scipy.ipynb

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      "version": "3.6.9"
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    "colab": {
      "name": "scipy.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mickeykubo/analytics_book/blob/master/scipy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "metadata": {
        "id": "b-34ql0Wvq5G",
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      "source": [
        "# 第6章 科学技術計算モジュールSciPy\n",
        "------------------\n",
        "\n",
        "SciPy(<http://www.scipy.org/>): 科学技術計算のためのパッケージ\n",
        "\n",
        "* optimize: 最適化と方程式の根\n",
        "* spatial: 計算幾何\n",
        "* stats: 確率・統計\n",
        "* interpolate: 補間\n",
        "* integrate: 積分\n",
        "* linalg: 線形代数，cluster: クラスタリング，sparse: 疎行列"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u-IHJoGWvq5H",
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      },
      "source": [
        "###  最適化\n",
        "------\n",
        "\n",
        "SciPyの最適化サブモジュールoptimize\n",
        "\n",
        "非線形最適化ならびに非線形方程式の根を求めるための解法群"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QwKbOa6Gvq5I",
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      "source": [
        "### optimizeに含まれる非線形最適化のための関数\n",
        "### 1 変数用のminimize_scalar"
      ]
    },
    {
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      "metadata": {
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      "source": [
        "### 例：関数 $f(x)= x/(1+x^2)$ の最小化\n",
        "----\n",
        "\n",
        " $x=-1$ のとき最小値 $-1/2$ \n",
        "\n",
        "* グラフによる確認\n",
        "\n",
        "* Brent法\n",
        "\n",
        "* 黄金分割法\n",
        "\n",
        "* 引数boundsで範囲を与えたBrent法\n"
      ]
    },
    {
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      "metadata": {
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        "colab": {},
        "outputId": "7743b0b5-0b5d-4a6d-8eae-a2fbff317a98"
      },
      "source": [
        "%matplotlib inline\n",
        "from scipy.optimize import minimize_scalar\n",
        "import numpy as np\n",
        "from pylab import *\n",
        "def f(x):\n",
        "    return x/(1+x**2)\n",
        "\n",
        "xx=np.linspace(-10,10,100)\n",
        "yy=f(xx)\n",
        "plot(xx,yy);None"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mVXSIGGgvq5O",
        "colab_type": "code",
        "colab": {},
        "outputId": "ac0a4401-7388-4a8c-b4b5-44c15e1eb1cb"
      },
      "source": [
        "print(\"Brent=========================\")\n",
        "print(minimize_scalar(f, method='Brent'))\n",
        "\n",
        "print(\"Golden=========================\")\n",
        "print(minimize_scalar(f, method='Golden'))\n",
        "\n",
        "print(\"Bounded=========================\")\n",
        "print(minimize_scalar(f, bounds=(-2,0), method='Bounded'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Brent=========================\n",
            "     fun: -0.5\n",
            "    nfev: 10\n",
            "     nit: 9\n",
            " success: True\n",
            "       x: -0.99999999152478614\n",
            "Golden=========================\n",
            "     fun: -0.5\n",
            "    nfev: 45\n",
            "     nit: 39\n",
            " success: True\n",
            "       x: -0.9999999904634918\n",
            "Bounded=========================\n",
            "     fun: -0.49999999999995526\n",
            " message: 'Solution found.'\n",
            "    nfev: 12\n",
            "  status: 0\n",
            " success: True\n",
            "       x: -0.99999957691832075\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6u3Rk8O8vq5Q",
        "colab_type": "text"
      },
      "source": [
        "### minimize(f,x0)\n",
        "-----\n",
        "\n",
        "複数の変数をもつ関数fを初期解x0からの探索で最小化\n",
        "\n",
        "引数methodで探索のためのアルゴリズムを設定 "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RGXB4XUgvq5R",
        "colab_type": "text"
      },
      "source": [
        "### 例： **Rosenbrock関数** \n",
        "\n",
        "$$f(x_1, x_2, \\ldots, x_n) = \\sum_{i=1}^{n-1} (100(x_i^2 - x_{i+1})^2 + (1-x_i)^2)$$\n",
        "\n",
        "----\n",
        "関数，Jacovian，Hessian\n",
        "\n",
        "    sum(100.0*(x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0)\n",
        "\n",
        "x=(1,1,...,1) のとき最小 \n",
        "\n",
        "* 2次元の場合の描画\n",
        "\n",
        "* 単体法(勾配情報なし）\n",
        "\n",
        "* BFGS（準Newton法）（勾配情報のみ使う）\n",
        "\n",
        "* 信頼領域Newton共役勾配法（勾配とHessian）\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_KUBl7Rvq5S",
        "colab_type": "code",
        "colab": {},
        "outputId": "98726225-1e57-4303-bc36-c7373109efc3"
      },
      "source": [
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from matplotlib.colors import LogNorm\n",
        "ax = Axes3D(figure())\n",
        "s = .05\n",
        "X = np.arange(-2, 2.+s, s) \n",
        "Y = np.arange(-1, 3.+s, s)\n",
        "X, Y = np.meshgrid(X, Y)   \n",
        "Z = (1.-X)**2 + 100.*(Y-X*X)**2 \n",
        "ax.plot_surface(\n",
        "    X, Y, Z, rstride=1, cstride=1,  norm=LogNorm(), cmap=cm.jet, linewidth=0.2); None"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XrVncRSIR6uvr6enp0SftaN8t40guY8uIy+XKGsm1GuupPi3mAboW2VKiWEgM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\nx6JIREQUx6JIREQUx6JIREQU9x+iDcakoycfrwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x203fbe58198>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KWvxucpyvq5V",
        "colab_type": "code",
        "colab": {},
        "outputId": "b011389e-87c4-4f26-873d-57946f2a4963"
      },
      "source": [
        "#5次元の場合の比較\n",
        "\n",
        "from scipy.optimize import minimize, rosen, rosen_der, rosen_hess\n",
        "\n",
        "x0 = [2.0 for i in range(5)]\n",
        "res = minimize(rosen, x0, method='Nelder-Mead')\n",
        "print(res.x)\n",
        "\n",
        "res = minimize(rosen, x0, jac=rosen_der, method='BFGS')\n",
        "print(res.x)\n",
        "\n",
        "res = minimize(rosen, x0, jac=rosen_der, hess=rosen_hess, method='trust-ncg')\n",
        "print(res.x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 1.00000047  1.00000079  1.0000027   1.00000292  1.00000473]\n",
            "[ 0.99999999  0.99999998  0.99999995  0.99999992  0.99999985]\n",
            "[ 1.          1.          1.          0.99999999  0.99999993]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rm6GA2l9vq5X",
        "colab_type": "text"
      },
      "source": [
        "## 制約付き最適化の例\n",
        "----\n",
        "\n",
        "半径 $\\sqrt{2}$ の円内の制約の下で $x+y$ を最大化\n",
        "\n",
        "* COBYLA (線形近似法;Constrained Optimization BY Linear Approximation）で求解\n",
        "\n",
        "* SLSQP（逐次最小2乗法;Sequential Least SQuares Programming）で求解"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "hejKIbRovq5Y",
        "colab_type": "code",
        "colab": {},
        "outputId": "9c2d633b-657c-490d-a6d7-8c51364c1764"
      },
      "source": [
        "def f(x):\n",
        "    return -x[0]-x[1]\n",
        "bnds = ((0, None), (0, None))\n",
        "f = lambda x: -x[0]-x[1]\n",
        "con  = {'type': 'ineq', 'fun': lambda x:  -x[0]**2 - x[1]**2 + 2 }\n",
        "res = minimize(fun=f, x0=[2,2], method='COBYLA',constraints=con)\n",
        "print(\"COBYLA========= \\n\",res)\n",
        "res = minimize(fun=f, x0=[2,2], method='SLSQP',constraints=con)\n",
        "print(\"SLSQP========== \\n\",res)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "COBYLA========= \n",
            "      fun: -2.000000006134913\n",
            "   maxcv: 2.0000961775679116e-08\n",
            " message: 'Optimization terminated successfully.'\n",
            "    nfev: 47\n",
            "  status: 1\n",
            " success: True\n",
            "       x: array([ 1.00006218,  0.99993783])\n",
            "SLSQP========== \n",
            "      fun: -2.000000092931198\n",
            "     jac: array([-1., -1.])\n",
            " message: 'Optimization terminated successfully.'\n",
            "    nfev: 20\n",
            "     nit: 5\n",
            "    njev: 5\n",
            "  status: 0\n",
            " success: True\n",
            "       x: array([ 1.00000005,  1.00000005])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jmN41I5fvq5b",
        "colab_type": "text"
      },
      "source": [
        "## 大域的最適化\n",
        "-----\n",
        "\n",
        "* basinhopping(f, x0): Basin-hopping法(焼き鈍し法ベースのメタヒューリスティクス)\n",
        "\n",
        "  - 引数で局所探索の解法を指定：\n",
        "  \n",
        "   minimizer_kwargs={'method':'Nelder-Mead'}\n",
        "\n",
        "\n",
        "* brute(f, ranges): 力ずく法\n",
        " \n",
        "  - ranges: 変数ごとに下限と上限のタプルを入力\n",
        "  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BqZdhV-Zvq5b",
        "colab_type": "text"
      },
      "source": [
        "### 曲線へのあてはめ（最小2乗法）\n",
        "\n",
        "* curve_fit(f,x,y): 配列  $x,y$ で与えたデータに対して誤差の最小2 乗和を最小にするような関数 $f$ のパラメータを求める\n",
        "\n",
        "  返値：\n",
        "  \n",
        " - 最小2 乗和を最小にするような推定パラメータを表す配列\n",
        " \n",
        " - 共分散行列を表す2 次元配列のタプル\n",
        "\n",
        "### 例：  2 次関数 $a x^2+ bx+c$  へのあてはめ"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIXzsuEevq5c",
        "colab_type": "code",
        "colab": {},
        "outputId": "9d18d252-bade-451f-f062-45384e24c724"
      },
      "source": [
        "from scipy.optimize import curve_fit\n",
        "def f(x, a, b, c):\n",
        "    return a*x**2 + b*x+ c\n",
        "    \n",
        "xdata = np.linspace(-10, 10, 20)\n",
        "ydata = f(xdata, 1, 1, 1) + np.random.normal(size=len(xdata))\n",
        "param, cov = curve_fit(f, xdata, ydata)\n",
        "print( param )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 0.9983613   0.96478899  1.17484689]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svUa15o7vq5f",
        "colab_type": "code",
        "colab": {},
        "outputId": "40fc6d56-a201-4719-ed2e-235833930378"
      },
      "source": [
        "plot(xdata,ydata,\"ro\")\n",
        "plot(xdata,f(xdata,param[0],param[1],param[2])); None"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7gn/d0Y+LdAwbj6WF3ou1Dw3i/bsHUFJeyaT3fuRYUZnVkZSHqKi08dDcjaxP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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x203fc891780>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXhCDZVdvq5i",
        "colab_type": "text"
      },
      "source": [
        "### 1 変数関数 $x^2 -4 \\sin x$ の根\n",
        "\n",
        "### $x=0$ だけでなく $x=1.9$ 付近でも根をもつ\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5moWzIFLvq5j",
        "colab_type": "code",
        "colab": {},
        "outputId": "410831a6-9609-471e-f10c-768e7316f407"
      },
      "source": [
        "def f(x):\n",
        "    return x**2 -4 * np.sin(x)\n",
        "\n",
        "x = np.linspace(-1, 3, 100)    \n",
        "plot(x,np.linspace(0, 0, 100) ) #x=0\n",
        "y = f(x) \n",
        "plot(x,y); None"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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O6JLPYMl0fyY9/2PYus30wZbrL91Uq+3nNLcccMVQUuRHsGxe4z/+2bGq+xWL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\nTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x203fdee8710>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPyNAMkSvq5l",
        "colab_type": "code",
        "colab": {},
        "outputId": "4c234dc6-b1e0-4031-86b5-3ae734df80fd"
      },
      "source": [
        "from scipy.optimize import root, brentq\n",
        "print(\"Brentq=\", brentq(f,1.0,3.0) )\n",
        "print(\"Root finding ======\\n \", root(f,2.0) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Brentq= 1.9337537628270214\n",
            "Root finding ======\n",
            "      fjac: array([[-1.]])\n",
            "     fun: array([ -4.44089210e-16])\n",
            " message: 'The solution converged.'\n",
            "    nfev: 7\n",
            "     qtf: array([ -1.96034300e-11])\n",
            "       r: array([-5.28766996])\n",
            "  status: 1\n",
            " success: True\n",
            "       x: array([ 1.93375376])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yHbrJ7nEvq5o",
        "colab_type": "text"
      },
      "source": [
        "# 計算幾何学\n",
        "------\n",
        "\n",
        "計算幾何とデータ構造に関するサブモジュール　spatial\n",
        "\n",
        "主なクラス\n",
        "\n",
        " * K次元木 KDTree\n",
        " \n",
        " * 凸包 ConvexHull\n",
        " \n",
        " * Delaunay 3 角形分割 Delaunay\n",
        " \n",
        " * Voronoi図 Voronoi\n",
        "\n",
        " * 距離計算のためのサブモジュール distance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "igHK2pl5vq5o",
        "colab_type": "text"
      },
      "source": [
        "    "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YiaZB01vvq5p",
        "colab_type": "code",
        "colab": {},
        "outputId": "6a87b426-c6ca-4e66-b9ca-aa29edbca295"
      },
      "source": [
        "%matplotlib inline\n",
        "from scipy.spatial import KDTree\n",
        "import numpy as np\n",
        "from pylab import *\n",
        "points = np.random.rand(1000, 2)\n",
        "tree = KDTree(points)\n",
        "ball =  tree.query_ball_point([0.5, 0.5], 0.1)\n",
        "plot(points[:,0], points[:,1], 'b+')\n",
        "plot(points[ball,0], points[ball,1], 'ro'); None"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vAHh9cqH6xqzMm/eXrm+gNBoHkXsLkkGnTLrHopVuNG3Y4BAsd0ps\nmDuGPaJoyZL2xcrDoNZrlRtx8FqvLa6X3iL3LWdVxWhI7jtZuA/AN/YE/uj+vccB3L//+/cB/CmA\nL+5/nuzJXJvcY7RLP7PYDNS4PwdbJHepTtzMhtxK1XhfnEtJu0sz33ppudaceB3wkoO1hd5htfzm\n7lOJPv7e5wl8iEmLNGxBgQcBc+pJwzIprA8cLqQ69Vzo/LnG5aZagZ6eDXeeKKQlOaSsrFtqec6Y\nahMkrPXR1OHJn+TehYd14kXuV67w/5OLtSttAe34XLlCiw1zyV2SQrvosgVQwkK1S0NqnRQWhoaF\nXGm9Se5HSO7e8UQPiyvGG88sZY6CVCdKvfSAppTXWnVWsXHr+aAYKq0DkbtOLMnUI9uoB8s7Gmkd\nKSa5d7A1EuwtTiq5a+vksPq/jwu0lllev/e+pi3rshb1evKoWSNUyz2F5GK6VL61prZyWC44ZMv9\nJpworl7d/Y7xxu8Yb7y/BVy9CoSw+1mwvG7pudTj1KnhD/+QX8cai95Ln9L+XLmye2/5bYFUVmnM\n8nnRjG9Jdqvtnh6PPfZyGSVdR+Dq1Rv7Crjxt8ea2tK+3QwoJ4DHz9qWe4yy9DVOzrFElxokVjjl\nk449WFshlNTFVj2OlWkBikdFBSfMxJVNKZuWqYWqLMMynAtcbvu9dq0ws2UOlNwXaGN73m7Y0obk\nEJLoVvsUn2WIZivhDG171v0YRe6aMpxyaXlJHFuC0WtkNKjkfrJhmRRbcelaely5wgs9PPaYXI+n\nny67008/faPMWmOmCb9QdeaEXSz0aYVOqCGfRY/eGrKCRFZpTbX01azhCUzLnYJe6l3JErH8BKwk\nhGF1WUyxMLV6WV/a1qC5HJOMXatftZCZ1HLX1BmRXFALPbVgHfIahRmWMSL30ZNHDctI4opaIqV8\nKRIXNYKyjOVz4+gj3fWljgfRboXcLetT4J1SKAlfWsJ7DE+G3LcQg+WSe36x1VvMEktHQ0o1eHkE\n3P6NtOhKdxeaS8eePpIDuieTE9feUrxaqgunnkd/J7kfEbn3vrsit34pFrqElEqEuaX83xQSEtMe\nWL16mo/rL+BcRvcMACvLnWpoxLidT9zGKJ9nzw8/1dqZH2IyInfNQEpTm7RES7GuKKRP1c07jm15\nqEr6J9k8PZ1bB6LU06CukdKzNch9bYyK/1u0IdmvFjhqcj/bUd/y0votMqhtQO33eIzcqNI89RK4\n/fOy6FryJYdLj6CpngL3Ky16sg7l8lF6wHGgkT/J/UTJvRV24FpXXt/3MRK9/lE+SFMjX+2FMYXo\nKB5WTTYFnrHvLVvuaftrHDzc/rc85VF6nwS556TQKmc18BbphL3LM2lIZc14aY+gqSGRVtnaB7Ms\nrC/qGqFYk9owXul1qQ0qtkru+UHJnQsrHaSYlrsjuUsGMV1Ao0BZBBRy2zJy0ku9FMom5fRfS+61\nTKTcM6jp6XFhx/VGOH32+BSoBtSL6hFhGS4oWWyT3Fcm95GLhhJLP0Zyz5/n6F2WpkRKKUsl3lrd\nxWMqkTynLWp5jtwtpyxK0ZrHe+7hhcB6sDrEUn1a8++JoyV3j7xlTj0tavpfuKDr1xrgEC7HGu/1\nW2u59w7S1hrRtEUt1/IYDm2N5Gj1oTQvpVx9i9CoFKV5Gj3+R0vuZzvJK6/ZHB6W0lpunQd6ljsn\nzJDLapUtvS6BGgrorZER5E45VA5xjeTI915rDDiHfw7OxXarvtRwscYkd+O6a5K71jIYYVn0yL2H\n0gdCKGVLr3uguNZ5OWlb1MvxEslRrdwtwMKapmaY9Q5/SjqoVM+SHpy1aoGTIHduKCaFxtqzDNFQ\n3tdu5BFEkGfLSMh9VNhBYk17t8X1GCzGxHJcJWPVO1g5dzItXXoE3EON0Es/1E+ja3D05K7NWNCc\n3jVZXqR/COQeI+8isVQvhafOGqOAC0k/KGEZC1jKTL0NKVJipJJ7aX0tJEsh3xpa+ewcj2qSu6iD\nPmUlMpZnVhPZWtgcYhrtdXByxfPXI8ndEqVx0H6gxcIYkbbDgfUaSw+InhdT8mRahOuVz57rYrF3\n+/qcOLlbDzAlNmtJ7q3XWnkW6MmkHIatOTqEe4YYy/3MNzsXnrpb7Yvaga1Za62xTJ/17hy4+4dz\n2Z/XA9qZbtNyJ0K6MD0GuOcySjaL1wLZArlLXWwv/azgQe6jwNWtdqeiOTB64ZPUEKDqnj/rhWI0\n5F+y3DmyJThKcj/bQZ+yEtSslt7irj3nyulhWdwW1jB1E3Pz1K3nyHPOqQdVSlJbgebSm0Ji1pY7\ndY9Q6nDa5dRtjcvMlhF1kF7W20WvLWxp6MKL6CzlWm5iK4t9VLZNip7lvjXkFje3bs+7tCb3FJbz\n27owldYddZhPch+IWraMlNypKZJU9DaeNIQkRZ7TvsXLQk07W1qbOSShmB6pWx2mHGL0MlQocnPv\nZ/R8T3JfCb0Fr9kQXG+lFS7QuudrxcY1GSce4IakNHI1sixImBKWGQVOCIcji9KPvPwkdwNyX8Pt\n1kBquVuVz+tZhYHWDO9Qy7fWxFbXSwqPQ09LRK2QzmiCo3x4iQpu6mo+DqPXkym5A7gXwLMAngPw\nSOH5KwB8Yv/8cwAu9mSuabmvmSbHeR6jzYFWIneK3Pxv73intffiKaMEiv5engfV4vTaN1s4MLXz\n2hofzZq1HhszcgdwDsDzAO4EcAuALwG4KyvzdwD8+v7vdwH4RE/umuTubWXUYvC1clRI9e7pw7Xo\nLQli1AHHbZMKbnirVcbj0Ds0b5cLy/5RD781Pd2dPDtyvxvAU8nrywAuZ2WeAnD3/u+bAfwZgNCS\nO/LrB3J4k7uX/NFyPchdG6vVHHAeJKeJ12rLcfs0OnQyGpL+SdbFMZH7AwA+lrx+EMATWZmvAnht\n8vp5ALe35Fr9D1UqRlowa7r9WrnccA0X+diMzEm2lJHKoozXFghkkrtNfWooxotvLMn9nQVy/1BW\n5msFcr+tIOshANcBXD9//ry+l0J4LPJjdH+txsk6Xq+Ftl+tTCSrtj0OvUNeixRYemGWWMtyD7uy\ndYQQ7gZwNcb4V/avLwNAjPEfJWWe2pf5oxDCzQD+K4A7YkP4pUuX4vXr15tteyGE3VY8VPmj4NGP\nLYzN1au7Hwuk/aH0zav/ln06VXiNofWchxA+H2O81Ct3E0HWfwLwhhDC60MIt2B3YfpkVuZJAH9z\n//cDAP6gRexr48qVtTU4DBzrOHmRIGW8vMZ0Ersea64LD3QtdwAIIdwH4Fexy5z5eIzxgyGEx7Fz\nD54MIfwEgN8C8GYA/w3Au2KML7Rkrmm5e2NaUXUc29gcW38mtg+q5U4idw8cM7lPTExMeMEyLDMx\nMTExcWCY5D4xMTFxhJjkPjExMXGEmOQ+MTExcYSY5D4xMTFxhFgtWyaE8H0AfyKsfjt2319zSph9\nPg3MPp8GNH2+EGO8o1doNXLXIIRwnZIKdEyYfT4NzD6fBkb0eYZlJiYmJo4Qk9wnJiYmjhCHSu4f\nXVuBFTD7fBqYfT4NuPf5IGPuExMTExNtHKrlPjExMTHRwKbJPYRwbwjh2RDCcyGERwrPXxFC+MT+\n+edCCBfHa2kLQp8/EEL4egjhyyGET4UQLqyhpyV6fU7KPRBCiCGEg8+soPQ5hPBL+7n+Wgjht0fr\naA3C2j4fQvh0COEL+/V93xp6WiGE8PEQwvdCCF+tPA8hhF/bj8eXQwhvMVWA8h891viB0z/m3vIP\nsc8/B+DW/d8Pn0Kf9+VeCeAzAJ4BcGltvQfM8xsAfAHAT+1fv3ptvQf0+aMAHt7/fReAb62tt7LP\nfxnAWwB8tfL8PgCfBBAAvA3A5yzb37Ll/hcBPBdjfCHG+H8A/HMA78jKvAPAb+7//lcAfj6EEAbq\naI1un2OMn44x/s/9y2cAvHawjtagzDMA/EMAvwzgf41UzgmUPv9tAB+OMf4AAGKM3xusozUofY4A\n/vz+71cB+M5A/cwRY/wMdv/fooZ3APhncYdnAPxkCOE1Vu1vmdx/GsC3k9cv7t8rlokx/l8APwRw\n2xDtfEDpc4r3YXfyHzK6fQ4hvBnA62KMvzdSMUdQ5vlnAPxMCOGzIYRnQgj3DtPOB5Q+XwXwnhDC\niwD+PYC/O0a11cDd7yzcbCXIASULPE/toZQ5JJD7E0J4D4BLAO5x1cgfzT6HEG4C8E8AvHeUQgNA\nmeebsQvNvB077+w/hBDeFGP87866eYHS53cD+I0Y4z/e/+/m39r3+cf+6q0CV/7asuX+IoDXJa9f\ni5e7aS+V2f9j7leh7QZtHZQ+I4TwCwAeBXB/jPF/D9LNC70+vxLAmwA8HUL4FnaxyScP/FKVurb/\nbYzxRzHGbwJ4FjuyP1RQ+vw+AP8CAGKMfwTgJ7D7DpZjBWm/S7Flcj+6f8xNQLfP+xDFR7Aj9kOP\nwwKdPscYfxhjvD3GeDHGeBG7e4b7Y4yH/D8aKWv7d7G7PEcI4XbswjTN/0u8cVD6/F8A/DwAhBB+\nFjty//5QLcfiSQB/Y5818zYAP4wxftdM+to3yp3b5vsAfAO7W/ZH9+89jt3mBnaT/y8BPAfgjwHc\nubbOA/r8+wD+FMAX9z9Prq2zd5+zsk/jwLNliPMcAPwKgK8D+Ap2/3R+db2d+3wXgM9il0nzRQC/\nuLbOyv7+DoDvAvgRdlb6+wC8H8D7kzn+8H48vmK9rucnVCcmJiaOEFsOy0xMTExMCDHJfWJiYuII\nMcl9YmJi4ggxyX1iYmLiCDHJfWJiYuIIMcl9YmJi4ggxyX1iYmLiCDHJfWJiYuII8f8BEHrOzHNF\njmgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x203fc619f60>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rSSeHXjAvq5s",
        "colab_type": "text"
      },
      "source": [
        "   "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A_UjEuLlvq5t",
        "colab_type": "text"
      },
      "source": [
        "### 凸包クラス ConvexHull\n",
        "\n",
        "------\n",
        "\n",
        "* 凸包：計算幾何学の基本となる概念；与えられたEuclid空間内の点を含む最小の凸多面体\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPWc0NkIvq5t",
        "colab_type": "text"
      },
      "source": [
        "   "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRhXYQ7vvq5u",
        "colab_type": "code",
        "colab": {},
        "outputId": "c20e2622-8823-4316-9baa-015e724b5a32"
      },
      "source": [
        "%matplotlib inline\n",
        "from scipy.spatial import ConvexHull, convex_hull_plot_2d\n",
        "import numpy as np\n",
        "points = np.random.rand(30, 2)   \n",
        "hull = ConvexHull(points)\n",
        "convex_hull_plot_2d(hull)\n",
        "print( hull.vertices )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[14 27  4 12 26 20  1 22]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Sah/GGGbNmsXq1at59tlnmTFjhtUlOVqr4W6MqRGR2cBGIBZ42RjziYg8ARQa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\n/x+MFalcSNYrIwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x203fdf8a320>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ARGhjZVxvq5x",
        "colab_type": "text"
      },
      "source": [
        "## Delaunay 3 角形分割クラス Delaunay\n",
        "\n",
        "------\n",
        "*  Delaunay3 角形分割: (2次元のときは）領域を点を通る3 角形に分割したとき，どの3角形の頂点を通る円も，他の点を含まない3 角形分割\n",
        "\n",
        "*  近い点同士を見つけるのに便利；Voronoi図と双対の関係\n",
        "\n",
        "*  応用事例：複数デポの大規模配送計画の前処理\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P8HE-f4Bvq5x",
        "colab_type": "text"
      },
      "source": [
        "   "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "np5AUZsRvq5y",
        "colab_type": "code",
        "colab": {},
        "outputId": "16a7a901-927a-4322-df44-d32d745dfb39"
      },
      "source": [
        "%matplotlib inline\n",
        "from scipy.spatial import Delaunay,delaunay_plot_2d\n",
        "import numpy as np\n",
        "np.random.seed(5)\n",
        "points = np.random.rand(8, 2)\n",
        "tri = Delaunay(points,furthest_site=False)\n",
        "fig = delaunay_plot_2d(tri)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NJGafHru3a8gJyPs6fJGI/KH1UDIkbZj37VCwqD02KTniSnEvgIRcegCRyILq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\nqLgriqL4ISruiqIofsj/A6HhtysakvZMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x203fdf8aef0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6vrAv1Ddvq51",
        "colab_type": "text"
      },
      "source": [
        "## Voronoi図クラス Voronoi\n",
        "\n",
        "------\n",
        "\n",
        "*  Voronoi図: 各点に近い空間で領域分けされた図\n",
        "\n",
        "* 点に近い領域を見つけるのに便利；Delaunay 3 角形分割の双対\n",
        "\n",
        "* 与えられた点を母点，各母点に近い空間から成る領域をVoronoi領域． Voronoi領域の境界をVoronoi境界，Voronoi境界の交点をVoronoi点\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0hOR-fErvq52",
        "colab_type": "code",
        "colab": {},
        "outputId": "1149136e-0a07-45dc-b863-f8c8afcde5e2"
      },
      "source": [
        "%matplotlib inline\n",
        "from scipy.spatial import Voronoi,voronoi_plot_2d\n",
        "import numpy as np\n",
        "points = np.array([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1],\n",
        "                   [1, 2], [2, 0], [2, 1], [2, 2]])\n",
        "vor = Voronoi(points)\n",
        "fig = voronoi_plot_2d(vor)\n",
        "print( vor.vertices )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0.5  0.5]\n",
            " [ 1.5  0.5]\n",
            " [ 0.5  1.5]\n",
            " [ 1.5  1.5]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x203fdead208>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NIWvPkXwvq54",
        "colab_type": "text"
      },
      "source": [
        "## 距離計算のためのサブモジュールdistance\n",
        "----\n",
        "\n",
        "*  euclidean(u,v): uとvの間のEuclid距離（直線距離）\n",
        "\n",
        " $\\|u-v\\|_2 = \\left(\\sum_{i} (u_i-v_i)^2 \\right)^{1/2}$\n",
        "\n",
        "*  chebyshev(u,v): uとvの間のChebyshev距離\n",
        "\n",
        " $(L_{\\infty}$ ノルム)  $\\max_{i} |u_i -v_i|$ \n",
        "\n",
        "* cityblock: uとvの間のManhattan距離（$L_{1}$ ノルム）\n",
        "\n",
        " $\\sum_{i} |u_i-v_i|$ \n",
        "\n",
        "*  minkowski(u,v,p): uとvの間のMinkowski距離（(Minkowski p-ノルム）\n",
        "\n",
        " $\\| u-v \\|_p  =\\left(\\sum_{i} |u_i-v_i|^p \\right)^{1/p}$\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eJrlDxGkvq55",
        "colab_type": "text"
      },
      "source": [
        "## 例題：最近点\n",
        "* ### 2 次元Euclid平面上にランダムに分布した10000個の点に対する最も近い点を求める\n",
        "* ### 最も近い点は，点を母点としたVoronoi図の隣接する領域の母点であることを利用\n",
        "* ### $K$-d木と比較"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7K8HXt0vq56",
        "colab_type": "code",
        "colab": {},
        "outputId": "fe68b861-e60a-4742-fa8c-22e33344c7ba"
      },
      "source": [
        "from scipy.spatial import Voronoi, KDTree\n",
        "from scipy.spatial.distance import euclidean\n",
        "import numpy as np\n",
        "\n",
        "def NN_KD(points):\n",
        "    tree = KDTree(points)\n",
        "    near={}\n",
        "    total_cost=0.0\n",
        "    for i,p in enumerate(points):\n",
        "        dis,near_list=tree.query(p,k=2)\n",
        "        near[i]=near_list[1]      \n",
        "\n",
        "def NN_Voronoi(points):\n",
        "    vor = Voronoi(points)\n",
        "    dis, near ={},{}\n",
        "    for i in range(len(points)):\n",
        "        dis[i]=np.inf\n",
        "    for (i,j) in vor.ridge_points:\n",
        "        d = euclidean(points[i],points[j])\n",
        "        if d<dis[i]:\n",
        "            dis[i]=d\n",
        "            near[i]=j\n",
        "        if d<dis[j]:\n",
        "            dis[j]=d\n",
        "            near[j]=i\n",
        "\n",
        "n = 1000\n",
        "points = np.random.rand(n, 2)\n",
        "%time NN_KD(points)\n",
        "%time NN_Voronoi(points)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Wall time: 144 ms\n",
            "Wall time: 55.1 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hmyvyff3vq59",
        "colab_type": "text"
      },
      "source": [
        "## 例題：Euclid最小木問題\n",
        "----\n",
        "* ### 点間の距離はEuclid距離\n",
        "\n",
        "* ### Euclid最小木問題の最適解に含まれる枝は，必ずVoronoi図の隣接する領域間の母点対の枝集合に含まれる\n",
        "\n",
        "* ### Voronoi図の隣接する領域間の母点対は ridge_points を用いて列挙\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOLqAsXavq5-",
        "colab_type": "code",
        "colab": {},
        "outputId": "c90f4915-b255-440c-8692-6eaf9dccf4f8"
      },
      "source": [
        "%matplotlib inline\n",
        "from scipy.spatial import Voronoi\n",
        "from scipy.spatial.distance import euclidean\n",
        "import networkx as nx\n",
        "import numpy as np\n",
        "\n",
        "def MST(points):\n",
        "    G=nx.Graph()\n",
        "    for i,p in enumerate(points):\n",
        "        for j,q in enumerate(points):\n",
        "            if i<j:\n",
        "                G.add_edge(i,j,weight=euclidean(p,q))            \n",
        "    E = nx.minimum_spanning_edges(G)\n",
        "\n",
        "def MST_Voronoi(points):\n",
        "    G=nx.Graph()\n",
        "    vor = Voronoi(points)\n",
        "    for (i,j) in vor.ridge_points:\n",
        "        G.add_edge(i,j,weight=euclidean(points[i],points[j])) \n",
        "    E = nx.minimum_spanning_edges(G)\n",
        "    return G, E\n",
        "    \n",
        "n = 200\n",
        "points = np.random.rand(n, 2)\n",
        "\n",
        "%time MST(points)\n",
        "%time MST_Voronoi(points)\n",
        "\n",
        "G,E=MST_Voronoi(points)\n",
        "pos={i: (points[i][0],points[i][1])   for i in range(n)}\n",
        "nx.draw(G, pos=pos, node_size=10, edgelist=list(E))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Wall time: 283 ms\n",
            "Wall time: 19 ms\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y60hrXBVYglWDc+fOISAgAAkJCVyHwrlXr14hNjZWmXA9AfyemgpTqVTR5ayp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\nCCGEGMH/A4YOYZ9AxkQbAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x203fa5230b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "99fgnmzxvq6D",
        "colab_type": "text"
      },
      "source": [
        "# 確率・統計\n",
        "------\n",
        "\n",
        "確率・統計に関するサブモジュール　stats\n",
        "\n",
        "* 確率分布の基礎\n",
        "* 共通のメソッド\n",
        "* 連続確率変数\n",
        "* 離散確率変数\n",
        "* データのあてはめ\n",
        "* 相関と回帰\n",
        "* 分布テスト\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7voYvA-vq6D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2b480bab-2b0d-4fb4-bd54-02e90c438620"
      },
      "source": [
        "from scipy.stats import norm\n",
        "print( norm(loc=100,scale=10).stats(moments='mvsk') )"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(array(100.), array(100.), array(0.), array(0.))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4xqYZXp4vq6G",
        "colab_type": "text"
      },
      "source": [
        "### 分布の**固定化**(freezing)\n",
        "* パラメータを固定した正規分布インスタンス n を生成\n",
        "* 固定化して分布 n に対して，分布関数メソッドcdfと密度関数メソッドpdfを用いる "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uE5MLWHxvq6G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e41fc778-d717-449f-b502-a0d28049cbd6"
      },
      "source": [
        "n = norm(loc=100,scale=10)\n",
        "print( n.cdf(80), n.pdf(80))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.022750131948179195 0.005399096651318806\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6iE6TWQHv1sl",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/colabwidgets/controls.css": {
              "data": "/* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

 /* We import all of these together in a single css file because the Webpack
loader sees only one file at a time. This allows postcss to see the variable
definitions when they are used. */

 /*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

 /*
This file is copied from the JupyterLab project to define default styling for
when the widget styling is compiled down to eliminate CSS variables. We make one
change - we comment out the font import below.
*/

 /**
 * The material design colors are adapted from google-material-color v1.2.6
 * https://github.com/danlevan/google-material-color
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/dist/palette.var.css
 *
 * The license for the material design color CSS variables is as follows (see
 * https://github.com/danlevan/google-material-color/blob/f67ca5f4028b2f1b34862f64b0ca67323f91b088/LICENSE)
 *
 * The MIT License (MIT)
 *
 * Copyright (c) 2014 Dan Le Van
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

 /*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.

Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:

* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations

Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/

 /*
 * Optional monospace font for input/output prompt.
 */

 /* Commented out in ipywidgets since we don't need it. */

 /* @import url('https://fonts.googleapis.com/css?family=Roboto+Mono'); */

 /*
 * Added for compabitility with output area
 */

 :root {

  /* Borders

  The following variables, specify the visual styling of borders in JupyterLab.
   */

  /* UI Fonts

  The UI font CSS variables are used for the typography all of the JupyterLab
  user interface elements that are not directly user generated content.
  */ /* Base font size */ /* Ensures px perfect FontAwesome icons */

  /* Use these font colors against the corresponding main layout colors.
     In a light theme, these go from dark to light.
  */

  /* Use these against the brand/accent/warn/error colors.
     These will typically go from light to darker, in both a dark and light theme
   */

  /* Content Fonts

  Content font variables are used for typography of user generated content.
  */ /* Base font size */


  /* Layout

  The following are the main layout colors use in JupyterLab. In a light
  theme these would go from light to dark.
  */

  /* Brand/accent */

  /* State colors (warn, error, success, info) */

  /* Cell specific styles */
  /* A custom blend of MD grey and blue 600
   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
  /* A custom blend of MD grey and orange 600
   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */

  /* Notebook specific styles */

  /* Console specific styles */

  /* Toolbar specific styles */
}

 /* Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

 /*
 * We assume that the CSS variables in
 * https://github.com/jupyterlab/jupyterlab/blob/master/src/default-theme/variables.css
 * have been defined.
 */

 /* This file has code derived from PhosphorJS CSS files, as noted below. The license for this PhosphorJS code is:

Copyright (c) 2014-2017, PhosphorJS Contributors
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

*/

 /*
 * The following section is derived from https://github.com/phosphorjs/phosphor/blob/23b9d075ebc5b73ab148b6ebfc20af97f85714c4/packages/widgets/style/tabbar.css 
 * We've scoped the rules so that they are consistent with exactly our code.
 */

 .jupyter-widgets.widget-tab > .p-TabBar {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
  margin: 0;
  padding: 0;
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  list-style-type: none;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='horizontal'] > .p-TabBar-content {
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
}

 .jupyter-widgets.widget-tab > .p-TabBar[data-orientation='vertical'] > .p-TabBar-content {
  -webkit-box-orient: vertical;
  -webkit-box-direction: normal;
      -ms-flex-direction: column;
          flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
  display: -webkit-box;
  display: -ms-flexbox;
  display: flex;
  -webkit-box-orient: horizontal;
  -webkit-box-direction: normal;
      -ms-flex-direction: row;
          flex-direction: row;
  -webkit-box-sizing: border-box;
          box-sizing: border-box;
  overflow: hidden;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
  -webkit-box-flex: 0;
      -ms-flex: 0 0 auto;
          flex: 0 0 auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel {
  -webkit-box-flex: 1;
      -ms-flex: 1 1 auto;
          flex: 1 1 auto;
  overflow: hidden;
  white-space: nowrap;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-hidden {
  display: none !important;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab {
  position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='horizontal'] .p-TabBar-tab {
  left: 0;
  -webkit-transition: left 150ms ease;
  transition: left 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging[data-orientation='vertical'] .p-TabBar-tab {
  top: 0;
  -webkit-transition: top 150ms ease;
  transition: top 150ms ease;
}

 .jupyter-widgets.widget-tab > .p-TabBar.p-mod-dragging .p-TabBar-tab.p-mod-dragging {
  -webkit-transition: none;
  transition: none;
}

 /* End tabbar.css */

 :root { /* margin between inline elements */

    /* From Material Design Lite */
}

 .jupyter-widgets {
    margin: 2px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    color: black;
    overflow: visible;
}

 .jupyter-widgets.jupyter-widgets-disconnected::before {
    line-height: 28px;
    height: 28px;
}

 .jp-Output-result > .jupyter-widgets {
    margin-left: 0;
    margin-right: 0;
}

 /* vbox and hbox */

 .widget-inline-hbox {
    /* Horizontal widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
    -webkit-box-align: baseline;
        -ms-flex-align: baseline;
            align-items: baseline;
}

 .widget-inline-vbox {
    /* Vertical Widgets */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widget-box {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    margin: 0;
    overflow: auto;
}

 .widget-gridbox {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    display: grid;
    margin: 0;
    overflow: auto;
}

 .widget-hbox {
    -webkit-box-orient: horizontal;
    -webkit-box-direction: normal;
        -ms-flex-direction: row;
            flex-direction: row;
}

 .widget-vbox {
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 /* General Button Styling */

 .jupyter-button {
    padding-left: 10px;
    padding-right: 10px;
    padding-top: 0px;
    padding-bottom: 0px;
    display: inline-block;
    white-space: nowrap;
    overflow: hidden;
    text-overflow: ellipsis;
    text-align: center;
    font-size: 13px;
    cursor: pointer;

    height: 28px;
    border: 0px solid;
    line-height: 28px;
    -webkit-box-shadow: none;
            box-shadow: none;

    color: rgba(0, 0, 0, .8);
    background-color: #EEEEEE;
    border-color: #E0E0E0;
    border: none;
}

 .jupyter-button i.fa {
    margin-right: 4px;
    pointer-events: none;
}

 .jupyter-button:empty:before {
    content: "\200b"; /* zero-width space */
}

 .jupyter-widgets.jupyter-button:disabled {
    opacity: 0.6;
}

 .jupyter-button i.fa.center {
    margin-right: 0;
}

 .jupyter-button:hover:enabled, .jupyter-button:focus:enabled {
    /* MD Lite 2dp shadow */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
            box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .14),
                0 3px 1px -2px rgba(0, 0, 0, .2),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .jupyter-button:active, .jupyter-button.mod-active {
    /* MD Lite 4dp shadow */
    -webkit-box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
            box-shadow: 0 4px 5px 0 rgba(0, 0, 0, .14),
                0 1px 10px 0 rgba(0, 0, 0, .12),
                0 2px 4px -1px rgba(0, 0, 0, .2);
    color: rgba(0, 0, 0, .8);
    background-color: #BDBDBD;
}

 .jupyter-button:focus:enabled {
    outline: 1px solid #64B5F6;
}

 /* Button "Primary" Styling */

 .jupyter-button.mod-primary {
    color: rgba(255, 255, 255, 1.0);
    background-color: #2196F3;
}

 .jupyter-button.mod-primary.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 .jupyter-button.mod-primary:active {
    color: rgba(255, 255, 255, 1);
    background-color: #1976D2;
}

 /* Button "Success" Styling */

 .jupyter-button.mod-success {
    color: rgba(255, 255, 255, 1.0);
    background-color: #4CAF50;
}

 .jupyter-button.mod-success.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 .jupyter-button.mod-success:active {
    color: rgba(255, 255, 255, 1);
    background-color: #388E3C;
 }

 /* Button "Info" Styling */

 .jupyter-button.mod-info {
    color: rgba(255, 255, 255, 1.0);
    background-color: #00BCD4;
}

 .jupyter-button.mod-info.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 .jupyter-button.mod-info:active {
    color: rgba(255, 255, 255, 1);
    background-color: #0097A7;
}

 /* Button "Warning" Styling */

 .jupyter-button.mod-warning {
    color: rgba(255, 255, 255, 1.0);
    background-color: #FF9800;
}

 .jupyter-button.mod-warning.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 .jupyter-button.mod-warning:active {
    color: rgba(255, 255, 255, 1);
    background-color: #F57C00;
}

 /* Button "Danger" Styling */

 .jupyter-button.mod-danger {
    color: rgba(255, 255, 255, 1.0);
    background-color: #F44336;
}

 .jupyter-button.mod-danger.mod-active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 .jupyter-button.mod-danger:active {
    color: rgba(255, 255, 255, 1);
    background-color: #D32F2F;
}

 /* Widget Button*/

 .widget-button, .widget-toggle-button {
    width: 148px;
}

 /* Widget Label Styling */

 /* Override Bootstrap label css */

 .jupyter-widgets label {
    margin-bottom: 0;
    margin-bottom: initial;
}

 .widget-label-basic {
    /* Basic Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-label {
    /* Label */
    color: black;
    font-size: 13px;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
    line-height: 28px;
}

 .widget-inline-hbox .widget-label {
    /* Horizontal Widget Label */
    color: black;
    text-align: right;
    margin-right: 8px;
    width: 80px;
    -ms-flex-negative: 0;
        flex-shrink: 0;
}

 .widget-inline-vbox .widget-label {
    /* Vertical Widget Label */
    color: black;
    text-align: center;
    line-height: 28px;
}

 /* Widget Readout Styling */

 .widget-readout {
    color: black;
    font-size: 13px;
    height: 28px;
    line-height: 28px;
    overflow: hidden;
    white-space: nowrap;
    text-align: center;
}

 .widget-readout.overflow {
    /* Overflowing Readout */

    /* From Material Design Lite
        shadow-key-umbra-opacity: 0.2;
        shadow-key-penumbra-opacity: 0.14;
        shadow-ambient-shadow-opacity: 0.12;
     */
    -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                        0 3px 1px -2px rgba(0, 0, 0, .14),
                        0 1px 5px 0 rgba(0, 0, 0, .12);

    box-shadow: 0 2px 2px 0 rgba(0, 0, 0, .2),
                0 3px 1px -2px rgba(0, 0, 0, .14),
                0 1px 5px 0 rgba(0, 0, 0, .12);
}

 .widget-inline-hbox .widget-readout {
    /* Horizontal Readout */
    text-align: center;
    max-width: 148px;
    min-width: 72px;
    margin-left: 4px;
}

 .widget-inline-vbox .widget-readout {
    /* Vertical Readout */
    margin-top: 4px;
    /* as wide as the widget */
    width: inherit;
}

 /* Widget Checkbox Styling */

 .widget-checkbox {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-checkbox input[type="checkbox"] {
    margin: 0px 8px 0px 0px;
    line-height: 28px;
    font-size: large;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -ms-flex-item-align: center;
        align-self: center;
}

 /* Widget Valid Styling */

 .widget-valid {
    height: 28px;
    line-height: 28px;
    width: 148px;
    font-size: 13px;
}

 .widget-valid i:before {
    line-height: 28px;
    margin-right: 4px;
    margin-left: 4px;

    /* from the fa class in FontAwesome: https://github.com/FortAwesome/Font-Awesome/blob/49100c7c3a7b58d50baa71efef11af41a66b03d3/css/font-awesome.css#L14 */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .widget-valid.mod-valid i:before {
    content: "\f00c";
    color: green;
}

 .widget-valid.mod-invalid i:before {
    content: "\f00d";
    color: red;
}

 .widget-valid.mod-valid .widget-valid-readout {
    display: none;
}

 /* Widget Text and TextArea Stying */

 .widget-textarea, .widget-text {
    width: 300px;
}

 .widget-text input[type="text"], .widget-text input[type="number"]{
    height: 28px;
    line-height: 28px;
}

 .widget-text input[type="text"]:disabled, .widget-text input[type="number"]:disabled, .widget-textarea textarea:disabled {
    opacity: 0.6;
}

 .widget-text input[type="text"], .widget-text input[type="number"], .widget-textarea textarea {
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    outline: none !important;
}

 .widget-textarea textarea {
    height: inherit;
    width: inherit;
}

 .widget-text input:focus, .widget-textarea textarea:focus {
    border-color: #64B5F6;
}

 /* Widget Slider */

 .widget-slider .ui-slider {
    /* Slider Track */
    border: 1px solid #BDBDBD;
    background: #BDBDBD;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    position: relative;
    border-radius: 0px;
}

 .widget-slider .ui-slider .ui-slider-handle {
    /* Slider Handle */
    outline: none !important; /* focused slider handles are colored - see below */
    position: absolute;
    background-color: white;
    border: 1px solid #9E9E9E;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    z-index: 1;
    background-image: none; /* Override jquery-ui */
}

 /* Override jquery-ui */

 .widget-slider .ui-slider .ui-slider-handle:hover, .widget-slider .ui-slider .ui-slider-handle:focus {
    background-color: #2196F3;
    border: 1px solid #2196F3;
}

 .widget-slider .ui-slider .ui-slider-handle:active {
    background-color: #2196F3;
    border-color: #2196F3;
    z-index: 2;
    -webkit-transform: scale(1.2);
            transform: scale(1.2);
}

 .widget-slider  .ui-slider .ui-slider-range {
    /* Interval between the two specified value of a double slider */
    position: absolute;
    background: #2196F3;
    z-index: 0;
}

 /* Shapes of Slider Handles */

 .widget-hslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-top: -7px;
    margin-left: -7px;
    border-radius: 50%;
    top: 0;
}

 .widget-vslider .ui-slider .ui-slider-handle {
    width: 16px;
    height: 16px;
    margin-bottom: -7px;
    margin-left: -7px;
    border-radius: 50%;
    left: 0;
}

 .widget-hslider .ui-slider .ui-slider-range {
    height: 8px;
    margin-top: -3px;
}

 .widget-vslider .ui-slider .ui-slider-range {
    width: 8px;
    margin-left: -3px;
}

 /* Horizontal Slider */

 .widget-hslider {
    width: 300px;
    height: 28px;
    line-height: 28px;

    /* Override the align-items baseline. This way, the description and readout
    still seem to align their baseline properly, and we don't have to have
    align-self: stretch in the .slider-container. */
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;
}

 .widgets-slider .slider-container {
    overflow: visible;
}

 .widget-hslider .slider-container {
    height: 28px;
    margin-left: 6px;
    margin-right: 6px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
}

 .widget-hslider .ui-slider {
    /* Inner, invisible slide div */
    height: 4px;
    margin-top: 12px;
    width: 100%;
}

 /* Vertical Slider */

 .widget-vbox .widget-label {
    height: 28px;
    line-height: 28px;
}

 .widget-vslider {
    /* Vertical Slider */
    height: 200px;
    width: 72px;
}

 .widget-vslider .slider-container {
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 6px;
    margin-top: 6px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .widget-vslider .ui-slider-vertical {
    /* Inner, invisible slide div */
    width: 4px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-left: auto;
    margin-right: auto;
}

 /* Widget Progress Styling */

 .progress-bar {
    -webkit-transition: none;
    transition: none;
}

 .progress-bar {
    height: 28px;
}

 .progress-bar {
    background-color: #2196F3;
}

 .progress-bar-success {
    background-color: #4CAF50;
}

 .progress-bar-info {
    background-color: #00BCD4;
}

 .progress-bar-warning {
    background-color: #FF9800;
}

 .progress-bar-danger {
    background-color: #F44336;
}

 .progress {
    background-color: #EEEEEE;
    border: none;
    -webkit-box-shadow: none;
            box-shadow: none;
}

 /* Horisontal Progress */

 .widget-hprogress {
    /* Progress Bar */
    height: 28px;
    line-height: 28px;
    width: 300px;
    -webkit-box-align: center;
        -ms-flex-align: center;
            align-items: center;

}

 .widget-hprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-top: 4px;
    margin-bottom: 4px;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    /* Override bootstrap style */
    height: auto;
    height: initial;
}

 /* Vertical Progress */

 .widget-vprogress {
    height: 200px;
    width: 72px;
}

 .widget-vprogress .progress {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    width: 20px;
    margin-left: auto;
    margin-right: auto;
    margin-bottom: 0;
}

 /* Select Widget Styling */

 .widget-dropdown {
    height: 28px;
    width: 300px;
    line-height: 28px;
}

 .widget-dropdown > select {
    padding-right: 20px;
    border: 1px solid #9E9E9E;
    border-radius: 0;
    height: inherit;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    outline: none !important;
    -webkit-box-shadow: none;
            box-shadow: none;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    vertical-align: top;
    padding-left: 8px;
	appearance: none;
	-webkit-appearance: none;
	-moz-appearance: none;
    background-repeat: no-repeat;
	background-size: 20px;
	background-position: right center;
    background-image: url("data:image/svg+xml;base64,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");
}

 .widget-dropdown > select:focus {
    border-color: #64B5F6;
}

 .widget-dropdown > select:disabled {
    opacity: 0.6;
}

 /* To disable the dotted border in Firefox around select controls.
   See http://stackoverflow.com/a/18853002 */

 .widget-dropdown > select:-moz-focusring {
    color: transparent;
    text-shadow: 0 0 0 #000;
}

 /* Select and SelectMultiple */

 .widget-select {
    width: 300px;
    line-height: 28px;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    -webkit-box-align: start;
        -ms-flex-align: start;
            align-items: flex-start;
}

 .widget-select > select {
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    -webkit-box-flex: 1;
        -ms-flex: 1 1 148px;
            flex: 1 1 148px;
    outline: none !important;
    overflow: auto;
    height: inherit;

    /* Because Firefox defines the baseline of a select as the bottom of the
    control, we align the entire control to the top and add padding to the
    select to get an approximate first line baseline alignment. */
    padding-top: 5px;
}

 .widget-select > select:focus {
    border-color: #64B5F6;
}

 .wiget-select > select > option {
    padding-left: 4px;
    line-height: 28px;
    /* line-height doesn't work on some browsers for select options */
    padding-top: calc(28px - var(--jp-widgets-font-size) / 2);
    padding-bottom: calc(28px - var(--jp-widgets-font-size) / 2);
}

 /* Toggle Buttons Styling */

 .widget-toggle-buttons {
    line-height: 28px;
}

 .widget-toggle-buttons .widget-toggle-button {
    margin-left: 2px;
    margin-right: 2px;
}

 .widget-toggle-buttons .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Radio Buttons Styling */

 .widget-radio {
    width: 300px;
    line-height: 28px;
}

 .widget-radio-box {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    margin-bottom: 8px;
}

 .widget-radio-box label {
    height: 20px;
    line-height: 20px;
    font-size: 13px;
}

 .widget-radio-box input {
    height: 20px;
    line-height: 20px;
    margin: 0 8px 0 1px;
    float: left;
}

 /* Color Picker Styling */

 .widget-colorpicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-colorpicker > .widget-colorpicker-input {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 72px;
}

 .widget-colorpicker input[type="color"] {
    width: 28px;
    height: 28px;
    padding: 0 2px; /* make the color square actually square on Chrome on OS X */
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    border-left: none;
    -webkit-box-flex: 0;
        -ms-flex-positive: 0;
            flex-grow: 0;
    -ms-flex-negative: 0;
        flex-shrink: 0;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    -ms-flex-item-align: stretch;
        align-self: stretch;
    outline: none !important;
}

 .widget-colorpicker.concise input[type="color"] {
    border-left: 1px solid #9E9E9E;
}

 .widget-colorpicker input[type="color"]:focus, .widget-colorpicker input[type="text"]:focus {
    border-color: #64B5F6;
}

 .widget-colorpicker input[type="text"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    outline: none !important;
    height: 28px;
    line-height: 28px;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    font-size: 13px;
    padding: 4px 8px;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    -ms-flex-negative: 1;
        flex-shrink: 1;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-colorpicker input[type="text"]:disabled {
    opacity: 0.6;
}

 /* Date Picker Styling */

 .widget-datepicker {
    width: 300px;
    height: 28px;
    line-height: 28px;
}

 .widget-datepicker input[type="date"] {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    min-width: 0; /* This makes it possible for the flexbox to shrink this input */
    outline: none !important;
    height: 28px;
    border: 1px solid #9E9E9E;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    font-size: 13px;
    padding: 4px 8px;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
}

 .widget-datepicker input[type="date"]:focus {
    border-color: #64B5F6;
}

 .widget-datepicker input[type="date"]:invalid {
    border-color: #FF9800;
}

 .widget-datepicker input[type="date"]:disabled {
    opacity: 0.6;
}

 /* Play Widget */

 .widget-play {
    width: 148px;
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .widget-play .jupyter-button {
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    height: auto;
}

 .widget-play .jupyter-button:disabled {
    opacity: 0.6;
}

 /* Tab Widget */

 .jupyter-widgets.widget-tab {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    /* Necessary so that a tab can be shifted down to overlay the border of the box below. */
    overflow-x: visible;
    overflow-y: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar > .p-TabBar-content {
    /* Make sure that the tab grows from bottom up */
    -webkit-box-align: end;
        -ms-flex-align: end;
            align-items: flex-end;
    min-width: 0;
    min-height: 0;
}

 .jupyter-widgets.widget-tab > .widget-tab-contents {
    width: 100%;
    -webkit-box-sizing: border-box;
            box-sizing: border-box;
    margin: 0;
    background: white;
    color: rgba(0, 0, 0, .8);
    border: 1px solid #9E9E9E;
    padding: 15px;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    overflow: auto;
}

 .jupyter-widgets.widget-tab > .p-TabBar {
    font: 13px Helvetica, Arial, sans-serif;
    min-height: 25px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {
    -webkit-box-flex: 0;
        -ms-flex: 0 1 144px;
            flex: 0 1 144px;
    min-width: 35px;
    min-height: 25px;
    line-height: 24px;
    margin-left: -1px;
    padding: 0px 10px;
    background: #EEEEEE;
    color: rgba(0, 0, 0, .5);
    border: 1px solid #9E9E9E;
    border-bottom: none;
    position: relative;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current {
    color: rgba(0, 0, 0, 1.0);
    /* We want the background to match the tab content background */
    background: white;
    min-height: 26px;
    -webkit-transform: translateY(1px);
            transform: translateY(1px);
    overflow: visible;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab.p-mod-current:before {
    position: absolute;
    top: -1px;
    left: -1px;
    content: '';
    height: 2px;
    width: calc(100% + 2px);
    background: #2196F3;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:first-child {
    margin-left: 0;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab:hover:not(.p-mod-current) {
    background: white;
    color: rgba(0, 0, 0, .8);
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon {
    margin-left: 4px;
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-mod-closable > .p-TabBar-tabCloseIcon:before {
    font-family: FontAwesome;
    content: '\f00d'; /* close */
}

 .jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabIcon,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabLabel,
.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tabCloseIcon {
    line-height: 24px;
}

 /* Accordion Widget */

 .p-Collapse {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Collapse-header {
    padding: 4px;
    cursor: pointer;
    color: rgba(0, 0, 0, .5);
    background-color: #EEEEEE;
    border: 1px solid #9E9E9E;
    padding: 10px 15px;
    font-weight: bold;
}

 .p-Collapse-header:hover {
    background-color: white;
    color: rgba(0, 0, 0, .8);
}

 .p-Collapse-open > .p-Collapse-header {
    background-color: white;
    color: rgba(0, 0, 0, 1.0);
    cursor: default;
    border-bottom: none;
}

 .p-Collapse .p-Collapse-header::before {
    content: '\f0da\00A0';  /* caret-right, non-breaking space */
    display: inline-block;
    font: normal normal normal 14px/1 FontAwesome;
    font-size: inherit;
    text-rendering: auto;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
}

 .p-Collapse-open > .p-Collapse-header::before {
    content: '\f0d7\00A0'; /* caret-down, non-breaking space */
}

 .p-Collapse-contents {
    padding: 15px;
    background-color: white;
    color: rgba(0, 0, 0, .8);
    border-left: 1px solid #9E9E9E;
    border-right: 1px solid #9E9E9E;
    border-bottom: 1px solid #9E9E9E;
    overflow: auto;
}

 .p-Accordion {
    display: -webkit-box;
    display: -ms-flexbox;
    display: flex;
    -webkit-box-orient: vertical;
    -webkit-box-direction: normal;
        -ms-flex-direction: column;
            flex-direction: column;
    -webkit-box-align: stretch;
        -ms-flex-align: stretch;
            align-items: stretch;
}

 .p-Accordion .p-Collapse {
    margin-bottom: 0;
}

 .p-Accordion .p-Collapse + .p-Collapse {
    margin-top: 4px;
}

 /* HTML widget */

 .widget-html, .widget-htmlmath {
    font-size: 13px;
}

 .widget-html > .widget-html-content, .widget-htmlmath > .widget-html-content {
    /* Fill out the area in the HTML widget */
    -ms-flex-item-align: stretch;
        align-self: stretch;
    -webkit-box-flex: 1;
        -ms-flex-positive: 1;
            flex-grow: 1;
    -ms-flex-negative: 1;
        flex-shrink: 1;
    /* Makes sure the baseline is still aligned with other elements */
    line-height: 28px;
    /* Make it possible to have absolutely-positioned elements in the html */
    position: relative;
}

