FLE_bright_conserve.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fFjrfreVHxqk"
      },
      "source": [
        "# Fokas Lennelles Equation"
      ]
    },
{
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/gautamksaharia/Fokas-Lenells/blob/main/FLE_bright_conserve.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mlnlayAzH8OK"
      },
      "source": [
        "$$u_{xt} - u  + 2 i|u|^2u_x=0$$\n",
        "\n",
        "$x \\quad ϵ \\quad  [-1,1]$\n",
        "\n",
        "$t \\quad ϵ \\quad  [0,1]$ "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oy-BrLSRtMwo"
      },
      "source": [
        "$$(r +im)_{xt} - (r+im)  + 2 i(r^2 + m^2)(r+im)_x=0$$\n",
        "**1. Real part** \n",
        "$$r_{xt} - r  -2 (r^2 + m^2)m_x=0$$\n",
        "2. **Imaginary part** \n",
        "$$m_{xt} - m  +2 (r^2 + m^2)r_x=0$$ "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bvh6DIjQr6uw"
      },
      "source": [
        "**Solution**\n",
        "\n",
        "$$U=\\frac{-2a}{a + i b}\\frac{e^{\\theta + i Χ}}{ e^{2\\theta }- b - ia}$$ \n",
        "$$U=\\frac{2a}{a + i b}\\frac{e^{\\theta + i Χ}}{b - e^{2\\theta } + ia}$$ \n",
        "\n",
        "$$U=\\frac{2a}{a + i b}\\frac{e^{\\theta + i Χ} (b - e^{2\\theta } - ia)}{(b - e^{2\\theta } + ia)(b - e^{2\\theta } - ia)}$$\n",
        "\n",
        "$$U=\\frac{2a(a - ib)}{(a + i b)(a-ib)}\\frac{e^{\\theta + i Χ} (b - e^{2\\theta } - ia)}{(b - e^{2\\theta } + ia)(b - e^{2\\theta } - ia)}$$\n",
        "\n",
        "$$U=\\frac{2a(a - ib)}{(a^2 + b^2)}\\frac{e^{\\theta + i Χ} (b - e^{2\\theta } - ia)}{(b - e^{2\\theta } + ia)(b - e^{2\\theta } - ia)}$$\n",
        "\n",
        "$$U=\\frac{2a(a - ib)}{(a^2 + b^2)}\\frac{e^{\\theta + i Χ} (b - e^{2\\theta } - ia)}{((b - e^{2\\theta })^2 + a^2)}$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "0GGyTllSo2sv",
        "outputId": "c155a4de-d478-4073-f0a4-fe8e9efa7b87"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "from mpl_toolkits import mplot3d\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "N =100\n",
        "x_min = -5\n",
        "x_max = 5\n",
        "t_min = -1\n",
        "t_max = 1\n",
        "x_space = np.linspace(x_min, x_max, N)\n",
        "t_space = np.linspace(t_min, t_max, N)\n",
        "XX, TT = np.meshgrid(x_space, t_space)\n",
        "\n",
        "a=1\n",
        "b=1\n",
        "v = 1/(a**2 + b**2)\n",
        "theta1 = a*(XX + v*TT)\n",
        "chi1 = b*(XX - v*TT)\n",
        "\n",
        "r = 2*a*np.exp(theta1)/( (a**2 + b**2)*( ( b-np.exp(2*theta1) )**2 + a**2)  ) *\\\n",
        "         (-a*np.exp(2*theta1)*np.cos(chi1) + (a**2 + b**2)*np.sin(chi1)- b*np.exp(2*theta1)*np.sin(chi1))\n",
        "\n",
        "\n",
        "m = 2*a*np.exp(theta1)/( (a**2 + b**2)*( ( b-np.exp(2*theta1) )**2 + a**2)  ) *\\\n",
        "         (b*np.exp(2*theta1)*np.cos(chi1) - (a**2 + b**2)*np.cos(chi1)- a*np.exp(2*theta1)*np.sin(chi1))\n",
        "\n",
        "UU = np.sqrt(r**2 + m**2)\n",
        "\n",
        "#fig = plt.figure(figsize=(8,6))\n",
        "ax = plt.axes(projection='3d')\n",
        "ax.plot_surface(XX, TT, UU, cmap=\"viridis\")\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"t\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2y2AVOGzJFOb"
      },
      "source": [
        "PINN to solve PDE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAVahzmUcK7f",
        "outputId": "a516185b-255c-4ee3-c166-77f30e0d595b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cpu\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import torch.nn as nn\n",
        "import matplotlib.pyplot as plt\n",
        "torch.manual_seed(123)\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AaDKufkLHwYT"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7VHejfMEL2Tz",
        "outputId": "4a3f2b68-f494-452b-91ae-d3a036593303"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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NO5hzOiEEeQi8tPK4UcVapSZhJdCQScMQenuJj5U1lOLaYRQi5FKF59TfLt12vbKVWD8HFgRgloahGDHbCLBI6Eg8KVk2YDxojUapZ6bH3JJsxXBJY1KmDVr7OCuSfLjtzcvvl0540eG6WmAZS2bDmOnA9c/A8fpiZBU1nypxku1Gxi0Pwzb2WmaXUsyyEJng1JIDgx0dODz7VsmBrHex1vgdYFZ5eWQS7mpoIE7pp81MEbq7qdtK0g6jYLPb4alagiNiqcRRaF29Bo4rrhB9N3plKzF7DioXcjHoncZulCdDaiFWw1PSqsWlZDQqPRM95hajNnZKoDUmVWfWMRwjYTgqKjD08stx4FuXS3vfdbiuWljGkhkxZjrwtamwHdqNzOPeUM244ZeGPE8MoZgKjdgJjqjIIsfJKgNLTSZ6Cssp7QAzJk5AxyuvEBmNuuy6o8C53aHMLkLj1QjY3crCb3LvRo1RTgKWAnqsRDX1TGM32pMhthALQR6HZ99KdZ4Ujweu666D0HcSXXsamRp4JM+E9dxCa3zpbeDSeCBVzV86Cs9yqanIX3G/LDlfb8FbUlgEb7OCswMjLwPGfi/0X8aGUhg0ZFIicmAwiNwlS0LaNmKQIPTqtSMnKZnAikyqCQOlB/KXLyNuh1HQ+m5YlSuhOi8FcubOZWZshLW0lMDLhC3FkKzaZ/EE3cySEsXSN3aPB8WbN6Fw7VrkzJsLQRDQtmEDk7JC0SB9JqznFhrji6SsktGkc8k5Pz8f2T+8NSFpR4/EAqL26HxdWlieJQvErlzSySTY0SHvqhfZyemxI6fZAWohkwbq6uB/7jnidiUgymBwVFTAxnHE7Yi+V712r7TvRqwttM+XFJL8MwUPZzRShw9Xde14hL0cJBwM7+o1yJIIW8ZD7xA1DUi80fk/uxdDy8oQqKtD28YndAlV0TwT1nML6TzYuWuXqGRI9P0DEppqVUtgd2fr5o2Sm/PzFi1SNZdomYOMTGhQC8tYskAM1lyZ6ElHj1ANrftdC5lUi7EUbzBklZeDy8pC955GAEBGaSkySy6WJcbrGZ6heTdibbF7PHBffx1Sh49AQfUqADbwx44xmxDF3lt/u0+8ZIMIWPRrWi4fTdjHbLXPSOv06Wng0TwT1nMLaX+RlAwZuP/mZctFy4b0t7QkcAT1CLdKhe/UJBawmIOSldBACstYskDc0YknndJS6hpotPwpEqhxv6sZsGq4M5zbjfylS2O4UID4u+h45RXJSccIoinpu+msrxdtC+/1om3Dxsjf4b7FcmIUfW/rgKZFP5X2MDHigBFx+URwUkrtOg5mrH2mtLHQ28CjeSas5xaSeZBzueQlQwSBSnXeaOI4DcxCdtcbFmfpDAcNF4KUe5JZcrEir0GssC7r2LVRNaPUcGeC7e1I8XhixNxoeSl6iT2KQendZJWXExsMevNswnBWVqLo0V+If8mQA6ZW6K+1uproGejVj7VyZeRECfU28GifCcu5hWQedE6fRnw+IiRJlFYJRs5ByYblWTqDocZVTso9UbuTYxm71isLSwxqtJuiFwo170Lv3buc3EL8u+na00huMBjIs3FUVgKP03PAaKB2wef9fsmdd/Sztw8bhhSPB/1SVdwJ+rHA8+hqfA/de/aEPrDb4f/d72IKQbMM9ei9UVEztlnOLXLzYF7VEvS3kHkNqWBwuDXUZxoT6AAAIn3pZFOTqULEesIyls5gqF1sSSYdLYReVrFrPUJ7cgg/F98zzxLVzIteKNS8Cz1377QcBOprGDiJ6k0eVe2ZlDAaReu1uVyR42n7caCuLsSPUQj7sAybkBgzdrcbJ71eVXICasc2S16MWL/i233wrl6jrqQIIYwIt4r1mWM1NbBlZACg14/T0mY+KKDxoE+3QrqksIylMxhaFluSSccMGQ56ZWFJwWa3I3vGzfBt2UK161XzLvTavavhIKg1GIzi2ehJHtWk9xRnNEo9+3B2Ked0xixgSv04UFeHI3fNJ24LK4+fop6PIID3+dC8eHHoPlR4tYwe22KI7leBujocWbhIuQ8MPA/O5RIleCtBb1FauT6jVmRXbZt3ftyMFdv3o7mjJ/JZgTMdy6eNQeX5BarOqRaWsXQGQCql0whOjxkyHIw22tTseu3DhhGdO/o4PcKMarOY1BoMZihjoBUshEr7jx4leva2tDQUb94E/phPsR8LPI+Wh1bRNYShx48mNK3Wq2Xk2JZLjach+YeNOQDU4pB6itKGQ7XN9y1jfm65ckhS2PlxM+54dl9CSfnmjh7c8ew+PHnzRYYaTJaxdJpDLpySVV5uGKcn2TDaaKPf9ZIusKeO0yPMqDY0S20wDLK+paQhI1keJDMTwa4uxfOn5OYSPXve64WNs8N59VTFc3a/vzeGk0QDVh6/GGPG60VLtYQOlQavlhFjWyksTUryz6uqQvaMm0/dHynPUWdRWjX1PmkgVw5JDHxQwIrt+yVnRQHAiu37ccWYfMNCcpaxdBpDKZxStH6doZye0wWk4ms0u17+GNnOK/441qEILaFZYk/CIOtbpPyt+Pfdd+iLGMkEUURrVL2+k6g9RkgJsPT4hY2Zrj2N8oKdJiUDk4Slhb6TROdKyclJMLLFNMJa43hPeoYW1db7pAHte2086IsJvYmhuaMHjQd9KBtF5pXXCstYOk1BGk4ZXf8WiiQKZmbPmIGs8nJd22hmxVYx0BKfSXe9WkKiLEMRWkOziQbDIfhffDHkvQz/1kBOiVbQ8rfC71vgeRwon0x0jbDRyDosrsrg0dHjZ0a9KCWQzqMhsVVliL0TsTkia9IktD//G/QdPozU4mK4f/B9cKmpqu5BDmo1wtSA5r22dJxgehwLWMbSaQqacEp4gWurqYFv6zOhciV+P9o2bID/pZd0WdiMLgqqBBLDTU/xNa38I1ahCBY8qPi25My5fdAZxYB6/hZArr0UXZeONQctY+IE2D0e8lCczh4/o3TPWIJ0HgVC2nFyx4ppy4lBbG70bdmiy9yoViNMDWjeq6+rj+lxLGCJUp6moN3FddbXo23jEwk13fQQEExWUVC59pAUu/Q+tEo38TW9is2aoR1y4oVmBs2GIx6k4y+6Lh3rZ2+z25H/s3uJjgX0L1waNgZpxWqTCdL3yB87BsfUq2SPcUy9SvHdGT03GuLFU/Fes4emMT2OBSxj6TQFzS7OSBVWsym+kk5ObTU1MaGkBMgsnKQwqvq2knKzmaqAG12RPRqkC0lXQ0NC+9R6UVg/e0dFBYoeXx/SaYqD3eVCzrx5KFy7FmfX1mJ0/Vu6vluzbAhoQPoe7cOGIbDjNdljAjtek+2/yZgbab14toyMiNZSzOeZmQP/w+a95jvSmR7HAlYY7jQFjUvfyEKdZioKShpmEYJBZaLuALTu1PROhVZLVk5G+ExUnNHpRPbMGciZM0f39PD+tjai30TXQWSRacr62YfPF63gnVFagswS4718pEV4zRK2JZ1HAUExnKU0ryVjbiSR/LBlZsJ9883IvOQSZJZcDIHn4Xv2OXTv2wd7Rgac10xH5iWXoLO+nlmiScnIbBQ402VJ3gXOkEClUbCMpdMUNGnlRhIvzUTyJJ2cWlasJD4nC76FXqnQasnKYtB7QZMTZ2zbsBG+Z55FwcoVTBdX0fRpSt0kokxTABAEZE0JpZyLtZd1H7DZ7RhadgmGll3C7JxqIWcMmo3LSDqPkmazxpc4ismCY1xYWQnh62dNqUB77dbEAwbur7B6VeTZi72f7sbGyPthZeTbORuWTxuDO57dF2prdLMG/rt82hhDlbwtY+k0BmlauZHESzORPEknHdl05yiYjW8RDS1k5XjovaCRZOgEB+qqnbh1NgI7XtPcFsn0aRWK3EqZpuA4IBhEe+1WtNduTaoxkCyIGYNmrV6vVAfO7nShq6GB6FzheU3Ua+p2U52DFtHGmViWarhfRq4Tt06Qvh9WRn7l+QV48uaLEhS885Ok4G0TBANyBk9zBAIBOJ1OdHR0wOFwJK0dUjtspZ13OM1ZydU8uv4t4l2CXFtYXovk3qS+79rTiC9nzSK6HxIUPb7etAse6b2eXVsbM9kl7H7bfWgSK+kwsAtlwWnS/F4k2qLYJxlnBYWfZfi6nbt2ob22lri9ZoERYTHFd6BiDmKN+OdAVQcuqv2d9fXqdI00PAMi0ckBz5l71ixkTZqUoFCerPejd2040vXb8iyZFUEeOLQbOO4FhnqA4ZcCnHQnVNrty1n7rJWgldrC6lpK11H6niRezw0diuDx44ptyZk3z5QLXRhqwp+iEyzHqfZOkS66msMMIm2R6wt2p4vYUOKcTrhuvAG+p36leGz4Pmx2OzImTkDTkiXE7TULjAqLkYbEuxobMbSsjNl1aaCqDhwQM68BINM1YigSTCw6OfB9YPt25P10Ucx1ksk1tXM2w4Qn5WBlw5kR+7cB684Haq8Gfv/D0H/XnR/6XAQs0k1ZZeGQtIXFtZSu433kEcV2RLJz5MI9x4+HMomk0p0B2D0e5My5XbHNyQRt+FPq+Ua76RMgkxFIIs9A21ZZRLVFqa907qonPm2wowMpbjJSafR9aJEhSBaMTGMnNZCPLFhouLRIGOHMTP+27WhZfj+xZyh6XiPVNbLHZS+qzYZUIzrJ+3z4z7cuj3nOZuKaJguWZ8ls2L8NeHEmEmqFBZpDn1+/FRgzPfIxSy6KVoIeTVu0XIvkOr5Nm2W/b3loFbisLAg9vbBlZkKQqt8VbSRJ7Pbyf3avqbwBYqDJjtSq6hs/YdJyUSJtZRAW6/d60froo7J9oWPbdqpz2rOzqTPdBttiw3JeIQGpgRzs6EgKf0lN7bRhc+Ygs6wsZl4jfb95S5fiLI9Hc+hTrehksL095jkbyTU1UzZkNAadZ+mJJ57AiBEjkJ6ejtLSUjQ2Nkoee/nll8NmsyX8mzr1VBHKW265JeH7yspKI24lEUEe2LkECYYScOqznVWh4wbAeseqRUCQti1qr0VyHdmFfqAg6eHZt6Jp8WJpQ2ng2KDfj5y5c02hO6QWNBo3WlV9oydMUu2Y4w1/i2gVhdvCAv0+n2JfCba3w05IrgWAs6KqxpPqypgpsYEERnvCFAUr42AGLTYlpI0enTCvkb7fszweJkKuWo3v8HM2SlCUxgNtNAaVsfTb3/4WixYtwvLly7Fv3z5ceOGFmDJlClpbW0WP/8Mf/oDm5ubIv48//hh2ux3XXXddzHGVlZUxx/3mN78x4nYScWg3EGiSOUAAAkdCxw3ATDtWo9qSjN136vDhGF3/Fs6urTVMxI81SMOfqp+vyIRJuugenj07ZnIEICmmSNMWezZZyMwxfbryQh11f7ShZK2LjcDzON7wN7SuW4/WdetxvKFBV2PB6HklxphXgkZDjUboVIuXVcwwMlrFXJPxHfWcjRAUlQv7/veu+fjVQ0/j6b9+jr5+GSqAjhhUYbhHH30Ut912G2bPng0AqKmpwY4dO7Bp0yZUVVUlHJ8dN1G+8MILyMjISDCW0tLSkJ+fr1/DSXGcsIZT1HFm2rEa1ZZk7L5TcnN10z8yEiThT7UFWIHECVPNYhqtVXTeu++greYp+LZuTSjFQ9IWu9NF9JOsSZOQMeEiNC9bjqDfL3vO8P3RhJK1JFEE6uoS2nWspgacyxXRmmIN+zAyQi3LsRhO0W++bxnRuz5JWvMuCrSEdbVeVs7lghAMQuD5mHfKOplGCSRJLEoIj2FSKRo1UPJACwC++rv/h9mBPDz02j9x22UjsfSqMaqvpwaDxrPU19eHvXv3YvLkU5W8OY7D5MmT0UCocfH000/jxhtvRGZYmn0Ab7/9NvLy8vDVr34Vd9xxB44dOyZ7nt7eXgQCgZh/TDDUo3xM3HFDxo+T1+cwsN6SUbsmWpe9VthdrqToJ+lV6kMp/En0fLnYqUPKo6JqMY0KzwFA7p0/wXm730Xx5s3gnE7Fn9uyspAz905klZdT9UlHRQXOe/cd5Mybm3AdqfujCSWrSWwI1NXhyF3zRQ24oN+PI3fNZxaiCPe3lurVaFq0SPkHHIch48cxuXYYjooKFK1bR3Rsa3U11b2rIayr9ZwF/X4cnj1bNIRkZDkhWY8QIaINZ0dFhS4ediWjlAOQd8KPr7d9jqAAPPWXg6h+bb+ma9Ji0OgsNTU1oaioCLt370ZZVOro4sWL8ec//xl7BmT8pdDY2IjS0lLs2bMHJSWnvANhb9PIkSPx2Wef4d5778XQoUPR0NAAu8TEd//992PFihUJn2vWWQryoay3QDPEeUs2wFEILPgI4MTToWMPN16/JULmBUR3TVraEi+q1rZxY+J1dADncuG8d99JeqkPI8ULFd/jY4/C7s5W9Kgo6mopIFr3iVZ/Kfy8AFD3SdYk0+jz2YdlA7CBP3ZM8dn9Z1I5eAUPSkp+vmZ9GzUEZiBRl4sFiPsMxZyiVidIL82vcJtYE5mlziklgkkiuFu8eZPucg0dr+5A0913Kx63euJN+PNXxgMAOBvwrweuRGqKNp+PpbMUh6effhpjx46NMZQA4MYbb4z8/9ixY3HBBRdg1KhRePvtt1FeXi56rqVLl2JR1M4rEAiguLhYeyM5O1C5ZiAbzgZRkffK1RFDSUk7g4V7VApSg1IvV63oYB/gs4iGTRgi6PcbUqsuDDMoGbN6j7JhBwJE7+xpd/nR4TwxJW25e2ERcj0lRlmPjm3bYxamsCEnd43u9/cqGkqAcs0xJRDr8IhdWwf+YEyfkQNFVp5anSAiLTa3G+B5BMUiDDJtZB3WV9pgxYeMT3q9aF68WPG8pKVctIDUA+1Ly4r8f1AAnmn4Aj+87By9mhWDQWMs5eTkwG63wxs3eXi9XkW+UVdXF1544QWsXKlc4+ucc85BTk4ODhw4IGkspaWlIS0tjbzxNBgzPSQPsHNJLNnbURgylMZMJyIdcllZyF2wAHanKyFurhVKg5J1IVC5OmEQBOTMm4vU4rPRUl0tu1OyuVywp6Whv7VV06KtJ4xO2ZYDq/coZXiRIHoSpQ7pxZUeMbIwsJKnhsTwpelzavunVpkIvfiDEf7S8vvlvR+EYohqCeskHKPsm2+SL7RtQHFwNaVIwpmnSjCCIzpk/LiEcivREAAEYcM/h42I+fyQr1v3toUxaIyl1NRUTJgwAfX19bj22msBAMFgEPX19Zg7d67sb1966SX09vbi5ptvVrzOf//7Xxw7dgwFBcbWnYnBmOnAeZXAe78G2r8A3COAi28DUlIBkJEOg52daB4gvbMM35AOSla7JhLjwf/S70Lu8/Q02XBL4cpQ6FRxxyoCrRMGqcs9mUq5YmD1HuMNL/uwbDRVLQUvZbiKaBWpIqvGPS8jnhmRp4bA8KXpc2r7pxaZCCkSMys4Kiog9PSiicD7oWQMaUk+kTL2OYcD2TNnILX4bKJz67XhUrvBotFe0xsnPvhQVvDWBsAOAf9z7At8lDs68vnw7Azd2xbGoCF4A8CiRYvw61//GrW1tfjnP/+JO+64A11dXZHsuJkzZ2Lp0sT006effhrXXnsthsVleBw/fhz33HMP/va3v+GLL75AfX09rrnmGowePRpTpkwx5J5EsX8b8PiFwBv3Ao2/Cv338QsjCt5qwxFaiaCkmjksU5ppjAcS4qSjogJFjz2aQFCWBANSOo12iJmkINRCipgeTYYeWlaG/J/dG/oBYSoyieK6FGhUuknuRe54Yk+NQgp8xsQJsHuUkz609E8t/UiOxMwK8WNZ8jgFY0hr8kmY2Jwzb16E/B/s6EDbho1oqa5m0ka1UKuJRSsHoFfCCUDeD7N7OyP/z9mAGWUjmLVBCYPGswQAN9xwA44ePYply5ahpaUF48aNw86dO+EZGFBffvkluLhF8NNPP8U777yDOpHBbLfb8Y9//AO1tbXw+/0oLCxERUUFHnjgAf3CbEqQUfAWXpiJ7vOXo7eJ0sZlFL5JhteD1nggCR3Z3dnyZTvioCWVl5Z/xFp+QS81XBoiqZRnU89U5Hi0125FxoQJVOekJdkLPA/fM89Se2qk+rjNbkf+z+7Fkbvmy/5eS/9ksYCr5dKR9E1W3g+ScJrruusQeH2nZFs66+tDSSXxdAAlkrTOHhotGyzSMah3wokaztJtl43UTO6mwaDJhjMzSNn0iohkwyUKUwYOp8O7z4n+E9oWOi3ZK6QZC4Vr18J59VTF40hAmo1Cc1+k98E5nSh4YKXqBcA+bBiaq6pCE70YRDJwFDOBKKp76zXBSZ3XMfUq8TIzCllLpAadYkaTAsJZYwAUrycZSpO4F7XZZIBy3xXTWQJCkhb5GnWWtGYrRkBZdZ6mb7LMsBW7rt3lCnFiop5vfFuI+56EIaZnRjKLOVJuDNKOBTVQ6odBAG1DXJhdcS+CNg5XjMnDr2derOmaYZCu35axxADMjKWDfw0VzY0//+F0HHk3rKWkTVtIiyGjh+EiBlGDQ4HbQpM6TXoftCmzrNKvWSwOek1wqjOnVLyneGhO4waQM28u/C/9TnaRpk0zN+KZCDyPrsb30D0gkZJRWoLMEvVlMKIh2d+iYM/OBu9TzooiGftq+iZLwz9WguQLcXJ2XFtI+57d7QYvkvWoZ/Yqyw2W5LkpJRfUQKofhv/vma9V4LdfnYygjYMNwJM3X4TK87Vziy3pgMEIEQVvIQh494UF8rSLMGpxuxtBCJSUCBgIJbJQvCW9j8wBmQkSzwfL9Gut4Sm9Muo0ZU4xCNGy4GmJLYzxYSSacHPGxAmasslI+67NbsfQskswtOwSVdeRgySB2e2Gc/o0ZE0qJ04zV3pHavsmywzbMHcubAiINzS2LUYXwKWBnqrgRlIvpPpheNWb+a86XPnFHtRccC0aCsdixfb9uGJMPuycMeLElrFkJogoeHcfTSUKvWXf/mP4X3xJOn7OwJDRW6pfViIAobBYjKtcJbdFUftHEOC67nuRNintaPVIv9ayOJBOcF2NjbBxduLzay2wC2gzeHRLYVa5MPYfPar6mRgpMEoCpf7GKs1cy+LLWpeIpi20BXCNhl78P6MTTsL98INVjyH9uacBxLoIhvV04OeNtXiwZBZ2YywaD/pQNoqsNI9WWMaSmTD80pCeUpSCd38PmeGRfu55KFhxv2z4hkXNIb0GJcmO05aWhuLNm8Af82netSlp/7Rt2AjfM8+KCl5SeyKkoGDAql0cSCeuIwsWxtTgUlrAWUyIuno2tUDFwpiSm0v1TOzZ2XBMmxaqQ2eAx4EWYv0t7FU96fWGQkx+vyavspmyPWna4riy0jRp9lKg2WCR8gSTVXs05fU/AkiMpXAI8Zdu/+iP+FvB19Ha2cP0urJtMuxKFpQRUfCeEfkoJZ0sPTMlNzc00RmQXcRadBIg2+XxXi9snJ0ZeTx8H201T6Ftw4aE7yWVwVV6ImKgQ9HMMEgnrvhipUpZTZoLIEukZpNO3MTKzmJeT0LjinZhlEr7j0deVRWyZ9xsOgNJDsQcPIq+PFgLfxtdAFctSDZYNPyvZGgxdb+/F2f52iS/j64Tl5d1KbPrKmFQ6SydicjI7UPKEB4JUgJhxOmDxBc6LN68GQXVqyD0nTS0GCst1Ow4Wel++F96if5HKjwR0dCjaGYYqgsNK2hlaS1gLLaYkGpQhd+10HcSWZWViTpZHIfsH96KosfXi+ps5cybR9TG6IURgKL+DKl+z2A0lMSKzoqBpi+T9CGzFv42sgCuXqAtJkyrxcQCpGvBKK4HJSOzmV1XCZZnyUwI8qEyJ1GwcYDnoo6BbDgBMY5JGfG+zNKSUMrx0qXMU8f10O6h3XGyyo7RysMh9UTY8/JQuLqaSQhRCZrqsSlwRtSe15aZiay48kGkGlREHg5BgG/TZhStX4fR9W8l9E8gZBST7pBJw82DxeNAA6JySm438pcujTwz0vsj8Q46pl5lyPNS8+708KobBS3keqP00ADyteCa8gsMI3cDlnQAE+gtHQCI6yzJGQd6po7rod0j8Dz+/Y1vSoe+olJUO+vrmd0bqeaSFMKp0iy1YFhBKrOQpPCwnMQEC4kE0pTkvKolaFq4iNgw41wuFD32GDJLLpbOWASI3xHpxkBv0T4jYYREiPeRR+B7epP4lzaboePldHp3ciB9r1IhY70EbuMRnhtOtrSI5n+HNZdObHkJlRd+RfP1LJ0lA8HMWProd8Dvfyj5tRAcyI674A6kXHS1evE+ldoYeoqTBerqFJWKix5fj6zycqb3plq7R+Q6Zpx04yc4IRjE4YHyQHJQWgijzxuoq8NxgnIX0QYY6XPn3G5lhWQRyAkc6vWOjFpM9Ibe4rNGavfQtOl0eHdyoNkYJnve8r9Rh6b58yEglisURCi28lDJLHz+PxfjnSWTNHuXLJ2lwQgR6YBo2Dgg09MHVF4BjJReyPTQxtBLuyfm3DKwZWZC6OlVLidBeW+qM6wEAXlVS0zvoo8nfAo8jxSPR1FVnKR8RPi8Kbm5RMZStHudlJegxlACpMnqer4j1qntyYLeJGyzFYsG2L87MxpfNO9LbQkbVvjn6AnYUDILc/7xCnJ7TiWitA1x4amx12B34Vigo8eSDjhjISIdEAtb6Pvh8hkAeqTnspzgxLwdSiEdoauLqPp4GKT3pshbEATJ0FXr6jWwcVzMZGL2BbOzvh7B3l7xLyk4NrEq69mwezzgKQww3bOdZAx4s7+jZEPvDCgzyQfoAdGSKm438pcvg6OyMmntotoYMqonqhatnT3YXTgWfyv4Or7e9jmyezvhS8vCJznnIGjjYo4zCpaxZCZEpANmIuRsjO7QA67GytWh42Sgx86Q1QQnyqNxOmV+oQ4096ZEYEQwiCMLFib8Ltm7L1ooqYzbnU6iWmOSKusAMVGWZEG2u91EJTYkkQQPxekA1qT1+M2RfRhZBpMR8gGsITXG+PZ2HFmwECd++BE899yTlLZRJ2gkcfzkZaUDAII2Dh/ljlY8zghY0gFmw5jpwPVbAUdczRtHYejzMdMVT0GbEksCFgaYVNpqvN6PJqi4N0BMcmETCqqrEezpRcuKleI/Uki1NxOIVMbT0hIy1uKh9A7jDV+ptGqSlOT8ZfdpkioIY7B6KJIJVmnyYtIQTVVLQ8a1wvwkBIOaZUGMBMkY8z29CR07dxrYqlhIvVc5dDU0GP78S0Zmo8CZLlngywagwJluqHSARfBmAGYE72j09wHv/Rpo/wJwjwAuvg1ISSVvE+PMH63FGrVWjCcCo8wzNdleWosH6w1WlclJiLkF1dXgjx0j4mooEa5JirxquScL8tDCvZFNCAl/Rhj6TjbhmASkY8zmcuErjz1qiIyIFASeh++ZZ9G6ejXR8WLPX29e1s6Pm3HHs/tC14r6PGxAWYV0LQD7twGvLwY6m099tnsDcOXDRJ4lgF4bQ2nR0uqaZ1FXTAksdD/UFsQ1u/eCRRiVlLdm4zjiLCklwrVUP7Z7PHB973tof/ZZac8kBbfGjIRcrWBxT2r5XSQJIZzTCS4tLSbZwO50gvf7EziCgyHkTTrGBL8fh2ffGvk7GYagzW5H9oyb4duyhYjDRKJ9xvo+Ks8vwJM3X4Tlf/wE3s5TPEuPIw33T/86E0OJBpaxZDbs3xZT7iSCzubQ59c/Q2UwkWT+kIoDahEnI51IOKeTKiyXV1WFlJwcJgucloK4ZudXsAij6kXMVVqQ5fpx+lfP01wP0YySD1qR7HsiMayDfj+KNm+GjeMiiQJNVUslj08m4ZgEaueAZBmCVBymqOcvBIOi2mf9Xi+O3DUf3bNmMat/+MGX7Th6PDYhpbWzFx982W64sWSF4RiAWRguyAOPjAJOyKRLD8kG7jmgSPImhRrNEzU7VlIXdfHmTbBxdpz0etFaXa1YuJOlFotazSW1asZGQmsYFTBGrFANtBgGemqHJQtmuCc1Wk1m7V+kEHge//nmZeDVyF0kQVsqDFraAan2mVbjvPq1/XjqLwclv7/9f0di6VVjVJ07GqTrt0XwNhMO/lXeUAKAE77QcYxAIwkQhpq6cKSk88ySEmSWlsA1fRryV9wf+S7+WCB5NYniEWxvR9PixZJ1zQB2dezUQpZQDQCCgKwpFeh+f69k2/RIHFADgedxvKEBrevWo3XdetgyM5H/0IMYNmcOhs2Zg+LNmzG6/i3FSVoxVITBQd6PBu09seiXYudQ48k8KSU9EQezhrxtdjvyly9T92ORedYohJNbhs2ZQ3Q8qfaZVL05EvT1B/Hrv0obSgDw678eRF9/kPrcamGF4cyEQ++QHzfqciaXNErzRG0dJjPWJJKDmEs92SGRMKSeJzgOCAbRXrsV7bVbJdtmhjpogbo6NC9bHstpqYk9puOVV4ierRnFEbWC5p74Dr/mfinVt/MW3yPvgYjjkwXq6tBaLS9MGzm/iUPejspKnPjhR9KlXBSQLEPQZrcjs6wMx2pqlA8mhYbQ6TMNXyCoEPMKCqHjfnjZORobSgbLs2QmkAZEGQZOSSee/rY2zTtsNenI8Sn9Z9fWYlTdG7A7Xcy9NCQV0RURt3unrfKtN6Kfpzsc8gjG7s7k2pbMyuvhkjhKte1In+3pKI5I2tbOXfWa+6Vk325pQdOin8oaSsApwzp8HpLwFedyMfFc6unp9dxzDwrXPQbO7ab+bTINQRLPsT2bMlVfpcfskK+b6XEsYHmWzISRlwF/fYTsOEYgVXVtXb0avi1bNHtD1JSbiCYAB+rq8FnFFF28NCQV0YkwMEF0Nb6nW4kYLbDZ7ciYOAFNS5aIH6DQtmSUdRF4Ht6HVhEeTPZs9S7rkQyQtrVj23ZN/VJTMkSUZ5j2PEG/H5319cylQVh7ep2VlXBccUWUyv0wNFdVob+1VRdVdBYg8RznL7sP3tVrqMtD0W44hmdnMD2OBSzPkpkw4pshArcchmSHjmMERS5LFFh5Q9RwngCZnSxDL03Yc8JCVbx7zx5qPphRUMNVi4bad6gW3e/vla5nJwaCZ2sWDhZLkHoHZHknBM9OrRSIPTsbo+reiBgl1OcJZ2Sp9AQZ6emNHiNDyy6B52f3DnxhDAdTDRQ9x5WVxOtFzO8pNxwzykZAqT4uZwsdZxQsY8lM4OzAtPXyx0xbzywTLgxiVdckkl6NJOM6KipQtG6d5vOQIhlhnsEWglLbDrnfkaiIm2EBowHJPTmmTSM6l9yzU/s+eJ8PJz74UP15NGwwSOeQ4w1/0yU8l8wQNg3EqA/RCRNUKuAqNxypKRxuu2yk7DG3XTYSqSnGmTCWsWQ2jJke0lJyFMZ+7iii0liiRXiAOP7v/+QPTJI3RKsnhBaZJRer5y+FJwhCUrCaMI9WzsVgC0GpbYfS7wbLAkYDpXvKmjSJ6Dxyz05Lv+jcVa/5PGqMNdI55PDs2ZHSLFLZrWqhZIjoDdJ5Q8lzHMt9nCl+MY0bjvFny3O+lL5nDYuzZEaMmQ58bSpwaDdw3AsM9QDDL2XuUYpHZ309Ai+/THSs0R4Hoz0h1EUnIz88NUFklpToUr2dBedC78ryrJExcQJSPB7yUBxF+5PBwdIbcvck8HxCOZEYEDw7qgr2cWiv3YqMCRPgqKhQfR41RpaauUEPwciwIRLWqwu8vtOQPseaqxW+j8zSEmRMmMA0a5kPClixfb/0tQGs2L4fV4zJh10pXscIlmfJAoAoFzUhjPY4JMMToqboZLRHQo8wDyvORaRtUguUIJgqBGWz209xPhQPpn+2LDhY8bv2YF9f0rW1xO6ps75ePqOQ4N3TcB3FEA6ZU59HA49M1dygE/VArMAway9W/PX05Gqx9pg1HvShuaNH8nsBQHNHDxoP+lS2mB6WZ8mM2L8N2LkECDSd+sxRCFSu0S0MR0O0TAbpNVmeEEdFBRAMomXFSsXUZs7txjmvv4aef3yEjld3ICU3F1nl5ShipBVFUm8rPovpdKp55qioAB5fn6izFAe9dLjkIKqCPKBfFWmXCUqokGyK7C4XssrLFc8lqdtFgGj9KuLzaAzrqPaGMdbbIi0vFbm8xjGsZt5QA7V1BMXQ2iltKKk5jgUsY8ls2L8NeHEmEsSUAs2hz6/fqspgUhpwNC5q0smK5UKdLEHEQF0djojUQRJDsL0dn00qjzGqwgvk6Pq3ND8LWhFFObd7Vnm5/KJp0jpc4fBSV2Mjuvc0AgCGXDwRNs4O/tixpBiEkuVFJPSrklkMlmRTxPv9xIaBWLgv8NZb8D/zjOJvo+ec+PP0HfoC7S++BD4q7KrVCFYdWhdpLyni58Ah48cRGy4AcPSXT8K3ZQuErq7IYbRG92AUX83LSmd6HAtYxpKZEORDHiVR1UkBgA3YWRXiM1Hwl0hi1aQu6px5c4kGqR5aJkYreqvRkon3PvW3tODI/AVMyMI0vC2l3WvO3DsH3QQahs1ux9CyMgwtK0t2U+j6iAmKwerB/RPzKJAYS/FzTvx5cubMYe4V1eINE5sj5TaEYnOgYl21gXHXVlODY5s2QehKFF0MzymkRvdgy3wFgJKR2ShwpqOlo0d0NbQByHemo2QkpUimBljGkplwaHds6C0BAhA4EjqOUJiS1OVL4qJOyc9HDkH9IFo3Mw2MJOOq1ZJJgCCgZdlyzQskqUFrH5aN5qX3yu5efVuVFzPAXBOoGUHdR5JshBrB/WMVMmcZ1olG/BxiH5aNpqql4CkFI+U2hABE50DSumptGzbKHyAIMR4oufnQSL4nq2iCnbNh+bQxuOPZfbAh1n0QZrYtnzbGMHI3YBlL5sJxwkwfwuNoY9UswlxGxMf1mkTjwdJQ4P1+tNXUIPfOO1WfI7IIKSzOJG73YEcH0TXNIh1gVqjtI5276pNiLBnB/TNDDUElxM8h+T+7l6q9shvCu+aDc7lUqZvTIOyB8r/0O1kPvtZ3TmoAsY4mVJ5fgCdvvggrtu+PIXvnO9OxfNoYVJ5fQH1OLbCy4cyEoYRZV4THkcaqj27YiK49jREyshbNGaP1kPQEa0PBt/UZ4owaMT0Um92OvCqJEiVRaH/uOaJrcE7naaVeHQ09a39FQ20f6di23fDsOMA4Ic7Bpl9F014ScUul+oWs0LZho2KGm5Z3Tpq1p5htt3Mn9XgUeB6XdR3Gq1/vxiOjT2JW6Vdw39T/wZ/v+bbhhhIA2ARBZ/OXMZ544gk88sgjaGlpwYUXXogNGzagpER8h7ZlyxbMnj075rO0tDT09JyyUgVBwPLly/HrX/8afr8f3/jGN/Dkk0/i3HPPJW5TIBCA0+lER0cHHA6HuhsDQpyldeeHyNxSkVpHIbDgIyLOUserO9B0993El48m/6p1pZJe0z1rFvKXVhG3LRkQeB4HyicrZs8o8hCicHZtraJHQW6HZne68GW4AK5G5Mybh7aNA+5+kd20GRc1ErDY4ZLupkn7iBhI+oJeMKI+GmDebEypdpG0t2tPI7MxGAMVpHO5c6V4PBhd/5Ysh0runUsmLsTND5ExILdJpswMFWvr0XQnai64Fgf/52KmniXS9XtQheF++9vfYtGiRaipqUFpaSnWrVuHKVOm4NNPP0VeXp7obxwOBz799NPI37Y4y/rhhx/G448/jtraWowcORL33XcfpkyZgv379yM93TimPYCQAVS5ZiAbTiJSW7mamNxNu+sN7wK0LJKk12yvrUXGhItMvRiTZM/kzJuHYbf9CP+57H+JQltKYRslvpfr5puI2s45nQgGArJu95w5tyPt3NHMCPNmWBhZ8OVoFhUtGVbJ5IMZxf0zKmROA6X3q9ReVu/N7nbHZs56PHBddx3aNmzQfnIRbhzNO6ehUxDx9igyQ8NjWBAERK/Ww3o68PPGWjwI4I6OHjx580WGepgGlWeptLQUF198MTYO7IaDwSCKi4sxb948VFUleim2bNmCBQsWwC/hEhUEAYWFhfjpT3+Kuwe8IR0dHfB4PNiyZQtuvPFG0d/19vait7c38ncgEEBxcbF2z1IY+7cBry8GOptPfZZVCFxJp7OkatcrsiOhAdEug8F1jATJ4nn0iV8STXJy3gSiZ0e4IOfMm4u2jU8MnFjea8TCyDHKUyEHxedH0OdId9Niv6PNsEqmZ+lMhdr3Gw3NnqWBfjiq7g2c+ODDmHEHAAcmldMVjZZB4dq1cF49lfp3pPd4dm0t+o8epYpgRCAyHpXGcBBA2xAXZlfcC48rA+8smaSZ5E3qWRo0nKW+vj7s3bsXkydPjnzGcRwmT56MhoYGyd8dP34cw4cPR3FxMa655hp88sknke8OHjyIlpaWmHM6nU6UlpbKnrO6uhpOpzPyr7i4WOPdiUE7y1+Vyq5GTlHMNXW8jpEgUafNmXN7iNQpBQIOENEOTclQGrhOzpw5xBwMrerVRlZyl4NWvpyWYs0xfeThh8G5ZepWmZgPZhTXKxlgVYw7TJhWWzcSCHGEuNTUhHFHpVRPAL1r74UNPVUQGY9KY5gDkHfCj6+3fW64gvegMZba2trA8zw8cZO/x+NBi8TD/epXv4pNmzbhj3/8I5599lkEg0Fceuml+O9//wsAkd/RnBMAli5dio6Ojsi/w4cPa7m1WOzfBrw4A+iMkxDobAp9vn8b1enUlOwAtLmaHRUV0sUVGV7HSCgZFDa7HQUrV0j8mIw4y+pZhK9jRNFOVgsQC2jVk9FqbEX6yPRpKFhxf+i960iiZg2jS3AYDVbJJ1pKvZAQ3B0VFSh6fL345istjfxaGgxyGrkBTcYjYscj6RjO7u0EALR0nFB1TTUYNMaSGpSVlWHmzJkYN24cvvWtb+EPf/gDcnNz8dRTT2k6b1paGhwOR8w/JgjywPb58sdsnx86jgLhRbN482Y4pk0j+o3SYFHagWZNUi6XQHKdwYTwJJeSnx/zOWkGkNZnwbndzL1GSjBT9qNWPRmW4n2DLRvMLN5BPWHE+5UD53ZjVN0bRO/eUVGB8959B8WbN2HYnDkYNmcOijdvRnHNk8TX02KQKxpAUd5RrXUCo8cj6Rj2pWWF/tvVR309tRg0BO+cnBzY7XZ442K5Xq8X+XGLkxTOOussjB8/HgcOHACAyO+8Xi8KCk4RxbxeL8aNG8em4TT44h3ghIJb8YQvdNw536I6dWd9PRmngkBnhYSfMtiq2mtFmPcj9J1EQfUqADbq8htaKrkDgOv664lqerGEmdSBtfY51uJ9RgqokkKMnwbAkNphyYae77eroQHHampkjw+2t+PEBx8S89TElOoFnleeIzgOhY/+QpNBTquVJamMHpcFF3uRxPEYHsMnW1pEiShhztInOecAALKHknvatGLQeJZSU1MxYcIE1NfXRz4LBoOor69HGWHZA57n8dFHH0UMo5EjRyI/Pz/mnIFAAHv27CE+J1Mc/Cvb4wYgtWtMAEGIgHQHapSeixkQH744PPtWNC9dClvqWVTeHK07NN9TTxkeNtFTHZiWP6O1z9Hspkmht2ePBlJhtraap0zjHdQTer7ftNGjiY7XumkgmSOKHv0FnJWVmq4D0HtHxcL+hY/+QjEcDSAyzrvf3wvPgJZcvIkVRIjJ+9TYaxC0hUyXfIdVG04UixYtwqxZszBx4kSUlJRg3bp16OrqimgpzZw5E0VFRaiuDhUIXblyJS655BKMHj0afr8fjzzyCA4dOoQf/ehHAEIyAgsWLMCDDz6Ic889NyIdUFhYiGuvvdb4GxTp+0IQ6D6aiv4eO1LSeWTk9lGtozS1q5RSxmnVuY2u5ZYMsC7tEnlmD61SlRFjdLFWvTyIarPrpPqcPS8P7uuvg9B3El17GkU9PINBeVot5Popaar6YOEXSkHP92tkSRHJeVWH7FNa76iYVISN4yTXAAAJ2W8p+flw33orPn/hD8ju9kc+bxviwlNjr8HuwrEAgAKDa8MNKukAANi4cWNElHLcuHF4/PHHUVpaCgC4/PLLMWLECGzZsgUAsHDhQvzhD39AS0sL3G43JkyYgAcffBDjx4+PnC8sSvmrX/0Kfr8f3/zmN/HLX/4S5513HnGbmIlSfvY28Mw1p857OB3efU70n4iq8zOEh2fhHXDMXEh0StIU0LyqKmTPuDlhEES77fvb2tC6erXiueJToqPPYR+WDTUhKjOCRaq6FI43/A2H4wRViWGwLENkIQaYiFuySO+O7nN9hw7B/+KLMcankhhfsmUQ5EAr9UAs56GA00XqQI/3qyjTosOYNIOuGSnE2tpZXy87zlsX3odf7GlFdm8njqVl4ZOccxC0cRGfAiudJdL1e9AZS2YEUwXvR0YBJ9oROJyOI++G04+jXUkCYOOIFyBSRW0xPQ412jFS55I6n5kWIVrQaJHQLjK06uusrqsWrN4tawNUreGVrIVI6bpqnjMrXSA9jW+jn7fW68ku/sBppYivB0jH+Wfrn8GK1z6NqQ1XwLg23Gmp4H3ag7MD0x6H8MIMePc5Bz6Mj7mF/iYlXKp1D0suMirOJXc+o8NGLKEnuZmFu97IsAkrMjNNdp2SIailqHMylKeVDCG1Y4iqHyQh/DjYNlFy7S06zWkHrEA6zi/racI7Syah8aAPrZ09yMsKhd60ClGqgWUsmQ1jpqP7/PvR/+KvpI+hWDDUcEpoeE5S54oPvbU8tEp20Wq5fwWCPb04a+D3ZnUnR0NPnoLWzDgAsA8bpup3asHCwGBpgHY1NjIzvPSGkiEkPPYoWlevUWX4kfa/nHlzEyvY67zQJ2MTpcU4U2pv0fp1GF3/1qAJjyULNOPcztlQNsrYuUwMlrFkQvRnkPGlSDqczW6HY+pV8D29SfwAQYDruutiPiJSkk640KkdKLFMQVQbeJ8PzYsXAzD3rjIaesojaKk5dgriv5FKH2cdlkgmUTZQV4fm+5YRnSvZxGUSD1jLipXyxZplDD/SfpozZw5y5swxbKHX4vlTCy3GGU17k218JwM0cwDNOOeDguVZsiAO0o7Ud+gLxWMCdXXwbdose0zbhg3wv/RSxEBRFTaKym5QG74LY7CE5vTOnpLKeuGcTqKivfyxRM0usV11WCk4GFVDkcZgZRlGYWGA0oaQ+9vaIPB80nb/JCEJWUMpCmJjl7afGrXQswy5kkCrcWZ0ewcTaOcA0nH+1/RCrFizS1fOEikGjc7SmQRS+fi2DRtlNXVowmlhA+XoE79E74BopxLyqqpiymhklZerC98lNNzYMhlaoLdSs5h2SdG6x4h+K8VDi5/wg35/jKEEkCs3s1Z+1qqVpCaE3Lp6taI+lZ4101h6tqQ2WmZUFDda0FSr2ryZBFjNgPCYaKmuxpG75lPNASTjvOnmObjjN3+PMZQAoKWjB3c8uw87P26GkbA8SyZEzE5Q9kCNO6FoDCwuRJorA1Z/vNRA1x4FjggNBtEuTW+l5ngukKKKLwseGsFOW68wCqk+l5jbX1UIGfLeTL0JyKSeZLvbDd7vV+1xM5uiuJHaRIB2Y8fo9poZRJnSCnOA3DjPXboUt+6zQ0BP/Fkj5IIV2/fjijH5hoXkLGPJpHBUVKB37lx540XBoNBlhyOzu9fjeoNll2Zk9pSa8J8qI0Khf+kZllBa2KUMmKwpKo0XiYndCAIyaUjCU7UERxYu0hTyTUaWnxSMLomk1dhh3d7BpJMUDaowt8IcIDXO//aFH81/+pvsqZs7etB40GcY+dsKw5kYqcOHEx0nZVD0HTrEsjkAQnwZKbe9HjuqM2GXpgY0YRWB59HV0KD6WlL9S++whFSpELnQX3vtVlXXApAQhlH0nIFNqJg09OiorDRdKE0LIvcttegKAlPJAq3lTliWcJIqPWP2gsVqM6Xl5gCxcd4SSPQoiYH0OBawPEsmhpadUKCuDm0bN7JuEoJ+P078/e+aSHsF1dXgjx5FS3W1NHH1NCu0qwdIwipqhUWjIdUPkxGWIDFgwHGh/1fJnQtP7EYSeklDjyxDaYPVs6EWLBIyWJRwGsyac2rD3PZhw0I0DcK+5jveS3Re0uNYwDKWTAy1bl/VOkmE8D29CeljxyYUaySdjIaWXRL6KD1NVvFWTyG802WhkAuraBEWDZ1c3mA1OowCEE7W4SrnKiUXwsad0YReUkOIRSgtmUKQ4bF30utF60AdT1HoIB3AwtjRYrAmQy6BJaj7us0GzulEc1UVcbkhAMjOTCU6PelxLGAZSyaG2p2QWuufBi0rVsJxxRVUpL34wZGsQruDTTFYDTQbzAQGazIKz5JO1q4ZMxB49dUYz6Xd44HQ2xuSXSAw7pLhOTOCU5RMzwaVp1OnJA8W3jm172mwyw9Q9fWBOSHo9yMY95VSX8t3DiG6BOlxLGAZSyaHGoPCCFJ0sL2dmrQnNhkZnaEzmF3gNKAxmEV1lggNVqMNXtLJujPeUHK7kb+0CuA4YuMuGZ4zvWGUZ4OqcKoCTnq9VCEcEiSL6D7Y5QdoKgvY8/JCm5M4WRIAin2tZGQ2CpzpCbIB0ShwhgQqjYJlLA0C0BoUpAtKXlUVUnJy0HfoC7Rt2EgdtiAh7ZHAqIlrsLvAaUA62Q6bMwe58+ZC4Hm0P/8b9B0+jNTiYrh/8H1wqWQubiMNXtLJmo/jwvF+P44sXISi9euI63clw3OmN4zwbIh6bj0eBHt7VXk6W6urY97nYPYC6+2t1JteoDgmBAHuWbOQNWkShCCPw7NvlWmsdF+zczYsnzYGdzy7T7QOgQ3A8mljDFXytoylQQIag4J0Rxytk5R27rnURGCja49pxWB3gdOAdLLNKC1BW00NfFufiVEF923ZQrUgGWXwqi4DE2UMhwVUST2fWj1nZuLHde6qJzpOrWdDznOrFvGG72D2AuvprTSKXkAyJgSex9ENZAlGUn2t8vwCPHnzRVixfb8pFLxtgqATC/gMQiAQgNPpREdHBxwOR7KbAyBq0gJEd8RiqcbhST3w1lvwP/OM4jVSPB54fnav6SYsqcWp49UdaLr7bsXfF65dC+fVUw1oqX4QeB4HyifLTsp2pxNBQYAgVjpFpp+YAaJlW9xuorIgZ9fWUht2ag0eM/HjBJ7Hv795ma7P6ED5ZN35kgAiRsXo+rcGlWcPUDc3E58zfqzrOI6lxgRtBq5SX9O7Nhzp+m0ZSwxgRmMJ0DZRex95RLr4bhgmXFDl7tnudOHLWbMUz5Ezby5y77xTz2YaAtlJmWTYm3xBip+s+71eNA0UY5aDUcZwMhYwOXTtaSTq//bsbJz7179Qv3PS87OEGqPODGBpRCsaqQaOY6oMXJPML6TrtxWGO42hhUviuecepI8dK1/tPAk8H7kdvhJ5u+ixR5Hi8SiGBNpffAk5c+aY0kCggZy7PNjTI068jIbJw5Lxob+uPY1Ev0vJzdU9NGZGfhxpaM0xbZqqNiWDlBx9TTOFO5XAkudHSy/Q6zlRZeAOQs6fZSyZFf19wHu/Btq/ANwjgItvA1JiCbcknV4Ll8RZWQm700lE0utqbMTQsjJV1yGF3G5MtohveHFavQbO730Px554QvY6vNdrWgMhDNIJT2xSFoJBHJ49m/haZs3MiQcpH6S/3ZewE2cdGjMjP46Ux5Y1aZKu52eJ8DXNFO4kBSueH02GnZ7PiSYDV4rzZ2aD1zKWzIi6+4CGjRD4ILqPpqK/x46UZ1YiY/ptsFU+CMC4yYE/5iM67siChSh4YKWu+ixyXqOcuXcSLU4gLE1hZgMhUFeHlodWgY/ykNk9HuRL8MfiJ+WOV3dQXW+wlJwhyV5zTL0KTQsX6S4dYcYUcZJMQrlyH5rPP8CTQ1paTN9VhSgi9JkiByIF+zCy9Pm+Q1+gbeMTuj0n2gzceCNIioeYv3xZggByMmDVhjMb6u4Ddj+OwJepOLDdgy//lIOmBje+3JWNA0tfROCRW2VrYx2Zv4BpfSHShTLY0cH82mGQlLjwbVUmpNPArAZCoK4OR+6an7DY8F4vjtw1n+j509yblsUzGZCtmffYowjseE33Wm+AOSvUK9Y2s9k0hUVIaqflr1yBc3fV4+zaWhSuXYuza2tRtO6xUM22KKTk5yP7h7dG2iV2Lk/VEnQ1NqL5vmWGvFMzIlBXh6aqpfIHDdS8a3/xJV2fE2lfziwrEzWUxNa0YHs7mhYshPeRR1S3ixUsgjcDMCN49/cBD+YhcDgNR951D3wYPVGEXhXndMWkeceAMWlOMatKp2tHu2P729rQunq1pvOFUbx5E5qX3it7P5zLhfPefUcX968WN7PA8/j3N74pyzWyZWTgq+81yp6T5p0WrnvMFLs6Wog95+739xIRkFmQhkmyEZNFbtXbK63m/DSZVSn5+XBMvQqBHa8xy7gajCAiUw8Yljlz56JtwwbFc2p5TiTZkCn5+Ql9nuR3AoDmBfch44opScuGs8JwZsKepyAEBXj3OQc+iO8QNgCCtKEEMOdCxIQ2lMDo2mqLv3JOJ4KBgOzilFlSErqfu+ZLnifo96Ozvp65617rItXV+J4iKVvo7sbRXz6JvHlzJY+h0SpqXb0GNo4bdGEMMT6IkaExowUtaYxwWnIxrYGvhrwsxd8RO1d/u080lCqHroYGU/FftIKUTB3mBgl9J4nOq6Xv2+x2OKZeJZtF7Zh6laryXDYAQ375KG46mAWPKyMpOktWGM5MOPy3EEfphB2JhlIYZBY1Ky6EwPOwO11wz5wBW2am7teWcseSIHvmzND/SLntBxanrPLySIkPUQxkKrF03ZOETgWeR9eeRnS8ugNdexoTrt+9Zw/RtXxbtii2XSpcFQ9WoV2lezMCRofGZEOCDHk0gbo6HCifjC9nzULT3Xfjy1mzcKB8suw7CxsnzqunIrO0RNKIUHNumvOTIPpcGRMnoHX1Gmol8GM1NUTtHiwgJVMXVFfDUVHBtO9LjWWB50NhbhkEdryWMPZJ1wt3Xxe+3vY5Wjp6cMez+7Dz42ai37GC5VkyE87KRH8Pm50PiwlfrYdHi1S/quKvA16jnDm3I+3c0Ypqy93v75X30DD2zpGkkTcvWw7vQ6uoKnNLXq+ri6jt4V17V2MjjixYKO6xZJDmbpZMpWTUetO7FIye5OZkEaflPFlaioSfToRvUgODP3YMALu+r6Rjp/RuxOZVmvUiu7cTAkIugxXb9+OKMfmGlTyxPEtmwoU3IiVd4457gMyndcJX5eHReG1VE2Gc18hRUYHR9W/FEEhH178VMzkanalEkkYe9PsT9J/ivToZFIZbV0MDkRfHZrfDxtmJQ7u0MDIZQQkkBGQ9dF9YelmiQZL4oNZDque55aDkydI0Jk8jwjetp4hF31cay527dhG1Kf4dZkycAM7tljg6Fr60LAADHKaOHjQeJMvWZgHLWDITzvkWMgo4pAzhAdHygQAgwB4OIek04avy8DC4tpqJUCykobQ4GR2OUT3Bx03umSUl4IYOJfrpsZoa4rCJXsZjshZcORgVGjMCpFpOvmeepX7GNDpRrEBiWGsekzq0W09IhbzCnqKENSAMkY2rlr5PMpYD27cT3VP8O7TZ7chfvkz2NwKA1iEufJJzTsznrZ094j/QAVYYzmSwpWXAc1HHQDZc2OEYRqhT5q9YDtg4TcU9I2eUyBqiDr2puHbCOQgnwryqKqTk5KgOaRgdjtE0wceFBAsefABHFiykOoVS+EEv49GMwoyA/qExo0BqvLauXk1dGNlo7yvJYtxy/wqMqn9LUSuKBGbWUQtDKXytJoHAUVGBoZdfjvbnf4O+w4eRWlwM9w++Dy41VvA4HiRjmff5YHO5QrUmKedVZ2Ulen74kSg5PHymp8Zeg6At1r+TMzRNtt0sYRlLZsKh3cAJHxzFAL7RDu8+5wDZO4SUDB6e8QE4zssERl6mecKXGoxZU6YQ/X7YnDlIGz2a2WJDasRkz7hZM2HUyEwlEjFAJYQnd0dlJbpn/x3tm7eQ/1iEdxRtJNuHZcPu8YBvbWVqPBIvuFoFClWAlXpyMkFjvNLydYz2vpJs0HifDwcmlcP1nf+Db9Nm8bFLOL7MqqMWBjFfTKKckZRhLDbnkxjSxMZlX19kvqGdVz333IMhAyW2+KgSW0eHuPDU2Guwu3Bs4o8MFD6yjCUz4fipRcNR3IOsop5TCt7pPDJy+2DjTh2nZcKXG4zttbVE58gsK2O64BhpxKiZaNSCJlVfCtGTe9bl36YzloAYLw7f4U9UynW5VE9yJG2WQ0t1NWzpaYMqBGYGUBnhlER9o72vpItxsL0dvk2bkX3r7ASdpRSPB56qJfCuXmMoiZ81aOoK0nhJtRD2Scey0N0NYEDGJSqJhnRedVRWIuuKK/DmCzvx3OsfwJeWhU9yzknwKIXR1tVL1C4WsIwlM2FobCzZxgGZnj7F42hBMhhhswHBoPgJdJxwjDRijAzHSN2X3eOB0NsbIlgTTu5aQgidu+rRvvWZhGuFCd5qJzkxkC7mwfb20yZLyUhQG+EUYU+jva+0np7Ajtcwqu4NnPjgw8Sxy3HiunAD7c6rWmLqECxt+Jpk06y1sDOVYW6zgUtPR9HmzeCPHaN+xja7HVmXXII/f6R8bF5WOtE5WcAylsyE4ZcCjkIg0Axx/6It9P3wSzVdhmQwRgaEARNlPIw0YowMx0jdV2d9PdWipCWE0LFtu+yEqWWSiwftYq5FnuBMhZQRLgdSY9vIjQutl6y/pQUnPvhQcuzGG/0AYHc64fzud9C6ek3SZSzkoAdfTCt/UI04sY3j4Lx6KnEbgVMc2q+1tqKs8xD2DD1b0qvkzjgLJSPJ6uKxwKDLhnviiScwYsQIpKeno7S0FI2NjZLH/vrXv8Zll10Gt9sNt9uNyZMnJxx/yy23wGazxfyrTFZ5B84OVK4Z+ENMvRtA5erQcRpALAI2a2bSsob0SrdONsTuizZLRTETRvTCNtizsxGM4gIkIG6Sk3vupCKT4Xuzy4mARl376IaNSROtHKwIy2XkVVURHU9jbJNIcbBATGo7IcTmsXCoSUxHjff74Xt6kylkLOSgB1+MhQEWHsuc0yl5jJprhhEtG9Fyzz1YVr8RW954CJc2ibuYjK7TNqg8S7/97W+xaNEi1NTUoLS0FOvWrcOUKVPw6aefIi8vL+H4t99+G9///vdx6aWXIj09HWvWrEFFRQU++eQTFBUVRY6rrKzE5s2bI3+npRnHsE/AmOnA9VuBnUuAQNOpzx2FIUNpzHTNlyAdZFmTyuFZvFjWw0NTCkFLXbTTHTTeNOrwy4BR5Zg2jYiPpjTJ0YpMOioqEOzpRfPixYrXPlZTg2M1Nabb7ZsdNrsd2TNuhm/LFuZ8HaO8r2FPVsvy+2MIvlKIn8dUi9oyEF5lCT34YqRzft+hQ7LfOyoqwGVl4fDsW5ldE5DmUw3r6cDPG2vxYMmsBIK3v/skGg/6UDZqGPF1tGBQFdItLS3FxRdfjI0bNwIAgsEgiouLMW/ePFQR7Kp4nofb7cbGjRsxc6A0xi233AK/349XXnmFuB29vb3o7T1FLAsEAiguLtZeSDcaQT6UHXfcG+IoDb9Us0cpDJJCqvbsbIx++0+yKaU0i6ZZVJyVMFgMOoHn0VbzFHxbt8YIStpdLghALO8oSmFXayFZyeKdAwaZlNexa08j0bVJz2dBHJH3A4iGdAfD8wz29eE/37pc2gsqUYSYuo+JwCwFd1m/R+Li2TYbkeaSliLR8XPskPHj8FnFFMkwYRBA2xAXZlfcmxCSe+yGcfi/8UWivyMFaSHdQROG6+vrw969ezF58uTIZxzHYfLkyWhoaCA6R3d3N06ePIns7Ng459tvv428vDx89atfxR133IFjAxLxUqiurobT6Yz8Ky4upr8hJXB2YORlwNjvhf7LyFACFNRcB8D7fPisYoqka5pGmdlMKs5yUFsHS2/Eh7wCO3fiQPlktG3YEEPMzpk3F+e++w7Oe/cd0bCJGiG7+HaoEZkUeB5CMEjsvlc6nwVpnA6im1xqKgpW3H8q0SQaMpxJFtpJZtFfYv0eI3M+gW9EacxpUQMXnWO/dbksn4oDkHfCj6+3fZ7wne+4cdlwg8az1NTUhKKiIuzevRtlZWWRzxcvXow///nP2ENQZPQnP/kJ3njjDXzyySdITw+x6F944QVkZGRg5MiR+Oyzz3Dvvfdi6NChaGhogF3Co2CIZ8kAKNZ+k9jFRHYWMr8L7ywAEB+bTA+OWo+JEe0iIu8StlPLjpV05x69O1dbX1DqfBbIYBYPqZZ20HqjTyfPUhis3+PRJ55A24aNiseRPAfa9yM5xxJi9cSb8OevjI/57LHrL8T/XfQVVeeLtIvQszSoOEtasHr1arzwwgt4++23I4YSANx4442R/x87diwuuOACjBo1Cm+//TbKy8tFz5WWlpZcXhMjhNVcJV3eErF82lIIZlRxjmmCxrRavUA1uRC2U0uGEy1JVOvkSHtdC6egJ8+IdAHXGnqnzYrVJP5qUv0l1u8xdfgIouNIxhzN+1HNJ4tCuC5cNPKdQ1SfjxaDxljKycmB3W6HN07t1+v1Ij8/X/a3a9euxerVq/HWW2/hggsukD32nHPOQU5ODg4cOCBpLBkCBpwlkkntxAcfEmVIRRszeqS2JnNBNGNZDlWTC2E71Uoz0GTpkLTflpkJoauL6HxnIsziHYoGqQGkRQAxGjTGgqI2FKUcihmfv1awzrQjfT+qiqQPIMxZiq8LZ7R0wKAxllJTUzFhwgTU19fj2muvBRAieNfX12Pu3LmSv3v44Yfx0EMP4Y033sDEiRMVr/Pf//4Xx44dQ0FBAaum02P/NolsuDWRbDiB59HV+B66B8KPGaUlyCw5lepNOqmpMXz0SG1N5oJI8wyMmkC1TC4k90OzCEXu2esF53YrEm9J6wsKXV2wu93g/f5Bq7asB6TI+8lOiCA1gJLpqVXynAIg8qoOloQUWuiRaUcyJ6rdDAcREs0RqwtnSQfIYNGiRZg1axYmTpyIkpISrFu3Dl1dXZg9ezYAYObMmSgqKkJ1dTUAYM2aNVi2bBmef/55jBgxAi0DHX/o0KEYOnQojh8/jhUrVuC73/0u8vPz8dlnn2Hx4sUYPXo0phDWR2OO/duAF2cioSsEmkOfX78Vgf+mo3nZ8piMp2M1NeBcLhSsXAEAxLs6NYYP7YAzsmyCFOQGNE1abTz/Sq8JVIunjaXhScuZCu/OiYUPp09H+9atSRE/NSMCdXUJYzuM/pYWHLlrPnrnzUPOnNsNfS40BlCyPbVKnlMlryorr5gZwVqZndSoJJ2T4jdjbTJ14YyWDhhUxtINN9yAo0ePYtmyZWhpacG4ceOwc+dOeAYyBr788ktw3Cnr88knn0RfXx++973vxZxn+fLluP/++2G32/GPf/wDtbW18Pv9KCwsREVFBR544IHkcJKCfMijJGozCwBsCDy5BEfelPi5348jd80/Vecr4RSJuzo1O43IgLtrvnhDBCFmwBlZNkEMSgOa5BlwTifaNm40bAJVZfAwNjxpOEfxu3NyLa9JyJhwETWH6nQMkQTq6qTHVBTaNmxA+4svIv9n9xq2aNMYQHqE6QG6dy7nOZX7zqz8RZZgpcxOY1SSrjPhEjZ73v8Uj+3zydaFA4DWzh6itrLAoDKWAGDu3LmSYbe333475u8vvvhC9lxDhgzBG2+8wahlDHBod2zoLQ5CUEDLX3kA8oNUbFd66iSJdYX0NmakBifncCB75gxk6cgNIx3QRFwHnSfQ6MXAPiwbdo8HfGsrGW+JseGpyDmy2WB3u5FXVYWzBgy06OvSGOE2u52KQ3U6hkgiz5sQvMFeDhoDSI8wvVHvXC+vmNmMe60lpWiNStJ1hktNRWZpCYbkjMJHh/+m2A4ja8MNGp2lMwLHvbJfdx9NBd/DZoBFT360mh6KE/vAQInW6giXTciZNy+itxPs6EDbho26aRnR6ALJPYOceXOJDdDwdUlKgUQjXn/k8OxbIfT2RiYeJbDW0SFZNHifD2d5PKJlUWi1WEjL2wwWzS5aqOWoGaVDRWMAadXzioeR77xzVz3RcTReMbPqt2kpKUWbEQ3QrTMlI7PhyjhLtg0WwftMxlCP7Nf9jAwlIHHyo9lpqN19ddbXaw5l0ezQaNsp9QwCr++UbVPkPo4eVbUDlvJ+RQtOxity51UtQYo7m3hXSLuzZVVLimUh1tM5RKKKo2Zgliatp1Cp6CqpB9TIdx6oq0N77VaiY0mNx8HMf5KbM9TODzTrTF9/UPbcvQrfs4ZlLJkJwy8NZb0FmiHGW0pJZ9A5ZHgtpBlSpAOlc1d95HwsJj1aQ0TNgBZ7BuQE8C/QtvEJqomR5Llw6eko2rwZ/LFjqlz4agw4VqEUre7+aCSbOKwntJDyjZDdoA3XOyoqcOLW2fBt3gIEo+YtjkP27FuIDQSt75x0k0AcBqXgBRpt3LMM9SnNGVrmB5J15m+fHUN3n7zHtLuPx98+O4ZvnJtD1BatsMJwZgJnD8kDDBhKQhDo8qai49AQdHlTMWRYL+zZygrhkQrvlFL0pCAdKB3btkdCBGrcttFQ44pnteCThhXaX3yJKOQXDdLnYuM