From 31c9fdc93d672bbedf2737ede2d65cf0a055dabf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Marco=20Mart=C3=ADnez=20Gonz=C3=A1lez?=
 <mmg870630@gmail.com>
Date: Mon, 27 Apr 2026 23:56:27 -0400
Subject: [PATCH 1/8] Move logo images to website

---
 {doc/images => website}/grid_logo_website.png | Bin
 {doc/images => website}/grid_logo_website.svg |   0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename {doc/images => website}/grid_logo_website.png (100%)
 rename {doc/images => website}/grid_logo_website.svg (100%)

diff --git a/doc/images/grid_logo_website.png b/website/grid_logo_website.png
similarity index 100%
rename from doc/images/grid_logo_website.png
rename to website/grid_logo_website.png
diff --git a/doc/images/grid_logo_website.svg b/website/grid_logo_website.svg
similarity index 100%
rename from doc/images/grid_logo_website.svg
rename to website/grid_logo_website.svg

From 096a98feb5d99585a13eb0ab461e74cb40d5c22e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Marco=20Mart=C3=ADnez=20Gonz=C3=A1lez?=
 <mmg870630@gmail.com>
Date: Tue, 28 Apr 2026 03:12:50 -0400
Subject: [PATCH 2/8] Move jupyter notebooks to notebooks folder

---
 examples/One_dimensional_grids.ipynb          | 404 ------------------
 {examples => notebooks}/Angular_grid.ipynb    |   0
 {examples => notebooks}/Atom_Grid.ipynb       |   0
 .../Atom_Grid_Construction.ipynb              |   0
 {examples => notebooks}/Cubic_Grids.ipynb     |   0
 .../Interpolation_and_Poisson.ipynb           |   0
 {examples => notebooks}/JCP_Paper.ipynb       |   0
 {examples => notebooks}/Molecular_Grid.ipynb  |   0
 .../Molecular_Grid_Construction.ipynb         |   0
 .../Multipole_Moments.ipynb                   |   0
 {examples => notebooks}/Quickstart.ipynb      |   0
 11 files changed, 404 deletions(-)
 delete mode 100644 examples/One_dimensional_grids.ipynb
 rename {examples => notebooks}/Angular_grid.ipynb (100%)
 rename {examples => notebooks}/Atom_Grid.ipynb (100%)
 rename {examples => notebooks}/Atom_Grid_Construction.ipynb (100%)
 rename {examples => notebooks}/Cubic_Grids.ipynb (100%)
 rename {examples => notebooks}/Interpolation_and_Poisson.ipynb (100%)
 rename {examples => notebooks}/JCP_Paper.ipynb (100%)
 rename {examples => notebooks}/Molecular_Grid.ipynb (100%)
 rename {examples => notebooks}/Molecular_Grid_Construction.ipynb (100%)
 rename {examples => notebooks}/Multipole_Moments.ipynb (100%)
 rename {examples => notebooks}/Quickstart.ipynb (100%)