/*# 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 */",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "text/css"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "b16e54c0-43e9-4e15-8eb4-636289024e14"
      },
      "source": [
        "#スライダーで平均と標準偏差を入力するウィジットを追加\n",
        "from ipywidgets import interact\n",
        "import numpy as np\n",
        "from matplotlib.pyplot import plot\n",
        "def f(a, b):\n",
        "    g = norm(loc=a,scale=b)\n",
        "    x = np.linspace(-100, 100, 100)\n",
        "    y = g.pdf(x)\n",
        "    plot(x,y);\n",
        "\n",
        "interact(f, a=(-50,50,1), b=(1,100,1))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "20e23781dc15440daf49658c2814b5ac",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=0, description='a', max=50, min=-50), IntSlider(value=50, description='b…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.f>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hKeTPjU2vq6I",
        "colab_type": "text"
      },
      "source": [
        "### ガンマ分布(形状パラメータの使用例）\n",
        "\n",
        "正のパラメータ $\\lambda ,a$ て定義される分布\n",
        "\n",
        "平均は $a/\\lambda$，分散は $a/\\lambda^2$ \n",
        "\n",
        "   - **形状** (shape)パラメータ a （分布によって名前は異なる；形状パラメータは必ず第1引数なので，名前を省略）\n",
        "   - 尺度パラメータ scale（$1/\\lambda$ に対応）\n",
        "   \n",
        "例：ガンマ分布を生成して，平均と分散を計算"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tWnwAmARvq6J",
        "colab_type": "code",
        "colab": {},
        "outputId": "f751ffc2-7b54-4804-9f5b-ba09aea028ad"
      },
      "source": [
        "from scipy.stats import gamma\n",
        "g = gamma(1,scale=10)             #a=1 (shape). 1/gamma=10 (scale)\n",
        "print( g.stats(moments='mv') )    #mean =a/gamma, variance =1/gamma^2"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(array(10.0), array(100.0))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vQWteqULvq6M",
        "colab_type": "text"
      },
      "source": [
        "## 共通メソッド\n",
        "-----------\n",
        "\n",
        "#### rvs: 確率変数にしたがう擬似乱数を生成するメソッド\n",
        "   * 引数size: 生成する乱数の数（タプルを引数としたときは配列）\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vcM0OcqXvq6M",
        "colab_type": "code",
        "colab": {},
        "outputId": "7c207891-692a-4ff6-f243-8b93c07e9d55"
      },
      "source": [
        "from scipy.stats import norm\n",
        "n = norm(loc=100,scale=10)\n",
        "print('array=', n.rvs(size=5) )\n",
        "print('matrix=', n.rvs(size=(2,3)) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "array= [ 108.73225712  108.45128881  113.66825351   89.11537976  108.10635549]\n",
            "matrix= [[  89.73824322  100.0276385   128.04029594]\n",
            " [ 123.51596258   92.09541691  104.73862244]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kJOQKGmQvq6O",
        "colab_type": "code",
        "colab": {},
        "outputId": "148438a4-860f-4e36-b081-8d57abd98f0d"
      },
      "source": [
        "from ipywidgets import interact\n",
        "def f(a, b):\n",
        "    g = norm(loc=a,scale=b)\n",
        "    x = np.linspace(-100, 100, 100)\n",
        "    y = g.pdf(x)\n",
        "    plot(x,y);\n",
        "\n",
        "interact(f, a=(-50,50,1), b=(1,100,1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "580ea6c89e72463f8bf13ab437bbc5ef",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=0, description='a', max=50, min=-50), IntSlider(value=50, description='b…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.f(a, b)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3FgGO1D7vq6Q",
        "colab_type": "code",
        "colab": {},
        "outputId": "d55d153e-5a24-46ce-c2db-a39772e9be7c"
      },
      "source": [
        "%matplotlib inline\n",
        "from pylab import *\n",
        "from scipy.stats import gumbel_r\n",
        "g = gumbel_r(loc=0,scale=1)\n",
        "print( g.stats(moments='mvsk') )\n",
        "\n",
        "x = np.linspace(-3, 5, 100)\n",
        "y = g.pdf(x)\n",
        "plot(x,y); None"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(array(0.5772156649015329), array(1.6449340668482264), array(1.1395470994046486), array(2.4))\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxKX4u3Xvq6S",
        "colab_type": "code",
        "colab": {},
        "outputId": "bc38e057-6c39-48a4-8db4-9331d6791b1a"
      },
      "source": [
        "from scipy.stats import mode\n",
        "print(g.mean(),g.median())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.577215664902 0.366512920582\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uB8oluKTvq6V",
        "colab_type": "text"
      },
      "source": [
        "### ppf: **パーセント点関数**(percent point function) （分布関数の逆関数）\n",
        "\n",
        "  - 例：平均 100，標準偏差 10 の正規分布にしたがう需要をもつ商品に対して品切れ率を 5% にするための在庫量\n",
        "\n",
        "    `from scipy.stats import norm\n",
        "    print( norm.ppf(0.95,loc=100,scale=10) )`\n",
        "    > 116.44853627\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qBYaop2vvq6V",
        "colab_type": "text"
      },
      "source": [
        "### sf: **生存関数**(survival function) （$1-F(x)$）\n",
        "\n",
        " - 例: 平均 100，標準偏差 10 の正規分布にしたがう需要をもつ商品に対して，123 個仕入れたときの品切れ確率\n",
        "\n",
        "    `from scipy.stats import norm\n",
        "    print( norm.sf(123,loc=100,scale=10) )`\n",
        "    > 0.0107241100217"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xSrLmukavq6W",
        "colab_type": "text"
      },
      "source": [
        "### expect(func) : 引数として与えられた関数funcの分布に対する期待値"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mr5DwPahvq6Y",
        "colab_type": "code",
        "colab": {},
        "outputId": "067c40b3-39b1-41aa-f72b-1872e23458e9"
      },
      "source": [
        "from scipy.stats import norm\n",
        "def f(x):\n",
        "    if x>113:\n",
        "        return 100*(x-113)\n",
        "    elif x<113:\n",
        "        return 10*(113-x)\n",
        "    \n",
        "print( norm.expect(f, loc=100, scale=10) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "180.08075830731676\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z8vK048Evq6b",
        "colab_type": "text"
      },
      "source": [
        "### 多変量正規分布\n",
        "* 平均を表す多次元ベクトル $\\mu$ \n",
        "* 共分散行列を表す尺度パラメータ $\\Sigma$ \n",
        "\n",
        "$$\n",
        "f(x) = \\frac{1}{\\sqrt{(2 \\pi)^k \\det \\Sigma}}\n",
        "       \\exp\\left( -\\frac{1}{2} (x - \\mu)^T \\Sigma^{-1} (x - \\mu) \\right)\n",
        "$$\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "owehVxD3vq6b",
        "colab_type": "code",
        "colab": {},
        "outputId": "71b01416-84d4-49de-8ff3-a164242ff220"
      },
      "source": [
        "%matplotlib inline\n",
        "from pylab import *\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from matplotlib.colors import LogNorm\n",
        "from scipy.stats import multivariate_normal\n",
        "ax = Axes3D(figure())\n",
        "s = .1\n",
        "X = np.arange(-3, 3.+s, s) \n",
        "Y = np.arange(-3, 3.+s, s)\n",
        "\n",
        "X, Y = np.meshgrid(X, Y)   \n",
        "pos = np.dstack((X, Y))\n",
        "\n",
        "#n = multivariate_normal([0.0, 0.0], [[1.0, -0.8], [-0.8, 1.0]])\n",
        "n = multivariate_normal([0.0, 0.0])\n",
        "Z =n.pdf(pos)  \n",
        "ax.plot_surface(X, Y, Z, rstride=1, cstride=1,  \n",
        "                norm=LogNorm(), cmap=cm.jet, linewidth=0.2); None\n",
        "#savefig(\"multivariate\",dpi=600)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Yi8VqRmNAe2YLaeTzeYyOjvo1lnpnHqOsKZqmiddeew0rKysYGBjAqVOnIhmh\naHVdzRxv9dVXMfLkk9B1HTGvcxRAgPDCiKfTsBQFPFwCtBGaXRQEJNNpGLkcAEDieZBPWMxLycqp\nFJZ/9COUX3gBw//u3/l/Skc2MzMzgXERslHbtr2jdEijHoughdNpkBSsqqq4du0aJiYmUCqV/O80\nuf7JZLKjesSNkjIxTd9MN1NRYseQIrBxR2K70qeVjtkoGdLHi4oU6ZRnoVBAJpOBbdtNeStGESma\npom5uTlcvXoVN998M06cONH0xrxZSPE7Dz8MThQh2rZPiGHQKVEAsD3lGg7Xo0L6zI5lwfaiQsAd\nyZA4DobjwCqXEUskYBcKAM/jXz/0IRz9t/8WvLfJVtIjpSObXC4HTdNw8eLFdfN96XS6q6m+dqFT\n0VqlFOzMzAxs20ZPT49/o1LJu7Id5sKtYjOtJUrsKFLcCO1Kn9IkRsiQdHDWS4YEUZOipml44YUX\nYBgGhoaG6i6eh9EKKZqmienpaV+Srb+/H4cOHWrqWFGspxoaJcWXP/1pFKan4eg6nNCa6JWFt5bw\nWQQAAs/D8P5WgJt6lWIxGF4qlhcECKYJ6Do4QQAcB3IiAXV0FN//jd/A//PJT1ZdJx3Z7N+/H6ur\nqzh//nxgvo92VqjmcrFVYdt2R0TfK4Gk1GsJp4fNhVuVFmwV210Wb0eR4kZ3++2YbSNES8/2JRKJ\nih2c9SAqUlQUBZlMBrlcDufOnWt56L6ZRhvbtteZDGua5o9btIJ2RIqNoJzP40d/+qfgAZjE7cJx\nwAuCO6NYKPhrdACIsgzTccCF0vdSOg1DUeDYNkSOc5/j/S78OZAAWNTjuqpCcBxMfetbWP3P/xl7\nTp1q6DVUm+8jRsOkE5Ns1nSdciNz282Ebm7y1RptaOH0at6VlWrFNGFuFNU3myIvFApd8WHtFHYU\nKXYDgiD4806tkCFBq9FssVhEJpNBqVTCkSNHwPN8y4QINKZEQ1tJhX0VoyKzWscxTROzs7MQRRE9\nPT11OV40uq5n3vMeWJoG24sSOQByMglDVWEVCi6xeQTHAYCuQ3JPBIs+D7GO8p5P6o0AYJumn3oV\nPHL11wt305N6e1FeWcE//sf/iH///e/Xvf5qoDdrWmItbDQ8MTHhjyzQm3WjIwudwGYkxWrYyLtS\nVdV13pV0+puO6pvtfN3OtlEAI8W2gUSGExMT4Hke586da4kMCZqNFIvFIsbHx6GqKgYHB7F3717o\nuo75+fmW1wTUl64MW0lV8lWMsos1fJ1s28b09DTm5uawf/9+lEolLC8vo1gsBtJS5F94bfWS4sIz\nz2Dun/4JZqkEIR6Ho2muKo3JJTc4AAAgAElEQVSqumMUsgwzJOsGuETGCwJgmuBCHaUEMs+jRP2/\nDSC+axdMr+mGRJNkpVqhAFmSkJ+YwNS3v41j999f12toFJU2a9LxTUc2Re/1hK91p1OANLYSKVZD\nLe9Kcv3DwunxeBy2baNcLtclnE6Qz+cZKW4X1Cs23MqXhAhTj4+PI5lM4vjx41BVNRJCBNzNp5EO\n2VKphPHxcRQKBQwODgbGGtphHVUJjuPg6tWrGB8fR19fX033jKj9FIFgZEo8FcMdltUiHWJRBLga\nmvWknJ799V8HvJEKQZLAaxqE3l5fxJujNkFBkgKjGbZpulFeuezLu9Ew8nnEe3uhUYLghBAB+Cla\n25ODcxzH11X9wW/9VttIsRJo1ZjwyAK51qurq5ienoau6yiVShgdHQ0MwneisWc7kGIl1Irqi8Ui\nVlZWYJomrly5Al3X/cYqEtmHhdMJstlsQzOKWw07ihTrARnLaNTyKEyGRAItm80iR29aLYI0x2wE\nesZvcHAQp0+fXreZt6uTlYBEy5lMBj09PXjd6163oXtGlKRo2zYWFxcxPj6OvXv3BiJTQnD0+itF\nOsSiaHl5GdlsFhcvXgzUz8g/snmMfOELUGZmYGgaErt2wcrlwAG+FyI4LuB2Qa8hPJjPw2uq8R6T\nZBmOrrv1SLgNOZIXifqSbwB4y4INwOE4t0kHAG+aUBcW8OPHH8fNHRiAroVKwt2O4+DChQvYu3cv\nVFXF7OysnwIkWqTkX9RapN10qujG8D5xdyFR4smTJwG43wmSgqUbq8iNYTwex+LiItbW1uruTm/W\nXPiFF17Ar/zKryCfz0MQBPze7/0e3vKWt0R7IapgR5FiPV8kUrOrlxRpy6JUKhXQA6WPFxU2Ol65\nXMb4+Diy2SyOHz9ec8Yvyo0lnK5cW1vD6Ogo4vG471xR73GiIEViSnvDDTc0bWVFWxRJkuT7VYYl\nv8bGxvzN48f//b+Di8UgaBoEy4IFgJMkf85Qjsd9IW8ydE+d0LeOAq4P6vs3LySidByIAAwAXLm8\nrlsVAHiOg+Q4iHlKOfBsqH70p3+Kk+94x6brFnUcB4IgbDguQg/Ch0UImu0g7eYAfTcVbcISb5Ik\nYffu3RW9K1VVxfz8PP7wD//Qd8WZnZ3FrbfeirNnz+K+++5bd/xWzIWTySQ+//nPY3h4GPPz87jj\njjtw3333dSRC3VGkWA/qJTGaDNPpdNWNP2pSrBbd6bqO8fFxrK6u4vjx47j55ps7uvERMsvlchgd\nHYUgCA2PmwCtN9rkcjmMjIzAtm3s378fpxrsuKwH1SS/XvrUp1CangYkKRgdUu+X5Um1AYDAcYH0\nKP2q6ajRtzaiOk8duF9ey3uMNPOQY0geCRqqCvA84DjuaEihgMv/83/i1l//9dYvRISolpYOj4sQ\nmKbpixCE7aBosqxnXKTb6dNunbseiTf6xrC/vx9f+9rX8JnPfAbxeBxveMMb8PLLL+PSpUsVSbEV\nc+ETJ074Px88eBD79u3DtWvXGClGjXpIYiNVG9pDsBYZElQb3m8WYVLUdR2Tk5NYXl7GTTfdhJMn\nT3YlCtA0DcvLyyiXyxgeHm54+J+g2bUrioLR0VHYto2TJ09C07RI09b1kPXY44/DsW04pRI4nofj\nRbyObUMCwMky4HWjAgA0DRIAoacHlmUFmmpoH0VT05AK1RGBYGqVBo+goLgoCDANA7wkwcjl8ONP\nfxpn3vc+f6B/M6DR8QBRFCuOi5B0d3hcpFYT1U41GW7FIePo0aO47bbbcNttt1V9XivmwjQuXLgA\nXdcxODjY8N82gx1FivWgWmTXKBnSx4uqbgdcJ0XDMDA5OYmrV6/i2LFjuOeee7ryxVZV1R/xSCaT\nNR242wEiFl4qlTA8POynfsrlckcVbV75H/8DyvQ04j090HI58I4DG8GbLCfUcerrmhYK4CUp0FgT\nTiFzlrWOBDkg8DcOXOFwx0vPEpFx0sjD8Txsx0F5dRXPf/CDOP+xjzV2EdqIKIiJjmrqHRdJpVIo\nlUoolUqQZbkr36FupbKb1VCu10uxFXNhgoWFBbztbW/DE0880bH3hpFiCGFSbJYMCdrxgc/n87hw\n4QKOHj2Ke++9tytf5FKphLGxMSiKgqGhIaRSKbz66qsdO7+u6xgbG0M2m60oFh61ok0tUrRtG6Of\n+xx4x4HuRaeON1NokhQqgpEdneoE3C5U2zAgwSU5uhkHuD7OQf5OjMXAlcvgQ59XoVTyz2F7jzu4\nTpBiMgmrWMT0V76Cc488AqkJ1/V2oJ1aq9WaqMhs39WrVzE3N4dMJgMA6xp7GhlX2EowTROJRKLh\nv6uXFFsxFybn+dmf/Vl89KMfxT333NPwOpvFjiLFetOnuq77ZDg2Nobe3l6cPXu2qQ9QVDBNE1NT\nU1hYWACAyMiw0REUupFncHAQZ86cAcdx0HU9clm1SjBN04+Qb7rppqq106gVbWrhtU99CuW1NUiA\nO0PIceAdJ0CIQFDOLUyKNAnGZBllKs3KU39DGmwELxK1TRMyAJ06vpxIQC+VXM/GVAq6qvoiAI5h\nwAFQzmbx3H/9r7j30Udbfv1RoNMpTNrhQpZlnD592r+RIibDa2trmJmZga7rTSnGbHY0mz6t1zaq\nFXNhXdfx5je/GW9/+9vxC7/wCw2vsRXsKFKsB4IgIJvNYmFhAb29vTh37lxXydCyLExPT2N+fh6H\nDx/GPffcgwsXLkS2gZB07EbHMwwDExMTWF5ertjI0w6tURqWZWFmZsavU2yULu6kIPhrn/kMnGIR\nfDwOWBaS6TT0QgFAkPiq6Z0KQEDFRuJ5cIA/pB/eekUEU7EO3NSobNv+KAe9bsAlXQ4uKUrxOBxB\nwMJ3vwvLMCBsAjm2brpy0I4P1UyGdV33U7C0YgztLkLGFjrh0RkFWjEYrocUWzEX/uu//ms888wz\nWFlZweOPPw4AePzxx3Hu3LmG19sodhQp1vqwkgHzkZERcByHO+64I1IybPRLT5MA0QVtx50pIcVq\nOpUkMltaWqpZu2wXKYYH7+u9DhutJyorqvHPfx5moQDesmCoKpKxGByPEAEAoghQXog+JMkfsQiv\nhLhpJAQBpQr1aCF8LABxSQK88Q6TECCAsqK4Yx0AxN5emPk8ZFmGrarQbRsvf/zjOPfII3VehfZh\ns1tVybIMWZbXjSvQot0LCwsNjYt0W1i7WYPhXC5Xdxdos+bCDz/8MB5++OGG1xYFdhQpVgKttrJr\n1y6cOnUKCwsLkRIiqVPWI5BMi2QT9ZV2KvhXI49whLpRujZqMXXHcbC0tFRx8L4edCp9+uonPgF4\nXaMxr17HxWI+QRFz4HA9kVaxCa/S8p4Db8TA9IbzCchAPt0j7ZTLgefIsZhvqi3BJVHL62bVVRWC\nZUEQRYz9n/+DI//lv6Cnp2fHSq01i2qi3fQca7VxkVQq5c++dgvNpk91XW9q7nerYMeRou9M4G26\nExMT2LVrl6+2UiqV2mY0XGtTt20bs7OzmJmZWSeS3U6ERzzodRw8eLBtEWotmKaJH/7wh+jp6Wlp\n8L7d6dO5b34TZi4Hh+fBWRYsjxxJKlTw3neR5yEJAhzTvD6cn05D82qOdCwox2KwqYF+keMgSBL0\n0GeSDPaTFYlwHTU0SiwccFOpoiBAtyw4luXOP1qWK0NXKsEqFnHl059G6md/1ndb0DQNi4uLHRXw\n3uyRYiOoNsdKxkVUVcXVq1ehqirK5TKuXLkSIMtO3Zw0kz7tZrq3U9hxpOg4DhYXF9eRIUEnjYaB\nYHpw//79DUdEUazNsizYtu2Lde/fv79jpEyDDN4bhoHbb7+94cF/GqSBKCpUIsUrf/InsLxUqSAI\nML2bC6I1Gu/pAb+2Btg2pGTSv9kSeB6WoiDp/Y0BitxiMegUKUqCEEiH0rqmvqqN98+ho0/LgiSK\n4E0TjmVdH93wVHP4dBpOoQAhHofyzW/i33zwgwDcruIXX3wR5XK5owLeWzFSbASVxkVUVcX4+DgO\nHTrkSwlOTk5C13XfXYTWgY36+jR7zTmO2zY3MJWw40jx5ZdfhiAIVXU4O2E0DCBAQvv27WuIDFsV\nLQ8f69q1a3j11Vexd+9enD9/vuNptPDg/ZUrV1pOz9STzm0lOln+/vehjo8DHAfOMMD39ACFAkRv\nDCMGwKLEAyxKr5aPxWCVSnAsC7IkQXAcGKYJE4AdcsVwvJpgDIAGQLBtn0BtuCQq8zycUgl2uQwJ\n19Oqkmmu62C1vWuiKwokjgMvyyjNzWH261/H4QcfRCwWgyiKOHbsmL8GWsB7ZWUFk5OTAVsoWj2m\nFSH97bzRVgIxN641LqKqKqanp6F6qe9uj4uQTtztjB1HirfddlvNzbIdHzCaaGn7pGZJiByvFfIi\nIydLS0vo7e2t6VzRLpDB+2KxiBMnTvhNDFE07WyUPlUUBbquo6enp670cPh4r/7BH8DI5yH39cHI\nZmF6mxYMA+QqWt5rEGTZjx6B4PiFEI/DKhQgwq0Thm/ISH2Rg1sb5ChbKA6A7KVBCXhB8GXlOJ4H\nb9uw4EaZAs/Dsm2/25WPxWCqKhzbxpWPfxyHH3ywIjlVE/DWdR2FQsEfiie2RBtZcFVCtyLFTowR\nVUO1Rhd6XCTsLkKPi8zOzqJcLgfcLdo9LrLdvRSBHUiKlXz22g1RFGEYBhYWFjAxMYE9e/a0REKt\nulsQ54pUKoUbb7wRfX19HSXEegbvW61dVCPFYrGI0dFR30OuWCzCcRz/Drynp6diepA+Xvbll5H7\n8Y/BAdCzWQieP6KcSsHxyJEXRb/JhveEwQFATCRgUiRmUx2oAtwvJEmehqmkkpWUqWmQOM6PAG0v\nVcrDkxCj/94jSEIDpmGA98ioODuLlR/8ALvuvLNuizWycff39/uPb2Q2TKcD6fN0K1KkxzE6jUYl\n3qqNixB3i/C4SCKRCFxzelyk2etd7+D+VsaOI8V6EdWX1HEcFItFzM7OYt++fU03jtBolhTX1taQ\nyWQgyzJuueUWpFIpTE5ORnq3XOu60eMdtUTLo6gHhqNNmoiHh4fR19cH0zTXDWyvrq5iamrKtw+j\n7YoIKb76yCOwTRPJ/n4Ul5fBiyIkXYdQKvmpS4eO+Kj3igtJvZlUWlWAG9GlkkmoxWJAFJy8pmQ8\nDpVKscoA5FQKJUoogMf1GiX9KXGo/woAYqmUW1fs6YGuKHj5934P/+Y739nw2tZCLbNhMrpAzG7p\nTd40za40cXTTpSKqc1dzt6g2LpJKpZBMJuE4TsPNNrlcjpHiTgTxBmzlA0sr4giCgEOHDmFoaCiy\n9TVCivl8HqOjo+A4DjfffHMgDRZl5EwIJnzdwoP39Yx3RJU+JUpAi4uLGBgY8ImYfs2V7sDD6cGl\npSXkcjn88O/+DmvPPw/HMPxuU6dYdCMyas3Vhvb5RALwCExOJqGHaogAYBWLSMRicGhbKQCyJMHw\nzkVWz8OtDwb8FL2fyXnJ8x240WcslYIEQCsUXPHwXA6CICCfySA/NhZ5xEabDYejShLhrK2toVQq\nYWVlpSmni2bRbYeMThgMVxoXWVtbg2maePHFF/1xETrtXa0+zEhxG6JeT0XDMJr6wIYtpc6dO4dc\nLucXyqNAvc4biqIgk8nANM2qzhWCIEQ2ghK+mWjX4H09IC3wP/zhD+uaswwjnB40DAOXL18GPvtZ\ngOMg2Tb0YhG8JEHwpNMC56eG9ulbDjp1ihAhcp6JMAcAoYgSuD6LGANQhJtyJaMZpBMVcN8HxzMa\nBoIELQPgVdWtVXrXmQcQ9yKH1z7yESR+67fqvUwtQRAEf3RBEATouo4jR45UdLogA/GpVAo9PT0t\n+SfS2K6kWA1kXEQURaiqijNnzgQieZL2LhaLgfpwIpGAYRgNDe43azAMAE888QQ++tGPAgA++MEP\n4h3veEd0F2ED7DhSrAfNdKASl/mxsTEkk8mAcLiqqpEbDdeK7orFYsA5gjZtDUMQBGhUCq8VkKhT\nFMWWBu9bqSmS+VNi/PsTP/ETkWyeHMfBMk3kvvc9CKYJm+dhWxbiXp0OCJIPqSeSV82nUrBV1R/b\nCD+fF0XYFBFKkgRH12FjfSrUARDz3DBof0XOXShs2wYnCG7k6mmkCnDNjnnD8McyJNsGOaNVLMKx\nbeQuXkQsFKF2AoScqjldkAiHpAKJK3ytulkj5+0GailJtRt02rRWJE/KChMTE/jABz6AlZUVxGIx\nqKqKW2+9FefOnatoH9WKwfDq6io+/OEP47nnnvPVxR588MFAerid2HGkWG+k2AiJETKMx+N+rY5G\nq40xYVQ7nqZpGBsbQ6FQwODg4LoGlmrHiqqmyPM8VlZWMDMz0/LgfTNrWllZwejoqN9Ne+nSpUhn\nLYtPPYXywoLrmSiKSKTTfirUwXXSEuGq2zimCc4jNqiq650IgE8mYRaLgaaZWDIJg/ZL9CLGZDwO\n1fNdpElU9MYt6Mfisgxb191UqWX5QuEAIPX0QPDWQmytyCeD8xqEeEmCtbaG3OOPA3ff3eLVagwb\n1fCrDcTXI7OWTqerRmTdJsVu1TPrkXgTBMHvOr7xxhvxD//wD/jUpz6FdDqNM2fO4KWXXsJf/dVf\nVSTFVgyGv/Od7+BNb3qTfzP/pje9Cd/+9rfx1re+tZWXXDd2HCnWg42MhgnoxpVaLvNRzz5WMhoe\nHx/H6uoqBgcHcfr06brvlqOqKeZyOaytrcG27YbttSqtqRFSLBQKGBkZgSAIuPXWW9fdlEQBjuNQ\n/u53Iff2Qs9m3UYaqrmFA+BwHGKe473ppUbFRAK2lzL1h+yLRVepxnGgwyPTkIEwabKxNQ3ktoL+\nBNmGASkWg0FFdaauo+r2XijAJpsPzwO27UaQkgRDVSGLIhzDAJ9MQv/e9xq+Pq2imS7QWnUzkgqc\nn59fJ95NOoxjsVjXSbGb525WDPzUqVN44xvfiDe+8Y1Vn9eKwXClv52bm2t4rc2CkWIFbERi2WwW\nmUwGoiiua1xp5niNgtQUaaPh48eP4+TJkw03JLQaxdKD9319fRgaGmqJEIH6SbFUKvnjFSdOnGhr\nA8Dq974Ha34esCxwggDeS006nrA3DyDpSblxVD3RqZIulVMplBUFMtxaoh2qIdI1yUrvqBSPr9M/\nJWMdvp8irouHc3DdNwzbduu+sRischkSx7nP53k48Mhc1zHzhS/gyNve1tzFagJkkD0KiKKIvr6+\nQO2LdIErioJcLoe5uTnfiJrneczPz3fcEqrVZr5W0G6HjFYMhqMwJ24FO44UW0mf5nI5ZDIZcByH\nkydPbkiGGx2vWRAVmrm5uZaNhpslxUqD96+++mokUedGNUUSGa+trVWcc2wHpv7sz/yoWk4mUS4U\n3A5Py4LA85DjcRhE+5T6O5PKOARonorwHF33zYUBdwDfCX1e6EYaABA0DRYQSJHGvAgw/HdiOg1b\nUXw9VMDVQ7UAn4wtL8rkOA6IxzH7uc91lBTbPadImkZSqRT279/vP76wsIC1tTVYluXP+DmOE6hV\nErH0qNfX7XGQZkix3kabVgyGDx8+jH/8x38M/O1P/dRPNbrUprHjSLEeEFFkgnw+j0wmA8dxMDQ0\n1HBEElVNkYw2TE1NIZlMRiLW3WhNsdbgfVT2UdVqipZlYXJyEouLi01Hxs2gtLCAwo9/DFgWJMoa\nSoAnuE1JrwHXxbjDPomB51BkSd7BJM+jaNuQQ58X8nuZ56GHrkvAuFiS4ISk3iwAInHswHV1HULg\nDtyo09Q0xNNp2JYFq1RCYWQE2eefR98dd9R1jVpFt9KYPM8jlUoF0nW2baNUKqFQKCCbzfpRJTEa\nppVjWllzt2uKzdT7O2EwfN999+F3f/d3sba2BgD427/9W3zsYx9reK3NYseRYiMjGYVCAZlMBpZl\nYWhoqO5W5DBaVWixbRtzc3OYnp7GjTfeiNOnT2N5eTmSL1S9NcV6Bu+jIsXwcejXf+jQoZYiY4JG\nyHT8kUcAw3AH7y0LXCrlNs709sLK56+nHQFf+xRwXSss8nMy6RMREKwPkhSnY9tI9/XBymYD5ye/\nl3gehufwQqdIJbiEZ3sjG3RUKcNzyfBIWIrHYXkNN5KnxJNOJmFoGkTbdsXH02k4to3xP/5j3P6V\nr9R9nVpBtxRtKpExIcpwbZrokSqKgpmZmap6pPWqQ3WbFNsZKbZiMLxnzx488sgjOH/+PADgQx/6\nUM0O+qix40gR2FgX0zAMLC4uIpfLYWhoqGOtwGEQndSJiYmAaHgul4usm3WjKLaRwfuoOlkJKRKv\ny7GxMfT393fcQQRwN821Z56Bkc9DiMdhaxps0wQPQDAMt25HS7fRnys6XSmKPlGFlWpoKrCyWVcD\nlV4EaYwxTaRSKeiqGhzn8P7R4xm8J/0mCEJg5rFMNQcJsRgEXYeVzboej8WiOwfpOHBUFdkXXoCu\nqpDb0LgUhm3bm4YUq0GWZezZsyewQYf1SGdmZnzRbDr9Wsnlotuk2My5FUWpW/u0WYNhAHj3u9+N\nd7/73Q2vLwrsSFKsBlVVMTY2BlVVkUgk/DuVToM22N2zZ8860fAoa5TVSLGZwfuoOll5noeiKLhw\n4QLS6XQk0njNYvYzn4GtKC7JeORilsuu7RMhQuq9oGuBtIQbrU5DIj//d6FzkmjPPxJFFpaqrqsv\n2nAjQpv6+4TjQBdFOKbpuml4UaFj2xB5HqZtgy+X3bEOjxhs2wYvy3BUFQLHwdF1TPzxH+PkH/1R\nXdeqFXRLg7TVBp9qeqS0GtLU1FRFCy4iM9gNNFtTdMiN1jYGI0W4w+5jY2MoFot+9+Srr74a+Xk2\nShERNZxMJoPe3t6qZBDl3GM4aqYJudHoLIr0aaFQ8Av0Z8+ebclTMQpc/eIXYVsWJJ6HblkQUikI\nqupeN+85tI+hUS6D43n3fSsWXWcLr9GFQEynYVD/T5wwAPcLSRwxLLgjE3ZoPCgW+ntwHGKShBIV\nEToAEtTsYzweh+L9XkqnETNN2JS+qpBMwlYUcLEYLF2HIIrgVBXzX/0qUr/yK5GqyFTCZkqfRgFZ\nlrF37951Lhe0BZemabh48SJkWW67d2IYzGC4OnYkKRIiKJVKGBsbg6IogWF30zTbYjRc6+5sdXUV\no6OjSCaTOHv2bM2xhqhJkYAefm8mOmuFFEk3a6lUwr59+/z0UzeRe/ZZlKenAbgD9ygUIBCPQmqD\nIJ+UGDzfQtuGUC77bhfw0pJJAJDlQNS4bigf17tXU54KDj2swQEwFSUwehEXBBi6HoguHbgD/oRO\nabsqWBYcL3LhvZQwiXr9x2MxcLYNp1BA4W//FurrXw9FUXwVGTLrR2porRJatxptOpnCDFtwZbNZ\n3HnnnYFaZdjYmb7OUZYOmnndhBS3u+/ljiRFsgHn83kMDg7izJkzgTc6agUa4HrKM0yK9MzjmTNn\n6iKCqNdnWZZ/x9rK4D3Rr2wEhmEEhAduuOEGLC4uokRv4m2Apmm+N1y1zWbmYx9zoynDgBWPg4/F\nwHtzfU7o+vckk+AolRqBEv4WbRsGPPLTdVd/FIAKQOQ4GBTBBuqLngoOfUXJpyfR0wPF64KVEgmY\nnicjOb8MgKfSt5Zh+OMbkjdXWYY7igGOg2NZLtFaFoR4HIaqQnAccLEYyl/+Ms559R1aRYae96un\nhlYL2y1SrBfVvBObteCqF828blVV2yKMsdmwI0lxaWkJ/f39VZVf2m00DLhpwtHRUTiOgxMnTjRk\n3BmF3yBwffC+XC7j7NmzLZuHNlJTtCwL09PTmJ+fx0033YQTJ04ERjvalaoxTRMTExO4du0aenp6\nMDU15ben036KfKEA9eWXIaRSsLJZWJoGWZJgealMPpkEikXE4EZslpcqJbBDajcEkifWLQDoBYCe\nnoC8WzjOFoB1rhg2ALNQ8EcvSPrW9M7lwP1ik+Fw8p6IsRi4chnQdfCe/qlj25A4Dgbg6qVaFniP\nqEVZhhiPo/TKK1AyGaSHhqqqyNDRDl1DC8utVbsB6abJcDfOu9HneyMLLlq4m3TLNmrs3Og+txMM\nhoEdSorHjx/vitEwETUeHR2FYRi+r1+nER68L5VKkXzY60mfOo6Dubk5TE1N4eDBgxUbeKLwU6TP\nR443OzuLmZkZHDlyBHfddRcsy/JT6cSZwRec/sM/hLm6Ck4UwcfjkHXdjRjhko5eLCLlpQ1tLyIL\nCILTa6B+Fi0r0Fkq5fM+uXEI1hc5uESXAEAolnTAErcMG25DD/mbGACN+vtEKgXFI11D169Lxnly\ndLZ7kdw1e/81vWifTyRg5nLgJAnzH/kITnz+81Wvc6XOTCIoXSgUAtFO+AYkHo/vuEixmddbS7i7\nUlRZy4KrmZvORhwytjJ2JCnWiyi/qI7jBGYe6VRJp1Br8D6K11qLFIm/ZCaT2dA5I2oRAGLldcMN\nN+Duu++GKIqwbdu/MQo7M9i2jRdeeQV8by+MXA6cZQG27ROPAFfA2y4WA4ROu1jQpBjwUwyt0YCb\n6uTicUDTAgRK6o02gEQ8Dq1chkGlRE24dUqaZB0AcW+OEgBs2pA4HodMGSHLcAmUELJj25B7euDo\nOkTLArz6KS/LyP3Lv8AqlyHUOYMHBAWl/fVVuAHRNA3lchmTk5Po6+vz06+dqPVth1pmtaiykgUX\n6ZY1DAPZbBbpdLruhpt8Ps8ixe2Kejb/jRpj6oWmaRgfH8e1a9dw6NAhDA8Pd/yOmKQMiUZqePA+\nqtdabU5xbW3NbyKqp4EnKlK0bRsXL15seKxj6bHH3Bqd48DgOPCeeDYhrDjgmwOb3nWkTX7pOUOO\n46qq2pAaoANAKpfB9/RA8+qEACBQKXfBMBATBN+SipxTRpAUbQCpWAw68e80TSTh+i/GbRtCIhFs\nuvEgAYAsQy4U3K5Xngevqoj39kKH26gz/9/+G4586EMbXb6aqGYN9aMf/Qj9/f0ol8vrBuPpZhN6\nNCkKbAdSrIRaFlz5fARTMTwAACAASURBVB6rq6sBi7VK0Xt4n2KR4g4Hccpolih0XcfExARWVlYw\nMDCARCIRqX4iiYJqfaHrHbyPUomGTksrioKRkREAqOkiEvV6VFXFyMgINE3D7bffXlV8oVp0vPbk\nk+BKJZimCTmRcKMz24YlikiYJrhYzNcu9X0TEwnolBsGgeQ5YRDQESRNno7jQDZNmNRjkGV/BtK2\nLCR37UIhlwus1UZophEA78ljEYiiCNE0YXtzieT5FtxGH9NxXAcPz3aKHJcDwNs2eFWFnUxi5atf\nbZkUq4HjOPT19QVIjwzGFwoFrKysYGpqCoZh+M0mZAOn04KNolui3N0a3BdF0VfrOXnyJICNLbhk\nWcbExASWlpbqlrjcyGC4XC7j7W9/O55//nns3bsXTz31FG666SYYhoH3vOc9uHTpEkzTxNvf/nb8\nzu/8TuTXoRZ2JCm2w1ORgI7K6AaS2dnZtthHVSK5Rgfvo+pmJWSmaRoymQxUVfXFwhvBRopD1UDS\nw7lcDsPDwzA9BZhq56j0OVAuXIA+N+faP3EcxGLRj+4kSQI8g2Eg+Bmhry/vpUKB67ql5OdaVG9b\nlt+448CVbaPBG0aAACWvcSaRTqNA5OTg1gtlSfKl3QzTRJo6Ny0+Hud5aKIIuVx2rargkaZtgxdF\nV/bNcSDbNozVVax+4xvY83M/V+NVNIdqcmvhwXi62aRQKPhpwUY8FGl0y76p22o29M3+RhZcs7Oz\neOKJJ3D58mWUSiVcunQJZ8+exfnz53H//fevO349BsOf/exnsXv3bmQyGTz55JP4wAc+gKeeegpf\n/vKXUS6X8fLLL6NYLOL06dN461vf6nsvdgI7khTrQaOkaFkWpqamsLCwUDEqEwQB5QgdzQmR0XW5\nZgfvoyJF27aRy+Vw6dKliqMu9aLRSJG+9nR6eHJysuGI8+onPuFGgY6DWDLpapc6DmRRhF0quTqn\nXkTIJ5O+DyJRshE4DoIsI65prqEvFVWSgXx/3Z7tFOB1mHoNLmkAJbgkScPwul39uchEAla5DFNR\nfLIkpCrF4z4pkjQrqUYG1mBZSHrnkUQRZfozL4pwNM0VPpdlcLqOxUcfbQsp1lvTrtZsQprYSKSj\nKIrvoUhHleFszXZNn2507noyYLQF1xNPPIFPfOITGB4ext13340XX3wR094Mbxj1GAw//fTT+IM/\n+AMAwEMPPYRf/dVf9T8DqqrCNE2USiXIstzxOiYjxSqo12jYtm3MzMxgZmYGhw4dqhqViaIYacdr\nmMhaGbxvlRTJeMXs7Cx4nsc999zT0kZTLyk6joP5+XlMTk7i4MGD625EGo04jWwWxWefhVUswrFt\n13S3rw9CoYB4Tw9Ka2sQPEcJALC9upcsy5A88rAdB1w+D5IEFOGRYU8PbK9eR0DXB+nozQSQSKeh\nUWMddNONzHHQHQcCdbwEgOvVSFfjlNQ5Y6IYsI0isnC6d1xCqDzpPiXXQ9Mgchw4noeZz4N3HOiv\nvYby/DxiddoA1YtWG71EUcSuXbsC6T3a7YLWJaUVZOr5jrcD3Y4Umzl3Pp/H7t27MTw8jOHh4arP\nq8dgmH4Oee9WVlbw0EMP4emnn8aNN96IYrGIP/uzP+uoGDiwQ0kxivQpnaI8cOAA7rnnnpp3X1F7\nKhKSzeVyGBkZaWnwvtkaHk1KN954I+68805cvny55TvvetazvLyM0dFR7N69e502LEGjpHjtT/7E\nHU+wbcR6e6Hn8zCLRSQtC5YXEQr0e2xZ6AEArxYIAKIkBTwUTS9K5AoFJFIpyKoKBR4pUWsLi4DH\nFAU2rg/uE71TDi7J6YYBk7qRsXDd9YIDAMdBHG7EKXr6p7T3ouAdi8P1jljTsnyCJGMivOPA8pw5\nxN5e8LaNuQ9/GAN//ud1X9d60I6RjGpuF/Ssn67ruHTpku+3SDf1tEvSDuhe2hZov0NGPSbB1Z5z\n4cIFCIKA+fl5rK2t4Sd/8ifxxje+0Y86O4EdSYr1oBqJEeeKyclJ9Pf3V92Q6z1es7BtG1euXIEo\nirj55pvrNjyuhEYjRVqjlSYl0zQjG6WoRmaFQgGvvfYaJEnaUA6vUbJXvvENwBPMNlTV1RT1UpqG\nd30cL3pL79oFw2t6EW37OikaxvWfYzGfFAHXQ9GCmx4Fgko1YTjwulzJa6F+ZxoG+gCUQ89PWVbw\nMVH0xyoAr+7pvQ7TO76DIBmTblbOEycgguFkjAWGgfL3v9+WtGOnurJpBZmrV6/i/Pnz/qwfqVOS\nrkxiNkzIMgpJO2DrRor1NNrUYzBMnnP48GGYpolcLoc9e/bgS1/6Eu6//35IkoR9+/bh9a9/PZ57\n7jlGiu1GPR9qSZL8tnAAvo3R+Pg4+vr6cMcdd9TtmwZEV7cjg/erq6s4duwYjh8/3vIxG1lbNpvF\nyMgI4vE4zp07h0Qi4f+uXX6KgDvaMjo6Ck3TcOLEibq+nI1EitkvfQl2LgeH4yDxvNt56qU8eVmG\nQxzqAexKJiHkcqh0xehPllgu+4TDC4I/FG8BSHqbkkKRFA0Sa/YCyAOwPILzj40gKQKuMAD9mGma\n6BVFGN7fmZblD+xzgoBYPI6yqgY6WMlV14tF8F6zjgS4PysKREGAtbqK1SeeQP+73lXxWm5FVJv1\nqyRpJ4piIKJsxmy4W12vQPMOGSR9uhHqMRh+8MEH8cQTT+Dee+/F3/zN3+Cnf/qnwXEcjh49ir//\n+7/Hww8/jGKxiB/84Ad4//vf3/BaW8GOJEVg4w2TRHaO42BlZQWZTAbpdHodEdSLViPF8OA9mUGK\nAvWQIhlzsG0bp06dqhiZNts1GgZNiqZpYnx8HMvLyxgaGsINN9xQ9516Q6T4l38J3mtDRzIJLp+H\nSAbdPTIT4DayOJ7AN4FT5WcasuNACzwgA6USegGooVnGmKdaA7jkmEinoXs1QgIe8OcP/XOLIlKp\nJNTcddk4iRIFB9zaowogaVkQVdUnUd7zbLTgNtwYpomYYYBPpyEoCgTTBNJpOIoCRxSR32akWAnV\nujKJAXnYbLgRAe9wk1wnQdRuGkWUBsO/9Eu/hLe97W0YGhrCnj178OSTTwIA3vve9+Jd73oXbrnl\nFjiOg3e961247bbbGl5rK9ixpLgRRFGEqqq4ePEi4vF4S0LZ5HitjnjQnZWqqkbWuFMrwtM0zXcS\nGR4erln0jir9ReYdp6amMDs7i6NHjzbVvFOvXFx5dBTW1JTbQappsCQJyXTa7TyFZ60kSegxDJ9E\n6KPS70JAXSaRAEinamgdRqnkN8Ls2rUL+WzW/53McwECTdoWwu0gFoIp1ZgkwjFMiNr1WJGDS6q0\ndqrjPW7DjTR9LVVqfaJpQuA4xB0HlqJA8EY/UqYJw5vbLL/2GkoXLiBx113YaZAkqaLZMEm/1iO1\n1u3u02bOXS6X674R38hgOB6P48tf/vK6v0un0xUf7yQYKVZALpfDa6+9hmKxiDvvvLOleh1BoyLX\nGw3et9tomBbOHhgYqCqeHjVImlpRFBiG4cuyNYN6r/nahz8MR1XBp1IwcznAMMB59T/AJY1k6PpY\nnqA2cJ0IeQRJkZZkoyGHPBJlw0CvICBPZOeKQcUZqVRCjyyj4EWsshcBWwDS6RQURUXMcCNCo1xG\nAm6DTQJe9ymuR5QGgB5cT8+SyNGGW2srl8vgRRG8abo6rJIEy0sZOqYJrlRyxz54Hisf/SgOf/3r\ntS7tpkaUovNhWyhyfFpqbXFx0R+KtywLlmVBlmWkUqmOEmQrXorb3TYK2MGkWCm1VigUkMlkYNs2\nBgcHMTk5GQkhNoJ6B++bsWmqBvpYtm374xXNRmjNYm1tDSMjI74lztDQUEvHqyd9aus6ys8+60aI\nmgY+FoPkOOBF0fVB5HnERBHQdTgUEdL6of4wPa7X+SQgYAkVaGaJx6BRpMg7FhzLQgouQYVvdRx4\nyjLwxik8QgQAXnFTd/RWJScSKJVKvu1UOJ9AOlnJsf11lMuIcRxk0wSSSZjFIgSedxtveN49DseB\nTyYhCgKKly/DuHoVEpVa3Epotwh5Nak1wzB8E/O5uTmoqgrHcSrOVLYDzXafdku0vdPYsaRIo1gs\nIpPJQNM0DA8PY/fu3bAsC5lMpmNraHTwPkpPRUEQYJom5ufnMTExUdeISZQg9UrHcXxPyX/9139t\n+bi1SFFVVczPz0P8i7+AUy5DSCahKwoSuu7Knnk3Cb22DdX72ZQkQNch4DrhkVocAN+OCbg+0kBA\n374IejCCNIquy4UIN3LTESQ503HFAXoBZAGIvT2w8u5UogGghwMM6mWWPUIk5GrBjRbL3rroRK4O\nQPRGOXQAKUGAY5q+SIBviEzqqo7jPuY4sB0HI+99L8wPftCPkqrpZm5GdGtwX5IkiKKIgwcP+uMi\nRNKOGA2HJe1a9U+k0Uz6tNmO1a2IHUuKHMehVCr59TLiXEE+cNXErVtFpbstMt7QyOB9lKRYKBQw\nNzeHAwcO1D1iEgV0XffNnk+cOBH5kG6lWiktBXfgwAEY3/qWP0QvyDJ4L4KzYjH0lMtAPAZ4dTqb\nEEM6DXiNL1LfLvStrQEOIPT2QszlfAUZOZWAVizBkWMBNSPHCHaR0vW+FACFem9j13kWFoB4TIZd\noMf0AdkJNtxwcMc+aOqVenpQLhSQ6OuDkc0inkxC80TNCQEm4Y6RGKYJ0/udBVfNxtB1SKLoplNL\nJYjxOGKaBrzyCvYNDfkapUQ3k+7QbMZ0uBPopsFweE6RlrQ7cOAAAHev0HXdb+qh/RPpiDKVSjV0\nA9tMpLhTHDKAHUyK09PTmJ6exsDAQNNyZI0i7EbRyuB9FDVFcn4A2Lt3L06dOtXS8Qg2SrNYloXJ\nyUksLi5iYGAAp06dasv1pyNFkhaem5vD8ePHcfLkSWT/7//F2tWrkCUJZi6HpCRBFwTANMFJIlAu\nwyA6p/EYTNLEYtvogTf8nstB90hLKLmpTAcAl0rBUlVIAOISj6KUguqlOssUT8c4+H8PAPKuHvQV\nClghv8f1iJMD0GvoKISCX5EDEokESlQtUt69C+VszidUrVBwCVh1m4dkreiTpsNzkMnYifeZsuB2\nwZY1DXIyAU3XIcgyzGIRvCy7NlfJJERVhf7Zz+KG971vXYqQbObEdLjagHy7DKU3QrdJcSNi4jjO\nn6lsRNKOEGY1A4JmhAN2ikMGsINJ8cCBAzh48GBH0zyEyMjMnW3bTQ/etxIpFotFjIyMwDRNXyl/\namqqqWOFQaKzSqkW2mD40KFDVV07ogLpPl1cXMT4+Dj279/v12gdx0Hpf/0vlzhzOfDpNDhPRUYA\nAI/AbNI96n1Oksk4ePO62bBp2eRk0PXrNymmdp2geN0Ar5vYw7sEqFEcINCeUwA4pQDdBnYLwJoV\nTKMCLgGmeEClA2AHEI3Q1GK5jF4eyFEfkV08UPSiVM0GeIGHbdnQtTJ29fZA13UYniKOAzetWtY0\n2F4XLokeJY5z1W5KJXCahtIXv4jd73tf4PSVOjRrDcjruo6VlRV/QL4T6DYpNnvuSpJ2juP46ddc\nLofZ2Vnoug5JkgI3IeTGu9F9L5fL1e2QsdWxY0kxFottGGnVY8/UKK5cuQLDMPzaZbNohhTL5TLG\nxsaQz+cxPDzsGx0rihL5eEeYFInB8J49e+oWKm8Vuq77UnBhsQVjZATGj38MQfv/2XvzILvO87zz\n933fWe7e3WigsRIAsYqgwEUAKcqkx86wFMVipaacKsuuGXqUv+KalFMJbUdyOXaJHnlKyzixk/GM\nIkee8ZKRM1PS2B7Zo0iyFMuiJC4gKQLE2tgb6AZ6vfs96/fNH+fce083GuiVgGTwrUKh+97TZ733\nPOd93+d9ng4RoDwPkcshPI/cwAB+rYawFFGUnBfpeQwIiNterydnKUEUJ4jmKEEQpX3GBVm8ti0I\nIkINRQFOuUi90c8qs9HtDcYx5EQqGh5kSDlKYkV9ROyOXRBq8nmXTsfHFhC0PdwFH9vsFTFA0bZo\nxAF5Aa5ILa6MwRko49caiaoPEEQxtqUIoxjHsohTRw1LSkQ+j5mYoPU3f0PxJ3/yjtfjdgPyzWaT\nt99+e54+6XraQ90u7qXUmjFmXbfdzcKLxSKbN2/uvZ4tv87MzNBut2m325w+fXpeVrlU1rpcNZu/\nC3HfguJyopvZrbXHlh2837t3L7t27VrzF3wloBhFEZcvX+bmzZuLlivXs3+6cF31ep1z585h2/aq\nhQ9WGp1Oh3PnzlGv19m+ffuiElG1f/WvEIGPRmDZNnEYEufz5DwP0ixPpnmak8the16iCSr6mZ4q\nliAlvDhG98g0ji3xMs9bQavf3VO2RdhoMWzDTDifIKMEdPHOAAXborNAsDqyHILAo6ygEUOhlEc3\nk/3NuTadjk9RgR9BoKEgoa1BCIh1ArTd/Td+ssc5AWGjldlI38MxJyWe1uTKZQrVKla5hJ6rIgYH\nCKMIE0VoIfB+53eWBMXFIut60WUbZ3tpC+2hsn3K1SjJZONeqsrA3RlvcByH4eHh3gOwMYZXX32V\n7du302w2eypdWUm77r8sYerdTPE+iJWIgq8WFBcO3gOUy+V1+TIsp6eotebatWuMjY3d0WR4PUk7\n3cH7TqfD6Ogovu8vW5ZtYayUAh5FERcuXGB2dpZ9+/YxMDCwaEYaNZuEr7+OReI2IfN54jDEslTi\nKWhZ4AeYKCLn2Die1+vrKUmPGSO9votFNuOzo6DXr3NsRRD2z622VJI1hrDJglrcr5/mUjDrrUcY\n7KJLvZmURiXgp0xVbAcRBzi21dtWp9rElSAyO+OKhIRTKpcw9SZuBhQ9A8VykajRQmAo5Fzano/f\n6vSFwQcr2I0mbquNb0CnBCPLDxCeh6lUiDyP+Phxohs3sFKSyEpi4XW+Uy9tKSWZ5WQ93biX5dN7\nFTrVsb2TpF2WMNXpdPjiF79IPp9nYGAA3/eXLG+v1mAY4Pjx4/zCL/wC9XodKWVPPOVuxn0LisuJ\n1ZJZbjd4v54qNHcaNzDGcOPGDS5dusTIyMiSA/DrCYoAFy9e7JVoN27cuKqHgO7xLedvs+C/c+dO\n9u/fj5SSTqezaAbcevETiHYLnQ5Ph+0W0rExc1UMELdSVmY5R9j0kxGMRbYbx/3zn/mR7GVxTDxv\nHEP7/d8UUB4oUq8m4GqJ+XqmlonpNGNcx8IPIvIK/PQyRV7AgM08EQABFPIufqu/Fi9O1murZN4w\nRCKVQMdxsrzj4JOAi3KdHtPWzdlEXohjNCZMXDZsIAwjXNtChyEmNR+2fZ/Ismj95m8y8LnPLXKm\n7hzLBSfLshgaGprXdlioJLMw6+mOiixGOrkfQfF2zNPbSdp1hQe+/OUvc+bMGX78x38cYwzPPvss\nn/70p29Zz1oMhqMo4vnnn+dP/uRPePTRR5mZmbknUnj3LSiuh33Uwlhq8H49VWhut/9ZX8Xlipav\nh5B3l905MzPDrl271s1T8U7r6Lp1jI6OsmnTplvAf7EHB2MM0V/9FUQxsphHtFroWFN0LVoB2DIp\nYRYUxC0fYQxysAzVpEwaiWSE3jCfMBNkTl+Y+Tl7mWxJrwcJIAYqmJk6Q6Ucc02Phc84Jl2P49j4\nQYQq5KHRJ/DoGKLm/JlHIyRSgM6sq+JAPJeaIWtNyYF6nJRrZa0vL+c32z2STSQtSirEzNVxZHJ8\n9kCZsNZAFgpEtTqqUEA3GgjLQtk20Te/SRQEWCusrKxlKPx2SjKLCXlnSSflcvme9RTvFdsWVj5v\nWCqVeO655zh+/Dgf+chH+Jmf+RmCIGBiYmLR5ddiMPz1r3+dRx55hEcffRSgV/K923HfguJyYrlG\nw8sdvF9v+6hsdHt3lmVx+PDhWzzk7hRrKedmj31kZIQtW7awadOmd9xTsWsh5TgOjz/++KK9ysW0\nTzv/5l8TTU2B56G8FiaXIz9Q6llCSQG5Yg7R9tDdm1eYZHeGPvg5aa8OwFEQpBmcYP7IRSy6OjTg\nJlXZ/v41E6CKmh4bbGjr+VTUbtU1anYoVgpoPzuNmOxDwYJaZp1O3sUlpNbsf86Unq+SI9L9K9sQ\nBoZ80aXT8hNVnZyi6cUYY7DjpFIscg60A3SaRQaehwSUJZPhf8BqtTBRhPdbv0Upo3G5nFjvjO12\nWU+2Tzk9PU099cgMgmBen/Kd7jPea9brar0Uuxm64zjs2rVr0eXWYjB87tw5hBB86EMfYmpqip/7\nuZ/jYx/72Ir3da3xLijeIZYDYisZvLcsi06nc9v3VxPtdpvR0VGCIFh17261kZVl62alZ86cWZdS\n7O1A0fd9RkdHabfbHDx48I7Huxgohv/xT5CWhSoXCKsNhOeBFkR24jUoCnlko4PlKoK0Vtklw1iu\nhZ82/ZyCiwp8LJGAQ2y50PGQxtDJF+jUEy9C32QITQvug5kZfrSWFEoO9VqS+dmuTexnHsja3rzs\nD5LMMwxBSIHpvlmtEmlBRlwHR4BKcA2AIAK34EIqKGC5LnRLrrYNXkxRB9gbKsSzdYKUNRT7ATkF\nnh9gC4gayYOEcl2ElFhKEn3lK+gXX1zRTf9uyYctJJ1MTEwQBAGDg4M9AYus5Fo2q1zPMt6PosHw\nctmnazEYjqKIl156iddee41CocCzzz7LkSNHePbZZ1e8v2uJ+xYU11o+rVarjI6Ormjwfj0zxSAI\n8DyPt956qzdecbdmLheTZevGenoqZr88cRz3SEt79+5lZGRkyeNduI7gz75EfOMmutnAtlSiKeq6\nmLZPEGqUABMkprymWAS/DgLCdkp0yTuUchaq5WHZNu2mTww45RLBdJJ12AUbq96mLMB2oKUUcTfl\nyzJNZc/vFwBnsEg006CQgpdbcPEzoGjlHJzQ62WkkOSf2kC5qKg3I5RMpjcEhpILDR8QEIew8D5Y\nFnGvP+nXGr2yqd/0GHQg9jVCJA9wRmvctJ9p2xZeHCEGK8RzdZxinljH6HYLbBtXzBB+4T/g/pNf\nuOO1yca90tTs2jctnPnrSq41Gg1mZma4fPnyPMeLtcrZrTZbW49YrUPGcr0U12IwvGPHDn7iJ36i\nR6768Ic/zBtvvPEuKN7NWEow2rZtvAVOB81mc9WD9+tBaImiiCtXrnDjxg1s2+bo0aN3rRndnXNs\nNBq3tZFaL1DsZnnGGMbHx7l8+TLbt29fUa9y4fUN/+3vYlkCWS4SNNq4CoK2j3RtRBBRyNl0OgkQ\n6TQLskp5wkaHUsnG9tp4XgpGrT7zVDf6/oUU8tAOE9k3IbDbAbmNZVpzjXmZoVMpElb7YxAqTgyJ\nnRgCCTKaP4zvmIDIh5wNXghCScK0lht3IoQU5B1DnH5cRfoxK9hgggQsbdUvydphhJ8ioY4NhYJF\nqx0hgBwJYzXyQpxkxBJKRai1eqActBLrK2FZRLUGjusglYJOm/gP/xBWAIr3qpzYZWIujKzkWjey\njhdrlbO7l+XT1WaKyx3JWIvB8Ic+9CE++9nP0m63cRyHb3/727zwwgsr3te1xn0NiktFNrPrOt53\nOp1VD96vJVPMMiy7ajBvvPHGuuqz3u6JfSWybOs18yilZG5ujuPHjzM0NLSqgf8sKEavfB9z5TI6\n8FFhMlvnbhgkmKxCOU+5XkfkXOiESe8wHRh0gg6uBboZYjL8kaA3tJCUMLuhsw9R5RLCa6CnG5QU\niKEy4XRC2FGE8xitxktdSmIoFxQL/U9UagCcVxIv1ORtjUkBSsdQHi4iW82ejmoYQdFJSDpxECZg\nN1QinG4ipCCODM5gkWAuVe5xctBuUrFBy/6xWTIRDffrbYQEHUYULPDjiJKbSMTlFZB3iesJCUdN\njBP+1Vewn/uHy7pO9ypTXAk43c7xIgiCHkNzMTm7LmBms7N76aW4WlBsNBrLAsW1GAwPDQ3xS7/0\nSzzxxBMIIfjwhz/Mc889t+J9XWu8C4p3CMuy8H2f06dP9xzvVzti0F3fSkExS2RZyLBc7/nChcPM\nq5Fl684priWazSazs7N4nsejjz66anPnbNaqP/U/IlotlJSYQp5iu06Ypm6210EHYGSS/dmOwAsM\nhY0DOPUafpSUFrvgpyyIUkk3peaXQcNWBs4ywt1WpUg83WCwAPUO8xk3QJT5Ne7E5IvzRznidNux\npykXEkLQvPerTfSCS2PlHEynv5SebSIEFF0DHXCisLeOsNrEsROA1UGE5VhEQYTfnaM0hpwDXgD5\n4SFyN+dw8i7hXB2nnMfUGviDFeJOwqLV//p/hmWC4r3MFNe6Xcdx7ihnt1Cb9G5b0S2MOI5XJaPX\nLTUvJ1ZrMAzw/PPP8/zzz694/9Yz7mtQvFP5NIoirl+/ztTUFIcOHeo53q8lVgqKs7OzjI6OUiqV\nFiXxWJa1rvZRXVDsjjqsRpZtLeXTrGtGpVJhz549qwZE6F/f+Mwp9PHjYDSi2cFsHET4htBrYpfz\nmFQRpitjKqWhPFRATNeIii4EfgKE6aVTGV8mq+AQNxJosZ0eURWYT6SxSPqPcTtlfbp5SElXtg06\nkzY6LsRNyOXA88BSEGfWpUIwC765SoI7PETzxlz/+L0AJfpWUVpDMQ9K2WhCokaA5SiitCZayivC\nevJzTkU0SWYxXSfBcKOhosBM1xBSEKUEHK0UGCjpkLCQI9Yaa2yM+I3XUO97Ysnr9KOQKa4kFpOz\n01rT6XRoNBo9E+1XX331rsjZZWO1BsP3cozkbsd9DYqLRXbwftu2bVQqlVsaxauNrm/hUtFoNDh3\n7hxSSt773vfedrxiuetbTmSVaM6ePYvruquSZZNSLmuMJRtaa65cucL4+DgPPvggDz30EGfOnFlz\nGbb30PPir4HnYRVzRB0fVa1hijloeuRMiGfScYsIpIS8gHCunZBUUlamtEVibAjzVLpl5htkWX1Q\nlK4i9rMpZOY6CUW+1cGkIGrb80uwlpUAmavBlwKn4mLm+mVZHUHemZ8pukWFrM23lHKLDioI5gG1\nMhA3k3KqAHKDcdvSbAAAIABJREFURZqTdYQARzqEJEAdxv1REpF3EDrCsRTCDzFaY7sQ+nGy79Um\nli3RQmG3m6icg+4YrM9+Cv7T/7Pkdfq7BoqLhZSyp00qhGBgYICdO3fi+36vT9mVs7Msa54u6Vrl\n7LKxWlCEuyNL98MQ9zUoZi/yYoP3Qghu3LixbttbyIZcGF1pNM/zOHDgwJJWLeutRHPq1CniOObg\nwYOr9k5bSabYLQ1fuHDhFrGD9SDsSCkRN2/AW2+AFJggxNo0SDRVxciAwmAR0U7VXCwQQjEgY2Ir\nB3goF8IUi0ylDJ2EUKPdPKQ2TSbO7GN2UF/F89Rp4gww2XaMbiUkmHbeQhRcaGe0R63EolgHUBkS\naKnnzRm6OQEdg3QUOs3yrDhGd6A05NCcC9LdsYjbAVIlZVFIZhvjgkXYTtZoasl2Szkw1U7PPjIO\ndO/noBlQtoGORlVcdD3jDVkqwlwL4SqiRhtLaKQUWCYkeuVVmm+8TuHQw7iue9ub6r0qn96r3l53\nu1nd16ycXRiGvT5lVs4uC5Rd263Vbnsl4XneXdEs/mGJ+xoUYenB+/UsG9zuphAEARcvXmRubm5F\nfcv1AMUwDLl48SLVapX9+/fzwAMPrOmJcLn7VK1WOXv2LKVSiaNHj97S51gPUBRCMPL7vws6xjE+\nfitEmSQlU66NqbeISwXw2pC3GQxDiDIqMQMV8FIgzJgEhxkZtWyG1wVTSHqN/WOZ3zNUMsnBTAh5\nBXrB6Y6j/gvxnMbe6hJl8kI1NIgZn6NciHuD+zodf1Xp30oFei5RqMkP5mnNpCLnTh5l0wNF7cfk\nhovYYQwdD6uQBz9Z1kpxqrChjNts4GfOQxiAkBBV22BLtFGIOERurCA7LaRrYzuS3Kde5Oyv/9Y8\nRZnuTb3rIn8/ZIrZWGokw7btW+Tsun3KZrN5i+1Wlv16Ow/FbqwmU7yfxMDhPgfFarXKyZMnV+R4\nv54RxzFXrlxhYmKC3bt3c/DgwRXdHNbSU8ya7u7atYstW7ZQqVTWfHNaCsy6Xo5xHN8y47hwPWt9\nIBHVOUrHX0cQg5vDthyiCEQu7omIRkGIUJAr5zGTIaZSgNlEOca0E+KNAeJGAgZqIIfSAcoBUa4g\nWnWMDbHlEGdSRe0WoJGsx3Ehymg26FwJ0pEPvAjXMkRW0jeUFkTNoLcmIcCpNvBlX/ZN+q3Ez7EG\nlgMyn4N06N80fApDLtg2YjLd/0YHBKi8jal2QIKwJCZl2OZchZhJWahNvzf474cK29XYMw1EyryN\nfbBciHywSjZhI8SxDEHLw7EhDiNMoEFG2MqneOYUjw4Pwo5d8xRlJicne84XUkps26bRaKxrqXCp\nuJeguFKyy+1st7p9yqyHouM48zLK7sMHrFzmDd4FxfsqLMtaseP9ekQcx0xMTHDlyhW2bdt2i0bq\ncmM1PcWuWPjFixfZsmVLb9vr5al4O1DsZqSzs7M9ofA7xWJqNCuN4md/A7tRxQQxcXkQ0ZglDiX5\noqZT0xhHYryQwqAD1XoyWy/6GVnkJdu3C0lFU2Fh4+GnrTuLOlGKbXZRY1oBBRvisksY93uAslKC\nTsZRo94vlVp5oBlT3JCnUfVwyhbRXD/9dApAC4o7N9C8OguArqb7aKCUh9ief55sIyBo9rQCTAD5\nCihXItqAhvygpD2bjp14HbQFJgJCTaEMrQaYKGawCNpPQLBbhpUW4CfWUgCatP84VCGerCNzAlEs\nYoTGMiH6xV8l/sKf3qIoA31bs06n0ysVLjXSsF5xL0FxPbablbPLeihm+5RTU1O0222UUpRKJTzP\no9Vqreic3k9einCfg2K5XL6rRsPGGOI45uWXX2bTpk1rNttVSuH7/tILptGVZSuXy7eULNerP7kQ\nFLXWjI2Nce3aNXbt2sWBAweWlY2uuXxancV59W/RORfht6DRwNgK1zHoQgVqdSjmKFhtRBj1en46\nHbXAFmApyhtz2F6TqAEQYTKMepG5pygrSvp+Iai6j4rArUDbKJD9jFfZoP3M725a+pztUB5WxCac\n1z+UxTy0OoixWeyiQGAwmfZjXANrm012gMNUPZzyfCKOihKBgO6WZSfZirSA2RB7pEAwmWS2Uigg\nJj/sYod+0hs1YG/I40916OoKxG2NtCHqaGwnUb5RZRdyDrrZAh0hijnUW68RX7kEux685TJZlkU+\nn6dQKPQIbYuNNGSl17pAuVbRins5CvJO9jK7tlsLHz6azSbT09OMj4/35OyKxeK8rHKxc1qtVt8F\nxfsllnNztm17XYyGu4AURRFHjhxZNZElG8sFsmaz2RPbvR2bdb2G7rv7ZIxhamqK8+fPL+pgsVSs\nFRTlb/4yqtUi1gaZl4haCCODWDNVvLR06cZtdAAip3tD71EASCgNGWhGiLkmZAoJJnO6dS5ReYGk\nv9bb9lABPdVG1KFIjCy0CB2FCWLUhiLmZh/V5n0EZ2LsTQvso8IOmsQjMV/IE0dtdAYUhQDXa5GV\nCreLYFkOQRYoI5A5RZweqe6AvbGEbUvERB2TAeq4EWM7kOv46ABkd2QkHV3REVgVh6ge4BbAdlys\nggvTdVQejNREKkIXimAiVLOB/T/9KuHv/1+LXquFyjK3G2noSq9NTU1x6dIloii6pae2krLk/UTw\nsSyLwcFBHMfhoYceAvq2W81mc945zeVyPXWertPIUqS/bqzFSxHg6tWrHDp0iBdffJFf+ZVfWbfj\nX0nc16C4nLAsizAMVw2KXUACePjhhzl//vy6fSGW6in6vs/58+dpNpscOHDgjio86zF0312P7/sc\nO3aMXC636l7tWkDRTE2iXv4bjGMhmj6hyiFUG6uW+iV2NO5IDtNK+3Dd+6KCQNoMV0KMtDA6FcJ2\nipB6DsZxwgwFMM0MCzNz6hTt3mygsEDNwFAupjlQxIpb85VshEsWBkUV3I05/Olk33SnPxrBVBt7\nVwV/ri8rZ5fAzIJdhrBb1nXBzAbYw3nClGDjDkpsHZOVo3d1E9lIPoum1sEakEQ1DQaKWyvI68l2\nnCJ4VYg7Sbk36oAgoLCjgtuoI9o+RKk8nu2i2j7OQAljIiJcIgPWiTcIL47Cnv23Xq9lEG2y0mtb\nt27t/V23p1atVrl27Rq+78/TKC2Xy7fVKL2fQBFunTfM2m5lz6nneTQaDcbHx/n4xz/O+Pg4rutS\nq9V47LHHOHr0KAcPHrxl/WvxUuzGCy+8wE/91E+9g2dh6bivQfGd8FTshud5nD9/nlarNQ+Q1nO2\n8Hbr6vZpJicn2bNnD4cOHVryWNejfOp5HqOjo9TrdZ544ok1ZcOr7SkaY5Cf+ZeY0Aevja1jjCeQ\nWwcRN+aIcwpRcskFbZpBMkURqiKCFmHJZZPnI0JoWRKHRHDbpFJosa3QrRTSHIFu9c997CclRwBD\n/2crxVPhQclrEY8Iwm4RU0Jc9XukGlUUiJbBbXhEOZKd68w/B047ILAT5iqAqiSEHltljJBFItNm\nq6j3mlI5zGwbmQOdtjtlCCIS/ZJqqQi1Bghw/aBXxtUmczxOAoquI3Bq9QSvKzmoe4gcmEaAcZLS\nsqgGuK5PlK8g2m3cz/wq/ue/fMs1Wy04LdZTM8bg+z6NRoNms8mNGzfmaZR2/3V5BPdKiPxegOJy\nyrZZObuRkRG+9rWv8Tu/8zsMDw9z4MAB3nzzTS5evMhv/MZv3PK3a/FSFELw53/+5+zZs2dFtnfv\nRNzXoLicWCkodgklMzMz7Nu3j02bNs374q23Ck12XcYYrl27xtWrV9mxY8eKxLNX2p/MRhRFXLp0\niampKXbt2kUYhmsuD680UzTGoLVGX79C4eW/QYQ+Ol8E3UHYCtWeS8qQLlQ6beKCg2gGyWuNFtZG\nRSwcRNd53g8wgM5bkHoT6oKCTlp+zFvIIM0YSw40M3JqGflTWbB7QCoAu6ko7MzTHmtgl4Faf1k1\noBKg9aE4BKEF2dROlUDMeRQ2u7Sup6ICcQJqpgrOEIRtiZlJhL31ZIhVShijYjoZz3AqiUoOgJMH\nUTD46e/xRANhgTsAVD2sAYhqYFoxdgXCegL+he0uTrWNKIJpgTBeAqwyoa2aUh4x1wJhMJVB7LCD\nNqBO/AAunIG977nl2q0XOGVn/xbTKO16KbbbbdrtNmfPnu0B5d1ivt7r+ciVRr1e5/HHH+eDH/wg\nH/zgB2+73Fq8FPP5PJ/5zGf4xje+wW//9m+veB/XM94FxSViuUbDcRxz9epVxsfH70goWU/7qGz/\nrivLNjw8vCoCz2p6illt1C4Id4XL1xrLBcUeGKbLup/5pWQgv9VGGIlWBjsM0fkCWG2cQh7ZbtLU\nFhAQ5x2KToDqxDiFRmKfZDvIVKDbdiPClDjq2P0unWv3Bb3tQtBjoRpboBuZBxU3Ty+Hy4FpR1jt\nBqVNEIsEdLohdOZzMQfOvhLhVJ+1apUAD+Skj7UhR1Tz0FOtXqZpCRDDDmI8QTkB2CUHqxgjZpJ9\nEm0FVqJlKn2ZWGeIGExiPiy3lVFtDwiRTl8YXDnJUdjDOQqmlcxZpnOZpgVIMD6gQHsaaQwM5jHV\nOkIaVMHBxAHuZ38V//N/Pu8a3o0y5mIapa+++iqbN2+m0WjcMiTf7VOudkj+TnGvQPGddshYi5fi\nJz7xCV544YXbjmjdzbivQXE9yqdZYFjOeMV6gmJXsPzYsWOrlmXrxkp7il2n7MW0Ud8JP8WF0X2v\n+1AghECceQt14hjGsWFDCXmjkSiudFqYpo8adJBzqTGu18Z3HAYGA8xsMpenWwmQuMWgp2STjaw+\nadOUcEnWlSXZRJU89kyG9lLN2EoNOnAjgVU5C2pfhWi63q1MYhZs06k3YQOEySQGwskDHYSBXF4Q\nCBAzmXMyC/aOgOxVNJMB9pbMC16MuwmMsRDVCPwYe8QivJm6YuAj22l/sB4jchLjaeKWRBU0Ba+N\nzKddzpoHJRuaIaICpm4QQwoz52OKIAINvkZvLCKUAcug3n4T+eq30U/+xLxreS/KmEIIBgcH55FI\nsszX7JB8lvm6VtNhY8w96WWuZkYRkkxxOUSbtXgpvvLKK3zpS1/iYx/7GNVqFSkluVyOX/zFX1zx\n/q417mtQhKU9FbtEm4XRZVdeuHBhRaLZ69VT7GqUNptNnnzyyTWXK1fCZD179ixKqUUdLJY6n8uN\nO/UUs9mhEKJ3g7H/za8khI8oRsQxpgSy1sKU8qDADyEfJxgUaIeBYkAYWSgiKIBIiSptVcROiTXV\nqESx0sHJx7TtEuVcE6Gh4xjifJG8aNG0i8ihNmbOkM+1e7044YDpJ3pYMpg3biFmmhS3QmucpL9Y\n6yvFCTvZH6cAoS0h1JDpYcqbHdydFtHM/M+Sq/Q8JqoQ4JSLhJN9yqrsgMj3/84S/d5jMQ7Qm1zi\nKR9igzUiCceBUFPcbSOnQmgCDhCAsJMOqbESwO72OnV5ADVdg4qDNgKr4yfyP4MF3H/3CTr/8W/m\nXc8fFl3NOzFfm83mLabDWaBcSk3mXsdqzY2Xyz5di5fid77znd4yL774IqVS6Z4AIrwLikuGbdu0\n2+15r1WrVc6dO0c+n19xdtbN7lYbYRhy4cKFniRcu92+K+MdXQeLRqNxRybret0UFiufLiyVCiH6\n2/ve/4e8MoqIgmTSPHIwlQFks0bsdLAbKekFMGUoBxEqAk9LFFDPDVBsJLVMO+ggy9CuDDBSqyEj\nCCKHoWaCcGHOopwaBBtgyG4jtSEegdnBCnajjq6DGFIw0z+nbV3ASSFL5IC6RgrQuytY7SbiRv94\nxSYbMRsiWlDYofGmSMZKMuHki0RWrefYIYdtrOkQeyuEE8lr9kZQN1pEacaXHIBCbXBhLlXuqYIc\ndBG2xGp30NucBBQBAhuIcUYgZ9JNGZAjOfR1D9NOraVmO8noSksjBwHHIHZUMErBjVri6lHMg+8h\nJi6i/uIPif+bfwzcGxboSh7csszXLVu29P6+y9Ks1Wpcv34d3/dxHGfeLOU77Xqxklht+bRery/L\nP3YtXoo/THHfg+JyMsVuZtdsNhkdHUVrzUMPPbQqbzTLsnq9i5VEVpYtKwk3Ojq64nUtFrfr4WWl\n6JYyGF7PyO7PHcEwWQDn87+JCHxwHUBgLA3tGn7BITYSC4+WKVEqNGmVBhiYrOHnHJxUPDSvkmsS\nbMhRVB5WAwq61vMxdCpBV9YUuxT1fhYVEPXk86N82FStYyRUD5bo2DalmcTKyQDWXIY1MySQU8nf\nDYzXYTd0Mtrzdr4PgGICrN0WnJ/nRYW6WiO3v4J3OinR2kMSJsB1coTpDlplC9GMsHaXCM8my1k7\n8rhBszcIIgBnwEflyzAB8mYDCgraMabqoXaUyIceohpCCWiCbEVJCdU3sKUEN5qoAVAFgxgqISfr\nmCEL6jGmYDDDA8hGO02fDc7/+b/Q+fB/B7Z9TzLFtW5zIUuzG13m60LXi+ws5b2yYVotKLbb7WWr\nfq3FS7EbXXbqvYr7HhSXim5m9/bbb9Nqtdi/f/+8Zv1q1reS8untZNnWOxZjsna3uxYputVGFxS7\n/3p9w0VuZOqPP4WcGEN4HoQxOidRzYiWyZPPdXCaAmOgGDYJHYsBP80I3Uz/TRv0LpdK7CGmk5di\nN82CgMixsdIiY700QGU6ZciUgLRtGBUsrGbC/By60YQdMHNoAPtsHVkxmLnMzbAoYap/vkNVRB4M\n0GeTbUTGwUlpPRJw8orYjXojjXKzQEwb1M0GpmIj6iGWSRmp0x7OTgiugmokDxJqok7oSAg0dthE\nzoIcBF1N1ifa4LTT+nEMzjZJcCnZP2eDQl1JFXA2ldHNBjQj5BDoORC6g7VDoEIrKZHOdZLZz9hG\n6AizeQA52wClMYUC+CGyOon1v/060T//zD3JFN+pbXbVZBa6XnRHRK5cuUK73ebYsWPzZimLxeI7\n/v1aDcGnC+D3ogd6r+JdULxDhGHI2NgY09PTHD58mIcffnjNT7Qr6SnOzs5y7tw5KpUKTzzxxJpV\ndZbary4oZuXg3unt3i6EELRaLebm5iiXy7f/MjfmsP/qj0AowoEc1AO0D7FlcO2AllWmXG/QGixg\nRxGWHxBHEoWmY+Ww8TA7FLl6jKrGVLcNMJgCXjtXokACFCLqZ9HFsJ/p+7kcbpqVBSMO1uX+tY3q\nFsP1GuF2RX0whzPX/7vIWKgMJaY014I21HZb6KsRajYr0gZONaS9v4h4u5vR5nGn2yjfUNgZ0u4I\n5HQfdIV0YYdApj6Mwgd7j0s0FqBm05nDTS5BNQFSe0sOITVcToG4LUCAKFq4k3WMKxC+QU620OnY\nohgsQaONVcxhtVrgh5iNCjEdYwYFotrBbCwgak0INWZDBaE9cMFoB+dv/1+i//Zf3JNM8W4yQG3b\n7jFf4zjG8zwee+yx3ojI9evX58muZbPK9WS+RlG0ap3nH5YS8N2I+x4Ub6d00S1Vbt++nXK5PE9w\ndy2xnDnFrCzb4cOHlxxmXY+bilKKIAj4wQ9+gNb6jg4W72R0S6Wu67J582auXbtGs9ns9XW6JIhS\nqZS4K/ybf0rUbhHoGBMYRE5QanrUR4pUZloU7QZxTqEqEc5EgF+xcSdTdqUUBA8WEbbATVmpg35/\nRqIwmwCiAahmlEBmM4QXvw+WBdnvPUeDFlY1Wc6eidk40qJ2OI84kZRQnal+X1lvVMh0jKPsxXQO\nCtSl/vZMWWLNasqzLWoPOqhLAW4rYyU1BtZhC3G2X3J1p3ysh4C5/rlV0x3iXTbiSjqecd1HFAWm\nZbBbEVJEPZgW1QBrO8iSixxvoR9wEZd9hK8ROyuYS3XEzRbWNoM120LvHEBerQEu0MbIPII2xlJI\nL0YPlxCNRkJbzVlQ8BC1APe3/ynm5z9512+691rNRinFwMDAvFGHruxaV8ru4sWLPeZrNqtc7UPq\natinXTLb/RT3PShmwxjDxMQEly5d6pUqpZSMj4+v2zbuVD5diSxbN7plxrU89YZh2FPfOXjw4Dwh\n4dXGSoF6Yd9QSsnOnTt778dxTKPRoF6vc/XqVZrNJtHYaY7+4HvYoU/O92jnK7hRSGu4gBsn59g3\nBZxyBxGm5sVOsv5oq6TQ7KAaEG/t72fQScqWOkdvsD7aaGNPJ4ATDjvYMwkgGQnWZB+cPJEnl07b\n680WVPvXWXdshq91aL9P0L5pYd3M9Ay3SkhB0eoY3PdYRDdjRDsBRr3LhtFE+aZMQGtQYI1ngNmA\nPaCA+UQclXcx0kekuC0aUNjdX0bGYPYnfT8rZbFGOx3E1eSYrIEy1niaKdcCjEg0WJXXIALE7gLC\nsaFW7VlPiek2ZsBFzrYxAwox2cTkBCL2EW2D2ZjHuBYowLVRp99k8NhXkfv+2S2fiXcy7jUoLhZZ\n2bVuGGN6mq9zc3NcvXqVMAxvkbK7k4lzdtsrzTwbjcaquBM/ynHfg2KXaNMdfh8aGnpHS4aLgeJq\nZNm60S17rgYUFzpYFAqFdQHE7ozhco6hq8e4ZN9Qqd5M2ayGf98UfPQLv4jtt9FK4ZdzVOaqtHJF\n8rSQNQgdhaV8RNNgd7U5tUW826CVwplL0EJHCkVEXJQ4adky3Gwjm6khcSWCtM9oDYWQzgZG22zs\naxmbp8mMhmk+k+m5YE8kyxUuGHhSETaiHujJwvybs6VsxCMW0cupYXHJpttMVJNgfyCP/M58RnSh\nGhE87CBPJvuvhyX5sz6d99qI46nyzqCElsTIqAeUzo2IaIcLM8m24rzs3RSifIRTAmoJmcjsUDAW\nI6YMvHcQa6yKSYnPohWht5eR1xuYAQdR8zGVErJWQ2+sIMbrmIqLUQpRa4ESUFRQctn59T+g84/+\nCdxFP9MfRlBcLLo2WtlqUZf52i2/jo+P32LiXC6Xb2G+roZoc785ZMC7oEij0eDtt99e8/D7ciPL\nqtRac/369VXJsnWjC7IrAXFjDJOTk1y4cIGRkZGeg8XVq1dXtO3bRVcIYKljud284e2ig+EPTcS3\nQsl//3/8c4ZrE8RFh+J0g6DgEOUUlh3jOwXytTZis8YaM/jDFu54hHbALvrISYi3p0LYEtR0kqXF\nwwqVegzqgkK4IXqbDdsM4aCFkAZTthDlCOEbxACQivfEW23URCb78/J0gSzeswVntE8tdcRhrIMN\nvAsTUK9hV3cAF3rvW+Nl3JlJWs88TfzSd3HmtgDne+/n5BHiPeNwMfkbs2kE+/okPPAAobqOiDUc\n2IM8fh6rUyCWdYQ2BPsGKJyeo/XeAs7x1CbKgFPqA7h70SMcUQhfkxvr4O/L4Z5IADPM2zjExBWF\nbScMHVEHvaOIvNZCpLI+YrKNUSAnapicgjiGrXlMXiGmOqAkFAQml3zXLL9J4fdfIP6V//2O1389\n40cFFBeLLPN1oZTdYibO3R6l7/srPub7zUsR3gVFhBAcPHhwyVm/9SIDdDPTyclJzp8/z8aNG1ds\nq5SNlQp512o1zp07tyYHi6ViKYm2JUcsFkSM4Uuqw9dkSM1Idp77Ps/+4C8RKkaFMcFwjsKNFuGI\nRe6mhzdUQG+TxMbCIsCyY+KSgu0x6nKyTuGlijibLayxlFmZN8TbFHqbQimDVQZZD4lKLtbVBBii\nfQWsi0mpMNz8GPLBCfS2PYjiIGbqG4gowkiJuNSXp7GG9gF9UJRTs1hXLiB276czV0Fcudg/N9t3\noKYTpM2ffpXW4+9DnH5z3vlQ0xOIco5QCIQxiL174cQk9tgY0ZM/Bt//Hnac3Mjc8RqdJ56CV17G\nDbcCc1DfCCQPQPVdhyhP3cTIAKEN0oB5cAAjIuSJOvaYj3FTos4lj3iDhG0GPSUQrkH4oHMBEmDS\nEA/aqGqI2a3QTh4ciRyro7fkENU2FMFsKGK0QE61wJXIokK99W38Y19DH/3QbT8H6xk/yqB4u7id\niXMXKFutFidOnEBKuWwT5/sxU7x/eLa3iXK5vCQgrtnwNhO1Wo12u83Nmzd53/vex4EDB9bEMFsu\nKHqex/Hjxzl37hwHDx7k8OHD7wggwu3PV9dkOYqiednh7QDRYPiGavLPnAn+s1VDyxa7zAT/8j+8\ngBM0yQce0hZYhESbbESQimO7BlnVWCnLMsy7mEFBnEsUh4wNViprpocUxgL/0RxRyUF5MdalAGtM\nJACRL6KuJ8say0Zd7Zct1WQVNXsT++3vY8UNxI4N1B55FG/PfpTfB0UZZCymSmXE1QQE7cuj5Pbt\nQAxk1EK29wWVZRiSG3ARpUyPacMw4vJ57PMnEU99INkP0T/X7tg5zIZhrPMne6/ZM+NEg0O4F84A\nUJy4inn0fQAMSo07NY1/6JHe8uK8QVxLzpVsGqJHHkte1xA9NoIa08iOITqYVCfUWIgeSPqN0RZJ\n+KBNVBDIm02oNZNrohMGr9lQQMw0kY0GjAjiDXlAIHSA80e/RlivEUURcRyv23dusVhOJePvwnYt\ny2JoaIidO3eSy+V44oknePzxx9m+fTtCCCYmJnjzzTd57bXXOHnyJFevXmVubq6n4rVciTdIvBQP\nHjzIvn37+PSnP33L+77v87M/+7Ps27eP97///Vy+fBmAb3zjGxw5coTDhw9z5MgRvvWtb63b8a8m\n7ntQXE4sVxT8TtFut3nrrbcYHR0ll8utGygtBYpRFDE6Osobb7zBli1bOHr06Loo4NwpFuqodjPD\n7o1uKTAEeE3W+WX3Al+2JrBos93MMBxe4h/9+19mZHYaaUtwoTDXQts2oLHmQsJdDvkbHeINFqod\nE2+TOFUfqxEhU05MvMNNNLBtidnyEOaBzTjXYtyLyU042LILmXotNjduQ6THEu/akwgEAKYygLh+\npbe//sQ17LlJNl55i/zOTZhnnsGksn/iWn85vXsvIjO8rRwLuWszJu0ZiQXfSOlKxOGH+i/s2dv7\n0rqXTmI2bsK60hdwkHPTWE8eQWZA2Zq4CkceR5o+yMjYx+zcjXXxNAB2RtjVOnQIsb/vexiOTyX7\nbjuYqSIxpw8HAAAgAElEQVTGSsGwnqi7iBji/cmNU5bAqofY1yOi7Tayrol22qhJj3iDwFQ7EGvC\njXm0b1C1OrLkILwIVZ+i+IVf6En8xXFMGIaEYbjuQLlWctpq416JgUO/2tWVstu+fTvvec97OHr0\nKEeOHGHXrl3Yts3U1BSvvPIKjz/+OL/7u7/L66+/zle/+lVu3Lhx23V3vRS/+tWvcurUKf70T/+U\nU6dOzVsm66X4wgsv8PGPfxyAjRs38pWvfIUTJ07wR3/0R/z8z//8O3oelor7HhTfSU9FSOr8Z86c\n4a233mL79u0cPXoUy7LW7ct9O1A0xjA2NsYrr7yC67o89dRTjIyM3PF4V+thuNg+LVSj6e7jUmB4\nRjb4NfckX3AuIOmw08yxOb6G377CM9/8v/mxN17BIiRX98C2iIsCp93GFC2i7Q7YybpNRRLvtdFb\nbGTHJL3DieTjLgaeJH7kCGb3DpwzV5GzN9E7DyCCVAVmy47e/uQ29tVK2k5/xqu9aXsP3GLbIX/z\neu89qSOcEy+h9m1HP/0Mcnqyf4ALx1xaNazLZ5AP7cM4LnJyYt7bYnYS++QrmPcnWaGw+1UF2ahh\nPXIIWZudf/6NRzy8ad5reTvqgTSANXoScbAPfOrCKfRD78UIgZgdw7pxFWMlN+/CzevEDz9CdOQo\nuesXaO1P/PHktas0HzyQ/HzVJvzAM9ijPvHD+5KVlt3kvfQ86ZKNasUE2x3UdAfZifCHHULpEVVi\nIhkizn0f62//gFwuh+M42LaNUmrdgfJelU/vFRgvFd2Rp61bt3LgwAGeeeYZjh07xrPPPssDDzzA\nt7/9bT760Y/y/PPPL/r3WS9Fx3F6XorZ+Iu/+As++tGPAomX4je/+U2MMTz++OM94fCHH34Yz/PW\nJIW51rjve4rLidWAotaaK1euMD4+Pk+WLbu+9WC4LrZv09PTjI6OrkioHPplz7XeLLqZYvaGtVTf\ncEw0+KI9ygVZY9A4HNQaYVq0O22syOOpxk0+/LUvI6VBuop4k8Ed8wh25nAvdtCbDFY1wATpR3pw\nC/LMOGx+AngJvfMQ6swp9Mg2TA6sM6+jt+9BdAGlkhl/yZxP2emXS4tO/zyafAYgN++gfK3fF+RG\n0hNU45dh82bip59GvnYsyTJbfYVwY9vIK+cAsEbfInzqaeSxl/vvVwYQVxOCjXXlBNGOB1ATfRcC\nAKU08cOPoE4e770WXzxJZ+s2BmaSDM8MDKKOfxf9vqdQr3w3eU1KpJ7PYDV5B/Po+1CXXk/W89j7\nUcdSP7xiAftyso2C1z8Gt5yAfGfbDuJgjgEgnPCwAHWug964BTV2g+iBQazLVeINEqsZITua6EEH\nuxkj2xFxWaJLiljH6L/8Lf7scJXcjiNs11vZobczbIYQiB5LOat2BH23lO5D11LErXvZU1xvK6p3\nKpRSSCn54Ac/yEc+8pE7LrsWL8Ws+s+Xv/xlHn/8cVzXXccjWVnc95nicuJ2ThmLhTGG8fFxvv/9\n72OM4amnnurV77PrW29PRUiYtK+//jrXr1/n0Ucf5eDBgyuyuFkpaWex6JZobt68Sa1Wm3ejWiym\naPF55xifyH2Lm3KSx3TETm4ggmvY1evstuZ4MN/kH3zhfyXfaWPHAULFSCmIdtvYswHxsIWaBTOw\nETkVET/2NNap64goQnRBpDxMfOTHEDkfderVZF+Ht/Z3JAN+YiIBNQPIqxczr/cBSbQavZ8LW/v2\nOFF5EDXR95PsRCHq7e8S738AvXM38kqfRWoe3I/I9h4J0GlGCKD37O9lo7LTRD6wCTk1P5OkNYcI\n6r0ssLXtAZz6DJUrb6MfTDI2ffAhRBwjJs5j3KRkrx96L+rEd9EHH+5v/+QbiWFy7xgzk/85CzOU\nEDjk1fPEDyU9SOvMD4ifeJri+AnK6eh/fvIa3t6HETqmPpSIXtTVNoSGYMsR1LQm3rcBVdPIakS8\nxcZULIQE5Rjs0OO5z30e453jgnqTLzlf4jP5z/Fn9l/zX5zXuWBfJ7YTgYdsRmlZ1rIzyr+LRJs7\nxWqP9254KXbj5MmTfPzjH+fzn//8ivdzPeNH45HlHYzllE9t214WiM3MzDA6OsrAwMAdZx3XGxQ7\nnQ4nT56k2Wxy8ODBZTfGF1vXakExO2/4wAMPMDU1xdjYGM1ms0cL76rRFItFGqLD161z/Bf7HAZ4\nKs4Tigm0jsnXfDYWPPJDDhXg6Oc+R7k6h7QTModzwyfaWUkMcxGYvY+jXv0m8Y73EA8ZUDFCx+iN\n25DXr2AGh5Ps8Pj3iPc+gppMs6p0djEBv2S8QW8YQaalULN9N/LK5eT14c290qYRgsJkRtAhA2xi\n91448Xrvd7eRjC5Y187T2LILCjsojyaEl2hwACc7BWM7qB+8RPz+p5OMLjf/aVnkc+j3fwD13cRm\nx5QryMunEXHM3MFHGXr7Ldxdu+DEGMIY9FAJLgFxqok6e5P4fc+gvv8SDBThGphCJvvdsQvhZpR7\nLp0jfvgwYmoKee5V9KGjMJ72R7sPW+VBzKCLiCPE5TPEB96LOvc2dvqkX75+kch2Gbo2irf/MJEF\ntYcfQUuDvWEj8YEyOT2BfeEG5EFbiWZ4vtng733u3/L6v/gfGJAuczrAU6cJTJlL8hwxZeoyZKfe\nxIPxFnbqzWw2FURqvpXNKLv/A73PdxiGuK5718HxR81guNFovONeit3lf/qnf5o//uM/Zu/evSve\nz/WM+x4UYWVOGYtF1mNwObJs6wWKcRwzPT3N7OwsDz300IqG/heL1YLiwnnDQqHArl27eu9HUUS9\nXqder3P6yjlODI5zbnsdX0Uc9CyUM4svIgZr4LpVckMWm8ihxHUOfvGrDF2dQFgG6QlwBoj3bMca\nPYE+8F+BuY6cSXp2pljAeuvrxHven/w+spu4tAkRzqBOp6WcXJ9kJMeSLNDs2IO8fBGjFHrfIcwD\ne8CxMeUyZtMwYRBSd/KUdu3AdfIYZaPOnoI0W5Rjl/onw+3PuZpiGWv8cu/3wo4HkG99D+/ok+SO\nvYpfr5J9bNI3r6EAeeZl4vc+hpi7ueBMx8jTr6D3HUSeP0u89wDW+WMADE6cRW/djqhP9ZZW535A\nfPQp5Pk+SMurpzFDw8iLJ3rL6AOHkOdOYR7YgTz1MnrrDmQ323VtzN69yFM3kKdfxwxtRMxNI0+9\ngd62E7NtBHXqFUypgmjWewP48vSbtIe3UpiZIHzm76ObN7AHK+SOv0T86I+jTn6HcM8jiJvjSG+S\n+p7DxCqm2LmGHTSxnZCB8Sne98d/wMV//DOUDASmjCdmyQuLaXGNbXozIYYfqFm+Yr+OLySPRnsY\nMCX26mH26A24ar75tdaaa9euMTU1xXve855emb93ftKqxjsFlD9qoLjcTHEtXorVapXnnnuOT33q\nUzz99NMr3sf1jndBcRlxOw9Ez/PmyaMtN0Nba5kyK0c3MDDA1q1bez