riskless-yield-construction-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "right-scheme",
   "metadata": {},
   "source": [
    "## Constructing a riskless yield curve for USD\n",
    "The first thing we need to do to price any option on a US equity is construct the so-called riskless yield curve for USD. This is because pricing of derivatives is done in _the risk-neutral world_ where all investors are assumed not to require a premium for additional risks, hence future cashflows are discounted using the riskless rate to obtain the NPV of the cashflow.\n",
    "\n",
    "I use the Fed Funds effective rate and US Treasury Yields as proxies for riskless rates. Ideally, using OIS swap rates might be better as treasury yields tend to be slightly lower given the preferred tax treatment of treasuries. However US Treasury Yields are available for free from [FRED](https://fred.stlouisfed.org) with daily frequency while OIS swap rates re not, and the difference is pretty small. Also it'd be better to use treasury spot rates (aka zero-rates) rather than treasury yields. However, treasury spot rates are not made avaliable by FRED.\n",
    "\n",
    "I use the following dataseries from FRED to accomplish the task:\n",
    "* DFF &mdash; Federal Funds Effective Rate (daily frequency)\n",
    "* DGS1MO &mdash; Market Yield on U.S. Treasury Securities at 1-Month Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS3MO &mdash; Market Yield on U.S. Treasury Securities at 3-Month Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS6MO &mdash; Market Yield on U.S. Treasury Securities at 6-Month Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS1 &mdash; Market Yield on U.S. Treasury Securities at 1-Year Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS2 &mdash; Market Yield on U.S. Treasury Securities at 2-Year Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS3 &mdash; Market Yield on U.S. Treasury Securities at 3-Year Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS5 &mdash; Market Yield on U.S. Treasury Securities at 5-Year Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS7 &mdash; Market Yield on U.S. Treasury Securities at 7-Year Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS10 &mdash; Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS20 &mdash; Market Yield on U.S. Treasury Securities at 20-Year Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "* DGS30 &mdash; Market Yield on U.S. Treasury Securities at 30-Year Constant Maturity, Quoted on an Investment Basis (daily frequency)\n",
    "\n",
    "In the `pricing.curves.YieldCurve` class I use cubic splines by default to interpolate when constructing the curve. See [this article](http://web.math.ku.dk/~rolf/HaganWest.pdf) for more details on interpolation methods if you want to know more. Cubic splines is pretty advanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "diverse-convertible",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment if running in Google Colaboratory, otherwise the import of the curves module in the cell below will fail\n",
    "#!git clone -l -s https://github.com/ilchen/options-pricing.git cloned-repo\n",
    "#%cd cloned-repo\n",
    "\n",
    "# Install the latest version of pandas datareader\n",
    "#!pip install pandas-datareader -U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "occupied-fundamentals",
   "metadata": {},
   "outputs": [],
   "source": [
    "import locale\n",
    "from datetime import date, datetime\n",
    "\n",
    "from dateutil.relativedelta import relativedelta\n",
    "import pandas_datareader.data as web\n",
    "import yfinance as yfin\n",
    "from pandas.tseries.offsets import BDay\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from volatility import parameter_estimators\n",
    "from volatility import volatility_trackers\n",
    "from pricing import curves, options\n",
    "\n",
    "_ = locale.setlocale(locale.LC_ALL, '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "brief-makeup",
   "metadata": {},
   "outputs": [],
   "source": [
    "today = date.today()\n",
    "\n",
    "\n",
    "# Constructing the riskless yield curve based on the current fed funds rate and treasury yields\n",
    "data = web.get_data_fred(\n",
    "    ['DFF', 'DGS1MO', 'DGS3MO', 'DGS6MO', 'DGS1', 'DGS2', 'DGS3', 'DGS5', 'DGS7', 'DGS10', 'DGS20', 'DGS30'],\n",
    "    today - BDay(3), today)\n",
    "data.dropna(inplace=True)\n",
    "\n",
    "cur_date_curve = data.index[-1].date()\n",
    "\n",
    "# Convert to percentage points\n",
    "data /= 100.\n",
    "\n",
    "# Some adjustments are required to bring FED Funds rate to the same day count convention and compounding frequency\n",
    "# as treasury yields (actual/actual with semi-annual compounding):\n",
    "# 1. https://www.federalreserve.gov/releases/h15/default.htm -> day count convention for Fed Funds Rate needs\n",
    "# to be changed to actual/actual\n",
    "# 2. Conversion to APY: https://home.treasury.gov/policy-issues/financing-the-government/interest-rate-statistics/interest-rates-frequently-asked-questions\n",
    "data.DFF *= (366 if curves.YieldCurve.is_leap_year(cur_date_curve.year) else 365) / 360 # to x/actual\n",
    "data.DFF = 2 * (np.sqrt(data.DFF + 1) - 1)\n",
