Lab1/mollaDefinitiva/molla-2/molla2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bdf377b8",
   "metadata": {},
   "source": [
    "Tutto quello che c'è da sapere sulla molla 2, possibilmente con la massima chiarezza."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3a34cc3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Heavy lifting\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "# Mostrare i dati\n",
    "import ipysheet\n",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Calcolo simbolico\n",
    "import sympy as sp\n",
    "\n",
    "\n",
    "G = 9.806\n",
    "uG = 0.004"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eaece9c",
   "metadata": {},
   "source": [
    "# Calibro"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "960e68a5",
   "metadata": {},
   "source": [
    "## Import dati e ricerca outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1e2fabea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Funzioni criterio di Chauvenet\n",
    "\n",
    "# Probabilità\n",
    "def p_t_student(valori, err_strumentale):\n",
    "  n   = len(valori)\n",
    "  GdL = n - 1\n",
    "  if n <= 2:\n",
    "    return np.ones(n)\n",
    "\n",
    "  media = valori.mean()\n",
    "  s     = valori.std(ddof=1)\n",
    "  if s == 0:\n",
    "    return np.ones(n)\n",
    "\n",
    "  s = np.sqrt(s**2 + err_strumentale**2)\n",
    "  return 2 * (1 - stats.t.cdf(np.abs(valori - media) / s, df=GdL))\n",
    "\n",
    "# Indice del peggiore outlier o None\n",
    "def trova_outlier(valori, err_strumentale):\n",
    "  n       = len(valori)\n",
    "  soglia  = 1 / (2 * n)\n",
    "  p       = p_t_student(valori, err_strumentale)\n",
    "  idx_min = np.argmin(p)\n",
    "\n",
    "  if p[idx_min] < soglia:\n",
    "      return idx_min\n",
    "  return None\n",
    "\n",
    "# Rimuove outlier\n",
    "def rimuovi_outlier(valori, err_strumentale):\n",
    "  rimossi  = []\n",
    "  campione = valori.copy()\n",
    "\n",
    "  while len(campione) > 2:\n",
    "    idx = trova_outlier(campione, err_strumentale)\n",
    "\n",
    "    if idx is None: # nessun outlier: stop\n",
    "      break\n",
    "\n",
    "    rimossi.append(campione[idx])\n",
    "    campione = np.delete(campione, idx)\n",
    "\n",
    "  return campione, rimossi\n",
    "\n",
    "# return media u rimossi []\n",
    "def stats_riga(valori_raw, err_strumentale):\n",
    "  # caso 0\n",
    "  if len(valori_raw) == 0:\n",
    "    return {\"media\": np.nan, \"u_strum\": err_strumentale, \"u_stat\": np.nan, \"u\": np.nan, \"n\": 0, \"rimossi\": []}\n",
    "\n",
    "  # caso 1\n",
    "  if len(valori_raw) == 1:\n",
    "    return {\"media\": valori_raw[0], \"u_strum\": err_strumentale, \"u_stat\": np.nan, \"u\": np.nan, \"n\": 1, \"rimossi\": []}\n",
    "\n",
    "  # caso n>1\n",
    "  puliti, rimossi = rimuovi_outlier(valori_raw, err_strumentale)\n",
    "\n",
    "  media = puliti.mean()\n",
    "\n",
    "  s = puliti.std(ddof=1)\n",
    "  n = len(puliti)\n",
    "  s = s / np.sqrt(n)\n",
    "  u = np.sqrt(s**2 + err_strumentale**2)\n",
    "\n",
    "  return {\"media\": media, \"u_strum\": err_strumentale, \"u_stat\": s, \"u\": u, \"n\": n, \"rimossi\": rimossi}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "264a5b1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accessor: aggiunta metodi ai df\n",
    "\n",
    "# Nasconde specificamente l'avviso di sovrascrittura dell'accessor\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", message=\"registration of accessor\")\n",
    "\n",
    "@pd.api.extensions.register_dataframe_accessor(\"acc\")\n",
    "\n",
    "class Miei_Metodi:\n",
    "\n",
    "    def __init__(self, pandas_obj):\n",
    "        self._df = pandas_obj\n",
    "    \n",
    "    # Calcola la statistica con Criterio di Chauvenet\n",
    "    def calcola_stats(self, df, prefix, err_strumentale):\n",
    "        cols = [col for col in df.columns if col.startswith(prefix)]\n",
    "\n",
    "        for i, row in df.iterrows():\n",
    "            valori = row[cols].dropna().values.astype(float)\n",
    "            ans    = stats_riga(valori, err_strumentale)\n",
    "\n",
    "            self._df.at[i, prefix]              = ans[\"media\"]\n",
    "            self._df.at[i, f\"u{prefix}_strum\"]  = ans[\"u_strum\"]\n",
    "            self._df.at[i, f\"u{prefix}_stat\"]   = ans[\"u_stat\"]\n",
    "            self._df.at[i, f\"u{prefix}\"]        = ans[\"u\"]\n",
    "            self._df.at[i, f\"n{prefix}\"]        = int(ans[\"n\"])\n",
    "            self._df.at[i, f\"out{prefix}\"]      = str(ans[\"rimossi\"])\n",
    "\n",
    "        return self._df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a5ff5ff5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F  = g*m\n",
      "sigma_F = sqrt(g**2*sigma_m**2 + m**2*sigma_g**2)\n",
      "\n",
      "Formula LaTeX:\n",
      "F = g m\n",
      "sigma_F = \\sqrt{g^{2} \\sigma_{m}^{2} + m^{2} \\sigma_{g}^{2}}\n"
     ]
    }
   ],
   "source": [
    "# Calcolo simbolico F\n",
    "\n",
    "# simboli\n",
    "m, x, g = sp.symbols('m x g', positive=True)\n",
    "sigma_m, sigma_x, sigma_g = sp.symbols('sigma_m sigma_x sigma_g', positive=True)\n",
    "\n",
    "F = m * g\n",
    "\n",
    "# derivate parziali\n",
    "dF_dm = sp.diff(F, m)\n",
    "dF_dg = sp.diff(F, g)\n",
    "\n",
    "sigma_F = sp.sqrt((dF_dm * sigma_m)**2 + (dF_dg * sigma_g)**2)\n",
    "\n",
    "print(\"F  =\", F)\n",
    "print(\"sigma_F =\", sigma_F)\n",
    "print(\"\\nFormula LaTeX:\")\n",
    "print(f\"F = {sp.latex(F)}\")\n",
    "print(f\"sigma_F = {sp.latex(sigma_F.simplify())}\")\n",
    "\n",
    "# funzioni numeriche\n",
    "F_fn  = sp.lambdify((m, g),  F,  'numpy')\n",
    "uF_fn = sp.lambdify((m, sigma_m, g, sigma_g), sigma_F, 'numpy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e649eddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dati, analisi statistica medie e ricerca outlier\n",
    "\n",
    "dfCalibro_raw = pd.read_csv(r'molla2Calibro.csv')\n",
    "\n",
    "# Costanti strumentali\n",
    "ERR_CALIBRO = 0.02 / np.sqrt(24)\n",
    "ERR_BILANCIA = 0.01 / np.sqrt(12)\n",
    "\n",
    "# Chauvenet e poi statistica di base\n",
    "dfCalibro = pd.DataFrame()\n",
    "dfCalibro = dfCalibro.acc.calcola_stats(dfCalibro_raw, \"dx\", ERR_CALIBRO)\n",
    "dfCalibro = dfCalibro.acc.calcola_stats(dfCalibro_raw, \"m\",  ERR_BILANCIA)\n",
    "\n",
    "# Aggiunta delle forze\n",
    "dfCalibro[\"F\"]  = F_fn(dfCalibro[\"m\"],  G)\n",
    "dfCalibro[\"uF\"] = uF_fn(dfCalibro[\"m\"], dfCalibro[\"um\"], G, uG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "42ff21d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "722fa004f851472cb356ab99ec9c396b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, row_start=0, squeeze_row=False, type='numeric', value=[212.08,…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheet = ipysheet.from_dataframe(dfCalibro)\n",
    "\n",
    "display(sheet)\n",
    "# dfCalibro.head(2).to_json(orient=\"records\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c58a6e26",
   "metadata": {},
   "source": [
    "## Regressione lineare\n",
    "Regressione e residui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dfab4331",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Funzioni regressione\n",
    "\n",
    "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",
    "  P = stats.chi2.sf(x2, dof)\n",
    "\n",
    "  return A, B, sigma_A, sigma_B, cov_AB, x2, P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d7ca0a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residuo:\n",
      "  r = x + B/A - y/A\n",
      "\n",
      "Formula LaTeX:\n",
      "  r       = x + \\frac{B}{A} - \\frac{y}{A}\n",
      "  sigma_r = \\sqrt{\\frac{A^{4} \\sigma_{x}^{2} + A^{2} \\left(\\sigma_{B}^{2} + \\sigma_{y}^{2}\\right) - 2 A cov_{AB} \\left(B - y\\right) + \\sigma_{A}^{2} \\left(B - y\\right)^{2}}{A^{4}}}\n"
     ]
    }
   ],
   "source": [
    "# Calcolo simbolico residui\n",
    "\n",
    "A, B, x, y = sp.symbols('A B x y', real=True)\n",
    "sigma_A, sigma_B, sigma_x, sigma_y = sp.symbols('sigma_A sigma_B sigma_x sigma_y', positive=True)\n",
    "cov_AB = sp.symbols('cov_AB', real=True)\n",
    "\n",
    "# Residuo: r = x - (y/A - B/A)\n",
    "r = x - (y / A - B / A)\n",
    "\n",
    "# Propagazione errore (con covarianza A,B)\n",
    "dr_dA  = sp.diff(r, A)\n",
    "dr_dB  = sp.diff(r, B)\n",
    "dr_dx  = sp.diff(r, x)\n",
    "dr_dy  = sp.diff(r, y)\n",
    "\n",
    "sigma_r  = sp.sqrt(\n",
    "  (dr_dA * sigma_A)**2 +\n",
    "  (dr_dB * sigma_B)**2 +\n",
    "  (dr_dx * sigma_x)**2 +\n",
    "  (dr_dy * sigma_y)**2 +\n",
    "  2 * dr_dA * dr_dB * cov_AB\n",
    ")\n",
    "\n",
    "# Funzioni numeriche\n",
    "r_fn      = sp.lambdify((x , y , A , B ), r, 'numpy')\n",
    "sigma_r_fn = sp.lambdify(\n",
    "  (x , y , A , B , sigma_x , sigma_y , sigma_A , sigma_B , cov_AB ),\n",
    "  sigma_r , 'numpy'\n",
    ")\n",
    "\n",
    "print(\"Residuo:\")\n",
    "print(f\"  r = {r }\")\n",
    "print(f\"\\nFormula LaTeX:\")\n",
    "print(f\"  r       = {sp.latex(r)}\")\n",
    "print(f\"  sigma_r = {sp.latex(sp.simplify(sigma_r))}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cd68b201",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AC      = 3.2009 ± 0.0033\n",
      "BC      = 1161.4139 ± 0.4685\n",
      "cov_ABC = 0.001384\n",
      "chi²    = 4.3285\n",
      "P(chi², ∞)= 0.1148\n"
     ]
    }
   ],
   "source": [
    "# Regressione lineare\n",
    "\n",
    "AC, BC, uAC, uBC, covABC, chiC, PC = reg_lin(-dfCalibro[\"dx\"], dfCalibro[\"F\"], dfCalibro[\"udx\"], dfCalibro[\"uF\"])\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\"chi²    = {chiC:.4f}\")\n",
    "print(f\"P(chi², ∞)= {PC:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "68eb66dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot regressione\n",
    "\n",
    "SCALA = 50\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "x_data = -dfCalibro[\"dx\"]\n",
    "y_data =  dfCalibro[\"F\"]\n",
    "\n",
    "x_fit = np.linspace(x_data.min(), x_data.max(), 300)\n",
    "y_fit = AC * x_fit + BC\n",
    "\n",
    "ax.errorbar(\n",
    "    x_data, y_data,\n",
    "    xerr=SCALA * dfCalibro[\"udx\"], yerr=SCALA * dfCalibro[\"uF\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=f\"Dati (barre errore x{SCALA})\"\n",
    ")\n",
    "ax.plot(\n",
    "    x_fit, y_fit,\n",
    "    color='red', linewidth=1.5,\n",
    "    label=f\"$A={AC:.4f}\\\\pm{uAC:.4f}$\\n\"\n",
    "          f\"$B={BC:.2f}\\\\pm{uBC:.2f}$\\n\"\n",
    "          f\"$P(chi², ∞)= {PC:.4f}$\\n\"\n",
    ")\n",
    "\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(r\"$- x$  [mm]\")\n",
    "ax.set_ylabel(\"F  [mN]\")\n",
    "ax.set_title(\"Fit lineare — Molla 2 (calibro)\")\n",
    "ax.legend(fontsize=9)\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f98a3612",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui\n",
