{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a8dfc0e",
   "metadata": {},
   "source": [
    "# Analisi della molla statica con calibro 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ff20b69",
   "metadata": {},
   "source": [
    "## Import delle librerie e set di variabili gloabali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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 = 9.807\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": 83,
   "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": 84,
   "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": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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": 86,
   "id": "2ad19283",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.18077741 3.20007153 3.19970803 3.21994047 3.20943506 3.19897326]\n",
      "[0.02199359 0.00788745 0.00602168 0.01779203 0.00988225 0.01119435]\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": 87,
   "id": "5f59d6c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-medio: 3.2015614337677185\n",
      "sigmaC:  0.003860508806765328\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": 88,
   "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": 89,
   "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:                Sat, 04 Apr 2026   Prob (F-statistic):           3.68e-10\n",
      "Time:                        19:38:07   Log-Likelihood:                -7.2422\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              3.2014      0.009    357.408      0.000       3.177       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  = 3.20145 ± 0.00896\n",
      "Bols  = 0.02655 ± 0.99076\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": 90,
   "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": [
    "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": 91,
   "id": "986ff4a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 2.77690\n",
      "GdL       = 4\n",
      "Chi² rid  = 0.69422\n",
      "P(0, chi²)= 0.40417\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    0.908593\n",
      "1    0.040832\n",
      "2    0.097969\n",
      "3    1.041094\n",
      "4    0.620684\n",
      "5    0.067728\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": 92,
   "id": "2d4b7144",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AC      = 3.2005 ± 0.0092\n",
      "BC      = 0.1195 ± 0.9524\n",
      "cov_ABC = -0.007942\n",
      "P(0, chi²)= 0.4020\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": 93,
   "id": "e2407a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3+HhRV5eUC5wPVV6Mv/Ojjz4qdI6M0YNflAvu7SioqNdVmtdb1DK0ZMyYMfj9998xdOhQNGrUCJ6enjAYDBg2bJhdli2AEl9i3torjhoMBgQGBmL+/PkWpz+4B6jgHqwHFTetJFxdXRWdk/egL7/8ElFRUejSpQuGDh2KwMBAqNVqrFq1yuKevtLWS+RM2HARUZkJCAiAl5cXDAaDxT06BdWoUQNXr16FLMtmX4pu3bplNp/xUKhbt26ZDg0E7p8gfvv2bbNmoHbt2vj777/Rpk0bxffyMf6emzdvmv21WqvVIj4+Hg0bNlQ0njWMNcTGxprtVcvPz0d8fLxpmRrnu379eqG/rF+/ft3s8DHg/pfO+fPn45133sF7772HmJgYU6OjJDNLxo8fb/WekpIs05s3bxZ67MaNG2Z7N/38/Cx+ITTuIbBGWd0L6sH6ZVnGzZs3Te9bY87e3t5WLf+yUtJlWNRySUtLw6lTpzB69GizQ/2Me/BKMkZJ1a5dGydPnkRqamqRe7lq1KgBg8GAmzdvon79+qbHExMTkZ6eXmjveGlqOXXqFB5//PEyb0CUrufF+ffff5GdnW3WvBuzMS6LI0eOoFatWli6dKlZRosXL7b2JRDR/8dzuIiozKjVanTr1g1HjhwpdOluwPzQtIiICNy7dw/fffed6bG8vDzs2LHD7DlhYWHw9/fHjh07oNPpTI9//fXXhQ736d69O+7du1doDADIzc0t9r40YWFhCAgIwLZt28yuHrh3716bHGpmSdu2baHRaLBp0yazPQK7du1CRkYGnnrqKVOtgYGBhWo9fvw4rl27hqeffrrQ2K6urli6dCnCw8MxYsQI02XQlWRmSVhYGNq2bWvVfyW5qtm3335rdgnsc+fO4c8//0SHDh1Mj9WqVQuxsbFmtf7999/47bffHjp+UTw8PACU/rC7ffv2mS7vDdw/5ychIcFUf1hYGGrXro1169aZLtFf0MOWf1kp6TIsarkUtYdow4YNhR4zjmHtYYZdu3aFLMtYunRpoWnG9ca4rjz4+9evX282vbS6d+8OvV5v8QqoOp2uVO8fa9bzouh0OrNDhvPz87F9+3YEBASgcePGAP6XYcFtz59//ok//vjD6tdARPdxDxcRKbZ79278+OOPhR4fPHgwPvjgA5w+fRr9+/fHiy++iAYNGiAtLQ1//fUXTp06ZTr8bMCAAdi8eTM++OADDB48GEFBQfj6669Nh0sZ/8Lq6uqK0aNHY9asWXjttdfQvXt33L59G3v27Cl0fkbv3r1x6NAhTJs2DadPn8bjjz8OvV6P2NhYHD58GGvWrDFdMvpBGo0GY8aMwdSpU/Haa68hMjIS8fHx2LNnT6nP4SqpgIAAvPXWW1i6dCmGDRuGTp064fr169i6dSvCw8PRq1cvU63jxo3DhAkTMHDgQPTo0cN0uegaNWoUeRl3d3d3rFq1CoMHD8bw4cOxadMmhISElDgze6hduzZefvllvPzyy8jPz8fGjRvh7++PYcOGmebp168fPv/8cwwdOhT9+vVDUlIStm3bhgYNGlhsYkrC+CV09uzZiIiIgFqtRo8ePRSP4+fnh1deeQV9+/Y1XRb+kUceMV0cRqVSYfbs2Rg+fDh69uyJvn37Ijg4GPfu3cPp06fh7e2NlStXWvUalCjpMnR3d0eDBg1w6NAh1KlTB/7+/nj00UcREhKCJ598EmvWrIFWq0VwcDB++ukn0/21CjIu2+joaERGRkKj0aBjx46FDp0sSuvWrdG7d29s2rQJN2/eRPv27WEwGHD27Fm0atUKAwcORMOGDdGnTx9s374d6enpePLJJ3H+/Hns3bsXXbp0MdtbXhotW7bEgAEDsGrVKly6dAnt2rWDRqPBjRs3cPjwYUyaNAnPPvusVWNbu55bUqVKFcTExOD27duoU6cODh48iEuXLmHWrFmmy9Y//fTTOHr0KEaOHImnn34a8fHxpvdARbvBNlF5Y8NFRIo9eMNMo759+6Jq1arYuXMnli1bhm+++QZffPEF/P390aBBA7MbcXp5eWHDhg2YPXs2Nm7cCE9PTzz//PNo3rw5Ro8ebXaeysCBAyHLMtavX49PPvkEDRs2xIoVKzB79myz+VQqFZYtW4bPP/8cX375Jb755ht4eHigZs2aGDRokMWTzwsaMGAA9Ho91q5di3nz5iEkJAQrVqzAokWLSrnESm706NEICAjA5s2bMWfOHPj5+aF///54//33ze7n07dvX7i7uyMmJgbz58+Hp6cnunTpgg8//LDIe3AB9w9dW7t2LQYOHIghQ4Zgy5YteOSRR0qUmT08//zzUKlU2LBhA5KSktCkSRNMmTIFVapUMc1Tv359fPLJJ1i8eDHmzJmDBg0aYN68edi/f7/VzWLXrl0xaNAgHDhwAF999RVkWbaq4RoxYgT++9//YvXq1cjKykKbNm0wbdo0014eAGjVqhW2b9+O5cuXY/PmzcjOzkZQUBCaNGlidmU5W1KyDGfPno1Zs2Zhzpw50Gq1GDVqFEJCQvDZZ59h1qxZ2Lp1K2RZRrt27RATE1PoqnxNmjTBe++9h23btuHHH3+EwWDAd999V+KGCwDmzJmD0NBQ7Nq1C/PmzYOPjw/CwsLMrtI4e/Zs1KxZE3v37sW3336LypUr46233rJ4dcPSmDlzJsLCwrBt2zZER0dDrVajRo0a6NWrFx5//PFSjW3tev4gPz8/zJ07F7Nnz8aOHTtQuXJlTJ061eyqsH379kViYiK2b9+OkydPokGDBvj0009x+PBhu/7RhagikGR7nclKRGTB559/jjlz5uDEiRNmlwN/kMFgQJs2bfDMM89g9uzZ5VghlYf4+Hh07twZH330EYYOHWrvchQ7ffo0Bg8ejEWLFlm9h4OIiCoGnsNFRHaTm5tr9nNeXh62b9+OOnXqmDVbeXl5ha5ytm/fPqSmpqJly5blUisRERGRNXhIIRHZzahRo1C9enU0bNgQmZmZ+OqrrxAbG1voEst//PEH5syZg2effRb+/v64ePEidu3ahZCQEO49ICIiIqGx4SIiu4mIiMCuXbvw9ddfQ6/Xo0GDBqYT6QuqUaMGqlatik2bNiEtLQ1+fn7o3bs3xo0b99Ab5xIRERHZE8/hIiIiIiIishGew0VERERERGQjbLiIiIiIiIhshOdwKfD7779DlmWze+EQEREREZHz0Wq1kCTJ7B6AlrDhUkCW5UKXpiYiIiIiIudT0r6ADZcCGo0GsiwjLCwMkiTZuxzC/Te6VquFRqNhJoJgJuJhJmJiLuJhJuJhJuJhJv9z/vz5Es3Hc7iIiIiIiIhsRMiGa+/evXj++ecRHh6OVq1aYdiwYcjNzTVN//7779GrVy+Eh4ejW7du2L17d6Ex8vPz8cknn6Bdu3Zo1qwZ3njjDcTGxpbnyyAiIiIiIicnXMO1YsUKzJo1C5GRkVi7di1mzpyJmjVrQq/XAwDOnDmDUaNGoVmzZoiJiUH37t0xadIkHD582Gyc2bNnY+fOnRg7diyWLFmC/Px8vP7668jIyLDHyyIiIiIiIick1DlcsbGxWLp0KZYvX46nnnrK9Hi3bt1M/16xYgWaNGmCmTNnAgBat26NuLg4LF68GM8++ywA4O7du9i1axemTZuGfv36AQDCw8PRsWNHbNu2DcOHDy/HV0VERERERM5KqD1ce/bsQc2aNc2arYLy8/Nx+vRpU2NlFBkZiWvXriE+Ph4AcPLkSRgMBrP5/P390a5dO5w4caJUNTr7yYEi4mX6xcNMxMNMxMRcxMNMxMNMxMNMlBFqD9eff/6JkJAQLF++HJs2bUJGRgbCwsIwYcIENG3aFLdu3YJWq0W9evXMnle/fn0A9/eQ1axZE7GxsQgMDISfn1+h+Xbt2lXqOh/WdOn1emi12lL/HqLyoNFooFary2w8/lFCPMxETMxFPMxEPMxEPMxEOaEaroSEBFy4cAGXL1/GtGnT4OHhgZUrV2LIkCE4evQo0tLSAAC+vr5mzzP+bJyenp4OHx+fQuP7+vqa5ikNg8FQ6M0mSRIMBgPu3r1bJr+DSsZ4/wOu/KXj5+eHatWqFTldkqQi7zVhXPbG6bIsQ6/XQ61WQ5IkRc9V+ns5bsmeazAYzDIpj5o47sOfCwA6na5QLrauydHGLc+ajNsvF5eivx6JVK8j12SrzxR711sRanrYuNZ+pjjLMrREqIZLlmVkZ2dj0aJFaNiwIQCgadOm6NSpEzZv3oyIiAg7V/i/ew8UfIOpVCq4uLjg7t27SE1NRVBQEDw9PU3zqFQq03MfDKfgxkPJtNI+11iTwWBw+HGNzwcKrxi2WobGaeX9Wst6XOM6l5iYCEmSUKVKFdMFago+z/jlw9KeW+N9OPR6PQwGA2RZhk6ng4uLi2ma8TFL4xrXKUvjAjCNW5BarYZarbY4riRJpufqdLpCy8I4zfiBUVbjuri4mDU3ll4rYHkZurq6FvlaSzJuSZZhfn6+aazSvlZbZWPtMrRlNg++v0v6WksyrrHeB/+IZ6tlaBzXUk32WobGcUXZRhT8t7NtI2yxnS2LcS19pjjTNqI8t7NKthFardbsM8VZthEFxzV+53nwD2aWCNVw+fr6wt/f39RsAffPvXrsscdw9epV9OjRAwAKXWkwPT0dAEyHEPr6+iIzM7PQ+Onp6YUOM7SGpRu96fV6pKWloUqVKggMDDSbVtE6d9HGfdib3Zn+qmLNuMY/DiQkJCAoKKjY47ItTbP0Bd44r3FawY2XpecX9zsLfgAofW5xf6VWqVSmJrS8xgWKP+69uNdamnrVarXZlxUlzy3utdojG6D4ZVjW4z74/rbkYa+1uGkqlarIm4dyGT58WmnGBQq/1oJf3JxtG2GLdbksxrX0mVKR3t9FjWsk6nZWlmWL266Kvo0oSKVSlajZAgRruBo0aIBbt25ZnJaXl4fatWtDo9EgNjYW7du3N00z3l/LeG5XvXr1kJiYiLS0NLMGKzY2ttD5X9YouHfDyLiB9vLyKnLhP6wpeNjvLOtpFWHcgs2DPeotzXNFGtfLywsJCQmmvyKWdlzjOlKw4VJaU2mnOdO4JXnug5nYuiaO+/Dpxj8WWcrFljU52rjlXVNJtlsi1euoNdnqM0WEeh29ppKMa81nijMtwwcJdZXCjh07IjU1FZcuXTI9lpKSgr/++guNGzeGq6srWrVqhSNHjpg97+DBg6hfvz5q1qwJAIiIiIBKpcLRo0dN86SlpeHkyZPo0KGDTV+DkoVPJAq+b4mIiIhsQ6g9XF26dEF4eDjeffddjB07Fm5ubli9ejVcXV3xyiuvAADefvttDB48GNOnT0f37t1x+vRp7N+/H9HR0aZxqlatin79+mHevHlQqVQIDg7GqlWr4OPjg5deeqlUNfKLqXiYiXjK8qqHVDaYiZiYi3iYiXiYiXiYiTJC7eFSqVRYvXo1mjVrhqlTp+L999+Ht7c3tmzZgqCgIADAE088gSVLluDs2bMYOnQo9u/fj9mzZ6N79+5mY02ePBn9+vXDZ599hpEjR8LFxQXr16+3ePVCpSr6F/wlS5YgNDS00H89e/YEAISGhmLt2rWm+ffs2YOvv/66RGN36tTJdNNqAIiKijKNa43iDsextzNnzuDtt99GmzZtEBYWhg4dOmDcuHE4f/58udaxZ88ehIaGIjk5uVx+nyRJFq+6RvbDTMTEXMTDTMTDTMTDTJQTag8XAAQEBODTTz8tdp7OnTujc+fOxc7j6uqK8ePHY/z48WVZHoCHX6ShInB3d8eGDRsKPQYA27dvR/Xq1U2P7927F56ennjuuecU/5533nkH2dnZVtdZ0nO4ytuWLVswa9YstG7dGpMmTUJwcDDu3buHr7/+GkOGDMGvv/5abrU8/fTT2L59e6HbKdiK8apSojbCzoiZiIm5iIeZiIeZiIeZKCdcwyU6Jdfcd2QqlQrNmjWzOK2ox61Ru3btUo9R1g2w8fLZxV1lpzh///03Pv74Y/Tu3Rtz5841q61nz544duxYqWvMzc01NcAPExAQgICAgFL/TiV0Oh3vQi8YZiIm5iIeZiIeZiIeZqKMUIcUkmMoeEjhoEGD8Msvv+CHH34wHXq4ZMmSEo/14CGFxsPfLl68iGHDhqFZs2bo2rUr9u3bV+i5P/zwA/r3749mzZqhTZs2mDZtmtnesuzsbMycORPdunUz3c9t6tSphW4rYDzMMSYmBh07dkSTJk2Qmppqque5555DeHg42rdvj+jo6EL3l3jQxo0bIUkSxo8fb7ER7Nixo+nf+/btw8svv4yWLVviySefxKBBg3Du3Dmz+ZcsWYLmzZvj3LlzGDBgAMLDw7FlyxbEx8cjNDQUe/fuxcSJE9GiRQu0bNkSc+bMMbunRHkfUkhERERE/8M9XFSkB28EZ+l43WnTpuHDDz+Eu7u76fDNqlWrlvp3jxs3Dv3798cbb7yBHTt2ICoqCuHh4ahfvz4A4PDhwxg7diz69u2LUaNGISEhAQsWLEB6errpAiq5ubnQ6/UYO3YsAgIC8M8//2DlypV45513sGnTJrPfd/ToUTzyyCOYNGkSVCoVPD09sX79enz66ad47bXXEBUVhWvXrpkarnHjxhVZ+6+//oqwsLAS7VWKj4/H888/j9q1ayM/Px8HDhzAq6++iq+++gp169Y1zafVavHBBx/g9ddfx9ixY+Hv72+atmDBAkRERGDhwoW4ePEiFi9eDI1GU2yNRERERKLLycnB1atX0aBBAwAw/dvDw8POlSnDhossys7ORuPGjc0emzdvHnr37m32WIMGDeDt7Q1PT88yPdTw1VdfxauvvgoAaN68OY4fP44jR47gnXfegSzLmDdvHiIjIzF79mzTIYVVqlTBm2++iXfeeQePPvooAgICMGPGDNOYOp0ONWvWxCuvvILr168XamhiYmLg6ekJAMjMzMTixYsxbNgwvP/++wCAdu3aQaPRYO7cuRg6dCgqVapksfZ79+4hPDy8RK9z1KhRpn8bDAa0a9cO586dw969e02/11jf2LFjERkZaXosPj4ewP3DMufMmQMAaN++PXJzc7F+/XoMHz68TG70TURERGQPOTk5uHDhAmrUqAEApn+z4argrD1XSHvjDgxpGQ+fsYyp/HygqVP94TM+wN3dHZs3bzZ7rFatWmVV1kNFRESY/u3p6Ynq1avj7t27AIDr16/j9u3bmDhxInQ6nanhatmyJVQqFS5cuIBHH30UwP1D9j7//HPcvHnT7HDDGzdumDVcrVq1MjVbAPD7778jOzsbzz77rNmevrZt2yI3NxdXrlxBy5Yti6y/pO+Ta9euYcGCBfj999+RlJRkVt+DnnrqKYtjPPPMM2Y/d+vWDcuXL8fly5fx5JNPlqiOssaTaMXDTMTEXMTDTMTDTMRTnplodXrE3k5DlUqO1WQVxIbLCkrfZPqkVNxq9TJgMNioomKo1ajz1z6oA/0VPU2lUpV4L40tPHj5fo1Gg/z8fAD3b4YNACNHjrT43H/++QcA8M0332D8+PEYMGCA6TC8hIQEjBw5Enl5eWbPCQwMNPvZ+Dv69OlT7O+wJDg4GHfu3ClyulFmZiaGDBmCgIAAREVFoXr16nBzc8PkyZML1efh4QEvLy+L4zx46GLlypUBAAkJCQ+twRYkSeKJtIJhJmJiLuJhJuJhJuIpj0xycnKQk5ODO3cTEHflH9z++nNktQ7DIz7ZSEtLg4eHh0Pt5WLDVQ7Ugf6offoLu+3hUtpsic54/tLUqVPRpEmTQtOrVKkC4P55Xo0aNTK779cvv/xiccwHm2jjoXhLly61eE5azZo1i6yvZcuW+Oqrr5Cammp2rtWD/vjjD9y9exerVq1Cw4YNTY9nZGQU+p3FNfkPXgwjMTERAEz3riMiIiJyJBcvXsSFc+fgf+IvPP/tbwCAXQ2Af3Pc8P3336NZs2Zo0aKFnassOTZcVrDmMuTWHNbnKDQaTaE9MrZUr149VK1aFXFxcXjllVeKvBdEbm5uob/AlPQGzc2bN4eHhwfu3r1b6JC9hxk0aBD27duHTz75xHRuVUE//PADnn76aeTm5gKAWY2//fYbbt++bToksiS++eYbvP7666afjxw5Ag8PD4SEhCiqu6zIsgydTgcXFxceBiIIZiIm5iIeZiIeZiKe8shE/d+baLr4K/jEJ+JaaHWcbt0QBk8fqNU6h7xFExsuhRwxZFurV68e9u3bh++//x5BQUGoUqUKgoODbfb7JElCVFQUxo0bh+zsbDz11FPw9PTEnTt3cPz4cYwdOxZ169ZF27ZtMXPmTCxbtsx04Y1Tp06V6Hf4+vri3Xffxaeffoq7d++iZcuWUKvViIuLw3fffYclS5YUuSu7YcOGmDhxImbNmoV79+7hhRdeMN34+MCBAzhz5gx++eUXNGvWDJ6enpgxYwbefPNN3Lt3D0uWLFG87G7duoUJEyYgMjISFy9exOrVq/Haa6/Z9YIZXE/Ew0zExFzEw0zEw0zEY6tM9EmpSJq9Cj5bDkAdWgeGmBGIS76Dpxq1QICfG87++jM6dOhQJlfELk9suKjUhg8fjlu3bmH8+PFIT0/HqFGjMHr0aJv+zu7du8PX1xcrVqww7bWqUaMG2rdvbzqH6aWXXkJ8fDw2b96MtWvXIiIiAp999hn69+9fot8xZMgQBAcHY/369di8eTNcXFxQu3ZtPP300w89dvnVV1813a9s5syZyMzMREBAAFq3bo3169cDuH+u1aJFizBv3jy88847qFOnDmbMmIE1a9YoWhZjx47FL7/8gvfeew9qtRqvvPIKxo4dq2gMIiIiInuR9Xqkb/wKyR/HALKMynPGwPe1XkhJT4fnkSMIC71/Ksd5V1f4+fk51PlbACDJ/LNBiZ0/fx6yLCM8PNzi4WvGS427u7vbqULnI8tykYcUVnTx8fHo3LkzFi1ahGeffbZUY5Xl+1eWZWi1Wmg0GqfLRFTMREzMRTzMRDzMRDxlnUnurxeQMH4B8s9fgc8rPRAw+S24BN2/9Y7o9+E6f/48ADz0QnPcw0VEREREROVKl5CC5JkrkLHtEFybhKDGoZVwf8L8HrAeHh5mzYw9r6BdGmy4FOJfV8TDTMTDS/iKh5mIibmIh5mIh5mIpzSZyDod0tfvQ/LctYBKQuVPP4DvoOcgqdVlWKFY2HBZgV/wxeHMWdSsWRP//e9/7V1GIc6ciaiYiZiYi3iYiXiYiXhKk0nOz+eQGLUA+Rdj4TvoOQRMHF7hbl9kCRsuK1hzWXiyjYKnIDITMciyDIPBAJVKxUwEwUzExFzEw0zEw0zEY00muruJSJq5Apk7j8KteSPUOLIK7s0b2bhScbDhUojXGBEPG2Dx6PV6qFQqe5dBBTATMTEX8TAT8TAT8ZQ0E1mrQ9ra3Uj+ZB0kVw2CFnwEn1d7QHKyPNlwlTE2ZOSI+L4lIiKispTz0+9InLAQ+X9fh+/rvREwYTjUlXztXZZdsOEqI8aTB7Ozs4W5VCVRSWVnZwPgiclERERUOrq7iUiatgyZe76F2xONUfObGLg1DbV3WXbFhquMqNVq+Pv7499//wUAeHp68jC3cuDM9+EqC7IsIzs7G//++y/8/f2hrsBXCCIiIiLbkbU6pK3eieRP10PycEPQoij4vNTd6Q4ftIQNl0LFfamvWrUqAJiaLiofPIer9Pz9/U3v37LAxk08zERMzEU8zEQ8zEQ8D2aSfeIMEicshPZqHPyG9EGlqKFQ+/nYqTrxsOGyQlFf7iVJQrVq1VClShVotdpyrorIOhqNpkw/zCRJ4oejYJiJmJiLeJiJeJiJeApmort9D4lTlyHrq2Nwb9UEwd9Nh1tYAztXKB42XFZ42B4VtVrNjUM54SGF4mEm4mEmYmIu4mEm4mEm4pFlGYbcPKSv3omUBRuh8vJElWWT4P1iN2ZUBDZcCvFqbuLR6XS82INgmIl4mImYmIt4mIl4mIlYso/9gsQJC6G7+Q/8hvVFpY+GQO3rbe+yhMaGi4iIiIiIiqWNu4ukKUuRdeA43No0RdX1s+H2WH17l+UQ2HAREREREZFFhtw8pC3bhpRFm6Dy9UaVlVPh2rMDXF1d7V2aw2DDRUREREREhWR9cwqJExdBF38Xfm+9iIBxb0Dy8uDF4RRiw6UQTwYUDzMRDzMRDzMRE3MRDzMRDzMpf9qbd5A4eQmyD5+ER/vHUW3zHLiG1gXA2/FYgw2XFfgmE4ckSTyRVjDMRDzMREzMRTzMRDzMpHwZcvKQunQrUhdvhirAH8FrZsKr19Nm332ZiXJsuIiIiIiInJgsy8g+8hMSJy+G7k4C/N8egEpjB0Pl7Wnv0ioENlxW4K5UcciyDJ1OBxcXF2YiCGYiHmYiJuYiHmYiHmZie9rYeCROWoTsb3+Gx9NPotq2+XBtULvI+ZmJcmy4FOJ9uMTDTMTDTMTDTMTEXMTDTMTDTGzDkJ2L1EWbkbJ0K1yqBCB4/Wx49ehQoiaKmSjDhouIiIiIyEnIsoysgz8iacoS6O4lwX/ky6g0ZhBUnu72Lq3CYsNFREREROQE8q/dQmLUQuT88Cs8O7dGtZ0L4Fq/lr3LqvDYcBERERERVWCGrBykLNiA1BXb4VItCFU3zYFnt3Y8B6ucsOFSiG9M8bi48G0sGmYiHmYiJuYiHmYiHmZiPVmWkfXVD0icuhSGpFRUGjMI/qNfhcrDrVTjMhNluLSswKZLHJIkMQ/BMBPxMBMxMRfxMBPxMBPr5V++gcQJC5Fz4iw8u7VD5dnvQlOneqnHZSbKseGyAi8LLw5ZlmEwGKBSqZiJIJiJeJiJmJiLeJiJeJiJcobMbCTPX4+0VTvhUrMqqm75BF5d25bZ+MxEOTZcCvEymOLR6/VQqVT2LoMKYCbiYSZiYi7iYSbiYSYlI8syMvd+h6Rpy2BIy0DAuDfgN/IlqNxLd/igJcxEGTZcREREREQOLO9SLBInLETuT7/Dq0cHBM4aDU2tqvYui/4/NlxERERERA7IkJGF5HnrkBazG5pHqqHa9vnw7NTK3mXRA9hwERERERE5EFmWkbnrKJKmL4chMxsBE4bBf0R/SG6u9i6NLGDDpRBPDhQPjyEWDzMRDzMRE3MRDzMRDzMxl3fhKhKjopF7+hy8enVE5Zkj4VIjuFxrYCbKsOGyApsucUiSxHtBCIaZiIeZiIm5iIeZiIeZ/I8+LQMpc9cibd1eaOrXQrVd0fB86olyr4OZKMelZQVeFl4cBa8ayUzEwEzEw0zExFzEw0zEw0wA2WBAxvbDSJ61EoasXARMeQv+b74IyVVjn3qYiWJsuBTiZeHFo9VqodHYZ6NDljET8TATMTEX8TAT8ThzJnnnLiMhKhp5v16Ad98uCJz+DlyqBdm7LKfOxBpsuIiIiIiIBKJPSUfynDVI3/AlNCGPoPq+xfBo19zeZZGV2HAREREREQlANhiQseUAkv5vFeQ8LQJnvAO/oS9A0vAruyNjekREREREdpb7x99IHL8Aeb9dgveLXRE49W24VK1s77KoDLDhIiIiIiKyE31yGpL/bzXSN30N18fqofpXS+HRpqm9y6IyxIZLIUmSeEUWgUiSBFdX3uRPJMxEPMxETMxFPMxEPBU5E1mvR/qmr5H8cQyg06Py/70L3zeehyT4Jdcrcia2InaiREREREQVTO7Zv5A4Php5f/4XPi91R8CUEXCpEmDvsshG2HBZgffhEocsy9Dr9VCr1cxEEMxEPMxETMxFPMxEPBUtE31iCpJmrULG1gNwDX8UNQ4sh3vLcHuXpUhFy6Q8sOFSiPfhEo/BYIBarbZ3GVQAMxEPMxETcxEPMxFPRchE1uuR/vmXSJ4TAwCo/Mn78H2tFyQHfV0VIZPyxIaLiIiIiMhGcn85j4Tx0ci/cAU+r/ZA4OS3oK5cyd5lUTliw0VEREREVMZ0/yYjeeYKZGw/DLdmDVHjyCq4P/6YvcsiO2DDRURERERURmSdDmlr9yLlk7WAixpBn30In1d7OOzhg1R6bLgU4smB4nER/PKpzoiZiIeZiIm5iIeZiMeRMsn5zx9InBCN/EvX4Tu4FwImDoc6wM/eZZU5R8pEBFxaVmDTJQ7eF008zEQ8zERMzEU8zEQ8jpKJ7m4ikmYsR+aub+DW4jHUOLoa7s0a2rssm3CUTETChssKvCy8OGRZhsFggEqlYiaCYCbiYSZiYi7iYSbiET0TWatD2ppdSJ63HpKbBkELo+DzcndIKpW9S7MZ0TMRERsuhXhZePHo9XqoKvCGzRExE/EwEzExF/EwE/GImknOyd+QEBUN7ZVb8H39eQRMGAa1v4+9yyoXomYiKjZcREREREQlpPsnAUlTlyJz3/dwfzIMwd+ugVv4o/YuiwTGhouIiIiI6CHkfC1SV+1AyvwNUHm5I2jJRPj071ahDx+kssGGi4iIiIioGNnHzyBxwkJoY+PhN7QvKn30BtR+znH4IJWeUC35nj17EBoaWui/+fPnm823c+dOdOvWDeHh4ejVqxeOHTtWaKyMjAxMnDgRLVu2RPPmzfHuu+/i33//LXWNPDlQPDyGWDzMRDzMREzMRTzMRDz2zEQbfw9335iMf/qNhTrQHzW/W4PK//eu0zdbXE+UEXIP15o1a+Dj8783cnBwsOnfBw4cwJQpUzBixAi0bt0aBw8exKhRo7BlyxY0a9bMNN+YMWNw9epVTJ8+HW5ubli4cCGGDx+O3bt3l/reAWy6xCFJEu8FIRhmIh5mIibmIh5mIh57ZSLn5SN1+TakLNwElbcnqqyYAu8XnuF3QHA9sYaQS6tx48YICAiwOG3x4sXo0aMHxowZAwBo3bo1Ll++jGXLliEmJgYA8Pvvv+PkyZNYu3YtIiIiAAB169ZFZGQkjh49isjIyHJ5HVQ+eJl+8TAT8TATMTEX8TAT8ZR3JtnfnUbixIXQ3vwHfm/2Q8CHb0Dl41Vuv98RcD1RxqH2B8bFxeHGjRvo3r272eORkZE4deoU8vPzAQAnTpyAr68v2rVrZ5qnXr16aNSoEU6cOFGqGmRZ5qXhBSLLMrRaLTMRCDMRDzMRE3MRDzMRT3lmor31D+6+NhH/vDQO6upVUOuH9ag8cxSbrQdwPVFOyD1cPXv2REpKCqpXr47+/ftj2LBhUKvViI2NBXB/b1VB9evXh1arRVxcHOrXr4/Y2FjUrVu3UOddr1490xhERERERIbcPKQu+wKpCzdB5e+L4NXT4fV8J+7BoTIjVMMVFBSE0aNHo2nTppAkCd9//z0WLlyIe/fuYerUqUhLSwMA+Pr6mj3P+LNxenp6utk5YEZ+fn64cOFCqeu01NFLklRkp29cYYubbqvnVvRxC+5xtEe9pXluRR3XmInxZxFqqujjluS5lvbOO+JrdbRxi3uucVp5f6Y42rjlWZO9P1MqwjIs63GVfqYorTf7m1NInLQYuvi78BvRH5Xefw0qb0+zeZlN4XGt+UxxlmVoiVANV/v27dG+fXvTzxEREXBzc8OGDRswYsQIO1ZmTqvVmv3VQ6VSmU4e1Gq1heZ3dXUFcP+u3AaDwWyai4sLJEmCwWCAXq83m2Yc17jr9kEajabIcdVqNdRqNWRZhk6nM5smSZLpuTqdrtAbxlbjluS1AoWXYXHjFlzhlY5rfK2SJCl+rSUZFyjfZWgc11JNtsrG+FoLLsOCr8s4rbhlaK/3d3HLsDzf30D5bCOMr8e47XKWbURpxjW+VltuIwwGQ6HPlIq+jbA0rijbiIL/drZthKjfIyx9ppTFNkJ38w5Spi5D7rc/w71DC1Tb+gnU9WpCr9dDX+D59t5GiPo94sHPFGfZRhQc1/idp+D2uyhCNVyWdO/eHevWrcOlS5fg5+cH4P4l34OCgkzzpKenA4Bpuq+vL+7evVtorLS0NNM8pWF8YxQ1rSgF38gPUqlURV5is2DwSsd92HOLu8qMrcYt7rUCxS/DB8ctuJJZM+6DXz4tedhrtUc2ZbkMSzuupS/wxnmN04p7rfZ6f5dmnbNVNrbcRri4uFjcdlX0bURpx7X1NkKlUhX5mcJl+PBppRkXsP4zpSJuI0T9HmHpM6U0y1Cl1SN18RakLd0KVWV/VFk7E149n4JKpYIsy8JtI0TdzsqybHHbVdG3EQWpVKoSNVuAAzRcBdWrVw8AEBsba/q38WeNRoNatWqZ5jt16lShrvP69esICQkpVQ2SJJn+szTtYc+1ZlppnusM4xr/8mePekvz3Io87oOZiFBTRR63JM8taj1xtNfqaOMWN12W5WK3X472WivKdtaenykVZRmW9bhKPlOKW9+yD59E4uQl0P2TAP93XkKlsYOh8vIo83rL67n2HNeazxRnWoYPEv4qhQcPHoRarcZjjz2GWrVqoU6dOjh8+HChedq0aWMKv0OHDkhLS8OpU6dM81y/fh0XL15Ehw4dSl2TkgVMtlVcA0z2wUzEw0zExFzEw0zEUxaZ5F+Lw92XP8LdwROhaVAbtX7cgMDJb5k1W1RyXE+UE2oP19ChQ9GqVSuEhoYCAL777jvs2LEDgwcPNh1COHr0aIwbNw61a9dGq1atcPDgQZw7dw6bN282jdO8eXNERERg4sSJGD9+PNzc3BAdHY3Q0FB07dq11HWW9HhNsj1ZlqHX66FWq5mJIJiJeJiJmJiLeJiJeEqTiSErBykLNyF1+Ta4BAei6ob/g2f39sy2lLieKCdUw1W3bl3s3r0bd+/ehcFgQJ06dTBx4kQMGjTINE/Pnj2Rk5ODmJgYrF69GnXr1sXSpUvRvHlzs7EWLlyIOXPmYOrUqdDpdIiIiMDkyZNLfWdsJVckofJhMBiKPDaX7IOZiIeZiIm5iIeZiEdpJrIsI2v/cSRNWQJ9YioqjX4F/u8OhMrT3YZVOheuJ8pIMjuIEjt//jxkWUZ4eDg7ekEYT3Au7kImVL6YiXiYiZiYi3iYiXiUZpJ/9RYSJyxEzg+/wvOZNqj8f+9BU7dGOVTqPLie/M/58+cBAOHh4cXOJ9QeLiIiIiIipQyZ2UhZsBGpK7fDpUYVVN0yF15d29m7LCIAbLiIiIiIyEHJsoysfd8jcdoyGFLSUOn9wfAf9QpU7m72Lo3IhA2XQs6+61REPIZYPMxEPMxETMxFPMxEPEVlkv/f6/cPH/zxN3h2j0DlWaOheaR6OVfnnLieKMOGywpsusQhSRJXesEwE/EwEzExF/EwE/FYysSQmY3kT9cjbfVOaGpVQ9UvPoVXl9Z2qtD5cD1Rjg2XFXhZeHHIsmzKg5mIgZmIh5mIibmIh5mIp2AmAJC551skTVsGQ3omAj4aAv93XoLk5mrnKp0L1xPl2HApxIs6iken00Gj0di7DCqAmYiHmYiJuYiHmYhHp9NBvnL/6oO5p/6EV8+nEDhrNDQ1g+1dmtPieqIMGy4iIiIiEpIhPRMpc9Yg8/MvoalbA9V2fAbPji3tXRaRImy4iIiIiEgosiwjc8cRJM1YAUNWNgImDIP/2wMguXKvCjkeNlxEREREJIy881eQGBWN3F/Ow6t3R/hOfgsej1Tn+ULksNhwKcSVXTzMRDzMRDzMREzMRTzMxH70aRlInrMG6ev3QdOgFqrtjoZH+xbQ6XT2Lo0ewPVEGTZcVuCbTBySJPGkTcEwE/EwEzExF/EwE/uQDQZkfHEISbNXQs7JQ+C0t+E3vB8kzf2vqcxELFxPlGPDRURERER2kffnf5EQFY28M3/B+4VnEDj9HbhUrWzvsojKFBsuK/A+XOKQZRk6nQ4uLi7MRBDMRDzMREzMRTzMpPzoU9KR/PFqpG/4Cq4N66L6vsXwaNe80HzMRDzMRDk2XArxPlziYSbiYSbiYSZiYi7iYSa2JRsMyNiyH0mzVwNaHQJnjYbfkD6mwwctPoeZCIeZKMOGi4iIiIhsLve3i0iMWoi83y/Bu/+zCJw6Ai7BgfYui8jm2HARERERkc3ok1KRNHsVMrYcgOtj9VF9/zJ4tGpi77KIyg0bLiIiIiIqc7Jej/SNXyH54xjAIKPyx+/B9/XekFz49ZOcC9/xCvHkQPHw0qTiYSbiYSZiYi7iYSZlI/fXC0iIikb+ucvweaUHAia/BZegSlaNxUzEw0yUYcNlBTZd4mAW4mEm4mEmYmIu4mEmpadLSEHyrJXI+OIgXJuEoMahlXB/orHV4zET8TAT5dhwWYGXhReHLMvQ6/VQq9XMRBDMRDzMREzMRTzMxHqyTof09fuQPHctoJJQ+dMP4DvoOUhqdenGZSbCYSbKseFSiJfBFI/BYIC6lBt0KlvMRDzMREzMRTzMRLmcn88hMSoa+RevwWdgTwROehPqQP8yG5+ZiIeZKMOGi4iIiIgU091LQtLMFcjccQRuzRuhxpFVcG/eyN5lEQmHDRcRERERlZis1SFt7R6kzFsHaFwQtOAj+LzaA5JKZe/SiITEhouIiIiISiTnp9+ROGEh8v++Dt/XeyNgwnCoK/nauywiobHhUognB4qHxxCLh5mIh5mIibmIh5lYprubiKRpy5C551u4PdEYNb+JgVvT0HL53cxEPMxEGTZcVmDTJQ5JkrjSC4aZiIeZiIm5iIeZFCZrdUhbvRPJn66H5OGGoEVR8Hmpe7kdPshMxMNMlGPDZQVeFl4csiyb8mAmYmAm4mEmYmIu4mEm5rJ/PIvEqGhor8bBb0gfVIoaCrWfT7nWwEzEw0yUY8OlEC8LLx6dTsc7nguGmYiHmYiJuYiHmQC6O/8iceoyZH35PdxbNUHwd9PhFtbAfvUwE+EwE2XYcBERERER5HwtUldsR8qCDVB5eaLKsknwfrEb92IQlRIbLiIiIiInl33sFyROWAjtjTvwG9YXlT4aArWvt73LIqoQ2HAREREROSlt/D0kTV6CrAPH4d6mKYLXz4Zbo3r2LouoQmHDpRB3q4uHmYiHmYiHmYiJuYjHWTKR8/KRumwbUhZuhMrXG1VWTYN3n85Cvn4Ra3J2zEQZNlxW4JtMHJIk8aRNwTAT8TATMTEX8ThLJlnfnELSpMXQxv0Dv7deRMC4N6Dy9rR3WRY5SyaOhJkox4aLiIiIyAlob95B4uQlyD58Eh7tH0fVTR/DNbSuvcsiqvDYcFmB9+EShyzL0Ol0cHFxYSaCYCbiYSZiYi7iqaiZGHLykLp0K1IXb4aqkh+CY2bAq3dHh3iNFTUTR8ZMlGPDpRDvwyUeZiIeZiIeZiIm5iKeipZJ1pGfkDhpEXR3EuD/9gBUGjtY2MMHi1LRMqkImIkybLiIiIiIKhhtbDwSJy9G9jen4PH0k6i2bT5cG9S2d1lETokNFxEREVEFYcjOReqizUhZuhUuVQIQvH42vHp04KFfRHbEhouIiIjIwcmyjKyDPyJpyhLo7iXBf+TLqDRmEFSe7vYujcjpseFSiH8hEo+LC9/GomEm4mEmYmIu4nHETPKv3ULihEXIOfYLPDu3RrWdC+Bav5a9yyozjphJRcdMlOHSsgKbLnFIksQ8BMNMxMNMxMRcxONomRiycpASvRGpy7fBpVoQqm6aA89u7RzqNTyMo2XiDJiJcmy4rMDLwotDlmUYDAaoVCpmIghmIh5mIibmIh5HyUSWZWR9/QOSpi6FPjEVlcYMgv/oV6HycLN3aWXOUTJxJsxEOTZcCvEymOLR6/VQqVT2LoMKYCbiYSZiYi7iET2T/Ms3kDhxEXKOn4Fnt3aoPPtdaOpUt3dZNiV6Js6ImSjDhouIiIhIcIbMbKR89jlSV+6AS82qqLrlE3h1bWvvsoioBNhwEREREQlKlmVk7vsOSVOXwZCWgYBxb8Bv5EtQuVe8wweJKio2XEREREQCyv/7OhImLETuyd/g1aMDAmeOgqZ2NXuXRUQKseFSiCcHiofHEIuHmYiHmYiJuYhHhEwMGVlInrcOaTG7oXmkGqptnw/PTq3sXZbdiJAJmWMmyrDhsgKbLnFIksR7QQiGmYiHmYiJuYjH3pnIsozMXUeRNH05DJnZCIgaCv+3B0Byc7VbTfZm70yoMGaiHJeWFXhZeHEUvGokMxEDMxEPMxETcxGPPTPJ++sqEqMWIvfnP+H13NP3Dx+sGVyuNYiI64l4mIlybLgU4mXhxaPVaqHRaOxdBhXATMTDTMTEXMRT3pno0zKQ8sk6pK3bC029mqi2KxqeTz1Rbr/fEXA9EQ8zUYYNFxEREVE5kw0GZGw/jORZK2HIykXA5Dfh/+aLkFz5JZaoomHDRURERFSO8s5dRkJUNPJ+vQDvPp0ROGMkXKoF2bssIrIRNlxERERE5UCfmoHkj2OQvuFLaB6tjep7F8Ej4nF7l0VENsaGSyGeHCgeZiIeZiIeZiIm5iIeW2QiGwzI2HoQSbNXQs7TInDGO/Ab+gIkDb+GlQTXE/EwE2W4pluBbzJxSJLEkzYFw0zEw0zExFzEY4tMcv/4G4njFyDvt0vwfrErAqe+DZeqlcv0d1RkXE/Ew0yUY8NFREREVMb0yWn3Dx/c+BVcG9VF9a+WwqNNU3uXRUR2wIbLCrwPlzhkWYZOp4OLiwszEQQzEQ8zERNzEU9ZZCLr9UjfvB/J/7ca0OkROPtd+A15HhJvFGsVrifiYSbKce1XiPfhEg8zEQ8zEQ8zERNzEU9pMsk9+xcSx0cj78//wuel7giYMgIuVQLKsDrnxPVEPMxEGTZcRERERKWgT0xB0uxVyNhyAK5hj6LGgeVwbxlu77KISBBsuIiIiIisIOv1SP/8SyTPiQEAVP7kffi+1guSWm3nyohIJGy4iIiIiBTK/eU8EsZHI//CFfi82gOBk9+CunIle5dFRAJiw6UQTw4UjwtPRBYOMxEPMxETcxHPwzLR/ZuM5JkrkLH9MNyahqLG4ZVwb9G4nKpzTlxPxMNMlFHZu4CiZGVloUOHDggNDcX58+fNpu3cuRPdunVDeHg4evXqhWPHjhV6fkZGBiZOnIiWLVuiefPmePfdd/Hvv/+WSW1susQhSRJUKhUzEQgzEQ8zERNzEU9xmcg6HVJX70Jcm1eRdfQ/qDx/HGocWcVmy8a4noiHmSgnbMO1fPly6PX6Qo8fOHAAU6ZMQffu3RETE4NmzZph1KhR+OOPP8zmGzNmDH766SdMnz4d8+fPx/Xr1zF8+HDodLpS18Yrs4hDlmXo9XpmIhBmIh5mIibmIp6iMsn5zx+I7zwUSZMXw7tPZ9T+eSv8XuvNc7XKAdcT8TAT5YTcH3jt2jVs3boV48ePx7Rp08ymLV68GD169MCYMWMAAK1bt8bly5exbNkyxMTcP2n1999/x8mTJ7F27VpEREQAAOrWrYvIyEgcPXoUkZGRVtfGN5d49Ho9VCph/3bglJiJeJiJmJiLeApmorubiKQZy5G56xu4tXgMNY6uhnuzhnau0PlwPREPM1FGyCU1e/ZsvPTSS6hbt67Z43Fxcbhx4wa6d+9u9nhkZCROnTqF/Px8AMCJEyfg6+uLdu3ameapV68eGjVqhBMnTtj+BRAREZHDkrU6pK7YhlttXkX2sV8QFD0eNQ6uYLNFRFYRruE6fPgwLl++jJEjRxaaFhsbCwCFGrH69etDq9UiLi7ONF/dunULHVtar1490xhERERED8o1Hj44fQV8+j+L2qe2wndgT0j8az4RWUmoQwpzcnIwd+5cjB07Ft7e3oWmp6WlAQB8fX3NHjf+bJyenp4OHx+fQs/38/PDhQsXSl2npcMKJUkq8nBDY+NX3HRbPbeijyvLsukxe9RbmudW1HGNmRh/FqGmij5uSZ5bMJPyqInjPvy5xmnl/ZniaOOWV026fxKQNG0ZsvZ9D7cnw1Djm9VwCw8pNJ8o9ZbXc+09rtLPFHvXWxFqKsm41nymOMsytESohmvFihUIDAzECy+8YO9SiqXVas32nqlUKtPlMbVabaH5XV1dAdw/3tVgMJhNc3FxgSRJMBgMhS4SYhxXlmWL42o0miLHVavVUKvVkGW50IVCJEkyPVen0xV6w9hq3JK8VqDwMixuXFmWTVkoHdf4WiVJUvxaSzIuUL7L0DiupZpslY3xtRZchrIsw2AwQKvVmqYVtwzt9f4ubhmW5/sbKL9tRMFtl7NsI0ozrvG12nIbYcyu4GdKRd9GWBrXntsIQ14+MtbsRnr0Jkie7ghYOB5+L3WHbOH1VPRtRFm91rIc19JnijNtI0T9HvHgZ0pF3kYU9z2i4PfQ4gjTcN2+fRvr1q3DsmXLkJGRAQDIzs42/T8rKwt+fn4A7l/yPSgoyPTc9PR0ADBN9/X1xd27dwv9jrS0NNM81pIkybThs8QYgiUF38gPUqlURZ58WDB4peM+7LnF3UfBVuMW91qB4pdhUeNKkmTVuA9++Sxq7OJqskc2tliG1o5b2mVor/d3adY5W2Vjq22Ei4tLseuOM2wjrB3XltsISZLg5uZW5PO4DB8+rTTjAkD+T38gaeJCaK/Fw3doH1T6aAjUfj6mL3jOso3g9wjrx3XG7xEajabI6RVtG/GwZViSZgsQqOGKj4+HVqvFm2++WWja4MGD0bRpU3z22WcA7p+jVa9ePdP02NhYaDQa1KpVC8D9c7VOnTpVqOu8fv06QkJCyqReSwv4YQu9uOm2em5FH7fgIQf2qLc0z62o4z546I0INVX0cUsyvaj5HO21Otq4xU23tK6UR02ONq4tatLdvofEKUuR9fUPcG/dFMExM+DWuIHdP1McaRmW17hKP1PsXW9FqKmknyeW5uUytEyYhqtRo0bYuHGj2WOXLl3CnDlzMGPGDISHh6NWrVqoU6cODh8+jC5dupjmO3jwINq0aWPa89ShQwcsX74cp06dQtu2bQHcb7YuXryIYcOGlapOJcdrUvkwHmZA4mAm4mEmYmIu5UvOy0fqiu1Iid4IlbcnqqyYAu8XnjH74sRMxMNMxMNMlBGm4fL19UWrVq0sTmvcuDEaN75/J/fRo0dj3LhxqF27Nlq1aoWDBw/i3Llz2Lx5s2n+5s2bIyIiAhMnTsT48ePh5uaG6OhohIaGomvXruXyeoiIiKj85eTk4OrVq2jQoAEAmP4t/+ccEicuhPbmP/B7sx8CPnwDKh8vO1dLRM5AmIarpHr27ImcnBzExMRg9erVqFu3LpYuXYrmzZubzbdw4ULMmTMHU6dOhU6nQ0REBCZPnlzssZhERETk2HJycnDhwgXUqFEDAHD5+H/g9/EGaL89Dfd2zVF1w8dwbVjXzlUSkTORZB4jV2Lnz5+HLMsIDw9XdNwm2Y7xyjTGK9yQ/TET8TATMTEX20hOTsaRI0fQtmU7ZKzaDddNX8IlwA9Bs0fD+/nOxS5rZiIeZiIeZvI/58+fBwCEh4cXOx939xAREZHDsXToYGBgIBITE+Fy+iKSp26GV1oGLj3eAMEThkKqXR3alBQEBATYuXIicjZsuBSSJMnpu3mRGC/nyUzEwUzEw0zExFxK58FDBy9cuACf9Bz4rT+Ix6/dw52aAfg+sjXSK3nB/dRJaH5V47HHHkNkZGSRYzIT8TAT8TAT5dhwkcPjCi8eZiIeZiIm5lI6Wp0esbfTEOShQo2Dv6DG93/C4O+NPwe0x291GuOfhCzU1GSiY6cOCA4KLNG9OJmJeJiJeJiJMmy4rFDSu0qT7cmyDL1eD7VazUwEwUzEw0zExFysk5ycjLS0NNxLSMK1m/dw58A6PHvmLKplZiG/fxe4vtUPeef+wJvtOyIhJQd//fETQhrUK9GhhMxEPMxEPMxEOTZcCvEaI+IxGAxF3kGc7IOZiIeZiIm5KPfzzz/j4sWLcEtIR6dvz6HmrSTcrh2IY8+FQ1vNDQ3+ewkA4O3pCm9PV1y+oGz5MhPxMBPxMBNl2HARERGRw2jVtDlCf/gL8qYTyPJww67OTyHjiUao4ZWJzu3aoHLlykhKSoKHhwcAICwszPRvIiJ7YMNFREREwpNlGVn7jyNz6lJICSlwf/MF/FzbG11aPo2gSh746cdjqFmzJgICAlC9enXT8x52uWYiIltjw0VERERCy796C4kTFiLnh1/h+UwbVN7zHjL8PKA+cgR1azz8QhhERPbEhkshnhwoHh5DLB5mIh5mIibmUjxDZjZSFmxE6srtcKkehKqb58KrWzsAgEdOjtnhgmV16CAzEQ8zEQ8zUYYNlxXYdIlDkiSu9IJhJuJhJmJiLkWTZRlZXx5D4rRlMCSnotL7g+E/8hWoPNxM83h4eJgdLlgWhw4yE/EwE/EwE+XYcFmBl4UXhyzLpjyYiRiYiXiYiZiYi2X5/71+//DBH3+DZ/cIVJ41GppHqj/8iWWAmYiHmYiHmSjHhkshXhZePDqdDhqNxt5lUAHMRDzMREzM5X8MmdlI/nQ90lbvhKZWNVT94lN4dWld7nUwE/EwE/EwE2XYcBEREZHdyLKMzD3fImnaMhjSMxHw4RD4vTMAKne3hz+ZiMgBsOEiIiIiu8i7FIvEqGjk/ucPePV8CoEzR0FTq6q9yyIiKlOKG674+Hh89913+O2333Dt2jWkpKRAkiRUqlQJ9erVw+OPP45OnTqhVq1atqiXiIiIHJw+PRMp89Yhbc0eaOrWQLUdn8GzY0t7l0VEZBMlbriOHTuGdevW4ezZs5BlGbVr10bNmjUREhICWZaRnp6Ov//+G0ePHsXcuXPRokULDB06FB07drRl/eWOJweKR6VS2bsEegAzEQ8zEZOz5SLLMjJ3HkHS9BUwZOUgYOJw+I/oD8lVnHNBnC0TR8BMxMNMlClRw9W/f3/8/fff6Ny5MxYuXIi2bdvC29vb4ryZmZn46aefcOTIEYwZMwYNGzbE9u3by7Roe2PTJQ5JkuDiwiNjRcJMxMNMxORsueRduIrE8QuQ+8t5ePXuhMozR8KlehV7l2XG2TJxBMxEPMxEuRItrVatWmH58uWoXLnyQ+f19vZGt27d0K1bNyQkJGDjxo2lLpKIiIgckz4tA8lz1iB9/T5oGtRCtd3R8OzwhL3LIiIqN5LM65yX2Pnz5yHLMsLDw7mXSxCyLEOr1UKj0TATQTAT8TATMVX0XGSDARnbDiFp1krIOXkI+GgI/Ib3g6QR9y/jFT0TR8RMxMNM/uf8+fMAHn7jdXG3ekREROSQ8v78LxKiopF35i94v/AMAqe/A5eqDz9KhoioIipxw/XXX38pHrxx48aKn0NERESOSZ+SjuSPVyN9w1dwbVgX1fcthke75vYui4jIrkrccL3wwguKdhtKkoSLFy9aVRQRERE5DtlgQMaW/UiavRrQ6hA4azT8hvQR+vBBIqLyUuIt4Zw5cx46T25uLnbs2IFLly6VqigiIiJyDLm/X0Li+Gjk/X4J3v2fReDUEXAJDrR3WUREwihxw9WnT58ip+Xn52Pbtm2IiYlBQkICnnzySYwePbpMChSNs58cKCKNRpz7t9B9zEQ8zERMjpyLPikVSf+3Ghmb98P1sfqo/vUyeLRuYu+ySs2RM6momIl4mIkypdrXn5+fjy+++AJr1qxBQkICWrZsiQULFuDJJ58sq/qExKZLHMxCPMxEPMxETI6ai6zXI33jV0j+OAYwyKj88Xvwfb03pApwXx5HzaQiYybiYSbKWbV1zM/Px9atW7F27VokJCSgVatWTtFoGcmyzDebIGRZhl6vh1qtZiaCYCbiYSZicsRccs/8hYTxC5B/7jJ8Xo5EwJQRcAmqZO+yyowjZlLRMRPxMBPlFDVceXl5pj1aiYmJaNWqFaKjo/HEE85zA0Petkw8BoMBarXa3mVQAcxEPMxETI6Siy4hBcmzViLji4NwbRKCGgdXwP3JMHuXZROOkokzYSbiYSbKlLjh+vzzz7FmzRokJSWhdevWWLRoEVq0aGHL2oiIiMiOZJ0O6Z9/ieS5awBJQuVPP4DvoOcg8YsWEVGJlbjhmjt3LiRJQqNGjVC/fn0cOnQIhw4dKvY5kydPLnWBREREVP5yTp9D4vho5F+8Bp+BPRE46U2oA/3tXRYRkcNRdEihLMu4ePFiie6vJUkSGy4iIiIHo7uXhKSZK5G54zDcmjdCjSOr4N68kb3LIiJyWCVuuP7++29b1uEweHKgeHgMsXiYiXiYiZhEykXW6ZC2Zg9S5q0DNC4IWvAhfF7tCUmlsndp5UqkTOg+ZiIeZqKM41/D1Q7YdIlDkiSu9IJhJuJhJmISKZec//yBxKho5P99Hb6v90bAhOFQV/K1d1nlTqRM6D5mIh5mohwbLivwsvDikGXZlAczEQMzEQ8zEZMIuejuJiJp+nJk7v4Gbk80Rs1vYuDWNNQutYhAhEzIHDMRDzNRzuqG68svv8Tu3bsRHx+PtLS0QpdLlyQJZ8+eLXWBouFl4cWj0+l4x3PBMBPxMBMx2SsXWatDWswuJM9bB8nDDUGLouDzUnenO3zQEq4r4mEm4mEmyljVcH366adYt24dgoODERYWBh8fn7Kui4iIiGwg+8ezSIyKhvZqHHzfeB4BUcOg9ufnOBGRrVjVcO3cuRNPP/00li1bBhX/GkZERCQ83Z1/kTh1GbK+/B7urZog+LvpcAtrYO+yiIgqPKsPKXzqqafYbBEREQlOztcideUOpHy2ASovD1RZNgneL3bjuRdEROXEqo7p6aefrpDnZ5UEP6DEw0zEw0zEw0zEZOtcso/9grgOryH54xj4DuqJWj9vgU//Z/l+KAaXjXiYiXiYiTKSbMVVIDIyMjBixAiEhobihRdeQLVq1Szu7fL39y+LGoVx/vx5AEB4eLidKyEiIiqaNv4ekiYvQdaB43Bv0xSV546F22P17V0WEVGFUtLewKpDCj08PNC8eXOsXbsWX3zxRZHzXbp0yZrhiYiIyApyXj5Sl21DysKNUPl6o8qqafDu05l/jSYisiOrGq6ZM2di586daNq0KZo2bep0VynkfbjEIcsydDodXFxcmIkgmIl4mImYyjqXrG9/RtLERdDG/QO/t15EwLg3oPL2LINKnQfXFfEwE/EwE+WsargOHTqE3r17Y+7cuWVdj/B4Hy7xMBPxMBPxMBMxlUUu2pt3kDhlCbIPnYRH+8dRddPHcA2tWwbVOSeuK+JhJuJhJspY1XC5uLigadOmZV0LERERlZAhJw+pS7cidfFmqCr5IThmBrx6d+RfnImIBGPVVQp79OiBY8eOlXUtREREVAJZR35CXIfBSIneCL/hL6L2fzbD+/lObLaIiARk1R6u7t27Y/bs2XjzzTdNVylUq9WF5mvcuHGpCyQiIqL7tNdvI3HSImR/cwoeTz+Jal98CtcGte1dFhERFcOqhuvVV18FcP8qhD/++GOh6caLSlTEqxTyr4ficXGx+v7dZCPMRDzMREwlzcWQnYvUxZuRuvQLqIMqIXj9bHj16MDPJBvguiIeZiIeZqKMVUtrzpw5ZV2HQ+EHnDgkSWIegmEm4mEmYipJLrIsI/vQj0icvAS6e0nwH/kyKo0ZBJWnezlV6Vy4roiHmYiHmShnVcPVp0+fsq7DofCy8OKQZRkGgwEqlYqZCIKZiIeZiOlhueRfu4XEiYuR8/1peHZujWo7F8C1fi07VOo8uK6Ih5mIh5kox/2BCvEymOLR6/VQqay6/gvZCDMRDzMRk6VcDFk5SIneiNQV2+FStTKqbvwYns9G8ItNOeG6Ih5mIh5mokyJltTUqVMRFxenePBbt25h6tSpip9HRETkbGRZRuZXxxDXbiDSVu5ApfcGotbJTfDq3p7NFhGRAyvRHq5//vkH3bt3R+vWrREZGYk2bdqgWrVqFueNj4/HqVOncOjQIZw+fRrt2rUr04KJiIgqmvwrN5E4YSFyjp+BZ7d2qDz7XWjqVLd3WUREVAZK1HDFxMTg7NmzWLduHaZOnQq9Xg9/f3/UqFEDfn5+kGUZaWlpiI+PR3p6OtRqNTp06IANGzbgiSeesPVrICIickiGzGwkLd6KtFU74FIzGFW3fAKvrm3tXRYREZWhEp/D1aJFC7Ro0QLJyck4duwY/vjjD8TGxuLu3bsAAH9/f3Tt2hXNmjXD008/jcDAQJsVbU88rEM8lu4BR/bFTMTDTMQiyzIy936HpGnLYEhNR6Vxr8N/5MtQubvZuzSnx3VFPMxEPMxEGUnmVSBK7Pz58wCA8PBwO1dCRESOKv/v60iYsBC5J3+DV48OCJw5Cpralg/TJyIicZW0N+BVCq3Ay8KLo+DfC5iJGJiJeJiJGAwZWUj+dD3SYnZBU7saqm6bD89OLe1dFhXAdUU8zEQ8zEQ5NlwKcYegeLRaLTQajb3LoAKYiXiYif3IsozMXUeRNH05DJnZCBg/FP5vDwBcNcxFQMxEPMxEPMxEGTZcRERENpL311UkRi1E7s9/wuu5p+8fPlgzGAD/gEdE5CzYcBEREZUxfVoGUj5Zh7R1e6GpWwPVdkXD8yletZeIyBmx4SIiIiojssGAjB1HkDxzBQxZuQiY/Cb833wRkisPvSEiclZsuBTiyYHiYSbiYSbiYSa2l3f+ChLHL0Durxfg3aczAmeMhEu1oGKfw1zEw0zEw0zEw0yUUZV0xgULFuDvv/+2ZS0Og28ycUiSBI1Gw0wEwkzEw0xsS5+agYTx0YjvMgz69ExU37sIwaunl6jZYi5iYSbiYSbiYSbKlbjhWr16Na5cuWL6OSUlBY0aNcKpU6dsUhgREZHIZIMB6Zv341brl5Gx4zACp7+NWsfWwyPicXuXRkREAinVIYXOeoUl3odLHLIsQ6fTwcXFhZkIgpmIh5mUvdw//kZiVDTyzl6E94tdETj1bbhUraxoDOYiHmYiHmYiHmaiXIn3cJWH48ePY+DAgWjdujXCwsLQuXNnzJkzBxkZGWbzff/99+jVqxfCw8PRrVs37N69u9BY+fn5+OSTT9CuXTs0a9YMb7zxBmJjY0tdo7M2mSJjJuJhJuJhJmVDn5yGhHHzcbvrm5BzclH9q6UIXj5FcbNlxFzEw0zEw0zEw0yUEeqiGampqWjSpAkGDRoEf39/XLlyBUuWLMGVK1ewbt06AMCZM2cwatQo9OvXDxMnTsTPP/+MSZMmwcvLC88++6xprNmzZ+PgwYOIiopCcHAwVq5ciddffx0HDhyAj4+PvV4iERE5IFmvR8aWA0iavQrQ6RE4+134DXkekotQH6NERCQgRZ8Ut2/fxl9//QUApr1ON2/ehK+vr8X5GzdurKiY3r17m/3cqlUruLq6YsqUKbh37x6Cg4OxYsUKNGnSBDNnzgQAtG7dGnFxcVi8eLGp4bp79y527dqFadOmoV+/fgCA8PBwdOzYEdu2bcPw4cMV1UVERM4r9+xfSIxaiLw//obPgGcRMPVtuFQJsHdZRETkIBQ1XIsWLcKiRYvMHpsxY0ah+YznOF26dKl01QHw9/cHAGi1WuTn5+P06dMYN26c2TyRkZHYv38/4uPjUbNmTZw8eRIGg8Fsj5e/vz/atWuHEydOsOEiIqKH0iemIGn2KmRsOQDXsEdR48ByuLcMt3dZRETkYErccM2ZM8eWdZjR6/XQ6XS4evUqli1bhk6dOqFmzZq4evUqtFot6tWrZzZ//fr1AQCxsbGoWbMmYmNjERgYCD8/v0Lz7dq1q1S18eRA8bjwkB7hMBPxMJOSk/V6pG/4CslzYgBZRuVP3ofva70gqdVl/ruYi3iYiXiYiXiYiTIlXlp9+vSxZR1mOnbsiHv37gEA2rdvj88++wwAkJaWBgCFDmE0/mycnp6ebvE8LV9fX9M8pfXgyYKSJBV5AqGxSStuuq2e6wzjSpJkt3pL89yKPG7Bx0SpqSKP+7DnFjWfI75WW4+b++sFJEZFI//8Ffi82gOBk9+CunIlyLJc5PantNlYmseRl2FZj1veNdnzM6WiLMOyHlfJZ4oI9Tp6TSXZZlnzmeIsy9ASIdvT1atXIycnB1evXsWKFSswYsQIrF+/3t5lAbi/4PPz883edCqVytTpa7XaQs9xdXUFcH/PncFgMJtmvKSmwWCAXq83m2YcV5Zli+NqNJoix1Wr1VCr1aZLdxZkvGEdAOh0ukJvGFuNW5LXChRehsWNK8uy6blKxzW+VkmSFL/WkowLlO8yNI5rqSZbZWN8rQWXofGLqfF3GjdKRS1De72/i1uG5fn+Bmy/jdDpdNDr9aYvkqV9rY60jSjpuLp/k5E4Yzmydx6FpkkIqny9FG6PN4LKytdakvXG+HlifA22XoaibCMsjSvKNkKWZdNznWkbIfL3CEufKfbYRlga1/hane17hFarhcFgMPtMcZZtRMFxjd95Cm6/iyJkw9WwYUMAQPPmzREeHo7evXvjm2++QYMGDQCg0GXi09PTAcB0CKGvry8yMzMLjZuenl7oMENrFHd3bWMIlhR8Iz9IpVJBpbJ8lf6CwSsd92HPLW6XsK3GLe61AsUvwwfHLbiSWTPug18+LXnYa7VHNmW5DEs7rqUv8Fqt1mw9Ke612uv9XZp1zlbZ2HIbYTAYLG67Kvo24mHjyjodUlfvQsonawG1CpU//QA+A3uaDh+09TbCON3SZ4qjLMOHjVvaZVie2wjj9kutVjvdNkLU7xGWPlMq0vu7qHGNRN3OPphJacZ1pG1EQSqVqkTNFiBow1VQaGgoNBoNbt26hU6dOkGj0SA2Nhbt27c3zWO8v5bx3K569eohMTERaWlpZg1WbGxsofO/rFGwo3/w8Yc9z5pppXmuM4xb8Et9WY5r6+dW5HGN60hJsimvmiryuCV57oOZ2LomRxg359SfSJwQjfyLsfAd3AsBE4dDHWD5j3K2qMn4l9Hy/kxxtHHLuyZ7fqZUlGVY1uMq+UwRoV5Hr6kk41rzmeJMy/BBQt342JI///wTWq0WNWvWhKurK1q1aoUjR46YzXPw4EHUr18fNWvWBABERERApVLh6NGjpnnS0tJw8uRJdOjQoVzrJyIisejuJuLeO7Nwp9coSG6uqHF0NYLmjyuy2SIiIioNofZwjRo1CmFhYQgNDYW7uzv+/vtvrF27FqGhoejSpQsA4O2338bgwYMxffp0dO/eHadPn8b+/fsRHR1tGqdq1aro168f5s2bB5VKheDgYKxatQo+Pj546aWX7PXyiIjIjmStDmlrdyP5k3WQ3DQIih