{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a8dfc0e",
   "metadata": {},
   "source": [
    "# Analisi della molla statica 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ff20b69",
   "metadata": {},
   "source": [
    "## Import delle librerie e set di variabili gloabali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "f34c5b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sc\n",
    "from scipy.stats import chi2\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "g = 9.806\n",
    "ug = 0.001"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d2ffea",
   "metadata": {},
   "source": [
    "## Lettura dei dati e calcolo delle deviazioni standard campionarie\n",
    "- Lettura del CSV\n",
    "- Creazione del data frame\n",
    "- Deviazioni standard\n",
    "- Permutazioni e calcolo dei Delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "08efb2be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'statica2.csv')\n",
    "\n",
    "def calcola_stats(df, prefix, err_arbitrario):\n",
    "  cols = [col for col in df.columns if col.startswith(prefix)]\n",
    "\n",
    "  def riga_stats(row):\n",
    "    valori = row[cols].dropna()\n",
    "    n = len(valori)\n",
    "\n",
    "    if n == 0:\n",
    "      return pd.Series({prefix: np.nan, f\"u{prefix}\": np.nan})\n",
    "    elif n == 1:\n",
    "      return pd.Series({prefix: valori.iloc[0], f\"u{prefix}\": err_arbitrario})\n",
    "    else:\n",
    "      media = valori.mean()\n",
    "      sigma = valori.std(ddof=1)\n",
    "      return pd.Series({prefix: media, f\"u{prefix}\": sigma})\n",
    "\n",
    "  stats = df.apply(riga_stats, axis=1)\n",
    "  df[prefix] = stats[prefix]\n",
    "  df[f\"u{prefix}\"] = stats[f\"u{prefix}\"]\n",
    "\n",
    "  return df\n",
    "\n",
    "df = calcola_stats(df, \"Dx\", err_arbitrario=0.01)\n",
    "df = calcola_stats(df, \"m\", err_arbitrario=0.0028867513)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "5494409f",
   "metadata": {},
   "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>m1</th>\n",
       "      <th>Dx1</th>\n",
       "      <th>Dx2</th>\n",
       "      <th>Dx3</th>\n",
       "      <th>Dx4</th>\n",
       "      <th>Dx5</th>\n",
       "      <th>Dx6</th>\n",
       "      <th>Dx</th>\n",
       "      <th>uDx</th>\n",
       "      <th>m</th>\n",
       "      <th>um</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49.246667</td>\n",
       "      <td>212.10</td>\n",
       "      <td>211.64</td>\n",
       "      <td>212.00</td>\n",
       "      <td>212.18</td>\n",
       "      <td>212.52</td>\n",
       "      <td>212.04</td>\n",
       "      <td>212.080000</td>\n",
       "      <td>0.284816</td>\n",
       "      <td>49.246667</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69.276667</td>\n",
       "      <td>150.92</td>\n",
       "      <td>150.26</td>\n",
       "      <td>150.02</td>\n",
       "      <td>150.16</td>\n",
       "      <td>150.40</td>\n",
       "      <td>150.18</td>\n",
       "      <td>150.323333</td>\n",
       "      <td>0.317847</td>\n",
       "      <td>69.276667</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88.966667</td>\n",
       "      <td>90.34</td>\n",
       "      <td>90.34</td>\n",
       "      <td>90.38</td>\n",
       "      <td>90.52</td>\n",
       "      <td>90.26</td>\n",
       "      <td>90.28</td>\n",
       "      <td>90.353333</td>\n",
       "      <td>0.092664</td>\n",
       "      <td>88.966667</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108.610000</td>\n",
       "      <td>29.82</td>\n",
       "      <td>30.18</td>\n",
       "      <td>30.10</td>\n",
       "      <td>30.20</td>\n",
       "      <td>30.10</td>\n",
       "      <td>30.40</td>\n",
       "      <td>30.133333</td>\n",
       "      <td>0.188750</td>\n",
       "      <td>108.610000</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           m1     Dx1     Dx2     Dx3     Dx4     Dx5     Dx6          Dx  \\\n",
       "0   49.246667  212.10  211.64  212.00  212.18  212.52  212.04  212.080000   \n",
       "1   69.276667  150.92  150.26  150.02  150.16  150.40  150.18  150.323333   \n",
       "2   88.966667   90.34   90.34   90.38   90.52   90.26   90.28   90.353333   \n",
       "3  108.610000   29.82   30.18   30.10   30.20   30.10   30.40   30.133333   \n",
       "\n",
       "        uDx           m        um  \n",
       "0  0.284816   49.246667  0.002887  \n",
       "1  0.317847   69.276667  0.002887  \n",
       "2  0.092664   88.966667  0.002887  \n",
       "3  0.188750  108.610000  0.002887  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "976d5531",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]\n",
      "6\n",
      "############################################################\n",
      "[ 61.75666667 121.72666667 181.94666667  59.97       120.19\n",
      "  60.22      ]\n",
      "[0.42678644 0.29951071 0.34168211 0.33107904 0.36966652 0.21026967]\n",
      "############################################################\n",
      "[-20.03       -39.72       -59.36333333 -19.69       -39.33333333\n",
      " -19.64333333]\n",
      "[0.00408248 0.00408248 0.00408248 0.00408248 0.00408248 0.00408248]\n"
     ]
    }
   ],
   "source": [
    "perm = [(x,i) for x in range(0,len(df)) for i in range(x+1,len(df))]\n",
    "\n",
    "este = np.array([df.Dx[x] - df.Dx[j] for x,j in perm])\n",
    "ueste = np.array([np.sqrt(df.uDx[x]**2 + df.uDx[j]**2) for x,j in perm])\n",
    "\n",
    "masse = np.array([df.m[x] - df.m[j] for x,j in perm])\n",
    "umasse = np.array([np.sqrt(df.um[x]**2 + df.um[j]**2) for x,j in perm])\n",
    "\n",
    "\n",
    "print(perm)\n",
    "print(len(perm))\n",
    "\n",
    "print(\"#\"* 60)\n",
    "print(este)\n",
    "print(ueste)\n",
    "\n",
    "print(\"#\"* 60)\n",
    "print(masse)\n",
    "print(umasse)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b3b6776",
   "metadata": {},
   "source": [
    "## Calcolo dei K e media pesata\n",
    "- Calcolo del K\n",
    "- Calcolo della media pesata (sui K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "22ee2969",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u_strum_m    = 0.004082\n",
      "u_strum_Dx   = 0.020412\n"
     ]
    }
   ],
   "source": [
    "res_b = 0.01\n",
    "res_c = 0.05\n",
    "\n",
    "# Incertezza strumentale su una differenza: res/sqrt(12) * sqrt(2) = res/sqrt(6)\n",
    "u_strum_m  = res_b / np.sqrt(6)\n",
    "u_strum_Dx = res_c / np.sqrt(6)\n",
    "\n",
    "# Massimo tra campionario e strumentale, punto per punto\n",
    "ueste_strum  = u_strum_Dx\n",
    "umasse_strum = u_strum_m\n",
    "\n",
    "# Worst-case scalare: prendi il massimo anche di ueste_strum\n",
    "uF_strum  = np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 )\n",
    "uDx_strum = ueste_strum\n",
    "\n",
    "# uK_strum  = np.average(np.sqrt( (1/este)**2 * uF_strum**2 + (F/este**2)**2 * uDx_strum**2 ))\n",
    "\n",
    "print(f\"u_strum_m    = {u_strum_m:.6f}\")\n",
    "print(f\"u_strum_Dx   = {u_strum_Dx:.6f}\")\n",
    "# print(f\"uF_strum     = {uF_strum:.6f}\")\n",
    "# print(f\"uK_strum     = {uK_strum:.6f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "2ad19283",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.06330605 0.07975344 0.10125841 0.06309237 0.07936924 0.06306327]\n",
      "[0.42727431 0.30020549 0.3422913  0.3317077  0.37022966 0.21125814]\n"
     ]
    }
   ],
   "source": [
    "F = masse * g\n",
    "uF = np.sqrt((g * umasse)**2 + (masse * ug)**2)\n",
    "\n",
    "uF = np.sqrt(uF**2 + uF_strum**2)\n",
    "ueste = np.sqrt(uDx_strum**2 + ueste**2)\n",
    "\n",
    "K = - F / este\n",
    "uK = np.sqrt((1/este)**2 * uF**2   +   (F / este**2)**2 * ueste**2  )\n",
    "\n",
    "\n",
    "print(uF)\n",
    "print(ueste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "5f59d6c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-medio: 3.2012390383768037\n",
      "sigmaC:  0.0038754337050082212\n"
     ]
    }
   ],
   "source": [
    "def mediaPesata(x, ux, dim = 0):\n",
    "  x_arr = x.to_numpy() if isinstance(x, (pd.Series, pd.DataFrame)) else np.asarray(x)\n",
    "  sigma_arr = ux.to_numpy() if isinstance(ux, (pd.Series, pd.DataFrame)) else np.asarray(ux)\n",
    "\n",
    "  w = 1.0 / (sigma_arr ** 2)\n",
    "\n",
    "  num = np.nansum(w * x_arr, axis=dim)\n",
    "  den = np.nansum(w, axis=dim)\n",
    "\n",
    "  media = num / den\n",
    "  sigma = np.sqrt(1.0 / den)\n",
    "\n",
    "  return media, sigma\n",
    "\n",
    "media, uA = mediaPesata(K, uK)\n",
    "print(\"K-medio:\", media)\n",
    "print(\"sigmaC: \", uA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02fc45a9",
   "metadata": {},
   "source": [
    "## Analisi con regressioni e grafici\n",
    "Ogni blocco ha:\n",
    "- Regressione\n",
    "- grafico\n",
    "- chi^2\n",
    "\n",
    "Nota: Ax + B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "7e75ec05",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "data = pd.DataFrame({\n",
    "    \"x\": este,\n",
    "    \"ux\": ueste,\n",
    "    \"y\": - F,\n",
    "    \"uy\": uF\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "313237da",
   "metadata": {},
   "source": [
    "### Regressione lineare \"OLS\"\n",
    "\n",
    "Non tiene conto dei nostri errori, un risultato puro e semplice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "aefe7756",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 1.277e+05\n",
      "Date:                Tue, 07 Apr 2026   Prob (F-statistic):           3.68e-10\n",
      "Time:                        16:16:38   Log-Likelihood:                -7.2416\n",
      "No. Observations:                   6   AIC:                             18.48\n",