diff --git a/examples/One_dimensional_grids.ipynb b/examples/One_dimensional_grids.ipynb
deleted file mode 100644
index 4e7f3f962..000000000
--- a/examples/One_dimensional_grids.ipynb
+++ /dev/null
@@ -1,404 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/theochem/grid/blob/master/examples/One_dimensional_grids.ipynb)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# One-Dimensional and Radial Grids\n",
-        "\n",
-        "Grid supports several quadrature rules for one-dimensional grids, most of them in the [-1, 1] interval, within the [OneDGrid](https://grid.qcdevs.org/pyapi/grid.basegrid.html#grid.basegrid.OneDGrid) module.\n",
-        "\n",
-        "### Example: Visualize One-Dimensional Grid Points\n",
-        "\n",
-        "Here, we showcase some of the quadrature rules available each with a unique set of points. Click [here](https://grid.qcdevs.org/pyapi/grid.onedgrid.html) for a full list of one-dimensional grid classes and [here](https://grid.qcdevs.org/onedgrids.html#one-dimensional-grids) for a condensed table containing most of the popular grids."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "ExecuteTime": {
-          "end_time": "2024-01-03T22:36:20.643610321Z",
-          "start_time": "2024-01-03T22:36:20.291101489Z"
-        }
-      },
-      "outputs": [],
-      "source": [
-        "from grid.onedgrid import *\n",
-        "\n",
-        "# Make several 1-dimensional grids for a given number of points\n",
-        "npoints = 31\n",
-        "\n",
-        "grids = [\n",
-        "    GaussChebyshev(npoints),\n",
-        "    GaussLegendre(npoints),\n",
-        "    TanhSinh(npoints),\n",
-        "    Simpson(npoints),\n",
-        "    Trapezoidal(npoints),\n",
-        "]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "ExecuteTime": {
-          "end_time": "2024-01-03T22:36:21.133162129Z",
-          "start_time": "2024-01-03T22:36:20.645646412Z"
-        }
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "names = []\n",
-        "for i, oned in enumerate(grids):\n",
-        "    plt.scatter(oned.points, np.repeat(i + 1, oned.size), marker=\"o\")\n",
-        "    names.append(oned.name)\n",
-        "\n",
-        "# set the y-ticks to be the 1D grid names, and set labels\n",
-        "plt.title(f\"{npoints} Grid Points in One Dimension\", fontsize=16)\n",
-        "plt.yticks(np.arange(1, len(grids) + 1), names)\n",
-        "plt.xlabel(\"Number of Points (Grid Size)\", fontsize=14)\n",
-        "plt.ylabel(\"Quadrature Rule\", fontsize=14)\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Example: Integration of One-Variable Function on $[-1, 1]$\n",
-        "\n",
-        "The one-dimensional grids can accurately integrate one-variable functions over an interval (defined by their domain), even with a small number of points. Here, we numerically compute the integral below and compute the relative percent error. The analytical value of this integral is $\\pi/2$.\n",
-        "\n",
-        "$$ \\int_{-1}^1 \\sqrt{1 - x^2} dx \\approx \\sum_{i=0}^{N-1} w_i \\sqrt{1 - x_i^2} $$\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "ExecuteTime": {
-          "end_time": "2024-01-03T22:36:21.338777861Z",
-          "start_time": "2024-01-03T22:36:21.146684630Z"
-        }
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Integral of sqrt(1-x^2) from -1 to 1:\n",
-            " 1.57079633 from Analytical integration (which is equal to pi/2)\n",
-            " 1.57079633 from Gauss-Chebyshev integration\n",
-            " 1.57082270 from Gauss-Legendre integration\n",
-            " 1.57067045 from Tanh-Sinh integration\n",
-            " 1.56683246 from Simpson integration\n",
-            " 1.56069571 from Trapezoidal-Lobatto integration\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 850x500 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "import numpy as np\n",
-        "\n",
-        "# define the function to integrate\n",
-        "function = lambda x: np.sqrt(1 - x**2)\n",
-        "\n",
-        "print(\"Integral of sqrt(1-x^2) from -1 to 1:\")\n",
-        "print(f\"{np.pi / 2: .8f} from Analytical integration (which is equal to pi/2)\")\n",
-        "\n",
-        "names = []\n",
-        "error_relative = []\n",
-        "for oned in grids:\n",
-        "    # integrate the function evaluated on the grid points\n",
-        "    integral = oned.integrate(function(oned.points))\n",
-        "    print(f\"{integral: .8f} from {oned.name} integration\")\n",
-        "    error_relative.append(np.abs(integral - np.pi / 2) / (np.pi / 2) * 100)\n",
-        "    names.append(oned.name)\n",
-        "\n",
-        "# plot the relative error as columns graph\n",
-        "plt.figure(figsize=(8.5, 5))\n",
-        "plt.bar(names, error_relative)\n",
-        "title = r\"Numerical Integration of $\\int_{-1}^1 \\sqrt{1-x^2} dx$ with \" + str(npoints) + \" Points\"\n",
-        "plt.title(title, fontsize=16)\n",
-        "plt.xlabel(\"Quadrature Rule\", fontsize=14)\n",
-        "plt.ylabel(\"Relative Percent Error\", fontsize=14)\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Changing the number of grid points increases the accuracy of each quadrature. Here we show how the relative integration error converges to zero when increasing the number of points.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "ExecuteTime": {
-          "end_time": "2024-01-03T22:36:21.461375714Z",
-          "start_time": "2024-01-03T22:36:21.349492787Z"
-        }
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "npoints = [3, 5, 11, 15, 17, 19, 21, 31]\n",
-        "grid_classes = [GaussChebyshev, GaussLegendre, TanhSinh, Simpson, Trapezoidal]\n",
-        "\n",
-        "for grid_class in grid_classes:\n",
-        "    errors = []\n",
-        "    for npoint in npoints:\n",
-        "        # make 1-dimensional grid for the given number of points\n",
-        "        oned = grid_class(npoint)\n",
-        "        # compute relative percent error of integration\n",
-        "        integral = oned.integrate(function(oned.points))\n",
-        "        errors.append(np.abs(integral - np.pi / 2) / (np.pi / 2) * 100)\n",
-        "    # plot relative percent error vs. number of grid points\n",
-        "    plt.plot(npoints, errors, label=oned.name)\n",
-        "\n",
-        "plt.legend(frameon=False, fontsize=12)\n",
-        "plt.title(r\"Numerical Integration of $\\int_{-1}^1 \\sqrt{1-x^2} dx$\", fontsize=16)\n",
-        "plt.xlabel(\"Number of Points (Grid Size)\", fontsize=14)\n",
-        "plt.ylabel(\"Relative Percent Error\", fontsize=14)\n",
-        "plt.xticks(npoints)\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Radial Grid: Transformation of 1D-Integration Intervals\n",
-        "\n",
-        "Generally, areas where the integrand changes more rapidly demand a denser concentration of points. So, the $[-1, 1]$ interval is not suitable for most applications encountered in quantum chemistry, where the integrals are usually performed on $[0,\\infty)$ domain. [Grid](https://github.com/theochem/grid) supports several transformations of the one-dimensional grids to other intervals. Some of these transformations have a parameter that can be used to control the density of points across different ranges of the new interval. Click [here](https://grid.qcdevs.org/pyapi/grid.rtransform.html) for a full list of radial transformations and [here](https://grid.qcdevs.org/radial_transf.html) for a condensed table with explicit formulas.\n",
-        "\n",
-        "### Visualize Radial Grid Points\n",
-        "\n",
-        "Here we visualize the OneDGrid points for different quadrature rules after a Becke transformation.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "ExecuteTime": {
-          "end_time": "2024-01-03T22:36:21.851432041Z",
-          "start_time": "2024-01-03T22:36:21.470246478Z"
-        }
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/var/folders/wk/kmp6fyrn3396qjpx40d2qk600000gn/T/ipykernel_75060/2869605059.py:18: UserWarning: Attempt to set non-positive xlim on a log-scaled axis will be ignored.\n",
-            "  ax2.set_xlim(0, 2500)\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 1000x500 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "from grid.rtransform import BeckeRTransform\n",
-        "\n",
-        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5), sharey=True)\n",
-        "names = []\n",
-        "for i, oned in enumerate(grids):\n",
-        "    # Becke R-transform (goes from 0 to \\infty) to transform 1D-grid\n",
-        "    rgrid = BeckeRTransform(0.0, R=1.5).transform_1d_grid(oned)\n",
-        "    # plot radial grid points for each transformed 1D-grid\n",
-        "    ax1.scatter(rgrid.points, np.repeat(i + 1, rgrid.size), marker=\"o\")\n",
-        "    # plot log plot of radial grid points\n",
-        "    ax2.semilogx(rgrid.points, np.repeat(i + 1, rgrid.size), marker=\"o\", linestyle=\"\")\n",
-        "    names.append(oned.name)\n",
-        "\n",
-        "# set the ticks, labels, and title\n",
-        "plt.suptitle(\"Plot & Log-Plot of Becke Radial Transformation of 1D-Grids\", fontsize=17)\n",
-        "ax1.set_yticks(np.arange(1, len(grids) + 1), names, fontsize=11)\n",
-        "ax1.set_xlim(0, 2500)\n",
-        "ax2.set_xlim(1.0e-4, 2500)\n",
-        "ax1.set_xlabel(\"Transformed Coordinate r\", fontsize=14)\n",
-        "ax2.set_xlabel(\"Transformed Coordinate r\", fontsize=14)\n",
-        "ax1.set_ylabel(\"Quadrature Rule\", fontsize=17)\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Example: Integration of One-Variable Function on $[0, \\infty]$\n",
-        "\n",
-        "This transformation tends to concentrate the points close to the origin. Because of this, it is usually used for defining radial grids for atoms where the function to integrate is rapidly varying close to the origin (e.g. the electron density is more concentrated close to the nucleus). Here, we integrate (which has the exact value of 2.0):\n",
-        "\n",
-        "$$ \\int_0^\\infty x^2 e^{-x} dx $$\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "ExecuteTime": {
-          "end_time": "2024-01-03T22:36:21.855941638Z",
-          "start_time": "2024-01-03T22:36:21.851891608Z"
-        }
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Integration of r^2 * exp(-r) from 0 to infinity:\n",
-            " 2.000000 Analytical integration\n",
-            " 2.000003 from BeckeRTransform radial grid of Gauss-Chebyshev\n",
-            " 1.999996 from BeckeRTransform radial grid of Gauss-Legendre\n",
-            " 1.999993 from BeckeRTransform radial grid of Tanh-Sinh\n",
-            " 1.998949 from BeckeRTransform radial grid of Simpson\n",
-            " 2.000006 from BeckeRTransform radial grid of Trapezoidal-Lobatto\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(\"Integration of r^2 * exp(-r) from 0 to infinity:\")\n",
-        "print(f\"{2.0: .6f} Analytical integration\")\n",
-        "\n",
-        "# Make several 1-dimensional grids for a given number of points\n",
-        "npoints = 21\n",
-        "grids = [\n",
-        "    GaussChebyshev(npoints),\n",
-        "    GaussLegendre(npoints),\n",
-        "    TanhSinh(npoints),\n",
-        "    Simpson(npoints),\n",
-        "    Trapezoidal(npoints),\n",
-        "]\n",
-        "\n",
-        "for oned in grids:\n",
-        "    rgrid = BeckeRTransform(0.0, R=1.5).transform_1d_grid(oned)\n",
-        "    x = rgrid.points\n",
-        "    func_vals = x**2 * np.exp(-x)\n",
-        "    integral = rgrid.integrate(func_vals)\n",
-        "    print(f\"{integral: .6f} from BeckeRTransform radial grid of {oned.name}\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {
-        "ExecuteTime": {
-          "end_time": "2024-01-03T22:36:22.028092961Z",
-          "start_time": "2024-01-03T22:36:21.856079509Z"
-        }
-      },
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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2V9b7cqv1qCyWCE0Azz77LHPmzOHff/8FTEHgySefvOXz/vDDDwDk5ubSu3fvEssUdpa2s7O75ecrb73GjRuHra1tiWUK5xMrbeRcVb1mtZGEJlEmDzzwAB988AGnTp3i22+/LdNv+RXl4OBQbFvh6KGS9l27X7lu1E/haJIzZ85w5syZGz5v4XDq65V3eHHhl3hZJ3k8e/Ysjz/+ODqdjpdffpmHHnqIpk2b4uTkhEqlYsmSJeb91aG06y2cl+f61/hmKvoe5OTkAJTYOlXI2dn5hqGptGuxxGteGBIaNmxYahlvb2/OnDlDVlZWsX2V9b7caj0qi6VCU0FBAc7OziQkJODl5cX7779fKefdunUrYBqhlpCQcMOyTZo0qZTnLIvCevXv37/UMoUt9KWFpqp6zWojuT0nykSj0fDOO+8A8N5776HX60ssV1p4KVT4RVhdCj+Mv/jii5sO5506dWqlPGfhl3xZl9n46aef0Ol0jBkzho8//pi2bdvi7Oxsfi3Pnz9fKfWylIq+B4Uh4frWw2tV9EvdEq954etw+fLlUsskJiYCNw6KdaUelghNWq2Wu+66i9OnT2NlZUVSUhIrV66slHMXtpKmpKRU22dNWRT+LDdu3LjE/SdOnCA+Ph4PD48Sp1OoytesNpLQJMpszJgxtG7dmujo6FLXLSv8oktKSipx/81aGipb4S2Io0ePVttzhoaGApibsm+mcC6fbt26lbi/uvoy3YobLbNT0fegefPmwNX5Y64XFxd3w1amG7mV17yiSwoVXs/x48dL3J+VlWX+gissWxVqSj0sEZomTJjA1q1bGThwIIsXLwZMc1AZDIZbPnfh9ZTWYm0phb9YlPYLa+GCv6W1MlXla1YbSWgSZaZWq3n33XcBeP/990u8deHh4YGrqyt5eXkcO3as2P4lS5ZUeT2vNWrUKACWL19e7g7MFTVy5EgAVq9ezdmzZ29avnB25cLf7q918uRJ1q5dW6n1qwqF11DSF0ZF34MBAwYA8Ouvv5bYonQrCw7fymt+o2u9kUGDBgHw888/F5motdCiRYsoKCggKCiIFi1alOvctbEe1R2a3nrrLZYvX054eDg///wz48aNo3nz5pw8eZLly5ff8vkDAwOBmreEjo+PDwD//fdfsX2HDx9mwYIFQMmhqapfs9pIQpMol3vvvZfw8HBiY2PZtWtXsf0qlcr8ofzSSy8V+WBctmwZX331VbXVFaBjx46MHj2alJQUBgwYUGz2ZIPBwLZt23jwwQcpKCiolOfs0KEDo0aNIj8/n8GDB7N3794i+8+cOcPHH39sftyjRw8A5s+fz6FDh8zbT58+zb333ouNjU2l1KsqFfbR+Pvvv4vtq+h70L9/fyIiIkhOTuaBBx4ocrtz9erVzJw5s8IL4t7Ka36ja72Rfv360alTJwoKCrj//vuL3B7btGkT06ZNA0ydbKtygeyaUo/qDE1LlixhxowZ+Pr6sn79elxcXNBoNLz99tsATJ8+vdQuB2VVuD7nSy+9xO+//15kn6IoHDhwgIkTJ5YYVKtSYV+m6dOnmwcBAGzZsoXBgwej1WqB4gMuquM1q5WqdEIDUStdP0/T9VauXHnD+UpOnDihODk5mSeqbN++veLj46NwZVbjwuOuV9p2Rbn5/E9bt24tNgFboaysLGXAgAHm8wcGBipdunRRwsPDFXt7e/P2a2fCvVl9Ct1oRvCuXbuazxEcHKx07NhR8fb2LnYdOp1Oue222xQwzUTdqlUrJSwszDz78/vvv68Ayvjx48v8/GWpc2nzNJX3WhVFUb755hvz8WFhYUrv3r2V3r17KwcPHlQUpeLvwbUzgjs4OCgdO3ZUgoODzfMWFdYpLi6uXNdyK6/5za71Rq9VVFSUefJDW1tbpX379uaJW8E0W3RpM4KXpiI/AxWph6JU7jxNhfNBAUp8fPwtn680GzZsUKysrBQnJ6cis7YrimnG7+bNmyuAsmjRolt6nqysLPOkoYDi5eWldOzYUYmIiFCcnZ0VQHF3d7+l56iI6Oho86zeVlZWSlhYmPn/UGhoqNK0aVMFKPLaVNdrVhtJaBLF3Cw0GY1GpW3btjec5O3AgQPKHXfcoTg7OyuOjo5Kt27dlLVr1yqKcvPJLUtyK6FJUUyzS3/33XfKoEGDFE9PT8Xa2lrx8fFRunTporz++uslLpFwK6FJURRFq9Uqn3/+udK9e3fF1dVVsbOzUxo3bqzcc8895teiUEZGhjJp0iTF19dXsba2Vvz9/ZXHHntMiY+PN08uWpNDk6IoymeffaZEREQUCUFbt24176/Ie6AoinLmzBllzJgxioeHh2JnZ6eEh4cr8+bNUxRFMU+6l5aWVq5rUZSKv+ZludYbvVZJSUnKK6+8ooSEhCi2traKi4uL0qtXL+Xbb78tMahURWiqSD0UpXJD0zvvvGO+tpImPa0MBw8eVJycnBSNRqOsX7++xDLffvutAigBAQFKfn7+LT1fXl6e8tFHHykdO3ZUnJycFBsbG8Xf31/p2rWrMmXKlCLBujpFRkYqAwcOVBwcHBQHBwclLCxMmTJlipKcnKzY2toqVlZW5muv7testlEpSjnHDwshRA2QkpKCp6cnbm5u9Wex0Drk5ZdfZs6cOYDpFm3h1AlC1GTyUyqEqJW+/vproPQRcKJmK+zT5ODgUKbAFBMTw4gRI3B2dqZBgwaMHTuW5OTkqq6mEEVIaBJC1FhHjhxh8eLFRToNK4rC8uXLmTJlCmBa9kPUfBkZGZw4cQKj0QhcnW29LJPAZmdn07dvXy5evMgPP/zA4sWL2b17N0OHDjWfT4jqIDOCCyFqrJSUFJ588kmeeeYZgoKC8PDw4Ny5c+apC5588kmGDx9u4VqKmzl16pR5OZYjR44QFhZmXiYnODj4pscvWrSIhIQEdu/ebR5CHxwcTOfOnfntt9/M01oIUdWkpUkIUWO1bt2a1157jfDwcDIyMjh48CCKonD77bfz448/snDhQktXUZRB8+bNzfMYzZo1i++//948aWnhfFw3sm7dOvr27WsOTACdOnWiefPmtWIeM1F3SEdwIYQQVa5wPb9rNW7cmH379uHu7n7DY729vRk7dmyR+c0A7rrrLhISEvjnn38qvb5ClERamoQQQlS5xx57jCVLlpgXRn7ooYf4559/bhqYANLS0krs++Tu7m7uGyVEdZA+TUIIIarFhAkTmDBhQoWOLWl2crlRIqqbtDQJIYSo0Ro0aFDiXFxpaWllaqkSorJIaBJCCFGjhYaGcvz48WLbjx8/TqtWrSxQI1FfSWgSQghRow0bNoytW7cWWex2//79nDp1SqacENVKRs8JIYSo0bKysoiIiMDLy4t3332X/Px8Xn/9dTw8PPjnn39kCRZRbeQnTQghRI3m7OzMli1baNSoEffddx8TJkzgtttuY926dRKYRLWSliYhhBBCiDKQiC6EEEIIUQYyT1MlMhqNxMfH4+zsXOKcIkIIIYSoeRRFISsrC19f3xve8pXQVIni4+MJCAiwdDWEEEIIUQHnz5/H39+/1P0SmiqRs7MzYHrRXVxcLFwbIYQQQpRFZmYmAQEB5u/x0khoqkSFt+RcXFwkNAkhhBC1zM261khHcCGEEEKIMpDQJIQQQghRBhKahBBCCCHKQEKTEEIIIUQZSGgSQgghhCgDCU1CCCGEEGUgoUkIIYQQogwkNAkhhBBClIGEJiGEEEKIMpDQJIQQQghRBhKahBBCCCHKQEKTEEIIIUQZSGiqBTK1mey7tM/S1RBCCCHqNQlNNVy2NpsnNz3Jk5ufZOfFnZaujhBCCFFvSWiq4WytbPF29EZr1PL8luclOAkhhBAWIqGphrNWWzO792xuD7xdgpMQQghhQRKaagEJTkIIIYTlSWiqJazV1szuVTQ47bq4y9LVEkIIIeoNCU21iLWmaHB6bstzEpyEEEKIaiKhqZYpDE79AvpJcBJCCCGqkYSmWshaY83HvT+W4CSEEEJUIwlNtVRhcOob0NccnHZf3G3pagkhhBB1loSmWsxaY80nvT8xB6dJWyZJcBJCCCGqiISmWk6CkxBCCFE9JDTVAdcHp+e2yq06IYQQorJJaKojCoNTn4A+FBgKTMEpXoKTEEIIUVkkNNUh1hpr5vSeczU4bZHgJIQQQlQWCU11jAQnIYQQompIaKoFtHl60i7llLm8OTj5Xw1O/8T/U4U1FEIIIeo+CU01nNFgZMOn/7Jy5l7ijlwu83HWGmvm9LkanCZtmSTBSQghhLgFEpquM2rUKBo0aMA999xj6aoAoNMayYmKQVtgZN3/HWbbXa8QN+ExLk2fTsrSpWRt2UrB2bMYCwqKHWutseaTPp9IcBJCCCEqgUpRFMXSlahJtm7dSnZ2NsuWLWPlypXlOjYzMxNXV1cyMjJwcXGptDqdfXAcBwsiSPRsD0Bg3CaanluDimveOpUKK59G2AQGYRMYiE1QEDZBgVgHBoKvN6/+9zbbLmzDVmPL//X7P7r6dq20+gkhhBC1WVm/vyU0lWDbtm3MmzevxoQmAKPRyH8/HePAtiQA/J3SaavfhSEuGl1sHMacG/d50ng3JM5Fy0mHDJLdrRjW+3HC2vTHJjAQtaNjpdZVCCGEqE3K+v1tVY11umXbt29n9uzZ7N+/n4SEBFatWsXIkSOLlJk/fz6zZ88mISGB0NBQPv30U3r27GmZClcitVpN1zHhuAcnsOXbk1zIdkMbfD9Dp0dg72yNITUVbWwc2rhYdHFxV/4dhzY2FmNmJobEy/glgh8AOtg6n2jmA6Dx8rymhSoQm8BArANNLVUaZ2dLXrYQQghRY9Sq0JSTk0ObNm145JFHuPvuu4vtX7FiBS+88ALz58+ne/fuLFq0iMGDB3P8+HECAwMB6NChAwUl9P/ZtGkTvr6+VX4Nt6rFbT44e9jx+8IjXI7JZOWH+xg6MQIPXw+sPDxwaN+u2DGG9HS0sbFo4+LIi4nm3z2rUF28RKN0cMkFQ1IyeUnJ5O3fX+xYTYMGpjAVHIR1YKApXF0JVho3t6q/YCGEEKKGqLW351QqVbGWpi5dutC+fXsWLFhg3taqVStGjhzJzJkzy3zust6eKygoKBLAMjMzCQgIqJLbc9dLT8xl3bzDZCTlYWOn4Y4nwglo7V6mY7UGLS9ue5HtF7bjrrNldrNXaJnrYmqZijGFK21cHIbk5BueR+3qagpUV1qorM19qYLQNGiASqWqjEsVQgghqlSdvD13I1qtlv379/PGG28U2T5w4EB2766ayR1nzpzJtGnTquTcN+Pm7cA9r3fk94WRJJzJYO28w/S+vzmhPf1ueqyNxoa5feaag9OzFz5h3u3z6DJkSJFyhuwcdOevudUXF4vuyr/1iYkYMzLIP3KE/CNHij2H2snJdJsvKLDIrT/rwECsvLwkUAkhhKh16kxLU3x8PH5+fuzatYtu3bqZy82YMYNly5Zx6tSpMp130KBBHDhwgJycHNzd3Vm1ahWdOnUqsawlW5oKGXRGtiw/wen/EgFoNyCQrqOaolLfPJRc2+Jkp7EzBSefLmV6XmNuLtrzF4r3oYqLRZ9wCW7wY6VycCjeQnXltp9Vw4ao1DIThhBCiOpT71qaCl3fgqEoSrlaNf74448yl7W1tcXW1rbM5StMUcBoAE3xt0tjrab/w61x9XJg77poDm6OIyM5j/6PtMbaRnPD017f4jTxr4llDk5qBwfsWjTHrkXzYvuMBQXoLlwwBanY2CItVLr4eJTcXApOnqTg5Mlix6psbbEJDLjSEb1o53SrRo1QaW58TUIIIURVqTOhydPTE41Gw6VLl4psv3z5Mt7e3haqVeVI2vghqjN/4fnoj+DoWWy/SqWi87DGuHrZs+XbE5w7mMTq1AMMeSYCR9cbh7rC4PTC1hfYcXFHuYJTadS2ttg2bYpt06bF9ilaLdqLF4u1Tuli49BevIhSUEBB1BkKos4Uv05ra6wDAoq3UAUHYe3jg8qqzvw4CyGEqIHqzO05MHUE79ChA/Pnzzdva926NSNGjChXR/CKqop5mjJTElH9XzucySHTphFOD/+E2rdNqeXjo9LZsPAI+Tk6nNxtGfZsGzz8nG76PFqD1hyc7DR2fH7753T26Vwp11BWik6HLiGhxKkTdOfPo+h0pR9sZYWNn1+Jfahs/PxQ2dhU34UIIYSoVerk5JbZ2dmcOWNqgWjXrh1z5syhb9++uLu7ExgYyIoVKxg7diwLFy6ka9euLF68mC+++IJjx44RFBRU5fWritBkNCp89dtG+h18gSbqS2hVtjDic2za3lvqMemXr4ysu2waWTfoiTACW3vc9LkKDAW8uPVFiwan0igGA/pLl8xzT5nDVFws2rjzKCVMI2Gm0WDt61tiHyprf3/U1XGLVQghRI1VJ0PTtm3b6Nu3b7Ht48ePZ+nSpYBpcstZs2aRkJBAWFgYc+fOpVevXtVSv6qcEXz1P8dosOFpeqsPA5DTaRKOg6eBuuQ+PvnZOjYsOkJ8VDoqtYpeY5oT1uvmI+uuD07z+8+nU6OSO8LXFIrRiP7y5eItVFfmplLy8ko/+PrlZ5o0xrl/f2z8/avvAoQQQlhUnQxNNV1VhiaAPWeTOPbtyzzCbwBkBfTF+YGlYO9WYnmDzsjW5Sc59Z+pn1fb/gF0u6vZTUfWFRgKeGHrC+y8uLPWBKfSKIqCPimp5D5UsbGlLj9j3749rsOH4XzHHVg1aFDNtRZCCFGdJDRZQFWHJoDYlByWL/mEl3L/D3uVlhynYBzH/wReLUosrygK+36PYc/aaACatPWi/6M3H1lXl4JTaRRFKbb8TN6hQ+T8+x8YjaZC1tY49eyJ653DcerTB7WdnWUrLYQQotJJaLKA6ghNAJn5OmZ9/RNPXXoHf1UyWo0j1vcuQdVySKnHnN57ib+WncCoV2gY5FymkXXXBid7K3s+v/3zOhecSqJLvEzm77+TsXYNBcdPmLernZxwHjQQ1+HDcejUSaY/EEKIOkJCkwVUV2gC0BuMzFm1i96Rr9BFfRIjKox9JmPV+1UoZV6qhDPp/L7gysi6BrYMm3jzkXUFhgKe3/o8uy7uqlfBqVDBmTNkrF1Hxto16OMTzNutvL1xGTYU1zvvxK5Fya18QgghagcJTRZQnaGp0Le7olA2TmacZhMA2ubDsbl7IdiWHIbSL+ey/vNI0hNzsbbTMOjxMIJCbzyyrr4HJzB1Ns87cICMNWvJ3LgRY2ameZ9t8+a4DB+G67BhWPv4WLCWQgghKkJCkwVYIjQB/H06iS3fzeYtlmCjMlDg0Qrbh36EBsElls/P0bFx0REunr4ysu6+EMJ633i0mASnq4xaLdl//03m2nVkb916df4olQqHTp1wvXM4zgMHoqnGnwEhhBAVJ6HJAiwVmgCiErOY+9VypuXPxEuVgc62Adb3LYMmvUssb9Ab2bb8JCf/NY2sa3NlZJ36BiPrCgwFPL/leXbFS3AqZMjIIHPTJjLXrCV3717zdpWNDU59+uB653Ace/VCLZNrCiFEjSWhyQIsGZoAUnO0TF66kacS36Wt+hxGlQb1oA+gy1Ml9nNSFIX9G2L5b805ABq38WTAo6FY25bewfn64DT/9vl0bNSxyq6pNtHFx5Oxfj2Za9YUWQZG7eqKyx134HrncOzbtZMFiYUQooaR0GQBlg5NAAV6A++s3E/nY9O5W7MDAGPE/aiHfwrWJQ+Xj9qbyF/LTmDQG/EKdGboMxE4upU+sk6C040pikLBqVOm/k/r1qG/fNm8z9rPD5dhw3C9c3iJa/MJIYSofhKaLKAmhCYwfWkv3HaWpL8+ZbLmO6xURgw+7dHc/z24lNxROeFsBr8viCQ/2zSybuizbfD0L31knQSnslEMBnL37CFj7Tqy/vijyGSadq1b43LncFyGDMG6YUML1lIIIeo3CU0WUFNCU6GNRy+xYsW3zFF/SgNVNgaHhmju/w4CSl5PLiMpl3Xzroyss70ysi6s9JF1EpzKx5ifT/bWrWSsWUv2jh2g15t2qNU43nYbLncOx7n/ADROjpatqBBC1DMSmiygpoUmgKMXM3hn6TpmFMykpfo8RrUN6mGfQPtxJZbPz9GxcfERLp5KR6WCnvc1J7xP6SPrCgwFPLflOXbH75bgVA76tDSyNm4kY81a8g4eNG9X2dnhfPvtpg7k3bqhsra2YC2FEKJ+kNBkATUxNAEkZuYzcekOHkmaxRDNHtPGzk/AoBmgKf6lbNAb2fb9KU7uNk3m2KZfAN3uKX1kXb4+n+e3Pm8OTgv6L6CDd4cqu566RhsXR8a6dWSuWYs2Jsa8XdOgAS5DhuB653DsIiJQlTJpqRBCiFsjockCampoAsjTGnjlpwM0ObGQl61XAqAE9UA1ehk4ehYrrygKB/6I5d/VppF1wRGeDHi0NTZ2ViWeP1+fz3NbnuOfhH8kOFWQoijkHz1Gxto1ZK7/HUNKinmfdVAgrsPvxHX4MGyCgixYSyGEqHskNFlATQ5NAEajwtw/T3Ni2wrmWs/HWZWH0TUA9ZjvwSeixGOi9iXy11LTyDrPACeGPdum1JF1Epwqj6LXk/PPP2SsWUvWn3+i5OWZ99m1icB1+J24DBmMlbu7BWsphBB1g4QmC6jpoanQqoMXWLxyI59rZtNEfQnFyh7VyM8h7O4Sy186ZxpZl5dVOLIuAk9/5xLLXh+cFvZfSHvv9lV5OXWeMSeHrC1byFizlpxdu8BoNO3QaHDq0cPUgbxfP9T29patqBBC1FISmiygtoQmgP2xqby0bDvTdHPoozls2tjjReg3BdTFJ7fMSMpj/eeHSbtkGlk38LFQgsOL39YDCU5VSZ+UROaGDWSsWUv+0aPm7WoHB5wHDMDlzuE43nYbKk3pE5QKIYQoSkKTBdSm0ARwPjWXx5f+y8jUr3jKaq1pY7MBcPcSsHcrVr4gV8eGRUe5eCoNlQp6jG5ORN+SR9bl6/OZtGUS/yb8K8GpihSciyZz3Voy1qxFd+GCebuVlxcuQ4eaJtBs1Uo6kAshxE1IaLKA2haaALLydUz64SAuUb8xy3oRdiodikczVGN+AK/mxcob9Eb+/v4UJ66MrIvo60/3e0NKHFl3bXBysHJgQf8FEpyqgKIo5B08ROa6tWT+vgFDerp5n03TprgOH47LsGHY+PtZrpJCCFGDSWiygNoYmgD0BiMf/H6CPbu3sshmLv6qZBRbZ1R3LYEWdxQrX2xkXbgHAyaEljiyToJT9VK0WrJ37iJj7Rqyt2xFKSgw77Pv2MHUgXzQQDRubparpBBC1DASmiygtoamQt/9F8tnv+3m/6w+pYv6JAoqVP3ehp4vl7jg75n9l/lz6XEMOtPIuqHPtMGpQfGRdXn6PJ7b8pw5OC0csJB2DdtVxyXVa4bsbLI2bSZj7Rpy//0PCv+rW1vj1LsXrsPvxKlPb9S2pa8zKIQQ9YGEJguo7aEJYGdUMs999x8v6L9mnNVm08bWI2HkfLApvrzHtSPrHN1MI+u8AoqPrMvT5zFpyyT+S/hPgpMF6BITyVy3noy1ayk4edK8Xe3sjMsdg3AZNhyHTh1RqdUWrKUQQliGhCYLqAuhCeBsUjYTlu7ltvR1vGf1NdYqA3iHwZjvoEFwsfKZyXmsm2caWWdlq2HQhFCCI4qPrJPgVDPknz5N5tp1ZKxbhz4hwbzdyscH12FDcRk+HLvmxfuzCSFEXSWhyQLqSmgCSMvR8vR3+9FF/8NCm0/xUmWg2LujuncpNOldrHxBro6Ni49y4aRpZF33e0No0y+gWLnrg9OiAYto27Bt1V+QKEYxGsndt4/MtWvJ3PgHxqws8z7bli2vdCAfirW3twVrKYQQVU9CkwXUpdAEoNUbmbL6KH/vO8Qim7m0UZ9DUWlQDZoBXZ4s1s/JYDCy/ftTHN9lar0I7+tPjxJG1uXp85j01yT+uyTBqaYwFhSQ/fffZK5dS9a2v0GnM+1QqXDo0gXX4cNxHjgAjXPJk5oKIURtJqHJAupaaALTSLklO6L5ZMNhPrBawt2anaYdbR+EoXPA2q5Y+YOb4vhn1VkAgsI9GFjCyDoJTjWXIT2dzD82kbF2DXn79pu3q2xtcerXF9fhw3Hq0QOVjY0FaymEEJVHQpMF1MXQVGjz8USe//EAYwzrecv6OzQYwa8j3LccXHyKlT974DKbvzaNrPPwd2LYsxE4NSgasK4NTo7Wjizsv1CCUw2ju3iRjHXryVizBu3Zs+btGldXnIcMxnX4ndi3aysTaAohajUJTRZQl0MTwPH4TB5btpfGWXuZb/M/XMkBp0am4BTQqVj5S9EZ/D7/ysg6VxuGPtsGr8Cit3ckONUOiqJQcOIEGWvWkrF+HYakZPM+a39/XIYPw3X4ndg2aWzBWgohRMVIaLKAuh6aAC5n5fP4N/tJu3CKL2zm0EJ1HjQ2plt17ccWK5+ZnMe6zyNJS8jBykbNwMfCaHzdyLo8fR4T/5rInkt7JDjVAorBQO5//5GxZi1ZmzZhzM0177MLC8P1zuG4DBmClWfJaxMKIURNI6HJAupDaALI1xl45efDbImM5hPrBQzW7DXt6PwEDJoBGusi5Qvy9Pyx+AjnT6SBCnrcE0JEP/8it3QkONVOxrw8srZsIXPNWrJ37QK93rRDo8GxWzdchw/D+fbbUTsWn+NLCCFqCglNFlBfQhOYbtd8+mcU//vrFBM1q3nZeqVpR3BPuHcZOHoUKW8wGNn+42mO74gHILy3Hz1Gh6DWXJ1MMVeXy6QtkyQ41VL61FQyN2wgc81a8g4fNm9X2dvj3L8/rncOx7FrV1RWxZfbEUIIS5LQZAH1KTQVWnM4nld+Pkwv417+ZzMfB/LANdA0EaZPRJGyiqJwaPN5dq86AwoEhnow6PGiI+skONUN2thYMtauI2PtGnSxcebtGg8PXIYMwfXO4diFhUkHciFEjSChyQLqY2gCOBCXxhPf7Mct5yxf2c4hkEtgZQ8jP4ewu4uVP3vwMn9+dRy9zoiHnxNDn43A2f3qyLpcXS4Tt0xk76W9OFo7smjAItp4tanOSxKVRFEU8o8cIWPNWjJ//x1Daqp5n327dnhOfBbHbt0kPAkhLEpCkwXU19AEcDE9jwlL9xJ/KYHPbT6np/rK7ZkeL0G/t0GtKVI+MSaT9fMjycvU4uBqw9BnImgYdPU1k+BU9yg6HTm7d5Oxdh1ZmzejFBQAYN++PV4Tn8Wha1cJT0IIi6jS0NSkSROGDBnCvHnzbqmSdU19Dk0A2QV6nv/hIFtPXuI1qx95ymqdaUfIQLh7Cdi5FimfmZLH+s8jSY03jawb8GgoTdp6mfdfG5ycrJ1YOGChBKc6Qp+URMqSJaT9uOJqeOrQwRSebrtNwpMQolqV9fu7QkuaJycn4yzLKYjrONlasXhcRyb0bMqH+gd4TvssWpUNRG2CL/pB0uki5V087Lnr1Q4EtHZHrzWyYdERDv0ZR2GOd7B2YF6/eXRq1IlsXTZPbX6Kw0mHS3pqUctYeXnh/eabNN28Cffx41DZ2pK3fz9xjzxK7Nix5Pz7L9IILoSoaSrU0tSrVy+8vLz45ZdfqqJOtVZ9b2m61o974nh79VFaKOdYav8pXsYksHUxtTg1H1SkrPHKyLpjV0bWhfXyo+d9V0fW5epyefavZ9mXuA8naycWDVhEhFdEsecUtZfu8mVSliwh/ccVKFotAA4dO+I5cSKOt3WxcO2EEHVdld6eW79+PaNGjeKPP/6gb9++t1TRukRCU1G7zybz9PIDWOUl86X9/9FWOQ6oTH2cer5cZMFfRVE49Od5dv9aOLLOnUGPhWFjbxpZJ8GpftAlXglPK64JT506mcJTl84Wrp0Qoq6q0tD0zTff8NNPP7Fx40ZGjRpFp06d8Pb2LrEfwrhx48p7+lpLQlNx0ck5TFi6l/PJGbxn+y1jVJtNO1qPhJHzwabopIfnDiWx+ctjV0bWOTL02TbmkXW5ulye+esZ9iful+BUx+kSE0n5YgnpP/1UNDxNmohjZwlPQojKVaWhSa1Wo1KpivU5UF3XcqBSqTAYDOU9fa0loalkGbk6nv5uP7vPpnC/Zgvv2yxFo+jBO8w0n1OD4CLlL8dmsv7zSHIztTi42DD02asj6yQ41S+6xERSFn9hCk86HQAOnTvjNWkiDp2Kr3cohBAVUaWhadmyZWUuO378+PKevtaS0FQ6ncHIO78d44c9cXRQnWKp4//hrE8Fe3cYvQwa9ypSPis1n/WfHyblYg5W1moGTLg6su764LR4wGLCvcItcVmimuguXTKFp59/vhqeunQxhaeOHS1cOyFEbSfzNFmAhKYbUxSFr3bF8MH64zRUUvjO+f9oqjsNKg3cMdO0dt01rZXaPD1/LDlK3LFUUEG3u5rRtn8AKpVKglM9pUtIIOWLL0j/eeXV8HTbbabw1KGDhWsnhKitJDRZgISmstl68jKTfjiIriCX/3NaykD9NtOOtg/B0E/A+urs4EaDkR0roji6/SIAoT196TWmOWqNukhwcrZ2ZtGARRKc6gldQgLJixeTvvIXKAxPXW/Da6KEJyFE+VVLaIqNjeW7777j0KFD5idq27YtDz74IMHBwRU9rcVkZWXRr18/dDodBoOB5557jscff7zMx0toKruTlzKZsHQfF9NzecbuD15VLUelGMGvI9y3HFx8zGUVReHwX+fZ9cuVkXWt3Rn0uGlkXa4ul6f/fJoDlw9IcKqHdPHxpvD0y6/m8OTYrSueEyfi0L69hWsnhKgtqjw0zZs3j1deeQWdTlesQ7i1tTWzZs3i+eefr8ipLcZgMFBQUICDgwO5ubmEhYWxd+9ePDw8ynS8hKbySc4u4Ilv9nEgLp3emiMsdvgcW10mODUyBaeAoh19zx1KYvNXx9Brjbj7OjL02QhcPOyLBafFAxcT5hlmoasSlqCLjyd50WLSf702PHW7Ep7aWbh2QoiarkpD0++//86wYcNo2LAhL774In369KFRo0YkJiaybds25syZQ1JSEmvWrGHo0KG3dCGWkpqaSrt27di/fz+enp5lOkZCU/nl6wy88Uskqw/FE6hKZKXr/9Ew/xxobGDYXGj3UJHyl2NNa9blZmixdzGtWecd7CLBSQCgu3jxanjS6wFw7N4dz4nP4tBOwpMQomRVuozKxx9/jIeHBwcOHOD111+nS5cuBAUF0blzZ1577TX279+Pu7s7c+bMqfAFlGT79u0MHz4cX19fVCoVq1evLlZm/vz5NG7cGDs7Ozp06MCOHTvK9Rzp6em0adMGf39/XnvttTIHJlExdtYa5t7XlpcHNCdO8aZv+lvsd+gBBi389iz8/hoYdObyDYNcuOf1jnj4OZGXqWX1Jwc4e/AyDtYOLOi/gPYN25Oly+KJTU9wNPmoBa9MWIK1nx8+06fRdONG3O69F6ysyNm1i9j7HyDuscfJO3TI0lUUQtRiFQpNBw4cYPTo0fj6+pa438/Pj9GjR7N///5bqtz1cnJyaNOmTakLBa9YsYIXXniBt956i4MHD9KzZ08GDx5MXFycuUyHDh0ICwsr9ic+3rSEh5ubG4cPHyY6Oprvv/+exMTESr0GUZxKpWLS7SF8/kB79FaO3JP6FN/YPmDauWcRfDsKclLM5Z3d7bjr1fYEhnqg1xnZuPgoBzfFYW9lL8FJAGDj74fPe9OvhKd7TOFp505ixtxP3ONPkHdY1jAUQpRfhW7POTg4MHHiRGbNmlVqmddee4158+aRm5t7SxUsjUqlYtWqVYwcOdK8rUuXLrRv354FCxaYt7Vq1YqRI0cyc+bMcj/H008/Tb9+/bj33ntL3F9QUEDBlRXawdS8FxAQILfnbsHh8+k8/s0+LmcVcLfDIWapP0ejzwG3QBjzPTS62snbaDCy86cojvxtGlnX+srIugJjfpFbdV8M/IJQz1BLXZKoAbQXLpC8cCEZq1bDlQl3HXv1xOvZZ7Fv08aylRNCWFyV3p5r3rw5a9euRX+lz8D19Ho969ato3nz5hU5fYVotVr279/PwIEDi2wfOHAgu3fvLtM5EhMTyczMBEwv4Pbt22nRokWp5WfOnImrq6v5T0BAQMUvQADQJsCN3yZ2p7WPC7/ktmV4/lSyHQMhPQ6+HAhHfzWXVWvU9BzTnB73hoAKju+IZ/28w2j0NszvP9/c4vT4psc5lnzMglclLM3G3x/f99+n6cYNuN5zN2g05GzfQcx9Y4h74gnyIiMtXUUhRC1QodA0fvx4Tp06xaBBg4rdgtu3bx+DBw/m1KlT1TobeHJyMgaDAW9v7yLbvb29uXTpUpnOceHCBXr16kWbNm3o0aMHEydOJCKi9CU63nzzTTIyMsx/zp8/f0vXIEx8XO35+amuDGztzXG9H91S3ibGrQvocmHlI/DnNDCaWgtUKhVtbg9gyNMRWNlqOH8ijV9n78eQoS4anDZLcBJgExBwNTzdfdfV8DT6PuKefJK8I0csXUUhRA1WodtzBoOB0aNHs2rVKlQqFfb29nh7e5OYmEheXh6KojBixAh++eUX1OoK5bKbuv72XHx8PH5+fuzevZuuXbuay33wwQd8++23nDx5skrqcS0ZPVe5jEaFWX+cYuHfZ1FjZJHPWgakrTDtDBkEd38Bdq7m8klxWaz//DA5GVrsna0Z+kwbnPw1PP3n0xy8fBBnG2e+GCC36sRV2rg4khcuIuO338y37Zx698Zz4kTsw2X0pRD1RZXentNoNPzyyy8sW7aMPn36YGtrS1xcHLa2tvTt25dly5axatWqKgtMJfH09ESj0RRrVbp8+XKx1idRO6jVKt4Y3JLZ90Sg0Wh4PGEEc5xeQdHYQdQf8MXtkBxlLu8V6Mw9b3TEM8CJvCwdq+Yc4NKRHBb0X0C7hu3I0l5pcUqRFidhYhMYiO+MD2j6+3pcR40CjYbsv/8m5t57Of/U0+QdlZ8VIcRVFWppiouLw8bGhkaNGlVFncqktI7gHTp0YP78+eZtrVu3ZsSIERXqCF5e0tJUdf47l8JTy/eTlqujj/NFFtvMxSYnHmxd4O4l0HyQuaw2X8+mL48Re8Q04q7rqKY07+vBM389c7XFaeAXhHpIi5MoShsbS/KChWSsWQNGIwBOffvi+eyz2IfJz4sQdVWVtjQ1btyYt956q8KVq6js7GwOHTrEoStzrURHR3Po0CHzlAIvvfQSS5Ys4auvvuLEiRO8+OKLxMXF8dRTT1V7XUXl6tLEg9XPdqeplyPbsvzomzmVVM+OUJAJ398H2z+GK/nfxs6KIU9HENHXH4B/Vp1l74rzfN73c9p6tTW1OG2SFidRnE1QEL4fzjS1PI0YAWo12Vu3EnPPPZx/5lnyjsnPjBD1WYVamry8vHj44YeZPXt2VdSpVNu2baNv377Fto8fP56lS5cCpsktZ82aRUJCAmFhYcydO5devXpVS/2kpanqZeTpmPj9AXZEJWOj0rOq8VpC43827QwdBSM+BxtHc/nIrefZ+VMUigL+LRvQ85EmPL9rIoeSDkmLk7ipguhoUhYuJGPtuqstT7ffjtezz2DXurWFayeEqCxVuozKXXfdRVJSUrln267rJDRVD73ByPR1x/nmn1gAPm58gLsTP0Nl1IF3OIz5DhoEmcvHHEnmjyXH0BcYaODjSL8nmvHqwRc4lHQIFxsXvhj4Ba095AtQlK7gXDTJCxeQuW791fDU/3a8nn0Wu1atLFw7IcStqtLQdOrUKW677Tbz7NtWVla3VNm6QkJT9Vq2O4Zpa49hVGCsXzzT8j5CnZsE9u4w+hto3NNcNul8Fus/jyQnvQB7Z2v6Ph7CO2deleAkyqXgXDTJCxaQuf5qeHIe0B/PZ5/FrmVLC9dOCFFRVRqaHn30UaKioti9ezeNGjWiTZs2eHt7o1Kpip5cpeLLL78sf+1rKQlN1e/v00lM/O4AWQV6OrrlsNz5/7BLigSVBu6YCZ2fgCs/l9lpBayff5jk89lorNX0eKgJH6W8JcFJlFvBuXMkz78Snq58hDoPGIDnxGexu8GEuEKImqlKQ1NZpxJQqVQYrsx9Uh9IaLKMqMQsHl22l/OpeXjaGVkf/DPeMb+ZdrZ9CIbNAStbwDSybvNXx4mJTAag/XB/Pmc6h5MPS3AS5VZw9qwpPP3++9XwNHAgns8+I+FJiFqkSkNTbGxsmcsGBQXdvFAdIaHJclKyC3hq+X72xqShUcOK8P10PD0XFCP4dYT7loOLD2CaNHPXyigit1wAIOQ2L771nMWhFFOL0yd9PuE2n9sseTmilik4e5bkz+eTuWHDdeHpWexaVN9yUkKIiqnS0PTNN9/g7e3NoEGDbl64HpHQZFkFegOTfz3KLwdMYWh62GXGXpiKKj8dnBqZOoj7dzSXP7LtAjtWnEZRoFFzF9Y0nc+B9L0AjG89nufaP4eNxsYSlyJqqYIzZ0ieP5/MDRuvhqdBg0wtT9W4FqcQonyqNDRZW1szadIk5syZc0uVrGskNFmeoigs/PscH200LZtzT2MdH+lmokk+CRobGDYX2j1kLh9zJJlNS46hKzDg2siemG7bWZGwHIDmDZrzYc8PCWkQYpFrEbVXQVQUSfPnk7Xxj6vh6Y478Hr2GWxD5OdJiJqmSie39PHxQavVVrhyQlQVlUrF032asvChDthba1gZbc2ogqnkNhkMBi389ixseB0MOgCCwz2569X2ODWwJeNSHj6buvGW8hkt89pzJuUsY9aN4dvj32JUjBa+MlGb2IaE4D93Lo1/W43z4DsAyNq4kXN3juDCiy9SEBV1kzMIIWqiCrU0Pffcc/zxxx9ERkZia2tbFfWqlaSlqWY5ejGDCcv2kphZgIeDFWsi/sHv0FzTzuCecO8ycPQAICe9gPXzI0mKyzIfb9DoiXM5wXm3E3g1t+edQW/S0KGhJS5F1HL5p0+TPH8BWRs3mjaoVLgMvgPPZ57Btlkzy1ZOCFG1t+cyMjLo168f3t7ezJ49m9BQmVEZJDTVRImZ+Ty2bB9HLmZgo1GzrOtlukZOBm02uAXCmO+hUTgAep2B6EPJxB1LIfZ4KnmZRVtTM+2TCAz1oGfXdviGuGFlo7HEJYlaLP/UaZLnzyfrjz9MG1QqXAYPxvOZpyU8CWFBVRqamjRpQkFBAZcuXQLAzs6Ohg0bljhP09mzZ8t7+lpLQlPNlKc18PLPh/j9iOnndUpnFY+en4wqLRqsHWDkfNMSLNdQjArJF7KJO57CqcMXSY3JQ6VcvZutsVbh17wBgaEeBIV64NrQvtjPvxClyT91iuTP55O1aZNpg0qFy5AhpvDUtKllKydEPVSloSk4OLjMXxDR0dHlPX2tJaGp5jIaFeZsPs28rWcAuKulA7NVn6GJ3moq0PNl6Ps2lDIHWU52Hl/+8SOnD8cTkN4SJ22DIvtdPO0IDPUgMNQDv+Zu2NjJLPni5vJPnjSFp82bTRtUKlyGDjWFpyZNLFs5IeqRKg1NomQSmmq+VQcv8PrKI2gNRiJ8Hfmh8R847p9v2hkyCO7+AuxcSz1+f+J+Jm+fTF6ykaD01nTR344m0Rmj/up/I7WVCt9mbgS29iAw1B13X0dphRI3lH/ihOm23eY/TRvUalN4evppbJs0tmzlhKgHJDRZgISm2mFfTCpPfruflBwt3i62rOx2gYCdr4M+HzxCTP2cvEqfUydLm8WM/2aw7tw6ANq4teM57zfIi9YQdyyFzOT8IuWdGtgS2NqdwFAP/Fu5Y2svrVCiZPnHj5M0fz7Zf/5l2qBW4zLsSnhqLOFJiKpi8dCUmZlJeno6gYGBVXH6GklCU+1xPjWXCcv2cjoxGztrNV8OsKL7vuch8yLYOMGIzyF05A3PsSF6A+/9+x5Z2izsrex5rdNr3NXsLjKT8ok9lkLcsVQunk7DoLs6XYFKraJRExdzXyhPfydUammFEkXlHz9O0ufzyf7ranhyHT4Mj6eekvAkRBWo9NDk7u7Om2++yauvvmretmLFClasWMGvv/5arPy0adOYPn26rD0naqysfB2TfjjItlNJALzT15NHEqajitlpKtB1IvSfBprSW4Yu5VzirZ1vsefSHgD6BvRlarepuNu5A6DXGoiPSifuWCqxx1JIT8wtcry9iw2Brd0JCvUgoJU7dk7WVXClorbKO3aM5M/nk71li2nDlfDk+fTT2AQHW7RuQtQllR6a1Go1U6dO5Z133jFvu1EwktAkoak20BuMfPD7Cb7eFQPAPW29mem2Gut//89UIKgH3PMVOHuXeg6jYuSbY9/w2cHP0Bv1eNh58F739+jp37NY2czkPOKOpxJ7NIULp9LQF1zz/0MF3sEuVzqUu9MwyAW1tEIJIO/oMZI//5zsrVcGLqjVuA4fjuczT2NTj9b3FKKqlPX7WzpXiHrNSqPm3eGhNPVy4t01x1h5KJF/3Poxv1tz2uybDLE7YVEvGL0MAktexFetUvNw2MN09e3K69tf52zGWZ756xnGtBjDSx1fwt7K3lzWxdOesF5+hPXyw6A3knA2g7ijKcQdTyHlYg6J0ZkkRmeyd100do7WBLR2JzDUncDWHji4yDp49ZV9WCgBC+abwtO8eWRv20bGb7+RsW6dKTw9/ZSEJyGqQYWWURGirnnotiC+fbQzfm72XEzPY8QWDyZ7/Q+de3PIvgRLh8K/C83riJWkhXsLfhz2Iw+1Mq1t9+OpHxmzbgwnUk6UWF5jpca/RQO63d2MMVO6MH5mN/qObUnT9l7Y2FuRn6Mjam8ify09wdev7eSnGXv5d/VZ4qPSMRpkWZf6yD4slICFCwj++WecevcGg4GM1as5O2Qo8W9ORhsXZ+kqClGnSWgS4opuzTzZ/FIvnu7TFGuNiu/P2tL58mROew4Aox42vg6/PAbanFLPYWdlx+udX2dh/4V42ntyLuMcD/z+AF8e+RKD8ca3qp0a2NG6uy93PBHOhI97MOqV9nQYHIRXoDMASXFZ7N8Yy6pPDvDlKzvZuOgIx3fGk52Wf8PzirrHPjyMgEULCf75Jxx79zKFp1WrODt4CPGT35LwJEQVkdAkxDUcbKx4/Y6WbHi+F92aepCmt2HghYeZZ/MYRpUVHF0JX9wOyWdueJ7uft359c5fuT3wdvRGPZ8e+JTHNj1GQnZCmeqh1qjxbebGbSOaMnpyJx6Z1YP+D7cipJM3do7WaPP0nD2YxNblJ1n25m5+mP4fu345w4WTqUVG64m6zT48nMBFiwj+aQWOvXqawtOvv5rC01tvoT1/3tJVFKJOkT5NQpSgWUMnvnusC2sjE3hv3XE+zuzHNpUvXzrMwzXpBCzuA6MWQKvhpZ6jgV0D5vaZy+ozq5m5Zyb7Evdx95q7eeu2txjaZGi56uPgYkOL23xocZsPRqNCUmzWlWkNUkiMySQ1PofU+BwObY7DylaDf4sGBIWa5oZy8bS/+ROIWs0+IoLAxYvJO3yYpM8/J2f7DjJ++ZWM39Zg26QJahdnNM4uqJ2dTH9feaxxcUbt7ILG2cn0t4szahcXNE5OqKxlJKcQ1yvX6DlPT088PT3N25KTk0lJSaFFixbFyhfuk9FzorbLzNcxd/Nplu2OwUNJY4Ht/9FRddK0s/sL0G/KDaclADifeZ43dr5BZFIkAIMbD+bt297GxebWf07ys3WcP5Fa6kLDbt4Ops7koR74yULD9ULeoUMkfT6fnB07KnwOlYMDGienq4Hr2qDl5Hw1cF37d+F2FxfUNjJwQdQeVTLlQHmpVCoJTaLOOB6fyZTfjnI4NonXrX7kcavfTTuCe8I9X4OT1w2P1xv1fBH5BYsiF2FQDDRybMSMHjPo1KhTpdXx2oWG446lknA2A8V49b+4xlqNX/OrS7y4eTvIEi91WMG5aHQJ8RizsjFkZWLMzLr6d3bWlcdZGDMzzX8bc3NvfuIyUNnamoKWk3Pxlq0Sg5czGhcX83aVnZ38bIpqY/EZwesjCU11n9GosHL/BT7ceJKueduZZb0IR1UBBicfNPd9CwE3D0CHkw7z5o43OZ91HhUqHg57mEltJ2GtqfzbIQV5ei6cTCXumKklKjutoMh+F087c4Dya9FAFhoWKHo9xuxsDFlZGDIzMZr/vjZ4XRO0soo/rhTW1micndE4X7lleO0txGuDlvM1geua4KV2lF8IRNlJaLIACU31R3qulll/nGLP3n9YaDWXZup4DCorVHd8iLrzY3CTD+tcXS6z9s7il6hfAGjp3pIPe35IU7emVVZnRVFITcgh7mgqccdTiD+TXnShYY0Kn2ZuBIaaZiiXhYZFRShGI8acHHOIuhq8CgPWNcErK/Oa7VeDF8ZKGMyg0Vy5vehSPHg5O1932/Fq8Cosq3Z0RFWBOyyidpLQZAESmuqfQ+fT+WDVfzyc9DFDNaalVFKbjsL9vvlg43DT4/+K+4upu6eSXpCOrcaWlzq8xP0t76+WsKLN13PxdDpxVzqUX7/QsKObrTlA+bdsgK2DdAwWVU9RFIw5uaZAVdiSdW3wyi4MYJnX/Z1lDmno9bdeEZUKtZPTNYHraif5q4+vBi8bf39sQ0JQWUlrbW0kockCJDTVTwajwnf/xpC4aQ4vKsuxUhlJsGuK49jvcfFredPjk3KTmLJrCrvidwGm6Qre7/4+nvaeNzmy8iiKQsblPFloWNR6iqKg5OdfDVTm4HVd0MrKNj++vp+XUlBw8ycqgcrODrvWrbEPD8cuIhz7iAis/f2lxbYWkNBkARKa6rekrAJ+WvkDo2PewUuVQRYOHO74Ed2GjL3pGnKKovDDyR+Ys38OBYYCGtg2YGq3qfQL7FdNtS+qrAsNB4a6E9jKQxYaFnWKsaCgaNC6JmAVCVqF/bzSMyg4d67E/lyaBg1MASo8AvuIcOzCw7Fq0MACVyVuREKTBUhoEgAHjh7HdtUjhBpM0xKsdBxD6AMf0srv5h+UZ9LO8ObONzmZajr27pC7ea3TazhY3/xWX1Uq00LDrd0JDPOQhYZFvaQYjWhjY8k/coS8yCPkRUZScOIEik5XrKx1QMA1rVFtsGvdCrWdnQVqLQpJaLIACU2ikE6bz8lvXiD8wg8A7DCG82+7WTw1uBPOdjduldEatMw7NI+lR5eioBDoHMjMnjOJ8IqojqrfVEkLDV/L1tGKwFamACULDYv6zKjVUnDqFHmRkeRHHiHvyBG0584VL6jRYNuieZHWKNumTVFpZE616iKhyQIkNInrpf33PQ4bX8RWyeei4sFbVq9y150jGB7hc9N+Dnsv7WXyzslcyrmERqXhyTZP8nj441ipa1ZH0+y0fOKOm6Y0OH8iDW1e0U64ngFOBIV6EBjqgXcTFzQaGZEk6i9DZib5x46RdziSvCNHyIs8jCEpuVg5lYMD9qGh5tYo+4hwrBo1kv5RVaRKQ9P06dPp06cPvXr1KrXMrl27+Ouvv3jnnXfKe/paS0KTKFHicXKX349DVgwFihXT9OOJCbqX6SPDadbQ6YaHZhRk8MG/H7AhZgMAbbzaMLPHTAJcAqqj5uVmNBi5FJ15ZUReKklxRft42NhbEdCyAYGhHgRHeEorlKj3FEVBn5hoao26cmsv/8iREicZ1Xh5FmmNsg8PRyPfNZWiSkOTWq1m6tSpNwxEH330EZMnT5YZwYUAyM/AsOoZNKfWAfCzvhfTlAmM69mSSf1CsL/J0ibrz63n/X/fJ1uXjYOVA290foORzUbW+N86czO1nD+eQuyxVM4fTyU/52r/DpVaRUDLBoR09qZJGy9s7GtWC5oQlqIYDGjPnTP1jTpiurWXf/p0iVMp2AQHY98mArsrYcq2ZUtZwqYCLB6a3nvvPaZPn46uhE5wdZWEJnFDigK7PkP5axoqxcgxYxBP6V7A6BrMu8NbM6C19w1DUHx2PJN3TmZ/4n4A+gf2592u7+Jm51ZNF3BrChcajjueQvTh5CKtUBprNcFhHoR09iYozAMra+nLIcS1jPn55J84YWqNunJrTxcXV7ygtTV2LVsW6WhuExwkE3XeRJWHpmnTpjFlypQS92u1WoYNG8bx48e5cOFCeU9fa0loEmVy7m+UlY+iyk0mE0ee0z7DNmM7+rVsyNThoQR6lD5SzmA0sPTYUuYdmofeqMfL3ov3u79PN79u1XgBlSM9MZeofYmc3pNYZEoDGzsNTdp5EdLJG/8WDVBLHyghSqRPSyP/6NGrHc0jIzGkpRUrp3Z2xj48zNwaZRcejnXDhhaocc1V6aGpSZMm5n/HxMTg5uaGm5tbsXIGg4Hk5GTy8/N5/PHHWbhwYflrX0tJaBJllnERfhoHF/cB8H+Gu5iruwtrKyue7duMJ3o1we4GrS3HU47zxo43iM6IBuChVg/xQocXsNXYVkv1K5OiKCSfzyZqbyJR+xKLrI9n72xNsw7ehHTyplETlxp/O1IIS1IUBd3FePKPRJpbo/KPHUPJzy9W1qpRo6LTHoSGonFytECta4ZKD03BwcHmD6y4uDhcXFxKDE0ajQZ3d3f69evHlClTcHSsP2+ChCZRLvoC+GMy7F0CwGHbDozPeIJ0nAn2cGD6iDB6Nfcq9fA8fR5z9s3hx1M/AtDMrRkf9vyQFu4tqqX6VUExKiSczeD03kTO7r9cpA+Us4cdIR29ad7ZGw+/G3egF0KYKHo9BWfOFOloXhAVVXx9P5UK22ZNi7RG2TVvjsq6fkxca/E+TfWRhCZRIYd/hLUvgD6PXAdfntK+wPZsfwCGhDdiyrDW+Ljal3r49gvbeWfXO6Tkp2Cttub59s8ztvVY1KrafVvLYDBy4UQap/deIvpQMrprJtR093UkpKOpBcrVq/TXRghRnDE3l/zjx6+2RkVGoouPL1ZOZWuLXatWRTqaWwcE1MkW3yoNTbGxsbi5ueHq6npLlaxrJDSJCrt0FFY8BGnRKBpb1vm/xAtRERiMCg42Gl7oH8Ij3RtjXUr/ntT8VN7d/S7bzm8DoEujLrzf430aOTaqvmuoQjqtgZjIZKL2JhJ7LAWj/urHlndjF0I6edOsQ0McXWvf7UkhagJ9crIpQBXOaH7kCMaMjGLlNK6u2EVEFFlfz8rd3QI1rlwyuaUFSGgStyQvHVY9BadNczKltbyfZ1LH8E+cacbt5t5OvDcijC5NPEo8XFEUfon6hVl7Z5Gnz8PZxpl3ur7DHcF3VNcVVIuCXB1nDyYRtTeRi6fSKPwEU6nAr0UDQjp507SdF7YO9eO2ghBVQVEUdLGxVybgNLVG5Z84gaLVFitr7edXpDXKrnVr1Pa1qwW4ykNTUlISX3/9NXv37iU9Pb3E+ZhUKhV//fVXRU5fK0loErfMaISdc2DrB6AYUXza8nurj5jydxapOaYPq7va+fHmkFZ4OZfcqhKbGcsb29/gaMpRAIY3Gc6bXd7E2ca52i6juuRkFHBm/2Wi9iaSGJ1p3q62UhEU6kFIJ2+CIzyxvsk8WEKIm1O0WvJPRxXpaK49dw6ujxEaDbYhIUU6mts2q9nLwlRpaIqMjKRfv36kpaVxo8NVKpVMbilERZzdAisnQF4q2Dcge9hCZpz244c9cSgKONtZ8eqgFjzYJQhNCYvj6ow6Fh1exBdHvsCoGPF19GVGzxl08O5ggYupHhlJeUTtSyRqbyKp8VfXw7O21dC4rSchHb0JaO0uy7gIUYkMWVmmZWEij5jCVOQR9ImJxcqpHBywb93adGsvwjSbuZWvb43pH1WloWnAgAH89ddfvP3220yYMAF/f380NThBVhcJTaJSpZ83TUsQfwBQQd/JHG78GG//dpwjF019DcL8XHhvRBjtAhuUeIpDlw/xxo43uJh9EbVKzYSwCTzd5mmsNXX71lXKxWxO7zUFqKyUq8Ot7RytadqhIc07NcSnqRuqEgKnEOLW6AqXhbnSNyr/yBGMOTnFymk8PLAPD796ay88DI2F+kpXaWhycnJi4MCB/Prrr7dUyZrIysqKsLAwADp27MiSJUvKfKyEJlHp9AWw4XXY/7XpccggDCMX8f2RTGZvPElmvh6VCsZ0CuT1O1rg5lB8+YRsbTYf7f2I1WdWA9DaozUze86kiWuTYmXrGkVRSIzO5PTeRM7sSyQv6+oUBk4NbGnW0ZvmnbzxDHCqMb/xClHXKEYj2ujoIq1R+adOQQkrhtgEBZk7mttHhGPbqhVq26of4FGlocnd3Z0JEyYwe/bsW6pkTeTp6UlycvEVp8tCQpOoMge/g/UvgT4fGgTD6G9Jdm7BzN9P8ssB06z77o42vHFHS+7p4I+6hBaUTTGbmPbPNDK1mdhp7Hi106vc2/zeehMWjAYjF0+lc3rvJc4dTEKbf7XrgJu3AyGdTAHKzbv0GdmFEJXDWFBAwYkT5pF6+ZGRaGNjixe0tsauRYsrc0eZbu3ZNG5c6cvCVGlouueee0hJSWHr1q23VMmaSEKTqLESIk3TEqTHgpUdDJsLbR9gT3QqU1Yf5VSiaS23DkENeG9EGK19i/8MJuYk8vaut/k34V8Aevn3Ylq3aXjae1brpViaXmcg9mgKUXsTiTmSgkF3daI/r0Bnmnf2plkHb5wayBQGQlQXQ3o6eUePFelobkhJKVYu6NtvcOjUqVKfu0pDU3R0NF26dOG1117jlVdeuaWKlsf27duZPXs2+/fvJyEhgVWrVjFy5MgiZebPn8/s2bNJSEggNDSUTz/9lJ49e5b5OWxsbAgPD8fe3p4PPviA3r17l/lYCU2iyuWlwa9PQNQm0+MOj8Dgj9CprFm6K4ZP/zxNjtaAWgXjuwXz0oDmONsV7b9kVIx8d+I7Pt3/KVqjFnc7d6Z3m07vgLL/rNcl2jw95w6bpjA4fyINxVg4hwH4hbhdmcKgIXZOdbsfmBA1jaIo6OPji057cPIkIX9vQ13Jq41UaWh69NFHiY6OZvv27TRu3Jg2bdqUONGlSqXiyy+/LO/pS7VhwwZ27dpF+/btufvuu4uFphUrVjB27Fjmz59P9+7dWbRoEUuWLOH48eMEBgYC0KFDBwoKCoqde9OmTfj6+hIfH4+vry9Hjx5l6NChHDlypMwBSEKTqBZGI2yfDdtmAgr4tofR34BbAJcy8nlv/XHWRyYA4OVsy9tDW3Fnm+KjVE6nneaNHW8QlRYFwOjmo3m548s4WNff21N5WVrTFAb7Ekk4c3ViP7VaRWCou3kKAxs7KwvWUoj6SzEaK/3WHFTDMiplUZVTDqhUqmKhqUuXLrRv354FCxaYt7Vq1YqRI0cyc+bMcj/H4MGDee+99+jYsWOJ+wsKCooEsMzMTAICAiQ0ieoR9Sf8MgHy08HeHe75Cpr2BWBHVBLv/HaM6GTTiJVuTT2YPiKUZg2LztVUYCjgfwf+xzfHvwEg2CWYD3t+SKhnaLVeSk2UmZLHmX2XOb03kZQL2ebtVjZqGkd4EtLJm8BQDzRWMoWBELVdlS+jUlZBQUHlPX2ZXB+atFotDg4O/Pzzz4waNcpc7vnnn+fQoUP8/fffNz1nWloaDg4O2NracuHCBbp3787BgwdxL2WK+KlTpzJt2rRi2yU0iWqTFgs/jYWEw6BSQ9+3oMdLoFZToDew+O9zzNt6hgK9EWuNisd6NmFSv2Y42BRtKfk34V/e2vkWl3MvY6Wy4pm2z/Bo2KNo1DKVCEBqQg5RexM5vTeRzKQ883ZbByuatvMipHMjfEPcSuyAL4So+er8MirXh6b4+Hj8/PzYtWsX3bp1M5ebMWMGy5Yt49SpUzc95+7du3nyySdRq9WoVCqmTp1arM/UtaSlSdQIunz4/RU4+K3pcYshMHIB2LsBcD41l2lrj/HnicsA+LnZ887w1gxs7V3kll1GQQbT/5nOplhTf6l2Ddsxo8cM/J39q/VyajJFUbgcm0XUnkSi9ieSm3F1SQkHVxtCOngT0tmbhkHO9WZUohB1QbWGptTUVHJycggICLjVU5VZaaFp9+7ddO3a1Vzugw8+4Ntvv+XkyZNVXifp0yQsav8y+P1VMBRAg8Zw33JoFGbevfl4IlPXHONiuqmlpG8LL6bdGUagx9U+TIqisPbcWmb8N4McXQ6O1o681eUthjUZJiHgOkajQnxUOlF7LnH2YBIFuXrzPlcve0I6eRPSyRt3n8rtsCqEqHxl/f6u8M34jIwMnn/+eby9vfHy8qJx48bmff/99x9Dhgxh//79FT19uXl6eqLRaLh06VKR7ZcvX8bb27va6iGExXQYDxP+ANdASIuGJf3h8Arz7gGtvfnzpd4827cp1hoVW08lMWDu33z2ZxT5OlPfQ5VKxZ1N72Tl8JW0a9iOHF0Ok3dO5tXtr5JRUHzF8/pMrVbh36IBfce24pGPejDk6XBCOjbEykZNRlIe+36P4Ydp//Hj+3s48EcsWan5Nz+pEKJGq1BLU2pqKt26deP06dO0b9+e/Px8Tpw4Ye70nZeXR6NGjZgwYQJz5syp9EpD6R3BO3TowPz5883bWrduzYgRIyrUEby8pKVJ1Ai5qfDLY3D2ymLZnR6HQTPA6ups4WeTsnn3t2PsPGOakyzYw4Gpd4bSp0VDcxmD0cBXR79i/qH56BU9DR0a8kGPD7jN57ZqvZzaRpuvJyYymai9icQdS8VovPoR69PMlZCO3jTr0BB75+KztwshLKNKb88999xzzJs3jx9++IH77ruPadOmMX369CIj5e68807i4uI4dOhQhS6gJNnZ2Zw5cwaAdu3aMWfOHPr27Yu7uzuBgYHmKQcWLlxI165dWbx4MV988QXHjh2rsg7p15LQJGoMowH+/sj0B8C/E9y7DFz9zEUURWFdZALvrz9OYqapb97gsEZMGdYaXzd7c7mjyUd5c8ebxGTGADC+9Xiea/8cNhr50r+Z/GwdZw9e5vSeROLPpEPhFFBqFQGtGhDSyZsmbbywsZcpDISwpCoNTcHBwURERLBmzRqAEkPTpEmT+OGHHyo8u3ZJtm3bRt++fYttHz9+PEuXLgVMk1vOmjWLhIQEwsLCmDt3Lr169aq0OtyIhCZR45z+A359HPIzwMET7v0aGhf9/5BdoOfTzaf5encMBqOCg42G528P4dEejbHWmO7g5+py+WTfJ/x0+icAmjdozoc9PySkQUi1X1JtlZ1WwJn9iZzek0hSXJZ5u8ZaTXC4B807NSIwzB0raxmxKER1q9LQZGtry4svvsiHH34IlByaXn31VebNm0deXl5pp6lzJDSJGik1GlaMhcQjpmkJbn8Xuj8P13XsPpGQyZTVR9kXmwZAc28npo8I47YmHuYy285v493d75Kan4qN2oYXO7zIA60eQK2SuYrKIz0xl9N7E4nam0h6Yq55u42dhibtvGjeqRF+LdxQa+R1FaI6VGlo8vX1pW/fvnz33XdAyaFp+PDhHDt2jHPnzlWg+rWThCZRY+nyYN1LcPh70+OWw0zTEtgV/Tk1GhV+OXCBDzecJCXHNJx+VDs/3hzSkobOdgAk5yXz7u532X5hOwBdfbryfo/3aejQEFE+iqKQfD6b03sTObMvkey0q1OY2Dtb06yjaRFh78YuMnpRiCpUpaFpzJgxrF+/npMnT+Ln51csNB0/fpw2bdrwyCOPsHjx4opfRS0joUnUaIoC+7+GDa+DQQsezUzTEjRsVaxoRq6O2ZtO8t1/cSgKONtZ8crAFjx0WxAatQpFUfj59M/M3jubfEM+rrauvNv1XQYEDbDAhdUNilEh4Ww6p/de5uz+y+Tn6Mz7nD3sCOlkClAefk4WrKUQdVOVhqYjR47QuXNnvL29mTFjBv/88w/z58/n6NGj7N69m7feeovs7GwOHjxISEj96fMgoUnUChf2w0/jIPMCWDvAnf8H4feUWPTw+XSm/HaUyAum6QbC/Fx4b0QY7QIbAHAu4xxvbH+DE6knABjZbCRvdH4DR2uZm+hWGAxGzh9PJWpfIucOJaMvuNqK7+7raA5QLp72NziLEKKsqnxyyzVr1jBu3DiyskwdGhVFQaUy/Qbq7OzMDz/8wJAhQypW+1pKQpOoNXJS4JdH4dw20+MuT8GA94pMS1DIYFT4fk8cszeeJDNfj0oFYzoF8NqgljRwtEFn0LHg8AKWHFmCgoK/kz8ze86kbcO21XpJdZVOazBPYRB7LAWj/upHtndjF0I6maYwcHS1tWAthajdqmVG8NTUVJYtW8Z///1HamoqLi4udOnShUceeQRPT8+KnrbWktAkahWjAbZ+ADs+MT0OuA3uXQouPiUWT84u4MMNJ1m5/wIADRyseWNwS+7tEIBarWJ/4n4m75hMfE48apWaJyKe4ImIJ7BWW1fTBdV9Bbk6zh5MImpvIhdPpVH46a1SgV8L0xQGTdt5Yesgr7kQ5VHn156riSQ0iVrp5O+w6kkoyATHhqbgFNy91OJ7Y1KZsvooJy+ZWpnbB7rx3sgwQn1dydJmMfO/maw9txaAcM9wZvacSZBL1c+TVt/kZBRwZt9lovYlkhidad6utlIRFOpB886NCA73wMpGpjAQ4mYkNFmAhCZRa6WcNU1LcPkYqDQwYBp0nVhsWoJCOoORZbtjmLv5NDlaA2oVjOsazEsDm+NiZ83G6I1M/3c6Wdos7K3seb3T69wVcpeMAKsiGUm5RO29zOm9iaQl5Ji3W9tq8G/ZAJ9mbvg2c8Mz0AmNTGMgRDFVGprmzJnDjBkziIyMxNfXt9j++Ph42rRpw5QpU3juuefKe/paS0KTqNW0ubDuBYi8sl5d6xEw4nOwdS71kEsZ+by//jjrIhMA8HK25e2hrbizjS+JuYm8vfNt/rv0HwB9A/oytdtU3O3cq/pK6i1FUUi5mEPUlTmgrl/vzspGjXdjV3ybueIT4kajxq5Y20pLlBBVGpq6du2Kvb09W7ZsKbXMgAEDyMnJYffu3eU9fa0loUnUeooCe5fAxjfBqAPP5qZpCbxa3PCwnVHJvPPbUc4lm1o5ujbx4L2RoTTxcuTb49/y2YHP0Bl1eNh58F739+jp37M6rqZeUxSFy7FZxJ9OJ/5MOgln0ynI0Rcpo1Kr8ApwwifE1BLl09RV1sQT9VKVhiYPDw8efPBB/ve//5Va5oUXXuC7774jKSmpvKevtSQ0iTrj/B74aTxkxYONE4yYB6GjbnhIgd7Akh3R/N+WKPJ1RqzUKh7r2YTnbm/G+eyzvLHjDc6km9aOvK/FffT0uxqcrr9tp0JVZLv58ZW/UZW97M3OfaNzXF+/YvUo6RjzXyXX4/pzl6XuN7zuMr5WilEh67KW1Oh8UqLzSI3OIy+9aIgCcGpojUdje9yv/HFoYCW3VUWN0sihEdaayh3sUKWhyd7enhdeeIGZM2eWWuaNN97gs88+k2VUhKitspNg5SMQs8P0uOtE6D8VbvJhdT41l2lrj/PniUQA/NzsmTKsNb1buPK/g/9j+YnlVVxxUVZOBQ1olNkEn6wm+GQ2xT2v+MjJbJt0EpzPcsnlHAnO50h1SACVdIUVlrNm5Boauzau1HNWaWhq2bIl3t7e/P3336WW6d27N/Hx8URFRZX39LWWhCZR5xj0sGU67PrM9DioO9zzNTh73/TQP48nMnXtMS6kmX5x6tPCi2l3hnIx/zBfHfuKXF0uhR8/Clc/hgr/ff1H0/XbzY+vPfb6Y0opW9LHXqnnL6GO15+jtPObj1FKLleW85fpmFLOX96PdxudPQ0zg2iYGYx3RmM8sv1QK0X7PBVY5ZHkEstllxgSXWJIcb6AUW0o5YxCVL4Vw1YQ7Bpcqees0tD0yiuvMHfuXL744gseffTRYvuXLFnCk08+yfPPP8+cOXPKe/paS0KTqLOOr4HVz4A2C5wamaYlCOp608PytAbmbzvDor/PoTUYsbFS80yfpjzVuyl21tIBuabTaQ1cjs409Yk6k86lc5noCooGJI2VmobBzqY+USFu+DRxxcbeykI1FqJiqjQ0JSUl0a5dOxISEujduzcDBgzAz8+PixcvsmnTJrZv346vry8HDhzAy8vrli6kNpHQJOq05CjTtARJJ0BtBQPfN80kXob+LueSsnl3zTF2RCUDEOThwPsjw+gZUn8+H+oCo8FI8oVsEs5kmINUXpauSBmVCjz8nczTHPg0c5XZykWNV+XzNEVFRfHQQw+xd+9e04muLKEC0LlzZ5YvX06zZs0qcupaS0KTqPMKsmHtc3D0F9PjsLth+P/A9uaLyCqKwu9HLjF93TESMwsAuKu9H1OGtqaBo4zYqo0URSHjcp4pQEWlE382g8yk4v1YXbzsTdMcXAlSrg3tpXO5qFGqbXLLffv2sWfPHtLT03Fzc6Nz58507NjxVk5Za0loEvWCosB/i2DTW2DUg1dL07QEnmVbnDu7QM/Hf5xi2T8xKAp4ONrwzvDW3NnGV75I64Cc9IIrrVAZJJxNJ/lCNtd118LexQbfpldCVIgbHn6OqGXSTWFBVRqaHn30USIiInjhhRdupY51joQmUa/E/WualiD7Etg4w8j50PrOMh9+MC6NN345wqlE03IsfVp48f7IMPwbOFRVjYUFFOTpuXT26u28yzFZGPTGImWs7TQ0anJl0s2mbng3dpHlX0S1qtLQZGdnx4svvnjDKQfqIwlNot7JSjRNSxC7y/S4+/PQ7x3QlK0jsFZvZPH2s/zvrzNoDUYcbDS8MrAF47sFo1FLq1NdpNcZuBybRYK5NSoDbV7R+aLUGhUNg5zxaeZm+tPUFTtHWYRYVJ0qDU1hYWF07NiRpUuX3kod6xwJTaJeMujgz6nwzzzT4+CecM9X4NSwzKc4m5TNm78eYU90KgBtAtz46O5wWjaS/0d1ndGokBqfTXyU6XZeQlQ6ORnaYuXcfR3NHct9mrnh7G5ngdqKuqpKQ9PcuXOZMWMGhw4dws/P75YqWpdIaBL12rFV8NtE0GaDsy+MXgYBnct8uNGo8OPe88z8/QRZBXqs1Cqe6t2Uif2ayfQE9YiiKGQm519piUon/kwG6Ym5xco5u9vhE2K6nefbzI0GPg7SJ05UWJWGppiYGCZOnMiRI0d47bXX6NSpE97e3iX+wAYGBpb39LWWhCZR7yWdghUPQfJpUFvDoBnQ+fEyTUtQKDEzn3d/O8bGY5cAaOLpyIy7wrmtiUdV1VrUcLmZ2iL9opLOZ6MYi3512Tlam1qhmrrhE+KKV6AzGulcLsqoSkOTWq02TzFwo2SvUqnQ64uvbVRXSWgSAijIMrU4HV9tehw+GoZ/CjaO5TrNxqMJvPPbMS5nmaYnuL9zIG8MbomrvfRtqe+0+XoSr5l0M/FcJnpd0c7lVtZqvJu4mKc58G7sgo2dTLopSlaloenhhx8uczPo119/Xd7T11oSmoS4QlHgn89h8zugGKBhKNz3LXg0LddpMvJ0fLTxJN//FwdAQ2dbpo8I5Y6w4mukifrLoDeSdD6LhKgrrVFn0ynIKfoLu0qtwivg6qSbjZq64uAi84MJk2qbp0lcJaFJiOvE7IKfH4acy2DrAqMWQsuh5T7Nf+dSePPXI5xLzgFgUKg300eE4e0inYFFcYpRIfVSjml03pl04s+kk51aUKycm7eDaZqDEDd8mrrh4mkn/aLqKQlNFiChSYgSZCaYgtP5f02Pe7wE/d4Gdfk6d+frDHy+9QwLtp1Fb1RwtrXijSEtub9TIGqZnkDcRFZqvrljecKZdFLjc4qVcXS1wSfk6vIv7r5O8rNVT1RLaLp06RK//vorJ0+eJCcnhy+//BIwrU0XHR1NeHg49vb2FT19rSOhSYhSGHSwaQr8t8D0uHFvuPtLcCr/2nMnL2Xyxi9HOHQ+HYDOwe7MuCucZg1vvpSLEIXyc3QknM0wLf9yJp2k2CyM13Uut7G3wqepq3maA+8gFzTW0rm8Lqry0DR//nxefvllCgpMTZ4qlQqDwbT69bFjx4iIiGDhwoU8/vjjFTl9rSShSYibOLIS1kwCXS44eMDQTyB0VLlPYzAqfPNPDLP/OEWu1oCNRs2kfs14sndTbKzkS02Un