{ "cells": [ { "cell_type": "markdown", "id": "6a739a84", "metadata": {}, "source": [ "# Tutorial 3: resolution of wave equation with hard constraint PINNs." ] }, { "cell_type": "markdown", "id": "2316f24e", "metadata": {}, "source": [ "## The problem definition " ] }, { "cell_type": "markdown", "id": "bc2bbf62", "metadata": {}, "source": [ "In this tutorial we present how to solve the wave equation using hard constraint PINNs. For doing so we will build a costum torch model and pass it to the `PINN` solver.\n", "\n", "The problem is written in the following form:\n", "\n", "\\begin{equation}\n", "\\begin{cases}\n", "\\Delta u(x,y,t) = \\frac{\\partial^2}{\\partial t^2} u(x,y,t) \\quad \\text{in } D, \\\\\\\\\n", "u(x, y, t=0) = \\sin(\\pi x)\\sin(\\pi y), \\\\\\\\\n", "u(x, y, t) = 0 \\quad \\text{on } \\Gamma_1 \\cup \\Gamma_2 \\cup \\Gamma_3 \\cup \\Gamma_4,\n", "\\end{cases}\n", "\\end{equation}\n", "\n", "where $D$ is a square domain $[0,1]^2$, and $\\Gamma_i$, with $i=1,...,4$, are the boundaries of the square, and the velocity in the standard wave equation is fixed to one." ] }, { "cell_type": "markdown", "id": "0a733b62", "metadata": {}, "source": [ "First of all, some useful imports." ] }, { "cell_type": "code", "execution_count": 1, "id": "d93daba0", "metadata": {}, "outputs": [], "source": [ "import torch\n", "\n", "from pina.problem import SpatialProblem, TimeDependentProblem\n", "from pina.operators import laplacian, grad\n", "from pina.geometry import CartesianDomain\n", "from pina.solvers import PINN\n", "from pina.trainer import Trainer\n", "from pina.equation import Equation\n", "from pina.equation.equation_factory import FixedValue\n", "from pina import Condition, Plotter" ] }, { "cell_type": "markdown", "id": "cbc50741", "metadata": {}, "source": [ "Now, the wave problem is written in PINA code as a class, inheriting from `SpatialProblem` and `TimeDependentProblem` since we deal with spatial, and time dependent variables. The equations are written as `conditions` that should be satisfied in the corresponding domains. `truth_solution` is the exact solution which will be compared with the predicted one." ] }, { "cell_type": "code", "execution_count": 2, "id": "b60176c4", "metadata": {}, "outputs": [], "source": [ "class Wave(TimeDependentProblem, SpatialProblem):\n", " output_variables = ['u']\n", " spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})\n", " temporal_domain = CartesianDomain({'t': [0, 1]})\n", "\n", " def wave_equation(input_, output_):\n", " u_t = grad(output_, input_, components=['u'], d=['t'])\n", " u_tt = grad(u_t, input_, components=['dudt'], d=['t'])\n", " nabla_u = laplacian(output_, input_, components=['u'], d=['x', 'y'])\n", " return nabla_u - u_tt\n", "\n", " def initial_condition(input_, output_):\n", " u_expected = (torch.sin(torch.pi*input_.extract(['x'])) *\n", " torch.sin(torch.pi*input_.extract(['y'])))\n", " return output_.extract(['u']) - u_expected\n", "\n", " conditions = {\n", " 'gamma1': Condition(location=CartesianDomain({'x': [0, 1], 'y': 1, 't': [0, 1]}), equation=FixedValue(0.)),\n", " 'gamma2': Condition(location=CartesianDomain({'x': [0, 1], 'y': 0, 't': [0, 1]}), equation=FixedValue(0.)),\n", " 'gamma3': Condition(location=CartesianDomain({'x': 1, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)),\n", " 'gamma4': Condition(location=CartesianDomain({'x': 0, 'y': [0, 1], 't': [0, 1]}), equation=FixedValue(0.)),\n", " 't0': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': 0}), equation=Equation(initial_condition)),\n", " 'D': Condition(location=CartesianDomain({'x': [0, 1], 'y': [0, 1], 't': [0, 1]}), equation=Equation(wave_equation)),\n", " }\n", "\n", " def wave_sol(self, pts):\n", " return (torch.sin(torch.pi*pts.extract(['x'])) *\n", " torch.sin(torch.pi*pts.extract(['y'])) *\n", " torch.cos(torch.sqrt(torch.tensor(2.))*torch.pi*pts.extract(['t'])))\n", "\n", " truth_solution = wave_sol\n", "\n", "problem = Wave()" ] }, { "cell_type": "markdown", "id": "03557e0c-1f82-4dad-b611-5d33fddfd0ef", "metadata": {}, "source": [ "## Hard Constraint Model" ] }, { "cell_type": "markdown", "id": "356fe363", "metadata": {}, "source": [ "After the problem, a **torch** model is needed to solve the PINN. Usually, many models are already implemented in `PINA`, but the user has the possibility to build his/her own model in `PyTorch`. The hard constraint we impose is on the boundary of the spatial domain. Specifically, our solution is written as:\n", "\n", "$$ u_{\\rm{pinn}} = xy(1-x)(1-y)\\cdot NN(x, y, t), $$\n", "\n", "where $NN$ is the neural net output. This neural network takes as input the coordinates (in this case $x$, $y$ and $t$) and provides the unknown field $u$. By construction, it is zero on the boundaries. The residuals of the equations are evaluated at several sampling points (which the user can manipulate using the method `discretise_domain`) and the loss minimized by the neural network is the sum of the residuals." ] }, { "cell_type": "code", "execution_count": 3, "id": "9fbbb74f", "metadata": {}, "outputs": [], "source": [ "class HardMLP(torch.nn.Module):\n", "\n", " def __init__(self, input_dim, output_dim):\n", " super().