5va4mVjp4sd97QsizKGyqc\n2TTOMesKddFmbyxQ8RxNp0qlZpETNZQTsDHOkw/nKKmYXX/5twycH4NCEfQ2yBUR1WZijGi50JmF\nTg1x4yLR0b+HOv7XGKmQl85gpMQMVFAXv4/e8xjyenKTF9VEjkZv3Y0cu4wpDRDvPYgZ3oAcP4ew\nQ9TJpK8XPPQkzujrKMB69Mew3kqANX7saUR4E/2eB9Gb9yDHxhBzyXpFvdo7bv3gPtSZzIxh5CO0\nJjf6KvHTz1C60GfnxQNDWBNJFibimLA+jhA23VzRCIEcG03Mky0f7bg0Qo9uJ1T4HvGBnagT359/\n8st2Mg/RXa42Q/T0P8B6+T/3r2PRxeQLyCsnEHGMfmAnpKAoZm5gNiQjISLwiR9+AvXaSwhjiA8e\nwno9WU/86DOoN15CnnkDf9NW3KkJrO27iPftRU2fRkyPw1QeUxpEXjiByZVQOkbOThAf/jEq1WuI\n+g2iB/bhlUcQ9UsoM8XA6Bi7/9MXGfuHP0lo2xTVCIGyKGMzJy9im2FcihR1gVnhUZeXmSXH6+oy\nUMYH9utB3qM3sLXtMnXyIoODgzz55JM9Qlg2o+z+HEVR77O8nkB5r0Bxtdu9G16Kv/d7v8f58+f5\n5Cc/ySc/+UkAvv71r8+z5Lqb8S4osnSmaNs2zWafXBBFEZcuXWJqaoq9e/eu2D1jLZni3NwcZ8+e\n7ZVoW60WExMTS//hMmK5YL2S4XuN5rg6y5vWWWq0KZuYbaZFR81R9GBTq02uEDNiFbBNhBvOYnsN\nNn7rNfKnGnSC7eRqHUzeYPIlLHMTeWmM+LGfwHrta8T7n8FsAWESwIl3HkZeu4Y+/BjqzPeSnlzK\nGjWFMiIltujd+zEjm5FXfoDszKDSjEtM92nnJkOekXP9DIxUV1VOXMJs2oKcOUd8+CFwh5CnT/SX\nW1AxkNf6GaWoT6APHkAeey0Z+di9D3Hytd771o6d6Pokcb2A6rRpb9lBcXYs3e5l6oceZ2Buviav\ncAT68BFUpnxL3EE//hTq2Hf7y4U1THkAkZoqq3NvEn3g72O9/vVk/aeOoTdtQU7dwOx+EGSGeHT+\nBCZfBDeHvPomZmAYUZtBXj2LtmxkFBJv2Y6pzaLyIG+eRtRniA89hTr1MvEjP446/p0ERE++RLz3\nMcT1i4hgFn3wCVSnRv7maahU0MWjCFFn+Mo4hb/+Djf/6/fSMHOYOIdWRTapEr57k5ys0FCaTcLG\n0dvwMbRkm7a4wRY9QpWYL0U3ubhBUHnG4QNa8ZaZ5JAusZ8iNmoeYCwk7yxkUBtjeozYpQhkC+NH\nLVNcyZziar0Uf/3X/3/23iy2jiy98/ydcyLuvvBy30VSJEXtSim1y1UuL1U97QEaYzfGL36Ylwbm\nZQYN+21gGB50w5iBH8ZtoIEeo+alUTAaM+hpzLjbVW27XOXMlFLKVCpTK0VKXMWdvJd3XyLinDMP\nQZFUVlWmlJXVHlfl96Kry9hu3Bvxj+/7/v//9/v8/u///hsf288qvgLF14iXIPay9PLixQuGhoa+\nkC3b4e29SdRqNWZnZ7HWvlKibTabPzU55mV8Hii+rk/py5iVc3zsPKYoSsSsZgyfpigR8QKSjTLR\nSEBnyiUufFKUSJgy8UaN+AcbqI1hiCms38TvTeEu3SfYzVOL9uD0JlHb6yghMFEHd/qH6P5QYG7b\ne7CNPBgfUQ8HHYq9/p8ZmIAhB3QJEZRQzz8I+4kre3ZpqTbEagiEfq6b6J6UwqYyiJUDGzaxfqA7\n3D93y9PooyexA1lM6gTy4w+gWt7/u+kfRm4eNBBtVy/q8U30hUvIe/fgU/1nEYsSmVtGn7qIvXuX\n6OAwFA56Nm59m1oiRTp/UGI1QQNV3sLGEohmPcz+Fh5BLIlNphG1Cjbbjpz+AHPyKurD9w52GD34\n3oXvYU4exeoA+fwuaB/TfwS5toSolNDnroPUqJnb6DNhhih2t6kePUt69j7xleeYc2dRz26jT99A\nPXwPUdgIs935B9h4CrkwjY3EEdpHFjbQZ64jX8yAX8ccPQmxKEL52FgngizxVcvwe1WCS2kabZqy\nqFL2A2qeoqwbJIMIJD2krRERaYadGIHK0AwKlBrbxOIuV0wPDWHYkFvM4PD/Og5JmyZAcMokOKuT\nnDQJuuSeefyha/uwNeOn+5TwKlB+et3D8fclyfgiEzIg7L3+fYyO+/uMr9inrxFKKarVKrdv36bV\nanH58mWGh4e/cDnlTUDR932mp6d5+PAhw8PDnD9//pWe5ZfBGH2dbb2J3nBVrvEX7l9y032fFjsM\n2hZZsYMyeXKlXZLeFoPJgOFYk26q9Og83bUt2pcaxN8XyK2BcAKucpGpJLHCMmLsbWK1bdJtKZK1\nFWJrz6hMXcKZ/jt8N4mYf0Rt/C3s5gxydw3iYR/E5HqRawvo4xew3VnUygfI9RnkSsgCtf1jiFJY\n+iy39R2wPftH9z+PGRpHvLwpdvUjD2WTYvOAaUpbO3JnBbV4B3P2+CtXl+0bePUk6dBJQM18gHnr\nHKLwav9QbIcAqmY/pHn+EvWdV4XTkeERkk4zzNoAE40hFx8jd1YpHQ21g62xYwiviSjnMafOhstN\nHkcYjZz5ENMZltzNyATO/e9jxg8xUafvoU+cQvhhBm77DjF1jY+c38tGZz/Gj4aZeLJRCvuykxOQ\n2tMoPv8Em8oiNxYxxy4iqiXMxLnwmMbPIxcfo4+eRS7NQLOGGbuA2F5Hzt1D7G4i6nlwBKIFaj2J\n836E9Jymt9pk0C0ynGgxlq4wloOjTpyc8kmbMq6/At5DIqV12mmRw8MTy3RQIGsrdFufhsgjxCYd\nVFgWVf7ELfL1xCq/Flvlj5wK31ZN7okAj5BBLaXEcRwikQjRaJRIJEIkEsFxHJRS+45NL5mvnuft\ns15fAmgQBP9gpCCfVT37eY6vMkU+m4FaKpWYnp6m2Wxy5cqVL82F5vNA8fBMx5GREaampn5iv+7L\nYrL+uJ7im5RKi6LIh85HrMk1hA3otRojSkgT0FaronWJzrhD2q2TQBG3FZItj1gJ2JSITQWFOrJS\ngFoT3G6s1oh0Arn8BH3u13A+/iuC0euIbo+kchFY5PBpAgRuaQV3O8ymWi/mSQD17iNE2nJE1j7C\neCPhZ+qfQC6ELjC6rXdfdhFPHyoTyUNP1bEDTaLtG4btvcyzvQu5eUg32DgoseMK5MYj9NUbyHt3\nXz2nQiDXDjJPubWAGRjDrqiwp9fTj9w62G5k+T7u0Ak4XC1tlJH5VfSZkKlqJ0+h5sP9ZF88wu8f\nRh8aYiym7+B19GDL2yjC/qCZGIWdDWx3N+w+C827X4bjQOwQE3X6LqZnALG9jtBFvKnzRB/eQTVr\n2NM34OP3kGuLBF/7Fs79/4yNp7HJDKJWPsgWq6HEQy5NY3I92GgEfeEaSIM1Lmb4GqK2jRmZACeC\nKO+EwNgsYtvbMCaGytewT+PYkkO2t0W8s0IiKmjIOgmZJS6hotuQjYBkykHlAhq6jBENcuVu6mYb\nzy2xm7AcC+JkTA9NpXghPepC8ys6h29jzMgq7+DwfxAhXU3hG8klBdcFnJWSI67dB7fXzSiDIMDz\nvFfe+y8FkF+0fAq/WAOG4StQ/IlRr9eZnZ1Fa83U1BQzMzNfmlfoZw0aPjzBoqenhytXrnxm2eNn\nlSm+CRi2aHHX+Yh5tYDBJ2cDXFFC2IB0s0nQypOJKLJJnxgeKdsg4deJ1iyy5CDyAnZ8RDkAr4UV\nSUhkUM/fx7aNYVUK+lKIWhkbTSKEh1r+ENM2iU21Q9wl8vRd9PgNWH2Bae8nXq7jnb2C8ptE1qdp\nJjuJbSwC0Ii18TLXru8WeMmtc0o7+59JHJJciPIhx5hDPmy2fxQKewJ5JZHLswfLZXOIJYOaeQ8z\nOrIv/wCwQ0eRWwd6RTM0jpq9g754BXnnDmbgCHLmEAIePY7aWsB09SG317HJNHIp9DZV0zfRJ89B\n7IBlKXSA7O4gsXJwPFL76MkR4tMHgmr55A7NwTEiS/fDbS08QE+dRT29jzlxFvX0TihRKWwhdIAZ\nOoLpG8RZvgP1LqzjIIIAtTyNjcQwU2eRpbBvKhoV9KkbqE/eQy48DEumK8/Qp65BVEJM4Dz9AfrE\nDdTsLfT4ZdSz97HpFCQSyKU72PYezOgp0AbhV5DNOjaSQpSrSJUE38WtG9wOTTTlE3HqBC2waoN0\ndpCalAQ2g+9IjNSYbJ5O20HR+GT8JGXRwrY28IQkbWMcczIIlScpE7RUjD7a2InUiWZ9cl6EuUac\n/1hIsug59EjDf6M0PcLydspwIWHIOvxYoIRQ2vD06VM6Ozv3zS0ODxk4TOT5WQBlEAQkEonPX/BQ\ntFqtX7jSKXwFisCrT0Ke5zE3N0exWGRycpKOjo79ssiXFT8puyuVSszMzJBIJLhw4cJruTp82aDo\n+/4rT7mfBYYWy0P1iMdqmqaok7EBSVHDiCYJz0c3d3CVoD9tiYgaaWuImhLJpo9TFVBUULJQDacw\noC1Yg5ACCluY8RvIx3+LGbqI2nyKbcXRE2dwHr2PaR8BbbE9OeRcKMZ/yQA1Y2HW5C7fxSWUSLhD\nk7AHemLPVcYiSGyvYIXA6xlCxBKoc1ew0Rgi0Oj+QawUCD/AjE4httYQWwd2bjiH6P7Dk6jlpwd/\naxyI+7EecvsB+u1rqLu3sF09cAgUccOHHjV7m+aFSzQrJV6hNsQTiOouZug4tpjHHJ1CzR2QckRt\nE+qfMmmIKszkadT9A4ecSCqCmTiNfBaSgYQ16OFB5OMDQlGzWSLuuNjNWYTXxExegMKeTd38I5od\nWVJAtLSNPnUN9cmtkNF68ddRS7cQrRr62Nuombv7YCiqJfTpG+A3INZCLXyETbZh4xnkwgNsIoco\nrELgY9vHkHN3MIMnIBpDLb2PTcQw/achaCLKu9ikQjRjiLwH1gFfYWJVrFunqztFEE9RY4cIKapy\nmpjpw1iJtll8fFqyRiO2yrDpx3fiJIjxTJVo9zXSS1DzPIrJCE2nxPGGQ9y0UZFZPolJiv0+3/Bc\nTDXGw4bkB1XJdzYVqSo0fMGFnOFrbZoTScuZDoMjworP5uYmU1NTr8gbPs18fXkdv/z3MJHnpwXK\nL0LwKZVKP3Of5P8/xleguBdaa5aXl1lbW2N0dPSVcuWXXT74dPm00Wjw7NkzPM/j+PHjbzTp+ss8\nNikllUqFRqNBNBr9zAtxRa5wV31EXu4StT79+HiihKMNTr0ItOhPCKJOjZSVOHabdABuM8CpuFAS\niIqGSgMqTUS5gqjUsfEhbAAyGsDGPPrkL+M8+BuC0W8g87MIE17Yun8SZ+5dTNdZpD+HyXRDoxLq\n+2pbiFoBPXwG9exVob5RLpHNJYLRE9A/iMivYvOLmN4c8fn7kIdS/wmyy6Fcwhs9Q2Qp3Ibp6QJl\n0JeuQdNHlA85vrR3vpzGhHUc5PKBSbftH0E+XkMt3EJfuQreq+5IYnNx/3V04UPk2a/DyoFcQ+yV\nhNWLafT566A/JZtpy2EzWdg65AQQUci1WWy6DbFnICB2XoDrhqOt9ioAsdYOZvw08nkIlMntRWqX\nvkHy8Q/Cdabv0Mx2EivtUBkaI5GLQv7F3vaWsSo0UBCUwIS/adEMWa2iVkKf+iXUJ+9i4y6q8Ali\nu4oev4h6/iH6+C+hpt9Fj1xHPbuJnrqOnLm150PrITamMUcvgLTIrSfgCkzXCBiJLG1h29IEuz7V\nYgOVc8j1tiHq0JI+TiRBQzZxTJSm2AUiVOU2cTuIsFGSJodPCyF2CWSNk6YdISNUYpKdRJ2MqPBW\n0EdFKrapsxZpMRhIJuoJtIihowk2RYJeG+U+ilja0qMt+SL8Lx+5LBUlCWW4JrboiPfwjWNjZD1L\n2lheXlYvr69PM18/y3TgiwLlFymfvgnz9OcpvgJFwplh9+/fp7+//3PLlV9GvCyfBkHA/Pw8Ozs7\nTExMvDIw9L9kvLz4stksxWKRR48e4fs+yWRy34Umk8ngOA4VUeWO+pAFtQD4DBhBS27gW0jXPQKz\nSzYaJeNCFEOCbSLGkAjArceQVQdqNpw63xLgOwgbBSkwuUHk1lOEb7GJYUR6C1EuoodOIYtziMoG\nlAP0yeuorScIvwFNHyskZvw8avYdRL4MpUb4wfaE+lZI7PIMlb5x5OAwiYW7iNITdF87aj0sQ0bi\nB0bdydTB03xTOPsC+3pbH6nFB5B/gU23g9dEX7wM5Qa0GvvrmCPHUEsHo5uwh8Thc7cx429h2zoQ\nxTymZwi5c9A/tEMTRKZ/iD5/HXXvJqZnEHkI7OTMLfTJa69+f23tqCfvoU9eQD3+CBuJIZceI5pV\n9NQ11Ee3MCPHkFszAOiz11Ef38QMjqFePEEPHguNwPeIFdFoax84pQ5oDvTi+i1S5Tlkvk6jrZt4\ncQu5vUJr6iIyFsGdv4k+HvYO5eoz9NTbqKd3kS+mCd6+gTP3g30QFKVtrJDIuY8wmS7k87uYXD9y\n+RHEMwjjILceoyevI7bnEKaKGT4JjkBWdiBmMJkstYqP0SXS/Z04IgalJliHmA5QqSZOVOAoi2sz\nRJEoYgRU0aKKL5ZJmRFqCAKTpCXquJRoYhi1Weo2gpA1YtLFFZKzNsdWBFKOoaqb1OpQwmHX9RiO\nemTLLq1mjHtenFqn5FxHFV0IqKsc2zWH6dtQ3RRsVwTnBg2/dlQz0mY5P24Y6zvUu/0ZAeUXYZ+W\ny+WvMsVf1EilUp9py/YyvqxBw1JKarUad+7c+amkHT9tfLpvGIlEmJyc3P9bvV6nVCqxtbXFs/ln\nLHWvsja8hS88hvwYyt2kpJp0N+MEehXpuByJpBCiQcrWUGKFpO7A9RtEK80QCOthdiiqATQdrMli\nA4uwVUQLbNsoYncZUVsDX4csRiuQLx4RDF4KgTAoIQsr2FgGUStiJo4jKmsIv44euYwqhH0zsb2C\nTrVTGDhK2+4i6dpzNL2Ixp5Uo1baPxdi80BmIQ+9Tvm1/ddu5KCcXcr00rb6BDV3ByslweAxOHMZ\n+ehDaGuHQ6oNuXZQnjRDx1Bz9zAdA+jYMJVkllzxECh2dMPmLHLuFvrURXAjUDxguNojx1Av7mP6\nhpHry3ufM/xX7i5h022Y4XHUYki6kU9vYcZPYdvaYHtm73imselsyCgtz6NWZtCnrqAe3saMn8aZ\nuUXzxEVij8ISbXb1KfrC13GefD/8nRwZhWJYUg38OrHtsCdpFh9CJI7yGlAvYqUI/VvjezKFhY+x\nqXbk9iL62FXUzJ6xwtNbmPYj0MhjhiehXkAf+UeIeh57ZAobiyNadWRtDZtuJwBahSKRVB03lwZc\nqNZDw3DXBTwcXUclBJFYgUhkm6bMAYbADuPiIMhgRBOHJlIW6dVHaRJQEVCUq3SYDozVRMiwIGu4\ntBi1KZpAzUkxl2tyxPgc8ZK0VJJnOsKcipJJ+Ixtlwl8hZNzWa9BjxPwYS3CYJ/l3JghWbX8X+8p\nnpQ2URwAACAASURBVM1LohLe7jGg4fwJw+WTmjMTlsGB1wfKTxujw48C5RdhnxaLxV/ITPErSQah\nOP/zAPHL6t1tb29z9+5dgiD4qaUdXzReZ76hEIJkMkl/fz/R40keXZ/j2bEl4hFDvylTiU1jak3S\n1V2aap524nQ4Hi5VspSIUidrJNFmnXh5HdGIQVNCS4FOYXUKK9ugVUYVHmKjfYjaAugWJjOAKC5h\neiZR6zehEWAGTiOUQW0+BJXBComevILwV5HVTeSLR+GBB+HNxB86TiWdRsQqtKVc3FpIiBH5PV/T\nWArxIuwBmo5B5N60e9M5jMyHJBebzCJWD8qgbv6gn5huO3D5aPWO4a5No1bv0BzuoWYOfiemfwRR\nPCT8z4XVAJlfBV0g2XUwJR2A+l7p0Vrk+gNs9NUbmc11IZpVSEZCckv/KHIPFEVpBzNxDCIHN08B\nIJqIzUM6y2oBPXUG+UqJdhEbjWMS4XUgt55jX7JukxmEc8i4/NmHmMGjWCmJxQ1MXQzPT6NEayw0\nCldrz9k+cRm1/D4sfIROtSOaVcxQOHZKbC5gnShy+QnBhV9FeC+wbQ7q+XeRwTbOygeoUpglOnPf\nQ4gSJpNE155Rs5poWx9urAfRsoh6AaGb2ABEM0BUmsiqh6rXcVoBUd/FsbvEAUfOExMF4uI5aeuT\nsYIu044jmkiRR8g5JnWUjFUEoskLtcAwRSaMQ8JK1mWNDbXO26bKCSPxhWEp0WSru8bFxC6dbov0\nUIbIQIZo2qUoFA9iDqMTVXoTeerVGh9tWx5pxdm3NUeOGJopaMbg9seC/+GfRzh2Ic7IqTj//PcU\n/+JfSP7jfxR82p9DSolSCtd1f8QY/SV4vrzGPc+j0QgrGW8yausXtXz6FSjy+qbgP82g4Uqlwt27\nd1lbW+Ott94iFot9aSNk3mQO4mEtFXy23rBMjX8X+S7fjv8H1mSRSZMCtUTgthioJIinl0jFFYPC\nQUXWkbUWXnkdr1hABfexRYlqNLHNdmiBaOyEpBDtIHQFoavhgNrei6gX38d0nEZuf4io7GLGL6EW\n3sGkhyGeAnaRy/ewTgxRLWCOHkdUNhBBE9N1DGENVkWg1aA2cYpqIkI2/xBpfNRueEcxuQHkZsiO\nNANTCL1nCN514NNquw5mKZrByUP6xKF9sISD7AzA7Tyw2Is1d0mtf0R98jjNjn5KsVf7w17hYLai\n8hs4O0/Qx8+H+06kkS+mDxYWArk7i+k5GMkjiuFnkevP0afPY3tf1T/K+U8g8qnpBKkUduRVk2Uh\nPWzuYGSP3N2gdvwCaukeAJHaLubUhfCzT5xAzdxEj+wBmjHYXA5z+ipqfRq5+hQbDfm88bVpbDKL\nPn2dTr2GVQ7Kq1PvGgnXnblDK92NLG7QOPV1bLuD0LvI0grCD8BawIH6LqZzEvXiLmbwEnZnFbvz\nnCAzTpstECk9QNhtbCqHiebAJBDlIqK6hQjq2EBg/Ryy2Ybbckl6GRJGEjdZYtYhQgxHlHHEJlGx\nQNa2SFlLl0njiBquKGLEKidNjH4Tx4g6JblJXKxzI3DpsGBEjYJbokdUGfSrmGyLoX5LkAQbwB0R\nIzkkuTxqGcvFKUXbeWDS2C44M1CmrOvIdJmibuDXmrz3yKGYEVz+lubsGc3Dacn/+e8Vv/u7ln/6\nT3cYG1vnt35rhz/5kwL/6T9ts7Z28KACPx4oPc/j4cOHdHd347ruG82k/Ipo81V8ZnxRPWCr1eLZ\ns2fU63WOHTv2Wua6X/TYPivbfVNrtpvOA/7G/QCPgCndCWKdiqjSW+tEiGWIwRF9hJbzMY7ppMPG\nkRlNxsYQpkGi0of2GgS1LaqVHI63g/RTKN2G6+eRtQImOoL0F8Ez6JEbqGd/i+6/gSo8xug2bCKL\n6R7HmftbdM8NhFhHT34NNf8OyBxiMxS0i1oJ0ztBo2uA5LMfkgRineEN3LQPIddDILRdI7C9l+05\nh5i9vnfo9aEHH/fgfJruIeQe4cV0DryiIxT1gzKsGZ5CzX1CYnM6BIThc9i1p4jAJ3CiRDcOMrZm\n3xjx/DNsrYA+fRkCva81BDAjx1HLH2M6j2DrbdhoArl2wFp1nt4iOPvLr353E2eQ+RlsW9dBhpqM\nIZc+CP1e1xf3lmxgk+4rvUQjWthcD2I3PK9y/iPM0DhyJQRKcehuITcXMQPhA4So5MN+4aN3EfUy\nwYVvoea+jzABeuIG6ul7pNYeYLqGkdvLiL6jNPt6ia6/h6+iRJfvUek+QXrzCc3Ry8Re3EGPX0ct\n3CQYPAtrTzDKQfYcJ75xD9N7DBvrRBbnsX4R2z4QSlKUwjop0C40ighTAR+coIg0Do5JI2PzBOII\nhjradiGJAxEMHoHcIQbEzRFqGOLCwYgSKWtpUaXHdgISJSrEidCwDplmnILTYiKZxKDw6vCOEYhO\ny6lOn4EyVIqSJ55gKyq4MWEo7ErSMklQh0jZcH9ZUW5Iet9qcYQK3q5kMR9jdSnK6dGA+acxfL+f\niYkWWm/xb//tMrOzuwgB3/qWxNoGb73VxaVLPXzzm8Phd7k3z3V7e/tHyHuf16OEEGBLpRKDgwcP\nib8o8VWm+JrxpqCotWZubo67d+/S2dnJxYsXfyaACJ/vRPOS1POTSqWHY1Fu8r9F/x/+2n1Ep8lx\nxsRRlIi2YmSLNaQs0x4ZoEcqPLlJh57EkfN7fUQPZSAVbOKoFgknRlSO0RaHpOPiihb429RKPgXZ\nidi8Q9UZQTSfQrmKGbuOLD2DwAt/mZko6sUdrHRCt5PRcUQtzA5tbjwcj9RzlCAZRXghuADYZCdy\nNcy4bG744MO1mthoAjNyCpuIo89dR5+5BG4TMzGGGRtCUMBMjqJPnMAmFPr8DfTpaxBLYPcE/bb7\nUGYZTSBWZg72ETsg7CDAmX+H5lA/jc4B7OhxpD70G8qGY3OEDpDLH1CMfuqhZm+QsdxZwgwPYYdf\nzfZMZz/qxYeYvpGDN6MSUd/FDIfZpY0lkKsPEIGHbU9hhQjJPS8eoFafUBsP7fFMLEl65yl24OB8\niVYdMzGB8MNZk/LFE/TxS+HyY5MIWti9n5FcuIfNdGIzHcjdx9hsaOYs1x5j4xmEDrDtvZjuERx/\nDdcNUH4dp/8YAIlWEaOiuKuPaEXasEuf0Ipk8XdWsSqCGHoLqZsEY78MkTZEs4TOnQYdQ27PQr0a\nvq5vI+tzCNMEKUAEIKNguxDWIE0WgcGlSlTsoOQ8KbFOkhrtNknWdoIIqMklUmKFPgMRK6gKn125\nSr9tkbAeeJoXqko0sstZR5OVgk0puJOpMjSww7cydXqEYU0K7jmCTCeczRmakbCC3nItfh3eXXUR\nA4KvXQ44PaQw0RxrXhvNWISRYZ+1gqV3rEwkUcLztnnnnYCZmR6OHx/n0qUMlUqU3d0o7767SV9f\nmK3XajU++ugjjDG8/fbbP8JmP5xRRiKRHym9SilZX1/nO9/5Dvl8nl+0+AoU+XLLp9ZaVldXuX37\nNkoprl69Sm9v74/s401Knp8XPw4UX6dveDjqePw79zbfidxG4zKhk3TQINAWVWoQ9XZJpdJ0RxLE\nZQXHJmmzEXy5Sk5PEBULKKtxRRX0KE5rE7wI0i5jgzREO4k2F4nE+8jGVmiLRNEDF0iVP6YWGcXX\n65SLu+hWlUrHWdTWe5jYMJgAPfEryPpDhG6gVvckFrUK/vGrVLNtRLY/AidKMh8SWkz35H72Q6uJ\nPvoW+uzXEE4Z0k2I1nHm/wa1fBNsA7X2EJmfD+3ItueQ+QWEX8GZewe19B5y/TZq+y70JdFnzmPb\n2rCZsBdoho8hgoNMUxQOmj+t3qOIZpV4aYmY3EH0v1rqjDQPJmoILG3VORpTb++/568c6B7VykNM\nJv7K+nZgFOHVIKWw0XiYSa7ujYRauoc+ew0zcQbRqu9t4xHmzFXs4AgvfwXx3efYdDt26i1Eq4J6\n9gH6aNgXtKkcauE99NDUwXGW19FHTiAX3keuTWOmwjFdolnDDB/DDA8jiyv7ZWhR28WMnNlbpozt\nb0eWlhAmwCJQ87fR/adQpTXs2CWUV0P0HKOeG6Y+cArVrmj2DePu/C2lCMj8ewTBCkHCRZVvYjs7\nMN1HsREX6ybRsSGMMwJNAQ0DtXAyiqyWcaoN4rVN4p4mbmK4NkbMdKNsBoRPIKdJUiBOgQ4bpV33\nYYSlLFdJilWO6QQJ47ARlFmJbzBh6hwjjrQus6pG0dnmmq1yVgvqjmY55rOc9bmYMzgxSHaBykAU\ny8NtxX0pmTxjuDKmsRrmK5KPdyUTRy3WkSSGXUbPuvT2plhfzfHBByNYO8bVqxKlGtRqDmtrkosX\n2/nOd64xNZVhaWmJR48ecezYMY4ePfrafIWXQOk4Dn/xF3/Bb//2b/PHf/zH/O7v/u5rrf/zFF+V\nT/fip52pCFAoFJidnf3cIcOHt/dlOEZ8GhRfZocv2bKfd2HcUYu868xhseRMjAw1AmrU6zUyXo1o\nOiAmI7ThY0QN1wpiog42StbG8eRz2uwQSm6gml049gXWG0eYEjboQvmP0fYsJnMStX0XnbqE2v47\nhPs2dvACye0VhC7i5iYRTp5k4QEGRaVSJZ7LYnbmSNgALzJAJFYkOHoFtfB3uNUWNjECgOk9g5rd\nE7T7PnryMsRd5NJHCL+BzryN3AlJMybbDxt7jND4oew9cUAqsF0HBtxm4DhqJZRYyNWPIZmFaAV9\n5i1spg+7/DB0fGnrQq4flEeDxEHWKIIWcuch+q1LyKdPwI0i1w4yTDM4idqeJVbeRJ+5jshvEMsf\n2lYshXr2fXbHz5J7vsf2rOfD+Ytbc+iTV6GlUSsf7K8j1+6jh0+/8l2LrWkascy+o49qVtDj1xCl\ngx6p8KtYx8GMn0ItvItwwYpwwLPceYF/8ZuoYkjSkYWQoCNaDXAkorU3eWP+A/TQKdSLR8j5D9GT\nV5CVJ+B3h+O9NmfDEunzm2HmLyRi7Qnl8WtE64+JdY4Q2byJHr5CevU2evAibdsPCNqHkLqBKC1S\nSk6RXv+EZnYAGU0Qrd/C5kYhFkfYLazSEMmCC7gW6zhY1wc8hNjEIYMUGyTtAJI6OTOIZ1MEwseX\nqySNwlIhZ+NkbB/1oEE+KKKSAeeDMRqkKEmPF7JGBsVU0EtNSOqqyXrcZ9CmCHSCWjqgMybRRUG+\nrLhdU3QMW96KaCjCxrZgoSTpTluODxqKgWD0tIWyRa8L/uaWAiu4+PWAqPHBdiBlkmi0xb/6Vy6D\ngzU2NlZ5+HAbpRS5XI7d3V2CINiXUr1O5PN5fu/3fg+lFD/4wQ/o6Oj4/JV+DuOrTPE147NAsVar\nce/ePZaWljh9+jTHjx//XLD7Mj1LX5oBHC6Vvs4Uiw1R5c/ce3zfWaaFIGs9tNhizWzhl4pEYptU\n23fJSAchlyjKF6QsILbAeiRpYnHJmi6M+JhoM4egCM0+FPexXgIcB2t6kM0HWBPBZEZRO++i228g\nvSfgA46D7r+Os/NDiI8h4inM+K+Ta93HTeRIlOYw0qUZaPyUoZxfQeoWrcwRIruL4YfREn3kHMHp\nryErj1D5OyB0KOEAEIds0CoHJSGRP5A7iN1DFD/vQHdI6oBpagaOI2pFhNGo1Y+Ru0+wQ13os9fR\nA8deOb8J79CUjL1+mlr+ADvUiZ56i8PfjM2F5UYByKWbmLFXtyXGTiGtpm33Pt7UBYJsJ876oUxy\n7n0ayU85ICVTCCr7ZV+ASlsPTvunyBOiFUo29kJuzqNP30Cuh+Ar155iTlwFQE9exFm6icn2hKuW\nNjHHLmB6RpBbH0DswKxeWA8rBLa9H9oUolVGbj/HjF8Jt7v6CJtsR249p3nq1/FTmgg7RHQZt7qG\njeWQG48xqX7kzjNsLIdqtXD8MiJ3jGzpAbbjbeL1AsorUhcj2J1ZaoVdGrUMtrBKUA2wNRdR3UHU\nI0QrddwmxLwMyrhEzFGwGXzhY8U2jqgg5LOQcGMjBEg8sQO1LQh26IhG6TM9WBEgRI2q3GbSROgx\nURxRR1DDp0XOOhTiLcbbKmTjLVxP8H7NYaFNcOao5kKbpuYLZpqSp1ZydsRgHEj2QluPIS4NH99X\n3FpSdJyAb/5qQMyFVuCwuJTgn/yTNv76r7u4dCmL1pparcaFCxf4pV/6JUZHR3Fdl62tLT7++GNu\n377NgwcPWFxcpFAo/EjFy1rLd7/7XX7jN36D3/qt3+LP//zPf2EBEb7KFPfjdWYq1uv1V957aQlX\nKpWYnJykvb39tff3s7BnewmynweGPobvOcvcVevUaJHBJy62mFdF0g1JLw1qHXkiJsmwEew6j8mZ\nTtIU2HU+pk+P4sglApsiY6NoESEZTKEjt4iWryFEA23Oo4J30PprkGwHT6Iq76MTN7Adw6idT7Ay\nGv4CVRm1fgvrZrARBynzyO29G77Tjh6/jhGK7Pw7BDJGrhqKAINoFyJeo9I2SLI6R8zboZG4iBPs\nAdqeoN4KhVwLMxub6kTsgYlpH0ZuLYavs73IPRKMdWP7PUkAUT5gjJI++I5NxyByO1yf8jql3rPE\nj18iNvcgZJKuH3K16R6G3T09YX4R2zewb4cGQPWQ7yogyvPoU1dRj/aGBotg/29u8TH65NcQD/7q\n4FjiGeKFT2h2DBHbc5yptA+QWb9HY+IS8Zkwg0ylY6iVT9AnrqKe7G3bCULT7UgM4YWMRhGx2EQS\n0QyBXW7OYNMdiNYmolXDjJ2EUjjZQy5/gjlyDLmyiFh9GPqYPr+D3JglOPUNZPkpavEmeuRt1OJd\n5Op9TLobWdnCn/glPL1LLP9DSHSidmdDh5uVm+iBS6jVD7C5EdhZh+wQojCNGfs6UCU49o+Rfgkz\nfAVli8SiLkSuktBFglg7vkngE1ALMrimgPZ94rEonqphnV2EyOCoFaQ5gScWUfotagjiZhhDFE9s\no+U2qVIXuDEasRhVmafdKFoEWDK41iEQFbptgl00Dh2syiaD1jCgo7RMhg/rMdaigo6hgIt18EqC\nNS147ksm2g1JAzUJA0MWXbaU1gXvzTtEBiy/PBwg8lApCTYKglzC8u//g8ep46GOeHp6mnQ6zcWL\nF/elGOl0mnQ6TX9/f/jbs5ZarUa5XGZ7e5u5uTm01vzZn/0ZPT09PH/+HGMMf/VXf/WlDCv/hx7q\nD//wD99k+Tda+B9SvGRi/aRoNptUq1U6OzsxxrC4uMjMzAy9vb1MTU29sdluPp8nmUwSj8c/f+Gf\nEC/nG3qex/Pnz9nd3aXVamGtxXXdH1s2fSzK/O+RGf7O2aQoWhy1AZtylbIIOFKKEk/UwXE5oruJ\nUUULQZ/uQ4o8GkWPaaMpHxCzXcTEApYGCZPBCE2i3otrf4htHQXZwtpJVOuHGH8UEYlg3UGc0g/Q\n6nQILiKH2rmJSZzHZnsxqQGctR9ism8ht2cwR64h6suo4mMaVUHEL2J6LiHzC5iBc6iEi9OaI9o1\nQmQnNL/WIoXTyOM5KdTOKgKL13MCZ3evFDp4Dpl/WRY9te8mY4bOHLw/fHr/tU13IgorB1mdG0NU\n9zxWj5xBFkKgs0IR1zXc4jy2swt99AJy/fn+ejbbjiyGrE6rHERrF1FaxBy7gWg0QsOCvWVNrg9V\nXESUVzFHryDKBURjE/HSRs0EmK4u8DVibzKHOXYBVZhDtuWgqRGBh0y4qGYJVV6jlh3BKgentBDu\np1HExjuwuW5U/imiUcJMXUFuLIfkIVHAdg8hd8LsWXgN9Lmvo16EI6Pk7ip6+ByyuIE5dgmiAfJl\nP1X7Yb1VSujMIOv5vYzdgrYIr4YZOIkWDgEbyFSCSG0dckeguo2obmHbhlDbj9GDl5E7T9HHvglu\nCztwFLX7A2znKE7h+9jMEKrwfuij2lpH2B2IZHAb95CpEWJmhmiyjYjwcJwqJkjh2A28ahLPVGi2\njuCZGFpGsSJFVS0QwSKNS80rouoDxJJdlCIlPLlFr8khbJqCEKzJbXpQpG0EbeI8UQEVUWPSSLI2\nTtkkuSktfqLJpUjAYCNKyZPMe5K8lZyJGKqBoDMdWr8lmpb3phUbgWRsxHC+0+BVBDslwdy65L//\n7wL+zf/q09NtWVlZ4fnz50xOTjIwMPCZLRIhBJFIhHQ6TWdnJwMDA/T391OpVPje976H4zg0m02+\n/e1v88477/Cbv/mbP6+TMf7n11lIvOHMrJ/bAVsvjbB/UhSLRVZWVujs7GRubo7e3l5GRka+sCXc\n7OwsuVzuC1u7fbpvCOFkj3K5TKlUolwuY4whnU6TyWQQbSn+71yBv3Q3sALOaQdfbKODgLZqQF/C\nw3UFESvIUgfRxLGWLA0QDVwrSYgSliZp64B8jMSSo4phi3TzPELUcBsR3Nb3Ma0rENSxpgdVfAcj\nTyHqeUxsArX9Q4w7GfbCui8i808Q2oOKAV1H9/wKsjaNxwCx9ds042PEtuex0Qy6/yqyPI1NdO3f\noE3mNHL9ISbdh8hvIKxFD11DPbuFiaapDpzD1ktoAUQSxP1dHNtCpLqRhTDrtB3jiNIW1klg2gaR\nlRI0m9hYFufR9/f204Wo7OyTeGr9x0luhhmlHjqDevFg//vRUxeh0UQUaojdTRD+PiFHj55Frd3f\nX9Y/+V/hfPQ3+7pJfeI6av5meFxCoqe+hfPwu/vLm7ZeRGMD2zWOWF1HNGvoqTOotXD/evgSurBL\npHSQqZrcIKZjDOf5O/vvldsnMG6ctp1wPSsVJjcB2U7U2nt7n+sy6ukdbDwN7RFMog+1HBolmI4j\nEEiE2Aj7toMX943K9fgNUAFq4zZ6+Apq/vbeZ7+BevYetc5JdDZCpvgIk+pFeHWEV0YP3UAtvIfp\nmgK/hBmYRAariOoCpusUqnAf3XsZtX0H3XcZWbyL6TiHrMxgMoOhDCNmIdqB0EvotgsIuYVJ9yNi\nHjYWQ8dWCNxempEHBP5ZGs4ifqubhnYJnIBqbYyKkARtJZL2KDVhKMkKCX2ahoiwIspURJMu0421\nvawLxZyqkbQuA7qbGm08kpIVCRkjGW11UGnG2G5GeNpwOdeybO9KOoQlosGWLGtzkoWiJBezvJ3R\nmAIUtgWLy5LxXsO/+R89poYsjUaD6elpkskk4+PjX+j+U6/X+YM/+AOePXvGt7/9bY4cCdnUL8fV\njYyMvPE2/4HEayH9V5niXrzMun5S7O7usrS0hJSSU6dO0dXV9VM50RSLRZRSb2T+/fI4Dw/7PWzn\n9OOeBuPxOP85WuZfduT5OOLT1pBMNeuYoIGoWfqtoDMFRkHUamKUqMkyESwxsUNdFkhYh4jYRuOH\nOkRRw7FdJPEIaJEyXeC+i9M8gmOqGHES5T8AFLI0j06cQuga0m4gSlsgo9j4KNLZAC+Oqs6h05ch\n241tG8ZZ+1t0YFC7G0jrQ+9FbGcvNpXAWbuF8EvYWB+yso5JDSA25xCA6TkH0sUMnMCm4hDXCJkn\naqrE6ovEVJN4ZRm3uYUBnMIC0quirUUWXiBr24jaJtIrI/PTyMYatr0T4XiYodPYvnFEeRfh1QlU\nlEirgNhzr7F9E8j8XtboRhHNHWR5FagQnPpV5MbCwbKD48jCoTmM2XS4n2o9lE5kc8hyWJoUWGx3\nJzbVjXxpQjB2Drm7jKgXQmanBlma37/iZWmVUt8J4ruHDML9OnbwCPKQ+XhEGpyBQdRetiyspR5N\nYBsbuMFeqyCog4xjxk4jd2dCmUi9jjAa0SihT15GbT3dW1+DUYigFZaLbR5ZyyNLK+jB88jSOqK0\nSr73PFkzQ1RqUFFkfQfT/xayvIKob4Xjwtq7sd0dODvvYBMdCL+K8GvYeAeyvIjJHkUWnmJyx5Dl\nZ+i244hINiQ+xRLYpItIuojIOiIWQ6pFkArJPNIkEXKTiIliHYUU7aCO4LmKZiBBClTbEvFmG/Wa\nperFCHAxWlBVRbSs0K8nCUixIWvsqAKDJkqX6aYoIsyqBjVZ54JOkrAJmgheKIMvDf2NCDUk3TGL\nsZZIGd5dc6jH4HS/4ZhrKJcEi3nJi5rkf/pvff71P/PpzIas9mfPnjE+Ps7g4OAXuv/cuXOH3/md\n3+Gb3/wmf/qnf0oud9AvF0L8vDvYfJUpvkm8dHb4dDQaDWZnZ2m1WhhjuHLlypeyv6WlJZRSry2O\nfRPx/cuYFx7/MpLnqWzRbSXHTRMTFKk3qrj4dIgq5VgFqwLGPUMpuYG0ginTRkk+QSAZNZ1U1QMi\nNkmPdWnJWdL6KAlKCGIkbAQrSiRaEWLB9yCYRNUaGDWIaGik/xhbG0I0Z9CxbyLMDjZI4Oy8g46+\njahsY9qPorbugVfEC0aI1hdppq/iuhYckKv3EMZDt11Brd7GJHoRu1sIawgGv4HwGuBaRLOIzM9g\nEt0hscIadPc51PInAOjhq6iF9/deX0PN3wLAG7hIZCnMcGqZEZLlRQCMiiEEiKC5t9w53NX7NDIT\nuB1jODO39n1UTecwcicEIX30Amr1o/3vQU9cQRS3oK6Qa8/CIcLFPSu5eAZkA6F9TPs41ARidw5h\nw+/ZRhKQBIzGpI+hFh+gjxxDbRwwV/2z/xj3/l/u/9+4MUQ2jo0OIpcf7h3DRdTmh+j+q6ine+fg\n5A3kxl1stB+5GbJx9fHrmBi40zf3t1foPkO2OovS4Xnwj1zDfXILPXEZWfoYmxzenw+pR68jitsI\nsYjNDiG2lhHaR6d7sZUirfYxYpkAWVhEaA89eGmfMav730aYFjYbR+bvg/EwvedROx+he6+iNt7H\nZCcQzRVsvBubG4QoEGkgveeYxCDSW8GkhpH+C0x6GGlWMPGh8N/EEYRYIkidxk85+G6cunqGsANU\ndJ1AlBD6BPXIDpjTNEmyoV4Akqg3wK71qHsdVATYIEY1DtWIocMfo6XilIVgQTbJ2DTKdqFxKaNY\nEtBd62DWjzBgJNJTqKLDg1WHgi8YjBhOaYOXF6zuwNyW5Oqg4V//1x5H2y3NZpMnT54Qj8eZvO86\n8wAAIABJREFUmJj4Qk5YrVaLP/qjP+LOnTt8+9vf3vc4/gWL18oUv2Kf7sWnASYIAmZmZvjkk08Y\nGBjgwoULX+r+Xpd9+qZ6Q4Amlj92a/yzaJUiUU7pGBNBnY1WgSWadCYUjbYSi20+OTdHlzJsxBvE\n6t10Vh22mgVkZYhcPUvZ1En4Z0mZBCVRIxGcQ8sn1OQOEVHEU7dxAhdknab7LSQGEhvIZgnpf4SW\n17GpHLrtBk7tewi/gCreR3dcgYhFRJYRrRrCL1IWx0FagtGvEWEBVb4N1kEYDxvtRG6Eziqm8xRm\n9Fqol2vOhMuZMjIfAoVtn9wHFeShwdDeIVus1oHRtzp0KmOHbN5aXZP7gBioGGr9MQJLojyLVCXI\nBOjT19ATV/YBEYBDczCtcpDr08jCPMJbIHj7m4jSxv7fzdCJ/bKpLDzHHul7xXbOHDkVlhaDFrI6\nFw7lPQSIAOxOUxw8c7DP0XOIxi7CW8e29eydh7B0K7c/wXSPYpWL2H2G8JuQUFgnEvY6G4s4L95/\nRcqR7Uhjhk4enK/lO1Q6j6KrsyGwKbvPcJXrDzB97QjtIQtz6NFQ7K8qG/jj10jYOVRhFjMU+qWq\nlQ/Qg5fDSSbpNDhbqO0PMD0XEFhk4TkmMYDaeB/dew2ERY9+HZEsIP4/9t48RrL0LPf8vd93Tuxb\nZkbuW2XWXtW1d1d1d1XbxjZmvSMZM8hIGDQayegKhAVj5BEIEAKJGY2Qr8ce2Yj+x9JlQAhGmmEQ\n9zJgvFS3e6/uru7a96yq3DNjj3PinO/75o8TlVk2xu5qfMeeSz9/RUWciDiZGXWeeN/3eZ7XW0T1\nLqA6t3HeEKpzB5uaRjWvY1M7UfXLWL0PnI8pnYSSxQw8hs2/gKIGcoGM6xG2W2S9O2R5HOMNEkoa\nJxGBukmOTapmEtFlWmmDX+gwnsuSKgUMSJlcr0IYNql3l6mZFUabWehpvLhLkzYq2dpJK9djfz7A\n04ag6fPVwKdXhacnDY8py3JTeKWhWPQV/+FnevzdJ0LmByz379/n3LlzzM7Osn///ndFiG+++SYf\n+chHqFarfOUrX/m3SojvGO+1T/t4uBJbWFjgwoULVKtVDhw4QD6fR0RYWFhgenr6e7/YO0C32yUI\ngu+qWH0wN3ynZAjw/1jD/2gj7ljIOMUobRbdKtf9kJJoRv2QW16LvCtw0GRQEpKmwJTLU9YWP5Wl\nonIUvQgDeIHgwk3CUJENcoiEiJ2mIGlCqZG2u9D+WZxEFDprWJ3FxUeRdA/n7cBr/xM4i6ovY4v7\ncXoYpe7iZApdewHj78HW7tAq7SefVaTMZZw/jd64gPNKSdani4jHTsPAKOQ9VOcOqnUJV5hB9wU2\nrrQHVX/QkvSQYBOn00h9HTE9XGYQ2VxIMlKzg8jGAuIczs8i9VXEbsvUH+wDVNU5VH9DRTD2GOlm\nUt1ZpbGdDXTcRjUXCIcnYWAIslWkuYoQIL2k/WinD23NLcVZ3Ng4rlRCIkHCNm5wDNXYzlR1pQoq\nXMaV55DGGm5kYutxsRFuag4khbSS9J7u0A7S3Vuko9WkBbm5iKsMolrLSNTBDs9CqoTavJioWm2c\nmPXH9qHXk3motDewe57ElcfRtYsIDlIeROBKo6jWJVTcwfnFhKBx6P2P421cRZxBB5vUR46QaS7R\nnNiHF95FrCC2hzTvEZbmULkBfK5jh/ahmotI4y527AiqtQzO4eaP4G18DVeeg+46qnkHM/YUqn4N\nlxvHFkahIFD08GrPYQefQDffxGV2IHENrAO/iOotYirHIZXGDe1G6+dw+R1o+zUcE2i5gjbVJN4u\nXma9vgdVKGPVCD2JCPWb5O0ssTQQV0DZXbQEVvU1ii5DwQ3Qc1nWlUW0ZUDlCNIxGX+Epk6RF422\nlpqJafdSLHqGsSAgbw2mm+dsq8CCD7sKlqPWYupwqam4Emo+tMPwf3wg5H0Tjl4v5Pz588RxzOHD\nhykUHkpKeoeIoog//uM/5gtf+ALPPvssH/vYx34g23h+iPCO2qfvWTIewurqKlevXmV4eJhTp059\ny7ey77ca67tViu+mVXrXCP9TW3NdhK4nZFNt4tQ6131HyhZ4nwlp6Ca4AodtlhwtIomxKEZcxIZa\nxEjAvM2y7l9CnDCvR2hmL+Gcz6QdIFAXiXsV/MhRj3p4YRWV6tLTT1PxVukUL5PuzJDiP2HjeVyQ\nxpSfgcAh5dfA5vDqX8N6c0jrKq3iMeIgplxuojNZ9PLzOJVHrb6NUz5m5CnEtpDgNl7jNSRcxww9\nieon1xD1W4xeDrXUXwQ8uAdZvYrLj2JGDiKdJvgpbLaC6jRAHDZTRnXq4ASbLqIam4l9Q6dQC4kA\nxikP10+HAUg/FMHmpg7h339969/SWcWvJee0ufc4mVadbLOfO5r51ouZ1O6gNu/gCoOY0VOohwQ3\nrjiMuv9mQkpeiNlzCrX4+rc9/wai2kRDM/jrd3CDw7ByK4kwa1xJWpr3X9w6Xq9cID70k3jnt4MA\n1OYN4uldsL30A3XrBcy+p6DvDFH1+5j5p8Aa1OICdDexk8eguYod2YW+/4/YXU+jLyeCnHLtbeKD\nH6K0lIiSauWDVNbfTlrcvoek26SDOtK+ic0Po9qrqPYSdmA+8TDaNZxKoTbewkwklgy19jpm+AgU\n8om6tf4S+EVsYTd67TnM8DPozW9gS4dABdjSNJIZRkcvYbPH0cHXMbnTqO43can9SLyAdUWC3A7q\npobzDpEeuYO4As7NEKtzlO0x2q5ER9qAxkmbQN2m6kbAzrAiEaveTQbtML7zaeKxgQLZZMaVaXkG\n61VYVCHDznLUCg2d5mKvxK0MDOgNdmzE6I0C13oZbpk0lUHHfzwc8NFqooBfXFzi1q1b7N69m2p1\nO7T9UXDp0iV+9Vd/lQ996EN8/etf/76EhPxbwXuk2EetVmNxcZFjx479q2wS7xTfLZrtUcgwcvCn\nG5r/M9Qs9Anx0HCN87mIjMnxZGTIqTZGFAWXZ9h12FSr3JEO8zZDpFa5IF2qNsuwi7mt71Gw44w5\nw6paImvnKNFlRd2naHeRSt2hl64xZPYjxbOIyZELMgRxQLRxkDRNtHeckl3Fz93AhGfQ4VksuyDe\nJK6eIWp2yORuo9wkFXc+IcLNK7jUIGbgJDK4gQS30Y2XkN4mpnwaFSYzLun07RC5KdTyG9jSNGb4\nACpMFLPOL6LdbcQuI94EOjgHAYjbj1rrV0bDB1Hr/SXAI4+j64lX0MycgXZEmJ6gkZqmJIJqbSLr\nC6j7Dy0NTm8b1G1lkkxte19ioZzGb1ynPXcYvbqMuvsWD/SBUXUefzM5VoINGHfYwuOoi88jzmLH\n96Bv91dcxR0oK2z2KPpaQnJ28iCqlpyHS4MZ3kG2uR0QLlELN5jHtce2WrQulUMvnU3arhcSArNT\nh/Bu/z1m/2l0f3Zo506gG28nfs3+c6W7hqtsdzL0vXOYPafB1lGbFnX3OczMMfSdc5ApI3oV62VQ\ncUCl/jZm+iTq/htkykKUHofgHirYoJnbRaG9RuzATIyTXn0OaYMZO41eeg699Bxm7CSIQzJNiO+j\n2quY4TPozbMQZbCZSdTqc8TTP4HIApLKoN0rEAo2sxfdfAFTOIPunMVkTuDSacjmabkefuHr5OMn\nsNmbgMO4Mdq0ceb9tEVoeK+Rszuw5KirO1TMYUJSrKr7BKrJtJ3A2EEWxeeeXmLIVsjbIh18bmgI\nWOeALeA7jwYlXsg4JNPhCRNRCCus6DRvZTzqKY//JrXCf7dxkcoNx9vLeZrNJplMhuPHj5NOf1sY\nwzuAMYYvfelL/MVf/AVf/OIXOXny5CO/xr91vNc+7SOTyTA8PIzv+//iMQsLC0xNTX1fqsY4jllf\nX2dsbGxL+frAK/lOW6Vna4rfvOvz5ZrPjY5wsBhSy3VodWFP7HEoFWFVjzoGRYSVNa7rBl00B22G\nNbWJdnl2mUGyxChyVG2VCoLDp2AHKDoNpCnYUbIIjipFO42RDsaNkyVDlLpDSuYopV/Bz6ySbZcQ\nWaXeOUAYhETpKhKH+PoKjWaFonkdp0p4YRubn8PmjyDpHuJ3Ua2bqOAmNnMMVb+CQ0MQIVETM3gM\n0lncyDyuOIFSKyi9imiNbpxD