{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4ed4f5f8",
   "metadata": {},
   "source": [
    "# Basic tutorial \n",
    "\n",
    "This tutorial demonstrates the usage of the sweights package.\n",
    "\n",
    "We will first cook up a toy model with a discriminant variable (invariant mass) and a control variable (decay time) and use it to generate some toy data.\n",
    "\n",
    "Then we will use a fit to the invariant mass to obtain some component pdf estimates and use these to extract some weights which project out the signal only component in the decay time. \n",
    "\n",
    "We will demonstrate both the classic *sWeights* and the *Custom Ortogonal Weight functions* (COWs) method. See [arXiv:2112.04575](https://arxiv.org/abs/2112.04574) for more details.\n",
    "\n",
    "Finally, we will fit the weighted decay time distribution and correct the covariance matrix according to the description in [arXiv:1911.01303](https://arxiv.org/abs/1911.01303)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7aa1bc84",
   "metadata": {},
   "outputs": [],
   "source": [
    "from types import SimpleNamespace\n",
    "\n",
    "# external requirements\n",
    "import numpy as np\n",
    "from scipy.stats import norm, expon\n",
    "import matplotlib.pyplot as plt\n",
    "from iminuit import Minuit\n",
    "from iminuit.cost import ExtendedUnbinnedNLL\n",
    "\n",
    "# from this package\n",
    "from sweights import SWeight # for classic sweights\n",
    "from sweights import Cow     # for custom orthogonal weight functions\n",
    "from sweights import cov_correct, approx_cov_correct # for covariance corrections\n",
    "from sweights.testing import make_classic_toy # to generate a toy dataset\n",
    "from sweights.util import plot_binned, make_weighted_negative_log_likelihood"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7bae46d",
   "metadata": {},
   "source": [
    "## Toy model and toy data\n",
    "\n",
    "We generate an m-distribution (for the discriminatory variable) and an independent t-distribution (the control variable). In particle physics, m is typically the invariant mass distribution of decay candidates, and t is the decay time distribution of these candidates. But any other two variables can be used, as long as they are independent in the pure signal and pure background."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eb61cca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a toy model\n",
    "true_yield = SimpleNamespace()\n",
    "true_yield.s = 1000\n",
    "true_yield.b = 1000\n",
    "\n",
    "# mass\n",
    "mrange = (0, 1)\n",
    "m_truth = SimpleNamespace()\n",
    "m_truth.mu = 0.5\n",
    "m_truth.sigma = 0.1\n",
    "m_truth.slope = 1\n",
    "\n",
    "# time\n",
    "trange = (0, 1.5)\n",
    "t_truth = SimpleNamespace()\n",
    "t_truth.slope = 0.2\n",
    "t_truth.mu = 0.1\n",
    "t_truth.sigma = 1.0\n",
    "\n",
    "toy = make_classic_toy(\n",
    "    1,\n",
    "    s=true_yield.s,\n",
    "    b=true_yield.b,\n",
    "    mrange=mrange,\n",
    "    trange=trange,\n",
    "    ms_mu=m_truth.mu,\n",
    "    ms_sigma=m_truth.sigma,\n",
    "    mb_mu=m_truth.slope,\n",
    "    ts_mu=t_truth.slope,\n",
    "    tb_mu=t_truth.mu,\n",
    "    tb_sigma=t_truth.sigma,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1eb58a1",
   "metadata": {},
   "source": [
    "## Make pdfs, plot ground truth and toy data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "45806a9e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# m-density for fitting and plotting (not normalized)\n",
    "def m_density(x, s, b, mu, sigma, slope):\n",
    "    ds = norm(mu, sigma)\n",
    "    snorm = np.diff(ds.cdf(mrange))\n",
    "\n",
    "    db = expon(mrange[0], slope)\n",
    "    bnorm = np.diff(db.cdf(mrange))\n",
    "\n",
    "    return s / snorm * ds.pdf(x) + b / bnorm * db.pdf(x)\n",
    "\n",
    "\n",
    "# t-density for fitting and plotting (not normalized)\n",
    "def t_density(x, s, b, slope, mu, sigma):\n",
    "\n",
    "    ds = expon(trange[0], slope)\n",
    "    snorm = np.diff(ds.cdf(trange))\n",
    "\n",
    "    db = norm(mu, sigma)\n",
    "    bnorm = np.diff(db.cdf(trange))\n",
    "\n",
    "    return s / snorm * ds.pdf(x) + b / bnorm * db.pdf(x)\n",
    "\n",
    "\n",
    "def plot(toy, bins=50, npoint=400, draw_pdf=True):\n",
    "    fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "    plt.sca(ax[0])\n",
    "    plot_binned(toy[0], bins=bins, range=mrange, color=\"k\")\n",
    "    plt.sca(ax[1])\n",
    "    plot_binned(toy[1], bins=bins, range=trange, color=\"k\")\n",
    "\n",
    "    if draw_pdf:\n",
    "        m = np.linspace(*mrange, npoint)\n",
    "        mnorm = (mrange[1] - mrange[0]) / bins\n",
    "\n",
    "        par = m_truth.mu, m_truth.sigma, m_truth.slope\n",
    "        s = m_density(m, true_yield.s, 0, *par)\n",
    "        b = m_density(m, 0, true_yield.b, *par)\n",
    "        p = s + b\n",
    "\n",
    "        ax[0].plot(m, mnorm * s, \"C0--\", label=\"signal\")\n",
    "        ax[0].plot(m, mnorm * b, \"C1:\", label=\"background\")\n",
    "        ax[0].plot(m, mnorm * p, \"k-\", label=\"total\")\n",
    "\n",
    "        t = np.linspace(*trange, npoint)\n",
    "        tnorm = (trange[1] - trange[0]) / bins\n",
    "\n",
    "        par = t_truth.slope, t_truth.mu, t_truth.sigma\n",
    "        s = t_density(t, true_yield.s, 0, *par)\n",
    "        b = t_density(t, 0, true_yield.b, *par)\n",
    "        p = s + b\n",
    "\n",
    "        ax[1].plot(t, tnorm * s, \"C0--\", label=\"signal\")\n",
    "        ax[1].plot(t, tnorm * b, \"C1:\", label=\"background\")\n",
    "        ax[1].plot(t, tnorm * p, \"k-\", label=\"total\")\n",
    "\n",
    "    ax[0].set_xlabel(\"m\")\n",
    "    ax[0].set_ylim(bottom=0)\n",
    "    ax[0].legend()\n",
    "\n",
    "    ax[1].set_xlabel(\"t\")\n",
    "    ax[1].set_ylim(bottom=0)\n",
    "    ax[1].legend()\n",
    "\n",
    "    fig.tight_layout()\n",
    "\n",
    "\n",
    "plot(toy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06fb26fd",
   "metadata": {},
   "source": [
    "## Fit toy data in the m-variable\n",
    "\n",
    "This provides us with estimates for the component shapes and the component yields, which we need to apply the sWeight method.\n",
    "\n",
    "We use an extended unbinned maximum-likelihood fit here, but an extended binned maximum-likelihood fit would work as well. We could use an ordinary fit in which the model pdf is normalized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "76702af5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Minimizer\"> Migrad </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:left\" title=\"Minimum value of function\"> FCN = -2.696e+04 </td>\n",
       "        <td style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 142 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 1.99e-05 (Goal: 0.0002) </td>\n",
       "        <td style=\"text-align:center\" title=\"Total run time of algorithms\">  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Minimum </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Below EDM threshold (goal x 10) </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> No parameters at limit </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Below call limit </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Hesse ok </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Covariance accurate </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th title=\"Variable name\"> Name </th>\n",
       "        <th title=\"Value of parameter\"> Value </th>\n",
       "        <th title=\"Hesse error\"> Hesse Error </th>\n",
       "        <th title=\"Minos lower error\"> Minos Error- </th>\n",
       "        <th title=\"Minos upper error\"> Minos Error+ </th>\n",
       "        <th title=\"Lower limit of the parameter\"> Limit- </th>\n",
       "        <th title=\"Upper limit of the parameter\"> Limit+ </th>\n",
       "        <th title=\"Is the parameter fixed in the fit\"> Fixed </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 0 </th>\n",
       "        <td> s </td>\n",
       "        <td> 960 </td>\n",
       "        <td> 50 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td> 2E+03 </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 1 </th>\n",
       "        <td> b </td>\n",
       "        <td> 1.02e3 </td>\n",
