From df82c3b106b285f78df9c54be4957d7efa1a0389 Mon Sep 17 00:00:00 2001 From: Greg Brunkhorst Date: Fri, 4 Oct 2024 17:46:08 -0700 Subject: [PATCH] added two example notebooks: energy-resource-build and sudoku --- examples/energy-resource-build.ipynb | 574 +++++ examples/sudoku.ipynb | 2996 ++++++++++++++++++++++++++ 2 files changed, 3570 insertions(+) create mode 100644 examples/energy-resource-build.ipynb create mode 100644 examples/sudoku.ipynb diff --git a/examples/energy-resource-build.ipynb b/examples/energy-resource-build.ipynb new file mode 100644 index 00000000..d544601b --- /dev/null +++ b/examples/energy-resource-build.ipynb @@ -0,0 +1,574 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Power Portfolio Build-Out Example with Linopy\n", + "\n", + "This notebook provides a simplified example of building a power generation portfolio using `linopy`. The goal is to demonstrate the process of setting up and solving a linear optimization problem where different power generation resources are selected and built to meet future energy demands. " + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "import linopy as lp\n", + "import xarray as xr\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problem Statement\n", + "This notebook presents an example portfolio expansion model used to meet an energy need. This was originally developed for a hydropower utility. Hydropower utilities can be energy limited (i.e., monthly energy balance) rather than capacity limited (i.e., hourly generating capacity), therefore, this algorithm addresses a monthly energy need rather than an hourly capacity need. A different algorithm would be needed to meet capacity needs and would include battery storage, etc. \n", + "\n", + "This notebook addresses the question: what is the least-cost way to meet an energy need in the future? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Dummy Data\n", + "\n", + "A lot of this code is here just to generate dummy data. I would not spend too much time on this code. The `Linopy` model is down below. \n", + "### Energy Need\n", + "\n", + "This analysis assumes that an energy need has been identified through modeling and simulation. For this example, we will assume the energy need is as follows: " + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_dummy_energy_needs(start_year=2024, years=10):\n", + " # Generate a monthly date range\n", + " dates = pd.date_range(start=f'{start_year}-01-01', periods=years*12, freq='M')\n", + " \n", + " # Create a sinusoidal pattern for energy needs with a downward trend over time\n", + " months = np.arange(len(dates))\n", + " \n", + " # Base seasonal pattern using sine functions (with phase shift for winter, spring, etc.)\n", + " seasonal_pattern = np.sin(2 * np.pi * (months % 12) / 12) # Basic seasonal pattern (positive in spring/fall, negative in winter/summer)\n", + " \n", + " # Adjust the pattern so that winter months trend more negative over time\n", + " winter_weight = 0.5 * (np.cos(2 * np.pi * (months % 12) / 12 - np.pi) + 1) # Emphasize winter more\n", + " \n", + " # Add a downward trend over time\n", + " trend = -0.01 * months # A small negative trend each month\n", + " \n", + " # Combine seasonal pattern, winter weight, and trend\n", + " energy_balance = seasonal_pattern * (1 - winter_weight) + trend\n", + " energy_balance *= 10\n", + " # Convert to pandas DataFrame\n", + " df = pd.DataFrame(data={'monthly_energy_balance': energy_balance}, index=dates)\n", + " \n", + " return df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate the dummy dataset\n", + "dummy_energy_needs_df = generate_dummy_energy_needs()\n", + "dummy_energy_needs_df.plot()\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The energy need is based on the negative monthly energy balance. This will be an input to the model, so it needs to be put in an xarray. " + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "dummy_energy_needs_df.index.name = 'datetime'\n", + "dummy_energy_needs_df['energy_needs_aMW'] = (-dummy_energy_needs_df.monthly_energy_balance).clip(lower=0)\n", + "energy_needs_xr = xr.DataArray(dummy_energy_needs_df.energy_needs_aMW)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Resource Generation Profiles\n", + "\n", + "We are looking for the least cost resource that can meet the energy need. We will make up some dummy generation profiles for wind and solar. " + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_resource_profiles(start_year=2024, years=10, noise_level=0.05):\n", + " # Generate a monthly date range\n", + " dates = pd.date_range(start=f'{start_year}-01-01', periods=years*12, freq='M')\n", + "\n", + " # Base profiles for wind and solar resources\n", + " wind_winter_peak = np.array([0.6, 0.55, 0.5, 0.45, 0.4, 0.35, 0.3, 0.25, 0.3, 0.4, 0.5, 0.55]) # Wind peaking in winter\n", + " wind_summer_peak = np.array([0.3, 0.25, 0.3, 0.4, 0.5, 0.55, 0.6, 0.55, 0.5, 0.45, 0.4, 0.35]) # Wind peaking in summer\n", + " solar_base = np.array([0.1, 0.2, 0.4, 0.6, 0.7, 0.9, 0.95, 0.85, 0.6, 0.4, 0.2, 0.1]) # Solar peaking in summer\n", + "\n", + " # Create dummy profiles with slight variations\n", + " profiles = {}\n", + " for i, location in enumerate(['A', 'B', 'C']):\n", + " # Set a unique random seed for each location's wind and solar profiles\n", + " wind_seed = hash(f'wind_{location}') % (2**32)\n", + " solar_seed = hash(f'solar_{location}') % (2**32)\n", + " \n", + " np.random.seed(wind_seed)\n", + " if i == 0:\n", + " wind_profile = wind_winter_peak + noise_level * np.random.randn(12)\n", + " elif i == 1:\n", + " wind_profile = wind_summer_peak + noise_level * np.random.randn(12)\n", + " else:\n", + " # Create a mix or a balanced profile as the third option\n", + " wind_profile = 0.5 * (wind_winter_peak + wind_summer_peak) + noise_level * np.random.randn(12)\n", + " \n", + " np.random.seed(solar_seed)\n", + " solar_profile = solar_base + noise_level * np.random.randn(12)\n", + " \n", + " # Set a unique random seed for the final noise adder\n", + " np.random.seed(wind_seed + 1)\n", + " profiles[f'wind_{location}'] = np.tile(wind_profile, years) + noise_level * np.random.randn(len(dates))\n", + "\n", + " np.random.seed(solar_seed + 1)\n", + " profiles[f'solar_{location}'] = np.tile(solar_profile, years) + noise_level * np.random.randn(len(dates))\n", + "\n", + " # Create a DataFrame with the profiles\n", + " df_profiles = pd.DataFrame(data=profiles, index=dates)\n", + " \n", + " return df_profiles\n" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generate the dummy dataset\n", + "df_profiles = generate_resource_profiles()\n", + "\n", + "# Plot the profiles for the first 12 months\n", + "df_profiles.groupby(df_profiles.index.month).mean().plot()\n", + "plt.xlabel('month')\n", + "plt.ylabel('normalized generation')\n", + "plt.grid()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before, convert to xarray for linopy. " + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "df_profiles.index.name = 'datetime'\n", + "df_profiles.columns.name = 'resource'\n", + "resource_profiles_xr = xr.DataArray(df_profiles)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Resource Cost Profiles" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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qCoem8OkRODwHImZA/EH4vQE0+AwajQVtA6kTCoIgCILwLzKZLN/DCaTk5+fH0qVLiY2NRalU4uLigp+fH+Hh4aSmpr6xVxdAqVS+9LVMJiO3ECfWL1myhAcPHqCnp5d3LDc3l9OnTzN58mTk8vcfhCCGMWgSpS74joehR8ExAHKz4NBM9YYUF3aIoQ2CIAiCIBTYi3G7v/32W15h+6LYDQ8Px8/P752uW6VKFZRKJVFRUXnHUlNT87182f379wkODmbdunXExMTkPaKjo0lNTWXv3r3vlOt/af7bkdKorD302KDeeW3XBEi7Aet7qgvgllOhbGWpEwqCIAiCUEyUKVOGmjVrsnr1aubNmwdAo0aN6NKlC1lZWW/t2X0bQ0NDBg4cyLhx4zAzM8PCwoKvv/46372xK1euxMzMjC5duiCTyV76XmBgIEuWLKFFixbvlO3fRM+uppLJwKUVDIsCn7Gg0IbLe2F+PfUKDllPpE4oCIIgCEIx4evrS05OTl4vbtmyZalWrRqWlpYFGmP7v6ZPn46Pjw9t2rTB39+fhg0b4unpma/nLl26lA4dOrxS6AJ06tSJbdu2ce/evXfO9oIodjWdtj40/VY9nrdyY8h5BgemovVnQ8qnRUudThAEQRCEYmDWrFmoVCpcXFzyjsXExJCcnJz3db9+/Xj48GHe15MmTSImJual64wcOZKEhIS8rw0NDVm5ciWZmZmkpKQwbtw4wsPDmTVr1n9mOn36NPPnz3/t97p06cKzZ88wNzfPV/veRhS7xYW5A/TeAl1WgHEFZA+vU+/abyjW94AHhbc8hyAIgiAIQkkiit3iRCaDau1g2DFy6n9OLgrkV/aqJ7CJoQ2CIAiCIGiQiIgIDA0N3/goKmKCWnGkY0huk+84+NCaxk93IY8/AAemQuxaaPELOAeqC2NBEARBEASJeHl5vTIMQgqi2C3GMnStyemwCfnlENjzFTy8Aet6gEMz9aoNZlWkjigIgiAIQimlp6eHg4OD1DHEMIZiTyaD6u3V2w43HA1yJVwJhQX1IOx7eJ4pdUJBEARBEATJiGK3pNA2AP+J6g0pqjSFnOfqndjm1YFzW8SGFIIgCIIglEqi2C1pzB2g11/QdTWYVIT0m7CxH6xoC3cuSp1OEARBEAShSIlitySSyaBqaxh+DHwngJYuxB+EP7xh91fwNE3qhIIgCIIgCEVCFLslmVIPGn+p3oXNpTXkZsPR+TDXC6JXQ26u1AkFQRAEQRA+KFHslgZl7KDbavXwBjMHyLwDwUNhaQDcOiV1OkEQBEEQNEBQUBCmpqbvfR0/Pz9Gjhz53tcpLKLYLU0c/NXbDjf7HrQN4eZxWNQEtn0GGXelTicIgiAIgoS6du1KXFxckd7Tz88PmUyW9yhfvjwfffQR169fL7R7SFrsHjx4kDZt2mBtbY1MJmPr1q1vPT85OZkePXrg5OSEXC7XqHcNxYaWNniPgOEnwLULoIJTK2CuJxz9A3KypE4oCIIgCIIE9PT0sLCwKPL7Dh48mOTkZJKSkggODiYxMZFevXoV2vUlLXYzMzNxc3Nj/vz5+Tr/2bNnlCtXjm+++QY3N7cPnK6EM7aCTotgwB6wrAnP0mD3F/CHD1wLlzqdIAiCIGg+lUq9nn1RPwqwnOiOHTswNTUlJycHgJiYGGQyGRMmTMg7Z9CgQfTq1euVYQyTJk3C3d2dlStXYmdnh4mJCd26dePRo0d552RmZtKnTx8MDQ2xsrJixowZBf5j1NfXx9LSEisrK+rVq8fw4cM5darwhllKuoNay5YtadmyZb7Pt7OzY/bs2QAsXbo0X8959uwZz549y/s6PT0dgJ3XdtLVqCtyWfEcyZGVlfXSf9+ZlSf0D0UWswpF+BRkdy/AinbkOrcmx/97MK34/mHfotDaITHRDs1SUtoBJactoh2apTi2QyOzZj2Gn6yL/r5fJanX188HHx8fHj16RHR0NF5eXhw4cABzc3PCw8Pzzjlw4ABffPHFa59/9epVtm7dyo4dO0hNTaVLly788ssvTJkyBYBx48Zx4MABgoODsbCw4KuvvuLUqVO4u7u/U9MePHjAhg0bqFu37js9/3VK/HbBP//8M5MnT37l+C8nf2HdpXW00muFjZaNBMkKR2hoaCFdqRxKhx9xSdmC3d0w5Jd2oIrbw5XygVy2aE2OQqeQ7vN6hdcOaYl2aJaS0g4oOW0R7dAsxakdjx8/ljpCsWRiYoK7uzvh4eF4eXkRHh7OqFGjmDx5MhkZGaSlpXHlyhV8fX2JjIx85fm5ubkEBQVhZGQEQO/evQkLC2PKlClkZGSwZMkSVq1aRdOmTQFYvnw5NjYFq6sWLFjA4sWLUalUPH78GCcnJ/bs2fP+jf9HiS92v/zyS0aPHp33dXp6Ora2tugp9EjMSWRhxkLaVm7LcLfhmOmZSZi0YLKysggNDaVZs2YolcpCvHIXcu5cgNCvUCRE4JwSjFPmcXKaTkJVrYN6Dd9C9OHaUbREOzRLSWkHlJy2iHZoluLYjhefzGoUpb66l1WK+xaAr68v4eHhjBkzhoiICH7++Wc2bNjAoUOHePDgAdbW1jg6Or622LWzs8srdAGsrKy4c+cOoO71ff78+Uu9sGXLlsXZ2blA+Xr27MnXX38NwO3bt/npp58ICAjg5MmTL937XZX4YldHRwcdnVd7Jde0WMPyhOVsv7ad4GvB/J34N0NqDqFn1Z4oFcXjHz6AUqks/B9UFWpC3+1wYRvs+QZZ2g20tn4Mp4Kg5S9gVfjjpT9IOyQg2qFZSko7oOS0RbRDsxSndmhkTpks38MJpOTn58fSpUuJjY1FqVTi4uKCn58f4eHhpKam4uvr+8bn/u+fu0wmI7eQ1+k3MTHBwcEBAAcHB5YsWYKVlRXr169n0KBB73394jlgtRCY65vzk89PrGy5kupm1cnMymTGyRl02NaBgzcPSh1PejIZVGun3oWt8degpQc3DsNCX9j2uViqTBAEQRCKiRfjdn/77be8wvZFsRseHo6fn987XbdKlSoolUqioqLyjqWmpr738mUKhQKAJ0+evNd1Xii1xe4L7hburGm1hh+8f8BM14zr6dcZFjaMT//+lGtp16SOJz2lHviOh89OQI3OqJcqWw5za8HheZD9XOqEgiAIgiC8RZkyZahZsyarV6/OK2wbNWrEqVOniIuLe2vP7tsYGhoycOBAxo0bx759+zh79iz9+vVDLi9Yefn48WNSUlJISUkhNjaWTz/9FF1dXQICAt4p1/+StNjNyMggJiaGmJgYAOLj44mJieHGjRuAerxtnz59XnrOi/MzMjK4e/cuMTExnD9//r1yyGVy2ju0Z0eHHfSv0R8tuRaHbh2iU3Anph6bSvpzDRwnVNRMbKDzEui/65+lytJh79fwe32I2yt1OkEQBEEQ3sLX15ecnJy8Yrds2bJUq1YNS0vLAo+x/bfp06fj4+NDmzZt8Pf3p2HDhnh6ehboGosWLcLKygorKysaN27MvXv3CAkJea9c/yZTqQqwWFshCw8Pp3Hjxq8c79u3L0FBQfTr14+EhISXlseQvWaCVKVKlUhISMjXPdPT0zExMeHevXuYmb1+Qtr19Ov8euJXwhPV9y2jU4bhHsPp5NgJhVyRr/t8aFlZWYSEhBAYGFj045hycyBmNYR9D5n/DGdwaAbNf4JyTgW6lKTtKESiHZqlpLQDSk5bRDs0S3Fsx4vf32lpaRgbGxf5/Z8+fUp8fDz29vbo6uoW+f2FV+X3NZF0gpqfnx9vq7WDgoJeOVYUtXkl40rMbTKXw7cOM+34NK6mXeWHoz+w7tI6vqj9BXWtCm/tt2JJroBafdRjeg9OV++8diUUru2H2oPB7wvQKyN1SkEQBEEQBDFm920aVGjAxrYbmVBnAsbaxlxOvcygvYMYsW8EN9JvSB1PeromEPAjDIsCp5aQmw1Rv8OcWnBsEeRkS51QEARBEASJREREYGho+MZHUSnxS4+9L6VcSc+qPWll34oFsQvYcGkD+xL3EXErgl5Ve/FxzY8x1C66F0wjmVWBHuvg6j7Y/RXcvQAhY+H4EmjxE1RpInVCQRAEQRCKmJeXV968LCmJnt18MtU15au6X7GpzSYaWDcgKzeLZeeW0WpLKzbFbSInN0fqiNKr0gSGHILAX0GvrLroXdkB1nSFe5elTicIgiAIQhHS09PDwcHhjY+iIordAnIo48Af/n8wv+l87IztePD0AZOPTKbrjq4cSz4mdTzpKbSgzmD4/BTU/RTkWhC3GxbUU/f6PkmVOqEgCIIgCKWIKHbfgUwmo5FNIza33cz42uMx0jbiUuolBu4dKMbzvqBXRr3b2qdHwLG5ejzv0fliPK8gCIIgCEVKFLvvQalQ0rtab3Z22El3l+4oZAr2Je6jXXA7ph+fLtbnBfVSZD03QK/NUK4qPHmgHs/7ewO4HCp1OkEQBEEQSjhR7BaCMrpl+KruV/zV9i+8K3iTnZvNivMraLW5FWsvriU7V/Ri4tBUPZ631QzQN4N7l2B1ZxRru2D05KbU6QRBEARBKKFEsVuIqphW4Q//P1jQdAGVTSrz8NlDfor6iU7bOhFxM0LqeNJTaEHtQfDZKWjwGciVyK/tw+/iN8h3jYPMe1InFARBEAShhBHF7gfgY+PDX23/4uu6X2OqY8q1tGsMDRvKkNAhXE4VqxKgZ5q3Pm+ucyvk5KI4tQzmeEDkbMh+JnVCQRAEQSh1goKCMDU1fe/r+Pn5MXLkyPe+TmERxe4HoiXXoptLN3Z23Enfan3RkmsRmRRJ5+2d+f7I99x/cl/qiNIzq0JO5+UccvgKlWVNeJYOod/BvNpwbitIt5O1IAiCIJQ6Xbt2JS4ursjve+XKFfr374+NjQ06OjrY29vTvXt3Tpw4USjXF8XuB2asbczY2mPZ1m4bzSo1I1eVy8a4jbTa0orFZxbzLEf0Yt43ciF7wN/Q/ncwsoKH12FjX1jaAm6elDqeIAiCIJQKenp6WFhYFOk9T5w4gaenJ3FxcSxcuJDz58+zZcsWXFxcGDNmTKHcQxS7RcTW2JaZfjMJahFEdbPqZGZlMvvUbNpuaUvItRBUpb0XUyYH9x7w2UnwnQBKfUg8CoubwKaB8FAs5yYIgiBoFpVKxeOsx0X+KEjNsGPHDkxNTcnJUW9+FRMTg0wmY8KECXnnDBo0iF69er0yjGHSpEm4u7uzcuVK7OzsMDExoVu3bjx69CjvnMzMTPr06YOhoSFWVlbMmDGjQH9+/fr1w9HRkYiICFq1akWVKlVwd3dn4sSJBAcH5/tabyO2Cy5inuU9WdNqDTuv7WT2qdkkZSbxRcQXrLqwinG1x+Fh4SF1RGlpG0DjL8GzL+z7EWLWwNlNcGE71B8KDUeBronUKQVBEASBJ9lPqLumbpHfN6pHFPpK/Xyd6+Pjw6NHj4iOjsbLy4sDBw5gbm5OeHh43jkHDhzgiy++eO3zr169ytatW9mxYwepqal06dKFX375hSlTpgAwbtw4Dhw4QHBwMBYWFnz11VecOnUKd3f3/8wWExPDuXPnWLNmDXL5q/2vhTF+GETPriTkMjltqrRhe4ftDHcfjp6WHmfunaHPrj6MDh9NYnqi1BGlZ2wN7RfAJwfAzgdynsGh39SbUhxfLDalEARBEIR8MDExwd3dPa+4DQ8PZ9SoUURHR5ORkcGtW7e4cuUKvr6+r31+bm4uQUFB1KhRAx8fH3r37k1YWBgAGRkZLFmyhF9//ZWmTZvi6urK8uXLyc7O3+/oy5fVk/ZdXFzev6FvIXp2JaSnpccnbp/QyakT86LnseXKFkKvh7I/cT/dnLsxxG0IJjqlvBfTyg36bldvObz3W7h/GXaOgaiF0OwHcGoOMpnUKQVBEIRSSE9Lj6geUZLctyB8fX0JDw9nzJgxRERE8PPPP7NhwwYOHTrEgwcPsLa2xtHRkcjIyFeea2dnh5GRUd7XVlZW3LlzB1D3+j5//py6df+/d7ts2bI4OzvnK1dRDeEUPbsawFzPnEkNJrGxzca8TSlWXVhFy80tWX5uOc9znksdUVoyGTi3hKFHIPBX0CsL9+JgbVdY3gaSYqROKAiCIJRCMpkMfaV+kT9kBezk8fPz49ChQ8TGxqJUKnFxccHPz4/w8HAOHDjwxl5dAKVS+Uqbc3Nz3+nP6385OTkBcPHixUK53puIYleDOJVx4g//P1jovxDHMo48ev6IX0/8StutbdmdsFtMYlMooc5gGBED3iNBoQMJEfCnL2z+BNLETmyCIAiC8L9ejNv97bff8grbF8VueHg4fn5+73TdKlWqoFQqiYr6/97t1NTUfC9f5u7uTrVq1ZgxY8ZrC+iHDx++U67/JYpdDdSgQgM2tt7I9w2+p5xeOW5l3GLcgXH02tWL6DvRUseTnq4JNJsMn50A14/Ux06vg7me8PckeJomaTxBEARB0CRlypShZs2arF69Oq+wbdSoEadOnSIuLu6tPbtvY2hoyMCBAxk3bhz79u3j7Nmz9OvX77WTzV5HJpOxbNky4uLi8PHxISQkhGvXrnH69GmmTJlCu3bt3inX/xLFroZSyBV0cOzAjg47GOo+FD0tPU7fPU2fXX0YtX8UN9LFUlyYVoROi2HwPqjkDdlP/5nE5gHHFkFOltQJBUEQBEEj+Pr6kpOTk1fsli1blmrVqmFpaZnvMbavM336dHx8fGjTpg3+/v40bNgQT0/PfD+/Tp06nDhxAgcHBwYPHkzVqlVp27Yt586dY9asWe+c699kqlL22Xh6ejomJibcu3cPMzMzqePk293Hd1kQu4DNlzeTq8pFS6aFl9KLKa2nYGFUtAtAF6asrCxCQkIIDAx8ZVxQgahUcGmXege2+/9syVy2CvhPgqptPvgktkJrh8REOzRPSWmLaIdmKY7tePH7Oy0tDWNj4yK//9OnT4mPj8fe3h5dXd0iv7/wqvy+JqJnt5gop1+OifUn8lebv2hk04hsVTZHnx+l7ba2LDq9iCfZT6SOKC2ZDFwC/38Sm745PLgKG3qrd2JLPCZ1QkEQBEEQJCCK3WLGoYwD85vO548mf2CtsCYzO5M50XNovaU1Wy5vISc3R+qI0noxie3zaGg0DrT01DuxLWkG63vD/atSJxQEQRCEUiEiIgJDQ8M3PoqKWGe3mKpjWYchhkNQVFcwP3Y+SZlJfHf4O1ZeWMloz9F4W3sXeGmSEkXXGJp8A14DYP8U9U5sF7bBpRD1sUbjwbCc1CkFQRAEocTy8vIiJiZG6hii2C3O5DI5Le1a0rxyc9ZdXMfC0wu5nHqZT//+lLpWdRntOZpqZtWkjiktY2toNx/qDVWv1HB5Lxz7E2LWQsMR6uPaBlKnFARBEIQSR09PDwcHB6ljiGEMJYGOQoe+1fuyq+Mu+lXvh1KuJCo5iq47ujL+4HhuPhLrz1K+OvTcCH22gZU7PH8E+35Ubz98crnYflgQBEEQSihR7JYgJjomjPEaw/YO22lduTUAu+J30WZrG6Yem0rq01SJE2qAyr4weD90WgKmlSAjBbZ/Dr83gIsh6lUdBEEQBEEoMUSxWwJVMKzAzz4/s6H1Bupb1c/bfjhwcyCLzywWKzfI5eDaGYYfh+Y/g14ZuHcJ1nWHZS3Fyg2CIAiCUIKIYrcEq2pWlT8D/mSh/0KcyziTkZXB7FOzab25NX/F/UV2bin/6F5LB+oPhc9joOEo0NKFG0f+WbmhF9y7LHVCQRAEQRDekyh2S4EGFRqwoc0Gfmr4E9YG1