05r4PI1ncsvnctEV2AoUkZjpaZhsPOVlihTvyhbe+lcXhdUaWhau3YtI0aMoGPHjrzzzjts2LCBhQsXmkMTQLt27fD19WX9+vUVu4JaSEKTEGVw+QT88hgkHjU9bj0ChnxSoVanC2m5vL36KNtOJQHQwtuZmXeH0z6wQWXWWNRDRoOR5AvZJJy5OtVBXpauSBmVCjz8nfBp6oa7ryP2ztbYO1lj52iDnZM1do5WsqZeLVGloalXr17ExcVx7NgxHB0dmTZtGtOnTy8SmsaNG8eOHTuIjo6u2BXUQhKahCgjvRZ2fAw7PjEt+uvgAUM+hrC7yn0qRVFYczie6WuPk5KjRaWC8V2DeWVQC5xspRVAVA5FUci4nGcKUFHpxJ/NIDMp76bH2TpYYed0JUw5mcKUvaO1KVRds93+ymNbeytU0o+q2pX1+7tCnyiHDh1i7NixODqWPu+Kn58fiYmJFTm9EKKus7KBvpNNI+lWP2NqdVr5iGlup3K2OqlUKka09aNXiBfvrz/BLwcusHR3DJuOXeL9UWH0a+ldddch6g2VSoWbtwNu3g607u4LQE56wZUpDjLISsknP1tHfo6OvGwtBbl6UKAgV09Brp6MyzcPWKbn4UorVWGosikasEoIXDZ2Ghn1V00qFJqMRiPW1jeeYC4pKQlbW9sKVUoIUU/4tIHHt15tdTr+G8TsNLU6hY4q10ziDRxt+GR0G0a282XyqiOcT83j0aX7uLONL+8Mb42nk3weicrl6GZLSEdvQjoWD+ZGg5GCXD152TpTmMo2hSlTqLp2m478bC352Tq0+QYUBfKydMVuBd6IWq26Lkhd03rleP12UxCzslFL0KqACoWmFi1asHPnzlL36/V6/v77b8LDwytcMSFEPVFaq9OxVTB0Trn7OvUM8eKPF3rx6Z9RLNlxjjWH49kelcTbQ1tzd3s/+aIQ1UKtUWPvbIO9c9kn0DTojeTnXBeockyhqnjQ0pGXo0NfYMBoVMjN1JKbWXyh49JorNVXg5TjdbcPna5rzXI0BTAZOVjB0PTggw/yyiuv8P777/P2228X2WcwGHjllVc4d+4cr7/+eqVUUghRD1zf6nRijanVaejHEHpXuVqdHGysmDykFcMjfHn9l0iOJ2Tyys+HWX3wIjNGhRPo4VCFFyJExWis1Di62uLoWvZWUb3WcMPWq7yc64JWthajXsGgM5KdVkB2WvFJP0tjbaspHqocbUpo5TK1Ztk6WtW59f8q1BFcp9MxcOBAtm/fTrNmzbC1teXYsWPcfffd7Nu3j5iYGAYOHMiGDRvq1W910hFciEqScBhWPwuJR0yPW91pmp7AqWG5T6UzGFmyI5pP/zxNgd6InbWalwY059HujbGqYx/oQtyMoijoCgzX9L+67vbhda1chfuvXyC5rGwdrEq4RWhTQivXlaDlYJmO8FU+T5NWq2XatGksXLiQtLQ083YXFxeefvpppk2bho1N/VrXR0KTEJVIrzW1OO342DTCzt69Qq1OhWKSc3jz1yP8cy4FgDA/Fz68K4IwP9fKrrkQdYpiVNDmX98/68q/c0q+dZifq4MKpAuVCmyvCVIl3ToMjvDEzrFyF+6utmVUFEXh1KlTpKam4uLiQqtWrdBoyrc8Ql0hoUmIKpAQeaWvU2Gr0/ArfZ3K3+qkKAo/77/AB+tPkJGnQ6NW8VjPxrxwe3Psbern55YQVcFoVCjIvS5IXd+add2tQ22e/uYnBsa80xkP38pdAaBKQlNaWhrz5s1j7969AHTu3JmJEyfi5uZ2yxWuCyQ0CVFF9FrYOQe2z77a6jRkNoTdXaFWp8tZ+Uxbe5z1kQkABLo7MPOucLo386zsmgshyqisHeH7P9oae6fKvZNV6aEpJSWFzp07ExMTQ+EhKpWKJk2a8N9//+Hu7l45Na/FJDQJUcUqsdUJ4M/jiUz57SgJGfkA3NvBn7eGtsLNoX51LRCivivr93eZe0HOmDGD6Oho+vTpw48//siPP/5Ir169OHv2LB9++GGlVFoIIW7IJwIe3wJ93gS1FZxYC593Ma1pV4GeBv1be7PpxV6M7xqESgU/779A/zl/s/ZwPLfYc0EIUQeVuaWpVatWGAwGTpw4Ye6zpNfradWqFdbW1hw/frxKK1obSEuTENWoklud9sem8sYvR4i6nA1Av5YNeW9kGH5u9pVVYyFEDVXpLU2xsbEMGjSoSCdvKysr7rjjDmJiYm6pskIIUW4ltjp1rnCrU4cgd9Y914MX+zfHWqNiy8nLDJzzN8t2x2Co4HBrIUTdUubQlJ+fj5dX8Zl5PT09KSgo++RYQghRaaxsoM8b8MQ2aBQOeWnwywRY8RBkXy736WytNDzfP4Tfn+tJh6AG5GgNvLvmGPcs3M3pxKzKr78QolaRmd2ucerUKdq2bWv+Y29vz+rVqy1dLSHEzTQKN80mXtjqdHLdLbU6hXg78/OTXXlvZBhOtlYcjEtn6P92MGfzaQr0hiq4ACFEbVDmPk1qtZr+/fvTv3//Its3b97Mli1b+PDDD0vsOPnaa69VTk2rWXZ2NsHBwcTGxuLo6FimY6RPkxA1wKUjsPpp098ALYeZ+jo5F19UtSwSMvKYsvoYf55IBKCplyMf3h1Bp2AZMSxEXVHpUw6o1eVvlFKpVBgMtfO3su+//57ffvuNFStWlPkYCU1C1BAGHeyYA9tnXZnXqQEM+bjC8zopisKGo5d457djJGebuiM8dFsgr93REhe7yp2ZWAhR/So9NC1btqxCFRk/fnyFjivJ9u3bmT17Nvv37ychIYFVq1YxcuTIImXmz5/P7NmzSUhIIDQ0lE8//ZSePXuW+7lGjhzJuHHjuOuuu8p8jIQmIWqYSm51ysjVMeP3E6zYdx4Abxdb3hsRxsDQRpVVYyGEBVTbMirVacOGDezatYv27dtz9913FwtNK1asYOzYscyfP5/u3buzaNEilixZwvHjxwkMDASgQ4cOJXZc37RpE76+voDpxWvatCnnz5/Hzs6uzPWT0CREDWTQwc658PcsMOpMrU6DZ0P4PRVqdQLYfTaZyb8eISYlF4Ah4Y2YOjyUhi5l/7wQQtQcdTI0XUulUhULTV26dKF9+/YsWLDAvK1Vq1aMHDmSmTNnlvnc3377LX/88QfLly+/YbmCgoIiASwzM5OAgAAJTULURJeOXml1ijQ9vsVWp3ydgf/9FcWi7ecwGBWc7ax4a0gr7usUgKqCYUwIYRmVPk9TTafVatm/fz8DBw4ssn3gwIHs3r27XOf66aefuO+++25abubMmbi6upr/BAQElOt5hBDVqFGYaV6nvm+B2to0wm5+F4j8uUIj7OysNbx2R0vWTuxBhL8rWfl63vj1CGMW/8u5pOwquAAhhKXVmdCUnJyMwWDA27vob43e3t5cunSpzOfJyMhgz549DBo06KZl33zzTTIyMsx/zp8/X+56CyGqkcYaer92ZV6nCNO8Tr8+Bj8+CFmJFTpla18Xfn26G28PbYW9tYb/olO547MdfL71DDqDsXLrL4SwqDoTmgpd3yyuKEq5mspdXV1JTEzExubmC3ba2tri4uJS5I8Qoha4vtXp1HrTvE6RP1Wo1clKo+axnk3Y9GIveoZ4otUbmf3HKYb/304On0+v/PoLISyizoQmT09PNBpNsValy5cvF2t9EkKIYq1O+enw6+NXWp3K3jp9rQB3B755tDNz72tDAwdrTl7KYtT8XUxfe5ycAn2lVl8IUf3qTGiysbGhQ4cObN68ucj2zZs3061bNwvVSghR45lbnd6+ptWpS4VbnVQqFaPa+fPnS70Z1c4PowJf7Ypm4NztbDtV/qVdhBA1R60KTdnZ2Rw6dIhDhw4BEB0dzaFDh4iLiwPgpZdeYsmSJXz11VecOHGCF198kbi4OJ566ikL1loIUeNprKH3q6ZWJ58217Q6PVDhVicPJ1vm3teWpY90ws/NnovpeTz89V5e+PEgKdmyXqcQtdEtTTlw6dIlfv31V06ePElubi5LliwBICkpiejoaMLDw7G3t6+0ym7bto2+ffsW2z5+/HiWLl0KmCa3nDVrFgkJCYSFhTF37lx69epVaXW4EZmnSYg6wKCDnZ/C3x+Z5nWyc4PBsyBidIXndcop0DNn82m+3hWNUYEGDtZMGdaaUe38ZHoCIWqAKp+naf78+bz88svmeYquXTLl2LFjREREsHDhQh5//PGKnL5WktAkRB2SeMw0r1PCYdPjFkNg2Fxwrvjs34fOp/PGL5GcvJQFQM8QT2aMCifA3aEyaiyEqKAqnadp7dq1TJw4kfDwcNasWcPTTz9dZH9oaCgRERGsXr26IqcXQgjL8w6Fx/6CfoV9nX439XU6vKJCfZ0A2ga4sXZSD14d1AIbKzU7opIZOHc7S3acQy/TEwhR41UoNM2ePZvAwEC2bt3KsGHDaNiwYbEy4eHhHD9+/JYrKIQQFqOxhl6vwpN/X+3rtOqJW+rrZK1R82zfZmx8viddGruTpzPw/voT3LVgN8fjMyu3/kKISlWh0HTo0CGGDh2Ko6NjqWX8/PxITKzYZHFCCFGjlNjq1BkO/1jhVqcmXk788PhtfHhXOM52VkReyGD4vJ18tPEk+TpDJV+AEKIyVCg0GY1GrK2tb1gmKSkJW1vbClVKCCFqnGKtThmw6kn44f4Ktzqp1SrGdA7kr5d6MzisEQajwoJtZ7nj0+3sPptcyRcghLhVFQpNLVq0YOfOnaXu1+v1/P3334SHh1e4YkIIUSNd3+p0esMttzo1dLFjwUMdWDS2A94utsSk5PLAF//x+spIMnJ1lXwBQoiKqlBoevDBBzlw4ADvv/9+sX0Gg4FXXnmFc+fOMW7cuFuuoBBC1DjmVqft4NP2mlanMZCZUOHTDgptxOaXevNgl0AAVuw7z+1z/ub3IwncwuwwQohKUqEpB3Q6HQMHDmT79u00a9YMW1tbjh07xt13382+ffuIiYlh4MCBbNiwoV7NQSJTDghRDxl0sOsz2PbhlXmdXK/M63Rfhed1AtgTncqbv0ZyNikHgP6tvHlvZCg+rpU3950QwqTK52nSarVMmzaNhQsXkpaWZt7u4uLC008/zbRp08q06G1dIqFJiHos8fiVeZ0OmR43vwOGfQouPhU+Zb7OwPytZ1jw91l0BgUnWytev6MFD3YJQq2uP7+QClHVqjw0FVIUhVOnTpGamoqLiwutWrVCo9HcyilrLQlNQtRzBj3s+rRoq9MdH0GbMbfU6nTqUhZv/BrJwbh0ADoGNWDmXeGEeDtXTr2FqOeqLTSJqyQ0CSEAU6vTb89A/EHT40podTIYFZb/G8usjSfJ0Rqw0ah5pm9Tnu7TFFur+vmLqhCVpUpnBG/evDnvvfce586dq3AFhRCizvJuDRP+hNvfAY0NnN4I87vAoR8qPMJOo1Yxvlswm17qTb+WDdEajHz6ZxTD/reT/bGplXwBQoiSVKilydXVlaysLFQqFV27dmXs2LGMHj2aBg0aVEUdaw1paRJCFHN9q1PIIBj+2S21OimKwrrIBKauOUZKjhaVCsbeFsSrg1rgbHfjOfSEEMVV6e25goICVq9ezfLly9m0aRN6vR5ra2uGDh3KQw89xLBhw246+WVdJKFJCFEigx52XxlhZ9Be6ev0IbS5/5b6OqXlaPng9xOs3H8BAB9XO94bEUb/1t6VVXMh6oVq69OUkpLCDz/8wPLly9mzZw8qlQo3NzdGjx7Ngw8+SI8ePW7l9LWKhCYhxA1dPmEaYVek1elTcPG9pdPujEpm8qojxKXmAjA0woepw0PxcpZVGYQoC4t0BD9z5gzLly/n+++/58yZM6jVavR6fWWdvsaT0CSEuKnrW51sXWHwrbc65WkNfPrnaZbsjMZgVHC1t+atIa24t6N/vZovT4iKsNjoue3bt/Ptt9/yzTffoNfrMRjqz8KTEpqEEGV2+QSsfgbiD5geV1Kr09GLGbz+SyTH4jMBaO7txMh2foxo64efm0yMKURJqjU0HTt2jO+++47vv/+e8+fPoygKISEhPPTQQ0yZMuVWT19rSGgSQpSLQQ+7/wfbZl5tdbpjJrR94JZanfQGI1/ujGbun6fJ1xnN27s0dmdkOz+GhPng6lD/+p0KUZoqD00JCQl8//33LF++nMjISBRFwdPTk9GjRzN27Fi6dOlS4crXVhKahBAVUqzVaeCVEXa31uqUkatjw9EEVh+6yL/nrk5LYKNR07elF6Pa+dGnRUPsrGWeJ1G/VWloGjBgANu2bcNgMGBra8vw4cMZO3YsgwcPxsrK6pYqXptJaBJCVJhBD//8H2ydUamtToXi0/NYczie1QcvcvJSlnm7s50VQ8N9GNHWjy6N3WV5FlEvVWlo0mg09OzZk7Fjx3LvvfdKQLhCQpMQ4pZdPnllhF3ltjpd60RCJqsPXWTNoXgSMvLN231d7bizrR8j2/nSspF8hon6o0pDU1xcHIGBgbdUwbpIQpMQolKU2Oo0A9o+WCmtToWMRoX/olNZffAivx9NICv/6mjnlo2cr3Qg98XHVTqQi7pN1p6zAAlNQohKVQ2tToXydQa2nrzM6kMX2XoyCa3B1IFcpTJ1IB/Vzo87wnxwtZcO5KLuqdTQtH37dgA6d+6MnZ2d+XFZ9OrVq8xlazsJTUKISldNrU7XysjV8fvRBFYdvMie6Gs6kFupub1lQ0a286NPCy9ZKFjUGZUamtRqNSqVihMnTtC8eXPz47KQeZqEEKISXD5pWsPu4n7T42YDTK1Orn5V+rQX0nLNHchPJ2abt7vYWTE0wpeRbX3pFCwdyEXtVqmhaerUqahUKiZNmoS7u7v5cVm8++67Za91LSehSQhRpSzQ6lRIURROJGSx+tBFfjt0kcTMAvM+Pzd7RrT1ZWQ7P5p7O1dpPYSoCtKnyQIkNAkhqkWxVqf+MPx/Vd7qVMhgVPjvXAqrD11kw5FLZBVc7UDe2seFke18ubONH41c7aqlPkLcqiofPefm5nbDE2dlZZGWllavRtlJaBJCVBuDHv6Zd6XVqQBsXWDQDGj3UJW3Ol0rX2dgy8nLrDp4kW2nLqMzmL5SVCro2sSDke38uCOsES520oFc1FxVPk/T1KlTb7hEykcffcTkyZOlT5MQQlSlpFOmEXYWanW6VnqulvVHElh98CJ7Y9LM222s1Axo5c2Itr70adEQGyt1tddNiBsp6/d3habvVhSFm2UtuesnhBDVwKsFPLoJ/v0ctnwAZ/6E+bdZpNXJzcGGB7sE8WCXIM6nmjqQrzp4kTOXs1l/JIH1RxJwc7BmaLgPI9v50SGwgXQgF7VKla15cuHCBZydpUOgEEJUOY0VdH8emt9hWsPu4j5YMxGOrYI7/88irU4B7g4827cZz/RpyrH4TFYfvMiaw/Fczirgu//i+O6/OPwbmDqQj2rnR7OG8n0har4y356bPn26+d9Tp06lT58+9OnTp1g5g8HAhQsX+PHHH+nSpQtbtmyptMrWdHJ7TghhcUaDqa/Tlg9MfZ0cG8IDP4JfB0vXDINR4Z+zpg7kG49eIvuaDuShvi6MaufH8Da+eLtIB3JRvSq9T5NaffUetEqluuntN19fX1atWkWnTp3KWOXaT0KTSWRkJJ999hnbtm0jPj4eAH9/f/r168fjjz9Ox44dLVzDW5OYmMjcuXNZv3490dHR6PV6AgICGDJkCBMnTiQkJASAhx9+mJUrV5KdnX2TM5ZdcHAwYWFhrFu3rtLOeTNVcR2iGiSdhpWPQOJRsLKHe76ElkMtXSuzfJ2BP08ksvrgRbadSkJvNH2nqFXQraknI9v5MSjUG2fpQC6qQaX3adq6dStg6qvUr18/Hn74YcaPH1+snEajwd3dnZYtWxYJWqJ+WLRoERMnTqRFixY8//zzhIaGmidG/eGHH+jUqRNnzpyhadOmlq5qhezZs4dhw4ahKAoTJ06ka9eu2NjYcOrUKZYvX07nzp1JS0u7+YmEqGpezeGRDfDzw3D2L/jxQbjjQ7jtKUvXDAA7aw3DInwZFuFLao6pA/lvBy+yLzaNnWeS2XkmmbdWqRnQ2puRbf3o1dxLOpALiytzaOrdu7f53++++y59+/atV0ukiJvbtWsXzzzzDEOHDmXlypXY2NiY9/Xr149nn32Wn3/+GXv72rn4Z2ZmJiNGjMDOzo7du3fj7+9v3tenTx+efPJJVq5cacEaCnEdOxd4YAWsfxkOLIONr0NaDAz6ANQ1ZwkUd0cbxt4WxNjbTB3Ifzt0kVUHL3I2KYd1kQmsi0yggYM1QyN8GNXOj/aBDco8wbIQlalCsf3dd9+VwCSKmTFjBhqNhkWLFhUJTNe699578fU1LTa6b98+xowZQ3BwMPb29gQHB3P//fcTGxtb5JjSZqBfunQpKpWKmJgY87YtW7bQp08fPDw8sLe3JzAwkLvvvpvc3FxzmQULFtCmTRucnJxwdnamZcuWTJ48+abX98UXX3Dp0iVmzZpVJDBd65577im27cyZMwwZMgQnJycCAgJ4+eWXKSgoKFJGq9Xy/vvv07JlS2xtbfHy8uKRRx4hKSmpxOdZtWoVERER2NnZ0aRJE/73v/+Z92VnZ+Pm5saTTz5Z7LiYmBg0Gg2zZ88GIDc3l1deeYXGjRtjZ2eHu7s7HTt25Icffqiy6xg5ciRBQUEYjcZiz9GlSxfat29f4jWLCtJYm5Zb6T/V9Pi/BbBiLGhzLFqt0gS4OzCxXwh/vtSbtRN7MKFHY7ycbUnL1bH83zjuXvAPvWdv45NNpzhzWW4Zi+p1y6Pnzp8/T3x8fLEPz0ISrm5OURTydDVnPit7a025f4szGAxs3bqVjh074uPjU6ZjYmJiaNGiBWPGjMHd3Z2EhAQWLFhAp06dOH78OJ6enuWqQ0xMDEOHDqVnz5589dVXuLm5cfHiRTZu3IhWq8XBwYEff/yRZ555hkmTJvHxxx+jVqs5c+YMx48fv+n5N23ahEajYfjw4WWuk06n484772TChAm8/PLLbN++nffeew9XV1feeecdAIxGIyNGjGDHjh289tprdOvWjdjYWN5991369OnDvn37irTOHTp0iBdeeIGpU6fSqFEjvvvuO55//nm0Wi2vvPIKTk5OPProoyxevJhZs2bh6upqPnb+/PnY2Njw6KOPAvDSSy/x7bff8v7779OuXTtycnI4evQoKSkpVXYdjz76KCNGjGDLli3079/f/BwnT55kz549RQKgqCQqFfR4EdwCYdXTcGo9LB0K968AZ29L165EKpWKcH9Xwv1dmTykFbvPJrPq4EX+OHqJuNRc/m/LGf5vyxnC/VwZ2c6P4W18aOgsHchF1arwMipr167l1VdfJSoq6oblZHLLm8vV6mn9zh9VWLPyOT59EA425cvTiYmJNGrUiDFjxhRrpTAYDEUGDmg0JYcyg8FAfn4+3t7ezJgxg+eeew4wtTRNmzat2OCDpUuX/n979x0V1fE2cPy7lF16RxEL2LEg9oYKdkVs0Rg1RkWNvZvXmggmxhr92UtibLEm9hor2GNs2Hsv2EBAQZAy7x8rG9cFXRRYlPmcc0+y987Ofe5lcR9m5s4QEBDAzZs3cXd3Z+3atbRu3ZrQ0FC8vLxSjbNfv34sW7bsg8YdlShRgsjISMLCwvQq37lzZ5YsWcKff/7Jl19+qdnfpEkTrl+/zqVLlwBYtWoV7dq1Y+3atXzxxReacsePH6dSpUrMmTOHXr16AeqB4Hfu3OHUqVNa19igQQOOHj1KWFgYFhYW3Lhxg6JFizJlyhQGDhwIQFxcHPny5aNZs2YsXLgQAE9PT4oUKcL69euz7DoSExPJly8fdevWZfny5Zpyw4YNY9q0aTx48ABHR0e97rH0AW4fgVXt4OUzsC0AX/8FuTwMHZXeXr5KYtfrAeT7r2gPIPcu4kSLsnlpWNoFK1WmzagjfYb0/f7+oO65kJAQWrZsyYsXL+jbty9CCGrVqkX37t0pWbIkQgiaNGmi+QtUytkqVKiAqampZpsyZQqg7kYaNmwYRYoUwcTEBBMTE6ysrIiJieHixYvpPk/ZsmVRKpV0796dJUuWcOPGDZ0ylStXJjIyknbt2rFx40aePn2qUyYxMVFr+5iJWhUKhU7LVJkyZbS6ILds2YKdnR1NmzbVOm/ZsmVxcXEhJCRE6/2lSpXSSQrbt29PdHQ0J0+eBKBQoUL4+/szZ84cTfwrVqwgPDycvn37at2P7du3M3z4cEJCQnj58mWmX4eJiQkdOnRg3bp1REVFAeqE+Y8//qB58+YyYcpsbtWg2x5wKARRd+D3BnBjn6Gj0pu50phmXq4s7FyJoyPr8mPzUpQvYEeygANXnzLkr9NUHLuL/itPsffSIxKSdLuBJelDfVAqPmHCBKysrDhx4gS5c+dm5syZ1K5dm9GjRyOEYMKECYwdO1Zrbicpbeamxlz4saGhw9AwN03/AFEnJyfMzc11xiOB+ss6NjaWsLAwmjVrptnfvn179uzZww8//EClSpWwsbFBoVDg5+eX5pf3uxQuXJjdu3czadIk+vTpQ0xMDIUKFaJ///4MGDAAgG+++YbExER+++03WrVqRXJyMpUqVWLs2LHUr18fAFNT7UecFy1aROfOnSlQoABXr14lJiYGS0tLvWKysLDAzEy7y0ClUhEXF6d5/ejRIyIjI9McB/Z2Yufi4qJTJmXfm91qAwYMoG7duuzatYsGDRowe/ZsqlWrpjVmaMaMGeTLl4/Vq1czceJEzMzMaNiwIZMnT9ZMnZAZ19GlSxemTJnCqlWr6NGjBzt27CAsLIyAgIBU3ytlMMfC0HW3usXp7lFY1ko9CWbZdoaOLF0crVR0rOZOx2ru3A6PYWPoAzacus+NpzFsOv2ATacf4GCpxL+Megbycvnt5ABy6aN8UNJ07NgxWrRoQe7c//WFpwzqVCgUjBgxgq1btzJ69Gg2bdqUMZF+xhQKRbq7w7IbY2Nj6tSpw86dOwkLC9Ma11SyZEkArQHbUVFRbNmyhcDAQIYPH67ZHx8fT0REhFbdKV/W8fHxqFQqzf7UWolq1qxJzZo1SUpK4vjx48ycOZOBAweSO3du2rZtC0BAQAABAQHExMSwf/9+AgMD8ff358qVK7i5uXHs2DGtOgsWLAhAw4YN2blzJ5s3b9bUlRGcnJxwdHTk77//TvX42zPrP3z4UKdMyr43W2nq1KlD6dKlmTVrFlZWVpw8eZJly5Zpvc/S0pIxY8YwZswYHj16pGl1atq0qabbLTOuo2TJklSuXJlFixbRo0cPFi1ahKurKw0aNEjXOaWPYOkIHTfBhp7qmcM39FQ/Wec7PEuXXskobo6W9K9blH51inD2fhTrT91n8+kwnr6IZ+mR2yw9chs3Rwual81Li7KuFHK2MnTI0ifog7rnYmNjyZv3v2n5VSoV0dHRWmWqVq3KoUOHPi466ZMyYsQIkpKS6NmzJwkJCe8smzJB6ptJEMCCBQt0xsG5u7sD6kkz37R58+Y06zc2NqZKlSrMnj0bQNNt9SZLS0saN27MqFGjePXqFefPnwegYsWKWltKItK1a1dcXFwYOnQo9+/fT/W869ate8dVp87f35/w8HCSkpJ0zl2xYkWKFy+uVf78+fOcPn1aa9+KFSuwtrbWefKsf//+bN26lREjRpA7d26tMUlvy507N507d6Zdu3ZcvnxZ64nDzLiOgIAAjh49ysGDB9m8eTOdOnXC2Dj7PAafI5iaQauF4D1Q/XrfBFjfExJfGTSsj6FQKCiTz47ApqX4Z0QdlnSpzBfl8mKhNOZ2eCwz9lylzpR9NJ91kEWHbvLkeeoPMUlSaj6oecPFxUXrEeK8efNqvnBSpPzjKeUc3t7ezJ49m379+lG+fHm6d+9OqVKlMDIyIiwsjLVr1wJgY2ODjY0NtWrVYvLkyTg5OeHu7s6+ffv4/fffsbOz06rXz88PBwcHunbtyo8//oiJiQmLFy/m7t27WuXmzZvH3r17adKkCQUKFCAuLk4z4DnlKa1vv/0Wc3NzvL29yZMnDw8fPmT8+PHY2tq+d/Z6W1tbNm7ciL+/P+XKldOa3PLq1assW7aM06dPaw2C1kfbtm1Zvnw5fn5+DBgwgMqVK2Nqasq9e/cIDg6mefPmtGzZUlPe1dWVZs2aERQURJ48eVi2bBm7du1i4sSJWFhYaNXdoUMHRowYwf79+/n+++91us6qVKmCv78/ZcqUwd7enosXL/LHH39QrVo1nboy+jratWvH4MGDadeuHfHx8XTu3Dld55MyiJER1B8D9u7q+ZzOrILo+/DVH2Bub+joPoqJsRE+xZzxKebM2FeJ7LrwegD51aecvhfF6XtRjN16kRpFnGhRzpUGJV2wlAPIpXcRH6B58+aiWrVqmtfdunUTpqamYuXKleLFixfi77//Fubm5qJmzZofUv0nKyoqSgAiKirK0KEYVGhoqAgICBAFCxYUKpVKmJmZiSJFioiOHTuKPXv2aMrdu3dPtGrVStjb2wtra2vRqFEjce7cOeHm5iY6deqkVee///4rqlevLiwtLUXevHlFYGCgWLBggQDEzZs3hRBCHDlyRLRs2VK4ubkJlUolHB0dhY+Pj9i0aZOmniVLlojatWuL3LlzC6VSKVxdXUWbNm3EmTNn9L6+hw8fimHDholSpUoJCwsLoVKpRJEiRUSPHj3E2bNnNeU6deokLC0tdd4fGBgo3v7VS0hIEL/88ovw8vISZmZmwsrKSnh4eIgePXqIq1evasq5ubmJJk2aiDVr1ohSpUoJpVIp3N3dxdSpU9OMt3PnzsLExETcu3dP59jw4cNFxYoVhb29vVCpVKJQoUJi0KBB4unTp5l6HSnat28vAOHt7Z1m/FIWurJLiJ9dhQi0EWJmJSEibhk6okzx5HmcWHzopmg+66BwG7ZFs3l8v130X3lS7L30SCQkJhk6TCkL6fv9/UFTDixcuJC+ffty8eJF3NzcuHnzJhUqVNA8CQPqJ2R27dr1Sc3T9Msvv7Bo0SIUCgXDhw+nQ4cO6Xq/XHtOym5evXqFu7s7NWrU4M8//zR0ONKn4OFZWN4Gnj8AS2f1XE75DL/Yb2a5+TSGjaH32XDqPrfC/+uSdrJS4l/GlRbl8uKVz1YOIP/MZfiCve9z/fp1pk6dyo0bN3Bzc6Nnz56ULVs2I6rOEmfPnqVTp04cPnwYgLp167J161adrqJ3kUmTlF08efKEy5cvs2jRIhYvXsyxY8fkTNuS/qLuw4o2/y3222oBlPA3dFSZSgjB6XtRbDh1n82nHxAe89+4LndHC1qUy0uLsnlxd9LvyVnp05LlSdOn7s8//2T//v3MmjULgD59+lCzZs10PSUlkyYpu0iZ+DNPnjwEBgamuqSKJL1TXDSsCYBruwEFNBoPVXsZOqoskZCUzMFrT9l46j47zj/SWrGhbH47WpR1xd/LFScr1TtqkT4lmTq5pSHs37+fpk2b4urqikKhYMOGDTpl5syZo1lDq0KFChw4cEDv+kuXLk1wcDCRkZFERkayd+/eNJ+QkqTsrnPnzgghePDggUyYpA9jZqPumqvQGRDw93DYPgySP/8HfEyNjahdPBfT2pbj+Pf1mPZVWXyKOWNspCD0biRBmy9QZdweOi/6l42h94l9lWjokKUsotdjAvv37//gE2TUmKaYmBi8vLwICAigVatWOsdXr17NwIEDmTNnDt7e3syfP5/GjRtz4cIFChQoAKhnpk5tjbydO3dSsmRJ+vfvT506dTRPUpmYyKcoJEnKwYxNwH8a2BeE3YFwdB5E3lF31ylzRjeVpcpE3TVXLi9Pnsez5Yx6As3T96IIufyEkMtPsFAaU7mgA24OFhRwtMTNwQI3RwvyO1hg9gGTBUvZl17dc0ZGRh88CC4zph1QKBSsX7+eFi1aaPalrI4+d+5czb4SJUrQokULxo8fn+5zdOvWjZYtW9KkSZM0y8THx2slYdHR0eTPn192z0mS9Pk5t049h1NSPLiWy9aL/WaFG09esCH0ARtD73M7PO05zXLbqHBzsKSAowUFXidT6v9aYm9hKgeYZxP6ds/p1ZQyevTobP2DffXqFSdOnNCaWRrUi5imDOzWx+PHj8mVKxeXL1/m33//Zd68ee8sP378eMaMGfNBMUuSJH1SSn8BNq6wsh08OAUL6sHXf0KuEoaOzCAKOVsxuH4xBtUrypl7UVwIi+ZORCx3wmO5HRHD7fBYnscl8ig6nkfR8fx7K0KnDmuVCflTEilHC9wcLDVJVR5bM0yMP5kRNDmGXklTUFBQJofxcZ4+fUpSUpLWsi6gnuE4tSUn0tKiRQsiIyOxtLRk0aJF7+2eGzFiBIMHD9a8TmlpkiRJ+iwVqArddsPy1hBxA35vqJ4Es5CPoSMzGIVCgVd+O7zy22ntF0IQGZvAnYhYbkfEcidcnUjdjojlbkQsYVFxPI9P5EJYNBfConXqNTFSkM/enAKOlhRwMNe0VqUkVZ/60lufqs/qrr/dGiaESFcLWXpapUC9fMzby4BIkiR91jSL/baHu//Asi9eL/bb3tCRZSsKhQJ7SyX2lkqdhAogLiGJe89i1YlUeKy6lSoiltvhMdx99pJXicncCo/VmjvqTU5WKtwcLV6Po/ovmSrgYImTlTJb9w59yj4qaXr48CHr1q3j0qVLxMTE8PvvvwPqOWJu3ryJp6cn5ubmGRLouzg5OWFsbKzTqvT48WOd1idJkiTpI1k6QseNsKEXnF+n/u+zW+A74pNc7NcQzEyNKZLLmiK5rHWOJScLHj2PUydTb3T33X3dahUZm8DTF/E8fRHPidvPdN5vqTQmv8MbY6jeGJzuameOqez2+2AfnDTNmTOHIUOGaAZCKxQKTdL0+PFjqlWrxrx58/j2228zJtJ3UCqVVKhQgV27dmmtbbVr1y6aN2+e6eeXJEnKcUzNoNXvYO8GB/8H+ybCs9vqVicT5fvfL6XJyEhBHltz8tiaU7WQo87xqJcJ3HndOnU7IkadWL1+/SDqJTGvkrj08DmXHj7Xea+xkQJXOzPtwema1ipLrOTae+/0QXdn8+bN9O3bl4oVKzJ69Gi2b9+uNWi6VKlSlClThg0bNmRY0vTixQuuXbumeX3z5k1CQ0NxcHCgQIECDB48mG+++YaKFStSrVo1fv31V+7cuUPPnj0z5PyS/s6cOcP06dMJCQnhwYMHAOTLl486derw7bffUrFiRQNH+GFu3bpFwYIFmTx5Mt99952hw8kUvr6+AISEhBg0DukTYWQE9YLUi/1uGfxZLfabndmam+KZzxbPfLY6x+ITk7j/7OXrcVRvdv3FcCcilriEZO5GvORuxEu4plu3o6VSMzg9ZQqFlBarXNaqHN/t90FJ0+TJkylQoADBwcFYWlpy4sQJnTKenp7pmlzyfY4fP07t2rU1r1MGYHfq1InFixfz1VdfER4ezo8//khYWBilS5dm27ZtuLm5ZVgM0vvNnz+fvn37Urx4cQYMGECpUqVQKBRcvHiRlStXUqlSJa5du0bhwoUNHaokSRmlQmewzQd/doZbB+D3BvD1X+pkSspSKhNjCjlbUcjZSueYEILHz+Nfj516PTj99f/fjYglPOaVZgu9G6nzfjNTI824qQJaT/1ZkM/eAqXJ59/t90FJU2hoKN988w2WlmlPbpY3b14ePXr0wYG9zdfXl/dNKdW7d2969+6dYeeU0ufQoUP07t2bJk2asGbNGpTK/5ro69SpQ58+ffjrr7+yZJyblHkSEhJQKBRy8ldJW5F60GW7erHfp1fUUxJ85ov9fmoUCgW5bczIbWNGJXcHnePP4xLemDYhVmsKhQeRccQlJHPl0QuuPHqRSt3gamuulUypu/7U3YC25qZZcYmZ7oPSwuTkZExN330Dnjx5Ip8sy2HGjRuHsbEx8+fP10qY3vTll1/i6uoKqFsP27Zti7u7O+bm5ri7u9OuXTtu376t9Z6goKBUm4QXL16MQqHg1q1bmn179+7F19cXR0dHzM3NKVCgAK1atSI29r8nUObOnYuXlxdWVlZYW1vj4eHByJEjM+AOqEVHR/Pdd99RsGBBlEolefPmZeDAgcTExGiVi4yMpGvXrjg4OGBlZUWTJk24ceMGCoVCZ5qPq1ev0r59e3LlyoVKpaJEiRLMnj1bq0xISAgKhYKVK1cyatQoXF1dsbGxoV69ely+fFmrrBCCSZMm4ebmhpmZGeXLl2f79u0615JS5x9//MGQIUPImzcvKpVK01W+e/du6tati42NDRYWFnh7e7Nnz54MuIvSJ8nFE77dA7k9IeYJLG4CFzcbOipJT9ZmppRytaWxZx56+hRmXEtPlnWrwoGhdbj0UyP2/Z8vS7tU5qcWpfm2ZkEalMyNh4s1FkpjhID7kS85ciOcVcfuMunvy/RdcYqmsw7iNWYnZX/cSfNZB+m74iSTd1ziz2N3+edGOGFRL0lO/nSWwP2gPxWLFy/OwYMH0zyemJjIvn378PT0/ODAchQhICHtGWWznKlFup+ASUpKIjg4mIoVK5InTx693nPr1i2KFy9O27ZtcXBwICwsjLlz51KpUiUuXLiAk5NTumK4desWTZo0oWbNmixcuBA7Ozvu37/P33//zatXr7CwsGDVqlX07t2bfv368csvv2BkZMS1a9e4cOFCus6VltjYWHx8fLh37x4jR46kTJkynD9/ntGjR3P27Fl2796NQqEgOTmZpk2bcvz4cYKCgihfvjxHjhyhUaNGOnVeuHCB6tWrU6BAAaZMmYKLiws7duygf//+PH36lMDAQK3yI0eOxNvbmwULFhAdHc2wYcNo2rQpFy9exNhYvaTDmDFjGDNmDF27dqV169bcvXuXb7/9lqSkJIoXL64Tw4gRIzQPdxgZGZErVy6WLVtGx44dad68OUuWLMHU1JT58+fTsGFDduzYQd26dTPknkqfGBtXdYvTX53Vi/2u/gYajlMv9pvDx8N8ykyNjXBztMTNUbeHSQjB0xevuPP6Kb83W6tuh8fy9EU8kbEJRMZGcfpelM77lSZG5Lc3x+31+KmU1io3R3W3X3ZaiuaDkqavv/6a7777jrFjx/L9999rHUtKSuK7777jxo0bDBs2LEOC/OwlxMI4V0NH8Z+RD9K9rtTTp095+fJlqmPIkpKStLpWjY2NUSgUtG7dmtatW2uV8/f3J3fu3KxYsYL+/funK4YTJ04QFxfH5MmT8fLy0uxv3/6/+WMOHTqEnZ0dM2bM0OzLyC/3GTNmcObMGY4ePaoZ8F63bl3y5s1L69at+fvvv2ncuDF///03Bw8eZO7cuZqHFerXr49SqWTEiBFadQ4ePBhra2sOHjyomd6/fv36xMfHM2HCBPr374+9/X+DbkuWLMmyZcs0r42NjWnTpg3Hjh2jatWqREZGMnHiRFq2bMmCBQs05UqVKoW3t3eqSVPhwoX566+/NK9jY2MZMGAA/v7+rF+/XrPfz8+P8uXLM3LkSI4ePfoxt1L6lKms1V1z276DE4tgxwj1lASNxoNR9vkClDKGQqHA2VqFs7WKCm663X6xrxLfGEf1+om/iJfcCY/h3us5qa4/ieH6k5hUagcXGzPN2Ck3RwtaV8iPi61ZZl9Wqj6oe65fv374+PgQGBhI8eLFWbt2LQBt2rShaNGizJgxg/r169O1a9cMDVb6NFWoUAFTU1PNNmXKFED9ROSwYcMoUqQIJiYmmJiYYGVlRUxMDBcvXkz3ecqWLYtSqaR79+4sWbKEGzdu6JSpXLkykZGRtGvXjo0bN/L06VOdMomJiVqbHsszamzZsoXSpUtTtmxZrToaNmyIQqHQPJW2b98+QP0786Z27dppvY6Li2PPnj20bNkSCwsLrTr9/PyIi4vjn3/+0XpPs2bNtF6XKVMGQNPteeTIEeLi4vj666+1ylWvXj3NByfeXiT78OHDRERE0KlTJ62YkpOTadSoEceOHdPpjpRyGGMT8P8f1P9R/frf+bDqa3glPxc5jYXSBA8XGxqWcuHbWoUY28KTpV0qE/J/tbn0UyMODK3Nsq5VGNfSkx4+hWhc2oWSeWw00x88jI7j35sR/HXiHr/svEJ0XILBruWDWppMTU3ZsWMHY8aMYd68eTx7pp5ca82aNdjY2DBs2DDGjBmT4x9N1Juphbp1J7swtUj3W5ycnDA3N9cZjwSwYsUKYmNjCQsL0/pCb9++PXv27OGHH36gUqVK2NjYoFAo8PPz4+XLl+mOoXDhwuzevZtJkybRp08fYmJiKFSoEP3792fAgAEAfPPNNyQmJvLbb7/RqlUrkpOTqVSpEmPHjqV+/frqy39rvN6iRYvo3LmzXjE8evSIa9eupTnmLyVJCw8Px8TEBAcH7b/K3p6MNTw8nMTERGbOnMnMmTPfWWcKR0fteV1Sxham3NPw8HAAXFxcdOpKbR+g0+Wa8pDHmy2Fb4uIiHjnwyJSDqBQgPcAsCsA63rAle2wyA/a/5mjF/uV/mNibER+BwvyO+h+7wgheBabwO3wmP9aqiJiKZBK2azywY+/KJVKfv75Z8aOHcvly5eJiIjAxsaGEiVKaMZNSHpSKNLdHZbdGBsbU6dOHXbu3ElYWJjWl2zJkiUBtAZsR0VFsWXLFgIDA7UWWo6PjyciQnthSzMzM82xNx8uSK2VqGbNmtSsWZOkpCSOHz/OzJkzGThwILlz56Zt27YABAQEEBAQQExMDPv37ycwMBB/f3+uXLmCm5sbx44d06qzYMGCet+HlORx4cKFaR4HdWKTmJhIRESEVuL09qz29vb2GBsb880339CnT59U60xPfCnnTu1cKfvc3d119r/9B1DKdcycOZOqVaumeh45G7+kUaolWLvCyrYQFgoL6qqnJMihi/1K+lEoFDhYKnGwVFKuQPaY9+ujnxlWKBR4eHjo7L958yZjxoxh8eLFH3sK6RMxYsQItm/fTs+ePVmzZs07n7BUKBQIIXSesFywYAFJSUla+1K+xM+cOUOlSpU0+zdvTvupHGNjY6pUqYKHhwfLly/n5MmTmqQphaWlJY0bN+bVq1e0aNGC8+fP4+bm9lGTb/r7+zNu3DgcHR3fmcz4+PgwadIkVq9eTa9evTT7V61apVXOwsKC2rVrc+rUKcqUKZPmU4npUbVqVczMzFi+fLlWt9vhw4e5fft2qknT27y9vbGzs+PChQv07dv3o2OScoACVV4v9vslRFx/vdjvUijka+jIJElvGT7Ryp07d/jpp59YunQpiYmJMmnKQby9vZk9ezb9+vWjfPnydO/enVKlSmFkZERYWJhm7JuNjQ02NjbUqlWLyZMn4+TkhLu7O/v27eP333/Hzs5Oq14/Pz8cHBzo2rUrP/74IyYmJixevJi7d+9qlZs3bx579+6lSZMmFChQgLi4OE2LT7169QD49ttvMTc3x9vbmzx58vDw4UPGjx+Pra2tVkL2LmfPnmXNmjU6+ytVqsTAgQNZu3YttWrVYtCgQZQpU4bk5GTu3LnDzp07GTJkCFWqVKFRo0Z4e3szZMgQoqOjqVChAkeOHGHp0qUAGBn9N9xw+vTp1KhRg5o1a9KrVy/c3d15/vw5165dY/Pmzezdu1e/H9Br9vb2mgc5unXrxpdffsndu3cJCgpKs3vubVZWVsycOZNOnToRERFB69atyZUrF0+ePOH06dM8efKEuXPnpisuKQdwLKxOnFa1hztHYFkraDoDyn39/vdKUnYg0uHAgQPC19dXWFtbC3t7e9GsWTNx6dIlIYQQMTExYtCgQUKlUgmFQiHy5s0rZs2alZ7qP3lRUVECEFFRUYYOxaBCQ0NFQECAKFiwoFCpVMLMzEwUKVJEdOzYUezZs0dT7t69e6JVq1bC3t5eWFtbi0aNGolz584JNzc30alTJ606//33X1G9enVhaWkp8ubNKwIDA8WCBQsEIG7evCmEEOLIkSOiZcuWws3NTahUKuHo6Ch8fHzEpk2bNPUsWbJE1K5dW+TOnVsolUrh6uoq2rRpI86cOfPe67p586YA0twWLVokhBDixYsX4vvvvxfFixcXSqVS2NraCk9PTzFo0CDx8OFDTX0REREiICBA2NnZCQsLC1G/fn3xzz//CEBMnz5d59xdunQRefPmFaampsLZ2VlUr15djB07VlMmODhYAOKvv/5KNe6U+IQQIjk5WYwfP17kz59fKJVKUaZMGbF582bh4+MjfHx83ltnin379okmTZoIBwcHYWpqKvLmzSuaNGmSZnlJEkII8eqlEH8FCBFoo972jBUiOdnQUUk5mL7f3woh9Hs06MSJE3h7e/Pq1Sut/S4uLuzfv58WLVpw4cIFXF1dGTZsGN27d89xk1tGR0dja2tLVFSU5tFwSUqPFStW8PXXX3Po0CGqV69u6HAkKfMkJ8Pen+DgVPXrMl+9Xuw3Z31vSNmDvt/fenfPTZo0iVevXjF+/HjNVALz5s1j9OjR1KxZkydPnvD9998zcuRIzcBdSZLStnLlSu7fv4+npydGRkb8888/TJ48mVq1asmESfr8GRlBvcDXi/0OgjOrIeo+tF0mF/uVsi29W5ry5cuHh4cHu3fv1tpfu3Zt9u/fz+TJkzWL6OZUsqVJSo8tW7YQFBTEtWvXiImJIU+ePLRo0YKxY8fKz4+Us1zbrV7s99VzcComF/uVspy+3996T275+PFjKlTQXXgxZfBsp06dPiBMScq5/P39OX78OJGRkSQkJHDnzh1mzJghEyYp5ylSD7r8DTZ5/1vs995xQ0clSTr0TpoSExNTnaguZd/bE+pJkiRJkt5cSqufrHORi/1K2dcHLaMiSZIkSRnOxhUCtkPRBpAYp17s98hs9aLmkpQN6D2mycjIiCJFilCkSBGt/deuXeP69es0bNhQt3KFgq1bt2ZMpJ8AOaZJkiQpAyQlwvb/g+OvZ9av3B0aTZCL/UqZRt/v73QlTemlUCh0Znf+nMmkSZIkKYMIAYdnwq4f1K+LNYJWv4PKyrBxSZ+lDJ9y4ObNmxkSmCRJkiS9l0IB3v3Vi/2u7wFX/obFKYv96jdzvSRlNL2TJjc3t8yMQ5IkSZJ0lWqhHuu0si2EnVY/WScX+5UMRA4ElyRJkrK3/JXVT9Y5FoGou/B7A7gebOiopBxIJk3SR1MoFHptISEhGXbOkJAQFApFqgvnpseOHTto0KABrq6uqFQqXF1d8fX1ZcKECVrl3N3d6dy58wedw9fXl9KlS39UnJKU4zkUgq67oEA1iI+G5a3h1DJDRyXlMHp3z0lSWo4cOaL1+qeffiI4OJi9e/dq7S9ZsmRWhvVe8+bNo1evXrRq1YpZs2bh4ODA3bt3OXz4MGvWrGH48OGasuvXr5eD+yXJ0Cwc4JsNsLEPnFuj/u+zW1B7lHoMlCRlMpk0SR+tatWqWq+dnZ0xMjLS2Z/djB8/nlq1aum0Vn3zzTckJydr7StXrlxWhiZJUlpMzeCL39TLrBz4BfZPhme3ofksudivlOlk95yUJWbPnk2tWrXIlSsXlpaWeHp6MmnSJBISErTKpXRlHTt2jJo1a2JhYUGhQoWYMGGCTiIDkJCQwKhRo3B1dcXGxoZ69epx+fJlvWIKDw8nT548qR57e4qNt7vnUroHV65cqff59b0mSZLew8gI6v4AzWaCwhjO/gl/tITYCENHJn3mZNKUDQghiE2IzTabnlN3pcv169dp3749f/zxB1u2bKFr165MnjyZHj166JR9+PAhX3/9NR06dGDTpk00btyYESNGsGyZ7viFkSNHcvv2bRYsWMCvv/7K1atXadq0qV7zg1WrVo21a9cSFBTE6dOnP2hOMX3Pn55rkiRJT+U7qp+kU1rD7UPqAeIRcnocKfPI7rls4GXiS6qsqGLoMDSOtj+KhalFhtY5depUzf8nJydTs2ZNHB0dCQgIYMqUKdjb22uOh4eHs23bNipXrgxAvXr1CAkJYcWKFXTs2FGr3pIlS2olHsbGxrRp04Zjx469t3tw3rx5tGjRgjFjxjBmzBjMzc2pXr06LVu2pHv37piamr73uvQ9f3quSZKkdChSF7rugOVfQvhV9ZQE7VdDvoqGjkz6DMmWJilLnDp1imbNmuHo6IixsTGmpqZ07NiRpKQkrly5olXWxcVFk1ykKFOmDLdv39apt1mzZjrlAE1ZIQSJiYlaW4rChQtz+vRp9u3bx5gxY6hXrx7Hjh2jb9++VKtWjbi4uPde1/vO/yHXJElSOuUuBd32gEsZiH2qXuz3wiZDRyV9hmRLUzZgbmLO0fZHDR2GhrmJeYbWd+fOHWrWrEnx4sWZPn067u7umJmZ8e+//9KnTx9evnypVd7R0VGnDpVKpVMutbIqlXogaErZJUuWEBAQoFXmze5HIyMjatWqRa1atQCIiYmha9eurF69moULF9K7d+93Xtv7zv8h1yRJ0gewyaNe7HdNAFzdCX92hAZjoVof+WSdlGFk0pQNKBSKDO8Oy042bNhATEwM69at05pZPjQ0NNPP3bRpU44dO6Z3eUtLS0aMGMHq1as5d+5cJkYmSVKGU1lB25WwfSgc/x12jlJPSdBoAhjLrzvp48lPkZTpFK//yktphQF1a89vv/2W6ed2dHRMtZUHICwsLNWn5y5evAiAq6trpsYmSVImMDaBJlPAoSDs/B6O/aaeRVwu9itlAJk0SZmufv36KJVK2rVrx9ChQ4mLi2Pu3Lk8e/bMoHGVKlWKunXr0rhxYwoXLkxcXBxHjx5lypQp5M6dm65duxo0PkmSPpBCAdX7gW3+/xb7XdRYvdivTerTjEiSPuRAcCnTeXh4sHbtWp49e8YXX3xBv379KFu2LDNmzDBoXCnzJP3888/4+fnRrFkzli5dSvv27Tl+/HiaczhJkvSJKNUCOm0BC0d4eEb9ZN2j84aOSvqEKURmTMqTQ0VHR2Nra0tUVJRcckOSJCm7iLjxekqCa6CygTZLoXBtQ0clZSP6fn/LliZJkiTp86ZZ7Lf6f4v9nvzD0FFJnyCZNEmSJEmfPwsH6LgBPL+E5ETY1Bf2jgXZ2SKlg0yaJEmSpJzBRKVe7LfW/6lf758M676FxHjDxiV9MmTSJEmSJOUcCgXU+R6azQIjEzj7l1zsV9KbTJokSZKknKf8N+rFflU2crFfSW8yaZIkSZJypsJ1oMvfYJPvv8V+7+q/goCU88ikSZIkScq5cpeCbrshj5d6sd8l/nBho6GjkrIpmTRJkiRJOZtNHui8DYo2hMQ4+LMTHJ4pn6yTdMikSZIkSZJUVtB2BVTqBgj1unXbvoOkRENHJmUjOTZpatmyJfb29rRu3TpdxyRJkqTPlLEJ+P0CDX4GFHBsAaxqD/EvDB2ZlE3k2KSpf//+LF26NN3HJEmSpM+YQgHV+6qXWjExg6s71Iv9RocZOjIpG8ixSVPt2rWxtrZO9zEpbUePHqVly5YUKFAAlUpF7ty5qVatGkOGDNGU8fX1xdfX13BBSpIk6aNks9eL/TrJxX4ljWyZNO3fv5+mTZvi6uqKQqFgw4YNOmXmzJlDwYIFMTMzo0KFChw4cCDrA5U0tm7dSvXq1YmOjmbSpEns3LmT6dOn4+3tzerVqzXl5syZw5w5cwwYqSRJkp7yV1I/WedYFKLvwe8N4fpeQ0clGZCJoQNITUxMDF5eXgQEBNCqVSud46tXr2bgwIHMmTMHb29v5s+fT+PGjblw4QIFChQAoEKFCsTH606Nv3PnTlxdXTP9GnKaSZMmUbBgQXbs2IGJyX8fq7Zt2zJp0iTN65IlSxoiPEmSpA/jUBC67oTVHdSTYC7/EvynqSfHlHKcbNnS1LhxY8aOHcsXX3yR6vGpU6fStWtXunXrRokSJZg2bRr58+dn7ty5mjInTpzg3LlzOltGJkzx8fFER0drbTlVeHg4Tk5OWglTCiOj/z5mb3fP3bp1C4VCweTJk5k4cSLu7u6Ym5vj6+vLlStXSEhIYPjw4bi6umJra0vLli15/PixVv3u7u74+/uzfv16ypQpg5mZGYUKFWLGjBla5ZKTkxk7dizFixfH3NwcOzs7ypQpw/Tp07XKHTx4kLp162JtbY2FhQXVq1dn69atWmUWL16MQqEgODiYXr164eTkhKOjI1988QUPHjz40NsoSVJ2ZOEA36wHzzb/Lfa75yc5JUEOlC2Tpnd59eoVJ06coEGDBlr7GzRowOHDh7M0lvHjx2Nra6vZ8ufP/0H1CCFIjo3NNpv4gH8IqlWrxtGjR+nfvz9Hjx4lISEhXe+fPXs2hw4dYvbs2SxYsIBLly7RtGlTunbtypMnT1i4cCGTJk1i9+7ddOvWTef9oaGhDBw4kEGDBrF+/XqqV6/OgAED+OWXXzRlJk2aRFBQEO3atWPr1q2sXr2arl27EhkZqSmzb98+6tSpQ1RUFL///jsrV67E2tqapk2banUzpujWrRumpqasWLGCSZMmERISQocOHdJ17ZIkfQJMVPDFr1BrqPr1gV/kYr85ULbsnnuXp0+fkpSURO7cubX2586dm4cPH+pdT8OGDTl58iQxMTHky5eP9evXU6lSpfcee9OIESMYPHiw5nV0dPQHJU7i5Usul6+Q7vdlluInT6CwsEjXeyZMmMClS5eYOXMmM2fOxNTUlEqVKtG0aVP69u2LlZXVO99vZ2fHhg0bNK1ST58+ZeDAgXh4eLBx43+z8166dIlp06YRHR2NjY2NZv+DBw84deoUXl5egLq18vHjx/z000/07t0bCwsLDh06hKenJ0FBQZr3NWzYUCuO4cOHY29vT0hIiCZmf39/ypYty3fffUebNm1QKBSa8o0aNdJq0YqIiGDo0KE8fPgQFxeXdN1DSZKyOYUC6owCezfYPEC92G/UfWi7XN0aJX32PrmWphRvfnGBurXm7X3vsmPHDp48eUJsbCz37t3TSoredexNKpUKGxsbrS2ncnR05MCBAxw7dowJEybQvHlzrly5wogRI/D09OTp06fvfL+fn59WN16JEiUAaNKkiVa5lP137tzR2l+qVClNwpSiffv2REdHc/LkSQAqV67M6dOn6d27Nzt27NDpTo2JieHo0aO0bt1aK8kzNjbmm2++4d69e1y+fFnrPc2aNdN6XaZMGQBu3779zuuVJOkTVq4DfL1GvdjvncPwe314es3QUUlZ4JNraXJycsLY2FinVenx48c6rU+fCoW5OcVPnjB0GBoKc/MPfm/FihWpWLEiAAkJCQwbNoz//e9/TJo0SWtA+NscHLT/SlMqle/cHxcXp7U/tVadlH3h4eGAumXQ0tKSZcuWMW/ePIyNjalVqxYTJ06kYsWKPHv2DCEEefLk0akrZSxcSl0pHB0dtV6rVCoAXr58mea1SpL0GShcG7rsUA8MD78GsypC/spQ3A88/MGpiKEjlDLBJ9fSpFQqqVChArt27dLav2vXLqpXr26gqD6OQqHAyMIi22zpabF7F1NTUwIDAwE4d+5chtSZltS6ZlP2pSQ2JiYmDB48mJMnTxIREcHKlSu5e/cuDRs2JDY2Fnt7e4yMjAgL053ELmVwt5OTUyZehSRJn5TcJeHbPVDQBxBw9yjsDoRZFWBWZdg9Bu4dh+RkQ0cqZZBsmTS9ePGC0NBQQkNDAbh58yahoaGaLpnBgwezYMECFi5cyMWLFxk0aBB37tyhZ8+eBow6Z0st0QC4ePEiQKZP83D+/HlOnz6ttW/FihVYW1tTvnx5nfJ2dna0bt2aPn36EBERwa1bt7C0tKRKlSqsW7dOq6UoOTmZZcuWkS9fPooVK5ap1yFJ0ifG2gU6bYJBF9RLsBSuA0Ym8PQyHJwKC+rC1BKwZRBc2w2JrwwdsfQRsmX33PHjx6ldu7bmdcpg606dOrF48WK++uorwsPD+fHHHwkLC6N06dJs27YNNzc3Q4Wc4zVs2JB8+fLRtGlTPDw8SE5OJjQ0lClTpmBlZcWAAQMy9fyurq40a9aMoKAg8uTJw7Jly9i1axcTJ07E4vWg9qZNm1K6dGkqVqyIs7Mzt2/fZtq0abi5uVG0aFFA/URk/fr1qV27Nt999x1KpZI5c+Zw7tw5Vq5cmWGtcJIkfWZs80Llb9Xby0h1gnRpK1zdBS8ewvGF6k1pDUXrg0cT9X/NbA0duZQO2TJp8vX1fe9j771796Z3795ZFJH0Pt9//z0bN27kf//7H2FhYcTHx5MnTx7q1avHiBEjNAO4M0vZsmUJCAggMDCQq1ev4urqytSpUxk0aJCmTO3atVm7di0LFiwgOjoaFxcX6tevzw8//ICpqSkAPj4+7N27l8DAQDp37kxycjJeXl5s2rQJf3//TL0GSZI+E+Z24NlavSXGw80DcGkLXN6uTqDOr1NvRqZQsKY6gSruBzZy4uXsTiE+ZFIeKVXR0dHY2toSFRWVo5+ky2ru7u6ULl2aLVu2GDoUSZKktCUnw4OT6gTq0lZ4ekX7uGt5dQLl4Q/OxdVTHEhZQt/v72zZ0iRJkiRJnx0jI8hXUb3VC4KnV9XJ06WtcO+YOqF6cBL2/gQOhcHj9ZN4+SqBkbGho5eQSZMkSZIkGYZTUagxUL09fwRXtqsTqBshEHEdDs9Ub5bOULwxFG8ChXzB1MywcedgsnsuA8nuOUmSJOmjxT9/PZB8G1zZAfFR/x0ztYQiddUtUMUagLm94eL8jOj7/S2TpgwkkyZJkiQpQyUlwK2D6haoy9sg+v5/xxTG4O6tTqCK+4Hdh61/KsmkySBk0iRJkiRlGiEgLPT1OKht8Pi89nGXMuoEyqMJ5C4lB5Kng0yaDEAmTZIkSVKWibihTp4ubYW7/4B4Y+ZxO7fXT+I1gfxVwVgOYX4XmTQZgEyaJEmSJIOIeQpX/lYnUNf3QuIb63OaO7weSO6nnrFcaWG4OLMpmTQZgEyaJEmSJIN7FQPXg9UJ1JXt8PLZf8dMzNWJk4cfFGsElnI9TZBJk0HIpEmSJEnKVpIS1V13l7aqJ9WMvPPfMYURFKj234zkDgUNF6eByaTJAGTSJEmSJGVbQsCjc6/HQW2Bh2e0j+cq9d84qDxeOWogub7f30ZZGJP0mVIoFHptISEhhg41U3Xu3Bl3d3e9yioUCoKCgtJ9jlu3bqFQKFi8ePF7y4aEhKBQKFizZk26z5OZdaW4cOECQUFB3Lp1S+fYihUrmDZtWoadS5Ik1EmQiyf4DoOeB2DgWWg0EQrWUk9f8Pg87J8Ev/rA/0rDtv9TT7SZlGDoyLMNOZxe+mhHjhzRev3TTz8RHBzM3r17tfaXLFkyK8PKcj/88AMDBgwwdBifjAsXLjBmzBh8fX11ks0VK1Zw7tw5Bg4caJDYJClHsCsAVXuqt9gIuLpT3QJ1bQ9E34N/f1VvZrZQtKG6BapIPVBZGTpyg5FJk/TRqlatqvXa2dkZIyMjnf1vi42NxcLi83mKo3DhwoYOQZIk6cNYOIBXW/WW8BJu7FMnUJe3Q+xTOPunejNWqZdy8fBTj4OyymXoyLOU7J6TsoSvry+lS5dm//79VK9eHQsLC7p06QLA6tWradCgAXny5MHc3JwSJUowfPhwYmJitOro3LkzVlZWnD9/nrp162JpaYmzszN9+/YlNjZWq6wQgjlz5lC2bFnMzc2xt7endevW3LhxQ1Mmpcspte3Nlo/k5GQmTZqEh4cHKpWKXLly0bFjR+7du6cT39stJtHR0Xz77bc4OjpiZWVFo0aNuHLlrZXNgWvXrhEQEEDRokWxsLAgb968NG3alLNnz37I7U6Xc+fO0bx5c+zt7TEzM6Ns2bIsWbIk1bJxcXEMHjwYFxcXzM3N8fHx4dSpU1pljh8/Ttu2bXF3d8fc3Bx3d3fatWvH7du3NWUWL17Ml19+CUDt2rU1933x4sX4+vqydetWbt++rfUzSREREUHv3r3JmzcvSqWSQoUKMWrUKOLj4zPh7khSDmRqDsUbQfNZ8N0V6LIDqvcD+4KQFA9Xd8DmAfBLMfi9ARyaDk+vGTrqLCFbmrIBIQSJr5LfXzCLmCiNtL6kMkpYWBgdOnRg6NChjBs3DiMjdc5+9epV/Pz8GDhwIJaWlly6dImJEyfy77//6nTxJSQk4OfnR48ePRg+fDiHDx9m7Nix3L59m82bN2vK9ejRg8WLF9O/f38mTpxIREQEP/74I9WrV+f06dPkzp2b8uXL63QtXr16la5du1KqVCnNvl69evHrr7/St29f/P39uXXrFj/88AMhISGcPHkSJ6fUH9kVQtCiRQsOHz7M6NGjqVSpEocOHaJx48Y6ZR88eICjoyMTJkzA2dmZiIgIlixZQpUqVTh16hTFixf/4Pv+LpcvX6Z69erkypWLGTNm4OjoyLJly+jcuTOPHj1i6NChWuVHjhxJ+fLlWbBgAVFRUQQFBeHr68upU6coVKgQoB53Vbx4cdq2bYuDgwNhYWHMnTuXSpUqceHCBZycnGjSpAnjxo1j5MiRzJ49m/LlywPq1rrKlSvTvXt3rl+/zvr167XOHxcXR+3atbl+/TpjxoyhTJkyHDhwgPHjxxMaGsrWrVsz5T5JUo5lZAwFqqq3+j/Bk0uvn8TbCg9Owt2j6m3XaHAq/t9ActfyYPT5tcvIpCkbSHyVzK8D9hk6DI3u030wVRlneL0RERH89ddf1KlTR2v/999/r/l/IQTe3t6UKFECHx8fzpw5Q5kyZTTHX716xZAhQ+jfvz8A9evXx9TUlFGjRnHo0CG8vb35559/+O2335gyZQqDBw/WvLdmzZoUK1aMqVOnMnHiRGxsbLS6EB8/fszXX39NsWLFWL58OQCXLl3i119/pXfv3sycOVNTtly5clSpUoX//e9//Pzzz6le744dOwgODmb69Ola8SqVSkaNGqVVtlatWtSqVUvzOikpiSZNmlCqVCnmz5/P1KlT9bvJ6RQUFMSrV68IDg4mf371ulV+fn5ERkYyZswYevToga2traa8s7Mz69ev1yTVNWrUoGjRoowfP57ffvsNgNatW9O6dWuta/H39yd37tysWLGC/v374+zsTNGiRQH1WLc3fw7Ozs7Y2dmhUql0uniXLFnCmTNn+PPPPzUtVfXr18fKyophw4axa9cu6tevnwl3SpIkFArIVUK91foOou6r18O7vA1u7oenl+HgZTg4Faxc1F14Hk3AvRaYKA0dfYb4/NJAKduyt7fXSZgAbty4Qfv27XFxccHY2BhTU1N8fHwAuHjxok75r7/+Wut1+/btAQgODgZgy5YtKBQKOnToQGJiomZzcXHBy8sr1af4YmJiaNKkCXFxcWzfvh07OzutOjt37qxVvnLlypQoUYI9e/akeb0p700r3jclJiYybtw4SpYsiVKpxMTEBKVSydWrV1O9B2+/980tPbOI7N27l7p162oSphSdO3cmNjZWpyWuffv2Wq2Qbm5uVK9eXXOtAC9evGDYsGEUKVIEExMTTExMsLKyIiYm5r3Xok+8lpaWWklZSrzAO38ekiRlMNu8UPlb+GY9/N91aPU7lPoClNbw4iEcXwjLWsGkQvBXAJxdA3FRho76o8iWpmzARGlE9+k+hg5Dw0SZObl0njx5dPa9ePGCmjVrYmZmxtixYylWrBgWFhbcvXuXL774gpcvX2rHZmKCo6Oj1j4XFxcAwsPDAXj06BFCCHLnzp1qHCndSCkSExNp3bo1V65cYf/+/VoJREqdqcXu6uqqNU7nbeHh4e+M902DBw9m9uzZDBs2DB8fH+zt7TEyMqJbt2469+BNt27domBB7QnpgoOD8fX1TfM9b8eY1rWlHH9f7C4uLpw+fVrzun379uzZs4cffviBSpUqYWNjg0KhwM/P753Xom+8Li4uOt3HuXLlwsTERCdeSZKyiLkdeLZWb4nxcPPAfwPJXzyE8+vUm5GpeoqDlIHkNq6GjjxdZNKUDSgUikzpDstuUhsntXfvXh48eEBISIimdQkgMjIy1ToSExMJDw/XSkQePnwIoNnn5OSEQqHgwIEDqFQqnTre3te9e3f27NnDtm3b8PLy0jqWUmdYWBj58uXTOvbgwYM0xzOlvPdd8b5p2bJldOzYkXHjxmntf/r0qabVKzWurq4cO3ZMa196xj85OjoSFhams//BgwcAOteXWuwPHz7UXF9UVBRbtmwhMDCQ4cOHa8rEx8cTERGhd1zvivfo0aMIIbQ+T48fPyYxMfGdPw9JkrKIiQqK1lNvTaaqxz5d2qIeB/X0Clzfo962DoG8FV7PSN4EnItn+wk1ZfecZFApX3xvJzLz589P8z0p441SrFixAkDTuuLv748Qgvv371OxYkWdzdPTU/Pe77//nkWLFrFgwQLq1aunc66U7sRly5Zp7T927BgXL16kbt26acZZu3btd8b7JoVCoXMPtm7dyv3799OsH0CpVOpcn7W19Tvf86a6detqEtc3LV26FAsLC50xRStXrtTq/rt9+zaHDx/W3HuFQoEQQudaFixYQFJSkta+lDKptT6pVKpU99etW5cXL16wYcMGnXhTjkuSlI0YGUG+ilAvCPoeg77Hod4YyFcZUMD9E7DnR5hTBWZWgJ0/wJ1/IDnpfTUbhGxpkgyqevXq2Nvb07NnTwIDAzE1NWX58uVa3T1vUiqVTJkyhRcvXlCpUiXN03ONGzemRo0aAHh7e9O9e3cCAgI4fvw4tWrVwtLSkrCwMA4ePIinpye9evXir7/+4ueff6Z169YUK1aMf/75R3MelUpFuXLlKF68ON27d2fmzJkYGRnRuHFjzdNz+fPnZ9CgQWleW4MGDahVqxZDhw4lJiaGihUrcujQIf744w+dsv7+/ixevBgPDw/KlCnDiRMnmDx5sk7r1od487re5OPjQ2BgIFu2bKF27dqMHj0aBwcHli9fztatW5k0aZLWIHBQt+i0bNmSb7/9lqioKAIDAzEzM2PEiBEA2NjYUKtWLSZPnoyTkxPu7u7s27eP33//XafFrHTp0gD8+uuvWFtbY2ZmRsGCBXF0dMTT05N169Yxd+5cKlSogJGRERUrVqRjx47Mnj2bTp06cevWLTw9PTl48CDjxo3Dz88v1cRXkqRsxKko1Bio3p4/Ui8ofGmreubxiOtweIZ6s3SG4o3Bwx8K+oCpmYEDf01IGSYqKkoAIioqytChGFSnTp2EpaWl1j4fHx9RqlSpVMsfPnxYVKtWTVhYWAhnZ2fRrVs3cfLkSQGIRYsW6dR75swZ4evrK8zNzYWDg4Po1auXePHihU69CxcuFFWqVBGWlpbC3NxcFC5cWHTs2FEcP35cCCFEYGCgAFLd3NzcNPUkJSWJiRMnimLFiglTU1Ph5OQkOnToIO7evatz3W++TwghIiMjRZcuXYSdnZ2wsLAQ9evXF5cuXRKACAwM1JR79uyZ6Nq1q8iVK5ewsLAQNWrUEAcOHBA+Pj7Cx8dHU+7mzZs69yUtwcHBaV4fIIKDg4UQQpw9e1Y0bdpU2NraCqVSKby8vHTqT6nrjz/+EP379xfOzs5CpVKJmjVrau5ninv37olWrVoJe3t7YW1tLRo1aiTOnTsn3NzcRKdOnbTKTps2TRQsWFAYGxtrXVdERIRo3bq1sLOzEwqFQrz5T1V4eLjo2bOnyJMnjzAxMRFubm5ixIgRIi4u7r33RJKkbCouWohz64VY002IcfmFCLT5bxubR4hVHYQIXSVEbESmnF7f72+5YG8Gkgv2Zq7OnTuzZs0aXrx4YehQJEmSpMySlAC3DqpboC5vg+g3hikojKHjBvVg8gyk7/e37J6TJEmSJCn7MDaFwrXVm99kCAt9PaHmNgi/Cq7lDBaaTJokSZIkScqeFAp1kuRaDup8rx4HpdL/YZcMD0d2z2Uc2T0nSZIkSZ8efb+/5ZQDkiRJkiRJepBJkyRJkiRJkh5k0iRJkiRJkqQHmTRJkiRJkiTpQSZNkiRJkiRJepBJkyRJkiRJkh5k0iRJkiRJkqQHmTRJkiRJkiTpQSZNkiRJkiRJepBJkyRJkiRJkh5k0iRJkiRJkqQHmTRJkiRJkiTpQSZNkiRJkiRJejAxdACfEyEEoF4tWZIkSZKkT0PK93bK93haZNKUgZ4/fw5A/vz5DRyJJEmSJEnp9fz5c2xtbdM8rhDvS6skvSUnJ/PgwQOsra1RKBQZVm90dDT58+fn7t272NjYZFi9kjZ5n7OOvNdZQ97nrCHvc9bIzPsshOD58+e4urpiZJT2yCXZ0pSBjIyMyJcvX6bVb2NjI38hs4C8z1lH3uusIe9z1pD3OWtk1n1+VwtTCjkQXJIkSZIkSQ8yaZIkSZIkSdKDTJo+ASqVisDAQFQqlaFD+azJ+5x15L3OGvI+Zw15n7NGdrjPciC4JEmSJEmSHmRLkyRJkiRJkh5k0iRJkiRJkqQHmTRJkiRJkiTpQSZNkiRJkiRJepBJUzayf/9+mjZtiqurKwqFgg0bNmgdX7duHQ0bNsTJyQmFQkFoaKhB4vzUjR8/nkqVKmFtbU2uXLlo0aIFly9f1ioj7/XHmzt3LmXKlNFMRFetWjW2b9+uOS7vceYYP348CoWCgQMHavbJe/3xgoKCUCgUWpuLi4vmuLzHGef+/ft06NABR0dHLCwsKFu2LCdOnNAcN+S9lklTNhITE4OXlxezZs1K87i3tzcTJkzI4sg+L/v27aNPnz78888/7Nq1i8TERBo0aEBMTIymjLzXHy9fvnxMmDCB48ePc/z4cerUqUPz5s05f/48IO9xZjh27Bi//vorZcqU0dov73XGKFWqFGFhYZrt7NmzmmPyHmeMZ8+e4e3tjampKdu3b+fChQtMmTIFOzs7TRmD3mshZUuAWL9+farHbt68KQBx6tSpLI3pc/X48WMBiH379ukck/c6Y9nb24sFCxZo7ZP3OGM8f/5cFC1aVOzatUv4+PiIAQMG6JSR9/rDBQYGCi8vr/eWk/f44wwbNkzUqFFDr7KGuNeypUnK8aKiogBwcHAwcCSfr6SkJFatWkVMTAzVqlUzdDifpT59+tCkSRPq1atn6FA+W1evXsXV1ZWCBQvStm1bbty4YeiQPjubNm2iYsWKfPnll+TKlYty5crx22+/GTosDZk0STmaEILBgwdTo0YNSpcubehwPjtnz57FysoKlUpFz549Wb9+PSVLljR0WJ+dVatWcfLkScaPH2/oUD5bVapUYenSpezYsYPffvuNhw8fUr16dcLDww0d2mflxo0bzJ07l6JFi7Jjxw569uxJ//79Wbp0qaFDA8DE0AFIkiH17duXM2fOcPDgQUOH8lkqXrw4oaGhREZGsnbtWjp16sS+fftk4pSB7t69y4ABA9i5cydmZmaGDuez1bhxY83/e3p6Uq1aNQoXLsySJUsYPHiwASP7vCQnJ1OxYkXGjRsHQLly5Th//jxz586lY8eOBo5OtjRJOVi/fv3YtGkTwcHB5MuXz9DhfJaUSiVFihShYsWKjB8/Hi8vL6ZPn27osD4rJ06c4PHjx1SoUAETExNMTEzYt28fM2bMwMTEhKSkJEOH+FmytLTE09OTq1evGjqUz0qePHl0/qgqUaIEd+7cMVBE2mRLk5TjCCHo168f69evJyQkhIIFCxo6pBxDCEF8fLyhw/is1K1bV+spLoCAgAA8PDwYNmwYxsbGBors8xYfH8/FixepWbOmoUP5rHh7e+tMAXPlyhXc3NwMFJE2mTRlIy9evODatWua1zdv3iQ0NBQHBwcKFChAREQEd+7c4cGDBwCaD5aLi4vWfCHSu/Xp04cVK1awceNGrK2tefjwIQC2traYm5sDyHudAUaOHEnjxo3Jnz8/z58/Z9WqVYSEhPD3338D8h5nFGtra53xeJaWljg6Omr2y3v98b777juaNm1KgQIFePz4MWPHjiU6OppOnToB8h5nlEGDBlG9enXGjRtHmzZt+Pfff/n111/59ddfNWUMeq+z7Dk96b2Cg4MFoLN16tRJCCHEokWLUj0eGBho0Lg/NandQ0AsWrRIU0be64/XpUsX4ebmJpRKpXB2dhZ169YVO3fu1ByX9zjzvD3lgLzXH++rr74SefLkEaampsLV1VV88cUX4vz585rj8h5nnM2bN4vSpUsLlUolPDw8xK+//qp13JD3WiGEEJmWkUmSJEmSJH0m5EBwSZIkSZIkPcikSZIkSZIkSQ8yaZIkSZIkSdKDTJokSZIkSZL0IJMmSZIkSZIkPcikSZIkSZIkSQ8yaZIkSZIkSdKDTJokSZIkSZL0IJMmSfrEde7cGYVCwa1btwwdSoZYtmwZZcuWxcrKCoVCQVBQkMFiUSgU+Pr6Guz86XHv3j0sLCyYNGlSlp0zvZ+9W7duoVAo6Ny5c6bEExISkqWfmcDAQKytrXn06FGWnE8yPJk0SdJrKf+gKxQK/P39Uy2T8o9yz549szi6nOHw4cN88803xMbG0qdPHwIDA9+btLi7u2t+bgqFAmNjY5ycnGjQoAEbN27MmsDfYfHixSgUChYvXpyp5xk5ciRWVlb06dMn1eNhYWH88MMPVKlSBUdHR0xNTXFwcKBatWqMHDlSZ5HU7OLQoUN8+eWX5M2bF6VSib29PR4eHrRv354lS5YYNLZBgwZhbGzM6NGjDRqHlHXkgr2SlIqtW7eyf/9+atWqZehQcpRt27YBsHTpUqpWrar3+4yNjfn+++8BePXqFZcuXWLTpk3s2rWLX375hSFDhnxQPBcvXsTCwuKD3puVrly5wrJlyxg9ejSWlpY6x1etWkXXrl2JjY2lbNmytGnTBgcHB6Kjozl58iSTJk1i4sSJbN68GT8/P73PO378eIYPH07evHkz8nI0Fi9eTJcuXTAxMcHPz4+iRYvy8uVLbty4wbZt29i/f79mwVyAypUrc/HiRZycnDIlnrfZ2dnRrVs3pk2bxsiRI3Fzc8uS80qGI5MmSXqLu7s7d+7cYdiwYRw5csTQ4eQoKauWp3elchMTE50umZ07d9KoUSNGjx5Nr169Pij58fDwSPd7DGH+/PkIIejQoYPOse3bt/P111/j6OjIxo0bqVevnk6Zu3fvMnbsWCIjI9N13jx58pAnT54PDfudYmNj6d+/P9bW1hw6dIjSpUtrHU9ISCAkJERrn4WFRZb/zDp06MCUKVP47bffGDt2bJaeWzKATF8SWJI+ETdv3hSAaNiwoejUqZMAxNq1a7XKBAcHC0D06NFDa7+bm5twc3NLtV4fHx/x9q9aYGCgAERwcLBYuHChKF26tDAzMxPu7u5i+vTpQgghkpOTxbRp00Tx4sWFSqUSRYsWFUuXLtWpPyXWa9euiXHjxonChQsLlUolihQpIiZNmiSSkpJSjWvfvn3C399fODo6CqVSKYoUKSJGjRolYmJiUr3mwMBAcfjwYdGgQQNha2urc01pOXTokPDz8xP29vZCpVKJ4sWLi8DAQK3zpJwjte193NzchEqlSvWYh4eHAMSxY8c0+zZv3ix8fX2FjY2NMDMzE15eXuJ///ufSExM1Hk/IHx8fLT2pdzvmzdvitmzZwsPDw+hUqlEgQIFRFBQkNb9Tin7rut68OCB6N+/vyhSpIgwMzMT9vb2onTp0qJXr14iKirqvdeflJQknJycRLly5XSOJSQkiIIFCwpA7N+//711JSQkaL1O+Vw/e/ZM9OvXT+TLl08YGxuLRYsW6dyLNyUmJooJEyZoPouFCxcW48aNE9evXxeA6NSp03tjOXr0qABE8+bN31s2xZuf1RQpv2tpbW//fOPj48WUKVNEuXLlhIWFhbCyshI1atQQGzduTPO8RYsWFXnz5tU7TunTJVuaJCkVP/74I6tWrWLkyJE0b94cY2PjTDnPtGnTCAkJoXnz5tSpU4e1a9cyYMAALCwsOH36NH/99Rf+/v7UqVOHVatW0bFjRwoWLEiNGjV06ho4cCD//PMPbdq0wczMjHXr1jF06FCuXbvG/PnztcrOmzeP3r17Y29vT9OmTXF2dubYsWP8/PPPBAcHExwcjFKp1HrP4cOHGTduHLVr16Z79+7cuXPnvde3du1a2rZti1Kp5KuvviJXrlzs3r2bMWPGsHPnToKDg1GpVLi7uxMYGMiGDRs4ffo0AwYMwM7O7qPubWqmT5/OwIEDcXBwoH379lhaWrJ582YGDRrEgQMHWLNmDQqFQq+6/u///o+QkBD8/f1p0KABGzZsICgoiFevXvHzzz8D0KJFCyIjI9m4cSPNmzenbNmyWnXExsbi7e3NrVu3aNCgAS1btuTVq1fcuHGDxYsXM3ToUGxsbN4Zx5kzZ3j69ClffvmlzrHg4GBu3rxJzZo1qVmz5nuvycRE9yshPj6eOnXq8Pz5c5o2bYpSqSR37tzvrKd79+4sXLiQggUL0qdPH+Li4pg6dSqHDx9+bwwpHBwcALh58ybJyckYGX3YENy0xsQdO3aMbdu2abVAxsfH06hRI0JCQihXrhxdu3YlISGBrVu30rx5c2bOnEnfvn116qpWrRpLly7l8uXLFC9e/IPilD4Rhs7aJCm7eLOlSQghBg8eLAAxf/58TZmMbmlycHAQ169f1+y/c+eOUCqVwtbWVhQrVkw8fvxYcyzlL+9mzZpp1ZXy137u3LnF/fv3NfufP38uPD09dVoZzp8/L0xMTES5cuVEeHi4Vl3jx48XgPjll190rhkQv//+e6rXmJro6GhhZ2cnVCqVOH36tGZ/cnKyaN++vQDETz/9lOq1vN1y8S5ptTTt2LFDKBQKYWFhIWJiYsT169eFiYmJyJUrl7hz546mXHx8vOZn9Mcff2jVwTtamgoWLCgePHig2f/kyRNhZ2cnrK2tRXx8vGb/okWLBKBpnXnTpk2bBCAGDRqkcyw6OlqrnrTMnj1bAOK3337TOTZmzBgBiNGjR7+3ntS4ubkJQDRo0EDExsbqHE/t55XyefHy8hIvXrzQ7L93755wcnLSu6UpOTlZlC9fXvMzWLRokbhw4UKqLYJvn/vNlqbU3Lx5Uzg7Owt7e3tx+fJlzf6RI0cKQAQFBYnk5GTN/ujoaFGxYkWhVCq1fsdSTJ8+XQBi4cKF770u6dMmn56TpDSMGjUKW1tbxowZQ2xsbKaco3///hQqVEjzOn/+/NSoUYOoqChGjRqFs7Oz5ljlypUpVKgQp0+fTrMuV1dXzWsrKyvNUz1vPmU0f/58EhMTmTFjhuav+RRDhw7F2dmZlStX6tRfrlw5unTpove1bdiwgcjISLp06UKZMmU0+xUKBRMmTMDExCTDnihLTEwkKCiIoKAgRo0axRdffIGfnx9CCMaOHYuFhQXLly8nMTGRIUOGkD9/fs17lUolEyZMAEhXPD/88IPWeB4nJyeaN2/O8+fP0/0kmrm5uc4+a2trnda+1Ny7dw8g1dafhw8fAmh9LlLcuHFDc89StmXLlqV6jsmTJ6caY2qWLl0KoDMoPW/evAwYMECvOkD9Ofnrr7+oVq0a+/btIyAggJIlS2JjY0O9evVYvHgxSUlJeteXIjo6Gn9/f549e8Zff/1FsWLFAEhOTmbu3LkUKVKE0aNHa7U4WltbM3r0aF69esW6det06ky59yk/C+nzJbvnJCkNDg4ODBs2jJEjR2qejslo5cqV09mX8kX8dldOyrGjR4+mWldq3S8p+0JDQzX7/vnnHwD+/vtvdu/erfMeU1NTLl26pLO/cuXKqZ43LadOnQJS7x7Jnz8/hQsX5vLlyzx//hxra+t01f22pKQkxowZA4CRkRH29vbUrVuXPn360KxZs/fGU7VqVczNzbXu0/uUL19eZ1++fPkA9B5QXatWLVxcXBg/fjyhoaE0adKEGjVq4OnpqXc3YXh4OAD29vY6x4QQab7vxo0bmnuWomHDhjqDyc3MzPD09NQrFkCT1L/r86ivQoUKcfjwYUJDQ9m9ezfHjh3j8OHD7Nmzhz179rB06VK2b9+OSqXSq76kpCTatWvH+fPnmTt3LnXr1tUcu3z5Ms+ePcPV1VXnvgA8efIEINXfjZQ/Pp4+fZqu65M+PTJpkqR3GDhwILNmzWLSpEn06NEjw+tPbbxKyriStI4lJiamWleuXLlS3WdkZERUVJRmX0REBIBm3I2+3jeO5W3R0dHvfJ+LiwuXL18mOjr6o5MmlUpFXFzcR8WTK1cu7t+/r/c5bW1tdfal/Oz0bQGxtbXlyJEjBAYGsnnzZs2UC/ny5WPEiBH07t37vXWktAC9fPlS51jKtaZ2XfXq1dMkVbdu3aJgwYKp1p8rVy69EziAqKgojIyMUn3sP72foRRly5bV+iMiJCSEDh06EBwczJw5cxg0aJBe9QwZMoRt27bRv39/nbnWUn4vzp8/z/nz59OsIyYmRmdfyr3/FKankD6O7J6TpHcwNzcnKCiIqKgoxo0bl2Y5IyOjNJOZNxOWzPT48eNU9yUnJ2t9wackY9HR0Qgh0tzelp4vzjfPk9ZsySn73zfQOaO8L57Hjx9nWSxvcnd3Z8mSJTx58oRTp04xceJEhBD06dMn1W7St6V04aZ86b+pevXqgHpA+IdK78/d1taW5OTkVFtdMmrmbF9fX3766ScA9u7dq9d75s+fz/Tp02nYsCFTp07VOZ7ys2/VqtU7fy8WLVqk896Ue/9md7r0eZJJkyS9R5cuXfDw8GD27NlpPjFmb2/P48ePdRKnmJgYrl69mhVhcuDAgTT3vflXepUqVYD/uukyS0rX49tz6YC65eP69esUKlToo1uZMiKef//9l5cvX6baJfqxUp68fF/rk7GxMWXLlmXo0KGaZGnTpk3vrT+l6yy1z1mdOnVwc3Pj4MGD7N+/P72hfxAvLy/g3Z/HjJDaJJ5p2bt3L3379qVkyZKsXr061adhS5QogY2NDcePHychISFdsaSMYUtPN6b0aZJJkyS9h7GxMePGjSM+Pp4ff/wx1TIVK1YkISGB5cuXa/YJIRgxYkSqzfmZYcaMGZrJIQFevHihibdjx46a/b1798bExIR+/fpx9+5dnXoiIyM1438+RvPmzbG1tWXRokVa3R0p9yUhISHT1iBLTfv27TExMWHq1Kla9ykhIYHhw4cDZEo8KeNdUhskfO7cOW7fvq2zP6VFRp/B1zVr1sTIyIh///1X55iJiQmzZs1CoVDQunXrVMewgf5jsPSR8ln78ccftT779+/fZ/r06XrXc/PmTWbNmsXz5891jsXExGjqSm36jTdduXKF1q1bY2try+bNm1PtVgX1verVqxe3b9/mu+++SzVxOnfuXKotukePHsXExETTsid9vuSYJknSQ8uWLalWrVqaM4T37duXRYsW0a1bN3bt2oWzszMHDhwgMjISLy+vNJ94y0iVKlXCy8uLr776CpVKxbp167h16xbffvut1nIwpUuXZs6cOfTq1YvixYvj5+dH4cKFiY6O5saNG+zbt4/OnTszb968j4rHxsaG3377jXbt2lGlShW++uornJ2d2bNnD8ePH6dy5cr83//938dett4KFy7MxIkTGTJkCGXKlKFNmzZYWlqyZcsWLl26RPPmzVOdUftjVatWDXNzc6ZNm0Z0dLSmC2f48OHs3r2bIUOG4O3tjYeHB46Ojty4cYNNmzZhbm6e6pxAb7O3t6dWrVocOHCA+Ph4nUHR/v7+LF26lO7du1O/fn3Kli1LtWrVcHBw4NmzZ1y/fp09e/ZgZGSUIV/6vr6+BAQEsGjRIjw9PWnZsiXx8fGsXr2aqlWrsmXLFr3qiYqKol+/fvzf//0fNWvWpFSpUpibm3P//n22bNlCREQEFSpUoF+/fu+sZ+DAgTx79ozGjRtrnux7k7u7uyZZHjNmDCdPnmTGjBls3boVHx8fnJ2duX//PmfPnuX06dMcOXJEa/zgixcv+Oeff6hfv366Wr+kT1TWznAgSdnX2/M0vW3//v2a+YrenqdJCCH27NkjqlSpIlQqlXB0dBTffPONePjw4XtnBH/bu+YqSq2ut2cEL1SokFAqlaJw4cJi4sSJac5r8++//4q2bdsKV1dXYWpqKpycnET58uXF8OHDxcWLFzXl9J37Ji379+8XjRs3FnZ2dkKpVIpixYqJH374QWsOH32uPS3vmhE8NRs3bhQ+Pj7C2tpaqFQq4enpKaZMmaIzG7YQ758R/G1p/Vy3bt0qKlWqJMzNzbVmBL9w4YIYMGCAKFeunHB0dBQqlUoUKlRIdO7cWVy4cEHva1q5cmWqM9i/6d69e2LkyJGiQoUKws7OTpiYmAh7e3tRpUoVMWzYMHHp0iWd97xr/jEh3j0j+Pjx4zWfxUKFColx48aJa9eu6T1PU1xcnFi7dq3o3r278PLyEk5OTsLY2FjY29uLGjVqiKlTp4qXL19qvSe1z2rK70xa29s/38TERDF//nzh7e0tbGxsNLO9N2rUSMydO1fnc7t48WIBiA0bNrz3mqRPn0KIdzyTKkmSJGV7r169olixYpQoUYLt27cbOpwcpVatWjx8+JCLFy9m2soBUvYhxzRJkiR94pRKJePGjePvv/9Ocx4vKeMFBwdz4MABJk6cKBOmHEKOaZIkSfoMtGvXjnv37skJFrNQVFQUv/zyCy1btjR0KFIWkd1zkiRJkiRJepDdc5IkSZIkSXqQSZMkSZIkSZIeZNIkSZIkSZKkB5k0SZIkSZIk6UEmTZIkSZIkSXqQSZMkSZIkSZIeZNIkSZIkSZKkB5k0SZIkSZIk6UEmTZIkSZIkSXr4f/u/5D1VTEP8AAAAAElFTkSuQmCC",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "npoints = [11, 21, 31, 41, 51, 61]\n",
-        "grid_classes = [GaussChebyshev, GaussLegendre, TanhSinh, Simpson, Trapezoidal]\n",
-        "\n",
-        "for grid_class in grid_classes:\n",
-        "    errors = []\n",
-        "    for npoint in npoints:\n",
-        "        # make 1-dimensional grid for the given number of points\n",
-        "        oned = grid_class(npoint)\n",
-        "        rgrid = BeckeRTransform(0.0, R=1.5).transform_1d_grid(oned)\n",
-        "        # calculate x^2 e^-x\n",
-        "        x = rgrid.points\n",
-        "        func_vals = x**2.0 * np.exp(-x)\n",
-        "        # compute relative percent error of integration\n",
-        "        integral = rgrid.integrate(func_vals)\n",
-        "        errors.append(np.abs(integral - 2.0) / 2.0 * 100)\n",
-        "    # plot relative percent error vs. number of grid points\n",
-        "    plt.semilogy(npoints, errors, label=oned.name)\n",
-        "\n",
-        "plt.legend(frameon=False, fontsize=12)\n",
-        "plt.title(r\"Numerical Integration of $\\int_0^\\infty x^2 e^{-x}dx$\", fontsize=16)\n",
-        "plt.xlabel(\"Number of Points (Grid Size)\", fontsize=14)\n",
-        "plt.ylabel(\"Relative Percent Error\", fontsize=14)\n",
-        "plt.xticks(npoints)\n",
-        "plt.show()"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.15"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 1
-}
diff --git a/examples/Angular_grid.ipynb b/notebooks/Angular_grid.ipynb
similarity index 100%
rename from examples/Angular_grid.ipynb
rename to notebooks/Angular_grid.ipynb
diff --git a/examples/Atom_Grid.ipynb b/notebooks/Atom_Grid.ipynb
similarity index 100%
rename from examples/Atom_Grid.ipynb
rename to notebooks/Atom_Grid.ipynb
diff --git a/examples/Atom_Grid_Construction.ipynb b/notebooks/Atom_Grid_Construction.ipynb
similarity index 100%
rename from examples/Atom_Grid_Construction.ipynb
rename to notebooks/Atom_Grid_Construction.ipynb
diff --git a/examples/Cubic_Grids.ipynb b/notebooks/Cubic_Grids.ipynb
similarity index 100%
rename from examples/Cubic_Grids.ipynb
rename to notebooks/Cubic_Grids.ipynb
diff --git a/examples/Interpolation_and_Poisson.ipynb b/notebooks/Interpolation_and_Poisson.ipynb
similarity index 100%
rename from examples/Interpolation_and_Poisson.ipynb
rename to notebooks/Interpolation_and_Poisson.ipynb
diff --git a/examples/JCP_Paper.ipynb b/notebooks/JCP_Paper.ipynb
similarity index 100%
rename from examples/JCP_Paper.ipynb
rename to notebooks/JCP_Paper.ipynb
diff --git a/examples/Molecular_Grid.ipynb b/notebooks/Molecular_Grid.ipynb
similarity index 100%
rename from examples/Molecular_Grid.ipynb
rename to notebooks/Molecular_Grid.ipynb
diff --git a/examples/Molecular_Grid_Construction.ipynb b/notebooks/Molecular_Grid_Construction.ipynb
similarity index 100%
rename from examples/Molecular_Grid_Construction.ipynb
rename to notebooks/Molecular_Grid_Construction.ipynb
diff --git a/examples/Multipole_Moments.ipynb b/notebooks/Multipole_Moments.ipynb
similarity index 100%
rename from examples/Multipole_Moments.ipynb
rename to notebooks/Multipole_Moments.ipynb
diff --git a/examples/Quickstart.ipynb b/notebooks/Quickstart.ipynb
similarity index 100%
rename from examples/Quickstart.ipynb
rename to notebooks/Quickstart.ipynb