    "\n",
    "offsets = [relativedelta(), relativedelta(months=+1), relativedelta(months=+3), relativedelta(months=+6),\n",
    "           relativedelta(years=+1), relativedelta(years=+2), relativedelta(years=+3), relativedelta(years=+5),\n",
    "           relativedelta(years=+7), relativedelta(years=+10), relativedelta(years=+20),\n",
    "           relativedelta(years=+30)]\n",
    "\n",
    "# Define the riskless yield curve\n",
    "curve = curves.YieldCurve(today, offsets, data[cur_date_curve:cur_date_curve + BDay()].to_numpy()[0, :],\n",
    "                          compounding_freq=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "olympic-front",
   "metadata": {},
   "source": [
    "### Optional: Visualizing the riskless yield curve for USD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "crucial-quest",
   "metadata": {},
   "outputs": [
    {
     "data": {
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+R90aBenWK+qqX4sweVpdY/UgoFiFwhVfX1+1atWqxDFvb28FBQU5jt91112KiIjQyy+/LEl68MEHNXnyZD366KN6+OGHtWvXLv3nP//RI4884riPxx57TMOHD1fHjh3VqVMnTZo0Senp6RoxYsRZNXzwwQcKCQnRoEGDJBWGN+PHj9eff/6pX375RS1atJDdbq/Qk3Ah/L1OhSsAAAAAAAAXwjAMbT6Sormbj2luzDEdiM9w3OZmNqlrwyD1axGmPi3C+OVOANVWo1BfPT2whR7v30wLt8XpizWH9MeuE1q+O17Ld8fLbrPqtivq6q6u9VTbzr+FcA0VClfK4+DBg44JFUmKjIzUvHnz9Pe//11t2rRRRESEHn30UT355JOOc2699VadOHFC48aNU2xsrKKjo/Xrr78qLCysxH3HxcXppZde0ooVKxzHOnXqpH/84x8aOHCgQkND9cknn1R2S6Wy29wlpSs5M+eyPB4AAAAAAKgeDMNQzJFk/RxzTL/ExOpgwqlAxdNq1tVNQ3Vtq3D1ahoqfy/XWy4FAC4VdzezBrSupQGta+lQQobmrDusOWsP6Vhylt5bskfT/tir/i3DdPeVUbqifgATfHCqiw5XFi9efM6PJalr1676888/z3k/Y8aMKXUZsNOFhYVp//79Zx0fN26cxo0bd75SK5W96JsbJlcAAAAAAMD5GIahvw4n65eYY5q7+ZgOJWQ6bvO0mnVNs1ANaF1LVzcNlbdHpf8uLABUOZGBNj3Wt4ke7d1YC7fF6ePl+7Vyb7zmxsRqbkysWtb2091X1tegtrVZMgxOwf/WF8jfVhSuZBKuAAAAAACAsxmGoY2HkjQ35pjmxsTqSNKpQMXLatE1zUM1oFUtXd0sRDZ3XqIBgNJYzCb1bxmu/i3DtT02RR8v369vNxzRlqMpevyrTXr5l+0a2qmu7uhST+H+ns4uFzUI/3NfILuXuyQmVwAAAAAAQEk741L1/cYj+uGvoyUmVGzuFl3TLFQDW9dSr6ah8nLnN60BoCKahfvplZva6Mlrm+mLNYf02cr9Opqcpcm/79Z7S/bouta1dPeV9dW6lrezS0UNQLhygexFkyvsuQIAAAAAAA4lZOjHTUf1w8aj2h6b6jhuc7eod/MwDWwdrp5NCFQAoDIEeLvrwV4Ndd9VUZq/tXDJsNX7E/TjX0f1419H1TrCT229TOqbXyArW1fhEiFcuUDF4QqTKwAAAAAA1Ewn07I1N+aYvt94VOsOJDqOWy0m9WwSquuja6t381CW/AKAS8TNYtaA1rU0oHUtbT6SrE9W7Nf3fx1VzJEUxcii395cpvt7NNCtV9Ql3Eal43/3C2S3sSwYAAAAAAA1TVae9O2Go/ppc5yW7z6p/AJDkmQySV0bBGlw29q6rlUtx16tAIDLo1WEv16/pa2euq6ZPlu5Xx8u3aWjyVka/+NWvfXbbo24sr7u6lqff59RaQhXLpDdiw3tAQAAAACoCXLyCrRk5wl9ve6QFm21KHfNZsdtbev4a1Db2hrUtrbC/NhIGQCcLcjHQ6N7NVBE6nalh7XWh8v361BCpt5YsFPvLdmjYV3q6Z7uUfybjYtGuHKBHHuuZLDnCgAAAAAA1Y1hGIo5kqxv1hduTJ+QXvzzv0kNgm26PrqOBkfXVlQwmyYDgCtyt0g3dIrUHV3q6+eYY3p38R5tj03V1KV79fHy/bqpQ4RG9Wio+vw7jgtEuHKB7F6Fy4IlsiwYAAAAAADVxtGkTH238Yi+WX9Eu4+nOY6H+HpoUOtwBaXt0X03d5O7u7sTqwQAlJebxazroyM0uG1t/b7juKb8vkdrDyTq89WH9OWaQxrQupYe7NVQLWv7O7tUVDGEKxeoeG2+zNx8ZeXmy9PKhkgAAAAAAFRF6dl5+nVzrL7ZcFgr9sTLKNxGRR5uZvVvGa7/ax+h7o2CZRTka+7cPTKZTM4tGABQYSaTSdc0C9M1zcK0Zn+Cpvy+W7/vOKGfNh3TT5uOqWeTED3Uq6E6NwhydqmoIghXLpCvh5vMJqnAkFIycwlXAAAAAACoQvILDK3cE69v1h/WL5tjlZmb77itc1SgbmpfR9e1Dpev56mNj3ML8ku7KwBAFXNF/UB9NKKTth5N0XtL9uinTUe1ZOcJLdl5Ql0bBOkf/ZqoY/1AZ5cJF0e4coHMZpP8vaxKzMhVUmauQtkACQAAAAAAl7f/ZLrmrDukr9cdUWxKluN4VLC3/q9dhG5oF6HIQJsTKwQAXC4tavvprdvb6R/9muj9pXs1Z+0hrdwbr5vfW6meTUL0WN8mahtpd3aZcFGEKxfBbnMvDFfYdwUAAAAAAJeVkZOnuTGxmr32kFbvS3Ac9/eyalDbWvq/9nXULtLOcl8AUEPVC/LWf25srYd6NdTk33ZrzrrDjkmWPs3D9FjfJmpR28/ZZcLFEK5cBH+vwtHgpIwcJ1cCAAAAAABOZxiGNhxK0py1h/TjX8eUlp0nSTKbpB5NQjSkY6R6Nw+VhxvLfAMACtUJsOmVm9rowV4N9b9Fu/TdhiNauC1OC7fFaUDrcP29TxM1DvN1dplwEYQrF8FetKl9UiaTKwAAAAAAuIKTadn6dv0RzV57SLuOpzmO1wuyaUjHSP1f+wjV8vdyYoUAAFdXL8hbE4dE66FejfS/Rbv006ajmhsTq182x2pw29p6tHdjNQjxcXaZcDLClYtgL5pcSWZZMAAAAAAAnCYvv0BLdp7Q7LWHtGjbceUVGJIkT6tZA1rX0pCOkepUP1BmM8t+AQDKr1Goj96+vZ1GX91Qby7YqXlb4vT9xqP6adMx/V+7CD3SuzH7dNVghCsXwW5zlyQlZbIsGAAAAAAAl9uhhAx9seag5qw9rOOp2Y7j0ZF2DekYqb+1rSU/T6sTKwQAVAfNwv30/p0dtflIsiYu2Knfth/XnHWH9e2GIxpyRaTG9m6sUD9PZ5eJy4xw5SKc2nOFyRUAAAAAAC6H3PwCLdoWp5mrDmrZ7pMyCodUFOjtrv9rF6FbOkaqaTjr4QMAKl+rCH9Nv/sKrT+YqDcX7NQfu05q1qqD+m7DEd3fo4Hu79FANndecq8p+ExfhAD2XAEAAAAA4LI4lJChz1cf1Oy1h3Uy7dSUylWNg3V7p7rq0zxM7m5mJ1YIAKgp2tcN0Gf3dNaqvfF65dft2nAwSZMW7tKsVQf1j35NdHOHSFlYirLaI1y5CMXLgrHnCgAAAAAAlS83v0ALt8Zp1uqD+mPXScfxYB8PDelYR7ddUVd1g1jrHgDgHJ0bBOmbB6/U3JhYvfLrNh1KyNSTX8do+rL9+vfA5urZJMTZJeISIly5CP6OyRX2XAEAAAAAoLIcjC/cS6W0KZWhneqqT4swWS1MqQAAnM9kMmlgm1rq0yJUn608oLd/260dcakaPn21rmocrH8PaK7mtfycXSYuAcKVi2Av2nMlMZ3JFQAAAAAALkZefoEWbjuumasOlJhSCfEtnFK5tSNTKgAA1+XhZtG9VzXQzR3qaPJvu/XJyv36Y9dJDXjrD93SoY4e69tU4f5sel+dEK5cBMeyYOy5AgAAAADABTmemqUvVh/SrFUHFZuSJUkymaSrGodoaKdI9W7OlAoAoOqw29z1zN9a6K6u9fXqvO36edMxzV57WD/+dUz39WigUT0ayNuDl+WrAz6LF6F4ciUtO0+5+QV8swcAAAAAQDkYhqHV+xL02Z8H9OvmWOUVGJKkIG93DbkiUkM71VVkIFMqAICqq26QTe8Mba97uifqPz9v09oDiXprUeGm94/1baJbr2DT+6qOcOUi+BWFK1Lh9Eqwj4cTqwEAAAAAwLWlZefp2/WH9dmfB7QzLs1xvEO9AN3VtZ6ubRUuDzeLEysEAKByta8boDkPdNW8LbF65Zft2h+foX9/G6OZqw7ohetbqUO9AGeXiAtEuHIRLGaT/DzdlJKVp6QMwhUAAAAAAEqzIzZVM/48oG/WH1Z6Tr4kyctq0Q3tInRHl7pqWdvfyRUCAHDpmEwmXduqlq5pFqYZfx7QpIU7teVoim56d4Vu7lBHT13XjNeWqyDClYtkt7krJStPyZk5zi4FAAAAAACXkZtfoA0nTZrx4Rqt2Z/oON4gxFt3dqmnmzrUkZ+n9Rz3AABA9eLuZtbI7lEaHF1br/26XbPXHtZX6w5r3pZY/aNvE93RpZ7c2HqiyiBcuUh2m1UHE6SkDDa1BwAAAADgRGq2Pl99UDP/PKC4VIukRFnMJvVrEaY7u9RT14ZBMplYYx4AUHMF+3jotZvb6rZOdTXu+83afCRF43/cqi/WHNKLN7TSFfUDnV0iyoFw5SL5F+27QrgCAAAAAKjJYg4n66MV+/TTX8eUk18gSfKzGrqrW0Pd0TVK4f6eTq4QAADX0r5ugL4f3V2frz6o1+ft0PbYVN3y3kr9X7sIPTWgmUJ9+b/TlRGuXCS7zV2SlJRJuAIAAAAAqFly8wv0y+ZYfbJiv9YdOLX0V7u6dt3RKVI6tEGDezeS1cryXwAAlMZiNumOLvU0oHUtvT5vu75Yc0jfbDiiBVvjNLZvE93VtZ6zS0QZCFcukr1ociU5gz1XAAAAAAA1w8m0bH2+6qBmrDqguJRsSZLVYtLf2tTW8CvrKzrSrtzcXM09ssHJlQIAUDUEervr5f9ro9uuKFwq7K/DyXrxp62aveaQnh3Y1NnloRSEKxcpwFa0LBiTKwAAAACAaq60pb9CfD00rHNdDe1cl+VLAAC4SG0j7fr2oW6avfaQXv11u3bEpeqO6WvVPsisK67KVu1ApkFdBeHKRfIvXhaMPVcAAAAAANVQXn6Bft0Sq4+Wl1z6KzrSrhHd6uu6VrXk7mZ2YoUAAFQvZrNJt3Wqq2tbheu/83do5qqDWh9v1rVvLdczA1volo51ZDKZnF1mjUe4cpGKlwVjcgUAAAAAUJ2kZOXqy9WH9PGK/TqSlCnp7KW/AADApWO3uWvCDa11c7vaGvPpSh1Oz9MTX2/SdxuP6OX/a616Qd7OLrFGI1y5SPbiZcHYcwUAAAAAUA0ciE/XR8v3a87aQ0rPyZckBXm7a1iXerqjC0t/AQBwubWs7afHWucrzr+F/vfbbq3YE69+by7VY32b6J7uUXKzMEHqDIQrF+lUuMLkCgAAAACgajIMQ6v3JejDZfu0YFucDKPweJMwH93TPUrXR0fI02pxbpEAANRgFpN0b/f6GtCmtv79bYyW747Xy79s1w9/HdWrN7VRqwh/Z5dY4xCuXCR/r+I9V5hcAQAAAABULTl5Bfo55qg+XLZPm4+kOI73bBKie6+KUvdGwazpDgCAC6kX5K0Z93TWV+sOa8LP27TlaIquf2e57r0qSmN7N5GXO78McbkQrlyk4smVlKw85RcYspj5phMAAAAA4NqSMnI0c9VBfbpyv+JSsiVJHm5m/V/7Orqne301CvV1coUAAKAsJpNJt3SMVK+moXr+xy36adMxvb9kr37dHKuXb2ytKxsFO7vEGoFw5SL5F21oL0kpmbkK8HZ3YjUAAAAAAJTtQHy6Pvhjn+asO6Ss3AJJUoivh4Z3raehnespkJ9pAQCoMkJ8PTR5aHvdEB2nZ7/frAPxGRr6wSoN6VhHTw9oIX+b9fx3ggtGuHKRrBazfDzclJadpyTCFQAAAACAC1p/MFHTlu7Vr1tiHfuptKjlp3u6R+lvbWvJw40lRAAAqKr6tAhT5waBen3eDn325wHNXntYv20/oecHt9SA1uEs8XmJEK5UAn8va2G4kpEjydvZ5QAAAAAAoIICQwu3xWnaH3u1Zn+i43ivpiG6/6oG6towiBdbAACoJnw9rXrh+lYa3La2nvomRruPp2n0rPUa0DpcE25ozXTqJUC4UgnsNquOJGUqKTPX2aUAAAAAAGq4rNx8fbP+iD74Y6/2nkyXJFktJt0QHaF7r2qgpuHspwIAQHXVsX6gfn6ku975fY+m/L5bc2NitXpfol69qbV6Nw9zdnnVCuFKJSje1D45g3AFAAAAAOAciek5+uzPA/pkxX7Fp+dIknw93XRHl3q6+8r6CvPzdHKFAADgcvBws+ixvk3Ut3mYHpu9UbuOp+meT9bq1o6ReuZvzeXryV4slYFwpRLYvQpHqgqXBQMAAAAA4PI5EJ+uD5ft0+y1pzapj7B7aWT3KN16RaR8PPjRHwCAmqh1HX/9+HB3vTF/hz5Ytk9frj2kZbtP6o0hbdWlQZCzy6vy+A6rEhRPrrAsGAAAAADgcok5nKz3luzRL5uPqaBok/pWEX66v0dDDWgVLjeL2bkFAgAAp/O0WvT0wBbq0zxM/5jzlw4nZur2aX9qZLcoPd6/qTytFmeXWGURrlQCR7jCsmAAAAAAgEvIMAwt3x2v95bs0bLdJx3HezUN0f09GqhrAzapBwAAZ+vcIEi/ju2hCT9t1RdrDunDZfu0ZOcJTRzSVm3q2J1dXpVEuFIJWBYMAAAAAHAp5RcY+nVzrN5bskcxR5IlSRazSYPb1taong3ULNzPyRUCAABX5+PhplduaqN+LcP05Ncx2n08TTdOWaExVzfSmGsaycrUa4UQrlQCf5YFAwAAAABcAlm5+fpm/RFNXbpH++MzJEmeVrNuu6Ku7r0qSnUCbE6uEAAAVDXXNAvT/LEBeub7zfp50zH9b9Eu/bb9uCYOaavGYb7OLq/KIFypBHYvlgUDAAAAAFSelKxczfjzgKYv26+TadmSCpekHt61voZfWV+B3u5OrhAAAFRlAd7uemdoe/VveVTPfrdZMUeSNfDtZXqif1ON7BYls5llRs+HcKUS2G2F39QmM7kCAAAAALgIyTnSa/N26os1h5WanSdJqu3vqXuvaqDbOkXK5s6P8QAAoPIMbltbnaMC9cRXm7Rk5wlN+Hmbluw8oTeGtFWor6ezy3NpfFdWCU5taM+eKwAAAACAijsQn64pv+/WV+styjf2S5KahPloVI+GGhxdmzXQAQDAJRPm56mPR1yhmasOasLPW/XHrpMa8L8/9MaQaPVsEuLs8lwW4UolKF4WLDkzVwUFBiNTAAAAAIBy2RGbqimLd+vHv46qwJAkkzrUteuhqxvp6qah/HwJAAAuC5PJpDu61FPnqEA9/PkGbY9N1fDpq3V/jwb6Z7+mcnfjFz3ORLhSCfyKwpUCQ0rNzpN/0ccAAAAAAJRm46EkvfP7bi3YGuc41rNxsNq6x+rh2zrJauXnSgAAcPk1DvPVd6O76aWft+mzPw9o6tK9WrU3Xm/d3k71grydXZ5LIW6qBJ5Wi7ysFklSMpvaAwAAAABKYRiGVuw+qWEf/Kkb3lmuBVvjZDJJA1qH66eHu+uDu9qroZ+zqwQAADWdp9WiF29opffu6CB/L6v+OpysgW8t0/cbjzi7NJfC5EolsdusykzOV1JmjurK5uxyAAAAAAAuoqDA0G/bj+udxbu14WCSJMnNbNIN7SL0QM+GahTqI0nKzeWX9QAAgOu4tlW4Wtfx19gvNmjN/kQ9+sVGLdt1Us9f31I2d6IFnoFK4u9l1bHkLCUxuQIAAAAAkJSXX6CfY47p3cV7tD02VZLk7mbWbVdE6v4eDVQngF/MAwAAri3C7qXP7+uit37brcm/7dKcdYe17mCi3r69nVrW9nd2eU5FuFJJ7LbC9XCTMglXAAAAAKAmy8kr0DfrD+vdJXt0ID5DkuTj4aY7utTTyO71Ferr6eQKAQAAys/NYtZjfZvoyoZBGvvFRu09ka4b31mhfw9opuFX1pfJZHJ2iU5BuFJJAmzukqTkjBwnVwIAAAAAcIas3HzNWXtI7y3ZqyNJmZIKfxFvZLcoDe9aX/42NqkHAABVV5cGQZr76FV64qu/tHDbcY3/cauW7Y7X6ze3UYC3u7PLu+wIVyqJY3KFZcEAAAAAoEbJzMnXzFUHNHXpXh1PzZYkhfh66P6rGmho57ry9uBHbwAAUD0Eertr2l0d9cmK/frP3O1auC1O1/3vD00e2k4d6wc6u7zLiu/wKom/V2Eyl0i4AgAAAAA1Qlp2nj5beUAf/LFX8emFqxjU8vfUAz0b6tYrIuVptTi5QgAAgMpnMpl0d7coXREVqIc/36C9J9J129Q/9e8BzTWiW81ZJoxwpZKc2nOFZcEAAAAAoDpLzszVx8v3a/ryfUou2nczMtBLD/VqpJva15G7m9nJFQIAAFx6LWv768cx3fXUNzH68a+jeuGnrVp/MFGv3tSmRkzuVv8OLxO7V2G4kszkCgAAAABUSwnpOZq+bJ8+WbFfqdl5kqQGwd566OpGuj66tqwWQhUAAFCzeHu46a3botW+rl0v/bxNP206ph2xqXr3jg5qFOrj7PIuKcKVSnJqcoVwBQAAAACqkxOp2frgj7367M8DysjJlyQ1CfPRmGsaa2DrWrKYa8bSFwAAAKUxmUwa0S1KrSP8NXrWeu06nqbrJy/T67e01YDWtZxd3iVDuFJJivdcScpgWTAAAAAAqA6Op2bp/SV7NXPVAWXlFkiSWtb208PXNFa/FmEyE6oAAAA4dKwfqJ8evkoPf75ef+5N0EMz1+u+q6L0xLXNquWEL+FKJSmeXElmcgUAAAAAqrTjKVl6d8kezVp1UNl5haFK20i7Hu3dSFc3Da0xm7QCAABUVIivh2bc01mvz9uh95fu1bQ/9umvQ8maPKydQn09nV1epSJcqSSOZcEycmUYBt9sAwAAAEAVE5eSpXcX79Hnq0+FKu3r2vVonybq0TiYn/MAAADKwc1i1r8GNFe7unb9c84mrd6foIFvLdOUYe11Rf1AZ5dXaQhXKom9aFmwvAJD6Tn58vHgqQUAAACAqiA2OUvvLt6tz9ccUk5RqNKxXoAe7dNY3RsRqgAAAFyIa1vVUpMwXz0wY512xqXptql/6l/XNdNdnes4u7RKQQJQSTytZrm7mZWTV6CkjBzCFQAAAABwcUeTMvXu4j36cs0h5eQXhiqd6gfq0T6NdWXDIEIVAACAi9QgxEffje6mf30To+83HtWEn7dp/YEE9bQ5u7KLRwJQSUwmk+xeVh1PzVZSRq7qBDi7IgAAAABAaY4kZerdxbs1e83hU6FKVKDG9mmsrg0IVQAAACqTzd1Nk26NVvu6AXrxp62auzlO67wsiu6Srqa17c4u74IRrlQiu60wXGFTewAAAABwPUeTMvXO77s1e+0h5eYbkqQuDQL1aO8m6towyMnVAQAAVF8mk0nDr6yvVhH+emjmOsWlZOvmqas0eWh79WwS4uzyLgjhSiWy2wr3XUnKIFwBAAAAAFdxLDlTU34vufzXlQ2D9GjvxurcgFAFAADgculQL0DfPdhFw6Ys1t7UPI34aLX+PaC57ukeVeWmhwlXKpHdyypJSsrMcXIlAAAAAIC4lCy9u3iPZq066AhVujYI0t/7NlGnqEAnVwcAAFAzBft4aHSLfK3Mraev1h/RhJ+3aUdsqibc2EoebhZnl1duhCuVyG4rCleYXAEAAAAApzmeeipUyc47tafKY32bqAuTKgAAAE7nZpb+c0MLtajtrwk/b9WcdYe192S63rujg0J8PZxdXrkQrlSiU8uCMbkCAAAAAJfbidRsvb9kjz7