       "        <td> 0.05e3 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td> 2E+03 </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 2 </th>\n",
       "        <td> mu </td>\n",
       "        <td> 0.494 </td>\n",
       "        <td> 0.005 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td> 1 </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 3 </th>\n",
       "        <td> sigma </td>\n",
       "        <td> 0.097 </td>\n",
       "        <td> 0.004 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td> 1 </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 4 </th>\n",
       "        <td> slope </td>\n",
       "        <td> 1.14 </td>\n",
       "        <td> 0.16 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td> 0 </td>\n",
       "        <td> 50 </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> s </th>\n",
       "        <th> b </th>\n",
       "        <th> mu </th>\n",
       "        <th> sigma </th>\n",
       "        <th> slope </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> s </th>\n",
       "        <td> 2.13e+03 </td>\n",
       "        <td style=\"background-color:rgb(180,180,250);color:black\"> -1.2e3 <strong>(-0.540)</strong> </td>\n",
       "        <td style=\"background-color:rgb(242,242,250);color:black\"> -12.228e-3 <strong>(-0.058)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,181,181);color:black\"> 91.430e-3 <strong>(0.458)</strong> </td>\n",
       "        <td style=\"background-color:rgb(231,231,250);color:black\"> -1.037 <strong>(-0.144)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> b </th>\n",
       "        <td style=\"background-color:rgb(180,180,250);color:black\"> -1.2e3 <strong>(-0.540)</strong> </td>\n",
       "        <td> 2.18e+03 </td>\n",
       "        <td style=\"background-color:rgb(250,241,241);color:black\"> 12.225e-3 <strong>(0.058)</strong> </td>\n",
       "        <td style=\"background-color:rgb(191,191,250);color:black\"> -91.418e-3 <strong>(-0.453)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,229,229);color:black\"> 1.037 <strong>(0.142)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> mu </th>\n",
       "        <td style=\"background-color:rgb(242,242,250);color:black\"> -12.228e-3 <strong>(-0.058)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,241,241);color:black\"> 12.225e-3 <strong>(0.058)</strong> </td>\n",
       "        <td> 2.06e-05 </td>\n",
       "        <td style=\"background-color:rgb(239,239,250);color:black\"> -0.002e-3 <strong>(-0.086)</strong> </td>\n",
       "        <td style=\"background-color:rgb(222,222,250);color:black\"> -0.155e-3 <strong>(-0.219)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> sigma </th>\n",
       "        <td style=\"background-color:rgb(250,181,181);color:black\"> 91.430e-3 <strong>(0.458)</strong> </td>\n",
       "        <td style=\"background-color:rgb(191,191,250);color:black\"> -91.418e-3 <strong>(-0.453)</strong> </td>\n",
       "        <td style=\"background-color:rgb(239,239,250);color:black\"> -0.002e-3 <strong>(-0.086)</strong> </td>\n",
       "        <td> 1.87e-05 </td>\n",
       "        <td style=\"background-color:rgb(236,236,250);color:black\"> -0.070e-3 <strong>(-0.104)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> slope </th>\n",
       "        <td style=\"background-color:rgb(231,231,250);color:black\"> -1.037 <strong>(-0.144)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,229,229);color:black\"> 1.037 <strong>(0.142)</strong> </td>\n",
       "        <td style=\"background-color:rgb(222,222,250);color:black\"> -0.155e-3 <strong>(-0.219)</strong> </td>\n",
       "        <td style=\"background-color:rgb(236,236,250);color:black\"> -0.070e-3 <strong>(-0.104)</strong> </td>\n",
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       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = -2.696e+04                 │              Nfcn = 142              │\n",
       "│ EDM = 1.99e-05 (Goal: 0.0002)    │                                      │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│          Valid Minimum           │   Below EDM threshold (goal x 10)    │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│      No parameters at limit      │           Below call limit           │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│             Hesse ok             │         Covariance accurate          │\n",
       "└──────────────────────────────────┴──────────────────────────────────────┘\n",
       "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
       "│   │ Name  │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
       "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
       "│ 0 │ s     │    960    │    50     │            │            │    0    │  2000   │       │\n",
       "│ 1 │ b     │  1.02e3   │  0.05e3   │            │            │    0    │  2000   │       │\n",
       "│ 2 │ mu    │   0.494   │   0.005   │            │            │    0    │    1    │       │\n",
       "│ 3 │ sigma │   0.097   │   0.004   │            │            │    0    │    1    │       │\n",
       "│ 4 │ slope │   1.14    │   0.16    │            │            │    0    │   50    │       │\n",
       "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌───────┬────────────────────────────────────────────────────────┐\n",
       "│       │          s          b         mu      sigma      slope │\n",
       "├───────┼────────────────────────────────────────────────────────┤\n",
       "│     s │   2.13e+03     -1.2e3 -12.228e-3  91.430e-3     -1.037 │\n",
       "│     b │     -1.2e3   2.18e+03  12.225e-3 -91.418e-3      1.037 │\n",
       "│    mu │ -12.228e-3  12.225e-3   2.06e-05  -0.002e-3  -0.155e-3 │\n",
       "│ sigma │  91.430e-3 -91.418e-3  -0.002e-3   1.87e-05  -0.070e-3 │\n",
       "│ slope │     -1.037      1.037  -0.155e-3  -0.070e-3     0.0244 │\n",
       "└───────┴────────────────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# m-model for an extended maximum-likelihood fit, must return...\n",
    "# - integral as first argument\n",
    "# - density as second argument\n",
    "# see iminuit documentation for more information\n",
    "def m_model(x, s, b, mu, sigma, slope):\n",
    "    return (s + b, m_density(x, s, b, mu, sigma, slope))\n",
    "\n",
    "\n",
    "mi = Minuit(\n",
    "    ExtendedUnbinnedNLL(toy[0], m_model),\n",
    "    s=true_yield.s,\n",
    "    b=true_yield.b,\n",
    "    mu=m_truth.mu,\n",
    "    sigma=m_truth.sigma,\n",
    "    slope=m_truth.slope,\n",
    ")\n",
    "mi.limits[\"s\", \"b\"] = (0, true_yield.s + true_yield.b)\n",
    "mi.limits[\"mu\"] = mrange\n",
    "mi.limits[\"sigma\"] = (0, mrange[1] - mrange[0])\n",
    "mi.limits[\"slope\"] = (0, 50)\n",
    "\n",
    "mi.migrad()\n",
    "mi.hesse()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6261600a",
   "metadata": {},
   "source": [
    "### Compute sWeights\n",
    "\n",
    "We first create an estimated pdfs of the signal and background component, respectively. Then we create the sWeighter object from the m-distribution, these pdfs, and the fitted yields.\n",
    "\n",
    "Note that this will run much quicker if `verbose=False` and `checks=False`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b09cc060",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialising sweight with the summation method:\n",
      "    PDF normalisations:\n",
      "\t 0 1.0000000000000002\n",
      "\t 1 1.0000000000000002\n",
      "    W-matrix:\n",
      "\t[[0.00072638 0.00029386]\n",
      "\t [0.00029386 0.00070361]]\n",
      "    A-matrix:\n",
      "\t[[1656.59790131 -691.86886983]\n",
      "\t [-691.86886983 1710.19865477]]\n",
      "    Integral of w*pdf matrix (should be close to the\n",
      "                identity):\n",
      "\t[[9.99871623e-01 2.30160489e-05]\n",
      "\t [9.35941868e-05 1.00003883e+00]]\n",
      "    Check of weight sums (should match yields):\n",
      "\tComponent  | sWeightSum |   Yield    |   Diff    |\n",
      "\t---------------------------------------------------\n",
      "\t  0        |   964.8295 |   964.8295 |     0.00% |\n",
      "\t  1        |  1018.1999 |  1018.1999 |     0.00% |\n"
     ]
    }
   ],
   "source": [
    "def spdf(m):\n",
    "    return m_density(m, 1, 0, *mi.values[2:])\n",
    "\n",
    "\n",
    "def bpdf(m):\n",
    "    return m_density(m, 0, 1, *mi.values[2:])\n",
    "\n",
    "\n",