      "Df Residuals:                       4   BIC:                             18.07\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0265      0.991      0.027      0.980      -2.724       2.777\n",
      "x              3.2011      0.009    357.408      0.000       3.176       3.226\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   1.155\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.275\n",
      "Skew:                          -0.058   Prob(JB):                        0.871\n",
      "Kurtosis:                       1.957   Cond. No.                         271.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "RISULTATI REGRESSIONE:\n",
      "Aols  = 3.20112 ± 0.00896\n",
      "Bols  = 0.02655 ± 0.99066\n",
      "R² = 0.99997\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jack/uni/lab/.venv/lib/python3.13/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "X = sm.add_constant(data[\"x\"])\n",
    "model = sm.OLS(data[\"y\"], X).fit()\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "# Estrazione parametri\n",
    "Bols = model.params[\"const\"]\n",
    "Aols = model.params[\"x\"]\n",
    "uBols = model.bse[\"const\"]\n",
    "uAols = model.bse[\"x\"]\n",
    "R2 = model.rsquared\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE:\")\n",
    "print(f\"Aols  = {Aols:.5f} ± {uAols:.5f}\")\n",
    "print(f\"Bols  = {Bols:.5f} ± {uBols:.5f}\")\n",
    "print(f\"R² = {R2:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "1d42b009",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WLwqTyWT38YKuLinnOR+qrFh+57vvvpvvHBmLe78o593bkVdBr6s4r7egZWjPmDFj8Ouvv+KVV15BvXr14OXlBbPZjKFDh6qybAEU+RLzjl5x1Gw2IygoCB9++KHd6ffuAcq7B+tehU0rCjc3N0Xn5N1r9+7diIqKwhNPPIFXXnkFQUFB0Gq1+Pjjj+3u6StuvUSuhA0XEZWYwMBAeHt7w2w2292jk1dISAguXrwIWZZtvhRdu3bNZj7LoVDXrl2zHhoI3D1B/MaNGzbNQM2aNfHHH3+gZcuWiu/lY/k9V69etflrtcFgwPXr1/HQQw8pGs8RlhpiY2Nt9qrl5ubi+vXr1mVqme/y5cv5/rJ++fJlm8PHgLtfOj/88EO8/vrrePPNNxETE2NtdJRkZs+4ceMc3lNSlGV69erVfI9duXLFZu+mv7+/3S+Elj0Ejiipe0HdW78sy7h69ar1fWvJ2cfHx6HlX1KKugwLWi4pKSk4efIkRo0aZXOon2UPXlHGKKqaNWvixIkTSE5OLnAvV0hICMxmM65evYo6depYH4+Pj0dqamq+vePFqeXkyZN4+OGHS7wBUbqeF+b27dvIzMy0ad4t2ViWxYEDB1CjRg0sXrzYJqOFCxc6+hKI6P/xHC4iKjFarRadO3fGgQMH8l26G7A9NC0yMhK3bt3C119/bX0sJycH27Zts3lOw4YNERAQgG3btsFoNFof/+KLL/Id7tOlSxfcunUr3xgAkJ2dXeh9aRo2bIjAwEBs2bLF5uqBn332WakcamZPq1atoNfrsWHDBps9Ajt27EBaWhoef/xxa61BQUH5aj169CguXbqEdu3a5Rvbzc0NixcvRkREBEaMGGG9DLqSzOxp2LAhWrVq5dB/Rbmq2VdffWVzCezTp0/j999/R9u2ba2P1ahRA7GxsTa1/vHHH/jll1/uO35BPD09ART/sLtdu3ZZL+8N3D3n586dO9b6GzZsiJo1a2L16tXWS/Tndb/lX1KKugwLWi4F7SFat25dvscsYzh6mGGnTp0gyzIWL16cb5plvbGsK/f+/jVr1thML64uXbrAZDLZvQKq0Wgs1vvHkfW8IEaj0eaQ4dzcXGzduhWBgYFo0KABgP9lmHfb8/vvv+O3335z+DUQ0V3cw0VEin366ac4fvx4vscHDx6Md955B6dOnULfvn3x3HPPoW7dukhJScG5c+dw8uRJ6+Fn/fr1w8aNG/HOO+9g8ODBqFSpEr744gvr4VKWv7C6ublh1KhRmDFjBl588UV06dIFN27cwM6dO/Odn9GzZ0/s27cPU6dOxalTp/Dwww/DZDIhNjYW+/fvx8qVK62XjL6XXq/HmDFjMGXKFLz44ovo2rUrrl+/jp07dxb7HK6iCgwMxKuvvorFixdj6NCh6NChAy5fvozNmzcjIiICPXr0sNY6duxYjB8/HgMHDkS3bt2sl4sOCQkp8DLuHh4e+PjjjzF48GAMGzYMGzZsQFhYWJEzU0PNmjXx/PPP4/nnn0dubi7Wr1+PgIAADB061DpPnz59sHbtWrzyyivo06cPEhISsGXLFtStW9duE1MUli+hM2fORGRkJLRaLbp166Z4HH9/fwwYMAC9e/e2Xhb+gQcesF4cRqPRYObMmRg2bBi6d++O3r17Izg4GLdu3cKpU6fg4+OD5cuXO/QalCjqMvTw8EDdunWxb98+1KpVCwEBAXjwwQcRFhaGxx57DCtXroTBYEBwcDC+/fZb6/218rIs2+joaHTt2hV6vR7t27fPd+hkQVq0aIGePXtiw4YNuHr1Ktq0aQOz2Yyff/4ZzZs3x8CBA/HQQw+hV69e2Lp1K1JTU/HYY4/hzJkz+Oyzz/DEE0/Y7C0vjmbNmqFfv374+OOPceHCBbRu3Rp6vR5XrlzB/v37MXHiRDz11FMOje3oem5P5cqVERMTgxs3bqBWrVrYu3cvLly4gBkzZlgvW9+uXTscPHgQb7zxBtq1a4fr169b3wPl7QbbRGWNDRcRKXbvDTMtevfujSpVqmD79u1YsmQJDh06hE8++QQBAQGoW7euzY04vb29sW7dOsycORPr16+Hl5cXnnnmGTRt2hSjRo2yOU9l4MCBkGUZa9aswfvvv4+HHnoIy5Ytw8yZM23m02g0WLJkCdauXYvdu3fj0KFD8PT0RPXq1TFo0CC7J5/n1a9fP5hMJqxatQpz585FWFgYli1bhgULFhRziRXdqFGjEBgYiI0bN2L27Nnw9/dH37598fbbb9vcz6d3797w8PBATEwMPvzwQ3h5eeGJJ57Af/7znwLvwQXcPXRt1apVGDhwIF5++WVs2rQJDzzwQJEyU8MzzzwDjUaDdevWISEhAY0aNcLkyZNRuXJl6zx16tTB+++/j4ULF2L27NmoW7cu5s6diz179jjcLHbq1AmDBg3Cl19+ic8//xyyLDvUcI0YMQJ//vknVqxYgYyMDLRs2RJTp0617uUBgObNm2Pr1q1YunQpNm7ciMzMTFSqVAmNGjWyubJcaVKyDGfOnIkZM2Zg9uzZMBgMGDlyJMLCwvDRRx9hxowZ2Lx5M2RZRuvWrRETE5PvqnyNGjXCm2++iS1btuD48eMwm834+uuvi9xwAcDs2bMRHh6OHTt2YO7cufD19UXDhg1trtI4c+ZMVK9eHZ999hm++uorVKxYEa+++qrdqxsWx3vvvYeGDRtiy5YtiI6OhlarRUhICHr06IGHH364WGM7up7fy9/fH3PmzMHMmTOxbds2VKxYEVOmTLG5Kmzv3r0RHx+PrVu34sSJE6hbty4++OAD7N+/X9U/uhCVB5Ks1pmsRER2rF27FrNnz8axY8dsLgd+L7PZjJYtW+LJJ5/EzJkzy7BCKgvXr19Hx44d8e677+KVV15RuxzFTp06hcGDB2PBggUO7+EgIqLygedwEZFqsrOzbX7OycnB1q1bUatWLZtmKycnJ99Vznbt2oXk5GQ0a9asTGolIiIicgQPKSQi1YwcORLVqlXDQw89hPT0dHz++eeIjY3Nd4nl3377DbNnz8ZTTz2FgIAAnD9/Hjt27EBYWBj3HhAREZHQ2HARkWoiIyOxY8cOfPHFFzCZTKhbt671RPq8QkJCUKVKFWzYsAEpKSnw9/dHz549MXbs2PveOJeIiIhITTyHi4iIiIiIqJTwHC4iIiIiIqJSwoaLiIiIiIiolPAcLgV+/fVXyLJscy8cIiIiIiJyPQaDAZIk2dwD0B42XArIspzv0tREREREROR6itoXsOFSQK/XQ5ZlNGzYEJIkqV0O4e4b3WAwQK/XMxNBMBPxMBMxMRfxMBPxMBPxMJP/OXPmTJHm4zlcREREREREpUTIhuuzzz7DM888g4iICDRv3hxDhw5Fdna2dfrhw4fRo0cPREREoHPnzvj000/zjZGbm4v3338frVu3RpMmTfDSSy8hNja2LF8GERERERG5OOEarmXLlmHGjBno2rUrVq1ahffeew/Vq1eHyWQCAPz0008YOXIkmjRpgpiYGHTp0gUTJ07E/v37bcaZOXMmtm/fjrfeeguLFi1Cbm4uhgwZgrS0NDVeFhERERERuSChzuGKjY3F4sWLsXTpUjz++OPWxzt37mz997Jly9CoUSO89957AIAWLVogLi4OCxcuxFNPPQUAuHnzJnbs2IGpU6eiT58+AICIiAi0b98eW7ZswbBhw8rwVRERERERkasSag/Xzp07Ub16dZtmK6/c3FycOnXK2lhZdO3aFZcuXcL169cBACdOnIDZbLaZLyAgAK1bt8axY8eKVaOrnxwoIl6mXzzMRDzMREzMRTzMRDzMRDzMRBmh9nD9/vvvCAsLw9KlS7FhwwakpaWhYcOGGD9+PBo3boxr167BYDCgdu3aNs+rU6cOgLt7yKpXr47Y2FgEBQXB398/33w7duwodp33a7pMJhMMBkOxfw9Rcej1emi12jL/vfyjhHiYiZiYi3iYiXiYiXiYiXJCNVx37tzB2bNn8ddff2Hq1Knw9PTE8uXL8fLLL+PgwYNISUkBAPj5+dk8z/KzZXpqaip8fX3zje/n52edpzjMZnO+N5skSTCbzbh582aJ/A4qGsv9D7jy2+fv748qVapAkiTrMironhGSJDk8Le+4sizDZDJBq9Vaf29Rn1taNZX3ce/3XLPZbJNJWdTEce//XAAwGo35cintmpxt3LKsybL90ukK/nokUr3OXFNpfaaoXW95qOl+4zr6meIqy9AeoRouWZaRmZmJBQsW4KGHHgIANG7cGB06dMDGjRsRGRmpcoX/u/dA3jeYRqOBTqfDzZs3kZycjEqVKsHLy8s6j0ajsT733nDybjyUTCvucy01mc1mpx/X8nwg/4pRWsswbwMj4jI0m83IzMzEnTt3YDabERwcbN39bzQa8z1Xp9PZfDG/tx7Llw97e24t9+EwmUwwm82QZRlGoxE6nc46zfKYvXEt65S9cQFYx81Lq9VCq9XaHVeSpEJfq2WavddanHGLswzd3NwKfK1FGbcoyzA3N9c6VnFfa2ll4+gyLM1s7n1/F/W1FmVcS733/hGvtJahZVx7Nam1DC3jirKNyPtvV9tGlMZ2tiTGtfeZ4krbiLLczirZRhgMBpvPFFfZRuQd1/Kd594/mNkjVMPl5+eHgIAAa7MF3D33qn79+rh48SK6desGAPmuNJiamgoA1kMI/fz8kJ6enm/81NTUfIcZOsLejd5MJhNSUlJQuXJlBAUF2Uwrb527aOPe783uSn9VyTvN0vTfuXMHVapUsc5T2F9uNRqNteGzx94x2/a+wFvmtUzLu/Gy9/zCjgXP+wGg9LmOvtbSGhco/Lj3wl5rcerVarU2X1aUPLew16pGNkDhy7C039/23O+1FjZNo9EUePNQLsP7TyvOuED+15r3i5urbSNKY10uiXHtfaaUp/d3QeNaiLqdlWXZ7rarvG8j8tJoNEVqtgDBGq66devi2rVrdqfl5OSgZs2a0Ov1iI2NRZs2bazTLPfXspzbVbt2bcTHxyMlJcWmwYqNjc13/pcj8u7dsLBsoL29vQtc+PdrCu73O0t6WnkYN2/joUa9xXluWYzr7e2NO3fuWP86WFLj3m+6ZR3J23AV9bmlVVN5Hrcoz703k9KuiePef7rlj0X2cinNmpxt3LKuqSjbLZHqddaaSuszRYR6nb2moozryGeKKy3Dewl1lcL27dsjOTkZFy5csD6WlJSEc+fOoUGDBnBzc0Pz5s1x4MABm+ft3bsXderUQfXq1QEAkZGR0Gg0OHjwoHWelJQUnDhxAm3bti3V16Bk4ROVNr4fiYiIiNQl1B6uJ554AhERERg9ejTeeustuLu7Y8WKFXBzc8OAAQMAAK+99hoGDx6MadOmoUuXLjh16hT27NmD6Oho6zhVqlRBnz59MHfuXGg0GgQHB+Pjjz+Gr68v+vfvX6wa+QVWPMxEPGpcHZEKx0zExFzEw0zEw0zEw0yUEWoPl0ajwYoVK9CkSRNMmTIFb7/9Nnx8fLBp0yZUqlQJAPDoo49i0aJF+Pnnn/HKK69gz549mDlzJrp06WIz1qRJk9CnTx989NFHeOONN6DT6bBmzRq7Vy9Uqrx/wV+0aBHCw8Pz/de9e3cAQHh4OFatWmWdf+fOnfjiiy+KNHaHDh2sN60GgKioKOu4jijscBy1HTt2DEOGDMGjjz6Kxo0b45lnnsH69evznZy5c+dOhIeHIzExscCxkpKSMGvWLHTq1AkRERFo2bIlnn/+eaxdu7aUX4VykiTZveoaqYeZiIm5iIeZiIeZiIeZKCfUHi4ACAwMxAcffFDoPB07dkTHjh0LncfNzQ3jxo3DuHHjSrI8APe/SEN54OHhgXXr1uV7DAC2bt2KatWqWR//7LPP4OXlhaefflrx73n99deRmZnpcJ1FPYerrK1evRrvv/8+nnzyScydOxeenp44duwY5s6di++//x6LFy8u9KTSvIxGI1588UWkpaVh+PDh1nMUf/nlFxw5cgRDhgwp3RejkOWqUqI2wq6ImYiJuYiHmYiHmYiHmSgnXMMlOiXX3HdmGo0GTZo0sTutoMcdUbNmzWKPUdINsOXy2UVtiO51/vx5fPjhh+jVqxfmzJljfbxly5aoW7cuJkyYgE2bNmHQoEFFGu+HH37An3/+iY0bN+Kxxx6zPt6tW7cCL5OvNqPRyLvQC4aZiIm5iIeZiIeZiIeZKCPUIYXkHPIeUjho0CD88MMP+Oabb6yHHi5atKjIY917SKHl8Lrz589j6NChaNKkCTp16oRdu3ble+4333yDvn37okmTJmjZsiWmTp1qs7csMzMT7733Hjp37my9n9uUKVPy3VbAcphjTEwM2rdvj0aNGiE5Odlaz9NPP42IiAi0adMG0dHR+e4vca8NGzZAkiSMGjUq37RevXqhVq1a+fYeFsZyI23LYbV5OdoUEhEREVHZ4Lc1KpDRaLT5z97evalTp6J+/fp4+OGHsXXrVmzduhXPPfdcsX/32LFjERkZiSVLlqBevXqIiorCpUuXrNP379+P1157DWFhYVi0aBHGjh2LQ4cOYeLEidZ5srOzYTKZ8NZbbyEmJgZvvvkmfvzxR7z++uv5ft/BgwfxzTffYOLEiVi6dCm8vLywZs0aTJo0CZGRkVi+fDmGDRuG9evX21ygxZ4ff/wR4eHhCAkJyTdNo9Ggffv2iIuLw61bt4q0LOrVqweNRoNJkybh5MmTyM3NLdLziIiIiEh9PKSQ7MrMzESDBg1sHps7dy569uxp81jdunXh4+MDLy+vEj3U8IUXXsALL7wAAGjatCmOHj2KAwcO4PXXX4csy5g7dy66du2KmTNnWg8prFy5MoYPH47XX38dDz74IAIDAzF9+nTrmEajEdWrV8eAAQNw+fJlhIaGWqcZDAbExMTAy8sLAJCeno6FCxdi6NChePvttwEArVu3hl6vx5w5c/DKK6+gQoUKdmu/desWwsPDC3xtVatWBQDcvHkTwcHB910WtWrVQlRUFD744AMMGTIEer0ejRo1QpcuXfD8888XelM+IiIiImeVlZWFixcvom7dugBg/benp6fKlSnDb2oKOXqukOHKPzCnpN1/xhKm8feFvla1+894Dw8PD2zcuNHmsRo1apRUWfcVGRlp/beXlxeqVauGmzdvAgAuX76MGzduYMKECdY9b5IkoVmzZtBoNDh79iwefPBBAMCuXbuwdu1aXL161eZwwytXrtg0XM2bN7c2WwDw66+/IjMzE0899ZTNVQVbtWqF7Oxs/P3332jWrFmpvf57vfjii+jatSsOHz6MH374ASdPnsTMmTNx8OBBrFu3TrhDC3kSrXiYiZiYi3iYiXiYiXjKKpOsrCycPXvWetSQ5d9suFyA0jeZKSEZ15o/D6hxgQOtFrXO7YI2KEDR0zQaDSIiIkqnpiK49/L9er3eeihdUlISAOCNN96w+9x///0XAHDo0CGMGzcO/fr1w1tvvYWAgADcuXMHb7zxBnJycmyeExQUZPOz5Xf06tWr0N9hT3BwcKHTLdOqVKlS4Dz2VKpUCf369UO/fv1gMBgwZcoU7Ny5E0eOHLnvVTvLkiRJPJFWMMxETMxFPMxEPMxEPGWRSVZWFrKyspCSkoLchBT8vXYPvDs3Q25uLlJSUuDp6elUTRcbrjKgDQpAzVOfqLaHS2mzJbqAgAAAwJQpU9CoUaN80ytXrgzg7nle9erVs7nv1w8//GB3zHubaH9/fwDA4sWL7TZG1atXL7C+xx57DF988QX+/fdf6+GDFrIs4+jRo6hRo0aRDicsiF6vx5AhQ7Bz505cunRJqIaLiIiIqDjOnz+Ps6fPIPC7c3h0/88AgJ0p/0DvKeHw4cNo0qQJHnnkEZWrLDo2XA5w5DLkjhzW5yz0en2+PUalqXbt2qhSpQri4uIwYMCAAu8FkZ2dne8vMEW9QXPTpk3h6emJmzdv4sknn1RU36BBg7B7924sXLgQs2fPtpm2e/duxMbGYvLkyUUeLzk5GT4+PvnO1bpy5QoA+1cvVJMsyzAajdDpdDwMRBDMREzMRTzMRDzMRDxlkYn2z6tovOhz+MbdwcXwqvi55YPIhQEak9Ypb9HEhkshZwy5tNWuXRu7du3C4cOHUalSJVSuXLlYe2/uR5IkREVFYezYscjMzMTjjz8OLy8v/PPPPzh69CjeeusthIaGolWrVnjvvfewZMkS64U3Tp48WaTf4efnh9GjR+ODDz7AzZs30axZM2i1WsTFxeHrr7/GokWLCtyVXb9+fYwdOxbvv/8+0tPT0bt3b3h4eODEiRNYt24dOnbsiAEDBuR73pEjR+Dt7W3z2IMPPoi///7bel+vRo0aQafT4cKFC/j4449RrVo1xQ1hWeB6Ih5mIibmIh5mIh5mIp7SysSUkIyEmR/Dd9OX0IbXQubil/HdpUu4kuyHGgG+qOyTgo4dOyg+LUNtbLio2IYNG4Zr165h3LhxSE1NxciRI+3eg6okdenSBX5+fli2bJl1r1VISAjatGmDihUrAgD69++P69evY+PGjVi1ahUiIyPx0UcfoW/fvkX6HS+//DKCg4OxZs0abNy4ETqdDjVr1kS7du3ue+zyyy+/jDp16mDNmjUYO3YsDAYDQkND8e6772LAgAF2L3IxYcKEfI+9+eab6NWrFzp37oyvv/4a69atQ05ODqpUqYKnn34aw4cPh4+PT5FeDxEREZGIZJMJqes/R+KsGMAso+KsN+E3pCeSUlNRJzcZPZ5ujUoVPPHt8SPw9/d3qvO3AECS+WeDIjtz5gxkWUZERITdw9cslxr38PBQqULXI8tygYcUkjrvS1mWYTAYoNfrmYkgmImYmIt4mIl4mIl4SjqT7B/P4k5UNHJP/wXfAd0QOOlV6CrdvfVOYmIiDhw4gM6dOwOA9d+BgYHF/r0l4cyZMwBw3wvNcQ8XERERERGVKeOdJCTOWI60T/bCrVEYQvYth8ejtveA9fT0RMOGDa17tPL+25mw4VKIf10RDzMRDy/hKx5mIibmIh5mIh5mIp7iZCIbjUhduxuJc1YCkoSKH7wDv0FPQ9Jq883r6elps/dIzVsWFQcbLgfwC744mIV4mIl4mImYmIt4mIl4mIl4ipNJ1venER8Vjdzzl+A7sDuCJg4vd7cvsocNlwMcuSw8lY68pyAyEzHIsgyz2QyNRsNMBMFMxMRcxMNMxMNMxONIJsZbCUh4bxnStx2Ae9N6CDnwMTya1ivlSsXBhkshXmNEPGyAxWMymexeiZHUw0zExFzEw0zEw0zEU9RMZKMRKSt3ImnuakCvQ6V578L3hW6QXCxPNlwljA0ZiYTvRyIiIlJD1re/In78fOT+cRl+Q3oicPwwaCv4qV2WKthwlRDLyYOZmZlOefUUKp8yMzMB8IRjIiIiKhvGm/FImLYU6Z8egvujDVD9UAzcG4erXZaq2HCVEK1Wi4CAANy+fRsA4OXlxcPcygDvw2WfLMvIzMzE7du3ERAQAK2dK/8QERERlRTZYERKzA4kzl0NydMdlRZEwbd/F5c7fNAeNlwKFfalvkqVKgBgbbqobPAcroIFBARY35dliQ2eeJiJmJiLeJiJeJiJeO7NJPP4z4iPiobhYhz8XnoGgVFDoQ3wVak68bDhckBBX+4lSULVqlVRuXJlGAyGMq6KyJZer1flQ0qSJH44CoaZiIm5iIeZiIeZiCdvJsZ/biN+yhJk7D4Mj+aNEPz1NLg3rKtyheJhw+WA++1R0Wq13DiUER5SKB5mIh5mIibmIh5mIh5mIh5ZlmHOyUXqx9uRNG89NN6eqLxkInye68yMCsCGSyFe9U08RqORF4UQDDMRDzMRE3MRDzMRDzMRS+aRHxA/fj6MV/+F/9DeqPDuy9D6+ahdltDYcBERERERUaEM128hYdIiZHx5FO4tGqHK6hlwb8DDB4uCDRcREREREdkl5+QieckWJM1fD42fDyovmwy3px+Hm5ub2qU5DTZcRERERESUT8ZX3yNhwgIY4v6F//DnEDh2CCQfL14cTiE2XArxZEDxMBPxMBPxMBMxMRfxMBPxMJOyZ7j2L+InLUTmvhPwiHwYVTbMglt4KADejscRbLgcwDeZOCRJ4om0gmEm4mEmYmIu4mEm4mEmZcucnYPkxZuRvGAjNBX8ERwzHd4929t892UmyrHhIiIiIiJycRkHv0X8xIUw3riNgBH9UOHtwdD4eKldVrnAhssB3JUqDlmWYTQaodPpmIkgmIl4mImYmIt4mIl4mEnpM1y+cffwwYPfwbPdY6j6yQdwq1uzwPmZiXJsuBTifbjEw0zEw0zEw0zExFzEw0zEw0xKhzkzG8kLNyJ58SfQVgxA8OoZ8O7+eJGaKGaiDBsuIiIiIiIXIcsyMvcdR/ykRTDeSkDAG8+jwpsDofH2VLu0cosNFxERERGRC8i9FIf4CQuQdfgUvDq2QNXt8+BWp4baZZV7bLiIiIiIiMoxc0YWkuZvQPLSLdBVqYgq62fB66lInoNVRthwKcQ3pnh0Or6NRcNMxMNMxMRcxMNMxMNMHCfLMjL2HEXC5EUwxSejwugXEDB6IDSe7sUal5kow6XlADZd4pAkiXkIhpmIh5mIibmIh5mIh5k4Lvfvq3cPH/zmR3h1aoWKM0dDHxpS7HGZiXJsuBzAy8KLQ5ZlmM1maDQaZiIIZiIeZiIm5iIeZiIeZqKcOT0TSfPWIXn5NuhCKqPKpjnw7tS6xMZnJsqx4VKIl8EUj8lkgkajUbsMyoOZiIeZiIm5iIeZiIeZFI0sy8jYdRjxU5fAnJSCCu+8iIA3nofGo3iHD9rDTJRhw0VERERE5MRy/7yM+PHzkXX8F3h3bYOgGaOgr1lV7bLo/7HhIiIiIiJyQub0TCR+sAYpK7ZDX6Mqqm75EF4dm6tdFt2DDRcRERERkRORZRnpO79CwtQlMKemI/DdlxHwen9I7m5ql0Z2sOFSiCcHiofHEIuHmYiHmYiJuYiHmYiHmdjKOX8J8VHRyD75O7y7P3738MHqwWVaAzNRhg2XA9h0iUOSJN4LQjDMRDzMREzMRTzMRDzM5H9MqelIen81UlbthD40BFW3z4NXu8fKvA5mohyXlgN4WXhx5L1qJDMRAzMRDzMRE3MRDzMRDzP5/8MHtx1AwvRlMGdkIXDiMAS82heSm161eixcNROl2HApxMvCi8dgMECvV2ejQ/YxE/EwEzExF/EwE/G4ciY5Z/6+e/jgD2fg80wHBE1/A7pqldUuy6UzcQQbLiIiIiIigZhS0pA4eyVS1+yC/sGaqLpzPrzaPKJ2WeQgNlxERERERAKQzWakfbIPCTOXQ87KQdDU1+A/rA8kPb+yOzOmR0RERESkspzf/8SdqGjk/HQOPs8+iaBpr0NXpaLaZVEJYMNFRERERKQSU1IqEmetQOq6z+FWLxTVdi+CZ6smapdFJYgNl0KSJPGKLAKRJAlubrzJn0iYiXiYiZiYi3iYiXjKcyay2Yy0TXuQMHMFYDAiaMYo+L/SC5Lgl1wvz5mUFrETJSIiIiIqZ7J/OY/4qPnI+fUCfPo+haApI6ALDlK7LColbLgcwPtwiUOWZZhMJmi1WmYiCGYiHmYiJuYiHmYinvKWiSkhGQkzP0bapi/hVr8Oqu1ZAs/mjdQuS5HylklZYMOlEO/DJR6z2QytVqt2GZQHMxEPMxETcxEPMxFPechENpmQuv5zJM6KAcwyKs56E35Degp/+GBBykMmZck5UyYiIiIicgLZP57Fnaho5J7+C74DuiFw0qvQVaqgdllUhthwERERERGVMOOdJCTOWI60T/bCrVEYQvYth8ejDdQui1TAhouIiIiIqITIRiNS1+5G4pyVgCSh4gfvwG/Q05B4CJ7LYsOlEE8OFI/OSY9/Ls+YiXiYiZiYi3iYiXicKZOsU6cRPy4auecvwXdgdwRNHA5tUIDaZZU4Z8pEBFxaDmDTJQ7eF008zEQ8zERMzEU8zEQ8zpKJ8VYCEt5bjvRt++HetB5CDnwMj6b11C6rVDhLJiJhw+UAXhZeHLIsw2w2Q6PRMBNBMBPxMBMxMRfxMBPxiJ6JbDQiZeVOJM1dDeh1qDTvXfi+0A2SRqN2aaVG9ExExIZLIV4WXjwmkwmacrxhc0bMRDzMREzMRTzMRDyiZpL13W+Ij4pG7h+X4TekJwLHD4O2gp/aZZUJUTMRFRsuIiIiIqIiMt6MR8K0pUj/9BDcH22A6odi4N44XO2ySGBsuIiIiIiI7kM2GJESswOJc1dD8nRHpQVR8O3fpVwfPkglgw0XEREREVEhMo//jPioaBguxsHvpWcQGDUU2gBftcsiJyFUS75z506Eh4fn++/DDz+0mW/79u3o3LkzIiIi0KNHDxw5ciTfWGlpaZgwYQKaNWuGpk2bYvTo0bh9+3axa+TJgeLhMcTiYSbiYSZiYi7iYSbiUTMT4z+3cXPoVPzbewy0AX6o/tVKVJrzlss3W1xPlBFyD9fKlSvh6/u/N3JwcLD1319++SUmT56MESNGoEWLFti7dy9GjhyJTZs2oUmTJtb5xowZg4sXL2LatGlwd3fH/PnzMWzYMHz66afFvncAmy5xSJLEe0EIhpmIh5mIibmIh5mIR61M5FwDkpdvQ9JH66Dx9kTlxRPh07czvwOC64kjhFxaDRo0QGBgoN1pCxcuRLdu3TBmzBgAQIsWLfDXX39hyZIliImJAQD8+uuvOHHiBFatWoXIyEgAQGhoKLp27YqDBw+ia9euZfI6qGzwMv3iYSbiYSZiYi7iYSbiKetMMr/5EfHj58Nw+Qb8h/ZGhXdfhtbPp8x+vzPgeqKMU+0PjIuLw5UrV9ClSxebx7t27YqTJ08iNzcXAHDs2DH4+fmhdevW1nlq166NevXq4dixY8WqQZZlXhpeILIsw2AwMBOBMBPxMBMxMRfxMBPxlGUmhuu3cPOlSfj3ubehrVQB1Q+vQsWZo9ls3YPriXJC7uHq3r07kpKSUK1aNfTt2xdDhw6FVqtFbGwsgLt7q/KqU6cODAYD4uLiUKdOHcTGxiI0NDRf5127dm3rGEREREREck4ukpdsQdL89dD4+aDy8inw6f0E9+BQiRGq4apUqRJGjRqFxo0bQ5IkHD58GPPnz8etW7cwZcoUpKSkAAD8/GxvKmf52TI9NTXV5hwwC39/f5w9e7bYddrr6CVJKrDTt6ywhU0vreeW93Hz7nFUo97iPLe8jmvJxPKzCDWV93GL8lx7e+ed8bU627iFPdcyraw/U5xt3LKsSe3PlPKwDEt6XKWfKUrrzfz6e8RPWABj3E34D+uDCmOHQOPrbTMvs8k/riOfKa6yDO0RquFq06YN2rRpY/05MjIS7u7uWLduHUaMGKFiZbYMBoPNXz00Go315EGDwZBvfjc3NwB378ptNpttpul0OkiSBLPZDJPJZDPNMq5l1+299Hp9geNqtVpotVrIsgyj0WgzTZIk63ONRmO+N0xpjVuU1wrkX4aFjZt3hVc6ruW1SpKk+LUWZVygbJehZVx7NZVWNpbXmncZ5n1dlmmFLUO13t+FLcOyfH8DZbONsLwey7bLVbYRxRnX8lpLcxthNpvzfaaU922EvXFF2Ubk/berbSNE/R5h7zOlJLYRxribSJ66BFkHvoNH66aoumE2tHVrwmQywZTn+WpvI0T9HnHvZ4qrbCPyjmv5zpN3+10QoRoue7p06YLVq1fjwoUL8Pf3B3D3ku+VKlWyzpOamgoA1ul+fn64efNmvrFSUlKs8xSH5Y1R0LSC5H0j30uj0RR4ic28wSsd937PLewqM6U1bmGvFSh8Gd47bt6VzJFx7/3yac/9Xqsa2ZTkMizuuPa+wFvmtUwr7LWq9f4uzjpXWtmU5jZCp9PZ3XaV921Eccct7W2ERqMp8DOFy/D+04ozLuD4Z0p53EaI+j3C3mdKcZahxmhCyuJPkLxwIzQV/FE5Zhq8e7SHRqOBLMvCbSNE3c7Ksmx321XetxF5aTSaIjVbgBM0XHnVrl0bABAbG2v9t+VnvV6PGjVqWOc7efJkvq7z8uXLCAsLK1YNkiRZ/7M37X7PdWRacZ7rCuNa/vKnRr3FeW55HvfeTESoqTyPW5TnFrSeONtrdbZxC5suy3Kh2y9ne63lZTur5mdKeVmGJT2uks+UwqZlHPwW8RMXwnjjNgJG9EOFtwdD4+NV4vWW1XPVHNeRzxRXWob3Ev4qhXv37oVWq0X9+vVRo0YN1KpVC/v37883T8uWLa3ht23bFikpKTh58qR1nsuXL+P8+fNo27ZtsWtSsoCpdBXWAJM6mIl4mImYmIt4mIl4SiITw+Ub+PeFcbj5QhT0tUJQ49g6BE0ZYdNsUdFxPVFOqD1cr7zyCpo3b47w8HAAwNdff41t27Zh8ODB1kMIR40ahbFjx6JmzZpo3rw59u7di9OnT2Pjxo3WcZo2bYrIyEhMmDAB48aNg7u7O6KjoxEeHo5OnToVu86iHq9JpU+WZZhMJmi1WmYiCGYiHmYiJuYiHmYinuJkYs7MRvLCjUhe/Am0FQMQvHoGvLs/zmyLieuJckI1XKGhofj0009x8+ZNmM1m1KpVCxMmTMCgQYOs83Tv3h1ZWVmIiYnBihUrEBoaisWLF6Np06Y2Y82fPx+zZ8/GlClTYDQaERkZiUmTJhX7zthKrkhCZcNsNhd4bC6pg5mIh5mIibmIh5mIR2kmsiwjc99xxE9eDOPNeAS88TwqvDkQGm/PUqzStXA9UUaS2UEU2ZkzZyDLMiIiItjRC8JygnNhFzKhssVMxMNMxMRcxMNMxKM0k9xLcYifsABZh0/Bq2MLBP13NNzq1CiDSl0H15P/OXPmDAAgIiKi0PmE2sNFRERERKSUOSMLSfM3IHnpFuiqVESV9bPg9VSkyzcEJAY2XERERETklGRZRsaeo0iYvAim+GRUGP0CAkYPhMbTXe3SiKzYcCnEv5SIh8cQi4eZiIeZiIm5iIeZiKegTHL/vnr38MFvfoRXp1aoOHM09KEhZVyda+J6ogwbLgew6RKHJElc6QXDTMTDTMTEXMTDTMRjLxNzeiaS5q1D8vJt0IVURpVNc+DdqbVKFboerifKseFyAC8LLw5Zlq15MBMxMBPxMBMxMRfxMBPx5M0EADJ2HUb81CUwJ6WgwjsvIuCN56Hx4OGDZYnriXJsuBTiRR3FYzQaodfr1S6D8mAm4mEmYmIu4mEm4jEajZBjbyBhwnxkHf8F3l3bIGjGKOhrVlW7NJfF9UQZNlxEREREJCRzeiaS31+FtFU7oa9RFVW3fAivjs3VLotIETZcRERERCQUWZaRvvMrJExdAnNKOiq8+zIqvN4fkrub2qURKcaGi4iIiIiEkXP+EuKjopF98nd4d38cflNehWet6jxfiJwWGy6FuLKLh5mIh5mIh5mIibmIh5mox5SajqT3VyNl1U7oQ0NQdfs8eD7+KIxGo9ql0T24nijDhssBfJOJQ5IknrQpGGYiHmYiJuYiHmaiDlmWkb7tABKmL4M5IwuBE4ch4NW+kNzuZsFMxML1RDk2XERERESkipwzf989fPCHM/B5pgOCpr8BXbXKapdFVKLYcDmA9+EShyzLMBqN0Ol0zEQQzEQ8zERMzEU8zKTsmFLSkDh7JVLX7IL+wZqounM+vNo8km8+ZiIeZqIcGy6FeB8u8TAT8TAT8TATMTEX8TCT0iWbzUj7ZB8SZi6HnJWDoKmvwX9YH0j6gr+SMhPxMBNl2HARERERUanL+f1P3ImKRs5P5+Dz7JMImvY6dFUqql0WUaljw0VEREREpcaUlIrEWSuQuu5zuNULRbXdi+DZqonaZRGVGTZcRERERFTiZLMZaZv2IGHmCsBgRNCMUfB/pRckHb9+kmvhO14hnhwoHl6aVDzMRDzMREzMRTzMpGRk/3oB8eOikfPrBfj0fQpBU0ZAFxzk0FjMRDzMRBk2XA5g0yUOZiEeZiIeZiIm5iIeZlJ8poRkJPx3BdI27oFb/TqotmcJPJs3cng8ZiIeZqIcGy4H8LLw4pBlGSaTCVqtlpkIgpmIh5mIibmIh5k4TjaZkLr+cyTOigHMMirOehN+Q3oW+/BBZiIeZqIcGy6FeBlM8ZjNZmi1WrXLoDyYiXiYiZiYi3iYiXLZP53DnXHzkHv6L/gO6IbASa9CV6lCiY3PTMTDTJRhw0VEREREihnvJCFxxnKkfbIXbo3CELJvOTwebaB2WUTCYcNFREREREUmG41IXbsbiXNWApKEih+8A79BT0PiHg8iu9hwEREREVGRZJ06jfhx0cg9fwm+A7sjaOJwaIMC1C6LSGhsuBTiyYHi4THE4mEm4mEmYmIu4mEm9hlvJSDhveVI37Yf7k3rIeTAx/BoWq9MfjczEQ8zUYYNlwPYdIlDkiSu9IJhJuJhJmJiLuJhJvnJRiNSVu5E0tzVgF6HSvPehe8L3SBpNGXy+5mJeJiJcmy4HMDLwotDlmVrHsxEDMxEPMxETMxFPMzEVtZ3vyE+Khq5f1yG35CeCBw/DNoKfmVaAzMRDzNRjg2XQrwsvHiMRiPveC4YZiIeZiIm5iIeZgIYb8YjYdpSpH96CO6PNkD1QzFwbxyuXj3MRDjMRBk2XEREREQE2WBESswOJM5dDcnTHZUWRMG3f5cyO3yQqLxiw0VERETk4jKP/4z4qGgYLsbB76VnEBg1FNoAX7XLIioX2HARERERuSjjP7cRP2UJMnYfhkezCAR/tRLuEQ+qXRZRucKGSyGeHCgeZiIeZiIeZiIm5iIeV8lEzjUgefk2JH20DhpvT1RePBE+fTsL+fpFrMnVMRNl2HA5gG8ycUiSxJM2BcNMxMNMxMRcxOMqmWR+8yPix8+H4fIN+A/tjQrvvgytn4/aZdnlKpk4E2aiHBsuIiIiIhdguH4LCZMXIWPPUXi0bIzgVe/BvX4dtcsiKvfYcDmA9+EShyzLMBqN0Ol0zEQQzEQ8zERMzEU85TUTOScXyUu3ICl6PTR+Pqi8fAp8ej/hFK+xvGbizJiJcmy4FOJ9uMTDTMTDTMTDTMTEXMRT3jLJ/PoU4ifMh+Hav/Af/hwCxw6Bxtdb7bIUKW+ZlAfMRBk2XERERETljOHav4iftBCZ+07AI/JhVFk/C27hoWqXReSS2HARERERlRPm7BwkL96M5AUboangj+CY6fDu2Z6HfhGpiA0XERERUTmQcfBbxE9cCOON2wgY0Q8V3h4MjY+X2mURuTw2XArxL0Ti0en4NhYNMxEPMxETcxGPM2ZiuHzj7uGDB7+DZ7vHUPWTD+BWt6baZZUYZ8ykvGMmynBpOYBNlzgkSWIegmEm4mEmYmIu4nG2TMyZ2UheuBHJiz+BtmIAglfPgHf3x53qNdyPs2XiCpiJcmy4HMDLwotDlmWYzWZoNBpmIghmIh5mIibmIh5nyUSWZWTuO474yYthvBmPgDeeR4U3B0Lj7al2aSXOWTJxJcxEOTZcCvEymOIxmUzQaDRql0F5MBPxMBMxMRfxiJ5J7qU4xE9YgKzDp+DVsQWqbvsIbnVqqF1WqRI9E1fETJRhw0VEREQkOHNGFpLmb0Dy0i3QVamIKutnweupSO5hIHICbLiIiIiIBCXLMjL2HEXC5EUwxSejwugXEDB6IDSe7mqXRkRFxIaLiIiISEC5f1+9e/jgNz/Cq1MrVJw5GvrQELXLIiKF2HApxF334uExxOJhJuJhJmJiLuIRIRNzeiaS5q1D8vJt0IVURpVNc+DdqbXaZalGhEzIFjNRhg2XA9h0iUOSJN4LQjDMRDzMREzMRTxqZyLLMjJ2HUb81CUwJ6WgwtuDETByADQernv4oNqZUH7MRDkuLQfwsvDiyHvVSGYiBmYiHmYiJuYiHjUzyf3zMuLHz0fW8V/g3bUNgt4bCf0D1cq0BhFxPREPM1GODZdCvCy8eAwGA/R6vdplUB7MRDzMREzMRTxlnYk5PROJH6xByort0NeoiqpbPoRXx+Zl9vudAdcT8TATZdhwEREREZUxWZaRvvMrJExdAnNqOgLffRkBr/eH5O6mdmlEVMLYcBERERGVoZzzlxAfFY3sk7/Du/vjCJoxCvrqwWqXRUSlhA0XERERURkwpaYj6f3VSFm1E/rQEFTdPg9e7R5TuywiKmVsuBTiyYHiYSbiYSbiYSZiYi7iKY1MZFlG+rYDSJi+DOaMLAROHIaAV/tCcuM5MEXB9UQ8zEQZNlwO4JtMHJIk8aRNwTAT8TATMTEX8ZRGJjlnLyJ+3Dxk/3AGPs90QND0N6CrVrlEf0d5xvVEPMxEOTZcRERERCXMlJKGxNkrkbpmF/QP1kTVnfPh1eYRtcsiIhWw4XIA78MlDlmWYTQaodPpmIkgmIl4mImYmIt4SiIT2WxG2if7kDBzOeSsHARNfQ3+w/pA0vMrlyO4noiHmSjHtV8h3odLPMxEPMxEPMxETMxFPMXJJOf3P3EnKho5P52Dz7NPImja69BVqViC1bkmrifiYSbKsOEiIiIiKgZTUioSZ61A6rrP4VYvFNV2L4JnqyZql0VEgmDDRUREROQA2WxG2qY9SJi5AjAYETRjFPxf6QVJx69XRPQ/3CIQERERKZT96wXEj4tGzq8X4NP3KQRNGQFdcJDaZRGRgNhwKcSTA8Wj418ShcNMxMNMxMRcxHO/TEwJyUj47wqkbdwDt/p1UG3PEng2b1RG1bkmrifiYSbKaNQuoCAZGRlo27YtwsPDcebMGZtp27dvR+fOnREREYEePXrgyJEj+Z6flpaGCRMmoFmzZmjatClGjx6N27dvl0htbLrEIUkSNBoNMxEIMxEPMxETcxFPYZnIJhNS1u7CtRYDkLH7CCrOehPVv4phs1XKuJ6Ih5koJ2zDtXTpUphMpnyPf/nll5g8eTK6dOmCmJgYNGnSBCNHjsRvv/1mM9+YMWPw7bffYtq0afjwww9x+fJlDBs2DEajsdi18cos4pBlGSaTiZkIhJmIh5mIibmIp6BMsn86h+udhiP+Px/Bu2tb1Ph+M/yHPstztcoA1xPxMBPlhNxSXLp0CZs3b8a4ceMwdepUm2kLFy5Et27dMGbMGABAixYt8Ndff2HJkiWIiYkBAPz66684ceIEVq1ahcjISABAaGgounbtioMHD6Jr164O18Y3l3hMJhM0GmH/duCSmIl4mImYmIt48mZivJOExBnLkfbJXrg1CkPIvuXweLSByhW6Hq4n4mEmygi5pGbOnIn+/fsjNDTU5vG4uDhcuXIFXbp0sXm8a9euOHnyJHJzcwEAx44dg5+fH1q3bm2dp3bt2qhXrx6OHTtW+i+AiIiInJZsNCJl5aeIazkAGfuOo+IH76D6wRVstojIIcI1XPv378dff/2FN954I9+02NhYAMjXiNWpUwcGgwFxcXHW+UJDQ/MdW1q7dm3rGERERET3yvnxLG48ORzxExbAu0d71Px+M/yHPANJq1W7NCJyUkIdUpiVlYU5c+bgrbfego+PT77pKSkpAAA/Pz+bxy0/W6anpqbC19c33/P9/f1x9uzZYtdp77BCSZIKPNzQ0vgVNr20nlvex5Vl2fqYGvUW57nldVxLJpafRaipvI9blOfmzaQsauK493+uZVpZf6Y427hlVZPxVgISZyxH+rYDcG/6EKrtXw6PpvXyzSdKvWX1XLXHVfqZona95aGmoozryGeKqyxDe4RquJYtW4agoCA8++yzapdSKIPBYLP3TKPRWC+PaTAY8s3v5uYG4O7xrmaz2WaaTqeDJEkwm835LhJiGVeWZbvj6vX6AsfVarXQarWQZTnfhUIkSbI+12g05nvDlNa4RXmtQP5lWNi4sixbs1A6ruW1SpKk+LUWZVygbJehZVx7NZVWNpbXmncZyrIMs9kMg8FgnVbYMlTr/V3YMizL9zdQdtuIvNsuV9lGFGdcy2stzW2EJbu8nynlfRthb1w1txFmgxHpa3Yh5aN1kHRaBH7wNvwH9YBs5/WU921ESb3WkhzX3meKK20jRP0ece9nSnneRhT2PSLv99DCCNNw3bhxA6tXr8aSJUuQlpYGAMjMzLT+PyMjA/7+/gDuXvK9UqVK1uempqYCgHW6n58fbt68me93pKSkWOdxlCRJ1g2fPZYQ7Mn7Rr6XRqMp8OTDvMErHfd+zy3sPgqlNW5hrxUofBkWNK4kSQ6Ne++Xz4LGLqwmNbIpjWXo6LjFXYZqvb+Ls86VVjaltY3Q6XSFrjuusI1wdNzS3EZIkgR3d/cCn8dleP9pxRkXAAw/nkV8VDQMf1yB34s9UWH8UGgr+Fm/4LnKNoLfIxwf1xW/R+j1+gKnl7dtxP2WYVGaLUCghuv69eswGAwYPnx4vmmDBw9G48aN8dFHHwG4e45W7dq1rdNjY2Oh1+tRo0YNAHfP1Tp58mS+rvPy5csICwsrkXrtLeD7LfTCppfWc8v7uHkPOVCj3uI8t7yOe++hNyLUVN7HLcr0guZzttfqbOMWNt3eulIWNTnbuKVRk/FmPBKmLUX6p4fg/mgDVD8UA/fG4ap/pjjTMiyrcZV+pqhdb3moqaifJ/bm5TK0T5iGq169eli/fr3NYxcuXMDs2bMxffp0REREoEaNGqhVqxb279+PJ554wjrf3r170bJlS+uep7Zt22Lp0qU4efIkWrVqBeBus3X+/HkMHTq0WHUqOV6TyoblMAMSBzMRDzMRE3MpW7LBiJSYHUicuxqSpzsqLYiCb/8ukPL8RZ6ZiIeZiIeZKCNMw+Xn54fmzZvbndagQQM0aHD3UqyjRo3C2LFjUbNmTTRv3hx79+7F6dOnsXHjRuv8TZs2RWRkJCZMmIBx48bB3d0d0dHRCA8PR6dOncrk9RAREVHZy8rKwsWLF1G3bl0A+N+/f76AO1HRMPx9DX4vPYPAqKHQBuS/wBYRUUkTpuEqqu7duyMrKwsxMTFYsWIFQkNDsXjxYjRt2tRmvvnz52P27NmYMmUKjEYjIiMjMWnSpEKPxSQiIiLnlpWVhbNnzyIkJAQA8Ofx7+H/wSYY9n0Lj2YRCP5qJdwjHlS5SiJyJZLMY+SK7MyZM5BlGREREYqO26TSY7kyjeUKN6Q+ZiIeZiIm5lI6EhMTceDAAbRqEYnUmF1wX7cTOj8fVJr2Bnz6di50WTMT8TAT8TCT/zlz5gwAICIiotD5uLuHiIiInI69QweDgoIQHx8PzU9/In7aZvglpeCPJrVRefwrkGpVhyEpCYGBgSpXTkSuhg2XQpIkuXw3LxLL5TyZiTiYiXiYiZiYS/Hce+jg2bNn4ZORA781e/HoX//iZrUAHOvXHMlBPvA49R30P2tRv359dO3atcAxmYl4mIl4mIlybLjI6XGFFw8zEQ8zERNzKR6D0YTYGymo5K1D1YM/o8ahX2H28cTpPq3xc50I/HsnA9X16WjfoS2CKwUV6V6czEQ8zEQ8zEQZNlwOKOpdpan0ybIMk8kErVbLTATBTMTDTMTEXByTmJiIlJQU3LqTgEvXbiNu/1p0+fFnhKSkwvBsB+hHPIfsc6cxvE173EnKwrnfvkVY3dpFOpSQmYiHmYiHmSjHhkshXmNEPGazucA7iJM6mIl4mImYmIty33//Pc6fPw99QhrafX0GNS/fwb8hFXCsawvkVvNC3Yt/AgB8vNzg4+WGv84qW77MRDzMRDzMRBk2XEREROQ0mjd9GA99+yfM648j002Lz9q1QXLzBgjxTkeH1i1RsWJFJCQkwNPTEwDQsGFD67+JiNTAhouIiIicQsbBb5E+cSHkG7fhMaQHvq8dgPbN26FSBU98e/wIqlevjsDAQFSrVs36nPtdrpmIqLSx4SIiIiKhGa78g/iJC5B58Dt4tnsMVT/5AOmBPtAeOIDQkPtfCIOISE1suBTiyYHi4THE4mEm4mEmYmIuhTNn5SB54UYkL9oMbcUABK+eAe/uj0OSJHhmZdkcLlhShw4yE/EwE/EwE2XYcDmATZc4JEniSi8YZiIeZiIm5lIwWZaRue844icvhvFmPAJe748KYwZB4/2/hsrT09PmcMGSOHSQmYiHmYiHmSjHhssBvCy8OGRZtubBTMTATMTDTMTEXOzLvRSH+AkLkHX4FDw7NEfVbR/BrU6NMvndzEQ8zEQ8zEQ5NlwK8bLw4jEajdDr9WqXQXkwE/EwEzExl/8xZ2Qhaf4GJC/dAl2Viqiyfha8noos8y90zEQ8zEQ8zEQZNlxERESkGlmWkbHnKBImL4IpPhkVRr+AgNEDofF0V7s0IqISwYaLiIiIVJF78Rrix89H1jc/wqtTK1ScORr60BC1yyIiKlGKG67r16/j66+/xi+//IJLly4hKSkJkiShQoUKqF27Nh5++GF06NABNWqUzfHWRERE5FzM6ZlImrceycu3QhdSGVU2zYF3p9Zql0VEVCqK3HAdOXIEq1evxs8//wxZllGzZk1Ur14dYWFhkGUZqamp+OOPP3Dw4EHMmTMHjzzyCF555RW0b9++NOsvczw5UDwajUbtEugezEQ8zERMrpaLLMvI2HUY8VOXwJyUggpvD0bAyAHQeIhz+KCrZeIMmIl4mIkyRWq4+vbtiz/++AMdO3bE/Pnz0apVK/j4+NidNz09Hd9++y0OHDiAMWPG4KGHHsLWrVtLtGi1sekShyRJ0Ol4ZKxImIl4mImYXC2X3D8v3z188Pgv8O7aBkHvjYT+gWpql2XD1TJxBsxEPMxEuSItrebNm2Pp0qWoWLHifef18fFB586d0blzZ9y5cwfr168vdpFERETknMzpmUj8YA1SVmyHvkZVVN3yIbw6Nle7LCKiMiPJvM55kZ05cwayLCMiIoJ7uQQhyzIMBgP0ej0zEQQzEQ8zEVN5z0WWZaTv/AoJU5fAnJqOCm8NRsDr/SG5u6ldWoHKeybOiJmIh5n8z5kzZwDc/8br3B9IREREJSrn/CXER0Uj++Tv8O7+OIJmjIK+erDaZRERqaLIDde5c+cUD96gQQPFzyEiIiLnZEpNR9L7q5Gyaif0oSGoun0evNo9pnZZRESqKnLD9eyzzyrabShJEs6fP+9QUUREROQ8ZFlG+rYDSJi+DOaMLAROHIaAV/tCctOrXRoRkeqK3HDNnj37vvNkZ2dj27ZtuHDhQrGKIiIiIueQc/Yi4sfNQ/YPZ+DzTAcETX8DumqV1S6LiEgYRW64evXqVeC03NxcbNmyBTExMbhz5w4ee+wxjBo1qkQKFI2rnxwoIr2ef0EVDTMRDzMRkzPnYkpJQ+LslUhdswv6B2ui6s758GrziNplFZszZ1JeMRPxMBNlinXRjNzcXHzyySdYuXIl7ty5g2bNmmHevHl47LHyfbw2my5xMAvxMBPxMBMxOWsustmMtC37kDBjOeSsHARNfQ3+w/pA0jv/dbicNZPyjJmIh5ko59DWMTc3F5s3b8aqVatw584dNG/e3CUaLQtZlvlmE4QsyzCZTNBqtcxEEMxEPMxETM6YS87vf+JOVDRyfjoHn2efRNC016Grcv97dDoLZ8ykvGMm4mEmyilquHJycqx7tOLj49G8eXNER0fj0UcfLa36hMPblonHbDZDq9WqXQblwUzEw0zE5Cy5mJJSkThrBVLXfQ63h0JRbddCeLZuqnZZpcJZMnElzEQ8zESZIjdca9euxcqVK5GQkIAWLVpgwYIFeOQR5z9Wm4iIiOyTzWakbdqDhJkrAIMRQTNGwf/lXuXi8EEiorJS5C3mnDlzIEkS6tWrhzp16mDfvn3Yt29foc+ZNGlSsQskIiKispf96wXEj4tGzq8X4NP3KQRNGQFdcJDaZREROR1Ff6KSZRnnz58v0v21JEliw0VERORkTAnJSPjvCqRt3AO3+nVQbc8SeDZvpHZZREROq8gN1x9//FGadTgNnhwoHh5DLB5mIh5mIiaRcpFNJqRu+AKJ/10BmGVUnPUm/Ib0hKRzrcMHRcqE7mIm4mEmyrjWVrSEsOkShyRJXOkFw0zEw0zEJFIu2T+dw51x85B7+i/4DuiGwEmvQlepgtpllTmRMqG7mIl4mIlybLgcwMvCi0OWZWsezEQMzEQ8zERMIuRivJOExBnLkfbJXrg1CkPIvuXweLSBKrWIQIRMyBYzEQ8zUc7hhmv37t349NNPcf36daSkpOS7XLokSfj555+LXaBoeFl48RiNRt7xXDDMRDzMRExq5SIbjUhduxuJc1YCkoSKH7wDv0FPQ+JfrbmuCIiZiIeZKONQw/XBBx9g9erVCA4ORsOGDeHr61vSdREREVEpyDp1GvHjopF7/hJ8B3ZH0MTh0AYFqF0WEVG55VDDtX37drRr1w5LliyBRqMp6ZqIiIiohBlvJSDhveVI37Yf7k3rIeTAx/BoWk/tsoiIyj2HDyl8/PHH2WwREREJTjYakbJyJ5Lmrgb0OlSa9x/4vtAdEj/DiYjKhENb23bt2pXL87OKgicHioeZiIeZiIeZiKm0c8n67jdc7/AKEqYshk+fJ1Hz+83wG9SDzVYhuK6Ih5mIh5koI8kOXAUiLS0NI0aMQHh4OJ599llUrVrV7t6ugICAkqhRGGfOnAEAREREqFwJERFRwYw345EwbSnSPz0E90cboNKct+DeOFztsoiIypWi9gYOHVLo6emJpk2bYtWqVfjkk08KnO/ChQuODE9EREQOkA1GpMTsQOLc1ZA83VFpQRR8+3fhHi0iIhU51HC999572L59Oxo3bozGjRu73FUKeR8ucciyDKPRCJ1Ox0wEwUzEw0zEVNK5ZJ34BXeiomH4+xr8XnoGgVFDoQ1wrc/n4uK6Ih5mIh5mopxDDde+ffvQs2dPzJkzp6TrER7vwyUeZiIeZiIeZiKmksjF+M9tJExdgvRdh+HRLALBX62Ee8SDJVCda+K6Ih5mIh5mooxDDZdOp0Pjxo1LuhYiIiIqIjnXgOSPtyHpw3XQeHui8uKJ8OnbmX9xJiISjEMHdXfr1g1Hjhwp6VqIiIioCDK/+RFxjw9B4n9j4DeoO2p8vwm+/Z5is0VEJCCH9nB16dIFM2fOxPDhw61XKdRqtfnma9CgQbELJCIiorsM128hYfIiZOw5Co+WjRG86j2416+jdllERFQIhxquF154AcDdqxAeP34833TLRSXK41UK+ddD8eh0Dt+/m0oJMxEPMxFTUXORc3KRvHQLkuZvgMbXG5WXT4FP7yf4mVQKuK6Ih5mIh5ko49DSmj17dknX4VT4AScOSZKYh2CYiXiYiZiKmkvm16cQP2E+DNf+hf/w5xA4dgg0vt5lUKHr4boiHmYiHmainEMNV69evUq6DqfCy8KLQ5ZlmM1maDQaZiIIZiIeZiKm++ViuPYv4ictROa+E/CIfBhV1s+CW3ioCpW6Dq4r4mEm4mEmynF/oEK8DKZ4TCYTNLypp1CYiXiYiZjs5WLOzkHy4s1IXrARmgr+CI6ZDu+e7fnFpoxwXREPMxEPM1GmSEtqypQpiIuLUzz4tWvXMGXKFMXPIyIickUZB79FXJvBSJq3Hv7DnkPN7zbC55kObLaIiJxYkfZw/fvvv+jSpQtatGiBrl27omXLlqhatardea9fv46TJ09i3759OHXqFFq3bl2iBRMREZU3hiv/IH7iAmQe/A6e7R5D1c1z4fbgA2qXRUREJaBIDVdMTAx+/vlnrF69GlOmTIHJZEJAQABCQkLg7+8PWZaRkpKC69evIzU1FVqtFm3btsW6devw6KOPlvZrICIickrmrBwkztuAlMWboa0YgODVM+Dd/XHu0SIiKkeKfA7XI488gkceeQSJiYk4cuQIfvvtN8TGxuLmzZsAgICAAHTq1AlNmjRBu3btEBQUVGpFq4kfguKxdw84UhczEQ8zEYssy8jYexwJkxfBeCsBAa/3R4Uxg6Dx9lS7NJfHdUU8zEQ8zEQZSeZVIIrszJkzAICIiAiVKyEiImeVeykO8RMWIOvwKXh2aI6Ks96EW50aapdFREQKFbU34FUKHcDLwosj798LmIkYmIl4mIkYzBlZSJq/AclLt0BXpSKC1/0XXk9FMhOBcF0RDzMRDzNRjg2XQtwhKB6DwQC9Xq92GZQHMxEPM1GPLMvI2HMUCZMXwRSfjAqjX0DA6IGQPNyYi4CYiXiYiXiYiTJsuIiIiEpJ7sVriB8/H1nf/AivTq1QceZo6ENDAPAPeEREroINFxERUQkzp2ciad56JC/fCl1IZVTZNAfenXibFCIiV8SGi4iIqITIsoyM3UcQP2UxzEkpqPD2YASMHACNh7vapRERkUrYcCnEkwPFw0zEw0zEw0xKX+6fl+8ePnj8F3h3bYOg90ZC/0C1Qp/DXMTDTMTDTMTDTJTRFHXGefPm4Y8//ijNWpwG32TikCQJer2emQiEmYiHmZQuc3om4qcuQVy7l2C8fhtVt3yIKutmFanZYi5iYSbiYSbiYSbKFbnhWrFiBf7++2/rz0lJSahXrx5OnjxZKoURERGJTJZlpH16CNdaDEDqms8Q+O7LqHF8Hbw6Nle7NCIiEkixDil01Sss8T5c4pBlGUajETqdjpkIgpmIh5mUvJwLsYiPikb2d7/Bu/vjCJoxCvrqwYrGYC7iYSbiYSbiYSbKFXkPV1k4evQoBg4ciBYtWqBhw4bo2LEjZs+ejbS0NJv5Dh8+jB49eiAiIgKdO3fGp59+mm+s3NxcvP/++2jdujWaNGmCl156CbGxscWu0VWbTJExE/EwE/Ewk5JhSk1H/MSFuN7+ZZhuJ6Lq9nmosmam4mbLgrmIh5mIh5mIh5koI9RFM5KTk9GoUSMMGjQIAQEB+Pvvv7Fo0SL8/fffWL16NQDgp59+wsiRI9GnTx9MmDAB33//PSZOnAhvb2889dRT1rFmzpyJvXv3IioqCsHBwVi+fDmGDBmCL7/8Er6+vmq9RCIickKyLCN92wEkTF8Gc0YWAicOQ8CrfSG58cafRERUOEUN140bN3Du3DkAsO51unr1Kvz8/OzO36BBA0XF9OzZ0+bn5s2bw83NDZMnT8atW7cQHByMZcuWoVGjRnjvvfcAAC1atEBcXBwWLlxobbhu3ryJHTt2YOrUqejTpw8AICIiAu3bt8eWLVswbNgwRXUREZHryjl7EfHj5iH7hzPweaYDgqa/AV21ymqXRURETkJRw7VgwQIsWLDA5rHp06fnm89yjtOFCxeKVx2AgIAAAIDBYEBubi5OnTqFsWPH2szTtWtX7NmzB9evX0f16tVx4sQJmM1mmz1eAQEBaN26NY4dO8aGi4iI7suUkobE2SuRumYX9HVroOrO+fBq84jaZRERkZMpcsM1e/bs0qzDhslkgtFoxMWLF7FkyRJ06NAB1atXx8WLF2EwGFC7dm2b+evUqQMAiI2NRfXq1REbG4ugoCD4+/vnm2/Hjh3Fqo0nB4pHpxPqyFgCMxERMyk62WxG2pZ9SJixHHJWDoKmvgb/YX0g6Ut+GTIX8TAT8TAT8TATZYq8tHr16lWaddho3749bt26BQBo06YNPvroIwBASkoKAOQ7hNHys2V6amqq3fO0/Pz8rPMU170nC0qSVOAJhJYmrbDppfVcVxhXkiTV6i3Oc8vzuHkfE6Wm8jzu/Z5b0HzO+FpLe9yc038iftx85Px8Dj69n7h7+GCVipBlucDtT3GzsTePMy/Dkh63rGtS8zOlvCzDkh5XyWeKCPU6e01F2WY58pniKsvQHiHb0xUrViArKwsXL17EsmXLMGLECKxZs0btsgDcXfC5ubk2bzqNRmPt9A0GQ77nuLm5Abi7585sNttMs1xS02w2w2Qy2UyzjCvLst1x9Xp9geNqtVpotVrrpTvzstywDgCMRmO+N0xpjVuU1wrkX4aFjSvLsvW5Sse1vFZJkhS/1qKMC5TtMrSMa6+m0srG8lrzLkPLF1PL77RslApahmq9vwtbhmX5/gZKfxthNBphMpmsXySL+1qdaRtR1HFNSamI/+8KZGz4AvrwWqi0Yx48WjaG1sHXWpT1xvJ5YnkNpb0MRdlG2BtXlG2ELMvW57rSNkLk7xH2PlPU2EbYG9fyWl3te4TBYIDZbLb5THGVbUTecS3fefJuvwsiZMP10EMPAQCaNm2KiIgI9OzZE4cOHULdunUBIN9l4lNTUwHAegihn58f0tPT842bmpqa7zBDRxR2d21LCPbkfSPfS6PRQKOxf5X+vMErHfd+zy1sl3BpjVvYawUKX4b3jpt3JXNk3Hu/fNpzv9eqRjYluQyLO669L/AGg8FmPSnstar1/i7OOlda2ZTmNsJsNtvddpX3bcT9xpXNZqRu+BwJM1dANhgRNGMk/F7qZT18sLS3EZbp9j5TnGUZ3m/c4i7DstxGWLZfWq3W5bYRon6PsPeZUp7e3wWNayHqdvbeTIozrjNtI/LSaDRFarYAQRuuvMLDw6HX63Ht2jV06NABer0esbGxaNOmjXUey/21LOd21a5dG/Hx8UhJSbFpsGJjY/Od/+WIvB39vY/f73mOTCvOc11h3Lxf6kty3NJ+bnke17KOFCWbsqqpPI9blOfem0lp1+QM42b/egHx46KR8+sF+PR9CkFTRkAXHFRmNVn+MlrWnynONm5Z16TmZ0p5WYYlPa6SzxQR6nX2mooyriOfKa60DO8l1I2P7fn9999hMBhQvXp1uLm5oXnz5jhw4IDNPHv37kWdOnVQvXp1AEBkZCQ0Gg0OHjxonSclJQUnTpxA27Zty7R+IiISiykhGbffnosbnV+FnGtAtT1LELxkYoHNFhERUXEItYdr5MiRaNiwIcLDw+Hh4YE//vgDq1atQnh4OJ544gkAwGuvvYbBgwdj2rRp6NKlC06dOoU9e/YgOjraOk6VKlXQp08fzJ07FxqNBsHBwfj444/h6+uL/v37q/XyiIhIRbLJhNQNXyBxVgxgMqPirDfhN6QnJF5ti4iISpFQnzKNGjXC3r17sWLFCsiyjJCQEDz33HN45ZVXrCeMPvroo1i0aBHmz5+PHTt2oFq1apg5cya6dOliM9akSZPg7e2Njz76CBkZGXj44YexZs0au1cvVELJ7kMqG4UdK0zqYCbicfVMsn86hzvj5iH39F/wHdANgZNeha5SBbXLcvlcRMRMxMNMxMNMlJFkJdc0vIfBYEBsbCzS0tLsXhrxscceK1Zxojlz5gwAICIiQuVKiIioKIx3kpA4YznSPtkLt0ZhqPT+2/B4tIHaZRERUTlQ1N7AoT1cZrMZH330ETZv3ozs7OwC57tw4YIjwwuvqJeApNJn7z4dpC5mIh5XzEQ2GpG6djcS56wEJAkVP3gHfoOehlTAlarU4Iq5iI6ZiIeZiIeZKOdQw7V8+XKsWrUK/fr1wyOPPIJ3330XY8eOhZ+fHzZv3gxJkvCf//ynpGsVQjF2CFIpsVyalMTBTMTjSplknTqN+HHRyD1/Cb4DuyNo4nBogwLULssuV8rFWTAT8TAT8TATZRw6APOzzz5Dly5dMH36dOvl2Rs0aIC+ffti27ZtkCQJ33//fYkWSkREVBjj7UTceuO/+Kf7G5Dc9Ag58DEqz3tX2GaLiIhcg0MN182bN9GiRQsA/7v7eW5urvXnHj16YPfu3SVUIhERUcFkoxHJH29HXIsByPzqJCrN+w9C9i+HR9N6apdGRETk2CGFAQEByMzMBAB4e3vDx8cHcXFxNvOkpqYWvzoiIqJCZH33G+KjopH7x2X4DemJwPHDoK3gp3ZZREREVg41XPXr17delQMAmjdvjnXr1qFevXqQZRnr169HeHh4iRVJRESUl/FmPBKmLUX6p4fg/mgDVD8UA/fG/NwhIiLxOHRIYd++fZGbm2s9jPCtt95CamoqBg4ciIEDByIjIwNRUVElWqgoJEniFVkEIkkS9Ho9MxEIMxFPecpENhiRvHQLrrUYgMyjP6LSgiiEfLnUKZut8pRLecFMxMNMxMNMlCvWfbjySktLw6lTp6DVatG0aVMEBASUxLBC4X24iIjUk3XiF9yJiobh72vwe+kZBEYNhTageDezJyIiclSp3ofLHl9fXzzxxBMlNZzQeB8ucciyDJPJBK1Wy0wEwUzE4+yZGP+5jYSpS5C+6zA8mkUg+KuVcI94UO2yis3ZcymPmIl4mIl4mIlyDh1S2LFjR/Tr1w+xsbF2p3/11Vfo2LFjsQoTFe/DJR6z2ax2CXQPZiIeZ8xEzjUgadEmXGs5EFnf/obKiyei2p4l5aLZsnDGXMo7ZiIeZiIeZqKMQw3XjRs3cO7cOTz33HP46quv8k3PzMzEP//8U+ziiIjINWV+8yPiHh+CxP/GwG9Qd9T4fhN8+z3Fv6YSEZHTcajhAoDx48fjsccew6hRozB//vwSLImIiFyV4fot3HxpEv597m1oK1VA9cOrUHHmaGj9fNQujYiIyCEOn8Pl5+eH5cuXY/HixVi6dCnOnz+Pjz76CL6+PIGZiIiUkXNykbx0C5Lmb4DG1xuVl0+BT+8nuEeLiIicnsN7uCxGjhyJ5cuX4/fff0efPn3w999/l0RdwuKHv3h0uhK79guVEGYiHpEzyfz6FOLavojEuavhN+QZ1Dy5Cb7PPukS21uRc3FVzEQ8zEQ8zESZYjdcANC2bVvs2LEDnp6e6Nu3L77++uuSGFZYrvAlwFlIkgSNRsNMBMJMxCNqJoZr/+LmixPwb/+x0FarjBrfrEHF6W9A4+utdmllQtRcXBkzEQ8zEQ8zUa5EGi4AqFGjBrZu3YpOnTrhwIEDJTWskHilQnHIsgyz2cxMBMJMxCNaJubsHCR+uAZxrQci+9c/EBwzHdV2zodbeKjapZUp0XIhZiIiZiIeZqKcQ/sD169fj7p16+Z73N3dHe+//z66du2KxMTEYhcnIr65xGM0GqHX69Uug/JgJuIRJZOMg98ifuJCGG/cRsCIfqjw9mBofLzULks1ouRC/8NMxMNMxMNMlFHccGVlZWHOnDl47rnn8Pzzz9ud5/HHHy92YUREVH4YrvyD+IkLkHnwO3i2ewxVN8+F24MPqF0WERFRqVPccHl6euL69es8bpOIiO7LnJWD5IUbkbxoM7QVAxC8ega8uz/OzxAiInIZDp3D1aZNG5w4caKkayEionJClmVk7DuOuMhBSFq4Cf6v9UONbzfC5+l2bLaIiMilONRwvf7667hy5Qr+85//4KeffsKtW7eQnJyc77/yiF8UxKPRlNi1X6iEMBPxlGUmuZficPP5d3Fz8ATo69ZEjWPrEDRxODTenmVWg7PguiIeZiIeZiIeZqKMQxfN6NatGwDg4sWL2LNnT4HzXbhwwbGqBMemSxySJPFeEIJhJuIpq0zMGVlImr8ByUu3QFelIqqsnwWvpyK5zSwA1xXxMBPxMBPxMBPlHFpab7zxBj9ASRiyLPP9KBhmIp7SzESWZWTsOYqEyYtgik9GhdEvIGD0QGg83Uvl95UnXFfEw0zEw0zEw0yUcajhGjVqVEnX4TRkWeabTCCyLMNgMECv1zMTQTAT8ZRmJrkXryF+/HxkffMjvDq1QsWZo6EPDSnR31FecV0RDzMRDzMRDzNRrkT2B2ZnZwMAPDw8SmI4IiISnDk9E0nz1iN5+VboQiqjyqY58O7UWu2yiIiIhONww/XPP/9g0aJFOHr0KJKSkgAAFSpUwOOPP46RI0ciJIR/4SQiKm9kWUbG7iOIn7IY5qQUVHh7MAJGDoDGg4cPEhER2eNQw3Xp0iUMGDAAaWlpaNWqFerUqQMAiI2Nxe7du3HkyBFs3rwZtWvXLtFiiYhIPbl/Xr57+ODxX+DdtQ2C3hsJ/QPV1C6LiIhIaA41XB999BE0Gg0+++wzhIeH20z766+/MGTIEHz00UdYsmRJiRRJRETqMadnIvGDNUhZsR36GlVRdcuH8OrYXO2yiIiInIJDDdePP/6Il156KV+zBQBhYWF44YUXsHbt2uLWJiSeHCgevV6vdgl0D2YiHkcykWUZ6Tu/QsLUJTCnpiPw3ZcR8Hp/SO5upVCha+K6Ih5mIh5mIh5mooxDDZfRaCz0Ahmenp4wGo0OFyU6Nl3iYBbiYSbicSSTnAuxiI+KRvZ3v8G7++MImjEK+urBpVCd6+K6Ih5mIh5mIh5mopxDt4muV68etm/fjrS0tHzT0tPTsWPHDtSvX7/YxYlKlmW1S6D/J8syjEYjMxEIMxGPkkxMqemIn7QQ19u/DNPtRFTdPg9V1sxks1UKuK6Ih5mIh5mIh5ko5/B9uIYNG4YuXbqgd+/eqFWrFgDg8uXL+Oyzz5CcnIwpU6aUZJ3C4JtLPGazGVqtVu0yKA9mIp77ZSLLMtK3H0DCtGUwZ2QhcMIwBIzoC8mNh42UJq4r4mEm4mEm4mEmyjjUcLVs2RIrVqzA3LlzsWLFCptp9erVwwcffIAWLVqUSIFERFS6cs5eRPy4ecj+4Qx8numAoOlvQFetstplERERlQsO34erVatW2LVrF+7cuYN//vkHAFCtWjVUqlSpxIojIqLSY0pJQ+LslUhdswv6ujVQded8eLV5RO2yiIiIypUiN1zz5s1D165d8dBDD9k8XqlSJTZZRERORDabkbZlHxJmLIeclYOgqa/Bf1gfSHqH/wZHREREBSjyRTNWrFiBv//+2/pzUlIS6tWrh5MnT5ZKYaLilVnEw2OIxcNMxGPJJOf3P3Gj2+u48+YceD3+GGp+v/nupd7ZbKmC64p4mIl4mIl4mIkyxfqEddULSLDpEockSVzpBcNMxCNJEpCagfjZMUhduxtuD4Wi2q6F8GzdVO3SXBrXFfEwE/EwE/EwE+X4J00HyLLMpksQsixb82AmYmAmYpHNZqRu3IPE/66AbDAiaMYo+L/ci3u0BMB1RTzMRDzMRDzMRDl+4irkqnv1RGY0GnnHc8EwEzFk/3oB8eOikfPrBXj1eRIVp74OfZWKapdFeXBdEQ8zEQ8zEQ8zUUZRw3Xjxg2cO3cOAKw3Pb569Sr8/Pzszt+gQYNilkdEREqZEpKR8N8VSNu4B27166DaF0ugffgh6PjhSEREVOYkuYi7bB566KF8uw0LOrTO8viFCxdKpkpBnDlzBrIsIyIigrtQBSHLMgwGA/R6PTMRBDNRj2wyIXXDF0icFQOYzAgcPxR+Q3oCWi0zERDXFfEwE/EwE/Ewk/85c+YMACAiIqLQ+Yq8h2v27NnFq4iIiEpN9k/ncGfcPOSe/gu+A7ohcNKr0FWqAICHQhMREampyA1Xr169SrMOp+HqnbyImIl4mEnZMd5JQuKM5Uj7ZC/cGoUhZN9yeDya/3BuZiIm5iIeZiIeZiIeZqIML5rhAL7JxCFJEk/aFAwzKRuy0YjUtbuROGclIEmo+ME78Bv0NCQ7l+plJmJiLuJhJuJhJuJhJsqx4SIicjJZp04jflw0cs9fgu/A7giaOBzaoAC1yyIiIiI72HA5gPfhEocsyzAajdDpdMxEEMyk9BhvJyJh+jKkb9sP96b1EHLgY3g0rXff5zETMTEX8TAT8TAT8TAT5dhwKcSTz8XDTMTDTEqWbDQiZdVnSHp/FaDXodK8/8D3he6QNJqij8FMhMRcxMNMxMNMxMNMlGHDRUQksKzvfkP8+GjkXrgMvxd7IHDCcGgr2L/3IREREYmHDRcRkYCMN+ORMH0p0nccgvsj9VH9UAzcG4erXRYREREpxIaLiEggssGIlJgdSJy7GpKnOyotiIJv/y6KDh8kIiIicbDhUognB4qHlyYVDzNxTNaJX3AnKhqGv6/B76VnEBg1FNoA3xIZm5mIibmIh5mIh5mIh5kow4bLAWy6xMEsxMNMlDP+cxsJU5cgfddheDSLQPBXK+Ee8WCJjc9MxMRcxMNMxMNMxMNMlGPD5QBeFl4csizDbDZDo9EwE0Ewk6KTcw1I/ngbkj5cB423Jyovngifvp1LfLkxEzExF/EwE/EwE/EwE+XYcCnEy2CKx2QyQcPzW4TCTO4v85sfET9+PgyXb8B/aG9UePdlaP18Su33MRMxMRfxMBPxMBPxMBNl2HAREZUhw/VbSJi8CBl7jsKjZWMEr3oP7vXrqF0WERERlRI2XEREZUDOyUXy0i1Imr8BGl9vVF4+BT69n+DhGEREROUcGy4iolKW+fUpxE+YD8O1f+E//DkEjh0Cja+32mURERFRGWDDpRD/Gi0erVardgl0D2Zyl+Hav3cPH9x7HB6RD6PK+llwCw9VpRZmIibmIh5mIh5mIh5mogwbLgew6RKHJElc6QXDTABzdg6SF29G8oKN0FTwR3DMdHj3bK/atoOZiIm5iIeZiIeZiIeZKMeGywG8LLw4ZFm25sFMxODqmWQc/BbxExfCeOM2Akb0Q4W3B0Pj46VqTa6eiaiYi3iYiXiYiXiYiXJsuBTiZeHFYzQaecdzwbhiJoYr/yB+4gJkHvwOnu0eQ9XNc+H24ANql2Xlipk4A+YiHmYiHmYiHmaiDBsuIqJiMGflIHnhRiQv2gxtxQAEr54B7+6P869+REREBAAQ6o5l+/btw2uvvYa2bduiSZMm6NmzJ3bs2JFvr9L27dvRuXNnREREoEePHjhy5Ei+sdLS0jBhwgQ0a9YMTZs2xejRo3H79u2yeilEVM7JsoyMfccRFzkISQs3wf+1fqjx7Ub4PN2OzRYRERFZCdVwrV27Fp6enoiKisKyZcvQtm1bTJ48GUuWLLHO8+WXX2Ly5Mno0qULYmJi0KRJE4wcORK//fabzVhjxozBt99+i2nTpuHDDz/E5cuXMWzYMBiNxjJ+VURU3uReisPN59/FzcEToK9bEzWOrUPQxOHQeHuqXRoREREJRqhDCpctW4bAwEDrzy1btkRycjLWrFmD119/HRqNBgsXLkS3bt0wZswYAECLFi3w119/YcmSJYiJiQEA/Prrrzhx4gRWrVqFyMhIAEBoaCi6du2KgwcPomvXrg7XyL9ci4eZiKe8ZmLOyELS/A1IXroFuioVUWX9LHg9FekUr9cZanRFzEU8zEQ8zEQ8zEQZofZw5W22LOrVq4f09HRkZmYiLi4OV65cQZcuXWzm6dq1K06ePInc3FwAwLFjx+Dn54fWrVtb56lduzbq1auHY8eOFbtOvsnEIUkS9Ho9MxFIecxElmWkf/EN4iIHIWXZVlQY/QJqnNgA7y5tnOJ1lsdMygPmIh5mIh5mIh5mopxQe7js+fnnnxEcHAwfHx/8/PPPAO7urcqrTp06MBgMiIuLQ506dRAbG4vQ0NB8b4TatWsjNja2zGonIueXe/Ea4sfPR9Y3P8KrUytUnDka+tAQtcsiIiIiJyF0w/XTTz9h7969GDduHAAgJSUFAODn52czn+Vny/TU1FT4+vrmG8/f3x9nz54tVk2yLMNsNudr5iRJKvCS8ZZ5C5teWs8t7+PKsnzfS5Mym7Id15KJTqez3qND7ZqK8tysrCxcvHgRDz74IDw8PJARn4i495bA7dMj0FWrjOCNc+DTubUw9Sp5rtlstsmkLGriuPd/LgAYDIZ8uZR2Tc42blnWpPZnSnlYhiU9rtLPFLXrLQ813W9cRz9TXGUZ2iNsw3Xz5k289dZbaN68OQYPHqx2OTYMBoPNG0yj0UCn01mn3cvNzQ0AYDKZYDabbaZZ3qxmsxkmk8lmmmVcWZbtjmv5QLA3rlarhVartW6o8rLsCgbu3kfh3jdMaY1blNcK5F+GhY1rufkeAMXjWl6rJEmKX2tRxgXKdhlaxrVXU2llY3mteZeh5XXJsmydVtgyVOv9fe9rTUtLw9mzZxESEgLD/m8RP3kxdInJcBveB5XfHgKNp7t13pJehmWxjTAYDJDl/9203VW2EcUZ1/JaS3sbkTeX+41bHrYR9sYVZRuR99+FLcOyfH8Drv09wt5niqttI0T8HnHvZ4qrbCPyjmv5znPvH8zsEbLhSk1NxbBhwxAQEIBFixZBo7l7qpm/vz+Au1+MKlWqZDN/3ul+fn64efNmvnFTUlKs8xRHYcetFvZXsbxv5HtpNBrr67xX3uCVjnu/51pWgLIct7DXChS+DO8dN+9K5si49375tOd+r1WNbEpyGRZ3XHtf4C3zWqYV9lrL8v2dlZWFP/74Aw8++CBkWcbFixdRt25deHp6QqfTwfNmIlIGT4L8w1lI7R/D6TZhaDfgObj5ehc6bl7FyaY0txE6nc7utqu8byOKO25pbyM0Gk2BnylchvefVpxxAcc/U9TKxhW/R9j7TClP7++CxrUQdTubtwEu7rjOtI3IS6PRFKnZAgRsuLKzs/Hqq68iLS0NW7dutTk0sHbt2gCA2NhY678tP+v1etSoUcM638mTJ/N1nZcvX0ZYWFixa7Ts0rb3+P2e58i04jzXFcbN+6W+JMct7eeW53Et60hRsimrmoC725dz586hYsWKuHjxovXfWfGJuDNnJRp+ehgp/n744slOyH2kDkJ0abhx4wYkSbK5qI9o2RTlufdmUto1cdz7T7d8RpX1Z4qzjVvWNan5mVJelmFJj6vkM0WEep29pqKM68hniistw3sJdZVCo9GIMWPGIDY2FitXrkRwcLDN9Bo1aqBWrVrYv3+/zeN79+5Fy5Ytrbvc27Zti5SUFJw8edI6z+XLl3H+/Hm0bdu29F8IEQktMTkNv585j9SUFGTvPoL4jsMgfXYEp5vVwef9HoEpTIY27W/cuXML+/fvx/fff692yUREROSkhNrDNX36dBw5cgRRUVFIT0+3uZlx/fr14ebmhlGjRmHs2LGoWbMmmjdvjr179+L06dPYuHGjdd6mTZsiMjISEyZMwLhx4+Du7o7o6GiEh4ejU6dOxapRSTdLZaOw3b2kDhEzSUxMxPXr15GcnIotX56Ef9w1tDl6FvI/SUCbJkh68Snc/uc6DJm+iLuVgxrB7qjilYbWrVuievXqapdfbCJmQsxFRMxEPMxEPMxEGUlWcomNUtahQwfcuHHD7rSvv/7a+qVn+/btiImJwT///IPQ0FC8/fbbaN++vc38aWlpmD17Ng4dOgSj0YjIyEhMmjQp314zJc6cOQMAiIiIcHgMIlKH5Y8z5tQMNPrhEsJPX0eavyd+bFsPiXWDUa1aNQBA+46dkW1yh4c2B98eP4LOnTvbvUcgERERubai9gZCNVyisyzUhg0bck+XIGT57mX6lZy4SKVL1EwSEhLww/T5qLH7O+gMJpx5NBQXGteEUaOFh7sbtFoJ7u7uGDx4MAIDA5GYmIgDBw6Ui4ZL1ExcHXMRDzMRDzMRDzP5n6I2XNwfqBD7U/GYTKZCr4hDZU+0THLOXkT2uHkI++EMbjWqhcyhz+LM339CNuVA9quLHl0egU4jIykpCZ6engAAT09PNGzY0PqzsxMtE7qLuYiHmYiHmYiHmSjDhouIyi1TShoSZ69E6ppd0NetAZ+1M/BD6r9o0aIxLqXdRmJiEp7u8gga1g/P91xPT08ePkxERETFxoaLiMod2WxG2pZ9SJixHHJWDoKmvgb/YX2QbTSg4cWLcHd3h5teDz9fHwQG+N5/QCIiIiIHseEionIl5/c/cScqGjk/nYPPs08iaNrr0FWpCADw1OsQERGBrKwsNGrUCABQoUIFNcslIiKico4Nl0KufnKgiHgMsXjUyMSUlIrE2TFIXbsbbg+FotquhfBs3dTuvJ6ennjkkUfKuEJ1cT0RE3MRDzMRDzMRDzNRhg2XA9h0iUOSJN4LQjBlnYlsNiNt0x4kzFwBGIwImjEK/i/3gqTn+8KC64mYmIt4mIl4mIl4mIlyXFoOkGWZTZcg8l41kpmIoSwzyf71AuLHRSPn1wvw6fsUgqaMgC44qFR/pzPieiIm5iIeZiIeZiIeZqIcGy6FeFl48RgMBuj1erXLoDxKOxNTQjIS/rsCaRv3wK1+HVTbswSezRuV2u8rD7ieiIm5iIeZiIeZiIeZKMOGi4ichmwyIXXDF0icFQOYzKg46034DekJiYc2EBERkaD4LYWInEL2T+dwZ9w85J7+C77Pd0Xg5BHQVeIVBomIiEhsbLiISGim+CQkzPgYaZu/hFujMITsXQaPxxqqXRYRERFRkbDhIiIhyUYjUtfuRuKclYAkoeIH78Bv0NOQtFq1SyMiIiIqMjZcCkmSxCuyCESSJLi5ualdBuVREplknTqN+HHRyD1/Cb4DuyNo4nBogwJKpkAXxPVETMxFPMxEPMxEPMxEOTZcRCQM4+1EJExfhvRt++HetB5C9i+Hx8P11S6LiIiIyGFsuBzA+3CJQ5ZlmEwmaLVaZiIIRzKRjUakrPoMSe+vAvQ6VJr3H/i+0B0S72RfIrieiIm5iIeZiIeZiIeZKMeGSyHeh0s8ZrMZWp7XIxQlmWR99xvix0cj98Jl+L3YA4EThkNbwa+UK3Q9XE/ExFzEw0zEw0zEw0yUYcNFRKow3oxHwvSlSN9xCO6P1Ef1QzFwbxyudllEREREJYoNFxGVKdlgRErMDiTOXQ3J0x2VFkTBt38XHj5IRERE5RIbLiIqM1knfsGdqGgY/r4Gv5eeQWDUUGgDfNUui4iIiKjUsOFSiCcHiken49tYNPdmYvznNhKmLkH6rsPwaBaB4K9Wwj3iQZWqc01cT8TEXMTDTMTDTMTDTJTh0nIAmy5x8L5o4smbiZxrQPLH25D04TpovD1RefFE+PTtzMzKGNcTMTEX8TAT8TAT8TAT5dhwOYCXhReHLMswm83QaDTMRBCWTLKP/4KECQtgiL0O/6G9UeHdl6H181G7PJfE9URMzEU8zEQ8zEQ8zEQ5NlwK8bLw4jGZTNDwggvCMF6/hTuTFiJr73F4tGyM4JXT4V6/jtpluTyuJ2JiLuJhJuJhJuJhJsqw4SKiEiHn5CJ56RYkzd8AydcblZdNhs+zT/KvX0REROTS2HARUbFlfn0K8RPmw3DtX/gP6wOfN1+Ae2AAmy0iIiJyeWy4iMhhhmv/ImHyImTsPQ6PyIdRZf0s6MNqwWAwqF0aERERkRDYcCnEv9iLh8cQlz1zdg6Sl3yC5PkboKngj+CY6fDu2R6SJEGWZWYiIGYiJuYiHmYiHmYiHmaiDBsuB7DpEockSbwXRBnLOPgd4icuhPHGLQSM6IcKbw+GxsfLOp2ZiIeZiIm5iIeZiIeZiIeZKMelRU6Pl+kvG4Yr/yB+4gJkHvwOnu0eQ9XN78PtwQfszstMxMNMxMRcxMNMxMNMxMNMlGHDpZAsy3yTCUSWZRgMBuj1emZSSsxZOUheuBHJizZDWzEAwatnwLv74wUub2YiHmYiJuYiHmYiHmYiHmaiHBsuIrJLlmVk7j+B+EmLYLwZj4DX+6PCmEHQeHuqXRoRERGR02DDRUT55F6KQ8LEhcj8+nt4dmiOqts+gludGmqXRUREROR02HARkZU5IwtJ8zcgeekW6IKDUGX9LHg9FclDBoiIiIgcxIaLiCDLMjL2HEXClMUw3UlChdEvIGDUC9B4eahdGhEREZFTY8OlEP/SLx69Xq92CU4t9+I1xI+fj6xvfoRXp1aoOHM09KEhxRqTmYiHmYiJuYiHmYiHmYiHmSjDhssBbLrEwSwcZ07PRNK89UhevhW6kMqosmkOvDu1Lva4zEQ8zERMzEU8zEQ8zEQ8zEQ5NlwO4GXhxSHLMkwmE7RaLTMpIlmWkbH7COKnLoE5MRkV3h6MgJEDoPFwL7HxmYlYmImYmIt4mIl4mIl4mIlybLgUkmVZ7RLoHmazGVqtVu0ynELuX1cQHxWNrOO/wLtrGwS9NxL6B6qV+O9hJuJhJmJiLuJhJuJhJuJhJsqw4SJyAeb0TCR+uAYpH2+HvkZVVN3yIbw6Nle7LCIiIqJyjw0XUTkmyzLSd36FhKlLYE5NR+C7LyPg9f6Q3N3ULo2IiIjIJbDhIiqnci7EIj4qGtnf/Qbv7o8jaMYo6KsHq10WERERkUthw6UQTw4UD48htmVKTUfS3NVIWbkT+tAQVN0+D17tHivTGpiJeJiJmJiLeJiJeJiJeJiJMmy4HMCmSxySJHGl/3+yLCN9+wEkTFsGc0YWAicMQ8CIvpDcyvZeGcxEPMxETMxFPMxEPMxEPMxEOTZcDuBl4cUhy7I1D1fOJOfsRcSPm4fsH87A55kOCJr+BnTVKqtSCzMRDzMRE3MRDzMRDzMRDzNRjg2XQrwsvHiMRqPL3vHclJKGpDmrkLL6M+jr1kDVnfPh1eYRtcty6UxExUzExFzEw0zEw0zEw0yUYcNF5IRksxlpW/YhYcZyyFk5CJr6GvyH9YGk5ypNREREJBJ+OyNyMjm//4k7UdHI+ekcfJ59EkHTXoeuSkW1yyIiIiIiO9hwETkJU1IqEmfHIHXtbrg9FIpquxbCs3VTtcsiIiIiokKw4VKIJweKp7xnIpvNSNu0BwkzVwAGI4JmjIL/y72EPnywvGfijJiJmJiLeJiJeJiJeJiJMuJ+YxMY32TikCSpXJ+0mf3rBcSPi0bOrxfg0/cpBE0ZAV1wkNplFaq8Z+KMmImYmIt4mIl4mIl4mIlybLiIBGRKSEbCf1cgbeMeuNWvg2pfLIFni0Zql0VERERECrHhcgDvwyUOWZZhNBqh0+nKRSayyYTUDV8gcVYMYDKj4qw34TekJySd86yq5S2T8oCZiIm5iIeZiIeZiIeZKOc83+IEwftwiae8ZJL90znER0Uj5/c/4ft8VwROHgFdpQpql+WQ8pJJecJMxMRcxMNMxMNMxMNMlGHDRaQyU3wSEmZ8jLTNX8KtURhC9i6Dx2MN1S6LiIiIiEoAGy4ilcgmE1LX7kbi7BhAklDxg3fgN+hpSFqt2qURERERUQlhw0WkgqxTpxEfNR+55y7Cd2B3BE0cDm1QgNplEREREVEJY8OlEE8OFI8zXZrUeDsRCdOXIX3bfrg3rYeQ/cvh8XB9tcsqcc6UiatgJmJiLuJhJuJhJuJhJsqw4XIAmy5xOEsWstGIlFWfIen9VYBeh0rz/gPfF7pD0mjULq3EOUsmroSZiIm5iIeZiIeZiIeZKMeGywG8LLw4ZFmGyWSCVqsVNpOs735D/Pho5F64DL8XeyBwwnBoK/ipXVapcYZMXA0zERNzEQ8zEQ8zEQ8zUY4Nl0K8DKZ4zGYztAJeaMJ4Mx4J05cifcchuD9SH9UPxcC9cbjaZZUJUTNxZcxETMxFPMxEPMxEPMxEGTZcRCVMNhiRErMDiXNXQ/J0R6UFUfDt36VcHj5IRERERIVjw0VUgrJO/II7UdEw/H0Nfi89g8CoodAG+KpdFhERERGphA0XUQkw/nMbCVOXIH3XYXg0i0DwVyvhHvGg2mURERERkcqEOsbp6tWrmDJlCnr27In69euje/fudufbvn07OnfujIiICPTo0QNHjhzJN09aWhomTJiAZs2aoWnTphg9ejRu375d7Bp5cqB41DyGWM41IGnRJlxrORBZ3/6GyosnotqeJS7fbPG4bvEwEzExF/EwE/EwE/EwE2WEarj+/vtvHD16FA888ADq1Kljd54vv/wSkydPRpcuXRATE4MmTZpg5MiR+O2332zmGzNmDL799ltMmzYNH374IS5fvoxhw4bBaDQWu042XeKQJEm1q+RkHv0Jce1eQuJ/Y+A3qDtqfL8Jvv2ecvn3h5qZkH3MREzMRTzMRDzMRDzMRDmhDins0KEDnnjiCQBAVFQUzp49m2+ehQsXolu3bhgzZgwAoEWLFvjrr7+wZMkSxMTEAAB+/fVXnDhxAqtWrUJkZCQAIDQ0FF27dsXBgwfRtWvXYtXJy8KLQ5Zlax5llYnh+i0kTF6EjD1H4dGyMYJXTod7fft/IHBFamRChWMmYmIu4mEm4mEm4mEmygm1h0tzn6u4xcXF4cqVK+jSpYvN4127dsXJkyeRm5sLADh27Bj8/PzQunVr6zy1a9dGvXr1cOzYsWLVyMvCi6ck9loWhZyTi6To9YhrPRDZP55F5eVTUG33IjZbdpRVJlR0zERMzEU8zEQ8zEQ8zEQZofZw3U9sbCyAu3ur8qpTpw4MBgPi4uJQp04dxMbGIjQ0NF/XXbt2besYREpkfn0K8RPmw3DtX/gPfw6BY4dA4+utdllEREREJDinarhSUlIAAH5+fjaPW362TE9NTYWvb/5Lcfv7+9s9TFEpe3u5JEkqcO+XpfErbHppPbe8j2vZrV2c31nYc41xNxE/aREy9x2HR2RTBK+bBbfwWvlqcOT3irIMS3pcSyaWn0WoqbyPW5Tn5s2kLGriuPd/rmVaWX+mONu4ZVlTaX+muMIyLOlxlX6mqF1veaipKOM68pniKsvQHqdquERhMBhs9p5pNBrodDrrtHu5ubkBAEwmE8xms800nU4HSZJgNpthMplsplnGlWXZ7rh6vb7AcbVaLbRaLWRZzrfbV5Ik63ONRmO+N0xpjVuU1wrkX4aFjZt3hVc6ruW1SpKU77XK2blI/3g7UhZuhKaCP4KWToJnj3aQJAkGg6FI4wJluwwt49qrqbSysbzWvMsw7+uyTLP3WtV+fxe2DMvy/Q2UzTbC8nos2y5X2UYUZ1zLa7W3jbjfay3qNsJsNuf7TCnv2wh744qyjcj7b1fbRoj6PcLeZ4orbSNE/R5x72eKq2wj8o5r+c6Td/tdEKdquPz9/QHcveR7pUqVrI+npqbaTPfz88PNmzfzPT8lJcU6T3FY3hgFTStI3jfyvTQaTYHnsOUNXum493uuZQUoy3ELe61A4cvw3nHzriiOjHvvl08AyDx0EvETF8J44xYCRvRFwFuDofHxcqheZ1iGxR3X3hd4SZKsG0nLPAXVpNb7uzjrXGllU5rbCL1eb5NJUZ/r7NuI4o5rbxthb57CarpfNvZyKcq4rrIMy3IbUdTPlPK4jRD1e4S9z5Ty9P4uaFwLkbez9rZd5X0bkZdGoylSswU4WcNVu3ZtAHfP5bL82/KzXq9HjRo1rPOdPHkyX9d5+fJlhIWFFasGSZIK3aDd77mOTCvOc8v7uJIkWf/y52g9lumGK/8gfuICZB78Dp7tHkPVze/D7cEHijW2MyzDkh7XXiZq11Tex73fdI1GU+B64myv1dnGvd/0wrZfzvZay8NnYEl+ppT0NLWeq/a4Sj9T1K63PNR0v3Ed/UxxpWV4L6GuUng/NWrUQK1atbB//36bx/fu3YuWLVtaw2/bti1SUlJw8uRJ6zyXL1/G+fPn0bZt2zKtmcRnzspB4vurEBc5CLnnLiJ49QxU3fZRkZotIiIiIqLCCLWHKysrC0ePHgUA3LhxA+np6dbmqlmzZggMDMSoUaMwduxY1KxZE82bN8fevXtx+vRpbNy40TpO06ZNERkZiQkTJmDcuHFwd3dHdHQ0wsPD0alTp2LXWdTjNan0WQ7/KOiQnPs9N3P/CcRPWgTjzXgEvN4fFcYMgsbbs5SqdQ3FyYRKBzMRE3MRDzMRDzMRDzNRTpKVXGKjlF2/fh0dO3a0O239+vVo3rw5AGD79u2IiYnBP//8g9DQULz99tto3769zfxpaWmYPXs2Dh06BKPRiMjISEyaNAnBwcEO13fmzBnIsoyIiAi+wQRhOVGysPPq7DHEXkf8hAXI/Pp7eHZojoqz3oRbnRqlWKnrcDQTKj3MREzMRTzMRDzMRDzM5H/OnDkDAIiIiCh0PqEaLtGx4RKP0pXenJmNpPkbkLzkE+iCg1Dxv6Ph9VQk8yxB3BCLh5mIibmIh5mIh5mIh5n8T1EbLqEOKSQqLbIsI2PPUSRMWQzTnSRUGP0CAka9AI2Xh9qlEREREVE5xoaLyr3ci9cQP34+sr75EV6dWqHizNHQh4aoXRYRERERuQA2XAq5+q5TERV0jwRzeiaS5q1H8vKt0IVURpVNc+DdqXUZV+eaCrtvBamDmYiJuYiHmYiHmYiHmSjDpeUANl3ikCQpXx6yLCNj9xHET10Cc2IyKrw9GAEjB0Dj4a5Sla7FXiakLmYiJuYiHmYiHmYiHmaiHBsuB/Cy8OKQZRlms9l6t+/cv64gPioaWcd/gXfXNgh6byT0D1RTu0yXcm8mpD5mIibmIh5mIh5mIh5mohwbLoV4UUfxmEwmIDMbSR+tRcrH26GvURVVt3wIr47N1S7NZZlMJmg0TnVf9XKPmYiJuYiHmYiHmYiHmSjDhoucmizLyPjsa6TM+Bjm1HQEvvsyAl7vD8ndTe3SiIiIiIjYcJHzyrkQi/ioaGR/9xu8uz+OoBmjoK/u+I2tiYiIiIhKGhsucjqm1HQkzV2NlJU7oQ8NQaVP3odvx5Y8jpiIiIiIhMOGSyF+qVePLMtI334ACdOWwZyRhcAJw+D/6nMwa3kMsWh4XLd4mImYmIt4mIl4mIl4mIkybLgcwKar7OWcvXj38MFTp+HdswMqvvcGdNUqAwC4yotFkiTen0MwzERMzEU8zEQ8zEQ8zEQ5Li0H8LLwZceUkoakOauQsvoz6OvWQNVPo+HV9lHr9LxXjWQmYmAm4mEmYmIu4mEm4mEm4mEmyrHhUoiXhS8bstmMtC37kDBjOeSsHARNfQ3+w/pA0ud/yxoMBuj1ehWqpIIwE/EwEzExF/EwE/EwE/EwE2XYcJFwcn7/E3fGz0fOj2fh8+yTCJr2OnRVKqpdFhERERGRYmy4SBimpFQkzo5B6trdcHsoFNV2LYRn66Zql0VERERE5DA2XKQ62WxG2qYvkTDzY8BgRNCMUfB/uZfdwweJiIiIiJwJv9EqxJMDS1b2b38gftw85PxyAT59n0LQlBHQBQcpGoOZiIeZiIeZiIm5iIeZiIeZiIeZKMOGywF8kxWfKSEZCf9dgbSNe+BWvw6qfbEEni0aKR5HkiSetCkYZiIeZiIm5iIeZiIeZvJ/7d17QFR1/jfw9zAMKIMDeAMljUuCmClUinhd8fIEWrqlq+atXUXMvIA/N/BG4iXRthTNTVOzdK3UtBREH7wFprblhpJuawpqmOsFgRkuojBznj/6OU84KDPKzPky83795XzncM7nzNvvDB/mXMTDTCzHhotsStLroduShqK31wN6A5q/PQOa14ZAwfs5EBEREZEd4m+5j4D34Xo0lSfPojBxBe6cPocmo6LRdP5kOLfweqx1SpKE6upqODs7MxNBMBPxMBMxMRfxMBPxMBPxMBPLseGyEO/DZTl9YTFuLVqH0k/3wqVTEHwzPkCjLh3rbf3MRDzMRDzMREzMRTzMRDzMRDzMxDJsuMhqJL0euo93o2jpekChQPN3/geasS9CoVTKXRoRERERkU2w4SKruP3PXBQmrsTdsxfQZMxgNJs7CcpmnnKXRURERERkU2y4qF5V3yjCreQPULZ9P1zDQuC7fy0aPdtB7rKIiIiIiGTBhstCPDmwdlJ1NbQbv0Txso2Ayhkt3vsrmoweDIWTk9W37cwrHAqHmYiHmYiJuYiHmYiHmYiHmViGr9YjYNNV0+3jp1A4ewXu/nQRmvEvoemcSVB6aWyybYVCwTwEw0zEw0zExFzEw0zEw0zEw0wsx4brEfCy8L+pvlaIW8l/R9kXB+D6XAc8cWA9XDsH27QGSZJgMBjg5OTETATBTMTDTMTEXMTDTMTDTMTDTCzHhstCvAwmIFVVQ7vhCxQt3wRFIxe0SE1Ek5FRNjl8sDZ6vR5OMm2basdMxMNMxMRcxMNMxMNMxMNMLMOGiyxy+5sfcDNxBarO/wLNn4eiaeJEKD2byF0WEREREZGQ2HCRWar/exO3kt5H2VeH0ajrM/A+uAGuz7STuywiIiIiIqGx4aKHku5WoWTddhT/7RM4qRuj5ftz4f6n/8NjdomIiIiIzMCGy0KO1GhUZJ1E4eyVqMq/Ao8JL8Mr4S9QatzlLssEjyEWDzMRDzMRE3MRDzMRDzMRDzOxDBuuR2DvTVf1r9dROP99lKd9jUYRneG9IRmuHQLlLqtWCoWC94IQDDMRDzMRE3MRDzMRDzMRDzOxHF+tR2Cvl4WX7txFyQfbULxiM5yaqNFybRLcX+4v9L7+/qqRItfpSJiJeJiJmJiLeJiJeJiJeJiJ5dhwWcheLwtfceifKJyzElW//Bcek4aj6azX4NRELXdZZqmqqoJKpZK7DPodZiIeZiIm5iIeZiIeZiIeZmIZNlwOruqX/+LW/NUozziKRj2fhc/mt+ES7C93WUREREREdoENl4MyVN5ByZrPULJyC5y8POD94QKoh0byq2EiIiIionrEhssBlWceR+HcVaj+9To8J/8JXjPHw8ndTe6yiIiIiIjsDhsuB1J16SoK56aiIvM4Gv+hC1p9ugwu7Z6UuywiIiIiIrvFhstCCoWiwR12Z7h9ByWr/oGS1Z9C2dwT3h8tgnpwnwa3H7VRKBRQqVR2sS/2gpmIh5mIibmIh5mIh5mIh5lYjg2XHZMkCRX7v0HhvNWovlYIzykj4RU3Fk7qxnKXVq844cXDTMTDTMTEXMTDTMTDTMTDTCzDhusRNIT7cFXlX0HhnFRUHPoWjSPD0Wr7u3AJbCN3WfVOkiTo9XoolUrhM3EUzEQ8zERMzEU8zEQ8zEQ8zMRybLgsJPp9uAwVlSheuQUlaz6Ds3cz+Gx+G24v9LTrCWEwGKBUKuUug36HmYiHmYiJuYiHmYiHmYiHmViGDZedkCQJ5XuzcWv+auhvFsNr+mh4ThsNJ7dGcpdGREREROSw2HDZgbsXfkHh7JW4/fX3cBvYHc0XT4fK31fusoiIiIiIHB4brgbMUFaB4vc2o2TtNjj7toTP1hSoB/aQuywiIiIiIvpfbLgsJMK5UJIkoXz3ERS+tQaGohJ4zRwHz6mvwqmRq9ylyYLHEIuHmYiHmYiJuYiHmYiHmYiHmViGDdcjkLPpuvvzpd8OH8z+F9TRvdBs4VSonmwtWz1yUygUnPSCYSbiYSZiYi7iYSbiYSbiYSaWY8P1COS4LLyhrAJFf9sE7bodULVphVaf/w1u/cJtWoOIJEky5iHCt4/ETETETMTEXMTDTMTDTMTDTCzHhstCtr4svCRJKPvyEG4lvQ+DrgxN3/wLPKeMhMLVxaZ1iKy6uhoqlUruMuh3mIl4mImYmIt4mIl4mIl4mIll2HAJ7M5P+ShMXIHK46egHtwHzRZNg+oJb7nLIiIiIiIiM7HhEpBeV4bi5R9Bu2EXVP6+aLX9Xbj17Sp3WUREREREZCE2XAKRJAllO/4vbi34AIby22g6Jwaek/8EhQu/siUiIiIiaojYcFnIWicH3jlz4bfDB/+ZC/WQSDRf+AacW7e0yrbsjZOTk9wl0H2YiXiYiZiYi3iYiXiYiXiYiWXYcD2C+my69NpSFKdshPajL6F6qg1a7VwBt97P19v67Z1CoYCzM/8bi4SZiIeZiIm5iIeZiIeZiIeZWI6vlkwkgwGln+/DrUVrId2+g2ZvvQ6PmGFQqBgJEREREZG94G/3Fvr9vQce1Z3T53Bz9krc+f4M3F8ZgGYLpsDZp3k9Vuk4JElCVVUVVCoV7wUhCGYiHmYiJuYiHmYiHmYiHmZiOTZcNqQv1qFo6XroPt4Nl/b+aP3VKjTuESZ3WUREREREZCVsuGxAMhhQunUvbi1ZB9ytRrNF0+Dxlz/y8EEiIiIiIjvH3/itrPLUf1CY8B7u/PAT3P/0ApolTYazdzO5yyIiIiIiIhtgw2Ul+iItipZ8CN2WNLh0CETrtDVo3K2T3GUREREREZENseGyUF0nB0p6PXRb0lD09npAb0Dzt2dA89oQKHj5TKtRqXhjaNEwE/EwEzExF/EwE/EwE/EwE8vYdReQl5eHxYsXIycnB2q1GkOGDEFcXBxcXFwea70ParoqT55FYeIK3Dl9Dk1GRaPp/MlwbuH1WNuih+PVccTDTMTDTMTEXMTDTMTDTMTDTCxntw2XVqvF+PHj4efnh9WrV+P69etISUlBZWUlkpKSHmvd918WXl9YjFuL1qH0071w6RQE34wP0KhLx8fdBTKDJEnQ6/VQKpV8AxAEMxEPMxETcxEPMxEPMxEPM7Gc3TZcn3/+OcrLy/H+++/D09MTAKDX65GcnIzY2Fh4e3s/0nolSfr//9broft4N4qWrgcUCjR/53+gGfsiFEplfewCmclgMEDJ11wozEQ8zERMzEU8zEQ8zEQ8zMQyTnIXYC3Z2dmIiIgwNlsAEBUVBYPBgGPHjj32+m//MxdX+segcPZKqF/qi7bffgqP14ay2SIiIiIiIiO7bbjy8/MREBBQY0yj0aBFixbIz89/9BUX6XBj6hJcHfwGFCpn+O5fi5bvvQllM8/HK5iIiIiIiOyO3R5SqNPpoNFoTMY9PDyg1WofaZ1VVVXQjE9GqbMSd2e+ioro7ih20gM//vi45dJjuP+cOpIfMxEPMxETcxEPMxEPMxEPM/nN3bt3zXod7LbhsgaFQgHdl8t4KUzBcMKLh5mIh5mIibmIh5mIh5mIh5n8RqFQOHbDpdFoUFpaajKu1Wrh4eHxSOsMCwt73LKIiIiIiMiB2O05XAEBASbnapWWluLmzZsm53YRERERERFZg902XL1798bx48eh0+mMY/v374eTkxN69OghY2VEREREROQoFNLvbyxlR7RaLQYNGgR/f3/ExsYab3z84osvPvaNj4mIiIiIiMxhtw0XAOTl5WHRokXIycmBWq3GkCFDEB8fDxcXF7lLIyIiIiIiB2DXDRcREREREZGc7PYcLiIiIiIiIrmx4SIiIiIiIrISNlxERERERERWwoaLiIiIiIjISthwERERERERWQkbLiIiIiIiIithw0VERERERGQlznIX0BDk5eVh8eLFNW6gHBcXxxso28C+ffuwZ88enD17FjqdDk8++STGjh2LV155BQqFAgAwduxYfPfddyY/m5GRgcDAQFuX7BB27dqF2bNnm4zHxMRg1qxZxsc7duzAhg0bcPXqVfj7+yM+Ph59+/a1ZakO40HzAADee+89DBo0iHPFyi5fvoyNGzfi9OnTOH/+PAICApCenm6ynDnzorS0FEuXLsXBgwdRVVWFXr16Yd68eWjZsqWtdscu1JVJWVkZNm3ahKysLFy6dAkuLi7o1KkT4uPjERwcbFzuypUr6Nevn8n6O3fujO3bt9tkX+yFOfPE3PcqzpP6U1cuD5oDAODi4oIff/zxocs5+lxhw1UHrVaL8ePHw8/PD6tXr8b169eRkpKCyspKJCUlyV2e3fv444/h6+uLxMREeHl54fjx45g/fz6uXbuGqVOnGpd79tlnkZCQUONnn3jiCVuX63A2bNiAJk2aGB97e3sb/713717Mnz8fkydPRrdu3ZCRkYGpU6di69atCA0NlaFa+/bWW2+hrKysxtgnn3yCzMxMREREGMc4V6zn/PnzyMrKQufOnWEwGCBJksky5s6LuLg4XLhwAQsWLICrqytWrlyJmJgY7Ny5E87O/Og2V12ZXL16Fdu2bcMrr7yCuLg43LlzBx999BFGjBiBnTt3mvwhYubMmQgPDzc+VqvVNtkPe2LOPAHMe6/iPKk/deXSsmVLbNu2rcaYJEmYOHEiunXrZrI+zpX7SPRQa9eulUJDQ6Xi4mLj2Oeffy6FhIRI165dk68wB3Hr1i2TsXnz5knPPvuspNfrJUmSpDFjxkiTJk2ydWkObefOnVJQUFCt+dwzcOBAaebMmTXGRowYIU2cONHa5dH/ioyMlGJiYoyPOVes6957kiRJUkJCgjRo0CCTZcyZFz/88IMUFBQkHT161DiWl5cnBQcHS3v37rVC5farrkzKy8ulioqKGmNlZWVS165dpYULFxrHCgoKpKCgIGnfvn3WLdgBmDNPzHmv4jypX+bkcr9vv/1WCgoKkjIyMoxjnCu14zlcdcjOzkZERAQ8PT2NY1FRUTAYDDh27Jh8hTmIpk2bmoyFhISgrKwMFRUVMlRE5igoKMClS5cQFRVVYzw6OhonTpzA3bt3ZarMcfzwww+4cuUKXnzxRblLcRhOTg//SDV3XmRnZ0Oj0aBHjx7GZQICAhASEoLs7Oz6L9yO1ZWJm5sbGjduXGNMrVajbdu2uHHjhjVLc1h1ZWIuzpP69Si5pKenw93dHZGRkVaoyL6w4apDfn4+AgICaoxpNBq0aNEC+fn5MlXl2P71r3/B29sb7u7uxrHvvvsOoaGheOaZZzBmzBh8//33MlboOAYPHoyQkBD069cP69atg16vBwDj3PD396+xfGBgIKqqqlBQUGDzWh1Neno63NzcTI6l51yRj7nzIj8/H/7+/sbzVO8JCAjg544N6HQ64zks91uwYAFCQkIQERGBefPmoaSkxPYFOoi63qs4T+RVVVWFzMxMDBgwAK6uribPc67UxANc66DT6aDRaEzGPTw8oNVqZajIsZ08eRIZGRk1juvu0qULhgwZAj8/P9y4cQMbN27En//8Z2zZsgVhYWEyVmu/WrRogWnTpqFz585QKBQ4fPgwVq5cievXryMpKck4N+6fO/cec+5YV3V1Nfbt24fIyEi4ubkZxzlX5GXuvNDpdDXOjbzHw8MDZ86csXKV9M4770ChUGDUqFHGMRcXF4waNQo9e/aERqPB6dOnsXbtWpw5cwY7duyASqWSsWL7Y857FeeJvLKzs1FSUoLBgwfXGOdcqR0bLmowrl27hvj4eISHh2PcuHHG8enTp9dY7g9/+AMGDx6Mv//971i/fr2ty3QIvXr1Qq9evYyPe/bsCVdXV3zyySeYPHmyjJURABw7dgxFRUUmH4ScK0QPt3PnTmzfvh0pKSnw8fExjrds2RILFiwwPu7atSvatWuH2NhYHDhwANHR0TJUa7/4XiW+tLQ0NG/evMZFmQDOlQfhIYV10Gg0KC0tNRnXarXw8PCQoSLHpNPpEBMTA09PT6xevfqhxxq7ubmhT58+OHv2rA0rpKioKOj1evz000/GuXH/3NHpdADAuWNl6enp8PT0RM+ePR+6HOeKbZk7LzQajckVJwF+7lhbVlYWkpKSMGXKFPzxj3+sc/k+ffrAzc2N88cGanuv4jyRT3l5OY4cOYKoqCgolco6l+dcYcNVp9qOBS4tLcXNmzdrPb6b6l9lZSViY2NRWlpqchlyEtO9uXH/3MnPz4dKpUKbNm3kKMshVFZW4uDBg3jhhRcc9tANUZk7LwICAnDx4kWTyzJfvHiRnztWcurUKcyYMQNDhw7FjBkz5C6HzMB5Ip8DBw6gsrKSF2WyABuuOvTu3RvHjx83/gUSAPbv3w8nJ6caV8Yh66iurkZcXBzy8/OxYcOGGvd5epCKigp8/fXXeOaZZ2xQId2TkZEBpVKJDh06oE2bNvDz88P+/ftNlomIiOBNw63o8OHDqKioMOuDkHPFtsydF71794ZWq8WJEyeMy1y8eBH//ve/0bt3b5vW7AguXLiA2NhYdOvWDcnJyWb/3JEjR1BRUcH5YwO1vVdxnsgnPT0dbdu2RefOnc1annOF53DVaeTIkdiyZQveeOMNxMbG4vr161i+fDlGjhxp1i//9HiSk5Nx5MgRJCYmoqysDKdOnTI+16FDB+Tm5mLDhg0YMGAAfH19cePGDWzatAk3b95EamqqfIXbuQkTJiA8PBzBwcEAgEOHDmH79u0YN24cWrRoAQCYNm0aZs2ahbZt2yI8PBwZGRnIzc3FP/7xDzlLt3tpaWlo3bo1nnvuuRrjJ0+e5Fyxstu3byMrKwsA8Ouvv6KsrMzYXHXt2hVNmzY1a16EhYWhZ8+emDNnDhISEuDq6ooVK1YgODgYAwcOlGXfGqq6MpEkCRMmTICrqyvGjx9f42IL7u7ueOqppwAAKSkpUCgUCA0NhUajQW5uLtatW4eOHTuif//+tt+xBqyuTO79gbWu9yrOk/plzvsXABQVFeHEiROIiYmpdT2cK7VTSPd/F0sm8vLysGjRIuTk5ECtVmPIkCGIj4/nX+ltIDIyEr/++mutzx06dAh6vR4LFy7EuXPnUFJSgsaNGyMsLAxTp05Fp06dbFyt41i8eDGOHj2Ka9euwWAwwM/PD8OHD8fYsWNrXKJ3x44dWL9+Pa5evQp/f3/MnDkTffv2lbFy+6bVatGjRw+MHz8ef/3rX2s8d/nyZc4VK7ty5YrJZfjv2bx5M8LDwwGYNy9KS0uxdOlSHDhwANXV1ejZsyfmzZvHP/RZqK5MANS4CNPvde3aFVu2bAHwW2afffYZLl++jMrKSnh7e6N///6YPn16jVuUUN3qysTHx8fs9yrOk/pj7vvX1q1bsXDhQmRkZCAwMNBkWc6V2rHhIiIiIiIishKew0VERERERGQlbLiIiIiIiIishA0XERERERGRlbDhIiIiIiIishI2XERERERERFbChouIiIiIiMhK2HARERERERFZCRsuIiKiWpSXlyMiIgJ79uyRuxSj4uJihIaGIisrS+5SiIjITGy4iIiowdm6dSuCg4MxfPhwq21j8+bNUKvVGDRokNW2YSkvLy8MGzYMqampcpdCRERmYsNFREQNTlpaGlQqFXJzc3H58uV6X39VVRU2b96M4cOHQ6lU1vv6H8eoUaNw9uxZnDhxQu5SiIjIDGy4iIioQSkoKEBOTg5ef/11qFQqpKWl1fs2vv76axQVFSEqKqre1/24AgMDERQUhC+//FLuUoiIyAxsuIiIqEFJS0uDUqnEiBEj0L1791obrlWrVqF9+/Ym3wLNnz8fHTt2xH/+85+HbuPgwYPw9fVF27Zta4wnJiYiLCwMV69eRWxsLMLCwtCrVy9s3boVAHDu3DmMGzcOoaGh6Nu3r0ltu3btQnBwME6ePInFixejW7dueP7555GUlIS7d+9Cp9PhzTffRJcuXdClSxcsX74ckiSZ1Ne9e3ccOXKk1ueIiEgsbLiIiKhBSUtLw/PPP4/mzZsjKioKly5dQm5ubo1lXn/9dYSEhGDu3LkoKysDABw9ehTbt2/HlClT0L59+4duIycnB08//XStz+n1esTExMDHxwezZs2Cr68vFi5ciF27dmHixIno2LEjZs2aBbVajYSEBBQUFJisY/Hixbh06RKmTZuGyMhIbNu2DampqZg8eTL0ej3i4+Px3HPPYePGjdi9e7fJzz/99NPQ6XQ4f/68uS8bERHJhA0XERE1GGfOnEF+fj6io6MBAP3796/1sEKVSoVly5bhxo0bSElJgU6nw9y5c9GxY0dMmjTpoduorq7GL7/8gieeeKLW5+/cuYOXXnoJycnJGD16ND788EM0atQIc+bMwezZs/Hmm29izJgxWLVqFfR6Pb766iuTdTRr1gzr16/H6NGjsXz5coSFhWHjxo1o164d3n33Xbz66qtYs2YNfHx8sHPnTpOfb9OmDQDgwoUL5rxsREQkIzZcRETUYKSlpcHZ2RkDBw4EADRp0gS9evVCRkYG9Hp9jWWDgoIwffp07NixAxMmTEBxcTGWLVsGZ2fnh25Dq9VCkiRoNJoHLvP7qyNqNBr4+/ujcePGNc75CggIgEajqfUbrmHDhkGhUBgfd+rUCZIkYdiwYcYxpVKJjh071vrz92orLi5+6L4QEZH82HAREVGDoNfrsXfvXnTr1g1NmzY1jkdHR6OwsLDWq/ZNmDAB7du3R25uLqZOnYqnnnrK7O096PwoV1fXGtsHfmv8fHx8ajRR98Z1Op3JOlq3bm2yHAC0atXKZFyr1T6wxvu3R0RE4mHDRUREDcK3336Lmzdvmlw5MDIyEo0aNar14hkFBQXGy8b//PPPZm3Hw8MDCoWi1kYJwAMvE/+g8doaNyen2j9+HzR+v3tNmJeXl1nLExGRfNhwERFRg3Dv3lsDBgyoMa5Wq9GnTx8cOHAAlZWVxnGDwYDExES4u7tj8uTJSE9PR2ZmZp3bcXZ2Rtu2bXHlypV634f6cq+2wMBAmSshIqK6sOEiIiLhVVZWIjMzE927d4eHh4fJ8y+88ALKy8tx+PBh49imTZuQk5ODhQsXYsaMGQgLC8OCBQtQVFRU5/ZCQ0Nx5syZet2H+nT27Fk0adIE7dq1k7sUIiKqw8PPHCYiIhLA4cOHUV5eDgD48MMPTZ6/ffs2AGDPnj2Ijo5GXl4eUlNT8fLLLyMyMhIAkJKSgqFDhyI5ORmpqakP3V6/fv2we/duXLx4Ef7+/vW8N4/v+PHj6Nu3L8/hIiJqANhwERGR8Pbs2QMAyMrKQlZW1gOX++abb1BcXIyEhAR4eXlhzpw5xuf8/Pwwc+ZMLFmyBBkZGcZLy9emb9++8PLywr59+zBlypT625F6kJeXh59//rnGvhERkbgUEm9TT0REZGLNmjXYtWsXMjMzH3hBDDksWbIEJ0+exK5du/gNFxFRA8BzuIiIiGrx2muvoaKiAnv37pW7FKPi4mJ88cUXiIuLY7NFRNRA8BsuIiIiIiIiK+E3XERERERERFbChouIiIiIiMhK2HARERERERFZCRsuIiIiIiIiK2HDRUREREREZCVsuIiIiIiIiKyEDRcREREREZGVsOEiIiIiIiKyEjZcREREREREVsKGi4iIiIiIyErYcBEREREREVnJ/wMKuerbUlpuPgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot( data=data,\n",
    "  x=\"x\",\n",
    "  y=\"y\",\n",
    "  s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "  data[\"x\"],\n",
    "  data[\"y\"],\n",
    "  xerr=data[\"ux\"],\n",
    "  yerr=data[\"uy\"],\n",
    "  fmt=\"none\",\n",
    "  ecolor=\"gray\",\n",
    "  elinewidth=1,\n",
    "  capsize=3,\n",
    "  alpha=0.7\n",
    ")\n",
    "\n",
    "# Linea di regressione\n",
    "xfit = np.linspace(0, data[\"x\"].max(), 200)\n",
    "yfit = Bols + Aols * xfit\n",
    "\n",
    "plt.plot(\n",
    "  xfit,\n",
    "  yfit,\n",
    "  color=\"crimson\",\n",
    "  linewidth=1,\n",
    "  zorder=10,\n",
    "  label=\"Fit lineare OLS\"\n",
    ")\n",
    "\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "\n",
    "\n",
    "plt.xlabel(\"Δx (mm)\")\n",
    "plt.ylabel(\"Forza F (mN)\")\n",
    "plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "986ff4a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 2.76283\n",
      "GdL       = 4\n",
      "Chi² rid  = 0.69071\n",
      "P(0, chi²)= 0.40173\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    0.905553\n",
      "1    0.040505\n",
      "2    0.097209\n",
      "3    1.035334\n",
      "4    0.617420\n",
      "5    0.066809\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "F_fit = Bols + Aols * data[\"x\"]\n",
    "sigma = np.sqrt(data[\"uy\"]**2 + (Aols * data[\"ux\"])**2)\n",
    "\n",
    "chi2_val = np.sum( ((data[\"y\"] - F_fit) / sigma)**2 )\n",
    "\n",
    "N = len(data)\n",
    "nu = N - 2\n",
    "\n",
    "chi2_red = chi2_val / nu\n",
    "Pols = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "\n",
    "print(f\"Chi²      = {chi2_val:.5f}\")\n",
    "print(f\"GdL       = {nu}\")\n",
    "print(f\"Chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {Pols:.5f}\")\n",
    "print(\"\\n\\n\" + \"#\"*60)\n",
    "print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
    "print(((data[\"y\"] - F_fit) / sigma)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6015e6fd",
   "metadata": {},
   "source": [
    "### Regressione lineare Carpi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "2d4b7144",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AC      = 3.2002 ± 0.0092\n",
      "BC      = 0.1201 ± 0.9568\n",
      "cov_ABC = -0.008013\n",
      "P(0, chi²)= 0.3995\n"
     ]
    }
   ],
   "source": [
    "def sigma_y_equiv(x, y, sigma_x, sigma_y):\n",
    "  # Stima iniziale di A con sola sigma_y\n",
    "  sy2 = sigma_y**2\n",
    "  Sw   = np.sum(1 / sy2)\n",
    "  Sx   = np.sum(x / sy2)\n",
    "  Sxx  = np.sum(x**2 / sy2)\n",
    "  Sy   = np.sum(y / sy2)\n",