784AjVLmifoD+3qdwT5WqttQEAABAdWYymTSye5QahvpozKz1WncgUddPXqapd3VUqwh/Z5d3XuaLufiVV16RyWTS2LFjyzzn448/lslkKvHm6elZ4hzDMDRu3DjVqlVLXl5e6tOnj3bt2uW4PTs7W3feeaf8/PzUpEkTLVy4sMT1r7/+uh5++OGLaaVS+HsxuQIAAAAAl1t8eo7+M3ebrnrtN32wbJ+y8wrUvq5dn93TSbNHddWVjYIJVgAAAFxUzyYh+n50NzUI9tbR5Czd8t5KzY055uyyzuuCJ1fWrFmj999/X23atDnvuX5+ftqxY4fj4zO/qX3ttdf01ltv6ZNPPlFUVJSeffZZ9e/fX1u3bpWnp6emTp2qdevWaeXKlfrll180dOhQxcXFyWQyad++fZo2bZrWrl17oa1UGseyYJmEKwAAAABwqSWk5+iHA2b9a+IfysjJlyRFR9r1975N1KMxgQoAAEBV0SDER9+O7qaHP9+gpTtP6KGZ6/Vo78Z6tHdjmc2u+T3dBU2upKWladiwYZo2bZoCAgLOe77JZFJ4eLjjLSwszHGbYRiaNGmSnnnmGV1//fVq06aNPv30Ux09elTfffedJGnbtm0aPHiwWrZsqdGjR+vEiRM6efKkJOnBBx/Uq6++Kj8/vwtppVLZvQqXBUtmcgUAAAAALpnkjFz9d94OXTPxDy06alZGTr7a1PHXR3dfoW8fulI9m4QQrAAAAFQx/l5WTR/eUfd0j5Ik/W/RLo2etV4ZOXlOrqx0FzS5Mnr0aA0cOFB9+vTRhAkTznt+Wlqa6tWrp4KCArVv317/+c9/1LJlS0nSvn37FBsbqz59+jjO9/f3V+fOnbVy5Urddtttatu2rT777DNlZmZq3rx5qlWrloKDgzVz5kx5enrqxhtvLFfd2dnZys7OdnyckpIiScrNzVVu7sUHIj7uhd+8J2ZkV8r9VVTxYzrjsS8F+nFt9OPa6Me10Y/rq2490Y9rox/XRj+uJTUrT5+sPKDpKw4oNavwh+w63ob+PaiN+rQIl8lkUl6ea/7wXR5V/fNzpurWj1T9eqIf10Y/ro1+XBv9uLbz9fNU/8ZqGGzTcz9u1S+bY7X/ZLreGxat2navy1rf+ZgMwzAqcsdffPGFXnrpJa1Zs0aenp7q1auXoqOjNWnSpFLPX7lypXbt2qU2bdooOTlZ//3vf7V06VJt2bJFderU0YoVK9StWzcdPXpUtWrVclw3ZMgQmUwmffnll8rNzdXYsWM1d+5cBQcH680331SLFi10xRVXaPHixXr//ff1xRdfqGHDhpo+fboiIiJKrWX8+PF6/vnnzzo+a9Ys2Wy2ijwNpTqcLr2+yU1+VkMvdsy/6PsDAAAAAEjZ+dIfsabCKZW8wl9qq+Vl6LrIArUJNMSQCgAAQPWzN0X6cKdFabkm+bgZuqdpvhpchgWsMjIyNHToUCUnJ59zxawKhSuHDh1Sx44dtWDBAsdeK+cLV86Um5ur5s2b6/bbb9eLL75YrnClNCNGjFB0dLSioqL073//W6tWrdJrr72mzZs36+uvvy71mtImVyIjI3Xs2DEFBQWV81ko25GkTPV64w+5u5m1eVzvyz6GnpubqwULFqhv376yWq2X9bEvBfpxbfTj2ujHtdGP66tuPdGPa6Mf10Y/zpWdm69Zaw7r/aX7FJ+eI0lqEGzTw1c31IBW4crPz6tS/ZxPVfv8nE9160eqfj3Rj2ujH9dGP66NflxbRfo5mpSpUTM3antsqqwWk166vqVubFf7ktaXkpKi4ODg84YrFVoWbN26dTp+/Ljat2/vOJafn6+lS5dq8uTJys7OlsViOed9WK1WtWvXTrt375YkhYeHS5Li4uJKhCtxcXGKjo4u9T5+//13bdmyRR988IEef/xxDRgwQN7e3hoyZIgmT55c5mN7eHjIw8Oj1Joq44sy2K8wTMnJK1C+LPKynvu5uFQqqx9XQT+ujX5cG/24NvpxfdWtJ/pxbfTj2ujn8srOy9fsNYc0+ffdiksp/AW5uoE2Pdq7sa6Pri03S+H2obm5hT+DuXo/FUU/rq+69UQ/ro1+XBv9uDb6cW3l6adeiFXfPHSl/jH7L/2yOVZPfLNZh5Oz9fc+jS/ZcEN5n+MKhSu9e/dWTExMiWMjRoxQs2bN9OSTT543WJEKw5iYmBgNGDBAkhQVFaXw8HAtWrTIEaakpKRo1apVevDBB8+6PisrS6NHj9bMmTNlsViUn5+v4uGb3Nxc5ec7bzkub3eL3Mwm5RUYSsrMkZf75VkDDgAAAACqg9z8An2z/rDeWrRbR5IyJUm1/T31cO/GurlDHVmLQhUAAADUHDZ3N70ztL1en79D7y7eo7cW7dKhhAy9clNrebg5Z8BBqmC44uvrq1atWpU45u3traCgIMfxu+66SxEREXr55ZclSS+88IK6dOmiRo0aKSkpSa+//roOHDige++9V5JkMpk0duxYTZgwQY0bN1ZUVJSeffZZ1a5dWzfccMNZNbz44osaMGCA2rVrJ0nq1q2bHn/8cY0YMUKTJ09Wt27dKvwkVBaTySS7zaqTaTlKyshVLX/CFQAAAAA4n/wCQ99vPKL/LdqlA/EZkqRQXw+NuaaRbr0i0qk/NAMAAMD5zGaTnry2meoG2vTMd5v17YYjOpKUqal3dpDd5u6UmioUrpTHwYMHZTaf+m2ixMRE3XfffYqNjVVAQIA6dOigFStWqEWLFo5znnjiCaWnp+v+++9XUlKSunfvrl9//VWenp4l7nvz5s2aPXu2Nm7c6Dh28803a/HixbrqqqvUtGlTzZo1q7JbqhC7zd0RrgAAAAAAylZQYGju5mOatHCXdh9PkyQFebvrwV4NdUeXevJ00lLLAAAAcE23d6qrOgFeemjGeq3el6D/m7JCH424QvWCvC97LRcdrixevPicH7/55pt68803z3kfJpNJL7zwgl544YVznteqVSvt2rWrxDGz2awpU6ZoypQp5a75UrJ7Fa7HlpyZ4+RKAAAAAMA1GYahBVvjNHHBTm2PTZUk+XtZdX+PBrr7yvry9qj03wMEAABANXFV4xB99eCVGvHRau09ma4bp6zQtLs6qEO9wMtaB9+xVjK7rTBcSWRyBQAAAABKMAxDS3ae0MQFO7XpcLIkydfDTfdcFaWR3aPk51l9NmgFAADApdM03Fffje6mez5Zq5gjybp92ipNHNJWf2tT+7LVQLhSyfy9Ctd3Y1kwAAAAADhlxZ6TemP+Tq07kChJsrlbdPeV9XV/jwZOWycbAAAAVVeon6e+HNVFj3y+UQu3xWnMrA06lJCpB3o2kMlkuuSPT7hSyYonV5JYFgwAAAAAtO5Agt6Yv1Mr9sRLkjzczLqzSz090Kuhgn08nFwdAAAAqjKbu5vev7ODJvy8VR8t369Xf92ugwnpeuH6VrJazOe/g4tAuFLJHHuuMLkCAAAAoAbbdDhJExfs1OIdJyRJVotJt3eqq9FXN1KYn6eTqwMAAEB1YTGb9NyglqoXaNMLP23V56sP6XBipt4Z1v6SLjtLuFLJHJMrhCsAAAAAaqCdcan677wdmr81TlLhD7u3dKijMdc0Up0Am5OrAwAAQHV1d7co1Qmw6eHPN+iPXSd1y7srNX3EFYqwe12SxyNcqWT+RWsFsywYAAAAgJrkYHyGJi3cqW83HpFhSCaTdGN0hB7p3Vj1g72dXR4AAABqgD4twjR7VFeN/GSNdsSl6oZ3luuju69Qqwj/Sn8swpVKVrwsGJMrAAAAAGqC4ylZevu33fpizUHl5huSpGtbhusf/ZqocZivk6sDAABATdO6jr++G91N93y8RttjU3Xb1D817a6O6towqFIfh3ClkhUvC5acSbgCAAAAoPpKysjRe0v26uMV+5SVWyBJuqpxsP7Zr6naRtqdWxwAAABqtAi7l+Y80FX3f7pOK/fGa/hHq/X27e3Uv2V4pT0G4Uols3sVLQvG5AoAAACAaig9O08fLd+n95fuVWpWniSpXV27nujfrNJ/GxAAAAC4UL6eVn004go98vkGzd8apwdnrNMr/9dGQ66IrJT7J1ypZP5FkyuZufnKys2Xp9Xi5IoAAAAA4OJl5+Vr1qqDeuf33TqZVrjHZLNwX/2zX1P1bh4qk8nk5AoBAACAkjytFk0Z1l5Pf7tZX649pCe+3qSEjBw90LPhRd834Uol8/Vwk9kkFRhSSmYu4QoAAACAKi0vv0DfbDii/y3cpSNJmZKkekE2Pda3iQa1qS2zmVAFAAAArsvNYtYrN7VWgLe73luyR6/8sl2J6Tl66rpmF/ULQoQrlcxsNsluc1dCeo6SMnMV6ufp7JIAAAAAoMIMw9C8LbF6fd4O7TmRLkkK8/PQo72b6JaOdWS1mJ1cIQAAAFA+JpNJT13XTIHeVv1n7na9v3SvEjNy9J8bW8vtAr+vJVy5BOxe1sJwhX1XAAAAAFRBy3ef1Gu/btdfh5MlSQE2qx7q1Uh3dq3HdD4AAACqrPt7NJTd5q6nvt6k2WsPKykjV2/d3u6CvsclXLkEivddSczIcXIlAAAAAFB+fx1K0uvzdmjZ7pOSJJu7Rfd2j9K9PRrIz9Pq5OoAAACAizekY6TsXlaNKdrofvj01Zo2vGOFv98lXLkE7F6Fn4RkJlcAAAAAVAG7j6fpjfk79MvmWEmS1WLSsM71NOaaRgr28XBydQAAAEDl6tcyXJ+O7KR7P1mrVfsSdPvUP/XJyE4V+t6XcOUSsNvcJUlJmUyuAAAAAHBdR5My9b+FuzRn3SEVGJLJJN3YLkJ/79NEkYE2Z5cHAAAAXDJdGgTpi/u76O6PVmvL0RTd8t5KfTqyk/zLmZoQrlwC/kWTK+y5AgAAAMAVJaTnaNqyXfr0zwPKySuQJPVtEaZ/9muqpuG+Tq4OAAAAuDxaRfhrzgNX6o4PVmnfyXTd/N4KvTukebmuJVy5BOxFe64kZRKuAAAAAHAd6dl5mnfYpH+/+YfSs/MlSZ2iAvXktc3UoV6Ak6sDAAAALr+oYG99/eCVumv6Ku2MS9NdH64u13WEK5cAe64AAAAAcCU5eQWateqA3vptlxLSLZLy1bK2nx7v31Q9m4TIZDI5u0QAAADAacL9PTV7VFeN/HiN1u46Wq5rCFcuAfZcAQAAAOAKCgoM/fDXUb2xYIcOJWRKkoI9DT0zuK0GR9eR2UyoAgAAAEiFr+vPuLeznvp8ld4ux/mEK5eAv409VwAAAAA4j2EYWrzzhF77dYe2HUuRJIX4emhMrwbyPR6jga3DCVYAAACAM9jc3TThxtZ6++7zn0u4cgnY2dAeAAAAgJOsP5ioV3/ZrlX7EiRJvh5ueqBXQ43oVl9Wk6G5c2OcXCEAAABQ9RGuXALFy4Ils6E9AAAAgMtk9/FUvT5vh+ZtiZMkubuZNbxrPT3Uq5ECvAt/RsnN5WcUAAAAoDIQrlwCxZMradl5ys0vkNVidnJFAAAAAKqrY8mZmrRgl+asO6QCQzKbpJva19HYvk0UYfdydnkAAABAtUS4cgn4eVllMkmGUTi9Euzj4eySAAAAAFQzSRk5enfxHn28Yr+y8wokSX1bhOnx/k3VJMzXydUBAAAA1RvhyiVgMZvk52lVcmaukjIIVwAAAABUnsycfH20Yp/eXbxHqVl5kqRO9QP15HVN1aFeoJOrAwAAAGoGwpVLxG4rDldynF0KAAAAgGogL79AX607rDcX7lRcSrYkqVm4r568tpl6NQ2RyWRycoUAAABAzUG4conYvaw6ICkpgw0jAQAAAFw4wzA0f2ucXvt1u/acSJckRdi99I9+TXR9dIQsZkIVAAAA4HIjXLlE/G3ukqSkTMIVAAAAABdm9b4EvfLLNq0/mCRJ/8/efYdHVaZvHL9n0hNSSKgJCYTee0cUlCauChZUFF1sa5fFtf0sq66uda1YsRfEruuKSBGQJr33kkYgCQFCejKZeX9/hIyigARJzpnh+7kuL8mZk+R5eA9zzsw973lVNzxINw9uqSv6NlVoUIC1xQEAAACnMMKVGhITFiRJ3BYMAAAAQLVtySrQU9M3a/bmHElSaJBT157WXNef0VxRoUEWVwcAAACAcKWGxIRXvuA5yMwVAAAAAMcpM69Ez87Yqi9X7ZIxUoDToUt7Jer2s1qpQVSo1eUBAAAAOIRwpYb8MnOFcAUAAADAsR0oKtcrc7frvcVpKq/wSJJGdmqkfwxro+b161hcHQAAAIDfIlypIay5AgAAAOCPlJS79c6iFL06d4cKSiskSX2bx+ruEW3VLamuxdUBAAAAOBrClRrCmisAAAAAjqbC7dEXK3fpuZnblJVfKklq2yhS95zdVme0ri+Hw2FxhQAAAACOhXClhrDmCgAAAIDfMsZo1qYcPTV9s7blFEqSEmLCdMew1hrVNUFOJ6EKAAAA4AsIV2pIVbjCmisAAAAAJGlF2n498f1mLUs9IKnyNcMtg1vqir5NFRoUYHF1AAAAAKqDcKWGRIcdWnOF24IBAAAAp7TtOYV6avpmzdiYLUkKCXTq6tOSdcMZLRR96HbCAAAAAHwL4UoNqZq5kl9aIbfHKIDp/QAAAMApJTu/VM/P2qpPlmXIYySnQxrTM1EThrRWo+hQq8sDAAAA8CcQrtSQmF99Ai2/xKW6EcEWVgMAAACgtuSXuvT6vB16a0GKSl0eSdLQ9g111/A2atUw0uLqAAAAAJwMhCs1JDDAqciQQBWUVehAcTnhCgAAAODnyirc+vDndE36cZsOHFp7sUfTurrn7Lbq1SzW4uoAAAAAnEyEKzUoOjxIBWUVyithUXsAAADAX3k8Rt+u3a1nZmxRxv4SSVKL+hG6a0RbDWvfUA4HtwgGAAAA/A3hSg2KCQ/SrgMlOlhMuAIAAAD4owXbcvXE9E1an5kvSWoQGaK/D22ti3s0UWCA0+LqAAAAANQUwpUaFBNWeSuwvJJyiysBAAAAcDJt2H1QT3y/WfO35UqS6oQE6oYzmuvq05IVHszLLAAAAMDfcdVfg6LDKxe1z2PmCgAAAOAXMvYX69mZW/X16kwZIwUFOHR5n6a69cyWiqsTYnV5AAAAAGoJ4UoNigkjXAEAAAD8wYHicr0xf5veX5ymcrdHknRel3j9Y1gbJcWFW1wdAAAAgNpGuFKDYg7NXDnIgvYAAACATyp1uTUz06H7n1uggtIKSdKAlnG6Z0Q7dWoSbXF1AAAAAKxCuFKDvGuuFLPmCgAAAOBL3B6jL1bs0n9mblF2foCkCrVrHKV7zm6r01vVk8PhsLpEAAAAABYiXKlB3jVXmLkCAAAA+ARjjOZsydET32/W1uxCSVLdYKP/O7eTLuyRJKeTUAUAAAAA4UqNYs0VAAAAwHeszsjT49M2aUnKfklSdFiQbjwjWfUObNT5XeMJVgAAAAB4Ea7UoJjwytuCseYKAAAAYF+puUV6+oct+m7dHklScKBT4/s3002DWio8SJo2baPFFQIAAACwG8KVGlS1oD1rrgAAAAD2k1tYppdmb9NHS9JV4TFyOKQLujXRxGGtlRATJklyufigFAAAAIDfI1ypQVXhysESlzwew20EAAAAABsoLq/Qm/NT9Pq8HSoqd0uSzmhdX/ec3VbtGkdZXB0AAAAAX0C4UoOiD6254jFSQWmFd4F7AAAAALWvwu3RJ8sz9PysbdpbUCZJ6pQQrXvPbqv+LetZXB0AAAAAX0K4UoNCAgMUHhyg4nK38krKCVcAAAAACxhjNGNjtp6avlk79hZJkhJjw3Tn8Lb6S6fGzDAHAAAAUG2EKzUsJiyoMlwpdqlpnNXVAAAAAKeWFWn79e9pm7Ui7YAkqW54kG49s5Uu75ukkMAAi6sDAAAA4KsIV2pYdHiwdh8sVV4JC2ECAAAAtWXH3kI9NX2zftiQLUkKDXLqmtOS9bczWigqlBnlAAAAAP4cwpUaFnNo3ZW84nKLKwEAAAD8X05BqZ6ftU2fLMuQ22PkdEhjeiZqwpDWahQdanV5AAAAAPwE4UoNizm0zspBZq4AAAAANaawrEJv/LRTb87fqeJytyRpSLsGuntEW7VqGGlxdQAAAAD8DeFKDasKV/KKCVcAAACAk83l9mjq0nS9MHubcgsrZ4t3TYzRvWe3VZ/mLHoIAAAAoGYQrtSw6LBgSYQrAAAAwMlkjNH367P09A9blJJbJElKrhehO4e30dkdG8nhcFhcIQAAAAB/RrhSw7wzV0pYcwUAAAA4GZbs3KfHv9+s1Rl5kqR6dYJ1+1mtdGnvJAUFOK0tDgAAAMApgXClhtWrEyJJys4vtbgSAAAAwLdtzS7Qk99v1uzNOZKk8OAAXTewua47vbnqhPDSBgAAAEDt4RVIDUuKDZckZewvsbgSAAAAwDdlHSzVczO36rMVGfIYKcDp0KW9EnX7kFZqEBlqdXkAAAAATkGEKzWsKlzJzCtRhdujQG5TAAAAAByX/FKXXpu7Q28vTFGpyyNJGtGhke4c0UYt6texuDoAAAAApzLClRrWIDJEwYFOlVd4tOdgqRIPhS0AAAAAjqyswq0Pf07XpB+36UCxS5LUq1ld3XN2O/VoWtfi6gAAAACAcKXGOZ0OJdYN0469RUrbV0y4AgAAAByFx2P07drdevqHLdp1oPK2ui0b1NHdI9pqSLsGcjgcFlcIAAAAAJUIV2pBUmy4duwtUvr+YqtLAQAAAGxpwbZcPTF9k9Zn5kuqnAE+cWhrXdSjCbfWBQAAAGA7hCu1oGlchKS9hCsAAADAb2zYfVBPfL9Z87flSpLqhATqhjOa6+rTkhUezMsVAAAAAPbEq5VaUHUrsAzCFQAAAEBS5bXxszO36uvVmTJGCgpw6PI+TXXrmS0VVyfE6vIAAAAA4JgIV2pB0qFwhZkrAAAAONUdKCrXpDnb9cHiNJW7PZKk87rE645hrQ/N+AYAAAAA+yNcqQWEKwAAADjVlbrcenthil6du0MFpRWSpAEt43TPiHbq1CTa4uoAAAAAoHoIV2pBYmyYJOlgiUsHi12KDg+yuCIAAACgdlS4Pfpi5S49N3ObsvJLJUntGkfpnrPb6vRW9eRwOCyuEAAAAACqj3ClFoQHB6penRDlFpYpfX+xOoXzyTwAAAD4N2OMZm3M1pPTN2tbTqEkKSEmTP8Y3lrnd0mQ00moAgAAAMB3Ea7UkqZx4b+EK9z2AAAAAH4stUAa+9YyLU/LkyTFhAfplsEtdUXfpgoNCrC2OAAAAAA4CZx/5pufeOIJORwOTZgw4bj2nzp1qhwOh0aNGnXYdmOMHnzwQTVu3FhhYWEaMmSItm3b5n28rKxM48aNU1RUlFq3bq1Zs2Yd9v1PP/20br311j/TSo1j3RUAAAD4ux17C3Xzx6v13PpALU/LU0igUzcOaqF5dw7WtQObE6wAAAAA8BsnPHNl2bJlev3119W5c+fj2j81NVX/+Mc/NHDgwN899tRTT+nFF1/Ue++9p+TkZD3wwAMaPny4Nm7cqNDQUL3xxhtasWKFFi9erO+//15jx45Vdna2HA6HUlJSNHnyZC1fvvxEW6kViYQrAAAA8FM5+aV6fvY2fbIsQ26PkUNGF3ZvojuGt1Hj6DCrywMAAACAk+6EwpXCwkJdfvnlmjx5sh599NE/3N/tduvyyy/Xww8/rPnz5ysvL8/7mDFGzz//vO6//36df/75kqT3339fDRs21Ndff61LL71UmzZt0nnnnacOHTqoefPmuvPOO5Wbm6v69evrxhtv1JNPPqmoqKg/rKOsrExlZWXer/Pz8yVJLpdLLpermn8L1ZMQHSxJSttXWGO/q+rn1nQvtYV+7I1+7I1+7I1+7M/feqIfe/PlfgpKXZq8IFXvLkpTicsjSRrcup76hGXpyr+0VlBQoE/29Wu+PD5HQj/2Rj/252890Y+90Y+90Y+90Y+92b2f463LYYwx1f3hV111lWJjY/Xcc89p0KBB6tq1q55//vmj7v/Pf/5Ta9eu1VdffaW//vWvysvL09dffy1J2rlzp1q0aKFVq1apa9eu3u8544wz1LVrV73wwgt6/fXX9cEHH2jmzJn64YcfdNNNNykzM1NTpkzRZ5995v1Zf+Shhx7Sww8//LvtU6ZMUXh4eDX+BqpvR7704oZAxYUYPdjdXaO/CwAAAKhJFR5pQbZDM3Y5VVRRuTB9cqTRuUlutfjjzzwBAAAAgG0VFxdr7NixOnjw4DEndVR75srUqVO1cuVKLVu27Lj2X7Bggd566y2tXr36iI9nZWVJkho2bHjY9oYNG3ofu/rqq7V27Vq1b99e9erV06effqoDBw7owQcf1Ny5c3X//fdr6tSpatGihd5++20lJCQc8Xfde++9mjhxovfr/Px8JSYmavDgwYqLizuufk5UVn6pXtzwk/JcTg0dPlRBAX9quZsjcrlcmjlzpoYOHaqgoKCT/vNrG/3YG/3YG/3YG/3Yn7/1RD/25kv9eDxG367L0vOztmlXXqkkqXm9CP1jaCsNaVdfDofDp/o5HvRjb/Rjb/7Wj+R/PdGPvdGPvdGPvdGPvdm9n6