{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "code",
   "execution_count": null,
   "id": "08efb2be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ndf[\"K\"] = df.m * g / df.Dx\\ndf[\"uK\"] = np.sqrt( (df.m * g / df.Dx**2)**2 * df.uDx**2 + (g/df.Dx)**2 * df.um**2 + (df.m / df.Dx)**2 * ug**2)\\n\\ndf[\"F\"] = df.m * g\\ndf[\"uF\"] = np.sqrt( df.m**2 * ug**2 + g**2 * df.um**2)\\nmedia_K, sigma_K = mediaPesata(df[\"K\"], df[\"uK\"])\\n\\n\\n#chi 2\\nchi2_val = np.sum((df[\"K\"] - media_K)**2 / df[\"uK\"]**2)   # formula corretta\\ndof      = len(df[\"K\"]) - 1                                # -1 perché stimi solo la media\\nchi2_rid = chi2_val / dof\\np_value  = 1 - sc.stats.chi2.cdf(chi2_val, dof)\\n\\nprint(\"#\"*60)\\nprint(\"Valori di K\")\\nprint(\"media pesata K:\", media_K)\\nprint(\"sigma K:\", sigma_K)\\nprint(f\"Chi2           : {chi2_val:.4f}\")\\nprint(f\"DOF            : {dof}\")\\nprint(f\"Chi2 ridotto   : {chi2_rid:.4f}   (ideale ~ 1)\")\\nprint(f\"p-value        : {p_value:.4f}\")\\n'"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'statica1.csv')\n",
    "\n",
    "def calcola_Dx_stats(df, err_arbitrario_DX):\n",
    "  dx_cols = [col for col in df.columns if col.startswith('Dx')]\n",
    "  \n",
    "  def riga_stats(row):\n",
    "    valori = row[dx_cols].dropna()\n",
    "    n = len(valori)\n",
    "      \n",
    "    if n == 0:\n",
    "      return pd.Series({'Dx': np.nan, 'uDx': np.nan})\n",
    "    elif n == 1:\n",
    "      return pd.Series({'Dx': valori.iloc[0], 'uDx': err_arbitrario_DX})\n",
    "    else:\n",
    "      media = valori.mean()\n",
    "      sigma = valori.std(ddof=1)  # sigma sperimentale S\n",
    "      return pd.Series({'Dx': media, 'uDx': sigma})\n",
    "  \n",
    "  stats = df.apply(riga_stats, axis=1)\n",
    "  df['Dx'] = stats['Dx']\n",
    "  df['uDx'] = stats['uDx']\n",
    "  \n",
    "  return df\n",
    "\n",
    "def calcola_m_stats(df, err_arbitrario_m):\n",
    "  m_cols = [col for col in df.columns if col.startswith('m')]\n",
    "  \n",
    "  def riga_stats(row):\n",
    "    valori = row[m_cols].dropna()\n",
    "    n = len(valori)\n",
    "      \n",
    "    if n == 0:\n",
    "      return pd.Series({'m': np.nan, 'um': np.nan})\n",
    "    elif n == 1:\n",
    "      return pd.Series({'m': valori.iloc[0], 'um': err_arbitrario_m})\n",
    "    else:\n",
    "      media = valori.mean()\n",
    "      sigma = valori.std(ddof=1)  # sigma sperimentale S\n",
    "      return pd.Series({'m': media, 'um': sigma})\n",
    "  \n",
    "  stats = df.apply(riga_stats, axis=1)\n",
    "  df['m'] = stats['m']\n",
    "  df['um'] = stats['um']\n",
    "  \n",
    "  return df\n",
    "\n",
    "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",
    "df = calcola_Dx_stats(df, err_arbitrario_DX=0.01)\n",
    "df = calcola_m_stats(df, err_arbitrario_m=0.0028867513)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>88.97</td>\n",
       "      <td>77.26</td>\n",
       "      <td>77.28</td>\n",
       "      <td>77.26</td>\n",
       "      <td>77.28</td>\n",
       "      <td>77.24</td>\n",
       "      <td>77.42</td>\n",
       "      <td>77.290000</td>\n",
       "      <td>0.065422</td>\n",
       "      <td>88.97</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108.61</td>\n",
       "      <td>68.92</td>\n",
       "      <td>68.94</td>\n",
       "      <td>69.00</td>\n",
       "      <td>68.98</td>\n",
       "      <td>68.90</td>\n",
       "      <td>69.02</td>\n",
       "      <td>68.960000</td>\n",
       "      <td>0.047329</td>\n",
       "      <td>108.61</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>128.64</td>\n",
       "      <td>60.88</td>\n",
       "      <td>61.00</td>\n",
       "      <td>60.82</td>\n",
       "      <td>60.94</td>\n",
       "      <td>61.08</td>\n",
       "      <td>60.94</td>\n",
       "      <td>60.943333</td>\n",
       "      <td>0.090701</td>\n",
       "      <td>128.64</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>148.38</td>\n",
       "      <td>52.96</td>\n",
       "      <td>52.96</td>\n",
       "      <td>52.78</td>\n",
       "      <td>52.88</td>\n",
       "      <td>52.88</td>\n",
       "      <td>53.00</td>\n",
       "      <td>52.910000</td>\n",
       "      <td>0.079750</td>\n",
       "      <td>148.38</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168.53</td>\n",
       "      <td>44.42</td>\n",
       "      <td>44.68</td>\n",
       "      <td>44.48</td>\n",
       "      <td>44.80</td>\n",
       "      <td>44.92</td>\n",
       "      <td>44.52</td>\n",
       "      <td>44.636667</td>\n",
       "      <td>0.196943</td>\n",
       "      <td>168.53</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       m1    Dx1    Dx2    Dx3    Dx4    Dx5    Dx6         Dx       uDx  \\\n",
       "0   88.97  77.26  77.28  77.26  77.28  77.24  77.42  77.290000  0.065422   \n",
       "1  108.61  68.92  68.94  69.00  68.98  68.90  69.02  68.960000  0.047329   \n",
       "2  128.64  60.88  61.00  60.82  60.94  61.08  60.94  60.943333  0.090701   \n",
       "3  148.38  52.96  52.96  52.78  52.88  52.88  53.00  52.910000  0.079750   \n",
       "4  168.53  44.42  44.68  44.48  44.80  44.92  44.52  44.636667  0.196943   \n",
       "\n",
       "        m        um  \n",
       "0   88.97  0.002887  \n",
       "1  108.61  0.002887  \n",
       "2  128.64  0.002887  \n",
       "3  148.38  0.002887  \n",
       "4  168.53  0.002887  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "976d5531",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 1), (0, 2), (0, 3), (0, 4), (1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]\n",
      "############################################################\n",
      "[ 8.33       16.34666667 24.38       32.65333333  8.01666667 16.05\n",
      " 24.32333333  8.03333333 16.30666667  8.27333333]\n",
      "[0.08074652 0.11183321 0.10315038 0.2075251  0.10230673 0.09273618\n",
      " 0.20255041 0.12077527 0.21682558 0.21247745]\n",
      "############################################################\n",
      "[-19.64 -39.67 -59.41 -79.56 -20.03 -39.77 -59.92 -19.74 -39.89 -20.15]\n",
      "[0.00408248 0.00408248 0.00408248 0.00408248 0.00408248 0.00408248\n",
      " 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": "code",
   "execution_count": null,
   "id": "2ad19283",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23.12002881 23.79714641 23.89558901 23.89236505 24.50072931 24.29810717\n",
      " 24.15686666 24.09590539 23.98781725 23.88286463]\n",
      "[0.22417698 0.16284102 0.10114355 0.15187011 0.31272214 0.1404374\n",
      " 0.20118598 0.36230687 0.31897871 0.61338857]\n"
     ]
    }
   ],
   "source": [
    "F = masse * g\n",
    "uF = np.sqrt((g * umasse)**2 + (masse * ug)**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(K)\n",
    "print(uK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f59d6c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-medio: 23.948686783351924\n",
      "sigmaC:  0.05731089207006433\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": "code",
   "execution_count": null,
   "id": "aefe7756",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      F   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 2.238e+04\n",
      "Date:                Wed, 01 Apr 2026   Prob (F-statistic):           4.46e-15\n",
      "Time:                        11:03:56   Log-Likelihood:                -27.237\n",
      "No. Observations:                  10   AIC:                             58.47\n",
      "Df Residuals:                       8   BIC:                             59.08\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0999      2.914      0.034      0.973      -6.621       6.821\n",
      "este          23.9663      0.160    149.602      0.000      23.597      24.336\n",
      "==============================================================================\n",
      "Omnibus:                        0.134   Durbin-Watson:                   0.595\n",
      "Prob(Omnibus):                  0.935   Jarque-Bera (JB):                0.285\n",
      "Skew:                          -0.202   Prob(JB):                        0.867\n",
      "Kurtosis:                       2.277   Cond. No.                         40.8\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "RISULTATI REGRESSIONE:\n",
      "k (pendenza) = 23.96627 ± 0.16020\n",
      "intercetta  = 0.09992 ± 2.91442\n",
      "R² = 0.99964\n"
     ]
    }
   ],
   "source": [
    "data = pd.DataFrame({\n",
    "    \"este\": este,\n",
    "    \"ueste\": ueste,\n",
    "    \"F\": - F,\n",
    "    \"uF\": uF\n",
    "})\n",
    "\n",
    "\n",
    "X = sm.add_constant(data[\"este\"])\n",
