1149 lines
375 KiB
Plaintext
1149 lines
375 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from scipy.integrate import odeint\n",
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"from scipy.interpolate import interp1d\n",
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"import cvxpy as cp\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"plt.style.use(\"ggplot\")\n",
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"\n",
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"import time"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"\"\"\"\n",
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"Control problem statement.\n",
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"\"\"\"\n",
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"\n",
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"N = 4 # number of state variables\n",
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"M = 2 # number of control variables\n",
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"T = 20 # Prediction Horizon\n",
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"DT = 0.2 # discretization step\n",
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"\n",
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"\n",
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"def get_linear_model(x_bar, u_bar):\n",
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" \"\"\"\n",
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" Computes the LTI approximated state space model x' = Ax + Bu + C\n",
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" \"\"\"\n",
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"\n",
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" L = 0.3 # vehicle wheelbase\n",
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"\n",
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" x = x_bar[0]\n",
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" y = x_bar[1]\n",
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" v = x_bar[2]\n",
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" theta = x_bar[3]\n",
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"\n",
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" a = u_bar[0]\n",
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" delta = u_bar[1]\n",
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"\n",
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" A = np.zeros((N, N))\n",
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" A[0, 2] = np.cos(theta)\n",
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" A[0, 3] = -v * np.sin(theta)\n",
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" A[1, 2] = np.sin(theta)\n",
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" A[1, 3] = v * np.cos(theta)\n",
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" A[3, 2] = v * np.tan(delta) / L\n",
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" A_lin = np.eye(N) + DT * A\n",
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"\n",
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" B = np.zeros((N, M))\n",
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" B[2, 0] = 1\n",
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" B[3, 1] = v / (L * np.cos(delta) ** 2)\n",
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" B_lin = DT * B\n",
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"\n",
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" f_xu = np.array(\n",
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" [v * np.cos(theta), v * np.sin(theta), a, v * np.tan(delta) / L]\n",
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" ).reshape(N, 1)\n",
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" C_lin = DT * (\n",
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" f_xu - np.dot(A, x_bar.reshape(N, 1)) - np.dot(B, u_bar.reshape(M, 1))\n",
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" )\n",
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"\n",
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" return np.round(A_lin, 4), np.round(B_lin, 4), np.round(C_lin, 4)\n",
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"\n",
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"\n",
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"\"\"\"\n",
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"the ODE is used to update the simulation given the mpc results\n",
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"I use this insted of using the LTI twice\n",
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"\"\"\"\n",
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"\n",
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"\n",
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"def kinematics_model(x, t, u):\n",
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" \"\"\"\n",
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" Returns the set of ODE of the vehicle model.\n",
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" \"\"\"\n",
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"\n",
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" L = 0.3 # vehicle wheelbase\n",
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" dxdt = x[2] * np.cos(x[3])\n",
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" dydt = x[2] * np.sin(x[3])\n",
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" dvdt = u[0]\n",
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" dthetadt = x[2] * np.tan(u[1]) / L\n",
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"\n",
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" dqdt = [dxdt, dydt, dvdt, dthetadt]\n",
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"\n",
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" return dqdt\n",
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"\n",
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"\n",
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"def predict(x0, u):\n",
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" \"\"\" \"\"\"\n",
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"\n",
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" x_ = np.zeros((N, T + 1))\n",
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"\n",
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" x_[:, 0] = x0\n",
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"\n",
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" # solve ODE\n",
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" for t in range(1, T + 1):\n",
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"\n",
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" tspan = [0, DT]\n",
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" x_next = odeint(kinematics_model, x0, tspan, args=(u[:, t - 1],))\n",
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"\n",
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" x0 = x_next[1]\n",
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" x_[:, t] = x_next[1]\n",
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"\n",
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" return x_\n",
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"\n",
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"\n",
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"\"\"\"\n",
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"MODIFIED TO INCLUDE FRAME TRANSFORMATION\n",
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"\"\"\"\n",
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"\n",
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"\n",
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"def compute_path_from_wp(start_xp, start_yp, step=0.1):\n",
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" \"\"\"\n",
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" Computes a reference path given a set of waypoints\n",
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" \"\"\"\n",
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"\n",
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" final_xp = []\n",
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" final_yp = []\n",
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" delta = step # [m]\n",
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"\n",
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" for idx in range(len(start_xp) - 1):\n",
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" section_len = np.sum(\n",
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" np.sqrt(\n",
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" np.power(np.diff(start_xp[idx : idx + 2]), 2)\n",
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" + np.power(np.diff(start_yp[idx : idx + 2]), 2)\n",
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" )\n",
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" )\n",
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"\n",
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" interp_range = np.linspace(0, 1, np.floor(section_len / delta).astype(int))\n",
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"\n",
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" fx = interp1d(np.linspace(0, 1, 2), start_xp[idx : idx + 2], kind=1)\n",
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" fy = interp1d(np.linspace(0, 1, 2), start_yp[idx : idx + 2], kind=1)\n",
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"\n",
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" # watch out to duplicate points!\n",
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" final_xp = np.append(final_xp, fx(interp_range)[1:])\n",
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" final_yp = np.append(final_yp, fy(interp_range)[1:])\n",
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"\n",
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" dx = np.append(0, np.diff(final_xp))\n",
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" dy = np.append(0, np.diff(final_yp))\n",
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" theta = np.arctan2(dy, dx)\n",
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"\n",
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" return np.vstack((final_xp, final_yp, theta))\n",
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"\n",
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"\n",
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"def get_nn_idx(state, path):\n",
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" \"\"\"\n",
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" Computes the index of the waypoint closest to vehicle\n",
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" \"\"\"\n",
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"\n",
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" dx = state[0] - path[0, :]\n",
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" dy = state[1] - path[1, :]\n",
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" dist = np.hypot(dx, dy)\n",
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" nn_idx = np.argmin(dist)\n",
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"\n",
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" try:\n",
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" v = [\n",
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" path[0, nn_idx + 1] - path[0, nn_idx],\n",
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" path[1, nn_idx + 1] - path[1, nn_idx],\n",
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" ]\n",
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" v /= np.linalg.norm(v)\n",
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"\n",
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" d = [path[0, nn_idx] - state[0], path[1, nn_idx] - state[1]]\n",
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"\n",
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" if np.dot(d, v) > 0:\n",
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" target_idx = nn_idx\n",
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" else:\n",
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" target_idx = nn_idx + 1\n",
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"\n",
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" except IndexError as e:\n",
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" target_idx = nn_idx\n",
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"\n",
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" return target_idx\n",
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"\n",
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"\n",
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"def normalize_angle(angle):\n",
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" \"\"\"\n",