tx5codJRybRaVsn9t3YRynbV+RVeqbqzSc+OwUevUEmhwvbYX5d2D4SHqVIHFAQBEEQhHclit1SQi6T06ZKG7Z32M44r3GY6JhwLe0aI/aPoM+uPpy6fUrqiNIzqQDt5sGnh8GpJahy4OQymO0OYd/D0zSpEwqCIAhCkUlISEAmk2nE8mHvQxS7pYy2Qps+1fuwq+MuBrkOQlehS8zdGPru7stnYZ9xOVV8dI9FVeixDvrvBtu6kP0EImaoi94j8yHrqdQJBUEQBKHEWbt2LQqFgmHDhhXqdUWxW0oZaRsxotYIdnbcSWenzihkCsJvhtNpWye+OfQNyRnJUkeUXqX6MGAPdFsD5k7w5AHs+Qrmeak3qSjtu9UJgiAIwn94/vx5vs9dsmQJ48ePZ+3atTx9WngdS6LYLeUs9C2YWH8iW9ptwb+iPypUBF8NpvWW1kw/Pp2HTx9KHVFaMhm4tIJPj0DbuWBkDWmJsPVT+KMhXNollisTBEEopVQqFbmPHxf5o6BzbTZt2oSrqyt6enqYmZnh7+9PZmYmubm5fP/999jY2KCjo4O7uzu7d+9+43VycnIYOHAg9vb26Onp4ezszOzZs186p1+/frRv354pU6ZgbW2Ns7NzvjLGx8dz+PBhJkyYgJOTE5s3by5QG99G0h3UDh48yPTp0zl58iTJycls2bKF9u3bv/U54eHhjB49mnPnzmFra8s333xDv379iiRvSWZvYs9vjX/j9N3TzDo1i+Mpx1lxfgWbL2+mf43+9KraC32lvtQxpaPQglp9wPUjiFoIh2bCnfOwthvY1kPW+BupEwqCIAhFTPXkCZdqeRb5fZ1PnUSmn7/fycnJyXTv3p1p06bRoUMHHj16REREBCqVitmzZzNjxgwWLlyIh4cHS5cupW3btpw7dw5HR8dXrpWbm4uNjQ0bN27EzMyMw4cP8/HHH2NlZUWXLl3yzgsLC8PY2JjQ0NB8t2nZsmW0atUKExMTevXqxZIlS+jRo0e+n/82kvbsZmZm4ubmxvz58/N1fnx8PK1ataJx48bExMQwcuRIBg0axJ49ez5w0tKjZrmaLAlYwu/+v+ctVzY3ei6BmwNZd3EdWblZUkeUllIPGo6EEbHgPVK9XFniUbRWtKbu1X8KYEEQBEHQEMnJyWRnZ9OxY0fs7OxwdXVl6NChGBoa8uuvv/LFF1/QrVs3nJ2dmTp1Ku7u7syaNeu111IqlUyePBkvLy/s7e3p2bMn/fv3Z8OGDS+dZ2BgwOLFi6levTrVq1f/z4y5ubkEBQXRq1cvALp168ahQ4eIj49/7/aDxD27LVu2pGXLlvk+/48//sDe3p4ZM2YAULVqVQ4dOsRvv/1G8+bNX/ucZ8+e8ezZs7yv09PTAUi4+whjY+P3SC+trKysl/5b2Opa1GV1i9Xsvb6XBacXcDPjJlOiprD83HKG1hxKQKUA5LL3f6/0odvxwWgZgt83UGsg8kPTkcesxjI9BtUiX3JdPyKn0RfqHdqKmWL7evyPktIOKDltEe3QLMWxHZqYVaanh/Opk5LcN7/c3Nxo2rQprq6uNG/enICAADp37oxCoSApKQlvb++Xzvf29iY2NvaN15s/fz5Lly7lxo0bPHnyhOfPn+Pu7v7SOa6urmhra+c7Y2hoKJmZmQQGBgJgbm5Os2bNWLp0KT/88EO+r/Mmkha7BXXkyBH8/f1fOta8eXNGjhz5xuf8/PPPTJ48+ZXjnf48RkNbPZrb5GKc/9dD4xTkI4J3NUgxiJN6J9n/dD83M27y1eGvmHN0Ds30muGk5YRMJnvvexRFOz4cfwxcqlM1+S8qPDyG7MwGOPsXCeZNiCvflmdKE6kDFljxfj3+X0lpB5Sctoh2aJbi1I7Hjx9LHeEVMpks38MJpKJQKAgNDeXw4cPs3buXuXPn8vXXX7/Ta79u3TrGjh3LjBkzqF+/PkZGRkyfPp2oqKiXzjMwMCjQdZcsWcKDBw/Q+1cRn5uby+nTp5k8eTJy+ft1rhWrYjclJYXy5cu/dKx8+fKkp6fz5MmTl/6QXvjyyy8ZPXp03tfp6enY2tqSq5Jx6Lackw+06Fe/EoMa2mGsp/zgbSgsWVlZhIaG0qxZM5TKD5+7LW2ZkD2B1RdXs+LCClKyUliZuRKPch585v4Z7uXc3+m6Rd2OD0XdDivMapRD+9BU5PHhVL4biv3Dw+TW+YTcesNBV/M/SShZr0fxbweUnLaIdmiW4tiOF5/MCgUnk8nw9vbG29ub7777jkqVKhEWFoa1tTWRkZH4+vrmnRsZGUmdOnVee53IyEgaNGjA0KFD845dvXr1vbLdv3+f4OBg1q1b99KQh5ycHBo2bMjevXtp0aLFe92jWBW770JHRwcdHZ1Xji/q7cEfR28TfeMhvx+MZ83xmwzxrUK/BnboaSskSPpulEplkf2gUiqVfOrxKd2rdmfp2aWsubiG6LvRDAgdQCObRnzu8TnOZfM36/J11y4uP3DfRlGxNvK+wXDtAPw9CVnSKRSRM1GcXAo+o6HOx+pxvxqupLweJaUdUHLaItqhWYpTO4pLTk0TFRVFWFgYAQEBWFhYEBUVxd27d6latSrjxo1j4sSJVKlSBXd3d5YtW0ZMTAyrV69+7bUcHR1ZsWIFe/bswd7enpUrV3L8+HHs7e3fOd/KlSsxMzOjS5cur3xSHBgYyJIlS9672C1WS49ZWlpy+/btl47dvn0bY2Pj1/bqvo1XpTJs/rQBf/b2xKm8IWlPspi6+yK+0/ez8uh1nmfnFmb0EsVU15TRXqPZ2eH/1+g9ePMgnbd3ZvzB8dxIvyF1ROlV9oXB+6DrKjB3hqcPIfQ7mOMBJ5ZCjuaNPRMEQRBKHmNjYw4ePEhgYCBOTk588803zJgxg5YtW/L5558zevRoxowZg6urK7t372bbtm2vXYkB4JNPPqFjx4507dqVunXrcv/+/Zd6ed/F0qVL6dChw2uHRHbq1Ilt27Zx796997pHserZrV+/PiEhIS8dCw0NpX79+u90PZlMRkB1S5pWLU9wzC1mhsZxM/UJ3249y6KD1xjVzJG2bhVQyN9/TGpJVN6gPBPrT6Rf9X7Mj57ProRd7Irfxd6EvXRw7MCQmkMob1D+vy9UUslkULUNOAfC6fWw/2dIuwE7RkHkHGj8NdToBO85FkkQBEEQ3qRq1apvXDtXLpczceJEJk6c+Nrv29nZvbSmr46ODsuWLWPZsmUvnffzzz/n/X9QUFCB8p0+ffqN3+vSpctLS5q9K0l/y2ZkZBATE5O353J8fDwxMTHcuKHuGfzyyy/p06dP3vlDhgzh2rVrjB8/nosXL7JgwQI2bNjAqFGj3iuHQi6jYy0b9o3x4/t21TE31OHGg8eMWh9L4OwI9p5LKfACzqVJJeNKTPOdxsY2G/Gp4EOOKodNcZsI3BzIr8d/JfVpqtQRpSVXgHsP+OwEtJwGBuUgNR42D1JvTHExRGxMIQiCIAgfiKTF7okTJ/Dw8MDDwwOA0aNH4+HhwXfffQeo14Z7UfgC2Nvbs3PnTkJDQ3Fzc2PGjBksXrz4jcuOFZS2lpw+9e04ON6P8S2cMdbV4tLtR3y88iTtFxwm8sr7daOXdC5lXVjgv4CgFkHUsqjF89znLD+/nJabW7IgZgEZzzOkjigtLR2o+wl8HgNNvgEdE7hzDtZ1h8X+6nG+giAIglCCREREYGho+MZHUZB0GIOfn99be0xf1xXu5+dHdHT0B0wF+tpaDPVzoGfdSiw6eI0lh+KJTXxIz8VR1K9sxtjmznhWKvNBMxRnnuU9CWoRxKFbh5gbPZcLDy7we+zvrL24lgE1BtDNpRt6Wpo/SeuD0TGERuPAayAcngNH/4BbJ2BFW7D3habfgY2X1CkFQRAE4b15eXnlfYIvFTFY8C1M9JSMbe7MwfGN6e9th7ZCzpFr9+n0+2EGBh3nXFKa1BE1lkwmw8fGh3Wt1/Gr76/Ym9jz8NlDZp6cSavNrdS7sZX2SVr6ZcF/EoyIUa/SIFdC/AFY3BTWdoeUs1InFARBEIT3oqenh4ODwxsfRUEUu/lQzkiHiW2qs3+cH129bFHIZYRdvEOrOYcYtuYUV+6U8o/n30Iuk9Pcrjmb227mB+8fsDaw5u6Tu0yJmkKbrW3Ydm0bOaocqWNKy8gSAqfDZyfBvRfI5HApRD2ed9NAuP9+axgKgiAIQmkmit0CqGCqx9TONQkd1Yg2btYA7DydTMBvBxi7MZbEB5q3u4um0JJr0d6hPds7bOerul9hrmfOrYxbTDo6ibmP5rL3+l5yVaV8ubcylaD9fBgaBdU7ACo4uwnm1YbgYfBQLOkmCIIgCAUlit13ULmcIXO7e7BrhA/+VcuTq4JNJ2/SZEY432w9w+30p1JH1FjaCm26u3QnpGMIoz1HY6Jtwr3ce0yInECX7V0ITwwXK1+Uc4KPguCTCHBqAaociF4Fc2rBzrGQnix1QkEQBEEoNkSx+x6qWhmzuK8XW4Y2wMfRnKwcFauO3qDRtP38uOM89zKeSR1RY+lp6dG/Rn+2t9tOE90mGGgZcCn1Ep/t+4yeIT05nHRYFL1WNaHHehgYqp64lpsFxxfBHHfY+w1kitVBBEEQBOG/iGK3EHhULMPKgXVZ93E9vCqV4Vl2LosPxdNo2n6m77nIw8fPpY6osQyVhjTRbcL2dtsZWGMgelp6nLl3hk9CP6H/nv6cSDkhdUTp2daBvtugzzawrQvZT+HwXJjtBvt+hCcPpU4oCIIgCBpLFLuFqF5lMzYOqU9Q/9rUtDHh8fMc5u+/is/U/cz6O45HT0v56gNvYapjykjPkYR0DKFX1V4o5UpO3j5J/z39+Xjvx5y+++YdVkqNyr4wYA/03ARWbvA8Aw5Oh1k14cB0ePZI6oSCIAhCCZKQkIBMJpN86bD3JYrdQiaTyfBztiB4mDd/9vbExdKIR8+ymfX3ZXym7WdB+BUyn2VLHVNjmeuZ80WdLwjpGEIXpy5oybQ4knyEniE9GRY2jPP3z0sdUVoyGTg2g48PQNfVYFENnqXB/h/VRe+hWfA8U+qUgiAIgpBvfn5+yGSyvEf58uX56KOPuH79eqFcXxS7H4hMJiOguiUhn/swr4cHlcsZ8PBxFtN2X6LRtP0sjrjG06xSvuTWW1gaWPJt/W/Z3mE77aq0Qy6Tc/DmQbru6MrI/SOJS42TOqK0ZDKo2hqGREKnJWDmCE8ewN8T1cMbjiyArCdSpxQEQRBKuefP8zeUc/DgwSQnJ5OUlERwcDCJiYn06tWrUDKIYvcDk8tltK5pTegoX2Z2caOSmT73M5/z484L+EzbT1BkvCh638LGyIYfG/5IcLtgWlVuhQwZYTfC6LytM+MOjOPaw2tSR5SWXA6unWHoUWj/O5hWgsy7sOdLmOMBxxZBtpgoKQiC8CGoVCqynuUU+aOgE7g3bdqEq6srenp6mJmZ4e/vT2ZmJrm5uXz//ffY2Nigo6ODu7s7u3fvfuN1cnJyGDhwIPb29ujp6eHs7Mzs2bNfOqdfv360b9+eKVOmYG1tjbOzc74y6uvrY2lpiZWVFfXq1WP48OGcOnWqQO18E0m3Cy5NFHIZHWvZ0MbNms2nbjIn7Aq3Hj5h0vbzLDx4jWGNHejiZYu2lnj/8Tp2Jnb84vMLg10HsyBmAXuv72V3wm72JOwhsHIgn7p9SiXjSlLHlI5CC9x7gOtHELNGPZY3LRFCxqqHNjQaC+49QUtb6qSCIAglRvbzXP4ccaDI7/vxbF+UOop8nZucnEz37t2ZNm0aHTp04NGjR0RERKBSqZg9ezYzZsxg4cKFeHh4sHTpUtq2bcu5c+dwdHR85Vq5ubnY2NiwceNGzMzMOHz4MB9//DFWVlZ06dIl77ywsDCMjY0JDQ19p/Y9ePCADRs2ULdu3Xd6/v8SlVURUyrkdK1dkf1j/ZjSoQZWJrokpz3lm61nafxrOOuO3SArp5RvrvAWVUyrMMNvBpvabKKJbRNUqNh5bSfttrbjm0PfkPgoUeqI0lIowbOveje2VjPAyBrSb8KOkTDPE06thNK+TbMgCEIpkpycTHZ2Nh07dsTOzg5XV1eGDh2KoaEhv/76K1988QXdunXD2dmZqVOn4u7uzqxZs157LaVSyeTJk/Hy8sLe3p6ePXvSv39/NmzY8NJ5BgYGLF68mOrVq1O9evV85VywYAGGhoYYGBhgZmbGpUuXWLp06fs2HxA9u5LR1pLTs24lOtWyYf3xRObvV/f0Tth8hvnhV/isiSMdPSqgpRDvR17Huawzs5vM5tz9c8yPnk/ErQiCrwarC1+HdgyuOZgKhhWkjikdLR2oPUi9/fDJIDg0U70D27bhEDEDfMeDaxd1j7AgCILwTrS05Xw821eS++aXm5sbTZs2xdXVlebNmxMQEEDnzp1RKBQkJSXh7e390vne3t7Exsa+8Xrz589n6dKl3LhxgydPnvD8+XPc3d1fOsfV1RVt7YJ9ktizZ0++/vprAG7fvs1PP/1EQEAAJ0+exMjIqEDX+l+ikpKYrlJB3wZ2HBzfmG9bV8PcUIfEB08Yv+k0TWce4K+TN8kWPb1vVN2sOgv8F7A6cDXe1t5kq7L56/JftN7SmslHJpOcUcp3G1PqQr0h8HkMBEwBg3KQGg9bP4X5dSB2HeSKMeOCIAjvQiaTodRRFPlDJpPlO6NCoSA0NJRdu3ZRrVo15s6di7OzM/Hx8QVu77p16xg7diwDBw5k7969xMTE0L9//1cmoRkYGBT42iYmJjg4OODg4IC3tzdLlizh8uXLrF+/vsDX+l+i2NUQukoFAxvaEzG+MV8HVsXMQJvr9x8zZmMszX47yJbom+TklvIdxd6iZrma/NHsD1a2XEk9q3pk52azKW4TgVsC+eHID6RkpkgdUVra+tBgOIyIhWbfg74ZPLgKWz6B+XXh9AZR9AqCIJRQMpkMb29vJk+eTHR0NNra2oSFhWFtbU1kZORL50ZGRlKtWrXXXicyMpIGDRowdOhQPDw8cHBw4OrVqx8ks0KhHpP85Mn7rywkil0No6etYHCjyhwc35gJLV0oo68k/l4mo9bH0uy3AwTH3BJF71u4W7izKGARQS2CqGtZl+zcbDbEbSBwcyA/Hv1RFL3aBuA9AkachqYTQa8M3L8MmwfDgnrIzv0FKvFJgiAIQkkRFRXFTz/9xIkTJ7hx4wabN2/m7t27VK1alXHjxjF16lTWr1/PpUuXmDBhAjExMYwYMeK113J0dOTEiRPs2bOHuLg4vv32W44fP14oOR8/fkxKSgopKSnExsby6aefoqurS0BAwHtfWwzY01AGOloM8a1C73qVWH4kgT8PXuPa3UxGrIthTthlhvlVRiZq3jfyLO/J4uaLOZ5ynAUxCzhx+wTrL61n8+XNdHLsxEDXgVgaWEodUzo6huAzWj2u99hCODwP7sWhtfUTmuhaI7PPAtdOIM/fbF9BEARBMxkbG3Pw4EFmzZpFeno6lSpVYsaMGbRs2ZLmzZuTlpbGmDFjuHPnDtWqVWPbtm2vXYkB4JNPPiE6OpquXbsik8no3r07Q4cOZdeuXe+dc9GiRSxatAiAMmXKULNmTUJCQvK9dNnbyFQFXaytmEtPT8fExIR79+5hZmYmdZx8y3iWzfLD6qI37Yl6Nn15PRVftK5JOw9bFPL8j9/RJFlZWYSEhBAYGIhSqfxg9zmecpz5MfM5efskAEq5ks5OnRlYYyDlDcq/9/WLqh0fzNN0iFqI6shcZE/T1MfKuagnslVrX+yK3mL/evxLSWmLaIdmKY7tePH7Oy0tDWNj4yK//9OnT4mPj8fe3h5dXd0iv7/wqvy+JmIYQzFhqKPFsMYOHPqiMWMDnDDR0+L2ExmjN54hQAxv+E+1LWsT1CKIpc2X4lnek6zcLNZeXEvLzS35KeonbmfeljqitHSNwXcc2cOiuWDVEZWuCdy9CJsGwO8N4MwmMaZXEARBKJZEsVvMGOkqGd7Ekf2jfWhlm4OJnhZX/xneEPDbAbZGi6L3bWpb1mZZ82UsCVjyStErxvQCusbEWbYne1g0NP4aXhS9fw2EBfVF0SsIgiAUSEREBIaGhm98FAUxZreYMtJVEmCj4oc+jVhz/CaLD8Vz9W4mI9erx/QOb+JAWzdrsU7va8hkMupY1aG2ZW31mN7YBZy8fTJvTG9Hx44Mch1Uusf06hqrhzDU/QSiFsKReXDvkrroPTAVGo2DGmJMryAIgvB2Xl5exMTESJpBFLvFnJGuFsObONK3gR0rjlxnUcQ1rt3LZPSGWObuu8Kwxg60dxdF7+u8KHrrWNXhWPIxfo/9PW8i21+X/6KjQ0cGug7E2tBa6qjS0TX5V9H75z9Fb5x69Ya8orez2JxCEARBeC09PT0cHBwkzSAqoBLCSFf5z5jeJoxv4Zy3ZNnYjbE0mXGADccTxTbEb1HHqg7LWixjafOl1LGsk7dkWastrZh0eBI3H92UOqK0dE3AdxyMPANNvvlnybIr/6zTWxuiV0NOttQpBUEQBOEVotgtYQx1tBjqpy56J7R0oayBNjcePGb8X6dp/Gs4a4/d4Hm2KHrfpLZlbZY0X8Ky5suoa6Vep/evy3/RZksbvov8jhvpN6SOKC1dY3Vv7sgz/6zTWxYeXIPgoTDPE06tgJwsqVMKgiAIQh5R7JZQL9bpPfSFekc2c0NtbqY+4cvNZ2j8azgrj17nWbaYaPQmXpZeLA5YzIqWK2hg3YBsVTZbrmyh7da2fH3oa+LTCr7NYomiY6Rep3fkGfCfDPrmkJoA2z6DObXgxDLIfiZ1SkEQBEEQxW5Jp6+txeBGlYkY34RvWlWlnJEOtx4+4dutZ/GdFk5QZDxPs0TR+yYeFh4sbLaQVYGr8KngQ44qh21Xt9E+uD3jD47nSuoVqSNKS8cQGo6EkachYAoYWEDaDdgxEuZ4qMf5Zj2VOqUgCIJQiolit5TQ01YwyKcyEeMbM7ltdSyNdUlJf8qk7efxmbafRQev8fi5GHP5Jm7l3Fjgv4B1rdbhZ+tHriqXXfG76LitI6PDRxOXGid1RGlpG0CD4eqit8VUMLKC9FuwaxzMrqneoe15ptQpBUEQhFJIFLuljK5SQd8GdhwY78eP7WtQwVSPu4+eMSXkAg2n7mf+/is8eirGXL5JdfPqzG0yl41tNtKsUjNUqAi9Hkq3Xd1YlbGK8/fPSx1RWko9qDcEPo+BVjPA2AYybsPer2FWTTj0Gzx7JHVKQRAEIR8SEhKQyWSSLx32vkSxW0rpaCnoVa8S+8f6MbWTKxXL6vMg8znT91yi4dT9zPo7jrTHouh9E5eyLsz0m8nmtptpadcSGTIuZl+k155eDPl7CNF3oqWOKC2lLtQeBJ9HQ5vZYFoJHt+DvyfBLFc4MA2ePJQ6pSAIgqAhrly5Qv/+/bGxsUFHRwd7e3u6d+/OiRMn3vvaotgt5bS15HStXZF9Y3yZ2cWNyuUMSHuSxay/L+M9dR/Tdl/kfoaYaPQmjmUcmeY7jb9a/4W70h2FTEHkrUj67OrDgD0DiEqOQqUqxTvaaWmDZz/47CS0/x3MHOBJKuyfoi56w36AzPtSpxQEQRA+kOfPn//nOSdOnMDT05O4uDgWLlzI+fPn2bJlCy4uLowZM+a9M4hiVwBASyGnYy0bQkf5Mq+HBy6WRmQ8y2ZB+FUaTt3PjzvOcztdTDR6EztjOzobdGZLmy10cuyEllyL4ynHGbR3EL139ebgzYOlu+hVKMG9Bww7Bp2WQLmq8CwdIn5VF717v4FHt6VOKQiCUCAqlYqsp0+L/FHQ3yebNm3C1dUVPT09zMzM8Pf3JzMzk9zcXL7//vu83lR3d3d27979xuvk5OQwcOBA7O3t0dPTw9nZmdmzZ790Tr9+/Wjfvj1TpkzB2toaZ2fn//wz7NevH46OjkRERNCqVSuqVKmCu7s7EydOJDg4uEBtfR2x7ZHwEoVcRuua1gTWsCL0wm3m77/C6ZtpLD4Uz4oj1+lS24YhvlWwKaMvdVSNZGNow6QGk/ik5icsPbuUzZc3E3s3lmFhw6hatiof1/yYJhWbIJeV0veZcgW4dobqHeHSTjg4HZJj4fBc9coNtfqA9wgwtZU6qSAIwn/KfvaMOX07F/l9P1++CaWubr7OTU5Opnv37kybNo0OHTrw6NEjIiIiUKlUzJ49mxkzZrBw4UI8PDxYunQpbdu25dy5czg6Or5yrdzcXGxsbNi4cSNmZmYcPnyYjz/+GCsrK7p06ZJ3XlhYGMbGxoSGhv5nvpiYGM6dO8eaNWuQy1/93Whqapqvdr6NKHaF15LLZTSvbklAtfIcvHyPuWGXOXE9lVVHb7DuWCIdPCrwqV8VKpczlDqqRrIytOLrel/zcc2PWXF+BesvrefCgwuMCh9FFZMqDKo5iBZ2LdCSl9J/gnI5VG0DLq3hcigcnAY3j8PxRXByGbh1h4ajwKyK1EkFQRCKteTkZLKzs+nYsSOVKlUCwNXVFYBff/2VL774gm7dugEwdepU9u/fz6xZs5g/f/4r11IqlUyePDnva3t7e44cOcKGDRteKnYNDAxYvHgx2tra/5nv8uXLALi4uLx7I/+DRvymnT9/PtOnTyclJQU3Nzfmzp1LnTp1XntuVlYWP//8M8uXL+fWrVs4OzszdepUWrRoUcSpSweZTIavUzkaOZoTFf+AefuucOjKPTaevMlfp24S6GrFsMYOVLUyljqqRiqnX44xXmMYUGMAK8+vZO3FtVxNu8qXEV+yIGYBA2oMoG2Vtmgr/vsHQokkk4FTADg2g4QIdU9v/EGIXgkxq9U9wD5joHw1qZMKgiC8QktHh8+Xb5Lkvvnl5uZG06ZNcXV1pXnz5gQEBNC5c2cUCgVJSUl4e3u/dL63tzexsbFvvN78+fNZunQpN27c4MmTJzx//hx3d/eXznF1dc1XoQsUyRA/yT9LXb9+PaNHj2bixImcOnUKNzc3mjdvzp07d157/jfffMPChQuZO3cu58+fZ8iQIXTo0IHo6FI++/0Dk8lk1KtsxqpBddkytAH+VS3IVcGO08m0nB3BoOXHib6RKnVMjVVGtwyf1/qcvZ338rnH55jqmJL4KJHJRybTcnNLVp1fxZPsJ1LHlI5MBvaNoO92GBgKjs1BlQtnN8Hv9WFtD7h5UuqUgiAIL5HJZCh1dYv8IZPJ8p1RoVAQGhrKrl27qFatGnPnzsXZ2Zn4+ILvBLpu3TrGjh3LwIED2bt3LzExMfTv3/+VSWgGBgb5vqaTkxMAFy9eLHCe/JK82J05cyaDBw+mf//+VKtWjT/++AN9fX2WLl362vNXrlzJV199RWBgIJUrV+bTTz8lMDCQGTNmFOi+ubli17B35VGxDIv71ibkcx9a1bRCJoO/L9yhw4LD9Fh0lMNX7pXuyVhvYaRtxOCag9nTaQ/jvMZhoWfBncd3mHp8Ki3+asHiM4t59LyUr0NrWwd6boBPIqBae0CmHt+7uAmsaAfxESD+fgmCIOSbTCbD29ubyZMnEx0djba2NmFhYVhbWxMZGfnSuZGRkVSr9vpP0yIjI2nQoAFDhw7Fw8MDBwcHrl69+l7Z3N3dqVatGjNmzCA3N/eV7z98+PC9rg8SD2N4/vw5J0+e5Msvv8w7JpfL8ff358iRI699zrNnz9D9n0HZenp6HDp06I3nP3v2/0tnpaenA7Du23H4dO6Gi7cvCi3l+zalyGVlZb30Xyk4ltNj1keufO5XmT8PxRMck8zhq/c5fPU+bjYmfNrInsbO5ZDL3/wOVBPaURgK2g4lSro7dadTlU5su7aN5eeXcyvzFrNPzWbJmSV0depKD+celNEt8yFjv0KjXg/zqtBhMfh8geLIbGRnNiK7Fg7XwsmtUJtc75GoHALUvcL/Q6Pa8Z5KSltEOzRLcWxHccqqSaKioggLCyMgIAALCwuioqK4e/cuVatWZdy4cUycODFv9YNly5YRExPD6tWrX3stR0dHVqxYwZ49e7C3t2flypUcP34ce3v7d84nk8lYtmwZ/v7++Pj48PXXX+Pi4kJGRgbbt29n7969HDhw4J2vDyBTSdgFl5SURIUKFTh8+DD169fPOz5+/HgOHDhAVFTUK8/p0aMHsbGxbN26lSpVqhAWFka7du3Iycl5qah9YdKkSS8Npn7hxw4B6CqVaOkbYFq1JsZVXJBracQQ5mLrwTPYlyTn6G0ZWSp1AWKlr6JZhVzczVQo8v+pS6mTo8rhdNZpIp5GcCdXPYRHiRIvbS+8db0xlZtKG1AD6D27i+OdECreP4hCpf6ll6Zry2XLNtwyrQOldYULQSglHj9+TI8ePUhLS8PYuOjniTx9+pT4+Hjs7e1f6XTTZBcuXGDUqFGcOnWK9PR0KlWqxGeffcbw4cPJzc3lhx9+YNGiRdy5c4dq1arxyy+/5M2DSkhIwN7enujoaNzd3Xn27BlDhgxhy5YtyGQyunfvjomJCbt27crbZa1fv348fPiQrVu3FihnXFwcU6ZM4e+//+bevXtYWVnRoEEDxo0bh4eHx2ufk9/XpNgVu3fv3mXw4MFs374dmUxGlSpV8Pf3Z+nSpTx58uqYx9f17Nra2rJ35VKuRuwj86F6nKmesTHuzVtT0z8QnQKMNZFKVlYWoaGhNGvWDKVSs3qm72U8Y9nh66w+lkjmM/VwEdsyenzsY08HD2t0tP6/KNHkdhREYbUjV5XLgZsHWHJuCecfqLce1pJr0cquFf2q9aOScaXCivxaxeL1yLiNPOp35KeWIXueCYCqjD059T9H5doFtHSKRzvyqaS0RbRDsxTHdqSnp2Nubi6KXSFPfl8TSbsyzc3NUSgU3L798mLyt2/fxtLS8rXPKVeuHFu3buXp06fcv38fa2trJkyYQOXKlV97vo6ODjqvmbVYq2VbGnfpyfmD+zgWvJG0O7c5snENJ3dswS2gFZ6B7TAwLdqPkN+FUqnUuB9UVmWUfNWqOsMaO7H8SALLIuNJTH3Ct9vOM3f/VQb52NOjbiUMdf7/r58mtuNdFEY7AioH0My+GUeSj7DkzBKOpRwj+Fow265to1mlZgxyHURVs6qFlPj1NPr1KGMDLaZAozFwbBFE/Y4sNR6tkFEQMR0aDIeaPQANb0cBlZS2iHZoluLUjuKSU9A8kn7up62tjaenJ2FhYXnHcnNzCQsLe6mn93V0dXWpUKEC2dnZ/PXXX7Rr167A99fS1qamfwsGzPqTwM/GYm5biedPnnA8eBOLhg/g78ULSLuTUuDrCmom+ko+b+pI5IQmfNe6GpbGutx59IyfQi7i/cs+Zu69xIPM/95GsDSSyWQ0sG7AkuZLWNlyJX42fqhQsff6Xrrs6MKQ0CEcTzleuicC6pcFvy9g5FkImAJGVvAoCfZ8hdY8D5yTt6i3JhYEQRAkExERgaGh4RsfRUHyQaqjR4+mb9++eHl5UadOHWbNmkVmZib9+/cHoE+fPlSoUIGff/4ZUA+0vnXrFu7u7ty6dYtJkyaRm5vL+PHj3zmDXKGgakM/XBo04uqp4xzbuoHky5eIDQ3hdNhuXBo0ona7zpSraFcYTS519LW1GNDQnl71KrEl+iZ/HLhG/L1M5uy7wqKIa9Qxk+OR9pSK5uJd++u4W7gzt+lc4lLjWHJmCbsTdhOZFElkUiRu5dwY5DqIRjaNSu+ubDqG6t7cOoMhdh1EzkL24BouT7agmrsXvPpDvaFgUkHqpIIgCKWOl5dX3nheqRS42L1x4wa2travrPGmUqlITEykYsWKBbpe165duXv3Lt999x0pKSl5+zKXL18+737/3j7u6dOnfPPNN1y7dg1DQ0MCAwNZuXJloWwnJ5PLcfCqSxXPOtw8f4aorRu5fjqaC4fCuXAonMq1alOn3UdUcBEL3L8LbS05XWtXpLOnLbvPprAg/ArnktI5kCKn6W8RtHevwCe+VXCwELuyvY5TGSemNprKcI/hBJ0NYuuVrcTejeWzfZ/hYOrAgBoDaGHfAqW8lL5p0NIBz77g0YvsM5vJ2P09pk9uwJF5ELUQ3LqC90gwf3ULTEEQBOHD0NPTw8HBQdIMBS527e3tSU5OxsLC4qXjDx48wN7enpycgq9fO3z4cIYPH/7a74WHh7/0ta+vL+fPny/wPQpCJpNhW70mttVrcvvaFY4FbyIuKpJrp45z7dRxKrhUp077zti7exVoYWdBTSGX0aqmFYGuluy/kMJPW09wJV3OxpM32XTqJs2rWTLErwrutqZSR9VItka2fFv/W4a4DWHlhZVsuLSBKw+v8NWhr5gXPY++1fvSwbEDelp6UkeVhlyBqlp7DsQraeWii9aROXA9EqJXQfRq9TbFDUdCBU+pkwqCUAyV6uFjGia/r0WBP/dUqVSvLfAyMjJK5OzE8pUdaDNqAv1n/oFrkwDkCi1uXTzHll8ms3L8Z1w4FE7uOxT4gvpNhY+jOZ9Vz2XDx3UIqFYelQp2n0uh/fxIuv95lINxd8UPljcop1+O0Z6j2dt5LyNqjaCsblmSMpP4+djPNN/UnD9i/yDtWZrUMaUjk6Gq0hT6h8CAveDUElDBhW2wqAksbwtX94sNKgRByJcXE+QeP34scRLhhRevxX9NXsx3z+7o0aMBdYHy7bffoq+vn/e9nJwcoqKiXtkbuSQpa12BgE8+p/5HPTgVso3Y0F3cvZFAyNxfObRuJV6t21OjcTOUOiWv4C8KHram/NmnHFfuPOL38GsEx9ziyLX7HLl2n+rWxgzxrULLGpZoKUrpuNS3MNY2ZpDrIHpV7cXWK1sJOhfErYxbzI+Zz9KzS+ns1Jk+1fpgafD6FU5KhYp1occ6uHMBImfDmY0Qf0D9sHJTD2+o1g7kCqmTCoKgoRQKBaampty5o14LXV9fX3y6KxGVSsXjx4+5c+cOpqamKBRv/9md72I3Ojo67wZnzpxBW1s773va2tq4ubkxduzYd4xdfBiVNce31wDqtu9CbGgIJ0OCSb97m33LFnJk01o8WrbBvXlr9AyNpI5aLDlYGDGjixtjApxYHBHP2mM3OJeUzmdro6lYVp/BjSrzkacNukpRlPwvXS1durl0o7NTZ/Ym7GXJ2SXEpcax8vxK1l5cSyv7VgyoMYDKpq9fpq9UsKgKHf6Axl/BkflwagUkx8Km/lDGHhp8Bu49QSnetAqC8KoXy6K+KHgFaZmamr5xqdp/y3exu3//fgD69+/P7NmzJVnQWZPoGhpSt0MXarVqx7n9f3Nix2bS7tzm8IbVHA/+C9emzfFs1Q5jc4v/vpjwCmtTPb5rU43Pmjiw/EgCyw8ncOPBY77depbZf8fRr4EdvevZYaJfSidjvYWWXIvAyoG0tG/JoVuHWHp2KSdunyD4ajDBV4Pxs/VjYI2BuFu4Sx1VOqYVoeVUaDQejv0JxxZCajzsHA3hP0PdIVB7IOhp/lrbgiAUHZlMhpWVFRYWFmL7Yokplcr/7NF9ocAT1JYtW/bS1+np6ezbtw8XFxdcXFwKerliT6mtg3vzVtT0b0Hc0UMcC97E3evxnAoJJmbPDvWyZW07YS6WLXsnZQy0GenvxMeNKrPheCKLIuK59fAJv+6NY0H4VbrXqcjAhvZYm5bSyVhvIZPJ8LHxwcfGh9i7sSw9s5R9ifsITwwnPDGcWha1GFBjAD42PqV32TIDM2j8JXh/ru7lPTwP0m/Cvh/g0G/g2U8sWyYIwisUCkW+Cy1BegX+DdelSxfmzZsHwJMnT/Dy8qJLly64urry119/FXrA4kKuUODi7UvvqXPo9OVkbKvXJDcnh/MR+1k+bjibf5nEzfNnxWSrd6SvrUU/b3vCx/kxq6s7LpZGPH6ew5JD8TSatp/RG2K4lPJI6pgay62cG7ObzCa4fTAdHTuiJdfi1J1TDN83nI7BHdl6ZStZOaW4l0LbAOp9CiNioMOfYFEdnmeoly2b7QZbh8Kdi1KnFARBEN5BgYvdgwcP4uPjA8CWLVtQqVQ8fPiQOXPm8OOPPxZ6wOJGJpNh5+5Jl+9+oueUmTjVa4hMJic++gTrJ09g7TdjiYuKJDdXrODwLpQKOe09KrBrhA9B/WtTr3JZsnNVbD51i+azDjIg6DhR1+6LNxVvUNmkMpMbTGZPpz30r9EfQ6UhV9Ou8m3kt7TY3IIVF1bwVPVU6pjSUSjV6/F+Ggk9NkKlhpCbBTGrYUFdWNMVrh8WKzgIgiAUIwUexpCWlkbZsmUB2L17N506dUJfX59WrVoxbty4Qg9YnFk6ONFm1ARSU5I4uWMLZ8P/JvnKJbbP/BlTSyu8Wnegmm9TlNo6UkctdmQyGX7OFvg5WxCT+JA/D15l19kU9l28w76Ld3C3NeWTRpUJqG6JQi5my/4vC30LRnuOZrDrYDbGbWTV+VXceXyHWdGz0EGHxOhE+tTog4V+KR1zLpOBU4D6cfOEegWHC9shbrf6YVMbGnwOLq3ECg6CIAgarsA9u7a2thw5coTMzEx2795NQEAAAKmpqSVynd3CUMbSGv9Bwxg8byn1OnZF18CQhynJ/L14AYuGDeDIX2t58ihd6pjFlrutKQt6erJvjB8961ZEW0tOTOJDPl19iqYzwll19DpPs0RP+usYaRsxoMYAdnfazfcNvsfO2I5nPGP5heU0/6s530Z+y9WHV6WOKS0bL+i6EoafAM/+oNCBm8dhQ2+YVxtOLIWsJ1KnFARBEN6gwMXuyJEj6dmzJzY2NlhbW+Pn5weohze4uroWdr4SxcC0DN5dezN4wTIa9x2MkXk5nqSncXjDav4c1p99yxaSdidF6pjFlr25AVM6uHJ4QhM+b+KAiZ6ShPuP+WbrWbx/2cfsvy/zIPO51DE1krZCmw6OHdjUahM9DXriXs6d7Nxstl7ZSvvg9gwLG8bxlOOle3iIuQO0mQWjzoLPWNA1gQdXYcco+K0GHJgGjx9InVIQBEH4HwUudocOHcqRI0dYunQphw4dQi5XX6Jy5cpizG4+aevqUSuwHQNnLyLws7GUs6tM9rNnRO/ezpLPP2bHrKmkXL0sdcxiy9xQh9EBzhz5sgmT2lSjgqke9zOf89vfcTT4JYxvt57l+v1MqWNqJLlMTlVlVZY2W8qqwFX4V/RHhoyDNw8yYM8AeuzswZ6EPeSU5jHnhhbQ9FsYdR5a/AImtvD4HuyfAr9Vh5Bx8CBe6pSCIAjCPwo8ZhfAy8sLLy8vVCpV3vbBrVq1KuxsJZ5CS4uqDf1w8fbl+pkYTmzfzPXT0Vw6EsGlIxHYVnPFq01H7N09kclL6dJQ7+HFCg696lUi5GwKfx68ytlb6aw8ep3VUddpUcOSwT6V8ago1lJ9HbdybvzW+DcS0hJYeX4lwVeDOXv/LGMPjMXG0Ibe1XrT3qE9+kr9/75YSaRjqF7BofYgOLcVDs+GlDPqdXuPL4aqbdVLmlXwlDqpIAhCqfZOFdSKFStwdXVFT08PPT09atasycqVKws7W6khk8mwq+lB569/oM+0uVTzaYxcoSDx/Bm2TJ1M0NhhnNm3l2yxgPU70VLIaetmzfbhDVkzuC5+zuXIVUHImRQ6LDjMR38cZu+5FHJzS/FH9G9hZ2LHt/W/ZU+nPQxxG4Kpjik3M27y87GfCfgrgLnRc7n35J7UMaWjUELNj+CTCOi9FRz8QZUL57fCoiawLBAu7YLcXKmTCoIglEoF7tmdOXMm3377LcOHD8fb2xuAQ4cOMWTIEO7du8eoUaMKPWRpUq6SPS2Hj6Fh976c2rWN03/v5sGtRPYunMOhdSvwaNEGt2Yt0dIVmygUlEwmo0EVcxpUMedSyiMWR1xja8wtjiekcjzhJJXNDRjoY0+nWmI74tcx0zNjmPswBtQYQPCVYFacX0Hio0T+PP0nQWeDaF2lNX2q9aGKaRWpo0pDJoMqjdWP2+fUG1Sc2QjXI9UPM0eoPwzcuoFS/PsVBEEoKgXu2Z07dy6///47U6dOpW3btrRt25Zp06axYMEC5syZ8yEyfhC7F57l+lnNXY/VyMwc314D+HjBMnx7DcDQzJzHaQ+JXL+SP4f1J3z5n2SJFRzembOlEdM/cuPQF0341K8KxrpaXLuXyddbztLgl338FhrH/YxnUsfUSHpaenRz6cb29tv5ze83aparyfPc52y+vDlvMtux5GMa+2+rSJSvDh1+h5GnwXsk6JjA/cuwY6R6Mlv4L5BZinvDBUEQilCBe3aTk5Np0KDBK8cbNGhAcnJyoYQqCslX0tkxL5YyVga4+9viVKc8WhrYm6ejb4BXm454tGxL3JEIju/Ywt2Ea5wO3QXAzuQEarftTAXnqhInLZ7KG+vyRQsXhjV2YMPxRJZGxnMz9Qmzwy7zx4GrdKxlw8CG9jhYGEodVeMo5Ar8K/njX8mfmDsxBJ0LYt+NfRy8eZCDNw9StWxV+lTvQ3O75ijlSqnjSsPYGppNhkZj4dRKOPo7pN2A8J/V2xG7dVf39po7Sp1UEAShxCpwz66DgwMbNmx45fj69etxdCw+P7Cr+1ij1FWQmpzJ/pUXWfHVYY7vjOdJhmYuTaXQ0qKqT2N6/zKbzt/8SCW3WgBcPRHFuu/GseabMVw6ckjszPaODHW0GNDQnvCxfszr4YGbjQnPsnNZe+wG/jMPMCDoOIev3ivdvZVv4W7hzqzGs9jeYTtdnbuiq9DlwoMLfBnxJS3/asmys8tIf16KP4nQMYL6Q+HzaOi8FKw9IPspnFymXqt3TTdIOCR2ZhMEQfgACtyzO3nyZLp27crBgwfzxuxGRkYSFhb22iJYU9Vpa0eTbiacj0ji9P5EMlKfcWx7PCd3X8elniVuTW0pY2kgdcxXyGQyKrm6Y+1SnS1rVmGUmcalyHCSL19ix6xfMLEoT62WbanRuBnaeqV0lvx70FLIaV3TmlauVhxPSGVRxDX+vnA7b2e2GhWMGdSwMq1qWqFUiBUy/lcl40p8U+8bhrsPZ/2l9ay9uJbbj28z8+RM/oj9g46OHelZtSc2RjZSR5WGQgtqdILqHdXbDh+Zp568FvfPw8od6g+H6u3VE98EQRCE91bg39adOnUiKioKc3Nztm7dytatWzE3N+fYsWN06NDhQ2T8YHT0tPAIqEivH+vTbEA1ylU0Iicrl3MRSayZFMXO+bHcvJSqsb15OqZl8R88jMHzl1GvUzd0jYxJu3Ob/csX8efQ/hxYtZT0e3eljlksyWQy6tiXZVEfL8JG+9KrXkV0lXLO3kpn5PoYfKbu5/fwq6Q9FitkvI6primfuH3Cns57+L7B9ziYOvA4+zGrLqyi1ZZWjA4fTezdWKljSkcmAztv6L5WvTOb1wDQ0oPkGNg8CGa7qbcofvJQ6qSCIAjF3juts+vp6cmqVasKO4tkFAo5TnUscaxdnqTLD4n5O5GEM/dIOHOfhDP3Mbc1xL2pLQ5e5VFoaV5vnoFpGby79KJOu86cP7ifkyHBpCbd5MT2zZzcuRXn+j54BrbD0sFJ6qjFUuVyhvzY3pXRzZxZE3Wd5Ueuk5L+lKm7LzJ332U+8rShd11bqWNqJB2FDh0cO9DeoT2Hkw6z4vwKDicdJvR6KKHXQ3Er50afan1oUrEJWvJ3+nFU/Jk7QOvfoPE36q2Hj/0J6bcg9DsIn4rcvSf6z8S/XUEQhHeV78otKSmJsWPHkp7+6ri7tLQ0xo0bx+3btws1XFGTyWRUcCpDq6E16TmpHjV8K6CllHMvMYO/gy6w4uvDnNydwNNMzezNU+ro4tasJf1nLKD9+O+wreaKKjeXi5EHWP31aNZNHM/lY4fFuN53VNZAm+FNHDn0RWOmd66Ji6URj5/nsPzIdZrNPsTii3KOJTzQ2E8CpCSTyfCu4M3CZgv5q+1ftHdoj1KuJPZuLGMOjKH1ltasOLeCjOcZUkeVjoEZ+I5Tb0fcbgFYVIesTBTH/8T//DgUm/qqhz6Iv1+CIAgFku9id+bMmaSnp2NsbPzK90xMTHj06BEzZ84s1HBSMi2vj293Z/r+7E3ddpXRN9Hmcdpzjm69xvIJkRxYc4nUFM3cclYml1PFsw5dJv5Mr19m521SceviebbN+ImlIz/hVEgwz588ljpqsaSjpeAjL1t2jfBh1cC6NHYuh0oFZ1Ll9FxygjbzDrEl+ibPs8UmAq/jVMaJH7x/YG/nvXxS8xPK6JThVsYtpp+Yjv8mf6Ydn8atjFtSx5SOlg549IRPI6H3VnIrN0WGCvmlnbCsJSxqDKc3Qo5mvukWBEHQNPkudnfv3k2fPn3e+P0+ffqwY8eOQgmlSXQNlXi1tKPPlAb496uKua0h2Vm5nD14izWTotgxP5bEi5rbm1fevgoth49h0Lwl1Gn/EbqGRqTdTmH/8kUs/LQf4SuXkH73jtQxiyWZTEZDR3OW9a/Drs8a0MAiFx0t9bjeUetj8Zm2j/n7r5CaqZkrfEjNXM+c4R7D2dt5L9/V/47KJpXJzMpk5fmVBG4OZHT4aKLvRGvsv60P7p9NKnK6r2efy8/kuvcGhQ4kRavH9c6qqV6+7Emq1EkFQRA0Wr6L3fj4eCpWrPjG79vY2JCQkFAYmTSSQkuOcz0runxVm3ajPLCraQ4yuH7mPttmxbD+x2Ocj0wiO0szhwgYlTXHp3tfPl6wDP9BQyljbcPzJ485uWMLiz8bxPaZP3Pr0oXSW1i8JwcLQ7pWyeXg2EaMaeZEOSMdbqc/Y/qeS9T/JYyvtpzhyp1S/BH9W+hq6fKR00dsabeF3/1/p75VfXJVuYReD6XPrj702NmDkGshZOWW3p7MR3oVyGn1G4w+D42/BgMLeJQEf0+CmdVgx2i4d1nqmIIgCBop3zNC9PT0SEhIeGPBm5CQgJ5eyd8CUyaTYeNcBhvnMjy8/ZjT+xK5cCSZ+7fU6/Ue3XqVGo0qUMPXBn1jbanjvkI9rjeQmk1bEB9zkpM7t3LjbCxxUZHERUVi6eCEZ2A7HOt6o9AqpROG3kNZA20+a+rIx76V2RGbzJJD8ZxPTmdN1A3WRN3Az7kcAxva09DBHJlMJnVcjSKXyWlYoSENKzTkcuplVl1YxY6rOzh7/yxfRHzBjJMz6O7Snc6OnTHVNZU6rjQMzMF3PHiPgLN/wZEFcPsMnFiifjg2h3qfQmU/dc+wIAiCkP9it27duqxcuZJGjRq99vsrVqygTp06hRasODAtr0+j7s7UaVuZ85FJnNl/k4zUZxzfmcDJPddxql2emk1sKWdrJHXUV8jkcirXqk3lWrW5eyOBUyHBXDgUTsqVOHbOmY5h2aW4N29NzabN0TN6dZy28HY6Wgo6edrQsVYFouIfsORQPH9fuE34pbuEX7qLo4UhAxra0969Anramrdzn9QcyzgyucFkPvf4nI1xG1l3cR13Ht9h9qnZLIxdSJsqbehVtReVTStLHVUaWjrg3kO9A1tChHpntku74PIe9cOimrrodf0IlCW/E0IQBOFt8l3sjh07lmbNmmFiYsK4ceMoX748ALdv32batGkEBQWxd+/eDxZUk+kaKKkVUAn3prZcjb5LbFgit+PTuXgkhYtHUrB2NMWtqS12Nc2RyzWvt6VcRTuaDxmBT/e+xIbuImbvTjIe3OfQ2uUc/Wsd1Ro1plbLtpjZvHkYi/B6MpmMepXNqFfZjOv3M1kWmcDGE4lcvpPBl5vPMG33RbrXqUif+nZYmuhKHVfjmOmZMcRtCANqDGB3wm5Wnl/JxQcX2Ri3kY1xG/G29qZXtV40sG6AXKZ5ywJ+cDIZ2DdSP+5fhaiFEL0K7pyHbZ9B6ET1Gr61B4GxldRpBUEQJJHvYrdx48bMnz+fESNG8Ntvv2FsbIxMJiMtLQ2lUsncuXNp0qTJh8yq8eQKOY5e5XH0Kk9KfBqn993k6sk7JF1+SNLlhxib6+LqZ0NVb2t09DRviIC+iSn1O3endrvOXDp8kJMhwdxNuMbpv3dz+u/dVKrpQa3Atti7eSKTl8LC4j1VMjNgUtvqjA5wYsPxRIIOJ3Az9QkLwq/y58FrtHS1YoC3HR4Vy0gdVeNoK7RpW6UtbSq34eTtk6w8v5L9ifuJTIokMikSO2M7elXtRZsqbdBXltKdA82qQOA0aPwVRK+EqD8h7QZE/AqRs6B6B6j7Kdh4Sp1UEAShSBWo4vrkk09o3bo1GzZs4MqVK6hUKpycnOjcuTM2NqV0+883sLQ3wXKgCRkdq3DmwC3ORdwi/d5TIjdd4dj2eFwaWFHTzwbT8pr3i1lLqaS6b1OqNWrCzQtnORWyjasnorh+Oprrp6MpY1UBjxatqe7nj7au+Ii0oIx1lQzyqUx/b3tCz99maWQ8x+IfsD02ie2xSbjbmtLf245AV7El8f+SyWR4WXrhZelF4qNE1l5cy+bLm0lIT+DHqB+ZHT2bTo6d6OzQWeqo0tEzhQafqQvbSzvV43oTj8KZjeqHTW31EIeqbcWWxIIglAoF7l6sUKECo0aN+hBZSiTDMrrUb18Fr0A74qJSiN13k9TkTM7sv8mZ8JtUqmFGzcY22FYtq3ETlmQyGbbVXLGt5kranRSid+/gzL69pCbfYt+yhRxatxLXJs3waNEGEwtLqeMWOwq5jBY1LGlRw5Kzt9JYFpnA9tgkYhIfMmJdDD+FXKB3vUp0r1MRM0MdqeNqHFsjW8bXHs9Qt6EEXw1m9YXVJD5KJOhcECvOr8BFy4Xyt8tTt0Jdjfu3VSQUWlCtnfqRFA1H/1BPart5HDYdByNrqDMIavVTb2ghCIJQQoluoyKi1FZQ3acC3b+rQ9vP3ankagYq9dJl2+fEsnZyFGcP3CTrmWYuXWZiYYlfn0F88nsQTfp/Qhkra/XSZTuDWfz5YLZO/5EbZ2PF0mXvqEYFE2Z0cSNyQhNG+f//0mW/7o2j/i/7GLcxlnNJaVLH1EiG2ob0rNqT7e23M7fJXOpa1SVXlcv5rPMMDhtMlx1d2HJ5C89ynkkdVTrWHtBxIYw6B74TwKCceumysO/ht2oQPBxSzkqdUhAE4YPQvIGjJZy6t7QsttXK8vD2Y86E3+TCkWRSUx5zYG0cR4OvUbWBFa5+Nhiba94QAW09fTxatME9oBXxsSc5FbKN66ejuXriKFdPHMW8oh0eLdpQtaEvSh0x4aqgyhnpMMLfkU/9qrDzTBLLIhM4fTONjSdvsvHkTerYl6V/AzuaVSuPlhji8BKFXIGfrR9+tn5cvHuRqX9P5WzOWS4+uMh3h7/jt5O/0dmpM12du1LeoLzUcaVhVB4afwk+o+HsZoj6HZJj1WN8o1eCnQ/U/QScA0EuVgkRBKFkEMWuhEzL6+PT1Ym6bStz4XAyZ8Jvknb3CTF/JxIblohdTXNqNrahgnMZjfsYViaXU9mjNpU9anP/ZiLRe3Zw/kAY924kEPrnXCLWBOHatDnuAYEYm1tIHbfY0daS08HDhvbuFTh14yHLIuPZdTaFY/EPOBb/gAqmevSuX4lutW0x1de89ZylVsW0Cu312zO96XS2JWxj7cW1pGSmsOjMIpadXYZ/JX96Vu2JWzk3jfu3VSS0dMC9O7h1gxtH1UXvhR3qZcwSIsCkItQZDLV6g56YMCkIQvGmEV1D8+fPx87ODl1dXerWrcuxY8feev6sWbNwdnZGT08PW1tbRo0axdOnT4sobeHT1tPCraktPSfXo9WwmthWK4tKBfGx9wieFcO6H45x9uAtjR3iYGZji//AT/n49yB8ew/ExKI8TzMecTx4E4uHD2LbjJ9IPHdaDHF4BzKZDM9KZZjXoxaHvmjMsMZVKGugza2HT/hl10Xq/RzGhL9OcyE5XeqoGslEx4QBNQawq+MuZvjOwLO8J9mqbHYn7Kb3rt503dGV4CvBpXeIg0wGlepDlxUw8jQ0HA16ZdWrOIR+CzOqwvYRcPu81EkFQRDeWYGL3cqVK3P//v1Xjj98+JDKlQu+wPv69esZPXo0EydO5NSpU7i5udG8eXPu3Lnz2vPXrFnDhAkTmDhxIhcuXGDJkiWsX7+er776qsD31jQyuQw7V3Pafu5O94l1qeFbAS0dBQ+SMjmw5hLLv4wkctNl0u89kTrqa+kaGOLVugMDZv9Ju3HfUrGGGypVLpePHWbD91+xYvxnnA7bTdaz4vvGREpWJnqMa+7C4QlNmN65JtWsjHmalcu644m0nB1B14VH2HUmmeycXKmjahwtuRYBdgEEtQhiY5uNdHDogI5ChwsPLvBN5DcEbApgzqk53M68LXVU6ZjYgP9E9ZbEbedCeVfIfgIng+D3+hDUGi5sh5xsqZMKgiAUSIGHMSQkJJCT82oP47Nnz7h161aBA8ycOZPBgwfTv39/AP744w927tzJ0qVLmTBhwivnHz58GG9vb3r06AGAnZ0d3bt3JyoqqsD31mRlrQzw7e5MvXb/P8Qh/d5TYv5OJCYskYrVy/JUT6GRvaVyuQIHr7o4eNXlXuJ1Yvbs4NzBff8McZhHxOogajQJwD0gEP0yYhZ4QekqFXzkZUtnTxuOJ6Sy/HACu8+lEBX/gKj4B1ib6NLzn1UcyhqIIQ7/y6WsC997f88oz1H8dfkv1l1cx+3Ht1l0ZhFLzy6lacWm9Kjag1oWtUrnEAelHtTqAx694cYRiPrjf4Y42ELtgVCrL+iXlTqtIAjCf8p3sbtt27a8/9+zZw8mJiZ5X+fk5BAWFoadnV2Bbv78+XNOnjzJl19+mXdMLpfj7+/PkSNHXvucBg0asGrVKo4dO0adOnW4du0aISEh9O7d+7XnP3v2jGfP/v8jyvR09ce9KT/8iM7AAei4uBQoc1GTK6G6rxVVfSxJPP+AcweSuHnxITfOPgD02XD9BDV8K+BYxwJtXc0bgm1iaY1v34+p27kH5w+EcTp0F+l3b3Ni+2ZO7NiCnZsnz8ta8Pz5c6mjvpesrKyX/ltUPGyM8OjiSnKaI2uPJbLuxE2S0p4yfc8lZoddprWrJX3qVaS6df62fJaqHYUtP+0wVBjS16UvPZ16cuDmAdbGreXUnVPsvb6Xvdf34mTqRDfnbjSv1Bw9Lekmi0r6mljXhg61oekt5CeXIY9egSwtEf6ehCr8F1TVO5HjNQgsXf/zUqXp71ZxUBzbUZyyCppFpspn16D8nx2zZDLZK72JSqUSOzs7ZsyYQevWrfN986SkJCpUqMDhw4epX79+3vHx48dz4MCBN/bWzpkzh7Fjx6JSqcjOzmbIkCH8/vvvrz130qRJTJ48+ZXjxxwcMVQoeGJXidQGDcioUQMUxWP2cVaGnIwbSh7fVKLKUfc8ybRUGFTIwqDSc5QGmtfb+4IqN5fHSYk8jDvHk5T//yRAaWyKiVM1jO0dkStFb+S7ysqF6HsyDqbIScz8/15JeyMVPpa5uJVVoaURI/U1U3JOMkefHeX089Nkof7FqifTw1PbkzradSirKN09mfLc51RIjaLy3VBMnyTkHb9v4Mi1cs1INvVCJdO8N91CyfD48WN69OhBWloaxsb5ewMvCFCAYvcFe3t7jh8/jrm5+Xvf/F2K3fDwcLp168aPP/5I3bp1uXLlCiNGjGDw4MF8++23r5z/up5dW1tbznwyBMWhQ5CtHn+mKFcOk486Y9y5M1rlyr132z60rKws9oSEYmvkysXI26Td+f9xvDZVy1C9kRW21coil2vux7APkm4Ss2cn5w6EocpWFxZKXT2q+jSmZrOWlLUuPrvyZWVlERoaSrNmzVAqpd+VSqVSEZOYxoqjN9hz/jZZOep/5uaG2nTzsqFbbRvKG7+6NJymteNdvW870p6lsfXqVjZe3khSZhIAMmT4VPChm1M36ljWQS4rmncNGvmaqFTIbh1HfnwRsovbkeWqf46qDMuT69GXXI8+YPTyRjMa2Y53INohnfT0dMzNzUWxKxRYgd+Cx8fHF9rNzc3NUSgU3L798qSQ27dvY2n5+h25vv32W3r37s2gQYMAcHV1JTMzk48//pivv/46rwf6BR0dHXR0Xt19ymrKjxjn5PBw/QZSN6wn5+5dHiz4nQd/LsI4IIAyvXqi5+Gh0WP25Epwa2KLZzN7Ei8+4Mz+myScvc/NC6ncvJCKkZkuNXwrUK2BNbqGmvfDrHwle5r0/4THZctjq6fN6b93kZp0k9OhIZwODaGiqzsezVtT2bM28mKy5qdSqdSYXxx1qpSjTpVy3El/ytpjiayOus6dR8+YF36NPw7G07yGJX3qVaKO/au792lSO97Hu7bDXGnOILdB9HftT8StCNZcWMOR5CMcvHWQg7cOYmdsRzeXbrSr0g5DbcMPkPxVGvea2HurH49S4MQyOLkMWcZtFBHTUETOVO/cVnswVKynXvXhHxrXjnck2lH0iktOQfPkq9idM2cOH3/8Mbq6usyZM+et537++ef5vrm2tjaenp6EhYXRvn17AHJzcwkLC2P48OGvfc7jx49fKWgV/ww/KOhkLaWFBeU+G475Jx+THhpK6uo1PDl1ivSQENJDQtBxcaFMzx6YtG6NXE/zNnh4QSaXUbGaGRWrmZF+7wlnD9zifGQSj+4/5cjmqxzbHo9j7fK4+lbAopLmvRuWK7VxCwjEM7AtN87EEr1nO9dOHufGmRhunInBuJwFNf1b4tokAH1jk/++oPASC2NdRvg7MrRxFXafTWHFkQSOJ6Sy83QyO08n42JpRJ/6drT3sEapue/tJPHvjSqupV1j/cX1BF8NJiE9gV+O/cLsU7NpW6Ut3Zy74VDGQeq40jCy/GejijFwYRscWwSJR9VbE5/9S72qQ53BULW91EkFQSil8jWMwd7enhMnTmBmZoa9vf2bLyaTce3atQIFWL9+PX379mXhwoXUqVOHWbNmsWHDBi5evEj58uXp06cPFSpU4OeffwbUY3BnzpzJn3/+mTeM4dNPP8XT05P169f/5/3S09MxMTHh3r17mJm9uhLA0wsXeLB6Nek7dqL6Z+1eubExph06UKZHd7QrVSpQ+z6UrKwsQkJCCAwMfO273aznOVw+fpsz4Te5l5iRd7y8vTGuvhVw8CyPQin94M03tSP97h1iQ0M4vW8vTx+pJxUqtLRwru+De/PWWDo4aVSv+3+9HprmfFI6K48msCX6Fk+z1EuVGelq0dHDGpsn1+jXqXi0400+5OuRmZXJ9qvbWXdxHVfTruYd9yrvRTeXbjSp2ASlvPDuWdz+bgHqXdmOLYIzGyFb/XNUpWvCVaN6VOr8A8ryzhIHfHfF8vV4jeLYjhe/v8UwBqGgCjxm90OYN28e06dPJyUlBXd3d+bMmUPdunUB8PPzw87OjqCgIACys7OZMmUKK1eu5NatW5QrV442bdowZcoUTE1N//Ne/1XsvpCTlsbDzVtIXbOGrMTEvOMGDRtSpkcPDH0bIZNwQlt+f1CpVCpux6dzev9Nrp66Q+4/Yzd1DZVU87ameiNrjM2knWn+tnZkP3/OpSMRxOzZQcrVy3nHy1d2wD2gFc7ejVBqvzpMpagVx18cAGmPs9h4MpGVR69z/f7jvOPeVczo28COplXLo9Dgcd9vUhSvh0ql4njKcdZdWse+G/vIUamXZLTQs6CzU2c6OXXCQv/9dw8srn+3AHj8AGJWw/HFkJrw/8erNFX39joGFLttiYv16/EvxbEdotgV3pVGFLtFKb/F7guq3FwyIyJ4sGYNmQcj4J8/LqW1NabdumHauRNaZYt+hva7/KDKTHvGhcgkzkUkkZGqnrQnk0ElV3NcfStgW7UssiIubArSjuQrl4jZs5NLRyLI+WcJGl0DQ6o3boZ7s0BMLa2KIvJrFcdfHP+Wm6vi4OW7LD8cT/ilu6hQ/z2oYKpHj7oV6VrbFnND6d9U5FdRvx4pmSlsjNvIprhNPHj6AAAtmRZNKjahm0s3vMp7vfMnEcX97xYAublkX9rNvV2/UD79NDL++bVjUhG8+qvX9TV4/0nPRaFEvB4Uz3aIYld4VwUudnNycggKCiIsLIw7d+6Qm/vybk379u0r1ICFraDF7r89T0wkde060v76i5y0NABkSiVGLVtQpnt39Nzdi+yj9ff5QZWbk0v86XucCb/FrUupecdNyulRw7cCLvWt0DUomh9+79KOx+lpnN0fSmxoCOl3/9lpTybD3q0WbgGtsPfwLPIJbcXxF8frZGVlsXJzCMkGDmw6dYvUx+o3FdoKOYGulvSuX4laFcto1BCS15Hq9cjKySL0eijrLq0j+k503nEHUwe6OneldeXWBZ7QVpL+boWEhBBYvxrKmOUQvQqe/PPzR6EN1TuoJ7TZeL00oU3TlLjXoxi1QxS7wrsq8GoMI0aMICgoiFatWlGjRg2N/6VXmLRtbSk/fhzlPv+M9JBdpK5Zw9OzZ0nftp30bdvRqVqVMt27qSe06etLHfeN5Ao5VTwsqOJhwYPkTM4evMWlI8mk3X1C5KYrHA2+ptET2vSNTajTrjNebToQH32SmL07SYg5Sfw/j7wJbY2boW9iKnXcYsdMF3o3d2JMcxd2nk5mxdHrxCY+ZGtMEltjkqhqZUzvepVo526NgY5YU/XflAolgZUDCawcyMUHF1l3cR0h8SFceXiFKVFT+O3kb7Sp0oYuzl1wKuMkdVxplLGDgB+h8ddwdjMcXwRJ0XB6vfph6Qq1B4HrR6BtIHVaQRBKgAL37Jqbm7NixQoCAwM/VKYP6n16dl/nyZkzpK5eQ/quXaj+Wc9XbmSESfv2lOnWFZ0qVd77Hq9T2O/Knz/NJu7Ybc4euMX9W/8/oc2ikhE1fG1w9LJAS7vwe0sLqx2pKUmc/ns3Z/eH8jTjEaCe0OZY1xv3gFZYO1f9oG/MimMvyeu8qR2nbz5k1dHrBMck8Sz7nwltOlp0rFWBXvUq4VjeSKrIr6VJr8ej54/YdnUb6y6uIyE9Ie94LYtadHXuSrNKzVAq3pxRk9ryPt7ajlsn4dhiOLc5b0IbOibg3h28BkA5zZnQVipeDw0lenaFd1XgYtfa2prw8HCcnIpnr0RhF7svZKemkrZlK6nr1pF140becf06dSjToztGTZsiK8QfKB/qB5VKpSLlWjpnD9zkyqk75Gar/3ro6GvhUt+KGo0qYFq+8HqtC7sdWc+fcelwBLGhIaRcics7Xq6iHW4BgVRt6Ie2XuH3uhfHXxyv81/tePj4OZtO3mTV0esk/GtCW137svSqV4nm1S3R1oAt2jTx9VCpVESlRLHh0oaXJrSV1S1LR8eOdHbqTAXDCq88TxPb8i7y1Y68CW1LIPVfa7rb+aiLXpfWoCXtDoul6vXQMKLYFd5VgT+DHDNmDLNnz2bevHmlagjDf9EqUwazAf0p268vmYePkLp2LRn79/P42DEeHzuGopw5pp07U6ZLF5RW0k2k+i8ymQyrKiZYVTGh4UeOnP9nQtuj+0+JDUskNiwRG5cy1GhUATs3cxQK6Qubf1Nq61DDz58afv6kXL1MbGgIFyMPcvdGAn8vXsCBVcuo5tMYt4BAylW0kzpusWOqr80gn8oM8LYn8uo9Vh29Tuj520TFPyAq/gHmhjp0rW1D9zoVsSmjuUN5pCCTyahnVY96VvW4nXmbzZc3syluE3ee3GHxmcUsObMEHxsfujp3xdvaG0UxW6WgUOiXhQafQb1hcG0fHF8KcbsgIUL9MCyvnszm2Q9Mis8Oi4IgSKvAxe6hQ4fYv38/u3btonr16q+8I9y8eXOhhSuOZHI5hg29MWzoTVZyMqkbNvBw0yZy7t7j/u9/cH/hnxj6+lKmezcMGjZEJtesYvHf9Iy08Wxhh0dAJW6cu8/Zg7e4fvY+Ny+mcvNiKgYm2lRtaE31htYYlnl161mpWVZxxLLKCHx7DeTcgTBi/9mhLTY0hNjQEKydq+HWrCVOdb3R0pa2t6i4kctl+DiWw8exHMlpT1h7LJF1x25w59Ez5u+/yu/hV2nsbEGvepVo5FSuWC5f9iGVNyjPp+6fMqjmIA4kHmD9pfUcTT7KwZsHOXjzINYG1nzk/BHtHdpjolUKN1KRy8HBX/1Iuwkng+Dkcsi4DQenQ8QMcGqh7u2t0lR9viAIwhsUuNg1NTWlQ4cOHyJLiaO0ssJixAjKDR3Ko7AwUteu43FUFBn795Oxfz9KGxtMu3TBtFNHtApxSEVhk8tl2LmaY+dqTvq9J5w7lMSFyCQy055zYmcCJ3ddx87VjBqNpFm+7L/oGhri2aodtQLbknjuNLF7Q7hy