    "model = sm.OLS(data[\"F\"], X).fit()\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "# Estrazione parametri\n",
    "intercetta = model.params[\"const\"]\n",
    "pente = model.params[\"este\"]\n",
    "u_intercetta = model.bse[\"const\"]\n",
    "u_pente = model.bse[\"este\"]\n",
    "R2 = model.rsquared\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE:\")\n",
    "print(f\"k (pendenza) = {pente:.5f} ± {u_pente:.5f}\")\n",
    "print(f\"intercetta  = {intercetta:.5f} ± {u_intercetta:.5f}\")\n",
    "print(f\"R² = {R2:.5f}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d42b009",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot(\n",
    "    data=data,\n",
    "    x=\"este\",\n",
    "    y=\"F\",\n",
    "    s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "    data[\"este\"],\n",
    "    data[\"F\"],\n",
    "    xerr=data[\"ueste\"],\n",
    "    yerr=data[\"uF\"],\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[\"este\"].max(), 200)\n",
    "yfit = intercetta + pente * xfit\n",
    "\n",
    "\n",
    "plt.plot(\n",
    "    xfit,\n",
    "    yfit,\n",
    "    color=\"crimson\",\n",
    "    linewidth=1,\n",
    "    zorder=10,\n",
    "    label=\"Fit lineare\"\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": null,
   "id": "986ff4a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi^2 = 25.02317\n",
      "Gradi di libertà = 8\n",
      "Chi^2 ridotto = 3.12790\n",
      "Probabilità P(0 → chi^2) = 0.99846\n"
     ]
    }
   ],
   "source": [
    "F_fit = intercetta + pente * data[\"este\"]\n",
    "sigma = np.sqrt(data[\"uF\"]**2 + (pente * data[\"ueste\"])**2)\n",
    "\n",
    "chi2_val = np.sum( ((data[\"F\"] - F_fit) / sigma)**2 )\n",
    "\n",
    "N = len(data)\n",
    "nu = N - 2\n",
    "\n",
    "chi2_red = chi2_val / nu\n",
    "P = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "\n",
    "print(f\"Chi^2 = {chi2_val:.5f}\")\n",
    "print(f\"Gradi di libertà = {nu}\")\n",
    "print(f\"Chi^2 ridotto = {chi2_red:.5f}\")\n",
    "print(f\"Probabilità P(0 → chi^2) = {P:.5f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef0817f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    13.640283\n",
       "1     1.141735\n",
       "2     0.543390\n",
       "3     0.255239\n",
       "4     2.911852\n",
       "5     5.525530\n",
       "6     0.872958\n",
       "7     0.105774\n",
       "8     0.002341\n",
       "9     0.024062\n",
       "dtype: float64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((data[\"F\"] - F_fit) / sigma)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cebe6742",
   "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>este</th>\n",
       "      <th>ueste</th>\n",
       "      <th>F</th>\n",
       "      <th>uF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.330000</td>\n",
       "      <td>0.080747</td>\n",
       "      <td>192.58984</td>\n",
       "      <td>0.044591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.346667</td>\n",
       "      <td>0.111833</td>\n",
       "      <td>389.00402</td>\n",
       "      <td>0.056359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.380000</td>\n",
       "      <td>0.103150</td>\n",
       "      <td>582.57446</td>\n",
       "      <td>0.071639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.653333</td>\n",
       "      <td>0.207525</td>\n",
       "      <td>780.16536</td>\n",
       "      <td>0.089064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.016667</td>\n",
       "      <td>0.102307</td>\n",
       "      <td>196.41418</td>\n",
       "      <td>0.044764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.050000</td>\n",
       "      <td>0.092736</td>\n",
       "      <td>389.98462</td>\n",
       "      <td>0.056429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24.323333</td>\n",
       "      <td>0.202550</td>\n",
       "      <td>587.57552</td>\n",
       "      <td>0.072063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.033333</td>\n",
       "      <td>0.120775</td>\n",
       "      <td>193.57044</td>\n",
       "      <td>0.044635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16.306667</td>\n",
       "      <td>0.216826</td>\n",
       "      <td>391.16134</td>\n",
       "      <td>0.056514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.273333</td>\n",
       "      <td>0.212477</td>\n",
       "      <td>197.59090</td>\n",
       "      <td>0.044818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        este     ueste          F        uF\n",
       "0   8.330000  0.080747  192.58984  0.044591\n",
       "1  16.346667  0.111833  389.00402  0.056359\n",
       "2  24.380000  0.103150  582.57446  0.071639\n",
       "3  32.653333  0.207525  780.16536  0.089064\n",
       "4   8.016667  0.102307  196.41418  0.044764\n",
       "5  16.050000  0.092736  389.98462  0.056429\n",
       "6  24.323333  0.202550  587.57552  0.072063\n",
       "7   8.033333  0.120775  193.57044  0.044635\n",
       "8  16.306667  0.216826  391.16134  0.056514\n",
       "9   8.273333  0.212477  197.59090  0.044818"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d4b7144",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "A      = 24.04493 +- 0.13159\n",
      "B      = -1.56967 +- 2.08087\n",
      "cov_AB = -0.246320\n",
      "p-value chi² = 0.9978741\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",
    "A, B, sA, sB, covAB, chi = reg_lin(data[\"este\"], data[\"F\"], data[\"ueste\"], data[\"uF\"])\n",
    "print(\"Ax + B : \")\n",
    "print(f\"A      = {A:.5f} +- {sA:.5f}\")\n",
    "print(f\"B      = {B:.5f} +- {sB:.5f}\")\n",
    "print(f\"cov_AB = {covAB:.6f}\")\n",
    "print(f\"p-value chi² = {chi:.7f}\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32e9948f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi^2 = 24.13715\n",
      "Gradi di libertà = 8\n",
      "Chi^2 ridotto = 3.01714\n",
      "Probabilità P(0 → chi^2) = 0.99783\n"
     ]
    }
   ],
   "source": [
    "F_fit = B + A * data[\"este\"]\n",
    "sigma = sigma = np.sqrt(data[\"uF\"]**2 + (A * data[\"ueste\"])**2)\n",
    "\n",
    "\n",
    "chi2_val = np.sum( ((data[\"F\"] - F_fit) / sigma)**2 )\n",
    "\n",
    "N = len(data)\n",
    "nu = N - 2\n",
    "\n",
    "chi2_red = chi2_val / nu\n",
    "P = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "\n",
    "print(f\"Chi^2 = {chi2_val:.5f}\")\n",
    "print(f\"Gradi di libertà = {nu}\")\n",
    "print(f\"Chi^2 ridotto = {chi2_red:.5f}\")\n",
    "print(f\"Probabilità P(0 → chi^2) = {P:.5f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2407a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o9XqLrxd2d0k53/VQZcX4Oz/44IMC18gYPfhFOf/ZjvwKW6/irG9h29CSsWPH4vfff8ewYcPQsGFDuLu7w2AwYPjw4Q7ZtgCsvsW8vXccNRgM8Pf3x2effWZx+oNngPKfwXpQUdOs4ezsbNM1eQ/65ptvEBkZiS5dumDYsGHw9/eHWq3G559/bvFMX3H7S1SRsOAiohLj5+cHDw8PGAwGi2d08qtRowYuXboEWZbNvhRdu3bNbD7jUKhr166ZhgYCeReI37hxw6wYqF27Nv7880+0bt3a5mf5GH/P1atXzf5ardVqcf36dTRo0MCm9uxh7ENsbKzZWbXc3Fxcv37dtE2N88XFxRX4y3pcXJzZ8DEg70vnZ599hrfeegv/+c9/EBUVZSp0bMnMkvHjx9t9psSabXr16tUCr125csXs7KaPj4/FL4TGMwT2KKlnQT3Yf1mWcfXqVdN+a8zZ09PTru1fUqzdhoVtl9TUVMTExGDMmDFmQ/2MZ/CsacNatWvXxrFjx5CSklLoWa4aNWrAYDDg6tWrqFevnun1hIQEpKWlFTg7Xpy+xMTE4PHHHy/xAsTW93lR7t69i8zMTLPi3ZiNcVvs378ftWrVwpIlS8wyWrRokb2rQET/4DVcRFRi1Go1unXrhv379xe4dTdgPjQtPDwcd+7cwaFDh0yv5eTkYMuWLWbLhIaGwtfXF1u2bIFOpzO9/t133xUY7tO9e3fcuXOnQBsAkJ2dXeRzaUJDQ+Hn54evv/7a7O6BO3fuLJWhZpa0adMGGo0GX331ldkZgW3btiE9PR1PPfWUqa/+/v4F+nrkyBFcvnwZHTp0KNC2s7MzlixZgrCwMIwcOdJ0G3RbMrMkNDQUbdq0seufNXc1O3jwoNktsE+fPo1Tp06hffv2ptdq1aqF2NhYs77++eef+O233x7afmHc3NwAFH/Y3a5du0y39wbyrvm5d++eqf+hoaGoXbs21qxZY7pFf34P2/4lxdptWNh2KewM0bp16wq8ZmzD3mGGXbt2hSzLWLJkSYFpxveN8b3y4O//4osvzKYXV/fu3aHX6y3eAVWn0xVr/7HnfV4YnU5nNmQ4NzcXmzdvhp+fHxo1agTg3wzzH3tOnTqFP/74w+51IKI8PMNFRDbbvn07fvzxxwKvDxkyBO+99x5OnDiB/v37o1+/fqhfvz5SU1Nx7tw5xMTEmIafDRgwAOvXr8d7772HIUOGICAgAN99951puJTxL6zOzs4YM2YMZs6ciaFDh6J79+64ceMGduzYUeD6jN69e2Pv3r2YNm0aTpw4gccffxx6vR6xsbHYt28fVq1aZbpl9IM0Gg3Gjh2LqVOnYujQoYiIiMD169exY8eOYl/DZS0/Pz+88cYbWLJkCYYPH45OnTohLi4OGzduRFhYGHr16mXq67hx4zBhwgQMGjQIPXr0MN0uukaNGoXext3V1RWff/45hgwZghEjRuCrr75CcHCw1Zk5Qu3atfHiiy/ixRdfRG5uLr788kv4+vpi+PDhpnn69u2LtWvXYtiwYejbty8SExPx9ddfo379+haLGGsYv4TOmjUL4eHhUKvV6NGjh83t+Pj44KWXXkKfPn1Mt4V/5JFHTDeHUalUmDVrFkaMGIGePXuiT58+CAwMxJ07d3DixAl4enpixYoVdq2DLazdhq6urqhfvz727t2LOnXqwNfXF48++iiCg4Px5JNPYtWqVdBqtQgMDMRPP/1ker5WfsZtO3/+fERERECj0aBjx44Fhk4WplWrVujduze++uorXL16Fe3atYPBYMDJkyfRsmVLDBo0CA0aNMDzzz+PzZs3Iy0tDU8++STOnDmDnTt3okuXLmZny4ujRYsWGDBgAD7//HNcuHABbdu2hUajwZUrV7Bv3z5MmjQJzzzzjF1t2/s+t6RKlSqIiorCjRs3UKdOHURHR+PChQuYOXOm6bb1HTp0wIEDBzBq1Ch06NAB169fN+0D5e0B20RljQUXEdnswQdmGvXp0wdVq1bF1q1bsXTpUnz//ffYtGkTfH19Ub9+fbMHcXp4eGDdunWYNWsWvvzyS7i7u+O5555Ds2bNMGbMGLPrVAYNGgRZlvHFF1/gk08+QYMGDbB8+XLMmjXLbD6VSoWlS5di7dq1+Oabb/D999/Dzc0NNWvWxODBgy1efJ7fgAEDoNfrsXr1asyZMwfBwcFYvnw5Fi5cWMwtZr0xY8bAz88P69evx+zZs+Hj44P+/fvj3XffNXueT58+feDq6oqoqCh89tlncHd3R5cuXfD+++8X+gwuIG/o2urVqzFo0CC89tpr2LBhAx555BGrMnOE5557DiqVCuvWrUNiYiIaN26MKVOmoEqVKqZ56tWrh08++QSLFi3C7NmzUb9+fcyZMwe7d++2u1js2rUrBg8ejD179uDbb7+FLMt2FVwjR47EX3/9hZUrV+L+/fto3bo1pk2bZjrLAwAtW7bE5s2bsWzZMqxfvx6ZmZkICAhA48aNze4sV5ps2YazZs3CzJkzMXv2bGi1WowePRrBwcGYO3cuZs6ciY0bN0KWZbRt2xZRUVEF7srXuHFj/Oc//8HXX3+NH3/8EQaDAYcOHbK64AKA2bNnIyQkBNu2bcOcOXPg5eWF0NBQs7s0zpo1CzVr1sTOnTtx8OBBVK5cGW+88YbFuxsWx4wZMxAaGoqvv/4a8+fPh1qtRo0aNdCrVy88/vjjxWrb3vf5g3x8fPDxxx9j1qxZ2LJlCypXroypU6ea3RW2T58+SEhIwObNm3Hs2DHUr18fn376Kfbt2+fQP7oQlQeS7KgrWYmILFi7di1mz56No0ePmt0O/EEGgwGtW7fG008/jVmzZpVhD6ksXL9+HZ07d8YHH3yAYcOGObo7Njtx4gSGDBmChQsX2n2Gg4iIygdew0VEDpOdnW32c05ODjZv3ow6deqYFVs5OTkF7nK2a9cupKSkoEWLFmXSVyIiIiJ7cEghETnM6NGjUb16dTRo0AAZGRn49ttvERsbW+AWy3/88Qdmz56NZ555Br6+vjh//jy2bduG4OBgnj0gIiIiRWPBRUQOEx4ejm3btuG7776DXq9H/fr1TRfS51ejRg1UrVoVX331FVJTU+Hj44PevXtj3LhxD31wLhEREZEj8RouIiIiIiKiUsJruIiIiIiIiEoJCy4iIiIiIqJSwmu4bPD7779DlmWzZ+EQEREREVHFo9VqIUmS2TMALWHBZQNZlgvcmpqIiIiIiCoea+sCFlw20Gg0kGUZoaGhkCTJ0d0hG8myDK1WC41Gw/wExPzExwzFxvzExvzExwyV58yZM1bNx2u4iIiIiIiISgkLLiIiIiIiolLCgouIiIiIiKiUsOAiIiIiIiIqJSy4bMSLFMXGW/qLjfmJjxmKjfmJjfmJjxmKSXF3KTx06BBWrFiBS5cuwcPDA82bN8e4ceNQq1Yts/m2bt2KVatW4ebNm6hbty7eeecddOzY0Wye9PR0zJ49GwcPHoRWq0W7du0wefJkVKlSpVh9fFjRpdfrodVqi/U7SEwajQZqtdrR3SiX+McO8TFDsTE/sTE/8TFDcUmygh4sdeLECbzyyit47rnn8OyzzyIlJQULFy6EwWDAd999B1dXVwDAnj178N5772HkyJFo1aoVoqOjsX37dmzYsAFNmzY1tTds2DBcunQJ48ePh4uLCxYsWACVSoXt27fDycn2WtN468fCbgsvyzJu376NlJQUu9afSp8sy6V+wPL19UXVqlV5YCxhsixDr9dDrVZz2wqKGYqN+YmN+YmPGSqPsTYICwsrcj5FneHas2cPqlevjo8++si0I/n5+WHo0KE4e/YsnnjiCQDAokWL0KNHD4wdOxYA0KpVK1y8eBFLly5FVFQUAOD333/HsWPHsHr1aoSHhwMA6tati4iICBw4cAARERF29bGo+tRYbFWpUgXu7u58MyiM8cHVkiSVSjayLCMzMxN3794FAFSrVq3Ef0dFZzAYeAZRcMxQbMxPbMxPfMxQTIoquHQ6HTw8PMy+DHt5eQH4t9CJj4/HlStX8P7775stGxERgTlz5iA3NxfOzs44evQovL290bZtW9M8QUFBaNiwIY4ePWp3wVUYvV5vKrb8/f1LtG0qGaVdcAGAm5sbAODu3buoUqUKD4pEREREFZyibprRp08fXL58GRs2bEB6ejri4+Mxb948PPbYY3j88ccBALGxsQDyzlblV69ePWi1WsTHx5vmq1u3boEv1kFBQaY2SpLxmi13d/cSb5vEYtwHeB0fERERESnqDNcTTzyBJUuW4L333sOMGTMAAA0bNsSqVatMZwpSU1MBAN7e3mbLGn82Tk9LSzOdHcvPx8cHZ8+eLVY/H3bZ24PTjUVfYctJklTktOIsy3YtTzP+XJrZlFa7StmGJdWutcsaz1Dmz060dXX0NnR0u/lfE6G/JdGuEvtU3GOapenchspu17ispfzK27qK1q4ty+bPUMR1VcI2LIt2LVFUwfXbb7/hgw8+QP/+/dGhQwekpKRg2bJleP3117Fx40bTTTMcTavVmp05U6n+PVFY2MEs/3RbpxX1AWdPuw+2XdHaLWo7l0R/8/+s1+thMBjMpqvVaqjVasiyDJ1OV6Bd4y1fdTpdgbadnJwgSRIMBgP0er3FdoGCZ9eK065KpTLdZMbSWTuNRgNJkiyuq7FdS+tqbFeW5ULbBf7dhsb3gVarhZOTk93b0DitqG1Y0tk8bBs6Ozubraut7Vq7DUtqXe1t1zi9sHbt3YaO2r+LWldr2gXEO0ZIkmTxM1AJxwhL68pjhPk2NP7XmJ/SjhGltQ3LyzHC+DloMBigUqkUeYxQ8vcIS+ta3GOEsfh9GEUVXLNmzUKrVq0QGRlpeq1p06bo0KEDvvnmGwwYMAA+Pj4A8m75HhAQYJovLS0NAEzTvb29cfv27QK/IzU11TSPPSRJMh348jOGZfxAKmp5W6dZE2RJ/s7Fixdj6dKlBV5/9NFH8d133yEkJATvv/8+hg0bBgDYsWMHNBoNnn322Yf+zk6dOuGpp57C1KlTAQCRkZE4d+4cvvvuu2Kviz3L5n+tpLZh/p/zH7wsLVfU8zSKupOmSqUyK/QfVJbtGtfX3nV92HZwxDZ0VDZFrWtx+lta29Cedo37S0XZvwtr10ikY8TD+lNYuzxGWN8uUHrHCEvfXaxpt6yPEda0y2OEde1WlG3oyGOENd/RAYUVXJcvX0bnzp3NXqtatSoqVaqEa9euAci7BgvIu0bL+P/GnzUajel5XUFBQYiJiSlQecbFxSE4OLjYfS2qOLKncCrpQqI47UqSBFdXV6xbt87sdVdXV0iShM2bN6N69eqmZXft2gV3d3f06tXL6j4Zp48aNQqZmZmmn0tzO+T/C0VRRZIt7T6sLVEyd2S71i6bfxhFWewvIrWrxD5Zmmb8y2xp/GGquMtW9GysmW7pPVgS7ZbmsmzXXGH5lad1Fa1dW5bN/x4szT6J1q5S+5Sfom6aUb16dZw/f97stRs3biA5ORk1atQAANSqVQt16tTBvn37zOaLjo5G69atTX/Bad++PVJTUxETE2OaJy4uDufPn0f79u3t7qMt4zVFplKp0LRpU7N/DRo0AJB31rG4D482ql27tqndsmBNfrm5uQVOPZMyWBqiRmJhhmJjfmJjfuJjhmJSVME1cOBAHDx4ELNmzcLPP/+M6OhojBw5Ev7+/ujevbtpvjFjxmD37t1YtGgRTpw4gWnTpuH06dN46623TPM0a9YM4eHhmDhxIvbu3YsffvgBb7/9NkJCQtC1a1dHrF65ERISgtWrVwMABg8ejP/973/473//i5CQEISEhGDx4sVWtxUZGYmePXuaft6xYwdCQkJw/vx5DB8+HE2bNkXXrl2xa9euAsv+97//Rb9+/dC4cWO0atUK06ZNQ2Zmpml6ZmYmZsyYgW7duqFJkybo1KkTpk+fjvT0dLN2OnXqhBkzZiAqKgodO3ZE48aNTQ+v3rFjB5599lmEhYWhXbt2mD9/foGxyUREREREhVHUkMIhQ4bA2dkZmzZtwvbt2+Hh4YGmTZtiwYIFqFSpkmm+nj17IisrC1FRUVi5ciXq1q2LJUuWoFmzZmbtLViwALNnz8bUqVOh0+kQHh6OyZMnFzkek/714F9RLD3ZfNq0aXj//ffh6uqK8ePHA8gbBlpc48aNQ//+/fHqq69iy5YtiIyMRFhYGOrVqwcA2LdvH9555x306dMHY8aMwb179zB37lykpaVh/vz5AIDs7Gzo9Xq888478PPzw61bt7BixQqMGjUKX331ldnvO3DgAB555BFMmjQJKpUK7u7u+OKLL/Dpp59i6NChiIyMxOXLl00F17hx44q9jkRERERU/imq8pAkCS+++CJefPHFh87br18/9OvXr8h5vLy88NFHH+Gjjz4qqS5WGJmZmWjUqJHZa3PmzEHv3r3NXqtfvz48PT3h7u6Opk2bltjvf/nll/Hyyy8DyDtbeeTIEezfvx9vvfUWZFnGnDlzEBERgf/7v/8zLRMQEIDXX38db731Fh599FH4+fnhww8/NE3XarWoUaMGXn75ZcTFxZk9y02r1SIqKsr0DK2MjAwsWrQIw4cPx7vvvgsAaNu2LTQaDT7++GMMGzbM7I8ARERERESWKKrgEoEtF8jlp71yE4bU9IfPWMJUPl7Q1Klu83Kurq5Yv3692WvGG5KUhfDwcNP/u7u7o3r16qa7TsbFxeHGjRuYOHGi2Vm4Fi1aQKVS4ezZs3j00UcB5N3QY+3atbh69arZcMMrV66YFVwtW7Y0e2j177//jszMTDzzzDNmv6NNmzbIzs7G33//jRYtWpT8ilOR7H3/kXIwQ7ExP7ExP/GV1wyzsrJw6dIl1K9fH25ubnbPo1QsuOxg686uT0zBtZYvAo64EYNajTrndkHt72vTYiqVCmFhYaXTJys8+NBqjUaD3NxcAEBycjKAvDscWnLr1i0AwPfff4/x48djwIABeOedd+Dr64t79+5h1KhRyMnJMVvG39/f7Gfj73j++eeL/B1UdiTp4bekJmVjhmJjfmJjfuIrzxlmZWXh7NmzqFGjBtzc3EwnKlyahBQ6j0hYcJUBtb8vap/Y5LAzXLYWW0rn6+sLAJg6dSoaN25cYLrxDor79u1Dw4YNMWPGDNO0//3vfxbbfLCINj6rbcmSJRavSatZs6ZdfSciIiIiy+SsHCR9vArJizfC8GRjBK7/BN6eLo7uVrGx4LKDtU+Vzs+eYX2i0Gg0Bc4YlaagoCBUrVoV8fHxpuu8LMnOzjb7S5Asy/j222+t+h3NmjWDm5sbbt++jaeffrrYfabiMz4N3vjUeRIPMxQb8xMb8xNfec5QlmW4//onEmdtgiopDadaNkd0UANUi/oOPdvWgburM2RZFvZO0Sy4bFRRnsNli6CgIOzatQs//PADAgICUKVKFQQGBpba75MkCZGRkRg3bhwyMzPRoUMHuLm54ebNmzhy5Ajeeecd1K1bF23atMGMGTOwdOlSNGvWDP/9739x/Phxq36Ht7c33n77bXz66ae4ffs2WrRoAbVajfj4eBw6dAiLFy8W7nR2ecD3n/iYodiYn9iYn/jKY4bauBvIeP9ThB45iZuP+OPkS62Q7OWOmrgFpN7C9/svQJIkuLm5wdPT09HdtQsLLiq2ESNG4Nq1axg/fjzS0tIwevRojBkzplR/Z/fu3eHt7Y0VK1bgu+++AwDUqFED7dq1Q+XKlQHkPdft+vXrWL9+PVavXo3w8HB8+umnGDhwoFW/47XXXkNgYCC++OILrF+/Hk5OTqhduzY6dOhQbsdQExEREZUFQ1YOUhatR8rijZD8fXB2SCcE9OmKVhoX7P7pCuJvpaNWNS90z3eG68yZM47utl0kuTyWyqXkzJkzkGUZYWFhBU7lZmdnm2417urq6qAeUlFkWTYNBy3NU/HcF0qHLMvQarXQaDTlbihFRcEMxcb8xMb8xFeeMry//yckTFwI3e0E+I56ERjaEweO/hfdunWDn58f0jJycDsxE1X93U3XcCUlJWH//v2meZTAWAA+7EZzPMNFRERERESlTht3AwmTFiLz+xi4dXgS1bZ8Bud6tZGUlGQ2n7enS7m4WYYRCy4bif4XhYqO+YmNQznFxwzFxvzExvzEJ2qGhqwcpCzegJRFG6Cu7IvAL2bBo0d70/cyNzc3hIaGFnl9vDXzKBULLjvwS7uYmJvYmJ/4mKHYmJ/YmJ/4RM3w/oF/hg/evAfftwai0jtDoPIwL5rc3NweOizPmnmUigWXHey5LTw5Xv7LFZmfeGRZhsFggEqlYn6CYoZiY35iY37iEy1D7dWbSJi0CJn7f8obPvj1Z3CuX9vR3XIIFlw24j1GxMZiWWx6vR4qlcrR3aBiYIZiY35iY37iEyFDQ3YOUpZsRMrC9VD5+SJwzUx49HyqQn//YsFVwliQEfcBIiIiqojuH/gZCZMWQnfjLnzfHIBK7w4tMHywImLBVUKMFzFmZmYKeTEflZzMzEwA4l7YSkRERGQL7dWbSJi8GJn7jsHtqSdQbdOnFXb4oCUsuEqIWq2Gr68v7t69CwBwd3ev0KdOlai0n8MlyzIyMzNx9+5d+Pr6Qq1Wl/jvICIiIlIKQ3YOUpZuQsqCr/KGD66eAY9nO/A78ANYcNmoqB2oatWqAGAqukh5yuIaLl9fX9O+QCWLRaz4mKHYmJ/YmJ/4lJTh/YPHkTBhAXTXb8P3zYGo9O4QqDzdHd0tRWLBZYfCvrBLkoRq1aqhSpUq0Gq1ZdwrUgKNRqOog2F5IkkSt63gmKHYmJ/YmJ/4lJKh9totJE5ZjPvRP8KtfXNU2/AxnIPrOLpbisaCyw4PO0uiVqsV8YYgc6U9pJBKF/MTHzMUG/MTG/MTn6MzNGTnIHXp10he8CVUvt4IjPoQHr07cn+yAgsuG/EOdGLT6XS8mYXAmJ/4mKHYmJ/YmJ/4HJVh5qETSJiwANr4W/Ad2R+V3nuFwwdtwIKLiIiIiIgK0Mbfzhs+uOcoXMMfR9WvPoJzSF1Hd0s4LLiIiIiIiMhEzslFinH4oI8XAldOh8dznTh80E4suIiIiIiICACQ+cM/wwev3YLPG/3gN+5VDh8sJhZcNmJlLzbmJzbmJz5mKDbmJzbmJ77SzFB7/Q4SJy/G/T1H4Nq2Gaqu+wjODTh8sCSw4LIDD1hikiSJFwsLjPmJjxmKjfmJjfmJr7QylHNykbJ8M5LnfwmVlweqfD4Nns935vfdEsSCi4iIiIioAso8/L+84YNXbv4zfPAVqLw8HN2tcocFlx0e9hwuUiZZlqHT6eDk5MT8BMT8xMcMxcb8xMb8xFeSGepu3EHClCW4/91/4dqmKQK/mAWXhkEl01EqgAWXjfgcLrExP7ExP/ExQ7ExP7ExP/EVN0M5V5s3fHDeOqg83VFlxVR49unCIryUseAiIiIiIirnMo/8ioTI+dDG3YDP633h9/6rHD5YRlhwERERERGVU7qbd/OGD357GK6tmyBwzUwOHyxjLLiIiIiIiMoZOVeLlM+3IPmzdVB5uKHKssnw7NuVwwcdgAWXjbiTis3Jibu8yJif+Jih2Jif2Jif+KzNMPPor0iIXABt7HX4DO+DSh+8BrW3Zyn3jgrDd54dWHSJSZIkZicw5ic+Zig25ic25ic+azLU3byLhKlLcf+bH+DasjECo6bDpVH9MuohFYYFlx14W3gxybIMg8EAlUrF/ATE/MTHDMXG/MTG/MRXVIZyrhYpK7ci+dO1ecMHl06CZ79uzFohWHDZiLdUFZter4dKpXJ0N8hOzE98zFBszE9szE98ljLM/PFk3t0HL8XDZ/gLqDSewweVhgUXEREREZFgdLfuIXHaUmTsPATXFmEIPLQaLqEcPqhELLiIiIiIiAQha3VIXbUdSXPWQOXuiipLJsGzP4cPKhkLLiIiIiIiAWT/9DtSpiyB9u9r8HnteVSKHAa1j5eju0UPwYLLRvzrgdg4dl1szE98zFBszE9szE9cutsJSJi2FPd3HITLk6GoeXAVXMIedXS3yEosuOzAoktMkiTxGSQCY37iY4ZiY35iY35iyhs+uA1Jn6yB5OaCgMUT4dW/GyQWz0LhO88OvC28mPLfYZL5iYf5iY8Zio35iY35iSfrp99xL3I+tBevwvvV5zh8UGAsuGzE28KLTavVQqPROLobZCfmJz5mKDbmJzbmJwbd7QQkTl+GjO3f5w0f/D4KLo2DIcsyMxQUCy4iIiIiIgeTtTqkrt6eN3zQRYOAhZHwGtidwwfLARZcREREREQOlPXzH0iInI/cv67Ae2hv+E0cAbUvhw+WF4oqmQcPHoyQkBCL//bs2WOab+vWrejWrRvCwsLQq1cvHD58uEBb6enpmDhxIlq0aIFmzZrh7bffxt27d8tydYiIiIiICqW7nYA7b83Ezd5jIHm4oeaBlQiY8y6LrXJGUWe4pk2bhoyMDLPX1q1bhwMHDqB169YAgD179mDKlCkYOXIkWrVqhejoaIwePRobNmxA06ZNTcuNHTsWly5dwvTp0+Hi4oIFCxZgxIgR2L59O+/SQ0REREQOI+t0SF29E8mfrAacnRCwIBJeL3L4YHmlqMqjfv36BV5777330LZtW/j5+QEAFi1ahB49emDs2LEAgFatWuHixYtYunQpoqKiAAC///47jh07htWrVyM8PBwAULduXURERODAgQOIiIiwu4+SJPHuPoKSJAnOzs6O7gbZifmJjxmKjfmJjfkpR1bMKSREzkPuhTh4v9IbfhNGQF3J+6HLMUNxKbqM/u2333D9+nU8++yzAID4+HhcuXIF3bt3N5svIiICMTExyM3NBQAcPXoU3t7eaNu2rWmeoKAgNGzYEEePHi27FSAiIiIiAqC7k4g7o2bhZq/RkNxcUfP7KATMec+qYovEpqgzXA/avXs33N3d0blzZwBAbGwsgLyzVfnVq1cPWq0W8fHxqFevHmJjY1G3bt