4Ve5ytSEMlqEULe7+mDbrRBw2A/h2H8Cpm46Nkt2gCaME6urge3pTrKEEAIIA36bNkX6nFLKm2ZjFn+/oE0/gP5PKicJfxGHQuOQVOWid82jAMtRHMmewDrWGEX5/h156GWOPHgAnyDsIGj5vozq/FlieJZMicDgd3n1O9J84NShThvBwThkP3yt/lv1t/oB8AKvwRzwyJk4I6eMoVAYPtrdHdnxaFjq1OzRWmiIku2rXddehbcMG6ZNI7ID1NAC07GyNIP7TQg/isBkQqKvDkYWLyIj80Yjqv0YRiEk8hQLPo3nZcvETRPU7IRhE6+o1sh5Pmo3ZkPHjE84XDykvL+n4cs+aRTx/GmXcswz1CTwP70OriOYM1vNDtDdrPIDxAI6mO1FzwbWi0gEDFyM+v1ZYniUzYUA6IHA4HUfedaP/ROzr6T/BwffKn5F96+yQPEAc7C4Xih5fD0dFRYRQLVZQVStsdjsc06cTHRueCLQsdGp3aFoKPsZDaVedOny44jmAxIlRTwNA687WbEVZ9drNJhOqy0BE9d/O+npmXoVgXx+ObalF8wMP4tiWWgT7+hIvreApbKt5iighomnBQkWPZ3hjptju9nbR84ldG0j08tJIXZDCCONebXFrKbTV1IQ2l5IXPDVnsJwfpLxZw3o68PPGWlza9JHo74zSWAIsz5K5cGg3BH8TvPvCnS9+UbABEBDY/grO/cuf0b13n6SCN6BvfaisSZPQXlureFx4IuDbfcrHSix0WnZoSiTjrPJydO1pJNqRy+2qu/Y0ErUxfmLUQ1E3DFYkbVacI60wo7dLK0jDP/FifdGK1Ky8Ct5HHkngGLU+/DCyZ98Czz33EJ1D4Hn4tpKRpMVPkOjxdEyfTjTX0Fwj3surxziMnFPh/fYTzI1SYMnjC9TVERXZBU7NGaSeRqXvpQw+DiEV79s/+iP+VvD1GM0ld8ZZuOQcy1g6M3Hci+6jqTGht0TY0H+0HSc++BBDyy7B0LJLDGteNEgmAntUiMC7eo3iOfOqlogudFp3aFIDOrwjpyE+SxmgaidbPQ0AVjtbVaVRdDKsWGfYRSMZWjikBm3+0qVI8Xhi2gYAB8onMyEQex95JMQxikcwGPlcymCKfm79bW0xJVpUIW6BJ92Y0SI+sYP1OLTZ7fBULcGRBQtlj2tdvQaOK65Q1ddYhfpodb6i5wy5+SFQV4eWh1aBj/JW2T2eGEFVJYOPA5B3wo+vt32Oj3JHRz6v/s5Y2DnjwnCWsWQmDPUQywMkm8SakB4sMmELvb3orK+H3eki2j2nuMU1M1js+uIHNOuUXi2TrV4GgJ5eKzEYJRzIwtsVbxjx7T544/kzHg9c11+P1OHDdTOeiA3aAT2raHTtaWTiVQj29YU8SjLwbd6C3PnzwaWmxnxOXA5HBcJzHKmHhhZi8imsx6FdYk6LhpYMTq0bovA46GpoIH6+pKFuKUV63usNfT5AGSFdy7J7OwEABc50LJ82BpXnG1u/1TKWzIThlyJlmHJ8HjAHiTU8uUjVsgp2dODI/AVwz5xBdD6pQcN61xchoDJO6dUy2eoR7jIybMXC+GRVzoKkrSQLfL/XG0Pa18Pw02LQsvIqtD//m8SstXgEg2h//jcYdsusyEc05XDUIDzHyW7M4vs1CWSeKetxqDfJW0v/UWvokswZsiT/ATQvW46s8nLiteymK8djziWXoGRktqEepTAsgreZwNmRMasaKUN4iOkshWEmEmtWeTk4qTp6A4O3Y9t2onNFD5r4lOKs8nJmZMK2mhoqRW4aaCHWs0qxj2+P3iRtRZKpIKB5+f3o2LZdUtHcKJVjKSIpCcKFbAM7yURKSaAlCYFVmLXv8GGi80Qfp5qYjlAiit3joSLqy/bjdY/JE//jzg3IL/gsx6HeJG+1/UftOMiZN49ozuhqfE9+jkWoLFdX43vEiRtX3FiJslHDkmIoAZZnyXSwjb0Wnnn7ceThZ4CB4rmxB9hMRWLtfn+vYvZEsL09JDXg9xPtfuTCOaPr39IcfvFtfYboWLW7vXCB4nA7u9/fm9RaUHqTtElIysH2djQtXgwg0UNjlMqxlgU+GkcW/RQCAGdlpeY2Aeo9kqzCrKnFxUTtjD5ObXkW2GwRaRNaj6dsP+Y48fPFgZV8CimMCIXT9h+148Du8SBnzu0x55GaU8KJR0ro3rMHQ8suGRSJG5axZEI4br0XKBgH78rl6G8/HvncjMVCSQ0Kx/TpaN+6VXEwKC2cWr0h3e/vJSagqt3tsebuiE1KAKiMHz0zI2mNymgjKKu83DCVY9ULfDyCQTQtWAjb4xyzsajGoGUVZnX/4Ptoffhh+VAcx8H9g+9H/lSzkUgYAyoMRKl+LGkw5OfDdd11uvLO5GBUKJxpuaqEmwi1M/9n90bOx3qO0zNxgxUsY8mM2L8Njn8uQdYVTaHsuB47Uoa5kTFrPmxjk99pokGjT5Ix4SLZwWBEeQDSSZ5zuVTt9lh7ScQmpbDGVny9uGRNKtRGZdS75LIchpUwYZ0UwboOnRqDlsUiw6WmInv2LeLZcAPInn1LhNwt8Dz628iUkx3/dy2GfuOboos3a4+nmWQu4ttlhCHAulxVGPHtJJnjMkpLcKymRvHcGVHtlXp/JwUbnv7r5zjk68bw7AzMKBuB1BTjGUSWsWQ27N8GvDgTgAAbB2R6wqJwPcDvbwHsW4ExZIKQYmCdHk1bYFNuMjOiDhTpwp49Ywb1c2Ft7EkW2BXhAiSzMKdiHxBD+F0SuutZGDqskyLMUoeOhZEQlgWQquUW/p6WFBx4+RVkffvbks+ItcdTTw+qFpjJkCMdB8PmzEFmWVlMO0nnuFF1b4BzuWR5S3aXC5klse8q/v1Vv7Yfv/7rQQSjLvfQa//EbZeNxNKrxhDdBytYxpKZMKDgLU7uHuAv7awCvjY1VEeOEnqkdtO6meUmMy2ZI6T3RrKwcy5XTGyeFKTGXlfje4r6WNS8Ah0Kc5JCtg8wAgtDR5VRpwC1ZWhYL5osjATPPfcgd/58tD//G/QdPozU4mK4f/D9iEdJVfZbkvqkGWEWQ450g5s7b27COyOd40588CEKVq4QlQ4II3/lCtk+Uf3afjz1l4MJnwcFRD430mCysuHMhEO7gUCTzAECEDgSOk7sW5milKQFVZUKW4qBVcaV2swRmmKxstkjAyhQGMRSIF04jyyQLmAbhip+DcPCnLSQ6gNKyCgtMayECcm7pwWtEWdU1h8Nosf8iQ8+RPaMm1Fw388x7JZZMaE3VeT4JPZJs0LNHMsSWjIwaTa0jooKFD2+PnFdyM+PlOWSQl9/EL/+a6KhFI1f//Ug+voVJC8YwvIsmQnHZbLKFI6T86yQkmhJCltKgYWbWU3miJrQlxwZVIuXjXThDOtPyYXMtISdjBQsjfeSjKp7Ayc++BAnvV60VlcrZkBmlpQYmgkj9+7zqpYgxZ2N/qNH0XfwINp++Utp40BFFpNRWX80IPXIaiXHa+mTRimrG3Edo4RblaCWR0W7oVW7LjzT8EVM6E0MQSF03A8vO4eoTVrBxFjy+/1wiRR2tUCJTMJdaqa4Z0VqEs6ZeyeR67RJRJafZiLX6mZWkzmiluekdhDLTai0YR658ISWsJNRgqVyE79r+jRw6WlE71IvAqzUuyJ996nnjhYdEyRGXPy1h4wfZ1jWHylojDetBjhJnxR7X5319YYYF0YYMWYzltXMgWo2tGrWhS+OdTM9jgWojaU1a9ZgxIgRuOGGGwAA119/PX7/+98jPz8fr732Gi688ELmjTxjQMNPCf8vgWeFVFdI8loGTuS0C6cWnhPtIFaaUBOUhuWgQFZXy6/hXC4IwSAEntf1XRFP/ITvkjUBluRdKb17Z2UlbI9zCeex5+XBff11EPpOomtPY0I7xa5td7vBRxXCTQDDrD8S0HpkNRnvBGFUqaxPI5IZjDBijMj0VQPaOdAoKQSBcM4jPY4FqDlLNTU1KB4QJ3vzzTfx5ptv4vXXX8eVV16JewgrU1uQQLd0Oq4QBLq8qeg4NARd770XiXOTeFZYFrY0AjQq2Hor5IZByosKc3c4p5PovEolXkJ/kPNrgn4/Ds+erRsPRuB5HG/4G5rvWyY98SPkNRN4nuhdhjkcgddDytiOKys1KSfTcNiUEN/+nHnzYAPQtmGjKOdI6tqyhlJ0Gw0KodJ4ZIFTxrsarpdj6lWy71LqmUlmUsX1MS1QNGIYXYf2eZsZRlQFGJpONvZJj2MBas9SS0tLxFh69dVXcf3116OiogIjRoxAaWkp8waeURgqTo4NHE6Hd58T/ScGOkbDJqTUvAbPvUsh9J0kOjXndCIYCGjKAgovCGbiDBihkEu7K3RUVIDLysLh2bcqnlvOiJPyzIjpLMVDD9c+cdp4nJdEqSq5lvCHEeGucPsDdXVo27hR0gMhPPYoWlev0TTGjAqh0npktWQ8+jZtxpALLxR9nyyI41o8cVrlSkihd404o6G3FEIKYbY36XEsQG0sud1uHD58GMXFxdi5cycefPBBACF3GG8wq/+0w/BLAUdhTEZc4HA6jrybWFz3FB9pLtGps2fODE30Yq5TwonK/9xz8D/3nKGcgWjSrdiA1OoWJjHQ1EyomSUlTIw4R0UFhl5+eUI6t81uR1djI44sWCjuOWTs2leTNq408WsNf4iGbtxuBHUId5EYzC0rVspfWw4MjHoakBpl/W1t6Hh1B1JycyP1GdUUX5Xqh8kkjtP83gjOFs1x0aDdZBpFmFeDcNu+9d/P8eejXnyScw6CNukAWNmoYYa1jdpY+s53voMf/OAHOPfcc3Hs2DFceeWVAIAPPvgAo0ePZt7AMwqcHTj/e8DuxwGEQm/efeFwTpz7e2CC9r/0EuweD/jWVtlFOWfO7Ug7d7Q4f6RqCbyr1xDzYwzjDIiQzsUMNbUEYVKvBulE2dXQEJl4WMX2xdro27IFnnuXwu50yYdYGe2K1e7+5SZ+rRwOScFOncJdROFutYbSANRwPdQufEScOI5D6+rVkT+j6zP6nnk25jv5Rkr3Q6OMEK2/13odvTzgtJ5ZVkR2PQjx0eccCuBhAEfTnai54FrsLhybcLw74yxcco5xxhI1Z+mxxx7D3LlzMWbMGLz55psYOnQoAKC5uRk/+clPmDfwjEKQBz7+XeTP7qOpA6E3CZ7AwCTkvv660N8KmhmS/JHKSjp+jFGcARFI8U5oeE4AHa+FdKI8VlMTw1/RGttXamPnrnqidmldkNTUklIi9WrhcLAoiEu7+OkZHuHcblVcDy2aTUScuLhacZF+V1+P7Bk3U3OYxJ6haiOEkf4WacV7qeuQaiZp0TaSAi03jxWXjyUnUOmcw3o68PPGWlza9FHCb6q/MxZ2jo1eGglsgpF08tMUgUAATqcTHR0dcDgc6k908K9A7dWRPzsODUFTQ2IILh6Fa9fClnqWZkuftpQBAJxdW6vJa9G1pxFfzppF96OBXdjo+rdUuY8FnseB8snS9xl3/sjxJJ63gYkvevFTs/snaSPnchF5M7S+o45Xd6Dp7rvJDha5fy3nHDZnDtJGj455bqr6TFT71PQd0mva3W5pbSkJFD78MJzTpxEfD8iERQmff/R5mpctl+W/xZ8//Pw66+upQrNi/ZBqbEW1ASC/RyVEniUg6gGWuo4a7worj4zqOYzweFbXZXEvQQBtQ1yYXXEvgjYOBc50LJ82BpXnFxCdXwmk6zdRGG7btm248sorcdZZZ2Hbtm2yx06frr5u2RmPOLHJlHQyr01Kbi4yS0s0E+6iSXuBujr4n3tO8TdGcQZioDG8RMtBoiK3ioSQ1OiMkIZ9ZBdnRjwYmt0/qTYSjbcu8puBRYU0qSEBGlKbScMonqolOLJwERUXkFb1nGUaelZ5OVoeWkVx8VNjIxz+bnloFXivjKCuTD+MjC2ZshjxYF2AVk0YXy3fjhUxmnYOY0Vk14MQr3RODkDeCT9qLuCQdcklKBmZbahHKQwiY+naa69FS0sL8vLycO2110oeZ7PZLJK3FsRlw2Xk9iFlCI/+ExxEQ3E2G+xuN056vRG9F7UFZiOnjFrYSYwlozgDYlBrqKkhdUpNqKJgwBUibaNj+nS0b92qq+YJUT09pxNF6x5DZglZyr8aHSnapIZ4bSMti6yiwSwIyJpSAbs7G4UDWXGK/USlMctywep+f6+8oSOBcP8ML/5tNU+hbcOGxAMJ+qGjogLds2ahvbZW8brD5swRrVmmFTRGjFZjlUWNONo5jBWRXQ9CPOmxpU7AaSChOx5ExlIwKnYdDBpXi+WMQyQbrhmAABsHeC7qGMiGGyikGw1BAO/zoXnxYgDi7ly1bl8jUvKJriMDtYaWWlJneEI9umFjjMdDClq8bqRtzJo0CRkTLmKufh0NErJ6wQMrMbSsjM05pUCZ1BAuvcIq60fSYOY4IBhEe+1WtNduDY2vqiWwu7PRuWtXyAhgaMyyXLDU9tHo/mmz25F750+kE0gI+mHWpElExlJmWZlumVukRoxRcgNyoJ3DWBHZ9SDEG0Wy1wqmhXS7u42THj8twdmByjUIGUYhOIp7UPSNdqQMUTZS4wl2Woh4ehASqa8j+SNt5E4tpE6b3Y5MQqNAy+CmaSMtuV0N9BCiU1V8lyKpgUtNRWZpCZxXT1UtchlP4M0qL488a3eYwyRGhF64CHyHH/lLq8SLiWp4biwXF+o+KjM2tPRDrURrKehRtNYMmkm0z4vV81V7Hrn3oNe7Zw1qgnd5eTm2bt2KoqKimM/37NmDGTNm4N///jfTBg4GMCN4A8D+bcCLMxI+FoID2XEX3ImWrXXSxN6oXfVnFVM0E/GMKvxITC5nRO5US+oECEipGgnoLNqoF/TQaIk+Z++BA0ReO1ZJDXJQKk5NS7Bl9dzU9D+p62tNXmAJ1v1dr7mLlOyvNbFCCbTPi9XzVXNdpfeQzLmOdP2m9iylp6fjggsuwG9/+1sAobDc/fffj8suuwxXXXWV+hYT4oknnsCIESOQnp6O0tJSNDY2yh7/0ksv4Wtf+xrS09MxduxYvPbaazHfC4KAZcuWoaCgAEOGDMHkyZPxn//8R89bkEaQB3YuEf3KxgGZnj6kfPZbItG99ud/w0Re3wivhdR1itY9FtpxREFqR067g9TiKTHK62ZEWYFgXx+ObalF8wMP4tiWWgT7+mSPD4cqtHhrZM9J4bXTs28qeWXbamqoxhfL50bb/+QkBmg8uyz7nRhY9nc90tvDMMoTojSn0T4vVs+X5jy0ZaL0nOu0QpV0wBNPPIHFixfjmmuuwRdffIFDhw5h8+bNqND5hn77299i5syZqKmpQWlpKdatW4eXXnoJn376KfLy8hKO3717N/73f/8X1dXVuPrqq/H8889jzZo12LdvH84//3wAocLA1dXVqK2txciRI3Hffffho48+wv79+5Genk7ULr2kA8RAKifguukmIoJ24dq1cF49lbiJSiDdQbM8jnQHKXYuAKp3/EZ53fRS3PU+8gh8m7fEhpE4Dtmzb4EnSXUejfLaEbVBTrbB4SCquch6fEWDaseuIDEgdS7XddchdfhwQ5WetfZ3PdLb46G3J4RmbkmWgrfSedS8h2Soi5Ou36p1lpYuXYo1a9YgJSUFb7/9Ni699FLVjSVFaWkpLr74YmzcuBFAyKtVXFyMefPmoaqqKuH4G264AV1dXXj11Vcjn11yySUYN24campqIAgCCgsL8dOf/hR3D2i+dHR0wOPxYMuWLbjxxhtF29Hb24ve3t7I34FAAMXFxdqNpX+8CPzhNtlDuryp+PJPOYqnyquqIlLYZekqJh3gLI0MrYuBVsPGzKUD5OB95BH4nt4k+X32D29NmsFEuxAJPI+uxvfQvWcPACCjtIQ4K08MmnSc4qBHKCa6z9mHZQOwgT92LKH/qdHiGYx9OR5Ghcn0mlNYaWglG6zeAx8U0HjQh9bOHuRlpTOXDmCqsxSN9vZ2/OhHP0J9fT2eeuop/PnPf0ZFRQUefvhhXRW8+/r6sHfvXixdujTyGcdxmDx5MhoaGkR/09DQgEWLFsV8NmXKFLzyyisAgIMHD6KlpQWTJ0+OfO90OlFaWoqGhgZJY6m6uhorVqzQeEci6FImBGbk9iHFPRT9/i7Znbf7B9+Hb8sW3bPZwiDVHdFaDywapCm8CAZD2jcMrhkPFmnARiPY1xfyKMnAt3kLcufPB5eaakyjokCjeyMmqnispgacy4WClStUvVdSYq5scWqdar3JLdDx/VCNnthg68tiMIqArUcxWZYaWskGi/ew8+NmrNi+H80dPZHPWItSkoKas3T++efD6/Xigw8+wG233YZnn30WTz/9NO677z5MnaqPuxkA2trawPM8PHExTY/HgxaJCaGlpUX2+PB/ac4JhLxqHR0dkX+HDx+mvh9RZCpnptg4wHPrwHNWyAQyglcDEAxwhEqjBPv6iI4jzVghXQxaVqxkdk1a6JGNoxXtz/8mIYMrAcFg6LgoGHkvJHykQF0djtw1X1R9Ouj348hd81VxU0gzxLJnzgz9jwR3hdX4CoOWh2OGrC1WoOl7Rqais+bwaSkDZDZofQ87P27GHc/uizGUAKClowd3PLsPOz9u1txGGlAbS3PmzMFf/vIXjBw5MvLZDTfcgL///e/oUyCHni5IS0uDw+GI+ccEWcqWshAE7HlfgXvmDHAuV8x38WQ4o0hzpAOcFek8DNJJniesQs8aWmp3AfoZJ32Exn30cVrvRQ3kFiKB54mUp9UYwooEXgB2lws5c25H0fp14JzOhO/FPtMC0g1J9L0OFv0aJdD2vcGSii6G08nA1fIe+KCAFdv3Q4wjFP5sxfb94IPGVWujNpbuu+8+cFziz77yla/gzTffZNIoMeTk5MBut8Mbpzbr9XqRH5cxFUZ+fr7s8eH/0pxTV4RFKSUQOJyOAzsK8eWSX6C9dmuk3IV71izJTCAjstlIBy7pIk16PpaTPOvJR2s2jp7GSWpxMdVxJPditAeNVHlajSEcyRCToXPyfj8660OFjEU9Wx0dmrOuoqHG46BmsTKbJ1TNODIqW1UPsDZwk/k+tbyHxoO+BI9SNAQAzR09aDzoY9VcRagWpezu7sa//vUv/OMf/4j5pxdSU1MxYcIE1NefqrQeDAZRX1+PMol047KyspjjAeDNN9+MHD9y5Ejk5+fHHBMIBLBnzx7Jc+qKiCilDfFq3YHDQ3DkXTf6u2J/wvv9aN+6FXyHX7GECct072iQDlzSRZr0fCSLAedWzhykuSYJ1HgBoqFn2rPA80g991xlAVCOg/sH3ye6l+Zly3FgUrmhXic9yilEI6u8PMFzGwObDS0PrYJXyrvFOMSrxuPAUmLACMQv7FrC9oMhFV0MLL1iyX6fgPr30NopbSipOY4FqAneR48exezZs/H666+Lfq9nbbhFixZh1qxZmDhxIkpKSrBu3Tp0dXVh9uzZAICZM2eiqKgI1dXVAID58+fjW9/6Fn7xi19g6tSpeOGFF/D+++/jV7/6FQDAZrNhwYIFePDBB3HuuedGpAMKCwtla+DpijHTgeu3hvSWAk0AQqE374cSi74JSH+ktcNSzx2NFI8H/QplKkjd4yRlOPKXLwvV6TKI6A5oK4egJ8GTWPgTQPbsW8ClpqJrT6NyQV+/H/EMKBbkeTnoUU4BOJVp1tXQIOoxOnWgoOzZGnjPvmeeRUpOjiYCsJYSPSRkeZaJF2og1jc5t5tIU06qrIgeBGy9QTKnkXjFkv0+o6HmPeRlkcn2kB7HAtTG0oIFC+D3+7Fnzx5cfvnlePnll+H1evHggw/iF7/4hR5tjOCGG27A0aNHsWzZMrS0tGDcuHHYuXNnhKD95ZdfxoQIL730Ujz//PP4+c9/jnvvvRfnnnsuXnnllYjGEgAsXrwYXV1d+PGPfwy/349vfvOb2LlzJ7HGki4YMx342lTg0G7guBfdn/vR/6LMs2Vci4g2hZikzlewowP//eGPQrv1gUWfRa0sksXAxnGaJx8aaOEd6FF3SuB5tNXUoG3DRuWD43SWVIcnKQw7NSnrGRMnhOrDKRgsNNwUGmOSBtESHmpTy7XUalRarJKdgSW1sMsaSlGQ66ODMcOPJhtUDMl+n2KgfQ8lI7NR4EyXDcUVOEMyAkaB2ljatWsX/vjHP2LixIngOA7Dhw/HFVdcAYfDgerqal0z4gBg7ty5mCtRdfztt99O+Oy6667DddddJ3k+m82GlStXYuXKlayayAacHRh5GQCg/9AOop+w4N2o1Q6RLDIah7CQH+d0xuzctRR+VVoMtE4+tOg79AXRcWLeAtYEz0BdHbwPrQotsjKwDRmCnLvmIfumm2LkAjSFJwkMO7X9zWa3w3n1VFmtKIDcEJbUtmEMtTt7rR4HucUqmYVhZRd2QpidoK4GWrxiZij0qxV2zobpFxbgqb8clDxm+oUFTPWWlEBtLHV1dUXUst1uN44ePYrzzjsPY8eOxb59+5g30IJxWS1aXbfhAd7V+B6OLFggrnA8sLOxpaWhePMm8Md8TNzjSjsXo1zygbo6ZQ+OjBeA9l3LeWVoDADhxAkMGfP1BF0lkhCrEqQMOy39LVBXB9+mzZLXtGVmorB6FZFBomrBttlgz8uDDZAOK4te7BTXhnZnr5fRn8wMLMWFXQFmzWpjAbVesdMho44PCtj2d3lpgG1/b8biyv8xzGCiNpa++tWv4tNPP8WIESNw4YUX4qmnnsKIESNQU1ODggJjRaLOFGhxwZOClevWZrfDxnHypSAG+B42zq5bKQiptmVMnBAxLrrf38vUYIo8QwJIeQFo3rVSoVdaA0Bs8iQJsSpBzADU0t+IjBu7HVxWFgSeV3y/1At2mA/3s3sBQNWzUbuz18PoT6bEgNYF23XddabmICUDp4NkhFI2HHAqG65s1DBD2kSdDTd//nw0N4csvuXLl+P111/H2WefjccffxyrVinrnlighxGpsCzF0My6s9E7O4R00c2ZO1fSC0D6rjvr6xUKvT5FvWOXmjylMlrsHk+Ig6Yic0dLfyN5zkIggMOzb8V/vnkZWqpXy6ZN0/bD6EweqWdDgs5du6h/A7DPbk2mLpHWBVs4edI0MgdmwWDWmQrjtMiGu/nmmyP/P2HCBBw6dAj/+te/cPbZZyMnR7lmmQV10Jt3w9LAMdPOJhym6ty1C+21tQnfs8wOIX2GqcOHy36v9K6zystxoHyyrFfG98wz5A0n8ExKeTTCRhstj0ZLf6Mxbvj2drTX1qK9tlaSC0XaDx3TpuGsoiJklJYis+TiU5/HPZv+tjaiuoyB7dvhWXyP5rpsWmu6scrAUoP+Y22afn+spiby/3oUsR6MoH2fcv0nWfUCT4tsuHhkZGTgoosuYtEWCwrQk3fD0sAxImxIAqLsJobZISyf4Sn+VyO69zQCQGSRJvHKyKa9i4BkMRTjUKg14rU8K7VGdn9Li6hhTMTL4jgEtm8HEFqg4xfm6Gcj8DzannpKMZuL9/lC4podftUFWVkVczU6CQIItb1p0U+ZnS8ZafFmBY1khFT/AaBLoWAShLPhWjp6RFW8bQDyDc6GswmCzukfZwBIqxabGZEK5QoGTrhCuRJoK8ezhprsJq1VyPV4hmKTVdaUCrTXblX8vWyh1wHYPR7k/+xeze+Cdgeq5VkJPI9/f+Ob1Aah3Hkl+6vMeQDpftxSXU30jtyzZqJ96zOJ1yQYJ3pUpzfKkxB5/4xlGmjH2OkCqfemKgFEjn8X1bf0TpgJ14YDEGMwhYOLT958EZNiuqTrt2UsMcDpYCwB7A0cVrteWqidiAvXrtVMOA8Xd5VC0ePrie5d1UQWh5x5c9G28YnQHyK/yZk3DzlzbjdsUYmfuPvbfWhauCixfQr9TekZk0DMMBaVWeA46aLDcQtz9P2RhuJkhRcVDEbZPm5yo6FrTyO+nDVLt/Nr3fgAyQtB0ULNPKvJWLXZwDmd4NLSYsaKHnP7zo+bsWL7/hiyd4EzHcunjWFiKAHk67fmMJwFnRDkI6KUGOoJ1Y3j9B2orF3xyVLQVZuObJbsEJISI+C40P/LeGVy5sxB2rnnJs2VHg2pCT371tkI7HiNuL/RZBzK4aTXG1Inj+qXABJd/lKGEhBDQhcLpSkZWna3G7xPpraVjB7OYNfSoeGd2T0eFK6uBn/Mh94DB2J4SizOL4ZkbfRooVZ+Q5Nkg4Gq/ZXnF+CKMfloPOhDa2cP8rJCoTcj9ZXCoDaWvvzySxQXF8MWx7QXBAGHDx/G2WefzaxxZyz2b4spdwIgVGC3ck1I3VtHsDZwkqGgSz1RMuJQKS7khNwoooksvAgrEDjNUPJBbkL3bdqMoscehd2dTdQ+rbo8YbRWV4OP8uhwLpe6sB5CWW3tW7cmGq4yhhIQIoyLJR3EQwvJ3axaOjQbk/yf3YuhA7U6u/Y0EhlLWjY+ZioVIgct8hu69IuBdrQ8tEo1/1PMm2e32w2TB5ADtbE0cuRINDc3R4Qpw/D5fBg5cqSuteHOCOzfBrw4Ewl73EBz6PPrt+puMA3GEgHRoJooGWb7sNrtk05k7lmz0PnGG4pemWS+T6IJffUa4nARq0mejwt9qTWUACCwbZt8aDTOwxR+R3ani8hYEuvP9mFki4dZvKXxICXVFz76CzoyvsaNjxlLhUhBy3yjZ7/gvV78d+EiZN90E9XGzOzePGpjSRCEBK8SABw/fjy59dROBwT5kEdJlP8vALABO6tCdeMYhOSSFZPX+7o0qtMss31Y7fZJJ7KsSZPgWXyPqXkVrMNFplr8bTZwLleC4ZWAYBB5VVUJxXQFnle18Ie5VUptY51xynLckoidFj36CzgqK8l/x2DjM5jCm1rmm8gcyZpgP4DjdXU4XldHldUp6s1racGRu+aj12B+pRiIjaVFi0JETJvNhvvuuw8ZGRmR73iex549ezBu3DjmDTyjcGh3bOgtAQIQOBI6bqBunFoky4o34rokE7F71kxkTSpnalywkg6g2T2b3QvIOlxEYghzbjecV1+NjldfjSFPK1axp8HAwuycPo0o6y0lJycheUDNwk+U5amDNpIe41aSI6lwXj1lDgZTeFPLfGOz2+GYepVibUW1qv1hSMl1RINEkb9twwa0v/gik8xdtSA2lj744AMAIc/SRx99hNSoGlKpqam48MILcffdd7Nv4ZmE4/LFTqmPk4BSTL537lykDh/O3FOhhQtAu6tVOxFrAasQAcvdc7IzetRM6HJtJnk2BSvuh6OiAp6qJbHZd14vmhYvZnNfMaE0AmNJRh2ddOEnrWFnz8tjuqjoyeFRy6nTi4tnJkFdJWiZbwSeR2DHa7Lnt7tcQFoa+KiMN7vHA6G3lzpsLRe6JOUh8knmjFFLB8yePRvr168f1CnyrMFMOuDgX4Haq5WPm/Wqas8SbcooK+NCS6qzll2t0caCkl4PTbq+1t28GTgAtHpKpG1Wc2+s0tXzqqqQPePmSCiNhbYWST8lbX/x5k0RQrRWmFGiQM8xzVorTW+olXuh6Us2zp6o2q9CuiMs5xD//k56vWgm3cTo8PwtnSUDwcxYCvLAuvNDZG4p3VJHIbDgI9WcJeoFg5GIJOl14/VR9BDeUwOaCVpJOZzGYFG7MJjlucW0BZCd0GnbrEYI8z/fvEyZYyQFWkFLxs+649UdaCLw3rPQCwtD7bjVC0ZsAJItqEsLNc9Ea186+sQTaNuwkaqdhWvXwpZ6VkJb7W439Zhk2d9I12/qQroWdARnD8kDyKFytSZyN3WsfWCy8K6q1lSoUg0XgERvSKldAs+ja0+jpmKbtAV4HRUVGF3/FnLmzRX9Phy+ICngq6ZoKovnxhJSxWajC9KqaTPts7HZ7XBMV5lJKhP+JLk/FtAzRCQ1TszE4QkbMVLFo1kVxDbqfbJCeL45u7YWhWvX4uzaWoyuf0u2nVr7Us6cOdTFo/sOHRJ9f7yKTNS9H/yb+jdaYYlSmg1jpgOXzgMaNgJClE6LjQPK5mqWDVAVa2eQAaJmcGrNTGGxC9XC1/C/9DvJduuZgmzGjB4ljolRbc6aNIkoXZ/LzESwqyvytxJ52Ag9K73S5uXGiVk4PEan9JtBn4wGtIkeWvuSzW6H52f3kpUIstlgz8uD/8UX5YV2KbD2vWPo/mYzMxVvEljGktmwfxuw+/GEjwU+iO6Xa9B/8CykjKtUPXAzJk5AiscTW9KBEFp2j2oGp5ZdrVRJjHgjR6nittIE3XL/CgR7enFWVHYaoN/iTxJ6MpM3IBpyE7pRbVZMmR7oh6Pq3sCJDz6kWij1zkzUI20+sHMnjixYmPB5eJwUPfaoKYpiJ2MDYPZMUy1g0ZekkhNiLxQ6l/v664jCdkoZq0EAbUNc+CTnHKzYvh9XjMk3TM3bMpbMhCAPbE9c4AOH0+Hd50T/CTvwp98A+I3qOH1nfT2Cvb2qmqdl96hmcKrd1Qo8j6aqpeIHR+1ChWAQravXSHqeSCZo3ueLkBOjf6vH4k/qKWOdgWYESNvce+AAuvY0qm5fTD8EJPshl5pqyoWSZdp8x86daFr0U/EvowRD86qWhGr4MdY1oulzZt0ADGaw6EvRHrjOXfXo2LY9xtgJn0voO0nUpvylS9F3+Esc3bARAmJ5QkGEiug+NfYa8DYOzR09aDzoM0zd2yJ4MwAzgvfnfwa2xobZAofTceRd98BfURa0CrIhkUaLGBhmINCExtRmpvx34SJ0vv66ugZGPVeh7yQRCVLst3aniykxlob8rFcGmhy0GluKbY6DVlKvGTIFtUDr86YpRnx2ba1o7Tstz4v2+ZuNaB6NZG80tIJl+6XORfv+6n/1As765Trk9nREvmsd4sJTY6/B7sKxkc/W3zgO14wrUtXWMKxsOAPBzFiqfxD46yORP4UgcGC7B/0nOMQYSmFQGDGqq0zrkAFCm1lGk5kS7OvDpxeO0ySkFn6uBdWrcHj2rap+O6ruDXxWMYVJCrKa9G0lCQPH//0fhpaVoe/wYbRt3Kgpa46V4aHUZrXtk0KyF7lkKujTzAXhjChW7VWTqUm7ATDq2Q52oxswph/Svr+Gz47hpl/txtfbPkd2byd8aVn4JOccBG2xOWm/ue0SzZ4l0vXbCsOZCXH2UPfR1FDoTQoUcXpS4a/4mDHLciBh0HABaF3F7c//RpuhBESeK2AjLpsS/9sTH3zIjF+ihq+hxCcIvPwyAi+/LHtOMdJs/MTKt/twZOEiJoKFRByI6PYBaF5+P4Se3hhFc1Ikk5OSzEWWthhxOEQa/bzULrAkWY9i75QmjG/Usx0sBXflYNSzoqVhlIzMhseVgY9to6VEdJDvTEfJyGxmbVSCZSyZCWdfGvNnfw/ZxE8SpyeN5ecvXRoigJvIpUyTmdJ3+DCz6/LHjimWTZFC/9GjIX0SBvwStXyN8HNrq6mh1kQBkGCEiepHcRzTDKXod93V0KBYYT7Y3h5R5R4sO/pkLLLRxk3vgQPEv0vJzxevTadygSUx1KLfqd3jgfv665A6fARScnNR9Nij8MbzDKPGk1HPdjAV3JWC0f2QZuNr52xYPm0M7nh2H2yIVR0M+xSWTxtjGLkbsIwlcyHOxZiSTqaDQ0KOJSb9ejymJLaSegFSi4uZXTMlNzd0TVJvR9xvATYpyFrTtyUlDAjRf/SodOgkGBT9DQDVGUrhd01L1h0MO/pkLLJKIqlyIK1NR/rsad8p7/XGGPop+fnIq1qCFHd2wngy8tmaUZ6DBsky9mjmw8rzC/DkzRdhxfb9aO7oiXye70zH8mljDJUNACxjyVzobov5MyO3DylDeEXOEknarl4aLSQwkpvh/sH30bpmDRPOUvhZxAxwrxct1dXS6a0iz1FruEfNuws/866GBs2Vxe3DhqF56VLVz1RthhJ19qUJdvRKfd3oRVZ1UgfHoejRX5DXpiN89lr1mPq9XjQtXISi9esSlKWNfLaDPTsvmcYezXxYeX4BrhiTj8aDPrR29iAvKxR6M9KjFIZlLJkJQ2MVUW0c4LmoYyAbToBYNhwp70UPjRYSkLjsWRpTXGoqsiorybPhCJ9F9AC3pacppp6zfI60706LJyEeKfn5AARN51K7QCoaiWJI4o6epK8buciSFt4VQ+Gjv4CjsjLmMxYLrKp3GncNKaPMyGdrFrFOtRhMxp6dsxkmDyAHq9yJmTD80lDttyg4intQ9