4ihJl86TdOk8+5cvooafPzX9W1DG0lrquMWOlYkeo5s58VkTB0LP32bV0escvnqfsIt3CLt4B5syenSvU5EuXraUMyo+y5cVBaVciX8lf/wr+ZOQlsCGuA0EXwkmKTOJ2admMz9mPk1smmCTZVPgHSJLDBMbaPINNBoPF3fAiaXqXt5LIeqHaSX18mXuvcCwnNRpBUHQQGKd3SL27No1Hq5fz8MtW8lNV+8MhlKJcbNmmHbrin7t2vkaHiL1eKuc7FyuRd/l7MFbJF1+mHfc2FyX6j4VqNrACj2j/+4tlaodGakPOLtvL6fD9vDo/t284xVd3XFvFkhlzzootPL/XlDq16OwFFY7rt7NYE3UDTadvEnaE/XyZUqFjObVLelZtxL1KpcVEwbf4Gn2U/Yk/B97fx0e53Um/v/vYY1gxMzMTJZlZst2bIfcQJN2t+22TWlzFbf7adrd/W13N/22KaTbbtOkmMSJY2ZGWSyLmZmZ4fn9MbZkJ05iO5Jmxjmv69LV+hnQufPMPHPrzH3uc4p3Kt6hsLtw7riPzoc9wXvY4b8Da43pzfYu6DnpqtQnvQVvwri+DSRyFYQ9op/t9U5dtPZlpvzaup0pxiFqdoUHJZJdA5kdG2PwxEn63n6b8cL5DzS1nx+2n9mD9c6dKKw//APNmC5Uva0jlFxpoTyjncmxaQDkChn+sY6Er3LHLdDmQxMbQ8cxOzND3Y0cCk4fp64gb27TEAtbOyLXbSJy3SZ0Dh+/C5ah41goCx3H+NQMRwpaeTOrkfzG/rnj/o4WPJ3szWNx7tiYL3wJycNyPsp6yni7/G2OVh9lkkkANAoNm30282Twk0Q5RJnM2olFOSeTo/oODjmv6zs63OIQpE96oz8DWtuF+V03PSyvLVOMQyS7woO652TX1vbujeStra0JCgri29/+Nhs3blzwAS40Y0l2bzdWUkL/23sZOHYMaVS/wl2m0aDbuhWbPU/edbMKY7xQTU3OUJXdQcmVVjrrB+eO27qYE77SneBlLh/YrMKY4hjobKfw3CmKL5xhdKAfAJlMjm9sPFEbtn7kZhXGFMcnsZhxlLQO8GZmIwfzWxiZ1Hci0CjlbIt05ZllXgu6WcXDcj5AH8v+Y/uZDZplX/U+Kvvmu4wE2QbxZNCTbPPbdt+bVSy1RT8nrTf0SW/RPpga0R9TmkH4o/oyB4/EBZntfVheW6YYh0h2hQd1z8nun//857se7+/vJzc3l71797Jv3z527NixoANcaMaY7N4yMzzM4JEj9L31NhOV8x9omuBgbPY8ifWOHSis9P1Mjf1C1dU4RPGVFiqzOpie0Cc2CpWcgHgnwle64+KnQyaTGWUcM9NTVGdnUHj2BI3F87PuVvaORK7bRMS6jVjZ3bm1qTHG8SCWIo7hiWkO5rfw98xGytrm/ygKcbHiqSQvdsW6Y639ZL/7YTkfcGcsSqWSgq4C3q18l1P1p5iY0ff21iq1pPmm8UTwE4Tbhxt4xHe3ZOdkfBCK3oGcN6CjeP64U7g+6Y16EswevAzkYXltmWIcItkVHtSClTH8/Oc/Z9++faSnpy/E0y0aY052b5EkibEbN+jf+84dm1XItFp0aVux3bMHRUgIJ06cMPoL1eTYNJXZHRRfbqGneX6zCjs3C8JXuuMXZ8+5i6eNNo7e1mYKz56k5NK5uc0qZHI5fnFJRG/Ygnd0LHK5wiQ/OO5mKeOQJIkbTf28mdnIkcJWxqf0m1WYqeTsiHLj6WQvYjw/vATmozws5wM+PJaBiQGO1Bzhncp3qBuY70kbZh/GE0FPkOabhrnKeNq/Lfk5kSRozoHcN6D4vfnNKlTmEPEoxP8DuMfd92zvw/LaMsU4RLIrPKgFS3YrKytZtmwZvb29C/F0i8YUkt3bzQwMMHDoEH1732GypmbuuDokhKbQEFK++100tgtbk7YYJEmio36QkiutVGd3MH0zsVGo5GicJti4JwH3wMVdtPRJTE9OUpl5jcKzJ2kpL5k7rnN0InLdZoJXrOHy9QyT+uC4G0N9AA6MTXEgr5k3sxqp7Jj/oyjUVcfTSZ7sjHVHZ3bv4zHFD/IP83GxSJJEbkcu71a+y5mGM0zN6hcEmivN2ea3jSeCniDUPnSph/0BBj0nY31QsFef+HaVzx93idT37I18EszuLXl6WF5bphiHSHaFB7VgyW5RUREbN26kvb19IZ5u0ZhasnuLJEmM5eXRt3cvQydPIU3qF6vItFqst2/D5sknMYuIMNpk8XYTo1NUZHZQcqWF3taRueP27haErXAnONkZjbnxXnx7mhv1s72XzzExoh+/TC7H3M2L9U99Fv+4hA+t7TV2hv4AlCSJ3IY+3sxs5GhRG5M3tybWqhQ8Eu3GU8leRHtYf+zr3NBxLKT7iaVvvI9D1Yd4r+q9O7YmDrcP5/Ggx9nquxULlcUij/jujOKcSBI0XteXOJQegptlIPrZ3sf0ZQ5uHz3baxRxLABTjEMku8KDWrBk91vf+hbl5eWcPHlyIZ5u0Zhqsnu76b4++g4coOWNP6Hpmm+bpQkNxfbJJ9Bt3z5X22vMJEmipaqXM3tzmOjUMHNztlepkhOQoK/tdfbVGW0CPzU5QVXGNQrOnqS1onTuuM7Rici1m4hYuxFLO9N6jRnTB2D/6CT781p4M6uR6s77m+01pjg+qQeJRZIkstuz2Ve5jzONZ5ie1XdJMVeak+aXxuNBjy95ba/RnZPRXih4S79hRff8GglcIiHu+Q+t7TW6OB6QKcYhkl3hQd1zsvviiy/e9fjAwAB5eXlUVlZy+fJl4uPjF3SAC+1hSHbh5oXq2DHWurgwtH//B2Z7dWlbsX3yScyijLs10a0L7vo1G6nN6/3AbK+dmwVhK9wITv5gJwdj0l5bzdE3/sB4cz0To/OzvX5xiUSt34JPTJxJzPYa4wegJEnkNPTx1vtme81UcrZHufFUkucHOjkYYxwP6pPG0jvey5GaI+yr3HfHbG+oXSiPBz1Omm/aknRyMNpzcmu2N/dPUHLwztne8Ef1ZQ4eCXOzvUYbx30yxThEsis8qHtOdteuXXvX4zqdjuDgYL7yla/g6+u7oINbDA9VsnvbhWqmv19f2/vuu0xWz9f2aoKCsHniCawf2fGRfXsN5f1xSJJER90gJVffV9urlOMf50jYCreP7NtrKLfi2LRhPXW5WRSeO0lL+fxsr5W9IxFrNxCxdhM6B+Pd5cnYPwD7Ryc5kN/CW++r7Q1ytuQziV48erNvr7HHcT8WKhZJksjpyOHdync523B2rrZXq9SyxWcLjwc9TqRD5KK9t0zinIz2QuFefeJ7e22vU5h+tjd6D1NKS+OP4x6YxPl4H5HsCg9KbCphoj7sQiVJEmP5+fpODidPzndy0Giw2rwJ2yeeQJuQYDTJ4kddcCdGp6jM0vft7WmZT2xsnM0JS3UjJMXlnnZpWwp3i6OnuYmi8ycpuXR+rpMDMhm+0XFErt+MX9z97dK2FEzlA1CSJPIa+3grq4mjt3VyUCvlbI1w4Yk4N7pLM9i2zbjjuBeLcU76xvv0s71V++7o5BBoG8hjgY+x3W/7gu/SZiqvLUA/29uUeXO298B8JwelGbMhO0ifCCT5iX9GpTaO68+DMKnzcZNIdoUHJZJdE3UvF6qZgQEGjhyl/913maiomDuu9vXF5vHHsd61E6WB/xvcSxySJNFZP0Tp1RYqczrn+vbKFTJ8ox0IW+GGZ4gdMrnhEviPimN6cpKq7OsUnT1JU2nR3HELG1vCV68nct1mbFxcl3rId2WSH4DjUxzKb+GtrCZKb+vb62gm8blVQTyZ6I2jlcaAI/xkFvOcSJJEfmc++yr3cbrh9FzfXrVczUafjTwW+BgJzgvzx7EpvrYAGOuHonf1ie9tfXsl+wBkcc9D9FNgabzf1nwYUzwfItkVHpRIdk3U/VyoJElivKiI/nffZeDY8bld2lCpsFq3DpvHH8cidTkyuXwJRn6n+73gTo5PU5XdQenVVjobhuaOW9mZEZrqSuhyVyxtzRZzyHd1r3H0tbVQdP40JZfOze3SBuAZHkXk+s0EJqagNOBskSl+AN4iSRJFLQO8ldXE4Rvzu7Qp5TI2hDrzmSRPVgY6ojDgH0UPYqnOycDEAMdqj/Fe1Xt37NLmrfPm0cBHecT/ERy0Dh/xDB/NlF9bgH62tyWP2ZzXmS18F+XszdpeuQpC0iDuOfBbCyZQmw+meT5Esis8KJHsmqgHvVDNDI8weOI4/e/uY7xwfncwlZsb1o8+is2ju1G5uS3GkO/qk1xwu5uHKL3SSkVWB5Nj+tXmMhl4RdgTluqGd6Q9CsXSJPD3G8fM9BQ1uVkUnT9NfUGe/oMUMLOwJHTVWiLXbcbRy2eRR/1BpvgBeDf9w2P815tnKJu0o6B5YO64m7UZTyR48kSCBx62xrPhwkdZ6nMiSRLF3cW8V/UeJ+pOMDqt/+NYKVOyxnMNjwY+ynK35SjuM6l7WF5bU1NTnD7yHps9RlEW/A1acudvtPaE2Gch5hmw8TTcIO+BKZ4PkewKD8q4CgaFRaewtMD2iSewfeIJxisq6H93HwOHDzPV2kr3b35D96uvYrFiBTaPP47V2jXIjLgmzcHDilVPBbP8sQBq8rsovdpKa1U/DUU9NBT1oNWpCU1xIXS5GzbOxpXYKJQqgpJTCUpOZbC7k+ILZyi+cJahni7yTxwh/8QRXAKCiFy3iZDlq1BrjWv8xs5CoyTFWeLf05Kp6Rnj7awmDuS30Dowzi/PVfGr81WsCnRkT6InG0KdUSuX/lsNYyWTyYh0jCTSMZLvJn6Xk/Unea/yPQq7CznbeJazjWdxNndmd+BudgXswt3S3dBDXnLTCi1S7GOQ9A/QXgx5f4HCt2GgCS7+FC7+FwSs18/2Bm0FpfFeRwXh00DM7JqohfyrfHZ8nKEzZ+l/911Gs7Lmjivs7LDeuRObxx9D4+//SYd8Vws9u9DfMUrp1VbKM9oYG5qaO+4WaENoqiv+cU6o1Av/NeNCxDE7O0ND4Q2Kzp+iJieT2Rn91/AqjRlBKSuIXLsJt+DQRV1caIqzPXdztzjGp2Y4VdLO3uwm0mt65u5rZ6Hm0Vh39iR6EuhsfP2pjeWcVPZVcqDqAEdqjzAwoZ8tlyEj2TWZxwIfY53XOtSKD0/qjCWOT+pD45gag7KjkPdnqL8yf9zcAaI/o098HYOXfsAfwhTPh5jZFR6UmNkVkJuZYb1jO9Y7tjPZ0ED/e/vpP7Cfma5uet94g9433kAbG4vN44+h27IFuYVhdmC6FzbO5ix/LIDkXX40FPZQmt5KY3EPrVX9tFb1c+XtSgKTXAhLdcXRy8poulIAyOUKfGPi8Y2JZ6S/j9LL5ym6cIa+1mZKLp6l5OJZ7Nw8iFi7kbBV67CwMf5too2JmUrBzhh3dsa409Azwt7sJvblNtM5NMFrV+t47WodsV427EnwZHu0G5YacXm8XZBtEN9L+h7fiv8W5xrOsb96P5ltmWS0ZZDRloG1xprtftvZHbCbYDvjSeqWjEoLUU/of3pqIP9vcOPvMNwB13+j//FMhtjPQvhu0Cx+b2NBEPTEzK6JWuy/yqXpaYYvX6b/3X0MX74MN2cZ5ebmWKVtxeaxx9DGxHziZHEpZheG+8Ypv95GWXobg93jc8ft3S0JTXUlOMkFM8tP9rsXKw5JkmipKKX4/BkqMq4wfbOVnFyhwC8ukYi1m/CNiUeuWJjZalOc7bmbe41jemaWS5VdvJ3dxPnyTmZm9ZdDc7WCbZGufOYuG1YsNWM+J01DTRysPsjB6oN0jnbOHY+wj2B34G62+m7FSq2fLTfmOO7HfcUxMw1VpyH/r1B5CiT9dRS1pT7hjXsOPBI/cnvixWKK50PM7AoPSkxdCHclUyqxWrcOq3XrmOrsZODgIQbee4/JhgYG9r3HwL73UPv7Y/Poo0bRwuyjWNqakZDmS/wWH5or+yi71kZtfhc9LcNcfaeK9P3V+EU7EprqikeIHXIjWq0vk8nwCAnHIySctZ/7EhXXL1N8/gxt1RVUZ2dQnZ2Bpa0dYavXE7F2I7YuS7e48GGgVMhZH+rM+lBnOofG2Z/XwjvZTdR2j/BubjPv5jbj72jBkwme7I5zx8lq6Tt9GDNPK0++Hvt1vhr9VdJb09lftZ+LTRcp7immuKeYl7NfZqP3RnYH7ibaLtrQw116CqW+U0NIGgy1w4039Ylvb63+f/P/Cg7B+kVtJtrCTBBMgZjZNVGG+KtckiTGcnLo3/ceg6dOIY3farSuxHLNamweewzLlSuR3cdGCYaaXRgfmdK3MLvWSnfT/IYVlnYaQlJcCU1xReegvefnW+o4uhvrKb54hpLLFxgfmu8t6xEaQcTajQQlp6Iyu//EzBRne+7mk8Rxa3vivdlNHCtsY2xKPxunkMtYF+LEkwmerAl2RGWknT4MrWesh6O1R9lftZ/agdq5456WngRPB/PtLd/G3dp0F7V94vMhSdCQri9zKD0IUzdbQcqVELRFX+YQsEGfKC8iU3tdgZjZFR6cSHZNlKEvVDPDwwweO07/e+/d0cJM4eiAzc6dWD/6KBo/v499HkPHAdDVOETZtVYqszuYGJ2eO+4ebEvoclf8Yh0/dlGboeK41cKs+Pxp6gvykaSbO4lptQQvX0XEmo24Bgbf89fwxnA+FsJCxTE8Mc3Rglb25jSR39g/d9zBUsNjce48keBBgNPiLmoz1XMiSRKF3YUcqDpwRwszuUzOcrfl7A7YzRrPNR+5qM0YLej5GB+Ekv2Q91doyZk/bumsX9QW8yw4Bn2y3/EhTPF1JZJd4UGJZNdEGdOFaqKqiv739jNw+DAzvb1zx7WxsVg/uhvd1q0oLO++GMOY4piemqHuRjdl6a00lffBzXeG2kxBYKIzocvdcPK5+6I2Y4hjqKebkkvnKLl4lv6Otrnj97OozRjiWAiLEUdVxxDv5jazP6+Z7uHJueOxXjY8meDJ9ihXrMwW/r/Zw3BORqdGOVF7gj9l/4n6mfq546a4qG3RzkdnmX62t+BtGO2eP+6ZrO/bG74bzBYuwTPF15VIdoUHJZJdE2WMFyppcpKhixcZ2H9Av6htVj/LKNNq0W3ahPWjj2KemHDHTm3GGAfAYM8YFRntlKW3MdQzv6jN1tWC0BRXgpe5YK6bn5EypjgkSaKlrISiC6epzLjG9KR+UZtMLsc3Jp6ItRvxi0tEofzgOI0pjk9iMeOYmpnlQnkn7+Q0c6FiflGbmUpOWoQrjyd4sMzXfsFqvx+2cxKxIoJjDcc4VH2IzrH5RW2hdqHsCtjFNr9tWGusDTjSj7bo52N6EipP6js5VJ2Gm9/WoDKHsJ36+l7v1E+8qM0UX1ci2RUelFigJiwYmVqNbtMmdJs2MdXZyeDhw/S/t5/JujoGDh1i4NAhVJ6eWO/ehc2uXUu6U9v90tlrSdzmS8JWH1oq+yhLb6Mmv4u+thHS91dz/WAN3hH2hC53xTvSuP5okslkeIRF4BEWwbrPf5mK61covniGtspyavOyqc3LRmulI3TlWiLWbMDR29fQQzYpKoWcTeEubAp3oXNonAN5Lbyb20x15zD781vYn9+Cp52Wx+I8eCzOA087sSHI7bx0Xnwj7hu8EPMC6a3pHKw+yIWmC5T1llGWVcbPcn7GWs+17AzYyXK35Sjln7KPKaUawh7R/wy162d68/8GPVVQ8Jb+x9ZHP9sb/ZTR79QmCMZAzOyaKFP5q1ySJMbybzBwYD+Dx08wOzKiv0EmwyJlGZY7HuHq9BRbd+0y6jgAJsamqc7poCy9jY66+UVhWisVAQlOdExWsuszW4w2jp6WJkounqX08nlG+vvmjjv5+hO+egOhK1ajNNOaxOvq4xhii938pn7ezWnmaEErQxPztd/L/e15IsGDLeGuaB9gQxNTea9/nI+Ko3+8n2N1+tnest6yueNOWie2+29nZ8BO/Kw/fg3AUjDI+ZAkaMqCG3+D4gMwOXTzBhn4rdbX9oZu1/f6vUem+LoSM7vCgxLJrokyxQvV7OgoQ2fO0L//AKOZmXPHZzQabLdvx/bxhenduxR6W0cov95GRWY7o4Pz9ZsOnpaELnclMNEZraVxLryZnZmhviCP4otnqMnJYnZGn5jJFUp84xIZNdfx2D98Ec0DdHMwFoZ8f4xN6ndqeze3iWvV8zu1WWqUbI9y5fF4D+K97713rym+1+/mXuMo7y3nYPVBjtUeo3+if+54lGMUuwJ2scVny1zvXkMw+PmYHIGyI/rZ3tt3atPoIOJR/YzvPfTuNXgcD0Aku8KDEsmuiTLFC9XtJpubGTh4iP4D+5luaZ07rvbxwXr3bqx37UTl7GzAEd6b2ZlZGkt7Kb3WQl1BN0j6Dxi5QoZvlAMhKa54htuhWKI2VfdrdHCA8muXKbl4ls76mrnj5ta2hK1aS/jq9Th4ehtwhA/GWN4fTb2j7M9rYV9eE029Y3PHfR0seDzeg92x7rjZfPRsnLHE8kndbxyTM5Ncbr7MweqDXG25yszNDRk0Cg3rvNaxK2AXyS7JKOQLv/33RzGq89FXry9zuPF36G+cP24fADFP68scdHcvFzOqOO6RSHaFB2UUye6rr77Kyy+/THt7O9HR0fz6178mKSnprvdds2YNly5d+sDxtLQ0jh079rG/SyS7xmVyYoKLr75KWHs7w2fOIo3dTAhkMiyWL8d6926sNqxHbuSzjFNTUxw5eAJf2ygqMzvv6N2r1akJSnImNMUVe3fj3SK0s76WogtnKLxwhtmJ+UV5zn6BhK9ZT0jqarSWhptRux/G9v6YnZXIqu/l3ZxmThS3MTqpT9xkMlgR4MDj8R5sCnO5a5mDscXyoD5JHF2jXRytPcqh6kPUDMz/UeZs7swO/x084v8IvtZLU3tulOdjdhYarumT3tJD8717kYH/Wv1sb8i2O8ocjDKOjyGSXeFBGTzZ3bt3L8899xy/+93vSE5O5pVXXuHdd9+loqICJyenD9y/t7eXycn5r417enqIjo7mtdde43Of+9zH/j6R7BqX2+OQT0wydOok/fsPMJabO3cfuaUluq1bsd69C21srFGWObz/fHQ3D1Ge3k5ldjtjQ1Nz93PwtCQkxZWgJOMsc5iamuLYkSOEuDlRcfUitXnZzN7cKlqhVOIfn0zY6vULukXxYjDm98fIxDQnitt5N6eJzLr5Vn1WGiXbolx5LN6DhNvKHIw5lvuxEHFIkkRpTykHqw9yvO44g5PztfNRjlHs9N/JFt8t6NSLlwgZ/fmYGIKSg/qFbA3X5o9rdPr2ZTFPg2cyU9PTxh3HXYhkV3hQBk92k5OTSUxM5De/+Q0As7OzeHp68vWvf53vf//7H/v4V155hR/96Ee0tbVhYWHxgdsnJiaYmJiY+/fg4CCenp60tbWZfLJ75swZNm7caDIXqrv5sDimmpoYPHyYocNHmG6dL3NQeXlhtWMHVo/sMKpuDh8Wh77MoY/KzA4ai3uZndG/3WRyGd4RdgQlOenLHJTGUebw/jhGBweoSL9M2ZULdDfUzd3P3NqG4OWrCF25FgcvH8MN+EOYyvujsXeUgzdaOZDfSnP//Gy6l52WXTFu7I5xw9lSaRKxfJyFPieTM5NcbrnM4drDXG+7PlfmoJarWeOxhh1+O0h2SV7wbg6m8toCoK8OeeFe5EV7kQ00zR2WbH2ZCn+ci32urNj2lPHHcdPg4CAODg4i2RXum0GT3cnJSczNzdm3bx+7du2aO/7888/T39/PoUOHPvY5IiMjSUlJ4f/+7//uevuPf/xjfvKTn3zg+Jtvvom5uWgJZPRmZ9HW1aHLzcWqqBj5bbP6o/5+DMbHMxQRgaTRGHCQ92ZmUsZYm5KRZhVTg/OzonLVLOZu05i7T6HSzX7S9pmLZqKvh8HaSoYbapgZn68/Vdvao/MNxNInAKXZva8GF+bNSlA7CJldcm70yJicnX8RBOgkEh1nibGXMDPeyXSDGpodomCygPzJfDpmO+aOW8osiVHHEKuOxVlh/GsAFo00i8NwOZ69V3Hrz0Y5Oz8B1GUZSpPdClptEplRGHe52OjoKE8//bRIdoX7ZtBkt7W1FXd3d9LT00lJSZk7/t3vfpdLly6ReduK/bvJysoiOTmZzMzMD63xFTO7xu1+4pgdHWX47FmGDh1iLCt77rhMa4blhg1YPfII2sREZAb4ev1+z0dv6wiVWR1UZXcyNjhf5mDrak5gkhOBCU5Y2Cx9An8vccxMT9NQmE/ZlQvU5WczO63v5iCTy/GOjiN05Vp8YxJQqg1XpmHK74/RyWlOl3ZyIL+V63W93LpCmynlbApzZnesGyl+digWaNOKpbIU50SSJMr7yjlae5QTDSfu6OYQYhvCdt/tbPHZgp2Z3QP/DlN+bQEwOYys/BiygjdRNM6XOUgqC6SQ7cxG7kHyToUlXvh3L8TMrvCgTLpb9x//+EciIyM/NNEF0Gg0aO4y66dSqUzzQvU+n6o4rK3RPPYY9o89xlRLCwOHD9N/8CBTDY0MHTnK0JGjKF1csN6xA+tdO9H4+y/N4G9zr+fD2dsGZ28bUh8NpKm8j4rrbdQWdNPXNkrWoXqyD9fjEWpHcLILfjGOqDRL+8HzUXGoVCqCk5cTnLycseEhKq5dpuTyOdqrK6nPz6E+PweNhQXBKSsJW7Uet6AQg9VZm+L7w1ql4olEb55I9Kalf4z3chr529UqOsdnOVzYxuHCNlx0ZuyKdeexOHcCnU1j0eAti31OopyjiHKO4jtJ3+Fyy2WO1BzhUvMlyvvKKe8r55X8V1jhvoId/jtY7bkajeLB/qg0xdcWACpbiH+Wqag9nDv4F9Y7dKEoegdZbw2yIn3JAzp3iNqj7+bgGGToEc8xyf/eglEwaLLr4OCAQqGgo6PjjuMdHR24uLh85GNHRkZ4++23+bd/+7fFHKJgpFTu7jh85SvYf/nL+k0rDh1i8MQJptvb6fnDH+j5wx8wi4zEeudOdNvSUNraGnrIdyVXyPEOt8c73J6JsWlqcjspz2ijrXqAptJemkp7UWkU+Mc7EZLsglugDTIjmtHTWloRs3kbMZu30dPSROnl85ReucBwTzeFZ09SePYkNi6uhK1aR9jKtVg7ffT7WriTu42Wr6z2w2u4HPeo5RwqbOdIQRvtg+P87lINv7tUQ5SHNY/GurMj2g17S+Mv51kqKoWK9V7rWe+1nr7xPk7Wn+Rw9WGKe4q52HyRi80XsVJbsdVnKzv8dxDtGG2Ui18X05jagdkVz6FY8z1oztYvait+DwZb4OrP9T9ucfqkN+IxsDDdb0OFTzeDJrtqtZr4+HjOnTs3V7M7OzvLuXPn+NrXvvaRj3333XeZmJjg2WefXYKRCsZKJpNhHheLeVwszv/yA4YvXGTg4EGGr1xhvKiI8aIiOv7rv7BcvRrrRx7Bcu0a5Ab8ev2jaLRKwla4EbbCjYGuUSoy2qnIbGewe5zy9DbK09uwtNMQnORC8DIXbF0+uCDTkOzdPVn51POk7nmWppIiSi+fpyoznf72NtLf+Tvp7/wd95BwwlatJWjZCswsjLcNm7GRySDG04ZEP0f+3/Ywzpd18l5eCxcrOilsHqCweYD/OFbGmmBHdsd6sD7UCTOV8X0NbSi2ZrY8FfIUT4U8RW1/LUdqj3Ck5ggdox28U/kO71S+g6eVJzv8drDdfzueVp+yLXhlMvBM0v9s/ilUnYIbb0H1GWjN0/+c+gEEboLoz0DQFlCKP6wE02HwMoYXX3yR559/noSEBJKSknjllVcYGRnh85//PADPPfcc7u7u/PSnP73jcX/84x/ZtWuXSdfdCgtLrtGg27IZ3ZbNTPf0MHjsOAOHDjFeUsLwuXMMnzuH3Noa3ZYtWO98xGjbmAFYO5qTtMOPxO2+tNUMUJHRTnVuJ8O9E+SebCD3ZANO3lYEL3MhMMEZrZXxJPByuQLvyBi8I2NY/49foSozndIrF2gsLqClvISW8hLOv/F7fRuzVevwiY5DoTT4pchkaJQKtka6sjXSlZ7hCQ4XtHIgv4XC5gHOlnVytqwTKzP9bm2Pxt3ZxkwAPxs/vhn3Tb4e+3Wy2rM4UnOEMw1naBpq4rcFv+W3Bb8l1imW7X7b2eyzGWuNtaGHvLRUZhC2U/8z3KWf6S14C9puQMVx/Y+Zjb6NWfRT+gRZvL4EI2fwT5g9e/bQ1dXFj370I9rb24mJieHkyZM439w9q7GxEbn8zrZMFRUVXL16ldOnTxtiyIIJUNrbY/fcZ7F77rNMVFczcOgQA0eOMt3eTv/evfTv3YvK01Nf37vzEdTexrlLmEwmwy3ABrcAG1Y+GUhdYTeVme00lPTS2TBEZ8MQ196txivcjqBkF3yjHVAa0Yye2kxL+Or1hK9ez1BPN2VXL1J6+Tw9zY1UZlylMuMqWp01IctXEbZyLc7+gSIxuw/2lho+n+rL51N9qe4cYn9eCwfzW2gdGOetrCbeymrC007L7hh3dsd54OtgXN8GGJJcJmeZ6zKWuS7jh8k/5FzjOY7UHCGzPZP8znzyO/P5r6z/Yo3nGrb7bWel+0pUik9ZzailIyz7sv6ns0y/W1vRu/oyh9w39D+2Pvr63qg9YL/06yQE4V4YvM/uUhObShiXpYxDmplhNCuLgUOHGTx9Gml0dO42bXQ0up2PoNu69YHqe5f6fIwOTlKV00FlZjudDUNzx9Vm+vre4GQX3ALuv753KeKQJInOuhpKr1yg/NolRgf6526zdfMgbOVaQleswdrpwVtFPSzvD7j/WGZnJTLqejiQ18LxojZGbu7WBvpSiN0363vtLJb22wBTOScdIx0crzvOkdojVPVVzR231lizxWcLW7y20JzVzLZt24w6jo/zwOdjdgbqr0DBXig7DJPzu0XikahPesMfXZT6XrGphPCgRLJrokzlg+PjGCqO2dFRhs6dY+DwEUauXdNvtwmgVGK5ahXWj+zAcu1a5PfYv9eQ56O3bYTKzHYqstoZ7p1vs2dppyEoyYXgJBfs3O5tRm+p45idmaGhMJ+Sy+epyc5gemq+j7J7SDhhK2/W91reX33vw/L+gE8Wy9jkDKdL2zmQ38KVqm5mZvWXe6VcxuogR3bHubMh1HlJ6ntN8ZxU9FZwpOYIx+uO0zXWNXfcTm7H42GPszNwJ146LwOO8MEtyPmYHIHy41D4NtScB+nmdVSu1Nf3Rj0JQVv1pRELQCS7woMSya6JMsUPjrsxhjimu7oYOHaMwcNHGC8tnTsut7LCavMmrHc8gnliAjL5h+9yZgxxSLMSrVX9VGS1U5PbyeT4/Iyeg6clwckuBCY6Y2H94Qm8IeOYGB2lKiudsivnaSwp4laDWYVSiV9cEqEr1+Abm4jyHsZlDOdjoSxULF1Dt+p7mylumd9m10qjZGukC7ti3Vnma498kbp9mPI5mZmdIbMtk6O1RznbeJax6flNVaIco+bqez9J/96ltuDnY6gDivdB4V5oK5g/rtHp63+j9oB3KnzEdfTjiGRXeFAi2TVRpvzBcTtji2OiupqBw0cYOHKE6ba2ueNKV1est29Dt2MHZkEf7DtpbHFMT85QX9RDRWY7jcU9zN6c0ZPJ0PfvTXLGN8YRtdmdZfvGEsdQTzfl1y5ReuUC3Y31c8c1FhYEL1tJ6Mo1uAeHfegfIMYSx0JYjFiqOoY4eKOFg/mttPTPJ26u1mY8Eu3Grlh3Ql0XNpl4WM7JwOgArxx7hRbrFjLbM5m9OZuplClJdU9lu9921niuwUxp3LuRLer56CzXJ72F78Bg8/xxnTtEPqGf8XUOv++nFcmu8KBEsmuiHpYPDmONQ5qdZTQnh8EjRxg8eYrZofm6WE1wMNY7tqPbvh3VzX7QxhoHwNjwJNU5nVRmtdNeOz+jp1TJ8Y1xJCjJGc8wOxQKuVHG0dVQN1ffO9zbM3dc5+hESOpqQleswcHzzgWGxhjHg1rMWGZnJbLrezmQ38KxojaGxqfnbgt2tmJXrDuPxLjhbvPJt4F+WM7J7XEMTA9wvPY4x+qOUdoz/62QhcqC9V7r2e63nSSXJBRGuBvZkpyP2VloTNcnviWHYGJg/jbnCH3SG/E4WLvf09OJZFd4UCLZNVEP4weHscYxOzGh79979AjDly7D1M3tfWUyzBMT0e3YjnbdOk5dvWrUcQAMdI1SmdVBZVYH/R3zC/TMLFUExDvhF+dAbtlVtm0zvjhmZ2doLi2m7OpFKjOuMTk2P35Hb19CV64lJHUVVnYOJvG6uldLFcv41AwXKzo5kN/ChfIuJmdm525L8rVjV4w72yJdsTZ/sDE8LOfkw+Ko7a/laO1Rjtcdp2W4Ze64o9aRLb5b2Oa3jTC7MKPpNrLk52NqHCpP6rs5VJ6C2VvbpMvAZ4V+xjdsJ2htPvQpRLIrPCiR7Jqoh/2Dw1jN9PczeOo0A0cOM5aTO3+DSsVQcBCB//iPWK9bd88L2wxFkiQ6G4aozGynKqeDsaGpudsU2lmiVnkTsswNO1fjbFU1NTlBbW42ZVcvUpefw+zMzRlJmQzP0AgCU1ZSNzDMjl27TeJ19VEM8R4ZGJ3ieHEbB/NbyKzrnTuuUshYE+zErhj3+964wtTe6x/m4+KQJIkbXTc4WnOUUw2nGLhtNtNH58M2v21s892Gp86wG1cY9HyM9kLpIX2ZQ2P6/HGFen5hW+DmDyxsE8mu8KBEsmuiPi0fHMZsqqWFgWPHGTxyhImq+RZFcisrrDZtxHr7dsyTkpApjO8rzNvNzszSXN5HZVYHNfmdTE/Oz+g5eFoSlKhf2GZpa5wJ/NjwEJXXr1J29SIt5SXzN8jl+MUmEr5qLX5xSSiNdOe8j2Po90hL/xiHb7Ry6EYL5e3z5TxWGiWbI1zYFeNOir89io9Z2GboOBbK/cQxNTPF1ZarHKs7xsWmi0zMzHdLiXKMIs03jS0+W7DXLv1nkdGcj/5GKNqnn/HtnC8FQaOD0Ecg6gnwWQlyhUh2hQcmkl0TZTQXqk/oYYljuLiEgl//GqeKCqbb2+eOKx0d0aWlodu+HbOIcKP5CvPDjI2Mc/Av57Cccae5tG9uYRsycA+0ISjJBb9YR8wsjPNcDXZ1UnbtEmVXLtDT3Dh3XK01JzBpOaEr1uAZEYncCGsoP4wxvUfK2wc5mN/K4Rv6jStucbTSsCPKjZ0xbkR5WN/1dW5McXwSDxrHyNQI5xrPcaz2GBltGXML2xQyBctcl5Hml8Z6r/VYqJbm2xSjPB/txVD0DhS9d+fCNksXiHiMQZ8tWIeuFsmucN9EsmuijPJC9QAetji2btnCdGEhA0eOMnjqFLMD819hqn180G3bhm77NjS+vgYc7Ye7/XxMT0jU5HVRmdVOW/V8HHKFDO8IewITnfGJckClNr7EcWpqigN//ytOConK61cZ6pnvkWpubUPI8lWErFiNi3+Q0f8BYozvkdlZiZyGPg7e0G9c0T86XwbjY2/OIzHu7Ixxw99xvj+yMcbxIBYiju6xbk7WneR43XGKuovmjmsUGlZ7rCbNL42V7itRKxbv2wijPh+zs9B4XZ/4lhyE8X4ABickrP9rSCS7wn0z+HbBgvAwkcnlmCcmYp6YiMu//pDhq9cYPHqEofMXmKyvp/vVV+l+9VXMwsPRbd+OLm0rKucH3ylsMWkt1USscidilTuDPWM3Ozp00NMyTF1BN3UF3ag0CnxjHAhKdMEj1BaF4sF7aC40ja09qWlprH7m87RUlM4tbBsd6CfvxGHyThzG2tmFkOWrCV2xGnsP09wcwBDkchlJvnYk+drx4x3hXK7s4nBBK2dKO6jvGeVX56r41bkqwt107IxxY3uUG44W4uPmFgetA8+GPcuzYc/SMNjA8drjHK87Tv1gPacbTnO64TRWais2eG0gzS+NROdEo+zosGjkcvBJ1f9sfRlqzunLHG4cBYY+9uGC8H7i6iMIi0SmVmO1bi1W69YyMzzC8PlzDBw9ysi1dMZLShgvKaHzf/4H84QEdNu3Y7Vp4wNtVbwUdPZa4jZ7E7fZm56WYaqyO6jM7mCoZ5zKzA4qMzv0HR3inAhMdMbV3/q+typeLDK5HI/QCDxCI1j3+X+iofAGZVcvUp2TwUBHO5kH9pJ5YC+OXj6ErFhDyPJV6BydDD1sk6FWytkQ5syGMGdGJqY5W9bBoRutXK7soqR1kJLWQf7zeDmJPrb4yGQsG5nE2cbIZhINyFvnzVdivsKXo79MWW8Zx2uPc6LuBJ1jnRyoPsCB6gM4aB3Y4rOFrb5biXSINPpvIxaUUg3BW/U/q1vg/+dh6BEJJkgku4KwBBSWFlg/8gjWjzzCdG8vgydPMnj0GGN5eYxmZzOanU37v/87lqmp6LZvw2rdOuQWxtkJwd7dEnt3S5J3+tFRN0hlZjvVeZ2MDU1RfLmF4sstWNpqCEhwJijRGQdPS6P5cFYoVfjFJeIXl8jU+DjVuZmUX7tE/Y1cuhrr6XrzT1x580+4BYUSkrqKoGUrsLAxzj9AjJGFRsnOGHd2xrjTOzLJ8aI2Dt9oJau+l+z6PrJRcOB/LrEy0IFHYtzYGOaCpUZ8DAHIZDLC7MMIsw/jxYQXye3I5XjdcU7Xn6Z7rJu/lf2Nv5X9DQ9LD7b6biXNN40A2wBDD3tpaawMPQLBRImrjCAsMaWdHXZPP43d008z1dLC4IkTDBw9xkR5OcOXLjF86RIyMzMs165Bl5aG5apVRtnKTCaT4eJnjYufNSueDKS5oo+q7A5q87sY7pvgxplGbpxpxMbZnMAE/YyvrYvxJPAqMzNCU1cTmrqaseEhqjKuUX7tEk1lxbRWltFaWcaFP/0Br8hoQpavIiApBTMLy49/YgEAOws1zy7z5tll3rT0j3Eov4k3r1bSPAIXKrq4UNGFRlnE+lAnHol2Y03w/bUye5jJZXISXRJJdEnkX5L+hWut1zhed5yLTRdpHm7mD0V/4A9FfyDQNpCtPlvZ4rsFTyvDtjITBGMmkl1BMCCVuzv2X/gC9l/4AhPV1QweP87AsWNMNTQydOIkQydOIre0xGrjRnRpaVikLEOmNL63rVwhxyvMHq8we1Y/PUNjcS+V2R3UF3XT3zFK9rF6so/V4+BpSWCCMwEJTujsP/muXAtFa2lF1IYtRG3YwlBvN5XXr1J+7RLtNVU0FObTUJjP2ddexScmgZDUVfjHJaEyM+7tYI2Ju42WL67wxX2wjODE1Zwo6eRwQSt13SMcL2rneFE7lholm8KdeSTajdQAB1RGVP9tSCqFijWea1jjuYbRqVEuNV/ieO1xrrZepaqviqq+Kn6V/ysiHSLZ4rOFzT6bcbYwznUAgmAoxvepKQifUpqAABy/8Q0cvv51xktKGTx2jMETJ5hub2fgwAEGDhxAYWeH1eZNWKeloY2PRyY3voRAqVLgF+uIX6wjk+PT1BV0U5XdQVNpL91Nw3Q3DXP9QA0uftYEJjrhH+eEhbXxzFxb2TkQv20X8dt20dfeSkX6FcqvXaKnuZGanAxqcjJQajT4xycTsnwVPjHxKI1tNbsR83e04J83BvGtDYGUtA5yuKCVowWttA6Msz+vhf15Ldiaq9ga6cqOKDeSfO0+tofvp4W5ypytvlvZ6ruVgYkBzjWe43jdcbLbsynqLqKou4if5fyMeOd4tvpuZYP3BuzM7Aw9bEEwOJHsCoKRkclkaCPC0UaE4/SdbzOWl8fg8eMMnjzFTG8v/W+9Tf9bb6N0ckK3dQu6tDTMoqKMpi72dmozJcHJLgQnuzA+PEVNfidVOR20VPbTXjtAe+0AV9+pwi3IlsAEJ/xjnTCzNJ7E0dbFjWWP7mHZo3voaqynIv0y5emXGehopyL9MhXpl9GYWxCQmELI8pV4RkSjMMKZd2Mkk8mIcLcmwt2a728JIbexjyMFrRwvaqN7eJI3Mxt5M7MRJysN26Jc2RHtRqynjVG+zg3BWmPNo4GP8mjgo3SPdXO6/jQn6k5wo+sGOR055HTk8J+Z/8ky12Vs8d3COq916NSiXZfw6SSuyoJgxGRyOeYJCZgnJOD8L//CyPUMBk+cYOjMGaY7O+n981/o/fNfULm7o0vbii4tDU1IiFEmBGaWKsJXuhO+0p2R/gmq8zqpzumgvXaQloo+Wir6uPxWJR6htgTEO+MX44DG3HgSX0cvHxy9fEjd81k6aqooT79ERfoVhvt6Kbl0lpJLZ9Fa6QhMXk5wyio8wsJNavMKQ5LLZST62JHoY8ePtoeRUdvL4YIWTha30zk0wRvX6nnjWj3uNlq2R+tnfMPddEb5OjcEB60DT4c+zdOhT9M63MrJ+pOcrDtJWW8Z11qvca31Gv92/d9IdU9li88W1nquRYXxvLcEYbGJZFcQTIRMqcRy5QosV65g9scvMXL1KoNHjzF08SJTLS30/OE1ev7wmn7ziluJb4Bxrta2sNEQvc6T6HWeDHaPUZ2rn/HtbhqmsaSXxpJeLr4pwyvMnsAEJ3yiHFCbGcflSiaT4RIQhEtAEKuf/Udaykspv36FyoyrjA0OUHj2JIVnT2JhY0vQshUEp6zELSjEKEtOjJFSIWdFoAMrAh34j12RXKnq4sjNHr4t/WP8/lItv79Ui6+DBdujXNke5Uawi1ilf4ubpRv/EPEP/EPEP1A/UM/J+pOcqDtB7UAtF5sucrHpImYKM1a4rcBh0oF10+uMb1MJQVhgxvHpIQjCfZGr1VitW4fVunXMjo0xfOkSg8eOM3zpkn7zit/+L92//V80gYFYbd2CbutWo921Tecw38O3v2OUqpwOqnI66Wsbob6wm/rCbpQqOd6R9gTEO+MdaW80u7bJ5HI8wiLwCItg3ee+RGNJIRXpV6jKusZIfx/5J4+Qf/IIlvYOBC9bQfDylSaxa5uxUCvlrA91Zn2oM+NTM1wo7+RIYSvnyjqp6x7h1+er+fX5agKdLNke5cb2aNc7dm37tPOx9uHL0V/mn6L+iar+Kk7WneRU/Skahxo523QWgEP7D7HWcy1bfLaQ6p66qLu2CYKhiGRXEEycXKtFt2ULui1b9JtXXDjP4PETDF+9ykRVFRNVVXT/6tdoQkP190vbitrTONsU2Tibk7jNl8RtvvS0DOtnfLM7GOgaoyavi5q8LpQaBb6R9gQkOOMVbofSSNpVyRUKfKJi8YmKZcMXvkJD4Q0q0i9TnZPBcE83uccOknvsIDpHZ4JT9DO+Tr7+IvG9R2YqBVsjXdka6crwxDTnyjo4UtDG5couqjqH+cXZSn5xtpJQVx3bo1zZFumKj4PxtLozJJlMRpBtEEG2QXw99uuU9ZZxrOYYh8sP0z/dz/E6/Q5ulipL1nmtY7PPZlJcU1ApxIyv8HAQya4gPEQUlhZY79iB9Y4dzAwOMnT2HIMnTjBy/ToTZWV0lZXR9YtfYBYRgW7rFqw2b0Ht4W7oYd/Vrc0rknb40t00TFVOB9W5nQz1jFOV00lVTicqMwW+UQ4ExDvhFWaPQmUcpQK3b14xPTlJXUEuFelXqM3NYrCrg+zD75F9+D1snF0Jupn4Onr7isT3HlnetnnFwNgUZ0o7OFrYytWqbsraBilrG+TlUxVEuOvYFunGtkhXvOzNDT1so3Br84pAXSCBrYF4Jntytuksp+tP0znWyeGawxyuOYyV2or1XuvZ4rOFJNckVHKR+AqmSyS7gvCQUuh02Dy6G5tHdzPd18fQ2bMMnTjBSEYm48XFjBcX0/nyzzCLiro5M7wZHB0NPewPkMlkOHpZ4ehlRcpufzrrh6jK7aAmt5PhvgkqszqozOpAbabAN9qRgHgnXAKNp4ZTqVYTmJhCYGIKUxPj1OXn6BPf/Bz6O9rIOvguWQffxdbVneCUFQSlrMTB01skvvfIWqvi8XgPHo/3oH90klMl7RwtbCO9pofilkGKWwb575PlRHlYsy3SlW1RrnjYisQX9O+tKIco4l3j+U7id7jReYOT9Sc503CG7rFuDlYf5GD1Qaw11mzw2sAmn00kuSShlIvUQTAt4hUrCJ8CSltbbJ94AtsnnmC6p4ehM2cYPHGS0exsxgsLGS8spPN//gdNVBQ2Xp5MxcWhMsJSB5lMhrOvDmdfHamPBtBRP0h1TifVeZ2M9E9QkdlORWY7aq0CpZ0Zjd69+EQ4olAax4yvSmNG0LIVBC1bweT4GLW5WVRcv0rdjRz62lrI2L+XjP17sXPzIChFfz9rFzdDD9tk2Jir2ZPoxZ5EL3qGJzhZ0s6xwjYyansobB6gsHmAn54oJ9rThu2RrmyNdBGJ701ymZw45zjinOP4XuL3yOvM41T9Kc40nKF3vJf3qt7jvar3sNXYzpU6JLokisRXMAkySZIkQw9iKQ0ODmJtbU13dzf29vaGHs4Dm5qa4vjx46SlpZn0SloRh2FNd3UxeOYMQydOMpqTA7ddDrTR0Vht2YJu8yZUbsadcEmzEm21A9Tk6hPf0YHJuds05kp8ox3wj3PCM9TOaBLf202OjVKTl03l9SvU3chlZmpq7jZbN3ewc2LrM8/jYsI1voZ8j3QN3Up8W8ms6739ZU6Mpw3b7iPxNdX3+vvdaxwzszPkduRysv4k5xrP0TveO3ebrcaW9d7r2eS9aUkS31uf3wMDA+h0omewcO9EsmuiPm0XXGP3MMQx1dlJ/8lTNL71Fub19XckvmbRUeg230x83Y2zxvcWaVaiqaKH8wdymO23YGxwPnHUmCvxjXLAP954E9+J0VFqczOpyLhK/Y1cZqan526zdfPQlzosW2FypQ7G8h7pHBrnVLG+1CGr/s7EN9rThm2RLmyNcMXT7u6Jr7HE8Uk9SBzTs9PkdORwqv4UZxvO0j/RP3fbrRnfxSx1EMmu8KBEsmuiPs0XXGP0sMWxKTGRsfMXGDp5ktHc3DsT36godJs3Y7V5s9EubrsVx5YtW+lpHKE6p5Oa/C5GB+dnfNXa+Rlfr1A7o1ncdruJ0REqM9O5duQgYx0tzL4/8V2Wqk98vXyMPvE1xvdI5+D4XKnDBxJfD2vSIl1Ji7wz8TXGOB7EJ41jenaa7PZsTjec5lzDOfom+uZus9HY6BNf700LurhNJLvCgxLFNoIgfIDS0RG7Z5/B7tlnmOrsZOjMGYZOnmI0J2e+xvfll/VdHbbcTHyNsMZXLpfhFmiLW6AtK/YE0V7TT3VuFzX5+lKHiox2KjLaUZsp8Im6mfgaUTszjbkFISvWUDs4yvo1q2kszKfy5oxvX2vzXI2vras7QctSCUxOxcnHz+gTX2PhpDPjuRQfnkvxmZvxPVbURlZdLwXNAxTcrPGNdL+V+LrgphN9aAGUciUpbimkuKXww+QffiDx3V+1n/1V+9GpdXOJ7zLXZaKdmWAQItkVBOEjqZycsHvmGeyeeWa+xvfUaf3itltdHX72/2EWFobV5s3oNm9C7eNj6GF/wO2J78onA2mrGaAmr5OavE5GBibnujqoNLcSX0e8wo1nAwuNuQVhK9cStnLtXKlDZeY16m7k0tfWQuaBd8g88A42zq4ELkslKDkVZ78AkfjeIycrMz6b4sNnU3zmanyPF7aRWddDUcsARS0D/PfJcsJcrfBVygjtHiHI1cbQwzYK7098czpyOF1/eq7G91ZXByu1FWs917LJexMpbiliAwthyYhkVxCEe6Z0dMTu6aexe/pppru7GTp7lsGTpxjNymK8tJTx0lK6fvELNMHBWG3ehG7TJqPcslgml+EWaINboA0rngikvW6QmtxOavL17cyqsjuoyu5AqZbjHaFPfL0j7I1my2KNuTmhK9cSunItk2Oj1OZlU5lxjbqb7cyyD+0j+9A+dI5OBCanEpS8HNeAYLFl8T1ytNLw2WXefHaZN93DE5wu6eB4URvXa3sobRuiFAXHfnmNYGcrtka6kBbpSqCTpfjDAn3iu8x1Gctcl/HD5B+S25HL6YbTnG04S894z1wfX0uVJas9V7PReyOpbqmYKc0MPXThIWYcV25BEEyO0sEB2898BtvPfIbp3l59H99TpxnJyGCiooKJigq6f/Vr1P7+WG3aiG7zZjTBwUaXEMjkMlz9rXH1tyb18QA6GgZv7tam38Di1uyvQiXHK8wO/zgnfKIc0GiN4/Kp1poTkrqakNTVTI6PUZefQ2VmOrV5WQx2dZJ79AC5Rw9gaWdPYNJyApOX4x4ShlxuHDPWxs7BUsPTyV48nexF78gkJwpb+NulYqoGFVR0DFHRMcQrZ6vwc7QgLcKVLREuhLvpjO51bggKuYIk1ySSXJP4QdIPyO/M50zDGc42nKVzrJNjtcc4VnsMrVLLKo9VbPDewCr3VZirRDs4YWEZx9VaEASTprSzw/bJJ7F98kmm+/oYPn+BodOnGU5PZ7Kmhp7/raHnf3+HyssL3aaNWG3ahFlkpNElBDK5DBdfa1x8rVn+qD9djUNzie9A1xh1Bd3UFXQjV8jwDLXDL9YRv2hHzCyNow5RbaYlOGUlwSkrmZoYp74gj8qMa9TmZTHc20P+ySPknzyCubUNAYnLCExOxTMsEoVSfBTcCzsLNU8meGDZWUjq2vVcrOrlRFEbV6q6qe0a4TcXqvnNhWq87MzZGuHClggXYjxtjO51bggKuYIElwQSXBL4XtL3KOwqnJvxbRtp41T9KU7Vn0Kj0LDcbTkbvTey2nM1OrVYiCZ8cuIKJwjCglLa2mLz2KPYPPYoM0NDDF+8qE98L19hqrGRntf+SM9rf0Tp6orVxg3oNm1CGxuLTGFcM40ymQwnbx1O3jqW7fKjp2V4LvHtax+lobiHhuIeLv69AvcgG/xiHPGLdcTCWmPooQP6DSwCk5YTmLSc6akpGgrzqcpMpzrnOqMD/RSePUnh2ZOYWVrhH59MYPJyvKNiUZpwh4GldPvObUPjU5wv7+R4URsXK7po7B3l95dr+f3lWlytzdgc7sLWCBcSfOxQyEXiK5fJiXGKIcYphu8kfIeSnhLONJzhTMMZmoaauNB0gQtNF+ZKIjZ4bWCt11qUImURHpB45QiCsGgUVlZY79iB9Y4dzI6MMHz5MoOnTzN86TLTbW30/eWv9P3lrygcHLBavx6rTRuxSEpCZmQJl0wmw8HDCgcPK5If8aO3bYTafH07s+6mYZrL+2gu7+Py3kpc/az1M74xjugctIYeOgBKlQr/+CT845OYmX6BpuJCKrPSqc7OYGxwgJJLZym5dBa1VotvbCJBycvxjUlAZSbqKO+FlZmKnTHu7IxxZ2RimosVXZwobuNCeSdtA+P8Kb2eP6XX42CpZmOYPvFN8bdHpRA11DKZjAiHCCIcIvhW3Leo7KucK3WoGajhastVrrZc5d8y/o0oyyhDD1cwUUaR7L766qu8/PLLtLe3Ex0dza9//WuSkpI+9P79/f388Ic/ZP/+/fT29uLt7c0rr7xCWlraEo5aEIT7IbewQLd1K7qtW5kdH2fk2jWGTp9m6PwFZrq76d+7l/69e5FbW2O1di1WmzZhkbocucY4ZkpvZ+dqgZ2rLwlpvgx0jVKT30VtfhcddYO01QzQVjPAtX3VOHpZ4RfriH+sI7YuFoYeNgAKpQqfmHh8YuLZ8IWv0lJWQmVmOtVZ6Qz39VKRfpmK9MsoVWp8YuIITFqOX1wSZpaWhh66SbDQKNkW5cq2KFfGp2a4UtXNieI2zpZ20D08yVtZjbyV1YjOTMmGMGe2hLuwKsgRMyNpd2dIMpmMYLtggu2C+Vrs16jtr+Vs41nONpylrLeM3M5cQw9RMFEGT3b37t3Liy++yO9+9zuSk5N55ZVX2Lx5MxUVFTg5OX3g/pOTk2zcuBEnJyf27duHu7s7DQ0N2NjYLP3gBUF4IHIzM/1M7vr1SJOTjGRm6RPfc+eY6e1l4OBBBg4eRG5ujsXqVeg2bsRi1WoUlsaRMN7O2tGcuE3exG3yZqh3nLqCLmryumir7qercYiuxiEyD9Vi62qB/80ZXwdP41i5L5cr8AyPwjM8inWf+xJt1ZVUZaVTlXmNgc4OqrMzqM7OQK5Q4BURTWDScvwTkrGwsTX00E2CmUrBxjBnNoY5MzUzy/WaHk4Ut3OmtJ3u4Un257WwP68Fc7WCNcGObA53YV2IE1ZmxvXNhqH42fjxJZsv8aWoL9E01MTRkqN8la8aeliCCTJ4svvzn/+cL37xi3z+858H4He/+x3Hjh3j9ddf5/vf//4H7v/666/T29tLenr63K4vPkbY01MQhHsjU6uxXLkCy5UrcPnxS4zm5jJ0+gxDZ88y3d7O0ImTDJ04iUytxiIlBatNG7Fctw6lrfElXFZ2ZkSt9SRqrSejg5PUFXRRe6OL5vI++tpGyGkbIed4PVZ2ZnOlDi7+1siNoI5TJpfjFhSCW1AIq575PF0NdTcT33R6mhupL8ijviCPM6+9intwKIFJywlITMHaydnQQzcJKoWcVUGOrApy5D92RZDb0MeJ4jZOFbfTOjDO8aJ2jhe1o1bISQ2wZ3O4CxvDnLG3NL5vNgzB08qTZ8KeEcmu8EAMmuxOTk6Sm5vLD37wg7ljcrmcDRs2cP369bs+5vDhw6SkpPDCCy9w6NAhHB0defrpp/ne976H4i4LXCYmJpiYmJj79+DgIKDfKnFqamqBI1o6t8ZuyjGAiMPYGEMc6thY7GNjsfvud5goKWH47DlGzp5lqqGB4UuXGL50CeRytPHxWKxfh8W6dahcXe94DmOIQ6WVEbTMiaBlTkyMTtNY0ktdQTfNZX0M9Y5TcK6JgnNNaK1UeEfa4xNlj3uQzQe2LTZULLbuniTt3kPS7j30tbZQk5NBTU4GHbXVtJSX0lJeysW/vIajtx/+Ccn4JyRj5+H1oTPWxnBOFsJCxRHrYUWshxU/2BxIUcsgZ8o6OV3aQW33KBcqurhQ0cW/HCgiwduWjWFObAp1ws1m4WrATfF8mNJYBeMik6TbdwNfWq2trbi7u5Oenk5KSsrc8e9+97tcunSJzMzMDzwmJCSE+vp6nnnmGb761a9SXV3NV7/6Vb7xjW/w0ksvfeD+P/7xj/nJT37ygeNvvvkm5uail58gmARJQt3RiWVxMZYlJZi1tt5x87i7O8MR4QyHhzPp5ARGUCLwYWZnYKJbyVi7krFOJdL0/FhlSgkzx2m0ztOYOU4jN/h3bx80NTLMSHM9I031jHW1w20fISpLHRaePlh6+qCxdzKKUg1T0z4Khb0yCnvlNI3c+d/Pw0Iiym6WKDsJF61Rv8wXxejoKE8//TQDAwPodKIlmXDvTC7ZDQoKYnx8nLq6urmZ3J///Oe8/PLLtLW1feD+d5vZ9fT0pK2tDXt7+0WIamlMTU1x5swZNm7cOFfOYYpEHMbFVOKYam5m5PwFhs+dYzw//86Ey8cb7Zo1FFtYsOLzn0dthAvcbpmZnqWtaoC6wm7qC3sYG5yfuVIoZbiH2OIZbkNtVyFbtm8wunMyOjhAXX42NTmZNBUXMHPbzJuFrR1+cUn4JSTjERrOrIRJvLY+zlK+R1r6xzhd2smZsk5yG/qYve3T2sfenA2h+hnfaI/7L4Uxlff67QYHB3FwcBDJrnDfDDpv4ODggEKhoKOj447jHR0duLi43PUxrq6uqFSqO0oWQkNDaW9vZ3JyErX6zr22NRoNmrt82KlUKpN5g38UEYdxEXEsDZWvL+b/6IvjP/6Dftvi8+cZOnuW0esZTNU3MPWnP+MFtLy7D6v167DasAGLxERk77s+GJpKBb5RTvhGOSHNSnTUD1Kbr6/zHegao7G4l8biXsCCk42l+MU4GVVLM2t7B2I2bCVmw1Ymx0apu5FHdfZ1avOyGenrpejcSYrOnURjboFPTDzDchXS9DSqh+BbtaV4j/g4qvjSah1fWh1A9/AE58o6OFnczrXqHup7Rnntaj2vXa3H0UrDxjBnNoe7kOJnj1p57y3NjP29fjtTGadgfAya7KrVauLj4zl37hy7du0CYHZ2lnPnzvG1r33tro9JTU3lzTffZHZ2FvnNfd4rKytxdXX9QKIrCMLDT+ngMLd728zwMCOXLzNw5gwD5y9AZyf9b71N/1tvI7eywnL1aqw2rMdixUqj6+wgk8tw8bPGxc+alEf96W0boe5G11wv37bqQdqqB7m2rxp7D0v8oh3wjXHEwcM4OjuoteYEp6wgOGUF01NTNJUUUp11neqcDEYH+qlIvwzAHzIv4x0Zg3/CMgISkjG3tjHswE2Eg6WGPYle7En0YnhimosVnZwq6eBCeSddQxO8mdnIm5mNWGmUrA1xYlO4M6uDHEVnB0HACLoxvPjiizz//PMkJCSQlJTEK6+8wsjIyFx3hueeew53d3d++tOfAvCVr3yF3/zmN3zzm9/k61//OlVVVfznf/4n3/jGNwwZhiAIRkBhaYkuLQ3txo3kHz7MKltbxi5eYuj8eWa6uxk8epTBo0eRqVSYL0/Rtz9buxalo6Ohh34HmUyGvZsl9m6WRG/04PB7J/Czj6KhqJfWqn56mofpaR4m+5i+s4PvzcTXLcAauRFsVKBUqfCNicf3Zi/f1qoKKjOuUnT5AlPDg9TmZVObl82ZP/wGt6BQAhL1ia+tq7uhh24SLDVKtke5sT3KjYnpGa7X9HC6tIMzpR10DU1wuKCVwwWtqBVylgfY69ufhTrjpBObhAifTgZPdvfs2UNXVxc/+tGPaG9vJyYmhpMnT+LsrG9n09jYODeDC+Dp6cmpU6f453/+Z6KionB3d+eb3/wm3/ve9wwVgiAIRkhSKrFYuRKbdetweelHjBUUMHTuHENnzzLV0MjIpcuMXLpMu+zHaKOjsdqwHst169H4+Rp66B+g1EpErHEndqMP48NT1Bd1U3uji6bSXoZ6xym80EzhhWY0Fkp8IhzwjXHAM9QOtZnBL/HI5HLcg0Nx8gtgwNqRZdGR1OfnUJ19nY7aalorSmmtKOXy317H3sML/4RkAhKX4eIXiExu+MTd2GmUCtYEO7Em2In/2BlBflM/p0vaOV3aQV33CBcrurhY0cUPDxQT42nDpnBnNoU542VjvLXsgrDQDH8lBL72ta99aNnCxYsXP3AsJSWFjIyMRR6VIAgPC5lCgXlcHOZxcTh9+9tM1tQwdPYcQ+fPM15YyNiNG4zduEHnz/4/1L6+NxPfdWijo40u4TKzVBGS4kpIiitTkzM0lfZSV9BFfWEP4yNTVGS2U5HZjkIpxyPUFt8oB3yiHLCwNnxyI5PJsPfwwsXXn2WP7mGwu4ua3EyqszNoLi2ip7mRnuZGsg6+i4WtHf7xSQQkLMMzIhqlqNf8WHK5jHhvW+K9bfn+1hBquoY5VdLB6dIOCpr6uXHz539OVuBrb46vRo5zQx+Jfo4ojKDXsyAsFqNIdgVBEJaKTCZDExCAJiAAhy//E1MdHQyfP8/Q2XOMZGUxWVdHzx9eo+cPr6FwcMBq7Ros167DYnkKcjPj+hpYpVbgF6PfnGJ2Zpb22gFqb3RTV9DFYPc4DUU9NBT1wJsVOPvo8I12wC/GeLYu1jk4Ert5O7GbtzM+PExdfjbVOZnU3chlpK+XwrMnKTx7EpWZFt/oOPwTl+EXmyi2Lr4HMpmMACcrApyseGFtAB2D45y5WeqQXtNNXc8odcg5/1o29hZq1oc6sTHMhRUBDmjVYuti4eEikl1BED7VVM7O2D71FLZPPcXM0BAjV64wdO48w5cvM9PdTf+7++h/dx8yrRaL1OVYrV2H5do1KO3sDD30O8gVctwCbXELtCX18QB6W0eoK+iirqCbzoYhOuoG6agbJONgLTbO5vhEOeAb7YCLn3Hs4GZmaUnoyrWErlyrX+BWXEB1TgY1uVmM9PVSmXmNysxryORyPEIjCLi5kYW109079wh3ctaZ8ewyb55d5s3Q+BTnS9v5y/kbVA6r6RmZ5J2cZt7JacZMJWdloCMbQ51ZF+qEg9jBTXgIiGRXEAThJoWVFbq0NHRpaUiTk4zm5DB07jxD588z3dbG8NlzDJ89BzIZ2thYrNavw3LtOqOr85XJZNi7W2LvbklCmi/DfePUF3brd3Cr6KO/Y5QbZxq5caYRM0sVPpH2+EY54hFqaxR1vkqVCt/YBHxjE9jwj7O011ZRk5NJTU4m3U0NNJUU0lRSyIU//wEHLx/845PxT0gSdb73yMpMRVqkCzTNsnHzGvKbh+ZmfVv6x+b+v0wGcV62bAh1ZmOYM/6OFkbR+UMQ7pfhr2qCIAhGSKZWY7F8ORbLl+P8rz9koqxMn/heOM9EaRljeXmM5eXR+fLPUPv4YLluHVbr1qKNiUGmNK5Lq6WtGRGrPYhY7cHk2DSNpb3UFXbRUNTD+PAU5dfbKb9+s843xBafKAd8Ih2wtDX8rJ5MLsc1IBjXgGBWfOY5+tvbqMnVJ77N5SV0N9bT3VhP5oG9NzeySMQ/PhmvyGhUasOP39ipFHJSAxxIDXDgpR1hlLUNcbZMn+wWtQyQ29BHbkMf/32yHF8HCzaEOrEh1Jl4b1uURtD5QxDuhXFdkQVBEIyQTCbDLCwMs7AwHL/+Naba2hg6f57hc+cZyc5msr6e3tdfp/f111HY2GC5epW+znfFCqPr56vWKgmIdyIg3omZmVnaqweoK+imrvBmnW9xDw3FPVyiAidvK33iG+VgNP18bVxcid+2i/htuxgbGqTuRi41OZnUF+Te3MjiFEXnTqFUa/COisU/Pgm/uEQsbGwNPXSjJ5PJCHPTEeam4xvrA2kbGONsWSdnSzu4XtNDXfcIf7hSxx+u1GFjrmJtsD7xXRXkIPr5CkZNJLuCIAj3SeXqit0zz2D3zDP6jSyuXtUnv5cuM9Pfz8ChwwwcOgwqFRZJSViuXYvV2jWo3I2rj6xCIcc92Bb3YFtSnwigr22UukJ9nW9H/SCdDUN0NgyRdaQOS1vNXOLrEWSLQmX4WT2tlY6wlWsJu1nn21xSSE1eFjU5WQz1dFGTk0FNTgbIZLj6B+GfkIxffBIOnt5GkbgbO1drLZ9d5s1nb9b5Xqnq5myZfiOLvtEpDuS3cCC/BZVCxjI/e9aHOLE+1BlPO9PfIU94uIhkVxAE4RNQWFqi27IF3ZYtSNPTjOXnM3T+AsPnzzPZ0MDItWuMXLtGx3/8B5qgoLnE1ywqyqjqS2UyGXZuFti5WRC/xYfRwUnqi7qpL+ymqayX4b4Jii+1UHypBaVGgVeoHT5R9nhHOGCuM/zulUqVCp+YeHxi4ln3+S/T1VBHdXYGtXlZdNRW01ZdQVt1BVff/gs6R+eb5Q5JeIRFirZm90Bf5+tKWqQr0zOz5DX2c66sgzNlHdR2jXClqpsrVd38+Egpwc5WrA/VJ74xnjairZlgcCLZFQRBWCAypRLzxETMExNx/t53maitZfjCBYYuXGAsL5+JykomKivp+f3vUdjbY7l6NZZr12C5fDlyC+MqdzDXqQlLdSMs1Y3pyRmaK/qoL9QnvyMDk9Te6KL2RhfIwNlHh0+kftbX3t3wi5hkMhlOPn44+fix/ImnGertpjY3m9q8LBqLChjs6uDGqaPcOHUUlZkWn+hY/OOT8Y1NwFxnbdCxmwKlQk6Srx1Jvnb8IC2U2q5hzpV1cqasg9yGPio6hqjoGOK3F2uwt1CzNsSJDaFOrAh0xFIj0g5h6YlXnSAIwiLR+Pmh8fPD/h//kem+PkYuX2bo4kVGrlxlpqeHgf37Gdi/X799cXIylmvXYLXG+ModlGqFPpmNdEB6WqKrcUif+Bb10NU439Ys83AtlnaaucTXPcgGpcrwPVut7ByI3riV6I1bmRofp6G4gNrcTGrzshnp76MqM52qzHR9uUNgMP5xSaLc4T74OVri52jJF1f50T86ycWKLs6WdXCpoouekUn25TazL7cZtUJOsp8dG0KdWRfiJModhCUjkl1BEIQloLS1xXrnTqx37tS3NcvNZejCBYYvXGSqqYmRq1cZuXqVjn+/We6wZg2Wa9agDAs19NDvIJPJcPLW4eStI2mHH8N9E9QXddNQ1E1TeR/DvXeWO3je7O7gHmwcM6YqMzMCEpIJSEhGmp2lo7aamrwsanOz6ayvoa2ynLbK8pvlDk74xSXiF5eEZ1gkSrXhyzWMnY25ml2x7uyKdWdqZpbsul7OlnVyrryDhp7RuXKHlw6XEORsyfqbiW+sp43o7iAsGpHsCoIgLDGZWo1FSgoWKSlIP/gBk7W1DF+8+MFyh//7P+Q2Nrj4+TIkl2O9ejUKnc7Qw7+Dpa2GiFXuRKxyZ2pyhpbyvrla35GBSX2nh4JuAFTW5uTSgF+ME46eVsgMXMspk8txCQjCJSCI1CefZainm9q828sdOrlx6hg3Th1DqdHgHRmDd3Qc06MjBh23qVAp5CwPcGB5gAP/b3soNV0jnC/v4FxZJzkNfVR2DFPZMcz/XqzBxlzFmiBH1oU6szrQEWtzUUctLByR7AqCIBiQTCZD4++Pxt8f+3/8R2b6+xm+cpXhixcZvnKF2f5+dHn5dOTl06FQYB4fr6/1XbMatZ+fUX3NrlIr5jo2SE9LdDcNzyW+nQ1DTA0oyD3RSO6JRsx1arwj7PGOtMcz1M4oNrOwsr+t3GFinMbiQmpzs6jNz2a4t2duYwuAt/Iz8E/QtzUTm1l8PP32xZYEOFnypVX+9I9Ocqmyi/PlnVys6KJ/dIqDN1o5eKMVhVxGvLct60OcWBfiRICTcbS9E0yX4a8ugiAIwhyFjQ3WO7ZjvWM70vQ0Q1nZFP35Tzg3NTNVW8toVhajWVl0vvwyKk9PfeK7ejXmSYnINcaziYJMJsPRywpHLysSt/ky0DPCsbcvYi33oKW8n9HBScrS2yhLb0OukOEWaINPpAPekfbYOBm+llOlMcM/Pgn/+CQkSaKroY7a3CyqczPpqKmiq6GWroZaMt57G3NrG3xj4vGNTcQnOhaNuXEtNjRGNuZqdsa4szPG/Y7uDufLO6nqHCarrpesul5+eqIcD1st60KcSPbQGnrYgokSya4gCIKRkimVaBMT6O7qJCktDam9neELFxm+dInRrCymmpro+9vf6Pvb35BptVikpNxMflehcnEx9PDvYK5TY+Exzaa0MOQoaK3qp75Yv8htsGuM5vI+msv7uPpuFTbO5nOzvm4BNiiUhp01vb27Q/wjj3F4/3sEOtjSUJBHfWEeowP9lFw6R8mlc8gVCtyDw/CNS8QvNhE7dw8xK/kx3t/doal3lPPlnZwr7ySjpofmvjH+cr2BP02MGnqogokSya4gCIKJUHt6YvfcZ7F77rPMjowwkpGhL3e4dJnpzk6Gz59n+Px5ADQhIXOJrzY6GpnC8F0RblGo5HiG2eEZZseKJyT6O0ZpKO6hvqiHtqp++jtG6e8YpeBcEyqNAs9QO7wj7fEOt8fCxvCz10ozLaGr1hG1fjMz01O0lJfqa33zc+hrbaaptIim0iIu/+11rJ2c8Y1NxC82AY/wSLGF8T3wtDPn+eU+PL/ch9HJaa5V93C+vJMzN+poMvTgBJMkkl1BEAQTJLewwGr9eqzWr0eSJCbKyhi+dInhi5cYKyxkorycifJyfU9fa2ssVqzAcvUqLFauRGlrPFvnymQybF0ssHWxIGaDFxNj0zSV9tJQ3E1DSS9jg7f19AUcPC31s74RDjj76pAbeJGbQqnCKyIar4ho1jz3Bfrb26jNz6Y2L5vm0iIGOud7+irVGrwiovCNScA3NgFrJ2eDjt0UmKuVbAxzZmOYM99f74XNvxt6RIIpEsmuIAiCiZPJZJiFhWEWFobDV77CdG8vI1evMnzxEsPXrjEzMMDgsWMMHjsGMhnaqCgsVq3EctVqzMLDjGpxlUarJCDeiYB4J6RZia6moblZ386GQbqbhuluGib3RAMaCyVeYfZ4R9jjFWaH1srwrcFsXFyJ2/oIcVsfmevpW5eXTe2NHIbnuj1kA2Dn7olvbAJ+sQm4h4ShUIoOBB9FlIMID0oku4IgCA8ZpZ0d1o88gvUjj+i3MC4sZPjSZYYvX2airIyxggLGCgro/vVv9Du53Zr1TU1FYW0c/XABZPL5nr6J23wZHZyksbSHhuIemkp7mRiZpiq7g6rsjrmd3LzC9cmvk5fhW5vd0dNXkuhuaqA2L5u6/BxaK8vobWmit6WJ3KMHUJlp8Y6Mxjc2Ad+YBKzsHQw6dkF4mIhkVxAE4SEmUyoxj4vDPC4Op3/+FlMdHQxfvszI5SuMpKfrd3I7dIiBQ4dALkcbE4PlqlVYrlqJJjTUqGbTzHVqQpa5ErLMldmZWdprB2ko0Se/Pc3Dczu5ZR+tQ2ulwivMHq8IO7xC7TGzNOysqb47hQ+OXj4k73qC8eFhGoryqcvPoe5GLqMD/VRnZ1CdnQGAg5fPzcQ3HregUBRK8XEtCA9KvHsEQRA+RVTOztg+8QS2Tzyh38ktL19f63vlMpPVNYzl5TGWl0fXK6+gcHTAcsVKLFetxGL5cqOa9ZUr5LgF2uAWaEPKLn+G+yZoLOmhoaSHprJexoamqMhspyKzHZkMnHx0+nKHcOOY9TWztCQ4ZSXBKSv1O7nV1VB3I4e6/Bzaqivpbqynu7Ge7EP7UGvN8Y6KwSc6Ht/YeKzsxKyvINwPkewKgiB8SsnUaiyWJWOxLBnn732XqZYW/YYWly8zkpHBTFc3AwcOMHDgwPys78oVWKxchVlYqFHV+lraaghb4UbYCjdmpmdprxmgoVif/Pa2jszN+mYd0c/6eobZ4R1uj1eYEcz6yuW4+Afi4h9IymNPMTo4QENhPnU3cqm/kcvY0CBVmelUZaYDN2d9Y+L1s77BoaLWVxA+hkh2BUEQBABU7u7YfmYPtp/Zw+zkJGO5uQxfvsLw5ctM1tw26/vLX92s9U3FYuUqLFKXG1WHB4VSjnuwLe7Btix/LICh3nEaS3poLOmlqVw/61uZ2UFl5m21vmF2+llfH8N3eDDXWRO6Yg2hK9YwOztDR201dfn6xLet5rZZ38PvodZq8YqI1s/6xsSjc3Qy6NgFwRiJZFcQBEH4ALlajUVKChYpKXfO+l65wuj16zdrfQ8zcOgwyGSYRUXqSx5WrsAsMtKo+vpa2ZkRvtKd8JXu87O+JT00lvTQ0zI/65t9rF7f4SFUn/h6htlhYW3YvrhyuQLXgGBcA4JZ/sTTc7O+9TdyqSvIY2xw4I5aXzt3T3xj4vCJjscjNAKl2vAdKgTB0ESyKwiCIHys22d9pclJRvNvMHLlMsNXrjJRUcF4QSHjBYV0v/oqcmtrLFOXY7FiJRYrUlE5Gc9s4x2zvo8G6Gt9S/WJb1NZn77DQ04nVTmdgL6vr1eYPV7hdrj4Gb5m+fZZX2l2ls76Wv0it4I82irL5zs8HDuEUq3BMzwSn2h98mvr6mZUCw4FYamIZFcQBEG4LzK1GovkJCySk3D69reZ6ujQ9/W9cpWR9HRmBwYYPH6CweMnANAEB6NdvhytUoE0OQkq46kxtbTVEJbqRliqG7Mzs3TUDdJY2ktjSQ+dDUNzfX3zTjWg0ihwC7JheEbFYPc49q6Gr/V19gvA2S+AZY995maHhxvU3cihoSCP4b5efSKcnwOAtZPzXOLrGhxm0LELwlISya4gCILwiaicnbF57DFsHnvsZl/fIoavXGbk6jXGi4uZqKhgoqICT6D2b3/HIikJi5UrsVyRisrb22hmG+UKOa4BNrgG2JD8iB+jg5M0lfXSWKrv6zs2NEVDUQ9gxts/ycbaSYtXuH5DC/cgW1Qaw5Zu6Ds8rCA4ZcVcX9/6gjzqb+TSXFbCQGcHBWdOUHDmBHKFArW9I9nTY/jHJuDk42dUCw4FYSGJZFcQBEFYMPq+vrGYx8XCN7/JdF8fI9fSGbp8ib4LF1EODelbnV26RAeg8vDAIjUVixWpWKSkoLC0NHQIc8x1aoKTXQhOdpnbza2uqIvCazVMDSgZ6ByjqLOZogvNyJUyXP1tbi50s8Pe3dKgSfztfX0TdzzK5PgYTSVF+uS3IJf+9jbGO9u5/s7fuP7O39DqrPGJisU7Khaf6DgsbIxnwaEgfFIi2RUEQRAWjdLWFuvt2zDfvIncY8dYHxDA+PXrjFy9xmheHlPNzfTv3Uv/3r2gUOjbm61IxWLFCszCwoxmodut3dxs3bS0zxSzYe0mOmqG9fW+pb0M9YzTUtFHS0Uf1w/UoNWp8Qq1wzPMDs9QO8x1hl0opjbT4h+fhH98EgDdLU0cf+tvWM5M0lxazNjgAGVXL1J29SIAjt6++ETH4R0Vi3twmFjoJpg0kewKgiAIS0MmQxMcjGVEBA5f/CKzIyOMZGUxcvUaI1evMtnQwFhuLmO5ufr2ZjY2WCxP0c/8Ll+OytXV0BHMUWuV+MU64hfriCRJDHSOzSW+LRV9jA1Ozm1qAbcWuukTX1d/GxQqw5YMWDu5YB0YRlpaGnIZtFaWU1+QR0NhPh211XQ11NHVUEf24ff0C93CIvCOisMnOg47dw+jKT0RhHshkl1BEATBIOQWFlitXYvV2rUATDY36xPfa1cZuZ7BTH//HQvd1P7+WKQux2L5ciwSE5FbWBhy+HNkMhk2zubYOJsTtdaTmalZ2moHaLqZ/N5a5KZf6NaIUi3HLdB2Lvm1dTU3aPKoUKrwDIvEMyySlU89P9ferKEwn/qCPEb6+6i7kUvdjVwALO0d5koevCKiMdcZvkuFIHwUkewKgiAIRkHt4YH6VnuzmwvdRq5eZeTaNcaKipisqWGypoa+v/wVVCrMY2PnZn3NwsOMZoGVQiXHI9gWj2BbUnYzt9CtqayXptJeRgcnb25y0QOAhY0Gz1BbfclDiB1aK8OWDNzR3uy2hW4Nhfk0lxUz3NNN8YUzFF84AzIZzr4B+u2Mo2JxDQpFaUTdNgQBRLIrCIIgGKHbF7o5fuPrzAwMMJKRycg1fcnDVGsro1lZjGZl0fWLX6CwscE8ZRmWt0oe3NwMHcKcOxa6SRK9rSM0luqT39aqfkb6Jyi/3k759TtLHjxC7XD1t0apMlzd8vsXuk1NTtBSVqJPfotu0N1YT0dtFR21VWQdfBelRoNnqL7kwTsqBnsPL1HyIBicSHYFQRAEo6ewtka3eRO6zZuQJImphgaGr11jJP06oxn6koehEycZOnESALWPj37WN3U55klJRtPlQSaTYe9uib27JbEbvZienKG1up+msj6aynrpaX5fyYNKjlugDR6h+pIHe3cLgyaPKrXmZq/eOACG+3ppLLpB/c2yh9GB/jtLHmzt8IrUz/p6RcaILg+CQRhFsvvqq6/y8ssv097eTnR0NL/+9a9JSkq6633/9Kc/8fnPf/6OYxqNhvHx8aUYqiAIgmBgMpkMtY8Pdj4+2D3zDNLUFGNFRfp63/R0fclDfT2T9fX0/f3v+i4PUVH6Wt/lKWijopAZyVftSrVCv0NbmD0AIwMTNJf3zZU9jA5M6je5KO0FQGulwiNEn/h6htpiaWtmyOFjaWtH2Kp1hK1ahzQ7S1djPQ1FN2gozKelrIThvl5KL5+n9PJ5ABy9fPCKjME7KhaPkHBUZoYdv/DpYPBkd+/evbz44ov87ne/Izk5mVdeeYXNmzdTUVGB04dsManT6aioqJj7t/iKRBAE4dNLplJhHheHeVycvuRhaIjRzExG0tMZuZau7/KQn89Yfr5+O2Nzc8yTkvTJb+py1H5+RvM5YmGt+UDJgz7x7aO1qo+xoSmqsjuoyu4AwNbF/Gbya4t7kC1qreE+1mVyOU4+fjj5+JG441GmJydpKS+loSifhsIbdNbX0NVYT1djPbnHDqJQKnELCr2Z/Mbg7BeAXG4creaEh4vBk92f//znfPGLX5ybrf3d737HsWPHeP311/n+979/18fIZDJcXFyWcpiCIAiCiVBYWWG1YQNWGzYAMNncwkj6nSUPwxcvMnzxIgBKJycsUlL0JQ/LlqH6kImWpXZ7yUPMBi9mpmZprxuYS367Ggbpax+lr32UoovNyOQynH10eITa4hlih7OfDoXCcIv2lGo13lH6RJZnYHRwgMbiAhoKb9BQlM9QdxdNpUU0lRZxbe9f0VhY4BUerU9+I6OxcXEzmj9CBNNm0GR3cnKS3NxcfvCDH8wdk8vlbNiwgevXr3/o44aHh/H29mZ2dpa4uDj+8z//k/Dw8Lved2JigomJibl/Dw4OAjA1NcXU1NQCRbL0bo3dlGMAEYexEXEYn4clFkPGIXN2wnL3bix370aanWWivJyxjAxGr2cwnpfHdGcnA4cOMXDoEKBvcaZdloz5smVoExKQ31bva+jz4eRriZOvJfFpXkyMTtFaOUBzRR8tFf0Mdo3TXjtAe+0AOcfqUarluAZY4x5sg3uwLXau5sjkMoPFodKa45+Ygn9iir43cUcbjcWFNBXfoKm0iImREaqy0qnKSgfAyt4Rz/BIPCOi8QyPYlpmHN02BNMjkyRJMtQvb21txd3dnfT0dFJSUuaOf/e73+XSpUtkZmZ+4DHXr1+nqqqKqKgoBgYG+NnPfsbly5cpKSnBw8PjA/f/8Y9/zE9+8pMPHH/zzTcxNzdf2IAEQRAEkyKbmsKsoQGLqmrMq6rQtLYiu+1jUZLLGff0ZDTAn9GAAMa8vEBp8C9F72p6VMZEj5LxHgUTPQpmJ+9MDuXqWTT2M5jZz6BxmEapNdjH/wdIs7NM9HYz2tHCWFsLY90dMDt7x31mzC353p/2MjAwgE6nM9BIBVNkcsnu+01NTREaGspTTz3Fv//7v3/g9rvN7Hp6etLW1oa9vf3CBGIAU1NTnDlzho0bN6IykoUWD0LEYVxEHMbnYYnFVOKYGRhgLCuL0YwMxjIymWpsvON2mZkZw15euG/dilXqctTBwUbT3/d20qy+3relop+Win7aagaYnrwzeVSYz+If5YJXmB1ugTaYWRrPeZkaH6elopTmkkKaSoroaqhlfGqKfz1wWiS7wn0z6J+nDg4OKBQKOjo67jje0dFxzzW5KpWK2NhYqqur73q7RqNBo9Hc9XHGfMG9VyIO4yLiMC4PSxzw8MRi7HGoHBwwS0vDNi0NgKmWFkYyMhhJv85IRgYzPT1YVFbSX1lJ/y9/qe/vm5yMxc2yB7WPj9HUmbr4qnHxtSV+C8xMz9JRN0BTeR/NZX101A8wMyqnMqOTyoxOQN/f1/3mZhhugTaozQyXIqhUKgITkglMSAb09b5lWdf51wOnDTYmwXQZNNlVq9XEx8dz7tw5du3aBcDs7Cznzp3ja1/72j09x8zMDEVFRaTdvDAJgiAIwkJRubtj89hj2Dz2GJIkMVJaSu4bf8JncJDxnBx9f99Tpxg6dQoApYsLFsnJmKcsw2LZMlRGsphaodRvUewWaEvyDhgZHOPI3nO4WgXQVjVAT8vIXH/fgrNNyOUynHx0eITY4hFii4uvNQqV4WawzXXWBCYtN9jvF0ybwQuPXnzxRZ5//nkSEhJISkrilVdeYWRkZK47w3PPPYe7uzs//elPAfi3f/s3li1bRkBAAP39/bz88ss0NDTwhS98wZBhCIIgCA85mUyGJiiI/pUrcEtLQwmMFRUzmpnByPUMxvLzmW5vv3Oxm48P5suSsViWgnlyEkpb49hUQa1VonWaYXmaPyqVitHBSVoq+mgu76W5oo/B7tsWux2vR6m6tdjNFo9gOxy9LJEbsNODINwPgye7e/bsoaurix/96Ee0t7cTExPDyZMncXZ2BqCxsRH5bfVQfX19fPGLX6S9vR1bW1vi4+NJT08nLCzMUCEIgiAIn0L6/r76LY0dvvIVZsfGGM3LYzQjk5HMTMaLi+c2t+h/ey8AmpAQ/czvsmTMExONZmc3c52awERnAhP1n72D3WM0V/TRXN5Hc0UfY4OTN3d56wNqUZspcAvSlzy4B9ti72Yx1+lBEIyNwZNdgK997WsfWrZw8WYfxFt+8Ytf8Itf/GIJRiUIgiAI906u1WKZmoplaioAM4ODjGZnM5KZyej1DCaqqpgoL2eivJzeP/8ZFArMIsKxSLqZ/MbFIddqDRyFns5BS5iDlrBUN/3mFm0jN2d++2it6mdidJr6wm7qC7sB/c5uboH6kgf3IBtsnM2NpnZZEIwi2RUEQRCEh41Cp8Nq/Xqs1q8HYLq7W5/4ZmYxkpnBVEMj4wWFjBcU0vOHP4BKhTY6Sp/8JiejjYlGfpcF1ktNJpNh72aJvZslUWs9mZ2V6GocoqWij5aKPlqr+xkbmqImr5OaPP1iNwtrNe43Z309gm3RORhHEi98OolkVxAEQRCWgNLBAett27Detg2AqdZWRjL1bc5GMjOZbm9nLCeXsZxc+O1vkWk0aGNjsUhO0ie/ERHI1GoDRwHymzu1OfvoiNvszcz0LJ31g/rNLSr7aK8ZZGRgksqsDiqz9N2WrOzM5ja3cA+yxcrOzMBRCJ8mItkVBEEQBANQublhs3sXNrt3IUkSU42N+pnfjExGsrKY6e5mNCOD0YwMAGRaLeZxcfpWZ8lJmIWHIzOCDS4USjmuATa4BtiQuM2X6ckZ2mvnd3brrB9kqHec8uvtlF9vB0DnqMUjaD75tbAx/Ay28PAy/LtEEARBED7lZDIZam9v1N7e2D75JJIkMVlbO1f2MJqVxUxfHyPXrjFy7RpdgNzcHG18vH7mNykJs7Awo0h+lWoFHiF2eITYATA5Pk17zQAtlX00V/TT1TDIYNcYpV1jlF5rA8DG2Ry3IBs8gmxxC7LBwlokv8LCMfy7QhAEQRCEO8hkMjT+/mj8/bF7+mn9drpV1YxmZjKSlclodg6zAwOMXLnCyJUrAMgtLNAmxGORdDP5DQ01iuRXbabEK9wer3D9rqWTY9O0Vvfra34r++luGqK/Y5T+jlFKr7QCYOtijluQfrGbW6BIfoVPxvDvAkEQBEEQPpJMLscsOAiz4CDsnvusPvmtqGA0K0tf95uTw+zgICOXLjNy6TJgxMmvVolPpAM+kQ4ATIxO0Vo9cDP57aO7eZi+9lH62kcpudwC6JNfaw/j3XlPMG6Gf9ULgiAIgnBfZHI5ZqGhmIWGYvf880gzM0xUVDCSlaUve8jNvXvyGx+HeWIimrg4mJkxcBR6GnMVvlEO+Ebpk9/xkSnaqvtpqeinpWo++W1tHDHwSAVTJZJdQRAEQTBxMoUCs7AwzMLCsP/c5+5MfrOy52d+L19h5LK+7CFArabl6DEsk5IwT0o0mm4PZhYqfKMd8Y12BPTJb2tVP1U3muENAw9OMEki2RUEQRCEh8yHJb+j2dmMZGUzmp0Ng4OMpaczlp6uf4yZGdrYGMwTE7FITMQsOhq5kSS/fjGOOPhp4POGHo1gikSyKwiCIAgPuduTX7vnn2dyYoJzr79OglbLRG4eozk5zPT1MXo9g9HrGXQDMrUabXQ05okJmCcmoo2JMZod3gThfohkVxAEQRA+ZWRyOZNubtikpaH63Of0rc6qqxnJ1s/6jmbn6Pv83vw3/C8olWgjIvTJb0IC2rg4FFZWhg5FED6WSHYFQRAE4VNOJpOhCQxEExiob3UmSUzW1TOao098R7Oz9Tu83bjB2I0b9PzhNZDL0YQEY56QMPejtLMzdCiC8AEi2RUEQRAE4Q4ymQyNny8aP9+5TS6mWlpvJr/6BHiqsZGJ0jImSsvo+8tfAVD7+2MeHz83+6tydTVwJIIgkl1BEARBED6GTCZD7eGO2sMdm127AJjq6GQsN4fRnBxGs3OYqKpisqaGyZoa+t95B9BviaxNiJ+b+VX7+iKTyQwYifBpJJJdQRAEQRDum8rZCVVaGrq0NACm+/oYy8vTlz3k5jJeWspUaytTh1sZPHwEAIWdnX7mNyEebXwCZiHBRrHRhfBwE68wQRAEQRA+MaWtLVbr12O1fj0AM8MjjBXcYDQnh7GcXMYKCpjp7WXozBmGzpwBQG5ujjY2Vp/8xsWjjY5CbmZmyDCEh5BIdgVBEARBWHAKSwssU1OxTE0FYHZykvHiYkZzchnNzWEsL5/ZoSFGrl1j5No1/YNUKrTh4fqd3uLj0cbGorS1NWAUwsNAJLuCIAiCICw6uVqNeVwc5nFxwBf1G11UVTGak8tYXi6jOblMd3bOdXzo/ePrAKgD/DGPi2c6NMSwAQgmSyS7giAIgiAsOZlCgVlICGYhIfDsMzc7PrToyx5y8xjNzWWytpbJ6homq2sYnpkx9JAFEyWSXUEQBEEQDE7f8cEDtYfHXMeH6d5exvLzGc3NYyrjOlRXGXaQgkkSya4gCIIgCEZJaWc3t+hNOzgIBw4YekiCCZIbegCCIAiCIAiCsFhEsisIgiAIgiA8tESyKwiCIAiCIDy0RLIrCIIgCIIgPLREsisIgiAIgiA8tESyKwiCIAiCIDy0RLIrCIIgCIIgPLREsisIgiAIgiA8tESyKwiCIAiCIDy0RLIrCIIgCIIgPLREsisIgiAIgiA8tESyKwiCIAiCIDy0RLIrCIIgCIIgPLREsisIgiAIgiA8tJSGHsBSkyQJgKGhIVQqlYFH8+CmpqYYHR1lcHBQxGEERBzG5WGJAx6eWEQcxsUU4xgcHATmP8cF4V596pLdnp4eAHx9fQ08EkEQBEEQ7tfQ0BDW1taGHoZgQj51ya6dnR0AjY2N9/VmSUxMJDs7e9Huf7+PGRwcxNPTk6amJnQ63aKNS8Qh4liMcYk47v0xDxLLUoxLxCHiWIzHfNT9JUliaGgINze3+xqDIHzqkl25XF+mbG1tfV8fggqFYlHv/6CP0el0Io5FGpOIQ8SxGGN6kMfA/cWyFOMScYg4FuMxH3d/MaMrPAixQO0evfDCC4t6/wd9zGL/DhHH4hJxLO5jFvt3PCxxPMhjRByL+ztEHIKwcGTSp6zSe3BwEGtrawYGBh7or2BjIeIwLiIO4/KwxAEPTywiDuPysMQhCPfiUzezq9FoeOmll9BoNIYeyici4jAuIg7j8rDEAQ9PLCIO4/KwxCEI9+JTN7MrCIIgCIIgfHp86mZ2BUEQBEEQhE8PkewKgiAIgiAIDy2R7AqCIAiCIAgPLZHsCoIgCIIgCA8tk0x2f/rTn5KYmIiVlRVOTk7s2rWLioqKO+4zPj7OCy+8gL29PZaWljz22GN0dHTM3V5QUMBTTz2Fp6cnWq2W0NBQfvnLX37o77x27RpKpZKYmBiTi+PixYvIZLIP/LS3t5tUHAATExP88Ic/xNvbG41Gg4+PD6+//rpJxfG5z33urucjPDzcpOIA+Pvf/050dDTm5ua4urryD//wD3NbcptSHK+++iqhoaFotVqCg4P5y1/+siAxLFQcPT09bNmyBTc3NzQaDZ6ennzta19jcHDwjue5ePEicXFxaDQaAgIC+NOf/mRycbS1tfH0008TFBSEXC7nW9/61oLFsJRx7N+/n40bN+Lo6IhOpyMlJYVTp06ZXBxXr14lNTUVe3t7tFotISEh/OIXv1iwOARhSUgmaPPmzdIbb7whFRcXSzdu3JDS0tIkLy8vaXh4eO4+X/7ylyVPT0/p3LlzUk5OjrRs2TJp+fLlc7f/8Y9/lL7xjW9IFy9elGpqaqS//vWvklarlX79619/4Pf19fVJfn5+0qZNm6To6GiTi+PChQsSIFVUVEhtbW1zPzMzMyYVhyRJ0iOPPCIlJydLZ86ckerq6qT09HTp6tWrJhVHf3//HeehqalJsrOzk1566SWTiuPq1auSXC6XfvnLX0q1tbXSlStXpPDwcGn37t0mFcdvf/tbycrKSnr77belmpoa6a233pIsLS2lw4cPG00cvb290m9/+1spOztbqq+vl86ePSsFBwdLTz311Nx9amtrJXNzc+nFF1+USktLpV//+teSQqGQTp48aVJx1NXVSd/4xjekP//5z1JMTIz0zW9+c0HGv9RxfPOb35T++7//W8rKypIqKyulH/zgB5JKpZLy8vJMKo68vDzpzTfflIqLi6W6ujrpr3/9q2Rubi79/ve/X5A4BGEpmGSy+36dnZ0SIF26dEmSJH0yoVKppHfffXfuPmVlZRIgXb9+/UOf56tf/aq0du3aDxzfs2eP9K//+q/SSy+9tKDJ7vstVhy3kt2+vr5FG/vtFiuOEydOSNbW1lJPT8/iDf42i/26uuXAgQOSTCaT6uvrF27wt1msOF5++WXJz8/vjvv86le/ktzd3Rc4Ar3FiiMlJUX69re/fcd9XnzxRSk1NXWBI9BbqDh++ctfSh4eHnP//u53vyuFh4ffcZ89e/ZImzdvXuAI9BYrjtutXr16wZPd91uKOG4JCwuTfvKTnyzMwN9nKePYvXu39Oyzzy7MwAVhCZhkGcP7DQwMAGBnZwdAbm4uU1NTbNiwYe4+ISEheHl5cf369Y98nlvPccsbb7xBbW0tL7300iKM/IO/HxYnDoCYmBhcXV3ZuHEj165dW+DR3/n7YeHjOHz4MAkJCfzP//wP7u7uBAUF8e1vf5uxsTGTiuP9/vjHP7Jhwwa8vb0XaOQf/P2w8HGkpKTQ1NTE8ePHkSSJjo4O9u3bR1pamknFMTExgZmZ2R330Wq1ZGVlMTU1tZAhzP1++GRxtLa2sn//flavXj137Pr163c8B8DmzZs/8r/FJ7FYcSy1pYpjdnaWoaGhj7wWfBJLFUd+fj7p6ekGPWeCcL9MPtmdnZ3lW9/6FqmpqURERADQ3t6OWq3Gxsbmjvs6Ozt/aJ1qeno6e/fu5Utf+tLcsaqqKr7//e/zt7/9DaVSuWgxwOLG4erqyu9+9zvee+893nvvPTw9PVmzZg15eXkmFUdtbS1Xr16luLiYAwcO8Morr7Bv3z6++tWvmlQct2ttbeXEiRN84QtfWNDx37KYcaSmpvL3v/+dPXv2oFarcXFxwdramldffdWk4ti8eTOvvfYaubm5SJJETk4Or732GlNTU3R3dxtVHE899RTm5ua4u7uj0+l47bXX5m5rb2/H2dn5A88xODi44H8QLmYcS2kp4/jZz37G8PAwTz75pEnG4eHhgUajISEhgRdeeGHRrlmCsBhMPtl94YUXKC4u5u23337g5yguLmbnzp289NJLbNq0CYCZmRmefvppfvKTnxAUFLRQw/1QixUHQHBwMP/0T/9EfHw8y5cv5/XXX2f58uWLsshgMeOYnZ1FJpPx97//naSkJNLS0vj5z3/On//85wX/MF/MOG735z//GRsbG3bt2vXAv+ejLGYcpaWlfPOb3+RHP/oRubm5nDx5kvr6er785S8vxNDvsJhx/L//9//YunUry5YtQ6VSsXPnTp5//nkA5PKFvUR+0jh+8YtfkJeXx6FDh6ipqeHFF19c0PHdKxGH3r3G8eabb/KTn/yEd955Bycnp08y5LtaijiuXLlCTk4Ov/vd73jllVd46623PumwBWHpGLqO4pN44YUXJA8PD6m2tvaO4+fOnbtrjaqXl5f085///I5jJSUlkpOTk/Qv//Ivdxzv6+uTAEmhUMz9yGSyuWPnzp0ziTg+zLe//W1p2bJln2jc77fYcTz33HOSv7//HcdKS0slQKqsrFyYIKSlOx+zs7NSQECA9K1vfWvBxn67xY7j2WeflR5//PE7jl25ckUCpNbW1oUJQlq68zE5OSk1NTVJ09PTc4vWFmoR50LFcbv3/7deuXLlB+pbX3/9dUmn0y3I+G9Z7Dhut5g1u0sVx1tvvSVptVrp6NGjCzb22y3l+bjl3//936WgoKBPNG5BWEommezOzs5KL7zwguTm5nbXJOdWYf6+ffvmjpWXl3+gML+4uFhycnKSvvOd73zgOWZmZqSioqI7fr7yla9IwcHBUlFR0R0rXo05jg+zYcOGBVs1v1Rx/P73v5e0Wq00NDQ0d+zgwYOSXC6XRkdHTSaOW24tHCwqKvrEY7/dUsXx6KOPSk8++eQdx9LT0yVAamlpMZk47mbVqlV3rEj/JBYqjve7dOmSBEh1dXWSJOkXqEVERNxxn6eeemrBFqgtVRy3W4xkdynjePPNNyUzMzPp4MGDCxqDJBnmfNzyk5/8RPL29v4kwxeEJWWSye5XvvIVydraWrp48eIdLZxuT3i+/OUvS15eXtL58+elnJwcKSUlRUpJSZm7vaioSHJ0dJSeffbZO56js7PzQ3/vQndjWKo4fvGLX0gHDx6UqqqqpKKiIumb3/ymJJfLpbNnz5pUHENDQ5KHh4f0+OOPSyUlJdKlS5ekwMBA6Qtf+IJJxXHLs88+KyUnJy/I2A0RxxtvvCEplUrpt7/9rVRTUyNdvXpVSkhIkJKSkkwqjoqKCumvf/2rVFlZKWVmZkp79uyR7OzsPvLDfqnjOHbsmPT6669LRUVFUl1dnXT06FEpNDT0jo4Rt1qPfec735HKysqkV199dUFbjy1VHJIkSfn5+VJ+fr4UHx8vPf3001J+fr5UUlJiUnH8/e9/l5RKpfTqq6/e8Xv6+/tNKo7f/OY30uHDh6XKykqpsrJSeu211yQrKyvphz/84YLEIQhLwSSTXeCuP2+88cbcfcbGxqSvfvWrkq2trWRubi7t3r1bamtrm7v9pZdeuutzfNRfqwud7C5VHP/93/8t+fv7S2ZmZpKdnZ20Zs0a6fz58yYXhyTpW+ds2LBB0mq1koeHh/Tiiy8uyKzuUsfR398vabVa6f/+7/8WZOyGiuNXv/qVFBYWJmm1WsnV1VV65plnpObmZpOKo7S0VIqJiZG0Wq2k0+mknTt3SuXl5QsSw0LFcf78eSklJUWytraW631XawAAAn5JREFUzMzMpMDAQOl73/veB76ivnDhghQTEyOp1WrJz8/vjt9hSnHc77XZGONYvXr1XX/P888/b1Jx/OpXv5LCw8Mlc3NzSafTSbGxsdJvf/vbBS3xEYTFJpMkSUIQBEEQBEEQHkIm341BEARBEARBED6MSHYFQRAEQRCEh5ZIdgVBEARBEISHlkh2BUEQBEEQhIeWSHYFQRD+/+3bIUtrYQDG8efo1bSVJYWFwYIfwTow+xVEMFgV2RdYFWFfwCCYBatB1GYxmQSDQVYEEWZQkN2222953cvv1855y5MOfw7nAFAtsQsAQLXELgAA1RK7AABUS+wCVRkMBjk4OCg9A4BfQuwC1er1ehmPx6VnAFCQ2AUAoFpiF1hYn5+f2dnZSavVyvr6ek5OTuZng8EgLy8vOTw8TNM0aZqm4FIAShG7wMIaDoe5vb3N5eVlrq6ucnNzk4eHhyTJxcVFut1uRqNRJpNJJpNJ4bUAlPCn9ACA/zGdTnN6eprz8/NsbW0lSc7OztLtdpMknU4ny8vLabfbWVtbKzkVgIK82QUW0vPzc76/v7O5uTm/1+l0srGxUXAVAL+N2AUAoFpiF1hI/X4/Kysrub+/n997f3/P09PT/Hp1dTU/Pz8l5gHwS4hdYCG1Wq3s7e1lOBzm+vo6j4+P2d3dzdLSv8dar9fL3d1dXl9f8/b2VnAtAKX4QQ1YWMfHx5lOp9ne3k673c7R0VE+Pj7m56PRKPv7++n3+/n6+spsNiu4FoASmpmnPwAAlfIZAwAA1RK7AABUS+wCAFAtsQsAQLXELgAA1RK7AABUS+wCAFAtsQsAQLXELgAA1RK7AABUS+wCAFCtvxZI7VR1k9+sAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def generate_unit_cost_profiles(start_year=2024, years=10, decay_rate=0.005):\n", + " # Generate a monthly date range\n", + " dates = pd.date_range(start=f'{start_year}-01-01', periods=years*12, freq='M')\n", + " \n", + " # Initial costs for wind and solar resources\n", + " initial_costs = {\n", + " 'wind_A': 1.0,\n", + " 'wind_B': 1.2,\n", + " 'wind_C': 1.1,\n", + " 'solar_A': 0.8,\n", + " 'solar_B': 0.85,\n", + " 'solar_C': 0.9,\n", + " }\n", + " \n", + " # Create dummy profiles with slow exponential decay\n", + " profiles = {}\n", + " for resource, initial_cost in initial_costs.items():\n", + " # Exponential decay function\n", + " decay = initial_cost * np.exp(-decay_rate * np.arange(len(dates)))\n", + " \n", + " # Assign decay curve to profile\n", + " profiles[resource] = decay\n", + " \n", + " # Create a DataFrame with the profiles\n", + " df_profiles = pd.DataFrame(data=profiles, index=dates)\n", + " \n", + " return df_profiles\n", + "\n", + "# Generate the dummy dataset\n", + "df_unit_cost_profiles = generate_unit_cost_profiles()\n", + "\n", + "# Plot the unit cost profiles\n", + "df_unit_cost_profiles.plot()\n", + "plt.xlabel('dt')\n", + "plt.ylabel('Unit Cost')\n", + "plt.legend(title='resource', bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "plt.grid()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert to xarray" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "df_unit_cost_profiles.index.name = 'datetime'\n", + "df_unit_cost_profiles.columns.name = 'resource'\n", + "unit_cost_profiles_xr = xr.DataArray(df_unit_cost_profiles)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimized Energy Portfolio Model\n", + "\n", + "Now we are finally to the porfolio model. We know our resource needs, the generation of each resource, and the cost of each resource, we can write an optimization model to select the least cost portfolio that meets the energy need. \n", + "\n", + "The model is contained in the cells below. I've broken up the code across cells so you can get an idea of what the model components look like. Because the variables are vectorized across multiple dimensions, a single constraint is actually a whole array of constraints. " + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the model\n", + "m = lp.Model()" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Constraint `build_out_increasing` (datetime: 120, resource: 6):\n", + "---------------------------------------------------------------\n", + "[2024-01-31 00:00:00, wind_A]: +1 build_out[2024-01-31 00:00:00, wind_A] ≥ -0.0\n", + "[2024-01-31 00:00:00, solar_A]: +1 build_out[2024-01-31 00:00:00, solar_A] ≥ -0.0\n", + "[2024-01-31 00:00:00, wind_B]: +1 build_out[2024-01-31 00:00:00, wind_B] ≥ -0.0\n", + "[2024-01-31 00:00:00, solar_B]: +1 build_out[2024-01-31 00:00:00, solar_B] ≥ -0.0\n", + "[2024-01-31 00:00:00, wind_C]: +1 build_out[2024-01-31 00:00:00, wind_C] ≥ -0.0\n", + "[2024-01-31 00:00:00, solar_C]: +1 build_out[2024-01-31 00:00:00, solar_C] ≥ -0.0\n", + "[2024-02-29 00:00:00, wind_A]: +1 build_out[2024-02-29 00:00:00, wind_A] - 1 build_out[2024-01-31 00:00:00, wind_A] ≥ -0.0\n", + "\t\t...\n", + "[2033-11-30 00:00:00, solar_C]: +1 build_out[2033-11-30 00:00:00, solar_C] - 1 build_out[2033-10-31 00:00:00, solar_C] ≥ -0.0\n", + "[2033-12-31 00:00:00, wind_A]: +1 build_out[2033-12-31 00:00:00, wind_A] - 1 build_out[2033-11-30 00:00:00, wind_A] ≥ -0.0\n", + "[2033-12-31 00:00:00, solar_A]: +1 build_out[2033-12-31 00:00:00, solar_A] - 1 build_out[2033-11-30 00:00:00, solar_A] ≥ -0.0\n", + "[2033-12-31 00:00:00, wind_B]: +1 build_out[2033-12-31 00:00:00, wind_B] - 1 build_out[2033-11-30 00:00:00, wind_B] ≥ -0.0\n", + "[2033-12-31 00:00:00, solar_B]: +1 build_out[2033-12-31 00:00:00, solar_B] - 1 build_out[2033-11-30 00:00:00, solar_B] ≥ -0.0\n", + "[2033-12-31 00:00:00, wind_C]: +1 build_out[2033-12-31 00:00:00, wind_C] - 1 build_out[2033-11-30 00:00:00, wind_C] ≥ -0.0\n", + "[2033-12-31 00:00:00, solar_C]: +1 build_out[2033-12-31 00:00:00, solar_C] - 1 build_out[2033-11-30 00:00:00, solar_C] ≥ -0.0" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# build out\n", + "build_out = m.add_variables(lower=0, dims=resource_profiles_xr.dims,\n", + " coords=resource_profiles_xr.coords, name='build_out')\n", + "## make the build-out increase\n", + "m.add_constraints(build_out >= build_out.shift(datetime=1), name='build_out_increasing')" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Constraint `meet_energy_need` (datetime: 120):\n", + "----------------------------------------------\n", + "[2024-01-31 00:00:00]: +0.5865 build_out[2024-01-31 00:00:00, wind_A] + 0.08098 build_out[2024-01-31 00:00:00, solar_A] + 0.2997 build_out[2024-01-31 00:00:00, wind_B] + 0.2 build_out[2024-01-31 00:00:00, solar_B] + 0.3958 build_out[2024-01-31 00:00:00, wind_C] + 0.09292 build_out[2024-01-31 00:00:00, solar_C] ≥ -0.0\n", + "[2024-02-29 00:00:00]: +0.5446 build_out[2024-02-29 00:00:00, wind_A] + 0.2505 build_out[2024-02-29 00:00:00, solar_A] + 0.1823 build_out[2024-02-29 00:00:00, wind_B] + 0.2713 build_out[2024-02-29 00:00:00, solar_B] + 0.4301 build_out[2024-02-29 00:00:00, wind_C] + 0.2522 build_out[2024-02-29 00:00:00, solar_C] ≥ -0.0\n", + "[2024-03-31 00:00:00]: +0.4962 build_out[2024-03-31 00:00:00, wind_A] + 0.4498 build_out[2024-03-31 00:00:00, solar_A] + 0.3503 build_out[2024-03-31 00:00:00, wind_B] + 0.4887 build_out[2024-03-31 00:00:00, solar_B] + 0.4543 build_out[2024-03-31 00:00:00, wind_C] + 0.4222 build_out[2024-03-31 00:00:00, solar_C] ≥ -0.0\n", + "[2024-04-30 00:00:00]: +0.2602 build_out[2024-04-30 00:00:00, wind_A] + 0.6699 build_out[2024-04-30 00:00:00, solar_A] + 0.4087 build_out[2024-04-30 00:00:00, wind_B] + 0.5831 build_out[2024-04-30 00:00:00, solar_B] + 0.4336 build_out[2024-04-30 00:00:00, wind_C] + 0.6248 build_out[2024-04-30 00:00:00, solar_C] ≥ -0.0\n", + "[2024-05-31 00:00:00]: +0.4571 build_out[2024-05-31 00:00:00, wind_A] + 0.7094 build_out[2024-05-31 00:00:00, solar_A] + 0.4777 build_out[2024-05-31 00:00:00, wind_B] + 0.663 build_out[2024-05-31 00:00:00, solar_B] + 0.4043 build_out[2024-05-31 00:00:00, wind_C] + 0.698 build_out[2024-05-31 00:00:00, solar_C] ≥ -0.0\n", + "[2024-06-30 00:00:00]: +0.3073 build_out[2024-06-30 00:00:00, wind_A] + 0.8444 build_out[2024-06-30 00:00:00, solar_A] + 0.4877 build_out[2024-06-30 00:00:00, wind_B] + 0.9785 build_out[2024-06-30 00:00:00, solar_B] + 0.5298 build_out[2024-06-30 00:00:00, wind_C] + 0.8308 build_out[2024-06-30 00:00:00, solar_C] ≥ 0.16506350946109716\n", + "[2024-07-31 00:00:00]: +0.3145 build_out[2024-07-31 00:00:00, wind_A] + 0.9329 build_out[2024-07-31 00:00:00, solar_A] + 0.5581 build_out[2024-07-31 00:00:00, wind_B] + 0.9354 build_out[2024-07-31 00:00:00, solar_B] + 0.4552 build_out[2024-07-31 00:00:00, wind_C] + 1.072 build_out[2024-07-31 00:00:00, solar_C] ≥ 0.6\n", + "\t\t...\n", + "[2033-06-30 00:00:00]: +0.3965 build_out[2033-06-30 00:00:00, wind_A] + 0.9507 build_out[2033-06-30 00:00:00, solar_A] + 0.484 build_out[2033-06-30 00:00:00, wind_B] + 0.8573 build_out[2033-06-30 00:00:00, solar_B] + 0.5333 build_out[2033-06-30 00:00:00, wind_C] + 0.9016 build_out[2033-06-30 00:00:00, solar_C] ≥ 10.9650635094611\n", + "[2033-07-31 00:00:00]: +0.2845 build_out[2033-07-31 00:00:00, wind_A] + 0.8437 build_out[2033-07-31 00:00:00, solar_A] + 0.6944 build_out[2033-07-31 00:00:00, wind_B] + 0.9712 build_out[2033-07-31 00:00:00, solar_B] + 0.4802 build_out[2033-07-31 00:00:00, wind_C] + 1.096 build_out[2033-07-31 00:00:00, solar_C] ≥ 11.400000000000002\n", + "[2033-08-31 00:00:00]: +0.2328 build_out[2033-08-31 00:00:00, wind_A] + 0.8538 build_out[2033-08-31 00:00:00, solar_A] + 0.6365 build_out[2033-08-31 00:00:00, wind_B] + 0.7901 build_out[2033-08-31 00:00:00, solar_B] + 0.3094 build_out[2033-08-31 00:00:00, wind_C] + 0.869 build_out[2033-08-31 00:00:00, solar_C] ≥ 11.834936490538903\n", + "[2033-09-30 00:00:00]: +0.2449 build_out[2033-09-30 00:00:00, wind_A] + 0.7632 build_out[2033-09-30 00:00:00, solar_A] + 0.476 build_out[2033-09-30 00:00:00, wind_B] + 0.6151 build_out[2033-09-30 00:00:00, solar_B] + 0.4305 build_out[2033-09-30 00:00:00, wind_C] + 0.5862 build_out[2033-09-30 00:00:00, solar_C] ≥ 13.765063509461093\n", + "[2033-10-31 00:00:00]: +0.4457 build_out[2033-10-31 00:00:00, wind_A] + 0.3404 build_out[2033-10-31 00:00:00, solar_A] + 0.3441 build_out[2033-10-31 00:00:00, wind_B] + 0.3422 build_out[2033-10-31 00:00:00, solar_B] + 0.4359 build_out[2033-10-31 00:00:00, wind_C] + 0.4658 build_out[2033-10-31 00:00:00, solar_C] ≥ 16.7\n", + "[2033-11-30 00:00:00]: +0.4749 build_out[2033-11-30 00:00:00, wind_A] + 0.3268 build_out[2033-11-30 00:00:00, solar_A] + 0.424 build_out[2033-11-30 00:00:00, wind_B] + 0.1894 build_out[2033-11-30 00:00:00, solar_B] + 0.5332 build_out[2033-11-30 00:00:00, wind_C] + 0.1307 build_out[2033-11-30 00:00:00, solar_C] ≥ 18.29519052838329\n", + "[2033-12-31 00:00:00]: +0.6292 build_out[2033-12-31 00:00:00, wind_A] + 0.05871 build_out[2033-12-31 00:00:00, solar_A] + 0.3618 build_out[2033-12-31 00:00:00, wind_B] + 0.1285 build_out[2033-12-31 00:00:00, solar_B] + 0.5603 build_out[2033-12-31 00:00:00, wind_C] + 0.05197 build_out[2033-12-31 00:00:00, solar_C] ≥ 16.5650635094611" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# gen\n", + "## multiply for each resource\n", + "gen = resource_profiles_xr * build_out \n", + "## sum across resources\n", + "total_gen = gen.sum(dim='resource')\n", + "\n", + "# Meet energy need\n", + "m.add_constraints(total_gen >= energy_needs_xr, name='meet_energy_need')" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearExpression\n", + "----------------\n", + "+1 build_out[2024-01-31 00:00:00, wind_A] + 0.8 build_out[2024-01-31 00:00:00, solar_A] + 1.2 build_out[2024-01-31 00:00:00, wind_B] ... -0.6067 build_out[2033-11-30 00:00:00, wind_C] + 0.4964 build_out[2033-12-31 00:00:00, solar_C] - 0.4964 build_out[2033-11-30 00:00:00, solar_C]" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# cost\n", + "build_month = build_out - build_out.shift(datetime=1)\n", + "cost = build_month * unit_cost_profiles_xr\n", + "total_cost = cost.sum()\n", + "total_cost\n" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Linopy LP model\n", + "===============\n", + "\n", + "Variables:\n", + "----------\n", + " * build_out (datetime, resource)\n", + "\n", + "Constraints:\n", + "------------\n", + " * build_out_increasing (datetime, resource)\n", + " * meet_energy_need (datetime)\n", + "\n", + "Status:\n", + "-------\n", + "initialized" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# objective\n", + "m.add_objective(total_cost)\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Solve" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('ok', 'optimal')" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m.solve()" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "sol = m.solution" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Least Cost Energy Portfolio')" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# convert to a Pandas DataFrame for plotting \n", + "sol_df = sol.to_dataframe().reset_index().pivot_table(values='build_out', columns='resource', index='datetime')\n", + "sol_df.plot()\n", + "plt.grid()\n", + "plt.title('Least Cost Energy Portfolio')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that a mix a resources are the least cost way to meet the average energy need. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Concusion\n", + "Linopy's vectorized variables and constraints allow for a concise representation of an optimization problen with multiple dimensions: in this case, a timeseries with a number of potential resources. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qtenv", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/sudoku.ipynb b/examples/sudoku.ipynb new file mode 100644 index 00000000..5c0fc970 --- /dev/null +++ b/examples/sudoku.ipynb @@ -0,0 +1,2996 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sudoku Solver with Linopy\n", + "\n", + "The linopy package is good at tracking indices and writing optimization problems as vectorized functions (i.e., functions that operate on all rows of a column at once). This notebook adapts the Medium article [Creating Sudoku Solver with Python and Pyomo in Easy Steps](https://medium.com/@dhanalakotamohan314/creating-sudoku-solver-with-python-and-pyomo-in-easy-steps-fe22ec916090) by Dhanalakota Mohan for linopy to demonstrate how indices (i.e., dimensions and coordinates) work." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import linopy\n", + "import xarray as xr\n", + "import numpy as np\n", + "import pandas as pd\n", + "pd.options.display.float_format = '{:.0f}'.format\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Puzzle\n", + "\n", + "Sudoku is a 9x9 grid where each of digits 1 through 9 appears once and only once in each row, columns, and 3x3 grid. The puzzle is initiated with a values in some of the rows and columns, which I'm calling hints. Here is an example Sudoku puzzle with hints as a Pandas dataframe. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1------2--
2-8---7-9-
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7--5---6-3
8-9-4---7-
9--6------
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" + ], + "text/plain": [ + "column 1 2 3 4 5 6 7 8 9\n", + "row \n", + "1 - - - - - - 2 - -\n", + "2 - 8 - - - 7 - 9 -\n", + "3 6 - 2 - - - 5 - -\n", + "4 - 7 - - 6 - - - -\n", + "5 - - - 9 - 1 - - -\n", + "6 - - - - 2 - - 4 -\n", + "7 - - 5 - - - 6 - 3\n", + "8 - 9 - 4 - - - 7 -\n", + "9 - - 6 - - - - - -" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# look at the hints for the puzzle \n", + "puzzle_hints = [(1, 7, 2), (2, 2, 8), (2, 6, 7), (2, 8, 9),\n", + " (3, 1, 6), (3, 3, 2), (3, 7, 5), (4, 2, 7),\n", + " (4, 5, 6), (5, 4, 9), (5, 6, 1), (6, 5, 2), \n", + " (6, 8, 4), (7, 3, 5), (7, 7, 6), (7, 9, 3),\n", + " (8, 2, 9), (8, 4, 4), (8, 8, 7), (9, 3, 6)]\n", + "puzzle_hints = pd.DataFrame(puzzle_hints, columns = ['row', 'column', 'digit'])\n", + "puzzle_hints_piv = puzzle_hints.pivot(index='row', columns='column', values='digit').replace(np.nan, '-')\n", + "puzzle_hints_piv\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Defining Dimensions and Coordinates in Xarray\n", + "In Pandas parlance, the `index` are the row labels and `columns` are the column labels. When converting to Xarray, it is handy to name the index and columns with `df.index.name = \"row_name\"` and `df.columns.name = \"column_name\"`. \n", + "\n", + "In Xarray parlance, the Pandas `df.index.name` and `df.columns.name` are both called `dimensions`. Then the array of values of the index and columns are called `coordinates`. Think of latitude and longitude as `dimensions` with `coordinate` values. Xarray will infer the dimensions and coordinates from a Pandas DataFrame, provided columns and the index have names. The following cell shows how this looks. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "  * column   (column) int64 72B 1 2 3 4 5 6 7 8 9
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+       "\n",
+       "...\n",
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The left and right margins cannot be made large enough to accommodate all axes decorations.\n", + " plt.tight_layout()\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data = np.random.rand(9, 9, 9)\n", + "array = xr.DataArray(data, dims=['column', 'digit', 'row'])\n", + "\n", + "# Function to create a single cube at a specific location\n", + "def create_cube(x, y, z, size=1):\n", + " # Define the vertices of a cube\n", + " vertices = [\n", + " [x, y, z],\n", + " [x + size, y, z],\n", + " [x + size, y + size, z],\n", + " [x, y + size, z],\n", + " [x, y, z + size],\n", + " [x + size, y, z + size],\n", + " [x + size, y + size, z + size],\n", + " [x, y + size, z + size]\n", + " ]\n", + " # Define the 6 faces of the cube\n", + " faces = [\n", + " [vertices[j] for j in [0, 1, 2, 3]],\n", + " [vertices[j] for j in [4, 5, 6, 7]],\n", + " [vertices[j] for j in [0, 3, 7, 4]],\n", + " [vertices[j] for j in [1, 2, 6, 5]],\n", + " [vertices[j] for j in [0, 1, 5, 4]],\n", + " [vertices[j] for j in [2, 3, 7, 6]]\n", + " ]\n", + " return faces\n", + "\n", + "# Create a 3D plot\n", + "fig = plt.figure(figsize=(7, 4))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "\n", + "# Colors for the slices\n", + "colors = plt.cm.viridis(np.linspace(0, 1, 9))\n", + "\n", + "# Plot each cube\n", + "for i in range(9):\n", + " for j in range(9):\n", + " for k in range(9):\n", + " faces = create_cube(i, j, k)\n", + " poly3d = Poly3DCollection(faces, color=colors[j], edgecolor='k') # Color by digit dimension\n", + " ax.add_collection3d(poly3d)\n", + "\n", + "# Set custom labels\n", + "ax.set_xlabel('Column')\n", + "ax.set_ylabel('Digit')\n", + "ax.set_zlabel('')\n", + "ax.set_title('Sudoku Dimensions and Coordinates')\n", + "\n", + "# Customize ticks and labels\n", + "ticks = np.arange(9)\n", + "labels = np.arange(1, 10) # Labels from 1 to 9\n", + "ax.set_xticks(ticks)\n", + "ax.set_yticks(ticks)\n", + "ax.set_zticks([]) # Remove z-axis ticks\n", + "ax.set_xticklabels(labels)\n", + "ax.set_yticklabels(labels)\n", + "\n", + "# Set the aspect ratio to be equal and adjust limits to fit the plot\n", + "ax.set_box_aspect([1, 1, 1]) # aspect ratio is 1:1:1\n", + "ax.set_xlim([0, 9])\n", + "ax.set_ylim([0, 9])\n", + "ax.set_zlim([0, 9])\n", + "\n", + "# Adjust layout to ensure labels are visible\n", + "plt.subplots_adjust(left=0.5, right=0.8, top=0.9, bottom=0.0)\n", + "\n", + "# Manually add z-axis labels\n", + "for z in range(9):\n", + " ax.text(x=-1, y=-1, z=z + 0.5, s=9- z )\n", + "ax.text(x=-4, y=-1, z=4.5, s='Row', ha='center')\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Indices for the 3x3 Squares" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you play Sudoku, you know that there are 3x3 squares that get each digit once and only once. There we need to get indices for the 3x3 squares. We will label each of the square indices within the three dimensions and make an xarray. Later, within the optimization model, we will use the square indices to mask out portions of the puzzle. This is how we are indexing the squares: " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "column 1 2 3 4 5 6 7 8 9\n", + "row \n", + "1 1 1 1 2 2 2 3 3 3\n", + "2 1 1 1 2 2 2 3 3 3\n", + "3 1 1 1 2 2 2 3 3 3\n", + "4 4 4 4 5 5 5 6 6 6\n", + "5 4 4 4 5 5 5 6 6 6\n", + "6 4 4 4 5 5 5 6 6 6\n", + "7 7 7 7 8 8 8 9 9 9\n", + "8 7 7 7 8 8 8 9 9 9\n", + "9 7 7 7 8 8 8 9 9 9" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define the function to get the 3x3 square index\n", + "def get_square_index(row, col):\n", + " return (row // 3) * 3 + (col // 3)\n", + "\n", + "# Create the grid with row, column, and square indices\n", + "square_index = pd.DataFrame({\n", + " 'row': np.repeat(range_coord, 9),\n", + " 'column': list(range_coord) * 9,\n", + " 'square': [get_square_index(row-1, col-1)+1 for row in range_coord for col in range_coord]\n", + "})\n", + "square_index = square_index.pivot(index='row', columns='column', values='square')\n", + "square_index\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We put that in an xarray and then duplicate it across the third dimension \"digit\" for use in the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray (digit: 9, row: 9, column: 9)> Size: 6kB\n",
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+       "\n",
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+       "        [4, 4, 4, 5, 5, 5, 6, 6, 6],\n",
+       "        [4, 4, 4, 5, 5, 5, 6, 6, 6],\n",
+       "        [7, 7, 7, 8, 8, 8, 9, 9, 9],\n",
+       "        [7, 7, 7, 8, 8, 8, 9, 9, 9],\n",
+       "        [7, 7, 7, 8, 8, 8, 9, 9, 9]],\n",
+       "\n",
+       "       [[1, 1, 1, 2, 2, 2, 3, 3, 3],\n",
+       "        [1, 1, 1, 2, 2, 2, 3, 3, 3],\n",
+       "        [1, 1, 1, 2, 2, 2, 3, 3, 3],\n",
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+       "        [7, 7, 7, 8, 8, 8, 9, 9, 9],\n",
+       "        [7, 7, 7, 8, 8, 8, 9, 9, 9]]], dtype=int64)\n",
+       "Coordinates:\n",
+       "  * row      (row) int32 36B 1 2 3 4 5 6 7 8 9\n",
+       "  * column   (column) int64 72B 1 2 3 4 5 6 7 8 9\n",
+       "  * digit    (digit) int32 36B 1 2 3 4 5 6 7 8 9
" + ], + "text/plain": [ + " Size: 6kB\n", + "array([[[1, 1, 1, 2, 2, 2, 3, 3, 3],\n", + " [1, 1, 1, 2, 2, 2, 3, 3, 3],\n", + " [1, 1, 1, 2, 2, 2, 3, 3, 3],\n", + " [4, 4, 4, 5, 5, 5, 6, 6, 6],\n", + " [4, 4, 4, 5, 5, 5, 6, 6, 6],\n", + " [4, 4, 4, 5, 5, 5, 6, 6, 6],\n", + " [7, 7, 7, 8, 8, 8, 9, 9, 9],\n", + " [7, 7, 7, 8, 8, 8, 9, 9, 9],\n", + " [7, 7, 7, 8, 8, 8, 9, 9, 9]],\n", + "\n", + " [[1, 1, 1, 2, 2, 2, 3, 3, 3],\n", + " [1, 1, 1, 2, 2, 2, 3, 3, 3],\n", + " [1, 1, 1, 2, 2, 2, 3, 3, 3],\n", + " [4, 4, 4, 5, 5, 5, 6, 6, 6],\n", + " [4, 4, 4, 5, 5, 5, 6, 6, 6],\n", + " [4, 4, 4, 5, 5, 5, 6, 6, 6],\n", + " [7, 7, 7, 8, 8, 8, 9, 9, 9],\n", + " [7, 7, 7, 8, 8, 8, 9, 9, 9],\n", + " [7, 7, 7, 8, 8, 8, 9, 9, 9]],\n", + "\n", + "...\n", + "\n", + " [[1, 1, 1, 2, 2, 2, 3, 3, 3],\n", + " [1, 1, 1, 2, 2, 2, 3, 3, 3],\n", + " [1, 1, 1, 2, 2, 2, 3, 3, 3],\n", + " [4, 4, 4, 5, 5, 5, 6, 6, 6],\n", + " [4, 4, 4, 5, 5, 5, 6, 6, 6],\n", + " [4, 4, 4, 5, 5, 5, 6, 6, 6],\n", + " [7, 7, 7, 8, 8, 8, 9, 9, 9],\n", + " [7, 7, 7, 8, 8, 8, 9, 9, 9],\n", + " [7, 7, 7, 8, 8, 8, 9, 9, 9]],\n", + "\n", + " [[1, 1, 1, 2, 2, 2, 3, 3, 3],\n", + " [1, 1, 1, 2, 2, 2, 3, 3, 3],\n", + " [1, 1, 1, 2, 2, 2, 3, 3, 3],\n", + " [4, 4, 4, 5, 5, 5, 6, 6, 6],\n", + " [4, 4, 4, 5, 5, 5, 6, 6, 6],\n", + " [4, 4, 4, 5, 5, 5, 6, 6, 6],\n", + " [7, 7, 7, 8, 8, 8, 9, 9, 9],\n", + " [7, 7, 7, 8, 8, 8, 9, 9, 9],\n", + " [7, 7, 7, 8, 8, 8, 9, 9, 9]]], dtype=int64)\n", + "Coordinates:\n", + " * row (row) int32 36B 1 2 3 4 5 6 7 8 9\n", + " * column (column) int64 72B 1 2 3 4 5 6 7 8 9\n", + " * digit (digit) int32 36B 1 2 3 4 5 6 7 8 9" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "square_index = xr.DataArray(square_index)\n", + "square_index_3d = xr.concat([square_index] * len(range_coord), dim='digit')\n", + "square_index_3d = square_index_3d.assign_coords(digit=range_coord)\n", + "square_index_3d" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model\n", + "\n", + "Now we get to the model. Initalize a Linopy model. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "model = linopy.Model()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make the binary `sudoku` variable for the model with the same dims and coords as above." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "sudoku = model.add_variables(name='sudoku', \n", + " dims=square_index_3d.dims, \n", + " coords=square_index_3d.coords, \n", + " binary=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is handy to look at what Linopy is doing. The code defined as single variable, but it is indexed on a 9x9x9 cube, so it is really 729 variables. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Variable (row: 9, column: 9, digit: 9)\n", + "--------------------------------------\n", + "[1, 1, 1]: sudoku[1, 1, 1] ∈ {0, 1}\n", + "[1, 1, 2]: sudoku[1, 1, 2] ∈ {0, 1}\n", + "[1, 1, 3]: sudoku[1, 1, 3] ∈ {0, 1}\n", + "[1, 1, 4]: sudoku[1, 1, 4] ∈ {0, 1}\n", + "[1, 1, 5]: sudoku[1, 1, 5] ∈ {0, 1}\n", + "[1, 1, 6]: sudoku[1, 1, 6] ∈ {0, 1}\n", + "[1, 1, 7]: sudoku[1, 1, 7] ∈ {0, 1}\n", + "\t\t...\n", + "[9, 9, 3]: sudoku[9, 9, 3] ∈ {0, 1}\n", + "[9, 9, 4]: sudoku[9, 9, 4] ∈ {0, 1}\n", + "[9, 9, 5]: sudoku[9, 9, 5] ∈ {0, 1}\n", + "[9, 9, 6]: sudoku[9, 9, 6] ∈ {0, 1}\n", + "[9, 9, 7]: sudoku[9, 9, 7] ∈ {0, 1}\n", + "[9, 9, 8]: sudoku[9, 9, 8] ∈ {0, 1}\n", + "[9, 9, 9]: sudoku[9, 9, 9] ∈ {0, 1}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sudoku" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add the digit constraints to the model. Each constraint sums across one of the dimensions, which holds the others constant for each summation. By making the total == 1, the constrains ensure that the variable is turned on once and only once across that dimension. In other words, we can't write a 1 and a 2 in the same square of the Sudoku puzzle. The last constraint is displayed in Jupyter, which is helpful for seeing what is going on. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Constraint `row_column_constraint` (row: 9, column: 9):\n", + "-------------------------------------------------------\n", + "[1, 1]: +1 sudoku[1, 1, 1] + 1 sudoku[1, 1, 2] + 1 sudoku[1, 1, 3] ... +1 sudoku[1, 1, 7] + 1 sudoku[1, 1, 8] + 1 sudoku[1, 1, 9] = 1.0\n", + "[1, 2]: +1 sudoku[1, 2, 1] + 1 sudoku[1, 2, 2] + 1 sudoku[1, 2, 3] ... +1 sudoku[1, 2, 7] + 1 sudoku[1, 2, 8] + 1 sudoku[1, 2, 9] = 1.0\n", + "[1, 3]: +1 sudoku[1, 3, 1] + 1 sudoku[1, 3, 2] + 1 sudoku[1, 3, 3] ... +1 sudoku[1, 3, 7] + 1 sudoku[1, 3, 8] + 1 sudoku[1, 3, 9] = 1.0\n", + "[1, 4]: +1 sudoku[1, 4, 1] + 1 sudoku[1, 4, 2] + 1 sudoku[1, 4, 3] ... +1 sudoku[1, 4, 7] + 1 sudoku[1, 4, 8] + 1 sudoku[1, 4, 9] = 1.0\n", + "[1, 5]: +1 sudoku[1, 5, 1] + 1 sudoku[1, 5, 2] + 1 sudoku[1, 5, 3] ... +1 sudoku[1, 5, 7] + 1 sudoku[1, 5, 8] + 1 sudoku[1, 5, 9] = 1.0\n", + "[1, 6]: +1 sudoku[1, 6, 1] + 1 sudoku[1, 6, 2] + 1 sudoku[1, 6, 3] ... +1 sudoku[1, 6, 7] + 1 sudoku[1, 6, 8] + 1 sudoku[1, 6, 9] = 1.0\n", + "[1, 7]: +1 sudoku[1, 7, 1] + 1 sudoku[1, 7, 2] + 1 sudoku[1, 7, 3] ... +1 sudoku[1, 7, 7] + 1 sudoku[1, 7, 8] + 1 sudoku[1, 7, 9] = 1.0\n", + "\t\t...\n", + "[9, 3]: +1 sudoku[9, 3, 1] + 1 sudoku[9, 3, 2] + 1 sudoku[9, 3, 3] ... +1 sudoku[9, 3, 7] + 1 sudoku[9, 3, 8] + 1 sudoku[9, 3, 9] = 1.0\n", + "[9, 4]: +1 sudoku[9, 4, 1] + 1 sudoku[9, 4, 2] + 1 sudoku[9, 4, 3] ... +1 sudoku[9, 4, 7] + 1 sudoku[9, 4, 8] + 1 sudoku[9, 4, 9] = 1.0\n", + "[9, 5]: +1 sudoku[9, 5, 1] + 1 sudoku[9, 5, 2] + 1 sudoku[9, 5, 3] ... +1 sudoku[9, 5, 7] + 1 sudoku[9, 5, 8] + 1 sudoku[9, 5, 9] = 1.0\n", + "[9, 6]: +1 sudoku[9, 6, 1] + 1 sudoku[9, 6, 2] + 1 sudoku[9, 6, 3] ... +1 sudoku[9, 6, 7] + 1 sudoku[9, 6, 8] + 1 sudoku[9, 6, 9] = 1.0\n", + "[9, 7]: +1 sudoku[9, 7, 1] + 1 sudoku[9, 7, 2] + 1 sudoku[9, 7, 3] ... +1 sudoku[9, 7, 7] + 1 sudoku[9, 7, 8] + 1 sudoku[9, 7, 9] = 1.0\n", + "[9, 8]: +1 sudoku[9, 8, 1] + 1 sudoku[9, 8, 2] + 1 sudoku[9, 8, 3] ... +1 sudoku[9, 8, 7] + 1 sudoku[9, 8, 8] + 1 sudoku[9, 8, 9] = 1.0\n", + "[9, 9]: +1 sudoku[9, 9, 1] + 1 sudoku[9, 9, 2] + 1 sudoku[9, 9, 3] ... +1 sudoku[9, 9, 7] + 1 sudoku[9, 9, 8] + 1 sudoku[9, 9, 9] = 1.0" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.add_constraints(sudoku.sum(dim=['column']) == 1, name='row_digit_constraint')\n", + "model.add_constraints(sudoku.sum(dim=['row']) == 1, name='column_digit_constraint')\n", + "model.add_constraints(sudoku.sum(dim=['digit']) == 1, name='row_column_constraint')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you play Sudoku, you know there is also the 3x3 square constraint. The general concept is the same as above, where we are setting the sum == 1 such that each digit occurs once and only once. This is not quite as elegant as the code above because it has some loops and we use `square_index_3d` to select the right coordinates in the puzzle. We do this with the `.where` function in Linopy. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "for digit in range_coord:\n", + " for square in range_coord:\n", + " model.add_constraints(sudoku.where(square_index_3d==square).sel(digit=digit).sum() == 1,\n", + " name=f'digit{digit}_square{square}_constraint')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A Sudoku puzzle starts with some values already filled in. These are also added to the model as constraints. We add these by finding the right coordinates for each hint and making it == 1. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "for _, datum in puzzle_hints.iterrows():\n", + " model.add_constraints(sudoku.loc[dict(row=datum.row, column=datum.column, digit=datum.digit)] == 1, \n", + " name=f'hint_{datum.row}_{datum.column}_{datum.digit}_constraint')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I have been able to solve this without an objective, since the constraints lead to only one solution, but having an objective seems to help the solver. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "model.add_objective(sudoku.sum(), sense='max')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Solve" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve the model with the default solver (HiGHs)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Dual values of MILP couldn't be parsed\n" + ] + }, + { + "data": { + "text/plain": [ + "('ok', 'optimal')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.solve()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The solution comes out as an xarray representing a cube of binary variables. 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" + ], + "text/plain": [ + "column 1 2 3 4 5 6 7 8 9\n", + "row \n", + "1 9 5 7 6 1 3 2 8 4\n", + "2 4 8 3 2 5 7 1 9 6\n", + "3 6 1 2 8 4 9 5 3 7\n", + "4 1 7 8 3 6 4 9 5 2\n", + "5 5 2 4 9 7 1 3 6 8\n", + "6 3 6 9 5 2 8 7 4 1\n", + "7 8 4 5 7 9 2 6 1 3\n", + "8 2 9 1 4 3 6 8 7 5\n", + "9 7 3 6 1 8 5 4 2 9" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_sudoku_grid = xr.zeros_like(solution.sel(digit=1), dtype=int)\n", + "for digit in solution.digit.values:\n", + " final_sudoku_grid += digit * solution.sel(digit=digit).astype(int)\n", + "\n", + "result = pd.DataFrame(data=final_sudoku_grid.values, \n", + " columns=puzzle_hints_piv.columns, \n", + " index=puzzle_hints_piv.index)\n", + "result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks like it was solved! " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qtenv2", + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}