4hqqsYiYDjY/jtq8juCwxbntiLfSDGozIRKXriD1u4gzOMBGPXTU\nxDNNMsPT+GsvIG4dO38cVx7GlaeQoI0EG1tJOXbqCKr2wKsoiAuRXptUuIxM7kQGZ5D6CmJjOsNz\npNsPZaPmB0itvIKdPowEPVzKR7W3dy+6ygBq7Rx25jRqfYHO0DSpTvJ8bbrYuceQbgMJE6+i0ymU\n24BCEUKDxCF250lU4xrSvo8dP4SqLWPHplDtRaSzlLQzG6swmEW17uOGpqFZS0h6bBZVv4jLTyLd\nJKfVVacR6skOQ0DEgGQw4zvw1l+nOXCAdLuvnI1bmPkTeJuv4XfvYyaeRDXuko426M18APFXSXcu\nUcsfIdtbRrUW6AycwIs2sCOzKG8N1bqCy02BCZKZavUMqnUJM3wShgp48XO4/B50+xWcN4NID+kt\nYnMnENckHjiKylxG9CqtQFNOv4WYkzj/KmLzBJykqRoYAYdPqM+TsxM4O0RbynTx0GSp6bsY2WTY\nHKHnqtzQq3RlkylbIeMqrEmay94mZQzzNo+4Cm8qxXXdZYSIY3Ee1ytyOda8LY7JnOF/r8T8xkiK\nnVOTiAiLi4uUy2VEhDt37rC4uEiz2aTX6yEi+L7/Xa8Jt2/f5hOf+ASFQoE/+7M/+6/ZWvFu8Z76\n9FHwwAT/3fDSSy9x7Nix70qc7xRBEPD2229z/Pjxb/EbvhPCXezC/3zL59llD4ewp2RIVXrENqSS\n9xio9nC5Jjd0gCeWUzbkstokj+aw0aSkjocj5SwDdAilRiQBE9anqa/QI2DejtFVb+HEMGlmCdQ5\nwDJu5+mpV9AUGbCjOOp4tkjGxVjlyEQWJW8hJkO2sY7QIGydJB2+RCccIeoNJOrIOEtJ3sTKALq+\nhrgQkzmNXn8Op0rQ1rh8FVuYR+Ia0ruLcyPojXM4rwShTS665cfQK4lvLq4+jXc3UZSa8WfQd7+R\n3B49g779wHd3Br3Qvz3xNPpOcnyrsJNCq59o4+VA2NoOYSaeQN/v++9Gj4LxoCeohddxoztRfUuC\nmTiULEHuw8wnr2/zkzg9jnSXUbV+QECmgkhry5DfyO/A70G2dit5vDyOxItbcrmN/FHKzevoqLn9\n+nNHkGAT2ewirVXM3Cn0SlJVmuHDqFuXcZOTqH4l6zJlbH4Pav3l7VCBwmi/HXl2+3WnTyPNBqqV\n/Cx2YB5ZXgSlcdUs+NnEmhIm59Kafj/5la9tvaYZexJ9+wXM/GnE30BWrydzXS+HS49C3IWKh8tU\nk2QjBDN4Am/lFXq5KToDo1SiV+mpCs6mSMcrRKXDeI23ceU92NFpvNbfY9MzID1UvIQpPoNufgOb\nmceWp1Gp+7jMKMo9R2THiJxPPnMH450mTHfoZqv0xCfQ51BuB9aVCfVFJH6GDlUW9duk3CC+m2RD\nLeDbHfTcEC1xLKkVFJqq2UuLMhf1OqEYRmyZtB1jiTzndRcQdtoSzg1wnxRXBHoIvxpl+PcmQ1aE\nXq/HpUuXUEqxd+/eb7m2xHFMo9Gg2WzSaDRot9tbNq4HmaaDg4Mopfjyl7/Mn/7pn/K5z32O97//\n/d/zGvJvFO+omnmvffoIeLAp4/tBilprut0utVqNfD7/jirD2MKfXPH4g7d8mrEwnrfsmgqouwAd\npvCKebIjDZ5PR+DSPBkL4jWp4bPHDlN1bWJpsimOiovoqSUWVYeSTTHt4IZ3B8/l2W1HWVa38Bhl\nylTYULdRbpYRl2NdXUExTtWlqOlLZO00nvcWgTTJ946g1FfB5vG7k/QyA4TtAlatI4NzZLoZctFF\njBxB1xIzd7e1g7y7R9tNYcMmunoSpYqkM99E7E1Ut4sK7mKye9Gr5wCwxcPo9oOL+HarW7WSKssh\nyINqUDRqta9KBaR2c/sX2t3eUuEXh6BPinb0EPpeQi4uVUItb5vy8bPo5b6lYedB8Adx3U2kuQyZ\n7UxRp33USj9EvH0PM1rClcZwcRdpreGmDqIWtm0PufEp9OLLdKaPk1t4jXp2mIHmdlWZq2gYOQKX\n+i3QyhRq5Y3Enzk4D86C244h1KtvEh/6MPrmP2zdJ0EdN1eG7gB0NpP7Wsu46f24WmorXEAvPEe8\n50OoVv/3unkDM/cUiEavnoUumPEn0LdeppceINN9jXjuNP7N5OdR629i5s6g2i8iLsJMJl8OJO5g\nB4qQSqObl3BxE1ucRTVvo5uXiCdO43OZstzE5mZJdW4TZ2YwQRHVvMLq4Emq6RfRrct00yfIhq9i\nU5NYfwrVfoV49KdQ6hW0fgPr7UCHZ2n0dpPLrZD1Yrq5nyQs3MVJG+cKiH6JnN1L5BQRPrH5EToS\n0tYvUrETiBugRYjYOQxZQqnT1CuMmAmc28GKGO6pW6TRzMc7EoJUIZuqw6jzmTATbJLhhupxX/U4\nYVL8bjjEXnwQWFlZ4fr16+zcuZORkRG+HQ9I72ExXhRFWyT5V3/1Vzz77LNEUUSpVOLXfu3XGB0d\nxVr7b11Q86/Ce5XiQwjD8Ls+fv78eWZnZ/9V26gftljcu3ePWq1Gu90mlUpRKpUol8uUy+V/Nk/4\n6oLif/haiqs1Yabk2DUZUVMdmimf9UyGfROOV3zoIhxIRewerCM6QojJug6e1LijmqQRTljLbbWI\nh+KALQKbeCiKTpGjDdJDu7i/FaOJYKg4wUoN5RwlBCsdPKfJEOIkJuM8lCzgaFCIMih5lV57nFK0\ngVZdTHAG3T6LI4eN94Dv4WQIiddRdgEbTqDb54gYQbdrKHrUzCEqQVKtRP5x/M3XcOLjZADE4XKT\nya48z8PpPNJuAVHSFm0vIC7GZqdQm7cAhS1MI+11kDQhOcJGg5TyyGCRXhu13o+KGz+GXkwI2Ew9\nhb6bkKDzsiAKiRI7h9mRVKNOPGz1JHQ76HsPPJGPo+9vLww2OxPxiEtXsLl9SNRCLScVrhNww1Oo\nRqJ0NSNnMBt3SLW2bR6tkd0U2lepZw9Run+BYOYE2bXtjRhm8hRq9QrSJzsAM38c0jn0xYRIXWEU\n/I3EsnL7GhKHmKnj6OZrmPEn0df6qTPTT6Da57GZHVvGfFvZgRudQt/arig3i4fJ5RXpZlLt2YHj\n6Luv4jIV7Mxc0r7uh6KbkadRi69jp2YgnUctnUtatIXZZP9jZSeSq0OvhQpXsNlJsD1UuIoZ/QCi\n76PiK8SFU+jWy4CjqQ5TtOfZTB0nXemQ9y4QeUfwuAmuRT0+TDF3jXjgFFGuh/VfBLsfq3o4fQtn\nTtJikKa0EDdAKE1CtUja7qJnR1hXiq7UKdgZNmWTUCJS5jECfFZVg7pqU7ZDeHaWFRFuqhZGYGc8\nSZsSyyJcVzEpFJ/qjfPvTBmFEEURly5dwjnHvn373pUIxjnHX/7lX/LZz36W3/7t36ZarfLqq6/y\n6quv8tGPfpSPf/zjj/ya3wtBEPC+972PMAyJ45if/dmf5fd//x11JH9Y8I4qxfdI8SF8L1K8ePEi\no6OjjxT8/QDfaW74cGXY6/Wo1+tbEW1hGJLL5ejoIT778hRnb2eZHbJUhw1htssNfJYly8FRS7ss\n1K0wmTZMDvVwxYCaH9HyQo5IiwuqgRM4aDQFmvhAHseg62KlRUxA2RkidY+OajBsyxRYo6M2KdgS\nFTp01RI5W6FMi0gtk7PjZOQeVhoUzV5SKtlKke8eRNw5wl6FvJtD+wZMAS+qIWoDglF0eBbnKtDx\nEbOKUU+gN/vtSe8Uuv4iTtIYvRsjQkgBG3ZR0iF2FSrR6yjXI84/jbfRb5fmH0ev9wUzpcPozX7k\nWe4weqN/e/AJ9GryPvXiMcrNPvGNnESvvITzSpjifsSkoNtNMlZLk+h+pWgmT21XkIAbmEQ1Ewmn\nmXgctfwKtvoEslFLdhH2TenOS0Euu7VWyZancflR1P2bSHs92Se48dbWZ6FV3on4Ptm1RVRQxw7M\noHrbBBkMHEfV7pEKlrefM3WcNDX08jqqW8cOzCLmNiJgxs+gL53F7D6DXum3jsdOoq68jJ3Zi649\naP+eQV37Jm5iGtW6hc2NQFdQrWXM/DFU4zxRbj+pfovYTD8JfoBeTr4IuFTSKnRDVXTzVczgUdTy\necQanJfDzDyFt56oU001+ZIAEM/+GLrzDcR2sLl5pLOOxHVsYTe2OI42z+Nye5HgNmJbmOIpVPMV\nXH4fpjqJH/09lhwBO8lxnk40gtFZXKWKyjl05g5KrWE4TexfxIkjcD9KXS8TyQope5iuuomhjWc+\nQJ00K95FsnaElBtmUy2j7T5aKCwpltQqBsugOUybFPdVhw3VpWyLpO0MTfG5Iz02lOFH4yr/fW+G\nKkl3aXV1lWvXrjE/P8/o6OgjXEG2sbq6ym/8xm+Qy+X43Oc+966uR+8GD4LFC4UCURRx5swZPve5\nz33fAk3+P8B7pPio6PV63zXq7erVq5TL5e/Y6vhu+Pac0ncyNwx6jv/l/4I//6ZPOddD+T28aswV\nv0xD0gxmDfvmYQNhMONI5S3ekOFVz1BXcDob0CrUMeKYssKs64B0iYnwCcjJBstqE98pHrMp7unE\nS7fHDBHKIh4eVVtAyzoC5F2alGwChqzzSckGDkPGZdHqLpYQr5PDz55DJKZidiDqNcSMkG8FCGvY\n+Al0u09+8ZOozis4fwLnZkA5nOSRaB2x61hm8OpJTqdRx9DNczh8nB1C9ZaI1ACq20ETEugJ0u0l\nBIvJ70I3ktapzc9v2TdsZjS52LoYJylI5ZEwqars4CHURv9CP/YMeimZRdrCNNgMTo8ijQ2czqAX\nkwABM3oIvf7Q/HDqcfRSQsouXcYO7EeW76NqdzCzJ9FLD1V182fQ98/iUoPY1E7wva1qFKC343FS\nq69g8zPQ0rjBSfTiQzO/qZNAgFq4gfRa2EwFsl2UDelkZ/CXN+hUd1LubNs9zMQHUCuvIHFr6754\n7sfxbv6n7c8pgpn/Cbw7D6XjDO7DqQq6mVSRsc6DDKF7bShbIE68p/25ZTz3YXTtZaSXfAEwI6fR\nC89hdpxGRRdwlFGtW8ljQ6fBxih5GVs6gVp7NakeiweQ1hJ2bDeSqiPdxYQkc/uR4B4QY8bfj3Zf\nQ+hg0k+hwpfBGerxEfLlLFK8Q0/GSaVeIYpLtMNJcsWrrMcfIEpnsdlzKFdF7Dwd9SZiDxG6adb1\nDWJpkTe7iPDoMkBNItKuQoeINb1K2eylS4kucFfVMDjGzX6akmEdw23VZcRl+fe9fRyzSYxaFEVc\nuXKFOI7f0Wq57wTnHH/7t3/LH/zBH/B7v/d7fOxjH/uBBXd3Oh3OnDnDF7/4RU6dOvUDOYd3gfdm\nit9vPJgpvlO8G4sFwN88p/lf/8pDpWA8DeQ9rhifjeU0ShynD7RopByt1RhSCpsRnlNFok2fYT/m\nmbE2oSiGuyWKfkBeNbmoO3To8aRV3FB1jCh2mUkGaNPEMmL2UHEhSBvfDVNxlp5aJKZNxeXp6Ddo\nSZdBM0mk3ySULiUzj9HniKWH1KbJV17EOY9CfBQrV7F2P5lonG66Ce4ovkSJ5SKuoOPzULC43jS6\nexbnNC6aS+K61Ci63a/6UofRm/1ZYv4ker0/t8ofRHcTolDpaaSdGNybvQwP0hsDO0iO5GJdZ5QB\nl1RWdvjYliDFFnZsEaIDpLk9c3SFWfTSWeAqNj+OGDC7TyO19W/xH7pcFbWy7Se0o4+hl5/DpTVm\nz9PwUM68E4WqJUpY6W2giAhzB3A6i2e6uGwZv181qvYdXGYA549/2yekja69jZ3YAyvruPH9W0KZ\nXPcOZudRCg+l5gA0wk300E5Ky9tEqaIbmF1n0Nf6hOtnUeEbmIntcHGp3yKcPU66KSgcnmlji0PY\nkX3ojX5ouV/EZau47BC6+TXcwF5YCxAToleeI9r7U/ibf5scm8liMyOoYAU8AwVBNi268TJm+CnU\nyjeRcCUJTe+8iHQ72MxuCDSqc5F44ANIbhXP/R3W34szm+jwmzSjnXjpAsXRBs7XKNclyysY8wRa\nXyNVqVLTc8SZ24i3gWnOEekuRu7SMqdwniJIv4JPmpLZR9ulWVeCoUXBVdlQ98GVyMeH6IlPQ+ps\nqCZjZprAVWkI3FEbhDg+Ee3ip+N50n0zztraGlevXmXHjh2MjY29KyKr1Wp85jOfod1u8w//8A/v\nusr818IYw4kTJ7h27Rq/8iu/8v8nQnzHeK9SfAjfa1PG3bt3McZspcr/S3i3ZHj5hvBH/5vPUkNo\nxhCmLNFgj2ubeXIpx5MHDW4Aup6wZoXSoGM5L9yLFCWJOViqE+QskoUgL8wVmlwohFiBOQtjroUR\nQ8kJYy5A0SKULpoeJamzptbwnGKfzbOqE9HJDjNBXV3CiWXSTGHUdcAxaMexcp3YhKTbQ+RK13ES\nULH7QCcXy6J5DPQ3waYotUdR7iLOjuIFPcRtYuxhVPM8gsOoM+hmv7WnTqG6l3B+Cat2IqYBKgUu\nD/RweMlWDReDpPuRbTGoPERtcBDbNHEY0AtjjFUolyYlFs9alC7hr72RbGUYO9MnPjBDR9Hr/Vag\neJCuIEHiZn/4OOcVsblJYAi1eAE7cnBrqwSAHdmJqvdFO/kxRLrYzCHUrZewY4+h11/bOrYxdJRS\n63VMZhKJy7jiAHppW4BjZp5Erb+ILZ9G3ziLre5BBVe23ys/h/ML6LWHqtbZZ5DeIrK+iXTWcakC\nlDTETYLcUbL3X6Ne3k+ZxLfZKTxO7t4rxHNn8BpncX4BxwRq7QqNkeOU4teIhp7Ev9OfOY4/gWTa\nyOqtxE/Z/92JbqHa/QzUwSdQi69ghw6j9AVs8QR69YX+Oe/C+aMo9wII2PwJdC2ppONIUw+OAAAg\nAElEQVShD6PkAsrdx2YOIt0FxDSw6Xlsfgatv4HzRkGyqPg6PTdEaIZIVyt4ubtYBtG8hWMEKxNE\n2tLIDeHEx+hXweVQ9ghduUfgDhBaRYdVjL8B3SGCTpVmKk9QWKPQm8Zqj4beRNsDdFEYNItqHU2G\nrN1FgMe69FhUHfaZYT4RHmeCRHMQxzFXrlyh1+uxf//+d+U7dM7xT//0T/zWb/0Wn/70p/mFX/iF\nHwoRTa1W46Mf/Sif//zneeyxx37Qp/NO8V779FHxvUhxaWmJdrvNzp07v+Pj32tu+C+hXof/8DmP\nb76uWGoo1juw43CHN+7nGKlYds8J2XHHshNudRTZvGNq1vFmUzFbdOwYslCB+55wRxS7il3Wi11W\ntGK4F3LILuNnDL4nKC9mXLW57iVKzcMmx5papEfMpC0y2P+vn0Ix4CKQDkJM0TmQJRw98i6DNdex\nBJTcCH76GhBRsHNodR5QZM0etLqEwycb7kDbe1hJ4ZkhoAnOx+8JEIBLI70NhAbOjqLaryDEGPUU\nutlXeqaeQTf6Fov0GXSjT2TZ0+haQiKmsH27nX6C/INWbflpdH/2GPg7yXQTwgrVMEaqKCniOQ1e\nDn33q4gzW3NGAIdOtjN0k2XDZvw0eqVfJak0ZugMavUOauMqZvgAunFh629rZp9BL/fbsZkZbH4f\n3o3t5cDR4Cx+u7++SjRm9oPoK/+4ld1qJ/egan0BUOUUOI1eeX779SeeRIXXsHYIvXoZ5xcgr5Go\nji3ugFoHV92TmN7772ErTyDRCqrVt2ogNHInyEdv4bl+6LdXoRONUPRvoVx/5dXwM6jFN2HAR6I1\nTOUoavFtxEaY2SfB76KWLyI2OT4e/RA6ehUxteR988fRay9jBo4gJYO07iBxIxEq5Y8hcRPJL2Oz\n+1CtlxBnsOndEPdw5SpK38VKBR1fxpKnGU2Szmbxh9Zw3g60ew6HxspT4C7TKBwm1h6xuoyTJtoc\nxEqXjpunLU2UG6CtLgCKtDlBmwpL+jKeK+AHIzS9NcLOBA1P4ds8nUxIx+9SiR+jpTL0gLuqTgqf\n/7Z3isfNLNK/7q6vr3PlyhVmZ2cZHx9/V9Vhq9Xid37nd7h16xbPPvvs9y1i8vuF3//93yefz/Pp\nT3/6B30q7xTvkeKj4l/alPEA6+vrrK6usm/fvn/22LuZG1rr+PM/t/zlX2rOv5ViZVVz4n0BNzYV\ngwMwNqHJjjsWRfH2msLz4MmjljuRMFJw5PKQrjrOo1i0imrasXPSsiDChGcZKQTkci2WvTarOuCx\nIGQl1SLwoBwY9vQ66JQlqz3KRPiqSVeaCCEjBGyoZGvGTltlQydijLHuOEHmEohjOJ4l1udxYhgw\nO3HqdRBDyezHqZdBHAVzBOlXjvnoKEolBJFtHcczL+CcQHgAZd7GuQwuHEGZOzipQFchZgOrRpGg\nmQgxvCmks4K4HtafQtrLiIuw3ijSqyE2JHY5xCm0beGc4NLTqG5/g0VxW9TTqzxNqtYnSzVCyq7h\nXJrIm8NlJkg1FpKtG8PbAh0AW5lD9duspnoEXUtakiZ/GJeexLuZ7D50SuPK22RqC5NItEhT9pKp\nr6FKo3j1bYGNGUtmp7byGKyu47KD6PZ2ko7NDeMqw8jqBqqVtEft6E5U6zrOy2Mze0Dn0esPVa3l\n3TjloTcvbr/PxFOgLXphOw4unj2NEG8JiQBq1eNk2gtketuBAuHMj5Ne2Z5DmsGTEGu06X95KZ9A\nLb2RrAcbHcVlJxIbB+AkhamcQdsXEdfGZvcg7VUk2sSUjkElh271v+DkTqBab+FS47iBQcTWUPEN\nnGRoxbvIqWvYgWNI3kO555J5spxEucu0M0cI0g4rLZy6gbhB6Fd1Temh3RiBuk0sNXw7Q2z3cl8v\nENOhZHdicAR4bEoFz2UwOFbVfVLhOI1ogCgWatmIIG04VJvmx4NjjBaqaK2J45irV68SBAH79+8n\nk3kokP4R8Pzzz/Obv/mbfPKTn+SXf/mXfyiqw9XVVXzfp1Kp0O12+chHPsJnPvMZfvqnf/oHfWrv\nFO+R4qPie5FivV5nYWHhW9oF77ZV+uKLbT7/+Zi/+zufIFDs26+InSKd1QwMp7BFoVUQ3ryTzCWe\nOGpwFcjkIPZBVxy3fMW9QAGOH9llaWQgm4Y4BZWBkJcLHUIFY2LZ7zepqYghpxh3ISnXomGbdKTF\nTDdgtZxI5ye6KVRqg0hFDLoiw84BPcQ4/GYd3w/IZlMURFCyiiMk67J46g6OLjk7hNLXgIC0ncVX\nbwMxKbOPNK8BCi88Qrr3BqCx5jAqvgz4OLMHFd1KyMTMoOJFQHBuHIlWQMDZMaS3BFicGkO6d8HG\nGDUFtddRpoPNHSNV76tSSyfRm0nFZzOzSGsBweJQyU7GoK8eHTy9NSMLM/Okg6SK6kmJTvox0mGL\nzNplbGUvuvbQMuHRE+j1ZE2USw+C6uBS07i4Aij0xjbJtIZOUGj1j1VZ4pEP4l39z1sGfjN9FL3R\nb9/6ZeLhp/AfEsKY2TPotbO49BDWjYNLoVvbrVjnFzBjJ/Bufm37OdPPIOFliEvJFg6dxg0NIb0l\nbOnJJFygshuRayCKOvuoNN7GjD6Bjl7GZiZwbUG37tEpHSCXvcCmO8xAI/kdxJkJ3Pg83urzW9Wt\nqZwCBTrsBwmUE9Wrzc5AOcT5M+j6y/2/ya5EqOOdA0Js9ml0s69KrXwY5V9G2QWcFAjNPBn7Jj01\nj4zModVXEQxWHgO3jlFCI38Aq3pY/QY4D22fIJL7NN1OYonBQU9fQ1wGZZ5mQ3o09G3StkrGjdKQ\ndSK3k5Z0yLgBVtUqBkXa7iXEo0XEstqg6gb5ic1TlNbTWwb7OI6Joohqtcr09DTFYvGRySwIAv7w\nD/+Q1157jWeffZZdu3Y90vP/S+LNN9/kl37pl7aWFPzcz/0cv/u7v/uDPq1HwXuk+Kj4XqTYbre5\nevUqR48efddkuLjY5Y//eJkvfamBc8L4eJqJiSGi2CebLdLupijMKV54KyHDI4cMQ/OOni80DHS0\nUJx2nFvT5DzH4zsMegCCtLCuhGzJsVkS7lhFGseHhjqE6YC0inE6ZEK3eEuv4QRmraZAi5oEDLoU\nc1FMbJu4uIeKWuSkRa/QwqkeY02IKknFMGumqetkhjVsZgnVJZzEVMw0oq7iJCBnpvDULZx0ydhp\nfLkJ0sWzO/DlBkiAZ3aTchdBIlS0n3TvfEJY5jBe0J81cgQd9CsxOYTup6wYbx8qvIzg6LkJvGAF\nJTFOSmB9QHCqjFXjyXJfp7GmjGrdQLXuYfP70Zt9e4VXAddDTH8+NnBq6zFT2IsOLvffP8uGO0w6\nqFFsXCNOj+DFy0hfSWMmnkGvf2Prb21G3pdYO+69jFFpJJNCmyQFxuYnEbOEy+3AddJIGKCi7QxV\nW5hBuIetnETdfgOUBzmH9BNtnPKJx34E/+Z2K9ZMJnNZM3AGdf0spMpQcIhp9OezM5CqbM1uHYKt\nnMbGG/jdC/37VJI127uGivpLjTMTEBYg30DFSYUa5Z/GW3yJoDpLVq5T4wCl9iUUlmDoSbxijF5/\nc7uVOvAjKLmKMneTFmf2FHrzeUzxGFKqQ2RR4a3k58idASwq/yJOj2KjPF58FecUvdKP4WW+ipIu\nVvaDa6FYoJ35MN2Uj/GSL0PKHMfKAqF7jLY0UG6YrjoP4kjHJ2hTZcU7j3IpinY3HVnHuFlWpUPe\nVelKi7papWAO0SKHQbivks//B6PTPBkfw+sLaYwxXL16lXa7zfT0NGEY0mg0aLVaiMhWAk2pVNoK\n6vhOOHfuHJ/61Kf4+Mc/zq//+q+jtf6Ox72Hd4331KePiu9Fag/Upw/apI9ksQhivvjFa/zRH12h\n3TZUq2n275+m2YQ4dqwsp9i91+P8BQ9uwJF9htEDlranuHZHsdoRTpywXFkURmI4PWIoTDiWlXBj\nReH5sGfa8uq6Yrbt+GA5xh8wrFjHUqSZTcfEfsgFpci6Ed4XGyIJSJNi0gVUXYdbqQ1C6XHQFFlT\na2wKZIMyg1GPtVyE15xjoGdZS8Wk9GFKWuF0E9/uIe1AywrWFUm5Kp5awkkXzw6RkhWcdFGuQop1\nnASILZNyqyAR2CLp3mpCiK6ADteSjfMug0RJALhzHtJr92+DOJVskwCiaIiU9Nc6pQ+jW/3Ul9RO\nvAe3/Wm8cAnxIlwFyIWY0nFweawtoddeSjJT0xOo2vaCYNIl6K9hlMwo1e5LSMZhCoN09D5Sm0K2\nex8rHq6+3eo05QPojcRS0h2ZRRX2kb73n7ced+U51Po9pH09sULM/SRya23L7O4GplAbd9Cb38SO\nzWIy+/AXt59vBw/g1/8eM/8k6vbrIAoVJi1uvXkWM38STAZdS85BogaSWcEVytBPihMcgWkQpXwq\n3Qf3WSQPrrQTFhNSVMF94skfQQUx9Be7+O3niXf/O7K1vwGgwgWikeOEnZiMehHpOJqZXeS69xDA\neQvY7DjSWEVciO4+TzT2E3juK0gc4nQ58ZR23kwWBxcC6BVR9j5OUnS9x/EGU2jv77AcwsVraHeR\nWMap5T5GL/WPiDiUOYiTOpGs0XYHgSyxXAd1l4ydoRfv5p63gJG7lM0uDNCWJk2mEBQ5V2JNXSft\nhkmbE4mQRm3QkYCdZo4f632Qqtv2BdZqNS5dusTU1BR79+79Z9cCY8xWAs3t27dptVpbUW1RFBHH\nMfv27eOzn/0sX/nKV/jyl7/MwYMHeQ8/OLxXKT4EY8x3XfxrjOEb3/gGu3btolKpvONtGn/zN1f5\nzGf+idu368zMlNm5c5pmM8PmZoWFhQyHD1dZWqpgjDA1ragMpghSmnM3FK1AeOKkYQMhn3eUyuBX\nHasZ4a26BhxP77GsaGEg68ikITPkuJBR3ENRVJbjI22uezGT4pjwepT9NpvSZl112WthU63TkpiM\nUxyxQse1kHaPsjMM5CFWHYx0GHEx6zqZpY21hwjyiWAl1xzAyy3hdI+sGSSrGkSyScoOUMLH0kCT\nJefSOGkjTpN3PkgbQZG2aYQWOME3OYQm4kCHeZTdBOeQuILqLYPtYWwV13ob7do4tQO/k5CR1WPJ\nXNEFODycHkf1kqxRk31yy2tnCqfQrX6VqEoggpg61p/Cpo8g3XWkdgNII9G97Upw4Cn0Zj/dxh8E\nAsR2MPnHiJkktfiPiEs+P7X0XiqmHy8nGjcwBt4ArmVR9fvgbVemNjeG6HWcP4Bzs6j1m+A1EZuE\nSTgvhysXcf5u1J3nEOew44+hGsk80uZ3Y72ZLWM8gMuMYgdHkfUVVCep7szkaVT3eWz+NHrhLEYy\n2MESvlvBlJ5C3X0JV9yJ+NeSGV3hGfS9b2AGj6PlNZxXxjKFrr+NKR9BqTexhdOoleeS4PXMNG54\nAlW/mCiGgSi1j57KkddJm7dl5knHq5jUNOnydWzqGKp9DnFhEthQ+VE8+b8BCOMq1hRJpeoE1UlE\nCnjRBRR1HGV6copG/j5WLaLtPoQ2Tt0kMB8mJEtPXcBJC+2GUGYXm5Kmoa+RtRP4rkxD3QD7OHXp\nkXYFNtQdLIaseZyueNSlQV3VyLosHwp/jIP2wJaQxhjD9evXaTabHDhw4JE26zzINH3hhRf4/Oc/\nz+XLl8lkMvzMz/wMJ0+e5PTp0z90opr/SvBe+/RRYa39jj7Eh1ultVqNzc1NGo0GQRCQy+Uol8tb\nEW3fqeXR7Ua8/voyZ8/e4sKFBmfPWu7fj9ixo8zg4CSdjmZoKINIGS9V5qtfTwGKHbssY/sd4oPO\nQscHxuC1e/3W6rwhP5oknfU8iPLQrAjXwqQ988xITJCDbNpgUjHlQpfr+Tpt5Sg6x3HbY1E6DDuP\nMWfJuBaN3jqB7rBTPBZTizhxDNs8OVrUVZ28yzJrChhp4aPJW0G5OsZ2IO6SdnUks9438Gex6Xvg\nhIqdp9dvuVbMQYxOCCprjoNOqjnfHEf179fRQVLRq0k1aOfxe/cQQmJTwQWWlNfAOQ/n5hG3gaOM\ntfOIbYHVuLiMBAtIsAo2h3Rubs8S0zPbrbp+kDSA9ceReB3pqy3j8oeQOIT2ZrIVIlxDXPL5MEPP\noGvJ85woXGkabJdIdtJbWaFgbm6RabN4mKLdnkM2Cx8kW7+K1+oT9uRpdH3bhhGP/BRq6dVtMpt4\nSG1bOAxxcUtlC+D8Em6gjLOjWwECZvwpdOebOL+KjUeRoIWk7yH9Uq+uDpP1SqR6D4UCFE+Ai9DB\n9rnG5fejwiv9+W4yC7WZ44i7grJJO9HkTqJqV3FDgyh7HevPQzdEhfeIh59Gebegl0MFiV0jzDyJ\nS6+SIflS1Y5m8EyILU2SLb5Gp3eUVHQFT3eIUvsIS7Mg/4iIBTeGjkfoelWa6Yt4di/IOk7dxdlR\nQnuKpn4bJ02UK5Kxu+iQYk2toF2WjBujqa7i2RkaDKHJsqmW6EmbgtlJz43QkIBVnVhxjkXHOR2d\noUBx63dSr9e5ePEiExMTTE9PvytlqTGGL3zhC/z1X/81f/Inf8Lc3ByvvvoqL7/8MnNzc/z8z//8\nI7/mO8HCwgK/+Iu/yNLSEkopPvnJT/KpT33qv8h7/RDiPVJ8VHw7KX6vuaFzjm63+y3xbM45isXi\nVoZpPp8niiKuX79Ou91mz549lEolVlZCXnst5KWXOrzySheRQb72tTRRBKOjcOhIgZ7TdCwst4XZ\nw44XbyZkt3+HZWze0U7BSqzYcMLeecs3NxWlFByoWvJDjmZaWNWCZCzZSsgFIAV8MBfi5zr4RBgC\nRl3AVbWCUY5SLOwUw31VZ9BlmDdJRackAhcwRI//l703D7IsK8t+f2utvfcZ85yTc2Zl1jxXd1V3\ndVdVT0W3IEhcwY97jWsYKqhXjTBCuRBwJVrsi4KGIIEG2BCiAgEhRjhhBKDGhdDvY7Cxu7qr55qn\nzqzKeTpDnnHvvdZ7/zinTlZpF9BFD6j5/NWdp3LtdfIMz37f93metWQu4lTEZjtGUZ9FlNDrBtBq\niVCt4kmCvjhNqJdRkY9fTaGDOr726SFJIqjjGfCdwtfLKOpoDL6aQ6s5lKRINQXFPCIefrgNLW1b\nQq26n6zfmSuqo5joqrfxNkzYOSCYPnAOJaXOY4fatgD6cXYA3ZxFVy+DjQGL6sz5bPZeTLU9k3KJ\nTSg70yWROPf6NkFWV9snXRiHiju5nn13YeprgpqwcA8GhVqcQVUmkYHN6MZEe13TgwSgpE7Z7iNY\nLZIKFtHS6lx3EKWroAwuuA09/yxkA1S00l3fjtwBLQ8z2xYQ2bGj3Zaxzd6HKi6iOI9S7Y+rqIB4\n8A3416hGXWorki+gVyZRYXttO3gU5c/CagvduJrDegS8VvvfxaX2HHL4IHgpzNI1RD78Y5jmU6i4\nTSZi+olTh/Dl6509pHHqFlS0iuqdAuVwsh9TP4YjoNl3Oy6ukfXbVX8zHKIu29DD59C6Cm4f2paA\nRVa9+7FK4/QFUEWQAOwDlPUMsZ5DS5aE20VTzVKTHdT0Ehk3Tk2fQ3Bg7yJSHhU1R1OXMZIiYe+g\npCLm9SwoGLaj3B29jl1uTWnunOPixYuUy2X27dtHOp3mZnDp0qWu8f13f/d3b1qhejOYnZ1ldnaW\nO+64g9XVVe68806+/OUvs2/fvldtD68h1knxpeIqKV7rNXwpc0NYmyGUy2VKpRLlcpk4junr62Ns\nbIx8Pv+iEU8iwvnzwvHjwpmzCb71LcPzz2tuv8cxUdIIwqZNQu9moZ5SnFzWlEK45xbHRFORT8FA\nXkgNORYSmlORBgV3jTmes5oNgWM4Zcn2hqxkGswlWvQqy4hd5QU/IrDC3c5D6wY+MdBkSEIum0ms\nsoy4LB6rlHUVLYp9tpemLpMQn5wYfOqg6gh18lhq+iyomEG7k6ppk1VPaysEJ0AJ1PpIJZZQXhNl\nMyRI4Uy7quxxuxE1gyJNEI0StuawodAT9JNUFbRtoGKHaU1j3AIiAcIo2rb9ftbciwk76lPvEKbZ\niV8jCzqF6lQ4cfB6lK1CHCCNCFN5DuXaA0SbvwtT66TeBOPtQ4s7VWLc8wZU3EKVFlDlC8Q9G/Gj\ntuXDJbeg5AoK254V5t6MWp3ElNqWCDu4FlIAEPXejw1DgsWn0BJSTOynlzUjfjj4JrzKJXSlXVXZ\ngSOYsEOG6btQxSWUnu7uGyAefQN6+Ry6PtW5xh347ilW1QGyqxfANnCDt2DCEzh/FAlzqKiByiy0\nW8+mF+c2AimM7vwN/HFoBEh6A0Z3CDhxD3rxSVzvEYz3SEcU049pnMJm70Knnsbpw12bhUvtQHLj\n6OYjazcb+n5spo7R7dcoiu4i4Z6jnryTRuY0UX0PqeBZjAmpN8epxLfi5Z5BqQglWTy3l7pkKJuT\nGHIk3RZa+lnEHaCsNL70sqov4lSThN1LS0ao6BkauoQSTa+9nTIZJs0kooSMZNkb38rd0VFSrJFe\npVLh9OnTjIyMsGnTppuqDp1zfO5zn+MLX/gCDz/8MK973ete8hovN972trfxzne+kze96U2v9VZe\nDayT4kuFiNBqtV6y3/DFsLS0xIULFxgcHGR0dJRqtdqtJqMoIpPJdKvJG0m3wxCeP6s5/pzmmVOa\nJYH/7wmDiGLHFkdhXNABBCmI0kKrT/FMudNaHbGYPCQToAIgK1wpCJdRKBwPZOvM+avk45DhhMeQ\n32LBlFhRdYbE0EeD6Q4BHrIZrKqSROFLRJ6Qsp6hoYpsdsOU9CWcsmRclhwxVb2IEsW43UKol/El\nQVZSGLWKJsJIi6Rq4tQkUMJvDqHTHeVleQfpfCd2rbGLIHkCpQTlhglUBVQZnMHYbaDPgGQw4WFg\nGW1TqDCNiZbR4QIqXALpR9tO1ZM4iml1hDdmK8pOoei0Q4ND6Og0Tu/CxQOYxgS62o5jsz13Y+rt\ntq54A6AbKNcW/RTdYYyLyTYm0FER23cEU+uY/pVBMmPo6DI2cQBpJjD1M93K1CXGUGqx47ncgNM7\nMcVHu+3byBRQ2RAtLar2AJnSOaRQwIunu++RePD1qHoZs9LJZO0/jImfQHS2XZktPkPYkyOpFjvX\n3IrT2/Aa18wfdYq4/3784jVCnsQ4LrcTs/KNtXMSc3dAwsesXuNvzL8BHZ9Fd/YkBNjk6zH6GyjV\nMf2bIxCV0bklFMs4tQdadZBlaoWtRLaIZ5OkvTbxrwY/QewvoTrtduXGcXY7FTOJ6BIuHMQ2s+jE\nIqV4O/gtAtdPlDiNwsfao0RKsarPIirESA+evZN5vUhdF1Gi6HNbiGSYc2YKpxwpSTFqxzlg72Cn\n3b32d3COS5cuUSwW2bdvH5lM5j98Tr8fTE9P8+u//uvs2LGDj33sYze9zsuJiYkJ7r//fk6cOPED\nnfzznwjrpPhS8Td/8zd8+ctf5tChQxw5coT9+/e/5ODeWq3GuXPn8DyPnTt3vmhr5GrafKlU6nqc\nlFJdkszn8ySTyRcl5EoNnr2oeXJK88RFzclpzdC48Oi0RoC9GxwbNgnNQFHWUNSKDaPC402DRrgj\nZ/GTdVqqjur1yGQjomSNGd2+c78vhpqukxdFlpheaVDUi1TUKiOSQrNKSddQAntdH6tqhYwkKIhH\nkvapG6JW6XUBJa8tBOm3m2joC4iKCaSHjChaeh4EBtwOQj2JJz1k7DBOiriwRdyskVB10qkVgqBE\nwg7gJdokZaJ7oFOtYPdg3GlQFlwSI4Mo2hUj8f1omUVcPyrMoKISunEBbctY/3ZM1Dnm6Zr/BtqR\nbPHziB7GcgDVKqKLJ1DSxObuw9Q7BnMJINGLcfNtY3riflRrCV16BgXYwn2YxjWRbYWjKLvSJsfl\np7F9d2Nqj6093n8XypWRqocpncCO3ItprKXX1FJHkeYK2Y59ou7vIB2053Rx8h708kXo8dDRTPd3\nSokfId96qtvqdcEQFEBkJ2ax8zyGjmLsI9jgLvTKKbA1ZHgfOjqB9e9EFy+B1jDgodw81tyLLj6J\nJEZRuRVQChfvxlQfxyW2ogorOHahaxfacX7eBqLeAbxI8OI20cVqhCW9j0z6W2glIAbcPTS8NK2g\n7WH07J1ofYbQ7aekL5Jwu9BqEaev4OytVBnGuVViv+MpLe1gNe4j7ruM0g7PZUnbXRS1z5KZQImi\n121CJMOMVhR1kZSk6HeDFFwfPxK98brqcHV1ldOnTzM0NMTmzZtvujr867/+ax5++GH+8A//kDe9\n6U2vWYj3tahWqzzwwAM89NBD/ORP/uRrvZ1XC+uk+FIRRRHPPvssjz32GMeOHePEiRNkMhkOHTrE\n4cOHOXLkCBs2bHjRN3UURVy6dIlyucyuXbsoFAovcoUb46oi7ep8stFokEwmKRQKXRGP5724g2ah\nCk9NaZ6f1Zxsab41bViow33bHCebmlwgbOgR0oUW0zpiJp0h9jVH+hsc8yx9wB4vZihVJ9INaqpJ\nWkI8tcqCbrflDtoUVbVKTjyyIuRpUVNL1HSJEZehrmcJVYgvHhtcnkVzhUASbLQjoMr4aDxxpIiJ\n9TRWLdPvxqibNhll7DZifRFRERIlyKgs1mt/uSdb+3DeeVzYg1TzJLwGCc+RMA1SyuGpZ1DKoe29\naDoePNmDsee7bUxhL5o2mVh5PcrVUaGHaiyiYouJO+3JxF0Ye83xUMEt7bQd0lh9F7ZRw5Sfx6NB\nnD2K17wmPSa7Hx09jzObcXYTunmlK+hxiU0oPbcm4kkfRYXNteOuMvswrMXDxZk3oBuXurNI8QYg\n1UK5VWxwGFWcxiZS+J19Ayz7h0hKg0yrPZeLktvxEpfB9OHCDZjS09iROzFR23JigzuhFqGDs6ir\nM01vHBvcgt+6pmo0G3DZvXitterSegcgkcTEa6d/ROYN6GACI52TSRjC2u3YnmmUuoyIQrn7UM3z\nLKZ6SWSnUHYPQVxGyTRl7yixXsSXQcQ8iYim5d6IBSL9DKgQxIP4jZTNBHHHNzZFogEAACAASURB\nVJi2O3AyyKyZxKoWge3Fb/VSjT2WMjVEOTL1QbQnhGqIyaBEkiT9rp+YJnfF97HHrtkgnHNMTEyw\ntLTEvn37yGbXwt9fChYWFnj3u99NoVDgE5/4BL29vTe1zsuNKIp461vfypvf/Gbe+973vtbbeTWx\nToo/KESElZUVjh07xqOPPsqxY8eYnZ1lx44dHD58mMOHD7N//34+85nPMD4+ztGjR29Imjdz7Waz\neZ2Ix1p7nYgnm83e8FrTNXiqaHhqRXOq5Jgg5IRrK+gOFCw2DelASCUsiZ6QUr7KZCLCiHCvE87o\nOkOiGROhX+q0VI2aqpEVi1ZVirptJ7jF5lnWS+QlScH5ZGkRqwqRWmXA+ZRMW4iTcj1kEGq6HXk2\nYnewqidISo6cLSBxmbBZJpV05P0UsXkKVEjGHiAybeLw3CCeqiKqLaCR2i68zGmc9bGru0n4LRLK\nJ6FbJGKH5x5D43Dch6aTVUofoFC0/Y+OoyieQdzOti8ubqLrT6ARbHA3xq5VcjXZTcY/i5DA6bsg\ntOjik+0KMnPkOoKw2aNo+xROHUSVZxG/D9O6OtsEye9Dx6dwZhdSz6Gkhm5dE8XWfwAdn8aZu9BL\nJ3E9ezCtteOlbOF+EIdeaJ9u79I7Uf4LKGIq4S0kanPEqQwZf+0cxjDzv+E3v9O1S4gKcP23QMvH\nrHbmlNkD6PQZnBxBrxxDEWEHjqLNd3ByL3r1SZAI17cfxWmcO4xp/BuiMzT6NoCq4zf7CaJnsKqf\nUu8oRgZIhM+jWSa0BebsPnoyBs/vnH4iAzTtUax5FFT7fWXs7TRkiLr3ePe192WUVXoomTMo8cm5\nXcRqnoZsY9FcoseNkZAEVX0F625hSc/T78ZpsUpDQsrxKHUVkqoHtBJNClGeB4o/wnBmhHQ6jVKK\narXKqVOnGBwcZPPmzTcVryYifPWrX+XDH/4wv/d7v8fb3va2H4rqENp7+4Vf+AX6+vr4xCc+8Vpv\n59XGOim+ErDWcvbsWR599FG+8pWv8O1vf5s9e/awf/9+jhw5wpEjR9i+ffsrklXonOuKeMrlMrVa\nDc/zrmu7XpvE75xjcnKS+fl5duzYQTM7yPN1xROh4emG5oVYUcgLT7j2XvcnIoZyNXwdo0xI0jRo\nmCKLul3dHLIB07pMn3gMiKbXtWjpCnVVpkcAXaKq6igRdrgRZvQkWcky6HIkaaFVC0WdrHiUzbOg\nHJl4AGQR8Rsgin63lVVzBkTT6/agKeORwENIiIA+j1LTJN3txKZd0Wk3iKIBun2GX6O0h1ThDGIT\nqOZuEhqSKsaPZzGuF0PntAa2oZhC0anezH0o9Z32Ach2P8Q+fu15TLxIsXULvZlrzPnBEYx7HCGL\nk9uRWGEqj7T9ev5WlLlmXpk4CNpBw6DLT+F67sa4a9qmqXtRLCLNArr0BC5/F4Zr5nbJQyiVQi8/\ngZImLtiASpfb+aF6E67eT2RLpMzasVe28CMgEXrlURSOlh7C5Ko4F9CqjdFjT9LK3UXC6yT3qMOo\n6goUVtHSyWpV27FuC76/ViE6tZlI7yXgmvg59tNMDKK8b3Z/JvEDtBIh4nUUwS5PY3kUm7OYRHv+\n6Nn9aOpUVJJIX0ZLLym3EasmKbGJppoi6/ai1DyWkDKbiGmRlgHq+jRaBigzgi9phBo1c4WE3UqF\nAklS1NQCDV0kY29hRfmkJcmKWiZUIa9vPsC2pa1UKhUqlQr1er0bX7Z582aGh4dvShVaLBb5jd/4\nDeI45k/+5E8YHBx8yWu8knjkkUd43etex/79+7vfUR/+8If58R//8dd4Z68K1knxlcT73vc+Lly4\nwMc+9jGGh4c5fvx4t5q8dOkSY2Nj3Wry0KFD5PP5V+RuMQzDLkmWy2XCMCSTyWCMoVQqMTo6ypYt\nW25I0jMWnok0Zx08n2hxXBwrCHcnI572Wxhgr4MxaYJqEdPEp4FSFZZ0OwblFtvDvF4hLwl6xdAr\nEZEqE6oyWdFEepGWaqBEsdGNMWcuocVQqPWi4yq5VIqEZ8mIoWlO4FSZjN2I01M41USJJuu2UTdt\nM3zG7kZUiUB68MWQQKH1aZS6gu9ub1eZAC4JcR4TtNuwjfIektkJVLSRQHpIiE8i/jcMJay6DdHP\ndW0MsboH9DFEFM36bpL+KInGFfzWOax3J4a11Js4cR+G7yCM4KKdSCT4zash2Ckk0Y+WttjH6T04\nM4opP46SGqIHIIhQ0jmU1xxAvF5M5dvtVB+VRLJDaHcZUcO4cDuiW3jx2vVX5DbSGUtQq6FbL+CS\nO1GZCRQRTu1EqglUCrRbCx+vyAMkOENCdc6YFE01cwCfBInwcTRC7O2k1bOAtgdI1J9Bs0ojfT9R\n4hg6PkKqcRKkxGrPUWLvOYy9Ez9+AlBU/FuxegHfbkebR4nCAnVvDK0SBKIQcwLnNlGil0AGQV1A\n9DK47VRlCI+Ahj6BUhZj99JkEEuRpml7O5PxYRqkWdVXCHUZEUXO3kVDwYq+QqRaeJIiZffRUsKC\nXiBUIWN2lP+j9eP0sZZKU61WOX36NLlcjt7e3u6NZ6vVIpVKdSPacrncDTUGIsK//Mu/8IEPfIAH\nH3yQn/3Zn/2hqQ7X0cU6Kb6SKBaLN5wROOe4fPkyjz76KI899hhPPPEEjUaD/fv3d4ly3759N5wR\n/iCo1WqcOnUK5xzpdJp6vY5SqjuXzOVy3VbRjbAsjlMm5qSOmVQxV3SVE7qFINztDGd1hQyajc4w\nIi1i1aCpqniEpGmwpNvtuZ22j2W9QI+kyElAToRYlWm5FUwjxMuUsKaFEsWI28SyaQtp+uw4PiEB\nPh6WlHjE+hROL5B0G7FqHqcaINDj9lHvqBSTdisGS0Aaj5hAfJT5JkqFKLcRUcvd9lytvJ9k/hQi\nGmlsIZACKV0mIaeAXYhZIx2r7oZOC1fcdrTdSNCYw2+eQMxulHe2a9SPvHsR798Qdwu6mUOFAb5d\nC+iOM+0qUMjh7AEkNPjhNY/n7saox3BsheZQO/TbreWpxul7EW8GqQ3g1Y7TYjPJwgyKCMHDuntB\nlfDsmgG/lX4d6BBvdRJj57DeGFGuCrRQ8WH8yhM0MrfjJdvVa722GRoJVF8F329XjcgQEh/CJf9n\nW9QEIL2E8QNI4uuozs/EbaRlb8d632ib7YGocQvijxB7a6IhL76fumrSMu2WsZIECXs3ZT1NpNtE\nHbhBtN3DgncR23ndeuwWnIxz2ZwD5VCiGXA7aEovE+YiKMETjyG7i4oKmNTTiHL44vGj4T3cbe9E\n075BFJFuJ2Xv3r3/QYF5dYRxddZfqVSI45h0Ok0ul6NWqzE2NoYxhoceeoiZmRk+85nPMDY2dsPP\n1jpeU6yT4g8TWq0WTz/9NI899hiPPfYYp0+fplAodEny8OHDDA8P3/TdpbWWF154gZWVFXbu3Hkd\nYVtrrxPx1Ot1EonEdW1X3/e/6/p1HJdUi3OmxnndZE41WVUVrnSqxVttinldISseA2IYkJhYlWmo\nEpqQHjRLum0SH1zNEmXKZEiTkxQ9onCqiFVFkqKweoGo8yU4aHd2j63qsRsIMAQYPGKSziPyngBV\nw7hePOWIVXtWmLYHqZunUeKTcJtJSA5fVTDqMr70EumOXxJwzX3oZFvkEjX6CFs5AkmST1QITBa8\ntVmh427EtNWR4sZQ0W6S4QxB9CyiRomDEkq17RpW3Ursz6GjvQTVZbRLYfw1sm35d2P9J1H2DvzV\nKooUJjjefTwy+4mCGl5zBL/6OGJGcdkVFO31w3gPyh8j1Vxrb9bTRxHzHCa6naD6JDbYgU0+j1IO\nkTREhxG/iFbXkKb+MUQ1COwjaARHknJwEBcnSdnTBN4C5fpOwlwNCUfJqoiEf4qa/Bg17xk8t5Gk\nJDH6BGV3iJY5hzRHMJEhma5SUnlivUjSbSMQi2BY0mUcTTJuF1qViGWUBf0CCkPBbceqOULZxpy5\nhC8ZCm6MkBVqDLFkZkhJjoIbJBTLtIaqrpJ1Wfqkl1AynDbLOOVIS5KtdoT749vZ7NbI6urNY29v\nL9u2bfu+xx1XleOVSoUvfOELfOlLX2J5eZmdO3fycz/3cxw5coQDBw7c1IHC63jFsU6KP8wQERYW\nFnjsscd49NFHefzxx1laWmLXrl1dpettt932PecaV9e5dOkS4+PjjI2NfV8f8GtFPOVyGWst2Wz2\nOhHP91rHIkypBpdVjRdMiRf0KouqQZaIK51qcbPNYFUNEzqCZsRwIoCgQl23A8AHXYZF044QG7TD\nNHWZpCTJSIK8GGK1SKzmCFAoYlq6LbLpt7spmZMgioyMkHU5PBViqOAjhPp5UA5EkXJ7aZg26flu\nFIBACgQofOeB9w2UsjhnCBsb8TPtFl3cGsKKg1aBlBKSxiPIrM36rL2PuEOQuCG8eD8Jd4HAPYcj\nSxQMgZrqvE79hGwjESVIN59CkaCZCtu+S0BkiNDbTqIlJFuPAxma6SyoTryajBOGt5K2/wvPtBXB\n9dQDWP9xlN1Nsu6hnEecvIbAZQ+R2kLK/i90J9W8GryB2LxAItpKIn4Eq3dRTSyAaqHddlKhoukV\nCL2ORUWS+NFhmt4E6GusHkv3otNTJNPtKDpxSSJ7FCsL6OBsZwNbqMs4Hi1i027dKnuYOoYAS0uf\nAhTK3UuTCA9LXZ/D0EPD7YbOzU9FXyTptrCgEmSkBx9LUU+Scfu5pJcZcP0kMVTUKk0Z4YopMuBy\n9EqKAZfhf0RHSZLo/B2FK1euMDMzw969e8nn89/1PX4jNBoNPvShD3HixAk+/elPU61WeeKJJzh+\n/DjveMc7eOCBB25q3e+FX/qlX+If//EfGRoa4sSJE9/7F9ZxLdZJ8T8b4jjm1KlT3bbrc889h+/7\n3Hnnnd1q8lpF3NTUFPPz86RSKXbs2PGSPZXXwjnXDRgol8vdNP9/7538flBUTSZVmVlV4WI8zQtq\niUbCspUcE6Yto+9zaXKi8IEMkJGIpl6kqubRwJgbZd5MANDjCvgoAvFJ4ZESiPUELTWLwZCTEaq6\n/W8zdhNNPY8nGdLSR8qlQV9G9EU0AUayRLpNNAm7l7q+hBIfV+0jqwdJpa6g9VkgQMkGrG57HiUc\no2VqbTFMGJDAke19eu1J29cRmjYhaTeKb/fi8xyevoCIIlSHsZ02r0gaHd9L4CYIeLp9JJJ/CHeV\nONwwEt1BQr6J7lS+pfBeTM/z4PIk4l141hGn1tSozm2nJf2kbIzvHgMSVP39WHMB5QZJxWMIijB4\n4po97yeWEYz5Jkq1RUFN+6PENElSQ5kTiNtAWWURWqTdNjz9FLG7g7I5CaJJxLsxbpGyzePS7RsA\namP4kqGWLmJ1FYCk24jnNrFgngfV9sSm3SbEbWbFPI90jP7Z+BZa9FI0l4g73YKcvZOmeFT1PHVd\nwpN0O/MURUSNJT1Pr9vCZeXTKykSQFmVeEt0iMN2V/fp1ut1Tp06RT6fZ9u2bTd9NNPx48d5z3ve\nw9vf/nbe9a53vapHPH37298mm83y8z//8+uk+NKxTor/2SEilMtlnnjiia6IZ3JykrGxMaIoolQq\n8bd/+7eMjo6+IkP9KIquqyZbrVY3AP3qfPJGXwjNZpNz584hIuzatQud8pjWRab0CvOqzIJeZEIv\nECvLLjvEZT2FKMhIkg0uhyYmIMKTGnEniQRgg93Kgmkb1gNJM2iHMCrGp4mmQaSnsKr9RVywt7Sr\nSUBLkrzbgofFp46hQqhmkY61JBnfRq0TNqAlS9buw1NlPHURpUpYGSLuVErGjRNRBpdGNwoQRmR6\nT6B1e67mwjuJgzYBem4Lvt2KMo+jdbuqcvZ+Wp0ZpXHj+PFOjP+dLgHG9n4a5mmQBF5rJ1GlTnpo\n7QtQ3C7qqkHSbSWpzqNUibqM4Lr72wLxDsT/n6gOATl7mJoqkZYRfH0MIWBVthDrKYwbIiODOAxV\ns9ZWDexBYlJEZq06VvHrsEqBPtm2xkiG1XAHYdORTYaQmIBwN0umjg57MC2Fl7tMs7af1Z4ZEjZP\nXgaJ9RIVeqnoeXxJ0+dGcaK5bJaJVYgRj0G3kVh6uWAm2q1ugTG7kzoZZs0cTdVEi2bA7aGJpkWL\nGV1ki+vjl8P7GJS2BUlEmJqaYnp6mj179rxkD/FVhGHIH/zBH/DII4/wmc98hr17997UOj8oJiYm\neOtb37pOii8d66T4Xw3OOT7/+c/zsY99jPvvvx9jDE8//TRRFHHbbbd1q8ndu3e/InevIkK9Xr9O\neHA1AP1qyEAqlWJqaqrr5xwYGLjhehbHoioxrReY0YvM6xWa1JntVJPjtp+yXiIioldyDNs8TteA\nErFeJicZirot7+9xQ4SqhiUm7/rJuSyil4n1FI4yObedSkfI47s+QiuIFfKqn4wJsPoFXGettD3I\n6lVyEEPG7cdg8aig1WUgdQ1BjtHsKGQT8SgqjPBSp9CmPWsNa7uJ0+11E247CdeL846jOm1TZQ9S\n0+cAj5TbRcJ5tLzj3cotrG/AJlv4bCAlHlqdp0kPruP3FPFR9nV4ahbdyZiN7QNUzbMYN0hGRjAU\nWdXziGq3ULUbQLnbsOapdqA2oOzdrOoFsm4U9HOAR1XGaekZUm4zKTE4Eizr86AELQmy8W6K9QaS\nXzscORPfS0NZavo0qBglAdg7sHFMZKawfgVXHaLkpcnYLL4X0ghmSLmDXNKzDMgAWfFpqApl+ljS\nRTKSZtAVsJLmpClilUWLYrMdJaLApF5hVTdRAv97tI+3xHsxHTFNo9Hg1KlTZLNZduzYcdOfi5Mn\nT/LOd76Tn/iJn+DBBx/8njP4VxLrpHjTWCfF/2qo1+t89KMf5b3vfe91s5B6vc6TTz7ZTeI5d+4c\nQ0ND3bi6Q4cO0d/f/4pUk9cGoC8uLlIul0kkEgwPD1MoFG4YgH4jCMKyKjGrF5nXs8zoOeb1AgOu\nnyumQ1qSpEdSWCLykiQjglNlanoCUTFDdke3mkQUI3Y7qJAEFhsvEtlVJN2eTWbsZmp6CataJF0v\nvW4TqCKiXkD0Eml7G6udtibiE7hdKCwpNIZZnIq67VjjhmkqgxCRdRsJXESkL4FpE09YGyZKhihl\nSUZbSGuP2DvfDSPQboyGsog1uNUecqli+wCIjhoTSWLdAXwMnjqP0tNYex/1DoEHbpzAbqbhHe+q\nbJUbp0aatAxg1AuImiN2d1IzZ1Hik3W7MGIomRPtGSzguWHE7cHqS9ir5G+PUFYlctJHrM8g1lAM\n+7GpCgW3mQQhVtLMmilEORKSI283UFM+i6aduqNEM2Bvp6oiVvQLiLIoG2BXN+PE4VI1WskaPa1N\nzPmKPukhwFFUK2jZwgVTJO9SDEsGcQGnvIiaijqRg738VLSDvW6o/acSYXp6mqmpKXbv3n3TaTJx\nHPPwww/z1a9+lT/7sz/j4MGDN7XOy4l1UrxprJPif1eICDMzM93Z5LFjx6hUKuzbt68r4rn11lt/\noBnktQjDkHPnzhFFUbdKvbbtGkXRdSKeGwWg3/D5IKyoEjN6llk9T0UVuWReIFIRI3aEZb1MpCKM\nGLbZjTjVxKNOrBbISYalzmzShCmUTWCTIQXXT49L4nSFpr6AqJC83c2KvthVpQ7GB9qqTGo4fZ7A\nbaPS8UoqCfBlM5Y6WSngU8KqBi3dnqsZ10tMilhV6XGbSGCJ1BJxpxqVME9TkqBD/OYQaVNH/DrO\nv4YAZSdadDuNUz+PuFuodSwMiCIZ34/oIuhnQEUYe4AVPY0mQY/bgkeRqmoS6aXO73gk7D2gVoj0\nCZRyePZ2FvQMWRkjJSDqMjVGqetZEEXObcGTPhbMc13SpD5AHI2SyjTaAhwgYQ+zrEpt9ae6DPgU\nKVBVZQbcCAkUVjJcMtOghKSkGLLjVPGZMhMo1X5O+fpOas5S98q0Ui2SjTxFM0AWn4xWFHWFQMZ4\nxjTIiGGzS7HN+bw92k0P7fdzs9nk1KlTpNNpdu7cedPV4fnz53nnO9/J0aNH+eAHP/hDoyhdJ8Wb\nxjoprmMNURTx3HPPdWeTJ06cIJ1Od6vJ75breiNcndVMTU2xfft2BgcHX/T3ReQ6Ec/q6ipa6+8r\nAP1GcDgW1SKzeo5pM8OMniEpKS6bdqyZLz79ro+KqtDTSqDrDXrShnpyAqdi8naUVV0hVE20GMbt\nDpwKMZRo6QkKbjtLHQIUURTcPkJVIyMBmiV8gm47VksCTzbSUEvk3SgJIoQGjc7jRjI4GaGplsm5\njSSJcTRpXK1mXZJWcwSrq/j1HMlgGaVyxB0VLEAQH2kHqlPE6tP47jBLpn3GpCc95OOdtPQCUacq\n066POv14JMiIh9VnwO2j2NlTwvWSddup6MtEnVas53qJZJwkAU7NEOlZfHuEWXOJpMuRbvYQtsrU\nc4rIdHyDbojAbWJFT9LqJApl7TYa9JDEUdGXEQTP7WJOrzDi+jGExPhc0hEtFdEjKUZtgRYZTpvZ\n7k3J5ng7FauoUGcpqJOpB5RVH54y9KGJPMv/cKO8xY5132ezs7NMTk6ye/du+vrWDPovBdZaPvvZ\nz/LFL36RT33qU9x77703tc4rhXVSvGmsk+I6bozvJ9f14MGDpFKpFyWrcrnM2bNnuz6vl3o3Hsdx\ndy5ZKpVoNpukUqnrRDwvNdwgJGRGzzKtZyirMmfMWRq6QaaZgQBquoYRwxY3jhCjWKWhZ+hzI8zp\nK0inEhqNd9DUNbLioykSSJJFr00mSgw9bhtlPU+fGyQpFg/pngiiJYmRcSpqlryM0iM+4LoCFi1J\ntGyiquYouHF8G9Fo1JB8hwBF49n91CmRaqYwZpq4WSDqXbNDpMM7cUYwaoZIXyZht1PUFaxqknFt\ne0pDNahdjX0TQ8IdBBweJZr6Akl3gBk9Cwi9bpw0hoqqdLNpEUOPPYRVLerqElbXMbVximlLTgpk\nxaep5mnJKPNmHiWKITdMwvUy5c3RvGq2dwNoN4JSISt6hpiIXreXS7rGmOvBEBPimNY+JdUiKz4b\nbY6YHCdMkbATCrA93siqChCJmFFV+iLF289qepbqBEFAJpOhVCqRzWbZs2fPTQdjXLlyhV/7tV9j\n3759fPSjH73pg4RfKfzMz/wM3/zmN1laWmJ4eJgPfehD/PIv//Jrva3/LFgnxXW8NDjnurmux44d\n46mn2nFpd9xxR7eizGazfPzjH+enfuqnfqDz5f49ftAA9Gthre2ekD5yyyjlnjJX9BRTZopAElzR\n87gOAW60m6ioKgVJ4lEnJYZZr12BKdH0u63M6RkG3ABZNIFoFjvzNyMBKTfGspmmx/XRJz1oMZTM\n84iKMJLElzGKepas66NXcmjRndxXC84jqo7R6inR50bIoNBiWPbWMlaz9naqqkzWpkCmcGGOYmah\n+/HO1rajjY8Kpon0Ar7ro06BuirR5zaQRIgJWLpalQIFe5AIwakFGnqGlNtAUSksEYNuGEONJlnm\nOjNc5TSDzZ1ECUdFT2JVSNoNUKGXlAQkESp6hqTbzgWzgBHNBtdH0iWZMg1Kun1+ZNZlybiNWBWz\nopep0WTUbeOkjtjokmRwtLBMqizzOsIXxQ6Xwpc8EzpmWjVBwc9EBf7vaIAA0+1WTExMUCgUiKKo\nq5K+muLU09PzPYUxzjn+8i//kk9/+tP80R/9ET/6oz+6HtP2Xw/rpLiOHwxX257Hjx/nO9/5Dn//\n93/P5OTkdS3XVzLX9aUGoAMsLi5y4cIFNm7cyNjY2H/YV4sWU3qGy2aaVeo8650hUjFaDMNujEkz\nT6/LMujSJDGU9ARNXWoflOyGmDcLBBIw4gZIiE9DT9PQc/iSxpNBVvQinngMuxF8SdLQV2jpeTxJ\n4csoy3oO3yVIlVMkdYYod5lY1VFiSLvdzOkp+mSQnPj4Ypi7hiDz9hZKqkFB0gjzGOuz6JVwnbMw\nk6VBlMpCegHrF9GSRLntLOslht0QSUJEElz21k7OGLG7icQj1HPU9QIJlyNkmCp1MhUfPwhJBANc\n8tp2El88xuxW6igWzRUiFZKUDMqNo1CkcJTUIj0yxgldRqEYlx56XIoFZbhs2qKilAT02XFEQZ0a\nM7rKFreBJzQMiWFUNDjFJZ1i0rSrxR3O4/9tZblH2tVbq9Xi9OnT+L7Prl27usQnIjQaje7N1dUb\nrKtz7Vwud91ce25ujne9610MDQ3x8Y9//KYN/ev4occ6Kb6c+MAHPsBXvvIVtNYMDQ3xhS98gQ0b\nNrzW23pVEIYhb37zmzl48CC//du/TalUetFc16tk+Urlul7dy4sFoGcyGYrFIkEQsHv37u9bFGFx\nzOp5ZtQyZ8xlJs0soPClh/lOes4G20dGUmhVp6Xm0DggRbHz+IDtJ0UOTYOmmkXhEHIUO97KATdA\n2uUQKlTVZZSDQA2z4i112o4jpCRPVU9T1wsgmoLbxRUzTd7l6Zc0gfO47J3vil367XZKKqRPUiiK\nCBFLSmh2Yvd6Vgu4ZhqbKROnKyjR9NidzJhlRlw/SWKUGC6apW7VPBZvwpKiGs/RSK7guQBftrOg\nq51WZxMhwRldwSnBiGa73UBTBSyqRaq6TkISpNw4deXoE01dreJLgad1hFXCsAvY6LKUVJJzukJL\nObLik3PDCIoEMYs0KNg8j4iHAXaiOKyE/yfO0E+7TT83N8cLL7zAzp07v6vt5yqcc9Rqte775vjx\n43zqU59ifHyckydP8lu/9Vv86q/+6qtqxF/Hq451Unw5UalUuoHBDz/8MKdOneJP//RPX+NdvXqY\nnZ1ldHT0RR9rtVo888wzXaJ8uXNdvxuutkrn5+fJZrOEYXhdAHo+n7/hXPTFIAgLqsRFM8clPceS\nWmVeN6l0PH5DtoAoj5xoAhoo6hR1i6pqk1Gf6wVJkUFjqCFUqSsod5JdMmGGnB4goSyxWiZSFYwM\nsqDbxv1eVyDnBolUhYqexhIz4HbwgpklJUlGXJ6kJJgxl4k61xywm1hSWWJiqQAAGkNJREFUjj5J\n4dHAUaOkPCqda/aGBXSzh5Yu00wXEQU99c1cTtcZc3l6UIBwXq0SdQIINtgBjOSJVI1FvYhCkXdb\nuKirbHIZ0jgQw3OmTtwRxuyOB4lVihp1ZnWZBB5pt4EpbdnsPDxiRJI8ZoRYQSBweytH1SVZ9kIm\nvZA+66GK/RSdZotnMUHE/+nD/yVtZWkYhpw5cwatNbt3775pv+Dy8jLvec97iOOYO+64g5MnT3L2\n7FkeeOAB/viP//im1vx+8bWvfY13v/vdWGv5lV/5FX7zN3/zFb3eOrpYJ8VXCh/5yEe4fPkyn/70\np1/rrfxQ4tpc16uWkJvJdf1euCr26evrY+vWrd27/DiOu22zqwHoyWTyOqJ8KV+my6rKBT3PFb3C\nCbPEdEdludkOMqlr5CRgSHySTlgyZcqd3NdRO8CCikg0hVzk6EkZil6NVd0O9C64PJYsGTEkaRGp\nEhEpFvTVFmOSAbcBiGiqJWqqyIDbznmzhBbFmMuTdxmWTZFyx3YxbEeZ1o4BlyKLw1JjWfksd66Z\ndxlyUT8tV6firxCaiJ5SP5MFyDcUIypJwgu46NWpdQIEBl0OX/rRRJRUmbpq0e/GedY02eACRkVj\nxONZE1LvCGO22TwhPfjELKs6IZbADfG8EraIYoMIppbkETTVjjvn7lqCWi3A94RFz5H2hM9lHHs6\nxdv8/DyXLl1i+/btDA0N3dR7RkT42te+xgc/+EEeeughfvqnf/q6G6ZGo0Eqlbqptb8fWGvZtWsX\n//zP/8z4+DiHDx/mr/7qr9i3b98rds11dLFOii83HnroIf7iL/6CfD7PN77xjR+6A0R/mPHdcl2v\ntl2/35PO4zjmwoUL1Go1du/eTTab/a7/XkRotVo/cAD6VZRocFEvccIsc8YsMaNW2eZGOKHLiIIR\nl2LMZijFFVbNClEQs8mOcL7T2tzo0vSJZlU1mNNLoIRB28uKMqQwDIhBS5NVLcx1CDYtAb1uCINg\naFLWS+TdCGc6M7pel2Cj66OmWqzoRVqqxagd4aIWsuIxLD6eRCxpmO0QZFoCesMBGvVVVCKknGpQ\nqPRxNmcIrGKspcjjMZ/wmPHaex9yGZA8aUBosaLqZKSfp4wlIbDDehSiJJNGMeGFOA27GmnmqxkG\njMMPYurEVIp9nI4MBuGAcQzXHAvWcF40NeDdhZjf7o8IVLs6PHu2bY/ZvXv3TftrK5UK73//+1la\nWuLP//zPb9j5eCXx6KOP8sEPfpCvf/3rQPsGG+D973//q76X/4ZYJ8WXije+8Y3Mzc39h5///u//\nPm9729u6//+Rj3yEZrPJhz70oVdze/+lcKNc1y1btnSryTvuuOM6xalzjvn5eSYmJti8efMPlPn6\ncgagV2hxyhQ5rYuc0itYm+Jk0CYRI4r9to9IWRQNirrIBtfLCb2KU5AVjz22QENZVlWJoq4wbgd4\nQQshjk0uyYBoGjgmzCKihJxLYuhFgD6BiCqGNKfMavea++wQTSWE1JjXRTa6QU5riBG2ugS9TrHQ\niricaYGCHgnIubavL0VMSaqk4wxPd/4EvQ3LaM3QSKSYS8VUPcemKM1c1EMWGFAWS0g9zPBk5zXp\nF9i/mqRqNQu+44J23FX3eHYhoNcIG1NCEkeprHm61S4HtyQcn90Zck+2Pee8Kpzatm0bw8PDN/Va\niwj/+q//yoMPPsi73/1ufvEXf/ElhUe8nPjSl77E1772NT772c8C8MUvfpFjx47xqU996jXZz38z\nrJPiK4XJyUne8pa3rJtnX2Y457h48WK37frkk08ShiG3334727dv56tf/Spvf/vbecc73vGypfFc\nix8kAP3q/icnJ5lcmcfuG+OFnGVaOb5pylgFaTEMuwIV5RgWwVAnieEp0579KYFb7AB1BSlCyqpC\nv6Q4qSNaSsiI5laXpSmakq6woKoMSZo6GZZVzDbnkxNB8HnalECBJ4qtdpCaUvQQs6pqpEOfZ3xN\nbBR50dwaJanjMWfqLJgWgzZBGBeoAFsRfEKwSb7ttYlKOeGWKUfd+UhaMZeDTY2AC8UcVQe7Eo4N\nJiSuezwaezRQ9Iqwp+poxZBOwArQ3xT+bdZgRTGcEH52W8z7tkfkvfZrcfbsWay17N2796Zf73q9\nzu/8zu9w9uxZPve5z7F58+abWuflwt/93d/x9a9//TpSfPzxx/nkJz/5mu7rvwnWSfHlxPnz59m5\ncycAn/zkJ/nWt77Fl770pVf8uu973/v4h3/4B4IgYPv27Xz+85+/6ZT//4wol8s8+OCD/NM//RMH\nDx5kYmKCoaEh7rzzTo4cOcLhw4dfsVzXqwHo10r7/30AeiaTQSlFuVzmzJkzDA4OsmXLlusqkQaW\nE7rGKR3xbdPgpG5REB8hxSXl2CKazVbQTnPGr7KqYxKi2OryXFbCZhES0iLhDI/5LVznqd4e91CX\n/7+9ew+K6r77OP4+Z3dZlrsIogIFFVHwEkfEaGJ9TFqTmEtnGjtJekk7Nc2j05hJx5jRNk2TTJ/G\nMtNmqE2n1CZa20naPnFiJ33SMulozaWPspJoohVXQC4qqNx3YdnbOb/nD2AfMV4QhOXyff0HM5zz\n27P89ru/3/n9PseCQwvQrnuJUTr1uoMmTZGoNOYHraiAjQZ7Nw0WH7HoJLY7qLXayNU1EqwhbCEr\nB2wKo/eYyzpj6PTZsdqDXIz24zA1mloTOWPoZFpMcq0Bonw6R5SV87qO3VQsaOnmQrdOku7DsOvE\nhHSOeWPxKx2bpvgPu0nIo/DoGtWGThIKezPUd2jkTFJMSzJ5NM/gwfSe+5HNzc1UVlYyY8aMIS3Q\ncjqdbNq0iW9/+9s88cQTERsdXkqmTyNKiuLNtHbtWlwuF7quk5WVRUlJCenp6df/wyF69913ufPO\nO7FarWzZsgWAoqKiYT/vaPHKK6/gdrvZvHkzUVFRI57rerlLA9D7pl0No+fDPDs7m7S0tOueO4Di\nmBagzBKi3BKgzdSoDEbRRE+vXRKyYPPbCNr9XHB40TVFbHc8nyqYCszGIDZopcoRoCE6iGbCgs54\nTnRHMdNmEBMVwIbB4WAUnt6PgVt8ikBLEEe8lc5E6NYVjrYEjgcspOgmc+0hEgMmLmWjStPQgBV+\nk+oOnUyHwmIHXSmOt1po763KS20G0V0QskIDGl4F090mR1usTLcHmGLzYu8KUO+Pp9F0gFLc7ghx\nsclCaoJC2SB1kuLllQGmxfaMDvsydPPy8gadNer3+3nppZcoKyvjt7/9LXPmzBnUcYZDKBQiNzeX\nffv2kZ6eTmFhIW+88Qbz5s2LdNMmAimK483evXvZs2cPr7/+eqSbMqr05br2TbvejFzXgegb0Uyd\nOpWYmJjwatcbDUA3UJxAcQiDUyF4xx1DXW/hWWIq3F06KXYTS3SQgNXgvDeWulDP8fJ0g6khg6AF\nzkcpLuiKed1WDnVamWRR5NhDRHvcnNPs1DliMTSNW02Ds+0aU6MVDjuENMX5Tp3aQM8xc60mU/0m\npq7RaoFGpTPXb3LwooVJNsWsBJMUU3GuU+OkTyeoNJbaDS6c10iNU0Q7IKgUrRd1Ktt6jjkz1s/k\nLj8Bw8SNhRYVzdO3trJ+GeE9pqdOnSI7O5upU6cO+r369NNPefLJJ3nwwQd55plnhm2/7FD87W9/\n43vf+x6GYbBu3TqeffbZSDdpopCiON488MADPPzww3zjG9+IdFNGtctzXZ1OJw0NDeTk5LBkyRIK\nCwtZvHjxDe1fvFTfakjTNJk7d+5nRjSXbxT3eDxYLJZ+W0KuF4BebUC5X+P9Dhv/261T5de4TTMp\na9FJskJ2rMkki8nFoM6nho6paczTDQIdGvFRihgHuM0gXR0BqlXP/tqpVpM8zSSIhtsGNabOLcrk\nYIOOXYecBJNpNkV7t8YJv47H1JhvN/Be0IiNgsR4RVBThDwaH13oubeaalcssBkEDA2vBnU+nbwo\nk0MndCw65ExVZMaYeNo1XM06zV6NZbMMXn6oheSoNtra2mhtbUUpRVpaGsnJyYMKiA8GgxQXF/P3\nv/+dHTt2sHDhwht+X8W4J0VxrBjIqtef/OQnlJeX89Zbb0km4yBcL9e1sLCQnJyca47oLn0Kw43u\nlQsGg+GRZEdHxw0HoDcH4ZDbwkG3zmG3Dm74V29hSrIqlsYbdIc03FaoCunkBD2cao3FRCcnSZEe\na+LzaRzz6rQZGtl2kxg3mApSEhRBC0QH4b2zPceMtihuTzDwBzSCVjjj18i2Kz6p1PEGYcZkxexk\nk6AHqtp06j062Qkmjg7o9sG0KT1TrvaAYt8RC6BhtyleejzIf94TQtehtbWVU6dOkZmZSWpqar9p\naZ/PR3R0dL/rc7W9pS6Xi40bN3LHHXfwox/9aNimzsWYJ0VxvNi9ezclJSXs27dv1KX2j1VKKbq6\nuigvLw8XyqqqKtLT08MLeC7NdXW5XLjdbuLi4pg9e/aQp+X6AtDb29vDxdI0zQEHoPsM+LhN51CL\nTs1FjberrTT7NJItIaZ4ugjgYFqKhVAUxGmK/TUWFBoaii+kGXR3aZhR0BjSmGxT1NfpNHk00pNM\n8qeZ4INaj061W2NytCLTUJy7qPO5qSbRceAwFAeOWQiEetJw7sg28LRqOOKgIwh2DWoqdJpbNRLi\nFF9cabB1fZB52QrDMKisrMTr9ZKfn3/FrS9XC4iPi4vDNE3a29spKCjgd7/7HX/605/49a9/zdKl\nS4f0nohxT4rieFBaWsqmTZt47733JCxgmJmmSX19fXgRT3l5OV1dXcTFxdHY2EhRURF33XXXsN2n\nujwAvbOzE5vNds0A9D5+v593j9Tiao3ntO9zOM9a0YIKb7NGbZNOWqIiP90gKgh1Ho0qt47DAvNj\nTU7W6MxMN4mJh2hN4XRZ6Oju+fy4PcuguwViEsALGCa01WvUN+jYoxQFc01iDEVHt0Zdi0a3X2N+\nksnhgzozZyrSMhSr7jZ4+rEQNhu0tbXhcrnIyMi4YmD79a5PZ2cnR44cYfv27Rw/fhzTNPnyl7/M\n7bffzrJly5gxY8bNeCsG5M033+SFF16goqICp9PJkiVLRuzcYlCkKI4HOTk5+P1+Jk+eDMCyZctG\nJHNVOjx8/PHHfPe732XBggXMmTOH8vLyEc11hasHoF86mmxqaqK2tpacnJx+X5w6vOA8reOstlDb\nCAc+sNDQ0jM9XJBloDwQEw9+C3QFQbk1Kqp1LBbFghyT1CiFx6txrl3jgltjaYbJoQ90MtIV0zMV\nsfEhGmp0TrgsKKVzy7wgga4g8fEaNruOxWrhv14MUVBgYhgGVVVVdHZ2kp+fP+goNdM02b17Nzt2\n7KC4uJiCggLKy8spKyvDNM0RXbRSUVGBruusX7+en/3sZxOyj4wxUhTF4EmHh127dnHrrbf2y6Xs\ny3W99OHMTU1NzJkz56bmul5N37RvR0cHra2tNDc3o2kaU6ZMYdKkSdcMQDdNqKjX+NSl8cG/rJQd\n1TlVq3HbbJPyD3UmT1ZkZCviJwdpP69z9JiVUEgnd1aIKC2I3Q6OWB2vz8SKibOsp8CmphosvKWN\ngB+6uqKpr4/ma1+z8PzzEB0N7e3tnDx5kvT0dDIyMgb9BaKxsZGNGzeSmZnJz3/+c+Lj44d0LW+W\nVatWTdg+MsZIURRDJx3++i7NdS0rK+OTTz7BZrOxePHicKEcaK7rQCilOHfuHGfPniU3N5eEhITw\nfUm3290vAL0vZOBqi1Q63OB06jgPWzhcrmEEfezf15NcExenWH6bB1+3ic/XU+xmzPBSUdFMZ6fJ\njBlRzJqlEwp1UV2tUVurMWtWFCUlU7jtNgeGYXD69Gk6OjrIz88f9P1wpRRvvvkmL7/8Mj/96U9Z\ns2bNqFpsJn1kzBjQP83o28QjxBhjtVpZuHAhCxcuZP369Z/Jdd2zZ0+/XNfCwkIKCgquuZDmarxe\nLxUVFcTFxVFYWBiOnUtOTiY5uSe79NIA9JaWFk6fPn3VAPTEBFj9RZPVXzR7/1bD5bJQVqaornZT\nWtpJRUWA5GSTrKwWams7yc2NJzragcPRygcftOH1KjQNnnpqBj/8YSYxMXo44WfatGkUFBQMuog1\nNTWxadMmHA4HBw4cCL/GkTLQPGQxfshIcQIbSIeXb8E3h2manD59OryI59Jc175COWfOnKtmqyql\nOHPmDA0NDcydO/eGo/6GEoDu8ZgcPdrBoUPNOJ1tNDW5uXixnrq6Dmw2ndtu+xxbt65g5crU8Ots\na2sjPz+f2NjYG75Wfa/3nXfe4cc//jHPP/88a9euHVWjw0tJHxkzZPpUDJ10+OHj9Xr56KOPKCsr\no6ysDJfLRWpqajiJpy/X9ZNPPqGqqopbbrmFmTNn3rSnww8lAL2mph2n8zz19W42bFhEfHwUbreb\niooK0tLSyMrKGnQRa29vZ8uWLXg8Hn7zm98M+ukYI0X6yJghRVEMnXT4kXOlXNeamhpsNhsPP/ww\n9913HwsWLBi2zemXBqD33Z8E+iXxxMTEfKbYmaZJTU0Nra2t5OXlXff5ltc6/4EDB/j+97/P008/\nzaOPPjoqQryvZu/evTz55JM0NTWRlJTEokWLwkHfYlSSoigGb6Q7fGlpKU899RSGYfCd73yHrVu3\nDtu5xoKKigoee+wxVq9ezZo1a/joo484dOgQx44dIzY2dthzXfsYhtEvicfr9WK328OF0mKxUFVV\nRWpq6pAWE3V1dfHcc89RU1PDq6++SmZm5k1+JUJIURRjhGEY5Obm8o9//IOMjAwKCwv54x//2G8r\nxERTWVmJ3+9n/vz5/X7fl+vqdDrDq10bGhqYNWtW+N7kUHJdB6IviefMmTN4PJ5+RXIgAeiXO3jw\nIJs3b+bxxx9nw4YNo3p0KMY0KYpibJBnzA3N5bmuR44cQSl1Q7muN6Kzs5OKigqSk5PDCTKXB6Dr\nuv6ZRTyXF2mfzxfO9H311VfDzysVYpjIlgwxNpw7d67fdFlGRgZlZWURbNHYous6eXl55OXlsW7d\nus/kur7wwgtUV1czffr0K+a6DpRSirq6Oi5cuEB+fn6/zfPx8fHEx8eTkZEB9A9Ab2xsDAegHzly\nhJSUFJKSkvjBD37AI488wv79+2/a4iEhhkqKooi4K81WjNbl92OBpmnExcWxatUqVq1aBfx/ruuh\nQ4fYv38/RUVFeL1e5s+fHw4YyM/Pv2qua1dXFydOnGDSpEkUFhZed9Rps9mYPHlyOJ5QKUV3dzf/\n/ve/2bFjB8eOHSM1NZVTp06xa9cu7r33XqZPn35Tr8O1PPPMM/z1r38lKiqKWbNmsWvXrhve5iLG\nJymKIuIyMjI4c+ZM+OezZ8+O6AfkRKDrOtnZ2WRnZ/PII48APSHiR48e5eDBgxQXF3Py5EkSExPD\nI8mlS5eSkpJCcXExhYWFLFq0iMTExEGdX9M0amtree2117jnnnsoLS3FNE2OHj1KWVkZFy5cGNH3\nfPXq1Wzbtg2r1cqWLVvYtm0bRUVFI3Z+MXrJPUURcaFQiNzcXPbt20d6ejqFhYW88cYbzJs3L9JN\nm1Auz3U9cOAAVVVV5Ofnc/fdd7N8+fJB5boahsGvfvUr9uzZQ0lJyajb3rN371727NnD66+/Humm\niOE1oOknWeYlIs5qtfLKK69w9913k5eXx0MPPTRiBXHdunVMmTLlM6s8JyJN00hLS+NLX/oSs2fP\nJhAIsHfvXn75y1+SkpLCH/7wB1avXs2dd97J5s2b+fOf/0xNTQ2maV71mDU1Ndx///20tLTw4Ycf\njrqCCLBz507WrFkT6WaIUUJGimJCe//994mLi+Ob3/wmx48fj3RzRo3Dhw8zf/78zzziSSmF2+0O\nbwlxOp3U1dWRlZUVnnItKCggNjaWnTt3snPnTrZv387KlStH/DUMJMawb/XrW2+9Jfexxz/ZkiHE\nQNTW1nL//fdLURykK+W6VldX88ADD7B9+/ZBJ9wMt927d1NSUsK+ffsG/QQPMaZIURRiIKQo3nzN\nzc0kJyeP2o34paWlbNq0iffee6/fg5nFuCb3FIUQkZGSkjJqCyLAxo0b8Xg8rF69mkWLFrFhw4ZI\nN0mMErIlQwgx4VRVVUW6CWKUGr1f5YQQQogRJkVRTGhf/epXWb58OS6Xi4yMDF577bURO/eZM2e4\n4447yMvLY968efziF78YsXMLIa5MFtoIESGNjY00NjayePFiPB4PBQUF/OUvf5nQTwcRYhjJQhsh\nRrNp06axePFioCdQOy8vj3PnzkW4VaPfc889x8KFC1m0aBF33XUXDQ0NkW6SGEdkpCjEKFBbW8vK\nlSs5fvw4CQkJkW7OqOZ2u8PXaPv27Zw4cYKSkpIIt0qMATJSFGIs6OzsZO3atRQXF0tBHIBLr1FX\nV5ck0YibSrZkCBFBwWCQtWvX8vWvf50HH3ww0s0ZM5599ll+//vfk5iYyD//+c9IN0eMIzJ9KkSE\nKKX41re+RXJyMsXFxZFuzqgykNxSgG3btuHz+XjxxRdHsnlibJKYNyFGsw8//JDPf/7zLFiwIJz+\n8tJLL3HvvfeOyPl9Ph8rV67E7/cTCoX4yle+MuaKS11dHffdd59E9ImBGFBRlOlTISJkxYoV3OCX\n0pvKbrezf/9+4uLiCAaDrFixgjVr1rBs2bKItWkgKisrmT17NgBvv/02c+fOjXCLxHgiRVGICUrT\ntPATLILBIMFgcEwsWtm6dSsulwtd18nKypKVp+KmkulTISYwwzAoKCigqqqKJ554gqKiokg3SYjh\nIlsyhBDXZrFYOHr0KGfPnsXpdMq9OTHhSVEUQpCUlMSqVasoLS2NdFOEiKgbnT4VQowTmqalAkGl\nVLumaQ7gXaBIKfU/EW6aEBEjC22EmLimAbs1TbPQM2v031IQxUQnI0UhhBCil9xTFEIIIXpJURRC\nCCF6SVEUQggheklRFEIIIXpJURRCCCF6SVEUQggheklRFEIIIXpJURRCCCF6/R+pP/Tuhi5VbwAA\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x203fc57d390>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e_oeMAGLvq6e",
        "colab_type": "code",
        "colab": {},
        "outputId": "3f331890-d4f9-440d-e2f9-81e7ccad4833"
      },
      "source": [
        "print(n.rvs(3))\n",
        "print(n.pdf([0.0,0.0]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 1.4459175  -0.88131263]\n",
            " [-0.67338593 -1.49557129]\n",
            " [-0.12533674 -0.09419818]]\n",
            "0.159154943092\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nfWruqgjvq6g",
        "colab_type": "text"
      },
      "source": [
        "## データのあてはめ\n",
        "---\n",
        "\n",
        "* ### fit(data): 与えられたデータdataに対するパラメータの**最尤推定値**\n",
        "  - 返値; 形状パラメータshape，位置パラメータloc，尺度パラメータscaleのタプル\n",
        "\n",
        "* 例: 平均 100，標準偏差 10 の正規分布に対する，10 個のランダムサンプルと 10000 個のランダムサンプルの比較\n",
        "\n",
        "    `from scipy.stats import norm\n",
        "    rvs = norm(loc=100,scale=10).rvs(size=10)\n",
        "    print( norm.fit(rvs) )`\n",
        "    > (95.753904782189622, 8.3575610605102142)\n",
        "\n",
        "    `rvs = norm(loc=100,scale=10).rvs(size=10000)\n",
        "    print( norm.fit(rvs) )`\n",
        "    > (99.948939025624099, 9.908908275994496\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AIWeaUwHvq6g",
        "colab_type": "text"
      },
      "source": [
        "* ### 平均や標準偏差を固定して推定（引数 floc, fscaleで指定）\n",
        "\n",
        "    `rvs = norm(loc=100,scale=10).rvs(size=10000)\n",
        "    print( norm.fit(rvs, floc=100) )`\n",
        "    > (100.0, 9.9532121919298557)  \n",
        "    \n",
        "* ガンマ分布のように形状パラメータを必要とする場合には，引数名は`f0`, `f1`, ... と番号で指定\n",
        "\n",
        "    `from scipy.stats import gamma\n",
        "    rvs = gamma(1,scale=10).rvs(size=10000)\n",
        "    print( gamma.fit(rvs) )`\n",
        "    > (0.9890002824686166, 0.0016179707988114146, 10.186757557396639)\n",
        "\n",
        "    `rvs = gamma(1,scale=10).rvs(size=10000)\n",
        "    print( gamma.fit(rvs, f0=1) )`\n",
        "    > (1.0, 0.001617970100331767, 10.013201292197056)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F0itVCbsvq6h",
        "colab_type": "text"
      },
      "source": [
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cmGIRTCpvq6h",
        "colab_type": "text"
      },
      "source": [
        "## 分布テスト\n",
        "-----\n",
        "\n",
        "### 例: 正規分布にしたがってランダムに発生させたデータに対する確率プロット"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qlYw-iw2vq6i",
        "colab_type": "code",
        "colab": {},
        "outputId": "a6bcb091-3cb8-40bf-d6c1-2b1fe3837d8a"
      },
      "source": [
        "from scipy.stats import norm, probplot\n",
        "import matplotlib.pyplot as plt\n",
        "rvs = norm(loc=100,scale=10).rvs(size=10000)\n",
        "xy_pair, corr = probplot(rvs, plot=plt)\n",
        "print( corr )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10.019842168865456, 99.956293279718381, 0.99993003197720121)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XLsVf8LYtvzCUi3iXQ6nJGo7mVboxJmE4+FyDc4FkTnMdQ7AnRMEC958AF6eq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\n4ZxzLpIHhHPOuUgeEM455yL9P6dP2sibccw2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x203fc5cc2e8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-pD61bWLvq6k",
        "colab_type": "text"
      },
      "source": [
        "### 補間\n",
        "-----\n",
        "\n",
        "### 例：1 次元の補間 $f(x)=\\sin x/(1+x^2)$\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AyZYfvrjvq6l",
        "colab_type": "code",
        "colab": {},
        "outputId": "bab5a3ef-30c0-4803-8ce0-41aac4dd96fc"
      },
      "source": [
        "%matplotlib inline\n",
        "from pylab import *\n",
        "import numpy as np\n",
        "from scipy import interpolate\n",
        "x = np.arange(-4,5)\n",
        "y = np.sin(x)/(1+x**2)\n",
        "plot(x, y, 'o'); None"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x203fc5ae550>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vi-w81zSvq6n",
        "colab_type": "code",
        "colab": {},
        "outputId": "b6052b42-df85-4a14-b998-0a2607e72147"
      },
      "source": [
        "f = interpolate.lagrange(x, y)\n",
        "print(f)\n",
        "xnew = np.arange(-4,4, 0.01)\n",
        "ynew = f(xnew)  \n",
        "plot(x, y, 'o', xnew, ynew, '-') ; None"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            8             7             6           5             4\n",
            "-1.249e-19 x - 0.0006892 x - 6.583e-18 x + 0.02123 x + 5.436e-17 x\n",
            "           3             2\n",
            " - 0.2016 x + 1.415e-17 x + 0.6018 x\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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i4nRkhogME5EMEckUkXEus5QkIv8WkX0iss51lpJEJFVEPheRjUX/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\nAy+JbxM818tqAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x203fdf00da0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-Kb-3o1vq6p",
        "colab_type": "code",
        "colab": {},
        "outputId": "5d61fde0-e776-4411-b064-c3bc7c7c28d4"
      },
      "source": [
        "#f = interpolate.interp1d(x, y, kind= 'linear')\n",
        "#f = interpolate.interp1d(x, y, kind= 'nearest')\n",
        "#f = interpolate.interp1d(x, y, kind= 'zero')\n",
        "#f = interpolate.interp1d(x, y, kind= 'slinear')\n",
        "#f = interpolate.interp1d(x, y, kind= 'quadratic')\n",
        "f  = interpolate.interp1d(x, y, kind= 'cubic')\n",
        "xnew = np.arange(-4,4, 0.01)\n",
        "ynew = f(xnew)   \n",
