From 55b49871801841171dd1f0e87c4e98f9f66e2c40 Mon Sep 17 00:00:00 2001 From: Jrenaud-Desk Date: Tue, 26 Nov 2024 11:02:36 -0500 Subject: [PATCH] Added in hooks to change EOS method for the future --- .../RadialSolver Benchmarks.ipynb | 94 ++++------ TidalPy/Material/eos/methods/__init__.pxd | 1 + TidalPy/Material/eos/methods/interpolate.pxd | 2 + TidalPy/RadialSolver/solver.pxd | 7 +- TidalPy/RadialSolver/solver.pyx | 160 +++++++++++------- pyproject.toml | 2 +- 6 files changed, 139 insertions(+), 127 deletions(-) create mode 100644 TidalPy/Material/eos/methods/__init__.pxd diff --git a/Benchmarks & Performance/RadialSolver/RadialSolver Benchmarks.ipynb b/Benchmarks & Performance/RadialSolver/RadialSolver Benchmarks.ipynb index 1e310d0e..ad83275a 100644 --- a/Benchmarks & Performance/RadialSolver/RadialSolver Benchmarks.ipynb +++ b/Benchmarks & Performance/RadialSolver/RadialSolver Benchmarks.ipynb @@ -37,36 +37,21 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "7d9c6658-deca-477c-8c9f-b32d0c7f0913", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Result Success: True\n", - "Result Message: RadialSolver.ShootingMethod:: completed without any noted issues.\n", - "\n", - "Shape: (6, 100).\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1.199040866595169e-12+1.8735013540549517e-14j)\n", - "[0.65847646-0.03056421j 0.19123156-0.00916519j 0.03061239-0.00072309j]\n" + "ename": "TypeError", + "evalue": "radial_solver() takes at least 10 positional arguments (7 given)", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[4], line 89\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m solution\n\u001b[0;32m 80\u001b[0m \u001b[38;5;66;03m# 0.5.4\u001b[39;00m\n\u001b[0;32m 81\u001b[0m \u001b[38;5;66;03m# New: 3.12ms; 3.07ms; 3.15ms\u001b[39;00m\n\u001b[0;32m 82\u001b[0m \u001b[38;5;66;03m# Old: 94ms; 96.6ms; 94.3ms\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 86\u001b[0m \u001b[38;5;66;03m# 0.6.0a6\u001b[39;00m\n\u001b[0;32m 87\u001b[0m \u001b[38;5;66;03m# New: 3.37ms; 3.14ms; 3.16ms\u001b[39;00m\n\u001b[1;32m---> 89\u001b[0m solution \u001b[38;5;241m=\u001b[39m \u001b[43mtest_1layer\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[1;32mIn[4], line 60\u001b[0m, in \u001b[0;36mtest_1layer\u001b[1;34m()\u001b[0m\n\u001b[0;32m 17\u001b[0m volume_array, mass_array, gravity_array \u001b[38;5;241m=\u001b[39m \\\n\u001b[0;32m 18\u001b[0m calculate_mass_gravity_arrays(radius_array, density_array)\n\u001b[0;32m 20\u001b[0m input_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[0;32m 21\u001b[0m radius_array\u001b[38;5;241m=\u001b[39mradius_array,\n\u001b[0;32m 22\u001b[0m density_array\u001b[38;5;241m=\u001b[39mdensity_array,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 57\u001b[0m perform_checks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m 58\u001b[0m )\n\u001b[1;32m---> 60\u001b[0m solution \u001b[38;5;241m=\u001b[39m \u001b[43mradial_solver\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minput_dict\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 62\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResult Success:\u001b[39m\u001b[38;5;124m\"\u001b[39m, solution\u001b[38;5;241m.\u001b[39msuccess)\n\u001b[0;32m 63\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResult Message:\u001b[39m\u001b[38;5;124m\"\u001b[39m, solution\u001b[38;5;241m.\u001b[39mmessage)\n", + "File \u001b[1;32m~\\anaconda3\\envs\\tpy6py311\\Lib\\site-packages\\TidalPy\\RadialSolver\\solver.pyx:379\u001b[0m, in \u001b[0;36mTidalPy.RadialSolver.solver.radial_solver\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mTypeError\u001b[0m: radial_solver() takes at least 10 positional arguments (7 given)" ] } ], @@ -98,9 +83,9 @@ " frequency=frequency,\n", " planet_bulk_density=planet_bulk_density,\n", " layer_types=('solid',),\n", - " is_static_by_layer=(False,),\n", - " is_incompressible_by_layer=(False,),\n", - " upper_radius_by_layer_array=np.asarray([radius_array[-1]]),\n", + " is_static_bylayer=(False,),\n", + " is_incompressible_bylayer=(False,),\n", + " upper_radius_bylayer_array=np.asarray([radius_array[-1]]),\n", " surface_pressure = 0.0,\n", " degree_l = 2,\n", " solve_for = None,\n", @@ -111,7 +96,7 @@ " integration_method = 'RK45',\n", " integration_rtol = 1.0e-9,\n", " integration_atol = 1.0e-12,\n", - " scale_rtols_by_layer_type = False,\n", + " scale_rtols_bylayer_type = False,\n", " max_num_steps = 100_000,\n", " expected_size = 500,\n", " max_ram_MB = 500,\n", @@ -121,6 +106,7 @@ " verbose = False,\n", " warnings = False,\n", " raise_on_fail = False,\n", + " eos_method_bylayer = None,\n", " eos_integration_method = 'RK45',\n", " eos_rtol = 1.0e-4,\n", " eos_atol = 1.0e-12,\n", @@ -171,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "9936e5b9-0a7a-4c77-bdc7-dc1bd04030e9", "metadata": {}, "outputs": [ @@ -179,29 +165,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Result Success: False\n", - "Result Message: RadialSolver.ShootingMethod:: Integration failed:\n", - "\tError in step size calculation:\n", - "\tRequired step size is less than spacing between numbers..\n", + "Result Success: True\n", + "Result Message: RadialSolver.ShootingMethod:: completed without any noted issues.\n", "\n" ] }, - { - "ename": "TypeError", - "evalue": "'NoneType' object is not subscriptable", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[4], line 102\u001b[0m\n\u001b[0;32m 87\u001b[0m yplot([ys], [radius_array], colors\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m 90\u001b[0m \u001b[38;5;66;03m# New\u001b[39;00m\n\u001b[0;32m 91\u001b[0m \u001b[38;5;66;03m# 0.5.3\u001b[39;00m\n\u001b[0;32m 92\u001b[0m \u001b[38;5;66;03m# 3.84ms; 3.91ms\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 99\u001b[0m \u001b[38;5;66;03m# New: 2.38ms; 2.41ms; 2.38ms\u001b[39;00m\n\u001b[0;32m 100\u001b[0m \u001b[38;5;66;03m# Old: 100ms; 97.8ms; 95.8ms\u001b[39;00m\n\u001b[1;32m--> 102\u001b[0m \u001b[43mtest_2layer\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[1;32mIn[4], line 87\u001b[0m, in \u001b[0;36mtest_2layer\u001b[1;34m()\u001b[0m\n\u001b[0;32m 84\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResult Success:\u001b[39m\u001b[38;5;124m\"\u001b[39m, solution\u001b[38;5;241m.\u001b[39msuccess)\n\u001b[0;32m 85\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResult Message:\u001b[39m\u001b[38;5;124m\"\u001b[39m, solution\u001b[38;5;241m.\u001b[39mmessage)\n\u001b[1;32m---> 87\u001b[0m \u001b[43myplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mys\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mradius_array\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\anaconda3\\envs\\tpy6py311\\Lib\\site-packages\\TidalPy\\utilities\\graphics\\multilayer\\yplot.py:95\u001b[0m, in \u001b[0;36myplot\u001b[1;34m(tidal_ys, radius, depth_plot, planet_radius, colors, labels, show_plot, use_tobie_limits, plot_tobie, plot_roberts)\u001b[0m\n\u001b[0;32m 93\u001b[0m num_y \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 94\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m array_num, (radius_array, tidal_y_array) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mzip\u001b[39m(radius, tidal_ys)):\n\u001b[1;32m---> 95\u001b[0m y1 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mreal(\u001b[43mtidal_y_array\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m)\n\u001b[0;32m 96\u001b[0m y2 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mreal(tidal_y_array[\u001b[38;5;241m1\u001b[39m, :])\n\u001b[0;32m 97\u001b[0m y3 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mreal(tidal_y_array[\u001b[38;5;241m2\u001b[39m, :])\n", - "\u001b[1;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable" - ] - }, { "data": { - "image/png": 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", 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qJ2K+KSUlBZUrV0bPnj1Rv359WFtb49ChQzhz5gzmz5+v0rZixYqYM2cO7t69i5o1a+LPP//E+fPnsXLlSpiamirb1a5dG35+fjp/aWOm38pCDl+4cAEfffQROnbsiNatW6NcuXJ4+PAh1qxZg0ePHmHRokXKAkTxpfebb75Bnz59YGpqii5duuQ5ryO32bNn4+jRo2jWrBmGDh0Kb29vJCQk4Ny5czh06BASEhKU6+Xq6oqWLVvCxcUF165dw88//4zOnTvDxsYGiYmJan9OMFaSSmI/OmnSJKxbtw4dOnTA6NGjlcMeuru7IyEhQeWA1uTJk7FmzRrExMSodfT5n3/+QXp6OrKzs/Hs2TOcOHECO3fuhJ2dHbZt2wZXV9cC/3bNmjVYunQpunXrhmrVqiElJQWrVq2Cra0tOnXqpNK2evXqaNWqFUaMGIGMjAwsWrQI5cuXx6RJk5Rttm3bhkGDBiEkJAQDBw7U+H3SKVIN78KKRjGEUH5DBmVnZ1O1atWoWrVqlJWVRTk5OTRz5kzy8PAguVxODRs2pN27d9OAAQPIw8ND5W+fPXtG/fv3J1tbW7Kzs6P+/ftTVFSUWkOm7dy5k+rVq0fm5uZUpUoVmjNnDv322295hlbSZNhDAASAZDIZ2drako+PDw0dOpQiIiLy/RvkGvYwIyODJk6cSPXr1ycbGxuysrKi+vXr09KlS1X+xs/Pj3x8fOjs2bPk6+tL5ubm5OHhQT///HO+y1cndsbepqzncFxcHM2ePZv8/PyoQoUKZGJiQg4ODtSuXTvasmVLnvb/93//R5UqVSIjIyOV1wNQ4JCEcXFxFBwcTG5ubmRqakqurq7Uvn17WrlypbLNihUrqE2bNlS+fHmSy+VUrVo1mjhxIiUlJRGR+p8TjGlbfsMeqpuDBQ0LnF9uRkVFUevWrUkul1PlypVp1qxZ9NNPPxEAio2NVbYbMGCAWkMhKoY9VNxMTU3JycmJ2rRpQzNmzKD4+Pg8f/PmsIfnzp2jvn37kru7O8nlcnJ2dqb33nuPzp49q/wbxbCH8+bNo/nz55ObmxvJ5XJq3bo1XbhwId/l5/6M01cyIonPAmBMIv7+/nj69GmZvvIXY4wxpjB27FisWLECqampOntJ+7t378LT0xPz5s3DF198IXU4pYb7kDPGGGOMlTFpaWkqj589e4a1a9eiVatWOluMGzLuQ84YY4wxVsb4+vrC398ftWvXRlxcHH799VckJydjypQpUofG8sEFOWOMMcZYGdOpUyds2bIFK1euhEwmQ6NGjfDrr7+iTZs2UofG8sF9yBljjDHGGJMQ9yFnjDHGGGNMQlyQM8YYY4wxJiHuQ66GnJwcPHr0CDY2Nhpf6pmxkkBESElJQcWKFWFkxN+ri4LzmukSzuni45xmukaTvOaCXA2PHj2Cm5ub1GEwlseDBw9QuXJlqcPQS5zXTBdxThcd5zTTVerkNRfkarCxsQEg3lBbW1uJo2EMSE5Ohpubm3LbZJrjvGa6hHO6+Dinma7RJK+5IFeD4qcvW1tbTnKmU/hn2aLjvGa6iHO66Dinma5SJ6+5oxpjjDHGGGMS4oKcMcYYY4wxCXFBzhhjjDHGmIQkL8gfPnyIjz/+GOXLl4eFhQXq1q2Ls2fPKp8nIkydOhUVKlSAhYUFAgICcPPmTZVlJCQkoF+/frC1tYW9vT0GDx6M1NRUlTYXL15E69atYW5uDjc3N8ydO7dU1o8xQ8M5zVjZw3nNWMmStCB//vw5WrZsCVNTU+zbtw9Xr17F/Pnz4eDgoGwzd+5c/PTTT1i+fDkiIiJgZWWFoKAgpKenK9v069cPV65cQWhoKHbv3o1jx45h2LBhyueTk5MRGBgIDw8PREZGYt68efjuu++wcuXKUl1fxso6zmnGyh7Oa8ZKAUnoyy+/pFatWhX4fE5ODrm6utK8efOU8xITE0kul9OGDRuIiOjq1asEgM6cOaNss2/fPpLJZPTw4UMiIlq6dCk5ODhQRkaGymt7eXmpFWdSUhIBoKSkJI3Wj7GSoqvbpL7kNJHuvofMMOny9qgvea3L7yEzTJpsk5IeId+5cyeaNGmCXr16wdnZGQ0bNsSqVauUz8fExCA2NhYBAQHKeXZ2dmjWrBnCw8MBAOHh4bC3t0eTJk2UbQICAmBkZISIiAhlmzZt2sDMzEzZJigoCNHR0Xj+/HlJryZjb0UE7NsHfPEFoM+bZFnJ6bAw4H//A+7dK/aiGNN7ZSWvddWFC8CIEUB2ttSRMClJWpDfuXMHy5YtQ40aNXDgwAGMGDECn3/+OdasWQMAiI2NBQC4uLio/J2Li4vyudjYWDg7O6s8b2JignLlyqm0yW8ZuV8jt4yMDCQnJ6vcGCtJMhkwYQIwf74oBvWVruY0oFle//wzMG0a4OkJBAQA69cDaWnqvguMlS26mtdlYV+dmio+Y5YvB776SupomJQkLchzcnLQqFEjzJw5Ew0bNsSwYcMwdOhQLF++XMqwMGvWLNjZ2SlvfCleVhratRPTI0ekjaM4dDWnAc3yumdPoG1b8cvF4cPAxx8Drq7AZ58Bp06J+YwZCl3N67Kwr7a2Bn76Sdz/4Qfgl1+kjYdJR9KCvEKFCvD29laZV7t2bdy/fx8A4OrqCgCIi4tTaRMXF6d8ztXVFfHx8SrPZ2VlISEhQaVNfsvI/Rq5TZ48GUlJScrbgwcPirqKjKmtLBTkuprTgGZ53aeP+D/cuSOOlHt4AMnJwMqVgK8vULcusGgR8PRpYe8GY2WDruZ1WdlX9+0LfPeduD9iBHDsmKThMIlIWpC3bNkS0dHRKvNu3LgBDw8PAICnpydcXV1x+PBh5fPJycmIiIiAr68vAMDX1xeJiYmIjIxUtjly5AhycnLQrFkzZZtjx44hMzNT2SY0NBReXl4qZ4kryOVy5aV3+RK8rLS0bi2mV68CCQnSxlJUuprTQNHy2tNT7Cjv3Hl9pNzcHLhyBRg3DqhUSRTvhw4BOTnqvUeM6RtdzeuytK+eOhXo3RvIygJ69ODzVwxSKZxkWqDTp0+TiYkJzZgxg27evEnr168nS0tLWrdunbLN7Nmzyd7ennbs2EEXL16krl27kqenJ6WlpSnbvPvuu9SwYUOKiIig48ePU40aNahv377K5xMTE8nFxYX69+9Ply9fpo0bN5KlpSWtWLFCrTj5zG1WWry8iACiXbsKb6er26S+5DRR0d/D58+Jli4latRI/K8Ut6pViWbNIoqN1WhxjBGR7uY0kf7ktS6/h+p48eL150qDBuIx02+abJOSFuRERLt27aI6deqQXC6nWrVq0cqVK1Wez8nJoSlTppCLiwvJ5XJq3749RUdHq7R59uwZ9e3bl6ytrcnW1pYGDRpEKSkpKm0uXLhArVq1IrlcTpUqVaLZs2erHaO+JznTHwMHig/jb78tvJ0ub5P6kNNE2nkPz50jCg4msrN7XZibmhL16kV06BBRdnaRF80MjC7nNJF+5LWuv4fquH+fyMlJfJb070+UkyN1RKw4NNkmZUR8etLbJCcnw87ODklJSXr9kxjTfUuXAsHBQFAQsH9/we14myw+bb6HL18Cf/4JrFgB/DeCGwCgRg3RJ3TgQKCAnjSMAeCc1oay8h4ePSpGXsnJAZYsAUaOlDoiVlSabJOS9iFnjKlq3FhMz5+XNAymIUtLYNAgMQLL+fNiB2prC9y8CYwfL/qaDxkCnDsndaSMMV3Xti0wZ464P24ccPastPGw0sEFOWM6pE4dMSZ5XJy4Mf1Tv744qvXwoThiXq+eGMP811/FFy5fX2DDBuDVK6kjZYzpqgkTgK5dxefEhx8CiYlSR8RKGhfkjOkQKyugWjVx/8oVaWNhxWNtDQwbJo6YHz8OfPQRYGoqjqJ/9JEYSvF//+MvXoyxvGQyICQEqFIFiIkR10DgDsZlGxfkjOmY2rXF9Pp1aeNg2iGTAS1biqt9PnggivAKFYDYWDHGuZsb8MknQFSU1JEyxnSJgwOwcSNgYgJs2iR+ZWNlFxfkjOkYLy8xvXFD2jiY9rm4AFOmAHfvim4rvr5AZiawdi3QqJHoO7pzJ49pzhgTmjUDZswQ98eMEeelsLKJC3LGdIyiy8qdO9LGwUqOmZm4oNDJk8Dp0+JKfcbGQFiY6Dfq5SVG3Hn5UupIGWNS++IL8WX95UugXz/xJZ6VPVyQM6ZjPD3FNCZG2jhY6XjnHeCPP8T/e9IkwN4euHVLDH/p4QFMnw48fSp1lIwxqRgZAWvWiM+GM2eA2bOljoiVBC7IGdMxbm5i+u+/0sbBSpebmxjq7MED4KefxMlcT58C330HuLsDo0aJri6MMcPj5gb8/LO4/7//8dC4ZREX5IzpmMqVxTQxkbssGCJra2D0aNFXdONG0bc8LU0MpVi9ujgB9OpVqaNkjJW2jz4CuncHsrLEdQ+460rZwgU5YzrGxgawsBD3eUg8w2ViAvTuLS4KcugQ0KEDkJ0tTgD18RE7Zr5gCGOGQyYDli0DypUTR8h/+EHqiJg2cUHOmI6RyQBnZ3GfC3ImkwHt2wMHD4oTQLt1E/O3bRP9zzt3FmObM8bKPmdn4Mcfxf3p03nUlbKEC3LGdFC5cmL6/Lm0cTDd8s47wNat4qJR/fuLk7327hXDJwYFASdOSB0hY6yk9esn8j0jAxgxgi8YVFZwQc6YDrK3F1MuyFl+vL2B338HoqNFX1JjY3EEvVUrsaM+fVrqCBljJUUmE8OimpsDhw+LUZqY/uOCnDEdZGMjpqmp0sbBdFv16sBvv4mLSA0ZIvqdHzwoLibSpQtf/ZOxsqpqVXGRMUCMU56cLG08rPi4IGdMB1lZiemLF9LGwfRD1arAqlXiiPmAAaIry+7dYoSWPn24nyljZdGECUCNGkBsrOhPzvQbF+SM6SDFKCsZGdLGwfRL1arA6tViWMS+fcVP23/+CdSuDQwfDjx6JHWEjDFtkcvFNQsAMb1xQ9p4WPFwQc6YDjI1FVMuyFlReHmJfqVRUUCnTmK4xBUrRBeXKVOAlBSpI2SMacO774ocz8oSXVeY/uKCnDEdZGwspjk50sbB9Fv9+sCePcDffwMtWogLDH3/vSjMly8XO3HGmH6bP1/sM3btErnO9BMX5IzpIJlM6ghYWdKmDXD8uBgysXp1ID5eDJdWrx5w4IDU0THGiqNWLWDYMHF/4kQeBlFfcUHOmA7iI+NM22QycVGhK1dEf9Py5YFr18RP3u+/zyd+MqbPpk0DrK2BM2fERcOY/uGCnDEdpOhKoOhLzpi2mJkBo0cDt24B48aJoRJ37QJ8fIAvv+ShNhnTRy4uIp8B4NtvxXkjTL9wQc6YDnr1Sky5IGclxd4eWLAAuHRJHCXPzATmzhUjsmzZwj97M6ZvJkwAHBzEL19//il1NExTXJAzpoNevhRTS0tp42BlX61awL59wM6dgKcn8O+/QK9eoki/dUvq6Bhj6rKzE0U5APzvf3yUXN9wQc6YDuKCnJW2Ll1E//KpU8X4xgcPAnXrArNni6PnjDHdN3q0OEoeHS1+6WL6Q9KC/LvvvoNMJlO51apVS/l8eno6goODUb58eVhbW6NHjx6Ii4tTWcb9+/fRuXNnWFpawtnZGRMnTkTWG2N5hYWFoVGjRpDL5ahevTpWr15dGqvHWJEpLoNsZydtHJrinNZvFhbiin+XLwMBAUB6OjB5MtC4MXD6tNTRMalwXusPW1tgzBhxf9Ys7nqmTyQ/Qu7j44PHjx8rb8ePH1c+N27cOOzatQubN2/G33//jUePHqF79+7K57Ozs9G5c2e8evUKJ0+exJo1a7B69WpMnTpV2SYmJgadO3dG27Ztcf78eYwdOxZDhgzBAR7ri+mwpCQxtbWVNo6i4JzWf9WriyPkv/8uRmO5dAnw9RUnfaanSx0dkwLntf4YPVqMuHLhAg9rqldIQtOmTaP69evn+1xiYiKZmprS5s2blfOuXbtGACg8PJyIiPbu3UtGRkYUGxurbLNs2TKytbWljIwMIiKaNGkS+fj4qCy7d+/eFBQUpHacSUlJBICSkpLU/hvGiqNSJSKA6MyZ/J/X1W1SX3KaSHffQ13z5AnRRx+J7REg8vIiOnlS6qjKHl3eHvUlr3X5PSxt48aJfA0IkDoSw6bJNin5EfKbN2+iYsWKqFq1Kvr164f79+8DACIjI5GZmYmAgABl21q1asHd3R3h4eEAgPDwcNStWxcuLi7KNkFBQUhOTsaVK1eUbXIvQ9FGsQzGdA0R8OyZuF++vLSxFAXndNni6AisXw9s3w64uoq+qa1aAVOmcN9yQ8J5rV/GjBFX7zx0SBwpZ7pP0oK8WbNmWL16Nfbv349ly5YhJiYGrVu3RkpKCmJjY2FmZgZ7e3uVv3FxcUFsbCwAIDY2ViXBFc8rniusTXJyMtLS0vKNKyMjA8nJySo3xkpLSsrrbgFvbLo6T1dzGuC8Lq6uXcVJn/36iQtXff+96MZy/brUkbGSpqt5zTldMA8PoGdPcX/xYmljYeoxkfLFO3bsqLxfr149NGvWDB4eHti0aRMsLCwki2vWrFmYPn26ZK/PDNt/+ydYW+vfKCu6mtMA57U2lCsHrFsnruw5fDgQGQk0agT8/DMwaJC4Gigre3Q1rzmnC/f552I88vXrgTlz9PMXV0MieZeV3Ozt7VGzZk3cunULrq6uePXqFRITE1XaxMXFwdXVFQDg6uqa50xuxeO3tbG1tS3wg2Ty5MlISkpS3h48eKCN1WNMLQ8fimmlStLGoQ26ktMA57U2ffihONEzIABISwMGDxZHzvkApWHQlbzmnC6cry/QsKH4xXXNGqmjYW+jUwV5amoqbt++jQoVKqBx48YwNTXF4cOHlc9HR0fj/v378PX1BQD4+vri0qVLiI+PV7YJDQ2Fra0tvL29lW1yL0PRRrGM/Mjlctja2qrcGCst//4rppUrSxuHNuhKTgOc19pWqZIYwWHWLNFXdcMGMTzixYtSR8ZKmq7kNed04WQy8UsWACxfzkMg6rxSOMm0QBMmTKCwsDCKiYmhEydOUEBAADk6OlJ8fDwREQ0fPpzc3d3pyJEjdPbsWfL19SVfX1/l32dlZVGdOnUoMDCQzp8/T/v37ycnJyeaPHmyss2dO3fI0tKSJk6cSNeuXaMlS5aQsbEx7d+/X+04+cxtVpr+7//E2fEDBxbcRle3SX3JaSLdfQ/10YkTRO7uYru1sCD6/XepI9I/urw96kte6/J7KJWUFCJra5Gbf/8tdTSGR5NtUtKCvHfv3lShQgUyMzOjSpUqUe/evenWrVvK59PS0mjkyJHk4OBAlpaW1K1bN3r8+LHKMu7evUsdO3YkCwsLcnR0pAkTJlBmZqZKm6NHj1KDBg3IzMyMqlatSiEhIRrFyUnOStPgweLDc/r0gtvo6japLzlNpLvvob56+pQoKOj18IijRhG98W9jhdDl7VFf8lqX30MpDRkicvKTT6SOxPBosk3KiPhHjLdJTk6GnZ0dkpKS+CcxVuLatgXCwsRFWfr3z78Nb5PFx++h9mVnA//7n7gBQPv2wKZN4mRQVjjeHouP38P8hYcDLVqIQQLi4sSAAax0aLJN6lQfcsYYcOuWmNaoIW0cjGnK2BiYPh3Ytg2wsgIOHwaaNQNu3JA6MsYMV/Pm4uq7L18CW7dKHQ0rCBfkjOmQFy9en9RZvbq0sTBWVB98AJw8CVSpIr5gtmghHjPGSp9M9vrX1g0bpI2FFYwLcsZ0SHS0mDo6ihtj+qpePeDUKeCdd8SVZ9u1E0fOGWOlr08fMQ0NBZ48kTYWlj8uyBnTIVevimnt2tLGwZg2uLgAR4+KCwllZIgrB65eLXVUjBmemjXFmOTZ2cD27VJHw/LDBTljOuTyZTGtU0faOBjTFisr0W918GAgJ0dc0fPHH6WOijHD06uXmP71l7RxsPxxQc6YDrl0SUy5IGdlibExsGoVMH68eDx2LLBwoaQhMWZwevQQ08OHgaQkaWNheXFBzpgOOX9eTBs0kDIKxrRPJgN++AH49lvxePx4PlLOWGmqWVN0h8zKAvbvlzoa9iYuyBnTEfHxwKNHonCpW1fqaBjTPplMjFH+zTfi8dixwG+/SRoSYwbl/ffFdNcuaeNgeXFBzpiOiIwU05o1ARsbaWNhrKTIZMD//R8wcaJ4PHQoj43MWGnp1ElMDxwQ53Qw3cEFOWM64uxZMW3cWNo4GCtpMhkwZw4wZIgoCj76iMcpZ6w0+PoCtrbA06ev9zlMN3BBzpiOiIgQ06ZNpY2DsdIgkwHLlwNdu4ohET/4AIiJkToqxso2U1OgfXtxPzRU2liYKi7IGdMBRMDp0+J+s2bSxsJYaTE2BtavF+MjP3ki+re+eCF1VIyVbQEBYsoFuW7hgpwxHXD7tihIzMx4hBVmWKysxAlmrq5iHP5hw8QXVMZYyVAcIT91CkhPlzYW9hoX5IzpAEX/2SZNAHNzaWNhrLRVqgRs3gyYmAB//CG6sjDGSkbNmkCFCqKr2KlTUkfDFLggZ0wH/POPmLZsKW0cjEmlVStg7lxxf/x44No1aeNhrKySyQA/P3H/+HFpY2GvcUHOmA44dkxM27SRNg7GpDRmDBAYKH5G79cPyMyUOiLGyibFwR8uyHUHF+SMSezxY+DGDXHUgo+QM0NmZASsXg2UKwdERQELFkgdEWNlk2Jfc+oUj0euK7ggZ0xif/8tpg0aAA4OkobCmOQqVHhdiH/3nTjhmTGmXXXrAhYWQFIScP261NEwgAtyxiR35IiYtm0rbRyM6YpPPgHatRNdVyZNkjoaxsoeExMxiAAAnDkjbSxM4IKcMYkdOiSmiqGoGDN0Mhnw44+iC8vWra/PsWCMaY+iII+MlDYOJnBBzpiE7twRVyc0MQFat5Y6GsZ0R506YkxyAPjmGx6bnDFta9xYTM+elTYOJnBBzpiEDhwQ0xYtABsbaWNhTNd8+624WNbx40BYmNTRMFa2NGwophcv8omduoALcsYkpCjIAwOljYMxXVSpEjB0qLivGKOcMaYdNWuKC9G9eMEnT+sCE3Ua/fTTTxoveNCgQbDhQ36MFejVK+DwYXH/3XdL//U5r5k+GDcOWLoU2L9fDA9as6bUEekuzmmmCRMTwNsbOHcOuHwZqFFD6ogMm1oF+dixY1G5cmUYGxurtdAHDx7gvffe4yRnrBDHjwOpqYCz8+ufDksT5zXTB9WqAZ06AXv2ACtXAj/8IHVEuotzmmmqTp3XBXm3blJHY9jU7rJy9uxZxMTEqHWzsLDQOJDZs2dDJpNh7Nixynnp6ekIDg5G+fLlYW1tjR49eiAuLk7l7+7fv4/OnTvD0tISzs7OmDhxIrKyslTahIWFoVGjRpDL5ahevTpWr16tcXyMaduePWLaqZMYTUIKnNdMHwwZIqYbNgDZ2dLGous4p5kmvL3F9No1aeNgahbk06ZNg7W1tdoL/frrr1GuXDm12585cwYrVqxAvXr1VOaPGzcOu3btwubNm/H333/j0aNH6N69u/L57OxsdO7cGa9evcLJkyexZs0arF69GlOnTlW2iYmJQefOndG2bVucP38eY8eOxZAhQ3BA0XmXMYns3i2mnTtL8/qc10xfdOwoLpr16BHwzz9SR6O7OKeZpmrXFlMuyHUASSwlJYVq1KhBoaGh5OfnR2PGjCEiosTERDI1NaXNmzcr2167do0AUHh4OBER7d27l4yMjCg2NlbZZtmyZWRra0sZGRlERDRp0iTy8fFRec3evXtTUFCQ2jEmJSURAEpKSirqajKm4vp1IoDI1JSoKJuVrm+TnNdM2wYMEDnzxRdSR1IydH175Jwum6KjRV5ZWhLl5EgdTdmjyTYp+SgrwcHB6Ny5MwICAlTmR0ZGIjMzU2V+rVq14O7ujvDwcABAeHg46tatCxcXF2WboKAgJCcn48qVK8o2by47KChIuYz8ZGRkIDk5WeXGmDbt2CGmbdsCtrbSxlISOK+ZtnXqJKb79kkbh6HinC6bPD3FyZ0vXwIPH0odjWHTuCB/9uwZgoOD4e3tDUdHR5QrV07lpomNGzfi3LlzmDVrVp7nYmNjYWZmBnt7e5X5Li4uiI2NVbbJneCK5xXPFdYmOTkZaWlp+cY1a9Ys2NnZKW9ubm4arRdjb7N9u5h27SppGEqc10zXtWsnpleuAAkJ0saiDzinmTpMTYEqVcT9W7ckDcXgqTXKSm79+/fHrVu3MHjwYLi4uEAmkxXphR88eIAxY8YgNDQU5ubmRVpGSZk8eTLGjx+vfJycnMyJzrQmNhY4dUrc15WCnPOa6TpHRzEs282bIn8UR8xZ/jinmbqqVxfF+K1bgL+/1NEYLo0L8n/++QfHjx9H/fr1i/XCkZGRiI+PR6NGjZTzsrOzcezYMfz88884cOAAXr16hcTERJVv3nFxcXB1dQUAuLq64vTp0yrLVZzZnbvNm2d7x8XFwdbWtsAzzOVyOeRyebHWj7GCbNsmLgPetKm48Iku4Lxm+qBJE1GQX7rEBfnbcE4zdVWtKqYxMdLGYeg07rJSq1atAn8+0kT79u1x6dIlnD9/Xnlr0qQJ+vXrp7xvamqKw4orpwCIjo7G/fv34evrCwDw9fXFpUuXEB8fr2wTGhoKW1tbeP83lo+vr6/KMhRtFMtgrLT99ZeY9ughbRy5cV4zfaAYou3qVWnj0Aec00xdnp5ieueOtHEYPE3PGD19+jS1a9eOwsLC6OnTp5SUlKRyK47cZ24TEQ0fPpzc3d3pyJEjdPbsWfL19SVfX1/l81lZWVSnTh0KDAyk8+fP0/79+8nJyYkmT56sbHPnzh2ytLSkiRMn0rVr12jJkiVkbGxM+/fvVzsuPnObacuTJ0TGxuKs9lu3ir4cbW+TnNdMH2zcKHKndWupI9E+zmnOaals2SLyqnlzqSMpezTZJjUuyG/cuEFNmjQhIyMjlZtMJiMjI6MiBazwZpKnpaXRyJEjycHBgSwtLalbt270+PFjlb+5e/cudezYkSwsLMjR0ZEmTJhAmZmZKm2OHj1KDRo0IDMzM6patSqFhIRoFBcnOdOWlSvFB1/DhsVbjra3Sc5rpg+OHRP5U7261JFoH+c057RUzpwReVWhgtSRlD2abJMyIiJNjqg3bdoUJiYmGDNmTL4nivj5+RX7qL2uSU5Ohp2dHZKSkmBbFseoY6WmQwfg0CFg5kxg8uSiL0fb2yTnNee1PoiOBmrVAuzsgMREqaPRLs7p4uOcLponTwBnZ3E/PR3gbvnao8k2qfFJnZcvX0ZUVBS8vLyKHCBjhiguDjhyRNzv3VvaWN7Eec30gZWVmL54IW0c+oBzmqnL0REwNxfF+MOHr0/yZKVL45M6mzRpggcPHpRELIyVaVu2ADk5YqQIXfvA47xm+kAx2EZWFpCdLW0suo5zmqlLJgMqVxb3eZORjsZHyEePHo0xY8Zg4sSJqFu3LkxNTVWer1evntaCY6ws2bBBTPv2lTaO/HBeM32Qk/P6vpHk15nWbZzTTBNubmIccr5ap3Q0Lsh7//db+6effqqcJ5PJQESQyWTI5sMWjOVx7x5w4oQ4EqFr3VUAzmumHzIzxdTISOQSKxjnNNMEHyGXnsYFeQyPHM+Yxv74Q0zbttWdiwHlxnnN9EFyspjy+XpvxznNNKHYL/ERculoXJA7ODgUeKborVu3ih0QY2UNEbBunbjfr5+0sRSE85rpg+fPxdTOTto49AHnNNMEF+TS07gXXufOnZGenp5nfnR0NPz9/bURE2Nlyrlz4sqC5uZAz55SR5M/zmumDx4/FtMKFaSNQx9wTjNNcEEuPY0Lcmtra3Tv3h1ZWVnKedeuXYO/vz966NK1wBnTEb//LqYffKC7P7VzXjN9oCgWuCB/O85ppomKFcWUC3LpaFyQb926FUlJSejXrx+ICJcvX4a/vz/69u2LH3/8sSRiZExvvXoFrF8v7n/yibSxFIbzmumDO3fEVNeGDdVFnNNME4oj5I8fq45mxEqPxgW5hYUF9uzZg+joaHz44Ydo3749PvnkEyxYsKAk4mNMr+3ZAzx7Jo7odeggdTQF47xm+kDR9bl6dWnj0Aec00wTrq5i9KLsbCA+XupoDJNaJ3UmK05t/4+RkRH+/PNPdOjQAT169MCUKVOUbfhytYy9FhIipv37AyYan0Jdsjivmb65dk1Ma9aUNg5dxTnNisrEBHBxEUfIHz4UBTorXTIiorc1MjIygiyfQV8Vf1rWxzZNTk6GnZ0dkpKS+EOMqe3xY3Gxhexs4Pp1QJtXsNbGNsl5zXmtT9LSACsrMWpRbKwoHsoSzuni45wunnfeAc6eBbZvB7p2lTqaskGTbVKtY3ZHjx7VSmCMGZLVq0Ux3qKFdotxbeG8ZvrkyhVRjDs6As7OUkejmzinWXFUriwKcj6xUxpqFeR+fn4lHQdjZUpODvDLL+L+0KHSxlIQzmumTy5cENP69fkqnQXhnGbFwVfrlJZaJ3VevHgRORqcdnvlyhWVoZYYMzSHD4sRIWxtgV69pI4mf5zXTJ/kLshZ/jinWXG4uYkpF+TSUKsgb9iwIZ49e6b2Qn19fXH//v0iB8WYvluxQkz79xf9XnUR5zXTJ1FRYtqwobRx6DLOaVYcHh5iypuENNTqskJEmDJlCiwtLdVa6KtXr4oVFGP67NEjYMcOcX/YMGljKQznNdMXRMDFi+I+HyEvGOc0Kw5FQX73rqRhGCy1CvI2bdogOjpa7YX6+vrCwsKiyEExps9++QXIygJatQLq1ZM6moJxXjN9cf8+kJwshmbTxROkdQXnNCuOKlXE9N9/xUXtzMwkDcfgqFWQh4WFlXAYjJUNmZmvu6uMGCFtLG/Dec30xdWrYurlxUVCYTinWXG4uACWlsDLl8C9e0CNGlJHZFg0vlInY6xgW7eKLiuurkDPnlJHw1jZcOOGmPLRccZKjkwGVKsm7iuuistKDxfkjGnRTz+J6bBhfCSPMW25c0dMFcUCY6xkVK8upoovwaz0cEHOmJacPg2cPAmYmup+dxXG9ElsrJhWqiRtHIyVdbVqien169LGYYi4IGdMSxYuFNO+fUWXFcaYdsTHiylfoZOxkuXtLaZXrkgbhyHigpwxLbh7F9i8WdwfN07SUBgrcxSj88nl0sbBWFlXp46YXrokhhtlpUfjgnzNmjXYs2eP8vGkSZNgb2+PFi1a4N69exota9myZahXrx5sbW1ha2sLX19f7Nu3T/l8eno6goODUb58eVhbW6NHjx6Ii4tTWcb9+/fRuXNnWFpawtnZGRMnTsxz5bGwsDA0atQIcrkc1atXx+rVqzVdbcYKtWgRkJ0NBAQADRpIHY3mtJXXnNOsJCguPimTSRuHPuF9NSuK2rXF8KKJiXyBoFJHGqpZsyYdPnyYiIhOnjxJlpaWtGLFCurSpQt169ZNo2Xt3LmT9uzZQzdu3KDo6Gj6+uuvydTUlC5fvkxERMOHDyc3Nzc6fPgwnT17lpo3b04tWrRQ/n1WVhbVqVOHAgICKCoqivbu3UuOjo40efJkZZs7d+6QpaUljR8/nq5evUqLFy8mY2Nj2r9/v9pxJiUlEQBKSkrSaP2YYXjyhMjSkgggOnCgdF5T29uktvJaX3KaiPNan3TqJPJr5UqpIyk5uprTRPqT15zT2tGggci3LVukjkT/abJNalyQW1hY0L1794iIaNKkSdS/f38iIrp8+TI5Ojpqurg8HBwc6JdffqHExEQyNTWlzZs3K5+7du0aAaDw8HAiItq7dy8ZGRlRbGysss2yZcvI1taWMjIylDH6+PiovEbv3r0pKChI7Zg4yVlhpkwRH16NGhHl5JTOa2p7myzJvNbFnCbivNYnw4aJHPv2W6kjKTn6lNNEupnXnNPaMXSoyLcvv5Q6Ev2nyTapcZcVa2trPHv2DABw8OBBdOjQAQBgbm6OtLS0Ih+pz87OxsaNG/HixQv4+voiMjISmZmZCAgIULapVasW3N3dER4eDgAIDw9H3bp14eLiomwTFBSE5ORkXPnvjITw8HCVZSjaKJaRn4yMDCQnJ6vcGMtPYiLw44/i/tdf6+9P6iWR17qU0wDntT6rW1dM3/IvZrnwvpoVVfPmYnrihLRxGBq1rtSZW4cOHTBkyBA0bNgQN27cQKdOnQAAV65cQRXFdVc1cOnSJfj6+iI9PR3W1tbYtm0bvL29cf78eZiZmcHe3l6lvYuLC2L/GwMrNjZWJcEVzyueK6xNcnIy0tLS8r1s8KxZszB9+nSN14UZnh9/FJf0rlMH6NZN6miKTpt5rYs5DXBe67P27cX0+HEgNRWwtpY2Hn3A+2pWVG3aiOnp00BaGlDARyrTMo2PkC9ZsgS+vr548uQJ/vrrL5QvXx4AEBkZib59+2ocgJeXF86fP4+IiAiMGDECAwYMwFXFdZIlMnnyZCQlJSlvDx48kDQeppsSEoAFC8T9KVMAIz0es0ibea2LOQ1wXuuzWrXEZbwzMoBVq6SORj/wvpoVVbVqYsz/V6/El2BWOjQ+Qm5vb4+ff/45z/yifks1MzND9f8uDdW4cWOcOXMGP/74I3r37o1Xr14hMTFR5Zt3XFwcXP8b5NnV1RWnT59WWZ7izO7cbd482zsuLg62trYFHkmTy+WQ8/ha7C3mzhVHx+vVA3r2lDqa4tFmXutiTgOc1/pMJgMmThRXwJ0/Hxg6lI+Svw3vq1lRyWRAYCAQEgLs3w/819uJlTCNC/Jjx44V+nwbxW8dRZSTk4OMjAw0btwYpqamOHz4MHr06AEAiI6Oxv379+Hr6wsA8PX1xYwZMxAfHw/n/64YERoaCltbW3j/N7q9r68v9u7dq/IaoaGhymUwVhQPHwI//STu/9//6ffRcaBk85pzmmnDJ58A338vhmILDgbWrJE6It3G+2pWHB07ioJ8507ghx/09/wovaLpGaMymSzPzcjISHnTxFdffUV///03xcTE0MWLF+mrr74imUxGBw8eJCIxlJK7uzsdOXKEzp49S76+vuTr66v8e8VQSoGBgXT+/Hnav38/OTk55TuU0sSJE+natWu0ZMkSHkqJFdvgweIs9JYtS29kldy0vU1qK6/1JaeJOK/10d9/ExkZidxbtkzqaLRLV3OaSH/ymnNae5KTieRykWsXL0odjf4q0WEPExMTVW5PnjyhgwcPUrNmzejQoUMaLevTTz8lDw8PMjMzIycnJ2rfvr0ywYmI0tLSaOTIkeTg4ECWlpbUrVs3evz4scoy7t69Sx07diQLCwtydHSkCRMmUGZmpkqbo0ePUoMGDcjMzIyqVq1KISEhGsXJSc5yO3eOSCYTH1QnTkgTg7a3SW3ltb7kNBHntb76/nuRewDR/PlSR6M9uprTRPqT15zT2vX++yLPcn1vYhrSZJuUEWnn4qh///03xo8fj8jISG0sTqckJyfDzs4OSUlJsLW1lTocJiEiwM8P+OcfoE8fYMMGaeIorW2S85rpmpwc0Z9ccUL1hAnAzJmAmZm0cRUX53TxcU5r15YtQK9e4gTPe/cAY2OpI9I/mmyTWuv56uLigujoaG0tjjGdtG6dKMYtLMRJnWUd5zXTNUZGok/r7Nni8fz5QJMmwNmz0salLzinmbq6dAHKlRPnTO3ZI3U0ZZ/GJ3VevHhR5TER4fHjx5g9ezYaNGigrbgY0zkJCeJoHABMnQq4uUkbjzZxXjN9IpMBX34phmcbMQK4dAlo1gwYP15coMvBQeoIpcc5zYpLLgcGDwbmzRPX3Hj/fakjKts07rJiZGQEmUyGN/+sefPm+O2331CrVi2tBqgL+GcwBgADBgC//w74+ABRUYCpqXSxaHub5LzmvNZXT54An38ObNwoHtvYiFFYxo0D/hvQQy9wThcf57T23b8PVK0KZGeLCwW9847UEekXTbZJjY+Qx8TEqDw2MjKCk5MTzM3NNV0UY3pj1y5RjMtkwMqV0hbjJYHzmukrJydxLsdHHwHffCOOls+eLY7oDR0KjB4N/Dd8tkHhnGba4O4ucmvtWuC777jrSknS2kmdZRl/6zZs8fHi4j9xcaLLyg8/SB0Rb5PawO9h2ZOTA+zeDcyYIY7mKTRvDvTvD3z4IeDoKF18heHtsfj4PSwZN28CtWuLo+R//w0Ucwh7g6L1I+Q//fQThg0bBnNzc/ykuBpKAT7//HP1I2VMx+XkAAMHimLcx0dcBKis4LxmZY2Rkejn2qULcPiwOOHz4EHg1ClxGzMG6NQJ+Phj4N13RfeWsoRzmpWEGjXEr03Ll4tfnCIjARON+1ewt1HrCLmnpyfOnj2L8uXLw9PTs+CFyWS4c+eOVgPUBfyt23DNmQN89RVgbg6cOQPUqSN1RII2tknOa85rQxAbK7q0rF0rzv1QMDUFWrQQlwgPCgIaNpT2iruc08XHOV1ynj0Thfnz5+JXYsUAB6xwmmyT3GVFDZzkhik0VBxFy8kRRwY++0zqiF7jbbL4+D00PFeuAOvXA3/+CbxZjzo5AR06iAK9QwegYsXSjY23x+Lj97BkrVoFDBsmRl+JjBS/GrPCSTIOOWNlSXS0uPBPTg7w6afiQ4gxpt98fMRFhG7fFv1ilywRXVysrcVoLX/8IbqoVaokTmbr1UsM+XbsGPDihdTRMyatIUOAjh2BjAygXz/g5UupIypb1OoFNH78eLUXuEBx+TTG9FRcnPjQSUgQYxsvWSJGVylrOK+ZIateXdxGjgRevQLCw0V/8wMHgHPngAcPxG3LFtHeyEh0WWvWTNyaNgW8vXXr6oWc06wkyWTAr78C9esDFy6IfuXr1pXN/aMU1CrIo3J3vANw7tw5ZGVlwcvLCwBw48YNGBsbo3HjxtqPkLFSlJQEdO4MxMSIsVd37hT9x8sizmvGBDMzwM9P3GbMAFJSxJU/T58GIiLE9OFD4OJFcVu1SvydhYUYfcLHRxTnPj7iVqWKNP3ROadZSatQAdi0CQgIEL8o1akDTJ4sdVRlg1oF+dGjR5X3FyxYABsbG6xZswYO/10O7fnz5xg0aBBat25dMlEyVgqSk0Wf8chIMTTavn36dWERTXFeM5Y/GxugbVtxU3j4ULVAP3MGSE0VR9PPnVP9+/wKdW9vwNOzZAt1zmlWGvz9gUWLxIgrX38NlCunW+dY6SuNT+qsVKkSDh48CJ83evNfvnwZgYGBePTokVYD1AV8oohhePdd8XO1gwNw5Aigy1eX1vY2yXnNec00k50t+qJfvSpOFr1yRdy/fl30sc2PhQXQt6/42f9NnNPFxzldur7+Gpg1S3RZ8fISvyZbWBR/WtBz5ua61UVMHSV6pc7k5GQ8efIkz/wnT54gJSVF08UxphNSU0UxDgD79+t2MV4SOK8Z04yxMVCzprh98MHr+VlZostb7iL9yhVRqKelAb/9Js5LKemucJzTrKQpunf9/LPYvkuDqWnxivqifDEorS8CGhfk3bp1w6BBgzB//nw0bdoUABAREYGJEyeie/fuWg+QsZKUnS0utX3kyOt5detKF49UOK8ZK5qMDCAxMf+biQng4QHY2QHVqgFbt4q/KY3BhjmnWUmTyYDFi4ERI8QoRWlpQHq69qeZma9fMzNT3JKTS3ddC/siUL68uGZJcYeB1LggX758Ob744gt89NFHyPzvXTIxMcHgwYMxb9684kXDWAl78UL0/zx+XNzCw8U3fAU7O5F4hobzmhmq9PSCC+r8bklJqo/T0zV7vXLlSuczhnOalRZv75JdflaW+OKr7UI/NRW4d0+MppSTU3gMb/si4O9f/IK8yBcGevHiBW7fvg0AqFatGqysrIoXiQ7jfmn6Kz0d+OcfMZzZsWPi5KusLNU2Njbiin2tWgE9eoiTsXRdSW2TnNdMX6WnA0+fiiN1T568vq+YPn+ef4H96lXxX1smE1/m7e3ffmvRQlzx8E2c08XHOc0UsrLEidh374qi++5d4P594NEj4PFjcXvy5O2FeG729mKUmdw3V1cxqlKXLmK0pjeVaB9yBSsrK9SrV6+of85YiSASF/U5cEDcwsLEt+HcKlcWxbfiVqeO/p0oUlI4r5kuIBJHovMrsAu6n5pa9NczMlK/oM6vnY2NNMMcqoNzmpVFr16JI9u5C+7c03//FV1S38bISIymll+h/ebjkj7vo0gF+dmzZ7Fp0ybcv38fr944vLBV0UmOsVKSkSEuc79rlyjC791Tfb5SJSAoCGjXDmjdWlyBj+XFec1KEpEonh8+FEepFNP8CuynT1X7jarLxEQMWerk9HqquF+uXMFFtrW17hbUxcE5zfRZQoI4WTQ6WlxZN3fB/ejR28/FMDMT+/sqVcS5HB4eQMWKqoW2k5PuHJDTuCDfuHEjPvnkEwQFBeHgwYMIDAzEjRs3EBcXh27dupVEjIzlkZYmuqFs2SIu3pO7X5eZGdCmjRjGMChI9OviK4kVjvOaFUda2usiO/ct97xHjzTvHmJllX9xnd99Jydx9JpzXeCcZvogK0sU2dHRovhWFODXr4sv6IWxsBBFtqLgfnPq6qpfX7Q1LshnzpyJhQsXIjg4GDY2Nvjxxx/h6emJzz77DBUqVCiJGBkDII6E79kDbN4M7N6t+hN1pUpAt25Ap07ianuWltLFqY84r1lBXr0SO8w7d8SRqTeL7ocPRf9sdTk5iaNUlSqJqbNzwYW2hUWJrVaZxznNdElWFnDtGnDhgpgqiu6bNwv/ou7mJsY4r1lTXFgrd8Ht5FS2voBrXJDfvn0bnTt3BgCYmZnhxYsXkMlkGDduHNq1a4fp06drPUhm2C5eFGP3rlsHPHv2er6bG9CzJ9CrF9CsmX59E9Y1nNeGLTFRFNy3b7++KR6rMwIBIPpXVqqU96YovitVEj8Ry+UlvjoMnNNMOunpwOXLYhCFqCgxvXix4BGJzM1FwV2rlrh5eYlpzZqiO5mh0Lggd3BwUF5UoFKlSrh8+TLq1q2LxMREvHz5UusBMsOUmAj88YcoxCMjX8+vWBH46CNRhL/zTtn6diwlzuuy7+lTcZGaW7fyFt8JCYX/raUlULWqODJVubJqka242dtzPuoSzmlWGjIyxD46MvJ18X3lSt7RzABRXDdoILqR5i6+3d11px+3lDQuyNu0aYPQ0FDUrVsXvXr1wpgxY3DkyBGEhoaiffv2JREjMyC3bgE//giEhIgxwwExZm/XrsCnnwKBgZy4JYHzuuxISxOF96VLqrfY2ML/ztlZXLxGcata9fV9FxcutvUN5zQrCamp4vodx46JW0SEKMrfVL480KiRuDVsKKbVqvEv2YUiDT179owePnxIRETZ2dk0a9Ys6tKlC40fP54SEhI0WtbMmTOpSZMmZG1tTU5OTtS1a1e6fv26Spu0tDQaOXIklStXjqysrKh79+4UGxur0ubevXvUqVMnsrCwICcnJ/riiy8oMzNTpc3Ro0epYcOGZGZmRtWqVaOQkBC140xKSiIAlJSUpNH6MfXk5BAdOULUpQuRTEYkzp0m8vEhWrSI6MkTqSPUPdreJrWV1/qS00T6n9c5OUQ3bxJt3Uo0fTpRz55EXl5ERkavc+jNm6cnUWAg0YgRRPPmib+9cIEoJUXqtWG6mtNE+pPX+p7TuujZM6IdO4gmTCB65x0iY+O8nyvOzkTvvUc0dSrR9u1E9+6Jzyem2TapcUFemJcvX2rUPigoiEJCQujy5ct0/vx56tSpE7m7u1NqaqqyzfDhw8nNzY0OHz5MZ8+epebNm1OLFi2Uz2dlZVGdOnUoICCAoqKiaO/eveTo6EiTJ09Wtrlz5w5ZWlrS+PHj6erVq7R48WIyNjam/fv3qxUnJ3nJyMkh2ruXqGlT1eTu3JkoNJQTujCluU1qktf6ktNE+pfXL14QhYURzZwpcqRcuYIL7/Llifz9iT7/nGjVKqJTp7jo1nW6mtNE+pPX+pbTuignh+jSJaJZs4hatsz/C36VKkT9+4vPluho3lcXptQL8vT0dJo/fz65uLgUaznx8fEEgP7++28iIkpMTCRTU1PavHmzss21a9cIAIWHhxMR0d69e8nIyEjlm/iyZcvI1taWMjIyiIho0qRJ5OPjo/JavXv3pqCgILXi4iTXrpwcoj17xLdtRYJbWIijdm8cdGEFKI1tUht5ras5TaT7ef3vv0SbNhGNGSNyxcQk745RLidq1IhowACiH34gOnCA6NEj3kHqI33JaSLdzWtdz2ldlZZGtG8fUXAwkYdH3s8ZLy+iYcOI1q0TR7+Z+jTZJtXuzZORkYHJkyejSZMmaNGiBbZv3w4ACAkJgaenJxYuXIhx48YVq/tMUlISAKBcuXIAgMjISGRmZiIgIEDZplatWnB3d0d4eDgAIDw8HHXr1oWLi4uyTVBQEJKTk3HlyhVlm9zLULRRLCO/dU1OTla5Me04cUJcOrpzZ+DMGXGy2MSJYli1pUvFCR6s9JR0XutKTivWVZfz+tUr4NAhYOxYcWn1ypWBDz8U51ScOSNOkqpQQYwstHCh6LuZnCxOplq9GpgwQZxjUaEC9/c2ZLyvZurIyhIX0vv4YzHMaMeOwJIlYmhTc3Oxj162TDy+fh1YsQLo148vrFeS1D6pc+rUqVixYgUCAgJw8uRJ9OrVC4MGDcKpU6ewYMEC9OrVC8bFONsuJycHY8eORcuWLVGnTh0AQGxsLMzMzGBvb6/S1sXFBbH/naEUGxurkuCK5xXPFdYmOTkZaWlpsHhjsNtZs2bxkFBadvs28OWXwF9/iceWlkBwMPDFF+JkMiaNksxrXcppQDfzOi4O2LdPjKt/8CDw36AYAMTJT/XqAS1bii+xLVuKnSEX26wwvK9mBSESo6CsWwds2CA+fxQqVQLee0/c2rXja3lIQe2CfPPmzfj999/x/vvv4/Lly6hXrx6ysrJw4cIFyLSwhwgODsbly5dx/PjxYi+ruCZPnozx48crHycnJ8PNzU3CiPRXairwv/8BixaJS2EbGQGDB4t5rq5SR8dKMq91KacB3cnrpCTgzz/FUe1Tp1Qv/+ziIo5Mde4MBAQAtralHh7Tc7yv5n31m1JTgTVrxK/QV6++nl++PNCnjzhK3qwZf9mXmtoF+b///ovGjRsDAOrUqQO5XI5x48ZpJcFHjRqF3bt349ixY6hcubJyvqurK169eoXExESVb95xcXFw/a+ac3V1xenTp1WWF/ff177cbeJyfxX8r42trW2+R9LkcjnkfPWKYiECtm8HxowRFxYBxKXs580D/juownRASeW1ruU0IG1e5+SIIcJ++w3YskUMTajQuLE4KtW5s7jPw4Kx4uB9NVO4cwf4+Wfg119F9zZAdEfp2lUU4UFBYlhhphvU/ujPzs6GmZmZ8rGJiQmsi3kJJSLCqFGjsG3bNhw5cgSenp4qzzdu3BimpqY4fPiwcl50dDTu378PX19fAICvry8uXbqE+Ph4ZZvQ0FDY2trC29tb2Sb3MhRtFMtg2vXwoUj47t1FMV6lCrBrl/hpnotx3aLtvOacVvXypejvXaMG0LYtsHatKMa9vYEffhC5cvYs8N134kJXXIyz4uJ9NYuKEvvf6tXF509ysvgMWrxYXI9g40ZxEICLcR2j7pmiMpmMOnXqRN26daNu3bqRiYkJBQYGKh8rbpoYMWIE2dnZUVhYGD1+/Fh5yz0k0/Dhw8nd3Z2OHDlCZ8+eJV9fX/L19VU+rxhKKTAwkM6fP0/79+8nJyenfIdSmjhxIl27do2WLFnCQymVgJwcot9+I7KzE2dmm5oSff21GK6NaZe2tklt57W+5DRRyeZ1aqoY59vZ+fVIBTY2YqSCU6d4FBSWl67mNJH+5LWh76svXSLq3l11hJTAQDGqWXa21NEZJk22SRlR7h6MBRs0aJBaBX5ISIjaXwYK+gktJCQEAwcOBACkp6djwoQJ2LBhAzIyMhAUFISlS5cqf+ICgHv37mHEiBEICwuDlZUVBgwYgNmzZ8PE5HWPnLCwMIwbNw5Xr15F5cqVMWXKFOVrvE1ycjLs7OyQlJQEW+7Uma/Hj4EhQ4C9e8Xjpk3F1Tb/O/DBtExb26S281pfchoombzOyhJHoWbNAp48EfOqVAG+/lqMUMAnSrGC6GpOA/qT14a6r46JEZ8xf/4pynCZDOjbF/jmG94HS02TbVLtgtyQGWqSq2vXLnFZ+6dPAblcnLA5fjxgovYZCkxTvE0Wn7bfw6goccJyVJR4XLWq2CH2788/DbO345wuPkN7D9PSgLlzgdmzgfR0Ma9nT9EFzsdH0tDYfzTZJrlkYkWWliaGLVy6VDxu0ABYv56/kTPDkpEBTJsm+oRnZwMODmIHOWgQF+KMsZKxaxfw+efiGh6AOEdlwQKxH2b6iQtyViS3bgG9egHnz4vHEyYAM2aII+SMGYonT8TJU4oR4Hr1An76iYf0ZIyVjMREMXrZ77+Lx5UrA/Pni88eHrZQv3FBzjS2das4+pecDDg5iZEjgoKkjoqx0nX5MtClizhCZWsrxhXv1k3qqBhjZVVoqNj3PnwoRmQaP178OlfMQXSYjuCCnKktO1v0iZ0zRzxu2VKcRFKpkrRxMVbaLl8G2rQBnj8HqlUTPx/Xri11VIyxsigrSxTeM2eKx9Wriwv9tGghbVxMu7ggZ2pJSAA++gg4cEA8njBBjCTBfWSZoYmJAQIDRTHerBmwZ4+44h1jjGlbXJwYMeXoUfF4xAhxgT0rK2njYtrHBTl7q+vXxUUEbt8GLCzE1Qb79JE6KsZK34sXQMeOYpjPOnXEMJ/lykkdFWOsLDp/Xux7Hz4UBfgvv/C+tyzjgpwV6uBB4MMPgaQkwMMD2LEDqF9f6qgYk8bYsUB0NFCxovi1iItxxlhJ2LlT/Cr94gXg5QVs28bd4so6vlAzK9Dy5UCnTqIYb9kSOH2ai3FmuHbvFkeoZDJg3TpRlDPGmLYtWwZ88IEoxgMCgFOnuBg3BFyQszxycoCJE0VftexsYMAA4PBhwNlZ6sgYk0ZmpjhvAhAjG7RtK208jLGyhwj4/ntg5Ehxf9gw0S3O3l7qyFhp4IKcqUhPB3r3Fhc5AcSHQ0gIjy/ODNsvvwA3bohhPqdOlToaxlhZQwRMmgRMmSIeT50qfqXmgRMMB/chZ0oJCeJnsn/+AczMxMmb/fpJHRVj0srOfv0FdcoUMeY4Y4xpC5H4BW7hQvF40SJx8R9mWLggZwCABw/ExX2uXQPs7IDt2wF/f6mjYkx6+/YBd+6In40//VTqaBhjZcmbxfiKFaKrCjM8XJAzXL0qivF//xUX+dm3D6hbV+qoGNMNiktUf/opj/3LGNOu777jYpwJ3IfcwJ06BbRuLYrxWrWAkye5GGdM4eVLceEfQAxBxhhj2jJ/PvC//4n7ixdzMW7ouCA3YAcPAu3bi77jzZoBx48D7u5SR8WY7jh6VBTlHh5Ao0ZSR8MYKyvWrgW++ELcnzkTGDVK2niY9LggN1CbN4srgL18KS4DfvgwX/6bsTcdOSKmgYFi/HHGGCuuAwden48yfjwwebK08TDdwAW5AfrtN3H53cxMcRXOXbu4byxj+Tl2TEx53HHGmDZcuAD07AlkZYlucPPmSR0R0xVckBuYRYuAwYPFxX+GDgX++EMMccgYU/XqFXDxorjfrJm0sTDG9N/Dh0DnzkBqqviSHxICGHEVxv7Dm4KBUFwBbNw48fiLL8QZ3cbG0sbFmK66dk0U5fb2gKen1NEwxvTZixdAly6iKK9dG9i6lQ+GMVVckBsAItFHTXEFsOnTgblzuU8sY4W5dk1MfXw4VxhjRZeTA3zyCRAVJa72u2eP+KLPWG48DnkZl5MDfP45sGSJeDx/vjiJhDFWuOhoMfXykjYOxph+mzbt9RHxbdv4FzeWPy7Iy7DsbOCzz4BffxVH+JYtE48ZY293966YVq0qaRiMMT22ebPoLgoAK1cCLVtKGw/TXVyQl1FZWcDAgcD69eKkkdWrgf79pY6KMf1x/76Y8tj8jLGiuHBB7IcB8cv0gAGShsN0HBfkZVBmphhOacsWwMREjKTSq5fUUTGmXx49EtPKlaWNgzGmf549A7p1E9f66NABmDNH6oiYrpP0pM5jx46hS5cuqFixImQyGbZv367yPBFh6tSpqFChAiwsLBAQEICbN2+qtElISEC/fv1ga2sLe3t7DB48GKmpqSptLl68iNatW8Pc3Bxubm6YO3duSa+aZDIyxBinW7aI/mpbt3IxzkpXWcnr2FgxdXHR6mIZ0ztlJadLS3a2OCgWEyP6i2/cKA6OMVYYSQvyFy9eoH79+liiOOPwDXPnzsVPP/2E5cuXIyIiAlZWVggKCkJ6erqyTb9+/XDlyhWEhoZi9+7dOHbsGIYNG6Z8Pjk5GYGBgfDw8EBkZCTmzZuH7777DitXrizx9Stt6eniG/nOnYC5ObBjhxhmibHSVBbyOjMTSEwU952ctLJIxvRWWcjp0jR1KnDwIGBhIU7iLFdO6oiYXiAdAYC2bdumfJyTk0Ourq40b9485bzExESSy+W0YcMGIiK6evUqAaAzZ84o2+zbt49kMhk9fPiQiIiWLl1KDg4OlJGRoWzz5ZdfkpeXl9qxJSUlEQBKSkoq6uqVuBcviDp0IAKILCyIDh2SOiJWkvRhmyTS37yOjxe5BBBlZqq9SMaKjHO6bOyrt29//dmxfr1kYTAdock2qbPjkMfExCA2NhYBAQHKeXZ2dmjWrBnCw8MBAOHh4bC3t0eTJk2UbQICAmBkZISIiAhlmzZt2sAs1wj8QUFBiI6OxvPnz/N97YyMDCQnJ6vcdNmLF8B77wGhoYCVFbBvH9C+vdRRMZaXvuS1YhG2tvxTM2OF0ZecLg03b4rxxgFgzBjRbYUxdelsQR77XwdOlzc6cLq4uCifi42NhbOzs8rzJiYmKFeunEqb/JaR+zXeNGvWLNjZ2Slvbm5uxV+hEpKaCnTqBBw9CtjYAAcOAH5+UkfFWP70Ja8V+3U7OzVXjDEDpS85XdJevgR69BCfHa1aAfPmSRYK01M6W5BLafLkyUhKSlLeHjx4IHVI+UpJAd59Fzh2TBzJO3iQxzhlrCCa5HVKipja2JRScIwxjenKvpoIGD4cuHRJnAT+55+AqakkoTA9prM/xrq6ugIA4uLiUKFCBeX8uLg4NGjQQNkmPj5e5e+ysrKQkJCg/HtXV1fExcWptFE8VrR5k1wuh1wu18p6lJSkJKBjRyA8XFyC9+BB4J13pI6KscLpS16/eCGmVlZqNWfMYOlLTpekVauAtWvFNT82bgQqVpQ6IqaPdPYIuaenJ1xdXXH48GHlvOTkZERERMDX1xcA4Ovri8TERERGRirbHDlyBDk5OWjWrJmyzbFjx5CZmalsExoaCi8vLzg4OJTS2mhXYiIQGCiKcQcH4PBhLsaZftCXvFYU5JaWxV4UY2WavuR0STl3Dhg9WtyfORPw95c0HKbPSuEk0wKlpKRQVFQURUVFEQBasGABRUVF0b1794iIaPbs2WRvb087duygixcvUteuXcnT05PS0tKUy3j33XepYcOGFBERQcePH6caNWpQ3759lc8nJiaSi4sL9e/fny5fvkwbN24kS0tLWrFihdpx6sKZ2woJCURNmogzuMuXJ4qKkjoiJgVd2ibfVBbyevVqkWPvvluMN4IxDXBO69+++vlzIk9P8VnRpQtRdnapvCzTI5psk5IW5EePHiUAeW4DBgwgIjGc0pQpU8jFxYXkcjm1b9+eoqOjVZbx7Nkz6tu3L1lbW5OtrS0NGjSIUlJSVNpcuHCBWrVqRXK5nCpVqkSzZ8/WKE5d+aB89oyoUSOR/I6ORBcuSBoOk5CubJP5KQt5vWyZyLMPPtBs3RkrKs5p/dpX5+SIzweAqEoVcbCMsTdpsk3KiIhK40i8PktOToadnR2SkpJga2srSQzPnonL70ZFiQuVHD4M1K0rSShMB+jCNqnvCnsPf/pJDFvWpw+wYYNEATKDwjldfKX5Hi5YAEyYIK6IfeIEkGtER8aUNNkmdfakTvba06dAQABw4QLg7AwcOQL4+EgdFWNlV0aGmOYaEpkxxgCI87e+/FLcX7iQi3GmHVyQ67inT8VFfi5e5GKcsdKiOK+Mhy5jjOX27BnQuzeQlSWmI0ZIHRErK7gg12G5i3EXF3Hxn9q1pY6KsbKPC3LG2JtycoABA4AHD4AaNYCVKwGZTOqoWFmhs8MeGronT4B27UQx7uoKhIVxMc5YacnKElMuyBljCj/8AOzZA8jlwObN4oJ8jGkLF+Q66MkTcWT80iWgQgVRjNeqJXVUjBkORUFubCxtHIwx3XDiBPD11+L+4sVA/frSxsPKHi7IdUx8vDgyrijGjx4FvLykjooxw6IoyE24Ux9jBu/pUzHiUnY28NFHwJAhUkfEyiLe3egQRTF+5Yq49O7Ro0DNmlJHxZjhyc4WUz5CzphhU/Qb//dfsT9evpz7jbOSwUfIdQQX44zpDi7IGWOA6De+dy9gbg5s2gTY2EgdESur+Ai5DnizGA8LE2dwM8akkZMjplyQM2a4Tp583W/8xx+53zgrWVyQSyw+HmjbFrh6lYtxxnSFoiDnn6YZM0zPnr3uN96nDzB0qNQRsbKOu6xIKC7udTFeqRIX44zpCj5CzpjhIgIGDXo93viKFfzlnJU8LsglEhcnuqkoivGjR7kYZ0xX8BFyxgzXwoXArl1ivPFNm3i8cVY6uMuKBN4sxsPCgOrVpY6KMaagKMiN+JAFYwYlIgL48ktxf+FCoEEDScNhBoR3N6WMi3HGdB+RmPIRcsYMx/PnQO/e4joEvXoBw4dLHREzJFyQlyIuxhnTD1yQM2ZYiMQFf+7dA6pWBVat4vxnpYsL8lLCxThj+oMLcsYMy88/A1u3AqamwJ9/AnZ2UkfEDA0X5KWAi3HG9AsX5IwZjshI4IsvxP1584AmTaSNhxkmLshLmOKiP7lHU+FinDHdpijIGWNlW3Ky6Df+6hXwwQfA559LHREzVFyQl6DcF/3hoQ0Z0z98hJyxsotIXPDn9m3A3R347TfOeSYdLshLCBfjjOkv7rLCWNm3apUYZ9zERPQbd3CQOiJmyLggLwFcjDNWNnBBzljZdPEiMGaMuD9zJtC8ubTxMMYFuZbl12eci3HG9Av3IWes7EpNBT78EEhPBzp1AiZMkDoixrgg1ypFMX7lClCxIhfjjDHGmK4JDgaio8VBszVr+Iq8TDfwZqglbxbjYWFcjDPGGGO6ZM0a4PffRRG+YQPg6Ch1RIwJBlWQL1myBFWqVIG5uTmaNWuG06dPa2W5XIwzJo2SymnGmHRKKq+vXQNGjhT3p08HWrfWymIZ0wqDKcj//PNPjB8/HtOmTcO5c+dQv359BAUFIT4+vljL5W4qjEmjpHI6Nz6pk7HSVVJ5/fKl6Df+8iUQEABMnqylgBnTEoMpyBcsWIChQ4di0KBB8Pb2xvLly2FpaYnffvutWMu9cAG4ceN1MV6zppYCZowVqqRymjEmnZLK64gIsa92cQHWrQOMjbUUMGNaYiJ1AKXh1atXiIyMxORcX4mNjIwQEBCA8PDwPO0zMjKQkZGhfJycnFzgsjt0ALZvF1ff5GKcsdKhaU4DmuX1lCnA8OGAh4f2YmaMFa4k99Vt2wKnTokRVlxctBs3Y9pgEEfInz59iuzsbLi8kYUuLi6IjY3N037WrFmws7NT3tzc3ApdfqdOXIwzVpo0zWlAs7yuUQNo0UKMwsAYKx0lva9u2JD7jTPdZRAFuaYmT56MpKQk5e3BgwdSh8QYKybOa8bKFs5pVpYYRJcVR0dHGBsbIy4uTmV+XFwcXF1d87SXy+WQy+WlFR5jTEOa5jTAec2YruN9NTNkBnGE3MzMDI0bN8bhw4eV83JycnD48GH4+vpKGBljrCg4pxkrezivmSEziCPkADB+/HgMGDAATZo0QdOmTbFo0SK8ePECgwYNkjo0xlgRcE4zVvZwXjNDZTAFee/evfHkyRNMnToVsbGxaNCgAfbv35/n5BHGmH7gnGas7OG8ZoZKRkQkdRC6Ljk5GXZ2dkhKSoKtra3U4TDG26QW8HvIdAlvj8XH7yHTNZpskwbRh5wxxhhjjDFdZTBdVopD8SNCYRcdYKw0KbZF/oGr6DivmS7hnC4+zmmmazTJay7I1ZCSkgIAb73oAGOlLSUlBXZ2dlKHoZc4r5ku4pwuOs5ppqvUyWvuQ66GnJwcPHr0CDY2NpDJZMr5ycnJcHNzw4MHDwyiv5qhrS+gu+tMREhJSUHFihVhZMQ9z4qioLwGdPf/rgv4vSlYcd4bzuni43219Pi9VqVJXvMRcjUYGRmhcuXKBT5va2trUBueoa0voJvrzEfRiudteQ3o5v9dV/B7U7Civjec08XD+2rdwe/1a+rmNX8NZ4wxxhhjTEJckDPGGGOMMSYhLsiLQS6XY9q0aZDL5VKHUioMbX0Bw1xnxv/3wvB7UzB+b3QT/19KD7/XRccndTLGGGOMMSYhPkLOGGOMMcaYhLggZ4wxxhhjTEJckDPGGGOMMSYhLsgZY4wxxhiTEBfkuSxZsgRVqlSBubk5mjVrhtOnTxfafvPmzahVqxbMzc1Rt25d7N27V+V5IsLUqVNRoUIFWFhYICAgADdv3izJVdCYttd54MCBkMlkKrd33323JFdBY5qs85UrV9CjRw9UqVIFMpkMixYtKvYyme4r6//PY8eOoUuXLqhYsSJkMhm2b9+u8rw6n10JCQno168fbG1tYW9vj8GDByM1NVWlzcWLF9G6dWuYm5vDzc0Nc+fOLelVK7ZZs2bhnXfegY2NDZydnfHBBx8gOjpapU16ejqCg4NRvnx5WFtbo0ePHoiLi1Npc//+fXTu3BmWlpZwdnbGxIkTkZWVpdImLCwMjRo1glwuR/Xq1bF69eqSXr0ywxD311IwxBpBMsSIiGjjxo1kZmZGv/32G125coWGDh1K9vb2FBcXl2/7EydOkLGxMc2dO5euXr1K3377LZmamtKlS5eUbWbPnk12dna0fft2unDhAr3//vvk6elJaWlppbVahSqJdR4wYAC9++679PjxY+UtISGhtFbprTRd59OnT9MXX3xBGzZsIFdXV1q4cGGxl8l0myH8P/fu3UvffPMNbd26lQDQtm3bVJ5X57Pr3Xffpfr169OpU6fon3/+oerVq1Pfvn2VzyclJZGLiwv169ePLl++TBs2bCALCwtasWJFaa1mkQQFBVFISAhdvnyZzp8/T506dSJ3d3dKTU1Vthk+fDi5ubnR4cOH6ezZs9S8eXNq0aKF8vmsrCyqU6cOBQQEUFRUFO3du5ccHR1p8uTJyjZ37twhS0tLGj9+PF29epUWL15MxsbGtH///lJdX31kiPtrKRhijSAlLsj/07RpUwoODlY+zs7OpooVK9KsWbPybf/hhx9S586dVeY1a9aMPvvsMyIiysnJIVdXV5o3b57y+cTERJLL5bRhw4YSWAPNaXudiUSyde3atUTi1QZN1zk3Dw+PfAvy4iyT6R5D+3++WZCr89l19epVAkBnzpxRttm3bx/JZDJ6+PAhEREtXbqUHBwcKCMjQ9nmyy+/JC8vrxJeI+2Kj48nAPT3338TkXgvTE1NafPmzco2165dIwAUHh5OROILj5GREcXGxirbLFu2jGxtbZXvx6RJk8jHx0fltXr37k1BQUElvUp6zxD311IwxBpBStxlBcCrV68QGRmJgIAA5TwjIyMEBAQgPDw8378JDw9XaQ8AQUFByvYxMTGIjY1VaWNnZ4dmzZoVuMzSVBLrrBAWFgZnZ2d4eXlhxIgRePbsmfZXoAiKss5SLJNJh/+f6n12hYeHw97eHk2aNFG2CQgIgJGRESIiIpRt2rRpAzMzM2WboKAgREdH4/nz56W0NsWXlJQEAChXrhwAIDIyEpmZmSrvT61ateDu7q7y/tStWxcuLi7KNkFBQUhOTsaVK1eUbdT5PGWqDHF/LQVDrBGkxgU5gKdPnyI7O1vlwxMAXFxcEBsbm+/fxMbGFtpeMdVkmaWpJNYZAN599138/vvvOHz4MObMmYO///4bHTt2RHZ2tvZXQkNFWWcplsmkw/9P9T67YmNj4ezsrPK8iYkJypUrp9Imv2Xkfg1dl5OTg7Fjx6Jly5aoU6cOABG7mZkZ7O3tVdq++f68bd0LapOcnIy0tLSSWJ0ywRD311IwxBpBaiZSB8DKlj59+ijv161bF/Xq1UO1atUQFhaG9u3bSxgZY4xpJjg4GJcvX8bx48elDoWxMoFrhILxEXIAjo6OMDY2znOWfFxcHFxdXfP9G1dX10LbK6aaLLM0lcQ656dq1apwdHTErVu3ih90MRVlnaVYJpMO/z/V++xydXVFfHy8yvNZWVlISEhQaZPfMnK/hi4bNWoUdu/ejaNHj6Jy5crK+a6urnj16hUSExNV2r/5/rxt3QtqY2trCwsLC22vTplhiPtrKRhijSA1LsgBmJmZoXHjxjh8+LByXk5ODg4fPgxfX998/8bX11elPQCEhoYq23t6esLV1VWlTXJyMiIiIgpcZmkqiXXOz7///otnz56hQoUK2gm8GIqyzlIsk0mH/5/qfXb5+voiMTERkZGRyjZHjhxBTk4OmjVrpmxz7NgxZGZmKtuEhobCy8sLDg4OpbQ2miMijBo1Ctu2bcORI0fg6emp8nzjxo1hamqq8v5ER0fj/v37Ku/PpUuXVL60hIaGwtbWFt7e3so2mn6eMsPcX0vBEGsEyUl9Vqmu2LhxI8nlclq9ejVdvXqVhg0bRvb29sqz5Pv3709fffWVsv2JEyfIxMSEfvjhB7p27RpNmzYt32GU7O3taceOHXTx4kXq2rWrTg2jpO11TklJoS+++ILCw8MpJiaGDh06RI0aNaIaNWpQenq6JOv4Jk3XOSMjg6KioigqKooqVKhAX3zxBUVFRdHNmzfVXibTL4bw/0xJSVFu1wBowYIFFBUVRffu3SMi9T673n33XWrYsCFFRETQ8ePHqUaNGirDHiYmJpKLiwv179+fLl++TBs3biRLS0udH/ZwxIgRZGdnR2FhYSpDs718+VLZZvjw4eTu7k5Hjhyhs2fPkq+vL/n6+iqfVwx7GBgYSOfPn6f9+/eTk5NTvsMeTpw4ka5du0ZLlizhYQ/VZIj7aykYYo0gJS7Ic1m8eDG5u7uTmZkZNW3alE6dOqV8zs/PjwYMGKDSftOmTVSzZk0yMzMjHx8f2rNnj8rzOTk5NGXKFHJxcSG5XE7t27en6Ojo0lgVtWlznV++fEmBgYHk5OREpqam5OHhQUOHDtW5QkaTdY6JiSEAeW5+fn5qL5Ppn7L+/zx69Gi+27Vi21fns+vZs2fUt29fsra2JltbWxo0aBClpKSotLlw4QK1atWK5HI5VapUiWbPnl1aq1hk+b0vACgkJETZJi0tjUaOHEkODg5kaWlJ3bp1o8ePH6ss5+7du9SxY0eysLAgR0dHmjBhAmVmZqq0OXr0KDVo0IDMzMyoatWqKq/BCmeI+2spGGKNIBUZEVGpH5ZnjDHGGGOMAeA+5IwxxhhjjEmKC3LGGGOMMcYkxAU5Y4wxxhhjEuKCnDHGGGOMMQlxQc4YY4wxxpiEuCBnjDHGGGNMQlyQM8YYY4wxJiEuyBljjDHGGJMQF+SMMcYYY4xJiAtyVioePHgAf39/eHt7o169eti8ebPUITHG9FRiYiKaNGmCBg0aoE6dOli1apXUITFWJvC+WjoyIiKpg2Bl3+PHjxEXF4cGDRogNjYWjRs3xo0bN2BlZSV1aIwxPZOdnY2MjAxYWlrixYsXqFOnDs6ePYvy5ctLHRpjeo331dLhI+SsSPz9/SGTySCTyXD+/Pm3tq9QoQIaNGgAAHB1dYWjoyMSEhJKNsh8DBw4UBn39u3bS/31GdMF+eWvv78/xo4dK2lc6jI2NoalpSUAICMjA0SE3MeWOM8ZE3hfrT+4IGdFNnToUDx+/Bh16tTR6O8iIyORnZ0NNze3t7YdNGgQvv3226KGmMePP/6Ix48fa215jOmrouZvUWk7lxMTE1G/fn1UrlwZEydOhKOjo/I5znPGXuN9tX7ggpwVmaWlJVxdXWFiYqL23yQkJOCTTz7BypUr39o2Ozsbu3fvxvvvv1+cMFXY2dnB1dVVa8tjTF8VJX+LqiRy2d7eHhcuXEBMTAz++OMPxMXFKZ/jPGfsNd5X6wcuyA1I5cqVsXTpUpV5J0+ehKWlJe7du1fs5W/ZsgV169aFhYUFypcvj4CAALx48UL5fEZGBj744AN89dVXaNGixVuXd/LkSZiamuKdd97J93l/f3+MHj0aY8eOhYODA1xcXLBq1Sq8ePECgwYNgo2NDapXr459+/YVe90Yk1pJ5++b9uzZAzs7O6xfvx4AkJKSgn79+sHKygoVKlTAwoUL1e7mUpK57OLigvr16+Off/4p1voypit4X22YuCA3IM2aNcOZM2eUj4kIY8eOxbhx4+Dh4VGsZT9+/Bh9+/bFp59+imvXriEsLAzdu3dX9uskIgwcOBDt2rVD//791Vrmzp070aVLF8hksgLbrFmzBo6Ojjh9+jRGjx6NESNGoFevXmjRogXOnTuHwMBA9O/fHy9fvizW+jEmtZLM3zf98ccf6Nu3L9avX49+/foBAMaPH48TJ05g586dCA0NxT///INz586ptTxt53JcXBxSUlIAAElJSTh27Bi8vLyKv+KM6QDeVxsoYgZj7ty55OPjo3y8Zs0acnV1pZSUFI2X5efnR2PGjFE+joyMJAB09+7dfNv/888/JJPJqH79+srbxYsXC32NGjVq0O7duwuNoVWrVsrHWVlZZGVlRf3791fOe/z4MQGg8PBwlb8FQNu2bSv09RnTJSWZv7nn/fzzz2RnZ0dhYWHK55KTk8nU1JQ2b96snJeYmEiWlpZ5lpMfbedyREQE1a9fn+rVq0d169al5cuX57tcznOmj3hf/Zoh5XDJdx5kOqN58+b46quvkJqaCplMhq+//hrff/89rK2tAQDdunVDWFgY2rdvjy1btmi07Pr166N9+/aoW7cugoKCEBgYiJ49e8LBwQEA0KpVK+Tk5Ki9vGvXruHRo0do3759oe3q1aunvG9sbIzy5cujbt26ynkuLi4AgPj4eE1WhzGdU1j+PnjwAP3790d8fDxMTEwwZcoU9OrVS+PX2LJlC+Lj43HixAmVn5/v3LmDzMxMNG3aVDnPzs5OraPSJZHL77//vlojRjCmjwrL9cTERAQEBCArKwtZWVkYM2YMhg4dqvayeV+tu7jLigFp3LgxjIyMcO7cOcyZMwdOTk4YNGiQ8vkxY8bg999/L9KyjY2NERoain379sHb2xuLFy+Gl5cXYmJiirS8nTt3okOHDjA3Ny+0nampqcpjmUymMk/xE5omHzCM6aLC8tfExASLFi3C1atXcfDgQYwdO1alT6i6GjZsCCcnJ/z2228qwwgWB+cyY5opLNdtbGxw7NgxnD9/HhEREZg5cyaePXum9rJ5X627uCA3IJaWlqhbty7++usv/PDDD1i4cCGMjF5vAv7+/rCxsSny8mUyGVq2bInp06cjKioKZmZm2LZtW5GWtWPHDnTt2rXIsTBW1hSWv9oaO7hatWo4evQoduzYgdGjRyvnV61aFaampir9WpOSknDjxo23LpNzmTHNFJbrbxuDXx28r9ZN3GXFwDRv3hyLFy9G165d4e/vr7XlRkRE4PDhwwgMDISzszMiIiLw5MkT1K5dW+NlxcfH4+zZs9i5c6fW4mOsLFAnfzUZOzg/NWvWxNGjR+Hv76888m5jY4MBAwZg4sSJKFeuHJydnTFt2jQYGRkVeiIX5zJjRVNYricmJsLPzw83b97EvHnzVMbgfxveV+suLsgNTP369WFqaop58+Zpdbm2trY4duwYFi1ahOTkZHh4eGD+/Pno2LGjxsvatWsXmjZtqtGHDGOG4G35qxg7eNWqVcV6HS8vLxw5cgT+/v4wNjbG/PnzsWDBAgwfPhzvvfcebG1tMWnSJDx48KDQn6o5lxkrmsJyXTEGf1xcHLp3746ePXsq+2C/De+rdRcX5AZm48aNGDVqFKpXr67V5dauXRv79+/XyrJ27Nih1gUGwsLC8sy7e/dunnna6gvLmNQKy19Nxw5+05v5VLt2bZWL7djY2CjHJAeAFy9eYPr06Rg2bFiBy+RcZqxo1NlX5x6Dv2fPnmotl/fVuov7kBuAnJwcxMXFYebMmbh58yamTZumleUuXboU1tbWuHTpklaWp9CqVSv07dtXq8tUGD58uHJUGcb0gTr5S0UYO1jT/I2KisKGDRtw+/ZtnDt3Tjk+eWH9R0sylwvDec70kTq5XpQx+HlfrR9kZOhfSQxAWFgY2rVrh1q1aiEkJATNmjXLt11AQAAuXLiAFy9eoFy5cti8eTN8fX3zbfvw4UOkpaUBANzd3WFmZlZi8WtTfHw8kpOTAYgT4aysrCSOiLHCqZO/x48fR5s2bVSGFlu7dq3KsGK5FSV/o6KiMGTIEERHR8PMzAyNGzfGggULCnwNKXGeM32kTq6fPn0aw4YNU57MGRwcjM8++6zAZfK+Wn9wQc4YY4wxxpiEuMsKY4wxxhhjEuKCnDHGGGOMMQlxQc4YY4wxxpiEuCBnjDHGGGNMQlyQM8YYY4wxJiEuyBljjDHGGJMQF+SMMcYYY4xJiAtyxhhjjDHGJMQFOWOMMcYYYxLigpwxxhhjjDEJcUHOGGOMMcaYhLggZ4wxxhhjTEJckDPGGGOMMSYhLsgZY4wxxhiTEBfkjDHGGGOMSYgLcsYYY4wxxiTEBTljjDHGGGMS4oKcMcYYY4wxCXFBzhhjrETJZDJ89913Gv9dWFgYZDIZwsLCtB6TrpLJZBg1apTUYbAyjPNRN3FBruNkMplaN31IkLt372LQoEGoVq0azM3N4erqijZt2mDatGkq7ZYuXYrVq1dLEyTTezExMRg1ahRq1qwJS0tLWFpawtvbG8HBwbh48aLU4RVq9erVKnltbm6OmjVrYtSoUYiLi9N4eaWZS3v37i3STl4btP2+FYY/nwwH52PRXbp0CT179oSHhwfMzc1RqVIldOjQAYsXL1ZpN3PmTGzfvl2aIHWMidQBsMKtXbtW5fHvv/+O0NDQPPNr165dmmFp7NatW3jnnXdgYWGBTz/9FFWqVMHjx49x7tw5zJkzB9OnT1e2Xbp0KRwdHTFw4EDpAmZ6affu3ejduzdMTEzQr18/1K9fH0ZGRrh+/Tq2bt2KZcuWISYmBh4eHlKHWqj//e9/8PT0RHp6Oo4fP45ly5Zh7969uHz5MiwtLdVeTmnm0t69e7FkyZJ8i4C0tDSYmJT87kZb71th+PPJ8HA+aubkyZNo27Yt3N3dMXToULi6uuLBgwc4deoUfvzxR4wePVrZdubMmejZsyc++OCDEotHX3BBruM+/vhjlcenTp1CaGhonvm6buHChUhNTcX58+fzFEPx8fFFXu6LFy9gZWVV3PBYGXD79m306dMHHh4eOHz4MCpUqKDy/Jw5c7B06VIYGRX+w6AubFMdO3ZEkyZNAABDhgxB+fLlsWDBAuzYsQN9+/aVNLaiMDc3L5XXKcn37eXLl1or6t9EREhPT4eFhUWJLJ8VD+ejZmbMmAE7OzucOXMG9vb2Ks/x/r5g3GWlDAgJCUG7du3g7OwMuVwOb29vLFu2LE+7KlWq4L333sPx48fRtGlTmJubo2rVqvj999/ztL148SL8/PxgYWGBypUr4/vvv0dISAhkMhnu3r2rcYy3b99G5cqV8z0y6ezsrBLjlStX8Pfffyt/JvT39wfw+ufDv//+GyNHjoSzszMqV66s/Nt9+/ahdevWsLKygo2NDTp37owrV66ovFZsbCwGDRqEypUrQy6Xo0KFCujatavKOp09exZBQUFwdHSEhYUFPD098emnn2q8zqx0zZ07Fy9evEBISEieYhwATExM8Pnnn8PNzU05b+DAgbC2tsbt27fRqVMn2NjYoF+/fgCAf/75B7169YK7uzvkcjnc3Nwwbtw4pKWlKf/+hx9+gEwmw7179/K83uTJk2FmZobnz58Xe93atWsHQHTHAYCsrCz83//9H6pVqwa5XI4qVarg66+/RkZGhvJvCsslAEhMTMTYsWPh5uYGuVyO6tWrY86cOcjJyVG2uXv3LmQyGX744QesXLlS+XrvvPMOzpw5o/I+LlmyBIBqNzuFN/us3rt3DyNHjoSXlxcsLCxQvnx59OrVq0ifLYV5830DgHXr1qFx48awsLBAuXLl0KdPHzx48EDl7/z9/VGnTh1ERkaiTZs2sLS0xNdff13oe/rdd9+prLOC4nMr97opPosPHDiAJk2awMLCAitWrFD5u/Xr18PLywvm5uZo3Lgxjh07pqV3hRUX52Phbt++DR8fnzzFOKC6v5fJZHjx4gXWrFmjjFHx64Ein65evYqPPvoIDg4OaNWqlfJv1cnjmzdvokePHnB1dYW5uTkqV66MPn36ICkpSdkmNDQUrVq1gr29PaytreHl5YWvv/66SOtdXHyEvAxYtmwZfHx88P7778PExAS7du3CyJEjkZOTg+DgYJW2t27dQs+ePTF48GAMGDAAv/32GwYOHIjGjRvDx8cHAPDw4UO0bdsWMpkMkydPhpWVFX755RfI5fIix+jh4YFDhw7hyJEjyg+z/CxatAijR4+GtbU1vvnmGwCAi4uLSpuRI0fCyckJU6dOxYsXLwCIrj0DBgxAUFAQ5syZg5cvX2LZsmVo1aoVoqKiUKVKFQBAjx49cOXKFYwePRpVqlRBfHw8QkNDcf/+feXjwMBAODk54auvvoK9vT3u3r2LrVu3FnndWenYvXs3qlevjmbNmmn0d1lZWQgKCkKrVq3www8/KI+Cbt68GS9fvsSIESNQvnx5nD59GosXL8a///6LzZs3AwA+/PBDTJo0CZs2bcLEiRNVlrtp0yYEBgbCwcGh2Ot2+/ZtAED58uUBiKN0a9asQc+ePTFhwgRERERg1qxZuHbtGrZt2wag8Fx6+fIl/Pz88PDhQ3z22Wdwd3fHyZMnMXnyZDx+/BiLFi1Sef0//vgDKSkp+OyzzyCTyTB37lx0794dd+7cgampKT777DM8evQo3+50+Tlz5gxOnjyJPn36oHLlyrh79y6WLVsGf39/XL16VWtHot9832bMmIEpU6bgww8/xJAhQ/DkyRMsXrwYbdq0QVRUlEoB8ezZM3Ts2BF9+vTBxx9/DBcXF/j7+7/180ld0dHR6Nu3Lz777DMMHToUXl5eyuf+/vtv/Pnnn/j8888hl8uxdOlSvPvuuzh9+jTq1KlTxHeDaQvnY+E8PDwQHh6Oy5cvF7q9rl27FkOGDEHTpk0xbNgwAEC1atVU2vTq1Qs1atTAzJkzQUQA1MvjV69eISgoCBkZGRg9ejRcXV3x8OFD7N69G4mJibCzs8OVK1fw3nvvoV69evjf//4HuVyOW7du4cSJExqtr9YQ0yvBwcH05r/t5cuXedoFBQVR1apVVeZ5eHgQADp27JhyXnx8PMnlcpowYYJy3ujRo0kmk1FUVJRy3rNnz6hcuXIEgGJiYjSO+/Lly2RhYUEAqEGDBjRmzBjavn07vXjxIk9bHx8f8vPzyzM/JCSEAFCrVq0oKytLOT8lJYXs7e1p6NChKu1jY2PJzs5OOf/58+cEgObNm1dgnNu2bSMAdObMGY3XkUknKSmJANAHH3yQ57nnz5/TkydPlLfc+TJgwAACQF999VWev8svr2bNmkUymYzu3bunnOfr60uNGzdWaXf69GkCQL///rtG66HYxg8dOkRPnjyhBw8e0MaNG6l8+fJkYWFB//77L50/f54A0JAhQ1T+9osvviAAdOTIEeW8gnLp//7v/8jKyopu3LihMv+rr74iY2Njun//PhERxcTEEAAqX748JSQkKNvt2LGDANCuXbuU8/L7bFIAQNOmTVM+zu+9DQ8Pz/OeHT16lADQ0aNH812ugjrv2927d8nY2JhmzJih8reXLl0iExMTlfl+fn4EgJYvX57ntQp6T6dNm5bv+itiy/25qfgs3r9/f572AAgAnT17Vjnv3r17ZG5uTt26dSv0fWDaxflYtHw8ePAgGRsbk7GxMfn6+tKkSZPowIED9OrVqzxtraysaMCAAXnmK/Kpb9++KvPVzeOoqCgCQJs3by4wzoULFxIAevLkSaHrU1q4y0oZkLvfYVJSEp4+fQo/Pz/cuXNH5acZAPD29kbr1q2Vj52cnODl5YU7d+4o5+3fvx++vr5o0KCBcl65cuWUP+UXhY+PD86fP4+PP/4Yd+/exY8//ogPPvgALi4uWLVqlUbLGjp0KIyNjZWPQ0NDkZiYiL59++Lp06fKm7GxMZo1a4ajR48CEO+TmZkZwsLCCuxGoDhCtnv3bmRmZhZtZVmpS05OBgBYW1vnec7f3x9OTk7Km+Kn3NxGjBiRZ17uvHrx4gWePn2KFi1agIgQFRWlfK53796IjIxUHjUDgD///BNyuRxdu3Yt0voEBATAyckJbm5u6NOnD6ytrbFt2zZUqlQJe/fuBQCMHz9e5W8mTJgAANizZ89bl79582a0bt0aDg4OKjkTEBCA7OzsPN0jevfurXKkX/EZkvtzQxO539vMzEw8e/YM1atXh729Pc6dO1ekZQKFv29bt25FTk4OPvzwQ5V1dnV1RY0aNZSfEwpyuRyDBg0qcixv4+npiaCgoHyf8/X1RePGjZWP3d3d0bVrVxw4cADZ2dklFhPLH+ejZjp06IDw8HC8//77uHDhAubOnYugoCBUqlQJO3fu1GhZw4cPV3msbh7b2dkBAA4cOICXL1/mu2zF/n7Hjh0qXYOkwl1WyoATJ05g2rRpCA8Pz7PhJSUlKTdMQHywv8nBwUGlQL137x58fX3ztKtevXqx4qxZsybWrl2L7OxsXL16Fbt378bcuXMxbNgweHp6IiAgQK3leHp6qjy+efMmABTYFcbW1haA2MHOmTMHEyZMgIuLC5o3b4733nsPn3zyCVxdXQEAfn5+6NGjB6ZPn46FCxfC398fH3zwAT766KNiddlhJcvGxgYAkJqamue5FStWICUlBXFxcfmeDG1iYqJyLoLC/fv3MXXqVOzcuTPPF7jcX3R79eqF8ePH488//8TXX38NIsLmzZvRsWNH5banqSVLlqBmzZowMTGBi4sLvLy8lCej3rt3D0ZGRnny0dXVFfb29vn2Z3/TzZs3cfHiRTg5OeX7/JsnXr35uaEoBoraPz4tLQ2zZs1CSEgIHj58qPwpGkCegwiaKOx9u3nzJogINWrUyPdvTU1NVR5XqlQJZmZmRY7lbd78HMstvxhr1qyJly9f4smTJ8rPK1Y6OB81984772Dr1q149eoVLly4gG3btmHhwoXo2bMnzp8/D29vb7WWk9/+Xp089vT0xPjx47FgwQKsX78erVu3xvvvv4+PP/5YWRP17t0bv/zyC4YMGYKvvvoK7du3R/fu3dGzZ8+3nvxfErgg13O3b99G+/btUatWLSxYsABubm4wMzPD3r17sXDhwjzf+nIfWc4tdwKWNGNjY9StWxd169aFr68v2rZti/Xr16tdkL85EoFiHdeuXZvvjir38E5jx45Fly5dsH37dhw4cABTpkzBrFmzcOTIETRs2BAymQxbtmzBqVOnsGvXLhw4cACffvop5s+fj1OnTuV7BJZJz87ODhUqVMDly5fzPKfoU17QCUpyuTzPh292djY6dOiAhIQEfPnll6hVqxasrKzw8OFDDBw4UCWvKlasiNatW2PTpk34+uuvcerUKdy/fx9z5swp8vo0bdpUOapDQfI7gVBdOTk56NChAyZNmpTv8zVr1lR5rO3PjdGjRyMkJARjx46Fr68v7OzsIJPJ0KdPn2IdqSrsfcvJyYFMJsO+ffvyXZ83c1vTEU8K+n8UdESbR1TRH5yPRWdmZoZ33nkH77zzDmrWrIlBgwZh8+bNea4/UpD89vfq5vH8+fMxcOBA7NixAwcPHsTnn3+OWbNm4dSpU6hcuTIsLCxw7NgxHD16FHv27MH+/fvx559/ol27djh48GCB73NJ4YJcz+3atQsZGRnYuXOnyrfmN39+1YSHhwdu3bqVZ35+84pL8SH3+PFj5TxNP9gUJ4E4OzurVdRXq1YNEyZMwIQJE3Dz5k00aNAA8+fPx7p165RtmjdvjubNm2PGjBn4448/0K9fP2zcuBFDhgzRKDZWejp37oxffvkFp0+fRtOmTYu1rEuXLuHGjRtYs2YNPvnkE+X80NDQfNv37t0bI0eORHR0NP78809YWlqiS5cuxYqhIB4eHsjJycHNmzdVrj8QFxeHxMRElZGMCsqlatWqITU1Ve0vwerQJG+3bNmCAQMGYP78+cp56enpSExM1Fo8b6pWrRqICJ6ennkKHE0UtJ6Ko5SJiYkqJ4eqc4T0TYpf/XK7ceMGLC0tCzyKyqTB+ag+be3vNcljxcG/b7/9FidPnkTLli2xfPlyfP/99wAAIyMjtG/fHu3bt8eCBQswc+ZMfPPNNzh69KhW/x/q4D7kek7xDe7Nn5hCQkKKvMygoCCEh4fj/PnzynkJCQlYv359nraPHz/G9evX39rf+p9//sm3jaL/Xe4RBqysrDT6IAgKCoKtrS1mzpyZ72s8efIEgDiTPT09XeW5atWqwcbGRjk81fPnz/McZVD0pc89hNXt27dV+gwz6U2aNAmWlpb49NNP872KniZHj/LLKyLCjz/+mG/7Hj16wNjYGBs2bMDmzZvx3nvvqYyXe//+fVy/fl3t1y9Mp06dACDPyAsLFiwAIL6YKBSUSx9++CHCw8Nx4MCBPM8lJiYiKytL47gU66tO7hobG+f5fyxevLhE+0d3794dxsbGmD59ep7XJiI8e/ZMreUU9J4qDgzk7u+rGNJNU+Hh4Sp9dx88eIAdO3YgMDBQuW2+fPkS169fx9OnTzVePtMezse8jh49mu/nrTb29+rmcXJycp73rW7dujAyMlLuyxMSEvIsP7/9fWnhI+R6LjAwEGZmZujSpQs+++wzpKamYtWqVXB2dlb5FqqJSZMmYd26dejQoQNGjx6tHPbQ3d0dCQkJKt9oJ0+ejDVr1iAmJkY5tGB+5syZg8jISHTv3h316tUDAJw7dw6///47ypUrh7FjxyrbNm7cGMuWLcP333+P6tWrw9nZudChEm1tbbFs2TL0798fjRo1Qp8+feDk5IT79+9jz549aNmyJX7++WfcuHED7du3x4cffghvb2+YmJhg27ZtiIuLQ58+fQAAa9aswdKlS9GtWzdUq1YNKSkpWLVqFWxtbZUfvADQvn17AAV3g2Clr0aNGvjjjz/Qt29feHl5Ka/USUSIiYnBH3/8ASMjo3z7i7+pVq1aqFatGr744gs8fPgQtra2+Ouvvwrso+ns7Iy2bdtiwYIFSElJQe/evVWe/+STT/D3339rpWtY/fr1MWDAAKxcuRKJiYnw8/PD6dOnsWbNGnzwwQdo27atsm1BuTRx4kTs3LkT7733nnLY0xcvXuDSpUvYsmUL7t69C0dHR43iUpyE+PnnnyMoKAjGxsbKvHrTe++9h7Vr18LOzg7e3t4IDw/HoUOHlMPIlYRq1arh+++/x+TJk3H37l188MEHsLGxQUxMDLZt24Zhw4bhiy++eOtyCnpPAwMD4e7ujsGDB2PixIkwNjbGb7/9pvws0kSdOnUQFBSkMuwhAJUrGp8+fRpt27bFtGnTJL1EuqHjfMxr9OjRePnyJbp164ZatWrh1atXOHnyJP78809UqVJF5WTpxo0b49ChQ1iwYAEqVqwIT0/PQoeuVTePjxw5glGjRqFXr16oWbMmsrKysHbtWhgbG6NHjx4AxBVYjx07hs6dO8PDwwPx8fFYunQpKleurDLmeakptfFcmFbkN5TRzp07qV69emRubk5VqlShOXPm0G+//ZbvUFudO3fOs0w/P788QzFFRUVR69atSS6XU+XKlWnWrFn0008/EQCKjY1VtlMMG/e2oRBPnDhBwcHBVKdOHbKzsyNTU1Nyd3engQMH0u3bt1XaxsbGUufOncnGxoYAKGNTDEFV0JCER48epaCgILKzsyNzc3OqVq0aDRw4UDl82NOnTyk4OJhq1apFVlZWZGdnR82aNaNNmzYpl3Hu3Dnq27cvubu7k1wuJ2dnZ3rvvfdUhiBTvJceHh6FrjOTxq1bt2jEiBFUvXp1Mjc3JwsLC6pVqxYNHz6czp8/r9J2wIABZGVlle9yrl69SgEBAWRtbU2Ojo40dOhQunDhAgGgkJCQPO1XrVpFAMjGxobS0tJUnlMMo/c2b9vGFTIzM2n69Onk6elJpqam5ObmRpMnT6b09HSVdgXlEpEYLnTy5MlUvXp1MjMzI0dHR2rRogX98MMPyuHJFMOs5TdUKN4YOi0rK4tGjx5NTk5OJJPJVNb3zbbPnz+nQYMGkaOjI1lbW1NQUBBdv36dPDw8VIZA03TYQ3WGK/3rr7+oVatWZGVlRVZWVlSrVi0KDg6m6OhoZRs/Pz/y8fHJ9+8Le08jIyOpWbNmZGZmRu7u7rRgwYIChz3M77OYSLxXwcHBtG7dOqpRowbJ5XJq2LBhnvdA8d7kfl+ZdnE+Fi0f9+3bR59++inVqlWLrK2tyczMjKpXr06jR4+muLg4lbbXr1+nNm3aKIdFVryeYtjDgoYkfFse37lzhz799FOqVq0amZubU7ly5aht27Z06NAh5TIOHz5MXbt2pYoVK5KZmRlVrFiR+vbtm2f4ydIiIyrFs/mYXhs7dixWrFiB1NTUUj/ZgTHGGGOsrOI+5CxfuS8PDoir1q1duxatWrXiYpwxxhhjTIu4DznLl6+vL/z9/VG7dm3ExcXh119/RXJyMqZMmSJ1aIwxxhhjZQoX5CxfnTp1wpYtW7By5UrIZDI0atQIv/76K9q0aSN1aIwxxhhjZQr3IWeMMcYYY0xC3IecMcYYY4wxCXFBzhhjjDHGmIS4D7kacnJy8OjRI9jY2Gh8mVfGSgIRISUlBRUrVoSREX+vLgrOa6ZLOKeLj3Oa6RpN8poLcjU8evQIbm5uUofBWB4PHjxQ68qTLC/Oa6aLOKeLjnOa6Sp18poLcjXY2NgAEG+ora2txNEwBiQnJ8PNzU25bTLNcV4zXcI5XXyc00zXaJLXXJCrQfHTl62tLSc50yn8s2zRcV4zXcQ5XXSc00xXqZPX3FGNMcYYY4wxCXFBzhhjjDHGmIS4IGeMMcYYY0xCkhfkDx8+xMcff4zy5cvDwsICdevWxdmzZ5XPExGmTp2KChUqwMLCAgEBAbh586bKMhISEtCvXz/Y2trC3t4egwcPRmpqqkqbixcvonXr1jA3N4ebmxvmzp1bKuvHmKHhnGas7OG8ZqxkSVqQP3/+HC1btoSpqSn27duHq1evYv78+XBwcFC2mTt3Ln766ScsX74cERERsLKyQlBQENLT05Vt+vXrhytXriA0NBS7d+/GsWPHMGzYMOXzycnJCAwMhIeHByIjIzFv3jx89913WLlyZamuL2NlHec0Y2UP5zVjpYAk9OWXX1KrVq0KfD4nJ4dcXV1p3rx5ynmJiYkkl8tpw4YNRER09epVAkBnzpxRttm3bx/JZDJ6+PAhEREtXbqUHBwcKCMjQ+W1vby81IozKSmJAFBSUlK+z796pdZiGNOat22TUtGXnCbS3feQlV05OUSpqfk/p8vbo77ktS6/h6zsSkkp+DlNtklJj5Dv3LkTTZo0Qa9eveDs7IyGDRti1apVyudjYmIQGxuLgIAA5Tw7Ozs0a9YM4eHhAIDw8HDY29ujSZMmyjYBAQEwMjJCRESEsk2bNm1gZmambBMUFITo6Gg8f/48T1wZGRlITk5WuRUkIwOoUQMYNAjI9esdYwZJV3Ma0CyvGdO2J0+AXr2A998HcnKkjkYzuprXnNNManv3Ap6ewKFDxV+WpAX5nTt3sGzZMtSoUQMHDhzAiBEj8Pnnn2PNmjUAgNjYWACAi4uLyt+5uLgon4uNjYWzs7PK8yYmJihXrpxKm/yWkfs1cps1axbs7OyUt8Ku/HXwIHDvHrB6NfDOO0Dz5sDatUCuX+kYMxi6mtOAZnnNmDZt3w74+AB//QUcOwZERkodkWZ0Na85p5lUsrOBb78FOncGnj4FFi4s/jIlLchzcnLQqFEjzJw5Ew0bNsSwYcMwdOhQLF++XMqwMHnyZCQlJSlvDx48KLDte+8BJ08C/foBpqZARATwySeAmxvw9dfA/fulGDhjEtPVnAY0y2vGtCExUewPunUTR8jr1AFOnxYHb/SJruY15zSTQlwcEBgIzJghHgcHA1u3Fn+5khbkFSpUgLe3t8q82rVr4/5/VayrqysAIC4uTqVNXFyc8jlXV1fEx8erPJ+VlYWEhASVNvktI/dr5CaXy5VX+nrbFb9kMsDXF1i3DnjwAPj+e6ByZfGNadYs8VNG9+7A0aMA0VvfEsb0mq7mNKBZXjNWXIcOAXXril9MjYyAr74S3RobNpQ6Ms3pal5zTrPSduIE0KgRcOQIYGUF/PEH8PPPgFxe/GVLWpC3bNkS0dHRKvNu3LgBDw8PAICnpydcXV1x+PBh5fPJycmIiIiAr68vAMDX1xeJiYmIzPUb4JEjR5CTk4NmzZop2xw7dgyZmZnKNqGhofDy8lI5S7y4XFyAb74BYmLEt6X27UVfwW3bgHbtxNGRZcuAN0Z5YqzMKGs5zZimXr4EPv8c6NAB+PdfoHp14PhxcYBGGzttKXBeM0NHBCxaBPj7A48eAbVrA2fOAH37avVFpHP69GkyMTGhGTNm0M2bN2n9+vVkaWlJ69atU7aZPXs22dvb044dO+jixYvUtWtX8vT0pLT/b+/O46Ko3ziAfxYEBHHBE7zIW8MDr1JKzZIkNfPKzMxM0zKPVDzK8sg0KSuPzOz0yl9qVl5pFKl4JGoeeOKNYipgJiConM/vj28srKKysMvs8Xm/XvPaY8bZh3WenWdmvvP93rxpWOapp56Spk2byu7du2XHjh1Sp04d6dOnj2F+YmKi+Pj4SL9+/eTIkSOyYsUK8fDwkC+//LJAcRblzu2jR0WGDhUpVUpE/ZeKeHmJhISInDlj8uqIRMR6exOwlZwWsd7vkGzX3r0i9evn/tYPHXr3XlVuZ83bo63ktTV/h2S7rl8X6d07N6979753zyp5mbJNalqQi4isX79eGjZsKG5ublK/fn356quvjOZnZ2fLpEmTxMfHR9zc3KR9+/Zy4sQJo2WuXr0qffr0EU9PT9Hr9TJgwAC5ftu3dfDgQWndurW4ublJlSpV5IMPPihwjOZI8sREkblzRerUyf1P1elEunQRCQ9X3WERFZQ173hsIadFrPs7JNuSkSEybZpIiRLqt71SJZGwMNPWYe3boy3ktbV/h2R7oqNFHnxQ5XWJEqqOM6VeM2Wb1ImwZfP9JCcnw8vLC0lJSUVuo5adDfz2G/Dpp0BYWO77DRqoy5wvvgh4eBQxYLJ75twmHRW/QzKHmBigXz/VthRQXRsuWACUK2faerg9Fh2/QzKnn38GXn4ZuH4dqFwZ+OEH4NFHTVuHKdukpm3IHZGTE9CxI/Drr8Dx48Dw4YCnJ3D0KPDaa6p3lrfeUjeIEhGRdRJRN2wGBKhivHRpYOlSYOVK04txIrIeWVmqDuvZUxXjbduqrkpNLcZNxYJcQ/XqAfPmqRt/Zs8GatYE/v0X+PBD1TvL888Du3ZpHSUREeWVmAi88ILq0vD6dbWjPnhQnSnX6bSOjogK6+pV4KmnVB0GACEhqseku3TeZVYsyK2AlxcwahRw8iSwdi3w+OPqCG3lStWlYqtW6nmeG8+JiEgDO3YATZoAK1YAzs7AtGlARIQ6iUJEtuvAAaB5c1WAe3gAy5cDn3yixpgpDizIrYizsxpWefNmdbZlwADA1VUNNvT88+oM+kcfqbMzRERUfDIzgalTgcceU6Mz16ypivOJE4ESJbSOjoiK4n//Ax55ROV2rVqqdcLzzxdvDCzIrVTjxsDChWqkz6lTVR/nf/8NjB+vBh4aORI4e1brKImI7N+FC2osiXffVTfm9+unzqa1aqV1ZERUFJmZqlnKiy8Ct26pe/z++ksN6lXcWJBbOR8fYPJk4Nw5VaA3agSkpqpeWurUUTcd7NypdZRERPZpzRp14+b27eoG/O++UzdvshMPItv2zz+qvfjs2er1O+8A69cDWo1BxYLcRpQsqZqwHDwIhIero7jsbNUtz6OPqkstP/2k2p4TEVHRpKWprmi7dweuXQNatFBnxV98UevIiKioDh4EHnoI2LQJKFVK1U/Tp6umw1phQW5jdDogKAjYuFF1lThokGpnHhkJPPssULcu8Nln6iw6ERGZ7vRpdUP9vHnq9ZgxqmvD2rW1jYuIim7VKnUS89w5dS/Irl1Ajx5aR8WC3Kb5+wNff63amU+apPq+PXsWGDEC8PNT78XHax0lEZHtWLkSaNZMnQ0vVw745Rfg44/ViQ8isl3Z2aoueu454MYN4MknVXvxhg21jkxhQW4HfHyA995Thfnnn6s7hP/9V11+eeABNeDQyZNaR0lEZL1u3QJef131rHD9OtCmjbqs3bmz1pERUVFdv67Ogk+frl6PGaNaGpQtq21cebEgtyMeHmqHcuIE8OOPQMuWqh3kV18B9eurG0B379Y6SiIi63L2rLoX54svVLPAd95R3c9WqaJ1ZERUVDExqonK2rWAmxuwZIm66mVt3ZWyILdDzs6q+I6MVD0DdOmihnn++WfVTVe7durIUETrSImItLVmjWqisn+/aqKycaM6i2ZtO2siMt3WrermzSNHgEqV1OuXXtI6qvyxILdjOh3QujWwbp26AXTAADXi1Nat6jJsQIDqDD8zU+tIiYiKV2amGtehe3cgKUmdQTtwQHWDRkS27+uvVScYV6+qXpL++ku1HLBWLMgdhL+/6sc8JgYYO1b1p3v4sOrCq04dYP58dZMDEZG9i4sD2rdXIx8DwOjRQEQEUK2apmERkRlkZgKjRgGvvqqeP/88sG2b9TdBY0HuYKpUUTuh2Fjg/feBChVU1z/DhwPVqwOhoepsERGRPfrzT9VEZds2oHRp1QXarFnq6iER2bakJNVMd+5c9XraNOD77wF3d23jKggW5A6qTBng7beB8+fV2fHq1YErV9R7fn7AhAnsMpGI7IeIGqOhXTvg8mV11fCvv9T4DURk+86eVU3PwsJUAf7jj8DEiar5ri1gQe7g3N2BoUOBU6fUkNANGgDJycAHH6gifcQIdTadiMhW3bwJvPyy+j3LzAR691Y9TtWrp3VkRGQOO3ao9uHHjqmWADt2qM4tbAkLcgKgehR48UXg0CHV60DLlqpf3s8+U6PTDRqkRq8jIrIl58+rm9uXLlU9UM2aBSxfru6jISLbt2yZuifkn3+A5s2BPXtUszRbw4KcjDg5AV27qi4T//gDePxxICMD+PZbdTapb1/VYwsRkbWLiFC9K+zfD5QvD4SHqxs4beUSNhHdnQgwZQrQrx+Qnq4G/tm6FahcWevICocFOeVLp1NHnJs3Azt3qm4Ss7PVzRGNGql2l1FRWkdJRHSnnPbiQUHqrFmzZsC+feoEAxHZvlu31AnC995Tr998U92gXaqUtnEVBQtyuq/AQOCXX9RZph491M7up5+Apk2BZ54B9u7VOkIiIiUtDRg8WLUXz8pSO+0dO9TN6kRk+/75R50wXL5cNbf95ht135uTjVe0Nh4+FaemTVUhfuQI0KeP2vjXr1ejYHXurG6SIiLSSkKC2lF/+636ffr4Y3Wzui10eUZE93fypBpxfOdOwMtL9ajyyitaR2UeLMjJZA0aqKYrx46ptltOTmq46Vat1Ch3u3ZpHSEROZqDB9XJgT//VDvqDRuAMWPYXpzIXuzYoa7YnzmjeoGLjFQH4PaCBTkVWr16queCEydUl2LOzsBvv6mE6diRZ8yJqHisWwc8+qjqorVOHfXb89RTWkdFROaycqW6J+Tff4GHH1Yn/h58UOuozEvTgvzdd9+FTqczmurXr2+Yf+vWLQwbNgzlypWDp6cnevbsifjbRquJjY1F586d4eHhgYoVK2LcuHHIzMw0WiYiIgLNmjWDm5sbateujcWLFxfHn+cwatcGFi1ShfnAgaowDwtTZ8w7dVJdEJFjYE5TcRJRzVK6dQNSU9XZMvYvbn7Ma9KKCDBzJvD88+r+kO7dgS1bAB8frSMzP83PkDdo0ACXL182TDt27DDMGz16NNavX49Vq1Zh69atuHTpEnr06GGYn5WVhc6dOyM9PR07d+7EkiVLsHjxYkyePNmwTExMDDp37ozHH38cUVFRGDVqFAYNGoTffvutWP9OR1Crlmq7eeIEMGCAKsx//VX1ad6lC3DggNYRUnFgTlNxyMgAXn0VGDdO7bRff1393pQpo3Vk9ol5TcUtMxMYNkz1oAIAo0apnlQ8PDQNy3JEQ1OmTJGAgIB85yUmJoqLi4usWrXK8F50dLQAkMjISBER2bhxozg5OUlcXJxhmQULFoher5e0tDQRERk/frw0aNDAaN29e/eW4ODgAseZlJQkACQpKanA/4ZETp0S6d9fxMlJRO0yRbp3Fzl4UOvIbJ+1bpO2ktMi1vsd0v1duybyxBPqN8XJSeTTT7WOqOiseXu0lby25u+QTJOSItKli8pxnU5kzhytIyocU7ZJzc+Qnzp1CpUrV0bNmjXRt29fxP43Tvu+ffuQkZGBoKAgw7L169eHn58fIiMjAQCRkZFo1KgRfPJcuwgODkZycjKO/jd6TWRkpNE6cpbJWQdZTu3awOLFQHS06npMpwNWrwaaNFG9tJw4oXWEZAnWmtNpaWlITk42msj2nDun2otv3qxG21y3TnVxSJZljXnNnLZPV66o5mfr1wMlS6qz4iNHah2V5WlakLds2RKLFy9GWFgYFixYgJiYGLRp0wbXr19HXFwcXF1d4e3tbfRvfHx8EBcXBwCIi4szSvCc+Tnz7rVMcnIybt68mW9cTHLzqltXDW175Ajw3HPqXPmKFYC/v2racu6c1hGSuVhrTgNAaGgovLy8DFO1atWK+udSMdu7V92bcuyYGo1v+3bV5SpZlrXmNXPa/pw5AzzyiLoXpGxZNWJ4z55aR1U8NC3IO3bsiF69eqFx48YIDg7Gxo0bkZiYiB9++EHLsJjkFuLvr+6UjopSbcqzs9UZ9Lp1geHDgf9+l8mGWWtOA8CECROQlJRkmC5cuKB1SGSCDRuAxx4D4uOBxo3VDrtJE62jcgzWmtfMafuyb58qxk+fVt0a/vmnuhrmKDRvspKXt7c36tati9OnT8PX1xfp6elITEw0WiY+Ph6+vr4AAF9f3zvu5M55fb9l9Ho93O8yWgST3LICAtRl5l27VDdGGRnA/PnqptC33wauXdM6QjIXa8lpAHBzc4NerzeayDZ8/bUaFfjGDeDJJ9WZ8apVtY7KcVlLXjOn7Ud4ONCunRrcq0kTNfBPno58HIJVFeQpKSk4c+YMKlWqhObNm8PFxQWbNm0yzD9x4gRiY2MRGBgIAAgMDMThw4eRkJBgWCY8PBx6vR7+/v6GZfKuI2eZnHXkh0lePFq2VEm4ebN6fuMGEBoK1KwJfPihek22zVpymmyTCPDuu6o3lexsNd7Bhg0Af5K1xbwmc/r+e9VFckqKaju+dStQqZLWUWmgGG4yvasxY8ZIRESExMTEyJ9//ilBQUFSvnx5SUhIEBGRIUOGiJ+fn2zevFn27t0rgYGBEhgYaPj3mZmZ0rBhQ+nQoYNERUVJWFiYVKhQQSZMmGBY5uzZs+Lh4SHjxo2T6OhomT9/vjg7O0tYWFiB4+Sd25aXnS2ydq1Iw4a5PbJUrizy5ZciGRlaR2d9rHWbtJWcFrHe75CUjAyRwYNzfw8mTVK/E/bKmrdHW8lra/4OKX+zZ+fm+PPPi/zX6Y7dMGWb1LQg7927t1SqVElcXV2lSpUq0rt3bzl9+rRh/s2bN2Xo0KFSpkwZ8fDwkO7du8vly5eN1nHu3Dnp2LGjuLu7S/ny5WXMmDGScVsFt2XLFmnSpIm4urpKzZo1ZdGiRSbFySQvPpmZIkuXijzwQG6S1q0r8tNP9r0zNpW1bpO2ktMi1vsdksjNmyLduuV2a7hggdYRWZ41b4+2ktfW/B2SsexskTffzN3PjxwpkpWldVTmZ8o2qRMR0ebcvO1ITk6Gl5cXkpKS2HylmKSlAV98AUyfDvzzj3ovMFCN2NW6tbaxWQNuk0XH79A6JSUBXbuqy9ZubsDy5Wp0PnvH7bHo+B3ahsxM4LXXgIUL1evQUDX4j06nbVyWYMo2aVVtyIlyuLmpfkfPnAEmTVIjc0VGAm3aqJ318eNaR0hE5nblCvD446oYL10aCAtzjGKcyFHcvAk8+6wqxp2c1Ojeb71ln8W4qViQk1XT64H33lPdIL36KuDsrHpoadhQDamb5x4hIrJhFy6oA+4DB4AKFYCICNXrAhHZh6QkoGNHYO1addLt55+BgQO1jsp6sCAnm1CpEvDll8Dhw6r7s6ws4PPP1WigoaHqqJuIbNPp06op2okTQLVqqlvDZs20joqIzCUhwfjq12+/qavdlIsFOdmUBx9UR9dbtgDNmwPXr6u+y+vXV6N/8o4IItty9Kg6Mx4bC9SpA+zYAdSrp3VURGQusbHGV7+2blWDfJExFuRkk9q1A/bsAZYtUwOExMYCffqoUb527dI6OiIqiAMH1I45Lg5o1EidGffz0zoqIjKXkyfV1a+TJ1Vu79gBNG2qdVTWiQU52SwnJ6BvX3WZe9o0oFQpVYwHBgL9+gEXL2odIRHdzV9/AU88AVy9Cjz0kGoz7uOjdVREZC4HD6oz4xcuqKteO3YAdetqHZX1YkFONs/DA5g4UR2BDxig7tZetkwl/rRpbF9OZG0iI4GgICAxUV3VCg8HypbVOioiMpddu9SV7IQEdUZ8+3Z1fwjdHQtyshuVK6uulPbsAR59FLhxA5g8GfD3B1avZvtyImuwcycQHAwkJwNt26quDb28tI6KiMxl8+bcA+5HH1X3fFWooHVU1o8FOdmdFi3U0fjy5ap9+blzQI8eqgiIjtY6OiLHlVOMX7+uelzYuFH1uEBE9uGXX4BOnYDUVKBDB9WbCg+4C4YFOdklnQ54/nk1gNA77wCuruqyeOPGwPjxQEqK1hESOZZdu4CnnlK598QTasddqpTWURGRuaxapQbySksDunVTY4YwxwuOBTnZtVKlgOnTgWPHgC5d1JC9H32kuklctYrNWIiKw969xmfG169X934QkX1YulSdBMvMBF54AfjhBzX4DxUcC3JyCLVqqaP19euBGjVUDyzPPaeKhNOntY6OyH4dOqQuXScnqx4XWIwT2ZevvgJefhnIzgYGDVLFuYuL1lHZHhbk5FCefloNRPLuu+roPTwcaNgQeO89dZmNiMzn+HF1c9e1a0CrVsCGDbyETWRPPvsMeO01dbV5+HA1orazs9ZR2SYW5ORw3N2BKVOAI0eAJ59UhfiUKap9+datWkdHZB/OnVPF+JUrqtuzX3/lDZxE9uSTT4ARI9TzceOATz9V44NQ4fCrI4dVu7a6A3zFCsDXV/Vj3q4dMHiwOqNHRIUTF6cOdi9eBB58EPj9d8DbW+uoiMhcQkOBsWPV84kTgQ8/VJ0pUOGxICeHptMBvXur7hBfe0299803qoj48UdtYyOyRYmJqjeV06eB6tVVs7Dy5bWOiojM5b33gLffzn0+bRqLcXNgQU4Edfbuiy+AbdtUDyzx8UCvXkDPnupsHxHd382bwDPPqCGzfXyAP/4AqlTROioiMgcRdf/VlCnq9YwZwKRJmoZkV1iQE+XRpg0QFaUuwZUoAfz8sxrpc+lSdpFIdC9ZWaq7s+3bAb1eNQerVUvrqIjIHERUIT51qno9cyYwYYK2MdkbFuREt3FzU5fg9u4FmjVT7cn791cDHfBsOdGdRNTNXWvWqPxZtw4ICNA6KiIyBxFg8mS1XwTUzZzjxmkbkz1iQU50FwEBwO7d6rKci4sqMho0AFau1DoyIuvy4YfAggWqHemyZcBjj2kdERGZQ04xPn26ej1rFhASom1M9ooFOdE9lCihLsvt26e6bvv3XzUaWd++6uY1Ike3YkXupes5c4Bnn9U0HCIyo3ffzS3GZ88GRo/WNBy7xoKcqAAaNVJnyydPVoMefP+96rd8yxatIyPSzp9/quZcgNpRv/GGtvEQkflMnap6UQHUmfFRozQNx+6xICcqIBcX9QO1Y4e6We3CBaB9e9X9U0aG1tERFa+zZ9V9FenpQPfuwEcfaR0REZnL+++rs+MA8PHHPDNeHEoUZKFPP/3U5BUPGDAApTksG9mhVq1UTyyjR6s+y0ND1Zny5ctVv8u2gnlNhZWcDHTpAvzzj7rx+bvvOFy2NWBOkznMnKl6GgPU/SFjxmgbj8OQAtDpdFKtWjWpXr16gSZnZ2c5c+ZMQVZtE5KSkgSAJCUlaR0KWZmVK0W8vEQA9bh6dfF8rjm2yeLI69DQUAEgI0eONLx38+ZNGTp0qJQtW1ZKlSolPXr0kLi4OKN/d/78eenUqZO4u7tLhQoVZOzYsZKRkWG0zJYtW6Rp06bi6uoqtWrVkkWLFpkUG/O6cDIzRTp3Vtt85coif/+tdUT2gTnNnLYGs2er3AZEpk/XOhrbZ8o2WeCCPD4+vsABeHp6MsnJYcTEiLRqlfsjNmaMSHq6ZT/TXDtvS+b1nj17pHr16tK4cWOjvB4yZIhUq1ZNNm3aJHv37pVWrVrJI488YpifmZkpDRs2lKCgIDlw4IBs3LhRypcvLxMmTDAsc/bsWfHw8JCQkBA5duyYzJs3T5ydnSUsLKzA8TGvC2fCBLWdlywpsmeP1tHYD+Y0c1pr8+fn7scmT9Y6Gvtg9oL83XffldTU1AIHMGPGDLl27VqBl2eSk61LSxMZPTr3x6x1a5Hbjh3NyhzbpCXz+vr161KnTh0JDw+Xxx57zJDXiYmJ4uLiIqtWrTIsGx0dLQAkMjJSREQ2btwoTk5ORgffCxYsEL1eL2lpaSIiMn78eGnQoIHRZ/bu3VuCg4ML/Pcwr023alXuNv6//2kdjX1hTjOntbRwYW5uv/mmSHa21hHZB7MX5JbEJCd78tNPInq9+lGrUsVyZxCtfZt86aWXZNSoUSIiRnm9adMmAXBHEeDn5yezZs0SEZFJkyZJQECA0fyzZ88KANm/f7+IiLRp08bo4F1EZOHChaLX6+8a061btyQpKckwXbhwwaq/Q2tz5IhIqVJq2x47Vuto7A9zmjmtleXLRXQ6ldsjR7IYNydT8lrzXlaGDRuGzp07IygoyOj9ffv2ISMjw+j9+vXrw8/PD5GRkQCAyMhINGrUCD4+PoZlgoODkZycjKNHjxqWuX3dwcHBhnXkJy0tDcnJyUYTUUH06AHs2QPUrw9cvAi0aaMGSnEkK1aswP79+xEaGnrHvLi4OLi6usLb29vofR8fH8T9NwxqXFycUU7nzM+Zd69lkpOTcfPmzXzjCg0NhZeXl2GqVq1aof4+R3T9OtCzJ5CaqnoWyue/luwYc9p+rV0LvPiiOjf+2muqr3GdTuuoHJPJBfnVq1cxbNgw+Pv7o3z58ihbtqzRZAomOdmjevVUn+XPPAOkpQH9+gFTpqgfPGtlrry+cOECRo4cif/9738oWbKkBSM23YQJE5CUlGSYLly4oHVINkEEGDwYOHECqFpV9SZUokD9c5GWmNN0P+HhwHPPAVlZaj/1+ecsxrVk8s9qv379cPr0abzyyivw8fGBrpD/ezlJHh4ebpVJHpJnbNjk5GQW5WQSvR5YvRp45x3ggw/U4AqnTgGLFgFublpHdydz5fW+ffuQkJCAZs2aGd7LysrCtm3b8Nlnn+G3335Deno6EhMTjQ624+Pj4evrCwDw9fXFnj17jNYbHx9vmJfzmPNe3mX0ej3c3d3zjc3NzQ1u1vjlW7kvvgBWrlRF+A8/ABUqaB0RFQRzmu5lxw6ga1c1jkDPnsDChYCT5m0mHJvJBfn27duxY8cOBAQEFOmDmeRk75yc1KX92rWBIUPUmcUrV4Cffwasrdtfc+V1+/btcfjwYaP3BgwYgPr16+PNN99EtWrV4OLigk2bNqFnz54AgBMnTiA2NhaBgYEAgMDAQLz//vtISEhAxYoVAQDh4eHQ6/Xw9/c3LLNx40ajzwkPDzesg8wjp799QPVNzK/XdjCn6W727wc6dwZu3gSeekqNPM2rXlbA1AbqLVq0MNxUWRTJycly+PBho6lFixby4osvyuHDhw03df7444+Gf3P8+PF8b+rM283Tl19+KXq9Xm7duiUi6qbOhg0bGn12nz59eFMnFavff8+9Ia5FC5ErV4q2PnNvk+bK6/zkvQFMRPWe5OfnJ5s3b5a9e/dKYGCgBAYGGubn9J7UoUMHiYqKkrCwMKlQoUK+vSeNGzdOoqOjZf78+ew9ycxSUkTq1lXb7DPP8EYvS2NOM6eLQ3S0SPnyKq/btBExoVMeKgSL9rKyZ88eeeKJJyQiIkL++ecfozuci5oETHKyZ3v2iJQrp34IGzUSSUgo/LrMvU0WZ17njC9QpkwZ8fDwkO7du8vly5eN/s25c+ekY8eO4u7uLuXLl5cxY8bkO75AkyZNxNXVVWrWrMnxBcxs8ODc3oL++UfraOwfc5o5bWnnz4tUraryunlzEX5NlmfRgvzkyZPSokULcXJyMpp0Op04OTkVKuAcTHKyd9HRIpUqqR/Ehg1FTBjDw4i5t0lL5rW1Yl7f3Zo1ahvV6UQ2b9Y6GsfAnC465vTdxcfnXvGqX7/oV2mpYEzZJnUipvX98PDDD6NEiRIYOXJkvjeKPPbYY4VvP2OlkpOT4eXlhaSkJOj1eq3DIRt38iTw+OPApUtAkyZARATg5WXaOsy9TTKvmdc5EhKAhg3V/Q7jxqm242R5zOmiY07nLzlZ7XP27wf8/IA//1Q9JpHlmbJNmtyM/8iRIzhw4ADq1atX6ACJHFndusCWLaqP8qgooFs34NdfAS07G2JeE6C6OBwyRBXjDRsC06ZpHREVFnOaAODWLbWP2b9f9ZAUHs5i3FqZ3MlNixYt2NcnURHVrQuEhaneViIigIEDte2nnHlNALBqlequs0QJ4LvvrLOLTioY5jRlZQF9+6oTQKVLq31O3bpaR0V3Y/IZ8hEjRmDkyJEYN24cGjVqBBcXF6P5jRs3NltwRPasaVM1SlqHDqpLxGbNgLFjtYmFeU3//AMMH66ev/OOak5Ftos57dhEgGHDVDe7rq5qX5Onl2myQia3IXfKp+d4nU4HEYFOp0NWVpbZgrMWbJdGljR/viqEnJyATZuAdu3u/2/MvU0yr5nXL78MLFmimqrs26d24lR8mNNFx5zONXUq8O67auTNH34Ann1W64gck0XbkMfExBQ6MCK609ChwN69wOLFqig6dEiN9FmcmNeObfNmVYzrdMA337AYtwfMacf11VeqGAfUCR8W47bB5IK8TJkyd63yT58+XeSAiByNTgd8+imwdSsQEwOMGQN8/XXxxsC8dlzp6eqgEABefx1o2VLbeMg8mNOOaf16lccAMHFi7nOyfibf1Nm5c2fcunXrjvdPnDiBdgW51k5EdyhdWp2hBIBvv1W9rxQn5rXjmjMHOHECqFgReP99raMhc2FOO55du4DevYHsbNVRwHvvaR0RmcLkgtzT0xM9evRAZmam4b3o6Gi0a9cOPXv2NGtwRI6kTRvg+efVzTjjxhXvZzOvHVNcXG7Xhh9+CHh7axoOmRFz2rGcPg106QLcvAl06gR88YW6+kq2w+SC/Oeff0ZSUhL69u0LEcGRI0fQrl079OnTB3PnzrVEjEQOY8YMwMUF+OOP4j1Lzrx2TJMmASkpwEMPAS+9pHU0ZE7Macdx5Qrw1FOqp6TmzYGVK9V+hGyLyQW5u7s7NmzYgBMnTuC5555D+/bt8dJLL2HWrFmWiI/IodSoAXTvrp5/8UXxfS7z2vEcPQosXKiez56tevkh+8Gcdgw3bwLPPAOcOQNUrw788gvg6al1VFQYBfoJTk5ONpqcnJywcuVK7N69Gz179sSkSZMM84ioaF59VT2uWqXaAloK89qxvf222r569AAefVTraMgcmNOOJTtbXdnatQsoU0aN+Ozrq3VUVFgF6ofcyckJunwaI+X8U/ZtSmQ+GRmqLe+NG8Dhw6pf6NuZY5tkXjtuXu/eDbRqpc6KHz0K1K+vdUTEnC46R8vpt95S9364ugLh4UDbtlpHRLczez/kW7ZsMUtgRHR/Li6qWNq8GdizJ/+C3ByY144rp4/il15iMW5PmNOO45tvVDEOqJ65WIzbvgIV5I899pil4yCiPCpVUo/XrlnuM5jXjmnPHiAsDHB2Vv0Uk/1gTjuGzZtz+xefMgV48UVt4yHzKFAb8kOHDiHbhMasR48eNepqiYhM4+GhHlNSLPcZzGvHlHNW7cUXgVq1tI2FzIs5bf9OngR69gQyM4E+fVRBTvahQAV506ZNcfXq1QKvNDAwELGxsYUOisjR5aRP5cqW+wzmteM5dQpYvVo9Hz9e21jI/JjT9u3aNeDpp4HERCAwUPWSxL7G7UeBmqyICCZNmgSPnNN295Genl6koIgcWXY2cOiQeu7vb7nPYV47ns8+UwNPde5s2W2LtMGctl+ZmcBzz6mDaj8/dWBdsqTWUZE5Faggb9u2LU6cOFHglQYGBsLd3b3QQRE5ss2bgcuXgdKlgSZNLPc5zGvHcv06sGiRev7GG9rGQpbBnLZfY8aoAeNKlQLWrQN8fLSOiMytQAV5RESEhcMgIkCdvZwzRz1/6SX142spzGvHsnKlKsrr1gWCgrSOhiyBOW2fFi4EPv1UPf/uOyAgQNt4yDI4NhuRFVmxAtiwAShRAhg2TOtoyJ588416HDyYo3IS2Ypdu3J7VJk6NXckZ7I//FkmshKnTgFDh6rnkyYBDz6obTxkP06eVIMBOTsD/fppHQ0RFURcnOpRJT1dFeLsptS+sSAnsgKnTgHt2qm75x96CJgwQeuIyJ4sX64en3ySbU+JbEF6OtCrF3DpkroBe8kSXtmyd/zvJdJYVJQqxi9dAho0AH75RY3WSWQuP/2kHp9/Xts4iKhgxo0DduwAvLyANWvUTf5k31iQE2lEBJg3D2jZMrcY37wZqFhR68gKb8GCBWjcuDH0ej30ej0CAwPx66+/GubfunULw4YNQ7ly5eDp6YmePXsiPj7eaB2xsbHo3LkzPDw8ULFiRYwbN+6OwUsiIiLQrFkzuLm5oXbt2li8eHFx/Hk2KSYGOHxYNVfp0kXraMgWMa+L1/Llxjdx1qmjbTxUPEwuyJcsWYINGzYYXo8fPx7e3t545JFHcP78eZPWxSQnR/X330C3bqr7ufR0VSht3apdMW6uvK5atSo++OAD7Nu3D3v37sUTTzyBrl274ujRowCA0aNHY/369Vi1ahW2bt2KS5cuoUePHoZ/n5WVhc6dOyM9PR07d+7EkiVLsHjxYkyePNmwTExMDDp37ozHH38cUVFRGDVqFAYNGoTffvvNDN+E/cn5SX30UaBsWW1joeJjzn0187r4REerG68B1WacB9EORExUt25d2bRpk4iI7Ny5Uzw8POTLL7+ULl26SPfu3U1a17p162TDhg1y8uRJOXHihLz99tvi4uIiR44cERGRIUOGSLVq1WTTpk2yd+9eadWqlTzyyCOGf5+ZmSkNGzaUoKAgOXDggGzcuFHKly8vEyZMMCxz9uxZ8fDwkJCQEDl27JjMmzdPnJ2dJSwsrMBxJiUlCQBJSkoy6e8jut316yITJ4q4u4sAIq6uIp9+KpKdbdp6zL1NmjOvb1emTBn55ptvJDExUVxcXGTVqlWGedHR0QJAIiMjRURk48aN4uTkJHFxcYZlFixYIHq9XtLS0kREZPz48dKgQQOjz+jdu7cEBwebFJej5HWPHmpbmz5d60joXmwpp0WsM69tPadTUkT8/VW+tm8vkpmpdURUVKZskyYX5O7u7nL+/HkRUQnUr18/ERE5cuSIlC9f3tTV3YFJTvbo5k2RL74Q8fFRP7aASOvWIlFRhVufubdJS+R1ZmamLF++XFxdXeXo0aOyadMmASDXrl0zWs7Pz09mzZolIiKTJk2SgIAAo/lnz54VALJ//34REWnTpo2MHDnSaJmFCxeKXq83KT5HyOvsbJFy5dT2tnOn1tHQvdhCTotYd17bek73769ytVIlkTxlDdkwU7ZJk5useHp64urVqwCA33//HU8++SQAoGTJkrh582ahz9RnZWVhxYoVSE1NRWBgIPbt24eMjAwE5RnBon79+vDz80NkZCQAIDIyEo0aNYJPnm4DgoODkZycbLiUFhkZabSOnGVy1kFkSfHxwLvvAg88AAwZol7Xrq1ustu2zXoGeDBnXh8+fBienp5wc3PDkCFDsHr1avj7+yMuLg6urq7w9vY2Wt7HxwdxcXEAgLi4OKN8zpmfM+9eyyQnJ98z1rS0NCQnJxtN9u7sWeDqVcDVFWjeXOtoqDiZe19tjXltTzn93Xe5PaksX87ekBxRgUbqzOvJJ5/EoEGD0LRpU5w8eRKdOnUCABw9ehTVq1c3OYDDhw8jMDAQt27dgqenpyHJo6KiiiXJ8xs2OC0tDWlpaYbXtpzkVPxEgAMH1A2b33+v2ogDQNWqwNixapAHV1dtY7ydOfO6Xr16iIqKQlJSEn788Uf0798fW7dutUDUpgkNDcXUqVO1DqNY/fWXemza1Pq2ObIsc++rrTGv7SWnT5/OHfxnyhTgsce0jYe0YfIZ8vnz5yMwMBBXrlzBTz/9hHLlygEA9u3bhz59+pgcQE6S7969G6+//jr69++PY8eOmbwecwoNDYWXl5dhqlatmqbxkG04exZ4/32gYUN1NnLxYlWMt2ypRuA8exYYOdI6CyNz5rWrqytq166N5s2bIzQ0FAEBAZg7dy58fX2Rnp6OxMREo+Xj4+Ph6+sLAPD19b3jxu2c1/dbRq/X53uAnWPChAlISkoyTBcuXDDp77JF/10oROPG2sZBxc/c+2przGt7yOn0dKBPHyA1VRXi77yjdUSkFZPPkHt7e+Ozzz674/3CHqXmJDkANG/eHH/99Rfmzp2L3r17G5I871ny25N8z549RuszV5KHhIQYXicnJ7Mop3zFxwOrVqkz4XlbQbm6qpHVRo0CWrXSLLwCM3de55WdnY20tDQ0b94cLi4u2LRpE3r27AkAOHHiBGJjYxEYGAgACAwMxPvvv4+EhARU/K/LmfDwcOj1evj7+xuW2bhxo9FnhIeHG9ZxN25ubnBzcyvy32NLjh9Xjxz11fFYMqcB68hre8jpqVOBvXuBMmWAZctU96TkmEwuyLdt23bP+W3bti10MACTnKxbVpb68fz1VzX99ZdqogKotn9PPAG88IIqxm9rbWXVzJXXEyZMQMeOHeHn54fr16/j+++/R0REBH777Td4eXnhlVdeQUhICMqWLQu9Xo8RI0YgMDAQrf47aunQoQP8/f3Rr18/zJw5E3FxcZg4cSKGDRtmyMkhQ4bgs88+w/jx4zFw4EBs3rwZP/zwg1EXb6TExqrHGjW0jYOKnzn31cxry9i5E/jgA/X8669Vs0ZyYKbeMarT6e6YnJycDJMp3nrrLdm6davExMTIoUOH5K233hKdTie///67iKhuD/38/GTz5s2yd+9eCQwMlMDAQMO/z+n2sEOHDhIVFSVhYWFSoUKFfLs9HDdunERHR8v8+fPZ7SGZJCFB5LvvRF54IbfHirzTQw+JzJkjculS8cVk7m3SXHk9cOBAeeCBB8TV1VUqVKgg7du3N+SziMjNmzdl6NChUqZMGfHw8JDu3bvL5cuXjdZx7tw56dixo7i7u0v58uVlzJgxkpGRYbTMli1bpEmTJuLq6io1a9aURYsWmfw3O0JeV62qttE9e7SOhO7HWnNaxHby2pZyOiVFpHZtlZ8vvaR1NGQpFu32MDEx0Wi6cuWK/P7779KyZUv5448/TFoXk5ys0YULIsuXiwwbJhIQIKLTGRfgXl4izz4r8u23IhcvahOjubdJc+a1rXCEvC5VSm2zp09rHQndD3O66Gwpp0eOVLlZtapIYqLW0ZClmLJN6kRyLrgXzdatWxESEoJ9+/aZY3VWJTk5GV5eXkhKSoJer9c6HDKj7Gzg2DFgx47cKb9B7AICgI4d1RQYCLi4FH+seRXXNsm8tl3Z2bntUePjtRsFlgqGOV10tpLTO3YAbduqUzxhYUBwsNYRkaWYsk2a3Ib8bnx8fHDixAlzrY7I7ERUTyf796vpwAFg927gts4B4OSkuolr3VoNN966NVCpkiYha455bbuys3OflzDbLz3ZOua0ttLTgVdfVfujAQNYjFMuk3+mDx06ZPRaRHD58mV88MEHaNKkibniIiqSzEzVw8SBA7nF94EDQH5dynt4qLPerVurqWVLoHTp4o9ZS8xr+5O3t4aMDO3iIG0wp63TRx8B0dHqitXHH2sdDVkTkwvyJk2aQKfT4faWLq1atcLChQvNFhhRQYgAFy6oH7hjx9TjoUPAwYPArVt3Lu/mBjRqBDRrps6Ct2ihmqNo3QRFa8xr+6PTAaVKqf6Nr1/nyH+Ohjltfc6fB6ZPV89nzwbKltU2HrIuJhfkMTExRq+dnJxQoUIFlCxZ0mxBEd0uIwM4c0YV3Hmn48dVwZEfT0+gSZPc4rtZM9Ufs6MX3/lhXtunihWBmBggIQH4b7gHchDMaeszZow6UdSunRoMiCgvkwvyBx54wBJxECE7G7h0SbXzPntWDSd8/LgqvE+duvtl9xIlgDp1AH9/VXA3bKgK8Nq1VXtwuj/mtX2qVEkV5LGxwCOPaB0NFSfmtHXZvh346SfVlGzePHUFiyivAhXkn376KV599VWULFkSn3766T2XfeONN8wSGNmnGzdyC+6c6cwZ9RgTA6Sl3f3feniogvv2qVYtnvUuDOa1/XvwQTX4SHS01pFQcWBOWycRYOxY9XzwYHXSiOh2Ber2sEaNGti7dy/KlSuHGvcY8k2n0+Hs2bNmDdAa2EpXStbg5k3g4kXVrvvChTuL7ri4e/97Z2fggQdUkV2zJlCvXm7hXa0az3jnMMc2yby2/7yePRsICQG6dAHWrdM6GroX5nTRWWtOr1mjRm8uVUpd+fX11ToiKi5m7/Ywb1u029ulkeNISzMutv/++87n//xz//V4e+cW3DlTzutq1dhFW3FhXtu/1q3V4/btQFaWcc8rZH+Y09ZHBJg6VT0fOZLFON0dSx8CAKSkqLPXly/fveBOSCjYujw8VGFdrRpQo4ZxwV2zJlCmjGX/FiJSmjZVXXgmJgJ796ouPYmo+Pz6KxAVpfIwJETraMiaFaggDzFhK5o1a1ahgyHzSk9XI/TFxeU+3m26W08ltytZMrfYrlYNqFrV+LFaNXUGnDesWD/mtf0rUQLo3BlYsQL44QcW5PaOOW19cr7mV18FypXTNhaybgUqyA8cOGD0ev/+/cjMzES9evUAACdPnoSzszOaN29u/gjJSFYW8O+/9y6uc6Z//zVt3Z6e6nJa3uL69oK7bFkW2/aCee0YevdWBfny5cAHH/AGaHvGnLYu0dHApk3q3qcRI7SOhqxdgQryLVu2GJ7PmjULpUuXxpIlS1Dmv7YH165dw4ABA9CmTRvLRGmnRICkJNXuuqDTv/+qf1dQJUqoIvt+k4+PKsjJcTCvHUPHjirHL18GVq0CXnhB64jIUpjT1mXxYvX49NOqswKieylQLyt5ValSBb///jsaNGhg9P6RI0fQoUMHXLp0yawBWoOC3CUropp9mFJcX72qhngvjAoV7l1c5zwvU4Y9k9gjc/cmwLy2nh4ZLGHaNGDyZOCxx4CICK2jofwwp4vO2nLaz0/dg7V6NdCtm9bRkBbM3svK7Su/cuXKHe9fuXIF169fN3V1Ni8jAwgOBiIj8x+qvSA8PYHy5Qs2Vaigmo2wJxIyJ+a1fXvsMfV4v25HyX4wp7WX8/U3baptHGQbTC7runfvjgEDBuCTTz7Bww8/DADYvXs3xo0bhx49epg9QGt3+jSQ5yohXF1V0ZxfIZ1fgV2unLpRkkhLzGv7dvCg1hFQcWNOW49Bg4CuXYFOnVRPY0T5EhOlpqbK66+/Lm5ubuLk5CROTk7i6uoqr7/+uqSkpJi6OpuQlJQkACQpKemOeceOiQAiZcqIXL8ukp2tQYDkcO61TRYG89o+ZWSIjBmjfqMAkTfe0DoiuhvmdNFZW0536ZKbezlT/foiISEif/whkpamdYRkaaZskya3Ic+RmpqKM2fOAABq1aqFUqVKmekQwfrcqw1QdDTg76/OdBdkUBwic7BUW0nmtf24ehV47jlg82b1+s03gfff5+BA1oo5XXTWltMiwNGjwMaNatqxQ/WUlqN0adUL0uDBwEMPsQcze2TKNlnogtyRsCAna2NtOx5bZM/f4eHD6hJ5TIwarnvJEqBnT62jonux5+2xuFj7d5iYCPzxR26BHh+fO69xY1WYv/iiGsuD7INFb+oEgL179+KHH35AbGws0tPTjeb9/PPPhVklEWmMeW0f1q4F+vZVvT7VrKleN2yodVSkBea0dfH2Bp59Vk3Z2eqM+ddfq+5IDx1SfZWPGwcMGABMmaJ6TCPHYXKHeCtWrMAjjzyC6OhorF69GhkZGTh69Cg2b94MLy8vS8RIRBbGvLZ9IsDMmUD37qoYf+IJYM8eFuOOijlt3ZycgLZtge++Ay5dAubOVbl66xawYAFQuzYwfTpw44bWkVJxMbkgnzFjBmbPno3169fD1dUVc+fOxfHjx/Hcc8/Bz8/PEjESkYUxr21bejowcKBqJy4CvP46EBbGobodGXPadpQtC7zxhjpLvmWLak+ekgJMmgTUqQOsWaN1hFQcTC7Iz5w5g86dOwMAXF1dkZqaCp1Oh9GjR+Orr74ye4BEZHnMa9uVmAg89ZQaFdDJCZg3D/j8c8DFRevISEvMaduj0wHt2gG7dgHLlwPVq6uz5927q4G9srO1jpAsyeSCvEyZMoZBBapUqYIjR44AABITE3GD11aIbBLz2jadPw88+qg6q+bpCWzYAAwfrnVUZA2Y07bLyQl4/nng+HFg1Cj13rRp6kbt1FRNQyMLMrkgb9u2LcLDwwEAvXr1wsiRIzF48GD06dMH7du3N3uARGR55srr0NBQPPTQQyhdujQqVqyIbt264cSJE0bL3Lp1C8OGDUO5cuXg6emJnj17Ij5vdwMAYmNj0blzZ3h4eKBixYoYN24cMjMzjZaJiIhAs2bN4Obmhtq1a2Px4sWF++NtVFQUEBgIHDsGVKmibhB76imtoyJrYc59NfNaG25uwOzZwNKl6vkvv6hBhtg3np0ytZPzq1evysWLF0VEJCsrS0JDQ6VLly4SEhIi//77r6mrswkFGRioXDkNAiOHZe4BMMyV18HBwbJo0SI5cuSIREVFSadOncTPz89oIJIhQ4ZItWrVZNOmTbJ3715p1aqVPPLII4b5mZmZ0rBhQwkKCpIDBw7Ixo0bpXz58jJhwgTDMmfPnhUPDw8JCQmRY8eOybx588TZ2VnCwsIKHKu1DSJiij/+ECldWv32NGwocuGC1hFRUVlrTovYTl7bck7fT0SESIkSKuc//ljraKigTNkmTS7I7+XGjRsmLT9jxgxp0aKFeHp6SoUKFaRr165y/Phxo2Vu3rwpQ4cOlbJly0qpUqWkR48eEhcXZ7TM+fPnpVOnTuLu7i4VKlSQsWPHSkZGhtEyW7ZskaZNm4qrq6vUqlVLFi1aVOA4WZCTtSnOHY+peZ1XQkKCAJCtW7eKiEhiYqK4uLjIqlWrDMtER0cLAImMjBQRkY0bN4qTk5NRni9YsED0er2k/Te03fjx46VBgwZGn9W7d28JDg4ucGy2uvNetUrExUX97rRrJ3LtmtYRkTnYSk6LWG9e22pOF9S8eSrvS5TgQbitMGWbNLnJSn7S0tIwa9Ys1KhRw6R/t3XrVgwbNgy7du1CeHg4MjIy0KFDB6TmaSQ1evRorF+/HqtWrcLWrVtx6dIl9OjRwzA/KysLnTt3Rnp6Onbu3IklS5Zg8eLFmDx5smGZmJgYdO7cGY8//jiioqIwatQoDBo0CL/99lvR/3giO1XYvM4rKSkJAFC2bFkAwL59+5CRkYGgoCDDMvXr14efnx8iIyMBAJGRkWjUqBF88nTCGxwcjOTkZBw9etSwTN515CyTsw579eWXavTNjAzVl3FYGAcRoYIzR04DzGutDBumukrMzAQ++0zraMjcClyQp6WlYcKECWjRogUeeeQRrPmvH55FixahRo0amD17NkaPHm3Sh4eFheHll19GgwYNEBAQgMWLFyM2Nhb79u0DoJL+22+/xaxZs/DEE0+gefPmWLRoEXbu3Ildu3YBAH7//XccO3YMy5YtQ5MmTdCxY0dMmzYN8+fPNwyE8MUXX6BGjRr45JNP8OCDD2L48OF49tlnMXv2bJPiJbI3lsjrHNnZ2Rg1ahQeffRRNPyvM+y4uDi4urrC+7Yq0sfHB3FxcYZlfG4bESPn9f2WSU5Oxs2bN+/6tyYnJxtNtmTmTGDIENV+9LXXgBUrVLtSorwsmdOAdeW1ree0qXQ6ICREPf/mG7YltzcFLsgnT56MBQsWoHr16jh37hx69eqFV199FbNnz8asWbNw7tw5vPnmm0UKxlqOuh0tyclxWTKvhw0bhiNHjmDFihVmjrpwQkND4eXlZZiqVaumdUgFIqL6I875b5gwQQ0c4uysbVxknSy9r7amvLbVnC6KTp2AEiWAq1eBCxe0jobMqcAF+apVq7B06VL8+OOP+P3335GVlYXMzEwcPHgQzz//PJyLuHewpqNuR0xyckyWyuvhw4fjl19+wZYtW1C1alXD+76+vkhPT0diYqLR8vHx8fD19TUsc3vvDDmv77eMXq+Hu7t7vjFNmDABSUlJhumCDezNRNRQ2tOnq9ehocCMGepMGVF+LLmvtra8tsWcLioXF6BuXfU8OlrbWMi8ClyQ//3332jevDkAoGHDhnBzc8Po0aOhM9OewZqOuh0xyckxmTuvRQTDhw/H6tWrsXnz5jvaqjZv3hwuLi7YtGmT4b0TJ04gNjYWgYGBAIDAwEAcPnwYCQkJhmXCw8Oh1+vh7+9vWCbvOnKWyVlHftzc3KDX640mayai+iD+5BP1et484K23NA2JbIAl9tXWmte2ltPmknNskpWlbRxkXiUKumBWVhZcXV1z/2GJEvD09DRLEDlH3du2bbvrUXfes+S3H3Xv2bPHaH1FPep2c3ODGxtnkgMwd14PGzYM33//PdauXYvSpUsbrlJ5eXnB3d0dXl5eeOWVVxASEoKyZctCr9djxIgRCAwMRKtWrQAAHTp0gL+/P/r164eZM2ciLi4OEydOxLBhwwx5OWTIEHz22WcYP348Bg4ciM2bN+OHH37Ahg0bivBtWA8RYMQIYP58dTb8yy+BwYO1jopsgSX21cxr6/Jf6144yPGH4yho1y06nU46deok3bt3l+7du0uJEiWkQ4cOhtc5kymys7Nl2LBhUrlyZTl58uQd83O6Uvrxxx8N7x0/fjzfrpTi4+MNy3z55Zei1+vl1q1bIqK6UmrYsKHRuvv06WOWrpTY7SFpwVzde5k7rwHkO+XtZjSnK9MyZcqIh4eHdO/eXS5fvmy0nnPnzknHjh3F3d1dypcvL2PGjMm3K9MmTZqIq6ur1KxZ06SuTEWst4u07GyRESPU74pOJ7JwodYRUXGw1pwWsZ28ttacNqeUFBFnZ/X7cP681tHQ/ZiyTepECnaf7oABAwpU4C9atKjABwNDhw41HHXXq1fP8H7OUTcAvP7669i4cSMWL15sOOoGgJ07dwJQZwOaNGmCypUrG466+/Xrh0GDBmHGjBkAVLeHDRs2xLBhwwxH3W+88QY2bNiA4ODg+8aZnJwMLy8vJCUl3XFJLDoa8PcHypUD/vmnwH86UZHca5s0hSXy2laY6zs0JxFg7Fhg1iz1euFCoID/RWTjmNNFZ405bW5btgBPPAFUrcqbOm2BSdukxQ8P7gF2cNTNM+SkBUc4E2Rp1vgdTpqkfk8Aka++0joaKk7WuD3aGkf4DocPV78PL76odSRUEBY5Q+7IeIacrI0jnAmyNGv7Dj/6CBg/Xj2fNw8YPlzbeKh4Wdv2aIvs/TtMTwcqV1ZdHoaFAQW4wE8aM2WbNMtInUREVHjffJNbjIeGshgnojstW6aK8cqVgfbttY6GzI0FORGRhlavViNvAmrwH3ZtSES3y8oCPvxQPR89Wg0ORPaFBTkRkUZ27AD69AGys4FXXlFnx4mIbrdkCXDyJFCmTO4BPNkXFuRERBqIjgaeeQZIS1OPX3zBETiJ6E4pKcA776jnEycCpUtrGw9ZBgtyIqJilpAAdOoEXLsGtGoFLF/OS9BElL+pU4G4OKBmTWDYMK2jIUthQU5EVIxu3gS6dgXOnQNq1QLWrwc8PLSOioisUVQUMHu2ev7ppwAHEbdfLMiJiIqJiGorvmuXagu6YQNQvrzWURGRNUpPBwYOVDd09uoFdO6sdURkSSzIiYiKyQcf5DZP+eknIM8AxURERqZPBw4cAMqWBebO1ToasjQW5ERExWDDhtwbs+bNAx5/XNt4iMh6RUYCM2ao5wsWAJUqaRsPWR4LciIiCzt9GujbVzVZGTJETURE+UlMVN2hZmUBL7wAPPec1hFRcWBBTkRkQTduAD16AElJwCOP8NIzEd2dCPDqq8D580CNGursODkGFuRERBY0YgRw+DDg4wOsWgW4umodERFZq88+U78TJUqo+030eq0jouLCgpyIyEKWLgUWLgScnNTOtXJlrSMiImu1axcwZox6/vHHQMuW2sZDxYsFORGRBZw8CQwdqp6/+y5v4iSiu4uPB559FsjIAHr2BN54Q+uIqLixICciMrP0dHUzVmoq0K4d8PbbWkdERNYqIwPo3Ru4eBGoXx9YtAjQ6bSOioobC3IiIjObOhXYt0/1H7xsGeDsrHVERGStQkKArVuB0qWB1avVIzkeFuRERGa0c6caAAgAvvoKqFJF23iIyHotXKhu5ATUwXv9+trGQ9phQU5EZCY3bwIDBgDZ2UC/fqotKBFRfnbsyB2TYOpU4JlntI2HtMWCnIjITKZMUTdzVq7M/saJ6O7On1fjE2RkqJs5J07UOiLSGgtyIiIz2L8f+OQT9fzLL4EyZbSNh4is0/XrwNNPA1euAE2bAosXq65RybFxEyAiKqLMTGDwYNVU5fnn1c6WiOh2WVlAnz7AkSOAry+wdi1QqpTWUZE1YEFORFRECxaoM+Te3sCcOVpHQ0TWauxYYMMGoGRJYN06oFo1rSMia8GCnIioCOLjgUmT1PMZMwAfH23jISLr9PnnuQfsS5YADz2kaThkZViQE5FZbdu2DV26dEHlypWh0+mwZs0ao/kigsmTJ6NSpUpwd3dHUFAQTp06ZbTMv//+i759+0Kv18Pb2xuvvPIKUlJSjJY5dOgQ2rRpg5IlS6JatWqYOXOmpf+0fE2cCCQlAc2aAa++qkkIRBblaDltCb/+CowYoZ6//z7w3HPaxkPWR9OCnElOZH9SU1MREBCA+fPn5zt/5syZ+PTTT/HFF19g9+7dKFWqFIKDg3Hr1i3DMn379sXRo0cRHh6OX375Bdu2bcOreard5ORkdOjQAQ888AD27duHjz76CO+++y6++uori/99eR08CHz7rXo+bx4HACL75Eg5bQmHDqkCPDsb6N8fmDBB64jIKomGNm7cKO+88478/PPPAkBWr15tNP+DDz4QLy8vWbNmjRw8eFCeeeYZqVGjhty8edOwzFNPPSUBAQGya9cu2b59u9SuXVv69OljmJ+UlCQ+Pj7St29fOXLkiCxfvlzc3d3lyy+/LHCcSUlJAkCSkpLumHfsmAggUq6c6X8/UWHda5u0JrfndXZ2tvj6+spHH31keC8xMVHc3Nxk+fLlIiJy7NgxASB//fWXYZlff/1VdDqdXLx4UUREPv/8cylTpoykpaUZlnnzzTelXr16BY7NHN/hk0+q/O/du9CrIBIR5rS15LS5XbwoUrWq+p14/HGRPH8eOQBTtklNC/K8bDXJWZCTFqxxx5Of2/P6zJkzAkAOHDhgtFzbtm3ljTfeEBGRb7/9Vry9vY3mZ2RkiLOzs/z8888iItKvXz/p2rWr0TKbN28WAPLvv//mG8utW7ckKSnJMF24cKFI32F4uMp9FxeRs2cLtQoiA+a09jltbsnJIk2aqN+J+vVF7vJnkB0zJa+ttg15TEwM4uLiEBQUZHjPy8sLLVu2RGRkJAAgMjIS3t7eaNGihWGZoKAgODk5Yffu3YZl2rZtC1dXV8MywcHBOHHiBK5du1ZMfw0RAUBcXBwAwOe2Ox99fHwM8+Li4lCxYkWj+SVKlEDZsmWNlslvHXk/43ahoaHw8vIyTNWK0L2BCPD22+r5668DNWoUelVENs1ectrcMjOB3r2BqCigYkVg40aOTUD3ZrUFuZZJnpaWhuTkZKOJiGzbhAkTkJSUZJguXLhQ6HVt3Aj89Rfg4QG8844ZgySiAjNnTpuTCDBsmLqR090dWL+eB+10f1ZbkGvJmo+6iWyZr68vACA+Pt7o/fj4eMM8X19fJCQkGM3PzMzEv//+a7RMfuvI+xm3c3Nzg16vN5oKQwSYOlU9Hz5cnf0iclT2kNPm9uGHwFdfATodsHw58PDDWkdEtsBqC3Itk9xaj7qJbF2NGjXg6+uLTZs2Gd5LTk7G7t27ERgYCAAIDAxEYmIi9u3bZ1hm8+bNyM7ORsuWLQ3LbNu2DRkZGYZlwsPDUa9ePZSx8HXhzZvV2XF3d2DMGIt+FJHVs4ecNqfvv8/tRWXuXKBrV23jIdthtQW5lklurUfdRLYgJSUFUVFRiIqKAqDuB4mKikJsbCx0Oh1GjRqF6dOnY926dTh8+DBeeuklVK5cGd26dQMAPPjgg3jqqacwePBg7NmzB3/++SeGDx+O559/HpUrVwYAvPDCC3B1dcUrr7yCo0ePYuXKlZg7dy5CQkIs/vd98IF6HDSIZ8fJMdh7TptLRATw8svq+Zgxuf2OExVIMdxkelfXr1+XAwcOyIEDBwSAzJo1Sw4cOCDnz58XEdXtobe3t6xdu1YOHTokXbt2zbfbw6ZNm8ru3btlx44dUqdOHaNuDxMTE8XHx0f69esnR44ckRUrVoiHhwe7PSSbZs09MmzZskUA3DH1799fRFQPSpMmTRIfHx9xc3OT9u3by4kTJ4zWcfXqVenTp494enqKXq+XAQMGyPXr142WOXjwoLRu3Vrc3NykSpUq8sEHH5gUZ2G+w4MHVb47O4ucO2fSxxHdE3Nam5w2l8OHRby81O/Ds8+KZGUVewhkhWym20N7SHIW5KQFa95524rCfIevvKLy/bnnLBgYOSTmdNFp9R3+/XduX+OtW4vkOWdIDs6UbVInIlI85+JtV3JyMry8vJCUlHRH85XoaMDfHyhXDvjnH40CJIdzr22SCsbU7/DaNaByZeDWLWDHDuDRR4shSHIYzOmi0+I7TEoC2rZVo3HWqwfs3AmULVssH002wJRt0mrbkBMRWZPvvlPFeOPGwCOPaB0NEWktPR3o2VMV476+QFgYi3EqPBbkRET3IQJ88416/tprqjszInJcIsArrwCbNgGenmpsgurVtY6KbBkLciKi+4iKAg4fBtzcgD59tI6GiLT29tvAsmVAiRLAjz8CTZtqHRHZOhbkRET38d136vGZZzj8NZGjmz8/t/vTr78GgoO1jYfsAwtyIqJ7yM4GfvhBPX/hBW1jISJtrV6d27/4tGm5/Y4TFRULciKie9i1C7h4EdDrgaee0joaItLKn3+qg3IR4NVXgXfe0ToisicsyImI7mHdOvXYqRNQsqS2sRCRNo4fB7p0UT0tdemimq3w5m4yJxbkRET3kFOQd+2qbRxEpI3Ll9XVsWvXgJYtgRUr1M2cRObEgpyI6C5iY9XgX05OvHGLyBElJwMdOwLnzwN16gC//AJ4eGgdFdkjFuRERHcRHq4eW7Zk7ypEjiZn4J+DB4GKFdXAP+XLax0V2SsW5EREdxERoR7bt9c0DCIqZtnZwMCBwB9/AKVKARs2ADVrah0V2TMW5EREd7Ftm3p87DFt4yCi4vXWW8D//pc78E+LFlpHRPaOBTkRUT4uXlRtyJ2cgFattI6GiIrL3LnARx+p5998w+5OqXiwICciysdff6nHBg0AT09tYyGi4rFqFTB6tHo+YwbQv7+28ZDjYEFORJSPAwfUIy9VEzmGrVuBF19UA/8MG6aarRAVFxbkRET5OHRIPTZurG0cRGR5R46osQbS04EePVSzFQ78Q8WJBTkRUT6OHVOPDRtqGwcRWdaFC6qdeFIS0Lo1sGwZ4OysdVTkaFiQExHdJjMTOHtWPa9bV9tYiMhyrl1TA/9cvAj4+6uRed3dtY6KHBELciKi21y4oIpyNzegalWtoyEiS7h1SzVTOXoUqFJFDfzDAcBIKyzIiYhuExurHqtVU90eEpF9ycoC+vYFtm8HvLyAX39V+U6kFe5qiIhu8/ff6pE7aCL7IwK88Qbw88+AqyuwZg3QqJHWUZGjY0FORHSbK1fUo4+PtnEQkfl98AHw+eeqF5Vly4B27bSOiIgFORHRHf75Rz2WL69tHERkXkuWAG+/rZ7PmQP06qVpOEQGLMiJyGbNnz8f1atXR8mSJdGyZUvs2bPHLOtNTlaPXl5mWR0RmcBSeR0WBrzyino+frxqtkJkLViQE5FNWrlyJUJCQjBlyhTs378fAQEBCA4ORkJCQpHXff26eixdusirIiITWCqv9+0Dnn1W3cz54otAaKiZAiYyE4cqyC1x1F2ihOoWrXJlMwRIRAU2a9YsDB48GAMGDIC/vz+++OILeHh4YOHChUVet16vcrpcOTMESkQFZqm8rlJFjSnQoQPw7bfsPYmsj8NskpY66q5TR/VZnDPMNhFZXnp6Ovbt24egoCDDe05OTggKCkJkZGS+/yYtLQ3JyclG093MnasGChk0yOyhE9FdmJrXpuS0ry8QEQH8+KPqWYXI2jhMQW7Js2lEVLz++ecfZGVlwee2blB8fHwQFxeX778JDQ2Fl5eXYarGPg2JrIqpeW1qTuv1bIZG1sshCnJLHnUTkW2YMGECkpKSDNOFCxe0DomIioA5TfakhNYBFId7HXUfP378juVDQ0MxderU4gqPiExUvnx5ODs7Iz4+3uj9+Ph4+Pr65vtv3Nzc4ObmVhzhEVEhmJrXzGmyJw5xhtxUPOomsm6urq5o3rw5Nm3aZHgvOzsbmzZtQmBgoIaREVFhMa/JkTnEGXIedRPZn5CQEPTv3x8tWrTAww8/jDlz5iA1NRUDBgzQOjQiKiTmNTkqhyjI8x51d+vWDUDuUffw4cO1DY6ICqV37964cuUKJk+ejLi4ODRp0gRhYWF3NE0jItvBvCZHpRMR0TqI4rBy5Ur0798fX375peGo+4cffsDx48fvm+jJycnw8vJCUlIS9Hp9MUVMdHfcJouO3yFZE26PRcfvkKyNKdukQ5whB4p21J1zzMLeVsha5GyLDnI8bRHMa7ImzOmiY06TtTElrx3mDHlR/P333+yzmKzShQsXULVqVa3DsEnMa7JGzOnCY06TtSpIXrMgL4Ds7GxcunQJpUuXhk6nu2N+cnIyqlWrhgsXLvAymZnwO703EcH169dRuXJlOHEM6EK5V15z+7Md9vJ/xZwuurvltL1sI7aA37UxU/LaYZqsFIWTk1OBzljo9XpugGbG7/TuvLy8tA7BphUkr7n92Q57+L9iThfN/XLaHrYRW8HvOldB85qH4UREREREGmJBTkRERESkIRbkZuDm5oYpU6ZwMCEz4ndKWuL2Zzv4f0X3w22k+PC7Ljze1ElEREREpCGeISciIiIi0hALciIiIiIiDbEgJyIiIiLSEAtyIiIiIiINsSAvovnz56N69eooWbIkWrZsiT179mgdktV69913odPpjKb69esb5t+6dQvDhg1DuXLl4OnpiZ49eyI+Pt5oHbGxsejcuTM8PDxQsWJFjBs3DpmZmcX9p5CdY15bv23btqFLly6oXLkydDod1qxZo3VIZKWYz5Z3v/073R8L8iJYuXIlQkJCMGXKFOzfvx8BAQEIDg5GQkKC1qFZrQYNGuDy5cuGaceOHYZ5o0ePxvr167Fq1Sps3boVly5dQo8ePQzzs7Ky0LlzZ6Snp2Pnzp1YsmQJFi9ejMmTJ2vxp5CdYl7bhtTUVAQEBGD+/Plah0JWjPlcfO61f6cCECq0hx9+WIYNG2Z4nZWVJZUrV5bQ0FANo7JeU6ZMkYCAgHznJSYmiouLi6xatcrwXnR0tACQyMhIERHZuHGjODk5SVxcnGGZBQsWiF6vl7S0NIvGTo6DeW17AMjq1au1DoOsEPO5eNxr/04FwzPkhZSeno59+/YhKCjI8J6TkxOCgoIQGRmpYWTW7dSpU6hcuTJq1qyJvn37IjY2FgCwb98+ZGRkGH2f9evXh5+fn+H7jIyMRKNGjeDj42NYJjg4GMnJyTh69Gjx/iFkl5jXRPaD+Vy87rZ/p4JhQV5I//zzD7KysoyKQwDw8fFBXFycRlFZt5YtW2Lx4sUICwvDggULEBMTgzZt2uD69euIi4uDq6srvL29jf5N3u8zLi4u3+87Zx5RUTGviewH87n43Gv/TgVTQusAyHF07NjR8Lxx48Zo2bIlHnjgAfzwww9wd3fXMDIiIiIqrHvt31955RUNI7MdPENeSOXLl4ezs/MdvYDEx8fD19dXo6hsi7e3N+rWrYvTp0/D19cX6enpSExMNFom7/fp6+ub7/edM4+oqJjXRPaD+aydvPt3KhgW5IXk6uqK5s2bY9OmTYb3srOzsWnTJgQGBmoYme1ISUnBmTNnUKlSJTRv3hwuLi5G3+eJEycQGxtr+D4DAwNx+PBho7vjw8PDodfr4e/vX+zxk/1hXhPZD+azdvLu36lg2GSlCEJCQtC/f3+0aNECDz/8MObMmYPU1FQMGDBA69Cs0tixY9GlSxc88MADuHTpEqZMmQJnZ2f06dMHXl5eeOWVVxASEoKyZctCr9djxIgRCAwMRKtWrQAAHTp0gL+/P/r164eZM2ciLi4OEydOxLBhw+Dm5qbxX0f2gnltG1JSUozOvsXExCAqKgply5aFn5+fhpGRNWE+F4977d+pgLTu5sXWzZs3T/z8/MTV1VUefvhh2bVrl9YhWa3evXtLpUqVxNXVVapUqSK9e/eW06dPG+bfvHlThg4dKmXKlBEPDw/p3r27XL582Wgd586dk44dO4q7u7uUL19exowZIxkZGcX9p5CdY15bvy1btgiAO6b+/ftrHRpZGeaz5d1v/073pxMR0fSIgIiIiIjIgbENORERERGRhliQExERERFpiAU5EREREZGGWJATEREREWmIBTkRERERkYZYkBMRERERaYgFORERERGRhliQExEREZFd2LZtG7p06YLKlStDp9NhzZo1Fv286tWrQ6fT3TENGzbMpPWwICciIiIiu5CamoqAgADMnz+/WD7vr7/+wuXLlw1TeHg4AKBXr14mrYcFOdm9xMREtGjRAk2aNEHDhg3x9ddfax0SERERWUDHjh0xffp0dO/ePd/5aWlpGDt2LKpUqYJSpUqhZcuWiIiIKPTnVahQAb6+vobpl19+Qa1atfDYY4+ZtB4W5GT3SpcujW3btiEqKgq7d+/GjBkzcPXqVa3DItJEu3btDJdUo6KizL7uUaNGFXkZc3n55ZcNf6ulL1sTWTNL5r25FFe+Dh8+HJGRkVixYgUOHTqEXr164amnnsKpU6eKvO709HQsW7YMAwcOhE6nM+nfsiCn+8ovkYtzp1pUzs7O8PDwAKCOjEUEImKYz502OZrBgwfj8uXLaNiwoVnX+/PPP2PatGmG11r/TsydOxeXL1/W7POJrMnteV/UttYDBgzAxIkTzRZfceRrbGwsFi1ahFWrVqFNmzaoVasWxo4di9atW2PRokVFXv+aNWuQmJiIl19+2eR/y4KcCsRSO/C7MXeiJyYmIiAgAFWrVsW4ceNQvnx5wzzutMnReHh4wNfXFyVKlDDresuWLYvSpUubdZ1F4eXlBV9fX63DILIKt+d9UdpaZ2Vl4ZdffsEzzzxjtviKI18PHz6MrKws1K1bF56enoZp69atOHPmDADg+PHj+d6kmXd666238