From 1eb49f9fe90a48cd618f6b9600d5dd4c3153469e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Marco=20Mart=C3=ADnez=20Gonz=C3=A1lez?=
 <mmg870630@gmail.com>
Date: Tue, 28 Apr 2026 09:49:19 -0400
Subject: [PATCH 3/8] Refactor website to jupyter books

---
 .github/workflows/website.yaml                |  90 +--
 doc/README.md                                 |  39 --
 doc/_static/css/override.css                  |  43 --
 doc/conf.py                                   | 170 ------
 doc/conventions.rst                           | 137 -----
 doc/gen_api.sh                                |   4 -
 doc/gen_table_onedgrids.py                    | 130 -----
 doc/gen_table_rtransform.py                   | 159 -----
 doc/images/grid_logo.png                      | Bin 48703 -> 0 bytes
 doc/images/grid_logo.svg                      | 544 ------------------
 doc/index.rst                                 | 115 ----
 doc/installation.rst                          | 142 -----
 doc/notebooks/angular_grid.nblink             |   3 -
 doc/notebooks/atom_grid.nblink                |   3 -
 doc/notebooks/atom_grid_construction.nblink   |   3 -
 doc/notebooks/cubic_grid.nblink               |   3 -
 doc/notebooks/interpolation_poisson.nblink    |   3 -
 doc/notebooks/molecular_grid.nblink           |   3 -
 .../molecular_grid_construction.nblink        |   3 -
 doc/notebooks/multipole_moments.nblink        |   3 -
 doc/notebooks/one_dimensional_grids.nblink    |   3 -
 doc/notebooks/quickstart.nblink               |   3 -
 doc/onedgrids.rst                             |  29 -
 doc/radial_transf.rst                         |  13 -
 doc/table_modules.csv                         |  12 -
 doc/table_rtransform.csv                      |  13 -
 website/One_dimensional_grids.ipynb           | 404 +++++++++++++
 website/README.md                             |  31 +
 website/_config.yml                           |  80 +++
 website/_toc.yml                              |  68 +++
 website/conventions.ipynb                     |  22 +
 website/installation.ipynb                    |  83 +++
 website/intro.md                              |  72 +++
 website/radial_transf.ipynb                   |  22 +
 website/requirements.txt                      |   5 +
 {doc => website}/table_angular_lebedev.csv    |   0
 {doc => website}/table_angular_spherical.csv  |   0
 {doc => website}/table_onedgrids.csv          |   0
 38 files changed, 837 insertions(+), 1620 deletions(-)
 delete mode 100644 doc/README.md
 delete mode 100644 doc/_static/css/override.css
 delete mode 100644 doc/conf.py
 delete mode 100644 doc/conventions.rst
 delete mode 100644 doc/gen_api.sh
 delete mode 100644 doc/gen_table_onedgrids.py
 delete mode 100644 doc/gen_table_rtransform.py
 delete mode 100644 doc/images/grid_logo.png
 delete mode 100644 doc/images/grid_logo.svg
 delete mode 100644 doc/index.rst
 delete mode 100644 doc/installation.rst
 delete mode 100644 doc/notebooks/angular_grid.nblink
 delete mode 100644 doc/notebooks/atom_grid.nblink
 delete mode 100644 doc/notebooks/atom_grid_construction.nblink
 delete mode 100644 doc/notebooks/cubic_grid.nblink
 delete mode 100644 doc/notebooks/interpolation_poisson.nblink
 delete mode 100644 doc/notebooks/molecular_grid.nblink
 delete mode 100644 doc/notebooks/molecular_grid_construction.nblink
 delete mode 100644 doc/notebooks/multipole_moments.nblink
 delete mode 100644 doc/notebooks/one_dimensional_grids.nblink
 delete mode 100644 doc/notebooks/quickstart.nblink
 delete mode 100644 doc/onedgrids.rst
 delete mode 100644 doc/radial_transf.rst
 delete mode 100644 doc/table_modules.csv
 delete mode 100644 doc/table_rtransform.csv
 create mode 100644 website/One_dimensional_grids.ipynb
 create mode 100644 website/README.md
 create mode 100644 website/_config.yml
 create mode 100644 website/_toc.yml
 create mode 100644 website/conventions.ipynb
 create mode 100644 website/installation.ipynb
 create mode 100644 website/intro.md
 create mode 100644 website/radial_transf.ipynb
 create mode 100644 website/requirements.txt
 rename {doc => website}/table_angular_lebedev.csv (100%)
 rename {doc => website}/table_angular_spherical.csv (100%)
 rename {doc => website}/table_onedgrids.csv (100%)

diff --git a/.github/workflows/website.yaml b/.github/workflows/website.yaml
index 4a6e6fd72..84831f566 100644
--- a/.github/workflows/website.yaml
+++ b/.github/workflows/website.yaml
@@ -1,9 +1,11 @@
-name: build_website
+name: deploy-book
 
