENCKDb55fgFsYdkMK4M2EmhPBnOVr0LHK4EQ5ZoY5SYqrnbKvwERYjCdjdD5ADDBz4O95gOozAiVsFAp9b0kdNbHe++9t00JntLYXsJqrFghHjFDhkDZjknsj/toLIYy6dmqK8kuFZt/wuaQLxDOrkYnmtX9OUERLBcdFFGmIy1uoQ+mLXJZU6NcA5UwUK7a4Un5ErYNtuXYXhrXv8eo72J/B8GEj8YBMGi3wRh2PckNDbqo0b6GQNbjhBoiuWnN6sRbWZfDRhfVOhKCHzCaGBjla65FFzVRdYjOh0jzy6rfZ1BuCrOdCRrCBSFRMYvhJtc6rgZDo2yFipuy3rVZnfJVws6JhozrW9uDpx0wd63/zwP2WPsBSqmfAD8BcN3t4/lRQuOjzrzhd4A81ppt5teiLAUuoA0wFHgRBJFEM2qDtd9QFXroWoKHKIJa2aWOIpSuVb2YEcq1wWHnHfwNwAHlKwhBe/CtF+Ma4FLaBG1DlMb2EnZ2GLFGWiJFEaqYkdQVJ14bZbL2WPRtkl75ebTlEkUaHflYjrM6AkUpH1xbCB6nRO6UsO1QGtdLANbEMW1lm4+tAq0xrAZsPpSEgaCQdacJhEqIH61REbLWriV80BodUUv6GKwzxgfFdYqQ65zGVijHgCBA++tdALX7jVVv+3pqfaV8cCyUW0vuOJRarr6n2NoKnpOBw7XWF9T+/yxgqNb6sg09viT33Plx882SXl1Ts/nirPY1NffWoFxF+QXrZ73uHBjaeiBOvJwH9r+OVr/dm4qujVDt3QCi6oj8GzX4Mzx0KD3CqlCNqlmIyi4XMse0CMvbECVaYMTXvY7Efh65UQ7G0ulELXpSqKkiufAjdGUnolZ9AY3VySY+LC5qqh0cOtQE8wP8yT7+TJ+oSlJojCXTsBZ9zvwWvWnRZgDuF3cTxvsSth2IjlWCBVZbC2egg93D/t56U5RatErYbnjiCTj9dFHx9O+/va9mg9h3931gccDwHz3Iwsoq+l2/1xYdL8pGVP+1Wv4TFDCXzgAvC4YpLVqxCnR5W/n/5jBvPJaXJqrsRNS0s/zOBqu1JX5i3WyMFkbJTPl7ilKLVgnbBNdfD7feujqFaWfB7m0G4sbKeXj/31Dxi91p1r/pZp+jI403vkBhdJqoZj3vtdBHeRnwcig/jwoKEBRX/8RyieJN0WUtv6XgVMtmopt3I3FsAneQi460pAx/mofAwFg6HSO/gqDjnviBxpn6NLr/KdKiBWSfzWJNeJhg8NlywOp5WMu+IipvK3ODWds6Z2jM5iZmKwuzrbm6RcuIfz/XvzsjGjKub+2d3Dygw1r/bw8s2MrnLGE74o03JChls+ROqMm+lCVaEZE6veHeBtsb8Z5NKExfSYvuexLdt4Kv5n+E16uSDmf3o6zDlrcf6EhTHLGI/HgbQh9n0vOYiyfgd+5Bukkv/GRvks17YtamR4WRpqY6S6UboGp7b53yefhVXUAXSb7xS5b87TXin1Zy6fgTeXDfh9B3XInX7hCKB/6M9EMBdj+b5NHJHa6VSfsaf5aPP83H+9IDDzA0ds0k3PefwJn9FsaFp8Otv2LuPw1aFAsU7ryGpvMmoK+9mOCrAv6gY/F3P4ncN/K3MduZOP0d7J42ZuWOafhaQgnfGaxYAQ8/DJa1w/rvAFhJm3zSR4cBbXVvFl8yhmV+FalDOtP++O6Ybv2WTkbSwBno4E3xgJh4I2wAVdmQ/JJZtHNqiNoNAR1g97DwvwJ31HWs3OsWEu2H8OrCKk5p8ij69Q/w0xWEHXcn2OVI8nPbkX8rj0qp1eOa1bGUnFVCCSU0IqIInnkG9t57pyJ3AJI9m5KbtoImXXeFJ9LM+OZjMr2a0eWCgVS23oBx6IwZZB7PEBidMedPwp01GhXkUSkHVZ6AVBk61QQdL0dXpoisFJ5uge8nMUlgqjUkShBqvp67kg7hNMqMkKDLvpgVBZwBlYQrQrLPZAiXRNhfvExs4v0Y55zMHP8syiN4N1fkh1PeJNv/FIwKY/X63F7yFZngbYrhIVRUtOf5Bc05wH2BTl/fTfjZAkKrFWGLHkQdB+G32wVv0pp9iVFpYLYzsdqIubLV9vvpSfldxdYmeD4FeiilugDzgVOB07fyOUvYTpg0CUaNgj/8YdOPC1eGZF/MEs4LiR8a36lj0pWhiPduyrJWc7E/XESzTgMgShA8UGTZiq/I1SwnF/n4MZOowkU1TWA0TWI2i2M2TaJSLtpxCDHxPY2X9SksyxLNX07qmyW0CcuINeuK9eVwJk4Yz3DnZM45/AzaVWjiwMrlHksXzqNFokiZUcAJPZq0HQyGgWFkiR1cjr+0C/5EH2fC41gH96fVrg41nxYIVsCI9j/h6E9PJX7RRcR+34vCH96g8EVfMrkMqdNT2z0qXGtNMCvA+8LDn+qjC1pMkruBPf557H9eg8pXw1VXwfCPoEULACyVByAEGDIE9dr/sN97D/sXv0C/8HPCYSfj//gGvFwH8q/nyb+ex+poYfeuTZ1JliobJZTQ6LjqKnjtNbjwQtjB2zvirRIkf9ODr379Pi3NStp23AOmQ80tS/FXzCOfT5MnxItZBGUuQZMUhcoKCs2bkTeT5PKKdBpWroRly2DBgiSL5iUwq0KSYUTSiSjXVfSypzMkOYne8RW0MBP4g08jsrtjNS2QPLMVmeczGIX5xBe8j3N1BbP/mma/ZpXc7f6ai0cZuJ9+Cg8+CLftRhhrQXDaL/B3+xHFTzTFMUWMJgZOfwd3iLuOz0MJJZRQQoPwxBNikn/jjdv7SuoPBYneTanquRT1+lyadxlEcx1H37WE+fOns7K6ihrto1WG5ssn0G3sEwS/mgGA16Y/YbshGzxspCFbVFRVKZZnFUszsHx5hnhuDt3dWXRNpimLCvTuvA9Rs70I8IkNDXH3bUXxkyL5d3Pg+SReuAK3dRXhi8+wZHicWD7iP4uS/KLJn8n1ORplR5itTZShMFsbeHudTZt/HknxkxnMftbi2D4xxs07k/sSP+XSEYo2Sz+XjdmIf6MffhftNCXsOJhw76MJnL0JZnfA/8Jf/d6YrWuNmNubYtZfmjN2WmyLmPQfAH9DuhUf0FrfsrHHluSeOy+0hiOOgDFjJAG36QZUj7qoKYwpUPiwAAYkj07i9Nt53Pc3hGHDhgEwcuRIALILs8z+z+cYX1eRxCFR0QyzojVYm9jMhD6E3hrTNSu2RqqfW8lnb73OL+ccidGuDbd0H8OQgRtx/Y88TDuD1SOOvXtzgiUh3ice0coI54vnSEz4C+qTMRRnxci9nOP4h1MMPd7mttuAMJQ/4KefUhwxl9zbAYljEri7bJ9NWFQTURxXxJvkEVVH4ILTw8EZYGONfAx1/bWwdKlELt96K7Rvv87zP7w9R+eaIuriJrRps/aBaytPv/oVzJkDJ5xAeNPf8FY0x/vSI1oagQFWZwt3oIvd2/5O+luUWrRK2C7Ye2/p3508eXtfySax/rgOMO+tb1j+0nTclR4p2yVe0QxjQ2N7FKLSiwirF1OsWkYxXYOXz0JQwFYF4oZH3IywLRvdtCtR086ELXqCnQB87M6K2KFNMFuZeBM8cq/miH/wR2Lls+CZZwhWhKy8q4YnJjoMuynJ6ulg+XKJsLzrLvA89GU/xzvtWrwvIZgTAGD3sIntE8Nqv+O34ZbQMJRatErYqli0SGT6nTvLgt/YuQiA9cf2wsI0c//zOdY3OZKJ5lhNO2wwzbBYKJLPZMims9RUZ8lmshRzBZSfI6aylFk5Kp0iZa4mHothJpqhy9oQVXZAJ2o3RLqI1TTA3a8pZg+X4KuAwugC0bIIa8EnJJ67DPP/LiA44ypqnsiSrdZcPzrFvx9OExuyC9U/G4u7R4rEEQm59g8L5N/OU3HHIIxTjkL/619Uf+KRfS0PgeamdxP0OdHlsstENEs+Dx99BC+8ACNGwNdfg1JE+x5KePS5BL0OJKhJECwIRCEPGBUGVgcLq6uF1ckqKd23E3bEFi201iOAEVv7PCVsX9x3n7Rn3Xnnt8mdKB9RHFuk+EkRndPYfWwShyW+k8xwsk2Sfr9Z168hCkJWfraUzLQVeIsy6OoiKh+gvAgj0hiIX4KhlJih2QYkTYxO5cQP7sEBf7iYj4sR3sm/IlzhoGd+g27RDsoSqKZJjM6tUO3KUXaCqKqcYGZA8bEsRGCmvyL56u9xrDnwynBCL0X+jTRmW5NJSy0GFWsv0jTh0kvh+ONx1BcUKvrgz/S3KcGjtSacG1IYU8Cf7oMGq5tF/MA4dh8b9eUkOPMn8PHHsO++MHy4JDhsAA6arKeIh+vdYRhwyilw3HHw5z/DrbdijhhB/Le/JX7VVYTVFt4kD+9Lj+yLWbDB6evgDHJKrQ4llLAl+PxzWVzestEazw6N9od0pP0hHdf5nZcuUPP5YgpfryRanMZM+5gFjaVszKYVxFt2IG6Xg7nuOKoBT2sMM4NRqXC7VmB1kZYqZSi01hQ/LYqy0F2C+/bf4bGHAbCamlj9HE4KPX7++wRPPFM7JjVrJvLZK66AX/8a9Zc/4r70DO5TTxEeM4ji2CLeRI/0f9NYHS1iw2LYnXZe9WwJJZSwjVEowEknQSYjqsGdjNzZEGJtyujxm31W/1/7mnBBgeJCn/xXK4m99AbaLcd0U8QqO1LZoRXtumx6XayjApo0Kh5it7Iwe7hYnR2M5hWEC0P8GT65N9LogsbIziP5yg04zEC//ChFdxC5hzJU5RRnP1XGv5+3SPzhevIDzgTDxtllTVHc6iJbeP/H1+P+41LUVVdRuUdPyvvaLH0sy58Oz/HQyIC9H01w932KIUPicNBBcrvrLvjyS3j2WYzhwzF+dRo2wB57oM86m/DAkwjS5QRzA/xZPt5kYXyMSgO7m72a8Cn5+Oy4KJVxSthifPQRXH45HHII/Oxn8jutNeHCUFQYkz0IwOpuEd8/jtXu+/WxMyyTZru2ptmurRt8jMzLOfyhvxYFytqT6sra24T8mvP5S3C/fBV7wktYyz9DXX89+mc/w5tukHuhBmUqvu6eZGW1Yh0xUFibrLUd2rL8r30KowoE3wSomCK2dwxnsIPZxJRFxW+vhz/9CSorZWFx1lmbXFw4WlOVV7jRRh4Qi8ENN8DZZ8PVV8O118Jjj2Heey/xg/cidlCMYE6AN9mT22ceRlMDd7CLM6jUwlVCCfXC5Mlw4IHQqhWcd972vppGg1MWo/k+nWCfTpt8nPY1uqilZ1QBJqiYQpnflrqGy0Lyb+XxZ/hYzXIkbzgYte9ecOKJqx+T7GPDFI8pH4UUChaxta0jWrWC+++HM86Q8W2vvTAfeojEqacS3z9OcWKRwgcFMg9nsHvaxA+LyzhbQgkllLAxBIGMKaNHw1NPQd++2/uKtgqUrbA6xbE6xTHmV1MYeNKa+xIKq4OJ3dFCVRqYCRPitWtmQ9KqVEzJztprTVQVESwJiJZHFD4oEMwJxGaACHvRGJx3/4297FPU9dcRnvQo+Q80/rQcX+dMjv1Xir/fbzBgydtEjw+ncPV47D42Vus1+yeztYmKK4LeJ+Dec5Wske+/H6PMoOWFKfJv5TmHIr1aRxy6f5KLrzS44QZwVnFEffvKOviGG2DGDHj+eXjkEdSlP8OyrsA6+mg4+2z0ZUcTrlAEcwKCWQHFz4sUxxVBgdVR1D1OT6dk7r+D4fu10y6h0fHFF3D00dIh88QTQDai8IVsiMMlIVjgDHBwd3exWpU+bg1FuFzIl1TVQ1iP3E5kVhKVtUY7SdARystgZJZiVM9D2Rr22w9uvgh9+NEUZzsUHykSLg4x25kkjk9y9Ukm5eVw8slrneT++6FJE/xmA4mqC8T22YDhXGO/rsUhuTdzBLMCVJkifngcdxd3jdHbxIlC5kyeLJuVO+7YcP/fenAjzcKcovXm5prOnWVSe/lluOQSUQZdeSXqlluwO8ewO4vazJvqURxXJP92nvzIPHYvm9jQGGZ7szShlVDCxpDPC6HzzjtCyI4eDa0bTnTvrFD2pqPMtdaE80MKnxTwp/hgQqzNbGK/PBRVUbbGmHoVavkYHcE330DPnhs46EEHwWefwQ9/KK2s6TTqwguJDY3hDnbF92FUHv9un9i+MWL7xLa751oJJZSwA8L34cwzZa10xx3wox9t9ik60ETpCJ0VcluHGmUolKNQZUqMgnfw8cZfWYHSWcru3J9g4JEEw04hmNOD/LT1lI+2jPEoIATtaVinuKgxopXYCydgj3oIa9ZojC5t4PLLiE58gvxEk+IjRQjhpWVxzr/L5dY/KU4a8jV633PInP042DbxA9eNVVdKYTQxiDxX/j6PPAJ//StUVKBMReLwBGZLkz1H5Hj3igwH3ZHitdcMnnhiAxkHPXqIdcEvfynr7YcfluO9+CKqTRusc87BuvhiGNoRHcp85X/l43/lU3i3QOHdAkYTA7uHjd3bxupg7fB/3+86SjvuEhqMzz6DQw+FFmURb/3Tx33No3p2ABrMtiaJHySw+9kYsZLaYUuROiFF+rE0Gc7FvPk8rPgyzNxczNwiVJSDeAzdojVBhx5EyZaEyyKC2QHhvwOIAsyWJoljE9gDHK68UvHuu9JWV7EqTf2uu2DECKI/30Xufx6qXOEM3Hr+SLqoyb+bpzi2iIop4ofGcXdzpUUNRKn097/LhNOiBbz6KvzgB3U+fiyMWJQ2GVDXwvQxx8CwYfCLX8gEOWIEPP44DB6MchTuQBd3oEu4LJRWh0ke6S/TmK1M3D1cnH7OmmsvoYQSYP58IWiffBLatJEe3m7dtvdV7TCI0hHBgoDg6wBvqofOaJSrcPuGxF6/BePav8Euu8Bzz0HHdVvDwqVC+M+tMshkNnGSZs3kfT/xRPjpT+XvcPTRKFsR2yeG098h92aOwkhpi02emCx5LJRQQglrkM0KoTNiBNx2G1x55Tp3a62JlspYFi4KCZeGhCtCdM1m/F0tsNpa2D1tUUUndrx9gjPQIb8wJHfjOyTe+A3ujfvJ623Xl2jwMKJ2fYgq2qBj5eiCDZFG+UXIVWEsm4cx5wvMGR9jLJ+J0gHsthscexD61D/il/eiOKGIf58PUQDdba58Ks7DL5rcdBP84uTZ6IMPI7f/bwlbDCBxdAKz2bfHZuUotK/h3HPh3nvhlVdEaVULd7CLSio6PJdl/I0Z9vtrGUOGKB54YL0C7+oDKhgwQP7Wt94qa+///EcsDf78Z5k/rroK64ADsDpaxA+KE6Uj/Ok+/gyf4nix41AJJaElfWvtDczS+nhbo0TwlNAgfPBWxEM3+tx/nMc+nQLUJxA1MWTROMDBbF5aJDYmzJYmFZdV4E2UlqHi/GYQNlvzgALSqjUdIA+GkGyxvWPY3W3M9iaZjOLHZ8h+66qr4PzzkbasW26B3/6W8Ec/IZM4i6g6ouycsq1mLuzP8Mm+mkWnNe5uLrEDYutO7itXwjnniKrmuOOEiapNx6oLtNYkwoi5Vfa6rQubQ1kZ3H03nHAC/PjHsMceMsFddZVMeoDZ3CRxRIL4QXG8yR6FTwrkhufIv53HHeri7uqWepJLKAGgTx9Ip0W58+GHopb7nkH7UsWOVkZE1fIzXBYSLgmJqmpLvBbY3W3sJtU4b92DOuN28Dz4v/+Dm26CRGLdY4Ya7zOP+ZFJddHYPGcWi4mx/L77igpy6lRo2RIQA83USSm8Lzyyr2RJ35cmeXISu3PJm6eEEr73WLpUil+ffipro5/+VNSGi0KCrwP8OT7BvABWeTnaYLYwsTvZGE0NjHIDI2mg3Nq2pUiKe1FNRLg0JJgdkH8rT/69PPED47hD3R1KER0bGgMNhfcUNbvfgXXMX7CDqdjT3saaPAr1zpsSkZhOSwubZUEqJcR6hw7Qpzsc+2N0v4EE3fYgSMdF+fK6jy5mwAF3V5cZjstJPzGZNQv+/W+4aNBH6H1PI7f/jXh9jiO2Twx3wIZ9f3RBo1JK/CgrKyUxay2CB8Dp6aBOUvB0hrG/z3DCf1P86EeK666D3/1uE24HpgnHHiu3OXOEQLrnHvG/HDxY5qgf/QijzMLdVda/uqjxv/LxpnniZzneQyWVeFn2dzDblVTv2wolgqeEOiPKRPjTfGa/7dErH/DnwyFKGcQGiILBbF364m5NKEvh7ubi7uaiQ71606ALtXJQS3wdjDIDo4mxDmP+2mvijzRrlvhwXnMNqC+/gEsuQY/6gOLP7iHf4WRUTpM6M7VVfJJ0UZN7I4c30cNobpA6eQPn+fxzIVjmzBEFz2WXrSZX6oqoOsICvlpukko14EIPOwwmTRIG7Oc/lwnzwQfXkjtJ1cQd4uIMdghmBRQ+EolqYXQBd4hLbK8YRqpE9JTwPcW778qiF0QJ9x0nd6JcRPBNQLggJFwZEi2PpD0ht14V2wCjqYHZxpS2ZbcK84MXUP9+BkaOlLHu5JOF2OnVa4Pnyr+bJ1oZ8Yc3Uuy33zrD0saRSMBjj0n6zQ03yGZtLTj9HMw2JpmnMmQez5A8IYnTe+dOuCyhhBK2AFOmwFFHwcKF6KdfxO99OP6LWfyZ/upxzWhu4PRzsNpbWO0sjGb192AJl4Tk3s6RfyNPVBORODSx+SfVEzrS3x6L64jYHqJ0LI4t4k3xyC/rS75ZXzjoMsxmprSaJZWQWCayFvchKsj4H9VERBMjGA+QRyUVdk8bu69N0Mrm5j8pbrtNOpfffStkv9G3Eh7/MNlTHiFs0Z/Y/jFi+2+4UqkjTbg8xO3oCkvTv7/83TYAu4dN4qgEuZdzjLixwNVPx7nlFpg5U5a37ubyVDp1kmLw9dfLnH7bbUIk/eY3orT/8Y/BcVCuwunn4PRz0H4t2TPZE2XPp0WMpobYdgxyMSpKa+StiRLBU8ImEa4M8af6+NN8grkStVqzzGB0LsYPf27TtFeJ1NkeUKbCbG5uUimlNXzwgewV3n5bfBpGjoT9Kz+Hs/6MfuoZ/N1+RP6WWURBGVY7i+Qxya0y6AbzArIvZImqI2J7x4gdEPt2S9Ozz8okUV4O778Pe+21wWNtDuESaV+YkzbXmMnVF82aSZTk3/4mPclDh4qiaD2zC6UUdlcbu6tNuDgkPzpP8WOZyNwhLrF9YhhlpUmshO8RPv4YfvITaN5cDGLi8c0/ZyeFP9sn/3aecEFtXJ8hqhijmYHdzpYKdrmBUWFgJnzUwpmoCePg/fFCgq2Ki+/TRxbOP/mJGNptBMVxRYofFRnvOTzxoc3o0fW42D59pE3rnntkUd6u3Tp3m01Nys4pI/NkhuyzWdQpCrtHSclTQgnfO7z5JvqM8/D6/AD/F9fjT0vBl1lUXGF3t7G6Wdid7UZZ25gtTVKnpsi/nqc4pijrqW6NM+4E8wIKHxTwZ/kQ1P5uToCyFdmXs1gdpUVsc6prI2kQPyBO/IA44bKQYH4gKszltcrMhZGY6EeAqvVciylUQomiqZeN1coSX5pyRRQpnnwSfv1rmSLPOVtz19Gvk7r81+SbHkzhkvdRcYfkUUmcfhtfxAbfBOCD1b52K9+69Zo5ZQNwd3EJvgnwxhT4129sune3uOYaqKoSe6U6TdXxuBQ/zz1X1sR/+IPMK7fcsia4xJa/n7IVTh8Hp4+DLmq8KR7e5x6F9woU3itgdbFwh7jYvexSC9dWQIngKWEdrOqn9aZ6+FN9wsWycA2bmDwxI8a/X3c44gyDP/9breP5WC/kcrBwoUgbV66UyMViURgJxxGJY/PmsgBt0+Y7EcUI8t5uCzJs+XJR5N93H4wfL91N/7nhG86ufAnrF48SzPPwB/6Q4vVfoY0URqVB8oDaKPJGvj4dagqjChRGFTAqDVJnp7A7rjd5RxHceCP8/vdC6jz7LLRt2+BzhvNDIg0rjC1sE1RK2rOGDJFo0KFDhfQ58MANPtxsZZI6IUU4LKQwukBxbJHi+CLuUJfY3rEdsse8hBIaDZ4n/lXXXy9S8euuq9OKcdW4qPW6FdYdvXBQ+LRA/rU8RqUitquPHV+BGS5EpatgyRKYsVgkkzNnwrx5okqMatuy4nFpmTr9dJG/9+u3yXPpsNaz7KMiC2yLI25McMEFsPfe9bzoCy+Ef/5TmP71ZPwARsKg7Iwy0g+lyb6YpeyCslLCVgklfE+gQ41/13C8j2vwL/kUTBfDN3B3s3F6OxIqsRWMc5USH0ZvmrS9NwbBE3wTkH4kjYqB219htHTBUBjPGWhP40/z8SZ6YAhBYvexcfo4myWtNldY3RQKBXjiQQm7mjYNdh/kMeKcF+jz9r14d/Sk+uin0fFm2H1tEockNltsLX4qHpZ2z9r3a/2U3Q0gcXiCYHZAbkSWX/6inGbNFD/5iQjnX3yxDkqeVTAMsVA49lh4/XVZw19wgby4W28Vc/+15nDlKtxdXNxdXMKVId7nHsWJRbLPZVEJJQm1Q5ySB1wjokTwlCA9tQtEqeNN9YhWyCLU7GASPyTO+/NsTrvIxPfFa6sOBvpC1syaJS03X3wBX34pMXxffy0MRF2RSIjh1z77wOGHy+ba3rGrijof4I1L488JCVcodF6hPQUacdlf1UoVN1AphZGSCu/qam9txVe5dZtIV73VI0aIH9q7b4V0CL7mhPaf8tCwMfTy5hC9WonXYSi5Ax8kSrYGpbG62cR2i2F12zpu9+GSkOzwLOHCEGegQ+KIxLdfUzotjP+LL0pF4F//on7GOd9GMDfgq2qTNh0b6TUdcID0oP/gB/IZfOQROOWUjT7cbGqSPCZJbJ8YhVEFih8V8cZ70kc91N1q3kYllLDdsHgxHHGEmCofdxw89NC3eodWeTeEi0KCBYGkq3iaYE6AUWZI0oqnZZzUYFTWKl/amChHYbYysdpaGOXbgSjVGmbPFmXhxInomjT5DrdgZJZQfsveqOJGnI7btIHu3WHPPSXlpHdvIYy7d6euFRJ/tk/+9TzhkpBJkcMh1yc44geKu+5qwOvo21eKKOPGbZDgAVmIJ09Okr6vluQ5p6yUhlJCCd9hhMtCiuPyeGNWoo39UT2qcXeN4QxObTPPFGUqnF4OxYnFhhdD83kxmXzpJQpNz0A170/5zXtiFGqEbGjeHCOfh3icivw/CLvuge/2xV8uCqL863nMNiZOHwerq9VothPTpklI7UMPwdIlEWd0HcPLRz5Dp2Vz8ScdSPXBD4GdxGpvEDsogd1p83ucYF6AP9Untl9szZpy3rzNplQqVxE/OE72+SzeZI8LLhBG58ILRZjzyCP1dEVQSub+ww8XRc8114ip/377SeLarrt+6ylmE5P4AXFi+8UIvg4ojitS+LBA4cMCdk8bd6iL1cna4Ys8OzpKBM/3FDrSMkBMEVJH12hQYHWxiO0Zw+5pUzANrv4/Mf0aMkTGzW9F663CihXw0UdipvnJJzB2rOj+VqFjR2lvOekk+XfbtiItadpUzG0dRwYKz5NN/7JlMHcuTJ8ui9F//lOqw82byyh09dWrjSK3OzIZkbyPHk1QnSBz8O3oslaoqoVYiyZJdHmhRpzyHRedrESnmqHLWhAlW+C7TdFWBaj1Ni5GgOH4GLEAwwlRTogyA/J5nxVLiyxfWGDl/Cz5pRniXoa9nCzHp3wqjragvDVRZXeiJgeTMUXiqdwIq7NLrKctstStpCjRoRZPmvcLslk4KYnTZwMy05kzZTM4dapMBFdcUW+/nW+du6AJ5ga8NdWlT58tOtS66NxZPtvHHitV90ym1qV64zCbmiSPSxLbK0b+nTz5d/IUxxWJHxzH7tv4aqkSStguuOMOqdqtXCnSwZNOAiQlKqqKCBZKuoo3xQNPnqJchSqXz7872EV7WuJz3dqoWSCsComWRRQ/LgrpA2sMiXvbkly3tYmHefMkLva++4TgAUgkUGVl2D86Cb/ZYIJf/g27XSBzWYsWQmy1aCG3OpdD14UONP5Un8LYAuHckCBucPPYJHcOdzjnHCm0NEhBa9vSqjV16iYfZjYxSRyZIPtiluK4IrHdt4x0L6GEEnYs6EjGmOK4IsFsSVu1p7yL0zmH/bsLUM623x4aTQ3wQec0KlnPsf3116VIuHChrNd+9HNU0kH9/gZAy75i0SKZo2pqUDf8BgvZBMdTKcJ9jsEbcCy+uRv5hc3gHVAJhdVZ/IXMNiZWa6tOhVetpab90kvw8lM5gs+/YH93HP/r/RW9+oTo9kMJOl1BNlaBMgOcvnHcPWJYber2nuuCJjs8iypXxPauHZsLBSmoX3LJZp9v97UxPzApjiniDnS54AIRnV53ndQgrr++TpexLpSS9fEPfgAPPCB7ot13F+boj3+U+XH9pxjS8md3t4mqI1G9TyjiT/MxW5nE9orJWrnUvtUglAie7xF0JNXS1aROVoOJ9LwOs9fpRR03TgqOU6eKz+wtt6y3Vq2uFpn322/De++JKa3W4ro+cKDIfHbdVWJe+/alYW63ayGfhzffFDew224TwuePfxTn4M1slHWkpVpcFBJrVW9sQzbYS5bIezJvHmRnLiL/ySTi+RWE4yeysPVgnLPuQZs2c6eNoKBrsF2DWLsUrjaJhTncYg1OfiHW4iqs6Stw0isxqlei0mkMFUOXtSWqaEdU0Z6ooi1ReVuisjYEyeZEiWYQa4YCmgHNWgPrk/U6ItRZDDfAbO7gdE5htLSk8t0AA7z6IpgbkBuRI1wSYve2SRyZ2LDZ8CuvyAfMNMUB+pBDGuX83gwPIhg+yeGy0xrlkGtQWSnXeuKJIkWFzZI8sKbH3J/tk38jT/b5LNY4i8QRCcyWJTlqCTsptBaDr5tuknaj229HDx5KMMuXjcPE4mrfA+UqnN4OVicLq5OocOq6aNNaxu9oZSSLv9rjV71RYI5p8+DkOItXKrJZERIVi3Jr2xaSSakJRBF07Sr+6e3bQ5cudeCSq6pkoxCGcOih8ItfiJJ0wAAwDBLpiPTDaTIrTsBKWDjdHexeDSfOo3wk8/N0fzUZFpUZvLgkzqV3u8TLFI89BqedtoU8+NKlkoCyGdj9bazx1mrj+NIiu4QSdn5E+QhvvEdhbAFdozFSEfHP78N5806M226CCy/abtemYjLG6KKGZD2eOHKkGEL37Stm8sOGYY/38Efk8I+5DKfXWgXGadPk5yuviCHxpEkwcSLm+PHE77uIeHU1UVlr/K77E/Q9An/ZXvhftlpzjSqLaecxXA9lByjTh8ijZoXHigV5Movz+CvzVJge5yXhsl3KiQ7uTtREVN8eYFg57F5J7D4J7B72t/0oNwEdaDLPZ4hWRqTOSKGc2ue+9pqQPIcdttljKKVwBjurlaFmS5NrrxVS6oYbxCnh4IPrfEnrwrLES+6UU2RtcOedYvDz979vcvIyKgziB8eJ7R/Dm+RR+LhA9sUs6h0hsdxdSur3+kKt3/e+PbHbbrvpsWPHbu/L+E5hFanjfSmeOjqnwZYqqNPHkcHFWfOlCQLhTX73O2jVSviUQw5BVsgTJsD//ie9QJ98IgvfeFwWvfvvL5K8oUO/Feva6Jg6Fa68Uhj7k08WTeFa7NOqljNvikcwW8zQCNc7hgNWOwu7l1SCN7YojyJ44w146il46y0hdgAsfD5hKFfwObPpRMH4lGW6GV9fU8Xo2RZnPpFkdSm6ztCkyFBODSkyJMkSo0Bl3KN184BWLTXt2ik6dHZp3y1B514JKjuWQ5NKVDIug5+7fbwrouqI/Lt5vEkeqlyRODwhxmnrX4vvS3ngz3+WTcbzzzdqwk768TTVX4d0vKGCWbMUnTo12qHXoFCA449f88E4+eQ6P1VHGm+CR/6dPNrTxPaShIT6TPCNAaXUOK31btvqfKWx/TuG0aMl4W7CBILzriE45zq8ryLC+bUDrQVOb0cqha1qk0a2YFx6910pOrz3Howfp9m9qc/FexXYrX3I7CqDv45PMDVj07atTAW2LcXabFb4DKXEzDKsvbzychGUDhokdYi+feXflZVy/7Bhw2DsWEa2aCHz3UbkgDrQYqo+sbi6tdloYQih3tTAbGJKMcFRYCBKpHCt+PRlEeGKkHCpGHYC4MD8mMM97zr862UL01T88peSSLvq+hqM5cuF8br5ZinXbgbeNI/s01lSp6Wwu+/YrdElCLbl2F4a13ceRNURhY8LFMcXxZi3i4XbYiH2+Qej0jXw3HONVmhrKLzJHtkXspRfVI7Zoo7FL61l8M7nxXSyrEx+HWjS/00TVUWkzkphtRY9w7BhwwAYOXLkho81f74wHVOnSgfB7NlESzKEujlBsjNR065ETTpJ8TXVApwNM1E6ClBBBtMpYpRrzK6VmN2bYrW1Gxxkon1N9rks/gyfxFEJ3CFrVd0POkiuec6cOtlYRJmI6juqiR0YI76veOVls7DbbiJ2+uKLOiY0bg6ffSaEzyefwDHHSIJjHTw2tdb4M3wKH4qKVSUVsT1juLu56+xZvy9oyLheUvB8B6EjLU7pX3r4U9YidXqKUZrdw94gEzplCpxzjtiNnHYa/PMvBZqMfxt+8pL0Vi5aJCvlXXeVPstDDhGqt4Ey9Aajd28hmm6/XRKOtIann0YD/hc+hdEFIXUMsDpYuENdzEoT5YqRpy5oouWRKCpey5N/K09saO1Ge6335fXXxWN3yhRZWB96qFgp9OsHfRaOouO5E1G9etO1dStGjmwGQPZtlx98WGD5S3lyg+JkC4pcTuaefF64Ac8TniMMhUBSSkhvx1HE42Ukk2WUlUGTJrIW39p82ZYgykUUPixQ/FTaKGJ7x6QneEMD8FdfiWpnVcrO3/++xX47ayNcERLMDBgxJ0bPnluJ3AG55hdekA/EmWfKZLXPPnV6qjIU7q4udh+b/Jt5CqMLeFM8kscmsTqUhuMSdgIsXEh49qUElf3xb7gH3+oO79VKqveLYbYxsbtueI6pDzIZGD5cfMUef1x+16YNHH64ondvh3wvh5oWPt3G5fhH0wzu7i7xw+MbJZJWrpRUwUWLxCroq6+EX77//jWP6dZNprelU5bTIptl6c330KxXHza2HFeWEm+tvV3ChSH+TEmb9Gf66M/qUDwzxGso45jMiDu8O93i709YVGcUnTtLNfWss0R91Cj47W/XSOnrALuLbBSChUGJ4CmhhJ0Q4UoJffA+80CD09/B3dPFmvqBtMiXl8vAOGDA9r5UVg+0UT2es2iRqHD++tfV5A7I2Jw8OUn6oTSZhzMkT0yuY96cy8kck80KoZFOQ02NoqqqPStXtmd59WEsM2FxChamhfdZ8HlI3KuijDRx8pTHV9C7xwr69HPoPTBG/z1SdB5SgRm3wAalWjTO+4K0LGefFU/L+BHxdcmd116TKsgdd9TZo9RISdrj6qIMonh96CHZ1l1/PQ3zeVsfgwaJvcGdd0pRYcAAuPdeUcJvAkopnJ4OTk8H/xufwvsF8m/nKYwpiKJnN3ebF0Z3NpR2FN8RaK0J54d4kz28KR46U0vq9LBx+jrY3Te+4A5D2Wv/+tfQIpFl9JWvsveC56DnCBkBy8rEROuYY8RIa0fwvlFKJPNaw69+RXjnY2RTxxIuCDGaGySOTmD3sTFim2bKg8UBxQ/F4Mub7pH6UQqjqbm686BXL9lcnHgi60Zuv+7LZbjresskDoqhPE1xbJHyhQGtDo1jD/7uLYyj6ojCJwWK46Qa5AxwiA2LbdgBP4okmvf//k/exKeeqqNTd/1QHFNEG3DjMy7nXdboh18X8bjsPvfYQ9ICxo/fZLzx+jASBsnjkjgDHHKv5Eg/mMbdyyU+LF6atErYIaHTGbybniL8JkPxtOHgpsCC+P5xnEHOhlsx63sODX/5iyS9vvGG2CnE43DttdKN27Ll+utXG71HOfk38xQ/LRLMC4jtF1tXkl+LJk1kClv/fPPmSbVy/HhRCX36KRSXFGgB7HrVfiy/TtQ+vXrJz44doUMHua0i4W1bYbWVVtjVx/Y0UVVElIuIippMDSxdDkuXK6ozMHWewfNvGIydoPBlOqGiAk49A049VQSxZmN2cI4eLa3NV1xR582ccsQrabW6qIQSStgpEFaFFEbVEjsGuENc3L2k2Mnw4bIG69ZNyIEOHbb35Qpqh08d1KOzxBNjN51IMq/WtnPWLLHwXLjQJFhZzmVd0nR8LMMjk1w+HA1+IGTG5lBeLp0MrVuLsqXjCSadOjWje/dm9O4tb9vWDvnVWuNN9Mi9kQMg+aPkuvNbdbXElPfqBRdfXK9jm81NwhXrtjcMHSqH+/e/Zc7t3XuLX4JMZFddJf48Z50lPn0/+Qn87W91Stq0O9rYZ9oEcwPy7+WlODqmQPwAWXuUQgA2jFKL1k6OcHEtqfOlR1QViadOd2k7Wr/9akOYMQN+enaeyjH/4+dtnmDvqldR+byspI8/XjavBx647VU6dYXWFH98G7nOZ6PKXOKHJXAGOPVuB/C/9sm+kAUNbyRSnPIza9OhTuk0dOrEsDCEXXZh5HvvrXO3N9Uj/2aeqCrCbG8S2zsmJNtO7GOgtSaYHVAcX8SfIjsSp79DbJ/YxuW0X3whk86oUaJ4uf/+rbKYCKtCav5ZwxeRw343JJk2TTZjWx1TpsiMOHCg9IE3IOFNFzW5N3N4EzzM1ibJE5KYzbauN0+pRauEukJrjT8+jffc5/jxfhCFWM1yxE9oK+l/ycZZ4Q4fLvzDG2+IZdv++wuHv8cem18Daq3FV2JMgWhFRPJ4IU8biv12GYL52QRuOPcRXq44k6lTxbph7aTztZFMitIymZQFfxRJu7Nti29bNvvt5xiGFDcPOwz69xdCp2PHLfaZ3zDSaXkj02mR8ddld1OLmvtqMMoMUqduoY9eCdsEpRat7zeiXCTpneOKgBA7sX1ia+K/n3xSlMe77irtp82abcerXRfB3ID0g2lSp6c2G5Wez0vL7vvvhvzijjaM0vtxXPDc6vuVkm1My5bQtqXmJ31zHNjC48j7j6Eag3Mveo9ESpFKSQ27rEwInYoKUew3a7ZeUXc7IPgmIPd2jnBeiNXJInFsYt0iahBI1eLNN0WFteee9Tp+9uUs/kyfyisr1/n90qXiU/fDH4oDRqPC80Sa+qc/yQT43HNCNNYD/iyf/Lt5wvkhRguDxCGJ77zCtNSi9T1BVB3hTfYoTioSLY0k/aqrRWz/GE5vp04u75Ef8tKV75K55zFeDJ+jnDQ6bIn68Y+F2W/08mHjQ2tN/q08xe4XYs14m2SfeRgDr2zQseyuNmXnllHzUJpBczKc8oMy/vMfc+PsfFkZ/OMfEjX75ZfSe7UWE+T0FtVUcXyR4sdFsk9nUSmFO8hd7Uuxs6QohctCvEke3hce0coIFVO4e7rEdo9tvJe4qkqMnO66S96rBx6AH/94K+1eIP9mHgz42QNxDjtsG5E7IN4c994ryVq33irJAfWEchXJo5PYPWxyL+eo+U8NyaOSOP238+qihO81dFHjz/TxPs/hz9AQ74e79HXid5yCMps3yjkKBel2/PhjUZFWVIip/2231W+oUEpaH51dHNIPpcm+miXKRLh7ug0aZ83KcnBdDpr0dw4ac9rqudDzYMECUf3MnSvD3JIlUFMjJE4uJ+SOYchTgkAUPqs2DR06CIlTUSHWY9tkA7FiBRx9tJS233ijXuQO1Bqe7jh7wBJKKGEDWOUHlh+dBw+cQQ7x/ePrrtEef1wUFPvuKybDa7U07QhQZTJWR9UbVgz6vrhFPPqoCI/yebAsk17Nz+OsxbfxzC8/penhu9O1K7Rrt3a9TQFJ/DkO9qOKZl7ET8triO0Vw+nv7FDmvVprglkBhQ8LBLMCVEqROCYhSpW157IwlDX1a6/JGrSe5I6cbMO/btFC8kP+9S+ZizeTvF4/OI6slfffX4jGoUNF1V8P/ye7i43V2cKf6pN/O0/miQxWd4vEYYmtXhzdmVAieHYS6ILGm+LhTfII5kg0idneJH5EHKevU/cq6tSpVP3tQbz/PsoPvfnkrDL0KSfB+aehDjywgdmr2x5aa/Jv5Cl+UhTvhTf/gRq7BK69ssHHNJuafNyqjF5L0/z14CwqKINNKaBOP12Y6JkzZcJ87DGRSdZCWYrY0Bjuri7+Vz7F8dIKVhhdwGhqYPeQeECro7VDteXoQBPMD/C/8vGn+SLPV2B1riUR+zobv958XrSdt9wiphfnny+u3c0bZ0O4IfgzJFVnnBXj868N7vrvVjvVhnHaaSI/+P3vpZevb98GHcbp5WC1scg8nyH7QpZgQUD8kHhJflrCNoUuirlh/r28GAdrj9i7fyW2q0JddxU0ggqxWBQbtZtvlrYow5C16t13b5lYVJmK1Ekpsq9myb+VJ1oZET9y4748m0TXrjB2rPhUPPUUJJM4jhAzjegLv3VRVSUSocmT5TUcdFC9nq4DSS/bEjVUCSWUsPWgtcaf5q9WjNs9bOIHx7+tqn7mGSF39t9fyJ16Er3bAkaFATbiobkWslkhG+64Q9p2W7eG884T3nr//SHhXwv9H+OkZ0+Fqz+UvqoNwO5kY7YxJYbdVOReyZF/K4/T38Hp72C2336F13BFiD/FpzihKIXUlCJ+UBx36AbSo7JZ+Vu+8AL84Q8SRd4A6JzeaMDMxReLbc4TT0h3VaPjBz+QfujjjxcLkH/9S9q26gillAQF9bQpflIkPypPzd01uHu6xPeLfy+NmNfHzrGb/55CR5pgZkDx8yL+dB8CMJoaxA6I4QxwMJvUkalMp+HJJ9EPPIAaM4YUJm9ZR+Be9FeG/eUYVGLzPZA7GopjikLuDHWJHxZHfXWSpLrMmAE9ejT4uJ/NNbn9mSTPnJ0hNyJH4rjEpgf89u1FuTNrlkTC/+pX0lew1uSpTIXTy8Hp5RDlotUx9cWxou7BlEQvq72F2d7EamOhyhoW494QRDlJvwkWBATzAoJvAok3NsDqZOHu5uL0cdZIfDeEdBruu0/o/kWLpB3rT3+qUxzvFl17NiL7Spaoqclpv4lx5JEy4W9z3HmnuHJffrnIZRv4tzPKDcrOKhNPkY+LhEtDkicmN+slVUIJW4pVqRW5F3Oi2lAByQ9/j/3ug6gfnw63390o51nVLTRlCjRtKkXlH/ygkRI7kO9Q6tQU+bfzFD8qggHxA+N1Uraug5YtZZV75ZWyk7jnnm0oDWwEPPkkXHqpeDS88IK8hnoiXCwbrVJVtIQSdjyEK0Jyr+UIZgYYLQxSZ6ZWG6Ovg9dek4LkXnvtsOQOyKbdamfJGhTxSHv0UclSWbRIRB733it8wLq16AohsA4+WB702msi4dnYeRKKsp+UEXwTyDp8YpHi2CKqTGF3s7G7SeG1MXzlNgbta4K5AcGcAH+Gv3qstTptppA6fbp0WXz+ucheL7+8wdcQLgkx2214bO/dWzqoXnxxKxE8IK1Zo0eL6dxPfyrS2Jtuqtf6WZlKlFgDHJnzPyzif+GTOFIi6L/PKBE8OyDCpSHFz4p4k8QsWcUV7i4uzkAHs20dGWatJZbuvvtkoZfNMifZl39wG3MPOIvbH2m1w/iq1Rf+LJ/8W3nsvraQO0qJ+TPA229vEcHjOPD2VzbR0BjeJwXM1iaxPTeT9NS8uUjff/5zGZzuvltcQc8/X8wk1oKRMHB3dXF3ddGexp/tE8wRUqUwprA6PUDFFWYLE6O5RO0alQZGuYFRZqCSql