I12pAyJDXeokd6nkfFnVSZEqTwBbSkREhStfQS2jAzpA2wh4nbRusdUlTTQsxyC1HMnvabUM1cLz71LwR/zqf69GOeFFGEjMfQH3U7S6EmetBSHkYssLZEbCL2vosfXwxlnKAFsFlgt7zSCKKMsGkY+27DRJ3kPA3OM3mKdaqFWi03rmjCYYXmWzATODpz/vQQFb0dxD7KKekLZcWdfhZRJd6r2vpDEjOV2yKTxZhKXffOy5Qh2dDAnGNrsdhSurhbXkYnyxDgqKpB1xRWqvFp6lENQ8kwoXVOLJ0EM7lkz4aioQNeeRtXncEy9ikgaQeqewqTQlodWgadQnTdScJMmPGVkOJzUuBk2Zw7SRo9WfDasjBGqrEcZdO7aFePBMvLZJstTzwq0z+p0kEjQCktniQGY6SwFeeCR0cAJmZ38kGzgngOaiunKQVYDRRDAuVwIRhU+lBowpCJkklDQISJZAAfTAFejPRMPzc88DmGBOFrByGjYPR6cu6teVkeL5B0db2gg1rxKyc9nKripBFrBPaOq2rMWclQrEit3vu739+Kk14vW6upQQVXK/lX0+PqYZ2XUs42+3mCZY+JB+qxYzE1mBun6bYXhzIQv3pE3lIDQ91+8o8vlSTRQgnEVoqUqfmsOg0i42gEQh+7UVONOBkieu3dVtaLbm1noKS6EoCV0wnu9ou8QoKsiTxMKjNbbMaJKPW14yqiq9qxDRbL9QIU3JcwDdE2fhvwV94ufV/YEtoRxYdSzjb4eqznG6DAXybNiNTedDrDCcGbCwb+SH3fOt5hfXg3HQYpwyYrYGL8Q0aYuD4ZimKwyU0ifefaPfxySAxATpZRY9LSETsLvMNobaB+WjZaHVhFnVpHemy0zE1nl5YamRtOEp8LPQOg7iYLqVQBs4I8d0yU8qEeoiGVtOpLzykJiXOgRIpcDizkmWR4qpWc12CUSWGLQGEs+nw/z5s3D9u3bwXEcvvvd72L9+vUYOnSo5PHLly9HXV0dvvzyS+Tm5uLaa6/FAw88AKfTGTnOJrKT+c1vfoMbb7xRt3uRBqkLWp/IqWrPhMiA0Zz1MoB47oleC2AyS1+wykwh5SHkzb8LNrsdWd++nGrRoxWMjJwzN5c+Qy+uT2VMnAC72w1epiI5AAhdXehqfA82jiOa5LsaGzG0rIysTRIgfe58uy+hzEh4QdRrodHDuNHLGIk/b+ebb6LzjTcUfyc2LgbDJikM1uKQtHOZ3LMaTFlzemPQGEs33XQTmpub8eabb+LkyZOYPXs2fvzjH+P5558XPb6pqQlNTU1Yu3YtxowZg0OHDmHOnDloamrC734XK9K3efNmVEZlf7hcLj1vRRpDCK9LehwlNGugRA0Y2V0tCUTImHrtcpLNO9BCnI2fGGkqxKtZ9MITa8bECeh4+eWQgSAD+4CRIFYShQThPmWz2+GYPh3ttbWKvzmyYAGc115LdP4jCxai4IGVmt6zYl8XBAwZP55ZWRha6GHc6GWMRJ83JTeXyFgya3o+CVhvAFnPZYNdIoElBgVn6Z///Cd27tyJ//f//h9KS0vxzW9+Exs2bMALL7yApqYm0d+cf/75+P3vf49p06Zh1KhRmDRpEh566CFs374d/f39Mce6XC7k5+dH/qWnpxtxW4mI01nSfBwlFDkOCogfMFIxcXBk3S4+RKDHLscoXosc1HJLxLhbravXIPvW2cScjfDi5Lx6KjJLS6j4Jp6f3at4nGdpFbyr1YuERveprEmTiH4T7OggMqrCx7J4z5J9fQCdr7/OlPdBy29R+56TicGenk8Cmg2gEljPZQLPQwjy4KIiMQk4Dd4BKQaFsdTQ0ACXy4WJEydGPps8eTI4jsOePXuIzxNmu6ekxDrU7rzzTuTk5KCkpASbNm2CUoJgb28vAoFAzD8myCKUbyc9jhKqibwyAyaeAJlXVSVfImMAOXPnJizsrHc5ZiEvqiHOyk2Mvk2b4alaojux3VFRgaLH14MT8cRyLheKHl8fKkuhJj1cpE9FFk/C35Ma5QCb9xzu6znz5tH9kGJBBMgTHAY7WBPKWYA1CZvVBpD1XBaoq8OBSeU4PPvWkLyLGAaBRAJLDIowXEtLC/Ly8mI+S0lJQXZ2NloIJ+K2tjY88MAD+PGPfxzz+cqVKzFp0iRkZGSgrq4OP/nJT3D8+HHcddddkueqrq7GihUr6G9ECcWlIdluQcaYsNlDx+kEKY5DRDJABVE02rXe8eoOonakDh+e8BkpN2TI+HHo2tMYIRJLkWjNRF6k4ZYQue5XryFO4dba7qzycnQ1NqJ7QI8po7QUmSUXw2a3E7/vGMioqEfCXUqeKkFImuq3/6WXVP2OZOE8HSrd00AvQrka6BGuZ7UBZDmXBerqxDXq4tuUhHeQTCTVWKqqqsKaNWtkj/nnP/+p+TqBQABTp07FmDFjcP/998d8d99990X+f/z48ejq6sIjjzwiaywtXboUixYtijl/MYsCrof3yBtKACDwoeNGXqb9ehKQ4jh01tdrnrTUKseG2+K67nsxhTVjIAhwTL0Kn1VMkZw4osU1uxoaiNpiFHmRlFtiJiMPCBkxQ8vKRInSargMSiRzrF+H5vuWSe94o+CeNRMdr/yR6FgW71lVRukAlJ7V6VDpPhqkRGSjs9vEQGukkt4bKyFNlh6q5mXLZY+xZWTgK088EdkQnSlIqrH005/+FLfccovsMeeccw7y8/PR2toa83l/fz98Ph/yFdzynZ2dqKysRFZWFl5++WWcddZZsseXlpbigQceQG9vL9LS0kSPSUtLk/xOE44TKhSTHqcBYgROFpMWC+VYOfie3iT7fb/XiyN3zU8Q15SDkeRFEuLsYMpQIX3fBdXVxCn0jooKcFlZRCKVWZPKMfTyb+Pw7NmKx7J4z6qeucKCSFwUeRClcdN6aaTGhRFZrLRGKs29sZJ3UOuhin9+wf6TivOi0N0NBJOXPZwsJNVYys3NRS7BSy4rK4Pf78fevXsxYUJoQtm1axeCwSBKS6VDUoFAAFOmTEFaWhq2bdtGRNz+8MMP4Xa79TGGlJBkgjcJtGbB0EwOqiumy0FCXFO8sezKI7CE2TNU4idgT9WSUCaYzPseWnYJ1TUyS0qojG5WZTCUFmfqZ66wIKopimwGIzke0c+t79AXot5h2lCiUVmsNJ5cvsNPHSZlEWpUQ1HoO/QF2l98KaaMEJeZqXgtAPjvXfMhdHWdausgUS3XgkFT7uTKK6+E1+tFTU1NRDpg4sSJEemAI0eOoLy8HFu3bkVJSQkCgQAqKirQ3d2Nl19+GZlRnSA3Nxd2ux3bt2+H1+vFJZdcgvT0dLz55pu4++67cffdd1NxkpiVO+nvAx7yKHKWhKoj6P7wI1Na9aQ7PaWJLlJagVFBWLWIL6dgBrAuO8ESUu/VMfUqBHa8xnRhoyltwaIMBsniTFsaRu4ZqN0skJYvkQJrbw2VwUfYd40swdHx6g403X234nGFDz+M1kcflb5PBiWc5KDUx7NvnZ0wBplBx9InfFBA40EfWjt7kJeVjpKR2bBzKoswi4B0/R40xpLP58PcuXNjRCkff/zxiCjlF198gZEjR+JPf/oTLr/8crz99tv49re/LXqugwcPYsSIEdi5cyeWLl2KAwcOQBAEjB49GnfccQduu+02cBSZNMyMpYN/BWqvlr/W4XR4Px2F/rZT4nwp+fnwVC2B3Z2dVAOKdqcnNznQ1ALTC5zLhfPefUfX56h2gjS6BhYJlBawwsceDWXHMeyjNH1OiyeCZnGWfTeCgJx5c5E6fIRiIWrqzQIDI5m1t0YPg0/x2TDeLJDW2MurqkLr6tWKx2k1ZuUgt1nxbdrM1ksfDx02aTs/bsaK7fvR3NET+azAmY7l08ag8nw2WeGnnbFkZjAzlv7xIvCH26SvczgdR951A1C2qo12i7Lc6QXq6ogJvHojGRMb6XtLtphmNIxewOKvTWpwqjFO1dyb1ndDXRSZgUHK2lujxTtcuHYtnFdPFf2OdYFgJZB6cnMXLULz4sWK55O7NxaI7+NDxo+TTXphDVbPfefHzbjj2X0J9SrCq9+TN1/ExGAiXb8HhXTAGYMuaa6BEAS8+2TEweJgZCoxyywdXXhKGqAX/4NFCrgZsoTCSGaGHg2PTg3nTs29aX03JxWU0eOR4vHAMfUqtK5eo8pA0yPTTq/MQKMTHEh5lnani+h8enMJ4/t4155GQ+kMLJ47HxSwYvt+0cJeAkIG04rt+3HFmHymITk5DApRyjMGmdKDqPtoKvpP2EHiVQJgqKgiKxVa2QlbCdGidSoVyMWgx8TGUkDOLMrMgylDjxZq703tuwnU1aG1upro2GFz5uDs2lp4qpbAt2kztXpzWGTx6IaNzJSkI9dW+a7tLpcs2T4ZCQ5SCu3RyvhDxo9TFkLluNBxBoLVmOPcbqLjWDz3xoO+mNBbPAQAzR09aDzo03wtUlieJTNBRpm7v0fFIiizm2dJ4mS1UGraiQ5kjgCQJZPaXS7wYuKa8dAxEy4ZXhi9U6zNnqGnBUbeG7FnNRz6mTcXAHCgfDK1V0jvTDu1z0Npq8RKm4gWSt7CEx98qFydIBjEiQ8+NFTaQUu/HDZnDtJGj44N5+n43MPz1In3P8XYoz58knMOgjZpA7S1U9qgYg3LWDIThl8KOAqBQGK9u5R09d6h+AnOrMUWiSfiOEPH7nbDU7Uk0vboCU1MwVtMXDMBggDXdd8jaw8lOnfVEx1HIiDHIvOQBZK1gBkBo+6N1rMalhtQDLOIGN9qw900C6/ic5NA0O+X3Siw0iZSA7kwrlm9q2rfAwBklpXF3K/n3qXS6t6CoOm5R89TBQAeBnA03YmaC67F7sKxor/JyzKujqsVhjMTODtQuQahUFtsKInvtUN5zyWO6AlOj8KxQ8aPk3fREhZbJJ6I4wY87/fjyMJFkbZHhz9CytKXxIRCouvVuWfNlGx724aNTGtuCTyPo0/8Eu21W4mOl3sepPXBjCoUHFPHSwKDtYaUUTXKSD2rnNsdQ7YmXXzDRrqqcLeKgqmqa01C+Z5IwmJGw6zeVVXvweACuVLz1LCeDvy8sRaXNn0U2zyEsuJKRmYb0j7AMpbMhzHTgeu3Ao5TITkhCHj/TpYFF4O4Dq9H4dhAXR0+q5iCYHu7+AEUi4lilXEpqGh72KDKX7oU573zV8nip6yMikBdHf4zqRxtGzYQNE5+oiI1gIwuFOyoqED2rbMTeRsch+xbZ6tawFgXLlULpcU5q7xccztJjZ78pbEeQdLFt2Pb9lPeSEpJAkCdQSj13JRAck/xRbr1KhhNCsX5y2ADJBpU70HifUfmE5nfqZlP5OYpDiEXwe0f/RHcgP5g+OkunzbGMHI3YIXhzIuojtN9NBX9XTLHikGkw7PmypC48mlUaGXd60rQyPORLH5KmQkkFhrrrK+nC3nIuLNJs5a4LAe69+wxlBsVqKsT13IRBPg2bcaQCy+kWsjMJI0AyNdMjE+RV9NOYs9E3IKXMXEC7G43eKkNywCC7e2RtlPBZkP27FtUP/OY5+b1oqW6WnZzRRPS1FpRgCWSGR6UQ3hOEvpOoqB6FaJpCXy7D974DEqJOVsvrqXSeTkAeSf8+Hrb5/godzTyGesskcIylsyG/duAF2ciOuSmhtwt1uFZxtRJXPn27GyMqnsDXGqq5DniFx4p6X/O6dStGCqrSUB0cfd4EOztpTL83LNmSS5MpG0lqYUWBgsOBevUc63SCnoR2uMXZxYSEGGo5UbZ7HY4pk9He22t4jXCz4MKwaAqYze+jeHnZktPkxVUHazhWoBN6RKWkNtwhN9H1hVXEI0VvThZpMcvvCgbQyovYa7gTQrLWDITgjywcwniuUmk5O68qiqk5OREMhdOfPAhOl7dERkALGPqJK583ueTzPxQ8hrE7+CFIE+k6K2GD8BiEpBbNGmRNWmSqjaoBQsOBQuDM2zgnPR6Q6nzKg0vozxSrA1ELZ6JrEmTiIylyFyggvBLq7MkBbMZFCwQb5yPqnsDJz74MOkVFUgMeVLvnF6cLNLjSyd+FZmjhlGdmyUsY8lMOLRbNBMuI7cPKUN49J/gIMpbGthxZs+4OVKANl6xNSU/H3lVS5hl9WgxMEgHcfQAFnhet4wkrZOAJn2oaBDcA2tyqD07mwmHQqvBSZXGLmN4sfT0KEGPsIRaQyJiACmojIcXbepwN+OQrZkEVbVCzjjXU6lbDnqIjOqVFTpYMmktgreZcFzcC2HjAM9F4RCUeCmC8I5TjvzbtHARHFOvivmd1HmUoNbAUEs61jMjiYRYLkfM1KIPFQ+le1BNgpeAY9o0JgsUaX/oO3Qo4TOpPquEeMPLaEK7XmEJR0UFRtW9gbyqKrhuugl5VVUYVfeGrJEXGR82G9H4UEu8JpGzICW6J1tQlUXygFHZprRgJRQcDb3mYKOyTbXCMpbMhIwcya8cxT0o+kY7UobEip5Fp8qSLBaBHa+h6LFHNafcqs380DKI9UoXJkl7H3LReARe3yk6qbIIjaXk5xPdg5Z0bDHIhfxoQGrEtW3cGLOAaPHKxRtopH3L98yzTAwmvcISYc9w6+rV8D/3HFpXr8ZnFVNEF97oBd/udFGN7ehssmFz5mi+F1I5CzOARVuNNs5poKchr0dWqBmlIOJhheHMBIWFxlHcg6yiHnRf9Aj6U4oSXNeki4XdnY3R9W9pcoGr5VdoHcR6ue8dFRU4cets+J7eJPp952uvo/O11wEk8l+0hsZy5s1Fzpw5xPcgFaqhBcs05kh/kBKsi0K0+1+VV07CLU/at1pXr4ZvyxbN/Bg9wgc0YUSp8E9e1RLiYrph707GxAnoeOUVVfci8DzaamrQtmFjwnfR7TZL2I1VqDaZ9RCVoKfmk15ZoWYPzVrGkpkgU0g3DBsHZI7KBsYmxsJpDBEWKbdq+BUsBrEe6cICz8P/+z8QHRs/qZIQZu0uF5CWBj6K8K2FdBw/sdiHZaOpain41lZlL41Orm1HRQV6580VXTQjiFtA1KSxA+Jtp5n4WXCYWKeK0/BMpOQowuH2ovXriPgy0cRk13XXoW3jRqp7CdTVwfvQKulEhoF2Ny9bnnBcMmQgWHJ51G789C49BOjPA9IrK9RMUhDxsIwlM2EoIX9A4rhkFZik2Q2YlczXVlODoN9PdrDIpBpZNCXg/O53kLdoEdNJMn5iyf/ZvUSkXT2zjlKHjyA6LryAUGfOyLS9v52iqKZKkms8WGZ2kXoquhobmSz4Yp4pzuUCgJixIHUvxCVTBAFBvx/xVdP0IN0rgaU3SM18a1SmppGaT3qQyaPBBwU0HvShtbMHeVnplnSABUTVhmtGApEbAGALfT/8UtGfszREaHY/NLsBMwq3CTwP39ZnKH8UO6kqhfG06tSQQFKjyu2G+wc/QNrIkbq7tmkXEBKvHOd2I3/p0phsrngIPI/W1WvoGssoTMIqfEDqqehWUQsuHlKGTrCjAxAE5Mybi9ThIyTvhUkGKCODlQYsuTy0862RmZqAcRINeoYjd37cjBXb96O541TB3IIkiVJaBG8zIVIbTmoCEoDK1aHjRKCYVSAIyJoyJaTmKkO805uoaTYyX/f7e4kEL8XQ1dAAgech8DwCO16TPVaK7MmypIejogKeqiWwR9W7C7a3o+P3v4ct9Szds45oif+KfdZmQ8GK++GcPk227VoyElkQ9FlkdrGWhZC6LxJPgP+l38FxZaXkvTDLAFWRlaUFLL3vNFlcySKDG1ESRi8y+c6Pm3HHs/tiDCUAaOnowR3P7sPOj5upzqcVlrF0mkEyJXhg8LbX1soaP0alwpqprpOWxfJYTQ0OlE9GW02Nqiw/GsOUxKgK1NXhyMJFCaUvjEplVpMGzMJ41vIOaYwUPWvVZUycEAmDScHuciGjtJTofFL3xSKtnLU4qh5iq2JgXb+NtO/qkcpPCr0lGvSgf/BBASu27xd1G4Q/W7F9P/igRm07ClhhODMhouAtBRuwswr42lRJ7xIQGxbo3FUfqnIfjGUMiLl+9Y49J9yNSch8Wnf0/V6vPKk5+tioRYFF5lO0O93o9ycFNe5/raEsVe+Qkh9nhlp1AgAhyMuX/1G4LxaeANZeMNbnk4IeNACSvquX98UM0IOH2njQl+BRioYAoLmjB40HfSgzSNXb8iyZCRIK3qcgAIEjoeMUYLPbQ+mcb0h4EkRcv8nc/SQTmoUeKXgb4UWBxi1P6u0z0/tT4znUsgOmfoeUC6MRHtfu9/cqJhkE/X7894c/kjWUAPn7YuEJIH3eXF5eyFvGyJPDAnrQAKL7bsbECeh+f2+M95HmmevpvdQDeohKtnZKG0pqjmMBy7NkJkgoeKs9jkakL3vGzaf17kcOqso/qADnckEIBk+R54kyn94j9haZ7f0Z6TmkfYc0JFejPHZMxE0J7ouFJ4DkeefMm4ecObefkjkwSUIHoJ+mj6z2lcIzt7vdCLz1Fo4sWBATRjfCe6lVzoA1mTwvK53pcSxgGUtmgoQkgBAEuo+mor/HjpR0HhlDcsUqxCWAVqTPdd33iI43ymVuJCQHe34+chffg5b7lkHo6iI7mcTiEfT7cXj2bKTk5yNrCtnk0b1nD7G3KBnSEWaC3DsMkd7JhBrjwSrbR2lB0vJeOJcLRY89hsySixXvi1UoSvZ5Ry2QZi2cGzbmw+8l8PpOTUaTXFi9aeEiZN86G75Nm8XnB0EA7/PB/0xiVq7eEguswstqDVCxcTGu2EV0TdLjWMAylswEEemAwOF0ePc50X8ialLdvxyen92r2JFpRfraNmwE53JF0ocTYIAGkhGCbVKQGuzd7+8lNpRy5s2D/6WXZBfXfq83xCNjiP6jR+G4stKUGlZGQg+PAQuPHcmClDFxAuweT4xwKSmCfj9sHKdZBZ7WgCF93mZVZ2ZlKJB4H8Olpryr19BlEurIN2QtZ0DrTZZ6/h//360AshV///yeQ/jhZecQX08LLGPJTAhLB7w4E0DIUDryrjvhsP7WVqKOTKJhE8HAgIwgCS5zMxBoxQY76WLJOZ3ImXM7cubcjq7GRhxZsFCcWxJ+rhwX+n8Z1e+M0hIcq6lRvHZKbq4pNaySAdbhP60eO9IFyWa3w339dcTJAvFQU+eLhQFD+rzNktARBktDQVWpKa8XLdXVCMZlrsr9Ptp7qXVjmeyEELnn/9UnV+HSklnYXThW9hyHfN3M2yUFi+BtNoyZDlw6D4LAwbvPOfBhXNCNUJeDuujqgNJuzty5hmsgmbV6N0C+WGbPnAGb3R76x9mVtZuCQVkjlvf7wXd0UKU6m03DSk/oQYQVO6eWdHNafR1SBXQxqAnj6Z1Wblaw0j0K9xfS+Sm61FSKx0NmKMX9HmCjhZfMhBCS53/7R38EJ8TrvsdieHYG87ZJwfIsmQ37twG7N6C79ayY0FsCCHkSaoqupg4frrnQLg2M2OFo2YWR1n7LiarcTrrLt2VkQOiW2B3ZbGhdvQaeqiU4snARkbdI4HnYnS7kLloE3udDSna2rPL1YAWtFzL6/duHDQMggD/mi+kLcudU67Gj5Tv1HTpE/SzOhPAqa7DgoYn1FyVEG7RqCP0pubnMPGLJTAhRev42AHkn/Ph62+f4KHe05HE/KB3OvG1SsIwlMyGisySgv4dsYSPpyGF3u++ZZ9G6erXi8eGQjlEuc72rd2sN7ylm/thsyF+5QhVZV9JQAmJc90UE/BK5+zzdDCWaxUJpUUvJz4dj6lUh8q3EOXPmzoV75kwEtm2LzVRS4PjQLEiBurpQIVsanEHhVZbQaigQ18ULQ8Sgpa6LmJ+PIePH4bOKKUw2lslMCCF9/tm9nbLff3jYb5jOkmUsmQlROksp6WQhBdKObLPbkT3jZvi2bDEdAVjPHQ6rXRhp5k8YJOnZnMNBVGal/+hROK+eKssvIb3PZBLoWYDWC0myqPV7vZI1/cK/a9uwIfIR53bDOX0asiaVKz4/0vFpHzYMzUuXUstWJDujzKxglXkodhx1XTwJgzZj4gR5cdE4eO5dihMffMhsY5nMouakz9+XliX7vaWzdKYiSj8pI7cPKUN49J/gkMBZAlR1ZLMSgPXa4bAO79EQYkmedfbMGURk3vB9S3n7BJ5H87LlivcpBINojcvEMZpArxU0XsiMiRPIFjVKAyXo96N96zPImKBsaJIuSIBAFM7JufNOZJSUDFpj1wiQZh6qNRRo6+JJGbQ2u514DsiZNxeOigp0vLqD6JokG8tkrgdKz18AcDTdCQ4CvvXfD+BLy8InOecgaIulWecMTWPeNilYBG8zIfOUMWDjAM9F4R2H+GSupiObkQBMogbMOZ0QgjwViVcPAiMNIVbpWefMmcOkTlVbTY288vPAfTYtWEhEoDezgjCNF5JZsdd4UBCASdWN+WM+okunjhxpakJ2svsOaaKIFtVp0j7ouukmReX6nDlzFOsBpuTnR/iQrDeWyVoPSJKPhp48gdXvPoWq95/Dw+/WYMsbD+HSpo9iDzKuNJzlWTIV4ixsR3EP8I32RJ2lHDc8y+5X3ZHDHpKuxvfQvWcPACCjtASZJclJ6yVRAw52dODw7FupPCFmULRW8kZp3dkJPA/f1kQhO2LEedg66+uTLt8gB5rFQlelcopwB4mmUdeeRqLLGi0oShO2Tbb0B60nWa3WFI0hotQ3bHY7ClauEA8Vi8wBeoTOWGtgkfYZyeQjmw02QUAG3xdz/LCeDvy8sRYPRkkKtHX1qmqjGgwaY8nn82HevHnYvn07OI7Dd7/7Xaxfvx5Dhw6V/M3ll1+OP//5zzGf3X777aiJ0q358ssvcccdd+BPf/oThg4dilmzZqG6uhopKUl4NN1tCR85inuQVdQTq+B9+wrYxmmbfOIXxWM1NUldFEmz9mi4RmZRtJYjy2sVB+x+fy8x50ESAwt/W81TIYIxI4E6PcC3+0L6VEGJlOKoxcKIGnikBpnSgpRM/ogUaIwf1uKGaqAmUUSNocD6XdHMAXqFzlgl9NAazLFF33ehvbZWMizOAQgiJCnwt4KvI2jjrHInYrjpppvQ3NyMN998EydPnsTs2bPx4x//GM8//7zs72677TasXLky8ndGxildBp7nMXXqVOTn52P37t1obm7GzJkzcdZZZ2HVqlW63YskJMqd2Dgg0xNlZTsLNF3GDBObGKI9XkcWLJAWdCTkGpFOakPGj0PXnkbNuyqaXXj8saPq3giRNynbwNJ74tu6NWkCdUoQeD5kzEURraUQXiyoRFlVgsbQlluQzMYnpJkjki1uGGmbSk8yraGgx7uiMdrMWj5G7boSHqtNS5YoXoPDKUmBttHno2Sksso3KwwKY+mf//wndu7ciffeew8TJ04EAGzYsAFXXXUV1q5di8LCQsnfZmRkID8/X/S7uro67N+/H2+99RY8Hg/GjRuHBx54AEuWLMH999+P1NRUXe5HEsMvBYZkAydk+AtDskPHqQQpGThZi2JI0JGT95bEFQCWaifJpOaYehU+q5iiOXRAuwuXOtZ59VTiawJsvWIkz5ylgjApAnV1aHlolXIZEI5D4aO/iDxv4uK6YcL9rbMR2PEaGc9JB0+PWRZBWuNHb+kPUhjpSdbjXdEYbWYrH6PVYKblF2b3duLSCwtg50iqpLLBoCB4NzQ0wOVyRQwlAJg8eTI4jsOeAc6NFJ577jnk5OTg/PPPx9KlS9EdpWvT0NCAsWPHwhNFbpsyZQoCgQA++eQTyXP29vYiEAjE/GMHfRlrpGRgrSEMLURPmgLASqq1cgTGcGFLrarhNOrjapXKpZ4nETne5YLd45ElktscDpJbZaogTILw8yKqlxYMIsUdu9OUev/RCJNZPffcM1BwN7HEUAx09PQ4Kiowuv4tnF1bi8K1axMIwkYQqGkTI8zADQQIxgJhwoQcop+/3enCqLo3RN+VEe/JTOrrWpNpaPuGLy0L2/7eDD5oHMN7UHiWWlpakJeXF/NZSkoKsrOz0SLzgn7wgx9g+PDhKCwsxD/+8Q8sWbIEn376Kf7whz9EzuuJm0TDf8udt7q6GitWrFB7O9I4tBs4oSB/f8IXOm7kZdSnpyEDa5nYtBI9aQsAK4UOxXZhJOJuLQ+tQu/hw+jZ9wG4jAw4rr0WQy8pjZmUaHZUAFTtvpSep5L3pGBlqK9KetgEAbZgkMhMZ6kgrARqPRuI99v49y+n4H1k4SLF6+nt6ZHyMBhFoKY1fszEDdQzlEnqEU420T0Z0Gowk/aNIIC2Ia6QjEBHDxoP+gwTpUyqZ6mqqgo2m03237/+9S/V5//xj3+MKVOmYOzYsbjpppuwdetWvPzyy/jss880tXvp0qXo6OiI/Dt8+LCm80VwnLDaOOlxcaAhA6ud2FjUeCPxlkRAUScvehdGIu7Ge71oe2QtjtfXI7B9O/77wx/i04tLYu6BZkelZvdF8jwlvWf5+Sh6fD0cFRWSx3DOUP3B4PHj0u0CIrvyIePHMampRQI1qf9S/Tb6/Q8tuwRDy8rgvHpqhAju37YdLcvvlzWUOJcLxZs3y6aC6wUjayfSGj9GeHRIoVcqPOnzN3ONSz2h1WAmmfMFhBQHnxp7TURv6YwRpfzpT3+KW265RfaYc845B/n5+WhtbY35vL+/Hz6fT5KPJIbS0lIAwIEDBzBq1Cjk5+ejsTE2Zdc74O6XO29aWhrS0nQQw5IgeKs+Lg6k1j/ncqma2FgRPYm5JlHnpuVEqPWcCd3dOHLXfGDACNEjBBE+luZ5knAYxDwszVVVkC9VeQqsFYSVQP2OOA797WR6RQB9ba+g3w8bxxke7jCaQE2b7WU2croeqfAkz3/o5ZebguieDGjNECSZ8wOpGXh83HUR2QAAhmbDJdWzlJubi6997Wuy/1JTU1FWVga/34+9e0/tuHft2oVgMBgxgEjw4YcfAgAKCkLZZGVlZfjoo49iDLE333wTDocDY8aMYXOTNBh+KeAohKhiNxD63FGkmuBNav1nz5ihajCzFIEk4ZrEg2Zx1RoSaH7wIQg8T7Wjot190T5PEg5D9DE2jgtNbgqwDR0K98wZsDtdOEnCHQIbfgr1OwoG0bRwEdHuXcoDoAS9eTdiMLo6vBrBRrOJ3bLk85A+//bnf0P8npIt3skaWkQ+w5DqQ4GzMrD1axX4wZX3xxhKroyzrGy4ePzP//wPKisrcdttt6GmpgYnT57E3LlzceONN0Yy4Y4cOYLy8nJs3boVJSUl+Oyzz/D888/jqquuwrBhw/CPf/wDCxcuxP/+7//iggsuAABUVFRgzJgxmDFjBh5++GG0tLTg5z//Oe688059PEdK4OxA5RrgxZkIGUynrGshaAtpLRXciJT39qraKZGkUnMuF3Lm3K6q+SQLL0CvTUNTAJgUWtPKg62tkZIaNDsqmmP1NkxIfyccP4722q1or92qTH4eQPhdaMmYU/WOBAEtD62S3b2r4UKFYbQoJJAcArWabC+zZWixAulz7SOkY3TuqkfTkiWnHaeJRYZguA8df+99/LSmHs32TNEyJwBwsp/UJ84Gg8JYAkJZbXPnzkV5eXlElPLxxx+PfH/y5El8+umnkWy31NRUvPXWW1i3bh26urpQXFyM7373u/j5z38e+Y3dbserr76KO+64A2VlZcjMzMSsWbNidJkMx5jpwPVbgZ1LIkV1A4fT4f0wG/1dAP70GwC/UTW4SFydBStXqJrcAnV1aCYkvdNq0+hRAJg61CeC/qNHqUMQpMcG6urQWl1N1A61C7ia3/FymZRAzLvQSnRV+454rxdtNU8h986fiH6vqgxKkopMA8kjUKsxfliJG5oJxM9VSig1Du21WxM+S7bGHSuwMJhtdjv+MWwU6vITRZqj0dXH42+fHcM3zs3R2myydgmCTmptZxACgQCcTic6OjrgIEzBVkSQBw7tRqB+F46sfSHx+4HFVY2Lm3W2RqCuLsTjIYDd48G5u+qpDbJIBhYgms3lnjULWZMmUQ9MWt5KNM6urY0sDCx0lvKqliDFnY3OXfWiE6oYOJcL5737jmoRzQPlk9mJNkb1SQCyJRxo+m2grg7eh1YRey7DCJPb49Hx6g403X03+Yk0jDUWUHxPA4bc6Pq3Br0Xx4ygGicK6vKw2RTV5633CKx941/Y+CflRKy53x6Fu6d8TdO1SNdvy1hiAL2MJeHzd3Bg1mL0H5PQcdIwuFgJCkYmEkJjI2feXOTeeSf1dQAJwyZuclJj9EU/C87twn9v+7HiLpHLy8N5f9qVICOgVsGbb/fBu3qNKqNNyigggaQRSgDO7Uaw/ZTURfjZZ5WXK/YJGqOZWJQyDin5+aJjo2tPI76cNYvqPFr6FOn4kvuN7GYBQNFjj8Luzj6twl9mgpZxAoDKMxq9CTtTsfaNT7HxTwcUj5v77dG4e8pXNV2LdP0eNGG4Mwr7twE7l6D7P23oPybjYtSQecTKXU4b0kgdPkL1tWLrCA14X+KMGjXu7PhnkT37Fvie3iT7m4Kf/yxhMaJ5ptHHkur7iJ9IW4YNaU0+MeQvXYoUjydhge7a06h4LqVQWRhSmk4kkBobRNw9tztyf+HQG2lJHDWeW6XfyPFBHFOvSjC0TwcOjJlAPU7iN3EeD7KmTAnVPlNA/9GjhqnjmxWlI7Ox8U9kxxkFy1gyG/ZvGyB4C+jvGUL0k2Rk6Ki9tlZehWIdIQYpup577gEA+DZtTlhMbRkZKFxdrWkRip4I7cOGwfvQKvVhMAap+vE8g/62NjJCvccjek3SPtG2YQPSzh0t+Sy1ELHl2kLCM8tfvgwpA56atpoatL/4Uoxni3O7kb98GZyVlTHnViPYSfobMT5If7sPTSKG9unCgTETqBJOgkHkVVUhJScnYux0v7+XyFg6/u67aHnggRhNvDPO+CUd8gbGxSxjyUwI8iFi90APSEknSydNRoaOmmuzEqYjTeXtanwPQ8suUXUNzz33IHf+fPieew4n3t8rqeBNCy0cKTloNZijPV0Cz2si1NP0CTGjNmxMdjU0aH5OUm1R8tS0KoREg+3taFqwED0//ChiXKvRQ6L9Tfx7OlA++YzU9UkWbHY7UnLICMUpOTkxyt5E2Z02GwIvv5zw8eli/JJ6zP528BjR+f528Bgu+6ox659lLJkJh3ZHMuAAICO3DylDePSf4CCqvZTEDJ0wiNO7bTZmwnSkhsGRBQtQ8MBK1ZMLl5qKnNmzgdmzVf0+HlpCSkqQMgqkJie5SUuryGCkTxAYOvFeMZbGpJJxLuap4dt9VCFR39ObMGTsWDgqK1UVlNVShNYsBWzPNKjNTiTK7pTqdzoZv0aG+2jC003+E0TnJD2OBSxjyUyIK2Ni4wDPRR048q4bp8Tew18ar4wrBpIJwO5yIX/lCmY7IuI6Qh0dptmNsQgpiULGYJaanBxTr0Jgx2uyk5YWzZRInyDMkIwuzsvSmCQZG8SeGhm0rHwAWVdcoUoPSYuGklkK2J5p0KJWLcl9ksuiC4Ox8SuWZZri8cDzs3uZz5e04elCNxkFhfQ4FkiqgreFOIiUMXEU96DoG+1IGRI7kJKljCsGubpjOfPm4tx332HaTqracWBXq0wLVGn7KEHGYJasUdXSAt/Tm0Q/P3LX/Bj1a0dFBUbXvyVaVV0JjooK5MybS3QbKbm5bI1JjkPRuseo+5zad8T7fOh+f68qj4MWDSWzFLA906BVrTp+XOVVVRFrNAFsjN+w3Eu8HEe/15swD2iFYqgZiXP0peeQhTpJj2MBy7NkJoTLnQSaEc1ccxT3IKuoB91H09BvG4aUmZuQcbE2CX/WMFK9N8abpQQGuzEWrmpVE1yUhpTd4YD/xRcTd4EiXh4thkfzsuUxbn4tWZM5c+bA/+JL0vpIUTtwlsake9ZMOOKI1yTQsgj1Hz0Kx5WV1B6H/nafojaPlJdCaz0uJZzpGVly0KpWHT2uOl7dQXVtrcavwPNoXrZc9piWuHlAC9SEiy8ZNQyujLPg7z4p+TNXxlm4ZNQwze0jhWUsmQky5U5snA2Znj7g+jXAmDLqUxsx8Rmp3huerJrvWxaTNSIFtQshKwFPNRNc/MSbM+d2Iv6REAyqNjyCfj9RSj8JbHY7PD+7V1YfKLwDpxWctGVkQBhQ649H+5ZaZIwfT+1Z0rIIpeTmKocfBSHG4xCoqxPNZIuHlJdCcdMgCHBMvUq1Ij9L4drTEaw2iDT9