    "\n",
    "dfCalibro[\"r\"] = r_fn(\n",
    "  -dfCalibro[\"dx\"],\n",
    "  dfCalibro[\"F\"],\n",
    "  AC,\n",
    "  BC\n",
    ")\n",
    "\n",
    "dfCalibro[\"ur\"] = sigma_r_fn(\n",
    "  -dfCalibro[\"dx\"], dfCalibro[\"F\"],\n",
    "  AC, BC,\n",
    "  dfCalibro[\"udx\"], dfCalibro[\"uF\"],\n",
    "  uAC, uBC, covABC\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "831a4ac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot residui\n",
    " \n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "# Residui con barre d'errore\n",
    "ax.errorbar(\n",
    "    dfCalibro[\"F\"], dfCalibro[\"r\"],\n",
    "    xerr=dfCalibro[\"uF\"], yerr=dfCalibro[\"ur\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=\"Residui\"\n",
    ")\n",
    "\n",
    "# Linea dello zero\n",
    "ax.axhline(0, color='red', linestyle='--', linewidth=1)\n",
    "\n",
    "# Estetica\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(\"F  [mN]\")\n",
    "ax.set_ylabel(r\"Residuo $x_i - \\left(\\frac{y_i}{A} - \\frac{B}{A}\\right)$  [mm]\")\n",
    "ax.set_title(\"Residui della regressione — Molla 2 (calibro)\")\n",
    "ax.legend()\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5643144a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Probabilità chi quadro con uR invece di sigmaY_equiv\n",
      "P(chi², ∞)= 0.1900\n"
     ]
    }
   ],
   "source": [
    "# Chi2 alternativo\n",
    "\n",
    "x2  = np.sum(dfCalibro['r']**2 / dfCalibro['ur']**2)\n",
    "dof = 4 - 2\n",
    "P = stats.chi2.sf(x2, dof)\n",
    "print('\\nProbabilità chi quadro con uR invece di sigmaY_equiv')\n",
    "print(f\"P(chi², ∞)= {P:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b104df9",
   "metadata": {},
   "source": [
    "# Sonar statico"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4efaca0d",
   "metadata": {},
   "source": [
    "## Sonar senza correzione $v_{suono}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "414d21e7",
   "metadata": {},
   "source": [
    "### Import dei dati, medie e incertezze\n",
    "- masse, $\\omega$, c, t trattati come prima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d6195b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfSonar_raw = pd.read_csv(r'molla2Sonar.csv')\n",
    "\n",
    "ERR_OMEGA = 0.000001 / np.sqrt(12)\n",
    "ERR_C = 0.000001 / np.sqrt(12)\n",
    "ERR_CRONOMETRO = 0.01 / np.sqrt(12)\n",
    "\n",
    "# Tutto trattato esattamente come prima\n",
    "dfSonar = pd.DataFrame()\n",
    "dfSonar = dfSonar.acc.calcola_stats(dfSonar_raw, \"m\",  ERR_BILANCIA)\n",
    "dfSonar = dfSonar.acc.calcola_stats(dfSonar_raw, \"w\",  ERR_OMEGA)\n",
    "dfSonar = dfSonar.acc.calcola_stats(dfSonar_raw, \"c\",  ERR_C)\n",
    "dfSonar = dfSonar.acc.calcola_stats(dfSonar_raw, \"t\",  ERR_CRONOMETRO)\n",
    "\n",
    "# Aggiunta delle forze\n",
    "dfSonar[\"F\"]  = F_fn(dfSonar[\"m\"],  G)\n",
    "dfSonar[\"uF\"] = uF_fn(dfSonar[\"m\"], dfSonar[\"um\"], G, uG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "02e2d183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ee5a718b7a14194847b0a7f62c703ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, row_end=4, row_start=0, squeeze_row=False, type='numeric', val…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheet = ipysheet.from_dataframe(dfSonar)\n",
    "\n",
    "display(sheet)\n",
    "# dfSonar.head(4).to_json(orient=\"records\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e07ecd",
   "metadata": {},
   "source": [
    "### Regressione lineare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "06f9ccef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AS      = 3.2192 ± 0.0027\n",
      "BS      = 2042.5203 ± 1.0326\n",
      "cov_ABS = 0.002760\n",
      "chi²    = 9.0319\n",
      "P(chi², ∞)= 0.0289\n"
     ]
    }
   ],
   "source": [
    "# Regressione lineare\n",
    "\n",
    "AS, BS, uAS, uBS, covABS, chiS, PS = reg_lin(-dfSonar[\"c\"], dfSonar[\"F\"], dfSonar[\"uc\"], dfSonar[\"uF\"])\n",
    "print(\"Ax + B : \")\n",
    "print(f\"AS      = {AS:.4f} ± {uAS:.4f}\")\n",
    "print(f\"BS      = {BS:.4f} ± {uBS:.4f}\")\n",
    "print(f\"cov_ABS = {covABS:.6f}\")\n",
    "print(f\"chi²    = {chiS:.4f}\")\n",
    "print(f\"P(chi², ∞)= {PS:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "75653d7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot regressione\n",
    "\n",
    "SCALA = 100\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "x_data = -dfSonar[\"c\"]\n",
    "y_data =  dfSonar[\"F\"]\n",
    "\n",
    "x_fit = np.linspace(x_data.min(), x_data.max(), 300)\n",
    "y_fit = AS * x_fit + BS\n",
    "\n",
    "ax.errorbar(\n",
    "    x_data, y_data,\n",
    "    xerr=SCALA * dfSonar[\"uc\"], yerr=SCALA * dfSonar[\"uF\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=f\"Dati (barre errore x{SCALA})\"\n",
    ")\n",
    "ax.plot(\n",
    "    x_fit, y_fit,\n",
    "    color='red', linewidth=1.5,\n",
    "    label=f\"$A={AS:.4f}\\\\pm{uAS:.4f}$\\n\"\n",
    "          f\"$B={BS:.1f}\\\\pm{uBS:.1f}$\\n\"\n",
    "          f\"$P(chi², ∞)= {PS:.4f}$\\n\"\n",
    ")\n",
    "\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(r\"$- x$  [mm]\")\n",
    "ax.set_ylabel(\"F  [mN]\")\n",
    "ax.set_title(\"Fit lineare — Molla 2 (sonar statico senza correzione)\")\n",
    "ax.legend(fontsize=9)\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "09445aac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui\n",
    "\n",
    "dfSonar[\"r\"] = r_fn(\n",
    "  -dfSonar[\"c\"],\n",
    "  dfSonar[\"F\"],\n",
    "  AS,\n",
    "  BS\n",
    ")\n",
    "\n",
    "dfSonar[\"ur\"] = sigma_r_fn(\n",
    "  -dfSonar[\"c\"], dfSonar[\"F\"],\n",
    "  AS, BS,\n",
    "  dfSonar[\"uc\"], dfSonar[\"uF\"],\n",
    "  uAS, uBS, covABS\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1ecf9ab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Residui\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "# Residui con barre d'errore\n",
    "ax.errorbar(\n",
    "    dfSonar[\"F\"], dfSonar[\"r\"],\n",
    "    xerr=dfSonar[\"uF\"], yerr=dfSonar[\"ur\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=\"Residui\"\n",
    ")\n",
    "\n",
    "# Linea dello zero\n",
    "ax.axhline(0, color='red', linestyle='--', linewidth=1)\n",
    "\n",
    "# Estetica\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(\"F  [mN]\")\n",
    "ax.set_ylabel(r\"Residuo $x_i - \\left(\\frac{y_i}{A} - \\frac{B}{A}\\right)$  [mm]\")\n",
    "ax.set_title(\"Residui della regressione — Molla 2 (sonar statico senza correzione)\")\n",
    "ax.legend()\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "affcb57a",
   "metadata": {},
   "source": [
    "## Sonar con correzione $v_{suono}$\n",
    "- La velocità del suono dipende dalla temperatura -> anche la distanza misurata dal sonar\n",
    "- Formula di Cramer 1993\n",
    "- !! Io (Giulia) ho usato un'altra formula, non capisco il perchè di quella di Jacopo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4088453c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c = 0.049862877088008*c_raw*v0/sqrt(T + 273.15)\n",
      "sigma_c = v0*sqrt(0.000621576627873448*c_raw**2*sigma_T**2 + 0.00248630651149379*sigma_c_raw**2*(T + 273.15)**2)/(T + 273.15)**(3/2)\n",
      "\n",
      "Formula LaTeX:\n",
      "c = \\frac{0.049862877088008 c_{raw} v_{0}}{\\sqrt{T + 273.15}}\n",
      "sigma_c = \\frac{v_{0} \\sqrt{0.000621576627873448 c_{raw}^{2} \\sigma_{T}^{2} + 0.00248630651149379 \\sigma_{c raw}^{2} \\left(T + 273.15\\right)^{2}}}{\\left(T + 273.15\\right)^{\\frac{3}{2}}}\n"
     ]
    }
   ],
   "source": [
    "# Funzioni c\n",
    "\n",
    "# simboli\n",
    "v0, T, c_raw = sp.symbols('v0 T c_raw', positive=True)\n",
    "sigma_T, sigma_c_raw = sp.symbols('sigma_T sigma_c_raw', positive=True)\n",
    "\n",
    "# velocità corretta e posizione\n",
    "# v = 331.3 * sp.sqrt(1 + T / 273.15)\n",
    "v = 20.055 * sp.sqrt(T + 273.15)\n",
    "c = c_raw * v0 / v\n",
    "\n",
    "# derivate parziali\n",
    "dc_dcraw = sp.diff(c, c_raw)\n",
    "dc_dT    = sp.diff(c, T)\n",
    "\n",
    "sigma_c = sp.sqrt((dc_dcraw * sigma_c_raw)**2 + (dc_dT * sigma_T)**2)\n",
    "\n",
    "print(\"c =\", c)\n",
    "print(\"sigma_c =\", sigma_c.simplify())\n",
    "print(\"\\nFormula LaTeX:\")\n",
    "print(f\"c = {sp.latex(c)}\")\n",
    "print(f\"sigma_c = {sp.latex(sigma_c.simplify())}\")\n",
    "\n",
    "# funzioni numeriche\n",
    "c_fn  = sp.lambdify((c_raw, T, v0), c, 'numpy')\n",
    "uc_fn = sp.lambdify((c_raw, sigma_c_raw, T, sigma_T, v0), sigma_c, 'numpy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1584a5d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico c\n",
    "\n",
    "Temperatura = (23.6+21.2) / 2\n",
    "Temperatura1 = 21.2\n",
    "Temperatura2 = 23.6\n",
    "uTemperatura = (23.6-21.2) / np.sqrt(12)\n",
    "Velocita_impostata = 343.0\n",
    "\n",
    "dfSonarCorrezione = dfSonar.copy()\n",
    "\n",
    "dfSonarCorrezione['c']  =  c_fn(dfSonarCorrezione['c'], Temperatura, Velocita_impostata)\n",
    "dfSonarCorrezione['uc'] = uc_fn(dfSonarCorrezione['c'], dfSonarCorrezione['uc'],\n",
    "                      Temperatura, uTemperatura,\n",
    "                      Velocita_impostata)\n",
    "dfSonarCorrezione['cA']  =  c_fn(dfSonarCorrezione['c'], Temperatura1, Velocita_impostata)\n",
    "dfSonarCorrezione['cB']  =  c_fn(dfSonarCorrezione['c'], Temperatura2, Velocita_impostata)\n",
    "dfSonarCorrezione['vecchioC'] = dfSonar['c']\n",
    "dfSonarCorrezione['vecchioUc'] = dfSonar['uc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ac4860fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b540b35e61894ffeb58f5f85aecfb401",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, row_end=4, row_start=0, squeeze_row=False, type='numeric', val…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheet = ipysheet.from_dataframe(dfSonarCorrezione)\n",
    "\n",
    "display(sheet)\n",
    "# dfSonar.head(4).to_json(orient=\"records\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74f92463",
   "metadata": {},
   "source": [
    "### Regressione lineare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8917049c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit completo:   chi²=9.03,  P=0.0289\n",
      "Temperatura :  chi²=0.952,  P=0.8129\n",
      "\n",
      "A:  3.2192 ± 0.0027  to  3.2390 ± 0.0076\n",
      "B:  2042.5203 ± 1.0326  to  2043.7514 ± 2.5703\n"
     ]
    }
   ],
   "source": [
    "#Regressione lineare\n",
    "\n",
    "AS2, BS2, uAS2, uBS2, covABS2, chiS2, PS2 = reg_lin(-dfSonarCorrezione[\"c\"], dfSonarCorrezione[\"F\"], dfSonarCorrezione[\"uc\"], dfSonarCorrezione[\"uF\"])\n",
    "\n",
    "print(f\"Fit completo:   chi²={chiS:.2f},  P={PS:.4f}\")\n",
    "print(f\"Temperatura :  chi²={chiS2:.3f},  P={PS2:.4f}\")\n",
    "print()\n",
    "print(f\"A:  {AS:.4f} ± {uAS:.4f}  to  {AS2:.4f} ± {uAS2:.4f}\")\n",
