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bcolloran/Simple_GP_regression-periodic-sklearn-Mauna-rev4-lbfgs.ipynb

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Simple_GP_regression-periodic-sklearn-Mauna-rev4-lbfgs.ipynb
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"source": [
"# Reproducing Sklearn results with Gpytorch #4\n",
"## Kernel initialization and optimization\n",
"\n",
"In this notebook, I'm trying to reproduce the quality of the fits I'm able to get in sklearn using gpytorch.\n",
"\n",
"Upon noticing that the fits I was getting were not as good as what I was seeing with sklearn (and after reading through previous GH discussions and issues), I tried:\n",
"\n",
"1. Initializing the noise level at a lower level (this helped substantially)\n",
"\n",
"2. Using the same kernel initialization in sklearn and gpytorch, and verifying that the initial kernels produce identical results before optimization. See:\n",
"\n",
" A. https://github.com/cornellius-gp/gpytorch/discussions/1646 and https://gist.github.com/bcolloran/8869c54f265606b66c09a56eb1241631\n",
" \n",
" B. https://github.com/cornellius-gp/gpytorch/discussions/1661 and https://gist.github.com/bcolloran/7686b63a5544295f0382415e553194ea\n",
"\n",
"\n",
"Having verified that I was starting the kernels from the same initialization (at least insofar as the kernels produce identical results in sklearn and gpytorch before any optimization), I tried optimizing the models further from this point. In sklearn, this is as simple as `gp.fit`; for gpytorch, I started with the basic optimization example using Adam. The results remained quite different, so digginf in a bit, I noticed that sklearn uses LBFGS as it's default optimizer. In what follows, I've attempted to use [gpytorch's LBFGS optimzer](https://github.com/cornellius-gp/gpytorch/blob/master/examples/LBFGS.py) based on [the example here](https://docs.gpytorch.ai/en/v1.1.1/examples/02_Scalable_Exact_GPs/Simple_MultiGPU_GP_Regression.html).\n",
"\n",
"So far, starting from the same initial kernels and using LBFGS in gpytorch still produces lower quality fits, so I'm hoping someone may have pointers about where to look next.\n",
"\n",
"### Outline:\n",
"* imports and loading data\n",
"* set up code for fitting sklearn models\n",
"* set up code for fitting gpytorch models\n",
"* define kernels with the same initialization\n",
"* verify that initialization is identitical\n",
"* compare results of optimizing gpytorch and sklearn models from same initialization"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 1,
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"source": [
"import torch\n",
"import gpytorch\n",
"from gpytorch.kernels import Kernel, PeriodicKernel, LinearKernel, RBFKernel, RQKernel, ScaleKernel, ProductKernel, AdditiveKernel\n",
"from matplotlib import pyplot as plt\n",
"from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
"from sklearn.gaussian_process import GaussianProcessRegressor\n",
"from sklearn.gaussian_process.kernels \\\n",
" import RBF, WhiteKernel, RationalQuadratic, ExpSineSquared, ConstantKernel\n",
"import numpy as np\n",
"import gc\n",
"from typing import Union\n",
"from multimethod import multimethod\n",
"\n",
"from sklearn.datasets import fetch_openml\n",
"\n",
"import sys\n",
"sys.path.append('../../gpytorch/examples')\n",
"from LBFGS import FullBatchLBFGS\n",
"\n",
"%matplotlib inline"
]
},
{
"source": [
"### Load Mauna Loa data"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"def load_mauna_loa_atmospheric_co2():\n",
" ml_data = fetch_openml(data_id=41187, as_frame=False)\n",
" months = []\n",
" ppmv_sums = []\n",
" counts = []\n",
"\n",
" y = ml_data.data[:, 0]\n",
" m = ml_data.data[:, 1]\n",
" month_float = y + (m - 1) / 12\n",
" ppmvs = ml_data.target\n",
"\n",
" for month, ppmv in zip(month_float, ppmvs):\n",
" if not months or month != months[-1]:\n",
" months.append(month)\n",
" ppmv_sums.append(ppmv)\n",
" counts.append(1)\n",
" else:\n",
" # aggregate monthly sum to produce average\n",
" ppmv_sums[-1] += ppmv\n",
" counts[-1] += 1\n",
" \n",
" months = np.array(months).reshape(-1, 1)\n",
" avg_ppmvs = np.array(ppmv_sums) / counts\n",
" return months, avg_ppmvs\n",
"\n",
"def to_torch_cuda(V):\n",
" return torch.tensor(V).flatten().double().cuda()\n",
"\n",
"X_raw, Y_raw = load_mauna_loa_atmospheric_co2()\n",
"\n",
"X_test = np.linspace(X_raw.min(), X_raw.max() + 30, 1000)[:, np.newaxis]\n",
"\n",
"f, ax = plt.subplots(1, 1, figsize=(14, 4))\n",
"ax.plot(X_raw, Y_raw)\n",
"\n",
"X_torch = to_torch_cuda(X_raw)\n",
"Y_torch = to_torch_cuda(Y_raw)\n",
"X_test_torch = to_torch_cuda(X_test)"
]
},
{
"source": [
"# Scikit learn code for fitting GPs"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def fit_sklearn_gp(kernel):\n",
" # SET `normalize_y=False` !!!\n",
" gp = GaussianProcessRegressor(kernel=kernel, alpha=0, normalize_y=False)\n",
" gp.fit(X_raw, Y_raw)\n",
"\n",
" log_likelihood = gp.log_marginal_likelihood(gp.kernel_.theta)\n",
" # print(\"\\nLearned kernel:\\n%s\" % gp.kernel_)\n",
" # print(\"Log-marginal-likelihood: %.3f\" % log_likelihood)\n",
"\n",
" y_pred, y_std = gp.predict(X_test, return_std=True)\n",
"\n",
" # Illustration\n",
" f, ax = plt.subplots(1, 1, figsize=(14, 5))\n",
" ax.plot(X_raw, Y_raw, 'k.', markersize=4)\n",
" ax.plot(X_test, y_pred,\"-\", color=\"steelblue\")\n",
" ax.fill_between(X_test[:, 0], y_pred - 2*y_std, y_pred + 2*y_std, alpha=0.3, color='steelblue')\n",
" ax.set_title(f\"log_likelihood: {log_likelihood:.2f}\")\n",
" return gp"
]
},
{
"source": [
"# Gpytorch code for fitting models"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# This block just contains function to print gpytorch kernel info\n",
"# in a format similar to sklearn, so that it's easy to check that kernels\n",
"# specs are the same\n",
"\n",
"def kernel_details_str(k: Kernel) -> str:\n",
" if isinstance(k, RBFKernel):\n",
" return f\"RBF(length_scale={k.lengthscale.item():.3f})\"\n",
"\n",
" elif isinstance(k, PeriodicKernel):\n",
" return f\"ExpSineSquared(length_scale={k.lengthscale.item():.3f}, periodcity={k.period_length.item():.3f},)\"\n",
"\n",
" elif isinstance(k, RQKernel):\n",
" return f\"RationalQuadratic(α={k.alpha.item():.3f},length_scale={k.lengthscale.item():.3f})\"\n",
"\n",
" elif isinstance(k, AdditiveKernel):\n",
" return f\"+\"\n",
"\n",
" elif isinstance(k, ProductKernel):\n",
" return f\"*\"\n",
"\n",
" else:\n",
" raise TypeError(\"Invalid kernel type\")\n",
"\n",
"@multimethod\n",
"def print_kernel(leaf: Union[RBFKernel,PeriodicKernel,RQKernel]) -> str:\n",
" return kernel_details_str(leaf)\n",
"\n",
"\n",
"@print_kernel.register\n",
"def print_kernel_scale(node: ScaleKernel) -> str:\n",
" scale = node.outputscale.item()\n",
" return f\"{np.sqrt(scale):.2f}**2 * {print_kernel(node.base_kernel)}\"\n",
"\n",
"\n",
"@print_kernel.register\n",
"def print_kernel_op_node(node: Union[AdditiveKernel, ProductKernel]) -> str:\n",
" children = f\" {kernel_details_str(node)} \".join([print_kernel(k) for k in node.kernels])\n",
" return f\"({children})\"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"class ExactGPModel(gpytorch.models.ExactGP):\n",
" def __init__(self, train_x, train_y, likelihood, kernel):\n",
" super(ExactGPModel, self).__init__(train_x, train_y, likelihood)\n",
" self.mean_module = gpytorch.means.ConstantMean()\n",
" self.covar_module = kernel\n",
" \n",
" def forward(self, x):\n",
" mean_x = self.mean_module(x)\n",
" covar_x = self.covar_module(x)\n",
" return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)\n",
"\n",
"\n",
"def log_marginal_likelihood(model, X, Y):\n",
" L = model.covar_module(X).detach().cholesky().clone().cpu().numpy()\n",
" Y = Y.clone().detach().cpu()\n",
" N = Y.shape[0]\n",
" sigma2 = model.likelihood.noise.item()\n",
"\n",
" K = L @ L.T + sigma2 * np.eye(N)\n",
" K_inv = np.linalg.inv(K)\n",
" _, log_det_K = np.linalg.slogdet(K)\n",
"\n",
" return -0.5 * (Y.T @ K_inv @ Y + log_det_K + N * np.log(2 * np.pi))\n",
"\n",
"\n",
"def plot_model(model, likelihood, train_x, train_y, test_x):\n",
"\n",
" model.eval()\n",
" likelihood.eval()\n",
"\n",
" # Make predictions by feeding model through likelihood\n",
" with torch.no_grad(), gpytorch.settings.fast_pred_var():\n",
"\n",
" observed_pred = likelihood(model(test_x))\n",
" # bigger plot of the last model\n",
" f, ax = plt.subplots(1, 1, figsize=(14, 5))\n",
" # Get upper and lower confidence bounds\n",
" lower, upper = observed_pred.confidence_region()\n",
" # Plot training data as black stars\n",
" ax.plot(train_x.cpu().numpy(), train_y.cpu().numpy(), 'k.', markersize=4)\n",
" # Plot predictive means as blue line\n",
" ax.plot(test_x.cpu().numpy(), observed_pred.mean.cpu().numpy(), \"-\", color=\"steelblue\")\n",
" # Shade between the lower and upper confidence bounds\n",
" ax.fill_between(test_x.cpu().numpy(), lower.cpu().numpy(), upper.cpu().numpy(), alpha=0.3)\n",
" log_likelihood = log_marginal_likelihood(model, train_x, train_y)\n",
"\n",
" ax.set_title(f\"log_likelihood: {log_likelihood:.2f}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def create_and_train_model(\n",
" kernel_gen_fn,\n",
" optimization_fn,\n",
" train_x,\n",
" train_y,\n",
" test_x,\n",
" likelihood_noise = None,\n",
" training_iter = 100, \n",
"):\n",
" likelihood = gpytorch.likelihoods.GaussianLikelihood().cuda()\n",
" if likelihood_noise is not None:\n",
" likelihood.initialize(noise=likelihood_noise) \n",
" model = ExactGPModel(train_x, train_y, likelihood, kernel_gen_fn() ).cuda()\n",
" # \"Loss\" for GPs - the marginal log likelihood\n",
" mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)\n",
"\n",
" model.train()\n",
" likelihood.train()\n",
" losses = optimization_fn(\n",
" model,\n",
" mll,\n",
" train_x,\n",
" train_y,\n",
" training_iter, \n",
" )\n",
"\n",
" plot_model(model, likelihood, train_x, train_y, test_x)\n",
" return model, likelihood, losses\n",
"\n",
"\n",
"def optimize_LBFGS(\n",
" model,\n",
" mll,\n",
" train_x,\n",
" train_y,\n",
" training_iter, \n",
"):\n",
" optimizer = FullBatchLBFGS(model.parameters(), lr=0.1)\n",
"\n",
" def closure():\n",
" optimizer.zero_grad()\n",
" output = model(train_x)\n",
" loss = -mll(output, train_y)\n",
" return loss\n",
"\n",
" loss = closure()\n",
" loss.backward()\n",
" losses = []\n",
"\n",
" for i in range(training_iter):\n",
" options = {'closure': closure, 'current_loss': loss, 'max_ls': 10}\n",
" loss, _, _, _, _, _, _, fail = optimizer.step(options)\n",
" losses.append(loss)\n",
" if fail:\n",
" print('Convergence reached!')\n",
" break\n",
"\n",
" return losses\n",
"\n",
"\n",
"def optimize_adam(\n",
" model,\n",
" mll,\n",
" train_x,\n",
" train_y,\n",
" training_iter, \n",
"): \n",
" optimizer = torch.optim.Adam(model.parameters(), lr=0.1) # Includes \n",
"\n",
" losses = []\n",
"\n",
" for i in range(training_iter):\n",
" # Zero gradients from previous iteration\n",
" optimizer.zero_grad()\n",
" # Output from model\n",
" output = model(train_x)\n",
" # Calc loss and backprop gradients\n",
" loss = -mll(output, train_y)\n",
" losses.append(loss.item())\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" return losses\n"
]
},
{
"source": [
"# Set up kernels\n",
"\n",
"In this section, we'll set up out kernels and make sure they look the same before the are optimized.\n",
"\n",
"Note that the sklearn `WhiteKernel` is equivalent to the `noise` in the likelihood of a gpytorch model.\n",
"\n",
"(See [this notebook](https://gist.github.com/bcolloran/7686b63a5544295f0382415e553194ea) for verification of this equivalence)"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# sklearn kernel definitions\n",
"\n",
"def sklearn_preoptimized_kernel():\n",
" # Kernel initialized with optimized parameters (from sklearn example)\n",
" mean_kernel = ConstantKernel(constant_value=0)\n",
" k1 = 50.0**2 * RBF(length_scale=50.0) # long term smooth rising trend\n",
" k2 = 2.0**2 * RBF(length_scale=100.0) \\\n",
" * ExpSineSquared(length_scale=1.0, periodicity=1.0,\n",
" periodicity_bounds=\"fixed\") # seasonal component\n",
" # medium term irregularities\n",
" k3 = 0.5**2 * RationalQuadratic(length_scale=1.0, alpha=1.0)\n",
" k4 = 0.1**2 * RBF(length_scale=0.1) \\\n",
" + WhiteKernel(noise_level=0.1**2,\n",
" noise_level_bounds=(1e-5, np.inf)) # noise terms\n",
" return mean_kernel + k1 + k2 + k3 + k4\n",
"\n",
"\n",
"def sklearn_default_kernel():\n",
" # Kernel with DEFAULT PARAMETERS - no scaling etc\n",
" mean_kernel = ConstantKernel(constant_value=0)\n",
" k1 = ConstantKernel() * RBF() # long term smooth rising trend\n",
" k2 = ConstantKernel() * RBF() * ExpSineSquared(periodicity_bounds=\"fixed\") # seasonal component\n",
"\n",
" # medium term irregularities\n",
" k3 = ConstantKernel() * RationalQuadratic()\n",
"\n",
" # noise terms -- we'll keep these\n",
" k4 = ConstantKernel() * RBF() + WhiteKernel(noise_level=0.1**2, noise_level_bounds=(1e-5, np.inf)) \n",
"\n",
" return mean_kernel + k1 + k2 + k3 + k4\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# translating optimize kernel initialization from sklearn example above\n",
"def gpytorch_preoptimized_kernel():\n",
" #### long term\n",
" k_long_term_RBF = RBFKernel()\n",
" k_long_term_RBF.lengthscale = 50.0\n",
"\n",
" k_long_term = ScaleKernel(k_long_term_RBF)\n",
" k_long_term.outputscale = 50.0**2\n",
"\n",
" #### seasonal\n",
" k_seasonal_RBF = RBFKernel()\n",
" k_seasonal_RBF.lengthscale = 90\n",
"\n",
" k_seasonal_periodic = PeriodicKernel()\n",
" k_seasonal_periodic.period_length = 1.0\n",
" # this must be squared, see https://github.com/cornellius-gp/gpytorch/issues/1020\n",
" k_seasonal_periodic.lengthscale = 1.0**2\n",
"\n",
" k_seasonal = ScaleKernel(k_seasonal_RBF * k_seasonal_periodic)\n",
" k_seasonal.outputscale = 2.0**2\n",
"\n",
" ### medium term\n",
" k_medium_RQ = RQKernel()\n",
" k_medium_RQ.alpha = 1.0\n",
" k_medium_RQ.lengthscale = 1.0\n",
"\n",
" k_medium = ScaleKernel(k_medium_RQ)\n",
" k_medium.outputscale = 0.5**2\n",
"\n",
" #### noise term\n",
" k_noise_RBF = RBFKernel()\n",
" k_noise_RBF.lengthscale = 0.1\n",
"\n",
" k_noise = ScaleKernel(k_noise_RBF)\n",
" k_noise.outputscale = 0.1**2\n",
"\n",
" return k_long_term + k_seasonal + k_medium + k_noise\n",
"\n",
"\n",
"# NOTE: because of the gpytorch's constraint transforms,\n",
"# to match skearn's defaults we do actually have to define initial values\n",
"def gpytorch_default_kernel():\n",
" #### long term\n",
" k_long_term_RBF = RBFKernel()\n",
" k_long_term_RBF.lengthscale = 1\n",
"\n",
" k_long_term = ScaleKernel(k_long_term_RBF)\n",
" k_long_term.outputscale = 1\n",
"\n",
" #### seasonal\n",
" k_seasonal_RBF = RBFKernel()\n",
" k_seasonal_RBF.lengthscale = 1\n",
"\n",
" k_seasonal_periodic = PeriodicKernel()\n",
" k_seasonal_periodic.period_length = 1.0\n",
" # this must be squared, see https://github.com/cornellius-gp/gpytorch/issues/1020\n",
" k_seasonal_periodic.lengthscale = 1\n",
"\n",
" k_seasonal = ScaleKernel(k_seasonal_RBF * k_seasonal_periodic)\n",
" k_seasonal.outputscale = 1\n",
"\n",
" ### medium term\n",
" k_medium_RQ = RQKernel()\n",
" k_medium_RQ.alpha = 1.0\n",
" k_medium_RQ.lengthscale = 1.0\n",
"\n",
" k_medium = ScaleKernel(k_medium_RQ)\n",
" k_medium.outputscale = 1\n",
"\n",
" #### noise term\n",
" k_noise_RBF = RBFKernel()\n",
" k_noise_RBF.lengthscale = 1\n",
"\n",
" k_noise = ScaleKernel(k_noise_RBF)\n",
" k_noise.outputscale = 1\n",
"\n",
" return k_long_term + k_seasonal + k_medium + k_noise\n",
"\n",
" return k_long_term + k_seasonal + k_medium + k_noise"
]
},
{
"source": [
"## Verify that initial kernels are identical (or very close)"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--- PREOPTIMIZED KERNEL ---\n0**2 + 50**2 * RBF(length_scale=50) + 2**2 * RBF(length_scale=100) * ExpSineSquared(length_scale=1, periodicity=1) + 0.5**2 * RationalQuadratic(alpha=1, length_scale=1) + 0.1**2 * RBF(length_scale=0.1) + WhiteKernel(noise_level=0.01) \n\n(50.00**2 * RBF(length_scale=50.000) + 2.00**2 * (RBF(length_scale=90.000) * ExpSineSquared(length_scale=1.000, periodcity=1.000,)) + 0.50**2 * RationalQuadratic(α=1.000,length_scale=1.000) + 0.10**2 * RBF(length_scale=0.100)) \n\n\n\n--- UNOPTIMIZED KERNEL ---\n0**2 + 1**2 * RBF(length_scale=1) + 1**2 * RBF(length_scale=1) * ExpSineSquared(length_scale=1, periodicity=1) + 1**2 * RationalQuadratic(alpha=1, length_scale=1) + 1**2 * RBF(length_scale=1) + WhiteKernel(noise_level=0.01) \n\n(1.00**2 * RBF(length_scale=1.000) + 1.00**2 * (RBF(length_scale=1.000) * ExpSineSquared(length_scale=1.000, periodcity=1.000,)) + 1.00**2 * RationalQuadratic(α=1.000,length_scale=1.000) + 1.00**2 * RBF(length_scale=1.000)) \n\n"
]
}
],
"source": [
"# VERIFY THAT THESE KERNELS LOOK THE SAME BEFORE OPTIMIZATION!!!\n",
"\n",
"print(\"--- PREOPTIMIZED KERNEL ---\")\n",
"print(sklearn_preoptimized_kernel(), \"\\n\")\n",
"print(print_kernel(gpytorch_preoptimized_kernel()), \"\\n\")\n",
"\n",
"print(\"\\n\\n--- UNOPTIMIZED KERNEL ---\")\n",
"print(sklearn_default_kernel(), \"\\n\")\n",
"print(print_kernel(gpytorch_default_kernel()), \"\\n\")\n"
]
},
{
"source": [
"for this verification, we need to set up a copy of the preoptimized kernel that has all parameters \"fixed\", because sklearn automatically optimizes the model when you initialize it."
