{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Practice 11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Python Practice will be dedicated to non-linear curve fitting using the `lmfit` library. After installing it, we will do a short introduction to the library and then we will fit a simple function using it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first install the `lmfit` library. You can do this directly from Jupyter Notebook by running the following command:\n",
    "\n",
    "```\n",
    "!pip install lmfit\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install lmfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import lmfit as lm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Theoretical background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `lmfit` package provides simple tools to help you build complex fitting models for non-linear least-squares problems and apply these models to real data. The way it works, is that for any data you want to fit, you need to define a model function, which has some free parameters to be optimized. The `lmfit` library will then use the Levenberg-Marquardt algorithm to minimize the residuals between the data and the model. The total fitting error can be expressed in a chi-square statistic:\n",
    "\n",
    "$$ \\chi^2 = \\sum_{i=1}^{N} \\left( \\frac{y_i - f(x_i)}{\\sigma_i} \\right)^2 $$\n",
    "\n",
    "where $y_i$ is the data, $f(x_i)$ is the model function, and $\\sigma_i$ is the uncertainty in the data. The goal is to minimize this statistic by adjusting the free parameters of the model function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore, while fitting the data, you need to follow these steps:\n",
    "\n",
    "1. Define the model function with free parameters.\n",
    "2. Estimate the uncertainties in the data.\n",
    "3. Define the residual function.\n",
    "4. Define the initial guesses for the free parameters.\n",
    "5. Minimalize the residuals w.r.t. the free parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this Python Practice we will generate synthetic data of a simple sinusoidal function with some noise. The function we will use is:\n",
    "\n",
    "$$ y = 3\\sin(5 x + \\pi/3)$$\n",
    "\n",
    "We only assume that we know in advance that the function is a sinusoidal one, but we don't know the amplitude, frequency, and phase. Therefore, our model function will have three free parameters: `a`, `b`, and `c`:\n",
    "\n",
    "$$ f(x) = a \\sin(b x + c) $$\n",
    "\n",
    "Let's define the data and plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "def target_function(x):\n",
    "    return 3*np.sin(5 * x + np.pi/3) \n",
    "\n",
    "def model_function(x, a, b, c):\n",
    "    return a * np.sin(b * x + c)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate some data\n",
    "num_points = 100\n",
    "noise_level = 1.0 # For modeling the experimental noise\n",
    "\n",
    "x = np.linspace(0, 5, num_points)\n",
    "data = target_function(x) + np.random.normal(loc=0.0, scale=noise_level, size=x.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
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g5C+RUM66gAqCkB4SiAiCkL9YbDnrApoL5HBTpFAALNT3K2uByFe+8hUURZkwqEgQBGFe2D1QtVITpU4+KeouoDWrs+4CajRWqxVgxrkrgjAf9O+X/n2bK1npmtm1axff//732bx5czaeThCEYkFRYO314O8ccwE1WzUXUH9nVlxAcxGz2Ux5eTnd3d0AOJ3OxLA4QZgvqqoyMjJCd3c35eXlmM3meT1exgORoaEhbrrpJn74wx/y5S9/OdNPJwhCvjNbg7nq1VqL7sGHof8EBP1jLqBrbyja1t36+nqARDAiFBGqqgXjAHb3VCH3AlFeXp74ns2HjAcit956K9dffz1XX331jIFIMBgkGAwm/u3z+TK9PEEQCoGaNbDjdhjtFxfQOIqi0NDQQG1tLeFw2OjlCNkkEoLdD2j/f8GHMuIWbbVa550J0cloIPLzn/+cPXv2sGvXrrTuf8899/CFL3whk0sSBKFQERfQpJjN5gW7YAh5QsQEsbg+yOHI+bEVGTtSz549y+23385Pf/rTtGcd3HXXXXi93sTP2bPS+y8IgiAIhUzGMiK7d++mu7ubrVu3Jm6LRqM8//zzfPvb3yYYDE6J0u12O3a7PVNLEgRBEAQhx8hYIPKWt7yF/fv3T7jtQx/6EGvXruUzn/mMpAoFQRAEQchcIOJyudi4ceOE20pLS6mqqppyuyAIgiAIxYlM3xUEwThm26orCELBkdVA5Nlnn83m0wmCIAiCkONIf5sgCIIgCIYhgQiAGoPAIAx1w+AZiMWMXpEgCIIgFAWiEelp1ayhT74AsYg2LrxmDay7sWitoQsK0SAIgjHIsSekSXEHIj2tsPN+GO4Ba4k2LKukEjr2adM8L75FghFBEARByCDFW5qJxeDw72CkD6rXgMWuWUTbXVCzVru95VEp0wiCIAhCBineQMR7FnqPgqdp6mRCRQF3E/Qc0e4nCIIgCEJGKN5AJDQEkQBYS5P/3ubUfh8ayu66hOwjYmVBEATDKF6NiK0MLA4IDycPRkIj2u9tZdlfm5A9RKwsCIJgKMWbEfE0Q/UqTZSqqhN/p6rga4Oa1dr9QFOAP3OP9hMJZX+9wlTm+5noYuXO/ZpY2Vk5Jlbeeb/2e0EQpiLnQ2EBKd5AxGTSdr3OKuhthUhQS9EHfdDTAqVVsPYG7X5C4SFiZUFYOCQwEeZBcV9la9ZoLbr1myA8CiP9MDoAjVtgewZbd+WgNR4RKwuCIOQExasR0alZAztuh9F+LSi45FaoXC6ZkEInHbGyv13EyrmKLjCOhDSBceVyo1ckCMIckUAEtJS8o1z7//LFEoQUAyJWzl9SCYxXX2f0yoTZIu6rAhKICMWKLlbu2AdVqyb+ThcrN24ZEysLC8PkC89smc4NefAMmKxQWr2waxYEIaMUZyAy35OhkP/oYmVv25hY2WzVxMr+ThEr5yKTBcaj/drtdhc4PNB9CAI+TYAuCELeIGdZoXgxSqwszI0ZBcaNMNILQa8x6xMEYU4UZ0ZEEHRErDw3jKjtzyQwtjo1zYh0ouUvohkpSiQQEQQRK+cHMwmMwyNgsoxdvOSiJgh5gZxxUyFeH4JgDKlm/8zohtwOzmqwe7K+ZEHIKfJsfpZkROaD7LgEYWGZafbPdALjshp4yz+PaXtkAyEUI3k4P0sCEUEQcoPeI7DrP6a25rbv1bplmrbBdfdoAuODD0P/CQj6xwTGa2/I2ROtIGSF6drbvW3asZODx4gEIoIgGI+qQsvvk7fmVq/RgpTeo1rKWQTGgjCVmdrbe1q0+VlVq3LuWMmt1QiCUJwEvdB3LHVrrt2lteZ6z8VviwuMy2pFYCwIkNfzs+ToFYTJiFA5+0RC07fmmq1j9W5BEKaSzvysSCAnjyEpzaSgwzfKgUNdVJfZ2BCNYTNLzJaP7D7dT/eBDlbVlrHS6MUIqbHYUrbmqqh4/cMERiMEhhSWGrNCYZ4EwlF2nujj3MAo77lgEQ6r2eglFRZ5PD9LApEkvHC0hzv+axfvD2kOjZ/+5gt89rq1vBkVBWWGvxZygf7hEF957DAPvXaKWy1+Wrv89JoP8E9/toUSm5wAcwa9zTAchNIaLW1ctRqAaEzlUJuXvWf62R7qpVst5/afneG7H1jE9sUuY9ctpMVQMMKJniG+/ZPXeP6El0BYayN98lAXP/jABdgNXl9BkcfzsyQQGYeqqnz76WPc98xJLGqEKo+d0VCEU30j3PLTPXy1to03rapBRmrlLrGYyi9eO8tXH29hcCSMFWiucHJ2YIRfvHaWV874+db7z2dtvdvopQq9RzSBqt5mWLEMhrtRAz66B3yc84V5I3KKBgYIWRz0WZfg88f46/94la//xXrebvT6hWk50ObluZdPEY7GeCbSQxgL9W4Hg6MhnjvSw9//1+t8570b5SK0UOTx/Cz5DsQZDUd5/EAn3+w+ioqF925bxPsqmonGVAZDy/nxn05xpn+En716hkvX97NtRb3RSxYmoaoqH/7xLp5p7QFgbb2Lf/2z1Ww9fYgz/SP84rCNo91DvOPbL3H3jRt4//bFBq+4iBnuhVd/oM330dsMK5ZCNIS3vxv/YC8uzDTZAlQvu5DVniBrS6t4/Fwtjx3u5xO/2Ev92gG2Lq4w+pUUPnPwS4pEY/x/v93PldEYVaU2PnXRaq5Y18S6BhcvHevjb368iycOdvHpX8e4t07FNFlcKcwNfX5WnrW3515oZAAqKr/d28bp/mEcVhP3/sUWvvzOTVhMJuwWM5++dg2P3X45zRVOYqrKPY+1oE52dhQM5/EDnTzT2oPdYuKfb1jPox+/jK2LKwFYXOnkdx+/jCvX1BCMxPjHh/bzp2O9Bq+4SFFVLRsy0q+1GVrsWheM3UW0cRv7fKUMqGUMVm/jLbd8jQv+6m5srhosJhPffv9WPnDJElQVnj/awwvHeox+NUISfvzyaQ62+7FbTLx76yI+dsUK1je6URSFy1ZV872btmIxKfzujQ6eOtwt59OFRG9vX3Y5LNkBV3wGdnwyZ4MQkEAEgOdae+j0BbCaTfzyby/hPRcsmnKfxZVOrttYj9VsYt85L08c7DRgpUIqoqrKvX9oBeBvr1jBhy9bhsVsmmB1XB3u4kcfuIC/3KZ9vl9/6qiBKy5igl7N68A9tc1wX5uXYyEPHnOILcvqsFQu1YKUOGZF4Qt/toE736qdVHefHmDv2YFsrl6YgQ7fKPf9oRWFGFctsVMa6p9iM/6WdXV846/Ox6TAwQ4vL8imYGHJs/b23F5dFlBR+c6zxwDY3ORh3TTagVKbhfObywH4tydaieS4f38x8fDeNo73DFPutPK/L1+m3djTCi99Q9MgnH4Jnvsqppf+nX/YqmAzm3j1ZD8vH+8zduHFSCSkaUKszgk3j4aj7DzZxyh2akst2Ikk/XNFUfjbNy1PHKv/98WTGV+ykD5fevQQ9eEzfKHqKdaM7k0ce7x4n3ZMxrl+cwP3vHsTAHvODHB2YMSgFQtGU/SByMvH+9h71ovZpLB1ycz15guWVFBeYuFEzzAPvd6WhRUKMxGJxfjmH7XsxseuWIHbYR2zOu7cr2kQnJUJu/CaJz/O/6v/JVYifOOpIwavvgix2LQpueGJF55XT/YTjMRocqp4Sktm1CJsW6odr3841MXJ3uGMLVdInxM9Q5w4/Dp/Y3mc62t7UMYfex37tGNyXDDy7vMXsaRSazX9ycunjVq2YDBFH4h8+xktG7Kp0UOpbWbtrt1i5mNXao4U3/jjUcmK5AAH2ny0ewPUuux84JKlU62Ox2kQqF4D4VHOK+nGZlbZeaKfV09KViSr2D3grNLaCePagNFQlP3tXkDl8roQShpTdKtK7SyrKkVV4YcvnMjCwoXpCEdjPNPSzdWm3WytjuFu2jjx2KtZqx2TLY9OKNOcF88y//eecwyHkmfBhMKmqAORtsFRdp0awGpWuGBp+ur7929fTKPHQZc/yN6zg5lboDAj4WiMV09qMxU+/pZVmkfITFbHdheO0AB/s1HbcX87XpoTsoSiQPVqbaccbzM80zeEUx1mh7uXqppGzQ8hjU6KbUs1MfKvdp+jxx/M9MqFaXjlZD9KyMtGezcrV65N22Z8aZWTihIbQ4EID0uWuSgp6kDklfhO+N3nL8Jlt6b9dw6LmU9eo5ku7To1QCAS1X4h1uBZZ+/ZQUbCERZVOHjvtrhRz0xWx3G78JvOr8RqVnj5eD/tg6PZW7QApdVw0UehfhNDQ36UwABVpmFWbL4MLvqI9vs0aCx3cF6zh1Akxo//dCqzaxZSEohE2dc2iJUI5zU4sDpSuHcmsRlXFIUt8azIj18+JR00M1GA15miDUQ6fQHO9I9gNil89E3LZ/6Dcd0XDJ7h3ec3srKmlGAkyu5Toto3Au9okNbTZ6nCy2cvKcWmf5vHWx0nIxoGk4X66upEh9ROKc9kn+rVsON2Xois45XYWs5u+nsq3/pp7fY0UVD435drx+//e/kUw0FJ7RvBc0e6CUViWG0O6io8qY+9FDbj6xvdlNktnOwd4XS/iFaLjaINRPR0/p9taWRxpXP6O/cemdJ9YX7p3/nnizVNyRvnBglGo5lecuEylwi/p5XDv/oSW2MHuNx6hGu6HxxT5etWx94xDUICVdVMfpzV4FnE3125EotJ4Uz/CB1eyYpkm57hMEe8Jvrw8I4rL55Tm+HV6+pYXl2KLxDh57tyb7JoQTJpY/bonrO4GWZVpRWlrFabkpzs2PO1Qc3qKTbjNrOJP9+qbQqk3F18FGUgcqjDx4leLTX4t1fMkA3RHSAnd1907OPi3t/QZB0hFI3Nzhxr0kGMCF5nR7wjJtK2jwA2nOU1mJxVY6r8vqOa1bGzaszqWI1pVse9rdrnWL0KFBPNlU7eeV4joJXZhOzyh4OdqECd20FzxQwbghSYFYWPxLOa//HCCcJROZ4yyqS2+NBjd/FXJz7Dm0xvsCFyEPqOa4FI22sTj72elmltxv/6ksUoCpzqG6Z/pDBKDkJ6FGUg8rt97QCsrnOxorpMaxO86i7tZ3zL4DQOkNSsxTTaz9bSHkDliQNd6T15Em+Lyf31wjTEO2KGBrvYE6glhI3KUsdUVX7VKs3quH4ThEe1z3B0ABo2Q9O2CRqEm3doviOn+4YZEtV+Vnl0fwcAq2vnN8TuXec3UV1mp90b4HdvtC/E0oRk9B6Z2BZvdTDSdYLlnKPSNIrd4dSs+h3lEPDC6ODYsde4BbbfktLhc0llKVetqQHgjfFZkQLURAgTKcpA5NPXruEd5zVx8bKq6e84jQOkpgBvpN4yjJsRnjzcRXimzEYqb4sk/fVCCuIdMccCHkDB47BgMcc/m8mq/GRWx5f+/RQh5Oq6MspLbERVleePiGV4tujyB9h9WstCraqb32hyh9XMzZcuAbQOGiEDqKo2pFBvizfbYGSAodEgZ6jFYVFQRgfBXgaLLtSOxZKq6W3GJ2WHP3ixNv/pUIcPXzCc9ZcoGENRBiIKCsuqSqksnWF4UwoHyARWJ06Lisui4gtE2DmdS+d03hYp+uuFJMQ7Ylr7NE3OlM9wsip/stWxMvUrr6CwokbrsHnyUJqZLWFujLvwvLBrD6gxGtwOzYRuntywWSuxvXqyH19ALmILTtALfcfG2uKjQaKhEXrCVkDBZndoJnUBn/Z7TxOEh7QsczKb8STZ4Us6f8qyklHC0RgP75FW3mKhKAORtDUaKRwgE4RHUEwWGqs1q+lp58/M5G2RpL9eSIKtjJGYFf+QFwWoGB+IxKJw4lntfTbbZ/WwK2q1HfmzrT2ERGOQGSZdeGp3f52Pmh9lU8XClMOWVpeyoqaUSEzluVbJbC04kdDEtvhYlFA4QhgLZTYLFrNFO7dG4+UTq1PbyCUrp6TIDiud+7nUcYoKfPyxRTYFxULxBSKz0WgkcYBMoKrgawdnNc31DYC2m46l6oGfydsiSX+9kARPMwfDdTTQT4Pbgc087is8qSNmNjS4HTitFoaCEV45McdW3mKvZU/3+iddeIK2co4N29hgOs3KcKsmCl8Arl5XB8BTh+UituBYbBPb4k1mAhEVKxGqy+ygRrWMozm+OQiPaBu5yVb9Mzgfe8wRVijt7D7Vx5C0YxcFxRWIzFajkcQBcoIC3FkF1atoqnTiKbHQPxKmLZUx1kzeFin664VJmEz8zLuZAVxsK+2etiMmbSw2lDf/Iyc3/T1hLPxRLmIzM5vOryQXnv6RCEOUMOxagS0W0LJY6vwzUVev1wKRZ1p7ZPzCQmP3QNXKRFv8aMyCP2bFRYBKp0ULPq1OcLgnbNSmWPVPlx0GrHYHi80DbIod4cUj3Vl6cYKRFE8gMleNxjgHyAndF41bEg6QZkXhmvhO7GhXiozGTN4WKfrrhYl0+wM83FbGA9HrqFhxwYwdMbPhrRu0z/DJQ13i7jgdqbKKvSkGCCa58PQNaXbsq+pc2jE40qu1fM6TrYsrqHBa8Y6G2XOqT9rkFxJFgbXXJ9riB3xDDFCK3WbD6j+r/b6kXMtKjtuoTQk2UmWHR/rg7Cso/g5qFS8fsfweXvz31N8rYd784WAnN37rRX5k8ATrmae8FQqz0WhULBnb8UVCWqbi0o/DaL/270tuhcrlWv0zzrUb6/nVnjaO9Qxx1ZoapsT5JpPmbeFtG8uumK3aTt7fOW1/fdERCcELX9P+//I7JqR2/3CwC1WFskXr8bzlA6D6xz4TzyLtxDVHLl1RRZndQs9QgE5fgAZPyXxfSeGhZxWHe7Tsk9k6llUcPAMm69RAcNKFJxCOMRSKoAAra8ugw6pdvBagLGk2KVy1ppY39r7KyLPPQORP2nEaGtI6NtbdmLJ9VEiD6tVw8S2oBx9mpHUfJUSwVK0Ep6IFkpHA2EZt1bXa5m8y47PDejASHoG23drGQjFhsjroC7up6t6H+uoPUJJ9r4R588LRXva3edm6uNzQdRRPIJKORsPfrt2vpxUOPqzt+PSTWNVKiEbGui9MJoiOBSuXrh/B5TAxEorQ7g3QlOw5atZo3hYHH4b+E9rJVz9o194gJ8g0ePyAJgi+bmPDWEcMaJ/JPLFbzFy5poYn9p3leM+wBCKTmZxVHNXcibG7wOGB7kNax4RzUlv8pAtP/7CWDWksL9EmXsct9xeqLPmOpiG27n8cU9cw1E8Klrxt2jEox9rcqVnDviUfZHd4NyWmGO94579AVTM89YWUG7UJ6Nnhjn2a34+qwnCf9h0pqYSRPiwlZfQFqmgPw7b+LjyMTv1eCfPmxbgR546VxgZ5xROIJIvCx6NrNIa64dAjU3d8nfu1bEnTNu3+k4IVW2iIf6kuo7vTx9Fuf/JABMa8LSZnVyQTMiODIyFejgtJ37axPiPP8dYN9fFAZIjLDD44c44Zs4qN8QDbO/F3ky48fcOakHV1rWtMYFxWn1pgPD47OXhGO15SEYuxPfQnRkx+9gYbuFDtokQvwTo8WslAN7yTY27OPHukD5VS6qpdOGs0Q8AJmwKTCVJVwiZnh4N+LSPi8GjnRbMFk7OKRTg53jfKqaCHLdGzU79Xwrw4NzDCyd5hzCaFi1cYG+QVTyAyOQofj67RaNgM53Yn3/FVr9Fqlb1HtZPZaw9MCVa22o/TpRznSJeFmKqmFuBM3snLCTEtnjzURTSmsrbexdLq0ox0ply1pgarWWFgJET/cIjKBX+GPGamrGKqds1xF56RjkOEQ6MomFnpic0sMO49oplojc9O6iWWq+6aen/vWRyDx7VS66DKwHCYknKz9rtkJVhhTuw80cd2mHlOVyrGZ4e7D2nfq1gEXHVgKwWbk6XOUo73jXJsMMaW0hRtwMKc+dMxbVO3ZZFnQXx85kPxXAH1k2Gy+SP6DISmC7Q5Jal2fHaXFny88fOkoteqJZtwmkLUh8+w72y/Ma+zgNHLMm/b2JCx53A5rFy8XNsdHO+RVuoJzNT5lapdExIXnqOmZTgI0WgNUBLxTS8wnmbOU0on4niw1FijfYYDk2eWSJv8vBkJRXjj3CAAzZXzKF/q2eHF26GsDhrPh+bt2mcELKnS/uvze4lgTv69EubMi8d6sRLhH2y/MdxyoHgCERiLwpN1wGy/RdN/TLfjM1u136cIVixmM2aHm0rFz+59B7LwgoqH4WCEF45q9cy3bcpMWUbnmnU1uBmmt+ucdFuMZ8bOrxTtmjo1a/gxf8bO2Hr6qy9MabmfeLxp5jyl7HKLB0vLPNqpzR+MEImOW6u0yc+bXacGCEdVXHYrnpJ57qQVE7ibtUxV0A/jPiq33Uq100Y9A/TjSv29EmZNLKbyUlwf0jzXrNYCUjylGZ3pNBoDp6fXkUTDWvARi6UMVkpLSgiP+mg905HhF1Jc7D49QCgao6m8hFW1GbyI9LRy49CvOGM6hGUkxugf/5WSxvXSbQEzd345q7SdbRJvCABVVdl5coDFlFJZ1zS9wFif81S7Pr0uN514sOTu2Ee10wYBlcHRENXaArQSbOMWaZOfB386rl/ASlCm9gfOHt2vKRae8r3aVtrF7hEXLWEXtSm+V8Lsaen00zccwmMz54Qov7gyIjqT54/oGo2ZdnxBP5RUQ0lFyvS0264SwcTrXREC4WhmX0cR8cpJrZ558fIqlEydkOKtqWX9hzFZnQxQxtmAQ4YSjme6rGLcVycVJ3qH6RkKYTYpNHgc0z/PTHOeUpVYxpVgLyjtwUaYwaFgWmPohfR4OT5Ta0F30in8mkqXbuMn0Ws45LWldq0WZo2eDblwaQVmk/EBXkaPxnvuuYcLL7wQl8tFbW0t73znO2ltzeGT+XQ6El1UV79x2mDFHh1hyFTO6WgFe84MGPM6CpBXTmiam+3LMiQfndSa6nQ6UVE4PWSWoYSTSTbVeMcntV3tNOyMdzw1uB1YZgoEZprzNF2JJR4suZaej4MQBPuJjvTPOIZemBnvaJj9bVr3SnPFAqf0q1dP+V41XP9Zuu1LCESidPoCC/t8RYzetnvpitzoDMxoIPLcc89x6623snPnTp588knC4TBvfetbGR5OIXbLBVLt+HRRXVnNBHfBycGKYi0h4FmOiilx8RTmx2gomhDHbV+eoUBkUmuqriJvHxzVUseuetj/a3jiLlHvQ+qs4jToO+lF6VzAZpzzNIMTcc0a6t/2GV43beLlyFoOrLwl+Rh6YWbG2fm/cXAfqDGWVzsps2egsj/pe2WxWLh8lXaxPNWbw9eNPCIYiSYyzJeuzA1vloxqRB5//PEJ/37wwQepra1l9+7dvOlNb8rkU8+PZDqS8a6dcXfBKcZkDZuhrI7yQQt0jZUThPnx+hlNHFfvdsy9XXAmJrWmljm0Q8MbCDMUjFA23SRRYUZUVWVnPDBfVDFDTdpigzf/45iL6xydiE0mM+WV1RztHuJPfaVskXLM7Jnkl+Q5cY6PmctxNl6ftSVcsbqGM4fhZO8wl2btWfMciy15ezuw5/QggXCM6jI7a+pckAMO+lkVq3q9WkqvsjL5rjYYDBIMBhP/9vl8WVlXUmZy7ZwmWFmEdrF6/cwggXAUh9WcvXUXIDtPxssyyyszpw+ZZHhnMSma62cA2gZHWeOOpG5NFWbkeM8QvUNByiwm6mfSh+gsgBNxo6eEo91D7D4t2clZk8TO/3iHjQ3KKTaFHoVhT1Zs19+0uob/BHqGgvQOB6n2yDE4H3R9yGUrqxZGbLwAZG2LEIvF+MQnPsGOHTvYuHFj0vvcc889eDyexE9zc44r2yenp+OGTBWlVqpKbQQjMd44O2joEguBV+Lagu3LMphGTCJUdsXLM239wzO3pgrTopdlzl9cPrM+ZDyp9Chpllgay7Xsy+4zg8RiInZMmyRDQkMxODdq4ShN1FtH4hOTM/+eVpfaqXRqwYfo7uZPrti6jydrgcitt97KgQMH+PnPf57yPnfddRderzfxc/bs2Wwtb0FRULgormV45aTsxOZDIBLl9XgwtyD6kFTj65MIld12E2WMYOk/knqSqJAWOxNi4zkEk3PQo+jUuOxYzSaGAhGOdPtn/9zFShI7f99oGICaMge2ikXaxOQs2a7rAeWe04NZeb6CJBIi8Icvc1XH/8VKhMtWFVkgctttt/Hoo4/yzDPPsGhRinkSgN1ux+12T/jJV7Yv1QMR0YnMh31nBwlFtHrm8uoURnPp0nsk+fh6vS13klDZrfqpUIZ5ebQZ76abZfrnHNH0IXr7dXZN803KWKvwLtkUpE8SO3/fqDbErrnSmdrOP0OMBSLyGc6HcwOjqMDyamdO+IfoZDQQUVWV2267jYceeoinn36aZcuWZfLpcooL422mu08PEIpIy+dc2XVKS8XOWx+Srl34uFKAZdllPOz+X9wfvZFXh3JDXZ6PHO0eom84hMNqYtOi7Je2GuMnXP27NCORkGZ5bbDttaEksfP3xjMizRXO6e38M0BjPJg82O4Xf6Z5cKZfa4ffkSNtuzoZDURuvfVW/vM//5Of/exnuFwuOjs76ezsZHR0NJNPaxzj0v6rbP1UOS0EwjH2tw0avbK85dVTC+AfMlu78HGlgMXL16JiYtfJSRcxuVilja4P2bakErs5+8Ltpvhu+rVTsptOm0maqUA4RjAaxaQoNJU7sq6Z8jitOK1mQtFYwsekaElVXk6Ds/FA5NIc0odAhrtmvve97wFw5ZVXTrj9gQce4Oabb87kU2efSVNCldAQ/+xx8a3Rtew80c8FS2SO62yJqiqvnxkElPkJVedqFw5cuKyCn+zq0LoukmushRmYtiwzTZvhQlHvcWAxKbR7A7QNjiYCE2EaJtn5Dw0PoxBjaWkEm66ZmsbOf6FRUGgoL4FReO3UABcuLdLz6aR26gnTqGcQcHf7AwyMhlAgoWHMFTJemkn2U3BBSIq0/3nW03zI/DhnWl83eoW5zeQIX9Ui/C5fgNFwlAqndX7zZeZqF462iwc42OEnFJUS22yJxcb0IZesMKa8ZTWbWN+o6c1EJzILxmmmgqNDVDDM8rJwWnb+mUAvsRVtK7beTj2badTj0EX/VaV23PZ5DitcYMThZ75Mk/YvXbSRCvzUtf+RcCRi9Epzk57WqQLSl74Bw720DWglvIuWVWKazzyEediFN3pKWFRRQjSm0jFYoCXFDHKk28/ASJgSq5lNTeWGreOCxRUA7JLyzOyoWYO64+95PryOV2JrCVx6Z1p2/pmgsVzTiew+PYBabHNnkrRTpzWNehx744FI2j4+WUQCkfmip/3dTVPSlNVldvotNSxRz3Gk9ZBBC8xhUkX4nfuh7TW8vZ3AAviHzNMu/KK4PqVNApFZk9CHLK3AZjHudHPBUi0Q2XuyW7Q9s6TDF6IrbKdfKWfduo3ZHxgYz5jWmvwss/QxOBLkeE+R2b0naadOMLm8nAI9EJlx4KQBSCAyX6ZJ+yuKQoWnHDthDp5qN2BxOcx0EX71GmLhUUqHTqAQm59/iG4Xfv29UFozdT5QGhNZL1oqgchcGdOHGNt1tG2JFogc6R6SrotZos95qi61UTIbl2hd/3PVXXPvrhmXMTWfeYn/r/QRPmZ+hCMHXpvb4+UrSdqpJzBNeRkgEo1xIC7ylUCkEJkh7b/EDUGsvNouu68JzBDhjyoleFQvqxxe1tYvgJ/MdOPrZ5jIqmdEOn0BIjJ9N21UVWV33IDqokxNTU6TqlI7y2u0k3i7VwLK2bD3zCBgQEq/98iUjGlJeQ0blFNU7P+PGTURBUWSduoJTDeNGmjp9BMIx7BZTFQ4c88iXwKR+TJD2n+xZYBjsSaeOGclKhbTY8wQ4XuDChZiXNRoxWxS0muXnamtbY524cuqS6kqtRGNqXT5gtPeVxijwxugdyiI2aSwsdF4a/wL48Lj9kEZJz8b9sUzIvXuLAYiqqp1IU7KmFZXVnGUJoK+nhk1EQVFkhEUCdIoL+tC1Xq3I3PzuuaBBCLzRVE04ZazMmna31VZzwvmi/AHYxzvSZ42K0pmiPBHA6NEMLG6uSG9x5vJNVVnDnbhiqJw4TIttT9teWYe/f0FwaTX/8YZTRi6tt5Fic34wY/b4jqRdimxpU04GuNAuzZ8NKsp/aAX+o5NyZg2lJcACseCHoIdLdNqIgqKJCMoZlNefj0+oyeX3FTHk9XpuwVLabX2JWn5/ZQpoaa1N+Bs74eT/ew9O8jqOpfRq80N9Ai/Yx9UrZr4O1VFCQ3Rr7pZuXLtzI+lt0+P9CemhCba2rxtWkkmzSFpqbhwSSX9LSQ6eaYwj/7+giDJ6y8ZqGSFsoItzUmmVxuAXh7qipfY5OQ3M616St+R5ZR+JJQ0Y+qwmKl02vCORBnw+alPoYkoSOYxjdqw8lqayLG4UFSvhh0rYbRfO4guuRUql4PJxHnNh3n1ZD/7zg3yl9tyfKJwtphkmEQkqAUQQR+B/rP4o1ZOqI28Z1H59I+jt087PFoKdzTenml3abf1tGgp3KpV81L7b1tawR+ADu8oMVXFND692XsEdv3HhHHpCx0I5TQpXr/9yAE+ZD5KlSf7rZ7JWFzppLrMRjSgldiajF5QHpBo+XRlOaVvsY1lTCcFI43lJYRGBukZVahPoYkoWPTycpLrTCoGhkOc6B3GSpbLa7NASjPpko4CPEXaf0v8YvrG2SK3Jp5MCgHpWdsKjqqNVJZYcQ63py5xWGxw/k3gboTy5jm3taXDqjoXZpNCKBrjVN+4clKKWvZs+vvzmhSvP2YvY0+gjgr8bAv8KSdev6IoifKMdEClh2HeE3YPVK1MqolocNtpog/V1w6v/7T42rBnWV7eG9f4LKtyzq7rKYtIRiQLbI4P+mrp9BEIR3HknlbIOCZH+GveTufzT7DedIZKxaRpPWrWwOrrkv99Om1t/vaUbW3pYjWZqC2z0+ELsL/Ny/Lq+E4s6NUCqHT6+yfZxxcEKV5//3CIcFSl11xN1ejpnHn9FyyuZLBFdCLpsnecyHECyaz5F9KqX1Fg7fXg75ySMV2inmMvLvqCZtarqlzEZuD1eFlmy+JyQ9cxHZIRyQKLKkqoKrURjqoc7vAZvZzcQ4/wFRO0/A573yEC2FDGWxi/+gNNCzKZeba1zYa6+Ml437lxma0Uteyx9U3f35/3pHj93fHuIleZG1M0d17/+fGTcZcvgIp0sSUl3qEWfPLLnOkZBAzSFlSvTpoxLV1yAY9Y305vzEW3X7rYZkIXqp7XXGHwSlIjgUgWUBQlkRV54+zgwhj9FBpxrYc60sfro7WEsFLmsI0rcfRrviOTW9fm2dY2G+riJ+MD46d/jq9lJ2MBA6GcJMXr7/RpLbJNZWpOvf419S5MisJoOEq7V9p4p6PLF0RVoancgdNmUN4hScu9ctknqVyqTaCUzNb0xGJqIqt13iLjW+hTIYFIltjSXA5M2k0LY8St8r22WoIRFUVRKNFPfoqi6UBGerX7jWeebW2zQc+IHOrwjRmbTVPLXuhAKCdJ8fq7fAFAZbFlMKdev8NipqpUC/4PtA0au5gcpzNu/KafuzLCHLV3W+OzgzolmJyWE71D+AMRHFYTa+qTdGzmiOWAlNeyhC5Y1YVDwiTiVvldo1rAUGo1M2HOndWptYUmE6bNo61tNlSUWLGZTQSCMY50DbEepq1l4+9c0EAoJ0ny+mMmC8HhQVYxgKty3cTXn0xbkGXq3A56hoLsP+fj2s2GLiWn0bNaWxaVg/Fa4wlsavKwE8RgcAb2xPUhmxeVY5l8Dsohy4HiDESyeTKMhOCFr7E9FMHKZk70DOMLhHE7cmsMs+HErfIHBrWMR5l90lczPKJZ6afaNc2hrW22KIpCrcsBQTjY7tUCERirZWc4EMpZJr3+YDCIBzMnbat4++V/l3Ovv87t4EC7lwPtkp1MhYqayDac1+yB0wYvaBLrm9zsBPzBMP0jISrdUuJOhi5UPX+yUDXHLAcKdJuWezhtFpriY6z3S3lmKnGrfNXXBqiUjg9EVBV87eCs1u6Xijm4ps6WOo8dSFJim6N9fMEw7vWfLDuPb0TezRuLb0apTcOQLsvUubXPcP85b/GNk08T32iEkXAUi0lhfQ7Y80/GbbdSUaKX2OR8mgpdqHr+eKFqDloOSCCSRTbHa61vSHlmKopCrHoVZ0YdrKIdlzU6UevhrNJEqQbPSdDbGPcn0xdkIRDKGZLV9uOv/1SglDa1hvOWGDvoLhVVpXbMioIvEOFsv4gdk6GXZdbWu3BYsug9MQshf208oDzYLp2IyRgKRjjS5QcmZURS2OcDC+q9NBsK+EyZe2xpKgfinTPCFPpibh4IX8Mx8zLsamjihNyLPqJZ6RtFXNTVYPHTpPRwpNMnk3hT0OUdpy0wkhQXNbNJobpMu4jJpiA5nbnyGU5D3XSbAoF95waJqdBUXpJ4r4CctBwoTo1IOmRAR7KpSRtnn1bnTFxbAsDldxRFm2+nL8BxtYldjRtQFv/PRK1HLGLcwsaJukpjEf7BfpLD4ToGekqoqROj8PGMhqN4A2EANuVwu2Cd2w4DsL/Ny41bGo1eTs6hZ0Q2N+fuZ1jrimdE2iQjkgxdH3LeZH3INPb5gCGWAxKIzIfJwcoMVsMbmjyYFG08epcvMDFKFeItn7BxUaVW4oCxEodRyYdJoi7FbMVSVsWGwdMo7cMQ31kLGvoFbFm1E09J7gqyx8zpBo1dSA4SjsXo9sczIpls3Z0nNfFApN0boG8oSJUcixPQv9vnTc5q2T2aMLXrYNKBo/jatCx0FlvupTSTRUptFlbVar3cUp6Zih6IbGl2G7ySOClEXZ7ySo7SSCQwHDdZkxKNTkbKMhkwANQDkQNtPmIxEayOp7XDTzSmYreYWFaVIn2fA9gt5sRE4P0iWJ3CgXimaGPTpKyW3nKfBe+ldJFAJMtsiac6xdhsIuFojN5hLaO0Ka6lMZwUoq46lx1Q6A/bNZM17znj1phjJFL6OawtAKgsteGwmhgKRjjZl8IVt0jRdTN1bsfEKdM5SJ1rrANKGGNgOJQY7LghLgmYYF5mccBFH51in0/jFtie/WnhUprJMpsXlfPL186JSG4SPUNBVFWlqtRGgxFzLZKRQtRVG99N+6MQjUYw58gcFaNRUceyWjmsDwEwKQrrG9y8cmaI/ee8rKjJDQv6XOBgm49acndk/Hhq3Q5ok4zIZPROoqVVTs2zKpV52Zq3ZdR7KV0kI5JlztNbeM8OiofBOBIp/WYPCgbvwvRSwGWf0BxdJ81RKbVZcNksWIkSiCo5M0fFaM4NjDIajmJWFNY0JLGTzjE2NqbITuaI7bVRHOzQ3g9dDJrLjHXOSCAyHt2sb0OTRwtCdt4Pnfs187Lxw0R3/V+IRgy3HJBAJMusqXdhs5jwBSKc6hsxejk5g57Sz5myDEw7UK+2zEo5fvxRs3bhKrKLVTL0LF91mR27OYveE3Nk4yItZT2h/bOnFV76hrZzPP0SPPdVePE+7fYiIBSJcbRby/DVuHI/I1LjsqPEGwB6ZBJvAj0w29jggsO/m8a8LMUw0SwjgUiWsZpNbGjU23gHjV1MDqHPjNBbnHOCVAP1Bk+zPbyTcoYxjQ7A8/9Hu1j1HjF6xYaip4N1o6lcZ1NcxHegzUc0pk6/c9x5f1EEI0e6/ISjKnaLGXdJ7lfubWYTy6u10qk4rI5xMP5eXFA+pAUaKc3LUgwTzTISiBhAYgCedM4A4B0NMziqCVVzTuSoD9TTRV2+Dmh7DYfVQgeVtEc8YxerV38Aw71Gr9gwDnfEA5E82EkDLKsuo9RmZjQc5Xi3b4adY/Ztr43gYPtYWcbwEmma6F0hUp7R8AXCiWz7mgplevOy6YaJZhEJRAxAP3DEmlhDP/m5HdZEO15Ooc9RWXqZlh2pWIZl/fX4KSUQUxk1OXMqzWkEqqpySM+I5IG2AMCsKFoNHTh65PAMO8fs214bgd7yOeEzXKj26UgInrlH+1nAC19KrU+Roh+HTeUleDwVY+ZlyZhpmGiWkEDEAPTSzOF28TAAONSRBxcwxaRdkCIBqFqFw2pNzODo9gdyKs1pBB3eAAMjYRRFoaosB4NJnUlC1C1Nmqj2ZHtXztleG4EucqzJ5WNxEhvj5VwpzWjo78OGRve0Ore0h4lmgdwvAhYgK2vLsFlM+IMRzg6MsCSHTYOywaEOH4vJ8UAEtF1cLKKlM4FSu4VAJEq3P8iSanImzWkEenavymnDkqvD/pK0ML472sjTShP7uqthUW7ZXmebaEzNu/IawLoGNyZFE7x3+wN5tfZMoB+Lm5o8Yzo3b9uYzs1s1czL/J1ahreszvBhojl6xihsrGYTa+q0nVjRlWeSpGf1VGLOq/QtNi2NGdbqr6U2LSOSUOvnSJrTCA7m+k46hRC1OXiUD5kf51x3L9Gq6XaObVCzesz2OkNlBiM50TNEIBzDaTNT7sxde/7JlNosCR8YyYqMvQcJR9XJOrdcGiYaRwIRg9DLM/oJvFgZDUU52avVL6fNiCyUt8N86t12j7aD8GkXq1K7llDs8QdzKs1pBEm1BblCLJZSiFratIFayzBXqK9xovKKnLK9zjZ6WWZdvSvnHVUnow9YLHadyEgowvEerXy4YXwHoq5zW3Y5LNkBV3wGdnwSqlcbtNKJSGnGIMYCkSLLiEzicKePmApOqyVxYZ9CKlfAdTdm14pYUbQDNxaG3lacpigKMaIBL6GuQ9hKcyPNaQSH9G6LXHTj9J5NKURVVJWl1gEWxdo42vsBVl18i/Zd6z8BQf/YznHtDVm3vc42+hTb9Q051EKfJpuaPPxmT1vRZ0QOd2jn01qXfWqJSjHlzjDRSUggYhDrG6VzBsZef8qU/qTpt5itY+2y3jYt5ZjNC0RptRYAtfwea/8JakwjVKjDdDq3sPiid2o77yKjfzhEuzeAFajORaFqaGhaIard5oDgEGc6uuCqy7SdYw7YXmcbPSOyvskDeTZ+Z7NkRIBpBt3lOBKILCR62j8N1jW4MClaWr/bF8jNnWQWODTOt2AKk6ffjvZrt9td4PBoKfOWR7VR1tm8UFSvhh0rYbSf06NtfKP3Sj5YdRUfqs7e2OxcQi8vLq1yYreMc1SNhOCFr2n/f/kdxmlnbGVjLYxJgpEyq8owJg72RbUbku0cCxxVVRObgvUNbjhm8IJmyfoGDyYFuv1BunyBhPV7QTLNdSahD2nMr6xW4R9hOYrTZmF5XGBVzFmRaTMiKabfAsZ7O8QvVo7yetrUGg52FHZb53Ton+G6XD35zdDCWMYo/aqLF3scRdtOf7Z/FH8ggs1sYlVdHnQGTdKMlViUhGD1UBGfTw/EX/sGyYgI6bKh0c2x7iEOtnu5am2t0cvJOuFYjJYOP2Es2K7+J4hbNSe6EFJMv01gc4K/3VBvBz2AKuZgUt+F5ay2YLoWRl871pJSTik1DIVUTvePsKw8fzpGFgq9LLOm3oU11zNAKTRjb65aydFua9GeTwPhKEe7/ED+lWZy/BtX2BS7YPVY9xChaAyX3cLiSufUO1hs07sC5oC3g15SOtrlJxiNGraOjDNNu6q+A92Qq4EIpG5hbNiMqWkb5tIaoHi72MZaPt25PX2490jKeUDvCD7CCqUtYZBYbBzp8hOJqVSW2mj05FdpSjIiBrKhyAWr+gljXaMbkylJp4k9Psel66CmAxmP7u3QuGXM28EAXA4LboeFvoDKsa4hNhi2EmMYCkY42acFiusb3dBp8IKmQ29hHC9E9SyCF/+dGlcXDGhB1Q3rjfdVyDb6OegSd9/Y9GEjO9TGo2siYjFtuGQKzVjt0Bu81fQaj7WtMGadBrN/nKOqkmede5IRMRA9I3KmfwRfIGzwarJPYiedSlugKLD2+pz2dlBQtAswFOVO7HCHD1WFereDqtIc9BCZjC5ELavVhKiK9t2pLeISm6qqHGjzskJp45Ke/87d6cPTtGGjKJTVLmGlqY3IwFn8RXg+zdeOGZBAxFDKnTaaykuA4hRY6RduPTOUlOrVqV0Bt2e5dTcFujbicBF+hgfHp/TzGF3rU4zBZJcvSP9wgOvMr1Gh+HN3+vAMbdgOpwuPJUYpo7R0+rO8OOPRy4obpzuf5ihSmjGY9Y1u2gZHOdju4+LlVUYvJ2uoqkpLh3aySJkR0UmWUk/m7TCL9umFZF2jZtd/qMMH5Vl/ekPRVfrr8/DkN57qMvtYO/1QgGKSOh5o89Kk9HFeSTeW8nUzd6hVLDFmoTO0YRMawel0Mhwo4WCblwuXVmZ/jQYRjsYS59N83BRIIJJpdOFXJKQJvyZdQDc0unnyUFfRieS8gTBDwQg2i42VtWmITXPY22F9g3YR3t85SuyWzybXuxQoB8eX1yYHgnk0g8VqNrG0yklrb4hD7b6iCkQOtvsoZZQah5rTHWqJNuyOfSk1Y9Gq1bT1VeVvZmuO3jtHu2YQ/uc