    "print(f\"B:  {BS:.4f} ± {uBS:.4f}  to  {BS2:.4f} ± {uBS2:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d5646b8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q1avH+vXri51f+D+09PR0unXrRnBwMM899xzNmjWjVq1aHDlyhFmzZjnd8AwU272pzWajbdu2vPrqq8W+VmRkZIl1/ve//3V0A1pW8+fPd7kr34iICLp3787SpUv58ccfK60nqH379nHffffRtWtXli9fTkREBDVr1uT9998vcmPrlRw7doz77ruPNm3a8Nlnn1GjhmuH/muuuabYL4ZDhgzhjjvu4IsvvuD777/nlVdeYfHixWzevNlxHXhZuPKZLOt74UpXuoBjHJKffvqJr7/+mu+++47Ro0ezdOlSfvrpp1Ibn0OGDOHAgQM89dRTtG/fnsDAQGw2G3369Cny+a/KPKknJF9f3xK77738c1GY0cMPP8yIESOKfU7h/TSFRo4cidFo5OeffyY4OLgcKv7/SnofL23gl/WYWbgPu3JflRDukoaFEB5Kz1+XmjVrxo4dO+jcufMVv6Dt2bOHlJQUNm/e7NRz0Llz58r0WseOHeOuu+4q8zY/+uijdOnSpUzPKXTppUquGDZsGGPHjiU0NJR77rmn2GXq1q2Lv78/Z86cKTLv9OnTeHl5XbGhdLnPP/+cWrVq8d133zn9ev7++++7vI4///yTPn36UK9ePb755huXz9KA/dfgkrKMiIhg8uTJTJ48mcTERG666SZefPFF+vbtS5MmTQD7+AyFl2cUOnPmjGN+WZTHe3Elt99+O7fffjsvvvgiGzZsYPjw4XzyySeMHTu2xM9lWloaO3fuZOHChcybN8/x+NmzZ4ssW5bPdrNmzfjtt9+uuEyTJk04fvw4NpvN6Ut04eWHV3qPmzVrBsBvv/3GddddV+L6oXwz9MRa6tatS1BQEFarlZ49e5a6/Msvv8yWLVvYvHkzLVq0KHX5S2u/9JiTl5fHuXPnXHrNy5X1mFm4DxeezRKiIsk9FkJ4qMJ+1/UYqXfIkCFYrVaef/75IvMKCgocNRX+unbpr2l5eXksX768TK8VGxvLu+++W2RednY2mZmZJT732muvpWfPnlf1V9aGxf3338/8+fNZvnx5kcu5Cnl7e9OrVy++/PJLp+4mExIS2LBhA126dCnTL5ze3t4YDAanLjvPnz/vck9Z8fHx9OrVCy8vL7777rsSL50oSadOnfjtt9+cLjuyWq1FLvGpV68eDRo0cCx38803U69ePVauXOn03G3btnHq1Kmr6s3K3feiJGlpaUUu92rfvj2Ao/bCXoUu3xeL+/yDvfegy5Vlfx48eDDHjh3jiy++KDKv8LXuuece4uPj2bhxo2NeQUEBb731FoGBgXTr1q3E9ffq1YugoCAWLVpUZIDHwvVXRIaeWIu3tzeDBw/m888/L7Yxl5SU5Pj3jh07ePbZZ5kzZw4DBgxwaf09e/bEx8eHN9980+lzsnr1ajIyMq6q9rIeM6OjozEYDHTq1KnMryVEWckZCyE8VMeOHQF47LHH6N27N97e3gwdOrRSXrtbt25MmDCBRYsWERMTQ69evahZsyZnz55l06ZNvPHGG9x///3861//onbt2owYMYLHHnsMg8HAhx9+WOx1+SV55JFH+PTTT5k4cSK7d++mc+fOWK1WTp8+zaeffsp3332n+30pACEhIS5dOvXCCy+wfft2unTpwuTJk6lRowbvvPMOubm5xfZHfyX9+vXj1VdfpU+fPgwbNozExESWLVvGdddd53TNdUn69OnDX3/9xcyZM9m/f7/Ttejh4eHcfffdV3z+v//9b55//nn27t1Lr169APsN+o0aNeL++++nXbt2BAYGsmPHDn755RfHQHo1a9Zk8eLFjBo1im7duvHQQw85ugdt2rQpjz/+eJneh/J4L0qybt06li9fzsCBA2nWrBlms5l3332X4OBgx5kpPz8/WrVqxcaNG7n++usJCwujTZs2tGnThq5du7JkyRLy8/Np2LAh33//fbFneQr35zlz5jB06FBq1qzJvffeW+zAbU899RSfffYZDzzwAKNHj6Zjx46kpqby1VdfsXLlStq1a8f48eN55513GDlyJNHR0TRt2pTPPvuMH3/8kddff73Ye4AKBQcH89prrzF27FhuueUWhg0bRu3atTl27BhZWVmsW7euQjL01Fpefvlldu/ezW233ca4ceNo1aoVqampHDlyhB07djjGf3jooYeoW7cuzZs356OPPnJax9133014eHiRddetW5enn36ahQsX0qdPH+677z7OnDnD8uXLueWWW5xu1HZVWY+Z27dvp3PnzlfsEleIcqNLX1RCVDOFXRCW1KVlcV3DFhQUaNOmTdPq1q2rGQyGUruedbW72YCAgCLPnT9/frHrX7VqldaxY0fNz89PCwoK0tq2bavNnDlTMxqNjmV+/PFH7fbbb9f8/Py0Bg0aaDNnztS+++67Il2pXtp96+Xy8vK0xYsXa61bt9Z8fX212rVrax07dtQWLlyoZWRkXHG7K8qV6i1UXHezmqZpR44c0Xr37q0FBgZq/v7+Wo8ePbQDBw4U+9zSuptdvXq11rx5c83X11dr0aKF9v7775eY1+UooYtjwOmzciU33nijo6tdTbN3f/nUU09p7dq104KCgrSAgACtXbt22vLly4s8d+PGjVqHDh00X19fLSwsTBs+fLh28eJFp2XK8pl09b0Artgd7qWOHDmiPfTQQ1rjxo01X19frV69elr//v21w4cPOy134MABrWPHjpqPj49T17MXL17UBg4cqIWGhmohISHaAw884Oje8/LuaZ9//nmtYcOGmpeXl1OXpJd3N6tpmpaSkqJNnTpVa9iwoebj46M1atRIGzFihFOXqAkJCdqoUaO0OnXqaD4+Plrbtm2d9vfSfPXVV9q//vUvzc/PTwsODtZuvfVW7eOPP3Zaprwz1KsWTbvy5yIhIUGbMmWKFhkZqdWsWVOrX7++dtddd2mrVq1yen5Jf4X78eXdzRZ6++23tRYtWmg1a9bUwsPDtUmTJmlpaWlOy5R0zCnuuODqMTM9PV3z8fHR3nvvvWK3W4jyZtC0Mvy0KIQQolr58MMPmTJlChcuXHAahEwI4flef/11lixZwp9//ulyhwZCuEPusRBCCFGi4cOH07hxY5YtW6Z3KUKIMsjPz+fVV1/l2WeflUaFqDRyxkIIIYQQQgjhNjljIYQQQgghhHCbNCyEEEIIIYQQbpOGhRBCCCGEEMJt0rAQQgghhBBCuE0aFi7SNA2TyVSmgb+EEEIIIYSoLqRh4SKz2UxISAhms1nvUipURkaG3iUIN0mG6pMM1ScZqk3yU59kqA9pWAgnXl7ykVCdZKg+yVB9kqHaJD/1SYb6kHddOMnJydG7BOEmyVB9kqH6JEO1SX7qkwz1IQ0L4SQ4OFjvEoSbJEP1SYbqkwzVJvmpTzLUhzQshJPk5GS9SxBukgzVJxmqTzJUm+SnPslQHwZNujlyiclkIiQkhIyMDGkFCyGEEEIIcZkaehdQVVitVvLz8/Uuw20JCQmEh4frXYZwQ0Vk6O3tTY0aNTAYDOW6XlG82NhYGjZsqHcZwg2SodokP/VJhvqQMxYuutIZC4vFwsWLF2WMC1Gl+fv7ExERgY+Pj96lVHmapkkjTnGSodokP/VJhvrQ9YzFDz/8wCuvvEJ0dDRxcXF88cUXDBgwwDF/wYIFfPLJJ/zzzz/4+PjQsWNHXnzxRW677TbHMqmpqUybNo2vv/4aLy8vBg8ezBtvvEFgYKBjmePHjzNlyhR++eUX6taty7Rp05g5c2a5bIPVauXixYv4+/tTt25d5T/EeXl58sVRceWdoaZp5OXlkZSUxLlz52jevLl041fBjEaj/NKmOMlQbZKf+iRDfejasMjMzKRdu3aMHj2aQYMGFZl//fXX8/bbb3PttdeSnZ3Na6+9Rq9evfjjjz+oW7cuAMOHDycuLo7t27eTn5/PqFGjGD9+PBs2bADsZxp69epFz549WblyJb/++iujR48mNDSU8ePHu70N+fn5aJpG3bp18fPzc3t9evPx8ZEvjYqriAz9/PyoWbMmf//9N3l5edSqVatc1y+c1alTR+8ShJskQ7VJfuqTDPXhMZdCGQyGImcsLld4OdKOHTu46667OHXqFK1ateKXX37h5ptvBuDbb7/lnnvu4eLFizRo0IAVK1YwZ84c4uPjHb/izp49my1btnD69GmX6yvpUqicnBzOnTtHVFRUlfiylZ+fT82aNfUuQ7ihojKsap91T5aUlOT48USoSTJUm+SnPslQH8r8NJ2Xl8eqVasICQmhXbt2ABw8eJDQ0FBHowKgZ8+eeHl5cejQIccyXbt2dbo0pHfv3pw5c4a0tLTK3QgFyNkK9UmG6vP19dW7BOEmyVBtkp/6JEN9eHyvUFu3bmXo0KFkZWURERHB9u3bHae34uPjqVevntPyNWrUICwsjPj4eMcyUVFRTssU9pgTHx9P7dq1i33d3NxccnNzHdMmk6nctkkIIYQQQoiqxuN/2uzRowcxMTEcOHCAPn36MGTIEBITEyv8dRctWkRISIjjLzIyErBfDmI0GrHZbMTGxgJQUFCAzWYjPz8fq9VKQUGB02OFN7+C/cxLccsWdldrs9mcli18rs1mK9Oyheu/fNnCZQYOHIjBYMBoNJZYy+V1a5p2xbonT55MZGQkwcHBNGzYkGnTppGTk1Nk2ezsbMaMGUNUVBRBQUG0aNGCVatWOdU9ZcoUp3X95z//wWKxOGo5e/Ysffr0oXbt2jRs2JCXX37Zqe7MzEymTp1K7dq1CQsLY8qUKeTm5pbbe1ja+suybE5ODpMnT3bMnzZtGllZWWiahsViYezYsU7v1bvvvutUd2BgoNNfrVq1uPHGG0vM/kqfw9KWtdlsZGRkkJaWRmZmJgkJCeTn5zv2hdjYWKxWK3FxcWRnZ5Oamkp6ejpms5nExETy8vKclrXZbBiNRnJyckhOTsZkMmEymUhOTi52X4uNjSUvL4/ExETMZjPp6emkpqaSnZ1NXFwcVqvVadn8/HwSEhLIzMwkLS3N5bpTUlLIyMhw1J2bm+u0rKZpGI1GcnNzSUpKKlPdFovFqe74+HgKCgqcls3KyiI+Pp6srCxSU1NLrdtoNBapOykpqdi6Y2Nji607Ozvb5boL67u87oKCgiJ1WyyWYusuzP5KdRfWe3ndZrOZjIwMUlJSSqy7MHuLxUJaWlqZ6k5PT8disVzxM5udne30mS2u7sLnXKnu0j6zl9Z9+XtYWPel+1ppdV++rxXWbTQai627cF+7tO7y3NfkGHF1x4j8/Hw5RpRyjEhLS5NjRDkeI1ymeQhA++KLL0pd7rrrrtNeeuklTdM0bfXq1VpoaKjT/Pz8fM3b21vbvHmzpmma9sgjj2j//ve/nZbZtWuXBmipqaklvk5OTo6WkZHh+Pvnn380QMvIyHBaLjs7Wzt58qSWnZ3twlZ6hu+//17z8/PTatWqpW3bts1pntVqver1njx5UrNYLJqmaVpSUpLWvXt37fnnny+ynMVi0ebOnav98ccfms1m0w4ePKiFhoZq3333nUvrKigo0Fq1aqU988wzWl5ennb69GktMjJSW79+veP58+bN09q1a6cZjUbNaDRq7dq10xYuXFjqNuzevVvr1q1bqcuVZf2lLXul+a68V5dr27at9uKLL5a6DWWl4mddVVlZWXqXINwkGapN8lOfZKgP5RoW1157rTZ//nxN0+xfPgHt8OHDjvnfffedZjAYtNjYWE3TNG358uVa7dq1tby8PMcyTz/9tHbDDTeUqb6MjIwq0bDIy8vTWrZsqT377LPaLbfcoi1atMhpfm5ubrm8TmJionbnnXdqjz76qEvLDxw4UJs7d65L6zpx4oTm7e3tVOuCBQucGgSNGjXSNm3a5Jj+9NNPtcaNG5dah6sNi7Ksv7Rly1rrld6rQ4cOad7e3o7Pf3lS7bOusorIT1QuyVBtkp/6JEN96HoplMViISYmhpiYGADOnTtHTEwMFy5cIDMzk2eeeYaffvqJv//+m+joaEaPHk1sbCwPPPAAAC1btqRPnz6MGzeOn3/+mR9//JGpU6cydOhQGjRoAMCwYcPw8fFhzJgxnDhxgo0bN/LGG28wY8YMvTZbV2+99Rbp6enMmjWLtm3bOt77QoW9CU2ePJnQ0NAS//bv31/s+l9++WUCAwOpV68ex44dY9q0aaXWlJOTw88//8yNN97o0rpsNhuA04CENpuN48ePA5CWlsbFixdp3769Y3779u25cOECGRkZpdZTmrKsv7Rly1prSe9VodWrV9O3b1/H51+oqX79+nqXINwkGapN8lOfZKgTPVs1u3fv1oAifyNGjNCys7O1gQMHag0aNNB8fHy0iIgI7b777tN+/vlnp3WkpKRoDz30kBYYGKgFBwdro0aN0sxms9Myx44d07p06aL5+vpqDRs21F5++eUy11oVzljEx8drwcHB2urVqzVN07TXXnutyJmb8jpjcfLkSW3OnDnaP//8c8XlbDabNnz4cK179+4lXoZ1+bry8vK0Zs2aaTNnztRycnK03377TWvUqJHm7e2taZqmXbhwQQO0pKQkxzoSExM1oNh6Jk2apIWEhGghISFaQECA5u3t7ZgOCQnR9u3b57R8WdZf2rJlWVdp75XFYtGCg4Odzn6UJ5U+66q7ePGi3iUIN0mGapP81CcZ6kPXXqG6d+/u9Kvz5TZv3lzqOsLCwhyD4ZXkxhtvZN++fWWur6qZPXs21157LSNHjgSgbdu2nD17lqysLPz9/QHKbcTmli1b0q5dO0aOHMmOHTuKXUbTNCZPnsyZM2fYsWNHid2kXr6umjVr8uWXX/L444/TsGFDGjVqxKhRo3jnnXcAHKOuZ2RkOHoQK/z1PygoqMj6ly9fzvLlywHYs2cPCxYsYM+ePSVuW1nWX9qyhWdfSluXK+/Vpk2b8Pf3v+JYMEINMlqs+iRDtUl+6nMnQ7PZ7OgwpjiBgYHFfp8QCnQ3K8rHzz//zLp16/D393dcJmO1Wh2XEN1+++2AvQcgHx8fJk6cyEcffVTi+rZt28Ydd9xxxdfMz8/n7Nmzxc7TNI0pU6Zw6NAhdu7cSUhISJnW1bp1a77//nvH9KxZs+jWrRsAtWvXplGjRsTExNCsWTMAYmJiiIyMLPV1XFGW9buybGnzXX2v3nvvPUaMGOForAh1xcbGyhcbxUmGapP81OdOhtHR0ezdu7fE+d26daN79+5XWVkVp+v5EoWofCmUzWbTbr31Vm3o0KFaXFyc01/Dhg21FStWOJa92l6hzGaztmbNGi0tLU2z2Wza8ePHtZYtW2rjxo0rdvnJkydrN954o5acnHxV6zp27JhmsVi03Nxc7fPPP9fq1KmjHTt2zDF/7ty5WocOHRzb2aFDB5d6hXJVWdZf2rKlzb/Se1Xo9OnTmsFg0H7//Xe3eva6EhU+61VFeV2SKPQjGapN8lOfOxmaTCZHT43Hjx/XFixYoB0/ftzxmMlkKsdKqxZpWLhI5YbFmjVrtPr162spKSlF5t13333ahAkTHNOX9p5VFhaLRevZs6cWFhamBQQEaFFRUdqTTz6pZWZmOpaZMGGCNmHCBO38+fMaoPn6+moBAQGOv8I6XFnXnDlztLCwMM3f31/r1KmTtn//fqd68vLytMmTJ2uhoaFaaGioNnXqVC0/P7/Y2idMmOBUx+V/P/zwQ5HnXGn9hdvpai1Xml/ae1Xoqaee0rp27epYX0VQ4bNeVSQkJOhdgnCTZKg2yU995ZWh0WjUFixYoBmNxnJZX1Vn0LQr3OQgHEwmEyEhIWRkZBAcHOx4PCcnh3PnzhEVFUWtWrV0rLB8WK1WvL299S5DuKGiMqxqn3VPZrFYHPfnCDVJhmqT/NRXXhnGxcWxatUqxo8fT0RERDlUVrV5/MjbonJJO1N9kqH6CgoK9C5BuEkyVJvkpz7JUB/SsBBCCA8jN+CrTzJUm+SnPslQH9KwEE4MBoPeJQg3SYbqk0vN1CcZqk3yU59kqA9pWAgn0sJXn2SoPpPJpHcJwk2SodokP89gNpvZs2cPZrO5zM8tjwzPJWey/MdY9uRFsfzHWM4lZ7q9zqvhzvtQ2aRhIZzUqCFDm6hOMlRf4WCJQl2SodokP89gsVjYu3fvFQerK4m7GX56+B/uWrqHDdEJnLeGsSE6gbuW7mHT4X/cWu/VcOd9qGzSsKhG8vPzqVWrFgEBAQQGBhIQEMCtt97KsWPHnJYpq9zcXMaNG0dUVBRBQUG0aNGCNWvWFHntqVOnUrt2bcLCwpg2bVqxN1ZlZ2dz3XXXERoa6vT4tGnTiIyMJDg4mIYNGzJ9+nTy8vJKrCk2NpYBAwZwzTXXUKdOHYYMGUJSUlKZt604b7/9NjfffDO+vr4ujXLt6raXl6vJUHiWhIQEvUsQbpIM1Sb5qc+dDM8lZzL78+PYNLBqoGHAqoFNg1mfH+e8TmcuVCA/bVYjJ06cIC8vD5PJRGBgINnZ2QwePJinn36ab775BgAfH58yr7egoICIiAh27NjBtddey6FDh+jbty+NGjWiV69eALzwwgvs37+fkydPAtC3b19eeukl5s2b57SuefPm0aRJE5KTk50enzx5Mi+//DIBAQEkJyfzwAMPsGTJEp599tlia5oyZQoAf//9N5qmMXz4cB577DE+/vjjErdjz549LFiwgD179lxxexs0aMCzzz7Ljh07uHjx4hWXBde3vbxcTYbCs8iIv+qTDNUm+XmWy78TuMLLy4u4uLirer01P8ZypbsVV+85xeTOlfcZuZrt14s0LKqRI0eOEBUV5ejX2c/Pj3bt2nH48GHHMnl5eWX+YhoQEMBzzz3nmL799tvp0aMH+/fvdzQs1qxZw2uvveboA3rOnDk8+eSTTl+uo6Oj+fbbb1m6dClDhgxxeo2WLVs6/q1pGl5eXpw9e7bEmv766y9mz57t2NYHH3yQRYsWlWm7SjJo0CAAYmJiXGpYuLLt5elqMhSeJTY2Vr7YKE4yVJvk51k2b95cqa+3Py8Km602GIpe2KNpGvuPnqTGif9Vak2qkIZFNXLkyBFuvPFGwL5jREdHs379elasWOFYpmbNmoD9DMGGDRtKXNfWrVvp0qVLsfNycnL4+eefGTZsGABpaWlcvHiR9u3bO5Zp3749Fy5cICMjg5CQEAoKChg3bhzLli0r8ebjl19+mRdeeIHMzEyuueYaFi9eXGJ9M2bMYNOmTfTr1w9N0/j444+59957S1y+oriy7eWtMEOhrvDwcL1LEG6SDNUm+XmWQYMGlfmeiYKCgqu75zA/H6/l21irhWIt5rSFwWCgS4dWjO98d9nXfZWSk5MrvXF1taRhUY0cOXKEo0ePEhoaSm5uLnl5ebz88sv069fPsUxBQQE1a9Zk+fLlLF++vMyvoWkaY8eOpXnz5o5f9gtvNrr0vonCf5vNZkJCQnjllVfo0KEDXbt2LfFSpNmzZzN79mxOnTrF+vXrqV+/fol1dO7cmXfffZfatWsD0KlTJ55++ukiy13agCooKCAnJ8epzis1oFzhyraXt8IMhbqSk5Ov+PkWnk8yVJvk51nq1KlT5lGv4+Pjy57hkSMwdiwPn0/g/bErQdOgmC7cx3RvSUSdgLKtu5qQm7erCavVyrFjx/jss89IT08nKyuLnTt3MmvWLH799VfHcl5eV/+R0DSNyZMnc+bMGbZs2eJYV+HlSBkZGY5lC/8dFBTEH3/8wcqVK3nllVdcep2WLVvSrl07Ro4cWex8m83G3XffTefOnbFYLFgsFjp37uy4LOtSy5cvJz09nfT0dEcjonA6PT3drUYFlL7tFcGdDIVnCA4O1rsE4SbJUG2Sn/rKlGF2NsyaBbfeCkePEmXIYXGjLLy8DHgbwICGlwG8DLB48I00lUZFieQbSDVx+vRpsrKyuOWWWwD7qbw77rgDHx8fzp0751hO0zQAJk6cSGBgYIl/+/btc1q/pmlMmTKFQ4cO8f333zv9El+7dm0aNWpETEyM47GYmBgiIyMJCQlh//79JCQkcP3111OnTh3+/e9/YzKZqFOnDocOHSp2e/Lz80u8xyI1NZW///6bxx57DH9/f/z9/Zk2bRqHDh2q9BugStv2ilCYoVBXTk6O3iUIN0mGapP8PENgYCDdunVz/EhXFi5nuGcP3HgjLFkCVis8+CCcPMkDjz3Irie6M6xjOE29UxneMZxdT3TngZsjy1yLu9x5HyqbNCyqiSNHjtCkSRPq1asH2AeOefrppwkODqZr165Fll+5cqXj1/7i/u644w6n5adOncqPP/7I9u3bHZcfXWrUqFG8+OKLxMfHEx8fz0svvcTYsWMBGDJkCH/88QcxMTHExMTw3nvvERQURExMDB06dMBisfD++++Tnp6Opmn8+uuvvPDCC/Tu3bvYba1Tpw7XXXcdy5YtIycnh5ycHJYtW0ajRo2ueI1m9+7dS+0RCv7/JVMFBQXYbDZycnKu2PXtlbZdiOLI6OnqkwzVJvl5hqCgILp3735VZ/hLzTA9HcaNgx494I8/oGFD+PJL+OQT+L97bJrWCWBy54Z09znH5M4NdTtT4c77UNnkHotq4siRI/zzzz8EBgbi7e1NcHAw3bp1Y//+/U7X/1/NwfTvv/9m+fLl+Pr60qRJE8fjDz/8MCtXrgRg7ty5pKSkOHp3evjhh3nmmWcAHGcVCtWtWxeDwUCjRo0A+9mJDRs28OSTT5Kbm0u9evUYPHgwCxcudDxn4sSJAI7X+/LLL3n88cdp2LAhNpuNDh068NVXXxWpfeLEiXz00Uclbtu