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"def sklearn_fixed_preoptimized_kernel():\n",
" # Kernel initialized with optimized parameters (from sklearn example)\n",
" mean_kernel = ConstantKernel(constant_value=0.0, constant_value_bounds = \"fixed\")\n",
" k1 = ConstantKernel(constant_value=50.0**2, constant_value_bounds = \"fixed\")\\\n",
" * RBF(length_scale=50.0, length_scale_bounds=\"fixed\") # long term smooth rising trend\n",
" k2 = ConstantKernel(constant_value=2.0**2, constant_value_bounds = \"fixed\")\\\n",
" * RBF(length_scale=100.0, length_scale_bounds=\"fixed\") \\\n",
" * ExpSineSquared(\n",
" length_scale=1.0, length_scale_bounds=\"fixed\",\n",
" periodicity=1.0, periodicity_bounds=\"fixed\"\n",
" ) # seasonal component\n",
" # medium term irregularities\n",
" k3 = ConstantKernel(constant_value=.5**2, constant_value_bounds = \"fixed\")\\\n",
" * RationalQuadratic(\n",
" length_scale=1.0, length_scale_bounds=\"fixed\",\n",
" alpha=1.0, alpha_bounds=\"fixed\"\n",
" )\n",
" k4 = ConstantKernel(constant_value=0.1**2, constant_value_bounds = \"fixed\")\\\n",
" * RBF(length_scale=0.1, length_scale_bounds=\"fixed\")\n",
" noise = WhiteKernel(noise_level=0.1**2, noise_level_bounds=\"fixed\") # noise terms\n",
"\n",
" return (\n",
" mean_kernel\n",
" + k1\n",
" + k2\n",
" + k3\n",
" + k4\n",
" + noise\n",
" )"
],
"cell_type": "code",
"metadata": {},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0**2 + 50**2 * RBF(length_scale=50) + 2**2 * RBF(length_scale=100) * ExpSineSquared(length_scale=1, periodicity=1) + 0.5**2 * RationalQuadratic(alpha=1, length_scale=1) + 0.1**2 * RBF(length_scale=0.1) + WhiteKernel(noise_level=0.01)"
]
},
"metadata": {},
"execution_count": 11
},
{
"output_type": "display_data",
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"gp = fit_sklearn_gp(sklearn_fixed_preoptimized_kernel())\n",
"gp.kernel"
]
},
{
"source": [
"Fir the gpytorch model with 0 training iterations (i.e., do no additional optimization)"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 1008x360 with 1 Axes>",
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495.332217 103.263433 \nL 496.04311 108.35906 \nL 496.754003 114.154664 \nL 497.464896 117.7789 \nL 498.175788 116.684616 \nL 498.886681 112.99761 \nL 499.597574 109.422178 \nL 500.308466 106.649025 \nL 501.019359 104.738918 \nL 501.730252 102.947056 \nL 502.441145 100.152158 \nL 503.152037 97.588105 \nL 503.86293 97.234406 \nL 504.573823 99.385261 \nL 505.284715 103.624264 \nL 505.995608 109.186688 \nL 506.706501 114.433214 \nL 507.417394 115.869483 \nL 508.128286 113.058272 \nL 508.839179 109.14999 \nL 509.550072 105.835155 \nL 510.260964 103.403534 \nL 510.971857 101.663842 \nL 511.68275 99.41636 \nL 512.393643 96.383682 \nL 513.104535 94.651161 \nL 513.815428 95.420768 \nL 514.526321 98.563989 \nL 515.237213 103.46869 \nL 515.948106 109.205931 \nL 516.658999 113.079347 \nL 517.369891 112.239725 \nL 518.080784 108.508602 \nL 518.791677 104.757764 \nL 519.50257 101.804764 \nL 520.213462 99.763054 \nL 520.924355 97.975865 \nL 521.635248 95.231216 \nL 522.34614 92.515925 \nL 523.057033 91.896588 \nL 523.767926 93.790737 \nL 524.478819 97.829615 \nL 525.189711 103.277161 \nL 525.900604 108.651843 \nL 526.611497 110.465728 \nL 527.322389 107.837831 \nL 528.033282 103.864738 \nL 528.744175 100.438534 \nL 529.455068 97.917213 \nL 530.16596 96.173297 \nL 530.876853 94.054485 \nL 531.587746 91.062179 \nL 532.298638 89.162642 \nL 533.009531 89.721931 \nL 533.720424 92.679444 \nL 534.431317 97.465829 \nL 535.142209 103.211593 \nL 535.853102 107.40818 \nL 536.563995 106.945641 \nL 537.274887 103.294419 \nL 537.98578 99.474891 \nL 538.696673 96.448805 \nL 539.407566 94.37582 \nL 540.118458 92.682354 \nL 540.829351 90.093294 \nL 541.540244 87.33764 \nL 542.251136 86.538187 \nL 542.962029 88.247862 \nL 543.672922 92.147245 \nL 544.383814 97.530029 \nL 545.094707 103.060471 \nL 545.8056 105.304015 \nL 546.516493 102.94033 \nL 547.227385 98.963057 \nL 547.938278 95.471616 \nL 548.649171 92.901233 \nL 549.360063 91.18506 \nL 550.070956 89.230367 \nL 550.781849 86.337812 \nL 551.492742 84.320168 \nL 552.203634 84.700024 \nL 552.914527 87.494097 \nL 553.62542 92.178306 \nL 554.336312 97.933328 \nL 555.047205 102.448671 \nL 555.758098 102.402694 \nL 556.468991 98.876286 \nL 557.179883 95.012201 \nL 557.890776 91.929231 \nL 558.601669 89.83617 \nL 559.312561 88.242959 \nL 560.023454 85.835694 \nL 560.734347 83.080542 \nL 561.44524 82.122166 \nL 562.156132 83.658818 \nL 562.867025 87.422756 \nL 563.577918 92.739451 \nL 564.28881 98.404821 \nL 564.999703 101.080501 \nL 565.710596 99.020532 \nL 566.421489 95.063934 \nL 567.132381 91.517602 \nL 567.843274 88.903165 \nL 568.554167 87.214181 \nL 569.265059 85.427628 \nL 569.975952 82.665967 \nL 570.686845 80.557186 \nL 571.397738 80.767282 \nL 572.10863 83.400588 \nL 572.819523 87.980347 \nL 573.530416 93.729847 \nL 574.241308 98.540724 \nL 574.952201 98.932332 \nL 575.663094 95.563751 \nL 576.373986 91.667833 \nL 577.084879 88.532194 \nL 577.795772 86.418318 \nL 578.506665 84.920265 \nL 579.217557 82.708074 \nL 579.92845 79.985794 \nL 580.639343 78.88338 \nL 581.350235 80.251546 \nL 582.061128 83.877512 \nL 582.772021 89.121247 \nL 583.482914 94.895081 \nL 584.193806 97.994658 \nL 584.904699 96.271536 \nL 585.615592 92.357298 \nL 586.326484 88.762361 \nL 587.037377 86.104328 \nL 587.74827 84.437344 \nL 588.459163 82.816654 \nL 589.170055 80.211074 \nL 589.880948 78.036719 \nL 590.591841 78.084627 \nL 591.302733 80.55766 \nL 592.013626 85.028181 \nL 592.724519 90.756516 \nL 593.435412 95.834813 \nL 594.146304 96.678307 \nL 594.857197 93.499583 \nL 595.56809 89.583683 \nL 596.278982 86.398088 \nL 596.989875 84.260633 \nL 597.700768 82.849971 \nL 598.411661 80.841194 \nL 599.122553 78.182497 \nL 599.833446 76.951516 \nL 600.544339 78.15512 \nL 601.255231 81.639882 \nL 601.966124 86.803445 \nL 602.677017 92.659069 \nL 603.38791 96.168304 \nL 604.098802 94.81239 \nL 604.809695 90.962972 \nL 605.520588 87.325486 \nL 606.23148 84.623685 \nL 606.942373 82.972425 \nL 607.653266 81.512411 \nL 608.364158 79.084657 \nL 609.075051 76.871323 \nL 609.785944 76.76519 \nL 610.496837 79.078623 \nL 611.207729 83.4348 \nL 611.918622 89.127509 \nL 612.629515 94.442769 \nL 613.340407 95.747073 \nL 614.0513 92.790584 \nL 614.762193 88.867313 \nL 615.473086 85.634574 \nL 616.183978 83.470371 \nL 616.894871 82.138194 \nL 617.605764 80.337534 \nL 618.316656 77.772112 \nL 619.027549 76.429785 \nL 619.738442 77.473207 \nL 620.449335 80.81388 \nL 621.160227 85.890479 \nL 621.87112 91.802272 \nL 622.582013 95.702008 \nL 623.292905 94.741007 \nL 624.003798 90.980358 \nL 624.714691 87.306837 \nL 625.425584 84.561263 \nL 626.136476 82.919088 \nL 626.847369 81.612461 \nL 627.558262 79.381053 \nL 628.269154 77.156516 \nL 628.980047 76.905695 \nL 629.69094 79.060843 \nL 630.401833 83.297694 \nL 631.112725 88.941846 \nL 631.823618 94.462308 \nL 632.534511 96.231006 \nL 633.245403 93.529399 \nL 633.956296 89.61254 \nL 634.667189 86.335877 \nL 635.378081 84.141772 \nL 636.088974 82.878458 \nL 636.799867 81.287338 \nL 637.51076 78.843543 \nL 638.221652 77.409051 \nL 638.932545 78.297394 \nL 639.643438 81.491699 \nL 640.35433 86.47501 \nL 641.065223 92.418914 \nL 641.776116 96.685647 \nL 642.487009 96.144116 \nL 643.197901 92.497779 \nL 643.908794 88.795359 \nL 644.619687 86.006389 \nL 645.330579 84.366509 \nL 646.041472 83.204226 \nL 646.752365 81.184304 \nL 647.463258 78.977204 \nL 648.17415 78.592484 \nL 648.885043 80.591384 \nL 649.595936 84.704184 \nL 650.306828 90.288337 \nL 651.017721 95.981673 \nL 651.728614 98.212868 \nL 652.439507 95.798642 \nL 653.150399 91.903303 \nL 653.861292 88.586389 \nL 654.572185 86.359415 \nL 655.283077 85.154805 \nL 655.99397 83.771552 \nL 656.704863 81.475904 \nL 657.415756 79.970329 \nL 658.126648 80.709516 \nL 658.837541 83.755866 \nL 659.548434 88.639938 \nL 660.259326 94.593715 \nL 660.970219 99.20014 \nL 661.681112 99.098874 \nL 662.392005 95.593906 \nL 663.102897 91.870387 \nL 663.81379 89.038852 \nL 664.524683 87.394433 \nL 665.235575 86.365936 \nL 665.946468 84.569085 \nL 666.657361 82.408443 \nL 667.368253 81.902173 \nL 668.079146 83.747585 \nL 668.790039 87.73192 \nL 669.500932 93.245965 \nL 670.211824 99.079917 \nL 670.922717 101.766156 \nL 671.63361 99.671097 \nL 672.344502 95.813811 \nL 673.055395 92.460734 \nL 673.766288 90.198222 \nL 674.477181 89.041739 \nL 675.188073 87.861765 \nL 675.898966 85.738438 \nL 676.609859 84.184587 \nL 677.320751 84.781429 \nL 678.031644 87.678953 \nL 678.742537 92.458103 \nL 679.45343 98.401471 \nL 680.164322 103.317071 \nL 680.875215 103.672505 \nL 681.586108 100.33729 \nL 682.297 96.601176 \nL 683.007893 93.728372 \nL 683.718786 92.072628 \nL 684.429679 91.166044 \nL 685.140571 89.600182 \nL 685.851464 87.514859 \nL 686.562357 86.901012 \nL 687.273249 88.596397 \nL 687.984142 92.448221 \nL 688.695035 97.883177 \nL 689.405928 103.826122 \nL 690.11682 106.954446 \nL 690.827713 105.208992 \nL 691.538606 101.407793 \nL 692.249498 98.023003 \nL 692.960391 95.722668 \nL 693.671284 94.603424 \nL 694.382176 93.619433 \nL 695.093069 91.689789 \nL 695.803962 90.111923 \nL 696.514855 90.574193 \nL 697.225747 93.322745 \nL 697.93664 97.991474 \nL 698.647533 103.906144 \nL 699.358425 109.097849 \nL 700.069318 109.921519 \nL 700.780211 106.78549 \nL 701.491104 103.046052 \nL 702.201996 100.133711 \nL 702.912889 98.460005 \nL 703.623782 97.662343 \nL 704.334674 96.331681 \nL 705.045567 94.349818 \nL 705.75646 93.644011 \nL 706.467353 95.193517 \nL 707.178245 98.909207 \nL 707.889138 104.257005 \nL 708.600031 110.278452 \nL 709.310923 113.830592 \nL 710.021816 112.463154 \nL 710.732709 108.737618 \nL 711.443602 105.32588 \nL 712.154494 102.985896 \nL 712.865387 101.892802 \nL 713.57628 101.095019 \nL 714.287172 99.377207 \nL 714.998065 97.800705 \nL 715.708958 98.137215 \nL 716.419851 100.737372 \nL 717.130743 105.290318 \nL 717.841636 111.159948 \nL 718.552529 116.592837 \nL 719.263421 117.890943 \nL 719.974314 114.984226 \nL 720.685207 111.251584 \nL 721.3961 108.301828 \nL 722.106992 106.603781 \nL 722.817885 105.901122 \nL 723.528778 104.806208 \nL 724.23967 102.954654 \nL 724.950563 102.174115 \nL 725.661456 103.582578 \nL 726.372348 107.158997 \nL 727.083241 112.41227 \nL 727.794134 118.483275 \nL 728.505027 122.435988 \nL 729.215919 121.472248 \nL 729.926812 117.843515 \nL 730.637705 114.409881 \nL 731.348597 112.028925 \nL 732.05949 110.950812 \nL 732.770383 110.327237 \nL 733.481276 108.835879 \nL 734.192168 107.286841 \nL 734.903061 107.507518 \nL 735.613954 109.96057 \nL 736.324846 114.3925 \nL 737.035739 120.202587 \nL 737.746632 125.840607 \nL 738.457525 127.613692 \nL 739.168417 124.966662 \nL 739.87931 121.251897 \nL 740.590203 118.267175 \nL 741.301095 116.538777 \nL 742.011988 115.916466 \nL 742.722881 115.054298 \nL 743.433774 113.358062 \nL 744.144666 112.521552 \nL 744.855559 113.794504 \nL 745.566452 117.229067 \nL 746.277344 122.380959 \nL 746.988237 128.47445 \nL 747.69913 132.79999 \nL 748.410023 132.262218 \nL 749.120915 128.752953 \nL 749.831808 125.302758 \nL 750.542701 122.880041 \nL 751.253593 121.805789 \nL 751.964486 121.342465 \nL 752.675379 120.088425 \nL 753.386271 118.593216 \nL 754.097164 118.709177 \nL 754.808057 121.017119 \nL 755.51895 125.322953 \nL 756.229842 131.060667 \nL 756.940735 136.867351 \nL 757.651628 139.110122 \nL 758.36252 136.752883 \nL 759.073413 133.068169 \nL 759.784306 130.051178 \nL 760.495199 128.286902 \nL 761.206091 127.729734 \nL 761.916984 127.093943 \nL 762.627877 125.575683 \nL 763.338769 124.703343 \nL 764.049662 125.847051 \nL 764.760555 129.13778 \nL 765.471448 134.181794 \nL 766.18234 140.272798 \nL 766.893233 144.939504 \nL 767.604126 144.845896 \nL 768.315018 141.480197 \nL 769.025911 138.019093 \nL 769.736804 135.554351 \nL 770.447697 134.473033 \nL 771.158589 134.154323 \nL 771.869482 133.144568 \nL 772.580375 131.729339 \nL 773.291267 131.752947 \nL 774.00216 133.918467 \nL 774.713053 138.093332 \nL 775.423946 143.747317 \nL 776.134838 149.686465 \nL 776.845731 152.387766 \nL 777.556624 150.349563 \nL 778.267516 146.708298 \nL 778.978409 143.661913 \nL 778.978409 47.94094 \nL 778.978409 47.94094 \nL 778.267516 51.389706 \nL 777.556624 55.410722 \nL 776.845731 57.828656 \nL 776.134838 55.510468 \nL 775.423946 49.959058 \nL 774.713053 44.697079 \nL 774.00216 40.916545 \nL 773.291267 39.146858 \nL 772.580375 39.523618 \nL 771.869482 41.346869 \nL 771.158589 42.773736 \nL 770.447697 43.523397 \nL 769.736804 45.044601 \nL 769.025911 47.922213 \nL 768.315018 51.759267 \nL 767.604126 55.491656 \nL 766.893233 55.954575 \nL 766.18234 51.660992 \nL 765.471448 45.947706 \nL 764.760555 41.284835 \nL 764.049662 38.376678 \nL 763.338769 37.618043 \nL 762.627877 38.881543 \nL 761.916984 40.799114 \nL 761.206091 41.844537 \nL 760.495199 42.826377 \nL 759.784306 45.010203 \nL 759.073413 48.408364 \nL 758.36252 52.449491 \nL 757.651628 55.162327 \nL 756.940735 53.278269 \nL 756.229842 47.834612 \nL 755.51895 42.464102 \nL 754.808057 38.52784 \nL 754.097164 36.590745 \nL 753.386271 36.849659 \nL 752.675379 38.726865 \nL 751.964486 40.371305 \nL 751.253593 41.237793 \nL 750.542701 42.726081 \nL 749.831808 45.539844 \nL 749.120915 49.343478 \nL 748.410023 53.195134 \nL 747.69913 54.077638 \nL 746.988237 50.100279 \nL 746.277344 44.359365 \nL 745.566452 39.563473 \nL 744.855559 36.486328 \nL 744.144666 35.572911 \nL 743.433774 36.774512 \nL 742.722881 38.843446 \nL 742.011988 40.087632 \nL 741.301095 41.106675 \nL 740.590203 43.230445 \nL 739.87931 46.574394 \nL 739.168417 50.621675 \nL 738.457525 53.599518 \nL 737.746632 52.160133 \nL 737.035739 46.859795 \nL 736.324846 41.391452 \nL 735.613954 37.303677 \nL 734.903061 35.195874 \nL 734.192168 35.323989 \nL 733.481276 37.228411 \nL 732.770383 39.082909 \nL 732.05949 40.081225 \nL 731.348597 41.546376 \nL 730.637705 44.295581 \nL 729.926812 48.059675 \nL 729.215919 52.005978 \nL 728.505027 53.289342 \nL 727.794134 49.659357 \nL 727.083241 43.915255 \nL 726.372348 38.992293 \nL 725.661456 35.747619 \nL 724.950563 34.67267 \nL 724.23967 35.791751 \nL 723.528778 37.988914 \nL 722.817885 39.437807 \nL 722.106992 40.508509 \nL 721.3961 42.576651 \nL 720.685207 45.863115 \nL 719.974314 49.904038 \nL 719.263421 53.116346 \nL 718.552529 52.126533 \nL 717.841636 47.005563 \nL 717.130743 41.45178 \nL 716.419851 37.217129 \nL 715.708958 34.936229 \nL 714.998065 34.922058 \nL 714.287172 36.826965 \nL 713.57628 38.880331 \nL 712.865387 40.024058 \nL 712.154494 41.476306 \nL 711.443602 44.160919 \nL 710.732709 47.879851 \nL 710.021816 51.897835 \nL 709.310923 53.559472 \nL 708.600031 50.304319 \nL 707.889138 44.583793 \nL 707.178245 39.540286 \nL 706.467353 36.130361 \nL 705.75646 34.888076 \nL 705.045567 35.905615 \nL 704.334674 38.205734 \nL 703.623782 39.86214 \nL 702.912889 40.998692 \nL 702.201996 43.016326 \nL 701.491104 46.24243 \nL 700.780211 50.265785 \nL 700.069318 53.681956 \nL 699.358425 53.140984 \nL 698.647533 48.235233 \nL 697.93664 42.610289 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582.772021 69.774166 \nL 582.061128 64.671437 \nL 581.350235 61.187464 \nL 580.639343 59.962062 \nL 579.92845 61.209736 \nL 579.217557 64.080554 \nL 578.506665 66.444538 \nL 577.795772 68.102597 \nL 577.084879 70.384454 \nL 576.373986 73.667827 \nL 575.663094 77.688132 \nL 574.952201 81.179304 \nL 574.241308 80.91221 \nL 573.530416 76.227391 \nL 572.819523 70.606298 \nL 572.10863 66.156559 \nL 571.397738 63.653725 \nL 570.686845 63.5756 \nL 569.975952 65.819222 \nL 569.265059 68.718829 \nL 568.554167 70.647485 \nL 567.843274 72.489387 \nL 567.132381 75.25384 \nL 566.421489 78.923134 \nL 565.710596 82.990438 \nL 564.999703 85.16319 \nL 564.28881 82.601492 \nL 563.577918 77.05224 \nL 562.867025 71.853827 \nL 562.156132 68.209186 \nL 561.44524 66.792412 \nL 560.734347 67.872677 \nL 560.023454 70.752461 \nL 559.312561 73.287015 \nL 558.601669 75.014199 \nL 557.890776 77.250316 \nL 557.179883 80.460061 \nL 556.468991 84.427855 \nL 555.758098 88.05547 \nL 555.047205 88.204638 \nL 554.336312 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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"model, likelihood, losses = create_and_train_model(\n",
" kernel_gen_fn = gpytorch_preoptimized_kernel,\n",
" optimization_fn = optimize_adam,\n",
" train_x = X_torch,\n",
" train_y = Y_torch,\n",
" test_x = X_test_torch,\n",
" likelihood_noise = 0.1**2,\n",
" training_iter = 0\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(50.00**2 * RBF(length_scale=50.000) + 2.00**2 * (RBF(length_scale=90.000) * ExpSineSquared(length_scale=1.000, periodcity=1.000,)) + 0.50**2 * RationalQuadratic(α=1.000,length_scale=1.000) + 0.10**2 * RBF(length_scale=0.100)) \n 0.0 \n 0.010000001639127731\n"
]
}
],
"source": [
"print(\n",
" print_kernel(model.covar_module), \"\\n\",\n",
" model.mean_module.constant.item(), \"\\n\",\n",
" likelihood.noise.item()\n",
")\n",
"\n"
]
},
{
"source": [
"Results above look close enough to feel comfortable that the starting points should be the same."