0CZ6KCgoJMbRQHn8MlJlmWodfroVarmZ+AmJ/4mKHYmJ/YmJ/jyDodUtfsQvLHqwCNEwLmj4fXSxE2Dx9khuJSbMGl0+mwd+9edOrUCe7u7gCA1NRUAIC3t/lfAow/G6enpaXBy6vgxYY+Pj44e/Zssfoly7LFZ3FJklToM7qMb4qippfWsmz332nGA5Uq3wGO2Ti+XWuXfTA/EdfV0dvQ0e3KsgyDwQC1Wi1Ef0uiXSX2yd52ARQ4hpZFf4uzLNs1X9ZSfuVtXZXWbvaJM0gYPw+5F2LhNaRX3t0HHzijZe3vzZ+hEtfV0fu3UvpkiWILrp9++glJSUno2bOno7tSgFarNfvLgkqlMt2IQ6vVFpjfON5Wr9fDYDCYTXNycoIkSTAYDNDr9WbTjO0aH3T3IOOD7yy1q1arTV9qdDqd2TRJkkzL6nS6AjtMabVrzboCBbdhSbZr7LMxP41GA0mSbF7Xh2XuiG1obNdSn0orG+O6FrYNje0WtQ2t3b/zt+Hk5FTs/buobViW+zdQcY4R+Sn1GPEgHiMKtmvrZ2BZHSMsrSuPEebb0NJnYGHt8nvEw9s1rqul/Vt/LwmpH63C/S374dy0AQL3LIVzkxAYABi0WruOEcZtplaroVKpFHuMUOr3CEvrWtxjhCxbN+pNsQXX7t274evra7rpBZB3hgrIu+V7QECA6fW0tDSz6d7e3rh9+3aBNlNTU03zFIdxxyhsWmHy78gPUqlUFv9qCJgHb2u7D1u2qDs2lla7Ra0rUPQ2LIl28+dn/G9x1tUR2Th6G+ZX3G1o7f5tPOA9mF9prKujsinvx4gHi+bClKf9u7B2jUQ8Rtj6GVhWxwh7lq2IxwhL+SnlGGFtu0o+Rsg6HdLWfoPkj1cDTmoEzHsfni/1KHL4oLWZGz8H84/0KI/b0BKlHiOsKbYAhRZc2dnZOHjwIHr16mW2EYKCggDkXctl/H/jzxqNBrVq1TLNFxMTU6DqjIuLQ3BwcLH7J0mW71T4sI1e1PTSWpbtmk8zZvfgvOVxXUVp15ZlH8xPtHVVwjZku2XbrhL7ZG+7xs9UfgaK165xOj8DS7fdrBOnkTB+PnLPX4b34GfhN+l1qP0e/od+W34vPwPF6VN+irxL4Q8//IDMzEzT3QmNatWqhTp16mDfvn1mr0dHR6N169amU+7t27dHamoqYmJiTPPExcXh/PnzaN++fbH6ZsvGJeXhM9jExvzExwzFxvzExvxKh+5eMu6O+Qg3e46CpHFCjX0rEDD3fauKLVsxQzEpMrXvvvsO1atXR/PmzQtMGzNmDMaNG4fatWujZcuWiI6OxunTp7F+/XrTPM2aNUN4eDgmTpyI8ePHw8XFBfPnz0dISAi6du1a7P6x6BJTYX+VJTEwP/ExQ7ExP7Exv5JnHD6YNHsVoFYhYO778Hq5B6RChq8VFzMUl+IKrtTUVPz4448YOnSoxZ2qZ8+eyMrKQlRUFFauXIm6detiyZIlaNasmdl8CxYswOzZszF16lTodDqEh4dj8uTJJfKXAWsvkCNlMd4hzZYxt6QczE98zFBszE9szK9kZf/vDO6Nn4/cc5fgNagn/Ce9DrW/b6n+TmYoLkm25Z6GFdyZM2cgyzLCwsK4owvIeBeboi74JuVifuJjhmJjfmJjfiVDdy8ZSTNXIH1TNFyahKDyJ+/AtXmjMvndzFB5zpw5AwAICwsrcj7FneEiIiIiIlISWa9H2rpvkfTRSkCSUPnT9+A9+NlSGz5I5QsLLiIiIiKiQmT/eg73xs9D7umLecMHJ79R6sMHqXxhwUVERERE9AB9QjISZ32O9A174Nw4GDX2roDrE2UzfJDKFxZcNuKYWbEV9TA+Uj7mJz5mKDbmJzbmZx1Zr0fal98i6aMoQJZRec678B7SSxHDB5mhmFhw2YFFl5gkSeLzKwTG/MTHDMXG/MTG/KyTffIc7n3wz/DBl3vkDR+sXMnR3QLADEXG1KhC4S39xcb8xMcMxcb8xMb8CqdPTMkbPrh+N5zDHkWN6OVwfTLU0d0qgBmKiQWXjWRZ5s4uKN5OVWzMT3zMUGzMT2zMzzJZr0fa+t1ImvV53vDBT96F91BlDB98EDMUFwsuIiIiIqpwsn87j4Tx85Hzx5/wejECflNGwilAGcMHqXxhwUVEREREFYY+MQWJ/7cyb/hgo/qosWcZXFsU/eBaouJgwUVERERE5Z6s1yN9wx4kzvoc0BtQ+aP/wPuV3pB4IwoqZdzDiIiIiKhcy/79Qt7wwd8vwGtg97zhg1X8HN0tqiBYcNmIFymKTaPROLoLVAzMT3zMUGzMT2wVMT99UiqSPopC2pffwvmxeqi+eyncWjZ2dLfsVhEzLA9YcNmBRZeYmJvYmJ/4mKHYmJ/YKlp+ssHw7/BBrQ6V/+9teL/6nNDDBytahuWJuHudA/G28GKSZRl6vR5qtZr5CYj5iY8Zio35ia0i5Zdz6i/c+2Aucn67AM/+z8B/2pvlYvhgRcqwvGHBZSNZlh3dBSoGg8EAtQKfrUHWYX7iY4ZiY35iK+/56ZPTkPTRSqSt+xbOjwWh+ndL4dZK3OGDlpT3DMsrFlxEREREJCzZYED6xmgkzlwBaHXwn/U2fF4Te/gglS/cE4mIiIhISDmn/sK98fOQc/I8PPt3g//UN+EU6O/obhGZYcFFRERERELRJ6chaXYU0tZ+A+eGdVH9m8Vwa9PU0d0isogFl414kaLYOO5ZbMxPfMxQbMxPbOUhP9lgQPqmvUicuRxyjhb+M0bDZ3ifCjN8sDxkWBFVjL2zhLHoEpMkSTxQCYz5iY8Zio35ia085Jdz6i/ci5yPnF/PwbPv0/Cf9hacqlZ2dLfKTHnIsKJiwWUH3hZeTLIsm7JjfuJhfuJjhmJjfmITOT99SjqSZq9C2tpd0AQ/guq7FsGtbTNHd6vMiZxhRceCy0a8LbzYdDodn9IuMOYnPmYoNuYnNtHykw0GpH+9F4kzV0DOzoX/h2/BZ9gLkDQV9+uraBlSnoq7xxIRERGRIuWc+Tvv7oO/nIXnC0/Df3rFGj5I5QsLLiIiIiJSBH3qP8MHv9gFzaO1K+zwQSpfWHARERERkUPJBgPSt+xH0ozlMGRmw3/6m/AZ3rdCDx+k8oN7sY14kaLYmJ/YmJ/4mKHYmJ/YlJpfztlLSBg/D9n/OwPPPl3g/+EoDh8shFIzpKKx4LIDd3YxSZLEC00FxvzExwzFxvzEpsT89KnpSP5kDVJX74Cmfi1U37kQbuGPO7pbiqXEDMk6LLiIiIiIqMzIsoyMLfuR+OGyvOGDU0fC5/V+HD5I5Rb3bDvwOVxikmUZOp0OTk5OzE9AzE98zFBszE9sSskv59wlJIyfj+wTp+H5fOe84YPVAhzWH5EoJUOyHQsuG/E5XGJjfmJjfuJjhmJjfmJzZH76tIx/hw/Wq4lqOxbAvV1zh/VHVHwPiokFFxERERGVClmWkbF1PxKnL4fhfhb8Jr8O39f7QXLmtUhUcbDgIiIiIqISl3P+ct7wweOn4NG7EyrPGAWn6lUc3S2iMseCi4iIiIhKjD4tA8lz1iB11Q5o6tZAtW3z4f7UE47uFpHDsOCyES9SFBtvpyo25ic+Zig25ie20s5PlmVkbP8eidOWwpCRCb+JI+A7sj+HD5YgvgfFxILLDiy6xMTcxMb8xMcMxcb8xFba+eVciM17eHHMKXj06pg3fLBGYKn+zoqG70FxseCyA28LLyZZlqHX66FWq5mfgJif+Jih2Jif2EorP0P6fSR9+gVSV26Dpk51VNs6D+4dniyx9ulffA+KiwWXjXg7TrEZDAao1WpHd4PsxPzExwzFxvzEVpL5ybKMjB0H84YPpt+H34ThecMHXZxLpH2yjO9BMbHgIiIiIiKr5f4Zh3vj5yH75z/g8WwH+M8YDU1NDh8kKgwLLiIiIiJ6KENG5j/DB7dC80h1VNsyF+4dWzi6W0SKx4KLiIiIiAolyzIydh1C4tSlMKSmw2/8MPi+OYDDB4msxILLRrxIUWwc9yw25ic+Zig25ic2e/LL/SsO9yIXIPvYb/Do8RT8Z43h8EEH4ntQTCy47MCiS0ySJPFAJTDmJz5mKDbmJzZb8zNkZCLpsy+Q+vlWaGpVQ7WvP4N755al2EN6GL4HxcWCyw68LbyYZFk2Zcf8xMP8xMcMxcb8xGZtfrIs4/6uH5AwdUne8MH3X4PvqIEcPqgAfA+KS+XoDliyc+dOPPfccwgLC0PLli0xfPhwZGdnm6b/8MMP6NWrF8LCwtCtWzds3769QBu5ubn45JNP0LZtWzRt2hSvvvoqYmNji9033hZebDqdztFdoGJgfuJjhmJjfmJ7WH65F6/g1gtjcef16XBt/hhq/bQeld4dwmJLQfgeFJPiznAtX74cUVFRGDlyJJo2bYrk5GTExMRAr9cDAH799VeMHj0affv2xcSJE3H8+HFMmjQJHh4eeOaZZ0ztzJo1C9HR0YiMjERgYCBWrFiBV155BXv27IGXl5ejVo+IiIhIUQwZmUieuxYpK7bAqWZVVN30KTy6tHJ0t4jKDUUVXLGxsViyZAmWLVuGp556yvR6t27dTP+/fPlyNG7cGDNmzAAAtGrVCvHx8Vi0aJGp4Lp9+za2bduGadOmoW/fvgCAsLAwdOzYEV9//TVGjBhRhmtFREREpDyyLOP+N4fzhg8mp8Jv3KvwGTUQKlcXR3eNqFxR1JDCHTt2oGbNmmbFVn65ubk4ceKE2ZksAIiIiMDly5dx/fp1AMCxY8dgMBjM5vP19UXbtm1x9OjR0lsBIiIiIgHk/n0Vt/q9izsjpsGlaUje8MH3hrLYIioFiiq4Tp06heDgYCxbtgytW7dGaGgoBg4ciFOnTgEArl27Bq1Wi6CgILPl6tWrBwCma7RiY2Ph7+8PHx+fAvMV9zouXqQoNuYnNuYnPmYoNuYnNkmSYMjIROKMFYh/6hVor95C1Y1zUO3L2dDUrubo7pEV+B4Uk6KGFN67dw9nz57FxYsXMW3aNLi5uWHFihV47bXXcODAAaSmpgIAvL29zZYz/mycnpaWZvE6LW9vb9M8xfXgzTMkSSr0hhrGN0dR00trWbZrPs3JycmsHWbj+HZtWTZ/fiKuqxK2oaPb1Wg0QvW3ImVjTbsPHkPLor/FWZbtmi+bvfcYEqcshiEpFZXeHQqfUQOhdnMtV+sqWru2Lmt8Dz5sueL0SbR2ldYnSxRVcMmyjMzMTCxcuBANGjQAADRp0gSdOnXC+vXrER4e7uAe5tFqtWY/q1Qq0xvgwWkA4Oycd3cfvV4Pg8FgNs3JySnvL04Gg+nGIA+2K8uyxXaNX1wstatWq6FWqyHLcoE72kiSZFpWp9MV2GFKq11r1hUouA1Lq13jukqSZPO6WtMuULbb0NiupT45ahsa2y1qGzpq/y5qG5bl/g3wGGHLugLK2b95jLB+XXmMcOwxIufvq7gXOR85R0/CtWsbVPrwLTjVrgYVjxHFate4rjxGVNxjhPEPwA+jqILL29sbvr6+pmILyLv26rHHHsOlS5fQo0cPAEB6errZcmlpaQBgGkLo7e2NjIyMAu2npaUVGGZoK1mWTeFbYgzBkvw78oNUKhVUKssjPPMHb2u7D1s2/19KyqrdotYVKHobFqddJycn6HQ6s/yM/y3OujoiG0dtQ0vtFncbWrt/Gw+KD+ZXGuvqqGzK+zHC+KHo5ORUYfbvwto1EukYIcsy9Hq9zZ+BZXWMsGfZ8n6MMNzPQuL8VUhZvhlO1aug8tpZ8OrerkB+SjlGWNtuRT1GGD8HS/sz8GF9Uto2dOQxwppiC1BYwVW/fn1cu3bN4rScnBzUrl0bGo0GsbGxaNeunWma8bos47VdQUFBSEhIQGpqqlmBFRsbW+D6L3tIkuUHzj1soxc1vbSWZbsFp1nKz9F9qsjt2rps/vxEW1elbENHtmv8S6Eo/S2PfbK33fzZ8TNQ2e3Ksoz7u48gccpi6BNTUOndIfAZ9SL0ahU/AxXWrj3L8jNQjD7lp6ibZnTs2BEpKSm4cOGC6bXk5GScO3cOjRo1grOzM1q2bIn9+/ebLRcdHY169eqhZs2aAIDw8HCoVCocOHDANE9qaiqOHTuG9u3bl83KEBERERVDVlYWzpw5g6ysLKvnyb18Dbf6v4c7r02Bc+ijqPXjV/Ab9yrvPkjkQIo6w9WlSxeEhYXh7bffxjvvvAMXFxesXLkSzs7OeOmllwAAb775JoYMGYLp06eje/fuOHHiBHbv3o358+eb2qlatSr69u2LOXPmQKVSITAwEJ9//jm8vLwwcOBAR60eERERkdWysrJw9uxZ1KhRA25ubkXOU72SPzLXfYWUpZvgVD0AVdd/DI9ubcu4x0RkiaIKLpVKhZUrV2L27NmYOnUqtFotnnjiCWzYsAEBAQEAgCeeeAKLFy/GggULsG3bNlSvXh2zZs1C9+7dzdqaPHkyPDw8MHfuXNy/fx+PP/44vvjiC4t3LyQiIiJSurSMHNxKvI9q/h7w9sw7YyXLMiqduozU2VuApDRUGjsYvmNehsqNZ7SIlEKSbbmnYQV35swZAEBoaKhN4zZJGWRZNt1NhvmJh/mJjxmKjfmVvaSkJOzatQsNGjSAXgYOHDuP64lAjWq+6Nm2Dpxv3YN6wSZ4no2D01PNUf3T96GpW8NiW8xPfMxQeYy1QVhYWJHzKeoMlyi4k4uJByixMT/xMUOxMT/HSEhIwKFDhyADUOsNqO0OqBPicWf6t2h48gqyPF1xcmA7dJ7+HjT+/oW2w/zExwzFxYLLDtbec5+URZZlGAwGm27jScrB/MTHDMXG/ByjcuXKaNCgAbJztDhy7Be4n0tBt19+g2dWFu7364QTIZWh8fR4aCbMT3zMUFyKukuhCDgCU2wPPoyPxML8xMcMxcb8yp6bmxuqVq2KgGwtuu/9H/oeOoIkv0rYPuxlJL7QETq1qsCDWwvD/MTHDMXEM1xERERECqXK1eLWjGUI2PcLDO7OONy9MeLrVgGkmzh27BYgy/Dw8HB0N4moCCy4iIiIiBRGlmXgx9/x+KfboUpKQ+6LXXG8ji/idZUQfycHtap54ammAbj41wU8+uijhd42nogcjwUXERERkYJo424gYeICZB48Do9OLVH5o/8go5IHvPfvx+vtOiJb74Kq/u7Q5d7Hnds30axZMxZcRArGgstGvEhRbCoVL1sUGfMTHzMUG/MrXYasHKQsWo+UxRuhDqiEquv+D+7d2+V990hKAgB4ujujtl8lAEBS0n2b2md+4mOGYmLBZQcWXWKSJAlOTtzlRcX8xMcMxcb8Stf9/T8hYeJC6G4nwHfUi6g0djBU7q6m6W5ubggNDTU7k2XptcIwP/ExQ3ExNTvwtvBiyn+HSeYnHuYnPmYoNuZXOrRxN5AwaSEyv4+BW4cnUW3LZ3CuV7vAfG5ubgUermrptcIwP/ExQ3Gx4LIRbwsvNq1WC41G4+hukJ2Yn/iYodiYX8kxZOUgZfEGpCzaAHVlXwR+MQsePdqX6hdp5ic+ZigmFlxEREREZej+gX+GD968B9+3BqLSO0Og8uBNL4jKKxZcRERERGVAe+UmEiYvQub+n/KGD379GZzrFxw+SETlCwsuIiIiolJkyM5BypKNSFm4Hio/XwSumQmPnk/xOhyiCoIFl414cBQb8xMb8xMfMxQb87Pd/QM/I2HSQuhu3IXvmwNQ6d2hDhs+yPzExwzFxILLDtzZxSRJEi80FRjzEx8zFBvzs4326k0kTF6MzH3H4PbUE6i26VOHDh9kfuJjhuJiwUVERERUQgzZOUhZugkpC77KGz64egY8nu3AP9YSVWAsuOzA53CJSZZl6HQ6ODk5MT8BMT/xMUOxMb+Hu3/wOBImLIDu+m34vjkQld4dApWnu6O7BYD5lQfMUFwsuGzE53CJjfmJjfmJjxmKjflZpr12C4lTFuN+9I9wa98c1TZ8DOfgOo7uVgHMT3zMUEwsuIiIiIjsYMjOQerSr5G84EuoKvkgMOpDePTuyLMPRGSGBRcRERGRjTIPnUDChAXQxt/69+6DChk+SETKwoKLiIiIyEra+Nt5wwf3HIVr+OOoun62IocPEpFysOCyEYcJiM3Jibu8yJif+Jih2CpyfnJOLlKMwwd9vBC4cjo8nusk1PeCipxfecEMxcTU7CDSwZX+JUkSsxMY8xMfMxRbRc4v84d/hg9euwWfN/rBb9yrwg0frMj5lRfMUFwsuOzA28KLSZZlGAwGqFQq5icg5ic+Zii2ipif9vodJE5ejPt7jsC1bTNUXfcRnBvUdXS37FIR8ytvmKG4WHDZiLfjFJter4dKpXJ0N8hOzE98zFBsFSU/OScXKcs3I3n+l1B5eaDK59Pg+Xxn4b/kVpT8yjNmKCYWXERERET/yDz8v7zhg1du/jN88BWovDwc3S0iEhgLLiIiIqrwdDfuIGHKEtz/7r9wbdMUgV/MgkvDIEd3i4jKARZcREREVGHJudq84YPz1kHl6Y4qK6bCs08X4YcPEpFysOCyEQ/AYuO4Z7ExP/ExQ7GVt/wyj/yKhMj50MbdgM/rfeH3/qvlevhgecuvImKGYmLBZQcWXWKSJInPrxAY8xMfMxRbecpPd/Nu3vDBbw/DtXUTBK6ZWe6HD5an/CoqZigupmYH3hZeTPnvMMn8xMP8xMcMxVYe8pNztUj5fAuSP1sHlacbqiyfAs8XnhZ2fWxRHvKr6JihuFhw2Yi3hRebVquFRqNxdDfITsxPfMxQbCLnl3n0VyRELoA29jp8hvdBpQ9eg9rb09HdKlMi50d5mKGYWHARERFRuaW7eRcJU5fi/jc/wLVlYwRGTYdLo/qO7hYRVSAsuIiIiKjckXO1SFm5FcmfroXKww1Vlk6CZ79uHIpFRGWOBRcRERGVK5k/nsy7++ClePgMfwGVxle84YNEpBwsuIiIiKhc0N26h8RpS5Gx8xBcW4Qh8NBquIRy+CARORYLLhtJksThCIKSJAkajYb5CYr5iY8Zik3J+claHVKjtiFpzhqo3F1RZckkePbn8MH8lJwfWYcZiosFF1UoPEiJjfmJjxmKTYn5ZR37Dfci50P79zX4vPY8KkUOg9rHy9HdUiQl5ke2YYZiYsFlBz6HS0yyLEOv10OtVjM/ATE/8TFDsSktP93thLzhgzsOwvXJUAQeXAWXsEcd3S3FUlp+ZDtmKC4WXDbic7jEZjAYoFarHd0NshPzEx8zFJsS8pO1OqSu2oakT9ZAcnNBwOKJ8OrfDZJK5dB+iUAJ+VHxMEMxseAiIiIiIWT99Hve8MGLV+H96nPwmzCcwweJSPFYcBEREZGi6W4nIHH6MmRs/x4uT4aiJocPEpFAWHARERGRIslaHVJXb88bPujqjIBFE+A14BkOHyQiobDgshEvUhQbxz2LjfmJjxmKrSzzy/r5DyREzkfuX1fg/co/wwd9OXywOPj+Ex8zFJOi/kS0Y8cOhISEFPj32Wefmc23detWdOvWDWFhYejVqxcOHz5coK309HRMnDgRLVq0QLNmzfD222/j7t27JdJPFl1ikiSJd/YRGPMTHzMUW1nlp7udgDtvzcTN3mMgebih5oGVCPjkHRZbxcT3n/iYobgUeYZr1apV8PL698AaGBho+v89e/ZgypQpGDlyJFq1aoXo6GiMHj0aGzZsQNOmTU3zjR07FpcuXcL06dPh4uKCBQsWYMSIEdi+fTucnIq32rwtvJhkWTZlx/zEw/zExwzFVtr5yTodUlftQNInqyG5aBCwIBJeL3bn8MESwvef+JihuBRZcDVq1Ah+fn4Wpy1atAg9evTA2LFjAQCtWrXCxYsXsXTpUkRFRQEAfv/9dxw7dgyrV69GeHg4AKBu3bqIiIjAgQMHEBERYXffeFt4sel0Omg0Gkd3g+zE/MTHDMVWWvllxZxCQuQ85F6Ig/crveE3YQTUlbxL/PdUdHz/iY8ZikmoPxvFx8fjypUr6N69u9nrERERiImJQW5uLgDg6NGj8Pb2Rtu2bU3zBAUFoWHDhjh69GiZ9pmIiIgs091JxJ1Rs3Cz12hIbq6o+X0UAua8x2KLiMoVRZ7h6tmzJ5KTk1G9enX0798fw4cPh1qtRmxsLIC8s1X51atXD1qtFvHx8ahXrx5iY2NRt27dAqdbg4KCTG0QERGRY8g6HVLX7ELyx6sAjRMC5o+H10sRHD5IROWSzQXX9evXcejQIfz222+4fPkykpOTIUkSKlWqhKCgIDz++OPo1KkTatWqZXNnAgICMGbMGDRp0gSSJOGHH37AggULcOfOHUydOhWpqakAAG9v8798GX82Tk9LSzO7BszIx8cHZ8+etblfD7I0rFCSpEKHGxoLv6Kml9aybPffafn/lVS7xe0T27V+2QfzE3FdHb0NHd1u/tdE6G9JtKvEPtnbrnFacT8Ds4+fzrv74IVYeA3pBf9Jr0P1zw0xHmyjvG1DR++HlvIrb+sqWru2LJs/QxHXVQnbsCzatcTqguvw4cNYs2YNTp48CVmWUbt2bdSsWRPBwcGQZRlpaWn4888/ceDAAXz88cdo3rw5hg0bho4dO1rdmXbt2qFdu3amn8PDw+Hi4oJ169Zh5MiRVrdT2rRardnZM5VKZboRh1arLTC/s7MzAECv18NgMJhNc3JygiRJMBgM0Ov1ZtOM7cqybLFd4xheS+2q1Wqo1WrIsgydTmc2TZIk07I6na7ADlNa7VqzrkDBbViS7RoMBrP8NBoNJEmyeV0flrkjtqGxXUt9Kq1sjOta2DY0tlvUNrR2/5Zl2ZSfk5NTsffvorZhWe7fQMU5Rhj7XFi7SjhGPIjHCPN2jcva8hlo3Ia5t+8haebnyNx6AM5NQxC4ZyncmzeCSqWCwWAo9jHC0rryGGG+DS19BhbWLr9HPLxd47qW1THC+DloMBigUqkUeYxQ8vcIS+ta3GOEsfh9GKsKrv79++PPP/9E586dsWDBArRp0waenp4W583IyMBPP/2E/fv3Y+zYsWjQoAE2b95sza+xqHv37lizZg0uXLgAHx8fAHm3fA8ICDDNk5aWBgCm6d7e3rh9+3aBtlJTU03z2EuSJNOBz5KiLmTMvyM/SKVSmb6IWPqd9rb7sGWLumNjabVb1LoCRW/D4rRrKTfjm6Q46+qIbBy1DS21W9xt6Kj9uzjvudLKpiIcI4z7S0XZvwtr10ikY8TDPv8KbVevR+oXu5D08WrASY3Kc9+H18s9zIYP8hjxr9I6RhSVnZKOEda0y2OEde1WlG3oyGOENcUWYGXB1bJlSyxbtgyVK1d+6Lyenp7o1q0bunXrhnv37uHLL7+0qiPWCAoKAgDExsaa/t/4s0ajMQ1jDAoKQkxMTIGqMy4uDsHBwcXuR2Eb92EbvajppbUs2y3ddpXYJ9HaVWKfRGtXiX1iu8rtU1m2m3XiNBLGz0fu+cvwHtILfhNHQO1X8A+f5WFdldyuEvvEdpXbJ9HaVWqf8rPq6tT33nvPqmLrQQEBAXjvvfdsXi6/6OhoqNVqPPbYY6hVqxbq1KmDffv2FZindevWpr/etG/fHqmpqYiJiTHNExcXh/Pnz6N9+/bF6k9h49dJ+WRZRm5uLvMTFPMTHzMUmy356e4l4+6Yj3Cz5yhIGifU2P85Aj4bZ7HYorLB95/4mKG4FHWXwmHDhqFly5YICQkBABw6dAhbtmzBkCFDTEMIx4wZg3HjxqF27dpo2bIloqOjcfr0aaxfv97UTrNmzRAeHo6JEydi/PjxcHFxwfz58xESEoKuXbs6ZN2IiIjKO1mnQ9rab5A0exWgViHAOHywkOE8REQVgdUF17lz52xuvFGjRjbNX7duXWzfvh23b9+GwWBAnTp1MHHiRAwePNg0T8+ePZGVlYWoqCisXLkSdevWxZIlS9CsWTOzthYsWIDZs2dj6tSp0Ol0CA8Px+TJk4sci0lERET2yf7fGdwbPx+55y7Ba1BP+E96HWp/X0d3i4jI4STZyvOSDRo0sGmsoiRJOH/+vN0dU6IzZ85AlmWEhYXZtC1IGYx3sTHeDYfEwvzExwzFVlh+unvJSJq5AumbouHSJASVP3kHrs1t+4MrlT6+/8THDJXnzJkzAICwsLAi57P6dM/s2bMfOk92dja2bNmCCxcuWNssERERCUjW65G27lskfbQSkCRU/vQ9eA9+lsMHiYgeYHXB9fzzzxc6LTc3F19//TWioqJw7949PPnkkxgzZkyJdFBp+BcFsRV1609SPuYnPmYoNmN+2b+ew73x85B7+mLe8MHJb3D4oAD4/hMfMxRTsS5oys3NxaZNm7Bq1Srcu3cPLVq0wLx58/Dkk0+WVP8UiUWXmJib2Jif+Jih2CRJgj4hGYmzPkf6hj1wbhyMGntXwPUJDh8UAd9/4mOG4rKr4MrNzcXGjRuxevVq3Lt3Dy1btqwQhZaRtU+VJmWRZRl6vR5qtZr5CYj5iY8Ziss0fHB2FCDLqDznXXgP6cXhgwLh+098zFBcNhVcOTk5pjNaCQkJaNmyJebPn48nnniitPqnOHz2gdgMBkOhTxsn5WN+4mOG4sk+eQ73PsgbPujxYndUnvImnAIqObpbZAe+/8THDMVkdcG1du1arFq1ComJiWjVqhUWLlyI5s2bl2bfiIiIyEH0iSl5wwfX74Zz2KOovmcZ1E1DoOY1JERENrG64Pr4448hSRIaNmyIevXqYe/evdi7d2+Ry0yePLnYHSQiIqKyI+v1SFu/G0mzPs8bPvjJu/Ae2gtQqaDVah3dPSIi4dg0pFCWZZw/f96q52tJksSCi4iISCDZv51Hwvj5yPnjT3i9GAG/KSNNwwc5pJ6IyD5WF1x//vlnafZDGLxIUWwc9yw25ic+ZqhM+sQUJP7fyrzhg43qo0b0crg+GVpgPuYnNuYnPmYopmLdFr6iYtElJkmSeKASGPMTHzNUHlmvR/qGPUic9TmgN6Dy7LHwfqW3xbsPMj+xMT/xMUNxseCyA28LLyZZlk3ZMT/xMD/xMUNlyf79Qt7wwd8vwGtg97zhg1X8Cp2f+YmN+YmPGYrL7oLrm2++wfbt23H9+nWkpqYWGNstSRJOnjxZ7A4qDcewi02n0/Ep7QJjfuJjho6nT0pF0kdRSPvyWzg/Vg/Vdy+FW8vGVi3L/MTG/MTHDMVkV8H16aefYs2aNQgMDERoaCi8vLxKul9ERERUgmSD4d/hg1odKv/f2/B+9TlIThzsQkRUmuw6ym7duhUdOnTA0qVLoVKpSrpPREREVIJyTv2Fex/MRc5vF+DZ/xn4T3uzyOGDRERUcuz+s9ZTTz3FYouIiEjB9MlpSPpoJdLWfQvnx4JQ/bulcGtl3fBBIiIqGXZVTB06dCiX12dZgxcpio35iY35iY8Zlg3ZYEDa+t241uolZGw/CP9Zb6PmwVXFLraYn9iYn/iYoZgk2Y67QKSnp2PkyJEICQnBCy+8gGrVqlk82+Xr61sSfVSMM2fOAADCwsIc3BMiIiLLck79hXvj5yHn5Hl49u8G/6lvwinQ39HdIiIqd6ytDewaUujm5oZmzZph9erV2LRpU6HzXbhwwZ7miYiIyEb65DQkzY5C2tpv4NywLqp/sxhubZo6ultERBWeXQXXjBkzsHXrVjRp0gRNmjSpcHcp5HO4xCTLMnQ6HZycnJifgJif+Jhh6ZANBqRv2ovEmcsh52jhP3MMfIY9X+J3H2R+YmN+4mOG4rLraLx371707t0bH3/8cUn3R/H4HC6xMT+xMT/xMcOSlXPqL9yLnI+cX8/Bs1/XvOGDVSuX2u9jfmJjfuJjhmKyq+BycnJCkyZNSrovREREZAV9SjqSZq9C2tpd0AQ/wuGDREQKZtddCnv06IHDhw+XdF+IiIioCLLBgLSNe3Ct9UtI37IP/h++hVo/rGGxRUSkYHad4erevTtmzZqF119/3XSXQrVaXWC+Ro0aFbuDREREBOScvpg3fPCXs/B84Wn4T3+rVIcPEhFRybCr4Hr55ZcB5N2F8Mcffyww3XhTifJ4l0JepCg2pxK+iJzKFvMTHzO0nT71n+GDX+yC5tHaqL5rEdzaNnNIX5if2Jif+JihmOxKbfbs2SXdD6Gw6BKTJEnMTmDMT3zM0DaywYD0LfuRNGM5DJnZ8J/+JnyG94WkccwXLuYnNuYnPmYoLruO2s8//3xJ90MovC28mGRZhsFggEqlYn4CYn7iY4bWyzl7CQnj5yH7f2fg2acL/D8c5fDhg8xPbMxPfMxQXDwvaSPejlNser0eKpVd94ohBWB+4mOGRdOnpiP5kzVIXb0Dmvq1UH3nQriFP+7obpkwP7ExP/ExQzFZldjUqVMRHx9vc+PXrl3D1KlTbV6OiIioIpFlGemb9yG+9ctI27gH/lNHotZ/1yqq2CIiIvtYdYbr1q1b6N69O1q1aoWIiAi0bt0a1apVszjv9evXERMTg7179+LEiRNo27ZtiXaYiIioPMk5dwkJ4+cj+8RpeD7fOW/4YLUAR3eLiIhKiFUFV1RUFE6ePIk1a9Zg6tSp0Ov18PX1RY0aNeDj4wNZlpGamorr168jLS0NarUa7du3x7p16/DEE0+U9joQEREJR5+W8e/wwXo1UW3HAri3a+7obhERUQmz+hqu5s2bo3nz5khKSsLhw4fxxx9/IDY2Frdv3wYA+Pr6omvXrmjatCk6dOgAf3//Uuu0I/EiRbFZel4ciYP5iY8Z5g0fzNi6H4nTl8NwPwt+k1+H7+v9IDlrHN21h2J+YmN+4mOGYpJk3gXCamfOnAEAhIWFObgnREQkopzzl/OGDx4/BY/enVB5xig4Va/i6G4REZEdrK0NeJdCO/C28GLK/7cF5ice5ie+ipyhPi0DyXPWIHXVDmjq1kC1bfPh/pRYQ+4rcn7lAfMTHzMUFwsuG/GEoNi0Wi00GuUP2yHLmJ/4KlqGsiwjY/v3SJy2FIaMTPhNHAHfkf2FGD5oSUXLr7xhfuJjhmJiwUVERFQKci7E5j28OOYUPHp1zBs+WCPQ0d0iIqIyxoKLiIioBBnS7yPp0y+QunIbNHWqo9rWeXDv8KSju0VERA7CgouIiKgEyLKMjB0H84YPpt+H34ThecMHXZwd3TUiInIgFlw24kWKYmN+YmN+4iuvGeb+GYd74+ch++c/4PFsB/jPGA1NzfI3fLC85ldRMD/xMUMxqaydcd68efjzzz9Lsy/C4M4uJkmSoNFomJ+gmJ/4ymOGhoxMJExbiviOr0J/JxHVtsxF1TUzy22xVd7yq0iYn/iYobisLrhWrlyJv//+2/RzcnIyGjZsiJiYmFLpGBERkVLJsoz0nQdxrfXLSPtiJ/zGD0OtI2vh3rGFo7tGREQKU6whhRX1Ful8DpeYZFmGTqeDk5MT8xMQ8xNfeckw96843ItcgOxjv8Gjx1PwnzWmXJ7RelB5ya+iYn7iY4bi4jVcNqqoRWZ5wfzExvzEJ3KGhoxMJH32BVI/3wpN7WqotvkzuHdq6ehulSmR8yPmVx4wQzGx4CIiIiqCLMu4v+sHJExdAkNqOvw+eA2+bw3k3QeJiMgqNhVcN27cwLlz5wAA6enpAICrV6/C29vb4vyNGjWyu2P3799H9+7dcefOHWzbtg1hYWGmaVu3bsWqVatw8+ZN1K1bF++88w46duxotnx6ejpmz56NgwcPQqvVol27dpg8eTKqVKlid5+IiKhiyb14BQmR85H142/w6NEe/jPHQFOrqqO7RUREArGp4Fq4cCEWLlxo9tqHH35YYD7jNU4XLlywu2PLli2DXq8v8PqePXswZcoUjBw5Eq1atUJ0dDRGjx6NDRs2oGnTpqb5xo4di0uXLmH69OlwcXHBggULMGLECGzfvh1OTjyxR0REhTNkZCJ57lqkrNgCp5pVUXXTp/Do0srR3SIiIgFZXXnMnj27NPth5vLly9i4cSPGjx+PadOmmU1btGgRevTogbFjxwIAWrVqhYsXL2Lp0qWIiooCAPz+++84duwYVq9ejfDwcABA3bp1ERERgQMHDiAiIsLuvvEiRbGx2BYb8xOf0jOUZRn3vzmcN3wwORV+416Fz6iBULm6OLpriqD0/KhozE98zFBMVqf2/PPPl2Y/zMyaNQsDBw5E3bp1zV6Pj4/HlStX8P7775u9HhERgTlz5iA3NxfOzs44evQovL290bZtW9M8QUFBaNiwIY4ePVqsggtg0SUqSZKYncCYn/iUnmHu31fzhg8ePQn37uGoPOttaGpXc3S3FEPp+VHRmJ/4mKG4rH4OV1nZt28fLl68iFGjRhWYFhsbCwAFCrF69epBq9UiPj7eNF/dunUL7JRBQUGmNoqDd4gRkyzL0Ov1zE9QzE98Ss3QkJGJxBkrEP/UK9Beu42qG+eg2pezWWw9QKn5kXWYn/iYobgUdV4yKysLH3/8Md555x14enoWmJ6amgoABW7SYfzZOD0tLQ1eXl4Flvfx8cHZs2eL1UdZli3u6JIkFfoGMBZ+RU0vrWXZ7r/TjM+v0Gg0JdZucfvEdq1f9sH8RFxXR29DR7dr/LKgUqkU0V9ZlnF/9xEkTlkCQ1IKKr071DR80DhvRcnGmnYBFDiGlkV/i7Ms2zVf1lJ+5W1dRWvXlmXzZyjiuiphG5ZFu5YoquBavnw5/P398cILLzi6K0XSarVmZ89UKpVpTK1Wqy0wv7Nz3q2D9Xo9DAaD2TTjw+sMBkOBm4QY25Vl2WK7xoOmpXbVajXUarXpzZmfJEmmZXU6XYEdprTatWZdgYLbsCTbNfbZmJ/xoGXruj4sc0dsQ2O7lvpUWtkY17WwbWhst6htaO3+nb8NJyenYu/fRW3Dsty/gYpzjMjP0ccI7aV4JE9ZjJyjJ+H+TDj8Z44GqgdAD0Cfr30eIwq2a+tnYFkdIyytK48R5tvQ0mdgYe3ye8TD2zWua1kdI4ztqNVq0x+ulHiMUOr3CEvrWtxjhCzLZsfDwiim4Lpx4wbWrFmDpUuXmm45n5mZafrv/fv34ePjAyDvlu8BAQGmZdPS0gDANN3b2xu3b98u8DtSU1NN8xSHcccobFph8u/ID1KpVFCpLI/wzB+8re0+bNmiLr4srXaLWleg6G1YEu3mz8/43+KsqyOycfQ2zK+429Da/dt4wHswv9JYV0dlU96PEQ8WzYWxZRtmZWXh0qVLePTRRwHA9OiSxx57DG5ubgXaRVYO7n26Bhkrt8GpehUErv8Ynt3aPvSvnDxG/NuurZ+BZXWMsGfZiniMsJSfUo4R1rZbnj4DC2vXKH+7xuOUsY/chg+fVpx2gYevqzXFFqCgguv69evQarV4/fXXC0wbMmQImjRpgrlz5wLIu0YrKCjIND02NhYajQa1atUCkHetVkxMTIGqMy4uDsHBwcXuqyRZvmjxYRu9qOmltSzbNZ9mzO7BecvjuorSri3LPpifaOuqhG1Y3trNzs7GuXPnULNmTQD/FlzVq1fH5cuXUb9+fbi5uUGWZSTv+B6JU5dAlZqBG08/jtBPPoBntaql2t/iLKu0do2fqfwMFK9d43R+BiqvXVuX5WegOH3KTzEFV8OGDfHll1+avXbhwgXMnj0bH374IcLCwlCrVi3UqVMH+/btQ5cuXUzzRUdHo3Xr1qZT7u3bt8eyZcsQExODNm3aAMgrts6fP4/hw4cXq5+2