o7Xv2RaoUrGRkZuv322zVz5kyFhob+4f4FBQUaN26cJk+erHr16lXnVx0mKChIL7/88mHbxo8fr9tuu02rVq3S119/rTVr1uipp57Sbbfdpi+++OKIPyckJEQhISFH/Pk1PYgJdQMVHOhUeYVHuUVuJcX9vo6TpTb6qU30Y2/0Y2/0Y2/0Y3/+1hP92Jud+zHG6KdtuXri+83atKfyhUbDqBD9fUhrXdSjiQKP8MEhO/dzIujH3ujH3vytH8n/eqIfe6Mfe6Mfe6Mfe7NrP8dbU7XClRUrVignJ0fdu3f3bnO73frpp580adIklZWVKSAgwPvYjh07lJqaqnPPPde7zeOpvBdzYGCgtmzZokaNGkmSsrOz1bhxY+9+2dnZh90m7NfmzJmjDRs26M0339Sdd96pkSNHKiIiQmPGjNGkSZOq01KtcTodSooN1/acQqXvL1ZSXM3ehgwAAAA4GdbuytMT32/Woh37JEmRoYG6cVALje+frLDggD/4bgAAAADwT9UKV8466yytW7fusG3jx49X27Ztdffddx8WrEhS27Ztf7f//fffr4KCAr3wwgtKTExUUFCQGjVqpNmzZ3vDlPz8fC1ZskQ33njj72ooLS3VzTffrI8++kgBAQFyu92qWjbG5XLJ7bbveia/DlcAAAAAO0vJLdIzM7bou7V7JEnBAU5d1b+pbhrUUnUjgi2uDgAAAACsVa1wJTIyUh07djxsW0REhOLi4rzbr7zySiUkJOjxxx9XaGjo7/aPiYmRpMO2T5gwQY8++qhatWql5ORkPfDAA4qPj9eoUaN+V8O//vUvjRw5Ut26dZMkDRgwQHfeeafGjx+vSZMmacCAAdVpqVYlxVbOViFcAQAAgF3l5Jfqhdnb9MmyDFV4jBwOaXS3BE0c2lpN6jL7GgAAAACkE1jQ/o+kp6fL6azeYu133XWXioqKdP311ysvL0+nnXaapk+f/rt1XdavX69PP/1Uq1ev9m676KKLNHfuXA0cOFBt2rTRlClTTkYbNSLxULiSQbgCAAAAm8kvdemNeTv11oIUlbgqZ4MPblNfd41oq3aNoyyuDgAAAADs5U+HK3Pnzj3m17/17rvv/m6bw+HQI488okceeeSY39uxY0dt27btsG1Op1OvvPKKXnnlleMp11LMXAEAAIDdlLrc+vDnNL08Z7sOFLskSd2SYnTPiLbq0zzO4uoAAAAAwJ5O+swVHF1VuJK2r8jiSgAAAHCqc3uMvlqVqedmblVmXokkqUX9CN05vK2Gd2goh8NhcYUAAAAAYF+EK7WoKlzJL63QwWKXosODLK4IAAAApxpjjH7cnKOnpm/RluwCSVKjqFBNGNJKF/VoosCA6t3iFwAAAABORYQrtSgsOED1I0O0t6BM6fuL1Sk82uqSAAAAcApZkbZfT3y/WctSD0iSokIDddPglvpr/2YKDQqwuDoAAAAA8B2EK7UsKTb8l3ClCeEKAAAAat7W7AI9/cMWzdyYLUkKCXTqrwOa6aYzWjKbGgAAAABOAOFKLUuKDdeKtAMsag8AAIAat+tAsZ6ftU1frtwlj5GcDuniHomaMLSVGkeHWV0eAAAAAPgswpValnho3RXCFQAAANSU/UXlennOdn2wOE3lbo8kaXiHhrpzeBu1bBBpcXUAAAAA4PsIV2pZ1aL2GYQrAAAAOMmKyir05vwUTZ6/U4VlFZKkvs1jdfeItuqWVNfi6gAAAADAfxCu1LKmcZXhStr+IosrAQAAgL8or/BoypI0TZqzXbmF5ZKkDvFRumtEW53eqp4cDofFFQIAAACAfyFcqWVVM1d255XK5fYoKMBpcUUAAADwVR6P0TdrMvWfGVu160CJJKlZXLjuGNZG53RqLKeTUAUAAAAAagLhSi2rXydEIYFOlVV4tCevVEmHZrIAAAAAx8sYozlbcvTU9C3anFUgSaofGaLbz2qlS3ol8gEeAAAAAKhhhCu1zOl0KDE2XNtzCpW+v5hwBQAAANWyLHW/np6+RUtT90uSIkMDdeOgFvpr/2YKD+byHgAAAABqA6++LJD0q3AFAAAAOB4bd+frmRlb9OPmHElSSKBTfx3QTDee0UIx4cEWVwcAAAAApxbCFQtUrbtCuAIAAIA/kppbpGdnbtV/1+yWJAU4HRrTM1G3ndVSjaPDLK4OAAAAAE5NhCsWSPSGK0UWVwIAAAC7ys4v1Quzt+nTZRmq8BhJ0rld4jVxaGsl14uwuDoAAAAAOLURrligKTNXAAAAcBR5xeV6de4OvbsoVWUVHknSoDb19Y9hbdQxIdri6gAAAAAAEuGKJaoWsU/fR7gCAACASkVlFfpwfqpen7dTBWUVkqSeTevqrhFt1Ts51uLqAAAAAAC/RrhigcS6leFKfmmFDha7FB0eZHFFAAAAsEpZhUc/7XHokecWaF9RuSSpbaNI3TWijQa3aSCHw2FxhQAAAACA3yJcsUBYcIDqR4Zob0GZ0vcXq1M4t3cAAAA41VS4PfpqVaZemLVVu/ICJJWraVy4Jg5trXM7x8vpJFQBAAAAALsiXLFIUmz4L+FKE8IVAACAU4XHYzR9Q5b+M2OLduwtkiRFBRndMaK9xvZtpqAAp8UVAgAAAAD+COGKRZJiw7Ui7QCL2gMAAJwijDGau3Wv/jNji9Zn5kuSYsKDdP3AZqp/YJNG9U4kWAEAAAAAH0G4YpGk2EOL2u8vsrgSAAAA1LSlKfv19A+btSz1gCSpTkigrjktWdcOTFZogDRt2iaLKwQAAAAAVAfhikV+CVeYuQIAAOCv1u06qGdmbNG8rXslSSGBTl3Vv5luOKOFYiOCJUkul8vKEgEAAAAAJ4BwxSJJcYQrAAAA/mpbdoGenblV36/PkiQFOh26pFeibj2zlRpFh1pcHQAAAADgzyJcsUjVzJXdeaVyuT3cXxsAAMAPZOwv1vOztumrVbvkMZLDIY3umqAJQ1p7P1wDAAAAAPB9hCsWqV8nRCGBTpVVeLQnr5QX2wAAAD5sz8ESTfpxuz5ZlqEKj5EkDe/QUHcMa6PWDSMtrg4AAAAAcLIRrljE6XQoMTZc23MKlb6/mHAFAADAB+UWlunVuTv0wc9pKq/wSJIGtqqnfwxroy6JMdYWBwAAAACoMYQrFmr6q3AFAAAAviOvuFxv/LRT7yxMVYnLLUnq3SxWdwxrrT7N4yyuDgAAAABQ0whXLJR4aN2VtP1FFlcCAACA41FQ6tI7C1M1+aedKiirkCR1aRKtO4a10cBW9eRwOCyuEAAAAABQGwhXLFS1qH0GM1cAAABsraTcrfcXp+q1eTt0oNglSWrbKFJ3DGujIe0aEKoAAAAAwCmGcMVCVeEKtwUDAACwp7IKtz5ekq5Jc3Yot7BMktS8foQmDm2tkR0by+kkVAEAAACAUxHhioWqFrFP30e4AgAAYCcut0efr9ill2Zv0+6DpZKkxNgw3X5Wa43qGq/AAKfFFQIAAAAArES4YqHEupXhSn5phQ4WuxQdHmRxRQAAAKc2l9ujL1fu0ks/bteuAyWSpEZRobr1rJa6uEeiggMJVQAAAAAAhCuWCgsOUP3IEO0tKFP6/mJ1Co+2uiQAAIBTksvt0VerMjXpx+3eW7bWqxOiGwe10OV9khQaFGBxhQAAAAAAOyFcsVjT2HDtLShT2v4idWpCuAIAAFCbKqpClTnblbavKlQJ1g1ntNDlfZoqLJhQBQAAAADwe4QrFkuKDdfytAMsag8AAFCLKtwefbN6t176cZtSD4UqcRHB+tsZzXVF36YKD+YyGQAAAABwdLxqtFhibOW6KxmEKwAAADXO7TH675pMvTh7u1JyiyRJsRHB+tvpzTWuH6EKAAAAAOD48OrRYkmHwhVmrgAAANQct8fo2zW79eLsbdp5KFSpGx6k609voSv7NVVECJfFAAAAAIDjx6tIiyXFEa4AAADUFLfH6H9rK0OVHXsrQ5WY8CBdN7C5rurfTHUIVQAAAAAAJ4BXkxarmrmyO69ULrdHQQFOiysCAADwfUcKVaLDgnTdwGRd1b+ZIkODLK4QAAAAAODLCFcs1iAyRCGBTpVVeLQnr9Q7kwUAAADV5/YYfbduj16cvU3bcwolSVGhgZUzVQY0UxShCgAAAADgJCBcsZjD4VBSbLi25RQqbX8R4QoAAMAJIFQBAAAAANQmwhUbqApXWHcFAACgeo4Wqlw7sLn+SqgCAAAAAKghhCs2kBjLovYAAADV4THSd+uy9PLcndpGqAIAAAAAqGWEKzZQtah9BuEKAADAMVXOVMnSk2sClPXzWkmEKgAAAACA2ke4YgNJzFwBAAA4JrfH6H9rd+ulH7cfuv2Xg1AFAAAAAGAZwhUbaHpoEfv0fYQrAAAAv1bh9uib1bv18pzt2plbJKlypkr/emV67MrBiosKt7hCAAAAAMCpiHDFBprUrXxTIL+0QgeLXYoO55OXAADg1OZye/TVykxNmrPdO7s3JjxI156WrLG9EjT/x5mKCuOaCQAAAABgDcIVGwgLDlCDyBDlFJQpbX+ROofHWF0SAACAJcoq3PpiRaZembtduw6USJJiI4J13cDmGtevqeqEBMrlcllcJQAAAADgVEe4YhNJseHKKShT+v5idW4SY3U5AAAAtarU5dZnyzP06twd2n2wVJJUr06I/nZ6c13eN0nhwVy2AgAAAADsg1epNpEUG67laQdY1B4AAJxSSl1ufbw0Xa/N26Hs/DJJUsOoEN1wRgtd1jtJoUEBFlcIAAAAAMDvEa7YRGJs5borGYQrAADgFFBUVqGPlqTpjZ9SlFtYGao0jg7VTYNa6OKeiYQqAAAAAABbI1yxiaRD4QozVwAAgD87WOLSe4tS9fbCFOUVV66dkhATppsHt9SFPRIUEkioAgAAAACwP8IVm2gaR7gCAAD8177CMr21IEXvL05TYVmFJCm5XoRuHNRCo7omKDjQaXGFAAAAAAAcP8IVm6iaubI7r1Qut0dBAbzBAAAAfF/WwVK98dNOTVmaplKXR5LUpmGkbj6zpc7p1FgBTofFFQIAAAAAUH2EKzZRPzJEIYFOlVV4tDuvRE3jIqwuCQAA4IRl7C/Wq/N26PPlu1TurgxVOjeJ1i2DW2pIu4ZyEqoAAAAAAHwY4YpNOBwOJcWGa1tOodL3FxOuAAAAn7Q9p1CvzN2ub1bvlttjJEm9m8XqljNbamCrenI4CFUAAAAAAL6PcMVGfh2uAAAA+JL1mQf16twdmrZ+j0xlpqLTW9fXLYNbqndyrLXFAQAAAABwkhGu2EhiLIvaAwAA32GM0ZKU/Xpl7g79tHWvd/uw9g118+CW6pIYY11xAAAAAADUIMIVG2kaVxmuZBCuAAAAG/N4jH7cnKNX5m7XyvQ8SZLTIZ3bJV43Dmqhto2irC0QAAAAAIAaRrhiI0nMXAEAADZW4fbo27W79drcndqSXSBJCg506uIeTfS301so6dAHRQAAAAAA8HeEKzZSFa6k7SuWMYYFXwEAgC2Uutz6bHmGXv9pp3YdKJEk1QkJ1BV9m+rq05qpQWSoxRUCAAAAAFC7CFdspEndynCloLRCB0tcigkPtrgiAABwKssvdenDn9P09oJU5RaWSZLiIoJ19WnJuqJvU0WHBVlcIQAAAAAA1iBcsZGw4AA1iAxRTkGZ0vcXE64AAABL5BSU6t2FqfpgcZoKyiokSQkxYbr+9OYa0zNRYcEBFlcIAAAAAIC1CFdsJik23BuudG4SY3U5AADgFLJzb6Emz0/RFyt3qbzCI0lq1aCObhzUQud2iVdQgNPiCgEAAAAAsAfCFZtJigvX8rQDLGoPAABqzcr0A3p93g7N2JgtYyq3dUuK0Y1ntNCQdg3ldLIOHAAAAAAAv0a4YjNVi9pnEK4AAIAa5PEYzdmSo9fn7dTS1P3e7UPaNdDfzmihnk3ryuEgVAEAAAAA4EgIV2ymKlxh5goAAKgJ5RUefbM6U2/8tFPbcgolSUEBDo3qmqDrT2+uVg0jLa4QAAAAAAD7I1yxmapwJW0f4QoAADh5SiukNxek6r3F6crKL5Uk1QkJ1OV9kjR+QLIaRYdaXCEAAAAAAL6DcMVmqsKV3Xklcrk9LBwLAAD+lKyDpXp7wQ69vzJApe6tkqQGkSG6+rRkje2TpKjQIIsrBAAAAADA9xCu2Ez9yBCFBDpVVuHR7rwSNY2LsLokAADggzbuzteb83fq27W75XIbSQ41rxehG85oofO7xSskMMDqEgEAAAAA8FmEKzbjcDiUFBuubTmFSt9fTLgCAACOmzFGc7fu1Zvzd2rh9n3e7T2bxqhr6D7dOba/QkKCLawQAAAAAAD/QLhiQ03jfglXAAAA/kipy62vV2XqrQUp3kXqA5wOnd2xka4d2FwdGkVo2rRpcjodFlcKAAAAAIB/IFyxocRD664QrgAAgGPZV1imD39O1wc/pyq3sFxS5SL1l/RK1PgBzdSkbuU1hcvlsrJMAAAAAAD8zp9aLf2JJ56Qw+HQhAkTjrrPl19+qZ49eyomJkYRERHq2rWrPvjgg8P2McbowQcfVOPGjRUWFqYhQ4Zo27Zt3sfLyso0btw4RUVFqXXr1po1a9Zh3//000/r1ltv/TOt2ErVovYbd+dbXAkAALCjHXsL9X9frVP/J37Uc7O2KrewXPHRobpvZDstuvdMPfCX9t5gBQAAAAAAnHwnPHNl2bJlev3119W5c+dj7hcbG6v77rtPbdu2VXBwsP73v/9p/PjxatCggYYPHy5Jeuqpp/Tiiy/qvffeU3Jysh544AENHz5cGzduVGhoqN544w2tWLFCixcv1vfff6+xY8cqOztbDodDKSkpmjx5spYvX36irdjOoDYN9PC3GzV/W6527i1U8/p1rC4JAABYzBijxTv26e2FKZq1Kce7vVNCtK4dmKyRnRorKOBPfW4GAAAAAAAcpxN6BV5YWKjLL79ckydPVt26dY+576BBgzR69Gi1a9dOLVq00O23367OnTtrwYIFkirfKHj++ed1//336/zzz1fnzp31/vvva/fu3fr6668lSZs2bdJ5552nDh066Oabb9bevXuVm5srSbrxxhv15JNPKioq6kRasaXkehEa0q6BJOmdhanWFgMAACxV6nLr02UZOvuF+Rr75hLN2pQjh0Ma0q6hpl7fV/+9ZYDO75pAsAIAAAAAQC06oZkrN998s8455xwNGTJEjz766HF/nzFGP/74o7Zs2aInn3xSkpSSkqKsrCwNGTLEu190dLT69OmjxYsX69JLL1WXLl30wQcfqKSkRD/88IMaN26sevXq6aOPPlJoaKhGjx59XL+/rKxMZWVl3q/z8ytvu+VyuWx3L/Kr+iZp1qYcfb4iQ7cNbq6Y8KA//J6qHuzWy4miH3ujH3ujH3ujH/uzQ0/Z+aWasnSXPl6WoQPFlXWEBTk1ulu8rurbVM3rR0iSKioq/vBn2aGfk4l+7I1+7I1+7I1+7M/feqIfe6Mfe6Mfe6Mfe7N7P8dbl8MYY6rzg6dOnarHHntMy5YtU2hoqAYNGqSuXbvq+eefP+r3HDx4UAkJCSorK1NAQIBeeeUVXX311ZKkRYsWacCAAdq9e7caN27s/Z4xY8bI4XDok08+kcvl0oQJEzRt2jTVq1dPzz33nNq3b69evXpp7ty5ev311zV16lS1aNFCb7/9thISEo5Yx0MPPaSHH374d9unTJmi8HB73ZfcGOnptQHKLHboL0luDU2o1jABAAAflVYozdvj1Kp9DnmMQ5JUN9jo9MYe9W1gFH7CN3UFAAAAAAB/pLi4WGPHjtXBgwePecesar08z8jI0O23366ZM2cqNDT0uL8vMjJSq1evVmFhoWbPnq2JEyeqefPmGjRo0HF9f1BQkF5++eXDto0fP1633XabVq1apa+//lpr1qzRU089pdtuu01ffPHFEX/Ovffeq4kTJ3q/zs/PV2JiogYPHqy4uLjj7qe2lMfv1l1frteyA+F6cvxABQce+3YfLpdLM2fO1NChQxUU9MczXeyOfuyNfuyNfuyNfuyvtnuqcHs0c1OO3l2crpXped7tPZvG6Kp+TTWkbX0F/onbfvnbGNGPvdGPvdGPvdGP/flbT/Rjb/Rjb/Rjb/Rjb3bvp+qOV3+kWuHKihUrlJOTo+7du3u3ud1u/fTTT5o0aZJ3ZspvOZ1OtWzZUpLUtWtXbdq0SY8//rgGDRqkRo0aSZKys7MPm7mSnZ2trl27HrGOOXPmaMOGDXrzzTd15513auTIkYqIiNCYMWM0adKko9YfEhKikJCQ320PCgqy5SCO6p6op2duU3ZBmWZuztWobkeekfNbdu3nRNGPvdGPvdGPvdGP/dV0T3nF5Zq6LEPvL0rV7oOllb8zwKFzO8dr/IBkdWoSfVJ/n7+NEf3YG/3YG/3YG/3Yn7/1RD/2Rj/2Rj/2Rj/2Ztd+jremaoUrZ511ltatW3fYtvHjx6tt27a6++67jxisHInH4/GufZKcnKxGjRpp9uzZ3jAlPz9fS5Ys0Y033vi77y0tLdXNN9+sjz76SAEBAXK73aq6s5nL5ZLb7a5OS7YWHOjUVf2a6pkZW/Xmgp06v2u8HA6H1WUBAIA/adOefL2/OE1fr8pUiavy2iUuIliX922qK/okqUHU8c8QBgAAAAAAta9a4UpkZKQ6dux42LaIiAjFxcV5t1955ZVKSEjQ448/Lkl6/PHH1bNnT7Vo0UJlZWWaNm2aPvjgA7366quSJIfDoQkTJujRRx9Vq1atlJycrAceeEDx8fEaNWrU72r417/+pZEjR6pbt26SpAEDBujOO+/U+PHjNWnSJA0YMKDafwl2NrZPU73043atz8zX0pT96tPcfrcvAwAAf8zl9mjGhmy9tzhVS1P2e7e3axylqwc007ld4hUadHwfVAEAAAAAANY66Uuipqeny+n85Z7gRUVFuummm7Rr1y6FhYWpbdu2+vDDD3XJJZd497nrrrtUVFSk66+/Xnl5eTrttNM0ffr0363rsn79en366adavXq1d9tFF12kuXPnauDAgWrTpo2mTJlysluyVGxEsC7o3kQfL03XmwtSCFcAAPAxOQWlmro0Qx8tSVN2fuXM3QCnQyM6NtKVfZuqd3IsM1MBAAAAAPAxfzpcmTt37jG/fvTRR/Xoo48e82c4HA498sgjeuSRR465X8eOHbVt27bDtjmdTr3yyit65ZVXjrtmX3PNac308dJ0zdqUrdTcIjWrF2F1SQAA4BiMMVqZnqf3F6dq2ro9crkrb2Far06IxvZJ0tjeSWoUza2/AAAAAADwVSd95gpOvpYNIjWoTX3N3bJX7y5K1UPndbC6JAAAcASlLrf+u2a33l+cqvWZ+d7tPZrW1ZX9mursjo0VHOg8xk8AAAAAAAC+gHDFR1x7WnPN3bJXny7P0N+HtlZ0WJDVJQEAgEPS9xXro6Vp+mRZhvKKXZKkkECnzu8aryv7NVPHhGiLKwQAAAAAACcT4YqPGNAyTm0bRWpzVoGmLk3X385oYXVJAACc0ircHv24OUcfLUnXT9v2ylTe+UtN6oZpXN+mGtMzUXUjgq0tEgAAAAAA1AjCFR/hcDh09WnJuuvztXp3UaquPi1ZQQHcVgQAgNqWdbBUU5ela+rSDGXll3q3D2xVT1f2a6Yz2zZQgJMF6gEAAAAA8GeEKz7kvC7xemr6Zu05WKrv12fpvC7xVpcEAMApweMxWrA9Vx8tSdOsTTlyeyqnqcRGBGtMz0SN7Z2kpLhwi6sEAAAAAAC1hXDFh4QGBeiKvk31/Kxtemv+Tp3bubEcDj4ZCwBATdlXVK5v1qRrytJ0pe0r9m7vnRyry/skaUTHRgoJDLCwQgAAAAAAYAXCFR9zRd+memXuDq3ZdVAr0g6oZ7NYq0sCAMCvGGO0PO2A3t/m1D+WzpPLXTlLJTI0UBd2b6KxfZLUumGkxVUCAAAAAAArEa74mHp1QjS6a4I+WZ6htxakEK4AAHCS7Css01erMjV1WYa25xRKckoy6twkWlf0aaq/dGms8GAunQAAAAAAAOGKT7pmYLI+WZ6hHzZkKWN/sRJjucc7AAAnwuMxWrgjV1OXZmjGxizvLJWwIKe61q3QnRf0U/dm9SyuEgAAAAAA2A3hig9q3TBSA1vV0/xtuXpnYaoePLe91SUBAOBT9hws0efLd+mT5RnadaDEu71zk2hd2itJI9rX1/wfZ6hTQrSFVQIAAAAAALsiXPFR15yWrPnbcvXJsnRNGNpKUaFBVpcEAICtVbg9+nFzjj5ZlqE5W3LkqZykosjQQI3ulqBLeiWqQ3xlmOJyuSysFAAAAAAA2B3hio86o3V9tWpQR9tyCvXpsgxdO7C51SUBAGBLafuK9MmyDH2+YpdyCsq823snx+rSXoka2amxQoMCLKwQAAAAAAD4GsIVH+VwOHT1acm698t1emdhqv7av5nVJQEAYBvF5RWati5Lny3P0JKU/d7tcRHBuqhHE43plagW9etYWCEAAAAAAPBlhCs+bHS3BD39wxZl5pXohw3ZGtaOBXcBAKcuY4yWpx3QZ8sz9N3aPSoqd0uSHA5pYKv6uqxXos5q11DBgU6LKwUAAAAAAL6OcMWHhQYF6Io+SXrxx+16c8FOwhUAwClpz8ESfbkyU5+v2KWU3CLv9qZx4bqoexNd0KOJEmLCLKwQAAAAAAD4G8IVH3dFv6Z6bd5OrUrP06qMPKvLAQCgVpS63Jq5MVufrdilBdv2ehenDw8O0MhOjXVxjybqnRwrh8NhbaEAAAAAAMAvEa74uAaRoTqva7w+X7FL7y5K0/BIqysCAKBmGGO0LvOgPlu+S9+szlR+aYX3sd7JsbqoRxOd06mxIkK4vAEAAAAAADWLdx/8wNUDkvX5il2aviFbvbpZXQ0AACdX1sFSfbUqU1+u3KVtOYXe7fHRobqwRxNd2L2JmtWLsLBCAAAAAABwqiFc8QPt46M0oGWcFm7fp7l7nLrC6oIAAPiTissrNGNDtr5YuUsLtufKHLrtV3CgUyM6NNLFPZuof4t6CnBy2y8AAAAAAFD7CFf8xPWnt9DC7fu0IMuh9P3FatEw2uqSAACoFo/HaEnKfn2xcpe+X7dHReVu72O9mtXVhd2b6OxOjRUdFmRhlQAAAAAAAIQrfuP0VvXUv0WsFu3Yr2dmbNOr43paXRIAAMdl597CQ7f9ylRmXol3e1JsuC7onqDR3RLUNI7bfgEAAAAAAPsgXPETDodD945oo/NeXqTvN2RrWep+9WoWa3VZAAAcUV5xuf63do++XLlLK9PzvNsjQwJ1TufGurBHE/VsWlcOB7f9AgAAAAAA9kO44kfaNopU3wZGi3McevR/G/XVTQPk5F70AACbKKtwa87mHH21KlNzNu9VudsjSXI6pNNb19eF3ZtoaPuGCg0KsLhSAAAAAACAYyNc8TMjEz1amxekNbsO6ps1mRrdrYnVJQEATmHGGC1PO6CvVmXqu7V7dLDE5X2sbaNIXdi9ic7vFq8GkaEWVgkAAAAAAFA9hCt+JipYuuH0ZP1n1nY9NX2LRnRorLBgPgEMAKhdO/cW6utVmfpqdaYy9v+yjkrDqBCN6pqg0d0T1LZRlIUVAgAAAAAAnDjCFT80vn9TTV1euSjw5Pk7ddtZrawuCQBwCthXVK4fNmbqy1WZWpOR590eERygER0b64LuCerbPE4B3LISAAAAAAD4OMIVPxQSFKC7z26r2z5epVfn7tAlvRLVMIrbrQAATr5Sl1s/rMvSG5udumPJPFV4jCQpwOnQwFb1NLpbgoa1b8QsSgAAAAAA4FcIV/zUuZ0b652FKVqVnqdnftiipy/uYnVJAAA/4fYY/bxzn75alanp67NUWFYhySnJqFNCtEZ3S9C5XeJVPzLE6lIBAAAAAABqBOGKn3I4HHrgL+11wSuL9PnKXbqqfzN1TIi2uiwAgI8yxmjTngJ9vTpT36zOVHZ+mfexhJhQtYso1h0XnKZ2CXUtrBIAAAAAAKB2EK74se5JdXVel3j9d81uPfrdRn18XV85HNznHgBw/Hbnleib1bv19apMbcku8G6PDgvSOZ0ba3S3BHVuXEfTp3+vlg3qWFgpAAAAAABA7SFc8XN3n91WP2zI0s8792vGxmwN79DI6pIAADZ3sMSl6ev36KtVmVqSsl+mchkVBQc4dVa7BhrVLUGD2tRXSGDlOioul8vCagEAAAAAAGof4YqfS4gJ07UDk/XynB16fNomDW7TQMGBTqvLAgDYTFmFW3O37NXXqzI1e3OOyis83sf6JMdqdLcEnd2psaLDgiysEgAAAAAAwB4IV04BNw5qqU+W7VLqvmK9vzhV1w5sbnVJAAAb8HiMVqQf0FerMvXd2j06WPLLDJTWDetodLcmOq9rvBJiwiysEgAAAAAAwH4IV04BdUIC9Y9hrXXPl+v04uxturB7E9WNCLa6LACARbbnFOirVZn6ZvVu7TpQ4t3eMCpE53dN0Pld49W+cRTrdAEAAAAAABwF4cop4uKeiXpvcZo27cnXC7O36aHzOlhdEgCgFuXkl+q/a3br69WZWp+Z791eJyRQIzo20uhuCerbPE4BTgIVAAAAAACAP0K4cooIcDp0/zntdPmbS/TBz2m6om9TtWxQx+qyAAA1qLCsQjM2ZOmrVZlauD1XnkML0wc6HRrUpr5GdUvQkHYNFRoUYG2hAAAAAAAAPoZw5RQyoGU9DWnXQLM25ejxaZv01l97WV0SAOAkc7k9WrAtV1+tytSMjVkqdf2yMH33pBiN7pagczrHK5bbQwIAAAAAAJwwwpVTzL0j22nulr2avTlHC7bl6rRW9awuCQDwJxljtGbXQX29KlPfrtmtfUXl3seS60VoVNcEjeoWr6ZxERZWCQAAAAAA4D8IV04xLerX0RV9m+rdRal69LuN+u62gdxfHwB8VNq+In29qnIdlZTcIu/2uIhgndslXqO7Jahzk2gWpgcAAAAAADjJCFdOQROGtNJXqzK1OatAny7P0GW9k6wuCQBwnPYVlum7dXv01apMrUrP824PCwrQsA4NNapbgk5rWU9BAU7rigQAAAAAAPBzhCunoJjwYN1+Vis98r+N+s+MLfpL58aKDA2yuiwAwFGUutyatSlbX63M1Lyte1VxaGV6p0M6rVV9je4Wr2HtGykihNM6AAAAAABAbeBdmFPUFX2b6oOf05SSW6RX5+7QXSPaWl0SAOBXjDFakXZAX6zM1P/W7lZBaYX3sU4J0RrVLUHndmmsBpGhFlYJAAAAAABwaiJcOUUFBzp179ltdf0HK/TOwlSNH5Cs+pEhVpcFAKe8jP3F+nJlpr5ctUtp+4q92xNiwjSqW7xGd2uilg3qWFghAAAAAAAACFdOYUPbN1SXxBitycjTq3N36MFz21tdEgCckgpKXfp+XZY+X7lLS1P2e7eHBwfo7I6NdWGPBPVNjpPTycL0AAAAAAAAdkC4cgpzOBz6x7DWGvfWUn24JE3XnZ6sxtFhVpcFAKcEj5Hmb8vVN2uz9MOGLJW6PJIkh0Ma0KKeLuieoBEdGyk8mFM1AAAAAACA3fCOzSnutJb11Ds5VktT9mvSj9v12OhOVpcEAH5te06hPlmapk9WBCj/55Xe7c3rR+jC7k00uluC4mMIugEAAAAAAOyMcOUU53A4dMfQ1rrkjZ/16fIM3XBGCyXGhltdFgD4lYJSl/63do8+W56hlel5h7Y6FBMWpPO6xuuC7k3UpUm0HA5u+wUAAAAAAOALCFegPs3jNLBVPc3flqsXZ2/T0xd3sbokAPB5Ho/Rzyn79PnyXZq2fo/3tl8BTodObxWnZJOtiZcNUZ2wEIsrBQAAAAAAQHURrkCSNHFoa83flqsvVu7SjYNaqHn9OlaXBAA+adeBYn2+Ype+WLlLGftLvNtbNqiji3tU3varbliApk2bppBAp4WVAgAAAAAA4EQRrkCS1C2proa0a6BZm3L0/KxtevGyblaXBAA+o9Tl1vT1WfpsRYYW7dgnYyq3R4YE6i9d4nVxzybqlhjjve2Xy+WysFoAAAAAAAD8WYQr8Pr70NaatSlH367drZsHt1SbRpFWlwQAtrY+86A+Xpqu/67erYKyCu/2AS3jdHGPRA3v0EhhwQEWVggAAAAAAICaQLgCrw7x0TqnU2N9t26Pnpu5Va+N62F1SQBgO/mlLn2zeremLk3Xht353u1N6obpoh5NdGH3JkqMDbewQgAAAAAAANQ0whUcZsKQVpq2fo+mb8jSul0H1alJtNUlAYDljDFakXZAHy/N0HfrdnsXpw8OcGpEx0a6tFei+jaPk9PpsLhSAAAAAAAA1AbCFRymVcNIjeqaoK9WZerZmVv0zvjeVpcEAJbZX1SuL1fu0tRlGdqeU+jd3qpBHV3aO0kXdEtQ3YhgCysEAAAAAACAFQhX8Du3n9VK/12zW3O27NWKtAPq0bSu1SUBQK3xeIwW7dinqcvSNWNDtsrdlbNUwoIC9JfOjXVp7yR1T/plcXoAAAAAAACceghX8DvN6kXo4h5NNHVZhp6duUUfXdvX6pIAoMblFpbpk2UZ+mRZhtL3F3u3d0qI1qW9E3Vel3hFhgZZWCEAAAAAAADsgnAFR3TrWa305cpMLdy+T4t25Kp/i3pWlwQAJ50xRstSD+iDn9M0ff0eudxGkhQZEqjzu8Xr0l5J6pjA2lMAAAAAAAA4HOEKjighJkyX9k7U+4vT9OyMrep3Qxy3wAHgNwpKXfpqVaY+/DlNW7N/WUulS2KMruiTpL90jldYcICFFQIAAAAAAMDOCFdwVDcPbqlPlmVoedoBzdu6V4PaNLC6JAD4UzbsPqgPf07XN6szVVzullS5lsr5XeN1Rd+mzFIBAAAAAADAcSFcwVE1jArVlf2aavL8FD0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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as mtick\n",
    "curve_points = curve.get_curve_points(120)\n",
    "ax = curve_points.plot(figsize=(20,10), grid=True, title='US Treasury Yield Curve on ' + cur_date_curve.strftime('%Y-%m-%d'),\n",
    "                       xlim=(curve_points.index[0], curve_points.index[-1]),\n",
    "                       xticks=curve_points.index[::4], xlabel='', rot=45)\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))\n",
    "_ = ax.set_xticklabels(list(map(lambda x: x.strftime('%Y-%m'), curve_points.index[::4])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "clean-athens",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert curve.date == BDay(1).rollforward(today).date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beneficial-round",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}