+# Only run this when master/main/website branches change
 on:
   push:
     branches:
       - master
+      - main
       - website
 
   pull_request:
@@ -12,48 +14,56 @@ on:
       - main
       - master
 
-permissions:
-  contents: write
-  deployments: write
-  pages: write
-
+# This job installs dependencies, builds the book, and pushes it to `gh-pages`
 jobs:
-  build:
+  deploy-book:
     runs-on: ubuntu-latest
-
     permissions:
-      contents: write
-      deployments: write
       pages: write
+      # https://github.com/JamesIves/github-pages-deploy-action/issues/1110
+      contents: write
 
     steps:
-    - uses: actions/checkout@v6
-
-    - name: Install development and distributions version
-      run: |
-        pip install --upgrade pip
-        pip install ".[doc]"
-        pip install pypandoc_binary
-
-    - name: Setup pandoc
-      uses: siacodelabs/setup-pandoc@v1
-      with:
-        xelatex: true.
-
-    # didn't need to change anything here, but had to add sphinx.ext.githubpages
-    # to my conf.py extensions list. that fixes the broken uploads
-    - name: Building documentation
-      run: |
-        sphinx-apidoc -a -o ./doc/pyapi/ ./src/grid ./src/grid/tests/ ./src/grid/test/ ./src/grid/data/ --separate
-        sphinx-build -M html doc _build
-
-    # still need to build and set the PAT to get a rebuild on the pages job,
-    # apart from that quite clean and nice
-    - name: GitHub Pages Action
-      if: ${{ github.event_name == 'push' && (github.ref == 'refs/heads/main' || github.ref == 'refs/heads/master') }}
-      uses: peaceiris/actions-gh-pages@v4
-      with:
-        github_token: ${{ secrets.GITHUB_TOKEN }}
-        publish_dir: _build/html
-        publish_branch: gh-pages
-        cname: grid.qcdevs.org
+      - uses: actions/checkout@v4
+
+      # Install dependencies
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          pip install -r website/requirements.txt
+
+      # Install Grid
+      - name: Install package
+        run: |
+          pip install -e .
+
+      # Build the API docs
+      - name: Build the API documentation
+        run: |
+          sphinx-apidoc -o website/_autosummary src/grid src/grid/tests/ src/grid/test/ src/grid/data/ -e -f
+
+      # Copy notebooks into the book so CI always builds from grid/notebooks
+      - name: Stage notebooks for website build
+        run: |
+          rm -rf website/tutorial
+          cp -r notebooks website/tutorial
+
+      # Build the book
+      - name: Build the website
+        run: |
+          jupyter-book build ./website/
+
+      # Push the book's HTML to github-pages
+      # only push to gh-pages if the master/main branch has been updated
+      - name: GitHub Pages Action
+        if: ${{ github.event_name == 'push' && (github.ref == 'refs/heads/master' || github.ref == 'refs/heads/main') }}
+        uses: peaceiris/actions-gh-pages@v4.0.0
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: ./website/_build/html
+          publish_branch: gh-pages
+          cname: grid.qcdevs.org
diff --git a/doc/README.md b/doc/README.md
deleted file mode 100644
index a9db04f8d..000000000
--- a/doc/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
-# Instructions On Documentation
-
-In-order to build the documentation locally, the following commands need to
-be runned. The html files are created in an folder called "build".
-
-```bash
-# Run one of the following commands to install the required dependencies:
-pip install qc-grid[doc]
-# Or if you're installing from a local setup:
-# pip install .[doc]
-
-cd doc
-# # Generates API html for grid while ignoring the test and data folders.
-# Stores it in pyapi/
-sphinx-apidoc -a -o pyapi/ ../src/grid ../src/grid/tests/ ../src/grid/test/ ../src/grid/data/ --separate
-# Build the html files
-sphinx-build -M html . ./build
-```
-Here inside the "./build/html/ folder, there are the html files to run the website locally.
-
-
-## Pushing To Website
-
-**WARNING: The website is automatically built everytime there is a push to the main branch and thus these sets of instructions must be taken with extreme care.**
-
-After running the commands above, you'll need to go inside the  "./build/html" folder and copy all the files.
-
-```bash
-git branch -a
-# Should be something like origin/doc/ where origin is the remote to the theochem/grid Github
-git checkout gh-pages
-cd ..  # Get out of doc and go back to grid folder
-```
-Now, paste and overwrite all of the files into the root. Then commit and push to the `gh-pages` branch of the theochem/grid
-```bash
-git add *
-git commit -m "Update website"
-git push origin gh-pages
-```
diff --git a/doc/_static/css/override.css b/doc/_static/css/override.css
deleted file mode 100644
index ae0da82d4..000000000
--- a/doc/_static/css/override.css
+++ /dev/null
@@ -1,43 +0,0 @@
-/** css/override.css **/
-
-
-/* This line is theme specific - it includes the base theme CSS */
-@import 'theme.css';       /* for the Read the Docs theme */
-
-table.docutils {
-    border-collapse: separate !important;
-}
-
-th.head {
-    position: sticky;
-    top: 0px;
-    z-index: 2;
-    background-color: white;
-    border-bottom-color: black !important;
-}
-
-th.head:nth-child(1) {
-    position: sticky;
-    top: 0px;
-    left: 0px;
-    z-index: 3;
-    background-color: white;
-    border-right-color: black !important;
-}
-
-td:nth-child(1) {
-    position: sticky;
-    left: 0px;
-    z-index: 1;
-    border-right-color: black !important;
-    border-right-width: 1px;
-    border-right-style: solid;
-}
-
-table.docutils tbody tr.row-odd td {
-    background-color: rgb(252, 252, 252);
-}
-
-div.wy-table-responsive {
-    max-height: 90vh;
-}
diff --git a/doc/conf.py b/doc/conf.py
deleted file mode 100644
index e5649a486..000000000
--- a/doc/conf.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# This file only contains a selection of the most common options. For a full
-# list see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import importlib
-import inspect
-import os
-import sys
-
-sys.path.insert(0, os.path.abspath("."))
-sys.path.insert(0, os.path.abspath("../"))
-
-# -- Project information -----------------------------------------------------
-
-project = "grid"
-copyright = "2024, QC-Devs"
-author = "QC-Devs"
-html_logo = "./images/grid_logo_website.svg"
-
-# -- General configuration ---------------------------------------------------
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
-    "sphinx_rtd_theme",
-    "sphinx.ext.autodoc",
-    "sphinx.ext.napoleon",
-    "sphinx_autodoc_typehints",
-    "sphinx.ext.autosummary",
-    "sphinx.ext.mathjax",
-    "nbsphinx",
-    "nbsphinx_link",
-    "IPython.sphinxext.ipython_console_highlighting",
-    "sphinx.ext.linkcode",
-]
-
-# List of arguments to be passed to the kernel that executes the notebooks:
-nbsphinx_execute_arguments = [
-    "--InlineBackend.figure_formats={'svg', 'pdf'}",
-    "--InlineBackend.rc=figure.dpi=200",
-]
-# nbsphinx_input_prompt = 'In [%s]:'
-# nbsphinx_output_prompt = 'Out[%s]:'
-# explicitly dis-/enabling notebook execution
-nbsphinx_execute = "never"
-
-# Don't add .txt suffix to source files:
-html_sourcelink_suffix = ""
-
-# This is processed by Jinja2 and inserted before each notebook
-
-# nbsphinx_prolog = r"""
-# nbsphinx_epilog = r"""
-# {% set docname = env.docname.split("/")[-1] %}
-# .. raw:: html
-# .. role:: raw-html(raw)
-#  :format: html
-# .. nbinfo::
-#  The corresponding file can be obtained from:
-#  - Jupyter Notebook: :download:`{{docname+".ipynb"}}`
-#  - Interactive Jupyter Notebook: :raw-html:`<a href="https://mybinder.org/v2/gh/theochem/procrustes/master?filepath=doc%2Fnotebooks%2F/{{ docname }}.ipynb"><img alt="Binder badge" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a>`    """
-
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ["_templates"]
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "**.ipynb_checkpoints"]
-
-
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_theme = "sphinx_rtd_theme"
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ["_static"]
-html_style = "css/override.css"
-
-# Theme options are theme-specific and customize the look and feel of a theme
-# further.  For a list of options available for each theme, see the
-# documentation.
-html_theme_options = {
-    "collapse_navigation": False,
-    "logo_only": True
-}
-
-
-# -- Configuration for autodoc extensions ---------------------------------
-
-autodoc_default_options = {
-    "undoc-members": True,
-    "show-inheritance": True,
-    "members": None,
-    "inherited-members": True,
-    "ignore-module-all": True,
-}
-
-
-add_module_names = False
-
-
-def autodoc_skip_member(app, what, name, obj, skip, options):
-    """Decide which parts to skip when building the API doc."""
-    if name == "__init__":
-        return False
-    if name.startswith("test_"):
-        return False
-    return skip
-
-
-def setup(app):
-    """Set up sphinx."""
-    app.connect("autodoc-skip-member", autodoc_skip_member)
-
-
-# -- Link to Github ------------------------------
-# Configure viewcode extension.
-code_url = f"https://github.com/theochem/grid/blob/master/src/grid"
-
-
-def linkcode_resolve(domain, info):
-    # Non-linkable objects from the starter kit in the tutorial.
-    if domain != 'py':
-        return None
-    if not info['module']:
-        return None
-
-    mod = importlib.import_module(info["module"])
-    if "." in info["fullname"]:
-        objname, attrname = info["fullname"].split(".")
-        obj = getattr(mod, objname)
-        try:
-            # object is a method of a class
-            obj = getattr(obj, attrname)
-        except AttributeError:
-            # object is an attribute of a class
-            return None
-    else:
-        obj = getattr(mod, info["fullname"])
-    try:
-        file = inspect.getsourcefile(obj)
-        lines = inspect.getsourcelines(obj)
-    except TypeError:
-        # e.g. object is a typing.Union
-        return None
-    file = os.path.relpath(file, os.path.abspath(".."))
-
-    # Get the file name e.g. /usr/conda/py37/grid/angular.py -> angular.py
-    split = file.split("grid/")
-    file = split[-1]
-
-    start, end = lines[1], lines[1] + len(lines[0]) - 1
-
-    return f"{code_url}/{file}#L{start}-L{end}"
diff --git a/doc/conventions.rst b/doc/conventions.rst
deleted file mode 100644
index 23ae5940c..000000000
--- a/doc/conventions.rst
+++ /dev/null
@@ -1,137 +0,0 @@
-.. _conventions:
-
-Conventions
-############
-
-Spherical Coordinates
-=====================
-
-Spherical coordinates :math:`(r, \theta, \phi)` is used extensively throughout the molecular, atomic,
-and angular grid modules.
-Particularly, the radius :math:`r \in [0, \infty)`, azumuthal :math:`\theta \in [-\pi, \pi]` and polar
-:math:`\phi \in [0, \pi)` angles are defined from Cartesian coordinates as:
-
-.. math::
-    \begin{align}
-        r &= \sqrt{x^2 + y^2 + z^2}\\
-        \theta &= \text{arctan2} \bigg(\frac{y}{x}\bigg)\\
-        \phi &= arc\cos \bigg(\frac{z}{r}\bigg)
-    \end{align}
-
-such that when the radius is zero :math:`r=0`, then the angles are zero :math:`\theta,\phi = 0`.
-
-Grid offers a :class:`utility<grid.utils.convert_cart_to_sph>` function to convert to spherical coordinates
-
-.. code-block::
-    python
-
-    import numpy as np
-    from grid.utils import convert_cart_to_sph
-
-    # Generate random set of points
-    cart_pts = np.random.uniform((-100, 100), size=(1000, 3))
-    # Convert to spherical coordinates
-    spher_pts = convert_cart_to_sph(cart_pts)
-    # Convert to spherical coordinates, centered at [1, 1, 1]
-    spher_pts = convert_cart_to_sph(cart_pts, center=np.array([1.0, 1.0, 1.0]))
-
-
-The :class:`atomic grid<grid.atomgrid.AtomGrid>` class can also be used to convert its points to spherical coordinates
-
-.. code-block::
-    python
-
-    from grid.atomgrid import AtomGrid
-
-    # Construct atomic grid object
-    atom_grid = AtomGrid(...)
-
-    # Convert atomic grid points centered at the nucleus to spherical coordinates
-    spher_pts = atom_grid.convert_cartesian_to_spherical()
-
-With the convention that when :math:`r=0`, then the angles within :math:`\{(0, \theta_j, \phi_j)\}` in that shell is
-obtained from a angular grid :math:`\{(\theta_j, \phi_j)\}` with some degree.
-
-Spherical Harmonics
-===================
-
-The real spherical harmonics are defined using complex spherical harmonics:
-
-.. math::
-    Y_{lm}(\theta, \phi) = \begin{cases}
-        i(Y^m_l(\theta, \phi) - (-1)^m Y_l^{-m}(\theta, \phi) & \text{if } m < 0 \\
-        Y_l^0 & \text{if } m = 0 \\
-        (Y^{-|m|}_{l}(\theta, \phi) + (-1)^m Y_l^m(\theta, \phi)) & \text{if } m > 0
-    \end{cases},
-
-where the degree :math:`l \in \mathbb{N}`, order :math:`m \in \{-l, \cdots, l \}` and
-:math:`Y^m_l` is the complex spherical harmonic.  The Condon-Shortley phase is not included.
-
-
-Alternatively, it can be written using the associated Legendre polynomials :math:`P_l^m` (without the Conway phase):
-
-.. math::
-    Y_{lm}(\theta, \phi) = \begin{cases}
-        \sqrt{\frac{(2l + 1) (l - m)!}{4 \pi (l + m)!}} \sqrt{2} \cos(m \theta) P_l^m(\cos(\theta)) & \text{if } m < 0 \\
-        \frac{1}{2} \sqrt{\frac{1}{\pi}} & \text{if } m = 0 \\
-        \sqrt{\frac{(2l + 1) (l - m)!}{4 \pi (l + m)!}}  \sqrt{2}\sin(m \theta) P_l^m(\cos(\theta))  & \text{if } m > 0
-    \end{cases}
-
-Grid offers :func:`generate_real_spherical_harmonics<grid.utils.generate_real_spherical_harmonics>` function
-to generate the real spherical harmonics:
-
-.. code-block::
-    python
-
-    from grid.utils import generate_real_spherical_harmonics
-
-    spher_pts = np.array(...)
-    theta = spher_pts[:, 1]
-    phi = spher_pts[:, 2]
-    # Generate all degrees up to l=2
-    spherical_harmonics = generate_real_spherical_harmonics(2, theta, phi)
-
-
-Ordering
---------
-
-The spherical harmonics are first ordered by the degree :math:`l` in ascending order.
-
-For each degree :math:`l`, the orders :math:`m` are in HORTON2 order defined as:
-
-.. math::
-   m = [0, 1, -1, 2, -2, \cdots, l, -l].
-
-
-Angular Grids
-=============
-
-The :class:`angular grids<grid.angular.AngularGrid>` is responsible for integrating functions over the unit-sphere.
-The quadrature weights are specifically chosen so that the spherical harmonics are orthonormal:
-
-.. math::
-    \int_{-\pi}^{\pi} \int_0^{\pi} Y_{l_1}^{m_1} Y_{l_2}^{m_2} \sin(\phi) d\theta d\phi = \delta_{l_1, l_2} \delta_{m_1, m_2}
-
-Further, the quadrature weights are all chosen that the weights sum up to :math:`4 \pi`.
-
-
-Nested Grids
--------------
-Angular grids of different degrees can be very close to one another. The following shows the mean with
-standard deviation and maximum distance between an angular grid of one degree and the consequent
-angular grid with higher degree.  The * indicates Lebedev-Laikov grids with negative weights.
-
-.. csv-table:: Lebedev-Laikov Grids
-   :file: ./table_angular_lebedev.csv
-   :widths: 11,11,11,11
-   :delim: ;
-   :header-rows: 1
-   :align: center
-
-
-.. csv-table:: Symmetric Spherical t-Design Grids
-   :file: ./table_angular_spherical.csv
-   :widths: 11,11,11,11
-   :delim: ;
-   :header-rows: 1
-   :align: center
diff --git a/doc/gen_api.sh b/doc/gen_api.sh
deleted file mode 100644
index bfb3cc5c2..000000000
--- a/doc/gen_api.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-# Generates API html for grid while ignoring the test and data folders.
-# Stores it in pyapi/
-
-sphinx-apidoc -a -o pyapi/ ../src/grid ../src/grid/tests/ ../src/grid/test/ ../src/grid/data/ --separate
diff --git a/doc/gen_table_onedgrids.py b/doc/gen_table_onedgrids.py
deleted file mode 100644
index 5d9c297f0..000000000
--- a/doc/gen_table_onedgrids.py
+++ /dev/null
@@ -1,130 +0,0 @@
-# GRID is a numerical integration module for quantum chemistry.
-#
-# Copyright (C) 2011-2019 The GRID Development Team
-#
-# This file is part of GRID.
-#
-# GRID is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License
-# as published by the Free Software Foundation; either version 3
-# of the License, or (at your option) any later version.
-#
-# GRID is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, see <http://www.gnu.org/licenses/>
-# --
-r"""
-Create a table of one-dimensional grids by parsing the grid data folder.
-This creates a "table_onedgrids.csv" file which is then added to index.rst file.
-
-"""
-import pandas as pd
-
-
-def generate_one_dimensional_grid_table_csv():
-    r"""
-    Parse the onedgrid.py file for all subclasses of OneDGrid, then find the __init__ line that contains domain info.
-    This creates a .csv file which is then added to index.rst.
-
-    This parses "Grid Name" from the documentation line.
-    class GridName():
-        "Grid Name integral quadrature class. "
-    The words "Integral" must seperate the name or else this will fail.
-
-    In order to parse the domain it assumes the following format:
-    def __init__(self, npoints):
-        r/"/"/"Generate 1-D grid on [l_bnd, u_bnd] interval using Interior Rectangle Rule for Sines.
-    The words on and interval should seperate the domain information.
-
-    """
-    df = pd.DataFrame(columns=["Grid", "Domain"])
-    with open(r"../src/grid/onedgrid.py", "r") as f:
-        line = f.readline()
-        found_class = False
-        while line:
-            line = line.rstrip()  # strip trailing spaces and newline
-            print("line: ", line)
-            # If you found a class that is a child of OneDGrid, then explore further.
-            if "class" in line and "OneDGrid" in line and "issubclass" not in line:
-                row_result = []
-
-                # Get the class name, by looking at what's after class
-                class_name = line.split("class")[1]
-                # Remove the OneDGrid
-                class_name = class_name.split("(OneDGrid)")[0].strip()
-
-                # Get the name of the Grid
-                line_document = f.readline()
-                print("linedoc", line_document.strip())
-                if line_document.strip() == 'r"""':
-                    line_document = f.readline()
-                    print("linedoc rev", line_document)
-                if "integral" in line_document:
-                    line_document = line_document.split("integral")
-                    line_name = line_document[0].strip()
-                    # Remove triple quotation marks: """
-                    line_name = line_name[:]
-                    line_name = line_name.replace('r"""', "")
-                    line_name = line_name.replace('"""', "")
-
-                    if "HORTON" in line_name:
-                        line_name = line_name.replace("HORTON", "Horton")
-                    # Store it in row_result plus the link to the funciton
-                    row_result.append(":func:`" + line_name + "<grid.onedgrid." + class_name + ">`")
-                else:
-                    raise RuntimeError(
-                        f"The word integral needs to be in the document, instead it is {line_document}."
-                    )
-
-                if found_class:
-                    raise RuntimeError(
-                        f"Found a class that it could not parse, it is the class before {line}."
-                    )
-
-                found_class = True
-
-            # If found a class then search for domain key.
-            if found_class:
-                if "def __init__" in line:
-                    # The next line should contain the domain information
-                    line_domain = f.readline()
-                    print(line_domain)
-
-                    if line_domain.strip() == 'r"""':
-                        line_domain = f.readline()
-                        print(line_domain)
-                    if "on" not in line_domain:  # or "interval" not in line_domain:
-                        raise RuntimeError(
-                            f"The words 'on' and 'interval' needs to be in {line_domain} for this to parse correctly."
-                        )
-
-                    # Seperate based on "on"
-                    line_domain = line_domain.split("on")[1]
-
-                    # Seperat ebased on "interval"
-                    line_domain = line_domain.split("interval")[0].strip()
-
-                    if "npoints" in line_domain:
-                        line_domain = line_domain.replace("npoints", "N")
-                    # Typo that too lazy to do pull request
-                    # if ":" in line_domain:
-                    #     line_domain = line_domain.replace(":", ",")
-
-                    line_domain = line_domain.replace(" ", "")
-                    # Store the result into pandas
-                    row_result.append(str(line_domain))
-                    df.loc[len(df.index)] = row_result
-                    # Move to the next class
-                    found_class = False
-
-            line = f.readline()
-    print(df)
-    df.to_csv("table_onedgrids.csv", index=False, sep=";")
-
-
-if __name__ == "__main__":
-    generate_one_dimensional_grid_table_csv()
diff --git a/doc/gen_table_rtransform.py b/doc/gen_table_rtransform.py
deleted file mode 100644
index 975cf9097..000000000
--- a/doc/gen_table_rtransform.py
+++ /dev/null
@@ -1,159 +0,0 @@
-# GRID is a numerical integration module for quantum chemistry.
-#
-# Copyright (C) 2011-2019 The GRID Development Team
-#
-# This file is part of GRID.
-#
-# GRID is free software; you can redistribute it and/or
-# modify it under the terms of the GNU General Public License
-# as published by the Free Software Foundation; either version 3
-# of the License, or (at your option) any later version.
-#
-# GRID is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, see <http://www.gnu.org/licenses/>
-# --
-r"""
-Create a table of one-dimensional grids by parsing the grid data folder.
-This creates a "table_onedgrids.csv" file which is then added to index.rst file.
-
-"""
-import pandas as pd
-
-
-def generate_rtransform_grid_table_csv():
-    r"""
-    Parse the rtransform.py file for all subclasses of BaseTransform,
-    then the next line should have a from and to that tells you the domain and codomain of
-    transformation.
-    This creates a .csv file which is then added to index.rst.
-
-    This parses "Grid Name" from the documentation line.
-    class Transformation(BaseTransform):
-        "Some Transformation from :math:`[-1, 1]` to :math:`[a, b]`. "
-    The words "from" and "to" must seperate the name or else this will fail.
-
-    In order to parse the transformation it assumes the following format:
-    def transform(self, npoints):
-       r\"\"\"
-       Blah blah
-
-       .. math::
-            r_i = trnasfomration here.
-        \"\"\"
-
-    """
-    df = pd.DataFrame(columns=["Transform", "Domain", "Co-Domain", ":math:`r(x)`"])
-    with open(r"../src/grid/rtransform.py", "r") as f:
-        line = f.readline()
-        found_class = False
-        while line:
-            line = line.rstrip()  # strip trailing spaces and newline
-            print("line: ", line)
-            # If you found a class that is a child of BaseTransform, then explore further.
-            if "InverseRTransform" in line and "BaseTransform" in line:
-                row_result = []
-                row_result += [
-                    ":func:`InverseRTransform<grid.rtransform.InverseRTransform>`",
-                    "Co-Domain",
-                    "Domain",
-                    ":math:`x_i(r_i)",
-                ]
-                print(row_result)
-                df.loc[len(df.index)] = row_result
-            elif "IdentityRTransform" in line and "BaseTransform" in line:
-                row_result = []
-                row_result += [
-                    ":func:`IdentityRTransform<grid.rtransform.IdentityRTransform>`",
-                    "Domain",
-                    "Domain",
-                    ":math:`x_i`",
-                ]
-                df.loc[len(df.index)] = row_result
-            elif (
-                "class" in line
-                and "BaseTransform" in line
-                and "issubclass" not in line
-                and "ABC" not in line
-            ):
-                row_result = []
-                # Get the class name, by looking at what's after class
-                class_name = line.split("class")[1]
-                # Remove the OneDGrid
-                class_name = class_name.split("(BaseTransform)")[0].strip()
-
-                # Get the name of the RTransform
-                line_document = f.readline()
-                print("linedoc", line_document.strip())
-                if line_document.strip() == 'r"""':
-                    line_document = f.readline()
-                    print("linedoc rev", line_document)
-                print(class_name, line_document)
-
-                # Add the correct link to the class name
-                row_result.append(":func:`" + class_name + "<grid.rtransform." + class_name + ">`")
-
-                if found_class:
-                    raise RuntimeError(
-                        f"Found a class that it could not parse, it is the class before {line}."
-                    )
-
-                found_class = True
-
-                # Get the domain and codomain information.
-                # The next line should contain the domain information
-                line_start = line_document  # f.readline()
-                print(line_start)
-                if line_start.strip() == 'r"""':
-                    line_start = f.readline()
-                    print(line_start)
-                if "from" not in line_start or "to" not in line_start:
-                    raise RuntimeError(
-                        f"The words 'from' and 'to' needs to be in {line_start} for this to parse correctly."
-                    )
-
-                # Line should be from domain to
-                # Seperat ebased on "to"
-                print(line_start.split("to"))
-                line_domain = line_start.split("to")[0].split("from")[1].strip()
-                line_codomain = line_start.split("to")[1].strip()
-                print("Line domain and codomain", line_domain, line_codomain)
-
-                # Remove white-space
-                line_domain = line_domain.replace(" ", "")
-                line_codomain = line_codomain.replace(" ", "")
-
-                # Remove period
-                line_codomain = line_codomain.replace(".", "")
-                # Store the result into pandas
-                row_result.append(str(line_domain))
-                row_result.append(str(line_codomain))
-
-            # If found a class then search for domain key.
-            if found_class:
-                # Get the transformation.
-                if "transform(self," in line:
-                    # Get the transformation from latex
-                    line_transf = f.readline()
-                    while ".. math::" not in line_transf:  # Keep going until you see  .. math::
-                        line_transf = f.readline()
-                    line_transf = f.readline().strip()  # Get the first line of transformation
-
-                    print("Transformation ", line_transf)
-                    row_result.append(":math:`" + str(line_transf) + "`")
-
-                    df.loc[len(df.index)] = row_result
-                    # Move to the next class
-                    found_class = False
-
-            line = f.readline()
-    print(df)
-    df.to_csv("table_rtransform.csv", index=False, sep=";")
-
-
-if __name__ == "__main__":
-    generate_rtransform_grid_table_csv()
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deleted file mode 100644
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-  </g>
-</svg>
diff --git a/doc/index.rst b/doc/index.rst
deleted file mode 100644
index 585e7496e..000000000
--- a/doc/index.rst
+++ /dev/null
@@ -1,115 +0,0 @@
-
-
-
-Welcome to grid's documentation!
-================================
-
-Grid is a free and open-source Python library for numerical integration,
-interpolation and differentiation of interest for the quantum chemistry community.
-Grid is intended to support molecular density-functional (DFT) theory calculations,
-but it also periodic boundary conditions.
-
-Please use the following citation in any publication using grid library:
-
-    **"Grid: A Python Library for Molecular Integration, Interpolation, Differentiation and More."**,
-    A. Tehrani, X. D. Yang, M. Martinez-Gonzalez,, L. Pujal, R. Hernandez‐Esparza, M. Chan,
-    E. Vohringer‐Martinez, T. Verstraelen, P. W. Ayers, F. Heidar‐Zadeh
-
-
-The `Grid source code <https://github.com/theochem/grid>`_ is hosted on GitHub and is released under the
-GNU General Public License v3.0. We welcome
-any contributions to the Grid library in accordance with our Code of Conduct; please see our Contributing
-Guidelines. Please report any issues you encounter while using Grid library on
-`GitHub Issues <https://github.com/theochem/grid/issues/new>`_. For further
-information and inquiries please contact us at qcdevs@gmail.com.
-
-
-Functionality
-=============
-* One-dimensional integration
-
-  * quadratures
-
-  * transformations
-
-* Multivariate integration
-
-  * Hypercubes:
-
-    * Tensor product of one-dimensional quadratures
-
-    * Rectilinear (a.k.a uniform) and skewed grids
-
-  * Integration over sphere:
-
-    * Lebedev-Laikov grids
-
-    * Symmetric Spherical T-Designs
-
-  * Molecular integration:
-
-    * Atom in molecule weights (nuclear weight functions)
-
-      * Hirshfeld weights
-
-      * Becke weights
-
-* Interpolation & differentiation
-
-* General ODE solver and Poisson solver
-
-
-Modules
-=======
-
-.. csv-table::
-    :file: ./table_modules.csv
-    :widths: 50, 70
-    :delim: ;
-    :header-rows: 1
-    :align: center
-
-
-.. toctree::
-    :maxdepth: 2
-    :caption: User Documentation
-
-    ./installation.rst
-    ./onedgrids.rst
-    ./radial_transf.rst
-    ./conventions.rst
-
-
-.. Notebooks that are external to the ./doc/ directory require
-   nbsphinx-link to link it to the table of contents. This
-   requires to create a ".nblink" for each external Jupyter notebook.
-
-.. toctree::
-    :maxdepth: 1
-    :caption: Example Tutorials
-
-    ./notebooks/quickstart
-    One-Dimensional Grids <./notebooks/one_dimensional_grids>
-    Angular Grids <./notebooks/angular_grid>
-    Atom Grid Construction <./notebooks/atom_grid_construction>
-    Atom Grid Application <./notebooks/atom_grid>
-    Molecular Grid Construction <./notebooks/molecular_grid_construction>
-    Molecular Grid Application <./notebooks/molecular_grid>
-    Cubic Grids <./notebooks/cubic_grid>
-    ./notebooks/interpolation_poisson
-    ./notebooks/multipole_moments
-
-.. toctree::
-   :maxdepth: 2
-   :caption: API Documentation
-
-   pyapi/modules.rst
-
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git a/doc/installation.rst b/doc/installation.rst
deleted file mode 100644
index 47779d434..000000000
--- a/doc/installation.rst
+++ /dev/null
@@ -1,142 +0,0 @@
-.. _usr_installation:
-
-Installation
-############
-
-Downloading Code
-================
-
-The latest code can be obtained through theochem (https://github.com/theochem/grid) in Github,
-
-.. code-block:: bash
-
-   git clone https://github.com/theochem/grid.git
-
-.. _usr_py_depend:
-
-Dependencies
-============
-
-The following dependencies will be necessary for Procrustes to build properly,
-
-* Python >= 3.0: http://www.python.org/
-* NumPy >= 1.16.0: http://www.numpy.org/
-* SciPy >= 1.4.0: http://www.scipy.org/
-* Sphinx >= 2.3.0: https://www.sphinx-doc.org/
-* SymPy >= 1.4.0: https://www.sympy.org/en/index.html
-* PyTest >= 5.4.3: `https://docs.pytest.org/ <https://docs.pytest.org/>`_
-* QA requirement: Tox >= 4.0.0: https://tox.wiki/en/latest/
-* Sphinx >= 2.3.0, if one wishes to build the documentation locally:
-  `https://www.sphinx-doc.org/ <https://www.sphinx-doc.org/>`_
-
-
-
-Installation
-============
-
-Installation via pip can be done by the following command:
-
-.. code-block:: bash
-
-    pip install git+https://github.com/theochem/grid.git@master
-
-
-With later release in PyPi, Grid can be installed via
-
-.. code-block:: bash
-
-    pip install qc-grid
-
-
-Grid can also be installed by cloning via git,
-
-.. code-block:: bash
-
-   git clone https://github.com/theochem/grid.git
-
-
-Then installation via pip can be done by going into the directory where Grid is downloaded to and running,
-
-.. code-block:: bash
-
-    cd grid
-    pip install .
-
-Successful installation can be checked by running the tests,
-
-.. code-block:: bash
-
-    pytest --pyargs grid
-
-
-
-
-
-
-
-
-
-..
-    The stable release of the package can be easily installed through the *pip* and
-    *conda* package management systems, which install the dependencies automatically, if not
-    available. To use *pip*, simply run the following command:
-
-    .. code-block:: bash
-
-        pip install qc-procrustes
-
-    To use *conda*, one can either install the package through Anaconda Navigator or run the following
-    command in a desired *conda* environment:
-
-    .. code-block:: bash
-
-        conda install -c theochem qc-procrustes
-
-
-    Alternatively, the *Procrustes* source code can be download from GitHub (either the stable version
-    or the development version) and then installed from source. For example, one can download the latest
-    source code using *git* by:
-
-    .. code-block:: bash
-
-        # download source code
-        git clone git@github.com:theochem/procrustes.git
-        cd procrustes
-
-    From the parent directory, the dependencies can either be installed using *pip* by:
-
-    .. code-block:: bash
-
-        # install dependencies using pip
-        pip install -r requirements.txt
-
-
-    or, through *conda* by:
-
-    .. code-block:: bash
-
-        # create and activate myenv environment
-        # Procruste works with Python 3.6, 3.7, and 3.8
-        conda create -n myenv python=3.6
-        conda activate myenv
-        # install dependencies using conda
-        conda install --yes --file requirements.txt
-
-
-    Finally, the *Procrustes* package can be installed (from source) by:
-
-    .. code-block:: bash
-
-        # install Procrustes from source
-        pip install .
-
-Building Documentation
-======================
-
-The following shows how to build the documentation using sphinx to the folder `_build`.
-
-    .. code-block:: bash
-
-        cd ./doc
-        ./gen_api.sh
-        sphinx-build -b html . _build
diff --git a/doc/notebooks/angular_grid.nblink b/doc/notebooks/angular_grid.nblink
deleted file mode 100644
index 90b079a97..000000000
--- a/doc/notebooks/angular_grid.nblink
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-    "path": "../../examples/Angular_grid.ipynb"
-}
diff --git a/doc/notebooks/atom_grid.nblink b/doc/notebooks/atom_grid.nblink
deleted file mode 100644
index f52411e76..000000000
--- a/doc/notebooks/atom_grid.nblink
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-    "path": "../../examples/Atom_Grid.ipynb"
-}
diff --git a/doc/notebooks/atom_grid_construction.nblink b/doc/notebooks/atom_grid_construction.nblink
deleted file mode 100644
index 1cdc4d6f7..000000000
--- a/doc/notebooks/atom_grid_construction.nblink
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-    "path": "../../examples/Atom_Grid_Construction.ipynb"
-}
diff --git a/doc/notebooks/cubic_grid.nblink b/doc/notebooks/cubic_grid.nblink
deleted file mode 100644
index d72622af2..000000000
--- a/doc/notebooks/cubic_grid.nblink
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-    "path": "../../examples/Cubic_Grids.ipynb"
-}
diff --git a/doc/notebooks/interpolation_poisson.nblink b/doc/notebooks/interpolation_poisson.nblink
deleted file mode 100644
index 02d370552..000000000
--- a/doc/notebooks/interpolation_poisson.nblink
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-    "path": "../../examples/Interpolation_and_Poisson.ipynb"
-}
diff --git a/doc/notebooks/molecular_grid.nblink b/doc/notebooks/molecular_grid.nblink
deleted file mode 100644
index 49197eed1..000000000
--- a/doc/notebooks/molecular_grid.nblink
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-    "path": "../../examples/Molecular_Grid.ipynb"
-}
diff --git a/doc/notebooks/molecular_grid_construction.nblink b/doc/notebooks/molecular_grid_construction.nblink
deleted file mode 100644
index da02adab1..000000000
--- a/doc/notebooks/molecular_grid_construction.nblink
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-    "path": "../../examples/Molecular_Grid_Construction.ipynb"
-}
diff --git a/doc/notebooks/multipole_moments.nblink b/doc/notebooks/multipole_moments.nblink
deleted file mode 100644
index 8be34dba5..000000000
--- a/doc/notebooks/multipole_moments.nblink
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-    "path": "../../examples/Multipole_Moments.ipynb"
-}
diff --git a/doc/notebooks/one_dimensional_grids.nblink b/doc/notebooks/one_dimensional_grids.nblink
deleted file mode 100644
index fd2c3846c..000000000
--- a/doc/notebooks/one_dimensional_grids.nblink
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-    "path": "../../examples/One_dimensional_grids.ipynb"
-}
diff --git a/doc/notebooks/quickstart.nblink b/doc/notebooks/quickstart.nblink
deleted file mode 100644
index 3360a123b..000000000
--- a/doc/notebooks/quickstart.nblink
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-    "path": "../../examples/Quickstart.ipynb"
-}
diff --git a/doc/onedgrids.rst b/doc/onedgrids.rst
deleted file mode 100644
index 058a226d8..000000000
--- a/doc/onedgrids.rst
+++ /dev/null
@@ -1,29 +0,0 @@
-
-One-Dimensional Grids
-======================
-
-There are various choices of one dimensional grids for integrating functions over some finite or infinite
-intervals.
-
-.. csv-table:: One-Dimensional Quadrature Grids With Explicit Solutions
-   :file: ./table_onedgrids.csv
-   :widths: 31,70,70,70
-   :delim: ;
-   :header-rows: 1
-   :align: center
-
-Grid also includes popular quadrature grids that can integrate polynomials up to
-degree :math:`2n - 1`:
-
-- :func:`Gauss-Legendre<grid.onedgrid.GaussLegendre>` (domain :math:`[-1, 1]`),
-- :func:`Gauss-Chebyshev<grid.onedgrid.GaussChebyshev>` (domain :math:`[-1, 1]`),
-- :func:`Gauss-Laguerre<grid.onedgrid.GaussLaguerre>` (domain :math:`[0, \infty)`).
-
-The Trefethen polynomial transformation of various grids on :math:`[-1, 1]` is also included:
-
-- :func:`Trefethen (Clenshaw-Curtis)<grid.onedgrid.TrefethenCC>`,
-- :func:`Trefethen (Gauss-Chebyshev)<grid.onedgrid.TrefethenGC2>`,
-- :func:`Trefethen (General)<grid.onedgrid.TrefethenGeneral>`,
-- :func:`Trefethen Strip (Clenshaw-Curtis)<grid.onedgrid.TrefethenStripCC>`,
-- :func:`Trefethen Strip (Gauss-Chebyshev)<grid.onedgrid.TrefethenStripGC2>`,
-- :func:`Trefethen Strip (General)<grid.onedgrid.TrefethenStripGeneral>`.
diff --git a/doc/radial_transf.rst b/doc/radial_transf.rst
deleted file mode 100644
index 6553cb909..000000000
--- a/doc/radial_transf.rst
+++ /dev/null
@@ -1,13 +0,0 @@
-Radial Transformations
-======================
-
-These one-dimensional grids can be transformed to other kinds of intervals using the
-:func:`radial transform <grid.rtransform>` module.
-
-
-.. csv-table:: Radial Transformation
-   :file: ./table_rtransform.csv
-   :widths: 31,70,70,70
-   :delim: ;
-   :header-rows: 1
-   :align: center
diff --git a/doc/table_modules.csv b/doc/table_modules.csv
deleted file mode 100644
index 245d08a8c..000000000
--- a/doc/table_modules.csv
+++ /dev/null
@@ -1,12 +0,0 @@
-Modules;Description
-:func:`One-dimensional Grids<grid.onedgrid>`; Integration of real-valued function over real line.
-:func:`Radial Transform<grid.rtransform>`; Transforming one-dimensional grids from one interval to another.
-:func:`Angular Grids<grid.angular>`; Integrating functions over sphere/angular coordinates.
-:func:`ODE<grid.ode>`; Solving linear ordinary differential equations.
-:func:`Poisson<grid.poisson>`; Solving the Poisson equation.
-:func:`Atomic Grid<grid.atomgrid>`; Integrating real-valued function over three-dimensional Euclidean space.
-:func:`Becke Weights<grid.becke>`; Type of nuclear weight function/atom in molecule weights.
-:func:`Hirshfield Weights<grid.hirshfeld>`; Type of nuclear weight function/atom in molecule weights.
-:func:`Molecular Grids<grid.molgrid>`;  Integration of general functions in three-dimensional space.
-:func:`Cubic Grids<grid.cubic>`;Hyper-Rectangular Grid in either two or three dimensions.
-:func:`Periodic Grids<grid.periodicgrid>`; Integrating periodic functions in 1, 2 or 3 dimensions.
diff --git a/doc/table_rtransform.csv b/doc/table_rtransform.csv
deleted file mode 100644
index 60a6fc27e..000000000
--- a/doc/table_rtransform.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-Transform;Domain;Co-Domain;:math:`r(x)`
-:func:`BeckeRTransform<grid.rtransform.BeckeRTransform>`;:math:`[-1,1]`;:math:`[r_{min},\infty)`;:math:`r_i = R \frac{1 + x_i}{1 - x_i} + r_{min}`
-:func:`LinearFiniteRTransform<grid.rtransform.LinearFiniteRTransform>`;:math:`[-1,1]`;:math:`[r_{min},r_{max}]`;:math:`r_i = \frac{r_{max} - r_{min}}{2} (1 + x_i) + r_{min}.`
-:func:`InverseRTransform<grid.rtransform.InverseRTransform>`;Co-Domain;Domain;:math:`x_i(r_i)`
-:func:`IdentityRTransform<grid.rtransform.IdentityRTransform>`;Domain;Domain;:math:`x_i`
-:func:`LinearInfiniteRTransform<grid.rtransform.LinearInfiniteRTransform>`;:math:`[0,\infty)`;:math:`[r_{min},r_{max})`;:math:`r_i = \frac{(r_{max} - r_{min})}{N - 1} x_i + r_{min},`
-:func:`ExpRTransform<grid.rtransform.ExpRTransform>`;:math:`[0,\infty)`;:math:`[r_{min},r_{max}]`;:math:`r = r_{min} e^{x \log\bigg(\frac{r_{max}}{r_{min} / (N - 1)}  \bigg)},`
-:func:`PowerRTransform<grid.rtransform.PowerRTransform>`;:math:`[0,\infty)`;:math:`[r_{min},r_{max}]`;:math:`r = r_{min}  (x + 1)^{\frac{\log(r_{max}) - \log(r_{min})}{N}},`
-:func:`HyperbolicRTransform<grid.rtransform.HyperbolicRTransform>`;:math:`[0,\infty)`;:math:`[0,\infty)`;:math:`r_i = \frac{a x_i}{(1 - bx_i)},`
-:func:`MultiExpRTransform<grid.rtransform.MultiExpRTransform>`;:math:`[-1,1]`;:math:`[r_{min},\infty)`;:math:`r_i = -R \log \left( \frac{x_i + 1}{2} \right) + r_{min}`
-:func:`KnowlesRTransform<grid.rtransform.KnowlesRTransform>`;:math:`[-1,1]`;:math:`[r_{min},\infty)`;:math:`r_i = r_{min} - R \log \left( 1 - 2^{-k} (x_i + 1)^k \right)`
-:func:`HandyRTransform<grid.rtransform.HandyRTransform>`;:math:`[-1,1]`;:math:`[r_{min},\infty)`;:math:`r_i = R \left( \frac{1+x_i}{1-x_i} \right)^m + r_{min}`
-:func:`HandyModRTransform<grid.rtransform.HandyModRTransform>`;:math:`[-1,1]`;:math:`[r_{min},r_{max}]`;:math:`r_i = \frac{(1+x_i)^m (r_{max} - r_{min})} { 2^m (1 - 2^m + r_{max} - r_{min}) - (1 + x_i)^m (r_{max} - r_{min} - 2^m )} + r_{min}`
diff --git a/website/One_dimensional_grids.ipynb b/website/One_dimensional_grids.ipynb
new file mode 100644
index 000000000..4e7f3f962
--- /dev/null
+++ b/website/One_dimensional_grids.ipynb
@@ -0,0 +1,404 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/theochem/grid/blob/master/examples/One_dimensional_grids.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# One-Dimensional and Radial Grids\n",
+        "\n",
+        "Grid supports several quadrature rules for one-dimensional grids, most of them in the [-1, 1] interval, within the [OneDGrid](https://grid.qcdevs.org/pyapi/grid.basegrid.html#grid.basegrid.OneDGrid) module.\n",
+        "\n",
+        "### Example: Visualize One-Dimensional Grid Points\n",
+        "\n",
+        "Here, we showcase some of the quadrature rules available each with a unique set of points. Click [here](https://grid.qcdevs.org/pyapi/grid.onedgrid.html) for a full list of one-dimensional grid classes and [here](https://grid.qcdevs.org/onedgrids.html#one-dimensional-grids) for a condensed table containing most of the popular grids."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-01-03T22:36:20.643610321Z",
+          "start_time": "2024-01-03T22:36:20.291101489Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from grid.onedgrid import *\n",
+        "\n",
+        "# Make several 1-dimensional grids for a given number of points\n",
+        "npoints = 31\n",
+        "\n",
+        "grids = [\n",
+        "    GaussChebyshev(npoints),\n",
+        "    GaussLegendre(npoints),\n",
+        "    TanhSinh(npoints),\n",
+        "    Simpson(npoints),\n",
+        "    Trapezoidal(npoints),\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-01-03T22:36:21.133162129Z",
+          "start_time": "2024-01-03T22:36:20.645646412Z"
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "names = []\n",
+        "for i, oned in enumerate(grids):\n",
+        "    plt.scatter(oned.points, np.repeat(i + 1, oned.size), marker=\"o\")\n",
+        "    names.append(oned.name)\n",
+        "\n",
+        "# set the y-ticks to be the 1D grid names, and set labels\n",
+        "plt.title(f\"{npoints} Grid Points in One Dimension\", fontsize=16)\n",
+        "plt.yticks(np.arange(1, len(grids) + 1), names)\n",
+        "plt.xlabel(\"Number of Points (Grid Size)\", fontsize=14)\n",
+        "plt.ylabel(\"Quadrature Rule\", fontsize=14)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Example: Integration of One-Variable Function on $[-1, 1]$\n",
+        "\n",
+        "The one-dimensional grids can accurately integrate one-variable functions over an interval (defined by their domain), even with a small number of points. Here, we numerically compute the integral below and compute the relative percent error. The analytical value of this integral is $\\pi/2$.\n",
+        "\n",
+        "$$ \\int_{-1}^1 \\sqrt{1 - x^2} dx \\approx \\sum_{i=0}^{N-1} w_i \\sqrt{1 - x_i^2} $$\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-01-03T22:36:21.338777861Z",
+          "start_time": "2024-01-03T22:36:21.146684630Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Integral of sqrt(1-x^2) from -1 to 1:\n",
+            " 1.57079633 from Analytical integration (which is equal to pi/2)\n",
+            " 1.57079633 from Gauss-Chebyshev integration\n",
+            " 1.57082270 from Gauss-Legendre integration\n",
+            " 1.57067045 from Tanh-Sinh integration\n",
+            " 1.56683246 from Simpson integration\n",
+            " 1.56069571 from Trapezoidal-Lobatto integration\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 850x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# define the function to integrate\n",
+        "function = lambda x: np.sqrt(1 - x**2)\n",
+        "\n",
+        "print(\"Integral of sqrt(1-x^2) from -1 to 1:\")\n",
+        "print(f\"{np.pi / 2: .8f} from Analytical integration (which is equal to pi/2)\")\n",
+        "\n",
+        "names = []\n",
+        "error_relative = []\n",
+        "for oned in grids:\n",
+        "    # integrate the function evaluated on the grid points\n",
+        "    integral = oned.integrate(function(oned.points))\n",
+        "    print(f\"{integral: .8f} from {oned.name} integration\")\n",
+        "    error_relative.append(np.abs(integral - np.pi / 2) / (np.pi / 2) * 100)\n",
+        "    names.append(oned.name)\n",
+        "\n",
+        "# plot the relative error as columns graph\n",
+        "plt.figure(figsize=(8.5, 5))\n",
+        "plt.bar(names, error_relative)\n",
+        "title = r\"Numerical Integration of $\\int_{-1}^1 \\sqrt{1-x^2} dx$ with \" + str(npoints) + \" Points\"\n",
+        "plt.title(title, fontsize=16)\n",
+        "plt.xlabel(\"Quadrature Rule\", fontsize=14)\n",
+        "plt.ylabel(\"Relative Percent Error\", fontsize=14)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Changing the number of grid points increases the accuracy of each quadrature. Here we show how the relative integration error converges to zero when increasing the number of points.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-01-03T22:36:21.461375714Z",
+          "start_time": "2024-01-03T22:36:21.349492787Z"
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "npoints = [3, 5, 11, 15, 17, 19, 21, 31]\n",
+        "grid_classes = [GaussChebyshev, GaussLegendre, TanhSinh, Simpson, Trapezoidal]\n",
+        "\n",
+        "for grid_class in grid_classes:\n",
+        "    errors = []\n",
+        "    for npoint in npoints:\n",
+        "        # make 1-dimensional grid for the given number of points\n",
+        "        oned = grid_class(npoint)\n",
+        "        # compute relative percent error of integration\n",
+        "        integral = oned.integrate(function(oned.points))\n",
+        "        errors.append(np.abs(integral - np.pi / 2) / (np.pi / 2) * 100)\n",
+        "    # plot relative percent error vs. number of grid points\n",
+        "    plt.plot(npoints, errors, label=oned.name)\n",
+        "\n",
+        "plt.legend(frameon=False, fontsize=12)\n",
+        "plt.title(r\"Numerical Integration of $\\int_{-1}^1 \\sqrt{1-x^2} dx$\", fontsize=16)\n",
+        "plt.xlabel(\"Number of Points (Grid Size)\", fontsize=14)\n",
+        "plt.ylabel(\"Relative Percent Error\", fontsize=14)\n",
+        "plt.xticks(npoints)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Radial Grid: Transformation of 1D-Integration Intervals\n",
+        "\n",
+        "Generally, areas where the integrand changes more rapidly demand a denser concentration of points. So, the $[-1, 1]$ interval is not suitable for most applications encountered in quantum chemistry, where the integrals are usually performed on $[0,\\infty)$ domain. [Grid](https://github.com/theochem/grid) supports several transformations of the one-dimensional grids to other intervals. Some of these transformations have a parameter that can be used to control the density of points across different ranges of the new interval. Click [here](https://grid.qcdevs.org/pyapi/grid.rtransform.html) for a full list of radial transformations and [here](https://grid.qcdevs.org/radial_transf.html) for a condensed table with explicit formulas.\n",
+        "\n",
+        "### Visualize Radial Grid Points\n",
+        "\n",
+        "Here we visualize the OneDGrid points for different quadrature rules after a Becke transformation.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-01-03T22:36:21.851432041Z",
+          "start_time": "2024-01-03T22:36:21.470246478Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/var/folders/wk/kmp6fyrn3396qjpx40d2qk600000gn/T/ipykernel_75060/2869605059.py:18: UserWarning: Attempt to set non-positive xlim on a log-scaled axis will be ignored.\n",
+            "  ax2.set_xlim(0, 2500)\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from grid.rtransform import BeckeRTransform\n",
+        "\n",
+        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5), sharey=True)\n",
+        "names = []\n",
+        "for i, oned in enumerate(grids):\n",
+        "    # Becke R-transform (goes from 0 to \\infty) to transform 1D-grid\n",
+        "    rgrid = BeckeRTransform(0.0, R=1.5).transform_1d_grid(oned)\n",
+        "    # plot radial grid points for each transformed 1D-grid\n",
+        "    ax1.scatter(rgrid.points, np.repeat(i + 1, rgrid.size), marker=\"o\")\n",
+        "    # plot log plot of radial grid points\n",
+        "    ax2.semilogx(rgrid.points, np.repeat(i + 1, rgrid.size), marker=\"o\", linestyle=\"\")\n",
+        "    names.append(oned.name)\n",
+        "\n",
+        "# set the ticks, labels, and title\n",
+        "plt.suptitle(\"Plot & Log-Plot of Becke Radial Transformation of 1D-Grids\", fontsize=17)\n",
+        "ax1.set_yticks(np.arange(1, len(grids) + 1), names, fontsize=11)\n",
+        "ax1.set_xlim(0, 2500)\n",
+        "ax2.set_xlim(1.0e-4, 2500)\n",
+        "ax1.set_xlabel(\"Transformed Coordinate r\", fontsize=14)\n",
+        "ax2.set_xlabel(\"Transformed Coordinate r\", fontsize=14)\n",
+        "ax1.set_ylabel(\"Quadrature Rule\", fontsize=17)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Example: Integration of One-Variable Function on $[0, \\infty]$\n",
+        "\n",
+        "This transformation tends to concentrate the points close to the origin. Because of this, it is usually used for defining radial grids for atoms where the function to integrate is rapidly varying close to the origin (e.g. the electron density is more concentrated close to the nucleus). Here, we integrate (which has the exact value of 2.0):\n",
+        "\n",
+        "$$ \\int_0^\\infty x^2 e^{-x} dx $$\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-01-03T22:36:21.855941638Z",
+          "start_time": "2024-01-03T22:36:21.851891608Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Integration of r^2 * exp(-r) from 0 to infinity:\n",
+            " 2.000000 Analytical integration\n",
+            " 2.000003 from BeckeRTransform radial grid of Gauss-Chebyshev\n",
+            " 1.999996 from BeckeRTransform radial grid of Gauss-Legendre\n",
+            " 1.999993 from BeckeRTransform radial grid of Tanh-Sinh\n",
+            " 1.998949 from BeckeRTransform radial grid of Simpson\n",
+            " 2.000006 from BeckeRTransform radial grid of Trapezoidal-Lobatto\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Integration of r^2 * exp(-r) from 0 to infinity:\")\n",
+        "print(f\"{2.0: .6f} Analytical integration\")\n",
+        "\n",
+        "# Make several 1-dimensional grids for a given number of points\n",
+        "npoints = 21\n",
+        "grids = [\n",
+        "    GaussChebyshev(npoints),\n",
+        "    GaussLegendre(npoints),\n",
+        "    TanhSinh(npoints),\n",
+        "    Simpson(npoints),\n",
+        "    Trapezoidal(npoints),\n",
+        "]\n",
+        "\n",
+        "for oned in grids:\n",
+        "    rgrid = BeckeRTransform(0.0, R=1.5).transform_1d_grid(oned)\n",
+        "    x = rgrid.points\n",
+        "    func_vals = x**2 * np.exp(-x)\n",
+        "    integral = rgrid.integrate(func_vals)\n",
+        "    print(f\"{integral: .6f} from BeckeRTransform radial grid of {oned.name}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-01-03T22:36:22.028092961Z",
+          "start_time": "2024-01-03T22:36:21.856079509Z"
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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2V9b7cqv1qCyWCE0Azz77LHPmzOHff/8FTEHgySefvOXz/vDDDwDk5ubSu3fvEssUdpa2s7O75ecrb73GjRuHra1tiWUK5xMrbeRcVb1mtZGEJlEmDzzwAB988AGnTp3i22+/LdNv+RXl4OBQbFvh6KGS9l27X7lu1E/haJIzZ85w5syZGz5v4XDq65V3eHHhl3hZJ3k8e/Ysjz/+ODqdjpdffpmHHnqIpk2b4uTkhEqlYsmSJeb91aG06y2cl+f61/hmKvoe5OTkAJTYOlXI2dn5hqGptGuxxGteGBIaNmxYahlvb2/OnDlDVlZWsX2V9b7caj0qi6VCU0FBAc7OziQkJODl5cX7779fKefdunUrYBqhlpCQcMOyTZo0qZTnLIvCevXv37/UMoUt9KWFpqp6zWojuT0nykSj0fDOO+8A8N5776HX60ssV1p4KVT4RVhdCj+Mv/jii5sO5506dWqlPGfhl3xZl9n46aef0Ol0jBkzho8//pi2bdvi7Oxsfi3Pnz9fKfWylIq+B4Uh4frWw2tV9EvdEq954etw+fLlUsskJiYCNw6KdaUelghNWq2Wu+66i9OnT2NlZUVSUhIrV66slHMXtpKmpKRU22dNWRT+LDdu3LjE/SdOnCA+Ph4PD48Sp1OoytesNpLQJMpszJgxtG7dmujo6FLXLSv8oktKSipx/81aGipb4S2Io0ePVttzhoaGApibsm+mcC6fbt26lbi/uvoy3YobLbNT0fegefPmwNX5Y64XFxd3w1amG7mV17yiSwoVXs/x48dL3J+VlWX+gissWxVqSj0sEZomTJjA1q1bGThwIIsXLwZMc1AZDIZbPnfh9ZTWYm0phb9YlPYLa+GCv6W1MlXla1YbSWgSZaZWq3n33XcBeP/990u8deHh4YGrqyt5eXkcO3as2P4lS5ZUeT2vNWrUKACWL19e7g7MFTVy5EgAVq9ezdmzZ29avnB25cLf7q918uRJ1q5dW6n1qwqF11DSF0ZF34MBAwYA8Ouvv5bYonQrCw7fymt+o2u9kUGDBgHw888/F5motdCiRYsoKCggKCiIFi1alOvctbEe1R2a3nrrLZYvX054eDg///wz48aNo3nz5pw8eZLly5ff8vkDAwOBmreEjo+PDwD//fdfsX2HDx9mwYIFQMmhqapfs9pIQpMol3vvvZfw8HBiY2PZtWtXsf0qlcr8ofzSSy8V+WBctmwZX331VbXVFaBjx46MHj2alJQUBgwYUGz2ZIPBwLZt23jwwQcpKCiolOfs0KEDo0aNIj8/n8GDB7N3794i+8+cOcPHH39sftyjRw8A5s+fz6FDh8zbT58+zb333ouNjU2l1KsqFfbR+Pvvv4vtq+h70L9/fyIiIkhOTuaBBx4ocrtz9erVzJw5s8IL4t7Ka36ja72Rfv360alTJwoKCrj//vuL3B7btGkT06ZNA0ydbKtygeyaUo/qDE1LlixhxowZ+Pr6sn79elxcXNBoNLz99tsATJ8+vdQuB2VVuD7nSy+9xO+//15kn6IoHDhwgIkTJ5YYVKtSYV+m6dOnmwcBAGzZsoXBgwej1WqB4gMuquM1q5WqdEIDUStdP0/T9VauXHnD+UpOnDihODk5mSeqbN++veLj46NwZVbjwuOuV9p2Rbn5/E9bt24tNgFboaysLGXAgAHm8wcGBipdunRRwsPDFXt7e/P2a2fCvVl9Ct1oRvCuXbuazxEcHKx07NhR8fb2LnYdOp1Oue222xQwzUTdqlUrJSwszDz78/vvv68Ayvjx48v8/GWpc2nzNJX3WhVFUb755hvz8WFhYUrv3r2V3r17KwcPHlQUpeLvwbUzgjs4OCgdO3ZUgoODzfMWFdYpLi6uXNdyK6/5za71Rq9VVFSUefJDW1tbpX379uaJW8E0W3RpM4KXpiI/AxWph6JU7jxNhfNBAUp8fPwtn680GzZsUKysrBQnJ6cis7YrimnG7+bNmyuAsmjRolt6nqysLPOkoYDi5eWldOzYUYmIiFCcnZ0VQHF3d7+l56iI6Oho86zeVlZWSlhYmPn/UGhoqNK0aVMFKPLaVNdrVhtJaBLF3Cw0GY1GpW3btjec5O3AgQPKHXfcoTg7OyuOjo5Kt27dlLVr1yqKcvPJLUtyK6FJUUyzS3/33XfKoEGDFE9PT8Xa2lrx8fFRunTporz++uslLpFwK6FJURRFq9Uqn3/+udK9e3fF1dVVsbOzUxo3bqzcc8895teiUEZGhjJp0iTF19dXsba2Vvz9/ZXHHntMiY+PN08uWpNDk6IoymeffaZEREQUCUFbt24176/Ie6AoinLmzBllzJgxioeHh2JnZ6eEh4cr8+bNUxRFMU+6l5aWVq5rUZSKv+ZludYbvVZJSUnKK6+8ooSEhCi2traKi4uL0qtXL+Xbb78tMahURWiqSD0UpXJD0zvvvGO+tpImPa0MBw8eVJycnBSNRqOsX7++xDLffvutAigBAQFKfn7+LT1fXl6e8tFHHykdO3ZUnJycFBsbG8Xf31/p2rWrMmXKlCLBujpFRkYqAwcOVBwcHBQHBwclLCxMmTJlipKcnKzY2toqVlZW5muv7testlEpSjnHDwshRA2QkpKCp6cnbm5u9Wex0Drk5ZdfZs6cOYDpFm3h1AlC1GTyUyqEqJW+/vproPQRcKJmK+zT5ODgUKbAFBMTw4gRI3B2dqZBgwaMHTuW5OTkqq6mEEVIaBJC1FhHjhxh8eLFRToNK4rC8uXLmTJlCmBa9kPUfBkZGZw4cQKj0QhcnW29LJPAZmdn07dvXy5evMgPP/zA4sWL2b17N0OHDjWfT4jqIDOCCyFqrJSUFJ588kmeeeYZgoKC8PDw4Ny5c+apC5588kmGDx9u4VqKmzl16pR5OZYjR44QFhZmXiYnODj4pscvWrSIhIQEdu/ebR5CHxwcTOfOnfntt9/M01oIUdWkpUkIUWO1bt2a1157jfDwcDIyMjh48CCKonD77bfz448/snDhQktXUZRB8+bNzfMYzZo1i++//948aWnhfFw3sm7dOvr27WsOTACdOnWiefPmtWIeM1F3SEdwIYQQVa5wPb9rNW7cmH379uHu7n7DY729vRk7dmyR+c0A7rrrLhISEvjnn38qvb5ClERamoQQQlS5xx57jCVLlpgXRn7ooYf4559/bhqYANLS0krs++Tu7m7uGyVEdZA+TUIIIarFhAkTmDBhQoWOLWl2crlRIqqbtDQJIYSo0Ro0aFDiXFxpaWllaqkSorJIaBJCCFGjhYaGcvz48WLbjx8/TqtWrSxQI1FfSWgSQghRow0bNoytW7cWWex2//79nDp1SqacENVKRs8JIYSo0bKysoiIiMDLy4t3332X/Px8Xn/9dTw8PPjnn39kCRZRbeQnTQghRI3m7OzMli1baNSoEffddx8TJkzgtttuY926dRKYRLWSliYhhBBCiDKQiC6EEEIIUQYyT1MlMhqNxMfH4+zsXOKcIkIIIYSoeRRFISsrC19f3xve8pXQVIni4+MJCAiwdDWEEEIIUQHnz5/H39+/1P0SmiqRs7MzYHrRXVxcLFwbIYQQQpRFZmYmAQEB5u/x0khoqkSFt+RcXFwkNAkhhBC1zM261khHcCGEEEKIMpDQJIQQQghRBhKahBBCCCHKQEKTEEIIIUQZSGgSQgghhCgDCU1CCCGEEGUgoUkIIYQQogwkNAkhhBBClIGEJiGEEEKIMpDQJIQQQghRBhKahBBCCCHKQEKTEEIIIUQZSGiqBTK1mey7tM/S1RBCCCHqNQlNNVy2NpsnNz3Jk5ufZOfFnZaujhBCCFFvSWiq4WytbPF29EZr1PL8luclOAkhhBAWIqGphrNWWzO792xuD7xdgpMQQghhQRKaagEJTkIIIYTlSWiqJazV1szuVTQ47bq4y9LVEkIIIeoNCU21iLWmaHB6bstzEpyEEEKIaiKhqZYpDE79AvpJcBJCCCGqkYSmWshaY83HvT+W4CSEEEJUIwlNtVRhcOob0NccnHZf3G3pagkhhBB1loSmWsxaY80nvT8xB6dJWyZJcBJCCCGqiISmWk6CkxBCCFE9JDTVAdcHp+e2yq06IYQQorJJaKojCoNTn4A+FBgKTMEpXoKTEEIIUVkkNNUh1hpr5vSeczU4bZHgJIQQQlQWCU11jAQnIYQQompIaKoFtHl60i7llLm8OTj5Xw1O/8T/U4U1FEIIIeo+CU01nNFgZMOn/7Jy5l7ijlwu83HWGmvm9LkanCZtmSTBSQghhLgFEpquM2rUKBo0aMA999xj6aoAoNMayYmKQVtgZN3/HWbbXa8QN+ExLk2fTsrSpWRt2UrB2bMYCwqKHWutseaTPp9IcBJCCCEqgUpRFMXSlahJtm7dSnZ2NsuWLWPlypXlOjYzMxNXV1cyMjJwcXGptDqdfXAcBwsiSPRsD0Bg3CaanluDimveOpUKK59G2AQGYRMYiE1QEDZBgVgHBoKvN6/+9zbbLmzDVmPL//X7P7r6dq20+gkhhBC1WVm/vyU0lWDbtm3MmzevxoQmAKPRyH8/HePAtiQA/J3SaavfhSEuGl1sHMacG/d50ng3JM5Fy0mHDJLdrRjW+3HC2vTHJjAQtaNjpdZVCCGEqE3K+v1tVY11umXbt29n9uzZ7N+/n4SEBFatWsXIkSOLlJk/fz6zZ88mISGB0NBQPv30U3r27GmZClcitVpN1zHhuAcnsOXbk1zIdkMbfD9Dp0dg72yNITUVbWwc2rhYdHFxV/4dhzY2FmNmJobEy/glgh8AOtg6n2jmA6Dx8rymhSoQm8BArANNLVUaZ2dLXrYQQghRY9Sq0JSTk0ObNm145JFHuPvuu4vtX7FiBS+88ALz58+ne/fuLFq0iMGDB3P8+HECAwMB6NChAwUl9P/ZtGkTvr6+VX4Nt6rFbT44e9jx+8IjXI7JZOWH+xg6MQIPXw+sPDxwaN+u2DGG9HS0sbFo4+LIi4nm3z2rUF28RKN0cMkFQ1IyeUnJ5O3fX+xYTYMGpjAVHIR1YKApXF0JVho3t6q/YCGEEKKGqLW351QqVbGWpi5dutC+fXsWLFhg3taqVStGjhzJzJkzy3zust6eKygoKBLAMjMzCQgIqJLbc9dLT8xl3bzDZCTlYWOn4Y4nwglo7V6mY7UGLS9ue5HtF7bjrrNldrNXaJnrYmqZijGFK21cHIbk5BueR+3qagpUV1qorM19qYLQNGiASqWqjEsVQgghqlSdvD13I1qtlv379/PGG28U2T5w4EB2766ayR1nzpzJtGnTquTcN+Pm7cA9r3fk94WRJJzJYO28w/S+vzmhPf1ueqyNxoa5feaag9OzFz5h3u3z6DJkSJFyhuwcdOevudUXF4vuyr/1iYkYMzLIP3KE/CNHij2H2snJdJsvKLDIrT/rwECsvLwkUAkhhKh16kxLU3x8PH5+fuzatYtu3bqZy82YMYNly5Zx6tSpMp130KBBHDhwgJycHNzd3Vm1ahWdOnUqsawlW5oKGXRGtiw/wen/EgFoNyCQrqOaolLfPJRc2+Jkp7EzBSefLmV6XmNuLtrzF4r3oYqLRZ9wCW7wY6VycCjeQnXltp9Vw4ao1DIThhBCiOpT71qaCl3fgqEoSrlaNf74448yl7W1tcXW1rbM5StMUcBoAE3xt0tjrab/w61x9XJg77poDm6OIyM5j/6PtMbaRnPD017f4jTxr4llDk5qBwfsWjTHrkXzYvuMBQXoLlwwBanY2CItVLr4eJTcXApOnqTg5Mlix6psbbEJDLjSEb1o53SrRo1QaW58TUIIIURVqTOhydPTE41Gw6VLl4psv3z5Mt7e3haqVeVI2vghqjN/4fnoj+DoWWy/SqWi87DGuHrZs+XbE5w7mMTq1AMMeSYCR9cbh7rC4PTC1hfYcXFHuYJTadS2ttg2bYpt06bF9ilaLdqLF4u1Tuli49BevIhSUEBB1BkKos4Uv05ra6wDAoq3UAUHYe3jg8qqzvw4CyGEqIHqzO05MHUE79ChA/Pnzzdva926NSNGjChXR/CKqop5mjJTElH9XzucySHTphFOD/+E2rdNqeXjo9LZsPAI+Tk6nNxtGfZsGzz8nG76PFqD1hyc7DR2fH7753T26Vwp11BWik6HLiGhxKkTdOfPo+h0pR9sZYWNn1+Jfahs/PxQ2dhU34UIIYSoVerk5JbZ2dmcOWNqgWjXrh1z5syhb9++uLu7ExgYyIoVKxg7diwLFy6ka9euLF68mC+++IJjx44RFBRU5fWritBkNCp89dtG+h18gSbqS2hVtjDic2za3lvqMemXr4ysu2waWTfoiTACW3vc9LkKDAW8uPVFiwan0igGA/pLl8xzT5nDVFws2rjzKCVMI2Gm0WDt61tiHyprf3/U1XGLVQghRI1VJ0PTtm3b6Nu3b7Ht48ePZ+nSpYBpcstZs2aRkJBAWFgYc+fOpVevXtVSv6qcEXz1P8dosOFpeqsPA5DTaRKOg6eBuuQ+PvnZOjYsOkJ8VDoqtYpeY5oT1uvmI+uuD07z+8+nU6OSO8LXFIrRiP7y5eItVFfmplLy8ko/+PrlZ5o0xrl/f2z8/avvAoQQQlhUnQxNNV1VhiaAPWeTOPbtyzzCbwBkBfTF+YGlYO9WYnmDzsjW5Sc59Z+pn1fb/gF0u6vZTUfWFRgKeGHrC+y8uLPWBKfSKIqCPimp5D5UsbGlLj9j3749rsOH4XzHHVg1aFDNtRZCCFGdJDRZQFWHJoDYlByWL/mEl3L/D3uVlhynYBzH/wReLUosrygK+36PYc/aaACatPWi/6M3H1lXl4JTaRRFKbb8TN6hQ+T8+x8YjaZC1tY49eyJ653DcerTB7WdnWUrLYQQotJJaLKA6ghNAJn5OmZ9/RNPXXoHf1UyWo0j1vcuQdVySKnHnN57ib+WncCoV2gY5FymkXXXBid7K3s+v/3zOhecSqJLvEzm77+TsXYNBcdPmLernZxwHjQQ1+HDcejUSaY/EEKIOkJCkwVUV2gC0BuMzFm1i96Rr9BFfRIjKox9JmPV+1UoZV6qhDPp/L7gysi6BrYMm3jzkXUFhgKe3/o8uy7uqlfBqVDBmTNkrF1Hxto16OMTzNutvL1xGTYU1zvvxK5Fya18QgghagcJTRZQnaGp0Le7olA2TmacZhMA2ubDsbl7IdiWHIbSL+ey/vNI0hNzsbbTMOjxMIJCbzyyrr4HJzB1Ns87cICMNWvJ3LgRY2ameZ9t8+a4DB+G67BhWPv4WLCWQgghKkJCkwVYIjQB/H06iS3fzeYtlmCjMlDg0Qrbh36EBsElls/P0bFx0REunr4ysu6+EMJ633i0mASnq4xaLdl//03m2nVkb916df4olQqHTp1wvXM4zgMHoqnGnwEhhBAVJ6HJAiwVmgCiErOY+9VypuXPxEuVgc62Adb3LYMmvUssb9Ab2bb8JCf/NY2sa3NlZJ36BiPrCgwFPL/leXbFS3AqZMjIIHPTJjLXrCV3717zdpWNDU59+uB653Ace/VCLZNrCiFEjSWhyQIsGZoAUnO0TF66kacS36Wt+hxGlQb1oA+gy1Ml9nNSFIX9G2L5b805ABq38WTAo6FY25bewfn64DT/9vl0bNSxyq6pNtHFx5Oxfj2Za9YUWQZG7eqKyx134HrncOzbtZMFiYUQooaR0GQBlg5NAAV6A++s3E/nY9O5W7MDAGPE/aiHfwrWJQ+Xj9qbyF/LTmDQG/EKdGboMxE4upU+sk6C040pikLBqVOm/k/r1qG/fNm8z9rPD5dhw3C9c3iJa/MJIYSofhKaLKAmhCYwfWkv3HaWpL8+ZbLmO6xURgw+7dHc/z24lNxROeFsBr8viCQ/2zSybuizbfD0L31knQSnslEMBnL37CFj7Tqy/vijyGSadq1b43LncFyGDMG6YUML1lIIIeo3CU0WUFNCU6GNRy+xYsW3zFF/SgNVNgaHhmju/w4CSl5PLiMpl3Xzroyss70ysi6s9JF1EpzKx5ifT/bWrWSsWUv2jh2g15t2qNU43nYbLncOx7n/ADROjpatqBBC1DMSmiygpoUmgKMXM3hn6TpmFMykpfo8RrUN6mGfQPtxJZbPz9GxcfERLp5KR6WCnvc1J7xP6SPrCgwFPLflOXbH75bgVA76tDSyNm4kY81a8g4eNG9X2dnhfPvtpg7k3bqhsra2YC2FEKJ+kNBkATUxNAEkZuYzcekOHkmaxRDNHtPGzk/AoBmgKf6lbNAb2fb9KU7uNk3m2KZfAN3uKX1kXb4+n+e3Pm8OTgv6L6CDd4cqu566RhsXR8a6dWSuWYs2Jsa8XdOgAS5DhuB653DsIiJQlTJpqRBCiFsjockCampoAsjTGnjlpwM0ObGQl61XAqAE9UA1ehk4ehYrrygKB/6I5d/VppF1wRGeDHi0NTZ2ViWeP1+fz3NbnuOfhH8kOFWQoijkHz1Gxto1ZK7/HUNKinmfdVAgrsPvxHX4MGyCgixYSyGEqHskNFlATQ5NAEajwtw/T3Ni2wrmWs/HWZWH0TUA9ZjvwSeixGOi9iXy11LTyDrPACeGPdum1JF1Epwqj6LXk/PPP2SsWUvWn3+i5OWZ99m1icB1+J24DBmMlbu7BWsphBB1g4QmC6jpoanQqoMXWLxyI59rZtNEfQnFyh7VyM8h7O4Sy186ZxpZl5dVOLIuAk9/5xLLXh+cFvZfSHvv9lV5OXWeMSeHrC1byFizlpxdu8BoNO3QaHDq0cPUgbxfP9T29patqBBC1FISmiygtoQmgP2xqby0bDvTdHPoozls2tjjReg3BdTFJ7fMSMpj/eeHSbtkGlk38LFQgsOL39YDCU5VSZ+UROaGDWSsWUv+0aPm7WoHB5wHDMDlzuE43nYbKk3pE5QKIYQoSkKTBdSm0ARwPjWXx5f+y8jUr3jKaq1pY7MBcPcSsHcrVr4gV8eGRUe5eCoNlQp6jG5ORN+SR9bl6/OZtGUS/yb8K8GpihSciyZz3Voy1qxFd+GCebuVlxcuQ4eaJtBs1Uo6kAshxE1IaLKA2haaALLydUz64SAuUb8xy3oRdiodikczVGN+AK/mxcob9Eb+/v4UJ66MrIvo60/3e0NKHFl3bXBysHJgQf8FEpyqgKIo5B08ROa6tWT+vgFDerp5n03TprgOH47LsGHY+PtZrpJCCFGDSWiygNoYmgD0BiMf/H6CPbu3sshmLv6qZBRbZ1R3LYEWdxQrX2xkXbgHAyaEljiyToJT9VK0WrJ37iJj7Rqyt2xFKSgw77Pv2MHUgXzQQDRubparpBBC1DASmiygtoamQt/9F8tnv+3m/6w+pYv6JAoqVP3ehp4vl7jg75n9l/lz6XEMOtPIuqHPtMGpQfGRdXn6PJ7b8pw5OC0csJB2DdtVxyXVa4bsbLI2bSZj7Rpy//0PCv+rW1vj1LsXrsPvxKlPb9S2pa8zKIQQ9YGEJguo7aEJYGdUMs999x8v6L9mnNVm08bWI2HkfLApvrzHtSPrHN1MI+u8AoqPrMvT5zFpyyT+S/hPgpMF6BITyVy3noy1ayk4edK8Xe3sjMsdg3AZNhyHTh1RqdUWrKUQQliGhCYLqAuhCeBsUjYTlu7ltvR1vGf1NdYqA3iHwZjvoEFwsfKZyXmsm2caWWdlq2HQhFCCI4qPrJPgVDPknz5N5tp1ZKxbhz4hwbzdyscH12FDcRk+HLvmxfuzCSFEXSWhyQLqSmgCSMvR8vR3+9FF/8NCm0/xUmWg2LujuncpNOldrHxBro6Ni49y4aRpZF33e0No0y+gWLnrg9OiAYto27Bt1V+QKEYxGsndt4/MtWvJ3PgHxqws8z7bli2vdCAfirW3twVrKYQQVU9CkwXUpdAEoNUbmbL6KH/vO8Qim7m0UZ9DUWlQDZoBXZ4s1s/JYDCy/ftTHN9lar0I7+tPjxJG1uXp85j01yT+uyTBqaYwFhSQ/fffZK5dS9a2v0GnM+1QqXDo0gXX4cNxHjgAjXPJk5oKIURtJqHJAupaaALTSLklO6L5ZMNhPrBawt2anaYdbR+EoXPA2q5Y+YOb4vhn1VkAgsI9GFjCyDoJTjWXIT2dzD82kbF2DXn79pu3q2xtcerXF9fhw3Hq0QOVjY0FaymEEJVHQpMF1MXQVGjz8USe//EAYwzrecv6OzQYwa8j3LccXHyKlT974DKbvzaNrPPwd2LYsxE4NSgasK4NTo7Wjizsv1CCUw2ju3iRjHXryVizBu3Zs+btGldXnIcMxnX4ndi3aysTaAohajUJTRZQl0MTwPH4TB5btpfGWXuZb/M/XMkBp0am4BTQqVj5S9EZ/D7/ysg6VxuGPtsGr8Cit3ckONUOiqJQcOIEGWvWkrF+HYakZPM+a39/XIYPw3X4ndg2aWzBWgohRMVIaLKAuh6aAC5n5fP4N/tJu3CKL2zm0EJ1HjQ2plt17ccWK5+ZnMe6zyNJS8jBykbNwMfCaHzdyLo8fR4T/5rInkt7JDjVAorBQO5//5GxZi1ZmzZhzM0177MLC8P1zuG4DBmClWfJaxMKIURNI6HJAupDaALI1xl45efDbImM5hPrBQzW7DXt6PwEDJoBGusi5Qvy9Pyx+AjnT6SBCnrcE0JEP/8it3QkONVOxrw8srZsIXPNWrJ37QK93rRDo8GxWzdchw/D+fbbUTsWn+NLCCFqCglNFlBfQhOYbtd8+mcU//vrFBM1q3nZeqVpR3BPuHcZOHoUKW8wGNn+42mO74gHILy3Hz1Gh6DWXJ1MMVeXy6QtkyQ41VL61FQyN2wgc81a8g4fNm9X2dvj3L8/rncOx7FrV1RWxZfbEUIIS5LQZAH1KTQVWnM4nld+Pkwv417+ZzMfB/LANdA0EaZPRJGyiqJwaPN5dq86AwoEhnow6PGiI+skONUN2thYMtauI2PtGnSxcebtGg8PXIYMwfXO4diFhUkHciFEjSChyQLqY2gCOBCXxhPf7Mct5yxf2c4hkEtgZQ8jP4ewu4uVP3vwMn9+dRy9zoiHnxNDn43A2f3qyLpcXS4Tt0xk76W9OFo7smjAItp4tanOSxKVRFEU8o8cIWPNWjJ//x1Daqp5n327dnhOfBbHbt0kPAkhLEpCkwXU19AEcDE9jwlL9xJ/KYHPbT6np/rK7ZkeL0G/t0GtKVI+MSaT9fMjycvU4uBqw9BnImgYdPU1k+BU9yg6HTm7d5Oxdh1ZmzejFBQAYN++PV4Tn8Wha1cJT0IIi6jS0NSkSROGDBnCvHnzbqmSdU19Dk0A2QV6nv/hIFtPXuI1qx95ymqdaUfIQLh7Cdi5FimfmZLH+s8jSY03jawb8GgoTdp6mfdfG5ycrJ1YOGChBKc6Qp+URMqSJaT9uOJqeOrQwRSebrtNwpMQolqV9fu7QkuaJycn4yzLKYjrONlasXhcRyb0bMqH+gd4TvssWpUNRG2CL/pB0uki5V087Lnr1Q4EtHZHrzWyYdERDv0ZR2GOd7B2YF6/eXRq1IlsXTZPbX6Kw0mHS3pqUctYeXnh/eabNN28Cffx41DZ2pK3fz9xjzxK7Nix5Pz7L9IILoSoaSrU0tSrVy+8vLz45ZdfqqJOtVZ9b2m61o974nh79VFaKOdYav8pXsYksHUxtTg1H1SkrPHKyLpjV0bWhfXyo+d9V0fW5epyefavZ9mXuA8naycWDVhEhFdEsecUtZfu8mVSliwh/ccVKFotAA4dO+I5cSKOt3WxcO2EEHVdld6eW79+PaNGjeKPP/6gb9++t1TRukRCU1G7zybz9PIDWOUl86X9/9FWOQ6oTH2cer5cZMFfRVE49Od5dv9aOLLOnUGPhWFjbxpZJ8GpftAlXglPK64JT506mcJTl84Wrp0Qoq6q0tD0zTff8NNPP7Fx40ZGjRpFp06d8Pb2LrEfwrhx48p7+lpLQlNx0ck5TFi6l/PJGbxn+y1jVJtNO1qPhJHzwabopIfnDiWx+ctjV0bWOTL02TbmkXW5ulye+esZ9iful+BUx+kSE0n5YgnpP/1UNDxNmohjZwlPQojKVaWhSa1Wo1KpivU5UF3XcqBSqTAYDOU9fa0loalkGbk6nv5uP7vPpnC/Zgvv2yxFo+jBO8w0n1OD4CLlL8dmsv7zSHIztTi42DD02asj6yQ41S+6xERSFn9hCk86HQAOnTvjNWkiDp2Kr3cohBAVUaWhadmyZWUuO378+PKevtaS0FQ6ncHIO78d44c9cXRQnWKp4//hrE8Fe3cYvQwa9ypSPis1n/WfHyblYg5W1moGTLg6su764LR4wGLCvcItcVmimuguXTKFp59/vhqeunQxhaeOHS1cOyFEbSfzNFmAhKYbUxSFr3bF8MH64zRUUvjO+f9oqjsNKg3cMdO0dt01rZXaPD1/LDlK3LFUUEG3u5rRtn8AKpVKglM9pUtIIOWLL0j/eeXV8HTbbabw1KGDhWsnhKitJDRZgISmstl68jKTfjiIriCX/3NaykD9NtOOtg/B0E/A+urs4EaDkR0roji6/SIAoT196TWmOWqNukhwcrZ2ZtGARRKc6gldQgLJixeTvvIXKAxPXW/Da6KEJyFE+VVLaIqNjeW7777j0KFD5idq27YtDz74IMHBwRU9rcVkZWXRr18/dDodBoOB5557jscff7zMx0toKruTlzKZsHQfF9NzecbuD15VLUelGMGvI9y3HFx8zGUVReHwX+fZ9cuVkXWt3Rn0uGlkXa4ul6f/fJoDlw9IcKqHdPHxpvD0y6/m8OTYrSueEyfi0L69hWsnhKgtqjw0zZs3j1deeQWdTlesQ7i1tTWzZs3i+eefr8ipLcZgMFBQUICDgwO5ubmEhYWxd+9ePDw8ynS8hKbySc4u4Ilv9nEgLp3emiMsdvgcW10mODUyBaeAoh19zx1KYvNXx9Brjbj7OjL02QhcPOyLBafFAxcT5hlmoasSlqCLjyd50WLSf702PHW7Ep7aWbh2QoiarkpD0++//86wYcNo2LAhL774In369KFRo0YkJiaybds25syZQ1JSEmvWrGHo0KG3dCGWkpqaSrt27di/fz+enp5lOkZCU/nl6wy88Uskqw/FE6hKZKXr/9Ew/xxobGDYXGj3UJHyl2NNa9blZmixdzGtWecd7CLBSQCgu3jxanjS6wFw7N4dz4nP4tBOwpMQomRVuozKxx9/jIeHBwcOHOD111+nS5cuBAUF0blzZ1577TX279+Pu7s7c+bMqfAFlGT79u0MHz4cX19fVCoVq1evLlZm/vz5NG7cGDs7Ozp06MCOHTvK9Rzp6em0adMGf39/XnvttTIHJlExdtYa5t7XlpcHNCdO8aZv+lvsd+gBBi389iz8/hoYdObyDYNcuOf1jnj4OZGXqWX1Jwc4e/AyDtYOLOi/gPYN25Oly+KJTU9wNPmoBa9MWIK1nx8+06fRdONG3O69F6ysyNm1i9j7HyDuscfJO3TI0lUUQtRiFQpNBw4cYPTo0fj6+pa438/Pj9GjR7N///5bqtz1cnJyaNOmTakLBa9YsYIXXniBt956i4MHD9KzZ08GDx5MXFycuUyHDh0ICwsr9ic+3rSEh5ubG4cPHyY6Oprvv/+exMTESr0GUZxKpWLS7SF8/kB79FaO3JP6FN/YPmDauWcRfDsKclLM5Z3d7bjr1fYEhnqg1xnZuPgoBzfFYW9lL8FJAGDj74fPe9OvhKd7TOFp505ixtxP3ONPkHdY1jAUQpRfhW7POTg4MHHiRGbNmlVqmddee4158+aRm5t7SxUsjUqlYtWqVYwcOdK8rUuXLrRv354FCxaYt7Vq1YqRI0cyc+bMcj/H008/Tb9+/bj33ntL3F9QUEDBlRXawdS8FxAQILfnbsHh8+k8/s0+LmcVcLfDIWapP0ejzwG3QBjzPTS62snbaDCy86cojvxtGlnX+srIugJjfpFbdV8M/IJQz1BLXZKoAbQXLpC8cCEZq1bDlQl3HXv1xOvZZ7Fv08aylRNCWFyV3p5r3rw5a9euRX+lz8D19Ho969ato3nz5hU5fYVotVr279/PwIEDi2wfOHAgu3fvLtM5EhMTyczMBEwv4Pbt22nRokWp5WfOnImrq6v5T0BAQMUvQADQJsCN3yZ2p7WPC7/ktmV4/lSyHQMhPQ6+HAhHfzWXVWvU9BzTnB73hoAKju+IZ/28w2j0NszvP9/c4vT4psc5lnzMglclLM3G3x/f99+n6cYNuN5zN2g05GzfQcx9Y4h74gnyIiMtXUUhRC1QodA0fvx4Tp06xaBBg4rdgtu3bx+DBw/m1KlT1TobeHJyMgaDAW9v7yLbvb29uXTpUpnOceHCBXr16kWbNm3o0aMHEydOJCKi9CU63nzzTTIyMsx/zp8/f0vXIEx8XO35+amuDGztzXG9H91S3ibGrQvocmHlI/DnNDCaWgtUKhVtbg9gyNMRWNlqOH8ijV9n78eQoS4anDZLcBJgExBwNTzdfdfV8DT6PuKefJK8I0csXUUhRA1WodtzBoOB0aNHs2rVKlQqFfb29nh7e5OYmEheXh6KojBixAh++eUX1OoK5bKbuv72XHx8PH5+fuzevZuuXbuay33wwQd8++23nDx5skrqcS0ZPVe5jEaFWX+cYuHfZ1FjZJHPWgakrTDtDBkEd38Bdq7m8klxWaz//DA5GVrsna0Z+kwbnPw1PP3n0xy8fBBnG2e+GCC36sRV2rg4khcuIuO338y37Zx698Zz4kTsw2X0pRD1RZXentNoNPzyyy8sW7aMPn36YGtrS1xcHLa2tvTt25dly5axatWqKgtMJfH09ESj0RRrVbp8+XKx1idRO6jVKt4Y3JLZ90Sg0Wh4PGEEc5xeQdHYQdQf8MXtkBxlLu8V6Mw9b3TEM8CJvCwdq+Yc4NKRHBb0X0C7hu3I0l5pcUqRFidhYhMYiO+MD2j6+3pcR40CjYbsv/8m5t57Of/U0+QdlZ8VIcRVFWppiouLw8bGhkaNGlVFncqktI7gHTp0YP78+eZtrVu3ZsSIERXqCF5e0tJUdf47l8JTy/eTlqujj/NFFtvMxSYnHmxd4O4l0HyQuaw2X8+mL48Re8Q04q7rqKY07+vBM389c7XFaeAXhHpIi5MoShsbS/KChWSsWQNGIwBOffvi+eyz2IfJz4sQdVWVtjQ1btyYt956q8KVq6js7GwOHTrEoStzrURHR3Po0CHzlAIvvfQSS5Ys4auvvuLEiRO8+OKLxMXF8dRTT1V7XUXl6tLEg9XPdqeplyPbsvzomzmVVM+OUJAJ398H2z+GK/nfxs6KIU9HENHXH4B/Vp1l74rzfN73c9p6tTW1OG2SFidRnE1QEL4fzjS1PI0YAWo12Vu3EnPPPZx/5lnyjsnPjBD1WYVamry8vHj44YeZPXt2VdSpVNu2baNv377Fto8fP56lS5cCpsktZ82aRUJCAmFhYcydO5devXpVS/2kpanqZeTpmPj9AXZEJWOj0rOq8VpC43827QwdBSM+BxtHc/nIrefZ+VMUigL+LRvQ85EmPL9rIoeSDkmLk7ipguhoUhYuJGPtuqstT7ffjtezz2DXurWFayeEqCxVuozKXXfdRVJSUrln267rJDRVD73ByPR1x/nmn1gAPm58gLsTP0Nl1IF3OIz5DhoEmcvHHEnmjyXH0BcYaODjSL8nmvHqwRc4lHQIFxsXvhj4Ba095AtQlK7gXDTJCxeQuW791fDU/3a8nn0Wu1atLFw7IcStqtLQdOrUKW677Tbz7NtWVla3VNm6QkJT9Vq2O4Zpa49hVGCsXzzT8j5CnZsE9u4w+hto3NNcNul8Fus/jyQnvQB7Z2v6Ph7CO2deleAkyqXgXDTJCxaQuf5qeHIe0B/PZ5/FrmVLC9dOCFFRVRqaHn30UaKioti9ezeNGjWiTZs2eHt7o1Kpip5cpeLLL78sf+1rKQlN1e/v00lM/O4AWQV6OrrlsNz5/7BLigSVBu6YCZ2fgCs/l9lpBayff5jk89lorNX0eKgJH6W8JcFJlFvBuXMkz78Snq58hDoPGIDnxGexu8GEuEKImqlKQ1NZpxJQqVQYrsx9Uh9IaLKMqMQsHl22l/OpeXjaGVkf/DPeMb+ZdrZ9CIbNAStbwDSybvNXx4mJTAag/XB/Pmc6h5MPS3AS5VZw9qwpPP3++9XwNHAgns8+I+FJiFqkSkNTbGxsmcsGBQXdvFAdIaHJclKyC3hq+X72xqShUcOK8P10PD0XFCP4dYT7loOLD2CaNHPXyigit1wAIOQ2L771nMWhFFOL0yd9PuE2n9sseTmilik4e5bkz+eTuWHDdeHpWexaVN9yUkKIiqnS0PTNN9/g7e3NoEGDbl64HpHQZFkFegOTfz3KLwdMYWh62GXGXpiKKj8dnBqZOoj7dzSXP7LtAjtWnEZRoFFzF9Y0nc+B9L0AjG89nufaP4eNxsYSlyJqqYIzZ0ieP5/MDRuvhqdBg0wtT9W4FqcQonyqNDRZW1szadIk5syZc0uVrGskNFmeoigs/PscH200LZtzT2MdH+lmokk+CRobGDYX2j1kLh9zJJlNS46hKzDg2siemG7bWZGwHIDmDZrzYc8PCWkQYpFrEbVXQVQUSfPnk7Xxj6vh6Y478Hr2GWxD5OdJiJqmSie39PHxQavVVrhyQlQVlUrF032asvChDthba1gZbc2ogqnkNhkMBi389ixseB0MOgCCwz2569X2ODWwJeNSHj6buvGW8hkt89pzJuUsY9aN4dvj32JUjBa+MlGb2IaE4D93Lo1/W43z4DsAyNq4kXN3juDCiy9SEBV1kzMIIWqiCrU0Pffcc/zxxx9ERkZia2tbFfWqlaSlqWY5ejGDCcv2kphZgIeDFWsi/sHv0FzTzuCecO8ycPQAICe9gPXzI0mKyzIfb9DoiXM5wXm3E3g1t+edQW/S0KGhJS5F1HL5p0+TPH8BWRs3mjaoVLgMvgPPZ57Btlkzy1ZOCFG1t+cyMjLo168f3t7ezJ49m9BQmVEZJDTVRImZ+Ty2bB9HLmZgo1GzrOtlukZOBm02uAXCmO+hUTgAep2B6EPJxB1LIfZ4KnmZRVtTM+2TCAz1oGfXdviGuGFlo7HEJYlaLP/UaZLnzyfrjz9MG1QqXAYPxvOZpyU8CWFBVRqamjRpQkFBAZcuXQLAzs6Ohg0bljhP09mzZ8t7+lpLQlPNlKc18PLPh/j9iOnndUpnFY+en4wqLRqsHWDkfNMSLNdQjArJF7KJO57CqcMXSY3JQ6VcvZutsVbh17wBgaEeBIV64NrQvtjPvxClyT91iuTP55O1aZNpg0qFy5AhpvDUtKllKydEPVSloSk4OLjMXxDR0dHlPX2tJaGp5jIaFeZsPs28rWcAuKulA7NVn6GJ3moq0PNl6Ps2lDIHWU52Hl/+8SOnD8cTkN4SJ22DIvtdPO0IDPUgMNQDv+Zu2NjJLPni5vJPnjSFp82bTRtUKlyGDjWFpyZNLFs5IeqRKg1NomQSmmq+VQcv8PrKI2gNRiJ8Hfmh8R847p9v2hkyCO7+AuxcSz1+f+J+Jm+fTF6ykaD01nTR344m0Rmj/up/I7WVCt9mbgS29iAw1B13X0dphRI3lH/ihOm23eY/TRvUalN4evppbJs0tmzlhKgHJDRZgISm2mFfTCpPfruflBwt3i62rOx2gYCdr4M+HzxCTP2cvEqfUydLm8WM/2aw7tw6ANq4teM57zfIi9YQdyyFzOT8IuWdGtgS2NqdwFAP/Fu5Y2svrVCiZPnHj5M0fz7Zf/5l2qBW4zLsSnhqLOFJiKpi8dCUmZlJeno6gYGBVXH6GklCU+1xPjWXCcv2cjoxGztrNV8OsKL7vuch8yLYOMGIzyF05A3PsSF6A+/9+x5Z2izsrex5rdNr3NXsLjKT8ok9lkLcsVQunk7DoLs6XYFKraJRExdzXyhPfydUammFEkXlHz9O0ufzyf7ranhyHT4Mj6eekvAkRBWo9NDk7u7Om2++yauvvmretmLFClasWMGvv/5arPy0adOYPn26rD0naqysfB2TfjjItlNJALzT15NHEqajitlpKtB1IvSfBprSW4Yu5VzirZ1vsefSHgD6BvRlarepuNu5A6DXGoiPSifuWCqxx1JIT8wtcry9iw2Brd0JCvUgoJU7dk7WVXClorbKO3aM5M/nk71li2nDlfDk+fTT2AQHW7RuQtQllR6a1Go1U6dO5Z133jFvu1EwktAkoak20BuMfPD7Cb7eFQPAPW29mem2Gut//89UIKgH3PMVOHuXeg6jYuSbY9/w2cHP0Bv1eNh58F739+jp37NY2czkPOKOpxJ7NIULp9LQF1zz/0MF3sEuVzqUu9MwyAW1tEIJIO/oMZI//5zsrVcGLqjVuA4fjuczT2NTj9b3FKKqlPX7WzpXiHrNSqPm3eGhNPVy4t01x1h5KJF/3Poxv1tz2uybDLE7YVEvGL0MAktexFetUvNw2MN09e3K69tf52zGWZ756xnGtBjDSx1fwt7K3lzWxdOesF5+hPXyw6A3knA2g7ijKcQdTyHlYg6J0ZkkRmeyd100do7WBLR2JzDUncDWHji4yDp49ZV9WCgBC+abwtO8eWRv20bGb7+RsW6dKTw9/ZSEJyGqQYWWURGirnnotiC+fbQzfm72XEzPY8QWDyZ7/Q+de3PIvgRLh8K/C83riJWkhXsLfhz2Iw+1Mq1t9+OpHxmzbgwnUk6UWF5jpca/RQO63d2MMVO6MH5mN/qObUnT9l7Y2FuRn6Mjam8ify09wdev7eSnGXv5d/VZ4qPSMRpkWZf6yD4slICFCwj++WecevcGg4GM1as5O2Qo8W9ORhsXZ+kqClGnSWgS4opuzTzZ/FIvnu7TFGuNiu/P2tL58mROew4Aox42vg6/PAbanFLPYWdlx+udX2dh/4V42ntyLuMcD/z+AF8e+RKD8ca3qp0a2NG6uy93PBHOhI97MOqV9nQYHIRXoDMASXFZ7N8Yy6pPDvDlKzvZuOgIx3fGk52Wf8PzirrHPjyMgEULCf75Jxx79zKFp1WrODt4CPGT35LwJEQVkdAkxDUcbKx4/Y6WbHi+F92aepCmt2HghYeZZ/MYRpUVHF0JX9wOyWdueJ7uft359c5fuT3wdvRGPZ8e+JTHNj1GQnZCmeqh1qjxbebGbSOaMnpyJx6Z1YP+D7cipJM3do7WaPP0nD2YxNblJ1n25m5+mP4fu345w4WTqUVG64m6zT48nMBFiwj+aQWOvXqawtOvv5rC01tvoT1/3tJVFKJOkT5NQpSgWUMnvnusC2sjE3hv3XE+zuzHNpUvXzrMwzXpBCzuA6MWQKvhpZ6jgV0D5vaZy+ozq5m5Zyb7Evdx95q7eeu2txjaZGi56uPgYkOL23xocZsPRqNCUmzWlWkNUkiMySQ1PofU+BwObY7DylaDf4sGBIWa5oZy8bS/+ROIWs