4EohkkjSsyfWyRLGVZvRBcWvqXFjNuRNUzIXl1aXYLSaGQ1FO9Q0n/GEKnWAkf9sFk7G+0UNrbw+HO/xcOf4rmSvGbJkgEmLZgW/yXnMXZU5nbk8fnmmSbGkVNN+AemSw6LQ+euC1Pg+FqiAakcyRYtrnZOGXnkY72j1EIFzA7Z+T6I4HIjOWZfIAi8nE2obia8U+0jlEJKZS7rTmXbtgMhJan3zdTc+RHn8AH07s9avTnz5sFNNNkt1+C0vXng/EMwSRHGo7zjCH2scCkXxEApFMMM20z8nCr3q3g8pSG9GYSut4gVUu9/KnYhaj0XumC0TmMyHXIDYUYeeMXk7Mx3bBZKxriGt9iqhMOjASwh+MAAoVF7wrpzvUEqSaJFuzhkUVJbgcFkLRGMe6i8ftuLW9l09YfsV7fD/Jq5KoTg58qwqQGdrMxgu/FEWZamzW0zrWy3/6JXjuq1r/vNHtcwuInhHJd5GjTjGa0x3IY5V+MvTv4un+UULRPAj8FwA9++MpsVLWuD7nO9QSTG7DjgdIiqIkMlvFsilQVZXDcaFqyuGhOY4EIplghjYzbE7t93Hh1/rERcybdkknnxkORRgJRVCUsV1ovpM4+bV7USentguUg3meDp5MpdNGQ7zEpGfsCp3EBawsfgGbJtuQLyR8fYpkU9A2qM0JMilKfnj5JEECkUwwvs0sGZOEXwnBattg2iWdfEbPhiyrcuK0FYZeem29G5MCvUOhxOubM7MocRlFNDa+/bowMiIwFlD2+AMGryQ7HO5MMnQyRbYhX9C/jy1tvTl/HC0E+oagstSGOU879vLrG5YvzDDtc7LwS0/r+7pOEOtJr6STz/QUWFkGoMRmTkz/LIZW7JO9w4yGo5RYzSyrTpH5y0P0Y7FoMiLTTb/OUxKi404/KoWfndQzP7Vz+QxzRI8ngUgm0NvM0hR+LasqxWkzY4mMMDzsT7ukk6/ou82cndY6RxI6kbbCTwnr9fe1Da683YUlQw+O553VygMC4SjHe7WsbSEFIitry7CaFYYCEXyjEaOXk3H0YzFRXstDJBDJFDO0mY2vuZpMCusa3AxTwkDYnNPTZheChFC14AKReImtCERyiXbBgvsMtdfTNxwiGivs3fTRriGiMRWHxUyZPc9LpOPKmTYirK7TtGfFkNk6VABZrTz/9uU46VqTo53Q95yu4oTayGJvW85Om50v/mAY76g2kGpdgYgcdYqpc2a8gVIhsaiihDKHhVhEpX84RI3RC8ogegmx1mVHoXCyWqCdT4+099MzFEzPuTlP8Y6EaRscxQpU53EgkvGMyHe+8x2WLl2Kw+Fg+/btvPrqq5l+ytwiTeHX+kY3KiYei2zLj17+OaJ7pZTZLVQ688MjJF3WF9E05ULNiCiKwrp6bTfdXeCC1URKP48vYKkY0/oUx2fYVO7AYTEbvJq5k9Er2i9+8Qs+9alPcffdd7Nnzx62bNnCtddeS3d3dyafNi/RT+h/7ClH3f63+dHLPwemtAvmC2mIusZPUy7kSbzdQwF6h4KYFK1bqNDQZwf1DBV2Wj/rKf0smjTqWp9CL83oWa11eb4hyGggct999/GRj3yED33oQ6xfv577778fp9PJj370o0w+bV6ypl4T/fUNh+h2LM37Xv5U6K6VhbgLg7ETQiHrRPQga3lNGSW2/N2FpWJ9Y+HrC2IxNWFmlpVjMcsmjbo/kT8YYbSAR2ckSqQN+d2BmLFAJBQKsXv3bq6++uqxJzOZuPrqq3n55ZeT/k0wGMTn8034KRYcVjMrarRumYPt3rzv5U+F7lswp1azPKAYzJQOxbNahVaW0VnXMNbCGytQc7oz/SMMh6LYLCYqMl0iNcCk0eWwsrhSy04WckCpn2fy3RgyY1e33t5eotEodXV1E26vq6ujs7Mz6d/cc889eDyexE9zc/6KMufCmDtnYV7EwtEYx7r8uBmmzuzPnxk6s6AYBKt6MFloQlWdlbVlmBWFUDTGuYHR5HfKx1lQ49B30mvqyjBlck7QLOZuLTTrcs2cboGNCoORaGKeTr4L/3Nqm33XXXfh9XoTP2fP5rdx12xZX+CD084eeZ2beYRLzYcp63ylIGfo6MHk0W5/wU7/bClQoaqO1WSiqkzLEhzuTGJOly+zoKa58CV20pnW+Mxi7tZCo38/894TJsXneLRLm37tKUlz+nWOmJclI2OBSHV1NWazma6urgm3d3V1UV9fn/Rv7HY7brd7wk8xkfChKMTddDw9u0E5jWJ1ohTgDB3Q2j/dDgvhqFqQ0z9D0Rin+keA/BfIASkzGzVl2on9ULt/4v17jxTELChd5JjxrNYs524tJHq5olBLM2P6EHfet19nLBCx2WxccMEFPPXUU4nbYrEYTz31FJdcckmmnjav0U/sp/pGGAoVkCNgPD0b8PZwlEbsdkdBztCB+PTP8UMMC4zeoSCqqml88l5w3HskeWZj4CTt53+Cr0few4HOcaUZVYWW3xfELCj9IpZxbcEs524tJHrnzMBIiECk8ASrhwpo6GRGSzOf+tSn+OEPf8iPf/xjDh8+zMc+9jGGh4f50Ic+lMmnzVsqS8emf7YUSnkmEoIn7oIDv+F0yA0oOMe7OObrDJ1p0py6gr0QS2xjc4LSOPnlso5iuBde/UHKzMZWp2YxMEHrE/RC37G8nwXVOxSkyxdEUWB1pkszs5y7tZDUuuyUWM2ojPkXFRJ6ILKhAAKRjDqrvve976Wnp4fPfe5zdHZ2ct555/H4449PEbAKY6xvcNPhDXC4w882oxezUERCqLEI50a0k3fp5JZPmxP87Xk/Q0enkDtn9EBkxpNfTyscfFjLNsQi2mdbs0abwWR0G7qqatkQh0fLbIz2a7fbXdptPS2sHngOhbV0+gL0DQepAi2onqnMkAffY71td2lVKWWZnn6tz93yto2ZNJqtmkmjvzOjJo0KCjUuO2f6RzjU4WPL0toFfw6jiMXUgnI3zrhY9bbbbuP06dMEg0FeeeUVtm/fnumnzGv0E3xBGWJZbIRVE+bIKCZFmeo9USAzdHTWj/MSUQus/TOREZnOt8CAds1ZEfRqZRR36syGfeAY2yu0ckJLvF0Zi82wMsNCknVX3FnM3Vpoal1ahjnt8+kCd7ZkinMDowwFI9jMpsTU73wmp7pmhLHoVm+RLAjsHvwmDw30U1limdgumOH0rBGsrC3DZjbhD0RSt3/mG2qM2Ogg6nA3TUoP6xtSnPwMbNdMm0hIy9JYncl/HxdQbqrRsgWJEpvdA1UrDSkzLCSG7KT1uVtZNmnUdUyFViY91KHpz1bXl2E15/9lXIbe5Rj6TvNI1xDRjSrmTPb4ZwtF4ax5EQP0sdHemdX0rBHYLCZW1ZVxsN3HwXYfzZUpLni5SiQEL3xN+//L74CBk3DwYULHnmMbPspsAZYcGoT1Scoss2nXrFiSlZczAYsNLvsEhEcgGgBTkjJLPLPR3FALR/zapqAebf1rr9e+s1kuMywkhs0J0k0aIWsmjXog0trlJxpTMZsW4Hw6+fgwoBW20GY95fYRU4QsqijBZbcQisYYGM7d1OBsORtw8v+ibyVUU5gzdCYzZk6X550z49pVR2JWBijDVFKJqTNFmcXAds20SVNA2bxsNTBJ61O92rAyw0IQCEc53qO994UgcpyJ8hIrVrOJQDjGyd7c1u7MhoMFFohIRiTHMJkU1jW6ef1kgB5/kOp8Gw6Xgp6hIMfVJiLb3wEnYtqu4pJboXJ5zu8g58KGRjf/vTsPUsLT7e4mtav6z51DRaHMXQE19dpE6JZHoWrV2Gc4vl0zWTCSCzqKNAWUG+zlAJzsHSYcjY2lwPUyw2h/7nyP09mlR0IM/s+/8PfmM/zc/hdatiBa2FOiTYpCdakNgtrFe2Vtfluh6+iByIam/J4xo1N4V4ACYP24WReFQCASxRfQTnjrGsvnNkMnh10Bk7G+EMzpJrWrjsS9bapd9tTtqga2a86KNASUNS471WU2Yir0Tc5O5uksKP2csrbBjVIIZd80qHHp5nR5fCyOo3coSKcvgKIUiKkgkhHJSXQR2YQx5DlQl5wr+smv0ePAU2I1eDXZYW3cKKrdG2BgOERFaf58XgnGtauqqAyHNFOohJFZsnZVA9s1Z80MmQ3NnM7Dy0dG6PYHqHenYaOd4+hzVzJu7Z5D6AM282L+k+69Ewlp3juVy6fcRX8dy6pKKbMXxiU8B84GwmTGz0hQyf/2z1mZYBUIboeVxXGRas6XZ1Ixrl11OBglEosBClV6UJWqzGJgu+asmSGzodlnx/AP9uamMdss0eeurG+KH4u5bDq3QNQkAhFvbrfTp5ph1Htkwt2yZs+fRQojnCowVtWVYTEpBCNR/IEI+f510wORQkkjpsuGRjcd/T4cL/4bnK3Iu0wWdo/m/9F1kB4aACixmrCYTGNllsYtycssuaijmAPby3qwmB+lceA1wD5mzLb6OqOXNmsisVgiy7qhwZ3adC4PX9t0VJXZMJsUBkbCdPoCNHhKjF7SVHTvneEezXvHbB3z3hk8AyYrlFYD4/QhjYWhDwEJRHISu8XMytoy6NUu4nlz+U6RVtRPfhmfa5FjrG9w88cDk0ps+cS4dtXoyVZshCm1zqLMYkC75oLS08rWzl8QUE4zGLGgllSipLg45AMneoeJxlSsZhNL1TbY+aO0Lnw5ia4Z05nGfMxiMrGyppQDXQEOtvlyLxCZ7L0z2em3+xAEfOCsAuBgm5YR2dg07sqQ7NybR8ebBCK5QBL9x7oGdyIQWWHs6tIjxe4qtOIa+uNCv2LLiCS0PvG6fF4Sb1c9cOK7rOYYVSZ1rMyy9obcKrMsJPGLgyvm5ZR5ERWqn0BEpSTFxSEf0FP6NaU2TK3/k/aFrxBY1+DWApF2H1evz7ERIzN67zRC/wkIevEHw5zq06ZfJzIiuTxOIU0kEMlR1jW48e4fq+kaznRi2WnSikNtx/CoXkYsFTSV59hOJMPoJ4r+kTCRWCx/D7aaNXx9+Do+EOvg7U1VcMUdebfjmjXxi4PJs4jq0n4YguFQhBKYcnHIFw62+SkDFjvD0w/v8zSDrR/Ovym/SonTsK7BDXu7E46kOcVM3jtWpxZgREKJcQMNHgeVpbbpSzreNk2rlQfBSAGfSfKbDfEyRs638M5g6T082M0KpZ2aUisKxdEuqFPntlPhtKKqKn1DszCnyzEB4cBwiDZfCB+llNc25WeZZbaMuzjoXj561xAw4eKQLxzs0DMiptw3nVsIxh1H57t8KMRys3NmvPdOMsIjYLKAxZYQvm9o9OTHOIU0ydtNWqGzrtHNnwB/MEz/SIhKZ47uTGZIK56LVFCpHMFaGjFmfQaiKIq2EzutZbbSSgjnYJpVP3l7Sqw4LOYZ7l0gjLs41LjsDHXCSHDcd3jcxSEfiKkqLR1+tgMVrlJQ1Nw2nZsvk46jDSM+PmY28cTghXhHL88tGwHde6djn2YOOB5VBV87OKvB7hknVHXn/jiFWVDg25r8xWW3UlGineR0cVJOMkNasW1EwUKMWmdxZUN01jXMQieSo1NrD8S1Bfok06JgnDFbTbxdeTgU1drpJ10c8oEz/SMMBSOYTQqeiuqCGN6XknFjCfTjyOaqZpv9LB8yP87Jw3uMXuFEdO8dZ9WY944a00ThPS3a7dWrQFESIyM2NLrzY5xCmkggksPUuLWU8IFcTCfqTJNWjKkqI8M+IpiocOfx7moebIgr27tmKrHlcJr1QJseiBTGuIG0GHdxqAqewkaEaCzCiH9wysUhH9DLMtWldswmk9YNlerCl0umc+mil2H8XbD3ZzDSO+U4GnatpAI/oYO/y71yxXTeOxd9BEqricRiHO/RzrMbmzwzl3TyKLMlpZlMM7nNbBbUuewc6fLnpsBKZ5q0Yv9QkJpYPz6zh3WewlHgz4aNjR6Oo9kyh2MxUiaEczjNqqeDa91FFIhA4uJgOfgwnqP7iUYi+Pu7KF11Pqy6Vgsc84SDbZrIMfEZ6sP7Dj4cF93687cbanwZJjwKahTK6rWM1ThqXTasA33Unf099N8M1SsNWW5KUnnvxLSSYN9QiEhMpcJppcHjAHWmks40Pj85hgQiOYyeCj/YlsMZkWksvUc7TtCPC6W0DiWfdlcLyJJKJzaziVA0xtGuIdY3pyhvpJNmnWynngX8wTAne4exUmSlGZ34xeHcG62c6RkkVvMhPrjjqsTFIV/QNzM147NahWA6N7lrxGSG4JAWVLXt1rIhNs3huMZlowcz4fBo7pYrknnvxJM3egflhkaPNidIUfJnnMIM5P4Kixj9pHF2YBTvaA5PyUyRVmxVVvCT6DWUeHKsbz+LmBQlUdKYduhWjqZZ9TU3ehyUWItEqDoZxYSrvJo+PLzc78yLE/t4VNTE5zglmMzT4X1A8nKm2aodJ3b32LkoLoOpcdmxEmU0qhA05Z+VgK4z2zDe2j2fxilMg2REchWLDcdb/z9+s/cZwv0jHGr3conRa5qOJLur7/+yk+NqH7XuszP/fQFT63ZwbnCUA21e3pPqTjMq541Js+plmUKaazEX6uID7w6cy+EyaQqGAhEGRsKYTQrVZfnR5ZMWycqZZjs4nTDcrQXtowMQ1S7gZTYzHlOQjqibSMDDJgOXPhcSGZGmSQLpAshs5c9KixTdxjenBas643ZXUU8zBzq09GdRiRyToL9+vfskKTMp5w1Ks07wLShi9Oxklz9Id5455eoXsJU1pdqcoEIhWTlTUbSA3lqi6V6iYe0n6EPpPYJiLeG42sjBjhSZxxwlpqr06nOCkm0K8jmzhQQiOc/GePR7MM8muJ7sHWI0HMVpM1ORqx4oWULfTbd0+olEp1Hr52CaVW8d39BYXHOCJmMzmxJePgdyuZ0+CYmJu4UWTKYqZzqroGkbOMsBVQtYRgegYTODFZsZwM2BtkHtvpEQPHOP9pNpc7p5GBUOjGhCVafNzLKqFDqyPEZKMznOxvjJ42Cbl/gA1LxA3/2vq3dhypMWx0xRXmLFZjYRDMY42j00/cydHEqzhqMxTvQOAxYtI9Kd9SXkFHVuB/hg/zkfb15ZYfRyZiZ+4QsMdtCk9LChYQ1EZ/6zvGG6cqazCkrrQDVB/QbtOPIswt35RWjvYF+2S2zzMSpUYwz091CFlzfVjGBChQJzqZZAJMfRMyKn+kYIRqLY88TZ8sA