    "sweight = SWeight(\n",
    "    toy[0],\n",
    "    [spdf, bpdf],\n",
    "    [mi.values[\"s\"], mi.values[\"b\"]],\n",
    "    [mrange],\n",
    "    method=\"summation\",\n",
    "    compnames=(\"sig\", \"bkg\"),\n",
    "    verbose=True,\n",
    "    checks=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db8c3132",
   "metadata": {},
   "source": [
    "### Construct the COW\n",
    "\n",
    "COW pdfs are weighted by a numerator, which we call the variance function $I(m)$. This function is arbitrary, but there is an optimal choice, which minimizes the variance of the weights. In case of factorizing signal and background PDFs, the optimal choice is $I(m) \\propto g(m)$, where $g(m)$ is the estimated total density of in the discriminant variable m. A solution close to optimal is to replace $g(m)$ with a histogram, with the advantage that $g(m)$ does not have to be explicitly constructed.\n",
    "\n",
    "But simply using $I(m) \\propto 1$ gives almost equivalent results in this case. To use a constant density for $I(m)$, we pass `Im=None` to the `Cow` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "57f2f1f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialising COW:\n",
      "    W-matrix:\n",
      "\t[[2.91611955 0.97740504]\n",
      "\t  [0.97740504 1.06300333]]\n",
      "    A-matrix:\n",
      "\t[[ 0.4956826  -0.45576779]\n",
      "\t  [-0.45576779  1.35979793]]\n"
     ]
    }
   ],
   "source": [
    "# unity:\n",
    "Im = None\n",
    "\n",
    "# sweight equivalent:\n",
    "# def Im(m):\n",
    "#     return m_density(m, *mi.values) / (mi.values['s'] + mi.values['b'] )\n",
    "\n",
    "# histogram:\n",
    "# Im = np.histogram(toy[0], range=mrange)\n",
    "\n",
    "# make the cow\n",
    "cow = Cow(mrange, spdf, bpdf, Im, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85b6c197",
   "metadata": {},
   "source": [
    "## Comparison of the sweight and COW methods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bbb12e0",
   "metadata": {},
   "source": [
    "We first compare the weight distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d8eff72",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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/0EREvNHv98PSj6DT/8HQf/rIRSbCvdvKpwgRr6JejOL9Lnwdbl4IHY8YabtjHrzZ2dlZWETEW5QUObuelOp4FQRHOYu5I6mo81oq7MQ3xLetOBx96YfOOfb2/21eJhGR6rT5D3i7Kyx+v3xb/a4wdgMMeMC8XOJSKuzEN13yPzj3JTjz0fJthdkVJ68UEfEk2cnOueRWfuucZqqUf5B5mcTl1MdOfJN/MPS8qeK26Y/B2onOuZqOXCpNRMQdGQYUZjkn+wXn0lr2YucEu5ovzmfpnRcBKC5wrjFYkOFWM4iLiBxTXjqMHw5fDitfgtFqdU7irnW5fZpa7ETAeani+hmwfQ406V++3WEHq820WCIix5R/GHbOh+I852oPDXqanUjchFrsRErZ/KH5WeX389Lhf2fA1pnmZRIROZbopnD553DDLBV1UoEKO5HjWfQOpK6FqXc7pxAQETHT0o/h4Nby+00HHnsJL/FpuhQrcjz973OukdjjRvALMDuNiPiypR/D1LEQ0QBGz4PgSLMTiZtSi53I8fgFwgXjIK51+bacA+blERHf1foiiGoKXa8uHwUrcgwq7EROVeo6eKcHzH3Z7CQi4mtqxTpb6s6417l8oshxqLATOVU75zsvzW74CYrzzU4jIt5u/jjn8oelNI2JnAL1sRM5VT1vcl4CaXmec4JjEZGasmEKzHgC/IJhzNKj13oVOQ4VdiKV0fG/Fe/bi53TpIiIVKemZ0KrCyC2pYo6qRRdihWpqrUTnX3uspLNTiIi3iYgBC7/AgY+YnYS8TAq7ESqwl4Mf74M6dvhr3fMTiMi3qAoF7ZML79vtWnNV6k0/cSIVIXNH674Cs64DwY9YXYaEfF0hgFTxsLXlzm/NIpUkUcVdn/++ScXXnghdevWxWKxMHny5BPuP2fOHCwWy1G3lJQU1wQW7xbdFM58GGzqqioip8kwnFOaWGzQIMnsNOLBPKqwy83NpWPHjrzzTuUufW3atInk5OSyW1xcXA0lFJ9lGLDkQ8jcZ3YSEfFEViuc/YxzBGyjvmanEQ/mUU0N5557Lueee26lnxcXF0dkZGT1BxIpNeNxWPAGrP8Jrv7J2TdGRORkDKPihMPRTc3LIl7Bo1rsqqpTp07UqVOHs846iwULFpgdR7xRl5EQEg1tLgaLT/xaiUh1mPsS/HgD5B82O4l4CY9qsausOnXq8P7779OtWzcKCwv56KOPGDBgAIsXL6ZLly7HfE5hYSGFhYVl97OyslwVVzxZdFO4YzUE1jI7iYh4iqz9MO8VsBdBy3Oh3TCzE4kX8OrCrmXLlrRs2bLsfu/evdm2bRvjxo3jyy+/POZznn/+eZ588klXRRRvcmRR53A4L69oTUcROZ7wunDNb7DpV2h7idlpxEv43DWjHj16sHXr1uM+/uCDD5KZmVl227NnjwvTiVc4uAU+Ox9WfmN2EhFxd/W7waDH9CVQqo1Xt9gdy8qVK6lTp85xHw8MDCQwMNCFicTrbJwKuxfC4Z3Q4XItOSYiFeUccA6wCokyO4l4IY8q7HJyciq0tu3YsYOVK1cSFRVFgwYNePDBB9m3bx9ffPEFAK+//jqNGzembdu2FBQU8NFHHzFr1iymTZtm1ksQX5A0BnIPQI8bVdSJyNF+uw+2z4FL3ocWQ8xOI17Gowq7ZcuWMXDgwLL7Y8eOBWDkyJF89tlnJCcns3v37rLHi4qKuPvuu9m3bx8hISF06NCBGTNmVDiGSLWz+cGQZ81OISLuqDAbUtdBQQaEHf/qkUhVWQzDMMwO4c6ysrKIiIggMzOT8PBws+OIJ8rc5+wkrT40IgLOtaZ3L4LGZ5idRDxEZWoRnxs8IeJSc1+GNzs7+92JiICzi4aKOqkhKuxEalJJPtgLYfPvZicRETMV58OGX5wrTYjUII/qYyficfrcAYm9oPlZZicRETMt+QCmPwbtLoXLPjE7jXgxFXYiNSkoAlqcbXYKETGbYYB/KDQdZHYS8XIq7ERcpaQIDm6GhHZmJxERV+t7J3QaDsG1zU4iXk597ERc4dA2eKc7fDkUivLMTiMiZqgV65wOSaQGqbATcYXIBmA4nP9/cJO5WUTEddb/7PxiJ+Ii+uog4go2f7hyPNRuDAEhZqcREVcoyISfboWiXLhhJtTtbHYi8QEq7ERcJb6t2QlExJUKMqFBEmTshoSOZqcRH6HCTsQM+1dAfDutJSvizSIbwPDvoTAHrOr5JK6hnzQRV5t4I3wwANb+aHYSEXGFwFpmJxAfosJOxNViWoDFBunbzU4iIjXBXgLLPnG21Im4mC7Firha9+uhwxUQmWh2EhGpCRt+gil3waJ3YcxSsFjMTiQ+RIWdiKsFRzpvIuKdbAEQ1dS5fJiKOnExFXYiZspLB3sxhMWbnUREqkvrC6Hl+WAvMjuJ+CD1sRMxy7JP4bU2MOc5s5OISHWzWsE/yOwU4oNU2ImYJaY5lORD6jpwOMxOIyKnKzsVts0CwzA7ifgwFXYiZmnYB26YBddN1xxXIt5g6Yfw5SUw+Wazk4gPUx87EbNYLFCvq9kpRKQ6BdSCFkPMTiE+TIWdiDtw2J3LD4VEmZ1ERKrqzEegz53gp751Yh5d/xEx27ZZ8GYn+Pk2s5OIyOkKrAU2tZmIefTTJ2K2sDrORcKL8qAoFwJCzU4kIpWRnQr2QufasCImU4udiNniWsOV38Fda1XUiXiiRW/B6x1gzotmJxFRi52IW2h5jtkJRKSqMvcBBtTpYHYSERV2Im7HYQerzewUInKq/vOpc+BEZEOzk4joUqyI29i3HD6/CCbdZHYSEams6KYaNCFuQT+FIu7CYoUdc51TJRTmOEfXiYj7KsoFqx/4BZqdRKSMWuxE3EWdTnDuS3DrEhV1Ip5gyYfwSgv46z2zk4iUUYudiLuwWKCnLsOKeIzts6EgA/yDzU4iUkaFnYiISFX830Rn9wktDShuRIWdiLtJXQ9LP3JOdtr3TrPTiMjxWG3Q9EyzU4hUoD52Iu7mwEZY9jEs/RgcDrPTiMi/GYbZCUSOS4WdiLtpeS50HgEXvm52EhE5lo1T4P2+sOJrs5OIHEWXYkXcjX8wXPy22SmkCg7nFrH9YA47D+axPyMfP5uVmwc0LXv8hd82ciC7kLAgP2LDAqkbGURi7RBaJoQRFuRvYnKplNXfQcoaZ+u6iJtRYScichq++msXi7YdYtXeDPYezq/wWEJ4UIXCbtH2Q6zak3HM4zSMDmHaXWcQ6KdVR9zehW9C4/7QZIDZSUSOosJOxF1lp8LaH6FOR2jUx+w0AhiGwe70PBpGh5Zt+2nlPpbuPFx2v15kMA2jQ0isHUJ8RFCF548+owk7D+WRXVBMalYh+zPy2Xkol+TMAsKC/CoUdQ9PWkNkiD8XdaxHy4Swmn9xcupCoqDHDWanEDkmFXYi7mrhm7DobWgzVIWdybILipm8Yh9fL97N1rQclj0ymMiQAACG92zIgJZxdE6MpF39CMJPcEn13PZ1jrk9I6+IjLzisvuZecV8t3QPJQ6Dd2Zvo2P9CC7vnsiwzvUJDlCLnogcnwo7EXfV4XLYsxiaDjQ7ic86mFPIx/N38OWiXeQUlgAQ6Gdl7b4s+jaPAWBo53qnfZ7IkICyQhEg0N/Kq5d35Nc1yczckMaqvZms2pvJK39sYkRSI0b1bkRUaMAJjig1IicN/ngI2g5zDnKyWMxOJHIUi2Fo3PaJZGVlERERQWZmJuHh4WbHEREXyMwv5t3ZW/l80U4Kip1TzjSNDeX/ejVkWJf6RAS7bqDDoZxCJq3Yx+eLdrIn3dmH794hLbl1YDOXZZB/LPkQfr0H6naBG2ebnUZ8SGVqEbXYiYj8S0ZeEZ8s2EGx3aBj/QhuO7M5g1rHYTGhhSa6ViDX92vCqN6N+H1dCt8t3cOo3o3KHj+UU0jtkACsVrUe1biGfaDXrRDX2uwkIselFruTUIudmM5eXL5sUXBts9N4rbSsAuLCywc7fDRvO41jQjmzlTkF3amwOwyGvbsAf5uVZy5pR6sE/Y0S8UaVqUU0QbGIu/tiKHx1KWyYYnYSr1RU4uCl3zfS98XZrN6bUbb9+n5NGNQ63m2LOoDNqdlsScth2a7DXPjWfN6dsxW7Q9/VRXyZCjsRd9d0AITGQkmB2Um8zvYDOVz2/kLenbONIruDGetTzY5UKa3rhDPz7v4Mbh1Hsd3gpd83cfn/FrH3cJ7Z0bzPsk/hwGazU4iclC7FnoQuxYrpivLAL9C54LhUmymr93PfhNXkFdmJCPbnhWHtjzsdibszDIMJy/fy1C/ryS4sITLEn3GXd2Jgqzizo3mHw7vgjQ5gscI9WyA0xuxE4mM0eELEmwSEmJ3Aq5TYHbz8xyb+9+d2AJKaRPPaFR2pExFscrKqs1gs/KdbIr2aRDPmm79ZtTeTl//YxBktYrFpUMXpK8qF5kPAUaKiTtyeWuxOQi124lZyD+qD5TR9v3QP9/24GoCb+jfh3rNb4mfznl4phSV2XvljE1f1bEjjmNCTP0FOncMBVu/5WRHPoRY7EW9TkAlfXAyp65yXgoIjzU7ksS7rWp9lu9Lp3yKO8zt45qXXEwn0s/Hw+W0qbPtl1X76NIvRpManS0WdeAD9lIp4gqAIKM53Xgrau8zsNB5nX0Y+xXbnRMNWq4WXLuvolUXdsczamMrt41dwxf8WkZKpATiVlr7deSlWxEOosBPxFEPfg7s3Q/PBZifxKFvTsrnknQXcOX4lJf8Ud76kQVQI8WFBbElzjgDedUhFSqX8NAZeagIbfzU7icgpUWEn4inqdYFasWan8CgbU7K44n9/kZZdyNa0nLL1Xn1Js7gwfhidRMPoEPYezue/H/zFnnRNh3JKSgohO8U51VBCO7PTiJwSFXYi4pW2H8jh/z5azKHcItrWDefbG3sRGeKbfcwSo0L4YXQSTWNDSc4s4MoP/2JfRr7ZsdyfXyDcthzGLIPIBmanETklKuxEPEnqOvjxBvjlDrOTuLV9Gfn830eLOZhTRJs64XxzfS+fHzgQFxbENzf0otE/LXdXffgXh3OLzI7l/iwWiGludgqRU6bCTsSTFBfAmu9hzQTnZSI5SnpuESM+Wsz+zAKaxIbyxXU9iAjxNzuWW4gPdxZ39WsHM6hVPBHB+nc5LocDNBuYeCBNdyLiSep2hv73Q5OBYNWH8rFsSM5iX0Y+9SKD+eq6nsTUCjQ7klupGxnM1Nv7qag7mXUTYc7z0P0G6DXa7DQip0yFnYgnsVph4ENmp3BrfZrF8O2NvQgL9KNupOeuJlGTjizqikoc/LommYs71cVi0SoVZTZOhUNbISfF7CQilaLCTkS8QkGxnSB/53q6XRrUNjmNZ7A7DEZ+soRF2w+RkVfEqD6NzY7kPi58A1pfAAkdzE4iUinqYyfiidI2wp8vOwdTCH+sS+HMV+awck+G2VE8is1q4cxWcQA8PXUDC7YeNDmRGwkKh3aXauCEeBwVdiKeaO4LMOsZ5yAKH7c1LYe7v1/F/swCpqzab3Ycj3N9v8YM61wPu8Pg1m/+1hx3Ih5OhZ2IJ2ozFJoPcU5a7MNyC0u46ctl5BSW0LNxFPef28rsSB7HYrHw3LD2dKwfQUZeMWO+XUFRie+t0FHG4YBJN8OKr5yj0EU8jAo7EU/UdigM/x5aX2h2ElM9+tNath3IJSE8iLev6oK/TX/SqiLI38Y7w7sQHuTHqj0ZvPDbRrMjmSd5Baz6Bn57ACz6eRLPo59aEfFIk1bsZeLf+7Ba4M0rOxMbpmlNTkf92iG8enknAL5ftoe0LB9traoVDwMehB43gJ9vT2otnkmjYkU8WWEO7FsOTfqbncSldh7M5ZFJawG4fVBzejSOMjmRdzirTTyPX9iG/i1iiQsPMjuOOSLqw4AHzE4hUmUq7EQ8VV46vNbauQLFvVshNMbsRC4TGeJPn2YxZOQXc9uZGrVYna7RlCciHk2FnYinColyTsVQmA2Hd/lYYRfA/0Z0JbfIjs2qSXVryl/bD5GSWcDQzvXMjuIae5cDBtTt4pwMXMQDqbAT8WQjf4GgSOdC5T4gp7CEWoHOP1sWi6Xs/6X6LdmRzpUf/kWgn5VOiZE0igk1O1LNm/sibPkDznoa+txudhqRKtFXEhFPFlzbZ4q6YruDK/63iNu+XUFGXpHZcbxet4a1SWoSTUGxgwcnrsEwDLMj1SzDcLaCB4ZDs0FmpxGpMhV2It7CXmx2ghr18fwdrNufxbwtByiy+