2bduKNKJeeOEFp9f28/OjW7dujkbJ5bVcadsrgvwPUX1yj4z6JEO1SX7qu2KGX3wBU6ZAYXe0EyfCyy9DBV1JUJ0YNLluwiUmk4mQkBAyMjKcrtvLycnh3LlzREVFUatWLR0rLB/5+flyQFVcRWVY1T7rniwhIUF6pVGcZKg2yU99xWYYHw9Tp8Lnn9unr78e3n0Xirlyo1BcXByrVq1i/PjxZb6BvDqSS6GEE29vb71LEG6SDNUXFhamdwnCTZKh2iQ/9TllqGmwZg20bGlvVNSoAc88A8eOXbFRIcpOGhbCSUFBgd4lCDdJhupLTEzUuwThJslQbZKf+hwZ/vkn9OwJY8bY76vo2BEOH4YXXwQ5+17u5B6L8qZpkJVVea/n719sH8tXS0ZsVp9kqD4Z8Vd9kqHaJD/1NQwPh//+F+bNs3cn6+cHzz0H06fbz1iICiHvbHnLyoLK7A7MYoGA8uulIC8vT76YKk4yVF9sbKx8sVGcZKg2yU9xx46R9+ij+Bw/bp++805YtQqaNXPp6Waz2THAbWE39Zd2Vx8YGKhED016kIaFcCI3bqtPMlSf3CCoPslQbZKfonJy4PnnYckSfAoKIDQUli6FUaPKdHVHdHQ0e/fudXps8+bNjn9369aN7t27l1PRVYs0LMqbv7/9LEJlvl45ys/Pl1+7FScZqi8+Pp4GDRroXYZwg2SoNslPQfv2wdix8PvvAGTfcw9+770HV9FI7NixIzfccEOJ81UYqE4vcvN2eTMY7JcmVdZfOd5f8cknnzB8+HCXl7/55pudWvCX69ixI7t27SqP0orVq1cvduzYUWHrV1UNuXZUecUNMinUIhmqTfJTiMkEkybZe3f6/Xd7Q2LzZvjss6tqVIB9QLqIiIgS/+QyqJJJw6Iayc/Pp1atWgQEBBAYGEhYWBiPPPIIubm52Gw2nnnmGZcHbisoKODEiRO0adOmxGWio6PJz8/nwQcf5O6772b8+PHltSkAzJkzh6eeeqrc1pefn8/UqVOpXbs2YWFhTJs27Yo9LMXGxjJgwACuueYa6tSpw5AhQ0hKSnLMf/vtt7n55pvx9fVlwIABTs/Nzc1l3LhxREVFERQURIsWLVizZo1j/p9//knfvn2pXbs2DRs2ZMmSJS5vh81mc32jhUfKqswOIESFkAzVJvkp4uuvoVUr+L8BaRk3Dk6ehIEDJUOdSMOiGjlx4gQFBQUkJCRgsVg4fPgwW7duZd26dXzzzTeEhYXRtm1bl9Z15swZAJqVciNUr1692LhxI9u3b+fAgQNkZGS4vR2FunbtSnp6Oj/++GO5rO+FF15g//79nDx5khMnTrBv3z5eeumlEpefMmUKYB95/Ny5c+Tk5PDYY4855jdo0IBnn32WcePGFXluQUEBERER7NixA5PJxNq1a3niiSf4/vvvsVqt3Hfffdx0000kJiaya9cu3n77bTZs2FAu2yk8n5x1Up9kqDbJz8MlJMCDD8J990FsLFx3HezaZb9BOzQUkAz1Ig2LauTIkSO0bt3acW3gtddeS3h4ODk5OXz11VfceeedGC67tGrbtm3ceuutBAQEEB4ezn//+18AYmJiaNGiBW+99RaNGzcmJCSERYsWOZ73xRdf0KFDB8f6li1bxr333ktISEi5bY/BYODOO+/kq6++Kpf1rVmzhmeffdZxqnPOnDmsXr26xOX/+usvhgwZ4ugd4sEHH+TXX391zB80aBADBgygTp06RZ4bEBDAc889R7NmzTAYDNx+++306NGD/fv3c+bMGc6cOcP8+fOpWbMmN9xwA2PGjGHVqlUubcflGQr1eHnJoVl1kqHaJD8PpWmwbp39LMWnn4K3N8ycCcePQ48eTotKhvqQd70aOXLkCLfffjsAOTk5LFu2DKPRSL9+/RwNhUsvo1m7di3jxo1j0aJFZGRkEBMTQ5cuXQA4duwYZ8+epW7duvz5559s3ryZuXPnkpubC8DRo0e56aabAFi0aBH//PPPFX/9v5TZbOaee+6hefPmLF26lPPnz3PzzTdz44038umnnzot26pVK2JiYoqsY/LkyYSGhpb4t3//fqfl09LSuHjxIu3bt3c81r59ey5cuFDiWZYZM2awadMmMjIySE9P5+OPP+bee+91aRsvl5OTw88//8yNN97oyEDTNMd8m83G8cJu80ohl0KpLycnR+8ShJskQ7VJfh7o3Dno0wdGjoTUVGjfHn7+GRYvto9RcRnJUB/SsKhGjhw5wocffkjt2rVp3rw53377LTt27KBZs2akpaURHByMt7c3ACaTienTp7Nu3TruuusuatSoQUREhKNhEhMTw8SJExk+fDg1a9akVatWaJrm+LX86NGjdOjQgXXr1rF48WJ+++037r33Xi5evFhqne+88w5PPvkkJ06c4LvvvmPgwIGsWLGCo0ePsmHDBqxWq2PZ4OBg0tLSiqxj+fLlpKenl/hX2EAqVNhfdej/nUK99N9ms7nYOjt37kxiYqLjnoy0tDSefvrpUrfvcpqmMXbsWJo3b86gQYO44YYbaNq0KfPmzSM3N5cTJ06wZs0aTCaTS+srzFCoKzg4WO8ShJskQ7VJfh7EaoXXXoM2beD77+2jZb/8sr1R8X8/YBZHMtSHNCyqCavVyrFjx9i3bx9paWn8888/fP3119x6662AvQcMk8nkuFl5165dREREcNdddxW7vmPHjjndkPzrr7/SvHlzRzenhQ2LESNGkJ6eztatW9m6dSuNGjUqtda//vqLO+64Ax8fH5566in8/f255ZZb8Pb2pkWLFqSkpDiWNZlM5dJ7R+HlYZeenSj8d3G9P9hsNu6++246d+6MxWLBYrHQuXNnevXqVabX1TSNyZMnc+bMGbZs2YKXlxc1a9bkyy+/5OjRozRs2JDhw4czatQorrnmGpfWeaUbzoUaLh2ISahJMlSb5Ochfv0V/vUvmDHDPgBxt272y55mzYJSxmySDPUhDYtq4vTp0+Tm5tK6deti57dv357Tp087BldLSUkp8Qt7XFwcSUlJTpcNRUdH06FDBwCSkpKIi4ujXbt2V1Vr+/bt+fDDD0lJSWHp0qX4+Pjw7rvvkpSUxPHjx6lbt65j2ZMnTzrVUWjixIkEBgaW+Ldv3z6n5WvXrk2jRo2cLquKiYkhMjKy2PtCUlNT+fvvv3nsscfw9/fH39+fadOmcejQIZcPZpqmMWXKFA4dOsT333/v9DqtW7fm+++/Jzk5mZiYGHJzc+nWrZtL65UB8tQn/eerTzJUm+Sns9xcmDfPfkbi558hONh+Y/auXdC8uUurkAz1IQ2LauLIkSNcd9111KpVq9j59957L7t37yY/Px+Am266iZiYGMe9CEajkQMHDgD2L9zNmzd3GiAmOjracU/F0aNHue666644gMyCBQtKHLVy1KhRHD9+nP79+zN69Gi2bNnCDz/8wMCBA3n66aedbk7evXs3/fv3L7KOlStXOs4kFPd3xx13FPu6L774IvHx8cTHx/PSSy8xduzYYmusU6cO1113HcuWLSMnJ8dxz0qjRo0cN2sXFBSQk5NDQUEBNpuNnJwc8vLyHOuYOnUqP/74I9u3by/SiDt+/DiZmZnk5eWxefNmx43lrijMUKjLaDTqXYJwk2SoNslPRwcOQIcO9hG0CwpgwAA4dcrelWwZbsiWDHWi6Wjv3r1a//79tYiICA3QvvjiC8e8vLw8bebMmVqbNm00f39/LSIiQnvkkUe02NhYp3WkpKRow4YN04KCgrSQkBBt9OjRmtlsdlrm2LFjWpcuXTRfX1+tUaNG2uLFi8tca0ZGhgZoGRkZTo9nZ2drJ0+e1LKzs8u8zso0ffp07YEHHihxfkFBgda0aVPt+PHjjsdWrFihRUVFaf7+/lrTpk219evXa5qmaYsWLdIefPBBp+c3bdpU2759u6Zpmvbyyy8XmX+5UaNGac8888zVbo6maZr2ww8/aO3atXNrHZfKy8vTJk+erIWGhmqhoaHa1KlTtfz8fMf8CRMmaBMmTHBMnzhxQuvVq5cWFhamhYaGaj169NCOHDnimD9//nwNcPrr1q2bpmmadv78eQ3QfH19tYCAAMdf4frnzJmjhYWFaf7+/lqnTp20/fv3u7wdNpvNzXeieKp81quCispQVB7JUG2Snw5MJk2bOlXTDAZNA00LD9e0TZs07SqzkAz1oWvD4ptvvtHmzJmjbd68uUjDIj09XevZs6e2ceNG7fTp09rBgwe1W2+9VevYsaPTOvr06aO1a9dO++mnn7R9+/Zp1113nfbQQw855mdkZGjh4eHa8OHDtd9++037+OOPNT8/P+2dd94pU62qNyxcsWHDBu3++++vlNdq06aNlpyc7NY6evXqpX3//fflVFHVkZubWyHrrUqfdU938eJFvUsQbpIM1Sb5VbL//U/TIiPtDQrQtFGjNC0lxa1VSob6MGjaJX1a6shgMPDFF18UGaH4Ur/88gu33norf//9N40bN+bUqVO0atWKX375hZtvvhmAb7/9lnvuuYeLFy/SoEEDVqxYwZw5c4iPj3fcWDx79my2bNnC6dOnXa7PZDIREhJCRkaGU08DOTk5nDt3jqioqBIvM1KJzWaTvp8VV1EZVrXPuifLzc3F19dX7zKEGyRDtUl+lSQpCaZPh8IBYKOi7PdS9Ozp9qolQ30o9Q0yIyMDg8Hg6Ab04MGDhIaGOhoVAD179sTLy4tDhw45lunataujUQHQu3dvzpw5U2w3pdXdpV25CjVJhupztWth4bkkQ7VJfhVM02D9emjZ0t6o8PKCJ56w9wJVDo0KkAz1osx45zk5OcyaNYuHHnrIccYgPj6eevXqOS1Xo0YNwsLCiI+PdywTFRXltEx4eLhjXkk9H+Xm5joGe4Pq8wGVsxXqkwzVJ2eE1CcZqk3yq0B//w2TJsG2bfbpG2+E996DW24p15eRDPWhxDeQ/Px8hgwZgqZprFixolJec9GiRYSEhDj+IiMjAXsDx2g0YrPZiI2NBXD0+pOfn4/VaqWgoMDpMU3THL0B5eXlFbus1WolPz8fm83mtGzhc202W5mWLVz/5cuWpZbLl9U0rVzrLlymuLovfQ/1qtud9/Bq6i6ulsLHy/p+V1TdNpuNjIwM0tLSyMzMJCEhgfz8fMe+EBsbi9VqJS4ujuzsbFJTU0lPT8dsNpOYmEheXp7TsjabDaPRSE5ODsnJyZhMJkwmE8nJycXua7GxseTl5ZGYmIjZbCY9PZ3U1FSys7OJi4vDarU6LZufn09CQgKZmZmkpaW5XHdKSgoZGRmOunNzc52W1TQNo9FIbm4uSUlJZarbYrE41R0fH09BQUGRZePj48nKyiI1NbXUuo1GY5G6k5KSiq07Nja22Lqzs7NdrjsrK6vYugsKCorUbbFYiq27MPsr1V1Y7+V1m81mMjIySElJKbHuwuwtFgtpaWllqjs9PR2LxXLFz2x2drbTZ/byugt/3Cqt7tI+s5fWffl7WFj3pftaaXVfvq8V1m00Gh3v86V1F+5rl9Zdnvuapx4jkpKSPPoYkZ+fr94x4sIFrK+/jq1VK9i2Dc3Hh+w5c7Ds2UNC48blfowwmUwefYy4fF/z9GOEqzz+HovCRsVff/3Frl27nAYJW7NmDU888YTTJU0FBQXUqlWLTZs2MXDgQB599FFMJhNbtmxxLLN7927uvPNOUlNTy3TGIjIyssrfY1FQUECNGsqcyBLFqKgMq9pn3ZOlpKS4PCCi8EySodokv3J28iSMHQsHD9qnu3SBd9+FFi0q7CUlQ3149BmLwkbF2bNn2bFjR5EPSKdOnUhPTyc6Otrx2K5du7DZbNx2222OZX744Qenvv23b9/ODTfccMURm319fQkODnb6qw7kMhr1SYbq8/f317sE4SbJUG2SXznJy4PnnrOPS3HwIAQFwfLlsHdvhTYqQDLUi67fQCwWCzExMY7Rjs+dO0dMTAwXLlwgPz+f+++/n8OHD7N+/XqsVqtj4LLCSzRatmxJnz59GDduHD///DM//vgjU6dOZejQoY4RF4cNG4aPjw9jxozhxIkTbNy4kTfeeIMZM2botdkeraCgQO8ShJskQ/VJxxLqkwzVJvmVg0OH7CNnz59vb2D07w8nTtjvr6iEH8AkQ33oeinUnj176NGjR5HHR4wYwYIFC4rcdF1o9+7djlGbU1NTmTp1Kl9//TVeXl4MHjyYN99802nU5+PHjzNlyhR++eUX6tSpw7Rp05g1a1aZaq0u3c1qmuY0srVQT0VlWNU+655Mun1Wn2SoNsnPDRYLPPssvPmmvfenunXt/37wQajE7xeSoT485h4LT1ddGhZ5eXlOXfMK9VRUhlXts+7JYmNjadiwod5lCDdIhmqT/K7Sd9/BhAn2np8AHn0UXn0VdLjXQTLUh9ylK5xIo0J9kqH65H+G6pMM1Sb5lVFKCjz+OHz4oX26SRN45x3o3Vu3kiRDfcg5IuGk8P4VoS7JUH2FXQAKdUmGapP8XKRp8Mkn9oHuPvzQfqnTf/4Dv/2ma6MCJEO9yBkL4US6mlWfZKi+ywf+FOqRDNUm+bng4kX7jdhbt9qnW7e2D3R3++361vV/JEN9yBkL4cRqtepdgnCTZKi+1NRUvUsQbpIM1Sb5XYHNBitWQKtW9kZFzZqwcCEcOeIxjQqQDPUiP20KJ9KDgvokQ/UFBAToXYJwk2SoNsmvBKdPw7hxsH+/fbpTJ/tZilat9K2rGJKhPuQbiHAinYSpTzJU36UDego1SYZqk/wuk58PL74I7drZGxUBAfYuZPft88hGBUiGepEzFkII4WGkcag+yVBtkt8lDh+GMWPg+HH7dN++sHIlNG6sb12lkAz1IWcshBMZHE99kqH6ZJwQ9UmGapP8gMxMePJJuO02e6Pimmvgo4/gf//z+EYFSIZ6kYaFcGKz2fQuQbhJMlSfyWTSuwThJslQbdU+v507oW1bWLrUfrP2sGFw6hQMH16po2e7o9pnqBNpWAgn0lWp+iRD9dWpU0fvEoSbJEO1Vdv80tJg9Gjo2RPOnYPISHvPT+vXQ926eldXJtU2Q51Jw0I4kZud1CcZqi8hIUHvEoSbJEO1Vbv8NA0++8w+0N3779vPSkydCidOQL9+eld3Vapdhh5CftoUTnx8fPQuQbhJMlRfw4YN9S5BuEkyVFu1ys9ohClTYMsW+3SLFrB6NfzrX7qW5a5qlaEHkTMWwkleXp7eJQg3SYbqi42N1bsE4SbJUG3VIj+bDVatsp+l2LIFatSAuXMhJkb5RgVUkww9kJyx0InZbMZisZQ4PzAwkKCgoEqsyK5mzZqV/pqifEmG6gsPD9e7BOEmyVBtVT6/s2ftA93t3WufvvVW+0B3bdvqW1c5qvIZeihpWOgkOjqavYU7dDG6detG9+7dK6+g/1NQUFCmL6YTJ04kJCSExYsXX3GZG2+8kcmTJ7Nnzx4GDBhAenp6OVQrilPWDAGsVivt27fn008/pWXLlhVUmXBVcnIy9evX17sM4QbJUG1VNr/8fHj1VViwAHJywN/fPvDdtGng7a13deWqymbo4aRhoZOOHTtyww03APYP/+bNmxk0aJCjF4PAwMByfb3u3btz8OBBfHx88PLyIjIykt69ezN79mzqXtLTg5dXyVfHLViwgJiYGLYUXocJrFy58oqv+8cff/C///2PN9980+1tEFdWmPGljYrff/+dBg0aAPau9yZOnMjWrVvx8/Nj6tSpzJ07FwBvb2+efPJJnnnmGb744gtd6hf/X3BwsN4lCDdJhmqrkvkdOQJjx8LRo/bpu++Gd96BqCh966ogVTJDBcg9FpXIbDazZ88ezGYzQUFBREREkFMzmE/P5LInL4pPz+SSUzOYiIgIx2VQlz7HXYsXL8ZsNpOens6nn35KbGwsHTt2dOo5obxHqly5ciUPPvhghd1QXFwPSAUFBRXyWld6zbL2xFRQUFAho4IuXryY9PR0LBYLFovF0agAmDZtGqmpqVy4cIF9+/bx7rvv8sEHHzjm33///ezcuZMLFy6Ue12ibHJycvQuQbhJMlRblcovOxtmzbJf7nT0KNSuDWvXwnffVdlGBVSxDBUiDYtKZLFY2Lt3r+Peik8P/8NdS/ewITqB89YwNkQncNfSPWw6/E+JzykPBoOBVq1a8dFHHxEcHMzSpUsdrzVo0CDq1atHSEgIXbt25dixYwBs2bKFl156ia1btxIYGOg4ozJy5EimT59e4mt99dVX3HnnnUUef+utt4iIiKB+/frMnz/f8SX7woUL3H333dStW5fatWvTr18/zp8/73jeyJEjGTNmDEOGDCE4OJiVK1fSvXt3Zs6cSa9evQgICGDbtm1YLBamTp1K48aNqVevHo8++igZGRkl1vnnn39y7733UrduXZo0acILL7zgGGhu7dq1tG/fnvnz51O/fn2GDh3KggUL6N+/P5MmTSIsLIzZs2eTn5/P008/TePGjalbty4PPvggSUlJTu/722+/TZs2bQgICMBisVzxdS/31FNP0a1bN8f8zz77jPr165OYmFjidhXKysrik08+4YUXXiA0NJTrr7+eadOmsXr1ascyAQEB3HLLLfzvf/8rdX2iYl3pzKFQg2SotiqT3549cOONsGQJWK3w4IP2ge5GjFBmoLurVWUyVIy86zo5l5zJ7M+PY9PAqoGGAasGNg1mfX6c88mZFV5DjRo1GDBggONeD5vNxkMPPcS5c+dISEigQ4cODBkyBE3TGDBgAM888wz9+/d3/BpemqysLM6ePUuLFi2cHjebzRw5coQ///yTPXv2sGbNGscv5zabjRkzZvDPP//w999/4+/vz7hx45ye//HHHzNmzBjS09MZM2YMYP/y/8ILL2CxWOjZsyejR48mNTWV48ePc+7cOfLz85k6dWqJdd51113cddddxMbGsm/fPj755BPef/99xzK//fYbNWrU4MKFC3z44YcAfPvtt9x2220kJiby/PPPs2jRIrZu3cr+/fs5d+4cBoOB4cOHO73Whg0b+P777zGZTHh7e5f6upd68cUXyczM5IUXXuDvv/9m/PjxfPDBB9SrV8+xzAsvvEC9evXo0KGD09mIM2fOkJeXR/v27R2PtW/fnuPHjzu9RqtWrYiJiSn29UXlkUEO1ScZqk35/NLTYfx46NED/vgDGjaEL7+ETz6BanJTs/IZKkoaFjpITk5mzZ5TXOm3gtV7ThEXF0dycnKF1tKwYUNSU1MB+/WI999/PwEBAdSqVYuFCxfy+++/YzQar2rdaWlpjvVeymazsXjxYvz9/WnRogVTp051fFlv2rQpffv2pVatWgQHBzNnzhz27dvn9Ct+r1696N27N15eXvj7+wMwbNgwbr31VgwGAxaLhc8//5xly5YRGhpKQEAAzz33HBs3bsRqtRap83//+x+1a9dm+vTp+Pj40LhxY/7zn/+wYcMGxzIhISHMmTMHHx8fx2u2adOGkSNHUqNGDfz9/fnwww959tlnady4MYGBgbz66qts377d6f2bOXMmDRo0wNfX16XXvZSPjw8ff/wxr732Gvfccw9jxoyhV69ejvmLFi3izz//5OLFi7z88stMmzbNcb+ExWIhICDA6UAbGhpa5BK74OBgR25CP1lZWXqXINwkGapN6fy++AJatYJ337VPT5xoH+juvvv0rauSKZ2hwqQ5p4PNmzezPy8KmxYGxTQvNE1j/9GT1DhR8ZekxMbGEhYWBkB2djYzZsxg27ZtpKamOk4jJicnX9VAM7Vr1wbsNw0X3pQOUKtWLadf2Zs0aeLobzopKYn//Oc/7Nu3z3HpUm5uLmazmZCQEAAaN25c5LUufez8+fPYbDaiLrt21MvLi/j4+CLbcv78eX777TdCQ0Mdj9lsNiIjIx3TDRs2LHJa9fI6Ll68SNOmTR3ThQ2IixcvOu51uLzO0l73cs2bN6d79+5s3bqVXbt2Oc3r1KmTYx29e/dmwoQJbNy4kYEDBxIYGEhWVhYFBQWOxkVGRkaRLo1NJpMjN6GfSz8TQk2SodqUzC8+3j5a9uef26evv97euOjaVd+6dKJkhlWAnLHQwaBBg+jSoRVeJVzfaDAY6NKhFePHj2fQoEEVVkdBQQFffvmlo1vbpUuXEh0dzf79+zGZTI57Gwrvfyjr9Yr+/v40b96c06dPOz2ek5PjdF/AhQsXHF/2n376abKysjhy5Agmk4kffvjBqYaS6rj0scjISLy8vDAajaSnpzv+cnJyim0gRUZG0rFjR6dlTSYTJ06ccPk1ARo1auR0P0h8fDy5ubk0atSoxDpLe93LffbZZ/z000/069ePKVOmFLtM4c3rl77WDTfcQM2aNR33zADExMTQ9rI+y0+ePOl0uZTQx6X35gg1SYZqUyo/TYM1a+wD3X3+ub3b2GeegWPHqm2jAhTLsAqRhoUO6tSpw+juLblSn0BjurckIiLC6Zf+8nT69GlGjBhBRkYGM2bMAOy/Vvv5+VG7dm0sFgvPPPOM03PCw8P5+++/y9Tr0r333svu3budHvPy8uLpp58mOzubM2fOsGzZMse9CCaTCX9/f0JDQ0lJSWHhwoVl3rb69eszYMAApk6d6riULD4+vsRuVPv3709CQgLLly8nJycHq9XKmTNn2LNnT5le9+GHH+all17in3/+wWKxMGPGDHr27OnUM5M7r3vhwgUmTJjAunXr+OCDDzh69CirVq0CID09nW+++YasrCy8vb3ZuXMnK1euZPDgwYC9kffggw8yd+5cMjIyOHv2LG+99RZjx451rD8rK4tffvmFe+65p0zbLcrf1ZwhFJ5FMlSbMvn9+Sf07Aljxtjvq+jYEaKj7WNT1Kqld3W6UibDKkYaFjqJqhPA4sE34mUAbwMY0PAygJcBFg++kaZ1Asr9NWfNmkVQUBAhISEMGjSI+vXrc/jwYcfolDNmzMDLy4vw8HDatGnjuLSm0AMPPEBwcDB169Z1+RTjhAkT+