5rV2w2FP6oDms1rjsEIT9bd6pgcjkOT45MrW2ZyhITNVKS0XZMTMJvfV1f9sgkNtOleMvfEv8Xm63DHBh/zmw+HN7qu5smKZjT+saqQZXvfbf8sXAWHbyaPcQgXAURzau5/OZBxP/HF3tO9luGmVz9L/hxTN5NdAuHaQ0k+NUltoSw+Jyfu7MOHQDpfVNxS1y1NEv5PmU1te/bxsni+OKFP0ilvXd9GwZ5ywaNtnpiZUyoJZRN3IU2l6D4V6jV7hwpGEENp4yhwWn1UwkpnI4G+Xu+RgVjvscvRELA5RRUl5juNNyJpBAJA/QxUk5M4l3BlRV5XB8SuSGhrKJdVE1xxwNs0RdYjed4xexcXT7NFHmxiLvmNGpKbNjUrTjsCtXBauqCi2/T1z4hqMWVBSsJW6stWu1i3Xv0cI6DvVy5rLLYckOuOIzsOOTmmnbJBQUauMBZVaOxdkYFY5n3OeoVq/GG1FQUaioqDTcaTkTSCCSB2yK70j1C0OuMzgaZjgYYZ2lg1VHf6TVRU+/BM99FV76RmHtyNJEz4gc7vBNL1jNIVK2CxYpVrOJlTWaj0vOtvEGvdB3LHHhGw5pxms1Lod24bO7NPtz7zmDF7rAzKJrJKuZrbnOgxn3OfqCUaIxFQWFylL79AFMniKBSB6gp8bnlBHJpio8TpcvwAqljdvL/oi568DEAW6d+wsvPZwGFU4rpTYzgXCMYz056uo4jkgsRt+w9n2R0swYelCWs+30kdCEC99wUA9E4i30ZuuYWLJIqYu/F/uTBSLz6GxJylyNCsd9jnq7uNNmxqxnVfJooF06SCCSB+gXgv6REOFc2k2nCHJ6fAGuNu1mkX10al20ek1hpodnQFGUhEj1wEJY9mc4wOwbChFTVcpLLDR6RKiqsykRiORoRsRim3DhGw5pbTI1ZfFAJBoGk8WYQWj63K2r7tL+3yBqE4JVPyPhcVb9vUe0jO34DO6L981PizFuivOsJh2P+xy7vNoGtNQ+rrckjwbapYMEInlAjctOrUs7cCcIVg3IdqSD39vPClMH1orm5HXRQk0Pz4DeQZTzgtVx7YJX1AVQJp9Aixhdr3WgzYtKDr4vdg9UrQRvG4FwhGAkHoi47NqFL+jXhsF5Fhm8UOMos1uoddmIqePGLgz3wqs/0DK24zO4yYShs8mazNWocNzn2OUdTaxbe/5pApg8Rdp384QNjR44Dt3+AI1GL2YaVFR8I8O4CVFRXp78TmardkIskLRiumxcpJmC5bRgNd4u6OncyXZTgPPDv4AXT8Lq64xeWU6wrsGN2aTQOxRiOBgduzjMlcmt2/PNFCgKrL0e/J0MnTuIjTAmk42S2DD0tmsX2epVWoayiNnYVE5bSz/723xsU1UtG+LwaBnb0X7tTnaXdltPiyYMrVoFfUdn7weid/bMZtJx/HOM+TtwDWmfY5nNlJcD7dJBApE8YUOjB/U4dPlyu3PGOxpmJGKixGSjyp5kQqnJDA3nawd7SR6YQi0gujndoXYf0ZiK2ZRhE4N0LnL67i4SghPPQevvYbiXwYgFH2XUeuLtgoNnwGQtHA+KOVJiNbOqtoyWTj8vL/4o16zPkYGO4z9HiwMu+ijH/ucB3BymwhrRLnz1m8Dq1O47eEYzyitSNjd5eKKlX3NYLfdqXSi166fvbDn5HBx6ZG5+IHMxKqxezemVH2Dfzu9wg6WTkoh35gAmT5FAJE/Y0OjmALnvJdLtC+LDia20BrOvPacGuBnNsuoynDYzI6Eox3uGWF1nsG36eLfHaBjOvQqxKNFF2/FFTKioVFRWgbMJug9BwKelmIucTU0eWjr97D83mBuBSArXzsdD57E4doqLa100bPpLaN8NXQcm7uSLNNOlz/Da3+aFspD2nlidye9sc2rnrCOPjfmBTJc1SRVczMGo8NWhan4QvYH1njBrl1Tl5UC7dCisV1PAbGx04WYYRnoI9p7M2f5xrbNHoX/R1Tk3wM1ozIqS0BgkVexnE93tUa+J20u1zykSJHjmNexqALPJhKfEGt8VNmq6nmAOl5XmwhwElJsWaZmtnCixTf4c49oGteMNNnf+CjNRql0l0PI76DwwVf/w6g+KroMNxhoATvaOEMSiCXjDI8nvHBoBNaplPWbrBzJP9p71omLCVVGdtwPt0qHwXlEh0tNK3f7vc5nlMBcphxn6w79oau7eIwvz+MlEr3MUwupeJ7XLN+fcALdcQD8BGnoRS+b2qMbnV5TVERodphI/LpsZRZ9pYXVqu8YcEkUbxaZxn6FqpJB3GtdOX9lKSsI+VpraqRo9pR1/SZ09++MdbDkovM0gVaX2hGN1d8iubZp803S2eJpAMc/eD2SevHF2EIB6d2F3rklpJteJ73iU4R5MNid9AZVQyElVDtbtY6qacJzc2OiGmpqcGeBmGONr94Nn2BSfYmto50wyt0eTWdsVxsIMRu2UKn1YbeOybuER7fezEVPq2YYCY7xgtdMXoMFTYsxCpnHt7PQH6aCCDeZ2zENhqNuQYiffGBdQ5kB2J8tsXuThj4N+uv1BmqtXQyycYmZNFax+G7zxc60tOlkwkoF22tFwlNYuPyagvsBb6IvoipCHTNrxlJQ4UVE4O2LOyd3Mid4hgpEYFpPCmoa4/mEWjocFR0/rFF+CN3X/JyuUNg7GBauGkMzt0RzfFQb9eIMqJlTKrPELl6qCr11r+7SLuZnDak7oewydOzONa2enN8Aodkotqqb/SaV/KOJMl15i6/IFtc3cRR9NncFddsXc/EDmwaF2L9GYSk2Zbf7dWTlOYb+6fGHSrjmRNZi049G/jJ2DgZzczew9o62jzu3AWkwBRzJSTNys9B3mI9YRfhi+liNd/qmTeLPBeLdH/SKmKFC9inDAhzvWAYpKid0ytit0VkFZ3dRddZGyqcnN4Q4fB9q8XLuh3phFJPsc43T6ApQQxGqzgzmuf7AnEUfPJdNVIGxuKgc0J2hAm02zY2XqDO60k34XXvf2RjzI3dxcPlYiLVAkEDGaFIp31t0Y36mM7Xj0QMQfijAUjFCWzd1MsmBpEq+fGaAGjEtV5wqTa/fjFPYmxzqWOl/irf7X2HP6GmMCEd3tsWPfxK4mZxWdZesJqb1gsmCJjBZsu+B82bSonF++ds7YjEiKzzGqqnT7R1nJABZPA9gs2o69etLnV+SZLl3r4w2ECYSjOGD6zpa5+IHMg33x8u2WReUL+ri5iAQiRpJi15zoS1//ZxN2PGaTgtNqgSB0eEdZ5YpkZzfTe0SbBDk5WJrU+vf62QHeCjQUeD1zRmaYuGkpX8TK4XMcPtYKFy/L/vp0t8cku7uRgS5eU1ezxm2haklzcep60mCyYFUxIlOU4nMc6O9leewcQxY3JY1LASW1/qGIM10ep5UllSXggy5/gCXp/NFc/EDmyBtnBwDN84SOBX/4nELOLkYxjeI9Mea5bbe20xlXlyxzaLFjx+BodnYz01kfj2v98wXDHOvR5lsUfSAyw8TNqspK7IQ50daZ5YWNQ9/dTaqJvx5Zwk+ib8Veu7I4dT1psq7Bhc1ion84xKm+FG2f2SDJ5+jr7+aAuow9de9BKa2ZXv9w0UdyRuxuBBvj2YZZGUVmQfc2EopwbiAu/F9U+NkqyYgYxQy7ZtxN2u+3/JWWVo3vZlw2E2WMYOnvgYZVmd3NzGR9PM7k6o0zg6gqeBxWnLYi/1pNU7sHaCiJsgcrRwahbyhIlcOg3eik3V304r/jX759jIAaot7Tbcya8gS7xczmJg+vnR7gtVP9LKtO0daZDSZ9jj8ffDM/jMLty5YBp7X7pNI/xJK4HxcRmxo9+A+N2Q7kCnpgtLymFI/DavBqMo9sdYxihl1zoi+9rHbCjseDnwplmD+NNBO64MOZ3c0E49bH7lTB0pjJ1evxfveiz4bAjBM3HSOdDDqX0aZWsefMoCFLTDBud3c8XIk/GMNpM1NVWnzixdlywVJtRMHu0wMGr4QJn+NzXQ5UTGxp9qS8j2S6NDbFHVY7vIGcGmLYGQ+MzmsuN3YhWUK+iUYxftecjPF96fqOZ9nlWJdfzgPmv+Q74Rs4HJ6k1l/oabyRGayPx4llE4FIeZELVSGtiZuDzW9FxZQbF7E4unnSpiYPpiLUDMyWbUsqAXgthz7DQCTKiV6tVLS5CESOsyLJ1NxNi8oxKQrDoQjnBkaNXmHCe+f7pr8kjKVoApEiz6EbSKrOBUg+jyW+m1GAuiWr2dc6wOtnBtmSyTVabGPWx9O0/qlmG3uLxAEwbWZQ2DefcsKBfezJoYvY3nh2plhOfmmTwpjtgiVaRuRY9xCDIyHKnVnMIk0eaBhHb0VdXOmkqtQ+8+MUqOncFFII7ktWX0ety06nL8Brp/tprkix6coiqqomNgXF0DEDkhExjjR2zan60s+Lfzn3nhvM7BrtnhmsjzWxbH/UwVAggsNqotqVxsmvWBiXyWLJDrjiM7Djk1Czhq3xi9gb5wYJ58jcID2YlEAkPSpLbSyv0UqruZLZ6vQWV0o/LWYQ3K8s1fQYu08NGrvOOGf7RxkYCWMzm1jbkGQDWIBIRsRI5tiXfv7icgBePz0ADRlcn6JoIrcZWv86vNqBvG5RNeY3/2MGF5SHpPAlWF5dSrnTyuBImMPtPjYbt0IAgpEoR3uGAAtbmsuh+OagzYltSyo40TPMa6cHeMs64yfxSiAyiTQE98uUHl6kgddO9xu71jivx9t21zW6sVvMEInO/Ed5ntmSjIjRTLNrTsXm5nJMCrR7AwwHM6x6T6P1r2NQO/ltXVyR2bUUECaTkni/DBesoqn0VRWaK0uoKZOsVrroOpHdp4zPiKioYyLH+Gal6ElDcO+J+XAzwvGeYfpHjLe6f+OsZmR2XhG07epIIJILzFLNXmazJGZddHizILCqXp08WKpePWENW+XkNyv092vPWeMvYp3xz/D8ZgkmZ4PeOfPGuUFCEWNLbL7RCKPhKFazwnojHHtzkZkE95YSLNEAy+xDNCk97DnVl931JWFv/HywpYiyWlKayVPOX1zB8c4BOrwBVtZmoY6YrMQQ01T6+i5C1z0I6aG/X6+fHoDGcb8wIM3aEU/pny/B5KxYXl1KZamN/uEQB9q9hmYF9WzIunoXDmuaKf1CxmKDyz6hieqjATBNskoY7oXON2Ckj42mILdbfoNlZys0xgwzeRsJRdgft3a/oIjOp5IRyVP03bR+ATEKvSbdXFFCtaT0Z8WWReWYTQpdvlGGvL0T2gqzyYSUfhHtwhYCRRkrsRldntGPxWLptEiLVJ4+I33Q9hp4z2kWCWV1DKhl2Hv3a7cPZ0kkNclyYc/pQcJRlUaPg8WVxnfwZAsJRPIUfTfd5Q8YN04e2UnPh1K7hWtqBvmo+VGiJ56H0y/Bc1+FF+/T5hBlCe9omNFwFJvZxPpGSenPlm3x8ozRYse2Qa28tkWOxTGSdSfGotC1Xwv8nTVQVourxM4QJewZrSMWGtFcrdXsl9pePqEFQBcvrzJmfpFBSCCSpyyrKsXtsBCNqfQOzWJOwgKj60POF6Hq7Olp5cOWx9mgnMYXsUxsK9x5f9aCEX0nvb7Rpan0hVmxbcmYw6o6uc09S3hHw3T7tc/x4uVVhqzBUPRy5lV3TR0COnkez1C39lPeDIu2gc2J3WqizGYhqsIQJZpjtPdc1l/GzhNaMHvxiuL6DCUQyVNMJiWhjM+KYDUJMXUspX++pPRnR3zoYaN9lKM0MhgyTR162PJoVso07fpOepEEk3NhY5MHm9lE71CI0wYNwHslLrKscNqoc4mp4BTGdyc2bNZMJJdcrmVKAAVoKNfeN19IGTM9yyLDoUjCyOySIgsmMxKInDp1ig9/+MMsW7aMkpISVqxYwd13300oZHxrVCGhdzhMqxNJYmu8UBzrGSIUiWExmVhTXxzGOwtGfOihq3YpoDASio6V2PShhz1HtPtlmDP9WiBy8fLKjD9XIeKwmtkcb7XcdcqY8swrx7XnzQVn0JxFF9y76rXsY2Ri0Njo0cZTjAYCmqO0rSyry9tzeoBITKWpvITmItKHQIYCkZaWFmKxGN///vc5ePAg//7v/87999/PP/6jmF0tJLpg9dzAaPKBTT2t8NI3NFvjDOgPXj/dj5thVpWOYPGdy7rIMq+JDz10udyUWs2oqBM9YfShh6l2ZQsUYJ4bHGFwNISiKFwkgcicMXoA3p+Oa9qCxZUy62lG7B6oWjlFwNpYXgKoKCE/MWcVeBal/5gLMOdr58l4WabIsiGQofbd6667juuuuy7x7+XLl9Pa2sr3vvc97r333kw8ZVGydbGHI6YRzKEwJ4+3sHz5OBO03iOw6z9guEezNTZbx/QH3jatZjqNadqM9LTifO17bDIdohaLFuTUrNGEYfN53GIhPvRQCY9ogwL7wB+MkJCKjh96OJmeVs2Nd9LcjLm89zvjO+l6twO3vfDHjWeKbUsq+T4nDBmA1z0U4FjPMNdaoEkyIjOjKLD2es0depxjdJU1wDpzB8MxGwMlS6lSsqtcePWkVl4rxsxk1t5pr9dLZWXxvcEZo6cVxyvf5s22VrabWog+fY+W/Rju1aL8lt9rOoPqNWCxL6z+oPcIsZ3fo6TvIAFs2F3Vhogs85pxbYUNbq3t2Tcaz4joQw9rVo8NPdTpadXe42RzM+bw3r+U2EnLBWw+TB6AlzYLkNnaeUK7gNWU2Smxitg4LapXTxSwjvRjGh3EW76OveoKzgazm1kKRWPsb/MBkhHJGMeOHeNb3/rWjNmQYDBIMDjWAeLz+TK9tPxkXLbDVlJGRyjE4LCdVZ37NU1B9RpABU8SW2M1pl3k+o7BundA9crZPXc8yPH2dXEo0sAlFj9lDtvY7IaeFi3IqVo1o0NsUaO3FXrbWBFo4xhhhoIqkdFBLMPdyYcexgWuiQBz8tyMWb73sZjKn471sgQJROaLPgDvRM8wu9OdO7NAma2EPkQ+w9mhC1hH+7VyyiW30rM3SPDZe2kbDHDeQj/fNEaF7YOjRGMqiyqKTx8Cs8yIfPazn0VRlGl/WlpaJvxNW1sb1113HX/xF3/BRz7ykWkf/5577sHj8SR+mpubp71/UTIp2+EuK0VF4bhPIVK1Sovu+45p/7WWJn8Ms3XuqvCgF/qOcSrkARQ8DutYrJNlkWXeE28rdC3ZSpkpgkcdoq+nS5vjsz