/A8ay5itVp4YVgHgvytLNp+iO+W7jE7Us2yWOCS9+HebRDXxuw0IlWmwk7E062ZAG91hZlPmp2kxuxJz+P1GZsBePi81sSF+eiITRdrEB3C3We1BODZXzf4xhQofgE+0wou3smjCrs///yTCy+8kLp162KxWJg8efJJnzNnzhy6dOlCYGAgzZo147PPPqvxnCIuZbHAoa2wbbbZSWqEYRg8MnktBcUOkppEc1nX+mZH8inX9GlEh/oRZBeU8OQv682OUzMMA/IPm51CpFp4VGGXm5tLx44deeedd05p/x07dnD++eczcOBAVq5cyZ133sn111/PH3/8UcNJRVyo2WC4/Au45jezk9SIKauTmbv5AAE2K89e0g6LWlNcys9m5YVhHbBaYOqaZP7e7YUFUPJKeKkpfH25s8gT8WAeNaTs3HPP5dxzzz3l/d9//30aN27Mq6++CkDr1q2ZP38+48aNY8iQITUVs1KKioooKPCByxtSgyxQfyAUAUVZZoepVrmFJTz541IchUVcO6ApMYEOsrK86zV6gvq14KZedakfFUTTCKv3vQcb50JBCRQC2dlmpxEPFRoais1mMzuGZxV2lbVo0SIGDx5cYduQIUO48847j/ucwsJCCgsLy+7X9B+wL774ghtuuKFGzyHiDR58HR40O4R4uR/guh/MDiEeau3atbRt29bsGJ51KbayUlJSiI+Pr7AtPj6erKws8vPzj/mc559/noiIiLJbYmKiK6KKiIiInDavbrGrigcffJCxY8eW3c/KyqrR4u6aa65hxIgRNXZ88RGGAW93hYw9cPmX0PIcsxOJl5q9KZWHJ63ljOaxvHBpB7PjiLgNf39/syMAXl7YJSQkkJqaWmFbamoq4eHhBAcHH/M5gYGBBAYGuiIeADabzS2uyYsX6HMLFOZA/Q7gwp/hmrB812F2p+dyccd6WLVkmFuJiwzjUL7B5DVpXD+ggLZ1I8yOdHqm3gOGHXrd4lyiT8TDefWl2KSkJGbOnFlh2/Tp00lKSjIpkUgNSroVBtwPUZ69iLthGDw9ZT13fbeKt2dvNTuO/EuXBrU5v0MdDAOe+3WDZ69IUVIEq8bDsk+gKMfsNCLVwqMKu5ycHFauXMnKlSsB53QmK1euZPfu3YDzMurVV19dtv/o0aPZvn079913Hxs3buTdd9/l+++/56677jIjvoicgj/WpbJyTwbB/jb+20N9XN3RA+e0IsBmZcHWQ8zZdMDsOFVnscBlH0PSGEjoaHYakWrhUYXdsmXL6Ny5M507dwZg7NixdO7cmcceewyA5OTksiIPoHHjxkydOpXp06fTsWNHXn31VT766CO3mepEpNo57LBnCeyYZ3aSKimxO3j5j40AXNe3sVaYcFOJUSGM6tMIgBd/34jD4aGtdjZ/aDEEhjwLVo/6OBQ5Lovh0e3oNS8rK4uIiAgyMzMJDw83O47Iif39Bfx8G9TvDtfPMDtNpX23dDf3/7iG2iH+zL1vIOFB7tEZWY6WkVdEvxdnk11Ywhv/7cTFneqZHUnEa1WmFtFXFBFv0nQQBEVCZANn650HKSi2M276FgBuHdhMRZ2biwwJ4MYzmgCwdl+myWmqIDsVlnwI6TvMTiJSrbx6VKyIz4moB/dtB6vnjbT+fOFOUrIKqBsRxP/1amh2HDkF1/RtzICWcbSv74EjY7dMg1/vgXpd4YZZZqcRqTYq7ES8jQcWdQDt60XQrWFtLu+eSJC/Z74GX1Mr0M8zizqAkCho2BeaDjA7iUi1Uh+7k1AfO/FY+RkQGO5RncJL/xxZLJq7ztPsy8gnOSOfbo2izI4i4nXUx07ElxkGfHUpvNQEUlabnaZSLBaLijoPNH/LQfq/NJux36+ixO4wO46IT1NhJ+JtLBawBThn09+71Ow0J/X9sj28Nn0zGXlFZkeRKurasDYRwf7sTs/jl9X7zY5zchm7wV5sdgqRGqHCTsQbDXoc7lwDPW4wO8kJFZU4eH36Zt6cuYVfViebHUeqKDjAxrV9nSuevDt7m/vPazd+OLzYGHb8aXYSkWqnwk7EG8W1ck554uYmrdjL/swC4sIC+U/X+mbHkdMwIqkhYUF+bEnLYdr61JM/wSyFOZC1H4qyIbaV2WlEqp0KOxExRYndwbtztgFw4xlNNBLWw4UH+TOqdyMA3pm91X3XkA2sBfdsgZsXQa04s9OIVDsVdiLeKmUt/HInzHrG7CTHNGV1MrsO5REVGsBVPd2/dVFO7po+jQn2t7FmXyZ/bjlodpzjs1ohvo3ZKURqhAo7EW+VkwLLP4WV3zhHyroRwzB4f66zte66vo0JCdCUmt4gKjSA4T0bEBJgY9/hfLPjiPgk/TUV8VYN+0DP0c5lxgzDOVrWTSzadoiNKdkE+9v4v55aZcKb3DqwGWPObEZkSIDZUY62Zyn8di+0HQZ9bjc7jUiNUGEn4q38g+HcF81OcUx1I4P5b/dEagX6ERGiNWG9Se1QNyzoSm2dDvtXQKS+TIj3UmEnIi7XKCaUFy7tYHYMqUGGYbB812EaRIcQFxZkdhynbtc5izoPGDEuUlXqYyfi7Q5tg8UfOKd5EHGRhyev5bL3F/HFwl1mRykXFg+dh0PjfmYnEakxKuxEvJlhwJeXOPsV7Zxndhoy84q554dVrNmbaXYUqWH9msUA8PXiXRQU201OI+I7VNiJeDOLBVqdD436gV+g2Wn4duluJizfy70TVrnvPGdSLc5qE0+9yGAO5xUzecU+s+PA8s9g7UQoyDI7iUiNUmEn4u2GPAejpkDTM02NUWx38NmCnQBc27cxFjcapSvVz89mLZuw+NMFO80t5B0OmPk0TLgGUlabl0PEBVTYiXg7Nymgfl+bQkpWATG1ArioY12z44gLXN49kZAAG5tSs1m47ZB5QYrzoON/oV5XSOxpXg4RF1BhJ+IrSoogK9m003+92NmJ/qoeDbR8mI+ICPYvWwP4039aa00RWAuGPAs3zAKbptcR76bCTsQXbPgFXmoMP99myum3puXw1/Z0rBb4bw9NNeFLRiQ554zbkpatQRQiLqB57ER8QVRTKMqBA5vAYQera1vMvl2yG4AzW8VRNzLYpecWczWLC+PHm5PolFgbm9WEbgGF2ZC5D2Jbuk23BJGapBY7EV8Q1xpunAt3rHJ5UQdQv3YwdSOCGK7lw3xS14ZR5hR1AFumwbs94ath5pxfxMXUYifiCywWqNvJtNNf06cxVyc1Qu0lvq2oxEFGXhFx4S5ciSJrP9gCIb6t684pYiIVdiLiEqa12IhbmL0xjXsnrKJj/Ug+HtXddSfufZtzKbGSAtedU8REuhQr4isMA2Y/D//rD4dds8zT9gM5/LommWK7wyXnE/fVIDqEgzlFzN6Uxv6MfNeePCAEQqJce04Rk6iwE/EVFgvsmAvJK2HrDJec8rOFO7nl6795aOIal5xP3FfT2Fr0ahKFw4DxS/e45qRa3UR8kAo7EV/S+zYY+h60vqjGT5VfZGfS386lpC7uVK/Gzyfu76p/Bs98t3Q3Ja5oxf36MvjqMkhdV/PnEnETKuxEfEmr86HTVVArtsZP9fu6ZLILS2gQFULvptE1fj5xf0PaxhMVGkBqViGzNx2o2ZMVZML2ObB1OgSE1uy5RNyICjsRqRETlu8F4LKu9bFq4IQAgX42Lu3ibL2dsLyGL8cGhsNN8+CCcVC7Uc2eS8SNqLAT8TX5h2HNBFg7scZOsS8jv2xt0Es66zKslLv0nyXGZm1MIzOvuOZOZLFAfBvodm3NnUPEDWm6ExFfs3Um/HgdxLWFdjUzaeukv/diGJDUJJrEqJAaOYd4plYJ4TxwbivOaB5LRIjWbRWpbirsRHxN0zMhvp3zvzW0vNiGlGzAeRlW5N9G929asyc4uBX+/hxangsNe9fsuUTcjAo7EV8TEgU3L6jRU7xzVRfuGJRN/dpaF1ZMsOlXWPgmpG1QYSc+R4WdiNSIFvFhZkcQN7Z2XyYfz99Bw+gQ7hzconoPXrcTdLwSGvWr3uOKeAAVdiK+yjCcLRoxzcFWPX2dCortFJY4iAhW3yk5sb2H85i0Yh/x4YHcdmbz6l1yrvEZzpuID9KoWBFf9b9+8F4S7F1WbYf8Y10K3Z+dwbNT11fbMcU7DWwVR2SIP6lZhczbUsNz2on4ELXYifiqmJZwcAukb4eGSdVyyAnL91JU4iAkQH9a5MQC/Wxc3LEuny/axY9/72NAy7jqOfDe5RCZCLWq6Xgu4HA4KCoqMjuGmMjf3x+brXoGsumvr4ivGvIsXPw2+FfPAIe07AIWbD0IwLAumrtOTu7SrvX5fNEupq1LIaugmPCg07yEbxgw4RrI2AUjf/GIy7FFRUXs2LEDh8MFS6yJW4uMjCQhIQGL5fS6JaiwE/FVYQnVeripq5NxGNApMZKG0VrCSU6ufb0ImsaGsu1ALtPWpZ7+9DgFmRAcCdkpULdLtWSsSYZhkJycjM1mIzExEatVvaN8kWEY5OXlkZaWBkCdOnVO63gq7ESkWvy8aj8AF3Wsa3IS8RQWi4WLOtZj3IzN/Lxq/+kXdsGRcNOfkJ8BgbWqI2KNKikpIS8vj7p16xISoom8fVlwsPPKSVpaGnFxcad1WVZfD0R82Z4l8NWlMPnW0ztMeh4rdmdgtcAFHU7v26b4los61aV5XC16No6qvoMGR1bfsWqQ3W4HICAgwOQk4g5Ki/vi4tNbak8tdiK+zHDA1hkQXBscb1Z5FYrS1rpeTaKJCw+qzoTi5RrHhDJ9bP/TP5DDDljAAy9nnm6fKvEO1fVzoMJOxJfV6wZnPwvNBoGl6h+Iw7rUw99moXGM+1/+Ei+1bTZMvhk6XQlnPWV2GhHTeN5XGxGpPjY/6D0G4lrDaXxbrBMRzI1nNOWsNvHVGE58SX6RnSmr95OWVVC1A2yfDblpzv51YppRo0YxdOhQl5/3iSeeoFOnTi4/rztSi52IiJjuxi+XMW/LQR4+rzU3nNGk8gcY9Di0GALB1dhXTyrtjTfewDAMs2P4NLXYiQhsnwvTHoHcQ5V6mmEY3PvDKn5cvpeCYnsNhRNfcPY/rb2/rN5ftQP4BTjnrUtoV42ppLIiIiKIjIw0O4ZPU2EnIvDHQ7DwLdg2q1JPW7U3kx+W7+WRyWtx6Fu6nIZz29fBZrWwem8mOw7mmh1HTmLChAm0b9+e4OBgoqOjGTx4MLm5uUddis3Ozmb48OGEhoZSp04dxo0bx4ABA7jzzjvL9mnUqBHPPfcc1157LWFhYTRo0IAPPvigwvnuv/9+WrRoQUhICE2aNOHRRx897dGj3kqFnYhA+/9Ap+FQu1GlnvbLP6Nhz2oTr2XE5LTE1Aqkd9NoAKasqmSr3eznYMEbzomJvUBeUclxb/9uGa+OfSsrOTmZK6+8kmuvvZYNGzYwZ84chg0bdsxLsGPHjmXBggX8/PPPTJ8+nXnz5vH3338ftd+rr75Kt27dWLFiBbfccgs333wzmzZtKns8LCyMzz77jPXr1/PGG2/w4YcfMm7cuEpn9wX6Sywi0PfOSj/FMAx+W5MMaO46qR4XdazLvC0H+XnVfm4b1PzUnlRSCAvfhuJcaDKg2ldUMUObx/447mMDW8by6TU9yu53fXoG+cfpBtGzcRTf3VS+DnTfF2eTnnv0mrQ7Xzi/UvmSk5MpKSlh2LBhNGzYEID27dsftV92djaff/4533zzDYMGDQLg008/pW7doycxP++887jlllsAZ+vcuHHjmD17Ni1btgTgkUceKdu3UaNG3HPPPYwfP5777ruvUtl9QZVa7J566iny8vKO2p6fn89TT2mYuYgvWLU3k/2ZBYQG2DijRazZccQLnN02AX+bhS1pOWxNyzm1JzlKYPDj0O5SiD+6uJDq17FjRwYNGkT79u35z3/+w4cffsjhw4eP2m/79u0UFxfTo0d5IRoREVFWrB2pQ4cOZf9vsVhISEgoW2IL4LvvvqNPnz4kJCRQq1YtHnnkEXbv3l3Nr8w7VKnF7sknn2T06NFHLYGSl5fHk08+yWOPPVYt4UTExbJTIffAKXVAL22tO7N1PEH+VV/+RqRURLA/fZrFMGfTAf7afohmcacwL2JAKPS8yXnzEuufGnLcx6z/mpZo+aODT3nf+fcPPL1g/7DZbEyfPp2FCxcybdo03nrrLR5++GEWL15c5WP6+/tXuG+xWHA4HAAsWrSI4cOH8+STTzJkyBAiIiIYP348r7766mm9Dm9VpcLOMIxjzpC8atUqoqI01FzEI62dCBOugcRecN3xLwWB82/Ar2udhd257Tz/0pe4j/vPacWTF7WlYXSo2VFMU5n+qjW178lYLBb69OlDnz59eOyxx2jYsCGTJk2qsE+TJk3w9/dn6dKlNGjQAIDMzEw2b97MGWecccrnWrhwIQ0bNuThhx8u27Zr167qeSFeqFLvcu3atbFYLFgsFlq0aFGhuLPb7eTk5DB69OhqDykiLlC/G2ABww72EufkxceRlV9CnYhg0nOKGNBSl2Gl+rSuE37qO2enQvIqaNwP/INrLpRUsHjxYmbOnMnZZ59NXFwcixcv5sCBA7Ru3ZrVq1eX7RcWFsbIkSO59957iYqKIi4ujscffxyr1Vqp5bOaN2/O7t27GT9+PN27d2fq1KlHFZFSrlKF3euvv45hGFx77bU8+eSTRERElD0WEBBAo0aNSEpKOsERRMRtRTaAe7dCaMxJd40I8ef7m5LILijWaFipMce7OlRmw8/w6z3QuD+M/Nl1wXxceHg4f/75J6+//jpZWVk0bNiQV199lXPPPZfvvvuuwr6vvfYao0eP5oILLiA8PJz77ruPPXv2EBR06mtKX3TRRdx1112MGTOGwsJCzj//fB599FGeeOKJan5l3sFiVGGK6Llz59K7d++jrol7o6ysLCIiIsjMzCQ8vBLfJEVEpEq2H8jhuV83kl1QXGFU51GWfAjzX4ceN1RpZLfZCgoK2LFjB40bN65UoePJcnNzqVevHq+++irXXXed2XHcyol+HipTi1Tpq3b//v1xOBxs3ryZtLS0sg6OpSpz7VxE3JDDDljAevTA+dLpEqJCA1wcSnxFrSA/Zm5MxTBgf0Y+dSOPc5m1xw3Q/Xqwa6Jad7VixQo2btxIjx49yMzMLJs54+KLLzY5mfeqUmH3119/cdVVV7Fr166jJiS0WCzY7VpaSMRj/fYArPkBrvgKGh7dWvL5wp28NWsLtw5sxt1nHz1tgcjpigsLolvD2izdeZjf16Zwbd/Gx9/ZYnEuJyZu65VXXmHTpk0EBATQtWtX5s2bR0zMybt8SNVUqbAbPXo03bp1Y+rUqdSpU6dSnSBFxM3lHXTets44ZmH3+9oUHAY08uFRi1LzzmlX58SFXUEmBEUcvV3cSufOnVm+fLnZMXxKlQq7LVu2MGHCBJo1a1bdeUTEbEljoMtIaNDrqIe2HchhU2o2/jYLg1vHmxBOfMU57RJ4esp6lu5KJy27gLiwI/ocGQa8mwSB4c6W5Rh9FomUqtLKEz179mTr1q3VnUVE3EHdTs7pI2xHD476fa1zLc7eTWOICPH+wVNinnqRwXSsH4FhwB/rUis+mL7duS7s4Z0QUc+UfCLu6pRb7I6cm+a2227j7rvvJiUlhfbt2x81OvbIpUFExHtMX+/8gD27rVrrpOad064Oq/ZmMm1dCiN6NSx/ILop3LcNUtdp/jqRfznlwq5Tp05YLJYKgyWuvfbasv8vfUyDJ0S8QHYqrPoG8jPgrCcBSMsqYOWeDABdhhWXOLttPLM3pnFmq7ijHwyuDY36uj6UiJs75cJux44dNZlDRNxJ3iGY8QT4BUH/+yEghOkbnK11nRIjiQ/3jTm3xFxNY2vx/WhNei9SGadc2DVs2PDkO4mId4hrDR2vhHpdwXDOUzmkbQI2i4XwYPWtExOt/8m5rnGnq6DFELPTiLidKo2K/fnnYy/dYrFYCAoKolmzZjRufIJ5h0TEvVkscMn7FTbF1Arkvz0amBRIfFl6bhGzN6ZxYce6BKz/CdZPdvazU2FnigEDBtCpUydef/31Gjn+qFGjyMjIYPLkyTVyfDPs3LmTxo0bs2LFCjp16lSj56pSYTd06NCj+ttBxX52ffv2ZfLkydSuXbtagoqIiO8xDINz3/iT1KxC4sOD6Js0BqKaQOuLzI4m4paqNN3J9OnT6d69O9OnTyczM5PMzEymT59Oz549mTJlCn/++SeHDh3innvuqe68vPPOOzRq1IigoCB69uzJkiVLjrvvZ599hsViqXDzlfX4RKpFUR5s+p0vf/uTT+bv4EB2odmJxMdYLBYGtHAOnpi+PgXqdYEzH4E6mn1BTl1RUZHZEVymSoXdHXfcwWuvvcagQYMICwsjLCyMQYMG8fLLL3PvvffSp08fXn/9daZPn16tYb/77jvGjh3L448/zt9//03Hjh0ZMmQIaWlpx31OeHg4ycnJZbddu3ZVayYRrzbxBvj2Cg4v/pqnpqxn56