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" Normalize an angle to [-pi, pi]\n",
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" \"\"\"\n",
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" while angle > np.pi:\n",
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" angle -= 2.0 * np.pi\n",
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"\n",
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" while angle < -np.pi:\n",
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" angle += 2.0 * np.pi\n",
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"\n",
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" return angle\n",
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"\n",
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"\n",
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"def get_ref_trajectory(state, path, target_v):\n",
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" \"\"\"\n",
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" modified reference in robot frame\n",
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" \"\"\"\n",
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" xref = np.zeros((N, T + 1))\n",
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" dref = np.zeros((1, T + 1))\n",
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"\n",
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" # sp = np.ones((1,T +1))*target_v #speed profile\n",
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"\n",
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" ncourse = path.shape[1]\n",
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"\n",
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" ind = get_nn_idx(state, path)\n",
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" dx = path[0, ind] - state[0]\n",
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" dy = path[1, ind] - state[1]\n",
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"\n",
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" xref[0, 0] = dx * np.cos(-state[3]) - dy * np.sin(-state[3]) # X\n",
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" xref[1, 0] = dy * np.cos(-state[3]) + dx * np.sin(-state[3]) # Y\n",
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" xref[2, 0] = target_v # V\n",
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" xref[3, 0] = normalize_angle(path[2, ind] - state[3]) # Theta\n",
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" dref[0, 0] = 0.0 # steer operational point should be 0\n",
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"\n",
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" dl = 0.05 # Waypoints spacing [m]\n",
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" travel = 0.0\n",
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"\n",
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" for i in range(T + 1):\n",
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" travel += abs(target_v) * DT # current V or target V?\n",
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" dind = int(round(travel / dl))\n",
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"\n",
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" if (ind + dind) < ncourse:\n",
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" dx = path[0, ind + dind] - state[0]\n",
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" dy = path[1, ind + dind] - state[1]\n",
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"\n",
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" xref[0, i] = dx * np.cos(-state[3]) - dy * np.sin(-state[3])\n",
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" xref[1, i] = dy * np.cos(-state[3]) + dx * np.sin(-state[3])\n",
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" xref[2, i] = target_v # sp[ind + dind]\n",
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" xref[3, i] = normalize_angle(path[2, ind + dind] - state[3])\n",
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" dref[0, i] = 0.0\n",
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" else:\n",
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" dx = path[0, ncourse - 1] - state[0]\n",
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" dy = path[1, ncourse - 1] - state[1]\n",
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"\n",
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" xref[0, i] = dx * np.cos(-state[3]) - dy * np.sin(-state[3])\n",
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" xref[1, i] = dy * np.cos(-state[3]) + dx * np.sin(-state[3])\n",
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" xref[2, i] = 0.0 # stop? #sp[ncourse - 1]\n",
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" xref[3, i] = normalize_angle(path[2, ncourse - 1] - state[3])\n",
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" dref[0, i] = 0.0\n",
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"\n",
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" return xref, dref"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## SMOOTHEN PATH\n",
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"\n",
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"I use a corner smoothing tecnique to help the line-finding proplem -> this way the line does not change from 0 to pi/2 instantly"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from scipy.signal import savgol_filter\n",
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"\n",
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"\n",
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"def compute_path_from_wp(start_xp, start_yp, step=0.1):\n",
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" \"\"\"\n",
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" Computes a reference path given a set of waypoints\n",
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" \"\"\"\n",
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"\n",
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" final_xp = []\n",
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" final_yp = []\n",
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" delta = step # [m]\n",
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"\n",
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" for idx in range(len(start_xp) - 1):\n",
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" section_len = np.sum(\n",
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" np.sqrt(\n",
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" np.power(np.diff(start_xp[idx : idx + 2]), 2)\n",
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" + np.power(np.diff(start_yp[idx : idx + 2]), 2)\n",
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" )\n",
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" )\n",
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"\n",
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" interp_range = np.linspace(0, 1, np.floor(section_len / delta).astype(int))\n",
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"\n",
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" fx = interp1d(np.linspace(0, 1, 2), start_xp[idx : idx + 2], kind=1)\n",
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" fy = interp1d(np.linspace(0, 1, 2), start_yp[idx : idx + 2], kind=1)\n",
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"\n",
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" # watch out to duplicate points!\n",
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" final_xp = np.append(final_xp, fx(interp_range)[1:])\n",
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" final_yp = np.append(final_yp, fy(interp_range)[1:])\n",
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"\n",
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" \"\"\"this smoothens up corners\"\"\"\n",
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" window_size = 7 # Smoothening filter window\n",
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" final_xp = savgol_filter(final_xp, window_size, 1)\n",
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" final_yp = savgol_filter(final_yp, window_size, 1)\n",
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"\n",
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" dx = np.append(0, np.diff(final_xp))\n",
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" dy = np.append(0, np.diff(final_yp))\n",
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" theta = np.arctan2(dy, dx)\n",
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"\n",
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" return np.vstack((final_xp, final_yp, theta))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## V3 Add track constraints\n",
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"inspried from -> https://arxiv.org/pdf/1711.07300.pdf\n",
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"\n",
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"explanation here...\n",
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"\n",
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"benefits:\n",
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"* add a soft form of obstacle aoidance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7f8432529970>]"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1080x720 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"def generate_track_bounds(track, width=0.5):\n",
|
|
" \"\"\"\n",
|
|
" in world frame\n",
|
|
" \"\"\"\n",
|
|
" bounds_low = np.zeros((2, track.shape[1]))\n",
|
|
" bounds_upp = np.zeros((2, track.shape[1]))\n",
|
|
"\n",
|
|
" for idx in range(track.shape[1]):\n",
|
|
" x = track[0, idx]\n",
|
|
" y = track[1, idx]\n",
|
|
" th = track[2, idx]\n",
|
|
"\n",
|
|
" \"\"\"\n",
|
|
" trasform the points\n",
|
|
" \"\"\"\n",
|
|
" bounds_upp[0, idx] = 0 * np.cos(th) - width * np.sin(th) + x # X\n",
|
|
" bounds_upp[1, idx] = 0 * np.sin(th) + width * np.cos(th) + y # Y\n",
|
|
"\n",
|
|
" bounds_low[0, idx] = 0 * np.cos(th) - (-width) * np.sin(th) + x # X\n",
|
|
" bounds_low[1, idx] = 0 * np.sin(th) + (-width) * np.cos(th) + y # Y\n",
|
|
"\n",
|
|
" return bounds_low, bounds_upp\n",
|
|
"\n",
|
|
"\n",
|
|
"track = compute_path_from_wp(\n",
|
|
" [0, 3, 4, 6, 10, 12, 14, 6, 1, 0], [0, 0, 2, 4, 3, 3, -2, -6, -2, -2], 0.05\n",
|
|
")\n",
|
|
"\n",
|
|
"lower, upper = generate_track_bounds(track)\n",
|
|
"\n",
|
|
"plt.figure(figsize=(15, 10))\n",
|
|
"\n",
|
|
"plt.plot(track[0, :], track[1, :], \"b-\")\n",
|
|
"plt.plot(lower[0, :], lower[1, :], \"g-\")\n",
|
|
"plt.plot(upper[0, :], upper[1, :], \"r-\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"the points can be used to generate the **halfplane constrains** for each reference point.\n",
|
|
"the issues (outliers points) should be gone after we are in vehicle frame...\n",
|
|
"\n",
|
|
"the halfplane constrains are defined given the line equation:\n",
|
|
"\n",
|
|
"**lower halfplane**\n",
|
|
"$$ a1x_1 + b1x_2 = c1 \\rightarrow a1x_1 + b1x_2 \\leq c1$$\n",
|
|
"\n",
|
|
"**upper halfplane**\n",
|
|
"$$ a2x_1 - b2x_2 = c2 \\rightarrow a2x_1 + b2x_2 \\leq c2$$\n",
|
|
"\n",
|
|
"we want to combine this in matrix form:\n",
|
|
"\n",
|
|
"$$\n",
|
|
"\\begin{bmatrix}\n",
|
|
"x_1 \\\\\n",
|
|
"x_2 \n",
|
|
"\\end{bmatrix}\n",
|
|
"\\begin{bmatrix}\n",
|
|
"a_1 & a_2\\\\\n",
|
|
"b_1 & b_2\n",
|
|
"\\end{bmatrix}\n",
|
|
"\\leq\n",
|
|
"\\begin{bmatrix}\n",
|
|
"c_1 \\\\\n",
|
|
"c_2 \n",
|
|
"\\end{bmatrix}\n",
|
|
"$$\n",
|
|
"\n",
|
|
"becouse our track points have known heading the coefficients can be computed from:\n",
|
|
"\n",
|
|
"$$ y - y' = \\frac{sin(\\theta)}{cos(\\theta)}(x - x') $$\n",
|
|
"\n",
|
|
"we have:\n",
|
|
"\n",
|
|
"$$\n",
|
|
"-tan(\\theta)x + y = - tan(\\theta)x' + y'\n",
|
|
"$$\n",
|
|
"where:\n",
|
|
"* $ a = -tan(\\theta) $\n",
|
|
"* $ b = 1 $\n",
|
|
"* $ c = - tan(\\theta)x' + y' $"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(-2.0, 2.0)"
|
|
]
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 360x360 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"plt.style.use(\"ggplot\")\n",
|
|
"\n",
|
|
"\n",
|
|
"def get_coeff(x, y, theta):\n",
|
|
" m = np.sin(theta) / np.cos(theta)\n",
|
|
" return (-m, 1, y - m * x)\n",
|
|
"\n",
|
|
"\n",
|
|
"# test -> assume point 10,1,pi/6\n",
|
|
"# coeff = get_coeff(1,-1, np.pi/2)\n",
|
|
"coeff = get_coeff(1, -1, np.pi / 4)\n",
|
|
"y = []\n",
|
|
"pts = np.linspace(0, 20, 100)\n",
|
|
"\n",
|
|
"for x in pts:\n",
|
|
" y.append((-coeff[0] * x + coeff[2]) / coeff[1])\n",
|
|
"\n",
|
|
"plt.figure(figsize=(5, 5))\n",
|
|
"plt.plot(pts, y, \"b-\")\n",
|
|
"\n",
|
|
"plt.xlim((-2, 2))\n",
|
|
"plt.ylim((-2, 2))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## WARN TANGENT BREAKS AROUND PI/2?\n",
|
|
