diff --git a/examples/component_example.ipynb b/examples/components.ipynb
similarity index 93%
rename from examples/component_example.ipynb
rename to examples/components.ipynb
index bdda8cf..9183e30 100644
--- a/examples/component_example.ipynb
+++ b/examples/components.ipynb
@@ -74,18 +74,16 @@
"source": [
"delta = DeltaFunction(name='Delta', center=0.0, area=1.0)\n",
"x1=np.linspace(-2, 2, 100)\n",
- "y=delta.evaluate(x1)\n",
+ "y1=delta.evaluate(x1)\n",
"x2=np.linspace(-2,2,51)\n",
"y2=delta.evaluate(x2)\n",
"plt.figure()\n",
- "plt.plot(x1, y, label='Delta Function')\n",
+ "plt.plot(x1, y1, label='Delta Function')\n",
"plt.plot(x2, y2, label='Delta Function (coarser)')\n",
"plt.legend()\n",
"plt.show()\n",
"# The area under the Delta function is indeed equal to the area parameter.\n",
- "xx=np.linspace(-2, 2, 10000)\n",
- "yy=delta.evaluate(xx)\n",
- "area= np.trapezoid(y, x1)\n",
+ "area= np.trapezoid(y1, x1)\n",
"print(area)\n"
]
},
@@ -114,7 +112,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "newdynamics",
+ "display_name": "easydynamics_newbase",
"language": "python",
"name": "python3"
},
@@ -128,7 +126,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.13"
+ "version": "3.12.12"
}
},
"nbformat": 4,
diff --git a/examples/convolution.ipynb b/examples/convolution.ipynb
new file mode 100644
index 0000000..2d7cac6
--- /dev/null
+++ b/examples/convolution.ipynb
@@ -0,0 +1,154 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f42e34d0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "from easydynamics.sample_model import DeltaFunction, Gaussian, Lorentzian, SampleModel, DampedHarmonicOscillator\n",
+ "from easydynamics.convolution import Convolution \n",
+ "from easydynamics.utils import _detailed_balance_factor as detailed_balance_factor\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib widget\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "600c0850",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Standard example of convolution of a sample model with a resolution model\n",
+ "sample_model=SampleModel(name='sample_model')\n",
+ "gaussian=Gaussian(name='Gaussian',width=0.5,area=1)\n",
+ "dho = DampedHarmonicOscillator(name='DHO',center=1.0,width=0.3,area=2.0)\n",
+ "lorentzian = Lorentzian(name='Lorentzian',center=-1.0,width=0.2,area=1.0)\n",
+ "delta = DeltaFunction(name='Delta',center=0.4,area=0.5)\n",
+ "sample_model.add_component(gaussian)\n",
+ "# sample_model.add_component(dho)\n",
+ "sample_model.add_component(lorentzian)\n",
+ "sample_model.add_component(delta)\n",
+ "\n",
+ "resolution_model = SampleModel(name='resolution_model')\n",
+ "resolution_gaussian=Gaussian(name='Resolution Gaussian',width=0.015,area=0.8)\n",
+ "resolution_lorentzian = Lorentzian(name='Resolution Lorentzian',width=0.025,area=0.2)\n",
+ "resolution_model.add_component(resolution_gaussian)\n",
+ "resolution_model.add_component(resolution_lorentzian)\n",
+ "\n",
+ "energy=np.linspace(-2, 2, 100)\n",
+ "\n",
+ "convolver = Convolution(sample_model=sample_model, resolution_model=resolution_model, energy=energy)\n",
+ "y = convolver.convolution()\n",
+ "plt.figure()\n",
+ "plt.plot(energy, y, label='Convoluted Model')\n",
+ "plt.xlabel('Energy (meV)')\n",
+ "plt.ylabel('Intensity (arb. units)')\n",
+ "plt.title('Convolution of Sample Model with Resolution Model')\n",
+ "\n",
+ "plt.plot(energy, sample_model.evaluate(energy), label='Sample Model', linestyle='--')\n",
+ "plt.plot(energy, resolution_model.evaluate(energy), label='Resolution Model', linestyle=':')\n",
+ "\n",
+ "\n",
+ "plt.legend()\n",
+ "# set the limit on the y axis\n",
+ "plt.ylim(0,6)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7d49acae",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "convolver.upsample_factor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fede1a58",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "# Use some of the extra settings for the numerical convolution\n",
+ "sample_model=SampleModel(name='sample_model')\n",
+ "gaussian=Gaussian(name='Gaussian',width=0.3,area=1)\n",
+ "dho = DampedHarmonicOscillator(name='DHO',center=1.0,width=0.3,area=2.0)\n",
+ "lorentzian = Lorentzian(name='Lorentzian',center=-1.0,width=0.2,area=1.0)\n",
+ "delta = DeltaFunction(name='Delta',center=0.4,area=0.5)\n",
+ "sample_model.add_component(gaussian)\n",
+ "sample_model.add_component(dho)\n",
+ "sample_model.add_component(lorentzian)\n",
+ "\n",
+ "resolution_model = SampleModel(name='resolution_model')\n",
+ "resolution_gaussian=Gaussian(name='Resolution Gaussian',width=0.15,area=0.8)\n",
+ "resolution_lorentzian = Lorentzian(name='Resolution Lorentzian',width=0.25,area=0.2)\n",
+ "resolution_model.add_component(resolution_gaussian)\n",
+ "resolution_model.add_component(resolution_lorentzian)\n",
+ "\n",
+ "energy=np.linspace(-2, 2, 100)\n",
+ "\n",
+ "\n",
+ "temperature = 10.0 # Temperature in Kelvin\n",
+ "offset = 0.5\n",
+ "upsample_factor = 5\n",
+ "extension_factor = 0.5\n",
+ "plt.figure()\n",
+ "plt.xlabel('Energy (meV)')\n",
+ "plt.ylabel('Intensity (arb. units)')\n",
+ "\n",
+ "convolver = Convolution(\n",
+ " sample_model=sample_model, \n",
+ " resolution_model=resolution_model, \n",
+ " energy=energy-offset, \n",
+ " upsample_factor=upsample_factor,\n",
+ " extension_factor=extension_factor,\n",
+ " temperature=temperature,\n",
+ " normalize_detailed_balance=True,)\n",
+ "y = convolver.convolution()\n",
+ "\n",
+ "\n",
+ "plt.plot(energy, y, label='Convoluted Model')\n",
+ "\n",