zq6yyHk0uhobEVRQ4uf9fnQ1NmJoGf1aEw814eL/396ZxzdVpX38d28KLS1pkkKbtlgRBBUURSytqDODtJaKI+oo6rhQQHHqACOCI8WtgMouIlVBHYECKiquLGJZRJ1XpkWW1wV0RNleoGkhbVNaaGly3z/ShOTmLuduSVrPdz58nCYn5z733HPPec5znvM8JpbB7L/0Q+GqXaLlZ/+lH0ws2e6CHlBlKdoQSHcCwGtxyp/t/V4hegx80bjKTMzLA2s248joMbJlmw8dVFy/mmPgYpDG9uk2fx7cNbWCbSykjAo9W9ZiIbtBEZwrV6Jr4d9UB9wMhHQF3uJ0EsvHxMeD6dgBnLCuBED4lJ0Q/DAOMXY7WqqqFFnlTElJiicwIusfy6Lbghck+1hiXh5OjxktGhfMuXQZOl1xhSIFR89+3xZR0tf1WCAqORChhzdfw3/Kico1luujLKndLs6/LA1L7huA4k9+hKO+yf95amIspg2/FPmXpWmWTQlUWYpG+g4HLrnJezrulMPry9T9Gq/lSSF6DHzRvMpMyMryTnAyloma995H18JCYgVPzTFwKUgc4T01NTj+5FOwPzGVaAAWe7YkljYpPLW1sifUlDx/khW4KYk8uBzX2CipKJFuvQoqmlar//mSKkypzzztt/A5Zkrk8wvoM0Tbjh4PTDbpduHcbrjWb5Aso6Sf6t3v2xqRGOuUHIjgv5tKcZWVoWbVKlW/VYuW7eLdh2tQFaAoAYDD1YTdh2vCrixRB+9ohTUBPf4A9LvD+18VipIaxzo+oo7CrcqWno6AamBMJljvvFO2nNvhQOO3O4nrVbLPToqYI3wgpO1q2Ok6nxy8E2pSz59zu9FQXoG6devRUF4h2J98K3DLn29CQnaov10HiTbReg9CiN2Xb3uC1DqX9MAYv3+Ukj5zlnDbUW4LQ+9+akS/bytEcqxLzMuDrWAkUVktLgVHH5kIrqGBqHx8draq6/BR6xA/a8NevPbVgRBrGgfgta8OYNaGvbrIRwpVltoxWgc+PZQtKUgmWRI6du9OVE7JIGPUsezEvDxcWPY5WJtNuABhuxpyui4A2RNqrZ9VPlOMX4bk4HBBAY499hgOFxRgf06u4onFf8JRR6RiT8kqmhyHjGVLkT5/PrpOmBB60tNmQ/rCF/0BKQHyvlC/dSuqZklYoAKQ28LQu5/+HsMRcG43Tm3fjuNPP2PYWEeCeUgOUTk1vnVKF1es1YqErIGS9SkZu8UWimKnuptbPHjj6wOSdb7x9QE0t5CfItQK3YZrZwTutzftl09ECIgPfKTKlnPlKiTdf58iHxc9zd1GHKE28lj26d17ghLQhkCwjUQ6WbEWS9C2nMluh+dUPbgGkX0sJSfUOA5uAUdRtfn5/NuUWq1lMqfASBRNT10dTu/a7Xd0F3OuD4S0L5CkvCA9yaZ3P/29hSMgDoIahgCfRp1u5NxuOFeuUrS4SpsxXdfch4Ayh/iV2w/CIzMMeDhvuQf+0JPspjRClaVoxeNW7LOkNvqx2MBHOiFXzZ4N5/Ll/pdF7mXS24HUiEHGyGPZeqzeSSerbgtfBMOaggan+i1bhJUS/gk1tdYDlb4tWpL6+iGIc0N6X4GO7iSOvETR7EmCDwIAx8E6YoRsMb37qdHhCPRCjwMnaoKgGmlR0xo5XwilcwKTmIj0556VTPisZewmdYg/5JRySlReTg/oNlw0svdTYOFlQOmfgQ8e8P534WXez0UQ22+XhGEkU0IoWT36XhbHvHnS+/4bN+q+tac1SFy46vRB2q6mLuLOvbKBOVufbUJWVoivkN8kztv24pvENVkPVPq2+AL2ZSxbpupUH0mwVuII8K3OtKSQ9BklwQdPlJTIbmnq3U+N7Pd64Sorw/6cXE1bv2p9/oy2qCndrpJCzZxwnsQ1jHbLCCTDFq9rOT2gylK0sfdT4L37g8MGAN6/37tfUGFS9eIryPFFFCmb4wCOg3PZcmkflxnPGuJAqucgY2SdAHm7HiuaKjoB6BpFeO5cpBQVIXnSJJgsVv9gpzRSuhBCK3E5fwfGZELnQVcj7dkZRNdgbTakz51LHGE8PvMqYkVMjU+aWJ8hdeANuj6Bc7He/dSofq8Hejlhq/H5Yy0WcB634dkAtETO96F4TghYXIkRTuf/S+xmXcvpAd2GiyY8bmCtzPHRtY94wwoEbMmpefEV5fiSSvzIR2rlzHFwE8bTUWPuNiKKuBF1kraru6pK0rStRxRhd10tqhYsEN0yVfz8efBX4kr8HRLz8tA0YTxOlLwseQ1PTQ1i7HZiXxLGZELSyPtl6xWSnwSxPtP47U7UlK5QVhnhlqbe/TScEflJ0TOsgZrxxVNXhyOjx6j2rQxn/CY1c4KcxVAv53+SdnCebia6Fmk5PaDKUjRx8N/AaRll4rTTW67nn/wfkXbiLoWFiO3VS5eggkaj1tytZJAhHbyMiEzub1deIstgAeUnAC2TGrH/gZBClpoKz5kzXudxQt8WNf4OHbtfIHsfgPg7IPaMuxYWwrlylXgkY42+OUJ9hsinSfAmyJyL9e6n4YzIT4KatBliaNlOU+NbGe74TYqUQYLgp4A+zv+k7ZBijiO6Fmk5PaDbcNHEga9VlSPtxAmDBonGuZHCZxZOKSoi/o0UrM0m62tjtAOpHn4PWknMy0Pa7NnShQhM23IxjASrVeB/ILYtkDZjeqsA8tuAav0dtAzQUs+YMZm88gv1QwN9c6wj7lB92q89HddXg55hDYi2mMW+a3U5IPXPCWf8Jt8WN+lJaABEwU8Bcj9JsbFbSTtk9UhCmkVaEUqzxCGrB3kwW61QZSmaIHUN4ZXT2omJLmkyIen++7T5sLTKkVr8jP9v/veA8Q6kagYvvWJC8XGfPElUTu+JUqn/gZBCpsS3Ra2/Q3zmVZJBPMX6NskzJnV01wOf4kay9ScGY7EY0gfbCnqGNSByxpdRakn8c7Q6RSsZdwIXByeXLJGUiw/J+KLFT1JpO5hYBsOvkI7QPfyKNJob7ndL9+sAzJMswnmAxroUtKxbH7StoPeRUyFU+TAJyJGYlweGZVX72ohBsq2mxu/BSBN6pOLakCpfDdu3S7Yn6TagWqtA/ZYt8DQ1CRcW6dtKnnE4fHPkjqh3GTcOtWvWwC2Tk+7o2LFBf0dLyqFwoXdYAymfv8433IDalStl65BLs6Rl61DJuKMmDEIgpi5d0FBeIfsOqPWTVNoObg+HT//3uKTMn/7vcTye3ydsChNVlqKJHn8AOtmA08IBC11H4uDYbUPLey/6Pwt6eTQ4+5Ki1oeJL4fekxTpwKL0pTU6qWik4tqQKl+BK1SxgZrEt0WNUig3AZgsFqTOmB4ij9JnbKRvjuypJIZB3QcfwD61CMcenaRoEaJHH4zGBNliGLEoFBuHnCvJ8qfJJYBWu0hQMu5oSn3EMGAtFhwvKgpS/KQUcTVjt9J2qDjgxPG6M5Jlj9edQcUBJwZd2IWobq3QbbhogjUBNy8S/Mp1JA5H/8eGlsZgLZq/raD1yCkJvut0KSwkKt+lsFBQDjW+NkIo2Var37qVqM6W6uqwxBWJVFwbNWEBtPhYKN0qJpoAYmNhzglNERFNKTtIFbcYWxK6vbQQrBILosY+GA1+e0oxIqyB0DhEmthZrpyaRYLScUd16qNWhdNTWxtiIZN715WO3UrboapeWlHyQVpOD6iyFG30HQ7cuRIwn9uv5TyAY08SBJ2aeC+PXgqIHIzJhIRBg4jKJgwaJCmHFn8gJQOLq6yMLNUEvC9tuOKKRCKujaSSJoaGyVmpUkgyAYglR46mlB1KFLfEvDx0GT1K2QVU9sFoT5AthTknB2mzZqJLYSG6FBYiY9ky3ReFpImd5cqp8SdVOu6oVfpNKSlgrVbRawD6BZlU2g7ReBqObsNFI32He2MptaY7afytFi3vvSBePgx5i4TQYwtJqz8Q6cDSULHDq1QR4HtpXZ9tJCqvh4XCZ9puqNiBxvJyAEB8dpZkkDgSOLcbDRUVaCyvaK0zGwlZA/0O2oq3VDX0NSVbxVp8quIzrwJrtRoWFkAJShW3s/93VNV1lPRBPeMVhRuh8aLu4491j3vkH9sk3guSQzOyW4ccB+uIO+BavwEtTidMSUlo/u03Ivl9z1yt0m+94w6cfOUV8QI6zitKt1B9p+Eq685AyLbMAEgN82k4qixFK6zJ68MEoOXQeqKfhPtosVYfAj38gUjvubG8nFgh8Mms1oSu1gekfsuWoIng5JIlmpx4XWVlOP5McZDScHLJEjAWC7qMvB8du1+AmORkXFj2OU7v3uNPvkxykkZtXyP1d9DiUwVAXFECAI4LW8oO0gVFpyv7o6G8Ap76elXXUdIH9YxXFE709B+UW6TJJnZmGOI+JLZI8EWRV3tC0vfMVcXvYhjUrCL0y9JpXlGyWDKxDIpv7ouHV+0CAwQpTD7bVPHNfelpOEow0bStwEetY7leq1up/GlqsBWM9Mus1HKmxUqmtyO5q6wMR/8hHA2eq6sLGqB9Mlr+fBMayiuIlCUtfU23pLQ8fG0ll8qEtVoFfZ2MgGRBkXjTMPyaN1S134nSPhhNPl2k6GkNI33XfGNb5fMz4Q7w6THZ7Uh98glF7yN/kdB86BBOvPyyaqdsvmXUOmIETpSUkNfBcd6AsgT43nU9DgMocQ7PvywNi+8bgGmf7kWl65xvUqolDsU390X+ZdKhBfSG+iy1AcIRR0kLahzL9fAHcpWV4VjRVFn5WJsN8QMzZcsBgHnIuUlUiZ+NFh8QUr8rT3MzkW8X53bD8fxMuVsVlDFa+pq/7RVGuvY5rEqhNEGuVqR80pLGjIZz6TJN0fGV9sFoXnyJoZf/oJpDG/w3Qa0tw7dISLwxH7Xvv69aUQKCn7k3fpcCRSkA1mIhetf1PAyg3K82uJ04leERtEKVpTZAW8gErvQF0Lq69U0Mbpk4J4A3d9ixJ570OjMqVALMOTnoOn4c2MTEoM8Dna+1npojnQh++dNgosGq8dudsvFfxGQEEFV9TdQBVSMk+av0DAAptKC4sOxzuNZvUB0bh7VYVPXBaFGIlaCXNUyJ0uVXPvknxVpzNqp1gld9eg3B446YcqyEpJGtyZ0l3vX6LVsichhg4w/H8fCqXah0BcdZc7ia8PCqXdj4g3QcJr2hylIbIZozgatBy+pWTVwRd1WV1+LQarIPQkQJCIy67DNZsxYLuk6YEGQ507rqJZ0IPDXB8bfEBitV2ycBMkZDX/NNBHJWIrXI5a8y4kg9f0FxevceTRNdt4ULVfXBtrD44qOXNYxY6XI4DAsbovT97FJYGGKxJ4nfBVZiem9ViLsW/k3yXTfn5BgePkUIt4fD9LV7BZ27fZ9NX7sXbk/4rExtxmfJ6XRiwoQJWLt2LViWxe23346XXnoJnTt3Fix/8OBB9OjRQ/C79957DyNGjAAAMAKrq3feeQd33323fsLrRDRmAleLlpN0qlZmrUoSa7GAjY0NDsAm4F8l5tfgcblw4uWXEdu7l24+IKq3O1plq3x+ZpCvhqYkoa0yRrKvaQqyJwfPoZp/b0YHIQ2qU4NPUExqKhKyBiquK/D5hiOIrV7oFbyV9N1ocToNc4JX+n4mDBoUcg0S5djfTjKHb6Te9YbyiogcBpALSskh/EEp24yydO+99+L48ePYtGkTzp49i9GjR+Ohhx7C22+/LVg+IyMDx48Hm+lef/11zJs3DzfeeGPQ58uWLUN+fr7/b6tBpn89iLZM4GrRcpJO9STT6s/SbdkyMCwrqgQodSbVuupVnY2+FbfDgRNLXkPyuL+fq89uV7YVJyBjpPqalm0KEoQcqmNSU2EvmgLH7DmGH6n3OcoqSnYaiMBJLDV9sC0tvvSK3t1SIx1xG/D2BdKglGrGIiXvu9h2KOl1bQUjUf95maxCLPauR+owQDQGpWwTytK+ffuwceNG7NixA5mZXkfdkpISDBs2DPPnz0d6enrIb0wmE1J5CTI/+ugj3HnnnSHWKKvVGlKWIo1eJyPUrG61Op66T56E5c83iX6v9Gi11lWvppx7rZwoKfFbuxiTCfYnnxA9DScshLiM4U6JYdQpLNZigfWO2+FcukzYcjTxUekKdFhFC51WkxaaBTwe/59ipyvV9kEShdjT3Iyat99B85Ej6JiRAds9fwXbsSOZ/Dqi1RrGud2omj1H9jr2oikw2ciUJTVjUdD7LllQPDwB6XXNQ3Jgf/xx1e9vpA4D0KCUKtm+fTusVqtfUQKA3NxcsCyL8vJy3HbbbbJ17Ny5E3v27MErAkG4xo0bhwcffBA9e/ZEYWEhRo8eLbg956OpqQlNAck9XS6XwjsiwOP2B6VEZzvQ/Rpv7KUoQM/EsmpWt/GZV4GxWMARHn3lI+YH5ZOBdMXvm9T1WPWKTQSmpCS4ZfJP+Qi0eiTm5QGLXgqJsySIhIx6PWslChdpOAjWZgvx45IifcELqHzyKUn/CxLUKnOKkp22PpNuC16AyZYk225GJdN2zJsH57LlQQpb1dy5SBo9CvZ//lNRXXqgxRpGarE02ZIMz9nof9+fnyloAZZ7x5TIp8VCHKnclTQopUoqKyuRkpIS9FlMTAySkpJQSbhCe/PNN9GnTx9cc801QZ/PmDEDQ4YMQXx8PMrKyvD3v/8dp06dwj/+8Q/RumbNmoXp06crvxFS9n4KbJwCuI6d+ywxHcif443uHUGM8OlQ+jJXLVigXlESMGsrXu376uJtaWj1ARGaCDpd2Z84Bg/f6nEuKvi5CN4wmVC3Zo2szxag37OWU7j4ihQXMDFLkT5/HtiYDjjrcKBq1iy4fQ78fFoHdIZlddneU7OKVuqHpcZ3SEkfJFFeHfPmwfnm0tALeTz+zyOhMKmd/JVGhbeOGOGNhaSj8smH3xuYxER0KRiJroWFknUbpRxH6jp8AoNSihHuoJQMF6mgBQCKioowZ460WXTfvn348MMPUVpaip9//jnou5SUFEyfPh0PP/ywZB2nT59GWloann76aUyePFmy7DPPPINly5bhyJEjomWELEsZGRmoq6tDIu+IuWL2fgq8NxICr5H3P3euiJjCxLnd2J+TKz7htE5KvbZsNmybxrVxo/x2iQRJD4wJGuAVrfZ9SNynEVtWUgEm+aTPny+5xSgkY6cr+/sjePtkBqDLsxZt39aBNmnMaG+qB15kY5KAeYH36r8OIDigd3tpIbjmszj22GOy9YqioX83lFfgcEGBbLkuhYVIGDRIU7+R64Mk1kJPczN+7n9lkEUpBJbFxXt2q96SM3p7V0gJPzJ6tKI6fKErAq2zWqLq+5B7L0hPnepp5Vd6HdZmg2X4zTAPyfGPGXLPM/CZeC3IDNwnT4qWn7VhL974+gACD72xDDD2Dz0wdVhffe7N5YLFYpGdvyNqWZo8eTJGjRolWaZnz55ITU1FVVVV0OctLS1wOp1EvkZr1qxBY2MjRvpiSkiQnZ2NZ599Fk1NTYiNjRUsExsbK/qdJjxur0VJ9MAkA2ws8uaNi8CWXKTTJHBuNyqnz9BUh3PpMnS64gqy47dCBKymAAieqNL73hPz8tA0YTxRWgQSq0egjK6yMkFnZ+uIEZqfNUnsHyHLhdLIwgCZVaXBZ10jQedVNKlVI7ZXL13ycInVQWotrHn7HWlFCQA8HtS8/Q66jJJXAoXkMHKSF6rfZLOBiY8H19hIXI+nrg7gOHSdMN6fHkirUqdnNPJwOOlzbjdYcyISb7kFLcePw3P6NBp37ICnpgY1pStQU7qCSKmUs+Dzy2/84The/+pAyGzIccDrXx3AlefbwhrFO6LKUnJyMpIJBvdBgwahtrYWO3fuxFVXeTXYrVu3wuPxIDs7W/b3b775JoYPH050rT179sBmsxmjDMlx6JvgrbcQOMB11FuuNW9cOIl0moTGb3fCrcBHRQzfQKTm1JVv8gVCLS9GrOh8dC0sRO1774ufcFPhOyA1cZJGBJZ61oadahO5V7mJg9T/IqVoCqpmz9H1SH00RM1WMkk3S1jWAyEtF4ieW/lC1ilfEEV+/arGjtZ2qX1/jW4Wc70XnUaeWhXKLymE0PeBzxOArAU/sHxC7g2ScZYYeOMs3dA3NWxbcW3CZ6lPnz7Iz8/H2LFjsWTJEpw9exbjx4/H3Xff7T8Jd/ToUeTk5GDFihXICsjUvn//fnz11VfYsGFDSL1r166Fw+HA1Vdfjbi4OGzatAkzZ87EY1pM9Vo4RXjUm7SczkR6wNdFCQsYiEjr61JYiNhevWQHYyNi8fjwn3CT2GpSYvUgsfqQIPWsDVGaZe5VauIg9b9IzMtD4g036LpaD5ejrNTWlpJJ2tPYQHS9jhkZiuXTM8dbiPXIbgfX1KRvjC6dLeaRXnSSomT7X5DW51n5/EyvE4ncMwl4/icu6B91cZbaTATvt956C5dccglycnIwbNgwXHfddXj99df93589exY///wzGnkm1qVLl+K8885DnsDk1aFDB7zyyisYNGgQ+vfvj9deew0LFixAcXGx4fcjSGe7fBkl5XRGNk0CvIOVUWkS9FTCfJMJCQmDBvmjLgOISERbQN8o7pqtPgQpMfR4XvykuFqjiJO2ofL8VdKEI2q2XORx0sn3rMOBU//zjXxBloXtnr8qklGvHG9iqT7cDoeiqO+s1Yqkv/2NqKxeykukF50kcG43KhXklxSviIPb4SCP+db6/JvffwcsJ3/Qg8ZZEiApKUk0ACUAXHDBBYIJ9mbOnImZM4Ufen5+flAwyojT/RrvqTfXcQj7LTHe77tfI/Cd8ZDEA+KamlC/ZYshW1HxmVfBZLPpshXnW3UrXe2rMaHr6ciql4+CooFfpf+O1mCbgDelhy+AaKBDaEN5hep21NvPg/T5Ghk1m2Rri3TydTud8PB8RIUwDx2q2LlbD6uKnhHePbW1iLHZiMrqpbxE6ji+Ehq/3UmUd9MoUlYuwfI4C5Zcfiu+Se8nXo7GWfqdwpq84QHeGwnvrmzgi9S6Gs2fHdF4S74BX2wf21NXZ+hWVGrxM5pOw/Hjjyg9Fqt0sFfqyEoy8erho0A68HedMB61769RNbnLtq/UZNf6nBKyBvrTkByf+oRuPmJ6+Xkofb5GOOSSbm1dWPY50SQdQxi92pyTo1hWPawqevvCmZKSwqq8kIw71hEj4PpsY8Siqkd6CxAAupypw1MVpXguq0BQYbLGdwhrnKU2sw33u6HvcG94gESel39iekTDBgRizskBK+YAb/RWVH4+kh4Yo74CjgtSgJRubSkZ7MW2CsQS4BqVwFUI0szzXQsL0WvLZpxfWhqSzJMEqfZNemCM9/oy21JK2zFcqJWLv8UHeE9V1q1bj4byCsXvDam18/TuPURbgfxnJYYaSwtpv5NSTPSeyN1OJ+xFU/zX58sD6B9LSOy9MFksYC0WnCgpMXwMkCKSW4A+WHjNBeP3rMHgIzvRr3p/0NZc+CIstV4vknGW2gukcRoU0dIM7HgDqDkI2C4ABo4FYsKfYkAI0pgx55eWGnZKo27jRlROn6EogjMA2AoKkDq1KORz0q0Uf6wpmVXohWWfSweT5MXs0SvuihJIYhPpdU2x9iUJWBmu2F5Ktkv1kkuPI/R169YTxY/yxaUibnOZPq62zbX2O9LxRwkxqalIvGlYSMwvI0+4AsF9rvnQQeHwIAaOAVJy/TIkJ6JbcUJU87bm3hl7tWYHb9L5mypLOqC7shTFEbwB5YOzUQQONC0nTqBq9mzZ3+ihwJEM9iaLlVihjM+8SneFgHTiD1dQO7WyhksxV9oOesill4KsRhaSwJWCfbyV9IUvwqLB31NNv/PL7HCgctYs8YUSw4C1WMDGxpI7Fre2efqLCxBDkF5Gb6Ih4C8fzafhDMADr0XJtzX30t39cUv/bprqbBNBKSkCiEXwdh33fh4FW3GkJtpTX34JU5cufr8TvQn0O+HcbjiXLw+L3wGJo27duvVEdbVUV+sed0UsIF9q8TNI5E1w0ZB5Xsp/KBzHrNXE/SG9Xv3WLYL3pucR+vjMq8BareKnwAT6vpzPllgf91E1ew4YlhVMV0PSf5T2O+KURK1KT9qM6f7667duQd2na6Wt0K1tXjV7TlgVEh+kY4Bz5SrEdO0alvfUl1/yWNFURYE85UjIzYUlPx+mLkk4s+8nVM+dS/xbFl6F6W/ff4L/pF1KHbx/t0R5BG8fpKecXGvXwrV2LVirFWkzphtuqbCOuEPSjK2n34HcYK/Et0lPhUBs4nfX1ODoxEdx+oHvQ/J56R3UTs/Tf0Yfs1artJBer6Z0BeKvuiqk7+upINdv2SJ9XJ7np0dKYl4e4PEIHqjwKZKnBdLVkFomSfudkpRE/MMHCdlZSMjOgv3xx+FcuUra+mxwBgIpSMeAQPnDYQE25+TAZDajRU9lKTPTv+OQkJWFmhUrFJ2YZQGknK7FH8/8H3Xw/t2iJIJ3BJGMGSOAp7YWR//xiGFOij7HaLF0IFpj84ghFYtHiSOrXgoByZFq55tLUbdxI9H11KC3k7oeDsFSqI37QxJzzIfQYYf6rVuJ5JObRP3PXAKT1arq5BrndsMxWyR3J8cBHAfnm0sNdbyX7dMMA1NSEtLmzpU8fMCYTIjp2pXompE4CaZG2TfigAPndgcdNmioqCDfyiSBF5tL6VwSyJg+5rAm0qXKUjShMYI3v6NrPY0mVZ/YaQ4pKp+fKSmTGvnFTiT56DphgqLTW3qhJAihXgoB6ZHqyukzDOkbRpxaMzqYo1qrnl8ugtUwX9lylZWhprSU6LpykyjJM3fX1soGeVRbtyA6noglUWbdTic62O2ywUOjORikEuXbj84nj4UWOprCtAiQNHpUSGwuNXMJAFx15UV6iiYL3YaLJgQic3MeoLG6I1rOmBAT50Z8cjMYgXJ6O+qS1OfbipI1b7fidjhETdxqHT7lVp2177+ProVkEXr1hjQIoZp4T0KQTvyemhpNWw2Cz8puh0cszYRCHxw+RgZz1DKBJublobGggEjx8T0bEkuQ/5oECrKRPl2aLCw6bWnpeX/RGgzSt21tHpqHmtIVCn+sTzuLbXWSJrX2wzBAXBxw5kxwXSyLpNGjQlwAfAS5NRA48EfiOVFlKZrofg3QyQac9nYS15E4OHZZ0HI6wFcinoP92gYk9jj3Mz0TUyqtT4l5GxAe1NTKr7djtBGQOrLqoRAoWRGrnQilnpUkGp+FUY7oWidQ85AhRMqS79kosdaQKMh6W0v4J0y1onVLS8/702tRoieCjussC3jkU30EoqWd9YyIDo5D1wcfQNKYMah99z00HzmCjhkZsN3zV9lo74E+bExcrG55MPWCKktRh7czuI7E4ej/hIbhb2kEjk6cBLx07iSKXqdqAHKH186DB+P07j2KB9WWEyfAud1+WbTI31YSUpI6spIoBFLO00rSwTQfOqT4PvQYVLU8CyOyq2udQJUqW6T+H7aCkUQKsp7WEr0m7kC0bmnpbQ0yykqp5lCDqON669+2ggJ0SEsjsto3HzqoSm5A/4joJ0peRu37a2B/Yiq6jCKLh8VvP3NODroZZE1WC1WWoolD3wCnneA8gGOXL4Eofw/bO6D7FAi9rSuk9f3yp8HBZlLCQbVq9mw4ly/3d3gt8kezD4JapBQCua1KJelgfNuTSlZnegyq0fgstEygSpQtV1kZKmeRbcGZh5A5ZOtlLRGduMXeacJ0NT4lRu0JSSOsQXpbKdW4EHiam1FZPE1ykVj/+ee4sOxz6ZAorZwoeRmxvXurUiJIFzCsxRK0LReTmopOV16J+s8+C61Twa6GVPv12rI5omFNAqHKUjTR6rjdWN0xaOtNCJ8Cobd1RYnfS/AH5KvPwBeJaz6rWq5o9UEwAtKtysT8fLiGlaF+Q+gAFvQ7FVtimix0Uf4stEygJMoW8fF3Fe2k1VpCZDHkLYZi7HYk3jQMzqXLWisJtY6Yh7YuhmqcqJo9Jzjul90O250j0LH7BbJtbYQ1SM/cgEpdCFxlZThePE027lNgihr/lpQYGnwCSRcwgUmtY5KT0enK/vg1b6io/EK7EPxnLdd+4YxaLgdVlqKJVsftszKKko+zDgc66JzHSfPKX27FCQS9SJbbbyeqVsjMHI0+CEagdKvSPCRHVlkClCs/qvtGG3kWWiZQKWWLePtSQzuRKntCFh4ii6HHg5SiopCAiJ2uuEJ0666mdIWow7Lb4QgK9SFniYmG4Kl81LgQKIkZBXjfUcufb0LT+PE4UVIiIYz6oJWki05+cOGG8gpVuxC+Z23OySFqv/jrh2DH4TpU1Z9BijkOWT2SwhoywC932K9IEaf7NUBiOtxnyE4guJ1OWG4apqt1hTTgpCgch+QpU9C0bx9cn34qWa6lshInX3mFqFoxM7ORJ6WiBaVblUYpxiSDqsliAWJjg3JKReOz0DNwpg8xZYt0+9JksyF1WrHqdpJT9sS2O8xDya4X07VrSPqiQCWmfutWr7O7Ch+nlsrKIEuM2POJ1EENIZS+l2p8/nzvaMfu3YnKqwlaqXbRqXYXwmc16jp+PFH7PfjYG9jW6Xz/x2mWOBTf3Bf5l6WJ/9YAqLIUTbAmIH8OYr57iKh4TFKS7tYVyfoI6ZCcjA7JydLKklIkzMxiq06gdfVDmBg1mlatgSjdQjVqe5Kkr6UGpJmIxrYEwp8Pj/T5pRQVGaZQSm13kB5XF1OufbHCjk2Zok3IVl9MzuMJ2bYLd75Cv0gS44LS91KRzx/vHdUStJLEb4h00anLaclWq5Fz5Uqy4idPAuedU5Yq687g4VW7sPi+AWFVmKiyFG30HY6YgeuB/3wlW9QXxMuck4Ou48fBuWJlsAOeyhW92ItjSkqC2+mUl8sIJ14ZR3X+qlPJhBgNyWSlUGopMnJ7knRQjSYLQCB6h9kggfT5dbDbDVHaZbeLAO/WWWtU7hBaJ27O40HduvWCcul1oqqlshLHJNKrGPF8xJAbF5S+l0q3vQPfUVUWf4WnoeW2OnU9Lclx0il6AnDGmoN/Cu+xp+lr9+KGvqlh25KjylK0sfdTxDtWI6aTHS2nWYSehvPiC1gn1IFZiwVJI0cqPu0UiNCL43PoI7VYaNrOE0FLfrSWykpvFu2FL/oTykZi8lSKGkuRkduTiXl56Dx4MGrefkdRHBUlRERpIHBIVQPp82upcYZkntdDaSf1SfLJEqJccxw8Z87gyOjRonIZHp5DYuI3oq+QjAvmnByi59rpyv5oKK9A0/79RNc2JSWFbMeqtvgrPA0tttWp+LQkIazFAo/LJXg/HgAnOlnxY9eeId9xAI7XnUHFAScGXdhFkwykMByn40z2O8XlcsFisaCurg6JiYnqK/K4gXkXAqdreHGWAhUmDmBYdHtpIQAId+BW64ERJwn8Lw0gaLEIvKZkWZXd7vzSUsmXnnO7QyacEFgW6QteQOINN0iXbR3sIpGFnI+Sdg/EqInESEucUfU3lFfgcIF83Bd+rCo9ri33/JLGjPaeLDPgXa5btx7HHntMtpytoAD1n38ebE22WuEWsgDw5CJtWz0IHAOM6CuyY0jAuFC/ZYvsc+UnGpaCSUjARdu/EV14CFp3CEifPz/E34wU0jE1UHEi3YXoOmE8Trzc6rca0H6+//dcVgG+Se8n+vuX7u6PW/p3k72OFKTzN80NF00c+NofvTsx4wy6XVuDmE7BmntMvBvdpo6VP0kA/XIGBSKWx0coWa1YWdZqhfX++2Cy2chzIemZH83jwbGJj+LEkiWqkqhGAiXtHohUsl81GJH/LVz1k1o/+EE99bi25PN7cQFc6zcY9i6TbheZhwxBry2bcX5pKdLnz0fGsqVAbKxwYZ5cqnKbqcT3HI3qK0oct6Weq08BVqLYcA0NOLVtm+j3iXl5Qc8opaiIqF5fH1CTf1PJacn0+fO9CY23fUGU77JrYaFg+3FdU/yKEst50K96P/70f7vRr3o/WO7cnJhijpOVXy/oNlw0cejfQX8mZpyBuduZ0Nxw5zWgIYKpPpQc4+WflnF9+incNTWoXbmK/IIG5EcDAOcKMgdD36AbKUdln3WIaz6LtFkzATBwnzwZdudpvaPFh7t+1b50OlwbEH9vjE7bo2QbN3AbpqG8IuhUo5xcWg+GkBKTnGxoX1HquC3lsqC4HQjkDnxGnNstHbQy4NmqddkgbQ/+aUkSv0kAMFmsSJk0CS1OJ0xJSehgtyN2wAAcmP8lrtm3A4XffYzkgBPi1XEWLLn8VhzoMxBZPZKIZNMDqixFE0J9nQUS7M0h5cKR6kNqC0fJMV7GZIK7rhY1K1aQDR4CAfCMyI9GmiSy9q23UPvWWxFx+pbaZgi3E7XRk3rElQYpdFp8CL03Rr/LRh8ND1QaBP3kUlNhL5qCpl9/k4wVFP+nP6Hpu++8234yE7+RfUVN6A3+c5WNQSSGCh8jkmfr3y7ktaunrg4nSkrgXLkSaTOmC45takORyPlNAhD10YvpEIO/MYeQVRGad7HLmTo8VVGKir4pYY23RJWlaKLHH4Cv54l+zXlao3sfikMLS3ZsU+1qWk9fAJL4IqzNhtSpU/0OkWodbP0TIuFAJeVgyCfcTt/R5nxu9KQeUaWBkIbt23UPiRCOgLFqHP7VyCVndY7t3SvU56b1WTR++aX4RXhKnZF9RY/QG1od3pX8PjEvD6fHjIZz2fIQZSlp9CiYc3KwPydXsr97amu9h18WvRTSF7S0h1h/EFPefGObe8EC9H7vDW/1vDpZeJ2/e733Blomj0RMh/CoMVRZiiYuuA6IiQNazoR85ToSB8cuizcNyhdvej+UOrapIb2E3pM0yZ63p6bGGyW2dTWldvXunxD/8QhR+aSRI3Hi5ZcVRx7Xsh1DgtFbUmowelKPpNLA2mzS6SdaOblkyTk5RBYPSp3qw5W2R2kUbLVySVmdg7flt3hjPBEorXylzsi+okfoDa3hUwJ/L9efXBs3wvnm0tBKPB44ly4DGx9PvHgUGlO0tge/P5CMbceKpyOpsVZUThZAl8Za7F73BQbedgPRvWmFOnhHEx430NIU8rHvZJw3lEBgeYkkl1AXS4ckJotSZ9NwbBkGkpiXh24LX/Qqk2L4HQz/JuhgKIoKp+9Ap8pT27fj1Pb/yDpYKtlmMBqf/C0OB1ibTbwgoRO+GLJOwhrr98F3kj2/tBS9v9ym2EFZyJHYVVaG/Tm5OFxQgGOPPYbDBQXYn5Mr6Wzsm4y8f/Cur3OqGCUO/0bJ5QtiWf+5tAM2a7Mhfe5cr8Pwls1BSqnRfUXtgQpi+cTgyS3Xn+o2bsTRSZMlqzxJGHQUgOiYorU9AiEZ21hXLVFd9ce0x/YihVqWookdb4DvuMR5AMcuS+tfIi+eBh8fPkb4AoQtL10Aifn54ADBAHf8gT5wtesqK0PtW2/J1k+q2Mkd9RWzToRbwRSD+KiyDpN6OHP9CVk/FG/R8Sx8clsLUhbZaE3bY5RcaqzNgYSjr2jJR6dqy5cnt5yF//SY0cIWpUA4DpzLJX/twPpFxhS98vPpOWaZ01N1q0sOqixFEzUHQz5qrO7o3XqTQiTJpRqMmKTDtc3Ax5KfD2YRSzTQB06eJMoSiWJHkjRTbCKNhILJR0nST70m9UgqDWLXlqR18dBQsUPztmkkk8VKbfUYIZce40w4+orW5MpiDu+JNw0Lib8UKDfJVpVz2XJVcskhNabokZ+PdMxyxXZG56ZTgttfHgA18VZc/efrNcmiBKosRRO2C0I+ajlDNiAJJblUgxGTdDgtBvxB35yTo6+fBrwB1zpd2V9WDqKkmSITaaQUTF/7nXU4UDVLQn6GgclmQ0pREToEHDvXg0gqDfxrN+3fH+SnJEZjebkuFlklk5FeAUdJDnPoncRWr3Emkn2FBCn5UiZNEpWbxMKve3gGg8YUPqRj25l7HoJ5wQx4EOwv5IF3j8X994lhc+4GqLIUXQwcC3z+JAK34mLiyHyD9LIwkCgLrMUCzuMG53YTD0rhWAXqcYKPxHzudjrxa95QyXoV5coSmEjDqWD6UBQdmOPgdjoRk9zVkBAGkcwwz481RKIskaLXFoRep1UjdeKSaJyx2WQXJYAxfUXPyPdi8knJbXj6mBBhQscUo5KLk45tv6b3w3P/PhQSZ+lEJyte63cLRl79R82yKJKbpjvRjq7pTmafDzSf8n/EeYD9ayXyxBmQkkM0NQMPNYMz53