"force the equation to x = val"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(-2.0, 2.0)"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<Figure size 360x360 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# def get_coeff(x,y,theta):\n",
|
|
"\n",
|
|
"# if (theta - np.pi/2) < 0.01:\n",
|
|
"# #print (\"WARN -> theta is 90, tan is: \" + str(theta))\n",
|
|
"# # eq is x = val\n",
|
|
"# m = 0\n",
|
|
"# return (1,1e-6,x)\n",
|
|
"# else:\n",
|
|
"# m = np.sin(theta)/np.cos(theta)\n",
|
|
"# return(-m,1,y-m*x)\n",
|
|
"\n",
|
|
"# test -> assume point 10,1,pi/6\n",
|
|
"coeff = get_coeff(1, -1, np.pi / 2)\n",
|
|
"y = []\n",
|
|
"pts = np.linspace(0, 20, 100)\n",
|
|
"\n",
|
|
"for x in pts:\n",
|
|
" y.append((-coeff[0] * x + coeff[2]) / coeff[1])\n",
|
|
"\n",
|
|
"plt.figure(figsize=(5, 5))\n",
|
|
"\n",
|
|
"plt.plot(pts, y, \"b-\")\n",
|
|
"plt.axis(\"equal\")\n",
|
|
"plt.xlim((-2, 2))\n",
|
|
"plt.ylim((-2, 2))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"becouse the controller uses vhicle reference frame this rquire adapting -> the semiplane constraints must be gathetered from **x_ref points**\n",
|
|
"\n",
|
|
"*low and up are w.r.t vehicle y axis*"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def get_track_constrains(x_ref, width=0.5):\n",
|
|
" \"\"\"\n",
|
|
" x_ref has hape (4,T) -> [x,y,v,theta]_ref\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" # 1-> get the upper and lower points\n",
|
|
" pts_low = np.zeros((3, x_ref.shape[1]))\n",
|
|
" pts_upp = np.zeros((3, x_ref.shape[1]))\n",
|
|
"\n",
|
|
" for idx in range(x_ref.shape[1]):\n",
|
|
" x = x_ref[0, idx]\n",
|
|
" y = x_ref[1, idx]\n",
|
|
" th = x_ref[3, idx]\n",
|
|
"\n",
|
|
" \"\"\"\n",
|
|
" trasform the points\n",
|
|
" \"\"\"\n",
|
|
" pts_upp[0, idx] = 0 * np.cos(th) - width * np.sin(th) + x # X\n",
|
|
" pts_upp[1, idx] = 0 * np.sin(th) + width * np.cos(th) + y # Y\n",
|
|
" pts_upp[2, idx] = th # heading\n",
|
|
"\n",
|
|
" pts_low[0, idx] = 0 * np.cos(th) - (-width) * np.sin(th) + x # X\n",
|
|
" pts_low[1, idx] = 0 * np.sin(th) + (-width) * np.cos(th) + y # Y\n",
|
|
" pts_low[2, idx] = th # heading\n",
|
|
"\n",
|
|
" # get coefficients ->(a,b,c)\n",
|
|
" coeff_low = np.zeros((3, x_ref.shape[1]))\n",
|
|
" coeff_upp = np.zeros((3, x_ref.shape[1]))\n",
|
|
"\n",
|
|
" for idx in range(pts_upp.shape[1]):\n",
|
|
" f = get_coeff(pts_low[0, idx], pts_low[1, idx], pts_low[2, idx])\n",
|
|
" coeff_low[0, idx] = f[0]\n",
|
|
" coeff_low[1, idx] = f[1]\n",
|
|
" coeff_low[2, idx] = f[2]\n",
|
|
"\n",
|
|
" f = get_coeff(pts_upp[0, idx], pts_upp[1, idx], pts_upp[2, idx])\n",
|
|
" coeff_upp[0, idx] = f[0]\n",
|
|
" coeff_upp[1, idx] = f[1]\n",
|
|
" coeff_upp[2, idx] = f[2]\n",
|
|
"\n",
|
|
" return coeff_low, coeff_upp"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## MPC INTEGRATION\n",
|
|
"\n",
|
|
"compare the results with and without"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"simpe u-turn test\n",
|
|
"\n",
|
|
"## 1-> NO BOUNDS"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"CVXPY Optimization Time: Avrg: 0.1602s Max: 0.1839s Min: 0.1493s\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"track = compute_path_from_wp([0, 3, 3, 0], [0, 0, 1, 1], 0.05)\n",
|
|
"\n",
|
|
"track_lower, track_upper = generate_track_bounds(track, 0.12)\n",
|
|
"\n",
|
|
"sim_duration = 50 # time steps\n",
|
|
"opt_time = []\n",
|
|
"\n",
|
|
"x_sim = np.zeros((N, sim_duration))\n",
|
|
"u_sim = np.zeros((M, sim_duration - 1))\n",
|
|
"\n",
|
|
"MAX_SPEED = 1.5 # m/s\n",
|
|
"MAX_ACC = 1.0 # m/ss\n",
|
|
"MAX_D_ACC = 1.0 # m/sss\n",
|
|
"MAX_STEER = np.radians(30) # rad\n",
|
|
"MAX_D_STEER = np.radians(30) # rad/s\n",
|
|
"\n",
|
|
"REF_VEL = 0.5 # m/s\n",
|
|
"\n",
|
|
"# Starting Condition\n",
|
|
"x0 = np.zeros(N)\n",
|
|
"x0[0] = 0 # x\n",
|
|
"x0[1] = -0.05 # y\n",
|
|
"x0[2] = 0.0 # v\n",
|
|
"x0[3] = np.radians(-0) # yaw\n",
|
|
"x_sim[:, 0] = x0 # simulation_starting conditions\n",
|
|
"\n",
|
|
"# starting guess\n",
|
|
"u_bar = np.zeros((M, T))\n",
|
|
"u_bar[0, :] = MAX_ACC / 2 # a\n",
|
|
"u_bar[1, :] = 0.0 # delta\n",
|
|
"\n",
|
|
"for sim_time in range(sim_duration - 1):\n",
|
|
"\n",
|
|
" iter_start = time.time()\n",
|
|
"\n",
|
|
" # dynamics starting state w.r.t. robot are always null except vel\n",
|
|
" x_bar = np.zeros((N, T + 1))\n",
|
|
" x_bar[2, 0] = x_sim[2, sim_time]\n",
|
|
"\n",
|
|
" # prediction for linearization of costrains\n",
|
|
" for t in range(1, T + 1):\n",
|
|
" xt = x_bar[:, t - 1].reshape(N, 1)\n",
|
|
" ut = u_bar[:, t - 1].reshape(M, 1)\n",
|
|
" A, B, C = get_linear_model(xt, ut)\n",
|
|
" xt_plus_one = np.squeeze(np.dot(A, xt) + np.dot(B, ut) + C)\n",
|
|
" x_bar[:, t] = xt_plus_one\n",
|
|
"\n",
|
|
" # CVXPY Linear MPC problem statement\n",
|
|
" x = cp.Variable((N, T + 1))\n",
|
|
" u = cp.Variable((M, T))\n",
|
|
" cost = 0\n",
|
|
" constr = []\n",
|
|
"\n",
|
|
" # Cost Matrices\n",
|
|
" Q = np.diag([20, 20, 10, 20]) # state error cost\n",
|
|
" Qf = np.diag([30, 30, 30, 30]) # state final error cost\n",
|
|
" R = np.diag([10, 10]) # input cost\n",
|
|
" R_ = np.diag([10, 10]) # input rate of change cost\n",
|
|
"\n",
|
|
" # Get Reference_traj\n",
|
|
" # dont use x0 in this case\n",
|
|
" x_ref, d_ref = get_ref_trajectory(x_sim[:, sim_time], track, REF_VEL)\n",
|
|
"\n",
|
|
" # Prediction Horizon\n",
|
|
" for t in range(T):\n",
|
|
"\n",
|
|
" # Tracking Error\n",
|
|
" cost += cp.quad_form(x[:, t] - x_ref[:, t], Q)\n",
|
|
"\n",
|
|
" # Actuation effort\n",
|
|
" cost += cp.quad_form(u[:, t], R)\n",
|
|
"\n",
|
|
" # Actuation rate of change\n",
|
|
" if t < (T - 1):\n",
|
|
" cost += cp.quad_form(u[:, t + 1] - u[:, t], R_)\n",
|
|
" constr += [\n",
|
|
" cp.abs(u[0, t + 1] - u[0, t]) / DT <= MAX_D_ACC\n",
|
|
" ] # max acc rate of change\n",
|
|
" constr += [\n",
|
|
" cp.abs(u[1, t + 1] - u[1, t]) / DT <= MAX_D_STEER\n",
|
|
" ] # max steer rate of change\n",
|
|
"\n",
|
|
" # Kinrmatics Constrains (Linearized model)\n",
|
|
" A, B, C = get_linear_model(x_bar[:, t], u_bar[:, t])\n",
|
|
" constr += [x[:, t + 1] == A @ x[:, t] + B @ u[:, t] + C.flatten()]\n",
|
|
"\n",
|
|
" # Final Point tracking\n",
|
|
" cost += cp.quad_form(x[:, T] - x_ref[:, T], Qf)\n",
|
|
"\n",
|
|
" # sums problem objectives and concatenates constraints.\n",
|
|
" constr += [x[:, 0] == x_bar[:, 0]] # starting condition\n",
|
|
" constr += [x[2, :] <= MAX_SPEED] # max speed\n",
|
|
" constr += [x[2, :] >= 0.0] # min_speed (not really needed)\n",
|
|
" constr += [cp.abs(u[0, :]) <= MAX_ACC] # max acc\n",
|
|
" constr += [cp.abs(u[1, :]) <= MAX_STEER] # max steer\n",
|
|
"\n",
|
|
" # Solve\n",
|
|
" prob = cp.Problem(cp.Minimize(cost), constr)\n",
|
|
" solution = prob.solve(solver=cp.OSQP, verbose=False)\n",
|
|
"\n",
|
|
" # retrieved optimized U and assign to u_bar to linearize in next step\n",
|
|
" u_bar = np.vstack(\n",
|
|
" (np.array(u.value[0, :]).flatten(), (np.array(u.value[1, :]).flatten()))\n",
|
|
" )\n",
|
|
"\n",
|
|
" u_sim[:, sim_time] = u_bar[:, 0]\n",
|
|
"\n",
|
|
" # Measure elpased time to get results from cvxpy\n",
|
|
" opt_time.append(time.time() - iter_start)\n",
|
|
"\n",
|
|
" # move simulation to t+1\n",
|
|
" tspan = [0, DT]\n",
|
|
" x_sim[:, sim_time + 1] = odeint(\n",
|
|
" kinematics_model, x_sim[:, sim_time], tspan, args=(u_bar[:, 0],)\n",
|
|
" )[1]\n",
|
|
"\n",
|
|
"print(\n",
|
|
" \"CVXPY Optimization Time: Avrg: {:.4f}s Max: {:.4f}s Min: {:.4f}s\".format(\n",
|
|
" np.mean(opt_time), np.max(opt_time), np.min(opt_time)\n",
|
|
" )\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
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"text/plain": [
|
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"<Figure size 1080x720 with 5 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# plot trajectory\n",
|
|
"grid = plt.GridSpec(4, 5)\n",
|
|
"\n",
|
|
"plt.figure(figsize=(15, 10))\n",
|
|
"\n",
|
|
"plt.subplot(grid[0:4, 0:4])\n",
|
|
"plt.plot(track[0, :], track[1, :], \"b+\")\n",
|
|
"plt.plot(track_lower[0, :], track_lower[1, :], \"g-\")\n",
|
|
"plt.plot(track_upper[0, :], track_upper[1, :], \"r-\")\n",
|
|
"plt.plot(x_sim[0, :], x_sim[1, :])\n",
|
|
"plt.axis(\"equal\")\n",
|
|
"plt.ylabel(\"y\")\n",
|
|
"plt.xlabel(\"x\")\n",
|
|
"\n",
|
|
"plt.subplot(grid[0, 4])\n",
|
|
"plt.plot(u_sim[0, :])\n",
|
|
"plt.ylabel(\"a(t) [m/ss]\")\n",
|
|
"\n",
|
|
"plt.subplot(grid[1, 4])\n",
|
|
"plt.plot(x_sim[2, :])\n",
|
|
"plt.ylabel(\"v(t) [m/s]\")\n",
|
|
"\n",
|
|
"plt.subplot(grid[2, 4])\n",
|
|
"plt.plot(np.degrees(u_sim[1, :]))\n",
|
|
"plt.ylabel(\"delta(t) [rad]\")\n",
|
|
"\n",
|
|
"plt.subplot(grid[3, 4])\n",
|
|
"plt.plot(x_sim[3, :])\n",
|
|
"plt.ylabel(\"theta(t) [rad]\")\n",
|
|
"\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 2-> WITH BOUNDS\n",
|
|
"if there is 90 deg turn the optimization fails!\n",
|
|
"if speed is too high it also fails ..."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"CVXPY Optimization Time: Avrg: 0.3174s Max: 0.3505s Min: 0.3006s\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"WIDTH = 0.18\n",
|
|
"REF_VEL = 0.4 # m/s\n",
|
|
"\n",
|
|
"computed_coeff = []\n",
|
|
"\n",
|
|
"track = compute_path_from_wp([0, 3, 3, 0], [0, 0, -1, -1], 0.05)\n",
|
|
"\n",
|
|
"track_lower, track_upper = generate_track_bounds(track, WIDTH)\n",
|
|
"\n",
|
|
"sim_duration = 200 # time steps\n",
|
|
"opt_time = []\n",
|
|
"\n",
|
|
"x_sim = np.zeros((N, sim_duration))\n",
|
|
"u_sim = np.zeros((M, sim_duration - 1))\n",
|
|
"\n",
|
|
"MAX_SPEED = 1.5 # m/s\n",
|
|
"MAX_ACC = 1.0 # m/ss\n",
|
|
"MAX_D_ACC = 1.0 # m/sss\n",
|
|
"MAX_STEER = np.radians(30) # rad\n",
|
|
"MAX_D_STEER = np.radians(30) # rad/s\n",
|
|
"\n",
|
|
"\n",
|
|
"# Starting Condition\n",
|
|
"x0 = np.zeros(N)\n",
|
|
"x0[0] = 0 # x\n",
|
|
"x0[1] = WIDTH / 2 # y\n",
|
|
"x0[2] = 0.0 # v\n",
|
|
"x0[3] = np.radians(-0) # yaw\n",
|
|
"x_sim[:, 0] = x0 # simulation_starting conditions\n",
|
|
"\n",
|
|
"# starting guess\n",
|
|
"u_bar = np.zeros((M, T))\n",
|
|
"u_bar[0, :] = MAX_ACC / 2 # a\n",
|
|
"u_bar[1, :] = 0.0 # delta\n",
|
|
"\n",
|
|
"for sim_time in range(sim_duration - 1):\n",
|
|
"\n",
|
|
" iter_start = time.time()\n",
|
|
"\n",
|
|
" # dynamics starting state w.r.t. robot are always null except vel\n",
|
|
" x_bar = np.zeros((N, T + 1))\n",
|
|
" x_bar[2, 0] = x_sim[2, sim_time]\n",
|
|
"\n",
|
|
" # prediction for linearization of costrains\n",
|
|
" for t in range(1, T + 1):\n",
|
|
" xt = x_bar[:, t - 1].reshape(N, 1)\n",
|
|
" ut = u_bar[:, t - 1].reshape(M, 1)\n",
|
|
" A, B, C = get_linear_model(xt, ut)\n",
|
|
" xt_plus_one = np.squeeze(np.dot(A, xt) + np.dot(B, ut) + C)\n",
|
|
" x_bar[:, t] = xt_plus_one\n",
|
|
"\n",
|
|
" # CVXPY Linear MPC problem statement\n",
|
|
" x = cp.Variable((N, T + 1))\n",
|
|
" u = cp.Variable((M, T))\n",
|
|
" cost = 0\n",
|
|
" constr = []\n",
|
|
"\n",
|
|
" # Cost Matrices\n",
|
|
" Q = np.diag([20, 20, 10, 20]) # state error cost\n",
|
|
" Qf = np.diag([30, 30, 30, 30]) # state final error cost\n",
|
|
" R = np.diag([10, 10]) # input cost\n",
|
|
" R_ = np.diag([10, 10]) # input rate of change cost\n",
|
|
"\n",
|
|
" # Get Reference_traj\n",
|
|
" # dont use x0 in this case\n",
|
|
" x_ref, d_ref = get_ref_trajectory(x_sim[:, sim_time], track, REF_VEL)\n",
|
|
"\n",
|
|
" # Prediction Horizon\n",
|
|
" for t in range(T):\n",
|
|
"\n",
|
|
" # Tracking Error\n",
|
|
" cost += cp.quad_form(x[:, t] - x_ref[:, t], Q)\n",
|
|
"\n",
|
|
" # Actuation effort\n",
|
|
" cost += cp.quad_form(u[:, t], R)\n",
|
|
"\n",
|
|
" # Actuation rate of change\n",
|
|
" if t < (T - 1):\n",
|
|
" cost += cp.quad_form(u[:, t + 1] - u[:, t], R_)\n",
|
|
" constr += [\n",
|
|
" cp.abs(u[0, t + 1] - u[0, t]) / DT <= MAX_D_ACC\n",
|
|
" ] # max acc rate of change\n",
|
|
" constr += [\n",
|
|
" cp.abs(u[1, t + 1] - u[1, t]) / DT <= MAX_D_STEER\n",
|
|
" ] # max steer rate of change\n",
|
|
"\n",
|
|
" # Kinrmatics Constrains (Linearized model)\n",
|
|
" A, B, C = get_linear_model(x_bar[:, t], u_bar[:, t])\n",
|
|
" constr += [x[:, t + 1] == A @ x[:, t] + B @ u[:, t] + C.flatten()]\n",
|
|
"\n",
|
|
" # Final Point tracking\n",
|
|
" cost += cp.quad_form(x[:, T] - x_ref[:, T], Qf)\n",
|
|
"\n",
|
|
" # sums problem objectives and concatenates constraints.\n",
|
|
" constr += [x[:, 0] == x_bar[:, 0]] # starting condition\n",
|
|
" constr += [x[2, :] <= MAX_SPEED] # max speed\n",
|
|
" constr += [x[2, :] >= 0.0] # min_speed (not really needed)\n",
|
|
" constr += [cp.abs(u[0, :]) <= MAX_ACC] # max acc\n",
|
|
" constr += [cp.abs(u[1, :]) <= MAX_STEER] # max steer\n",
|
|
"\n",
|
|
" # Track constrains\n",
|
|
" low, upp = get_track_constrains(x_ref, WIDTH)\n",
|
|
" computed_coeff.append((low, upp))\n",
|
|
" for ii in range(low.shape[1]):\n",
|
|
" # constr += [low[0,ii]*x[0,ii] + x[1,ii] >= low[2,ii]]\n",