FcsxOJDzqrjXMU9vT1qUddKRJzlJSUMGbMGCIiIoiJieHRRx8te2++/PJLunXrRlhYGAkJCVx11VVHfU6vW7eOCy64gPDwcMLCwujXrx/btm075rmWLl1KbGwsL774Ytm2Z555hri4OMLCwrj++ut54IEHKlziHDVqFEOHDuXZZ5+lbt26tGzpXP5wzZo1nHnmmQQHBxMdHc2NN95ITk5O2fMGDBjAnXfeWeH8Q4cOZdSoUWX3GzVqxHPPPce1115LWFgYDRo04IMPPqjwnCVLltC5c2eCgoLo1q0bK1asOOV/29NVpcJu27ZthIeHH7U9PDyc7du3A9C8eXMOHjx4eun+5bXXXuOGG27gmmuuoU2bNrz//vuEhITwySefHPc5FouFhISEslt8vKZpEDllzQZREFqXzGI/okMD6NJAXSvE9fo2jyHY38a5ORPZtmq+c+UJb1aU67wd+TpLipzbSgqPva/DUb7NXuzcVlxwavtWweeff46fnx9LlizhjTfe4LXXXuOjjz4CoLi4mKeffppVq1YxefJkdu7cWaEw2rdvH2eccQaBgYHMmjWL5cuXc+2111JSUnLUeWbNmsVZZ53Fs88+y/333w/A119/zbPPPsuLL77I8uXLadCgAe+9995Rz505cyabNm1i+vTpTJkyhdzcXIYMGULt2rVZunQpP/zwAzNmzGDMmDGVfv2vvvpqWcF2yy23cPPNN7Np0yYAcnJyuOCCC2jTpg3Lly/niSeeqJErmMdlVEGfPn2Mc845x0hLSyvblpaWZpxzzjlGv379DMMwjOnTpxstWrSoyuGPqbCw0LDZbMakSZMqbL/66quNiy666JjP+fTTTw2bzWY0aNDAqF+/vnHRRRcZa9euPeF5CgoKjMzMzLLbnj17DMDIzMysrpci4jlKio3HJ68xGt4/xbj3h5VmpxEf9vCHEw3j8XCj5InahpF32Ow41SI/P99Yv369kZ+fX/GBx8Odt5wD5dvmvuTc9tOYivs+k+Dcnr6zfNvCd5zbJlxXcd8XGzu3p64v37bs00rn7t+/v9G6dWvD4XCUbbv//vuN1q1bH3P/pUuXGoCRnZ1tGIZhPPjgg0bjxo2NoqKiY+4/cuRI4+KLLzYmTpxo1KpVyxg/fnyFx3v27GnceuutFbb16dPH6NixY4VjxMfHG4WFhWXbPvjgA6N27dpGTk5O2bapU6caVqvVSElJKXttd9xxR4VjX3zxxcbIkSPL7jds2ND4v//7v7L7DofDiIuLM9577z3DMAzjf//7nxEdHV3hfX3vvfcMwFixYsUxX7NhnODnwTCMzMzMU65FqtRi9/HHH7Njxw7q169Ps2bNaNasGfXr12fnzp1lFXtOTg6PPPJItRWgBw8exG63H9XiFh8fT0pKyjGf07JlSz755BN++uknvvrqKxwOB71792bv3r3HPc/zzz9PRERE2S0xMbHaXoOIpzGsNqZvcF5COatNgslpxJf1axrJb/buLPbrDsGRZsfxeb169cJisZTdT0pKYsuWLdjtdpYvX86FF15IgwYNCAsLo3///gDs3r0bgJUrV9KvX7+jVq060uLFi/nPf/7Dl19+yRVXXFHhsU2bNtGjR48K2/59H6B9+/YEBASU3d+wYQMdO3YkNDS0bFufPn1wOBxlrW2n6sgVtkqvDJZebt6wYQMdOnSo0Kc/Kcl18zFWaVRsy5YtWb9+PdOmTWPz5s1l28466yysVmetOHTo0GoLWVVJSUkV/jF79+5N69at+d///sfTTz99zOc8+OCDjB07tux+VlaWijvxWRuSs9mXkU+Qv5W+CVpRRszTvUcfuk27C3IM/souIC7MiwfCPbTf+V//kPJtve+AXreA9V8f2/f+s2673xFLq/W4AbqOBIut4r53rjl6307DqyfzPwoKChgyZAhDhgzh66+/JjY2lt27dzNkyJCyAQzBwSdfBq5p06ZER0fzySefcP7555+wCDyeIwu4U2W1Wo/qx1lcfPTl6n/nsVgsOI68xG2iKrXYgfPFn3POOdx+++3cfvvtDBkypKyoqwkxMTHYbDZSUysuBp2amkpCwqm1JPj7+9O5c2e2bt163H0CAwMJDw+vcBPxVbM3pRFFFjOD7if43c7O/jkiJogKDeB/I7qx+KGzvLuoAwgIdd6OaBHDL8C5zS/w2Pse+flr83du8w86tX2rYPHixRXu//XXXzRv3pyNGzdy6NAhXnjhBfr160erVq2OGjjRoUMH5s2bd8yCqVRMTAyzZs1i69atXH755RX2bdmyJUuXLq2w/7/vH0vr1q1ZtWoVubnlf8cWLFiA1WotG1wRGxtLcnJy2eN2u521a9ee9Nj/Ps/q1aspKCjv4/jXX39V6hin45QrsTfffLMs5JtvvnnCW00ICAiga9euzJw5s2ybw+Fg5syZp9zEabfbWbNmDXXq1KmRjCLe5nBuETm2CCL8isFRAsmrzI4kvujwTijI5Kw28cSGBZ50d6l5u3fvZuzYsWzatIlvv/2Wt956izvuuIMGDRoQEBDAW2+9xfbt2/n555+PukI2ZswYsrKy+O9//8uyZcvYsmULX3755VGXQ+Pi4pg1axYbN27kyiuvLBtccdttt/Hxxx/z+eefs2XLFp555hlWr15d4dLwsQwfPpygoCBGjhzJ2rVrmT17NrfddhsjRowo6+Z15plnMnXqVKZOncrGjRu5+eabycjIqNS/zVVXXYXFYuGGG25g/fr1/Prrr7zyyiuVOsbpOOVLsePGjSv7Rxk3btxx97NYLNx+++3VEu7fxo4dy8iRI+nWrRs9evTg9ddfJzc3l2uuuQaAq6++mnr16vH8888D8NRTT9GrVy+aNWtGRkYGL7/8Mrt27eL666+vkXwi3uaRC9pw11ktsKZ8BXFN1bdJzPH7Q7BlGlz0FnS60uw0gvPzNj8/nx49emCz2bjjjju48cYbsVgsfPbZZzz00EO8+eabdOnShVdeeYWLLiqfUDo6OppZs2Zx77330r9/f2w2G506daJPnz5HnSchIYFZs2YxYMAAhg8fzjfffMPw4cPZvn0799xzDwUFBVx++eWMGjXqhPPaAoSEhPDHH39wxx130L17d0JCQrj00kt57bXXyva59tprWbVqFVdffTV+fn7cddddDBw4sFL/NrVq1eKXX35h9OjRdO7cmTZt2vDiiy9y6aWXVuo4VWUx/n0x2c29/fbbvPzyy6SkpNCpUyfefPNNevbsCTjnn2nUqBGfffYZAHfddRcTJ04kJSWF2rVr07VrV5555hk6d+58yufLysoiIiKCzMxMXZYVEXE1w4APB8L+FXDTPCbsj2Li33u5oV8TBraKMzvdaSkoKGDHjh00btxYk+efprPOOouEhAS+/PJLs6NU2Yl+HipTi5xWYVdUVMSOHTto2rQpfn5VGofh9lTYia/KLigmLKhq/W9Eqt2hbRDVhMd+XscXi3ZxZY8GPD+svdmpTosKu6rJy8vj/fffZ8iQIdhsNr799lueeuoppk+fzuDBg82OV2XVVdhVabRDXl4e1113HSEhIbRt27ZsCPNtt93GCy+8UJVDiogbKbY76PvibM5/cx4pmf90AN79F3w3AmY9Y2448U3RTcFiKWulm7MpTatQ+CiLxcKvv/7KGWecQdeuXfnll1/48ccfPbqoq05VKuwefPBBVq1axZw5cypUlYMHD+a7776rtnAiYo7luw6TmV9McmZBeWf1nFTY8DOs+cH7Z/4X92AYR/2sJTWJJtjfRnJmARuSs00KJmYKDg5mxowZHDp0iNzcXP7++2+GDRtmdiy3UaXCbvLkybz99tv07du3wiiUtm3bHnetNxHxHLM2OqcnGNAiFpv1n9/xpoPgjHvhsk9NTCY+Zc9ieL1DhVbiIH8bfZpFAzBrY+rxninis6pU2B04cIC4uKM7rebm5p50uLGIuL/Swu7M1kf8ngfWgjMfgXpdKs6vJVJTNv0Kmbud050cofRybOnPqYiUq1Jh161bN6ZOnVp2v7SY++ijj1y6bIaIVL/dh/LYmpaDzWqhX/NYs+OILxvwIPz3G+h5c4XNA1s6C7sVezJIzy0yI5mI26rSUNbnnnuOc889l/Xr11NSUsIbb7zB+vXrWbhwIXPnzq3ujCLiQqWXt7o3qk1E8DFGxSavhvWToesoiGzg0mziY/yDodX5R22uGxlMp8RIIkP8ycgrIio04BhPFvFNVWqx69u3L6tWraKkpIT27dszbdo04uLiWLRoEV27dq3ujCLiQjNLL8Meb46wPx6Cea/C+p9cmEqkook39+aza3rQJLaW2VFE3EqVWuyuvvpqBg4cyAMPPEDTpk2rO5OImOi/3RsQFRrAoNbxx96hw+XOFSji27o0l/gQw4CfxkD9rtD+cmf/zn+xWtXPU+RYqlTYBQQE8Pzzz3P99ddTt25d+vfvz4ABA+jfvz/Nmzev7owi4kLnd6jD+R1OsJ5yl6udN5GakrYBVn4Fa76HdpedcNf9GflYLRYSIjTBrwhU8VLsRx99xObNm9m9ezcvvfQStWrV4tVXX6VVq1bUr1+/ujOKiIgvCY2BwU9Az9EQdPxZ9l/8fSO9X5jFpwt2uC6biJurUmFXqnbt2kRHR1O7dm0iIyPx8/MjNlaj6EQ8kcNh8NG87WxIzjq1Gf3zM2DztBrPJT6oVhz0vQvOfvqEu7Wp4yz6NO2JSLkqFXYPPfQQvXv3Jjo6mgceeICCggIeeOABUlJSWLFiRXVnFBEXWJ+cxTNTN3DpewspsjtOvHNeOrzSHL65HLI1SayY44x/JtDekpbDnvQ8s+OcNsMwyM3NNeVW2eXZJkyYQPv27QkODiY6OprBgweTm5vLgAEDuPPOOyvsO3ToUEaNGlV2v1GjRjzzzDNcffXV1KpVi4YNG/Lzzz9z4MABLr74YmrVqkWHDh1YtmxZNfyr+p4q9bF74YUXiI2N5fHHH2fYsGG0aNGiunOJiIvN3XwAgN5Nown0s51455AoSOgARbmQtRfCjjPQQqSyts0Cqz807A3WE/8cRgT7061hbRbvSGf2pjSuTmrkmow1JC8vj1q1zBnlm5OTQ2ho6Cntm5yczJVXXslLL73EJZdcQnZ2NvPmzatUcThu3Diee+45Hn30UcaNG8eIESPo3bs31157LS+//DL3338/V199NevWrdPCB5VUpcJuxYoVzJ07lzlz5vDqq68SEBBQNoBiwIABKvREPNDcTc7Crn/L40xz8m8jJp2w/5NIlcx4EpJXwkVvndIgnQEt41i8I525mw54fGHnKZKTkykpKWHYsGE0bNgQgPbt21fqGOeddx433XQTAI899hjvvfce3bt35z//+Q8A999/P0lJSaSmppKQkFC9L8DLVamw69ixIx07duT2228HYNWqVYwbN45bb70Vh8OB3W6v1pAiUrMy84tZvvsw4Fwf9pSoqJPqZi+B+HaQuRdanHtKT+nfIpYXf9/Iwm2HKCyxn7y12Y2FhISQk5Nj2rlPVceOHRk0aBDt27dnyJAhnH322Vx22WXUrl37lI/RoUOHsv+Pj3e2+B9ZHJZuS0tLU2FXSVUq7AzDYMWKFcyZM4c5c+Ywf/58srKy6NChA/3796/ujCJSwxZuPYjdYdAkNpTEqFP/Aw+AwwH5hyE0umbCie+w+cHQd8BhP+ll2FKt64QRGxbIgexClu44TN/mMTUcsuZYLJZTvhxqJpvNxvTp01m4cCHTpk3jrbfe4uGHH2bx4sVYrdajLskWFxcfdQx///JVbUovtR5rm8Nxkv6+cpQqFXZRUVHk5OTQsWNH+vfvzw033EC/fv2IjIys5ngi4gql/ev6n2prXakd82DyzRDVGEb+UgPJxCedYlEHzgLgnrNbEBLgR/v6ETUYSo5ksVjo06cPffr04bHHHqNhw4ZMmjSJ2NhYkpOTy/az2+2sXbuWgQMHmpjWt1SpsPvqq6/o168f4eG6FCPiDZbsTAec/ZUqJTIRMvdAQRYUZkNgWA2kE5+QcwDsRRBRr9JPvaK71ix2pcWLFzNz5kzOPvts4uLiWLx4MQcOHKB169aEhoYyduxYpk6dStOmTXnttdfIyMgwO7JPqVJhd/75Ry/KLCKe67c7+rF0x2G6NTr1PjIA1G4EIyZDg17OBdtFqmrphzD3JehzB5z1pNlp5ATCw8P5888/ef3118nKyqJhw4a8+uqrnHvuuRQXF7Nq1Squvvpq/Pz8uOuuu9Ra52IWo7KT1/iYrKwsIiIiyMzMVAuliEhNmTQaVn0Ll3wAHa+o9NO3pGbzx7oUWtcJP/46x26moKCAHTt20LhxY4KCtCSarzvRz0NlapHTWnlCRKQCfU+UqrrkfbhzLbS+oEpP/21tCq9M28z3y/ZUczARz6LCTsSH5RSWcM7rf/L0lPUUlZzG6LOtM+Dzi2DB69WWTXxQZCIEVG1U6ICWzoE/C7YeovhkK6eIeDEVdiI+bOHWg2xMyWbGhlQC/E7jz0FWMuyYC6u+q75w4hscDigpOu3DtKsbQVRoADmFJfy963A1BBPxTCrsRHxYlac5+bfWF8KZj8KV31ZDKvEpe5c41x3+4+HTOozVauGMf+awm/PPz7WIL1JhJ+KjDMMoK+xKL2NVWXAknHGPcz47kcrYOBUKMiDv0GkfqnS6ntLl8TyFxjAKVN/PQZWmOxERz7ftQC57D+cTYLPSq4lWjRCTDH4Cmp8FwVGnfah+zWOwWGB9chZpWQXEhbv3SFObzTkRc1FREcHBmi7I1+Xl5QEVV+CoChV2Ij6qtLWuZ5MoQgKq6U/BniWwajx0Hg71ulbPMcW7WW3Q+IxqOVR0rUA61Itga1oOW9Jy3L6w8/PzIyQkhAMHDuDv74/VqotovsgwDPLy8khLSyMyMrKs4K8qFXYiPmrOpjSgGvrXHWnZJ865yAy7CjsxxTvDuxAXFnR6g4FcxGKxUKdOHXbs2MGuXbvMjiMmi4yMJCEh4bSPo8JOxEc1iQllS2rO6fevO1LnEc657NpfXn3HFO+Ulw5fXQrtLoVeN1dqfdgTqV87pFqO4yoBAQE0b96coqLTHxksnsvf3/+0W+pKaeWJk9DKE+LNSn/9LRaLyUnE5yz9CKbeDfHt4eb5NXKKErsDP5v7t9yJnExlahG12In4MBV0Ypp2lwIWCK7k+sSn4Idle3hn9lYu6lSPsWe1qPbji7gzfZUR8UFr92Vid9RgY31OGix8G7L219w5xLMF14bu10G7YdV+aAPYeSiPuf/0IxXxJSrsRHzMzoO5XPDWfJKen3l6y4idyA/XwLSHYbVWohDXG/DPgKDV+zI5lFNochoR11JhJ+JjSqc5aRIbWnMjBzteAfW7Q21NWCz/4rDDz7fBpt/AXlIjp4gLD6J1nXAMA+ZtOVgj5xBxVyrsRHxM+WoTcTV3ks4j4PoZ0HZozZ1DPNOOufD3FzBptHNanBpSOtp7rpYXEx+jwk7EhxQU21m4zdmCUa3z1/2bBmXI8dRuBL1uhR43gF9gjZ2m9Of7z80HcNRkf1IRN6NRsSI+ZOnOdAqKHcSHB9IqIazmT2gvgc2/Q7NB4K8lkwSIagLnPFfjp+nasDa1Av04lFvE2v2ZdKgfWePnFHEHKuxEfEjp4uhnNI91zVQnn50Pe/6Coe9Dpytr/nwi//C3WRnWpR4lDqP6lswT8QD6aRfxIXNc0b/uSC3OhkNboaTANecT92UYMO9VaDMUYpq55JRPXdzOJecRcSdaeeIktPKEeJOlO9OZu+kAN/RrQkSIf82fsCgXrP7gF1Dz5xL3tmMefH4BBITBvVt0aV6kErTyhIgcU/dGUXRvFOW6EwaEuu5c4t4CQqH5EIio79KirsTuYOWeDEID/WhdR1/OxftpVKyIuEbqemcLnvimel1g+Pdw3isuPe0r0zZz2fuL+Hj+DpeeV8QsKuxEfEBRiYMnfl7H9PWplNhraLWJE5l4E7yXBGt+cP25xb1YXfux07dZDOCcz07TnogvUGEn4gP+3n2Yzxbu5IEfV2M1Y465+LZgscHhXa4/t5jLMGDVd1CYbcrpuzeuTbC/jQPZhWxIyTIlg4grqbAT8QFzSqc5aRGL1WpCYdd1JNy1DgY/7vpzi7l2/AmTboS3u9fYEmInEuhno3fTaKD890DEm6mwE/EB5cuI1eBqEycSFAHhdcw5t5jLXgxRTaHleWAzZ7yelhcTX6JRsSJeLjWrgA3JWVgs5f2NTJWXDhYrBEeanURcoflgaLoMis0bONO/RRywjuW7DpNVUEx4kAum+hExiVrsRLzcn/+0UnSoF0F0rZpbm/OULHwLXmsDi/9nbg5xLasVAl2whN1xNIgOoUlMKHaHwcKtB03LIeIKarET8XKlq02ULopuqloJUJIPe5eanURqWkEW7FsGTQaCGQN2/uWxC9sQEeyvNWPF66mwE/Fye9PzAOjvqmXETqTtUAivCw17m51EatrKr+H3B6DVBfDfr81O47pl9ERMpsJOxMv9NKYvOw/mUr+2GyzhZPOHRn3MTiGuUJwPAbWg2SCzk4j4FK0VexJaK1akhthLoCADQt1gQIfUjIJMsAW4zbqwy3am8+Pfe+mUGMkV3RuYHUfklFWmFtHgCREvZnfXmfa3z4U3O8NPY8xOIjUpKMJtijqAtfsy+XbJHn5aud/sKCI1RoWdiJc6lFNI56emMfrL5RSbsYzYiYTXhczdzkEUBVoNwKsc2ua8uaHSfqZLd6aTU+j6yZJFXEGFnYiXmr/1IFkFJexKz8Pf5ma/6jHN4aof4K61EKQuDl5lxuPwdjdY8qHZSY7SOCaUhtEhFNsNFm07ZHYckRrhZn/tRaS6zN3kRtOcHEuLs93qMp1UA3uJ82YY0Kiv2WmOqfT3Yc6mNJOTiNQMFXYiXsjhMMxfRqwyMveZnUCqg80PrhoPd6yEuNZmpzmm0t+HOZsOoLGD4o1U2Il4oXX7sziUW0StQD+6NKhtdpzjK8iCLy+Bt7pCjlpQvEbtRmYnOK5eTaIJsFnZl5HPtgPmLXMmUlNU2Il4obmbnUVS76bRBPi58a95YJizuLMXwc75ZqeR07FtNhS5f6EUEuBHzyZRNIkN5WBOodlxRKqdJigW8UKll2H7u/tlWIsFLhjnHEDhxq08chJZ++Gby50TEt+8EMLrmJ3ohD4Y0Y3gAJvZMURqhAo7ES80oGUcDgPOaO7mhR1AnQ5mJ5DTlbkPIuo71wJ286IOUFEnXk0rT5yEVp4QcaHcg+AX6LxEK57FXgJ5ByEswewkp6ywxE5eoZ3aoQFmRxE5Ia08ISKeZ+HbMK4dLHjD7CRSFTY/jyrqvly0k85PTef1GZvNjiJSrVTYiXgRwzD4fW0KmXnFZkepvMgGUJIPe5c550ET95exGzb97pHvV3x4EHlFdub80x9VxFuosBPxIhuSsxn91XL6vDjL/ZYRO5nWF8LVP8OISc5BFeL+5rwI314BfzxkdpJK690sBn+bhV2H8th50P1H84qcKhV2Il5k1sZUAHo1iXK/ZcROxmKBJv1V1HkKw3BeevUPgXaXmp2m0moF+tGtYRSgVSjEu3jYX34ROZFZG50fUANbxZmc5DTZi2HbLLNTyIlYLDDoURi7Hup3MztNlZSuQjFXl2PFi6iwE/ES6blFrNiTAcDAlh5c2BXlwru9nCtSJK8yO42cTLAbr2xyEqXzPC7afoiCYrvJaUSqh8cVdu+88w6NGjUiKCiInj17smTJkhPu/8MPP9CqVSuCgoJo3749v/76q4uSirjW3M1pGAa0SgijbmSw2XGqLiAU6nSCkBjnxLfiXhwOmPEEpG83O8lpaxkfRkJ4EAXFDpbsSDc7jki18KjC7rvvvmPs2LE8/vjj/P3333Ts2JEhQ4aQlnbs/hELFy7kyiuv5LrrrmPFihUMHTqUoUOHsnbtWhcnF6l5szY6LycNau3BrXWlznnBuZB8y3PNTiL/tnYCzB8HHw2G4nyz05wWi8XCdX0bc985LWkSG2p2HJFq4VETFPfs2ZPu3bvz9ttvA+BwOEhMTOS2227jgQceOGr/K664gtzcXKZMmVK2rVevXnTq1In333//lM6pCYrFE9gdBl2enk5mfjE/3pxE1386hYtUu9T1MO1haNQX+t1tdhoRn+CVExQXFRWxfPlyBg8eXLbNarUyePBgFi1adMznLFq0qML+AEOGDDnu/gCFhYVkZWVVuIm4O5vVwm939OP5Ye3plOi5fZ6OafdiWP292SmkVHwb55Q0fe40O4mIHIPHFHYHDx7EbrcTHx9fYXt8fDwpKSnHfE5KSkql9gd4/vnniYiIKLslJiaefngRF6gbGcyVPRpgs3rRdCE7F8AnZ8OUuyD7+L+34gL/vrhj9Z71VjPzivlp5T5mbkg1O4rIafOYws5VHnzwQTIzM8tue/bsMTuSiO9qkAT1u0PbS8Cm9TxNYxjww0iY/zqUFJmdptr9vGofd4xfyftzt5kdReS0+Zkd4FTFxMRgs9lITa34jSo1NZWEhGOvT5iQkFCp/QECAwMJDAw8/cAiLrInPY9HJq/l7LbxDO/Z0Ow41ctqhZFTwD/I7CS+bec8WP8TbPrNuUJIdFOzE1Wrga3i4Kd1LN91mMO5RdQO1ZcI8Vwe02IXEBBA165dmTlzZtk2h8PBzJkzSUpKOuZzkpKSKuwPMH369OPuL+KJZm9KY+7mA/y0wkunBvl3Uec54728R6N+cPG7cOYjXlfUAdSvHUKrhDAcBszZrFUoxLN5TGEHMHbsWD788EM+//xzNmzYwM0330xubi7XXHMNAFdffTUPPvhg2f533HEHv//+O6+++iobN27kiSeeYNmyZYwZM8aslyBS7WZu8JLVJk4mPwOmjIWZT5mdxPdYLNB5OPS5w+wkNaZ0mqDS3ycRT+Uxl2LBOX3JgQMHeOyxx0hJSaFTp078/vvvZQMkdu/ejdVaXqv27t2bb775hkceeYSHHnqI5s2bM3nyZNq1a2fWSxCpVnlFJSzafgjwkvnrTmTPYlj2MVj9oPt1EFHf7ETeL32H89/Z5m92kho3qHU878zextzNByi2OzxvrWWRf3jUPHZm0Dx24s5mrE/l+i+WUS8ymPn3D8Ri8aIRsccy7VFofjY07md2Eu9XmA3v9YGgcLj8C4hqYnaiGmV3GPR4dgaHcov45vqe9G4WY3YkkTJeOY+diBxt1ibnZaMzW8V5f1EHcPbTKupc5eBmZ3GXn+Fc3s3L2ayWsu4Ma/dnmpxGpOo86lKsiJQzDIPZG8sLO5+Tewiy90NCe7OTeKd6XeGWRc75A4N842rFHYOac++QlsSHaxS2eC4VdiIeKiu/hKaxtcgpLCGpabTZcVwrdR18dSlYbDB6HoRoCbUaEZbgvPmIxKgQsyOInDYVdiIeKiLEn6+u70lBsZ0gf+9ZBeCURDYA/2BnYZeXrsKuujjs8Mvt0PVaqN/V7DSmcjgMrN60iov4DBV2Ih7O54o6gMAwGD7B2ZoUEGp2Gu+x5ANY8RVs+AXuXOszl2CPtH5/Fs//tgGAL6/raXIakcpTYSfigTLziikssRPny32B/j1RrsPuVeuXmqLjlbBrIbQ81yeLOoCwID/mbTmIzWohM6+YiBDvn+pFvItGxYp4oIkr9tLjuZk8Onmt2VHcw4Zf4L3ekHPA7CSeLTjSObVJp6vMTmKaxKgQWsTXwu4wtAqFeCQVdiIeaNo65xrIDaPV2Rt7sXM1igMbYeGbZqfxPEV5sGVG+X1fmDbnJAa1dk56P2196kn2FHE/KuxEPExGXhFLdqYDcHYb3xmxeFw2f7jiK+hzJwx63Ow0nsUw4Ocx8PWlsEBFcamz2zgLuzkb0ygotpucRqRyVNiJeJhZG9OwOwxaJYTRQC12TrEt4awnwaZuw5ViOKBWgnOZtnq+PQr2SB3rR5IQHkRukZ2F2w6aHUekUlTYiXiY6f9cHiptVZB/MQyYPw7W/2R2EvdntcE5z8Eti6FRH7PTuA2r1cLZbZ2/X7+vTTE5jUjl6OutiAcpKLYzd7NzgMBZugx7bGt/hBlPgC0Q6nSC2g3NTuR+0jY6WzlL+9PFNDM3jxs6t10dth/IpVcTH5v8WzyeCjsRD7Jw20HyiuzUiQiiXT3fnI7ipNoMhfWToWEfFXXHsn0OfHMFtLkYLnob/ALMTuSWkppG+96KLuIVVNiJeJCuDaMYd0VHikocWDR68dhsfnD5lxrdeTx5h5wjiQuy9G8k4oVU2Il4kIhgfy7pXN/sGO7vyIKlpMi5TFb3G3x+mSwA2l0KteKhfnfniGI5obSsAuZsOsB/utXXlynxCBo8ISLebd6rsOpb+PYKKMo1O405dvwJ+Rnl9xv1Bb9A0+J4iqISB2e+Opf7flzN2n1ZZscROSUq7EQ8xAd/buO9OdtIySwwO4pn6T0Gmp4JQ9/3zXVlN0yBLy9x9qsryjM7jUcJ8LNyRosYAH5fl2xyGpFTo8JOxAPYHQYf/LmdF3/fyKbUbLPjeJbAMPi/idB8cPm2Qh/6N4xuBv4hEFFfl16rYEhb5+jzP9ZpFQrxDCrsRDzAkh3pHMwpIiLYn94aqVd5R/aNykuHDwbC1Lud/e+8kWGU/39cK7huOgz7QIVdFQxsFYe/zcLWtBy2pvnQFwLxWCrsRDzAr2ucl4HObhOPv02/tqdl6ww4tAU2T4OiHLPTVL/0HfDZ+c656krFtXJORiyVFh7kT59mzsuxU1drsmJxf/qEEHFzdofB7+ucHyjndahjchov0OFyuPI7uOJLCIkyO031m/4o7FoAU8eancRrXNChLgC/rN6PcWRrqIgbUmEn4uaW7UznQHYh4UF+9GkaY3Yc79DyHKjbqfz+5j/g13u9Y9Ts+eOg9YXOS69SLc5uG0+AzcquQ7nsy8g3O47ICWkeOxE3V3oZ9qw2CQT46btYtSvKhZ/GQG4ahNeDvneanejUGQZsnAqZe6DXzc5ttWLhiq/MzeVlwoP8+XhUNzrUiyQiRP0Uxb2psBPxAMH+Ns5rr7Vha0RAKFzyHix8C3qOLt9uGO6/MsP+v+G74WCxQWJPqNfF7EReq1/zWLMjiJwSi6EOAyeUlZVFREQEmZmZhIdrbU4xR15RCf42qwZOuNK3V0F0E+g71r364tmLK45unXAt1G4E/e6BgBDTYvkSh8PAanXzol+8SmVqEbXYiXiAkAD9qrrU3mWwaSpY/aHnzWancSrIghmPw9aZcMui8smWL/3Y/VsWvcTsjWm8MXMLPZtE8eC5rc2OI3JM+vov4qaK7Q42azJic9TrCsMnwKBHIaJe+fa/3nNOl+Kwuz5TQChsmwUZu2DdpPLtKupcpqDYzso9GUxZlazRseK2VNiJuKn5Ww5y9rg/GfHxYrOj+B6LBZqfBX3uKN+WexCmPwZfXQoHt9Ts+XMOwM+3wyfnlE82bLXBuS/DyCnQaXjNnl+OaWCrOEIDbOzLyGfFngyz44gckwo7ETc1eeU+AJrG1jI5iQDOVrqu10DDvs4Jf0v98TB8MRS2z63acbNTYf3PzpbAUoG1YM0E2L0I9hxR2Lc4Gxr3UyudSYL8bZzVJh6AKau0dqy4JxV2Im4ot7CEaf+sTTm0c72T7C0uERYP570E10ytuH3Tr7B9NhTnlW/bswTe6AQTb6q47/TH4MthkLK2fNuWP+D7Ec5RuaX8g+Gc52DEJOdlYXEbpZMVT12zH7tDl2PF/aiwE3FD09ankF9sp1F0CB3rR5gdR07kiq/hvFegfo/ybQe3wOEdkLWv4r5bZ8G2mZB9RGtPnY7OW3y7ivt2HQVNz9T6rm6mX4sYIoL9Sc0qZOG2g2bHETmKhtqJuKFJK/YDztY6iy67ubf4Ns7bkVqeC9f8BrbAitv73gn2Iog7Yv86HeGmP2s8plSPQD8bF3asw1d/7Wbi3/s0v524HRV2Im7mQHYh87ccAGBoJ12G9UghUdCw99Hb21/m+ixS7S7rmkhKZgHnt9fazeJ+VNiJuJlf1yTjMKBTYiSNYkLNjiMi/9IpMZKPRnY3O4bIMamwE3EzV/ZoQN3IYPxtugQrIiKVo8JOxM0E+FnLplQQEfe1Jz2PH//ey6Vd6pMYpeXcxD2osBMREamChyatYd4W58jYOwe3MDmNiJOmOxFxEyV2Bxe/s4DXpm0ip7DE7DgichKXdqkPwMS/92mJMXEbKuxE3MTsTQdYtSeDrxfvJsCmX00RdzekbQK1Av3YnZ7H0p2HzY4jAqiwE3Eb3y3dA8CwLvUI8NOvpoi7Cw6wcV77BAAmLN9jchoRJ316iLiBtKwCZm9KA+CK7okmpxGRU3VZV+fv65TVyWQXFJucRkSFnYhb+PHvfdgdBl0aRNIsLszsOCJyiro3qk2zuFrkFdmZvHK/2XFEVNiJmM3hMBi/dDeg1joRT2OxWLiqRwNCA2zkFGjQk5hP052ImGze1oPsOpRHWJAfF3asa3YcEamk//ZI5PLuidQK1EeqmE8/hSImS6wdzJU9GlA7xJ+QAP1Kinga/d6KO9FPo4jJmsTW4vlh7c2OISKnyTAMVuzJoGlsLSKC/c2OIz5KfexERESqwR3jVzLs3YV8v1RTn4h5VNiJmKSoxMFjP61l1Z4Ms6OISDVIahoNwOeLdmJ3aCUKMYcKOxGT/LJqP18s2sWNXy6jxO4wO46InKahneoRGeLP3sP5TF+fanYc8VEq7ERMYBgGH83fAcDVSY3w0xJiIh4vOMDGlT0aAPDpgh0mpxFfpU8TERMs2naIDclZBPvbGN6zgdlxRKSaXJ3UEJvVwuId6azbn2l2HPFBKuxETFDaWvefbvWJDAkwOY2IVJc6EcGc2865fuynC3aaG0Z8kgo7ERfbmpbDrI1pWCxwTZ/GZscRkWp2bV/n7/XqvRkUq/+suJjmsRNxsY/nbwdgcOt4GseEmpxGRKpblwa1+fr6nvRqEo3NajE7jvgYFXYiLta6TjhxYYHc0K+J2VFEpIb0aRZjdgTxUSrsRFzs6qRGXNE9kUA/m9lRRKSGFRTb2ZKaQ/v6EWZHER+hPnYiJlBRJ+L9th/Iod9LsxnxyWJyC0vMjiM+QoWdiIt8sWgnv6zarxnpRXxEw+hQQgNsZOQV88WiXWbHER+hwk7EBQ7nFvHibxu57dsVzN960Ow4IuICNquF285sDsD//txGdkGxyYnEF6iwE3GB9+ZuI7fITtu64ZzRXJ2qRXzF0M71aBIbSkZesea1E5dQYSdSw5Iz8/l84U4A7hnSEotF0x+I+Aqb1cKdg1sA8OG87WTmqdVOapYKO5Ea9ubMLRSWOOjROIoBLWLNjiMiLnZB+zq0jA8ju6CED+dtNzuOeDkVdiI1aNuBHL5ftheA+89Ra52IL7JaLdx1lrPVbn9mPoahAVRSczSPnUgNeuWPTdgdBoNbx9G1YZTZcUTEJEPaxjPltr60q6f57KRmqbATqUGjejdi7+F87h3SyuwoImIii8Wiok5cQpdiRWpQzybR/DymDy0TwsyOIiJuYn9GPu/M3qpLslIj1GInUgOKShwE+Dm/N6lfnYiUyi+yc96b88jIKyYxKoSLOtY1O5J4GY9psUtPT2f48OGEh4cTGRnJddddR05OzgmfM2DAACwWS4Xb6NGjXZRYfFV6bhH9X57NmzO3UFTiMDuOiLiR4AAb1/RuDMDTU9aTma/pT6R6eUxhN3z4cNatW8f06dOZMmUKf/75JzfeeONJn3fDDTeQnJxcdnvppZdckFZ82VO/rCM5s4Bf1yRjVWOdiPzL6AFNaBITyoHsQl75Y5PZccTLeERht2HDBn7//Xc++ugjevbsSd++fXnrrbcYP348+/fvP+FzQ0JCSEhIKLuFh4e7KLX4otmb0pi8cj9WC7xwaQf8bB7xKyYiLhToZ+OZS9oB8NXiXazck2FuIPEqHvGps2jRIiIjI+nWrVvZtsGDB2O1Wlm8ePEJn/v1118TExNDu3btePDBB8nLyzvh/oWFhWRlZVW4iZyKnMISHp64BoBr+jSmU2KkuYFExG31bhrDsM71MAy4b8IqCortZkcSL+ERhV1KSgpxcXEVtvn5+REVFUVKSspxn3fVVVfx1VdfMXv2bB588EG+/PJL/u///u+E53r++eeJiIgouyUmJlbLaxDv9+JvG9mfWUBiVDB3n93C7Dgi4uYePr81MbUC2Jyao3VkpdqYOir2gQce4MUXXzzhPhs2bKjy8Y/sg9e+fXvq1KnDoEGD2LZtG02bNj3mcx588EHGjh1bdj8rK0vFnZzUzA2pfPnXLgCev6QDIQEacC4iJxZdK5Dnh3Vg8fZDXNOnkdlxxEuY+ulz9913M2rUqBPu06RJExISEkhLS6uwvaSkhPT0dBISEk75fD179gRg69atxy3sAgMDCQwMPOVjigAczCkkwGbl/3o1pG/zGLPjiIiHOKtNPGe1iTc7hngRUwu72NhYYmNPvih6UlISGRkZLF++nK5duwIwa9YsHA5HWbF2KlauXAlAnTp1qpRX5Hiu6N6Azg1q0yAqxOwoIuKhSuwOflm9n6Gd6mn+S6kyj+hj17p1a8455xxuuOEGlixZwoIFCxgzZgz//e9/qVvXObnjvn37aNWqFUuWLAFg27ZtPP300yxfvpydO3fy888/c/XVV3PGGWfQoUMHM1+OeJHCkvIOzy3iwwjyt5mYRkQ8lWEYXPPZUu76bhUfz99hdhzxYB5R2IFzdGurVq0YNGgQ5513Hn379uWDDz4oe7y4uJhNmzaVjXoNCAhgxowZnH322bRq1Yq7776bSy+9lF9++cWslyBeZsb6VAa/Npe1+zLNjiIiHs5isTC4tfOS7Au/bWTpznSTE4mnshharO6EsrKyiIiIIDMzU3PgSZltB3IY+vYCsgtLuLZPYx67sI3ZkUTEwxmGwW3frmDK6mRiagUw6ZY+JKp7h1C5WsRjWuxE3MWB7EKu+XQp2YUl9GgUxYPntTI7koh4AYvFwouXdqBNnXAO5hRxzWdLteSYVJoKO5FKyCks4ZrPlrA7PY8GUSG8M7wL/lpdQkSqSWigH5+M6k5CeBBb03K4+avlFfryipyMPpFETlFBsZ2bv1rO2n1ZRIcG8Pm1PYgN09Q4IlK9EiKC+HhUN0IDbKzZm8mOg7lmRxIPollURU7RW7O2MG/LQUICbHwyqjuNY0LNjiQiXqpt3Qg+vLoboYF+tEpQ/245dSrsRE7RzQOasXpvJjf3b0pHrQMrIjWsd7OKk51vTMmicUwogX6aVkmOT5diRU4gp7CE0oHjtQL9+OLaHkf9sRURqWnr92fxn/cWce1nS8kpLDE7jrgxFXYix7HrUC4XvTWft2dtLdum2eBFxAwZeUXYDYMFWw/x3w8WkZyZb3YkcVMq7ESOYd6WA1zy7kK2H8xl/NI9+oYsIqbq3SyG8Tf2Iio0gLX7srjwrQWaxFiOSYWdyBHsDoNx0zdz9SdLSM8ton29CCbd0ptageqOKiLm6lA/ksm39KFVQhgHcwq58oO/+GjedhwOrTMg5VTYifxj58FcrvzgL96YuQXDgCt7NOCH0UnEhQeZHU1EBIAG0SFMvKU353eoQ4nD4JmpG5i4Yp/ZscSNqBlCBOccdZe9v4iDOYWEBNh4+uJ2XNq1vtmxRESOEhLgx9tXdqZXk2h+W5PM0E51zY4kbkRrxZ6E1or1HV/9tYupq5N56bIOWp9RRDyCYRhlg7oKiu3c/cMqruvbmC4NapucTKpTZWoRFXYnocLOO63ck8HrMzbzfz0bMrhNPOD8A2kYYLVq5KuIeJ43Z27htembATirTTz3nN2SlglhJqeS6lCZWkSXYsVnOBwGc7cc4LMFO5m7+QAAe9LzOLNVHFarBYvFgmYzERFP9d/uiew9nMeE5XuZvj6V6etTGdw6jhvPaEr3RrU1XZOPUIvdSajFzvOl5xYxYfkevvprN7vT8wCwWuCSzvW57cxmNNLSYCLiRbamZfPa9M38tjaF0k/4DvUjmHhzb/xsGjPpidRiJ3KEhyau4fd1KQCEBfnxn66JXJ3UUAWdiHilZnFhvDu8K9sO5PDRvB38+Pde6kYEVyjqfluTTM8m0USFBpiYVGqCCjvxCgXFdv7efZiFWw+xYNtBnh3anjZ1nd9qzm2fwL6MfIb3bMBFneoSEqAfexHxfk1ja/H8sPY8cE4rsgqKy7bvOJjLzV//jdUCXRrUZkDLWHo0jqZD/QiC/LUOrafTJ5x4pMO5RczYkMqafZms2pvJhuQsikocZY8v3HawrLC7qGNdLu5Uz6yoIiKmigjxJyLEv+x+em4hreuEsyE5i2W7DrNs12EAAmxWOtSP4M7BLejbXGtieyoVduJ2iu0ODmQXkpxZQGpWAcmZBew+lMsZLWIZ1No5gnXHoVzunbC6wvNiwwLp0zSa3s1i6N8itmy7OgyLiJTr2jCK3+7ox76MfGZtSGXR9kMs2XGYgzmFLNt1mMISe9m+v61J5sXfN9IgOpSGUSE0jA6hQVQI8eFBxIYFEhsWiL/67bkVFXZSJQ6HQbHDgd1hUGw3sDsMSuwOShwGoQF+Zd8OcwpLWLoznYIiO3lFdvKL7RQUO/8/M7+YXk2iOeuf6UZ2HMzlsvcWkp5XxLGG9FitlrLCrklMKD0aR9GxfgTt60fSoV4EDaNDVMSJiJyiepHBjEhqxIikRhiGwa5DeSzdmU7nI+bA23Ygh52H8th5KO+Yx/j82h5lX6TnbErj2yW7CQvyp1agH2FBfv/8159aQX70ahJFXJhzJZ9DOYWkZRcS4GclwGYl0M9KgJ+VQD8b/jYLtn9mKpDKU2HnBmZvSuOl3zdx5ABlwwAD5/37z2lVVtAs2HqQR39a+89OYEDZ8wzg7rNbclFH5yzkS3emc9d3KysUSYZRelS4fVBzruzRAIBVezK4/otl/+xrlD2n9Pi3DmzG9f2aALB2XyYXvDX/uK9nzMBm3DOkJQApmflc8+nS4+5rGJQVdiEBNg7lFgHgZ7UQHx5EQoTzVj8ymN7Nyi8NRIYE8P1NScc9roiInDqLxUKjmNCjBpVd2aMBXRtGsTs9l12H8tiVnsee9DwOZBdyMKeQ2FqBZfuuT87ij3Wpxz3HN9f3LCvsfl2TzKM/rTvuvp+O6s7AVnEA/Lh8Lw9NWoPNasH2z7RUNqsFq8WC1WrhhWHtyz4jZ25I5akp652v6YjXVnr//nNbMaRtAuDssvPo5LVH7eO8D7ed2ZwL//k8Xbkngwd+rHiV6MjnfTqqOwkR7rH8pAo7N5CVX8yG5KzjP35Ep9fcwhK2H8g97r7ZR+xbUGxn7+H84+6bW1hS9v8lDuflz+PvW940bzvOBL5WC/hZrRXmggsL8qddvXCC/W0EB/gR7G8lJMCPIH8b4cF+9GwcVbZvdGgAv93Rj5hagUSHBmiiYBERk0XXCiSpViBJTaOPeqx0UvdSZzSPJSzIn5yCEnIKi8kpKCG7sITsghJyCkqIDSsvAv1tVmJqBVJUYqfI7qCoxIHjiGMd+TlSbHdQeEQf6n8rtpc/lltkZ9dxWhcBcgrKP/fyCu1sO8HnaWZ+8RH7lrAxJfuUMphN89idhCvmsUvLKmBjSnbZD7IF5zeS0p/rZvG1yr7lpOcWsSXV+cN15IS6Fpy/CIlRIWX7ZhUUlxWBpY8fefzSPhIAeUUl7DyY989x/7n9k8BigZhagWXD4kvsDrIKSvCzWfCzWvCzWvGzWlSIiYhIlRiGQYnDoKjEQbHdQUiAHwF+zr57uYUlHM4rwuEAu2HgMAwcDgO74ewGVL92CBHBzu4/6blF7DiY888x/zn2EedpFB1a9rl3OLeITf98npbvW753k5haZa1wGXlFrNufddR+pfd7NI6q0RHFWlKsGmmCYhERETFTZWoRDWURERER8RIq7ERERES8hAo7ERERES+hwk5ERETES6iwExEREfESKuxEREREvIQKOxEREREvocJORERExEuosBMRERHxEirsRERERLyECjsRERERL6HCTkRERMRLqLATERER8RIq7ERERES8hAo7ERERES/hZ3YAd2cYBgBZWVkmJxERERFfVFqDlNYkJ6LC7iSys7MBSExMNDmJiIiI+LLs7GwiIiJOuI/FOJXyz4c5HA72799PWFgYFoulRs6RlZVFYmIie/bsITw8vEbOIadG74V70fvhPvReuBe9H+7DFe+FYRhkZ2dTt25drNYT96JTi91JWK1W6tev75JzhYeH6xfUTei9cC96P9yH3gv3ovfDfdT0e3GylrpSGjwhIiIi4iVU2ImIiIh4CRV2biAwMJDHH3+cwMBAs6P4PL0X7kXvh/vQe+Fe9H64D3d7LzR4QkRERMRLqMVORERExEuosBMRERHxEirsRERERLyECjsRERERL6HCzkXeeecdGjVqRFBQED179mTJkiUn3P+HH36gVatWBAUF0b59e3799VcXJfV+lXkvPvzwQ/r160ft2rWpXbs2gwcPPul7J5VT2d+NUuPHj8disTB06NCaDehDKvteZGRkcOutt1KnTh0CAwNp0aKF/lZVo8q+H6+//jotW7YkODiYxMRE7rrrLgoKClyU1nv9+eefXHjhhdStWxeLxcLkyZNP+pw5c+bQpUsXAgMDadasGZ999lmN5yxjSI0bP368ERAQYHzyySfGunXrjBtuuMGIjIw0UlNTj7n/ggULDJvNZrz00kvG+vXrjUceecTw9/c31qxZ4+Lk3qey78VVV11lvPPOO8aKFSuMDRs2GKNGjTIiIiKMvXv3uji5d6rs+1Fqx44dRr169Yx+/foZF198sWvCernKvheFhYVGt27djPPOO8+YP3++sWPHDmPOnDnGypUrXZzcO1X2/fj666+NwMBA4+uvvzZ27Nhh/PHHH0adOnWMu+66y8XJvc+vv/5qPPzww8bEiRMNwJg0adIJ99++fbsREhJijB071li/fr3x1ltvGTabzfj9999dkleFnQv06NHDuPXWW8vu2+12o27dusbzzz9/zP0vv/xy4/zzz6+wrWfPnsZNN91Uozl9QWXfi38rKSkxwsLCjM8//7ymIvqUqrwfJSUlRu/evY2PPvrIGDlypAq7alLZ9+K9994zmjRpYhQVFbkqok+p7Ptx6623GmeeeWaFbWPHjjX69OlTozl9zakUdvfdd5/Rtm3bCtuuuOIKY8iQITWYrJwuxdawoqIili9fzuDBg8u2Wa1WBg8ezKJFi475nEWLFlXYH2DIkCHH3V9OTVXei3/Ly8ujuLiYqKiomorpM6r6fjz11FPExcVx3XXXuSKmT6jKe/Hzzz+TlJTErbfeSnx8PO3ateO5557Dbre7KrbXqsr70bt3b5YvX152uXb79u38+uuvnHfeeS7JLOXM/gz3c8lZfNjBgwex2+3Ex8dX2B4fH8/GjRuP+ZyUlJRj7p+SklJjOX1BVd6Lf7v//vupW7fuUb+0UnlVeT/mz5/Pxx9/zMqVK12Q0HdU5b3Yvn07s2bNYvjw4fz6669s3bqVW265heLiYh5//HFXxPZaVXk/rrrqKg4ePEjfvn0xDIOSkhJGjx7NQw895IrIcoTjfYZnZWWRn59PcHBwjZ5fLXYip+iFF15g/PjxTJo0iaCgILPj+Jzs7GxGjBjBhx9+SExMjNlxfJ7D4SAuLo4PPviArl27csUVV/Dwww/z/vvvmx3NJ82ZM4fnnnuOd999l7///puJEycydepUnn76abOjiYupxa6GxcTEYLPZSE1NrbA9NTWVhISEYz4nISGhUvvLqanKe1HqlVde4YUXXmDGjBl06NChJmP6jMq+H9u2bWPnzp1ceOGFZdscDgcAfn5+bNq0iaZNm9ZsaC9Vld+NOnXq4O/vj81mK9vWunVrUlJSKCoqIiAgoEYze7OqvB+PPvooI0aM4Prrrwegffv25ObmcuONN/Lwww9jtaodx1WO9xkeHh5e4611oBa7GhcQEEDXrl2ZOXNm2TaHw8HMmTNJSko65nOSkpIq7A8wffr04+4vp6Yq7wXASy+9xNNPP83vv/9Ot27dXBHVJ1T2/WjVqhVr1qxh5cqVZbeLLrqIgQMHsnLlShITE10Z36tU5XejT58+bN26tay4Bti8eTN16tRRUXeaqvJ+5OXlHVW8lRbdhpaEdynTP8NdMkTDx40fP94IDAw0PvvsM2P9+vXGjTfeaERGRhopKSmGYRjGiBEjjAceeKBs/wULFhh+fn7GK6+8YmzYsMF4/PHHNd1JNanse/HCCy8YAQEBxoQJE4zk5OSyW3Z2tlkvwatU9v34N42KrT6VfS92795thIWFGWPGjDE2bdpkTJkyxYiLizOeeeYZs16CV6ns+/H4448bYWFhxrfffmts377dmDZtmtG0aVPj8ssvN+sleI3s7GxjxYoVxooVKwzAeO2114wVK1YYu3btMgzDMB544AFjxIgRZfuXTndy7733Ghs2bDDeeecdTXfijd566y2jQYMGRkBAgNGjRw/jr7/+Knusf//+xsiRIyvs//333xstWrQwAgICjLZt2xpTp051cWLvVZn3omHDhgZw1O3xxx93fXAvVdnfjSOpsKtelX0vFi5caPTs2dMIDAw0mjRpYjz77LNGSUmJi1N7r8q8H8XFxcYTTzxhNG3a1AgKCjISExONW265xTh8+LDrg3uZ2bNnH/NzoPTff+TIkUb//v2Pek6nTp2MgIAAo0mTJsann37qsrwWw1AbrYiIiIg3UB87ERERES+hwk5ERETES6iwExEREfESKuxEREREvIQKOxEREREvocJORERExEuosBMRERHxEirsRERERLyECjsRERERL6HCTkRERMRLqLATETmBAQMGcNttt3HnnXdSu3Zt4uPj+fDDD8nNzeWaa64hLCyMZs2a8dtvvwFgt9u57rrraNy4McHBwbRs2ZI33nijwjHnzJlDjx49CA0NJTIykj59+rBr1y4AVq1axcCBAwkLCyM8PJyuXbuybNkyl79uEfFMKuxERE7i888/JyYmhiVLlnDbbbdx880385///IfevXvz999/c/bZZzNixAjy8vJwOBzUr1+fH374gfXr1/PYY4/x0EMP8f333wNQUlLC0KFD6d+/P6tXr2bRokXceOONWCwWAIYPH079+vVZunQpy5cv54EHHsDf39/Mly8iHsRiGIZhdggREXc1YMAA7HY78+bNA5wtchEREQwbNowvvvgCgJSUFOrUqcOiRYvo1avXUccYM2YMKSkpTJgwgfT0dKKjo5kzZw79+/c/at/w8HDeeustRo4cWbMvTES8klrsREROokOHDmX/b7PZiI6Opn379mXb4uPjAUhLSwPgnXfeoWvXrsTGxlKrVi0++OADdu/eDUBUVBSjRo1iyJAhXHjhhbzxxhskJyeXHWvs2LFcf/31DB48mBdeeIFt27a54iWKiJdQYScichL/vhRqsVgqbCu9jOpwOBg/fjz33HMP1113HdOmTWPlypVcc801FBUVle3/6aefsmjRInr37s13331HixYt+OuvvwB44oknWLduHeeffz6zZs2iTZs2TJo0yQWvUkS8gQo7EZFqtGDBAnr37s0tt9xC586dadas2TFb3Tp37syDDz7IwoULadeuHd98803ZYy1atOCuu+5i2rRpDBs2jE8//dSVL0FEPJgKOxGRatS8eXOWLVvGH3/8webNm3n00UdZunRp2eM7duzgwQcfZNGiRezatYtp06axZcsWWrduTX5+PmPGjGHOnDns2rWLBQsWsHTpUlq3bm3iKxIRT+JndgAREW9y0003sWLFCq644gosFgtXXnklt9xyS9l0KCEhIWzcuJHPP/+cQ4cOUadOHW699VZuuukmSkpKOHToEFdffTWpqanExMQwbNgwnnzySZNflYh4Co2KFREREfESuhQrIiIi4iVU2ImIiIh4CRV2IiIiIl5ChZ2IiIiIl1BhJyIiIuIlVNiJiIiIeAkVdiIiIiJeQoWdiIiIiJdQYSciIiLiJVTYiYiIiHgJFXYiIiIiXkKFnYiIiIiX+H9qVV8RKvxVCQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_weights(x, sw, bw):\n",
    "    plt.figure()\n",
    "    plt.plot(x, sw, \"C0--\", label=\"signal\")\n",
    "    plt.plot(x, bw, \"C1:\", label=\"background\")\n",
    "    plt.plot(x, sw + bw, \"k-\", label=\"sum\")\n",
    "    plt.xlabel(\"mass\")\n",
    "    plt.ylabel(\"weight\")\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "\n",
    "\n",
    "for method, weighter in zip( ['sWeights','COWs'], [sweight, cow] ):\n",
    "    x = np.linspace(*mrange,400)\n",
    "    swp = weighter.get_weight(0, x)\n",
    "    bwp = weighter.get_weight(1, x)\n",
    "    plot_weights(x, swp, bwp)\n",
    "    plt.title(method)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6383d0e5",
   "metadata": {},
   "source": [
    "The weights are not identical, because in this case we used a simple (non-optimal) variance function for the COWs.\n",
    "\n",
    "### Fit weighted t-distribution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d9f5711a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sWeights : naive 0.202 +/- 0.007, corrected 0.202 +/- 0.017\n",
      "COWs     : naive 0.206 +/- 0.007, corrected 0.206 +/- 0.016\n"
     ]
    }
   ],
   "source": [
    "# signal pdf in t-domain\n",
    "def t_signal_pdf(x, slope):\n",
    "    return t_density(x, 1, 0, slope, 0, 1)\n",
    "\n",
    "\n",
    "fitted_slopes = []\n",
    "for method, weighter in ((\"sWeights\", sweight), (\"COWs\", cow)):\n",