0+IoLAxYvJO3yYpM8/J2f7DjJ++ZWM39Zg26QJahdnNM4uqJ2dTH9feaxxcUbt7ILG2cn0t4szahcXNE5OqKxlJKcQ1yvX6DlPT088PT3N25KTk0lJSaFFixbFyhfuk9FzorbLzNcxd/Nplu2OwUNJY4Ht/9FRddK0s/sL0G/KDaclADifeZ43dr5BZFIkAIMbD+bt297GxebWf07ys3WcP5Fa6kLDbt4Ops7koR74yULD9ULeoUMkfT6fnB07KnwOlYMDGienq4Hr2qDl5Hw1cF37d+F2FxfUNjJwQdQeVTLlQHmpVCoJTaLOOB6fyZTfjnI4NonXrX7kcavfTTuCe8I9X4OT1w2P1xv1fBH5BYsiF2FQDDRybMSMHjPo1KhTpdXx2oWG446lknA2A8V49b+4xlqNX/OrS7y4eTvIEi91WMG5aHQJ8RizsjFkZWLMzLr6d3bWlcdZGDMzzX8bc3NvfuIyUNnamoKWk3Pxlq0Sg5czGhcX83aVnZ38bIpqY/EZwesjCU11n9GosHL/BT7ceJKueduZZb0IR1UBBicfNPd9CwE3D0CHkw7z5o43OZ91HhUqHg57mEltJ2GtqfzbIQV5ei6cTCXumKklKjutoMh+F087c4Dya9FAFhoWKHo9xuxsDFlZGDIzMZr/vjZ4XRO0soo/rhTW1micndE4X7lleO0txGuDlvM1geua4KV2lF8IRNlJaLIACU31R3qulll/nGLP3n9YaDWXZup4DCorVHd8iLrzY3CTD+tcXS6z9s7il6hfAGjp3pIPe35IU7emVVZnRVFITcgh7mgqccdTiD+TXnShYY0Kn2ZuBIaaZiiXhYZFRShGI8acHHOIuhq8CgPWNcErK/Oa7VeDF8ZKGMyg0Vy5vehSPHg5O1932/Fq8Cosq3Z0RFWBOyyidpLQZAESmuqfQ+fT+WDVfzyc9DFDNaalVFKbjsL9vvlg43DT4/+K+4upu6eSXpCOrcaWlzq8xP0t76+WsKLN13PxdDpxVzqUX7/QsKObrTlA+bdsgK2DdAwWVU9RFIw5uaZAVdiSdW3wyi4MYJnX/Z1lDmno9bdeEZUKtZPTNYHraif5q4+vBi8bf39sQ0JQWUlrbW0kockCJDTVTwajwnf/xpC4aQ4vKsuxUhlJsGuK49jvcfFredPjk3KTmLJrCrvidwGm6Qre7/4+nvaeNzmy8iiKQsblPFloWNR6iqKg5OdfDVTm4HVd0MrKNj++vp+XUlBw8ycqgcrODrvWrbEPD8cuIhz7iAis/f2lxbYWkNBkARKa6rekrAJ+WvkDo2PewUuVQRYOHO74Ed2GjL3pGnKKovDDyR+Ys38OBYYCGtg2YGq3qfQL7FdNtS+qrAsNB4a6E9jKQxYaFnWKsaCgaNC6JmAVCVqF/bzSMyg4d67E/lyaBg1MASo8AvuIcOzCw7Fq0MACVyVuREKTBUhoEgAHjh7HdtUjhBpM0xKsdBxD6AMf0srv5h+UZ9LO8ObONzmZajr27pC7ea3TazhY3/xWX1Uq00LDrd0JDPOQhYZFvaQYjWhjY8k/coS8yCPkRUZScOIEik5XrKx1QMA1rVFtsGvdCrWdnQVqLQpJaLIACU2ikE6bz8lvXiD8wg8A7DCG82+7WTw1uBPOdjduldEatMw7NI+lR5eioBDoHMjMnjOJ8IqojqrfVEkLDV/L1tGKwFamACULDYv6zKjVUnDqFHmRkeRHHiHvyBG0584VL6jRYNuieZHWKNumTVFpZE616iKhyQIkNInrpf33PQ4bX8RWyeei4sFbVq9y150jGB7hc9N+Dnsv7WXyzslcyrmERqXhyTZP8nj441ipa1ZH0+y0fOKOm6Y0OH8iDW1e0U64ngFOBIV6EBjqgXcTFzQaGZEk6i9DZib5x46RdziSvCNHyIs8jCEpuVg5lYMD9qGh5tYo+4hwrBo1kv5RVaRKQ9P06dPp06cPvXr1KrXMrl27+Ouvv3jnnXfKe/paS0KTKFHicXKX349DVgwFihXT9OOJCbqX6SPDadbQ6YaHZhRk8MG/H7AhZgMAbbzaMLPHTAJcAqqj5uVmNBi5FJ15ZUReKklxRft42NhbEdCyAYGhHgRHeEorlKj3FEVBn5hoao26cmsv/8iREicZ1Xh5FmmNsg8PRyPfNZWiSkOTWq1m6tSpNwxEH330EZMnT5YZwYUAyM/AsOoZNKfWAfCzvhfTlAmM69mSSf1CsL/J0ibrz63n/X/fJ1uXjYOVA290foORzUbW+N86czO1nD+eQuyxVM4fTyU/52r/DpVaRUDLBoR09qZJGy9s7GtWC5oQlqIYDGjPnTP1jTpiurWXf/p0iVMp2AQHY98mArsrYcq2ZUtZwqYCLB6a3nvvPaZPn46uhE5wdZWEJnFDigK7PkP5axoqxcgxYxBP6V7A6BrMu8NbM6C19w1DUHx2PJN3TmZ/4n4A+gf2592u7+Jm51ZNF3BrChcajjueQvTh5CKtUBprNcFhHoR09iYozAMra+nLIcS1jPn55J84YWqNunJrTxcXV7ygtTV2LVsW6WhuExwkE3XeRJWHpmnTpjFlypQS92u1WoYNG8bx48e5cOFCeU9fa0loEmVy7m+UlY+iyk0mE0ee0z7DNmM7+rVsyNThoQR6lD5SzmA0sPTYUuYdmofeqMfL3ov3u79PN79u1XgBlSM9MZeofYmc3pNYZEoDGzsNTdp5EdLJG/8WDVBLHyghSqRPSyP/6NGrHc0jIzGkpRUrp3Z2xj48zNwaZRcejnXDhhaocc1V6aGpSZMm5n/HxMTg5uaGm5tbsXIGg4Hk5GTy8/N5/PHHWbhwYflrX0tJaBJllnERfhoHF/cB8H+Gu5iruwtrKyue7duMJ3o1we4GrS3HU47zxo43iM6IBuChVg/xQocXsNXYVkv1K5OiKCSfzyZqbyJR+xKLrI9n72xNsw7ehHTyplETlxp/O1IIS1IUBd3FePKPRJpbo/KPHUPJzy9W1qpRo6LTHoSGonFytECta4ZKD03BwcHmD6y4uDhcXFxKDE0ajQZ3d3f69evHlClTcHSsP2+ChCZRLvoC+GMy7F0CwGHbDozPeIJ0nAn2cGD6iDB6Nfcq9fA8fR5z9s3hx1M/AtDMrRkf9vyQFu4tqqX6VUExKiSczeD03kTO7r9cpA+Us4cdIR29ad7ZGw+/G3egF0KYKHo9BWfOFOloXhAVVXx9P5UK22ZNi7RG2TVvjsq6fkxca/E+TfWRhCZRIYd/hLUvgD6PXAdfntK+wPZsfwCGhDdiyrDW+Ljal3r49gvbeWfXO6Tkp2Cttub59s8ztvVY1KrafVvLYDBy4UQap/deIvpQMrprJtR093UkpKOpBcrVq/TXRghRnDE3l/zjx6+2RkVGoouPL1ZOZWuLXatWRTqaWwcE1MkW3yoNTbGxsbi5ueHq6npLlaxrJDSJCrt0FFY8BGnRKBpb1vm/xAtRERiMCg42Gl7oH8Ij3RtjXUr/ntT8VN7d/S7bzm8DoEujLrzf430aOTaqvmuoQjqtgZjIZKL2JhJ7LAWj/urHlndjF0I6edOsQ0McXWvf7UkhagJ9crIpQBXOaH7kCMaMjGLlNK6u2EVEFFlfz8rd3QI1rlwyuaUFSGgStyQvHVY9BadNczKltbyfZ1LH8E+cacbt5t5OvDcijC5NPEo8XFEUfon6hVl7Z5Gnz8PZxpl3ur7DHcF3VNcVVIuCXB1nDyYRtTeRi6fSKPwEU6nAr0UDQjp507SdF7YO9eO2ghBVQVEUdLGxVybgNLVG5Z84gaLVFitr7edXpDXKrnVr1Pa1qwW4ykNTUlISX3/9NXv37iU9Pb3E+ZhUKhV//fVXRU5fK0loErfMaISdc2DrB6AYUXza8nurj5jydxapOaYPq7va+fHmkFZ4OZfcqhKbGcsb29/gaMpRAIY3Gc6bXd7E2ca52i6juuRkFHBm/2Wi9iaSGJ1p3q62UhEU6kFIJ2+CIzyxvsk8WEKIm1O0WvJPRxXpaK49dw6ujxEaDbYhIUU6mts2q9nLwlRpaIqMjKRfv36kpaVxo8NVKpVMbilERZzdAisnQF4q2Dcge9hCZpz244c9cSgKONtZ8eqgFjzYJQhNCYvj6ow6Fh1exBdHvsCoGPF19GVGzxl08O5ggYupHhlJeUTtSyRqbyKp8VfXw7O21dC4rSchHb0JaO0uy7gIUYkMWVmmZWEij5jCVOQR9ImJxcqpHBywb93adGsvwjSbuZWvb43pH1WloWnAgAH89ddfvP3220yYMAF/f380NThBVhcJTaJSpZ83TUsQfwBQQd/JHG78GG//dpwjF019DcL8XHhvRBjtAhuUeIpDlw/xxo43uJh9EbVKzYSwCTzd5mmsNXX71lXKxWxO7zUFqKyUq8Ot7RytadqhIc07NcSnqRuqEgKnEOLW6AqXhbnSNyr/yBGMOTnFymk8PLAPD796ay88DI2F+kpXaWhycnJi4MCB/Prrr7dUyZrIysqKsLAwADp27MiSJUvKfKyEJlHp9AWw4XXY/7XpccggDCMX8f2RTGZvPElmvh6VCsZ0CuT1O1rg5lB8+YRsbTYf7f2I1WdWA9DaozUze86kiWuTYmXrGkVRSIzO5PTeRM7sSyQv6+oUBk4NbGnW0ZvmnbzxDHCqMb/xClHXKEYj2ujoIq1R+adOQQkrhtgEBZk7mttHhGPbqhVq26of4FGlocnd3Z0JEyYwe/bsW6pkTeTp6UlycvEVp8tCQpOoMge/g/UvgT4fGgTD6G9Jdm7BzN9P8ssB06z77o42vHFHS+7p4I+6hBaUTTGbmPbPNDK1mdhp7Hi106vc2/zeehMWjAYjF0+lc3rvJc4dTEKbf7XrgJu3AyGdTAHKzbv0GdmFEJXDWFBAwYkT5pF6+ZGRaGNjixe0tsauRYsrc0eZbu3ZNG5c6cvCVGlouueee0hJSWHr1q23VMmaSEKTqLESIk3TEqTHgpUdDJsLbR9gT3QqU1Yf5VSiaS23DkENeG9EGK19i/8MJuYk8vaut/k34V8Aevn3Ylq3aXjae1brpViaXmcg9mgKUXsTiTmSgkF3daI/r0Bnmnf2plkHb5wayBQGQlQXQ3o6eUePFelobkhJKVYu6NtvcOjUqVKfu0pDU3R0NF26dOG1117jlVdeuaWKlsf27duZPXs2+/fvJyEhgVWrVjFy5MgiZebPn8/s2bNJSEggNDSUTz/9lJ49e5b5OWxsbAgPD8fe3p4PPviA3r17l/lYCU2iyuWlwa9PQNQm0+MOj8Dgj9CprFm6K4ZP/zxNjtaAWgXjuwXz0oDmONsV7b9kVIx8d+I7Pt3/KVqjFnc7d6Z3m07vgLL/rNcl2jw95w6bpjA4fyINxVg4hwH4hbhdmcKgIXZOdbsfmBA1jaIo6OPji057cPIkIX9vQ13Jq41UaWh69NFHiY6OZvv27TRu3Jg2bdqUONGlSqXiyy+/LO/pS7VhwwZ27dpF+/btufvuu4uFphUrVjB27Fjmz59P9+7dWbRoEUuWLOH48eMEBgYC0KFDBwoKCoqde9OmTfj6+hIfH4+vry9Hjx5l6NChHDlypMwBSEKTqBZGI2yfDdtmAgr4tofR34BbAJcy8nlv/XHWRyYA4OVsy9tDW3Fnm+KjVE6nneaNHW8QlRYFwOjmo3m548s4WNff21N5WVrTFAb7Ekk4c3ViP7VaRWCou3kKAxs7KwvWUoj6SzEaK/3WHFTDMiplUZVTDqhUqmKhqUuXLrRv354FCxaYt7Vq1YqRI0cyc+bMcj/H4MGDee+99+jYsWOJ+wsKCooEsMzMTAICAiQ0ieoR9Sf8MgHy08HeHe75Cpr2BWBHVBLv/HaM6GTTiJVuTT2YPiKUZg2LztVUYCjgfwf+xzfHvwEg2CWYD3t+SKhnaLVeSk2UmZLHmX2XOb03kZQL2ebtVjZqGkd4EtLJm8BQDzRWMoWBELVdlS+jUlZBQUHlPX2ZXB+atFotDg4O/Pzzz4waNcpc7vnnn+fQoUP8/fffNz1nWloaDg4O2NracuHCBbp3787BgwdxL2WK+KlTpzJt2rRi2yU0iWqTFgs/jYWEw6BSQ9+3oMdLoFZToDew+O9zzNt6hgK9EWuNisd6NmFSv2Y42BRtKfk34V/e2vkWl3MvY6Wy4pm2z/Bo2KNo1DKVCEBqQg5RexM5vTeRzKQ883ZbByuatvMipHMjfEPcSuyAL4So+er8MirXh6b4+Hj8/PzYtWsX3bp1M5ebMWMGy5Yt49SpUzc95+7du3nyySdRq9WoVCqmTp1arM/UtaSlSdQIunz4/RU4+K3pcYshMHIB2LsBcD41l2lrj/HnicsA+LnZ887w1gxs7V3kll1GQQbT/5nOplhTf6l2Ddsxo8cM/J39q/VyajJFUbgcm0XUnkSi9ieSm3F1SQkHVxtCOngT0tmbhkHO9WZUohB1QbWGptTUVHJycggICLjVU5VZaaFp9+7ddO3a1Vzugw8+4Ntvv+XkyZNVXifp0yQsav8y+P1VMBRAg8Zw33JoFGbevfl4IlPXHONiuqmlpG8LL6bdGUagx9U+TIqisPbcWmb8N4McXQ6O1o681eUthjUZJiHgOkajQnxUOlF7LnH2YBIFuXrzPlcve0I6eRPSyRt3n8rtsCqEqHxl/f6u8M34jIwMnn/+eby9vfHy8qJx48bmff/99x9Dhgxh//79FT19uXl6eqLRaLh06VKR7ZcvX8bb27va6iGExXQYDxP+ANdASIuGJf3h8Arz7gGtvfnzpd4827cp1hoVW08lMWDu33z2ZxT5OlPfQ5VKxZ1N72Tl8JW0a9iOHF0Ok3dO5tXtr5JRUHzF8/pMrVbh36IBfce24pGPejDk6XBCOjbEykZNRlIe+36P4Ydp//Hj+3s48EcsWan5Nz+pEKJGq1BLU2pqKt26deP06dO0b9+e/Px8Tpw4Ye70nZeXR6NGjZgwYQJz5syp9EpD6R3BO3TowPz5883bWrduzYgRIyrUEby8pKVJ1Ai5qfDLY3D2ymLZnR6HQTPA6ups4WeTsnn3t2PsPGOakyzYw4Gpd4bSp0VDcxmD0cBXR79i/qH56BU9DR0a8kGPD7jN57ZqvZzaRpuvJyYymai9icQdS8VovPoR69PMlZCO3jTr0BB75+KztwshLKNKb88999xzzJs3jx9++IH77ruPadOmMX369CIj5e68807i4uI4dOhQhS6gJNnZ2Zw5cwaAdu3aMWfOHPr27Yu7uzuBgYHmKQcWLlxI165dWbx4MV988QXHjh2rsg7p15LQJGoMowH+/sj0B8C/E9y7DFz9zEUURWFdZALvrz9OYqapb97gsEZMGdYaXzd7c7mjyUd5c8ebxGTGADC+9Xiea/8cNhr50r+Z/GwdZw9e5vSeROLPpEPhFFBqFQGtGhDSyZsmbbywsZcpDISwpCoNTcHBwURERLBmzRqAEkPTpEmT+OGHHyo8u3ZJtm3bRt++fYttHz9+PEuXLgVMk1vOmjWLhIQEwsLCmDt3Lr169aq0OtyIhCZR45z+A359HPIzwMET7v0aGhf9/5BdoOfTzaf5encMBqOCg42G528P4dEejbHWmO7g5+py+WTfJ/x0+icAmjdozoc9PySkQUi1X1JtlZ1WwJn9iZzek0hSXJZ5u8ZaTXC4B807NSIwzB0raxmxKER1q9LQZGtry4svvsiHH34IlByaXn31VebNm0deXl5pp6lzJDSJGik1GlaMhcQjpmkJbn8Xuj8P13XsPpGQyZTVR9kXmwZAc28npo8I47YmHuYy285v493d75Kan4qN2oYXO7zIA60eQK2SuYrKIz0xl9N7E4nam0h6Yq55u42dhibtvGjeqRF+LdxQa+R1FaI6VGlo8vX1pW/fvnz33XdAyaFp+PDhHDt2jHPnzlWg+rWThCZRY+nyYN1LcPh70+OWw0zTEtgV/Tk1GhV+OXCBDzecJCXHNJx+VDs/3hzSkobOdgAk5yXz7u532X5hOwBdfbryfo/3aejQEFE+iqKQfD6b03sTObMvkey0q1OY2Dtb06yjaRFh78YuMnpRiCpUpaFpzJgxrF+/npMnT+Ln51csNB0/fpw2bdrwyCOPsHjx4opfRS0joUnUaIoC+7+GDa+DQQsezUzTEjRsVaxoRq6O2ZtO8t1/cSgKONtZ8crAFjx0WxAatQpFUfj59M/M3jubfEM+rrauvNv1XQYEDbDAhdUNilEh4Ww6p/de5uz+y+Tn6Mz7nD3sCOlkClAefk4WrKUQdVOVhqYjR47QuXNnvL29mTFjBv/88w/z58/n6NGj7N69m7feeovs7GwOHjxISEj96fMgoUnUChf2w0/jIPMCWDvAnf8H4feUWPTw+XSm/HaUyAum6QbC/Fx4b0QY7QIbAHAu4xxvbH+DE6knABjZbCRvdH4DR2uZm+hWGAxGzh9PJWpfIucOJaMvuNqK7+7raA5QLp72NziLEKKsqnxyyzVr1jBu3DiyskwdGhVFQaUy/Qbq7OzMDz/8wJAhQypW+1pKQpOoNXJS4JdH4dw20+MuT8GA94pMS1DIYFT4fk8cszeeJDNfj0oFYzoF8NqgljRwtEFn0LHg8AKWHFmCgoK/kz8ze86kbcO21XpJdZVOazBPYRB7LAWj/upHtndjF0I6maYwcHS1tWAthajdqmVG8NTUVJYtW8Z///1HamoqLi4udOnShUceeQRPT8+KnrbWktAkahWjAbZ+ADs+MT0OuA3uXQouPiUWT84u4MMNJ1m5/wIADRyseWNwS+7tEIBarWJ/4n4m75hMfE48apWaJyKe4ImIJ7BWW1fTBdV9Bbk6zh5MImpvIhdPpVH46a1SgV8L0xQGTdt5Yesgr7kQ5VHn156riSQ0iVrp5O+w6kkoyATHhqbgFNy91OJ7Y1KZsvooJy+ZWpnbB7rx3sgwQn1dydJmMfO/maw9txaAcM9wZvacSZBL1c+TVt/kZBRwZt9lovYlkhidad6utlIRFOpB886NCA73wMpGpjAQ4mYkNFmAhCZRa6WcNU1LcPkYqDQwYBp0nVhsWoJCOoORZbtjmLv5NDlaA2oVjOsazEsDm+NiZ83G6I1M/3c6Wdos7K3seb3T69wVcpeMAKsiGUm5RO29zOm9iaQl5Ji3W9tq8G/ZAJ9mbvg2c8Mz0AmNTGMgRDFVGprmzJnDjBkziIyMxNfXt9j++Ph42rRpw5QpU3juuefKe/paS0KTqNW0ubDuBYi8sl5d6xEw4nOwdS71kEsZ+by//jjrIhMA8HK25e2hrbizjS+JuYm8vfNt/rv0HwB9A/oytdtU3O3cq/pK6i1FUUi5mEPUlTmgrl/vzspGjXdjV3ybueIT4kajxq5Y20pLlBBVGpq6du2Kvb09W7ZsKbXMgAEDyMnJYffu3eU9fa0loUnUeooCe5fAxjfBqAPP5qZpCbxa3PCwnVHJvPPbUc4lm1o5ujbx4L2RoTTxcuTb49/y2YHP0Bl1eNh58F739+jp37M6rqZeUxSFy7FZxJ9OJ/5MOgln0ynI0Rcpo1Kr8ApwwifE1BLl09RV1sQT9VKVhiYPDw8efPBB/ve//5Va5oUXXuC7774jKSmpvKevtSQ0iTrj/B74aTxkxYONE4yYB6GjbnhIgd7Akh3R/N+WKPJ1RqzUKh7r2YTnbm/G+eyzvLHjDc6km9aOvK/FffT0uxqcrr9tp0JVZLv58ZW/UZW97M3OfaNzXF+/YvUo6RjzXyXX4/pzl6XuN7zuMr5WilEh67KW1Oh8UqLzSI3OIy+9aIgCcGpojUdje9yv/HFoYCW3VUWN0sihEdaayh3sUKWhyd7enhdeeIGZM2eWWuaNN97gs88+k2VUhKitspNg5SMQs8P0uOtE6D8VbvJhdT41l2lrj/PniUQA/NzsmTKsNb1buPK/g/9j+YnlVVxxUVZOBQ1olNkEn6wm+GQ2xT2v+MjJbJt0EpzPcsnlHAnO50h1SACVdIUVlrNm5Boauzau1HNWaWhq2bIl3t7e/P3336WW6d27N/Hx8URFRZX39LWWhCZR5xj0sGU67PrM9DioO9zzNTh73/TQP48nMnXtMS6kmX5x6tPCi2l3hnIx/zBfHfuKXF0uhR8/Clc/hgr/ff1H0/XbzY+vPfb6Y0opW9LHXqnnL6GO15+jtPObj1FKLleW85fpmFLOX96PdxudPQ0zg2iYGYx3RmM8sv1QK0X7PBVY5ZHkEstllxgSXWJIcb6AUW0o5YxCVL4Vw1YQ7Bpcqees0tD0yiuvMHfuXL744gseffTRYvuXLFnCk08+yfPPP8+cOXPKe/paS0KTqLOOr4HVz4A2C5wamaYlCOp608PytAbmbzvDor/PoTUYsbFS80yfpjzVuyl21tIBuabTaQ1cjs409Yk6k86lc5noCooGJI2VmobBzqY+USFu+DRxxcbeykI1FqJiqjQ0JSUl0a5dOxISEujduzcDBgzAz8+PixcvsmnTJrZv346vry8HDhzAy8vrli6kNpHQJOq05CjTtARJJ0BtBQPfN80kXob+LueSsnl3zTF2RCUDEOThwPsjw+gZUn8+H+oCo8FI8oVsEs5kmINUXpauSBmVCjz8nczTHPg0c5XZykWNV+XzNEVFRfHQQw+xd+9e04muLKEC0LlzZ5YvX06zZs0qcupaS0KTqPMKsmHtc3D0F9PjsLth+P/A9uaLyCqKwu9HLjF93TESMwsAuKu9H1OGtqaBo4zYqo0URSHjcp4pQEWlE382g8yk4v1YXbzsTdMcXAlSrg3tpXO5qFGqbXLLffv2sWfPHtLT03Fzc6Nz58507NjxVk5Za0loEvWCosB/i2DTW2DUg1dL07QEnmVbnDu7QM/Hf5xi2T8xKAp4ONrwzvDW3NnGV75I64Cc9IIrrVAZJJxNJ/lCNtd118LexQbfpldCVIgbHn6OqGXSTWFBVRqaHn30USIiInjhhRdupY51joQmUa/E/WualiD7Etg4w8j50PrOMh9+MC6NN345wqlE03IsfVp48f7IMPwbOFRVjYUFFOTpuXT26u28yzFZGPTGImWs7TQ0anJl0s2mbng3dpHlX0S1qtLQZGdnx4svvnjDKQfqIwlNot7JSjRNSxC7y/S4+/PQ7x3QlK0jsFZvZPH2s/zvrzNoDUYcbDS8MrAF47sFo1FLq1NdpNcZuBybRYK5NSoDbV7R+aLUGhUNg5zxaeZm+tPUFTtHWYRYVJ0qDU1hYWF07NiRpUuX3kod6xwJTaJeMujgz6nwzzzT4+CecM9X4NSwzKc4m5TNm78eYU90KgBtAtz46O5wWjaS/0d1ndGokBqfTXyU6XZeQlQ6ORnaYuXcfR3NHct9mrnh7G5ngdqKuqpKQ9PcuXOZMWMGhw4dws/P75YqWpdIaBL12rFV8NtE0GaDsy+MXgYBnct8uNGo8OPe88z8/QRZBXqs1Cqe6t2Uif2ayfQE9YiiKGQm519piUon/kwG6Ym5xco5u9vhE2K6nefbzI0GPg7SJ05UWJWGppiYGCZOnMiRI0d47bXX6NSpE97e3iX+wAYGBpb39LWWhCZR7yWdghUPQfJpUFvDoBnQ+fEyTUtQKDEzn3d/O8bGY5cAaOLpyIy7wrmtiUdV1VrUcLmZ2iL9opLOZ6MYi3512Tlam1qhmrrhE+KKV6AzGulcLsqoSkOTWq02TzFwo2SvUqnQ64uvbVRXSWgSAijIMrU4HV9tehw+GoZ/CjaO5TrNxqMJvPPbMS5nmaYnuL9zIG8MbomrvfRtqe+0+XoSr5l0M/FcJnpd0c7lVtZqvJu4mKc58G7sgo2dTLopSlaloenhhx8uczPo119/Xd7T11oSmoS4QlHgn89h8zugGKBhKNz3LXg0LddpMvJ0fLTxJN//FwdAQ2dbpo8I5Y6w4mukifrLoDeSdD6LhKgrrVFn0ynIKfoLu0qtwivg6qSbjZq64uAi84MJk2qbp0lcJaFJiOvE7IKfH4acy2DrAqMWQsuh5T7Nf+dSePPXI5xLzgFgUKg300eE4e0inYFFcYpRIfVSjml03pl04s+kk51aUKycm7eDaZqDEDd8mrrh4mkn/aLqKQlNFiChSYgSZCaYgtP5f02Pe7wE/d4Gdfk6d+frDHy+9QwLtp1Fb1RwtrXijSEtub9TIGqZnkDcRFZqvrljecKZdFLjc4qVcXS1wSfk6vIv7r5O8rNVT1RLaLp06RK//vorJ0+eJCcnhy+//BIwrU0XHR1NeHg49vb2FT19rSOhSYhSGHSwaQr8t8D0uHFvuPtLcCr/2nMnL2Xyxi9HOHQ+HYDOwe7MuCucZg1vvpSLEIXyc3QknM0wLf9yJp2k2CyM13Uut7G3wqepq3maA+8gFzTW0rm8Lqry0DR//nxefvllCgpMTZ4qlQqDwbT69bFjx4iIiGDhwoU8/vjjFTl9rSShSYibOLIS1kwCXS44eMDQTyB0VLlPYzAqfPNPDLP/OEWu1oCNRs2kfs14sndTbKzkS02Un05r4PI1ncsvnctEV2AoUkZjpaZhsPOVlihTvyhbe+lcXhdUaWhau3YtI0aMoGPHjrzzzjts2LCBhQsXmkMTQLt27fD19WX9+vUVu4JaSEKTEGVw+QT88hgkHjU9bj0ChnxSoVanC2m5vL36KNtOJQHQwtuZmXeH0z6wQWXWWNRDRoOR5AvZJJy5OtVBXpauSBmVCjz8nfBp6oa7ryP2ztbYO1lj52iDnZM1do5WsqZeLVGloalXr17ExcVx7NgxHB0dmTZtGtOnTy8SmsaNG8eOHTuIjo6u2BXUQhKahCgjvRZ2fAw7PjEt+uvgAUM+hrC7yn0qRVFYczie6WuPk5KjRaWC8V2DeWVQC5xspRVAVA5FUci4nGcKUFHpxJ/NIDMp76bH2TpYYed0JUw5mcKUvaO1KVRds93+ymNbeytU0o+q2pX1+7tCnyiHDh1i7NixODqWPu+Kn58fiYmJFTm9EKKus7KBvpNNI+lWP2NqdVr5iGlup3K2OqlUKka09aNXiBfvrz/BLwcusHR3DJuOXeL9UWH0a+ldddch6g2VSoWbtwNu3g607u4LQE56wZUpDjLISsknP1tHfo6OvGwtBbl6UKAgV09Brp6MyzcPWKbn4UorVWGosikasEoIXDZ2Ghn1V00qFJqMRiPW1jeeYC4pKQlbW9sKVUoIUU/4tIHHt15tdTr+G8TsNLU6hY4q10ziDRxt+GR0G0a282XyqiOcT83j0aX7uLONL+8Mb42nk3weicrl6GZLSEdvQjoWD+ZGg5GCXD152TpTmMo2hSlTqLp2m478bC352Tq0+QYUBfKydMVuBd6IWq26Lkhd03rleP12UxCzslFL0KqACoWmFi1asHPnzlL36/V6/v77b8LDwytcMSFEPVFaq9OxVTB0Trn7OvUM8eKPF3rx6Z9RLNlxjjWH49kelcTbQ1tzd3s/+aIQ1UKtUWPvbIO9c9kn0DTojeTnXBeockyhqnjQ0pGXo0NfYMBoVMjN1JKbWXyh49JorNVXg5TjdbcPna5rzXI0BTAZOVjB0PTggw/yyiuv8P777/P2228X2WcwGHjllVc4d+4cr7/+eqVUUghRD1zf6nRijanVaejHEHpXuVqdHGysmDykFcMjfHn9l0iOJ2Tyys+HWX3wIjNGhRPo4VCFFyJExWis1Di62uLoWvZWUb3WcMPWq7yc64JWthajXsGgM5KdVkB2WvFJP0tjbaspHqocbUpo5TK1Ztk6WtW59f8q1BFcp9MxcOBAtm/fTrNmzbC1teXYsWPcfffd7Nu3j5iYGAYOHMiGDRvq1W910hFciEqScBhWPwuJR0yPW91pmp7AqWG5T6UzGFmyI5pP/zxNgd6InbWalwY059HujbGqYx/oQtyMoijoCgzX9L+67vbhda1chfuvXyC5rGwdrEq4RWhTQivXlaDlYJmO8FU+T5NWq2XatGksXLiQtLQ083YXFxeefvpppk2bho1N/VrXR0KTEJVIrzW1OO342DTCzt69Qq1OhWKSc3jz1yP8cy4FgDA/Fz68K4IwP9fKrrkQdYpiVNDmX98/68q/c0q+dZifq4MKpAuVCmyvCVIl3ToMjvDEzrFyF+6utmVUFEXh1KlTpKam4uLiQqtWrdBoyrc8Ql0hoUmIKpAQeaWvU2Gr0/ArfZ3K3+qkKAo/77/AB+tPkJGnQ6NW8VjPxrxwe3Psbern55YQVcFoVCjIvS5IXd+add2tQ22e/uYnBsa80xkP38pdAaBKQlNaWhrz5s1j7969AHTu3JmJEyfi5uZ2yxWuCyQ0CVFF9FrYOQe2z77a6jRkNoTdXaFWp8tZ+Uxbe5z1kQkABLo7MPOucLo386zsmgshyqisHeH7P9oae6fKvZNV6aEpJSWFzp07ExMTQ+EhKpWKJk2a8N9//+Hu7l45Na/FJDQJUcUqsdUJ4M/jiUz57SgJGfkA3NvBn7eGtsLNoX51LRCivivr93eZe0HOmDGD6Oho+vTpw48//siPP/5Ir169OHv2LB9++GGlVFoIIW7IJwIe3wJ93gS1FZxYC593Ma1pV4GeBv1be7PpxV6M7xqESgU/779A/zl/s/ZwPLfYc0EIUQeVuaWpVatWGAwGTpw4Ye6zpNfradWqFdbW1hw/frxKK1obSEuTENWoklud9sem8sYvR4i6nA1Av5YNeW9kGH5u9pVVYyFEDVXpLU2xsbEMGjSoSCdvKysr7rjjDmJiYm6pskIIUW4ltjp1rnCrU4cgd9Y914MX+zfHWqNiy8nLDJzzN8t2x2Co4HBrIUTdUubQlJ+fj5dX8Zl5PT09KSgo++RYQghRaaxsoM8b8MQ2aBQOeWnwywRY8RBkXy736WytNDzfP4Tfn+tJh6AG5GgNvLvmGPcs3M3pxKzKr78QolaRmd2ucerUKdq2bWv+Y29vz+rVqy1dLSHEzTQKN80mXtjqdHLdLbU6hXg78/OTXXlvZBhOtlYcjEtn6P92MGfzaQr0hiq4ACFEbVDmPk1qtZr+/fvTv3//Its3b97Mli1b+PDDD0vsOPnaa69VTk2rWXZ2NsHBwcTGxuLo6FimY6RPkxA1wKUjsPpp098ALYeZ+jo5F19UtSwSMvKYsvoYf55IBKCplyMf3h1Bp2AZMSxEXVHpUw6o1eVvlFKpVBgMtfO3su+//57ffvuNFStWlPkYCU1C1BAGHeyYA9tnXZnXqQEM+bjC8zopisKGo5d457djJGebuiM8dFsgr93REhe7yp2ZWAhR/So9NC1btqxCFRk/fnyFjivJ9u3bmT17Nvv37ychIYFVq1YxcuTIImXmz5/P7NmzSUhIIDQ0lE8//ZSePXuW+7lGjhzJuHHjuOuuu8p8jIQmIWqYSm51ysjVMeP3E6zYdx4Abxdb3hsRxsDQRpVVYyGEBVTbMirVacOGDezatYv27dtz9913FwtNK1asYOzYscyfP5/u3buzaNEilixZwvHjxwkMDASgQ4cOJXZc37RpE76+voDpxWvatCnnz5/Hzs6uzPWT0CREDWTQwc658PcsMOpMrU6DZ0P4PRVqdQLYfTaZyb8eISYlF4Ah4Y2YOjyUhi5l/7wQQtQcdTI0XUulUhULTV26dKF9+/YsWLDAvK1Vq1aMHDmSmTNnlvnc3377LX/88QfLly+/YbmCgoIiASwzM5OAgAAJTULURJeOXml1ijQ9vsVWp3ydgf/9FcWi7ecwGBWc7ax4a0gr7usUgKqCYUwIYRmVPk9TTafVatm/fz8DBw4ssn3gwIHs3r27XOf66aefuO+++25abubMmbi6upr/BAQElOt5hBDVqFGYaV6nvm+B2to0wm5+F4j8uUIj7OysNbx2R0vWTuxBhL8rWfl63vj1CGMW/8u5pOwquAAhhKXVmdCUnJyMwWDA27vob43e3t5cunSpzOfJyMhgz549DBo06KZl33zzTTIyMsx/zp8/X+56CyGqkcYaer92ZV6nCNO8Tr8+Bj8+CFmJFTpla18Xfn26G28PbYW9tYb/olO547MdfL71DDqDsXLrL4SwqDoTmgpd3yyuKEq5mspdXV1JTEzExubmC3ba2tri4uJS5I8Qoha4vtXp1HrTvE6RP1Wo1clKo+axnk3Y9GIveoZ4otUbmf3HKYb/304On0+v/PoLISyizoQmT09PNBpNsValy5cvF2t9EkKIYq1O+enw6+NXWp3K3jp9rQB3B755tDNz72tDAwdrTl7KYtT8XUxfe5ycAn2lVl8IUf3qTGiysbGhQ4cObN68ucj2zZs3061bNwvVSghR45lbnd6+ptWpS4VbnVQqFaPa+fPnS70Z1c4PowJf7Ypm4NztbDtV/qVdhBA1R60KTdnZ2Rw6dIhDhw4BEB0dzaFDh4iLiwPgpZdeYsmSJXz11VecOHGCF198kbi4OJ566ikL1loIUeNprKH3q6ZWJ58217Q6PVDhVicPJ1vm3teWpY90ws/NnovpeTz89V5e+PEgKdmyXqcQtdEtTTlw6dIlfv31V06ePElubi5LliwBICkpiejoaMLDw7G3t6+0ym7bto2+ffsW2z5+/HiWLl0KmCa3nDVrFgkJCYSFhTF37lx69epVaXW4EZmnSYg6wKCDnZ/C3x+Z5nWyc4PBsyBidIXndcop0DNn82m+3hWNUYEGDtZMGdaaUe38ZHoCIWqAKp+naf78+bz88svmeYquXTLl2LFjREREsHDhQh5//PGKnL5WktAkRB2SeMw0r1PCYdPjFkNg2Fxwrvjs34fOp/PGL5GcvJQFQM8QT2aMCifA3aEyaiyEqKAqnadp7dq1TJw4kfDwcNasWcPTTz9dZH9oaCgRERGsXr26IqcXQgjL8w6Fx/6CfoV9nX439XU6vKJCfZ0A2ga4sXZSD14d1AIbKzU7opIZOHc7S3acQy/TEwhR41UoNM2ePZvAwEC2bt3KsGHDaNiwYbEy4eHhHD9+/JYrKIQQFqOxhl6vwpN/X+3rtOqJW+rrZK1R82zfZmx8viddGruTpzPw/voT3LVgN8fjMyu3/kKISlWh0HTo0CGGDh2Ko6NjqWX8/PxITKzYZHFCCFGjlNjq1BkO/1jhVqcmXk788PhtfHhXOM52VkReyGD4vJ18tPEk+TpDJV+AEKIyVCg0GY1GrK2tb1gmKSkJW1vbClVKCCFqnGKtThmw6kn44f4Ktzqp1SrGdA7kr5d6MzisEQajwoJtZ7nj0+3sPptcyRcghLhVFQpNLVq0YOfOnaXu1+v1/P3334SHh1e4YkIIUSNd3+p0esMttzo1dLFjwUMdWDS2A94utsSk5PLAF//x+spIMnJ1lXwBQoiKqlBoevDBBzlw4ADvv/9+sX0Gg4FXXnmFc+fOMW7cuFuuoBBC1DjmVqft4NP2mlanMZCZUOHTDgptxOaXevNgl0AAVuw7z+1z/ub3IwncwuwwQohKUqEpB3Q6HQMHDmT79u00a9YMW1tbjh07xt13382+ffuIiYlh4MCBbNiwoV7NQSJTDghRDxl0sOsz2PbhlXmdXK/M63Rfhed1AtgTncqbv0ZyNikHgP6tvHlvZCg+rpU3950QwqTK52nSarVMmzaNhQsXkpaWZt7u4uLC008/zbRp08q06G1dIqFJiHos8fiVeZ0OmR43vwOGfQouPhU+Zb7OwPytZ1jw91l0BgUnWytev6MFD3YJQq2uP7+QClHVqjw0FVIUhVOnTpGamoqLiwutWrVCo9HcyilrLQlNQtRzBj3s+rRoq9MdH0GbMbfU6nTqUhZv/BrJwbh0ADoGNWDmXeGEeDtXTr2FqOeqLTSJqyQ0CSEAU6vTb89A/EHT40podTIYFZb/G8usjSfJ0Rqw0ah5pm9Tnu7TFFur+vmLqhCVpUpnBG/evDnvvfce586dq3AFhRCizvJuDRP+hNvfAY0NnN4I87vAoR8qPMJOo1Yxvlswm17qTb+WDdEajHz6ZxTD/reT/bGplXwBQoiSVKilydXVlaysLFQqFV27dmXs2LGMHj2aBg0aVEUdaw1paRJCFHN9q1PIIBj+2S21OimKwrrIBKauOUZKjhaVCsbeFsSrg1rgbHfjOfSEEMVV6e25goICVq9ezfLly9m0aRN6vR5ra2uGDh3KQw89xLBhw246+WVdJKFJCFEigx52XxlhZ9Be6ev0IbS5/5b6OqXlaPng9xOs3H8BAB9XO94bEUb/1t6VVXMh6oVq69OUkpLCDz/8wPLly9mzZw8qlQo3NzdGjx7Ngw8+SI8ePW7l9LWKhCYhxA1dPmEaYVek1elTcPG9pdPujEpm8qojxKXmAjA0woepw0PxcpZVGYQoC4t0BD9z5gzLly/n+++/58yZM6jVavR6fWWdvsaT0CSEuKnrW51sXWHwrbc65WkNfPrnaZbsjMZgVHC1t+atIa24t6N/vZovT4iKsNjoue3bt/Ptt9/yzTffoNfrMRjqz8KTEpqEEGV2+QSsfgbiD5geV1Kr09GLGbz+SyTH4jMBaO7txMh2foxo64efm0yMKURJqjU0HTt2jO+++47vv/+e8+fPoygKISEhPPTQQ0yZMuVWT19rSGgSQpSLQQ+7/wfbZl5tdbpjJrR94JZanfQGI1/ujGbun6fJ1xnN27s0dmdkOz+GhPng6lD/+p0KUZoqD00JCQl8//33LF++nMjISBRFwdPTk9GjRzN27Fi6dOlS4crXVhKahBAVUqzVaeCVEXa31uqUkatjw9EEVh+6yL/nrk5LYKNR07elF6Pa+dGnRUPsrGWeJ1G/VWloGjBgANu2bcNgMGBra8vw4cMZO3YsgwcPxsrK6pYqXptJaBJCVJhBD//8H2ydUamtToXi0/NYczie1QcvcvJSlnm7s50VQ8N9GNHWjy6N3WV5FlEvVWlo0mg09OzZk7Fjx3LvvfdKQLhCQpMQ4pZdPnllhF3ltjpd60RCJqsPXWTNoXgSMvLN231d7bizrR8j2/nSspF8hon6o0pDU1xcHIGBgbdUwbpIQpMQolKU2Oo0A9o+WCmtToWMRoX/olNZffAivx9NICv/6mjnlo2cr3Qg98XHVTqQi7pN1p6zAAlNQohKVQ2tToXydQa2nrzM6kMX2XoyCa3B1IFcpTJ1IB/Vzo87wnxwtZcO5KLuqdTQtH37dgA6d+6MnZ2d+XFZ9OrVq8xlazsJTUKISldNrU7XysjV8fvRBFYdvMie6Gs6kFupub1lQ0a286NPCy9ZKFjUGZUamtRqNSqVihMnTtC8eXPz47KQeZqEEKISXD5pWsPu4n7T42YDTK1Orn5V+rQX0nLNHchPJ2abt7vYWTE0wpeRbX3pFCwdyEXtVqmhaerUqahUKiZNmoS7u7v5cVm8++67Za91LSehSQhRpSzQ6lRIURROJGSx+tBFfjt0kcTMAvM+Pzd7RrT1ZWQ7P5p7O1dpPYSoCtKnyQIkNAkhqkWxVqf+MPx/Vd7qVMhgVPjvXAqrD11kw5FLZBVc7UDe2seFke18ubONH41c7aqlPkLcqiofPefm5nbDE2dlZZGWllavRtlJaBJCVBuDHv6Zd6XVqQBsXWDQDGj3UJW3Ol0rX2dgy8nLrDp4kW2nLqMzmL5SVCro2sSDke38uCOsES520oFc1FxVPk/T1KlTb7hEykcffcTkyZOlT5MQQlSlpFOmEXYWanW6VnqulvVHElh98CJ7Y9LM222s1Axo5c2Itr70adEQGyt1tddNiBsp6/d3habvVhSFm2UtuesnhBDVwKsFPLoJ/v0ctnwAZ/6E+bdZpNXJzcGGB7sE8WCXIM6nmjqQrzp4kTOXs1l/JIH1RxJwc7BmaLgPI9v50SGwgXQgF7VKla15cuHCBZydpUOgEEJUOY0VdH8emt9hWsPu4j5YMxGOrYI7/88irU4B7g4827cZz/RpyrH4TFYfvMiaw/Fczirgu//i+O6/OPwbmDqQj2rnR7OG8n0har4y356bPn26+d9Tp06lT58+9OnTp1g5g8HAhQsX+PHHH+nSpQtbtmyptMrWdHJ7TghhcUaDqa/Tlg9MfZ0cG8IDP4JfB0vXDINR4Z+zpg7kG49eIvuaDuShvi6MaufH8Da+eLtIB3JRvSq9T5NaffUetEqluuntN19fX1atWkWnTp3KWOXaT0KTSWRkJJ999hnbtm0jPj4eAH9/f/r168fjjz9Ox44dLVzDW5OYmMjcuXNZv3490dHR6PV6AgICGDJkCBMnTiQkJASAhx9+mJUrV5KdnX2TM5ZdcHAwYWFhrFu3rtLOeTNVcR2iGiSdhpWPQOJRsLKHe76ElkMtXSuzfJ2BP08ksvrgRbadSkJvNH2nqFXQraknI9v5MSjUG2fpQC6qQaX3adq6dStg6qvUr18/Hn74YcaPH1+snEajwd3dnZYtWxYJWqJ+WLRoERMnTqRFixY8//zzhIaGmidG/eGHH+jUqRNnzpyhadOmlq5qhezZs4dhw4ahKAoTJ06ka9eu2NjYcOrUKZYvX07nzp1JS0u7+YmEqGpezeGRDfDzw3D2L/jxQbjjQ7jtKUvXDAA7aw3DInwZFuFLao6pA/lvBy+yLzaNnWeS2XkmmbdWqRnQ2puRbf3o1dxLOpALiytzaOrdu7f53++++y59+/atV0ukiJvbtWsXzzzzDEOHDmXlypXY2NiY9/Xr149nn32Wn3/+GXv72rn4Z2ZmJiNGjMDOzo7du3fj7+9v3tenTx+efPJJVq5cacEaCnEdOxd4YAWsfxkOLIONr0NaDAz6ANQ1ZwkUd0cbxt4WxNjbTB3Ifzt0kVUHL3I2KYd1kQmsi0yggYM1QyN8GNXOj/aBDco8wbIQlalCsf3dd9+VwCSKmTFjBhqNhkWLFhUJTNe699578fU1LTa6b98+xowZQ3BwMPb29gQHB3P//fcTGxtb5JjSZqBfunQpKpWKmJgY87YtW7bQp08fPDw8sLe3JzAwkLvvvpvc3FxzmQULFtCmTRucnJxwdnamZcuWTJ48+abX98UXX3Dp0iVmzZpVJDBd65577im27cyZMwwZMgQnJycCAgJ4+eWXKSgoKFJGq9Xy/vvv07JlS2xtbfHy8uKRRx4hKSmpxOdZtWoVERER2NnZ0aRJE/73v/+Z92VnZ+Pm5saTTz5Z7LiYmBg0Gg2zZ88GIDc3l1deeYXGjRtjZ2eHu7s7HTt25Icffqiy6xg5ciRBQUEYjcZiz9GlSxfat29f4jWLCtJYm5Zb6T/V9Pi/BbBiLGhzLFqt0gS4OzCxXwh/vtSbtRN7MKFHY7ycbUnL1bH83zjuXvAPvWdv45NNpzhzWW4Zi+p1y6Pnzp8/T3x8fLEPz0ISrm5OURTydDVnPit7a025f4szGAxs3bqVjh074uPjU6ZjYmJiaNGiBWPGjMHd3Z2EhAQWLFhAp06dOH78OJ6enuWqQ0xMDEOHDqVnz5589dVXuLm5cfHiRTZu3IhWq8XBwYEff/yRZ555hkmTJvHxxx+jVqs5c+YMx48fv+n5N23ahEajYfjw4WWuk06n484772TChAm8/PLLbN++nffeew9XV1feeecdAIxGIyNGjGDHjh289tprdOvWjdjYWN5991369OnDvn37irTOHTp0iBdeeIGpU6fSqFEjvvvuO55//nm0Wi2vvPIKTk5OPProoyxevJhZs2bh6upqPnb+/PnY2Njw6KOPAvDSSy/x7bff8v7779OuXTtycnI4evQoKSkpVXYdjz76KCNGjGDLli3079/f/BwnT55kz549RQKgqCQqFfR4EdwCYdXTcGo9LB0K968AZ29L165EKpWKcH9Xwv1dmTykFbvPJrPq4EX+OHqJuNRc/m/LGf5vyxnC/VwZ2c6P4W18aOgsHchF1arwMipr167l1VdfJSoq6oblZHLLm8vV6mn9zh9VWLPyOT59EA425cvTiYmJNGrUiDFjxhRrpTAYDEUGDmg0JYcyg8FAfn4+3t7ezJgxg+eeew4wtTRNmzat2OCDpUuX/n979x0V1fE2cPy7lF16RxEL2LEg9oYKdkVs0Rg1RkWNvZvXmggmxhr92UtibLEm9hor2GNs2Hsv2EBAQZAy7x8rG9cFXRRYlPmcc0+y987Ofe5lcR9m5s4QEBDAzZs3cXd3Z+3atbRu3ZrQ0FC8vLxSjbNfv34sW7bsg8YdlShRgsjISMLCwvQq37lzZ5YsWcKff/7Jl19+qdnfpEkTrl+/zqVLlwBYtWoV7dq1Y+3atXzxxReacsePH6dSpUrMmTOHXr16AeqB4Hfu3OHUqVNa19igQQOOHj1KWFgYFhYW3Lhxg6JFizJlyhQGDhwIQFxcHPny5aNZs2YsXLgQAE9PT4oUKcL69euz7DoSExPJly8fdevWZfny5Zpyw4YNY9q0aTx48ABHR0e97rH0AW4fgVXt4OUzsC0AX/8FuTwMHZXeXr5KYtfrAeT7r2gPIPcu4kSLsnlpWNoFK1WmzagjfYb0/f7+oO65kJAQWrZsyYsXL+jbty9CCGrVqkX37t0pWbIkQgiaNGmi+QtUytkqVKiAqampZpsyZQqg7kYaNmwYRYoUwcTEBBMTE6ysrIiJieHixYvpPk/ZsmVRKpV0796dJUuWcOPGDZ0ylStXJjIyknbt2rFx40aePn2qUyYxMVFr+5iJWhUKhU7LVJkyZbS6ILds2YKdnR1NmzbVOm/ZsmVxcXEhJCRE6/2lSpXSSQrbt29PdHQ0J0+eBKBQoUL4+/szZ84cTfwrVqwgPDycvn37at2P7du3M3z4cEJCQnj58mWmX4eJiQkdOnRg3bp1REVFAeqE+Y8//qB58+YyYcpsbtWg2x5wKARRd+D3BnBjn6Gj0pu50phmXq4s7FyJoyPr8mPzUpQvYEeygANXnzLkr9NUHLuL/itPsffSIxKSdLuBJelDfVAqPmHCBKysrDhx4gS5c+dm5syZ1K5dm9GjRyOEYMKECYwdO1Zrbicpbeamxlz4saGhw9AwN03/AFEnJyfMzc11xiOB+ss6NjaWsLAwmjVrptnfvn179uzZww8//EClSpWwsbFBoVDg5+eX5pf3uxQuXJjdu3czadIk+vTpQ0xMDIUKFaJ///4MGDAAgG+++YbExER+++03WrVqRXJyMpUqVWLs2LHUr18fAFNT7UecFy1aROfOnSlQoABXr14lJiYGS0tLvWKysLDAzEy7y0ClUhEXF6d5/ejRIyIjI9McB/Z2Yufi4qJTJmXfm91qAwYMoG7duuzatYsGDRowe/ZsqlWrpjVmaMaMGeTLl4/Vq1czceJEzMzMaNiwIZMnT9ZMnZAZ19GlSxemTJnCqlWr6NGjBzt27CAsLIyAgIBU3ytlMMfC0HW3usXp7lFY1ko9CWbZdoaOLF0crVR0rOZOx2ru3A6PYWPoAzacus+NpzFsOv2ATacf4GCpxL+Megbycvnt5ABy6aN8UNJ07NgxWrRoQe7c//WFpwzqVCgUjBgxgq1btzJ69Gg2bdqUMZF+xhQKRbq7w7IbY2Nj6tSpw86dOwkLC9Ma11SyZEkArQHbUVFRbNmyhcDAQIYPH67ZHx8fT0REhFbdKV/W8fHxqFQqzf7UWolq1qxJzZo1SUpK4vjx48ycOZOBAweSO3du2rZtC0BAQAABAQHExMSwf/9+AgMD8ff358qVK7i5uXHs2DGtOgsWLAhAw4YN2blzJ5s3b9bUlRGcnJxwdHTk77//TvX42zPrP3z4UKdMyr43W2nq1KlD6dKlmTVrFlZWVpw8eZJly5Zpvc/S0pIxY8YwZswYHj16pGl1atq0qabbLTOuo2TJklSuXJlFixbRo0cPFi1ahKurKw0aNEjXOaWPYOkIHTfBhp7qmcM39FQ/Wec7PEuXXskobo6W9K9blH51inD2fhTrT91n8+kwnr6IZ+mR2yw9chs3Rwual81Li7KuFHK2MnTI0ifog7rnYmNjyZv3v2n5VSoV0dHRWmWqVq3KoUOHPi466ZMyYsQIkpKS6NmzJwkJCe8smzJB6ptJEMCCBQt0xsG5u7sD6kkz37R58+Y06zc2NqZKlSrMnj0bQNNt9SZLS0saN27MqFGjePXqFefPnwegYsWKWltKItK1a1dcXFwYOnQo9+/fT/W869ate8dVp87f35/w8HCSkpJ0zl2xYkWKFy+uVf78+fOcPn1aa9+KFSuwtrbWefKsf//+bN26lREjRpA7d26tMUlvy507N507d6Zdu3ZcvnxZ64nDzLiOgIAAjh49ysGDB9m8eTOdOnXC2Dj7PAafI5iaQauF4D1Q/XrfBFjfExJfGTSsj6FQKCiTz47ApqX4Z0QdlnSpzBfl8mKhNOZ2eCwz9lylzpR9NJ91kEWHbvLkeeoPMUlSaj6oecPFxUXrEeK8efNqvnBSpPzjKeUc3t7ezJ49m379+lG+fHm6d+9OqVKlMDIyIiwsjLVr1wJgY2ODjY0NtWrVYvLkyTg5OeHu7s6+ffv4/fffsbOz06rXz88PBwcHunbtyo8//oiJiQmLFy/m7t27WuXmzZvH3r17adKkCQUKFCAuLk4z4DnlKa1vv/0Wc3NzvL29yZMnDw8fPmT8+PHY2tq+d/Z6W1tbNm7ciL+/P+XKldOa3PLq1assW7aM06dPaw2C1kfbtm1Zvnw5fn5+DBgwgMqVK2Nqasq9e/cIDg6mefPmtGzZUlPe1dWVZs2aERQURJ48eVi2bBm7du1i4sSJWFhYaNXdoUMHRowYwf79+/n+++91us6qVKmCv78/ZcqUwd7enosXL/LHH39QrVo1nboy+jratWvH4MGDadeuHfHx8XTu3Dld55MyiJER1B8D9u7q+ZzOrILo+/DVH2Bub+joPoqJsRE+xZzxKebM2FeJ7LrwegD51aecvhfF6XtRjN16kRpFnGhRzpUGJV2wlAPIpXcRH6B58+aiWrVqmtfdunUTpqamYuXKleLFixfi77//Fubm5qJmzZofUv0nKyoqSgAiKirK0KEYVGhoqAgICBAFCxYUKpVKmJmZiSJFioiOHTuKPXv2aMrdu3dPtGrVStjb2wtra2vRqFEjce7cOeHm5iY6deqkVee///4rqlevLiwtLUXevHlFYGCgWLBggQDEzZs3hRBCHDlyRLRs2VK4ubkJlUolHB0dhY+Pj9i0aZOmniVLlojatWuL3LlzC6VSKVxdXUWbNm3EmTNn9L6+hw8fimHDholSpUoJCwsLoVKpRJEiRUSPHj3E2bNnNeU6deokLC0tdd4fGBgo3v7VS0hIEL/88ovw8vISZmZmwsrKSnh4eIgePXqIq1evasq5ubmJJk2aiDVr1ohSpUoJpVIp3N3dxdSpU9OMt3PnzsLExETcu3dP59jw4cNFxYoVhb29vVCpVKJQoUJi0KBB4unTp5l6HSnat28vAOHt7Z1m/FIWurJLiJ9dhQi0EWJmJSEibhk6okzx5HmcWHzopmg+66BwG7ZFs3l8v130X3lS7L30SCQkJhk6TCkL6fv9/UFTDixcuJC+ffty8eJF3NzcuHnzJhUqVNA8CQPqJ2R27dr1Sc3T9Msvv7Bo0SIUCgXDhw+nQ4cO6Xq/XHtOym5evXqFu7s7NWrU4M8//zR0ONKn4OFZWN4Gnj8AS2f1XE75DL/Yb2a5+TSGjaH32XDqPrfC/+uSdrJS4l/GlRbl8uKVz1YOIP/MZfiCve9z/fp1pk6dyo0bN3Bzc6Nnz56ULVs2I6rOEmfPnqVTp04cPnwYgLp167J161adrqJ3kUmTlF08efKEy5cvs2jRIhYvXsyxY8fkTNuS/qLuw4o2/y3222oBlPA3dFSZSgjB6XtRbDh1n82nHxAe89+4LndHC1qUy0uLsnlxd9LvyVnp05LlSdOn7s8//2T//v3MmjULgD59+lCzZs10PSUlkyYpu0iZ+DNPnjwEBgamuqSKJL1TXDSsCYBruwEFNBoPVXsZOqoskZCUzMFrT9l46j47zj/SWrGhbH47WpR1xd/LFScr1TtqkT4lmTq5pSHs37+fpk2b4urqikKhYMOGDTpl5syZo1lDq0KFChw4cEDv+kuXLk1wcDCRkZFERkayd+/eNJ+QkqTsrnPnzgghePDggUyYpA9jZqPumqvQGRDw93DYPgySP/8HfEyNjahdPBfT2pbj+Pf1mPZVWXyKOWNspCD0biRBmy9QZdweOi/6l42h94l9lWjokKUsotdjAvv37//gE2TUmKaYmBi8vLwICAigVatWOsdXr17NwIEDmTNnDt7e3syfP5/GjRtz4cIFChQoAKhnpk5tjbydO3dSsmRJ+vfvT506dTRPUpmYyKcoJEnKwYxNwH8a2BeE3YFwdB5E3lF31ylzRjeVpcpE3TVXLi9Pnsez5Yx6As3T96IIufyEkMtPsFAaU7mgA24OFhRwtMTNwQI3RwvyO1hg9gGTBUvZl17dc0ZGRh88CC4zph1QKBSsX7+eFi1aaPalrI4+d+5czb4SJUrQokULxo8fn+5zdOvWjZYtW9KkSZM0y8THx2slYdHR0eTPn192z0mS9Pk5t049h1NSPLiWy9aL/WaFG09esCH0ARtD73M7PO05zXLbqHBzsKSAowUFXidT6v9aYm9hKgeYZxP6ds/p1ZQyevTobP2DffXqFSdOnNCaWRrUi5imDOzWx+PHj8mVKxeXL1/m33//Zd68ee8sP378eMaMGfNBMUuSJH1SSn8BNq6wsh08OAUL6sHXf0KuEoaOzCAKOVsxuH4xBtUrypl7UVwIi+ZORCx3wmO5HRHD7fBYnscl8ig6nkfR8fx7K0KnDmuVCflTEilHC9wcLDVJVR5bM0yMP5kRNDmGXklTUFBQJofxcZ4+fUpSUpLWsi6gnuE4tSUn0tKiRQsiIyOxtLRk0aJF7+2eGzFiBIMHD9a8TmlpkiRJ+iwVqArddsPy1hBxA35vqJ4Es5CPoSMzGIVCgVd+O7zy22ntF0IQGZvAnYhYbkfEcidcnUjdjojlbkQsYVFxPI9P5EJYNBfConXqNTFSkM/enAKOlhRwMNe0VqUkVZ/60lufqs/qrr/dGiaESFcLWXpapUC9fMzby4BIkiR91jSL/baHu//Asi9eL/bb3tCRZSsKhQJ7SyX2lkqdhAogLiGJe89i1YlUeKy6lSoiltvhMdx99pJXicncCo/VmjvqTU5WKtwcLV6Po/ovmSrgYImTlTJb9w59yj4qaXr48CHr1q3j0qVLxMTE8PvvvwPqOWJu3ryJp6cn5ubmGRLouzg5OWFsbKzTqvT48WOd1idJkiTpI1k6QseNsKEXnF+n/u+zW+A74pNc7NcQzEyNKZLLmiK5rHWOJScLHj2PUydTb3T33X3dahUZm8DTF/E8fRHPidvPdN5vqTQmv8MbY6jeGJzuameOqez2+2AfnDTNmTOHIUOGaAZCKxQKTdL0+PFjqlWrxrx58/j2228zJtJ3UCqVVKhQgV27dmmtbbVr1y6aN2+e6eeXJEnKcUzNoNXvYO8GB/8H+ybCs9vqVicT5fvfL6XJyEhBHltz8tiaU7WQo87xqJcJ3HndOnU7IkadWL1+/SDqJTGvkrj08DmXHj7Xea+xkQJXOzPtwema1ipLrOTae+/0QXdn8+bN9O3bl4oVKzJ69Gi2b9+uNWi6VKlSlClThg0bNmRY0vTixQuuXbumeX3z5k1CQ0NxcHCgQIECDB48mG+++YaKFStSrVo1fv31V+7cuUPPnj0z5PyS/s6cOcP06dMJCQnhwYMHAOTLl486derw7bffUrFiRQNH+GFu3bpFwYIFmTx5Mt99952hw8kUvr6+AISEhBg0DukTYWQE9YLUi/1uGfxZLfabndmam+KZzxbPfLY6x+ITk7j/7OXrcVRvdv3FcCcilriEZO5GvORuxEu4plu3o6VSMzg9ZQqFlBarXNaqHN/t90FJ0+TJkylQoADBwcFYWlpy4sQJnTKenp7pmlzyfY4fP07t2rU1r1MGYHfq1InFixfz1VdfER4ezo8//khYWBilS5dm27ZtuLm5ZVgM0vvNnz+fvn37Urx4cQYMGECpUqVQKBRcvHiRlStXUqlSJa5du0bhwoUNHaokSRmlQmewzQd/doZbB+D3BvD1X+pkSspSKhNjCjlbUcjZSueYEILHz+Nfj516PTj99f/fjYglPOaVZgu9G6nzfjNTI824qQJaT/1ZkM/eAqXJ59/t90FJU2hoKN988w2WlmlPbpY3b14ePXr0wYG9zdfXl/dNKdW7d2969+6dYeeU0ufQoUP07t2bJk2asGbNGpTK/5ro69SpQ58+ffjrr7+yZJyblHkSEhJQKBRy8ldJW5F60GW7erHfp1fUUxJ85ov9fmoUCgW5bczIbWNGJXcHnePP4xLemDYhVmsKhQeRccQlJHPl0QuuPHqRSt3gamuulUypu/7U3YC25qZZcYmZ7oPSwuTkZExN330Dnjx5Ip8sy2HGjRuHsbEx8+fP10qY3vTll1/i6uoKqFsP27Zti7u7O+bm5ri7u9OuXTtu376t9Z6goKBUm4QXL16MQqHg1q1bmn179+7F19cXR0dHzM3NKVCgAK1atSI29r8nUObOnYuXlxdWVlZYW1vj4eHByJEjM+AOqEVHR/Pdd99RsGBBlEolefPmZeDAgcTExGiVi4yMpGvXrjg4OGBlZUWTJk24ceMGCoVCZ5qPq1ev0r59e3LlyoVKpaJEiRLMnj1bq0xISAgKhYKVK1cyatQoXF1dsbGxoV69ely+fFmrrBCCSZMm4ebmhpmZGeXLl2f79u0615JS5x9//MGQIUPImzcvKpVK01W+e/du6tati42NDRYWFnh7e7Nnz54MuIvSJ8nFE77dA7k9IeYJLG4CFzcbOipJT9ZmppRytaWxZx56+hRmXEtPlnWrwoGhdbj0UyP2/Z8vS7tU5qcWpfm2ZkEalMyNh4s1FkpjhID7kS85ciOcVcfuMunvy/RdcYqmsw7iNWYnZX/cSfNZB+m74iSTd1ziz2N3+edGOGFRL0lO/nSWwP2gPxWLFy/OwYMH0zyemJjIvn378PT0/ODAchQhICHtGWWznKlFup+ASUpKIjg4mIoVK5InTx693nPr1i2KFy9O27ZtcXBwICwsjLlz51KpUiUuXLiAk5NTumK4desWTZo0oWbNmixcuBA7Ozvu37/P33//zatXr7CwsGDVqlX07t2bfv368csvv2BkZMS1a9e4cOFCus6VltjYWHx8fLh37x4jR46kTJkynD9/ntGjR3P27Fl2796NQqEgOTmZpk2bcvz4cYKCgihfvjxHjhyhUaNGOnVeuHCB6tWrU6BAAaZMmYKLiws7duygf//+PH36lMDAQK3yI0eOxNvbmwULFhAdHc2wYcNo2rQpFy9exNhYvaTDmDFjGDNmDF27dqV169bcvXuXb7/9lqSkJIoXL64Tw4gRIzQPdxgZGZErVy6WLVtGx44dad68OUuWLMHU1JT58+fTsGFDduzYQd26dTPknkqfGBtXdYvTX53Vi/2u/gYajlMv9pvDx8N8ykyNjXBztMTNUbeHSQjB0xevuPP6Kb83W6tuh8fy9EU8kbEJRMZGcfpelM77lSZG5Lc3x+31+KmU1io3R3W3X3ZaiuaDkqavv/6a7777jrFjx/L9999rHUtKSuK7777jxo0bDBs2LEOC/OwlxMI4V0NH8Z+RD9K9rtTTp095+fJlqmPIkpKStLpWjY2NUSgUtG7dmtatW2uV8/f3J3fu3KxYsYL+/funK4YTJ04QFxfH5MmT8fLy0uxv3/6/+WMOHTqEnZ0dM2bM0OzLyC/3GTNmcObMGY4ePaoZ8F63bl3y5s1L69at+fvvv2ncuDF///03Bw8eZO7cuZqHFerXr49SqWTEiBFadQ4ePBhra2sOHjyomd6/fv36xMfHM2HCBPr374+9/X+DbkuWLMmyZcs0r42NjWnTpg3Hjh2jatWqREZGMnHiRFq2bMmCBQs05UqVKoW3t3eqSVPhwoX566+/NK9jY2MZMGAA/v7+rF+/XrPfz8+P8uXLM3LkSI4ePfoxt1L6lKms1V1z276DE4tgxwj1lASNxoNR9vkClDKGQqHA2VqFs7WKCm663X6xrxLfGEf1+om/iJfcCY/h3us5qa4/ieH6k5hUagcXGzPN2Ck3RwtaV8iPi61ZZl9Wqj6oe65fv374+PgQGBhI8eLFWbt2LQBt2rShaNGizJgxg/r169O1a9cMDVb6NFWoUAFTU1PNNmXKFED9ROSwYcMoUqQIJiYmmJiYYGVlRUxMDBcvXkz3ecqWLYtSqaR79+4sWbKEGzdu6JSpXLkykZGRtGvXjo0bN/L06VOdMomJiVqbHsszamzZsoXSpUtTtmxZrToaNmyIQqHQPJW2b98+QP0786Z27dppvY6Li2PPnj20bNkSCwsLrTr9/PyIi4vjn3/+0XpPs2bNtF6XKVMGQNPteeTIEeLi4vj666+1ylWvXj3NByfeXiT78OHDRERE0KlTJ62YkpOTadSoEceOHdPpjpRyGGMT8P8f1P9R/frf+bDqa3glPxc5jYXSBA8XGxqWcuHbWoUY28KTpV0qE/J/tbn0UyMODK3Nsq5VGNfSkx4+hWhc2oWSeWw00x88jI7j35sR/HXiHr/svEJ0XILBruWDWppMTU3ZsWMHY8aMYd68eTx7pp5ca82aNdjY2DBs2DDGjBmT4x9N1Juphbp1J7swtUj3W5ycnDA3N9cZjwSwYsUKYmNjCQsL0/pCb9++PXv27OGHH36gUqVK2NjYoFAo8PPz4+XLl+mOoXDhwuzevZtJkybRp08fYmJiKFSoEP3792fAgAEAfPPNNyQmJvLbb7/RqlUrkpOTqVSpEmPHjqV+/frqy39rvN6iRYvo3LmzXjE8evSIa9eupTnmLyVJCw8Px8TEBAcH7b/K3p6MNTw8nMTERGbOnMnMmTPfWWcKR0fteV1Sxham3NPw8HAAXFxcdOpKbR+g0+Wa8pDHmy2Fb4uIiHjnwyJSDqBQgPcAsCsA63rAle2wyA/a/5mjF/uV/mNibER+BwvyO+h+7wgheBabwO3wmP9aqiJiKZBK2azywY+/KJVKfv75Z8aOHcvly5eJiIjAxsaGEiVKaMZNSHpSKNLdHZbdGBsbU6dOHXbu3ElYWJjWl2zJkiUBtAZsR0VFsWXLFgIDA7UWWo6PjyciQnthSzMzM82xNx8uSK2VqGbNmtSsWZOkpCSOHz/OzJkzGThwILlz56Zt27YABAQEEBAQQExMDPv37ycwMBB/f3+uXLmCm5sbx44d06qzYMGCet+HlORx4cKFaR4HdWKTmJhIRESEVuL09qz29vb2GBsb880339CnT59U60xPfCnnTu1cKfvc3d119r/9B1DKdcycOZOqVaumeh45G7+kUaolWLvCyrYQFgoL6qqnJMihi/1K+lEoFDhYKnGwVFKuQPaY9+ujnxlWKBR4eHjo7L958yZjxoxh8eLFH3sK6RMxYsQItm/fTs+ePVmzZs07n7BUKBQIIXSesFywYAFJSUla+1K+xM+cOUOlSpU0+zdvTvupHGNjY6pUqYKHhwfLly/n5MmTmqQphaWlJY0bN+bVq1e0aNGC8+fP4+bm9lGTb/r7+zNu3DgcHR3fmcz4+PgwadIkVq9eTa9evTT7V61apVXOwsKC2rVrc+rUKcqUKZPmU4npUbVqVczMzFi+fLlWt9vhw4e5fft2qknT27y9vbGzs+PChQv07dv3o2OScoACVV4v9vslRFx/vdjvUijka+jIJElvGT7Ryp07d/jpp59YunQpiYmJMmnKQby9vZk9ezb9+vWjfPnydO/enVKlSmFkZERYWJhm7JuNjQ02NjbUqlWLyZMn4+TkhLu7O/v27eP333/Hzs5Oq14/Pz8cHBzo2rUrP/74IyYmJixevJi7d+9qlZs3bx579+6lSZMmFChQgLi4OE2LT7169QD49ttvMTc3x9vbmzx58vDw4UPGjx+Pra2tVkL2LmfPnmXNmjU6+ytVqsTAgQNZu3YttWrVYtCgQZQpU4bk5GTu3LnDzp07GTJkCFWqVKFRo0Z4e3szZMgQoqOjqVChAkeOHGHp0qUAGBn9N9xw+vTp1KhRg5o1a9KrVy/c3d15/vw5165dY/Pmzezdu1e/H9Br9vb2mgc5unXrxpdffsndu3cJCgpKs3vubVZWVsycOZNOnToRERFB69atyZUrF0+ePOH06dM8efKEuXPnpisuKQdwLKxOnFa1hztHYFkraDoDyn39/vdKUnYg0uHAgQPC19dXWFtbC3t7e9GsWTNx6dIlIYQQMTExYtCgQUKlUgmFQiHy5s0rZs2alZ7qP3lRUVECEFFRUYYOxaBCQ0NFQECAKFiwoFCpVMLMzEwUKVJEdOzYUezZs0dT7t69e6JVq1bC3t5eWFtbi0aNGolz584JNzc30alTJ606//33X1G9enVhaWkp8ubNKwIDA8WCBQsEIG7evCmEEOLIkSOiZcuWws3NTahUKuHo6Ch8fHzEpk2bNPUsWbJE1K5dW+TOnVsolUrh6uoq2rRpI86cOfPe67p586YA0twWLVokhBDixYsX4vvvvxfFixcXSqVS2NraCk9PTzFo0CDx8OFDTX0REREiICBA2NnZCQsLC1G/fn3xzz//CEBMnz5d59xdunQRefPmFaampsLZ2VlUr15djB07VlMmODhYAOKvv/5KNe6U+IQQIjk5WYwfP17kz59fKJVKUaZMGbF582bh4+MjfHx83ltnin379okmTZoIBwcHYWpqKvLmzSuaNGmSZnlJEkII8eqlEH8FCBFoo972jBUiOdnQUUk5mL7f3woh9Hs06MSJE3h7e/Pq1Sut/S4uLuzfv58WLVpw4cIFXF1dGTZsGN27d89xk1tGR0dja2tLVFSU5tFwSUqPFStW8PXXX3Po0CGqV69u6HAkKfMkJ8Pen+DgVPXrMl+9Xuw3Z31vSNmDvt/fenfPTZo0iVevXjF+/HjNVALz5s1j9OjR1KxZkydPnvD9998zcuRIzcBdSZLStnLlSu7fv4+npydGRkb8888/TJ48mVq1asmESfr8GRlBvcDXi/0OgjOrIeo+tF0mF/uVsi29W5ry5cuHh4cHu3fv1tpfu3Zt9u/fz+TJkzWL6OZUsqVJSo8tW7YQFBTEtWvXiImJIU+ePLRo0YKxY8fKz4+Us1zbrV7s99VzcComF/uVspy+3996T275+PFjKlTQXXgxZfBsp06dPiBMScq5/P39OX78OJGRkSQkJHDnzh1mzJghEyYp5ylSD7r8DTZ5/1vs995xQ0clSTr0TpoSExNTnaguZd/bE+pJkiRJkt5cSqufrHORi/1K2dcHLaMiSZIkSRnOxhUCtkPRBpAYp17s98hs9aLmkpQN6D2mycjIiCJFilCkSBGt/deuXeP69es0bNhQt3KFgq1bt2ZMpJ8AOaZJkiQpAyQlwvb/g+OvZ9av3B0aTZCL/UqZRt/v73QlTemlUCh0Znf+nMmkSZIkKYMIAYdnwq4f1K+LNYJWv4PKyrBxSZ+lDJ9y4ObNmxkSmCRJkiS9l0IB3v3Vi/2u7wFX/obFKYv96jdzvSRlNL2TJjc3t8yMQ5IkSZJ0lWqhHuu0si2EnVY/WScX+5UMRA4ElyRJkrK3/JXVT9Y5FoGou/B7A7gebOiopBxIJk3SR1MoFHptISEhGXbOkJAQFApFqgvnpseOHTto0KABrq6uqFQqXF1d8fX1ZcKECVrl3N3d6dy58wedw9fXl9KlS39UnJKU4zkUgq67oEA1iI+G5a3h1DJDRyXlMHp3z0lSWo4cOaL1+qeffiI4OJi9e/dq7S9ZsmRWhvVe8+bNo1evXrRq1YpZs2bh4ODA3bt3OXz4MGvWrGH48OGasuvXr5eD+yXJ0Cwc4JsNsLEPnFuj/u+zW1B7lHoMlCRlMpk0SR+tatWqWq+dnZ0xMjLS2Z/djB8/nlq1aum0Vn3zzTckJydr7StXrlxWhiZJUlpMzeCL39TLrBz4BfZPhme3ofksudivlOlk95yUJWbPnk2tWrXIlSsXlpaWeHp6MmnSJBISErTKpXRlHTt2jJo1a2JhYUGhQoWYMGGCTiIDkJCQwKhRo3B1dcXGxoZ69epx+fJlvWIKDw8nT548qR57e4qNt7vnUroHV65cqff59b0mSZLew8gI6v4AzWaCwhjO/gl/tITYCENHJn3mZNKUDQghiE2IzTabnlN3pcv169dp3749f/zxB1u2bKFr165MnjyZHj166JR9+PAhX3/9NR06dGDTpk00btyYESNGsGyZ7viFkSNHcvv2bRYsWMCvv/7K1atXadq0qV7zg1WrVo21a9cSFBTE6dOnP2hOMX3Pn55rkiRJT+U7qp+kU1rD7UPqAeIRcnocKfPI7rls4GXiS6qsqGLoMDSOtj+KhalFhtY5depUzf8nJydTs2ZNHB0dCQgIYMqUKdjb22uOh4eHs23bNipXrgxAvXr1CAkJYcWKFXTs2FGr3pIlS2olHsbGxrRp04Zjx469t3tw3rx5tGjRgjFjxjBmzBjMzc2pXr06LVu2pHv37piamr73uvQ9f3quSZKkdChSF7rugOVfQvhV9ZQE7VdDvoqGjkz6DMmWJilLnDp1imbNmuHo6IixsTGmpqZ07NiRpKQkrly5olXWxcVFk1ykKFOmDLdv39apt1mzZjrlAE1ZIQSJiYlaW4rChQtz+vRp9u3bx5gxY6hXrx7Hjh2jb9++VKtWjbi4uPde1/vO/yHXJElSOuUuBd32gEsZiH2qXuz3wiZDRyV9hmRLUzZgbmLO0fZHDR2GhrmJeYbWd+fOHWrWrEnx4sWZPn067u7umJmZ8e+//9KnTx9evnypVd7R0VGnDpVKpVMutbIqlXogaErZJUuWEBAQoFXmze5HIyMjatWqRa1atQCIiYmha9eurF69moULF9K7d+93Xtv7zv8h1yRJ0gewyaNe7HdNAFzdCX92hAZjoVof+WSdlGFk0pQNKBSKDO8Oy042bNhATEwM69at05pZPjQ0NNPP3bRpU44dO6Z3eUtLS0aMGMHq1as5d+5cJkYmSVKGU1lB25WwfSgc/x12jlJPSdBoAhjLrzvp48lPkZTpFK//yktphQF1a89vv/2W6ed2dHRMtZUHICwsLNWn5y5evAiAq6trpsYmSVImMDaBJlPAoSDs/B6O/aaeRVwu9itlAJk0SZmufv36KJVK2rVrx9ChQ4mLi2Pu3Lk8e/bMoHGVKlWKunXr0rhxYwoXLkxcXBxHjx5lypQp5M6dm65duxo0PkmSPpBCAdX7gW3+/xb7XdRYvdivTerTjEiSPuRAcCnTeXh4sHbtWp49e8YXX3xBv379KFu2LDNmzDBoXCnzJP3888/4+fnRrFkzli5dSvv27Tl+/HiaczhJkvSJKNUCOm0BC0d4eEb9ZN2j84aOSvqEKURmTMqTQ0VHR2Nra0tUVJRcckOSJCm7iLjxekqCa6CygTZLoXBtQ0clZSP6fn/LliZJkiTp86ZZ7Lf6f4v9nvzD0FFJnyCZNEmSJEmfPwsH6LgBPL+E5ETY1Bf2jgXZ2SKlg0yaJEmSpJzBRKVe7LfW/6lf758M676FxHjDxiV9MmTSJEmSJOUcCgXU+R6azQIjEzj7l1zsV9KbTJokSZKknKf8N+rFflU2crFfSW8yaZIkSZJypsJ1oMvfYJPvv8V+7+q/goCU88ikSZIkScq5cpeCbrshj5d6sd8l/nBho6GjkrIpmTRJkiRJOZtNHui8DYo2hMQ4+LMTHJ4pn6yTdMikSZIkSZJUVtB2BVTqBgj1unXbvoOkRENHJmUjOTZpatmyJfb29rRu3TpdxyRJkqTPlLEJ+P0CDX4GFHBsAaxqD/EvDB2ZlE3k2KSpf//+LF26NN3HJEmSpM+YQgHV+6qXWjExg6s71Iv9RocZOjIpG8ixSVPt2rWxtrZO9zEpbUePHqVly5YUKFAAlUpF7ty5qVatGkOGDNGU8fX1xdfX13BBSpIk6aNks9eL/TrJxX4ljWyZNO3fv5+mTZvi6uqKQqFgw4YNOmXmzJlDwYIFMTMzo0KFChw4cCDrA5U0tm7dSvXq1YmOjmbSpEns3LmT6dOn4+3tzerVqzXl5syZw5w5cwwYqSRJkp7yV1I/WedYFKLvwe8N4fpeQ0clGZCJoQNITUxMDF5eXgQEBNCqVSud46tXr2bgwIHMmTMHb29v5s+fT+PGjblw4QIFChQAoEKFCsTH606Nv3PnTlxdXTP9GnKaSZMmUbBgQXbs2IGJyX8fq7Zt2zJp0iTN65IlSxoiPEmSpA/jUBC67oTVHdSTYC7/EvynqSfHlHKcbNnS1LhxY8aOHcsXX3yR6vGpU6fStWtXunXrRokSJZg2bRr58+dn7ty5mjInTpzg3LlzOltGJkzx8fFER0drbTlVeHg4Tk5OWglTCiOj/z5mb3fP3bp1C4VCweTJk5k4cSLu7u6Ym5vj6+vLlStXSEhIYPjw4bi6umJra0vLli15/PixVv3u7u74+/uzfv16ypQpg5mZGYUKFWLGjBla5ZKTkxk7dizFixfH3NwcOzs7ypQpw/Tp07XKHTx4kLp162JtbY2FhQXVq1dn69atWmUWL16MQqEgODiYXr164eTkhKOjI1988QUPHjz40NsoSVJ2ZOEA36wHzzb/Lfa75yc5JUEOlC2Tpnd59eoVJ06coEGDBlr7GzRowOHDh7M0lvHjx2Nra6vZ8ufP/0H1CCFIjo3NNpv4gH8IqlWrxtGjR+nfvz9Hjx4lISEhXe+fPXs2hw4dYvbs2SxYsIBLly7RtGlTunbtypMnT1i4cCGTJk1i9+7ddOvWTef9oaGhDBw4kEGDBrF+/XqqV6/OgAED+OWXXzRlJk2aRFBQEO3atWPr1q2sXr2arl27EhkZqSmzb98+6tSpQ1RUFL///jsrV67E2tqapk2banUzpujWrRumpqasWLGCSZMmERISQocOHdJ17ZIkfQJMVPDFr1BrqPr1gV/kYr85ULbsnnuXp0+fkpSURO7cubX2586dm4cPH+pdT8OGDTl58iQxMTHky5eP9evXU6lSpfcee9OIESMYPHiw5nV0dPQHJU7i5Usul6+Q7vdlluInT6CwsEjXeyZMmMClS5eYOXMmM2fOxNTUlEqVKtG0aVP69u2LlZXVO99vZ2fHhg0bNK1ST58+ZeDAgXh4eLBx43+z8166dIlp06YRHR2NjY2NZv+DBw84deoUXl5egLq18vHjx/z000/07t0bCwsLDh06hKenJ0FBQZr3NWzYUCuO4cOHY29vT0hIiCZmf39/ypYty3fffUebNm1QKBSa8o0aNdJq0YqIiGDo0KE8fPgQFxeXdN1DSZKyOYUC6owCezfYPEC92G/UfWi7XN0aJX32PrmWphRvfnGBurXm7X3vsmPHDp48eUJsbCz37t3TSoredexNKpUKGxsbrS2ncnR05MCBAxw7dowJEybQvHlzrly5wogRI/D09OTp06fvfL+fn59WN16JEiUAaNKkiVa5lP137tzR2l+qVClNwpSiffv2REdHc/LkSQAqV67M6dOn6d27Nzt27NDpTo2JieHo0aO0bt1aK8kzNjbmm2++4d69e1y+fFnrPc2aNdN6XaZMGQBu3779zuuVJOkTVq4DfL1GvdjvncPwe314es3QUUlZ4JNraXJycsLY2FinVenx48c6rU+fCoW5OcVPnjB0GBoKc/MPfm/FihWpWLEiAAkJCQwbNoz//e9/TJo0SWtA+NscHLT/SlMqle/cHxcXp7U/tVadlH3h4eGAumXQ0tKSZcuWMW/ePIyNjalVqxYTJ06kYsWKPHv2DCEEefLk0akrZSxcSl0pHB0dtV6rVCoAXr58mea1SpL0GShcG7rsUA8MD78GsypC/spQ3A88/MGpiKEjlDLBJ9fSpFQqqVChArt27dLav2vXLqpXr26gqD6OQqHAyMIi22zpabF7F1NTUwIDAwE4d+5chtSZltS6ZlP2pSQ2JiYmDB48mJMnTxIREcHKlSu5e/cuDRs2JDY2Fnt7e4yMjAgL053ELmVwt5OTUyZehSRJn5TcJeHbPVDQBxBw9yjsDoRZFWBWZdg9Bu4dh+RkQ0cqZZBsmTS9ePGC0NBQQkNDAbh58yahoaGaLpnBgwezYMECFi5cyMWLFxk0aBB37tyhZ8+eBow6Z0st0QC4ePEiQKZP83D+/HlOnz6ttW/FihVYW1tTvnx5nfJ2dna0bt2aPn36EBERwa1bt7C0tKRKlSqsW7dOq6UoOTmZZcuWkS9fPooVK5ap1yFJ0ifG2gU6bYJBF9RLsBSuA0Ym8PQyHJwKC+rC1BKwZRBc2w2JrwwdsfQRsmX33PHjx6ldu7bmdcpg606dOrF48WK++uorwsPD+fHHHwkLC6N06dJs27YNNzc3Q4Wc4zVs2JB8+fLRtGlTPDw8SE5OJjQ0lClTpmBlZcWAAQMy9fyurq40a9aMoKAg8uTJw7Jly9i1axcTJ07E4vWg9qZNm1K6dGkqVqyIs7Mzt2/fZtq0abi5uVG0aFFA/URk/fr1qV27Nt999x1KpZI5c+Zw7tw5Vq5cmWGtcJIkfWZs80Llb9Xby0h1gnRpK1zdBS8ewvGF6k1pDUXrg0cT9X/NbA0duZQO2TJp8vX1fe9j771796Z3795ZFJH0Pt9//z0bN27kf//7H2FhYcTHx5MnTx7q1avHiBEjNAO4M0vZsmUJCAggMDCQq1ev4urqytSpUxk0aJCmTO3atVm7di0LFiwgOjoaFxcX6tevzw8//ICpqSkAPj4+7N27l8DAQDp37kxycjJeXl5s2rQJf3//TL0GSZI+E+Z24NlavSXGw80DcGkLXN6uTqDOr1NvRqZQsKY6gSruBzZy4uXsTiE+ZFIeKVXR0dHY2toSFRWVo5+ky2ru7u6ULl2aLVu2GDoUSZKktCUnw4OT6gTq0lZ4ekX7uGt5dQLl4Q/OxdVTHEhZQt/v72zZ0iRJkiRJnx0jI8hXUb3VC4KnV9XJ06WtcO+YOqF6cBL2/gQOhcHj9ZN4+SqBkbGho5eQSZMkSZIkGYZTUagxUL09fwRXtqsTqBshEHEdDs9Ub5bOULwxFG8ChXzB1MywcedgsnsuA8nuOUmSJOmjxT9/PZB8G1zZAfFR/x0ztYQiddUtUMUagLm94eL8jOj7/S2TpgwkkyZJkiQpQyUlwK2D6haoy9sg+v5/xxTG4O6tTqCK+4Hdh61/KsmkySBk0iRJkiRlGiEgLPT1OKht8Pi89nGXMuoEyqMJ5C4lB5Kng0yaDEAmTZIkSVKWibihTp4ubYW7/4B4Y+ZxO7fXT+I1gfxVwVgOYX4XmTQZgEyaJEmSJIOIeQpX/lYnUNf3QuIb63OaO7weSO6nnrFcaWG4OLMpmTQZgEyaJEmSJIN7FQPXg9UJ1JXt8PLZf8dMzNWJk4cfFGsElnI9TZBJk0HIpEmSJEnKVpIS1V13l7aqJ9WMvPPfMYURFKj234zkDgUNF6eByaTJAGTSJEmSJGVbQsCjc6/HQW2Bh2e0j+cq9d84qDxeOWogub7f30ZZGJP0mVIoFHptISEhhg41U3Xu3Bl3d3e9yioUCoKCgtJ9jlu3bqFQKFi8ePF7y4aEhKBQKFizZk26z5OZdaW4cOECQUFB3Lp1S+fYihUrmDZtWoadS5Ik1EmQiyf4DoOeB2DgWWg0EQrWUk9f8Pg87J8Ev/rA/0rDtv9TT7SZlGDoyLMNOZxe+mhHjhzRev3TTz8RHBzM3r17tfaXLFkyK8PKcj/88AMDBgwwdBifjAsXLjBmzBh8fX11ks0VK1Zw7tw5Bg4caJDYJClHsCsAVXuqt9gIuLpT3QJ1bQ9E34N/f1VvZrZQtKG6BapIPVBZGTpyg5FJk/TRqlatqvXa2dkZIyMjnf1vi42NxcLi83mKo3DhwoYOQZIk6cNYOIBXW/WW8BJu7FMnUJe3Q+xTOPunejNWqZdy8fBTj4OyymXoyLOU7J6TsoSvry+lS5dm//79VK9eHQsLC7p06QLA6tWradCgAXny5MHc3JwSJUowfPhwYmJitOro3LkzVlZWnD9/nrp162JpaYmzszN9+/YlNjZWq6wQgjlz5lC2bFnMzc2xt7endevW3LhxQ1Mmpcspte3Nlo/k5GQmTZqEh4cHKpWKXLly0bFjR+7du6cT39stJtHR0Xz77bc4OjpiZWVFo0aNuHLlrZXNgWvXrhEQEEDRokWxsLAgb968NG3alLNnz37I7U6Xc+fO0bx5c+zt7TEzM6Ns2bIsWbIk1bJxcXEMHjwYFxcXzM3N8fHx4dSpU1pljh8/Ttu2bXF3d8fc3Bx3d3fatWvH7du3NWUWL17Ml19+CUDt2rU1933x4sX4+vqydetWbt++rfUzSREREUHv3r3JmzcvSqWSQoUKMWrUKOLj4zPh7khSDmRqDsUbQfNZ8N0V6LIDqvcD+4KQFA9Xd8DmAfBLMfi9ARyaDk+vGTrqLCFbmrIBIQSJr5LfXzCLmCiNtL6kMkpYWBgdOnRg6NChjBs3DiMjdc5+9epV/Pz8GDhwIJaWlly6dImJEyfy77//6nTxJSQk4OfnR48ePRg+fDiHDx9m7Nix3L59m82bN2vK9ejRg8WLF9O/f38mTpxIREQEP/74I9WrV+f06dPkzp2b8uXL63QtXr16la5du1KqVCnNvl69evHrr7/St29f/P39uXXrFj/88AMhISGcPHkSJ6fUH9kVQtCiRQsOHz7M6NGjqVSpEocOHaJx48Y6ZR88eICjoyMTJkzA2dmZiIgIlixZQpUqVTh16hTFixf/4Pv+LpcvX6Z69erkypWLGTNm4OjoyLJly+jcuTOPHj1i6NChWuVHjhxJ+fLlWbBgAVFRUQQFBeHr68upU6coVKgQoB53Vbx4cdq2bYuDgwNhYWHMnTuXSpUqceHCBZycnGjSpAnjxo1j5MiRzJ49m/LlywPq1rrKlSvTvXt3rl+/zvr167XOHxcXR+3atbl+/TpjxoyhTJkyHDhwgPHjxxMaGsrWrVsz5T5JUo5lZAwFqqq3+j/Bk0uvn8TbCg9Owt2j6m3XaHAq/t9ActfyYPT5tcvIpCkbSHyVzK8D9hk6DI3u030wVRlneL0RERH89ddf1KlTR2v/999/r/l/IQTe3t6UKFECHx8fzpw5Q5kyZTTHX716xZAhQ+jfvz8A9evXx9TUlFGjRnHo0CG8vb35559/+O2335gyZQqDBw/WvLdmzZoUK1aMqVOnMnHiRGxsbLS6EB8/fszXX39NsWLFWL58OQCXLl3i119/pXfv3sycOVNTtly5clSpUoX//e9//Pzzz6le744dOwgODmb69Ola8SqVSkaNGqVVtlatWtSqVUvzOikpiSZNmlCqVCnmz5/P1KlT9bvJ6RQUFMSrV68IDg4mf371ulV+fn5ERkYyZswYevToga2traa8s7Mz69ev1yTVNWrUoGjRoowfP57ffvsNgNatW9O6dWuta/H39yd37tysWLGC/v374+zsTNGiRQH1WLc3fw7Ozs7Y2dmhUql0uniXLFnCmTNn+PPPPzUtVfXr18fKyophw4axa9cu6tevnwl3SpIkFArIVUK91foOou6r18O7vA1u7oenl+HgZTg4Faxc1F14Hk3AvRaYKA0dfYb4/NJAKduyt7fXSZgAbty4Qfv27XFxccHY2BhTU1N8fHwAuHjxok75r7/+Wut1+/btAQgODgZgy5YtKBQKOnToQGJiomZzcXHBy8sr1af4YmJiaNKkCXFxcWzfvh07OzutOjt37qxVvnLlypQoUYI9e/akeb0p700r3jclJiYybtw4SpYsiVKpxMTEBKVSydWrV1O9B2+/980tPbOI7N27l7p162oSphSdO3cmNjZWpyWuffv2Wq2Qbm5uVK9eXXOtAC9evGDYsGEUKVIEExMTTExMsLKyIiYm5r3Xok+8lpaWWklZSrzAO38ekiRlMNu8UPlb+GY9/N91aPU7lPoClNbw4iEcXwjLWsGkQvBXAJxdA3FRho76o8iWpmzARGlE9+k+hg5Dw0SZObl0njx5dPa9ePGCmjVrYmZmxtixYylWrBgWFhbcvXuXL774gpcvX2rHZmKCo6Oj1j4XFxcAwsPDAXj06BFCCHLnzp1qHCndSCkSExNp3bo1V65cYf/+/VoJREqdqcXu6uqqNU7nbeHh4e+M902DBw9m9uzZDBs2DB8fH+zt7TEyMqJbt2469+BNt27domBB7QnpgoOD8fX1TfM9b8eY1rWlHH9f7C4uLpw+fVrzun379uzZs4cffviBSpUqYWNjg0KhwM/P753Xom+8Li4uOt3HuXLlwsTERCdeSZKyiLkdeLZWb4nxcPPAfwPJXzyE8+vUm5GpeoqDlIHkNq6GjjxdZNKUDSgUikzpDstuUhsntXfvXh48eEBISIimdQkgMjIy1ToSExMJDw/XSkQePnwIoNnn5OSEQqHgwIEDqFQqnTre3te9e3f27NnDtm3b8PLy0jqWUmdYWBj58uXTOvbgwYM0xzOlvPdd8b5p2bJldOzYkXHjxmntf/r0qabVKzWurq4cO3ZMa196xj85OjoSFhams//BgwcAOteXWuwPHz7UXF9UVBRbtmwhMDCQ4cOHa8rEx8cTERGhd1zvivfo0aMIIbQ+T48fPyYxMfGdPw9JkrKIiQqK1lNvTaaqxz5d2qIeB/X0Clzfo962DoG8FV7PSN4EnItn+wk1ZfecZFApX3xvJzLz589P8z0p441SrFixAkDTuuLv748Qgvv371OxYkWdzdPTU/Pe77//nkWLFrFgwQLq1aunc66U7sRly5Zp7T927BgXL16kbt26acZZu3btd8b7JoVCoXMPtm7dyv3799OsH0CpVOpcn7W19Tvf86a6detqEtc3LV26FAsLC50xRStXrtTq/rt9+zaHDx/W3HuFQoEQQudaFixYQFJSkta+lDKptT6pVKpU99etW5cXL16wYcMGnXhTjkuSlI0YGUG+ilAvCPoeg77Hod4YyFcZUMD9E7DnR5hTBWZWgJ0/wJ1/IDnpfTUbhGxpkgyqevXq2Nvb07NnTwIDAzE1NWX58uVa3T1vUiqVTJkyhRcvXlCpUiXN03ONGzemRo0aAHh7e9O9e3cCAgI4fvw4tWrVwtLSkrCwMA4ePIinpye9evXir7/+4ueff6Z169YUK1aMf/75R3MelUpFuXLlKF68ON27d2fmzJkYGRnRuHFjzdNz+fPnZ9CgQWleW4MGDahVqxZDhw4lJiaGihUrcujQIf744w+dsv7+/ixevBgPDw/KlCnDiRMnmDx5sk7r1od487re5OPjQ2BgIFu2bKF27dqMHj0aBwcHli9fztatW5k0aZLWIHBQt+i0bNmSb7/9lqioKAIDAzEzM2PEiBEA2NjYUKtWLSZPnoyTkxPu7u7s27eP33//XafFrHTp0gD8+uuvWFtbY2ZmRsGCBXF0dMTT05N169Yxd+5cKlSogJGRERUrVqRjx47Mnj2bTp06cevWLTw9PTl48CDjxo3Dz88v1cRXkqRsxKko1Bio3p4/Ui8ofGmreubxiOtweIZ6s3SG4o3Bwx8K+oCpmYEDf01IGSYqKkoAIioqytChGFSnTp2EpaWl1j4fHx9RqlSpVMsfPnxYVKtWTVhYWAhnZ2fRrVs3cfLkSQGIRYsW6dR75swZ4evrK8zNzYWDg4Po1auXePHihU69CxcuFFWqVBGWlpbC3NxcFC5cWHTs2FEcP35cCCFEYGCgAFLd3NzcNPUkJSWJiRMnimLFiglTU1Ph5OQkOnToIO7evatz3W++TwghIiMjRZcuXYSdnZ2wsLAQ9evXF5cuXRKACAwM1JR79uyZ6Nq1q8iVK5ewsLAQNWrUEAcOHBA+Pj7Cx8dHU+7mzZs69yUtwcHBaV4fIIKDg4UQQpw9e1Y0bdpU2NraCqVSKby8vHTqT6nrjz/+EP379xfOzs5CpVKJmjVrau5ninv37olWrVoJe3t7YW1tLRo1aiTOnTsn3NzcRKdOnbTKTps2TRQsWFAYGxtrXVdERIRo3bq1sLOzEwqFQrz5T1V4eLjo2bOnyJMnjzAxMRFubm5ixIgRIi4u7r33RJKkbCouWohz64VY002IcfmFCLT5bxubR4hVHYQIXSVEbESmnF7f72+5YG8Gkgv2Zq7OnTuzZs0aXrx4YehQJEmSpMySlAC3DqpboC5vg+g3hikojKHjBvVg8gyk7/e37J6TJEmSJCn7MDaFwrXVm99kCAt9PaHmNgi/Cq7lDBaaTJokSZIkScqeFAp1kuRaDup8rx4HpdL/YZcMD0d2z2Uc2T0nSZIkSZ8efb+/5ZQDkiRJkiRJepBJkyRJkiRJkh5k0iRJkiRJkqQHmTRJkiRJkiTpQSZNkiRJkiRJepBJkyRJkiRJkh5k0iRJkiRJkqQHmTRJkiRJkiTpQSZNkiRJkiRJepBJkyRJkiRJkh5k0iRJkiRJkqQHmTRJkiRJkiTpQSZNkiRJkiRJejAxdACfEyEEoF4tWZIkSZKkT0PK93bK93haZNKUgZ4/fw5A/vz5DRyJJEmSJEnp9fz5c2xtbdM8rhDvS6skvSUnJ/PgwQOsra1RKBQZVm90dDT58+fn7t272NjYZFi9kjZ5n7OOvNdZQ97nrCHvc9bIzPsshOD58+e4urpiZJT2yCXZ0pSBjIyMyJcvX6bVb2NjI38hs4C8z1lH3uusIe9z1pD3OWtk1n1+VwtTCjkQXJIkSZIkSQ8yaZIkSZIkSdKDTJo+ASqVisDAQFQqlaFD+azJ+5x15L3OGvI+Zw15n7NGdrjPciC4JEmSJEmSHmRLkyRJkiRJkh5k0iRJkiRJkqQHmTRJkiRJkiTpQSZNkiRJkiRJepBJUzayf/9+mjZtiqurKwqFgg0bNmgdX7duHQ0bNsTJyQmFQkFoaKhB4vzUjR8/nkqVKmFtbU2uXLlo0aIFly9f1ioj7/XHmzt3LmXKlNFMRFetWjW2b9+uOS7vceYYP348CoWCgQMHavbJe/3xgoKCUCgUWpuLi4vmuLzHGef+/ft06NABR0dHLCwsKFu2LCdOnNAcN+S9lklTNhITE4OXlxezZs1K87i3tzcTJkzI4sg+L/v27aNPnz78888/7Nq1i8TERBo0aEBMTIymjLzXHy9fvnxMmDCB48ePc/z4cerUqUPz5s05f/48IO9xZjh27Bi//vorZcqU0dov73XGKFWqFGFhYZrt7NmzmmPyHmeMZ8+e4e3tjampKdu3b+fChQtMmTIFOzs7TRmD3mshZUuAWL9+farHbt68KQBx6tSpLI3pc/X48WMBiH379ukck/c6Y9nb24sFCxZo7ZP3OGM8f/5cFC1aVOzatUv4+PiIAQMG6JSR9/rDBQYGCi8vr/eWk/f44wwbNkzUqFFDr7KGuNeypUnK8aKiogBwcHAwcCSfr6SkJFatWkVMTAzVqlUzdDifpT59+tCkSRPq1atn6FA+W1evXsXV1ZWCBQvStm1bbty4YeiQPjubNm2iYsWKfPnll+TKlYty5crx22+/GTosDZk0STmaEILBgwdTo0YNSpcubehwPjtnz57FysoKlUpFz549Wb9+PSVLljR0WJ+dVatWcfLkScaPH2/oUD5bVapUYenSpezYsYPffvuNhw8fUr16dcLDww0d2mflxo0bzJ07l6JFi7Jjxw569uxJ//79Wbp0qaFDA8DE0AFIkiH17duXM2fOcPDgQUOH8lkqXrw4oaGhREZGsnbtWjp16sS+fftk4pSB7t69y4ABA9i5cydmZmaGDuez1bhxY83/e3p6Uq1aNQoXLsySJUsYPHiwASP7vCQnJ1OxYkXGjRsHQLly5Th//jxz586lY8eOBo5OtjRJOVi/fv3YtGkTwcHB5MuXz9DhfJaUSiVFihShYsWKjB8/Hi8vL6ZPn27osD4rJ06c4PHjx1SoUAETExNMTEzYt28fM2bMwMTEhKSkJEOH+FmytLTE09OTq1evGjqUz0qePHl0/qgqUaIEd+7cMVBE2mRLk5TjCCHo168f69evJyQkhIIFCxo6pBxDCEF8fLyhw/is1K1bV+spLoCAgAA8PDwYNmwYxsbGBors8xYfH8/FixepWbOmoUP5rHh7e+tMAXPlyhXc3NwMFJE2mTRlIy9evODatWua1zdv3iQ0NBQHBwcKFChAREQEd+7c4cGDBwCaD5aLi4vWfCHSu/Xp04cVK1awceNGrK2tefjwIQC2traYm5sDyHudAUaOHEnjxo3Jnz8/z58/Z9WqVYSEhPD3338D8h5nFGtra53xeJaWljg6Omr2y3v98b777juaNm1KgQIFePz4MWPHjiU6OppOnToB8h5nlEGDBlG9enXGjRtHmzZt+Pfff/n111/59ddfNWUMeq+z7Dk96b2Cg4MFoLN16tRJCCHEokWLUj0eGBho0Lg/NandQ0AsWrRIU0be64/XpUsX4ebmJpRKpXB2dhZ169YVO3fu1ByX9zjzvD3lgLzXH++rr74SefLkEaampsLV1VV88cUX4vz585rj8h5nnM2bN4vSpUsLlUolPDw8xK+//qp13JD3WiGEEJmWkUmSJEmSJH0m5EBwSZIkSZIkPcikSZIkSZIkSQ8yaZIkSZIkSdKDTJokSZIkSZL0IJMmSZIkSZIkPcikSZIkSZIkSQ8yaZIkSZIkSdKDTJokSZIkSZL0IJMmSfrEde7cGYVCwa1btwwdSoZYtmwZZcuWxcrKCoVCQVBQkMFiUSgU+Pr6Guz86XHv3j0sLCyYNGlSlp0zvZ+9W7duoVAo6Ny5c6bEExISkqWfmcDAQKytrXn06FGWnE8yPJk0SdJrKf+gKxQK/P39Uy2T8o9yz549szi6nOHw4cN88803xMbG0qdPHwIDA9+btLi7u2t+bgqFAmNjY5ycnGjQoAEbN27MmsDfYfHixSgUChYvXpyp5xk5ciRWVlb06dMn1eNhYWH88MMPVKlSBUdHR0xNTXFwcKBatWqMHDlSZ5HU7OLQoUN8+eWX5M2bF6VSib29PR4eHrRv354lS5YYNLZBgwZhbGzM6NGjDRqHlHXkgr2SlIqtW7eyf/9+atWqZehQcpRt27YBsHTpUqpWrar3+4yNjfn+++8BePXqFZcuXWLTpk3s2rWLX375hSFDhnxQPBcvXsTCwuKD3puVrly5wrJlyxg9ejSWlpY6x1etWkXXrl2JjY2lbNmytGnTBgcHB6Kjozl58iSTJk1i4sSJbN68GT8/P73PO378eIYPH07evHkz8nI0Fi9eTJcuXTAxMcHPz4+iRYvy8uVLbty4wbZt29i/f79mwVyAypUrc/HiRZycnDIlnrfZ2dnRrVs3pk2bxsiRI3Fzc8uS80qGI5MmSXqLu7s7d+7cYdiwYRw5csTQ4eQoKauWp3elchMTE50umZ07d9KoUSNGjx5Nr169Pij58fDwSPd7DGH+/PkIIejQoYPOse3bt/P111/j6OjIxo0bqVevnk6Zu3fvMnbsWCIjI9N13jx58pAnT54PDfudYmNj6d+/P9bW1hw6dIjSpUtrHU9ISCAkJERrn4WFRZb/zDp06MCUKVP47bffGDt2bJaeWzKATF8SWJI+ETdv3hSAaNiwoejUqZMAxNq1a7XKBAcHC0D06NFDa7+bm5twc3NLtV4fHx/x9q9aYGCgAERwcLBYuHChKF26tDAzMxPu7u5i+vTpQgghkpOTxbRp00Tx4sWFSqUSRYsWFUuXLtWpPyXWa9euiXHjxonChQsLlUolihQpIiZNmiSSkpJSjWvfvn3C399fODo6CqVSKYoUKSJGjRolYmJiUr3mwMBAcfjwYdGgQQNha2urc01pOXTokPDz8xP29vZCpVKJ4sWLi8DAQK3zpJwjte193NzchEqlSvWYh4eHAMSxY8c0+zZv3ix8fX2FjY2NMDMzE15eXuJ///ufSExM1Hk/IHx8fLT2pdzvmzdvitmzZwsPDw+hUqlEgQIFRFBQkNb9Tin7rut68OCB6N+/vyhSpIgwMzMT9vb2onTp0qJXr14iKirqvdeflJQknJycRLly5XSOJSQkiIIFCwpA7N+//711JSQkaL1O+Vw/e/ZM9OvXT+TLl08YGxuLRYsW6dyLNyUmJooJEyZoPouFCxcW48aNE9evXxeA6NSp03tjOXr0qABE8+bN31s2xZuf1RQpv2tpbW//fOPj48WUKVNEuXLlhIWFhbCyshI1atQQGzduTPO8RYsWFXnz5tU7TunTJVuaJCkVP/74I6tWrWLkyJE0b94cY2PjTDnPtGnTCAkJoXnz5tSpU4e1a9cyYMAALCwsOH36NH/99Rf+/v7UqVOHVatW0bFjRwoWLEiNGjV06ho4cCD//PMPbdq0wczMjHXr1jF06FCuXbvG/PnztcrOmzeP3r17Y29vT9OmTXF2dubYsWP8/PPPBAcHExwcjFKp1HrP4cOHGTduHLVr16Z79+7cuXPnvde3du1a2rZti1Kp5KuvviJXrlzs3r2bMWPGsHPnToKDg1GpVLi7uxMYGMiGDRs4ffo0AwYMwM7O7qPubWqmT5/OwIEDcXBwoH379lhaWrJ582YGDRrEgQMHWLNmDQqFQq+6/u///o+QkBD8/f1p0KABGzZsICgoiFevXvHzzz8D0KJFCyIjI9m4cSPNmzenbNmyWnXExsbi7e3NrVu3aNCgAS1btuTVq1fcuHGDxYsXM3ToUGxsbN4Zx5kzZ3j69ClffvmlzrHg4GBu3rxJzZo1qVmz5nuvycRE9yshPj6eOnXq8Pz5c5o2bYpSqSR37tzvrKd79+4sXLiQggUL0qdPH+Li4pg6dSqHDx9+bwwpHBwcALh58ybJyckYGX3YENy0xsQdO3aMbdu2abVAxsfH06hRI0JCQihXrhxdu3YlISGBrVu30rx5c2bOnEnfvn116qpWrRpLly7l8uXLFC9e/IPilD4Rhs7aJCm7eLOlSQghBg8eLAAxf/58TZmMbmlycHAQ169f1+y/c+eOUCqVwtbWVhQrVkw8fvxYcyzlL+9mzZpp1ZXy137u3LnF/fv3NfufP38uPD09dVoZzp8/L0xMTES5cuVEeHi4Vl3jx48XgPjll190rhkQv//+e6rXmJro6GhhZ2cnVCqVOH36tGZ/cnKyaN++vQDETz/9lOq1vN1y8S5ptTTt2LFDKBQKYWFhIWJiYsT169eFiYmJyJUrl7hz546mXHx8vOZn9Mcff2jVwTtamgoWLCgePHig2f/kyRNhZ2cnrK2tRXx8vGb/okWLBKBpnXnTpk2bBCAGDRqkcyw6OlqrnrTMnj1bAOK3337TOTZmzBgBiNGjR7+3ntS4ubkJQDRo0EDExsbqHE/t55XyefHy8hIvXrzQ7L93755wcnLSu6UpOTlZlC9fXvMzWLRokbhw4UKqLYJvn/vNlqbU3Lx5Uzg7Owt7e3tx+fJlzf6RI0cKQAQFBYnk5GTN/ujoaFGxYkWhVCq1fsdSTJ8+XQBi4cKF770u6dMmn56TpDSMGjUKW1tbxowZQ2xsbKaco3///hQqVEjzOn/+/NSoUYOoqChGjRqFs7Oz5ljlypUpVKgQp0+fTrMuV1dXzWsrKyvNUz1vPmU0f/58EhMTmTFjhuav+RRDhw7F2dmZlStX6tRfrlw5unTpove1bdiwgcjISLp06UKZMmU0+xUKBRMmTMDExCTDnihLTEwkKCiIoKAgRo0axRdffIGfnx9CCMaOHYuFhQXLly8nMTGRIUOGkD9/fs17lUolEyZMAEhXPD/88IPWeB4nJyeaN2/O8+fP0/0kmrm5uc4+a2trnda+1Ny7dw8g1dafhw8fAmh9LlLcuHFDc89StmXLlqV6jsmTJ6caY2qWLl0KoDMoPW/evAwYMECvOkD9Ofnrr7+oVq0a+/btIyAggJIlS2JjY0O9evVYvHgxSUlJeteXIjo6Gn9/f549e8Zff/1FsWLFAEhOTmbu3LkUKVKE0aNHa7U4WltbM3r0aF69esW6det06ky59yk/C+nzJbvnJCkNDg4ODBs2jJEjR2qejslo5cqV09mX8kX8dldOyrGjR4+mWldq3S8p+0JDQzX7/vnnHwD+/vtvdu/erfMeU1NTLl26pLO/cuXKqZ43LadOnQJS7x7Jnz8/hQsX5vLlyzx//hxra+t01f22pKQkxowZA4CRkRH29vbUrVuXPn360KxZs/fGU7VqVczNzbXu0/uUL19eZ1++fPkA9B5QXatWLVxcXBg/fjyhoaE0adKEGjVq4OnpqXc3YXh4OAD29vY6x4QQab7vxo0bmnuWomHDhjqDyc3MzPD09NQrFkCT1L/r86ivQoUKcfjwYUJDQ9m9ezfHjh3j8OHD7Nmzhz179rB06VK2b9+OSqXSq76kpCTatWvH+fPnmTt3LnXr1tUcu3z5Ms+ePcPV1VXnvgA8efIEINXfjZQ/Pp4+fZqu65M+PTJpkqR3GDhwILNmzWLSpEn06NEjw+tPbbxKyriStI4lJiamWleuXLlS3WdkZERUVJRmX0REBIBm3I2+3jeO5W3R0dHvfJ+LiwuXL18mOjr6o5MmlUpFXFzcR8WTK1cu7t+/r/c5bW1tdfal/Oz0bQGxtbXlyJEjBAYGsnnzZs2UC/ny5WPEiBH07t37vXWktAC9fPlS51jKtaZ2XfXq1dMkVbdu3aJgwYKp1p8rVy69EziAqKgojIyMUn3sP72foRRly5bV+iMiJCSEDh06EBwczJw5cxg0aJBe9QwZMoRt27bRv39/nbnWUn4vzp8/z/nz59OsIyYmRmdfyr3/FKankD6O7J6TpHcwNzcnKCiIqKgoxo0bl2Y5IyOjNJOZNxOWzPT48eNU9yUnJ2t9wackY9HR0Qgh0tzelp4vzjfPk9ZsySn73zfQOaO8L57Hjx9nWSxvcnd3Z8mSJTx58oRTp04xceJEhBD06dMn1W7St6V04aZ86b+pevXqgHpA+IdK78/d1taW5OTkVFtdMmrmbF9fX3766ScA9u7dq9d75s+fz/Tp02nYsCFTp07VOZ7ys2/VqtU7fy8WLVqk896Ue/9md7r0eZJJkyS9R5cuXfDw8GD27NlpPjFmb2/P48ePdRKnmJgYrl69mhVhcuDAgTT3vflXepUqVYD/uukyS0rX49tz6YC65eP69esUKlToo1uZMiKef//9l5cvX6baJfqxUp68fF/rk7GxMWXLlmXo0KGaZGnTpk3vrT+l6yy1z1mdOnVwc3Pj4MGD7N+/P72hfxAvLy/g3Z/HjJDaJJ5p2bt3L3379qVkyZKsXr061adhS5QogY2NDcePHychISFdsaSMYUtPN6b0aZJJkyS9h7GxMePGjSM+Pp4ff/wx1TIVK1YkISGB5cuXa/YJIRgxYkSqzfmZYcaMGZrJIQFevHihibdjx46a/b1798bExIR+/fpx9+5dnXoiIyM1438+RvPmzbG1tWXRokVa3R0p9yUhISHT1iBLTfv27TExMWHq1Kla9ykhIYHhw4cDZEo8KeNdUhskfO7cOW7fvq2zP6VFRp/B1zVr1sTIyIh///1X55iJiQmzZs1CoVDQunXrVMewgf5jsPSR8ln78ccftT779+/fZ/r06XrXc/PmTWbNmsXz5891jsXExGjqSm36jTdduXKF1q1bY2try+bNm1PtVgX1verVqxe3b9/mu+++SzVxOnfuXKotukePHsXExETTsid9vuSYJknSQ8uWLalWrVqaM4T37duXRYsW0a1bN3bt2oWzszMHDhwgMjISLy+vNJ94y0iVKlXCy8uLr776CpVKxbp167h16xbffvut1nIwpUuXZs6cOfTq1YvixYvj5+dH4cKFiY6O5saNG+zbt4/OnTszb968j4rHxsaG3377jXbt2lGlShW++uornJ2d2bNnD8ePH6dy5cr83//938dett4KFy7MxIkTGTJkCGXKlKFNmzZYWlqyZcsWLl26RPPmzVOdUftjVatWDXNzc6ZNm0Z0dLSmC2f48OHs3r2bIUOG4O3tjYeHB46Ojty4cYNNmzZhbm6e6pxAb7O3t6dWrVocOHCA+Ph4nUHR/v7+LF26lO7du1O/fn3Kli1LtWrVcHBw4NmzZ1y/fp09e/ZgZGSUIV/6vr6+BAQEsGjRIjw9PWnZsiXx8fGsXr2aqlWrsmXLFr3qiYqKol+/fvzf//0fNWvWpFSpUpibm3P//n22bNlCREQEFSpUoF+/fu+sZ+DAgTx79ozGjRtrnux7k7u7uyZZHjNmDCdPnmTGjBls3boVHx8fnJ2duX//PmfPnuX06dMcOXJEa/zgixcv+Oeff6hfv366Wr+kT1TWznAgSdnX2/M0vW3//v2a+YrenqdJCCH27NkjqlSpIlQqlXB0dBTffPONePjw4XtnBH/bu+YqSq2ut2cEL1SokFAqlaJw4cJi4sSJac5r8++//4q2bdsKV1dXYWpqKpycnET58uXF8OHDxcWLFzXl9J37Ji379+8XjRs3FnZ2dkKpVIpixYqJH374QWsOH32uPS3vmhE8NRs3bhQ+Pj7C2tpaqFQq4enpKaZMmaIzG7YQ758R/G1p/Vy3bt0qKlWqJMzNzbVmBL9w4YIYMGCAKFeunHB0dBQqlUoUKlRIdO7cWVy4cEHva1q5cmWqM9i/6d69e2LkyJGiQoUKws7OTpiYmAh7e3tRpUoVMWzYMHHp0iWd97xr/jEh3j0j+Pjx4zWfxUKFColx48aJa9eu6T1PU1xcnFi7dq3o3r278PLyEk5OTsLY2FjY29uLGjVqiKlTp4qXL19qvSe1z2rK70xa29s/38TERDF//nzh7e0tbGxsNLO9N2rUSMydO1fnc7t48WIBiA0bNrz3mqRPn0KIdzyTKkmSJGV7r169olixYpQoUYLt27cbOpwcpVatWjx8+JCLFy9m2soBUvYhxzRJkiR94pRKJePGjePvv/9Ocx4vKeMFBwdz4MABJk6cKBOmHEKOaZIkSfoMtGvXjnv37skJFrNQVFQUv/zyCy1btjR0KFIWkd1zkiRJkiRJepDdc5IkSZIkSXqQSZMkSZIkSZIeZNIkSZIkSZKkB5k0SZIkSZIk6UEmTZIkSZIkSXqQSZMkSZIkSZIeZNIkSZIkSZKkB5k0SZIkSZIk6UEmTZIkSZIkSXr4f/u/5D1VTEP8AAAAAElFTkSuQmCC",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "npoints = [11, 21, 31, 41, 51, 61]\n",
+        "grid_classes = [GaussChebyshev, GaussLegendre, TanhSinh, Simpson, Trapezoidal]\n",
+        "\n",
+        "for grid_class in grid_classes:\n",
+        "    errors = []\n",
+        "    for npoint in npoints:\n",
+        "        # make 1-dimensional grid for the given number of points\n",
+        "        oned = grid_class(npoint)\n",
+        "        rgrid = BeckeRTransform(0.0, R=1.5).transform_1d_grid(oned)\n",
+        "        # calculate x^2 e^-x\n",
+        "        x = rgrid.points\n",
+        "        func_vals = x**2.0 * np.exp(-x)\n",
+        "        # compute relative percent error of integration\n",
+        "        integral = rgrid.integrate(func_vals)\n",
+        "        errors.append(np.abs(integral - 2.0) / 2.0 * 100)\n",
+        "    # plot relative percent error vs. number of grid points\n",
+        "    plt.semilogy(npoints, errors, label=oned.name)\n",
+        "\n",
+        "plt.legend(frameon=False, fontsize=12)\n",
+        "plt.title(r\"Numerical Integration of $\\int_0^\\infty x^2 e^{-x}dx$\", fontsize=16)\n",
+        "plt.xlabel(\"Number of Points (Grid Size)\", fontsize=14)\n",
+        "plt.ylabel(\"Relative Percent Error\", fontsize=14)\n",
+        "plt.xticks(npoints)\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.15"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 1
+}
diff --git a/website/README.md b/website/README.md
new file mode 100644
index 000000000..a4be2ea1b
--- /dev/null
+++ b/website/README.md
@@ -0,0 +1,31 @@
+# Instructions On Documentation
+
+In-order to build the documentation locally, the following commands need to
+be runned. The html files are created in an folder called "_build".
+
+```bash
+# # Generates API html for grid while ignoring the test and data folders.
+# Stores it in pyapi/
+sphinx-apidoc --separate -o website/_autosummary grid -M -f
+# Build the html files
+jupyter-book build ./website/
+```
+Here inside the "./website/_build/html/ folder, there are the html files to run the website locally.
+
+
+## Pushing To Website
+
+After running the commands above, you'll need to go inside the  "./website/_build/html" folder and copy all the files.
+
+```bash
+git branch -a
+# Should be something like origin/gh-pages/ where origin is the remote to the theochem/gbasis Github
+git checkout gh-pages
+cd ..  # Get out of gh-pages and go back to gbasis folder
+```
+Now, paste and overwrite all of the files in gh-pages with the html. Then commit and push to the `gh-pages` branch of the theochem/gbasis
+```bash
+git add ./*
+git commit -m "Update website"
+git push origin gh-pages
+```
diff --git a/website/_config.yml b/website/_config.yml
new file mode 100644
index 000000000..53f2649ed
--- /dev/null
+++ b/website/_config.yml
@@ -0,0 +1,80 @@
+# Book settings
+# Learn more at https://jupyterbook.org/customize/config.html
+
+title: Welcome to Grid Documentation
+author: The QC-Devs Community
+logo: grid_logo_website.svg
+
+# Force re-execution of notebooks on each build.
+# See https://jupyterbook.org/content/execute.html
+execute:
+  execute_notebooks: 'off'
+
+# Define the name of the latex output file for PDF builds
+latex:
+  latex_documents:
+    targetname: book.tex
+
+sphinx:
+  extra_extensions:
+  - 'sphinx.ext.githubpages'
+  - 'sphinx.ext.autodoc'
+  - 'sphinx.ext.autosummary'
+  config:
+    mathjax_path: https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
+    autosummary_generate: True
+    add_module_names: True
+    html_theme_options:
+      show_navbar_depth: 1
+      max_navbar_depth: 4
+      collapse_navbar: False
+    exclude_patterns:
+      - README.md
+      - markdown.md
+      - markdown-notebooks.md
+      - tutorial/JCP_Paper.ipynb
+      - tutorial/Ngrid.ipynb
+
+
+#Read LaTeX
+parse:
+  myst_enable_extensions:  # default extensions to enable in the myst parser. See https://myst-parser.readthedocs.io/en/latest/using/syntax-optional.html
+    # - amsmath
+    - colon_fence
+    # - deflist
+    - dollarmath
+    # - html_admonition
+    # - html_image
+    - linkify
+    # - replacements
+    # - smartquotes
+    - substitution
+    - tasklist
+  myst_url_schemes: [mailto, http, https] # URI schemes that will be recognised as external URLs in Markdown links
+  myst_dmath_double_inline: true  # Allow display math ($$) within an inline context
+  myst_dmath_allow_space: false
+
+# Add a bibtex file so that we can create citations
+bibtex_bibfiles:
+  - references.bib
+
+# Information about where the book exists on the web
+repository:
+  url: https://github.com/theochem/grid  # Online location of your book
+  path_to_book: website  # Optional path to your book, relative to the repository root
+  branch: master  # Which branch of the repository should be used when creating links (optional)
+
+# Add GitHub buttons to your book
+# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository
+
+# Add GitHub buttons to your book
+launch_buttons:
+  thebe                  : true
+  colab_url: "https://colab.research.google.com"
+
+copyright: "2022-2024"
+
+html:
+  use_issues_button: true
+  use_repository_button: true
+  favicon: favicon.png
diff --git a/website/_toc.yml b/website/_toc.yml
new file mode 100644
index 000000000..63943c422
--- /dev/null
+++ b/website/_toc.yml
@@ -0,0 +1,68 @@
+# Table of contents
+# Learn more at https://jupyterbook.org/customize/toc.html
+
+format: jb-book
+root: intro
+parts:
+  - caption: User Documentation
+    chapters:
+      - file: installation
+        title: Installation
+      - file: tutorial/onedgrids
+        title: One-Dimensional Grids
+      - file: radial_transf
+        title: Radial Transformations
+      - file: conventions
+        title: Conventions
+
+  - caption: Example Tutorials
+    chapters:
+      - file: tutorial/Quickstart
+      - file: One_dimensional_grids
+        title: One-Dimensional Grids
+      - file: tutorial/Angular_grid
+        title: Angular Grids
+      - file: tutorial/Atom_Grid_Construction
+        title: Atom Grid Construction
+      - file: tutorial/Atom_Grid
+        title: Atom Grid Application
+      - file: tutorial/Molecular_Grid_Construction
+        title: Molecular Grid Construction
+      - file: tutorial/Molecular_Grid
+        title: Molecular Grid Application
+      - file: tutorial/Cubic_Grids
+        title: Cubic Grids
+      - file: tutorial/Interpolation_and_Poisson
+        title: Interpolation and Poisson
+      - file: tutorial/Multipole_Moments
+        title: Multipole Moments
+
+  - caption: API Documentation
+    chapters:
+      - file: _autosummary/grid
+        title: grid
+        sections:
+          - file: _autosummary/grid.onedgrid
+            title: grid.onedgrid
+          - file: _autosummary/grid.rtransform
+            title: grid.rtransform
+          - file: _autosummary/grid.angular
+            title: grid.angular
+          - file: _autosummary/grid.atomgrid
+            title: grid.atomgrid
+          - file: _autosummary/grid.molgrid
+            title: grid.molgrid
+          - file: _autosummary/grid.becke
+            title: grid.becke
+          - file: _autosummary/grid.hirshfeld
+            title: grid.hirshfeld
+          - file: _autosummary/grid.cubic
+            title: grid.cubic
+          - file: _autosummary/grid.periodicgrid
+            title: grid.periodicgrid
+          - file: _autosummary/grid.ode
+            title: grid.ode
+          - file: _autosummary/grid.poisson
+            title: grid.poisson
+          - file: _autosummary/grid.utils
+            title: grid.utils
diff --git a/website/conventions.ipynb b/website/conventions.ipynb
new file mode 100644
index 000000000..f3fdaa1c0
--- /dev/null
+++ b/website/conventions.ipynb
@@ -0,0 +1,22 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "# Conventions\n\n## Spherical Coordinates\n\nSpherical coordinates $(r, \\theta, \\phi)$ are used extensively throughout the molecular, atomic,\nand angular grid modules.\nThe radius $r \\in [0, \\infty)$, azimuthal $\\theta \\in [-\\pi, \\pi]$ and polar\n$\\phi \\in [0, \\pi)$ angles are defined from Cartesian coordinates as:\n\n$$\n\\begin{align}\n    r &= \\sqrt{x^2 + y^2 + z^2}\\\\\n    \\theta &= \\text{arctan2} \\bigg(\\frac{y}{x}\\bigg)\\\\\n    \\phi &= \\arccos \\bigg(\\frac{z}{r}\\bigg)\n\\end{align}\n$$\n\nsuch that when the radius is zero $r=0$, the angles are zero $\\theta, \\phi = 0$.\n\nGrid offers a {class}`utility<grid.utils.convert_cart_to_sph>` function to convert to spherical coordinates:\n\n```python\nimport numpy as np\nfrom grid.utils import convert_cart_to_sph\n\n# Generate random set of points\ncart_pts = np.random.uniform((-100, 100), size=(1000, 3))\n# Convert to spherical coordinates\nspher_pts = convert_cart_to_sph(cart_pts)\n# Convert to spherical coordinates, centered at [1, 1, 1]\nspher_pts = convert_cart_to_sph(cart_pts, center=np.array([1.0, 1.0, 1.0]))\n```\n\nThe {class}`atomic grid<grid.atomgrid.AtomGrid>` class can also convert its points to spherical coordinates:\n\n```python\nfrom grid.atomgrid import AtomGrid\n\natom_grid = AtomGrid(...)\nspher_pts = atom_grid.convert_cartesian_to_spherical()\n```\n\nWith the convention that when $r=0$, the angles within $\\{(0, \\theta_j, \\phi_j)\\}$ in that shell are\nobtained from an angular grid $\\{(\\theta_j, \\phi_j)\\}$ with some degree.\n\n## Spherical Harmonics\n\nThe real spherical harmonics are defined using complex spherical harmonics:\n\n$$\nY_{lm}(\\theta, \\phi) = \\begin{cases}\n    i(Y^m_l(\\theta, \\phi) - (-1)^m Y_l^{-m}(\\theta, \\phi)) & \\text{if } m < 0 \\\\\n    Y_l^0 & \\text{if } m = 0 \\\\\n    (Y^{-|m|}_{l}(\\theta, \\phi) + (-1)^m Y_l^m(\\theta, \\phi)) & \\text{if } m > 0\n\\end{cases},\n$$\n\nwhere the degree $l \\in \\mathbb{N}$, order $m \\in \\{-l, \\cdots, l\\}$ and $Y^m_l$ is the complex spherical harmonic.\nThe Condon-Shortley phase is not included.\n\nAlternatively, using the associated Legendre polynomials $P_l^m$ (without the Condon-Shortley phase):\n\n$$\nY_{lm}(\\theta, \\phi) = \\begin{cases}\n    \\sqrt{\\frac{(2l + 1)(l - m)!}{4\\pi(l + m)!}} \\sqrt{2} \\cos(m\\theta) P_l^m(\\cos(\\phi)) & \\text{if } m < 0 \\\\\n    \\frac{1}{2}\\sqrt{\\frac{1}{\\pi}} & \\text{if } m = 0 \\\\\n    \\sqrt{\\frac{(2l + 1)(l - m)!}{4\\pi(l + m)!}} \\sqrt{2} \\sin(m\\theta) P_l^m(\\cos(\\phi)) & \\text{if } m > 0\n\\end{cases}\n$$\n\nGrid offers the {func}`generate_real_spherical_harmonics<grid.utils.generate_real_spherical_harmonics>` function:\n\n```python\nfrom grid.utils import generate_real_spherical_harmonics\n\nspher_pts = np.array(...)\ntheta = spher_pts[:, 1]\nphi = spher_pts[:, 2]\n# Generate all degrees up to l=2\nspherical_harmonics = generate_real_spherical_harmonics(2, theta, phi)\n```\n\n### Ordering\n\nThe spherical harmonics are ordered by degree $l$ in ascending order.\nFor each degree $l$, the orders $m$ are in HORTON2 order:\n\n$$m = [0, 1, -1, 2, -2, \\cdots, l, -l].$$\n\n## Angular Grids\n\nThe {class}`angular grids<grid.angular.AngularGrid>` integrate functions over the unit sphere.\nThe quadrature weights are chosen so that the spherical harmonics are orthonormal:\n\n$$\n\\int_{-\\pi}^{\\pi} \\int_0^{\\pi} Y_{l_1}^{m_1} Y_{l_2}^{m_2} \\sin(\\phi)\\, d\\theta\\, d\\phi = \\delta_{l_1, l_2} \\delta_{m_1, m_2}\n$$\n\nThe weights also sum to $4\\pi$.\n\n### Nested Grids\n\nAngular grids of different degrees can be very close to one another. The following shows the mean with\nstandard deviation and maximum distance between an angular grid of one degree and the next higher degree.\nThe * indicates Lebedev-Laikov grids with negative weights.\n\n```{csv-table} Lebedev-Laikov Grids\n:file: table_angular_lebedev.csv\n:widths: 11,11,11,11\n:delim: ;\n:header-rows: 1\n:align: center\n```\n\n```{csv-table} Symmetric Spherical t-Design Grids\n:file: table_angular_spherical.csv\n:widths: 11,11,11,11\n:delim: ;\n:header-rows: 1\n:align: center\n```\n",
+   "id": "main-cell"
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/website/installation.ipynb b/website/installation.ipynb
new file mode 100644
index 000000000..d786837f2
--- /dev/null
+++ b/website/installation.ipynb
@@ -0,0 +1,83 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f1be9e2f",
+   "metadata": {},
+   "source": [
+    "# Installation\n",
+    "\n",
+    "## Dependencies\n",
+    "\n",
+    "The following dependencies are required for Grid:\n",
+    "\n",
+    "- Python >= 3.10\n",
+    "- NumPy >= 1.23.5\n",
+    "- SciPy >= 1.15.0\n",
+    "- SymPy >= 1.4.0\n",
+    "- PyTest >= 8.0.0 for running tests\n",
+    "- Sphinx >= 2.3.0 for building the documentation\n",
+    "\n",
+    "## Installation\n",
+    "\n",
+    "Install the latest release from PyPI:\n",
+    "\n",
+    "```bash\n",
+    "pip install qc-grid\n",
+    "```\n",
+    "\n",
+    "Install the latest development version directly from GitHub:\n",
+    "\n",
+    "```bash\n",
+    "pip install git+https://github.com/theochem/grid.git\n",
+    "```\n",
+    "\n",
+    "Install from a local clone:\n",
+    "\n",
+    "```bash\n",
+    "git clone https://github.com/theochem/grid.git\n",
+    "cd grid\n",
+    "pip install .\n",
+    "```\n",
+    "\n",
+    "Check the installation by running:\n",
+    "\n",
+    "```bash\n",
+    "pytest --pyargs grid\n",
+    "```\n",
+    "\n",
+    "## Building Documentation\n",
+    "\n",
+    "Build the documentation locally with Jupyter Book:\n",
+    "\n",
+    "```bash\n",
+    "sphinx-apidoc -o website/_autosummary src/grid src/grid/tests src/grid/test src/grid/data -e -f\n",
+    "jupyter-book build ./website/\n",
+    "```\n",
+    "\n",
+    "The built HTML files are written to `website/_build/html/`.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/website/intro.md b/website/intro.md
new file mode 100644
index 000000000..db4cacefa
--- /dev/null
+++ b/website/intro.md
@@ -0,0 +1,72 @@
+# Welcome to Grid's Documentation!
+
+Grid is a free and open-source Python library for numerical integration,
+interpolation and differentiation of interest for the quantum chemistry community.
+Grid is intended to support molecular density-functional (DFT) theory calculations,
+and it also supports periodic boundary conditions.
+
+## Citation
+
+Please use the following citation in any publication using Grid:
+
+> **Grid: A Python library for molecular integration, interpolation, differentiation, and more.**
+> Alireza Tehrani, Xiaotian Derrick Yang, Marco Martínez-González, Leila Pujal,
+> Raymundo Hernández-Esparza, Matthew Chan, Esteban Vöhringer-Martinez,
+> Toon Verstraelen, Paul W. Ayers, Farnaz Heidar-Zadeh.
+> *J. Chem. Phys. 160 (17), 172503 (2024).*
+> https://doi.org/10.1063/5.0202240
+
+```bibtex
+@article{grid,
+    author = {Tehrani, Alireza and Yang, Xiaotian Derrick and Martínez-González, Marco and Pujal, Leila and Hernández-Esparza, Raymundo and Chan, Matthew and Vöhringer-Martinez, Esteban and Verstraelen, Toon and Ayers, Paul W. and Heidar-Zadeh, Farnaz},
+    title = {Grid: A Python library for molecular integration, interpolation, differentiation, and more},
+    journal = {The Journal of Chemical Physics},
+    volume = {160},
+    number = {17},
+    pages = {172503},
+    year = {2024},
+    month = {05},
+    doi = {10.1063/5.0202240},
+    url = {https://doi.org/10.1063/5.0202240},
+}
+```
+
+## License and Contributions
+
+The [Grid source code](https://github.com/theochem/grid) is hosted on GitHub and is released under the GNU General Public License v3.0. We welcome any contributions to Grid in accordance with our Code of Conduct and Contributing Guidelines. Please report issues on [GitHub Issues](https://github.com/theochem/grid/issues/new). For further information and inquiries please contact us at qcdevs@gmail.com.
+
+## Key Features
+
+### Integration
+- **One-dimensional integration**: quadratures and transformations
+- **Multivariate integration**:
+  - Hypercubes with tensor product grids
+  - Integration over spheres with angular grids
+  - Molecular integration with various weight functions
+
+### Transformations
+- **Radial transformations** for converting one-dimensional grids between intervals
+- Support for finite and infinite domains
+
+### Analysis
+- **Interpolation** on constructed grids
+- **Differentiation** through grid-based methods
+- **ODE solver** for linear ordinary differential equations
+- **Poisson solver** for the Poisson equation
+
+## Available Modules
+
+| Module | Purpose |
+| --- | --- |
+| [`grid.onedgrid`](_autosummary/grid.onedgrid) | One-dimensional quadrature grids and integration |
+| [`grid.rtransform`](_autosummary/grid.rtransform) | Radial transformations between intervals |
+| [`grid.angular`](_autosummary/grid.angular) | Angular grids for spherical integration |
+| [`grid.atomgrid`](_autosummary/grid.atomgrid) | Atomic grids for 3D integration |
+| [`grid.molgrid`](_autosummary/grid.molgrid) | Molecular grids with atom-in-molecule weights |
+| [`grid.becke`](_autosummary/grid.becke) | Becke weight functions |
+| [`grid.hirshfeld`](_autosummary/grid.hirshfeld) | Hirshfeld weight functions |
+| [`grid.cubic`](_autosummary/grid.cubic) | Hyper-rectangular grids in 2D or 3D |
+| [`grid.periodicgrid`](_autosummary/grid.periodicgrid) | Periodic grids for 1D, 2D, or 3D |
+| [`grid.ode`](_autosummary/grid.ode) | ODE solver |
+| [`grid.poisson`](_autosummary/grid.poisson) | Poisson equation solver |
+| [`grid.utils`](_autosummary/grid.utils) | Utility functions for coordinate transformations and spherical harmonics |
diff --git a/website/radial_transf.ipynb b/website/radial_transf.ipynb
new file mode 100644
index 000000000..9dc84d577
--- /dev/null
+++ b/website/radial_transf.ipynb
@@ -0,0 +1,22 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "# Radial Transformations\n\nThese one-dimensional grids can be transformed to other kinds of intervals using the\n{func}`radial transform <grid.rtransform>` module.\n\n| Transform | Domain | Co-Domain | $r(x)$ |\n|-----------|--------|-----------|--------|\n| {func}`BeckeRTransform<grid.rtransform.BeckeRTransform>` | $[-1,1]$ | $[r_{min},\\infty)$ | $r_i = R \\frac{1 + x_i}{1 - x_i} + r_{min}$ |\n| {func}`LinearFiniteRTransform<grid.rtransform.LinearFiniteRTransform>` | $[-1,1]$ | $[r_{min},r_{max}]$ | $r_i = \\frac{r_{max} - r_{min}}{2} (1 + x_i) + r_{min}$ |\n| {func}`InverseRTransform<grid.rtransform.InverseRTransform>` | Co-Domain | Domain | $x_i(r_i)$ |\n| {func}`IdentityRTransform<grid.rtransform.IdentityRTransform>` | Domain | Domain | $x_i$ |\n| {func}`LinearInfiniteRTransform<grid.rtransform.LinearInfiniteRTransform>` | $[0,\\infty)$ | $[r_{min},r_{max})$ | $r_i = \\frac{r_{max} - r_{min}}{N - 1} x_i + r_{min}$ |\n| {func}`ExpRTransform<grid.rtransform.ExpRTransform>` | $[0,\\infty)$ | $[r_{min},r_{max}]$ | $r = r_{min} e^{x \\log\\left(\\frac{r_{max}}{r_{min}} / (N - 1)\\right)}$ |\n| {func}`PowerRTransform<grid.rtransform.PowerRTransform>` | $[0,\\infty)$ | $[r_{min},r_{max}]$ | $r = r_{min} (x + 1)^{\\frac{\\log(r_{max}) - \\log(r_{min})}{N}}$ |\n| {func}`HyperbolicRTransform<grid.rtransform.HyperbolicRTransform>` | $[0,\\infty)$ | $[0,\\infty)$ | $r_i = \\frac{a x_i}{1 - bx_i}$ |\n| {func}`MultiExpRTransform<grid.rtransform.MultiExpRTransform>` | $[-1,1]$ | $[r_{min},\\infty)$ | $r_i = -R \\log\\left(\\frac{x_i + 1}{2}\\right) + r_{min}$ |\n| {func}`KnowlesRTransform<grid.rtransform.KnowlesRTransform>` | $[-1,1]$ | $[r_{min},\\infty)$ | $r_i = r_{min} - R \\log\\left(1 - 2^{-k}(x_i + 1)^k\\right)$ |\n| {func}`HandyRTransform<grid.rtransform.HandyRTransform>` | $[-1,1]$ | $[r_{min},\\infty)$ | $r_i = R \\left(\\frac{1+x_i}{1-x_i}\\right)^m + r_{min}$ |\n| {func}`HandyModRTransform<grid.rtransform.HandyModRTransform>` | $[-1,1]$ | $[r_{min},r_{max}]$ | $r_i = \\frac{(1+x_i)^m (r_{max} - r_{min})}{2^m(1 - 2^m + r_{max} - r_{min}) - (1+x_i)^m(r_{max}-r_{min}-2^m)} + r_{min}$ |\n",
+   "id": "main-cell"
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/website/requirements.txt b/website/requirements.txt
new file mode 100644
index 000000000..c7e4e4e3f
--- /dev/null
+++ b/website/requirements.txt
@@ -0,0 +1,5 @@
+jupyter-book==1.0.2
+matplotlib
+numpy>=1.23.5
+scipy>=1.15.0
+sympy>=1.4.0
diff --git a/doc/table_angular_lebedev.csv b/website/table_angular_lebedev.csv
similarity index 100%
rename from doc/table_angular_lebedev.csv
rename to website/table_angular_lebedev.csv
diff --git a/doc/table_angular_spherical.csv b/website/table_angular_spherical.csv
similarity index 100%
rename from doc/table_angular_spherical.csv
rename to website/table_angular_spherical.csv
diff --git a/doc/table_onedgrids.csv b/website/table_onedgrids.csv
similarity index 100%
rename from doc/table_onedgrids.csv
rename to website/table_onedgrids.csv