1J6SwucE0aYCZ772fYbbd0+ukfCWM1m6h3S6fFfNHbeF85mYZgdQEzW3+Ke09MEapO19IqaEwar3FBfHZQ+2AK3V2GODegCcaTdssUwec4q4zIHXfcwc033zztfZYvX574//b2dq666iouvfRSfvCDH8z4+HfddRef+tSnEv/2+XwSjEwm6NUGz8UvRiU2M1aTiUhEpcMbpNnugoAX1HoIDycPRqLhuavCIyGIBDg+qJ30PCWTdAU2J/jbs976lrfUrEHZcTtd+1o51T2It+wmbtvx1uQZjbjANWWAOf69T2O3/dIx7QLWVF6C2VQ85kmZ4rJVNfzytXM829rNP759Xeo7LmBmyzsa5txAAItJoalChKrzZXNzOTsVheFQhLP9oyyuzY5b9NkBrYOnGMsyMMtApKamhpqamrTu29bWxlVXXcUFF1zAAw88gCmNVLHdbsduF5vwaYkHAvrFSCEeDPjhTP8IzRardqu7UROlVq2a+PeqCkE/lNWPqcL1nXMkpO2cK5dPvU2/r8VGWLHh9XsBO56SSRH6dCJLITmKibraenZ3qzx+xsJtqY6VuMA1ZYCpv/dD3XDokRmFyi8eE33IQnLFqhpMChzpGuLcwAiLkglHIyF48p+1AHHp5TNntiqWTPuc5+IXsM2LNC8TYX44LGbq3A46vKO8erKfxbXlGX/OYCRKt1+TJBSbkZlORjQibW1tXHnllSxZsoR7772Xnp6exO/q6+sz8ZTFg8U25WJU7hwLRHZUhbULz6profV/JqjCCfrA165dnKpXaWnJZDtnZ6UWiJx6aey2qpWaENZZxakhG/XqTvoci3FYx538dJFl45YxkaUBA9zykcWVThTgeM9w6ouYLnDt2Jc8wPS1aWZN53bPuNsOli/n1XgJQQKRhcHjtHLBkgp2nRrgmdYe/npbA7zwNe2Xl98xllafaSLsLLKKZ/uLeyedCRZXOunwjvJ0axfv2b585j+YJ+3eAKqq0lxRQlN5cWa1MhJCP/nkkxw7doynnnqKRYsW0dDQkPgR5kmSHnh3vDzSMxQgMuoFZ7XmIDhJFc7ogHahatqmTZfsPTK1To0CrY/DkSe059Nr153xYVAjfTwRvoABXFzg7NGCHDWmBTk9LclFlsKMOKxm6uOGSs8fSTFwK9ncjMnvfdMF0DezjuTQ4UOMhqNUldqoKivMurMRXLW2FoBnWrpT38li00qj4RQurGlmFVVUzg6IGd1Cs6JG2+C9dKyPQDjzE4zPxYPJ7cuK9zPMyNXi5ptvRlXVpD/CPNF74MddjGwmWOyMsop2/FHbWLZjvK3xkh1wxWfg0r/XgpBkLb4oWsbEYgeTDYJDQLztt3qNFtD0HuXhsw4eiF6HvXnLxCAnlchSSIulVdoO+bkj01zEJs/NmPzel9XOrCOJBNh3XJujsWNlFQqiD1ko3hwPRP50vJdAJMVFzO7Rjl/fpImwMH3r9iQGhkMMhyLYLCaZ9bRQWGzU3Ph5fu78X3hDCi/HW6MziS5U3b6ieAORrLTvCguM3gN/8GHoPwFBPyvL3Dw+vBS3KcyO0uqx++qqcIDyxWO3TxK9AhANQmhUCzxUVdOGROP28YoCdhchfzeRwXOcUxZR//b3w/OhROsblcslEzIPllaN7cRCkRg2y7j3MhKamOaf1HaYeO8HTqelI3npbBCwc8mKahjO/GsrFtbUuWjwOOjwBth5oo8rk91JUbRjOBaeWjr1d6adVdSzIVsXl+OQYYULhqIovHldLT975QxPHe7iqjW1GXsuX3BsWOH2ZcVbXpOrRr4yKdsRuPQf+EH0eg55bem1nU0SvQLaeOxYRDspmq1a2j82bldntjISCFJKgK1LKiiz2ya0vkkQMj9qXXYqnFaGghH2nJnBoXNS22HivR9nlJZqtz1avpKnOrRy3qXJxHFF0C6YKRRFSZRnnmvtSX3H0mq46KOpM1tpZBV1fcilK6pnuKcwW66J+8A8dbgbNRKcaN++gMfHayf7UYHyEisN7uLUh4AEIvnNuIvRxo0bsVksDIci9A+n4ew4XvSqYzJrtetoWPtRTNptOtEw/hAM4+BNq+Tkt9AoisLl8ff1uSPTXMSmIw0dye6SS4mqJpbXlCYGfQkLh76Dfqa1e/pNQfXqqaXTHZ+EimUzzi2JqaIPySSXrKiixGqmwxvgUHvmjDX/cLALgMWVKUqpRYIEIgWCw2LmwrjYKa1R5MkGP5nt2gUs6IeAT1P1m+NGV6pKLOinLeSkXa3iTavTa+MWZsflq+OByHS76ZmYQUfyh25tUuxlK6sl+7FQjBt6tmOZC5vFRNtggIGZNgWpMlszcLDdSzASxWY2sanJk97nKJ912jisZi6LbwqebpnHsTgNoUiMPxzWApHVdcVtdyCBSAFxWTxFe6Y/jUAkiegVVHA3aP8fC4G9DIjvpntbGVFttEbr8ZTY2NDoyehrKVYuW6kFeIc6fHT7AnN/oGRC5R2fnOAfsmOlZLUygdNqSbTTnuxN41icA4/u6wBgSZUTi5REM4JennmmtSsjj//SsV78gQhOmyU++bd4kW9wvpDGbmbHygrcDDM60EGw7+TMA7SSDH4CYO3bYPW12v+Pa/s9Zl3DAG4uXVktTpwZorrUru1wgeePpmjjTZcku+3TfcOc6BnGpIj3RCa5ao0WUJ7sXXglcDSm8ui+dgDW1LtnuLcwV65aW4uiwP42H0PByII//u/in+Hq2jJMk1vtiwzpmikUeo+w5tjvMVlaIBZh8H++TN2KLbD6uun/LsngJyqXQzQEf7x77DbPIlpe+SQQ4PJVUpbJJFesrmF/m5fnjvTwngsWLehj/3pPG6DZkU+x5xcWjKvW1PKvv4N27yjBSJR5+UVP6ph69bSfbn8Im8OUaPkWFp4al50ti8o5eLaXk73DiQ3CQhCMRHkyrg9ZVedasMfNVyQjUggM98KrP0Dp3I/dUcoAZbR4LZoD56s/0H4/Hcnq1JNuO+cN0BkvFexYKTvptJlDXf6K+G76haM9RGML570Ti6n8Zo/mH/LnW5sW7HGFqSytLmVZlZOYqqZXKp0Fv93bhkKMTZVgGelNOtBQWBiuWa+VZ070LOzsrBeO9OIPRqhz2Wn0yMBJCUTyHVXVHFJH+qF6DZUeFyoKB/tVghWrtNt7j05t5Zwlv9qt7aSbK5xF3WaWDc5vLsflsDA4EuaVkwtnqPTqqX7ODYzislu4doOMWsg0V8S7ZxayPBOMRjm8fxcfNT/K1tgBOP0SPPdVePG+WU3sFdLjLeu0z/DMwAjh6MIFe3pp7bqNDShFXpYBCURyg/mo2YNezR3VrRmTlTkslFjNRGIqrV1D2vC7kV7tfnMkqqr8erc2Wn7jAqYni5Jx3RWpWjMtZhM3bG4E4L9ePbtgT/3r3Vo25PrNDTisYoCVaXSdyKneEWIL5Cr92muv8BeR37PVdhans2xsBEPHPm1cgwQjC8qaOheNHgfR2MJltgLhKE8e0soyb98kGwKQQCT/mTRASwFqXVqq72B7vAU3Fkl50UuHF472cNYX5UHb+1jy51+S1r8scNN2zQX38QMd9MQnc86HkXCE/9mvdVr8+QLrToTkbFtaic1iYiQc4fm5+sKMR1Xp3/0wFfix1q1Fsdq1EqrdBTVrtQ1Jy6NSpllAFEXh6nV6eWZhMlvPHelhOBSl0ePgvObyBXnMfEcCkXwnyQCt6jIbZkWhyx+gb3BQ+/08gof/fk3bSb/r/CbZSWeJjU0etjSXE46q/Pfu+WdFnjjYyXAoypIqJ9uWyFySbGAzm9jYoGUQH3z51LwfLzg8QKz3CB1UTu2WGTfQEO/CZdEEuGqtltk60TuUen7QLNBbr6/f3FD03TI6EojkO0kGaFnNJpZVlwIqXedOaNN47XMrqQyHIjwdnyT63gunH8IlLCz/K54V+dkrZ4jOM7X/0Ouaxufd5y+SmnQW2dJcjoI2P+hYt39ej3Wu14tVDWEvcVHjStKHEx9oSGhhhZXFzvblVbjsFkbDUX67t31ejzUaivJU3MTs+s2NmutxYBCGuotadCyBSL6jD9ByVk6w9N5UrbCKNg55LUSqVk4dCZ8mhzt8RGIq5zWXs1Y8C7LKDZsbcTssnBsY5YWjc0/t+wNhdp7QPGLeLd0yWcVTYmV5teaa+cBLp+b1WEf7ggSxsbHGknxicnygIbbidulcaKymsenG//HCiXl1sj3b2s1IKMqiihK22DvhpW/AyReKXnQsgUghkGSAVpM9wGn7Kv5v8GpODM+tPUxF5WCbNmfhryQbknVKbGbec4H2vv/Xq2fm/DiHO/2oKmxfVklzpfhOZJvzFpcD8Js9bXhHw3N6jOFQhNZBheOxBlaX+FIONKRmtTb4UFhQNjZ5sFtMnOwb4clDnXN+nEfjOq0PrAqgvPJ96NwP1pKiFx1LIFIoTBqgZbrys4xe9HGOq00caJ9bx0z7YICB0RClNjM3bmlc4AUL6fD+eHnm2dYefIHZX8RUVA7Hh3aJSNUYFlWUsKaujNFwlF++Njf9xpEuPyoKZ2uupLS8LuVAQ9beIFOwUzGP7kSb2cSWReUAfO+5E6hzKJWe6RvhyYNdKMS40bZHExdXrwGLiI7lG1tITDIh+4ttSwBt9sxcLmIH2rQA5vpNDZTaxYTXCFbWlnHJ8ipi6tjnMRs6vVowWWI18/ZNDRlYoTATCgo3X7oMgP/ceXrWrbzhWIz957TPfusF26cdaEjNmgVfv6Cxpbkcm8XEG2cHefVk/6z//p7HDhOKxnjnshj1obPgaZpaMi9S0bEEIgXM4ion2+MTeWc7ytobCHM0Lq77i22ykzaSmy7WsiIH2n2zFq3uPTsIwFs31FEmwaRh3LClgcpSG+3eAMdn6dL5oxdP0T+iBZPvPK9p2oGGQuYotVl49/maxur+545P/OUMotNXTvTx2IFOTAr8/WUNKJEAWEuTP1ERio4lEClw9CBi3zkvvcPp+1H8/NWzRGIqVaV2tkivu6G8dX09VaU2RkKRWVlNP3W4i9YuLZjUO3AEY3BYzLz/Iu0z2HtmMO2/OzswwrefOQrA5ePnAyUbyyBknA9ftgxFgWdae2jpjG/uelqnFZ3GYipf/v1hAN530WKWNdZpouJwCl+SIhQdy7e3wLluYz2VThsj4Qif/tW+tNLCr58Z4BtPHQHg/MXlyRX6QtawWUyJgHLniX6GQzNPAu0fDvFPDx8A4ILFFZzXLN4hRvPXlyzBYlJo847S7Q/MeH8VlS8+eohAOMai8hLWNchwNKNZWlXK2zZqbqg/eO6EFmzsvH9a0elDr7exv82Ly27hU9fExcTVq8DbJqLjOBKIFDh2s6YNMJsUXjjay/998eS09x8YCXHrT/cQjqqsqi1jQ6O07OYCf33JEpxWC33DQe747zemDShVVeX/e3g/fcMhqkptXLJChhTmAnVuB9fFL2JPt3QzNENAebx7iGdbe7CaFd68tk42BJlgDgLWv33TCgB+98Y5vK8/NK3oNHTgEf7P44cAuO3NK6kqs2vZq3U3av5PIjoGJBApCqrL7Fy1Whve9O9PttDV3ZW0lqmqKnf+6g3avQGWVjm5ep2c/HKF2jIHN27RAsqnDndz7x+OpLzvI2+08z/7O7GYFK7dUI+liE5ouc5tb16J3WKm0xfgo//vNUbDyZ06Q9EYz7Zq3jEfuXw5laUyViFX2NJcziXLq6hTe9m1aydDjrqUotNjh/ZgGWqnubKEm3csHft9zRoRHY9DzlBFwoYmN3+zJsSHld/hO/w00ZMvjtUye7WL2q5T/Tx/pBe7xcQ333c+dovYuWeFNHdlDZ4SronPvfjhCyc4mKQtu8sX4J/jJZm/u3JlYu6QkBusqC7jXec3YTOb2HVqgI/9dDeRJG2aLx/vYygUYXFlCR+7coUBKxWm4wvv2MCi0iiBwAi/3NuXVH/XGzJzuqufUka5623rpp5PRXScQGT0RYIy3Mcd5e287DiHP2zh2JCNWsqwnd6D2nWCgaDKyye0uvWX3rmRdfVuOGrwooUprK1383cVK/jus8d5qqWbcqeNqkiUM31+TvUO86OXTuILRNjU5OGWK5fDS0avWJiAGqPeFuA9a+z8rqWfPx2L8T9DHVy/qRHfSIjnjvfwzKFzLD87AMDnb9yAQzYEOcfqOhf3/a/LOPCfvyMWGOZXu8/xvrow7hIrHd5RXjvjo7u3mwrFwsqm+oSuZAq66BiKWnQsgUgxoKrQewSbw8O6jds4+fpTDAQiPPF6PzFsrOEILmUYlTX8+flN/OW25nlN6xUyy+1vWcWJ3iFiLSq/3nOOb73yB8LjDmWbxcR9f7kFa5Ge1HIGvaUzEtLKoNEwHH4ETr5AbSzCj5f7+dlxOwO9A/z45RBf/eNTBFULViLcaoGNjR4uX1Vj9KsQUtC4eBUVl1zK3lefY9ewnZZOP06bmWeOnyOGwiqln2D1Jv7pprfKfKcZkECkGAh6NUFV7Xpq7SWYako50z+K3WQioproVitZSicry2L87Z9tMHq1wgyYFIV/+/MtPPxNB13x7guXw8Ky6lIWVzp5//bFrKpzSTBpJD2tcPBhraUzFgFfOwx3g92tdVeYrdTWNnJT5DidZ46zJwAxFdbWu7hmTQV/GWymwSNltZzGZKJk8zu4cKgdx5FjqP4QIyEzHtMoW8sDLG5eg+fKv4WKFH4hQgIJRAodiw22fgD8nZqaG028Wl1mZ+viFWAya6rt1lOsW1IFVkkD5wMlVjPvuWARAyMhPnjlWyh3lcquK1foPQK7/gOGe7Sgw2SB0X7tGIxGtGPOYge7i5qlWyjzn6Ta7Oct77ucRTWVWgD5QonRr0JIh5o1WC79GFs8DzOw678hFmbLkjIcTdu1zpci1HvMBQlEigFb2ZiBTjI3v/CIdrKc5fyFhMhSyDyT0/yVy7Fe/U/UGr0uYSKqCi2/H2vpHO2Pu2SOgqtRa9GMBMEaHz6oKJSUlVMSHgKlH6g0dPnCHKhZg2nH7VSN9mvH5yW3QuXyotV7zAUJRPKZyYFAqlS8bqDTsQ+qVk38napqaWNnNdg9mVurMHcmp/lDQ9pOa92Ns99xRULwwte0/7/8jtkHn8L0BL1aK+b4OSKxqPa5WayAS2vTjI4zNDNbIegvKkvvvCLJJmBKkCGi03khgUgxoBvoeNvGDHTMVm135u/UjHXKkvTCC8ajOzfqaX6zdcy50dumeRFI+jd3iIS0DMj4zKPJrGUco2Ht81NjWnCi/67hfC1zUhJ3v5184fPIrCfDWMhNgJASCUSKBd1A5+DD0H9C24HpBjqrroXDvzN6hcJkYjHtcxmf5gdN6+PwaC6MLY9qWS7ZgRmLHjwE/VqQERoamxVitoPTqYlVraXa7tkU12Lplt6NW7TMZbILX9VKGO6F0mqjXl1xIpuArCGBSDGhG+hMrmXGZp5dIhiA9yz0Hk1vXHjFkql/Lxqe7DA+eIiGtfe9/wQ0X6z9XlG00mjID0MdYLaByTaWkdQtvfuOJr/wde7XPuOmbYa+zKJCNgFZRd7BYkOmduYPoaGpaf7xFOG48Jxj8tCz0iqo3aDZdp94GkYHtWyJHlSU1Wv3G51k6V21auKFb/zckuo12uP1HtUeS8g8s9kECPNGMiJC8p2z7KaNZ6ZupyIcF55TpNo1ly8GexmcelHLbsQiWtCx7DJYeQ3s/enU7oqB06kvfKBlUXzn4OyrsPSy7L7OYiSdTYC/XTYBC4QEIoKQq8zY7TROWwDSEZNtpts1l9bAsivgxDPQsFmbI6KXQZN1V6S68I30ac/h79B+//K34NyrsPq6jL+8omYhNgFyPKaNBCKCkKvM1O1UhOPCc4oZd81xYarNNRZ0pKqsJLvwhUegbbdWllFMYHZobfYd+7RuGpNVBKyZYrabAGFeyBlMELKJ3l0x1K1dTJJMXp2AjAvPXcYHD8mYjVGgfuHztmkXOlWF4T7tMy+p1DpxbKXgboSatdr3oPeodj9h4dE3Ac6qsU2AGtM2AT0tsglYYCQjIgjZYq6eBKm6neQkaCwLaRQ4OfsV9GuBjMOjfe5mCzgrASUulmyMt+F7M/LSBKa3PBD79gVFAhFByAbz9SQQ58bcY6GNAsdf+LoPaWWfWARcdVo2xOYcu6/Vqf1OBhtmFtkEZAV5NwUh00zurhjfmlmzVru95dGZyzRC7jFd6eyij8xew6Ff+BZv14KYxvOhefvEIATmPh9KmD1ieZBxJCMiCJlmvsZkQm6z0EaBignczeA+p5UDJstAZD6UUGBIICIImUY8CQqfZKWz+SS4FAWqV0MsLPOhhIJHApFCQkzIchMxJhPmQmm1pkFp+b3MhxIKGglEBCHTiCeBMFeqV8OOlTIfSihoJBARhEwjxmTCfEhW9jFJ9lMoHOTMJwjZQIzJBEEQkiIZEUHIFuJJIAiCMAUJRAQhm4gxmSAIwgTkLCgIgiAIgmFIRkQQBEEQjEAsFwDJiAiCIAiCYCASiAiCIAiCYBgSiAiCIAiCYBgSiAiCIAiCYBgZD0SCwSDnnXceiqKwd+/eTD+dIAiCIGQXXXR61V3a/wuzIuOByKc//WkaGxsz/TSCIKSDGoPAIAx1w+AZiM1nRKwgCML8yWj77mOPPcYf/vAHfv3rX/PYY49l8qkEQZiJnlY4+DCcfEEbmhYa0txe190oFvOCIBhGxgKRrq4uPvKRj/Dwww/jdDrT+ptgMEgwGEz82+fzZWp5glBc9LTCzvthuAesJdrQvZJKbSKwt02bgyPBiCAIBpCR0oyqqtx8883ccsstbNu2Le2/u+eee/B4PImf5mYZi77gSC2z+IjF4PDvYKQPqteAxa5ZzdtdULNWu73lUSnTCIJgCLMKRD772c+iKMq0Py0tLXzrW9/C7/dz112zc4y766678Hq9iZ+zZ8/O6u8FQUiC9yz0HgVPEyjKxN8pCriboOeIdj9BEIQsM6vSzB133MHNN9887X2WL1/O008/zcsvv4zdbp/wu23btnHTTTfx4x//OOnf2u32KX8jCMI8CQ1BJADW0uS/tznB367dT8gsYuktCFOYVSBSU1NDTU3NjPf75je/yZe//OXEv9vb27n22mv5xS9+wfbt22e/SkEQZkbviImEtI6YyuXadF9bGVgcEB5OHoyERrTf28qyvuSCRoKO4ibV8ShMISNi1cWLF0/4d1mZdoJbsWIFixYtysRTCkJxM11HTNUqqF6lCVOrVk38O1UFXxs0bgGPaLIEYUGQDrVZIdN3BSHfSacjZt2N2v/3tkIkqN0n6AN/J5RWwdobZLcmCAuBdKjNmqwEIkuXLkVV1Ww8lSAUJqnS/JM7Ykb7tdvtLnB4oKdF64jZ8UntBHjwYeg/AUE/jA5omZC1N8iJURAWgnSPx6pVEviPQzIigpDPzKYjpmYN7LhdOzlGQnDJrVK3FoSZmI3WZzbHY8WShV9rniKBiCDkM7PtiFFM4CjX/r98sQQhucjkC18kZNxahNkhHWpzQgIRQchnpCMmN5AOGQHkeJwjsh0ShHzG06x1xHjbtA6Y8egdMTWrpSNGELKBHI9zQjIigpDPmEzSESMIuYIcj3NCAhFByHdq1khHTCEjZZ/8Qo7HWSOBiCAUAtIRIwi5gxyPs0ICEUEoFKQjRhByBzke00YCEUHIJpJmFwRBmICEaIIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIYEIoIgCIIgGIbF6AUIgpBFLDa46i6jVyEIgpBAMiKCIAiCIBiGBCKCIAiCIBiGBCKCIAiCIBiGBCKCIAiCIBiGoqqqavQiUuHz+fB4PHi9Xtxut9HLEQRBEAQhDWZz/ZaMiCAIgiAIhiGBiCAIgiAIhiGBiCAIgiAIhiGBiCAIgiAIhiGBiCAIgiAIhiGBiCAIgiAIhiGBiCAIgiAIhiGBiCAIgiAIhiGBiCAIgiAIhiGBiCAIgiAIhiGBiCAIgiAIhiGBiCAIgiAIhiGBiCAIgiAIhiGBiCAIgiAIhiGBiCAIgiAIhmExegHToaoqAD6fz+CVCIIgCIKQLvp1W7+OT0dOByJ+vx+A5uZmg1ciCIIgCMJs8fv9eDyeae+jqOmEKwYRi8Vob2/H5XKhKMqCPrbP56O5uZmzZ8/idrsX9LGFMeR9zg7yPmcHeZ+zg7zP2SNT77Wqqvj9fhobGzGZpleB5HRGxGQysWjRoow+h9vtli96FpD3OTvI+5wd5H3ODvI+Z49MvNczZUJ0RKwqCIIgCIJhSCAiCIIgCIJhFG0gYrfbufvuu7Hb7UYvpaCR9zk7yPucHeR9zg7yPmePXHivc1qsKgiCIAhCYVO0GRFBEARBEIxHAhFBEARBEAxDAhFBEARBEAxDAhFBEARBEAyjKAOR73znOyxduhSHw8H27dt59dVXjV5SwfH8889z44030tjYiKIoPPzww0YvqSC55557uPDCC3G5XNTW1vLOd76T1tZWo5dVcHzve99j8+bNCdOnSy65hMcee8zoZRU8X/nKV1AUhU984hNGL6Wg+PznP4+iKBN+1q5da9h6ii4Q+cUvfsGnPvUp7r77bvbs2cOWLVu49tpr6e7uNnppBcXw8DBbtmzhO9/5jtFLKWiee+45br31Vnbu3MmTTz5JOBzmrW99K8PDw0YvraBYtGgRX/nKV9i9ezevvfYab37zm3nHO97BwYMHjV5awbJr1y6+//3vs3nzZqOXUpBs2LCBjo6OxM+LL75o2FqKrn13+/btXHjhhXz7298GtHk2zc3NfPzjH+ezn/2swasrTBRF4aGHHuKd73yn0UspeHp6eqitreW5557jTW96k9HLKWgqKyv5P//n//DhD3/Y6KUUHENDQ2zdupXvfve7fPnLX+a8887j61//utHLKhg+//nP8/DDD7N3716jlwIUWUYkFAqxe/durr766sRtJpOJq6++mpdfftnAlQnCwuD1egHtIilkhmg0ys9//nOGh4e55JJLjF5OQXLrrbdy/fXXTzhXCwvL0aNHaWxsZPny5dx0002cOXPGsLXk9NC7haa3t5doNEpdXd2E2+vq6mhpaTFoVYKwMMRiMT7xiU+wY8cONm7caPRyCo79+/dzySWXEAgEKCsr46GHHmL9+vVGL6vg+PnPf86ePXvYtWuX0UspWLZv386DDz7ImjVr6Ojo4Atf+AKXX345Bw4cwOVyZX09RRWICEIhc+utt3LgwAFDa72FzJo1a9i7dy9er5df/epXfPCDH+S5556TYGQBOXv2LLfffjtPPvkkDofD6OUULG9729sS/79582a2b9/OkiVL+OUvf2lIqbGoApHq6mrMZjNdXV0Tbu/q6qK+vt6gVQnC/Lntttt49NFHef7551m0aJHRyylIbDYbK1euBOCCCy5g165dfOMb3+D73/++wSsrHHbv3k13dzdbt25N3BaNRnn++ef59re/TTAYxGw2G7jCwqS8vJzVq1dz7NgxQ56/qDQiNpuNCy64gKeeeipxWywW46mnnpJar5CXqKrKbbfdxkMPPcTTTz/NsmXLjF5S0RCLxQgGg0Yvo6B4y1vewv79+9m7d2/iZ9u2bdx0003s3btXgpAMMTQ0xPHjx2loaDDk+YsqIwLwqU99ig9+8INs27aNiy66iK9//esMDw/zoQ99yOilFRRDQ0MTouuTJ0+yd+9eKisrWbx4sYErKyxuvfVWfvazn/Hb3/4Wl8tFZ2cnAB6Ph5KSEoNXVzjcddddvO1tb2Px4sX4/X5+9rOf8eyzz/LEE08YvbSCwuVyTdE3lZaWUlVVJbqnBeTOO+/kxhtvZMmSJbS3t3P33XdjNpt53/veZ8h6ii4Qee9730tPTw+f+9zn6Ozs5LzzzuPxxx+fImAV5sdrr73GVVddlfj3pz71KQA++MEP8uCDDxq0qsLje9/7HgBXXnnlhNsfeOABbr755uwvqEDp7u7mAx/4AB0dHXg8HjZv3swTTzzBNddcY/TSBGHWnDt3jve973309fVRU1PDZZddxs6dO6mpqTFkPUXnIyIIgiAIQu5QVBoRQRAEQRByCwlEBEEQBEEwDAlEBEEQBEEwDAlEBEEQBEEwDAlEBEEQBEEwDAlEBEEQBEEwDAlEBEEQBEEwDAlEBEEQBEEwDAlEBEEQBEEwDAlEBEEQBEEwDAlEBEEQBEEwDAlEBEEQBEEwjP8fzuEjuIRe7ggAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, target_function(x), label='Target function')\n",
    "plt.errorbar(x, data, yerr=noise_level, alpha=0.5, fmt='o', label='Data')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We indeed see some noisy oscillations, but we can still see the sinusoidal behavior. Our goal is to fit this data with the model function and extract the best-fit parameters.\n",
    "\n",
    "To do this, we need to minimize the residual between the data and the model function. The residual is usually defined as a difference between the data and the model prediction, scaled by the uncertainty in the data:\n",
    "\n",
    "$$ \\text{residual} = \\frac{y_i - f(x_i)}{\\sigma_i} $$\n",
    "\n",
    "where $y_i$ is the data, $f(x_i)$ is the model function, and $\\sigma_i$ is the uncertainty in the data.\n",
    "\n",
    "In the case of `lmfit`, it is usually convenient to provide all parameters of the model in a single dictionary. Let's define the model function and the residual function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "def residual(params, x, data, uncertainty):\n",
    "    a = params['a']\n",
    "    b = params['b']\n",
    "    c = params['c']\n",
    "    model = model_function(x, a, b, c)\n",
