From 621f391e1e7918ea8a62e6928005e65c3bffbaea Mon Sep 17 00:00:00 2001 From: LemurPwned Date: Fri, 27 Dec 2024 20:36:52 +0100 Subject: [PATCH] better resistance computation --- cmtj/models/general_sb.py | 26 ++++- cmtj/utils/parallel.py | 38 ++++++- cmtj/utils/resistance.py | 184 +++++++++++++++++++++++++-------- docs/tutorials/SBModel.ipynb | 192 +++++++++++++++++++++++++++++++++-- view/simulation_fns.py | 85 +++++++++++++++- view/streamlit_app.py | 13 ++- 6 files changed, 482 insertions(+), 56 deletions(-) diff --git a/cmtj/models/general_sb.py b/cmtj/models/general_sb.py index 50978d4..03b68a0 100644 --- a/cmtj/models/general_sb.py +++ b/cmtj/models/general_sb.py @@ -861,8 +861,32 @@ def _compute_numerical_inverse(self, A_matrix): A_inv_np = np.linalg.inv(A_np) return sym.Matrix(A_inv_np) - @lru_cache(maxsize=1000) # noqa: B019 def _compute_A_and_V_matrices(self, n, Vdc_ex_variable, H, frequency): + A_matrix = sym.zeros(2 * n, 2 * n) + V_matrix = sym.zeros(2 * n, 1) + U = self.create_energy(H=H, volumetric=False) + omega = sym.Symbol(r"\omega") if frequency is None else 2 * sym.pi * frequency + for i, layer in enumerate(self.layers): + rhs = layer.rhs_spherical_llg(U / layer.thickness, osc=True) + alpha_factor = 1 + layer.alpha**2 + V_matrix[2 * i] = sym.diff(rhs[0] * alpha_factor, Vdc_ex_variable) + V_matrix[2 * i + 1] = sym.diff(rhs[1] * alpha_factor, Vdc_ex_variable) + theta, phi = layer.get_coord_sym() + fn_theta = (omega * sym.I * theta - rhs[0]) * alpha_factor + fn_phi = (omega * sym.I * phi - rhs[1]) * alpha_factor + # the functions are only valid for that row i (theta) and i + 1 (phi) + # so we only need to compute the derivatives for the other layers + # for the other layers, the derivatives are zero + for j, layer_j in enumerate(self.layers): + theta_, phi_ = layer_j.get_coord_sym() + A_matrix[2 * i, 2 * j] = sym.diff(fn_theta, theta_) + A_matrix[2 * i, 2 * j + 1] = sym.diff(fn_theta, phi_) + A_matrix[2 * i + 1, 2 * j] = sym.diff(fn_phi, theta_) + A_matrix[2 * i + 1, 2 * j + 1] = sym.diff(fn_phi, phi_) + return A_matrix, V_matrix + + @lru_cache(maxsize=1000) # noqa: B019 + def _compute_A_and_V_matrices_old(self, n, Vdc_ex_variable, H, frequency): A_matrix = sym.zeros(2 * n, 2 * n) V_matrix = sym.zeros(2 * n, 1) U = self.create_energy(H=H, volumetric=False) diff --git a/cmtj/utils/parallel.py b/cmtj/utils/parallel.py index ffeeeae..ef55dfc 100644 --- a/cmtj/utils/parallel.py +++ b/cmtj/utils/parallel.py @@ -5,7 +5,9 @@ from multiprocess import Pool from tqdm import tqdm -__all__ = ["distribute"] +from ..models.general_sb import LayerDynamic + +__all__ = ["distribute", "parallel_vsd_sb_model"] def distribute( @@ -47,3 +49,37 @@ def func_wrapper(iterable): iterable, output = result indx = indexes[iterables.index(iterable)] yield indx, output + + +def parallel_vsd_sb_model( + simulation_fn: Callable, + frequencies: list[float], + Hvecs: list[list[float]], + layers: list[LayerDynamic], + J1: list[float] = None, + J2: list[float] = None, + iDMI: list[float] = None, + n_cores: int = None, +): + """ + Parallelise the VSD SB model. + :param simulation_fn: function to be distributed. + This function must take a tuple of arguments, where the first argument is the + frequency, then Hvectors, the list of layers and finally the list of J1 and J2 values. + :param frequencies: list of frequencies + :param Hvecs: list of Hvectors in cartesian coordinates + :param layers: list of layers + :param J1: list of J1 values + :param J2: list of J2 values + :param n_cores: number of cores to use. + :returns: list of simulation_fn outputs for each frequency + """ + if J1 is None: + J1 = [0] * (len(layers) - 1) + if J2 is None: + J2 = [0] * (len(layers) - 1) + if iDMI is None: + iDMI = [0] * (len(layers) - 1) + args = [(f, Hvecs, *layers, J1, J2, iDMI) for f in frequencies] + with Pool(processes=n_cores) as pool: + return list(tqdm(pool.imap(simulation_fn, args), total=len(frequencies))) diff --git a/cmtj/utils/resistance.py b/cmtj/utils/resistance.py index 94c3fff..10e624c 100644 --- a/cmtj/utils/resistance.py +++ b/cmtj/utils/resistance.py @@ -1,3 +1,4 @@ +from functools import lru_cache from typing import Union import numpy as np @@ -8,9 +9,7 @@ EPS = np.finfo("float64").resolution -def compute_sd( - dynamic_r: np.ndarray, dynamic_i: np.ndarray, integration_step: float -) -> np.ndarray: +def compute_sd(dynamic_r: np.ndarray, dynamic_i: np.ndarray, integration_step: float) -> np.ndarray: """Computes the SD voltage. :param dynamic_r: magnetoresistance from log :param dynamic_i: excitation current @@ -51,11 +50,7 @@ def compute_resistance( for i in range(number_of_layers): w_l = w[i] / l[i] SxAll[i] = Rx0[i] + (AMR[i] * m[i, 0] ** 2 + SMR[i] * m[i, 1] ** 2) - SyAll[i] = ( - Ry0[i] - + 0.5 * AHE[i] * m[i, 2] - + (w_l) * (SMR[i] - AMR[i]) * m[i, 0] * m[i, 1] - ) + SyAll[i] = Ry0[i] + 0.5 * AHE[i] * m[i, 2] + (w_l) * (SMR[i] - AMR[i]) * m[i, 0] * m[i, 1] return SxAll, SyAll @@ -77,10 +72,7 @@ def calculate_magnetoresistance(Rp: float, Rap: float, m: np.ndarray): if not isinstance(m, np.ndarray): m = np.asarray(m) if m.shape[0] != 2: - raise ValueError( - "The magnetoresistance can only be computed for 2 layers" - f". Current shape {m.shape}" - ) + raise ValueError("The magnetoresistance can only be computed for 2 layers" f". Current shape {m.shape}") return Rp + 0.5 * (Rap - Rp) * np.sum(m[0] * m[1], axis=0) @@ -179,6 +171,46 @@ def angular_calculate_resistance_gmr( return compute_gmr(Rp, Rap, m1, m2) +@lru_cache(maxsize=5) +def Rxx_symbolic(id: int, AMR: float, SMR: float): + """Compute the Rxx resistance for a given layer. + :param id: layer id + :param AMR: anisotropic magnetoresistance + :param SMR: spin Hall magnetoresistance + """ + theta1 = sym.Symbol(r"\theta_" + str(id)) + phi1 = sym.Symbol(r"\phi_" + str(id)) + m = sym.Matrix( + [ + sym.sin(theta1) * sym.cos(phi1), + sym.sin(theta1) * sym.sin(phi1), + sym.cos(theta1), + ] + ) + return AMR * m[0] ** 2 + SMR * m[1] ** 2, theta1, phi1, m + + +@lru_cache(maxsize=5) +def Rxy_symbolic(id: int, AMR: float, SMR: float, AHE: float, w_l: float): + """Compute the Rxy resistance for a given layer. + :param id: layer id + :param AMR: anisotropic magnetoresistance + :param SMR: spin Hall magnetoresistance + :param AHE: anomalous Hall effect + :param w_l: width to length ratio + """ + theta1 = sym.Symbol(r"\theta_" + str(id)) + phi1 = sym.Symbol(r"\phi_" + str(id)) + m = sym.Matrix( + [ + sym.sin(theta1) * sym.cos(phi1), + sym.sin(theta1) * sym.sin(phi1), + sym.cos(theta1), + ] + ) + return (0.5 * AHE * m[-1]) + w_l * (SMR - AMR) * m[0] * m[1], theta1, phi1, m + + def calculate_linearised_resistance( GMR: float, AMR: list[float], @@ -189,32 +221,103 @@ def calculate_linearised_resistance( :param GMR: GMR :param AMR: AMR :param SMR: SMR - :param stationary_angles: stationary angles [t1, p1, t2, p2] - :param linearised_angles: linearised angles [dt1, dp1, dt2, dp2] """ - theta1 = sym.Symbol(r"\theta_1") - phi1 = sym.Symbol(r"\phi_1") - theta2 = sym.Symbol(r"\theta_2") - phi2 = sym.Symbol(r"\phi_2") - m1 = sym.Matrix( - [ - sym.sin(theta1) * sym.cos(phi1), - sym.sin(theta1) * sym.sin(phi1), - sym.cos(theta1), - ] + + Rxx1, theta1, phi1, m1 = Rxx_symbolic(1, AMR[0], SMR[0]) + Rxx2, theta2, phi2, m2 = Rxx_symbolic(2, AMR[1], SMR[1]) + GMR_resistance = GMR * (1 - (m1.dot(m2))) / 2.0 + return Rxx1, Rxx2, GMR_resistance, theta1, phi1, theta2, phi2 + + +def Rxx_parallel_bilayer_expr(): + """Get the symbolic expressions for the parallel and linearised resistance of a bilayer system. + :returns: linearised and parallel resistance functions + Signals: + - GMR: GMR + - AMR1: AMR of layer 1 + - SMR1: SMR of layer 1 + - AMR2: AMR of layer 2 + - SMR2: SMR of layer 2 + - stationary angles: [t1, p1, t2, p2] + - linearised angles: [dt1, dp1, dt2, dp2] + + Function signatures + - Rlin_func: linearised resistance function + f(GMR, AMR1, SMR1, AMR2, SMR2, [t1, p1, t2, p2], [dt1, dp1, dt2, dp2]) + - R_func: series resistance function + f(GMR, AMR1, SMR1, AMR2, SMR2, [t1, p1, t2, p2]) + """ + AMR_1 = sym.Symbol(r"\mathrm{AMR}_1") + SMR_1 = sym.Symbol(r"\mathrm{SMR}_1") + AMR_2 = sym.Symbol(r"\mathrm{AMR}_2") + SMR_2 = sym.Symbol(r"\mathrm{SMR}_2") + GMR_s = sym.Symbol(r"\mathrm{GMR}") + R_1, t1, p1, m1 = Rxx_symbolic(1, AMR_1, SMR_1) + R_2, t2, p2, m2 = Rxx_symbolic(2, AMR_2, SMR_2) + gmr_term = GMR_s * (1 - m1.dot(m2)) / 2 + + Rparallel = gmr_term + (R_1 * R_2) / (R_1 + R_2 + EPS) + linearised_terms = sym.symbols(r"\partial\theta_1, \partial\phi_1, \partial\theta_2, \partial\phi_2") + dRdtheta1 = sym.diff(Rparallel, t1) * linearised_terms[0] + dRdphi1 = sym.diff(Rparallel, p1) * linearised_terms[1] + dRdtheta2 = sym.diff(Rparallel, t2) * linearised_terms[2] + dRdphi2 = sym.diff(Rparallel, p2) * linearised_terms[3] + + linearised_R = dRdtheta1 + dRdtheta2 + dRdphi1 + dRdphi2 + + Rlin_func = sym.lambdify( + [GMR_s, AMR_1, SMR_1, AMR_2, SMR_2, [t1, p1, t2, p2], linearised_terms], + linearised_R, ) - m2 = sym.Matrix( - [ - sym.sin(theta2) * sym.cos(phi2), - -sym.sin(theta2) * sym.sin(phi2), - sym.cos(theta2), - ] + R_func = sym.lambdify([GMR_s, AMR_1, SMR_1, AMR_2, SMR_2, [t1, p1, t2, p2]], Rparallel) + + return Rlin_func, R_func + + +def Rxx_series_bilayer_expr(): + """Get the symbolic expressions for the series and linearised resistance of a bilayer system. + + :returns: linearised and series resistance functions + Signals: + - GMR: GMR + - AMR1: AMR of layer 1 + - SMR1: SMR of layer 1 + - AMR2: AMR of layer 2 + - SMR2: SMR of layer 2 + - stationary angles: [t1, p1, t2, p2] + - linearised angles: [dt1, dp1, dt2, dp2] + + Function signatures + - Rlin_func: linearised resistance function + f(GMR, AMR1, SMR1, AMR2, SMR2, [t1, p1, t2, p2], [dt1, dp1, dt2, dp2]) + - R_func: series resistance function + f(GMR, AMR1, SMR1, AMR2, SMR2, [t1, p1, t2, p2]) + """ + AMR_1 = sym.Symbol(r"\mathrm{AMR}_1") + SMR_1 = sym.Symbol(r"\mathrm{SMR}_1") + AMR_2 = sym.Symbol(r"\mathrm{AMR}_2") + SMR_2 = sym.Symbol(r"\mathrm{SMR}_2") + GMR_s = sym.Symbol(r"\mathrm{GMR}") + R_1, t1, p1, m1 = Rxx_symbolic(1, AMR_1, SMR_1) + R_2, t2, p2, m2 = Rxx_symbolic(2, AMR_2, SMR_2) + gmr_term = GMR_s * (1 - m1.dot(m2)) / 2 + + Rseries = gmr_term + R_1 + R_2 + linearised_terms = sym.symbols(r"\partial\theta_1, \partial\phi_1, \partial\theta_2, \partial\phi_2") + dRdtheta1 = sym.diff(Rseries, t1) * linearised_terms[0] + dRdphi1 = sym.diff(Rseries, p1) * linearised_terms[1] + dRdtheta2 = sym.diff(Rseries, t2) * linearised_terms[2] + dRdphi2 = sym.diff(Rseries, p2) * linearised_terms[3] + + linearised_R = dRdtheta1 + dRdtheta2 + dRdphi1 + dRdphi2 + + Rlin_func = sym.lambdify( + [GMR_s, AMR_1, SMR_1, AMR_2, SMR_2, [t1, p1, t2, p2], linearised_terms], + linearised_R, ) - GMR_resistance = GMR * (1 - (m1.dot(m2))) / 2.0 + R_func = sym.lambdify([GMR_s, AMR_1, SMR_1, AMR_2, SMR_2, [t1, p1, t2, p2]], Rseries) - Rxx1 = AMR[0] * m1[0] ** 2 + SMR[0] * m1[1] ** 2 - Rxx2 = AMR[1] * m2[0] ** 2 + SMR[1] * m2[1] ** 2 - return Rxx1, Rxx2, GMR_resistance, theta1, phi1, theta2, phi2 + return Rlin_func, R_func def calculate_linearised_resistance_parallel( @@ -236,19 +339,20 @@ def calculate_linearised_resistance_parallel( t02, p02 = stationary_angles[2:] dt1, dp1 = linearised_angles[:2] dt2, dp2 = linearised_angles[2:] - Rxx1, Rxx2, GMR_resistance, theta1, phi1, theta2, phi2 = ( - calculate_linearised_resistance(GMR, AMR, SMR) - ) + Rxx1, Rxx2, GMR_resistance, theta1, phi1, theta2, phi2 = calculate_linearised_resistance(GMR, AMR, SMR) Rparallel = GMR_resistance if any(AMR) or any(SMR): Rparallel += (Rxx1 * Rxx2) / (Rxx1 + Rxx2 + EPS) + elif GMR == 0: + return 0, 0 dRparallel = ( sym.diff(Rparallel, theta1) * dt1 + sym.diff(Rparallel, phi1) * dp1 + sym.diff(Rparallel, theta2) * dt2 + sym.diff(Rparallel, phi2) * dp2 ) - + if isinstance(dRparallel, (list, np.ndarray)): + dRparallel = dRparallel[0] dRparallel = dRparallel.subs( { theta1: t01, @@ -287,9 +391,7 @@ def calculate_linearised_resistance_series( t02, p02 = stationary_angles[2:] dt1, dp1 = linearised_angles[:2] dt2, dp2 = linearised_angles[2:] - Rxx1, Rxx2, GMR_resistance, theta1, phi1, theta2, phi2 = ( - calculate_linearised_resistance(GMR, AMR, SMR) - ) + Rxx1, Rxx2, GMR_resistance, theta1, phi1, theta2, phi2 = calculate_linearised_resistance(GMR, AMR, SMR) Rseries = GMR_resistance + Rxx1 + Rxx2 dRseries = ( sym.diff(Rseries, theta1) * dt1 diff --git a/docs/tutorials/SBModel.ipynb b/docs/tutorials/SBModel.ipynb index 6cb6ef1..1621629 100644 --- a/docs/tutorials/SBModel.ipynb +++ b/docs/tutorials/SBModel.ipynb @@ -15,14 +15,16 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 50/50 [00:27<00:00, 1.80it/s]\n" + "/opt/homebrew/Caskroom/miniforge/base/envs/.cmtj/lib/python3.9/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.4)\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n", + "100%|██████████| 50/50 [00:26<00:00, 1.86it/s]\n" ] } ], @@ -88,17 +90,19 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -165,14 +169,14 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 50/50 [00:14<00:00, 3.36it/s]\n" + "100%|██████████| 50/50 [00:14<00:00, 3.53it/s]\n" ] } ], @@ -249,12 +253,12 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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v/z9Fy+32u4qWve99d5bc7s/8TPHy++67o2hZd3dHye1u29ZetPw97xGKlm3durlo2d13317yNf/Fv9hUtPynfmpj0bK77tpQcrt33tlWtLy9vbVo2ebNt5Xc7qZNtxYt37DhlqJlra3ri5bdequ15GuuX7+uaPm6dS1FyywWS8ntAsgr/63fksrT6fyy3/xNqWx5OZ1X9uu/vlPZ5uLiSl75o4/eDwB49938sk98QrqZ9847y3ll/f0/p2z3nXeW8sofeUQqX1hYzCs7ePD9AIDr1/PLDhz4WQDA22+/m1e2f/+/VF7z6tWf5JV7vVL5889/NK9s377tRct+7dfsynZLlRcqq6XruibStCaxWCwNIB2Lxap63muvpdNWazotHZrptBXvpu/GTNqKd5Vl69al0x/9aDrd1ib93daWTj/6qPRcIiKqzWuvSefS7HPrRz9a/pxstVZ//q31GkH6Uut9SybfTn/724l0Mvl2VWVrea6Rtmuk/0Wr7Rrpf9Fqu0b6X5ptu832v1RD7es7J3pbo7UM8i+UE1fW0gJYLMDysjTxUCdmMYtOLGE9rFbmMCciqkX2eXf1uTX7vLtareddTvTWnPi+ERFRKZzozUAOHgQmJoCBAWmcIiD9fvhhKShfXpZS88xgK97EVsxgKx7Ay8xhTkRUg+xc5IXOrSs3h/8+/HDuOXlgQDpX80ao+UxNTSEejyMejyOZTOpdHSIi0lEymVSuCVNTU6pum0G5znbskHLevv02cO2a9Lu9PdOKI49rBKQZgM/hAKxYVPLlEhFRZeRc5KXOrcvLQEdH7jn51CnpXE3m09/fD6fTCafTiUAgoHd1iIhIR4FAQLkm9Pf3q7ptTvSmkuy7JV1dXejq6qrq+S0twIYN0sRD4bC0rFRqnrewBaEQcPIkJx0iIiqnlnPrhg21vVYymVRaVdW+k071FQwGYbdLEwRVe10nIiJjEUURvb3SbPdTU1OqBuYMylWS/aYMDw9jZGSkpu1k58stl5pHzpdb6xdHIiKzqOe5NRAIYHR0dK1VpgZgt9s5ppyIiADU1vBaKbaxqiQYDCIWiyEWi0EUxZq3I+fLBTKpeZI3vyiuTs3T1gbcequULm1lpdgWiYjMa2VFOkfeemt159a15CIXRVG5HgSDwbX+C0RERGRwbClXiVp307NzmAPAK9iNrZjJmSFY1tUFbN4steq0tUnPO3KEYx+JiCYnpTHk4XDmHNnZCSQSUnmpc+tac5FreSediIiIjIct5Q3oyBEp/Y5sCevxFrbkfGkEgOnpTHfMhQUpkN+1S0r5Q0RkVmfPSufC06dzz5FyQC4rdG61WoHDh+tYWSIiIjI9BuUNaMcO6cuktYJ+DFYs4m68CSsWAYDp0ojI1LLTngH558hS5Fzk7G1ERERE9cSgvEEVy2He3Z1Zp1CeXQBMl0ZEpiWnPQOKnyMB6VzKXORERETUCBiUN7DVOcyvXAFuZtkpmWcXAEIhTv5GROaSnfas3DkymZTOqcxFTkRERHozzERvqVQKx44dQyqVAgAkEgm43W4MDQ0VXH9sbAyXLl1CR0cHAMDpdGJwcLBe1a2KnMP8+vXM+MhyeXaZLo2IzCY77Vkl58if/ITnSCIiItKfYYJyr9eLQCAAm80GQArSt23bhkgkgkgkkrOu2+2GzWZDKBTKeX4sFkMgEKhrvashp0tbWCifZ3etKX2IiJoNz5GktqmpKeUxZ9UnIjK3ZDKJ5M1uy9nXBzUYovt6PB5HNBpFPB5XlgmCAJfLlbc8Go0iGo3C7/fnbOPEiRMYHx/PWbfRyOnSgPJ5dr1eaT3mMCcio5NzkQPVnSPXkvaMzKG/vx9OpxNOp7Ohb9oTEZH2AoGAck3o7+9XdduG+EoiCAIEQcDc3FzOcrlrevbyUCikrF9oG41+0c1Olybn2d2CGWzFDF7BbgDAunXA5cvApk3Axo3S74EBzshORMYyOSmd27LPdXNz5c+RTHtGlQoGg4jFYojFYhBFUe/qEBGRjkRRVK4JwWBQ1W0bovu6zWbD/Px83vJoNAqbzQaXy5W3rJCOjg5MTEzUVIdSXRjU7PImp0uTU/7IeXZlcsvPSy9lniPnMD9zRvrN2YWJqNmdPZub+gyQznUvvSSdB9etA5aX88+RWqQ9y+7Otpra3duovux2OxwOh97VICKiBqDlMCZDBOWrJRIJ+Hw+CIKAixcv5pUVu8AKgoBEIlHTa5bqwjA8PIyRkZGatlvIwYPA9u1S2rNQSPoi2tYG7N0LfOMbufl5OzGLWXRiCeuVHObbt3OWYSJqXoVykWef61ZWpKD84YeBixcz50ivV2ohV/v8FwgEMDo6qu5GiYiIyDQMFZRnz8CeSCTQ19dX0zZqEQwGYbfbC5ZpcUdFTpd28qQ043BrK/Abv5Gbn1dOBySPpXwFu5Uc5qdOqV4lIqK6WJ2LvNC5bnkZ6OiQ0p3J50itxpCLooje3t6CZVNTU6qPOyMiIiJjMVRQLghCzgRubrcbx44dQywWK9plPVutATmgXxc3OV1aJfl5t2IGS1iPUEgK5jnJERE1m1rOdVqnPeOs3ERERLQWhg7LfD4fUqlUzuQspYLzubm5ioL3RlRpfl4ASg5zIqJmw3MdERERGY0hgnKv14vu7u685XKAnT1O3OFwFB03nkqlciaFayZyfl4gk583W6H8vHIaIaZMI6JGln2uquVcR0RERNTIDBGUx+NxzM3N5XU/l4Pv7G7lfX19SKVSeevKf3vlBN9Nppoc5nv3SuPPmTKNiBpZoZRnv/EbwJ49UjlzkRMREZERGGJMuc/nw/T0dF7ucXkG9hMnTijLPB4PXC4XfD5fTk7yQ4cOweVyNW1LOSDlMD9zRpoASc7Pmz0jMSB9Qc2eoR1gyjQiajzFUp6dPi3NrC6nPCt2rmMuciIiImoWhgjKBwcHEY1Gc8aOJxIJuFwuHD16NC9Yj0Qi8Pl88Hq9sNlsSCQS6OnpwdDQUJ1rrq5yOczXrZN+M2UaETWycinPlpelG4xWa+FznRa5yImIiIi0YoigHEDVrdzZs7QbSbEc5l4vcPky8NJL0npMmUZEjaqSlGcrK8BDD0lpz1af67TIRU7mNDU1pTzmLPtEROaWTCaRTCYB5F4f1GBJp9NpVbdoMvF4HE6nE7FYTJeUaKWsrGTy8wLSeMyFBanVaQZbc2YtTqJTSSPU1ibl9uVYTCKqt5WV6s9VgPa5yGvVyNcIKk5+37INDw9jZGREnwoREZHuRkZGMDo6mrNMreu7YVrKKZ+cwxyQZi6uJI3QW9iipBHSOrcvEdFqlaY8W32u4vmKtBAMBmG32wGAreRERCYniiJ6e3sBSC3l/f39qm2bQblKGr2Lm5xGaGEhk0ZodesT0wgRkd6McK7Ssnsb1ZfdbmcPByIiAqBtjNdgHf2aV39/P5xOJ5xOZ86s7o2impRpclY45jAnonqRc5ED1Z2rGq27OgAEAgHleqDmXXQiIiIypgb8OtOcgsEgYrEYYrFYzizwjeTIEWlWYiCTRmgLZrAVM3gFuwFIM7Rfvswc5kRUH4Vykc/NlT9XNXLKM1EUletBMBjUuzpERETU4Nh9XSXN0MWtXMo0ucVJnqEdYA5zItJOsVzkL70knY/kXOTNlvKsEYcwERERUeNiUG4yxVKm7d0LfOMbzGFORPVRLhf5yooUlD/8MHDxIlOeGY3P50MikUAikQAg9S4YHBwsuO7Y2BguXbqEjo4OAIDT6VRlXSIiokbBoNyEduyQ8pCfPJlJI/Qbv1E+LzBzmBORWirJRb68LOUhf/vtxk15RtVzu93w+/1K77JoNAq3241IJIJQKJS3rs1my1nu9XoRi8Xy5m+pZl0iIqJGwq83JpadMi0cln5bsah8OQak9EPncABWLAKQWtc5+RsRrcXKSnXnHEA6VzEgb35jY2MQRTFnuJfL5cLQ0BDC4TDC8gcDUrAejUbh9/tztnHixAmMj48jHo/XtC4REVGj4VccqjgvMAAlLzARUa14zjGvSCQCr9eLVCqVs7yvr08pl4VCIQiCAEEQctaVl2W3flezLhERUaNh93UyRF5gImoePOeYl8PhwMTERN5yOZiWx5gDUuu3zWYruJ2Ojo6c7VSzbjVK5ZnnhH5ERMaWTCaRTCYLlpW6PtSCLeXEHOZEVBdGykVOtfH7/Zifn89r0Y5GowCkceGy7AB9NUEQcsqrWbca/f39Ss751T9sfSciMrZAIFD0GtDf36/qa7GlnABIOczPnJEmXpLzAmfPhAzk5jCXZ0L2eKTnciZkIipmclKa2C0czpw79uyRUpuVOuc0ci5yUpff74fNZsPQ0FDFz1ndBV6tdbMFg0HY7faCZWwlJyIyNlEU0dvbW7BsampK1cCcQTkBYA5zItKGUXORk3q8Xi8EQcDFixcrfk49AnIAsNvtOZPSERGRedRzmBI7BapkamoK8Xgc8Xi86NiDRnfwIDAxAQwMSC1ZgPT74YelL8/Ly9IyKxZxN95UZkeWc5hPTupUcSJqSIVykWefO+QhMA8/nHvOGRiQzkXNeqMvmUwq1wO1x5wZjffmmKhYLJbXpb3YGHEAmJubyymvZl0iIqJGw6BcJdnjzpp5nJmcw/ztt4Fr16Tf7e25+YRnsBVvYitmsBUP4GUAUHKYExHJVuciL3TuyM5FLp9zTp1q7hby7DFoao85MxKv1wu3252TV1weWw5Ik8IVGwueSqXgcrlqWpeIiKjRMChXSTAYRCwWQywWgyiKeldnzZjDnIjWwsy5yEVRVK4HwWBQ7+o0JK/Xi6NHj2JwcFBZlkqlcgL0vr4+pFKpvO7n8t9yK3u16xIRETUajilXiVHHnVWaT/gtbFHyCcvBPBGZl5nPHUyVVZrT6QQAHDt2LGd5IpFQ8pUDgMfjgcvlgs/ny+mBdujQIbhcrpzW72rWJSIiajQMyqkk5hMmolrw3EGFeL1exONxAFB+Z/P7/Tl/RyIR+Hw+eL1e2Gw2JBIJ9PT0FJylvZp1iYiIGgmDcipJzmF++nQmn7DcDbVYPuGVFanVq7XVGF1Riagyq4/9as8dZHzZ3dMrtTpQV2tdIiKiRsGvQVTWkSNSeiIgk094C2awFTN4BbsBSOUf+Yg0a/KmTcDGjdLvgQHOyk5kdJOThY/9hx6q7NzBXORERERkZmwpp7LK5TC3WoHHHgMeeSQ/FzHzmBMZW7E85PKx/9hjwPPPFz93MBc5NbLslHacK4CIyNySyaSS+lrtlKdsKaeKFMthPjAAvPBC5ks3wDzmRGZRLg/50pJ0bnjhhcLnjmbORU7mYJR0p0REtHZapjxlUE4VK5TD/NQp4GtfYx5zIjOqJA/50hLw9a8XPnewhZwandHSnRIRUe20THnK7utUtewc5pXkIt6KGSxhPUIh4ORJTuhEZAS1HvtGSXtG5mDUdKdERFQ9LYcxMShXiVnHnZk5FzGRmfHYL07LMWdERERkPGyzVIlZx53JuYiBTC7ibMxFTGRMPPaL03LMGRERERkPg3KVmHXcmZyLGMjkIpa/nBfKRQwA169LXV+JqPmsrEjHMFDdsW+mYStajjkjIiIi42H3dZWYedzZkSNS6qOlpUwu4k7MYhadypfydeuAy5el/MULC1LLmccjPZeTPRE1vslJaWK3cDhzDO/ZI6U1K3XsmzEPuZmGMBEREdHamajtgrQi5zG33rzFI+cilr+Uyy1kL70EvLsgpUx6d2ERp08Du3ZJeY6JqHGdPSsdq6dP5x7DL70ktZyvWyett/rYZx5yIiIiovIYlJMqiuUxf/hhKShfXi6cMok5zIkaW3Yu8kLHsDwU5eGHmYeciIiIqBYMykk1hfKYt7dLX+aLpUyyYpE5zIkamJyLvNQxvLwMdHQwDzkRERFRLRiUk+qycxHLeYxLpUwCgFCIk78RNZrsXOSVHMOAdOybaVI3IiIiorXiRG+kmew8xnLKpOwv9dkpk8yWx5ioGfAYJrPLzjPPCfyIiMwtmUwimUwCyL0+qIHtGaSZ7DzG5VImtbUBt97KdGlEjUBOe3brrdUdw2bKRU7m0N/fr+ScDwQCeleHiIh0FAgElGtCf3+/qttmSzlpRs5hfvq09HexlEkA0NUFbN7MdGlEeiqU9qyzE0gkpPJSx7DZcpGTOQSDQdjtdgBgKzkRkcmJooje3l4AUku5moE5g3LSVHYOcyCTMmm16enM44UFKZA/c0b6zdmbibR39mxmlnXZwkImIJcVOobNmIuczMFut8PhcOhdDSIiagBaDmNiUK4SjjsrTM5hvvrLfiFWLOa0wMnp0rZvZ4s5kZay054B+cdiKcxFnk/LMWdERERkPOxsqBKOOyuuWA7z7u7MOoXyHwNgujSiOpDTngHFj0VAOmaZi7w8LcecERERkfFY0ul0Wu9KNLN4PA6n05k37owt5YWtrEgzNN96a2YMuRWLmMHWvFmdt2IGS1iPtjYp7zHHqxKpb2UF2LSp8mPxyhXgJz+RJnXjMVnY6pby/v5+xGIxdoNuIvK1ne8bEREVovZ1gt3XVcJxZ5WRc5hfv55JtVQq//Fb2MJUS0Qayk57Vsmx+JOf8FgshzdmiYiIqBps5yBdZKdLk/MfZ8vOf8xUS0Ta4bFIREREpC8G5Q1Czgtslhzdcro0oHz+YznVktn2EZFWso+lWo5FM+D5hoiIiOrFJF+vGtfkpDRZ0qZNwMaN0u+BAWm50R05Is3cDGTyH2/BDLZiBq9gNwCp/CMfMe8+IlJTsfPNQw9VdiyaIe2Zmc/JREREpA9TBuWJ1Yl3yyzXytmzwK5dUjoheUynnKN71y6p3MjkdGlyMCDnP5Zb5axW4LHHgEceMe8+IlJLqfPNI49Ix1qpY9EMac/Mfk6mfFNTU4jH44jH48rkfUREZE7JZFK5Jqid8tRQQbnP54PX61VS0YyPjxdcTxRFWCwWOJ1OuN1uOJ1OtLe31zWVWaG8wHfjTVixCABKjm6jt84US5c2MAC88ALw/PPcR0RrVcn55vnnpWOu0LFohrRnPCdTIUx3SkREMi1TnhomKHe73ejr60MoFEIsFoPf74coivB6vQXXt9lsiMfjmJiYQEdHB0KhEPx+f93qW0leYLPk6N6xAzh1Skp7du2a9PvUKeBrX+M+IlJDpeebr3+98LFo9BZygOdkKiwYDCIWiyEWi0EURb2rQ0REOhJFUbkmBINBVbdtiDzlY2NjsNls8MizFd3k8/kwNjaGUCiUU+Z2uxGJRFR57Vpy1FWbF9iMObq5j4jUwWOpPC33EfNdNye+b0REVIra1wlDfPWKRCLwer1IpVI5y/v6+pTyRlJpXmAASo5us+E+IlIHj6XyuI+IiIhIT1a9K6AGh8OBiYmJvOWCIAAoPoFbOBxGIpGAzWaDy+VS1q9FqcH+XV1d6OrqUv6W8wIvLGTyAq9ulTF7XmDuIyJ18Fgqb637KJlMFp0ETO2JYIiIiMh4DNFS7vf7MT8/nxdUR6NRAFJ39dV8Ph9sNhuGhoYgCELJieEqkT0ZzOqf1ZPDVJsXGDBfvlzuI6K1kfNsA8xDXoxa+yh74pfVP2pPBENERETGY4gx5cV0d3cDAKanp3OWy63j2cLhMLxeb9XjAuTxBMFgEHa7veA6q1vKAWkG3127cmf67cQsZtGpfPlbtw74lV8Bvv1tqQWnrU364njkiDkmXuI+Iqre5KQ0aVk4nDkm9uwB/uIvSh9LVqs0y7oZjhu191G5lvL+/n6OTW4yHFNORESlqH2dMET39UK8Xi8EQcDFixfzylYH5ADgcrkASC0etaQ9sdvtVb0hco5uOQWPnBdYJrfEvPRS5jlyvtwzZ6TfRk9RxH1EVJ2zZ3PTegHSMfHSS9Lxsm4dsLycfyyZJQ85oM0+KnTjlYiIiKhShuyoKKdBi8VieV3ax8bG4HQ6iz632PhzLRTL0f3ww9KXw+VlaZmZ8+VyHxFVplyebXlox8MPmzMPOcB9RERERI3JcEG51+uF2+1GKBRSlsljywFpJvbVs7QDwNzcHADUvZtaoRzd7e3Ml5uN+4iovErybC8vAx0d5sxDDnAfERERUWMyVFDu9Xpx9OhRDA4OKstSqVROgO52uwt2Tw+HwwCkpPB6aGkBNmyQ6yL9tmIR53BAmQW4C7M4hwNKq04oZK6JzbiPiApbWanumACkY8lsk7pxHxEREVEjMsyYcrlL+rFjx3KWJxIJJV85AAwNDcHtdsNmsyljy+PxOI4dO4ZAIFBwvHk9VZov9y1sUfLlyoGqWXAfEeXiMVEe9xHVIjulHecOICIyt+yJXdVOeWqIoNzr9SIejwOA8jub3+/P+TsSicDn8yGVSmFubg6pVAoXL15siBlWmVO4PO4jolw8JsrjPqJaZKe0Gx4exsjIiH6VISIiXQUCAYyOjmqybUN0zAuFQkin00V/5JnVs/n9fgQCAYRCIUQikYYIyAHm564E9xGRhLnIy+M+orUIBoOIxWKIxWK6DW8jIqLGIIqick0IBoOqbptfOxrQkSNS+h0AeAW7sRUz2IIZbMUMXsFuAFLansuXgU2bgI0bpd8DA+aZbZz7iMxsclL6LGd/tufmyh8TVitw+LCOFa8j7iNSg5zu1OFwsOs6EZHJdXV1KdcEu92u6rYZlDcgOT+3/OVRzpcrt+Jk5+eWx0jK+bl37ZLy8Bod9xGZ1dmz0mf49Oncz/ZLL0mtwuvWSctWHxNmy0XOfURERETNgkG5SqamphCPxxGPx5UJANaC+bnL4z4is2Ge7fIaYR8lk0nleqD2RDBERERkPAzKVdLf3w+n0wmn01kw5VotmJ+7PO4jMhPm2S6vEfZRIBBQrgfZE4URERERFWJJp9NpvSvRzOLxOJxOJ4LBoDK2QKu0KSsr0rjIhQWp9WcGW/NmD96KGSxhPdrapC+aZpusiPuIjIqf7fIaZR+tTpnS39+PWCzWMBOKNoJUKgWv1wuv14vBwcGi642NjeHSpUvo6OgAIKU/LbZ+NeuWI1/b+b4REVEhal8nDJESrRHIk8FoiXl2y+M+IqPiZ7u8RtlHzGddnCiKmJubAwBEo1G43e6i67rdbthsNoRCIWWZ1+tFLBbL65FWzbpERESNhkF5E2Ge3fK4j8io+Nkuj/uo8ckBciqVQjgcLrpeNBpFNBrF/Px8zvITJ06gvb0doigqN8KrWZeIiKgRmaxzY3Njfu7yqt1HLS2ZPMZm2k/UHLI/m7V8ts2CuciNJxQKQRAECIKQs1xelt36Xc26REREjYgt5U3myBHgzBlpIiM5z24nZjGLTuXLZnZ+7oUFqUXI45Gea4bJnirZR1Yr8JGPSDMuh8Pm3E/UuCYnpQnLVn82H3qoss+2WfJsF9pPe/ZI+4D7qLlFo1HYbLaCZR0dHZiYmKhp3WqVmj2fwxSIiIwte46Y1dTOrsKgvMnI+bnllD9ynl1Zdn5umZyf+8wZ6bfR0yKV20dWK/DYY8Ajj2RmaQbMt5+oMZ09m5vSC8j9bD72GPD888U/22bJs11sP730knQeXLdOmmXdzPuomSUSiaJdzgVBQCKRqGndapWaPX94eBgjIyM1b5uIiBpbIBDA6OhoXV6LQXkTOngQ2L5dSukVCmVaiPbuBb7xjdz8vNktRHJ+7u3bjf+FtNg+8nqlFvLsgNzM+4kaS6Ec26s/m88/D7zwAvD1r+d/tg8fNsdnttx+WlmRgvKHHwYuXjTnPjK6VCqlybqrZWdWWY2t5ERExiaKInp7ewuWydlV1MKgvEnJ+blPnpRmEG5tBX7jN3Lz857DAXRhVhlL+Qp2K/m5T53Ss/b1UWgftbRIXda5n6gRrc6xXeyz+fWvF/5sm0Ul+yk7F7kZ95GR1SsgB+qTWYWIiBpTPYcp8StKk2tpyaT0kSeytWJR+ZIKSGmAzuEArFgEILWumWlSM3kfyZO6cT9RI6rls5n92TaLavcTYL59ZATFxogDwNzcXE55NesSERE1IraUG0Sj5OdtdNxP1Kj42awM95M5OBwORKPRgmWpVAr79++vad16+vM//wf86q+eL7uexbL6b0vRsmLrldvOWl6z3OtUum419a2mDmptV4vXLPScSutQahtrec1KlhV6nWqXqVGPcnVbvW412y1W/0JlhV6/0s9DI3w+V6vHcZn/mqXqW/x51dahXH2y1//N39yJD3zgp0u/eB0xKFdJ9gx8eszIyvy8leF+okbFz2ZlmmE/Zc/WqvbsrGbR19eHcDiMVCqVk+pM7o7ulfN+Vrluva2spGt4Vi3PISKiajzwwHsYlBtR9kB/PWZklXMYnz6dyc+7epxlofzcZhtrWct+IqoHfjaLW32uavT9VM/ZWpvV3NwcAODy5csFyz0eD1wuF3w+X06e8UOHDsHlcsHlctW0bj0Jwm3o6fkXOcvSq+Lt9OoFJdYt9by1bDd73XLbqbVO1dS3mjqotV0tXrPQcyqtQ6Xb0GK7hZ5b6TIiqp0lXerMTWXF43E4nc6cGVr1yl06OQns2lV8RmJASgf0wgvA175m3vzcle6niQng/e83340Lqq/sgPN736v8s2mWY7VYvvZSGRQAfffT6pby/v5+xGIxThgGwOfzIZFIIB6PI5FIQBAE7Nq1C4Ig4MSJEzkt3dnr22w2JBIJ9PT0YGhoqOS2K1m3HPnazveNqHprCexLLav05km57a7eVrU3Wup9s0erm1qrqVWH/O0WX7dcFFrN/1bqNQutv3XrZrS3196dTu3rBIPyNWq0C3eh3L0yOT+3nOO4ULlZ8nOX20+f/SwwNWXeGxekvWIBp90OPPUUj1GjnMsa7RpBleH7RkREpah9nWDbn8EcPCi1Dg0MSF/yAen3wIDUQp79JdaKRdyNN5WZiuX83JOTOlW+jkrtp89+VgqKTp8G3l2Q9tG7C4s4fVpqxTx7Vt+6U/M7e1b6LBX6jD31lPQZLPTZnJhojEBTa4XykK8+V8n52s28n4iIiMgYGJQbkJyf++23gWvXpN+nTkld1rNz+85gK97EVsxgKx7AywCg5Oc2g0L76fDhTCtloX1kphsXpI3sgLPYZ+ypp6TP4upj2Cy9NFbnIS92rpLztZt1P5H2pqamEI/HEY/HlSEJRERkTslkUrkmqD2RK4NyA2N+7spk7yc5GCi1j8x044LUV81njHnIma+d9NXf3w+n0wmn05kziRwREZlPIBBQrgnZk3yrgV9hTKLS3L4AlNy+ZpMdDJTbR2a7cUHq4GesPJ6rqJEEg0HEYjHEYjGIoqh3dYiISEeiKCrXhGAwqOq2mRLNJJoht6/esoOBcvtIDgY2bNCjptSs+Bkrj+cqaiR2u50TvREREQBtM2yxpdwk5Ny+QCa3b/LmF9tCuX0B4Pp1c7XUycEAUH4fMRigWvAzVtzKinTOAao7V7HLOhERETU7fp0xkSNHpFRBAPAKdmMrZrAFM9iKGbyC3QCAdeuAy5eBTZuAjRul3wMD5pjYLPvGBVB8HwHmvXFBtSkUcALlP2NmCDgnJ6VzTPY5Z26u/LnKapUmwyMiIiJqdib4ylcfzTBD644dUgom+cvuEtbjLWxRWp3kAOCllzJdbBcWYKpUYNk3LoD8fQSY+8YFVadcwAkU/oyZJeDMTg2Xfc556SXpRsa6ddKy1ftIzkPeqLOsazk7KxERERkPg3KVNMsMrcXycz/8sBSULy9Ly8yaw3z1jYvVeOOCKlVpwLlaowecaimXi1zugfLww82Xh1zL2VmJiIjIeBiUq6SZZmgtlJ+7vZ05zGW8cUFrZeSAUy2V5CJfXgY6OpovD7mWs7MSERGR8TAoV4k8Q6vD4dBsVj61ybl9AeYwX403LmgtjBxwqqHaXORAc+Uh7+rqUq4Hdrtd7+oQERFRg2uSrzikJeYFLo43LqhaRg841cBzDhEREVGGib4GUjHZaZrkvMDZmBeYQQRVjp+V8njOISIiIspgUE5V5zA3U4uejEEEVYqflfJ4zqFm0QyZVYiIqD60zK7CrzoEoLIc5nKaJjnnspm6aFcbRADm20dmVygXOQPOXNnnjmrOOUR6aZbMKkREpD0ts6uY6OsglVIuh7nVCnz2s9IEVmbNz11JEMEc5uZTLhc5A87C++j4cemcUuqcY4bUcNTYmimzChERaUvL7CpFsjGTGR08CGzfLs0eHgpJ413b2qTWvPe9D3jqKWlGaSm90yxmFzpx+vR6nDkjfXk2ehon+caFnOpKDiJk2TnMZXIOc7PsI7M5ezY39RmQyUXe0iLdpFlezv+smCngzN5Hq88d8s2+738//5xz+LA59g81NjmzChERUVdXl2ZZtthSTjkKpQI7fDgTkBdK72Sm/NzMYU4y5iIvL3sfFTt3PPWUdI4xY2o4IiIiIoBBORUhpwJracnkXC6V3slM+bmZw5wA5iKvRDXnjuxzDhEREZGZ8OuPSow6Q2t2zuVy6Z3Mlp+bOczNi7nIyzPzuUPL2VmJiIjIeEz0FVFbRp2hNTvncrn0TmbNucy81ObD97w8M587tJydlYiIiIyHQblKjDpDa3bO5XLpncyac5l5qc2H73l5Zj53aDk7KxERERkPg3KVyDO0OhwOzWbl00N2fm6geHonIJNz2Wx5zKvNYW6mLsxGxfe8sOxjv5Zzh1F0dXUp1wO73a53dYiIiKjBGehrEGklOz83kJ9PGJDKP/KR/FzEZsnRXUkO8+y81Ga7cWEEq9+zat9zIyuUh3xgAHjoocrOHWbYR0RERETFMCinsuT83NYiWe2tVuCxx4BHHpHWk8eRyjm6d+2SchUb2ep9tDr4kPNSA+a9cdGsigWcQGXvudFnWj97VjrGCx37jzwinRtKnTvMsI+oeRl1ElciomYXj8cxPj5e19fUciJXBuVUkWL5uQcGgBdeAJ5/vni+ZrPk6C61jyYmpL/l4OXdBWkfvbuwaJobF80oO+As9J4Bpd9zo+ciL5erfWlJOje88IJ59xE1N6NO4kpE1Oyi0Sh2yV/G6kTLiVwZlFPFCuXnPnUK+NrXmKNbVmwfAZngpdA+MsuNi2aSHXCWes+Awu+5GVp/K8nVvrQEfP3r5t1H1NyMOokrEVGzu3TpEhwOR11fU8uJXIt0KmxOPp8PiUQCiUQCgLTjBgcHC647NjaGS5cuoaOjAwDgdDqLrku5svNzV5KveStmsIT1CIWAkyeNNaFTMdn7CMgELyX30dJ6PPtsJognfVX7nq1+z42u1mPfTPuo3q5evYpEIoG5uTmkUinYbDYIgoB77rlH76o1LXkSVyIioq6uLs0m9DZMUO52u+H3+5WLZzQahdvtRiQSQSgUylvXZrPlLPd6vYjFYuyeVqVK8zW/hS1KLmKzfSnPDl7K7SMz3bhoZHzPyuOx3xhee+01BAIBRKNR5YZ0IS6XCw8++CAOHTqE22+/vY41JCIiUlcikUBPT4/e1VCVIYLysbExiKKYczfb5XJhaGgIY2NjCIfD8NzMzRONRhGNRjE/P5+zjRMnTqC9vT1vO1SanIt4YSGTrzn7yznzNecGL+X2EYOXxsD3rDwe+/p64403IIoiotEo0uk0HA4HnnjiCdxxxx0QBAEdHR1Ki/l3v/tdvPrqq3jiiScwNDQEn8+Hp59+Wu9/gYiIqGKpVArHjh1DKpXCxMQEbDYbRFGE2+1W4rxmZoigPBKJKIG2IAjK8r6+PoyNjSESiShvVigUgiAIOesBUJYFAgG2lldBzkV8+nQmX7PcjbVQvmZASivV2mqelsXs4KXcPmLw0hj4nhW3siLdhGhtre7YN8vxXg/f/va34fF4YLPZcOHCBezbt6+i573++usIhUJ45plnEI1GcfHiRWzatEnj2hIRkVY+9qcfw4+v/1jvapR054Y78dVPfHVN2xgfH4ff70coFILD4YDX61V6PIuiiEgk0vTxmyGCcofDgQl5eusscuCd3aUvGo3CZrMV3E5HR0fB7VSi1LT4Wo4/aARHjgBnzkjjb+V8zZ2YxSw6lS/l69YBly9L6aQWFqRAxuORnmv0yZ6yb1wAxfcRYN4bF42iUMAJlH/PzPA+TU5K4+zD4cwxvGePlNas1LFvhjzkyWSyaLostVOmvP766/B4PDhx4kTFwbhs27ZtGBoawtDQEERRxJ49e3Dp0iVV60dERPXz4+s/xuy12fIrNrHx8XH4fD68/vrreY2qAOD3+wv2do7H4zh27Bh6enowNDRUxxrXxhBBud/vh9/vz1sejUYBSGPIZYlEomj3dEEQSo7JK6XUtPjDw8MYGRmpabvNQM7RLc9ULedrlskBy0svZZ4j5zE+c0b6bfS0SNk3LoD8fQSY+8aF3soFnEDh98wMAScgpYbLTn0GSPvppZek43vdOmB5OX8fmSUPeSAQwOjoaF1eK5VKIRaLYdu2bWvaTiAQwJe//GWVakVERHq4c8OdelehrLXUMZFIQBRFpaezvCy7gVVeHo1GlRhPFEU4nU7E4/GmGXtuiKC8GL/fD5vNVtXdkVQqVdNrBYNB2O32gmVGbiWXHTwIbN8upT0LhTKBzd69wDe+kZvHOLslTU4rtX27sb+4r75xsRpvXOin0oBzNbMEnIVykWcfwysr0j56+GHg4sXMse/1SjcsjL5/AOni39vbW7BsampK1VymO3fuVG1b1ba0ExFRY1lrt/BGJ3dJzx4zLk/mLZNjt+xWdPl5zdSl3bCdLr1eLwRBQCwWq/g5tQbkQCZtSqEfMwTlQOEc3e3tzGEuO3gQmJgABgakoAWQfj/8sBT8yYGfFYu4G2/CikUAYA5zDRUKOLP3/cqKtPzhh3Pfs4EB6b00w42SSnKRLy8DHR3mzUPe1dVV9Pxf7Gat2np6evDtb3+7aPnVq1dx9OhRPP7443jttdfqUiciIqK1kNN7ZotEInC5XMrf4+PjAID9+/fXtW5qM2RQ7r05MDcWi+WNPSg2nhwA5ubmSpZTZbJzEZfLYywHP6FQJgAyMt64aCwMOEurJBd59jEMSMe+GcbYN5rp6emS5R6PB36/H+fPn8fevXvxxhtv1KdiTW5qagrxeBzxeLzovAFERKQNp9OJubm5ouWJRAI+nw+RSKTgeHO1JZNJ5Zqg9pwxhvvq5PV64Xa7c3KQy2PLAWlSuGLjxlOpVM6dF1qbSvMYA5m0UmbBGxf6Y8BZHo/h5uFyuRAKhdDT04Oenh788R//sVL26quvIhqNYnx8HHNzc9i2bRvGxsZ0rG3z6O/vh9PphNPpbKpukERERjA4OAibzaZcs7LHk8vd2EOhUN3it0AgoFwT1ByaBhgsKPd6vTh69CgGBweVZalUKidA7+vrQyqVyuuqLv8tt7LT2slppYBMHuNszGPMoEdP3Pfl8RhuHj09PQgEAmhvb0d7ezsOHTqEz3zmMwCAiYkJWCwWpWtfX18fIpGIntVtGsFgELFYDLFYDKIo6l0dIiLTkYcie71eiKKIeDyu/J6enq5rjnJRFJVrQjAYVHXbhpnozel0AgCOHTuWszyRSKCvr0/52+PxwOVywefz5dz1PnToEFwuF1vKVVRtDnMztUDKsvNhy0FPdnDIoEc73Pfl8RhuHoFAAKIo4rnnngMAhMNh9PX14emnn1ZuOt9+++0ASvcYo1zyfDFERKQfedJuURTh9/vr0lW9EC3TXBsiKPd6vYjH4wCg/M62Ol1aJBKBz+eD1+uFzWZDIpFomhx2zaaSHOZyWqnsHNFm+XLPoEc/3PfFZR+L1RzDpJ9EIpHT08vtdiOdThcdO67XFxoiIqJazc3NGfb6ZYigPLt7eqUK5TUn9ZXLYW61Ap/9bH6OaDPl5+aNi/piwFlcoXztHo90jD71VPFj2Ayp4Rqdw+FAOBzGnj17AAAXLlyAxWLBPffcg8uXL+esG4lEOKkpERE1lVQqhY6OjorW9fl8SKVSSCQSCAQCmJ6ehtPpzBni3GgMEZRTYyuWw9zrBd73vsyXfZnZ8nPzxkV9MOAsrVi+9tOnM5/B738//xg2Sy7yRvfMM8/gwQcfVMaKT09PQxAEPP744zh//jwA4Atf+AL27duH8fFxZbw5ERFRM5iYmMjJT16K3PjaTBN0WtLpdFrrF7l69SoSiQTm5uaUfHOCIOCee+7R+qU1F4/H4XQ6EYvFOO6sAtmtlN/7HrBrV26O6EKtlBMT5vjSPzlZ+Y0LmRw0Gv3GxVoVCjhlDDilz16lx+L738/eGtWo5zUiHo8jEAhgfn5emUslEomgu7sbly9fxtjYGCwWC2w2G/7pn/5J07o0O17biYioFLWvE5q1lL/22msIBAKIRqMlJ5RxuVx48MEHcejQIWUSmmaUnatOy0kAml12KrDVOaJXj+d9BbuV/NynTulW5bqRc5ifPFn5jYulJSnY3L7dHMFjLSYncwPyQvvwqaekgDN735sp4Kz2WJSPYSosmUwqOa3VzmNaisPhyGsV2Ldvn/K4r68PiUQiZ5nRjI2N4dKlS0oXx0bvrkhERARo0FL+xhtvQBRFRKNRpNNpOBwOuFwu3HHHHRAEAR0dHUqL+Xe/+128+uqrSCQSsFgs8Pl8ePrpp9WsjubkuyTZhoeHMTIyok+FmsTKCrBpk9QqacUiZrA1b+brrZjBEtajrQ14+21zBUmygQGpJRwoHizJ65nhxkUtuA9L47GovpGREYyOjuYsq1eL69WrV5Wb4Z/+9KcBAF/60pewf//+pr7xXQm32w2bzZZzY8Lr9aKjo6PqLoxsKSciolJUv06kVXTx4sV0e3t72ul0psPhcMXPSyQSab/fn25vb0/39PSkr169qma1NBWLxdIA0sFgMB2LxdKxWCz9wx/+UO9qNbxr19JpQPq5GzOZP7J+7saM8ue1a3rXuP6Wl9Pptjbp/7fi3fQP0Zmzf36IzrQV76YBab3lZb1r3Hi4D8vjsai+H/7wh8r1IBgMpgGkY7GY5q+7f//+dEtLS7q7uzvd0tKiLHc6nenPf/7zmr++niKRSBpAen5+Pmf5/Px8Tftfvrav9X37v9f+b/pv/vlv0v/32v+tqmwtzzXSdo30v2i13Ub8X4jMQK3rhEy17uuvv/46PB4PTpw4UXXXuG3btmFoaAhDQ0MQRRF79uzBpUuX1KpaXTCXaXWYI7q8Gzek/QMAnZjN2T8A0IVZdGIWb2ELFhak9dmtOBf3YXk8FtWnxxCmJ598EpFIBBMTE9i8eTPuu+8+pWz//v04d+4cPvWpT9W1TvUUCoUgCEJeqhx5WSAQqPuEP//pr/8T/vPf/mekkYYFFnzwPR/Ee+98LwDgH3/8j/ibf/6bgmXlymstq/d2f+bOnwEA/MOP/0H1MqNv90Pv+VBO2V//819XXZZXbrHgQ1s/hJ+56+Zzf/QP+OuZv0Y6LZX90nt+Ce+7632wwILv/+j7+M4/fwcr6RW0WFrwy/f9Mn7xPb+ItvVtaLW24rtvfRd/+uqfYjm9DGuLFb/v+n30/Vxf2WOCiIpTrfv6q6++CkEQsG3btjVv68tf/nLTjHljF7faVdut2GzpwNiteO24DwtbfSyxi7926nWNuPfee/Hkk0/ik5/8JF5//XXce++9WF5eBgBcvHgRDz74oPK3EXV3d0MQBMRisarKipHft2AwCLvdXnCdUjdffnT9R/jg8x/ESnql4tckalbWFiu+I34Hd224S++qEKkqe46Y1aamptDf36/a9V21r587d+5UJSAH0DQBOa3NkSPSjM5AJkf0FsxgK2aUIMBqBT7yESkY2LQJ2LhR+j0wIE3gZWQtLVLKLkBK1XUA55C82WIpB0vyzNher7T+ygpw/br026yy90Et+9DIJicLH0sPPVTZsWiWfO3NaG5uDnfccUfBskQiYfi85KUmlBUEoWR5Kf39/XA6nQV/SrW8/+DyDxiQk2ksrSzhB5d/oHc1iFQXCASKXgP6+/tVfS3N85S/9tpruP/++wuWXblyBbFYDHv27NG6GtSAKsnP/dhjwCOPmDeP+ZEj0v+5tJQJlgqlqpJvXJg5j3mxPOQPPVTZPjR6wFkqD/mZM9Kx9vzzzNferPbu3Yunn34aH//4x/PKAoGA6XtypVKpmp5XrqW8mHvvuBfWFiuWVjIH3DrLOjz/q88DAB77ymNYTi/nlbW3tmP+xnzR8lLPLVb23K8+p2z38a88XrAcQE1lWm1Xj9dsuO1+7GbZV6svE1oFpG6kCpZ/8WNfBAD89ld/O6/sjx7+IwDA7/z57+SUtVhacHT3UaxrWYcfX/8xvvj3X0Q2a4sV995xL4iMRhRF9Pb2FiyTW8pVo8rI9BIsFku6r6+vYFk0Gs2ZjKYZqT3I34xeey2dHhjITMjV1ib9ff58Om21ZuabsuLd9N2YUSbmAqTy117T+z/Q1pkzufsh+8dqTad/53dKl585o/d/oD3uo9Jee62yY+n8+cLHotGPMS3V6xqRSCTS7e3t6fvuuy/95JNPpltaWtIXL15MP/jgg+mWlpb066+/runr6w1A2uFwFCyz2Wzpar/uqPG+nZs8l37vF96btn3Oln7vF96bPjd5rqKytTzXSNs10v9itH306IVH07bP2dK2z9nS933+vrxyIjNQ+/pel6DcYrGk77vvvvQbb7yRU8agnLItL0szO8szYD/6aCaIeAD/XZk5+4foTD+A/66UDQzoWu264I2L4hhwllftsbT6WKTa1fMaMT09nXa5XMp112KxpNvb29PxeFzz19abzWYrGpQLgpC22WxVbY+zrzfGdo30v2i1XT1e84/+9o+UoDz8vcqzLREZSVMG5T6fLy0IQrqlpSX94osvKmUMyqkYprIqjDcu8jHgLI3Hkr70uEakUql0NBo1RTAu83g8aUEQCpYBSA8ODla1PV7biYoLvhpUgvLQ/wjpXR0ykVAolPZ4POnBwcG03+/XtS5qXyfqMq3RgQMHEIvFcP/998Pj8eC3f/u36/Gy1MQqTWUFQEllZQYtLVLKLnlSt3BYWm7FojJjNiDtn3M4ACsWAQChkDEnf6tlH2TvQzPgsWQ+mzdvxt69e7Fz5069q1I3fX19SKVSeWPH5b+9Xm/9K0VkUJtv26w8Tr2T0q8iZCpjY2MIBAIIhUJKmst4PK53tVRTt6+lNpsNsVgMn/zkJ/H888/jAx/4AF5//fV6vTw1GTl3MpDJnZyNuZMZbAHcB5XgsURm4PF44HK54PP5cpYfOnQILpcLLpdLp5oRGU97a7vymEE51UM0GoXP50MoFFKWuVyuklkwmk3d24oCgQCef/55TExMQBTFer+8ZqamphCPxxGPx4vms6PKMZVVeQy2uA8qwWOp/pLJpHI9mJqaUn37LS0tWLduXVU/H/jAB1SvR6OJRCIQBAFerxc+nw9erxc9PT2IRCJ6V43IUNhSTvXm9XoxNDQEQRBylk9MTOhTIQ1onhKtkMHBQbhcLrjdbrzxxht6VEF12VPiDw8PY2RkRL/KGESl6cAOH5a6Jd+4IQVdZgkq5GDr9OlMsCV33zZLsMV9UFz2MVHNsURrFwgEMDo6qtn29+3bB4vFkrc8HA7D4XCgo6NDWZZIJJBIJOB0OjWrTyPx+/16V4HI8ITbBOXxlRtX9KsImcL4+DhSqVReY+7c3FzN6S4bkeZB+fT0NLZt25a33GazYXp6GidOnNC6CnWRncu0VO5Sqlwlecw/+9nCuanNkp/brDcuGHAWVyxf+2c/Czz1FPOQ10N2XlPV85gCOd33ZJ/73OcAABcuXMgr27VrF8dUE5FqhFZBecyWcn3t2jWO2dlrelejpM7OjZiYGKz5+YFAADabDTabLWd5PB7PazlvZpoH5YUC8myHDh3Sugp1Ybfb4XA49K6G4Rw8CGzfDjz7rDRRlxxkeL3A+96XCTJkCwtScHHmjPT74EH96l4PZrtxwYCztLNnM58FmXxMyJ+F738//1g6fNgc+6deurq66n5z9sKFCzh69GjBMlEU4ff7sWfPnrrWyQiyhx/o8b4SNaIN6zfA2mLF0soSrrzDlnI9zc5ew//+32/rXQ3NyEPBhoaG8soSiQQ88vi8Okkmk8owZbWHp+nSfZ2oGjt2AKdOASdPZlpHv/c9YNeuTPBhxWJO6+jSkhScbN9u/GDDLDcuGHCWNjmZu38KHRNPPQVMTOQeS83ea4IksVis5OSpRhp3V08cmkaUz2KxYPNtm3F54TLmb8zrXR1T6+zcqHcVylpLHaPRqPLb7XYry+fm5gAAPT09ec+Jx+M4duwYenp6Cgbza6Hl8DRVg/LHH3+86udYLBZ88YtfVLMaZFByKitAai2Vg48H8HLeOOJXsBtLS1KgeuqUblWuG6PfuGDAWV61x4R8LJEx7Ny5E08//TQGBwexadOmnDK/358zzpwqx6FpRIUJtwm4vHCZLeU6W0u38GZw6dIlANKN52w+nw/xeByDg7n/vyiKcDqdiMfjBQP2tdJyeJqqQXmxaektFgvS6XTRMgblVI1KclNvxQyWsB6hkBSkmSU4M+qNCwacpfGYoKNHj2L//v245557IIqiMm+LPEFOoXHoVB6HphEVJo8rv754He8uv4tb1t2ib4XIkFKpVN5YckCa2HRwcDBvTLkci2qVKk3LYUyqBuWFLvrpdBr79+/H0NCQJncsyHwqzU39FrYouakZpDVvkGak/0UrPCbI4/HgwoUL8Pl8eOaZZ5TlgiDgwoUL+LVf+zUda0dERpMzA/s7V3DXhrv0qwwZ2uqgPBqNIpFIwOfz6VQjbagalO/bt69o2YMPPshJZkgVcm7qhYVMbursIIS5qY0VpBnpf9EKjwkCpMDc4/Hg9ddfRyKRgM1mKzvZKhFRLXJyld9IMSgnTdhsNiQSiZxlPp8PQ0NDBVvQm5nJ2pPICOTc1EAmN3XyZsBh9tzUMjlIAzJBWrZmCtKM9L9ohccEZdu2bRv27t3LgJyINNN+W7vymOPKSSuiKOZMVCqKIjo6OuD3+3WslTb41Yya0pEj0ozbQCY39RbMYCtm8Ap2A8jNz339uvTbLIwUpBnpf1Fb9me7mmOCmtdrr72Gq1evqrKtF198UZXtEJH5bG7NailnrnLSiMPhgN/vhyiKEEUR3d3diEQieldLEyb6+kpGIufnloMQOTe1HJxl5+fetAnYuFH6PTAgzeRtBs1+44IBZ3GTk9JnOfuzffy49JkvdUyYJV+7kaXTaWzbtg1/+Zd/uabtPPnkkzh27JhKtSIis8keU566kdKtHmR8g4ODCAQCCAQCqqc4ayQMylUyNTWlJLiXk8qTtg4elFJgDQxkuje3tUl/f/azUoqs06cz45HlnNa7dkk5r42uWW9cMOAs7exZ6TNc6LP91FPSfip0TExMNE9O+maXTCaV68HU1JSq2965cyfOnz+PvXv34pd/+ZerCs6vXr2Kz3/+87jjjjtw8eJFJf8rEVG1csaUs6WcaM1UneitFIvFUq+X0kV2nrrh4WGMjIzoVxkTMXp+7rU6eFD6P599FgiFpOCtrU3q5v2+90lBnLyfgExwd+aM9LveQdzZs7n5yLPrJN9E+P738/+Xw4eN/14CzNfeLAKBAEZHRzXbvsvlwsTEBHw+H/bu3QuLxQKXywWHw4Hu7m4lJ/nc3BxSqRSmp6eV2WrT6TSGhoZyZmgnIqpWe2tmTDmDcmoUPp8PqVQKiUQCgUAA09PTcDqdefnMG5ElXSyBeA3uu+++gssTiQQEQVC+KORUwGLBP/7jP6pVhbqLx+NwOp0IBoOw2+0AtM1hR+UNDEhBHFA8p7W8XqPn51bTykrlNy4AKQiemKhfsDs5WXmd3v9+cwac/Gw3h2QyqfSYmpqaQn9/P2KxmCb5ruPxOAKBAEKhEFKplLLcYrEg+/LucDjgcrlw9OhRbN68ucCWKJt8bdfqfSNqdv/z//xPfOxPPwYAOLjjIP5f9/+rc42I6kvt64SqLeXT09NFy+bn5zE/P5+33Cgt6Ha7nRfuBsCc1sW1tGRShR0/ngl+iwV3S0tSC3u9grtq62S2tGf8bDePet6YdTgcyli7K1euIJFIKC3k8s3wnTt31qUuRpQ9/IA33IkycvKU3+Ds62QOq2+6q0nVoLxQ0E1UT8xpXV4jBneNWKdGw882lbN582YG4Crj0DSiwoRWQXnM7utkFloOT1M1KGeXONKbnNN6YSGT0zo7eGFO68YM7hqxTo2Gn22i+ls9NI2IJBvWb4C1xYqllSXmKSfTEEURvb29ADLD09RisrYmMjrmtC5PDu6ATHCXTY/grhHr1Gj42SaqP3lomsPhYFBOlMVisSgzsM/fYE9ZMoeuri7lmiDfsFWL6l/brl69WrTsxRdfzPshUlu1Oa0bMUe3lhoxuGvEOjWC1Z9N5msnIqJGIY8rZ0s50dqp+tX24sWLaG9vx+c///mC5R6PB16vF16vV3n8Z3/2Z2pWgaii/NzyDNar82HrnaO7XhoxuGvEOumlUK72gQGprJLPthnSwxERkb7kceXXF6/j3eV39a0MUZNTNSgPBAIQBAGf/vSni67zxBNP4MKFC7hw4QJ27tyJc+fOqVkFIgBSfu2JCSmQkbtFt7VJf09MSH/v2iUFMPJYZjkf9q5dUr5sI6v0xoUc3GnRm2D1Nqutk1GdPVv6swmU/mzXO7c8ERGZU84M7GwtJ1oTVYPyeDyO/fv3l1znwQcfxL59++DxeOByuRCPx9WsApFixw4pddbbbwPXrkm/5fRejz6amw/7brwJKxYBSMsffdT4LeblblwcPFi8xXYt+6bUNiupk5FNTlb22QQKf7aNfsOCiIgahzymHABSN1L6VYTIAFQNyhOJBLq7uytev7u7G4lEQs0q6GZqagrxeBzxeFzJX0eNQc7PLY9DXp0PewZb8Sa2YgZb8QBeBgAlH7bRFbtxsWNH+RbbWnoTVLLNUnUyumo/m6s/29QYksmkcj1QO49pOVevXsWLL76YM4zsS1/6Usn5XoiIatF+W7vymC3lRGuj6lc5QRAgCELR8pWVFezZs0f5O5VKqfnyuurv74fT6YTT6UQgENC7OlREJfmw5VbJUMhck79lB3eVtthW02Je7TbNFnDys2kcgUBAuR6omS6lnL6+PrS3t2NoaAg+n09Z/vzzz+PEiRN1qwcRmcPm1qyWcuYqpzqLx+MYHx/XuxqqUfXrrs1mQzQarXj9SCQCh8OhZhV0EwwGEYvFEIvFIIqi3tWhIirNhw1AyYdtRlr0JmAPhdL42TQOURSV60EwGKzLaz755JOIRCKYmJjAt771rZyy/fv3c/4WIlJd9phydl+neotGo9glT7ZjAKoG5YODgwiFQhXNqH7x4kVEo1H09fWpWQXdMJdpc2A+7PK0aLFlK3B5/Gwah5Z5TIsJh8MYGxvDzp07YbFYcsqcTifnb6kRh6YRFZczppwt5VRnly5dqnvjrpbD01QPyu+//354PJ6SgfmLL76IBx98EE6ns+RM7URqYz7s8rRosWUrcHn8bNJazM3N4Y477ihYlkgkYLPZ6lwjY+DQNKLi2lszY8oZlBvDj67/CH8787f40fUf6V2VhqTl8DSrqlsDEAqF4HQ64fF40N3djcHBQeXLQCKRwPnz5xGPx7F582aEQiG1X56orCNHgDNnpK7Scj7sTsxiFp056bfMkA+7ELnFdmEh02KbHUTX0mKrxTaNiJ9NqtXevXvx9NNP4+Mf/3heWSAQMMxQsXoLBoNKbwf2giPKld1Szonemt/5/3Eevxf9PSytLMHaYsXvu34ffT/XmD2aE4kEenp66v66oiiit7cXgNSTSs3AXPWg3Gaz4Y033sAnP/lJfPnLX86ZbAYA0uk0PB4PTpw4gc2bNxfZCpF25HzY8qRjcj5sWXY+7JUVqdW2tdU8LZNyi+3p05kWW7m7ea0ttlps0yiyP2PVfDaJso2NjcHpdOK9730v9u3bBwD49re/Db/fj1dffRVhefwIVUUemkZE+TimXH8f+9OP4cfXf7zm7SyvLONHC5nW8aWVJXzmW5/Bs995Futa1q1p23duuBNf/cRX11pFpFIpHDt2DKlUChMTE7DZbBBFEW63Gx65q6HGurq6NLtBq3pQDkBpBX/11Vdx/vx5Je2ZzWZDX18fdu7cqcXLVqRYNz527zOXgweB7dulScVCIakFt61NCgjlVsiBAWkctFzm8UgtmWYIiKppsa30xgVbgXNNTkqT3xX6jE1MFP9smuHzR9Xbtm0bJiYmIIoi/H4/AMDlckEQBExMTOCee+7Rt4JEZDgcU66/H1//MWavzZZfsUbZgbqexsfH4ff7EQqF4HA44PV6lR7XoigiEok0/RAjTYJy2c6dO+sagKdSKXi9Xni9XgwODhZcRxRFRKNROBwOdHR0YG5uDolEAoODg8oXGTIHOR/2yZO5QeXZs5mWSilt1yxmFzpx+vR6nDkjtVQePKh37bVVSYvtZz9bPKgsFDiyFTijks9Yoc8mUSk2mw2RSARXrlzBxMQEOjo6dL0JTkTGtvGWjbC2WLG0ssTu6zq5c8OdqmxndUu57K62u1RpKV+L8fFx+Hw+vP766wVTb/v9frS3t0MURaVnUzgcxvnz5wFIDa99fX0YGhpaUz20pmlQXi+iKGJubg6AND2+2+0uub7NZkM8HocgCNi1axf8fj9cLlc9qkoNSM6HDeTm0n4AL+d1sX5laTcefVRqZTd68FiqN8H73gc89VQmxRkglZ8+jZI3Lsr1UDD6PgWq/4zJn02iSm3evBl79+7VuxpEZHAWiwWbb9uMywuXMX9jXu/qmJIa3cJljTimPJFIQBRFhEIhJSBf3btZXi43uobDYVy6dElpSU+lUti2bRump6cbujVdtaD8tddeg81mw+23377mbb344ov4tV/7tYrXl3dwKpWqaNzc9PR0zXUjY5NzaRdL27UVM1haWo9nn5VaMo2uUG+C730P2LUrE5BbsZjTDX1pCSVvXBTroWAW/IzRWr322ms1Pe/+++9XtR5ERMJtAi4vXGZLuQH0/Vwf9nTvwQ8u/wD33nEv7tpwl95VUmK87DHjqxtgU6kUgExwLreQywRBwNGjR+Hz+cwRlKfTaWzbtg3hcBj/+l//65q38+STT+LixYtVBeVEasjOpV0qbddb2IJQSAoqzRJMZvcmkINKoEhLL3ZjaQllg8rsbZoFP2OkBofDkZeLvJR0Og2LxYLl5WUNa0VEZiS0CgCA64vX8e7yu7hl3S36VojW5K4NdzVEMC5LpVJ5c35FIpGcDF7j4+MAgP379wOQelDLgbqsULf3RqNaUL5z506cP38ee/fuxYMPPgifz1dxcH716lWMj4/j2LFjsNlsiEajalWrqHA4rHR/kCfDWYtSCeS1nKmP1JOdS7tc2i45l7aZg8qSLb1Yz6CyAH7GjCmZTCKZTBYsK3VtqBXTiRJRo8iegf3qO1dVG+NMBABOpxMXLlwoWp5IJODz+RCJRJRYrtCQ5EAg0PBDlVUdU+5yuTAxMQGfz4e9e/fCYrHA5XLB4XCgu7sbHR0dAIC5uTmkUilMT08jGo0ikUggnU5jaGgIzzzzjJpVKsjn86Gvrw8ejwfRaBROpxM+n6/o5HCVKJWnbnh4GCMjIzVvm+ojO5d2ubRdZs2lnR1UlmvpZVCZj58xYwoEAhgdHa3b68kpz4iI9JY9A/v8jXkG5aSqwcFBBAIBjI2NYWhoKGc8eTQaVcablwq45fTcjX5DW/WJ3hwOByKRCOLxOAKBAEKhECKRiFJusViQTqdz1n/iiSdw9OjRuuQtDwQCOd0gXC4X/H4/vF4vdu3aVXM+0mAwCLvdXrCMreTNITuXNlA8bRdgvlzasuygslxLL4PKfPyMGZMoiujt7S1YNjU1VfKmrVp6enrg9/uxZ8+eguVXr15V8ruKosjx5RXK7unAXm9E+bJbyjmunLQQi8UwNjYGr9erdEsXRRHd3d1l5wkbGxtDIpFALBZTpS7ZPePU7gmn2ezrDocDgUAAgUAAV65cQSKRUFrIBUHQLVVLoVzk8t0Vub61sNvtNQf01Diyc2kD+Wm7gOrzcxtJdlBZrqWXQWVG9melms8YNYdGCNbKfTGRe4YJgoALFy4gFosxd3kFsm+osNcbUT55TDnAXOWkHTmdmSiK8Pv9FQ079vl8uOOOO5QW8vHx8TX1iga07RlXl6/Mmzdvxs6dO7F3717s27cPe/fu1SUgHxsbg9PpLFqeSCTqWBtqRHIubWuR21XZ+bk3bQI2bpR+DwxIqa7M4MiRzP6RW3q3YAZbMYNXsBsAg0rZ5KT02cj+rBw/Ln2GSn3GzJKvndTjcrkQCoXQ09ODnp4e/PEf/7FS9uqrryIajWJ8fBxzc3PYtm0bxsbGdKxt8wgGg4jFYojFYhBFUe/qEDWc7Jby1I2UbvUgc5ibm6soIJcne5NTpIXDYVW6r4uiqFwTgsHgmreXzRB5yisViUTyZuMDoOQ4Z0s3Adrk5zYS+caFnGt7dUtvdlBpxt4EsrNnM/tIJn9W5Js73/++efO1k7p6enrg8/mUnl+HDh3C9PQ0nn76aUxMTMBisSgz0/b19Smz1VJp7AVHVFr2mHK2lJOWUqmUMj9ZKaIoKte47GudGhO9adkzzlRfk91ud8Hu6XJuc94FJ5mcS/vtt4Fr16Tfhw/nBuRWLOJuvAkrFgFAyc9thhbzgweBiQmpFbitTVrW1ib9PTEh3dRY3UJspt4Ek5O5AXmhz8pTT0mfqezP2KlTDMipNoFAAKIo4lvf+ha+9a1v4cKFC/D7/QAyOVxvv/12ANINaPYMIyI1tLe2K48ZlJOWJiYmcvKTFxMIBJBOp/N+suc4a0SGCsrlFu/Lly8XLB8aGoLf78/5MhKPx3Hs2LG8CeCIgEwu7ZaW/PzcM9iKN7EVM9iKB/AyACj5uc2g0I2LU6eA//W/gF27pBZheaZ2uYV41y6pBdnoqvmsZH/GiGqVSCTg9XqVv91uN9LpNN54442C6zdDzlYianzZLeWc6I205HK54PF49K6GZgzRfd3n8yGRSCAejwOQuirE43EIgoATJ07kfPmIRCLw+XxIpVLKxHMXL15k9zQqifm5i5ODSqBwC3H2zOJyb4Lt243bIszPCulBHjcnz75+4cIFWCwW3HPPPXk3qiORCG9CE5EqOKacSB2GCMrlLnparU/E/NyVWd1CvHpm9lewW2khPnVK16pqhp8V0sMzzzyDBx98UOmeNz09DUEQ8Pjjj+P8+fMAgC984QvYt28fxsfH8ZnPfEbP6hKRQXBMOZE6VA3Kr169CiAzbo3IKJifuzy2EEv4WSE9uFwuTExMIBAIYH5+Hs888wwAqVX86NGjuHz5Mp544gkMDQ3BZrPh05/+tK71TaVS8Hq98Hq9JVPUjI2N4dKlS8rkPk6ns+j61axLROrYeMtGWFusWFpZYvd1ojVQNSh3Op0QRVH3iz2R2pifuzy2EEv4WSG9OByOvMlM9+3bpzzu6+tDIpHIWVZvoigq879Eo9GSk/a43W7YbLacNDZerxexWCzv/6xmXSJSj8ViwebbNuPywmXM35jXuzpETUvVoHx6ejpvnNodd9yBixcv4v7771fzpRrO1NSU8ljL6fJJP0eOSGnPlpYy+bmzx0sD5s7PzRbiDH5WzC2ZTCKZTALIvTbobefOndi5c6eudZAD5FQqpWQ+KSQajSIajWJ+PvdL/okTJ9De3g5RFJW5YKpZl4jUJ9wm4PLCZbaUE62Bqm00DocDExMTOctWXySNqr+/H06nE06nk3flDUrOz229eStLzs+dHWRl5+e+fl36bRZyCzGQaSFO3gzCzdBCnP2eV/NZIeMJBALK9aC/v1/v6jSlUCgEQRDyZomXl2VfZ6tZl4jUJ7QKAIDri9fx7vK7+laGqEmp2lL+5JNPYv/+/YjFYjkt5j6fr2j6FYvFgnPnzqlZDV0Eg0HY7XYAYCu5gR08KM0c/uyzQCgktQq3tUlBptzqOTAgja2WyzweqeXUDAGYGVuIJyelCe4KvecTE8U/K2b4PJiVKIro7e0FILWU1yMwv3LlCvbv34+JiQklL3k2i8WCJXkWxiYQjUaLzhDf0dGR0wBQzbrVKtXTgb3iiCTZM7Bffecq7txwp36VIVJRds+31dTuCadqUO7xeHDhwgU888wzygywFoulZLJ2owTldrud3eNMQs7PffKkNC66tVVq9T17NjcdGJDJz33mjPT74EHdql0XcguxvB/kFmKZ0VqIK3nPC31WyNj0CNa8Xq8SnDqdzqbPQ55IJIpeUwVBQCKRqGndapW6oTI8PIyRkZGat01kFNkzsM/fmGdQToYRCAQwOjpal9dSPSWax+PJSeze0tKCeDxu+DHlZD7Mz11Yud4ERvn/q33PjTipHTWOiYkJiKKI5557Tu+q1EWh3gBqrLtadi+41dhKTiTJbinnuHLSUjgcxvnz59HR0YHu7m4MDQ1p+nrZPd9WU7snnOZ5yv1+f9FuZURGwfzcuYr1JjASvufUSDo6OkrOZG4k9QrIAfaCI6qEPKYcYK5y0s7Y2BgikYjSA7u7uxsul0vTc3Q9e75p/jX5iSeeYN5yMrRK8nNbsQhAajk22+RvGzYYLyDne06NZt++fSWHiqnB7XbDYrFU/NPe3l7za5W6mT83N5dTXs26RKS+7Jby1I2UbvUg44pGo/D5fDlpL10ul6Em8tS8pZzI6Jif23z4nlOjeeyxx+B2u3HgwAHs37+/4JjyPXv2rOk1tA76szkcDkSj0YJlqVQK+/fvr2ldIlJf9phytpQ3t9nZa5ia+hHs9rvQ2blR7+oovF4vhoaG8q5ta5nIs9EwKCdaI+bnNh++59RonE4nUqkUEolETksCAKTTaVgsFiwvL+tUu+r19fUhHA4jlUrlfAmTu6N7vd6a1iUi9bW3ZnrFMChvXl/6UhyPP/41LC2twGptwXPPPYRPflL/4Tvj4+NIpVIQRTFn+dzc3JqHKDUSBuVEayTn5z59OpOfe/X4YiPn5zYjvufUaPx+v95VqMrc3BwA4PLlywXLPR4PXC4XfD5fTvfEQ4cOweVyweVy1bQuEakvu6WcE73V165d45idvbbm7Swvr2B29rry99LSCg4d+nM89dS3sW7d2r7EdHZuxMTEYM3PDwQCsNlseUOR4vF402caycagnEgFZszPbXZ8z6mRHDp0SO8qVMTn8yGRSCAejwOQWkDkL1YnTpzI+YIViUTg8/ng9Xphs9mQSCTQ09NTcLbdatYlInVxTLl+Zmev4X//77c13P718itpKB6PIx6PFzyXJxKJnIxfzY5BOZEKqs3PvbJi3FnJjWr1e2a2nOxEaqi2Rb+a9ZuttwCRUXBMuX7UGve9uqU8s/0NqrSU10qeLyQajeZkGJF7W/X09OSsL6dMA6Sgva+vr2luzjIoV8nU1JTyuJ7T51PjqCQ/9+SklEorHM6UezxSqyuDt8ZU6j0zS052qk4ymUQymQSQe22oh6tXryKRSBQsu//+++taFyIyh423bIS1xYqllSV2X6+ztXQLX60Rx5RfunQJABCLxXKW+3w+xONxDA5m/v9wOIxLly4p86qkUils27YN09PTTTFLO4NylWQnjx8eHsbIyIh+lSHdlMrPffZsplVVtrAgtaaeOSP9PnhQl2pTEZW+Z0bPyU7VCQQCGB0drfvryhOeFeJwOJQvN1Q53nAnKs9isWDzbZtxeeEy5m/M610dqtEnP+nARz/63oaafT2VShVMaxkOhzE4OJgz5EluIZcJgoCjR4/mzTeyFlredGdQrpJgMAi73Q4AvGiTkp9bNjmZG9xZsZgz/nhpSSrfvp2tq42i2vds9XtO5iWKInp7ewFIF+3sm7ZaefLJJxEKhTA4OAibzYYnn3wSQ0NDSKfT+NznPpc3ay1VhjfciSoj3Cbg8sJltpQ3uc7OjQ0RjGdbHZRHo1EkEgn4fL6c5aIo5s3GrvZEcFredGdQrhK73Q6HQ/+0AdSYjh/PBHcP4OW8mbpfwW4sLUndoE+d0rWqdBPfM6qVHi2q4XAYY2Nj+PSnPw1AmkDtwIEDuP/++2GxWDA9PV3X+hgFb7gTVUZoFQAA1xev493ld3HLulv0rRAZgjxxZzafz4ehoaG8YL1Qpo1AIKBqBg4tb7qzkyWRxlZWpPHIgNTaKgd3ANCFWZzDAVixCEAal7yyoldNScb3jJpNIpHIuTGc/UXG7XYX7dZOpck33B0OB4NyohKyZ2C/+s5V/SpChiKKIiYmJnL+7ujoqGhiT7klXR5jroauri7lmiDfsFULg3Iijd24IY1DBoBOzCrBnawLs+i8uWxhQVqf9MX3jJqNzWbDq6++qvztcDgQiUQASCllik3+RkSkhuwZ2DmunNTicDjg9/shiiJEUUR3d7dybStlbGwMiUQCsVisaXKZs/s6kcZaW6UZuRcWgFl0IonOnCAviU7MohOAtF5rq141JRnfM2o2+/btw7lz5/CpT30KALB//37s2rULgiAgEAgUnCiHiEgt2S3lHFdOasqeYb0SPp8Pd9xxh9JCPj4+XvU29MCWciKNtbRIKbQAKZf1AZxD8mZAJ49PXsJ6AFIqLc7crT++Z9RsPvOZz+DJJ59U/nY4HDh06JDSxU/N7ntERKvJY8oB5ion/ciTvTkcDoTDYYTD4aa5/rGlnKgOjhyRUmgtLQGvYDe2YiZnJm8AsFql3NbUGPieUTPZvHkz9u3bl7MsEAhgbGwMmzdvLvIsIiJ1ZLeUp26kdKsHmZcoihgfHwcA5TdQeAK4RsT2HaI62LFDymltvXkbbAnr8Ra25AR3p08zHVoj4XtGRsCAnIjqIXtMOVvKSQ+BQADpdDrvp5Ix6I2AQTlRnRw8CExMAAMD0jhkQPo9MCAtP3hQWrayAly/zhm99bB631f6nhEREZlZe2u78phBOVH1GJQT1dGOHVJO67ffBq5dk36fOiUtn5yUgr1Nm4CNG6XfAwPSctJWqX1f6j0jIiKi3JZyTvRGVD2OKVfJ1NSU8rirq4v5TKmklhZgw4bM32fPAo8+Ko1fli0sSN2jz5yRfrNVVhuV7vvV7xlRMclkEslkEkDutYGIyKg4ppxobRiUq6S/v195PDw8jJGREf0qQ01lcjI3KLRiMWdCsaUlqXz7drbOqo37nrQQCAQwOjqqdzVIBbzhTlQZjiknM9Dypju7r6skGAwiFoshFotBFEW9q0NN5PjxTFD4AF7GDLbiTWzFDLbiAbwMQCp/9lkdK2lQ3PekBVEUletBMBjUuzq0Bv39/XA6nXA6nQgEAnpXh6hhbbxlI6wtUlsfu6+TUQUCAeWakN0gqwa2lKvEbrfD4XDoXQ1qMisrQDgsPbZiEedwAF2YBQB0YRbncABbMYMlrEcoBJw8yZzYauG+J62wRdU4gsEg7HY7APA9JSrBYrFg822bcXnhMuZvzOtdHSJNiKKI3t5eAFJLuZqBOYNyIh3duCGNXwaATswqQaGsC7PoxCzewhYsLEjrc1yzOrjviagc3nAnqpxwm4DLC5fZUk6GpeVNd7b7EOmotTWTamsWnUiiM6c8iU7M3lzW1iatT+rgviciIlKP0CoAAK4vXse7y+/qWxmiJsOgnEhHLS2AxyM9XsJ6HMA5JThMohMHcA5LWA8A8HrZfVpN3PdERETqyZ6B/eo7V/WrCFET4tdMIp0dOQJYbw4keQW7sRUz2IIZbMUMXsFuAFL54cM6VtKguO+JiIjUkT0DO8eVE1WHQTmRznbskHJhy8HhEtbjLWxRWmmtVqmcKbnUx31PRESkjuyWco4rJ6oOg3KiBnDwIDAxAQwMZMY5t7VJf09MSOWANGP49evSb6rN6n1Y6b4nIiKi4uQx5QBzlRNVi0E5UYPYsQM4dQp4+23g2jXp96lT0vLJSSlI3LQJ2LhR+j0wIC2nypTah6X2PREREZWX3VKeupHSrR5EzYgp0VQyNTWlPGaOWlqLlpbc1FtnzwKPPgosLWWWLSxI3arPnJF+szW3tEr34ep9T1SLZDKJZDIJIPfaQERkZNljytlSTlQdBuUqyU4ePzw8jJGREf0qQ4YxOZkbTFqxiE7MYhadWMJ6LC1J5du3s1W3GO5DqrdAIIDR0VG9q0Eq4A13osq1t7YrjxmUkxFpedOd3ddVEgwGEYvFEIvFIIqi3tUhgzh+PBNMPoCXMYOteBNbMYOteAAvA5DKn31Wx0o2OO5DqjdRFJXrQTAY1Ls6tAb9/f1wOp1wOp0IBAJ6V4eooWW3lHOiNzKiQCCgXBOyG2TVwJZyldjtdjgcDr2rQQaysgKEw9JjKxZxDgfQhVkAQBdmcQ4HsBUzWMJ6hELAyZPMpb0a9yHpgS2qxhEMBmG32wGA7ylRGRxTTkYniiJ6e3sBSC3lagbmDMqJGtSNG9K4ZwDoxKwSTMq6MItOzOItbMHCgrQ+x0Pn4j4korXgDXeiynFMORmdljfd2SZE1KBaWzMpumbRiSQ6c8qT6MTszWVtbdL6lIv7kIiIqD423rIR1hapvY/d14mqw6CcqEG1tAAej/R4CetxAOeUoDKJThzAOSxhPQDA62W360K4D4mIiOrDYrEoreXzN+Z1rg1Rc+FXUKIGduQIYL05yOQV7MZWzGALZrAVM3gFuwFI5YcP61jJBsd9SEREVB/yuHK2lBNVx1BBeSqVgtvtxvj4eMn1xsbG4PV6IYoiRFEsuz6RXnbskHJoy0HlEtbjLWxRWnetVqmcqbyK4z4kIiKqD6FVAABcX7yOd5ff1bcyRE3EEBO9iaKIubk5AEA0GoXb7S66rtvths1mQygUUpZ5vV7EYjGmO6GGdPCglEP72WeBUEiauKytTepuffgwg8lKcB8SERFpL3sG9qvvXMWdG+7UrzJETcQQLeWBQAChUAgnTpwouV40GkU0GoXf789ZfuLECYyPjyMej2tZTaKa7dgBnDoFvP02cO2a9PvUqfxgcmUFuH5d+m1WxfZBpfuQiIiIapM9A/v05em88h9d/xH+duZv8aPrPyr4/FLlWpRxu9put9n+Fz0ZIiivVCgUgiAIEAQhZ7m8jC3l1OhaWqSUXasnJJucBAYGgE2bgI0bpd8DA9Jys6h0HxTbh0RERLQ2/+fa/1EefyL0CZz/H+eVv8//j/P4pcAvof9CP34p8Es5ZeXKtSjjdrnvVz9XT5Z0Op3WuxJqSaVSaG9vh9/vx9DQUF55d3c3BEFALBarqqyUeDwOp9OJYDAIu91ecB0tc9oRnT0LPPoosLSUXyaPlz54sP71qifuA9JTMplEMpksWDY1NYX+/n7EYjHmu24iha7t1V7Lvz39bTz2lce0qiJpzGKx6F2FqlhQeX0L/W/yMnk7FljQYmlB6/pWtN3Shg3rN6B1fSs23LIBbevbsOnWTfj49o/jF7b+grKNH13/ET74/Aexks7tqra+RZrDZXFlMe9117esh8ViQTqdLlhutUgjbZfS+Rf4WsvWWdYpr7mcXi5YDqCmMm63uf4Xa4sV3xG/g7s23JX3nEKyr/dqX98NMaa8UolEouhOEwQBiUSi5m339/cXLRseHsbIyEjN2yYqZnIyNxi1YhGdmMUsOrGE9Vhaksq3bzduN23uA9JbIBDA6Oio3tUgDWRf26u9lqdR+MshNQnDNFmtzfXF68BC4bJv/dO38HeP/x1utd4KAPjB5R/kBeRA4WC8kjKgcFC91rLl9HLJ97fUcVu2jNttqv9laWUJP7j8g4qDci2v96YKystJpVI1P7dcSzmRFo4fzwSjD+BlnMMBdGFWycH9CnZjaUma4OzUKV2rqhnuA9KbKIro7e0tWCbfSafmtLqlvBqbbtmE9//U+7WoFt2UZuQMoMr9kF79Zxpyp1l5O/LfyyvLuLF0AwuLC7j+bv5s6ld/chU/vv5j/PTmnwYA3HvHvbC2WLG0khsQv/eO9wIA/vHyP+ZV5713vBfr163H4vJi4fI7bz73x+qWKa9ZoPxn7vwZAMA//Pgfqi6Tt1vLc4203Wb5X6wtVtx7x7156xeTfb1X+/rOoPymtQTkAGC329k1kepqZQUIh6XHViwqwSgAdGEW53AAWzGDJaxHKAScPGm8cdTcB9QIOETJuNZybf/Alg/gK5/4iroVItLR0soSbizewOjFUfzZ//ozAEDqnZQSlN+14S78vuv38XvR38PSyhKsLVb8vuv30fdzfQCk8bzFysqVa1HG7XLf/77r9ytuJQe0vd5zTPlN7e3t6OjowPR0/kyRpcjjzjhekOrt+nVpQjMAuBtv4k1szVtnC2bwFrYAkGYc37ChnjXUHvcBNTpeI5oT3zei4j7/V5/Hc3//HADgtPc0PvSeD+WU/+j6j/CDyz/AvXfcmxfwlCpby3Mb7TW53eb8X6qh9nXCVC3lDocD0Wi0YFkqlcL+/fvrXCOi2rW2Srm2FxaAWXQiiU6llRgAkujELDoBSOu1tupVU+1wHxAREdVXdtqz1I1UXvldG+4qGuyUKlvLcxvtNbld/V5zrc/Vi6k6cvb19SGVSuV1VZf/9nq99a8UUY1aWgCPR3q8hPU4gHNI3gxA5fHUS5BmPPV6jdltm/uAiIiovtpva1ceX3nnio41ITIOQ31FnZubAwBcvny5YLnH44HL5YLP58tZfujQIbhcLrhcLs3rSKSmI0eklF8A8Ap2YytmsAUz2IoZvILdAKTyw4d1rKTGuA+IiIjqZ3NrVkv5Oyn9KkJkIIbovu7z+ZBIJBCPxwEA4+PjiMfjEAQBJ06cgCAIyrqRSAQ+nw9erxc2mw2JRAI9PT0Fx6ATNbodO6Qc3HJKsCWsV8ZPA5kc3UZOBcZ9QNRc5Gu2nIZUFEUMDg4WXHdsbAyXLl1CR0cHAMDpdKqyLhHVTrhNUB4X6r5ORNUzRFDu9/s1XZ+okR08KOXgfvZZIBSSxle3tUndtQ8fNkcwyn1A1Bzcbjf8fr8yKU40GoXb7UYkEkEoFMpb12az5Sz3er2IxWIIBAI1r0tEa5Mzppwt5USqMERQTmR2O3ZIObhPngRu3JAmNDPb+GnuA6LGNjY2BlEUc2apdblcGBoawtjYGMLhMDw3J4mIRqOIRqOYn5/P2caJEyfQ3t6es51q1iWitWtv5ZhyIrXxKyuRgbS0SCm/CgWjKytSCrGVlfrXS22l/pdS+4CI9BOJROD1evMmW+3r61PKZaFQCIIg5Aw/A6Asy279rmZdIlq722+9XXnMlnIidbClXCVTU1PKYy0TyxNVa3ISOH4cCIcz3bo9HmmCtGbr1m2k/4WMK5lMIplMAsi9Npidw+HAxMRE3nI5mJbHmANS67fNZiu4nY6OjpztVLNutUq9f7zWk1ndar0VbevbsLC4wDHlZGjZ1/PV1L6+MyhXSX9/v/J4eHgYIyMj+lWG6KazZzMToMkWFqSJz86ckX4fPKhf/aphpP+FjC0QCGB0dFTvajQcv99fcE6XaDQKQBoXLkskEkW7nAuCkBPAV7NutbKv7avxWk9mJtwmSEE5W8rJwOp5PWdQrpJgMAi73Q4AvHNODWFyMjeItWIRnZjFLDqxhPVYWpLKt29v/FZmI/0vZHyiKKK3txeAdCe9VGBHUrBus9mqyoKyugu8Wuuuln1tX43XejIzoVXAD9/+Ia68cwXpdBoWi0XvKhGpLvt6vpra13cG5Sqx2+2cSIYayvHjmSD2AbyMcziALswiiU4cwDm8gt1YWpJmLD91SteqlmWk/4WMj92aK+f1eiEIAi5evFjxc+oVkAO8thMVI8/AvriyiOuL17Hxlo0614hIffW8nnMqJCIDWlmRxl0DUquyHMQCQBdmcQ4HYMUiACmFWCNP/mak/4Wombndblgslop/2tvbS27P6/UCAGKxWN4kbcXGiAPA3NxcTnk16xKROrJzlV+5wRnYidaKQTmRAd24IY23BoBOzCpBrKwLs+i8uWxhQVq/URnpfyFqZpFIBOl0uuKf1SnKsnm9Xrjd7py84vLYckCaFK7YWPBUKgWXy1XTukSkDqFVUB5zXDnR2jEoJzKg1lZpZnIAmEUnkujMKU+iE7M3l7W1Ses3KiP9L0QkBeRHjx7F4OCgsiyVSuUE6H19fUilUnndz+W/5Vb2atclInVkt5QzKCdaOwblRAbU0iKlCgOAJazHAZxTgll5HPYS1gMAvN7GzultpP+FyOycTicSiQSOHTsGr9er/Ozduxfd3d3Keh6PBy6XCz6fL+f5hw4dgsvlymn9rmZdIlKHPKYcANOiEamAE70RGdSRI1KqsKUl4BXsxlbM5MxYDgBWK3D4sM4VrYCR/hcis/J6vYjH4wCg/M62Ol1aJBKBz+eD1+uFzWZDIpFAT09PwVnaq1mXiNau/bbMnBFX3uGYcqK1YlBOZFA7dki5u+VUYktYj7ewRSm3WqXyZkghZqT/hcissrunV6pQXnM11iWitdncmtVSzu7rRGvGjp5EBnbwIDAxAQwMZMZlt7VJf09MSOXNwkj/CxERUTPLGVPO7utEa8aWciKD27FDyt198qQ0M3lra/OOuzbS/0JEjW9qakp5zPzzRBk5Y8rZUk4mkUwmkUwmAeReH9TAoFwlvHBTo2tpATZs0LsW6jDS/0LGo+VFm+qrv79feTw8PIyRkRH9KkPUQNpbOaaczCcQCGB0dFSTbTMoVwkv3NTMVlYar+W5EetEVAktL9pUX8FgEHa7HQB4s50oy+233q48Zks5mYUoiujt7QUg3XTPjv/WikG5SnjhpmY0OQkcPw6Ew8DCgjRG2+ORZjvXa9K0RqwTUTW0vGhTfdntdjgcDr2rQdRwbrXeirb1bVhYXOCYcjINLXtDMyhXCS/c1GzOns3MZi5bWJBmMT9zRvpd78nTGrFORNXiECYiMgPhNkEKytlSTrRm7BRKZEKTk7nBrxWLuBtvwopFANLyRx+V1jNznYiIiKgwoVUAII0pT6fT+laGqMkxKCcyoePHM8HvA3gZM9iKN7EVM9iKB/AyAKn82WfNXSciIiIqTJ6BfXFlEdcXr+tcG6LmxqCcyGRWVqTx2oDUGn0OB9CFWQBAF2ZxDgeU1ulQSFrfjHUiIiKi4rJzlV+5wRnYidaCQTmRydy4IY3TBoBOzCrBr6wLs+i8uWxhQVrfjHUiIiKi4uTu6wBnYCdaKwblRCbT2irNaA4As+hEEp055Ul0YvbmsrY2aX0z1omIiIiKy24pZ1BOtDYMyolMpqVFSjEGAEtYjwM4pwTBSXTiAM5hCesBAF5vfXKEN2KdiIiIqDh5TDkApkUjWiN+tSUyoSNHAOvNhIivYDe2YgZbMIOtmMEr2A1AKj982Nx1IiIiosLab2tXHl95h2PKidaCecqJTGjHDinnt5yCbAnr8Ra2KOVWq1S+Y4e560RE5jY1NaU8Zv55olybW7Naytl9nUwgmUwimUwCyL0+qIFBuUp44aZmc/AgsH27lGIsFJImUGtrk7qHHz6sT/DbiHUiqpaWF22qr/7+fuXx8PAwRkZG9KsMUYPJGVPO7utkAoFAAKOjo5psm0G5Snjhpma0Ywdw6hRw8qQ0o3lrq/7jtRuxTkTV0PKiTfUVDAZht9sBgDfbiVbJGVPOlnIyAVEU0dvbC0C66Z4d/60Vg3KV8MJNzaylBdiwQe9a5GrEOhFVQsuLNtWX3W6Hw+HQuxpEDam9lWPKyVy07A3NoFwlvHATERHAIUxEZA6333q78pgt5URrw06hRERERERUlVutt6JtfRsAjiknWisG5URU1soKcP269LuRt0lERET1I0/2xpZyorVhUE5ERU1OAgMDwKZNwMaN0u+BAWl5I22TiIiI6k9oFQBIY8rT6bS+lSFqYgzKiaigs2eBXbuk3OALC9KyhQXp7127pPJG2CYRERHpQ56BfXFlEdcXr+tcG6LmxaCciPJMTgKPPgosLUl/W7GIu/EmrFgEIC1/9NHqWre12CYRERHpJztX+ZUbnIGdqFYMyokoz/HjmeD5AbyMGWzFm9iKGWzFA3gZgFT+7LP6bpOIiIj0I3dfBziunGgtGJQTUY6VFSAclh5bsYhzOIAuzAIAujCLczigtG6HQpVN1KbFNomItDY1NYV4PI54PI5kMql3dYgaTnZLOYNyMrpkMqlcE6amplTdNvOUqyT7jWGOWmpmN25kxnt3YlYJnmVdmEUnZvEWtmBhQVp/w4b6b5OoUSWTSSWAU/uiTfXV39+vPB4eHsbIyIh+lSFqQPKYcoBp0cj4AoEARkdHNdk2g3KV8MJNRtHaCrS1SUH0LDqRRGdOEJ1EJ2bRCUBar7VVn20SNSotL9pUX8FgEHa7HQB4s52ogPbb2pXHV97hmHIyNlEU0dvbC0C66Z4d/60Vg3KV8MJNRtHSAng80ozoS1iPAzindDdPohMHcA5LWA8A8Hql9fXYJlGj0vKiTfVlt9vhcDj0rgZRw9rcmtVSzu7rZHBa9oZmUK4SXrjJSI4cAc6ckSZeewW7sRUz6MQsZtGpBM9WK3D4sL7bJGpEHMJERGaRM6ac3deJasb2KCLKs2OH1KptvXnbbgnr8Ra25ATPp09L6+m5TSIiItJPzphytpQT1YxBOREVdPAgMDEBDAxI47wB6ffAgLT84MHG2CYRERHpo72VY8qJ1MDu60RU1I4dwKlTwMmT0ozora1rH++txTaJiIio/m6/9Xbl8fw78zrWhKi5mfKrcCKRqGo5kdm1tEgpytQMnrXYJhEREdXPrdZb0bZe6vp25QZbyolqZcqvw6IowmKxwOl0wu12w+l0or29HYFAQO+qERERERE1DXmyN44pJ6qdKYNyALDZbIjH45iYmEBHRwdCoRD8fr/e1SIiIiIiahpCqwBAGlOeTqf1rQxRkzLtmPLp6Wm9q0BEREQNbGpqSnnMVHdEhckzsC+uLOL64nVsvGWjzjUi0kYymUQymQSQe31Qg2mDciIiIqJS+vv7lcfDw8MYGRnRrzJEDar9tqwZ2G9cYVBOhhUIBDA6OqrJtk0dlIfDYSQSCdhsNrhcLgiCUPO2St0t4d11IiLjyr5zvprad9KpvoLBIOx2OwDwOk5UxObW3FzlP735p3WsDZF2RFFEb28vAOn6nn3jdq1MG5T7fD709fXB4/EgGo3C6XTC5/NhcHCwpu2VelN4d704r9eLcDhsiDFIPp8PY2NjCAQCNX+OiKj5aHnnnPRlt9vhcDj0rgZRQ5MnegM42RsZm5YNraYMygOBAGw2m/K3y+WC3++H1+vFrl27aroAZ99NX63ed9dXVpj/mYioXrLvnK+m9p10IqJGI48pB4DUjZR+FSFqYqYMyrMDcpnL5QIgBey1pEZrhLvpk5PA8eNAOAwsLABtbYDHAxw5AuzYoWvVTMHv95tyBn/eBCKz4xAlIjKznDHl7zBXOVEtTPcVemxsDE6ns2h5IpGoY23Uc/YssGsXcPq0FJAD0u/Tp6XlZ8/qWz8ynslJYGAA2LQJ2LhR+j0wIC0nIiIic1g9ppyIqme6oDwSiSCVSuUtn5ubAwDdW7trMTkJPPoosLRUuHxpSSpnsERq4U0gIiIiAlaNKWf3daKamC4od7vdBbunh8NhANLYwGZz/HjxgFy2tAQ8+2x96lNMOByG0+mExWKB0+nE2NhY3jpjY2OwWCwFeyyIooj2dqmL1Pj4ONrb25FIJODz+dDd3Y329na43e68my6pVEpZx2KxoLu7Gz6fL2/7hbZpsVjgdruRSCSQSCTgdrthsVjQ3t6et41wOAyLxYJ4PJ73+qIoKttrb2+HKIoFbw41g9U3gaxYxN14E1YsAuBNICIqTj4fi6IIURThdrsLXgtkY2Nj8Hq9yvrj4+OqrEtE6uFEb0RrZ7ox5UNDQ3C73bDZbMrY8ng8jmPHjuVNANcMVlakMeSVCIWAkyf1GfcbDofh9XohCAL8fj9sNhvOnz+v3AyRDQ4Owufzwe/35908GR8fx9DQkPJ3KpWC2+1WJuqLRCIYHx+H1+tFJBJR1otGo4hGoxBFEQ6HA/F4HD6fD4lEAqFQKOc1Sm1T/jLp9XoRCAQwNjaG7u7ukjOtJxIJZbjE4OAguru7MT09jXA4jGg0Co/HU/M+1Uv2TaAH8DLO4QC6MIskOnEA5/AKdis3gU6d0rWqRNRg5POnfK1NpVLYtm0bIpFIznkbgHKtzj5Pe71exGKxvOtDNesSkbqEVkF5zDHlRDVKm9TQ0FB6cHAw7fF40i6XKx2LxWraTiwWSwOo+flrde1aOg1U/nPtmi7VTNtstrQgCOn5+fmc5Q6HI736Yzg4OJgGkLNuIBBIA0hPT0/n/O3xeAq+TjmlXqPQNgGkQ6GQsmx6ejpv3VAolPdZcDgcBf/vZrW8nE63tUmfJSveTf8QnTkfsB+iM23Fu2lAWm95We8aE+lL72tEI5H3Rfa5NJ1Opz0eT94+ikQieefodDqdnp+fX9O61daV7xtRee8svpO2fc6Wtn3Olvae8epdHaK6UPs6Ybru6zK5JTYUCiESiTTlWHJAmvG6ra2yddvapPXrTe76PTg4CEEQcso6Ojry1pe7hWd3PQwEAnC5XHk9Gfr6+nL+ttlsFXUL7+7uVuq2WqFtApkZ+rOXlXqtVCqFeDxe8P9uVjduZMaQd2IWXZjNKe/CLDpvLltYkNYnIgIAQRAgCIIyh4tMvg5kLw+FQsr6hbaR3fpdzbpEpL5brbeibb30ZfTKDbaUE9XCdN3XjaalRUp7dvp0+XW9Xn26rsuBrxwIl2Oz2eByuRAIBDA0NIR4PI54PJ7XtRFAwS9hhcTjcZw/fx7xeFy5SVBMsW1WG1hPTEwAqPz/bgbyTaCFBWAWnUiiMycwT6ITs+gEoN9NICJqTDabDfPz83nLo9Goct5fvayQjo4O5fxa7brVmpqaKlrGVHhEGcJtAhYWFzimnAwlmUwimUwWLCt1faiFaVvKjeTIEcBa5vaK1QocPlyf+qwmf1manp6u+Dl+vx+JRALRaBTnz5/P+8JWDVEU4XQ6lQnXQqFQzth0rdTyfzc6+SYQACxhPQ7gHJI3g3B5TPkS1gPQ7yYQETWHRCKhzDUSi8XyyooRBCGnvJp1q9Xf3w+n01nwhy3wRBnyuPIr71xBOp3WtzJEKgkEAkWvAf39/aq+FlvKVZJ9t6Ted8937JBayoulRbNapfIdO+pWpRw2mw2CIGB8fBx+vz+nrNiXJYfDAYfDgVAohGg0WnC29EqkUillgrjs1z5//nxN26uGzWaDw+HA+Pg4jh49mtfSnkqlmrJb+5EjwJkz0mftFezGVsygE7OYRacSkOt5E4hIb9l31tW+k24EqVQKx44dQyqVQiKRyBsyVOk2tFh3tWAwCLvdXrCMreREGZtvk3KVL64s4vridWy8ZaPONSJaO1EU0dvbW7BsampK1cCcQblKst+U4eFhjIyM1PX1Dx4Etm+XZrwOhaTuxW1tUmvl4cP6BeSyEydOwOv15qQjCwQCJVswjh49ikOHDiGVSpWc4bwUeUzh+Pg47rjjDjgcDkQikZIpeNQUCoXgdDqxbds2JTCXZ18XRbEuLfZqW30TaAnr8Ra2KOV63wQi0lsgEMDo6Kje1WhYchYOmdvtxrFjxxCLxSrKgFKvgBwA7HZ70845Q1RP7be1K4+v3LjCoJwMoZ4NrexcqpJgMIhYLIZYLKZbrvMdO6QUVG+/DVy7Jv0+daoxgiOPx6OkqvH5fAiFQvD7/RgcHCz6hUdOF1ZrQC67ePEiOjo6lNy4gPSluR5ftGw2G15//XVljLwoigiHw/B4PGv+v/R08CAwMQEMDGQmGmxrk/6emJDKicxKFEXlehAMBvWujmrcbjcsFkvFP+3t7eU3CumaIA8vkpUKzufm5nLKq1mXiLSxuXWz8pjjyomqZ0lz4MeaxONxOJ1OxGIx3k3XQHt7e8WtJ6SPlRVplvXWVo4hJ1qN14gMr9eLeDyeN89GIpFAd3c3bDabUub1ehGNRgtODGexWDA4OKiM6a5m3UrxfSOqzuf/6vN47u+fAwCc9p7Gh97zIZ1rRKQtta8T/ApNDSscDsNmszEgb3AtLcCGDQzIiai0eDyOubm5vC7l8jCm7C81fX19SKVSeevKf3u93prWJSJtyGPKASB1I6VfRYiaFL9GU0NJpVKIRqOIx+M4dOhQ3sRwRETUnHw+HwYHB/MmuPT5fBAEASdOnFCWeTweuFyuvEk+Dx06BJfLlZONo5p1iUgbOWPK32GucqJqcaI3aihzc3Nwu90AgKGhIX6ZIiIyiMHBQUSj0Zyx44lEAi6Xq2CGikgkAp/PB6/XC5vNhkQigZ6enoITZFazLhGpj2PKidaGQTk1FJvNxvyWREQGVW3LdTW9pdizikg/wm2C8pjd14mqx+7rRERERERUs5ygnC3lRFVjSzkRERFRAVNTU8rjeuarJWo2QqugPOaYcjKqZDKJZDIJIPf6oAYG5UREREQF9Pf3K4+Hh4cxMjKiX2WIGtjtt96uPJ5/Jz89IZERBAIBjI6OarJtBuVEREREBQSDQdjtdgBgKzlRCbdab0Xb+jYsLC7gyg22lJMxiaKI3t5eAFJLefaN27ViUK4SdnEjIiJA2+5tVF92uz0nfzoRFSfcJmBhcYFjysmwtIzxONGbSvr7++F0OuF0OhEIBPSuDhER6SQQCCjXAzXvohMRNTJ5XHnqnRQz6RBViS3lKmEXNyIiArTt3kZE1Kg23yblKl9aWcL1xevYeMtGnWtE1DwYlKuEXdyIiAjgECYiMqf229qVx1duXGFQTlQFdl8nIiIiIqI12dy6WXnMceVE1WFQTg0jHA7DYrEgHo/X/bV9Ph8sFgvGx8c12b6e/xsRERGR1oTbBOUxg3Ki6jAoJyIiIiKiNZHHlANA6kZKv4oQNSEG5UQA/H4/0uk0BgcH9a4KERERUdPJGVP+DnOVE1WDQbkRLS4Cb74p/SYiIiIi0lj2mPL5d+Z1rAlR8+Hs60bz8svAgQPA7CzQ2QmcOwfs3q13rYiIiJrO1NSU8piz6hOVlj2m/MoNtpST8SSTSSSTSQC51wc1sKXcSBYXMwE5IP0+cKBhWsxTqRREUUR3dzcsFgva29shiiJSqVTJ5/h8PuU53d3d8Pl8eevF43G43W5YLJaC65UrLzYRW7k6V1o/IiJqPv39/XA6nXA6nQgEAnpXh6ihcaI3MrpAIKBcE/r7+1XdNlvKjWR2NhOQr162ZYs+dbopkUjA6XQCAAYHB9Hd3Y3p6WmEw2FEo1F4PJ6Cz4tGo4hGoxBFEQ6HA/F4HD6fD4lEAqFQSFnP6XTC5XIhEokglUohkUggEolUXF5rnSutHxERNZ9gMAi73Q4AbCUnKkNoFZTHHFNORiSKInp7ewFILeVqBuYMylXSEF3cOjuln+zAXF6mM6/XCwB4/fXXIQiCstzv95d8nsfjyQnYXS4XpqenMT4+jlQqBUEQEI1GAUhpzVwul7Lu0NAQAJQtX0udK6kfEZmLlt3bqL7sdjscDofe1SBqCrffervymGPKyYi0jPHYfV0lDdHFbf16aQy5HITLY8rXr9enPjelUinE43EMDg6qEqR2d3cDkFqyAWDXrl0ApCBaFEWEw+GcLvHlytWu8+r6EZG5aNm9jYioUd1qvRVt69sAcEw5UbXYUq6Shunitns3MDOTmehN54AcACYmJgBkgtVqxeNxnD9/HvF4HIlEIi/YFQQBsVgMhw4dwvj4OMbHxwFILdpDQ0Nly9da53L1IyJz0bJ7GxFRIxNuE7CwuMAx5URVYku5SuQubg6HQ/9xZ+vXS2PIGyAgBwCbzQYAmJ6ervq5oijC6XQqE66FQqGCgbTD4UAsFsP8/DxCoRAcDgd8Pp8ycVu58lrrXGn9zGplBbh+XfpNZBZdXV3K9UC+WUtEZAbyuPLUOymk02l9K0PURBiUk+ZsNhscDocyznq1Yl3JU6kUxsfHMTQ0hEAgAI/HU3ZsnyAI8Hg8yiRrhVrVS5VXU+da6mcWk5PAwACwaROwcaP0e2BAWk5ERETGtPk2KVf50soSri9e17k2RM2D3depLkKhEJxOJ7Zt24ajR49CEARlJnNRFAu2LguCAEEQMD4+jjvuuAMOhwORSARjY2M560WjUXi9Xuzfvx9OpxMdHR3KuH6Xy1W2fC11rqR+ZnP2LPDoo8DSUmbZwgJw+jRw5oz0++BB/epHRERE2mi/rV15fOXGFWy8ZaOOtSFqHmwpp7qw2Wx4/fXX4XK5EAgElAnXPB4PBgcHiz7v4sWL6OjogM/ngyiKAKRJlLJbpHft2oXBwUFMTEzA5/PB6/Vibm4OkUgEgiCULV9LnSupn5lMTuYG5FYs4m68CSsWAUjLH32ULeZERERGtLl1s/KY48qJKmdJc8DHmsTjcTidTsRiMdMGYkSygQGpJRwAHsDLOIcD6MIskujEAZzDK9itrHfqlH71JKoXXiOaE983otp8/q8+j+f+/jkAwG85fwvbOrblrZNGjaEHIxZS0S++5xexrT3/81kpta8T7L5ORKpYWQHCYemxFYtKQA4AXZjFORzAVsxgCesRCgEnTwIt7KtDRA0sO8+8lvlpiYxCHlMOAH8c+2Mda0JU2rMPPVt1UJ5MJpFMJgHkXh/UwK/ERKSKGzekseMA0IlZJSCXdWEWnTeXLSxI6xMRNbL+/n4l57w8FwkRFfeBuz+gdxWINBMIBJRrgtrpTtlSTkSqaG0F2tqkgHsWnUiiMycwT6ITs+gEIK3X2qpXTYmIKhMMBpW0dmwlJypvR9cOfPUTX8X3/+/3S69oqW37llqfSLTK/V33V/0cURTR29sLQGopVzMwZ1CuEnZxI7NraQE8HmlM+RLW4wDO5Y0pX8J6AIDXy67rZFxadm+j+rLb7RxTTlSln/2pn8XP/tTP6l0NItVpGeMxKFdJ9p2S4eFhjIyM6FcZIp0cOSKlPVtaAl7BbmzFDDoxi1l0KgG51QocPqxzRYk0FAgEMDo6qnc1iIiIqEkwKFcJu7gRATt2SC3lclq0JazHW9iilFutUvmOHTpWkkhjWnZvIyIiIuNhUK4SdnEjkhw8CGzfDjz7LBAKSWPM29qkLuuHDzMgJ+PjECYiIiKqBoNyIlLdjh1SHvKTJ6VZ1ltbOYaciIiIiKgQBuVEpJmWFmDDBr1rQURERETUuNh2RURERERERKQTBuVEREREREREOjFtUD42Ngav1wtRFCGKIsbHx/WuEt2UTCYxMjKi5Pml5sH3rrnx/SPKNTU1hXg8jng8zuNijXh+aW58/5ob3z91JJNJ5ZowNTWl6rZNGZS73W5MT08jFAohEAggEAggEolAFEW9q0aQPvCjo6M8cTQhvnfNje8fUa7+/n44nU44nU4EAgG9q9PUeH5pbnz/mhvfP3UEAgHlmqB2ulPTTfQWjUYRjUYxPz+fs/zEiRNob2+HKIpMbUZEREQIBoOw2+0AwDR3REQmJ4oient7AUg9qdQMzE0XlIdCIQiCAEEQcpbLy+SWcyIiIjI3u93OG/VERARAujmr1Q1a03Vfj0ajsNlsBcs6OjowMTFR03azx52t/qmkq4hWYz2abbtaasZ90Yx11koz7otmrLNWmm1fVLPd7DFmq3/UHnNGzaURPp+NtG2tNNt+5vuX0Yz7otm2qyW+fypKmwyAtMPhKFjmcDjSgiBUtb1YLJYGUPJneHi44u3EYrGqXt9o29Vy2822XS233Wzb1XLbrLP229Vy242w3eHh4bLXAS32KWlHrc9VI3w+G2XbrLP229Vy2822XS233Wzb1XLbZq6z2vUzXff1clKpVE3Pyx53thrHoRERGVf2GLPV1B5zRkRERMbDoDxLLQH5jRs3yq6TTCbLdpGQuziq3dWx2bar5babbbtabrvZtqvltlln7ber5babZbuVXCuoccjv11rf/2b5fNZj26yz9tvVctvNtl0tt91s29Vy22aus/x8ta7vlnQ6nVZlS02iu7sbgiAgFovllbW3t6OjowPT09MVb++FF15gKwgREZUUDAbxyCOP6F0NqhCv7UREVAm1ru+mayl3OByIRqMFy1KpFPbv31/V9j784Q8jGAzinnvuQWtrqxpVJCIig7hx4wbeeOMNfPjDH9a7KlQFXtuJiKgUta/vpmspD4fD8Hq9mJ+fz0mLlkql0N7ejkgkApfLpV8FiYiIiIiIyDRMF5QDgNvths1my8lH7vV6kUqlEIlEdKwZERERERERmYkpg3IA8Pl8SCQSsNlsSCQS6OnpwdDQkN7VIiIiIiIiIhMxbVBOREREREREpLcWvStAREREREREZFYMyomIiIiIiIh0wqCciIiIiIiISCcMyomIiIiIiIh0wqCcGloikahqORFVh8cYEemB5x4i7fD4aj6cfZ10Iaekk08OoihicHAwbz23241oNAqHw4GOjg7Mzc0hkUhgcHAQfr8/b/2xsTFcunQJHR0dAACn01lwu6Qu7vfGw2OMiPTAc4+xcL83Fh5fBpYmqjOXy5WOxWLK35FIJA0g7fF4Cq5rs9nSANKCIKRdLlc6EokU3e7g4GDOMo/Hk7eM1MX93nh4jBGRHnjuMRbu98bC48vYGJRTXfn9/nQoFMpbPjQ0lAaQV+ZyuSrarnximp+fz1k+Pz+fBpBzEiP1cL83Hh5jRKQHnnuMhfu9sfD4Mj6OKae6ikQi8Hq9SKVSOcv7+vqU8lqEQiEIggBBEHKWy8sCgUBN26XSuN8bD48xItIDzz3Gwv3eWHh8GZ9V7wqQuTgcDkxMTOQtl08GxSagCIfDSCQSsNlscLlceSePaDQKm81W8LkdHR0FX5PWjvu98fAYIyI98NxjLNzvjYXHl/GxpZzqyu/3Y35+vuBJAZAmpljN5/PBZrNhaGgIgiDA6XRifHw8Z51Ss0kKgsDZJjXC/d54eIwRkR547jEW7vfGwuPLBPTuP0+UTqfTNpstbbPZ8pZPT0/nLQuFQnnjXACkHQ5HwW07HI40P+ra4H5vHjzGiEgPPPc0J+735sDjyzjYUk6683q9EAQBsVgsr6xQlxqXywUAFY9zWT3+huqD+71x8BgjIj3w3GNM3O+NgceXsTAop6q43W5YLJaKf9rb20tuz+v1AgBisVhel5yxsTE4nc6iz83uUlNsPAwAzM3NlSyn2nG/Nz4eY0RUCV7fKRv3e2Pj8WU8DMqpKpFIBGkplV5FP/Pz80W35fV64Xa7EQqFlGXy2Bj5tQrdpZubmwMgTXohczgcRce9pFIp5e4gqYv7vbHxGCOiSvH6Ttm43xsXjy9jYlBOuvB6vTh69CgGBweVZalUKucE43a7C3axCYfDAABRFJVlfX19SKVSeSch+W/5jiKpi/u9cfEYIyI98NxjDNzvjYnHl3FZ0ul0Wu9KkLnIXWpWd4lJJBLo6+vD0NCQskw+scjrxuNx7N27F36/P+eEJK9rs9lyTkRyTsda8zdSedzvjYfHGBHpgeceY+F+byw8voyNQTnVldfrVe7UFRKJRPK6yvh8PqRSKczNzSGVSsHv9+d0vVm9rpyPMZFIoKenJ+ckRdrgfm8cPMaISA889xgT93tj4PFlfAzKiYiIiIiIiHTCMeVEREREREREOmFQTkRERERERKQTBuVEREREREREOmFQTkRERERERKQTBuVEREREREREOmFQTkRERERERKQTBuVEREREREREOmFQTkRERERERKQTBuVEREREREREOmFQTkRERERERKQTBuVEREREREREOmFQTkRERERERKQTBuVEREREREREOmFQTkQAgHg8jng8rnc1AACJREK1bcXjcVW3R0RE1Cx4bSdqDgzKiZpAKpWCxWJBd3d30XXC4TAsFgtEUax6+9FoFHv37oXNZstZZrFYqr6Yy89rb2+vuh7///bu9yiNNYoD8HHmFgBJB0sHS0pgO5CkgmAHMpYAHYAdwHbAWoFKB24JcTvwfsgsN14VJRE2K88zw0yU/euXX87Z93231u/3f3vf/+t0OtHv96Moinc7JgD8Kdn++2Q7H42iHI7cer2OLMtiuVxGp9P54+PNZrPodDpRVVXkeb7z/nmex9evX//4OmpJksTl5WUMh0NddQCOgmyHdlGUw5Ebj8cxGAxiMBj88bHqsL68vIyInyG+q9ls9ltPBLY5PT2NJEne/bgA8DeS7dAuinI4Yuv1OoqiiPF4/C7HWywWEfEzKAeDQRRFEVVVvXn/siyjLMtI0/RdrudXFxcXURTFXzO3DgD2QbZD+yjK4YjV3e736KTXxzs9PY2I2HSu5/P5Tvvvq+NdX9fvdPgBoC1kO7SPohyO2GKx2Cm0y7KMbrcbWZY9+916vd4Eb33cXYIyz/MYjUaPfjefz6Pb7UZZljEej6PX68XJyUlkWbbpvmdZtlmAZtuTgTRNLQoDwIcm26F9FOXQImVZxsnJybOf4XC407Gqqoqqqt48nKwsy+j3+5EkSaxWqyffTyaT6HQ6m8Cu/12W5ZvCsiiKSNP02QVpqqqKLMuiqqqYTCYxGo2iKIoYDoeRZVkMh8OYzWaRJElMp9MXO/j19ewy7A4A9km2y3b4p+kLAN6u0+nEcrl89rvVahXT6fTNx6pXK932KpZft61D+/b29tltFovFk5VVh8NhFEURs9ns1a79a8Pb0jTddOZPT083c8iWy+Vm+NpgMIherxer1epJVz4i4vPnz5v72cfcNgDYlWyX7aAohxb59OnTiwG4a4f4x48fm2NuU5ZlfP/+PaqqejG08zyPqqqi3+8/ejXJly9fNt9vU1VVrNfrreH+7du3Rz8nSRJlWT7ap34X60t/i7pTX987ADRNtv9HtnOsDF+HI/XWoB8Oh5twf6lbX3e5z87OotfrbT79fn+zzbZFYRaLxaYj/pL/D32rf97l/av1fRjiBsBHJNuhnRTlcKTe2llO0zTu7u7i/Pw8xuPxk9eOVFUVRVHEZDKJh4eHJ596jtq2RWH2uTLrr+p7rbvuAPCRyHZoJ0U5HKm3dpbreW6TySTSNH2y6EzdJX9unlfEz7lgSZLEer1+NPytVv9H4BBhWt/rLh14AGgL2Q7tpCiHI1UvhnJ3d7d1u1/npS2XyyjL8lHnu17oZVsY1ts/11E/VCc9IuL6+joidNMB+JhkO7STohyO2K7v9kySJGazWczn88jzfNMhfy146077c3PPFovFi5349/bagjMA0HayHdrn5OHh4aHpiwCaMR6PYzqdxv39fSPDvvI8j9VqtXVO2nspyzJ6vV5MJpM4Pz/f+/kAoAmyHdrHk3I4YhcXFxGxffXUfTrk8Lb61S2H6twDQBNkO7SPJ+Vw5Mbjcczn87i/vz/oeet3n7427+29dLvdGI1GMZlMDnI+AGiKbId2UZQD0e/3YzAYHDTU6veiHmK42dnZWdzc3MTt7e3ezwUAfwPZDu1h+DoQV1dXURTFZhjYIVxfXx9kuFme53FzcxNXV1d7PxcA/C1kO7SHJ+UAAADQEE/KAQAAoCGKcgAAAGiIohwAAAAaoigHAACAhijKAQAAoCGKcgAAAGiIohwAAAAaoigHAACAhijKAQAAoCGKcgAAAGiIohwAAAAaoigHAACAhijKAQAAoCGKcgAAAGiIohwAAAAaoigHAACAhijKAQAAoCGKcgAAAGjIv+TUNpXSAeKXAAAAAElFTkSuQmCC", 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" ] @@ -340,7 +344,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 30/30 [00:21<00:00, 1.42it/s]\n" + " 0%| | 0/30 [00:00" ] @@ -505,6 +516,165 @@ " ax2.set_ylabel(\"angle (deg)\")\n", " fig.tight_layout()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Voltage FMR \n", + "\n", + "The dynamic model can be used to compute the voltage FMR. This is done by computing the linearised resistance of the system and then using the voltage drop across the system to compute the frequency shift.\n", + "\n", + "We need both the stationary and linearised angles to compute the linearised resistance, so we find the energy minimum first, and then compute the linearisation using standard Jacobian method. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/18 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from cmtj.utils.resistance import Rxx_parallel_bilayer_expr\n", + "\n", + "GMR = 0.0\n", + "SMR = 0.01\n", + "AMR = 0.1\n", + "Vdc = 1\n", + "Rlin_func, R_func = Rxx_parallel_bilayer_expr()\n", + "with plt.style.context([\"science\", \"nature\"]):\n", + " fig, ax = plt.subplots(dpi=200)\n", + " for findx, fdata in enumerate(spin_diode_data):\n", + " V = []\n", + " for linearised, stationary in zip(fdata[\"lineshape\"], fdata[\"pos\"]):\n", + " Rparallel = R_func(GMR, AMR, SMR, AMR, SMR, stationary)\n", + " dRparallel = Rlin_func(GMR, AMR, SMR, AMR, SMR, stationary, linearised)\n", + " Vline = (Vdc / Rparallel) * dRparallel\n", + " V.append(Vline)\n", + " V = np.asarray(V)\n", + " V = (V - V.min()) / (V.max() - V.min()) / 2\n", + " ax.plot(Hscan / 1e3, V + findx, color=\"gray\")\n", + " ax.scatter(\n", + " Hvals,\n", + " result_dictionary[\"frequency\"],\n", + " color=\"crimson\",\n", + " label=r\"f\",\n", + " )\n", + " ax.set_xlabel(\"H (kA/m)\")\n", + " ax.set_ylabel(\"V (a.u.)\")\n", + " ax.set_title(\"Voltage FMR\")\n", + " ax.set_xlim(-hmin / 1e3, hmin / 1e3)\n", + " ax.set_ylim(-0.1, fmax)" + ] } ], "metadata": { diff --git a/view/simulation_fns.py b/view/simulation_fns.py index 07f8bf7..28de75c 100644 --- a/view/simulation_fns.py +++ b/view/simulation_fns.py @@ -1,7 +1,11 @@ from collections import defaultdict from itertools import groupby from typing import List +from venv import create +from colorama import init + +from new_sb import LayerDynamic import numpy as np import streamlit as st @@ -45,7 +49,9 @@ def create_single_layer(id_: str) -> tuple: ] demag_sum = nxx + nyy + nzz if abs(demag_sum - 1.0) > 1e-5: - st.warning(f"Warning: Demagnetization tensor components should sum to 1.0 (Layer {id_})") + st.warning( + f"Warning: Demagnetization tensor components should sum to 1.0 (Layer {id_})" + ) Kdir = FieldScan.angle2vector( theta=st.session_state[f"theta_K{id_}"], phi=st.session_state[f"phi_K{id_}"] ) @@ -74,6 +80,27 @@ def create_single_layer(id_: str) -> tuple: return layer, rp +def create_sb_layer(id_: str) -> tuple[LayerDynamic, list[float]]: + nxx = st.session_state[f"Nxx{id_}"] + nyy = st.session_state[f"Nyy{id_}"] + nzz = st.session_state[f"Nzz{id_}"] + Ks = st.session_state[f"Ks{id_}"] + Kv = st.session_state[f"Kv{id_}"] + kphi = st.session_state[f"phi_K{id_}"] + demag = VectorObj.from_cartesian(nxx, nyy, nzz) + layer = LayerDynamic( + _id=int(id_), + thickness=st.session_state[f"thickness{id_}"] * 1e-9, + Kv=VectorObj(np.deg2rad(0.0), np.deg2rad(kphi), Kv), + Ks=Ks, + Ms=st.session_state[f"Ms{id_}"] / mu0, + demagTensor=demag, + damping=st.session_state[f"alpha{id_}"], + ) + ktheta = 90 if Kv > Ks else 0 # if Kv is smaller than Ks, assume in plane + return layer, [np.deg2rad(ktheta), np.deg2rad(kphi)] + + def get_axis_cvector(axis: str): if axis == "x": return CVector(1, 0, 0) @@ -131,12 +158,68 @@ def prepare_simulation(): j = Junction(layers=layers) for jvals in range(N - 1): J = st.session_state[f"J{jvals}"] * 1e-6 # rescale GUI units + J2 = st.session_state[f"J2{jvals}"] * 1e-6 # rescale GUI units l1_name = layers[jvals].id l2_name = layers[jvals + 1].id j.setIECDriver(l1_name, l2_name, ScalarDriver.getConstantDriver(J)) + j.setQuadIECDriver(l2_name, l1_name, ScalarDriver.getConstantDriver(J2)) return j, rparams +def prepare_sb_simulation() -> tuple: + layers = [] + init_pos = [] + N = st.session_state["N"] + for i in range(N): + layer, init_pos_i = create_sb_layer(i) + layers.append(layer) + init_pos.append(init_pos_i) + Js = [] + Js2 = [] + for jvals in range(N - 1): + J = st.session_state[f"J{jvals}"] * 1e-6 # rescale GUI units + J2 = st.session_state[f"J2{jvals}"] * 1e-6 # rescale GUI units + Js.append(J) + Js2.append(J2) + return layers, init_pos, Js, Js2 + + +def get_spectrum_sb_data(H_axis, Hmin, Hmax, Hsteps, run_vsd: bool = False): + layers, init_pos, Js, Js2 = prepare_sb_simulation() + + htheta, hphi = get_axis_angles(H_axis) + hmin, hmax = min([Hmin, Hmax]), max([Hmin, Hmax]) # fix user input + _, Hvecs = FieldScan.amplitude_scan(hmin, hmax, Hsteps, htheta, hphi) + force_single_layer = not any(Js) and not any(Js2) + all_sb_data = defaultdict(list) + for H in Hvecs: + Hvec = VectorObj.from_cartesian(*H) + solver = Solver(layers=layers, J1=Js, J2=Js2, H=Hvec) + eq, frequencies = solver.solve( + init_position=init_pos, + perturbation=1e-4, + force_single_layer=force_single_layer, + ) + for freq in frequencies: + all_sb_data["Hmag"].append(Hvec.mag / 1e3) + all_sb_data["frequency"].append(freq) + + if run_vsd: + res = solver.linearised_N_spin_diode( + H=Hvec, + frequency=freq * 1e9, + Vdc_ex_variable=LayerDynamic.get_Vp_symbol(), + Vdc_ex_value=st.session_state["Hoe_mag"] * 1e3, + zero_pos=eq.tolist(), + phase_shift=0, + cache_var="H", + ) + + all_sb_data["pos"].append(eq.tolist()) + init_pos = eq.tolist() + return all_sb_data + + # @st.cache_data def get_pimm_data( H_axis, diff --git a/view/streamlit_app.py b/view/streamlit_app.py index 65e5606..e60b148 100644 --- a/view/streamlit_app.py +++ b/view/streamlit_app.py @@ -47,7 +47,8 @@ def import_session_state(file): mime="application/json", type="primary", help="Export the current session state to a JSON file. " - "You can use this to save your current settings and load them later or ""share them with others.", + "You can use this to save your current settings and load them later or " + "share them with others.", ) st.file_uploader( @@ -173,6 +174,16 @@ def import_session_state(file): format="%.3f", help="Interlayer exchange coupling constant", ) + + st.number_input( + f"$J_2$ ({j+1}<-->{j+2}) (uJ/m^2)", + min_value=GENERIC_BOUNDS["J"][0], + max_value=GENERIC_BOUNDS["J"][1], + value=0.0, + key=f"J2{j}", + format="%.4f", + help="Biquadratic interlayer exchange coupling constant", + ) with st.expander("Simulation & control parameters"): st.selectbox( "H axis", options=["x", "y", "z", "xy", "xz", "yz"], key="H_axis", index=0