ajoWIHGsvLAQDx2VlIyNIvsrRe6SJkFQeZeklTTAQilJLAtXEjKqfP0D39Bh8lW26BsBYL0p6dEfH8eUbhT/UgswpOmzUTR0aPka1PLl0PCXr1dSVpPYyw1JCMM9EW2yxccoQzfQwQen/haAPJGHK5N+C6OVtxvO4MWM6DS0/8hqSmejhjzfixa09wDItUSxz+PWWI5lhLpPM3VZZ0QDdl6cDXQOmfQ+sXyxMXMDgm5OZgV9UuVDdWIzk+GQNSBsDEKh/g3B43dlXtQsOmLUh+fS3Y6nP5fTjw1DWJ61/R9Qr874n/DZFH6AXxJCeh+qGbEZczGAzD4OTpk0T34JO1ut6B7mNmBckaBMPAk2zDoTenItlsD6nX7XFjR+UO7HDsADggKy0LmfZMMC1u/PKnwaJHyjkAHVJTBScTNYPdecuXwnz1IP/fghF3bTakPPMU9l+ZrPhZ+9sr4HcAsOv4t4i76xGYTtSJHSGQhmGCJmih65D2xcDf2uJs+K/zvzh66ii6de6GHpYe+OzgZ2g824gB9gH468V/RccY+eS9UvKItknrZ106dYHpyx2In+bNX8XL0ggAODNjAi6/7UEcuEE8yTQHgEtJwsVbvkBMh46ybST2fcvZZvw85How1U7BZ8UBcHe14sy7CzEgLVOw3d0eN751fItftnyEq577RLb9js9+GOarB6keU6TQuijRm9rPN+LYI95DIULPOvXFF/Arwbun1ioTPKbNBFNdo+6dJKRLYSESBg0Kkk+LMi73PvHbTKydtv96En994z+y8r8z9mrNiXTbnbL0/PPPY/369dizZw86duyIWqHkjjw4jkNxcTHeeOMN1NbW4tprr8XixYvRu3dvfxmn04kJEyZg7dq1YFkWt99+O1566SV07tyZWDbdlKXv1wAfPCB8jcA4S634tPCKi1nMrpgNR+O51AT2eDuKsoqQ2z2X+PKbD20OqofxcLjWYUXh+6fQoaFJ+KVlGLR0tWDS+HhUnqnyf8wyLDwBOXzs8XZMa85Hl+eWhkaRhXdgeuEvLCouZoN+I3YPgbL2PeTBtLfls19Pu4fF3u5sUL2bD23GtO3TUNcUHM3b0tGCmQn3IvnxRbL1Vs/9B/44/OGgz2QtEgF4ADjNwIzH0jHl6qnI7Z4rOmD5/gpsK5JnzX+2vnsEA3T7bw1R+4kSYIHY8n9fqO6LQjJKXhYMRl06CpMyJymq0ycPgNA2ibUAHFDXHNwfsn72YNQmD7rWn/vshBlYfoP3Ofj6d9Kzb7bKdo7A/n3oyjQM6zEMGw5sEG0jMZmH9RiG/279CI8slQ9IO+0eFif7pIW0++ZDmzHtm2moa67DtT968Min8s/9peEs/udSVtWYQsLmXzYi8a7J6NzoER1jwpHQevNvZYi/+1FYXcJyeAA4E4FxD5vAtVozhNpErVWG/9yzfvZg8ocecFAe44cD0NCJQfxpTvi3Im2qxdooOMYIvE8k/eiTPUfxyOo9svdJE+kK0NzcjBEjRuDhhx+WL9zK3LlzsWjRIixZsgTl5eVISEjA0KFDcebMuaCP9957L3788Uds2rQJ69atw1dffYWHHnrIiFuQp7N4rJ/EjDPodbMD519/AulTHvTHHqm4mMWkbZNCJpiqxipM2jYJmw9tJrr05kObQ+rhWAbO5hp0FFOUAIDjEFNdi6Sfg1+uQEUJAKpPOcAtfBNCujkL78s9apMHjOfc92L3wJfVdgpE+Mr56l3w7QI8uu3REEUJ8L7cb//7ZaJ63/73KyEySsaoCcA3kS6/gYXjTLX3fn8rE3UOZxDaVnLPWujZ+u6xrqmOuP1EafW5+nr966r7opiMkpcFh2U/LsOCbxcoqrOqsQqPbnsUj257NLRNmupCFCUAqLiYxbi/mzDtHhYvDWcx7R7v3z6FtaqxCg+3lOKFv7A4aQ7+rdN8Trl1NDqw7Mdlom204NsFgjL7focT8pncAW9f57f75kObvf299f5qCNeDvnJKxxQSNh/ajH+98xjMYooSEJaYYpsPbca/Vk2CTURRArzjVFcX0OeI+BilNrmvUF+tuJjFC39hcUphrtiWZCsW/MWEJTcyYOAdYwLxSS/k86g2vpvoGCPwPpH0I9IEueFMpNtmlKXp06fj0UcfRb9+/YjKcxyHhQsX4qmnnsItt9yCyy+/HCtWrMCxY8fw8ccfAwD27duHjRs34l//+heys7Nx3XXXoaSkBKtXr8axY8cMvBsRul8DJKZDLJ4SwzJI6J0MS8FEJGRnwcN4V8acQFI532dzKubA7ZF2End73KL1KFVExLjkiHdVLjkQ1QcPREL3ICSr0kGfa/1f6d7QvENC5eXr5QTbWSyQWyCBE6nvnj5Y86zkgMVvK6lnLfVsz8kveXvErN+xUlVfJJFRitIfS9HcEpw/UapOtdfhWAZ7u3utLHu7s37rQmCdckqVaN0BfVKPZ1XTObjdm1uaMat8VlCZfRkMTphDJ1MfHnitZ/symKB7JBlTSPA9I+spsudhlNOzUjkCx7rANmk526wqoK9UX624mMWLt5FN010KC3He8qWYND4e5RczfmXLyVPeay0mpC1cIGjlUhPSQen7S9KPsnokIc0SJzpfMADSLHFhTaTbZpQlpRw4cACVlZXIzT1n6rNYLMjOzsb27dsBANu3b4fVakVmZqa/TG5uLliWRXmrA7IQTU1NcLlcQf90gTUB+XNa/+B3k9a/82d7y8G7Dyy1EufAobKxEruqdkleVqoepYqIGGqVLv49CMmqdND3f85Jb0EoqVesnfnRok+/MAXT72ZEJ1IOHDgF1oPA3wnJINdHSO6TVLU42EE8MbFUXySRUQoPPHj3v+/qWqcWpJQqOfTsk8C5dn/3v++i6nRVUFmOZbD8BlbQ+hBo8eQrhSRjCgm+Z0Q6xhgVU0ypHPxyvjb5bvNqVVYZub6693zpZ+6L+p08YTx+uiAmyB1CSHkvLAR+uULYz0dNSAc175pcPzKxDIpv7gtAdCZE8c19w5pIt90qS5WtndbOW9Xb7Xb/d5WVlUhJSQn6PiYmBklJSf4yQsyaNQsWi8X/LyMjQz/B+w4H7lwBJKYFf56Y7v2873D/R9WNZKsAuXJS36tVRPhoVbp8MgrJqmbQJ0FpvWLtGJhiorJPCn7sYZKcSLW0FV8Gkj4id58A4IqTUJoYBi1drbJ9QEwe0n4sxRHXEd3rjEbU9nV++/gQsz4EWjyF0KN9fXWQKOt6pLnRKofcWFd//DDR9fjWG7m2lHrm/CP/YuMjX3kXu6aaNDJa+oLUb/MvS8Pi+wYg1RK81ZZqicPi+wYg/7I0kV8aQ0SVpaKiIjAMI/nvp59+iqSIgkydOhV1dXX+f0eOCA9Equk7HJj4A1CwDrj9Te9/J34fpCgBQHI82SpArpzU93opIloHIp+MYrKqHfTlUFIvyfMgKbMvg4EnOUl0wJJqK379pH1E7j5fH9Z6nyI5ws7+434iZVRIHlIZpchIDF6w6FFntKKmr/Pbh1+f0q1DPdrXVweJsq53TDE1csiNdea084mux7fekLSl2DPn52bTOieoyQeopS/I/Tb/sjT8e8oQvDP2arx0d3+8M/Zq/HvKkLArSkCEg1JOnjwZo0aNkizTs2dPVXWnpnpzxjgcDqSlnWtYh8OB/v37+8tUVQWbpltaWuB0Ov2/FyI2NhaxsbGq5CKGNQE9/iBZZEDKANjj7ahqrBLcL2bAwB5v9x/fVFuP90VFyGkgpxkovSEGOy72uR2Lw7EMSm8wYdKH7pBAZFIDEf8epGStuJjFt71ZXFNtxXOXPIYnf5qP7cm18IiM+fwTe0Iw8O797+jNoM8RDrZTXovOvgwmSNbU+FTZdpaT33+/nVPR7anJOD5xEnFbiT1ruesFInafYL0nodL/NBnVs+YIBhVNyM2B/YMPVfVFJTIKwYLFXRfdpWudkYJlWHAcp/pZib0/d110F5b/sDxkK86H1/rAIKVTChiGARqrIPROk44pJAQ+I7Exps5iQt8Z8w0NG0AihzPg5CMfX5tcnns3DqQuUxx1n7SvBo5vM/s8jo4poZHz9ZgTlAYQVvOuKelHJpbRHB5ADyJqWUpOTsYll1wi+a9jR/k4KkL06NEDqamp2LJli/8zl8uF8vJyDBrkjWUzaNAg1NbWYufOc3vIW7duhcfjQXZ2trabCwMm1uQ/As0gdJAEgClZU2Rjo0jV44O/+px+jwnj/x6Dvn8ZLfm7QHkqLmbhfPqBEIdnsRWx0D3I3TPHMrj9rmJ0GX4L7rirGBzLCJZjwKCgr3wcpFGXjvLXK+aHwoAhamcS+X33ax2aL+gc7jQDC/4SvOqXetYkzzYQ/n2CZYNkCvS/CswGr6UvKpWRT8GlBSHxlkjkUXs9IbTWye+TpM9qX3dTyLPiyzElawo6xnTE1OypsnVOzZ6qy5hCAv8ZBY8xJky/x4TGdxbAOjRf87XUyyFtaQtsk5gOHRVbZYSuL0bg+Ga7+WYkZIcG89VrTuD7Wga+63yUvr9696Nw0WbiLB0+fBhOpxOffvop5s2bh6+//hoA0KtXL39MpEsuuQSzZs3CbbfdBgCYM2cOZs+ejdLSUvTo0QNPP/00vvvuO+zduxdxcd590BtvvBEOhwNLlizB2bNnMXr0aGRmZuLtt98mlk23OEsqEYpvkRqfiilZUzTFWfLVc2OPG0PiwgTWL/Q7vtUmsLwvENmefVuxsmo9vkmuETRtS90D6T3LlROLs2TtaEXxNcWi90cioxSk8vODtm1PrsXsnXMVP2uh61k7WsExXNC9Sz03Pe+L9LdSqI2z5JMHCI2zZI21guM4wfABUkjVyS+n5n2S+53QdcXeB1+cpUCssVYUDyr2l9VrTCEhnNdSKgf/fSB5P/SKs8RHSZtEok0FxxiB9ykSz1aKdheUctSoUSgtDT3q/cUXX2Dw4MEAAIZhsGzZMv/Wni8o5euvv47a2lpcd911ePXVV3HRRRf5f+90OjF+/PigoJSLFi2KTFBKDWiJmkxSj9KIw2IRvKWulxSXpC6Ct4JriMkuFMFb6P4cDQ7UNNXAFmuDPSE0GrgebR3O3wFQ9dyMkI//22iJ4M1xHKoaq/D9ie/BcRy6W7rjjl534AfnD7J1+n7vPOPU9D6R/E7J+/Ct41tUHK8AGGCgfSAGpg4MKavXmEJCOK+lRA7++0D6fmiO4K1iPJS7l3C0qdII3tFAu1OWoploUJYoFAqFQqEoo91F8KZQKBQKhUKJBFRZolAoFAqFQpGAKksUCoVCoVAoElBliUKhUCgUCkUCqixRKBQKhUKhSECVJQqFQqFQKBQJqLJEoVAoFAqFIgFVligUCoVCoVAkoMoShUKhUCgUigQxkRagPeALgu5yuSIsCYVCoVAoFFJ887ZcMhOqLOlAfX09ACAjIyPCklAoFAqFQlFKfX09LBaL6Pc0N5wOeDweHDt2DGazGQzDRFqciOJyuZCRkYEjR47QPHkGQts5fNC2Dg+0ncMDbedgOI5DfX090tPTwbLinknUsqQDLMvivPPOi7QYUUViYiJ9EcMAbefwQds6Ao4fjwAACWpJREFUPNB2Dg+0nc8hZVHyQR28KRQKhUKhUCSgyhKFQqFQKBSKBFRZouhKbGwsiouLERsbG2lR2jW0ncMHbevwQNs5PNB2Vgd18KZQKBQKhUKRgFqWKBQKhUKhUCSgyhKFQqFQKBSKBFRZolAoFAqFQpGAKksUCoVCoVAoElBliRIWmpqa0L9/fzAMgz179kRanHbFwYMH8cADD6BHjx7o1KkTLrzwQhQXF6O5uTnSorV5XnnlFVxwwQWIi4tDdnY2KioqIi1Su2LWrFkYOHAgzGYzUlJScOutt+Lnn3+OtFjtntmzZ4NhGEycODHSorQZqLJECQuPP/440tPTIy1Gu+Snn36Cx+PBa6+9hh9//BEvvvgilixZgieeeCLSorVp3n33XUyaNAnFxcXYtWsXrrjiCgwdOhRVVVWRFq3d8OWXX2LcuHH4z3/+g02bNuHs2bPIy8tDQ0NDpEVrt+zYsQOvvfYaLr/88kiL0qagoQMohvPZZ59h0qRJ+OCDD3DppZdi9+7d6N+/f6TFatfMmzcPixcvxm+//RZpUdos2dnZGDhwIF5++WUA3hyQGRkZmDBhAoqKiiIsXfukuroaKSkp+PLLL/HHP/4x0uK0O06dOoUBAwbg1VdfxXPPPYf+/ftj4cKFkRarTUAtSxRDcTgcGDt2LFauXIn4+PhIi/O7oa6uDklJSZEWo83S3NyMnTt3Ijc31/8Zy7LIzc3F9u3bIyhZ+6aurg4AaN81iHHjxuGmm24K6tcUMmgiXYphcByHUaNGobCwEJmZmTh48GCkRfpdsH//fpSUlGD+/PmRFqXNcuLECbjdbtjt9qDP7XY7fvrppwhJ1b7xeDyYOHEirr32Wlx22WWRFqfdsXr1auzatQs7duyItChtEmpZoiimqKgIDMNI/vvpp59QUlKC+vp6TJ06NdIit0lI2zmQo0ePIj8/HyNGjMDYsWMjJDmFopxx48bhhx9+wOrVqyMtSrvjyJEjeOSRR/DWW28hLi4u0uK0SajPEkUx1dXVOHnypGSZnj174s4778TatWvBMIz/c7fbDZPJhHvvvRelpaVGi9qmIW3njh07AgCOHTuGwYMH4+qrr8by5cvBsnQtpJbm5mbEx8djzZo1uPXWW/2fFxQUoLa2Fp988knkhGuHjB8/Hp988gm++uor9OjRI9LitDs+/vhj3HbbbTCZTP7P3G43GIYBy7JoamoK+o4SClWWKIZx+PBhuFwu/9/Hjh3D0KFDsWbNGmRnZ+O8886LoHTti6NHj+L666/HVVddhVWrVtGBTweys7ORlZWFkpISAN5tovPPPx/jx4+nDt46wXEcJkyYgI8++gjbtm1D7969Iy1Su6S+vh6HDh0K+mz06NG45JJLMGXKFLrtSQD1WaIYxvnnnx/0d+fOnQEAF154IVWUdOTo0aMYPHgwunfvjvnz56O6utr/XWpqagQla9tMmjQJBQUFyMzMRFZWFhYuXIiGhgaMHj060qK1G8aNG4e3334bn3zyCcxmMyorKwEAFosFnTp1irB07Qez2RyiECUkJKBLly5UUSKEKksUShtn06ZN2L9/P/bv3x+ihFLDsXruuusuVFdX45lnnkFlZSX69++PjRs3hjh9U9SzePFiAMDgwYODPl+2bBlGjRoVfoEoFBHoNhyFQqFQKBSKBNQDlEKhUCgUCkUCqixRKBQKhUKhSECVJQqFQqFQKBQJqLJEoVAoFAqFIgFVligUCoVCoVAkoMoShUKhUCgUigRUWaJQKBQKhUKRgCpLFAqFQqFQKBJQZYlCoVB0YNu2bWAYBrW1tZEWhUKh6AyN4E2hUCg60NzcDKfTCbvdDoZhIi0OhULREaosUSgUCoVCoUhAt+EoFEq7orq6GqmpqZg5c6b/s2+++QYdO3bEli1bRH83ZcoUXHTRRYiPj0fPnj3x9NNP4+zZswC8CYlzc3MxdOhQf3Jip9OJ8847D8888wyA0G24Q4cO4eabb4bNZkNCQgIuvfRSbNiwwaC7plAoRhITaQEoFApFT5KTk7F06VLceuutyMvLw8UXX4z7778f48ePR05OjujvzGYzli9fjvT0dHz//fcYO3YszGYzHn/8cTAMg9LSUvTr1w+LFi3CI488gsLCQnTr1s2vLPEZN24cmpub8dVXXyEhIQF79+5F586djbptCoViIHQbjkKhtEvGjRuHzZs3IzMzE99//z127NiB2NhY4t/Pnz8fq1evxrfffuv/7P3338fIkSMxceJElJSUYPfu3ejduzcAr2Xp+uuvR01NDaxWKy6//HLcfvvtKC4u1v3eKBRKeKHKEoVCaZecPn0al112GY4cOYKdO3eiX79+kuXfffddLFq0CL/++itOnTqFlpYWJCYmoqqqKqjcPffcg3feeQeLFy9GYWGh/3O+svSvf/0LDz/8MLKyspCbm4vbb78dl19+uSH3SqFQjIX6LFEolHbJr7/+imPHjsHj8eDgwYOSZbdv3457770Xw4YNw7p167B79248+eSTaG5uDirX2NiInTt3wmQy4ZdffpGs88EHH8Rvv/2G+++/H99//z0yMzNRUlKi9bYoFEoEoMoShUJpdzQ3N+O+++7DXXfdhWeffRYPPvhgiIUokG+++Qbdu3fHk08+iczMTPTu3RuHDh0KKTd58mSwLIvPPvsMixYtwtatWyXlyMjIQGFhIT788ENMnjwZb7zxhuZ7o1Ao4Yc6eFMolHbHk08+ibq6OixatAidO3fGhg0bMGbMGKxbt06wfO/evXH48GGsXr0aAwcOxPr16/HRRx8FlVm/fj2WLl2K7du3Y8CAAfjnP/+JgoICfPfdd7DZbCF1Tpw4ETfeeCMuuugi1NTU4IsvvkCfPn0MuV8KhWIs1LJEoVDaFdu2bcPChQuxcuVKJCYmgmVZrFy5El9//TUWL14s+Jvhw4fj0Ucfxfjx49G/f3988803ePrpp/3fV1dX44EHHsC0adMwYMAAAMD06dNht9uD/JYCcbvdGDduHPr06YP8/HxcdNFFePXVV/W/YQqFYjjUwZtCoVAoFApFAmpZolAoFAqFQpGAKksUCoVCoVAoElBliUKhUCgUCkUCqixRKBQKhUKhSECVJQqFQqFQKBQJqLJEoVAoFAqFIgFVligUCoVCoVAkoMoShUKhUCgUigRUWaJQKBQKhUKRgCpLFAqFQqFQKBJQZYlCoVAoFApFgv8HcY1U5z01k5MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "\n",
        "N_0 = 50\n",
        "N_b = 50\n",
        "N_r = 1000\n",
        "np.random.seed(2)\n",
        "\n",
        "#initial at U(x, t=0)\n",
        "x_0 = np.random.uniform(low=x_min, high=x_max, size=(N_0, 1))\n",
        "t_0 = np.ones((N_0, 1), dtype=np.float64)*t_min\n",
        "\n",
        "\n",
        "# real initial at t=t_min\n",
        "\n",
        "theta = a*(x_0 + v*t_min)\n",
        "chi = b*(x_0 - v*t_min)\n",
        "\n",
        "\n",
        "r_0 = 2*a*np.exp(theta)/( (a**2 + b**2)*( ( b-np.exp(2*theta) )**2 + a**2)  ) *\\\n",
        "         (-a*np.exp(2*theta)*np.cos(chi) + (a**2 + b**2)*np.sin(chi)- b*np.exp(2*theta)*np.sin(chi))\n",
        "\n",
        "m_0 = 2*a*np.exp(theta)/( (a**2 + b**2)*( ( b-np.exp(2*theta) )**2 + a**2)  ) *\\\n",
        "         (b*np.exp(2*theta)*np.cos(chi) - (a**2 + b**2)*np.cos(chi)- a*np.exp(2*theta)*np.sin(chi))\n",
        "\n",
        "\n",
        "t_b1 = np.random.uniform(low=t_min, high=t_max, size=(N_b, 1))\n",
        "x_b1 = np.ones((N_b, 1), dtype=np.float64)*x_min\n",
        "#Dirichlet BC\n",
        "U_b1 = np.ones((N_b, 1), dtype=np.float64)*0\n",
        "\n",
        "t_b2 = np.random.uniform(low=t_min, high=t_max, size=(N_b, 1))\n",
        "x_b2 = np.ones((N_b, 1), dtype=np.float64)*x_max\n",
        "#Dirichlet BC\n",
        "U_b2 = np.ones((N_b, 1), dtype=np.float64)*0\n",
        "\n",
        "\n",
        "# uniform distribution\n",
        "x_c = np.random.uniform(low=x_min, high=x_max, size=(N_r, 1))\n",
        "t_c = np.random.uniform(low=t_min, high=t_max, size=(N_r, 1))\n",
        "\n",
        "\n",
        "# traingular distribution \n",
        "#x_c = np.random.triangular(x_min, 1, x_max, size=(N_r, 1))\n",
        "#t_c = np.random.triangular(t_min, 0, t_max, size=(N_r, 1))\n",
        "\n",
        "\n",
        "plt.scatter(x_0, r_0)\n",
        "plt.xlabel(\"x axis\")\n",
        "plt.ylabel(\"r\")\n",
        "plt.show()\n",
        "\n",
        "plt.scatter(x_0, m_0)\n",
        "plt.xlabel(\"x axis\")\n",
        "plt.ylabel(\"m\")\n",
        "plt.show()\n",
        "\n",
        "plt.scatter(x_0, np.sqrt(m_0**2 + r_0**2))\n",
        "plt.xlabel(\"x axis\")\n",
        "plt.ylabel(\"U\")\n",
        "plt.show()\n",
        "\n",
        "plt.scatter(x_b2, t_b2)\n",
        "plt.scatter(x_b1, t_b1)\n",
        "plt.scatter(x_0, t_0)\n",
        "plt.scatter(x_c, t_c)\n",
        "plt.xlabel(\"x axis\")\n",
        "plt.ylabel(\"t axis\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NyYTf3WXNh81"
      },
      "source": [
        "#Neural Network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k1So22Jgg2xh"
      },
      "source": [
        "## We consider Neural Network as our solution u(x,t)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0kOUv98Mb4D0"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "class Neural_network(nn.Module):\n",
        "  def __init__(self):\n",
        "    super(Neural_network, self).__init__()\n",
        "    self.input_layer = nn.Linear(2,40)\n",
        "\n",
        "    self.dropout = nn.Dropout(0.2)\n",
        "    self.hidden_layer1 = nn.Linear(40,40)\n",
        "    self.hidden_layer2 = nn.Linear(40,40)\n",
        "    self.hidden_layer3 = nn.Linear(40,40)\n",
        "    self.hidden_layer4 = nn.Linear(40,40)\n",
        "    self.hidden_layer5 = nn.Linear(40,40)\n",
        "    self.hidden_layer6 = nn.Linear(40,40)\n",
        "    self.hidden_layer7 = nn.Linear(40,40)\n",
        "    self.hidden_layer8 = nn.Linear(40,40)\n",
        "    self.output_layer = nn.Linear(40,2)\n",
        "\n",
        "  def forward(self, x):\n",
        "\n",
        "    CELU = torch.nn.CELU()\n",
        "    hardswish = torch.nn.Hardswish()\n",
        "    softsign = torch.nn.Softsign()\n",
        "\n",
        "    #\n",
        "    \n",
        "    x = self.input_layer(x)\n",
        "    x = torch.tanh(x)\n",
        "    #x = self.dropout(x)\n",
        "    x = torch.tanh(self.hidden_layer1(x))\n",
        "    x = torch.tanh(self.hidden_layer2(x))\n",
        "\n",
        "    \n",
        "    x = torch.tanh(self.hidden_layer3(x))\n",
        "    x = torch.tanh(self.hidden_layer4(x))\n",
        "    x = torch.tanh(self.hidden_layer5(x))\n",
        "    x = torch.tanh(self.hidden_layer6(x))\n",
        "    x = torch.tanh(self.hidden_layer7(x))\n",
        "    #x = self.dropout(x)\n",
        "    x = torch.tanh(self.hidden_layer8(x))\n",
        "    x = self.output_layer(x)\n",
        "    return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O4TtIwiutwUs"
      },
      "outputs": [],
      "source": [
        "Net = Neural_network()\n",
        "Net = Net.to(device)\n",
        "loss_fu = nn.MSELoss()\n",
        "optim = torch.optim.Adam(Net.parameters(), lr=0.001)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jXfCxPPMcu90"
      },
      "outputs": [],
      "source": [
        "#initail\n",
        "x_01 = torch.autograd.Variable( torch.from_numpy(x_0).float(), requires_grad=False).to(device)\n",
        "t_01 = torch.autograd.Variable( torch.from_numpy(t_0).float(), requires_grad=False).to(device)\n",
        "R_01 = torch.autograd.Variable( torch.from_numpy(r_0).float(), requires_grad=False).to(device)\n",
        "M_01 = torch.autograd.Variable( torch.from_numpy(m_0).float(), requires_grad=False).to(device)\n",
        "X_0 = torch.cat([x_01, t_01], axis=1)\n",
        "\n",
        "#BC1\n",
        "x_b11 = torch.autograd.Variable( torch.from_numpy(x_b1).float(), requires_grad=True).to(device)\n",
        "t_b11 = torch.autograd.Variable( torch.from_numpy(t_b1).float(), requires_grad=False).to(device)\n",
        "U_b11 = torch.autograd.Variable( torch.from_numpy(U_b1).float(), requires_grad=False).to(device)\n",
        "X_b1 = torch.cat([x_b11, t_b11], axis=1)\n",
        "\n",
        "#BC2\n",
        "x_b21 = torch.autograd.Variable( torch.from_numpy(x_b2).float(), requires_grad=True).to(device)\n",
        "t_b21 = torch.autograd.Variable( torch.from_numpy(t_b2).float(), requires_grad=False).to(device)\n",
        "U_b21 = torch.autograd.Variable( torch.from_numpy(U_b2).float(), requires_grad=False).to(device)\n",
        "X_b2 = torch.cat([x_b21, t_b21], axis=1)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iXRuDW12hMg7"
      },
      "source": [
        "# PDE Loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FIcwGBJGh7oX"
      },
      "outputs": [],
      "source": [
        "def pde(x, t, Net):\n",
        "  x_1 = torch.autograd.Variable( torch.from_numpy(x).float(), requires_grad=True).to(device)\n",
        "  t_1 = torch.autograd.Variable( torch.from_numpy(t).float(), requires_grad=True).to(device)\n",
        "  X = torch.cat([x_1, t_1], axis=1)\n",
        "  \n",
        "  rr = Net.forward(X)[:, 0:1]\n",
        "  mm = Net.forward(X)[:, 1:2]\n",
        "\n",
        "  r_x = torch.autograd.grad(rr.sum(), x_1, create_graph=True)[0]\n",
        "  r_xx = torch.autograd.grad(r_x.sum(), x_1, create_graph=True)[0]\n",
        "  r_xxx = torch.autograd.grad(r_xx.sum(), x_1, create_graph=True)[0]\n",
        "  r_t = torch.autograd.grad(rr.sum(), t_1, create_graph=True)[0]\n",
        "  r_xt = torch.autograd.grad(r_t.sum(), x_1, create_graph=True)[0]\n",
        "\n",