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# Optimizing from identical initial kernels\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"## Starting from PREOPTIMIZED kernels (sklearn, gpytorch (adam), gpytorch (LBFGS))"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/home/bc/miniconda3/lib/python3.9/site-packages/sklearn/gaussian_process/kernels.py:271: RuntimeWarning: divide by zero encountered in log\n",
" return np.log(np.hstack(theta))\n",
"/home/bc/miniconda3/lib/python3.9/site-packages/sklearn/gaussian_process/kernels.py:402: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__k1__k1__constant_value is close to the specified lower bound 1e-05. Decreasing the bound and calling fit again may find a better value.\n",
" warnings.warn(\"The optimal value found for \"\n",
"/home/bc/miniconda3/lib/python3.9/site-packages/sklearn/gaussian_process/kernels.py:411: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__k1__k2__k1__constant_value is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
" warnings.warn(\"The optimal value found for \"\n"
]
},
{
"output_type": "display_data",
"data": {
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248.717461 \nL 230.169236 244.241498 \nL 230.880129 242.525474 \nL 232.301914 239.522305 \nL 233.012807 237.266104 \nL 233.7237 235.408884 \nL 234.434593 235.306344 \nL 235.145485 237.032496 \nL 235.856378 240.046473 \nL 236.567271 243.812793 \nL 237.278163 246.858017 \nL 237.989056 247.326536 \nL 238.699949 245.419689 \nL 240.121734 240.755328 \nL 242.965305 233.786462 \nL 243.676198 232.839278 \nL 244.38709 233.731853 \nL 245.097983 236.160829 \nL 246.519769 243.546949 \nL 247.230661 245.609025 \nL 247.941554 244.75139 \nL 249.363339 239.740336 \nL 250.074232 237.681381 \nL 251.496018 234.220207 \nL 252.917803 229.548526 \nL 253.628696 229.138657 \nL 254.339588 230.617214 \nL 255.050481 233.426825 \nL 256.472267 240.282081 \nL 257.183159 241.016352 \nL 257.894052 239.181733 \nL 259.315837 234.182622 \nL 260.02673 232.436962 \nL 260.737623 231.042819 \nL 261.448516 229.214979 \nL 262.159408 226.984077 \nL 262.870301 225.675766 \nL 263.581194 226.124613 \nL 264.292086 228.137967 \nL 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226.091111 \nL 307.656542 219.102991 \nL 309.078327 215.710648 \nL 310.500113 210.723702 \nL 311.211005 209.547756 \nL 311.921898 210.404394 \nL 312.632791 212.883661 \nL 314.054576 220.491992 \nL 314.765469 222.180292 \nL 315.476362 220.746269 \nL 316.898147 215.352621 \nL 317.60904 213.524885 \nL 318.319932 212.09569 \nL 319.030825 210.172517 \nL 319.741718 207.538027 \nL 320.452611 205.711653 \nL 321.163503 206.003394 \nL 321.874396 208.126421 \nL 322.585289 211.501502 \nL 323.296181 215.619292 \nL 324.007074 218.682734 \nL 324.717967 218.697594 \nL 326.850645 211.496729 \nL 328.27243 207.924653 \nL 329.694216 203.33832 \nL 330.405109 202.572027 \nL 331.116001 203.84303 \nL 331.826894 206.660967 \nL 333.248679 214.306428 \nL 333.959572 215.991217 \nL 334.670465 214.676622 \nL 336.09225 209.665758 \nL 336.803143 208.030396 \nL 337.514036 206.762956 \nL 338.224928 205.020477 \nL 338.935821 202.415269 \nL 339.646714 200.304949 \nL 340.357606 200.223942 \nL 341.068499 202.124262 \nL 341.779392 205.284784 \nL 342.490285 209.113486 \nL 343.201177 212.155561 \nL 343.91207 212.545173 \nL 344.622963 210.462313 \nL 345.333855 207.768393 \nL 346.044748 205.538175 \nL 348.177426 200.386301 \nL 348.888319 197.824838 \nL 349.599212 196.443502 \nL 350.310104 197.151153 \nL 351.020997 199.484759 \nL 352.442783 206.44194 \nL 353.153675 208.237676 \nL 353.864568 207.107482 \nL 355.997246 199.295325 \nL 358.129924 193.325747 \nL 358.840817 191.30494 \nL 359.55171 190.932127 \nL 360.262602 192.463493 \nL 360.973495 195.429463 \nL 361.684388 199.276644 \nL 362.395281 202.467653 \nL 363.106173 202.986834 \nL 363.817066 200.907694 \nL 364.527959 198.125231 \nL 365.238851 195.802488 \nL 365.949744 194.181184 \nL 366.660637 192.972353 \nL 367.371529 191.204003 \nL 368.082422 188.864664 \nL 368.793315 187.515305 \nL 369.504208 188.148655 \nL 370.2151 190.481395 \nL 371.636886 198.184439 \nL 372.347778 200.532549 \nL 373.058671 199.764789 \nL 375.191349 192.40669 \nL 377.324027 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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"gp = fit_sklearn_gp(sklearn_preoptimized_kernel())"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 1008x360 with 1 Axes>",
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523.057033 95.816265 \nL 523.767926 97.705342 \nL 524.478819 101.754294 \nL 525.189711 107.19445 \nL 525.900604 112.530603 \nL 526.611497 114.243328 \nL 527.322389 111.638157 \nL 528.033282 107.829713 \nL 528.744175 104.447351 \nL 529.455068 101.999146 \nL 530.16596 100.223555 \nL 530.876853 97.9737 \nL 531.587746 94.964739 \nL 532.298638 93.119848 \nL 533.009531 93.691886 \nL 533.720424 96.660539 \nL 534.431317 101.466418 \nL 535.142209 107.204363 \nL 535.853102 111.336093 \nL 536.563995 110.788675 \nL 537.274887 107.275446 \nL 537.98578 103.571891 \nL 538.696673 100.59842 \nL 539.407566 98.579757 \nL 540.118458 96.773209 \nL 540.829351 94.078511 \nL 541.540244 91.355009 \nL 542.251136 90.594724 \nL 542.962029 92.314803 \nL 543.672922 96.24184 \nL 544.383814 101.644184 \nL 545.094707 107.156989 \nL 545.8056 109.294273 \nL 546.516493 106.941464 \nL 547.227385 103.145879 \nL 547.938278 99.715789 \nL 548.649171 97.225688 \nL 549.360063 95.483587 \nL 550.070956 93.372418 \nL 550.781849 90.431205 \nL 551.492742 88.464634 \nL 552.203634 88.865692 \nL 552.914527 91.683807 \nL 553.62542 96.404857 \nL 554.336312 102.178087 \nL 555.047205 106.635673 \nL 555.758098 106.488259 \nL 556.468991 103.096859 \nL 557.179883 99.3705 \nL 557.890776 96.34752 \nL 558.601669 94.316861 \nL 559.312561 92.601842 \nL 560.023454 90.050609 \nL 560.734347 87.304564 \nL 561.44524 86.3873 \nL 562.156132 87.943482 \nL 562.867025 91.747987 \nL 563.577918 97.106096 \nL 564.28881 102.77495 \nL 564.999703 105.33736 \nL 565.710596 103.269739 \nL 566.421489 99.507092 \nL 567.132381 96.038203 \nL 567.843274 93.509355 \nL 568.554167 91.799735 \nL 569.265059 89.830366 \nL 569.975952 86.980449 \nL 570.686845 84.911858 \nL 571.397738 85.147757 \nL 572.10863 87.815771 \nL 572.819523 92.447482 \nL 573.530416 98.240892 \nL 574.241308 103.004539 \nL 574.952201 103.27574 \nL 575.663094 100.034613 \nL 576.373986 96.297295 \nL 577.084879 93.229438 \nL 577.795772 91.186292 \nL 578.506665 89.559749 \nL 579.217557 87.163177 \nL 579.92845 84.419444 \nL 580.639343 83.356044 \nL 581.350235 84.750663 \nL 582.061128 88.429484 \nL 582.772021 93.734929 \nL 583.482914 99.536707 \nL 584.193806 102.518417 \nL 584.904699 100.765378 \nL 585.615592 97.054952 \nL 586.326484 93.554667 \nL 587.037377 90.98823 \nL 587.74827 89.307937 \nL 588.459163 87.480065 \nL 589.170055 84.741714 \nL 589.880948 82.590487 \nL 590.591841 82.666311 \nL 591.302733 85.183709 \nL 592.013626 89.720625 \nL 592.724519 95.518874 \nL 593.435412 100.565558 \nL 594.146304 101.269208 \nL 594.857197 98.20642 \nL 595.56809 94.469889 \nL 596.278982 91.36121 \nL 596.989875 89.304064 \nL 597.700768 87.761478 \nL 598.411661 85.527653 \nL 599.122553 82.810183 \nL 599.833446 81.612079 \nL 600.544339 82.847183 \nL 601.255231 86.396908 \nL 601.966124 91.641308 \nL 602.677017 97.552593 \nL 603.38791 100.943316 \nL 604.098802 99.53204 \nL 604.809695 95.893805 \nL 605.520588 92.369573 \nL 606.23148 89.766405 \nL 606.942373 88.111676 \nL 607.653266 86.42311 \nL 608.364158 83.814363 \nL 609.075051 81.600626 \nL 609.785944 81.521913 \nL 610.496837 83.888251 \nL 611.207729 88.324927 \nL 611.918622 94.113397 \nL 612.629515 99.418112 \nL 613.340407 100.563829 \nL 614.0513 97.70751 \nL 614.762193 93.984147 \nL 615.473086 90.838891 \nL 616.183978 88.76597 \nL 616.894871 87.302129 \nL 617.605764 85.236674 \nL 618.316656 82.568778 \nL 619.027549 81.248559 \nL 619.738442 82.326457 \nL 620.449335 85.743908 \nL 621.160227 90.91934 \nL 621.87112 96.917116 \nL 622.582013 100.702712 \nL 623.292905 99.658114 \nL 624.003798 96.11301 \nL 624.714691 92.572698 \nL 625.425584 89.933778 \nL 626.136476 88.300609 \nL 626.847369 86.747746 \nL 627.558262 84.284508 \nL 628.269154 82.0292 \nL 628.980047 81.802287 \nL 629.69094 84.01743 \nL 630.401833 88.348658 \nL 631.112725 94.113697 \nL 631.823618 99.650082 \nL 632.534511 101.243303 \nL 633.245403 98.621515 \nL 633.956296 94.924533 \nL 634.667189 91.747332 \nL 635.378081 89.656808 \nL 636.088974 88.266046 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101.613614 \nL 693.671284 100.40476 \nL 694.382176 99.025756 \nL 695.093069 96.76837 \nL 695.803962 95.110254 \nL 696.514855 95.591954 \nL 697.225747 98.455633 \nL 697.93664 103.285644 \nL 698.647533 109.416613 \nL 699.358425 114.575692 \nL 700.069318 115.189876 \nL 700.780211 112.243635 \nL 701.491104 108.728288 \nL 702.201996 105.962026 \nL 702.912889 104.372353 \nL 703.623782 103.304748 \nL 704.334674 101.536318 \nL 705.045567 99.332349 \nL 705.75646 98.595356 \nL 706.467353 100.21152 \nL 707.178245 104.074374 \nL 707.889138 109.628868 \nL 708.600031 115.816073 \nL 709.310923 119.168927 \nL 710.021816 117.752353 \nL 710.732709 114.310419 \nL 711.443602 111.060309 \nL 712.154494 108.882076 \nL 712.865387 107.720478 \nL 713.57628 106.50655 \nL 714.287172 104.397023 \nL 714.998065 102.697356 \nL 715.708958 103.040964 \nL 716.419851 105.76172 \nL 717.130743 110.489902 \nL 717.841636 116.606513 \nL 718.552529 122.044448 \nL 719.263421 123.104083 \nL 719.974314 120.366765 \nL 720.685207 116.882655 \nL 721.3961 114.090225 \nL 722.106992 112.500174 \nL 722.817885 111.535234 \nL 723.528778 109.956432 \nL 724.23967 107.819079 \nL 724.950563 106.980321 \nL 725.661456 108.449931 \nL 726.372348 112.185285 \nL 727.083241 117.662333 \nL 727.794134 123.943334 \nL 728.505027 127.707713 \nL 729.215919 126.6523 \nL 729.926812 123.315558 \nL 730.637705 120.062958 \nL 731.348597 117.859069 \nL 732.05949 116.737628 \nL 732.770383 115.682731 \nL 733.481276 113.735102 \nL 734.192168 112.012571 \nL 734.903061 112.223731 \nL 735.613954 114.800378 \nL 736.324846 119.421091 \nL 737.035739 125.506724 \nL 737.746632 131.193894 \nL 738.457525 132.703427 \nL 739.168417 130.198898 \nL 739.87931 126.757067 \nL 740.590203 123.941489 \nL 741.301095 122.346396 \nL 742.011988 121.475339 \nL 742.722881 120.087588 \nL 743.433774 118.036971 \nL 744.144666 117.108748 \nL 744.855559 118.433788 \nL 745.566452 122.037922 \nL 746.277344 127.429066 \nL 746.988237 133.777392 \nL 747.69913 137.934171 \nL 748.410023 137.259655 \nL 749.120915 134.046066 \nL 749.831808 130.798006 \nL 750.542701 128.567371 \nL 751.253593 127.478782 \nL 751.964486 126.575432 \nL 752.675379 124.801365 \nL 753.386271 123.075035 \nL 754.097164 123.160094 \nL 754.808057 125.591728 \nL 755.51895 130.099523 \nL 756.229842 136.138799 \nL 756.940735 142.044422 \nL 757.651628 144.004069 \nL 758.36252 141.755775 \nL 759.073413 138.367772 \nL 759.784306 135.532453 \nL 760.495199 133.927638 \nL 761.206091 133.141188 \nL 761.916984 131.943539 \nL 762.627877 129.998534 \nL 763.338769 128.994064 \nL 764.049662 130.176907 \nL 764.760555 133.64631 \nL 765.471448 138.943667 \nL 766.18234 145.333838 \nL 766.893233 149.860462 \nL 767.604126 149.583798 \nL 768.315018 146.511848 \nL 769.025911 143.275691 \nL 769.736804 141.017514 \nL 770.447697 139.954294 \nL 771.158589 139.193743 \nL 771.869482 137.602397 \nL 772.580375 135.89146 \nL 773.291267 135.857507 \nL 774.00216 138.143504 \nL 774.713053 142.533096 \nL 775.423946 148.51185 \nL 776.134838 154.604412 \nL 776.845731 157.010103 \nL 777.556624 155.040744 \nL 778.267516 151.718612 \nL 778.978409 148.867347 \nL 778.978409 48.081328 \nL 778.978409 48.081328 \nL 778.267516 51.339396 \nL 777.556624 55.027577 \nL 776.845731 57.364567 \nL 776.134838 55.336484 \nL 775.423946 49.629142 \nL 774.713053 44.042602 \nL 774.00216 40.050663 \nL 773.291267 38.16485 \nL 772.580375 38.604191 \nL 771.869482 40.731266 \nL 771.158589 42.748779 \nL 770.447697 43.945366 \nL 769.736804 45.460303 \nL 769.025911 48.14609 \nL 768.315018 51.752888 \nL 767.604126 55.177389 \nL 766.893233 55.815591 \nL 766.18234 51.659015 \nL 765.471448 45.646035 \nL 764.760555 40.732429 \nL 764.049662 37.650678 \nL 763.338769 36.858335 \nL 762.627877 38.261314 \nL 761.916984 40.61596 \nL 761.206091 42.231423 \nL 760.495199 43.449609 \nL 759.784306 45.490803 \nL 759.073413 48.714241 \nL 758.36252 52.447205 \nL 757.651628 55.040311 \nL 756.940735 53.435299 \nL 756.229842 47.891674 \nL 755.51895 42.221202 \nL 754.808057 38.087749 \nL 754.097164 36.033063 \nL 753.386271 36.329728 \nL 752.675379 38.448014 \nL 751.964486 40.623831 \nL 751.253593 41.937632 \nL 750.542701 43.452641 \nL 749.831808 46.090591 \nL 749.120915 49.689371 \nL 748.410023 53.232839 \nL 747.69913 54.245558 \nL 746.988237 50.435361 \nL 746.277344 44.440511 \nL 745.566452 39.409327 \nL 744.855559 36.16922 \nL 744.144666 35.210875 \nL 743.433774 36.513232 \nL 742.722881 38.948735 \nL 742.011988 40.729089 \nL 741.301095 42.005322 \nL 740.590203 44.013221 \nL 739.87931 47.19729 \nL 739.168417 50.962397 \nL 738.457525 53.788205 \nL 737.746632 52.609584 \nL 737.035739 47.260436 \nL 736.324846 41.519415 \nL 735.613954 37.248922 \nL 734.903061 35.025205 \nL 734.192168 35.171364 \nL 733.481276 37.260906 \nL 732.770383 39.585103 \nL 732.05949 41.024185 \nL 731.348597 42.545615 \nL 730.637705 45.135302 \nL 729.926812 48.718088 \nL 729.215919 52.361494 \nL 728.505027 53.731135 \nL 727.794134 50.289235 \nL 727.083241 44.337333 \nL 726.372348 39.195749 \nL 725.661456 35.800072 \nL 724.950563 34.67267 \nL 724.23967 35.860523 \nL 723.528778 38.357315 \nL 722.817885 40.302903 \nL 722.106992 41.645761 \nL 721.3961 43.623823 \nL 720.685207 46.764289 \nL 719.974314 50.549565 \nL 719.263421 53.584104 \nL 718.552529 52.831094 \nL 717.841636 47.706643 \nL 717.130743 41.909861 \nL 716.419851 37.507155 \nL 715.708958 35.114712 \nL 714.998065 35.10346 \nL 714.287172 37.144614 \nL 713.57628 39.604935 \nL 712.865387 41.176297 \nL 712.154494 42.710563 \nL 711.443602 45.251996 \nL 710.732709 48.811286 \nL 710.021816 52.536321 \nL 709.310923 54.242699 \nL 708.600031 51.18777 \nL 707.889138 45.30481 \nL 707.178245 40.060761 \nL 706.467353 36.512687 \nL 705.75646 35.213741 \nL 705.045567 36.274313 \nL 704.334674 38.811762 \nL 703.623782 40.920733 \nL 702.912889 42.338532 \nL 702.201996 44.2904 \nL 701.491104 47.383529 \nL 700.780211 51.177668 \nL 700.069318 54.396736 \nL 699.358425 54.064512 \nL 698.647533 49.194001 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669.500932 50.287684 \nL 668.790039 44.865968 \nL 668.079146 41.024165 \nL 667.368253 39.376761 \nL 666.657361 40.149084 \nL 665.946468 42.70322 \nL 665.235575 45.12417 \nL 664.524683 46.715202 \nL 663.81379 48.626126 \nL 663.102897 51.615322 \nL 662.392005 55.39591 \nL 661.681112 58.911975 \nL 660.970219 59.391355 \nL 660.259326 55.110367 \nL 659.548434 49.249309 \nL 658.837541 44.482875 \nL 658.126648 41.590642 \nL 657.415756 41.074206 \nL 656.704863 42.858967 \nL 655.99397 45.632863 \nL 655.283077 47.629611 \nL 654.572185 49.241025 \nL 653.861292 51.641331 \nL 653.150399 55.089982 \nL 652.439507 58.958584 \nL 651.728614 61.533795 \nL 651.017721 59.715129 \nL 650.306828 54.202878 \nL 649.595936 48.707879 \nL 648.885043 44.725511 \nL 648.17415 42.902188 \nL 647.463258 43.515895 \nL 646.752365 46.045465 \nL 646.041472 48.610284 \nL 645.330579 50.298484 \nL 644.619687 52.194822 \nL 643.908794 55.128327 \nL 643.197901 58.887795 \nL 642.487009 62.517259 \nL 641.776116 63.380253 \nL 641.065223 59.430724 \nL 640.35433 53.587183 \nL 639.643438 48.711971 \nL 638.932545 45.656784 \nL 638.221652 44.963509 \nL 637.51076 46.628918 \nL 636.799867 49.469625 \nL 636.088974 51.612313 \nL 635.378081 53.262176 \nL 634.667189 55.617566 \nL 633.956296 59.01758 \nL 633.245403 62.903397 \nL 632.534511 65.716315 \nL 631.823618 64.321631 \nL 631.112725 58.988677 \nL 630.401833 53.432151 \nL 629.69094 49.313938 \nL 628.980047 47.314202 \nL 628.269154 47.761166 \nL 627.558262 50.244346 \nL 626.847369 52.941807 \nL 626.136476 54.733217 \nL 625.425584 56.61895 \nL 624.714691 59.494821 \nL 624.003798 63.224444 \nL 623.292905 66.944902 \nL 622.582013 68.172386 \nL 621.87112 64.575358 \nL 621.160227 58.770905 \nL 620.449335 53.794005 \nL 619.738442 50.578443 \nL 619.027549 49.70522 \nL 618.316656 51.236305 \nL 617.605764 54.122525 \nL 616.894871 56.410129 \nL 616.183978 58.104612 \nL 615.473086 60.416464 \nL 614.762193 63.76276 \nL 614.0513 67.652098 \nL 613.340407 70.675382 \nL 612.629515 69.70376 \nL 611.918622 64.577632 \nL 611.207729 58.972444 \nL 610.496837 54.723501 \nL 609.785944 52.547327 \nL 609.075051 52.820513 \nL 608.364158 55.235927 \nL 607.653266 58.052512 \nL 606.942373 59.952234 \nL 606.23148 61.831355 \nL 605.520588 64.647993 \nL 604.809695 68.339652 \nL 604.098802 72.129672 \nL 603.38791 73.699709 \nL 602.677017 70.473192 \nL 601.966124 64.730457 \nL 601.255231 59.659545 \nL 600.544339 56.286645 \nL 599.833446 55.230953 \nL 599.122553 56.614076 \nL 598.411661 59.523481 \nL 597.700768 61.952917 \nL 596.989875 63.697978 \nL 596.278982 65.967779 \nL 595.56809 69.255725 \nL 594.857197 73.135872 \nL 594.146304 76.341925 \nL 593.435412 75.788331 \nL 592.724519 70.895785 \nL 592.013626 65.255943 \nL 591.302733 60.881793 \nL 590.591841 58.529626 \nL 589.880948 58.62303 \nL 589.170055 60.950021 \nL 588.459163 63.870038 \nL 587.74827 65.882068 \nL 587.037377 67.758563 \nL 586.326484 70.514664 \nL 585.615592 74.160831 \nL 584.904699 78.000108 \nL 584.193806 79.888341 \nL 583.482914 77.047005 \nL 582.772021 71.38949 \nL 582.061128 66.232889 \nL 581.350235 62.706159 \nL 580.639343 61.466021 \nL 579.92845 62.688942 \nL 579.217557 65.598503 \nL 578.506665 68.164535 \nL 577.795772 69.965851 \nL 577.084879 72.195161 \nL 576.373986 75.420555 \nL 575.663094 79.279769 \nL 574.952201 82.641117 \nL 574.241308 82.496518 \nL 573.530416 77.863145 \nL 572.819523 72.203829 \nL 572.10863 67.710434 \nL 571.397738 65.183212 \nL 570.686845 65.091799 \nL 569.975952 67.310686 \nL 569.265059 70.31645 \nL 568.554167 72.44353 \nL 567.843274 74.321391 \nL 567.132381 77.015909 \nL 566.421489 80.609646 \nL 565.710596 84.479129 \nL 564.999703 86.65922 \nL 564.28881 84.214151 \nL 563.577918 78.666032 \nL 562.867025 73.432794 \nL 562.156132 69.756247 \nL 561.44524 68.330227 \nL 560.734347 69.382162 \nL 560.023454 72.268517 \nL 559.312561 74.963739 \nL 558.601669 76.826653 \nL 557.890776 79.017124 \nL 557.179883 82.176198 \nL 556.468991 86.003711 \nL 555.758098 89.493367 \nL 555.047205 89.744949 \nL 554.336312 85.394821 \nL 553.62542 79.732415 \nL 552.914527 75.126242 \nL 552.203634 72.425465 \nL 551.492742 72.145145 \nL 550.781849 74.237499 \nL 550.070956 77.309604 \nL 549.360063 79.553144 \nL 548.649171 81.436443 \nL 547.938278 84.068636 \nL 547.227385 87.603632 \nL 546.516493 91.485651 \nL 545.8056 93.929774 \nL 545.094707 91.888335 \nL 544.383814 86.474266 \nL 543.672922 81.174306 \nL 542.962029 77.352555 \nL 542.251136 75.739835 \nL 541.540244 76.611557 \nL 540.829351 79.451471 \nL 540.118458 82.266414 \nL 539.407566 84.195978 \nL 538.696673 86.349508 \nL 537.98578 89.439034 \nL 537.274887 93.225094 \nL 536.563995 96.816935 \nL 535.853102 97.448434 \nL 535.142209 93.403814 \nL 534.431317 87.755983 \nL 533.720424 83.044159 \nL 533.009531 80.17205 \nL 532.298638 79.699761 \nL 531.587746 81.648778 \nL 530.876853 84.766576 \nL 530.16596 87.126749 \nL 529.455068 89.019935 \nL 528.744175 91.58966 \nL 528.033282 95.060394 \nL 527.322389 98.938771 \nL 526.611497 101.618148 \nL 525.900604 99.983966 \nL 525.189711 94.728947 \nL 524.478819 89.373283 \nL 523.767926 85.411784 \nL 523.057033 83.612427 \nL 522.34614 84.296555 \nL 521.635248 87.067642 \nL 520.924355 89.991262 \nL 520.213462 91.992647 \nL 519.50257 94.112087 \nL 518.791677 97.129943 \nL 518.080784 100.865907 \nL 517.369891 104.534994 \nL 516.658999 105.527164 \nL 515.948106 101.808365 \nL 515.237213 96.194293 \nL 514.526321 91.38505 \nL 513.815428 88.345105 \nL 513.104535 87.679407 \nL 512.393643 89.470899 \nL 511.68275 92.613885 \nL 510.971857 95.090814 \nL 510.260964 97.00038 \nL 509.550072 99.510257 \nL 508.839179 102.913104 \nL 508.128286 106.773368 \nL 507.417394 109.659172 \nL 506.706501 108.432927 \nL 505.995608 103.363191 \nL 505.284715 97.965698 \nL 504.573823 93.873109 \nL 503.86293 91.891113 \nL 503.152037 92.386153 \nL 502.441145 95.072572 \nL 501.730252 98.099541 \nL 501.019359 100.18696 \nL 500.308466 102.289219 \nL 499.597574 105.24869 \nL 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129.307996 \nL 443.43705 131.549187 \nL 442.726157 133.311173 \nL 442.015264 135.765008 \nL 441.304371 139.054218 \nL 440.593479 142.651176 \nL 439.882586 144.617957 \nL 439.171693 142.080962 \nL 438.460801 136.620895 \nL 437.749908 131.531486 \nL 437.039015 128.074208 \nL 436.328122 126.808686 \nL 435.61723 127.759319 \nL 434.906337 130.436165 \nL 434.195444 133.082614 \nL 433.484552 135.206338 \nL 432.773659 137.836404 \nL 432.062766 141.267941 \nL 431.351873 145.106567 \nL 430.640981 148.545929 \nL 429.930088 148.818205 \nL 429.219195 144.635084 \nL 428.508303 139.11221 \nL 427.79741 134.527905 \nL 427.086517 131.92032 \nL 426.375624 131.841234 \nL 425.664732 134.268262 \nL 424.953839 137.735181 \nL 424.242946 140.226393 \nL 423.532054 142.194444 \nL 422.821161 144.826197 \nL 422.110268 148.316494 \nL 421.399376 152.244721 \nL 420.688483 154.89745 \nL 419.97759 153.246034 \nL 419.266697 148.326558 \nL 418.555805 143.303419 \nL 417.844912 139.509288 \nL 417.134019 137.79722 \nL 416.423127 138.553864 \nL 415.712234 141.437865 \nL 415.001341 144.423547 \nL 414.290448 146.581956 \nL 413.579556 149.037504 \nL 412.868663 152.37254 \nL 412.15777 156.257864 \nL 411.446878 159.950216 \nL 410.735985 160.796662 \nL 410.025092 157.061752 \nL 409.314199 151.477928 \nL 408.603307 146.420441 \nL 407.892414 143.160141 \nL 407.181521 142.49076 \nL 406.470629 144.464516 \nL 405.759736 147.744217 \nL 405.048843 150.240124 \nL 404.33795 152.184284 \nL 403.627058 154.612048 \nL 402.916165 157.603602 \nL 402.205272 160.856772 \nL 401.49438 163.094753 \nL 400.783487 161.320779 \nL 400.072594 156.038015 \nL 399.361701 150.379302 \nL 398.650809 145.949346 \nL 397.939916 143.819191 \nL 397.229023 144.387644 \nL 396.518131 147.304273 \nL 395.807238 150.50561 \nL 395.096345 152.69231 \nL 394.385452 154.796629 \nL 393.67456 157.588846 \nL 392.963667 161.046227 \nL 392.252774 