l3/t99+i44dO6Jy5comx8aCnArEUjvw/Fgi0b29vXHw4EHExMTg+++/R3x8vGEed9pk7apWrYrPP//c6L2dO3fCw8MD58+fL/L6q1evjjlz5hi916RJE7z77ruG1+3atcMbb7yB8ePHo2zZsvD19TWan7NMzhnxl19+GVu3bsXcuXMNO7Fz587d8dnZ2dkIDQ1FjRo14O7ujoCAAPz4449Gy/z4449o1KgR3N3dUa5cOQQFBSE1NfW+84hsmaXz/n5tre9l586dcHFxwUMPPZTv/Hbt2mHEiBEYNWoUypQpAx8fH3z99ddITU3FgAEDULp0adSuXRu//vprUf8Mk6SkpMDZ2Rn79u1DVFSUYYqOjsbcuXMBADVr1kR0dPQ9pzFjxtyx7vPnz+OPP/7AoEGDChUbC3I7ZelEvt2GDRvg5eWF//3vfwCA69evo2/fvihVqhQqVaqE2bNnF/jytSUT3cfHBwEBAdi+fXuR/l6i4tSyZUv89ddfhtciglGjRmH06NF44IEHii2OJUuWoFSpUti9ezdmzpyJ9957z9CjwO3mzp2LwMBAw9W1y5cvo1q1ancsFxoaiqVLl+KLL77A0aNHMXr0aLz44ovYunUrAODy5cvo06cPBg4ciOjoaERERKBHjx4QkXvOI7J11pL3+Vm3bh26dOlyz3bSS5YsQfny5bFnzx6MGDECr7/+Onr16oVHHnkE+/fvR4cOHdCvXz/cuHGj2OJu2rQpsrKykJCQgNq1axtNOSfmXF1dUb9+/XtOFSpUuGPdixYtQsWKFdG5c+dCxcaC3E4VZyJ///336NOnD/73v/+hb9++AICQkBD8+eefWLduHcLDw7F9+3bs37+/QOszd6LHx8fj+vXrAICkpCRs27YN9erVK/ofTlRMWrVqZZTP3333HS5cuIAJEyYAUGe4GzdujCZNmuDxxx+3WByNGzfGlClTUKdOHbz00kto0aIFNm3alO+yXl5ecHV1NVxd8/X1hbOzs9EyaWlpmDFjBhYuXIjg4GDUrFkTL7/8Ml588UV8+eWXAFRBnpmZiR49eqB69epo1KgRhg4dCk9Pz3vOI7J198v7mJgYPP744/D390ejRo2K9crQ2rVr73sVOyAgABMnTkSdOnUwYcIElCxZEuXLl8fgwYNRp04dTJ48GVevXsWhQ4fMGltKSorhzDegvqeoqCjExsaibt266Nu3L1566SX8/PPPiImJwZ49exAaGooNGzYU+jOzs7OxaNEi9O/fv9AtCViQ26n7JbK5zJ8/H0OHDsX69evx9NNPA1Bnx5csWYKPP/4Y7du3R8OGDbFo0SJkZWUVaJ3mTvTz58+jTZs2CAgIQJs2bTBixAg0atSoyH87UXFp1aoVoqOjkZKSgtTUVLz99tuYPn26UeG5c+dOREVFYcuWLRaLo3HjxkavK1WqhISEhEKv7/Tp07hx4waefPJJo/acS5cuNbTnDAgIQPv27dGoUSP06tULX3/9Na5du3bfeUS27n55//LLL+O9997DsWPHsHXrVri5uRVLXNHR0bh06RLat29/z+Xy/l44OzujXLlyRvteHx8fACjSb0h+9u7di6ZNm6Jp06YA1AnCpk2bYvLkyQDUmeyXXnoJY8aMQb169dCtWzf89ddf8PPzK/Rn/vHHH4iNjcXAgQMLvQ7LNwgmTbRq1QpvvfUWUlJSoNPp8t2B37hxAw8++CB69eqFjz/+2OTP+PHHH5GQkIA///zTqHnJ2bNnkZGRgYcfftjwnpeXV4HOSlsi0Z955hmr7eaJqCCaN28OJycn7N+/H3/88QcqVKiAAQMGmG39Tk5OdzTzyMjIuGM5FxcXo9c6nQ7Z2dmF/tyUlBQAqslblSpVjOblFBfOzs4IDw/Hzp078fvvv2PevHl45513sHv3btSoUeOe84hs2b3y/ujRo3BxcUGbNm0AqBuqi8u6devw5JNPomTJkvdcLr/fi7zv5VwFL8pvSH7atWt3z2ZrLi4umDp1KqZOnWq2z+zQoUORm8rxDLmdypvIH374Yb478Pfffx+tWrUq9Gc0bdoUFSpUwMKFC83WZtPaE51ICx4eHmjUqBF++uknfPzxx5g9ezacnHJ/vnU6HR577DE89NBDhvs4TFGhQgWjnoaSk5MRExNT5LhdXV3veWXM398fbm5uiI2NvaM9Z9725jqdDo8++iimTp2KAwcOwNXVFatXr77vPCJbdq+8P3XqFDw9PdGlSxc0a9YMM2bMKLa41q5di65duxbb5zkKniG3U3kT+euvv8bGjRuNduCnTp3C8ePH0aVLFxw5cqRQn1GrVi188sknaNeuHZydnfHZZ58BUHcou7i4GF0CSkpKwsmTJ9G2bdt7rnPt2rV49dVXCxUPkT1r1aoV5s2bh65du6Jdu3ZG83bs2IEqVarg8uXLCAoKQqNGje5oXnIvTzzxBBYvXowuXbrA29sbkydPvqO9d2FUr14du3fvxrlz5+Dp6XnHWbzSpUtj7NixGD16NLKzs9G6dWskJSXhzz//hF6vR//+/bF7925s2rQJHTp0QMWKFbF7925cuXIFDz744D3nEdmDu+V9ZmYmtm/fjqioKFSsWBFPPfUUHnroITz55JMFXndKSgpOnz5teJ3T1rps2bJ3bb6RkJCAvXv3Yt26dYX+myh/LMjt2L124GPHjsVHH32EnTt3Fukz6tatiy1btqBdu3YoUaIE5syZg9KlS6N///4YN24cypYti4oVK2LKlClwcnK6542aTHSiuwsICICLiws++uijO+blNPeoVKkSOnXqhP3795tUkE+YMAExMTF4+umn4eXlhWnTppnlDPnYsWPRv39/+Pv74+bNm/muc9q0aahQoQJCQ0Nx9uxZeHt7o1mzZnj77bcBAHq9Htu2bcOcOXOQnJyMBx54AJ988gk6duyI6Ojou84jsgd3y/sqVaqgRYsWhitJnTp1QlRUlEkF+d69e41uAg8JCQEA9O/fH4sXL87336xfvx4PP/yw0VgeZB4syO3Y3RJ57dq1qFu3LurWrVvkghwA6tWrh82bNxvOlH/yySeYNWsWhgwZgqeffhp6vR7jx4/HhQsX7tkUhYlOdHcrVqzA8OHDUbt2baP3U1NTkZ2djdKlSyMlJQWbN2/Gc889Z9K69Xo9VqxYYfRe//79jV5HRETc8e9uH9nv9mXq1q2LyMjIey6j0+kwcuRIjBw5Mt/YHnzwQYSFhZk8j8ge3C3vH3roISQkJODatWvw8vLCtm3b8Nprr5m07vu1tc5PQTpdAPL/vchvHAJ2UZqLBbkdu1si79q1CytWrMCqVauQkpKCjIwM6PV6wx3IBXF7sj344INGg+2ULl3aqC1ramoqpk6des/mKEx0ImPZ2dm4cuUKvv32W5w6dQpr1669Y5n4+HjDwB5ZWVkYPHjwXfvwz/H555/jm2++QWRkpF33ODRkyBAsW7ZM6zCITFKQvC9RogRmzJiBtm3bQkTQoUMHQ09nd2OOvG/dujX69OlTqH97P46erzph1WJX8iby559/jmPHjkGv1991+cWLF+PIkSP37GWlXbt22LlzJ1xdXQucyAcOHMDx48fx8MMPIykpCe+99x4iIiJw+vTpu54BnzlzJvr06ZPv4CGWlPMjkJqaitWrV6Nbt27F+vlEdxMREYEnnngC9evXx6JFi9CyZcsir/PixYu4efMmAMDPzw+urq5FXqe1SkhIQHJyMgDVnKdUqVIaR0R0f46a946eryzI7YypiVyQgrwwiXzgwAEMGjQIJ06cgKurK5o3b45Zs2ZZ5dk4R/8RICIiIm2xICciIiIi0hD7ISciIiIi0hALciIiIiIiDbEgJyIiIiLSEAtyIiIiIiINsSAnIiIiItIQC3IiIiIiIg2xICciIiIi0hALciIiIiIiDbEgJyIiIiLSEAtyIiIiIiINsSAnIiIiItIQC3IiIiIiIg2xICciIiIi0hALciIiIiIiDbEgJyIiIiLSEAtyIiIiIiINsSAnIiIiItIQC3IiIiIiIg2xICciIiIi0hALciIiIiIiDbEgJyIiIiLSEAtyIiIiIiINsSAnIiIiItIQC3IiIiIiIg2xICciIiIi0hALciIiIiIiDbEgJyIiIiLSEAtyIiIiIiINsSAnIiIiItIQC3IiIiIiIg2xICciIiIi0hALciIiIiIiDbEgJyIiIiLSEAtyIiIiIiINsSAnIiIiItIQC3IiIiIiIg2xICciIiIi0hALciIiIiIiDbEgJyIiIiLSEAtyIiIiIiINsSAnIiIiItIQC3IiIiIiIg2xICciIiIi0hALciIiIiIiDbEgJyIiIiLSEAtyIiIiIiINsSAnIiIiItIQC3IiIiIiIg2xICciIiIi0hALciIiIiIiDbEgJyIiIiLSEAtyIiIiIiINsSAnIiIiItIQC3IiIiIiIg2xICciIiIi0hALciIiIiIiDbEgJyIiIiLSUAmtAyAiIvuUlZWFjIwMrcOwGi4uLnB2dtY6DCKyQizIiYjIrEQEcXFxSExM1DoUq+Pt7Q1fX1/odDqtQyEiK8KCnIiIzCqnGK9YsSI8PDxYfEIdpNy4cQMJCQkAgEqVKmkcERFZExbkRERkNllZWYZivFy5clqHY1Xc3d0BAAkJCahYsSKbrxCRAW/qJCIis8lpM+7h4aFxJNYp53th23oiyosFORERmR2bqeSP3wsR5YcFORERERGRhliQExERERFpiAU5ERFRIYgIJk+ejEqVKsHd3R1BQUE4deqU1mERkQ1iQU5ERFQIM2fOxKeffoovvvgCu3fvRqlSpRAcHIxbt25pHRoR2RgW5ERE5PCWLl2KcuXKIS0tzej9bt26oV+/fncsLyKYM2cOJk6ciK5du6Jx48ZYunQpLl26hDVr1hRT1ERkL1iQExGRxYgAqanaTCIFj7NXr17IysrCunXrDO8lJCRgw4YNGDhw4B3Lx8TEIC4uDkFBQYb3vLy80LJlS0RGRhbpOyMix8OBgYiIyGJu3AA8PbX57JQUoFSpgi3r7u6OF154AYsWLUKvXr0AAMuWLYOfnx/atWt3x/JxcXEAAB8fH6P3fXx8DPOIiAqKZ8iJiIgADB48GL///jsuXrwIAFi8eDFefvllfP/99/D09DRM27dv1zhSIrI3PENOREQW4+GhzlRr9dmmaNq0KQICArB06VJ06NABR48exYYNG+Dt7Y2WLVsalqtSpQouX74MAIiPj0elSpUM8+Lj49GkSRNzhE9EDoQFORERWYxOV/BmI9Zg0KBBmDNnDi5evIigoCBUq1YNAFC6dGmj5WrUqAFfX19s2rTJUIAnJydj9+7deP3114s7bCKycWyyQkRE9J8XXngBf//9N77++ut8b+bModPpMGrUKEyfPh3r1q3D4cOH8dJLL6Fy5cro1q1b8QVMRHaBZ8iJiIj+4+XlhZ49e2LDhg33LazHjx+P1NRUvPrqq0hMTETr1q0RFhaGkiVLFk+wRGQ3WJATERHlcfHiRfTt2xdubm73XE6n0+G9997De++9V0yREZG9YkFOREQE4Nq1a4iIiEBERAQ+//xzrcMhIgfCgpyIiAiql5Vr167hww8/RL169bQOh4gcCAtyIiIiAOfOndM6BCJyUOxlhYiIiIhIQyzIiYjI7ERE6xCsEr8XIsoPC3IiIjIbFxcXAMCNGzc0jsQ65XwvOd8TERHANuRERGRGzs7O8Pb2RkJCAgDAw8MDOp1O46i0JyK4ceMGEhIS4O3tDWdnZ61DIiIrohNePyMiIjMSEcTFxSExMVHrUKyOt7c3fH19eZBCREZYkBMRkUVkZWUhIyND6zCshouLC8+ME1G+WJATEREREWmIN3USEREREWmIBTkRERERkYZYkBMRERERaYgFORERERGRhliQExERERFpiAU5EREREZGGWJATEREREWno/3hdRPWV/tzPAAAAAElFTkSuQmCC", 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" ] @@ -257,9 +228,9 @@ " frequency=frequency,\n", " planet_bulk_density=planet_bulk_density,\n", " layer_types=layer_types,\n", - " is_static_by_layer=is_static_by_layer,\n", - " is_incompressible_by_layer=is_incompressible_by_layer,\n", - " upper_radius_by_layer_array=upper_radius_by_layer,\n", + " is_static_bylayer=is_static_by_layer,\n", + " is_incompressible_bylayer=is_incompressible_by_layer,\n", + " upper_radius_bylayer_array=upper_radius_by_layer,\n", " surface_pressure = 0.0,\n", " degree_l = 2,\n", " solve_for = None,\n", @@ -270,7 +241,7 @@ " integration_method = integration_method,\n", " integration_rtol = integration_rtol,\n", " integration_atol = integration_atol,\n", - " scale_rtols_by_layer_type = False,\n", + " scale_rtols_bylayer_type = False,\n", " max_num_steps = 5_000_000,\n", " expected_size = len(radius_array),\n", " max_ram_MB = 1500,\n", @@ -280,6 +251,7 @@ " verbose = False,\n", " warnings = False,\n", " raise_on_fail = False,\n", + " eos_method_bylayer = None,\n", " eos_integration_method = 'RK45',\n", " eos_rtol = 1.0e-4,\n", " eos_atol = 1.0e-12,\n", @@ -403,9 +375,9 @@ " frequency=frequency,\n", " planet_bulk_density=planet_bulk_density,\n", " layer_types=layer_types,\n", - " is_static_by_layer=is_static_by_layer,\n", - " is_incompressible_by_layer=is_incompressible_by_layer,\n", - " upper_radius_by_layer_array=upper_radius_by_layer,\n", + " is_static_bylayer=is_static_by_layer,\n", + " is_incompressible_bylayer=is_incompressible_by_layer,\n", + " upper_radius_bylayer_array=upper_radius_by_layer,\n", " surface_pressure = 0.0,\n", " degree_l = 2,\n", " solve_for = None,\n", @@ -416,7 +388,7 @@ " integration_method = integration_method,\n", " integration_rtol = integration_rtol,\n", " integration_atol = integration_atol,\n", - " scale_rtols_by_layer_type = False,\n", + " scale_rtols_bylayer_type = False,\n", " max_num_steps = 100_000,\n", " expected_size = 500,\n", " max_ram_MB = 500,\n", @@ -426,6 +398,7 @@ " verbose = False,\n", " warnings = False,\n", " raise_on_fail = False,\n", + " eos_method_bylayer = None,\n", " eos_integration_method = 'RK45',\n", " eos_rtol = 1.0e-4,\n", " eos_atol = 1.0e-12,\n", @@ -555,8 +528,8 @@ " frequency=frequency,\n", " planet_bulk_density=planet_bulk_density,\n", " layer_types=layer_types,\n", - " is_static_by_layer=is_static_by_layer,\n", - " is_incompressible_by_layer=is_incompressible_by_layer,\n", + " is_static_bylayer=is_static_by_layer,\n", + " is_incompressible_bylayer=is_incompressible_by_layer,\n", " upper_radius_by_layer_array=upper_radius_by_layer,\n", " surface_pressure = 0.0,\n", " degree_l = 2,\n", @@ -568,7 +541,7 @@ " integration_method = integration_method,\n", " integration_rtol = integration_rtol,\n", " integration_atol = integration_atol,\n", - " scale_rtols_by_layer_type = False,\n", + " scale_rtols_bylayer_type = False,\n", " max_num_steps = 100_000,\n", " expected_size = 500,\n", " max_ram_MB = 500,\n", @@ -578,6 +551,7 @@ " verbose = False,\n", " warnings = False,\n", " raise_on_fail = False,\n", + " eos_method_bylayer = None,\n", " eos_integration_method = 'RK45',\n", " eos_rtol = 1.0e-4,\n", " eos_atol = 1.0e-12,\n", diff --git a/TidalPy/Material/eos/methods/__init__.pxd b/TidalPy/Material/eos/methods/__init__.pxd new file mode 100644 index 00000000..742a2538 --- /dev/null +++ b/TidalPy/Material/eos/methods/__init__.pxd @@ -0,0 +1 @@ +from TidalPy.Material.eos.methods.interpolate cimport EOS_INTERPOLATE_METHOD_INT, preeval_interpolate, InterpolateEOSInput \ No newline at end of file diff --git a/TidalPy/Material/eos/methods/interpolate.pxd b/TidalPy/Material/eos/methods/interpolate.pxd index 0dc19134..4a4dc56b 100644 --- a/TidalPy/Material/eos/methods/interpolate.pxd +++ b/TidalPy/Material/eos/methods/interpolate.pxd @@ -2,6 +2,8 @@ from libcpp cimport bool as cpp_bool from TidalPy.Material.eos.ode cimport EOS_ODEInput +cdef int EOS_INTERPOLATE_METHOD_INT = 0 + cdef struct InterpolateEOSInput: size_t num_slices double* radius_array_ptr diff --git a/TidalPy/RadialSolver/solver.pxd b/TidalPy/RadialSolver/solver.pxd index 3222fbf4..ae7970d2 100644 --- a/TidalPy/RadialSolver/solver.pxd +++ b/TidalPy/RadialSolver/solver.pxd @@ -16,8 +16,8 @@ cdef void cf_radial_solver( double planet_bulk_density, size_t num_layers, int* layer_types_ptr, - bint* is_static_by_layer_ptr, - bint* is_incompressible_by_layer_ptr, + bint* is_static_bylayer_ptr, + bint* is_incompressible_bylayer_ptr, double surface_pressure, int degree_l, size_t num_bc_models, @@ -29,13 +29,14 @@ cdef void cf_radial_solver( int integration_method_int, double integration_rtol, double integration_atol, - cpp_bool scale_rtols_by_layer_type, + cpp_bool scale_rtols_bylayer_type, size_t max_num_steps, size_t expected_size, size_t max_ram_MB, double max_step, cpp_bool nondimensionalize, cpp_bool use_prop_matrix, + int* eos_integration_method_int_bylayer_ptr, int eos_integration_method, double eos_rtol, double eos_atol, diff --git a/TidalPy/RadialSolver/solver.pyx b/TidalPy/RadialSolver/solver.pyx index c83727bf..84227ad9 100644 --- a/TidalPy/RadialSolver/solver.pyx +++ b/TidalPy/RadialSolver/solver.pyx @@ -27,7 +27,7 @@ from TidalPy.RadialSolver.matrix cimport cf_matrix_propagate # EOS Imports (note these will change in a future release) from TidalPy.Material.eos cimport EOS_ODEInput, solve_eos from TidalPy.Material.eos.eos_solution cimport EOSSolutionCC -from TidalPy.Material.eos.methods.interpolate cimport InterpolateEOSInput, preeval_interpolate +from TidalPy.Material.eos.methods cimport InterpolateEOSInput, preeval_interpolate, EOS_INTERPOLATE_METHOD_INT ctypedef EOS_ODEInput* EOS_ODEInputPtr @@ -47,8 +47,8 @@ cdef void cf_radial_solver( double planet_bulk_density, size_t num_layers, int* layer_types_ptr, - bint* is_static_by_layer_ptr, - bint* is_incompressible_by_layer_ptr, + bint* is_static_bylayer_ptr, + bint* is_incompressible_bylayer_ptr, double surface_pressure, int degree_l, size_t num_bc_models, @@ -60,13 +60,14 @@ cdef void cf_radial_solver( int integration_method_int, double integration_rtol, double integration_atol, - cpp_bool scale_rtols_by_layer_type, + cpp_bool scale_rtols_bylayer_type, size_t max_num_steps, size_t expected_size, size_t max_ram_MB, double max_step, cpp_bool nondimensionalize, cpp_bool use_prop_matrix, + int* eos_integration_method_int_bylayer_ptr, int eos_integration_method, double eos_rtol, double eos_atol, @@ -205,7 +206,7 @@ cdef void cf_radial_solver( frequency_to_use = frequency surface_pressure_to_use = surface_pressure - # Solve the equaiton of state for the planet + # Solve the equation of state for the planet # TODO: For now there is only one accepted EOS, the interpolated kind. In the future additional EOS will be supplied # either via arguments to this function or a more OOP approach where they are built into the layers. @@ -216,36 +217,43 @@ cdef void cf_radial_solver( for layer_i in range(num_layers): # TODO: Below is specific to interpolate EOS. For now we are only storing the interpolate version of the EOS for each layer. printf("DEBUG-cf_radial_solver EOS setup 1\n") - eos_function_bylayer_vec[layer_i] = preeval_interpolate - - # Build EOS input - printf("DEBUG-cf_radial_solver EOS setup 2\n") - bottom_slice_index = first_slice_index_by_layer_vec[layer_i] - - # Build EOS input for specific EOS model - # TODO: Below is specific to interpolate EOS. - printf("DEBUG-cf_radial_solver EOS setup 3; bottom slice = %d\n", bottom_slice_index) - specific_eos_input_bylayer_vec.emplace_back( - num_slices_by_layer_vec[layer_i], # Number of slices for this layer [size_t] - &radius_array_in_ptr[bottom_slice_index], # Radius array pointer [double*] - &density_array_in_ptr[bottom_slice_index], # Density array pointer [double*] - &complex_bulk_modulus_in_ptr[bottom_slice_index], # Complex bulk array pointer [double complex*] - &complex_shear_modulus_in_ptr[bottom_slice_index], # Complex shear array pointer [double complex*] + + if eos_integration_method_int_bylayer_ptr[layer_i] == EOS_INTERPOLATE_METHOD_INT: + + eos_function_bylayer_vec[layer_i] = preeval_interpolate + + # Build EOS input + printf("DEBUG-cf_radial_solver EOS setup 2\n") + bottom_slice_index = first_slice_index_by_layer_vec[layer_i] + + # Build EOS input for specific EOS model + # TODO: Below is specific to interpolate EOS. + printf("DEBUG-cf_radial_solver EOS setup 3; bottom slice = %d\n", bottom_slice_index) + specific_eos_input_bylayer_vec.emplace_back( + num_slices_by_layer_vec[layer_i], # Number of slices for this layer [size_t] + &radius_array_in_ptr[bottom_slice_index], # Radius array pointer [double*] + &density_array_in_ptr[bottom_slice_index], # Density array pointer [double*] + &complex_bulk_modulus_in_ptr[bottom_slice_index], # Complex bulk array pointer [double complex*] + &complex_shear_modulus_in_ptr[bottom_slice_index], # Complex shear array pointer [double complex*] + ) + printf("DEBUG-cf_radial_solver EOS setup 4\n") + specific_eos_void_ptr = &specific_eos_input_bylayer_vec.back() + + # Build input for generalized EOS solver + printf("DEBUG-cf_radial_solver EOS setup 5\n") + eos_inputs_bylayer_vec.emplace_back( + G_to_use, # Gravitational constant [double] + radius_planet_to_use, # Planet radius [double] + specific_eos_void_ptr, # void-casted pointer to model specific input (created just above) + False, # Final solve flag [bool] (will be updated by EOS solver) + False, # Final update shear flag [bool] (will be updated by EOS solver) + False # Final update bulk flag [bool] (will be updated by EOS solver) ) - printf("DEBUG-cf_radial_solver EOS setup 4\n") - specific_eos_void_ptr = &specific_eos_input_bylayer_vec.back() - - # Build input for generalized EOS solver - printf("DEBUG-cf_radial_solver EOS setup 5\n") - eos_inputs_bylayer_vec.emplace_back( - G_to_use, # Gravitational constant [double] - radius_planet_to_use, # Planet radius [double] - specific_eos_void_ptr, # void-casted pointer to model specific input (created just above) - False, # Final solve flag [bool] (will be updated by EOS solver) - False, # Final update shear flag [bool] (will be updated by EOS solver) - False # Final update bulk flag [bool] (will be updated by EOS solver) - ) - # TODO: update bulk/shear flags are overwritten by EOS solver regardless of layer type. They should not ever need to be updated, for example shear for liquid layers? + # TODO: update bulk/shear flags are overwritten by EOS solver regardless of layer type. They should not ever need to be updated, for example shear for liquid layers? + else: + # Unknown EOS method + solution_storage_ptr.error_code = -250 + break printf("DEBUG-cf_radial_solver EOS Solver\n") if solution_storage_ptr.error_code == 0: @@ -287,8 +295,8 @@ cdef void cf_radial_solver( bulk_density_to_use, # Planet bulk density [double] # TODO: In the future the propagation matrix should take in layer types and multiple layers # int* layer_types_ptr, - # int* is_static_by_layer_ptr, - # int* is_incompressible_by_layer_ptr, + # int* is_static_bylayer_ptr, + # int* is_incompressible_bylayer_ptr, num_bc_models, # Number of boundary conditions requested by user [size_t] bc_models_ptr, # Boundary condition model int array pointer [int*] G_to_use, # Gravitational constant [double] @@ -300,12 +308,12 @@ cdef void cf_radial_solver( else: printf("DEBUG-cf_radial_solver - Pre shooting method\n") cf_shooting_solver( - solution_storage_ptr, # (Modified) Final radial solution storage struct pointer [RadialSolutionStorageCC*] + solution_storage_ptr, # (Modified) Final radial solution storage struct pointer [RadialSolutionStorageCC*] frequency_to_use, # Forcing frequency [double] bulk_density_to_use, # Planet bulk density [double] layer_types_ptr, # Layer type int array pointer [int*] - is_static_by_layer_ptr, # Layer is_static flag array pointer [int*] - is_incompressible_by_layer_ptr, # Pointer array of layer is_incompressible flag array pointer [int*] + is_static_bylayer_ptr, # Layer is_static flag array pointer [int*] + is_incompressible_bylayer_ptr, # Pointer array of layer is_incompressible flag array pointer [int*] first_slice_index_by_layer_vec, # First radial slice of each layer array pointer [size_t*] num_slices_by_layer_vec, # Number of radial slices in each layer array pointer [size_t*] num_bc_models, # Number of boundary conditions requested by user [size_t] @@ -318,7 +326,7 @@ cdef void cf_radial_solver( integration_method_int, # Integration method int (0=RK23, 1=RK45, 2=DOP853) [unsigned char] integration_rtol, # Integration relative tolerance [double] integration_atol, # Integration absolute tolerance [double] - scale_rtols_by_layer_type, # Flag for if tolerances should vary with layer type (using pre-defined scaling) [cpp_bool] + scale_rtols_bylayer_type, # Flag for if tolerances should vary with layer type (using pre-defined scaling) [cpp_bool] max_num_steps, # Maximum number of integration steps allowed [size_t] expected_size, # Expected number of integration steps required for the average layer [size_t] max_ram_MB, # Maximum amount of ram allowed for each layer's integration (note if parallized then radial solver will exceed this value; there is also overhead of other functions) [size_t] @@ -376,9 +384,9 @@ def radial_solver( double frequency, double planet_bulk_density, tuple layer_types, - tuple is_static_by_layer, - tuple is_incompressible_by_layer, - double[::1] upper_radius_by_layer_array, + tuple is_static_bylayer, + tuple is_incompressible_bylayer, + double[::1] upper_radius_bylayer_array, double surface_pressure = 0.0, unsigned int degree_l = 2, tuple solve_for = None, @@ -389,13 +397,14 @@ def radial_solver( str integration_method = 'RK45', double integration_rtol = 1.0e-6, double integration_atol = 1.0e-12, - cpp_bool scale_rtols_by_layer_type = False, + cpp_bool scale_rtols_bylayer_type = False, size_t max_num_steps = 500_000, size_t expected_size = 500, size_t max_ram_MB = 500, double max_step = 0, cpp_bool nondimensionalize = True, cpp_bool use_prop_matrix = False, + tuple eos_method_bylayer = None, str eos_integration_method = 'RK45', double eos_rtol = 1.0e-3, double eos_atol = 1.0e-5, @@ -432,9 +441,9 @@ def radial_solver( Indicator of layer type. Current options are: - "solid" - "liquid" - is_static_by_layer : tuple[bool, ...] (Size = number of layers) + is_static_bylayer : tuple[bool, ...] (Size = number of layers) Flag declaring if each layer uses the static (True) or dynamic (False) assumption. - is_incompressible_by_layer : tuple[bool, ...] (Size = number of layers) + is_incompressible_bylayer : tuple[bool, ...] (Size = number of layers) Flag declaring if each layer is incompressible (True) or compressible (False). upper_radius_by_layer : tuple[float64, ...] (Size = number of layers) Tuple of the upper radius of each layer. @@ -482,7 +491,7 @@ def radial_solver( Relative integration tolerance. Lower tolerance will lead to more precise results at increased computation. integration_atol : float64, default=1.0e-12 Absolute integration tolerance (when solution is near 0). - scale_rtols_by_layer_type : bool, default=True + scale_rtols_bylayer_type : bool, default=True If True, then each layer will be imparted with a different relative tolerance. Liquid layers will have a lower rtol which has been found to improve stability. max_num_steps : uint32, default=500,000 @@ -512,6 +521,11 @@ def radial_solver( Note that many of the parameters set by this function are only applicable to the shooting method and may not be passed to the propagation matrix solver. See more about the prop-matrix method in `TidalPy.RadialSolver.PropMatrix`. + eos_method_by_layer : tuple, default = None + Tuple of EOS methods for each layer. This is a tuple of strings. + If `None` then will use default for each layer (interpolation) + Currently supported methods: + - "interpolation" eos_integration_method : unsigned int, default = 2 Integration method used to solve for the planet's equation of state. eos_rtol : double, default = 1.0e-6 @@ -557,11 +571,11 @@ def radial_solver( assert complex_bulk_modulus_array.size == total_slices assert complex_shear_modulus_array.size == total_slices # Check that number of assumptions match. - if len(is_static_by_layer) != num_layers: - raise AttributeError('Number of `is_static_by_layer` must match number of `layer_types`.') - if len(is_incompressible_by_layer) != num_layers: - raise AttributeError('Number of `is_incompressible_by_layer` must match number of `layer_types`.') - if upper_radius_by_layer_array.size != num_layers: + if len(is_static_bylayer) != num_layers: + raise AttributeError('Number of `is_static_bylayer` must match number of `layer_types`.') + if len(is_incompressible_bylayer) != num_layers: + raise AttributeError('Number of `is_incompressible_bylayer` must match number of `layer_types`.') + if upper_radius_bylayer_array.size != num_layers: raise AttributeError('Number of `upper_radius_by_layer` must match number of `layer_types`.') if radius_array[0] != 0.: raise AttributeError('Radius array must start at zero.') @@ -580,8 +594,8 @@ def radial_solver( cdef vector[bint] layer_assumptions_vec = vector[bint]() layer_assumptions_vec.resize(2 * num_layers) - cdef bint* is_static_by_layer_ptr = &layer_assumptions_vec[0] - cdef bint* is_incompressible_by_layer_ptr = &layer_assumptions_vec[num_layers] + cdef bint* is_static_bylayer_ptr = &layer_assumptions_vec[0] + cdef bint* is_incompressible_bylayer_ptr = &layer_assumptions_vec[num_layers] printf("DEBUG-RadialSolver Point 4\n") @@ -594,11 +608,11 @@ def radial_solver( for layer_i in range(num_layers): layer_type = layer_types[layer_i] - is_static_by_layer_ptr[layer_i] = is_static_by_layer[layer_i] - is_incompressible_by_layer_ptr[layer_i] = is_incompressible_by_layer[layer_i] + is_static_bylayer_ptr[layer_i] = is_static_bylayer[layer_i] + is_incompressible_bylayer_ptr[layer_i] = is_incompressible_bylayer[layer_i] if not dynamic_liquid: - if (layer_type == 1) and not is_static_by_layer_ptr[layer_i]: + if (layer_type == 1) and not is_static_bylayer_ptr[layer_i]: # There is at least one dynamic liquid layer dynamic_liquid = True @@ -629,7 +643,7 @@ def radial_solver( # Convert integration methods from string to int printf("DEBUG-RadialSolver Point 8\n") cdef str integration_method_lower = integration_method.lower() - cdef unsigned char integration_method_int + cdef int integration_method_int if integration_method_lower == 'rk45': integration_method_int = 1 elif integration_method_lower == 'rk23': @@ -641,7 +655,7 @@ def radial_solver( raise UnknownModelError(f"Unsupported integration method provided: {integration_method_lower}.") cdef str eos_integration_method_lower = eos_integration_method.lower() - cdef unsigned char eos_integration_method_int + cdef int eos_integration_method_int if eos_integration_method_lower == 'rk45': eos_integration_method_int = 1 elif eos_integration_method_lower == 'rk23': @@ -651,6 +665,25 @@ def radial_solver( else: log.error(f"Unsupported EOS integration method provided: {eos_integration_method_lower}.") raise UnknownModelError(f"Unsupported EOS integration method provided: {eos_integration_method_lower}.") + + # Convert EOS methods from string to int + cdef str eos_method_str + cdef vector[int] eos_integration_method_int_bylayer = vector[int]() + eos_integration_method_int_bylayer.resize(num_layers) + cdef int* eos_integration_method_int_bylayer_ptr = eos_integration_method_int_bylayer.data() + + if eos_method_bylayer is None: + # Use default method + for layer_i in range(num_layers): + eos_integration_method_int_bylayer[layer_i] = EOS_INTERPOLATE_METHOD_INT + else: + # Step through each layer and check the method + for layer_i in range(num_layers): + eos_method_str = eos_method_bylayer[layer_i].lower() + if eos_method_str == 'interpolate': + eos_integration_method_int_bylayer[layer_i] = EOS_INTERPOLATE_METHOD_INT + else: + raise NotImplementedError("Unknown EOS method provided.") # Clean up what values the solver is solving for. printf("DEBUG-RadialSolver Point 9\n") @@ -694,7 +727,7 @@ def radial_solver( printf("DEBUG-RadialSolver Point 11\n") # Build solution storage - cdef RadialSolverSolution solution = RadialSolverSolution(num_bc_models, upper_radius_by_layer_array, radius_array) + cdef RadialSolverSolution solution = RadialSolverSolution(num_bc_models, upper_radius_bylayer_array, radius_array) printf("DEBUG-RadialSolver Point 11b\n") solution.set_model_names(bc_models_ptr) @@ -713,8 +746,8 @@ def radial_solver( planet_bulk_density, num_layers, layer_types_ptr, - is_static_by_layer_ptr, - is_incompressible_by_layer_ptr, + is_static_bylayer_ptr, + is_incompressible_bylayer_ptr, surface_pressure, degree_l, num_bc_models, @@ -726,13 +759,14 @@ def radial_solver( integration_method_int, integration_rtol, integration_atol, - scale_rtols_by_layer_type, + scale_rtols_bylayer_type, max_num_steps, expected_size, max_ram_MB, max_step, nondimensionalize, use_prop_matrix, + eos_integration_method_int_bylayer_ptr, eos_integration_method_int, eos_rtol, eos_atol, diff --git a/pyproject.toml b/pyproject.toml index c25df858..81f42f08 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name='TidalPy' -version = '0.6.0a7.dev8' +version = '0.6.0a7.dev9' description='Tidal Dynamics and Thermal-Orbital Evolution Software Suite Implemented in Cython and Python' authors= [ {name = 'Joe P. Renaud', email = 'TidalPy@gmail.com'}