6KHx1pdE4TZSKimohopdzC5RLPq9NrdJpmSxN3sIvVxcLuZKNimznP7NlinPGf/0i1+MADpVK8DVgWHWmyL2bRec3N4xPUZBV33LHVT7thtGghf/vLL5fPwqrPYwOgTEXiiARmK5PcCDFgLjutrMHRmiWUsDmEy0KyL2cJ54WYzSEx+x+Y9/8J1aU9/PJKidRoBPzzn3DjjdI19OijcMopW0c0qpRUP/Gh+GmRcElI6pRU/Umeyy4T5umSS0SZd+ml0oa5A/lWfAsffyzX+OabsPfeUvYeOrRBh/ImeWCB1a20VCyhhB0FOtQUPipQGFUQAvuwOO7u7obVvp9+Ksri/v0lknAHJXdWwe5mk387z8o5IedeYfLSSzJ8PfecDGcbxZ57Sv/WcceJI/Jm1qFKKexONnYnG13UeFM9UYNP8fEminGzUWlgtjExW5mYzU2MJgZGpYFy616A1YEmSteuuZfKmjtcFAqhEwGqthvj0Dh2L3vjhfsoEnX8L38pReVXX4Ujj6zTNWwI4cqQqFramDeGww4TH+T1HCgaF+XlooC/6CJRwVdVCXFVzyKpkZLgEnewS/bVLJknM9j9bBJHJDaqUvquozRr7yCIChH+Fz7FiUXCBRLxbXe3cQbVmiXXVRafTkur0N13w2efoZNJpg8+lUvGX8An7MFf7lHcduFWs0LZ6ogKEdmXshjNDJLHJNcMst27S4buBx/IQNFADBwoPz/yYgzrFZJ/K4/Z3Ny8gVfbtqJlvPRSiQK88krZ8J9/vsgON0A6KWdNDCAIox8uCgkWBYRLZCLwp/h4ee/b57NAxZSknFgKTKTNWAORTCra11KJL27geh1pSbM725gtTcy2tcqhumyA8nmZSO+/XzYRhgEnnCApWQ3cSDQE+bfyBF8HTKhM8LdHLW69dZ0OuW2Pn/4Ubr9dJqktIHhWwR3sSoTyMxlqHqyh7IwyzOalSnoJjQdd1ORHSaurcpTEr/7zMtRD/5EHPP98g1sO10YQiFnj5ZfLGHvvvWLguDWhDEXiyARmG5Pcyzmq/1VN6kcprHb1XPacfbZ8n2+4QZR6998v/QE//7kY6uwoWJWS9eSToj76wx/g6qsb3PMW5SO8qR52Fxsj/v1cIJdQwo6GYFFAbniOcHGI3bt2A7sxdfWcOWLC27Kl9MQ2lkxyK8Lua5N7O88D/+cxYkScv/xFltN1Sqs66CCJ5D7hBBg2TCacm27a7OtWrvhjuoNcdKQJF4SirpkfEC4MV4eKrIYFRtKQAqgjRTkMZP0dsnrtrXMaXVjX6EYllBRS93SxO9ZaNGxu3T16tMhoPv1UWJf7769XauuG4H8pr2lTKpc99hDfo8mThTPbarAsESNUVkqMZhDIXNaAjarV0aL8J+UUPihQ+KBAzawaEkclcHp//9qMSwTPdoTWmuCbAG+CRJsTiIIifmgcZ0A9fHVA9O7/+IesotNpGDSI6lv/zQXvnsGzr5cxbBhM+u9O5B2wERTeLaAzmtR5qXV7LJUSev/DD7fo+PvuK4Kc+/6jOOqZJOkH02Sey1B2dhlWmzp8XfbZR9KUxoyRzf7f/iY/995bvFpOPFGIqA1A2Qqrg4XVYd3z6IImrBKlTZSJiLIROl87gRQ1BKJmQQNKNjaYtRG3rhIiKKEwUrUqoEoDlahnC1guJ8qU558XuX8mI46hv/kNXHDBNo/ZLHxUoPhxkaqOLkdd5HLwwcIvbVc4jmyorrxSvDsaYUa0u9iUnV1G5vEM6YfTlJ1ZhtmyRPKU0HCs8mzwZ/h4X3jg1xpyDvExrvspPPwwXHON9PV37brF56uqgqOOkqF5l11keNyW+wx3FxezpUnm8QyZpzPED4rXOYxgNVq1khatK64Qf7G775bbPvuIUeTJJ28fw9IpU6Qd4fnnpbixyqX6hhu26Hp0qMk8nkHn9CYrvCWUUMK2gY40hdEFCu8XUAlF8uTkpjet2ayoWQoFePfdRnbK3XpYkDYZO8fmhz2K7PNmjL0OqOcmf8AAmDBB7BJWmchcf73MZ3WQoShDYbUXy4RV0J4mXB6K+r06IspEQt7kawupoQYfWX+bCpVUGE0NjLgo7letu83mpijw67r2/vBDmW9GjJB9wyOPSLDLFlbodagpjC1gdbI2afWxqi49a9ZWJnhAXtNtt0k6wZ//LH+rv/ylQa9VmYr4AXHs3ja54Tmyz2TxB/kkDk/UX8W7E6NE8GwHRJkI7zOP4sQi0YoI5SqcQY4sRNvUw+QrDEWmd+ed0prkutKbecklPPPNHlx0sSKXE47hssvqyIDvwAiXhhTHFXF3dbHabuCju9deouNctKjBk5nrigjnxhvhg48V+5yWIv3fNJnHMqTOSmG1quNXZs894dlnxRHuoYfEaOKyy+S2++6y4znkEFG8bCZaW8UUVmsLtuX8HEWSDvbWW6LSefddUe5UVspn7PTTpUKyHZLWCp8UyL+Vx+tos++1cVq3ljl8hwh9+/GPpT3v/vsbbUa0WluUnV1G+pE06UfSlJ1dtvFY+hJK2AyK44rk/5cHB5y+Du5uLlYbU+Teb7wBv/2t3LZwEblkiXAP11wjsasPPyxr0+0xD1ltLVKnpci9kiM3PEdhVIGy88vqr0zp21cUsn/8o8jIR4wQleb558t9xx0nVYJhwyQ3vbGxaJEs+t98E95/X8ZokPaL226TVrJGOG/xE1ESJ09MYnf+fvsYlFDC9ka4MiT7QpZwfihtJ0cmNj12aS2K4s8/lzGqT59td7FbgEIBjj0WUrkYw89M0zYsAA3wCE2lRAFy7rniiXn55UKUXHqpRCHWM75QOUoKvBuuzTYu8nnZO/z73/DRR2JU94c/yGtopPa64vgiukYTO2rThNeqbJTlyxvltJuHUpKwVShIa3HLlrKAaCCsVhZl55VReF/CbYJvApInJDe8f/wOQmm9kZy07YDddttNjx07dntfxlbBqui74vgi/jQfIpGSObs4YqZVn5i+dBr++18hdmbOFKneJZfA+edT5bTk0ktlDbr77rKo7t17672ubYnM8xn8GT4Vl1VsuKfyo49EKfPss6KUaSCyWejXT1SD48ZBKgpJP5QGH1KnryvxHzZsGAAjR47c/IG/+EIcy15+WfyRtJbF+F57CSG0224wZIioYbZlD53WsnH47DNRnnz0kSiQVqyQ+3v0kM3fscdKT/NmCKmtd5mawqgChfcKBB1t9vl9kmUrFB980ChdJI2HU04RmcKCBY3KOoXLaz+HBpSfW75FnjxKqXFa661dk1mN7/LYvrMgXBqSeyNH8HWA1cUidXpK1H5BIG2lf/6zLCSvvXaLz1VTI37EixfL/PPggyL33t7Qkcb/0pc236YGyeOTmK2/XVSp87iutShnRo6Ed96RfweBVAo6d5b5qF07qb726iUL9E6dZNHuukKkg4z3YSjjxZIl8sZNniw/v/lG5o6ZM+Hrr+XxqZQc+5hj5NapU6O9R94Uj+zzWexuNqlTU5t/Qgk7FLbl2F4a17c+vC88sq9mUSgSP0jg9K8DOXH//aKs/t3vRGW9k+DXvxbu/OWX4cAgi/eFR9n5ZVLgbCi0lvH5z3+G115jGECzZoy8804xni8vb5yL3xKsUlk99ZQo5FdNoD/7mRQPGtE3KUpH1Nxdg9nGJHVGapOCgqVLhWO56y7hxrYZokiUsU88AU8/LQrZLUTwTUD2xSxROiJ+aK1n1U7kVdKQcf37QWNtR0S5CG+iR3F8bfRdXOEOdXEHu/X305g3T0ide++VZIy99pIF+Q9/CLbNyJFiF7BggShQrrtup0k93yzCqhD/Sx93T3fjhlm77grxuFQ2t4DgSSbFAPTAA8Xcffhwk7Jzysg8miH9SJrkD5M4vRrQz9mvn9yuu04o8ffek0H9gw+EtQ5royErK2VX1KuXuMx37iykT9u2MtpWVNSPANJaSMHFi+XDMX++mCN//TVMmyZV4FVkDki15/jjpRJ90EGNunloKHSgyY3I4X3mUejssP9vEyxZrnjzzR2M3AEhwp5+GiZOlM9kI8FsJhNy+qE06cfTlJ9bvnkT7BK+99C+FtXOyDzKUsQPiePuWmvImc3K4ul//5OK769+tcXnC0NRxC9eLMmn55yzdcQsDYEyFE5/B5VSZJ7OkP5PGqe/Q+KoRMNiVZWC/faT229+I+Psxx9Lot60abJTWbZsw89NJIQMCkM5zipiqLieaVo8LvPGoEGyyt59d2HLtgLRXpxYJPdyDrOtSfL4HduMtYQSvsvQoZY0zU+LmO1MkickMSvrsGf46itRexx8sDAmOwnmz5eOnLPOEt4lysXxZ/lkn89Sfn55w1trlJLF/IEHSgLVIYfI5HTGGaLk2X9/SX0dNkxSX7dFATMIRF31wQeikn/7bbFBKC8X/6Czz5braWQCQkea7EtZdKBJHLmZhGBkOoPt4MttGCJi+OYbWUD06CH93VsAq6NF2QVl5IbnyL+eJ5gXkDw6+Z2OU/+ObP93LGitCeeHFD8tirdOKB+u+AFx7D52vRKQAJg0SeTXTzwhm/UTTxSvj9qSqOfBb34lD+neXRTc29DrdpvAm+iBBne3TfgBOI4stN98c4vPt+++opC88ELpRnrsMZOyc8vIPJUh+3SW8ICQ2H5bYCvfrJkM5CecIP/P5WTAnzBBfk6dKgP/Qw99+7mWJSRPeblsEmIx+Z1hyOfD92WTkMtJJaC6Wn63Plq2lCrBqoSFgQNlgtvBjPjCqpDs8yJPXtYlxj5Xx4gixdtvb4O+4IbggAPk5+jRjUrwgEhOUyenxE/k+QypU1MbTs4ooQTAn+mTey1HtCLC6mqRPDa5xpDzs8+EwZ4+XfxkfvrTLT5fLifj5UsviQ/xxRdv8SG3CuzONhU/q6D4cZHC6AL+1z7Jk5LYnbZwcV9WJhuIQw5Z8zutZaE6a5YQarNny5i8bJmM27YtFctYTMbrzp1F8dOjh5g4V1ZudUWnLmry74nZttXVksSx+q5TSiihhEZBlInIPJshnBvi7uESPzhet6CVKJK2JMeRteMO0bdeN9xzj/AeN90k/zcSBskfJsk8WrvWOaUR1jo9e4qvXNeu0rL1wgvSR7yqsBGLCZEweLCsiXv1kse2b98w4sf3hbmaOXNNMfWzz2Sdn83KY7p2ldb+o44SUq6Bpvh1Qf7tPMGsgMTRCcxmm/9szJ0rPzeROL/14LpiuTFkCJx0Eowfv8VqKyNhkDwlKV5W7xZIL02TPKWOxOlOiBLB04jQvsab7Eks6+IQXHCHuLhD3IYZo44aJcqOESNkI3/JJeKkvpZT8tSpsqCeMEHCmv761x0+BbHe0FreV6urtfkv4pFHyns0c6aoX7YAF1wgDPbVV8u6++mnDcrPLiP3Sk7ahOYGECIJMNwhcgAAZQlJREFUVluKRELatPbcc93fFwqShDB3rvj5LFki6p+qKrm4bFbInCCQyV0pmYhcV45ZViYbhGbNxCi0dWuZrDp23Ck+KN4XHrkRObTWfFSZ5IfnO3ToIF+Jnj2399VtBO3bS2z6559vlcPbXaQHP/dqjsL7BeLDGtCjXsJ3GlE6IvtClmBOgNHUIHVGCrvrWgvUUaNk0WSa4rtz8MFbfM7PPhMf+SlTRGi6TSXdDYCRNIgfFMfqbpF7OUfm4Qx2L0mubFQoJSrIHUAJuT50pPFn+OTfyBNVRThDHBKHJErkTgklbCcECwMyT4vBefKEJE6/eoxH994rqpAHH9xOu/KG47nnRLTSpcua39mda9c6I3LkXsqROC7ReAWtffaR2+23i0XB+++LNcHYseJxUVOz5rFKSUG0RQtpry0vF2Wl40hhNYqk0p7Lybp8xQrpb1qyRAj+VSgrExPo886TFtu9995mSYz5D/IUxxRxd5cOkrpg0iT5ud0snFq1kra1Aw6QBcXDD2/xIZVSxPeNY7W2yD6fJf1AWtI123/36JDv3ivaDgirQopji3gTPHRBY7Y0pVd2gFN/+ZfWIu++5RYZqJs3l9jlSy6RgWWth91/v4R6xONi7XLccY37unYUREsjopURsb3roJg59lgheF54oVFila66SgQtP/2pCKaee07R9/gEVieL3Os5ggWygdJab51+zlhMqgjbNf972yPKRORey0k8ZSuTGz9McudDJoceKkK2Zs229xVuBt27S8V+K8EZ7BDMCyiMkiQEu0vJCLWE2qSVUZIwp7UmflhtO9baG/Ynn5SqQLt2Qu40wurtrbdEVt+kiQgo1xaw7OiwO9qUn19O4cMCxYnikRcuDDEqDHSkv5MKuVUq49xrOXmtTQ3Kflz2rQTHEkooYdvBn+GTeS6Diiv5PtYluXUVli+XlqwDD5QWn50INTUibjn99G/f5+7qEuUjSdANNMkfJhufgG7dWsJDfvQj+b/WoryZPl3Wcd98s6bAunKl3JfLiUInioTkcRzZjJWXy/pvr73kuB07ikqnVy+xWdjGvi9aawrvFSiMKuD0d4gfXveC4MiRUq/cwkT2LcO++0rP9+9+J90GjbTRtbvblJ1XRubJWuuN45M4fb5bUeql2byB0FoTzAkoflzEnyHtL3ZvG3d3F6ujVf/NfhTB8OFC5owfL54rd94pBlvrGRhUVYla55lnpPD68MMybnxX4c+qfX+71WET27WreBQ88ojExTbCYHreeSIGOuUUOfRttykuvtilvKOFekgRLYvIPJYhcXiilG60hdCR+IWsmszntotx3E0x5s5X/O53sn7ZKVTHLVpIK8ZWglKKxBEJgrkB2eFZKi6q+F7FP5bwbYQrQrIvZQnnhdi9bWL7x76d+nfxxdKOteeewso0gopv9mxZmPfoIT7DLVps8SG3OVRMET8oTmxYDG+yh35IEy4NyTyWIbZnTOb078D3S2tN8I0Qw8GsABVTJI5L4PRz6tYCUkIJJWwVFCcUyb2aw2xlkjo1taaVtq74/e+l9fPOO7c5ibClWNUK1L37hu+P7xtH2Yr8G3nSD6dJndyA96c+UGoHYDa2HNrX5F7N4U3ycHap9Zqr42cjn5f6z6mn7gAfp+uvl77viy8WArORjLHN5mtZbzybRf9A4+669VrktjV28uDsbQ8daIoTi6TvTZN5JEMwNyC2d4yKyytInZTC7mTXj9yJIkl9GjxYzJJrakSa89VXEqm9HrkzZoy0iL7wgrjNv/HGd5vcAQjmBahyVffUoHPPlfaYRkx3OOAAaYNbpRQ86CCYvtTEbG1iNDUIF4TU3FND7n85okzUaOf9vkBrjTfNo+aeGvKv5Qmamtw4rZxB58eJJxUffigepjsFuQOivFrfLLWRoRxF8vgkOq3Jv5PfqucqYceGN9mj5t4aoqURyeOTpE5OfZvcefFFIXd+/GPxHWgEcufJJ0Vxns+Lsm5nJHfWhjIU7kAXq72F0dQgmBeQeTJD1V+qyDybwZvkob0dJ3m0rohyEcVxRWr+VUPm4QzhwpD4YXEqLqvAHeiWyJ0SStiOKHxYIPdKDqurRdk5ZfUnL+bOFdPIc88V75idDKuWSpuyn4ntESN5UpJwSUjNfTX4MzfgK1nCaoTLQtL/TeNN8ogdECNxdP3a2559VrrNTjttK15kXWHbYtK0cKF0tzQijKRB2Vll2D1sciNyFD4sNOrxtydKCp46IspGFMcWKY4rorMao4VB4miJLKxXxPnqA0bC0tx0kzQ69uolqpNTT91g9FUUib/OtdcKqTxq1LftWr6rCJeE9YtJPP10+OUv4e9/lzisRkKbNuL98sAD0v01cKC0iHbqZFD+s3IK7xUojitS/KyIu5tLbM8YRqrEoW4KWosHROH9AuHCEJoYvBIluegKmzBU3HgjXHPNVvWd2zrwPJHsbmVY7Szc3VyKnxZxBjtbFidawk4Hb7KHP9PHm+RhdbBIHp/cMBG+aJEUDAYNkoVSI3w2v/hC9hODB8vUtYWWZzscjDKDyp9XEiwI8Kf6eFM8/Ck+ylVYHS3sXjZ2d3vrVpK3AFE+wp/u433pEXwVAGC2MUkcm8Dp04D28RJKKKFRobWm8H6BwvsF7H42yeOSDSNbb7tNNgk7UST62mjSRH5uLHBwFZw+DmYzk8xzGTKPZ3B2dUgclCilia4FHWmKnxbJv5NH2YrUqSnsHvVr4Y8iSTTr3Vt8kXYI7LGHJGr97W8SH9+I3kXKViRPTpJ9KUv+7Tw61MT32/m9LUu7gc0gXBZSGFPA+7w2Dau7RWyPGFaXBrRhgfR2vvIK3HCDxCj36iWGXqecslF5wooV8rl+5RUJXbr/fvHN/T5Aa020MqrfAFVRIfFXd94pstW1Xdu2EEpJ19yxx0q70H/+I3unm/9icMUVCcr3dIXoGVOk+EkRZ4BDbI9Yw0y2v8PQvsb7wqMwpkC0NIJyg1F2gotudli4WHHSSfCnP0nH3U6JpUvX8czamljVVpJ/K0/ZmWXb5JwlbF/ogqbwSYHCewWwwBnokDhsIwvdf/wDrrtOjNiff75RyJ2//U38ycrLxQOxQ4ctPuQOCeUo7M42dmeb+OFxgm8CvIkewbwA/xWpIBtNDaxOFmZzE6uDhdnSbFjRZwuhfU2wMMD/widYFBAuCcEDbIjtF8PuaWO2MbeOV1wJJZRQb6wid5xBTr0VFqtRVSWbgtNP3yGN3OuCjh2lWaEuuRRmS5PyC8vJv5sXi4xpPvGD4+J5+j0f24K5gfiqLQqxulskj042qADxzDMSmvDwwztAe9ba+N3vRCr8hz+IGrkRoUxRxOeMHIWRBZShiO2zBUnJOwBKBM8GoLUmmBtQ/LDWX8cCZ1DtRr35FmzU335bFtoffyzlzocflkF5E30nn3wCJ58syrRVySQ71BduK0PnNYRglNdzkPr5z+Ff/4Ibb9xw1PgWokULuO8+4ehmzYLf/lYY74svNrn00iRt9o+tJga9iR5WJwt3iIvdy94ui/8dBeHSkOKEIt7nHjqviZqavBEmuOoWh8VLFYccAi+8JGT9To2vv4bDDtsmpzJiBrH9YuTfyOPP8bc86rmEHRphVUj6oTS6RpSk5ReWb7zqO3y4KHcOPlgmkL59t/j8VVVSn9h1V5nCvqvkzvpQSmF3srE72WJSvCAkmBvgf+3jT/PxJnirH2t1tTAqDay2FkYTA6PMEDWnwxZvQnSgCReHRDUR4fKQcElItDIiXBRCBFhgtbdw+ju4gyTBs6TWKaGEHQuFD9cid46puzfKt/Dww2L4e8UVjXuB2xCmCfvtB//7n9TAN/dWKEuROFS8w3L/k4St4pgi8QMlFfH7RvSES0Ly7+Xxp/qoMkXyhCR233rahdSipka2T4MGbdj0eruiY0eRDf/3v9L90qpVox5eGYrEsQnQiO2BA7Hdd16Sp0TwrAWtNf40n8JHBcJ5ISquiO0Xw93dxUhugQz7k09E7vH227Iavu8+keTYG9+IaS38xFVXicfO6NFi8Pt9g86L30G9JZjt2sHll0sE4hVXwJAhW+HqxMaif3+44w5JtL/tNjnlCSeY/PSnSQ64NE4wyaM4rkj2hSy4IjN1+jqiAvsOJrSsj6gmwpvi4U3ypA3LgOpmNg9Oc/njzRa+rzjmGGnF2muv7X21jYDFi4WRHThwm53SHeJS+KBA4cNCieD5DkNHmuzzWXRRkzg6gdXZ2ji588gjcMEF0LMnvPpqo/Q5ep7UKNJp6fRqBL5op4RSCqudhdXOIranLACjTEQwOyBYFOBP9wkXhHjjvfWeKG1Syq31lIvkdyouf0MjYRDlI9ASdY8GNOiiFDrCZSE6u64HkFFpYFQaxPaMYbY3sTpaGPEds22shBJKgOLEIvm389h9bVHubAkh8eCDwrZvpTXutsKPfiTq+A8+ELKnLrDaWpSdV4Y32aMwskDmyQxmG5PYnjHsPvZ32ltsVRJi4cMC/jQfXIgdECO2Z2yLCP0rr5Tl63PP7aCel1dfLYuPe+6RSlMjQxkSPKA9Tf61PEbSwOm7c6ZrlQgeQIcab5JH4cMC0fIIo9IgfmQcd5C7ZWqL6dNlNfzssxJ3fscdcNFFYsC6CWSzEsv92GNw1FFC0G+jbo8dD6H8aFAs4q9/LeqdSy4RhmwrjlaDB0urwuzZ0hHx3/+KzLFzZ4Ozzopx+mku3WIB3mce3pei6lFxhd3DlltX+zvTR6y1VJj9r3zZ6MyXP2LQxGQMcW5+yuGTSQbl5fI5/9nPvmMp8O+9Jz/33nubnVLZCnc3V7yMVoSYTXfEmbmELUFYFZJ7JUc4PyRxXAJ34CYImxkzJP5v331lIGoEciedlo/05MmS4rjrrlt8yO8UjJSB09/B6e/AIbXtxVWR3NIROquJshHhghDtafyv/NUxFzqvhewJARMwhOzBRAigmAIFdg8bo9zAbGGKMqiJgRErkTkllLCzwP/KF0PlbuKZtkVFvunTJf3jjjsa7wK3E045BX71K3FVeOONuj9PKYU7wMXp68g+bnSB7AtZ1FsKd7CLu4tb94CWnQC6KPYGxXFFwkUhKlYrRNjD3WJi/8EHZe9y3XU7sIq+Z0849FAxQ73+eomob2QoQ1RQ6cfSZF/MYpQZWB12Prpk57viRoT2NMUJRQpjCugajdnKJPnDWmnblgy6ixdLr+A990A8Lv07P/85lG3eH2PmTAnTmjxZBrpf/3qrfH53Hqz6MzQkmKqyUvqmzjpL2hOuuqoRL2zD6NxZFDw33yx2Fw8+KKbvv/+9on9/mxNOsDnuqAT9ynyCaT7+DF/8nRSY7UzszjZWRwur/c4Ty6u1JloREXwTEMwO8Gf76IxUmb1Kk/Eqxj1vObz0npAO++8vk8jJJzdKkM+Oh+HDhZHdbbdtelp3sEthVAHvM4/4gTu/QVwJAn+OjBH+FPF8iR8ifgMbxeuvC2uaSMDjj0txoRHw8MMyLz31lFRbS9g0lFKYTUzMJnUjW7XW4pnTCG1cJZRQwo6HcElI5rkMZkuT1ImpLVeYDB8uP084YcsvbjsjmZT9ztVXS/7MD39Yv+crU+Hu4uIMcvBn+BTHFld7HFmdpGXV7rlzqpu1ryVM4UtP1DoBGM0NEkcmcAY2jmH+e+9JwfWgg8TZYofGOefAmWfChx9KEWsrQNmK1I9SpP+bJvN0hrLzyzArd67C6feS4NEFTWFsgeLHRXROY3W0iB0Vw+q2hb2budyaXp18Xr4tN9xQ5z7B116TSDqlpBf18MMbfinfFawauBocTXvGGfD00xI/dsghkum7DRCLSf/q6afDggUi4nr2WSHtfvc7RZs2Docf7nDowZoD+wRU1gT4s3wKowvwgRzDbGlitpUodquVhdFy+1drdSRV6XBJSLgoJFgQSEW6tpVOxxWLbYtRK2zufc1m3FS53qFD4c9/lipNI5rf73hIpyWO+rTTNpiGtzVhlBtYnS28Lz1iw2KlTeJ3ALqoyTyegRDsPjbxYXHMZptYZEyeDCedJKvlRx+V6L9GwPDh0vI+cGCJ3NlaUEr9f3v3HS9HWT1+/PPMbL8lvYcQ0kNCNYReQkdBQETAQhFREUT9oiCiX376tXyVLxakSQfpSpUaek3oCSUhJCEEEkJC6i1bZ+b5/XE2JGL63TK7e96v177m5t6bnee5d+/szJnznAO11i1QKbVJgkxAxx0dmJh0NirJDbxHH5V1snVyUnXmmXIj4TvfkQySgQM3/zmMMcRGxYiNiuGv9KUO5pt50g+k4QEk6yVp8BZ6slw2hGUSVjeX8eZ5kgk/rwAFWcob275YW21Q6Qrmv/KKNI4ZNkwSfit86rr5jjhCmkXcc0/ZAjwgWbTNxzXTdm0bnf/opOXklpqqoRr2X2NJBZmA3IvS3cjmLJEREZJ7JbueehUEsp7qZz+DBQvgqKOkBdCoUZv0362Vi9/zzpP4wz33lLTxU00zKfljCjq3JIUHiZZdfbVcmRx7rNRDam0t4Qg3buBAKQd01lnSXOnBB+Vx331w/fUGiDJsWJQ990yy10TLHsM9to55sNiT4p3T1tRxMC0Gt1cxPb+7g9PNwWmVAp6lKOJpAytLCToCbIeVQp4rpYhnsCzAX+6Dt3owkG9x+chEeXV5hDunRHj4RQcwtLTInYDTfgiHHy4lkRrC9dfLGstvfasqu4+OjpJ5OEOwPNhwIECFXtAekH0xCx40n9RMdMhG7j4++6ykLnfrJne2StSC7tln4cgjJQH1178uyVMqpVTDsNaSvjdNsCqg5aSWzW8asi6+L8f5b3yj688VEtGoXEpNnChJSU88IYmoW8rt7pLcJ0li74SUDZhVgJsgWBnQfm07xKUgfWSw1FNz+7tdq7e6hWxeyhp4izy8BR7ehx62TW6aOt0c4jvEiY6KlqVu5/PPSymQXr1kaVxNlANpbZVCTZuzlm8Lub1dmo5qovP2TtKT0zR9oXaWHTREgCfoDMhNzZF9JQt5uQhK7J0gMqAE03/hBalK9fLLsiTj5ptlDcomSqelsNhtt0l2wzXX1OmylS1kogaTMgSrtjDAA9C3r/yADzwQTjxR1k5Vad1bnz6SXXjSSRIXnDYNnnpKCstNngx//7sEfCKRKGPGwHbbWXYZa9lxsM/WLT49jY9t8ym8W/iPYpsAuFKvwSQMJmYk2hyR9FXWel+wgRTttAVZFmDzFpuVx39wwEs5tDkuC2yEmZ+4PDfT5cGpLis65Em7d5cCyb/9Ley7rxQE30AN8fqUy8n6vD32qNoC5uiwKBkyeO97GuCpUas7NHXe3UmwIsA0m43fhOjslFpjffrIe1H//iUbzyOPyOFy4cJNWmWslFJqLbkXpSNv8pAkkcEluux6913o6JBoSB3ZdlvpD3DMMZKMevfdXS8hZ4wh0j9CpH8Ed4ALPjQd3URhfgHvQ4/s09k139tkcPu4uL3XqnPWKp0QTcpsUYDFWovNWWy73DQNVgb4K3y5abrUJ1i+5vrGNBsp07B1hOjQKE4vp2zZ2P/8p8QHt9oKHnusxjpi7rcf/OIXsGIF9OhR1l3FRsXw95CC1tFtojVTdLmuAzxBZ0B2apbcyzkoQHTbKMm9k7h9S3Dhs2ABnHMO3HqrpGjceKMsB9qMwMHqZJ/XXoPf/U4KjOmqiv/k9nLxP/G79iT77Qd//KN01PrJT6Q2T5U5jjQ+2HlnWXdsLXzwgVyfvf46TJ8Ozz9vuPVWg1TjlIhJ795yIB42xDJmUMDQ3gH9WwN6Jiwt0YCUY4lhiVqLkwUnsBgrRTxXh29W1/P0MeStIeMbOjCswLCkzfDhMofZixzemOvwxlyDH6x5YfbrJ53DTvm2jH3CBBg5ssFrRYG0vfvgA8kYqxKnp5yEeAs94p/T9R61xhYsnXd2Upgt9XZi28eIfy6+8RO8H/wA3n5b1vaWMLjz3HPw5z9LAqQGd5RSavN4iz0yT2SIjooS36WE78kzZsi2QmUHKunoo6WE6be/Latx7ryzxO8/LmsK4iNLob1FHv7HPv5iH3+pT/7NvHQv/AyTLN5AjctNVFx5PmOM1FErnlzbQjGok7NSwuCzlzAuOD0c3L4usfExKcUwIIJpMWVfXu/7Uhr2N7+RG7P33iv3hmrK6puor70GBxxQ9t0l9ktQeL9A+oE0ka0iOC3hv+CpywBPkA7ITikGdjyIjYuR2CuB26cEgZ1sVoIDv/2tpGD8/OcSmWlu3qyneeklCe60t8tSncMP7/rQ6pXb3yU3LYcNbNfSE7//fZgzRwI9ffvK7y1EjIGtt5bHl7+85vMdHdIUZ/ZsKcI9f77EEWbPMzzzgsvSpS52C0sUrUuPHnKNOHAg7LAnfPGrMHy4BHFGjy57sLw2LVwo75iHHSbLZKrEGIPbrwQBUVUV2RezFGYXSOyXIDY2htt7I+9Zq1bJjYZrrpFtCQu3vfGGvEd1dsqyYaWUUpvO+rI0yyRM19uhf9a8ebIdPrx0zxkip50mWeDf+paUWSln6QoTN0SHRokOXZN2bq0EZoKVgWTdFDsh2rQlyAZrMt/zxZun1kqWvCMZ8yZpcLo5n2bUO03FLKBWg9vNxbSWP5CzLvPny0KGZ56R1SOXXlqSJpuVN368bGfMqEiAx7iGpqOaaLuyjfQDaZqOawp9ncu6CvAEmeJSrJeKS7HGRUnuk9z4SfKmeuABuVM6d64sEL3oImmbtJn+8Q/5A+vfX5blrH6dqnWLDI6Qe1laAkYGduEla4wUwf7kE/jpT+Wo9sMflmyc5dLcLG3Yd9pp3V/3fVi+XB6rVkFbm1yUpdOyasjz5HuMkSwb15WpJxKyHLClRZZY9egh63BjtZF9GB7WylmI58Ff/1rt0eD0dCjMKFR7GGoz+Ut9clNzRIZHSO69CV3Q2tslhW7+fDj9dAkwlmIcvtSl/+535d+/+50EnZVSSm263NQc/mKfpi83lb62y+LFchLXvXtpnzdETj5Z+gQcf7y81V177eZ319pSxkh5CCflwBYUew6bIICrrpIFDNbCDTfIdWjN6t9fulS//37Fdun2cknulyTzWIbCzELol2rVRYDH5izZl7Lkpkjx5OjYKMl9k6XJ2AGJlP/gB/Cvf8GYMVK5/sADN3+cVk6Wzz9fynTcc08NpsVVQWSovEwLcwtdC/CARDduvBEKBWmbns1KsKeGua68jvS1VCUXXSQt8C65JBR305wmB5uxXc94UxXhfeiRfiyNv8DHJAzJAzaxxf1dd8F770kK6BFHlGw8zz8v3f9AlmjtuWfJnloppRpCsCog80yG6OgosbFluBBctaqugzurHXKIdHk67ji5r/6Nb0gSfu/e1R5Z7Xj1VVnAMGWKND+5+uo6aORjjAR5Fi+u6G7ju8alK9ujaaIjoiVpUV8u4V9EtgG2YMlOzbLqklVkn8oS2TpCy2ktNH+5uTTBnVxO2oZsu62Uc//DH6QwyhYEdwoFSTk8/3w5eX78cb0g31ROs4M70JUK+KUQjUrtpBNOkNZlZ58t4W2lNtfkybLU78tfliK3IbC6/eq61o+rcMk8l6H9+naCVQHJ/ZO0freVSL9NCGJfcIHc3uzbFw49tGTjyecl7g1w//0a3FFKqS2RfjwNQPLgTQzYb65crkbX1my+4cOln80vfiGn7mPGSI0eX1eib9C8eZKls8susvDkuuukmHLNB3dW69ZNlixUkHEMqcNS2DZL9oXsxv9DFdVkgMcGltzrOVZduorMoxncfi4tp7bQfFwzkf4lSkp68knYYQc5ohx+OLzzjuS2bcH6lbY2eYprrpGnu+kmyaxUmy62bQx/kY+/rERH9GhUfhFnnim3A77yFVnTpNSmmjZNAjvjxknucFjW464+qmt8J9RsYMm/nscd6NLte91I7JnYtMJ9L78M//M/8v50330lbVf30ENSs/C666R1qlJKqc3jLfIovF0gsVsCt3uZulk6TkNFOGIx+NWvpAHJuHGyhHj77SWRVe/P/rs5cyShYNQoKQlyzjnSdO3kk8NzmloSkYiURqj0breKEB0bJTs1S9AZ3hdfTQV4rLXkZ+Rpu7yN9P1pnFaH5m800/L1lq4v3Vlt6VL5K9h/f0m7eegh+QsZPHiLnu6jj6Rr+hNPyDXgr35VZ39gFRIbHwMDuddzpXtSx4GLL5YAz113ybq51YXrlNqQt9+WYsrdu8ODD4arxVDx/c5E9EATVoW5BVb+ZiXByoDEbonNS/O99FJ53T3zzJpOEiXy8MOyrH31Ei2llFKbJ/NUBpM0JPYo453c5mYptthgxo+Hp56SFt++L+3Ud9xR7tfm89UeXfVYK6cExxwjgZ2//x2+8x0J9vzv/0qyS90pFEp6g2tzJCclwSPUWTw1E+ApvF+g/dp2Ou/sBAeavtJEyykt/1b1vEuslb+IMWPg5pvhZz+Dt97qUvr7zJnSgm7uXEl3P+WU0gy1ETktDtFRUfLT8thCCVMTjJE1CQ88IMVKd95Z7oortT7TpsGkSfLG8vjjWxz8LZcgHUjrzuq876mNyL6YpePWDgDcgS7RMZvxi5o6Vc5sDzsMWltLNiZr5a3uiiskbqmF1pVSavN5izy8OR6J3ROfLpcui969YcWKhoxqGCOBjLfekpKavi+1eYYOldXLH3xQ7RFWzpIl0jtm/HjYd18Jfp17rtyrvuQSGDSo2iMso7a2qt1cdXtJe/vcqzk55w6h0Ad4/CU+7be20/H3DoKOgNQRKVq/00psdKx0LcrmzZNKXieeKKHP11+H3/xGbmVuoalTpbVfNgtPP13S7rUNK75bHJuxpc3iWe2ww6QS2bBhcOSRsnRLl2ypz3rsMXkXTSTkD3vkyGqP6D8EqwKcVif0LRwbkb/UJ/NUBre/S/dzutN6aivG3cTf0803yx2DZFKq9ZfQsmXwyCNyGLzuupI+tVJKNYzsC1mIQ3xCmevjDBki20aKZnxGJCKBnTfflETqHXeU1ctDh0qp1GuvlRhYvVm+HK6/Xt6vBw6E//oviXNcfTV8+KGcHgwYUO1Rlpm18PHHUmi5ShJ7JKAAudfKcE1aAqEN8ATtAZ3/6qTtyjb8BT7JA5J0+1434jvGS9cZxvfhz3+W0OfUqRLufO65Lvctf/hhOOAAaTv9wguSFKK6LrJVhMiQCNkXsqXN4llt2DD5hf3oR7IMYqed5HWhlLVw2WXyjjpkiLxOQhjcAQiWBji9Qntob1jehx7t17VjIobUEanNu7v75JOSb7377jB79pqT+xJ54AHZnnce9OxZ0qdWSqmGELQHFN4pEN8pXt7sHZCb0SD1QRuc48ip2YMPSmPJCy6Q7tmnnip9CA46SKoxzJ5d7ZFuGWvhjTekz8+kSTKnU05ZUxr2rbfkUuXUUyGVqvZoK2TRIshkJJpXJW5fl8g2EXKv5rBB+Ipehu4qwOYtmacyrLp0Ffk38sQnxmk9s5XEHglMtIQHzHfegb33lov5/faTmhpnnCFHii647TbpWDt6tLSbDUHX5LphjCGxXwLbbsm+WKZ1j/G41OR57DFJv9pjD/jBD6C9vTz7U+HX0QEnnSTHh0MPlT/skC3LWs3mLf4nfumKzauS8D7waL+5HZMytHyzZdM6Za126aVSE65HD0mvKXFr3EWLpDbc2LGSdaqUUmrz5abnIID45yrQ3Wr77WWt0quvln9fNWT1Mq3Zs6Ufwdlnw4IFcho/apQEQt55R5rezJwZzgLNuRy89BL85S/Sx6NfP+mpcO65km177rkyt/fek2ydceOqPeIqeOst2VZ58vHPxbFtFm9u5Ys9b0yorgJs2rLq0lXYDkt02yjJ/ZO4PUpcgd734aKL4L//G5qapO7O175WksrHf/sbnH66xI3uu69Oi1pVWXTrKNFRUbLPZYlvH8dpLVOM8oAD5ADy05/CX/8Kd94J//d/cNxxWiW7kbzyihwfZs+GX/4Sfv7zLgeBy8n7wAMLkSGhOrQ3tCATkH4sDQG0nNSC07wZr59cTlpgHHQQ3Htvl5YNr8/pp0uQZ/JkPbQppdSWsNaSfyNPZEgEt2eZOmetraUFtttOVh2o/2AMTJggj//9X6mFOnmyXPotWwbf+pZ8X2urJOvvsIP8OMeOlUBQ797lfz/0PFlh9+67MGOGXHJMmybbQkG+Z8gQua84aRIcfHCd19TZHC++KNuddqrqMKKjopikIfdmjujIcBW+DNVVgM1anG4OqWNTRAaXYWjvvCMdsl58EY4+Gi6/XEKjJXDhhXIe/oUvSNOtMpyHq6LkwUkKVxRIP5Km+djm8u2opUXunp94olwFnXCCLNP54x/lXUPVr2wWfv1rOTMYMECKKU+aVO1RbVRhdgEiGuAJC5uzdNzagb/QJ3lwcvOCOyDZYuk0nHVWWd5UFi+WtPazztLsHaWU2lL+Yp9gWUBi1zJ2zvqs/feXyvjpdAOtzdkyw4fLafztt8u/r7gCpkyRTJjXXpP6NWuX3WxtlWygwYMlqNKvH/TpA716yc37lhb5kcfj0m/DcWQple9L3etsVpK/29pg5UoJKn3yidxMWbhQAjsffPDvXb779pVA03/9l1xi7LZbaJPFq++ppySLrcQZzZvLuIbo2Cj5N/NYz4aqe22orgKcHg4tp7SUvjhoEEiu289+Jn+Rt9wCxx9fkvCstXJj/5e/hK98RVr1ValrW8Nwe7gk90mSeSJD/u08sXFlbvmy667yLnDNNfCLX8Auu0gmz69+Vd79qup47DFZjvXuu7I0609/kuUxIWcDS35mnuiIaGmXs6ot4q/w6bipg2BVQNOXm4iN3czj1LRp8MMfyse7717q4QHy0vY8+Pa3y/L0SinVEArvSspFdHQFLwC+8AWpI/rII3LTWm2yMWPksbq7cRBI3Z533pFTv7lzpbHuhx9KIvcnn8j1XlckElITeNAguaw47jgJPI0aJWPp06fL02oMq1bBs8+uOT+qstjoGPnX8njzvFBl8YQqwIOh9MGd99+XrJ2nn5biOFdeWbKq29ZKUcrf/14OElddBW4FMjMVxHePk5+VJ/1gmsjgCE63Mi+bcV25Cjr+eEnX+uMfJVWrTx/Yeuvy7ltVxuzZkoZ3zz0wYoTk8x50ULVHtckKswvYTktsO+1xXW1BW0Dn3Z0E6YDmE5uJDtnMN/10Wu7Orlgha8x79Srp+FbfmPj97+GrX11Tr1MppdTmK8wt4A50Nz9Lsyv220/OQW+9VQM8XeQ40mdl2DD4/Of/8+u+L92rVqyQjJyODnmbzuVkOVUQSM6A60IsJsGcpibJBOreXd7Cm5p0GXRJ3Hef/NBD8pqPDI1ABArvFeonwGOMuRA4AsgDc4FTrLUri187DzgV8IGzrLWPdG2om8lauPFG+P735d/XXiuBnhL9dVkraXR//rOk/V1ySahLc9Qd4xiajmqi7ao2Ou7qoOXElk1vN9wVra3Sh/H735fqZhdfLOscjj9eKp9VeT2o2gIffSTLsa66SvJtf/Mb+eNOVDDVugRyL+UwrSZUbzCNyF/q03ZlG/iQ+kJq84M7ILnjK1ZI56zTTy/5GB98UAI8xx0n9zyUUkptGZu3+At9aZtcSZGIROgvvxyWLJE1PqosXFdiaZplEwI33CDr53bdtdojAcBEDJGtInjvh6vQcldDEo8C46212wPvAucBGGO2BY4HxgGHApcZYyqX27JsmZQeP/lkueB+4w1JsSlhcOcHP5DgzllnSZkWDe5UntvTpenwJvwFPpnJmcruvG9fWd+w666w1VZyxbTzzlKc+b77JNyvwu3DD+UPeNgwCe6cdhrMmSNLOWssuOMt8PDe90hMTFQm0KnWyWYtnXd3goGWk1uI77wF3VQefFD6nTY1SbbgDjuUfJxXXSU1Bf7+d9mNUkqpLeN9VMXmBt/5jhR9+dvfKr9vpSpt1iypifmtb4XqwjsyOIK/xMfmw9MuvUs/HWvtZGvt6pDVVGB1OagjgdustTlr7TxgDjCxK/vaZI89JoWX/vUvyT9/4gmJ9JWItXJN+Ne/rsng0ZS76omNixHfLU7ulRy5V3JVGEBMAgQffAB/+IMs3j3ySFni87vfSXaPCpdp06S2zvDhcufra1+TN43LLivZ8s1KstaSeTyDaTKVac+q1skGls57OvGX+DQf20xkqy042X/qKamrEATw0ENSybHEli+H+++X2vFaL04ppbrGXyQ39NwBVajRMHYsHHaYZJN3dlZ+/0pV0kUXSab9aadVeyT/xh0of/v+4vDc3C9l+OubwEPFjwcBH671tQXFz/0HY8y3jTGvGGNe+eSTT7Z87/k8/OQnUjOjtRVeeknqaZSwKI61UtPpkkvg7LOla7YGd6oveUCS6Mgo6YfT5N/NV2cQ3bvL62/ePKnNM3SoZIIMHgxHHSUtjvNVGpuShdK33Qb77CNZfXfeKUtf5s6V4tnDhlV7hFusMLOA94FHcp8kJhaeA1LJju01wFpL5rEMhdkFkgcniY7YwsjJk0/K9o03YO+9SzfAtTzzjCQYrqvOgFJKbUgjHdc3lf+Jj2kyOE1Vyij4+c9h6VK586xUvZo/H66/XjKcQ7Yc0e1bDPB8UkMBHmPMY8aYt9bxOHKt7zkf8ICbV39qHU+1zrwla+2V1toJ1toJfbZ0cePs2dJl5P/+D777XXj1Vdhxxy17rvWwVq7fL74YfvQjyZzX4E44GMfQ9KUm3P4unXd2UphfqN5gIhFZHvjkk1KO/4c/hKlTJcgzcKAEFZ5+WpdwVYK18Prr8jsYNEja3C9cKH+8H34onfWGDKn2KLskyAakH0nj9nOJ7Ryu4solObbXAGst6QfT5F7MEZ8QJ7HLFi7ve/VVWTs1YUJZW3/edx/07Al77FG2XSil6lSjHNc3R7A8wO1VxQ4re+wBhx8uWeNLllRvHEqV089/Lsuyzjuv2iP5D06rA450Tw2LjQZ4rLUHWmvHr+NxL4Ax5iTgcOBr1n7aRG4BsNVaTzMY+KjUgwekiMBOO0m3rHvukSUXqVTJd/Pzn0tm2JlnylaDO+FiYobmE5pxujt03NaB92EIil2NHr0mmHD//XDwwVIcbL/9JOBw+unSqUkze0pr1iwpmjx+vNRFuvxymDRJWonOng0//nFNtD3fFJmHM9hOS+qIFMbRg1KlWWvJvZQj/1qe+G5xkocmt/SJZM3UkiWy7rdMHntMGq7ssIOsLlVKKdU1QVtQ/k6uG3PhhdLW6dxzqzsOpcphyhS46SbJsBg8eOPfX2HGMTitDratTmrwGGMOBc4FvmitTa/1pfuA440xcWPMNsBI4KWu7Os/dHRIEeUTT4TPfQ6mT5faJ2Xw29/K47TTJINHgzvh5DQ5tHy9BafZof2W9upm8qwtGpW6GrfcIhdwt90myy9uvBEOOQR694ZjjoGrr4YFC6o92trj+3LwP/98CeqMGQO/+IWkKVx+OSxaJMvmDj44VEXZuio3PUf+zTyJvRNEBlShuGODs9aSvidNZnKGyNAIyUlJzJa+OUyeDDNmwBVXwJ57lnagazn3XGnXqpn8SilVGkFngGmq8oXBmDFy8+r66yWSr1S9yOelmPjgwXKeH1KmyRCkg2oP41NdvSq4BIgDjxZPbKdaa79rrX3bGHMHMANZunWGtbZ0eUtvvglf+Yrcqb/gArmYK2GtnbVdfLG8nr7+dTn31uBOuDktDi0nttB+Uzsdt3TQdEwTsVEhulXd3Cy9iY87DjIZqQZ/333SOeeuu+R7Ro+Wblz77y+BoJCtNQ2F+fPlZ/foo3Iys3SpHAP23lv+aI8+OpRR/lLxFnmkH0wT2TpCYu/a6vhVL7z3PPJv5UnsmSAxKbHlwZ3nn5elxYMHyw2LMpk9G157TW70jhtXtt0opVTDsL4FD0wiBBcH//3fcPfd8M1vyk3vOslUVg3uV7+S6/777pNrqJAyCYPNhCeDp0sBHmvtiA187TfAb7ry/Ot03XVwxhnQrZtc2O2/f8l3sdoNN0g79KOOkt3W0c3/urY6yNNxawedd3RiP2+3rF1xuSWTsm768MNlicZbb8md/McflxffZZfJ940aJXf1d9tN2rKPGye1fhqF70t2w5Qp8Nxz8OyzsiQTpNfzoYdKhtQhhzTECU3QFtBxewcmJbWndGlW5QWdAenJaZweDol9uhDcmTtXXrvdusHNN5d13dS998r22GPLtgullGosxWoAJhKC9+FkUspW7LGHtJH+5z/1rrSqbU8/LbWlTj4Zjjii2qPZIOMaCfiGRO1cJabTUgDnuuskqHPzzWVtaXzPPRIEP+ggWVHTSNfT9cBpKgZ5/tlB+oE0/nKf5P7J8F4MGwPbbSePs8+GQgFeeUVa3jz/PPzrX/LaB0gkpIjGTjvB9tvL/xk/vqyFWSsmm4WZM6WL0LRpUnj2tdfWtP/s2xf22kvW4U6aJPNuoBOYIBPQcWsHNmdpOUmWI6rKsjlL29Vt2E5L81eau3Zi/8tfyt/6k0+WvZPbnDkSP6rxuuJKKRUeq6/nwnIasssuckH8k59IwdAf/7jaI1JqyyxaJM1Rhg+XzPxaEJ74To0EeObMkRolb74py7EuuKBsS7JAAobHHy/HybvugngIkz/UxpmYofn4ZjIPZ8hNyeF/4tN0dBNOogYuiqNR6Qy3++7yb2vhvffgxRcl8PP66xJ5vOKKNf+nf39Zhz1yJIwYIQfFoUNh662l8EZYAiG5nBSenjdP5jRnjiy3fOcdyWgIimtYk0kJZJ1yimQu7babzCks86gwm7V03NKBv8yn+YRmIv1r4/BdT6y1pCensW2W5q81Ex22he3Q5cng5ZclO6/MwZ2PP5Z7Irvv3rB/PkopVXqrL0XC0zxHbhK++KIUXdt2W/j856s9IqU2TzYLX/oSrFolDVJaWqo9oo2yvl1zPAiB8F8h3Huv1CWIRKROyaGHlnV306fDF78o59sPPBDq5X5qExjHkPp8CrefS/rhNO1Xt9N0TFPtFaU1RoIbw4fDV78qn7NWAiVvvglvvy2ZL7NmSVRy2bJ////JpHTuGjgQBgyQTJg+faTAc48ekv3T2ioH0eZm+f54XG75R6MSUF29RjEIwPMk8yCfl1pC6bQUPm9vh5Ur5bFsGXzyiRSWXrxYovELF8rV5tricQlIbb+9RFbHj5ePR44sayC3lgSZQII7H/s0fbmJ6DZdCCyoLWI9S+bRDPlpeRJ7JLoW3AH5e33nHVkHXEavvy4lvTo7pY67UkqpEokABmw+RLfujZFiy3PnSr3Sp5+WZjRK1YIggJNOgqlTZZnhdttVe0SbxOYtJh6eO2jhvcr1fSkY9tvfwoQJ8kveeuuy7nLePIkftbZKwLBXr7LuTlVQ/HNx3H4uHf/soP26dpIHJYlPiG957YwwMEbWWwwZInU81rZypbyg582TgsQffijBlUWLZMnTkiUSGS8315VAUr9+EljacUfYaisZ8zbbSMBq0CAtcLUBQVtA+y3tBMsDmr7cRGx0iIqGN5DMUxlyr+SIT4yT2L+Lha3b2qRwIEiqaBndfz+sWCEJfyPWWzVPKaXU5jLGYJLh6p4DQFOTHPz32EMubJ55BsaOrfaolNowa+Gss+COO6QjxDHHVHtEm8x2Wpz+4bmWCWeAZ/lyyVJ45BEpFPbXv0rdkTJatkyOgbmc1Ljdaquy7k5VQWRwhNZvt9J5byeZhzMU5hRoOqKpPuuYdO8uNXp22mn935PPywt/xQoJCLW3y6OzUzJysln5nkJBIuqrl045jmTURaOSfZNIQColmT8tLVIwtkcPaVPerZsGb7rA+9ij4zapudN8QrNm7lSJzVtyr+SIjY+ROiTV9Sf84Q/h9ttledb48V1/vg2YNk3ujRx3XFl3o5RSDclpcQjaQhbgAcnYfvRR6S56wAFS62306GqPSql1sxbOOQcuvVRqR519drVHtMmstQTtAdGR4TlHD1+A5403pMXxggVw5ZVw2mll32UmI8uy5s+XY+G225Z9l6pKnJRD8/HN5F7OkXk8Q9sVbaQOSxHdNlrb2TxbIhaTrJoBA6o9ErUO+Rl5Ou/rxCQNLSe1aM2dKsq+kIUCxHYsQfbUCy/ATTdJFf9rrun6821ALidlfjRzRymlysPp4eB/EqYiPGsZOVLuWu+/P+y7r3Rq3X77ao9KqX8XBPBf/wV/+Yt0yv7DH2qqYKDtsFCQY0FYhGckIJkEu+8umQNPP12R4E4QSImfKVPknHvvvcu+S1VlxhgSExO0ntaK08Oh865OOu/oDOcdGNVwrG9JP5qm885O3L4urd9s1eBOldjAknkqQ/bZLLHxMSJDu/h7WLoUvvxlWaJ44YWlGeQGTJ0qqzO/+92y70oppRqS28slWB5gvRDV4VnbuHFyTRWJSJDnueeqPSKl1igU5IbXX/4i2c1//WtNBXeATwO8bu/w1A0NV4Bn4ULpmvPKK9IxpwLOO0/K+1x4oZx3q8bh9nZpOaWF5IFJCu8VWHX5KrJTs9ggpG/Squ75y33ab2gnNzVHfEKclhNbcFrCdZhuJLlXcmSfzRIZHiF5cLLrWX4XXij1r/75T1nCWGaTJ8t2n33KviullGpIbj8XLOHN4gHpsPrcc1IP8cAD4dZbqz0ipaQW6OGHww03wC9/CX/8Y80FdwD8RcUAT7/wBHjCdVt41ChZI1qhvuRXXSVZYKefLplhqvEYx5DYPUF0TJT0w2kyj2bITcuROiSl9U5UxVhryb+eJz05jXENTcc0EdtWiylXk7fAI/tMlsiQCM0nNHc9uJPLwd13S9OAHXcsyRg3ZPJkeX879ljo37/su1NKqYbkDpKLOm+BF+4OrUOHwvPPSxmMr34V3npLiv1rt1JVDbNnw5FHyvaaaySLp0Z5Czycng5OKjw3ZMMzEpCaIBUK7jzxBHzve1JY+eKLazJgqErI7eHSfHwzTcc2QQE6buqg4/YO/KUhviOj6oK/wqfj5g7SD6Q/LQSuwZ3qCjIBHXd2YGKG1OdTpanPdeONciJz/vldf65NcNFF8paqrdGVUqp8nG4OTjcH732v2kPZuF69pNjot74lXYoPP1yWDitVSf/6l3QQXbxY7kbVcHDHBhbvA4/IkHAFd8MV4KmQd9+V5VijR0vr2Ei4fieqSowxxMbEaD29lcSkBIX3C7Rd0Ubn/VqfR5We9SyZZ6XQt7fQI/X5FM1fa8bp1pCH5VDp/GcntsOSOjyF26cEdzeDAP7f/4Ndd5UT6jJbuVJuYhx1FLS2ln13SinVsIwxRLaJ4M3zsH4NLPGPx6WJzRVXyBvFTjtJG3Wlyi2fh5/8RDobDRsGr74KkyZVe1Rd4i/0sVlLdFi4Vn003JXEypXyunJdCSB261btEamwMRFDcq8k3c7sRnxCnPz0PKsuWUX6kTRBuwZ6VNdYa8nPytP2tzayT2WJjojS7fRuxD8Xb7xObiEUdAZ473sk9k6U7g170SL46CP4xjcqki76zjvgeXD88WXflVJKNbzoyCg2Z/Hm10AWD8j70He+Ix1mkknYbz8pSprLVXtkql7NnCmNlP7v/6Q2ygsvyLLBGpeflQcHIsPDlS3SUAEe35cT3rlz4a67YJttqj0iFWZOk0Pq0BStZ7QS2y5G7uUcq/66ivRDafyVunRLbT5voUfH3zvovKMTHGj+ajPNxzbjtDbUoTi0/CU+HTd1ABAdXsK7MVdeKdvu3Uv3nBuweLFs9QaGUkqVX3R4FKKQn5Gv9lA2z847w2uvyRKZ//1fWTbz6qvVHpWqJ54nDSZ22gk++EBqEV52GSQS1R5Zl1lrKcwoEBkWwUmE6zw+XKMps5/+FB55RF5X2g5dbSq3u0vTEU0S6Nk+Ru61HG2XtNF5dyc2XwPpuKrqvEUeHbd30H5tO/5Sn9RhKVq/01raIILqssxTGfxPfJIHJIkMKtHdmCeekEKWxx4LJ5xQmufciBdflKXHFWpGqZRSDc1EDbGxMfIz8thCjZ0XNjdLsbZ//Uvq8UycCD/+MXR0VHtkqta9/rpk7ZxzDhx2GLz5pqwdrxPe+x7BqoDY+PDVzWyYAM+tt67JCjvttGqPRtUit4dL0+FNdPt+N+IT4+TfzeMv8vEX++TfyWt7dfUfvAUeHbd10H51O958j8R+iU+X/hlHl2OFhQ0smacyFGYVSOyZILFHie4s+b6cKG+9NVx/PTjlf8t95x146CE5Z4+F75xDKaXqUmzHGORqMItntcMPhxkzpADzRRfB2LFwxx1g9dxWbaYVK+Css6Rr6AcfSMHbu+6qu5aeuddymITUbw2bhgjwTJ8Op54qWTt/+Uu1R6NqndPqkDo4RbcfdMPp4WA9S+c/Oln111Vkns1onZ4Gt7rGTvsN7bRf1463wCOxb4LWs1pJ7p3ExDSwEzbeex7ZZ7NEtooQ372EnRwnT5Y7WL/+NaRSpXveDfjqV2HaNCmnoJRSqjIiQyI4vR1yL+WwtRoU6d4d/vY3aafeuzccd5zU53nllWqPTNWCQkGWyYwaBZdeKnWeZs2S11Gd1ZgM2gIK7xSI7RDDRMM3t7oP8CxfDkcfDT17wj/+AVFdEaFKxEk4OK0OkUERmo5twu3lkn0qy6q/rKLj9g7ys/K10VFBlUSQCchOzdJ2aRudd3QSrApIHpSk21ndSO6TDN36XCVsYMm+mAUjNZFK9nvyPOlSAnDggaV5zk3w+utw8MGSEa2UUqoyjDEkdkvgf+zjzauRYsvrs8ceEtS54gopjrvLLrLEeM6cao9MhZG1cOedsN12cMYZMH681HK67LKK1R6stOyLWbAQn1jCm4IlFK6SzyUWBNK0ZMEC6QDYr1+1R6TqVWxMjNiYGP4yn9zrOfJv5Cm8W8CkDLHxMWLbxXAHuNolqc5Ya/E+8MhPy0tatgfuVi5Nk5qIjo3qMqwakJ+Wx3vPI7FXorTZVZddBvfdJ+vN+/Yt3fNuwIIFsh01qiK7U0optZbYdjEyT2ckI3SbSG2f87muZGCccAL84Q/wpz/JnfKTToLzz5c216qxWQsPPAAXXCDFuseOhXvvhSOOqLuMnbUFHQG5V3PExsdwu7vVHs461XWA5ze/gQcflCwxLTapKsHt5ZI6MEVyUpLCnAL5N/LkXs2ReymH09Mhtm2M6Ngobj8N9tQyf4VP/s08+TfzBMsDiEFshxjxneNE+tf1YbWu2JwspzNJQ3JSsrRPPmuW3Lm6++7SPu8GvPGGbLU9ulJKVZ6JGBJ7Jsg8nMGb6xEdUQfLBlpbZZnxmWfC734nS7huuAG+9jU491zYdttqj1BVmu/Luc1vfytpw9tsI3UGv/51CQzWuexzWfAgsXd4O4HV7ZXIY49JQPHrX5fCykpVknENsdExYqNjBBlZp5l/O0/2+SzZ57I4PR2io6PERsdwB7ma6VED/JX+p79H/yMfgMjQCIm9EsTGxrS2To0pzC3QcWcH5CAyvMRvhZ4Hjz4qacoV9NBDcm6lGTxKKVUd8Z3j5F7MkX4sTeuw1vo5v+vfXwqZnnOOdK258kq48Ub4whfg7LOlVo/euKxvnZ0S3PvTn2S53siRcM01slymQWqg+Mt8yd7ZKYbbK7zBrLoM8Hz0kRSaHDtWlo/q8UZVk5N0iO8UJ75TnKAzoDCrQH5mntyLOXJTcpiUITo8SnRElMiwCE5Ka7WEgbUWf4lP4d0ChVkF/EUS1HH7uyQPSBIbF8Pppr+rWpV7JYcxhtSXU0SHlfjE5KqrYPZsuPDC0j7vBixdCpdcItk7ffpUbLdKKaXWYlxD8oAknf/sJPdqjsQu4b3Lv0UGDZIL/PPPlzedSy+F/feX+itnnikXYM3N1R6lKqU5c+SC+pprYOVKmDhROqx96UsNkbGzmrWW9CNpiEJy3xJnfZdY3QV4PE+Wi3Z2wtNPQ1NTtUek1BpOk0N85zjxnePYrKUwp0BhdkGWc70prTXdgS7RYVEiQyNEBkdCWZ29XgXZAO99j8LcAt5cj2CVdERzB7kk90/K8rqejfNmVq+stXjzPaJjo8TGlri9ZSYDF18sJz2HHlra596AqVNlu3BhxXaplFJqHaJj5IZd5skMsdExnNY6vBnUuzf8v/8ny7RuuQX++lep2fPjH8vyrW99C3beWe+y16pcTurpXH21ZCRHIhLQOessKcLdgL/Xwgy5NkgenMRpDvffdN0FeH71KymofOONksGjVFiZRLEA8/gYNrD4H/kU5hYovFcg+3wWngNciAyKEBlSfAyOYOKNd1AtF5uzeB96FOYX8N73JEvHAjGIbhMlsXeC6IgoTku4D+Rq8xRmFrA5S3RoGVKKzzsP3nlH0pjjleuucNVVsr388ortUiml1DoYY0gdlqLtb22kH0zTdFxT/dZdTCbh1FPhm9+EKVOkRs/110vGx/jxUpT5hBMk80eFm7Xw0kvw97/DrbdKK+ohQ+CXv5SA3cCB1R5h1QSdAemH07gDXOK7hLNz1trqKsDzxBNSB+zkk2U5oFK1wjiGyGAJ4CT3TUrg4QOPwvsFvPnemoAP4PSR1uyRQRHcAS5uXxfj1umJQwlZawmWB3gLPfyFPt6HHv6SYkDHkSydxJ4JIsOKgTT9mdYlf6VP+qE0bj+X6LgSB3hmz5YU5uOOgxNPLO1zb0TPnnKDbeTIiu5WKaXUOrg9XZKTkmQezZCfnie+Y/gvCrvEGMns2GMPqdVz220S6PnJT6Ruzz77yHvjl76kbY3DxFqYPl06pN12G7z3HiQS0gH05JPhwAMbahnWulhrST+QxuYsTUc01URdrboJ8HzyiRRUHj1aloQqVctM3BAdGSU6Ui5Abd7iLfDwPvTwFnpSx2eaLOnCBbePi9vfxe0nAR+3j4vT1LhZJ9az+Et9/CU+/mIf/2Mff5GPzVn5hhhEBkqB5E8zo7RIckPIvpDFZixNJ5XhTfrEEyEWk9oEFeR5ch79xS/K7pVSSlVffNc4hXcLpB9OExkcwe3dIBfK3bvDd78rj9mz4eab4fbb4XvfgzPOgL32kgDCF78II0ZUe7SNx/Mk2+ree+Gee2DuXHAcqaX0859LEK5bt2qPMjTyr+UpzCqQPCCJ2682/obrIsBjrWQGLlsmXUS07o6qNyZmiA6LfloM1lpLsCLA/8jH+9jD/1iKAX8a9AFMyuD2cnF6O7g9XZyeDm4PF6eHUxfBDGsttrP4c1juEywLJKiz1JfW5cVYDi6SrTE+SmRABHegBMBqIQKvSssWLPnX89L9oNQn2itXSmrzz38uxSYr6I47ZPu5z1V0t0oppTbAGEPTUU20XdlG552dtHyzpfHqKo4cKbV6LrgA3noL/vlPabF99tnyGD0aDjtMatbtvTekUtUecX36+GOYPBkefhgeeUSWX8ViEtQ591wJuGmHhv/gLfJIP5ImMixCfPfaycKriwDPZZfB/fdLRuAOO1R7NEqVnzEGt6eL29MlNl5u2VtrsR0W/5Ni5spSn2BpsWtXOv/v/z9lcLo5OK2ObFscTLPBaXZwmhxMk8GkTNWCINa32LQl6AgkiNMRELQFBO3F7cpACiAX1vpPDjg9HNzeLrExMclk6ufi9HI0mKOwQbH7QQCx0WVIc3ntNQgCOUGtsJtuku33v1/xXSullNoAp9Wh6egmOm7pIH1/mtRRqfqtx7MhxsjNj+22k5ou8+bBv/4FDz4oxeP+/GcJOOy5J0yaJG3XJ06saC27urJ0KTz7LDz1FDz+OLz9tny+b1844ghpb3/IIdDaWtVhhlmQDuj8ZyemSQK1tfR3W/MBnhkzpGD7YYfpya1qbMYYTIvBaXH+o+1zkA0IlgcEKyQ44q/0CVYF+Mt8CvMKkF/PcyYMJmlkGy8+YgZiYKIGEzEQkbagOIALGDDIQdBiIUCyaXwJ3ODJEiqbt1CQQsc2b7FZeQSZAHLrGU/K4LQ6uL1cosOjON2LWUk9HZzujtbNUeuVn5aX7J2dY0SGleGtr71dtj16lP65N2LuXOlKq+dpSikVPtHhURKTEmSfzOL0cUjuFe4WyxWxzTbSkemssyCdlmDE5MlSUPWCC2R5RjwOu+widX122w123bWhC/2ul+9Lc4epU+Xx/PMwc6Z8LZmUoNk3vgEHHQQ77ijLsdQGWc/S+Y9OgvaAlpNbaq7sRU0HePJ56cTX0gLXXdeQHduU2iROwsEZ6MB63hdtrpgl0xFgOyxBOsCmLTYjjyAbyPcsl6wZm7fYgv33DJrNES0GiGLFgFEcySLqUwwoJc2nmUROUzG7qMWRgJJSm8lb5JF+KA0RSB2UKn1G1wsvwA9+IG9CAwaU9rk34u234d13Ydiwhq+DqJRSoZXYM0HwSUD2ySxu9zXZ1wpZlnXIIfIAWT707LPyeO45+NOfoFA84RwwQNYj77yzLNvYfnt5A2yUoEU2K8Gb6dPh9dfXPDo65Ovdu8Puu0tAZ++9JUCmWVCbxVpL+v403gceTUc3ERlYe+GS2hvxWi64AKZNkxpRWpBdqS1n4gY37uL22rwrRGuLGTq+LIHBZ03tm9UcJKvHNZLh41BTaY6qtllryTyewSQNLSe2lKf+1A9/CPPnS5p5he8uPvCAbL/73YruViml1GYwxpA6IkXQFtB5bycmZf4j21oV9ewJRx4pD5Cgxuuvw8svy+O112RpVxDI11MpGDMGxo6Vmj6jRkntn2HDJOBRa4JAaubMnStFqt99VzJ0ZsyQz6097x12kG5Xu+wiS9pGjWqcYFcZrD5nzL+ZJ7FfomYDsTUb4Hn+efj97+Fb35Ii7EqpyjOmGLRx1yzLUipMci/k8OZ5JA9KlqeDydKlcsK5446SUlph770n24MPrviulVJKbQYTMTQd10THDR103NFBy9daiGxVs5dilZNISFbK7ruv+Vw6LUWbVz/efhueeUY6dq2te3cYOhSGDJFgSTwON9wA/ftLdkCfPtCrl+yj3KyVTJtPPpHH4sUSyFm4UB4ffig3i+bPl6DWatGodBvbfns44QQYN04COyNGaOpuiWWfzZKbkiM+IU5irwq8JsqkJo8qnZ1w0kmw9dbwxz9WezRKKaXCKMgEZJ7KEB0TJT6xTCnKTz8t28svl/XCFfb223KzrsKNu5RSSm0BJ+HQ/LVm2m9op/2WdgnyDK7Jy7HqSqUkY2XixH//fDoNc+bIY+5ceP99ebz3ngRUfF8yXtb1fD16SHvw1lZ5P0+l5JFISGAoFoNIRIIqxkjAxlp5Ts+DXE4e2ayMo6NDHqtWSafNFSvWLDVbmzESbBo8GMaPh8MPlxpFw4dLJtLWW8t+VVlln8+SfTpLbPsYyUOTNb3aoCZfLeeeK3+nTz5ZlfNppZRSNcB734MA4jvHy9dJbckS2Va49g7IeeRzz8nNSc3IVkqp2uA0O7R8o4X2G9tpv7md5hOaiQ7R5VolkUpJpsv22//n1/bbT5Y3XXutZM4sWSKZNEuXSt2fFSskGNPeLo+PP5ZATTYrhV/zeQnk+L4EdoyRh+tKACYWk0BQMinjaGqS7KBhwyRw1KOH/Lt3b8kc6tdPzh369ZMsHVUV1lqyzxaDO+NjpI6o/U53NRfgefJJuPRSqWe5777VHo1SSqmwyk7JYpoM7oAypTB3dsItt0jdnSFDyrOPDfjOd2S7ukyBUkqp2uC0OrSc2EL7Te103NxB87HNREfoRX7ZOY4sbRoxotojUSFgrSXzWIbc1Byx7YvBnXLdEKygmrrn19EB3/ymZKv99rfVHo1SSqkwstaSeSaDv9AnsWcCJ1WGt7r33pO7cs89B7/+dcXbOFoLDz0E++wDf/5zRXetlFKqBJxWh5aTWnB7u3Tc3kFueq7aQ1KqYVjfkr43TW5qjvgucVJfrI/gDtRYgOe886Tu1LXXSuabUkop9Vn5t/Jkn87idHOI71Cm2jv33y/p3XffDaecUp59bMBHH8nuv/Sliu9aKaVUiThNkskT2TpC+r40macz0qFUKVU2QSag45aOT7tlJQ+p7Zo7n1UzAZ7nnoNLLoEzz4S99qr2aJRSSoVVsERaiLZ+uxWTKNMbdv/+sq3SuvnOTtm2tlZl90oppUrExA3NJzQT2yFG9pksnXd1Ygsa5FGqHPylPu3XtuN96JE6MkVy7/oK7kCNBHiyWWmHPnSoLs1SSim1ftaz5F7PSevZMiXvADB5smyrUHsHpOwPwCOPVGX3SimlSsi4htQRKZL7JynMKNB+XTv+Cr/aw1KqruRn5Wm7pg2btbR8vYX49uU8Uayemiiy/Otfw6xZciLb3Fzt0SillAqrwnsFbMaS2CtRvjsy8+fDNdfAt79dtf7kq7uzf/WrVdm9UkqpEjPGkNgzgdvXpfOeTtqvbid1ZIrYqFi1h6ZUTbOBJfNkhtwLOdwBLs3HNuN0q4k8ly0S+pm9+Sb8/vdw4olw8MHVHo1SSqkwy72QwzQZIkPLeP9i5kzZHn98+faxEdOmSV3nPfes2hCUUkqVQXRklJZTW3C6OXTe3kn60TTW1yVbSm2JYFVA+43t5F7IEds5RsvJLXUd3IGQZ/AEgbSB7d4dLrqo2qNRSikVZrlXc3gfeiT3T2IiZVxP/fLLst1ll/LtYyNWroRIBJLJqg1BKaVUmbg9XVq+2ULmUWnh7L3v0XRUE24ft9pDU6pm5N/Kk34wjbWWpqOaiG3XGNlwoQ5fXXklTJkiwZ3evas9GqWUUmEVdASkH04T2SZCfGIZ11TncjB9ukRXYtU5UVi1Srq0FwprkomUUkrVFxMxpA5L0fSVJoJVAW1Xt5F9MatdtpTaiCAd0HFnB513d+L0dmg9rbVhgjsQ4gyexYvhpz+FSZPgG9+o9miUUkqFWe6lHASQOjSFiZYxe+foo+Ghh+D006sW4PniF+GZZ+TjnXaqyhCUUkpVSGx0jMigCOn702QmZ8jPzNN0eBNub83mUWpt1loKMwukH05LPcb9EiT2TGCc+uqStTGhDfD8+MeQycDll0udAaWUUmpd/KU+uVdzREdFy3/CO2cO7LgjXHppefezAW1taz52Qp2Hq5RSqhScZoem45rIv5EnMzlD25VtJPZKkNgjUd4lyUrViGCVZHIX3i3g9ndJfTVFpH9oQx1lFcpZP/003HQTnH8+jB5d7dEopZQKK+8jj45bOsCF5IFlLkizfDnMnQvnnlvVOw+dnbLdf/+qDUEppVSFGWOI7xAnOjxK+pE02aez5N/Kkzo0RXRYtNrDU6oqrG/JvZQj83QGLCQPSBLfLd5wWTtrC12Ap1CAM86AoUPhZz+r9miUUkqFWf6NPDZjaf1eK26vMmfvvPKKVP/fa6/y7mcjFi+GUaPglluqOgyllFJV4DQ7NB/TTGEHWYrScXMH0TFRkgcmcXvosi3VOApzCqQnpwmWBURHRkkemsTtrn8DoQvwXHIJvP023HMPpFLVHo1SSqkwC1YFOD2c8gd3cjn40Y+gW7eqFr7xfXlvnDAB+vWr2jCUUkpVWXRElNbvtpKdkiX7fJbC7ALxiXESeyVwErp+V9Uvf4lP+rE03lwPp6dD8/HNREdqFttqoQrwFApwwQVw6KFSRFIppZRaH2sthXmFynRGuPZamDEDHngABgwo//7WY+ZM+Phj2HPPqg1BKaVUSJiIIbl3kvgOcTJPZshNyZGfliexV4L4hLjW51F1JVgVkHk6Q/6NPCZuSB6YJL6Lvs4/K1QBnoULIZuFv/xFCysrpZTaMO99DwpUpojeE0/A4MHw+c+Xf18bMGOGbKN6o0oppVSR0+rQdGQT8YlxMk9kyDyaIftiluReSWI7xjCuXlip2hW0B2Sfz5J7LQdAfNc4iT0TOCnNVFuXUAV4li2Dc86R2gJKKaXU+gTpgI47pLhyRTJ4nnlG0kurbOZM2R51VFWHoZRSKoQiAyK0fK2FwvsFMk9mSD+YJvt8lsSeCWI7xDTTQdWUoC0gO6UY2PEhtmOM5N5JnG4a2NmQUAV4xo6VzllKKaXUhgRLA8hDdGQUE6vACWtnJ/TsWf79bMQ++8j2uefg6KOrOxallFLhFB0aJXJyBG+uR+YZCfRkns2Q2DVBfOc4Jq6BHhVe/jKf7JQs+el5sBDbPkZi74QWEd9EoQrwpFLQ2lrtUSillAq7zLMZTMqQPLjMrdEB7r9fAjz9+5d/Xxtx/fWy3Xbbqg5DKaVUyBljiI6IEhkewZvnkX0+S+axDNlns8R2jpGYmMBp1UwIFQ7WWvwPfbJTsxRmFSRDe6cYid01sLO5ShLgMcb8GLgQ6GOtXVr83HnAqYAPnGWtfaQU+1JKKdXYrLV48z3iE+K4PSvwpj9limyPO678+9qIZ56Bvn1h9Ohqj0QppVQtMMYQHRYlOiyK95FH9oUsuak5ci/miI6JkpiYwB3sYrQAqqoC61nyb+fJvZzDX+RjkkaKhO8Sx2nWAOSW6HKAxxizFXAQ8MFan9sWOB4YBwwEHjPGjLLW+l3dn1JKqcbmzfXAB7dfhe7oJBKydat/B2ngQIhVoOSQUkqp+hMZGKH5y834K31yL+fIv56nfUY7bj+X+OfixMbHdPmWqgh/uU/utRz56Xls2uL0ckgdlpJaUVF9DXZFKTJ4/gScA9y71ueOBG6z1uaAecaYOcBEYEoJ9qeUUqpBWWvJPJnB6eEQG1ehSMc990iRuK22qsz+1iOXgzlzYO+9qzoMpZRSNc7t7pI6KEVy3yT5N/PkXsmRfjBN+rE0sXEx4jvGcQdpVo8qLVuwFGYVyE3L4c3zwEB0VJT4hDiRbSL6eiuRLgV4jDFfBBZaa6d/5hcyCJi61r8XFD+3ruf4NvBtgCFDhnRlOEoppUKiXMf2/Bt5/I99UoenKtcNpFCAfv0qs68NmD8fliyBceOqPRKlVCPSc/b6Y2JGMnd2juEvLGZUvJUn/3oep5dDbPsY8e3i2rVIbTFrLf4Cn9wbOQpvF7A5i9PNIbFvgviOca0DVQYbDfAYYx4D1lVZ8nzgZ8DB6/pv6/icXdfzW2uvBK4EmDBhwjq/RymlVG0px7Hdepb0Q2ncwS6xHSqUvXP55fDmm/CnP1Vmf+vx0kuw667y8aB13i5RSqny0nP2+mWMITI4QmRwhNQhKfIz8uSn58k+mSX7ZJbIkAixcTGiY6M4TXpBrjbMWou/xKfwdoH823mClQFEIDYmRmyHmGbrlNlGAzzW2gPX9XljzHbANsDq7J3BwGvGmIlIxs7aueyDgY+6PFqllFINy1voQQHiO8YxTgVODBYvhh/8AJJJOOOM8u9vA8aOlcLKs2bBvHlVHYpSSqk6ZuKG+E5x4jvF8Vf4ktHzVp70Q2l4GCJDI8TGxoiOjmoRXPUpay3+xz6FdwrkZ+YJlgVgILJNhMQ+CWJjtL5TpWzxEi1r7ZtA39X/Nsa8D0yw1i41xtwH3GKM+SNSZHkk8FIXx6qUUqqBFWYVwKFytXfuv1+WZz38MESjldnnerz8sgR3Wlvh7LOrOhSllFINwu3hktw7SWKvBP5in8IMuXhPP5iGB5GM2tExoiOjOL0dzcpoMNa3eB94FN4tUHi3IJk6BiJbR0hMTGjGV5WUpE36Z1lr3zbG3AHMADzgDO2gpZRSaktZaynMKeD2dzGxCp1AdnTIdvz4yuxvAwoF2ba1aRctpZRSlWWMIdI/QqR/hMSkBMGSgPysPIVZBTKPZ8g8Ls0PosOjREdEiWwdqdx7taqooC2gMLdAYU6BwrwC5AAXosOiJPZKEB2lQZ1qK1mAx1o79DP//g3wm1I9v1JKqcbkr/DJPJEhWBaQ+kKqMjtNp+HSS+Xj1tbK7HMD0mnZGgO+3i5RSilVJcYY3H4uyX5JkvskCVYFFGbLBX9uWo7cKzlwIbJVhMg2EaJDo7gD3cosrVYlZ7OWwgcFvHkehfcKBEsDAEyLIbZtjOiIKNFhUQ3ohUhZMniUUkqpUsm/macwo4BpNZUrrvzWWzB7tnycSFRmnxswebJsTzkFevSo7liUUkqp1ZxuDvEJceIT4livuGTnvQLee54UaSYLsWLAZ0iE6JBiwKdSnTDVZgk6A7wPPXnM9/A/9qVVUgQiQyLEd4wTHRbF6atL8sJKAzxKKaVCLVghd4tSB6cwbgVOJqyFiy+Wj885p/z72wSrV4tpBy2llFJhZSKG6DDJ6IBisGC+h/e+R2F+YU3AxwV3gCuduwZGcAe5ON00YFBp1pduV/5CH2+hh7fAI1gu51y4EBkUIbFXgshQ6bCmQbnaoAEepZRSoWWtJf92nujoKLGxFcremTYNbr4ZvvIV+N3vKrPPTdCzJ/zqV9UehVJKKbVpnCaH2LYxYtvK+3fQGeAtKGaHLPDIvZIj5+UAMCkjQZ/+Edz+Lm4/F6enBn1KxRYs/ic+/sfy8BZ5+It9KC77NilDZLBk6ESGRHAHaJZVrdIAj1JKqfAK5I5gRdd2jxgh23wenHAUCmxrg3i82qNQSimltpzT5BAbHSM2WgI+/5ZBssjD/8gnOy8LxSQSouD2cXH7urLt7eL0djTbZwNsweIv9wmWBvhLffn5LvElG9oWvykOkf4R4rvEJYNqoIvTXX+m9UIDPEoppUIpWBXQdm0bNmeJjq1gm/K//U22+Xzl9rkRL74Ihx1W7VEopZRSpWNcQ2RAhMiACHHkLob1ipkmi4uPJT6Fdwvkp631nhwBp4eD21OyfNzuLk4PB6d7MfhTx5kn1lps1hKsDNY8VgQS1FkeEKwK1nyzKf6c+rjExsVw+xUzo3poMKeeaYBHKaVUKPlLfGyHJbGvtN2smJtuku0NN1RunxsRj8OSJdUehVJKKVVeJrIm6LO2IC0ZKcHSAH+ZT7BMtoU5hU+XGX36HCmD0+rgtDqYFoPT7BB0BBjH4C305OspB2KEJtBhA4vNWGzaEqQDbKcl6AiwHbIN2iV4E7QFUPj3/2sSBqeHQ2SrCM4ODm4vyXRye7mYaDjmpypHAzxKKaVCyfvYAyCxa6JyJ2CrVsH06XDMMdCrV2X2uREffwwffACnnlrtkSillFLV4aQcnCEODPn3z1trse0Wf6Uv2Syrio/2YnbLhwE2YwmWSWZL+7Xtaz0pmKTBxA0mUXzE1jyISsCJSHHrIu3eHfm/ABjWLH2yxYcvARt8yUjCk6VTtmAhDzZvsTnJxLG5YmAnZ1knB0yTwWmRTJzo8ChON8lUcroXH8lwLCdX4aABHqWUUqFk01bursUrePcplYJu3WQbkrt6fvHOpF3PuZ9SSinVqIwxmFbJ2Pls8Gc161kij0SwgaX5+GbJkElLYCXIBGsCLVn7aYaMzRcDMl4pBokEi6JrBZDiMuZPA0sJ82lmkWkqbpvlc2HJMlK1QQM8SimlQslf5OO0VPiuVFBcux6ik6lBgySZaOHCao9EKaWUqj2fZuFgiI7cvCXf1komDp4UhSYAgmKGzto3Xow8jClm+LhSY4hIcatUhWiARymlVOh4H0kb1cSkRGV3/PLLskzrmGMqu98NuPpqWLYMxo2r9kiUUkqpxmKMBGlWB4iUCjtdsKeUUipUgo6Azrs6wYHYtrHK7nzBAtkOHlzZ/a7H88/Dd74Du+4KJ59c7dEopZRSSqkw0wCPUkqpUMk8mSFoC2j+ejNuT7eyO58xAxwHttuusvtdjwcflFVjF10kpYGUUkoppZRaHw3wKKWUChXbaXGaHSJDKryK2PPgySelJ3m0gm3ZN+C442R77LHVHYdSSimllAo/DfAopZQKDWst/ic+Qad0saioiy+G556D5uYK73j9/vhH2QaBdtFSSimllFIbpgEepZRSoVF4t0CwMiB1aAoTq3Axw5dfhlgMpk+v7H434OGHZfs//xOqxl5KKaWUUiqENMCjlFIqNHIv5zDNhtgOFS6uDDBlChx2GAwYUPl9r8fWW8t2woTqjkMppZRSSoWfBniUUkqFgvVkeZbb38U4VUhXcZxQLc8CWLQITjgBdtqp2iNRSimllFJhpwEepZRSoZB9JovtsCR2TVR+554HuZwUuwmRTz6BhQurPQqllFJKKVULNMCjlFIqFHKv5oiOiRIdVoUOVpdeCh99BJ/7XOX3vR6FAmSz1R6FUkoppZSqFRrgUUopVXVBZ4DNWtx+bnUGsLqC8VZbVWf/65DJ/PtWKaWUUkqpDdEAj1JKqarLPp8FA7GxVSiuDHD77bDNNlJkOSSmTZPtj35U1WEopZRSSqkaoQEepZRSVecv9XH7ubh9qpDB09kJr70mAZ6Wlsrvfz1GjJDtBx9UdxxKKaWUUqo2aIBHKaVU1QUrApyWKr0lffObUuzmG9+ozv7XI1ZMZioUqjsOpZRSSilVGzTAo5RSqqpszhIsD3AHVyF7x1q44w75+LjjKr//DZg5U7ajR1d3HEoppZRSqjZogEcppVTVOX2c6izPMgYOPBAGDwbfr/z+N6BHDxlW797VHolSSimllKoFxlpb7TF8yhjTDsyq9jgqoDewtNqDqACdZ31phHk2whwBRltrK1ZsRo/tdaUR5gg6z3rTKPOs2LFdj+t1R+dZXxphno0wR9iC43qkXCPZQrOstROqPYhyM8a8ovOsHzrP+tEIcwSZZ4V3qcf2OtEIcwSdZ71ppHlWcHd6XK8jOs/60gjzbIQ5wpYd13WJllJKKaWUUkoppVSN0wCPUkoppZRSSimlVI0LW4DnymoPoEJ0nvVF51k/GmGOUPl56s+1fjTCHEHnWW90nrW9r2rSedYXnWf9aIQ5whbMM1RFlpVSSimllFJKKaXU5gtbBo9SSimllFJKKaWU2kwa4FFKKaWUUkoppZSqcaEI8Bhj/scY84YxZpoxZrIxZuBaXzvPGDPHGDPLGHNINcfZVcaYC40x7xTnercxpvtaX6uLeRpjjjXGvG2MCYwxEz7ztbqY42rGmEOLc5ljjPlptcdTKsaYa40xS4wxb631uZ7GmEeNMbOL2x7VHGMpGGO2MsY8aYyZWXzN/qD4+bqZqzEmYYx5yRgzvTjHXxY/X/Y56nG97uapx/Ya1wjH9kY4roMe2ytBj+11N089rtcwPbZv5jyttVV/AK1rfXwWcEXx422B6UAc2AaYC7jVHm8X5nkwECl+/Hvg9/U2T2AsMBp4Cpiw1ufrZo7F+bjFOQwDYsW5bVvtcZVobvsAOwNvrfW5PwA/LX7809Wv3Vp+AAOAnYsftwDvFl+ndTNXwADNxY+jwIvAbpWYox7X626eemyv8UcjHNsb4bhenIMe28v/M9Zje53MU4/rtX28K85Dj+2bMc9QZPBYa9vW+mcTsLry85HAbdbanLV2HjAHmFjp8ZWKtXaytdYr/nMqMLj4cd3M01o701o7ax1fqps5Fk0E5lhr37PW5oHbkDnWPGvtM8Dyz3z6SOCG4sc3AEdVckzlYK1dZK19rfhxOzATGEQdzdWKjuI/o8WHpQJz1ON63c1Tj+01rhGO7Y1wXAc9tleCHtvrap56XK9xemzfvHmGIsADYIz5jTHmQ+BrwH8XPz0I+HCtb1tQ/Fw9+CbwUPHjep7navU2x3qbz8b0s9YuAjnIAn2rPJ6SMsYMBXZCIuV1NVdjjGuMmQYsAR611lZsjnpcr9t5rq3e5llv89mYujrera2ej+ugx/YK02N7bc+znuayKerueLc2PbZvfJ4VC/AYYx4zxry1jseRANba8621WwE3A2eu/m/reKpQ93Xf2DyL33M+4CFzhRqb56bMcV3/bR2fC+0cN0G9zadhGWOagTuBH37mzmRdsNb61todkbuPE40x40v13Hpcr5/jOuixvaje5tOQ6v24DnpsLwU9tjfMsb2e5tLQ9Ni+aSIlH9V6WGsP3MRvvQV4ALgAibButdbXBgMflXhoJbWxeRpjTgIOBw6wxYV01Ng8N+N3ubaamuMmqLf5bMxiY8wAa+0iY8wAJKpc84wxUeSN4mZr7V3FT9flXK21K40xTwGHUqI56nFd1MNxHfTYXlRv89mYujveNdJxHfTY3hV6bN+gmpvnBtTTXDZFXR7v9Ni+6fMMxRItY8zItf75ReCd4sf3AccbY+LGmG2AkcBLlR5fqRhjDgXOBb5orU2v9aW6mud61NscXwZGGmO2McbEgOOROdar+4CTih+fBNxbxbGUhDHGANcAM621f1zrS3UzV2NMH1Ps/GGMSQIHIsfXss9Rj+v1Nc8NqLd56rG9hjXCcR302F4Jemyvq3nqcb3G6bF9M+dpw1Ex+k7gLeAN4F/AoLW+dj5S+XwWcFi1x9rFec5B1oBOKz6uqLd5AkcjkfIcsBh4pN7muNZ8Po9UcZ8LnF/t8ZRwXrcCi4BC8Xd5KtALeByYXdz2rPY4SzDPvZAU3TfW+pv8fD3NFdgeeL04x7eA/y5+vuxz1ON63c1Tj+01/miEY3sjHNeL89Rje/l/xnpsr6956nG9hh96bN+8eZrif1JKKaWUUkoppZRSNSoUS7SUUkoppZRSSiml1JbTAI9SSimllFJKKaVUjdMAj1JKKaWUUkoppVSN0wCPUkoppZRSSimlVI3TAI9SSimllFJKKaVUjdMAj1JKKaWUUkoppVSN0wCPUkoppZRSSimlVI37/wrvvOxcgoW6AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from