4Pn1ciIRVz+AsREVFpCdVwNWnSBAcPHsTq1ashyzJq1KiBF198EUOHDjWdMPrEE09gyZIlWLhwIXbt2oXq1atj9uzZ6N69u9lYkydPhpeXFz777DNkZWXh8ccfx/r16y1evVAJJbsPqXwUd6ww2QczEY+zZ5Lz0+9IiIqG9vJN+L7+PAKihkJdyffhT7QxZ89FRMxEPMxEPMxEGUlWck3DB2i1WsTGxiIjI8PipRGffPLJUhUnmvPnzwMAwsN540siIkeg+ycBSdOWIXPvd3B7MgxBc8fCrUmIvcsiIqIKoKS9gVV7uAwGAz777DNs3boVubm5Rc536dIla4YXXkkvAUm2Z+k+HWRfzEQ8zpiJnK9F6uqdSJn/OVSe7ghaMhE+/bsJdfigM+YiOmYiHmYiHmainFUN18qVK7F27VoMGDAALVq0wEcffYRx48bB19cXW7duhSRJ+PDDD8u6ViGUYocg2Yjx0qQkDmYiHmfKJPvEGSRGLYT2Whz8hvZFpfFDoPYr3eHktuJMuTgKZiIeZiIeZqKMVX/q27t3L7p3744ZM2aYLs/euHFj9O/fHzt27IAkSfj555/LtFAiIqLi6G7fw92hU/HPC2OhDvRHze/XovLH7wnbbBERkXOwquG6e/cuWrduDeB/dz/Pz883/dyrVy98+eWXZVQiERFR0eS8fKQs3IRbbQci9+c/UWXFFFT/agncGjewd2lERETWHVLo7++P7OxsAICXlxe8vb0RFxdnNk96enrpqyMiIipG9venkThhIbQ3/4Hfm/0Q8OEbUPl42bssIiIiE6sarscee8x0VQ4AaNWqFTZs2IBGjRpBlmVs3LgRoaGhZVYkERFRQdq4u0iasgRZB07AvV1zVN3wMVwb1rV3WURERIVYdUhh//79kZ+fbzqMcOzYsUhPT8fAgQMxcOBAZGVlISoqqkwLFYUkSbwii0AkSYJGo2EmAmEm4qlImRhy85Dy2QbEtRuI3LMXUWX1NFTfu8ghm62KlEtFwUzEw0zEw0yUK9V9uArKyMjA6dOnoVar0bx5c/j7+5fFsELhfbiIiOwn6+h/kDhpMXTxd+E/oj8qffA6VN6e9i6LiIiclE3vw2WJj48PunTpUlbDCY334RKHLMvQ6/VQq9XMRBDMRDyOnon2xh0kTl6M7CM/weOpJ1Bt6ydwffQRe5dVao6eS0XETMTDTMTDTJSz6pDCzp07Y8CAAYiNjbU4/dtvv0Xnzp1LVZioeB8u8RgMBnuXQA9gJuJxxEwMOXlInrcOcRGDkH/hCoLXzkS1nQsqRLNl5Ii5VHTMRDzMRDzMRBmrGq7bt2/jr7/+wosvvohvv/220PTs7GzcuXOn1MUREZHzkWUZWYdPIq79IKQs3AS/Ef1R66fN8O7VkX9NJSIih2NVwwUAEyZMwJNPPonRo0dj4cKFZVgSERE5K21sPO6+/BHuDpoATb1aqPXjBgROfgsqLw97l0ZERGQVqxsuX19frFy5EiNHjsTq1avx5ptvIiMjoyxrIyIiJ2HIzkXSxzG41X4w8i/fQNUN/4dq2+fDtX5te5dGRERUKlY3XEajRo3CypUr8eeff6Jfv364cuVKWdQlLB7OIh4XlzK79guVEWYiHlEzkWUZmfuPI67dQKQt34ZKo19BrZOb4BXZwSm2t6Lm4syYiXiYiXiYiTKlbrgAoEOHDti1axc8PDzQv39/fPfdd2UxrLCc4UuAo5AkCSqVipkIhJmIR9RM8q/ewj/9P8C9NybDtVE91DqxAQFRw6DydLd3aeVC1FycGTMRDzMRDzNRrkwaLgCoVasWtm/fjq5du+LIkSNlNayQeKVCcciyDIPBwEwEwkzEI1omhsxsJM1cibgOr0F7PR5VN89Fta3zoKlX096llSvRciFmIiJmIh5mopxV+wM3btyIBg0aFHrczc0Nn3zyCSIjI5GcnFzq4kTEN5d4dDodNBqNvcugApiJeETIRJZlZH15DInTlsGQnIpK7w+G/8hXoPJws2td9iRCLmSOmYiHmYiHmSijuOHKycnB3Llz8eKLL+Lll1+2OM9TTz1V6sKIiKjiyL98A4kTFiLnxFl4do9A5VmjoXmkur3LIiIisjnFDZeHhwfi4+N53CYRET2UITMbyfPXI23VTmhqVUPVrfPg9Uwbe5dFRERUbqw6h6t9+/Y4efJkWddCREQVhCzLyNjzLW61eRXp6/Yi4MMhqHniczZbRETkdKxquN555x3cuHEDH374Ic6cOYN79+4hNTW10H8VEffsiUelKrNrv1AZYSbiKc9M8i7F4s7z7+Lft2bAvUVj1PppMyq9Pxgqd+c9V6soXFfEw0zEw0zEw0yUseqiGT169AAAXL16Ffv37y9yvkuXLllXleDYdIlDkiTeC0IwzEQ85ZWJPj0TKfPWIW3NHmjqVEe1HZ/Bs2NLm/9eR8V1RTzMRDzMRDzMRDmrltbIkSPZdJAwZFnm+1EwzEQ8tsxElmVk7jyCpOkrYMjKQcDE4fAf0R+SK69g9TBcV8TDTMTDTMTDTJSxquEaPXp0WdfhMGRZ5ptMILIsQ6vVQqPRMBNBMBPx2DKTvAtXkRgVjdzT5+DVuxMqzxwJl+pVyvR3VFRcV8TDTMTDTMTDTJQrk/2Bubm5AAB3d/eyGI6IiASnT8tAyty1SFu3F5oGtVBtdzQ8Ozxh77KIiIiEY3XDdefOHSxZsgTHjx9HSkoKAKBSpUp46qmnMGrUKNSoUaPMiiQiIjHIBgMyth1C0qyVkHPyEDh1BPyG9+Phg0REREWwquG6du0aXnnlFWRkZKBt27aoX78+ACA2NhZffvkljh07hq1bt6JevXplWiwREdlP3p//RcKEhcj79QK8X3gGgdPfgUvVyvYui4iISGhWNVyfffYZVCoV9u7di9DQULNply9fxuuvv47PPvsMy5YtK5MiiYjIfvQp6UieE4P0z7+Ea8O6qL5vMTzaNbd3WURERA7Bqobr119/xRtvvFGo2QKAkJAQvPrqq/j8889LW5uQeHKgeDQaHsokGmYiHmsykQ0GZGzZj6TZqwGtDoEzR8FvaF9IGl4OuKxwXREPMxEPMxEPM1HGqk9NnU5X7AUyPDw8oNPprC5KdGy6xMEsxMNMxGNNJrm/X0Li+Gjk/X4J3v27IXDq23AJDrRBdc6L64p4mIl4mIl4mIlyVt0mulGjRti5cycyMjIKTcvMzMSuXbvw2GOPlbo4UcmybO8S6P+TZRk6nY6ZCISZiEdJJvqkVPz7/jzc7vYW5Hwtqn+9DMHLJrPZsgGuK+JhJuJhJuJhJspZfR+u4cOHo3v37ujbty/q1KkDALh+/Tr27t2L1NRUTJ06tSzrFAbfXOIxGAxQq9X2LoMKYCbieVgmsl6P9E1fI/njGEBvQOWP34Pv670hufDwQVviuiIeZiIeZiIeZqKMVZ+kbdq0werVqzFv3jysXr3abFqjRo3w6aefonXr1mVSIBER2Vbumb+QMH4B8s9dhs/LkQiYMgIuQZXsXRYREVGFYPWfLtu2bYt9+/YhISEBd+7cAQBUr14dQUFBZVYcERHZjj4xBUmzViFj6wG4NglBjYMr4P5kmL3LIiIiqlBK3HAtWLAAkZGRaNiwodnjQUFBbLKIiByIrNMh/fMvkTx3DSBJqDzvffgO7gWJh4cQERGVuRJfNGP16tW4cuWK6eeUlBQ0atQIp06dsklhouKVWcTDY4jFw0zEY8wk5/Q5xHcZjsSJi+DVqyNq/7wVfm/0YbNlJ1xXxMNMxMNMxMNMlCnV2dDOegEJNl3ikCSJK71gmIl4JEmCnJSGf2esQOaOw3Br3gg1Dq+E++MV92qyjoDriniYiXiYiXiYiXK8/JQVZFlm0yUIWZZNeTATMTATscg6HdLW7kHyJ+sgaVwQtOBD+LzaE5LKqruCUBniuiIeZiIeZiIeZqIcGy6FnHWvnsh0Oh3veC4YZiKGnP/8gcSoaOT/fR1eA3ui8uS34BLgZ++yqACuK+JhJuJhJuJhJsooarhu376Nv/76CwBMNz2+efMmfH19Lc7fuHHjUpZHRERK6e4mImn6cmTu/gZuLR5DjaOroXqsHtT8cCQiIip3klzCXTYNGzYstNuwqEPrjI9funSpbKoUxPnz5yHLMsLDw7kLVRCyLEOr1UKj0TATQTAT+5G1OqTF7ELyvHWQPNwQOGUEfF7qDkgSMxEQ1xXxMBPxMBPxMJP/OX/+PAAgPDy82PlKvIdrzpw5pauIiIhsJufkb0iIiob2yi34vvE8AqKGQe3vA4CHQhMREdlTiRuuPn362LIOh+HsnbyImIl4mEn50d35F4lTlyHry+/h3jIcwd+ugVv4o4XmYyZiYi7iYSbiYSbiYSbK8KIZVuCbTBySJPGkTcEwk/Ih52uRunIHUj7bAJWXB6osnQTv/t0sbp+YiZiYi3iYiXiYiXiYiXJsuIiIHEz2D78iccJCaK/fht+wvqj00RCofb3tXRYRERFZwIbLCrwPlzhkWYZOp4OLiwszEQQzsR1t/D0kTVmCrP3H4d6mKYLXzoTbY/Uf+jxmIibmIh5mIh5mIh5mohwbLoV48rl4mIl4mEnZkvPykbp8G1KiN0Ll640qK6fCu28XRR90zERMzEU8zEQ8zEQ8zEQZNlxERALL/u40EicuhPbWP/B780UEjHsdKh8ve5dFREREJcSGi4hIQNpb/yBx8mJkHzoJj/aPo+rGj+EaWtfeZREREZFCbLiIiARiyM1D6tKtSF20GapKfgiOmQGv3h15nDwREZGDYsOlEL/0iIeXJhUPM7FO1tGfkDhpMXS3/4X/iAGo9P5gqLw9y2RsZiIm5iIeZiIeZiIeZqIMGy4rsOkSB7MQDzNRTnv99v3DB4/+Bx5PP4lqX3wK1wa1y2x8ZiIm5iIeZiIeZiIeZqIcGy4r8LLw4pBlGQaDASqVipkIgpmUnCE7F6mLNyN16RdQV/ZH8PrZ8OrRocyXGzMRE3MRDzMRDzMRDzNRjg2XQrwMpnj0ej1UKpW9y6ACmEnxZFlG9qEfkTh5CXT3kuA/8mVUem8gVF4eNvudzERMzEU8zEQ8zEQ8zEQZNlxEROUo/1ocEicuQs73p+HZuTWq7VwA1/q17F0WERER2QgbLiKicmDIykHKwk1IXb4NLlUro+rGj+H5bAQPxyAiIqrg2HAREdmQLMvI+voHJE1dCn1iKiq9+yr83x0IlYebvUsjIiKicsCGSyH+NVo8arXa3iXQA5jJfflXbiJxwkLkHD8Dz27tUHnWaGjq1rBLLcxETMxFPMxEPMxEPMxEGTZcVmDTJQ5JkrjSC4aZAIbMbKQs2IDUlTvgUqMKqm75BF5d29qtHmYiJuYiHmYiHmYiHmaiHBsuK/Cy8OKQZdmUBzMRgzNnIssysvZ9j8Rpy2BISUOlD16D/8iXoXK37+GDzpyJyJiLeJiJeJiJeJiJcmy4FOJl4cWj0+l4x3PBOGMm+X9fR8KEhcg9+Ru8ItsjcNZoaGpXs3dZJs6YiSNgLuJhJuJhJuJhJsqw4SIiKgVDRhaSP12PtJhd0NSuhmrb5sOzcyt7l0VERESCEOqOZYcOHcLbb7+NDh06oFmzZujduzd27dpVaK/Szp070a1bN4SHh6NXr144duxYobEyMjIwceJEtGzZEs2bN8e7776Lf//9t7xeChFVcLIsI2PXUdxq8yrSN3yJgPFDUevEBjZbREREZEaohuvzzz+Hh4cHoqKisGLFCnTo0AFTpkzBsmXLTPMcOHAAU6ZMQffu3RETE4NmzZph1KhR+OOPP8zGGjNmDH766SdMnz4d8+fPx/Xr1zF8+HDodLpyflVEVNHkXbyGO71H49+3Z8G9ZThq/bQZlcYMguTmau/SiIiISDBCHVK4YsUKBAQEmH5u06YNUlNTsX79erzzzjtQqVRYvHgxevTogTFjxgAAWrdujcuXL2PZsmWIiYkBAPz+++84efIk1q5di4iICABA3bp1ERkZiaNHjyIyMtLqGnlyoHiYiXgqaib69EykfLIOaWv3QFO3BqrtXADPp5+0d1klUlEzcXTMRTzMRDzMRDzMRBmh9nAVbLaMGjVqhMzMTGRnZyMuLg43btxA9+7dzeaJjIzEqVOnkJ+fDwA4ceIEfH190a5dO9M89erVQ6NGjXDixIlS18k3mTgkSYJGo2EmAqmImcgGA9K3HUJc61eQvnk/Aia/iVrHP3eoZquiZVIRMBfxMBPxMBPxMBPlhNrDZcnZs2cRHBwMb29vnD17FsD9vVUF1a9fH1qtFnFxcahfvz5iY2NRt27dQm+EevXqITY2ttxqJyLHl3f+ChLHL0Durxfg3aczAmeMhEu1IHuXRURERA5C6IbrzJkzOHjwIMaPHw8ASEtLAwD4+vqazWf82Tg9PT0dPj4+hcbz8/PDhQsXSlWTLMswGAyFmjlJkoq8ZLxx3uKm2+q5FX1cWZYfemlSZlO+4xozcXFxMd2jw941leS5OTk5uHr1Kh599FG4u7sj6+6/iJ+yGJqvT0LzaG1U27MQnu1bCFOvkucaDAazTMqjJo778OcCgFarLZSLrWtytHHLsyZ7f6ZUhGVY1uMq/Uyxd70VoaaHjWvtZ4qzLENLhG247t69i7Fjx6JVq1YYPHiwvcsxo9Vqzd5gKpUKLi4upmkPcnW9fyK9Xq+HwWAwm2Z8sxoMBuj1erNpxnFlWbY4rvEDwdK4arUaarXatKEqyLgrGLh/H4UH3zC2GrckrxUovAyLG9d48z0Aisc1vlZJkhS/1pKMC5TvMjSOa6kmW2VjfK0Fl6HxdcmybJpW3DK01/v7wdeakZGBCxcuoEb16sjf/S2SZq2ElJUD1w8GocrIV6By/d8XsLJehuWxjdBqtaYbVZbVMnSEbURpxjW+VltvIwrm8rBxK8I2wtK4omwjCv67uGVYnu9vwLm/R1j6THG2bYSI3yMe/Exxlm1EwXGN33ke/IOZJUI2XOnp6Rg+fDj8/f2xZMkSqFT3TzXz8/MDcP+LUVBQkNn8Baf7+vri7t27hcZNS0szzVMaxR23WtxfxQq+kR+kUqlMr/NBBYNXOu7DnmtcAcpz3OJeK1D8Mnxw3IIrmTXjPvjl05KHvVZ7ZFOWy7C041r6Am+c1zituNdanu/vnJwc/P3333j00UchyzKuXr2KBg0awMPDAy4uLvCMS0Ba/48gn78CKbI9zrWui079+8DV06PYcQsqTTa23Ea4uLhY3HZV9G1Eace19TZCpVIV+ZnCZfjwaaUZF7D+M8Ve2Tjj9whLnykV6f1d1LhGom5nCzbApR3XkbYRBalUqhI1W4CADVdubi7eeustZGRkYPv27WaHBtarVw8AEBsba/q38WeNRoNatWqZ5jt16lShrvP69esICQkpdY3GXdqWHn/Y86yZVprnOsO4Bb/Ul+W4tn5uRR7XuI6UJJvyqgm4v33566+/ULlyZVy9etX07+x//kXi7FUIO3ASSZUD8FX3SMhNHkENZOD27duQJMnsoj6iZVOS5z6Yia1r4rgPn278jCrvzxRHG7e8a7LnZ0pFWYZlPa6SzxQR6nX0mkoyrjWfKc60DB8k1FUKdTodxowZg9jYWKxZswbBwcFm02vVqoU6derg8OHDZo8fPHgQbdq0Me1y79ChA9LS0nDq1CnTPNevX8fFixfRoUMH278QIhJacmoG/jx/EempacjddhjJnYcDR3/G2fahONCvGVT1tFBnXEFCwj0cPnwYP//8s71LJiIiIgcl1B6uGTNm4NixY4iKikJmZqbZzYwfe+wxuLq6YvTo0Rg3bhxq166NVq1a4eDBgzh37hw2b95smrd58+aIiIjAxIkTMX78eLi5uSE6OhqhoaHo2rVrqWpU0s1S+Shudy/Zh4iZJCcnIz4+Hqmp6dh24BQCr19Hx+MXIP+bDkPXVkh99Rkk37wBXbYP4u7loVawG6p6ZqBduzaoWbOmvcsvNREzIeYiImYiHmYiHmaijCQrucSGjXXq1Am3b9+2OO27774zfenZuXMnYmJicOfOHdStWxfvv/8+OnbsaDZ/RkYG5syZg2+++QY6nQ4RERGYPHlyob1mSpw/fx4AEB4ebvUYRGQfxj/OIDUDzU5dRYOLd5BS2Ru/PPUY0h+pjOrVqwMAOnbuhly9G9zVefjpx2Po1q2bxXsEEhERkXMraW8gVMMlOuNCDQsL454uQcjy/cv0KzlxkWxL1EySEhJwdvJnqHngNADgj1b1caVxTeglFdzdXKFWS3Bzc8PgwYMREBCA5ORkHDlypEI0XKJm4uyYi3iYiXiYiXiYyf+UtOHi/kCF2J+KR6/XF3tFHCp/omWS++sFZI9fgPrnr+DOE48id2hf/H3xL8j6PMi+DdCrewu4qGSkpKTAw+P+FQk9PDwQFhZm+tnRiZYJ3cdcxMNMxMNMxMNMlGHDRUQVli4hBckzVyBj2yG4NQ2Fz455iL97A61bNEZs8h0kJ6fgue4tEPZYaKHnenh48PBhIiIiKjU2XERU4cg6HdLX70Py3LWAWoXK88fBd2BP5ObnI+yqN9zc3OCq0cDXxxsB/j4PH5CIiIjISmy4iKhCyTn1JxInRCP/Yix8Bz2HgElvQh1w/4bnxr1WOTk5aNKkCQCgUqVK9iyXiIiIKjg2XAo5+8mBIuIxxOKxRya6u4lImrkCmTuPwu3xRqhxdDXcmzW0OK+HhwdatGhRzhXaF9cTMTEX8TAT8TAT8TATZdhwWYFNlzgkSeK9IART3pnIWh3S1u5G8ifrILlpEBQ9Hj6vRELih4EJ1xMxMRfxMBPxMBPxMBPluLSsIMsymy5BFLxqJDMRQ3lmkvPT70iIiob28k34vtYbAROGQV3J16a/0xFxPRETcxEPMxEPMxEPM1GODZdCvCy8eLRaLTQajb3LoAJsnYnubiKSpi1D5p5v4fZkGGp+EwO3JiE2+30VAdcTMTEX8TAT8TAT8TATZdhwEZHDkPO1SF29EynzP4fK0x1BSybCp383Hj5IREREwmLDRUQOIfvEGSRGLYT2Whz8hvZFpfFDoPbjJd2JiIhIbGy4iEhoutv3kDh1GbK+Ogb3Vk0QHDMdbo0b2LssIiIiohJhw0VEQpLz8pG6cgdSFmyAytsTVZZPhne/rjxBl4iIiBwKGy6FJEniFz6BSJIEV1dXe5dBBZRFJtnfn0bixEXQ3rgDv+EvIOCjIVD5eJVRhc6H64mYmIt4mIl4mIl4mIlybLiISBjauLtImrIEWQdOwL1tMwSvnw23RvXsXRYRERGR1dhwWYH34RKHLMvQ6/VQq9XMRBDWZGLIzUPasm1IWbQJKj8fVFk9Dd7Pd2amZYTriZiYi3iYiXiYiXiYiXJsuBTifbjEYzAYoFar7V0GFaAkk6xvTiFx4iLo4u/Cf0R/VPrgdai8PW1cofPheiIm5iIeZiIeZiIeZqIMGy4isgvtjTtInLwY2Ud+gkeHFqi2ZS5cQ+rYuywiIiKiMsWGi4jKlSEnD6lLtiB18RaoAv0RvGYmvHo9zcMSiIiIqEJiw0VE5UKWZWQf+QmJkxdDdycB/u+8hEpjB0Pl5WHv0oiIiIhshg2XQvwrvHhcXPg2Fs2DmWhj45E4aRGyv/0ZHh1botr2+XCtX9tO1TknridiYi7iYSbiYSbiYSbKcGlZgU2XOHhfNPEUzMSQnYvURZuRsnQrXKoEIPjz/4NXZHtmVs64noiJuYiHmYiHmYiHmSjHhssKvCy8OGRZhsFggEqlYiaCMF4uNvfwT0iauhS6e0moNOoV+L83ECpPd3uX55S4noiJuYiHmYiHmYiHmSjHhkshXhZePHq9HiqVyt5l0P+Xfy0OiVHRyD1+Bp7PtEH1XdHQ1Ktp77KcHtcTMTEX8TAT8TAT8TATZdhwEVGZMGTlIGXBBqSu2A51tSAEb5oD72cj7F0WERERkV2x4SKiUpFlGVlf/YDEqUthSE5FpTGD4PnWi3Dz9bZ3aURERER2x4aLiKyWf/kGEicsRM6Js/B8NgKVZ4+GS+1q0Gq19i6NiIiISAhsuBTiyYHi4THE5c+QmY3k+euRtmonNLWqoerWefB6pg2A+3u8mIl4mImYmIt4mIl4mIl4mIkybLiswKZLHJIk8V4Q5UiWZWTu/Q5J05bBkJaBgA+HwO+dAVC5u5nmYSbiYSZiYi7iYSbiYSbiYSbKcWmRw+Nl+stH3qVYJE5YiNyffodXj6cQOGsUNLWqWpyXmYiHmYiJuYiHmYiHmYiHmSjDhkshWZb5JhOILMvQarXQaDTMxEb06ZlI+XQ90mJ2Q1OnOqrt+AyeHVsWOT8zEQ8zERNzEQ8zEQ8zEQ8zUY4NFxFZJMsyMnceQdL0FTBkZSNgwjD4j+gPyc3V3qUREREROQw2XERUSN6Fq/dvXnz6HLx6d0LlGe/ApUawvcsiIiIicjhsuIjIRJ+WgZS5a5G2bi80DWqh2u5oeHZ4wt5lERERETksNlxEBNlgQMb2w0ietRKG7FwETh0Bv+H9ILlq7F0aERERkUNjw6UQTw4Uj0bDpqA08v78LxImLETerxfg3bcLAmeMhEvVyqUak5mIh5mIibmIh5mIh5mIh5kow4bLCmy6xMEsrKdPSUfynDVI3/AlNCGPoPq+xfBo17zU4zIT8TATMTEX8TAT8TAT8TAT5dhwWYGXhReHLMvQ6/VQq9XMpIRkgwEZWw4g6f9WAfk6BM4YCb+hfSFpymZzwEzEw0zExFzEw0zEw0zEw0yUY8OlkCzL9i6BHmAwGKBWq+1dhkPI/eNvJI5fgLzfLsG7fzcETn0bLsGBZf57mIl4mImYmIt4mIl4mIl4mIkybLiInIA+OQ3J/7ca6Zu+hutj9VD962XwaN3E3mURERERVXhsuIgqMFmvR/qmr5H8cQygN6Dyx+/B9/XekFy46hMRERGVB37rIqqgcs/8hcSoaOT9+V/4vByJgCkj4BJUyd5lERERETkVNlwK8eRA8fAYYnP6xBQkzVqFjK0H4Br+KGocXAH3J8PKtQZmIh5mIibmIh5mIh5mIh5mogwbLiuw6RKHJElc6f8/Wa9H+udfInlODCBJqDzvffgO7gWpnJcPMxEPMxETcxEPMxEPMxEPM1GODZcVeFl4cciybMrDmTPJOX0OiVELkf/XVfgM7InASW9CHehvl1qYiXiYiZiYi3iYiXiYiXiYiXJsuBTiZeHFo9PpnPaO57p/k5E0YwUydxyGW7OGqHF4Jdwff8zeZTl1JqJiJmJiLuJhJuJhJuJhJsqw4SJyQLJOh7S1e5HyyVpA44KgBR/C55Ue5X74IBEREREVjw0XkYPJ+c8fSJwQjfxL1+H7Wi8ETBgOdYCfvcsiIiIiIgvYcBE5CN3dRCTNWI7MXd/ArcVjqPlNDNyahtq7LCIiIiIqBhsuhXhyoHgqeiayVoe0mF1InrcOkrsrghZFweel7pBUKnuXVqSKnokjYiZiYi7iYSbiYSbiYSbKsOGyAt9k4pAkqUKftJlz8jckREVDe+UWfN94HgFRw6D297F3WcWq6Jk4ImYiJuYiHmYiHmYiHmaiHBsuIgHp7vyLpGnLkLnve7i3DEfwt2vgFv6ovcsiIiIiIoXYcFmB9+EShyzL0Ol0cHFxqRCZyPlapK7agZT5G6Dy8kCVpZPg3b+bQ722ipZJRcBMxMRcxMNMxMNMxMNMlGPDpRDvwyWeipJJ9vEzSJywENrYePgN7YtK44dA7ett77KsUlEyqUiYiZiYi3iYiXiYiXiYiTJsuIjsTBt/D0lTliBr/3G4t2mK4DUz4PZYfXuXRURERERlgA0XkZ3IeflIXb4NKQs3QeXjhSorp8K7bxfuniciIiKqQNhwEdlB9nenkThxIbS3/oHfmy8iYNzrUPl42bssIiIiIipjbLgU4t4H8TjSpUm1t/65f/jgwR/hHvE4qm78GK6hde1dVplzpEycBTMRE3MRDzMRDzMRDzNRhg2XFdh0icNRsjDk5iF16VakLtoMVSU/BMfMgFfvjg5TvxIV8TU5OmYiJuYiHmYiHmYiHmaiHBsuK/Cy8OKQZRl6vR5qtVrYTLKO/oTESYuhu/0v/EcMQKX3B0Pl7WnvsmzGETJxNsxETMxFPMxEPMxEPMxEOTZcCvEymOIxGAxQq9X2LqMQ7Y07SJy0CNlH/wOPp59EtS8+hWuD2vYuq1yImokzYyZiYi7iYSbiYSbiYSbKsOEiKmOGnDykLt6M1CVboa7sj+B1s+DV8yn+FYiIiIjICbHhIiojsiwj+9CPSJyyFLq7ifAf+TIqvTcQKi8Pe5dGRERERHbChouoDORfi0PixEXI+f40PDu3RrUdn8G1fi17l0VEREREdqaydwEF3bx5E1OnTkXv3r3x2GOPoWfPnhbn27lzJ7p164bw8HD06tULx44dKzRPRkYGJk6ciJYtW6J58+Z499138e+//5a6Rh4WJh57HkNsyMpB0v+tRlyH16C9egtVN36Mql/Mc/pmi8d1i4eZiIm5iIeZiIeZiIeZKCNUw3XlyhUcP34cjzzyCOrXr29xngMHDmDKlCno3r07YmJi0KxZM4waNQp//PGH2XxjxozBTz/9hOnTp2P+/Pm4fv06hg8fDp1OV+o62XSJQ5Iku1wlR5ZlZH79A+LaDUTaiu2o9O6rqHVyE7y6t3f694e9MqGiMRMxMRfxMBPxMBPxMBPlhDqksFOnTujSpQsAICoqChcuXCg0z+LFi9GjRw+MGTMGANC6dWtcvnwZy5YtQ0xMDADg999/x8mTJ7F27VpEREQAAOrWrYvIyEgcPXoUkZGRpaqTl4UXhyzLpjzKK5P8q7eQOGEhcn74FZ7d2qHyrNHQ1K1RLr/bEdgjEyoeMxETcxEPMxEPMxEPM1FOqD1cKlXx5cTFxeHGjRvo3r272eORkZE4deoU8vPzAQAnTpyAr68v2rVrZ5qnXr16aNSoEU6cOFGqGnlZePGUxV7LkjBkZiNp5sr7hw/euI2qWz5Btc1z2WxZUF6ZUMkxEzExF/EwE/EwE/EwE2WE2sP1MLGxsQDu760qqH79+tBqtYiLi0P9+vURGxuLunXrFuq669WrZxqDqKRkWUbWvu+ROG0ZDClpqPTBa/Af+TJU7m72Lo2IiIiIBOdQDVdaWhoAwNfX1+xx48/G6enp6fDx8Sn0fD8/P4uHKSplaS+XJElF7v0yNn7FTbfVcyv6uMbd2qX5ncU9V3v5BhImLETuj7/Bs3t7BM4aBU3taoVqsOb3irIMy3pcYybGn0WoqaKPW5LnFsykPGriuA9/rnFaeX+mONq45VmTrT9TnGEZlvW4Sj9T7F1vRaipJONa85niLMvQEodquESh1WrN9p6pVCq4uLiYpj3I1dUVAKDX62EwGMymubi4QJIkGAwG6PV6s2nGcWVZtjiuRqMpcly1Wg21Wg1Zlgvt9pUkyfRcnU5X6A1jq3FL8lqBwsuwuHELrvBKxzW+VkmSCr1WQ2Y2MqI3IX3NbrjUqorKm+fAo2NL0zglGRco32VoHNdSTbbKxvhaCy7Dgq/LOM3Sa7X3+7u4ZVie72+gfLYRxtdj3HY5yzaiNOMaX6ulbcTDXmtJtxEGg6HQZ0pF30ZYGleUbUTBfzvbNkLU7xGWPlOcaRsh6veIBz9TnGUbUXBc43eegtvvojhUw+Xn5wfg/iXfg4KCTI+np6ebTff19cXdu3cLPT8tLc00T2kY3xhFTStKwTfyg1QqVZHnsBUMXum4D3uucQUoz3GLe61A8cvwwXELrijWjGvpy2fWnm+RNH05DOmZCPhoCPzeHgDJzdWqeh1hGZZ2XEvLUJIk00bSOE9RNdnr/V2adc5W2dhyG6HRaMwyKelzHX0bUdpxH3x/W/Kw1/qwbCzlUpJxnWUZluc2oqSfKRVxGyHq9whLnykV6f1d1LhGIm9nLW27Kvo2oiCVSlWiZgtwsIarXr16AO6fy2X8t/FnjUaDWrVqmeY7depUoa7z+vXrCAkJKVUNkiQVu0F72HOtmVaa51b0cSVJMv3lz9p6jNPzLl5DYlQ0ck/9Ca/nnkbgzFHQ1Awu1diOsAzLelxLmdi7poo+7sOmq1SqItcTR3utjjbuw6YXt/1ytNdaET4Dy/Izpayn2eu59h5X6WeKveutCDU9bFxrP1OcaRk+SKirFD5MrVq1UKdOHRw+fNjs8YMHD6JNmzam8Dt06IC0tDScOnXKNM/169dx8eJFdOjQoVxrJvHp0zOROGkx4jsNhT4hBdV2LkDVdbNK1GwRERERERVHqD1cOTk5OH78OADg9u3byMzMNDVXLVu2REBAAEaPHo1x48ahdu3aaNWqFQ4ePIhz585h8+bNpnGaN2+OiIgITJw4EePHj4ebmxuio6MRGhqKrl27lrrOkh6vSbZnPPyjqENyHvbczB1HkDRjBQxZOQiYNBz+b/WH5Fr0rmV6uNJkQrbBTMTEXMTDTMTDTMTDTJSTZCWX2LCx+Ph4dO7c2eK0jRs3olWrVgCAnTt3IiYmBnfu3EHdunXx/vvvo2PHjmbzZ2RkYM6cOfjmm2+g0+kQERGByZMnIzjY+r0W58+fhyzLCA8P5xtMEMYTJYs7r86SvPNX7h8++Mt5eD/fCYEzRsKlehUbVuo8rM2EbIeZiIm5iIeZiIeZiIeZ/M/58+cBAOHh4cXOJ1TDJTo2XOJRutLrUzOQPGcN0j/fB82jtRE0dyw8Ih4vh0qdBzfE4mEmYmIu4mEm4mEm4mEm/1PShkuoQwqJbEU2GJDxxSEkzV4JOTcfgdPfht+wfpA0XAWIiIiIyHb4bZMqvLw//4uEqGjknfkL3v2eQeC0d+BStbK9yyIiIiIiJ8CGSyFn33UqoqLukaBPSUfyx6uRvuEruDaqi+pfLoFH22blW5yTKu6+FWQfzERMzEU8zEQ8zEQ8zEQZLi0rsOkShyRJhfKQDQZkbNmPpNmrAa0OgbPfhd+Q5yFx41AuLGVC9sVMxMRcxMNMxMNMxMNMlOM3UCvwsvDikGUZBoPBdLfv3N8uIjFqIfJ+vwSfAc8iYOrbcKkSYO8yncqDmZD9MRMxMRfxMBPxMBPxMBPl2HApxIs6ikev10NOSUfy/61GxpYDcG3cANX3L4NHqyb2Ls1p6fV6qFQOdV/1Co+ZiIm5iIeZiIeZiIeZKMOGixyarNcjc8NXSJu3DpBlVJ4zBr6v94akVtu7NCIiIiIiNlzkuHJ/vYCEqGjkn7sMn1ciEThlBNSVK9m7LCIiIiIiEzZc5HB0CSlInrUSGV8chGuTEFT5agm8WzflccREREREJBw2XArxS739yDod0tfvQ/LctYBahcrzx8Hn1R4w2LswKoTHdYuHmYiJuYiHmYiHmYiHmSjDhssKbLrKX87P55AYtQD5F2PhO+g5BEx6E+oAPwAAV3mxSJLE+3MIhpmIibmIh5mIh5mIh5kox6VlBV4Wvvzo7iUhaeYKZO44ArfHG6HG0dVwb9bQNL3gVSOZiRiYiXiYiZiYi3iYiXiYiXiYiXJsuBTiZeHLh6zVIW3tHqTMWwdoXBC04CP4vNoDkoVd2FqtFhqNxg5VUlGYiXiYiZiYi3iYiXiYiXiYiTJsuEg4OT/9jsQJC5H/3xvwfa03AiYMg7qSr73LIiIiIiJSjA0XCUN3NxFJ05Yhc8+3cHsyDDW/iYFbkxB7l0VEREREZDU2XGR3slaHtNU7kfzpekgebghaPAE+A561ePggEREREZEjYcOlEE8OLFvZJ84gccJCaK/GwW9oX1QaP+T/tXfncVHVex/APwMMyDYsLpBbLAliLlCpoKKJyxNq6U29ZrnUVcK8LuBjgQsEijf0PqW4lClmbt3UsBJEH9xCr9rj9UaR3hYFNcyrgsAMiyjMnOePrvMKB2VGGc6Pmc/7L+c3h3O+w8ffGb4zZ4Gtm6tJ62Am4mEm4mEmYmIu4mEm4mEm4mEmpmHD9RD4n+zR1V29gZKEtajaexSt+vaE15EkODz5hMnrUSgUPGlTMMxEPMxETMxFPMxEPMxEPMzEdGy4qFlJd2pR/sFOlL23BTYuTmj3/mK4jBvOJpaIiIiILBIbrofA+3A9nOqjp387fPDSVbhFjYXnW3+CjavzI61TkiTU1dXBzs6OmQiCmYiHmYiJuYiHmYiHmYiHmZiODZeJeB8u09UWXcPNhLWo2peLVv2C4bU5BQ5Bfk22fmYiHmYiHmYiJuYiHmYiHmYiHmZiGjZcZDa6mttQr/sUZWnbYOPminYb3obLmCH8NISIiIiIrAYbLjKLqoOncHPRatQW/RvuM/4Ij/9+FTYuTnKXRURERETUrNhwUZOqvXwVJYvXoPrA3+E48Gl4b38H9gE+cpdFRERERCQLNlwm4uFwDdPduo3ytZ+gfPV22Hi6wyt9CZxfeLZZfl92dvxvLBpmIh5mIibmIh5mIh5mIh5mYhr+th4Cm676qv73BEoWpaHuajHc35gAj3lTYePs2CzbVigUzEMwzEQ8zERMzEU8zEQ8zEQ8zMR0bLgeAi8L/5vawisoWbwa1QdPwfHZ3njs0/+B/ROdm7UGSZKg0+lgY2PDTATBTMTDTMTEXMTDTMTDTMTDTEzHhstEvAwmoKuuQXnadpSt/QR27Tzh9fEyOI8Il23SabVa2NjYyLJtahgzEQ8zERNzEQ8zEQ8zEQ8zMQ0bLjKaJEmoyj6OmwlrUHf9JjxmvQz3uZNg49RK7tKIiIiIiITEhouMcqfgF5QsSMOto6fhNDQU7T9bCaVfR7nLIiIiIiISGhsueiBd1S2UvbcF5R/shF37tvDengqn4f14zC4RERERkRHYcJnIWhoNSZJQtfcrlCSuha60HB6xU+A+62XYODrIXZoBHkMsHmYiHmYiJuYiHmYiHmYiHmZiGjZcD8HSm647P19CycI03Mo9A6fnBqBNymwoH28vd1kNUigUvBeEYJiJeJiJmJiLeJiJeJiJeJiJ6fjbegiWell4XWU1yt79GOXrd8Guoze8P1kB52Fhcpf1QL+/aqQlZtISMRPxMBMxMRfxMBPxMBPxMBPTseEykSVeFl6SJFR+fhg3314HnboCnm/+CW4zJ8CmlXiHDzaktrYWSqVS7jLod5iJeJiJmJiLeJiJeJiJeJiJadhwWbk7P15EcfxK1JzIg/PIQWi9dBaUnbzlLouIiIiIyCKw4bJSuooqlK74COqNGVD6tMdju96F0+A+cpdFRERERGRR2HBZGUmSUPlZDm4mvQ9dZTU8F0yH+4w/QuFgL3dpREREREQWhw2XFbl97gJK4leh5uvv4Dw6Am2SZ8Kug5fcZRERERERWSw2XCZSKBQt7oosWnUFypZ/BPVHn0Pp3xGPZayE08Bn5C6rSSgUCiiVyhaXiSVjJuJhJmJiLuJhJuJhJuJhJqZjw2XBJJ0OFTsPoHTpeuiqa9A6IRpuUeOgsLesq8pwwouHmYiHmYiJuYiHmYiHmYiHmZiGDddDaAn34bqd/zOK41fi9j/OwuXFoWidNBN2j7WVu6wmJ0kStFotbG1thc/EWjAT8TATMTEX8TAT8TAT8TAT07HhMpHo9+HSlleg9C8bodnyJZQBj6P9F6vh2D9E7rLMSqfTwdbWVu4y6HeYiXiYiZiYi3iYiXiYiXiYiWnYcFkISadDxSfZuJmyHtLtWrRO/jPcpr0IhZIRExERERHJhX+NW4Cab39ESdx7uP3ND3D543+hdeIbsPNqLXdZRERERERWjw1XC6YtVaN02QZotmXCvpsf2meug2NoT7nLIiIiIiKi/2DDZSIRTg6UtFpotmehdNkGQKtDm2VzoHptDBR21hknjyEWDzMRDzMRE3MRDzMRDzMRDzMxjXX+hf6I5Gy6av55DiVxK3H7u5/g+lIkPBNmwK6dp2z1yE2hUHDSC4aZiIeZiIm5iIeZiIeZiIeZmI4N10OQ47Lw2pIy3Fz6ISo+2Qf7Hl3QIfsDtOrdvVlrEJEkSfo8RPj2kZiJiJiJmJiLeJiJeJiJeJiJ6dhwmai5LwsvabXQfPwlSt/ZCCgUaLNiHlRTXoCCnyzo1dXVQam0rJs5t3TMRDzMREzMRTzMRDzMRDzMxDRsuARWc/p7FMetxJ1zF+D6yki0XhwN29bucpdFRERERERGYsMloLobpShd8gEqdh6AQ3BXdDiwHq2e6iZ3WUREREREZCI2XAKR6uqg/ugLlKWmA0o7tH3vTbi+PJKHDxIRERERtVBsuExkrpMDb538FiULVuLODxehmvoCPBdEwdbTzSzbsjQ2NjZyl0D3YCbiYSZiYi7iYSbiYSbiYSamYcP1EJqy6aq7VoKbye+j8rODcHi6Gzoe3AiHXoFNtn5Lp1AoYGel9x8TFTMRDzMRE3MRDzMRDzMRDzMxHX9bMpFq66BO/wylKzZD4aBE21XxcJ0YCQU/MSAiIiIishhsuEz0+3sPPKxbJ/JQHPceas//AtVrY+AZPx227q5NWKX1kCQJtbW1UCqVvBeEIJiJeJiJmJiLeJiJeJiJeJiJ6dhwNaO6fxfj5tvrUPn5YbTq0wNeh9Lh0KOL3GUREREREZGZsOFqBtKdWpR/uAtl/7MFNs6OaLd2EVz++F/8VICIiIiIyMKx4TKz6twzKFmwCrWFV+A27UV4xP0JtioXucsiIiIiIqJmwIbLTOp+vY6ShLWoyvwKrUJ7wSs9GQ7d/OUui4iIiIiImhEbLhM1dhigdPsOyj/YibKVW2Hj4oR2HyTAZewwHj5oRkqlUu4S6B7MRDzMREzMRTzMRDzMRDzMxDQW3XAVFBQgJSUFeXl5cHZ2xujRoxETEwN7e/tHWu/9mqfqw/+HkoWrUPvLv+H2+nh4zn8VNq7Oj7QtejA2suJhJuJhJmJiLuJhJuJhJuJhJqaz2IZLrVZj6tSp8PHxwZo1a3D9+nWkpqaipqYGiYmJj7Tuey8LX/vLv3EzYQ2qso+j1YCn4L31L7AP9H3Ul0BGkCQJWq0Wtra23AEIgpmIh5mIibmIh5mIh5mIh5mYzmIbrk8//RRVVVVYu3Yt3N3dAQBarRbJycmIjo6Gl5fXQ61XkiT9v3U1t1G+7m8oX7UNNh5u8NqQBOcxEfzP18x0Oh1sbW3lLoN+h5mIh5mIibmIh5mIh5mIh5mYxkbuAszl2LFjCAsL0zdbABAZGQmdTocTJ0488vqrck6iKHwqyt7dAreoceh8cjtc/jCEzRYREREREelZ7DdchYWFGDt2bL0xlUqFtm3borCw8KHXq7hagmvL41GdcxKOz/bGY58sh32Xxx+1XCIiIiIiskAW23BpNBqoVCqDcTc3N6jV6odaZ21tLVR/WopKD1fUJkWhOjwYN2s0wPffP2q59AjuPaeO5MdMxMNMxMRcxMNMxMNMxMNMfnPnzh2jfg8W23CZg0KhgCbrXV4KUzCc8OJhJuJhJmJiLuJhJuJhJuJhJr9RKBTW3XCpVCpUVFQYjKvVari5uT3UOkNCQh61LCIiIiIisiIWe9EMPz8/g3O1KioqUFxcDD8/P5mqIiIiIiIia2KxDdfAgQNx8uRJaDQa/diBAwdgY2OD/v37y1gZERERERFZC4X0+xtLWRC1Wo2RI0fC19cX0dHR+hsfP//8849842MiIiIiIiJjWGzDBQAFBQVYunQp8vLy4OzsjNGjRyM2Nhb29vZyl0ZERERERFbAohsuIiIiIiIiOVnsOVxERERERERyY8NFRERERERkJmy4iIiIiIiIzIQNFxERERERkZmw4SIiIiIiIjITNlxERERERERmwoaLiIiIiIjITOzkLqAlKCgoQEpKSr0bKMfExPAGys1g//792Lt3L86dOweNRoPHH38ckydPxtixY6FQKAAAkydPxunTpw1+Njs7G/7+/s1dslXYs2cPFixYYDAeFRWF+fPn6x/v3r0b6enpuHr1Knx9fREbG4vBgwc3Z6lW437zAADee+89jBw5knPFzC5fvoxNmzbhu+++w/nz5+Hn54esrCyD5YyZFxUVFXjnnXdw6NAh1NbWIjw8HIsXL0a7du2a6+VYhMYyqaysxObNm5Gbm4tLly7B3t4ePXv2RGxsLAIDA/XLXblyBUOGDDFYf69evbBr165meS2Wwph5Yuy+ivOk6TSWy/3mAADY29vj+++/f+By1j5X2HA1Qq1WY+rUqfDx8cGaNWtw/fp1pKamoqamBomJiXKXZ/E+/vhjdOjQAfHx8fDw8MDJkyeRkJCAa9euYdasWfrlnnrqKcTFxdX72Y4dOzZ3uVYnPT0drq6u+sdeXl76f+/btw8JCQmYMWMGQkNDkZ2djVmzZmHHjh0IDg6WoVrL9vbbb6OysrLe2JYtW5CTk4OwsDD9GOeK+Zw/fx65ubno1asXdDodJEkyWMbYeRETE4MLFy4gKSkJDg4OWLVqFaKiopCRkQE7O751G6uxTK5evYqdO3di7NixiImJwe3bt/HRRx9hwoQJyMjIMPggYt68eejbt6/+sbOzc7O8DktizDwBjNtXcZ40ncZyadeuHXbu3FlvTJIkTJ8+HaGhoQbr41y5h0QPtH79eik4OFgqKyvTj3366adSUFCQdO3aNfkKsxI3b940GFu8eLH01FNPSVqtVpIkSZo0aZL0+uuvN3dpVi0jI0MKCAhoMJ+7hg8fLs2bN6/e2IQJE6Tp06ebuzz6j4iICCkqKkr/mHPFvO7ukyRJkuLi4qSRI0caLGPMvPjmm2+kgIAA6fjx4/qxgoICKTAwUNq3b58ZKrdcjWVSVVUlVVdX1xurrKyU+vTpIy1ZskQ/VlRUJAUEBEj79+83b8FWwJh5Ysy+ivOkaRmTy72+/vprKSAgQMrOztaPca40jOdwNeLYsWMICwuDu7u7fiwyMhI6nQ4nTpyQrzAr4enpaTAWFBSEyspKVFdXy1ARGaOoqAiXLl1CZGRkvfERI0bg1KlTuHPnjkyVWY9vvvkGV65cwfPPPy93KVbDxubBb6nGzotjx45BpVKhf//++mX8/PwQFBSEY8eONX3hFqyxTJycnODo6FhvzNnZGZ07d8aNGzfMWZrVaiwTY3GeNK2HySUrKwsuLi6IiIgwQ0WWhQ1XIwoLC+Hn51dvTKVSoW3btigsLJSpKuv2z3/+E15eXnBxcdGPnT59GsHBwejRowcmTZqEf/zjHzJWaD1GjRqFoKAgDBkyBB9++CG0Wi0A6OeGr69vveX9/f1RW1uLoqKiZq/V2mRlZcHJycngWHrOFfkYOy8KCwvh6+urP0/1Lj8/P77vNAONRqM/h+VeSUlJCAoKQlhYGBYvXozy8vLmL9BKNLav4jyRV21tLXJycjBs2DA4ODgYPM+5Uh8PcG2ERqOBSqUyGHdzc4NarZahIut25swZZGdn1zuuu3fv3hg9ejR8fHxw48YNbNq0Ca+99hq2bduGkJAQGau1XG3btsXs2bPRq1cvKBQKHDlyBKtWrcL169eRmJionxv3zp27jzl3zKuurg779+9HREQEnJyc9OOcK/Iydl5oNJp650be5ebmhrNnz5q5SvrrX/8KhUKBiRMn6sfs7e0xceJEDBgwACqVCt999x3Wr1+Ps2fPYvfu3VAqlTJWbHmM2Vdxnsjr2LFjKC8vx6hRo+qNc640jA0XtRjXrl1DbGws+vbtiylTpujH58yZU2+5Z599FqNGjcL777+PjRs3NneZViE8PBzh4eH6xwMGDICDgwO2bNmCGTNmyFgZAcCJEydQWlpq8EbIuUL0YBkZGdi1axdSU1Ph7e2tH2/Xrh2SkpL0j/v06YMuXbogOjoaBw8exIgRI2So1nJxXyW+zMxMtGnTpt5FmQDOlfvhIYWNUKlUqKioMBhXq9Vwc3OToSLrpNFoEBUVBXd3d6xZs+aBxxo7OTlh0KBBOHfuXDNWSJGRkdBqtfjhhx/0c+PeuaPRaACAc8fMsrKy4O7ujgEDBjxwOc6V5mXsvFCpVAZXnAT4vmNuubm5SExMxMyZM/GHP/yh0eUHDRoEJycnzp9m0NC+ivNEPlVVVTh69CgiIyNha2vb6PKcK2y4GtXQscAVFRUoLi5u8Phuano1NTWIjo5GRUWFwWXISUx358a9c6ewsBBKpRKdOnWSoyyrUFNTg0OHDuG5556z2kM3RGXsvPDz88PFixcNLst88eJFvu+Yybfffou5c+dizJgxmDt3rtzlkBE4T+Rz8OBB1NTU8KJMJmDD1YiBAwfi5MmT+k8gAeDAgQOwsbGpd2UcMo+6ujrExMSgsLAQ6enp9e7zdD/V1dX46quv0KNHj2aokO7Kzs6Gra0tunXrhk6dOsHHxwcHDhwwWCYsLIw3DTejI0eOoLq62qg3Qs6V5mXsvBg4cCDUajVOnTqlX+bixYv417/+hYEDBzZrzdbgwoULiI6ORmhoKJKTk43+uaNHj6K6uprzpxk0tK/iPJFPVlYWOnfujF69ehm1POcKz+Fq1EsvvYRt27bhz3/+M6Kjo3H9+nWsWLECL730klF//NOjSU5OxtGjRxEfH4/Kykp8++23+ue6deuG/Px8pKenY9iwYejQoQNu3LiBzZs3o7i4GGlpafIVbuGmTZuGvn37IjAwEABw+PBh7Nq1C1OmTEHbtm0BALNnz8b8+fPRuXNn9O3bF9nZ2cjPz8f27dvlLN3iZWZmon379nj66afrjZ85c4Zzxcxu3bqF3NxcAMCvv/6KyspKfXPVp08feHp6GjUvQkJCMGDAACxcuBBxcXFwcHDAypUrERgYiOHDh8vy2lqqxjKRJAnTpk2Dg4MDpk6dWu9iCy4uLnjiiScAAKmpqVAoFAgODoZKpUJ+fj4+/PBDdO/eHUOHDm3+F9aCNZbJ3Q9YG9tXcZ40LWP2XwBQWlqKU6dOISoqqsH1cK40TCHd+10sGSgoKMDSpUuRl5cHZ2dnjB49GrGxsfyUvhlERETg119/bfC5w4cPQ6vVYsmSJfjpp59QXl4OR0dHhISEYNasWejZs2czV2s9UlJScPz4cVy7dg06nQ4+Pj4YP348Jk+eXO8Svbt378bGjRtx9epV+Pr6Yt68eRg8eLCMlVs2tVqN/v37Y+rUqXjzzTfrPXf58mXOFTO7cuWKwWX479q6dSv69u0LwLh5UVFRgXfeeQcHDx5EXV0dBgwYgMWLF/ODPhM1lgmAehdh+r0+ffpg27ZtAH7L7G9/+xsuX76MmpoaeHl5YejQoZgzZ069W5RQ4xrLxNvb2+h9FedJ0zF2/7Vjxw4sWbIE2dnZ8Pf3N1iWc6VhbLiIiIiIiIjMhOdwERERERERmQkbLiIiIiIiIjNhw0VERERERGQmbLiIiIiIiIjMhA0XERERERGRmbDhIiIiIiIiMhM2XERERERERGbChouIiKgBVVVVCAsLw969e+UuRa+srAzBwcHIzc2VuxQiIjISGy4iImpxduzYgcDAQIwfP95s29i6dSucnZ0xcuRIs23DVB4eHhg3bhzS0tLkLoWIiIzEhouIiFqczMxMKJVK5Ofn4/Lly02+/traWmzduhXjx4+Hra1tk6//UUycOBHnzp3DqVOn5C6FiIiMwIaLiIhalKKiIuTl5eGNN96AUqlEZmZmk2/jq6++QmlpKSIjI5t83Y/K398fAQEB+Pzzz+UuhYiIjMCGi4iIWpTMzEzY2tpiwoQJ6NevX4MN1+rVq9G1a1eDb4ESEhLQvXt3/Pjjjw/cxqFDh9ChQwd07ty53nh8fDxCQkJw9epVREdHIyQkBOHh4dixYwcA4KeffsKUKVMQHByMwYMHG9S2Z88eBAYG4syZM0hJSUFoaCieeeYZJCYm4s6dO9BoNHjrrbfQu3dv9O7dGytWrIAkSQb19evXD0ePHm3wOSIiEgsbLiIialEyMzPxzDPPoE2bNoiMjMSlS5eQn59fb5k33ngDQUFBWLRoESorKwEAx48fx65duzBz5kx07dr1gdvIy8vDk08+2eBzWq0WUVFR8Pb2xvz589GhQwcsWbIEe/bswfTp09G9e3fMnz8fzs7OiIuLQ1FRkcE6UlJScOnSJcyePRsRERHYuXMn0tLSMGPGDGi1WsTGxuLpp5/Gpk2b8OWXXxr8/JNPPgmNRoPz588b+2sjIiKZsOEiIqIW4+zZsygsLMSIESMAAEOHDm3wsEKlUonly5fjxo0bSE1NhUajwaJFi9C9e3e8/vrrD9xGXV0dfvnlF3Ts2LHB52/fvo0XXngBycnJeOWVV7Bhwwa0atUKCxcuxIIFC/DWW29h0qRJWL16NbRaLb744guDdbRu3RobN27EK6+8ghUrViAkJASbNm1Cly5d8O677+Lll1/GunXr4O3tjYyMDIOf79SpEwDgwoULxvzaiIhIRmy4iIioxcjMzISdnR2GDx8OAHB1dUV4eDiys7Oh1WrrLRsQEIA5c+Zg9+7dmDZtGsrKyrB8+XLY2dk9cBtqtRqSJEGlUt13md9fHVGlUsHX1xeOjo71zvny8/ODSqVq8BuucePGQaFQ6B/37NkTkiRh3Lhx+jFbW1t07969wZ+/W1tZWdkDXwsREcmPDRcREbUIWq0W+/btQ2hoKDw9PfXjI0aMQElJSYNX7Zs2bRq6du2K/Px8zJo1C0888YTR27vf+VEODg71tg/81vh5e3vXa6Lujms0GoN1tG/f3mA5AHjssccMxtVq9X1rvHd7REQkHjZcRETUInz99dcoLi42uHJgREQEWrVq1eDFM4qKivSXjf/555+N2o6bmxsUCkWDjRKA+14m/n7jDTVuNjYNv/3eb/xed5swDw8Po5YnIiL5sOEiIqIW4e69t4YNG1Zv3NnZGYMGDcLBgwdRU1OjH9fpdIiPj4eLiwtmzJiBrKws5OTkNLodOzs7dO7cGVeuXGny19BU7tbm7+8vcyVERNQYNlxERCS8mpoa5OTkoF+/fnBzczN4/rnnnkNVVRWOHDmiH9u8eTPy8vKwZMkSzJ07FyEhIUhKSkJpaWmj2wsODsbZs2eb9DU0pXPnzsHV1RVdunSRuxQiImrEg88cJiIiEsCRI0dQVVUFANiwYYPB87du3QIA7N27FyNGjEBBQQHS0tLw4osvIiIiAgCQmpqKMWPGIDk5GWlpaQ/c3pAhQ/Dll1/i4sWL8PX1beJX8+hOnjyJwYMH8xwuIqIWgA0XEREJb+/evQCA3Nxc5Obm3ne5v//97ygrK0NcXBw8PDywcOFC/XM+Pj6YN28eli1bhuzsbP2l5RsyePBgeHh4YP/+/Zg5c2bTvZAmUFBQgJ9//rneayMiInEpJN6mnoiIyMC6deuwZ88e5OTk3PeCGHJYtmwZzpw5gz179vAbLiKiFoDncBERETXg1VdfRXV1Nfbt2yd3KXplZWX47LPPEBMTw2aLiKiF4DdcREREREREZsJvuIiIiIiIiMyEDRcREREREZGZsOEiIiIiIiIyEzZcREREREREZsKGi4iIiIiIyEzYcBEREREREZkJGy4iIiIiIiIzYcNFRERERERkJmy4iIiIiIiIzIQNFxERERERkZmw4SIiIiIiIjKT/wdQPAxDaMn61gAAAABJRU5ErkJggg==",
      "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": 94,
   "id": "32e9948f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 2.76835\n",
      "GdL       = 4\n",
      "Chi² rid  = 0.69209\n",
      "P(0, chi²)= 0.40269\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    0.961357\n",
      "1    0.033834\n",
      "2    0.061166\n",
      "3    0.968819\n",
      "4    0.642604\n",
      "5    0.100572\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": 95,
   "id": "bfb895c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AY        = 3.2006138 ± 0.0091837\n",
      "BY        = 0.1126344 ± 0.9516509\n",
      "cov_ABY   = -0.007930461708887697\n",
      "chi²      = 2.76829\n",
      "chi² rid  = 0.69207\n",
      "P(0, chi²)= 0.40268\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": 96,
   "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": 97,
   "id": "caf23dbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI senza ERRORE STRUMENTALE INCLUSO:\n",
      "Media pesata K  = 3.20156 ± 0.00386\n",
      "\n",
      "RISULTATI REGRESSIONE OLS:\n",
      "Aols  = 3.20145 ± 0.00896\n",
      "Bols  = 0.02655 ± 0.99076\n",
      "P(0, chi²)= 0.40417\n",
      "\n",
      "RISULTATI REGRESSIONE Carpi:\n",
      "Ac  = 3.20054 ± 0.00919\n",
      "Bc  = 0.11948 ± 0.95235\n",
      "P(0, chi²)= 0.40269\n",
      "\n",
      "RISULTATI REGRESSIONE York:\n",
      "Ay  = 3.20061 ± 0.00918\n",
      "By  = 0.11263 ± 0.95165\n",
      "P(0, chi²)= 0.40268\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}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7692f792",
   "metadata": {},
   "source": [
    "## Aggiunta degli errori strumentali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "8f5c8bb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u_strum_m    = 0.004082\n",
      "u_strum_Dx   = 0.020412\n",
      "uF_strum     = 0.071603\n",
      "uK_strum     = 0.022946\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  = np.maximum(ueste,  u_strum_Dx)\n",
    "umasse_strum = np.maximum(umasse, u_strum_m)\n",
    "\n",
    "# Worst-case scalare: prendi il massimo anche di ueste_strum\n",
    "uF_strum  = np.max(np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 ))\n",
    "uDx_strum = np.max(ueste_strum)\n",
    "\n",
    "uK_strum  = np.max(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": "markdown",
   "id": "8706126d",
   "metadata": {},
   "source": [
    "### Propapazione dell'errore strumentale max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "a1dc24c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Media pesata\n",
    "uA_fin   = np.sqrt(uA**2      + uK_strum**2)\n",
    "\n",
    "# OLS\n",
    "uAols_fin = np.sqrt(uAols**2  + uK_strum**2)\n",
    "uBols_fin = np.sqrt(uBols**2  + uK_strum**2)\n",
    "\n",
    "# Carpi\n",
    "uAC_fin  = np.sqrt(uAC**2     + uK_strum**2)\n",
    "uBC_fin  = np.sqrt(uBC**2     + uK_strum**2)\n",
    "\n",
    "# York\n",
    "uAY_fin  = np.sqrt(uAY**2     + uK_strum**2)\n",
    "uBY_fin  = np.sqrt(uBY**2     + uK_strum**2)\n",
    "\n",
    "\n",
    "# Nuovo uy e ux per ricalcolare i chi^2\n",
    "uy_fin = np.sqrt(data[\"uy\"]**2 + uF_strum**2)\n",
    "ux_fin = np.sqrt(data[\"ux\"]**2 + uDx_strum**2)\n",
    "\n",
    "# OLS\n",
    "F_fit_ols = Bols + Aols * data[\"x\"]\n",
    "sigma_ols = np.sqrt(uy_fin**2 + (Aols * ux_fin)**2)\n",
    "chi2_ols  = np.sum(((data[\"y\"] - F_fit_ols) / sigma_ols)**2)\n",
    "GdL       = len(data) - 2\n",
    "\n",
    "# Carpi\n",
    "F_fit_c   = BC + AC * data[\"x\"]\n",
    "sigma_c   = np.sqrt(uy_fin**2 + (AC * ux_fin)**2)\n",
    "chi2_c    = np.sum(((data[\"y\"] - F_fit_c) / sigma_c)**2)\n",
    "\n",
    "# York\n",
    "F_fit_y   = BY + AY * data[\"x\"]\n",
    "sigma_y_   = np.sqrt(uy_fin**2 + (AY * ux_fin)**2)\n",
    "chi2_y     = np.sum(((data[\"y\"] - F_fit_y) / sigma_y_)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e57c7d8",
   "metadata": {},
   "source": [
    "## Risultati della propagazione dell'errore strumentale massimo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "c8e264fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI CON ERRORE STRUMENTALE INCLUSO:\n",
      "Media pesata K  = 3.20156 ± 0.02327\n",
      "\n",
      "RISULTATI REGRESSIONE OLS:\n",
      "Aols  = 3.20145 ± 0.02463\n",
      "Bols  = 0.02655 ± 0.99102\n",
      "Chi² OLS   = 1.17582  |  rid = 0.29396  |  P = 0.11794\n",
      "\n",
      "RISULTATI REGRESSIONE Carpi:\n",
      "AC  = 3.20054 ± 0.02472\n",
      "BC  = 0.11948 ± 0.95263\n",
      "Chi² Carpi = 1.17417  |  rid = 0.29354  |  P = 0.11767\n",
      "\n",
      "RISULTATI REGRESSIONE York:\n",
      "AY  = 3.20061 ± 0.02472\n",
      "BY  = 0.11263 ± 0.95193\n",
      "Chi² York  = 1.17400  |  rid = 0.29350  |  P = 0.11764\n"
     ]
    }
   ],
   "source": [
    "print(\"RISULTATI CON ERRORE STRUMENTALE INCLUSO:\")\n",
    "print(f\"Media pesata K  = {media:.5f} ± {uA_fin:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE OLS:\")\n",
    "print(f\"Aols  = {Aols:.5f} ± {uAols_fin:.5f}\")\n",
    "print(f\"Bols  = {Bols:.5f} ± {uBols_fin:.5f}\")\n",
    "print(f\"Chi² OLS   = {chi2_ols:.5f}  |  rid = {chi2_ols/GdL:.5f}\" f\"  |  P = {chi2.cdf(chi2_ols, df=GdL):.5f}\")\n",
    "\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
    "print(f\"AC  = {AC:.5f} ± {uAC_fin:.5f}\")\n",
    "print(f\"BC  = {BC:.5f} ± {uBC_fin:.5f}\")\n",
    "print(f\"Chi² Carpi = {chi2_c:.5f}  |  rid = {chi2_c/GdL:.5f}\" f\"  |  P = {chi2.cdf(chi2_c, df=GdL):.5f}\")\n",
    "\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE York:\")\n",
    "print(f\"AY  = {AY:.5f} ± {uAY_fin:.5f}\")\n",
    "print(f\"BY  = {BY:.5f} ± {uBY_fin:.5f}\")\n",
    "print(f\"Chi² York  = {chi2_y:.5f}  |  rid = {chi2_y/GdL:.5f}\" f\"  |  P = {chi2.cdf(chi2_y, df=GdL):.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e38b0bd",
   "metadata": {},
   "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\n",
    "- \\>95%: Fit troppo piccolo\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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