From 76517ada090930fb443a3699f55da3289c7c93ee Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Marco=20Mart=C3=ADnez=20Gonz=C3=A1lez?=
 <mmg870630@gmail.com>
Date: Tue, 28 Apr 2026 11:01:43 -0400
Subject: [PATCH 4/8] Fix issues with webpage

---
 .../One_dimensional_grids.ipynb               |  4 +-
 website/_toc.yml                              |  5 +-
 website/onedgrids.ipynb                       | 54 +++++++++++++++++++
 3 files changed, 60 insertions(+), 3 deletions(-)
 rename {website => notebooks}/One_dimensional_grids.ipynb (99%)
 create mode 100644 website/onedgrids.ipynb

diff --git a/website/One_dimensional_grids.ipynb b/notebooks/One_dimensional_grids.ipynb
similarity index 99%
rename from website/One_dimensional_grids.ipynb
rename to notebooks/One_dimensional_grids.ipynb
index 4e7f3f962..629b66204 100644
--- a/website/One_dimensional_grids.ipynb
+++ b/notebooks/One_dimensional_grids.ipynb
@@ -87,11 +87,13 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
+        "## One-Dimensional Grid Integration\n",
+        "\n",
         "### Example: Integration of One-Variable Function on $[-1, 1]$\n",
         "\n",
         "The one-dimensional grids can accurately integrate one-variable functions over an interval (defined by their domain), even with a small number of points. Here, we numerically compute the integral below and compute the relative percent error. The analytical value of this integral is $\\pi/2$.\n",
         "\n",
-        "$$ \\int_{-1}^1 \\sqrt{1 - x^2} dx \\approx \\sum_{i=0}^{N-1} w_i \\sqrt{1 - x_i^2} $$\n"
+        "$$ \\int_{-1}^1 \\sqrt{1 - x^2} dx \\approx \\sum_{i=0}^{N-1} w_i \\sqrt{1 - x_i^2} $$"
       ]
     },
     {
diff --git a/website/_toc.yml b/website/_toc.yml
index 63943c422..658f125ad 100644
--- a/website/_toc.yml
+++ b/website/_toc.yml
@@ -8,7 +8,7 @@ parts:
     chapters:
       - file: installation
         title: Installation
-      - file: tutorial/onedgrids
+      - file: onedgrids
         title: One-Dimensional Grids
       - file: radial_transf
         title: Radial Transformations
@@ -18,7 +18,8 @@ parts:
   - caption: Example Tutorials
     chapters:
       - file: tutorial/Quickstart
-      - file: One_dimensional_grids
+        title: Quickstart
+      - file: tutorial/One_dimensional_grids
         title: One-Dimensional Grids
       - file: tutorial/Angular_grid
         title: Angular Grids
diff --git a/website/onedgrids.ipynb b/website/onedgrids.ipynb
new file mode 100644
index 000000000..9247ab872
--- /dev/null
+++ b/website/onedgrids.ipynb
@@ -0,0 +1,54 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d57f4021",
+   "metadata": {},
+   "source": [
+    "# One-Dimensional Grids\n",
+    "\n",
+    "There are various choices of one dimensional grids for integrating functions over some finite or infinite\n",
+    "intervals.\n",
+    "\n",
+    "## One-Dimensional Quadrature Grids With Explicit Solutions\n",
+    "\n",
+    "| Grid | Domain | $x_i$ | $w_i$ |\n",
+    "|------|--------|-------|-------|\n",
+    "| {func}`UniformInteger(Horton2)<grid.onedgrid.UniformInteger>` | $[0,N]$ | $i - 1$ | $1$ |\n",
+    "| {func}`Gauss-Chebyshev Type2<grid.onedgrid.GaussChebyshevType2>` | $[-1,1]$ | $\\cos\\left( \\frac{i}{n+1} \\pi \\right)$ | $\\frac{\\pi}{n+1} \\sin^2 \\left( \\frac{i}{n+1} \\pi \\right)$ |\n",
+    "| {func}`Gauss-Chebyshev Lobatto<grid.onedgrid.GaussChebyshevLobatto>` | $[-1,1]$ | $\\cos\\left( \\frac{(i-1)}{n-1}\\pi \\right)$ | $w_{1} = w_{n} = \\frac{\\pi}{2(n-1)}, \\quad w_{i\\neq 1,n} = \\frac{\\pi}{n-1}$ |\n",
+    "| {func}`Trapezoidal Lobatto<grid.onedgrid.Trapezoidal>` | $[-1,1]$ | $-1 + 2 \\left(\\frac{i-1}{n-1}\\right)$ | $w_1 = w_n = \\frac{1}{n}, \\quad w_{i\\neq 1,n} = \\frac{2}{n}$ |\n",
+    "| {func}`Rectangle-Rule Sine End Points<grid.onedgrid.RectangleRuleSineEndPoints>` | $[-1,1]$ | $\\frac{i}{n+1}$ | $\\frac{2}{n+1} \\sum_{m=1}^n \\frac{\\sin(m \\pi x_i)(1-\\cos(m \\pi))}{m \\pi}$ |\n",
+    "| {func}`Rectangle-Rule Sine<grid.onedgrid.RectangleRuleSine>` | $[-1,1]$ | $\\frac{2 i - 1}{2 N_{pts}}$ | $\\frac{2}{n^2 \\pi} \\sin(n\\pi x_i) \\sin^2(n\\pi /2) + \\frac{4}{n \\pi} \\sum_{m=1}^{n-1} \\frac{\\sin(m \\pi x_i)\\sin^2(m\\pi /2)}{m}$ |\n",
+    "| {func}`Tanh Sinh<grid.onedgrid.TanhSinh>` | $[-1,1]$ | $\\tanh\\left( \\frac{\\pi}{2} \\sinh(i\\delta) \\right)$ | $\\frac{\\frac{\\pi}{2}\\delta \\cosh(i\\delta)}{\\cosh^2(\\frac{\\pi}{2}\\sinh(i\\delta))}$ |\n",
+    "| {func}`Simpson<grid.onedgrid.Simpson>` | $[-1,1]$ | $-1 + 2 \\left(\\frac{i-1}{N_{pts}-1}\\right)$ | $w_i = 2 / (3(N - 1))$ if $i = 0$, $8 / (3(N - 1))$ if $i \\geq 1$ and odd, $4 / (3(N - 1))$ if $i \\geq 2$ and even |\n",
+    "| {func}`MidPoint<grid.onedgrid.MidPoint>` | $[-1,1]$ | $-1 + \\frac{2i + 1}{N_{pts}}$ | $\\frac{2}{n}$ |\n",
+    "| {func}`Clenshaw-Curtis<grid.onedgrid.ClenshawCurtis>` | $[-1,1]$ | $\\cos (\\pi (i - 1) / (N_{pts} - 1))$ | complex weight formula |\n",
+    "| {func}`Fejer First<grid.onedgrid.FejerFirst>` | $(-1,1)$ | $\\cos\\bigg(\\frac{(2i - 1)\\pi}{2N_{pts}}\\bigg)$ | complex weight formula |\n",
+    "| {func}`Fejer Second<grid.onedgrid.FejerSecond>` | $(-1,1)$ | $\\cos(i \\pi / N_{pts})$ | complex weight formula |\n",
+    "\n",
+    "Grid also includes popular quadrature grids that can integrate polynomials up to degree $2n - 1$:\n",
+    "\n",
+    "- {func}`Gauss-Legendre<grid.onedgrid.GaussLegendre>` (domain $[-1, 1]$),\n",
+    "- {func}`Gauss-Chebyshev<grid.onedgrid.GaussChebyshev>` (domain $[-1, 1]$),\n",
+    "- {func}`Gauss-Laguerre<grid.onedgrid.GaussLaguerre>` (domain $[0, \\infty)$).\n",
+    "\n",
+    "The Trefethen polynomial transformation of various grids on $[-1, 1]$ is also included:\n",
+    "\n",
+    "- {func}`Trefethen (Clenshaw-Curtis)<grid.onedgrid.TrefethenCC>`,\n",
+    "- {func}`Trefethen (Gauss-Chebyshev)<grid.onedgrid.TrefethenGC2>`,\n",
+    "- {func}`Trefethen (General)<grid.onedgrid.TrefethenGeneral>`,\n",
+    "- {func}`Trefethen Strip (Clenshaw-Curtis)<grid.onedgrid.TrefethenStripCC>`,\n",
+    "- {func}`Trefethen Strip (Gauss-Chebyshev)<grid.onedgrid.TrefethenStripGC2>`,\n",
+    "- {func}`Trefethen Strip (General)<grid.onedgrid.TrefethenStripGeneral>`."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 9b5a6a85d8a7dcd5de5b6b6c7d5a93250a147f51 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Marco=20Mart=C3=ADnez=20Gonz=C3=A1lez?=
 <mmg870630@gmail.com>
Date: Tue, 28 Apr 2026 13:02:31 -0400
Subject: [PATCH 5/8] Add fixes to website generation

---
 .github/workflows/website.yaml      |   3 +-
 website/_config.yml                 |   2 +-
 website/_toc.yml                    |  40 ++++---
 website/conventions.ipynb           | 134 ++++++++++++++++++++++-
 website/intro.md                    |  24 ++--
 website/table_angular_lebedev.csv   |  32 ------
 website/table_angular_spherical.csv | 163 ----------------------------
 website/table_onedgrids.csv         |  13 ---
 8 files changed, 165 insertions(+), 246 deletions(-)
 delete mode 100644 website/table_angular_lebedev.csv
 delete mode 100644 website/table_angular_spherical.csv
 delete mode 100644 website/table_onedgrids.csv

diff --git a/.github/workflows/website.yaml b/.github/workflows/website.yaml
index 84831f566..74909c0de 100644
--- a/.github/workflows/website.yaml
+++ b/.github/workflows/website.yaml
@@ -44,7 +44,8 @@ jobs:
       # Build the API docs
       - name: Build the API documentation
         run: |
-          sphinx-apidoc -o website/_autosummary src/grid src/grid/tests/ src/grid/test/ src/grid/data/ -e -f
+          rm -rf website/pyapi
+          python -m sphinx.ext.apidoc -o website/pyapi src/grid src/grid/tests/ src/grid/test/ src/grid/data/ -e -f
 
       # Copy notebooks into the book so CI always builds from grid/notebooks
       - name: Stage notebooks for website build
diff --git a/website/_config.yml b/website/_config.yml
index 53f2649ed..ccf49ce43 100644
--- a/website/_config.yml
+++ b/website/_config.yml
@@ -77,4 +77,4 @@ copyright: "2022-2024"
 html:
   use_issues_button: true
   use_repository_button: true
-  favicon: favicon.png
+  favicon: grid_logo_website.svg
diff --git a/website/_toc.yml b/website/_toc.yml
index 658f125ad..9003197f9 100644
--- a/website/_toc.yml
+++ b/website/_toc.yml
@@ -40,30 +40,34 @@ parts:
 
   - caption: API Documentation
     chapters:
-      - file: _autosummary/grid
+      - file: pyapi/grid
         title: grid
         sections:
-          - file: _autosummary/grid.onedgrid
-            title: grid.onedgrid
-          - file: _autosummary/grid.rtransform
-            title: grid.rtransform
-          - file: _autosummary/grid.angular
+          - file: pyapi/grid.angular
             title: grid.angular
-          - file: _autosummary/grid.atomgrid
+          - file: pyapi/grid.atomgrid
             title: grid.atomgrid
-          - file: _autosummary/grid.molgrid
-            title: grid.molgrid
-          - file: _autosummary/grid.becke
+          - file: pyapi/grid.basegrid
+            title: grid.basegrid
+          - file: pyapi/grid.becke
             title: grid.becke
-          - file: _autosummary/grid.hirshfeld
-            title: grid.hirshfeld
-          - file: _autosummary/grid.cubic
+          - file: pyapi/grid.cubic
             title: grid.cubic
-          - file: _autosummary/grid.periodicgrid
-            title: grid.periodicgrid
-          - file: _autosummary/grid.ode
+          - file: pyapi/grid.hirshfeld
+            title: grid.hirshfeld
+          - file: pyapi/grid.molgrid
+            title: grid.molgrid
+          - file: pyapi/grid.ngrid
+            title: grid.ngrid
+          - file: pyapi/grid.ode
             title: grid.ode
-          - file: _autosummary/grid.poisson
+          - file: pyapi/grid.onedgrid
+            title: grid.onedgrid
+          - file: pyapi/grid.periodicgrid
+            title: grid.periodicgrid
+          - file: pyapi/grid.poisson
             title: grid.poisson
-          - file: _autosummary/grid.utils
+          - file: pyapi/grid.rtransform
+            title: grid.rtransform
+          - file: pyapi/grid.utils
             title: grid.utils
diff --git a/website/conventions.ipynb b/website/conventions.ipynb
index f3fdaa1c0..fa4a49774 100644
--- a/website/conventions.ipynb
+++ b/website/conventions.ipynb
@@ -2,21 +2,143 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "main-cell",
    "metadata": {},
-   "source": "# Conventions\n\n## Spherical Coordinates\n\nSpherical coordinates $(r, \\theta, \\phi)$ are used extensively throughout the molecular, atomic,\nand angular grid modules.\nThe radius $r \\in [0, \\infty)$, azimuthal $\\theta \\in [-\\pi, \\pi]$ and polar\n$\\phi \\in [0, \\pi)$ angles are defined from Cartesian coordinates as:\n\n$$\n\\begin{align}\n    r &= \\sqrt{x^2 + y^2 + z^2}\\\\\n    \\theta &= \\text{arctan2} \\bigg(\\frac{y}{x}\\bigg)\\\\\n    \\phi &= \\arccos \\bigg(\\frac{z}{r}\\bigg)\n\\end{align}\n$$\n\nsuch that when the radius is zero $r=0$, the angles are zero $\\theta, \\phi = 0$.\n\nGrid offers a {class}`utility<grid.utils.convert_cart_to_sph>` function to convert to spherical coordinates:\n\n```python\nimport numpy as np\nfrom grid.utils import convert_cart_to_sph\n\n# Generate random set of points\ncart_pts = np.random.uniform((-100, 100), size=(1000, 3))\n# Convert to spherical coordinates\nspher_pts = convert_cart_to_sph(cart_pts)\n# Convert to spherical coordinates, centered at [1, 1, 1]\nspher_pts = convert_cart_to_sph(cart_pts, center=np.array([1.0, 1.0, 1.0]))\n```\n\nThe {class}`atomic grid<grid.atomgrid.AtomGrid>` class can also convert its points to spherical coordinates:\n\n```python\nfrom grid.atomgrid import AtomGrid\n\natom_grid = AtomGrid(...)\nspher_pts = atom_grid.convert_cartesian_to_spherical()\n```\n\nWith the convention that when $r=0$, the angles within $\\{(0, \\theta_j, \\phi_j)\\}$ in that shell are\nobtained from an angular grid $\\{(\\theta_j, \\phi_j)\\}$ with some degree.\n\n## Spherical Harmonics\n\nThe real spherical harmonics are defined using complex spherical harmonics:\n\n$$\nY_{lm}(\\theta, \\phi) = \\begin{cases}\n    i(Y^m_l(\\theta, \\phi) - (-1)^m Y_l^{-m}(\\theta, \\phi)) & \\text{if } m < 0 \\\\\n    Y_l^0 & \\text{if } m = 0 \\\\\n    (Y^{-|m|}_{l}(\\theta, \\phi) + (-1)^m Y_l^m(\\theta, \\phi)) & \\text{if } m > 0\n\\end{cases},\n$$\n\nwhere the degree $l \\in \\mathbb{N}$, order $m \\in \\{-l, \\cdots, l\\}$ and $Y^m_l$ is the complex spherical harmonic.\nThe Condon-Shortley phase is not included.\n\nAlternatively, using the associated Legendre polynomials $P_l^m$ (without the Condon-Shortley phase):\n\n$$\nY_{lm}(\\theta, \\phi) = \\begin{cases}\n    \\sqrt{\\frac{(2l + 1)(l - m)!}{4\\pi(l + m)!}} \\sqrt{2} \\cos(m\\theta) P_l^m(\\cos(\\phi)) & \\text{if } m < 0 \\\\\n    \\frac{1}{2}\\sqrt{\\frac{1}{\\pi}} & \\text{if } m = 0 \\\\\n    \\sqrt{\\frac{(2l + 1)(l - m)!}{4\\pi(l + m)!}} \\sqrt{2} \\sin(m\\theta) P_l^m(\\cos(\\phi)) & \\text{if } m > 0\n\\end{cases}\n$$\n\nGrid offers the {func}`generate_real_spherical_harmonics<grid.utils.generate_real_spherical_harmonics>` function:\n\n```python\nfrom grid.utils import generate_real_spherical_harmonics\n\nspher_pts = np.array(...)\ntheta = spher_pts[:, 1]\nphi = spher_pts[:, 2]\n# Generate all degrees up to l=2\nspherical_harmonics = generate_real_spherical_harmonics(2, theta, phi)\n```\n\n### Ordering\n\nThe spherical harmonics are ordered by degree $l$ in ascending order.\nFor each degree $l$, the orders $m$ are in HORTON2 order:\n\n$$m = [0, 1, -1, 2, -2, \\cdots, l, -l].$$\n\n## Angular Grids\n\nThe {class}`angular grids<grid.angular.AngularGrid>` integrate functions over the unit sphere.\nThe quadrature weights are chosen so that the spherical harmonics are orthonormal:\n\n$$\n\\int_{-\\pi}^{\\pi} \\int_0^{\\pi} Y_{l_1}^{m_1} Y_{l_2}^{m_2} \\sin(\\phi)\\, d\\theta\\, d\\phi = \\delta_{l_1, l_2} \\delta_{m_1, m_2}\n$$\n\nThe weights also sum to $4\\pi$.\n\n### Nested Grids\n\nAngular grids of different degrees can be very close to one another. The following shows the mean with\nstandard deviation and maximum distance between an angular grid of one degree and the next higher degree.\nThe * indicates Lebedev-Laikov grids with negative weights.\n\n```{csv-table} Lebedev-Laikov Grids\n:file: table_angular_lebedev.csv\n:widths: 11,11,11,11\n:delim: ;\n:header-rows: 1\n:align: center\n```\n\n```{csv-table} Symmetric Spherical t-Design Grids\n:file: table_angular_spherical.csv\n:widths: 11,11,11,11\n:delim: ;\n:header-rows: 1\n:align: center\n```\n",
-   "id": "main-cell"
+   "source": [
+    "# Conventions\n",
+    "\n",
+    "## Spherical Coordinates\n",
+    "\n",
+    "Spherical coordinates $(r, \\theta, \\phi)$ are used extensively throughout the molecular, atomic,\n",
+    "and angular grid modules.\n",
+    "The radius $r \\in [0, \\infty)$, azimuthal $\\theta \\in [-\\pi, \\pi]$ and polar\n",
+    "$\\phi \\in [0, \\pi)$ angles are defined from Cartesian coordinates as:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align}\n",
+    "    r &= \\sqrt{x^2 + y^2 + z^2}\\\\\n",
+    "    \\theta &= \\text{arctan2} \\bigg(\\frac{y}{x}\\bigg)\\\\\n",
+    "    \\phi &= \\arccos \\bigg(\\frac{z}{r}\\bigg)\n",
+    "\\end{align}\n",
+    "$$\n",
+    "\n",
+    "such that when the radius is zero $r=0$, the angles are zero $\\theta, \\phi = 0$.\n",
+    "\n",
+    "Grid offers a {class}`utility<grid.utils.convert_cart_to_sph>` function to convert to spherical coordinates:\n",
+    "\n",
+    "```python\n",
+    "import numpy as np\n",
+    "from grid.utils import convert_cart_to_sph\n",
+    "\n",
+    "# Generate random set of points\n",
+    "cart_pts = np.random.uniform((-100, 100), size=(1000, 3))\n",
+    "# Convert to spherical coordinates\n",
+    "spher_pts = convert_cart_to_sph(cart_pts)\n",
+    "# Convert to spherical coordinates, centered at [1, 1, 1]\n",
+    "spher_pts = convert_cart_to_sph(cart_pts, center=np.array([1.0, 1.0, 1.0]))\n",
+    "```\n",
+    "\n",
+    "The {class}`atomic grid<grid.atomgrid.AtomGrid>` class can also convert its points to spherical coordinates:\n",
+    "\n",
+    "```python\n",
+    "from grid.atomgrid import AtomGrid\n",
+    "\n",
+    "atom_grid = AtomGrid(...)\n",
+    "spher_pts = atom_grid.convert_cartesian_to_spherical()\n",
+    "```\n",
+    "\n",
+    "With the convention that when $r=0$, the angles within $\\{(0, \\theta_j, \\phi_j)\\}$ in that shell are\n",
+    "obtained from an angular grid $\\{(\\theta_j, \\phi_j)\\}$ with some degree.\n",
+    "\n",
+    "## Spherical Harmonics\n",
+    "\n",
+    "The real spherical harmonics are defined using complex spherical harmonics:\n",
+    "\n",
+    "$$\n",
+    "Y_{lm}(\\theta, \\phi) = \\begin{cases}\n",
+    "    i(Y^m_l(\\theta, \\phi) - (-1)^m Y_l^{-m}(\\theta, \\phi)) & \\text{if } m < 0 \\\\\n",
+    "    Y_l^0 & \\text{if } m = 0 \\\\\n",
+    "    (Y^{-|m|}_{l}(\\theta, \\phi) + (-1)^m Y_l^m(\\theta, \\phi)) & \\text{if } m > 0\n",
+    "\\end{cases},\n",
+    "$$\n",
+    "\n",
+    "where the degree $l \\in \\mathbb{N}$, order $m \\in \\{-l, \\cdots, l\\}$ and $Y^m_l$ is the complex spherical harmonic.\n",
+    "The Condon-Shortley phase is not included.\n",
+    "\n",
+    "Alternatively, using the associated Legendre polynomials $P_l^m$ (without the Condon-Shortley phase):\n",
+    "\n",
+    "$$\n",
+    "Y_{lm}(\\theta, \\phi) = \\begin{cases}\n",
+    "    \\sqrt{\\frac{(2l + 1)(l - m)!}{4\\pi(l + m)!}} \\sqrt{2} \\cos(m\\theta) P_l^m(\\cos(\\phi)) & \\text{if } m < 0 \\\\\n",
+    "    \\frac{1}{2}\\sqrt{\\frac{1}{\\pi}} & \\text{if } m = 0 \\\\\n",
+    "    \\sqrt{\\frac{(2l + 1)(l - m)!}{4\\pi(l + m)!}} \\sqrt{2} \\sin(m\\theta) P_l^m(\\cos(\\phi)) & \\text{if } m > 0\n",
+    "\\end{cases}\n",
+    "$$\n",
+    "\n",
+    "Grid offers the {func}`generate_real_spherical_harmonics<grid.utils.generate_real_spherical_harmonics>` function:\n",
+    "\n",
+    "```python\n",
+    "from grid.utils import generate_real_spherical_harmonics\n",
+    "\n",
+    "spher_pts = np.array(...)\n",
+    "theta = spher_pts[:, 1]\n",
+    "phi = spher_pts[:, 2]\n",
+    "# Generate all degrees up to l=2\n",
+    "spherical_harmonics = generate_real_spherical_harmonics(2, theta, phi)\n",
+    "```\n",
+    "\n",
+    "### Ordering\n",
+    "\n",
+    "The spherical harmonics are ordered by degree $l$ in ascending order.\n",
+    "For each degree $l$, the orders $m$ are in HORTON2 order:\n",
+    "\n",
+    "$$m = [0, 1, -1, 2, -2, \\cdots, l, -l].$$\n",
+    "\n",
+    "## Angular Grids\n",
+    "\n",
+    "The {class}`angular grids<grid.angular.AngularGrid>` integrate functions over the unit sphere.\n",
+    "The quadrature weights are chosen so that the spherical harmonics are orthonormal:\n",
+    "\n",
+    "$$\n",
+    "\\int_{-\\pi}^{\\pi} \\int_0^{\\pi} Y_{l_1}^{m_1} Y_{l_2}^{m_2} \\sin(\\phi)\\, d\\theta\\, d\\phi = \\delta_{l_1, l_2} \\delta_{m_1, m_2}\n",
+    "$$\n",
+    "\n",
+    "The weights also sum to $4\\pi$.\n",
+    "\n",
+    "### Nested Grids\n",
+    "\n",
+    "Angular grids of different degrees can be very close to one another. The following shows the mean with\n",
+    "standard deviation and maximum distance between an angular grid of one degree and the next higher degree.\n",
+    "The * indicates Lebedev-Laikov grids with negative weights.\n",
+    "\n",
+    "```{csv-table} Lebedev-Laikov Grids\n",
+    ":file: ./data/table_angular_lebedev.csv\n",
+    ":widths: 11,11,11,11\n",
+    ":delim: ;\n",
+    ":header-rows: 1\n",
+    ":align: center\n",
+    "```\n",
+    "\n",
+    "```{csv-table} Symmetric Spherical t-Design Grids\n",
+    ":file: ./data/table_angular_spherical.csv\n",
+    ":widths: 11,11,11,11\n",
+    ":delim: ;\n",
+    ":header-rows: 1\n",
+    ":align: center\n",
+    "```\n"
+   ]
   }
  ],
  "metadata": {
-  "language_info": {
-   "name": "python"
-  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/website/intro.md b/website/intro.md
index db4cacefa..438eb8751 100644
--- a/website/intro.md
+++ b/website/intro.md
@@ -58,15 +58,15 @@ The [Grid source code](https://github.com/theochem/grid) is hosted on GitHub and
 
 | Module | Purpose |
 | --- | --- |
-| [`grid.onedgrid`](_autosummary/grid.onedgrid) | One-dimensional quadrature grids and integration |
-| [`grid.rtransform`](_autosummary/grid.rtransform) | Radial transformations between intervals |
-| [`grid.angular`](_autosummary/grid.angular) | Angular grids for spherical integration |
-| [`grid.atomgrid`](_autosummary/grid.atomgrid) | Atomic grids for 3D integration |
-| [`grid.molgrid`](_autosummary/grid.molgrid) | Molecular grids with atom-in-molecule weights |
-| [`grid.becke`](_autosummary/grid.becke) | Becke weight functions |
-| [`grid.hirshfeld`](_autosummary/grid.hirshfeld) | Hirshfeld weight functions |
-| [`grid.cubic`](_autosummary/grid.cubic) | Hyper-rectangular grids in 2D or 3D |
-| [`grid.periodicgrid`](_autosummary/grid.periodicgrid) | Periodic grids for 1D, 2D, or 3D |
-| [`grid.ode`](_autosummary/grid.ode) | ODE solver |
-| [`grid.poisson`](_autosummary/grid.poisson) | Poisson equation solver |
-| [`grid.utils`](_autosummary/grid.utils) | Utility functions for coordinate transformations and spherical harmonics |
+| [`grid.onedgrid`](pyapi/grid.onedgrid) | One-dimensional quadrature grids and integration |
+| [`grid.rtransform`](pyapi/grid.rtransform) | Radial transformations between intervals |
+| [`grid.angular`](pyapi/grid.angular) | Angular grids for spherical integration |
+| [`grid.atomgrid`](pyapi/grid.atomgrid) | Atomic grids for 3D integration |
+| [`grid.molgrid`](pyapi/grid.molgrid) | Molecular grids with atom-in-molecule weights |
+| [`grid.becke`](pyapi/grid.becke) | Becke weight functions |
+| [`grid.hirshfeld`](pyapi/grid.hirshfeld) | Hirshfeld weight functions |
+| [`grid.cubic`](pyapi/grid.cubic) | Hyper-rectangular grids in 2D or 3D |
+| [`grid.periodicgrid`](pyapi/grid.periodicgrid) | Periodic grids for 1D, 2D, or 3D |
+| [`grid.ode`](pyapi/grid.ode) | ODE solver |
+| [`grid.poisson`](pyapi/grid.poisson) | Poisson equation solver |
+| [`grid.utils`](pyapi/grid.utils) | Utility functions for coordinate transformations and spherical harmonics |
diff --git a/website/table_angular_lebedev.csv b/website/table_angular_lebedev.csv
deleted file mode 100644
index b36164844..000000000
--- a/website/table_angular_lebedev.csv
+++ /dev/null
@@ -1,32 +0,0 @@
-Degree;Number Points;Mean (std) Distance; Max Distance
-3;6;0.0(0.0);0.0
-5;18;0.0(0.0);0.0
-7;26;0.14(0.15);0.31
-9;38;0.19(0.15);0.31
-11;50;0.15(0.15);0.31
-13*;74;0.11(0.082);0.2
-15;86;0.052(0.042);0.12
-17;110;0.065(0.047);0.14
-19;146;0.048(0.048);0.14
-21;170;0.035(0.027);0.089
-23;194;0.061(0.047);0.16
-25*;230;0.058(0.036);0.12
-27*;266;0.045(0.035);0.13
-29;302;0.055(0.036);0.14
-31;350;0.042(0.028);0.1
-35;434;0.054(0.021);0.092
-41;590;0.048(0.019);0.082
-47;770;0.043(0.017);0.074
-53;974;0.038(0.015);0.065
-59;1202;0.035(0.014);0.06
-65;1454;0.032(0.012);0.054
-71;1730;0.03(0.011);0.05
-77;2030;0.027(0.011);0.047
-83;2354;0.026(0.0099);0.043
-89;2702;0.024(0.0093);0.041
-95;3074;0.023(0.0088);0.039
-101;3470;0.021(0.0083);0.036
-107;3890;0.02(0.0078);0.035
-113;4334;0.019(0.0075);0.033
-119;4802;0.018(0.007);0.031
-125;5294;0.018(0.0066);0.03
diff --git a/website/table_angular_spherical.csv b/website/table_angular_spherical.csv
deleted file mode 100644
index fee7a8942..000000000
--- a/website/table_angular_spherical.csv
+++ /dev/null
@@ -1,163 +0,0 @@
-Degree;Number Points;Mean (std) Distance; Max Distance
-1;2;0.0(0.0);0.0
-3;6;0.34(0.24);0.55
-5;12;0.22(0.13);0.35
-7;32;0.16(0.076);0.26
-9;48;0.14(0.071);0.26
-11;70;0.14(0.045);0.23
-13;94;0.12(0.047);0.2
-15;120;0.11(0.041);0.19
-17;156;0.088(0.038);0.16
-19;192;0.078(0.036);0.14
-21;234;0.077(0.033);0.14
-23;278;0.075(0.027);0.13
-25;328;0.066(0.029);0.11
-27;380;0.061(0.023);0.11
-29;438;0.057(0.024);0.1
-31;498;0.06(0.021);0.1
-33;564;0.047(0.02);0.094
-35;632;0.05(0.019);0.085
-37;706;0.048(0.018);0.085
-39;782;0.039(0.017);0.074
-41;864;0.045(0.017);0.079
-43;948;0.043(0.015);0.074
-45;1038;0.041(0.015);0.071
-47;1130;0.037(0.014);0.068
-49;1228;0.033(0.014);0.064
-51;1328;0.034(0.013);0.062
-53;1434;0.034(0.013);0.061
-55;1542;0.03(0.012);0.054
-57;1656;0.034(0.011);0.057
-59;1772;0.03(0.011);0.054
-61;1894;0.025(0.011);0.05
-63;2018;0.029(0.011);0.052
-65;2148;0.029(0.011);0.052
-67;2280;0.025(0.01);0.05
-69;2418;0.027(0.0095);0.048
-71;2558;0.027(0.0093);0.048
-73;2704;0.022(0.0095);0.045
-75;2852;0.024(0.0091);0.044
-77;3006;0.02(0.0085);0.041
-79;3162;0.023(0.0086);0.041
-81;3324;0.023(0.0084);0.042
-83;3488;0.02(0.0081);0.04
-85;3658;0.022(0.0083);0.04
-87;3830;0.022(0.0081);0.039
-89;4008;0.018(0.0076);0.036
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-93;4374;0.018(0.0075);0.036
-95;4562;0.02(0.0074);0.036
-97;4756;0.019(0.007);0.034
-99;4952;0.016(0.0067);0.031
-101;5154;0.018(0.0067);0.034
-103;5358;0.016(0.0064);0.031
-105;5568;0.018(0.0064);0.031
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-109;5998;0.015(0.0062);0.03
-111;6218;0.017(0.0062);0.03
-113;6444;0.017(0.0061);0.029
-115;6672;0.014(0.006);0.027
-117;6906;0.016(0.006);0.029
-119;7142;0.016(0.0056);0.029
-121;7384;0.016(0.0056);0.028
-123;7628;0.015(0.0056);0.027
-125;7878;0.013(0.0054);0.027
-127;8130;0.015(0.0056);0.027
-129;8388;0.014(0.0053);0.027
-131;8648;0.012(0.0051);0.025
-133;8914;0.014(0.0054);0.025
-135;9182;0.012(0.005);0.025
-137;9456;0.014(0.0052);0.025
-139;9732;0.014(0.0048);0.024
-141;10014;0.013(0.005);0.025
-143;10298;0.011(0.0047);0.023
-145;10588;0.013(0.0049);0.023
-147;10880;0.01(0.0047);0.022
-149;11178;0.013(0.0047);0.023
-151;11478;0.013(0.0045);0.023
-153;11784;0.01(0.0044);0.022
-155;12092;0.012(0.0045);0.022
-157;12406;0.01(0.0043);0.021
-159;12722;0.012(0.0044);0.022
-161;13044;0.012(0.0044);0.021
-163;13368;0.0096(0.0042);0.02
-165;13698;0.011(0.0043);0.021
-167;14030;0.011(0.0042);0.02
-169;14368;0.0092(0.004);0.02
-171;14708;0.011(0.004);0.02
-173;15054;0.011(0.004);0.02
-175;15402;0.0086(0.0039);0.02
-177;15756;0.01(0.004);0.019
-179;16112;0.0085(0.0039);0.018
-181;16474;0.01(0.0038);0.019
-183;16838;0.011(0.0037);0.019
-185;17208;0.01(0.0038);0.018
-187;17580;0.0087(0.0036);0.018
-189;17958;0.0081(0.0036);0.017
-191;18338;0.0098(0.0036);0.018
-193;18724;0.0099(0.0036);0.018
-195;19112;0.0098(0.0035);0.018
-197;19506;0.0078(0.0034);0.017
-199;19902;0.0076(0.0034);0.017
-201;20304;0.0095(0.0034);0.017
-203;20708;0.0094(0.0033);0.017
-205;21118;0.0075(0.0033);0.016
-207;21530;0.0091(0.0033);0.017
-209;21948;0.009(0.0033);0.016
-211;22368;0.0089(0.0033);0.016
-213;22794;0.0072(0.0032);0.016
-215;23222;0.009(0.0032);0.016
-217;23656;0.0071(0.0031);0.016
-219;24092;0.0087(0.0032);0.016
-221;24534;0.0069(0.0031);0.015
-223;24978;0.0086(0.0031);0.016
-225;25428;0.0084(0.003);0.015
-227;25880;0.0066(0.003);0.015
-229;26338;0.0083(0.003);0.015
-231;26798;0.0082(0.003);0.015
-233;27264;0.0065(0.003);0.015
-235;27732;0.0081(0.003);0.015
-237;28206;0.0064(0.0028);0.015
-239;28682;0.0079(0.0029);0.015
-241;29164;0.0062(0.0028);0.014
-243;29648;0.006(0.0028);0.014
-245;30138;0.0078(0.0028);0.014
-247;30630;0.0077(0.0028);0.014
-249;31128;0.006(0.0027);0.013
-251;31628;0.0076(0.0028);0.014
-253;32134;0.0076(0.0027);0.014
-255;32642;0.0075(0.0027);0.014
-257;33156;0.0059(0.0026);0.013
-259;33672;0.0074(0.0026);0.013
-261;34194;0.0058(0.0026);0.013
-263;34718;0.0071(0.0027);0.013
-265;35248;0.0055(0.0025);0.013
-267;35780;0.0071(0.0026);0.013
-269;36318;0.0071(0.0026);0.013
-271;36858;0.0054(0.0024);0.012
-273;37404;0.007(0.0025);0.013
-275;37952;0.007(0.0025);0.012
-277;38506;0.0054(0.0024);0.012
-279;39062;0.0068(0.0025);0.012
-281;39624;0.0052(0.0024);0.012
-283;40188;0.0068(0.0025);0.012
-285;40758;0.0068(0.0024);0.012
-287;41330;0.0066(0.0025);0.012
-289;41908;0.0051(0.0023);0.011
-291;42488;0.0065(0.0024);0.012
-293;43074;0.0051(0.0023);0.011
-295;43662;0.0065(0.0023);0.012
-297;44256;0.005(0.0023);0.011
-299;44852;0.0064(0.0023);0.011
-301;45454;0.0064(0.0023);0.011
-303;46058;0.0063(0.0023);0.011
-305;46668;0.0049(0.0023);0.011
-307;47280;0.0062(0.0023);0.011
-309;47898;0.0047(0.0022);0.011
-311;48518;0.0061(0.0022);0.011
-313;49144;0.0047(0.0022);0.011
-315;49772;0.0061(0.0022);0.011
-317;50406;0.0059(0.0022);0.011
-319;51042;0.0059(0.0022);0.011
-321;51684;0.0046(0.0021);0.011
-323;52328;0.0047(0.0021);0.01
diff --git a/website/table_onedgrids.csv b/website/table_onedgrids.csv
deleted file mode 100644
index 91f885f85..000000000
--- a/website/table_onedgrids.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-Grid;Domain;:math:`x_i`;:math:`w_i`
-:func:`UniformInteger(Horton2)<grid.onedgrid.UniformInteger>`;:math:`[0,N]`;:math:`i - 1`;1
-:func:`Gauss-Chebyshev Type2<grid.onedgrid.GaussChebyshevType2>`;:math:`[-1,1]`;:math:`\cos\left( \frac{i}{n+1} \pi \right)`;:math:`\frac{\pi}{n+1} \sin^2 \left( \frac{i}{n+1} \pi \right)``
-:func:`Gauss-Chebyshev Lobatto<grid.onedgrid.GaussChebyshevLobatto>`;:math:`[-1,1]`;:math:`\cos\left( \frac{(i-1)}{n-1}\pi \right)`;:math:`w_{1} = w_{n} = \frac{\pi}{2(n-1)}, \quad w_{i\neq 1,n} = \frac{\pi}{n-1}`
-:func:`Trapezoidal Lobatto<grid.onedgrid.Trapezoidal>`;:math:`[-1,1]``;:math:`-1 + 2 \left(\frac{i-1}{n-1}\right)`;:math:`w_1 = w_n = \frac{1}{n}, \quad w_{i\neq 1,n} = \frac{2}{n}``
-:func:`Rectangle-Rule Sine End Points<grid.onedgrid.RectangleRuleSineEndPoints>`;:math:`[-1,1]`;:math:`\frac{i}{n+1}`;:math:`\frac{2}{n+1} \sum_{m=1}^n \frac{\sin(m \pi x_i)(1-\cos(m \pi))}{m \pi}`
-:func:`Rectangle-Rule Sine<grid.onedgrid.RectangleRuleSine>`;:math:`[-1,1]`;:math:`\frac{2 i - 1}{2 N_{pts}}`;:math:`\frac{2}{n^2 \pi} \sin(n\pi x_i) \sin^2(n\pi /2) + \frac{4}{n \pi} \sum_{m=1}^{n-1} \frac{\sin(m \pi x_i)\sin^2(m\pi /2)}{m}`
-:func:`Tanh Sinh<grid.onedgrid.TanhSinh>`;:math:`[-1,1]`;:math:`\tanh\left( \frac{\pi}{2} \sinh(i\delta) \right)`;:math:`\frac{\frac{\pi}{2}\delta \cosh(i\delta)}{\cosh^2(\frac{\pi}{2}\sinh(i\delta))}`
-:func:`Simpson<grid.onedgrid.Simpson>`;:math:`[-1,1]`;:math:`-1 + 2 \left(\frac{i-1}{N_{pts}-1}\right)`;:math:`w_i = 2 / (3(N - 1)) \text{ if } i = 0, \quad  8 / (3(N - 1)) \text{ if } i \geq 1 \text{ and is odd}, \quad 4 / (3(N - 1)) \text{ if } i \geq 2 \text{ and is even}.`
-:func:`MidPoint<grid.onedgrid.MidPoint>`;:math:`[-1,1]`;:math:`-1 + \frac{2i + 1}{N_{pts}}`;:math:`\frac{2}{n}`
-:func:`Clenshaw-Curtis<grid.onedgrid.ClenshawCurtis>`;:math:`[-1,1]`;:math:`\cos (\pi (i - 1) / (N_{pts} - 1))`;:math:`w_i = \frac{c_k}{n} \bigg(1 - \sum_{j=1}^{\lfloor n/2 \rfloor} \frac{b_j}{4j^2 - 1} \cos(2j\theta_i) \bigg), \quad  b_j =  1 \text{ if } j = n/2, \quad 2  \text{ if } j < n/2, \quad  c_j = 1  \text{ if } k \in \{0, n\}, \quad  2  \text{ else}`
-:func:`Fejer First<grid.onedgrid.FejerFirst>`;:math:`(-1,1)`;:math:`\cos\bigg(\frac{(2i - 1)\pi}{2N_{pts}}\bigg)`;:math:`\frac{2}{n}\bigg(1 - 2 \sum_{j=1}^{\lfloor n/2 \rfloor} \frac{\cos(2j \theta_j)}{4 j^2 - 1} \bigg)`
-:func:`Fejer Second<grid.onedgrid.FejerSecond>`;:math:`(-1,1)`;:math:`\cos(i \pi / N_{pts})`;:math:`\frac{4 \sin(\theta_i)}{n} \sum_{j=1}^{\lfloor n/2 \rfloor} \frac{\sin(2j - 1)\theta_i}{2j - 1}`

From b12292628ac53282abef3887a2d6d2554af5c8c2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Marco=20Mart=C3=ADnez=20Gonz=C3=A1lez?=
 <mmg870630@gmail.com>
Date: Tue, 28 Apr 2026 13:57:09 -0400
Subject: [PATCH 6/8] Fix inconsistency between notebook execution and website
 actions

---
 .github/workflows/website.yaml                |   6 +-
 {notebooks => examples}/Angular_grid.ipynb    |   0
 {notebooks => examples}/Atom_Grid.ipynb       |   0
 .../Atom_Grid_Construction.ipynb              |   0
 {notebooks => examples}/Cubic_Grids.ipynb     |   0
 .../Interpolation_and_Poisson.ipynb           |  27 +-
 {notebooks => examples}/JCP_Paper.ipynb       |   0
 {notebooks => examples}/Molecular_Grid.ipynb  |   0
 .../Molecular_Grid_Construction.ipynb         |   0
 .../Multipole_Moments.ipynb                   |   0
 .../One_dimensional_grids.ipynb               |   0
 examples/One_dimensional_grids.ipynb.orig     | 406 ++++++++++++++++++
 {notebooks => examples}/Quickstart.ipynb      |   0
 src/grid/basegrid.py                          |   4 +-
 src/grid/ngrid.py                             |   4 +-
 website/_config.yml                           |  13 +-
 website/_toc.yml                              |  32 +-
 website/data/table_angular_lebedev.csv        |  32 ++
 website/data/table_angular_spherical.csv      | 163 +++++++
 19 files changed, 625 insertions(+), 62 deletions(-)
 rename {notebooks => examples}/Angular_grid.ipynb (100%)
 rename {notebooks => examples}/Atom_Grid.ipynb (100%)
 rename {notebooks => examples}/Atom_Grid_Construction.ipynb (100%)
 rename {notebooks => examples}/Cubic_Grids.ipynb (100%)
 rename {notebooks => examples}/Interpolation_and_Poisson.ipynb (94%)
 rename {notebooks => examples}/JCP_Paper.ipynb (100%)
 rename {notebooks => examples}/Molecular_Grid.ipynb (100%)
 rename {notebooks => examples}/Molecular_Grid_Construction.ipynb (100%)
 rename {notebooks => examples}/Multipole_Moments.ipynb (100%)
 rename {notebooks => examples}/One_dimensional_grids.ipynb (100%)
 create mode 100644 examples/One_dimensional_grids.ipynb.orig
 rename {notebooks => examples}/Quickstart.ipynb (100%)
 create mode 100644 website/data/table_angular_lebedev.csv
 create mode 100644 website/data/table_angular_spherical.csv

diff --git a/.github/workflows/website.yaml b/.github/workflows/website.yaml
index 74909c0de..b14df4001 100644
--- a/.github/workflows/website.yaml
+++ b/.github/workflows/website.yaml
@@ -47,11 +47,11 @@ jobs:
           rm -rf website/pyapi
           python -m sphinx.ext.apidoc -o website/pyapi src/grid src/grid/tests/ src/grid/test/ src/grid/data/ -e -f
 
-      # Copy notebooks into the book so CI always builds from grid/notebooks
-      - name: Stage notebooks for website build
+      # Copy examples into the book so CI always builds from grid/examples
+      - name: Stage examples for website build
         run: |
           rm -rf website/tutorial
-          cp -r notebooks website/tutorial
+          cp -r examples/ website/tutorial
 
       # Build the book
       - name: Build the website
diff --git a/notebooks/Angular_grid.ipynb b/examples/Angular_grid.ipynb
similarity index 100%
rename from notebooks/Angular_grid.ipynb
rename to examples/Angular_grid.ipynb
diff --git a/notebooks/Atom_Grid.ipynb b/examples/Atom_Grid.ipynb
similarity index 100%
rename from notebooks/Atom_Grid.ipynb
rename to examples/Atom_Grid.ipynb
diff --git a/notebooks/Atom_Grid_Construction.ipynb b/examples/Atom_Grid_Construction.ipynb
similarity index 100%
rename from notebooks/Atom_Grid_Construction.ipynb
rename to examples/Atom_Grid_Construction.ipynb
diff --git a/notebooks/Cubic_Grids.ipynb b/examples/Cubic_Grids.ipynb
similarity index 100%
rename from notebooks/Cubic_Grids.ipynb
rename to examples/Cubic_Grids.ipynb
diff --git a/notebooks/Interpolation_and_Poisson.ipynb b/examples/Interpolation_and_Poisson.ipynb
similarity index 94%
rename from notebooks/Interpolation_and_Poisson.ipynb
rename to examples/Interpolation_and_Poisson.ipynb
index f5d982590..7c1921ef6 100644
--- a/notebooks/Interpolation_and_Poisson.ipynb
+++ b/examples/Interpolation_and_Poisson.ipynb
@@ -18,18 +18,21 @@
         "#### Details\n",
         "It is known based on the properties of the real spherical harmonics $Y_{lm}$ that any $L_2$ function $f : \\mathbb{R}^3 \\rightarrow \\mathbb{R}$ can be decomposed as\n",
         "\n",
-        "$$\n",
-        "    f(r, \\theta, \\phi) = \\sum_{l=0}^\\infty \\sum_{m=-l}^l w_{lm}(r) Y_{lm}(\\theta, \\phi),\n",
-        "$$\n",
+        "```{math}\n",
+        "f(r, \\theta, \\phi) = \\sum_{l=0}^\\infty \\sum_{m=-l}^l w_{lm}(r) Y_{lm}(\\theta, \\phi),\n",
+        "```\n",
         "\n",
         "where the unknown $w_{lm}$ is a function of the radial component and is found as follows. For a fixed $r$, the radial component $w_{lm}$ is computed as the integral\n",
-        "$$\n",
+        "\n",
+        "```{math}\n",
         "w_{lm}(r) = \\int \\int f(r, \\theta, \\phi) Y_{lm}(\\theta, \\phi) \\sin(\\phi) d\\theta d\\phi\n",
-        "$$\n",
+        "```\n",
+        "\n",
         "over various values of $r$ and then interpolated $\\tilde{w}_{lm}$ using a cubic spline. This integral can be done using the [angular grid](../pyapi/grid.angular.rst) module. The interpolation of $f$ is simply then\n",
-        "$$\n",
-        "    f(r, \\theta, \\phi) \\approx \\sum_{l=0}^{L_{max}} \\sum_{m=-l}^l \\tilde{w}_{lm}(r) Y_{lm}(\\theta, \\phi),\n",
-        "$$\n",
+        "\n",
+        "```{math}\n",
+        "f(r, \\theta, \\phi) \\approx \\sum_{l=0}^{L_{max}} \\sum_{m=-l}^l \\tilde{w}_{lm}(r) Y_{lm}(\\theta, \\phi),\n",
+        "```\n",
         "\n",
         "where $L_{max}$ is the maximum chosen degree $l$ of the real spherical harmonics.\n",
         "\n",
@@ -40,7 +43,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": null,
       "metadata": {
         "ExecuteTime": {
           "end_time": "2024-01-08T20:58:10.192004233Z",
@@ -61,7 +64,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": null,
       "metadata": {
         "ExecuteTime": {
           "end_time": "2024-01-08T20:58:10.461254804Z",
@@ -85,7 +88,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": null,
       "metadata": {
         "ExecuteTime": {
           "end_time": "2024-01-08T20:58:10.840072685Z",
@@ -150,7 +153,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": null,
       "metadata": {
         "ExecuteTime": {
           "end_time": "2024-01-08T20:58:17.855028023Z",
diff --git a/notebooks/JCP_Paper.ipynb b/examples/JCP_Paper.ipynb
similarity index 100%
rename from notebooks/JCP_Paper.ipynb
rename to examples/JCP_Paper.ipynb
diff --git a/notebooks/Molecular_Grid.ipynb b/examples/Molecular_Grid.ipynb
similarity index 100%
rename from notebooks/Molecular_Grid.ipynb
rename to examples/Molecular_Grid.ipynb
diff --git a/notebooks/Molecular_Grid_Construction.ipynb b/examples/Molecular_Grid_Construction.ipynb
similarity index 100%
rename from notebooks/Molecular_Grid_Construction.ipynb
rename to examples/Molecular_Grid_Construction.ipynb
diff --git a/notebooks/Multipole_Moments.ipynb b/examples/Multipole_Moments.ipynb
similarity index 100%
rename from notebooks/Multipole_Moments.ipynb
rename to examples/Multipole_Moments.ipynb
diff --git a/notebooks/One_dimensional_grids.ipynb b/examples/One_dimensional_grids.ipynb
similarity index 100%
rename from notebooks/One_dimensional_grids.ipynb
rename to examples/One_dimensional_grids.ipynb
diff --git a/examples/One_dimensional_grids.ipynb.orig b/examples/One_dimensional_grids.ipynb.orig
new file mode 100644
index 000000000..629b66204
--- /dev/null
+++ b/examples/One_dimensional_grids.ipynb.orig
@@ -0,0 +1,406 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/theochem/grid/blob/master/examples/One_dimensional_grids.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# One-Dimensional and Radial Grids\n",
+        "\n",
+        "Grid supports several quadrature rules for one-dimensional grids, most of them in the [-1, 1] interval, within the [OneDGrid](https://grid.qcdevs.org/pyapi/grid.basegrid.html#grid.basegrid.OneDGrid) module.\n",
+        "\n",
+        "### Example: Visualize One-Dimensional Grid Points\n",
+        "\n",
+        "Here, we showcase some of the quadrature rules available each with a unique set of points. Click [here](https://grid.qcdevs.org/pyapi/grid.onedgrid.html) for a full list of one-dimensional grid classes and [here](https://grid.qcdevs.org/onedgrids.html#one-dimensional-grids) for a condensed table containing most of the popular grids."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-01-03T22:36:20.643610321Z",
+          "start_time": "2024-01-03T22:36:20.291101489Z"
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from grid.onedgrid import *\n",
+        "\n",
+        "# Make several 1-dimensional grids for a given number of points\n",
+        "npoints = 31\n",
+        "\n",
+        "grids = [\n",
+        "    GaussChebyshev(npoints),\n",
+        "    GaussLegendre(npoints),\n",
+        "    TanhSinh(npoints),\n",
+        "    Simpson(npoints),\n",
+        "    Trapezoidal(npoints),\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-01-03T22:36:21.133162129Z",
+          "start_time": "2024-01-03T22:36:20.645646412Z"
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "names = []\n",
+        "for i, oned in enumerate(grids):\n",
+        "    plt.scatter(oned.points, np.repeat(i + 1, oned.size), marker=\"o\")\n",
+        "    names.append(oned.name)\n",
+        "\n",
+        "# set the y-ticks to be the 1D grid names, and set labels\n",
+        "plt.title(f\"{npoints} Grid Points in One Dimension\", fontsize=16)\n",
+        "plt.yticks(np.arange(1, len(grids) + 1), names)\n",
+        "plt.xlabel(\"Number of Points (Grid Size)\", fontsize=14)\n",
+        "plt.ylabel(\"Quadrature Rule\", fontsize=14)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## One-Dimensional Grid Integration\n",
+        "\n",
+        "### Example: Integration of One-Variable Function on $[-1, 1]$\n",
+        "\n",
+        "The one-dimensional grids can accurately integrate one-variable functions over an interval (defined by their domain), even with a small number of points. Here, we numerically compute the integral below and compute the relative percent error. The analytical value of this integral is $\\pi/2$.\n",
+        "\n",
+        "$$ \\int_{-1}^1 \\sqrt{1 - x^2} dx \\approx \\sum_{i=0}^{N-1} w_i \\sqrt{1 - x_i^2} $$"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-01-03T22:36:21.338777861Z",
+          "start_time": "2024-01-03T22:36:21.146684630Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Integral of sqrt(1-x^2) from -1 to 1:\n",
+            " 1.57079633 from Analytical integration (which is equal to pi/2)\n",
+            " 1.57079633 from Gauss-Chebyshev integration\n",
+            " 1.57082270 from Gauss-Legendre integration\n",
+            " 1.57067045 from Tanh-Sinh integration\n",
+            " 1.56683246 from Simpson integration\n",
+            " 1.56069571 from Trapezoidal-Lobatto integration\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 850x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# define the function to integrate\n",
+        "function = lambda x: np.sqrt(1 - x**2)\n",
+        "\n",
+        "print(\"Integral of sqrt(1-x^2) from -1 to 1:\")\n",
+        "print(f\"{np.pi / 2: .8f} from Analytical integration (which is equal to pi/2)\")\n",
+        "\n",
+        "names = []\n",
+        "error_relative = []\n",
+        "for oned in grids:\n",
+        "    # integrate the function evaluated on the grid points\n",
+        "    integral = oned.integrate(function(oned.points))\n",
+        "    print(f\"{integral: .8f} from {oned.name} integration\")\n",
+        "    error_relative.append(np.abs(integral - np.pi / 2) / (np.pi / 2) * 100)\n",
+        "    names.append(oned.name)\n",
+        "\n",
+        "# plot the relative error as columns graph\n",
+        "plt.figure(figsize=(8.5, 5))\n",
+        "plt.bar(names, error_relative)\n",
+        "title = r\"Numerical Integration of $\\int_{-1}^1 \\sqrt{1-x^2} dx$ with \" + str(npoints) + \" Points\"\n",
+        "plt.title(title, fontsize=16)\n",
+        "plt.xlabel(\"Quadrature Rule\", fontsize=14)\n",
+        "plt.ylabel(\"Relative Percent Error\", fontsize=14)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Changing the number of grid points increases the accuracy of each quadrature. Here we show how the relative integration error converges to zero when increasing the number of points.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-01-03T22:36:21.461375714Z",
+          "start_time": "2024-01-03T22:36:21.349492787Z"
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "npoints = [3, 5, 11, 15, 17, 19, 21, 31]\n",
+        "grid_classes = [GaussChebyshev, GaussLegendre, TanhSinh, Simpson, Trapezoidal]\n",
+        "\n",
+        "for grid_class in grid_classes:\n",
+        "    errors = []\n",
+        "    for npoint in npoints:\n",
+        "        # make 1-dimensional grid for the given number of points\n",
+        "        oned = grid_class(npoint)\n",
+        "        # compute relative percent error of integration\n",
+        "        integral = oned.integrate(function(oned.points))\n",
+        "        errors.append(np.abs(integral - np.pi / 2) / (np.pi / 2) * 100)\n",
+        "    # plot relative percent error vs. number of grid points\n",
+        "    plt.plot(npoints, errors, label=oned.name)\n",
+        "\n",
+        "plt.legend(frameon=False, fontsize=12)\n",
+        "plt.title(r\"Numerical Integration of $\\int_{-1}^1 \\sqrt{1-x^2} dx$\", fontsize=16)\n",
+        "plt.xlabel(\"Number of Points (Grid Size)\", fontsize=14)\n",
+        "plt.ylabel(\"Relative Percent Error\", fontsize=14)\n",
+        "plt.xticks(npoints)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Radial Grid: Transformation of 1D-Integration Intervals\n",
+        "\n",
+        "Generally, areas where the integrand changes more rapidly demand a denser concentration of points. So, the $[-1, 1]$ interval is not suitable for most applications encountered in quantum chemistry, where the integrals are usually performed on $[0,\\infty)$ domain. [Grid](https://github.com/theochem/grid) supports several transformations of the one-dimensional grids to other intervals. Some of these transformations have a parameter that can be used to control the density of points across different ranges of the new interval. Click [here](https://grid.qcdevs.org/pyapi/grid.rtransform.html) for a full list of radial transformations and [here](https://grid.qcdevs.org/radial_transf.html) for a condensed table with explicit formulas.\n",
+        "\n",
+        "### Visualize Radial Grid Points\n",
+        "\n",
+        "Here we visualize the OneDGrid points for different quadrature rules after a Becke transformation.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-01-03T22:36:21.851432041Z",
+          "start_time": "2024-01-03T22:36:21.470246478Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/var/folders/wk/kmp6fyrn3396qjpx40d2qk600000gn/T/ipykernel_75060/2869605059.py:18: UserWarning: Attempt to set non-positive xlim on a log-scaled axis will be ignored.\n",
+            "  ax2.set_xlim(0, 2500)\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from grid.rtransform import BeckeRTransform\n",
+        "\n",
+        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5), sharey=True)\n",
+        "names = []\n",
+        "for i, oned in enumerate(grids):\n",
+        "    # Becke R-transform (goes from 0 to \\infty) to transform 1D-grid\n",
+        "    rgrid = BeckeRTransform(0.0, R=1.5).transform_1d_grid(oned)\n",
+        "    # plot radial grid points for each transformed 1D-grid\n",
+        "    ax1.scatter(rgrid.points, np.repeat(i + 1, rgrid.size), marker=\"o\")\n",
+        "    # plot log plot of radial grid points\n",
+        "    ax2.semilogx(rgrid.points, np.repeat(i + 1, rgrid.size), marker=\"o\", linestyle=\"\")\n",
+        "    names.append(oned.name)\n",
+        "\n",
+        "# set the ticks, labels, and title\n",
+        "plt.suptitle(\"Plot & Log-Plot of Becke Radial Transformation of 1D-Grids\", fontsize=17)\n",
+        "ax1.set_yticks(np.arange(1, len(grids) + 1), names, fontsize=11)\n",
+        "ax1.set_xlim(0, 2500)\n",
+        "ax2.set_xlim(1.0e-4, 2500)\n",
+        "ax1.set_xlabel(\"Transformed Coordinate r\", fontsize=14)\n",
+        "ax2.set_xlabel(\"Transformed Coordinate r\", fontsize=14)\n",
+        "ax1.set_ylabel(\"Quadrature Rule\", fontsize=17)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Example: Integration of One-Variable Function on $[0, \\infty]$\n",
+        "\n",
+        "This transformation tends to concentrate the points close to the origin. Because of this, it is usually used for defining radial grids for atoms where the function to integrate is rapidly varying close to the origin (e.g. the electron density is more concentrated close to the nucleus). Here, we integrate (which has the exact value of 2.0):\n",
+        "\n",
+        "$$ \\int_0^\\infty x^2 e^{-x} dx $$\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-01-03T22:36:21.855941638Z",
+          "start_time": "2024-01-03T22:36:21.851891608Z"
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Integration of r^2 * exp(-r) from 0 to infinity:\n",
+            " 2.000000 Analytical integration\n",
+            " 2.000003 from BeckeRTransform radial grid of Gauss-Chebyshev\n",
+            " 1.999996 from BeckeRTransform radial grid of Gauss-Legendre\n",
+            " 1.999993 from BeckeRTransform radial grid of Tanh-Sinh\n",
+            " 1.998949 from BeckeRTransform radial grid of Simpson\n",
+            " 2.000006 from BeckeRTransform radial grid of Trapezoidal-Lobatto\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Integration of r^2 * exp(-r) from 0 to infinity:\")\n",
+        "print(f\"{2.0: .6f} Analytical integration\")\n",
+        "\n",
+        "# Make several 1-dimensional grids for a given number of points\n",
+        "npoints = 21\n",
+        "grids = [\n",
+        "    GaussChebyshev(npoints),\n",
+        "    GaussLegendre(npoints),\n",
+        "    TanhSinh(npoints),\n",
+        "    Simpson(npoints),\n",
+        "    Trapezoidal(npoints),\n",
+        "]\n",
+        "\n",
+        "for oned in grids:\n",
+        "    rgrid = BeckeRTransform(0.0, R=1.5).transform_1d_grid(oned)\n",
+        "    x = rgrid.points\n",
+        "    func_vals = x**2 * np.exp(-x)\n",
+        "    integral = rgrid.integrate(func_vals)\n",
+        "    print(f\"{integral: .6f} from BeckeRTransform radial grid of {oned.name}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-01-03T22:36:22.028092961Z",
+          "start_time": "2024-01-03T22:36:21.856079509Z"
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "npoints = [11, 21, 31, 41, 51, 61]\n",
+        "grid_classes = [GaussChebyshev, GaussLegendre, TanhSinh, Simpson, Trapezoidal]\n",
+        "\n",
+        "for grid_class in grid_classes:\n",
+        "    errors = []\n",
+        "    for npoint in npoints:\n",
+        "        # make 1-dimensional grid for the given number of points\n",
+        "        oned = grid_class(npoint)\n",
+        "        rgrid = BeckeRTransform(0.0, R=1.5).transform_1d_grid(oned)\n",
+        "        # calculate x^2 e^-x\n",
+        "        x = rgrid.points\n",
+        "        func_vals = x**2.0 * np.exp(-x)\n",
+        "        # compute relative percent error of integration\n",
+        "        integral = rgrid.integrate(func_vals)\n",
+        "        errors.append(np.abs(integral - 2.0) / 2.0 * 100)\n",
+        "    # plot relative percent error vs. number of grid points\n",
+        "    plt.semilogy(npoints, errors, label=oned.name)\n",
+        "\n",
+        "plt.legend(frameon=False, fontsize=12)\n",
+        "plt.title(r\"Numerical Integration of $\\int_0^\\infty x^2 e^{-x}dx$\", fontsize=16)\n",
+        "plt.xlabel(\"Number of Points (Grid Size)\", fontsize=14)\n",
+        "plt.ylabel(\"Relative Percent Error\", fontsize=14)\n",
+        "plt.xticks(npoints)\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.15"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 1
+}
diff --git a/notebooks/Quickstart.ipynb b/examples/Quickstart.ipynb
similarity index 100%
rename from notebooks/Quickstart.ipynb
rename to examples/Quickstart.ipynb
diff --git a/src/grid/basegrid.py b/src/grid/basegrid.py
index 9f9906b7b..c87045671 100644
--- a/src/grid/basegrid.py
+++ b/src/grid/basegrid.py
@@ -114,7 +114,7 @@ def __getitem__(self, index):
     def integrate(self, *value_arrays):
         r"""Integrate over the whole grid for given multiple value arrays.
 
-        Product of all value_arrays will be computed element-wise then integrated on the grid
+        Product of all input arrays will be computed element-wise then integrated on the grid
         with its weights:
 
         .. math::
@@ -122,7 +122,7 @@ def integrate(self, *value_arrays):
 
         Parameters
         ----------
-        *value_arrays : np.ndarray(N, )
+        value_arrays : tuple[np.ndarray]
             One or multiple value array to integrate.
 
         Returns
diff --git a/src/grid/ngrid.py b/src/grid/ngrid.py
index 687844424..a3539e776 100644
--- a/src/grid/ngrid.py
+++ b/src/grid/ngrid.py
@@ -124,8 +124,8 @@ def weights(self):
         For a MultiDomainGrid formed from two grids, [(x11, y11), (x12, y12) ... (x1n, y1n)] and
         [(x21, y21), (x22, y22) ... (x2m, y2m)], the combined weights are calculated as follows:
         For each combination of points (x1, y1) from the first grid and (x2, y2) from the second
-        2D grid, the combined weight `w` is:
-            w = w1 * w2
+        2D grid, the combined weight ``w`` is ``w1 * w2``.
+
         where `w1` and `w2` are the weights from the individual grids corresponding to
         (x1, y1) and (x2, y2), respectively.
 
diff --git a/website/_config.yml b/website/_config.yml
index ccf49ce43..eb111124f 100644
--- a/website/_config.yml
+++ b/website/_config.yml
@@ -17,23 +17,12 @@ latex:
 
 sphinx:
   extra_extensions:
-  - 'sphinx.ext.githubpages'
   - 'sphinx.ext.autodoc'
   - 'sphinx.ext.autosummary'
   config:
     mathjax_path: https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
-    autosummary_generate: True
+    autosummary_generate: False
     add_module_names: True
-    html_theme_options:
-      show_navbar_depth: 1
-      max_navbar_depth: 4
-      collapse_navbar: False
-    exclude_patterns:
-      - README.md
-      - markdown.md
-      - markdown-notebooks.md
-      - tutorial/JCP_Paper.ipynb
-      - tutorial/Ngrid.ipynb
 
 
 #Read LaTeX
diff --git a/website/_toc.yml b/website/_toc.yml
index 9003197f9..2789d8cc1 100644
--- a/website/_toc.yml
+++ b/website/_toc.yml
@@ -40,34 +40,4 @@ parts:
 
   - caption: API Documentation
     chapters:
-      - file: pyapi/grid
-        title: grid
-        sections:
-          - file: pyapi/grid.angular
-            title: grid.angular
-          - file: pyapi/grid.atomgrid
-            title: grid.atomgrid
-          - file: pyapi/grid.basegrid
-            title: grid.basegrid
-          - file: pyapi/grid.becke
-            title: grid.becke
-          - file: pyapi/grid.cubic
-            title: grid.cubic
-          - file: pyapi/grid.hirshfeld
-            title: grid.hirshfeld
-          - file: pyapi/grid.molgrid
-            title: grid.molgrid
-          - file: pyapi/grid.ngrid
-            title: grid.ngrid
-          - file: pyapi/grid.ode
-            title: grid.ode
-          - file: pyapi/grid.onedgrid
-            title: grid.onedgrid
-          - file: pyapi/grid.periodicgrid
-            title: grid.periodicgrid
-          - file: pyapi/grid.poisson
-            title: grid.poisson
-          - file: pyapi/grid.rtransform
-            title: grid.rtransform
-          - file: pyapi/grid.utils
-            title: grid.utils
+      - file: pyapi/grid.rst
diff --git a/website/data/table_angular_lebedev.csv b/website/data/table_angular_lebedev.csv
new file mode 100644
index 000000000..b36164844
--- /dev/null
+++ b/website/data/table_angular_lebedev.csv
@@ -0,0 +1,32 @@
+Degree;Number Points;Mean (std) Distance; Max Distance
+3;6;0.0(0.0);0.0
+5;18;0.0(0.0);0.0
+7;26;0.14(0.15);0.31
+9;38;0.19(0.15);0.31
+11;50;0.15(0.15);0.31
+13*;74;0.11(0.082);0.2
+15;86;0.052(0.042);0.12
+17;110;0.065(0.047);0.14
+19;146;0.048(0.048);0.14
+21;170;0.035(0.027);0.089
+23;194;0.061(0.047);0.16
+25*;230;0.058(0.036);0.12
+27*;266;0.045(0.035);0.13
+29;302;0.055(0.036);0.14
+31;350;0.042(0.028);0.1
+35;434;0.054(0.021);0.092
+41;590;0.048(0.019);0.082
+47;770;0.043(0.017);0.074
+53;974;0.038(0.015);0.065
+59;1202;0.035(0.014);0.06
+65;1454;0.032(0.012);0.054
+71;1730;0.03(0.011);0.05
+77;2030;0.027(0.011);0.047
+83;2354;0.026(0.0099);0.043
+89;2702;0.024(0.0093);0.041
+95;3074;0.023(0.0088);0.039
+101;3470;0.021(0.0083);0.036
+107;3890;0.02(0.0078);0.035
+113;4334;0.019(0.0075);0.033
+119;4802;0.018(0.007);0.031
+125;5294;0.018(0.0066);0.03
diff --git a/website/data/table_angular_spherical.csv b/website/data/table_angular_spherical.csv
new file mode 100644
index 000000000..fee7a8942
--- /dev/null
+++ b/website/data/table_angular_spherical.csv
@@ -0,0 +1,163 @@
+Degree;Number Points;Mean (std) Distance; Max Distance
+1;2;0.0(0.0);0.0
+3;6;0.34(0.24);0.55
+5;12;0.22(0.13);0.35
+7;32;0.16(0.076);0.26
+9;48;0.14(0.071);0.26
+11;70;0.14(0.045);0.23
+13;94;0.12(0.047);0.2
+15;120;0.11(0.041);0.19
+17;156;0.088(0.038);0.16
+19;192;0.078(0.036);0.14
+21;234;0.077(0.033);0.14
+23;278;0.075(0.027);0.13
+25;328;0.066(0.029);0.11
+27;380;0.061(0.023);0.11
+29;438;0.057(0.024);0.1
+31;498;0.06(0.021);0.1
+33;564;0.047(0.02);0.094
+35;632;0.05(0.019);0.085
+37;706;0.048(0.018);0.085
+39;782;0.039(0.017);0.074
+41;864;0.045(0.017);0.079
+43;948;0.043(0.015);0.074
+45;1038;0.041(0.015);0.071
+47;1130;0.037(0.014);0.068
+49;1228;0.033(0.014);0.064
+51;1328;0.034(0.013);0.062
+53;1434;0.034(0.013);0.061
+55;1542;0.03(0.012);0.054
+57;1656;0.034(0.011);0.057
+59;1772;0.03(0.011);0.054
+61;1894;0.025(0.011);0.05
+63;2018;0.029(0.011);0.052
+65;2148;0.029(0.011);0.052
+67;2280;0.025(0.01);0.05
+69;2418;0.027(0.0095);0.048
+71;2558;0.027(0.0093);0.048
+73;2704;0.022(0.0095);0.045
+75;2852;0.024(0.0091);0.044
+77;3006;0.02(0.0085);0.041
+79;3162;0.023(0.0086);0.041
+81;3324;0.023(0.0084);0.042
+83;3488;0.02(0.0081);0.04
+85;3658;0.022(0.0083);0.04
+87;3830;0.022(0.0081);0.039
+89;4008;0.018(0.0076);0.036
+91;4188;0.02(0.0073);0.036
+93;4374;0.018(0.0075);0.036
+95;4562;0.02(0.0074);0.036
+97;4756;0.019(0.007);0.034
+99;4952;0.016(0.0067);0.031
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+115;6672;0.014(0.006);0.027
+117;6906;0.016(0.006);0.029
+119;7142;0.016(0.0056);0.029
+121;7384;0.016(0.0056);0.028
+123;7628;0.015(0.0056);0.027
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+139;9732;0.014(0.0048);0.024
+141;10014;0.013(0.005);0.025
+143;10298;0.011(0.0047);0.023
+145;10588;0.013(0.0049);0.023
+147;10880;0.01(0.0047);0.022
+149;11178;0.013(0.0047);0.023
+151;11478;0.013(0.0045);0.023
+153;11784;0.01(0.0044);0.022
+155;12092;0.012(0.0045);0.022
+157;12406;0.01(0.0043);0.021
+159;12722;0.012(0.0044);0.022
+161;13044;0.012(0.0044);0.021
+163;13368;0.0096(0.0042);0.02
+165;13698;0.011(0.0043);0.021
+167;14030;0.011(0.0042);0.02
+169;14368;0.0092(0.004);0.02
+171;14708;0.011(0.004);0.02
+173;15054;0.011(0.004);0.02
+175;15402;0.0086(0.0039);0.02
+177;15756;0.01(0.004);0.019
+179;16112;0.0085(0.0039);0.018
+181;16474;0.01(0.0038);0.019
+183;16838;0.011(0.0037);0.019
+185;17208;0.01(0.0038);0.018
+187;17580;0.0087(0.0036);0.018
+189;17958;0.0081(0.0036);0.017
+191;18338;0.0098(0.0036);0.018
+193;18724;0.0099(0.0036);0.018
+195;19112;0.0098(0.0035);0.018
+197;19506;0.0078(0.0034);0.017
+199;19902;0.0076(0.0034);0.017
+201;20304;0.0095(0.0034);0.017
+203;20708;0.0094(0.0033);0.017
+205;21118;0.0075(0.0033);0.016
+207;21530;0.0091(0.0033);0.017
+209;21948;0.009(0.0033);0.016
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+213;22794;0.0072(0.0032);0.016
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+221;24534;0.0069(0.0031);0.015
+223;24978;0.0086(0.0031);0.016
+225;25428;0.0084(0.003);0.015
+227;25880;0.0066(0.003);0.015
+229;26338;0.0083(0.003);0.015
+231;26798;0.0082(0.003);0.015
+233;27264;0.0065(0.003);0.015
+235;27732;0.0081(0.003);0.015
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+245;30138;0.0078(0.0028);0.014
+247;30630;0.0077(0.0028);0.014
+249;31128;0.006(0.0027);0.013
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+263;34718;0.0071(0.0027);0.013
+265;35248;0.0055(0.0025);0.013
+267;35780;0.0071(0.0026);0.013
+269;36318;0.0071(0.0026);0.013
+271;36858;0.0054(0.0024);0.012
+273;37404;0.007(0.0025);0.013
+275;37952;0.007(0.0025);0.012
+277;38506;0.0054(0.0024);0.012
+279;39062;0.0068(0.0025);0.012
+281;39624;0.0052(0.0024);0.012
+283;40188;0.0068(0.0025);0.012
+285;40758;0.0068(0.0024);0.012
+287;41330;0.0066(0.0025);0.012
+289;41908;0.0051(0.0023);0.011
+291;42488;0.0065(0.0024);0.012
+293;43074;0.0051(0.0023);0.011
+295;43662;0.0065(0.0023);0.012
+297;44256;0.005(0.0023);0.011
+299;44852;0.0064(0.0023);0.011
+301;45454;0.0064(0.0023);0.011
+303;46058;0.0063(0.0023);0.011
+305;46668;0.0049(0.0023);0.011
+307;47280;0.0062(0.0023);0.011
+309;47898;0.0047(0.0022);0.011
+311;48518;0.0061(0.0022);0.011
+313;49144;0.0047(0.0022);0.011
+315;49772;0.0061(0.0022);0.011
+317;50406;0.0059(0.0022);0.011
+319;51042;0.0059(0.0022);0.011
+321;51684;0.0046(0.0021);0.011
+323;52328;0.0047(0.0021);0.01

From aaffac38cc5cb6ac6a4f0d43596522e3029dd492 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Marco=20Mart=C3=ADnez=20Gonz=C3=A1lez?=
 <mmg870630@gmail.com>
Date: Tue, 28 Apr 2026 14:09:24 -0400
Subject: [PATCH 7/8] Add bibliography placeholder to avoid warnings

---
 website/references.bib | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 website/references.bib

diff --git a/website/references.bib b/website/references.bib
new file mode 100644
index 000000000..d2b94b6e2
--- /dev/null
+++ b/website/references.bib
@@ -0,0 +1 @@
+% Placeholder bibliography file for Jupyter Book builds.

From 81333222d94e9708b9e557a0ee09408bd63b2e61 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Marco=20Mart=C3=ADnez=20Gonz=C3=A1lez?=
 <mmg870630@gmail.com>
Date: Wed, 29 Apr 2026 11:05:27 -0400
Subject: [PATCH 8/8] Add small fixes to documentation

---
 src/grid/basegrid.py       | 4 ++--
 website/README.md          | 8 ++++----
 website/conventions.ipynb  | 2 +-
 website/installation.ipynb | 8 +++++++-
 4 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/src/grid/basegrid.py b/src/grid/basegrid.py
index c87045671..f24275d99 100644
--- a/src/grid/basegrid.py
+++ b/src/grid/basegrid.py
@@ -122,8 +122,8 @@ def integrate(self, *value_arrays):
 
         Parameters
         ----------
-        value_arrays : tuple[np.ndarray]
-            One or multiple value array to integrate.
+        *value_arrays : np.ndarray of shape (N,)
+            One or more value arrays to integrate.
 
         Returns
         -------
diff --git a/website/README.md b/website/README.md
index a4be2ea1b..6e0ea5af5 100644
--- a/website/README.md
+++ b/website/README.md
@@ -6,7 +6,7 @@ be runned. The html files are created in an folder called "_build".
 ```bash
 # # Generates API html for grid while ignoring the test and data folders.
 # Stores it in pyapi/
-sphinx-apidoc --separate -o website/_autosummary grid -M -f
+sphinx-apidoc --separate -o website/pyapi grid -M -f
 # Build the html files
 jupyter-book build ./website/
 ```
@@ -19,11 +19,11 @@ After running the commands above, you'll need to go inside the  "./website/_buil
 
 ```bash
 git branch -a
-# Should be something like origin/gh-pages/ where origin is the remote to the theochem/gbasis Github
+# Should be something like origin/gh-pages/ where origin is the remote to the theochem/grid Github
 git checkout gh-pages
-cd ..  # Get out of gh-pages and go back to gbasis folder
+cd ..  # Get out of gh-pages and go back to grid folder
 ```
-Now, paste and overwrite all of the files in gh-pages with the html. Then commit and push to the `gh-pages` branch of the theochem/gbasis
+Now, paste and overwrite all of the files in gh-pages with the html. Then commit and push to the `gh-pages` branch of the theochem/grid
 ```bash
 git add ./*
 git commit -m "Update website"
diff --git a/website/conventions.ipynb b/website/conventions.ipynb
index fa4a49774..cfa6ee0df 100644
--- a/website/conventions.ipynb
+++ b/website/conventions.ipynb
@@ -24,7 +24,7 @@
     "\n",
     "such that when the radius is zero $r=0$, the angles are zero $\\theta, \\phi = 0$.\n",
     "\n",
-    "Grid offers a {class}`utility<grid.utils.convert_cart_to_sph>` function to convert to spherical coordinates:\n",
+    "Grid offers a {func}`utility<grid.utils.convert_cart_to_sph>` function to convert to spherical coordinates:\n",
     "\n",
     "```python\n",
     "import numpy as np\n",
diff --git a/website/installation.ipynb b/website/installation.ipynb
index d786837f2..92c10e960 100644
--- a/website/installation.ipynb
+++ b/website/installation.ipynb
@@ -51,12 +51,18 @@
     "Build the documentation locally with Jupyter Book:\n",
     "\n",
     "```bash\n",
-    "sphinx-apidoc -o website/_autosummary src/grid src/grid/tests src/grid/test src/grid/data -e -f\n",
+    "sphinx-apidoc -o website/pyapi src/grid src/grid/tests src/grid/test src/grid/data -e -f\n",
     "jupyter-book build ./website/\n",
     "```\n",
     "\n",
     "The built HTML files are written to `website/_build/html/`.\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a2e7e5d",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