__init__()\n", "\n", " self.layers = torch.nn.Sequential(torch.nn.Linear(input_dim, 20),\n", " torch.nn.Tanh(),\n", " torch.nn.Linear(20, 20),\n", " torch.nn.Tanh(),\n", " torch.nn.Linear(20, output_dim))\n", " \n", " # here in the foward we implement the hard constraints\n", " def forward(self, x):\n", " hard = x.extract(['x'])*(1-x.extract(['x']))*x.extract(['y'])*(1-x.extract(['y']))\n", " return hard*self.layers(x)" ] }, { "cell_type": "markdown", "id": "f79fc901-4720-4fac-8b72-84ac5f7d2ec3", "metadata": {}, "source": [ "## Train and Inference" ] }, { "cell_type": "markdown", "id": "b465bebd", "metadata": {}, "source": [ "In this tutorial, the neural network is trained for 3000 epochs with a learning rate of 0.001 (default in `PINN`). Training takes approximately 1 minute." ] }, { "cell_type": "code", "execution_count": 4, "id": "0be8e7f5", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/u/n/ndemo/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML\n", " warnings.warn(\"Can't initialize NVML\")\n", "GPU available: True (cuda), used: True\n", "TPU available: False, using: 0 TPU cores\n", "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n", "Missing logger folder: /u/n/ndemo/PINA/tutorials/tutorial3/lightning_logs\n", "2023-10-17 10:24:02.163746: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", "2023-10-17 10:24:02.218849: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "2023-10-17 10:24:07.063047: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", "/opt/sissa/apps/intelpython/2022.0.2/intelpython/latest/lib/python3.9/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.0)\n", " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n", "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", "\n", " | Name | Type | Params\n", "----------------------------------------\n", "0 | _loss | MSELoss | 0 \n", "1 | _neural_net | Network | 521 \n", "----------------------------------------\n", "521 Trainable params\n", "0 Non-trainable params\n", "521 Total params\n", "0.002 Total estimated model params size (MB)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4ce601ec7f9f4661be3c4d4fe789baa1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Training: 0it [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "`Trainer.fit` stopped: `max_epochs=3000` reached.\n" ] } ], "source": [ "pinn = PINN(problem, HardMLP(len(problem.input_variables), len(problem.output_variables)))\n", "problem.discretise_domain(1000, 'random', locations=['D','t0', 'gamma1', 'gamma2', 'gamma3', 'gamma4'])\n", "trainer = Trainer(pinn, max_epochs=3000)\n", "trainer.train()" ] }, { "cell_type": "markdown", "id": "c2a5c405", "metadata": {}, "source": [ "Notice that the loss on the boundaries of the spatial domain is exactly zero, as expected! After the training is completed one can now plot some results using the `Plotter` class of **PINA**." ] }, { "cell_type": "code", "execution_count": 5, "id": "c086c05f", "metadata": {}, "outputs": [ { "data": { "image/png": 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9k08+KaNHj5ZLLrlE2rRpIzfddJP+//vuuy/r+wRMZHJlI5WXoIqyfJm2Qffff79cdNFFcuutt8qpp54qEydOlDPOOEMeeuihlO02bdokP/vZz+Spp56SY445JuVn27dvl/fff19GjhwpnTt3lpNPPll+/etfy5dffpk26AwCAaXnXAq08A2eVwCZMqmTkinCK3/Y/Fzb/Ddmgl27dqVc9u/fn3a7AwcO6CqPPn36lNxWvXp1fX3JkiVpf0fdlxoCV1qtWrVk0aJFWd8nYAJTw8hMuHAMqDyfQsow27UlxbeX3l5RFZfq9qSioiK59tprdYh52mmnHXEfxx9/vLRr106eeOIJ2bt3r66kVKMNGjduLN26dcvmkI+KRXI8rfAilPRD8nm2dUEdVvUGUBFbgyoE99zbtqCO66t6z9ndRfISqRUYVbVvz8Hi/74sLVu2TLldDXMbP378Edurio/Dhw9LkyZNUm5X19esWZP2MVSnTVWnfOc739HzUC5YsECee+45fT/Z3icQBx8CvLLH6PJ7KuJna7u2efPmtNur25N+85vfSM2aNeXnP/952vtQc1K+8soremh43bp1dSiqwkk1R3PDhg0rc6gZI6D0LJwkmPSTzUElISUQPts6NLsJJlHqtWBbSInsbNiwQU/Qn5SbG9z7lhoKp4bOqcUEVIdMhZRqGB3Dt2EL29rxIBFYullFmbNmY9y7YXW7djSqIlO1fWo+S9XupZNIJOSnP/2pDiX//ve/65EFjz76qFx22WXyz3/+U5o1a3a0h8kYQ7w9wZBf8DoA4EIYRTgJ218XPgcJVaE6caUv5XXk1EqjNWrUkC1btqTcrq43bdo07e+ccMIJMmfOHD2E7eOPP9YVKXXq1NHzUWZ7n0DYGPbMeYHdwmzXmhbfXtH2KnBUC+y0atVKV1Gqi2r/fvnLX+qVwhW1MM4LL7wgs2bNkrPPPlvPYfnwww/roHLmzJlVPfy0CCg9qJ6kahLpXhM2vS5s+5sDbGJLWGJTAIV42BRU2vJ3Z6OcnBw9N5Yapl16ni11vVevXhX+rpqHskWLFnqerb/85S9y+eWXV/k+gaAxFyPnygc+zUV5NDlZtEHq9tLbK/Pnzy/ZXs09+fbbb8vKlStLLmoVbzUf5UsvvaS3UYvhKGpod2nqunr8MBBQOhyU2BZCIXq8PpA0depU/W2Z6pz17NlTli1bVu7JOXjwoEyYMEEPgVPbd+nSRc9FUpX7RDxsCElsCp1gBl4vKCgokOnTp+sKj9WrV+tVuVV1pBq2rQwaNEhGjRpVcqLeeOMNPefkhx9+qKtK1MqnqvN12223Vfo+gbARTFb93NnwuQffIKTMvl0bMWKE7p/dd999elSAmtvyzTfflOHDh5csgNOxY8eUi1rFW1VYqoVxFBVmqrkmBw8eLP/617/kvffe0wHmRx99JJdeeuk3Oxcg5qB0FMETXJufkrkowzN79mzd6E2bNk0HiVOmTNELBqxdu1bPOVLWmDFj5I9//KNuJNV8XepbtiuuuEIWL14sp59+elb3CaRD0ISqvnZMnp/S9QVz4jRgwADZtm2bjB07Vi8I0LVrV91RSy4YUFhYmFIRsm/fPt22qYBSDe2+5JJL5Mknn5QGDRpU+j6BsBCqhXM+ef+Fy+3aWWedJU8//bRu20aPHi0nn3yynspEBZGVpYaWq8e444475Lvf/a4uUlGrff/v//6vLlAJQ7WEmvnScGrJ9fr160ufZjdIzerxVQTaUD1JMImqMD2krLVqU6yPf6jogLzy6e9l586dKRMaV+V9bdGq5lKnbrDF7Ht2F0nvjp9Uej9VgHjmmWfKQw89pK+rqhG1qtzPfvYzGTly5BHbq/J/1VCpSZOTrrzySj0fiQous7lPnySf++/0His1a+bFth+md3gIJxEUk0PKuDvIhw7tk9cXTahyu5Z8XxuztK/k1Ql+tdO7v/1yIG0v3G7XXGN6O+2SuN+LUbFMFszR/bWtj9KuWYoh3g4hnITrryEbviQwrcNQ+rJ//5Efvg4cOKBXcevTp0/JberbN3V9yZIlae9X3Y8atl2aCicXLVqU9X0CSQzphk9hNwEEgLIYihw9zjlgBoZ4OxCMmB4qwS62DPl2xawdPSX3ULDVJvuLq01E/qorFksbN26cnn+ktO3bt8vhw4ePGKKmrqv5StJRQ7UnT54s3/nOd/Q8lGoCZjV3l7qfbO8T0TI1FDE5SILdbBjyDcBvprbNPmH4t7lzUWZSRQl7UUFpOcJJ+PbaMvnLAtNs2LBBD29IXkpPnFwV999/v57HRM0/qVaVU5Mtqwmay67wBmSCcBK+vs4IJQDwPmAWKirNw4I5fqCC0uJAxNQACe5QrzEqKe2l5uo62nxdavLjGjVqyJYtW1JuV9fVKm7pnHDCCXqSZbWowGeffabnpFTzSrZp0ybr+4TfnSATQyMT7W919Mr23EI+G1Tm9UYlJQATmNgm4xtUVALRotzFUoST8Pm1ZuqXBjZSFZDdunXTw7ST1II26nqvXr0q/F01D2WLFi3k0KFD8pe//EUuv/zyKt8n/ONzOKkCx0wuQd+nz3x+3QEwA+GkPaioNANVlO6jgtJCJgZGptjdtqjK91F3Hbl9WVRSuq2goEAGDx4s3bt3lx49esiUKVNk7969eti2MmjQIB1ETpo0SV9/4403ZNOmTdK1a1f9r5rXUgWQt912W6XvE/ApIDI1DCxvv3ypwjSpklJ1fllFFvAH4aSdeK+OH/NRuo2A0rJKLZ/DySDCx6Aex8cQk5DSXQMGDJBt27bJ2LFjZfPmzTp4nDdvXskiN4WFhSnzS6qh3WPGjJEPP/xQ6tSpI5dccok8+eST0qBBg0rfJ/zuELkeTpoaSFZl/10NLU0KKQG4z5R2GNlj2DcQHgJKi/gSTkYVRAa9jz6EliaFlOrLg1qrNsW9G85QC92oSzoLFy5MuX7uuefKv//97yrdJ+AS2wPJbI7RpcDSlJCSyhzAbYSTbiGojA9VlO4ioLSketLlcNKGQDKb43A1sDQppARgX8fIlepJH0LJyh6/C2GlKSElADeZ0gYjeHy5FA9CSjcRUFrAtXDSlUDS58DSlJCSKkrALraHk76Hkq6HlYSUAMJAOOk+qimBYLiTmDjKlXBShXXJi69cOweuvDYBRMPWcJJVr7M7X7aK+3VKkAG4hb9pv/B8R4tVvd1DBaXBw7ttD4BcCeLCPjc2V1aaUkkJwOwPzHGHPtmwOWQzgc1VlVRSAnCh7UU8qKaMFkO93WJvMuI4m8NJl6oEo2D7+Yr7tWrClwkA3AgnqZYM97yicgg1APvxdwxeA0DmCCgNFHfgkw3Xhi/HweZzaONrFkD4bAknCdA4z7a+dgGYh2AKvBaixVBvdxBQpkFFVuXZGqiZjvMKICh0lMpHMBkPW847ISWATNHmIt1rgtdF+Agp3UBAaRhbKtEI0DjPJr12+VIBMI/p4Y4NAZnrbAgqTX8dAzAHIRR4fQBVQ0BpEBvCSYJJzrvtr2EAfoc6NoRivuE5ORJBB2AX/mbB6yR+VFHaj4DSkEosG4IdhnLHz4aA2IbXMuCLODpMpoaThGDmMzU4NvU1DQCwD2F2uA6c0jzkR0CYCCjhRCjmG56PIzHMG4BtwRfsCZIJKQGUh8AJmWJeSiA9AkoDmFpxRjBpNpOfH1Nf0wD8CnFMDbtwdDxvhB6ADQgnwesHCA4BZcwVWKYGOaYGX7DnuTL1tQ3An3ASdjMtYDbtNQ4gXoST4HUEBIuAEtZU5aF8PG8AyvK542RSqAW3nk9CSgC+t7EIHq8n4GsElDEyrcKMYNJ+pj2HUb/GmYcS8Du0Ma3iDsHheQUAuIyQEsgyoJw6dark5+dLXl6e9OzZU5YtW1bh9lOmTJF27dpJrVq1pGXLlnLzzTfLvn37vD7/hJPwJaQEbEC7Zj8CLPeZ8hxHGcjTYUU2aNPCxd8leG0BhgSUs2fPloKCAhk3bpysWLFCunTpIv369ZOtW7em3f7pp5+WkSNH6u1Xr14tjz32mL6P0aNHV3nng+Rr5RVDg91k0vNqWhgP+NKu+VQ9aUpwhfBRJQtUjDYtXISTCBuvMfgs44By8uTJMmzYMBkyZIh06NBBpk2bJrVr15YZM2ak3X7x4sVy9tlny49+9CNdddm3b18ZOHDgUasuXWZKYGNKgAX3n2NTXvOAL+2aTx9uCSf9FPfzbkIwD/jSpgG+8elzHJB1QHngwAFZvny59OnT55s7qF5dX1+yZEna3znrrLP07yQbuQ8//FDmzp0rl1xySSYPDUeDK4TPt+fa12poZId2ze6QJu6QCvHi+QdS0aaFi9AIUeL1Bh/VzGTj7du3y+HDh6VJkyYpt6vra9asSfs76ts49Xu9e/eWRCIhhw4dkhtvvLHCoXD79+/Xl6Rdu3ZlsptGM6GSzLfACl8/53XXVY/9td9wLWECzBJFu+ZymxYnwikkXwe5hTmxBfR1P05E0kmtv+6b9xCgPPTVwv07BKLG+z98E3pisXDhQvnVr34lDz/8sJ7b67nnnpMXX3xRJk6cWO7vTJo0SerXr19yUQvrhMmniivCSX/x3APxtGtRt2k+VE8SToLXA+BPXw3mtf9hXpCKcBw+yaiCslGjRlKjRg3ZsmVLyu3qetOmTdP+zp133inXXnut/PjHP9bXO3XqJHv37pXrr79e7rjjDj1EvKxRo0bpBQtKV5u40PDFXT1JQIW4KympooRpomjXom7TXP8g63M4mf+tbRX+fP3GEyLaE/PEVUkZVRUlUBn01cLhertakTgCw/Ie0+f3Wiop4YuMAsqcnBzp1q2bLFiwQPr3769vKyoq0teHDx+e9ne+/PLLIzprqjOoqKFx6eTm5uoLgkM4CZOGewOmiKJdc7FNi6vCweVw8mjhY5D34WqQGedwb8AE9NVQVSZXMJbdN98CS0JK+CCjgFJRVSCDBw+W7t27S48ePWTKlCm6ckStFKcMGjRIWrRooUv/lcsuu0yvJnf66adLz5495YMPPtDVJ+r2ZIfOB3FWTxJOwqSQMooqSjVtQ61Vm0J9DLiDds0OLoWTQYSRQT6+S4FlHCFlFFWUdExRWbRpwfKhetLkULKy++1LWElbANdlHFAOGDBAtm3bJmPHjpXNmzdL165dZd68eSULDBQWFqZUlowZM0aqVaum/920aZOccMIJOpz87//+7+COAuUinERFrw0qKQHaNRs6MraHk3EHkr4FllRSwmf01YLjejhpazDpe1hJSAmXVUuUN87aIGq+LjUBc59mN0jN6jnWLZATV/WkD+FkndY7Q73/PR/VD/X+TRBHSBnFat5hVFAeKjogr3z6e9m5c6fUq1cvkPe14YuukNw6xwS0h1/bv+egPNT7r4HsJ4KXfO6/03us1KyZZ12HKuoOja3hpOmhZGXZGlbGMdQ77E5xGCt5Hzq0T15fNKHK7UXyfW3M0r6SF3Cbtq+4Tbv72y/TpnncrsXFxYDSpVCyMlwOK8NoE1xAu+ZZBSXs4GI4GXYYWdnHdC20jKOSksVygHAQTsbLlVCyvGOyKaykihJAVbgWTvoWTJY9bheDSiop4SICSgerJ10JJ+MIJLPZLxcCS4Z7AzCNTZWTLgaTLoSVUYeUrOgNwDS+BpM+BZWASwgoYRRTQ8nK7rMLYaUrVZQslAMEi06Ov8FkRcduelDpUiUl1TJAdH9rtqPNPvp5cSGspF2AawgoHWNj9aSNoaSLYSVVlABMYXr1pM/BpI1BpUshJQAcDeGkX1WVO4sDdeajhCuiXx3DIGEvkBP18G7bwkkV5rkUTrpwfFG/huJaQAqAuUwOJ1UYRzjJuakIwQAg1oc9Nr//8B6U3Xmznc2vW6A0rwNKl9gUTtoY3AVxvLYcs02vJQDRfDiN6sO7qeEkwWRm58pEpr62ACAILoRscXIh3CWkhAsY4h0SKsOOZEtAF8U5sG34NwD4yNSwzXSmDvuOaqg3i+UAdrIx4LE9VDON7cO+Ge4N21FB6QDTK95sqh6MiunnI8rXVJhhftjTOAA+8LV6knAymHPIeQSAcBBOhseFikrARgSUljM5nCSYtPv8mPzaAuAWk8JJQrVwzqkpTHqt+VLhBdjAtr8twjPOsyuvZaA0AsoQ+D682/TgzTScLwAwg0lBmmtMCn6jCCkJDwCE9d7C+0u0bDznhJSwlbcBpQtDP3cbWOFGMOnWuYvqNeZ7qA+YKooP5KZUtJkSnrmO8wwA2bEtJHONbeefkBI28jagtJ1p4SRVgO6eR9NeawCOxIdQNyr7fGHC+TYlGAcQPxvaUNvCMVfZWE0J2ISAMmA+VoKZFqi5gHMKwAdxh0QmBGW+8uHc04mN19SpUyU/P1/y8vKkZ8+esmzZsnK3Pe+886RatWpHXC699NKSba677rojfn7RRRdFcSjwHO8l5rHlObEhfEc47ZryzDPPSPv27fX2nTp1krlz56qb07rxxht1uzZlypSU2z///HO55pprpF69etKgQQMZOnSo7Nmzp9z7qSoCSguZVNFGkObHuY3iNedjuA/4/OGbcBJxh5RxvwYRntmzZ0tBQYGMGzdOVqxYIV26dJF+/frJ1q1b027/3HPPyaefflpyWbVqldSoUUOuuuqqlO1UIFl6uz/96U88jZYzPcCxJQjzkS3PjemvcYTTri1evFgGDhyoA8W33npL+vfvry+qfSvrr3/9qyxdulSaN29+xM9UOPnuu+/K/Pnz5YUXXpDXX39drr/++tCeNgJKy5gSTpo4FNlFnOeqc2G+WQBuBWMwZ4g9IaWbJk+eLMOGDZMhQ4ZIhw4dZNq0aVK7dm2ZMWNG2u2PO+44adq0aclFdcTU9mUDytzc3JTtGjZsGMXhwFO2BGA+s2XINyGlf+3a/fffr79Uu/XWW+XUU0+ViRMnyhlnnCEPPfRQynabNm2Sn/3sZ/LUU0/JMccck/Kz1atXy7x58+TRRx/VFZu9e/eWBx98UGbNmiWffPJJKMdJQBkgXyrACCb9POemhOMA7BdnKEQ4aSZXn5cwOq50NCt24MABWb58ufTp06fkturVq+vrS5YsqdQ5fuyxx+Tqq6+WY489NuX2hQsXSuPGjaVdu3Zy0003yWeffZbp0weDmPy3ZEPohW/wfMG0dm1J8e2lt1dUxaW6PamoqEiuvfZaHWKedtppae9DDevu3r17yW3qPtVjv/HGG1U9rLQIKC1iQkBkQlDmKx/OvS8hP4B4uBqCuSKu54cqSjvs2rUr5bJ///60223fvl0OHz4sTZo0SbldXd+8efNRH0fN6aWGwP34xz9OuV1VojzxxBOyYMEC+c1vfiN/+9vf5OKLL9aPBQSJsMtOpj9vJgfyvgqzXdtcfPvRtldtWc2aNeXnP/95ufehvpQrTW2vRh2Uvp8g1QzlXg3HkM/s+BCQ2fAc7Pmofqwhed11fK8BuC7MD9lxhUGEk3ZQz9P6jSfEvRvI0mtbTpGae4LtBB/aqzpsL0vLli1TblfzcI0fPz7Qx0pWT6rFBHr06JFyu6qoTFI/79y5s7Rt21ZXVV5wwQWB7wf8ZHrIhaM/f3U/ThgdUtZflz4Eg7vtWjqqIlMNA1fzWarFcUzhZUBpY+VX3NWThJPmiDukBGAWvhE/OsJJu8QRUqrgPLcwx8sOqy02bNigVxEtPR9kOo0aNdIL3GzZsiXldnVdzRtZkb179+q5tSZMmHDU/WnTpo1+rA8++ICA0kImtp2Ek27gPR8mtGtNi2+vaPu///3veoGdVq1alfxcVWn+8pe/1Ct5r1+/Xm9bdhGeQ4cO6ZW9k/cTNEqhcFSEk+aJ8zmJOywHYK84qicJJ+3E84ayVCeu9KW8jlxOTo5069ZND8UuPc+Wut6rV68KT+wzzzyjh9j953/+51GfgI0bN+o5KJs1a8aTBcCaxXNMDOd9FWa71qv49tLbK2oBOHW7ouaefPvtt2XlypUlF7WKt5qP8qWXXiq5jx07duhqy6RXX31VP7ZaNCcMVFBaIM5AiHDSXK5WUqpq5IZr41tAA/CdqR+os0HIBVOqKBGtgoICGTx4sJ7YXw3VVtUgqjpSrX6qDBo0SFq0aCGTJk06Ynh3//795fjjj0+5fc+ePXLXXXfJlVdeqatG1q1bJ7fddpucdNJJetEBoKpcanthfjUlQ73db9dGjBgh5557rtx3331y6aWX6tEBb775pjzyyCP656qdK9vWqVW8VRunFoJT1Orfav5ltXq4WjX84MGDMnz4cD3liQozw0BAiXIRTpovrpDStrko1byztVZtins3AK9FXT1JOGk/5qNEtgYMGCDbtm2TsWPH6on8u3btKvPmzStZMKCwsFCvQlra2rVrZdGiRfLyyy8fcX9qaJ2qNJk5c6auJlEds759+8rEiRPLrXiBuUyrICOcdJupISXcbtfOOussefrpp2XMmDEyevRoOfnkk2XOnDnSsWPHjB73qaee0qGkmmtZ3b/6ou6BBx4I9NhKI6A0fP7JuKonCSft4WolJQBki3DSHVGHlGFVUQbdQaX65ehUh0pd0lEL25SlKkYSifTPUa1atUqGvAFBIpz0g4khJe2I++3aVVddpS+VpeadLEut2K2CzqgQUOIIhJP2iSOkDLOKkmHeAIAkKikBuMjVcDKIERMuTrdhYkgJmMa7gFIN9bRFHNWThJP2opISgKmdpSiHd1M96SZCSgAuDu+2XVjte7r7dSG0NC2kpIoSpvEuoLRpeHfUCCfh+lyUANxGOIkgsFgOgLDZWj0Z9XzSFT22rYElISVQPgJKwCFUUQJ+saESJKrOjEvh5IVN1wR+n/M3tw/8PqNmexWlaZ1SAPGwLZyMM5Ss7H7ZFlbSHgDpEVAaKurh3VRPuoOQEgDsEkYgebTHsDWwjCqkpIoScI8NX+qZxNRg0pWw0qSQkqHeMAUBJQgnHRRlSBnWMG8WygHsr+igejLeULKyj29bWGl7JSUAf5lePWlTKHm0Y7AhqDQppARMwORxnldPUjkJQJk6dark5+dLXl6e9OzZU5YtW1buiTnvvPOkWrVqR1wuvfTSkm2uu+66I35+0UUXcbLh9dBuFQomLyYxdb8AwCUmh5Mq1HMhnEx3TK4dV1ioMIYJCCirwKUFcuCeKMPnOFacz9RXHVvEvQvGmj17thQUFMi4ceNkxYoV0qVLF+nXr59s3bo17fbPPfecfPrppyWXVatWSY0aNeSqq65K2U4FkqW3+9Of/hTF4QDGsSn8s2Vfowimw+jUmhxQAC4jfCmfDwGeyUEl7QLwDQJKj1E96T6eY1TG5MmTZdiwYTJkyBDp0KGDTJs2TWrXri0zZsxIu/1xxx0nTZs2LbnMnz9fb182oMzNzU3ZrmHDhjwhHomiI2B69aQtYZ+t+2768x82AhfAHiaGUCaHdr4dsymvD9oVxI05KA0TVSWaa8FV31ZrA72/lwvbBXp/yA7zUFbNrl27jggM1aW0AwcOyPLly2XUqFElt1WvXl369OkjS5YsqdTjPPbYY3L11VfLsccem3L7woULpXHjxjqY/O53vyt33323HH/88VkeDWBPOGV6sJfNsdg2T2VQWCwHgCvhU5KJAV3UTHxvN2U+ShbMQZwIKGGloAPJo92/zYFlVAvmhLVYjute3Xiy1Kgd7KqSh7/cr/9t2bJlyu1qCPf48eNTbtu+fbscPnxYmjRpknK7ur5mzdFDFjVXpRrirULKssO7v//970vr1q1l3bp1Mnr0aLn44ot16KmGg8MspnWebOVSMJnu2EwMKVkwB8DRUBX2DcLJI8+FSUGlKSElEBevAsog56ALY/5JqifjDSUr+9g2hpVRruoNc2zYsEHq1atXcr1s9WQQVDDZqVMn6dGjR8rtqqIySf28c+fO0rZtW11VecEFFwS+H/CrA2Ri9aTL4WSS79WUAGDzF4CEk3YElSaElDvb5kr9dV8XPABRotwJxlPhYJzhZFmm7Q9QHhVOlr6kCygbNWqkKxq3bNmScru6ruaNrMjevXtl1qxZMnTo0KM+CW3atNGP9cEHH/CEwSk2zNUYNNOON+zAOuhOvUmBBQD3mTrvomlMOke0E/AVAaVnbJl7MhkCmhwEmr5/cTz3NqzmjVQ5OTnSrVs3WbBgQcltRUVF+nqvXr0qPF3PPPOM7N+/X/7zP//zqKd148aN8tlnn0mzZs14CgL8dttHJlVPmhbURcnHYBaAfeJsK00ImUwK3WxAmPsNXz9nIl4ElIaIItixIZy0LfRTbNxnm4QxnQJSFRQUyPTp02XmzJmyevVquemmm3R1pFrVWxk0aFDKIjqlh3f379//iIVv9uzZI7feeqssXbpU1q9fr8POyy+/XE466STp168fp99xPnSGCOdSz4UJTAquAcAEPrTHLp87EwJuIGpezUEJs9ke8iX33+Q5KpmLEukMGDBAtm3bJmPHjpXNmzdL165dZd68eSUL5xQWFuqVvUtbu3atLFq0SF5++eUj7k8NGX/77bd14Lljxw5p3ry59O3bVyZOnBjKPJjw5wOwCSGUKYGcSUxZQCfMBXNMXPEVgLnibltNCNhsZ8L7ftzzUTIXJaJGQOlJRZfJ1ZO2B5M2BpVhYjVvOw0fPlxf0lEL25TVrl07SSTSf2CqVauWvPTSS4HuH0A4aTZTQkoA8B3hZPDnMs6gMu6QEogSQ7wN4PO8fa6FkzYcm8lhNQC7udwponKycuco7vNkQpBtQ3UV4IO45tCL8+/b5XY4Tj6fV+aiRJQIKD1Qx8BAypd5G305Tlt81bFF3LsAwMLQKe7QzTacLwCIns8hmuvnly+14AsCSkTOx8DOtGMOO7T2uSoYgFsI2zhvJnf+qWwBzBJXkGTi+5OLfA0paWsQFQJKx5lWPWlaUBcln4/dx3lfARsE+WHXxc4R4aSd5y/uilsAiJKL7a/p55tzDoSDgDJmvlSaMdT5m/NgCtPCawAwKWwinOQ8ArBXHBVfcVS4EZTFJ45zz1BvuM6bgDKouedsquQyJYAyKZQzAecDAMxGOGm/sIJtwgAApuD9KH4+hZQM80YUvAkoEQ/COLPPS5ghti/VwQDcqp4knOScRoVKGMAdUf89E06ag+cCCA4BZYzCDHBMqJ40JYQzFecHgCtc+XBOOOnWuWUuSgAucqXNdUnUzwlVlHAVASVCQfhmz3kyIcwGkBkfhtkQLrmHABiAi20k1dDwKaQEwkRA6aC4AycTQjebcL7cm/8VgH0Iz9w8z2EE3VQvAYiL7e8/6j25oovtbH9+KsOHL8kRn5rxPTSA0iHly4XtnJzGoO46vgcBTMQ3798gnAQAmN6W2hR+ZRs2VvR76zeekO3uREo9T7mFOZG9/up+nIjksYAokBzEVMHl6gIiVAPaee7irroFYK8wOkxRVlEQTkaPcw4AboWTUVRC2lRpGeXzxRfOcAkBpWPiDJoIJzmHAACYFlLa0JkFANvEGRjaFFa6iGHeCAsBJQJBOGn/uaSKEoAJqJ6EDYKqjqHyBbA3OInq79e06knTgkFTw0qqKIHMEVCiyggn4eN0BgDsxjDj+PEcAIA94aSJIaDp+2jS8xc0qigRBgJKh1AB5w5C3/B81bFFiPcO+MXlD95wK6Q0qcMKADYxLfSzbZ+j+qxERT5cQEAZA5cqygjS3Dq3pofcQS5UBcA8UXUmqNyDS6hiAaIXRRgU95eAJoV8th8DISVQOQSUjogjWCKcBAA78S07TEJgDADmhZMuMSGojPs5DQNfkCFoBJSAwVwJgV2qGgZgN8Iwf5+XoDunLnY2AZtFFZa4/CWfCUFemFw+Nh9en3AfAWUlMbTUzeDMBlGfa9OHeQNwUxSdBsJJAIDp4vryw4fwLu4Qli+2gIp5EVCatChGGJVkUQdKhJMAAD5kI2gEyAAQD1/CydJcDimjrKJkmDeC5EVACdiOUBgAqobwC6Z2whmOB9gj7L/XOL78M/F90fVqSr7kBdIjoERGCMr8wDBvAEAcCJIBIBquzzeZCRfPA1WUsBEBJWAJwmEArgq7Y0DohbBQBQOYwYVhplG+n7gYyNl2Tmg/gCMRUFouyko3AjJUBSt5A8FwoRMGxBko0zEH4PN0DLwHVnxuojw/YYeUrrxm4Q8CyggR0KCqCIlFvmiXwwsJqALfPqy6Wj35g3orSi4AALtFVU1HOMl5CgtfoCMINYO4E7iPYMzP6tw9H9WPezcAON6RorNUvsqGj+Vt9+yuM7J4RswJludvbh/3bgCAM2hvMz9f6zeeEMpzUfYzVW5hTqhfTNf9OBHa/QNBIqC0uGKLhUz8DYtfLmwX924AgPFsrJ4MsiKy9H3ZHFYCgMujD5iL0FyuhJSALQgocVRUTwIAEK6wh2oTVsbT6QQQPoaWHh3Vk363F1RRwhbMQQlYKKrQOOgqXeZhBYBUccwjacu8lbZUwFL9BMDk9w/CSTvOoQttCV8WoKoIKFEhqicBADZ+4Dc93Ip7gZu4Hx9+L14VhalTp0p+fr7k5eVJz549ZdmyZRVuv2PHDvnpT38qzZo1k9zcXDnllFNk7ty5VbpPIG6Ek3adyzBDStoZ+03NsA165plnpH379nr7Tp06HdGmjR8/Xv/82GOPlYYNG0qfPn3kjTfeKPn5+vXrZejQodK6dWupVauWtG3bVsaNGycHDoT3OiWgjEjQlWPMPwnCYwCwk0nBoOlBpelBM8w0e/ZsKSgo0B2pFStWSJcuXaRfv36ydevWtNurztaFF16oO2PPPvusrF27VqZPny4tWrTI+j7hjrCCnbAr5ggnOadwx+wM26DFixfLwIEDdcD41ltvSf/+/fVl1apVJduoL+Ieeugheeedd2TRokU6/Ozbt69s2/Z1GL9mzRopKiqS3//+9/Luu+/K//zP/8i0adNk9OjRoR0nASXKRQAGALBxCJLJoZapYaCp+xUmOu/umjx5sgwbNkyGDBkiHTp00B2q2rVry4wZM9Jur27//PPPZc6cOXL22WfrTtq5556rO4DZ3icAN4XddtjwOasiDPMOx+QM26D7779fLrroIrn11lvl1FNPlYkTJ8oZZ5yhA8mkH/3oR7pqsk2bNnLaaafpx9i1a5e8/fbb+ufq9//whz/o0FJt873vfU9uueUWee6550I6SgJKwGpRhMhU6wKA+5WKiun7B1SGqoZcvny57nQlVa9eXV9fsmRJ2t95/vnnpVevXnqId5MmTaRjx47yq1/9Sg4fPpz1fQIVoXrSbrZ+wcUwbzsdyKINUreX3l5RFZflba8e45FHHpH69eunfDlX1s6dO+W4447L4igqhwpKpEX1JAAgTLZ+uHc9+DNxX02uiEV0VFVH6cv+/fvTbrd9+3YdLKqgsTR1ffPmzWl/58MPP9RDu9XvqTm67rzzTrnvvvvk7rvvzvo+gbj41L66yvYqSsTfrm0uvr0y27/wwgtSp04dPU+lGsI9f/58adSoUdr7/OCDD+TBBx+UG264IbSnuGZo94zQUNEG333RLkcars2+4f6qYwuptWpTgHsE2MGHb85NC7NMDPwqs8/P7joj7t2wrjOZW5gT65C6+uvSd2yiVPhJI6leKy/Q+yz6ap/+t2XLlim3q3m41AT/gTxGUZE0btxYV4/UqFFDunXrJps2bZLf/va3+nHg73BS29pNwsloz/X6jSdY166o13TdjxOB36+JbZLP7VplnX/++bJy5Uodgqq5l3/4wx/qhXJUm1iaahPVkO+rrrpKDzUPCwEl4EC168uF7eLejYwWjKq7juJtAO6zMZxMIqSEaTZs2CD16tUrua5W2k5HVX6okHHLli0pt6vrTZs2Tfs7auXuY445Rv9ekpqzS1WaqGFv2dwnUB6q447+heL8ze2teAGFHVLCbWG2a02Lb6/M9moF75NOOklfvv3tb8vJJ58sjz32mIwaNapkm08++UQHmWeddZb+Ii9MpAQ4AsO7AQDwN5w08RjCqoyl2sgeqhNX+lJeRy4nJ0dXQC5YsCClQlJdV/NMpqMWxlFD19R2Se+9954OLtX9ZXOfQNRMfT9T79/pLkH/jovnnTDbbWG2a72Kby+9vaKGb6vbK6Lut/RQc1U5ed555+nHVwvmqLkvw0RAWYmhpEFUjAE2Y1oBADYwpfNiUrBXVS4dC/xRUFCgh6rNnDlTVq9eLTfddJPs3btXr36qDBo0KKU6RP1creI9YsQIHUy++OKLepEctWhOZe8TwDfCChVNDStNDYddmboAknG7ptqzefPm6fmU16xZo4eOv/nmmzJ8+HD9c/W7o0ePlqVLl8rHH3+sF+H5r//6Lx1IqmHcpcPJVq1ayb333ivbtm3TIwvKzmMZJIZ4Aw6wbZg3ADsF9U2+bR/kfQ/0GO4N2wwYMEB3pMaOHas7Ul27dtUdteSCAYWFhSlVIGoesJdeekluvvlm6dy5s7Ro0UJ37m6//fZK3yfcE0aIE1ZFnCntapTBYfKxbBkObuscx9lyaR5KG9u1s846S55++mkZM2aMDiLV0O05c+ZIx44d9c/VkHEVXKrAU80/efzxx8uZZ54pf//73+W0004rqbhUowvU5Vvf+lbK/iQS4cxjSkBpmbAr2RjeDQAATAwpVWfU9Y4ogqOqRJKVImUtXLjwiNvUsDdVSZLtfQJxMSGcjLOisfRjx9lG2DYfZRSL5SDedu2q4kpIdUlHrdr93HPPVfh41113nb5EyfmAUq3WC39d23BxhT9/8ouzItoTAECYTBju5WL1pGkhpcvoLAJmreAdNBfnEzSh7TXpi6ywQkpbqyiBSOagnDp1quTn5+vUtWfPnrJs2bIKt9+xY4eew0VNNK0m/jzllFNk7ty52Tw0cNRAsvTlaDLZ1nRUvwLZo11DVbkeTrrMhOojIEi0af6I6/3LxHkgk0zeNwABB5SzZ8/WE3SOGzdOVqxYIV26dJF+/frJ1q1b025/4MABufDCC2X9+vXy7LPPytq1a/XknmpuF5jF5oAriJDRpbAyDCyUA1fRrgH2BLEmdzpdrI6CfWjTgsEiIna+D5sQVIYVGofRxoT9Oretyhnxy3iI9+TJk2XYsGElqwVNmzZNr3Q3Y8YMGTly5BHbq9vVqniLFy+WY445Rt+mqi+BIIQVJqr7Zfg34AfatWi5WK0Wd2gXNYZ6A+aiTTNTGOFSHO2pLeFk3MO+bZuPErCyglJVQ6rlx/v06fPNHVSvrq8vWbIk7e88//zzetJpNcRbrTCkVg361a9+JYcPHy73cfbv3y+7du1KuYAKttKiqHS0sZrSlirY3W2L4t4FILJ2jTbN7Q6Tb+EkAHPRV0NYbB82bfv+J1GpD9dlFFCq5cdVByy5lHmSuq6WOk/nww8/1EO71e+peSfvvPNOue++++Tuu+8u93EmTZok9evXL7m0bNkyk900ii1BjC3BlhJ1aGhbSAnArHbNpTYNSCKYBcxDX80fUVZPuhDsxXEstowYYToDWL9ITiaKioqkcePG8sgjj0i3bt1kwIABcscdd+ih4eUZNWqU7Ny5s+SyYcOGsHcTlogrLCSkBJBtu+ZKm8a39kcipHOng2lLRxIIGn21IxHYuBlOuhJS2vZ5jHkoEdoclI0aNZIaNWrIli1bUm5X15s2bZr2d9TK3WruSfV7SaeeeqquTFHDEHJyco74HbXSt7oAJoWEzEsJuCeKdi3INo0PeTAJc1ECZqGvZqagA6WovlBxMZyMc15K00P5uh8n4t4NILMKStXpUtUiCxYsSPnWTV1X83Glc/bZZ8sHH3ygt0t67733dAcvXTgJmBhOJpmyH64M16+KL9rx/oGq86ldc7UiJK4OFNWTAEzjU5vGF3bhcjmcjPoYqdAHQhziXVBQINOnT5eZM2fK6tWr5aabbpK9e/eWrOo9aNAgPZwtSf1creI9YsQI3dipFb/VYgJqcQGYwfRAy7RQ0LT9iVKd1jvj3gUgcLRrQPYIagGz0KahqnwIJ20PKW0b5g2EMsRbUXNtbdu2TcaOHauHs3Xt2lXmzZtXssBAYWGhXgE1SS0G8NJLL8nNN98snTt3lhYtWuiw8vbbb8/0ob3mazBkahjIcG/AHbRr0XGlioBQLn4MzwPSo00zi63Du31CewJYHFAqw4cP15d0Fi5ceMRtakjB0qVLs3moWDGENF6mhpNJhJSAO3xp14AwMBdlahiRW2jusFj4gTYte65Oh1JZPlVPRh1SqnB5/cYTjG1vwpyHUk3JUH/d/lDuG24JfRVvwGWmhqimD9sHAFs7UlRPAgDiEHb1pK/hZJLvxw+YgIDSc6YGWaYGfwAAwM3glqGTAHxFOBfNeaCdASpGQAnj2BZO2ra/ptjd9pvVIhG/qVOnSn5+vuTl5UnPnj1l2bJlFW6/Y8cOvdiZWuUzNzdXTjnlFJk7d26V7hMwnQshHAAgGixkYiebwtqgX2O+T3GA+BFQAgEgpITNZs+erVf9HDdunKxYsUK6dOki/fr1k61bt6bd/sCBA3LhhRfK+vXr5dlnn5W1a9fK9OnT9SJo2d4nAPtFHeDa1IkEAFMr73gvjfacUEUJlI+AMkRUiGWOoM98vq4o77LJkyfLsGHDZMiQIdKhQweZNm2a1K5dW2bMmJF2e3X7559/LnPmzJGzzz5bV0mee+65OoTM9j4BAFSvAIgW4aT9bKnUVQvlAEdDQAk4Gq6aOr8oorNr166Uy/79+9NWQy5fvlz69OlTclv16tX19SVLlqS93+eff16vYq2GeDdp0kQ6duwov/rVr+Tw4cNZ3yfc//AbdMVA1J0qhncDgNtBh6nDW6m4i4evVZSm/h3ADzXj3gHEV7FmWoBlWsAHRGHvx/Wkel5eoPdZtG+f/rdly5Ypt6vh1uPHj0+5bfv27TpYVEFjaer6mjXpP5h9+OGH8uqrr8o111yj55384IMP5Cc/+YkcPHhQP0Y29wnADSrIfXbXGXHvBgDEyoaqNqonK3eO5m9uH/pzAeBrBJRAwCHrk1+cxTmFETZs2CD16tUrua4WswlCUVGRNG7cWB555BGpUaOGdOvWTTZt2iS//e1vdUAJAAAAcxFOxh9SqirK9RtPCCwQzy3MCeS+gDgRUMIIVE8CwVPhZOmAMp1GjRrpkHHLli0pt6vrTZs2Tfs7auXuY445Rv9e0qmnniqbN2/Ww7uzuU/AZAzvNhfVLQBcZvJQYAAIGnNQAoDHcnJydAXkggULUiok1XU1z2Q6amEcNaxbbZf03nvv6eBS3V829wnAHQS6AGAuqifNOWemBtBhzUPJQjk4GgJKxO5ax+aedO144L6CggKZPn26zJw5U1avXi033XST7N27V6/ArQwaNEhGjRpVsr36uVrFe8SIETqYfPHFF/UiOWrRnMreJwCYyNTOIgB72DD/JNwLdnndwQUM8QYAzw0YMEC2bdsmY8eO1cO0u3btKvPmzStZ5KawsFCvwp2kFt956aWX5Oabb5bOnTtLixYtdFh5++23V/o+AVtQDQgAcIXpIRsAvxFQAg4vlqNWan+5sF3cuwELDB8+XF/SWbhw4RG3qaHaS5cuzfo+gaqggwWTsVgBYJ+whrRWBRXdfsx7HORiOYDtGOLtKRVcmYDh0AAAuIfKUwAwC1/uuX8egxzmbWJoD/cRUAIAAAAAAMSAalngawSUQEhcrg6t03pn3LsAAKGjChAAEEcFW9CBlclVfzbifALhYA5KxMblAA8A8A0qA2DbnGAAzLSzbW7cu4AIvwx8dtcZnG/H5kFWf8P11+2PezdgKAJKw1GpBgCoKuYRQlydTjqXAICybUMm7UhpJrUpQX85ZuJiOerzY92PE3HvBjxCQAkAAABYjqoUACYPRw5i2pTS92FSWAkgGMxBiVj4Mrzbl+PM1u62RXHvAgAAADxm2igD16ZFUaFiGHM6h3W/cYa/QT33Qa7mDUSJgBIAAFgjqkqQuDs9AADYLoq2lPYacAcBZUioDIMp+rZaG/cuAAAAAE4zsWotruHdUVc3xllNacoQel8qjOE2AkoPEVjBFV+0M3eFOgAAAMA3cVY0ulBN6doQfyATBJQAAMCqihDYw4XOIgDAnvf8OPbBxCpKkz+/qUXdgHQIKBE53xaO8e14AQCwGdUrAFx4/4k6NDMhnEwyaV8AVB4BJQAAAAAAcCYQjHqfggyE+aIMviKgBAAAAAAAToSTSSbvm01YKAdRIaAEAAAohQ6NfUyc/wsA4sJ74jd8btNNnocSSIeAshysDgwAAAAAOBpfgyBbwr+o9pNgGKgaAkoAAAAAgHdMGrpq27yDtoSTtrLt9QAEgYASgJe+6tgi7l0AAAAAEAGqKM2ys21u3LsAAxFQAgAAAACMRqBhzjBjW6snbd1vE6YfMKnaGO4ioAQAAAAAAM6zKaRkmDd8Q0AJAAAAAACcCvjiwmI5QHYIKAEAABAaOrMAAJPQLgFmIqAEAAAA4JSpU6dKfn6+5OXlSc+ePWXZsmWV+r1Zs2ZJtWrVpH///im3X3fddfr20peLLroojF0HjK3cI9gD7GnXnnnmGWnfvr3evlOnTjJ37tySnx08eFBuv/12ffuxxx4rzZs3l0GDBsknn3yS9r72798vXbt21W3fypUrAz2u0ggoAQAAADhj9uzZUlBQIOPGjZMVK1ZIly5dpF+/frJ169YKf2/9+vVyyy23yDnnnJP25yqQ/PTTT0suf/rTn8LYfXiIuQajF3bYGlRYHMRrI6iFcmBPu7Z48WIZOHCgDB06VN566y39pZu6rFq1Sv/8yy+/1Pdz55136n+fe+45Wbt2rXzve99Le3+33XabDjHDRkAJAAAAwBmTJ0+WYcOGyZAhQ6RDhw4ybdo0qV27tsyYMaPc3zl8+LBcc801ctddd0mbNm3SbpObmytNmzYtuTRs2DCsQ4BFfAl/qJ4E7GnX7r//fv2l2q233iqnnnqqTJw4Uc444wx56KGH9M/r168v8+fPlx/+8IfSrl07+fa3v61/tnz5ciksLEy5r//7v/+Tl19+We69997Qj5OAEgAAAIDRdu3alXJRw83SOXDggO5g9enTp+S26tWr6+tLliwp9/4nTJggjRs31tUm5Vm4cKHeRnXmbrrpJvnss8+yPyAAsSN0zczuE6uF8jz4Ksx2bUnx7aW3V1TFpbq9PDt37tRDuBs0aFBy25YtW3Qw+uSTT+pANGw1Q38EAAAAAM7L2ZAjNfJyAr3Pw/uK9L8tW7ZMuV0Ncxs/fvwR22/fvl1XQzZp0iTldnV9zZr0Qy4XLVokjz32WIXzaqlKlO9///vSunVrWbdunYwePVouvvhi3dmrUaNGpocFALCAre3a5s2b026vbk9n3759ek5KNSy8Xr16+rZEIqHnX77xxhule/fuehqUsBFQAgAAADDahg0bSjpNyeHWQdi9e7dce+21Mn36dGnUqFG521199dUl/68WFejcubO0bdtWV1VecMEFgewLYCqXKw3VsT2764zQ5qGcv7l9KPftgp1tc6X+uvRVgz4Iq13LlFowRw31VoHk7373u5LbH3zwQd1Gjho1KtO7zBoBJQAv1Vq1Ke5dAAAAlaQ6caU7cuVRIaOqaFTD0kpT19W8kWWpakhVFXLZZZeV3FZU9HV1S82aNfWiASqILEvNU6ke64MPPiCghBcreCM+aqGc9RtPqPJcqbmFwVYCwsx2TVG3V2b7ZDj58ccfy6uvvpqyP+q6GiVQNjhV1ZRqzuaZM2em3B4E5qAEAAAA4IScnBzp1q2bLFiwICVwVNd79ep1xPbt27eXd955Rw/vTl7UKqbnn3++/v+yQ/CSNm7cqOegbNasWWjHAiAaLleIwr92TVG3l95eUYvilN4+GU6+//778sorr8jxxx+fsv0DDzwg//rXv0raxrlz55asKP7f//3fKdsGhQpKAAAAAM4oKCiQwYMH6yqPHj16yJQpU2Tv3r169VNl0KBB0qJFC5k0aZLk5eVJx44dU34/uUBA8vY9e/bo1b2vvPJKXX2iqi5vu+02Oemkk/SiA7ATC35UDuEdYFe7powYMULOPfdcue++++TSSy+VWbNmyZtvvimPPPJISTj5gx/8QFasWCEvvPCCnuMyOT/lcccdp0PRVq1a6etJderU0f+qUQXf+ta3Un4WFAJKAAAAAM4YMGCAbNu2TcaOHas7XF27dpV58+aVLBhQWFioV0CtLDW07u2339bD2Xbs2CHNmzeXvn37ysSJE2ObMwyAHZiHEnG0a2eddZY8/fTTMmbMGL2o28knnyxz5swp+eJt06ZN8vzzz+v/V/dV2muvvSbnnXdeLE8cASUAAABCE9biA0BFhg8fri/pqIVtKvL444+nXK9Vq5a89NJLnHCENr8g3F4sx7XK47ofJ+LeDS8Nz7Bdu+qqq/Qlnfz8fL0oTiay+Z1MMQclAAAAAACAoQiy4QMCSgAAAAAA4PX8k74dL2AaAspyNFx7IMrnAQ578ouz4t4FAAAAAEBM81CaYH8rMg6YjYASgLX4IgEAAAC+MyUAA4CqIKBE5KgoBAAAAAAAQBIBJeC4lwvbxb0LAGAVVvEEAMBPJs9DyUI5cB0BJYDY1F3HWxAAoOrmb27PaQQAwDI72+bGvQswCOkAECKGswMAAACwjcmVhDZyaZ7Q3SdWi3sX4CgCSg8x5BcAACC99RtP4NQAAABEjIAyJAxdrRiVhQBgh9zCnLh3ARZjPk8AgG1crh7d3+pA3LsAlIuAEgAAAAAAAEBsCCiBkLhcJbrno/px7wIAAAAAAHAEASUAALAGqzUDAAAA7iGgNJzLlWouVxgCAOzG3IkAAMA0+d/aFvcuAKEhoAQcDl9ZsR0AAD/UX7c/7l0AAGeEsVDOhU3XBH6fgEsIKAEAcFzdjxNx7wI8RBUqAAAAKouAErEypdIQAAAAAAAA8SCg9BRDf8ND6AoAqdZvPMHKU0IFoB1YOAkAAMB+BJSIHYEeAAAAAAB22H1itbh3AQ4ioAQCRNgKAAAAADDV/lYH4t4FIC0CShiBYA8AAHf4PDw+tzAn7l0AAACwDgEl4GjIyjyjAFwV5ZyDPgdtAAAAQFQIKGEM0wI+pLfno/qcGgdNnTpV8vPzJS8vT3r27CnLli2r1O/NmjVLqlWrJv3790+5/brrrtO3l75cdNFFYew6AIjvCzoBAADYjoDSY1TYBYdwFTabPXu2FBQUyLhx42TFihXSpUsX6devn2zdurXC31u/fr3ccsstcs4556T9uQokP/3005LLn/70pzB2H4BhqDoFAABApggoLeBTxRpBnz/qruPtxxSTJ0+WYcOGyZAhQ6RDhw4ybdo0qV27tsyYMaPc3zl8+LBcc801ctddd0mbNm3SbpObmytNmzYtuTRs2DCsQwDgqSiH+wMA/PKDeivi3gXAKyQEQBURqsJUu3btSrns37//iG0OHDggy5cvlz59+pTcVr16dX19yZIl5d73hAkTpHHjxjJ06NByt1m4cKHepl27dnLTTTfJZ599VrUDAmJCRSAAADBF/re2xb0LgdrZNjfuXYAhasa9A65XiO1uWxT3blgZ+F3bcHHcu2F1OPlyYbu4dwGVVOfD6lIjN9jvig7v//r+WrZsmXK7GsI9fvz4lNu2b9+uqyGbNGmScru6vmbNmrT3v2jRInnsscdk5cqV5e6DGt79/e9/X1q3bi3r1q2T0aNHy8UXX6xDzxo1amRzWAAsQJgLAED5Lmy6hup/oBwElDCSTSElYKoNGzZIvXr1UoZcV9Xu3bvl2muvlenTp0ujRo3K3e7qq68u+f9OnTpJ586dpW3btrqq8oILLqjyfiBauYU5sr/VgSovPhLkN/5qaK/6kA8AAADAfgzx9hyVdu5VTwJJKpwsfUkXUKqQUVU0btmyJeV2dV3NG1mWqoZUi+NcdtllUrNmTX154okn5Pnnn9f/r36ejpqnUj3WBx98wBMEK1EZiCjU/TjBiQaQMebjBeACAkoYy+QA0OR9C5NPCzb5IicnR7p16yYLFiwoua2oqEhf79Wr1xHbt2/fXt555x09vDt5+d73vifnn3++/v+yw8qTNm7cqOegbNasWWjHAiBehLgA4A7e0wFEjYASRjMxCDRxn3ysim24tmrDTfGNgoICPWR75syZsnr1ar2gzd69e/Wq3sqgQYNk1KhR+v/z8vKkY8eOKZcGDRpI3bp19f+rwHPPnj1y6623ytKlS3W1pQo7L7/8cjnppJOkX79+nHpYi86aWagYAuACNQUKzES7D0SLOSiPEoB80S4nqucCFsxHaXo4CWRjwIABsm3bNhk7dqxs3rxZunbtKvPmzStZOKewsFCv7F1Zasj422+/rQPPHTt2SPPmzaVv374yceLEQObBBIAwEBIAAADEh4DSoqG1dVrvDK3irm+rtaHct0shJeEkXDZ8+HB9SUctbFORxx9/POV6rVq15KWXXgps34DysFCOOagyAQAAQFUwxBvWiDMgJJwEACgEcTjaivcAAADIHAElrBJHUGhTOOnL/JMAAHMQ2gKwUd2PE3HvAgCgqgHl1KlTJT8/Xy+W0LNnT1m2bFmlfm/WrFlSrVo16d+/fzYPC5QEhlGEhlE9jo/qruO7EZiFdi0arszxRyAXPxbIAcpHm4ag0N4BiFLGKcHs2bP1iq/jxo2TFStWSJcuXfSqrFu3bq3w99RKrrfccoucc845We8swmNj5V2Y4SHBZPp5UAEX0a4B2aPzCpiFNg0A4E1AOXnyZBk2bJgMGTJEOnToINOmTZPatWvLjBkzyv2dw4cPyzXXXCN33XWXtGnTpko7bBsqxeyqcqRqEvCPTe1a/XX7s/5dV4eyxVVJRzAHwEQ2tWmuMGXuWSrL7cDzBAQUUB44cECWL18uffr0+eYOqlfX15csWVLu702YMEEaN24sQ4cOrdTj7N+/X3bt2pVyARVslQkWswkrq/K7JrGxChaIWxTtmittmikdMJjDpZDWlekH4Df6agAAm9XMZOPt27frb9iaNGmScru6vmbNmrS/s2jRInnsscdk5cqVlX6cSZMm6W/wEH3A1bfVWutPu+1BI4DoRNGu0aa5HdD9oN6KuHfDO1SfAOnRV0MYaOsARCXUlSp2794t1157rUyfPl0aNWpU6d8bNWqU7Ny5s+SyYcOGEPcSAIDw2jXaNLe5VEWYCV+PG3AJfTVzUMVtHto5wPAKStUZq1GjhmzZsiXldnW9adOmR2y/bt06vTjOZZddVnJbUVHR1w9cs6asXbtW2rZte8Tv5ebm6gsAM4Z3s0AOXBVFu0abdmQnLP9b24J5AktV1F3YNH3FK4JHpw0wE301AIA3FZQ5OTnSrVs3WbBgQUrHTF3v1avXEdu3b99e3nnnHT0MLnn53ve+J+eff77+/5YtW1b9CBAo5jEE4BPaNQSBwA6ACWjTEBbaOQDGVVAqBQUFMnjwYOnevbv06NFDpkyZInv37tUrxSmDBg2SFi1a6Dm38vLypGPHjim/36BBA/1v2dvDUmvVJvmqY4tIHisKqpKtTuudce8GkDVWtodpbGvXYCZf5uiKu5Nq8vyTLCQFE9Cm+Y0RBQC8CigHDBgg27Ztk7Fjx8rmzZula9euMm/evJIFBgoLC/UKqACi4WPVa8O1B6r8xQWQRLvmBjpl7oeTrs/9VvfjRNy7AAfQpgEAvAkoleHDh+tLOgsXLqzwdx9//PFsHtL6irHdbb+eo8wGrqzmDQCVRbuGILhcRelqOAm4iDYNYXC5jSuLNg+IB6WOIVdqAbZXT7JADgBXK9bCQKfGz+HdAABExeXPUfAbASUAAHBijj1TAizXQkrXjgd+mDp1quTn5+u5g3v27CnLli0rd9vnnntOz0Os5hQ+9thj9RRWTz75ZMo2iURCT3HVrFkzqVWrlvTp00fef//9sA8DHrWPQYVOprSFAOJr15RnnnlGL1yttu/UqZPMnTtX3ZzS9vXt21eOP/54qVatml7IOp0lS5bId7/7Xd0+1qtXT77zne/IV199lXbbqiKgtFAUFW0+zmsIAEBQXAn1XDkO+GX27Nl6sZhx48bJihUrpEuXLtKvXz/ZunVr2u2PO+44ueOOO3Qn7O2339aLpKnLSy+9VLLNPffcIw888IBMmzZN3njjDd1RU/e5b9++qA4LIWDu18zQJgB2tGuLFy+WgQMHytChQ+Wtt96S/v3768uqVatKtlGLgvbu3Vt+85vflPu4ql286KKLdJCpAtF//vOfehqRsNadIaAELEWIDABms70jZ9L+h1URxDA5N02ePFmGDRumQ8YOHTroULF27doyY8aMtNufd955csUVV8ipp54qbdu2lREjRkjnzp1l0aJFJdWTU6ZMkTFjxsjll1+uf/bEE0/IJ598InPmzIny0ABY3PZR3Yqo2rX7779fB4u33nqrbtsmTpwoZ5xxhjz00EMl21x77bV6ZIAaEVCem2++WX7+85/LyJEj5bTTTpN27drJD3/4Q8nNzQ3lySSgRLkIwBB0ta5aMApAfHyoFDHtw79JIV8mbN3vuJg+/YELdu3alXLZv39/2u0OHDggy5cvT+lwqUoPdV1VghyNCiMXLFgga9eu1cPYlI8++kg2b96ccp/169fXQ+wqc5+AS2gfAPPbtSXFt5cNHlXFpbq9slR1phox0LhxYznrrLOkSZMmcu6555Z8eWfMKt4A4kV4DMB2qnIt/1vb4t6NSNi28imdT/vUX5e+UxO1uhsSUiMn2C9CDh/4+v5atmyZcrsa5jZ+/Pgjtt++fbscPnxYd6RKU9fXrFlT7uPs3LlTWrRooTuINWrUkIcfflguvPBC/TMVTibvo+x9Jn8GAHCPre3a5uK2qapt1ocffqj/Vft077336vmZ1eiBCy64QA8VP/nkkyt9X5VFQBkRVTm2u21RoJVtdVrvDOz+AABAeGwJKU0MJ02rikU8NmzYoCfnTwp6eFndunX1AgF79uzRFZRqrq82bdro4d+AbV/eqffNC5uWH8j70qa50AbCXWG3a1VVVPR1fnXDDTfooeXK6aefrttINbR80qRJVX2IIxBQ4qiVen1breUsGYTqSQCIt2PmYofOx04Z80/aRXXiSnfkytOoUSNdAblly5aU29X1pk2blvt7arjcSSedpP9fVYmsXr1ad75UQJn8PXUfahXv0veptgUAwKR2rWnx7Zm2g2Ul2zs152Vpak7LwsLCSt9PJpgQDkBsq8UDsA9z7VU9CDQtDDRtf0qjehKZysnJkW7duukKj9JVIOp6r169Kn0/6neS84G1bt1ad+pK36eaL0zNzZXJfcKNqQzK41v7aHLbkQ3Xjgd+t2u9im8vvb0yf/78jNqs/Px8ad68uZ6TubT33ntPTjzxxAyOoPKooMRRUUVpDqonRRquPRD30wAATlRT0hmDq9Tw7MGDB0v37t2lR48eegXuvXv3lgxRGzRokJ5vMjk8Tf2rtlUreKtQcu7cufLkk0/K7373O/3zatWqyS9+8Qu5++679ZxbKrC88847dcetf//+sR0nYONoAl/xhRuibNdGjBihF7S577775NJLL5VZs2bJm2++KY888kjJfX7++ee6EvKTTz7R15NBpPpCTl1U26dWAVdzY3bp0kWPGJg5c6ae9/LZZ58N5QkloKxkIPJFO/O+EWMeStiEFbwBRLlQjg0ds2RAGEdQSThplrofBzsBv+8GDBgg27Ztk7Fjx+oFAVSnat68eSULBqgOmRrSnaQ6eT/5yU9k48aNUqtWLWnfvr388Y9/1PeTdNttt+ntrr/+etmxY4f07t1b32deXl7kxweYwoQv2wAfDMiwXVOrbj/99NMyZswYGT16tP5ybc6cOdKxY8eSbZ5//vmSgFO5+uqrj1isR305t2/fPrn55pt1oKmCSlWJqb7QCwMBJSqFKkq/qif3MLwbAJwMKm0KJm2pNvFtWKcthg8fri/pLFy4MOW6qoxUl4qoSpIJEyboC2Drl3ewv22Ev4Zn0K4pV111lb6U57rrrtOXoxk5cqS+RIE5KCNEBRkAANGxJeAKe37K5P3SAfsGC+QA8KGCOYp20Pa2xbb9p/2Cy6igtJyqdKvTemckj0UVZXyYexKAq6gUOXpnKdPKSts6Wy6EywDgM4Z6AwgCASUyQkgJAPZXiuw+sVqVh7Tub2XHglU2zEXpQ+AIAD6wqX1EdO2raV+6MTUJTMUQb8BwUVdPhjH/JNMbAMGpv24/pxNOC7sjx/A4AKYL8n0qqnCML9MAVBUBJTLGcOPocK4BwL3KBQAAXGRTSGnTvgK+IKCMWBiVZKy4DF80XMuQGcBF6wOsFIHdbAuTGSYHAPYFfzbsI+AjLwLKWqs2xb0LzqGyz81z7EPYzfsB4Cfbgi8AAGxtA00OAKPeNz5/AJXnRUAZBCq3jkRIGR7OLQDTUTkGGztxJlbrqoWrALjzdxhU+2ji+5UrIaWtbH9NAEdDQOmIuCrfCNJwNCyQA8CUD91UMQAAfBVHG2haSGna/gBIRUAJGCau0NeH4d0AADMRHgOAm0wJBePYD9o2IDMElA5VlFFFaT8qUgEgXHQW/BV0lS7THAAImytDeuMMKdVjmxKSAqgYASUCQbBm9zmkehLwi6lz3rnSEUNmCI0BVFb9dfutOFmmfoER5/ttHEEhwWR4r01TP0vCbgSUCAwhJeeuLOafBNxmagfsaAjEzMFz4U9gAwBRhoZxh5NBt298iQsfEFA6hko4+xDsVk7DtQdCfR4A+IVgzC907ADYWhEW9PuXCe1fmNWUDOkG7EVAGVNA4mplGWGbfQi1AZiEIMkfJnSSfaseBgCTJMPEIMJKgknAfjXj3gG4GVL2bbU27t2wAoEuAMQbkF3YdA1PQUznHuZWbAEI5ouM/a3MHAFkYvtXOqT8Qb0VGW1vGtq4zDBVCZIIKB2tiKvTemes+0BIWblz5HL1pKtVwgDs6YDZ2klDsKjKBeDC+1j+t7bFvRuRMTl8BBAeEgQ4HcCZinMDAOUjUHIblSUAED/ei+3B5yL4goAyRmFWmJkyryBBnLnnxJTXCIB4MLT0G3TS3D3XYXTqmH8SgCto//w6p7RfMB0BJbwJ5Ew4D5yL7LCCN+DfXD1RVwuY3KEAANj55V2QgRBVdABcR0DpMJMq5HwP50w79rBfG6bOP1lr1aa4dwFwkivfyBNScn4BwDe0fZxLwBRmpggGBxNUcrkV1EXBx2MGAFvRUXPnvJpebcQ0C0B2fBhFANjSlgFB8iagNFXYlWYmVVH6GNiZeKwmviYAIB0+lLvBpdDXlWphAHYKq1106X06Lj6dQ75gQ1gIKBEL14d8u358Ng7vBoDK8qmT4eq5JNwG4OI8lGGi7QMQN5IED5hcMediiGfyMZn8WgBgdycsrA5YXEETHTXOIQCYiC9gzBPWZ4Ygn2tbgnL4jYDSAL5XnLlSbejKcZiGeV8BxIWQ0s5zR+cdALJDuwcgTn4nYx4FJjZUztka8Nmy31G8BnwP2wG4FzjRWeOcmVx9wmIhgJ/vH2G2i7R7nC8gLjXjemCgPMmwr2+rtUafJBtCSQBAMJ21C5uu4VRW8lzFyZbqSRYYAAD7hdnm2dKeAUGi3MkQUVSe2VBFaXplYnKfTNsv1557AHYKs8Is7g/qqhMSd/hmOs4PAFfY8CUCVZQAXENA6RkbgyoTQsG4H98GDO8G4ANCOHPPS5iddROHdwM+s3F6A9veR0x4XzeZTefHttce/EVACWvDyjADw6geJwo2htI2z/dqq6lTp0p+fr7k5eVJz549ZdmyZeVu+9xzz0n37t2lQYMGcuyxx0rXrl3lySefTNkmkUjI2LFjpVmzZlKrVi3p06ePvP/++2EfBhytEjGlitLGTkkUOB8A4Ga7yPt7POfFlM87tn9uhH2Yg9KwCrTdbYsiCazqtN4Z+uNEIV14mMnclbaHjy6Hk0GrtWpT3LtgrNmzZ0tBQYFMmzZNh5NTpkyRfv36ydq1a6Vx48ZHbH/cccfJHXfcIe3bt5ecnBx54YUXZMiQIXpb9XvKPffcIw888IDMnDlTWrduLXfeeaf+2b///W8dgiKY6pGdbXON/JZ+fyv3v1xgXkqzOq4md+YAwGa0dwCiUtO3gOKrji0Cq+z6oh2l0iZyPXQ0EcO77TZ58mQZNmyYDhkVFVS++OKLMmPGDBk5cuQR25933nkp10eMGKGDyEWLFukQUlVPqpBzzJgxcvnll+ttnnjiCWnSpInMmTNHrr766vAPCs5SQVT+t7bFvRspAZ2PC+iYFE7aNjSO6hPADupvdfeJ1Yz/Ei+KdpGQMvVcAAgHQ7w9RWWd+3iOsWvXrpTL/v1Hztd04MABWb58uR6CnVS9enV9fcmSJUc9iSqMXLBgga62/M53vqNv++ijj2Tz5s0p91m/fn1dnVmZ+wRs41NnxcTFgqieBIDwmfbe7+o5CLpNM33+SRvnk0V4vKqgtEFUw7xdG+qNVIST9mjwwQGpWTPY74oOHfr6m/mWLVum3D5u3DgZP358ym3bt2+Xw4cP6+rG0tT1NWvKrwrbuXOntGjRQoeeNWrUkIcfflguvPBC/TMVTibvo+x9Jn8Gt4U9zNukKkqfqilN7JwSTgKwja1VlL5XUprYBgKuIaAEHBN1OBnW8G4WyKm6DRs2SL169Uqu5+YGN19h3bp1ZeXKlbJnzx5dQanmsGzTps0Rw7/h7zC2sJkYUroaVPrcKTO98gQAouZjSBlVO8iXbvAdQ7wNDFCinM+PSjvAXSqcLH1JF1A2atRIV0Bu2bIl5XZ1vWnTpuXetxoGftJJJ+kVvH/5y1/KD37wA5k0aZL+WfL3Mr1PuMX3YGe+A6GeOgaTj4OOHOC3KIaG2jRnbJTviSa3DUHz6Vhd+nuAnQgoQUjpEFeqJxEdtQp3t27ddBVkUlFRkb7eq1evSt+P+p3kHJdq1W4VRJa+TzUH5htvvJHRfQK2B1TJgM+2zo2N++xj5455uwB7ufAlng/tRJTHGMZnGhdeZ/ALQ7yhMR+l/aiGRbbU8OzBgwdL9+7dpUePHnoF7r1795as6j1o0CA932SyQlL9q7Zt27atDiXnzp0rTz75pPzud7/TP69WrZr84he/kLvvvltOPvlkHVjeeeed0rx5c+nfvz9PFLwY6l1eJ8fUYXG2dTSjCKfp2AGwTdRtomo7TG3XfGsXARcQUBoqysVyYD/Xwknmn4zWgAEDZNu2bTJ27Fi9iI0atj1v3rySRW4KCwv1kO4kFV7+5Cc/kY0bN0qtWrWkffv28sc//lHfT9Jtt92mt7v++utlx44d0rt3b32feXl5ER8d4pyHMuzFcmzv8JjQqbOxA2Z65SwAt4Q1T7MrbaSLIaWNbSPgAgLKAIKUL9q5UTpNFSUywfButwwfPlxf0lm4cGHKdVUZqS4VUVWUEyZM0BcgbLZUUZoQVtLpqhyqJwHYKo420fRRAqa3kwzvBjwNKGut2iRfdWwR924YW0VJSGkf16onw/q7B1yk5qDb2Ta41dltZmtIWVGHqKodPRfDSKonAcDcNtH2akoX203AJt4FlDg6Qkp7xBVOUj0JwLchbHGgo+RWOMnqp4C9X9QxzNvtaso421tb2jbaMESBJXgNF1cQRFWe+Vx9jph/EoDrH/Jh9nPL8G4ALoi7TbTlSzYXw0naMdiKgDIArgYqrgZgLojzuaF6EnBXWN+OR/lBOe4OGQAAprSRcbeJKvwzNag0ed98qoAGSiOgRIUIKc3DcwIAZnfIECyqJ49Epw6IHkNc3QgDTdkXPqsARyKgtEDcFWsEYuaI+7kI+7XoajUygOiHG/HB3w2uPI8EG9GbOnWq5OfnS15envTs2VOWLVtW7rbvvvuuXHnllXr7atWqyZQpU47YZvz48fpnpS/t28cfcsANLldRpgsH4wgITQkmw8bwbndNzaBdU5555hndTqntO3XqJHPnzk35eSKRkLFjx0qzZs2kVq1a0qdPH3n//fdTtnnvvffk8ssvl0aNGkm9evWkd+/e8tprrwV+bEkElLAiGAPPAYBoEKTAFFF3qunUuWP27NlSUFAg48aNkxUrVkiXLl2kX79+snXr1rTbf/nll9KmTRv59a9/LU2bNi33fk877TT59NNPSy6LFi0K6xAAZ0PKKMPKOANR256PivDZ0L52bfHixTJw4EAZOnSovPXWW9K/f399WbVqVck299xzjzzwwAMybdo0eeONN+TYY4/V97lv376Sbf7jP/5DDh06JK+++qosX75cP666bfPmzaEcJwGlJZVfcVdRKoSUfp97E16D2ai1alPcuwDg/6GKEq523mCWyZMny7Bhw2TIkCHSoUMH3fmqXbu2zJgxI+32Z555pvz2t7+Vq6++WnJzy18RumbNmjrATF5URQn8mvIgzKAm7DbS5PfV0kFiVcLEoO7H1+cB7rRr999/v1x00UVy6623yqmnnioTJ06UM844Qx566KGS6kk1WmDMmDG6QrJz587yxBNPyCeffCJz5szR22zfvl1XVI4cOVL//OSTT9Zf5Kkv9UoHnUGqGcq9WhBYfNWxRdy7kVVAtLttUexBWZ3WO2PdB5+YEEwCML9jtrNt+R1q36mOQP63tsW9GzC88xZmMEDlSbQOHDigqzxGjRpVclv16tX10LUlS5ZU6b5VR6158+Z6uFyvXr1k0qRJ0qpVq6ruMhAZW9pEU8NF0zESwE0HsmjX1O2q4rI0VR2ZDB8/+ugjXQWp7iOpfv36eui4+l31hd3xxx8v7dq108GlCjfVF3i///3vpXHjxtKtW7cQjtTTgBJVQ0jpXzgZRfUk808C/nx43t8q2vlmbemQgcoSlG/Xrl0p11VHKV21o6r4OHz4sDRp0iTldnV9zZo1WZ9i1Wl7/PHHdWdNDe++66675JxzztFVJHXr1uWpg7VtJKJD9SSiatc2F4eP6bZXtyd/nrytvG3UXMuvvPKKHhqu2jkViqpwct68edKwYcNQnkwCyoADli/a5ThfRVk6PKOaMtzzawJbh3YDqFrF1+4Tqzl1CgkpzRdXx42Kk+DU+2i/1KwZ7HvHoUNfD99t2bJlyu1qHi61cE1ULr744pL/V0PdVGB54oknyp///Gc9xxf8YXsbSXsY33m3ke+jAFxu145GDQP/6U9/qkPJv//973ohnUcffVQuu+wy+ec//6kX1wkaASWqhGpKt8NJAHCpQoROmbls7bghOhs2bNAriCaVN1ekmheyRo0asmXLlpTb1fWKFsDJVIMGDeSUU06RDz74ILD7BKJqI2kP3cOXbfYJs11rWnx7Rdsn/1W3lQ4a1fWuXbvq/1cL47zwwgvyxRdflOznww8/LPPnz5eZM2fquSmDRmmUhUyraFOBGqGam+cxqtcaw7sBRIUgzDxxPidhd+jCqDyJckEQk6jOUelLeR25nJwcPTfWggULSm4rKirS19W8kUHZs2ePrFu3LpQKEmTO17+LqqA95FzHib/ZcNu1XsW3l95eUcGiul1p3bq1DilLb6OGnKvVvJPbqMVwFDW0uzR1XT1+GMxKuhzgc9BiWrhmE84dAN+G9MT5Tb/qlNExMwPPA8KgFgaYPn26rvBYvXq13HTTTbJ37169+qkyaNCglMUG1AIEK1eu1Bf1/5s2bdL/X7o68pZbbpG//e1vsn79elm8eLFcccUVuqJl4MCBPIkecqWN5D3YjXNM9aT7CjJs10aMGKHnirzvvvv0PJVq6Pibb74pw4cPL5lf8he/+IXcfffd8vzzz8s777yj70MtBKfmnFRUUKnmmhw8eLD861//kvfee0+vCq4W2Ln00ktDOU6GeFvKlLkoy2JuyuzOl4lMq9TNRq1Vm+LeBQAGY4hb/Oc/TnTo3DVgwADZtm2bjB07Vk/2r4arqY5acjGAwsLClIqQTz75RE4//fSS6/fee6++nHvuubJw4UJ928aNG3UY+dlnn8kJJ5wgvXv3lqVLl+r/B2yeDoW2MNxzazPf55+0uV0766yz5Omnn5YxY8bI6NGj5eSTT9YreHfs2LFkm9tuu02HnNdff73s2LFDt2vqPvPy8kqGlqvrd9xxh3z3u9+VgwcPymmnnSb/+7//K126dAnlOL0NKFVw8VXHFnHvhpMhpcLclEc/PyaLMpz0ueoYCHKYzM626YeFmMyE1UrpmMV33l1Hxy5eqkokWSlSVjJ0TMrPz9eLAVRk1qxZge0b3GD7Yjml0RaGc06jwJdt/hieQbumXHXVVfpSHlVFOWHCBH0pT/fu3eWll17KfGezZH+JlIEIXMydUzFunBMANokiYDHhg7UPYZkpTBleb8LrDkCwXJzTLsr3KhPemwH4jYDScjYMwyWUs+sc2PCaAgBXgzOXmXJ+CScBwOz3adu5UD3JKADEgSTCAbYESjaFdEGx7Zijfi1RbQzAtMCIzlk459S380rHDvCDiyMNfHu/DhrnD8ieHcmWhQhejh7a2RTcZcrG47Ml6AYQLR+DFh8DtbCYdh5NCsOz4eIQVgBHR0hphyjbPJvbM9oylIdEwhG2hks2Bnk+B682hfis4A3YxcQP2qaFazYxMeSN6jXmY6gP+Bx8uPo3b9p7uOlcOl+uvqZhPm9X8XZlJW9bVvU+mtKhXp3WO2Pck8y4EkbaGnADcIsJq3qX1+HI/9a2mPfEHi510gDA5zaSNrDy58j3L3WBIJBKODbM24WgqXQlookBoMn75utrBkC4VSNRfpNu6oduEysCTWPyOTL1dQXADVG1k3G9l5n63h43zgsQLK8rKF1lcyVlOmWDwCgrLF0JIU0LJ5mjFYCtqCYp/5yYKsoOPcPiALg62oD2L/35cK1Nox1DnAgoIwhivmjHt/ZRh4aVCTFdDx+PhspJAJlWh+w+sZq3Q73LoqNmfjDpWuUkiwoAlf9b2dk21+l2Mu73ft+nPXE1nATiRkDpKNeqKDPle/hosiiqJ1kgB7CfDSGlj0GlDaFkXKg6AeBLG+lb25dEG1h1fNmGing/+VwUQUZcw1mpkAOvDQA2s6lSYP3/m39RXVxk47HZ9PoB4Abf5my2rV2w+VijeL75og1x8z6gdB0hJUx6TTD3JGD3t9J8cHU3zHMtdDWh4w4gPr5UaZnwXmdrO1FZrh8fYBKGeHswF6Xvw73xDQJrALaJexhbVZTt0NgwFM6FTlgcHXbCewBxzUVpSjuZbD9saOtsaw9NCKKBKGRVSjV16lTJz8+XvLw86dmzpyxbtqzcbadPny7nnHOONGzYUF/69OlT4fYIB8EU4n4NUD0Jk9GumR3EuPLBvHRFogkdn7L7Y8I+VZUrrxVfq8EQDNo0xMn29sS0/Y+qXeOLNpgg48Ri9uzZUlBQIOPGjZMVK1ZIly5dpF+/frJ169a02y9cuFAGDhwor732mixZskRatmwpffv2lU2bwp/70TRxBzRxB1SIj0/PPQvkIFO0a3ZwMXhKFxCG0SmK6nF8fY3QqYNJaNPMCPajfl8wsY20rb0xcV9NfF6rgi/bEPgQ78mTJ8uwYcNkyJAh+vq0adPkxRdflBkzZsjIkSOP2P6pp55Kuf7oo4/KX/7yF1mwYIEMGjQo04cPLdD4qmOLuHcjEgz39ospwWTc4TzgW7vm2vA104axhc20DpINXOvEAdmiTfOXyW2kqcO/aW+/xhdtMEVG6cWBAwdk+fLleph2yR1Ur66vq+rIyvjyyy/l4MGDctxxx5W7zf79+2XXrl0pF1eYENSYElohXDzPgBntWtRtmuvfThNEwaTXBJ06mOQAfTWjMCXKkUyo5I/78SuLzzvwUUZJ1fbt2+Xw4cPSpEmTlNvV9c2bN1fqPm6//XZp3rx5SmewrEmTJkn9+vVLLmpYOIIPrwiw3GXSc2tCKA/E2a652qbFGczwoR28FoAj0Vcz70s7Qsr4A0sTQlGTP+fwRRu8XcX717/+tcyaNUvPS6kW2CnPqFGj9DyXSaraxJUOXdwrepfFkG/3mBRORo35JxG1yrRrrrdpcTF5KBv8CKqj6tTFHbDAH/TV3GFrG2lLgOhy2wZYE1A2atRIatSoIVu2bEm5XV1v2rRphb9777336kbvlVdekc6dO1e4bW5urr5Eyad5KMsipHSHaeEk1ZMwXRTtWhxtmutzUdreAUPV0YED/Oqr2Yx5m1FZLrdtfNmGysgozcjJyZFu3brphQCSioqK9PVevXqV+3v33HOPTJw4UebNmyfdu3fP5CGdZVpwY1qwBfuH7Jv2Ggd8atd8+hDo8od5mPucMyQOJnK1TXOhTYzrPcOE90uYi7YM1g/xVsPUBg8erBuvHj16yJQpU2Tv3r0lq5+qFUxbtGih59xSfvOb38jYsWPl6aeflvz8/JI5verUqaMvMEcy4NrdtijmPUEmTAsmAdvQrtldRVm6A0Y1pfvobAMVo01DuvdN2kfz0b4BWQSUAwYMkG3btunQUYWNXbt21d+2JRcYKCws1CugJv3ud7/TK8r94Ac/SLmfcePGyfjx471+Dkyai7I0hnzbweRgMo7qSeafRLZo19wIKRU6YW4zpfMWZcWJCZVfsAttmrnibCtpH80WR/tWN8bFDoFAF8kZPny4vqSjFgoobf369dk8RCzimIfS5JBSoZrSTISTQLBcbdd8RCfMPaYEkwodOtiANi192L+zba4R7yGElDC1jQsLX7ahsswtwYIRTA7CfGTiXJMAzBT1h0GTghsfPuz7gucSAIJ9T+V91RxxPRcmfWYDSiPpMIDpi4kQisXPlufA9NcyAH/QCbOfaZ1oOnSA3Uyp4jLhvcS091cfEU4CRzI/8fCEDcGOLSGZS2w653G+hpl/EjCDCZ2usuiE2cfEcDmO17YpYQoAN9tL095nfWFiGweYwo7kw5Ogw4aQ0rbQzFacYwC2BhwmdLrKojNgDzptABDtey7vu9Ge7zjxZRtMR8qErBGiBc/Wc2pLuA7Ab3TEzGXyc2Ni6A7A/spkk95bTH3/dQnnGDg6+5IQxzW0MOixNVQzic3nMO7XLMO7AfOY1OlKh06COUwOJuN8LZsUogDwo700+b3YdiacW5Nea0B57ExEHA884g58qhqy2Rq0Rc2F82XraxVA+Ez/IGx6MOY6zj+AuJj2BYBJ7SXvzW6eT75sgy3sTUYcZ3vw40L4FhZXzovtr1HAF3F2xEzqdJneefCFTefbhtcvADeY9n5j03u1qTh/QOZqZv4riDIA+qKd/Q1D6TBud9uiGPckHi6EkaaGk3FXOwNwryOxv5UZ72+usa2jFmdYYFp1F+Ai9Xe2s21u3LthxXs37WLm58wkpoXfQEUIKCsIPr7q2KKic4cs+BJWuhhKAkC2H4x3n1jNyo4FnbJgz6ct6MwBiOu9x8T2ki/wMjtPJuHLNtiGgNJwrlRRVibEszmw9CmQpHoSsE/clSKmdrqOhk5Z1c6bjQgnAX/E3Tba1l7SJh793ACoGgJKC6ooXQ4pKxPymRRc+hREmhxOArCPyZ2uo6GqMvPzZCMTwkmGdwMwvb0kqDzyXJjIhDYNyBQBpSV8CSmzCQWDDDB9DyArQjgJ2M2EShHTO12VQViZ/lwAgI1MaBttbS99bQ9taPviDif5sg3ZIqC0iM8hZUUIFf0LJ1kcB7CXDZ2uyvKxc2ZDx8zGzhwA2Nxe+lBVaUv7R3sGmxFQWjLMO4mQElG/3gDA505XVTouLnTUbOmQudCZo+IEiO9vz8QqShvbS9e+uLOtDTSlPQOyxXhWS0MjgiNE8TozEdWT4Zg6dark5+dLXl6e9OzZU5YtW1butu+++65ceeWVevtq1arJlClTjthm/Pjx+melL+3btw9p72FjAOLDh2jVsSl7MZlt+xsEH16HAOxm6/uUrW2JrfttyuvEpM+asA8VlBZWUSZRTYkwX1vwx+zZs6WgoECmTZumw0kVOPbr10/Wrl0rjRs3PmL7L7/8Utq0aSNXXXWV3HzzzeXe72mnnSavvPJKyfWaNWlyYHdlSBAq6vBEUW1iW4fL9Y5cEh06IF4mV1G60F6WbXtMq66kbQTMQG/RcoSUCOM1ZSqqJ8MxefJkGTZsmAwZMkRfV0Hliy++KDNmzJCRI0cesf2ZZ56pL0q6n5cOJJs2bRrOTsOZTpjtna4g0UHyM5wEYAbT2keX28s4p0Vxsa01pV3jyzZUFQGl5VWUCiElgnwtwR27du1KuZ6bm6svpR04cECWL18uo0aNKrmtevXq0qdPH1myZEmVHv/999+X5s2b62HjvXr1kkmTJkmrVq2qdJ9wk0udLpjNlE5caXToAGT6HuZim+licOhruwZki4DSsWCJVb5RldePyVytnqz170+lZvVgP5AdKvr6+WzZsmXK7ePGjdNzQ5a2fft2OXz4sDRp0iTldnV9zZo1We+DGir++OOPS7t27eTTTz+Vu+66S8455xxZtWqV1K1bN+v7hbtVIi53umAGOnEAbGwf0+GLPSRfB6bgyzYEgYDSkSrKJKopkc1rBm7asGGD1KtXr+R62erJMF188cUl/9+5c2cdWJ544ony5z//WYYOHRrZfsA+dLoQ1uvKRHToAPMQUsIGprZrQFUQUDqIkBKVfZ3YwtXqybCpcLJ0QJlOo0aNpEaNGrJly5aU29X1IOePbNCggZxyyinywQcfBHafcLcDRkiJIF9LAOAq2kv/mNiu8WUbglI9qDvyhS1BiQqfbAqgEC1eG0jKycmRbt26yYIFC0puKyoq0tfVvJFB2bNnj6xbt06aNWvGyUelP4Cb+CEc9jD99UOHDjCXTX+ftJf+ML1dA6qKgNJxBJWw/fVgy5cCNisoKJDp06fLzJkzZfXq1XLTTTfJ3r17S1b1HjRoUMoiOmphnZUrV+qL+v9Nmzbp/y9dHXnLLbfI3/72N1m/fr0sXrxYrrjiCl2pOXDgwMiPD3Z3wPgwDl43yMbUqVMlPz9fL9SmphlZtmxZhds/88wz0r59e719p06dZO7cuSk/TyQSMnbsWP1FW61atfRicmoxOLjLhjayNNpLt5n6/Nr2d2KzqTG0a59//rlcc801elSeGhGnpupShSdhIaD0JDCxLZRCsGwMJhGdAQMGyL333qsbqK5du+qwcd68eSUL5xQWFuqFbpI++eQTOf300/VF3a5+V/3/j3/845JtNm7cqMNItUjOD3/4Qzn++ONl6dKlcsIJJ/DUImNUh8C11wodunDNnj1bf/mmFodbsWKFdOnSRfr16ydbt25Nu736Ik21Warj9dZbb0n//v31RS3slnTPPffIAw88INOmTZM33nhDjj32WH2f+/btC/logMqz4f0PmeN5xeyY2jUVTr777rsyf/58eeGFF+T111+X66+/PrQnpFpxamr8u9iuXbukfv360qfZDYGvdpst0xfLqQgrffvD9lDStC8D1OrYr3z6e9m5c+dR53aM830tyP1E8JLP/Xd6j5WaNfOMOMWmzkWZDqt8w4XOmykB5aFD++T1RROq3F6E+b6WzT6qypIzzzxTHnrooZKpS1q2bCk/+9nPZOTIkWm/qFMjB1TnK+nb3/62/tJOddxUd6l58+byy1/+Uo8QUNT+qC/yHn/8cbn66qsDOFJ7mdiu+dpGlkZ7aT/T2zZT2rLSaNeCa9fUyLoOHTrIP//5T+nevbveRhWxXHLJJboYRf1+0KigdCQ4yQTVdO5z4Tm2+W8MQHhsqZBDdGx7PZjYoXOJmnpk+fLleqhaUvXq1fX1JUuWpP0ddXvp7RVVRZLc/qOPPpLNmzenbKMCORWElnefcIetf7O0l3azrW2De+3akuJ/1bDuZDipqO3VY6uKyzAQUHocoLgQYiEVzykAXzpfdLxg42vAxr81k6r0Sl/2709/Lrdv3y6HDx8umaYkSV1XnbF01O0VbZ/8N5P7hFts/tu17X0SdjxnNv9NmML0dm1z8b+NGzdO+XnNmjXluOOOC63tqxnKvcIqpUNKhn/bybWg2fbwH7CR+qBp4zC25Id4hrL5w4aOm69y3vsk8GlLqhd9/RlHDdEuTc3DNX78+EAfC3CxnVRoK+1gS/vmUzhJuxYtAsoAghSb56MsL+giqDSfa6FkEuEkgGzQ+XKfLR238vjUoQvDhg0bUuagzM1NHxQ1atRIatSoIVu2bEm5XV1v2rRp2t9Rt1e0ffJfdZta7bT0Nmo+L8AWtJVmsr19g5vtWtPibcouwnPo0CG9srf6WRgY4h0AFwOV5FBhV0Mwm/G8AAiLCwGKjcN+4f5z6sLfVtxUJ670pbyOXE5OjnTr1k0WLFhQcptaJEdd79WrV9rfUbeX3l5RK5Ymt2/durXujJXeRg3HU3NwlXefcJMrf8suvK+6wrbnwZW/AROY3q71Kv53x44dev7LpFdffVU/tpqrMgxUUAbEtUrK0qiqjJ8vQbGLYT9gG5uHsJVGlYjdbOuwwSwFBQUyePBgPbF/jx49ZMqUKXo10yFDhuifDxo0SFq0aCGTJk3S10eMGCHnnnuu3HfffXLppZfKrFmz5M0335RHHnlE/7xatWryi1/8Qu6++245+eSTdcfuzjvv1CuY9u/fP7bjRDxcaScV2sr4z71NCCf9atdOPfVUueiii2TYsGF65e+DBw/K8OHD9QrfYazgrRBQIuuQjGHg4fIllEwinATM4WLnS2GeSvPZ2GE7Gjp00RswYIBs27ZNxo4dqyfyV8PV5s2bV7IYQGFhoV6FNOmss86Sp59+WsaMGSOjR4/WnbU5c+ZIx44dS7a57bbbdGfw+uuv1xUlvXv31veZl5cX+fEhfi61kwpBZfTnGrChXXvqqad0KHnBBRfo+7/yyivlgQceCO3Jq5YoFtq9B0SVmqolz/s0uyHwibeD5moV5dEQVladb4GkjeHkoaID8sqnv5edO3emzBdi2vtakPuJ4CWf++/0His1a5rbsXWp45UOYaU5XO6smR5OHjq0T15fNKHK7UVJm9b4x+G0aVsfpU0zmC3tWhhcbStpI4Nne1tnenuWRLtmNyooA+byUO+KUF1Z9XPmK1vCScA3rlWHlEVlpTnn31W2dOYAZM/VtpI2MvjzaDPaM0SFgDIEvoaURwvffK6yJIxMj3ASMJurHa+y6IhFf55dR2cO8IfrbSVtZNXOme1ozxAlAsqQEFJWPqRzKbgkiKw8wknADq53vI7WqWCYW3DnEgBc5UtbSVhZuXPjCsJJRI2AMkSElMGEeiYFmASQwSCcBOziS8crHQLL7M6Tz+jQAX7/7fvSXhJW0vYBQSOgDBkhZdURCrqFcBKwk88h5dGCON+qLAkjy0c4CcDH9tKXL/N8av9ozxAHAsoIEFIC3/wtALCXj52uqnZYbO6k+dQRCwKdOQCl3w98bi9d+DLP5zaQ9gxxIaCMCCElfEc4CbjB905XWB2cKDtuPne6wkBHDkB57w20l0dve+IOLmkTU9GmIU4ElDEENL6v8A3/EE4CbqHTFTw6SHaiIwegMu8RBJXBtn/lhZq0pVVDm4a4EVDGgGpK+IJgEnAXISV8R0cOQCbvF4SUwSGIDBbtGUxRPe4d8BXBDVzHaxzw4wMtH2rhI173AHjfgAtoz2ASKihjxJBvuIhgEvAPlSHwBR05AEG8h1BNCRPQpsE0VFAagEAHruC1DPiLakq4jo4cAN5P4AraNJiICkpDUE0JmxFMAkiimhKuoRMHIMz3FqopESXaNJiMgNIwBJWwCcEkgHTodMEVdOQARPU+Q1CJqF5rgKkIKA1FUAmTEUwCqAw6XbAVnTgAcb3vEFQirNcWYDoCSsMRVMIkBJMAssGwb9iCThyAuBFUIozXE2ADAkpLEFTChNcfAGSLDhdMRgcOgGloNxHE6wewCQGlZQgqEcfrDQDC+MDMMDbEjQ4cANMRVCKb1wtgIwJKB4Kjrzq2iHFP4BpCSQBRodOFONB5A2Aj2kxU5vUB2IyA0gGElQjyNQQAUaOqElG/zgDAVrSZKO/1ANiOgNIxhJXI5rUCAKagQgRhvJ4AwEW0mX6ibYOrCCgdRliJ8l4PAGDbh2/mq0S2rx0AcB1VlX6gfYPrCCg9DaeYt9J9BJIAXELnC5V9fQCAz2gv3UL7Bp8QUHoqXXhFaGk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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotter = Plotter()\n", "\n", "# plotting at fixed time t = 0.0\n", "plotter.plot(trainer, fixed_variables={'t': 0.0})\n", "\n", "# plotting at fixed time t = 0.5\n", "plotter.plot(trainer, fixed_variables={'t': 0.5})\n", "\n", "# plotting at fixed time t = 1.\n", "plotter.plot(trainer, fixed_variables={'t': 1.0})" ] } ], "metadata": { "interpreter": { "hash": "56be7540488f3dc66429ddf54a0fa9de50124d45fcfccfaf04c4c3886d735a3a" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }