diff --git a/papers/alan_lujan/main.md b/papers/alan_lujan/main.md index ffacee30d3..e18f43b564 100644 --- a/papers/alan_lujan/main.md +++ b/papers/alan_lujan/main.md @@ -488,23 +488,14 @@ The benchmarks were conducted using the following setup: The first set of benchmarks compares the performance of `multinterp` with `scipy.interpolate.RegularGridInterpolator`, a widely-used interpolation library in the scientific Python ecosystem. The benchmarks were conducted for both 2D and 3D grids, and the results are presented in the following figures. -```{embed} #fig:performance_comparison_2d +```{embed} #fig:multivariate_speed_2d :remove-input: true -``` -```{embed} #fig:performance_comparison_3d -:remove-input: true ``` -### Backend Comparison - The second set of benchmarks compares the performance of different backends in `multinterp`. The benchmarks were conducted for both 2D and 3D grids, and the results are presented in the following figures. -```{embed} #fig:backend_comparison_2d -:remove-input: true -``` - -```{embed} #fig:backend_comparison_3d +```{embed} #fig:multivariate_speed_3d :remove-input: true ``` diff --git a/papers/alan_lujan/notebooks/Curvilinear_Interpolation.ipynb b/papers/alan_lujan/notebooks/Curvilinear_Interpolation.ipynb index c358c0c294..bc81c2f8a3 100644 --- a/papers/alan_lujan/notebooks/Curvilinear_Interpolation.ipynb +++ b/papers/alan_lujan/notebooks/Curvilinear_Interpolation.ipynb @@ -25,11 +25,10 @@ }, { "cell_type": "markdown", - "metadata": { - "lines_to_next_cell": 2 - }, + "metadata": {}, "source": [ - "Suppose we have a collection of values for an unknown function along with their respective coordinate points. For illustration, assume the values come from the following function:\n" + "Suppose we have a collection of values for an unknown function along with their respective coordinate points. For illustration, assume the values come from the following function:\n", + "\n" ] }, { @@ -231,7 +230,7 @@ } ], "source": [ - "#| label: fig:curvilinear_original\n", + "# | label: fig:curvilinear_original\n", "\n", "plt.imshow(function_1(grid_x, grid_y).T, extent=(0, 1, 0, 1), origin=\"lower\")\n", "plt.plot(rand_x.flat, rand_y.flat, \"ok\", ms=2, label=\"input points\")\n", @@ -271,7 +270,7 @@ } ], "source": [ - "#| label: fig:curvilinear_result\n", + "# | label: fig:curvilinear_result\n", "\n", "fig, axs = plt.subplots(1, 3, figsize=(9, 6))\n", "titles = [\"Original\", \"WarpedInterp\", \"CurvilinearInterp\"]\n", @@ -289,18 +288,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In short, `multinterp`'s `Warped2DInterp` and `Curvilinear2DInterp` classes are useful for interpolating functions on curvilinear grids which have a quadrilateral structure but are not perfectly rectangular.\n" + "In short, `multinterp`'s `Warped2DInterp` and `Curvilinear2DInterp` classes are useful for interpolating functions on curvilinear grids which have a quadrilateral structure but are not perfectly rectangular.\n", + "\n", + "\n", + "\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { "jupytext": { - "formats": "ipynb,py:percent" + "formats": "ipynb,md" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", diff --git a/papers/alan_lujan/notebooks/Curvilinear_Interpolation.md b/papers/alan_lujan/notebooks/Curvilinear_Interpolation.md index 94017035a2..bdbf235dec 100644 --- a/papers/alan_lujan/notebooks/Curvilinear_Interpolation.md +++ b/papers/alan_lujan/notebooks/Curvilinear_Interpolation.md @@ -1,6 +1,7 @@ --- jupyter: jupytext: + formats: ipynb,md text_representation: extension: .md format_name: markdown @@ -32,7 +33,7 @@ def function_1(x, y): return x * (1 - x) * np.cos(4 * np.pi * x) * np.sin(4 * np.pi * y**2) ** 2 ``` -The points are randomly scattered in the unit square and therefore have no regular structure. This is achieved by randomly shifting a well structured grid at every point. +The points are randomly scattered in the unit square and therefore have no regular structure. This is achieved by randomly shifting a well structured grid at every point. ```python @@ -93,6 +94,8 @@ Now we can compare the results of the interpolation with the original function. ```python +# | label: fig:curvilinear_original + plt.imshow(function_1(grid_x, grid_y).T, extent=(0, 1, 0, 1), origin="lower") plt.plot(rand_x.flat, rand_y.flat, "ok", ms=2, label="input points") plt.title("Original") @@ -104,6 +107,8 @@ Then, we can look at the result for each method of interpolation and compare it ```python +# | label: fig:curvilinear_result + fig, axs = plt.subplots(1, 3, figsize=(9, 6)) titles = ["Original", "WarpedInterp", "CurvilinearInterp"] grids = [function_1(grid_x, grid_y), warped_grid, curvilinear_grid] @@ -117,7 +122,8 @@ plt.show() ``` -In short, `multinterp`'s `Warped2DInterp` and `Curvilinear2DInterp` classes are useful for interpolating functions on curvilinear grids which have a quadrilateral structure but are not perfectly rectangular. +In short, `multinterp`'s `Warped2DInterp` and `Curvilinear2DInterp` classes are useful for interpolating functions on curvilinear grids which have a quadrilateral structure but are not perfectly rectangular. + diff --git a/papers/alan_lujan/notebooks/Multivalued_Interpolation.ipynb b/papers/alan_lujan/notebooks/Multivalued_Interpolation.ipynb index 1e7a48f3bf..6bf4cf290d 100644 --- a/papers/alan_lujan/notebooks/Multivalued_Interpolation.ipynb +++ b/papers/alan_lujan/notebooks/Multivalued_Interpolation.ipynb @@ -88,7 +88,7 @@ } ], "source": [ - "#| label: fig:multivalued\n", + "# | label: fig:multivalued\n", "\n", "mult_interp = MultivaluedInterp(z_mat, [x_grid, y_grid], backend=\"cupy\")\n", "z_mult_interp = mult_interp(x_new, y_new).get()\n", @@ -130,6 +130,9 @@ } ], "metadata": { + "jupytext": { + "formats": "ipynb,md" + }, "kernelspec": { "display_name": "multinterp-dev", "language": "python", diff --git a/papers/alan_lujan/notebooks/Multivalued_Interpolation.md b/papers/alan_lujan/notebooks/Multivalued_Interpolation.md index 2c9ad2e058..9d9f5d2799 100644 --- a/papers/alan_lujan/notebooks/Multivalued_Interpolation.md +++ b/papers/alan_lujan/notebooks/Multivalued_Interpolation.md @@ -1,6 +1,7 @@ --- jupyter: jupytext: + formats: ipynb,md text_representation: extension: .md format_name: markdown @@ -22,6 +23,7 @@ from multinterp.rectilinear._multi import MultivaluedInterp Consider the following multivalued function: + ```python def squared_coords(x, y): return x**2 + y**2 @@ -35,7 +37,8 @@ def multivalued_func(x, y): return np.array([squared_coords(x, y), trig_func(x, y)]) ``` -As before, we can generate values on a sample input grid, and create a grid of query points. +As before, we can generate values on a sample input grid, and create a grid of query points. + ```python x_grid = np.geomspace(1, 11, 1000) - 1 @@ -53,7 +56,10 @@ x_new, y_new = np.meshgrid( `MultivaluedInterp` can easily interpolate the function at the query points and avoid repeated calculations. + ```python +# | label: fig:multivalued + mult_interp = MultivaluedInterp(z_mat, [x_grid, y_grid], backend="cupy") z_mult_interp = mult_interp(x_new, y_new).get() z_true = multivalued_func(x_new, y_new) diff --git a/papers/alan_lujan/notebooks/Multivariate_Interpolation.ipynb b/papers/alan_lujan/notebooks/Multivariate_Interpolation.ipynb index 687d4a7553..1e8cda2df3 100644 --- a/papers/alan_lujan/notebooks/Multivariate_Interpolation.ipynb +++ b/papers/alan_lujan/notebooks/Multivariate_Interpolation.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2023-08-09T15:43:37.689978Z", @@ -35,16 +35,15 @@ }, { "cell_type": "markdown", - "metadata": { - "lines_to_next_cell": 2 - }, + "metadata": {}, "source": [ - "Suppose we are trying to approximate the following function at a set of points:\n" + "Suppose we are trying to approximate the following function at a set of points:\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2023-08-09T15:43:40.032035Z", @@ -55,8 +54,8 @@ }, "outputs": [], "source": [ - "def squared_coords(x, y):\n", - " return x**2 + y**2" + "def squared_coords(*x):\n", + " return sum(xi**2 for xi in x)" ] }, { @@ -68,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2023-08-09T15:43:40.036113Z", @@ -95,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2023-08-09T15:43:40.040670Z", @@ -122,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2023-08-09T15:43:40.045082Z", @@ -144,7 +143,7 @@ } ], "source": [ - "#| label: fig:multivariate_regular\n", + "# | label: fig:multivariate_regular\n", "\n", "interp = RegularGridInterpolator([x_grid, y_grid], z_mat)\n", "z_interp = interp(np.column_stack((x_new.ravel(), y_new.ravel()))).reshape(x_new.shape)\n", @@ -156,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2023-08-09T15:43:41.230752Z", @@ -170,7 +169,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "44.5 μs ± 824 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n" + "44.5 μs ± 914 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n" ] } ], @@ -188,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2023-08-09T15:43:51.720509Z", @@ -210,7 +209,7 @@ } ], "source": [ - "#| label: fig:multivariate_interp\n", + "# | label: fig:multivariate_interp\n", "\n", "mult_interp = MultivariateInterp(z_mat, [x_grid, y_grid])\n", "z_mult_interp = mult_interp(x_new, y_new)\n", @@ -223,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2023-08-09T15:43:51.801148Z", @@ -237,7 +236,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "18.1 μs ± 377 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n" + "17.6 μs ± 412 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n" ] } ], @@ -255,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2023-08-09T15:44:06.960397Z", @@ -272,14 +271,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "766 μs ± 130 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" + "655 μs ± 166 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" ] } ], @@ -297,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2023-08-09T15:44:07.591118Z", @@ -313,7 +312,7 @@ "True" ] }, - "execution_count": 26, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -331,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2023-08-09T15:44:07.597018Z", @@ -344,7 +343,7 @@ "outputs": [], "source": [ "n = 35\n", - "grid_max = 300\n", + "grid_max = 500\n", "grid = np.linspace(10, grid_max, n, dtype=int)\n", "fast = np.empty((n, n))\n", "scipy = np.empty_like(fast)\n", @@ -354,16 +353,15 @@ }, { "cell_type": "markdown", - "metadata": { - "lines_to_next_cell": 2 - }, + "metadata": {}, "source": [ - "We will use the following function to time the execution of the interpolation.\n" + "We will use the following function to time the execution of the interpolation.\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2023-08-09T15:44:07.602053Z", @@ -374,18 +372,18 @@ }, "outputs": [], "source": [ - "def timeit(interp, x, y, min_time=1e-6):\n", + "def timeit(interp, *coords):\n", " if isinstance(interp, RegularGridInterpolator):\n", " start = time()\n", - " points = np.column_stack((x.ravel(), y.ravel()))\n", - " interp(points).reshape(x.shape)\n", + " points = np.column_stack([coord.ravel() for coord in coords])\n", + " interp(points).reshape(coords[0].shape)\n", " else:\n", " interp.compile()\n", " start = time()\n", - " interp(x, y)\n", + " interp(*coords)\n", "\n", " elapsed_time = time() - start\n", - " return max(elapsed_time, min_time)" + " return max(elapsed_time, 1e-6)" ] }, { @@ -397,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2023-08-09T15:44:07.607206Z", @@ -432,7 +430,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2023-08-09T15:52:10.349570Z", @@ -441,6 +439,149 @@ "shell.execute_reply": "2023-08-09T15:52:10.697671Z" } }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0.75, 'Benchmarks for 2D Interpolation')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# | label: fig:multivariate_speed_2d\n", + "\n", + "fig, ax = plt.subplots(1, 3, sharey=True)\n", + "\n", + "\n", + "ax[0].imshow(\n", + " scipy,\n", + " cmap=\"RdBu\",\n", + " origin=\"lower\",\n", + " norm=colors.SymLogNorm(1, vmin=0, vmax=10),\n", + " interpolation=\"bicubic\",\n", + " extent=[0, grid_max, 0, grid_max],\n", + ")\n", + "ax[0].set_title(\"scipy\")\n", + "\n", + "\n", + "ax[1].imshow(\n", + " parallel,\n", + " cmap=\"RdBu\",\n", + " origin=\"lower\",\n", + " norm=colors.SymLogNorm(1, vmin=0, vmax=10),\n", + " interpolation=\"bicubic\",\n", + " extent=[0, grid_max, 0, grid_max],\n", + ")\n", + "ax[1].set_title(\"numba\")\n", + "\n", + "cbar = ax[2].imshow(\n", + " gpu,\n", + " cmap=\"RdBu\",\n", + " origin=\"lower\",\n", + " norm=colors.SymLogNorm(1, vmin=0, vmax=10),\n", + " interpolation=\"bicubic\",\n", + " extent=[0, grid_max, 0, grid_max],\n", + ")\n", + "ax[2].set_title(\"cupy\")\n", + "\n", + "\n", + "cbar = fig.colorbar(\n", + " cbar,\n", + " ax=ax,\n", + " label=\"Relative Speed (faster - slower)\",\n", + " location=\"bottom\",\n", + ")\n", + "cbar.set_ticks([0, 0.1, 0.5, 1, 2, 5, 10])\n", + "cbar.set_ticklabels([\"0\", \"0.1\", \"0.5\", \"1\", \"2\", \"5\", \"10\"])\n", + "ax[0].set_ylabel(\"Data grid size (squared)\")\n", + "ax[1].set_xlabel(\"Approximation grid size (squared)\")\n", + "\n", + "fig.suptitle(\"Benchmarks for 2D Interpolation\", y=0.75)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "n = 20\n", + "grid_max = 200\n", + "grid = np.linspace(10, grid_max, n, dtype=int)\n", + "fast = np.empty((n, n))\n", + "scipy = np.empty_like(fast)\n", + "parallel = np.empty_like(fast)\n", + "gpu = np.empty_like(fast)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "for i, j in product(range(n), repeat=2):\n", + " data_grid = np.linspace(0, 10, grid[i])\n", + " x_cross, y_cross, w_cross = np.meshgrid(\n", + " data_grid,\n", + " data_grid,\n", + " data_grid,\n", + " indexing=\"ij\",\n", + " )\n", + " z_cross = squared_coords(x_cross, y_cross, w_cross)\n", + "\n", + " approx_grid = np.linspace(0, 10, grid[j])\n", + " x_approx, y_approx, w_approx = np.meshgrid(\n", + " approx_grid,\n", + " approx_grid,\n", + " approx_grid,\n", + " indexing=\"ij\",\n", + " )\n", + "\n", + " fast_interp = RegularGridInterpolator([data_grid, data_grid, data_grid], z_cross)\n", + " time_norm = timeit(fast_interp, x_approx, y_approx, w_approx)\n", + " fast[i, j] = time_norm\n", + "\n", + " scipy_interp = MultivariateInterp(\n", + " z_cross,\n", + " [data_grid, data_grid, data_grid],\n", + " backend=\"scipy\",\n", + " )\n", + " scipy[i, j] = timeit(scipy_interp, x_approx, y_approx, w_approx) / time_norm\n", + "\n", + " par_interp = MultivariateInterp(\n", + " z_cross,\n", + " [data_grid, data_grid, data_grid],\n", + " backend=\"numba\",\n", + " )\n", + " parallel[i, j] = timeit(par_interp, x_approx, y_approx, w_approx) / time_norm\n", + "\n", + " gpu_interp = MultivariateInterp(\n", + " z_cross,\n", + " [data_grid, data_grid, data_grid],\n", + " backend=\"cupy\",\n", + " )\n", + " gpu[i, j] = timeit(gpu_interp, x_approx, y_approx, w_approx) / time_norm" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, "outputs": [ { "data": { @@ -448,13 +589,13 @@ "Text(0.5, 0, 'Approximation grid size (squared)')" ] }, - "execution_count": 30, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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q6LVKeOqpp0AIwfjx4xOtv3DhQjz22GM44YQTAiazeiNK7dhvv/0waNAg/P3vf9f234kTJ0aqpDqIXFACd955JwD46RLENVfvyX333Yf29vbYe2IYBkzTBKXUn9fZ2YnbbrsttG5SJa2trQ2TJk3C/fffH1ifMYbbb78dW265JbbbbruK+8mQoVZkSlKGPoc33ngDtm0DcCX3+++/H0uWLMHXvvY1jBs3DgDw9a9/HXfccQemTZuG733ve9h7772Ry+Xw4Ycf4qmnnsJXv/pVfO1rX0t13EsuuQSPPfYYDjjgAPzoRz/ChAkT8Nlnn2Hx4sU477zzsMMOO9T9XONwxBFHYNGiRdhhhx2wyy674OWXX8bPf/7zxCZBFf3798fixYtx9NFH49BDD8VDDz2EAw88EK+//jrOOeccHHfccdh2221hWRaefPJJvP766/jhD3+Y+jiDBg3CT3/6U/zoRz/CKaecghNOOAGrV6/G3LlzUSgUMHv27KraDwDf/OY3MWDAAOy9994YPnw4Pv30U9x77724++678f3vfz+kInV2dvqEsLOzE++88w4efPBBPPLII5g8eXJqlSwtdt55ZwDATTfdhP79+6NQKGDcuHEYMmQIrr32WsyYMQNr1qzBsccei8033xyrVq3CX//6V6xatQq//vWvEx3Dsiz88pe/xMaNG7HXXnvhueeew6WXXoqpU6di//33BwAceuihOPzww/GDH/wA69evx3777YfXX38ds2fPxu67746TTz45cv9f+cpXcOWVV+LEE0/EN7/5TaxevRq/+MUvQgQeACZMmIC77roLd999N8aPH49CoYAJEyZo93v55Zfj0EMPxYEHHogLLrgAlmVhwYIFeOONN/C73/2uR/J8ZciQkaQMfQ6nnXaa/33gwIEYN24crrzySpx99tn+fEopHnroIVxzzTW47bbbcPnll8M0TWy55ZaYPHly5IM5DltssQVeeOEFzJ49Gz/72c+wevVqDBs2DPvvv7/Wt6jRuOaaa5DL5XD55Zdj48aN2GOPPXD//feHEiamQUtLC/7whz/gxBNPxLRp03Dfffdh4sSJ2HrrrbFgwQJ88MEHMAwD48ePxy9/+Ut85zvfqeo4F110ETbffHP86le/wt13342WlhZMmTIF8+bN89MOVIN99tkHCxcuxC233ILPPvsM/fr1w6677orbbrtNGw7+zjvvYJ999gHgqhfDhw/HHnvsgXvvvRdHH300CGms2D5u3DhcffXVuOaaazBlyhQ4joOFCxfi1FNPxUknnYTRo0dj/vz5OOuss7BhwwbfqV91sI6DMIV997vfxaWXXoqWlhaceeaZ+PnPf+6vYxgGHnzwQcyZMwcLFy7EZZddhqFDh+Lkk0/GvHnztIRH4KCDDsJvf/tbXHHFFZg+fTq22GILnHnmmdh8881xxhlnBNadO3culi9fjjPPPBMbNmzAmDFjIkuyTJ48GU8++SRmz56NU089FYwx7LrrrnjooYdwxBFHJD7/DBlqgcFrDeHIkCFDhgxNiVNPPRW///3vsXHjxt5uSoYMfRKZT1KGDBkyZMiQIYMGGUnKkCFDhgwZMmTQIDO3ZciQIUOGDBkyaJApSRkyZMiQIUOGDBpkJClDhgwZMmTIkEGDjCRlyJAhQ4YMGTJokJGkDBkyZMiQIUMGDTKSlCFDhgwZMmTIoEFGkjJkyJAhQ4YMGTTISFKGDBkyZMiQIYMGGUnKkCFDhgwZMmTQICNJGTJkyJAhQ4YMGmQkKUOGDBkyZMiQQYOMJGXIkCFDhgwZMmiQkaQMGTJkyJAhQwYNMpKUIUOGDBkyZMigQUaSMmTIkCFDhgwZNMhIUoZYzJkzB4Zh9HYzMmRoKJ5++mkYhoHf//73vd2UDBkyNBEykpQhFjNnzsTSpUt7uxkZMmTIkCFDj8Ps7QZkaG5sueWW2HLLLXu7GRkyZMiQIUOPI1OSNmGsWrUK3/zmN7HVVlshn89j2LBh2G+//fDEE0/46yxevBgHH3wwBg4ciNbWVuy44464/PLL/eU6c9vYsWNxxBFH4IEHHsAuu+yCQqGA8ePH41e/+pW/zsaNGzFo0CCcddZZoXYtW7YMlFL8/Oc/b8BZZ+gLEP3qzTffxAknnICBAwdi+PDhOP3007Fu3ToAbj8xDAOLFi0KbW8YBubMmRPa3+uvv47jjjsOAwcOxODBg3HeeefBtm289dZb+PKXv4z+/ftj7NixmD9/vrZdXV1dOO+88zBixAi0tLRg8uTJePXVVwPrvPTSS/j617+OsWPHoqWlBWPHjsUJJ5yA9957r27XJ8OmgX/+85844YQTMHz4cOTzeYwePRqnnHIKuru7I10ZFi1aBMMwsGzZMn9e9sztPWQkaRPGySefjAcffBAXX3wx/vjHP+I3v/kNDjnkEKxevRoAcPPNN2PatGlgjOGGG27Aww8/jO9+97v48MMPK+77tddew6xZs3DuuefigQcewL777ovvfe97+MUvfgEA6NevH04//XTccccd/qAnsGDBAliWhdNPP73+J52hT+GYY47Bdttth/vuuw8//OEPceedd+Lcc8+ten/HH388dt11V9x3330488wzcdVVV+Hcc8/FUUcdha985St44IEHcNBBB+EHP/gB7r///tD2P/rRj/DOO+/gN7/5DX7zm9/g448/xpQpU/DOO+/46yxbtgzbb789rr76ajz++OO44oorsHz5cuy111749NNPq257hk0Lf/3rX7HXXnvh+eefxyWXXILHHnsMl19+Obq7u1EsFlPvL3vm9hJ4hk0W/fr147NmzdIu27BhAx8wYADff//9OWMsch+zZ8/majcZM2YMNwyDv/baa4H5hx56KB8wYABvb2/nnHP+n//8hxNC+FVXXeWv09nZyYcMGcJPO+20Ks8qw6YA0a/mz58fmH/22WfzQqHAGWP83Xff5QD4woULQ9sD4LNnzw7t75e//GVgvd12240D4Pfff78/r1Qq8WHDhvGjjz7an/fUU09xAHyPPfYI/B+WLVvGc7kcnzlzZuS52LbNN27cyNva2vg111yT9BJk2MRx0EEH8UGDBvGVK1dql+uerZxzvnDhQg6Av/vuu/687Jnbe8iUpE0Ye++9NxYtWoRLL70Uzz//PEqlkr/sueeew/r163H22WdXFb220047Yddddw3MO/HEE7F+/Xq88sorAIDx48fjiCOOwIIFC8A5BwDceeedWL16Nc4555wazizDpoIjjzwy8HuXXXZBV1cXVq5cWdX+jjjiiMDvHXfcEYZhYOrUqf480zSxzTbbaM1jJ554YuD/MGbMGOy777546qmn/HkbN27ED37wA2yzzTYwTROmaaJfv35ob2/HP/7xj6ranWHTQkdHB5555hkcf/zxGDZsWF32mT1zewcZSdqEcffdd2PGjBn4zW9+g3322QeDBw/GKaecghUrVmDVqlUAULVT9ogRIyLnCXMeAHzve9/D22+/jSVLlgAArr/+euyzzz7YY489qjpuhk0LQ4YMCfzO5/MAgM7Ozqr2N3jw4MBvy7LQ2tqKQqEQmt/V1RXaPqpfy336xBNPxHXXXYeZM2fi8ccfxwsvvIAXX3wRw4YNq7rdGTYtrF27Fo7j1DXoJXvm9g6y6LZNGEOHDsXVV1+Nq6++Gu+//z4eeugh/PCHP8TKlStx3nnnAUAi/yMdVqxYETlPHvgOOugg7LzzzrjuuuvQr18/vPLKK7j99turOmaGzxcEsenu7g7MlweEeiOqX4s+vW7dOjzyyCOYPXs2fvjDH/rrdHd3Y82aNQ1rV4a+hcGDB4NSGvt8lfu3eDkAEOnXlj1zeweZkvQ5wejRo3HOOefg0EMPxSuvvIJ9990XAwcOxA033ODLsmnw5ptv4q9//Wtg3p133on+/fuH3li++93v4tFHH8VFF12E4cOH47jjjqvpXDJ8PjB8+HAUCgW8/vrrgfl/+MMfGnbM3/3ud4H/w3vvvYfnnnsOU6ZMAeBG1XHOA4MaAPzmN7+B4zgNa1eGvgURGXnvvfdGkp6xY8cCQKh/P/zww9r1s2du7yBTkjZRrFu3DgceeCBOPPFE7LDDDujfvz9efPFFLF68GEcffTT69euHX/7yl5g5cyYOOeQQnHnmmRg+fDj+/e9/469//Suuu+662P2PGjUKRx55JObMmYORI0fi9ttvx5IlS3DFFVegtbU1sO5JJ52Eiy66CM8++yx+8pOfwLKsRp56hk0EhmHgpJNOwm9/+1tsvfXW2HXXXfHCCy/gzjvvbNgxV65cia997Ws488wzsW7dOsyePRuFQgEXXXQRAGDAgAE44IAD8POf/xxDhw7F2LFj8cwzz+Dmm2/GoEGDGtauDH0PV155Jfbff39MmjQJP/zhD7HNNtvgk08+wUMPPYQbb7wR06ZNw+DBg3HGGWfgkksugWmaWLRoET744APt/rJnbu8gFUlat24dHnjgAfz5z3/GsmXL0NHRgWHDhmH33XfH4Ycfjn333bdR7cyQEoVCAZMmTcJtt92GZcuWoVQqYfTo0fjBD36ACy+8EABwxhlnYNSoUbjiiiswc+ZMcM4xduxYzJgxo+L+d9ttN5x22mmYPXs23n77bYwaNQpXXnmlNny7paUF06dPx+23345vfetbdT/XDJsufvnLXwIA5s+fj40bN+Kggw7CI4884r+F1xvz5s3Diy++iNNOOw3r16/H3nvvjbvuugtbb721v86dd96J733ve7jwwgth2zb2228/LFmyBF/5ylca0qYMfROC1M+ePRsXXXQRNmzYgBEjRuCggw6CZVnI5/NYvHgxZs2ahZNOOgmDBg3CzJkzMXXqVMycOTO0v+yZ2zsweAJby/Lly3HxxRfjjjvuwIgRI7D33ntjiy22QEtLC9asWYM33ngDL7/8MsaMGYPZs2fjv/7rv3qi7Rl6CWPHjsXOO++MRx55JNH6xWIRY8eOxf7774977rmnwa3LkCFDhk0L2TO395BISdp1111xyimn4IUXXsDOO++sXaezsxMPPvggrrzySnzwwQe44IIL6trQDH0Pq1atwltvvYWFCxfik08+CTi6ZsiQIUOG+iJ75tYfiUjSm2++WTHXQ0tLC0444QSccMIJfnh5hs83Hn30UZx22mkYOXIkFixYkIWgZsiQIUMDkT1z649E5rYMGTJkyJAhQ4bPGxIpSQ899FDiHaoZdONw+eWX4/7778c///lPtLS0YN9998UVV1yB7bff3l+Hc465c+fipptuwtq1azFp0iRcf/312Gmnnfx1uru7ccEFF+B3v/sdOjs7cfDBB2PBggVZ9foMGTJkyJAhQ9VIpCQREkynJHKFyL8F0uQK+fKXv4yvf/3r2GuvvWDbNn784x/jb3/7G/7+97+jra0NAHDFFVfgsssuw6JFi7Dddtvh0ksvxbPPPou33noL/fv3BwB8+9vfxsMPP4xFixZhyJAhOP/887FmzRq8/PLLoJQmbk+GDBkyZMiQIYOPtMXelixZwvfYYw++ePFivm7dOr5+/Xq+ePFiPnHiRP7HP/6xpkJyK1eu5AD4M888wznnnDHGR4wYwX/2s5/563R1dfGBAwfyG264gXPO+WeffcZzuRy/6667/HU++ugjTgjhixcvrqk9GTJkyJAhQ4bPL1Ink5w1axZuuOEG7L///v68ww8/HK2trfjmN79ZU4HHdevWASjXX3r33XexYsUKHHbYYf46+XwekydPxnPPPYezzjoLL7/8MkqlUmCdUaNGYeedd8Zzzz2Hww8/PHSc7u7uQKkDxhjWrFmDIUOGVFXsNUMGwDUNb9iwAaNGjQqpr/VC1nczNAJZ383QV9HovpuaJP3nP//BwIEDQ/MHDhyIZcuWVd0QzjnOO+887L///n6aAVGXZvjw4YF1hw8f7lfwXrFiBSzLwmabbRZaR1frBnB9oebOnVt1WzNkiMMHH3zQMH+4rO9maCSyvpuhr6JRfTc1Sdprr70wa9Ys3H777Rg5ciQAl6icf/752HvvvatuyDnnnIPXX38df/nLX0LL1LcMznnFN4+4dS666CK/wCvgKlijR4/GH/abhH55CzRHYFADNEdBLeJ+FiwYORPUNEEsCoMSUNOE4fk8GQqD5Yy5n56PFvN+y0xX3VbehjEGzhhY0QZ3uL9c7EeHkO+YhlVzZfvA/hymXY874WOq89T9AgDTbccaE0zJnPJ+ucP93+VrWvm4Bk33Nqte3/aSjamPP+v7yjUCUX33pQtOQr+8BYMQt2/mKGhrAdZmA2EOHgFzq21hDxmPNczC+iLD+i4bqztL2Fh0sLFoo+TdlxwxUDAJ+udNbFbIYXCriQEWxRCzBHPFWygt+ztKq1age816lDq64ZRK4IyBO2LicGwbcFhwfky/rQTRjzhz76u4l8zh3jHKv8V6OhgkeH9JyvvNEvQhFWmOIbdf/L+4d471gu6ZAADtto2jnn+hV/ruHj/4Hay2fqCUIG+ZGNBqYnCbhaH9C9h8QB4j+uUxos3CZl5fHOSsB131Dkof/hv2px+j45O16FqzHt1rNqDrs050ru5E94ZudG8ooXtDNzodji7G0MU4HA6UvE8AcDjgeP61DgeY9L0WOBpXX3mf6vJqjscSBqYTaRysZpskx+3NqoVFMNyGjxrWd1OTpN/+9rf42te+hjFjxmD06NEAgPfffx/bbbcdHnzwwaoa8Z3vfAcPPfQQnn322QATHDFiBACXhAlCBrj1lYS6NGLECBSLRaxduzagJq1cuTKyTEo+nw8VqASAfnkL/fM5EGqA5CgINWAWTNCcCWKZILngd0IJDEojHzxBouF2I0NyJI8iMdxxwMQgY5ragUYmIISG9xPVJrVd/gDkMIBSaRktEyGpzbp5uv2qbSxv36CMEzlpkCEMjCgkiSQ7bhKiFHdtgTCpryei+m7/lgIGtLlVxV2SZMJsKyDXWoDZ1gKzJQ+nrQCH9IdRdEAKDmyrCFJ0kCs56LLd65QjBlpzFP3yJgYVchjYYqK/RTAA7TBbCyi1tqDUkkdXIe8ONsTwr7FL6Bl4jsIp2qlJkvqyIMAkwsAIByfe8YhHkohHionXv2Juj3x/kxIYnxxVoeSnIkmizzoczPDO2egZkuQv74W+m2tpQ66lH0yLIp+jKLTm0NLPQmv/Avr1z6O1LY+2fnkMaDXRP08xoOSAtBdgt7WgtN4CLeRhmiZMSkEIBTFNUMMG4TYooWCMwTAMGAaHA4AaHA48MgREfq8FYj/+bw6YRszyag5i6MmYCurdU4dzIOH9pTHrqW13951otw1Fo/puapK0zTbb4PXXX8eSJUvwz3/+E5xzfOELX8AhhxySupGcc3znO9/BAw88gKeffhrjxo0LLB83bhxGjBiBJUuWYPfddwfgplt/5plncMUVVwAA9txzT+RyOSxZsgTHH388ALeMyhtvvIH58+enao/6pmko5MMlRdEPmdhlmoeTuj53mL8eAaB7NIoHpo4YxR1LhwBBgqy6xKtISUEo0RKlaqAjL40gXNzhsUQp6bXtaQh1k3hKErE8Ek88MsscwCmCmu4DMEcJ8iZFyeFgjPtvjtQwkDcJCpQgbxJQA8hRA0Z3N3ixyyUlXj8xCHHvMdy+I/qz/F1F1IAvX1eDkIrEQKfqyP2h0ktEUlSjHmVIB0IJqElAiAHLJP6UNwlyhCBHDIjuQQwAnAGMAY7jE3EBuX/WEw7nscRBXTf4O355LaCGkWh/9Tzm5w2pSRLgMrbDDjsMBxxwAPL5fNUM7r//+79x55134g9/+AP69+/v+xANHDgQLS0tMAwDs2bNwrx587Dtttti2223xbx589Da2ooTTzzRX/eMM87A+eefjyFDhmDw4MG44IILMGHCBBxyyCGp20SoAYMagTdAg5LgQ1yn3ND45VHrxkIhGPJ2ugdBLQO4SpCqIUdJBrdqEEVaDGpEEiVCjaoHuEpESXcsACCs95xPqadyGh6RN0iwz3K7BMMpwiIGct7UmiNgzCVRsrktbxK05ijypgGLEpjEgFHqArdLLtlCuS8alAYIvSD6BIAj9SGDksBLQD0gm9oEdKScOcwnSmnvbeB4Ef2p2v1Ve7xNDdR0SVJLjvrkyDIpKDFADIAQAzlCXDUI8EiSS5CYE1Qry6bXYD/QXcqg+Su6fWVzXDxRqpaI1HqbKxGljCDVhtQkiTGGyy67DDfccAM++eQT/Otf/8L48ePx05/+FGPHjsUZZ5yReF+//vWvAQBTpkwJzF+4cCFOPfVUAMCFF16Izs5OnH322X4yyT/+8Y8B++NVV10F0zRx/PHH+8kkFy1alDpHkiBIvppDjXjFxjO1lQeM8rqEECDlgMAQJCfiLV1FksFGR8QqER/xsAGi3/iBeDKmEqVa1aRKA1AcUaoFusFUPe+0Pi2NhJEzQQuW+52SsupJKDhzwO0ijFI38oYDixKUmGtWA9xBqOTdoxx1VaQ2i8IiBkwCtBgOjFInWHcnOCsbBgL9HYAToyZxh/lEqd6Q/ZTKx5PyuFEjQJTk7eLuoernFgWxTNdXKx0jA2CZ1CdILRb1laSWHEWOukoSJQYocX1lDGa7/ZA5PjlySjaYw8AC/oi87r5F8u+AGUu7bZL91dY+tS1x7clQHVKTpEsvvRS33HIL5s+fjzPPPNOfP2HCBFx11VWpSBJPcDMNw8CcOXMwZ86cyHUKhQKuvfZaXHvttYmPrT2Woh6FlkuDpPrAFeuTGMUp0gThDRw6E1voLV0ZeHRIrFR5+9BJ1lHEhlCXBKUhStUi7Ru6Tj0Sbak3mdINfI1SFJKAeCqS6JckZ8LIuaQJzAHsEoxSB0jXBljWIDjcAOcGiEGRIwSMczicu+YNaqBAPbObSUA618Do2ghe7HLNHCj7PTmw/TZQwP9tOCTUnxtJlNzTDCtL7nF5gCilVZOSmvFqUal0x/q8oGBRnxy1et9bLYqc1wdzlLiKEgyYxAAcG7BL4LYbNOAUS+AOg1N07z/XkOY0SLppvHqTbv16QlW76n3cpCa+TQWpte9bb70VN910E77xjW8ElJpddtkF//znP+vauN6A62fhdjKhKgkThjuv8iXzTR5U8g+xzNB83USk47nHpp5vSbgNcrt0v3XtioMgTLKjrPrQYQmUJiCeUNYbIcWnwcdTCVKl694T8M1thHiESVJRHQe8uwvo7gTpXIc2dCNPDbTkCFpMgjaLoJ9FMTBvos0iaMsRb5mBNtYJ0rEWrGO9b26T/xO+ic/rq+I3sUzfZ8mI6L9pkGbQE34psn9KPciH+uLgKhdKlGeTk5xGmMNrRYtFfGLUYlG0WCZaLIq8pyLliAFquEqSRQDDKYGXiuB20Q0WYAys5AYNMH9KT5TqMfC70XLJ9p3ExNdXsCmLpamVpI8++gjbbLNNaD5jDKVSqS6N2hQQIFYkenDQvVUnVZTEtvUaoMs2/mBItSAFcW/KOoWrET5KMuGSB6hGmd38/UcoZ71NjgQMagT8hECob+7ljLkDSscGECsPsoGif9sQ5HIt6CIcjmSWoAZAiaskFZxOkI2rYHSuB+tsB+/u8s1tBnGJECvaoDDBqKtGMgCG2i91vkl1VJTclA96FUmcv7h/OrNbEsSZjKvdpw6N7sfNiH55E/0KpmduM9G/YKJgEhRM6n0SUOIqGIbdBcMpeoTdM7N5UZVOyQm91KnmMdevKJlalIaopCU8UevL64rvSR3GMzQGqUnSTjvthD//+c8YM2ZMYP69997rR6B9HsAcFlDSVFOYgHiDjnqIRvkcxaHeA43si+S3K2BiCJKlSoNCgMBVIErqvtNA5+8km9xqcd5OgmYhSAJGlA8ec9zItM52MM+p2mAOWgr9Uci3gVsF2N5lMg3AKHbA6GwH6doAo+MzsPWrwTo2gNtF7zgE1DLBPN84zhiMou2H5gOAUyyVTdCs7FuXhDRHpbpQ76WcD0vdVtev6mEOE/sBggpmWufwJGRIrFPvl41K5vKeRlvB9MxtFP0LJlpyroqUN90IzBwlvpJklLpdJanYBdbVBVa0wUo2nJKbT84pOX6eLLnvUAOQ02e5xKM2n6V6E62+jqTks68hNUmaPXs2Tj75ZHz00UdgjOH+++/HW2+9hVtvvRWPPPJII9rYVJDJEHf0TqwyZIKkezAJhYgrb9txalK1D0zuBAefJPlrwn5QGofmiGi/kBO6E+0bpHNwVY+lI2YyUarnW3jUINfMTriBXFzCwZo54DYAQsG62mEI/6TWDtDu9eBmAaCW/7ZqMAeGU4Rhd4O1r4fT2Q7escE11/n+SG5Em0EYOGU+WWIlO9QmBhuGQ0KRmtr2JyFQwpSSgHiL74QaiYiBISmmPYHeVI3kNA69jRbLRL9CDnnPcTvvqUhuCgA3eECY3GB3+aqmU7LBSiU4RRtOseyTpDODCtQ6kNfDXyntNmnSD+jQkz5EmyJRSk2Spk+fjrvvvhvz5s2DYRi4+OKLsccee+Dhhx/GoYce2og29hhEdlsGAgIG7hjgnhmBewOCGsosEyXAJTEEGqdtEvZb8R22KamJ/CRBNYpTVWkAFDMgl9SGSqgcbaRXsHSKkk7NSjooVYpqa0awkgNuldM3GJTAADyCVCZMKBXB7BLQ2Q7DzLnO3WbOz6fkOsO6PkzcLroKVHcw/F+sb+TcfRLHjTJyFN8s7jjgTESKkkBKADVQQHtOkm9cvaEScPU79/piWZGqPkpT7de6Y7nHaKzyqYPvs9WLvkotOdcfyfJMawU/R5Kb/V2oSHnqpaIoupPTVYTdVYTTVYRTdPzJfY67+b9kEPRMZuhKhKSaW1wrUerJ421qRCkVSbJtG5dddhlOP/10PPPMM41qU6+h7OzHtBmlZbhkyov20hAlQzaHxahMYl+AN5jXYD5Lsm