+ "plt.plot(energy, sample_model.evaluate(energy-offset)*detailed_balance_factor(energy-offset, temperature), label='Sample Model with DB', linestyle='--')\n",
+ "\n",
+ "plt.plot(energy, resolution_model.evaluate(energy ), label='Resolution Model', linestyle=':')\n",
+ "plt.title('Convolution of Sample Model with Resolution Model with detailed balancing')\n",
+ "\n",
+ "plt.legend()\n",
+ "plt.ylim(0,2.5)\n",
+ "plt.show()\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "newdynamics",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/detailed_balance.ipynb b/examples/detailed_balance.ipynb
index 172422f..b4ca072 100644
--- a/examples/detailed_balance.ipynb
+++ b/examples/detailed_balance.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "97050b3e",
"metadata": {},
"outputs": [],
@@ -17,36 +17,10 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "c1654720",
"metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "7cfd67c54e984f0bbf333f80d81e1929",
- "version_major": 2,
- "version_minor": 0
- },
- "image/png": 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Z3qXhjhTHgu0ysC9x/FkuA3f/uMvEcVeQY9XZ61eQtCF3B9tDp+cQ4Q4dOfiW03FnilwWSyhNsMxpRs7TwIED1d3y2bNnC5Ume8dNQc8PzmCbDnwncqaR24EcSH09kLOBYwS5ofr2Qc4HcmAwzVmwvZBboUPOJra3fhxhW6I4HrmRKA7U4dyP3DrkfNlW+0CuBbatLfwWkFOjQ84G1gk5Xpa5iPq45bFseY5BUTi2D4pk8fmi7CscT7iGIbcXxa06R48vnGdwTFvm9mDd9etYYSEnCb8NnK8sz+PYttg/tlVynM1yeyPHGuuOUi1w1m8DOcU7duxQ10u9uBq50fgt4vdgud3xPqo+2J5LkYuOXLWiXFNscz5jY2PVZy2/H/sUx6Xt9wNyfi3huLE8dp1xfUDpB0oIUBKnQ04ejgdbOFeixA8lkzrksqMqA+KmvPgjwMHKI6sXgz0oW9ezbvEDtA0q8KMuCOxUSzgZ4YC3rEeALFKUba9ZsyZXJU8EQZYXG1sovkSWLOrY4MRhuZFt2QZ1+gVWL6fXdyrqHeSlINuwoFDU+/rrr8vWrVut6lpa7gPsdGw/1JsqDOxX2yIdy2IJvG+5/va2Y0G/D8eAbbBq+X2WHP0+fA7LtLy4Feb4dAQuyDgOLYsx8tvf9tYBy0D1gryCW9tlXOtYdaaipk3/feIEZG+6bZrtnVeuu+469RfnBZzcC5qmvI6bgpwfnMF2uSimwo0iirww5LUuOPmjaAfFjdgW+A2iiw8U+RS2ONrevtKPJX2f4HyGc6693w1+o7hYo6gWVXTyWkcdim5t9yuOAUeOCxTrofgbRegIiLMzMLPllwmQHwT7CDQQ0CHwsGwo4ujxhfMMgmFcyJ15ntHPe7bLwQ0LAnHb86Kz4cYExc+oZmT7Wyrs9raEKgTYl/irB5b6dse+tY0LdLbF3vhd2FY7Kug1xdKBnJsrFLfag+DbEoIw22PE8vfjrOsD0oxrme3vB+dKW8gcwI0Dgj5kBOG7ETugClZ+mUD+euVW3KnnVYehKCcbR9gmEAEN6hrWr19f3n33XXWywA5HlI96B/m1OkW9DpT74+4VlVWxAxDJ40RiGR3r7N21guXJ5lqKaxuuXr1aRf+oB4E7VZx08GPAjwh1AYxSlNw/d/g+R/c5ji384OyxPUHYWwcsA3eIqGNljx4AOfNYdZSz0ubMNBc0Tfa2eUHPD3mdr+yl37YSeF7p0M8XqAtpe9dve5LHbx/pWrRokcqVQ/1FnANnzJiRq06bo4rjOMrrN1qU4wI5ajjX4cLWtm1bFSRi2yNHsbBdvGCfI1MBuW16Y5XCHl+eBoEx6jxiGyF3FAEutgluOorapQ7q16IeG45Z2xJDLBv7FXXm88pFLs7rgSln3VAP0F4jGssSjPyOXWdfHwoCASjq2uoBIOr+IcMIMUl+/PXWkjh5Ias7PzVq1JCdO3eqH6ll0IYKjAWB6Ngy4sUdMTaYXtESjSuQeLSIs7xbtZcVawsrjrsl3N1ZprGw/dDpRSBY77wUZBsWBLLJcbeBisqWd6o4KVrCXQK2H4pqbRu5WMrrTgD71d4+RA6J/r4zYXm400aaLe/Yivp9+ByWiQum5V10QY9PR2Cb4yKCivyF/fFiGSiecOYx40iRvyPzF0fa8qPnilmmB5XNQT8vOCNNjp4f8tuOONnaK3Z3NIdGP6fgZs6RdUGjERR5YcD6IyhEY4HCBoDXgvMZqvbkdU7Ab9Y2B684YF/hhhq5tZZFk4XtgBg5W2jIgcFesZijxxfOM8uWLVPzWgYnjp5n8jsP68uxLHpHsTAaEBTnbxG5V1gn5AAigNDZq3pQ0HMMcpT1Ine9lwLb7Y7fPmKCwgbZRbmm1MkpMULA5qxt7IzrA9KMa7rteRHnSntQDIzSAjQYQiCIRoyWufT2+CKaRR0VBBv2ghzLpsU9evRQrQwtuyFBUUFexZ55sT0I0NoH0LLHMsK2zfK3DXzssfdZlI3jrq+wJ0OccNFqEa3kLOnfUZBtWBBYLna8Zc4CisNQ588ScjNw0KO4yPZOzXI7oKWhvZMn9ivu0Cy3EVogYb/i4lvYouW84PtQDwetliyLZnAc4ISaX52F/OjHD1rYWbJtHeoMejESWkTbwro4cpHCMrDNEeDbwuexnILCPtY/7+j89uYtjrTlB+cVtKTUoY4ZegXARUO/K3dGmhw9P+gtTO1tG5zccWGx/F1v27ZNVVtxBC40aLGIojC08LNluVzLLjgAvw/kDha0662CwDZCzwHIdbSsloPeAlDygLrCtsVixZUO21xJnCPyymnND87LCJiRI5JXi1BHjy+cvzBu2S0Z0qRfxwr7G0XwgZIunL8s1xut1HH9c6Q3g8Ky97vI69xZkHMMtgtybBHE4vpor8cItCzG9yP4tP1+vLb9DTj7mtK1a1d1POPJT5ZVQopy/XbG9QHpQtUHy66hcAOETs7zuv6hVTZaMaPO47Vy/0Dlbb755psqdw31wFDBEBd81AdAxU9EsXq/XXgPzZgRaaIfHhRJItu0IM3xAXczKNpE1jJ+cHq3Hei3B3DywYGCRhVoFo07Law0Tpz2TpiWkA2Ku3tUKMcPBt+F4hKsE5ZTGPhB4qTXsmVLlX2NOxWcGFEpF3XzCrINCwLpRxE4thO2D+oNIHjGBQB3Ozq8RjcHONhQGRU/KOQY4k4Alev17ktQARUnLdQpxGewPVHvAd1QoFk/DiBUmkWOA+pJYdvhR2tbr6KosA1x8UNRHI4jBJm4qcAFFCccvRuIgkKwgIq8KC7HCRPdwOCuNq87pqLACQXHJrYtjgEcs8jRwR0zKgCj+x7LxlL2oKgFP24cs9gW2D8IvFFJGtsDx5jezYKjsAzAfsQJBCdWy0r49ua3d0wUR9rygzt/dJmEYxbdOuCGCwGH5U2fM9Lk6PkBd+2YhgsK0obfBOrgYUCDMPwusX2RZvwusQzcbdvrE9Me/I5xTmnSpIk6XyDHB+uL8yH6rURACUgDgkWsK9KALmCwrpbdPWC9cU5CbpmzHv2F4wGV45FGVNxHMRh+swg80cVPScC+wvUFRb/YDnrRrd5VTUHojQZwM4/rjSWcJ7D9HT2+cF1Czg7Om5im91npaD05nKfwu8SFGp/BuVrv8xZdziAQwjkf10jkBuJ8hj4/Hbmg4/qM4AI3VHppmt4PKorU86o7jwAI2wb7FkEQ6tmhygF+H3mdY3DNwbkF5z1sEz0wtITfBRo8odGEbQkefucocscNFY43rDu2JzI0cA3Ad+OmENcLVJcormtKRESEOgeibi2u8VgnZPwgwwfXeOxrbNeSvj7g8/heXNNw04J4Czl7epcutjmxWD7Sjs/g+LJsEJMnvTnwuXPnVBcS0dHRqnl8pUqVVFcFn376qVWzYXSFgm5cQkNDtfLly2vPPvusuWmzo93A7N69W7v33ntVlwJlypTRhg0bppo3W/r555+1pk2basHBwaorkrfeeks1g7dtfm7bJQO6UnjjjTdU0+qgoCDVPPvXX3/Ns4sGNGm3Za/7h507d6puVtBcHGmqV6+eNm7cOKt5HN2GBekG5vPPP1fdQmBd0Pwbzfr17WgL2wfri3mxXbHMpUuXWjVrR5cS2O74vOV2Q3cP2Cf6+rVu3VptN0t6dwvoxsBR9rqB0bfV4MGD1TGErg/QJYZtlwX57aO84Dh65plnVJcp6NKhZ8+e2okTJ5zeDYwO+xbN/0NCQtR2xXqMGjVKdZlxrW2gd0GALlPQlRK2A7YHugKZOnWq6sbgWtvBdr3QHcXTTz+tumXx8fG5Zpcw+R0TRUlbXseKvt3RTYXt9kFXIPjN68e6veOsqNvL0fMD/Pfff2rf4ntstzO6m0B3SHivefPmKu0FOcfov7mBAweq8wTOF1WrVtXuvPNObd68eeZ5Xn/9dfVbxO8Sxxi2C7rF0NdV7y4D34PuSq4F8+EcZQvpRvotbd68WXW9hW40cL7v1KmT2ibX2p/X+t3k9XuwTRu659HPEUgD0oJuZGzT6kg3MHpXG/YGy/OOI8cXXLp0SXvooYdUFz/oKgTjW7ZscbjrlZkzZ6rjB92f2KYd3b5gP+OYQPdETz31lNoWjshvPa/VbcvJkyfN1zisU9++fdV5zN71EF3m4HhFdz75LVu/VtkbbLtSQjdQ6E4I52AM2AY4Hvbt2+fQudiRa0p+li9fro4xrDuugXXq1NEefvhhbePGjde8RuR1TS7q9eHw4cPqPXwe5/QRI0ao7YTvsuxGSbd+/Xr1HroScoQP/leg0JaIyIlwt46cNbRao4JDDhEaLqDuq96RNxF5pmnTpqnWvcjZRU6tJZQcIIcZ1WeQo3ktfBYwEZEbQ9EaivwZ/BF5lhSbZwKjDiCKutHljW3wB6gqhzqP+tOLrsW6fTMREbkV1CkiIs9z9913q55QkKuH+qKov4oGaLbdy6CuJ1oMo+Em6gfbq49pDwNAIiIiIheDhmbo+xMBH1oVo8ERujNCx8+W0MAHjcjQGhqNiBzFOoBEREREXoZ1AImIiIi8DANAIiIiIi/DAJCIiIjIy7ARSBHgsWvocR29jBf0+YhERERkDE3TJCEhQT0ty9lPu3IbmodauXKl6lW/cuXKqmfsBQsWmN9Dj+7ojbtx48aqh3vMg57cT506VaDv0J8wwYEDBw4cOHBwv+HEiROat/LYHEA8wxHPFsZzO207RUxOTlbP6B03bpya58qVK+pZe3j2Ip616Sj9+YInTpwokYejExERUdHFx8dLdHR0oZ897wm8ohsYFM/iodJ4yHRe8BD61q1by7Fjx1THi44eQHi4NjpoZABIRETkHuJ5/WYjEB0OAgSKkZGRRieFiIiIqFh5bBFwQeD5eqNHj5b7778/3zuBtLQ0NVjeQRARERG5G6/PAczIyJB+/fqpFkEff/xxvvNOnjxZZRnrA+oPEBEREbkbr84B1IM/1Pv7+++/r1kPYOzYsfL888/nqkSaHwSWmZmZ6jl+5Nr8/PzE39+fXfoQEZHH8/f24O/AgQOyfPlyKVeu3DU/ExQUpAZHpaeny5kzZ1SrY3IPoaGhUrlyZQkMDDQ6KURERMXGYwPAxMREOXjwoPn1kSNHZOvWrVK2bFl1gb/33ntVVzC//vqryp07e/asmg/vO+Pij06i8Z3IVUJHk1gmc5ZcF3JqEbBfuHBB7be6det6b+egRETk8Ty2G5gVK1ZIp06dck0fNGiQTJw4UWrVqmX3c8gNvOWWW4rcjBwNSxBI1KhRQ+UqkXtAbi2qBOD4CA4ONjo5RERUDOLZDYzn5gAiiMsvti2puJe5SO6F+4uIiLwBr3ZEREREXoYBIBEREZGXYQBIZmikkt+AupMFtWvXLrnnnnukZs2aahnTpk1zqP4m5o2NjTVPO336tDRp0kQ6dOig6mwQERFR4XlsHUAqOHRZo/vhhx9k/Pjxsm/fPvO0UqVKFapRRe3ataVv374yfPjwQqXr0KFDcvvtt0vDhg3lxx9/lJCQkEIth4iI3Avq67MHjeLBAJDMKlWqZB5H6yj86CynFcYNN9ygBhgzZkyBP799+3bp2rWr3HrrrfLVV1+pjpqJiMjzbTl+RSb8vEveuqepNKjsnS11ixOvpiV8J5OSUfJPBAkJ8HPqHdS1cgIffPBBmTFjRpG/57///pMBAwao4YMPPuBdIBGRl0jNyJKRP26TQxeSZObqw/Juv+ZGJ8njMAAsQQj+Go5fUuLfu/vVrhIa6LxdjQ618+OsPpXuuusu6d+/v3z44YdOWR4REbmH95buV8FfhfAgGX9nQ6OT45EYAFKBxcTElMj39O7dWxYsWCCrV6+W9u3bl8h3EhGRsTYduyyfrj6sxiff1UQiQ/lozuLAALCEi2KRG2fE9zpTSRUBf/LJJzJq1Cjp3r27/P7776oFMBERea6UdBT9bhc8q+HullWlc8Moo5PksRgAliDUYXNmUaxRSqoIGNvr008/VU/n6NGjh/z222/SsWNHpyybiIhcz9Q/98mRi0kSFREkE+5sZHRyPJr7RyPk0kXA6enpsnv3bvP4qVOnVACJXERHloMgELmJfn5+5iDQ0Wc1ExGR+1h/5LJ88e8RNf7m3U2ldGiA0UnyaOwImooVOnBu0aKFGtDP4NSpU9X4Y4895vAyEAROnz5dBg8eLHfccYcsX768WNNMREQlKzk9U0bN26aKfvteX0061a9odJI8no+GvkmoUOLj41V/eXgyhW2xZ2pqqhw5ckRq1aolwcHBhqWRCob7jYio5E38eZd8+d9RqRQRLEuGd5DSIQGGXb+9BXMAiYiIyDBrD19SwR+8dW/TYg/+KBsDQCIiIjJEUlqmvDBvmxq/v3W0dLyugtFJ8hoMAImIiMgQby3eKycup0jVyBB5sUcDo5PjVRgAEhERUYn77+BF+XrNMTWO5/2GB7PotyQxACQiIqISlaiKfrer8QFtqku7uuWNTpLXYQBIREREJeqN3/fIqdgUqVYmRMay6NcQDACJiIioxKw+cEHmrDuuxt++t6mUCuIzKYzAAJCIiIhKREJqhozOKfod1LaG3FSHRb9GYQBIREREJWLSb3vkdFyqVC8bKqO71zc6OV6NASAREREVuxX7zsv3G06Ij4/I1L7NJDSQRb9GYgBIVs/czW+YOHFigZe5a9cuueeee6RmzZpqGdOmTbM7H571i3nw+LU2bdrI+vXr810u0tK8eXOraatXr5bIyEh57rnnhE84JCJyHXEpGTLmpx1q/OGbakrrWmWNTpLXYwBIZmfOnDEPCNTwfETLaSNHjizwMpOTk6V27dry5ptvSqVKlezO88MPP8jzzz8vEyZMkM2bN0uzZs2ka9eucv78eYe/57ffflOfwXKQdgSbRETkGl77dbecjU+VmuVCZVRXFv26Aua/kpllgIaHZCOIyitoc9QNN9ygBhgzZozded599115/PHHZfDgwer1jBkzVED3xRdf5PkZS3PmzFGffeedd2TYsGFFSi8RETnX33vPybxNJ81FvyGBfkYniRgAljAUS2Ykl/z3BoSifNdpiytVqlS+7z/44IMqiHNEenq6bNq0ScaOHWue5uvrK507d5Y1a9Zc8/MoOkauH4LFAQMGOPSdRERUMuKSrxb9PnpzLWlVk0W/roIBYElC8PdGlZL/3hdPiwSGOW1xW7duzfd9FB076uLFi5KVlSVRUVFW0/F67969+X52z549Ksfv888/Z/BHROSCXvlll5xPSJPaFcJkZNd6RieHLDAApAKLiYkRV1CtWjXV6GPKlCnSvXt3qVy5stFJIiKiHH/uOivzt5wS35yi3+AAFv26EgaAJV0Ui9w4I77XiZxZBFy+fHnx8/OTc+fOWU3H62vVPwwPD5e//vpLbr/9dunUqZMsX76cQSARkQu4kpQuLy7YqcYf71BbWlYvY3SSyAYDwJKEenhOLIo1ijOLgAMDA+X666+XZcuWSZ8+fdQ0k8mkXjvSoKNMmTIqCOzSpYvccsstKgisUsWAYnYiIjKb+MsuuZiYJjEVS8nwztcZnRyygwEgFWsRMBp57N692zx+6tQpFUAiF1FfDhpxDBo0SFq1aiWtW7dW3bgkJSWZWwVfC4qBly5dqrqBQRC4YsUKBoFERAZZvPOsLNp6Wvx8feQdFv26LPYDSMXq9OnT0qJFCzWgL8GpU6eq8ccee8w8T//+/dX08ePHq86dESAuXrw4V8OQ/KDbmj///FMVKXfs2FEFmkREVLIuJabJSwuyW/0+0aG2NIuONDpJlAcfjY9MKLT4+HgVeMTFxeUq9kxNTZUjR45IrVq11NMtyD1wvxERFd7QOZvlt+1n5LqoUvLL0+0kyN/P7a7f3oI5gERERFRkCPwwZBf9NnfZ4I+yMQAkIiKiIkGDj3GLslv9Dr2ljjSpVtroJNE1MAAkIiKiQkNNsnELd8rlpHSpXylcht1a1+gkkQMYABIREVGh/bL9jPyx86z4o+i3XzMJ9Gdo4Q64l4iIiKhQziekyvicot9ht8ZIoyos+nUXDACJiIioUEW/Ly3YKbHJGdKwcoQM7eQajwklxzAAJCIiogJbuPWULN19TgL8sot+A/wYUrgT7i0iIiIqkHPxqTLx5+ynPD17W11pUNk7+9JzZwwAiYiIqEBFvy/O3yFxKRnSpGppebJjHaOTRIXAAJCIiIgc9tPmU7Js73kJ9PNVRb/+LPp1S9xrZObj45PvMHHixAIvc9euXXLPPfdIzZo11TKmTZtmd77p06erefD4tTZt2sj69etzPaJt6NChUq5cOSlVqpRa5rlz5/L97ltuuUWee+45q2n/93//J0FBQfL9998XeF2IiLzdmbgUeeWXXWr8udvrynVR4UYniQqJASCZnTlzxjwgUMPzES2njRw5ssDLTE5Oltq1a8ubb74plSpVsjvPDz/8IM8//7xMmDBBNm/eLM2aNZOuXbvK+fPnzfMMHz5cfvnlF/nxxx9l5cqVcvr0abn77rsLlBYs/8UXX5RFixbJfffdV+B1ISLy9qLfMT/tkITUTGkWHSlD2tc2OklUBP5F+TB5FssADQ/JRo5dXkGbo2644QY1wJgxY+zO8+6778rjjz8ugwcPVq9nzJghv/32m3zxxRfqM3hY9+effy5z5syRW2+9Vc0za9YsadCggaxdu1ZuvPHGa560nnnmGfn2229l6dKlctNNNxVpnYiIvNHcjSdk5f4LqqPnd/o2ZdGvm/PYvbdq1Srp2bOnVKlSRQUyCxcuzBUUjB8/XipXriwhISHSuXNnOXDgQLGmCd+ZnJFc4gO+15lQBJvf8OSTTzq8rPT0dNm0aZPa/jpfX1/1es2aNeo13s/IyLCap379+lK9enXzPHnJzMyUBx98UObNm6dyDhn8EREV3KnYFHnt1z1qfGSX6ySmIot+3Z3H5gAmJSWposRHHnnEblHh22+/Le+//7589dVXUqtWLRk3bpwqdty9e7eqh1YcUjJTpM2cNlLS1j2wTkIDQp22vK1bt+b7PoqOHXXx4kXJysqSqKgoq+l4vXfvXjV+9uxZCQwMlMjIyFzz4L38zJw5U/3dtm2bChqJiKgwRb/bJTEtU1pWj5RH27Ho1xN4bADYvXt3NeR1MKOO28svvyy9e/dW077++msVUCCnkPXD8hcT4z69vbdr104FrAjwv/vuO/H399hDnoioWHy3/oSsPnBRgvx9ZWrfZuLn62N0ksgJvPJqeOTIEZVzZFmkiDpvaH2KIsXiCgBD/ENUblxJw/c6E4p584MiV9Tjc0T58uXFz88vV4tevNbrH+IviopjY2OtcgEt58lLkyZN5J133lH7un///qrBCYNAIiLHnLicLJN+y+7weVS3+lK7Qv7nf3IfXnkl1IsN7RU75lekmJaWpgZdfHx8gb4XdRGdWRRrFGcWAaNo9/rrr5dly5ZJnz591DSTyaReDxs2TL3G+wEBAWoaun+Bffv2yfHjx6Vt27bX/I7mzZurzyII7NevnwoCsTwiIsqbyaTJ6J+2S1J6ltxQs4wMvqmm0UkiJ/LKALCwJk+eLK+88op4u4IUASPnDvUq9fFTp06pABK5iPpy0AXMoEGDpFWrVtK6dWtVPI86nHqrYOTOPvroo2q+smXLqgDz6aefVsHftVoA61Af9O+//5bbbrtNBYFz585lEEhElI/Z647Jf4cuSXCAr0y5t5n4sujXo3hsK+D86MWG+RU72jN27FjVJYk+nDhxotjT6u7QX1+LFi3UgL4Ep06dqsYfe+wx8zwomsV0tMpGbh0CxMWLF1vl0L733nty5513qhzADh06qP00f/78AqUFxcEIAv/77z/p27evCkiJiCi345eS5Y3fsxvije5WX2qWDzM6SeRkPpqz+whxQSh6XbBggbmIEauM7mHQsfGIESPMxbkVK1aUL7/80uE6gPgMcqcQDNoWe+LJFahriBbGxdWqmJyP+42IvB2Kfu+fuVbWHbksbWqVle8ev9Hjcv/i87l+ewuPLQJOTEyUgwcPml/joo6cJRQhov84PCLs9ddfl7p165q7gUFQqAeJRERE3ujrNUdV8Bca6MeiXw/msQHgxo0bpVOnTubXqD8GqGuGXL5Ro0apemZDhgxRrUvRXQiKHZnrQ0RE3uroxSR5c3F20e/YHg2kejn3b7hIXhYA3nLLLfk+AQPFwq+++qoaiIiIvF2WSZORP26T1AyT3FSnnAxoXd3oJFEx8spGIERERGRt1r9HZOOxKxIW6Cdv3dOURb8ejgEgERGRlzt0IVGmLNmnxl+6o6FEl2XRr6djAFjMvKCRtUfh/iIibyz6feHHbZKWaZL2dcvL/a2jjU4SlQAGgMVE72Q4OTnZ6KRQAej7i51EE5G3+Pyfw7L5eKyEB/mrol/UkSfP57GNQIyG59viubXnz59Xr0NDQ/mjcvGcPwR/2F/Yb9h/RESe7uD5BJn65341Pu7OhlIl0rnPjifXxQCwGOlPFdGDQHJ9CP7yexoMEZGnyMwyyYgft0t6pkluqVdB+raqZnSSqAQxACxGyPGrXLmyesJIRkaG0cmha0CxL3P+iMhbfLr6sGw7ESvhwf7y5t0s+vU2DABLAIIKBhZEROQq9p1NkGlLD6jxCT0bSaXSfAiCt2EjECIiIi+SkWVSHT6nZ5nktvoV5Z6WVY1OEhmAASAREZEXmbHikOw4FSelQwLkjbubsOjXSzEAJCIi8hJ7zsTL+39nF/2+0quRREWw6NdbMQAkIiLykqLfEXO3SUaWJl0aRknv5lWMThIZiAEgERGRF5i+/KDsPhMvZUIDZNJdLPr1dgwAiYiIPNzOU3Hy4d8H1firvRtLhfAgo5NEBmMASERE5MHQ0TNa/WaaNOnRpJLc2bSy0UkiF8AAkIiIyIN98PcB2Xs2QcqGBarcPxb9EjAAJCIi8lDbT8bKRysOqfFJfRpL+VIs+qVsDACJiIg8UFpmlmr1m2XSpGezKtK9CYt+6SoGgERERB5o2l8H5MD5RJXr92qvRkYnh1wMA0AiIiIPs+X4FflkZU7R712NpUxYoNFJIhfDAJCIiMiDpGZkqVa/Jk2kT/Mq0rVRJaOTRC6IASAREZEHeXfpfjl0IUn19TeRRb+UBwaAREREHmLTscsyc/VhNT75riYSGcqiX7KPASAREZEHSElH0e920TSRe1pWk84No4xOErkwBoBEREQeYMqSfXLkYpJERQTJ+J4NjU4OuTgGgERERG5u3eFLMuu/I2r8zXuaSumQAKOTRC6OASAREZEbS07PlBfmZRf99m8VLZ3qVTQ6SeQGGAASERG5sbf+2CvHLydLldLB8tKdDYxODrkJBoBERERu6r9DF+WrNcfU+Fv3NpWIYBb9kmMYABIREbmhxLRMGTVvuxq/v3V1aV+3gtFJIjfCAJCIiMgNTf59j5y8kiJVI0PkpTtY9EsFwwCQiIjIzaw+cEFmrzuuxqfc21RKBfkbnSRyMwwAiYiI3EhCaoaMzin6Hdi2htwUU97oJJEbYgBIRETkRib9tkdOx6VK9bKhMrpbfaOTQ27KZQJATdPk+PHjkpqaanRSiIiIXNKKfefl+w0nzEW/YSz6JU8IAGNiYuTEiewDm4iIiK6KS8mQMT/tUOODb64pbWqXMzpJ5MZcJgD09fWVunXryqVLl4xOChERkct57dfdcjY+VWqWC5VRXVn0Sx4SAMKbb74pL7zwguzcudPopBAREbmMZXvOybxNJ8XHR2Rq32YSEuhndJLIzblU5YGBAwdKcnKyNGvWTAIDAyUkJMTq/cuXLxuWNiIiIiPEJqfL2PnZRb+PtaslrWqWNTpJ5AFcKgCcNm2a0UkgIiJyKa/8slvOJ6RJ7QphMqJLPaOTQx7CpQLAQYMGGZ0EIiIil7Fk11lZsOWU+OYU/QYHsOiXPDAAhKysLFm4cKHs2bNHvW7UqJH06tVL/Px40BMRkfe4nJQuLy3ILvod0qGOtKxexugkkQdxqQDw4MGD0qNHDzl16pTUq5edzT158mSJjo6W3377TerUqWN0EomIiErEhJ93ycXEdKlbsZQ817mu0ckhD+NSrYCfeeYZFeShL8DNmzerAZ1D16pVS71HRETkDX7fcUZ+2XZa/Hx9WPRLnp8DuHLlSlm7dq2ULXu1hVO5cuVU9zA333yzoWkjIiIqCRcT0+TlhdndoT3VsY40i440OknkgVwqBzAoKEgSEhJyTU9MTFTdwhAREXkyPBVr3MKdqv5f/Urh8vRtMUYniTyUSwWAd955pwwZMkTWrVunfgQYkCP45JNPqoYgREREnuyX7Wfkj51nxT+n6DfIn0W/5AUB4Pvvv6/qALZt21aCg4PVgKJfPCPY2X0EorXxuHHjVP1CdDiN733ttddU0ElERFTSziekyvhF2UW/QzvFSOOqpY1OEnkwl6oDGBkZKYsWLVKtgfVuYBo0aKACQGd766235OOPP5avvvpKdTWzceNGGTx4sJQuXZoNToiIqEQh8+GlBTslNjlDGlaOkGG3suiXvCgH8NVXX1WPgkPA17NnTzVgPCUlRb3nTP/995/07t1b7rjjDqlZs6bce++90qVLF1m/fr1Tv4eIiOhaFm49JUt3n5MAPx95p18zCfBzqcszeSCXOsJeeeUV1eDDFoJCvOdMN910kyxbtkz279+vXm/btk3++ecf6d69u1O/h4iIKD/n4lNlwqJdavzZ2+pKg8oRRieJvIC/q2WB+/j45JqO4MyyaxhnGDNmjMTHx0v9+vXVU0ZQJ3DSpEkyYMCAPD+TlpamBh0+T0REVJTr3piftkt8aqY0qVpanuzIBx6QFwWAZcqUUYEfhuuuu84qCERghlxBtAR2prlz58rs2bNlzpw5qg7g1q1b5bnnnpMqVark+UxiPJXE2TmRRETkvX7cdFKW77sggX6+qujXn0W/VEJ8NBdo9oqGGEjGI488olr7oiGGDv3/oY4eWgY7Ex4vh1zAoUOHmqe9/vrr8u2338revXsdzgHEcuLi4iQigln2RETkuNOxKdL1vVWSkJYpo7vVl6duYe5fSYmPj1exhjdfv10iB1DPcUOXLOj2xd+/+JOFeoW+vtZ3WigKNplM+XZUjYGIiKgokOkx+qftKvhrHh0pj7evZXSSyMu4VF5zUlKSaphha8mSJfLHH3849bvQwhh1/n777Tc5evSoLFiwQN5991256667nPo9REREtr7fcEJWH7goQf4s+iVjuNQRhyJZ1PmzW0l2zBinftcHH3ygun753//+p/oaHDlypDzxxBOqM2giIqLicuJysrz+6241PrJLPalToZTRSSIv5BJ1AHV4Igc6gEadP0vIoUNDDeQQuhLWISAiooIwmTR58PN18t+hS9KqRhn54Ym24uebu/cLKl7xvH67Vg4gdsbhw4dzTceTQcLCwgxJExERkbPMXndMBX/BAb4ypW8zBn9kGJcKAPFkDnTFcujQIavgb8SIEdKrVy9D00ZERFQUxy4lyRu/Z/cyMaZbfalVnhkbZByXCgDffvttldOHzpnRIhgD6ueVK1dOpk6danTyiIiICl30+8KP2yUlI0va1CorA9taV3Ui8spuYCyLgPGM3qVLl6qnf6BOYNOmTaVDhw5GJ42IiKjQvvzvqKw/ellCA/1kyr3NxJdFv2QwlwoAAU8B6dKlixqIiIjc3eELifL2kuyi3xd7NJDq5UKNThKR6wWAaOm7cuVKOX78uKSnp1u998wzzxiWLiIiooLKMmky8sdtkpphkptjysmANtWNThKR6wWAW7ZskR49eqindCAQLFu2rFy8eFFCQ0OlYsWKDACJiMitfP7PYdl8PFZKBfnL2/c2s3rWPZGRXKoRyPDhw9UTOq5cuaLq/61du1aOHTsm119/PRuBEBGRWzlwLkGm/rlfjY+7s4FUjQwxOklErhkAbt26VXX5gmf04rm8aWlpEh0drVoHv/jii0Ynj4iIyCGZWSZV9JueaZKO11WQfq2ijU4SkesGgAEBASr4AxT5oh6g3jr4xIkTBqeOiIjIMZ+sOizbTsZJeLC/vHlPExb9kstxqTqALVq0kA0bNkjdunWlY8eOMn78eFUH8JtvvpHGjRsbnTwiIqJr2ns2Xqb9lV30O7FnI6lcmkW/5HpcKgfwjTfekMqVK6vxSZMmSZkyZeSpp56SCxcuyKeffmp08oiIiPKVkWWSEXO3SUaWJp0bVJS7W1Y1OklErpkD+PPPP0v37t1V8W+rVq3M01EEvHjxYkPTRkREVBDTlx+UXafjpXRIgLxxF4t+yXUZngN41113SWxsrBpHw4/z588bnSQiIqIC23kqTj78+6Aaf7V3I6kYEWx0kohcNwCsUKGC6u4FNE3j3RIREbkdtPZFq99MkybdG1eSXs2qGJ0kItcuAn7yySeld+/eKvDDUKlSpTznzcrKKtG0EREROeL9ZQdk79kEKRsWKK/1aczMDHJ5hgeAEydOlPvuu08OHjwovXr1klmzZklkZKTRySIiInLIthOx8vHKQ2p8Up/GUr5UkNFJInL9ABDq16+vhgkTJkjfvn3Vo9+IiIhcXWpGlir6xTN/ezarIt2bZPdkQeTqXCIA1CEAJCIichfv/bVfDpxPVLl+r/ZqZHRyiNynEQgREZE72nTsisxcdViNT767iZQJCzQ6SUQOYwBIRERUQCnp2UW/Jk1UZ8+3N4wyOklEBcIAkIiIqIDeXrJXjlxMkqiIIJnQk0W/5H5cNgBMTU01OglERES5rD18SWb9e1SNv3VPU/XUDyJ341IBoMlkktdee02qVq0qpUqVksOHs+tWjBs3Tj7//HOjk0dERF4uKS1TXpi3TY3fd0O03FKvotFJInL/APD111+XL7/8Ut5++20JDLxambZx48by2WefGZo2IiKiyX/skROXU6RqZIi8dEcDo5ND5BkB4Ndffy2ffvqpDBgwQD0XWNesWTPZu3evoWkjIiLv9s+Bi/Lt2uNq/O17m0p4MIt+yX25VAB46tQpiYmJsVs0nJGRYUiaiIiI4lMzZFRO0e9DN9aQm2PKG50kIs8JABs2bCirV6/ONX3evHnSokULQ9JERET0+q+75XRcqlQvGypjutc3OjlEnvUkkPHjx8ugQYNUTiBy/ebPny/79u1TRcO//vqr0ckjIiIv9PfeczJ340nx8RGZ2reZhAW51KWTyP1zAHv37i2//PKL/PXXXxIWFqYCwj179qhpt99+u9HJIyIiLxObnC5jftqhxh+5uZa0rlXW6CQROYXL3ca0b99eli5danQyiIiIZOLPu+R8QprUrhAmL3StZ3RyiDwzB3DDhg2ybt26XNMxbePGjYakiYiIvNPinWdl4dbT4usj8k7fZhIccLV3CiJ351IB4NChQ+XEiRO5pqNOIN4jIiIqCZcS0+SlBdlFv0M61JEW1csYnSQizw0Ad+/eLS1btsw1HS2A8R4REVFx0zRNxi3aKZeS0qVeVLgMv72u0Uki8uwAMCgoSM6dO5dr+pkzZ8Tf3+WqKxIRkQf6ZfsZ+X3HWfH39ZF3+jWTIH8W/ZLncakAsEuXLjJ27FiJi4szT4uNjZUXX3yRrYCJiKjYnU9IlfGLdqrxYbfGSOOqpY1OElGxcKlstalTp0qHDh2kRo0a5o6ft27dKlFRUfLNN98YnTwiIvLwot8X5++Q2OQMaVw1QoZ2yv1kKiJP4VIBYNWqVWX79u0ye/Zs2bZtm4SEhMjgwYPl/vvvl4AAPnORiIiKz0+bT8lfe85LoJ+vvNO3uQT4uVQhGZHnBoCADqCHDBlidDKIiMiLnI5NkVd+3qXGn7u9rtSrFG50koi8KwA8cOCALF++XM6fP68eB2cJTwYhIiJydtHv6J+2S0JaprSoHilD2tc2OklE3hUAzpw5U5566ikpX768VKpUSXzw4MUcGGcASEREzjZn/XFZfeCiBPn7qmf9+rPol7yASwWAr7/+ukyaNElGjx5tdFKIiMgLHL+ULJN+26PGR3erL3UqlDI6SUQlwqVuc65cuSJ9+/Y1OhlEROQFTCZNRs7bJsnpWdKmVll5+KaaRieJyDsDQAR/f/75p9HJICIiLzDrv6Oy/shlCQ30U0W/vnjoL5GXcKki4JiYGBk3bpysXbtWmjRpkqvrl2eeecawtBERkec4dCFR3l68V42/dEcDiS4banSSiEqUj4bmTy6iVq1aeb6HRiCHDx8WVxIfHy+lS5dWTy6JiIgwOjlEROSAzCyT3DtjjWw9ESvt65aXrx9pbdXokDxfPK/frpUDeOTIEaOTQEREHu6TVYdV8Bce7C9v39uUwR95JZeqA1jSTp06JQ8++KCUK1dOPXUExc4bN240OllERFRM9pyJl2l/7VfjE3s2ksqlQ4xOEpEhXCoHEE6ePCk///yzHD9+XNLT063ee/fdd53a4vjmm2+WTp06yR9//CEVKlRQnVCXKVPGad9BRESuIz3TJCPmbpOMLE1ubxgld7esanSSiAzjUgHgsmXLpFevXlK7dm3Zu3evNG7cWI4ePap6aW/ZsqVTv+utt96S6OhomTVrlkN1EImIyL19+PcB2X0mXsqEBsgbdzVh0S95NZcqAh47dqyMHDlSduzYIcHBwfLTTz/JiRMnpGPHjk7vHxC5jK1atVLLrVixorRo0UI9iYSIiDzPthOxMn3FITX+ep8mUiE8yOgkERnKpQLAPXv2yMCBA9W4v7+/pKSkSKlSpeTVV19VOXbOhBbFH3/8sdStW1eWLFmiHkGHbma++uqrPD+TlpamWg5ZDkRE5NpSM7JkxI/bJMukSc9mVeSOppWNThKR4VwqAAwLCzPX+6tcubIcOpR9twYXL1506neZTCZVrPzGG2+o3L8hQ4bI448/LjNmzMjzM5MnT1bNxvUBRchEROTa3vlznxw8n6hy/V7t1cjo5BC5BJcKAG+88Ub5559/1HiPHj1kxIgR6tnAjzzyiHrPmRBgNmzY0GpagwYNVOOT/Iqo0WeQPqB4moiIXBee9PHZP9ldjL15dxMpExZodJKIXIJLNQJBK9/ExEQ1/sorr6jxH374QRXTOrMFMKAF8L59+6ym7d+/X2rUqJHnZ4KCgtRARESuLyktU0b+uE3wuIN+rarJbQ2ijE4SkctwqQAQrX8ti4PzK44tquHDh8tNN92kioD79esn69evl08//VQNRETk/t74fY8cv5wsVSNDZNyd1iU+RN7OpYqAS9INN9wgCxYskO+++051N/Paa6/JtGnTZMCAAUYnjYiIimjl/gsye112lZ4p9zaV8GDrZ8sTeTvDcwDR8bKjfTFdvnzZqd995513qoGIiDxHXEqGjJ63XY0/fFNNuSmmvNFJInI5hgeAyHUjIiJylld+3iVn41OlVvkwGd2tvtHJIXJJhgeAgwYNMjoJRETkIRbvPCvzt5wSXx+RqX2bSkign9FJInJJhgeAeUlNTc31LOCIiAjD0kNERK7tYmKavLRghxp/omMdub5GWaOTROSyXKoRSFJSkgwbNkw9mg2tgFE/0HIgIiKyB8+Mf3nBTrmUlC71K4XLc53rGp0kIpfmUgHgqFGj5O+//1aPaEN/e5999pnqD7BKlSry9ddfG508IiJyUQu3npLFu85KgJ+PvNOvmQT5s+iXyG2KgH/55RcV6N1yyy0yePBgad++vcTExKjOmWfPns0uWoiIKJczcSkyftEuNf7sbXWlUZXSRieJyOW5VA4gunnRO4NGfT+925d27drJqlWrDE4dERG5YtHvqHnbJSE1U5pFR8qTHesYnSQit+BSASCCvyNHsp/ZWL9+fZk7d645ZzAyMtLg1BERkatBZ8+rD1yUIH9feadvM/H3c6nLGpHLcqlfCop9t23bpsbHjBkj06dPl+DgYPXYthdeeMHo5BERkQs5ejFJJv22R42jv7+YiqWMThKR2/DRkH/uoo4ePSqbN29W9QCbNm0qriY+Pl5Kly4tcXFx7KKGiKgEZZk06ffJGtl07Iq0rV1OZj/WRnzR+R+RA+J5/XatRiC2atasqQYiIiJLn646rIK/UkH+MqVvUwZ/RO5cBAzLli1Tz+etU6eOGjD+119/GZ0sIiJyEXvPxst7S/er8fE9G0q1MqFGJ4nI7bhUAPjRRx9Jt27dJDw8XJ599lk1IGu2R48eqj4gERF5t/RMkwz/YZukZ5mkc4OK0vf6akYnicgtuVQdwGrVqqnGH3gaiCUEf2+88YacOnVKXAnrEBARlawpS/bK9OWHpExogCwZ3kEqhgcbnSRyQ/G8frtWDmBsbKzKAbTVpUsXtZOIiMh7oc7fxysOqfFJdzVh8EfkKQFgr169ZMGCBbmmL1q0SNUFJCIi75Scnikj5m4VkyZyV4uq0qNJZaOTROTWDG8F/P7775vHGzZsKJMmTZIVK1ZI27Zt1bS1a9fKv//+KyNGjDAwlUREZKTJv++Vo5eSpVJEsEzs1cjo5BC5PcPrANaqVcuh+Xx8fOTw4cPiSliHgIio+K3cf0EGfbFejX/zaGtpX7eC0UkiNxfP67fxOYD6o9+IiIhsxSVnyKh52U+IGtS2BoM/Ik+sA0hERGRp/M875Vx8mtQuHyZjujcwOjlEHoMBIBERuaRft5+WRVtPi5+vj7zbv7mEBPoZnSQij8EAkIiIXM65+FR5acFONT70ljrSPDrS6CQReRQGgERE5FLQNvGFedslLiVDmlQtLU/fVtfoJBF5HAaARETkUmavOy6r9l+QIH9fea9/Mwnw46WKyONaAW/fvt3heZs2bVqsaSEiImMduZgkk37bo8ZHd6svMRXDjU4SkUcyPABs3ry56uMPWf74m5+srKwSSxcREZWszCyTPD93q6RkZMnNMeXk4ZtqGp0kIo/l6wr9AKKDZ/z96aefVMfQH330kWzZskUNGK9Tp456j4iIPNeMlYdky/FYCQ/2lyn3NhNf3/wzBYjIjXMAa9SoYR7v27evejRcjx49rIp9o6OjZdy4cdKnTx+DUklERMVpx8k4mfbXATX+Sq9GUiUyxOgkEXk0w3MALe3YscPuo+Ewbffu3YakiYiIildqRpY898MWyTRpckeTynJXi6pGJ4nI47lUANigQQOZPHmypKenm6dhHNPwHhEReZ43/9grhy4kScXwIHm9T+Nr1gcnIg8oArY0Y8YM6dmzp1SrVs3c4hethHEy+OWXX4xOHhEROdnqAxfky/+OqvEpfZtJmbBAo5NE5BVcKgBs3bq1ahAye/Zs2bt3r5rWv39/eeCBByQsLMzo5BERkRPFJqfLyB+3qfGHbqwhHa+rYHSSiLyGSwWAgEBvyJAhRieDiIiK2bhFu+RcfJrULh8mY3vUNzo5RF7FpeoAwjfffCPt2rWTKlWqyLFjx9S09957TxYtWmR00oiIyEkWbT0lv2w7LX6+PvJu/+YSGuhy+RFEHs2lAsCPP/5Ynn/+eenevbtcuXLF3PFzmTJlZNq0aUYnj4iInOBUbIq8vHCnGn/61hhpHh1pdJKIvI5LBYAffPCBzJw5U1566SXx9796N9iqVSvVRQwREbk3k0mTkXO3SUJqpgr8hnWKMTpJRF7JpQJAPA2kRYsWuaYHBQVJUlKSIWkiIiLn+fyfI7Lm8CUJDfST9/o3F38/l7oMEXkNl/rlocPnrVu35pq+ePFi9gNIROTm9pyJlylL9qnxcXc2lFrl2bsDkVFcqtYt6v8NHTpUUlNTRdM0Wb9+vXz33XeqI+jPPvvM6OQREVERnvYx/Ietkp5lks4NouS+G6KNThKRV3OpAPCxxx6TkJAQefnllyU5OVn1/4fWwP/3f/8n9913n9HJIyKiQnrnz32y92yClC8VKG/e04RP+yAymI+GrDYXhAAwMTFRKlasKK4qPj5eSpcuLXFxcRIREWF0coiIXNK/By/KgM/WqfHPBraSzg2jjE4Sebl4Xr9dKwfQUmhoqBqIiMi9n/YxYm720z4GtKnO4I/IRRgeAKLVr6NFAZs3by729BARkXOggOmlBTvlbHyqetrHS3ewMR+RqzA8AOzTp4/RSSAiomLw0+ZT8tuOM+Lv6yPT7uPTPohcieG/xgkTJhidBCIicrLjl5JlwqLsp30Mv/06aVqNT/sgciUu1Q8gERG5v8wskzz3wxZJSs+S1jXLypMd6xidJCJytRzAsmXLyv79+6V8+fLqmb/51Qe8fPlyiaaNiIgKbvryQ7L5eKyEB/nLO/2aiZ8vu3whcjWGB4DvvfeehIeHq/Fp06YZlo4333xTxo4dK88++6yh6SAicmebjl2R9/8+oMZf7dNIosuyNwciV2R4ADho0CC74yVpw4YN8sknn0jTpk0N+X4iIk+QkJqhin6zTJr0bl5F7mpRzegkEZG71QHE4+DQUaPlUBzQ2fSAAQNk5syZqgiaiIgKZ8LPu+TE5RSpGhkir/VpbHRyiMhdAsCkpCQZNmyYevpHWFiYCsgsh+KAZw/fcccd0rlz52JZPhGRN/h522mZv/mUoLofunyJCA4wOklE5MpFwJZGjRoly5cvl48//lgeeughmT59upw6dUoVz6KOnrN9//33qnNpFAE7Ii0tTQ264sqVJCJyJ6diU+SlBTvU+LBOMXJDzbJGJ4mI3CkH8JdffpGPPvpI7rnnHvH395f27dvLyy+/LG+88YbMnj3bqd914sQJ1eADyw0ODnboM5MnT1bPDtSH6Ohop6aJiMjdoL7f8B+2SkJqpjSPjpSnb6trdJKIyN0CQHTzUrt2bTWOhzPr3b60a9dOVq1a5dTv2rRpk5w/f15atmypgk0MK1eulPfff1+NZ2Vl5foMWgnjwdH6gCCSiMibfbT8oKw/clnCAv1kWv/mEuDnUpcVInKHImAEf0eOHJHq1atL/fr1Ze7cudK6dWuVMxgZ6dxe5G+77TbZsSO7yEI3ePBg9b2jR48WPz+/XJ8JCgpSAxERZXf5Mm1ZTpcvvRtLzfJhRieJiNwxAEQAtm3bNunYsaOMGTNGevbsKR9++KFkZGTIu+++69TvQt+DjRtbt1JDw5Ny5crlmk5ERNbiUzPk2e+vdvlyd8uqRieJiNw1ABw+fLh5HK1y9+7dq4pqY2Ji2EcfEZGL0DRNXl6wU05eSZFqZbK7fMnvKU5E5HpcKgD8+uuvpX///uZi1ho1aqghPT1dvTdw4MBi/f4VK1YU6/KJiDwBuntBty94xNv/3deCXb4QuSFfVysCRuMKWwkJCeo9IiIy1tGLSTJ+0U41/txtdeX6GuxAn8gd+bpasYK9YoSTJ0+qbleIiMg46Zkmeeb7LZKUniWta5WV/3WKMTpJROTORcAtWrRQgR8GtM5FNyw6dMeClsHdunUzNI1ERN5u6p/7ZPvJOCkdEqC6fEERMBG5J5cIAPv06aP+bt26Vbp27SqlSpUyvxcYGCg1a9ZUnUMTEZExVuw7L5+uOqzGp9zbVKpEhhidJCJy9wBwwoQJ6i8CPTQCcfTJHEREVPzOJ6TKyB+3qfGBbWtIl0aVjE4SEXlSHcBBgwZJamqqfPbZZ+qpG/qTQPC8XjwTmIiISpbJpMnzP2yTi4npUr9SuLzYo4HRSSIiJ3CJHEDd9u3bVf9/aPBx9OhRefzxx6Vs2bIyf/58OX78uOoKhoiISs4nqw7LPwcvSnCAr3z4QAsJDsj9lCQicj++rtYR9MMPPywHDhywKgbu0aOH058FTERE137U2zt/7lPjE3s2kpiK4UYniYg8MQdw48aN8umnn+aaXrVqVTl79qwhaSIi8kZxyRnyzHdbJNOkyZ1NK0v/G6KNThIReWoOIJ4AEh8fn2v6/v37pUKFCoakiYjI26BP1lE/bZNTsSlSo1yoTL67CR/1RuRhXCoA7NWrl7z66quSkZGhXuOEg7p/o0ePZjcwREQl5Os1x2TJrnMS4OcjH97fUsL5qDcij+NSAeA777wjiYmJUrFiRUlJSZGOHTtKTEyMhIeHy6RJk4xOHhGRx9t5Kk4m/bZHjY/t3kCaVONTmIg8kUvVAUTr36VLl8o///yjWgQjGGzZsqVqGUxERMUrMS1Ths3ZLOlZJuncIEoG31zT6CQRkTcEgLp27dqpgYiISq7e34vzd8jRS8lSpXSwTO3blPX+iDyYywSAJpNJvvzyS9XnH/oAxImnVq1acu+998pDDz3EExERUTGas/64/LzttHq+7wcPtJDI0ECjk0REnl4HEHeeaADy2GOPqSd+NGnSRBo1aiTHjh1T/QLeddddRieRiMij6/298stuNT66Wz25vkZZo5NERN6QA4icP3T0vGzZMunUqZPVe3///bf06dNHPQVk4MCBhqWRiMgTJaRmZNf7yzTJbfUrymPtahudJCLylhzA7777Tl588cVcwR/ceuutMmbMGJk9e7YhaSMi8lQofRnzU3a9v6qRIfJOv2bi68vqNkTewCUCQLT47datW57vd+/eXbZt21aiaSIi8nTfrj0mv+04I/6s90fkdVwiALx8+bJERUXl+T7eu3LlSommiYjIk20/GSuv/Zrd39+Y7vWlZfUyRieJiLwtAMzKyhJ//7yrI/r5+UlmZmaJpomIyFNdSUqXp77N7u+vS8MoebRdLaOTRETe2AgE9VDQ2hfPArYnLS2txNNEROSJTCZNhs/dan7O75S+zdjNFpEXcokAcNCgQdechy2AiYiKbvryg7Ji3wUJ8veVjwdcL6VD+JxfIm/kEgHgrFmzjE4CEZHH++fARXn3r/1q/LU+jaVhlQijk0RE3lwHkIiIiteZuBR55vstomki/VtFS79W0UYniYgMxACQiMjDpWVmyf9mb5bLSenSsHKEvNK7kdFJIiKDMQAkIvJwr/26W7Ycj5WIYH/5+MGWEhzgZ3SSiMhgDACJiDzYjxtPyLdrjwsa+v7ffS2kRrkwo5NERC6AASARkYfaeSpOXlq4U40/d9t10ql+RaOTREQuggEgEZGHdvb8xDebJD3TJLfVryhP3xpjdJKIyIUwACQi8jBZJk21+NU7e363f3Px9WVnz0R0FQNAIiIPM2XJPll94KKEBPjJjAfZ2TMR5cYAkIjIg/y87bTMWHlIjb91b1NpUJmdPRNRbgwAiYg8xK7TcTJq3jY1/mTHOtKrWRWjk0RELooBIBGRB7iUmCZDvt4kqRkm6XhdBXmhaz2jk0RELowBIBGRm8vIMsmwOdmNPmqWC5X372shfmz0QUT5YABIROTmJv22R9YcviRhgX4yc2ArKR3KRh9ElD8GgEREbmz2umPy5X9H1Ti6e6kbFW50kojIDTAAJCJyU/8duigTFu1S4yO7XCddG1UyOklE5CYYABIRuaGjF5PkqW83S6ZJk97Nq8jQTnzSBxE5jgEgEZGbiUvJkEe/2qD+NouOlLfuaSo+Pmz0QUSOYwBIRORGMrNM8vR3W+TQhSSpXDpYZj50vQQH+BmdLCJyMwwAiYjchKZp8sovu2XV/gsSHOCrWvxWjAg2OllE5IYYABIRuYkv/j0q36w9Jijtnda/uTSuWtroJBGRm2IASETkBv7cdVZe/223Gn+xewPp1riy0UkiIjfGAJCIyMVtPxkrz36/VTRNZECb6vJY+1pGJ4mI3BwDQCIiF3bySrI8+tVGScnIUs/4faVXI7b4JaIi8+oAcPLkyXLDDTdIeHi4VKxYUfr06SP79u0zOllEREpccoY88uUGuZCQJvUrhcuHD7QQfz+vPm0TkZN49Zlk5cqVMnToUFm7dq0sXbpUMjIypEuXLpKUlGR00ojIy6VmZMnj32yU/ecSJSoiSL54+AYJD+YzfonIOfzFiy1evNjq9ZdffqlyAjdt2iQdOnQwLF1E5N2yTJo8P3errD9yWcKD/OWrR1pLlcgQo5NFRB7EqwNAW3Fxcepv2bJl7b6flpamBl18fHyJpY2IvKevv9d+3S2/7zgrgX6+8snA66V+pQijk0VEHsari4AtmUwmee655+Tmm2+Wxo0b51lnsHTp0uYhOjq6xNNJRJ7tk1WH5cv/jqrxd/o1k5vqlDc6SUTkgXw03G6SPPXUU/LHH3/IP//8I9WqVXM4BxBBIHIOIyJ4h05ERTNv00kZ+eM2NT7uzobyaDt290JUHOLj41VGjjdfv1kELCLDhg2TX3/9VVatWpVn8AdBQUFqICJytiW7zsron7ar8cfb12LwR0TFyqsDQGR+Pv3007JgwQJZsWKF1KrFEy4Rlbx/D16Up+dsUY0/+l5fTV7s0cDoJBGRh/PqABBdwMyZM0cWLVqk+gI8e/asmo5s4ZAQtrgjouK39USsPP71RknPMkm3RpVk8t1N2NEzERU7r64DmNdJdtasWfLwww9f8/OsQ0BERbH/XIL0+2SNxCZnSLuY8vL5w60kyN/P6GQRebx4Xr+9OwfQi2NfIjLYkYtJ8uBn61Tw1zw6Uj556HoGf0RUYtgNDBFRCTt+KVkemLlWziekSb2ocPly8A0SFuTV9+NEVMJ4xiEiKkEnryTL/TPXypm4VImpWEpmP95GIkMDjU4WkbFQIpdyRSThTM5w9urfet1FYjobnUKPwwCQiKiEnIlLkQdmrpNTsSlSu3yYzHmsjZQvxa6lyMMDu9RYkYRzVwO6xLPWAZ7+Nyvd/jJCyzEALAYMAImISsD5+FQV/B2/nCw1yoXKnMdvlIoRwUYni8g5gV2iRYBnHnKmZ6Y6vtyQsiIRVUTCK+UMlUVqti/ONfFaDACJiEoo5w8NP6qVCVHBX6XSDP7IBZlMIimXLXLqztn8zRkKGtgFR2YHc+bArpJIqUoiEZWvTi8VJeLPHPGSwgCQiKgYnbicLA98tlZOXE5Rwd93j98oVSPZzyiVsKxMkaTzV4M3lWNnJ6hLPC9iyihgYGcR0Om5duFR1oFdAI95V8MAkIiomBy9mKRa+56OSzUX+zL4I6dKT8oJ3s5b5NTpAZ5FsJd0EeW2ji8X9e5UQJcTyCGIswr0ohjYuTkGgERExeDg+UQZ8NlaORefJnUqhKngL4p1/sgRpqzsgE3PkUNgZ86xs5x2XiQ90fHl+viJlKp4NZhT45VyB3ZhFUX82TLd0zEAJCJysp2n4uThWevlYmK66ufv28faSIVw1m3yaqrRRFxO8IYcufNXx/W/eoCXfFFEMzm+7IDQ3EEd/trm1iFXz5edjVM2BoBERE609vAlefyrjZKQlimNqkTIN4+2kbJhzE3xWGmJFsGcHtxdyAns9L8507PSCrBgH5GwCjmBXU4AZw7uMF3PtasoEhRejCtInooBIBGRkyzZdVae/m6LpGeapE2tsjJzUCuJCA4wOllUmHp1ekCnB3fmoM5y/IJIRlLBlh1UOieAQ1FrBZtcu6irwR1y6/x4iabiw6OLiMgJfthwXMbO3yEmTaRLwyh5//4WEhzA4jaXKX5NS8gJ3GyDuvM20wsR1KEIVs+tU8Fcxex6dHp9O3065gkMLa61JCoQBoBEREWgaZpMX35Qpv65X73u3ypaJt3VWPz9+Kj1Ym8ogUeHWQZx+Y0XpM868A/Jzo0zB3KWQV1OYKcHfUGlimstiYoNA0AiokJCUe9LC3bIj5tOqtdPdqwjo7vVEx8fH6OT5p7Sk7MbQCAXLimPQX+voA0lICDMOqhTAZzlX4sAL7CUCPcjeTAGgEREhRCXkiFPfbtJ/jt0SXx9RF7p1UgealvT6GS5lqwMkeRL2V2aqABO/3vB+jWCOYwXpEsTXUiZ7MBNBXF6cIe/FWymo/g1rDjWksgtMQAkIirE0z0Gf7lB9fUXFugnHz7QUjrVryheUeyafNkiaEMQdynv1yiiLSi/IOugDYPqlDgnhy6sfM6QM+7HRjZEhcEAkIioADYcvaxy/tDHX6WIYPn84VbSqEppcfscOj0XLq/XCOpUQFeAp0mAj292AKcHcnpQp4ZyV3PpVFBXIbtLExa9EhU7BoBERA429pi97rhM/HmXZJo0aVA5Qr54uJVULh3iWt2X6EGbOZDDuB7I5YzrAR46Ji4MVeyKgE7PjSufPa5y6XLG9Vy6kEh2PkzkghgAEhFdQ1pmlkxYtEu+33BCvb6jaWWZcm9TCQ0sxlNoVqZIyuWrwZw5oLOcZhnYXRLJTCnEF/mIhJa9GrSZc+n0QK6cRUBXQSSkLPunI/IA/BUTEeXjXHyqPPntJtlyPFaVTI7qWl+e7Fi7YC19VZclsbkDOnMwZzv9kkhqbOES7BeYHbCpQM4ieMNrNS0nsNPHkZvHHDqvztnGP5NmshpXry3HNU1MYjHuwPv4Z3e8gMuuWbqm1C5d2+hN5XEYABIR5WH1gQsy/Ietqr5fRLC/6tz5lpiy2Tlx5mDusvV4rteXCld3TocATQVvehBX1iKQK2fxXtnsaQ50X2J9Uc4SU2aG1UVXhQE2F2tnX/ShqMvJFbBcK0ixmdfeew4FLIUIjKz+OrLOltu9CMHTtb4H87q6/zX/nzzV7Cmjk+FxGAASeaiC3tlf62Ka7wVSTCq+KcidPjhykSrIhdlqmZZBjL3PmEyiZaWJKSNFTJnJomWkiJaZql5nZaTKhdg4SUhKkpYR6RJSJlPC/E2y9O90WfJXBtZWXTbx1+Tjc3U8Z9tjmprHX8RU2k9MpStkz+PrLyYUn+Kvb4Aa13z8xOTnJyYfP9F8/bJf+/qK5uObvRyr/XJZtIxLYorbJ6bYvC/4loFcXtueqDB88M/HR3x9fEX98/G1eq2PYz41Led9/PPz8bs677XmsVh2VGiU0avtkRgAuqm87uRyXTzzuTt05MKa6641r5yBvL7D9u7V5jugKEFJXmlwpcDnWmm41sU6VxryWn83vLN3+bNjrsa9gTlDUWVmD1kWL12c5YXZ8iKuv8ag5sm5qKu/vhbv5wQOeS3H3ufwWn23r1+u4MIy6EA1RtsAwzK4sPyMs77fctmQ13IsX+f3/ZbbCd+X13bMa90sAyl7y8Y2wndbpdFmefYCNatl56wDeQYGgC7osx2fqcE26FCBBu/gqRjldcK3d1Gwveu3DQDMF8+cz+V1h48Lk/luXzTxMWWJnylL/fVVfzPFx5QhfviblSm+WRnqtW9Wuvhk5vzFe0g/Bk0zj6u/mmYex+Cn4a/FPLgo+gerIU0LlHN4GIUpUDJ9gqV6VDmpVK6s+PiHim9gmPgE4m8p9ZgwP78AuzkfeV3sc62rHhQV4YKvL8Ne4OLIcvR9nisosBNsEZFnYQDogjKyMiSpoA8jvwZ7FwyrC7nNBQzs3QXaXtzzvOu0uJu1nd+2+EC/M80rLfrda35FDPnehdvc6VpemO1dgHMFLnldzG3WP1cRh9i/4FsGRHkFDo7chec1T17rml+Og/45pzCZRNLicho9XMluzIC/+mvzNIvX+lDQ57Vawj4JjsyuC4d6c2itqurP6a/LWLwue/VvYJjEp2Wq7l3mbz6lFtWoSoTq3LlWeT45gog8EwNAF/RAgwfkztp35grILAMV2+Aq1wXfZh7ewVOBg7j0hOwgTQ/WbP/aBnLm99G3XBGKn338rAM2NUTmDuhUsGcR6AVFiPhm/1YK4t+DF+WFH7fJ6bhU9Ui3JzrWkec615Ugf7aMJSLPxQDQBZUOKq0GoiL3I5cWfzU4Q2BmFcjF2Qnucqbhb1GrGgSEZgdplkGcOWjLCeBsgzsVyJXMkyASUjNkypJ98vWaY+p1jXKh8k7fZtKqZtli/24iIqMxACRy9Vw4c+AWZzHYvLb3fnpi0dOA57JaBm+5/uoBnB7Q6eOlRfyDxFUt3nlWFfmejc8uch7Qprq82KOBhAXxlEhE3oFnO6LifM5qanx2MIacOBWY6X/j7E8zv5cz3RktedFowRy0lc49jr9WgZ3FPAEu9JgzJzgTl6Ke6PHn7nPmXL/X+zSW9nUrGJ00IqISxQCQKL/iUxWk2f7NCc70IM3ue/EiGcnOSYt/cHb9NnNwZjnYTLMK7DBEiPgFiLfDo9y+/PeofPD3QUlMyxR/Xx8Z0qG2PHNbXQkOYF0/IvI+DADJs2iaSEZKTvCWYBGc5Yzjrx6g2Q3unBy8mXPgSmcHcSo40//q0zDogVvOuB7w4W9AsPPS4mXQjRJy+974fY8cu5S9T1tWj5Q37m4i9StFGJ08IiLDMAAk16nvhq5v0hJzgjWLgM1qiM97XA/0NL13XSfwD8kOyvRAzeqvTRBnG+DhNQY8+YFK3J4z8fLar7vlv0OX1OsK4UEyqms9uadlNfFFc18iIi/GKxMVMbctOScAQ+AWn93wQA/i0IDB/B5e58yTa1rOfM58cgW6zkFr0sDwq8FZkD4ebhG0IVCzN0/OdH9nPPWBStLhC4ny3l8H5Nftp9UhGujvK4+1qyX/6xQjpdjIg4hI4dnQm+BqmJl2NehKT7oasKlgLdH+a8xnFazp0xOL3lWIvT7ggkpZBG7hFkOEzd9SFkFbTsCmvx8YViJdiZDrOHklWd5fdkB+2nxKskzZNxN3NKksY7rXl+iyoUYnj4jIpTAAdPVWpHoApgdrKhjTp1kEaObAzWK6vdfOLB61zG1DPTeV44a/luMWAZzta6tpOYEcWp0ycKMCOHIxST5ddUh+2nRK0rOyb0hurV9Rnr/9Omlclf1pEhHZwwDQFa18W2TVFJGs9OL7joCw7FwylduG8ZwcNT2Asw3orN63DN5KZXf4y6CNStj2k7EyY+Uh+WPnWZW5DW1rl5ORXa+T62uwM2ciovwwAHRFyFGzDP78ArODLRWo6X/1oZR1EGcO6izn04M5i8/7susLcj+ZWSb5a895+XrNUXPjDj3H78mOdaR1LQZ+RESOYADoilo9ItLsvuxADTl1bIhAXu5CQpr8sOG4zFl3XD2zF/x8faRXsyryRMfa7NKFiKiAGAC6IjzsXpiTQd4tI8skK/ddkPlbTsrS3eckIyu7nLdsWKD0vyFaPb6tWhk27iAiKgwGgETkUh037z4TL/M3n5JFW0/JxcSrVSGaR0fKwLY1pEeTynx6BxFRETEAJCLDg76dp+Ll951n5I8dZ+RozhM7oHypQOndvKrqvLlhFRbzEhE5CwNAIipxqRlZsu7IZVmx77wq3j15JcX8XpC/r9zWoKIK+jpcV0EC/HwNTSsRkSdiAEhEJZLLd+hComq5i3p9+JuScbVPypAAP9WSt3uTStKpXkUJ4xM7iIiKFc+yROR0JlN2wLf+6GVZc+iSrD18WS4mplnNExURpIK9W+pVlI7XVZCQQNbrIyIqKQwAiajIziekyq5T8bLl+BXZciJWth6PlYS0TKt5ULR7fY0ycnNMeZXbV79SuPiwA3EiIkMwACSiAtXdO3whSQ6cT5C9ZxNk9+l42XU6Plfunl6s2yy6tNxYu5x6Qkez6Ei23iUichEMAIko19M2zsSlytFLSapF7rGL+JskB88nyvHLyWLKeeyaJWTk1S4fpoK8FtXLSMvqkVIvKlz82YCDiMgleX0AOH36dJkyZYqcPXtWmjVrJh988IG0bt3a6GQRFVtwdykpXc7Fp8rZuFQ5l5Am5+JS5VRsipy6kqL+no1PlSx7UV6OiGB/uS4qXOpGhUujKhGqexYU54YGev3phIjIbXj1GfuHH36Q559/XmbMmCFt2rSRadOmSdeuXWXfvn1SsWJFo5NHlG+r2uT0LElIzZT41AyJT8lQf2OTM+RKMv6myxU1ZMilxDS5lJiuAj9M0/KO7cwC/XylerlQqVkuVGqUC5Ma5UIlpkIpiYkqJRVKBbHuHhGRm/PRcCXxUgj6brjhBvnwww/Va5PJJNHR0fL000/LmDFjrvn5+Ph4KV26tMTFxUlEBDup9caWrpkmTeWWZZhMkpWV/RePLENOm/prMkl6JsZNkqb+aup1WmaWpGVkT8N4aoZJ1a/TB3SRggAvJT37b3JGliSlZaohMedvPpl0+cIzdBHEoRVuVESwGqpEhkjVMiFSNTJYqkaGSoXwIDUfEZEniuf123tzANPT02XTpk0yduxY8zRfX1/p3LmzrFmzxu5n0tLS1GB5ABWHxTvPyOKdZ3NNL8z13pHw3t4stvcFdhdjZ6KWM1H/uJbHPOb3c81z9fPZ72Uv0Xp+y3myl6WPq6DIYrop5/OYjs+p15r1a4wjmMvSX5tEBXXqtSl7Gl5jPgR0WTmBnyvcOvn7+khESIAqlg0PDpDIUAyBEhkSIGVyxsuVCpTypYLUgOfoYmBwR0Tk3bw2ALx48aJkZWVJVFSU1XS83rt3r93PTJ48WV555ZViTxtaVy7cerrYv4ecDyWjeHJFgK+PagAR4OejilMD/H2z//r5SqC/r+oSJSjAT00LCvCVYH8/CQnM/ouWsugTD61oQ/E3EH/9JSzIT8KDAtTfUkH+UirYX83D4lgiIioorw0ACwO5hagzaJkDiCJjZ2tft4K6wDuisBd/2085shiffL7f8vO55tPnsfN9PjlTr762eS/7P/U9+ufxvhrwT583531f/LX8jE/2NN+cv/rykAN2dV4f9RoNVvEZPzW/j/j6Zs+nXiOgU/Pgr2/2ez5Xgzx9OnPWiIjIHXhtAFi+fHnx8/OTc+fOWU3H60qVKtn9TFBQkBqKGzrLxUBERERUHLy2k67AwEC5/vrrZdmyZeZpaASC123btjU0bURERETFyWtzAAHFuYMGDZJWrVqpvv/QDUxSUpIMHjzY6KQRERERFRuvDgD79+8vFy5ckPHjx6uOoJs3by6LFy/O1TCEiIiIyJN4dT+ARcV+hIiIiNxPPK/f3lsHkIiIiMhbMQAkIiIi8jIMAImIiIi8DANAIiIiIi/DAJCIiIjIyzAAJCIiIvIyDACJiIiIvAwDQCIiIiIvwwCQiIiIyMt49aPgikp/iAp6FCciIiL3EJ9z3fbmh6ExACyChIQE9Tc6OtropBAREVEhruOlS5cWb8RnAReByWSS06dPS3h4uPj4+Dj97gSB5YkTJzzyOYVcP/fn6evI9XN/nr6OXL/C0zRNBX9VqlQRX1/vrA3HHMAiwEFTrVq1Yv0OHPSe+MPWcf3cn6evI9fP/Xn6OnL9Cqe0l+b86bwz7CUiIiLyYgwAiYiIiLwMA0AXFRQUJBMmTFB/PRHXz/15+jpy/dyfp68j14+Kgo1AiIiIiLwMcwCJiIiIvAwDQCIiIiIvwwCQiIiIyMswACQiIiLyMgwADTJp0iS56aabJDQ0VCIjIx36DNrrjB8/XipXriwhISHSuXNnOXDggNU8ly9flgEDBqhOM7HcRx99VBITE6WkFTQdR48eVU9TsTf8+OOP5vnsvf/999+LEQqzrW+55ZZc6X/yySet5jl+/Ljccccd6tioWLGivPDCC5KZmSmuvn6Y/+mnn5Z69eqp47N69eryzDPPSFxcnNV8Ru7D6dOnS82aNSU4OFjatGkj69evz3d+HHv169dX8zdp0kR+//33Av8mS1JB1m/mzJnSvn17KVOmjBqQdtv5H3744Vz7qlu3buIO6/fll1/mSjs+58r7r6DraO98ggHnD1fch6tWrZKePXuqp28gHQsXLrzmZ1asWCEtW7ZULYFjYmLUfi3q75pyoBUwlbzx48dr7777rvb8889rpUuXdugzb775ppp34cKF2rZt27RevXpptWrV0lJSUszzdOvWTWvWrJm2du1abfXq1VpMTIx2//33ayWtoOnIzMzUzpw5YzW88sorWqlSpbSEhATzfDhkZ82aZTWf5fqXpMJs644dO2qPP/64Vfrj4uKstkPjxo21zp07a1u2bNF+//13rXz58trYsWM1V1+/HTt2aHfffbf2888/awcPHtSWLVum1a1bV7vnnnus5jNqH37//fdaYGCg9sUXX2i7du1S+yEyMlI7d+6c3fn//fdfzc/PT3v77be13bt3ay+//LIWEBCg1rMgv8mSUtD1e+CBB7Tp06er42zPnj3aww8/rNbl5MmT5nkGDRqkjgPLfXX58mXNCAVdPxxjERERVmk/e/as1TyutP8Ks46XLl2yWr+dO3eqYxbr7or7EOezl156SZs/f746DyxYsCDf+Q8fPqyFhoaq6yR+gx988IFav8WLFxd6m9FVDAANhh+qIwGgyWTSKlWqpE2ZMsU8LTY2VgsKCtK+++479Ro/EPyoNmzYYJ7njz/+0Hx8fLRTp05pJcVZ6WjevLn2yCOPWE1z5KThyuuIAPDZZ5/N9wTp6+trdaH6+OOP1YUsLS1Nc7d9OHfuXHVyzsjIMHwftm7dWhs6dKj5dVZWllalShVt8uTJdufv16+fdscdd1hNa9OmjfbEE084/Jt05fWzhZuP8PBw7auvvrIKHnr37q25goKu37XOra62/5yxD9977z21DxMTE11yH1py5DwwatQorVGjRlbT+vfvr3Xt2tVp28ybsQjYTRw5ckTOnj2riigsn2OI7O41a9ao1/iLorpWrVqZ58H8eGbxunXrSiytzkjHpk2bZOvWrarY0dbQoUOlfPny0rp1a/niiy9UMU5JK8o6zp49W6W/cePGMnbsWElOTrZaLooao6KizNO6du2qHoq+a9cuKSnOOpZQ/IsiZH9/f0P3YXp6ujqmLH8/WBe81n8/tjDdcn59X+jzO/KbLCmFWT9bOA4zMjKkbNmyuYrgUBUBRftPPfWUXLp0SUpaYdcPVRZq1Kgh0dHR0rt3b6vfkCvtP2ftw88//1zuu+8+CQsLc7l9WBjX+g06Y5t5M+uzMrksnKjAMjDQX+vv4S9+5JZw4cUJXZ+npNJa1HTgRNagQQNVT9LSq6++KrfeequqH/fnn3/K//73P3WSR12zklTYdXzggQfUBQl1YLZv3y6jR4+Wffv2yfz5883LtbeP9ffcaR9evHhRXnvtNRkyZIjh+xBpycrKsrtt9+7da/czee0Ly9+bPi2veUpKYdbPFo5FHJeWF1PUFbv77rulVq1acujQIXnxxRele/fu6uLq5+cnrrx+CHZwc9G0aVN1IzJ16lR1PkEQWK1aNZfaf87Yh6j3tnPnTnXutOQq+7Aw8voN4oY4JSVFrly5UuTj3psxAHSiMWPGyFtvvZXvPHv27FGVyj15/YoKP+w5c+bIuHHjcr1nOa1FixaSlJQkU6ZMcVrwUNzraBkMIacPlc9vu+02dWKuU6eOeMo+xAkaFdEbNmwoEydOLNF9SAX35ptvqoY4yCmybCiB3CTL4xXBFI5TzIfj1pW1bdtWDToEf7ip/OSTT9SNiadB4Id9hFx1S+68D6l4MQB0ohEjRqgWV/mpXbt2oZZdqVIl9ffcuXMqaNDhdfPmzc3znD9/3upzaD2K1pn650ti/Yqajnnz5qniqIEDB15zXhTX4GSelpbmlOdFltQ6WqYfDh48qE7K+KxtCzbsY3CXfZiQkKByHcLDw2XBggUSEBBQovvQHhQ3I7dD35Y6vM5rfTA9v/kd+U2WlMKsnw45YwgA//rrLxUcXOvYwHfheC3J4KEo66fDcYgbDqTd1fZfUdcRN1EI4JG7fi1G7cPCyOs3iGolaLWN7VXU48KrGV0J0dsVtBHI1KlTzdPQetReI5CNGzea51myZIlhjUAKmw40lLBtOZqX119/XStTpoxW0py1rf/55x+1HLRAtGwEYtmC7ZNPPlGNQFJTUzVXXz8ckzfeeKPah0lJSS61D1FZfNiwYVaVxatWrZpvI5A777zTalrbtm1zNQLJ7zdZkgq6fvDWW2+pY2vNmjUOfceJEyfUMbBo0SLNHdbPtpFLvXr1tOHDh7vk/ivKOuI6gnRfvHjRpfdhYRqBoFcES+iJwLYRSFGOC2/GANAgx44dU90v6F2dYByDZZcnOFmhubxllwVo3o4f7vbt21XLLnvdwLRo0UJbt26dCi7QDYdR3cDklw50NYH1w/uWDhw4oE5OaHFqC92LzJw5U3XDgfk++ugj1UUAutQxQkHXEV2jvPrqqyqoOnLkiNqPtWvX1jp06JCrG5guXbpoW7duVd0dVKhQwbBuYAqyfrh4opVskyZN1LpadjuB9TJ6H6K7CFwkv/zySxXgDhkyRP2e9BbXDz30kDZmzBirbmD8/f1VgIBuUiZMmGC3G5hr/SZLSkHXD2lHC+158+ZZ7Sv9HIS/I0eOVMEhjte//vpLa9mypToOSvJmpLDrh3MrbloOHTqkbdq0Sbvvvvu04OBg1VWIK+6/wqyjrl27dqp1rC1X24dIj36tQwCIrtAwjushYN2wjrbdwLzwwgvqN4hui+x1A5PfNqO8MQA0CJrm4wdgOyxfvjxXf2k63LGOGzdOi4qKUgf8bbfdpu3bty9Xv1C4SCOoxJ394MGDrYLKknKtdOBkZLu+gEAnOjpa3cXZQlCIrmGwzLCwMNVH3YwZM+zO64rrePz4cRXslS1bVu0/9KuHE5tlP4Bw9OhRrXv37lpISIjqA3DEiBFW3ai46vrhr71jGgPmdYV9iH7EqlevrgIf5Bygj0Mdci3xu7Ttxua6665T86M7it9++83qfUd+kyWpIOtXo0YNu/sKgS4kJyerGxHcgCDwxfzoY83IC2tB1u+5554zz4v906NHD23z5s0uvf8Kc4zu3btX7bc///wz17JcbR/mdY7Q1wl/sY62n8E5A9sDN8yW10RHthnlzQf/M7oYmoiIiIhKDvsBJCIiIvIyDACJiIiIvAwDQCIiIiIvwwCQiIiIyMswACQiIiLyMgwAiYiIiLwMA0AiIiIiL8MAkIjIic6ePSu33367hIWFSWRkZLF8x0MPPSRvvPGGuILFixerZ+eaTCajk0JEBcAAkMiLPPzww+Lj45Nr6Natm7irW265RZ577jlxFe+9956cOXNGtm7dKvv373f68rdt2ya///67PPPMM1KcmjRpIk8++aTd97755hsJCgqSixcvqmMnICBAZs+eXazpISLnYgBI5GVwwUaAYjl89913xfqd6enpYiQ88CgzM7NEvuvQoUNy/fXXS926daVixYpO314ffPCB9O3bV0qVKiXF6dFHH5Xvv/9eUlJScr03a9Ys6dWrl5QvX958Y/H+++8Xa3qIyLkYABJ5GeTcVKpUyWooU6aM+X3kCH722Wdy1113SWhoqApkfv75Z6tl7Ny5U7p3766CkKioKFUkidwgy1y5YcOGqZw5BAldu3ZV07EcLC84OFg6deokX331lfq+2NhYSUpKkoiICJk3b57Vdy1cuFAVpyYkJORaFwQeK1eulP/7v/8z52YePXpUVqxYocb/+OMPFYxhnf/55x8VnPXu3VulGWm/4YYb5K+//rJaZs2aNVXx6iOPPCLh4eFSvXp1+fTTT62CM6xb5cqV1XrUqFFDJk+ebP7sTz/9JF9//bX6fqQPsH6PPfaYVKhQQa3jrbfeqnLydBMnTlTFqNjutWrVUsu1JysrS22fnj175krz66+/LgMHDlTrhTRhW1+4cEGtL6Y1bdpUNm7caPU5bJP27dtLSEiIREdHq1xF7Ad48MEHVfCH9bF05MgRtX0RIOqQHiwb25eI3EQ+zwkmIg+Dh6337t0733lwWqhWrZo2Z84c7cCBA9ozzzyjlSpVSrt06ZJ6/8qVK+rh8mPHjtX27Nmjbd68Wbv99tu1Tp06mZeBB7rjMy+88IJ6WD2Gw4cPqwfSjxw5Ur3+7rvvtKpVq6rvwzIBD6rv0aOHVXp69eqlDRw40G5aY2NjtbZt26rPnTlzRg2ZmZnmh843bdpU+/PPP7WDBw+q9G/dulWbMWOGtmPHDm3//v3ayy+/rAUHB2vHjh0zL7NGjRpa2bJltenTp6v1nzx5subr66vSDFOmTNGio6O1VatWaUePHtVWr16tthWcP39e69atm9avXz+VFqQPOnfurPXs2VPbsGGD+t4RI0Zo5cqVM2/TCRMmaGFhYeqz2J7btm2zu754D+t19uxZq+l6mrFuWP5TTz2lRUREqOXNnTtX27dvn9anTx+tQYMGmslkUp/BNsF3vvfee+oz//77r9aiRQvt4YcfNi+3b9++VvsVxo8fr9Y/KyvLanpUVJQ2a9asPI4qInI1DACJvCwA9PPzUxd+y2HSpEnmeRBgIDDSJSYmqml//PGHev3aa69pXbp0sVruiRMn1DwINPQAEMGEpdGjR2uNGze2mvbSSy9ZBYDr1q1T6Tt9+rR6fe7cOc3f319bsWJFnuuE73r22WetpukB4MKFC6+5TRo1aqR98MEHVsHUgw8+aH6NgKlixYraxx9/rF4//fTT2q233moOpGwhwMZ21iFARDCWmppqNV+dOnW0Tz75xBwAIjhGAJmfBQsWqO1j+922aUbwifUfN26cedqaNWvUNLwHjz76qDZkyBCr5SCtCHZTUlLU68WLF2s+Pj4qeNe3Bb7L8vjQYX9PnDgx3/QTketgETCRl0HRKxooWA62lf1RXKhD8SuKLc+fP69eo+hy+fLlqlhRH+rXr6/esywCRNGrpX379qkiV0utW7fO9bpRo0aqaBi+/fZbVZzZoUOHQq1rq1atrF4nJibKyJEjpUGDBqqFLtK+Z88eOX78eJ7rj6JcFJPr649iXWyzevXqqSLTP//8M980YHvhe8uVK2e1zVCUarm9sJ4oIs4PimRRnI002bJMM4q49YYcttMs9+OXX35plSYU1aM1L9IGaM1crVo1VecPli1bprbV4MGDc30/ipGTk5PzTT8RuQ5/oxNARCULAV1MTEy+86BVpyUEHHo3HwhmUOfrrbfeyvU51Iuz/J7CQF256dOny5gxY1TggWDDXsDjCNs0IPhbunSpTJ06VW0DBC333ntvrkYX+a1/y5YtVYCE+oWoP9ivXz/p3LlzrrqLOmwvbBfUm7Nl2U2MI9sL9SkRZCG9gYGBeaZZ3172plnuxyeeeMJua2LUewRfX18V8CIgRz1F7A/cQNSuXTvXZy5fvnzNAJaIXAcDQCIqEARAaBiAhgf+/o6fQpBjhu5LLG3YsCHXfGh8MGrUKNWqdPfu3TJo0KB8l4tACI0jHPHvv/+qgAYNXPQgCI1GCgo5ov3791cDAki0rEYAVLZsWbvbC30DYlthmxUFGooAtos+XlhIF5ZzrZsBBOBoYDJ//nxZsGCBaqhiKzU1VeVmtmjRokhpIqKSwyJgIi+TlpamAhLLwbIF77UMHTpUBTv333+/CuBw4V+yZIkKFPILxJDbtHfvXhk9erTqH2/u3LmqCBIsc/jQIvnuu++WF154Qbp06aKKIPODoGrdunUqkMN65NchMVogI5BBES6KQB944IECd2D87rvvqm5zsC5Yjx9//FEVEefV6TNyB9u2bSt9+vRRxcVI53///ScvvfRSrla514IcNgRuaL1bVNgPSAdaNGN7HDhwQBYtWqReW0KrZLRaHjJkiCp+xr6xtXbtWvUe1pOI3AMDQCIvgyc3oEjScmjXrp3Dn69SpYrKSUOwhwAN9czQ3QsCIBQZ5gWBBIpJEYChvtrHH3+sgiBA8GAJXYygmBNdsVwLinX9/PykYcOGKkCyrc9nG7whwLzppptUMTbqvCGgKgh0DfP222+r+oWo04iADjmbea07glu8j3qMCJKvu+46ue++++TYsWPmenkFLSJ3RqfL2AfoQgdBLLqCQe7d+PHj1f61hf1x5coVFTDb66IGAfGAAQNUt0FE5B580BLE6EQQkXeaNGmSzJgxQ06cOJHrSRPDhw+X06dP56rr5u3QEATF6T/88INL5Lgh1xXpQW4mgnwicg+sA0hEJeajjz5SuWZoEYtcxClTplgVOaKBA55M8uabb6oiYwZ/uaHhCjqaLkixfXFCDij2K4M/IvfCHEAiKjHI1UPOFeoQoqUpniAyduxYc2MStDRFriCKS1Efrbgfd0ZE5K0YABIRERF5GTYCISIiIvIyDACJiIiIvAwDQCIiIiIvwwCQiIiIyMswACQiIiLyMgwAiYiIiLwMA0AiIiIiL8MAkIiIiMjLMAAkIiIiEu/y/7hzk/GXCQdIAAAAAElFTkSuQmCC",
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