        "plot(x, y, 'o', xnew, ynew, '-'); None"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pL8CaFB+Zqyj3N3vb9fW7nYqkmUjLfxjQ18zmA30rX2NmBWb2ROUx5wEnAZeZ\n2fTKX10iPK/Eygk3+DJ8977QSWJryaewZbUf7xdJQ/sc898b59wa4L+WRTrnioArKn8/AhgRyXkk\njuoeBt1+Bp88Br1vhfqHh04UG3PGQkaWv/IXSUNa4Sv/7fhrwTLgw7+GThI7c8f5sf5a9UInEQlC\n5S//rV4edLkQPvsXbFweOk30rZoHaxZA+zNDJxEJRuUvVet1A1SUwUcPh04SfXPG+K8a75c0pvKX\nqh3SBjoMgaKnYMva0Gmi68v/QNNjoF6z0ElEglH5y56deCNs3+Rv/qaKbxf5/fs7/NdOJCJpReUv\ne3ZoB7/6dcrfYdvG0Gmi48vKpShHDQqbQyQwlb/s3Yk3wdZ1MPXp0EmiY9YoaNIlPbavENkLlb/s\nXbMCaHUyfPg3KNsaOk1k1n0DSz/TkI8IKn/ZHyfeBJtWwPQkX6v33ZCPyl9E5S/71uokaHYsfPAg\nlJeFTnPgZo2CJkdDg1ahk4gEp/KXfTPzV//rvoGZ/w6d5sCsWwwlRbrRK1JJ5S/754j+cGhHeP8v\nUFHl4xgSm4Z8RH5A5S/7x8zP+189D2aPDp2m+ma+7Id8DmkTOolIQlD5y/47ajAc0hbe+zM4FzrN\n/ls11y/s6nxB6CQiCUPlL/svI9M/7Wv5DJj/Zug0+2/GS36X0o7nhk4ikjBU/lI9nc+Dei3gvfuS\n4+q/osKXf+tToO6hodOIJAyVv1RPZhb0ug4WfwyL3g+dZt8WT4H138DRGvIR2ZXKX6qv6yVQ51B/\n9Z/oZrwIWbW1d7/IblT+Un1ZteC4a2Dh27Bkaug0e7ZjG8x6DY48C7Jrh04jklBU/nJgCn4OOfUT\n++p/zljYut7fpxCRH1D5y4GpWQd6/NI/C3fFrNBpqvbZM1Cvub/ZKyI/oPKXA9fjSsiu6+f9J5q1\nX/lhqa6X+CmqIvIDKn85cDn14djL/bj6qnmh0/zQtBF+bn/Xi0MnEUlIKn+JzPHXQtZBMPnu0Em+\nV74Dpj8HbftCvbzQaUQSkspfIlO7IfT8FXw5CpZ9HjqNN38CbFwG3S4NnUQkYan8JXLHXwO1cmFS\nglz9f/IY1G0K7fqFTiKSsFT+Erla9aDX9TC/EL75OGyWlbNh4WTofgVk1gibRSSBqfwlOnpcBbUb\nw6S7wu75M+XvUKMWdPtZuAwiSUDlL9GRXRtO+g0ses9feYeweY3fzuHoC+CgBmEyiCQJlb9ET7fL\n/I6fE+6AivL4n3/qP2HHVuhxdfzPLZJkVP4SPTVqQt87YcVMP88+nspK4eNH/WrexkfG99wiSUjl\nL9HV4RxocZwf+9+6IX7nnfqqQP7XAAAHmklEQVQMbF7ph55EZJ9U/hJdZtD/Hti8On6bvu3YBh88\nCC2Oh5YnxOecIklO5S/R17QrdLnIz7xZvSD255s2AjYuhZNvjv25RFKEyl9i47TfQ40cGHNDbKd+\nbt8M7w6HZt2hde/YnUckxaj8JTbqHgqn3+Wnfk77V+zO89HDfiuH0+/yQ04isl9U/hI7x/wUDj8B\nJvwONi6P/vtvXAHvPwBHDoQWPaP//iIpTOUvsWMGZz8IZVth9LXRH/6ZeCeUb4M+f4ju+4qkgYjK\n38wamNmbZja/8mv9vRx7sJmVmNlDkZxTkkzDttDvbr/T5sf/iN77LnzHb9t83DVwSJvova9Imoj0\nyv9WYKJzrh0wsfL1ntwFvBPh+SQZHXsF5A+AN++ApdMif7/tm/2N5PqtoPfe/siJyJ5EWv6DgGcq\nf/8MMLiqg8ysG3AoMCHC80kyMoOBD/mN3164EDYsi+z9xt3sH9M48K+QlROdjCJpJtLyP9Q5twyg\n8mvj3Q8wswzgz8DQCM8lyaz2IXDRi37V7wsX+Kv3AzH9BZg+wq/kbXVSdDOKpJF9lr+ZvWVmX1Tx\na9B+nuNXwDjn3OL9ONeVZlZkZkWrVq3az7eXpHFYR/jRk7B8Bjx/fvU/AL56D16/zs8gOlnDPSKR\nMBfBDAwzmwv0ds4tM7MmwNvOufzdjnkOOBGoAOoA2cAjzrm9/u0tKChwRUVFB5xNEtiMl+C1q6B5\nDzh/hH8U5G5GTStheOFclq4rpWluDvcUbOakT38NdQ+Dn7+hLZtF9sDMpjrnCvZ1XKTDPqOBnQ9K\nvRT4z+4HOOd+4pxr4ZxrCfwGeHZfxS8prvN5cO6T/ubvY6fAog9+8ONR00q47dWZlKwrxQHdNkyk\n+3s/Y2ON+nDxKyp+kSiItPyHAX3NbD7Qt/I1ZlZgZk9EGk5SWMch8LPxYMDTA+Dfl8PiT6CinOGF\nc9laVsYxNo/Hs+7jr9kP8YVrxfk77oTc5qGTi6SEiIZ9YknDPmli+xa/N8/Hj0LZZsiuw9dbD6KB\nbaSulbLeHcTfdwzk8fIzqSCTr4adGTqxSELb32EfPeFawso+CPr8Hk64AeYVwpJPmVs0m2Xba/FZ\nRTsmVhzDJg4CIC9X0zpFokXlL4mhVj1/L6DzeWxpUsKwV2dSusujIHOyMhnaL38vbyAi1aHyl4Qz\nuGsewA9m+wztl//d90Ukcip/SUiDu+ap7EViSLt6ioikIZW/iEgaUvmLiKQhlb+ISBpS+YuIpCGV\nv4hIGkrY7R3MbBXwdQRv0RBYHaU40aRc1aNc1aNc1ZOKuQ53zjXa10EJW/6RMrOi/dnfIt6Uq3qU\nq3qUq3rSOZeGfURE0pDKX0QkDaVy+T8WOsAeKFf1KFf1KFf1pG2ulB3zFxGRPUvlK38REdmDlC9/\nM/uNmTkz+++nhAdiZneZ2Qwzm25mE8ysaQJkGm5mcypzvWZmuaEz7WRmPzazWWZWYWZBZ2aYWX8z\nm2tmC8wsYZ5FbWZPmdlKM/sidJZdmVlzM5tsZrMr/xteHzoTgJnVMrNPzOzzylx3hs60k5llmtk0\nMxsTy/OkdPmbWXP8s4W/CZ1lN8Odc52dc12AMcAdoQMBbwIdnXOdgXnAbYHz7OoLYAjwbsgQZpYJ\nPAycARwFXGhmR4XMtIungf6hQ1RhB3CTc+5IoCfw6wT5/2wbcKpz7migC9DfzHoGzrTT9cDsWJ8k\npcsfuB+4GUioGxvOuQ27vKxNAuRzzk1wzu2ofDkFaBYyz66cc7Odc3ND5wC6Awuccwudc9uBkcCg\nwJkAcM69C6wNnWN3zrllzrnPKn+/EV9qwR/U4LxNlS+zKn8F/3toZs2AM4EnYn2ulC1/MxsIlDjn\nPg+dpSpmdreZLQZ+QmJc+e/q58D40CESUB6weJfXS0iAIksWZtYS6Ap8HDaJVzm8Mh1YCbzpnEuE\nXA/gL1grYn2ipH6Sl5m9BRxWxY9uB34LnB7fRN/bWzbn3H+cc7cDt5vZbcA1wO9DZ6o85nb8P9Wf\ni3We6mZLAFbF94JfLSYDM6sDvALcsNu/fINxzpUDXSrvb71mZh2dc8HumZjZWcBK59xUM+sd6/Ml\ndfk75/pU9X0z6wS0Aj43M/BDGJ+ZWXfn3PKQ2arwPDCWOJT/vjKZ2aXAWcBpLs5zgKvx/1dIS4Dm\nu7xuBiwNlCVpmFkWvvifc869GjrP7pxz68zsbfw9k5A3zHsBA81sAFALONjMRjjnLo7FyVJy2Mc5\nN9M519g519I51xL/l/aYeBX/vphZu11eDgTmhMqyk5n1B24BBjrntoTOk6A+BdqZWSszywYuAEYH\nzpTQzF99PQnMds79JXSencys0c4ZbWaWA/Qh8N9D59xtzrlmlZ11ATApVsUPKVr+SWCYmX1hZjPw\nQ1OJMP3tIaAu8GblFNR/hA60k5mdY2ZLgOOAsWZWGCJH5Q3xa4BC/I3Ll5xzs0Jk2Z2ZvQB8BOSb\n2RIzuzx0pkq9gEuAUyv/XE2vvLINrQkwufLv4Kf4Mf+YTq1MNFrhKyKShnTlLyKShlT+IiJpSOUv\nIpKGVP4iImlI5S8ikoZU/iIiaUjlLyKShlT+IiJp6P8DYZNiZAl6sfwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x203fc88c4e0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0u4kRErvq6r",
        "colab_type": "code",
        "colab": {},
        "outputId": "c1eacc36-c5e3-47c9-c32b-36b58d5a97e0"
      },
      "source": [
        "#テーマパークの待ち時間の補間\n",
        "y = np.array([80,90,90,100,110,110,90,90,90,90,90,80,\n",
        "              80,60,60,60,60,70,70,70,60,35,40,50,45])\n",
        "x=np.arange(0,len(y))\n",
        "f  = interpolate.interp1d(x, y, kind= 'cubic')\n",
        "xnew = np.linspace(0,len(y)-1,100)\n",
        "ynew = f(xnew)  \n",
        "plot(x, y, 'o', xnew, ynew, '-', lw=1,ms=7); None"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sYAtwtTHm65CiVCoK1Jx2BWbuRJ6Iv5HMXiltWvBja1klWW1cJKS1bRruf87B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\nUl2AJnullOoCNNkrpVQXoMleKaW6gP8PUkfgfQrMYNIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x203fc871438>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHwNNPRZvq6t",
        "colab_type": "text"
      },
      "source": [
        "   "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zyfS_L-Ivq6t",
        "colab_type": "text"
      },
      "source": [
        "### 積分\n",
        "----\n",
        "\n",
        " 例１：テーマパークの待ち時間の平均値\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWEIeu1zvq6u",
        "colab_type": "code",
        "colab": {},
        "outputId": "5e747748-0f2d-4b8a-f95f-d68c97ef88a9"
      },
      "source": [
        "from scipy import integrate\n",
        "(total,error) =  integrate.quad(f, 0,len(y)-1,limit=1000)\n",
        "print(total/len(y))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "72.4373621070184\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YC0vK9Bmvq6w",
        "colab_type": "text"
      },
      "source": [
        "例２：円錐の面積"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0QaZCtF2vq6x",
        "colab_type": "code",
        "colab": {},
        "outputId": "ab9406f7-01ae-4550-b134-f6ee5403f7a4"
      },
      "source": [
        "f = lambda x: 2*math.pi*x**2\n",
        "print( integrate.quad(f, 0, 1./math.sqrt(math.pi)) )\n",
        "print( 2./3./math.sqrt(math.pi) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(0.37612638903183754, 4.175841772722649e-15)\n",
            "0.37612638903183754\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JzPhrAHNvq60",
        "colab_type": "text"
      },
      "source": [
        "例３：面積 1 の正方形の都市に一様に住んでいる人の，正方形の中心にあるスーパーに行くときの距離の期待値（理論値の導出は結構面倒；拙著「はじめての確率論」（近代科学社）参照）"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rppXxPB2vq60",
        "colab_type": "code",
        "colab": {},
        "outputId": "aeeb6f6c-e855-4453-8f47-08d5a73410a8"
      },
      "source": [
        "f=lambda x,y: math.sqrt( (x-0.5)**2 +(y-0.5)**2 )\n",
        "g=lambda x: 0.0\n",
        "h=lambda x: 1.0\n",
        "print(integrate.dblquad(f,0,1,g,h))                #2重積分\n",
        "print((math.sqrt(2)+math.log(1+math.sqrt(2)))/6.0) #理論値"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(0.3825978582314068, 1.3339283230929342e-08)\n",
            "0.38259785823210635\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rAFLKF9-vq62",
        "colab_type": "text"
      },
      "source": [
        "## 線形代数"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-B4Pqsfvq63",
        "colab_type": "code",
        "colab": {},
        "outputId": "19c4d10f-8a67-463c-cf21-39ee1c27e8f2"
      },
      "source": [
        "import numpy as np\n",
        "import scipy.linalg as linalg\n",
        "A = np.array([[1,-1,-1],[-1,1,-1],[-1,-1,1]])\n",
        "print(linalg.inv(A))\n",
        "print(np.dot(A,linalg.inv(A)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0.  -0.5 -0.5]\n",
            " [-0.5 -0.  -0.5]\n",
            " [-0.5 -0.5  0. ]]\n",
            "[[ 1.  0.  0.]\n",
            " [ 0.  1.  0.]\n",
            " [ 0.  0.  1.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WTeqahIQvq64",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if False:\n",
        "    B = np.array([[2,-1,-1],[-1,2,-1],[-1,-1,2]])\n",
        "    print(linalg.inv(B)) #エラーする例\n",
        "    print(np.dot(B,linalg.inv(B)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WIxjR7h5vq66",
        "colab_type": "code",
        "colab": {},
        "outputId": "6cd92e9f-03c4-4a1b-ffe9-0a4cedabb318"
      },
      "source": [
        "A = np.array([[1,3,-2],[2,2,-1],[3,-1,1]])\n",
        "b = np.array([-2,1,5])\n",
        "\n",
        "print(linalg.solve(A,b))\n",
        "print(np.dot(linalg.inv(A),b)) # A^-1 bの計算"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 1.  1.  3.]\n",
            "[ 1.  1.  3.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3NlZSEzvq68",
        "colab_type": "code",
        "colab": {},
        "outputId": "5a2af90d-bc89-4a9d-ec6d-6b01a651c7fd"
      },
      "source": [
        "A = np.array([[1,3,-2],[2,2,-1],[3,-1,1]])\n",
        "b = np.identity(3)\n",
        "\n",
        "x = linalg.solve(A,b)\n",
        "print(x)\n",
        "print(np.dot(A,x))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0.5 -0.5  0.5]\n",
            " [-2.5  3.5 -1.5]\n",
            " [-4.   5.  -2. ]]\n",
            "[[ 1.  0.  0.]\n",
            " [ 0.  1.  0.]\n",
            " [ 0.  0.  1.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7XdyUrPVvq6_",
        "colab_type": "code",
        "colab": {},
        "outputId": "c89a72dc-23b0-46bf-9620-f88473dcd5d0"
      },
      "source": [
        "#数値的に不安定な例\n",
        "A = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
        "b = np.array([-2,1,5])\n",
        "\n",
        "if False:\n",
        "    print(linalg.inv(A)) # エラー\n",
        "    print(linalg.solve(A,b)) # エラー\n",
        "print(linalg.det(A))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PNAn3JUvq7B",
        "colab_type": "code",
        "colab": {},
        "outputId": "3f53ac7e-4092-4e57-85be-e856d391db67"
      },
      "source": [
        "L = np.arange(5.0)\n",
        "a = np.array([L[j]-L[i] for i in range(len(L)) for j in range(len(L)) if j>i])\n",
        "print( a.prod() )\n",
        "\n",
        "A = np.vander(L)\n",
        "print(np.linalg.det(A))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "288.0\n",
            "288.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTJ-VoFovq7C",
        "colab_type": "code",
        "colab": {},
        "outputId": "cc2950fd-4911-4c89-ad8c-8c231f5087c8"
      },
      "source": [
        "x = np.array([0,0])\n",
        "y = np.array([3,4])\n",
        "A = np.arange(9).reshape(3,3)\n",
        "\n",
        "print('Euclid-norm(x-y):',linalg.norm(x-y))\n",
        "print('1-norm(x-y):',linalg.norm(x-y,1))\n",
        "print('inf-norm(x-y):',linalg.norm(x-y,np.inf))\n",
        "print('2-norm(A):',linalg.norm(A,2))\n",
        "print('1-norm(A):',linalg.norm(A,1))\n",
        "print('inf-norm(A):',linalg.norm(A,np.inf))\n",
        "print('fro-norm(A):',linalg.norm(A,'fro'))\n",
        "print('root(trace(A*A)):',np.sqrt(np.trace(np.dot(A.T,A))))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Euclid-norm(x-y): 5.0\n",
            "1-norm(x-y): 7.0\n",
            "inf-norm(x-y): 4.0\n",
            "2-norm(A): 14.2267073908\n",
            "1-norm(A): 15.0\n",
            "inf-norm(A): 21.0\n",
            "fro-norm(A): 14.2828568571\n",
            "root(trace(A*A)): 14.2828568571\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CyPmfEMRvq7E",
        "colab_type": "code",
        "colab": {},
        "outputId": "a9a1e8b6-39d7-4912-e548-d899c522df76"
      },
      "source": [
        "A = np.array([[1,1,1],[1,2,3],[1,4,9]])\n",
        "(v,X) = linalg.eig(A)\n",
        "print(v)\n",
        "print(X)\n",
        "V = np.diag(v)\n",
        "B = np.dot(X,np.dot(V,linalg.inv(X)))\n",
        "print(B)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 10.60311024+0.j   1.24543789+0.j   0.15145187+0.j]\n",
            "[[-0.132363   -0.72999807  0.57300039]\n",
            " [-0.34005127 -0.56448038 -0.76916357]\n",
            " [-0.9310452   0.38531119  0.28294516]]\n",
            "[[ 1.+0.j  1.+0.j  1.+0.j]\n",
            " [ 1.+0.j  2.+0.j  3.+0.j]\n",
            " [ 1.+0.j  4.+0.j  9.+0.j]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ruh6LnFevq7G",
        "colab_type": "code",
        "colab": {},
        "outputId": "335a39fb-8283-4c85-e406-b58fb7c37477"
      },
      "source": [
        "A = np.array([[1,2,3],[4,5,6]])\n",
        "print('A=',A)\n",
        "m,n = A.shape\n",
        "U, s, Vs = linalg.svd(A)\n",
        "S = linalg.diagsvd(s,m,n)\n",
        "print('U=',U); \n",
        "print('S=',S)\n",
        "print('V*=',Vs)\n",
        "print('U . S. V*=\\n', U @ S @ Vs)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A= [[1 2 3]\n",
            " [4 5 6]]\n",
            "U= [[-0.3863177  -0.92236578]\n",
            " [-0.92236578  0.3863177 ]]\n",
            "S= [[ 9.508032    0.          0.        ]\n",
            " [ 0.          0.77286964  0.        ]]\n",
            "V*= [[-0.42866713 -0.56630692 -0.7039467 ]\n",
            " [ 0.80596391  0.11238241 -0.58119908]\n",
            " [ 0.40824829 -0.81649658  0.40824829]]\n",
            "U . S. V*=\n",
            " [[ 1.  2.  3.]\n",
            " [ 4.  5.  6.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1YXgnjMpvq7H",
        "colab_type": "code",
        "colab": {},
        "outputId": "8d34b690-60a7-472f-a620-47bd4c78d422"
      },
      "source": [
        "A = np.identity(5)\n",
        "A[0,:] = 1; A[:,0] = 1; A[0,0] = 5;\n",
        "b = np.ones(5)\n",
        "print(A)\n",
        "(LU,piv) = linalg.lu_factor(A)\n",
        "print((LU,piv))\n",
        "L = np.identity(5)+np.tril(LU,-1)\n",
        "U = np.triu(LU)\n",
        "P = np.identity(5)[piv]\n",
        "print(np.dot(np.dot(P,L),U))\n",
        "x = linalg.lu_solve((LU,piv),b)\n",
        "print(x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 5.  1.  1.  1.  1.]\n",
            " [ 1.  1.  0.  0.  0.]\n",
            " [ 1.  0.  1.  0.  0.]\n",
            " [ 1.  0.  0.  1.  0.]\n",
            " [ 1.  0.  0.  0.  1.]]\n",
            "(array([[ 5.        ,  1.        ,  1.        ,  1.        ,  1.        ],\n",
            "       [ 0.2       ,  0.8       , -0.2       , -0.2       , -0.2       ],\n",
            "       [ 0.2       , -0.25      ,  0.75      , -0.25      , -0.25      ],\n",
            "       [ 0.2       , -0.25      , -0.33333333,  0.66666667, -0.33333333],\n",
            "       [ 0.2       , -0.25      , -0.33333333, -0.5       ,  0.5       ]]), array([0, 1, 2, 3, 4], dtype=int32))\n",
            "[[  5.00000000e+00   1.00000000e+00   1.00000000e+00   1.00000000e+00\n",
            "    1.00000000e+00]\n",
            " [  1.00000000e+00   1.00000000e+00   0.00000000e+00   0.00000000e+00\n",
            "    0.00000000e+00]\n",
            " [  1.00000000e+00   0.00000000e+00   1.00000000e+00   0.00000000e+00\n",
            "    0.00000000e+00]\n",
            " [  1.00000000e+00   0.00000000e+00   1.38777878e-17   1.00000000e+00\n",
            "    0.00000000e+00]\n",
            " [  1.00000000e+00   0.00000000e+00   1.38777878e-17   0.00000000e+00\n",
            "    1.00000000e+00]]\n",
            "[-3.  4.  4.  4.  4.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Emn5hhdOvq7J",
        "colab_type": "code",
        "colab": {},
        "outputId": "c6725ff3-c36f-49ad-d26b-2aab94b8d791"
      },
      "source": [
        "A = np.array([[2,1],[1,3]])\n",
        "U = linalg.cholesky(A)\n",
        "print(U)\n",
        "print(np.dot(U.T,U))\n",
        "B = np.array([[2,1+1j],[1-1j,3]])\n",
        "L = linalg.cholesky(B,lower=True)\n",
        "print(L)\n",
        "print(np.dot(L, np.conj(L).T))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 1.41421356  0.70710678]\n",
            " [ 0.          1.58113883]]\n",
            "[[ 2.  1.]\n",
            " [ 1.  3.]]\n",
            "[[ 1.41421356+0.j          0.00000000+0.j        ]\n",
            " [ 0.70710678-0.70710678j  1.41421356+0.j        ]]\n",
            "[[ 2.+0.j  1.+1.j]\n",
            " [ 1.-1.j  3.+0.j]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0B9YXbnvq7L",
        "colab_type": "code",
        "colab": {},
        "outputId": "0eabccc2-85b1-4862-9c11-e73b07641bd7"
      },
      "source": [
        "A = np.array([[1,2,3],[4,5,6]])\n",
        "print('A=',A)\n",
        "Q, R = linalg.qr(A)\n",
        "print('Q=\\n',Q)\n",
        "print('R=\\n',R)\n",
        "print('Q.R =\\n', Q @ R)\n",
        "print('Q.Q*=\\n', Q @ np.conj(Q).T)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A= [[1 2 3]\n",
            " [4 5 6]]\n",
            "Q=\n",
            " [[-0.24253563 -0.9701425 ]\n",
            " [-0.9701425   0.24253563]]\n",
            "R=\n",
            " [[-4.12310563 -5.33578375 -6.54846188]\n",
            " [ 0.         -0.72760688 -1.45521375]]\n",
            "Q.R =\n",
            " [[ 1.  2.  3.]\n",
            " [ 4.  5.  6.]]\n",
            "Q.Q*=\n",
            " [[  1.00000000e+00   1.94289029e-16]\n",
            " [  1.94289029e-16   1.00000000e+00]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_VUEsa6Kvq7N",
        "colab_type": "code",
        "colab": {},
        "outputId": "07a78a70-eb9c-42ef-9267-169064788284"
      },
      "source": [
        "import numpy as np\n",
        "import scipy.linalg as linalg\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "np.random.seed(0)\n",
        "a3, a2, a1, a0 = 1.0, -3.0, -6.0, 2.0\n",
        "xi = np.linspace(-3,4,10)\n",
        "yi = a3*xi**3 + a2*xi**2 + a1*xi + a0\n",
        "zi = yi +2.0*np.max(yi)*np.random.randn(len(yi))\n",
        "A = np.vander(xi,4)\n",
        "c, resid, rank, sigma = linalg.lstsq(A,zi)\n",
        "\n",
        "xi2 = np.linspace(-3,4,100)\n",
        "yi2 = c[0]*xi2**3 + c[1]*xi2**2 + c[2]*xi2 + c[3]\n",
        "\n",
        "plt.plot(xi,zi,'x',xi2,yi2)\n",
        "plt.xlabel('$x_i$')\n",
        "plt.title('Data fitting with linalg.lstsq')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AeprZ8aEzlcTMBgIbnHPz4nG8GvE4SDQ55/qX86E5wBTgrhjGOazD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\nqOCLiKSJ/w/PBI7RAdIIXgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x203fcd89160>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efBvjCtvvq7P",
        "colab_type": "code",
        "colab": {},
        "outputId": "015752cf-dc0a-41d3-c3ca-4578277fa0d7"
      },
      "source": [
        "A = np.array([[1,1,2],[1,3,-1],[0,1,1]])\n",
        "OA = linalg.orth(A)\n",
        "U, S, V = linalg.svd(A)\n",
        "\n",
        "print('A= ',A)\n",
        "print('orth(A):', OA)\n",
        "print('orth(A).T dot A \\n',np.dot(OA.T,OA))\n",
        "print('U: ',U)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A=  [[ 1  1  2]\n",
            " [ 1  3 -1]\n",
            " [ 0  1  1]]\n",
            "orth(A): [[-0.39543728  0.80500544 -0.44226191]\n",
            " [-0.87521453 -0.47631006 -0.08442901]\n",
            " [-0.27861961  0.35368767  0.89290321]]\n",
            "orth(A).T dot A \n",
            " [[  1.00000000e+00   2.29535095e-16  -3.47750653e-16]\n",
            " [  2.29535095e-16   1.00000000e+00   1.02157332e-16]\n",
            " [ -3.47750653e-16   1.02157332e-16   1.00000000e+00]]\n",
            "U:  [[-0.39543728  0.80500544 -0.44226191]\n",
            " [-0.87521453 -0.47631006 -0.08442901]\n",
            " [-0.27861961  0.35368767  0.89290321]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5ZlV7DKvq7Q",
        "colab_type": "code",
        "colab": {},
        "outputId": "83129657-8cf9-459d-cf22-67d4b07bd81a"
      },
      "source": [
        "from scipy import sparse\n",
        "row = np.array([0,1,1,1,2,3,3])\n",
        "col = np.array([2,0,1,2,2,1,3])\n",
        "data = np.array([2j, 1, 1+1j,1+2j, 2+2j,3+1j,3+3j])\n",
        "COO = sparse.coo_matrix((data,(row,col)))\n",
        "print(COO)\n",
        "print(COO.toarray())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  (0, 2)\t2j\n",
            "  (1, 0)\t(1+0j)\n",
            "  (1, 1)\t(1+1j)\n",
            "  (1, 2)\t(1+2j)\n",
            "  (2, 2)\t(2+2j)\n",
            "  (3, 1)\t(3+1j)\n",
            "  (3, 3)\t(3+3j)\n",
            "[[ 0.+0.j  0.+0.j  0.+2.j  0.+0.j]\n",
            " [ 1.+0.j  1.+1.j  1.+2.j  0.+0.j]\n",
            " [ 0.+0.j  0.+0.j  2.+2.j  0.+0.j]\n",
            " [ 0.+0.j  3.+1.j  0.+0.j  3.+3.j]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uc7Kzcasvq7S",
        "colab_type": "code",
        "colab": {},
        "outputId": "a9c3c738-a7a5-4d79-b5fa-9c3892b6dd0b"
      },
      "source": [
        "N = 3000\n",
        "m = sparse.rand(N, N,density=0.01)\n",
        "\n",
        "COO = m.copy()\n",
        "LIL = m.tolil()\n",
        "CSC = m.tocsc()\n",
        "CSR = m.tocsr()\n",
        "AR = m.toarray()\n",
        "print(COO.nonzero)\n",
        "\n",
        "print('The sparse matrix: COO data size: ' + str(COO.data.nbytes/1000) + ' kbytes')\n",
        "print('The sparse matrix: LIL data size: ' + str(LIL.data.nbytes/1000) + '  kbytes')\n",
        "print('The sparse matrix: CSC data size: ' + str(CSC.data.nbytes/1000) + ' kbytes')\n",
        "print('The sparse matrix: CSR data size: ' + str(CSR.data.nbytes/1000) + ' kbytes')\n",
        "print('The numpy array: AR data size: ' + str(AR.nbytes/1000) + ' kbytes')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<bound method spmatrix.nonzero of <3000x3000 sparse matrix of type '<class 'numpy.float64'>'\n",
            "\twith 90000 stored elements in COOrdinate format>>\n",
            "The sparse matrix: COO data size: 720.0 kbytes\n",
            "The sparse matrix: LIL data size: 24.0  kbytes\n",
            "The sparse matrix: CSC data size: 720.0 kbytes\n",
            "The sparse matrix: CSR data size: 720.0 kbytes\n",
            "The numpy array: AR data size: 72000.0 kbytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3FIHId6vq7U",
        "colab_type": "code",
        "colab": {},
        "outputId": "0668917f-28bf-4e2a-db6f-67109780f9c0"
      },
      "source": [
        "import numpy as np\n",
        "import scipy.sparse as sparse\n",
        "from scipy.sparse.linalg import spsolve\n",
        "from scipy.linalg import solve\n",
        "\n",
        "#N = 5000\n",
        "N = 1000\n",
        "m1 = sparse.rand(N, N, density=0.01)\n",
        "m2 = sparse.rand(N, N, density=0.01)\n",
        "\n",
        "LIL1 = m1.tolil(); LIL2 = m2.tolil();\n",
        "CSC1 = m1.tocsc(); CSC2 = m2.tocsc();\n",
        "CSR1 = m1.tocsr(); CSR2 = m2.tocsr();\n",
        "AR1 = m1.toarray(); AR2 = m2.toarray();\n",
        "\n",
        "print('和の計算時間')\n",
        "%timeit LIL1+LIL2\n",
        "%timeit CSC1+ CSC2\n",
        "%timeit CSR1+ CSR2\n",
        "%timeit -n 100 AR1+ AR2\n",
        "print('積の計算時間')\n",
        "%timeit LIL1.dot(LIL2)\n",
        "%timeit -n 100 CSC1.dot(CSC2)\n",
        "%timeit -n 100 CSR1.dot(CSR2)\n",
        "%timeit -n 3 AR1.dot(AR2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "和の計算時間\n",
            "2.21 ms ± 181 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "221 µs ± 8.99 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
            "206 µs ± 14.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
            "3.79 ms ± 485 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "積の計算時間\n",
            "3.84 ms ± 570 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "1.16 ms ± 172 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "803 µs ± 32.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "26.9 ms ± 2.49 ms per loop (mean ± std. dev. of 7 runs, 3 loops each)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ly-DV86Pvq7X",
        "colab_type": "code",
        "colab": {},
        "outputId": "1654ab39-d63e-412d-c6fa-6204d9a3f57d"
      },
      "source": [
        "m = sparse.rand(N, N, density=0.01)\n",
        "b = sparse.rand(N, 1, density=0.01)\n",
        "\n",
        "print('方程式を解く計算時間')\n",
        "CSC = m.tocsc(); bcsc = b.tocsc();\n",
        "CSR = m.tocsr(); bcsr = b.tocsr();\n",
        "AR = m.toarray(); bar = b.toarray().reshape(-1)\n",
        "\n",
        "%timeit spsolve(CSC,bcsc)\n",
        "%timeit spsolve(CSR,bcsr)\n",
        "%timeit solve(AR,bar)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "方程式を解く計算時間\n",
            "94 ms ± 4.13 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
            "98 ms ± 11.6 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
            "37.7 ms ± 2.92 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}