OSTT5y6Yw0KCqJ9+/Zce+21dO3alUcffZQRI0YAsHDhQv744w9q165N586d6du371Vt69q1awkNDeWWW24hODiYO+64g+jo6GKXDQwMZMeOHezcuZOmTZtyzTXXMGzYMOLj48v0mk8//TS9e/emU6dONG3alPz8fD766KMSly/L61qtVoYPH86oUaPo1asXwcHBfPzxx8ycOZNTp06Rn5/PwoULqV+/PrVr1+bxxx/n1Vdf5YEHHnCs4+233yYkJIRGjRrRuXNnxowZw6OPPuqY//nnn9OjRw+aNGlSpu0W5a/w0kChLslQbR6fX0EB/Pe/9pGyd+0CPz945RX46Sdo107v6jyCx2dYRRm0iuhMvwoymUyEhISQkZHhdDNyTk4O586dIyoqilql/DpgNpuJjo6mY8eOjmvbzydnsnrPKfYfPUmXDq0Y072lU6OiuOdUJE3TMJRzF3QTJkygffv2TJo0qVzXK4p3NRnabDbat2/PJ598QqtWrYpdpiyfdeEem80mXSUqTjJUm0fnd+yYfaC7w4ft03feCatWQbNm+tblYTw6wypMGhYuKo+GxaXMZrOjy9aSRt6ujIbE5fLy8ipsMDtROSoqQ2lYVB6j0VjiJXRCDZKh2jwyv5wceP55+5gUBQUQGgpLl8KoUVV+TIqr4ZEZVgPSK5ROoqOjHeNHFNq8ebPj3926dXPcVF2ZpN9n9UmG6pOeudQnGarN4/Lbtw/GjYMzZ+zTgwfDW29BRIS+dXkwj8uwmpBvIDrp2LEjN9xwQ4nzC0e2rmxWq1VOHSpOMlRfZmYmfn5+epch3CAZqs1j8jOZYNYsWLnSPh0RAcuWwcCB+talAI/JsJqRhoVOgoKCdLnUqTTyhVR9kqH65HJE9UmGavOI/L7+GiZNgsKbkMeNs18GJeMzuMQjMqyGpGEhhBBCCOEpEhPhscdg40b79HXX2W/O7tFD37qEcIH8tFlOqso98DabTe8ShJsqKkP5bFSe3NxcvUsQbpIM1aZLfpoG69bZB7rbuNE+0N3MmXD8uDQqroLsg/qQMxZuqlmzJgaDgaSkJOrWrVvuXbVWNpvNhtVq1bsM4YbyzlDTNPLy8khKSsLLy0tOL1eCS3ueE2qSDNVW6fmdPw8TJsD339un27eH1avhppsqt44qRPZBfUjDwk3e3t40atSIixcvcv78eb3LcVtBQYH0KqS4isrQ39+fxo0byz0clSA5OVlGjVWcZKi2SsvParX37jRnDmRl2UfLXrAAZsyAmjUr/vWrMNkH9SHjWLiopHEsClmtVqfRpVVVEQPkicpVERl6e3tTo0YN+WxUEtkP1ScZqq1S8vvtN/tAd4cO2ae7dYN334XmzSv2dasJ2Qf1oetP0z/88AOvvPIK0dHRxMXF8cUXXzBgwADH/M2bN7Ny5Uqio6NJTU3l6NGjtG/f3mkdOTk5PPHEE3zyySfk5ubSu3dvli9fTnh4uGOZCxcuMGnSJHbv3k1gYCAjRoxg0aJF5fqrrre3N97e3uW2Pr3ExsZKC19xkqH6jEajZKg4yVBtFZpfbi689BIsWgT5+RAcDK+8Ym9kyBnhciP7oD50/QRnZmbSrl07li1bVuL8Ll26sHjx4hLX8fjjj/P111+zadMm9u7di9FoZNCgQY75VquVfv36kZeXx4EDB1i3bh1r165l3rx55b49VYHshOqTDNUnGapPMlRbheV34AB06ADPPWdvVPz733DyJIwfL42Kcib7oD485lIog8FQ5IxFofPnzxMVFVXkjEVGRgZ169Zlw4YN3H///QCcPn2ali1bcvDgQW6//Xa2bdtG//79MRqNjrMYK1euZNasWSQlJbl8I2ppl0JVFfJrt/okQ/VJhuqTDNVW7vmZzfDMM/bB7TQNwsPh7bftI2jL5ToVQvZBfSjdPI6OjiY/P5+ePXs6HmvRogWNGzfm4MGDABw8eJC2bds6XRrVu3dvTCYTJ06cqPSaPV2dOnX0LkG4STJUn2SoPslQbeWa3zffQOvW9oaEpsGoUfazFPffL42KCiT7oD6UbljEx8fj4+ND6GWjUIaHhxMfH+9Y5tJGReH8wnklyc3NxWQyOf1VB9VlO6syyVB9kqH6JEO1lUt+SUkwfDj06wf//ANRUbB9O6xZA2Fh7q9fXJHsg/pQumFRkRYtWkRISIjjLzIyErDfLG40GrHZbMTGxgL20215eXkkJiZisVhIT08nNTWV7Oxs4uPjKSgocFo2Pz+f+Ph4srKySE1NJS0tjczMTBISEsjPz3da1mq1YjQayc7OJiUlhYyMDMxmM0lJSeTm5jotq2kasbGx5ObmkpSU5GgQJScnk52d7VLdubm5ZGVlFVt3QUFBkbotFkuxddtstlLrLqz38rrNZjMZGRmkpKSUWHd+fj4JCQlYLBbS0tJITU11ue709HQsFguJiYnk5eWVWHdycrLjPSyu7sLnXKluq9VabN2ZmZlF6r78PSysOzs72+W6c3JyyM7OLlK30Wgstu7ExMQidcfFxZVad2mf2bi4OKe6zWZzqXVf+n4nJyeXuq+ZzWanfa086770M5uYmFjsvmY0Govd18rjGFGjRg2PPUaUZV+rzseIjIwMjz1GFFe3HCOc687MzLz6Y8TFi2gffYS1RQvYsAHNy4vcqVMx/fgjye3bV/nvEZ5yjCgoKPDoY4Qnf48orm5XKX2Pxa5du7jrrrtIS0tzOmvRpEkTpk+fzuOPP868efP46quviImJccw/d+4c1157LUeOHKFDhw7F1pObm+v0RppMJiIjI6v8PRYZGRmEhIToXYZwg2SoPslQfZKh2q46v7//hkmTYNs2+/SNN8J778Ett5RvgaJUsg/qQ+kzFh07dqRmzZrs3LnT8diZM2e4cOECnTp1AqBTp078+uuvJCYmOpbZvn07wcHBtGrVqsR1+/r6Ehwc7PRXHRQUFOhdgnCTZKg+yVB9kqHaypyfzWYf6K51a3ujwscHXngBDh+WRoVOZB/Uh67jWFgsFv744w/H9Llz54iJiSEsLIzGjRuTmprKhQsXMBqNgL3RAFC/fn3q169PSEgIY8aMYcaMGYSFhREcHMy0adPo1KkTt99+OwC9evWiVatWPPLIIyxZsoT4+HieffZZpkyZgq+vb+VvtIfz9/fXuwThJslQfZKh+iRDtZUpv5Mn7WNQ/F+nMXTpYh/orkWLiilOuET2QX3oesbi8OHDdOjQwXE50owZM+jQoYNjjImvvvqKDh060K9fPwCGDh1Khw4dWLlypWMdr732Gv3792fw4MF07dqV+vXrs3nzZsd8b29vtm7dire3N506deLhhx/m0Ucf5bnnnqvELVVHWlqa3iUIN0mG6pMM1ScZqs2l/PLy7ONRdOhgb1QEBcHy5bB3rzQqPIDsg/rwmHssPF11GcfCarVWiRHEqzPJUH2SofokQ89mNpuxWCwlzvfz8yvS46STQ4fsZyl++80+3b+/vVHxfx29CP3JPqgPXS+FEp4nPj5eBpRRnGSoPslQfZKhZ4uOjmbv3r0lzu/QoQP33Xdf0RkWCzz7LLz5pn1Mirp17f9+8EEZk8LDyD6oDzlj4aLqcsZCCCGEqOouPWORnJzM5s2bGTRokGNQtcDAQIKCgpyf9P33MH68vecngEcfhVdfhWuuqczShfBoSvcKJcpfYZ/GQl2SofokQ/VJhp4tKCiIiIgIIiIiHI2JOnXqOB5zGlwtJQVGjIDeve2NiiZN4NtvYd06aVR4MNkH9SENC+GkXr16epcg3CQZqk8yVJ9kqLZ69erZL3XauBFatoQPPrBf6vSf/9jvq+jdW+8SRSlkH9SHNCyEk9TUVL1LEG6SDNUnGapPMlRb+m+/wb//DUOHQlKSfXyKAwfg9dchMFDv8oQLZB/UhzQshJNAOWAqTzJUn2SoPsnQzmw2s2fPHsxms96lFOtccibLf4xlT14Uy3+M5VyiGVasoE63bvD111CzJixcCEeOwP+Nj6UHT38fPZHsg/qQhoVwkpeXp3cJwk2SofokQ/VJhnYWi4W9e/desWtXvXx6+B/uWrqHDdEJnLeGsSE6gbuW7mHT8s8xmM3QqRPExMC8efaRtHXkye+jp5J9UB/S3axwIp2EqU8yVJ9kqD7J0LOdS85k9ufHsTliMmDV7P+d1fc/tB56D61m/wdkHARlyT6oD2lYCCe1atXSuwThJslQfZKh+iRDZ8nJyXqX4GTNj7EUO+qEwYDBy8BHzf7FtMTEyi6rRJ72/qlA9kF9SMNCODGZTPj7++tdhnCDZKg+yVB9kqGzzZs3612CkwM5TbDZrgFD0SvCbZrGwV9/x/fMdzpUJsqL7IP6kIaFcHKN9MmtPMlQfZKh+iRDZ5cOPqc3n3378PloP++16Im1mNMWBoOBf7Vrwfg77q784kpQOIifcJ3sg/qQhoVwkpiYSMOGDfUuQ7hBMlSfZKg+ydBZ4eBzukpLgyeegPffZ2jtBrzbsuSGw32tPaBe4RbZB/UhvUIJJ7ITqk8yVJ9kqD7J0INoGnz2mX2gu/ffB4OBqGEDWXxvC7wM4G0AAxpeBvAywOLBN3Jb62v1rlq4SfZBfUjDQjiJjY3VuwThJslQfZKh+iRDu8DAQLp166bfmAJGIwwaBA88AAkJ0KIF7NsHb7/NA12as+uJ7gzrGE5T71SGdwxn1xPdeeDmSI/LT/f3UUGelmF1YdCkPy6XmEwmQkJCyMjIIDg4WO9yKkxBQQE1asgVciqTDNUnGapPMtSZzQbvvQdPPQUmE9SoAU8/DXPmgK+v06JxcXGsWrWK8ePHOy5/kvzUJxnqQ85YCCfSpZ36JEP1SYbqkwx1dPYs3HUXTJhgb1Tceqt95OznnivSqCiJ5Kc+yVAf0rAQTkJCQvQuQbhJMlSfZKg+yVAHBQWweDHceCPs2QP+/vDaa3DgALRtW6ZVSX7qkwz1IQ0L4SQ7O1vvEoSbJEP1SYbqkwwr2dGj9jMTs2dDTg7cfTf89htMn35Vo2dLfuqTDPUhF58JJ15e0tZUnWSoPslQfZJhJcnOhgULYOlSsFqhdm37WYpHHwVDsWNrA2A2m7FYLMD/v2Tm0ktnbDZbhZYtKp7sg/qQhoVwIjc6qU8yVJ9kqD7JsBLs2QPjxsEff9inH3wQ3ngDwsNLfWp0dDR79+51euzSAeg6deok3ZUqTvZBfci7LpxkZWVJd3aKkwzVJxmqTzKsQOnpMHMmvPuufbphQ1i+HO67z+VVdOzYkRtuuKHE+Tk5OW4WKfQm+6A+pGEhnISGhupdgnCTZKg+yVB9kmEF2bIFJk+GuDj79MSJ8PLLUMYbdYOCgggKCipxfl5enhtFCk8g+6A+5AI04SQpKUnvEoSbJEP1SYbqkwzLWXy8fZC7gQPtjYrrr4e9e2HFijI3Klwh+alPMtSHDJDnouoyQJ4QQgjhMTQN3n8fnnjCfgmUtzfMmgVz50KtWnpXJ4S4jJyxEE5iY2P1LkG4STJUn2SoPsmwHPz1l73b2DFj7I2Kjh0hOhpefLHCGxWSn/okQ33IGQsXVZczFjabTbpoU5xkqD7JUH2SoRsKCuy9O82da+9O1s/PPmr29OlQST39SH7qkwz1Ie+4cBIfH693CcJNkqH6JEP1SYZX6dgx6NQJnnzS3qi480749Vf7dCV2Hyr5qU8y1Ic0LISTsLAwvUsQbpIM1ScZqk8yLKOcHJgzB26+GQ4fhtBQWL0aduyAZs0qvRzJT32SoT6kYSGcFI5EKtQlGapPMlSfZFgG+/ZB+/bw0kv2y6AGD4aTJ2H06CuOnl2RJD/1SYb6kIaFcOLj46N3CcJNkqH6JEP1SYYuMJlg0iTo2hXOnIGICNi8GT77zP5vHUl+6pMM9SED5AkhhBCicn39tb1RUdhzz9ix8Mor9kughBDKkoaFcJKbm6t3CcJNkqH6JEP1SYYlSEyExx6DjRvt082awbvvQo8e+tZ1GclPfZKhPuRSKOGkKnelW11IhuqTDNUnGV5G0+CDD6BlS3ujwtsbZs609/jkYY0KkPyqAslQH9KwEE5SUlL0LkG4STJUn2SoPsnwEufPQ58+MGIEpKbab9T++WdYvNg+RoUHkvzUJxnqQ9eGxQ8//MC9995LgwYNMBgMbNmyxWm+pmnMmzePiIgI/Pz86NmzJ2fPnnVaJjU1leHDhxMcHExoaChjxowp0hPA8ePHueOOO6hVqxaRkZEsWbKkojdNWRE63zAn3CcZqk8yVJ9kCFit8Prr0Lo1fP+9fbTsl1+2Nypuuknv6q5I8lOfZKgPXRsWmZmZtGvXjmXLlhU7f8mSJbz55pusXLmSQ4cOERAQQO/evcnJyXEsM3z4cE6cOMH27dvZunUrP/zwA+PHj3fMN5lM9OrViyZNmhAdHc0rr7zCggULWLVqVYVvn4qMRqPeJQg3SYbqkwzVV+0z/O036NwZHn8csrKgWzc4fhxmzYKaNfWurlTVPr8qQDLUh0HTNE3vIgAMBgNffPEFAwYMAOxnKxo0aMATTzzBk08+CUBGRgbh4eGsXbuWoUOHcurUKVq1asUvv/zCzTffDMC3337LPffcw8WLF2nQoAErVqxgzpw5xMfHO7oemz17Nlu2bOH06dMu12cymQgJCSEjI0Ou2xNCCCGKk5trH49i0SLIz4fgYHtvT2PHgpdcfS1EVeexe/m5c+eIj4+nZ8+ejsdCQkK47bbbOHjwIAAHDx4kNDTU0agA6NmzJ15eXhw6dMixTNeuXZ36M+7duzdnzpwhLS2tkrZGHbGFXf8JZUmG6pMM1VctMzxwADp0gOeeszcq/v1v+0B348cr16iolvlVMZKhPjx2T4+PjwcgPDzc6fHw8HDHvPj4eOrVq+c0v0aNGoSFhTktU9w6Ln2N4uTm5mIymZz+qoM6deroXYJwk2SoPslQfdUqQ7MZpk2DLl3g1CmoVw82bYIvvoCGDfWu7qpUq/yqKMlQHx7bsNDbokWLCAkJcfxFRkYCkJOTg9FoxGazOVrDsbGx5OXlkZiYiMViIT09ndTUVLKzs4mPj6egoMBp2fz8fOLj48nKyiI1NZW0tDQyMzNJSEggPz/faVmr1YrRaCQ7O5uUlBQyMjIwm80kJSWRm5vrtKymacTGxpKbm0tSUpKjQZScnEx2drZLdV+8eJGsrKxi6y4oKChSt8ViKbZum81Wat2F9V5et9lsJiMjg5SUlBLrzs/PJyEhAYvFQlpaGqmpqS7XnZ6ejsViITExkby8vBLrTk5OdryHxdVd+Jwr1W21WoutOzMzs0jdl7+HhXVnZ2e7XHdOTg7//PNPkbqNRmOxdScmJhapOy4urtS6S/vMxsXFOdVtNptLrfvS9zs5ObnUfc1sNjvta+VZ96Wf2cTExGL3NaPRWOy+Vh7HiNTUVI89RpRlX6vOx4g///zTY48RxdV91ceIbdsoaNEC3n4bNI38Rx4h/eBB0u66i8ysLGWPEefPn/foY4Qnf4/wlGOE0Wj06GOEJ3+PKK5uV3nsPRZ//fUXzZo14+jRo7Rv396xXLdu3Wjfvj1vvPEGa9as4YknnnC6pKmgoIBatWqxadMmBg4cyKOPPorJZHLqcWr37t3ceeedpKamUrt27WLryc3NdXojTSYTkZGRVf4eC7PZTFBQkN5lCDdIhuqTDNVX5TNMSrLfmL1+vX06KgpWrYJLLl9WWZXPrxqQDPXhsWcsoqKiqF+/Pjt37nQ8ZjKZOHToEJ06dQKgU6dOpKenEx0d7Vhm165d2Gw2brvtNscyP/zwA/n5+Y5ltm/fzg033FBiowLA19eX4OBgp7/qwGaz6V2CcJNkqD7JUH1VNkNNszcmWrWy/9fLC2bMsA90V0UaFVCF86tGJEN96NqwsFgsxMTEEBMTA9hv2I6JieHChQsYDAamT5/OCy+8wFdffcWvv/7Ko48+SoMGDRxnNVq2bEmfPn0YN24cP//8Mz/++CNTp05l6NChNGjQAIBhw4bh4+PDmDFjOHHiBBs3buSNN95gxowZOm21ZysoKNC7BOEmyVB9kqH6qmSGFy5Av37w8MOQnAw33gg//QRLl0JAgN7VlasqmV81IxnqQ9dLofbs2UOPHj2KPD5ixAjWrl2LpmnMnz+fVatWkZ6eTpcuXVi+fDnXX3+9Y9nU1FSmTp3K119/jZeXF4MHD+bNN98kMDDQsczx48eZMmUKv/zyC3Xq1GHatGnMmjWrTLVWl+5ms7Oz8fPQkVCFayRD9UmG6qtSGdpssHw5PP00WCzg4wPz5sHMmUqMSXE1qlR+1ZRkqA+PucfC01WXhkVcXJyMVqk4yVB9kqH6qkyGJ0/ax6D4v27e6dIF3n0XWrTQt64KVmXyq8YkQ31Iw8JF1aVhYbVa8fb21rsM4QbJUH2SofqUzzAvD15+GV580f7voCBYvBgmTFBuTIqroXx+QjLUSdU/OogyudLYHkINkqH6JEP1KZ3hoUPQsSPMn29vVPTvDydOwKRJ1aJRAYrnJwDJUC9yxsJF1eWMhRBCiGoqMxOefRbeeMPe+1PduvDmm/Dgg2Aw6F2dEEIB1eOnB+GywsFShLokQ/VJhupTLsPvv4c2beD11+2NikcesY+iPXRotWxUKJefKEIy1IecsXBRdTljkZ+fT80q2stHdSEZqk8yVJ8yGaak2Meh+OAD+3STJrByJfTpo29dOlMmP1EiyVAfcsZCOElNTdW7BOEmyVB9kqH6PD5DTYONG+0D3X3wgf2sxH/+A7/9Vu0bFaBAfqJUkqE+auhdgPAsl47/IdQkGapPMlSfR2d48SJMngxff22fbt0a3nsPbr9d37o8iEfnJ1wiGepDzlgIJ3l5eXqXINwkGapPMlSfR2Zos8GKFfazFF9/bR/cbsECOHJEGhWX8cj8RJlIhvqQMxZCCCFEVXfmDIwbB/v22advv91+lqJ1a33rEkJUKXLGQjjx8fHRuwThJslQfZKh+jwmw/x8eOklaNfO3qgICLB3Ibt/vzQqrsBj8hNXTTLUhzQshBOLxaJ3CcJNkqH6JEP1eUSGhw/DzTfDnDmQm2u/KfvECZg2DWRE4ivyiPyEWyRDfUjDQjgJCwvTuwThJslQfZKh+nTNMCsLnnwSbrsNjh+Ha66BDz+Eb76xdycrSiX7oPokQ31Iw0I4SUxM1LsE4SbJUH2Sofp0y3DnTmjbFpYutd+sPWyYfaC7hx+ulgPdXS3ZB9UnGepDBshzUXUZIE8IIYSC0tLsZynWrLFPR0bae4Dq10/fuoQQ1YqcsRBOYmNj9S5BuEkyVJ9kqL5Ky1DT4LPPoGVLe6PCYIApU+z3Ukij4qrJPqg+yVAfcsbCRdXljIXVasVbbupTmmSoPslQfZWSodFob0Rs2WKfbtHC3oVs584V+7rVgOyD6pMM9SFnLIQTuSZRfZKh+iRD9VVohjYbvPuufaC7LVugRg2YOxdiYqRRUU5kH1SfZKgPGSBPOAkNDdW7BOEmyVB9kqH6KizDP/6wD3S3Z499+tZb7Wcp2ratmNerpmQfVJ9kqA85YyGcZGdn612CcJNkqD7JUH3lnmFBASxebG9A7NkD/v7w2mtw4IA0KiqA7IPqkwz1IWcshBMvL2lrqk4yVJ9kqL5yzfDoURgzxv5fgLvvhnfegaio8nsN4UT2QfVJhvqQd104kRud1CcZqk8yVF+5ZJidDbNnwy232BsVtWvD2rXw3XfSqKhgsg+qTzLUhzQshBM5dag+yVB9kqH63M5wzx648Ub75U9WKwwZYh/obsQIGeiuEsg+qD7JUB/SsBBO5GYn9UmG6pMM1XfVGaanw/jx0KOH/UbtBg3gyy9h40YIDy/PEsUVyD6oPslQHy7dY/Hmm2+WecWjRo0iKCiozM8T+kpKSqJhw4Z6lyHcIBmqTzJU31VluGULTJ4McXH26YkT4eWXISSk3OsTVyb7oPokQ324NECel5cXjRo1cvl6tX/++Yfff/+da6+91u0CPUV1GSBPCCFEJYuPh2nT7CNoA1x/vX2ciq5d9a1LCCHKyOVeoQ4fPky9evVcWlbOVKgrNjZWWviKkwzVJxmqz6UMNQ3efx+eeMJ+CZS3N8ycCfPmQa1alVKnKJ7sg+qTDPXhUsNi/vz5BAYGurzSZ555hrCwsKsuSugnIiJC7xKEmyRD9UmG6is1w7/+st9LsXOnffqmm2D1amjfvsJrE6WTfVB9kqE+XLoUSlSfS6GMRiMNGjTQuwzhBslQfZKh+krMsKAA3ngD5s61dyfr5wfPPQfTp0MNGVrKU8g+qD7JUB9yFBNO5EyT+iRD9UmG6is2w+PH7QPdHT5sn77zTli1Cpo1q9ziRKlkH1SfZKgPlxsWPXr0wFBK39kGg4Gdhad1hZIsFgu15NpepUmG6pMM1eeUYU4OPP88LFliP2MREgJLl8Lo0TImhYeSfVB9kqE+XG5YtL/CdZ9ms5kNGzaQm5tbHjUJHfn4+OhdgnCTZKg+yVB9jgz37YNx4+DMGfv04MHw1lsg1397NNkH1ScZ6sPlhsVrr71W5LGCggKWLVvGiy++SMOGDXn++efLtTghhBBCSSYTzJ4NK1bYpyMiYNkyGDhQ37qEEKICXfU9FuvXr2fevHlkZ2ezYMECxo8fTw258Ux5eXl5epcg3CQZqk8yVNzXXxMwYcL/H+hu7Fh45RWQkYCVIfug+iRDfZS5JfDtt98ye/Zszp07x5NPPsmMGTMICAioiNqEDsrSrbDwTJKh+iRDRSUmwmOPwcaNeIP9pux334UePfSuTJSR7IPqkwz14eXqgj///DM9evRg4MCB9OjRgz///JO5c+dWeKPCbDYzffp0mjRpgp+fH//617/45ZdfHPM1TWPevHlERETg5+dHz549OXv2rNM6UlNTGT58OMHBwYSGhjJmzBgsFkuF1q2q1NRUvUsQbpIM1ScZKkbT4IMPoGVL2LgRvL0xT54Mv/4qjQpFyT6oPslQHy6PY+Hl5YWfnx/jx48nKiqqxOUee+yxcisO4MEHH+S3335jxYoVNGjQgI8++ojXXnuNkydP0rBhQxYvXsyiRYtYt24dUVFRzJ07l19//ZWTJ086egPo27cvcXFxvPPOO+Tn5zNq1ChuueUWNmzY4HId1WUcC5vNhpeXy+1N4YEkQ/VJhgo5fx4mTIDvv7dPt28Pq1dja99eMlSY7IPqkwz14XLDomnTpi51N/vXX3+VS2EA2dnZBAUF8eWXX9KvXz/H4x07dqRv3748//zzNGjQgCeeeIInn3wSgIyMDMLDw1m7di1Dhw7l1KlTtGrVil9++YWbb74ZsF/Odc8993Dx4kWXB0+pLg2L2NhYGjZsqHcZwg2SofokQwVYrfD22zBnDmRmQq1asGABzJgBNWtKhoqT/NQnGerD5Xsszp8/X4FlFK+goACr1VqkH2I/Pz/279/PuXPniI+Pp2fPno55ISEh3HbbbRw8eJChQ4dy8OBBQkNDHY0KgJ49e+Ll5cWhQ4cYWEIPHbm5uU7d55pMpnLeOs8kO6H6JEP1SYYe7rff7DdkHzpkn+7WzX4vRfPmjkUkQ7VJfuqTDPXh0eeIgoKC6NSpE88//zxGoxGr1cpHH33EwYMHiYuLIz4+HoDw8HCn54WHhzvmxcfHU69ePaf5NWrUICwszLFMcRYtWkRISIjjLzIyEoCcnByMRiM2m43Y2FjA3irOy8sjMTERi8VCeno6qampZGdnEx8fT0FBgdOy+fn5xMfHk5WVRWpqKmlpaWRmZpKQkEB+fr7TslarFaPRSHZ2NikpKWRkZGA2m0lKSiI3N9dpWU3TiI2NJTc3l6SkJEwmEyaTieTkZLKzs12q+8yZM2RlZRVbd0FBQZG6LRZLsXXbbLZS6y6s9/K6zWYzGRkZpKSklFh3fn4+CQkJWCwW0tLSSE1Ndbnu9PR0LBYLiYmJ5OXllVh3cnKy4z0sru7C51ypbqvVWmzdmZmZReq+/D0srDs7O9vlunNycjh9+nSRuo1GY7F1JyYmFqk7Li6u1LpL+8zGxcU51W02m0ut+9L3Ozk5udR9zWw2O+1r5Vn3pZ/ZxMTEYvc1o9FY7L5WHseICxcueOwxoiz7WpU7Rvz1F9q8eWg33QSHDmELCiL79dcxbdlCUmioU92//vqrxx4jiju2yTHCue6TJ0969DHCk79HeMox4uzZs/I9ohyPEa5y+VKoS/3yyy/s3r2bxMREbDab07xXX321rKu7oj///JPRo0fzww8/4O3tzU033cT1119PdHQ0q1evpnPnzhiNRiIuGWxoyJAhGAwGNm7cyEsvvcS6des4Uzg40f+pV68eCxcuZNKkScW+bnFnLCIjI6v8pVB5eXkyqIziJEP1SYYe6MAB+1mKU6fs0//+t31cihJ+FZUM1Sb5qU8y1EeZu5t96aWXePbZZ7nhhhsIDw93uu+itHswrkazZs3Yu3cvmZmZmEwmIiIiePDBB7n22mupX78+AAkJCU4Ni4SEBMdI4fXr1ycxMdFpnQUFBaSmpjqeXxxfX198fX3LfXs8XXp6epEzPEItkqH6JEMPYjbDM8/YGxGaBvXq2e+tuP9+uML/8yRDtUl+6pMM9VHmhsUbb7zBmjVrGDlyZAWUU7KAgAACAgJIS0vju+++Y8mSJURFRVG/fn127tzpaEiYTCYOHTrkOBPRqVMn0tPTiY6OpmPHjgDs2rULm83GbbfdVqnboAI/Pz+9SxBukgzVJxl6iG3bYOJEuHDBPj1qFPz3vxAWVupTJUO1SX7qkwz1UeaGhZeXF507d66IWor13XffoWkaN9xwA3/88QdPPfUULVq0YNSoURgMBqZPn84LL7xA8+bNHd3NNmjQgAEDBgDQsmVL+vTpw7hx41i5ciX5+flMnTqVoUOHutwjVHVitVr1LkG4STJUn2RYfsxm8xXHLQoMDCQoKMj5waQkePxxWL/ePh0VBatWwSUdhZRGMlSb5Kc+yVAfZW5YPP744yxbtozXX3+9AsopKiMjg6effpqLFy8SFhbG4MGDefHFF6lZsyYAM2fOJDMzk/Hjx5Oenk6XLl349ttvnXqSWr9+PVOnTuWuu+7Cy8uLwYMH8+abb1ZK/aq5/J4ZoR7JUH2SYfmJjo5m7969Jc7v1q0b3bt3t09oGmzYANOnQ3IyeHnZ//3cc1DGwWAlQ7VJfuqTDPVR5pu3bTYb/fr14/fff6dVq1aOL/iFNm/eXK4FeorqMo5Fdna2nD5UnGSoPsmw/Fx6xiI5OZnNmzczaNAg6tSpA1xyxuLCBZg0Cb75xv7Etm1h9Wq45Zarel3JUG2Sn/okQ32U+YzFY489xu7du+nRowfXXHNNhdywLfSTnp4uO6LiJEP1SYblJygoqMilTnXq1Pn/HX7YbPabsZ9+GiwW8PGBefPgqafs/75KkqHaJD/1SYb6KHPDYt26dXz++edOI2GLqkN6UFCfZKg+ybCSnDpl70L2wAH7dJcu9oHuWrRwe9WSodokP/VJhvoo8wB5YWFhNGvWrCJqER7gSoMGCjVIhurz1AzNZjN79uzBbDbrXUqZnUvOZPmPsezJi2L5Dxc4t2AxtG9vb1QEBtq7k927t1waFVAxGar8/qvGU/dB4TrJUB9lblgsWLCA+fPnk5WVVRH1CJ01LGGwJ6EOyVB9npqhxWJh7969V+xlyRN9evgf7lq6hw3RCZwvqM2G6ATuymrFpuvvgH794ORJmDzZfrN2OamIDFV9/1XkqfugcJ1kqI8yXwr15ptv8ueffxIeHk7Tpk2L3Lx95MiRcitOVL7Y2FjZGRUnGapPMiw/55Izmf35cWyF3ZQYvLAaAE1jVr/p3PJEd5rWDSz315UM1Sb5qU8y1EeZGxaF40OIqkmuSVSfZKg+T88wOTlZ7xJctubHWAyaBlzW0YjBAAZYvfc0kzuX/5cPm81GXFxcua5TpfdddZ6+D4rSSYb6KHPDYv78+RVRh/AQqamphIeH612GcINkqD5Pz1CVbsX9srI4Hx+CFt4GvLyLzNc0jf1HT1LjxP90qE54Mk/fB0XpJEN9lLlhIaq2wMDyvyRAVC7JUH2enuGl40B4JE2j1ldfETx3Lv9t1Y8D4W2KXcxgMNClQyvGd7673EuoiD70C8fhEBXP0/dBUTrJUB8uNSzCwsL4/fffXf4fSePGjdm3bx9NmjRxqzhR+fLy8ggo4wizwrNIhurz9AydxoHwNBcv2m/E/vprAIbknOOdYs5WFBrTvSURdcr/vU5LS6N27drlvl5ROTx9HxSlkwz14VLDIj09nW3bthESEuLSSlNSUrBarW4VJoQQQrjMZoNVq2DmTDCboWZNmDOHqKefZvHxBGZ9fhwDYNM0x8CuiwffSNMKaFQIIUR15fKlUCNGjKjIOoSH8HFjpFnhGSRD9XlqhoGBgXTr1s3zLjE4cwbGjYN9++zTt98O770HrVsD8MDNkdzSNIzVe06x/+hJunRoxZjuLSu0UVERGXrs+18Feeo+KFwnGerDpU67bTZbmf+uvfbaiq5dVADpH119kqH6PDXDoKAgunfvTlBQkN6l2OXnw0svQbt29kZFQAC8+Sbs3+9oVBRqWieAyZ0b0t3nHJM7N6zwMxUVkaHHvf9VmKfug8J1kqE+5OZt4SQsLEzvEoSbJEP1SYYuOHwYxo6FY8fs0336wMqV4CH39kmGapP81CcZ6qP8hhkVVUJiYqLeJQg3SYbqkwyvICsLnnwSbrvN3qi45hr48EP45ptiGxVms5m4uDji4uIc40AkJyc7HjObzRVSpmSoNslPfZKhPgyapmmlLyZMJhMhISFkZGQQHBysdzlCCFH97NwJ48fDX3/Zp4cNg9dfh7p1S3zKnj172Lt3b4nzu3XrRvfu3cu3TiGEqKbkUijhJDY2loYNy38UWlF5JEP1SYaXSUuzn6VYs8Y+HRkJK1ZAv36lPrVjx47ccMMNJc6vqBuhJUO1SX7qkwz1IWcsXFRdzlhYrVa8vUvu8114PslQfZLhJT7/HKZMgYQEMBjsY1QsWgQefgOzZKg2yU99kqE+5B4L4USuSVSfZKg+yRAwGmHQILj/fnujokULe89Pb7/t8Y0KkAxVJ/mpTzLUhzQshJPQ0FC9SxBukgzVV60z1DR4911o1Qq++AJq1IC5c+HoUejcWe/qXFatM6wCJD/1SYb6kIaFcJKVlaV3CcJNkqH6qm2Gf/wBd95pv0E7IwNuvRWOHIHnnoNatfSurkyqbYZVhOSnPslQH9KwEE5q1JD7+VUnGaqv2mVYUACLF0PbtrBnD/j7w6uvwoED9scUVO0yrGIkP/VJhvpw+V3/66+/iIqKwmAwVGQ9QmdeXtLWVJ1kqL5qleHRozBmjP2/AHffDe+8A1FR+tblpmqVYRUk+alPMtSHy+968+bNSUpKckw/+OCDJCQkVEhRQj/Z2dl6lyDcJBmqr1pkmJ0Ns2fDLbfYGxW1a8PatfDdd8o3KqCaZFiFSX7qkwz14XLD4vJeab/55hsyMzPLvSChr5CQEL1LEG6SDNVX5TPcuxfatbNf/mS1wpAhcOoUjBhh71K2CqjyGVZxkp/6JEN9yHki4SQ5OVnvEoSbJEP1VdkM09NhwgTo3h3OnoUGDeDLL2HjRggP17u6clVlM6wmJD/1SYb6cLlhYTAYitxfIfdbVD0ySqX6JEP1VckMt2yxdyG7apV9euJEOHkS7rtP17IqSpXMsBqR/NQnGerD5Zu3NU1j5MiR+Pr6ApCTk8PEiRMJCAhwWm7z5s3lW6GoVLGxsbIzKk4yVF+VyjA+HqZNg88+s09ff719nIquXfWtq4JVqQyrIclPfZKhPgza5TdPlGDUqFEurfD99993qyBPZTKZCAkJISMjg+DgYL3LqTCapsmZKMVJhuqrEhlqmv1m7CeegLQ08PaGmTNh3jzlxqS4GlUiw2pM8lOfZKgPlxsW1V11aVgYjUYaNGigdxnCDZKh+pTP8K+/7IPc7dxpn77pJli9Gtq317WsyqR8htWc5Kc+yVAfcvO2cHLNNdfoXYJwk2SoPmUzLCiApUuhTRt7o8LPD155BQ4dqlaNClA4QwFIflWBZKgPaVgIJyaTSe8ShJskQ/UpmeHx49CpEzz5pH2Mih494Ndf7dPVcARcJTMUDpKf+iRDfUjDQjgpvDlfqEsyVJ9SGebkwJw50LEjHD4MISHw3nv2MxbNmuldnW6UylAUIfmpTzLUR/X7GUkIIUT52LcPxo2DM2fs04MHw1tvQUSEvnUJIYTQhZyxEE7y8vL0LkG4STJUn8dnaDLB5Mn2LmPPnIH69WHzZnuXstKoABTIUFyR5Kc+yVAfHt2wsFqtzJ07l6ioKPz8/GjWrBnPP/88l3ZkpWka8+bNIyIiAj8/P3r27MnZs2ed1pOamsrw4cMJDg4mNDSUMWPGYLFYKntzlBAYGKh3CcJNkqH6PDrDrVuhdWtYscI+PXYsnDoFAwfqW5eH8egMRakkP/VJhvrw6IbF4sWLWbFiBW+//TanTp1i8eLFLFmyhLfeesuxzJIlS3jzzTdZuXIlhw4dIiAggN69e5OTk+NYZvjw4Zw4cYLt27ezdetWfvjhB8aPH6/HJnm81NRUvUsQbpIM1eeRGSYmwkMPwb33wsWL9vsndu2yD3YXGqp3dR7HIzMULpP81CcZ6sOjx7Ho378/4eHhrF692vHY4MGD8fPz46OPPkLTNBo0aMATTzzBk08+CUBGRgbh4eGsXbuWoUOHcurUKVq1asUvv/zCzTffDMC3337LPffcw8WLF13u47i6jGNhs9nw8vLo9qYohWSoPo/KUNPgww/h8cchNdU+0N0TT8D8+eDvr3d1HsujMhRlJvmpTzLUh0e/4//617/YuXMnv//+OwDHjh1j//799O3bF4Bz584RHx9Pz549Hc8JCQnhtttu4+DBgwAcPHiQ0NBQR6MCoGfPnnh5eXHo0KESXzs3NxeTyeT0Vx3ExcXpXYJwk2SoPo/J8Px56NsXRoywNyrat4eff4bFi6VRUQqPyVBcFclPfZKhPjy6YTF79myGDh1KixYtqFmzJh06dGD69OkMHz4cgPj4eADCw8OdnhceHu6YFx8fT7169Zzm16hRg7CwMMcyxVm0aBEhISGOv8jISABycnIwGo3YbDZiY2MBiI2NJS8vj8TERCwWC+np6aSmppKdnU18fDwFBQVOy+bn5xMfH09WVhapqamkpaWRmZlJQkIC+fn5TstarVaMRiPZ2dmkpKSQkZGB2WwmKSmJ3Nxcp2U1TSM2Npbc3FySkpIcDaLk5GSys7NdqtvPz4+srKxi6y4oKChSt8ViKbZum81Wat2F9V5et9lsJiMjg5SUlBLrzs/PJyEhAYvFQlpaGqmpqS7XnZ6ejsViITExkby8vBLrTk5OdryHxdVd+Jwr1W21WoutOzMzs0jdl7+HhXVnZ2e7XHdOTg6+vr5F6jYajcXWnZiYWKTuuLi4Uusu7TMbFxfnVLfZbC617kvf7+Tk5FL3NbPZ7LSvlWfdl35mExMTi93XjEZjsftaeRwj6tWrp+8x4sIFeOMNbK1bw3ffofn6krtgAenff09q06ZyjHDhGFHIE48RxdUtxwjnumvUqOHRxwhP/h5Rln2tIo8RgYGBHn2M8OTvEcXV7SqPvhTqk08+4amnnuKVV16hdevWxMTEMH36dF599VVGjBjBgQMH6Ny5M0ajkYhLeiIZMmQIBoOBjRs38tJLL7Fu3TrOFHaH+H/q1avHwoULmTRpUrGvnZub6/RGmkwmIiMjq/ylULGxsTRs2FDvMoQbJEP16Zrhb7/Zb8guPKPbtav9Porrr9enHkXJfqg2yU99kqE+PHoci6eeespx1gKgbdu2/P333yxatIgRI0ZQv359ABISEpwaFgkJCbRv3x6A+vXrk5iY6LTegoICUlNTHc8vjq+vb7UcXKVu3bp6lyDcJBmqT5cMc3PhpZdg0SLIz4fgYHjlFXsjQ65TLjPZD9Um+alPMtSHR//fIisrq8iNN97e3thsNgCioqKoX78+O3fudMw3mUwcOnSITp06AdCpUyfS09OJjo52LLNr1y5sNhu33XZbJWyFWtLT0/UuQbhJMlRfpWd48CB06ADPPWdvVPz733DyJIwfL42KqyT7odokP/VJhvrw6DMW9957Ly+++CKNGzemdevWHD16lFdffZXRo0cDYDAYmD59Oi+88ALNmzcnKiqKuXPn0qBBAwYMGABAy5Yt6dOnD+PGjWPlypXk5+czdepUhg4d6nKPUNWJv9yQqTzJUH2VlqHZDHPmwNtv23t/qlfP/u/77weDoXJqqKJkP1Sb5Kc+yVAfHt2weOutt5g7dy6TJ08mMTGRBg0aMGHCBObNm+dYZubMmWRmZjJ+/HjS09Pp0qUL3377LbVq1XIss379eqZOncpdd92Fl5cXgwcP5s0339RjkzxeQUGB3iUIN0mG6quUDLdtg4kT4cIF+/TIkbB0KYSFVfxrVwOyH6pN8lOfZKgPj75525NUl3EsUlNTCZMvFkqTDNVXoRkmJ8P06bB+vX06KgpWrYJLuu0W7pP9UG2Sn/okQ33IxbPCiZ+fn94lCDdJhuqrkAw1DTZsgJYt7Y0KLy+YMQN+/VUaFRVA9kO1SX7qkwz1IQ0L4SQjI0PvEoSbJEP1lXuGFy5A//4wfLj9jEXbtvDTT/ZLnwICyve1BCD7oeokP/VJhvqQhoVwUqdOHb1LEG6SDNVXbhnabPabsVu3hm++AR8feOEFOHwYbrmlfF5DFEv2Q7VJfuqTDPUhDQvhJCEhQe8ShJskQ/WVS4anTsEdd8C0aWCxQOfOcOyYvRcoHx/31y+uSPZDtUl+6pMM9SE3b7uouty8LYRQXF4eLF5sPzORlweBgfbpiRNlTAohhBAVSv4vI5zExsbqXYJwk2SovqvO8NAh6NgR5s2zNyr69bMPdDd5sjQqKpnsh2qT/NQnGepD/k8jnNSrV0/vEoSbJEP1lTnDzEx4/HHo1Al++w3q1oWPP4avv4bIyIopUlyR7Idqk/zUJxnqQxoWwklKSoreJQg3SYbqK1OG338PbdrA66/bu5R95BH7WYqhQ2X0bB3Jfqg2yU99kqE+PHrkbVH55P4R9UmG6nMpw5QUeOIJWLfOPt24MbzzDvTpU7HFCZfIfqg2yU99kqE+5IyFcJKTk6N3CcJNkqH6rpihpsHGjdCqlb1RYTDAY4/BiRPSqPAgsh+qTfJTn2SoDzljIZwY5NIJ5UmG6isxw4sX7Tdif/21fbp1a3jvPbj99sorTrhE9kO1SX7qkwz1IWcshBMf6d9eeZKh+opkaLPBypX2sxRffw01a8KCBXDkiDQqPJTsh2qT/NQnGepDGhbCicVi0bsE4SbJUH1OGZ45Az16wKRJYDbbGxJHj8L8+TLQnQeT/VBtkp/6JEN9SMNCOAkLC9O7BOEmyVB9YWFhkJ8PL70E7drBDz9AQAC8+Sbs32+/BEp4NNkP1Sb5qU8y1Ic0LISTxMREvUsQbpIM1Ze2fTvccgvMmQO5ufabsk+cgGnTwNtb7/KEC2Q/VJvkpz7JUB8GTdM0vYtQgclkIiQkhIyMDOnCTAhRMbKy7Jc4vfqq/b6Ka66xj08xfLiMSSGEEMLjyRkL4SQ2NlbvEoSbJENF7dwJbdvCf/9rb1Q89BCcOgUPPyyNCgXJfqg2yU99kqE+5IyFi6rLGQur1Yq3XGqhNMlQMWlp8OSTsGaNfToyEuvbb+N933361iXcIvuh2iQ/9UmG+pAzFsJJQkKC3iUIN0mGCvn8c3sXsmvW2M9KTJkCJ06QcPPNelcm3CT7odokP/VJhvqQAfKEk9q1a+tdgnCTZKgAoxGmToUvvrBPt2hhH+iuc2cAateQQ7PqZD9Um+SnPslQH3LGQjjJysrSuwThJsnQg2kavPuu/SzFF19AjRowd659XIr/a1SAZFgVSIZqk/zUJxnqQ34WE05qyC+lypMMPdQff8C4cbBnj336lltg9Wr7DduXkQzVJxmqTfJTn2SoDzljIZx4eclHQnWSoYcpKIAlS+wNiD17wN/f3p3swYPFNipAMqwKJEO1SX7qkwz1Ie+6cJKTk6N3CcJNkqEHOXoUbr0VZs2CnBy4+2747Td4/PErDnQnGapPMlSb5Kc+yVAf0rAQTqpyV7rVhWToAbKzYfZs++VOR49C7dqwdi189x1ERZX6dMlQfZKh2iQ/9UmG+pCGhXCSnJysdwnCTZKhzvbuhXbtYPFisFphyBD7QHcjRrg80J1kqD7JUG2Sn/okQ33IAHkuqi4D5AkhrlJ6uv2Sp1Wr7NMNGsCKFSAD3QkhhKgm5IyFcBIbG6t3CcJNkqEOtmyxdyFb2KiYMAFOnrzqRoVkqD7JUG2Sn/okQ33IGQsXVZczFpqmYXDxcg3hmSTDShQfD9OmwWef2aevv94+TkXXrm6tVjJUn2SoNslPfZKhPuSMhXBiNBr1LkG4STKsBJoG779vP0vx2Wf2Hp6efhqOHXO7UQGSYVUgGapN8lOfZKgPGT1EOKlTp47eJQg3SYYV7K+/7Jc67dhhn77pJvtAd+3bl9tLSIbqkwzVJvmpTzLUh5yxEE5MJpPeJQg3SYYVpKAAli6FNm3sjQo/P3jlFTh0qFwbFSAZVgWSodokP/VJhvqQMxbCia+vr94lCDdJhhXg+HEYMwYOH7ZP9+hhv5eiWbMKeTnJUH2SodokP/VJhvqQMxZCCFGSnBx49lno2NHeqAgJgffeg507K6xRIYQQQqjK4xsWTZs2xWAwFPmbMmUKYB+yfcqUKVxzzTUEBgYyePBgEhISnNZx4cIF+vXrh7+/P/Xq1eOpp56ioKBAj83xeHl5eXqXINwkGZaT/fvtlzi9+KL9MqhBg+wD3Y0Z4/JAd1dLMlSfZKg2yU99kqE+PL5h8csvvxAXF+f42759OwAPPPAAAI8//jhff/01mzZtYu/evRiNRgYNGuR4vtVqpV+/fuTl5XHgwAHWrVvH2rVrmTdvni7b4+kCAgL0LkG4STJ0k8kEU6bAHXfAmTNQvz58/rn9LyKiUkqQDNUnGapN8lOfZKgP5caxmD59Olu3buXs2bOYTCbq1q3Lhg0buP/++wE4ffo0LVu25ODBg9x+++1s27aN/v37YzQaCQ8PB2DlypXMmjWLpKQkfHx8XHrd6jKOhdFopEGDBnqXIdwgGbph61aYNAkuXrRPjx1rv0E7NLRSy5AM1ScZqk3yU59kqA+PP2Nxqby8PD766CNGjx6NwWAgOjqa/Px8evbs6VimRYsWNG7cmIMHDwJw8OBB2rZt62hUAPTu3RuTycSJEydKfK3c3FxMJpPTX3VQv359vUsQbpIMr0JiIjz0ENx7r71R0awZ7Nplv0G7khsVIBlWBZKh2iQ/9UmG+lCqYbFlyxbS09MZOXIkAPHx8fj4+BB62f/4w8PDiY+PdyxzaaOicH7hvJIsWrSIkJAQx19kZCRgv6fDaDRis9kcw8XHxsaSl5dHYmIiFouF9PR0UlNTyc7OJj4+noKCAqdl8/PziY+PJysri9TUVNLS0sjMzCQhIYH8/HynZa1WK0ajkezsbFJSUsjIyMBsNpOUlERubq7TspqmERsbS25uLklJSY4GUXJyMtnZ2S7VffbsWbKysoqtu6CgoEjdFoul2LptNlupdRfWe3ndZrOZjIwMUlJSSqw7Pz+fhIQELBYLaWlppKamulx3eno6FouFxMRE8vLySqw7OTnZ8R4WV3fhc65Ut9VqLbbuzMzMInVf/h4W1p2dne1y3Tk5Ofz+++9F6jYajcXWnZiYWKTuuLi4Uusu7TMbFxfnVLfZbC617kvf7+Tk5FL3NbPZ7LSvXVXdFy/Chx9iu+EG+OQTNG9v8mfMIGX3bjJuuslRd3H7mtFoLHZfK49jxMWLFz32GFGWfa06HyMKf7jyxGNEcXXLMcK57tOnT1+x7ks/s3ocIzz5e4SnHCP+/PNPjz5GePL3iOLqdpVSl0