pySDC.implementations.collocation_classes.gauss_legendre import CollGaussLegendre\n", + "from pySDC.implementations.collocation_classes.equidistant_right import EquidistantNoLeft\n", + "from pySDC.implementations.collocation_classes.gauss_lobatto import CollGaussLobatto\n", + "fig, axs = plt.subplots(1, 3, figsize=(16, 5), sharex=True, sharey=True)\n", + "coll_classes = [CollGaussLegendre, CollGaussLobatto, EquidistantNoLeft]\n", + "titles = ['Gauss-Legendre', 'Gauss-Lobatto', 'Equidistant']\n", + "for i in range(len(nodes)):\n", + " desc = {'sweeper_params': {'QI': 'LMM', 'collocation_class': coll_classes[i]}}\n", + " axs[i].set_title(titles[i])\n", + " stats, _, _ = run_dahlquist(custom_description=desc)\n", + " plot_stability(stats, ax=axs[i], iter=ks)\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "ab5f1288", + "metadata": {}, + "source": [ + "We were using Gauss-Radau collocation points before and again, they seem to be the best choice, although equidistant nodes lead to the same stability polynomials, strangely enough." ] } ], diff --git a/pySDC/playgrounds/Preconditioners/dahlquist.py b/pySDC/playgrounds/Preconditioners/dahlquist.py new file mode 100644 index 0000000000..a365743f57 --- /dev/null +++ b/pySDC/playgrounds/Preconditioners/dahlquist.py @@ -0,0 +1,138 @@ +# script to run a simple advection problem +from pySDC.implementations.collocation_classes.gauss_radau_right import CollGaussRadau_Right +from pySDC.implementations.problem_classes.TestEquation_0D import testequation0d +from pySDC.implementations.sweeper_classes.generic_implicit import generic_implicit +from pySDC.implementations.controller_classes.controller_nonMPI import controller_nonMPI +from pySDC.core.Hooks import hooks +from pySDC.helpers.stats_helper import get_sorted +import numpy as np +import matplotlib.pyplot as plt + + +class log_data(hooks): + + def post_iteration(self, step, level_number): + + super(log_data, self).post_iteration(step, level_number) + + # some abbreviations + L = step.levels[level_number] + + L.sweep.compute_end_point() + + self.add_to_stats(process=step.status.slot, time=L.time + L.dt, level=L.level_index, iter=step.status.iter, + sweep=L.status.sweep, type='u', value=L.uend) + self.add_to_stats(process=step.status.slot, time=L.time, level=L.level_index, iter=0, + sweep=L.status.sweep, type='dt', value=L.dt) + + def pre_run(self, step, level_number): + super(log_data, self).pre_run(step, level_number) + L = step.levels[level_number] + self.add_to_stats(process=0, time=0, level=0, iter=0, sweep=0, type='lambdas', value=L.prob.params.lambdas) + + +def run_dahlquist(custom_description=None, num_procs=1, Tend=1., hook_class=log_data, fault_stuff=None, + custom_controller_params=None, custom_problem_params=None): + + # initialize level parameters + level_params = dict() + level_params['dt'] = 1. + + # initialize sweeper parameters + sweeper_params = dict() + sweeper_params['collocation_class'] = CollGaussRadau_Right + sweeper_params['num_nodes'] = 3 + sweeper_params['QI'] = 'LMM' + + # build lambdas + re = np.linspace(-30, 30, 400) + im = np.linspace(-50, 50, 400) + lambdas = np.array([[complex(re[i], im[j]) for i in range(len(re))] for j in range(len(im))]).\ + reshape((len(re) * len(im))) + + problem_params = { + 'lambdas': lambdas, + 'u0': 1., + } + + if custom_problem_params is not None: + problem_params = {**problem_params, **custom_problem_params} + + # initialize step parameters + step_params = dict() + step_params['maxiter'] = 5 + + # initialize controller parameters + controller_params = dict() + controller_params['logger_level'] = 30 + controller_params['hook_class'] = hook_class + controller_params['mssdc_jac'] = False + + if custom_controller_params is not None: + controller_params = {**controller_params, **custom_controller_params} + + # fill description dictionary for easy step instantiation + description = dict() + description['problem_class'] = testequation0d # pass problem class + description['problem_params'] = problem_params # pass problem parameters + description['sweeper_class'] = generic_implicit # pass sweeper + description['sweeper_params'] = sweeper_params # pass sweeper parameters + description['level_params'] = level_params # pass level parameters + description['step_params'] = step_params + + if custom_description is not None: + for k in custom_description.keys(): + if k == 'sweeper_class': + description[k] = custom_description[k] + continue + description[k] = {**description.get(k, {}), **custom_description.get(k, {})} + + # set time parameters + t0 = 0.0 + + # instantiate controller + controller = controller_nonMPI(num_procs=num_procs, controller_params=controller_params, + description=description) + + # insert faults + if fault_stuff is not None: + raise NotImplementedError('No fault stuff here...') + + # get initial values on finest level + P = controller.MS[0].levels[0].prob + uinit = P.u_exact(t0) + + # call main function to get things done... + uend, stats = controller.run(u0=uinit, t0=t0, Tend=Tend) + return stats, controller, Tend + + +def plot_stability(stats, ax=None, iter=None): + lambdas = get_sorted(stats, type='lambdas')[0][1] + u = get_sorted(stats, type='u', sortby='iter') + + if ax is None: + fig, ax = plt.subplots(1, 1) + + iter = [1] if iter is None else iter + colors = ['blue', 'red', 'violet', 'green'] + + for i in iter: + # isolate the solutions from the iteration you want + U = np.reshape([me[1] for me in u if me[0] == i], (len(np.unique(lambdas.real)), len(np.unique(lambdas.imag)))) + + # get a grid for plotting + X, Y = np.meshgrid(np.unique(lambdas.real), np.unique(lambdas.imag)) + ax.contour(X, Y, U, levels=[1], colors=colors[i - 1]) + ax.plot([None], [None], color=colors[i - 1], label=f'k={i}') + + # decorate + ax.axhline(0, color='black') + ax.axvline(0, color='black') + ax.legend(frameon=False) + + +if __name__ == '__main__': + custom_description = None + stats, controller, Tend = run_dahlquist(custom_description=custom_description) + plot_stability(stats, iter=[1, 2, 3]) From 33932a7754d2ac6716626c21bb286a1793687e05 Mon Sep 17 00:00:00 2001 From: Thomas Baumann Date: Thu, 25 Aug 2022 13:26:41 +0200 Subject: [PATCH 09/10] Got a better grasp on preconditioners on Dagobah --- .../Preconditioners/LMM_preconditioner.ipynb | 89 ++++++++++++++++--- 1 file changed, 76 insertions(+), 13 deletions(-) diff --git a/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb b/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb index 711ddfd702..98c7c9b2bb 100644 --- a/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb +++ b/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "63acb33f", + "id": "e81e7e3a", "metadata": {}, "source": [ "# High Order Linear Multistep Preconditioners" @@ -10,7 +10,70 @@ }, { "cell_type": "markdown", - "id": "287d6ed6", + "id": "4f83ae03", + "metadata": {}, + "source": [ + "## Interpretaion of preconditioners in SDC\n", + "In SDC, the interpretation of the preconditioner can become quite obscure when looking only at the simplified derivation of SDC as shown for instance in the [pySDC paper](https://doi.org/10.1145/3310410).\n", + "Here, SDC is derived in four steps to solve the initial value problem $\\partial_t u=F(t, u)$, $u(0)=u_0$:\n", + " - Integrate both sides of the initial value problem with respect to time\n", + " - Discretize with a quadrature rule\n", + " - Form Picard iteration\n", + " - Precondition the system\n", + "\n", + "Finally, one arrives at this formula for performing a sweep:\n", + "$$(I - \\Delta t Q_\\Delta)(u^{k+1}) = u_0 + \\Delta t(Q-Q_\\Delta)F(u^k),$$\n", + "where $k$ is the iteration index, $Q$ realizes the quadrature rule and $Q_\\Delta$ is the preconditioner.\n", + "\n", + "It becomes perfectly obvious that a lower triangular $Q_\\Delta$ is useful, such that we can solve the system with forward substitution and it is clear how to implement this from this equation, but since the preconditioner appears on both sides, it is difficult to interpret.\n", + "Why would implicit Euler be a good preconditioner for this system?\n", + "What are we actually integrating with implicit Euler here?\n", + "\n", + "To understand this, it makes sense to go through the [original derivation](https://doi.org/10.1023/A:1022338906936) of SDC from Dutt et al.\n", + "We do not need to understand all the nuances, but the crucial point is that the update between sweeps is\n", + "$$u^{k+1} = u^{k} + \\delta^{k+1},$$\n", + "where $\\delta^k$ is the error of iteration $k$.\n", + "This is the \"deferred correction\" part in spectral deferred corrections.\n", + "Instead of solving the original equation, we are solving an equation for the error and adding correction terms to the approximate solution to make it more accurate.\n", + "\n", + "In section 2.1, of the Dutt et al. paper, are the crucial steps outlining how to arrive at the equation for the error.\n", + "We only present the result here:\n", + "$$\\delta^{k+1}(t) - \\int_0^t\\left(F(\\tau, u^k(\\tau) + \\delta^{k+1}(\\tau)) - F(\\tau, u^{k}(\\tau))\\right) d\\tau = r^k(t),\\\\\n", + "r^k(t) = u_0 + \\int_0^t F(\\tau, u^k(\\tau))d\\tau - u^k(t)$$\n", + "where $r^k$ is the residual after $k$ iterations.