164.608937 \nL 391.541882 165.648411 \nL 390.830989 162.004594 \nL 390.120096 156.152723 \nL 389.409204 150.582325 \nL 388.698311 146.63666 \nL 387.987418 145.375611 \nL 387.276525 147.209693 \nL 386.565633 151.007996 \nL 385.85474 154.250175 \nL 385.143847 156.562114 \nL 384.432955 159.0007 \nL 383.722062 162.077987 \nL 383.011169 165.734467 \nL 382.300276 168.669642 \nL 381.589384 167.682857 \nL 380.878491 163.000949 \nL 380.167598 157.876065 \nL 379.456706 153.847844 \nL 378.745813 151.763432 \nL 378.03492 151.975623 \nL 377.324027 154.336139 \nL 376.613135 157.122802 \nL 375.902242 159.141168 \nL 375.191349 161.327588 \nL 374.480457 164.272824 \nL 373.769564 167.706629 \nL 373.058671 171.139672 \nL 372.347778 172.320928 \nL 371.636886 169.038303 \nL 370.925993 163.671648 \nL 370.2151 158.876567 \nL 369.504208 155.782708 \nL 368.793315 155.02524 \nL 368.082422 156.720322 \nL 367.371529 159.875118 \nL 366.660637 162.241958 \nL 365.949744 163.801417 \nL 365.238851 165.951067 \nL 364.527959 169.015167 \nL 363.817066 172.572124 \nL 363.106173 175.493657 \nL 362.395281 174.83347 \nL 361.684388 170.453064 \nL 360.973495 165.44999 \nL 360.262602 161.479481 \nL 359.55171 159.512422 \nL 358.840817 159.929993 \nL 358.129924 162.568276 \nL 357.419032 165.739328 \nL 356.708139 168.098392 \nL 355.997246 170.556744 \nL 355.286353 173.737994 \nL 354.575461 177.324035 \nL 353.864568 180.919804 \nL 353.153675 182.604954 \nL 352.442783 180.064421 \nL 351.73189 175.302678 \nL 351.020997 170.840509 \nL 350.310104 167.772425 \nL 349.599212 166.951245 \nL 348.888319 168.683442 \nL 348.177426 172.130952 \nL 347.466534 174.950576 \nL 346.755641 176.790296 \nL 346.044748 178.966421 \nL 345.333855 181.914732 \nL 344.622963 185.312169 \nL 343.91207 188.191002 \nL 343.201177 187.731916 \nL 342.490285 183.511498 \nL 341.779392 178.482097 \nL 341.068499 174.249255 \nL 340.357606 171.8604 \nL 339.646714 172.009437 \nL 338.935821 174.772612 \nL 338.224928 178.312415 \nL 337.514036 180.591921 \nL 336.803143 182.278597 \nL 336.09225 184.528675 \nL 335.381357 187.546819 \nL 334.670465 191.015938 \nL 333.959572 192.93382 \nL 333.248679 190.558172 \nL 332.537787 185.48344 \nL 331.826894 180.400747 \nL 331.116001 176.575692 \nL 330.405109 174.923184 \nL 329.694216 175.852689 \nL 328.983323 178.952715 \nL 328.27243 182.076533 \nL 327.561538 184.411775 \nL 326.850645 186.847527 \nL 326.139752 189.817225 \nL 325.42886 193.291756 \nL 324.717967 196.536858 \nL 324.007074 196.546735 \nL 323.296181 192.329679 \nL 322.585289 186.929988 \nL 321.874396 182.310425 \nL 321.163503 179.504956 \nL 320.452611 179.157748 \nL 319.741718 181.532293 \nL 319.030825 185.085289 \nL 318.319932 187.611795 \nL 317.60904 189.561138 \nL 316.898147 192.111327 \nL 316.187254 195.426368 \nL 315.476362 199.065927 \nL 314.765469 201.091608 \nL 314.054576 198.717575 \nL 313.343683 193.534519 \nL 312.632791 188.596507 \nL 311.921898 185.232472 \nL 311.211005 184.218795 \nL 310.500113 185.757979 \nL 309.78922 189.30065 \nL 309.078327 192.60306 \nL 308.367434 194.831649 \nL 307.656542 197.123531 \nL 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mEaxAaYlfrfHT+axIOJmuCO7Krwtql/qVl7zNdS6g6pAHCWqEEEIIIcSzLpvVjPjjjJk0wRevyd/sVrvpTWeyhV4ZMJ+qFYilKoKi8qxJKJ4yLSMrDjC01lX7SIpLoKrtr1ISYCTmFxTUCkTyQVIobp4NASPgymefak1zy58rlclW3bcGZj+3+VxX/tqqryvN6Mx1rlrXVrwuWmV89pFIghohhBBCiCNUukofRbHp3OaVU1VKsPJr8jf+1bIaI4F4ScmYWR+MWQN6RdN7ZO5AJJLMmDbQG+ebvamull1JpWcDjFqfT2l2Ze6gYK7pX7HCurlfc649X2LJuc8VT88vKMu/ZjyVqSjzyyvuvakW+BSfC2qX2R1pJKgRQgghhDhCDflj7BgO1lyT7yep9a36eNH44XgqY7qufCSy2SSsyVBlQ37lJLIqk7ySc2cTis+XrVEKVryu1s1+PhsRT2VMm+efyrmM86VJZbKmAV9ePsM117ni8wh+Mhljz6BUJltzMllsHlkfrWd7h2oGSEkJaoQQQgghxGE0FkwQTqRL9j4plw9qslnz6WGZrC7Z58Vs88pgPFVRbpYsyoaAcbNrdv7yTE3Vno75Zh2Sc/eaFL9utd4fmJ3OViuIKj7HXFmxeNLYULRWRVY6YwRjc/U4RZMZMlk957pwIj3n9c/ns4D5BT/Ff07B2MLZt0aCGiGEEEKI58Du0VDNfoV86ZnW1QOFRDpT8k2/WR/MaDBekaUoz4CYZWBgfhO/ipv7E+lMxZjgwrmS8ysFy1/bfMq3tNZzrosmM3NmHELxlLHpZo3MEBjDFmoNWsibCier7neTF4ynmI4kawZuYHwO8y2Lm+t95oOemkMTcn9OWmvCCcnUCCGEEEKIKkLxFAPT0Zo3of6ijEC1yVcj/tIel/INFgFGA5WTyMozLjPzaMiveSOcnruUqiRTUyOjkL+2uW7QA7EU4US6JJtkJhhLzXmubBZ2jATnDDAmQgkGpqO1FwH7J8I1y93ACAZ3jtQuLQSjH2pgpvZrJlJGADxTo68KjH6qZLp2KVv+zymSzMz5eRxJbM/1BQghhBBCHGvy44wnwwkaPQ7TNcV7xphlYKBydHL5zWoqkzXNLBSPHc5mddXyptg8syvxVBaPo3ZZU34PFK2pOiQAZnuD5iq5mo4kcdmtNdeAkRGbj2ojjsvNFUQBcwY0edWa+ovN97q2DwfnvLZwPD2vAC+eysxZinekkUyNEEIIIcRhNDAdrbrDfd50xDheKwiYKCoJM2tM90eTFRmX8nXTkaRp/0csOXszHUlWz3bMd5+U+TbHx5Jz3yxns0Yvx1ylYMl0ds4MxrFkrjI8MDJku0bnzg4FYqmSoHohkEyNEEIIIcRh1D8Vpc5lo8XnND1evGdMJGFeBuSPJksCCrMG/dF5jFeuumfJUxwVHKsxghlmx0TPFbBMRZIlWaJqHu6bnnMN1M74iEqZjJ7XZzbsjy2oyWcgQY0QQgghxGETS2aIpzJkawwA8MdShV4FY9RwFpu1tHimfLxyfoqXvWid2X4w+XHNSimgel9KaVnZ3JPIQnM0jEcSmTk3mwQYD8ZJzaN8Szy3FlqWBqT8TAghhBBiXmLJDHvGavdnzESNm8FkOluRNckr38CyPFuTyeqS0rPC6xedL5nOmvbZaD07rllrXTVgSaZnJ5bVmnA13/1bIon0nNO+wMg4pebRRyLEUyVBjRBCCCHEPIyH4gzOROe1ZwyYTwvLZnVFUFPeBzMVSZj2uBSPRK62wWXx+fL7oFQTza2r1SuTzm3+ONdUrWQ6y/ahuXs1hHimSFAjhBBCCDEPk+EE2awxYteM1pqpopt/s7G546FExWSs8qDGrKwMSjfMnE8fzFwbMYbjaaLJ9Jw9FnONnhbiSCBBjRBCCCGOeUP+WKEcy0xxc3+1KVOBWKqktMqsv2TEZM+Y8uCnah9MUfBTa9LVfHaNB6PsbK41AIemo6YT1GptHCrEs02CGiGEEEIc01KZLLtHg6bTxPICRc391Zrhh8s2wixfl81q0z1j4mV7xlTrcSnu0ZlXc39Z+VsilebQ+GyJWDCeZqhsnxutNffvGuaPjxwsBC1mscvgZIje27ey49BU1esA2H5oip/+bSeROaai9Y0FiC6g3evFkUeCGiGEEEIc02aiSbJZTAOOvOKSM7OyroxZr0xZUBOMp+bslQkl0lV3cc9nalKZ0iEEWmt2D04zOBkqrNNaV2R8/vBwH/979x6e7Js03lM4WbGx4/6RAPdsH2Zb/xQj0xHzCwEe3D1KLJnmT4/2kalywaFYkj88fJDh6UjhNc0MTob4xT17ueEPT5CoMlwBIJpIccfj/QQicw8kEMceCWqEEEIIcdRKZbJz7tqeD2bMGvvzigOW/I73xabClc39iXSmpKStWtAUT2cK5ysPmAYmQuwemgGqTyLbcmCC2x48wK/v24fWRqanvHcnFEuyb8QPwCN7x6q+z/25NRal2DFgvldMNJHiwGiAJp/TmNRmUlJnnCsAgMdpY3uNjM7WgxOAERjmAzMztz2wn8cPTHDPjuGqawD6xoI1zyOOThLUCCGEEOKo1T8VZd94uOaa/GSvaDJt2lcTjKeIJopLxCBRFiiNm4xg1rq0tKw8aMoHMtns7BCA4l6ZVDrDz/+5m9se2M/ARIhMRhNJpCvKyvbkgp5EKsPQVJhsFnaOlE4iOzhqBBhru5uYDMZMMyJaaw6MBljT3cSSNh8j0+afW/+4ETCcvWExAIOT5uv2j/hp8Do5ZWU706E4SZNNNzPZLPuG/axf0ozVojg0YR6MBCIJBnKvs3tw2vRcYARcv7hnD//zj90cmqg9jS1ZFEyKhU+CGiGEEEIctSZCCaZqlCsl0plC5iObhUiysrRsxF/ZazPf5v7iiWXFAcuTfZN857eP88CuEcAIqKC0D2ZnUaZkW/9k7ni65LUSqQyDk2FOWdmGUkaWAqiYsNY/HsLrsnPiilbjPZmUlo0HYoTjKVZ1NtBa72YqGDe96e8bC+CyWzluUSP1HgejM5XnSqYz9I8HOa6rgfYGNwCTwcqMzvBUhGQ6y5ruJrqavQxVCZDyWaaXnri4ZnaouMdn37DfdA3AVDBG7++28LcnBqquEQuLBDVCCCGEOCrFUxkiiTSJVLYQNJSbLtt/pXy8stbadIBA8bpEOlMS5Gw9MMHARKhkXTqTLRkc8NCeUVKZLE/2TaK1LvTBhIrKz/rHg/hcdlZ2NhSCkOFArGRjzv7xIFmtWbe4mSafiwmTwCGb1fSPB1naVkdXkxeAMX9lIHIgFzisyAU1qUwWf1lAmMpk2TvsZ0VnAxalaPa5mA5Vfj79Y0EyWc2qrkbaGjwApoHIvhE/SsGy9jraGzxMhWKmgdTeYT8tdS7WLm4GYNwfrVgDsHtoho5GD8va6woZJTP/eHKQdFbz6L5xwjX2/EllssTmGI0tjgwS1AghhBBiwclmNXvHavdNFPewVNuzZTRQu7k/lEiXjGnOK27UD8Zmzz0yHeYvj/fz83/uJppIFdYVv34gkmA6FKfZ58IfSTAVihNLZggnZveM0VpzaCLE0vY6upq9TIXiJFJppsPJkmlkB0cDOGxWFrV4c9mVysChfzxILJlmTXcTLocNj9OGP1warGit2TEwzaJmL16XnbZ8dqUsENk7NEMileHE5UbGp8nnZCacqAhEHj8wgcdpY3GrjwavA7vVUnGuVCbLtv5JVi9qwmm30eRzkUxniZT9WcUSaQYnQqzubqLObcdltzIeqAxqookUw1MRVnU1sKS1jslgjKRJmV0ynaFvPMiStjqAqiVvAL+9fx/X3r6lJGsmjkwS1AghhBBiwZkIJ+ifilb0lxTzF30DH0lU3tzGU5mKTE35GObi6WCHJoI8vGeUeDJdkqkpvoZH940Xft475DfdM+bgmNHf8tKTjJ6U0ekIM9FUSRA27o8STaRZ1lZflF0pvZHPZLPsG/GzvKMeq8VCS70LfzhBOlMahG3rn8Rpt7KyswGARp+TmXBpMNc3HmQ6FOeUVe0ANNe5AJgpytRorXlw9ygtdS6W5gKCpjoXyXSGaFEgMjoToX88yPPXdGK1WFBK0ehzEoiWBlLb+iaJJzOcuqotdy6n8ZplmZ+tByfQwJruJpRStDV6KgIkgN2DRm/Rqq5GWuqNoGw6XD2LdOaaTqwWVfG55gUiCQ7myvme7JswXZOXSGVqTm4TzzwJaoQQQgix4MzkRizX2ul+Klwc1FRmasaDiYo9WMrLz/y582ezmt/cv5+/PznIlgMTpnvGaK0ZmAixbnETLruV0ZlIIUgqHhJwcDRIg8fBis4GrBbFZChGMJZi/8RsP8meoRmUgpVdDTTlAozy7Mq+YT/RRJoNy1oAaK13oym9kfdHEuwenOGk5a3YrMZtX5PPVRKsADy2bxyP08ba7iYAnHYbTru1ZHzy0FSYyWCMM9Z0opQqnAsoCZK2HJjAbrVwcq5/B6DB4yRQFEDmA6TuFh9LWo0AqdnkXMl0hof2jLKqs4GORqOMrdHrJBAtDUYz2SwP7Rmlq9lLR6OnEJSZlcbtHJjG7bCxtL2OtgZ31VK2fHZmTXcTg5NhUlWGEyRSGTb/8Ql+dOcOsjJ44DkjQY0QQgghjihme7mUm4kYQUK1TSgDsVRJKZnZhpnjuRveTDbLvTuGmAnFS/aMMc5vvM7AZKjwTfyB0UDJ+fKlZTPhBOF4iiVt9XQ0eRmdiRBOpMhmdeE605ks/RNBlnfkelLqXEzlenbyzf1Zrdk5MM3Stjo8Tjv1bgcWpZgpC2oePzBRCI7AuNkHCBSt23FoCg0877iOwmNNXifhWKpwkx6OJTkwGuDkFW2FwAegweskWBQ87B32Y7UoVucCn/y5gELvjdaaPUMzrFlslJTl1XscBKOzZWrD0xFCsWRuwIERINV5HFgtpe9z18A0iVSGM9Z2lpwrEk+VZKR2HJomGE3yonVdRmbI60RRmamJxFPsG8lPW7PQ3uAxLWUz3u8MXc1eTlzWQiarGZ0xX/fI3jESqQyBSILdg1Km9lyRoEYIIYQQR5QnhwIVG1kWS6QzhcxLtaCm/PnlgwIyWV3Injyyd4z7do7wmwf2EUvN7kGTSGdI5KaX7R/xY7MoTl/dwfBUmGA0SSarSWeyheCpL1dWtqy9jq4mDxPBGMlUluFArDD5bO/wDKnctC+AlrrKPpi9w34C0SSnrDRKwSwWRYPXUdK0PxWMMTAR4uSVbVhyQUFDPqgpKvPaO+xnUYuXeo+j8FhjLiPiz2VO+nM9JasXNZZcR4PHUZKpOTASYGlbHU67tfBYXe68oVzwY/T+ZFiay74UzuV1kEzPbhq6b9iPRalCSRwYe+PUuY3gJ2/nwDTNdS66W3xF12W8z+KA6/H947Q1uAsBns1qod7rrChlu2f7EFrD84rK7OLJDPGyvx8z4TijM1GOKy5lM8n6GO9lhu4WHy67lUM1hhMEIgke2j3KRJUgSjw9EtQIIYQQ4oiRymSZjiRKSsfK5bM0YF5WprUuDADIao3WmnRGl3yzH4qnyGaNtY/vN/olJoNxJgLxwh40xSOYD02EWJQrldK5tdFkmmDRmn0jfprrXDT5XDTVudDauPHeW7RPztYDEzR4nSxrz5Vc1bkIRJMl17a9f5I6t53jioKMJp8Lf1HWYX9u35njl7YUHnPZrThslkKZVySeYtxv3JgXqy8EIkbwMDARwmm30pYr78pryJV5aa1zQwriLC4LVmxWCx6nrRBg5Ke0dTV7y14zF4jkgqShqTBdzV5cDlvZOkfhXFmtGZmJsKytrpDNMa7LuP588BaMJhjzRzl+SUvJukavsyQQjCZSbD80xUkrWgslffWFQLD079v9O0ewWRQblrVQ73Fgs1qYMglqApEE44EYxy1qpL3RU7U/B+Du7UP8Y9sgf36sv+oa8X8nQY0QQgghjhgz0STZ7GzPjJnifWcyWU2irNfBH02RTGdJpjNc//ut3PTnbUTiqZJ+mXwvzkw4QSiW5LTjjG/uR2cihWxCPgsUS6aZCMRY2lZHS/1sr0YsmSmcJxxPMTARLgQQhVKwSKIw0Ww6FGdgMszJK1oLN9/5dfkb+WQqQ99YkDXdTYUMTH7dTGS2fOvQeJDmOhd17tkMjFLKCERyn09+/5hFzbNZDpgNavI38oOTIRa3+kpeD4xMTTqTJZpIM5YrveooC3yMdc6SoMZptxZ6WgprioKHrNaM+6N0NlWeq97jIJgb8DAVjJFKZ+mquP58gGSs2z9iBHiruhpK1jUUBUgAOw9Nk8lqTlnZVrLGOFdpFmzHoSlOWdWOz+1AKUWzz8l0qHI4wZN9xv5Ba7ub6GzyMhGIVQxqAKM3KL9vzsh0hEiNARfi/0aCGiGEEEI8K4zekto3c4HcBLBYMkPK5OYwk9Ul+7Tk1xabyt3sHhgJEE2k8UcS7B/xlwQ1+THM+c0qT13Zjt1qYWwmWliXD2oO5G6al7bX0+B1YrUopoIxJsIJ/Lmb5q0HJshqzUm5UccNZb0mYPS3KAUblhU10OeyDvl1A5MhMlltml1J5cq3slnN4GS4MIGsWINntok+nzUoD0R8LjsWpQjFkiRSGWbCCTqbvBXnygcPgWiC0fy5TAKRuqLgYWQ6TGeTpyRjYlxXLpDKjbNOZbJ0mLxmncdBOJYik80Wsj6LyrI+Prdx/flMzch0BK/LXhFI1XkcRBPpQpDRPx6kyecs7JtjvMfSAA/gkX1j2KwWzizq42muc1eUn8WTabYenGBlZwMNXiftjR6yWptOXNs37CeVyfKyk5YAxrS5ah7fP86v791LJlv5919UJ0GNEEIIIZ4VAzNRdo/OsbdM0TQzs+b+iVCCdEYTjCa5c8shYol0xcSyfJZnz9AMHqcNr8tO31iQeLK0/AyMG90Gj4OmOhdtjW7G/NHZMcwJY82TfRM0ep0savZiUYomn4upUJwRf5ypsFGetb1/kmXt9YWyJp/LjtWiSvpb+saDdDYZ+8DkFWd0YDa70llevlWU0ZkOG0FBeQYGjCApkMvojM5EaK5z4SjqgQEjo1PnthOMJguTvzrNMjDe2YzI2EyEeo8Dj9NesS6fXUmmM0wEYxWZFQCn3YrDZiUYTc6+xypZH4BQLMXwdASXw0qjz1myxqJUSSA1OhOho7EykKov6vfJas3gVLgwaS3P7bBht1oKfTxaa/aPBFjR2VDyXpt8ToKxJNmiIRYP7Bohlkhz1vGLgKKsW6Qyy7hzYJo6t4NTV7UZE+9MRlKDMeHuji2H2D8a4NG946ZrhDkJaoQQQgjxrJiKJAulYWZSmSzBkqCmsl8mPwDgtw/s47H94zy4Z6Qk+Mlng/KbV67obGB5ez0DkyHiuTK1TFYTy2U8Dk2EWNZeDxgjkadDcQKxVGEAwEzYKBk7cflsyVhLnatk7PC4P0ogmmT9kubCY0op6j2OQn9LIpVhdCZSeK08bz74yQU1YzNRWupcOGylgUihTCqamC0FM8maNHicpDJZYkmjZMysXAyMICkYTTLmj+TOVZk1Ke5dGa1xrgaPk3Qmy4HRAFpT2FenmFEa5yCQu3671VIIAEuuqyijMzIdpqvJWxGs5D+PQCRBMp1hOhSvWsoGEIwlmQzESKQyLC7LbhWuKzKb3YrEUxWlbPUeh9EjlSuNS2WyPNk3ydrFTYXPbjbrU5pJnAzGODga4PilxsQ1Iyg2D2p2DEwBUOe2s2/Eb7pGmJOgRgghhBDPuGxWF0rLqpWgTYZL940p3zAzk9VMR5IEIonCeN0nDk6WDAuIJNNks8YUrlgyzZLWOlob3EQTaSZyU8bC8TRaG6VeyXSG5R1GoNHodRJLphkPxhkPGdeyrX8KBZywbLYhPz/qOL8nyYFc0355yVhxf8vIdAStYUlraRYj3wfjz2dX/FHTAKNwg54LRGxWS0W5Vf41AYanIoTjKdOyMjCCgplwnDF/FJ/LXpI9ynPYrLgdNsb9UfwR8xI1mN2oc1uuv6R8SMDsaxp71YzOROho8lT08ICREQEjuJsMxlnUUpn1yb/PQC7TpMG8fM49m90q9BdVKbPLZ2qGp4x1S9tKg8/ZrJWx7sBogHgqw0krZvtzPE4bNqulZD8egH88OYjDbuX5q41ytpaiMd7l9g7NsKjFy5ruJsb80ZLMUDGtNdsPTRU2chUS1AghhBDiMBgLxgtN7GaC8VRh/5mwycQy4xwJookUtz2wn92D0xWZmmDMOMeh3Aji045rJ5HKFHovjNcxnpMfrbu41UdL7qZ7IHfDmh/lvOXABC6HlZW5YCR/4zodihc2wtw37GdJW11JQ36j10kmqwnnvrUfmgrTWu/C7Syd5GVsEpnLwNTKiHgcBKJJwvEUkXjKNOvgdhg3zEZQE6W9wW0aFOSzK7uHZgBMzwVGViqaSHNwNGia8Sk+3+5B41zVMjWtueEJB8eMUj6zAAmMwMwfSTDur571qXMbk8byGYtqAVJ+r5qhqXDVa6vz2FHK6Fca80dx2CwVpWww+/kDjPojeJw26tz2sjWlU9IGJkLYrRYWFwWpSqnccILZTM3gZIgDowFesK6r8Pejud5FIJKoGCgwOhNhPBBjXXcznU1e0pksk0HzjM6WAxP84eGD/OqevVU3BT3WSFAjhBBCiKclkc6wbShQMt643Ex0NjtjtrdMIp1hKpxge/8Uu4dmuO3BA0yVDQTI99scmgjidtgK44z7J2dHJufHMO8YmKK13kWj11nIJIzOGN98B2Ip4sk0+4b9nLC0FXtuw8n8jas/kiCRyhKOp5gMxgqZnLziIQBaa0amI+b9LR4n8WSGRCrDuD9Kg8eBu2yEcf58gchsWZlZ1iF/wzwTjtcMCvLXtmtgGgW0N5iva2sw9l6JJdN0NJoHDmAEP/lQtbvVPGvizQ0egOpBCBjlculMlnRWl+w7U0wpRZPPyUSu58SslA1mM1d7hmbwuez4ioLOPKvFQoPHiT+XkWo36bsBoxQvkTL2qhmbidBpUvJW57GjmO19GpwM0dXsxWopvZWuLwqQwOilsVstnJrbFweg2edCUzpEAuD+XSM4bBY2LG8p/B2otjHo9n4j6NPUHjpwLJGgRgghhBBPy0SuVMtfYwxz8YhmswEA40HjHLuKdmTfW9ZTEMhNxTowEmBFZz2t9Ua2YmgyXCjTCcVTTIfijExHOGFpay4YMCaWTQZjRFMZgvEU+4b9ZLVm/ZKmwvkLPSS58qFDuZvF8j6Y2eb+JDPhBPFUpuRmvsFjx2ZVRedLFG6q8ywWOGlJQ+F8iVSG/vFgLhBxF9Y1ee0sbTGe19bgYf9IgGS6cnJYi8+B1apw2Kx4nDayWtPZ7K0YEpDXWhTs1ApEivuEyvt88pRStDca17yqrASvWPFGmys6G6qua85tDtpc56rYxyYvP8FsdCZaM9PU6HMyFYozEYjV6Aky/pwmgzGmgub9OVaLhbpcwJJMZZgIxEqyNIVzeZ2FEjWtNQdGAyxrry8Ezvk1ULp56P4RP/uG/Zy5tgun3UaD14HCGBxQbjoUZ2QmwtkbunHYrBwcrR3UHCuZnDmDGqWUSyn1kFJqq1Jqu1LqC7nHT1FKPaCU2qKUekQpdUbRc65WSu1TSu1WSr3ymXwDQgghhHhu+XNZmOJsTLFUJos/mmRb3yS33r2nYqNDgOlIklgizehMlBet78JutXBgJFBSohOMpTg0ESKeyrC2uznXV+JkMhgvDAEIJdJs759EAccvNW7ILZZc30o4wbahANFEhv2jAerc9pKsiNthw26zFE0ii2KzWgrZDo/Dyoo2b0lZU/6b9OIgY21nHctavIWb1/FAlJlwouSmutHjoL3ORb3bPptdGZympd6NvSh4WNrsZWmz8bx84GD8PHsupWBDdwOtufPkg4fym3irVbG6w7gR9zpttNS5OK6rsSITVWxpez0rOxt4wwtWVV0D8Lrnr+TCl60r2Qy0nMdpZ0lbHWu6m6oGSGAEPFaLYk3R5qPligO/FR3VA6Qmr7Owd0y17FD+89837K/anwO5LEzRn7n5GGwH8ZSRnfNHEgSjSVZ0lmX6ioYh5D26b5w6t4Pnr+kAjCAqX65XbvuhXJ/X0hbaG91VS9TAGA/937/bwuBk7amDRwPz8LdUAni51jqslLID9yil/ghcA3xBa/1HpdRrgP8EXqqUOh54O3ACsAi4Qym1Rmt9bISJQgghxFEkGE9hVQqvs/otQ34DykiVXpnRQJypYJw/PtoHGM33L13XXvj2WmvNdDRZ6I9Y1l7PwETIGK+cylBntZBIZ0ims+wZnMFusxRuxPNN9rFkBoUinc6yc2CaZe31JSVJ+Y0Y8+VpozMRult8hTKjVe0+Dk6Gc/u8GDeS44EobQ1uLBZjTUeDi6XNHvomI9R7nIXRyRalCn0lTruFepcdnZ3N6Owd8hvPLwoy2uuMY60+R+GmOppIs6Z7NnNksUCz14HVonDYLCXPb6ufvalv9DiwWy201TkZC8Z58fHdjAdinFy0ySRAd6ObZS1e+qeiJNNZ3vuKE0z7cpp9DrJZjT+awqIUb37xatM/12Jmk8zMvP3stXOuOXF5Kycub625xhhL7SAUK506V67RN3td1YKa/J/TtlxJl1nfExh/1w6NBwtDKkw3Ii1kYRKFYKN8xHV+4l0+UxOOJekfD/Ki9YtKytnyf7eLxRJpthwYZ0VnAz63g5Y6N7sHp9FaV5TMJVIZ7to6QFZr7txyiIvOO8H0fR0t5szUaEO+WNWe+6Vzv/KhZwMwnPv5fODnWuuE1vogsA84AyGEEEIsOHvHQgzOVP8mOJ7KFPZ1yW8MWW4sGC9scmm1KLb3T5bsLRNOpMlkNIOTIawWRWeT19jsMBwnnjIyNeF4mmxWs2/Ez6rORmy5gCjfjD8TTeKPJfGHEwSiSY7LfcvvdliNQKNo1/tIPEUwmix80+5xWFnR6qW9zujBCUSMvWfKRyK31TmxWy00uO00emeb3lvqXYWb0abct/A+lw2300ad21EYzdtedLPckrv59blshYlfQMk+Kg1u4+YXoN5tZ1GzjyWtPt5xztpCoAXQlnt+vdsIPOs9Di469/iKfprWwjqjCd4soAFY2eqlu8lteqyY3fbcdjG845y1vP3stVVL1IDCaOaWOpdp3w2Ay2Gjuc5FLJnG57bjqzLooMHjIBRLMTIdxlulj2d2JHXSyPRZZgPevHxAlh8oMJDrCTuubIx0o89ZUX529/YhEqkMZ29YbLyvehfxVIaIyUTB/SNGieXyjnrGAzESqeo9b0eDef1tVEpZlVJbgHHgr1rrB4GrgP9SSg0AXweuzi3vBgaKnj6Ye6z8nJfmytYemZiY+L+/AyGEEEI8IzK5pvqJUGUJTN5kOIE/nODvTwwQjqWIlm2EGU9lCOTKxuo9Dk5c3sqYP0a0KKuTHxywfzRAd4uvUFYWT2YYzzWMhxNpBidDRjZjcVEfjMdJKp1lYCrKeDDBwVwfTD6Ts7TZw8o2Hw1eB7FkmmRuvxiYLR9qzN2INnrshc0r/bk9UPJlXnabkYEBCiVj/nBlr0w+cLBaFB6HrdCvUnyz7HFYcTuM8iuvw4bDZuUVpy7DYbOypGgflbaiLEOdy4bDbuXt56xjcdkGki0+4/rddiuWKnd2VouiMRfMNLjNb9oBHDYjaGs1mRJWzGKBM1c043FULyN7pjV4nSWfl5kmn4sPnX8q73jpuprr6nMByuoaPUGzZYIzVafK5YdNBKMJRmcitDV6KoYJGOeaHSgwOBnCbrMU+oTy8iPGE7l/UzPhOFsPTnDqyvbCoIfWXMZuKlQ5InrX4DQ+t53TjzNK2kamzYcO5NWaXrgQzCuo0VpntNanAIuBM5RSG4APAh/WWi8BPgx8P7fcLOyv+JS01jdqrU/XWp/e1tZm8hQhhBBCPJcCsRTZrBGYlI+fzZsMJ7lv5zAP7x3jtgf3lwQrYPTKZLOagYkQS9rqaGvwkExnGJwqHsNsTBmbDsUL5Vf5ZvGDufHNwVia3UMz2K0WVnbUY7UoVrTN9q0MToWZiiQYmgxR57bT6HWiFHTUu2jxOqgvGsk7MhNBMbt5ZT4oqHMawUoqky1klvKZmmbP7LfyHoe1cMMZTaQLk88sltlzgRGI5LMwa7tnS6Tai7659ziMQOSUlW1c+YZTSsYht9c7S9aZ8TithdJApYxAykyLz1HI7tS5qmc2Wn1OlFLYrRZcVYYMALTXuXDZrTR5zbMfxWqd59mQ32+nlnwJ2/NyAYCZfMACpRm1Yvm9ambCxjS7atPbGjyzexgNTYVZ1Owryb4Vv14+w7g/l/E7ffXsNTYWTeIrNhGIsX8kwIalLYXAemQ6jJlEKsMtf9vJtbdvIValhHQheEp5Q621H/g78CrgIuDXuUO/YLbEbBBYUvS0xcyWpgkhhBDiCKC1ZtJkslKx6aJNBM32lkmms4wGouzJ7YkyNBlmquyc/miKiUCMWDLN0ra6QoP3/rHZiU2heJodh6ZQClbnysbyPRqDRXvLHBwLsqyjHrvNSludk5WtXjpyzfOBSJJs1tgRviM3ktfntOGwGTfn7Q3G+YLRBKPTEVrq3ThsVpQy+lbAKAXL3yTuGZrJ9crkvhGvm7159zpm1wF0txg3jc1eZ8mUK5fdyskr2li/pJkXre8CjKb+xUWlXUqpwk1/cU9EW52zJBhwVwkMlrWU3jT7qvQ+dRU11tfKrrTVzb4vr7P6unzwVivrA7C42c36rtrZlCPBCcta+ND5p5puaJrXVvQZVssQKaVoqXOxe2iGVCZbdTJbvddBNJEmlkwzGYyxyGQCXV0ukA7l9kM6MBqgpc5VCOTB2NdHqdJJalmt+evj/ThsVk5f3YnLYaPe4zDN5oAxdnpkOkI8mSn8W16I5jP9rE0p1Zj72Q2cB+zCCFTOyS17ObA39/NtwNuVUk6l1ApgNfDQYb5uIYQQQjwNE+EETw4FapacBGJJQrEkM6G46d4yY8E4gxMRUpksp6/uQAM7B/1l50hxaMIIYJa21dOauzEsydTEUmzrn2JVZyM+t4PlrR66Gj0oZQQpqUyWqVCcQCRBdy4r0ux1oJRiaW6srj+SIJnKlEwZK84iLM41iU+H4ozORAvlQw1ueyEQsVoUS3LrDk2EaKl3YbNasFkV7UU3ux6nleUd9ThsVlx2ayHwWdZcegPrcVhp8Dp53RkrC30f5cEKGEFSueVlwYrbJBCx2yx0lfVrmK3zOm20FmWQXLlgrpzTbqGl6DOrFiAVB4KNntpBTXudi2avA8dz3H8zF6VUzYlsYPTevPUlazh+aXNJyWG5jkZPocel+j47RmCyf8SP1pieL18SF4wmyWSzDE1FKibVWSxGf07xJLW9w36GpsKce/KSwoafjSZDB/K29U3SWu+myedc0EHNfKafdQE/UkpZMYKgW7XWtyul/MB3lFI2IA5cCqC13q6UuhXYAaSBTTL5TAghhDiyjAcTZDKaUCJd6BUplslqDo6H+N6ftmFRiuMXv7hizUQ4wdBUGAU8f3UHj+4dy+0tswyAdCZLJGFsctlc5yo0UXtd9kKvTDSZZmAiRCSe4vilzVgs+eyDcbPmjyTYOuCf3ZgyFzjkb6pb65x4nLaSUbuFkrGiG/T2ehcuh5W+8SCxZLpQktNRFhR0Nnuw2yyk0tlCiVFXg7vQsA/gtFnxuuxset3JJFIZo+zLWVmKZZZd6W6sbMAvD3I8DisNZcGC02bFalVkMrNBaHeju6JkyazUa3lr6aaTFouRHYqV7Re0rNlbcr5qE+9afE6cuQAg38eTNalOtFkVTR67kTVz2ZgOV9/HaF1XHWPBODMR87HgR4pl7fUV+xaVKw5QqmV+8mOdzSbj5XndxujwUCzJZDBOOpM13VOoPjfZL+/JgxP43HaOXzY7XrvJ5zINWCYCMUZmIrzspMVMBePsHfabTlJbCOYz/ewJrfWpWuuTtNYbtNbX5B6/R2t9mtb6ZK31mVrrR4ue82Wt9Sqt9Vqt9R+fyTcghBBCiKcuv7dMoMreMpPhBNv6jBG3Wa159EDpUJ9MVuOPJhmaCtHa4MbnNkYTj/lnm5HDiTTBaIKByTDHL2nGbrPQ7HPQ4HEwHYqjtSYcT9M3Zmw6uay9vjCeuMljL4xh9kdTjOSb+xu9eBzWws27z2krjL7Nv3ZHoxGYlPTBOG00+1yFXpnOJi9Wq6KrofSm0+Ow4nUaAUV+rPBik0lgLrsVm9VS6IEpD46gMmtitajCdLTSc5XejhX30pScryxgaTNp5i9fY7FUWVd2bTarYlFjWdanSsnb0qKMlFIKV5UMR3udq3BzXFdjJLjNqljU4C7Jhi1k+dK0V5y6rGpwkO+X2Tfix+O0FQL+YpbClLRkxXCL8nPlg5pUJkv/eIj1S5pLptvle8DiydKM60N7RowvLZa20FrvLvSJLURHdi5QCCGEEE9Ztf1i8uKpDPHcRKWgyShYMPaWOTQRoqPRg9thY/uh6ZLjgViKeDLD4GSYpbmbuCafk4mijQBD8TQ7B4znrV/SwvIWD6vafLN7y+Qmo/VPBOls9uJy2Ao3/Y0eR24vGONmbXQmQkudC4fdSkvRTbrPZcvtLZNk3B/F47ThddnprHeVZB08dmvhW3OF0R/RUecqjIbOc9utnP+CVZxz4mLaGz0ljfjl64qZTQtzlU0ja/TYKzIrZudqNLnBzZ8vz2ZVhRHOpWtK30+z11nxHs1es6O+8rMwy/o0eR0lGTAwL3kD6CwKGGvtc9Tqc2KxqDlL2apd05Gmtd7NlW84hVNWVh+E5S0a1720rb5q8JMf/Tw6HcFlt5b0cuXVexyEY0aJ2thMhKzWFXvyNPoqBwrsH/Gz49A0Z67txOO0F0pDa23meSSToEYIIYQ4yjzSP4M/Wr3UJxBL8fj+cX51716mTMY1pzNZRgJRRqYjLGuvp63BzdhMpOIcfWNBMlnNcV2NxkaRdS5mQgliuW+D8wMAult8NPqcNHkdNLjttNS5CEWTjPhjjAcTjPtnG6XzY4etFkVznatwszY6Ey1siljczO60WWnyOQnlvs3uaDRKrcr3WXE7rIV9WzqbvVgtlpKb7pJ1jR7OWNMJmAcrUHpzbbUo6qtMFCvOYphlaQCcZTfq1ZrviwOWRo/D9Ea4vF+mseq5Sl+zxWSKmctuqei9MSufM5u4Vh78+GpMXMv/eda57Nis1cuefC4bZ6xorjq2+kjitNfu8LAoxcpOY1+axa3mm4KC8W9qKhhnZCZCZ24ARrl6jwMNuT10jH+n5Rt+5ocL5MdIa635x5ODNPmcvDA3xKKlXoIaIYQQQhwhQvEUqXSWsWD1yWYj/hh3bDnEgdEAf3y0v+L4RDjBwHiYrNYsbaujrcHNRDBekgEKxVPsGpzG5bCytL2OM1e00NrgJpXJMpzbD2NoOsJkMM6a7kYjs5Av1Wp0o4HtgwEGpsKkM1k6Gr3YrKrkhj6/bmgqTCSeoqvJyJyUZwoWNXvJas1kME5Ho4d6t72iyd1lt3LamnYuOGs1rz19Rcm+LcUqsyZzBxj1blvVb9qLA5b6KgFG8Wt6nNaSCWrV1lUby2yxqJKm/Plcv1KYjmZWShV6Z4xzUzJwoHDNJpmaFa1lgw6qZFgcNktJeVy1kdQA7XVOHDZLYePShe5fnreMU1e2FcZJm2lrMErCJgIx034amA1YgpEEIzMR6j2Oig1E80MHQrmgZjIYYyoU5/lrOgv76Hhz0wLLN/xcKCSoEUIIIY4i+THMoSplZVprHt5n9MdYlKJvPEiqbA+aqXCSQxMhLErR3eqjrcFDOpPl0GSosGbUH2PfsJ8TlrbQVufC67SxNFfycmAiRDar2T8WAIxvjYsDkcW5m7PxQLTQB9Pe6KGtzllSnpWfRLYjV/rW2eRlUUNlpuD4JbObca7saizZK6aYx25jeUcDTXUu6t0201Kw8gyG2RAFKL1JrzXWeD6BiMNmwZrLUFR7vfJrq7Uu/5pKGdkP03MVBSseh616IOWYfbzBbTctZXOWlbyZBZ723BS5ckubPaVlgjXGTef396m1zw6wIDI5AB6nnfNOXVaYjGemrX7277tZPw1Q6McJRpOMTEdMgx+Xw+gBy/feHBg1/m3ms0VgBLH1Rf05C80C+WMXQgghxHzMRFNMBWOMBsxLSGaiKfrGg9gsihes62I6FGfMX7o2EEvRPx6kq9lLndteGH+8f8wIalKZLNsPTZPJak5Y2lK4gV2e660Zmo4SSqQZmYpgUYr2Rk/JTe7StvwYZqMPxmYx9vboKgtYVrQb67b3TxXOY1Yy1l70vEXNXtNSKjBu7PIa3OZrim++nfbqG1AWn8vnrBWIGLdaHkf1DAzMBiI1gxrb3AGS8ZrGOq/TVjK1reT1HMUZpOrnKs7UVC+LKy9lm7tkD4zPd2nZGOxq/Tl1Llsh+1arlA2MQGlNx5G/N858tDXMfj5Lq+yNU5f7uzw8HSEYTZqOkVZKUe92FPa86R8P0VrvLjw3r8HjIBBdmJma+Yx0FkIIIcQRIBBNEU9nTCdt5f1z+zC/f/ggyzvqOe/4zoobyWF/jKGpMJ3NXpa21XHfTtg2MMPi3L4o8VSGoakIY/4oLz1xMcd31VOXu9EcyY1VDsXTHBwL4HXZaW/0FEqcVuSClTF/lIlQgpGZKK31LuzW0pKh7kYvFqUI5CaWtTV68LlsFd/ut9S58LrsROIp43W8DtMgw2238v5XnEA6q7EWlbmVKw4Kat3Iu+xWwpl01SxH/jXz5hNg1DpX/nzheLrmDbsrlzWpFWzBbGBQ67qcNqNfRus5sj4lwc/cnytAk7d6ditctN9RV4OrIltmtmcPUNIjVVcjiATobvSg0ewZq7lsQXA7bbz69OUsbqnDUeXP3Ga14HPZ2TVoZDSrlanVeeyEokm01ozORFi3uLLsrd7jYHAqfPjewLNIMjVCCCHEAjHoj3JwMlL1eDiRZutBo7SsbyzI0HTpWq01I4EYYzNRult8hWlHA0U3McF4ih0Dxijn45c10+hxsKLdh8NmYTRXKhaMpegfC7Kiox67zVL4Br3ObafObexBc3AizFiuubnJ6yi5Ofa6bDR4jT1oxmaidDR6CqVFxVx2Ky25iWVdTd4a/SFWWurddDR68DrMy8qg7AZ9HgFLraDAbrVgt1mwWlTNkinPPAKM+a5z2qzYbZZ5BUhQ++a/uF9mPkEZVP/MHLnPIa/aQITyQKzNZISzWe+N12krGVDgdlirZqDq3XbcDiseh62iLG6h2rCstTC9rJomn5NEKmNsyFotqHE7CMaSTIfiJFKZKnveGOdJpIzg85G9Y/zy/gNP/008C46OP20hhBDiGNA/GWE6bGzCZ2ZoxphYtjy3OWC+dyYvGEuzfzhAVmtWdNazuNmDy2EtCX5CsRQ7D02ztK2Oxc0+rBZFndNOo8/JRK6krW8yRDyVMQIjn7PQJO+wWWjwGuOV/ZEE8VSGzmZvxf4nDpuFRq+T/vEgyXSGjkaPaQO6227lheu6eMHaTl64rovGKiVj88km5M8HxjjkWpkO1zyCGjACkVpDAoxz2HO9LbXP5bJb8bmq97bk+Zy2qpPW8vLvs1avD4DXaa3Zd1N8LoetdnYoH0C4a5TZFQcs5UMhys9TrHzzUKg+2rl4Ml6twBWMkdMLcI9JU+tywwY8Dluh8b9cg9dJJJ7i0IRRRmoa1OSypfkpaU/2TbC1f+qZuOTDToIaIYQQYgEIxVNcd/tWem9/grFg3HTNowcmyWQ1p65qx2ZR9E2ESo5PR5PsH/HjsFk4dXkrG7obaPK5SjbM3D0cwB9JsH5Jc6EczGJRtNS5mcxtmLl7yGgy7mjy0l5X+g1yS52LQCTBaK5UrbvFZ7r5Y1ezl0Rur5wl7XWmN7hWi2LVokZesmExdR5H1Zv0+ZaCeZzzKwXLZ03muil2261Ve3PyrBZFY41rz2urc1YdH12swW0r2afHTH6KVa0SOzACpFp9NzDbEzSfoAzmGnRQPDGuetat+J7calV0mGR0yvfjASNQKt4otVYGzWGzcMKi+pIgaCE7fkkL7Q1uzj1ladU1+ZHN2w9N4bBZCvs2FWvIDx2IJEmkMkwG46xb1PiMXPPhJkGNEEIIsQD8ZesgqUyWdCbLX7YMVhyPJNLsHvKjlNFQ3FLvZmiqtPxsOpxgz/AMKzsbWdTkxm610FbvZrIoSNoxOAPAis4GmorKvToa3ATCCfqnogxOhrEoRWeju+JGvKPRQzCaZGAihM2iWNNVbzox69QVrYWfV7T5qmY7irMr1ZrIrRaF3Za/+a5+U+2ZZwaj0WOfs28FjJ6Q8j4gM+u76kw/g2Iuu5WVreZlQ8WWtXjnvH6HzcK6rrqaGSQwgp+5zpXf96ZWBgzmVz5XOqih+vmKhxM0uqtsWGryd2F5i7ckS+SZY8NPpdScAeJC4bBbuei8E1jV1Vh1TWsuiBmZNspCLaZ73uTGQ+f2fQJY1139nEcSGRQghBBCLAAP7xunzm1HA7uH/RXHx4JxDk0E6WzysqbL2Fumf7w0U/PI/gniyQwblrcUSrm6W7w80TdJLJnGZrXQNxakweuk0ecs+da9q8lDOqvZOjBtNPc3uFnc7K244VzRbkxo2n5oirZGD60+86EGa7sbafA4WLekuWa2w2W3EIzNnSlw262kM9mK/WmK2ayWeWUw6lx2llXpSyg23xviWnuvFKvWC1RsrvK0vHaTb+HLNXsdc2aHLBZFvdtedaJcXr7Bv1awUjKoYY6MTiyZKVyjmfLeG7utcpKap0ZQmp+iN9ffq6NJcV9Otb4bj9OGzaIIRhMk08afwdoFEtRIpkYIIYQ4AmzcuAmbzcamTZsqjmmt2TcSYHFrHYuaffSNByvWHJqKMDodZXl7PavafCxt9RGKJQnn9quJpzLsHJjG47SxvrupsEFjfupZ/2SYYCzF0FSYxS0+OupKJ1N1526CZkIJxv255n6T0p18UJPOZI3m/iqTsDwOG5e++iTO3rC4ZhYgHxDMp2TM6CeoHRg0e+cuBQNY0ly5H87RxmW3lmzUWc2SJs+cn5nHYZ2zd6h4oMB8x0hX7aMqC1jay/Y4gtlyQ7NrzQdLPoetZl+NzapMx4gvRFaLpTD+fK3J5DMwBkjUeRwEokmGp8M017kqNvI8UklQI4QQQjzHxoMx/rxjEntdC5s3b644PjAVJhRLsWFpM6u7GpgJJwqNvACZrObR/eNktebMNe0opVjZYQwL6M/11QSiKQ6OBVjR0UBr/Wwwsji3p0X/RJiBqTDRRJrOZi9dZTdyS3LBz8BEiFjSWGM2jSy/ASfAkjYfdVUyJ6559sF4nfPrb2nyOqpuullsQ3dDyU1zNXOVbh1LjIb6uUvZvE7bnGV2TrvF+FXjzyD/d0Op6nvSOCsmqVUG2E6b1XQjzuM6Zv+OWiyqZiZtUaObtZ11R81AgbectYbLX3cKHY2eqmuMDTgTVTfyPFIdOzk3IYQQ4gj1lV89znHnvgdPcxdnd6Uqjt+909hw46XrOzk0Geb3j8ChyTAnLjW+bQ3EUmzvn6alzsWGJU0ALM/tGXNoMsIJS5rZNewnnsyworO+pORoSa6PY2AqwmggnnvMR1NZ2c/i3LrdQ0bPzequBtMbXa/LxgvWdWFRcNqq9uq9MrmeCKu19k1l/thcJWMtXkfN0jPxzHLZrZy2rGnOdT6nDa1rr8n359QaYlA+KKDqxqA2K9FcKRsYe+iUl+Z5HFYiiXT5UwGjb8puNcZoB2OV/zYXGu88si4NHgdP9BnZYLONPI9UkqkRQgghnkORRIqduX1h2ta9EG3yn+aH94/T5HOyqrO+UN51aHJ2b5kD4yGGpsKsX9pMc26Ty+W53cfzG+ntGTYmli1u9dFctI9IV5MHq0VxaDJMX64H58QllTenPqedeo+DiUAMm0WxtqvB9P04bRbO3tDNi4/vntd45WqZnDyvw4ptjsAHjJvq+ZSViWfOfPp9WnxOWueYODbfPXvyWRivs/oo7PKMzpLmygxFtQEUHqe1UPbYVGWPpKPR0tz/xwCs7DT/d34kkqBGCCGEeA492T8NykL//bdhd/v439//reR4KJZk77CfDUtbUErRnSsDK55s9mBu6/STlrfiLdoI0+u0MTJj7C1zcDyI025l3aKGkt4Di1I0ep34I0avTEudi85G836S/P437Y0eWqrcmCql5rXPi9WicMxjI0mb1TKvDIBYGFq8DtNerGL5ALaxyiaeefnBAzWHExRldCwWaPFWvrarSinckqbZAKjWJDUwMo5VtodZcFZ2NhZ+bjD5vI5UR8nHL4QQQhyZIok02Wz1epuvf/9WMsk4Aw//AYBXv+XCkuN/2jJIOqP5l5MXA+CwWWnwOBgp3ltmaIZGr5PVZd+qttS7GQtESaQzDE2F6Wj0sKih8pvqtgY306E4Y/4I7Y0eGqvcJJ51fBcA3a2+mj0uhX1e5sicNM+zD2auwEcsHC579c058xw2S9XNOYs55xE8F/du1bvspuVsLkfl9fhctpI9b7w19rwBOL6rns76o2O4hNNu5dWnL+ftZ699ri/lKZGgRgghhHiGJNMZPvrj+7n573uqrhmJWJg5tIPEzChOu5UXnPu6kuP/2DFMa72b01e2FB5ra3AXNsxMpjP0j4dY0lZX0dzf3uBiIhhjNBBnIhBjUYuXBrPm/lYfE4EYoViKJW2+qs3e6xc38Y5z1nLeyUtrTs3yOW1YLMZkqVq6GlwlpXBC5K1q883ZI5XPwtQKnouDmmqZn/JJagBrOkr3+alWogbGOOn2OieLGo+OKWkAG5a1sqStbu6FRxAJaoQQQohnyHs/8XUOjAa59d591HeuqBjXPBWK4+tYzszBJ+jpuZSlrT4OFI1rDsVS7B8J8Pzj2rAU1bZ0NnqYyG2YuWsoQDyVYWVHfUVzf2ejh2AkyT27x8hkdUUmJy8/KQ3guI7qNfRuu5XFrXU0zrVnidOG12Gbc9+VFl/lGF4hwLz3pVyd047FUrsvqzhgqTZsojyocdmtFfvjOG1Gb5eZttxGnj5n7fHQ4pklQY0QQgjxf7B/LMSe0cr9YoodiDiIB40hAE0rT6oY17zp898F4NxTVtLb28vIvid5bMeBQvDz+MFJshrO3bCo5HmLmjwEo0kSqQw7h/wAnLisct+JRU1eY7POQWNi2fEmAwAAVnXOBjUnVFkDs996z/UNus9lo6vh6CjFEUeuBo+dBrejZmDsLis/M2OzWkoClvZ68z6SasMq8nsa2awW06yPeHZIUCOEEEI8RR/cdAWXfPv3XPG9u7n4Q58wXRNNpGlYdBzDj/+V2MwoTcs20NPTU7LmUMxNYGgPN1/3DQC23P83nL4mbrr5xwBc+6NfkE2nuP4/P1fyvPyeMUPTEfaNBrBZLWxY0lhxDUtajbHOuwamcdisrOowLydZ3ubjheu7eO95x9NWYyf62V6Z2kFNvcvO0pa5v2kX4umod9lYOkdGx2W3YLEY++O4agQcJRt+Vpl05jQpuexscJX0fM25SWyVDUHF0ydBjRBCCPEU/fburdg9RoDwzx2jpms2ffKLoCycdeJKXvniU1lz+kvp7e0tHD8wFqSuayWjW/9WCHbOOm0DABdecjkAB8fDhMYOcuPmG0rOvTgXrBwYD9E3HqK90W268/qy3LpANMmSturN/R6HjbOO76a90VN1s0MwvvV22i3SByOOCEop0003y9e4bHOP+y6eklZtbXlfjdWqWNtZ+kVBraDFZbfywpUtOO1y+/1MkE9VCCGEeIrOeO2FJELTzPRvp2X1aRW9MgD37x4mm07x0xu+zuquBgLRJJH47OZ9n/jGD8lmUrzqtJWFYOdzn7gKgPdcegVaa1qWrSc8eqAiw9OV2w38yYEZRmciLGurM93ksq3BVSjNOa7TfLNMMMpmXHYrHkf1zQ7B2H19XWf9nLvGC3EkcTuspkF/sXwWx2W3lmRtStaUPd7d6K6Y5Far/GxRowulVEW/jjg85P+VhBBCiKcgqzXW5uW84swTmDmwlfquVXzvBz+sWLfy9POY6XuSS97/Xn7/y58CcOUnZsvIhqJWZvq2c9MN1xYeW5TbF2NoOsKYP4a2OviPz3y0JMMD0Oh14LRb2TkwQzKd5bgqAwAsSvG605cDcNLyFtM1eT6XreZo3Ly5vhkX4kjT3eSmyTtXpmbuPW/KMyxtvsp/C7WCmnyfmdleOeLpk6BGCCGEKBMsyqiUOzQRJhxPccNXPkmjIwPAuy+9smTNB668mozdx5o2B729vfzmf34IwJ/+cT8APVd8mLqO5Uwf2FKShXE5bFjSMb767et5/Ts+AMCvf1w6XACMkpolrT76c5PS1nc3Vr3eczZ0s/G1J7N+ce0NLOtctqq9BEIsZO11rjl7XfLBSK1+seJMjdVivo9OtdHPbXXOwjHpq3lmSFAjhBBCFBmZjvDWL/6C7lNeblpWdt/uMQAm929h95YHAPjz3Q+XrH1oOEU6EeWX130JgHe+6bVoneXsf3kDAL/7x2MAzPQ9WZGFmRrch7upE3/aTjaT4Sc3fMv0OvNjmBWwrkZQ43FY8brsc2ZhuhpcdDfKxDJxbMr/+6i1qWzxJp31brvp1DWnzRhMUMxW1nvjnWP/JqDiHGJu8pEJIYQQRX5+3360q4ENb/4IN/7gRxXHf/qXh/AP7CIRnOTCN7+ObCaNu6W7MK45kkjRcfyLGN5yF6lYGIDre/+b1noPJ77gHADOevWbyaZTvOP151Wcf0lbA96WbrrWnEJk4hCXXPx+0+tc0W7cJK1d3IS3xohlj8OG1apq7uWRX1et50aIo53XacNlt9YM/p02a6HnrKFKRic/mKDYylZfyeQ1q0XVnMTW2eDiuAW28eWRQIIaIYQQIierNX9+ZP/s3jLLTig53jceIu1oYOSJv2OxWLiu91oc2Rh17UsLZWT37RrDancyvu1uNm7cWHhud7OXoamI8fP60zmuu4Xreq+l3Ftf/y84fI14F63lTa98SUUmJ+8l6zt50fou3nnOmprvqdFjp9njkIBFiDkcv2juIRj5YKRWRqe490YpI0gpV6sEbWmLh+4mt2zk+RRJUCOEEELkDEyG0VYHB/7+czKpJC89/8KS4x/7+vfJZjKMbb+nEMScun4lzzvrvELw8eDecerdDiYPlpaW9e18jJ0Hh9i4aRM7+8f55+0/Ny1v687tu5HV8J0vfMx0DUCLz8VLNnTTXl+7ZMxlt1aMnRVCVJrPVDJ3Ya+mGkFN2Z43DpP9baoNFPA4rNS77Fgtqupmn8KcBDVCCCFEzo7BGQD8/dtIzQzRPx0vCSpGEm6mD24lEwsVApbFLV7G/FG01gDc98Re9j32Ty6//PKScz9y9x3Y3XX8/Pa/oS12AkN7CyVrxY7rmp1kNt233XQNGOOVvQ4b9fOYWFar1EUIMX/5vZpq/ZsqPtZYZU+nas9f1uot/DyfaYRilgQ1QgghRM5N//sHktEg777g9UwN7MHTvKgQVEyF4nhbFzNTNrFsUZOXZDrLdDhBKJYiY/cRGNpdEYycc8bJAJz3DqMkzWz/GYDORg9bfvZF+u79NdHpYdM1eYubPbLnhRDPIo/DStMcm886bXNv5Oky2YCzyWtnUVGpWq0SN1FJghohhBAiZyym8B/ayY2bN3PS6qU4fI1cfJmRqfnw578BwMuet6akrKwrt7fMyEyUPSN+AELD+yuCkS9/+qMAJJqOw261MHloT9V+mbe8/DT23/EjNm7cWHUNGJv/Sa+MEM+eRY3uOacEFmdhqgUm5cMElIL1XfUl/56rjYcuPi5T0mbJRyGEEOKYcfGll2Gz2Uz7VILRJN7WxQQHd9PT08OVl7wbgKuu/jwAj+4dJh2P8qPrvlHyvEVNRrnIq974NjZ94hoA3vzKsyqCkUVNHho8DuLJDBMHt/OhK6+oep29vb2k0+maAY0Q4tlntSia5siO5rMwHofVtJ/GWFMasLT4nBU9NJ4aQY1ScNqyJlpNNgA9VklQI4QQ4pjQc8W/sbfhRWx4y8e58Xs3VRy/8tNfBeCcU41MTHezEawM5iaWrTzpBYRGD9Bz6SUlz2tvdKOzGVyNnSTsDUQmB7nphspgRClFNGBMVRveelfVXhkhxMLmddiwWGoPE3DZLSXTzVp9lYFStWECYARBLruVFglqCiSoEUIIcUz408N7cdW30HHCi3n3xo9VHH+0b4ZUPMJPrvsvADqbPFiUYnAqQiabRXtbed9bX1eRPbFbLdiyCbwtXTQvO4Hg0N6qfTAP3/otAkN7Gdn695q9MkKIhcuSm1xWrZ8GjC85irM4Zn06FosqGQ9dbFGj0XvTPEd/z7FEghohhBBHhXAiXfP4yS9/E8mIH7Tm7PPfXXIsk9V0n3Q2U3se4dJLPgAYwYolGeLbm3/I6S/5F1LpLPf/5Tem5z5+1WKOf+G/YPPUc83HN1XvlTn3dB656aP0XPw+KS0T4ijW4LbTYpJ9KZbPxDhslqob6Jplaxo8dtpyGRq3Y3ZD0GOdBDVCCCEWvHdc+XnO+9B/c+nl/2Z6PJPNous6eePZJ2FP+rn+p78t6au59MOfQludnLSkviTYGD24C3fzIsYixrjm3/60smwNYHGzl+lwAoC1ixqrXqf0yghxbDiu3TfnPjOFjTxrlqmVBjVWq+LE7oaSgQK1em+OJRLUCCGEWNDC8RTTbc+noXsN9w6kTYcA9I2HSKaznLS0hYNPPoi3bWlJT8vft+wH4Jc3favkeau6W/C2LGLFiWeSScZ515tfY3oN+f4bgO9+9bOH420JIRYwu3XuW+xCUFNjP5ry0c9Lmz0VgU61LE+ezXpsZHIkqBFCCLGgbTk4CUB0eoT2419o2oD/2a/fAMDPbvwmp6xZit1Txwc+ODt9bMNZryI81sf73/32kue954LXY3W4WHbauaxf3sF1VTIsf/2f64j5x5nc9xjf23z94XprQoijWH5kc61MjbNs9HN7XeVggFqZmnq3ndOXNx8To5+PgbcohBDiaPbQvgnSiRj99/0Gh6ee92+8qmLN9kE/8eAUP7ju2/zbZe8F4Fd/uKuQ1Una6wkO76t43rpcKdlUKM4dv/6JaRYI4AfXf4e7v/l+HvvJ52UAgBBiXtx2Y5+ZxlpBTVGmxmW3Umey702t/WyWNLvxOW34nEf/Rp4S1AghhDiiTYQSpDLZqsfveHgnM31PEhjcDcA7e0r7apLpDG1rTmdi90OsX7+eJa0+ANzN3WzevBl/JEHW6iI4erAiy7Oys77w88iT/6w6hrmnpwer1crGD14m/TJCiHlpdNtpr3Nhq1GqVlxq1uiZ30aeeTaror3OmJJW765donY0kKBGCCHEEWvvSIB3/+dvOOVNV5pmSUb9UTJ2H1P7txCdOAQ6Q89HPl2y9v49Y1jsLsZ33s/OnTtpq3dBNo23rZuenh4OjocAiE4MVGRZLEoxsvVvJMN+AgO7qmZhZACAEOKpslgU6zrraq4pDliqjYgu77HJW9XmK0xGqzfJ8BxtJKgRQghxxLr62z9GO3wsPv3V3HjzjysCm4988TsATB/YyqWXXExkagR3Y2dJRuUbt/yZeGCSmYNb6enpQSlFd2sDb7uoh97e3kJQs3fLfaZByVkdMe751vvZuHGjBC1CiMOqVpYGjHHPhcCkalBTupEnQEe9iyXNnsLv62oMIzhaSFAjhBDiiDWWcJGKhQFoWXFyRfnXzrE4Mf84salBent7afU58DR3FTIq/kiCuKuVocfvQEEhKOls8jA6EwVg/2iAeredRq/5ztzX9faSSiYkoBFCPCc8DqP3pq7KlLPyjTyVglXt3pI1ZvvdHG0kqBFCCHFEGg/E8LQs4uA//5dsKk7LqpNLyr8i8RRta05jfOd9hcdfcdbzaV+2mmuvvRaAh/dNoJSFqT0PlTx33xMPs29glI2bNnFwLMSy9tolIEII8VzxuWzUu+xYamyy6S7rvSnfI8dmtWC3Vb/tr3fb8S3wbI4ENUIIIY5Ie4b9APzxf7/P2qUdvOrN7yrJlmztnwJl5Rc3/Efh8a5mL7FkhmAsBcB1P/8DifAM73jDeSXPffieO7G5vNz845+xf2Sa3//vD6tONhNCiOeSz2mr2k+TV9xX0+RxmK6pNfp5TYePpUXlaguRBDVCCCGeE+lMllgyU/X47mE/FotiZUc9I/u38ej2/SWBx7e+/3OymTTX/cfnCo91NRr/UR7JlZZNJazM9G3nxrKytbPPOAWACy79CFhsBEcqJ58JIcSRoNXnZHmrt+aa4k06qwU11UrQ6lw2Gj0OWn3Oit6chUSCGiGEEM86rTVv/th3OOviL7OxSobkt3c9iH9oHx/+0JVsue8unHXN3HTzjwvH+yaihMf6Sja77GrKBzURAtEk7qZOQiP7KqaWffHfjbHPq858FQDRyUOyv4wQ4ojkddqwzzFQIL9Jp8Xy1KekLWsxAiaHzbKge28WdvGcEEKIBel9//Z54s1n0NoEv/zJZ7mu7Hg8lSFqa2D64L1s/uvNvOvKzzAKXHjJFQBktaZl+fEMPPrXkmCkMxfUXHLFRznrlDXQfAa//vH1nLK8teT8i5o8WC2K+3aPoYCR/dur/gdfCCGOdPn///I6bFV7b8w26exqdNHZ4Cr83uu0Ea2RQT+SzZmpUUq5lFIPKaW2KqW2K6W+UHTsCqXU7tzj/1n0+NVKqX25Y698pi5eCCHEwvTA7mEA0okoHSe+tKKf5bJPfBGrzcHU3kfp6enhmquNzMq7LjbW9XzoE2iLneet7i7plXHZrSRC07gaO7j78T0A3PD1L1a8vs1qoTNXqhaZGuIjV115+N+kEEI8S/IBS7WxzwDOskEBLruVdUUbDAN4nQv3y535lJ8lgJdrrU8GTgFepZR6gVLqZcD5wEla6xOArwMopY4H3g6cALwKuE4ptXA/ISGEEIfdcc97KTN925jp20Z916qKfpZH+wKk41GCAzvp7e2lIxeAjPqNXpk/3bsFgN/+9MaKc9c7wNPcSUP3aiITA9x0g/ko5qWtPgBCo33STyOEWNBcNmOvmkZP9aCmPBvd2eAq7IGT560yNnohmDOo0YZw7rf23C8NfBD4mtY6kVs3nltzPvBzrXVCa30Q2AeccdivXAghxBFp46bL8TZ1VJ0mFo6nSLsa+eC7zmfNoiY8bYu5pOeDheOJVIbuk89hfOf9XHLx+wHjP8YNHkdhb5kzzjufdDzChW9+bcX5zzr9RFZuOJ2O404mOFzZT5N36P7fMPDQHzjwj59LP40QYkGzWS00eR20VNlvCyozNW11lWs99qM4qAFQSlmVUluAceCvWusHgTXAS5RSDyql/qGUen5ueTcwUPT0wdxj5ee8VCn1iFLqkYmJiaf1JoQQQjw7wok02ayuuebv/WnO+rcf8Ku/PW56fNO/fw2t4e7bbmHT+96GxWLlw5+aLRHb2jeFttgZ2/bPkud1NLgZzmVqWlacyPPWLuU6kw0xV3TUMRNOkLW5uObjm6pumvnja7/GztuvIzpxSDbWFEIseGs76ko24Sxnt1qwWo3MjM2qTAcKOO0Ld4bYvK5ca53RWp8CLAbOUEptwBgy0AS8APgYcKtSSgFm3UkV/wXUWt+otT5da316W1vb//X6hRBCPEuCsSRv/czNrH/V+6pmYVKZLItPfzUAy178JtN1jx6cIhUL85Pe/2Rlu1HPfe4b3lZY+9C+cTKpBFMHnygpCxs6sIOHtuzgg5uuoH88wG9vucH0/KevnP1vyuevvKjq++np6cFqtUqWRghxVJhP6Vg+W1Ot98Zlt2JZoHHNU7psrbUf+DtGr8wg8OtcedpDQBZozT2+pOhpi4Hhw3GxQgghnhnv3/QR6juW1dyA8uLPXU+mbhHLX3IBN3zv+6Zrn+yfRlltRCaHqOtaxY3f+37Jca01S046i+kDW7n00kvoaHSTzWZwNXYUApg/3vckM/3b0Zl0ScCx7ZH7cNa38Ys//ROUBf/ALtNemOXtdYWftz54d9X309vbSzqdliyNEOKY4XEYgU+tzTxdtoXZCj+f6WdtSqnG3M9u4DxgF/Ab4OW5x9cADmASuA14u1LKqZRaAawGHnomLl4IIcTTp7VmoP0cXnj5dSX7wJTrn0kWfm5dfZppQLFjYBq0pu+eX2G1O3jPB/+t9BwTYbI2N9P7jdI0m9WCIxPD29pNT08PsWSalN2H/9AOLBZLScDxghNXY7FaedlbLwMgXKVfRinFyB++wT++/l42bNjw1D4MIYQ4inlyU9Iaa01JW6Dj7efTDdQF/Cg3wcwC3Kq1vl0p5QB+oJTaBiSBi7TWGtiulLoV2AGkgU1a64U58FoIIY4BF3/401haXgTAWzZ91nRNMp2hcclaDj1wG0ue/xoal6zlLS89pWLdL/58D+FQgte8aAP9wL++uzTouPq/boD64xnf8wibH/8rvb29nLR2OYu7u+jtOZvtA9MoZSEy1lcRsHz2o5ez6aZ7CHuX0eJzEA1MVn1PT9x/11P7EIQQ4hjgddpQao5MzQLtq5nP9LMntNanaq1P0lpv0Fpfk3s8qbW+MPfY87TWdxU958ta61Va67Va6z8+k29ACCHE0/PPbYfQ2QypaIht/ROmZWV7RwKgrHz/G19gSXsDF1x0WUXZVlZrglkXgcHd/OD67+KwWRiaCpeeZyJJeKyfRHCyELQsbvEyOhNFa83+0SAATz5wV8X5l7T6QGuyWhMbP3g4PwIhhDgmeB1WvE4bNmv1EKB49LMy38fziLQwQzEhhBCHzQlnvJTo1DD+QzvwdSw3LSv70ndvBuDH1/4HS1t9HJoMV6zZPxrE4W3A3/ckPT2X0tXkYWAqUjgeT6ZpWXkiU/sfY+PGjYWgZXGzl3gqgz+SZPdwAJ/LTmudq+L8TruVRHgGgEfvuq1m/48QQohKXqeNNR11NdcUBzX1bjtqgUQ2EtQIIcQxztO+jFedfQZrFrfgaS3dMyZv90iQyOQgN93w3yxvr2M8ECOZLq0svnfXKADb7vkDvb29TB7awz8f2lIIPvaOBkFZ+dF3v1yShVnU7AXguBNP5w9/f4D+7Q9x+eWXm17r0sRedvyul8GH/yAbZgohxFNkt1po9jpqrnEVjYVuqrGZ55FGghohhDjKvffiHmw2m2lmIxhLMhVK8IPvfhVHOoTFYuWjn/lSyZp0Jkv7cacQGNhJT08Pd/7uF2gNGz/y7yXrfnHnIwQG9/CpjxvDAbY9fA+uxk5u/N5NAOwb8QOwuquh5Hn5oMbd0o2tvoPg8P6qAcuPv3MNrz99BRalZBSzEEI8A4ozNY2e2gHQkUSCGiGEOIrd/mg/fc1n0XXqv5gGCnuGAwAEhvbyu1t/AsCYP1ay5uKPfgFtdfC85U309vZy+y9/Zpz7r7ObY178oU+SdjYyvPWuwuucedJaLFYr773sQwD85Dd/IRGa5jOf+EjJ+Tsb3aCzdKx/ARab3XRIQLHrrpNRzEII8UwpDmpqDRQ40khQI4QQR7Gf37sfZ10zS898rWmgsGNgBrQmPLKft51vbJo5GigNah7pC5COR/mf3q8BcMEbXgnAua99Y2HNP7YNorNZxrbdXXidf/+wUcb2gcuNIGYslCE4vK8iuLJaLHQ2+Whd83wAHvrb7RKwCCHEc8RqUdhtFjwOK/YaAwWONAvnSoUQQpRIZ7I1j4/MRJkIxIhMDuLrWE7S6q1Y8+TANItbfSSiIa7/72+hsmk+dc3XCqVq6UyWzhNexMTuh1i75jgAbvjuN3HYLLzkFa8rnGftC19FYGgPF1/0rkJA0t1ivN7QdIR4KoOvfRnh0YOmwVVwZB8AidAMX/7Mx/8Pn4YQQojDxee0Ur+AsjQgQY0QQixYF3zsm3RtOKvqFLCH940DsOcvxuSyP977eMnxVCbLkwdGeeDPv2LTpk0opQhNDOJqaCtkU57on8bq9DK6/R527twJGJtbdjR4GJ42Jpv5IwlSziYuf9frSzIs9W4HPpedwakIfeNBUIofXvufplmYe395AwDxwDg3ygAAIYR4TrV4nbTXO5/ry3hKJKgRQogF6OIPfZJo03pOetvV/PS3fzFd8/1f30HMP87knofJpJK86Lw3lBx/on8KbbEzsfuhQhDT1uDG3dRRyKY8vH8cdBZ/3xMlGZbOJjcjM1EAPvQZoyztb//vxxXXkAyMccuvbueiTR8D4Ie9/2V6rW95xQvYd9dP2f6b78oAACGEeI511Lto9UpQI4QQ4hl234Fg4efmVc+ryNakM1nClnqm9j2GRSlWdbewfMPzS9bcs3MUspncvjJGIPGKl5xBx9I1hWzK7/7xGP6BXVzy/veWZFj2bH2IA0MTbNy0iQd29JNNp7jl+m9UXGffzsfxNC8ijIdkJMAPrv+O6fu5rreXfXf9lNBYn/TTCCHEc8ztsGKxLIz9afIkqBFCiAVoxSkvYXLvI4THD9G4dH1F8/3FH/kCNpeXyb2P0tPTw2TfDh56cm8h+NFa86cHdzK57zEu+cD7CoFER4OHaDJNOJ4ilkyTtNczdeCJivM/fPcd2Jxubv7xz1hz2ksJjeznkovfX3GdJ6zsxtXQSsfq5xEaPVAzC7NQNngTQghx5JGgRgghjjCXbdxIXdtiNlbplQnHU6Qd9Vz1gXfQ6YXGJeu5tCxYeKTfmFg2s/9xent72XLfXTjrmrnpZqNE7NIPfZKs3cv4rgdLApaORjdgjHXeNxpEWayEhvdUBCPnnHkqAO/4wCaSjkb8g7tMr3XT+94JgL2+jcsufLNkYYQQQjwjJKgRQogjzH1jbl505WZuu888UNg5OAPAhqXNXPTmV2N3+/jhT28tmVi2+ORzmNj9YCF7ctZpGwC48JLLAfjLg9sAmNq/pbRXptEDwIvPfRVf++/vG6/38D8qgpHPfeLDAJzxmneCxcpM33bTfXDyE9AA7v7DL5/KxyCEEELMmwQ1QghxBAnGknSe/FIAuk4513Sy2X9873/JpJPc+I1r6GoyghBnY0chqLj4o9cYm2UurSsEI5//xFUAvPuSKwA47WWvIxkN8t53vKkkYOloMDI1zvo2njw4TjwwWbFZJkBn7nXvfGIQgNDwXtPSskXNs0HNzzd/ff4fhBBCCPEUSFAjhBBHkK0Hp1DKwtT+LTQtP5Hv/+iWkuNaa8aSbqYPbOV7N1xXCBp8LYsKQcVD+8ZJJ2L8tPerhed1Nc/uGQNGydqZJ6zkurIMTJ3bjsqm8TR3Ut99HIGhvaYZGLfDRrPPSTCWIjzez/ve9VbT0jKX3cq2X3+Le//7gySjwYrjQgghxOEgQY0QQhxBHj0wgcqmOPTAbSiLhXdefEXJ8fd9+DN4mrsY33Ef69evp6PBjUUprvmv7xaCipUnv5jg8F4uvfgDhee57FYs6Rj/8d3NfPDyKxmejnDCkqaK11dKsbyziZf/64V4WxcTHt1ftbk/OWqUx02bDBIo9q8vWkt8epiNGzc+5c9DCCGEmA8JaoQQ4giyfWCGDcs72PnwPwA4/10Xlxx/YkKRjAQYeeIf7Ny5E5vVQlu9i8FcBiaRypBy1BMc3F1x7smBvbibu/jf3/8dDdz5m5+ZXsPqrgb2jRpZlZ/f9N2qzf1/uvEapg9spf/+39Scatbb20s6nZYhAUIIIZ4xEtQIIcQRIppIc2gixG9/ch2f+eRHUJkkn/ryNwt9NeF4ira1ZzC85U50JlUIJLqaPQzngpp9owFQFmYO7azInixrb8DTsoiGxWsA+N+bzPeMefyffyr8fNO3vlz1et/7zgt4/Cef5X3vkKlmQgghnlsS1AghxLPosg9uxGazmQ4A2PTJL4JSzPTv4MbNm5ke2I23fVkhOHlgzxgoC7/9/tfJZrOFQGJJi4/RmRhaa3YMGJPRwiP7KrInbzv/lTg89Zx49uuJzYzy/ve80/Qaf3/rzQBM7n2Um264tup7kQyMEEKII4Xtub4AIYQ4VsSSae6f9FG/xNgsszwYuH/3MItPT+I/tIOenh7G6xqYUo2F4OSxA5N4XTbWLmosfd5dfyRafzwfvOLDdLzkQlrrXUQDUxWv350bFpBwtfKq0xbx72/6QMUagHe8/lx+ccvnmTn4RM2yMiGEEOJIIZkaIYR4lvx5ywCdJ53D89//NY4/6ZSK4ytPPw9//zZ6Ln4/vb29vONfX4ndU89X/utbAPzj0Z30P/kAl19+ecnz7vz9rwG49Xd/4aEdB9l2719MM0GLW3yFn2/46r+brgG4rreXid0Pk04mJAsjhBBiQZCgRgghniU33XZv4eexaOn//Q5NRcjYfXxq44WFQCIfhAxOhYkm0qTsPvwDuyt6ZV5/3tkAvOqt7ydr8+Af3GU6jWxJixdrKgzA1MHaE8uEEEKIhUSCGiGEeBYk0xkStjr67/8t2UyGl57/rpLjH/vqdQD88aezmZHFLUa52OBUhD0jfpSymG5y2fvNr2CzKBzLTgMgNGS+EaZSiju/cQmP3fIFolPDUlomhBDiqCFBjRBCHAa1BgAA7B8NYrHaCBzagTMdonv980uO75mIEx4/xA+u+3bhsY5GN+gsV37yc1z20c8D8KZXvLiiJMxhs7KsvY6RmSjZdIq3v+7cqmVjF7/33czsf4yNGzdKaZkQQoijhgQ1QgjxNGWyWR6YbuD0i/+L7//oFtM1e4b9ADxx31952Zkb6J8IF45prWldsYHA4K6S7InVYiEyOYSneRFxeyORyaGq08jGDmwHYKZ/Ozduvr7qtcrEMiGEEEcjCWqEEOJpuvTfPkPHhrNo6F5Dy+rTTbM1N//mLmL+cT539Ud59J9/JRxPcdnlVwEwMhNFWx3852c+UhFstPrseFsX07LiBIJDe6qWjN396+8DsOfP35eyMiGEEMccCWqEEGIOiXSm5vG/b9kPQDadonXN8ysa8OOpDGFbE+O7HuDGzZu58/ZfAPCL2+8AYM9IAIA1XQ0V5379uS/C27YEm7uek5a3Vs2wXHDeGdx5zRt595teLVkYIYQQxxwJaoQQooaLrvgkL3jP5+m54sNV15zw4lcRGusjNrCVpuUbKjIlj+2fwGp3MrX7IXp6enjDK4xpZa95izEsoPfHvyKTTvLNL32q4tzFY5h/8+Prql5Db28v6VRSAhohhBDHJAlqhBCiikw2y0DdqbSvfwF37Zw0XaO1xlLfxQWveglXXfJunL4mvprbVybv6z/8DalYmAteaTT53/Dt/8BmUZzzqn8FoH8yQmjkADduvqHi/C9c00EmlSARmiEyMXDY36MQQghxNJCgRgghqugbD2F3G5mSRaeca9orMzwTJZZMs667kWVtxtr+iVDh+MZNlzOjGpjY/VAhaLFaLJAIct3NP2Pjpk00L11PaGSfaS9MvcfB0rE7ePCGK9m4ceMz8TaFEEKIBU+CGiHEMSub1TWPbxuYBqD//ttwNbRx8y0/r1hz9ZeNrMyvfngdy9rqAHjd295bCIBu/dM9ODz1jO96oCRoGd63DU9LNz/91R/QFhvB4X1Vr+MHm68jFpyW0jIhhBCiCglqhBDHpA9efiWrz30XdZ3Lq+4t88Nf/5V4cApvYgyAt73/8oo1j+4dIZOM86Prv0VbvYt0IoqndXFhWMDL3vIBMukkb3rZaSVBydolbbibu3jDhZcC4B/YXTFgQAghhBDzI0GNEOKY9PuH9nHcy9/FCz/4XW783vdN10wlbPgP7WT7g38H4NUXvKfkeFZrlp76Mqb2Pcall1yMUgq3SuJtWVTIyjQuP4kTV3Rw/bXfLXnuB975ZixWG4tOfhkqmyY+MyqjmIUQQoj/IwlqhBDHpFPPezMAymLF17WyIlszFYrjbuogOLiLD1z0Lpp9Tg6MBUvWbDs0TdbmZmL3g4XHzjx5HcefZgwEiCbSHJoI8ftbbqg4/3Gd9QBs6Zti3ZJWmVwmhBBCPA0S1AghjjnpTJZM3SJevK4TgKblJ1aUfm3P9dP88dab6e3tJTI1xP/7410lwclnr/8lqViY0e33Fp6/uNnLRDBOOpNlz7AflGK6f3vF+Ze31xV+vu2H36laAieEEEKIuUlQI4Q45vRPhEmls5y1rhNbMkjjknUVpV/f/tFvSCeifPcrnwbgwLZH8TR1FoKTaCJNxNnByNa/kUnGC8/vbvGS1ZqxQIztAzMAhE0mmymlmOnbBsDgo3+WfhohhBDiaZCgRghx1Lnk8n+j+9Rz2Vgl+7FvNADAmkUNnHHiak4886UlpV9aa6Z1HZN7HyuMYT5pzTIcvkYuvsw452X//lUsNjtjO+5l48aNhef//GYjOPnUF/+LW/90N5GJAT5w0YWmpWXPc49w/3VXkI6FpJ9GCCGEeBokqBFCHHW2ZZez4U3/xm/u2WFa1vW9/7mNdCLKVz77CXY+ei/D0yE2bpqdbLZvNIizrpmpvY8Ugo2N73snAB/51DUAbOnzk4wECA7uLglYfnbTtQD889HthLSbmUM7qmZhbrj22wRHDpDNZqWfRgghhHgaJKgRQhxVLrnyY9R3rQJgyZmvMw0oRqIK/8Aubty8mXv+chsWi5VbfnV74finv/kDtM7yuhdtKAQbi5q9xnNnogAsPuEM/Id20nPpJSXn/sBF7yIRmmbDOedj99QTHNojWRghhBDiGSZBjRDiqPKn+54AYHzn/dR3reLSnstKjo8HYvjalzG9/3F6enp45dlnAvDGd76/sKY/BIGB3dx0w38XHutq8gAwPB0hGE2Ssfv45KaLKjIsvb291NszJBxNADz4519JFkYIIYR4hklQI4RYUBLpTM3jZ553Pql4mBO7XFhsdq761JdKjl91zXcAOO95q+nt7eU7//EFAF53wbsAY1PO+q5VTB/cWpJhcTtsWDJxvvzNa3n5+UYp2p9/8SPTa9i75X4AMsl4IRgSQgghxDNHghohxIKxtX+KN37yRtz1LVVHILes2MCpa5byn5/5NwBe8aYLS9buD1oJj/Vz83XfAMDnsuN12hjNlZX94o//QFmsBAf3VGRYpgf342rqxJ91obMZbtn8TdNreMlKH1P7t9B376+5/PLLTdcIIYQQ4vCRoEYIsWB86sY/kGlYyvo3XG7aK5NMZzg0GWZ9dyPtDW4yqQSupq7C2vFAjMalxzP8xN9Yv3594XkdjR6Gpo2g5hVveg8AbzzvhRXnX9xWj6e5i+51pxEe6+eS97/X9Dpv/O9vsOWWz7H/bz+TUc1CCCHEs0CCGiHEgjA8HSHlagageeXJrD9hQ8WaA2NBslnNZ666hCsuvxyXTuBtWVQoI3t43zgAEzsfYOfOnYXnjfXt4p6Ht7Jp0yZWnvoSOhvd3HDttyvO/5bXnoervhVX5xre+pqX1uyV6enpwWq1ypAAIYQQ4lkgQY0Q4ojwjks/hLepo2pZ2db+KQD2/vVHWO1OhgPpijW7Bv0AzAzsZvPmzTzvhOM45YUvKwQf3/vlX4n5x4lODZUEG9sfvR93YzubN9/IY3sGePzvvze9jlOWtxR+/um1X6n5fnp7e0mn0zIkQAghhHgWSFAjhHjOvffDn2N6yb9w6nuuqVqudeOtfyQZ8VMXPQTAKy94b8WaH/6/v5AITZMMTdHT08OSVh9j/iiZrEZrTSDrZvrgE1gslpJg4/knrsFis/PuKz5J1upiZmCn6XWsWdRY+PnRO3759N60EEIIIQ4bCWqEEM+5xwaNfhZv62K87csrsiRaa6ZTTmb6trN9yyM0eBycetZ5JWvSmSxBayOTex8tBC2Lmjyks5r61i56rvwoDl8joeF9FSVhV3/oUgDWn/NmAELDe03LxqwWxdT9/8MTv/w6J6xfd9jevxBCCCGeHglqhBDPqazWLDrhBUztewydzdC69oyKLMmekQCuhjam9j5CT08P8ZkRfnn7HSXBz9b+KewuH5O7HyoEJPkNM12NHfzubw8C8Odf/aSiJGxZWx0Adz45hMNmYerQ7qplYw///icMb7mLJ5988vB8AEIIIYR42iSoEUI8owLRJNsG/FWPX3rVv6OtLta3WrFlotR3razIknz62z8im0nz2hceT29vLwe2PYKnZVFJ8PP3bcM4bBb6t95dCEgW5faI8bZ2c/ZrL8BiUazsqKu4Bp/LTqPXCcDYvq1cecUVVa9XKYVSat7vXwghhBDPPAlqhBDPqHd/8Sd85If38r6rPmN6/G9bDgDwq5u+yfNPWMVJLzq3JEuitWY06WH64BPcdMO1AJy0eikObwMXX2bsAZPVmjse3cvgE/fwb1ddWXhua70bm0Xx2a98g5itAf/QPj78oSsxE58yenVm+rfLGGYhhBBigZkzqFFKuZRSDymltiqltiulvlB2/KNKKa2Uai167Gql1D6l1G6l1CufiQsXQhz5Lr3iIyQ8nQBsGdemE8VOOOs1hEYP8L4L38aqjgbG/FESqUzh+CVXXY2nZRHjO+4tZHCuvOTdAFzxic8CcGgiTNbmZnz3wyUBidWiaG/0cGgqTFi7CQzuqRqw/PXaj/Pojz/LwX/+QsYwCyGEEAvMfDI1CeDlWuuTgVOAVymlXgCglFoCvAI4lF+slDoeeDtwAvAq4DqllPUwX7cQYgH4yyN7AZg++CRNyzawefONJcdjyTQJZzNT+x8HYHl7HVpDx4p1hQAon8mZ2vNIIYOzpMUHwNB0BIBP/6fxeGBgR0VA0t3k4dF9E9jdPsKj+6sGLO+78G34D26l55IPyBhmIYQQYoGZM6jRhnDut/bcL537/beAjxf9HuB84Oda64TW+iCwDzjj8F2yEGKhWHz884kHp/Dvvhe7p473bvpoyfGtfVOgLEzueZTNmzezuCXX2N/UVcionHDWawiN9fG+C99WeF5nkxurRTE4aQQ1Ww+Okwz7ic+MVgQkJy1vIZ01/i/qlS84sWrAIvvKCCGEEAvXvHpqlFJWpdQWYBz4q9b6QaXUG4AhrfXWsuXdwEDR7wdzj5Wf81Kl1CNKqUcmJib+b1cvhHhOxYvKxMyouk4Cg3sY3WNkYi5438aS4w/sHYNsmuDQbnp6egqN/b7Wbnp6ekimM2S87bzrteeUBBtWi4WOBjcDU8b3LUtOOJPA4C7TLMxffz5bbnbzdd/8v71RIYQQQhzRbPNZpLXOAKcopRqB/6eUOgn4FPAvJsvNxgLpige0vhG4EeD000+vOC6EOLJF4iku+NSNbL3zl/zri4+vyHD4Iwk8LYsYfvyvvPut57M/m+FDV3+Bc1c6C2sf3T/JySs7+HMiXnhek9dJz1Wf4GPnn8KeYT/pjObLH9/IjttLsywzwwfYszvJ8N0/JdN+Hldvei8XvGhVxXXefN03WX9+hondD0qvjBBCCHGUekrTz7TWfuDvGCVmK4CtSqk+YDHwmFKqEyMzs6ToaYuB4cNwrUKII8j1f9lBpnE5619/OdffUNl8v+XgFAC//9mNXNfbS2isH0/rkkJZ2chMlPFAjFuv/2rJAIHOJg+DU0ZZ2Z6RAAAzg7srGvx3PnYvnuYufnPXQwD88dYfmF5nT08PO3/7Hd58zslSWiaEEEIcpeYz/awtl6FBKeUGzgMe11q3a62Xa62XYwQyz9NajwK3AW9XSjmVUiuA1cBDz9QbEEI8N+58ZDfZdAqrw0Xr6tMqjj+wdxy3w8bqrgYAOhuc+NqXFLIlT/QbQc/kvsdLApYlLV5GZqIA7BycQWUSpEJTFVmWMzasxmKz03XSS8mmU/zk+m+YXqf0ygghhBBHv/lkarqAvymlngAexuipub3aYq31duBWYAfwJ2BTrnxNCLFAxFMZosl01eOj/ihZu5d9d92CzmZ4+RveUXI8qzWPHpjgpGXNWC1GRepbXvMy3I0dfP1b3wFgS98UKpMkPj1SErA8es8dBKJJPnj5h/j7IzuYPLidnp6eiqDkE1caz+k4/kUEh/dx6cUfOCzvXQghhBALz3ymnz2htT5Va32S1nqD1voakzXLtdaTRb//stZ6ldZ6rdb6j4f7ooUQzxytNRd85mbOet81fPDyD5mu+diXvg3A5N5HsKejrDzlhSXHdwzMEIwm+f5XP14oLVveVgfAoUmjuf8fj+1ifN8WenouLQlY/vbH3wBw6+13krT5CAyZ7y2Tn5QGsKzZIZkYIYQQ4hj2lHpqhBBHv4s//CmS3i7a1p3JHx7rN90wc+vBKZKRALHJIV586lr2j4ZKjl/TewvZdIqxXQ8WApKlbcbeMue+/gIuu/zDZOx1zPRvrwhYXvvyFxvr3n4ZFouV0MgB0wb/Bo+DdNzovbnj1ptMr1MIIYQQxwYJaoQQJe7f7yeTjBMPTtGy6pSKoENrTfeGFzDTt42enkvZdv+d+CMJLrv8qsKagRD4B3aSScYKAUlXk4dMOomndQm/+ZvRZhc0GcN87X99CQDVthaAJ++/wzQLo5RiceAR7r/+Q8z0PWmazRFCCCHEsUGCGiFEiRWnnIV/YBfpsT00Ld/ApT2XlRw/MBYia/Pw5Y8ZfS53/f5XANz6u78AEIgmqe9axczBJ9i4cWMhILFaLDizMeral7LylBeTSSd522teVhGwuBw2mrxO/NEk0ekRPn916YadxX507X/y7je+EqvVKuOahRBCiGOYBDVCHEMu27iRzhNezAcvv9L8+OUfImWv47h2D1/82AexOdx8/HNfLVnzyW/+gGw2w+9u/hYArz/vJQC89i3vAuBDnzbWn33yqoqA5cyT1rL+9JeQ9XYQHNrLjZuvN72O1PgeAEIjB+bMwMh0MyGEEEJIUCPEMeTeURcnv/3f+dv+qOnx/3fHAyhl4c7/dwtLW40emBed99pCv8rGTZsYTdUxvX8LN91wLQDXfetrKAXnve4tANz3xH50NsMtm79Vcf4V7XVMBOM0LFlHYKCy9Czvz9/7EvHABIceuE0yMEIIIYSYkwQ1QhwjMtksnRvOAqDzxHPYuOnyijVnnf8eMsk4b/mXF7G41YfWWdwtiwvZkp//7i48zZ2MPvnPQrBht1qwJCN864YfsmnTJtaf8TLCEwNc8v73Vpz/jt/+vPDzC09YVjW7ctHb38i9376Yd7z2HMnACCGEEGJOEtQIcYzoGw9hsTsZ3/Ugzrpm/ue3fyk5rrXG3rmWM9Yt4fre/8Zlt2JLx6hrX1YIYF7x9h50NsPrX3xCSbAx1rcTd0sXmzdvxtbUzRtf8WLTYOTXt9xU+Pl/NptvlglSUiaEEEKIp8b2XF+AEOLZsX1gBoCBB2+nfd2ZvO7t7ys5vuXgFDPhBD/+8dUE7ltHb28vp6xbTndXB70fPAeAznXPpzGV4YbPfafkuasWtTBl7+T9G6+iP55iXXeT6TW8+4LX85tffZPAwA4ufu+7n4F3KYQQQohjkWRqhDhK/PrhPiaCsarHnzw0TYPXwdieR3HYLLzwvNeXHL/mxl+RioUZ2XZPodxsRXsdw9MRMtksqUyWXQNT/PO2n1bsCXPhm1+HzeEm4uwA4Dc/MW/uv663l6HH7yAyNSJZGCGEEEIcNhLUCHEUuGfnCJv/tJ03fOJGNlbZhPLerfvY+/DfufKKyyEyyQ9/8ftCcJLJZglYGhnfeT/ZdLJQbvbPP/4/0lnNZVd9kr0jAbBYme7bVjGRrLvFC8D+oI1sOsWPr/9m1WtVSh2OtyyEEEIIUSBBjRBHga/99A4A6hcdxy2//mNFJmUmnCBj9zJzaCebN2/m4LaH8bYtKQQn2wdmsLt9TO19pGRvmd//4icA/O7Oe3myfxqAkMmGmYubjaCmYfEagsN7ufSSDzxzb1YIIYQQoowENUIc4S6+bCMuX2NFoJIXSaRI2BsY3nIXAA1L1lVkUh49MAFAcGAnPT09nLR6KQ5vAxdfZkxA+3Lvj9HZDP/60tNKysLe9oZXAPCy176Zn/3+70SnhnjfhW+vKB1ra3CjsmkAJvY8chjetRBCCCHE/ElQI8QRLJ3Jstd3Bmd/7Cf87Ld/NV2z6eovY7FYGd5yJ5l4iKYl6ysyKf/cOUqDx8Fk/w56e3u56tL3AHDFJz4LwP6xMOHxQ3zvhtJg5fprv0uzz8lJL3gpIe1hum+76WaYFqXYc4eR1ZnY/dCcG2YKIYQQQhxOEtQI8RyKpzI1j1/6kc/hamhDWSy0n3CWabbmoT2jZFIJQkN7OOOElZz28teVZFKS6QwP7hpkx923ccXlRmZmSW5jzYHJMFmtaV2xgeDQbtONLrtbvNy7awy7p56gSelZ3qtO6uCf33gfkfF+2TBTCCGEEM8qCWqEeI5sG5jhjZ/5MfUdy6qWlt27c4hsJo3/0E7a1r2gIgOitWbZ817O9IGtXPKB97F/6/0MToZKNta89GPXgMXO2M4HCs9vb3CDznDV1V+g50OfQFsdfOPzHzedSLairY5UJgvAA3/5ddWpZdf19hLzj5PNZmWymRBCCCGeVRLUCPEMiKcyxJLVszBaaz7+vb+S9baz8uXvrlqutfr55xIY3I2a6cPb2s3Fl5UGP5dcdTVZu5f1nW56e3u5747fY7FY+ckvfltY8/D+STLJONMHthYyKFaLIjw+gLulmz/duwWA9d2Nptdw/523A5BNp/jyZz4+349ACCGEEOJZI0GNEIdZKpPlrVdfx4vff01JxqTYwFSEjN1LKhqifd2ZHH/iKRVr4sk0GVcjPe94A9/56mcAuOITnytZc99+P9l0il9t/g8AXvGS5wPw5nfNTh9bdspLmOnbxmWXXlKSQWmvd+BrW8zSDWeSiof52uc/aXqtv7n5O0zseYT7r/8QN0qvjBBCCCGOQBLUCHGYPbx3nETDStrWnsEv73zYtLRsa98kAPvu/AnKYmEknK1Ys2vYT1bDv2+6iO9/9z8BeM1b3l2yt8yik1/G+M77Wb18MQDf/LLR+H/+2y8CYCoUJ2Ov4xMfvLCiJOz8V5yNr3UxqnExgcE9VQOWi97+Jh6/5fNEJwekV0YIIYQQRyQJaoQ4zL7xw1+TSSVIRUN0nfwy09KyH/zqr8QDkzSmjVHL573xwoo19+waJZtJM92/nR9c/x3SiSietqWF8+0Y9GN1+RjbcS87d+4EoMHjwOOwMTgVBuDDn/86AH/+3+9XnH9pq4+s1nhbFzNz8MmqAUtvby9aa+mVEUIIIcQRS4IaIQ6z8YSDqX2PMbH3ERqXVo5XzmqNX3uZPrCVHU88hjUVZtfgdElGJ5PV/P7+XUzueYRMIkpPTw8eEvjalhTOd++uUdBZZop6ZZRSdDV5GJiKAPDwrgEyqQQ/vv4bFde5rqiHZnLfYxKwCCGEEGLBkqBGiMNoJpzA09xFYGAnJ67oxFXfypf/85sla/rHQzi8Dfj7t9HT08PIvm14WrpLMjqPH5gga3MzsvVvWCwWent7ecnpG1h14hmF4OP2fz6G/9BOLn7fe0oCkrG+nTzw+A42bdrEcaeeRWhkP5decnHFtS5q9rLnzz8gMLib8NjBZ+gTEUIIIYR45klQI8Rh9OHPfg2Al562Hmc6AMBVn/5KyZqr/+tGAF79ohPp7e1lzZI2PC1dXNpzWWHNHx8fQGWSTO97tJCFWdZWx0wkQSSeIpHKkLD5mDm0o6K87YkH/4mroZWbfvAjkvYGAoO7q17va07u5JHvf5wPXnZZ1TVCCCGEEEc6CWqEeAo2bdqEzWaruq/M/dsOks2kueWGb/I/3/suAHc/trNkTX/YRmBw
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gpleiss commented Jul 5, 2021

Have you tried using a ZeroMean instead of a ConstantMean?

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