bFxSnqL+6kLKx/zEV1oZZmTm4m5yFnR6A5zUKuTk5ODs2bPwqRSArNhLcFuwGtofTyI1tA78Pp+Em/GxCHNxLpW+lGd8D4qN+YmPGYqpWAWXVqtFbGws0tPTLQ7HePLJJ61uy9vbGy1btrQ4rVGjRmjUqBEAYMyYMRg3bhxq166Nli1bIjo6GqdPn8b69etN8zdr1gzh4eGYOHEixo8fDxcXF8yfPx8hISHo2rWrjWtZEIsuMUmSxOdXCIz5ia+kM0xKSsL169eRkpKGlZt/wK27GajpmQIfDyecPHkSSTdu49e3ZiP0xB9I9PJExnsv4ZyvhMYqfYFrDOjh+B4UG/MTHzMUl12pGQwGzJ07Fxs3bkR2dnah8124cMHujhWmZ8+eyMrKQlRUFFauXIm6detiyZIlaNasmdl8CxYswOzZszF16lTodDqEh4dj8uTJJbKjWnuBHClL/j8KMD/xMD/xlXSGx48fx9mzZ5GdnQ214TZqugGSHkhPk5H27WG0+/EvuGZpcbb5Izj7eB3IOXeBO8DRo0ct3smWisb3oNiYn/iYobgk2ZZ7Gv5j2bJlWLRoEQYMGIDmzZvjgw8+wLhx4+Dt7Y2NGzdCkiS8//77puF85cWZM2cgyzLCwsK4owvIeBeboi74JuVifuIr6QyNZ7h++ikGNzK9cOtuBhrqrqPNj+cQGHcXNx7xx//ahyDd2x3GXych76JtNzc3DB06FH5+fsXuR0XB96DYmJ/4mKHynDlzBgAQFhZW5Hx2ne7ZuXMnunfvjg8//BDJyckA8ob9tW7dGs899xwGDhyI48ePl7uCi4iIlMNYLPn6eiOiSxtkLN0C56+PINfbHVkzXsefqkyE1g3GhfgMXL+bATdnGTXc09GkSRj+/vtvB/eeiIgqCruuvLt9+zZatWoF4N9nQ+Tm5pp+7tWrF7755psS6iIREZFlsizD93QsDH3fgevWvbjeqQl++6AvPJ4Jh5e3N1o80QTDBnTFGy89g9cHdIKvrzeqVatm+uwiIiIqbXad4fL19TU9I8vDwwOenp6Ij483m8f4bCwiIqLSoI29jqzIeQg+/As0HZ+Ez9dvISUzFY8BcHFxMc3n7ekCb08XJCUlAf9MCw0NLfCcLiIiotJgV8H12GOPmcYsAkDLli2xbt06NGzYELIs48svv0RISEiJdZKIiMjIkJmNlEXrkbx4I5wC/VH1y4/g/kw4JElC83/mycrKKlBUubm5ITQ0FJUqVUL16tUd03kiIqpw7Cq4+vfvj507dyI3NxfOzs5455138PLLL2PQoEGQZRk+Pj6IjIws6b4qQmEPfCTlkySJF5oKjPmJr7gZyrKMzH3HkDBpEXR3ElFp9Evw/c8gqNxdC8zr5uZW4CJmS6+R9fgeFBvzEx8zFJdddym0JD09HSdOnIBarUazZs3g6+tbEs0qirV3IiEiopKljbuBhIkLkHnwONw6tUTlj/4D53q1HN0tIiKqwEr1LoWWeHl5oUuXLiXVnKLxOVxikmUZer0earWa+QmI+YnPngwNWTlIWbQeKYs3Qh1QCVXX/R/cu7fjPuAAfA+KjfmJjxmKy667FHbu3BkDBgxAbGysxekHDx5E586di9UxpSqhE4LkIAaDwdFdoGJgfuKzJcP7+39CfPhgJC/aAJ+3BqLWT+vhEdGeXzQciO9BsTE/8TFDMdlVcN24cQPnzp1Dv379cPDgwQLTMzMzcfPmzWJ3joiIKh5t3A3ceukD3B4UCU29Wqh1dB38J46weK0WERGR0tlVcAHAhAkT8OSTT2LMmDFYsGBBCXaJiIgqIkNWDpLmrEF8uyHIvRCLwLX/h2qbP+O1WkREJDS7r+Hy9vbGihUrsGTJEixbtgznz5/H3Llz4eXlVZL9IyKiCuD+gZ+QMHEhdDfvwXfUi6g0djBUHnxOFhERic/uM1xGo0ePxooVK3Dq1Cn07dsXf//9d0n0S7F47YDYnJxK7D4x5ADMT3wPZqi9chO3BkXi9suR0NStmTd8cNLrLLYUiu9BsTE/8TFDMRW74AKA9u3bY9u2bXBzc0P//v1x6NChkmhWsVh0iUmSJKhUKuYnKOYnvvwZGrJzkPTZF4hvNxg5Z/5G4JqZqLZlLpzr13Z0N6kQfA+KjfmJjxmKq0QKLgCoVasWNm/ejK5du2L//v0l1awi8U6FYpJlGQaDgfkJivmJz5jh/QM/I77dECTP+xI+r/dD7Z/Xw/PZDvwSoXB8D4qN+YmPGYrLrvOSX375JerXr1/gdRcXF3zyySeIiIhAUlJSsTunRNzJxabT6aDRaBzdDbIT8xOb9upN3Ju0CNkHfobbU0+g2qZPeUZLMHwPio35iY8ZisnmgisrKwsff/wx+vXrhxdffNHiPE899VSxO0ZEROWDITsHKUs3IWXBV1BV8kGV1TN4RouIiCoMmwsuNzc3XL9+nR+URET0UPcPHkfChAXQXb8NnzcHwHPMS3Dx9eZnCBERVRh2XcPVrl07HDt2rKT7QkRE5YT22i3cHjoRt198H5raVVHryFr4TxnJuw8SEVGFY1fB9dZbb+HKlSt4//338euvv+LOnTtISUkp8K884l9lxaZSldh9YsgBmJ/yGbJzkDx3HeLbDkL2738iMOpDVNs2H87BdQAwQ9ExP7ExP/ExQzHZddOMHj16AAAuXbqE3bt3FzrfhQsX7OuVwrHoEpMkSXx+hcCYn/JlHjqBhAkLoI2/Bd83B6DSu0Oh8nQ3TWeGYmN+YmN+4mOG4rIrtVGjRrHoICHJssx9V2DMT5m08beROGUx7u85Crd2j6Pq+tmmM1oPYoZiY35iY37iY4ZisqvgGjNmTEn3QxiyLHNnF5Qsy9BqtdBoNMxPQMxPeeScXKQs/RrJC76EyscLgSunw+O5ToXmwwzFxvzExvzExwzFVSLnJbOzswEArq6uJdEcEREpXOYP/wwfvHYLPm/0g9+4V82GDxIREVEeuwuumzdvYvHixThy5AiSk5MBAJUqVcJTTz2F0aNHo0aNGiXWSSIiUgbt9TtInLwY9/ccgWvbZqi67iM4N6jr6G4REREpll0F1+XLl/HSSy8hPT0dbdq0Qb169QAAsbGx+Oabb3D48GFs3LgRQUFBJdpZIiJyDDknFynLNyN53jqovD1R5fNp8Hy+M4e1EBERPYRdBdfcuXOhUqmwc+dOhISEmE27ePEiXnnlFcydOxdLly4tkU4SEZHjZB7+X97wwSs3/xk++ApUXh6O7hYREZEQ7Cq4fvnlF7z66qsFii0ACA4Oxssvv4y1a9cWt2+KxL/mik2j0Ti6C1QMzK9s6W7cQcKUJbj/3X/h2qYpAr+YBZeGxRu5wAzFxvzExvzExwzFZFfBpdPpirxBhpubG3Q6nd2dUjoWXWJibmJjfmVHztX+O3zQ0x1VVkyFZ58uxc6AGYqN+YmN+YmPGYrLrsdVN2zYEFu3bkV6enqBaRkZGdi2bRsee+yxYndOqWRZdnQXyA6yLEOn0zE/QTG/spF55FfEP/UKkmavgvfQ3qh9fCO8Xni6RD7omaHYmJ/YmJ/4mKG47H4O14gRI9C9e3f06dMHderUAQDExcVh586dSElJwdSpU0uyn4rBnVxsBoMBarXa0d0gOzG/0qO7eTdv+OC3h+HaugkC18ws9vBBS5ih2Jif2Jif+JihmOwquFq3bo2VK1dizpw5WLlypdm0hg0b4tNPP0WrVq1KpINERFR65FwtUj7fguTP1kHl6YYqy6fAs4TOaBEREVExnsPVpk0b7Nq1C/fu3cPNmzcBANWrV0dAQECJdY6IiEpP5tFfkRC5ANrY6/AZ3geVPngNam9PR3eLiIioXLG64Jo3bx4iIiLQoEEDs9cDAgJYZBERCUR38y4Spi7F/W9+gGurJghc9SFcHqvn6G4RERGVS1bfNGPlypX4+++/TT8nJyejYcOGiImJKZWOKRWH2YiN457FxvyKR87VInnJRlxrPQjZP/+BKssmo/q3i8u02GKGYmN+YmN+4mOGYrJ7SCFQcW8gwaJLTJIk8UAlMOZXPJk/nkRC5HxoL8XDZ/gLqDS+7IcPMkOxMT+xMT/xMUNxFavgqqhkWWbRJSBZlk3ZMT/xMD/76G7dQ+K0pcjYeQiuLcIQeGg1XELrO6QvzFBszE9szE98zFBcLLhsVFHP6pUXOp2OT2kXGPOznqzVITVqG5LmrIHK3RVVlkyCZ/9uDv+QZoZiY35iY37iY4ZisqngunHjBs6dOwcApoceX716Fd7e3hbnb9SoUTG7R0REtso69hvuRc6H9u9r8HnteVSKHAa1j5eju0VERFQhSbKVp2waNGhQ4C+jhQ2tM75+4cKFkumlQpw5cwayLCMsLMzhfyUm28myDK1WC41Gw/wExPweTnc7IW/44I6DcH0yFJU/eRcuYY86ulsmzFBszE9szE98zFB5zpw5AwAICwsrcj6rz3DNnj27eD0iIqJSIWt1SF21DUmfrIHk5oKAxRPh1b8bJJXVN6IlIiKiUmJ1wfX888+XZj+Ewb8oiI35iY35FZT10+95wwcvXoX3q8/Bb8JwRQ8fZIZiY35iY37iY4Zi4k0z7MCdXUySJPFCU4ExP3O62wlInL4MGdu/h8uToah5cJWihg9awgzFxvzExvzExwzFxYKLiEggslaH1NXb84YPujojYNEEeA14hsMHiYiIFEpRn9BHjhzBoEGD0KpVK4SGhqJz586YPXu26Y6IRj/88AN69eqFsLAwdOvWDdu3by/QVm5uLj755BO0bdsWTZs2xauvvorY2NgS6SdvDS8m48WmzE9MzA/I+vkPXO88DInTlsGr/zOoHbMR3i9GCFNsMUOxMT+xMT/xMUNxKeoMV0pKCho3bozBgwfD19cXf//9NxYvXoy///4ba9asAQD8+uuvGD16NPr27YuJEyfi+PHjmDRpEjw8PPDMM8+Y2po1axaio6MRGRmJwMBArFixAq+88gr27NkDLy/7r2/gTi425ie2ipqf7nYCEmcsR8bWA3B5ohFqHlgJlyYhju6WXSpqhuUF8xMb8xMfMxSTogqu3r17m/3csmVLODs7Y8qUKbhz5w4CAwOxfPlyNG7cGDNmzAAAtGrVCvHx8Vi0aJGp4Lp9+za2bduGadOmoW/fvgDybtfYsWNHfP311xgxYkTZrhgRkR1knQ6pq3Yg6ZPVkFw0CFgQCa8XuwtzRouIiIgUNqTQEl9fXwCAVqtFbm4uTpw4YXYmCwAiIiJw+fJlXL9+HQBw7NgxGAwGs/l8fX3Rtm1bHD16tMz6TkRkr6yYU3nDB6cugVe/rnnDB1/uwWKLiIhIMIr85Nbr9cjJycG5c+ewdOlSdOrUCTVr1sS1a9eg1WoRFBRkNn+9evUAwHSNVmxsLPz9/eHj41NgvpK6jouIqDTo7iTizqhZuNlrNCQ3V9T8PgoBc96DupK3o7tGREREdlDUkEKjjh074s6dOwCAdu3aYe7cuQCA1NRUAIC3t/kXD+PPxulpaWkWr9Py9vY2zWMvSZIsjp8t7HXjNKDwcbeluSzb/XeaLMtwcnIym4/ZOL5da5d9MD8R17XIaTodUtfsRNLHqyFpnFB5/gfwyndDjPKwrrIsm25pLEJ/S6JdJfbJ3nYBFDiGlkV/i7Ms2zVf1lJ+5W1dRWvXlmXzZyjiuiphG5ZFu5YosuBauXIlsrKycOnSJSxfvhwjR47EF1984ehumeh0OrOfVSoVnJzyNqVWqy0wv7OzM4C8M3cGg8FsmpOTEyRJgsFggF6vt9iu8a40DzJ+cbHUrlqthlqthizLBfqb/zkOOp2uwA5TWu1as65AwW1YUu0+2F/jukqSZPO6PixzR2xDY7uW+lRa2RjXtbBtaGy3qG3oqP27qG1Ylvs3AOh/+xMJkfOQez4WHoN6wmf8a1BX8oZOr4eTJJXLY4SlO205+hhh6/5d0Y4RKpWKxwgHHSNK4ntEYZ+BhbXr6GOEEr9HKOkYAUBxx4iK+D3CWPw+jCILrgYNGgAAmjVrhrCwMPTu3Rvff/896tevDwAFbhOflpYGAKYhhN7e3sjIyCjQblpaWoFhhvYwhm9JUQ+ky78jP0ilUkFVyLUZ+YO3td2HLWt8A5Rlu0WtK1D0NixOu05OTjAYDFCpVKb8jP8tzro6IhtHbUNL7RZ3G1q7f8uybDG/0ljXsspGdzcJSTNXIGPzPrg0a4ga+z+HS9MGJd5fpRwjZFmGXq83+/C1pDzt34W1ayTSMUKW/x0pYMtnYFkdI+xZVunHiAcV53uEpc9Aa9rl94ii2y3LY4Txc7C0PwMf1ielbUNHHiOsKbYAhRZc+YWEhECj0eDatWvo1KkTNBoNYmNj0a5dO9M8xuuyjNd2BQUFISEhAampqWYFVmxsbIHrv2xlrGQtbeCHbfSippfWsmzXfJrBYIBarS4wb3lcV1HatWXZB/MTbV1NQxR0OqR9sQtJH68GnNQImPc+vF7uWeQNMURd1wfpdLqHfkgpqb/lsU/2tmv8smfpGFqcdktzWbZrPp2fgcpr19ZljRmWZp9Ea1epfcpPkTfNyO/UqVPQarWoWbMmnJ2d0bJlS+zfv99snujoaNSrVw81a9YEAISHh0OlUuHAgQOmeVJTU3Hs2DG0b9++TPtPRJRf1onTuN5lBBImLYLn851RO2YDvAf34t0HiYiIyilFneEaPXo0QkNDERISAldXV/z5559YvXo1QkJC0KVLFwDAm2++iSFDhmD69Ono3r07Tpw4gd27d2P+/PmmdqpWrYq+fftizpw5UKlUCAwMxOeffw4vLy8MHDjQUatHRBWY7l4ykmYsR/rXe+HStAFq7P8crs0aOrpbREREVMoUVXA1btwY0dHRWLlyJWRZRo0aNdCvXz8MGzbMdMHoE088gcWLF2PBggXYtm0bqlevjlmzZqF79+5mbU2ePBkeHh6YO3cu7t+/j8cffxxffPGFxbsXEhGVFlmnQ9rab5A0exWgViFg7vvwerkHpELGkxMREVH5Ism23NOwgjtz5gwAIDQ01KZxm6QMlm66QOIQMb/s/53BvfHzkXvuErwHPwu/Sa9D7Vf8G/eISsQM6V/MT2zMT3zMUHmMtUFYWFiR8ynqDJcouJOLSZKkQu9SQ8onUn66e8lImrkC6Zui4dIkBDX2rYDr4485ulsOJ1KGVBDzExvzEx8zFBcLLjtYe899UhbjLY0Lu8skKZsI+cl6PdLWfYukj1YCkoTKn74H78HPcvjgP0TIkArH/MTG/MTHDMXFgstGHIEpNp1OV+TzFkjZlJxf9q/ncO+Ducg98ze8BvWE/+Q3oPb3dXS3FEfJGdLDMT+xMT/xMUMxseAiIioGfUIyEmd9jvQNe+DcOBg19q6A6xONHN0tIiIiUggWXEREdpD1eqR9+S2SPooCZBmV57wL7yG9OHyQiIiIzLDgIiKyUfbJc7j3wTzknr4Ir5d75A0frFzJ0d0iIiIiBWLBZSNepCg25ic2R+enT0zJGz64fjecwx5FjejlcH0y1KF9Eo2jM6TiYX5iY37iY4ZiYsFlB+7sYpIkiReaCsyR+cl6PdLW70bSrM/zhg9+8i68h3L4oK34HhQb8xMb8xMfMxQXCy4ioiJk/3YeCePnI+ePP+H1YgT8poyEUwCHDxIREZF1WHDZgc/hEpMsy9DpdHBycmJ+Airr/PSJKUj8v5V5wwcb1efwwRLA96DYmJ/YmJ/4mKG4WHDZiM/hEhvzE1tZ5Cfr9UjfsAeJsz4H9AZUnj0W3q/05vDBEsL3oNiYn9iYn/iYoZhYcBER/SP79wt5wwd/vwCvgd3hN/VNDh8kIiKiYmHBRUQVnj4pFUn/txJpX30H58fqofrupXBr2djR3SIiIqJygAUXEVVYssGA9A27kTjzc0CnR+X/exverz4HyYmHRiIiIioZ/FZhI16kKDYnfpEWWknml3PqL9z7YC5yfrsAz/7PwH/am3Cq4ldi7ZNlfA+KjfmJjfmJjxmKianZgUWXmCRJYnYCK6n89MlpSPpoJdLWfQvnx4JQ/bulcGvF4YNlge9BsTE/sTE/8TFDcbHgsgNvCy8mWZZhMBigUqmYn4CKm59sMCB9YzQSZ64AtDr4z3obPq9x+GBZ4ntQbMxPbMxPfMxQXPymYSPejlNser0eKpXK0d0gO9mbX86pv3Bv/DzknDwPz/7d4D/1TTgF+pdCD+lh+B4UG/MTG/MTHzMUEwsuIiq39MlpSJodhbS138C5YV1U/2Yx3No0dXS3iIiIqAJhwUVE5Y5sMCB9014kzlwOOUcL/5lj4DPseQ4fJCIiojLHbx9EVK7knPoL9yLnI+fXc/Ds1zVv+GDVyo7uFhEREVVQLLhsxIsUxcZxz2IrKj99SjqSZq9C2tpdcA6pw+GDCsX3oNiYn9iYn/iYoZhYcNmBRZeYJEni8ysEVlh+ssGA9K/3InHmCsjZufD/8C34DHsBkoZZKw3fg2JjfmJjfuJjhuJianbgbeHFlP8Ok8xPPJbyyzl9MW/44C9n4fnC0/Cf/haHDyoY34NiY35iY37iY4biYsFlI94WXmxarRYajcbR3SA7GfPTp/4zfPCLXdA8WhvVdy2CW9tmju4eWYHvQbExP7ExP/ExQzGx4CIiYRiHDybNXAFDZjb8p78Jn+F9OXyQiIiIFIvfUohICDlnL+He+HnI/eUsPPt0gf+Hozh8kIiIiBSPBRcRKZo+NR3Jn6xB6uodcKpXE9V2LIB7u+aO7hYRERGRVVhwEZEiybKMjC37kfjhMhgys+E35Q24v/ocnN3dHN01IiIiIqux4LKRJEm8M4ygJEmCs7Ozo7tBVsg5dwkJ4+cj+8RpeD7fOW/4YLUAR3eLionvQbExP7ExP/ExQ3Gx4CIixdCnZZiGD2o4fJCIiIjKARZcduBzuMQkyzL0ej3UajXzUxhZlpGxdT8Spy+H4X4W/Ca/Dt/X+0Fy1pjNw/zExgzFxvzExvzExwzFxYLLRnwOl9gMBgPUarWju0H55Jy/nDd88PgpeD7XKW/4YPUqFudlfuJjhmJjfmJjfuJjhmJiwUVEDqFPy0DynDVIXbUDmqCaqLZ9PtzbP+HobhERERGVKBZcRFSmZFlGxvbvkThtKQwZmfCbNAK+b/Q3Gz5IREREVF6w4CKiMpNzIRYJ4+chO+YUPHp1ROUZo+BUI9DR3SIiIiIqNSy4bMSLFMXm5MRd3hEM6feRNGcNUqO2Q1OnOqptnQf3Dk/a3A7zEx8zFBvzExvzEx8zFBNTswOLLjHxGWplT5ZlZOw4mDd8MP0+/CYMh+/I/pBcbH+OCPMTHzMUG/MTG/MTHzMUFwsuO/C28GKSZRkGgwEqlYr5lYHcP+Nwb/w8ZP/8Bzye7QD/GaOhqWn/8EHmJz5mKDbmJzbmJz5mKC4WXDbibeHFptfroVKpHN2Ncs2QkYmkT79A6sqt0DxSHdW2zIV7xxYl0jbzEx8zFBvzExvzEx8zFBMLLiIqEbIsI2PXISROXQpDajr8xg+D75sD7Bo+SERERFResOAiomLL/SsO9yIXIPvYb/Do8RT8Z40p1vBBIiIiovKCBRcR2c2QkYmkz75A6udboaldDdU2fwb3Ti0d3S0iIiIixVDUINC9e/fizTffRPv27dG0aVP07t0b27ZtK3Dd1NatW9GtWzeEhYWhV69eOHz4cIG20tPTMXHiRLRo0QLNmjXD22+/jbt37xa7j7xIUWwc91wyZFlGxs5DuNb6ZaSt2Qm/D15DraPrSr3YYn7iY4ZiY35iY37iY4ZiUlRqa9euhZubGyIjI7F8+XK0b98eU6ZMwdKlS03z7NmzB1OmTEH37t0RFRWFpk2bYvTo0fjjjz/M2ho7dix++uknTJ8+HZ999hni4uIwYsQI6HS6YveTRZeYJEmCk5MT8yum3ItXcOuFsbjz+nS4Nn8MtX5aj0rvDCn1a7WYn/iYodiYn9iYn/iYobgkWUG33UtKSoKfn5/Za1OmTEF0dDR++eUXqFQqdOvWDaGhoZg7d65pnoEDB8LLywtRUVEAgN9//x0DBw7E6tWrER4eDgCIjY1FREQE5s2bh4iICLv6d+bMGQBAWFiYXcuT4/GW/vYzZGQiee5apKzYAk2tavD/6D/w6NKqTPvA/MTHDMXG/MTG/MTHDJXF2tpAUWe4Hiy2AKBhw4bIyMhAZmYm4uPjceXKFXTv3t1snoiICMTExCA3NxcAcPToUXh7e6Nt27ameYKCgtCwYUMcPXq0WH2UZZm3hheULMvQarXML5+srCycOXMGWVlZhc6TmZmJ84vX4mrrl5G6ajv8xr2KmkfXOqTYYn5iY4ZiY35iY37iY4biUlTBZcnJkycRGBgIT09PxMbGAgDq1q1rNk+9evWg1WoRHx8PIO9sVt26dQv8BSAoKMjUBlFFlb/IysrKwqlTp/DHH39YLLpy/76KewPfh8uM1VA3qpc3fPC9oVC5ujig50RERETiUfRdCn/99VdER0dj/PjxAIDU1FQAgLe3t9l8xp+N09PS0uDl5VWgPR8fH5w9e7bY/bL0lwVJkgr9i4Ox8Ctqemkty3b/nZb/X0m1W9w+OaLdzMxMnDlzBtWrVwcA6HQ6XLx4ESEhIXB1dUVaRg5uXU+E94adyFqzHapqlfHXGz3Q8p0RcPLzK9B+WW3DB/Mrj9mUpz5Zmpb/NRH6WxLtKrFP9rZrnMbPQPHaNS5rKb/ytq6itWvLsvkzFHFdlbANy6JdSxRbcN2+fRvvvPMOWrZsiSFDhji6O2a0Wq3Z2TOVSgUnJyfTtAc5O+fdTECv18NgMJhNM178aDAYoNfrzaYZ2zWeQn6QRqMptF21Wg21Wg1ZlgvcKESSJNOyOp2uwA5TWu1as65AwW1Yku0a+2zMT6PRQJIkm9f1YZk7Yhsa27XUJ2O7WVlZuHDhAnQ6HVLTs5GQmoXcXB10ulwcPnwYPr5+uLLhEJof+gVSbg7uv9AJcW2DkZSRXuS6FrYNjf0tahtau3/nb8PJyanY+3dR27As92+g4hwj8lPqMeJBFe0YYU27tn4GltUxwtK68hhhvg0tfQYW1i6/Rzy8XeO6ltUxwtiOWq2GSqVS7DGiJLeh0o8RxuL3YRRZcKWlpWHEiBHw9fXF4sWLTbfA9PHxAZB3y/eAgACz+fNP9/b2xu3btwu0m5qaapqnOIw7RmHTCpN/R36QSqUq9Faf+YO3td2HLWt8A5Rlu0WtK1D0NiyJdvPnZ/xvcdbVEdnYuw1v3bqFs2fPQqvVYc32I7h1NwM13ROhlrTIuXgFTxz9Cx3jkxBfpzIOtnscWZUkyBf/gouLS6HtFncbWrt/Gw94D+ZXGtvQUft3eT9GPFg0F8bRx4j8Ktoxwpp2bf0MLKtjhD3LVsRjhKX8lHKMsLbdinqMMH4OGvvIbfjwacVpF3j4ulpTbAEKLLiys7PxxhtvID09HZs3bzYbGhgUFAQg7xot4/8bf9ZoNKhVq5ZpvpiYmAJVZ1xcHIKDg4vVP0mSTP8sTXvYsvZMK86ybNd8mvGvhA/OWx7X1ZI//vgD9+7dg0GW4SQnobY7oNbqEPZLHBr+cRU5Ph449GwrnAh4FIFVKqF9WGVcungOzs7Ohe73xe2TLcs+mF95yqa89elhX8hF6W957JO97cqyXOgxtDjtluaybNd8ekX/DFRiu7Yua8ywNPskWrtK7VN+irpphk6nw9ixYxEbG4tVq1YhMDDQbHqtWrVQp04d7Nu3z+z16OhotG7d2rQTtm/fHqmpqYiJiTHNExcXh/Pnz6N9+/bF7qctG5iUo6hiuaIICgqCl5cX3N3ckav2h++f6Xh2Qwwanr6Gi21CcPXj16F9IgiPBfkhJ1eHY2duQZbzhgikpKQ4tO/MT3zMUGzMT2zMT3zMUFyKOsP14Ycf4vDhw4iMjERGRobZw4wfe+wxODs7Y8yYMRg3bhxq166Nli1bIjo6GqdPn8b69etN8zZr1gzh4eGYOHEixo8fDxcXF8yfPx8hISHo2rVrsftp7XhNUhZZlqHX66FWqytsfrGxsUhLS4NnUga6H/0L1U3DB4OR4eMO9flzeeOvDXfhCQnIkZGsyju4//HHH2Znlssa8xMfMxQb8xMb8xMfMxSXoh583KlTJ9y4ccPitEOHDqFmzZoAgK1btyIqKgo3b95E3bp18e6776Jjx45m86enp2P27Nn4/vvvodPpEB4ejsmTJxc4a2aLM2fOQJZlhIWFcUcXkPGiyqKuPyjvLp+7gMtTFqDuT3/ivpcHop94EllNqsHfPRdqlYSGDRvi778v40amF+Lv5KBWoAuquabgkUdq4/HHHzfd2dARmJ/4mKHYmJ/YmJ/4mKHyWPvgY0UVXErHgktsFflAJcsy7u85irsTF0B/LxlZfTsi8IORSDU4w9ddwuVLF3D58mW0bdsWx48fR9t2HZGtd4GrOgc//XgY3bp1s/hg8rJeh4qaX3nBDMXG/MTG/MTHDJXH2oJLUUMKiajk5V6OR8KEBcg6/D9oOjyBP4Z3Q4dB/eHn5wfj+V4ndQiuXLliWsbT3Rm1/SohKSnJIX0mIiIiKi9YcBGVU4bMbCQv+AopSzfBqWplVF3/MVTtH8ejly7Bzc3NbF43NzeEhobCx8cHoaGhpunG1x+cn4iIiIisw4LLRjyFK7bCnsNQnsiyjMy9PyJh0iLo7yWj0tsvw/ftQVC55T1Ly9Jpbzc3N9Pr+YcO5n9dCSpCfuUdMxQb8xMb8xMfMxQTCy47sOgSkyRJ5f5ApY29joSJC5F56DjcO7dC5e0LoAmq6ehulYiKkF95xwzFxvzExvzExwzFxYLLDrwtvJhkWTZlV97yM2RmI2XReiQv3ginQH9U/fIjuD8TXq7WszznV1EwQ7ExP7ExP/ExQ3Gx4LIRb+ooNp1OB41G4+hulBhZlpG57xgSJi2C7k4iKo1+Cb7/GQSVu6uju1Yqylt+FREzFBvzExvzEx8zFBMLLiJBaeNuIGHiAmQePA63Ti1Rbes8ONer5ehuEREREVE+LLiIBGPIykHKovVIWbwR6oBKqLru/+DevR2HFxAREREpEAsuIoHc3/8TEiYuhO52AnxHvYhKYweX2+GDREREROUBCy4b8SyC2ETNTxt3AwmTFiLz+xi4dWyBalvmVsjhg6LmR/9ihmJjfmJjfuJjhmJiwWUH7uxikiRJuAtNDVk5SFm8ASmLNkAdUAmBa/8PHhEVc/igiPmROWYoNuYnNuYnPmYoLhZcRAp1/8A/wwdv3vt3+KCHm6O7RUREREQ2YMFlBz6HS0yyLEOn08HJyUnR+Wmv3ETC5EXI3P8T3Do8iWqbP4NzvdqO7pbDiZIfFY4Zio35iY35iY8ZiosFl434HC6xKTk/Q1YOUpZsRMrC9VD5+yJwzUx49HyKB9V8lJwfWYcZio35iY35iY8ZiokFF5EC3D/wMxImLYTuxl34vjkAld4dyuGDREREROUACy4iB9JevYmEyYuRue8Y3J56AtU2fQrn+hw+SERERFResOAicgBDdg5Slm5CyoKvoPLzReDqGfB4tgOHDxIRERGVMyy4bMQvxGJTwu1U7x88joQJC6C7fhu+bw5EpXeHQOXp7uhuCUEJ+VHxMEOxMT+xMT/xMUMxseCyA4suMTk6N+21W0icshj3o3+EW/vmqLbhYzgH13Fon0Ti6Pyo+Jih2Jif2Jif+JihuFhw2YG3hReTLMvQ6/VQq9Vlmp8hOwepS79G8oIvoarkg8CoD+HRuyP3IRs5Kj8qOcxQbMxPbMxPfMxQXCy4bMTbcYrNYDBArVaX2e/LPHQCCRMWQBt/69+7D3L4oN3KOj8qecxQbMxPbMxPfMxQTCy4iEqBNv523vDBPUfh1u5xVF0/m8MHiYiIiCogFlxEJUjOyUWKcfigjxcCV06Hx3OdeOqfiIiIqIJiwUVUQjJ/+Gf44LVb8B3ZH5Xee4XDB4mIiIgqOBZcNuKZCrGVxrhn7fU7SJy8GPf3HIFr22ao+uVHcA6pW+K/h0onPypbzFBszE9szE98zFBMLLjswKJLTJIkleiBSs7JRcryzUietw4qb09U+XwaPJ/vzP2jlJR0flT2mKHYmJ/YmJ/4mKG4WHDZgbeFF5Msy6bsiptf5uH/5Q0fvHITPm/0g9+4V6Dy8iihnpIlJZkfOQYzFBvzExvzEx8zFBcLLhvxtvBi0+l0xXpKu+7GHSRMWYL73/0Xrm2aIvCLWXBpGFRyHaQiFTc/cjxmKDbmJzbmJz5mKCYWXERWkHO1/w4f9HRHlRVT4dmnC//CRERERERFYsFF9BCZR35FQuR8aONuwOf1vvB7/1UOHyQiIiIiq7DgIiqE7ubdvOGD3x6Ga+smCFwzk8MHiYiIiMgmLLhsxCFkYrMmPzlXi5TPtyD5s3VQebqhyvIp8HzhaWavAMxAfMxQbMxPbMxPfMxQTCy47MCdXUySJD30QtPMo78iIXIBtLHX4TO8Dyp98BrU3p5l1EMqijX5kbIxQ7ExP7ExP/ExQ3Gx4CLCP8MHpy7F/W9+gGurJghc9SFcHqvn6G4RERERkeBYcNmBz+ESkyzL0Ol0cHJyMuUn52qRsnIrkj9dC5WHG6osmwzPvl2ZrwJZyo/EwgzFxvzExvzExwzFxYLLRnwOl9jy55f548m8uw9eiofP8BdQaTyHDyod33/iY4ZiY35iY37iY4ZiYsFFFY7u1j0kTV+GjJ2H4NoiDIGHVsMltL6ju0VERERE5RALLqowZK0OaSu2IG3el1C5u6LKkknw7N+Np+WJiIiIqNSw4KIKIevYb7gXOR/av6/B+7Xn4Rc5DGofL0d3i4iIiIjKORZcNuLZELHobicgcdpSZOw4CJcnQ1H9+5VwDQtmjoJycuIhS3TMUGzMT2zMT3zMUExMzQ78sq58slaH1FXbkPTJGkhuLghYPBFe/btBUqkc3TWykyRJfO8JjhmKjfmJjfmJjxmKiwWXHXhbeGXL+un3vOGDF6/C+9Xn4DdhONQ+XpBlGXq9HiqVivkJSJZlGAwG5icwZig25ic25ic+ZiguFlw24u04lUt3OwGJ05chY/v3cHkyFDUProJL2KNm8xgLLhIT8xMfMxQb8xMb8xMfMxSTohK7evUqpk6dit69e+Oxxx5Dz549Lc63detWdOvWDWFhYejVqxcOHz5cYJ709HRMnDgRLVq0QLNmzfD222/j7t27pb0K5ACyVoeUFZtxrfXLyDzyCwIWTUCN3UsLFFtERERERGVNUQXX33//jSNHjuCRRx5BvXr1LM6zZ88eTJkyBd27d0dUVBSaNm2K0aNH448//jCbb+zYsfjpp58wffp0fPbZZ4iLi8OIESOg0+nKYE2orGT9/Aeudx6GxGnL4NX/GdSO2QjvFyN4rRYRERERKYKihhR26tQJXbp0AQBERkbi7NmzBeZZtGgRevTogbFjxwIAWrVqhYsXL2Lp0qWIiooCAPz+++84duwYVq9ejfDwcABA3bp1ERERgQMHDiAiIqJsVohKje52AhJnLEfG1gNweaIRah5YCZcmIY7uFhERERGRGUWdBnjYmNT4+HhcuXIF3bt3N3s9IiICMTExyM3NBQAcPXoU3t7eaNu2rWmeoKAgNGzYEEePHi1WH3mRomPJOh1SVmzJGz74wwkELIhEjT3LrC62OO5ZbMxPfMxQbMxPbMxPfMxQTIo6w/UwsbGxAPLOVuVXr149aLVaxMfHo169eoiNjUXdunULFEdBQUGmNoqDRZdjZMWcQkLkPOReiIP3K73hN2EE1JW8rV5ekiQ+v0JgzE98zFBszE9szE98zFBcQqWWmpoKAPD2Nv+SbfzZOD0tLQ1eXl4Flvfx8bE4TNFWBoOhQNElSVKhdzA0zlvU9NJatjy0q7uTiKSZK5CxZT9cmj+GGvmGDxrnt6bd/NON/WQ2jm/X2mUfzE/EdXX0NnR0u9Y8UkNJ/S2JdpXYJ3vbzT+Nn4FitQvkfXd58DVH9ont2r5s/v9XqVTCrasStmFZtGuJUAWXEsiyDK1Wa3awUqlUpr84aLXaAss4OzsDyLuVZ/4DHpD3xHBJkmAwGKDX682mGds1/s4HaTSaQttVq9VQq9WQZbnAjUIkSTItq9PpCuwwpdWuNesK/LsNZZ0eGeu+QeqnayFpnBAwfzxc+z0NSJLZ9rClXZ1OZ5rfuK6SJNm8rg/L3BHb0NiupT6VdDYPrmth29DYblHb0Nr929iGk5MTnJycir1/F7UNy2L/zq+iHCPyL1+S29BR+3dFPEbk5OQUeAaQUo4RltaVxwjzbWjpM7CwdsvD94jSbte4rmV1jDC24+LiYiq4lHaMUPL3CEvrWtxjhDV/SAQEK7h8fHwA5N3yPSAgwPR6Wlqa2XRvb2/cvn27wPKpqammeYrDuGMUNq0w+XfkB6lUqkLH5eYP3tZ2H7ZsUaemS6vdotYVyNuG2cdPIyFyPnIvxMJrSC/4T3od6kreRf41wZp2jf815mf8b3HW1RHZWLuuZdFucbehtfu3MfsH8yuNdXVUNuX9GJH/g62i7N+FtWsk4jHC1s/AsjpG2LNsRTxGWMpPKccIa9utqMcI4+egsY/chg+fVpx2gYevqzXFFiBYwRUUFAQg71ou4/8bf9ZoNKhVq5ZpvpiYmAJVZ1xcHIKDg4vdD0mSLG7gh230oqaX1rKitau/l4ykGcuRvnkfXJo1RI0DK+HatEGJ9deY3YPzlqdtKFq7tiz7YH6irasStiHbLdt2ldgne9s1fqbyM1C8do3T+RmovHZtXZafgeL0KT+hbnVSq1Yt1KlTB/v27TN7PTo6Gq1btzadcm/fvj1SU1MRExNjmicuLg7nz59H+/bty7TPZB1Zp0Nq1DbEt34Z97+PQcC891Fj3wqzYouIiIiISDT/3969x1VV5nsc/25w4wUFBc27B7VCDQ2aDO+FZoaadmZ0cia1Gstb2qjjEdN08jaapzJzrNTMsjxTWkzesHLUqEmzLIp0zAtkar1UFOSm3Pbe5w9GJgTkul37gc/79fIP1l6u9dt+fWD92M96lkd9wnX58mXFxsZKkn766SdlZGQUNFd33HGHAgICNHnyZE2fPl1t2rRReHi4YmJiFB8fr7feeqvgOGFhYerVq5dmzZqlqKgo1a5dW8uWLVNwcLDuueeeStVYnm4WZXN5f7zORy1Tzr8S5Dd6iAJmPSbvgMpP/SwO+ZmN/MxHhmYjP7ORn/nI0Ew2V3mW2HCz06dPq1+/fsW+tn79eoWHh0uSNm3apDVr1ujnn39W27ZtNW3aNEVERBTaPz09XYsXL9bOnTuVl5enXr166amnnlLTpk0rXN93330nSercuXOFj4H/yLsyffDtHaod2kGNl05TnbCOVpcFAAAAlKqsvYFHNVyejoararjy8pT2+mYlL35V8vZS4FPj1ODBQbKVcEMjAAAA4GnK2ht41JRCU5R1CUgUlfXFd0qKWqacQ8flN+o+Bcwe67bpg1f75bLi5Gce8jMfGZqN/MxGfuYjQ3PRcJUTHwhWTF5SipIXvKL0v8WodmiH/AUxbut03esgP7ORn/nI0GzkZzbyMx8ZmomGC27lcjiU9sYWJf9ltWSzqfGz0+U3cjDTBwEAAFAj0HDBbbIOHFLSjOeU890xNRg5WIFPjZN3YEOrywIAAACuGxouVDnH+RRdWLhK6Ru2y6fLzWq54xXVuf0Wq8sCAAAArjsarnLiJsWSuRwOpa3fouS/rJFcLjVeOk1+o4d41PTBWrX4L28y8jMfGZqN/MxGfuYjQzORWgXQdBWV9dUhJc14XjnxR9XgwUH50wcbN7K6rEJsNhvZGYz8zEeGZiM/s5Gf+cjQXDRcFcCy8P/huHAxf/rgW9vk0/kmtYx5WXW6hlhdVrFcLpecTqe8vLzIz0DkZz4yNBv5mY38zEeG5qLhKieW48zncjiU9tY2JS9clT998Jlp8nvIs6YPFsfhcMjLy8vqMlBB5Gc+MjQb+ZmN/MxHhmai4UK5ZX39L52PWqbsb75Xg98NVMCc8arVxLOmDwIAAACegIYLZea4cFEXFq3Onz54y40ePX0QAAAA8AQ0XCiVy+FQ+obturBwleRwqvHiKfJ7eKjHTx8EAAAArEbDVU417SbFrLjD+dMH4w6rwYhIBcydYPT0QeY9m438zEeGZiM/s5Gf+cjQTDRcFVATmi5HcqqSF61W2ptb5dOpvVpsW6m64V2sLqtSbDYbz68wGPmZjwzNRn5mIz/zkaG5SK0CqvOy8C6nU+kbtunCglVSnkONFz0hv0ful60aDPBfrjBZXfOrzsjPfGRoNvIzG/mZjwzNZf5V9HVWnZeFz/72iJJmPKfsrw+r/m/vVeCfJ6jWDQFWl1WlcnNzZbfbrS4DFUR+5iNDs5Gf2cjPfGRoJhouyJGSpuS/rFbaG1vk06mdWmxdqbrdzJ4+CAAAAHgCGq4azOV0Kv3/YnRhwStSbp4CFz4h/z9Uj+mDAAAAgCfgyrqGyv72iJKinlf2V/9S/d8OUODcCarVNNDqsgAAAIBqhYarhnGkpCl58Rqlvb5ZPh3bqsXmFarbI9TqsgAAAIBqiYarnGw2m5Erw7icTqX/bYcuLHhZruxcBS6YLP8x/12jpg/abDbZ7XYj8wP5VQdkaDbyMxv5mY8MzVVzrrZrsOxvjyhp5jJlHzik+sPvyZ8+2Kyx1WVZgm9SZiM/85Gh2cjPbORnPjI0Ew1XBZjyHC7HxXQlL35Vaa+/L5/goBo/fdDlcsnhcMjb29uI/FAY+ZmPDM1GfmYjP/ORoblouMrJhOdwuZxOpb+9QxcWvCJXVo4C502U/5jfyGYnbqfTKW9vb6vLQAWRn/nI0GzkZzbyMx8Zmokr8GomO/5o/vTBLw+q/rD+CvzzxBo7fRAAAACwGg1XNeFI/ff0wXXvy37zf6nF+y+qbs8wq8sCAAAAajQaLsO5nE6lb/xQF+a9JNflbAU+PUH+jw5j+iAAAADgAbgqLydPukkx++BxnY96XllffKf6v75bgfMeZ/pgKZj3bDbyMx8Zmo38zEZ+5iNDM9FwVYDVTZcjNV0pz7ym1LXRst/YWi3+vlx1e91maU0msNlsfKMyGPmZjwzNRn5mIz/zkaG5aLgqwKpl4V0ulzL+PX3QeSlLgXPHy3/scKYPlpHL5SrIzuqmGeVHfuYjQ7ORn9nIz3xkaC6u1MvJqmXhsw8d1/moZcraH6/6/90vf/pg8yaW1GKyvLw82e12q8tABZGf+cjQbORnNvIzHxmaiYbLwznSMv4zfbB9KzWPfkH1ev/K6rIAAAAAlAENl4dyuVzK2PShLjz9spyZlxXw1Fg1HDtcNh9+qwEAAACYgobLA2X/KyF/+uDn36r+/X3zpw+2uMHqsgAAAACUEw1XObnzJkVHWoZSlr6m1FejZW/XSs3fW6Z6fW532/lqIi8vL6tLQCWQn/nI0GzkZzbyMx8ZmomGqwKquulyuVzKeG+nLvx5pZwZlxUw+zE1HPdbpg9WMZvNplq1+C9vKvIzHxmajfzMRn7mI0NzkZrFsg8n5j+8eN+38h0SocbzH1etlk2tLgsAAABAFaDhKqdfPgOhMpzpmUpe+ppS17wne1ALNd/0vOrd1bWKqkRxXC6XcnNzZbfbeX6FgcjPfGRoNvIzG/mZjwzNRcN1nblcLmVE/yN/+mB6pgKefFQNx/9Wtto+VpcGAAAAoIrRcF1HOd//oKSo55W19xv53neXAudPkr0V0wcBAACA6oqG6zpwZlxS8v+uU+rqTbL/Vws13/ic6kXcYXVZAAAAANyMhsuNXC6XMt7fpQtzV8qZmq6AqDFqOOEBpg8CAAAANQQNVzmV9SbFnCM/KGnmC8r659fyHXSnAhdOZvqgB7DbWWrfZORnPjI0G/mZjfzMR4ZmqtYNV0JCghYuXKi4uDj5+vpq6NChmjJlinx8KvcJ07WaLmfGJSU/u06pqzbJ3qa5mr/zrOr1Da/U+VA1WNHHbORnPjI0G/mZjfzMR4bmqrYNV2pqqh566CEFBQVpxYoVOnv2rJYsWaKsrCzNnTu3Uscubll4l8ulzPd36/zcv+ZPH5zxBzWcOILpgx7E5XLJ4XDI29ubb1oGIj/zkaHZyM9s5Gc+MjRXtW243n77bWVmZuqvf/2rGjZsKElyOByaN2+exo0bp6ZNKza9z+VyFdmWc/SEzs9cpsuffi3fQX0UuGCy7K2bVaZ8uInT6ZS3t7fVZaCCyM98ZGg28jMb+ZmPDM3kZXUB7vLJJ5+oe/fuBc2WJEVGRsrpdOqzzz6rknM4My7pwryXdOrOh5V3+pya/e1/1ez1RTRbAAAAACRV40+4EhMT9Zvf/KbQNj8/PzVp0kSJiYmVOrbL5VLm5j350wcvpingf/4g/4kPyKtO7UodFwAAAED1Um0brrS0NPn5+RXZ7u/vr9TU1AodMzc3V7aTZ3T8f16U99dHlNfzVuU+/oQymwXq9LGjlS0Z10Fx99/BHORnPjI0G/mZjfzMR4aeJScnp0x5VNuGyx1sNptcbZop+9k/Wl0KKohvUmYjP/ORodnIz2zkZz4y9Cw2m61mN1x+fn5KT08vsj01NVX+/v4VOmZYWFhlywIAAABQg1TbRTPatWtX5F6t9PR0JSUlqV27dhZVBQAAAKAmqbYNV58+fbR3716lpaUVbPvggw/k5eWlnj17WlgZAAAAgJrC5iruwVLVQGpqqgYNGqS2bdtq3LhxBQ8+vu+++yr94GMAAAAAKItq23BJUkJCghYsWKC4uDj5+vpq6NChmjp1qnx8fKwuDQAAAEANUK0bLgAAAACwUrW9hwsAAAAArEbDBQAAAABuQsMFAAAAAG5CwwUAAAAAbkLDBQAAAABuQsMFAAAAAG5CwwUAAAAAbkLDVQYJCQl65JFHFBoaqp49e2rp0qXKycmxuiyUQXR0tIKDg4v8efbZZ60uDcX48ccfNXfuXA0dOlSdOnXS4MGDi91v06ZNGjBggDp37qwhQ4Zoz54917lSlKQsGY4aNarYcZmQkGBBxbhix44dmjBhgvr06aPQ0FANHTpU7777rq5+XCfjz3OVJUPGn+eKjY3VyJEj1a1bN4WEhKhfv35avHix0tPTC+23e/duDRkyRJ07d9aAAQP03nvvWVQxyqqW1QV4utTUVD300EMKCgrSihUrdPbsWS1ZskRZWVmaO3eu1eWhjF599VU1aNCg4OumTZtaWA1KcuzYMcXGxurWW2+V0+kscqEnSdu3b9ecOXM0fvx4devWTTExMZo0aZI2bNig0NDQ6180CilLhpJ02223KSoqqtC2Vq1aXY8SUYLXX39dLVu21MyZM9WoUSPt3btXc+bM0ZkzZzRp0iRJjD9PV5YMJcafp7p48aK6dOmiUaNGqWHDhjp27JhWrFihY8eO6bXXXpMkHThwQJMmTdKwYcM0a9Ysff7555o9e7Z8fX117733WvwOUBKbq6SfhpAkrVq1Sq+88or27Nmjhg0bSpLeeecdzZs3T3v27OHC3cNFR0frySef1L59+xQQEGB1OSiF0+mUl1f+B+8zZ87UwYMHtW3btkL7DBgwQCEhIXruuecKto0YMUINGjTQmjVrrmu9KKosGY4aNUr16tXTqlWrrCgRJUhOTi7yfXLOnDmKiYnRl19+KS8vL8afhytLhow/s2zcuFFz5szRJ598oqZNm2rMmDHKzMzU22+/XbDPn/70Jx0+fFgxMTEWVoprYUphKT755BN17969oNmSpMjISDmdTn322WfWFQZUQ1cu1Ety6tQpnThxQpGRkYW2Dxw4UPv27WOqrwcoLUN4ruJ+KdWxY0dlZGTo0qVLjD8DlJYhzHPl+jM3N1c5OTnav39/kU+yBg4cqISEBJ0+fdqCClEW/GQsRWJiotq1a1dom5+fn5o0aaLExESLqkJ5DR48WB07dlS/fv20atUqORwOq0tCBVwZc23bti20vX379srNzdWpU6esKAsV8MUXXyg0NFSdO3fWyJEj9eWXX1pdEorx1VdfqWnTpqpfvz7jz1C/zPAKxp9nczgcys7O1qFDh7Ry5Ur17dtXrVq10smTJ5Wbm1vkurR9+/aSxHWpB+MerlKkpaXJz8+vyHZ/f3+lpqZaUBHKo0mTJpo8ebJuvfVW2Ww27d69Wy+88ILOnj3LPXgGujLmrh6TV75mTJqha9euGjp0qIKCgnTu3DmtXbtWjzzyiN58802FhYVZXR7+7cCBA4qJiSm414fxZ56rM5QYfyaIiIjQ2bNnJUm9e/cumMLLGDQXDReqtd69e6t3794FX/fq1Uu1a9fWG2+8ofHjx+uGG26wsDqgZnriiScKfX3XXXdp8ODBeumll7gPyEOcOXNGU6dOVXh4uEaPHm11OaiAkjJk/Hm+1atX6/Llyzp+/LhefvlljR8/XuvWrbO6LFQCUwpL4efnV2Q5Tin/twj+/v4WVITKioyMlMPh0OHDh60uBeV0ZcxdPSbT0tIKvQ6z1KtXT3feeacOHTpkdSlQ/nh67LHH1LBhQ61YsaLgvjzGnzlKyrA4jD/P06FDB4WFhWn48OF66aWXtH//fu3cuZMxaDAarlK0a9euyJzY9PR0JSUlFZlDC8C9roy5q8dkYmKi7Ha7WrdubUVZQLWRlZWlcePGKT09vcjjNBh/ZrhWhjBPcHCw7Ha7Tp48qTZt2shutxc7BiVxXerBaLhK0adPH+3du7fgtweS9MEHH8jLy0s9e/a0sDJUVExMjLy9vdWpUyerS0E5tW7dWkFBQfrggw8KbY+JiVH37t3l4+NjUWWojEuXLunjjz9W586drS6lRsvLy9OUKVOUmJioV199tchjTxh/nq+0DIvD+PNs3377rXJzc9WqVSv5+PgoPDxcH374YaF9YmJi1L59e56l5sG4h6sUI0aM0JtvvqnHH39c48aN09mzZ7V06VKNGDGCZ3AZYMyYMQoPD1dwcLAkadeuXdq4caNGjx6tJk2aWFwdrnb58mXFxsZKkn766SdlZGQUXNzdcccdCggI0OTJkzV9+nS1adNG4eHhiomJUXx8vN566y0rS8e/lZbhlQvB/v37q2XLljp37pzWrVunpKQkLV++3MrSa7wrz5ecOXOmMjIy9M033xS81qlTJ/n4+DD+PFxpGcbHxzP+PNikSZMUEhKi4OBg1alTR99//73Wrl2r4OBg3X333ZKkCRMmaPTo0Xr66acVGRmp/fv3a9u2bVq2bJnF1eNaePBxGSQkJGjBggWKi4uTr6+vhg4dqqlTp/LbPAMsXLhQn376qc6cOSOn06mgoCANHz5co0aNks1ms7o8XOX06dPq169fsa+tX79e4eHhkqRNmzZpzZo1+vnnn9W2bVtNmzZNERER17NUlKC0DJs1a6b58+fryJEjunjxourWrauwsDBNmjRJXbp0uc7V4pf69u2rn376qdjXdu3aVfDbc8af5yotQ4fDwfjzYKtXr1ZMTIxOnjwpl8ulli1bqn///hozZkyhZf137dqlF154QT/88INatGihsWPHatiwYRZWjtLQcAEAAACAm3APFwAAAAC4CQ0XAAAAALgJDRcAAAAAuAkNFwAAAAC4CQ0XAAAAALgJDRcAAAAAuAkNFwAAAAC4CQ0XAADFyMzMVPfu3bVlyxarSymQkpKi0NBQxcbGWl0KAKCMaLgAAMbZsGGDgoODNXz4cLedY/369fL19dWgQYPcdo7yatSokYYNG6bly5dbXQoAoIxouAAAxtm6davsdrvi4+P1448/Vvnxc3NztX79eg0fPlze3t5VfvzK+N3vfqdDhw5p3759VpcCACgDGi4AgFFOnTqluLg4TZgwQXa7XVu3bq3yc3z88cdKTk5WZGRklR+7stq3b6+bb75Zf//7360uBQBQBjRcAACjbN26Vd7e3nrggQfUo0ePYhuuF198UR06dCjyKdCcOXMUEhKi77///prn+Mc//qGWLVuqTZs2hbbPnDlTYWFh+vnnnzVu3DiFhYWpd+/e2rBhgyTpyJEjGj16tEJDQxUREVGktujoaAUHB+vAgQNauHChunXrpttvv11z585VTk6O0tLSNGPGDHXt2lVdu3bV0qVL5XK5itTXo0cP7dmzp9jXAACehYYLAGCUrVu36vbbb1fjxo0VGRmpEydOKD4+vtA+EyZMUMeOHTV79mxlZGRIkj799FNt3LhREydOVIcOHa55jri4ON1yyy3FvuZwOPTYY4+pWbNmmj59ulq2bKn58+crOjpajz76qEJCQjR9+nT5+voqKipKp06dKnKMhQsX6sSJE5o8ebL69u2rd955R8uXL9f48ePlcDg0depU/epXv9LatWu1efPmIn//lltuUVpamo4dO1bWfzYAgEVouAAAxjh48KASExM1cOBASdLdd99d7LRCu92uZ555RufOndOSJUuUlpam2bNnKyQkRGPHjr3mOfLy8nTy5Em1atWq2Nezs7M1ZMgQzZs3Tw8++KBWr16tOnXqaNasWXryySc1Y8YMjRw5Ui+++KIcDofef//9IscIDAzUmjVr9OCDD2rp0qUKCwvT2rVrddNNN+m5557T73//e61cuVLNmjXTe++9V+Tvt27dWpJ0/PjxsvyzAQAsRMMFADDG1q1bVatWLd1zzz2SpAYNGqh3796KiYmRw+EotO/NN9+sJ554Qps2bdKYMWOUkpKiZ555RrVq1brmOVJTU+VyueTn51fiPr9cHdHPz09t27ZV3bp1C93z1a5dO/n5+RX7CdewYcNks9kKvu7SpYtcLpeGDRtWsM3b21shISHF/v0rtaWkpFzzvQAArEfDBQAwgsPh0Pbt29WtWzcFBAQUbB84cKDOnz9f7Kp9Y8aMUYcOHRQfH69JkybpxhtvLPP5Sro/qnbt2oXOL+U3fs2aNSvURF3ZnpaWVuQYLVq0KLKfJDVv3rzI9tTU1BJrvPp8AADPQ8MFADDC559/rqSkpCIrB/bt21d16tQpdvGMU6dOFSwbf/To0TKdx9/fXzabrdhGSVKJy8SXtL24xs3Lq/gfvyVtv9qVJqxRo0Zl2h8AYB0aLgCAEa48e6t///6Ftvv6+urOO+/Uzp07lZWVVbDd6XRq5syZql+/vsaPH69t27bpo48+KvU8tWrVUps2bXT69Okqfw9V5Upt7du3t7gSAEBpaLgAAB4vKytLH330kXr06CF/f/8ir997773KzMzU7t27C7atW7dOcXFxmj9/vv74xz8qLCxMTz/9tJKTk0s9X2hoqA4ePFil76EqHTp0SA0aNNBNN91kdSkAgFJc+85hAAA8wO7du5WZmSlJWr16dZHXL1++LEnasmWLBg4cqISEBC1fvly//vWv1bdvX0nSkiVLdP/992vevHlavnz5Nc/Xr18/bd68WT/88IPatm1bxe+m8vbu3auIiAju4QIAA9BwAQA83pYtWyRJsbGxio2NLXG/f/7zn0pJSVFUVJQaNWqkWbNmFbwWFBSkadOmadGiRYqJiSlYWr44ERERatSokXbs2KGJEydW3RupAgkJCTp69Gih9wYA8Fw2F4+pBwCgiJUrVyo6OlofffRRiQtiWGHRokU6cOCAoqOj+YQLAAzAPVwAABTj4Ycf1qVLl7R9+3arSymQkpKid999V1OmTKHZAgBD8AkXAAAAALgJn3ABAAAAgJvQcAEAAACAm9BwAQAAAICb0HABAAAAgJvQcAEAAACAm9BwAQAAAICb0HABAAAAgJvQcAEAAACAm9BwAQAAAICb0HABAAAAgJvQcAEAAACAm/w/XSbQxBhZ0zsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot(\n",
    "    data=data,\n",
    "    x=\"este\",\n",
    "    y=\"F\",\n",
    "    s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "    data[\"este\"],\n",
    "    data[\"F\"],\n",
    "    xerr=data[\"ueste\"],\n",
    "    yerr=data[\"uF\"],\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[\"este\"].max(), 200)\n",
    "yfit = B + A * xfit\n",
    "\n",
    "\n",
    "plt.plot(\n",
    "    xfit,\n",
    "    yfit,\n",
    "    color=\"crimson\",\n",
    "    linewidth=1,\n",
    "    zorder=10,\n",
    "    label=\"Fit lineare\"\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"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}