L17t0bHx8fvv76awA2bNjAqFGjimzwrbfeSo8ePVi8eDHjx4/n77//5rvvvnPMz8rKIiAggG+++Ya+ffsW+1q5ublO6zWZTERGRlb5S6GEqDbOn4eJE6Hw2NC+vX2gu5tu0rMqIYQQQlnKnLH4+++/2bFjB2PHjnU8Vr9+ffLy8khPT3daNiEhwXEKrH79+kV6iSqcvtJpMl9fX4KDg53+qoPC1qtQV3XM0Gw2O3XycPmf2Wz+/wtbrfDGG/aB7r77Dnx9YdEi+Plnj2lUVMcMqxrJUG2Sn/okQ30oM0De+++/T7169ejXr5/jsY4dO1KzZk127tzJ4MGDAThz5gwXLlygU6dOAHTq1IkXX3yRxMRE6tWrB8D27dsJDg6mVatWlb8hHq5u3bp6lyDcVB0zjI6OZu/evSXO79atG927d4cTJ+zdxR46ZJ/Rtav9Porrr6+cQl1UHTOsaiRDtUl+6pMM9aHEpVA2m42oqCgeeughXn75Zad5kyZN4ptvvmHt2rUEBwczbdo0AA4cOADYu5tt3749DRo0YMmSJcTHx/PII48wduxYXnrpJZdrqC69Ql3aABNqqo4Zms1mLBYLAMnJyWzevJlBgwZRp04dAAJr1iTorbfsZyby8yE42N7b09ix4OV5J26rY4ZVjWSoNslPfZKhPjzv/6jF2LFjBxcuXGD06NFF5r322mv079+fwYMH07VrV+rXr8/mzZsd8729vdm6dSve3t506tSJhx9+mEcffZTnnnuuMjdBGf7+/nqXINx0pQzNZjN79uxxvjSoCggKCiIiIoKcmsF8eiaXPXlRfHoml5yawUScP09Qt27w3HP2RsW//w0nT8L48R7ZqIDS98OqmmNVIsdStUl+6pMM9aHEGQtPUF3OWKSnpxfpZUuo5UoZxsXFsWrVKsaPH09EJQ32Vlk+PfwPsz8/jgGwaRpeBgOaTWPxtjd44NcdUK8evP023H9/hY+c7a7S9sOqnGNVIcdStUl+6pMM9aHMPRaicthsNr1LEG6qjhmeS85k9ufHsTl+JjFg/b9/z+rzGLfcegNNlzwHYWF6lVgm1THDqkYyVJvkpz7JUB/SsBBOatWqpXcJwk2uZJicnFwJlVSeNT/GUuw5CIMBgwFW9xnJ5NxciIur7NKuSk5OzhX7Da9q+VVFcixVm+SnPslQH9KwEE5MJpNcl6g4VzK89D6kqmB/bhQ2W20wFL1nwgbs/3/t3Xl4VOXZBvB7shNCwhKyQAOCyiqIAo1hKaHGAqVVioVPPoRi2WUpSxGwKrhFRHGjRQUpUFtU+CwFWZStoEIQBSKrKEouZCaTECZk3+d8f5xmyoGETJgk73lm7t915ZIz58zMM3PnHXl455z32GkEnNrW8IWRz+JnqWzMTz5mqAYbCzKovIoOyeVOhldfMUk6v4sXEfDnrfhrdE9UVDFtYbFY0O+uLpjU976GL+4mlZeXIyCg+o/nyitfkXl5y/jyVcxPPmaoBhsLMsjIyEDr1q1Vl0EecCfDyMhI+Sf9Op3AihXAwoX438BwrJ7QC4AGVPGlqPGJnREb2bjBS7xZVqtVfj4+jp+lsjE/+ZihGua81iIpw0Eon09keOYM0L8/MGMGkJ+Pdl3a4cX+0fCzWOBvASzQ4GcB/CzAiw92xy2CmgrARzL0csxQNuYnHzNUg40FGVitVtUlkIdulGFYWBgGDBiAsLCwBqyoDpWWAs8+C/ToARw8CISFAX/5C/Dppxjx659i79xE/G/PaNzi78DontHYOzcRI3rFqa661moah+Jz9AH8LJWN+cnHDNXgOhZu8pV1LGr6bjeZn9dmePgwMH48cPKkvj10KPDmm0CcsXHwhjUevDZDH8IMZWN+8jFDNThjQQa8jKV8XpdhQQEwZw6QkKA3FZGRwPr1wEcfuZqKvLw8pKenIz093fX6s7KyXLdJW6Ha6zL0QcxQNuYnHzNUg60cGXjzbIyv8KoMd+4EJk8G0tL07TFjgFde0ZuLqxw5cgT79+833Hb1VZMGDBiAxMTEei627nhVhj6KGcrG/ORjhmqwsSCD4uJiXvdZOK/I0OHQZynWrdO327QB3n4bGDy4ysN79uyJjh07Vvtw0s5F8IoMfRwzlI35yccM1WBjQQYWS5XrF5MgojPUNGDjRv1qT5mZgMWi//n55/UTtavRpEkTNGnSpAELrV+iMyQAzFA65icfM1SDjQUZBAYGqi6BPCQ2w4sXgUcf1c+dAIAuXYB33tHPrfAxYjMkF2YoG/OTjxmqwZO3yaCgoEB1CeQhcRk6ncBbbwFdu+pNRWAgsHgxcPSoTzYVgMAM6TrMUDbmJx8zVIMzFmTQvHlz1SWQh0RlePYsMGkS8Omn+vY99+izFF27qq1LMVEZUpWYoWzMTz5mqAZnLMggMzNTdQnkIREZlpUBycnAnXfqTUXjxsDrrwOff+7zTQUgJEO6IWYoG/OTjxmqwQXy3OQrC+QR1buvvgImTAC+/lrfHjxY/ypU27Zq6yIiIiKPcMaCDKxWq+oSyEOmzbCwEJg3D4iP15uKFi2Ad98Ftm9nU3EN02ZIbmOGsjE/+ZihGpyxcJOvzFg4nU74+bHflMyUGe7dC0ycCPzwg749ahTw2mtAVJTSsszKlBlSrTBD2ZiffMxQDb7jZGC321WXQB4yVYbZ2frXnu69V28q4uKArVuB9evZVNyAqTKkm8IMZWN+8jFDNdhYkEGzZs1Ul0AeMk2GH36or0WxerW+PW0acOoUMHSo2roEME2GdNOYoWzMTz5mqAYbCzIoLCxUXQJ5SHmGNhswfDjw298CdjvQqZN+tac//xnwotWx65PyDMljzFA25icfM1SDjQUZBARwaRPplGWoacCqVfosxaZNQEAA8OSTwLFjQN++amoSiuNQPmYoG/OTjxmqwXedDHiik3xKMjx3Tj85e98+fbt3b/0rUN26NXwtXoDjUD5mKBvzk48ZqsF3nQyKi4tVl0AeatAMy8uBpUv1BmLfPiA0FHjlFSAlhU2FBzgO5WOGsjE/+ZihGpyxIANvvpSur2iwDFNTgfHjgaNH9e2kJGDlSqBdu4Z5fi/GcSgfM5SN+cnHDNXgjAUZZGVlqS6BPFTvGRYVAQsXAr166U1Fs2bAmjXAzp1sKuoIx6F8zFA25icfM1SDC+S5yVcWyNM0DRaLRXUZ5IF6zXD/fv1ciu++07dHjgTeeAOIjq6f5/NRHIfyMUPZmJ98zFANzliQgc1mU10CeaheMszJASZPBhIT9aaiVStg82bggw/YVNQDjkP5mKFszE8+ZqgGZyzcxBkLkqLOM9y8GXj0UX19CkBvMF58EYiIqLvnIAOOQ/mYoWzMTz5mqAZnLMiAHb58dZah3Q6MGAEMG6Y3Fbffrl/56a232FTUM45D+ZihbMxPPmaoBhsLMoiMjFRdAnnI4ww1TT8Zu0sX4P/+D/D310/W/vprYMCAuimSbojjUD5mKBvzk48ZqsHGggxyc3NVl0Ae8ijDH34AfvEL4Pe/B7KzgbvvBr76CkhOBho1qrsi6YY4DuVjhrIxP/mYoRpsLMggJCREdQnkoZvKsKJCX9jujjuA3buBkBB94bsvvgB69KjzGunGOA7lY4ayMT/5mKEapm8srFYrHn74YbRo0QKNGjVCt27d8NVXX7n2a5qGp556CrGxsWjUqBGSkpLwXeWlMP/D4XBg9OjRCA8PR9OmTTF+/Hjk5+c39EsRwel0qi6BPFTrDI8fBxISgLlz9TUqBg4ETpwA5s0DAriGpgoch/IxQ9mYn3zMUA1TNxbZ2dno27cvAgMDsWPHDpw+fRrLli1Ds2bNXMcsXboUb7zxBt566y188cUXaNy4MQYNGmRYyn306NE4deoUdu3aha1bt+LTTz/FpEmTVLwk0ysvL1ddAnnI7QyLi4EnngB69gS+/FI/Ifudd4A9e4DbbqvfIumGOA7lY4ayMT/5mKEapr7c7IIFC3DgwAF89tlnVe7XNA2tWrXC3Llz8cc//hEAkJOTg+joaKxduxYPPfQQzpw5gy5duuDLL79Er169AAAff/wxfvnLX+LixYto1aqVW7X4yuVmi4qK0IjfpRfNrQw//1xf6O6bb/Tt4cOBP/8ZiI2t/wKpRhyH8jFD2ZiffMxQDVPPWGzZsgW9evXCiBEjEBUVhbvuugurVq1y7T9//jzsdjuSkpJct0VERCA+Ph4pKSkAgJSUFDRt2tTVVABAUlIS/Pz88MUXX1T73CUlJcjNzTX8+ILs7GzVJZCHbphhbi4wbRrQv7/eVMTEAB9+qP+wqTANjkP5mKFszE8+ZqiGqRuLH374AW+++SZuv/12fPLJJ5g6dSpmzpyJdevWAQDsdjsAIPqalX+jo6Nd++x2O6Kiogz7AwIC0Lx5c9cxVXnhhRcQERHh+omLiwMAFBcXw2azwel0wmq1AtDPAyktLUVmZiby8/Nx5coVOBwOFBUVwW63o7y83HBsWVkZ7HY7CgsL4XA4kJ2djYKCAmRkZKCsrMxwbEVFBWw2G4qKinD58mXk5OQgLy8Ply5dQklJieFYTdNgtVpRUlKCS5cuuRqirKwsFBUVuVV3UFAQCgsLq6y7vLz8urrz8/OrrNvpdNZYd2W919adl5eHnJwcXL58udq6y8rKkJGRgfz8fGRnZ8PhcLhd95UrV5Cfn4/MzEyUlpZWW3dWVpbrPayq7sr73KjuioqKKusuKCi4ru5r38PKuouKityuu7i4GIGBgdfVbbPZoH30Eco7dQJWrAAAVDzyCC59+iny7rvPUHd6enqNddf0O5uenm6oOy8vr8a6r36/s7KyahxreXl5hrFWl3Vf/TubmZlZ5Viz2WxVjrW6+IyIjIw07WdEbcaaL39GVFRUmPYzoqq6bTZblZ9tlWPN1z4jLBaLqT8jzPz3CLN8RoSGhpr6M8LMf4+oqm53mfqrUEFBQejVqxcOHjzoum3mzJn48ssvkZKSgoMHD6Jv376w2WyIvepfW0eOHAmLxYIPPvgAycnJWLduHc6ePWt47KioKDz99NOYOnVqlc9dUlJieCNzc3MRFxfn9V+FslqtaN26teoyyAPXZZiZCfzhD8D77+vbt94KrFwJ/PznagqkGnEcyscMZWN+8jFDNUw9YxEbG4suXboYbuvcuTMuXLgAAIiJiQEAZGRkGI7JyMhw7YuJiUFmZqZhf3l5ORwOh+uYqgQHByM8PNzw4ws4COVzZahpwLvvAp07602Fvz/w2GP6VaDYVJgax6F8zFA25icfM1TD1I1F3759r5tp+Pbbb9G2bVsAQLt27RATE4M9e/a49ufm5uKLL75AQkICACAhIQFXrlzBkSNHXMfs3bsXTqcT8fHxDfAqZKmcFiO5rFYrkJYGDBkCjB0LOBz6WhSHDwMvvgiEhqoukWrAcSgfM5SN+cnHDNUwdWMxe/ZsHDp0CMnJyTh37hzWr1+PlStXYtq0aQAAi8WCWbNm4bnnnsOWLVtw4sQJjB07Fq1atcKwYcMA6DMcgwcPxsSJE3H48GEcOHAA06dPx0MPPeT2FaF8ybXno5AwFRWI+eADfaG7Tz4BgoOBF17Qm4q771ZdHbmJ41A+Zigb85OPGaph6nMsAGDr1q1YuHAhvvvuO7Rr1w5z5szBxIkTXfs1TcOiRYuwcuVKXLlyBf369cOKFSvQoUMH1zEOhwPTp0/HRx99BD8/Pzz44IN44403EBYW5nYdvnK52YyMjOtOhichTp0Cxo/XV8sGgJ/9DFi1CrhqLJAMHIfyMUPZmJ98zFAN0zcWZuErjUV+fn6tGi4ygZISfVYiORkoK4MWHg7L0qX6OhV+pp6UpGpwHMrHDGVjfvIxQzUCVBdA5lJWVqa6BKqNlBRgwgTg9Gl9+4EHkPP882jatavausgjHIfyMUPZmJ98zFAN/nMmGXACS4j8fGDmTKBvX72piIoCNmwANm2CkwvdicdxKB8zlI35yccM1eCMBRmEhISoLoFq8vHHwOTJwH8uu4xx44Bly4DmzQEwQ2/ADOVjhrIxP/mYoRqcsSCD3Nxc1SVQdbKygDFj9MvIXrgA3HILsHMnsGaNq6kAmKE3YIbyMUPZmJ98zFANNhZkEBkZqboEupamAevX6wvd/f3v+gnZc+YAJ08C99133eHMUD5mKB8zlI35yccM1WBjQQbXrmJOil24APzqV8Do0fqMRbdu+gnby5YBjRtXeRdmKB8zlI8Zysb85GOGavBys27ylcvNkkk4ncCbbwILFugnagcFAU8+CTz2mP5nIiIiIpPhjAUZWK1W1SXQmTNA//7A9Ol6U9G3L5CaCjzxhFtNBTOUjxnKxwxlY37yMUM1OGPhJl+ZsSgvL0dAAC8WpkRpKfDii8Bzz+l/DgvTt6dMqdVCd8xQPmYoHzOUjfnJxwzV4IwFGWRlZakuwTcdPgz06gU89ZTeVAwdqq9P8eijtV49mxnKxwzlY4ayMT/5mKEabCzIwJtnY0ypoEC/wlNCAnDiBBAZqV8B6qOPgLi4m3pIZigfM5SPGcrG/ORjhmqwsSCD4uJi1SX4jl27gDvuAF59VT9Ze8wY/fyKUaMAi+WmH5YZyscM5WOGsjE/+ZihGvzyGRn41fJrN3QTHA59lmLdOn27TRvg7beBwYPr5OGZoXzMUD5mKBvzk48ZqsF3nQx4olM90jRgwwZ9obt16/RZiZkzgVOn6qypAJihN2CG8jFD2ZiffMxQDTYWZFBYWKi6BO9ktQLDhgH/8z9AZibQpQtw4ADw+uv61Z/qEDOUjxnKxwxlY37yMUM12FiQQdOmTVWX4F2cTuCtt/RGYssWIDAQWLwYOHpUP2G7HjBD+ZihfMxQNuYnHzNUg40FGVy6dEl1Cd7j22+BgQOBqVOB3FzgnnuAY8eARYuA4OB6e1pmKB8zlI8Zysb85GOGanCBPDf5ygJ5VAfKyoCXXwaefhooKQEaNwaSk4Fp0wB/f9XVEREREdULzliQgdVqVV2CbEeOAL17A48/rjcVgwfrJ2fPnNlgTQUzlI8ZyscMZWN+8jFDNThj4SZfmbFwOp28RNvNKCzUz51Ytkw/r6JFC+C114DRoz1ak+JmMEP5mKF8zFA25icfM1SD7zgZ2O121SXIs3cv0L078NJLelMxahRw+jTw8MMN3lQAzNAbMEP5mKFszE8+ZqgGGwsyaNasmeoS5MjOBiZMAO69F/j+e+AnPwG2bgXWrweiopSVxQzlY4byMUPZmJ98zFANNhZkUFBQoLoEGT78UL+E7OrV+va0afq5FEOHqq0LzNAbMEP5mKFszE8+ZqgGlyUkg6CgINUlmJvNBkyfDmzapG936gS88w7Qt6/auq7CDOVjhvIxQ9mYn3zMUA3OWBC5Q9P0BqJLF72pCAgAnnhCX5fCRE0FERERkSqcsSCDkpIS1SWYz7lzwKRJwL//rW/37q03Gd27q62rGsxQPmYoHzOUjfnJxwzV4IwFGXjzpXRrrbwcWLoU6NZNbyoaNQJeeQVISTFtUwEwQ2/ADOVjhrIxP/mYoRpsLMggKytLdQnmkJoKxMcD8+cDxcVAUhJw8iQwe7bpV89mhvIxQ/mYoWzMTz5mqAYXyHOTryyQp2kaLArWXjCNoiLgmWf0NSkqKoBmzfRZit/9TsmaFDfD5zP0AsxQPmYoG/OTjxmqwRkLMrDZbKpLUGf/fuDOO4ElS/SmYsQI4MwZYNw4MU0F4OMZeglmKB8zlI35yccM1eCMhZt8ZcbCJ+XkAI89BqxcqW+3agWsWAE88IDauoiIiIgE4YwFGVitVtUlNKzNm/VLyFY2FZMnA6dPi24qfC5DL8QM5WOGsjE/+ZihGrzcLBlERkaqLqFhZGQAM2YAGzfq27ffDqxaBQwYoLauOuAzGXoxZigfM5SN+cnHDNUw/YzF4sWLYbFYDD+dOnVy7S8uLsa0adPQokULhIWF4cEHH0RGRobhMS5cuIChQ4ciNDQUUVFRmDdvHsrLyxv6pYiQm5uruoT6pWnAmjVA5856U+HvDyxcCHz9tVc0FYAPZOgDmKF8zFA25icfM1RDxIxF165dsXv3btd2QMB/y549eza2bduGjRs3IiIiAtOnT8fw4cNx4MABAEBFRQWGDh2KmJgYHDx4EOnp6Rg7diwCAwORnJzc4K/F7EJCQlSXUH9++EH/qlPl79LddwOrVwM9eigtq655dYY+ghnKxwxlY37yMUM1RDQWAQEBiImJue72nJwcrF69GuvXr8fPf/5zAMCaNWvQuXNnHDp0CPfccw927tyJ06dPY/fu3YiOjkaPHj3w7LPPYv78+Vi8eDGCgoIa+uWYmtPpVF1C3auoAF5/HXjySaCwEAgJ0S8pO3s2ECBiCNSKV2boY5ihfMxQNuYnHzNUw/RfhQKA7777Dq1atUL79u0xevRoXLhwAQBw5MgRlJWVISkpyXVsp06d0KZNG6SkpAAAUlJS0K1bN0RHR7uOGTRoEHJzc3Hq1KmGfSECeN1XxI4fBxISgLlz9aZi4EDgxAlg3jyvbCoAL8zQBzFD+ZihbMxPPmaohun/ZhUfH4+1a9eiY8eOSE9Px9NPP43+/fvj5MmTsNvtCAoKQtOmTQ33iY6Oht1uBwDY7XZDU1G5v3JfdUpKSlBSUuLa9pXv6oWGhqouoW4UFwPPP6+vSVFeDkREAMuWAb//vag1KW6G12Tow5ihfMxQNuYnHzNUw/QzFkOGDMGIESPQvXt3DBo0CNu3b8eVK1ewYcOGen3eF154AREREa6fuLg4APrJ4jabDU6n03UpM6vVitLSUmRmZiI/Px9XrlyBw+FAUVER7HY7ysvLDceWlZXBbrejsLAQDocD2dnZKCgoQEZGBsrKygzHVlRUwGazoaioCJcvX0ZOTg7y8vJw6dIllJSUGI7VNA1WqxUlJSW4dOkScnNzkZubi6ysLBQVFblV948//ojCwsIq6y4vL7+u7vz8/CrrdjqdNdZdWe+1defl5SEnJweXL1+utu6ysjJkZGQgPz8f2dnZcDgc/6173z6UdesGPPccUF4O529+g8z9+1E4ahQc2dm4cuUK8vPzkZmZidLS0mrrzsrKcr2HVdVdeZ8b1V1RUVFl3QUFBdfVfe17WPl+FxUVweFwuFV3cXExLly4cF3dNputyrozMzOvqzs9Pb3Gumv6nU1PTzfUnZeXV2PdV7/fWVlZNY61vLw8w1iry7qv/p3NzMyscqzZbLYqx1pdfEZkZWWZ9jPCMNakfkbUUHddfEacO3fOtJ8RVdXNzwhj3efPnzf1Z4SZ/x5hls8Iq9Vq6s8IM/89oqq63SVygbzevXsjKSkJ9913H+69915kZ2cbZi3atm2LWbNmYfbs2XjqqaewZcsWpKamuvafP38e7du3x9GjR3HXXXdV+RxVzVjExcV5/QJ5FRUV8Pf3V13GzcnN1a/wtGKFvh0TA/zlL8Dw4WrramCiMyQAzNAbMEPZmJ98zFAN089YXCs/Px/ff/89YmNj0bNnTwQGBmLPnj2u/WfPnsWFCxeQkJAAAEhISMCJEyeQmZnpOmbXrl0IDw9Hly5dqn2e4OBghIeHG358wY2+HqZCXl4e0tPTq/3Jy8vTD9y2Deja9b9NxYQJ+kJ3PtZUAObLkGqPGcrHDGVjfvIxQzVMP2Pxxz/+Eb/+9a/Rtm1b2Gw2LFq0CKmpqTh9+jRatmyJqVOnYvv27Vi7di3Cw8MxY8YMAMDBgwcB6B1rjx490KpVKyxduhR2ux1jxozBhAkTanW52dzcXERERHj9jIXZ7Nu3D/v37692f1L37ui7YQPw/vv6Dbfeqq+i/Z+rhBERERFRwzD9ydsXL17EqFGjcPnyZbRs2RL9+vXDoUOH0LJlSwDAq6++Cj8/Pzz44IMoKSnBoEGDsKLyX60B+Pv7Y+vWrZg6dSoSEhLQuHFj/O53v8Mzzzyj6iXdlLy8PBw5cgQ9e/ZEkyZN6u15rFYrWrduXW+PX1s9e/ZEx44d8WN2MTZ8dRFHzp5Hz47tMLJXa3TYsw3hEyYADgfg56df+WnxYsDLT9iq6XfBbBlS7TFD+ZihbMxPPmaohulnLMxC9YxFeno6Vq5ciUmTJiE2NrbenqesrAyBgYH19vg3Y8NXP2LBh8dhAeDUNPhZLNCcTry4/XWMOLkHuPNOfaG7nj1Vl9ogavpdMGOGVDvMUD5mKBvzk48ZqiHuHAuqXw6HQ3UJBuezCrDgw+NwakCFBmiwoEIDnLBg/pA/IO3Zl4Evv/SZpsIdZsuQao8ZyscMZWN+8jFDNUz/VSgyysrKqtfHr7wkn1n89YAVVa46YbHA4mfB6k4D8Gg9vydmU9PvQFhYWANVQvWFGcrHDGVjfvIxQzXYWAjzz3/+U3UJDerz0nZwas2BKtoLp6bh82OnEXBqW8MXZmKlpaVo3Lix6jLIA8xQPmYoG/OTjxmqwcZCmOHDhyMyMrLeHj8nJwcRERH19vi1VX7AigtHMlBRxZlAFosF/e7qgkl972v4whTKysq6YYPJ06bkY4byMUPZmJ98zFANNhbCREZG1uvJ2xEREQg10VWVfp8Yjn8cyah2//jEzoiN5L9IXC0kJER1CeQhZigfM5SN+cnHDNXgydtkkJubq7oEg8hgJx5Pags/C+BnASzQXH9+PKktWgQ7VZdoOmbLkGqPGcrHDGVjfvIxQzU4YyFEWFgYBgwYUO8nI7Vo0aJeH7+2jhw5Auvn+/GboGB8WxGJfC0IYZZSdPDPgvXzr3DEfwASExNVl9mgavpdMFuGVHvMUD5mKBvzk48ZqsF1LNykeh2LhmK2BWXy8vKQn59f7f6wsLB6XTBQIrNlSLXHDOVjhrIxP/mYoRpsLNzkK40FEREREdHN4DkWZGC1WlWXQB5ihvIxQ/mYoWzMTz5mqAZnLNzkKzMW5eXlCAjgqTeSMUP5mKF8zFA25icfM1SDMxZkUN8re1P9Y4byMUP5mKFszE8+ZqgGGwsyMNPieHRzmKF8zFA+Zigb