2ah2Fac57qmKuaBOuZXFKFvO9qBptqzDBRJtbegFO0YdOir1xyxrw+6MAQmdSZ45KdYpdLiog+W7y/LnNc9UgO+xfJKc1y5JxLxkowaBE2PLWHMVfRKtluH4jod5WytKv+cT0Fmbyo5DpJX0lj2tOR90b+V5oNLRbxCZLrrF0mSK6pzVVDKPMUzs528GIX7C7XcdtVlLzEkixsggWCakqUqa1yvqH480iq1lRLImojLunVpIwouUhFkkzTxM9//nPMmDGjUe3pVYg/GIFbIsBwuDvQBMwKykPd+1SHGk6Yr6Iwh4H6ztveG7ujf6dJQ0zSZsau5m0xKQmoZHKrVk1K60eiDjgqYVIHv4r7U0hEuH3yPei9pwIv2WAmhcEIWEnyhWPEJ0s+2SG0XFiAhfthqISOIEaEwDAt91PMQ668DiEw4V4T6rh1B5OUx4kiA9Vez7QkOa4fpFGT6uXArSNN1folMeW/0IzIecSIGG4+JJMayFEDxHCTSVJiwDAAwy56TttF8FKZIAlTm1MsB57I108lSPrv4UE9DbHoC75H9SRKSfa1qRCl1Oa2Qw45BE8//bSf7HFTA3c44PkwcMdAXJaEgI+Psiaj7pu8MNGVt3GiHWxj21Wu5xbXDu22CWqqqahF+lcf6Crxa5QvRtK373rUZ2s22N1FMFEwWYTdMwLuBMkSAHDWDSDeL00OOAjkXIKrJhlmrqy2Ok6ZSDEGarnOtGJ7RgngBb6qtdyS1PfzkwPWSVmJIjOBDOWSeZZQw/Mb5LF9rBaCFPWfqOW/ov5/df/nZiBOxDOnUcMlRvJneR3AcBzXp06E/Eu1AYUfkvBJ0kE4b8uDt3v6wQE/7eBeL4KUhHj0hn+SLoFmtfvqi0hNkqZOnYqLLroIb7zxBvbcc0+0tbUFlh955JF1a1xPgzsMMN2HCZWdOHPx2whzgkyUfD8myZ8JEIQh2jKuGwjUQSyQGTth3iKBOBIhykkwMH9wCK0jqygaZ1g/3YHvBxSvjCUhY7KalOZtvZrovyTkKKq9vVnnixUd2KTokyPiuCH6/nLAd56WE4j6g4ycJV3Kg8S93EsGddyUAsTN4g0qzHUUnIjBy52IVB4lts0JfI5Sm029flfr4K/zYyuTpnI6AYFKfTJp5u2e9ksSx+rVvqsZaNV5jAOcUHARYCCltxBuEnK0o/qcE8RH+CV9XgZ4HWo5d5WkVdrXpqAmpSZJ3/72twEAV155ZWiZYRhwYghAX0GUk2UUVKKUthxKpX1XWlYNQVKj6eR5riN5Y5xmK4Xt1+84KU0ufUw5UsFKJXDLdAkCTPfuFW2fKAlC6ys4Xm4ZxlyzWGAdj2hRmG7gAmOuAmq6bwuGR5QMz8RmEOJH0IFQiHI9JOKaqglLI88pIgpSB9FnI+urVXF/5esl+lPQ0bpv95lmAeMcJc8XlDhAiRhwTIISY2CcwmEcnAM8VwA3CzCsAoiV8+sThqJiPYftnhicm4VoRQ1TjbgGaYlSX0dVtds2Vbhvv+X6bYHM1iQ615EO3GFglIGgPCiIrVVS4m9TxbWt5NeRFuU38XLIfqJ2SEQxMvGiNOjofIf6KpohQoh5RAYAuF8yXYk0VIoYM8YkVcnb1nFgMAoC01dCmcNAJDWVM4Zq7lao/I0mF1clqORXNoUJVPLfqWRqi/Jj0xGlalCvfE2V0BvRctWgs8hASg4c7t4DSgyQkuMWW7Yd5IiJosPRbVLQfBuMtv4wCm0wCxaoX8mgPtezrw34lU5bLK/F30qHNGa/vq4mpSZJmzJojoBaLiGiFgW1SLiyOiWRpgSZSKkRXjJ0ddLSOGsn8euoBmrIvs6sAFR+g05KFqoNyU+CRg8OzVjeoRrH4ah+J0i+IfU3gzHALrmKUakInrPcyDbJ1Abm+IQryQtVLfdJ6+AsJWCsBT4hUsxuMlEC6q+0Rl2PavtbOQFs845SG7ttsJyNvEngWGUVPkcMt+CyyVF0GDptA4W2ITAHrgXpPwi5AZ/CbCsg12KCWhQGNSJftqo9/UrO3T2NoIITvQxQfYkaQ5Q+D6iKJLW3t+OZZ57B+++/j2KxGFj23e9+ty4N6w2QHAXNUbgZhYN1pwgNE6RwVfjgfNncIFIByEpL2gefbn2dCS1NWLrqOxTKA8UqD7yB7WIIoYCaANJdL8JMUsMbYhp/pDRZiMM+Yjzw2Vtw+2jZeVt8ij4MhNuuC8/njuOno+CkXGXdoEXXJ8l2vbANAJxStzYcY25+JbsUcgYXalTwGLVdq3BwgOFmN3Oi82FV25fUY8kKTbX7VNWkyJIqqUyOyaL0mg3rO4ookSJaLBPdNoPjmcuE83aOGCCGCdNhaKd59G/dDHSzzZFbuxK51s9ACxao1QlqUZTaSzBI+To4PDiYM39+70RmNWKfUYqOmN8MJK8ZCGa1SE2SXn31VUybNg0dHR1ob2/H4MGD8emnn6K1tRWbb755nyZJuRYKs2AGCsqSnOlKukrhWa1kL/ki6YiKX4U8ba6iBFmxdb+jyFIUeVCJjkqWkqDaCKQ0g02UYqJT1HSlLKKOW025ht4mRgI0l/N9geRCyYDkTK9cM+K4CR7VxKhq9KUchGDYJT99AGeO67jtRRyJ0GxRT4sV7Yh7IhTQ+GunqiCif2pNbkqYfqXcRurvpBm5a0n8qEtvoUO1juyVnMOblSht6LJhmzaKNkOLFex71HDTARRMAosa6LIZ2tqGwBw0FLTfIFgDPnXNbjkK4rlKCKh10MSp9/QlSHq8NMqMOE1BhHS3vRzBF60Y9TXzYm8gtSZ97rnnYvr06VizZg1aWlrw/PPP47333sOee+6JX/ziF41oY4+B5twq5iRnglhmgCARy0RQVaLhiQSrnQPhgUlXSbyZEFDKYlQzAdnPJc50I68L1N/ptdrszJXWjxt0VJ+a3hyA5L4XNQFl/zr9MhoiSLLfkkt+Sm4Gbi/rMetq97/z7i6wklRLi5Wj6NJCvu5x9yAqa7b4bSgDZ60wlHaJqR4IllMJmuTV5dUiqq29Sfbbux1s7LKxocvGxi4bG7tK2NBlo73ooKPkoKPE0FFyUHQ4ioyjw8iD5weA9N8MVv9WmG0FmC0mSI6GnisyOWKov4kozi/HVbEadywdQYozx0XNyxCP1ErSa6+9hhtvvBGUUlBK0d3djfHjx2P+/PmYMWMGjj766Ea0s0dAC5ZLhqQcMZEESQ19V4gRUchS2WG5Ppl05QihWqFzdE1TXiRgQkzRpsph07WnNQBkIhf95p7YQV2zr2Z4Ozeo20+JSoCE6Ve61gzwTazEIV6G+Jis2CJNANwouHKCyqA52REEqbPobeMEUlXUkueo7AukT1CaNOljEnKuu59xQQfi+FHbVos0SpMhHT8taettNbS9ywbPOb6ZDXCdt0UNt27bJUmtOYoCNVB0OFihP0j/QSCtrTALeffllpKAqU1AJkryb3V+b6IaFUn+rkabiX0mMXP1lJrUV01uqUlSLpeD4d2E4cOH4/3338eOO+6IgQMH4v333697A3sSBnHNawASEaRQfTYa9kVSkdRvp1FIkodJIC1RAvSKU/VOp3rH8ah55WX1+SeKwSbOTNEMBAnwfHI0BEkm+0DwPvl9kenLq/hRcABQtP0+wKR9i/WEYlQuE2HHJquMQ9p8QlG5jNIGHKRBlN9TtWSpnDAzvp/Vg+g3G0qMobNUJkmUGOgsOmixGErMmxw3TYDD4aYEyLXAsLx0ALlghBuhxK/bpqpIusuXlqAkubVN8ljwkZnVqkdqkrT77rvjpZdewnbbbYcDDzwQF198MT799FPcdtttmDBhQiPa2HOgUnSapiCtjCiTGtEQKDU8vplqflUawNImZGxE1FccWWomcNZ7D6E4Yh4F38eHeL5JEffaVZGCiVINGs6iLUxzrGTDKZbcrMiK03al/qESJJ0fUNI+GUWKoshEWp+0uOjM3vT/iVOTKvno9QZEMVrH4HBYxMTdiXG4PnEiqSmCbgEyqvFBEkQiaptG5R3K0LxI/WSdN28eRo4cCQD4n//5HwwZMgTf/va3sXLlStx00011b2Czolb1Rw7lrwZJ8japtd3qkZepN0E8EktoupxVPYFmGGziIPyDAvMS9D3ZXMY8vySnZMP2KrDbXUV/HivaPkHyM3rH5JCqpBgJMhOnkKj5jSqtrzt+knsXrbbGO0on9a1Ke1ztuhVMwM1IkKJAFbMZ9cuVuFGVcGwv5UT4ORr1MiWb2PoaMamlFAnQHOfbxN0tFqmVpIkTJ/rfhw0bhv/v//v/6tqg3oZfSV63TJovK0KyMsSY+2YeFRaf9m01yUAWlZwycl6qB2/ydRtR0yoO9VKYdANdX0tuKXIS6e6XOkc1ganbqqbTpFnf5fqCfkZvaQCr1JfSZrqPU2uqTQMQMEfW6SUijfkwFJVXQzvilKxmIkiGlw/JMssTJYb7nRK3AC51o9yo4RW8LXb6QQNOV7enWvJAtm2hPrm/w+cb9FPSty1JLbVmRJQi1kS3vc8gSyYpgZdsMMPNti0TIhlxRAnwHLM9ohTYdwNMbKLWGgAv7Lmyr1M1akKl9SqRFN3y3lCCopIP+t99J2c9aWoW/yMduMN9sqNeWZWYq3Xb1HUjt4vpTzJBUpOd6iLc5OSMKiqpM1rH6pjEkkn2G3Veses0YX+IO99mbC8A5E0aIEh5k6DFMpH3vrsTRd4jTS0mAVm/Ac6Gz1DasNFVM4sOnKLog8HzrJde3oyXz+HCTyqaBDYrkesrSE2Sxo0b5ztu6/DOO+/U1KDeBCva4JS6hEaqoE6VyySIEnccqLmRQnmGEvg1VQqdB8pEI+TrJAZ2ZT3t/pQEf0nRCMdyX7GrA1lSnXRDZSUi3sZ1BKkS6qkw1BPCadqgROqfzPfX0OXSkv2IohyskyifcsFmlXil7TtRRCZpdnZdSZGo9WQkcXyuJ8nQEZiocihAtNLUU+1tJFosioLlEqX+BRP9CiZaLIp+eROtOepNbp4kixrIF9cD61fCWbsSpfUdKLV3odRpwyk5EIVue4IY9HZhV1EapJwPScwPrlP+Hr2fRqOPdEUtUpOkWbNmBX6XSiW8+uqrWLx4Mb7//e/Xq129AqdkwzYMNxrII0gEJhzYXthzubK6ePRHDR8MZWdamSzpIuOSOKISb/CTUwlo1xPHVxSuSgQpsaKkDKS1mrrSVFKPQ1zYtwzdQKjza0kLgxrwIuR7BbLPEXeYW6SWkgBZ8tcVUWs61ScioWQUwokoE/ZlJ3ldwJ5CT5CKJCRQzh4et48k7e1pE3g1aLEI2gqmpyBRtFgm+hfKBKm/Rb3wf4J+FgH9dCWc1Stgr/kUXZ9tQGlDB+wu21OTwqlRCIA0Zdd1CkxvDvLhciMyIeJSyL+8Dg9toyJTmJIhNUn63ve+p51//fXX46WXXqq5Qb0Jp8TggIFTDoMyEOYRGkYAmCCA/7YuJtUsl0SRiCI4wnwXuy2pTK7kArWhZVW85acun1KFLwhQH8KkK5wbZyaLI0hpI/t0OVp6Cm5OIiekboqoNXgm4JD/kYYgyYhKd6EDAeCw6Ci5zzuq9bMq11KMVppkJHlZqNfLST3QL++qR5ZnZhvUmkP/vIl+FvUmEy0mQUvOgNW+Cli7HM6qj9C5ej26P9uI4sYi7E4brBSdOy5JCHwa0pBk3aRqUjXh+SpRilsvPK9+5OjzkFqgbv+OqVOn4r777qvX7noFTtF9q3aKrmzLvHBmEdYc8rkQTqq+g6oTWM4k1UdO7CcgR2kZVK8wqesDUnZlL6+TSCQYdRwdosiVboqCrh5XXB6XNG/qlR704vyTQrduJYLUl8Ck5KJqP9SZ1tz14gmSgNw3ac4MTSI7Pcnl9AWhlZI+QLDvy2i2CMZ6ZFLXZf2W/8NlIhpch0i5f8rz4surpEUzXOO2gokWy0S/Qg79CybaLCqpSCYKJkHBNNCfMtANn8D+5H10f7oaXavXuSSpveQrSSKdgICagdqNkit/JkEtt79WsTRtRm8xT7+sPmZIEWlYzvjdXIpwvVG3f8jvf/97DB48ONU2zz77LKZPn45Ro0bBMAw8+OCDgeWnnnoqDMMITF/84hcD63R3d+M73/kOhg4dira2Nhx55JH48MMPqzoHt/SCA+bwAFEKDDQRRCkpDM0AoCNK4TISbskIdRAh3sAVKjUhFeVNct5Jz0EQwmoLlqYlSzoE1DQSvo7+sgYOAOXEdc3xgCiXrggSJfd7OLosSunRldiRyZGf2Vua3NpZgizl/O9+Ikv1vij3rBpSVOu9FeSn0qRbP1n7okuiVCL3ag0ydRs1tUC1RKkZCBIAtOQ885pF0WaZyFNS9kWyqOuHRA3QjavAVi+Hs3YVulavR3F9B4obS7A7bc9pO3hv5LIdBOmIURyaTTmJIkXuMl4zOZJJUaViupsiqkomKTtuc86xYsUKrFq1CgsWLEi1r/b2duy666447bTTcMwxx2jX+fKXv4yFCxf6vy3LCiyfNWsWHn74Ydx1110YMmQIzj//fBxxxBF4+eWXQRP6Vfjn4nBwwgEwMBAYDnd9Oqg3yNB0EWpEGcwNGkxWGVgXnh+R5MdEvCg7GVEPWIN6IdikXD4CACAcvlM4HKf1M1JJj/qwqoZIaAsIR517wnMLlJaoIvliX4FcADhpyolAAAIJknWRyVsu2QME/cBIznTVVkLghO6dDe7oAw4qJ5es7GtWb2d62TdIh7hkjUlzOumWuaViVHMxAQNzHcuVaxaVdbzSNWsWciTQ4jltt+SoV8yW+lFtJgEsaqAtR0DWrENp3WoUP1uP4oZ22B1droJUcgJO20DZDOTeDgOAm4XSgRjQe64kSU+XBql1P5sy4akGqUnSUUcdFfhNCMGwYcMwZcoU7LDDDqn2NXXqVEydOjV2nXw+jxEjRmiXrVu3DjfffDNuu+02HHLIIQCA22+/HVtttRWeeOIJHH744anawxwO5Mq/ucMASsEcBiqRFwb4v/31AEBjKpNVIbk8hI5sEZQdwv1tpHkydI64Yn3ADNTZYohWDmpxxK5EjuT5aaKJ0hCkahA2e4bb0dd8aqIUtWhSTQJkRV0mkyP/t18fS8yjoBD+UAyOtK4M7jBQy4RTtEPz1dQEPY04VTNt+oBIp+wq+66u3IrYXxKi1FdACWCZXi4kYsD0Pgsm8dUf0r0R6NwA3tkOp6sIp6uIUqcdUqYNaoA4BhxHIUjeJ0OZIG1K/jT1Oo9qCVKl4/dEtF+jkJokzZ49uxHtiMTTTz+NzTffHIMGDcLkyZNx2WWXYfPNNwcAvPzyyyiVSjjssMP89UeNGoWdd94Zzz33XCRJ6u7uRnd3t/97/fr1idoS9QYc5QckR7Spvkbym7vrbFsmRb6zrQeVKIVKUBACRplP4AgAzsqDoNinOHYUAUhKkCqRIzlnlLxOsqR6PUuQgGRta5YcSXF9N0COImoMqjm9dFAJkvB78/2NchZACAxRFgIAZw6IXYLT1Q3HC0AIZOv2Ah4E1PxiACIJkxwNV48ItOj6aEpUVKC96Wuj1dJvdQV70xKlyHbpTIC88epBVN+1PDJEDDepZDm7tpQ80i6BF7vAi11eXqRyslLdfaPMAWD4g3ORuUTJ4RwwxPxkBWDrgUYdZ1Mhec2M1CQpKaEAgAEDBqTdfQBTp07FcccdhzFjxuDdd9/FT3/6Uxx00EF4+eWXkc/nsWLFCliWhc022yyw3fDhw7FixYrI/V5++eWYO3duojYwh4PGSeg6nwuNoqS+gQfXpwHHWd3gRWk4e7ecZ0kQKQLTq+ou5Wtiwe9i23S5kqJVovI64Vw8cjurqVDeF8xiPZloMqrvEhLsVypBigoICJAU0VdV/zaPIBErB5g5lxyJTwHmAMRVlgCAM8t1FGcMJGe6fZQFzcc6oiS3P62yJCeoTFqCI75QcvJcXnE+REkQV/ZELtgbV1hXJkrNiLjnLpH8XYgBEGK45MgrRQJmgzMHnMUFGBgwHO96EcN9JnrkyL0kLikSft2CuJRNc5uOstSTSHrN+qqalJokDRo0KDaZJOD6KRmGAScmYiYJ/uu//sv/vvPOO2PixIkYM2YMHn30URx99NEVjx+Fiy66COedd57/e/369dhqq62061Y7qAf8kQKmNyp2XN6IBcO2ZeVHbCcnBlT3q24nnLaj1CS1vXGDUdxbaRxBCu4jmS9XyPG6CQlS1LUi1ABhjR+covouyeVC6SHkTyDYJ+XISxkhgiQHABBaJkim5R6PUsBxAEbdwqPMAck5IA4D9cgRdxww6uYZQwpi3mjEBR/oiEaUmlQrQYqCmkBSJUrqf1d9VpWVqAQKbg8oSZF9NxCBVp3/IqEEDpj33OOglHr3kwGsbHqLUpD64gBeb0Knu/Zq16n1GvXF65yaJC1cuBA//OEPceqpp2KfffYBACxduhS33HILLr/8cowdO7bebfQxcuRIjBkzBm+//TYAYMSIESgWi1i7dm1ATVq5ciX23XffyP3k83nk8/nQ/NSEqMLAL5vhAg9OojiUE+q+iUfsO0pB0v02BDGi1FeTdOaVWn1ukhIk3Xb1jghTI7vqjSTZu3sKUX03jkCHzLMR89TtgiH8FKLyukFomSARdzJKRfe7aQF2ySdW4sXAN92xclLUeuRTilLxKql7cl/RvQjIhEh2hK8EHUGS+7uuTbX0p7j+2CyRlwJRfbcSXDGIeP2u3JeoRUEtCrvLhkEMUIvAKTJQUKkGp6uxUw4IJSkJmkVRkpNFNiOquU59jSilJkm33norrrzySpxwwgn+vCOPPBITJkzATTfdhKeffrqe7Qtg9erV+OCDDzBy5EgAwJ577olcLoclS5bg+OOPBwAsX74cb7zxBubPn9+wdlRCRdXEM00Eftf5+KrPFIDQW7w6SCXNWl3JtJSUCMX5edTjbTwqF1S93vTr5SNTD7jh5mEVs7p9JUsd0ShEBRPEXWtddFiUr04lgiQvqz0PUQXVPVRLMb4/RfknpUWzldaJ6m5cDMIG8Uh4TkpH4aZCoDkKp+iAMAJOued24EYFwvtOmRMo38F8h+4ymoUYNSvq+aiLIkry36VJHq3p8yQtXboUEydODM2fOHEiXnjhhVT72rhxI1577TW89tprAIB3330Xr732Gt5//31s3LgRF1xwAZYuXYply5bh6aefxvTp0zF06FB87WtfAwAMHDgQZ5xxBs4//3z86U9/wquvvoqTTjoJEyZM8KPd0iJQz6uRAwVzyhPKEUIiOWVc/qWoOlxa3w5ZUaCyMlA5AkrfbL2TtoyejBZLM8jIBVfTIJTAL2DWis6H05OQkzuq/kT1ID3ccQDGAM8vhDPmmtlKRaBU9HJtOeB2EaLYbeV96kmsjDTqoC4pY73UlKRETUaU+UugUt+t9DJSS59Tk1k2o2k7AIP4wQIiHQXNmSA5CoMaoBb11CTq55ly/UA9PycSneMHaJ4BWYeeIm/xRXKTrZ8GblLP4NSMSP3P2GqrrXDDDTeE5t94442Rfj1ReOmll7D77rtj9913BwCcd9552H333XHxxReDUoq//e1v+OpXv4rtttsOM2bMwHbbbYelS5eif//+/j6uuuoqHHXUUTj++OOx3377obW1FQ8//HDqHEkyXD+MJEpICsfniMzG5XISZYKkO4ZKhNRsyvL64YrryW9znFkhCUFKgiROzomqsEvryNdNTQKYdH9xiCNK/rxe/JeHkolWuOeMsdAEKP2KBT8FQYIXacSKXeB2yf0sdgF2CbBLbr6kku0WjPZ8kmRUk46irALx0D4CiRUlolQp4WcSFbBSwEKycjvhJJS1EiQBmaRHTX0VjudhzTnADcP1gzNzfrSlbHKjOeoTJaEwiXPXXYOoAb6RZKnSrWhWs1ozE8ieQGpz21VXXYVjjjkGjz/+uJ/9+vnnn8d//vOf1GVJpkyZUpZTNXj88ccr7qNQKODaa6/Ftddem+rYjQR3mOu/ASWyzPcRClZNF+sxmQAxPQGSP2Ww0EM37OOkIwpyvpzeylMT9SDXmcZ0bdS95achcEnVhlDtrIQJEXsECjFSI8fk6yH3FdlfTcxX6RUT0ZPFkptegjCAeP2LUJc4MQZeKoKVbDhFlyAxJpXzccoZ62XI/V8+XnCdIEHSIWBaC/z39LmG6o2o1BI6xBGkJFF5Sc3iftu8/t0spuGqYHh+cZ5/m6sima65zSJwimVS6Pq9McBzdncquDP0pJmtWn8c1TepUW1O6gP1eTJNpiZJ06ZNw7/+9S/ccMMN+Mc//gHOOb761a/iW9/6VmolqdlQzVtXXORWYKCSiFJoPWXQ8t/cpXkCKhmSl+sGHBkiyk0Nw1aR5CFcD8fvRGpdBQISH8JdHxVJRlQBUvmzN0C80jQydCH2jOnNsipZkvfEvIzZgmjL5loube9ICpLjK0lOQJHSIcrxPoo8RPU91UG63sQ/jQN3FGolSAJqOxoVtNATIFLoPxChqBgE3DDcNBM5EwaloJbpqkedrqIkEktyhwOO2x8c6RqKgb23uaLopnqfnOg0BD1pdos3TTa+Hb19j2SkJkmAa3K77LLL6t2WPgs1yaQYaKj0IBNv41HKiO4hJytJavX20HfFnMdiBiUVadWkZslGrbtmtRcjjSa8AmIwbpYEk0DZ3wxQ+mMEQap0D13n1yBcsuMdpyTNF33UKwYtCBIrlQIqklhXp5JGOVNXG0Xph88T97WAoH73S1Y/kzp3pzGv6chRpZxNtZKmplBDFVCiXFdD8kkS9SqJS5BYyQmYGAkjgRQ0ctHbZkEUWWqGfE3VHl9Nq7ApIPVr0eLFi/GXv/zF/3399ddjt912w4knnoi1a9fWtXE9jVrk6MRO1sp6clX0KMjV232zRcn2C9OyUsn3bVIz0NaL0KQZYKJ8Y+rXluoL7Prrp05UGPbzUQuN9jZEvphK/kiy4hia5D7m/WbFsn+R72/kTU5XsfwpK0gsWAwaiHgRSKisJOk7ap/ryXsTm2ssZQSbDLWvJyU/aRSvZiBIqpoUXoH4n3JKCYOSgKO2PmhEVpPq3fLaoeumzeqfFAWHh6+tbl5fRGqS9P3vf9/Puv23v/0N5513HqZNm4Z33nknkCjs8wyd+Ssq8syPNpOS9slOuOp+y4OZS4qcYsl/kDInOOAlQSjnkkLa1LfkSoOVboBOk0iyUsRNI9+Qk5CgKLLU26g2w3M5cMApm8YksuSU7BBJF6Sp7JgdJFWif7r7jzcBA3qfsjiCpHOEjssdpj3/Ojy90wYgqHXGdPsJXoNagw0q94lmIEi1IO4+u/2kfH69OWCLUiu6yV0eJkt9hShVuq5pr3uzEavU5rZ3330XX/jCFwAA9913H6ZPn4558+bhlVdewbRp0+rewN4AZwwMBGkD5JjD/Kg64bwdSgQp+XOILNuGv40DgzBwyvxioAYNZ8uWQ6yTKFhJ38LTJoUUSEMSQtdDE5kUaFeNfiXVbK+ejy4ZYDjHlBEwQfUGKmdPD/oGyaZZwB1UdQ7/AHw/pSSE1yDh/aRBFEFqhHnTL9NTj7xcVWau16EnHLObjSDJ5rXQIyXBc859UQznwgLiB17ZvKWWKqkVKtHRZbDWHV/XtmZDmu7mnmfj2tJIpCZJlmWho6MDAPDEE0/glFNOAQAMHjw4VV23ZkXQ34AhNVNKAMPPVCzUE+8YzIHhRQkBgCMVBRWZtANt1fgpNRJls0nYn4JI1yxtTh5R1w4ID8Kqr1SUU3m9IpcC4eTSd12CQhnN4J8Uq9ZERD/qTDlCgdAmJEU0UZKDAgxGYwstJ0G9CRL3lAVxLwMqTw1JRmMjNKtsd9oINqC5kpumQeLHBWduFKWXMkWAKQqd7MDNGU90HUXZEh0hqTYijRph0qdGqJW7TTRRajY0c9sagdQkaf/998d5552H/fbbDy+88ALuvvtuAMC//vUvbLnllnVvYF+DqwZF/+v9GlhmzidKMkkCcxUoSsuyBIMNlPRh/JUIUpyjbpTyEKdIBN7ypYElLlpNPnacv4hcb0yOtkpKlMR+qxlQw+Ya1czW3ANQvVUB7gTrCQrI5sZAP895/aFouykCgMhCy/Vqq67PJSWxujboiFIc+TG8YxElzYBYnqE6EMUxyTDgEiQPqn8m9xQkmRgB8AkxYxwO5xEEKEyMKqlJSdQd2Ywm/5bnAe6XMlnaNFSjuH1UUpOa8W+T+tXpuuuug2ma+P3vf49f//rX2GKLLQAAjz32GL785S/XvYE9ibSDa6WkfUx5Ew/VwDJzIFYBRt6diFWA4U3wkqZRy02clqTUhC6RZNLzkD8F4nwaqs1eHTyu4atIqtOt+l2FcFJW9xdcpzp9N5ixuXycSskJexu+X1oVfSAuTxGgFL4lxO+bZsGCWbBALTdvDS1YUjZkE9SSkv9FpEqI6mfVZAmv9B92B9GyIlpvBTDO7yjqeFHqZQYECpUbnPvZ3uXJJ0is7JuZ5L4G/YEM/7NWRBGkcmZp/W/dtvK8TQFxt6UZCRJQhZI0evRoPPLII6H5V111VV0a1NtgDvdC9cu/qU71UCqulx2vK5jnRJFGM+eqSTnLVZIIcVUku+gd2JWWDaecl8ZgbiX1Sv4etfggJfFNqiYyLOnALV9XtQiqTuESA6w8sBNqgCF+uySQB29dzSyBZjC1MUUxVCPVAJ0ps9yPKqqfKPdzapkSsS33d+I4YCW73CbGYDjEq8xeLnDrKCbZ8D0Nq0FR85JAV7NNzcquVzaT7V/sq1IZkjjozy+9yW2TBfeCAaQyTnKwClPVpISmtijTVjUmryjzml5NCqtIAG9KolDvNqmKUjOes4xEJKm9vR1tbW2Jd5p2/WYEZ2F/pLKpKNkbrqr+uKY24ldOR87yyJLlHVMy1Xl13YjlwCi5vknEIeCUwGDUL1armqZC56FZT374lguDBolMGmKhmil0fklRqoChKBTVIo7EVLu/voSwU3byUHF1IBFkPxhkoChJVtBc7PZdGpCmqdcmg1Hf7GY45X0GHd/dLMnqvQv223J+qqD6Et3vy9ej/n1C9UWqF1lO24f7umlPdAOHcYBWyGkkB6145jSVGIU2qZC00SUsHED5M8oEJ28fhTiCpJIDvbmvec1u9UJf6rKJRoJtttkG8+bNw8cffxy5DuccS5YswdSpU/GrX/2qbg3sSah/sFoeerGmKz/EnboEiRBAKEvevLLPEtXmvolzdo5rh6qAyfPizFux56ojgzEQA1wlIhLX1qRoVtNYvaGG7gNJ0jW4LwHCpKg3YZKyqU1KUwFfDbUCfdbIWYHaWjryS6hCvJSCwe460SQo6T2t7f+rmm4bT5p159XXyHq1YB4piM35yMppJdIobFHdJUk3SmvqilKN5BB/HWHqCahpB5Jv16AG9REkUpKefvpp/OQnP8HcuXOx2267YeLEiRg1ahQKhQLWrl2Lv//971i6dClyuRwuuugifPOb32x0uxsG7nAwQGtiUxF4s9a8ffvr6AZ2Wo5ug3DcJtRNAeDXxVJyB0kmN6AcTaTWh9O1Uy5RISKNILaXHKOTmMbqUe4hre9FEmVEVkVqNbnVowRFj0IxrQlE9QdA9Jty+gl3mZeWIkZFkv3q/D6sMYMyj2Ax77cwuen6q18yR1IDZeUoqn8nIfSVyJI4jlwcVz6PeqISwfMzhfdAoEBv1WtUoSNI4tQDtT1J5UhjIzIbZVmhoUb5WPUL9Q8eR8wLpwDom4xDVtLSPLr7kmIUhUQkafvtt8e9996LDz/8EPfeey+effZZPPfcc+js7MTQoUOx++6743//938xbdq0RA7GfQHc4b5fUnle8jBhQsLqTwCOVxSUOTCYN+CIQozyg0tRU4TJzW2P45kpvPaxsJkrFCWmvpVDqeXGyuuXl3tlHUBAwKRyD9J+xECTUEXSIWS2i4hAqoRqBpikqQua1bTBSjZYhD+crt4fUCYFgiyF/OwkFUm/4yCZDy5SIhpL0n6dcjoAmfwEyXvZTKYjS3EI5gjT369QklBCGkqOtMfUpJioJ5KoLb1Zb1CGw4AcgR+Fxhh3zW8wwDkgP4wNRfGUy5EEfjtuYAhjToAgOdx9rgmPvFr9kqIUorgIt9hr0cDitbWgGkVJt02TPkIjkcpxe8stt8S5556Lc889t1Ht6VVwh2mvCHc4OFWTIJZVHlVFSpJTBkCZIDHqEyM3ekNyzJZJizTQuIO6Zy6B9wauqUofGIQ03wPby860MQ/YKKIT2KfGVyOWIAmlK2WKAyD5G7G8XtzgUA7v7v237KRgjEWSIRk6shzlPyYTpND1YqxM7IUZxPuuM/cZ1PVLku+zjtCLeyT7mLnLws7VcQoRk6LYQtdA0w/KZr6w6a8eiCNI4nc0oWu8A3dvkiXGOZgUoi9UHsZ5+QXO8BRMSsvPW0r0BW4ZB6ccnBJwxkEcVzqqNf+QbvukBElGsxTa7S2Ia9JXzr+qArefJ8QllCybI4IEiShkKTAYeFFr3C65f3pGwUVEm7/cAbdLIROGv0/ZXOGpAP4buKNXYyoqPKj85ilMWP51Ua6DWEe3XRTk9qlEzl9HccKVofN3CTsiJ8+dJOe9CZdk4f461e6/UXAHCP09URG3jJBgn5X7sZ9k0nFcF1cbQXMbk0qbJDDZqipm4HxY2dwpkyX3+GHSLa5/ZT8s795GkKNGhOBXIkhpkcQU3MiEq/WGI/IaSWRJEAnOPXMcJeCG4bkkeFGWORNOzgS1vDqWDvUi3US/4QE1CSj7B4lK99WqK1HmNd1vMU8+lrgN7rmWP9XlmzJqJa09hYwkKWCOa1QCNUIRbjpHU9UJNZBPJsofiTngjAClkthJcFmp5L+Vg4XD/YWfh4AgSsL8Js4DqKSYaBQlZbk4RxF5VPaZqM6ZFtAPErLPlHp8cY6x+0yhJlWCmiBQXaYDoQYI6z1/AzXUX414TGIG1yqlUh92/xvS+u5M7/iOH13nFG2fKDGFMEX5vPkqqCY9QKhPS+qSDpUIa5RTdlQerFoIcBqCFEVi0qpIfUkBBVyS5HAOwowAYfKJEgBOTICYXm65HIhlulPORK7F9CPdmENBvWtIHeIrS8QxQHnjTE61EKTAtdC0b1OOdOsLRCkjSRK4wyOviC6CSzix+kVhNea2EDyCZAgFiTn+25EfrmwXfX+lUDs0AyAQJEpAvEQfRRRUkiGrVe7voEOtdt8Ry9SMxOpx3Xpf1fkgxSGJI3q5jeUBWq1N19tKUSWoRY0D5LeK6MWoyEdxLCBMYkUiS85YoPitu66Uk0lDzHW+c7pM60CQLEURJZekxecdSpokVPaJSookeZd05nGxbaVjRalJlQhS1P/T4L1H8B3mEqUccU1tIgUAY5IzNzHBielGU5o5P4kp8wi5GQpY4KA5ClGKhlDi+oFWgTjFKS4RpM4HSUeQPo8qUl9CRpISQHXo9FUjjUmivE05Ik3A9yNiDNwuuvllCAOIA/9/IcwWzAHskm++UPdNoGT09qPTgkQJCD9Q1RxJ8j4Q8ZBVfUTSotJ2upIQPRV5o9aeA4IDuW7A6ql6eUnhKjnBUiKVTL9xTvKhe+ERF38Q1kTRqQksRf9Meh+TlsmJegFIGgEZ2LdCkKJMrEmRNCllwEm+yiizvqYYxcFhPBDl5ke3gfu/TWK61QmsAmjBAi3kQUu2dO263O0dDlZy/OcVoQSsFL5WjfAN0hEk9ThRBKmvK0YqKUx6Ps2uJmUkKQIi03bUoCKXWEhsZkPZnwMopy8TalLQATZYyDFuAEijllQCoSRUhFR2pnWb25iHc71JUS0KULORoErwCYlSOzAcvh/0L2IKKYw77zilT+6nspktcab1Cn04KZFQHbaZpGDKhCf44qMnSGmRRj2KQ7OrlvWGMLHJvkii3hrg+iXZjMOiORiWW75JKEmqeZ47HLTkwCm6ZjeXHFWnICVFlHO2ToEKmt3CBOlzduv7BKoiSX/+859x44034j//+Q9+//vfY4sttsBtt92GcePGYf/99693G3sU3OGA9zKuHWxIWUUSERaB5TGmjIBqI88jJBz+7xOmyonTogYXXYX3Sogz0almj6TbNgohE2gNeV/iCvT2BYh+EiilIvqqqAGoCec3SFDtEVcvqelTJfEqQeIe2WdOWWnStr9SH4+5r0xxWnf3Fx5t4mr7NcJhuxqC1BsglMDgzdO2cpSbqyIxr1wHNy2v1mULSEsbzLZiaFtR7NYpMrCSA4MYofvgSFFz9VBv5DIbwe9BPySZCKlkScwP77vvsKZa2trMalLqf8Z9992Hww8/HC0tLXj11VfR3d0NANiwYQPmzZtX9wb2JKIexMLE5n/mTMgRbaHyIxUehu4A4gSJkT+JMGon+JYUYcpQ2y5MbRWJFQubR7SmjgglTYc0uWVqzUPTiAGnEQVPewsGJb5jqyg+SwrlYspGvuBmx7Y8R9ic6aezUCH7OwX6i2ZSHbV1iOpvUf8/tfhuuSxF8H7Fhf2rSEuQkiakjNpntf1V/R/LJThqQVSG9WYD53KEmwVYeRiFVhhWAbm2FphtBeTaCsi1tngmOAvUIqCWXs3XOVA3GlH+RpvIo2aTR+p/yKWXXoobbrgB//u//4tcLufP33ffffHKK6/UtXG9BfeNvOyr4Ge8llWkCDNb3D6B4APfJ0pKJJscKRRX1V01Z+gIkloxWx3w0kK+FtWi2R/MfZ0oyf2SWiZoIe/7cugmEIqowswqOfLnVzClRZFuFWr/DilTCkFyl/EAQRLqgQ4qeSHUSEyQqiUiaQmSrr6c7tjqvFDdsoTkqdn/f0BYleAAOM0B1IKRb4HR0gaj0AbTI0amNNGcUE31PjI6Jafav3yUIiRP6jF020RBLiXSiGzdurZmCCK1ue2tt97CAQccEJo/YMAAfPbZZ/VoU68hbnAM+HaIRGZKjSogbKLrCQiTBqAnY/56Gmdb8VmJkOlQjXmrHg/onjBbJEmA2UwoO5x7ZUU8xZPkTLcuoFVw66vJEVXMAbcBw3RzHhnUUzaVPlTJLy6eLAVNbbKKFCLzCQmS+5sH1g22V41qS38P5X3UVAOuTn01bcHbKPLXFwiSCsa9KDdqglMrQPAJc2B6/Yl0FUEs03uRDfqZif4TDrmvTxuFmS1YNDdMgqJMa0nbUY8yKhkZSofU/5iRI0fi3//+d2j+X/7yF4wfP74ujepNhCPJys7bPjFSnbQTEKR6PZzinGKjIn6CodgaM4lmwJLRU2SvEmpVsKpBXzLByYk5fcUzZ3nFZy0YuRyQs4Cc+92QiipD1GRT95kwcKDqNiuKk+44cQRJ3J+0ZF0uY1ErGu3bVC/onkF+CY+YmmeNBiUidD4cTi/+eZwDnJjghJZJUr4AmDmQQsF9IbDCL62yuib+xjp/JPW2pSkpoipIYp5ufj1QraKUpB195FHXo0itJJ111ln43ve+h9/+9rcwDAMff/wxli5digsuuAAXX3xxI9rYa5DrkZXD/WnYzFZBPQqUOggUDi0XtgWgTRwpI8qZliv5PyJ9OyLe1FnCgTAqCWC9otLqTYDqSW76mrLk90tCXDKUy7k5ZvwM2dQtpMwYQGqP/hH9Q010CkCbViLKzOb/DvXNMkES6/pkKUCy4u95EhKjU6KSmvMCx+olR+2kRK2ZCJ1MlGQSEGghMQHq+YNSCoNQL42K519Fwi+rAkyKoAsc13CPohImVxlKrtrIClIlB+yeDPnPSE/tSE2SLrzwQqxbtw4HHnggurq6cMABByCfz+OCCy7AOeec04g29hjiJHZdVXQxX8yTEcrHEkroR8NVrb3fgQeDNHjUEuofR5D0Jgu9E3etofFRSfCaNfJHha4kSTMg6n4ZxOtnhLpmN6+PcebAEGVFvOWcMAClQBoItVxMNe0glMBh4T4W+l6hb6lO2uHtaydIUUhyn+tBOoLnWIXPYB80s1HikiSSUCER5Un8vgvPoV/KyyXSAXDGJVObSpAMxTzGIQiTHG0VRZrk5eV58X1wUyctumvS11HVP+eyyy7Dp59+ihdeeAHPP/88Vq1ahf/5n/9JvZ9nn30W06dPx6hRo2AYBh588MHAcs455syZg1GjRqGlpQVTpkzBm2++GVinu7sb3/nOdzB06FC0tbXhyCOPxIcffljNaQEQb6r6nizMPVEJ+tSIEdXhu+zPRKU/uTIpINJxxLFC0XSKmSQ6R1P5ISKihOR5icp6NPHDtqeST8oQ5p5mN8sZKiEXUPuO/CJAgi8FSSedylqpPE4cgg7NLESQKjkspzWtqVXldfN1U2DdOpL+JOdWT/NhT4MSzzGZAMTwJh1p4h5x59wLdHGT8rpJd73oyqLtTp4J1vWHi3bMFrXc5O9lR+n4QrUCSc1pTfx4qBpRjt9pHcKb+dqk/iffeuut+Mc//oHW1lZMnDgRe++9N/r164euri7ceuutqfbV3t6OXXfdFdddd512+fz583HllVfiuuuuw4svvogRI0bg0EMPxYYNG/x1Zs2ahQceeAB33XUX/vKXv2Djxo044ogj4FSZgj4N5Gg3P+JNQ4yIlXPJUc7y3+hBiC8Xy5N4OxJmPTVppX9sjalPNeuJ9aKgc5CV5zcKPZlPqdK5iGgneeqr8FNLVAOJRIlUAIauT8dNGqIEBJ3Jxf51iOqrUaVs3HNunHoktk9NsuqsimozdqdoU5SK1CzFboWpjRiGqyiRMkExPNLkCj2snGS3VPQJEiuW4HQV4XR1g5VsOCXby7itJ88EUeQomjT5bY31TYqfguvWdu2T+CVVH7GXbJ00+48jTE3SDSOR2tx26qmnoq2tDYsWLcIxxxzjz1+3bh1OO+00nHLKKYn3NXXqVEydOlW7jHOOq6++Gj/+8Y9x9NFHAwBuueUWDB8+HHfeeSfOOussrFu3DjfffDNuu+02HHLIIQCA22+/HVtttRWeeOIJHH744anOTc09lEYZ0JrTBAT5gfRGH/FmL0Rf8R0ACCv7efiqFVA2ichEyW97ue6VWhpFRhpzR19AUgIWR4b6Qq02FaraIj45czO8u5+5iK09eM7bYnu3sHHEqsrAq7vuBACn5WztsslNZ9KTv8tZ3qsl1TUTJK2y27t9Q3dOtZjSRL/hrPfOiRgJTW2ceUTJAbdL4N1d4MUu2F3FwOQUmTdFEyVANrEBCNVB4L6JTZ1XK/pSgkgdar0GfezRWp25be7cuTj55JMxZ86cOjenjHfffRcrVqzAYYcd5s/L5/OYPHkynnvuOQDAyy+/jFKpFFhn1KhR2Hnnnf11dOju7sb69esDU1LoTGxBs4RsTlNUI1NRkkw5wkiY3DSKEpEdxyv5RlG/XXLbZMiFSIHKOW8agVCSvBpNZWm2ryokPCLXVE8jad8NRI2JHFx2CVzUBRSfGuVJVpBkEzLxojv9CE8SXE8krQz10Tg1VKN4VlKcyucYb4aqFnIUpaoyurmnaK+oj+GM4c2fDFJGVN9lKUiDwTm4XXL7sl2C09UNu70LpfZOT00qwu4qwe4qgZUYRIFb2feIGu7AV1aUwkqSrCipqCVfUU8SpD4sijcVqvqHnXTSSXjyySdx44034thjj0VnZ2e924UVK1YAAIYPHx6YP3z4cH/ZihUrYFkWNttss8h1dLj88ssxcOBAf9pqq61StU0lSuJ7IFpNRBbJ5EgQo8A8d3Jz2NAIoiSZM6RBJeQb5Zs9aIAsVSv/V0qQCYTJSW88tMPmwhQPXcWfJsn+5fk9TZYq9d1QpCJj7qDilIsmC38OLWRTr0yWckECFJokshQwNfvfyyY39TrHEaVqUCtBEpCjW9V2q/MaTZR0BEnM7yt+SVF9VxS3ZZy73z0fIodzL9u2mwIAhqSIMwfcLsIp2q561FVEqb0LpU4bdqfjliXx/C6ZRiUTl0lPluI/1X0kRV9XkD6vSP0kMjwW/cUvfhH/7//9P/z73//Gvvvui2XLltW7bYHjCXDOQ/NUVFrnoosuwrp16/zpgw8+qEtbAYRNawo58kmRxqcjRI6k/DV+3ps4J9kIVcn9Hu/M3RtIoyZFZQ7XJRx0v1cO2Y4biNMMej2pLiXpu+UQeVYuc+ORIy5MFb6a5JrkVKikxg+7FlPOcicaJFX+Nmo0qKqGSn5PQJAoRW3XaEQRJPE7yn8tjig1om/IBEl7Hk1KlKL6btHmKDoMJYejxLzJYSgxDtsrdMs49/Mk+WAMrGSDlUrepw27qwSn6HgT05oRZWdsmSzVCpEbKWrqK6jUfZq0ezUMqX2SuHSzR48ejeeeew7f+MY3cOihh9a1YSNGjADgqkUjR470569cudJXl0aMGIFisYi1a9cG1KSVK1di3333jdx3Pp9HPp+v2Ab3QVh+4EeFrgdUJH++RJDEd0gPYk3ivqD12wNz3G1FdXfhr6EQDDlUW/bpMCh1/ZdYeVlSaPMyxSSdFKjFj6S3zFgC9Uhz0EhE9V0xMMp9NFAjkDkwVNNaoH5gNFHyofOjk9JWcK+PCn85n/D4SS4pDM+0a9Byfi21z8rzAueXQCGsB0GIIj/yvpOolXG16QKZzxsAgxraNkb9L3uiz0f13c6ig1zRgWOWTWI5YqDbdtBNCfKmAZsBnTZHv1zBLXIrUll4apFTtOEUHS/s3yVHIupRd6uC4f9u1yKA8BCH/DRuRoLTiDal+esk/DtuEkhNoGfPno1+/fr5v1tbW/HAAw/g3HPP1ZYrqRbjxo3DiBEjsGTJEn9esVjEM8884xOgPffcE7lcLrDO8uXL8cYbb8SSpCj4ycgS+BloTVhRYdbyNpSWCZKcwwYAPP8jX0VC2dFbNp+5vyWpP+Q0nsynQ0aSh2QSgiTQW74SOgdmXX6dNOgLOZxUBSYEyewmprLPUoIBMpTTiyCQtkIy08ltUCPdAssUdUnMi25COCQ/7nc9UK0ZrdL/I05d0tVoi0NSv6iejCpNg43dJXzWUcSGLhsbu210lBxvYugoObAZfIWJW21AvtUtcmvm3CLOUt+PSsXREwN6I2qr1Yqk3beabv55UZRSK0mzZ8/Wzp87d27qg2/cuDFQ4uTdd9/Fa6+9hsGDB2P06NGYNWsW5s2bh2233Rbbbrst5s2bh9bWVpx44okAgIEDB+KMM87A+eefjyFDhmDw4MG44IILMGHCBD/aLS105pgoZ+maiIAukaT6Nk+IpyS5n+LtW/cWrqIaRSRp0sDkldarV5TSohHHkSOZKmUW742SKYHj+4pnsF9xh8GISN7oO2/7v2OuoVA0k7QlQvEUpEiomgIEXjSmRlGKO4b7X4gwOcUoNTLpqESsmMN7xDG7EhGKq8eWpI3NSpAAYF1HCSVSQtFmKNomHM9ERomrKG3otpEjORQdju58K4jVBtI2AEahDSS3FiSX84hScid6XVJIhnIhWvkzDWSilDxbd6pD1F1F+ryQnWqRiCQ99NBDmDp1KnK5HB566KHI9QzDwPTp0xMf/KWXXsKBBx7o/z7vvPMAADNmzMCiRYtw4YUXorOzE2effTbWrl2LSZMm4Y9//CP69+/vb3PVVVfBNE0cf/zx6OzsxMEHH4xFixaBRlQ1j4Nbw4j4fzgg+GYbVSm9rqDUD9kGUCZPnsktKWo1q0WtE+UDBOhJI/Hf8PSZmBsN3VulGFSiSGEk8WxiRUnnNB04N+aAMxLI5s6FA7dMliTSJHIu+f1eJkqpa6UJc7BrcnP3X05hoRIlsY3bThbYDwGLVATjk1aGt5HJB2cs1fZqkV1dFnB1u6Rql1qOpfL66dSnZsL6ThtFUkK3zVC0GRzGXIJEDeQIQd4kaM1RWNRAp82QbxmIXNsAkLb+yLW1oNvaCCKiLhMQpSiCVG/Uoxitimr2F2caq5UgfR7MbolI0lFHHYUVK1Zg8803x1FHHRW5nmEYqZI4TpkyJeDjpNvfnDlzYlMNFAoFXHvttbj22msTHzcKsu+BO+gYQYdpEo4oSw3H0fojeQ0oD1I6ZakHoCMOSQiSmBdFfHrS/FbPPDa9nRcnKYjs4F8jmQvVApTJktw/E0L4JQkFVFWTAnmZlHpusfUEFcWpWhKrqjQqidYpNUn7RJTpTOffVGmfop3ifxanLiWFnyepF/t4e5cNm9pwGJdUJOIpSQQtJeISKJPAZhysMAAs399Xk3JtG1AsWCCWCZoTQTH6SD+56KwAU+br1KRqVCWgMlFKl5Cx+nukIzPJTXHN6ZfVU0hEkuQki2rCxU0J7lu4bG4zfHIUvX7wbT0weHgDSsANUOwrhfkiKXT12bjjJCpgq+5HHcBkNLN0HwXfPEnKvgtJ1SQxQDYzWRL5tEKRjoH+ybw+SMMRbVqzVFDBCZElTV8HkpljfXOY3NeqeLYkJUZRdRmTmmei7n21xXbrRUp0RCmseCXzfeotdBdtcMvxSRIlBizTRkeRos1i6LYZuhyGNs8vqdPmMK0W19xWaIVZsECtnJ+iglq0bAmgsvkr+Mn8+fEESUY9L1lPX/60fFqXaVxHljZ1Nakur/efffZZPXbTFHATtIUl28AApDyYdWY4XVi1jzqWTOFSYcfg8VXJP6wGxZEm9S1ercrezIgb+PQ+Kkl9rJq3fIkfgRkIY4/K+yRMa2FTmyBCoczscp0/RzLNicmbX+lahv87PWvCTEt0o85Hvh49SZ7LqlOQjMmTjGYnSABglxzYRQfFkoOizdBZdNBZdL+XmJsKoNt23HQAnhM3z7WCtLTByLeAFvKgBctNdGoJ3yTi/0d1f9Wy/5EgQ2GCJKCSpXqoKr3th1QJUU7ozeic3mikfkJdccUVuPvuu/3fxx13HAYPHowtttgCf/3rX+vauJ6G/Pbhm9kiwv+1A1AlZYg5IcdZdXnctgKqYqTOK2fSdrwCjyxynXoiMkVCDeaf5GVG0h9DHtyinN8r5VJqFuKkyy9UD6h5fkJEqcK29Ti+myG+9woIC0IkTwKBPtTDKnvVaTaaiCABAGMcnHNw5uZLcrz8SEXbI0oOg8PdzNzcSzDJaQ6c5mBYBcDM+W4QghxFJdZ0eJR5LUyQVDWpyS5bw1CJCH3eiFLqJ+mNN97oZ0pdsmQJnnjiCSxevBhTp07F97///bo3sKcRZ+PXmTICWbZjEKss6aB1pK0QVhxBkIJvvXqFqVaoJEUlR9WSpbTkJ4mPRtC3KkiUosjSJgsp3YSKuDw/aZA2iEDts9XuCwgrVzKRrZXUxpG2RhKRoOmQRfoH9gXVV4U6AIvityFoFMlKLwc605nO1CbW1RGkz4OKlCGI1CkAli9f7pOkRx55BMcffzwOO+wwjB07FpMmTap7A3sSunIJUWpC4M8oDzJytu04CAdu4dshyJDjhAcjjcN0vKksrCDVSz3ShfXrCFIUkoR316ttciRUmuPq/JTifLT6LEg50aPfbyE5atfgGK1TO4FooiUUI3n7KBWpqlxXUnLFqHxLcr22tFDPKyqZo3w8IJpMib4ctZ+ws3nf7ZfUJKCUgJjE80dyI9osk3pRbsStt2YYMAzDzffo2AAPZ4vX9S+Z7Khh/upyIEiWxHIdknTDzwvB2ZT9klK/Im+22WZ+OvnFixf7+Yg456ki2/oa5DINgXmCDEmJ9XyCJBGlWNIUpTIF/If0jrFMeutWHbXd7cq+SCywXrqitobi6yJP8jpJBtJaTUHaki7ycsX0RaRBULe+btCNU5WaUVkS7fX7A4tWEioSeISTOybxw0vSvkrzRd+M2kc1KJc8KZtg1FpnUQRJLQOU6rg0XFdNlwCznC097Gys+72pwcxREJOgkKNosSgsk6DFomi1KAomQY4YyJsUJjFAiTsgG3Y3UCq6tQjtEpySDVa0A/tNoual8T1KS3h062cqUt9DaiXp6KOPxoknnohtt90Wq1evxtSpUwEAr732GrbZZpu6N7A3EDWIE/9BqjwwRekR/3dYWUoL8YbEY6KGYtUkpvehqAVRakw1pKdaRSku0lCEl6tqUpLzj0rIp+bskfcbagPrvcFM3O+kA7lf1kH8Vr87Tjm3USBXmLJ/P29SuhcklQwxqZ/qVKRanaSDJU+MwPzyqVSu66dGPdbLT0ooRvI+5b6sU5R06QT6IgqWiULeRIvlkqT+BRMtlvs7T90cSQVKXJJkGCiYBMbGDvCudvDuTjhd3WBFG5wxOEXH6y/xqQ2SKkdRZra42x5FbDZ1grSpqkmpSdJVV12FsWPH4oMPPsD8+fP9EiXLly/H2WefXfcG9gbch2mCN2W5Npv47S+TFZZkfku+qU31R0owAMkqkkqQdCpStahEiBpZSLfisZWB0E1QWDaxVBrQ5HVVRJGlZgN3GDjx7nHOMx3mvIUqmYeXDkB8h54oqUjqhyfAFEVUZ1YDgsSpkpmtGjNg5MuPRj2K3Y+GeCcl/WoborYpk6Z4ogTo0wD0JQxsNVFozcEyCfoXTPQr5DCoNYf+eROtOepOlptM0qIGck4XSLEdTvt6sK4O2F1F2F3dcIo2mMM9ohSdcFRNBVCenyyCLSNI0Yjqhn2ZPKUmSblcDhdccEFo/qxZs+rRnj4D3/xGFJObWK4rOwLpIVmFuUL3qX7vTcQNLrUMLGLduGPpsjcDHjmCe+ykb/5xZR7SqDU9Ce4R7ERt0+U3kn2SvEWRw25EoVuXWMWrm8E26/twJcU0eVmcyvc7Tj3S9QG/TA1N16fiFFAAfokVVU0Krb8JEqWBLTm0tOY8JclE/4KJAQWXIPXziFLOK1FiUQNGxzqgcwNYxwbwrnY4nUWwkg2nq1gucutNzGGx0Wn1yoNUr4SRlfbVVyG6Zl8kS6lJ0qaMpGTDf+AFnLdjTG7qdg1GlIrUSCQZnKupJ5fm+PK+CSVgYJJJIj1RcvejV5WakSjp4LdTRLERGuiHHAiWwUFQXfKhJo9U+7JcSzABiVFNbbr5UQgohhXUoahEoJWcs6OWpzWxpf3PyyRIpyap62wKGNBqobXVQqtnbmuzXPWonxVWkVoMB6RrA9jGz8A721Fq74TdVXQJUskGKzleygo9+UnjJ1SPS5wRpL6PvvGkbyKEHno6k5ukGmmdhXuiBpwCbRX2GGfUnnJQrsqfKabNun2qA16a6KW4TMvNouABrrKp+g75TvVKKgYAfh80CJEIFPH7b/l3mWT5+0rinC/dI+HLV8+XhDQpJeqdX6nsYB0f1RlH4HTJanXHKP8OByfEOYRHoRnJVb+8iUGtOfQrmD5BahFmthzxVSSTAKRrA4xSB1hnO1hXOxzJ1OYUmWduY34KBMYaf76bmomtWdrRLMiUpHpCNanJqJEYiTf0wKejf3sPmQ1QflsvV08PFxKtBUn31Ug1SX88I6QmpUVf8VUKRGIJck4VAqTxJ/JL5nifYMw3nwEIq0qQHL81/nIGIYDcNx0mfVLAYZH3Qu3LqrkUqEx6kqwXd091iFNvZHWpkglZjbqsdC71Uo2akRwJtFgELTmKHDWQpwQ5QlCgBDlKUDCpG9FGgBaTwOjoAO8STttdsLtcUxsr2eAO8/2RuMN9NSltqH5GEhqHvujcnZGkaiEl4dOZMWJJkWqKi3LMFvmTCAUcxx98DFIeeCCZHeKyILvmp8pI41MiQ1eLK3JdZYBMG+lWD3NXNZFJlchSbw5Eom4VIQTEMstKoKQMCdUICJvUQkRJWRYFvw6c6PteH3QLsAb7qa7ArUt+3L4pm9AI4JtLBVECECBLaaFz9pbvaVy/SnNvG2VWF2pS2pxIzUyQADdhJDHciRIDJjVAPPXIgJtgkhoGKBgMuwRe7AIvlVxyVPSmkh2IasuQoV7ISFKtUB+IKjlKEgEkJZM0CPHe5sVv6ik/1B9QHNn3hpR/G5R6g0vEYbxP3XLOgopIlLOs7gFNhLrVQKIk77+3kVaF6AkY1CgTJEJAc6arIpk5f0LOKqtKQEgpChAlQXw8VclHhUhLEdAg1EqDeWkzHLefCjIvE/0o/yJBlMR3AD5ZEkiiDGrr9SmESS14HOe839vQZaGvtkabHFnY9ODMnbwIYD+SV/hd+slze6+ETS2ghtE0KpbDeWT5kWZpY08hEUnabLPNYCSs17JmzZqaGtRXofVVSgtK/Tdx/03fH6ycgKlNDD68gulChziyBCBkjhMoZ/su/0kMapQjyZDuLboW01uj1aRQBmXNeTUTWaI5E8QyQXImqKckwcy5k2dmC5jdJIRC/xEmUOXM3MnyIulUT9FnDRY0uZWVozDKfTVIltwm8EAfqFy2J3yvDWr4BLwniFE1x0hickurMjUTKXIYB+McjHvfxcQ5ONxExYxzgJiAEU0QXRObHATAe9W00xdNSzLiiFIt6GvXJRFJuvrqq/3vq1evxqWXXorDDz8c++yzDwBg6dKlePzxx/HTn/60IY3sKVQ96IqBQ+fvoQxIqWu4JTS5BRxlEU2AAruWvov1tVmRlQcPAOnNO5jQTgw41ZCY3lCTkprd4qKpmHRNegtGzvRNbgYhMHIWDNNVjoxcDoZpAWYuFIEp+qOOKIE5YXOaTJYq9GVhRtOpSf6yCv3WN8l5vwVZ4hLZUMPyZVRSFwiCRMldN15NqvU+N7Kf6MoGqWgmggQARZujs+QAoCAGR4l5k8Pd4rbUfWF0OECJq47GvYSqkW2NRpwCJHefvkQMBMR5NZPK1dNIRJJmzJjhfz/mmGNwySWX4JxzzvHnffe738V1112HJ554Aueee279W9mXIPspaSDPT0qYokxu/gBCwv5GldQiGWqmah1ZEc6Q7vfgpyBLDExrCgAQ+cZfi39SVURMM5DqiFJUOyqFnvcWRJ1BV1HyzGuEeKY2jyCpgwtzgj5IgTQAJEyUgLIJLq4tssnZKZMPVU0iADjTmdiiUYksifsbLFwcvLdyTiHmcJ8o+b8DNdGSKT/1zL4dh7gotnJtumii1GwECQA6iw5ydrld1HD9kUqMoeRwNyM2A4qMI2dabp/2iLb4TIokg71YJ0rxqFYJ6cu5gupNkPqSmpT6Sf/444/jy1/+cmj+4YcfjieeeKIujeptxA2APfaQkcOwAd9M4isF8qfnsBuIbPIGKvEgEesCCH/GlZ1QoBsIKkfoqOHLwfQClUKpZcQqTUJ21yhflZDWBNJsg40gSL6ZjVCXHOXKBMnIWeWJUhg5q5y+QsqjFPCrq0D6K4HQYF8lJNhH5T5sSIOeofRbebnYr3veye6b6KNyuYoQeQr0GxbaVv3ek0gT5t/X0FmysaHLRkfRQWfJQZfN0O0wdNkMXQ6Dw9xB2mYcMAsg+RbPvy6q+Hj5OiW9ZLWaldJsH9emRpi3mhV9pTunJklDhgzBAw88EJr/4IMPYsiQIXVpVG9CTUJXDZIMKKnMbnIknUx+lCmOKPltiyBK6no9gWqIUqisQx3TCVTKXaNCOI02A8qkoUyuXTNbmSBBduIWRCpAlEiAKJWTpmr6s1pbUFfTT3nTD/VTSQ2IIkoAQoOhSpTqgUpkmiVwBu5t37S+SqK6ig46izY2dpXQUXTQZXtEyWYoOQxFh6HEXDWJ5/LgNAfDKriKaaC/NK4wcBLy8nkiOPUCNZqfLKWObps7dy7OOOMMPP30075P0vPPP4/FixfjN7/5Td0b2JOQ30AqgjkAchVXU6ElR2JeTAg/gIADt7u+xkdHiVID4KcG0OVISgudWUE3OOhIWCDTs5yh2Ql/l9tbCbpziY72id+fen5J2tAsEUJ+yD/gqkiC8BDqESSrHNnGXJ8ibgNGzgIvFT0/oyrOIUnNMpWcK2a3KKdthnAuqlB5mypzCQUK3Tb4Sd0I36YkUK9NtYWlG4kNXTZgOXAYByVuIVuLEnSbgihxX01iZgHcLMCwCoCZ8wMWtEl7PYhLTw2A8bCppxrTT7Qpzj1YZZNe9DE/j/4/zWx+S02STj31VOy444741a9+hfvvvx+cc3zhC1/A//3f/2HSpEmNaGPfQQUFKUSQ5N8qQYrJnSQet4QFfZFk/6Sw301lh85K0A1G5ezDRuybvS4bsY4oVdquGqQdjKohSk0FyUTmm9k8Xw45Oo0z4vok2fDUSgZOnECZktC5yyapuP4sQZdcUvRTw/FUIQBOZLFXjxxFLE9CkMQ9VclQvdWf3laTkqDZ/Om6Sg5o0YHNOChxcyVZJkHeJOhyXJNbP+46c3c5HGYu7/ZpqxBQIIGyWVK9D2FiVI7jlAlJPQiU2H8zER1Z5WqmdsloVqJUVZ6kSZMm4Y477qh3W5oC8h+snlmUAwOKOphIBCnNYKyGTov26t7M4xJNlpsVfWyZZIWrpktv5Br/pjjTWSjUvgaVC0jvjxSlTuiIku4YzQo3caTnxyaZ3Lj3sDS4Gy3JGQOI4wYF2KWymiTIlK7fMCdsZosiSIIUUaLtp1xWGnU5uJAueaI+FYa+cGwUoVFzJjUKcc7eOsfzNGpXIBAjgdLWm8Sps8hAiq6SZEokqSVHYXsRbkJNshkHpxaIVYBhWm5Ep/B3C5nL3aSUkKLdCIAkjg7JHLzjB/VaiFK9SJbOBJhc7ep5YtWMRCkRSVq/fj0GDBjgf4+DWK8vIlgRvMIDSVWNYjJslweUhORI9fcIHNcjHvIswI92A5Lki9E7qEZtG1R9vFB/5a1c5/fUbAibaYJtTl3gOOH8noAoYmzIFmBCXb80z+TGCYXIM8M5cxUjzw+Jo+Sbc30IEhWlakYQpEqEXO6nskrkZ95OeB9k8pRULdSbhptH/YkjM+p/Lg2auShuyWboLLp9pqPogBIDLZbri9RlOygxEyXGwDlx+Q4tR7i55IgEvlfrq1aJmFRDXHqTKFXykUrnbN5cylhPInEyyeXLl2PzzTfHoEGDtIklOecwDANOAsWimaE6iYYKS6YYCLXqkXJ9QqpEAodYHUIDT5oyH8q6UUqJ+nYqzwd6X8bXZQVPVbFdIUo9FdZdV0QQa4NScIP4JAkAuMEkgpTAVKwjTNLvSuRIVpNEP/UJvug7MclRa0FactFTapJAUlNirUQpchnvPaJYtB1XRSy6JUq6bYai7aBoMzAOlBwGxlyFgXO4jts5C6DUc9gO9135XN2yJkKl4HUd8JtR+WgUeoooNds1TUSSnnzySQwePBgA8NRTTzW0Qb0JdeCXnU214aZS1JmMSF8NRxAgzQNRHmwSmjLiIJcrqSfUzL66t7ZgQdNgyZJKqNfgGB5c6ueo3ozgTkSknShE673YcIPA4Cy4nFAApeB2MX3OJ0yB4+vX952uFdOqSup1Kl+ikiMBs5QgydX7k8l9tRqilMScp1s3DWohSs0Ix2ZwbIYigKLN/MlhHCXmEiXHy8jNOHer3QIViX0aNNvALPB5VnCaBYn++ZMnT4ZpmrBtG08//TTGjx+PyZMna6d6Ys6cOTAMIzCNGDHCX845x5w5czBq1Ci0tLRgypQpePPNN+vaBl2OITeKSBO1VSFyLRQyzpzyBHfwiSJISZWlJA/zJOH2laPAwrK2Ls+MnLsosJzJtZaCn9UgrrZc5DYpSrk0O/z6VU65P8l9y/AetAGCJK9TBRH3+2YFgiTaFwffT6kB1z+twin3zXJ+pXD71VxKKumJSxvQCOLdrOa0SuCcw7EZGOM+OXIYR7ddzpHEZKIgPRv9Ph+BqP60CXHMDA1GqqeHaZr4xS9+0aMmtZ122gnLly/3p7/97W/+svnz5+PKK6/EddddhxdffBEjRozAoYceig0bNlR9POI5bqvkCEDZ/k2VRI86iIHHccq+HfJ8RTkKmuYqEKS4t3wn/LZePrdgiKya1LEeiCI/OrIUmO+ECZQ8VTpm4LeSETwN+dKt2xcilmS4/UlcV1f14Y4Dv0CoNxmch/ud1Be5/FtZ5h9L9qvz7qd8Xyuhp0hpXIi4SCwZmjRESddfKxEeQZbkKXB8qXRK8Hjx/kmbChybgXMO7pEjMQGucsTUMiOcgZeK4KWSl13de040QDnPkCH1E+rggw/G008/3YCm6GGaJkaMGOFPw4YNA+C+fVx99dX48Y9/jKOPPho777wzbrnlFnR0dODOO++s6lihMHXp7TaQ8VepfxWATIBU85qGGIUHqcoEiTtikipgR5hb1MSS4jzrMTgx7wGlTuKcxYNLJUu6efI2OugIk/q7mhQHUQNf8Dz7wICkkhOZ2NhF97dCknwCJfqU7HcU9yKkK9cSc+3jVL56D2xRBCTyt0RMVBKjI0pqmwPzU5J7XXuioL92QVJXDQgNJu7sLXDmkiQAIaIkQAyAGAYM5oDbJXC7CFayAwSp2jQnPf0X7wuPlN5Cs12b1CkApk6diosuughvvPEG9txzT7S1tQWWH3nkkXVrHAC8/fbbGDVqFPL5PCZNmoR58+Zh/PjxePfdd7FixQocdthh/rr5fB6TJ0/Gc889h7POOityn93d3eju7vZ/yxF7hpe5NWheo26WYCKZ2iL8kXxEEKTITNtx0W2Sw6x4c4/0QYmBQWkgpDqQcDJm4NPVcYuDvDzJo7eSqSzOrKces1oVqa+8hcb1XUCoPx7xsYswWMH9bZfcvEkeWeaORJ7skk/utSqSDgnNc2FSElYa5e+sAmFNg0pJPuW+opIMsYXnnQSDhAveqgQprg0V2xqjIjHl2tTLJNnTGfaj+q4gR4xxrf8NIYZLjgzAMADYXeDFLsAugRVtsKINuV6fXGeyt9Gb/kTCST3JegJZ1vAwUpOkb3/72wCAK6+8MrSs3tFtkyZNwq233ortttsOn3zyCS699FLsu+++ePPNN7FixQoAwPDhwwPbDB8+HO+9917sfi+//HLMnTu34vHVulOGamojVO/vkIYgJYluiyFIsooUZ+aQHVj9UGvZkVZK+CejEoGpWM7BG27iHsdJSVfUQz3N22PSgqXNiqi+G1AuhCIkiFAp5/VXAjCPJHnkiNslX1Hy+2Iosanm+hJawbk7nlg3mpRWNNFqCFKQ3LsvS6IArkyU4o6l/h9qdbCuNQFsFHqaIAHxz13GuNYPm3iDNjUMELi+RIZdBO/uBC92wSnZZTWJhcmRev1E1u0MLlQSJ35XS5bURJybAlL/UxhjkVO9fZWmTp2KY445BhMmTMAhhxyCRx99FABwyy23+Ouo6QhEKoI4XHTRRVi3bp0/ffDBB6F1VF8k4Y+kLfgZqHpeA0GKWe6b1yQTW5SZjTvu/dCfi1L4NuJhqRKx8HKuvO3q/S7Eeqo5TuxX/q3zCwlcjhoHDKa8rafZpllQqe/KzttCVYJd8ifuTbBLnnktqCBV5cBdAbI6FCJPTDZ3Jj92VL0uoDaCxGPUCK3aGkOQxLxK5rC0qkejiFOjEdd3iaYkFCUSQSLus54SA4ZTAi92gRVLYCUbzGFwio5vHs1UpGSIa59uWaXzUS+7w9Obzprk1gVQVcbt3kJbWxsmTJiAt99+G0cddRQAYMWKFRg5cqS/zsqVK0Pqkop8Po98Ph+5XM77I5QiuQJ53UJP/ai4mIee5y8iIAgSEDZdVHo7L9duo7EDEleIjDsvuvdWiuAhXiI7MaCFVYXkCfRUVSnpgKGLPOqLilKlvivAmeP5bhTdYraOZ1YjQYWSl4qRDts6uHmV4PdZ0ZfkkH2dn5hqWovrrxX7sSY9gGxucX9XdqYOt7FM7F0ViYHAVV0J4lWhJANz2szZcX2bOSxWDaqUPb83UPG5Sww3p5GXdRtwo/2J4RElA8gRA4ZnbrO7inB8olS5ALGKRiaHbBaCFGVyS3MOtaYhELelUrdvRoIEVEGSfvWrX2nnG4aBQqGAbbbZBgcccABoTAbqatHd3Y1//OMf+NKXvoRx48ZhxIgRWLJkCXbffXcAQLFYxDPPPIMrrrii7sfWIWRqiyEeBqHR/h0JyJL6Fh6VNTvS3CbyFlUoBSL7cSSx7YccZaXjGz6R4T5RcueXs3YnQVxemFpq0vVVolQJ3GEwPL8kwydHLrmRHfhlx+2Qw7ZsTiNeclJ1HgAwJ5J0qwQpLgIpqe+OjiCFnKc1iqHuPvMUpEqHqDZXKgCdNM9Rpb5diSj1FRjE8CeqTMQwPILk+iSZBgBmu5FtUnCIQJxarMuF5N4Gt45bVBeoXH6kcQN8PciWSpTS7rNehE9/7ZuXHAmkJklXXXUVVq1ahY6ODmy22WbgnOOzzz5Da2sr+vXrh5UrV2L8+PF46qmnsNVWW9XUuAsuuADTp0/H6NGjsXLlSlx66aVYv349ZsyYAcMwMGvWLMybNw/bbrsttt12W8ybNw+tra048cQTazoucxiSUDw/8VycmUAqKBrn6M2ZExh44tpWDxiU+j5I+rdy5pnJmi+fizowyKpSXJ0u3bnIapf+WEZgvT4F4afkmdQMRhA4iwQmNtFn5e3ElfLneUQJ0PclIEiQhIokTG1pysGkMaXJqHT/kpbt0CVGVc3OlaASpFpKhkT56+kIVrMSKkK8PHgKQcqbxDO1eYQJBuAUAdv2FHa1TyW7hpXqtzWyJlvSYwD1VaPqnWm8Hugrj9TU/5p58+Zhr732wttvv43Vq1djzZo1+Ne//oVJkybhmmuuwfvvv48RI0bg3HPPrblxH374IU444QRsv/32OProo2FZFp5//nmMGTMGAHDhhRdi1qxZOPvsszFx4kR89NFH+OMf/4j+/fvXfGzfGVo2Z6WIJgupTCKzsW4C4iPlegCy47d4G3d9NtLlvlGrcgO15xmKqhGnwjeTBmrwpc+UrN93uLJ4n4KsFvkTk0xs5WEjYFZW+6dIgUGCAQzhw7FIgpQWSUPU5XD9qNxHcRD9RnwKnyetCpXwWRBXDFo3nyhtSAqdz58I76+lnllPgFACahKfDAWVJM9ZG25km8GjTcECXOOZrV5OUaokvJ6h/a5bL7hu9H6aBY0mSH358RiH1ErST37yE9x3333Yeuut/XnbbLMNfvGLX+CYY47BO++8g/nz5+OYY46puXF33XVX7HLDMDBnzhzMmTOn5mMJuA9x9U1ReiD6jq5eYVDNm6XfvqSlOMT66nfNg1j3hpikhEP4DT/8oBGDGfP8O9QyD7oSDZWUliSDRNTgpa4bV0cvELnnv12nKymSxPwWFQLe29D2tUDi0pxrfgO0SVB9XyMAcBx/f1yY2aAoSN78UJ9V+5nGRJzE5yhqHypU5UgQpCgVRWfq0tUkDPW1uLpnkhIk/x8q9SW1/+pUUPX/ntavqS+AeKY2ixJYJkHeJLBMCkoM5CjxTW3EQMglIZiqxSOYGidwAVVNcckMR7UmN3l/ac1uces3k+KTBnK3TPt41HXpZnjEpiZJy5cvh23bofm2bfth+aNGjaop63VvQc58WzY5sQB5II4wXXj+GXFEKaFfljz4+IOObH7zCI1sbojyMUpbHNQ3fQTe+qNNFvKDP+q79hwrDDKVUKnQcGBQR3DgEtek3nXY1HMmrPcHLu4wIAefGPmQyE7FqErRbyWyBOjJvLs/SYWiYZOYLmEnS6HKyvsO7Dd0HH2kGoByKgpKfKIUJDd6UlSteqjbLqBwavqvTJTE+lEvEJtS/TZiEuQ8gmSZxFeRLEpCik1AydRE6BLpvjnwVCrmhEPTObykS3pi1Ju13PoqQVKRhvREdeVm8FtKrcEeeOCBOOuss/Dqq6/681599VV8+9vfxkEHHQQA+Nvf/oZx48bVr5U9BGFiYn4oqZik8HuHBaOB5IggGTJBijO1EeqvG3hwSiYMg1LlQVB+eyqvk84koT9/zVs/C5od1fXKxzdip2qhmgrU0hLqeYvQ8M8bAiYt9R47lX2PfKipLaTJv/ZKItWKJXoSIE51rXRPuUZpCRc4jlYrdZNsXq0HGYkjSLr5wReQqDQdehNitZnnews5EiRI4jvxItqIZ3YDAG4QwDRh5HIgOdNPZVKLKVyY3uRP/XrlSd2+XthUCFIU9Nevd9qSFKmfbDfffDMGDx6MPffc0w/pnDhxIgYPHoybb74ZANCvXz/88pe/rHtjGw2ZHDFFQWIyWZDqWclESQvJp0M3+evQ8ndD2kaFeJCqxCEOUaY2HfERpjZ1eaANAXJWPQlSfSZ0U+C4EWVjxPdQ4d4q/IiayYSWBqHoRCdY9kbne6Qi0B8VnzkAQTJPNPULiYbsa9oYOq5P+qk/6Ol82+IQlcOoku8QkQgR0ZCjpHmY4nyNdAQp8phEn/sp7jjNkheoWuQkgpT3Pk1iQL31jAMgJkBMGKYFmjNBcjlQy0z0X68nmamkfNT7eJsaoghn3Pq9hdTmNhF2/9Zbb+Gtt94C5xw77LADtt9+e3+dAw88sK6N7Clw5vkzEOHoWSZGrGiD5WywnAnieJFqskkM+siXSohMDUBIYP++SU4ysfmZs53wcQnCJo2o/DW6MhFJHJmj/DfqDd01jRs8DUIC5Vfq0ga5hEsvvnVHgXl9lFimG+VH3DQAIsKt4jPGC/PX9kcpQhOUhqM5K2TfFpBNwYQQMMYSmYf9GopAKFN8NdCZ0uqtFmmXp3w2EGp4/2+u9UWsJ3qzdpusILmf1M+TJJJMCl9sBwQmtWBYBZBCAcQyA4lFk15j1eQG6CO/4iLCZJNcs0WOZagfqk4muf322weI0aYA7jA4RQZRjkDnlyQmg3rO24z6vkSx/kmVoPMX0Qw+qi+STJTqCfcBLflARUjacuJNHVLXl0tw7ap5oKf11eqpfdUL3OEu6XAYWMl270sgks0BmKIMCSj9TEuUGpCJWyZKgfkA5KurXmuDlK+/qLNoODwUjBA0y6rO2npTWii8v0ZCpttnXLoJlsAfSbesL/soUYJARJs7z/BLkgAA564pqsg4LDMPo6UNhlVw1SSPKJUT/8Y5bgPRhKg5HIUzNBc+f84bMRBRMU7RJUJ+XSDPLylgchMQodXiu0DaEi0xGY79OnHC3FGBmMRBNrWFBh/fzBU0D8gEyfCWu/J25Xaoy3ShylHravdX5zdenUkmWa6b3q+cLoOV3EKfjvgs2kGTW71KBjlOmDho+m7YEVqOQpJ8b1T/MkISm930CqPhqwryPNEm2SxTjoYiIV+3uGMkQdw+U++rCsW2L5UuEeTIVIiSAGMcHB5RYhzcaoVRaHVJUsFyfZMsM6Bu+9s6LEB8HN6zfj+bksnNvXa9d+zeQp8qS9JoOCUH3DTBHQ6n5IBa1FWTvPpAciSYb3ITyfakZJEBNcmPgBMRalLGY3VwqTSQiZBrqQyEb1pSVlUzX+tKjajryTCoKMhQ/u02QU+QQpFHCR7SaTIGN4KQBHyyFAVQjeRTUx/o2mX0YnSb3d0NlvPq8nkqIyvZoDnmk3guJZTU5uUSmbVlRBF/yS8vCqqJUvxWvxMSVEN9vx0Ajhy56fV1xwmb6Qg1wJnhLffMU1JEm9sePUGS9+GeWjCDtwxZwdEpFpVIUZzfjI6cp4nI7Ktqko4YCTDPzuYwDuYN0tzMuyQpX4BZsECtXCIympnDqodKNOvZzcomy8rH7g1kJEmCKJDIHAPcIUGTm2JuC5jcxPYom90AuIMKlUs5RPgfaebJeWvKaQGYlijp4JIcpDbFuW/vzJUYKQ1lpPbf1CPIkXz8cPmIlKa3lMQoMror6fbVmkqbAEJFAgDHU15YzgT183ol8EvyoC12Kzv7pzS9BdIIaMiSTJTEfN224vhymgFCCTjlcByxLgO13P+uWhop4JAdEUkmfuuISSUTVy0EqRLKOZRIILN8HPpS2RI1iaQMh3MwzuFw7ilJBUD4JVlulFugCHkCRIeiV5/3KEN6qOSrGZGRJAmMuZFdBpGyTSsZt2Vnbq6oSeUdOd7bOim/fdMKzq3yW7rOF0kuLCoRJQAhNalafxlfLWIEoO5DlmpCkXUESR740iDqQV6rclSJkEURqTiiFKUmNQNYyQEruiTJoBQ8Z3qmYpHXK+iXJCuf5Z1EXDPZxCZHyWkIk1wbTibp5TxAYYKkI0rqPiDtB96nIEvCL8nVjtwtdHdJVY/SkJaK2bo1fSbp/qOUomrKlfRFNYlUMEkxj7Rw7r4scmKCeyQJZk7rJqAibZJH2W8pyim7WQf1nkAlNUk1M/ZlFa9qktTR0YH3338fxWIxMH+XXXapuVHNBjkZHmPhum5BU1uE2U0mSyoizGyBLMjeflWiJNoX2E72+YA0yGhDl4ODl7uN6Q5aofYEyVGU+UlHlNSswfFhztUpSICa1oBr10mKvlQAl3n+cwDgEOL7abCSDZr3+qXjuP94xQSs36G3TDGxAWEzcdx+RGBBcP0gQfLnVyBKMolnAAxGQGACsAHLLeslEyUVUeSoEsFPS1RqJUe6/VWbmqIvqEk6fySdmgTAN7mZno+mQShIlb5f9fTaqoYEVMrgnaE5kJokrVq1Cqeddhoee+wx7XKnXg6iTQjf6dkzwRnENbn54foAAkVqvWg3AGGyFAE/g7anJgWIEhTTW0pU8vVQ96gbyMT8pIjz6dC1LS2aLcqst+Caid0+KZuGAQT6ikvilWstkRweZWJTl1fofz7xEBGYsnIUoSqJ71FEiTOPnDtuf+VO8LcgSpQC3AkTiyhyJP+u1J8qEeckBCmO8ESR/E0ZZsDURiL9kwSYK9OH83gp0NVwqxeS3ppqCE8zEqXPs+kx9cg0a9YsrF27Fs8//zxaWlqwePFi3HLLLdh2223x0EMPNaKNPQZC5MitHlIQREZjCYECo5Ai3PzlwYg3ERUkCoGqUUGEhCOEdJFDuu2JZQbWkyPr6hHBExUFVQm6grvlAqfJ8z1tKjC87MN+OHRc9F0VfkVx6wfMdkoUppr8VO0/gXlyX5T7pxfxJs5LnCMVOXKkXDnUEv9hAmrRULJG9TjpLkE8wa/1mRFlAk7bh/sauVL9kXzSZBhetu1yaRJiuBO4JqI4JZL0AHFL46LUGkFo+npUXJJr0le6aWol6cknn8Qf/vAH7LXXXiCEYMyYMTj00EMxYMAAXH755fjKV77SiHb2CKglkw0jQBjqfzAa/VsuMOrNMqTviRL3hdSCcOSQGlEk511qFGo1s0WaRGIUK7VQb7WO3c3qjwQAw3bZGgPbWtw+m7MAQmCYFgwzB8PMuWVFTKtMtuUEkUBQGdImN5WUTZHkVKd4aoILAL2iBCCgKlVSlKiVA3ccOHCTZspZ4sU2gO0b3IAo76TqkKRYcyWkiVTTb89il/dFqCSJEgM5QkD8UiGGX+TWgEtcDOaAlYrgrJyapdYXIrXYrfBLEipKEoWnnrdFEKVmUZXSqknN0u5akZoktbe3Y/PNNwcADB48GKtWrcJ2222HCRMm4JVXXql7A3sSJEdBLSqRJUN5y42QduXioUlQqfCttNx9aZIGHnW+BNlsppIJnQkDEY7IbmRbfIZfbYK/lOQjSZ6lJNBlEq8HVPWhmVE68lzYgwYiR9z6U4bdBaPYCaPUAaPUDaPUAXR3gnW1A3YJvLsrZDqL9VHSIY4owXMgV4oz64iSDnHO3BQAIwwMdtknCab/2yAMDlz/LPmuNVJVbLSKtCmRIhVEMbUFE0pKywVZcoownCJgl9y+LOVdS/r/V7NlQ1PoVnbgVglCFGEIrxefsRuoTDyayQm6ngaWvmLCS02Stt9+e7z11lsYO3YsdtttN9x4440YO3YsbrjhBowcObIRbewxuCSJSGSJ+pET2jxAUpH1KOftygeNWc8rA6EjRO62HsmQ2+QhivzIe4lTjcSA5jZD7/CtHjOwvUrSFNJVTaRc+sg5rv0eh76aAuCpZevQrz9DjhK05gjyJkV/qwWtuX5oaTXQYhIMzAF0wycg7avBN6wFa18PXlScsRWHba2zv+z0rRAlf560r6QZytVoN5Uoid/cS1MBAJyK/ZaJEgBQmK66AOYTDJXIqH0iOp1GOMpMl2epXvg8qUhAOReSDErgm9mopyARA66aZBcBpwhe7AIrlqSEv+qLW/l+R5UQkZ+JghRJyVygKkrQZOtWiZCOKMkIbx++JnG3OI0pLqrUSi3b1xPquTdj105NkmbNmoXly5cDAGbPno3DDz8cd9xxByzLwqJFi+rdvh6FIEiEGr6aFOdzE0gDAEQ6b0cijfoUBeZALccgExJtTTcIZ1kaEQdUhkyW3MPFm7viBsOogrU6ghTav2a/kWH8MVFtSdWuvqQiAcDvXvgAbf37o8Wi6F8wMbA1hyH98hjSZmHzNgvD2/IY3GpiYGE4BhUGgtIPYTAH3C6B2yVtNFsos3yVSEpudevJRAlAyPzmFEsglniM2b4zt18jjpaTSqrkIk3UWFziyHpHs6nrx5GiUGBISvhkrxcToTqMo2i75yGK3DoMfm4kwCNHhmdqs0tAsRvcLvrP4CT/a1UxEmSGiAMACJrbgCjSVP5eNsX55+MTKs258niSIudoqhZxBC0telrFqua8aYOJVWqS9I1vfMP/vvvuu2PZsmX45z//idGjR2Po0KF1bVxPg3oKEvFLGwSdR3UQSSUBzV/JL4JbQ023CKjZjuMGGB0i0wQIciVFIrnHKw86QHJZO8oMl4Yg6aDbZ62mtmrujzxAkl4caN7+2wrkWjfCzBHk8iYKbTm09stjyMACxgxtxfhh/TB2UAvGDGqB3ZLH0IGjYNpd4J3t4MWucLkRDSnSJ0LVONBqHMNVp/o0iT8r+Sk5xZL7H3WkwAIa7LfuvPR5hyLblLKvVGvqE+3VqUi11JVrplxKG7ttcMuB4ylKothtwSTlLNvy5WO2S+5LJThFO/Z/TyhxE6rCUAhIkOiUOZFLilx1SCwwPEJVJlCyj1L4UkZf27KJLao/1H5fym2tL3RkMFl70p9TM/kzpSZJl1xyCS644AK0trYCAFpbW7HHHnugs7MTl1xyCS6++OK6N7KnIKLaREbeqAyu3GHg1CtPAs9BVSyD9PdjzHWglZeJBJNKJu4QdHlqgNjIpKiHZpSaVD4fJ2R6k80dYh9VOzwr2+prbtVGkHoazZY/aflfnwTN9wO1WmC1DYTVfzMUBgzD6s1a8MmnrVj+WRfWbTkQJcaxzeBWmG2tGNyyGYzcJ64aaZcAVCBCOqjpA6T+yR0nkJCVKSYRnfpYyU8JCOf+IpS4me8pAWEkULbE99Pzyb/hHadsgosjMOpLgjxPbK+2r5a0FGVS1DyDRKOxrsOGTUuwTBIgSgWTottmKDkMNuNwGALpUEQmeRXlGn0EnHEQx4DjBM1lsjO2C++4AMQTW6xTrvcWJFDufoyQiSjJo0HdRi3NkZYkhE1+MplJsr1+ftgPK6yexbVVJUhJr00aYkVgKB2jvkhNkubOnYtvfetbPkkS6OjowNy5c/s0SZLrkkWpR65PBPM/3b+oDcNTlAxKwQkDSDCppCBLXJjgKilLmjIQwiwiD0bqQASkV1RkB9vyPBIiSun2GdxOPdc0ClJUssh6oq/6IgkwuwTO22F3taN7/afAciDXOgAbh2yB9pHj0dVeQqfnf5QjBvJmKwYOHAlz9QeuiiSb3CQkcuZWs3HHkKOkxEhWNHXzBQxCQn3XXyb1QdUnTu9nJK8fJE+VMmqr/TlYK7ExT/BaTWzNgo0dRXSjG1aOorPooNszvZnEQI4aaMkRdNsMjuUqSyBmICWKDJkgGZTBcIyAmqQzlwEIkB2ZqBBPRQIMFBn3SZPsdxTlwK1Ded0gwQiW5+CJSUJwvTBpSVtnLc5HKEgKK5MluW26VArysaLIWKI2l5vTEKQmSZxzGJob+Ne//hWDBw+uS6OaAQG1KFf2P+KEgFEGw3GjawxGwJ3yW2yILKHkEiQiyBNRQqlp9Ju6pgSE+M5LxcDA0xNVv2tRkwL7qaOJTUaSula6c6g02DRzOZI4lDrWY13HehQ71sEp7ggAaLEoBrbkMLTVwsB8HkNb+wEAuF1MnT9JJSe6/qiW9lHXVb+Hj6Exq7J40iyyyPu/lci68nqiHlqZEMURJRl9zWetmdGxoRsWy6GUo+jOURRt5itKwuzW3zLRz6GwGQe3WkDyLTBMt26bqCXpBtp41gDifnJPTTJBgZJLlMQgrKpAOrOZwwUx4iDEAAvMiz+vJL5HDi8TjDjSVWnfwUi5aAIWv8+otpY/ZYIYRZZ0+xTLqBH8rh5DtLuSEiZvy7lR3/TpChKTpM022wyGYcAwDGy33XYBouQ4DjZu3IhvfetbDWlkT8Gt18bBwEClgVNETjDCYFDm18gijuSzxAhYCQEzXTmpX1ldAlAmTO5OIhqjvJ1HqEeqj4cOPWmeCpjoFPNDmszdXDmvWnIb6dSHSlAzK/dVogQAnas/BgBQa3d80JbDO4MK2GJAASP658Ctfu4LQHdXSJEUSErAo9SiKELEqryn8n6544TNeAmCB2TzWyWi5K7PA78FKvWJJCpSmr5djV9VpdIkhBq96k/Xvr4bJScHM0eRy1M4jkySKNosExvyNgbaJooOQSlfgGnmYeRbPMXIS6jrBdzYnS5hkjNuO3BggoI4zIumCw/UAirhcAlMmRwVWbi+m7ud5J+o7EuG2C9DkCyViVJc2gC9EqPzFZIJWPm4elTel970qJovdfsUEYryp3pMuX3qZyUljNbBjysOiUnS1VdfDc45Tj/9dMydOxcDBw70l1mWhbFjx2KfffZpSCN7Eszhrp8RLZvU/O+UwSmWQK0cWNEG98iRKxYFswYziSRpCZOnMAnIKQN4hBOsSo7iCITqSxFl0hAKWRLUMwljTxOOaupfqQNjHFEyqAHDaS4zhozO1R9jQ+tArBu4I977tAM7jiyis+S+lYM5sLuKfu03oDq/miRkKM60piLO1Cb6LfP7cDgEXOwjKgBBR5QA976r5LpePmiVfPOioLYXQGqypN9v7/fZro3dcJgFM0dhlyiYzcEZByUGWr1ozf55ig1FB/0sik6bIZ9rgWEVQAt5kJwJ6k0GKYFaFE7JgUE8/1JqgILCgQORSY4gmHqAeOkCZDIpBkfmESuHuya3KFObTi0Rv2WUtw2SL1mlUtWgONOV2ouYtK1KuuL8p/TKl7yvoMmxyIAoohRHkORP/XUJmwl118E/X94kJGnGjBkAgHHjxmHfffdFLpersEXfg1MUfkae2cxLWCeDWKYbdkwJDEYBTz0Sjz5dKRBBmKhlAiU7kMVbJKjkKIXaI9eK05kw0g5kviKm2VYMNtVCNmMlSRTYKMiDiUqMqi0UWqleV7P5eEShY/VH2Lh2LFat7cTKDd3oKDlg/fuDMwdOV9FXSCuRmrSoNg1DdIoHp/xfYK6yq/5HoiCTJVlVkomH6CeVHLHT9uNQVnitqdBQ2lBWjnTmwsD+Aqkv4otIu/trjn7b3b4WjDGUTAt2qcVXgAglWG1RDGzNYbNWCx0lB0WHo+Rw8Hw/GC1tMKwCzILll0+iFoXdZYPmaOB6OEUGCupZA8Q1DrbD8Mx04tgC7rORw3Q4aMkJqT4CqkqiI15ifwA84gWPeJTJh0uY/FYFtiUIExAdwvuKzxYeRbgAeIpXmbgUmdtmixhaoiTvUyZEFjEC3+XjquZGmUjK6+na3TQkSWDy5Mn+987OTpRKwcF9wIABtbeqlyBS2xMwcMeAA9tNSkfLxUNZ0fZ8HlwiJYNQV1UyKAWTSBLNmb6fhE+QSmJ9d2CS3yp1TtgqMdKZMuTw5ySJGHUqUtz+1TbFQWvmSjio1MPUJg8uSf1M4iCIklCTKhGnZkSpYz06P/sEnRsHYvXGbmwoOuD5fgBjrpJUtCXlsWcKVaepUs+U/iBIv6quhnzOIkyulVQlnflN3mca1MvkHZ/g1YgkSmmuc0+juOEzMJuB5gueit4PBjFgWgTrOk2s6yihvWh7JInBZhQ83wba2h+GVQCxTNc3KWf6Oe6covvJHAbCiFf8mIGgnDdLhuvDJJy+3WsmrieF+393ig5y1IDpcF9dkkGkfYj9if3I4N72gnjlHIaS4xKlIgsTBAGZFEWZrqJUKodz5DSRePK2cWYwhwMlxQ9LqGplgqQnYmWiZHhEqUyY5GPIxxJtTuLAXodsg7FITZI6Ojpw4YUX4p577sHq1atDy50eerg2AtzhXpZeAhQd166tmN3K64ZNL6zkPTw9tYhaORhOOUpMkCWUxIO3gsNwhG+HUINUqHtT34RlFSkYfVNZRaqOqNT/oZz2Ya8jRTK50RGmtI7dfQnFDWvQ3VnCZx0ldJQcdDscOQCsaMPu6gYAVKNSykh7350ECRF1/wXVUTyJeqmLuNSpSkmIUk9B54eUNF9ZlKIUdDw3ejWZZKljPRjnML3kkAahICaBmaOwCjY2dLlTZ8lBl81QYhw2LcC08jDyBdfcZuVALdN13hY+SQ4HtSQ3BsrBAC1REuTGLZBMytm6cwBnHIbD/P7gFB2XTEn3RCZGciqZcmUBT6n0iJXbXzmcIoNTckAcDrvk+ERDdQxXTXmqUiXusemU9y9MhCphkhFFkNSItSJzkzYWGeAYoh3lSDRhalPNgsF2wydIOUUF06ddSOZHpUnYXlekJknf//738dRTT2HBggU45ZRTcP311+Ojjz7CjTfeiJ/97GeNaGOPQThuA8Lkxn2zm+uPZLsmMw9xMrxQjmjOhMEoDI8QCWfvJI7AKjliykARaj9c3yhd6H6SB3ycetQMiBoQ4vxN/HUiBrkkypJQAHXqUV8xtQmUOjei2GljY5ft5qBhHkkqhcs7pCXGtSYIjUMcOQosr/AfiSJLYfVJT5TSoNaCysFj6/2QdH09DZqh/zrFTjesH65vZsnMgVoF2CUHdslBZ9FBZ9FGyeFuKgDO0e1w5M0CDNMC9VQkUR2BWhSs5BEZ5kW4CcJUdHyiJKCSGuHLJL+MOUUHnHLfesAdDkbKzxt1PzRHgyRJus5CSWIl5vtMOUUHJgDiMMAJphgQkMmRTrECAOTKRJ84HMRhoEyoPtFKkkqSBAGTdAFfObJI2bRWJkrR91eoSEJJyhlhvyTfIZwDIqGnHImo7k8GaRbHbYGHH34Yt956K6ZMmYLTTz8dX/rSl7DNNttgzJgxuOOOOwIZuXsSCxYswM9//nMsX74cO+20E66++mp86UtfSrUPzrjvuG0Q8UA2fDXJHSjDdcXVmmScMRgOcf2XvBxKqrO3TGa0bdE4vsaRGEECVKIUaCcLmidkx9dKbegpJPFfiVKTQkkrlTfwehClvg6n2OkPPCWH+elFnJLtm9uSREyqMCgBhEmTibfnZC8DYvskqQBkc1pU8EKSdociLyPSBDQbdGRJZy7sS3BKXT5JIoSC5lvgFLvg2Dm/rxZthhJzJ4e5JUtATMDMgVg5/8XUCBAW4ilpnm8Xcz/VZLoCgiDJqQQAl+xSi5b7heUpdFKghkqygukIlAAEKX+TUwxbXnIIRuABYR8nkiMBBSmgDDpGgMQBDBaCIfvl/cb7ULlFI9ztgaAZzD2k7DOkV6qCU5kgCb8k76rAj5gLECUEroO8X/97aGl9kZokrVmzBuPGjQPg+h+tWbMGALD//vvj29/+dn1blxB33303Zs2ahQULFmC//fbDjTfeiKlTp+Lvf/87Ro8enWpf3PNO44wBVJJqnbK5Tf2DyQ9lke2XwDVhiLpSqo+H2CJq+NA5z0a94auDEWMsUlGqFfIAEufnUSuaVc0CoqPcmtXnQwazi3BsMehwV6pmyaImo8i5WFcMBpX6tm4fPlnRXMN6E6RNAX2RDEWBMwfMLsIgFIw5YKWiN4+DM8+/hnHPbwfg4OAAOKF+VLAcKCP7FAkIwgSNN0i5ykI5Gk4mN8I4R+DuQ5jr5J4aR5BkRclVeSgMwuFAqF0EnHI/p5OIwIMSfSf2IRMk0fbA+QDl9ob2F1xXVo6ifKjE9pQD1OCB7QTp0RXtDedyCpI++VNOKSATpbg6eLr9NgKpSdL48eOxbNkyjBkzBl/4whdwzz33YO+998bDDz+MQYMGNaCJlXHllVfijDPOwMyZMwG46Qoef/xx/PrXv8bll18eWr+7uxvd3d3+73Xr1gEANnYXAQAGc23TFBwGc0Ad15+IgLt/HPlBrnsoi+g2QmDYLlEySjZoLqdEtsXfXF2h1rjSI4G3KI3q4SpJPKgkMb1DeOj4YnCSj68591pJUhQhVN/uZUKic3ovO/kKG71n+ogYWJKYRgxC/HwyBnOlfPltskMofg2sOxTVd7kTjo7UgXEbTtcG2J3taN+wHuvXUfCOLmzoLsLpLvlpANL6Jfl9T9wL7z/gL6+gwsn/BZkolftD+f4wxvy+Vw+CpKYWAMoRTe739PfTNalUZ25T+2jU8eU8QFH/WwAwvOgfg3v3iBmBfgwA7eK+90LfZaVuN3ecTcCKHXCoCaergFLBhN3poNjB0LkR6Gw30GE5WE+KsEomSEcHeGcXSl0ldHZ1o6O7iGKxhGKp5JYyKdlg3AHjDhybwXEccJT9Zv0UHzBgwAAlHJRQGDYH4UEFiHMGx2ZgcIN75Fp6rjpFYHAHBgwQm4MS12JgGNy97nawn3KHu/vkDA534IDBgbd/pvE5ZQAxuOc/xt39wvDbHojGAwMHh8MYOBi4weEwJ+Ro7jbGU5Dk/StESmxfYhxFzlEEQxfn6OIcRQ4UOUeJ84DPE4Wbv4jCDdE3YACcg3MD3DDAuRHKlO06h5f3wxCdeVsmRl28wc9dnhJXXnklv+aaazjnnD/55JO8paWFW5bFCSH86quvTru7mtHd3c0ppfz+++8PzP/ud7/LDzjgAO02s2fP5t7tyaZsqvv0n//8p2H9Peu72dTIKeu72dRXp0b1XYPz2ujX+++/j5deeglbb701dt1111p2VRU+/vhjbLHFFvi///s/7Lvvvv78efPm4ZZbbsFbb70V2kZ9o/nss88wZswYvP/++4EkmRnKWL9+Pbbaait88MEHfTrNQyOxbt06jB49GmvXrm2Yqpr13fTI+m5lZH23OZH13cpodN9NbW5TMXr06NR+P42AWk+OR9SYA4B8Po98Ph+aP3DgwKwjVsCAAQOya1QBOlNnvZD13eqR9d3KyPpucyLru5XRqL6biiQxxrBo0SLcf//9WLZsGQzDwLhx43Dsscfi5JNPjiQljcTQoUNBKcWKFSsC81euXInhw4f3eHsyZMiQIUOGDJsGElMvzjmOPPJIzJw5Ex999BEmTJiAnXbaCe+99x5OPfVUfO1rX2tkOyNhWRb23HNPLFmyJDB/yZIlAfNbhgwZMmTIkCFDGiRWkhYtWoRnn30Wf/rTn3DggQcGlj355JM46qijcOutt+KUU06peyMr4bzzzsPJJ5+MiRMnYp999sFNN92E999/H9/61rcSbZ/P5zF79mytFJzBRXaNKqM3rlF2Xyoju0aVkfXd5kR2jSqj0dcoseP2YYcdhoMOOgg//OEPtcvnzZuHZ555Bo8//nhdG5gUCxYswPz587F8+XLsvPPOuOqqq3DAAQf0SlsyZMiQIUOGDH0fiUnSiBEjsHjxYuy2227a5a+++iqmTp0a8g3KkCFDhgwZMmToi0jsk7RmzZpYR+jhw4dj7dq1dWlUhgwZMmTIkCFDbyMxSXIcB6YZ7cJEKYVt23VpVIYMGTJkyJAhQ28jseM25xynnnpqpHOUnCQsQ4YMGTJkyJChryMxSZoxY0bFdXojsi1DhgwZMmTIkKEhaEixkz6E66+/no8dO5bn83m+xx578Geffba3m9RjeOaZZ/gRRxzBR44cyQHwBx54ILCcMcZnz57NR44cyQuFAp88eTJ/4403Aut0dXXxc845hw8ZMoS3trby6dOn8w8++KAHz6JxmDdvHp84cSLv168fHzZsGP/qV7/K//nPfwbW6c1rlPXdrO/GoZn77//f3p3HRVXufwD/nGEbZBVQGBQVXHDBQMUFVFzCrdzS0qum0PXWT02UXNKbpWhq7qVZKWag5YI33DJFsQQxriugKIiJKF6FSEXNBZGZ7+8PmSNn5gygAkPyfb9evGbO8zzneb7nmQf4Muecgdcur93SVKe1W6OTpK1bt5KZmRmtW7eO0tLSaPLkyWRlZUVXrlwxdmhVYu/evTRr1iyKjo6W/WZdtGgR2djYUHR0NKWmptLw4cNJpVLR3bt3xTbjxo2jevXqUWxsLCUlJVGPHj3I29ubioqKqvhoKl6fPn0oIiKCzp49SykpKfT6669TgwYN6N69e2IbY80Rr11eu2WpruuX1y6v3bJUp7Vbo5OkDh060Lhx4yRlzZs3p5kzZxopIuPR/WbVaDTk4uJCixYtEssKCgrIzs6O1qxZQ0REt2/fJjMzM9q6davY5tq1a6RQKCgmJqbKYq8qeXl5BIDi4+OJyLhzxGv3KV675VNd1i+v3ad47ZaPMddu5f03w2qusLAQp06dQu/evSXlvXv3RmJiopGiqj6ysrKQm5srmR8LCwt069ZNnJ9Tp07h8ePHkjaurq7w8vJ6Kefwzp07AAAHBwcAxpsjXrul47UrrzqsX167peO1K8+Ya7fGJkk3btyAWq3W++wnZ2dn/kBMQJyD0uYnNzcX5ubmqF27tsE2LwsiwpQpU9ClSxd4eXkBMN4c8dotHa9dfdVl/fLaLR2vXX3GXrvlvrvtZSUIgmSbiPTKarLnmZ+XcQ4nTpyIM2fO4MiRI3p1xpojXrul47X7VHVbv7x2S8dr9yljr90a+06Sk5MTTExM9DLKvLy8Uj9ZvKZwcXEBgFLnx8XFBYWFhXqftP6yzWFISAh2796NQ4cOoX79+mK5seaI127peO1KVaf1y2u3dLx2parD2q2xSZK5uTnatWuH2NhYSXlsbCz8/f2NFFX14e7uDhcXF8n8FBYWIj4+Xpyfdu3awczMTNImJycHZ8+efSnmkIgwceJEbN++Hb/++ivc3d0l9caaI167peO1+0R1XL+8dkvHa/eJarV2n/0685eH9lbU9evXU1paGoWGhpKVlRVdvnzZ2KFVib/++ouSk5MpOTmZANCKFSsoOTlZvBV30aJFZGdnR9u3b6fU1FQaMWKE7C2W9evXp4MHD1JSUhL17NnzpbkVdfz48WRnZ0dxcXGUk5Mjfj148EBsY6w54rXLa7cs1XX98trltVuW6rR2a3SSRPTkQ80aNmxI5ubm1LZtW/EWw5rg0KFDBEDvKygoiIiefliXi4sLWVhYUEBAAKWmpkr6ePjwIU2cOJEcHBzI0tKS+vfvT9nZ2UY4moonNzcAKCIiQmxjzDnitctrtzTVef3y2uW1W5rqtHaF4oAYY4wxxlgJNfaaJMYYY4yx0nCSxBhjjDEmg5MkxhhjjDEZnCQxxhhjjMngJIkxxhhjTAYnSYwxxhhjMjhJYowxxhiTwUkSY4wxxpgMTpIYewkFBwdj8ODBRhn78uXLEAQBKSkpRhm/PARBwM6dOw3Wv+gxdO/eHaGhoc+1b3kVFhaiSZMm+O233yp1HGPSncf27dtj+/btxguI1Timxg6AseoqMTERXbt2Ra9evRATE2PscJ7JypUrURUfph8cHIzbt29LEg43Nzfk5OTAycmp0sd/Xjk5Oahdu3al9b99+3aYmZlVWv8AEB4ejoYNG6Jz586VOk518sknn2DatGkYPHgwFAr+G59VPl5ljBnw3XffISQkBEeOHEF2dnaVjPn48eMK6cfOzg729vYV0tezMjExgYuLC0xNq9/fYIWFhQAAFxcXWFhYVNo4Dg4OsLGxqbT+AeDLL7/Ev/71r0odoyJo57wivP7667hz5w72799fYX0yVhpOkhiTcf/+fWzbtg3jx49H//79ERkZKamPi4uDIAj4+eef4e3tDaVSiY4dOyI1NVVsExkZCXt7e+zcuRPNmjWDUqlEr169cPXqVbFNWFgYfHx88N1338HDwwMWFhYgImRnZ2PQoEGwtraGra0thg0bhj/++AMAcP78edSqVQubN28W+9m+fTuUSqU4vu7ptu7duyMkJAShoaGoXbs2nJ2dER4ejvv37+Odd96BjY0NGjdujH379on7qNVqjB07Fu7u7rC0tISnpydWrlwpiX3Dhg3YtWsXBEGAIAiIi4uTPVUVHx+PDh06wMLCAiqVCjNnzkRRUZEkvkmTJuHDDz+Eg4MDXFxcEBYWVuprVFRUhEmTJsHe3h6Ojo6YMWMGgoKC9I574sSJmDJlCpycnNCrVy8A+qfbjh8/jjZt2kCpVMLX1xfJycmljg0AX3/9NZo2bQqlUglnZ2e8+eabknG1p4m0a0X3Kzg4WGz/008/oV27dlAqlfDw8MDcuXMl86MrKSkJFy9exOuvvy6WFRYWYuLEiVCpVFAqlWjUqBE+++wzsf73339HQEAAlEolWrZsidjYWMk8aOO8ffu2uE9KSgoEQcDly5cBADdv3sSIESNQv3591KpVC61bt8aWLVsksRma87S0NLz22muwtraGs7MzRo8ejRs3boj73b9/H2PGjIG1tTVUKhWWL1+ud9wmJiZ47bXX9MZkrLJwksSYjKioKHh6esLT0xNvv/02IiIiZE9fTZ8+HcuWLcOJEydQt25dDBw4UPJu0IMHD7BgwQJs2LABv/32G+7evYt//OMfkj4uXryIbdu2ITo6WkwsBg8ejFu3biE+Ph6xsbHIzMzE8OHDAQDNmzfHsmXLMGHCBFy5cgXXr1/Hu+++i0WLFqF169YGj2nDhg1wcnLC8ePHERISgvHjx+Ott96Cv78/kpKS0KdPH4wePRoPHjwAAGg0GtSvXx/btm1DWloaZs+ejY8++gjbtm0DAEybNg3Dhg1D3759kZOTg5ycHPj7++uNe+3aNbz22mto3749Tp8+jW+++Qbr16/H/Pnz9eKzsrLCsWPHsGTJEsybNw+xsbEGj2fx4sXYtGkTIiIixLmVu85ow4YNMDU1xW+//Ya1a9fq1d+/fx/9+/eHp6cnTp06hbCwMEybNs3guABw8uRJTJo0CfPmzUNGRgZiYmIQEBAg29bf31+cn5ycHPz6669QKpVi+/379+Ptt9/GpEmTkJaWhrVr1yIyMhILFiwwOP7hw4fRrFkz2NraimWrVq3C7t27sW3bNmRkZOCHH35Ao0aNADx5LYcMGQITExMcPXoUa9aswYwZM0o9RjkFBQVo164d9uzZg7Nnz+K9997D6NGjcezYMUk73TnPyclBt27d4OPjg5MnTyImJgZ//PEHhg0bJu4zffp0HDp0CDt27MCBAwcQFxeHU6dO6cXQoUMHJCQkPHPsjD0XYozp8ff3py+++IKIiB4/fkxOTk4UGxsr1h86dIgA0NatW8WymzdvkqWlJUVFRRERUUREBAGgo0ePim3S09MJAB07doyIiObMmUNmZmaUl5cntjlw4ACZmJhQdna2WHbu3DkCQMePHxfLXn/9deratSu9+uqr1KtXL9JoNGJdUFAQDRo0SNzu1q0bdenSRdwuKioiKysrGj16tFiWk5NDAOi///2vwXmZMGECDR061OA4RERZWVkEgJKTk4mI6KOPPiJPT09JfF999RVZW1uTWq2WjY+IqH379jRjxgyDsTg7O9PSpUslx9SgQQO94/bx8dHbFwDt2LGDiIjWrl1LDg4OdP/+fbH+m2++kRyDrujoaLK1taW7d+/K1nfr1o0mT56sV37jxg1q3LgxTZgwQSzr2rUrLVy4UNLu+++/J5VKJds3EdHkyZOpZ8+ekrKQkBDq2bOnZJ619u/fTyYmJnT16lWxbN++fZJ50K7p/Px8sU1ycjIBoKysLIOxvPbaazR16lRxW27OP/nkE+rdu7ek7OrVqwSAMjIy6K+//iJzc3PZ7yfdedy1axcpFApx7TBWmarfRQOMGVlGRgaOHz8u3kVjamqK4cOH47vvvkNgYKCkrZ+fn/jcwcEBnp6eSE9PF8tMTU3h6+srbjdv3hz29vZIT09Hhw4dAAANGzZEnTp1xDbp6elwc3ODm5ubWNayZUtxv/bt2wN4cs1Us2bNoFAocPbsWQiCUOpxvfLKK+JzExMTODo6St55cnZ2BgDk5eWJZWvWrMG3336LK1eu4OHDhygsLISPj0+p4+hKT0+Hn5+fJL7OnTvj3r17+N///ocGDRroxQcAKpVKEktJd+7cwR9//CHOofaY2rVrB41GI2lbcv4Nxeft7Y1atWqJZSVfVzm9evVCw4YN4eHhgb59+6Jv37544403JH3oevz4MYYOHYoGDRpITlueOnUKJ06ckLxzpFarUVBQgAcPHsj2+fDhQyiVSklZcHAwevXqBU9PT/Tt2xf9+/dH7969xWNs0KAB6tevX+5jlKNWq7Fo0SJERUXh2rVrePToER49egQrKytJO905P3XqFA4dOgRra2u9PjMzM8W1Jff9pMvS0hIajQaPHj2CpaXlMx8DY8+CkyTGdKxfvx5FRUWoV6+eWEZEMDMzQ35+fpl3RekmK3LJS8ky3V8wRCS7j2756dOncf/+fSgUCuTm5sLV1bXUuHTvthIEQVKm7VubZGzbtg0ffPABli9fDj8/P9jY2GDp0qV6p1bKInc8VHzqsmS5XHy6CY8uQ/2WpDu/cvE9KxsbGyQlJSEuLg4HDhzA7NmzERYWhhMnThi8YH78+PHIzs7GiRMnJBe1azQazJ07F0OGDNHbRzcR0nJycpJc/wYAbdu2RVZWFvbt24eDBw9i2LBhCAwMxI8//ih7jLpzp71brGRb3RsJli9fjs8//xxffPEFWrduDSsrK4SGhupdnK075xqNBgMGDMDixYv14lCpVPj9999lj1POrVu3UKtWLU6QWJXga5IYK6GoqAgbN27E8uXLkZKSIn6dPn0aDRs2xKZNmyTtjx49Kj7Pz8/HhQsX0Lx5c0l/J0+eFLczMjJw+/ZtSRtdLVu2RHZ2tuQC77S0NNy5cwctWrQA8OQXRXBwMGbNmoV33nkHo0aNwsOHD1/4+EtKSEiAv78/JkyYgDZt2qBJkybIzMyUtDE3N4darS61n5YtWyIxMVHyyzcxMRE2NjaSRPRZ2NnZwdnZGcePHxfL1Gp1uS64lovv9OnTkvkr+boaYmpqisDAQCxZsgRnzpzB5cuX8euvv8q2XbFiBaKiorB79244OjpK6tq2bYuMjAw0adJE78vQbe5t2rTB+fPn9ZIfW1tbDB8+HOvWrUNUVBSio6Nx69YtcU1dv35dbPvf//5Xsq/23cycnByxTPdzohISEjBo0CC8/fbb8Pb2hoeHR7kSnLZt2+LcuXNo1KiR3jFaWVmhSZMmMDMzk/1+0nX27Fm0bdu2zDEZqwicJDFWwp49e5Cfn4+xY8fCy8tL8vXmm29i/fr1kvbz5s3DL7/8grNnzyI4OBhOTk6Su6vMzMwQEhKCY8eOISkpCe+88w46deokOU2kKzAwEK+88gpGjRqFpKQkHD9+HGPGjEG3bt3E0xjjxo2Dm5sbPv74Y6xYsQJEVObFxs+qSZMmOHnyJPbv348LFy7gk08+wYkTJyRtGjVqhDNnziAjIwM3btyQ/QiDCRMm4OrVqwgJCcH58+exa9cuzJkzB1OmTHmhz7oJCQnBZ599hl27diEjIwOTJ09Gfn5+macddY0cORIKhQJjx45FWloa9u7di2XLlpW6z549e7Bq1SqkpKTgypUr2LhxIzQajezpoYMHD+LDDz/EsmXL4OTkhNzcXOTm5uLOnTsAgNmzZ2Pjxo0ICwvDuXPnkJ6ejqioKHz88ccGx+/Rowfu37+Pc+fOiWWff/45tm7divPnz+PChQv4z3/+AxcXF9jb2yMwMBCenp4YM2YMTp8+jYSEBMyaNUvSZ5MmTeDm5oawsDBcuHABP//8s94dZk2aNEFsbCwSExORnp6O//u//0Nubm6Zc/z+++/j1q1bGDFiBI4fP45Lly7hwIED+Oc//wm1Wg1ra2uMHTsW06dPl3w/ya2PhIQE8TQiY5WNkyTGSli/fj0CAwNhZ2enVzd06FCkpKQgKSlJLFu0aBEmT56Mdu3aIScnB7t374a5ublYX6tWLcyYMQMjR46En58fLC0tsXXr1lJj0N6WXbt2bQQEBCAwMBAeHh6IiooCAGzcuBF79+7F999/D1NTU9SqVQubNm3Ct99+i71791bQTDxJxIYMGYLhw4ejY8eOuHnzJiZMmCBp8+6778LT0xO+vr6oU6eO7Kc/16tXD3v37sXx48fh7e2NcePGYezYsaUmAeUxY8YMjBgxAmPGjIGfnx+sra3Rp08fg6eoDLG2tsZPP/2EtLQ0tGnTBrNmzZI9LVSSvb09tm/fjp49e6JFixZYs2YNtmzZglatWum1PXLkCNRqNcaNGweVSiV+TZ48GQDQp08f7NmzB7GxsWjfvj06deqEFStWoGHDhgbHd3R0xJAhQyTvbFpbW2Px4sXw9fVF+/btcfnyZezduxcKhQIKhQI7duzAo0eP0KFDB/zrX//Su3vOzMwMW7Zswfnz5+Ht7Y3Fixfr3YH4ySefoG3btujTpw+6d+8OFxeXcn2yu6urK3777Teo1Wr06dMHXl5emDx5Muzs7MREaOnSpQgICMDAgQMRGBiILl26oF27dpJ+rl27hsTERLzzzjtljslYRRDoeU7IM1bDxcXFoUePHsjPzzd4DUpkZCRCQ0MlnzvDKo9Go0GLFi0wbNgwfPrpp8YOp9KlpqYiMDAQFy9efO4PrhQEATt27DDav7B5VtOnT8edO3cQHh5u7FBYDcEXbjPG/pauXLmCAwcOoFu3bnj06BFWr16NrKwsjBw50tihVYnWrVtjyZIluHz5cqmfj/UyqVu3boWfVmasNJwkMcb+lhQKBSIjIzFt2jQQEby8vHDw4EHx4vaaICgoyNghVKnp06cbOwRWw/DpNsYYY4wxGXzhNmOMMcaYDE6SGGOMMcZkcJLEGGOMMSaDkyTGGGOMMRmcJDHGGGOMyeAkiTHGGGNMBidJjDHGGGMyOElijDHGGJPBSRJjjDHGmAz+tyTFCgoKUFhYaOwwGGOMMfYMzM3NoVQqK6VvTpLwJEFytLTGA6iNHQpjjDHGnoGLiwuysrIqJVHiJAlAYWEhHkCNUagHy+IzkCaCAABQPHkQt01KbJvo1BVvyrTVfSyrb53y4n4VJdrJxVOyjVBcoRAbPDkuQSEtFxQK6baJ/P6Ctn8ThUwbhSTwpzEY6ltbDmlMxbHotnsao6LEceg8mkgfFQbKBcFAe535kdtPMDGRr9MdSyGdW0F7oOKjtr2B8uJHCE/rxTKDfUjrhaeTqxODwsD+Jjr7l4ih+PXU9kWCQrIt1isM1St09hfk95frvyL6KNFe+58qNcVPtP+4UlP8RPuvLDUl/qOlXh2022XUi/vLj6Ut0BQ/KdkfGax78kStkW5rdOLWrVfrxKbWaPvV1j9tr63TyNQBgEYjHUtvjOIK7Z+cBuv1HvXLxLYkv0/RM7YXH0mjF0uRBqXuoxs36RyPdpt02hcPpd/+6QQ+XSN6fUCnHLJ9iO119tftTy5G0qiLy7SPTwYhdfE26dbLt9cYqIdePxqxvuTzko9iX8ULudQY1I+Rm7YNhYWFnCRVNnMoYC5IkyS9hKWUJEl7gVd59i19u+x2ZSZJYuKh8wtbN/kxmCTplIvJleEkSdxWGOijHH3LtSuZJBlOUKQJjMEkyVBSVUa9oFBAUd4kSS+RK2+SpJOgKEpLkkx0+jCQJFVEDHoJx4smSQb2161XlJYklbOPF0iSDNZBu13eekNjFW9XQJIkJiBl1qO43nAiVN4kSbdcLwkyUG8wcdFQqXVyjyYGk6Cy9tdIthUaglDiudyjtq22nV4y9JxJEmkIgm4yozuWTpKkG4PY3kA/hmKEbJKkTVDKSo50kiFD9QppveRRmwXq1AnabUFnH0H/Uft9VVn4wm3GGGOMMRmcJDHGGGOMyeAkiTHGGGNMBidJjDHGGGMyOElijDHGGJPBSRJjjDHGmAxOkhhjjDHGZHCSxBhjjDEmg5MkxhhjjDEZnCQxxhhjjMngJIkxxhhjTAYnSYwxxhhjMjhJYowxxhiTwUkSY4wxxpgMTpIYY4wxxmRwksQYY4wxJoOTJMYYY4wxGZwkMcYYY4zJ4CSJMcYYY0wGJ0mMMcYYYzI4SWKMMcYYk2Fq7ACqk0JoYEJPnptAAPA0i9Rum5TYNtGpEwy21X0sq2+d8uKYFELxtgCYlHj+5FGQtBF0tiFQcYzasYvbEUm3xUeNtFxTvB8pIJB236dlAADSiUHzpFyhkfbxtLw4NEXxfuon5ULxQQkmxduK4vYmCkCnTHw0UUv2UYjlOo+CfLlC7FcwuJ9gYiJfpzuWtg8xxuJXVHzUtjdQXvwI4Wm9WGawD2m9OKagG4PCwP4mOvuXiEFcQ0/KSFBItsV6haF6hc7+gvz+cv1XRB8l2hcvd2iKnxRvQlP8hIrLtduyddBul1Ev7i8/lrZAU/ykZH9ksO7JE7VGuq3RiVu3Xq0Tm1qj7Vdb/7S9tk4jUwcAGo10LL0xiivUKKNe71G/TGxL8vsUPWN78ZE0erEUaVDqPrpxk87xaLdJp33xUPrtn07g0zWi1wd0yiHbh9heZ3/d/uRiJI26uEz7+GQQUhdvk269fHuNgXro9aMR60s+l30sXsilxqB+jMrESRKeLBxra2tsunetRKHOI2OMMcaqHWtrazEBrGicJOHJuy737t3D1atXYWtrW2nj3L17F25ubpU+Dns+/PowLV4LhvHcVD2ec8O0c6M9e1LROEkqwdbWtkoWYFWNw54Pvz5Mi9eCYTw3VY/nvOrxhduMMcYYYzI4SWKMMcYYk8FJEgALCwvMmTMHFhYWL8U47Pnw68O0eC0YxnNT9XjODavsuRGosi4JZ4wxxhj7G+N3khhjjDHGZHCSxBhjjDEmg5MkxhhjjDEZnCQxxhhjjMmo8UnS119/DXd3dyiVSrRr1w4JCQlV1l9OTg5GjhwJT09PKBQKhIaGvtDYrGzP8vrExcVBEAS9r/Pnz1dhxKwqHT58GAMGDICrqysEQcDOnTuNHVK18Nlnn6F9+/awsbFB3bp1MXjwYGRkZBg7rJdeWFiY3s8fFxcXY4dlFGV9bxIRwsLC4OrqCktLS3Tv3h3nzp174XFrdJIUFRWF0NBQzJo1C8nJyejatSv69euH7OzsKunv0aNHqFOnDmbNmgVvb+8XORRWDs/7emdkZCAnJ0f8atq0aRVFzKra/fv34e3tjdWrVxs7lGolPj4e77//Po4ePYrY2FgUFRWhd+/euH//vrFDe+m1atVK8vMnNTXV2CEZRVnfm0uWLMGKFSuwevVqnDhxAi4uLujVqxf++uuvFxuYarAOHTrQuHHjJGXNmzenmTNnVnl/3bp1o8mTJz/XuKx8nvX1OXToEAGg/Pz8KoiOVTcAaMeOHcYOo1rKy8sjABQfH2/sUF5qc+bMIW9vb2OHUe3ofm9qNBpycXGhRYsWiWUFBQVkZ2dHa9aseaGxauw7SYWFhTh16hR69+4tKe/duzcSExON3h+rWC/y+rRp0wYqlQqvvvoqDh06VJlhMva3cOfOHQCAg4ODkSN5+f3+++9wdXWFu7s7/vGPf+DSpUvGDqnaycrKQm5uruTnu4WFBbp16/bCv39rbJJ048YNqNVqODs7S8qdnZ2Rm5tr9P5YxXqe10elUiE8PBzR0dHYvn07PD098eqrr+Lw4cNVETJj1RIRYcqUKejSpQu8vLyMHc5LrWPHjti4cSP279+PdevWITc3F/7+/rh586axQ6tWtD/DK+P3r+kL7f0SEARBsk1EemXG7I9VrGd5fTw9PeHp6Slu+/n54erVq1i2bBkCAgIqNU7GqquJEyfizJkzOHLkiLFDeen169dPfN66dWv4+fmhcePG2LBhA6ZMmWLEyKqnyvj9W2PfSXJycoKJiYlelpmXl6eXjRqjP1axKur16dSpE37//feKDo+xv4WQkBDs3r0bhw4dQv369Y0dTo1jZWWF1q1b888gHdo7/irj92+NTZLMzc3Rrl07xMbGSspjY2Ph7+9v9P5Yxaqo1yc5ORkqlaqiw2OsWiMiTJw4Edu3b8evv/4Kd3d3Y4dUIz169Ajp6en8M0iHu7s7XFxcJD/fCwsLER8f/8K/f2v06bYpU6Zg9OjR8PX1hZ+fH8LDw5GdnY1x48ZVSn///ve/ce3aNWzcuFHcJyUlBQBw7949/Pnnn0hJSYG5uTlatmz5wsfHpJ719fniiy/QqFEjtGrVCoWFhfjhhx8QHR2N6OhoYx4Gq0T37t3DxYsXxe2srCykpKTAwcEBDRo0MGJkxvX+++9j8+bN2LVrF2xsbMS/2O3s7GBpaWnk6F5e06ZNw4ABA9CgQQPk5eVh/vz5uHv3LoKCgowdWpUr63szNDQUCxcuRNOmTdG0aVMsXLgQtWrVwsiRI19s4Be6N+4l8NVXX1HDhg3J3Nyc2rZt+8K3tJbWX1BQEHXr1k3SHoDeV8OGDV8oBmbYs7w+ixcvpsaNG5NSqaTatWtTly5d6OeffzZC1KyqaD/2QfcrKCjI2KEZldycAKCIiAhjh/ZSGz58OKlUKjIzMyNXV1caMmQInTt3zthhGUVZ35sajYbmzJlDLi4uZGFhQQEBAZSamvrC4wpERC+WZjHGGGOMvXxq7DVJjDHGGGOl4SSJMcYYY0wGJ0mMMcYYYzI4SWKMMcYYk8FJEmOMMcaYDE6SGGOMMcZkcJLEGGOMMSaDkyTGGGOMMRmcJDFmJJcvX4YgCOK/pjF2P39nYWFh8PHxKbPdJ598gvfee0/cJiK89957cHBwqDFzWN65qmqrV6/GwIEDjR0GYxKcJDH2HIKDgyEIAgRBgKmpKRo0aIDx48cjPz+/0scdPHiwpMzNzQ05OTnw8vKq1LEPHTqEHj16wMHBAbVq1ULTpk0RFBSEoqKiSh23ovzxxx9YuXIlPvroI7EsJiYGkZGR2LNnT4XNoSAI2Llz5wv3U9O8++67OHHiBI4cOWLsUBgTcZLE2HPq27cvcnJycPnyZXz77bf46aefMGHChCqPw8TEBC4uLjA1rbz/V33u3Dn069cP7du3x+HDh5Gamoovv/wSZmZm0Gg0lTZuRVq/fj38/PzQqFEjsSwzMxMqlQr+/v6VPofP6vHjx8YOoUoQEYqKimBhYYGRI0fiyy+/NHZIjIk4SWLsOVlYWMDFxQX169dH7969MXz4cBw4cEDSJiIiAi1atIBSqUTz5s3x9ddfG+xPrVZj7NixcHd3h6WlJTw9PbFy5UqxPiwsDBs2bMCuXbvEd7Hi4uIkp9s0Gg3q16+PNWvWSPpOSkqCIAi4dOkSAODOnTt47733ULduXdja2qJnz544ffq0wdhiY2OhUqmwZMkSeHl5oXHjxujbty++/fZbmJubAwAiIyNhb2+PnTt3olmzZlAqlejVqxeuXr0q6eunn35Cu3btoFQq4eHhgblz50rejSpPbIsWLYKzszNsbGwwduxYFBQUGIxda+vWrZLTOcHBwQgJCUF2djYEQRCTp5iYGHTp0gX29vZwdHRE//79kZmZKe5XWFiIiRMnQqVSQalUolGjRvjss88AQOzjjTfekPRZnuMWBAFr1qzBoEGDYGVlhfnz55d5TIbExcWhQ4cOsLKygr29PTp37owrV67IttVoNJg3bx7q168PCwsL+Pj4ICYmRqwfOnQoQkJCxO3Q0FAIgoBz584BAIqKimBjY4P9+/cDeJL0LFmyBB4eHrC0tIS3tzd+/PFHSWyCIGD//v3w9fWFhYUFEhISAAADBw7Ezp078fDhw+c+dsYq1Av/i1zGaqCgoCAaNGiQuJ2ZmUktW7YkZ2dnsSw8PJxUKhVFR0fTpUuXKDo6mhwcHCgyMpKIiLKysggAJScnExFRYWEhzZ49m44fP06XLl2iH374gWrVqkVRUVFERPTXX3/RsGHDqG/fvpSTk0M5OTn06NEjvX6mTp1KXbp0kcQ7depU8vPzI6In/y27c+fONGDAADpx4gRduHCBpk6dSo6OjnTz5k3Z492yZQtZWFhQfHy8wTmJiIggMzMz8vX1pcTERDp58iR16NCB/P39xTYxMTFka2tLkZGRlJmZSQcOHKBGjRpRWFhYuWOLiooic3NzWrduHZ0/f55mzZpFNjY25O3tbTC2W7dukSAIdPToUbHs9u3bNG/ePKpfvz7l5ORQXl4eERH9+OOPFB0dTRcuXKDk5GQaMGAAtW7dmtRqNRERLV26lNzc3Ojw4cN0+fJlSkhIoM2bNxMRUV5eHgGgiIgISZ9lHTcREQCqW7curV+/njIzM+ny5csGj6c0jx8/Jjs7O5o2bRpdvHiR0tLSKDIykq5cuUJERHPmzJHM1YoVK8jW1pa2bNlC58+fpw8//JDMzMzowoULRES0atUq8vLyEtv7+PiQk5MTffXVV0RElJiYSKampvTXX38REdFHH31EzZs3p5iYGMrMzKSIiAiysLCguLg4Inr639xfeeUVOnDgAF28eJFu3LhBRET37t0jQRDEtowZGydJjD2HoKAgMjExISsrK1IqlQSAANCKFSvENm5ubuIvT61PP/1UTFZ0kxs5EyZMoKFDh0rGLZmcyfWTlJREgiCIv2TVajXVq1dP/KX2yy+/kK2tLRUUFEj6ady4Ma1du1Y2jqKiIgoODiYA5OLiQoMHD6Yvv/yS7ty5I7aJiIggAJJEJD09nQDQsWPHiIioa9eutHDhQknf33//PalUqnLH5ufnR+PGjZPUd+zYsdQkKTk5mQBQdna2pPzzzz+nhg0bGtyP6Gnik5qaSkREISEh1LNnT9JoNLLtAdCOHTskZWUdt3a/0NDQUmMpj5s3bxIAg4mGbpLk6upKCxYskLRp3749TZgwgYiIzpw5Q4Ig0J9//km3bt0iMzMzmj9/Pr311ltERLRw4ULq2LEjET1JcpRKJSUmJkr6Gzt2LI0YMYKIniZJO3fulI2vdu3a4h8SjBkbn25j7Dn16NEDKSkpOHbsGEJCQtCnTx/xtMSff/6Jq1evYuzYsbC2tha/5s+fLzl1o2vNmjXw9fVFnTp1YG1tjXXr1iE7O/uZ4mrTpg2aN2+OLVu2AADi4+ORl5eHYcOGAQBOnTqFe/fuwdHRURJbVlaWwdhMTEwQERGB//3vf1iyZAlcXV2xYMECtGrVCjk5OWI7U1NT+Pr6itvNmzeHvb090tPTxbHnzZsnGffdd99FTk4OHjx4UK7Y0tPT4efnJ4lPd1uX9vSNUqksc/4yMzMxcuRIeHh4wNbWFu7u7gAgvg7BwcFISUmBp6cnJk2apHeKVU5Zx61Vcu7kLFy4UNKH3NpwcHBAcHAw+vTpgwEDBmDlypWS16iku3fv4vr16+jcubOkvHPnzuJr5uXlBUdHR8THxyMhIQHe3t4YOHAg4uPjATw5fdatWzcAQFpaGgoKCtCrVy9JnBs3btRbW4aO1dLSUjInjBlT9blKkbG/GSsrKzRp0gQAsGrVKvTo0QNz587Fp59+Kl7MvG7dOnTs2FGyn4mJiWx/27ZtwwcffIDly5fDz88PNjY2WLp0KY4dO/bMsY0aNQqbN2/GzJkzsXnzZvTp0wdOTk4AnlyDolKpEBcXp7efvb19qf3Wq1cPo0ePxujRozF//nw0a9YMa9aswdy5c8U2giDo7act02g0mDt3LoYMGaLXRqlUvlBspdEee35+PurUqVNq2wEDBsDNzQ3r1q2Dq6srNBoNvLy8UFhYCABo27YtsrKysG/fPhw8eBDDhg1DYGCg5LobXWUdt5aVlVWpsY0bN05MdgHA1dVVtl1ERAQmTZqEmJgYREVF4eOPP0ZsbCw6deok2173NSMisUwQBAQEBCAuLg7m5ubo3r07vLy8oFarkZqaisTERISGhorHCQA///wz6tWrJ+nTwsJCsm3oWG/dulXma8RYVeEkibEKMmfOHPTr1w/jx4+Hq6sr6tWrh0uXLmHUqFHl2j8hIQH+/v6SO+R0//o2NzeHWq0us6+RI0fi448/xqlTp/Djjz/im2++Eevatm2L3NxcmJqaSi4sfla1a9eGSqXC/fv3xbKioiKcPHkSHTp0AABkZGTg9u3baN68uTh2RkaGmFzqKk9sLVq0wNGjRzFmzBix7OjRo6XG2rhxY9ja2iItLQ3NmjUz2O7mzZtIT0/H2rVr0bVrVwCQvSXd1tYWw4cPx/Dhw/Hmm2+ib9++uHXrFhwcHGBmZqb3GpV13OXl4OAABweHcrVt06YN2rRpg3//+9/w8/PD5s2b9ZIkW1tbuLq64siRIwgICBDLExMTxdcQALp3747w8HCYm5tj3rx5EAQBXbt2xbJly/Dw4UPxnaiWLVvCwsIC2dnZ4rtLzyIzMxMFBQVo06bNM+/LWGXgJImxCtK9e3e0atUKCxcuxOrVqxEWFoZJkybB1tYW/fr1w6NHj3Dy5Enk5+djypQpevs3adIEGzduxP79++Hu7o7vv/8eJ06cEE/3AE/untq/fz8yMjLg6OgIOzs72Vjc3d3h7++PsWPHoqioCIMGDRLrAgMD4efnh8GDB2Px4sXw9PTE9evXsXfvXgwePFj2NMjatWuRkpKCN954A40bN0ZBQQE2btyIc+fOSW7ZNjMzQ0hICFatWgUzMzNMnDgRnTp1En/hzp49G/3794ebmxveeustKBQKnDlzBqmpqZg/f365Yps8eTKCgoLg6+uLLl26YNOmTTh37hw8PDwMvjYKhQKBgYE4cuSI3udMlVS7dm04OjoiPDwcKpUK2dnZmDlzpqTN559/DpVKBR8fHygUCvznP/+Bi4uL+E5Xo0aN8Msvv6Bz586wsLBA7dq1yzzuipSVlYXw8HAMHDgQrq6uyMjIwIULFyRJZUnTp0/HnDlz0LhxY/j4+CAiIgIpKSnYtGmT2KZ79+6YPHkyTE1NxeSxe/fumDp1Ktq2bQtbW1sAgI2NDaZNm4YPPvgAGo0GXbp0wd27d5GYmAhra2sEBQWVGntCQgI8PDzQuHHjCpoNxl6QsS+KYuzvSO4CaiKiTZs2kbm5uXiB8KZNm8jHx4fMzc2pdu3aFBAQQNu3byci/QuuCwoKKDg4mOzs7Mje3p7Gjx9PM2fOlFxkm5eXR7169SJra2sCQIcOHTJ4AfhXX31FAGjMmDF6cd69e5dCQkLI1dWVzMzMyM3NjUaNGqV3YbNWUlISvf322+Tu7k4WFhbk6OhIAQEBtHv3brFNREQE2dnZUXR0NHl4eJC5uTn17NlT7y6tmJgY8vf3J0tLS7K1taUOHTpQeHj4M8W2YMECcnJyImtrawoKCqIPP/yw1Au3tePWq1dPvEuNSP7C7djYWGrRogVZWFjQK6+8QnFxcZKLscPDw8nHx4esrKzI1taWXn31VUpKShL33717NzVp0oRMTU0lfZd13JC54Pt55Obm0uDBg0mlUpG5uTk1bNiQZs+eLR637oXbarWa5s6dS/Xq1SMzMzPy9vamffv2SfrUaDRUp04d8vX1Fcu0F8NPmzZNr+3KlSvJ09OTzMzMqE6dOtSnTx/xzkjthdv5+fl6sffu3Zs+++yzF54DxiqKQERkvBSNMfayiIyMRGhoKG7fvm3sUGQRETp16oTQ0FCMGDHC2OEwHWfPnsWrr76KCxcuGHyHlLGqxne3McZqBEEQEB4e/rf5Nyo1zfXr17Fx40ZOkFi1wtckMcZqDG9vb3h7exs7DCajd+/exg6BMT18uo0xxhhjTAafbmOMMcYYk8FJEmOMMcaYDE6SGGOMMcZkcJLEGGOMMSaDkyTGGGOMMRmcJDHGGGOMyeAkiTHGGGNMBidJjDHGGGMy/h+uEWkKRqZQKgAAAABJRU5ErkJggg==", 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" ] @@ -464,10 +605,12 @@ } ], "source": [ - "#| label: fig:multivariate_speed\n", + "# | label: fig:multivariate_speed_3d\n", "\n", "fig, ax = plt.subplots(1, 3, sharey=True)\n", "\n", + "fig.suptitle(\"Benchmarks for 3D Interpolation\", y=0.75)\n", + "\n", "\n", "ax[0].imshow(\n", " scipy,\n", @@ -523,18 +666,16 @@ "\n", "For `backend=\"numba\"`, `MultivariateInterp` is slightly faster when the number of data points with known function value are greater than the number of approximation points that need to be interpolated. However, `backend='parallel'` still suffers from the high overhead when the number of approximation points is small.\n", "\n", - "For `backend=\"cupy\"`, `MultivariateInterp` is much slower when the number of data points with known function value are small. This is because of the overhead of copying the data to the GPU. However, `backend='numba'` is significantly faster for any other case when the number of approximation points is large regardless of the number of data points.\n" + "For `backend=\"cupy\"`, `MultivariateInterp` is much slower when the number of data points with known function value are small. This is because of the overhead of copying the data to the GPU. However, `backend='numba'` is significantly faster for any other case when the number of approximation points is large regardless of the number of data points.\n", + "\n", + "\n", + "\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { "jupytext": { - "formats": "ipynb,py:percent" + "formats": "ipynb,md" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", diff --git a/papers/alan_lujan/notebooks/Multivariate_Interpolation.md b/papers/alan_lujan/notebooks/Multivariate_Interpolation.md index d52cbf32f0..7dc0f060e7 100644 --- a/papers/alan_lujan/notebooks/Multivariate_Interpolation.md +++ b/papers/alan_lujan/notebooks/Multivariate_Interpolation.md @@ -1,6 +1,7 @@ --- jupyter: jupytext: + formats: ipynb,md text_representation: extension: .md format_name: markdown @@ -34,8 +35,8 @@ Suppose we are trying to approximate the following function at a set of points: ```python -def squared_coords(x, y): - return x**2 + y**2 +def squared_coords(*x): + return sum(xi**2 for xi in x) ``` Our points will lie on a regular or rectilinear grid. A rectilinear grid may not be evenly spaced, but it can be reproduced by the cross product of $n$ 1-dimensional vectors. For example, let's assume we know the value of the function at the following points: @@ -64,6 +65,8 @@ We can use scipy's `RegularGridInterpolator` to interpolate the function at thes ```python +# | label: fig:multivariate_regular + interp = RegularGridInterpolator([x_grid, y_grid], z_mat) z_interp = interp(np.column_stack((x_new.ravel(), y_new.ravel()))).reshape(x_new.shape) @@ -81,6 +84,8 @@ Here we introduce `MultivariateInterp`, which brings additional features and spe ```python +# | label: fig:multivariate_interp + mult_interp = MultivariateInterp(z_mat, [x_grid, y_grid]) z_mult_interp = mult_interp(x_new, y_new) @@ -108,7 +113,7 @@ z_gpu_interp = gpu_interp(x_new, y_new).get() # Get the result from GPU z_gpu_interp = gpu_interp(x_new, y_new).get() # Get the result from GPU ``` -We can test the results of `MultivariateInterp` against `RegularGridInterpolator`, and we see that the results are almost identical. +We can test the results of `MultivariateInterp` against `RegularGridInterpolator`, and we see that the results are almost identical. ```python @@ -120,7 +125,7 @@ To experiment with `MultivariateInterp` and evaluate the conditions which make i ```python n = 35 -grid_max = 300 +grid_max = 500 grid = np.linspace(10, grid_max, n, dtype=int) fast = np.empty((n, n)) scipy = np.empty_like(fast) @@ -134,18 +139,18 @@ We will use the following function to time the execution