        "  m_x = torch.autograd.grad(mm.sum(), x_1, create_graph=True)[0]\n",
        "  m_xx = torch.autograd.grad(m_x.sum(), x_1, create_graph=True)[0]\n",
        "  m_xxx = torch.autograd.grad(m_xx.sum(), x_1, create_graph=True)[0]\n",
        "  m_t = torch.autograd.grad(mm.sum(), t_1, create_graph=True)[0]\n",
        "  m_xt = torch.autograd.grad(m_t.sum(), x_1, create_graph=True)[0]\n",
        "\n",
        "\n",
        "  pde1 = r_xt - rr - 2*(rr**2 + mm**2)*m_x\n",
        "  pde2 = m_xt - mm  + 2*(rr**2 + mm**2)*r_x\n",
        "\n",
        "\n",
        "  #H1 = integrate (r_x**2 + m_x**2) dx\n",
        "\n",
        "  def integral_approximation(f, aa):\n",
        "    return (aa.max() - aa.min() )*torch.mean(f)\n",
        "\n",
        "\n",
        "  # H1\n",
        "  H = (r_x**2 + m_x**2)\n",
        "  H1 = integral_approximation(H, x_1 )\n",
        "  H1_t = torch.autograd.grad(H1.sum(), t_1, create_graph=True)[0]\n",
        "\n",
        "  #H3\n",
        "  H3_real = (r_x**2 + m_x**2)**2 - r_xx*m_x + r_x*m_xx\n",
        "  H3_imag = -(r_x*r_xx + m_x*m_xx)\n",
        "\n",
        "  H3_real_integrate = integral_approximation(H3_real, x_1 )\n",
        "  H3_imag_integrate = integral_approximation(H3_imag, x_1 )\n",
        "\n",
        "  H3r_t = torch.autograd.grad(H3_real_integrate.sum() , t_1, create_graph=True)[0]\n",
        "  H3m_t = torch.autograd.grad(H3_imag_integrate.sum() , t_1, create_graph=True)[0]\n",
        "\n",
        "  #H5\n",
        "  H5_real = -(r_x*r_xxx + m_x*m_xxx) + 2*(r_x**2 + m_x**2)**3 + 3*(r_x**2 + m_x**2)*(r_x*m_xx -r_xx*m_x)\n",
        "  H5_imag = -(-r_xxx*m_x + r_x*m_xxx) - 3*(r_x**2 + m_x**2)*(r_x*r_xx + m_x*m_xx)\n",
        "  \n",
        "  H5_real_integrate = integral_approximation(H5_real, x_1 )\n",
        "  H5_imag_integrate = integral_approximation(H5_imag, x_1 )\n",
        "\n",
        "  H5r_t = torch.autograd.grad(H5_real_integrate.sum() , t_1, create_graph=True)[0]\n",
        "  H5m_t = torch.autograd.grad(H5_imag_integrate.sum() , t_1, create_graph=True)[0]\n",
        "\n",
        "  #H-1\n",
        "  H11_real = -rr*m_x + r_x*mm\n",
        "  H11_imag = -r_x*rr - mm*m_x\n",
        "\n",
        "  H11_real_integrate = integral_approximation(H11_real, x_1 )\n",
        "  H11_imag_integrate = integral_approximation(H11_imag, x_1 )\n",
        "\n",
        "  H11r_t = torch.autograd.grad(H11_real_integrate.sum() , t_1, create_graph=True)[0]\n",
        "  H11m_t = torch.autograd.grad(H11_imag_integrate.sum(), t_1, create_graph=True)[0]\n",
        "\n",
        "  #H-3\n",
        "  H33_real = -(rr**2 + mm**2) - (rr**2+mm**2)*(rr*m_x - r_x*mm)\n",
        "  H33_imag = -(rr**2 + mm**2)*(rr*r_x + mm*m_x)\n",
        "\n",
        "  H33_real_integrate = integral_approximation(H33_real, x_1 )\n",
        "  H33_imag_integrate = integral_approximation(H33_imag, x_1 )\n",
        "\n",
        "  H33r_t = torch.autograd.grad(H33_real_integrate.sum() , t_1, create_graph=True)[0]\n",
        "  H33m_t = torch.autograd.grad(H33_imag_integrate.sum(), t_1, create_graph=True)[0]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  return pde1, pde2, H1_t, H3r_t, H3m_t, H5r_t, H5m_t, H11r_t, H11m_t, H33r_t, H33m_t"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e9tvoW6BhJtk"
      },
      "source": [
        "#Loss "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tm3lCgfPw1oe",
        "outputId": "1e2e5421-bdf7-4400-9525-37dda72c96c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch:0, loss:0.48572227, Loss_conserve1:0.00000070,Loss_conserve3:0.00000065, Loss_pde:6.19792747\n",
            "epoch:100, loss:0.04653510, Loss_conserve1:0.06269637,Loss_conserve3:0.16709521, Loss_pde:15.68184280\n",
            "epoch:200, loss:0.02619890, Loss_conserve1:0.07207061,Loss_conserve3:0.36651081, Loss_pde:12.64720535\n",
            "epoch:300, loss:0.01890757, Loss_conserve1:0.11175935,Loss_conserve3:0.60601103, Loss_pde:11.02468681\n",
            "epoch:400, loss:0.01020516, Loss_conserve1:0.18464698,Loss_conserve3:0.75926876, Loss_pde:7.29818773\n",
            "epoch:500, loss:0.00664604, Loss_conserve1:0.22603352,Loss_conserve3:0.92837608, Loss_pde:5.10925007\n",
            "epoch:600, loss:0.00601369, Loss_conserve1:0.23866379,Loss_conserve3:0.96554530, Loss_pde:4.01295090\n",
            "epoch:700, loss:0.00600852, Loss_conserve1:0.23174432,Loss_conserve3:1.02758527, Loss_pde:4.16863632\n",
            "epoch:800, loss:0.00401931, Loss_conserve1:0.25060004,Loss_conserve3:1.06093824, Loss_pde:3.14873767\n",
            "epoch:900, loss:0.00408402, Loss_conserve1:0.24965477,Loss_conserve3:1.11938667, Loss_pde:3.02537274\n",
            "epoch:1000, loss:0.00390315, Loss_conserve1:0.27266765,Loss_conserve3:1.12944055, Loss_pde:2.65857315\n",
            "epoch:1100, loss:0.00438147, Loss_conserve1:0.28168070,Loss_conserve3:1.15353513, Loss_pde:2.45241475\n",
            "epoch:1200, loss:0.00362639, Loss_conserve1:0.26087254,Loss_conserve3:1.24086845, Loss_pde:2.55149055\n",
            "epoch:1300, loss:0.00338463, Loss_conserve1:0.28752992,Loss_conserve3:1.21958089, Loss_pde:2.28885174\n",
            "epoch:1400, loss:0.00279514, Loss_conserve1:0.28339842,Loss_conserve3:1.24970818, Loss_pde:2.12445831\n",
            "epoch:1500, loss:0.00285869, Loss_conserve1:0.28674045,Loss_conserve3:1.27870417, Loss_pde:2.10625768\n",
            "epoch:1600, loss:0.00277947, Loss_conserve1:0.27744752,Loss_conserve3:1.31178021, Loss_pde:2.13165331\n",
            "epoch:1700, loss:0.00245586, Loss_conserve1:0.29007125,Loss_conserve3:1.30291986, Loss_pde:1.95969713\n",
            "epoch:1800, loss:0.00261863, Loss_conserve1:0.30046010,Loss_conserve3:1.30026293, Loss_pde:1.84919024\n",
            "epoch:1900, loss:0.00292799, Loss_conserve1:0.28903931,Loss_conserve3:1.32917023, Loss_pde:1.87628865\n",
            "epoch:2000, loss:0.00271203, Loss_conserve1:0.27762088,Loss_conserve3:1.35383320, Loss_pde:1.98253787\n",
            "epoch:2100, loss:0.00258046, Loss_conserve1:0.30160445,Loss_conserve3:1.32309699, Loss_pde:1.84314215\n",
            "epoch:2200, loss:0.00240792, Loss_conserve1:0.30621642,Loss_conserve3:1.31829667, Loss_pde:1.71912789\n",
            "epoch:2300, loss:0.00218951, Loss_conserve1:0.29555175,Loss_conserve3:1.32806289, Loss_pde:1.73262191\n",
            "epoch:2400, loss:0.00238276, Loss_conserve1:0.29666311,Loss_conserve3:1.34497976, Loss_pde:1.63655901\n",
            "epoch:2500, loss:0.00210088, Loss_conserve1:0.29721120,Loss_conserve3:1.33807945, Loss_pde:1.67974257\n",
            "epoch:2600, loss:0.00230681, Loss_conserve1:0.29543626,Loss_conserve3:1.36606288, Loss_pde:1.60751665\n",
            "epoch:2700, loss:0.00247368, Loss_conserve1:0.28527248,Loss_conserve3:1.38806331, Loss_pde:1.81693888\n",
            "epoch:2800, loss:0.00196788, Loss_conserve1:0.29777420,Loss_conserve3:1.37085438, Loss_pde:1.63299084\n",
            "epoch:2900, loss:0.00227880, Loss_conserve1:0.30148867,Loss_conserve3:1.36845756, Loss_pde:1.70336199\n",
            "epoch:3000, loss:0.00187653, Loss_conserve1:0.29887018,Loss_conserve3:1.38423169, Loss_pde:1.48872042\n",
            "epoch:3100, loss:0.00196605, Loss_conserve1:0.29834592,Loss_conserve3:1.36629534, Loss_pde:1.57789886\n",
            "epoch:3200, loss:0.00195152, Loss_conserve1:0.30372983,Loss_conserve3:1.36098742, Loss_pde:1.49296105\n",
            "epoch:3300, loss:0.00209562, Loss_conserve1:0.29776102,Loss_conserve3:1.39691794, Loss_pde:1.50664651\n",
            "epoch:3400, loss:0.00203884, Loss_conserve1:0.29240158,Loss_conserve3:1.40478468, Loss_pde:1.55683458\n",
            "epoch:3500, loss:0.00198080, Loss_conserve1:0.29783595,Loss_conserve3:1.40836918, Loss_pde:1.50234342\n",
            "epoch:3600, loss:0.00168939, Loss_conserve1:0.30758107,Loss_conserve3:1.38662589, Loss_pde:1.43757617\n",
            "epoch:3700, loss:0.00171326, Loss_conserve1:0.30749738,Loss_conserve3:1.38968408, Loss_pde:1.46191001\n",
            "epoch:3800, loss:0.00188370, Loss_conserve1:0.30139679,Loss_conserve3:1.40495312, Loss_pde:1.53463638\n",
            "epoch:3900, loss:0.00168792, Loss_conserve1:0.30454713,Loss_conserve3:1.38045478, Loss_pde:1.43514752\n",
            "epoch:4000, loss:0.00192850, Loss_conserve1:0.30093896,Loss_conserve3:1.41727519, Loss_pde:1.43066442\n",
            "epoch:4100, loss:0.00223284, Loss_conserve1:0.30594632,Loss_conserve3:1.39946342, Loss_pde:1.58167863\n",
            "epoch:4200, loss:0.00181577, Loss_conserve1:0.32101172,Loss_conserve3:1.37932432, Loss_pde:1.26473725\n",
            "epoch:4300, loss:0.00212891, Loss_conserve1:0.31355023,Loss_conserve3:1.40630949, Loss_pde:1.38457847\n",
            "epoch:4400, loss:0.00171277, Loss_conserve1:0.30616349,Loss_conserve3:1.42347431, Loss_pde:1.37751067\n",
            "epoch:4500, loss:0.00186091, Loss_conserve1:0.29985157,Loss_conserve3:1.41917133, Loss_pde:1.45660353\n",
            "epoch:4600, loss:0.00198127, Loss_conserve1:0.30190468,Loss_conserve3:1.41558433, Loss_pde:1.52110767\n",
            "epoch:4700, loss:0.00170609, Loss_conserve1:0.32386813,Loss_conserve3:1.37692428, Loss_pde:1.24300373\n",
            "epoch:4800, loss:0.00175858, Loss_conserve1:0.32409418,Loss_conserve3:1.39109445, Loss_pde:1.20095491\n",
            "epoch:4900, loss:0.00176519, Loss_conserve1:0.31656161,Loss_conserve3:1.40837765, Loss_pde:1.21958017\n",
            "epoch:5000, loss:0.00176518, Loss_conserve1:0.32123494,Loss_conserve3:1.40120769, Loss_pde:1.19955301\n",
            "epoch:5100, loss:0.00179733, Loss_conserve1:0.30169976,Loss_conserve3:1.43979895, Loss_pde:1.41134453\n",
            "epoch:5200, loss:0.00172271, Loss_conserve1:0.31409895,Loss_conserve3:1.42805564, Loss_pde:1.22900009\n",
            "epoch:5300, loss:0.00166546, Loss_conserve1:0.32892033,Loss_conserve3:1.38827527, Loss_pde:1.20174742\n",
            "epoch:5400, loss:0.00149778, Loss_conserve1:0.31766468,Loss_conserve3:1.42271626, Loss_pde:1.29441762\n",
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            "epoch:8700, loss:0.00123563, Loss_conserve1:0.33306274,Loss_conserve3:1.45929885, Loss_pde:1.02117097\n",
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            "epoch:10700, loss:0.00152722, Loss_conserve1:0.33254966,Loss_conserve3:1.51493132, Loss_pde:1.16004515\n",
            "epoch:10800, loss:0.00120214, Loss_conserve1:0.33012596,Loss_conserve3:1.53191590, Loss_pde:1.06792140\n",
            "epoch:10900, loss:0.00130434, Loss_conserve1:0.32028344,Loss_conserve3:1.53705287, Loss_pde:1.11019230\n",
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            "epoch:11200, loss:0.00135008, Loss_conserve1:0.32191652,Loss_conserve3:1.53225875, Loss_pde:1.14620209\n",
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            "epoch:12100, loss:0.00111873, Loss_conserve1:0.34208211,Loss_conserve3:1.50284362, Loss_pde:0.93265092\n",
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            "epoch:19600, loss:0.00104804, Loss_conserve1:0.33304653,Loss_conserve3:1.58036447, Loss_pde:0.92215693\n",
            "epoch:19700, loss:0.00095236, Loss_conserve1:0.34239072,Loss_conserve3:1.57531142, Loss_pde:0.88001728\n",
            "epoch:19800, loss:0.00103808, Loss_conserve1:0.33497044,Loss_conserve3:1.58251524, Loss_pde:0.91749662\n",
            "epoch:19900, loss:0.00090702, Loss_conserve1:0.34907892,Loss_conserve3:1.55463910, Loss_pde:0.81394970\n",
            "epoch:20000, loss:0.00099622, Loss_conserve1:0.33446971,Loss_conserve3:1.56638312, Loss_pde:0.85670912\n",
            "epoch:20100, loss:0.00088461, Loss_conserve1:0.34892944,Loss_conserve3:1.56659818, Loss_pde:0.80773675\n",
            "epoch:20200, loss:0.00093996, Loss_conserve1:0.34275717,Loss_conserve3:1.57535934, Loss_pde:0.86543238\n",
            "epoch:20300, loss:0.00095724, Loss_conserve1:0.34269148,Loss_conserve3:1.57524550, Loss_pde:0.87860245\n",
            "epoch:20400, loss:0.00096559, Loss_conserve1:0.34135354,Loss_conserve3:1.57900262, Loss_pde:0.88808322\n",
            "epoch:20500, loss:0.00101274, Loss_conserve1:0.33581012,Loss_conserve3:1.58269429, Loss_pde:0.89827687\n",
            "epoch:20600, loss:0.00093967, Loss_conserve1:0.35200614,Loss_conserve3:1.55393028, Loss_pde:0.82167858\n",
            "epoch:20700, loss:0.00102600, Loss_conserve1:0.33265662,Loss_conserve3:1.56599355, Loss_pde:0.94503927\n",
            "epoch:20800, loss:0.00102085, Loss_conserve1:0.33306569,Loss_conserve3:1.57970238, Loss_pde:0.90680647\n",
            "epoch:20900, loss:0.00083592, Loss_conserve1:0.34698850,Loss_conserve3:1.56969488, Loss_pde:0.77012056\n",
            "epoch:21000, loss:0.00099035, Loss_conserve1:0.33663017,Loss_conserve3:1.56934333, Loss_pde:0.92083031\n",
            "epoch:21100, loss:0.00101352, Loss_conserve1:0.33527216,Loss_conserve3:1.58271801, Loss_pde:0.89673781\n",
            "epoch:21200, loss:0.00091812, Loss_conserve1:0.34479541,Loss_conserve3:1.57355809, Loss_pde:0.84920853\n",
            "epoch:21300, loss:0.00098819, Loss_conserve1:0.34085035,Loss_conserve3:1.56731832, Loss_pde:0.90658790\n",
            "epoch:21400, loss:0.00087190, Loss_conserve1:0.35069090,Loss_conserve3:1.54033053, Loss_pde:0.75382113\n",
            "epoch:21500, loss:0.00090248, Loss_conserve1:0.34248742,Loss_conserve3:1.54216766, Loss_pde:0.83692145\n",
            "epoch:21600, loss:0.00101732, Loss_conserve1:0.33273974,Loss_conserve3:1.57534575, Loss_pde:0.89586031\n",
            "epoch:21700, loss:0.00100245, Loss_conserve1:0.33696592,Loss_conserve3:1.58540010, Loss_pde:0.89867008\n",
            "epoch:21800, loss:0.00106167, Loss_conserve1:0.35150933,Loss_conserve3:1.55955458, Loss_pde:0.88274693\n",
            "epoch:21900, loss:0.00095363, Loss_conserve1:0.33957332,Loss_conserve3:1.56826258, Loss_pde:0.77981889\n",
            "epoch:22000, loss:0.00083095, Loss_conserve1:0.35142934,Loss_conserve3:1.54435098, Loss_pde:0.72015810\n",
            "epoch:22100, loss:0.00080795, Loss_conserve1:0.35382470,Loss_conserve3:1.55570412, Loss_pde:0.69825947\n",
            "epoch:22200, loss:0.00102208, Loss_conserve1:0.34842056,Loss_conserve3:1.56520689, Loss_pde:0.86623168\n",
            "epoch:22300, loss:0.00085847, Loss_conserve1:0.34423500,Loss_conserve3:1.54149914, Loss_pde:0.78489417\n",
            "epoch:22400, loss:0.00080020, Loss_conserve1:0.35272628,Loss_conserve3:1.55424643, Loss_pde:0.69757956\n",
            "epoch:22500, loss:0.00095578, Loss_conserve1:0.33771595,Loss_conserve3:1.58166933, Loss_pde:0.84988630\n",
            "epoch:22600, loss:0.00090388, Loss_conserve1:0.34833103,Loss_conserve3:1.57247972, Loss_pde:0.81929791\n",
            "epoch:22700, loss:0.00075889, Loss_conserve1:0.34894106,Loss_conserve3:1.57263625, Loss_pde:0.70262694\n",
            "epoch:22800, loss:0.00096429, Loss_conserve1:0.34033671,Loss_conserve3:1.58236909, Loss_pde:0.88454068\n",
            "epoch:22900, loss:0.00086337, Loss_conserve1:0.34541836,Loss_conserve3:1.56630635, Loss_pde:0.80853820\n",
            "epoch:23000, loss:0.00085437, Loss_conserve1:0.34844530,Loss_conserve3:1.57425833, Loss_pde:0.79124904\n",
            "epoch:23100, loss:0.00100575, Loss_conserve1:0.33400345,Loss_conserve3:1.57716894, Loss_pde:0.91699481\n",
            "epoch:23200, loss:0.00103344, Loss_conserve1:0.33966833,Loss_conserve3:1.57049370, Loss_pde:0.93713814\n",
            "epoch:23300, loss:0.00094657, Loss_conserve1:0.34274319,Loss_conserve3:1.58069718, Loss_pde:0.87188911\n",
            "epoch:23400, loss:0.00099841, Loss_conserve1:0.33472559,Loss_conserve3:1.58352923, Loss_pde:0.89443254\n",
            "epoch:23500, loss:0.00100532, Loss_conserve1:0.33640346,Loss_conserve3:1.58040428, Loss_pde:0.92967474\n",
            "epoch:23600, loss:0.00079430, Loss_conserve1:0.35246086,Loss_conserve3:1.54976594, Loss_pde:0.68652439\n",
            "epoch:23700, loss:0.00089944, Loss_conserve1:0.34266353,Loss_conserve3:1.54741406, Loss_pde:0.84318805\n",
            "epoch:23800, loss:0.00081911, Loss_conserve1:0.34677193,Loss_conserve3:1.54419875, Loss_pde:0.74776316\n",