    "    # get signal weights\n",
    "    w = weighter(toy[0])\n",
    "\n",
    "    # do the minimisation\n",
    "    tmi = Minuit(\n",
    "        make_weighted_negative_log_likelihood(toy[1], w, t_signal_pdf),\n",
    "        slope=t_truth.slope,\n",
    "    )\n",
    "    tmi.limits[\"slope\"] = (0, 10)\n",
    "    tmi.migrad()\n",
    "    tmi.hesse()\n",
    "\n",
    "    # do the correction\n",
    "    fitted_slopes.append(tmi.values[\"slope\"])\n",
    "\n",
    "    # first order correction\n",
    "    ncov = approx_cov_correct(\n",
    "        t_signal_pdf, toy[1], w, tmi.values, tmi.covariance, verbose=False\n",
    "    )\n",
    "\n",
    "    # second order correction\n",
    "    hs = t_signal_pdf\n",
    "    ws = weighter\n",
    "    W = weighter.Wkl\n",
    "\n",
    "    # these derivatives can be done numerically but for the sweights / COW case\n",
    "    # it's straightfoward to compute them\n",
    "    ws = lambda Wss, Wsb, Wbb, gs, gb: (Wbb * gs - Wsb * gb) / (\n",
    "        (Wbb - Wsb) * gs + (Wss - Wsb) * gb\n",
    "    )\n",
    "    dws_Wss = (\n",
    "        lambda Wss, Wsb, Wbb, gs, gb: gb\n",
    "        * (Wsb * gb - Wbb * gs)\n",
    "        / (-Wss * gb + Wsb * gs + Wsb * gb - Wbb * gs) ** 2\n",
    "    )\n",
    "    dws_Wsb = (\n",
    "        lambda Wss, Wsb, Wbb, gs, gb: (Wbb * gs**2 - Wss * gb**2)\n",
    "        / (Wss * gb - Wsb * gs - Wsb * gb + Wbb * gs) ** 2\n",
    "    )\n",
    "    dws_Wbb = (\n",
    "        lambda Wss, Wsb, Wbb, gs, gb: gs\n",
    "        * (Wss * gb - Wsb * gs)\n",
    "        / (-Wss * gb + Wsb * gs + Wsb * gb - Wbb * gs) ** 2\n",
    "    )\n",
    "\n",
    "    tcov = cov_correct(\n",
    "        hs,\n",
    "        [spdf, bpdf],\n",
    "        toy[1],\n",
    "        toy[0],\n",
    "        w,\n",
    "        [mi.values[\"s\"], mi.values[\"b\"]],\n",
    "        tmi.values,\n",
    "        tmi.covariance,\n",
    "        [dws_Wss, dws_Wsb, dws_Wbb],\n",
    "        [W[0, 0], W[0, 1], W[1, 1]],\n",
    "        verbose=False,\n",
    "    )\n",
    "    print(\n",
    "        f\"{method:9}: \"\n",
    "        f\"naive {tmi.values[0]:.3f} +/- {tmi.errors[0]:.3f}, \"\n",
    "        f\"corrected {tmi.values[0]:.3f} +/- {tcov[0,0]**0.5:.3f}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "857bddaf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot the weighted decay distributions and the fit result\n",
    "sws = sweight(toy[0])\n",
    "scow = cow(toy[0])\n",
    "\n",
    "bins = 50\n",
    "t = np.linspace(*trange, 400)\n",
    "\n",
    "for i, (w, method, slope) in enumerate(\n",
    "    zip(\n",
    "        (sws, scow),\n",
    "        (\"sWeights\", \"COWs\"),\n",
    "        fitted_slopes,\n",
    "    )\n",
    "):\n",
    "    color = f\"C{i}\"\n",
    "    plot_binned(\n",
    "        toy[1],\n",
    "        bins=bins,\n",
    "        range=trange,\n",
    "        weights=w,\n",
    "        label=method,\n",
    "        color=color,\n",
    "    )\n",
    "    tnorm = np.sum(w) * (trange[1] - trange[0]) / bins\n",
    "    plt.plot(t, tnorm * t_signal_pdf(t, slope), color=color)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Weighted Events\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "209f4f45",
   "metadata": {},
   "source": [
    "## Write sWeights into a file\n",
    "\n",
    "There are many ways to store the sweights for later use. In the Python world, you can just pickle the arrays. Or you can use Numpy's npz format. A slightly more organized way is to use a Pandas data frame. We show that option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6eb670a9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m</th>\n",
       "      <th>t</th>\n",
       "      <th>sweight_ws</th>\n",
       "      <th>sweight_wb</th>\n",
       "      <th>cow_ws</th>\n",
       "      <th>cow_wb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.927542</td>\n",
       "      <td>0.075108</td>\n",
       "      <td>-0.678891</td>\n",
       "      <td>1.679019</td>\n",
       "      <td>-0.303654</td>\n",
       "      <td>0.906145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.468974</td>\n",
       "      <td>0.005580</td>\n",
       "      <td>1.217367</td>\n",
       "      <td>-0.217423</td>\n",
       "      <td>1.523312</td>\n",
       "      <td>-0.464189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.449125</td>\n",
       "      <td>0.152979</td>\n",
       "      <td>1.180231</td>\n",
       "      <td>-0.180283</td>\n",
       "      <td>1.374161</td>\n",
       "      <td>-0.310642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.558792</td>\n",
       "      <td>0.079876</td>\n",
       "      <td>1.171464</td>\n",
       "      <td>-0.171515</td>\n",
       "      <td>1.213886</td>\n",
       "      <td>-0.250459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.400035</td>\n",
       "      <td>0.028505</td>\n",
       "      <td>0.992147</td>\n",
       "      <td>0.007819</td>\n",
       "      <td>0.793680</td>\n",
       "      <td>0.264917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>0.930051</td>\n",
       "      <td>1.093816</td>\n",
       "      <td>-0.678957</td>\n",
       "      <td>1.679085</td>\n",
       "      <td>-0.302998</td>\n",
       "      <td>0.904166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>0.653689</td>\n",
       "      <td>0.081157</td>\n",
       "      <td>0.618199</td>\n",
       "      <td>0.381803</td>\n",
       "      <td>0.137224</td>\n",
       "      <td>0.670526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>0.382352</td>\n",
       "      <td>0.095900</td>\n",
       "      <td>0.881958</td>\n",
       "      <td>0.118018</td>\n",
       "      <td>0.560982</td>\n",
       "      <td>0.494386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>0.564565</td>\n",
       "      <td>1.170659</td>\n",
       "      <td>1.155841</td>\n",
       "      <td>-0.155891</td>\n",
       "      <td>1.149193</td>\n",
       "      <td>-0.195338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>0.718651</td>\n",
       "      <td>0.169675</td>\n",
       "      <td>-0.085891</td>\n",
       "      <td>1.085961</td>\n",
       "      <td>-0.226854</td>\n",
       "      <td>0.961263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1983 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             m         t  sweight_ws  sweight_wb    cow_ws    cow_wb\n",
       "0     0.927542  0.075108   -0.678891    1.679019 -0.303654  0.906145\n",
       "1     0.468974  0.005580    1.217367   -0.217423  1.523312 -0.464189\n",
       "2     0.449125  0.152979    1.180231   -0.180283  1.374161 -0.310642\n",
       "3     0.558792  0.079876    1.171464   -0.171515  1.213886 -0.250459\n",
       "4     0.400035  0.028505    0.992147    0.007819  0.793680  0.264917\n",
       "...        ...       ...         ...         ...       ...       ...\n",
       "1978  0.930051  1.093816   -0.678957    1.679085 -0.302998  0.904166\n",
       "1979  0.653689  0.081157    0.618199    0.381803  0.137224  0.670526\n",
       "1980  0.382352  0.095900    0.881958    0.118018  0.560982  0.494386\n",
       "1981  0.564565  1.170659    1.155841   -0.155891  1.149193 -0.195338\n",
       "1982  0.718651  0.169675   -0.085891    1.085961 -0.226854  0.961263\n",
       "\n",
       "[1983 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame()\n",
    "df['m'] = toy[0]\n",
    "df['t'] = toy[1]\n",
    "df['sweight_ws'] = sweight.get_weight(0, toy[0])\n",
    "df['sweight_wb'] = sweight.get_weight(1, toy[0])\n",
    "df['cow_ws'] = cow.get_weight(0, toy[0])\n",
    "df['cow_wb'] = cow.get_weight(1, toy[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1630d021",
   "metadata": {},
   "source": [
    "Pandas data frames can be saved in multiple formats (see Pandas documentation). The feather format is particularly good.\n",
    "\n",
    "However, in the particle physics world, you probably want to save the table as a ROOT file. This is easy with uproot. It will convert the data frame into a ROOT TTree automatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4507ad31",
   "metadata": {},
   "outputs": [],
   "source": [
    "import uproot\n",
    "\n",
    "with uproot.recreate('outf.root') as f:\n",
    "   f['tree'] = df"
   ]
  }
 ],
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