    "  Sxy  = np.sum(x * y / sy2)\n",
    "  delta = Sxx * Sw - Sx**2\n",
    "  A_est = (Sxy * Sw - Sx * Sy) / delta\n",
    "\n",
    "  # Propagazione\n",
    "  sigma_eq = np.sqrt(sigma_y**2 + A_est**2 * sigma_x**2)\n",
    "  return sigma_eq\n",
    "\n",
    "\n",
    "def reg_lin(x, y, sigma_x, sigma_y):\n",
    "  x = np.asarray(x, dtype=float)\n",
    "  y = np.asarray(y, dtype=float)\n",
    "  sigma_x = np.asarray(sigma_x, dtype=float)\n",
    "  sigma_y = np.asarray(sigma_y, dtype=float)\n",
    "\n",
    "  # Propagazione errore x → y\n",
    "  sigma_y_eq = sigma_y_equiv(x, y, sigma_x, sigma_y)\n",
    "\n",
    "  # Somme pesate\n",
    "  w    = 1.0 / sigma_y_eq**2\n",
    "  Sw   = np.sum(w)\n",
    "  Sx   = np.sum(w * x)\n",
    "  Sxx  = np.sum(w * x**2)\n",
    "  Sy   = np.sum(w * y)\n",
    "  Sxy  = np.sum(w * x * y)\n",
    "  delta = Sxx * Sw - Sx**2\n",
    "\n",
    "  # Parametri\n",
    "  A      = (Sxy * Sw - Sx * Sy)  / delta\n",
    "  B      = (Sxx * Sy - Sxy * Sx) / delta\n",
    "  sigma_A = np.sqrt(Sw  / delta)\n",
    "  sigma_B = np.sqrt(Sxx / delta)\n",
    "  cov_AB  = -Sx / delta\n",
    "\n",
    "  # Chi quadro\n",
    "  x2  = np.sum((y - A * x - B)**2 / sigma_y_eq**2)\n",
    "  dof = len(x) - 2\n",
    "  chi = sc.stats.chi2.cdf(x2, dof)  # P(X² > x2)\n",
    "\n",
    "  return A, B, sigma_A, sigma_B, cov_AB, chi\n",
    "\n",
    "\n",
    "AC, BC, uAC, uBC, covABC, chiC = reg_lin(data[\"x\"], data[\"y\"], data[\"ux\"], data[\"uy\"])\n",
    "print(\"Ax + B : \")\n",
    "print(f\"AC      = {AC:.4f} ± {uAC:.4f}\")\n",
    "print(f\"BC      = {BC:.4f} ± {uBC:.4f}\")\n",
    "print(f\"cov_ABC = {covABC:.6f}\")\n",
    "print(f\"P(0, chi²)= {chiC:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "e2407a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot(\n",
    "  data=data,\n",
    "  x=\"x\",\n",
    "  y=\"y\",\n",
    "  s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "  data[\"x\"],\n",
    "  data[\"y\"],\n",
    "  xerr=data[\"ux\"],\n",
    "  yerr=data[\"uy\"],\n",
    "  fmt=\"none\",\n",
    "  ecolor=\"gray\",\n",
    "  elinewidth=1,\n",
    "  capsize=3,\n",
    "  alpha=0.7\n",
    ")\n",
    "\n",
    "# Linea di regressione\n",
    "xfit = np.linspace(0, data[\"x\"].max(), 200)\n",
    "yfit = BC + AC * xfit\n",
    "\n",
    "\n",
    "plt.plot(\n",
    "  xfit,\n",
    "  yfit,\n",
    "  color=\"crimson\",\n",
    "  linewidth=1,\n",
    "  zorder=10,\n",
    "  label=\"Fit lineare Carpi\"\n",
    ")\n",
    "\n",
    "\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "\n",
    "\n",
    "plt.xlabel(\"Δx (mm)\")\n",
    "plt.ylabel(\"Forza F (mN)\")\n",
    "plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "32e9948f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 2.75428\n",
      "GdL       = 4\n",
      "Chi² rid  = 0.68857\n",
      "P(0, chi²)= 0.40025\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    0.958978\n",
      "1    0.033756\n",
      "2    0.060884\n",
      "3    0.962374\n",
      "4    0.638537\n",
      "5    0.099753\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "F_fit = BC + AC * data[\"x\"]\n",
    "sigma = np.sqrt(data[\"uy\"]**2 + (AC * data[\"ux\"])**2)\n",
    "\n",
    "\n",
    "chi2_val = np.sum( ((data[\"y\"] - F_fit) / sigma)**2 )\n",
    "\n",
    "N = len(data)\n",
    "nu = N - 2\n",
    "\n",
    "chi2_red = chi2_val / nu\n",
    "PC = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "\n",
    "print(f\"Chi²      = {chi2_val:.5f}\")\n",
    "print(f\"GdL       = {nu}\")\n",
    "print(f\"Chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {PC:.5f}\")\n",
    "print(\"\\n\\n\" + \"#\"*60)\n",
    "print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
    "print(((data[\"y\"] - F_fit) / sigma)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad1c81fc",
   "metadata": {},
   "source": [
    "### Regressione lineare pesata Yorkfit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "bfb895c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AY        = 3.2002863 ± 0.0092220\n",
      "BY        = 0.1132724 ± 0.9560993\n",
      "cov_ABY   = -0.008001359496108713\n",
      "chi²      = 2.75422\n",
      "chi² rid  = 0.68856\n",
      "P(0, chi²)= 0.40024\n"
     ]
    }
   ],
   "source": [
    "def york_fit(x, y, sx, sy, max_iter=50, tol=1e-15):\n",
    "  # Pesi iniziali\n",
    "  wx = 1 / sx**2\n",
    "  wy = 1 / sy**2\n",
    "\n",
    "  # Stima iniziale della pendenza (OLS y|x)\n",
    "  A = np.cov(x, y, aweights=wy)[0,1] / np.cov(x, x, aweights=wy)[0,1]\n",
    "\n",
    "  for _ in range(max_iter):\n",
    "    A_old = A\n",
    "\n",
    "    # 1) Pesi combinati\n",
    "    Wi = (wx * wy) / (A**2 * wy + wx)\n",
    "\n",
    "    # 2) x e y pesati (eq. 10)\n",
    "    X_bar = np.sum(Wi * x) / np.sum(Wi)\n",
    "    Y_bar = np.sum(Wi * y) / np.sum(Wi)\n",
    "\n",
    "    # 3) Variabili centrate\n",
    "    Ui = x - X_bar\n",
    "    Vi = y - Y_bar\n",
    "\n",
    "    # 4) beta\n",
    "    beta_i = Wi * (Ui / wy + A * Vi / wx)\n",
    "\n",
    "    # 5) Aggiornamento pendenza\n",
    "    A = np.sum(Wi * beta_i * Vi) / np.sum(Wi * beta_i * Ui)\n",
    "\n",
    "    # Convergenza\n",
    "    if abs(A - A_old) < tol:\n",
    "      break\n",
    "\n",
    "  # 6) Intercetta\n",
    "  B = Y_bar - A * X_bar\n",
    "\n",
    "  # S = somma Wi (xi - x)**2\n",
    "  S = np.sum(Wi * Ui**2)\n",
    "\n",
    "  sA = np.sqrt(1 / S)\n",
    "  sB = np.sqrt(1 / np.sum(Wi) + X_bar**2 * sA**2)\n",
    "\n",
    "  # cov(A,B)\n",
    "  cov_AB = -X_bar * sA**2\n",
    "\n",
    "  # CHI QUADRATO\n",
    "  chi2 = np.sum(Wi * (Vi - A * Ui)**2)\n",
    "  dof = len(x) - 2\n",
    "  chi2_red = chi2 / dof\n",
    "\n",
    "  return A, B, sA, sB, cov_AB, chi2, chi2_red\n",
    "\n",
    "AY, BY, uAY, uBY, cov_ABY, chi2_val, chi2_red = york_fit(data.x, data.y, data.ux, data.uy)\n",
    "PY = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "\n",
    "print(f\"AY        = {AY:.7f} ± {uAY:.7f}\")\n",
    "print(f\"BY        = {BY:.7f} ± {uBY:.7f}\")\n",
    "print(f\"cov_ABY   = {cov_ABY}\")\n",
    "print(f\"chi²      = {chi2_val:.5f}\")\n",
    "print(f\"chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {PY:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "202de438",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot( data=data,\n",
    "  x=\"x\",\n",
    "  y=\"y\",\n",
    "  s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "  data[\"x\"],\n",
    "  data[\"y\"],\n",
    "  xerr=data[\"ux\"],\n",
    "  yerr=data[\"uy\"],\n",
    "  fmt=\"none\",\n",
    "  ecolor=\"gray\",\n",
    "  elinewidth=1,\n",
    "  capsize=3,\n",
    "  alpha=0.7\n",
    ")\n",
    "\n",
    "# Linea di regressione\n",
    "xfit = np.linspace(0, data[\"x\"].max(), 200)\n",
    "yfit = BY + AY * xfit\n",
    "\n",
    "plt.plot(\n",
    "  xfit,\n",
    "  yfit,\n",
    "  color=\"crimson\",\n",
    "  linewidth=1,\n",
    "  zorder=10,\n",
    "  label=\"Fit lineare York\"\n",
    ")\n",
    "\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "\n",
    "\n",
    "plt.xlabel(\"Δx (mm)\")\n",
    "plt.ylabel(\"Forza F (mN)\")\n",
    "plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63442336",
   "metadata": {},
   "source": [
    "## Raccolta finale dei dati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "caf23dbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI senza ERRORE STRUMENTALE INCLUSO:\n",
      "Media pesata K  = 3.20124 ± 0.00388\n",
      "\n",
      "RISULTATI REGRESSIONE OLS:\n",
      "Aols  = 3.20112 ± 0.00896\n",
      "Bols  = 0.02655 ± 0.99066\n",
      "P(0, chi²)= 0.40173\n",
      "\n",
      "RISULTATI REGRESSIONE Carpi:\n",
      "Ac  = 3.20021 ± 0.00923\n",
      "Bc  = 0.12013 ± 0.95680\n",
      "P(0, chi²)= 0.40025\n",
      "\n",
      "RISULTATI REGRESSIONE York:\n",
      "Ay  = 3.20029 ± 0.00922\n",
      "By  = 0.11327 ± 0.95610\n",
      "P(0, chi²)= 0.40024\n"
     ]
    }
   ],
   "source": [
    "print(\"RISULTATI senza ERRORE STRUMENTALE INCLUSO:\")\n",
    "print(f\"Media pesata K  = {media:.5f} ± {uA:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE OLS:\")\n",
    "print(f\"Aols  = {Aols:.5f} ± {uAols:.5f}\")\n",
    "print(f\"Bols  = {Bols:.5f} ± {uBols:.5f}\")\n",
    "print(f\"P(0, chi²)= {Pols:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
    "print(f\"Ac  = {AC:.5f} ± {uAC:.5f}\")\n",
    "print(f\"Bc  = {BC:.5f} ± {uBC:.5f}\")\n",
    "print(f\"P(0, chi²)= {PC:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE York:\")\n",
    "print(f\"Ay  = {AY:.5f} ± {uAY:.5f}\")\n",
    "print(f\"By  = {BY:.5f} ± {uBY:.5f}\")\n",
    "print(f\"P(0, chi²)= {PY:.5f}\")"
   ]
  },
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   "id": "1e38b0bd",
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   "source": [
    "## Minima interpretazione (Mio commento -> Non necessario)\n",
    "Sovrastimando l'errore in modo molto marcato ovviamente il Chi² viene ridotto.\n",
    "Il Chi² che stiamo usando serve per stimare quale sia la probabilità di ottenere un Chi² minore o uguale a quello osservato.\n",
    "Sapendo che il valore buono è ~50% stiamo un po' allargando le distribuzioni in modo un po' esagerato.\n",
    "\n",
    "In generale con il Chi² vale:\n",
    "- \\~50%: Errori ottimamente stimati\n",
    "- \\<5% : Fit troppo grande (errori sovrastimati)\n",
    "- \\>95%: Fit troppo piccolo (errori sottostimati)\n",
    "\n",
    "In generale mi sembra che nei paper se è presente un Chi² si riporta la probabilità complementare (probabilità di ottenere gli stessi risultati o peggio)"
   ]
  }
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