            "epoch:23900, loss:0.00100005, Loss_conserve1:0.33955339,Loss_conserve3:1.57343817, Loss_pde:0.90459228\n",
            "epoch:24000, loss:0.00083919, Loss_conserve1:0.34651306,Loss_conserve3:1.58123422, Loss_pde:0.78413838\n",
            "epoch:24100, loss:0.00088365, Loss_conserve1:0.34931368,Loss_conserve3:1.56747556, Loss_pde:0.80636179\n",
            "epoch:24200, loss:0.00083357, Loss_conserve1:0.34630832,Loss_conserve3:1.57259917, Loss_pde:0.72602057\n",
            "epoch:24300, loss:0.00089159, Loss_conserve1:0.33984891,Loss_conserve3:1.55596590, Loss_pde:0.81781512\n",
            "epoch:24400, loss:0.00081682, Loss_conserve1:0.34656262,Loss_conserve3:1.54424191, Loss_pde:0.75333345\n",
            "epoch:24500, loss:0.00084862, Loss_conserve1:0.34674776,Loss_conserve3:1.54649377, Loss_pde:0.77666771\n",
            "epoch:24600, loss:0.00089136, Loss_conserve1:0.34567887,Loss_conserve3:1.57642221, Loss_pde:0.72249675\n",
            "epoch:24700, loss:0.00093629, Loss_conserve1:0.34510273,Loss_conserve3:1.58074188, Loss_pde:0.87485886\n",
            "epoch:24800, loss:0.00080878, Loss_conserve1:0.34633073,Loss_conserve3:1.58397007, Loss_pde:0.74652082\n",
            "epoch:24900, loss:0.00091108, Loss_conserve1:0.34976801,Loss_conserve3:1.56372690, Loss_pde:0.80818975\n",
            "epoch:25000, loss:0.00079810, Loss_conserve1:0.34749177,Loss_conserve3:1.54588103, Loss_pde:0.72704697\n",
            "epoch:25100, loss:0.00089120, Loss_conserve1:0.33786926,Loss_conserve3:1.57082546, Loss_pde:0.77853209\n",
            "epoch:25200, loss:0.00097033, Loss_conserve1:0.35209331,Loss_conserve3:1.56706154, Loss_pde:0.82344627\n",
            "epoch:25300, loss:0.00099320, Loss_conserve1:0.35023162,Loss_conserve3:1.56765747, Loss_pde:0.84731245\n",
            "epoch:25400, loss:0.00074498, Loss_conserve1:0.35140035,Loss_conserve3:1.57484996, Loss_pde:0.69451398\n",
            "epoch:25500, loss:0.00100081, Loss_conserve1:0.33778295,Loss_conserve3:1.57764161, Loss_pde:0.89993346\n",
            "epoch:25600, loss:0.00097121, Loss_conserve1:0.35331160,Loss_conserve3:1.56638670, Loss_pde:0.82480109\n",
            "epoch:25700, loss:0.00095824, Loss_conserve1:0.33822933,Loss_conserve3:1.58334959, Loss_pde:0.86742640\n",
            "epoch:25800, loss:0.00092118, Loss_conserve1:0.34177968,Loss_conserve3:1.58848369, Loss_pde:0.84546608\n",
            "epoch:25900, loss:0.00097199, Loss_conserve1:0.33520764,Loss_conserve3:1.58323979, Loss_pde:0.87854737\n",
            "epoch:26000, loss:0.00084809, Loss_conserve1:0.34605908,Loss_conserve3:1.58417726, Loss_pde:0.78952491\n",
            "epoch:26100, loss:0.00078105, Loss_conserve1:0.34988880,Loss_conserve3:1.54552960, Loss_pde:0.69529867\n",
            "epoch:26200, loss:0.00080847, Loss_conserve1:0.35465434,Loss_conserve3:1.55340624, Loss_pde:0.69616961\n",
            "epoch:26300, loss:0.00082535, Loss_conserve1:0.34711629,Loss_conserve3:1.58514833, Loss_pde:0.77352649\n"
          ]
        }
      ],
      "source": [
        "import time\n",
        "\n",
        "number_of_epoch =30000\n",
        "\n",
        "loss_value = []\n",
        "loss_value1 = []\n",
        "loss_value2 = []\n",
        "loss_value3 = []\n",
        "\n",
        "start_time = time.time()\n",
        "\n",
        "\n",
        "for epoch in range(number_of_epoch):\n",
        "  optim.zero_grad()       # make the gradient zero\n",
        "  \n",
        "  r_pred_0, m_pred_0  = Net.forward(X_0)[:, 0:1], Net.forward(X_0)[:, 1:2]      #forward pass\n",
        "  Loss_initial = loss_fu(r_pred_0, R_01 ) + loss_fu(m_pred_0, M_01 )    # loss of initial\n",
        "\n",
        "  r_pred_b1, m_pred_b1 = Net.forward(X_b1)[:, 0:1], Net.forward(X_b1)[:, 1:2]\n",
        "  Loss_b1 = loss_fu(r_pred_b1, U_b11) + loss_fu(m_pred_b1, U_b11)             # left boudary loss\n",
        "\n",
        "  r_pred_b2, m_pred_b2 = Net.forward(X_b2)[:, 0:1], Net.forward(X_b2)[:, 1:2]\n",
        "  Loss_b2 = loss_fu(r_pred_b2, U_b21) + loss_fu(m_pred_b2, U_b21)            #right boundary loss\n",
        "\n",
        "  r_pred_b1_x = torch.autograd.grad(r_pred_b1.sum(), x_b11, create_graph=True)[0]\n",
        "  m_pred_b1_x = torch.autograd.grad(m_pred_b1.sum(), x_b11, create_graph=True)[0]\n",
        "  \n",
        "  r_pred_b2_x = torch.autograd.grad(r_pred_b2.sum(), x_b21, create_graph=True)[0]\n",
        "  m_pred_b2_x = torch.autograd.grad(m_pred_b2.sum(), x_b21, create_graph=True)[0]\n",
        "  \n",
        "  loss_b3 = loss_fu(r_pred_b1_x, r_pred_b2_x)\n",
        "  loss_b4 = loss_fu(m_pred_b1_x, m_pred_b2_x)\n",
        "\n",
        "\n",
        "  pdee1, pdee2, H1_t, H3r_t, H3m_t, H5r_t, H5m_t, H11r_t, H11m_t, H33r_t, H33m_t= pde(x_c, t_c, Net)                # pde losss\n",
        "  Loss_pde = torch.linalg.norm(pdee1) + torch.linalg.norm(pdee2)\n",
        "  \n",
        "  Loss_conserve1 = torch.linalg.norm(H1_t)\n",
        "  Loss_conserve3 = torch.linalg.norm(H3r_t) + torch.linalg.norm(H3m_t)\n",
        "  Loss_conserve5 = torch.linalg.norm(H5r_t) + torch.linalg.norm(H5m_t)\n",
        "  Loss_conserve11 = torch.linalg.norm(H11r_t) + torch.linalg.norm(H11m_t)\n",
        "  Loss_conserve33 = torch.linalg.norm(H33r_t) + torch.linalg.norm(H33m_t)\n",
        "  \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  total_loss =  (Loss_initial +  Loss_b1 + Loss_b2)  +  0.001*( Loss_pde) +  0.00001*Loss_conserve11 #Loss_conserve5 +  + Loss_conserve33) ) (Loss_conserve1 + Loss_conserve3)\\\n",
        "  \n",
        "  total_loss.backward()    # computing gradients using backward propagation  dL/dw\n",
        "  optim.step()             # This is equivalent to : Weight_new = weight_old - learing_rate * derivative of Loss w.r.t weight\n",
        "\n",
        "  loss_value.append(total_loss.cpu().detach().numpy())\n",
        "  loss_value1.append(Loss_conserve1.cpu().detach().numpy())\n",
        "  loss_value3.append(Loss_conserve3.cpu().detach().numpy())\n",
        "  \n",
        "  loss_value2.append(Loss_pde.cpu().detach().numpy())\n",
        "  with torch.autograd.no_grad():\n",
        "    if epoch%100==0:\n",
        "      print(f'epoch:{epoch}, loss:{total_loss.item():.8f}, Loss_conserve1:{Loss_conserve1.item():.8f},Loss_conserve3:{Loss_conserve3.item():.8f}, Loss_pde:{Loss_pde.item():.8f}')\n",
        "\n",
        "print(\"total time:\",time.time() - start_time, \"seconds\")\n",
        "\n",
        "plt.plot(loss_value)\n",
        "plt.xlabel(\"Loss value\")\n",
        "plt.ylabel(\"No of iteration\")\n",
        "plt.title(\"Total loss vs iteration\")\n",
        "plt.show()\n",
        "\n",
        "plt.plot(loss_value1)\n",
        "plt.xlabel(\"Loss value\")\n",
        "plt.ylabel(\"No of iteration\")\n",
        "plt.title(\" loss of conserve 1 vs iteration\")\n",
        "plt.show()\n",
        "\n",
        "plt.plot(loss_value2)\n",
        "plt.xlabel(\"Loss value\")\n",
        "plt.ylabel(\"No of iteration\")\n",
        "plt.title(\" loss of PDE vs iteration\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vo1lOCyykMnJ"
      },
      "outputs": [],
      "source": [
        "#a = np.array(loss_value)\n",
        "#b = np.array(loss_value2)\n",
        "\n",
        "#np.savetxt('loss_value_tanh_cons.csv', a, delimiter=',')\n",
        "#np.savetxt('loss_value_pde_tanh_cons.csv', b, delimiter=',')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wpzHPftza55g"
      },
      "source": [
        "## loss curve\n",
        "## total time need to train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d30bOoskljHR"
      },
      "outputs": [],
      "source": [
        "#plot\n",
        "\n",
        "\n",
        "X = np.hstack((XX.flatten()[:, None], TT.flatten()[:, None]))\n",
        "X_tensor = torch.from_numpy(X).float().to(device)\n",
        "\n",
        "R_pred = Net.forward(X_tensor)[:, 0:1] # if use GPU\n",
        "R_pred1 = R_pred.detach().cpu().numpy().reshape(N, N)\n",
        "\n",
        "M_pred = Net.forward(X_tensor)[:, 1:2] # if use GPU\n",
        "M_pred1 = M_pred.detach().cpu().numpy().reshape(N, N)\n",
        "\n",
        "\n",
        "\n",
        "from mpl_toolkits import mplot3d\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig = plt.figure(figsize=(8,6))\n",
        "ax = plt.axes(projection='3d')\n",
        "ax.plot_surface(XX, TT, np.sqrt(R_pred1**2 + M_pred1**2), cmap=\"viridis\")\n",
        "plt.title(\"exact 3D bright conserve\")\n",
        "plt.savefig(\"exact_bright_conserve.png\", dpi='figure', transparent = True)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "fig = plt.figure(figsize=(8,6))\n",
        "ax = plt.axes(projection='3d')\n",
        "ax.plot_surface(XX, TT, np.sqrt(r**2 + m**2), cmap=\"viridis\")\n",
        "plt.title(\"datadriven 3D bright conserve\")\n",
        "plt.savefig(\"datadriven_bright_conserve.png\", dpi='figure', transparent = True)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "fig = plt.figure(figsize=(8,6))\n",
        "ax = plt.axes(projection='3d')\n",
        "ax.plot_surface(XX, TT, np.sqrt(r**2 + m**2)-np.sqrt(R_pred1**2 + M_pred1**2), cmap=\"viridis\")\n",
        "plt.title(\"error bright conserve\")\n",
        "plt.savefig(\"error_bright_conserve.png\", dpi='figure', transparent = True)\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R8lIV04laEF2"
      },
      "outputs": [],
      "source": [
        "plt.pcolor(XX, TT, np.sqrt(R_pred1**2 + M_pred1**2), cmap=\"viridis\")\n",
        "plt.title(\"predicted\")\n",
        "plt.savefig(\"predicted_density_bright_conserve.png\", dpi='figure', transparent = True)\n",
        "plt.show()\n",
        "\n",
        "plt.pcolor(XX, TT, np.sqrt(r**2 + m**2), cmap=\"viridis\")\n",
        "plt.title(\"exact\")\n",
        "plt.savefig(\"exact_density_bright_conserve.png\", dpi='figure', transparent = True)\n",
        "plt.show()\n",
        "\n",
        "error = np.sqrt(R_pred1**2 + M_pred1**2) - np.sqrt(r**2 + m**2)\n",
        "plt.pcolor(XX, TT, error, cmap=\"viridis\")\n",
        "plt.title(\"error\")\n",
        "plt.savefig(\"error_bright_conserve.png\", dpi='figure', transparent = True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8zTjnPfEiFIA"
      },
      "outputs": [],
      "source": [
        "U_predicted= np.sqrt(R_pred1**2 + M_pred1**2)\n",
        "for ml in range(0 , N, 10):\n",
        "  plt.plot(x_space, U_predicted[ml, :])\n",
        "  plt.xlabel(\"x\")\n",
        "  plt.ylabel(\"|U|\")\n",
        "  print((  (x_space[-1]- x_space[0] )/N ) * np.sum(U_predicted[ml, :]**2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hzp2ZXY3ciCd"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "for mk in range(0 , N, 10):\n",
        "  plt.plot(x_space, UU[mk, :])\n",
        "  plt.xlabel(\"x\")\n",
        "  plt.ylabel(\"U\")\n",
        "  print((  (x_space[-1]- x_space[0] )/N ) * np.sum(UU[mk, :]**2))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bZ6L4GqtbUCC"
      },
      "outputs": [],
      "source": [
        "plt.plot(x_space, UU[75,:], 'b-', linewidth = 2, label = 'Exact')       \n",
        "plt.plot(x_space, U_predicted[75,:], 'r--', linewidth = 2, label = 'Prediction')\n",
        "plt.xlabel('$x$')\n",
        "plt.ylabel('$|u(t,x)|$')    \n",
        "plt.title('$t = %.2f$' % (t_space[75]), fontsize = 10)\n",
        "plt.savefig(\"compare11_bright_conserve.png\", dpi='figure', transparent = True)\n",
        "plt.show()\n",
        "\n",
        "plt.plot(x_space, UU[35,:], 'b-', linewidth = 2, label = 'Exact')       \n",
        "plt.plot(x_space, U_predicted[35,:], 'r--', linewidth = 2, label = 'Prediction')\n",
        "plt.xlabel('$x$')\n",
        "plt.ylabel('$|u(t,x)|$')    \n",
        "plt.title('$t = %.2f$' % (t_space[35]), fontsize = 10)\n",
        "plt.savefig(\"compare22_bright_conserve.png\", dpi='figure', transparent = True)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "plt.plot(x_space, UU[15,:], 'b-', linewidth = 2, label = 'Exact')       \n",
        "plt.plot(x_space, U_predicted[15,:], 'r--', linewidth = 2, label = 'Prediction')\n",
        "plt.xlabel('$x$')\n",
        "plt.ylabel('$|u(t,x)|$')    \n",
        "plt.title('$t = %.2f$' % (t_space[15]), fontsize = 10)\n",
        "plt.savefig(\"compare33_bright_conserve.png\", dpi='figure', transparent = True)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IK0pjO4YqIz6"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits import mplot3d\n",
        "from scipy.interpolate import griddata\n",
        "import matplotlib.gridspec as gridspec\n",
        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
        "\n",
        "\n",
        "H_pred =griddata(X, U_predicted.flatten(), (XX, TT), method='cubic') # U_predicted\n",
        "Exact_h=UU\n",
        "x=x_space\n",
        "t=t_space\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.axis('off')\n",
        "    \n",
        "    ####### Row 0: h(t,x) ##################    \n",
        "gs0 = gridspec.GridSpec(1, 2)\n",
        "gs0.update(top=1-0.06, bottom=1-1/3, left=0.15, right=0.85, wspace=0)\n",
        "ax = plt.subplot(gs0[:, :])\n",
        "    \n",
        "h = ax.imshow(H_pred.T, interpolation='nearest', cmap='YlGnBu', \n",
        "                  extent=[t_min, t_max, x_min, x_max],\n",
        "                  origin='lower', aspect='auto')\n",
        "divider = make_axes_locatable(ax)\n",
        "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
        "fig.colorbar(h, cax=cax)\n",
        "    \n",
        "ax.plot(X[:,1], X[:,0], 'kx', label = 'Data (%d points)' % (X.shape[0]), markersize = 0.01, clip_on = False)\n",
        "    \n",
        "line = np.linspace(x.min(), x.max(), 2)[:,None]\n",
        "ax.plot(t[75]*np.ones((2,1)), line, 'k--', linewidth = 1)\n",
        "ax.plot(t[10]*np.ones((2,1)), line, 'k--', linewidth = 1)\n",
        "ax.plot(t[25]*np.ones((2,1)), line, 'k--', linewidth = 1)    \n",
        "    \n",
        "ax.set_xlabel('$t$')\n",
        "ax.set_ylabel('$x$')\n",
        "leg = ax.legend(frameon=False, loc = 'upper right')\n",
        "#    plt.setp(leg.get_texts(), color='w')\n",
        "ax.set_title('$|h(t,x)|$', fontsize = 10)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "gs1 = gridspec.GridSpec(1, 3)\n",
        "gs1.update(top=1-1/3, bottom=0, left=0.1, right=0.9, wspace=0.5)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "ax = plt.subplot(gs1[0, 0])\n",
        "ax.plot(x, Exact_h[75,:], 'b-', linewidth = 2, label = 'Exact')       \n",
        "ax.plot(x, H_pred[75,:], 'r--', linewidth = 2, label = 'Prediction')\n",
        "ax.set_xlabel('$x$')\n",
        "ax.set_ylabel('$|h(t,x)|$')    \n",
        "ax.set_title('$t = %.2f$' % (t[75]), fontsize = 10)\n",
        "ax.axis('square')\n",
        "ax.set_xlim([-5.1,5.1])\n",
        "ax.set_ylim([-0.1,2.1])\n",
        "    \n",
        "ax = plt.subplot(gs1[0, 1])\n",
        "ax.plot(x, Exact_h[10, :], 'b-', linewidth = 2, label = 'Exact')       \n",
        "ax.plot(x, H_pred[10, :], 'r--', linewidth = 2, label = 'Prediction')\n",
        "ax.set_xlabel('$x$')\n",
        "ax.set_ylabel('$|h(t,x)|$')\n",
        "ax.axis('square')\n",
        "ax.set_xlim([-5.1,5.1])\n",
        "ax.set_ylim([-0.1,2.1])\n",
        "ax.set_title('$t = %.2f$' % (t[10]), fontsize = 10)\n",
        "ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.8), ncol=5, frameon=False)\n",
        "    \n",
        "ax = plt.subplot(gs1[0, 2])\n",
        "ax.plot(x, Exact_h[25, :], 'b-', linewidth = 2, label = 'Exact')       \n",
        "ax.plot(x, H_pred[25, :], 'r--', linewidth = 2, label = 'Prediction')\n",
        "ax.set_xlabel('$x$')\n",
        "ax.set_ylabel('$|h(t,x)|$')\n",
        "ax.axis('square')\n",
        "ax.set_xlim([-5.1,5.1])\n",
        "ax.set_ylim([-0.1,2.1])    \n",
        "ax.set_title('$t = %.2f$' % (t[25]), fontsize = 10)\n",
        "\n",
        "plt.savefig(\"alll.png\", dpi='figure', transparent = True)\n",
        "\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}