\n", + "\n", + "The next step is to descretize the integrals with quadrature rules.\n", + "Since the residual only depends on the iteration that we already know, we do not need to solve any system but just evaluate the right hand side at the quadrature nodes.\n", + "While this is, of course, also not computationally free, evaluating this with very high accuracy is crucial to solving the equation for the error accurately, so we do this with the full, dense quadrature rule $Q$.\n", + "\n", + "Now for solving the error equation at $t=\\Delta t$, we get a system of equations that requires solving and this is precisely where we put in the preconditioner $Q_\\Delta$ as a simplified quadrature rule that is easier to apply.\n", + "The resulting system is:\n", + "$$\\delta^{k+1} = r^k + \\Delta t Q_\\Delta(F(u^k + \\delta^{k+1}) - F(u^k)),\\\\\n", + "r^k = u_0 + \\Delta t QF(u^k) - u^k.$$\n", + "\n", + "Now let's plug this into the update formula where we add the corrections:\n", + "$$u^{k+1} = u^{k} + \\delta^{k+1} = u^{k} + u_0 + \\Delta t QF(u^k) - u^k + \\Delta t Q_\\Delta(F(u^k + \\delta^{k+1}) - F(u^k)).$$\n", + "We see that the $u^k$ on the right hand side cancel, and we plug in the update formula on the right to replace the argument of $F(\\Delta t, u^k + \\delta^{k+1})$ with $F(\\Delta t, u^{k+1}).$\n", + "We end up with\n", + "$$u^{k+1} = u_0 + \\Delta t QF(u^k) + \\Delta t Q_\\Delta(F(u^{k+1}) - F(u^k)),$$\n", + "which becomes exactly the formula from the simple derivation if we move both $u^{k+1}$ terms to the left hand side.\n", + "\n", + "Following these extra steps makes clear that we are still solving an equation for the defect with the preconditioner and then correcting the solution with the computed defect, even though it becomes obscure in the final equation.\n", + "Another fact becomes apparent: The accuracy of computing the corrections is limited by how well we can compute the residual, which, in turn, depends on the accuracy of the full quadrature rule.\n", + "\n", + "One final thing we need to mention is that the full quadrature rules are build on stages, like a Runge-Kutta system.\n", + "That means we compute the solution at multiple intermediate time points.\n", + "An SDC sweep with a preconditioner which is lower triangular (which we require to solve the system with forward substitution), will then proceed to successively compute approximations at each of the time points like a time stepping scheme within the time step.\n", + "This can be whatever time stepping scheme you like!\n", + "Implicit Euler is well liked for its stability properties, but other (higher order) choices are also possible.\n", + "Linear multistep methods allow to get higher order and we will show some of their properties for SDC here.\n", + "As a side note: Interpretation of the preconditioner as a time stepping scheme is not required.\n", + "In fact, the LU preconditioner, which is algebraically motivated, is a very popular choice." + ] + }, + { + "cell_type": "markdown", + "id": "bf678305", "metadata": {}, "source": [ "## Constructing linear multistep methods (LMMs)\n", @@ -52,7 +115,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "925e4c85", + "id": "b7f3b325", "metadata": {}, "outputs": [], "source": [ @@ -69,7 +132,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "e9955fe6", + "id": "47f5a303", "metadata": {}, "outputs": [ { @@ -111,7 +174,7 @@ }, { "cell_type": "markdown", - "id": "f0d03d0f", + "id": "8794a853", "metadata": {}, "source": [ "What you see above is the order for the pi-line problem, a non-linear ordinary differential equation, which we integrate with an IMEX scheme such that we only need to solve linear systems in each step.\n", @@ -130,7 +193,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "6e821b7c", + "id": "ee33c537", "metadata": {}, "outputs": [ { @@ -153,7 +216,7 @@ }, { "cell_type": "markdown", - "id": "3e4b3e97", + "id": "ecb5ec87", "metadata": {}, "source": [ "Cowabanga! Looks like we need to talk stability...\n", @@ -182,7 +245,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "192885eb", + "id": "8b139a6d", "metadata": {}, "outputs": [ { @@ -220,7 +283,7 @@ }, { "cell_type": "markdown", - "id": "412007eb", + "id": "b58bba8c", "metadata": {}, "source": [ "The above experiments shows why implicit Euler is such a popular preconditioner for SDC.\n", @@ -234,7 +297,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "603195fd", + "id": "b229df24", "metadata": {}, "outputs": [ { @@ -264,7 +327,7 @@ }, { "cell_type": "markdown", - "id": "5da3555b", + "id": "d14491d7", "metadata": {}, "source": [ "When we use only one node, we have an A-stable method!\n", @@ -280,7 +343,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "640133b0", + "id": "c5b46d4c", "metadata": {}, "outputs": [ { @@ -321,7 +384,7 @@ }, { "cell_type": "markdown", - "id": "ab5f1288", + "id": "8b070ac6", "metadata": {}, "source": [ "We were using Gauss-Radau collocation points before and again, they seem to be the best choice, although equidistant nodes lead to the same stability polynomials, strangely enough." From ec466be1e6bc68a6ec6df01caa2c1f66d63293ad Mon Sep 17 00:00:00 2001 From: Thomas Baumann Date: Thu, 25 Aug 2022 16:41:05 +0200 Subject: [PATCH 10/10] Added parallel LMM a.k.a. trapezoidal rule --- pySDC/core/Sweeper.py | 18 +++++++ .../Preconditioners/LMM_preconditioner.ipynb | 50 ++++++++++++++++++- .../playgrounds/Preconditioners/dahlquist.py | 3 +- 3 files changed, 68 insertions(+), 3 deletions(-) diff --git a/pySDC/core/Sweeper.py b/pySDC/core/Sweeper.py index d267263a75..c2176d4bbd 100644 --- a/pySDC/core/Sweeper.py +++ b/pySDC/core/Sweeper.py @@ -240,6 +240,24 @@ def rho(x): u_coeff, f_coeff = get_linear_multistep_method(h, u_signature, f_signature) + QDmat[i, 0: i + 1] = f_coeff + elif qd_type == 'LMMpar': + ''' + Trapezoidal rule between initial conditions and the node you want to compute the solution at. + ''' + for i in range(1, len(self.coll.nodes) + 1): + t_expand = self.coll.nodes[i - 1] + h = np.append([0], self.coll.nodes[:i]) - t_expand # time difference to where we expand about + + u_signature = np.zeros_like(h) + u_signature[0] = 1 + + f_signature = np.zeros_like(h) + f_signature[-1] = 1 + f_signature[0] = 1 + + u_coeff, f_coeff = get_linear_multistep_method(h, u_signature, f_signature) + QDmat[i, 0: i + 1] = f_coeff else: raise NotImplementedError(f'qd_type implicit "{qd_type}" not implemented') diff --git a/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb b/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb index 98c7c9b2bb..8319dad809 100644 --- a/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb +++ b/pySDC/playgrounds/Preconditioners/LMM_preconditioner.ipynb @@ -342,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "id": "c5b46d4c", "metadata": {}, "outputs": [ @@ -387,7 +387,53 @@ "id": "8b070ac6", "metadata": {}, "source": [ - "We were using Gauss-Radau collocation points before and again, they seem to be the best choice, although equidistant nodes lead to the same stability polynomials, strangely enough." + "We were using Gauss-Radau collocation points before and again, they seem to be the best choice, although equidistant nodes lead to the same stability polynomials, strangely enough.\n", + "\n", + "## Parallel preconditioners\n", + "If the preconditioners only have entries on the diagonal, all nodes can be evaluated in parallel, which is what has been done [here](https://doi.org/10.1007/s00791-018-0298-x), but one thing they neglect is that you can also utilize the initial conditions and still stay parallel.\n", + "\n", + "We try this here by putting LMMs in the preconditioners which depend on $u_0$, $f(u_0)$ and $f(u_{\\tau_i})$, where $u_{\\tau_i}$ is the solution at collocation node $i$.\n", + "What we get is the trapezoidal rule, but unlike the usual implementation, it does not march over previous nodes.\n", + "The idea is the same as the parallel implicit Euler from the aforementioned paper, but with the trapezoidal rule.\n", + "What do we get?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a2fdf9fd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "LMM_par_desc = {'sweeper_params': {'QI': 'LMMpar', 'num_nodes': 3}}\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(9, 4))\n", + "plot_orders(axs[0], ks, True, Tend_fixed=0.1, custom_description=LMM_par_desc, dt_list=0.1 * 2.**(-np.arange(7)), prob=run_piline)\n", + "stats, _, _ = run_dahlquist(custom_description=LMM_par_desc)\n", + "plot_stability(stats, ax=axs[1], iter=ks)\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "b3350e82", + "metadata": {}, + "source": [ + "This is curious! We get a second order prediction, but then we get only first order correction sweeps.\n", + "Also, this implementation of the trapezoidal rule seems to be A stable again for multiple iterations, which is good news!" ] } ], diff --git a/pySDC/playgrounds/Preconditioners/dahlquist.py b/pySDC/playgrounds/Preconditioners/dahlquist.py index a365743f57..02b0c397e3 100644 --- a/pySDC/playgrounds/Preconditioners/dahlquist.py +++ b/pySDC/playgrounds/Preconditioners/dahlquist.py @@ -42,7 +42,7 @@ def run_dahlquist(custom_description=None, num_procs=1, Tend=1., hook_class=log_ sweeper_params = dict() sweeper_params['collocation_class'] = CollGaussRadau_Right sweeper_params['num_nodes'] = 3 - sweeper_params['QI'] = 'LMM' + sweeper_params['QI'] = 'LMMpar' # build lambdas re = np.linspace(-30, 30, 400) @@ -136,3 +136,4 @@ def plot_stability(stats, ax=None, iter=None): custom_description = None stats, controller, Tend = run_dahlquist(custom_description=custom_description) plot_stability(stats, iter=[1, 2, 3]) + plt.show()