|
|
" constr += [\n",
|
|
" upp[0, ii] * x[0, ii] + x[1, ii] <= upp[2, ii]\n",
|
|
" ] # <-- CAUSES ISSUES Y?\n",
|
|
"\n",
|
|
" # Solve\n",
|
|
" prob = cp.Problem(cp.Minimize(cost), constr)\n",
|
|
" solution = prob.solve(solver=cp.ECOS, verbose=False)\n",
|
|
"\n",
|
|
" # retrieved optimized U and assign to u_bar to linearize in next step\n",
|
|
" u_bar = np.vstack(\n",
|
|
" (np.array(u.value[0, :]).flatten(), (np.array(u.value[1, :]).flatten()))\n",
|
|
" )\n",
|
|
"\n",
|
|
" u_sim[:, sim_time] = u_bar[:, 0]\n",
|
|
"\n",
|
|
" # Measure elpased time to get results from cvxpy\n",
|
|
" opt_time.append(time.time() - iter_start)\n",
|
|
"\n",
|
|
" # move simulation to t+1\n",
|
|
" tspan = [0, DT]\n",
|
|
" x_sim[:, sim_time + 1] = odeint(\n",
|
|
" kinematics_model, x_sim[:, sim_time], tspan, args=(u_bar[:, 0],)\n",
|
|
" )[1]\n",
|
|
"\n",
|
|
"print(\n",
|
|
" \"CVXPY Optimization Time: Avrg: {:.4f}s Max: {:.4f}s Min: {:.4f}s\".format(\n",
|
|
" np.mean(opt_time), np.max(opt_time), np.min(opt_time)\n",
|
|
" )\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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dc/1jnXTOrQYut9ZeWemFnHO3AbeNOHZ10fZ7gPdUep1Rps2o7jKq2b7gOZ+vXp8yKcXCDkBERERERGSqKE5eWGv/X6k21tov7S7JEXnTZuCruYxqrpC4iOnr61SnnwAREREREZFwvGOM42+rZxBVN3067KzeEBLyueA5Fq9enzIpaQiJiIiIiIhIHVlr31XYbCraHvQCYHKvuzl9JmxYV73+BoeOxJXAmOqUwBAREREREamvwQqLZoZXW3hgLcEKJZOW6TgA/9c/4nfuwEybXnmHucEKDA0gmOqUwBAREREREakj59zLAay1lzrnLgo7nmozhx6J/8mN8MQjcMRxlXc4mMDAVN6XTGpKYYmIiIiIiNSJtbZ5cHt3yQtrbUt9IqqBgw8HwD/59+r0119YhWQgW53+ZNJSBYaIiIiIiEj9PGCtvRb4dmHJ1GGstQsJhpW8AziizrFVhZk+I5gHo3dzdTrsK0wImp28C7NIdSiBISIiIiIiUj8vAT4M3G+t3Qg8AvQCbcChwBzgm8CpIcVXHa1t0LulOn317QyelcCY8iKTwLDWng5cCcSBa5xzl404fxpwM/Bk4dBNzrlP1zVIERERERGRCjjneoAPWms/CpwMHE2QtNgIXAbc45yb/GMlWmfht1YngeGVwJCCSCQwrLVx4Crg1cAq4F5r7S3OuYdGNP2tc+51dQ9QRERERESkipxz/cBvC4/G0zoLtmyquBu/dQvsHBxCksXn89DfV53VTWTSiUQCAzgJeMw59wSAtfYG4GxgZAJDRERERESkYVhr5xN8H0pStMyGc+7a0IKqAtPahl/9TNmv871bggTFvL3xj/yF/Bc+tutcth9+czv+lu8Q+8K3MPF4NUOWSSAqCYwO4Nmi/VUE5VQjpay19wOrgQ865x4s1Zm1dimwFMA5RzKZrHK4Y2tqaqrr9aS29Hk2Fn2ejUOfZWPR59lY9Hk2Dn2WtWetfQPwbeBR4EjgQeAo4HfApE5gMHMWTGAISX7Zv4LPE//fW/Crnh5+MtuPf/g+2NobrEiiBMaUE5UERqkFff2I/T8BBzjntlprzwR+CCwq1ZlzbiWwcrCfnp6easW5R8lkknpeT2pLn2dj0efZOPRZNhZ9no1Fn2fj0Gc5Wnt7e7W7vBR4p3Pue9bajc6546217yRIZkxurW3QtxOf7cckmvfcHvCbNoDPB9v5PDSN+Lqa7Yc1q4LtgQGYvAvNygTFwg6gYBWwX9H+vgRVFkOcc1ucc1sL27cBCWutUsIiIiIiIjJZ7e+c+96IY9cB/xpGMFXVOit4fuDeIBkxDv7hB3Ztf/sro+fQ2LAONhaSarnJP8+plC8qCYx7gUXW2oOstc3AOcAtxQ2stQustaawfRJB7BvqHqmIiIiIiEh1rCvMgQHwlLU2BRxMsDLjpGbm7QNA/urPkf/0f+IfHXt6Q79hHfmbroNNu77e+d/+FP/H3+9qdHQnPF9UETQwUPWYJfoikcBwzg0A5wN3AA8Hh9yD1tpzrbXnFpq9CfhrYQ6M/wec45wbOcxERERERERksvhf4CWF7S8CvwLuB74SWkTVcuTxxD75Zcw7/xN2bCe/8vNjNs1//Qr8T76Pf/DPw0/s2AZ7JYld/QPM9BnDzymBMSVFZQ6MwWEht404dnXR9peBL9c7LhERERERkVpwzn2uaPtb1tpfAzOdcw9Xo39r7enAlQQVHdc45y4bcd4Uzp8JbAfe4Zz7UzWubYyBjv0xHfuTf74Hf/N38NksJpEY3Xj7tuB5/XMwZy5sej7Y3/g8dByAicfxI1+XKz+B4bdthc3PY9r3L/u1Eg2RqMAQERERERGZ6pxzz1QxeREHrgLOAI4A3mytPWJEszMIFkZYRLCK41erce1RZu8VPG9+Hv+nNLkPvZPcpRfifaGgfnCOjOfXQ3I+5g3/Euz7fDAZKDC07kPb7OB5RAWGX7eG3KfeR+6rn9117C8Zcl/8OPk//AK//jnyH/k38p84n9xXL8P3bq7BG5Vai0wFhoiIiIiIiFTNScBjzrknAKy1NwBnA8WTUZwNfKswNP8ua+0ca+1C59yaagZi5szDA/7e3+Fv/jbkcsF8F1t7oW3WrgQGwKy9MC9+Jf6H3w5eOzNIYPhHHwz2T3wp/pc/HpbA8N6Tv2Y5rHoqeAD5n92Md18Pzsfi8PTj0LcDc+rp+DtvJ//gn4hd/s3RQ1PK4Pt2Do+9hPz2bfgd2yd8jYbSXPmyMUpgiIiIiIiINJ4O4Nmi/VXAyeNo0wFUNYHBnLkA+Juug70XYM54E/5bX4bNzxcSGLmhpmb2HGiZtuu1hQoM84rX429YiTnqhCCBUbwKyTOPw5N/h+R86FmLf/h+/Pe+ASekYOdOWP00/u9/xZx8GrG3/Qf5WbPxP+4KKj46Dij77fh1a8h/40p4bOyJSQetL7v3xhW78BKYP3/PDXdDCQwREREREZHGY0ocG7kIwnjaAGCtXUowzATnHMlkclSbpqamksfzTbGhL/Kz3/5eYnP3ZiMwyw/Qkkyyblvv0EVnLOxgZkcH6wb3955PazIJS94BS95B/1//zEZg9syZNBeutfWnN7EtFqPtH8+h99or4RtfIjZnLvMu/BRb/++r7HgomBx07pJ30pRM0n/yqWz8cRezjKelRLy7k9v0PM9/8eOYnduZYd+FmTFzt+1j8Rj53PiWkW100w47csyfkfFSAkNERERERKTxrAL2K9rfF1g9gTYAOOdWAisLu76np2dUm2QySanjvmiYRe8hRwWVD8Dmp5+Ee3+PH5zEE9jR8QJ2btwETQkYyLI91sTOoj79tqDt5g0bMIXjuT/8Cg45gm1zg9/u5zduwCx+F8/v2El+eiHBsFeSTTNnQ08P3gd5m83PPk2s/cBSb3dM+f/9An7T88Q+8nl27n/wHtuPdU+moh1AcmBg1P1ob28fdx9KYIiIiIiIiDSee4FF1tqDgG7gHOAtI9rcApxfmB/jZGBztee/ADCxGBx/CuagQ4MVRQaHlFy3Imhw0KHBEBCAQw4LnqdNg61ZmNk2vLOmwlfYwiokft0a6H4as+TdsNe8Xdf8h1cFG4PHZs3Z1cfg9pZNZb0P//e/4u+5E/O6czDjSF5I9SmBISIiIiIi0mCccwPW2vOBOwiWUb3WOfegtfbcwvmrgdsIllB9jGAZ1XfWKp74f3x0aNskmndtv24J5h/fAn97APr7MLF4cKJlOmztxbSOSGDEC19hC5N4+vvuDvo59uShuTYAzMzWYGPwWsUJjBkzg0RImQmM/Pevg7l7Y07/57JeJ9WjBIaIiIiIiEgDcs7dRpCkKD52ddG2B95b77iKmdefgzEGDj92+InBFSvGqMDwA1kM4O+7C/Y9CLP3gqC/V5+NObpzV/9HHI9/wQuJvekdu44ZA21z8Ld/H/+y0zHJPU8s6Z95HJ54BHPOv2FaKl9NQyZGCQwRERERERGpq9glX4Xmll0VFyNNmx48j0xgxHcNIfEP3AuPPYx53ZJd/dp3D2tuZrYS/8jlo/vfGMzDkL/qf4h/4so9xut/cwckmjGnvHyPbaV2YmEHICIiIiIiIlOLWdCBmbub1SgGl1IdOYSkKRE896wjf80VsP/BmFedXf71X/vGYGPVk/hnnthtW79zB/7u32BOfOmuoSkSCiUwREREREREJFpapoExwXwVxeJBxYZP/xJ2bCP2ngv3uJRpKbE3vZPYFd+GeBx/z2923/hv90PfDkxK1RdhUwJDREREREREIsW0TIMZraOHmAyuQrJhfTDR5/yOiV+jbRYcfhz+z3fvtp3/yx+DIS2HHDHha0l1aA4MERERERERiRRz/Ckwb5/RJ+KFISQ+D3vNCybkrOQ6hx6J/+sf8Vu3YFpnjTrvvQ8SGEcch2nS1+ewqQJDREREREREIsV0voTYP/3r6BPFSYS95lV+nRe8MNh48tHSDbqfho09mKNeVPG1pHJKYIiIiIiIiMjkEIsFc2MAZk7lCQwOOASMwT9dOoHhH/xTcC0lMCJBCQwRERERERGZFIwxu5ZS3Ws3q5iMt79p04NKjrVrSp73j/8N9l6AqUK1h1ROCQwRERERERGZPAaHkew1tzr97b0Q3/Nc6XNPPoo56IXVuY5UTAkMERERERERmTwKCQxThQoMAJOcD+vXjjruN26ATRvgoEVVuY5UTgkMERERERERmTwGVyKp1rCOvRfA5ufxfX3Djz/5dwDMQYdW5zpSMSUwREREREREZPJoqt4cGAAk5wfP64fPg+Gf+jvE47D/C6pzHamYEhgiIiIiIiIyecSbgiRG66yqdGf2OwgA/8zjw477J/4O+x6ESTRX5TpSOSUwREREREREZPJoaoI584IVSaphwb4wbTo8uWspVe89PPsk5oCDq3MNqYqmsAMQERERERERGbemJpjZWrXuTCwGBy7CF+a8AKB3E2zfCu37V+06UjklMERERERERGTSMK86G9Myrbp9HrQI/9Ob8dn+YMjI6meD4wv3q+p1pDJKYIiIiIiIiMikEUu9vOp9mgMPxecG4Jkn4ODD8GuCBAbtSmBEiRIYIiIiIiIiDcRaOxfoAg4EngKsc27jiDb7Ad8CFgB5YKVz7sr6RhohhaVS/VOPYg4+LKjAmD4TZs8NOTAppkk8RUREREREGsuHgV845xYBvyjsjzQALHPOHQ6cArzXWntEHWOMFLPXvCBZ8VQwkadf8yy071e9iUKlKpTAEBERERERaSxnA9cVtq8D3jCygXNujXPuT4XtXuBhoKNeAUbSAQfjny4spbr6Gc1/EUEaQiIiIiIiItJY5jvn1kCQqLDW7rO7xtbaA4Hjgbt302YpsLTQJ8lkclSbpqamkscni61HHMO2rgzNN13Hjt7NzDzkMGZW+H4m+z2ptkrvhxIYIiIiIiIik4y19ucE81eM9LEy+2kFvg+83zm3Zax2zrmVwMrCru/p6RnVJplMUur4ZOH3bgfv2fGT7wOwfcF+7Kjw/Uz2e1Jtpe5He3v7uF+vBIaIiIiIiMgk45x71VjnrLVrrbULC9UXC4F1Y7RLECQvvuOcu6lGoU4eh+yaAsS8Z1kwmadEiubAEBERERERaSy3AG8vbL8duHlkA2utAb4OPOycu6KOsUWWmTFz1/ZRJ4QYiYxFFRgiIiIiIiKN5TLAWWvfDTwDLAaw1rYD1zjnzgT+AXgb8Bdr7X2F133UOXdbCPFGRuw/Por/+18xM9vCDkVKUAJDRERERESkgTjnNgCvLHF8NXBmYft3gNYIHcEcfwrm+FPCDkPGoCEkIiIiIiIiIhJ5SmCIiIiIiIiISOQpgSEiIiIiIiIikacEhoiIiIiIiIhEnhIYIiIiIiIiIhJ5WoVEREREREREytLe3l7W8alM92S4Su6HKjBERERERESkHKbUw1r7x7HOTdWH7sm478e4KIEhIiIiIiIiIpGnBIaIiIiIiIiIRJ4SGCIiIiIiIlINK8MOIIJ0T4ar6H4Y7321Aokqv3r16rpdLJlM0tPTU7frSW3p82ws+jwbhz7LxqLPs7Ho82wc+ixHK0w+OO7x+iJSXarAEBEREREREZHI0zKqIiIiIiIiUhFr7enAlUAcuMY5d1nIIdWctfZa4HXAOufcUYVjc4Eu4EDgKcA65zYWzn0EeDeQA97nnLsjhLBrxlq7H/AtYAGQB1Y6566s5j1RBYaIiIiIiIhMmLU2DlwFnAEcAbzZWntEuFHVxTeB00cc+zDwC+fcIuAXhX0K9+Mc4MjCa75SuG+NZABY5pw7HDgFeG/hfVftniiBISIiIiIiIpU4CXjMOfeEc64fuAE4O+SYas45dyfw/IjDZwPXFbavA95QdPwG51yfc+5J4DGC+9YwnHNrnHN/Kmz3Ag8DHVTxniiBISIiIiIiIpXoAJ4t2l9VODYVzXfOrYHgCz2wT+H4lLpH1toDgeOBu6niPVECQ0RERERERCpRamWWhl/uskxT5h5Za1uB7wPvd85t2U3Tsu+JEhgiIiIiIiJSiVXAfkX7+wKrQ4olbGuttQsBCs/rCsenxD2y1iYIkhffcc7dVDhctXuiVUhERERERESkEvcCi6y1BwHdBBMzviXckEJzC/B24LLC881Fx6+31l4BtAOLgHtCibBGrLUG+DrwsHPuiqJTVbsnSmCIiIiIiIjIhDnnBqy15wN3ECyjeq1z7sGQw6o5a+13gdOApLV2FfAJgi/pzlr7buAZYDGAc+5Ba60DHiJYreO9zrlcKIHXzj8AbwP+Yq29r3Dso1TxnhjvG3LYTTG/enX9KnOSySQ9PT11u57Ulj7PxqLPs3Hos2ws+jwbiz7PxqHPcrT29nYoPW5fROpAc2CIiIiIiIiISOQpgSEiIiIiIiIikacEhoiIiIiIiIhEnhIYIiIiIiIiIhJ5SmCIiIiIiIiISOQpgSEiIiIiIiIikacEhoiIiIiIiIhEnhIYIiIiIiIiIhJ5SmCIiIiIiIiISOQpgSEiIiIiIiIikacEhoiIiIiIiIhEnhIYIUpkMrSuWEEik6l6+1r2Xev2UYql1u2jFEu57aMUS7ntoxRLrdtHKZZy20cplnLbRymWWrePUizlto9SLOW2j1IstW4fpVjKbV/rWERC5PXQo8qPcWkab0OprkQmw7wlSzDZLK2JBBu6ush2dlalfS37nmqxm7vumjLvVbFHP/ap9F6jFMtUin0qvdcoxdJosefuuAMOOWRKvNcwYxcJ2+rVq0cdSyaT9PT0hBBNNOl+DDfW/Whvbx93H6rACElLOo3JZjG5HCabpSWdrlr7WvY91WI3d945Zd6rYm+89lGKRbFPjtjLbR+lWBR7dGI3d95Ztf6j/l7DjF1EZCpSAiMkfakUPpHAx+P4RIK+VKpq7WvZ91SL3Z966pR5r4q98dpHKRbFPjliL7d9lGJR7NGJ3Z96atX6j/p7DTP2sWgYiog0MuP9uIebTFa+VHlTrZRTJpTIZGhJp+lLpcZVIlhO+1r2PZViTyaTbL799inxXqdC7IN/PqMQS73aRymWasZe6u/ayRJ7o7WvRt+7+39n1GMPq32UYhnZfvbpp+/x30KN8l7Djr3U6weHofgqDENR+ftohVJ3E3YcEVDyO5Z+ZobT/RhuD0NIxvXnSgmMKtMPaWPR59lY9Hk2Dn2WjUWfZ2PR5xme1hUraLv8ckwuh4/H6f3Qh9h6wQUT7k+f5WhKYAxRAmMcdD+Gq0YCQ0NIREREREQaQLWGoYhEXf6Om/CPPhR2GBICrUIiIiIiItIAsp2dbOjqqmgYikjU+Z078Dd+Ew/E//eWsMOROlMCQ0RERESkQWQ7O5W4kMb2zBNhRyAhUgJDRERERESGSWQyxB54gMQxxyghIpHin3o02JgzL9xAJBRKYIiIiIiIyJDi1UzmVWE1E5Fq8o8+OLgVahwSDk3iKSIiIiIiQ1rSaUw2i8nlMNksLel02CGJAOD7dsKDfw52ejfj8/lwA5K6UwJDRERERESGaDUTiSLf34e/+TuQ7YfjT4FcDrZtDTssqTMNIRERERERkSGDq5ns9cADbNQcGBIRvusa/J13YE46FY47Gf/nu2DLRmibFXZoUkdKYIiIiIiIyDDZzk7yp59Otqcn7FBEAPDrn4PZczHv/gA89nAwA8am56HjgLBDkzrSEBIRERERERGJtt7NcOAhmFgckguAQlJDphQlMERERERERCTaejdjZs0JtufMhaYEKIEx5SiBISIiIiIiIpHl8/mgAqNtNgAmFoO9F+DXrwk5Mqk3JTBEREREREQkurZvhXx+KIEBwD4LYZ0SGFONEhgiIiIiIlJViUyG1hUrSGQyYYcijaB3S/BclMAw8ztg7Wp8tj+koCQMWoVERERERESqJpHJMG/JEkw2S2siwYauLi3FGhJr7enAlUAcuMY5d9mI86cBNwNPFg7d5Jz7dF2DHI/eTQC75sAAzKFH4n/6A3j8b3DYMeHEJXWnBIaIiIiIiFRNSzqNyWYxudzQvhIY9WetjQNXAa8GVgH3Wmtvcc49NKLpb51zr6t7gGXwPWuDjTlzdx089CiIxfAPP4BRAmPKUAKjivI/uoGNq54kl80GB4wJHoPbwQaYwX1T+M9APA6xOMRjwXOs8Dzq+Ihj8QQkmoJZeJsSmKYEJBJD+zQVnU80jzpuYvEQ7pSIiIiINKq+VIrWRAIAn0jQl0qFHNGUdRLwmHPuCQBr7Q3A2cDIBEb0PfxAMHxkfsfQITN9Bhy4CP+3+4F/CS82qSslMKppxzbyWzZCdgC8B3zw7AcbFPahcNwXNn0wKU0+Fzxyg9uF51zRcZ/fbQh+t2dLiMeDxEZzy65Hohladu2bwWPFbZpboLl51/nmFmiZBtOmB88thedp05QkEREREZlCsp2dbOjqoiWdpi+VUvVFeDqAZ4v2VwEnl2iXstbeD6wGPuice7BUZ9bapcBSAOccyWRyVJumpqaSxyvh83l6HvkLzceeyJx99hl2busJp7Dtpm8zd8Z0YjNmVvW61VCL+zGZVeN+KIFRRTH7buYlk/T09NTsGj6fH57YGBiAgeyuR7b0ti8+PpANkiwDWcj2B4/+Pujvw/fv2mZrL/T3BBPjDB7r2xlcvzimPQXd3FyU0ChKcEybjikkOQb3g3NBOzMqGTI9aNs8LVg6SUREREQiKdvZqcRF+EyJYyP/6f4n4ADn3FZr7ZnAD4FFpTpzzq0EVg72U+o7T7IG34X8Yw+R39hD/6FHj+rbH3go5HNs+N0vMSdEr9KnFvdjMhvrfrS3t4+7DyUwJhkTiwXDS8r86Er97TVRfmBgWNJjWHJj5w58307o2wE7g30G9/t24gf3d2yDTRt27fftCJIxxdfZXRDNLaOSIUEiZBqmRDJkV7Jk+q5Hy3SYNiM4VyhzFBERERFpEKuA/Yr29yWoshjinNtStH2btfYr1tqkcy70b935u34V/EJ21VOQaMYcX6J45ODDYcZM/H13l53A8PkcYPSL0UlGCQwpm2lqgqYmmD6j9PkJ9usHsoUkyK6ER5AA2YHfuXNXomMw6VFoN5QE2bYVNqzHDyZP+nYEVSqD/e/u4vGm4cmNQoJj0+zZ5E285Dkz8ljxubiGzYiIiIhIqO4FFllrDwK6gXOAtxQ3sNYuANY657y19iQgBmyoe6Qj+HwO//UvBjv7HgSLjsRMG/3dwzQ1YY45EX//3UO/RPU/ugHzmjdi9l4wdt+//DH+lu8G7V63pJZvRapMCQyJDDM4uejMttHnJtinH8gGCY+dRQmRwsMPbW/flRgpPr5jGwO9m/Dbene9rmj4zO4rRJqLKkMGHzN2JT1GnRuRECmuFtGQGREREREpk3NuwFp7PnAHwTKq1zrnHrTWnls4fzXwJuA8a+0AsAM4xzlX9rR6VffIX3dtr3oSc6Yds6k57Uz8Xb/GX7cC/+wT8Fw39O3EvOsDAOTv+jX+9z8ndu6Hg2qNb38V/9ufQnMz/u7fgBIYk4oSGNLQTFMCWhPQOmv0uXG8vniclvc+GDpTlATZVSGyY/TxwYTIYHJkyyb8ujXDXldszP9TGAPN00pWe5gSiZBhCZFS5xPNGFPNQUUiIiIiEkXOuduA20Ycu7po+8vAl+sRi8/nhib391s2kf/qZzFHvYjYWXZEuzz5G78x7Jg56JAx+zUHH4Y56WX4e34TVIgfcRz+nt/ijzkR/+xT+Ntc0O9v74BtW/G//SnmzMXQNhvfdQ1+3WrMPuOfg0HCpQSGyDgZY3atwDJrzvBzE+jP5/PQv3NEMmTniOqQ4YmSXee2w/Prd7Xr2wH9/cP7H+vCsdiIeUDGqgIpmiOk5FCZwvwjTU1KiIiIiIjImHw2S/7T78Mcdgzm9DeRX/Fp6H4av/pZ/GvegEk072rc/TQ88wRm8bvwN387+DfuCw7bbf/m3R/AnHYGLNgXgPxl/0X+a58PzqVeju9Zi//+dcH+qadj3vAvsP65IIHxQAbzqn+syfuW6lMCQyQkJhYrJAhKjOebQH8+lxs2FGbUcJkS54YlQLZsGp44ye2aVHW3dYSxWGGlmJai52lDD9NctNLMsDbTMYP7g5OyDj1Pg2ZVioiIiIg0An/vnfBcN/65bvyvfxL8O/BMi7/N4e/6Nealr9nV9vGHATDHn4I56VSYPiOYjH83TCwGi44Y2o9dfAU8dB8k52P2Pxjfuxn/yx/D7LmYU18T/Btzn4WwcD/8A/eCEhiThhIYIg3CxOMwozV4jDw3gf58dnD+kO2jkh+7Vo8Z/fCD21t7C5OqFp0fyA6/xm7f0ODQmWlBYmNEAiQYPhMsq1ucMAlWohl5rAWap5GfPg0/MBBMRCsiIiIideHvvAPmd2Be+0Z4fj0m9QrYewH+kQfwP/w2/sSX7Jqk8/FHYPZeQfJhgr/MMtNmwAkv3rXfNhtz9ltHtzumE//zH+F3bMeMsUCBRIv+FS8iJZlEAhIJaJvY/CGlBFUipRMf9O3A9/UVVqAZ/ez7dgTL9e7YBpuf35VE6d857uEz6wc34vFdw4FKPpoxg9st00q2Mbt9fQskEpp8VURERKY8v241PP43zD+/nVhRpQVAbMl7yP/PB/G3fBdfGKLsH74fc8gRdanENceciL/jB0G1xotevMf2Ej4lMESkboIqkZnBo9T5Cfbr87kgubGzkNAY8ex37oRsPzMTTWx7fkPQtujhi/e3bBp9rL8P/PC0yLim525uLkpqjE6EmOaWQnVIy/C2ieahhxnaTuw63lx8LEiWENdcJCIiIhI9Pv1rMAZz8mmjzpmDDsWcdgb+ZzcPP3F0Zz1Cg4MPD1YmefBPGCUwJgUlMERk0jOx+JjzicCuxMjMZJIdhVVlyuG9D4a/9PcFVSH9fYXKj6IkyNDx/lEJklGJkp3bYcvGXfuDry2ad2To2uO/CcOTHIlEkAxpSgxLepiRyZCRiZDmZmgqtG0eca74NUPtVGkiElXe+2D573yeMv42KU88PrSqgIjISD6fx6d/CYcfi9lrXsk2Zsl78M8+CY//LTiQaMYcfUJd4jPxOLzghfgnHqnL9aRySmCIiOyBMWbXF/eZbaXbVOE6fmAgWKp32KOQOBnIBsmRbD+++Fy2r/Bc9Jr+4Jwf6B96DTt3QO/mYG6Tka8fGJ04gTK+7sRi0NQE8UTw3FR4jjcNf24qOh9vCuYiGTpX/LrErmMjXxtPYJqa6Js7F79texmvU4VKoxv2ZT2fB58bvp/PQa7wPLSf27WfKzo+tJ8bep0v9Zo99Vl8PpcDX9Rvbldcfuh1pa4/YntkbLuNPV+fmz84AXPh8XzrLHLxpl0rWJVY0nv0qle7VrjSn9WpK5HJ0JJO05dKke2s02/gpbYeewg2rAtW/RiDaUoQ+89PwrrVsO9BsH0rpm123UI0Bx6Kf9Dhd+4I/m6SSFMCQ0QkIszgl+89TCJV7X/a+3w+SGYMDE+AlEqO+P7+QrtdCRVyA0ESJDcQJFqGtgeCpMxAdte57X1jnBvsIztmQmUoXmDTRN5ovAniMYjFg6RLLB7MhzK0HxuxP3i+cNwUnR/ajwW/fTam8IgVbZvgtSPPlTxW/NrYiA+5aKf4i9142oy6eT64g/nCs2fEftFj8LzP72o38vywBEE+aFvY9qOO5Uq2I59nQyxGrr9/t23I50ocK2rna1RhMFGFn4+SP2/x2Bg/T0U/b7FYkIAr/jmMFX7ein9O40X9D/sZHtwvXKsWlVLeB39eiyZ69oOrWG3eiF+7ete5vp3DXzrmfTPB3ENts2F+O2Z+ByzoCJ7nd8Be85TgaFCJTIZ5S5ZgsllaEwk2dHUpidEAfPpXwYojx5+y23Zm+gw44JBgp47JCwDzgkPxPg/PPA6HHlXXa0v5lMAQEZniTCxWWOGlZc9t6xCP9z74DfJYyY2BAea0zmRTT09R8iS7KymSGwgSLEOvLUqg5PNj/8Y8F3wZ9kP7xb8xzxfFVHQ+nw/+0ZP3hS/6g1/w80Vf/v2uL9i+qN2w14w4Vk/GACb4cIeSJ0VJmMHzsaJ2FCVoYkVfkIsfw47FRx9vSgwdi7VMCz6rwS/oI/uKxcdxjeJjI/sonTQww5ILu0luDZ2LD08Y7OY1U/lL9txkkp4Rw/V8Ph8kMYqX6+4rsaT34Pbmjfi13fhHHwpWuBrsqGUa7LMQs2DfIKExvx2zIEhuaAWBya0lncZks5hcbmhfCYzJzff34TO/w7zoxXtcBjVUHQcA4Fc/i1ECI/KUwBARkUgxxuwaCjKGRDKJmTt6PpNG+8roi6sKhlUY+JKbu20zmHgYSkSYyHzJ3qvEF15pLCYWC6rLSiQZdvdT6L2HjRtgbTd+bTesXY1/rhv/1KOQ+X2QdBxsPHuvIJGxoJDYmL8vLOiAefto+exJoC+VojWRAMAnEvSlUiFHJBV76M+wcwfmlNPCjmT35swLkqPPrQo7EhkH/W0uIiISUcMSDBFJNojUkzEG5iZhbhJz+LHDzvlsFtavgee6g+Eqa1cFyY0/pWHrll2JjXgc9l4QJDcOPRJz7MmY+e11fy+ye9nOTjZ0dWkOjAbi13QHGwctCjeQPTCxGMzvCJKkEnlKYIiIiIjIpGMSCWjfH9r3H1XF4bf1FhIb3bsSHKufwd9/D/5734AF+2KOPQlz3EnwghdqJZWIyHZ2KnHRSHqeg9ZZmDFWiYsSs2Bf/OMPhx2GjENk1r6z1p5urX3EWvuYtfbDJc4ba+3/K5x/wFpbn7V1amz58tIrGlSjfS37rnX7KMVS6/ZRiqXc9lGKpdz2UYql1u2jFEu57aMUS7ntoxRLrdtHKZZy20cplnLbRymWWrcvt+8rrm7HHHwYsRe/ktg//Svx8z5M/JKvELvsGsybl8LcJP7nt5D/3IfJL3s7D3zwK/g/pYP5OEKOvdz2IlHl1z8XVD9NBvsshOfXB3NpSaQZH4FZu621ceDvwKuBVcC9wJudcw8VtTkTuAA4EzgZuNI5d/I4uverV6+uftBjSJY5jrejo53u7vHHV077WvZd6/ZRiWXw85wK73Ui7aMUy3jaF//5DDuWeraPUizlth+r7Vh/106G2BuxfaV97+n/nVGOPcz2UYqluP14/y0U9nv127fhH/wz3H83m373J2YneoPJZQ8/NqjOOPZEzJx5kYy9Xsr9d+1U0N7eDo035dJElPyOVfwzk/voUsyBi4gt/VC9Yytb/s478P93FbHPfR0zd++q9as/Q8ONdT/K+XMVlSEkJwGPOeeeALDW3gCcDTxU1OZs4FvOOQ/cZa2dY61d6JxbU/9wRURERGQyMzNmYk58CZz4Eo7/5D489Ytf4O+7B3//3fi/ZPDfBg44BHPcSZhjT4Z9D4zMxLcQVGosW9YbdhgScdba04ErgThwjXPusjHanQjcBSxxzt1Y6XV9PgfPr4fOl1TaVV2YvZLBvDkbN0AVExhSA9770B+LFy9+0+LFi68p2n/b4sWLvzyizY8XL178kqL9XyxevLhzjP6WLl68OLN48eKM99739fXV7ZHL5fbY5qKLBnzRenpDj4suGqi4fS37Vux6r1GLpdz2H/94PjKx6HOqLPbiv2snW+yN0r6afZf6f+dkiX0y3/eotw8tlo9l/bbH/uafv/5//ZoPvN0/c1anf+bMF/kH33CW/9QRn/MvTaZ9wvSHGntfX5+H2v8bdzz/rp1qjwImw2Px4sXxxYsXP7548eIXLF68uHnx4sX3L168+Igx2v1y8eLFty1evPhN4+zfd3d3j3r09fX57u5uv+qvD/hnznyRf/b6r5dsF7XHqrt/758580V+1c1dVe138H7osfv7Uc6fq6gMIVkMvNY5957C/tuAk5xzFxS1uRX4rHPud4X9XwD/5Zz74x661xCSCMRSbvuoxKIhJJMnlvG01xCS8GMpt72GkEyO9hpCEk77KMVS3H6yDCEZb3u/ZSP+gQz+/nuCZSH7+9mSncns1PFw7EmYY07EzJhZ99jrMdRE5e+jTaYhJNbaFPBJ59xrC/sfAXDOfXZEu/cDWeBE4MfjrMDY7RAS/8Qj5D/7IWIXXIw55sQK30nt+W1byb//LRj7bmKvPrtq/erP0HCNNIRkFbBf0f6+wMg/EeNpIyIiIiJSNWbWXpiXvBpe8mp8fx88/AA//q+/8pZHfw2Z3+Gnz8S8+mzMq/4RM722qy0sX97GFVfsmuSzoyNYDvbCC3tLDifRMJMprwN4tmh/FcFcgkOstR3AG4FXECQwxmStXQosBXDOkUwmR7VpamoimUyy89G/sBmYc9AhJEq0ixo/bx7rWqYxfec22qoY7+D9kEA17kdUEhj3AoustQcB3cA5wFtGtLkFOL8wP8bJwOZGmP/iwgvL+59KOe1r2Xet20cpllq3j1Is5baPUizlto9SLLVuH6VYym0fpVjKbR+lWGrdPkqxlNs+SrGU2z5KsdS6fVRiMc0tcOyJrHvNK4h94O3w5N/J334T/pbr8b/4Eea1/4R5xVmYlmk1iWXZsl2JivFUYFxxhRIYk5G19lvjbNrnnPu33Zwv9RvtkeX3XwL+2zmXs9bu9mLOuZXAysF+Sv0mffA37PmnnwRgk4ljJksFwuy57Fi9ir4qxqsKjOH2UIExLpEYQgJDq4x8iWCCmWudc5+x1p4L4Jy72lprgC8DpwPbgXc65zLj6DrSQ0gk2vR5NhZ9no1Dn2Vj0efZWKbi5+mfepT8zdfDX/8IbbMxZ74J87IzMInmml1zPAmMSoeZTMXPck/qMYTEWtsH/M84mi5zzs3aTT97HEJirX2SXe8nSfA9a6lz7od7uPZuh5Dkv3ct/le3Ebvqe5Ga/HZ3cp//MJgY8Q+N59aPj/4MDddIQ0hwzt0G3Dbi2NVF2x54b73jEhERERHZHXPgIuL/+Qn8Yw+Tv/k7+K6v4+/4AeYsi3nJqzFNiapfc6xKjXKHmUgkPeuc+9SeGllr37yHJnuscnfOHVTU3zcJ5sD4YbkBj7JxA+w1b9IkLyAYLua7nw47DNmDWNgBiIiIiIg0AnPI4cSXXUps2aWQnI//ztXkLzqP/G9/ih8YqOq1xkpGLFvWS3f36qHKi8FtJS8mD+fcIeNsd9gezg8A5wN3AA8Hh9yD1tpzByvda8X3boa22bW8RPXNmgNbNoYdhexBZCowREREREQagTnsGGIvPBoe/HNQkfGtL+Nv/z7m9edgTjoVE4uHGp8m95y8rLUvAHLOuXGVCuypyn3E8XdUHOCg7dtg9l5V664uZu8F27fhs/01Hf4llVEFhoiIiIhIlRljMEedQOyjXyD23o9B8zT8179I/pPvw2d+h8/nax7DWMNMioeYSLRZa79rrX1xYfudwIPAQ9bad4cb2R7s2Lbb5YUjadac4HnL5lDDkN1TAkNEREREpEaMMZjjTiZ28ReJnfvfAOS/9nnyl3wAf9/d1HJC/UasskhkMrSuWEEiM565/BvCK4HBN3sh8CrgJODDoUU0Htu2wiRLYJjBihENI4k0DSEREREREakxE4vBi/6B2PGn4O/5Lf5H3yV/1WfgwEXEzn4LHHlCTSc8bITJPROZDPOWLMFks7QmEmzo6iLb2Rl2WLXW7Jzrt9Z2AHOdc78HsNbODzmuMfl8HnZshxmtYYdSnsEKjM1KYESZEhgiIiIiInViYnHMKafhT3wpPv1L/I+7yF/5KTjkcGJv+BfMC4+uyXWXLduVqKh0edWwtKTTmGwWk8sN7U+BBMZ9heVPDwBuBSgkM7aEGtXu9O0En590FRi0BivS+m29tV0nVyqiISQiIiIiInVm4nFiL3k1sUu/innrudCzlvwXPkbuS5/Aq4S9pL5UCp9I4ONxfCJBXyoVdkj18G7gaGA6cFHhWAr4TmgR7cn2bcHz9MmWwChUKG2dHBVJU5UqMEREREREQmKaEpjTzsS/+JX439yO/+H/kb/kA8TO/TDm4N2ukjlhY03uGXXZzk42dHXRkk7Tl0o1dPWFtfY9wG3OuceBtxSfc87dCNwYSmDjsWMrAGbmJBtC0jId4k2wLbrFLaIEhoiIiIhI6ExzC+bVZ+MPP4b8Vz5L/vKPYpa8B3PaGVWfG6PUnBeTZWnVbGdnQycuipwIXGyt3UgwdOQ24A/OudrN+lotk7QCwxgDM1tVgRFxGkIiIiIiIhIRZt+DiH3sCjjiOPz1V+O/8SV8f1/Nr6ulVaPFOffvzrkDgLcRzHfxWeA5a+311tp/sdYmw41wN7YHFRiTbhJPgJlt+G1KYESZKjBERERERCLEzGwldv5F+Fsd/kffxa96ith5H8HsvSDs0KTOnHN/Af4CfM5aOxt4LXAW8Hlr7TPAJ5xzd4QZ40h+qAJjRriBTERrmyowIk4JDBERERGRiDGxGOb15+APXET+mi+Qv/RCYu9Zhjn6RVW7RiMsrTqVOOc2A67wwFp7YrgRjaGvUDE0bXq4cUzEzFmwfk3YUchuKIEhIiIiIhJR5ugXEbvoi8G8GCs+jXn9mzFnWUys8pHgjbC06lRgrX0pcDwwbEyGc+5/woloD/p3Bs8t08KNYwJMaxv+yb+HHYbshhIYIiIiIiIRZvZeQOzDn8d/+yv4W67HP/UosXd/ADMZ5xiQslhrVwAW+C2wo+hUdCfzHKzASDSHG8dEzGyDbVvw3ld98lypDiUwREREREQizrS0wLveDy84FN91TTCk5D8+gtn3oKr0P1mXVp0C3goc5ZybPOUx/Tsh0VyVKqG6a22DgQHo75uUFSRTwST8qRIRERERmXqMMcRefhaxD/4PZPvJf/ZD5O/6dVX61pwXkfUsUPtlaKqpvw9aWsKOYmJmFuaE0USekaUKDBERERGRScQccngwL8bKz+O/fgX5px7FvOmdmCb9074BvRv4X2vtd4G1xSecc3fu6cXW2tOBK4E4cI1z7rIR588GLgHywADwfufc7yqKuK8PmidnAsO0zgrG5mzbAvP2DjscKUEVGCIiIiIik4yZvRexD1yCedXZ+F/8iPzyi/Cbnq9a/8uXt+25kdTDi4AzgK8C3yl6fHtPL7TWxoGrCq8/AniztfaIEc1+ARzrnDsOeBdwTcUR90/eBIYqMKJPaVoRERERkUnINDVhlryb/EGL8NetIH/pB4j9+39jFo38jlq+K65o47Of7a9ClFKh/wFe75z7+QReexLwmHPuCQBr7Q3A2cBDgw2cc1uL2s+kCpOD+v4+aJ6k80cUEhh+Wy+awjOalMAQEREREZnEYiediu84IFhqdfnHMIvfhXnF67SKQmPYBuxxqMgYOgjm0Bi0Cjh5ZCNr7RuBzwL7AGeN1Zm1dimwFMA5RzKZHNWmqamJRD4HM1uZW+J81OXihh6glTwzqhB/U1NTyfs0VVXjfiiBISIiIiIyyZmOA4h9bDn5a7+Iv+F/4cm/w9vfh0kkxt3H8uVtXHHFrqEjLS3NQDsXXtirST7D83HgS9baTwPrik845/J7eG2pDNaoCgvn3A+AH1hrTyWYD+NVpTpzzq0EVg7209PTM6pNMpkku20rzJhJqfNR5weyAGxdu4btVYg/mUxOyvtQK2Pdj/b29nH3oTkwREREREQagJkxk9h/fBTzhn/B3/0b/P99Ge/HPyJg2bJeurtX090drNjZ19dPd/dqJS/CdS1wLtANZAuPgcLznqwC9iva3xcYcznWwqSgB1trK/sV+SSeA8M0JWDadM2BEWGqwBARERERaRAmFsOcZcn7PP7m62GfhZjXnRN2WDWTyGRoSafpS6XIdnaGHU4tHFTBa+8FFllrDyJIgJwDvKW4gbX2EOBx55y31p4ANAMbKrgm9PdhJmkCAwjmwdimBEZUKYEhIiIiItJgzFlLYN0a/M3Xk997IbGTX1bW6y+8sBeI9pfQRCbDvCVLMNksrYkEG7q6Gi6J4Zx7uoLXDlhrzwfuIFhG9Vrn3IPW2nML568G/hn4V2ttFtgBLHHOVTaRZ99OaJmkk3gCzGzDb9u653YSCiUwREREREQajDEG3nY+fsM6/DevxM/bG3PI+FcnCYaNRDuB0ZJOY7JZTC43tN8ICQxr7SXOuYvH0e5TzrlP7K6Nc+424LYRx64u2v4c8LmJxlrSJB5CAsDMVtiuBEZUKYEhIiIiItKATCJB7LyPkP/sf5G/6n+IfeRyzD4Lww6ravpSKVoLk5T6RIK+VCrkiKrm/dbaayk9CWex9wG7TWDUm/e+kMCYxBUYM2bCxspG0UjtKIEhIiIiItKgTOssYu/7OPnPfoj8ik8T+/DnMTPb9vzCSSDb2cmGrq5GnANjJvAYe05g7KxDLOUZyEI+D83NYUcyYWZGK14VGJGlBIaIiIiISAMz89uJ/cdHyX/xYvJfvYzY+z8ZrLawB5dcEue88+oQYAWynZ2NlLgAwDk3aVeK9H2FnMqkHkLSBtu34r0PhmJJpEzaPxwiIiIiIjI+5tAjMW9/HzzyF/y3rhrX8qqXXhqvQ2TSSHy2sLprYs8Jssia0QoDA9DfH3YkUoIqMEREREREpoDYKaeRX7cG/6Pvwvx2zFk27JCk0WQLX/rHUeETWTNnBs/beqFlEleSNChVYIiIiIiITBHm9edgTjkN/8Nvk7/3t6POL1/eRkdHOx0d7QBD28uXN8a8GVJbvgESGGZGa7CheTAiSRUYIiIiIiJThDEG/vWCYHnVa7+E3yuJOeTwofPLlvUWllANkhfd3avDClUmocEhJCYxeSfxRAmMSFMFhoiIiIjIFGISCWL/8VGYuzf5qz6DX7cm7JBkN6y1rdbafa21rWHHskcNUIHBTCUwokwVGCIiIiIidZZZmyG9Jk1qYYrO+fVfRWP08qqXY2YO/3580UW5usclAWvtUcC/A2cBBxAsqeqttU8BPwG+5pz7S3gRlrZrEs9J/DWzUIHht23b4zq2Un+qwAhRZm2GFfetILM2U/X2tey71u2jFEut20cplnLbRymWcttHKZZat49SLOW2j1Is5baPUiy1bh+lWMptH6VYym0fpVhq3T5KsZTbfqy2mbUZlty6hMszl7P4x4v58O8+TGZtpuxYKhUsr/oRWL+W/Fc/ix/IDjt/8cVKYITBWvtd4HpgDfAvQBJoLjy/DegGvmOtvSG0IMcw9DOkCgypkUmcGpvcBv/Hlc1nScQSdJ3Vtdvseznta9n3VIv9rlV3TZn3qtijH/tUeq9RimUqxT6V3muUYmm02O+YfQeHTDtkSrzXicZ+46M30pfrw+PJ+RzffvjbdD3SRd7nyfkciViCS158CRv7Nta8QsMcehTmHRfgv/5F/Le/Am9/XzBPhoTpeufcj0oc3wj8ofD4rLX2dfUNaxwaYQjJtBlgjBIYEaUKjJCk16TJ5rPkfI5sPkt6Tbpq7WvZ91SL/c5n7pwy71WxN177KMWi2CdH7OW2j1Isij06sd/5zJ1V6z/q73UisWfWZuh6pAuPH2rr8WTzWQb8AB5Pf76fj/zuI3zu3s+x+MeLa16RETvl5ZjXnYP//S/wP7mxpteSPStOXlhrTy7Vxlp7knPux/WLanx8/+RPYJhYDKbPhG1KYESRKjBCklqYIhFLQB4SsQSphamqta9l31Mt9lP3P3XKvFfF3njtoxSLYp8csZfbPkqxKPboxH7q/qdWrf+ov9eJxJ5ekybng6EZBkPM7Pp94uBxgDx5APrz/dz46I01nyfD/OObYd0a/A/+j/zeC4md+JKaXk/G7WfArBLHbwfm1jmWPfIDhQTGZF6FBIJhJEpgRJLx3u+51eTmV6+u3/JPyWSSnp6ecbXNrC1v8qZy2tey76kUezKZ5PYHb58S73UqxD745zMKsdSrfZRiqWbspf6unSyxN1r7avS9u/93Rj32sNpHKZaR7U8/8vQ9/luoUd7rRGIfObTkU6lPsbFvI3u17MXFf7iYbD6LMYa8zw/187bD38ZlL7lsj7FVymf7yV/xcXjqUWIf/Ax7n/yScf+7dqpob28Haj+3o7U2VrjOJoIERvE1DwZ+75zbp9Zx7EbJ71gzH7ibLSs+Q+x/VmL2XhBCWNWRu/RCaJtN/D8/UVE/5Xw3nArGuh/l/LlSAqPK9EPaWPR5NhZ9no1Dn2Vj0efZWPR57tlYSZDB48XJjEQswfde9726rVTie7eQ/+wHob+Pvb/qeH7bjrpcd7KoYwIjD4z1RS0PfMY598lx9HM6cCUQB65xzl024vxbgf8u7G4FznPO3T+OEEt+x5rxx9/Re/XniV3+DcyceePoJppyV1wMfTuJf+TyivrR34fDVSOBoSEkIiIiIiJ11Dm/s2RCovj4YXMPK6sypFpM2yxi//Yh8v+zjDsuvJ4TL3lj3a4twxxE8IXuN0DxuCwPrHfO7TGzZK2NA1cBrwZWAfdaa29xzj1U1OxJ4GXOuY3W2jOAlUDJeTfGpREm8QTMjFb8RiUeokgJDBERERGROhrPMJSxkhz1YA5ahDnxpRx+1/X4jadi9orub9ITmQwt6TR9qRTZznDuVy04554ubB5QQTcnAY85554AKCy7ejYwlMBwzv2hqP1dwL4VXA+fbYBlVEFzYESYViEREREREamTwTkwLs9czpJbl9R8hZGJMm98G3GTw9/8nbBDGVMik2HekiW0XX4585YsIZGJ5r0sl7X2CmvtbieQsNYusNZesYeuOoBni/ZXFY6N5d3AT8YXZWm+QSowmNkK27cxBaZbmHRUgSEiIiIiUifFy6uSD/bDqrQoZfnyNq64og1o5+LDLe8yN3D6Ze/hrH+bz7JlvWGHN0xLOo3JZjG53NB+g1RhPALcY619mGAIySNAL9AGHAqcBrwQuHQP/ZSaU6DkN3Jr7csJEhhjLj9jrV0KLAVwzpFMJke12Z4bgFiM5Pz5GFPzqUJqZtve89maG2Be60xi02dMuJ+mpqaS92mqqsb9UAJDRERERKROyl26td6WLesdSlQcceC7+bcjb+Zn511e8WoMtdCXStGaCH7T7xMJ+lLRupcT5Zz7mrX2WoLhHmcAbwDmABuBB4CrgR855wb20NUqYL+i/X2BUTNvWmuPAa4BznDObdhNXCsJ5sgA8KUmY2zp64OmJjZsGLObSSFfGKiw4dmnMXP3nnA/msRzuD1M4jkuSmCIiIiIiNRJ5/xOus7qCmWCznJtzs7GnGnxN34D//D9mMOPDTukYbKdnWzo6mrUOTCywI2Fx0TdCyyy1h4EdAPnAG8pbmCt3R+4CXibc+7vFVwLAD/QP/mHjwBmZmtQqrJtK1SQwJDq0xwYIiIiIiJ11Dm/kwuOuyDSyQuAiy7KYV5xFszbh/yN38Dn82GHNEq2s5OtF1zQUMmLYtbaL1lrT5zIawsVGucDdwAPB4fcg9bac6215xaafRyYB3zFWnuftbayiUSy2YZIYDCzLXjeFq1hU6IKDBERERERKeHii3P09DRj3vg2/DXL8Xf/BpN6edhhTTUGuNlauw24HrjeOffIeF/snLsNuG3EsauLtt8DvKdKsQarkDREAqM1eNZKJJGjCgwRERERERmTOfGlcMAh+B/+H76/L+xwphTn3H8SzF3xHwTzWdxlrf2jtfbCcCMrzWf7GiOBMSNIYPjtSmBEjRIYIiIiIiIyJhOLEXvTO+D5Hvwvfxx2OFOOcy7vnPuZc+5dwFHABuDykMMqLZuFRAMkMDSEJLI0hERERERERHbLHHYMHN2Jv+1G/EtejWmdFXZIU4a1tpVgJZI3Eyyh+hvg7SGGNKaGGULS3AJNTRpCEkGqwBARERERkT2KvekdsHMH/sddYYcyZVhrvwc8BywFfgwc4Jw70zn37XAjG0O2vyEqMIwxQRWGKjAiRxUYIiIiIiIyyiWXxDnvvF37pn1/zEtehf/1T/CveB1mn4XhBTd1ZIBlzrlnwg5kPHxuAOIN8hVzRiteCYzIUQWGiIiIiEgdZdZmWHHfCjJrK1uxstYuvTQ+6pj5x7dAUxP+pm+FENFoiUyG1hUrSGSifS8nyjn3ucmSvABgYADio39uJqXWNtiqBEbUNEh6TEREREQk+jJrMyy5dQnZfJZELEHXWV10zu8MO6xxM3PmYl7zBvyPbsA//jfMwYeFFksik2HekiWYbJbWRIINXV1kOyfPvWxEPp+D2LSww6iOttmwZlXYUcgIqsAQEREREamT9Jo02XyWnM+RzWdJr0mHHdIwy5e30dHRTkdHO8DQ9vLlbUNtzGveCLP3In/jN/DehxUqLek0JpvF5HKYbJaWdLTu5ZQ0kGuYCgzTOhu2bgk7DBlBCQwRERERkTpJLUyRiCWImziJWILUwlTYIQ2zbFkv3d2r6e5eDTC0vWzZrlJ6M2065h/fDI89DPfdHVao9KVS+EQCH4/jEwn6UtG6l1NRQ82BMStIYPh8LuxIpEiD/HSJiIiIiERf5/xOus7qIr0mTWphalINHylm/uHV+J//iPz3ryN2dCemqf5fK7KdnWzo6qIlnaYvldLwkSjI5TANUoFB62zwPlhKtW122NFIgRIYIiIiIiJ11Dm/c1IkLi66aOzfPJt4nNg/v538ly/F//anmJefWcfIdsl2dipxESW5BprEs21W8Ny7WQmMCNEQEhERERERGeXii/dQOn/MiXDoUfgffRe/Y3t9gpJI8w20CokZTFr0bg43EBlGCQwRERERESmbMYbY4ndC72b87TeFHY5EQT4HscZIYKAERiQpgSEiIiIiIhNiDlyEOelU/M9/iN+pKoypLqjAaJBZCmbNAcBv3hRqGDKcEhgiIiIiIjJh5mWnQ38//i9/DDsUCVsjzYHROit4L5ufDzsSKdIg6TEREREREQnFIYcH5fZ//AOc+NKwo5Ei1trTgSuBOHCNc+6yEecPA74BnAB8zDn3hUqu5xtoFRITi8HsvWCTEhhRogoMEREREREZ5ZJLxvdF1MTimONT+L/+Ed/fV+OoZLystXHgKuAM4AjgzdbaI0Y0ex54H1BR4mJII1VgAMyei9+8MewopIgSGCIiIiIidZRZm2HFfSvIrM2EHcpuXXrp+L+ImheloG8nPPjnGkY0WiKToXXFChKZaN/LkJwEPOace8I51w/cAJxd3MA5t845dy+QrcoVc7nGmQMDYPZcDSGJmAb66RIRERERibbM2gxLbl1CNp8lEUvQdVYXnfM7ww6rcoceDTNa8X/6A+b4U+pyyUQmw7wlSzDZLK2JBBu6ush2NsC9rJ4O4Nmi/VXAyRPtzFq7FFgK4JwjmUwOO+9zOdZ5z4zWNlpHnJustixoZ+djD416r+PV1NQ04dc2omrcDyUwRERERETqJL0mTTafJedzkA/2o5TAWL68jSuuaBva7+hoB+DCC3tZtqx3zNeZpibMcSfj/3wXfiCLaUrUPNaWdBqTzWJyuaF9JTCGMSWO+Yl25pxbCawc7Kenp2d4x9l+ALb39bFzxLnJKj9tBn7rFtav7sY0t5T9+mQyycj7NJWNdT/a29vH3YeGkIiIiIiI1ElqYYpELEHcxEnEEqQWpsIOaZhly3rp7l5Nd/dqgKHt3SUvBpkTXgw7tsHDD9Q6TAD6Uil8IoGPx/GJBH2paN3LCFgF7Fe0vy+wumZXKySSGmoIyV6FaoGNG8KNQ4Y00E+XiIiIiEi0dc7vpOusLtJr0qQWpiJVfVGxI46FadPxf05jjn5RzS+X7exkQ1cXLek0famUqi9GuxdYZK09COgGzgHeUrOrDSUwGud35Ca5T1CysmEtzB9/lYDUjhIYIiIiIiJ11Dm/c1IkLi66KFdWe5NoxhzdGQwjeet5dVlOM9vZqcTFGJxzA9ba84E7CJZRvdY596C19tzC+auttQuADDALyFtr3w8c4ZzbUvYFcwPBcyNVYMybD4DvWVdyPI7UXwP9dImIiIiISLVcfHGOcofvmxe9GH/vb+HRB+GwY2oTmIybc+424LYRx64u2n6OYGhJ5fKDFRgNtIzqnLnB+9mwLuxIpKBx6ntERERERCRcR70Impvxf/pD2JFIvQ0OIYk1TgLDxOPBPBg9SmBEhRIYIiIiIiJSFaZlGhx5Av5Pd+Hz+bDDkXpqxCEkAMn5+J7nwo5CCpTAEBERERGRqjEnvBg2Pw9PPBJ2KFJPuULCqpGGkABmQQc81433E16BVqpICQwREREREakac8yJEG/SMJKpplCBUY/JW+tqwb6wfSv0bg47EkEJDBERERERKeGSSyb2RdTMmAlHHIf/U1q/tZ5Kcg04iSdg5ncEG8+tCjcQAZTAEBERERGpq8zaDCvuW0FmbSbsUHbr0ksn/kXUnJAKVm545vEqRjRaIpOhdcUKEplo38spYWgVkgabA2NhsEiLX6MERhQ02E+XiIiIiEh0ZdZmWHLrErL5LIlYgq6zuuic3xl2WFVnjjsZ/39X4f/4B8wBh9TkGolMhnlLlmCyWVoTCTZ0dZHtbLx7OWkMTuLZQKuQADB3b5gxE555IuxIBFVgiIiIiIjUTXpNmmw+S87nyOazpNekww5pmOXL2+joaKejox1gaHv58ray+jGts+CFR9d0GElLOo3JZjG5HCabpSUdrXs55TTqEBJjYP+D8TWuJpLxUQJDRERERKROUgtTJGIJ4iZOIpYgtTAVdkjDLFvWS3f3arq7VwMMbS9b1lt2X+aEFKzthtXPVjtMAPpSKXwigY/H8YkEfalo3cspJ9egQ0gAs//BsOop/MBA2KFMeY330yUiIiIiElGd8zvpOquL9Jo0qYWphhw+Msgcdwr++q/h//QHTMf+Ve8/29nJhq4uWtJp+lIpDR8J22ACI9Z4vyM3By3CD2SDOV1e8MKww5nSlMAQEREREamjzvmdkyJxcdFFuYpeb+bMhYMPC5ZTff05VYpquGxnpxIXUTE4B0ZTA37FfOHRAPi/PYBRAiNUjZceExERERGRil18cWUJDABzwouD0vt1q6sQkURavjHnwAAwbbOh4wD8w/eHHcqUpwSGiIiIiIjUhDkhmJfC/1ETbDY6PzSEpPESGADmmBPh73/Fb9kUdihTmhIYIiIiIiJSE2bePsEKDn/NhB2K1NrgEJIGnMQTwJz0Usjn8ff+NuxQprTG/OkSEREREZFIMAcegs/8Hu99sCSl1I219nTgSiAOXOOcu2zEeVM4fyawHXiHc+5PE7pYLh88N+AQEgA6DgzmdPnZzfiXnY5pSoQd0ZSkCgwREREREamd9v1h+1bYvDHsSKYUa20cuAo4AzgCeLO19ogRzc4AFhUeS4GvTviCgxUYjTqExBhirzsHNqzD3/QtvPdhhzQlqQJDRERERERGueSSOOedV3k/pn1/PMDqZ2DO3Mo7lPE6CXjMOfcEgLX2BuBs4KGiNmcD33LOeeAua+0ca+1C59yasq82OAdGU2MmMADMUSdgTjszqMJ45C+QnI/ZzZCZTS0t5Pv66hdgxA28dSnMnF1RH0pgiIiIiIjUUWZthvSaNKmFqUgvp3rppdVJYNCxPwB+9dOYI46rQoe7JDIZWtJp+lIpLac6WgfwbNH+KuDkcbTpAEYlMKy1SwmqNHDOkUwmh53fsfc+bO/Yn732mU+sdVbl0UeUv+Cj7Dj0cHb+7hfk1z+3K3FTQs5ATIUaQ0y2b9TPTbmUwBARERERqZPM2gxLbl1CNp8lEUvQdVZXpJMYVdE2B1rbYPWze2xajkQmw7wlSzDZLK2JBBu6upTEGK7UhCMjv06Ppw0AzrmVwMrBNj09PcMbHNVJ8rTT6enpgZ09I1/eWE58WfDYg3nJJKPu0xQWH+N+tLe3j7sPzYEhIiIiIlIn6TVpsvksOZ8jm8+SXhOt5UWXL2+jo6Odjo7gC8Xg9vLlbRPu0xgD7QfgVz9TrTABaEmnMdksJpfDZLO0pKN1LyNgFbBf0f6+wOoJtBGJDFVgiIiIiIjUSWphikQsAXlIxBKkFqbCDmmYZct6WbasFwiSF93d1fkua9r3x9/966quRNKXStGaCFaC8IkEfalo3csIuBdYZK09COgGzgHeMqLNLcD5hfkxTgY2T2j+C5E6UQJDRERERKROOud30nVW16SYA6Oq2veHHdthYw/M3bsqXWY7O9nQ1aU5MMbgnBuw1p4P3EGwjOq1zrkHrbXnFs5fDdxGsITqYwTLqL4zrHhFxkMJDBERERGROuqc3zkpEhcXXTT25ITlMh2FlUi6n6laAgOCJIYSF2Nzzt1GkKQoPnZ10bYH3lvvuEQmSnNgiIiIiIjIKBdfXL0EBu2DK5FUdx4MEZlaVIEhIiIiIiI1ZVpnwey9QAmMhjHWyhHlrCgxFeh+DFfp/VAFhoiIiIiI1F77/vjup8OOQqrDlHpYa/841rmp+ND9KOt+jIsSGCIiIiIiUnOmfX9Y8yw+nw87FBGZpJTAEBERERGR2mvfH/r7YMO6sCMRkUlKCQwREREREak5U5jIU/NgNLSVYQcQMbofw1V8P5TAEBERERGRUS65JF7dDtv3A7QSSSNzzukLexHdj+GqcT+UwBARERERqaPM2gwr7ltBZm0m7FB269JLq5vAMDNaYa9kVSswEpkMrStWkMhE+16KSHVoGVURERERkTrJrM2w5NYlZPNZErEEXWd10Tm/M+yw6qd9v6qtRJLIZJi3ZAkmm6U1kWBDVxfZzil0LyPGWns6cCUQB65xzl0Wckg1Z629FngdsM45d1Th2FygCzgQeAqwzrmNhXMfAd4N5ID3OefuCCHsmrDW7gd8C1gA5IGVzrkrq30/VIEhIiIiIlIn6TVpsvksOZ8jm8+SXpMOO6Rhli9vo6OjnY6OdoCh7eXL26rSv+k4ANaswudzFffVkk5jsllMLofJZmlJR+teTiXW2jhwFXAGcATwZmvtEeFGVRffBE4fcezDwC+cc4uAXxT2KdyPc4AjC6/5SuG+NYoBYJlz7nDgFOC9hfdc1fuhBIaIiIiISJ2kFqZIxBLETZxELEFqYSrskIZZtqyX7u7VdHevBhjaXrastzoXaN8fBrKw7rmKu+pLpfCJBD4exycS9KWidS+nmJOAx5xzTzjn+oEbgLNDjqnmnHN3As+POHw2cF1h+zrgDUXHb3DO9TnnngQeI7hvDcE5t8Y596fCdi/wMNBBle9H6ENIdldSMqLdU0AvQXnJgHNO9WEiIiIiMql0zu+k66wu0mvSpBamptbwEYKVSDwE82As6Kior2xnJxu6umhJp+lLpTR8JFwdwLNF+6uAk0OKJWzznXNrIPhSb63dp3C8A7irqN2qwrGGY609EDgeuJsq348oVGCULCkZw8udc8cpeSEiIiIik1Xn/E4uOO6CyCcvLrqo8mEeoyys7kok2c5Otl5wgZIX4TMljvm6RxFtU+IeWWtbge8D73fObdlN0wndj3EnMKy1V1hrjxtv+zKMVVIiIiIiIiIhufji6icwzLTpMG+fqq5EIpGwCtivaH9fYHVIsYRtrbV2IUDheV3heMPfI2ttgiB58R3n3E2Fw1W9H+UMIUkAd1hr1wP/VwhqVRmvH8tYJSUjeeCn1loPfE1r6oqIiIiITEIdB1StAkMi415gkbX2IKCbYHLGt4QbUmhuAd4OXFZ4vrno+PXW2iuAdmARcE8oEdaAtdYAXwceds5dUXSqqvfDeD/+qpXCrKBnAG8lWC7mboKlUm5yzm3dzet+TrCcykgfA65zzs0parvRObdXiT7anXOrCwmOnwEXFCZNKXW9pcBSAOfci/r7+8f5DivX1NTEwMBA3a4ntaXPs7Ho82wc+iwbiz7PxqLPs3HU6rPsve4qtv+4i33cbzCmVBV5dDU3N0Pp0vcpz1p7JvAlgmVUr3XOfSbciGrPWvtd4DQgCawFPgH8EHDA/sAzwGLn3POF9h8D3kWwYsf7nXM/qX/UtWGtfQnwW+AvBMuoAnyUIGdQtftRVgJjRIBHAtcDRwPbCWaa/YRzrrvMfh4BTitUXywEfu2ce+EeXvNJYKtz7gvjuIRfvbp+lTnJZJKenp66XU9qS59nY9Hn2Tj0WTYWfZ6NRZ9n46jVZ5m/7Xv4H/wfsau+h2luqXr/tdTe3g5KYIiEpqxVSKy1s4DFwL8AxxCMb/kPgkzKMuAnhePlGKukpPi6M4GYc663sP0a4NNlXkdERERERMLWMi147uuDSZbAEJFwjTuBYa29EXgtcCdwNfBD51xf0fkLgc0TiOEywFlr302hpKTQXztwjXPuTGA+8ANr7WDM1zvnbp/AtUREREREZBwuuSTOeefVoOPBpEV/3+7biYiMUE4Fxl3A+c6550qddM7lrbXzyw3AObcBeGWJ46uBMwvbTwDHltu3iIiIiEjUZNZmSK9Jk1qYivRSqpdeWusExs6Ku0pkMrSk0/SlUlpKVWQKGHcCYzzzTTjntlcWjoiIiIhI48qszbDk1iVk81kSsQRdZ3VFOolRC6ZlGh4qrsBIZDLMW7IEk83SmkiwoatLSQyRBhcLOwARERERkakivSZNNp8l53Nk81nSa9JhhzTM8uVtdHS009HRDjC0vXx5W/UuMliB0VdZBUZLOo3JZjG5HCabpSUdrXspItVX1iSeIiIiIiIycamFKRKxBOQhEUuQWpgKO6Rhli3rZdmyXiBIXnR312A1v8FJPCuswOhLpWhNJADwiQR9qWjdSxGpPiUwRERERETqpHN+J11ndU2KOTBqZqgCo7IERrazkw1dXZoDQ2QKUQJDRERERKSOOud3TorExUUX5WrTcUuQwPD9fZgKu8p2dipxITKFaA4MEREREREZ5eKLa5TAaC4MIalwDgwRmXqUwBARERERkfoZWka1siEkIjL1KIEhIiIiIiL1M5TAUAWGiJRHCQwREREREakb09QE8aaKJ/EUkalHk3iKiIiIiEh9tbRoCMnk5sMOQBrOuOb0VQJDRERERERGueSSOOedV6POm6dpEs9JbvXq1aOOJZNJenp6QogmunRPhit1P9rb28f9eg0hERERERGpo8zaDCvuW0FmbSbsUHbr0kvjteu8uToVGIlMhtYVK0hkon0vRaQ6VIEhIiIiIlInmbUZlty6hGw+SyKWoOusLjrnd4YdVv21tOArTGAkMhnmLVmCyWZpTSTY0NVFtnMK3kuRKUQVGCIiIiIidZJekyabz5LzObL5LOk16bBDGmb58jY6Otrp6AhKuge3ly9vq+6FqlCB0ZJOY7JZTC6HyWZpSUfrXopI9akCQ0RERESkTlILUyRiCchDIpYgtTAVdkjDLFvWy7JlvUCQvOjuHj3PQVW0TIOdOyrqoi+VojWRAMAnEvSlonUvZc/82tX4n/4QDjkcc+AhmIX7hR2SRJwSGCIiIiIiddI5v5Ous7pIr0mTWpiamsNHIJjEc8umirrIdnayoauLlnSavlRKw0cmGd+3k/yVn4T1z8Gdt+OB2Ke+jGnfP9y4crkgpnn7YAoJMokOJTBEREREROqoc37npEhcXHRRrmZ9m+YWfBVWIcl2dipxMQn57dvw3/0arH8O86/n47/15eD4PXdi3vAv1b/e+ufw99+DOfxYaN8fY0av2Om9x9/1a/z3r4PNz8OCDmLnfST0hIoMpwSGiIiIiIiMcvHFOWq2+mNLdVYhkcnDew/PPgG5PPlvfwWeeRwW7It5yasxL34l+Ss/if9TGqqcwMjfcyf+W1dB3w48wNy9Mcd0Yo4/BWa0QqIZtvWS/+kP4f574ODDMK96Pf6nPyT/Px/EvP19xE58SVVjkolTAkNEREREROqrSsuoSnVYa08HrgTiwDXOucuq1bfP5/G/uhX/u5/BqqeCg83NmFe8DvOy04NqiHgcc9SL8N+7lvyvboOmJmIvfU3F186nf4W/9osAmLe9N4jnLxl8+lf4X/9keOPmZox9N+aVr8fEYviTTyP/tc/hV36e/BOPYP757ZgmfX0Omz4BERERERGpr5Zp0LcT733Jcn6pH2ttHLgKeDWwCrjXWnuLc+6havTvr78a/5vbYfZemNf+E+z/AsyBizD7LBzWzhx5Av571+Kvvzo4UGECw/f14b//TTj4MGLLPrNrPotTX4vPZoOEytYtwVwXrW1B5cXMXavtmL3mEfvgZ/Df+wb+5zfjH/wT5uVnYQ4/pjA/RnNF8cnEKIEhIiIiIiL11dwC+TzkBqBJEyWG7CTgMefcEwDW2huAs4GKExj+j7/H/+Z2zGveiHnTO3afrGrfD47uhL9kgtf2bsa0zZ74tf/8B9i8kdh7lo2ajNMkEpiXn7nHPkxTAvPmpfgXHk3+xzcEyZjBk83NEN/z1+l1xgTDZ4TYez8GyZdX1IcSGCIiIiIiUl+DpfgDWSUwwtcBPFu0vwo4eWQja+1SYCmAc45kMjmqo6ampmHHn7/zDkzHAcxb+gHMOL7s+098kR0/u4Xer13O7G1baD7o4LLfzKBND9xLdt7eJF98GiYWm3A/ALzm9fhXv47cqqfJ/v2v5DduIL91S5CE2wOjBMaQ6QcePOpnpFxKYIiIiIiIyCiXXBLnvPNq1PnGDTBtOrRMr9EFpAylyiJGfeN2zq0EVg6e7ykxw2symWTwuN+5nfwjf8G85g1s2Lhp3MH4FxwOwKaH/0JswX7jft2wPnI58vffi/mHV7Lh+ecn1EdJ01vh2FPKeknxPZnq+oDkwMCo+9He3j7uPipMRYmIiIiISDkyazOsuG8FmbWZsEPZrUsvjdesb7/+OUguqHj+i0QmQ+uKFSQy0b6XEbcKKM4U7AusLreT/O9+xrq3n4nfvjU48NB9kMthDj+uvI72mhcMzXh+fbkh7NL9VDBJ7MGHT7wPiSRVYIiIiIiI1ElmbYYlty4hm8+SiCXoOquLzvmdYYdVf+ufgwUdFXWRyGSYt2QJJpulNZFgQ1cX2c4peC8rdy+wyFp7ENANnAO8ZSId+S2bMDu2w4xW8r+5A+bMg0OPKqsPY0ywzG62fyIhBHE8+WjQ1wteOOE+JJpUgSEiIiIiUifpNWmy+Sw5nyObz5Jekw47pGGWL2+jo6Odjo6gpHtwe/nytj28cvy899CzFrP3gor6aUmnMdksJpfDZLO0pKN1LycL59wAcD5wB/BwcMg9WG4/ZsbMYGP7NvyO7fDwfZgXvxITn0AlT3ML9O0s/3WDnn4MZrZBcv7E+5BIUgWGiIiIiEidpBamSMQSkIdELEFqYSrskIZZtqyXZct6gSB50d1d9kiCPdu8MfjterKyBEZfKkVrYXUJn0jQl4rWvZxMnHO3AbdV1Mn0QgJjx7ZgCIf3E6+AaG4JhoBMkF+3BhZ0aIneBqQEhoiIiIhInXTO76TrrC7Sa9KkFqam5vCRnucAMHtX9tvxbGcnG7q6aEmn6UulNHwkbMUVGJs2BNv7Hjixvppb8BUkMFi3BnPY0RN/vUSWEhgiIiIiInXUOb9zUiQuLrooV5N+/fq1wUaFFRgQJDGUuIiI6TMAguEjq54KEhpzJ7hcZsu0CVdg+P4+2NgD+yyc2LUl0jQHhoiIiIiIjHLxxbVJYLD+OTAG5u1Tm/4lHNNbg+ft2/CrnoJ9D5z4EI5KhpD0FBJkeyuB0YiUwBARERERkfrpeQ72mocpzF8hDaJQgcH2rbDqaUzHgRPvq5IExoZ1ABhN4NmQlMAQEREREZG68evXVmX4iESLaWqClmn4Z5+Avh0Tn/8CMBUkMPy2rcHGzOqtnCPRoQSGiIiIiIjUT89zFU/gKdEUm9EKjwYrsJr9Dpp4R83NE6/A2LEteJ7ZOvHrS2QpgSEiIiIiInXh+/tg0/OqwGhQZmYrbO2FpiboOGDiHTW3QN8EExiDFRiDQ1qkoSiBISIiIiIio1xySbz6nRbmJ2BvJTAaUWxwKdUDDw2GgUxUJXNg7NgGzS2YJs2x0oiUwBARERERqaPM2gwr7ltBZm0m7FB269JLa5DAWP8cUL0JFhOZDK0rVpDIRPteThWJo18EgFl0eGUdNbdAth+fz5f/2m1bYYaGjzSqprADEBERERGZKjJrMyy5dQnZfJZELEHXWV10zu8MO6y68esHl7isvAIjkckwb8kSTDZLayLBhq4usp1T515GUetb/52dCw+AFx5ZWUeD1RvZfmiZVtZL/Y5tMFgJIg1HFRgiIiIiInWSXpMmm8+S8zmy+SzpNemwQxpm+fI2Ojra6ehoBxjaXr68Sis69DwXfCFtm11xVy3pNCabxeRymGyWlnS07uVUZIzBHHsiZlqF808MJjAmMoxEFRgNTRUYIiIiIiJ1klqYIhFLQB4SsQSphamwQxpm2bJeli3rBYLkRXf36qr279c/B8n5GGMq7qsvlaI1Ecxz4BMJ+lLRupdSgcGqi4kkMHZsgznzqhuPRIYSGCIiIiIiddI5v5Ous7pIr0mTWpiaUsNHAOhZW7UJPLOdnWzo6qIlnaYvldLwkQmw1n4S+DdgfeHQR51zt4UXUUElFRjbt2HaK1gBRSJNCQwRERERkTrqnN85KRIXF12Uq2p/3ntY/xzm8OOq1me2s1OJi8p90Tn3hbCDKGYSzXiYYAJjq+bAaGCaA0NEREREREa5+OLqJjDo3RR8Id27OiuQSAOLF37PPjBQ1st8Pg87tiuB0cBUgSEiIiIiIrVXWIHEVGkIiVTN+dbafwUywDLn3MZSjay1S4GlAM45ksnkqDZNTU0lj5erb+5cNgGz21ppLqO//LZe1nvPzL3nM7MKcVRDte5Jo6j0fiiBISIiIiIiNefXPxdsJJXAqCdr7c+BUjf9Y8BXgUsAX3heDryrVD/OuZXAysKu7+npGdUmmUxS6ni5/NZtAGzesAFTRn++J0iSbfOwowpxVEO17kmjKHU/2tvbx/16JTBERERERKT2egYTGPuEG0dEWGtfMM6meefcUxO9jnPuVeOM53+BH0/0OlUVjwfPuTKHMW0PEh9Gy6g2LCUwRERERESk9tavhTnzMInmsCOJiscIKh/2tKbsDqAmkzpYaxc659YUdt8I/LUW1ynb4BwYZScwtgbPmgOjYSmBISIiIiIio1xySZzzzqtef37dakhqAs8i25xzbXtqZK0tOSdFlXzeWnscQSLlKeDfa3it8RuqwChvEs/BCgxUgdGwlMAQEREREamjzNoM6TVpUgtTkV5O9dJLq5fA8Fs2wROPYF77xup0WJDIZGhJp+lLpSbjcqr/Oc52H6hVAM65t9Wq74oUKjB8LrfH8pRiXhUYDU8JDBERERGROsmszbDk1iVk81kSsQRdZ3VFOolRLT7zO8jnMSefVrU+E5kM85YswWSztCYSbOjqmlRJDOfcteNs980ahxI9lVZgTFcCo1EpgSEiIiIiUifpNWmy+Sw5n4N8sB+lBMby5W1cccWuUQ0dHcHqABde2MuyZb0T7tffcyd0HIDpOKDiGAe1pNOYbBZTmCehJZ2eVAkMa+0rxtPOOffLWscSOROexHMrmBhMm179mCQSlMAQEREREamT1MIUiVgC8pCIJUgtTIUd0jDLlu1KVHR0tNPdvbriPv365+Dxv2H+6e0V91WsL5WiNZEIrpFI0JeK1r0ch6+P2O8gmItiAzCPYHLPVcB4VytpHJVUYEyfgYnFqh+TRIISGCIiIiIiddI5v5Ous7omxRwY1eLvuRMAc9JLq9pvtrOTDV1dk3YODOfcQYPb1tqPEiQtLnbObbfWzgA+TZDMmHomugrJjm2a/6LBKYEhIiIiIlJHnfM7J0Xi4qKLyvzyWIL3Hn/3b2DREZh5+1QhquGynZ2TLnExhg8A7c65LEAhifERYDXw2VAjC8NgBUa+vJ9B398PLdNqEJBEhWprRERERERklIsvrjyBwbNPwppnMSe9rPK+Gts24KQRx04EtocQS/iGKjDKHEKS7YemRPXjkchQBYaIiIiIiNSEv+c3EI9jOv8h7FCi7mLgdmvtj4Bngf2A1wHvDTWqsEx0Es+BLCSaqx+PRIYqMEREREREpOp8Po+/57dw5AmY1llhhxNpzrn/A04GHgZmAX8DTikcn3oqqcBIqAKjkakCQ0REREREqu/Rh2BjD+ZN7wg7kknBOfcQ8FDYcUTC4Coi5VZg9PfDjNbqxyORoQSGiIiIiIhUnb/719AyDXPsyKkdpBRr7T8CLwOSBEuoAuCc+9fQggqJMSaowlAFhoygISQiIiIiIlJVPpvF//EPmONPwWhViD2y1n4C+BrB97PFBMunvhbYFGJY4YrHJzQHhtEcGA1NCQwRERERERnlkkviE3/xg3+E7Vu1+sj4vQt4tXPuA0B/4fn1wIGhRhWmeFP5CYxsvybxbHAaQiIiIiIiUkeZtRnSa9KkFqbonN8ZdjhjuvTSOOedN7HX+rvvhLbZcPix1Q1qhEQmQ0s6TV8qRbYzuvdyHOY45/5a2O631iacc/dYa6uSAbLWLgY+CRwOnOScyxSd+wjwbiAHvM85d0c1rlmxeHwCQ0iyWka1wakCQ0RERESkTjJrMyy5dQmXZy5nya1LyKzN7PlFk4zfsR1//z2Yzn/ANNXu96WJTIZ5S5bQdvnlzFuyhERmUt/Lx621Rxa2/wqcZ619G7CxSv3/Ffgn4M7ig9baI4BzgCOB04GvWGsrKL2pIlVgSAlKYIiIiIiI1El6TZpsPkvO58jms6TXpMMOaZjly9vo6Gino6MdYGh7+fK2cffh/3wXZPsxJ59WoygDLek0JpvF5HKYbJaWdLTuZZkuAuYVtj8MvA+4HLiwGp075x52zj1S4tTZwA3OuT7n3JPAY0A0Zl2Nx2Fg/BUY3vugAkOTeDY0DSEREREREamT1MIUiVgC8pCIJUgtTIUd0jDLlvWybFkvECQvurtXl92Hv+c3kJwPL3hhtcMbpi+VorXwZdUnEvSlonUvx8taGwN2AncBOOfuAQ6p0+U7Bq9bsKpwbBRr7VJgKYBzjmQyOapNU1NTyeMT0ZNIkEg0MXuc/fmBAdb5PDNmz6G1SjFUQzXvSSOo9H4ogSEiIiIiUied8zvpOqtrUsyBMRF+y0Z46H7MGW8KlsKsoWxnJxu6uib9HBjOuby19mbn3PjLXEqw1v4cWFDi1MecczeP8bJSH5Iv1dA5txJYOdimp6dnVJtkMkmp4xORM4b8tm3j7s/v3A7A9myWnVWKoRqqeU8aQan70d7ePu7XK4EhIiIiIlJHnfM7J0Xi4qKLypx/APD3/h58HnPyqTWIaLRsZ+ekTVyMcKe19hTn3F17blqac+5VE3jZKmC/ov19gfLLbmoh3oTPl/EzmM0Gz5oDo6EpgSEiIiIiIqNcfHGOcn9x7O/+Nex3EKZ9/5rE1MCeBn5irb0ZeJaiKgjn3MdreN1bgOuttVcA7cAi4J4aXm/84vHyJvHM9gfPWoWkoWkSzyrKrM3w+T98ftyzSWfWZlhx34qatK9l37VuH6VYat0+SrGU2z5KsZTbPkqx1Lp9lGIpt32UYim3fZRiqXX7KMVSbvsoxVJu+yjFUuv2UYql3Pal2pZ7vcnEr1sDT/4dc3JVVv6caqYDPyRIXOxLUBWxX2G7YtbaN1prVwEp4FZr7R0AzrkHAQc8BNwOvNc5V37pTS3Em8pbRlUVGFOCKjCqZHBJrGw+SyKWoOusrt2WBtayfZRimeyx37XqrinzXhV79GOfSu81SrFMpdin0nuNUiyNFvsds+/gkGljzz/YSO+13NgBlty6hP5cPzET4zP/8Bn+5fB/GfN6k42/5zdgDObE+gwfaSTOuXfWuP8fAD8Y49xngM/U8voTMsEKDKMERkNTBUaVlLskVi3bRymWyR77nc/cOWXeq2JvvPZRikWxT47Yy20fpVgUe3Riv/OZO6vWf9Tfa7mxp9ek6c/1kyfPgB/gY7//WMNUYviBAXz6V7DoSMxcrbgwHtba6dVs13AmWoHRrARGI1MFRpWUuyRWLdtHKZbJHvup+586Zd6rYm+89lGKRbFPjtjLbR+lWBR7dGI/df/d//a9kd7rRGI3xgzNbpD3edJr0pNiQs898b+6FdatIbb4XWGHMpmsBWaNo103MLfGsURPPL5rXovx0BwYU4LxvuQqOY3Er15dn4l0M2szPLD5AY6Zfcy4/keUWZspawmtctrXsu+pFHsymeT2B2+fEu91KsQ+uGxTFGKpV/soxVLN2EstwTVZYm+09tXoe3dLzEU99rDaRymWke1PP/L0PS4Z2CjvtdzYM2sz/POP/pkBH/xWuTnWzPde973IJjC++tX5nHfe2j2285ueJ3/xebDoSGIXXFzz5VPDVFjusSpv0FrbD9wwjqZvrHSJ1Roo+R2rqsuo/r9Pw5ZNxC+6YnwBPfRn8l/8BLH//hzmkMOrEkM1aBnV4XazjOq4/lwpgVFl+gFtLPo8G4s+z8ahz7Kx6PNsLPo8x7bivhVcnrmcnA/G9Z9+wOl8/TVfDzmqsXV0tNPdved/R+evWY7/4++JferLmH3a6xDZLolMhpZ0mr5Uqi7LqVY5gfGJcTYdKMxTESW1T2Bc9RnoWUv8E/9vfAHdfw/5L19K7KIvYg44uCoxVIP+Thyu0gSGhpCIiIiIiNTBXi17Ddv/5bO/JLM2E9kKjPHwf/8r/u7fYM6yoSQv5i1ZgslmaU0k2NDVVZckRrU45z4VdgyRpmVUpQRN4ikiIiIiUmOZtRk+kf7EUPUFQM7n9jhxaL0tX95GR0c7HR1BMmJwe/ny0SMYfC5H/vqvwbx9MGcsrneotKTTmGwWk8thslla0tG6l1IZU+Yknn4w2RGP1ygiiQJVYIiIiIiI1NjgqiSDDGZcE4fW27JlvSxb1gvseQiJ/9Wt0P00sfM+gmlpqVeIQ/pSKVoTwW/bfSJBXypa91IqVG4FxmCyQwmMhqYEhoiIiIhIjaUWpoibOHmfxxjDa/Z/Decde96kHT7iN2/E33I9HHUCHH9KKDFkOzvZ0NVV1zkwpI7KXUZ1qAJDX3EbmYaQiIiIiIjUQd7n8XjyPs8vn/1l2OHs0UUXjf3bb3/jNyHbT+ycpaGuOpLt7GTrBRcoedGIYhOswGhSBUYjU3pKRERERKTG0mvSw+a/yOazpNekI12BcfHFOUotnuAffQh/168wZy7GzK/vxJ2NyFo7H3gNcCwwB9gE3A/8zDn3XHiRhazcISQDg0NI9BW3kenTFRERERGpsdTCFIlYgv58sFJCFOe/GI9g4s6rYW4Sc2b9J+5sJNbaw4FLgJcDfwQeBp4D2oC3AV+y1v4K+Lhz7qHQAg1LvKnMCgxN4jkVKIEhIiIiIlJjnfM7+d7rvseNj94IwJsWvSnS1Rdj8b/+Cax6iti5H8a0TAs7nMnum8DlwFudc30jT1prm4Gzga8Dky/bVal4vMw5MFSBMRXo0xURERERqYPO+Z2TJmmxfHkbn/3s8GN+y0b8zd+BI46DE6be9+lqc86dvIfz/cD3Co+KWGsXA58EDgdOcs5lCscPJKj8eKTQ9C7n3LmVXq8qVIEhJWgSTxERERERGeaKK9pGHfM3Xgf9fcTeHO7EnY3IWnvzGMdvqtIl/gr8E3BniXOPO+eOKzyikbyAIBHh8/h8fnztcwNgDCamBEYjUwWGiIiIiIjsln/sYXz6l5jT/xmzYN+ww2lELx/j+GnV6Nw59zCAtbYa3dXHYCVFLgexcfzePZdT9cUUoASGiIiIiIiwfHnbsMqLlpZmoJ1lH9jEf26/GvZKYs6aRF+AJwFr7acLm81F24NeADxdhzAOstb+GdgCXOSc+22pRtbapcBSAOccyWRyVJumpqaSxydi26zZbAXmzZlNbPqMPbbvbU6woylRtetXSzXvSSOo9H4ogSEiIiIiIixb1suyZb0AdHS009fXT09PD/lf3Yq//kli//5fmGnTQ46y4exXeI4VbQN44FmCeSvGxVr7c2BBiVMfc86VHKICrAH2d85tsNa+CPihtfZI59yWkQ2dcyuBlYPx9ZRYYzeZTFLq+ETk+3YCsGHdOszM1j2337oVH4tV7frVUs170ghK3Y/29vEvx6wEhoiIiIiIlOS3bML/8Ntw+LHwon8IO5yG45x7J4C19g/Ouf+tsK9XTeA1fUBfYfuP1trHgUOBTCWxVMXgaiLjXYkkN6AVSKYATeIpIiIiIiLDXHhhUInhb7oO+nZq4s4asNbuM7i9u+SFtXZ+DWPY21obL2y/AFgEPFGr65WleA6M8dAcGFOCUlQiIiIiIjLMsmW99P/t7/jf/wLz2jdiFu635xdJuX5lrf0N8H/A3c65oeU2rLUx4CTgX4FTgaMquZC19o3ACmBv4FZr7X3OudcW+v60tXYAyAHnOueer+RaVaMKDClBn7CIiIiIiAzj+/voXfkFmDMX87olYYczSiKToSWdpi+VItvZGXY4E3U8waSYK4EXWGufAHqBNoIJPB8Fvga8v9ILOed+APygxPHvA9+vtP+aUAWGlKAEhoiIiIjIFLd8edvQBJ4+nyN/zXLyTz5K7L0fxUzb8woQ9ZTIZJi3ZAkmm6U1kWBDV9ekTGI45/qBLwNfttbuBxwNzAE2Ag8457pDDC98scEExvgqMLwqMKYEfcIiIiIiIlPcFVcECQzvPf76r8Gf76LtXf/J9uNOCTu0UVrSaUw2iyn8Zr4lnZ6UCYxizrlnCVYdkQITb8KDKjBkGCUwREREREQEAP/jLvxvbsec/s/MeP0Stkdw+ce+VIrWRAIAn0jQl0qFHFF1WGuPA14KJIGhGVOdcx8PK6ZQDSYj8uUkMPT1ttFpFRIRERERkSlo+fI2Ojra6ehoB+CDp9yNv+V6Hpr1Gsw//WvI0Y0t29nJhq4uej/0oUk7fGQka+1S4PfAK4D/JhhOsgw4JMy4QjWYjBgoZxJPVWA0OiUwRERERESmoGXLeunuXk1392pevc+v+dwxn4WjXsRRnzs38kumZjs72XrBBQ2RvCj4L+B059wbgR2F5zcB2XDD+v/t3XmcHFd97/1P9cxoJM0i2Rp5kQ2xAwYMZhcOIgmBsMR2AANBxwYChAAOuSxJTHITYgKE5QmE4ISwhDjADTyB2OeGcM1uIAvmyZUDAgwGm8V4leVFiyWNllm7nj+qWtMz0yPNSN1dNd2f9+vV7lpOV/+myq2Z+c45pwrkJJ5qwABDkiRJ6mLpzTfywcdeBmc8mMqr/5ik1274BTgpxviNfLkaQqjEGL8EPLvIogrlbVTVgAGGJEmS1KXSu+6g+v53cHDVSVRe92aS/pVFl9SttoUQzsiXfwJcGEL4ZWCiuJIK1qAHRrp/H9Ovv5j0pzfOb28PjK5ggCFJkiR1kfe+dwiAdPcOqu97K/T1MfLWPyMZGi62sO72l8DZ+fLbgH8C/j1f7k6He2DUDSG54xY4dJDq1Z+c394eGF3BAEOSJEnqIpdfPkR6YD/Vv3krjB2k8ntvJVl/StFldbUY4z/mQ0bIn08ATogxfqjYygp0uAdG3RCS1QPZ8/5989vbA6MrGGBIkiRJXaS/Mkb1A++AHXdT+R9/SvKAM4suqeuFEL5bvx5jnIgx7g8hbC2qpsLlvSnS+h4YteWGAcYUiT0wOp4BhiRJktThardMfeDpJ/GBx1xG9ac38bvffAeXf+EXiy5NmXm3Sw0hJMDPF1BLOTTqgVFbHt07v709MLqCEZUkSZLU4d7whlEuvXQf6T99iPTar5NcfAlXfOThwGjRpXW1EMIn8sUVdcs1ZwA/bG9FJdLoNqpTeYBRrc5v7xwYXcErLEmSJHWg9753iDe8YSagSD/3z6TXXsMHbn45v/e0ZxVYmer8bIHlFPgv4H83401CCO8huyXrRP4+L48x7sn3vRF4BTANvD7GeE0z3vO4NbqNan2YMZc9MLqCAYYkSZLUgS6/fCbAqH79y6Sfu5LkSU9j6qEvAfYXW5wAiDH+OUAI4boWBwdfBd4YY5wKIbwbeCPwxyGEhwMXA48ANgBfCyE8JMZ4hKSgTXob9MCYnjy8mE5NkfTW/TprD4yu4BWWJEmSOlj63etIP/lheORGkpe8hjf0Gl6UQQjhV+tWJ+esHxZj/Pfjfa8Y41fqVq8DXpAvXwhcGWMcB24NIdwMnAtsOd73PG6VBnNgTM3pjTErwLAHRjcwwJAkSZI6xHvfO8Tllw8dXn/eOffyT+f+FfcPPJTTfud/zv6LtYr20UW0SWn+RJ6/DVyVL59GFmjUbMu3zRNCuAS4BCDGyMjIyLw2vb29Dbcfi+rqVewABlatZCA/5qHVq6ndf2TdCWup1G6rCtw7Pc2qwSGGmvT+zdLMc9IJjvd8+C+YJEmStMzMnd+i5g1vGD28/fyH7+Ffnvb7MLye0/7nZST9K9tc5fHr27qV/i1bGN+0icmNG4sup6lijE29f20I4WvAKQ12XRZjvDpvcxkwBXwy35c0aJ82On6M8QrgilqbnTt3zmszMjJCo+3HIp0YB+DAvn0cyo9Zvf/+w/t37biPZGAmrGN6ikMTE4w36f2bpZnnpBM0Oh8bNmxY9OsLv41qCGFzCOGHIYRqCGHBf5VCCOeFEH4cQrg5hPAn7axxKd7+9qV1W3rve4eO3ugY27fy2K1uX6ZaWt2+TLUstX2Zallq+zLV0ur2Zaplqe3LVMtS25eplla3L1MtS21fplqW2r5MtbS6fZlqWWr7Vh27vpfFXGmaUv2PL/LpJ74CVq6m8ntvJRkaXlIdZdC3dSvrLrqIofe8h3UXXUTf1q1Fl9RSIYS+EMIvhxAuytcHQggDR3tdTYzx6THGcxo8auHFy4BnAS+OMdZCim3AA+oOczqwvTlf0XFqOInnZN3yzNwYaZrmQ0j8+3ynKzzAAH4APB+4dqEGIYQe4IPA+cDDgRfmE86UzjvesbQA40jffI63fSuP3er2Zaql1e3LVMtS25eplqW2L1MtrW5fplqW2r5MtSy1fZlqaXX7MtWy1PZlqmWp7ctUS6vbl6mWpbZvdS1zpYcOkv7DX5F+6sNsH34clTf9NcnIycd1zKL0b9lCMjlJMj1NMjlJ/5bip2VolRDCI4GfAP/AzNCSXwE+1qTjnwf8MfCcGOPBul2fBS4OIfSHEM4EzgK+2Yz3PG6V/FfVRrdRBajWT+6Zb3cOjI5XeIARY7wpxvjjozQ7F7g5xnhLjHECuJJswhlJkiSpK7z3vUOcdtoGTjst625dW6712kjvvJXqOy4l3fpfJM97CQ+6/I3LsudFzfimTaR9faQ9PaR9fYxv2lR0Sa30d8CbY4wPA2rdDL4O/FKTjv8BYAj4agjh+hDChwFijD8EInAj8GXgNaW4AwmQJEnWo2Kh26hOV+dv7+trT3EqTpqmpXhs3rz5Pzdv3rxxgX0v2Lx580fq1l+yefPmDxzhWJds3rx56+bNm7emaZqOj4+39PGmN02lkM57vOlNU21vX6Zauqn2bvpay1TLUtu/+c3V0tTidTq+2qenp5dt7Z3SvpnHrr+ey6325Xzey96+TLWUrfbx8fEUZn7GHRsbS+///P9O73jupnTbi38tHf32dQu+bimPRp/Ndj8mvv71dPLtb08nvv71wmsZHx9Pc7Tgd6H7N2/enOTLu+u2727F+zXhkd51113zHuPj4w23H+vjjuc+Kb3zb95+eP3Oj/xtescFj0/vuODx6bbrv314+7af/Di944LHp3d+/O+a+v7NeDT7nCz3R6PzsZTPVZKmactDkkVOKPOfwB/GGOcNbgshbAZ+Lcb4ynz9JcC5McbXLeLt0+3b2zeM67TTNnDXXYt/v1a2L1MtS21fllpqk8x0w9d6LO3LVMti2tdPGlR0Le1sX6Zaltp+obYLTYi1HGrvxPbHe+yjTXBW5tqLbF+mWurbL3bCuk74Wos8dq1dOj5G+k9/R3rdf8DZj6byyktJhk9Y9PsdiZMPzpdPNtho4svjEkL4LvCqGOPWEMLuGOOJIYRzgQ/EGM9t9vs1QcPfsZr9/8z0772Q5IlPpfLCSwCofv5K0qs/BUDl7X9Hckp2w5R03/1U3/Aykhe/mspTLmja+zeDn6PZjjCJ56I+V22Z5STG+PTjPER5J5eRJEmS2uzSS0dJt99B9cPvhnu2kTz7hSTPCiQV5wBYpv4M+EI+tGNFCOGNwKuBVxVbVsF6emfPe7HQHBi17U7i2fGWyxX+FnBWPrHMXcDFwIuKLamxN71paUPGLr10/u2vmtW+lcdudfsy1dLq9mWqZanty1TLUtuXqZZWty9TLUttX6Zaltq+TLW0un2Zallq+zLVstT2Zaql1e3LVMtS27fq2H/wpM9SfeeHoH8llT94G8nZj17S+6hcYoyfDyGcD7ySbO6LnwOeH2P8drGVFayvD6Ya33mkYYDR6xwYna4tQ0iOJITwPOD9wHpgD3B9jPHXQggbgI/EGC/I210A/A3QA3wsxvjORb5Fy4eQpLf8mOoV7wGgp7eX6WoVkgRIsueEuuX8AXVtgKSSrVcq2aO2nBxhPd+WHKnNUteTBCo9UEmy7T092aPSkyWaPRXo6c3S/Z76R2/epmf+ayqV/LVzj5WtJ0nTe+E1jV2+OovXs3N4LTuL17OzeD1bK50YJ/3nK0j/v6/CQ86h8qo3kKxd15L38lrO16ohJMtQe4aQXPY7JGecReVVfwhA9aqPkn7taoDsDjs/96CsmLvvpPrm15Bc8kdUnvDLTXv/ZvBzNNuyGEJyJDHGzwCfabB9O3BB3foXgS+2sbTFWzVA8pBHQAp9/Suojo1BCqT5zLj5XEwp6cz2FCCfowmgmretTmfbqtX8eTqbeffwejV7fd16mlahmtZtP9J6Cun0nPXq/K/pKJoae1Uq8wISenqhN3/09GZpam/+XLcvObyc7+/pm/26vrr2dcdJDrefu2/266sr+kjHDh5+3zKHLZIkqbOl99xF9e/fDdtuI7lgM8lzXkTibSOXrRDC2xbTLsb45lbXUlo9vaT1w0am63pjNOiBkTiEpON5hZsgOfV0kt/+AwDWLMOELa1Ndl0LRWrBR7Wa/cNQnYap/Hl6eiZUmZ6eeRzeVs0Dl2nS+v35tqO95nDb6ensH6LpKdLJyWzbVP48drDBvqnsH7SpfLm+q1mjr3mR52bH3A19K7Iwo6+vwXLvrG1J/f7evM0Cy0mjYzZ6TW+fP6hIktSFqt/6BunHPwB9vVRe/xaSRz6+6JJ0/Orn+FsJ/AbZ0PnbgQcC5wKfLqCu8jjSEJLpRkNI/PW203mFlfUqqA0naeZxm3q0pUnTdCYMmRVu1IUc01Ozt+Vt0rp9g/0r2L93b95mEiYnsraTEzCZbUsnJ/J9kzA+Bvv3weQkaa39ZN1rq417uyypR0ulcuQgZUV/For0rYAVK6CvP3/O11f0z9qW1Nos1L6vP+u1Yu8TSZLaLp2cII0fJf3PL8GDHkblkj8iOXF90WWpCWKML68thxCuBF4YY/x03bbnA5uLqK00eucEGAtN4lnrmWGA0fG8wupISZLMDAXpX+Jr65ZXj4xwsIk9atLp6bogZHJ2uDFnOZ3b7givSScm8vUJmBiH/fuyYKW2fWICJscbBiiLCk+SZHagcXh5JjBhRS006Z+3fV5gsqI/e/SvzJfz5/48fDEskSSJdMc92V1G7vgZyTOfS/K8l5L4C1qnOh948ZxtVwP/q4BayuNIAUajHhg9TuLZ6fwXUGqjpDaRaf/Ko7dtwfunh3uPjM8PN/L1dGLu/vE5z7V2detje2FiPAtN6o9Z/02mVsNRv/BkJuCYG3T0r8yCkhUrs7CjPvioa5esyNrNfl0/1YFVpNVpbzEnSSq99DtbqP7j30ICldf8Kcljnlh0SWqtm4HXAH9bt+1/AD8rppyS6O2FsUOHV9NZc2DU/WHOISRdwyssdZGk1itl1eqF2zTx/dLqdNZLpFEoMjEO42NZEDI+lu2bGDu8PXsez/bXtu/bkwUl9W0mxue/7wL1HJ7TpLdvTjDSP6snSNI/t4dIP6xcla33ryJZmT1Te+5fmS2vWEnS5KFYkqTukt51O+nnriT99n/BGWdlQ0bWn1J0WWq9VwKfCSH8T+Au4DRgCnh+oVUVrbcPpvbNrC90G9VpA4xu4RWW1DJJpQf6j9zj5HgDk7RazUOS+eEHefiRjmfPA329HNi9e3a7+kBk7CDs3Z2HJjMhy9yhN0fsRTIn7MhCjjz46F+Z7asPPvJtSf1raoFI/yro77fHiCR1gXTbbVQ/fyV8+/9m3xeedRHJBYGkzy7x3SDG+N0QwlnAE4ENwN3AlhjjkWem73S9fdnPeTVTU9l8cNXqAkNI/PW203mFJS1rSaWS9aDoX3iyk1pIMjAywqElzmmSpmn2TXFiDMbGYPxQHnYcgvFDpGNj2fr4oXx/3b7xfP3gAbh/V7ae75s7vOaooUgt/OhfOSscSRr1BOlflYciq2HVKli5Out1s2p1tt1ARJJKI73z1iy4+M6W7N/oXw8kz7iQZGCo6NJaqm/rVvq3bGF80yYmN24supxSyMOKbxRdR5kkvX3ZvHA101PZ3Grjh2b1wDjcxh4YHc8rLElHkCRJfoeXPmjww+Sx9iBJpyazXiL1gchYtpwuEIgwPpYHJodmeovU9o2Nzbt98IKhSP/KPNSoCzcOBx6r5+07HISsGshClFr7Xv8qKEnHKr3jZ1Q/dxVcf132b+2zLiZ5+nNIBgaLLq3l+rZuZd1FF5FMTjLY18euq64yxGixEMJ7gGcDE2Tzarw8xrgnhHAGcBPw47zpdTHGVxdTZQO9vbP/6DM1mc1zNn6IdLo683PYtD0wuoVXWJIKkPT2Zd0iG/ygeuyhSF1PkbGDeShyEA4dJD10MF+e2cbYoZnt+/ZkYcihfF86M2xmwSCkb0VdoFEXhDQIQVi5muTw+sDsfb193nlGUtdIb7+Z6ueuhO99E1YNkDz7YpKndUdwUdO/ZQvJ5CRJPgSgf8sWA4zW+yrwxhjjVAjh3cAbgT/O9/0sxviYwio7krl3IZmeznqmwuw5MA5P4ukfVzqdAYYkdYhsktZBWH18oUiapvndZQ7CoQOzQo90XgiSradj+fLuHbODkOmZv5osGIT05BPLrh7Iwo2BQZJVA9n66oHs68n3Jfny1NjppOOTsHrQ8eGSloX0tp9mwcX3vwWrB0ie8yKSpz0r+3ety4xv2sRg/m932tfH+KZNBVfU+WKMX6lbvQ54QVG1LElf35weGFMzAca0k3h2I6+wJGmWJElm5hVZc8LsfUs8Vjo5MSvoaByEHMjaHDxAevAAHDpAuntntv3ggezONbXj5c+76t+kb8Ws8CMLOhqFH3XrqwdgVbac9DgniKTWSW/9SRZc3LA1C10vfDHJrz4r+zepS01u3Miuq65yDozi/DZwVd36mSGE7wL7gDfFGBvOwxFCuAS4BCDGyMjIyLw2vb29Dbcfq9GhYQ5OTx4+5i7S7A8ZwNDq1azKtx/o72c/sO6kk6mUrDdTs8/Jcne858MAQ5LUMknfiixgGF47e/sSjpFOTmRBxsEDcHA/HDrAYE/C6L33zGzL96eHDsDoXtL7ts/sy+8is/CcIKvqentkIcdMD5CZwCOpC0gYGMqW+1c6/EVSQ+nPfpRNzvmD78DAEMlzfzMLLo5wK/NuMrlxo8FFk4UQvgY0uufuZTHGq/M2l5HdnvWT+b67gQfGGHeFEB4P/J8QwiNijPvmHiTGeAVwRb6a7mwwMfrIyAiNth+r6sQkTE6yY8cOkiRhenz88N3tRvft4UD+XtW9ewHYtXcvyaGxpr1/MzT7nCx3jc7Hhg0bFv16AwxJUqklfStgzYpZvUFWjYwc/qHlSNI0nbkTzKGZECStBSKH9sOB7Pnwtt07SA/eNtMzJM2ij4YBSG9vFmasHoTBoeyXlIFBGBjOAo6BIZLBfP9Atp/BIVjRb/Ahdaj05puyHhc3fhcGh0ie/1KSp16QTYgstVCM8elH2h9CeBnwLOBpMcY0f804MJ4vfzuE8DPgIcDWFpe7OH292ffhahV6emByPJtDC2C67jb3096FpFt4hSVJHStJkmyi0ZWrgJnuiouNDtJqNRvmUt8D5OB+0gP74cAo5M9pbXnnvaS3/wwO7MvmEeEowUetJ8fAUHbLxPr1+uAjD0cMPqTySm++MQ8urofBYZLfeBnJUy4gWbmq6NIkQgjnkU3a+SsxxoN129cDu2OM0yGEnwfOAm4pqMz5apNyTk1mAcbEBMnKVdn31rmTeCYVbxXfBQwwJElaQFKp5MNIZo+nXUyEkE6MZ4HH/rlBxwLBx203w8HRRQQftd4dDYKPwXx99SAMzvQCMfiQmi+dnISf/oD0+1tJb9gK990NQ2tIXvBykqecT5J3c5dK4gNAP/DVEALM3C71ycDbQghTwDTw6hjj7uLKnKM+wOhfmc2LVQsFp+cEGPa+6ApeZUmSWiBZ0Z/NlL523cy2RbwunRg/HHBkz/tmenzsH817gOT7dtyzuOCjb0UeaAzB0DBJbXlwOH8MZdvyZQaHDT2kBtL7d5HesJX0hm/DTddnQ9R6++BhjyR5xnNJNv0qSX9/0WVK88QYH7zA9k8Dn25zOYtXCyUmJ2fuklYLB6tz7kJigNEVvMqSJJXI4eDjhOMJPkbzHh61HiD7YP8+0v2j2fMdt8D+fVkPkYXm+DD0kEir03DrT/NeFt+CO2/Ndpw4QvLEp5A88gnwsEcZWkitUuuBMT2V9bhIqwv3wOjxV9tu4FWWJKkDHEvwkVans0lM92cBBwf2kY7ua3voIZVJemCU9IffhRu2kv7gO9n/90kFHvSwbELOR26E037OsE5qh1qAMTk5c1v1WoBRrZ/E0wCjW3iVJUnqUkmlB4aGs0dt21Fe04rQY8fwGqqrBrPQozZp6eHwY4hkYHj2tlWr/eVRTZOmKdx1ezY05Ptb4Wc/yv7KOzhEcs7j4ZEbSR7xuOwOQ5LaKunry75vTE1mdyABWLESkmTOJJ6TDiHpEl5lSZK0aK0IPVZMTjC2a0cWeuzemQUhB44QevT0zExUOjgEA8PZXVvynh+H7+JSH3ysHiTpcXZ6ZdLxcfjR90lv+FY2Aefu/LbMDziT5PwXkDxqI5x5lnc0kIpWP4lnPtcTK1ZApcchJF3KqyxJklrqaKHHmpERJnfunPWatDqd3bp2/+jhCUzTPPyobav19GDH3aS3/iTbNzWVvb5RIasHZg1pSepvUVsb3jIwp/eHQ1yWrTRNs/8/7rub9N7tsOPumeXtd2Td0ftXwtmPIXnWxSTnPJ6kbgiWjq5v61b6t2xhfNMmJjduLLocdaJaKDE1NTOEpG8F9FRm9cBIncSza3iVJUlS6SSVnpm5M2rbjvKaNE2zu0Ls3zcn9MiDjvrgY98e0u13ZNvGD2Wvb3TQFf11IUf9vB6129cOkaweyG+3OwCrBrLn/pUOc2mDNE1hdE8eTGQBBTvqAotDB2caJxVYtx5OOpXkV84neeTj4KxzSPr6Cqt/OevbupV1F11EMjnJYF8fu666yhBDzVcLkSfGsp4XQNK3grTSA9N1c2BMTs701lBHM8CQJEkdIUmSbHK3latg5ORs2yJel05Ozty9JQ860lpPj8PrefixmHk9IBvmUgsz8nAjmbNeW05qwcdALQQZ9JfqOmmawt77s5Divu1ZSHF4+Z7DARQAlQqsOykLKR700Oz5pA1w0qmw7mTPaxP1b9lCMjlJknfj79+yxQBDzTcwBEB64ADJivz2qX35EJL6OTAO7s/+DVXHM8CQJEldLenrg7UnZo/atqO85vAQl4P7Zz2n9dsOHYAD+0kPHcj27d4x0zYf6gILBSC90N8P/auy5xUrYeVKWLGSpH9lNvRhof35M/31j7xtbx8kCUml0pRzd8RzNDWZ9YiZ+xgbI50Yg7FDMDGePY+PzSxPjJPWth0YhR33ZPsOn5seWHdyFk6c9Yg5IcV6Ev8K2xbjmzYxmAdCaV8f45s2FVyROtLAQPZ8YDTr+QZZT4yeOQHGgVGStQ4B6wYGGJIkSUvUaIgLLK7HB0A6MT4TcuShRhZ+5AHH+CEYG8u6TY+NZe3Hx2B0L+nOew9vZ2JsVhgCCwQijVQq2SOpzFlO8ueebKb/ytz9DdqmaVbL+Dj31QKI6amj11BfSy2M6V81E7qsO4nkYY+aHVKcuJ7Ese6Fm9y4kV1XXeUcGGqt1XmvioP7Zybx7OufP4nngf2He2uos/mvvyRJUpslK/qzsd1L6PWxkHRq6nB4MK+3w8QYaf361BRUq9ltQuufa480rVuuzllOIa1mvU/mtk2SvGdIPyvXnsBYlTk9QOp7jtQedYFFb69zhixDkxs3GlyopZIV/dmQkQP7626jOnsSzzRNsx4aDiHpCgYYkiRJy1jS2wu9gzN/qZy7v831DI+MMDHnrjKSdMwGBrNeahN1dyGpn8Rz7FAWpA7aA6MbtH4ApCRJkiRJx2L1YDaR8lQeYKyYM4nngdHs2SEkXcEeGJIkSZLUwUIIbwcuBKrAfcBvxRi35/veCLwCmAZeH2O8prBCGxkYzOYHqu+B0VM3B8aB/QAkDiHpCvbAkCRJkrpI39atDL7//fRt3Vp0KWqf98QYHxVjfAzweeDNACGEhwMXA48AzgM+FELoKazKRlYPZr0sJuuHkFSy+XhgpgfGantgdAN7YEiSJEldom/rVtZddBHJ5CSDfX3suuoqJ+LsAjHGfXWrA8zcsOhC4MoY4zhwawjhZuBcYEubS1xQMjBE+qMbSLf+V7ah1gPj9p9R/cQHsjszgUNIuoQBhiRJktQl+rdsIZmcJMm73/dv2WKA0SVCCO8EXgrsBZ6abz4NuK6u2bZ8W6PXXwJcAhBjZGRkZF6b3t7ehtuPx6EnPIn9P/oejO6l97G/wAnr1zP6mHMZ+8bX4AffIQEqZ57FiQ89m2Tlqqa+dzO04pwsZ8d7PgwwJEmSpC4xvmkTg319AKR9fYxv2lRwRWqWEMLXgFMa7Losxnh1jPEy4LJ8zovXAm+h8Y2K0gbbiDFeAVxRa7Ozwd2GRkZGaLT9uJzzBJJ3PwHIJunYuXMnPPtFJM9+0ayCd+0/APsPNPe9m6Al52QZa3Q+NmzYsOjXG2BIkiRJJde3dSv9W7YwvmnTcfWYmNy4kV1XXdWUY6lcYoxPX2TTTwFfIAswtgEPqNt3OrC9yaVJTeMknk3Ut3Urlb/8y0VPiLTUCZSW0r6Vx251+zLV0ur2Zaplqe3LVMtS25eplla3L1MtS21fplqW2r5MtbS6fZlqWWr7MtWy1PZlqqXV7ctUy1LbN2uyzNq8FUPveQ/rLrrouI83uXEj+1/3OsOLLhJCOKtu9TnAj/LlzwIXhxD6QwhnAmcB32x3fdJi2QOjSeonRFq3iAmRljqB0lLat/LY3VZ7ct11XfO1Wnv5a++mr7VMtXRT7d30tZaplk6rffqaa+DBD+6Kr7WZtR+J81aoCd4VQngo2W1UbwdeDRBj/GEIIQI3AlPAa2KM08WVKR2ZPTCapP4bSzI5Sf+WI0/c28r2ZapludeeXHtt13yt1t557ctUi7Uvj9qX2r5MtVh7eWpPrr22accv+9fazNqPZHzTJtK+PtKeHuet0DGJMf5GjPGc/Faqz44x3lW3750xxgfFGB8aY/xSkXVKR2OA0SRL/cbSyvZlqmW5154++cld87Vae+e1L1Mt1r48al9q+zLVYu3lqT198pObdvyyf63NrP1IavNWjP7RH3nbU0ldLUnThpPMdpJ0+/b2zEPTt3UrJ3z/+9z/qEct6htL39alTca0lPatPHY31T4yMsLeL3+5K77Wbqi9NutxGWppV/sy1dLM2hvNYL1cau+09s049pFmaC977UW1L1Mtc9uvOe+8o8643ylfa7NrLxvvnjBffreERnfu6DYd/0uk2m5RnysDjCbzH/rO4vXsLF7PzuG17Cxez87i9ewcXsv5DDCOLISwNca4/NK6FvKczHa858MhJJIkSZIkqfQMMCRJkiRJUukZYEiSJEmSmuGKogsoIc/JbMd1PpwDo8kcK9hZvJ6dxevZObyWncXr2Vm8np3Dazmfc2BIxbIHhiRJkiRJKj0DDEmSJEmSVHq9RRcgSZIkSVreQgjnAe8DeoCPxBjfVXBJLRdC+BjwLOC+GOM5+bYTgauAM4DbgBBjvD/f90bgFcA08PoY4zUFlN0yIYQHAJ8ATgGqwBUxxvc185zYA0OSJEmSdMxCCD3AB4HzgYcDLwwhPLzYqtriH4Hz5mz7E+DfYoxnAf+Wr5Ofj4uBR+Sv+VB+3jrJFPCGGOPZwBOB1+Rfd9POiQGGJEmSJOl4nAvcHGO8JcY4AVwJXFhwTS0XY7wW2D1n84XAx/PljwPPrdt+ZYxxPMZ4K3Az2XnrGDHGu2OM38mXR4GbgNNo4jkxwJAkSZIkHY/TgDvr1rfl27rRyTHGuyH7hR44Kd/eVecohHAG8Fjgv2niOTHAkCRJkiQdj0a3lk3bXkW5dc05CiEMAp8Gfj/GuO8ITZd8TgwwJEmSJEnHYxvwgLr104HtBdVStHtDCKcC5M/35du74hyFEPrIwotPxhj/Nd/ctHPiXUgkSZIkScfjW8BZIYQzgbvIJmZ8UbElFeazwMuAd+XPV9dt/1QI4XJgA3AW8M1CKmyREEICfBS4KcZ4ed2upp0TAwxJkiRJ0jGLMU6FEF4LXEN2G9WPxRh/WHBZLRdC+GfgKcBICGEb8BayX9JjCOEVwB3AZoAY4w9DCBG4kexuHa+JMU4XUnjr/CLwEuCGEML1+bY/pYnnJEnTjhx2Uy/dvr19PXNGRkbYuXNn295PreX17Cxez87htewsXs/O4vXsHF7L+TZs2ACNx+1LagPnwJAkSZIkSaVngCFJkiRJkkrPAEOSJEmSJJWeAYYkSZIkSSo9AwxJkiRJklR6BhiSJEmSJKn0DDAkSZIkSVLpGWBIkiRJkqTSM8CQJEmSJEmlZ4AhSZIkSZJKzwBDkiRJkiSVXm/RBUiSJEmSlpW06ALUcZLFNDLAkCRJkiQtyfbt2+dtGxkZYefOnQVUU06ej9kWOh8bNmxY9DEcQiJJkiRJkkrPAEOSJEmSJJWeQ0gkSZIkqYuFEM4D3gf0AB+JMb6r4JKkhgwwJEmSJKlLhRB6gA8CzwC2Ad8KIXw2xnhjO+tI791OeuP1sGc3HNoPp59J8svPJEkWNbejuoQBhiRJkiR1r3OBm2OMtwCEEK4ELgSWFGBUv/EV7vvXT5A+4Ewq4RUkp59x1NekE+Ok372O9BtfgR/fkG1MKtDbC5MTUKmQ/NIzlvjlqJMZYEiSJElS9zoNuLNufRvwC3MbhRAuAS4BiDEyMjIya//EQ85m/JeexqH/+x9U/+IPGfrN32XVM59L0t8PQDo9TXXXfUzdcQuTN9/E5E9vYuIH34GJcXpO3sCq33w1K3/5GVRGTgZg959cQvrvn2fkuS9syRfdDr29vfPOUzdrxvkwwJAkSZKk7tVojEY6d0OM8Qrgitr+ebfDPPkBjPzOHzH2jOeR/q/3Mfqx9zH6iQ/B+lNgahJ274TpqfwdK3Dq6SRPehrJ4zaRPvSRHKpUOASwezcA1Yc/lvTqT7LjjttIVg8262ttK2+jOlszbqNqgCFJkiRJ3Wsb8IC69dOB7cd6sGR4LZXXvxl+9H3SG7aS7rqPpKcXNv4irD+V5JTT4QFnkqxcdeTjPOhhWYpyy0/gnMcdaznqMAYYkiRJktS9vgWcFUI4E7gLuBh40fEcMEkSOPvRJGc/+tgPcuZZAKS330xigKFcpegCJEmSJEnFiDFOAa8FrgFuyjbFHxZbFSQrV8OaE2DHPUWXohKxB4YkSZIkdbEY4xeBLxZdxzzrTyHdeW/RVahE7IEhSZIkSSqdZOQU2HF30WWoRAwwJEmSJEnls/5kuH8X6eRk0ZWoJAwwJEmSJEnlM3IypCns3lF0JSoJAwxJkiRJUukka0/MFvbeX2whKg0DDEmSJElS+QyfkD3vM8BQxgBDkiRJklQ+a7IAI927p9g6VBoGGJIkSZKk8hkYgkrFHhg6zABDkiRJklQ6SaUCQ2udA0OHGWBIkiRJksppzVrSfXuKrkIlYYAhSZIkSSqn4RPAAEM5AwxJkiRJUikla9Y6hESHGWBIkiRJkspp+AQY3UNarRZdiUrAAEOSJEmSVE5rToDpaTiwv+hKVAIGGJIkSZKkcho+IXv2VqrCAEOSJEmSVFLJ8NpswXkwhAGGJEmSJKms1qwFILUHhjDAkCRJkiSVVW0Iyd49hZahcjDAkCRJkiSV06rV0NsHo3uKrkQlYIAhSZIkSSqlJElgeA3s21t0KSoBAwxJkiRJUnkNrSUdNcCQAYYkSZIkqcyG1oABhjDAkCRJkiSVWDK0xjkwBEBv0QVIkiRJkmaEEN62yKaTMca3t7SYMsjnwEjTNJsTQ13LAEOSJEmSyuVPgE8uot0LgM4PMIbWwtQkjB3K7kqirmWAIUmSJEnlMh5jfPnRGoUQntuGWoo3tCZ7Ht1jgNHlnANDkiRJkspl3SLbndzSKkoiGc4DDG+l2vUMMCRJkiSpRGKME81st+wNrc2evRNJ13MIiSRJkiSVSAjh/wXSo7WLMb60DeUULx9Cko7uwSk8u1vhAUYIYTPwVuBs4NwY49YF2t0GjALTwFSMcWO7apQkSZKkNrq5bnkEeBnwOeB24IHAs4GPF1BXMYYcQqJM4QEG8APg+cDfL6LtU2OMO1tcjyRJkiQVJsb457XlEMI1wK/HGL9Rt+2XgD8rorYiJH19sGrAISQqPsCIMd4EEEIouhRJkiRJKpsnAtfN2fbfwKYCainO0BoDDBUfYCxBCnwlhJACfx9jvGKhhiGES4BLAGKMjIyMtKlE6O3tbev7qbW8np3F69k5vJadxevZWbyencNrWRrfBf6fEMKbY4yHQgirgD8Hrj/eAy92OH8pDK8h3ben6CpUsLYEGCGErwGnNNh1WYzx6kUe5hdjjNtDCCcBXw0h/CjGeG2jhnm4UQs40p072zfqZGRkhHa+n1rL69lZvJ6dw2vZWbyencXr2Tm8lvNt2LChiLf9LeBTwN4Qwv3ACcBW4EVNOPZShvMXa3gt3HNX0VWoYG0JMGKMT2/CMbbnz/eFED4DnAs0DDAkSZIkqRPEGG8DnhRCeCBwKnB3jPGOJh172QznT4bWkP70xqLLUMEqRRewGCGEgRDCUG0ZeCZZWihJkiRJHS8PLb4JbAshVEIIy+J3uaYZWgv795FWp4uuRAUqfA6MEMLzgPcD64EvhBCujzH+WghhA/CRGOMFwMnAZ/JksBf4VIzxy4UVLUmSJEltkP9e9EHgycDaObt7FvH6ZgznX9Q8g62cN+XgqacxmqasW9FHZe2JLXmPZnMemdmacT4KDzBijJ8BPtNg+3bggnz5FuDRbS5NkiRJkor298BB4GnA18mCjLcCX1zMi5sxnD8/zlHnGWzlvClpJctqdt1+K8lUtSXv0WzOIzPbQudjKXPLdFe3I0mSJElaXp4E/HaM8XogjTF+D3gF8IZCq2q3obXZs3ci6WoGGJIkSZJUXtPAVL68J4SwHjgAnHa8Bw4hPC+EsA3YRDac/5rjPWbLDK8BIB3dW3AhKlLhQ0gkSZIkSQv6b7Kh9Z8BrgGuAg6R3Ur1uCw0nL+UhrIAAwOMrmaAIUmSJEnl9RJmes7/PtnQkSHgbwqqpxirB6FSgX0GGN3MAEOSJEmSSiiE0AO8j5m7fxwC3lFoUQVJKpWsF8bonqJLUYGcA0OSJEmSSijGOA08E1get91otaE1zoHR5QwwJEmSJKm8/hr48xBCX9GFFG5ojXNgdDmHkEiSJElSeb0OOAW4NISwA0hrO2KMDyysqgIkQ2tJb/1x0WWoQAYYkiRJklRev1l0AaUxbA+MbmeAIUmSJEklFWP8etE1lMbQGhg7RDoxTrKiv+hqVADnwJAkSZKkEgkhvHKR7V7R6lpKZWhN9jy6r9g6VBh7YEiSJElSuVweQvgokByhTQK8B/hoe0oqXjK8NpsAZN/9sG590eWoAPbAkCRJkqRyGQSmgMmjPFYWVWAh8tAi3XlfwYWoKPbAkCRJkqRyOXOR7dKjN+kgIydnzzvvLbYOFcYAQ5IkSZJKJMZ4e9E1lFGycjUMDsPOe4ouRQVxCIkkSZIkaXlYfwrpDgOMbmWAIUmSJElaFpKRkx1C0sUMMCRJkiRJy8PIybB7B+n0dNGVqAAGGJIkSZKk5WH9KTA9DffvLLoSFcBJPCVJkiSpZEIII8BLgV8HHg2sAfYC3wO+BHw8xrijuAqLkYycnN16Zee9M3clUdewB4YkSZIklUgI4S+A7wIPBT4KPAM4O3/+KHAW8J0QwrsKK7IoeWjhRJ7dyR4YkiRJklQu24EHxxjHG+z7LvCpEMJK4JXtLasETlwPlYoTeXYpe2BIkiRJUonEGN9fCy9CCKcs0GxtjPEDbSyrFJKeHlhzIuzZXXQpKoABhiRJkiSV108W2H5jW6sok6Fh0tG9RVehAhhgSJIkSVJ5JXM3hBCGgWoBtZTD4DDs31d0FSqAc2BIkiRJUsmEEO4EUmBVCOGOObvXAf/c/qrKIRlcQ3rf3UWXoQIYYEiSJElS+fwmWe+LLwIvqdueAvfGGH9cSFVlMDQMo/bA6EYGGJIkSZJUMjHGrwOEEEZijAeLrqdUhtbA+CHSyQmSvhVFV6M2cg4MSZIkSSqREMLrQwj9AAuFFyGE/hDC69tbWUkMDWfP9sLoOvbAkCRJkqRyOQW4OYTwReDrwI+BUWAIeAjwFOB84BNFFVikZHANKcD+vXDiSNHlqI3sgSFJkiRJJRJj/FPgscBPgVcAXwJ+QDYfxm8DPwIeG2N8U2FFFmloTfZsD4yuYw8MSZIkSSqZGONO4K/yh+rlQ0jS0b3z7zGrjmYPDEmSJEnS8lHrgbF/b7F1qO3sgSFJkiRJJRVCGAbeCvwKMAIznQ5ijA8sqKxirRqASsUhJF3IHhiSJEmSVF4fAh4HvA04EXgdcAfw10UWVaSkUoGBIRi1B0a3sQeGJEmSJJXXM4GzY4y7QgjTMcarQwhbgc9xnCFGCOE9wLOBCeBnwMtjjHuOt+C2GFpDag+MrmMPDEmSJEkqrwpQ62qwP4SwFrgbeHATjv1V4JwY46OAnwBvbMIx22NoDew3wOg29sCQJEmSpPL6Htn8F/8GfAP4ILCfLHA4LjHGr9StXge84HiP2S7J4DDpXbcVXYbazABDkiRJksrrVcxM3Pl64C+AtcBLm/w+vw1ctdDOEMIlwCUAMUZGRkbmtent7W24vRX2rT+ZsR/f0Lb3OxbtPB/LQTPOhwGGJEmSJJXX+hjjfwPEGHcArwQIIZy7mBeHEL4GnNJg12UxxqvzNpcBU8AnFzpOjPEK4Ip8Nd25c+e8NiMjIzTa3grVvhWk+/ex4957SXp62vKeS9XO87EcLHQ+NmzYsOhjGGBIkiRJUnl9FRhusP3LZHclOaIY49OPtD+E8DLgWcDTYozpMVVYhKE12fOBfTB8QrG1qG0MMCRJkiSpZEIIFbKhI0kIIWFmGAnAg8h6TBzve5wH/DHwKzHGg8d7vLYazDOdUQOMbmKAIUmSJEnlMwWkdcv1qsA7m/AeHwD6ga+GEACuizG+ugnHbblkcDg7OaN7j9ZUHcQAQ5IkSZLK50yyXhdfB56cL6f5Y0eM8dDxvkGMsRm3Yi1GPoQkHd03q2uKOpsBhiRJkiSVTIzx9nzx5+DwkJKTY4x3F1dVidTmwNhvD4xuYoAhSZIkSSUVQlgLfAh4ATAJDIQQngOcG2N8U5G1FWpgKHt2CElXqRRdgCRJkiRpQR8G9pL1xJjIt20BLiqsohJIenth9WA2iae6hgGGJEmSJJXX04DX50NHUoAY4w7gpEKrKoPhNfbA6DIGGJIkSZJUXnuBkfoNIYQHAs6FMbSGdHRP0VWojQwwJEmSJKm8PgJ8OoTwVKASQtgEfJxsaEl3G14L+/YUXYXayABDkiRJksrr3UAEPgj0AR8DrgbeV2RRZZAMr4V9DiHpJt6FRJIkSZJKKsaYAn+TP1RvaC0c3E86NUnS21d0NWoDAwxJkiRJKrEQwkOBRwOD9dtjjB8rpqKSGF6bPY/ugxPWFVqK2sMAQ5IkSZJKKoTwp8Cbge8BB+t2pWTDSbpWMrQmuy3Lvj0GGF3CAEOSJEmSyuv3gXNjjN8vupDSqfXAcCLPruEknpIkSZJUXoeAHxVdRCnlAYa3Uu0e9sCQJEmSpBIJIdT/ofnPgPeHEN4K3FvfLsZYbWddpTO8Jnu2B0bXMMCQJEmSpHKZIpvjAiDJn19Ztz/J9/e0s6jS6V8FK1YYYHQRAwxJkiRJKpcziy5gOUiSJLuV6ujeoktRmxhgSJIkSVKJxBhvry2HEP4wxvhXc9uEEC4FLm9rYWU0vJbUHhhdw0k8JUmSJKm83rzA9je1tYqyGl7rEJIuYg8MSZIkSSqZEMKv5os9IYSnMjMXBsDPA6Ptr6p8kqE1pLf9tOgy1CYGGJIkSZJUPh/Nn1cCH6vbngL3AK9re0VlNLwWRveSVqskFQcYdDoDDEmSJEkqmRjjmQAhhE/EGF9adD2lNbwWqlU4sB+GhouuRi1mRCVJkiRJJWV4cRRDa7Jn58HoCgYYkiRJkqRlKRlemy3su7/QOtQeBhiSJEmSpOUpDzDS0b3F1qG2MMCQJEmSJC1PQ2uzZ4eQdAUDDEmSJEnS8jQwCJWKAUaXMMCQJEmSJC1LSaWSTeRpgNEVDDAkSZIkScvX8FpSA4yuYIAhSZIkSVq+1q6DvbuLrkJtYIAhSZIkSVq2krUnwl5vo9oNDDAkSZIkScvXmhNg3x7S6emiK1GLGWBIkiRJkpavNSdCmsLonqIrUYsZYEiSJEmSlq1k7QnZwh7nweh0vUUXIEmSJElqvxDC24ELgSpwH/BbMcbtxVZ1DNasy54NMDqePTAkSZIkqTu9J8b4qBjjY4DPA28uuJ5js249AOmu+wouRK1mgCFJkiRJXSjGuK9udQBIi6rluAytgZWr4L67i65ELeYQEkmSJEnqUiGEdwIvBfYCTz1Cu0uASwBijIyMjMxr09vb23B7O+w69XQqe3dxQkHv30iR56OMmnE+DDAkSZIkqUOFEL4GnNJg12UxxqtjjJcBl4UQ3gi8FnhLo+PEGK8ArshX0507d85rMzIyQqPt7TB9wnq46/bC3r+RIs9HGS10PjZs2LDoYxhgSJIkSVKHijE+fZFNPwV8gQUCjLJLTjqV9HvfJJ2aIun119xO5RwYkiRJktSFQghn1a0+B/hRUbUctwc+CKan4M5biq5ELWQ0JUmSJEnd6V0hhIeS3Ub1duDVBddzzJKzziYF0p/eSHLmQ4ouRy1igCFJkiRJXSjG+BtF19Asydp1sP4U0puuh2c+95iPk1arcP9OuPcu0nvugt07Ia1Cms5+LMK+VSupHho75lo6zdTzXwz9A8d1DAMMSZIkSdKylzzhyaRf+hfSXTtI1q0/Ytt0ahLu2Ua67Ta4exvpvXfBPXdlt2KdnJhp2NsLlR5IKlBJgAQSyP9zRGOVhLS6PO9M2wrVp54PpxhgSJIkSZK6XPLkZ5Je86+k//oJeOWlJMlMyJDu2U36kx/AT35AevNNcM82mJ7OdlYqMHIKnHIaySMeCydvIDn5dDh5A6w5YdZxlsK7kMy2YmQEjvN8GGBIkiRJkpa9ZN1JJBdsJv3cP5PuupdkwwNJR/fCnbfCrvuyRitXwYPPJnnUE+D0M0hOOyMLLLxzybLgVZIkSZIkdYTk2RfD0DDptdeQfv9bsGqA5OcfCk/9dZKHnAMP/HmSnp6iy9QxMsCQJEmSJHWEJElInvrr8NRfL7oUtUCl6AIkSZIkSZKOxgBDkiRJkiSVngGGJEmSJEkqPQMMSZIkSZJUegYYkiRJkiSp9JI0TYuuodU6/guUJEmS1DZJ0QWUgL9jqdkW9bnqhh4YSTsfIYRvt/s9fXg9fXg9u+3hteysh9ezsx5ez855eC0XfMj/Zxb18Hws6XwsSjcEGJIkSZIkaZkzwJAkSZIkSaVngNF8VxRdgJrK69lZvJ6dw2vZWbyencXr2Tm8lloq/5+ZzfMx23Gfj26YxFOSJEmSJC1z9sCQJEmSJEmlZ4AhSZIkSZJKr7foAparEMJ5wPuAHuAjMcZ3zdmf5PsvAA4CvxVj/E7bC9VRLeJaPgW4Grg13/SvMca3tbVILVoI4WPAs4D7YoznNNjvZ3OZWMS1fAp+NpeNEMIDgE8ApwBV4IoY4/vmtPHzuQws8lo+BT+fy0IIYSVwLdBP9rvBv8QY3zKnjZ9NHdXRfqbuRI1+VgkhnAhcBZwB3AaEGOP9+b43Aq8ApoHXxxivKaDslljoe0Ozz4c9MI5BCKEH+CBwPvBw4IUhhIfPaXY+cFb+uAT4u7YWqUVZ5LUE+EaM8TH5wx/Ayu0fgfOOsN/P5vLxjxz5WoKfzeVkCnhDjPFs4InAa/zeuWwt5lqCn8/lYhz41Rjjo4HHAOeFEJ44p42fTR3REn6m7jT/yPyfVf4E+LcY41nAv+Xr5OfjYuAR+Ws+lJ+3TrHQ94amng8DjGNzLnBzjPGWGOMEcCVw4Zw2FwKfiDGmMcbrgLUhhFPbXaiOajHXUstIjPFaYPcRmvjZXCYWcS21jMQY7679xTbGOArcBJw2p5mfz2VgkddSy0T+edufr/blj7mz/PvZ1NF05c/UC/ysciHw8Xz548Bz67ZfGWMcjzHeCtxMdt46whG+NzT1fDiE5NicBtxZt74N+IVFtDkNuLu1pWmJFnMtATaFEL4HbAf+MMb4w3YUp5bws9lZ/GwuQyGEM4DHAv89Z5efz2XmCNcS/HwuG/lfPb8NPBj4YIzRz6aWarE/U3eDk2OMd0P2S30I4aR8+2nAdXXtap+jjjPne0NTz4c9MI5N0mDb3KR6MW1UvMVcp+8AP5d3rXw/8H9aXZRays9m5/CzuQyFEAaBTwO/H2PcN2e3n89l5CjX0s/nMhJjnI4xPgY4HTg3hDB33iE/mzoa/x85uq44R0f53lDvmM6HAcax2QY8oG79dLK/Liy1jYp31OsUY9xX61oZY/wi0BdCGGlfiWoyP5sdws/m8hNC6CP7oeaTMcZ/bdDEz+cycbRr6edzeYox7gH+k/lj+v1s6mj8f2TGvbUhVvnzffn2jj9HC3xvaOr5cAjJsfkWcFYI4UzgLrLJR140p81ngdeGEK4k6z61t9Z1RqVy1GsZQjgFuDfGmIYQziUL/na1vVI1i5/NDuFnc3nJ72LwUeCmGOPlCzTz87kMLOZa+vlcPkII64HJGOOeEMIq4OnAu+c087Opo1nM70fd4rPAy4B35c9X123/VAjhcmAD2aS43yykwhY4wveGpp4PA4xjEGOcCiG8FriG7DZBH4sx/jCE8Op8/4eBL5LdaupmsttNvbyoerWwRV7LFwC/G0KYAg4BF8cYO667V6cIIfwz8BRgJISwDXgL2YRkfjaXmUVcSz+by8svAi8BbgghXJ9v+1PggeDnc5lZzLX087l8nAp8PJ8HowLEGOPn/blWS7HQz9QFl9VyC/ys8i4ghhBeAdwBbAbIf8eIwI1kd+x4TYxxupDCW2Oh7w1NPR9Jmvq9RJIkSZIklZtzYEiSJEmSpNIzwJAkSZIkSaVngCFJkiRJkkrPAEOSJEmSJJWeAYYkSZIkSSo9AwxJkiRJklR6BhiSJEmSJKn0DDAkSZIkSVLp9RZdgCRJRQghPAj4FvD0GON3QggbgO8DL4gx/mehxUmSJGmeJE3TomuQJKkQIYRXAZcCjwc+A9wQY/zDYquSJElSIw4hkSR1rRjjPwA/Bf4bOBW4rNiKJEmStBADDElSt/sH4Bzg/THG8aKLkSRJUmMOIZEkda0QwiDwPeA/gPOBR8YYdxdblSRJkhqxB4YkqZu9D/h2jPGVwBeADxdcjyRJkhZggCFJ6kohhAuB84BX55suBR4XQnhxcVVJkiRpIQ4hkSRJkiRJpWcPDEmSJEmSVHoGGJIkSZIkqfQMMCRJkiRJUukZYEiSJEmSpNIzwJAkSZIkSaVngCFJkiRJkkrPAEOSJEmSJJWeAYYkSZIkSSq9/x+UxtoAHLu71QAAAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<Figure size 1080x720 with 5 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"%matplotlib inline\n",
|
|
"# plot trajectory\n",
|
|
"grid = plt.GridSpec(4, 5)\n",
|
|
"\n",
|
|
"plt.figure(figsize=(15, 10))\n",
|
|
"\n",
|
|
"plt.subplot(grid[0:4, 0:4])\n",
|
|
"plt.plot(track[0, :], track[1, :], \"b+\")\n",
|
|
"plt.plot(track_lower[0, :], track_lower[1, :], \"g.\")\n",
|
|
"plt.plot(track_upper[0, :], track_upper[1, :], \"r.\")\n",
|
|
"plt.plot(x_sim[0, :], x_sim[1, :])\n",
|
|
"plt.axis(\"equal\")\n",
|
|
"plt.ylabel(\"y\")\n",
|
|
"plt.xlabel(\"x\")\n",
|
|
"\n",
|
|
"plt.subplot(grid[0, 4])\n",
|
|
"plt.plot(u_sim[0, :])\n",
|
|
"plt.ylabel(\"a(t) [m/ss]\")\n",
|
|
"\n",
|
|
"plt.subplot(grid[1, 4])\n",
|
|
"plt.plot(x_sim[2, :])\n",
|
|
"plt.ylabel(\"v(t) [m/s]\")\n",
|
|
"\n",
|
|
"\n",
|
|
"plt.subplot(grid[2, 4])\n",
|
|
"plt.plot(np.degrees(u_sim[1, :]))\n",
|
|
"plt.ylabel(\"delta(t) [rad]\")\n",
|
|
"\n",
|
|
"plt.subplot(grid[3, 4])\n",
|
|
"plt.plot(x_sim[3, :])\n",
|
|
"plt.ylabel(\"theta(t) [rad]\")\n",
|
|
"\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## VISUALIZE THE COMPUTED HALF-PLANES"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1080x1080 with 9 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"plt.style.use(\"ggplot\")\n",
|
|
"\n",
|
|
"\n",
|
|
"def plot_low_upp(low, up):\n",
|
|
" \"\"\"\n",
|
|
" low and upp arre arrays of shape (3,N)\n",
|
|
" each column represent the couefficient at each\n",
|
|
" step of the optimization\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" # for low,up in zip(low, up):\n",
|
|
" for idx in range(low.shape[1]):\n",
|
|
"\n",
|
|
" ll = low[:, idx]\n",
|
|
" uu = up[:, idx]\n",
|
|
"\n",
|
|
" x = np.linspace(-2, 2, 100)\n",
|
|
"\n",
|
|
" # low\n",
|
|
" y = []\n",
|
|
" for xx in x:\n",
|
|
" y.append((-ll[0] * xx + ll[2]) / ll[1])\n",
|
|
"\n",
|
|
" plt.plot(x, y, \"b-\")\n",
|
|
"\n",
|
|
" # high\n",
|
|
" y = []\n",
|
|
" for xx in x:\n",
|
|
" y.append((-uu[0] * xx + uu[2]) / uu[1])\n",
|
|
"\n",
|
|
" plt.plot(x, y, \"r-\")\n",
|
|
"\n",
|
|
" plt.xlim((-2, 2))\n",
|
|
" plt.ylim((-2, 2))\n",
|
|
"\n",
|
|
"\n",
|
|
"def plot_lines():\n",
|
|
" \"\"\"\n",
|
|
" sample randomly from computed coeff\n",
|
|
" and plot grid\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" plt.figure(figsize=(15, 15))\n",
|
|
" indices = np.random.choice(len(computed_coeff), 9)\n",
|
|
"\n",
|
|
" for i in range(len(indices)):\n",
|
|
" plt.subplot(3, 3, i + 1)\n",
|
|
" plt.title(\"t = \" + str(indices[i]))\n",
|
|
" plot_low_upp(computed_coeff[indices[i]][0], computed_coeff[indices[i]][1])\n",
|
|
"\n",
|
|
" plt.tight_layout()\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"\n",
|
|
"plot_lines()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### NOTE \n",
|
|
"There seems to be some flaws in the impementation\n",
|
|
"the plane to the LEFT of the vehicle causes issues in the optimization...\n",
|
|
"\n",
|
|
"*maybe the \"hanging\" points?*\n",
|
|
"\n",
|
|
"different approach -> compute those points from the path directly ? Then from contour get semiplanes consrtains?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|