    "    return (data - model) / uncertainty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create the initial guess of the parameters, we need to use the `lm.create_params()` function, and put the initial guess as arguments. Since we don't know anything the parameters yet, we can but $a=b=1$ for amplitude and frequency, and $c=0$ for the phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = lm.create_params(a=1.0, b=1.0, c=0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, once the params are created, we can use the `lm.minimize()` function to minimize the residuals w.r.t. the free parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "out = lm.minimize(residual, params, args=(x, data, noise_level))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2>Fit Result</h2> <table class=\"jp-toc-ignore\"><caption class=\"jp-toc-ignore\">Fit Statistics</caption><tr><td style='text-align:left'>fitting method</td><td style='text-align:right'>leastsq</td></tr><tr><td style='text-align:left'># function evals</td><td style='text-align:right'>73</td></tr><tr><td style='text-align:left'># data points</td><td style='text-align:right'>100</td></tr><tr><td style='text-align:left'># variables</td><td style='text-align:right'>3</td></tr><tr><td style='text-align:left'>chi-square</td><td style='text-align:right'> 452.676707</td></tr><tr><td style='text-align:left'>reduced chi-square</td><td style='text-align:right'> 4.66677017</td></tr><tr><td style='text-align:left'>Akaike info crit.</td><td style='text-align:right'> 157.000801</td></tr><tr><td style='text-align:left'>Bayesian info crit.</td><td style='text-align:right'> 164.816312</td></tr></table><table class=\"jp-toc-ignore\"><caption>Parameters</caption><tr><th style='text-align:left'>name</th><th style='text-align:left'>value</th><th style='text-align:left'>standard error</th><th style='text-align:left'>relative error</th><th style='text-align:left'>initial value</th><th style='text-align:left'>min</th><th style='text-align:left'>max</th><th style='text-align:right'>vary</th></tr><tr><td style='text-align:left'>a</td><td style='text-align:left'> 0.42132511</td><td style='text-align:left'> 0.86511276</td><td style='text-align:left'>(205.33%)</td><td style='text-align:left'>1.0</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr><tr><td style='text-align:left'>b</td><td style='text-align:left'> 0.45876507</td><td style='text-align:left'> 1.45342513</td><td style='text-align:left'>(316.81%)</td><td style='text-align:left'>1.0</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr><tr><td style='text-align:left'>c</td><td style='text-align:left'> 2.35859715</td><td style='text-align:left'> 2.55400903</td><td style='text-align:left'>(108.29%)</td><td style='text-align:left'>0.0</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr></table><table class=\"jp-toc-ignore\"><caption>Correlations (unreported values are < 0.100)</caption><tr><th style='text-align:left'>Parameter1</th><th style='text-align:left'>Parameter 2</th><th style='text-align:right'>Correlation</th></tr><tr><td style='text-align:left'>b</td><td style='text-align:left'>c</td><td style='text-align:right'>-0.9652</td></tr><tr><td style='text-align:left'>a</td><td style='text-align:left'>b</td><td style='text-align:right'>-0.9101</td></tr><tr><td style='text-align:left'>a</td><td style='text-align:left'>c</td><td style='text-align:right'>+0.8549</td></tr></table>"
      ],
      "text/plain": [
       "<lmfit.minimizer.MinimizerResult at 0x112907e20>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chi-square statistic is pretty large, which means that the fit is not optimal. Let's print the best-fit parameters and plot the data and the model function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_opt = out.params['a'].value\n",
    "b_opt = out.params['b'].value\n",
    "c_opt = out.params['c'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, target_function(x), label='Target function')\n",
    "plt.errorbar(x, data, yerr=noise_level, alpha=0.5, fmt='o', label='Data')\n",
    "plt.plot(x, model_function(x, a=a_opt, b=b_opt, c=c_opt), label='Fit')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't learn any thing... This is because the least-squares algorithm is sensitive to the initial guess of the parameters. Therefore, we need to provide better initial guesses. Let's try to do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = lm.create_params(a=1.0, b=4.5, c=1.0)\n",
    "out = lm.minimize(residual, params, args=(x, data, noise_level))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2>Fit Result</h2> <table class=\"jp-toc-ignore\"><caption class=\"jp-toc-ignore\">Fit Statistics</caption><tr><td style='text-align:left'>fitting method</td><td style='text-align:right'>leastsq</td></tr><tr><td style='text-align:left'># function evals</td><td style='text-align:right'>30</td></tr><tr><td style='text-align:left'># data points</td><td style='text-align:right'>100</td></tr><tr><td style='text-align:left'># variables</td><td style='text-align:right'>3</td></tr><tr><td style='text-align:left'>chi-square</td><td style='text-align:right'> 87.4261696</td></tr><tr><td style='text-align:left'>reduced chi-square</td><td style='text-align:right'> 0.90130072</td></tr><tr><td style='text-align:left'>Akaike info crit.</td><td style='text-align:right'>-7.43755247</td></tr><tr><td style='text-align:left'>Bayesian info crit.</td><td style='text-align:right'> 0.37795809</td></tr></table><table class=\"jp-toc-ignore\"><caption>Parameters</caption><tr><th style='text-align:left'>name</th><th style='text-align:left'>value</th><th style='text-align:left'>standard error</th><th style='text-align:left'>relative error</th><th style='text-align:left'>initial value</th><th style='text-align:left'>min</th><th style='text-align:left'>max</th><th style='text-align:right'>vary</th></tr><tr><td style='text-align:left'>a</td><td style='text-align:left'> 2.72194591</td><td style='text-align:left'> 0.13404111</td><td style='text-align:left'>(4.92%)</td><td style='text-align:left'>1.0</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr><tr><td style='text-align:left'>b</td><td style='text-align:left'> 5.02306883</td><td style='text-align:left'> 0.03419869</td><td style='text-align:left'>(0.68%)</td><td style='text-align:left'>4.5</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr><tr><td style='text-align:left'>c</td><td style='text-align:left'> 1.03098860</td><td style='text-align:left'> 0.10132277</td><td style='text-align:left'>(9.83%)</td><td style='text-align:left'>1.0</td><td style='text-align:left'>       -inf</td><td style='text-align:left'>        inf</td><td style='text-align:right'>True</td></tr></table><table class=\"jp-toc-ignore\"><caption>Correlations (unreported values are < 0.100)</caption><tr><th style='text-align:left'>Parameter1</th><th style='text-align:left'>Parameter 2</th><th style='text-align:right'>Correlation</th></tr><tr><td style='text-align:left'>b</td><td style='text-align:left'>c</td><td style='text-align:right'>-0.8729</td></tr></table>"
      ],
      "text/plain": [
       "<lmfit.minimizer.MinimizerResult at 0x1128d69e0>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time chi-square is much smaller, which means that the fit is better. Let's print the best-fit parameters and plot the data and the model function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_opt = out.params['a'].value\n",
    "b_opt = out.params['b'].value\n",
    "c_opt = out.params['c'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, target_function(x), label='Target function')\n",
    "plt.errorbar(x, data, yerr=noise_level, alpha=0.5, fmt='o', label='Data')\n",
    "plt.plot(x, model_function(x, a=a_opt, b=b_opt, c=c_opt), label='Fit')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time our fit is perfectly fine. We can see that the best-fit parameters are very close to the true values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "metatrain",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