of the interpolation. ```python -def timeit(interp, x, y, min_time=1e-6): +def timeit(interp, *coords): if isinstance(interp, RegularGridInterpolator): start = time() - points = np.column_stack((x.ravel(), y.ravel())) - interp(points).reshape(x.shape) + points = np.column_stack([coord.ravel() for coord in coords]) + interp(points).reshape(coords[0].shape) else: interp.compile() start = time() - interp(x, y) + interp(*coords) elapsed_time = time() - start - return max(elapsed_time, min_time) + return max(elapsed_time, 1e-6) ``` For different number of data points and approximation points, we can time the interpolation on different backends and use the results of `RegularGridInterpolator` to normalize the results. This will give us a direct comparison of the speed of `MultivariateInterp` and `RegularGridInterpolator`. @@ -175,8 +180,119 @@ for i, j in product(range(n), repeat=2): ``` ```python +# | label: fig:multivariate_speed_2d + +fig, ax = plt.subplots(1, 3, sharey=True) + + +ax[0].imshow( + scipy, + cmap="RdBu", + origin="lower", + norm=colors.SymLogNorm(1, vmin=0, vmax=10), + interpolation="bicubic", + extent=[0, grid_max, 0, grid_max], +) +ax[0].set_title("scipy") + + +ax[1].imshow( + parallel, + cmap="RdBu", + origin="lower", + norm=colors.SymLogNorm(1, vmin=0, vmax=10), + interpolation="bicubic", + extent=[0, grid_max, 0, grid_max], +) +ax[1].set_title("numba") + +cbar = ax[2].imshow( + gpu, + cmap="RdBu", + origin="lower", + norm=colors.SymLogNorm(1, vmin=0, vmax=10), + interpolation="bicubic", + extent=[0, grid_max, 0, grid_max], +) +ax[2].set_title("cupy") + + +cbar = fig.colorbar( + cbar, + ax=ax, + label="Relative Speed (faster - slower)", + location="bottom", +) +cbar.set_ticks([0, 0.1, 0.5, 1, 2, 5, 10]) +cbar.set_ticklabels(["0", "0.1", "0.5", "1", "2", "5", "10"]) +ax[0].set_ylabel("Data grid size (squared)") +ax[1].set_xlabel("Approximation grid size (squared)") + +fig.suptitle("Benchmarks for 2D Interpolation", y=0.75) +``` + +```python +n = 20 +grid_max = 200 +grid = np.linspace(10, grid_max, n, dtype=int) +fast = np.empty((n, n)) +scipy = np.empty_like(fast) +parallel = np.empty_like(fast) +gpu = np.empty_like(fast) +``` + +```python +for i, j in product(range(n), repeat=2): + data_grid = np.linspace(0, 10, grid[i]) + x_cross, y_cross, w_cross = np.meshgrid( + data_grid, + data_grid, + data_grid, + indexing="ij", + ) + z_cross = squared_coords(x_cross, y_cross, w_cross) + + approx_grid = np.linspace(0, 10, grid[j]) + x_approx, y_approx, w_approx = np.meshgrid( + approx_grid, + approx_grid, + approx_grid, + indexing="ij", + ) + + fast_interp = RegularGridInterpolator([data_grid, data_grid, data_grid], z_cross) + time_norm = timeit(fast_interp, x_approx, y_approx, w_approx) + fast[i, j] = time_norm + + scipy_interp = MultivariateInterp( + z_cross, + [data_grid, data_grid, data_grid], + backend="scipy", + ) + scipy[i, j] = timeit(scipy_interp, x_approx, y_approx, w_approx) / time_norm + + par_interp = MultivariateInterp( + z_cross, + [data_grid, data_grid, data_grid], + backend="numba", + ) + parallel[i, j] = timeit(par_interp, x_approx, y_approx, w_approx) / time_norm + + gpu_interp = MultivariateInterp( + z_cross, + [data_grid, data_grid, data_grid], + backend="cupy", + ) + gpu[i, j] = timeit(gpu_interp, x_approx, y_approx, w_approx) / time_norm +``` + +```python +# | label: fig:multivariate_speed_3d + fig, ax = plt.subplots(1, 3, sharey=True) +fig.suptitle("Benchmarks for 3D Interpolation", y=0.75) + ax[0].imshow( scipy, @@ -232,3 +348,4 @@ For `backend="cupy"`, `MultivariateInterp` is much slower when the number of dat + diff --git a/papers/alan_lujan/notebooks/Multivariate_Interpolation_with_Derivatives.ipynb b/papers/alan_lujan/notebooks/Multivariate_Interpolation_with_Derivatives.ipynb index 4fa07a40e0..4cc4e09e64 100644 --- a/papers/alan_lujan/notebooks/Multivariate_Interpolation_with_Derivatives.ipynb +++ b/papers/alan_lujan/notebooks/Multivariate_Interpolation_with_Derivatives.ipynb @@ -102,7 +102,7 @@ } ], "source": [ - "#| label: fig:multivariate\n", + "# | label: fig:multivariate\n", "\n", "mult_interp = MultivariateInterp(z_mat, [x_grid, y_grid], backend=\"cupy\")\n", "z_mult_interp = mult_interp(x_new, y_new).get()\n", @@ -150,7 +150,7 @@ } ], "source": [ - "#| label: fig:multivariate_dx\n", + "# | label: fig:multivariate_dx\n", "\n", "dfdx = mult_interp.diff(0)\n", "z_dfdx = dfdx(x_new, y_new).get()\n", @@ -196,7 +196,7 @@ } ], "source": [ - "#| label: fig:multivariate_dy\n", + "# | label: fig:multivariate_dy\n", "\n", "dfdy = mult_interp.diff(1)\n", "z_dfdy = dfdy(x_new, y_new).get()\n", @@ -227,6 +227,9 @@ } ], "metadata": { + "jupytext": { + "formats": "ipynb,md" + }, "kernelspec": { "display_name": "multinterp-dev", "language": "python", diff --git a/papers/alan_lujan/notebooks/Multivariate_Interpolation_with_Derivatives.md b/papers/alan_lujan/notebooks/Multivariate_Interpolation_with_Derivatives.md index a142d655a6..4b0a6575eb 100644 --- a/papers/alan_lujan/notebooks/Multivariate_Interpolation_with_Derivatives.md +++ b/papers/alan_lujan/notebooks/Multivariate_Interpolation_with_Derivatives.md @@ -1,6 +1,7 @@ --- jupyter: jupytext: + formats: ipynb,md text_representation: extension: .md format_name: markdown @@ -20,7 +21,8 @@ import numpy as np from multinterp.rectilinear._multi import MultivariateInterp ``` -Consider the following function along with its analytical derivatives: +Consider the following function along with its analytical derivatives: + ```python def trig_func(x, y): @@ -37,6 +39,7 @@ def trig_func_dy(x, y): First, we create a sample input gradient and evaluate the function at those points. Notice that we are not using the analytical derivatives to create the interpolation function. Instead, we will use these to compare the results of the numerical derivatives. + ```python x_grid = np.geomspace(1, 11, 1000) - 1 y_grid = np.geomspace(1, 11, 1000) - 1 @@ -45,7 +48,8 @@ x_mat, y_mat = np.meshgrid(x_grid, y_grid, indexing="ij") z_mat = trig_func(x_mat, y_mat) ``` -Now, we generate a different grid which will be used as our query points. +Now, we generate a different grid which will be used as our query points. + ```python x_new, y_new = np.meshgrid( @@ -55,9 +59,12 @@ x_new, y_new = np.meshgrid( ) ``` -Now, we can compare our interpolation function with the analytical function, and see that these are very close to each other. +Now, we can compare our interpolation function with the analytical function, and see that these are very close to each other. + ```python +# | label: fig:multivariate + mult_interp = MultivariateInterp(z_mat, [x_grid, y_grid], backend="cupy") z_mult_interp = mult_interp(x_new, y_new).get() z_true = trig_func(x_new, y_new) @@ -78,11 +85,14 @@ ax2.set_title("True Function") plt.show() ``` -To evaluate the numerical derivatives, we can use the method `.diff(argnum)` of `MultivariateInterp` which provides an object oriented way to compute numerical derivatives. For example, calling `mult_interp.diff(0)` returns a `MultivariateInterp` object that represents the numerical derivative of the function with respect to the first argument on the same input grid. +To evaluate the numerical derivatives, we can use the method `.diff(argnum)` of `MultivariateInterp` which provides an object oriented way to compute numerical derivatives. For example, calling `mult_interp.diff(0)` returns a `MultivariateInterp` object that represents the numerical derivative of the function with respect to the first argument on the same input grid. + +We can now compare the numerical derivatives with the analytical derivatives, and see that these are indeed very close to each other. -We can now compare the numerical derivatives with the analytical derivatives, and see that these are indeed very close to each other. ```python +# | label: fig:multivariate_dx + dfdx = mult_interp.diff(0) z_dfdx = dfdx(x_new, y_new).get() dfdx_true = trig_func_dx(x_new, y_new) @@ -103,10 +113,12 @@ ax2.set_title("True Function") plt.show() ``` -Similarly, we can compute the derivatives with respect to the second argument, and see that it produces an accurate result. +Similarly, we can compute the derivatives with respect to the second argument, and see that it produces an accurate result. ```python +# | label: fig:multivariate_dy + dfdy = mult_interp.diff(1) z_dfdy = dfdy(x_new, y_new).get() dfdy_true = trig_func_dy(x_new, y_new) @@ -127,4 +139,5 @@ ax2.set_title("True Function") plt.show() ``` -The choice of returning object oriented intepolation functions for the numerical derivatives is very useful, as it allows for re-usability without re-computation and easy chaining of operations. For example, we can compute the second derivative of the function with respect to the first argument by calling `mult_interp.diff(0).diff(0)`. +The choice of returning object oriented intepolation functions for the numerical derivatives is very useful, as it allows for re-usability without re-computation and easy chaining of operations. For example, we can compute the second derivative of the function with respect to the first argument by calling `mult_interp.diff(0).diff(0)`. + diff --git a/papers/alan_lujan/notebooks/Unstructured_Interpolation.ipynb b/papers/alan_lujan/notebooks/Unstructured_Interpolation.ipynb index 8723ad0ebd..38cd615b2a 100644 --- a/papers/alan_lujan/notebooks/Unstructured_Interpolation.ipynb +++ b/papers/alan_lujan/notebooks/Unstructured_Interpolation.ipynb @@ -23,11 +23,10 @@ }, { "cell_type": "markdown", - "metadata": { - "lines_to_next_cell": 2 - }, + "metadata": {}, "source": [ - "Suppose we have a collection of values for an unknown function along with their respective coordinate points. For illustration, assume the values come from the following function:\n" + "Suppose we have a collection of values for an unknown function along with their respective coordinate points. For illustration, assume the values come from the following function:\n", + "\n" ] }, { @@ -188,7 +187,7 @@ } ], "source": [ - "#| label: fig:unstructured_original\n", + "# | label: fig:unstructured_original\n", "\n", "true_grid = function_1(grid_x, grid_y)\n", "plt.imshow(true_grid.T, extent=(0, 3, 0, 3), origin=\"lower\")\n", @@ -229,7 +228,7 @@ } ], "source": [ - "#| label: fig:unstructured_interpolated\n", + "# | label: fig:unstructured_interpolated\n", "\n", "fig, axs = plt.subplots(2, 2, figsize=(6, 6))\n", "titles = [\"Nearest\", \"Linear\", \"Cubic\", \"Radial basis function\"]\n", @@ -267,7 +266,7 @@ } ], "source": [ - "#| label: fig:unstructured_gp\n", + "# | label: fig:unstructured_gp\n", "\n", "from multinterp import RegressionUnstructuredInterp\n", "\n", @@ -295,7 +294,7 @@ ], "metadata": { "jupytext": { - "formats": "ipynb,py:percent" + "formats": "ipynb,md" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", diff --git a/papers/alan_lujan/notebooks/Unstructured_Interpolation.md b/papers/alan_lujan/notebooks/Unstructured_Interpolation.md index ea1031ef97..dd4f5eacf6 100644 --- a/papers/alan_lujan/notebooks/Unstructured_Interpolation.md +++ b/papers/alan_lujan/notebooks/Unstructured_Interpolation.md @@ -1,6 +1,7 @@ --- jupyter: jupytext: + formats: ipynb,md text_representation: extension: .md format_name: markdown @@ -80,6 +81,8 @@ Now we can compare the results of the interpolation with the original function. ```python +# | label: fig:unstructured_original + true_grid = function_1(grid_x, grid_y) plt.imshow(true_grid.T, extent=(0, 3, 0, 3), origin="lower") plt.plot(rand_x, rand_y, "ok", ms=2, label="input points") @@ -91,6 +94,8 @@ Then, we can look at the result for each method of interpolation and compare it ```python +# | label: fig:unstructured_interpolated + fig, axs = plt.subplots(2, 2, figsize=(6, 6)) titles = ["Nearest", "Linear", "Cubic", "Radial basis function"] grids = [nearest_grid, linear_grid, cubic_grid, rbf_grid] @@ -104,9 +109,12 @@ plt.show() ``` -Finally, `multinterp` also provides a set of interpolators organized around the concept of *regression*. As a demonstration, below we use a `RegressionUnstructuredInterp` interpolator which uses a Gaussian Process regression model from `scikit-learn` (@Pedregosa2011) to interpolate the function defined on the unstructured grid. The `RegressionUnstructuredInterp` class takes the same arguments as the `UnstructuredInterp` class, but it additionally requires the user to specify the regression `model` to use. +Finally, `multinterp` also provides a set of interpolators organized around the concept of _regression_. As a demonstration, below we use a `RegressionUnstructuredInterp` interpolator which uses a Gaussian Process regression model from `scikit-learn` (@Pedregosa2011) to interpolate the function defined on the unstructured grid. The `RegressionUnstructuredInterp` class takes the same arguments as the `UnstructuredInterp` class, but it additionally requires the user to specify the regression `model` to use. + ```python +# | label: fig:unstructured_gp + from multinterp import RegressionUnstructuredInterp gaussian_interp = RegressionUnstructuredInterp(