85OPGarBxoIMioqKVJdAHmKG8jFD+ZihbMxPPmaoBhsLMvDz46+EdMxQPmYoHzOUjfnJxwzV4LtOBjzRST5mKB8zlI8Zysb85GOGarCxIIPCwkLVJZCHmKF8zFA+Zigb85OPGarBxoIMmjZtqroE8hAzlI8ZyscMZWN+8jFDNdhYkMGlS5dUl0AeYobyMUP5mKFszE8+ZqgGF8hzk68skEdEREREdDM4Y0EGVqtVdQnkIWYoHzOUjxnKxvzkY4ZqcMbCTb4yY+F0OnmJNuGYoXzMUD5mKBvzk48ZqsF3nAzsdrvqEshDzFA+ZigfM5SN+cnHDNXgRX7dVDmxk5ubq7iS+hUQEOD1r9HbMUP5mKF8zFA25icfM6x7TZo0gcViueExbCzclJeXBwCIi4tTXAkRERERUcNy53QAnmPhJqfTCZvN5la3JlVubi7i4uLw448/evV5JN6MGcrHDOVjhrIxP/mYYf3gjEUd8vPzw09+8hPVZTSI8PBwDkThmKF8zFA+Zigb85OPGTY8nrxNREREREQeY2NBREREREQeY2NBLsHBwVi0aBGCg4NVl0I3iRnKxwzlY4ayMT/5mKE6PHmbiIiIiIg8xhkLIiIiIiLyGBsLIiIiIiLyGBsLIiIiIiLyGBsLIiIiIiLyGBsLH3TLLbfAYrEYfpYsWWI45vjx4+jfvz9CQkIQFxeHpUuXXvc4GzduRKdOnRASEoJu3bph+/btDfUS6D9KSkrQo0cPWCwWpKamum5PS0u7LmOLxYJDhw4Z7s8M1asuQ4Dj0Ozuv/9+tGnTBiEhIYiNjcWYMWNgs9lc+zkOza+mDAGOQzNLS0vD+PHj0a5dOzRq1Ai33norFi1ahNLSUsMxHIcNSCOf07ZtW+2ZZ57R0tPTXT/5+fmu/Tk5OVp0dLQ2evRo7eTJk9p7772nNWrUSHv77bddxxw4cEDz9/fXli5dqp0+fVp74okntMDAQO3EiRMqXpLPmjlzpjZkyBANgHbs2DHX7efPn9cAaLt37zbkXFpa6jqGGZpDdRlyHJrfK6+8oqWkpGhpaWnagQMHtISEBC0hIcG1n+PQ/GrKkOPQ3Hbs2KGNGzdO++STT7Tvv/9e27x5sxYVFaXNnTvXdQzHYcNiY+GD2rZtq7366qvV7l+xYoXWrFkzraSkxHXb/PnztY4dO7q2R44cqQ0dOtRwv/j4eG3y5Ml1Xi9Vbfv27VqnTp20U6dOVdtYXH3btZihejfKkONQns2bN2sWi8X1FxaOQ3muzZDjUJ6lS5dq7dq1c21zHDYsfhXKRy1ZsgQtWrTAXXfdhZdeegnl5eWufSkpKfjZz36GoKAg122DBg3C2bNnkZ2d7TomKSnJ8JiDBg1CSkpKw7wAH5eRkYGJEyfi3XffRWhoaLXH3X///YiKikK/fv2wZcsWwz5mqFZNGXIcyuJwOPCPf/wDffr0QWBgoGEfx6EMVWXIcShPTk4Omjdvft3tHIcNg42FD5o5cybef/99/Pvf/8bkyZORnJyMxx57zLXfbrcjOjracJ/KbbvdfsNjKvdT/dE0DePGjcOUKVPQq1evKo8JCwvDsmXLsHHjRmzbtg39+vXDsGHDDB+mzFAddzLkOJRh/vz5aNy4MVq0aIELFy5g8+bNrn0chzLcKEOOQ1nOnTuH5cuXY/Lkya7bOA4bFhsLL7FgwYIqT066+uebb74BAMyZMweJiYno3r07pkyZgmXLlmH58uUoKSlR/Cp8m7sZLl++HHl5eVi4cGG1jxUZGYk5c+YgPj4evXv3xpIlS/Dwww/jpZdeasBX5HvqMkNSozafpQAwb948HDt2DDt37oS/vz/Gjh0LTdMAcByqUpcZkhq1zRAArFYrBg8ejBEjRmDixImu2zkOG1aA6gKobsydOxfjxo274THt27ev8vb4+HiUl5cjLS0NHTt2RExMDDIyMgzHVG7HxMS4/lvVMZX7qfbczXDv3r1ISUlBcHCwYV+vXr0wevRorFu3rsr7xsfHY9euXa5tZlj36jJDjkM1avtZGhkZicjISHTo0AGdO3dGXFwcDh06hISEhCrvy3FY/+oyQ45DNWqboc1mw8CBA9GnTx+sXLmyxsfnOKw/bCy8RMuWLdGyZcubum9qair8/PwQFRUFAEhISMCf/vQnlJWVub5numvXLnTs2BHNmjVzHbNnzx7MmjXL9Ti7du2q9n+mVDN3M3zjjTfw3HPPubZtNhsGDRqEDz74APHx8dXeLzU1FbGxsa5tZlj36jJDjkM1PPksdTqdAHDD2V+Ow/pXlxlyHKpRmwytVisGDhyInj17Ys2aNfDzq/nLOByH9UjtuePU0A4ePKi9+uqrWmpqqvb9999rf//737WWLVtqY8eOdR1z5coVLTo6WhszZox28uRJ7f3339dCQ0Ovu7xeQECA9vLLL2tnzpzRFi1axEuzKVLVFS/Wrl2rrV+/Xjtz5ox25swZ7fnnn9f8/Py0v/71r65jmKF5VJUhx6G5HTp0SFu+fLl27NgxLS0tTduzZ4/Wp08f7dZbb9WKi4s1TeM4NDt3MuQ4NLeLFy9qt912m3bvvfdqFy9eNFxOthLHYcNiY+Fjjhw5osXHx2sRERFaSEiI1rlzZy05Odn1IVrp66+/1vr166cFBwdrrVu31pYsWXLdY23YsEHr0KGDFhQUpHXt2lXbtm1bQ70Mukp1jUXnzp210NBQLTw8XPvpT3+qbdy48br7MkNzqO5yiByH5nX8+HFt4MCBWvPmzbXg4GDtlltu0aZMmaJdvHjRdQzHobm5k6GmcRya2Zo1azQAVf5U4jhsWBZN4xlKRERERETkGV4VioiIiIiIPMbGgoiIiIiIPMbGgoiIiIiIPMbGgoiIiIiIPMbGgoiIiIiIPMbGgoiIiIiIPMbGgoiIiIiIPMbGgoiIiIiIPMbGgoiIiIiIPMbGgoiITCkxMREWiwUWiwWpqalKahg3bpyrhn/9619KaiAikoKNBRERmdbEiRORnp6OO+64Q8nzv/7660hPT1fy3ERE0gSoLoCIiKg6oaGhiImJUfb8ERERiIiIUPb8RESScMaCiIjq3HvvvYdGjRoZ/rX/kUceQffu3ZGTk+PRYycmJmLGjBmYNWsWmjVrhujoaKxatQoFBQV45JFH0KRJE9x2223YsWOHR/chIqLaYWNBRER17qGHHkKHDh2QnJwMAFi0aBF2796NHTt21MkMwLp16xAZGYnDhw9jxowZmDp1KkaMGIE+ffrg6NGj+MUvfoExY8agsLDQo/sQEZH7LJqmaaqLICIi77N161b89re/xZNPPolly5bhs88+Q9euXd2+f2JiInr06IHXXnvtutsrKirw2WefAQAqKioQERGB4cOH429/+xsAwG63IzY2FikpKbjnnntu6j5Xs1gs2LRpE4YNG3aT7wYRkffjjAUREbllwYIFriskVffzzTffuI7/1a9+hS5duuCZZ57Bpk2batVU1KR79+6uP/v7+6NFixbo1q2b67bo6GgAQGZmpkf3ISIi9/HkbSIicsvcuXMxbty4Gx7Tvn17158//vhjfPPNN6ioqHD9pb2uBAYGGrYtFovhNovFAgBwOp0e3YeIiNzHxoKIiNzSsmVLtGzZ0q1jjx49ipEjR2L16tVYu3YtnnzySWzcuLGeKyQiIpXYWBARUZ1KS0vD0KFD8fjjj2PUqFFo3749EhIScPToUdx9992qyyMionrCcyyIiKjOOBwODB48GA888AAWLFgAAIiPj8eQIUPw+OOPK66OiIjqE2csiIiozjRv3txwAnelbdu21dlz7Nu377rb0tLSrrvt6ose3sx9iIiodjhjQUREprVixQqEhYXhxIkTSp5/ypQpCAsLU/LcRETScB0LIiIyJavViqKiIgBAmzZtEBQU1OA1ZGZmIjc3FwAQGxuLxo0bN3gNRERSsLEgIiIiIiKP8atQRERERETkMTYWRERERETkMTYWRERERETkMTYWRERERETkMTYWRERERETkMTYWRERERETkMTYWRERERETkMTYWRERERETkMTYWRERERETkMTYWRERERETksf8HCxzRXzpwZ8gAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot regressione\n",
    "\n",
    "SCALA = 50\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "x_data = -dfSonarCorrezione[\"c\"]\n",
    "y_data =  dfSonarCorrezione[\"F\"]\n",
    "\n",
    "x_fit = np.linspace(x_data.min(), x_data.max(), 300)\n",
    "y_fit = AS2 * x_fit + BS2\n",
    "\n",
    "ax.errorbar(\n",
    "    x_data, y_data,\n",
    "    xerr=SCALA * dfSonarCorrezione[\"uc\"], yerr=SCALA * dfSonarCorrezione[\"uF\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=f\"Dati (barre errore x{SCALA})\"\n",
    ")\n",
    "ax.plot(\n",
    "    x_fit, y_fit,\n",
    "    color='red', linewidth=1.5,\n",
    "    label=f\"$A={AS2:.4f}\\\\pm{uAS:.4f}$\\n\"\n",
    "          f\"$B={BS2:.1f}\\\\pm{uBS:.1f}$\\n\"\n",
    "          f\"$P(chi², ∞)= {PS2:.4f}$\\n\"\n",
    ")\n",
    "\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(r\"$- x$  [mm]\")\n",
    "ax.set_ylabel(\"F  [mN]\")\n",
    "ax.set_title(\"Fit lineare — Molla 2 (sonar statico con correzione)\")\n",
    "ax.legend(fontsize=9)\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e35a9456",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui\n",
    "\n",
    "dfSonarCorrezione[\"r\"] = r_fn(\n",
    "  -dfSonarCorrezione[\"c\"],\n",
    "  dfSonarCorrezione[\"F\"],\n",
    "  AS2,\n",
    "  BS2\n",
    ")\n",
    "\n",
    "dfSonarCorrezione[\"ur\"] = sigma_r_fn(\n",
    "  -dfSonarCorrezione[\"c\"], dfSonarCorrezione[\"F\"],\n",
    "  AS2, BS2,\n",
    "  dfSonarCorrezione[\"uc\"], dfSonarCorrezione[\"uF\"],\n",
    "  uAS2, uBS2, covABS2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8de2a48e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8fv36GDVqFH766Sf7z7/M5MmTHd5O2bNnT1itVpw9e9Z+WVVaOHuuOhMQEABfX1/s3LkT6enpl13+Un5+fvYPp7BarUhNTbW/lbCy+5VLbdiwASaTCXPmzCl3XVmjsufatGnTHK6fPn06AOC///1vhetPTk7GDz/8gAceeADXXnut0/XX1M9Qq7FMmjTJ6XEN1dmHXyw3Nxd33XUXwsPD8emnn1Z4DMWVss8MDw9HSkqK0zGSe/HtT6S5ZcuW4brrrkNmZibee+89/PDDDw4H6p08eRJKKcyaNQuzZs1yuo6kpCQ0bNgQ8+bNw5133onrrrsObdq0wcCBA3H//fejXbt2FW7/7Nmz8PLyQtOmTR0ub9GihXvuoAtnz55F/fr1ERQU5HLbVWlQWV9//TVeeOEFHDp0yOG9spU9h0fDhg3LvUf9xIkTOHr0KOrWrVvhGIH/NY+NjXW4vlmzZhVu79JlT5w4AaUUmjdv7nT5soPkY2NjMW3aNCxevBgff/wxevbsiTvuuAP33XeffcJxzz334J133sHEiRMxc+ZM9O3bF0OHDsXdd99d4Sd4lf1y4Oxx0qpVK2zZsgW5ubkIDAy0X37pLwplv6Smp6cjJCSk0vfJmaKiIqSlpVV4vSu+vr4O/5N3pUOHDmjZsiU++eQThIWFITo62v4Lx6XOnj2L5s2bl2vYqlUr+/VVceTIETz33HPYvn17uV+cLj4+5nIsFgsGDx6M0NBQfP7555U+kFT9/7fdXWzGjBn4/vvv0blzZzRr1gz9+/fHqFGj0L17dwClvyjm5eVV+Dix2Ww4f/68w1uwXD1OylSlhbPnqjN+fn5YuHAhpk+fjnr16uGmm27CbbfdhjFjxiA6Ovqyty879mH58uWIi4tzOL6jTp06l729M6dOnUKDBg1cPj7L9ieX7j+io6MRFhbm8nF2+vRpAHD56V819TPUaiyX7jvLVGcffrFJkybh1KlT2L17t8uf85Wyz1RKVfr/c1Q9nFSQ5jp37mx/X+yQIUPQo0cPjBo1CseOHUNQUJD9L8dPPPEEBgwY4HQdZf8z6dWrF06dOoUvv/wS3333Hd555x0sWbIEK1aswMSJE6s91op2RJU5ULQ6qtKgMn788Ufccccd6NWrF5YvX4769eujVq1aWL16dbkDTCty8V9KLx5n27ZtsXjxYqe3iYmJqfQYL7c9m80Gk8mEb7/91ukvhRdP1F577TWMGzfO/riYOnUqFixYgF9++QXXXHMNAgIC8MMPP2DHjh3473//i82bN2Pt2rX4xz/+ge+++85tn15S0XrKflmtyn261O7du8Ufddq7d2+HkyVezqhRo/DWW28hODgY99xzjyYfnZyRkYHevXsjJCQE8+bNQ9OmTeHv74+DBw9ixowZlX6FKTMzE4MGDUJGRgZ+/PFHNGjQoFK3q1OnjtNfCFu1aoVjx47h66+/xubNm7FhwwYsX74cs2fPxty5c6t0H8tc7nFS1RbOnqsVeeyxx3D77bdj06ZN2LJlC2bNmoUFCxZg+/bt5Y4nudRLL72EWbNm4YEHHsD8+fMREREBLy8vPPbYY5p8TOyV9Ivi5X6GWqro5++Offjrr7+OTz/9FB999FG5jwd2h5rYZ6anp1c4CSH34qSCdOXt7Y0FCxbg5ptvxr///W/MnDnT/hJvrVq1nH7yyqUiIiIwfvx4jB8/Hjk5OejVqxeef/75CicVjRo1gs1mw6lTpxz+gnLs2LFyy4aHhzv9ZKaq/sX14m1v27YNOTk5Dju/S7dd1QaXs2HDBvj7+2PLli0OrwqtXr26Wutt2rQpfv/9d/Tt29fl/+DLmsfFxTns3E+ePFmlbSmlEBsbi+uuu+6yy7dt2xZt27bFc889h927d6N79+5YsWIFXnjhBQCAl5cX+vbti759+2Lx4sV46aWX8Oyzz2LHjh1Omzdq1AiA88fJ33//jcjISIe/uNXEfbrYDTfcIP5EE2dv63Fl1KhRmD17NhISEvDhhx9WuFyjRo1w+PBh2Gw2h4lH2dtzyhpWxs6dO5GamoqNGzeiV69e9svj4uIqvY6CggLcfvvtOH78OL7//nu0bt260rdt2bIlPv74Y2RmZtpf4SoTGBiIe+65B/fccw+KioowdOhQvPjii3j66adRt25d1K5du8LHiZeXV5Un2+5o4UrTpk0xffp0TJ8+HSdOnED79u3x2muv4aOPPgJQ8S/vn3/+OW6++Wa8++67DpdnZGQgMjLS/n1Vfvlv2rQptmzZgrS0tApfrSjbn5w4ccL+KhgAJCYmIiMjw+XjrGzf+ueff1a4TE38DK/UsVR2Hw6U/nHqiSeewGOPPYbRo0dfdt1Xwj6zpKQE58+fxx133FGl7ZAMj6kg3fXp0wedO3fG0qVLUVBQgKioKPTp0wcrV65EQkJCueUv/pi+1NRUh+uCgoLQrFkzlx8rOGjQIADAG2+84XD50qVLyy3btGlTZGZm4vDhw/bLEhISLvvpIhW59dZbUVJSgrfeest+mdVqxZtvvumwXFUaVIa3tzdMJpPDKyxnzpxx+HQmiREjRiA+Ph5vv/12uevy8/PtnzxS9mrL8uXLHZa59H67MnToUHh7e2Pu3Lnl/vqnlLI/FrKyslBSUuJwfdu2beHl5WV/XDh721DZX90qeuzUr18f7du3x5o1axwmmn/++Se+++473HrrrZW+L1W9T86Eh4ejX79+oq+OHTtWaZxNmzbF0qVLsWDBAnTu3LnC5W699VZYLBaHT7wpKSnBm2++iaCgIPTu3bvS2yz7K+TFXYqKiso9hipitVpxzz33YM+ePVi/fj26du1a6W0DQNeuXaGUwoEDBxwuv/Rn4uvri9atW0MpheLiYnh7e6N///748ssv7Z9sB5T+wvvJJ5+gR48eCAkJqdJYqtuiInl5eSgoKHC4rGnTpggODnZ4HgQGBjr944q3t3e5x+369evLHe9V9otjRR+dfbFhw4ZBKeX0VZ+ybZU91y7dZ5f9td3Vp8rVrVsXvXr1wnvvvYdz5845XX9N/Ayv1LFUdh+ekJCAESNGoEePHpX+5LUrYZ/5119/oaCgwOWnjZH78JUKuiI8+eSTGD58ON5//308+OCDWLZsGXr06IG2bdti0qRJaNKkCRITE7Fnzx5cuHDB/jnorVu3Rp8+fdCxY0dERERg//79+PzzzzFlypQKt9W+fXvce++9WL58OTIzM9GtWzds27bN6V/NR44ciRkzZuCuu+7C1KlTkZeXh7feegvXXXed6EDE22+/Hd27d8fMmTNx5swZtG7dGhs3bnT6/vDKNqiMwYMHY/HixRg4cCBGjRqFpKQkLFu2DM2aNXOYMFXV/fffj3Xr1uHBBx/Ejh070L17d1itVvz9999Yt24dtmzZgk6dOqFjx44YNmwYli5ditTUVPtHyh4/fhxA5f6S2bRpU7zwwgt4+umncebMGQwZMgTBwcGIi4vDF198gcmTJ+OJJ57A9u3bMWXKFAwfPhzXXXcdSkpK8OGHH8Lb2xvDhg0DUHpW9x9++AGDBw9Go0aNkJSUhOXLl+Oaa65xOCfGpV555RUMGjQIXbt2xYQJE+wfjxgaGupwro3Kqux9uhJU5uNgJ0+ejJUrV2LcuHE4cOAAGjdujM8//xw///wzli5diuDg4Epvr1u3bggPD8fYsWMxdepUmEwmfPjhh5V+O8n06dPx1Vdf4fbbb0daWpr9r+5l7rvvPpe379GjB+rUqYPvv//e4RiS/v37Izo6Gt27d0e9evVw9OhR/Pvf/8bgwYPt9++FF16wnwfl4Ycfho+PD1auXInCwkIsWrSo0g3KVLdFRY4fP46+fftixIgRaN26NXx8fPDFF18gMTERI0eOtC/XsWNHvPXWW3jhhRfQrFkzREVF4R//+Aduu+02zJs3D+PHj0e3bt3wxx9/4OOPP3Y4oBgofZyHhYVhxYoVCA4ORmBgILp06eL0vf8333wz7r//frzxxhs4ceIEBg4cCJvNhh9//BE333wzpkyZghtuuAFjx47FqlWr7G8N27dvH9asWYMhQ4Zc9m2Bb7zxBnr06IEbb7wRkydPRmxsLM6cOYP//ve/OHToEAD3/wyv1LFUdh8+depUJCcn46mnnsJnn33msI527dpVeByj3vvMrVu3onbt2i4/Yp7cSINPmCJSSv3v4+5+/fXXctdZrVbVtGlT1bRpU/vH0506dUqNGTNGRUdHq1q1aqmGDRuq2267TX3++ef2273wwguqc+fOKiwsTAUEBKiWLVuqF198URUVFdmXcfbxr/n5+Wrq1KmqTp06KjAwUN1+++3q/Pnz5T7mUimlvvvuO9WmTRvl6+urWrRooT766CPxR8oqpVRqaqq6//77VUhIiAoNDVX333+//WNQL/3Ixco0qOxHyr777ruqefPmys/PT7Vs2VKtXr3a6f1wpnfv3hV+jGZRUZFauHChuv7665Wfn58KDw9XHTt2VHPnzlWZmZn25XJzc9UjjzyiIiIiVFBQkBoyZIg6duyYAqBefvll+3IXf5ypMxs2bFA9evRQgYGBKjAwULVs2VI98sgj6tixY0oppU6fPq0eeOAB1bRpU+Xv768iIiLUzTffrL7//nv7OrZt26buvPNO1aBBA+Xr66saNGig7r33XnX8+HH7Ms4+HlEppb7//nvVvXt3FRAQoEJCQtTtt9+u/vrrL4dlKroPzj42tTL3SWuX+xmUwSUfKauUUomJiWr8+PEqMjJS+fr6qrZt2zr9KNFLn2vO2vz888/qpptuUgEBAapBgwbqqaeeUlu2bCn3eHem7CM0K/qqjKlTpzp8nK5SSq1cuVL16tVL1alTR/n5+ammTZuqJ5980uGxrpRSBw8eVAMGDFBBQUGqdu3a6uabb1a7d+92WKaifaKz53RlW7h6rl4qJSVFPfLII6ply5YqMDBQhYaGqi5duqh169Y5LGexWNTgwYNVcHCwAmD/eNmCggI1ffp0Vb9+fRUQEKC6d++u9uzZo3r37l3uI2i//PJL1bp1a+Xj4+PwvHK2ryopKVGvvPKKatmypfL19VV169ZVgwYNUgcOHLAvU1xcrObOnatiY2NVrVq1VExMjHr66acr/VHDf/75p7rrrrtUWFiY8vf3Vy1atFCzZs1yWMbdP0O9xqJU9ffhrp5PZc/jK3Gf2aVLF3Xfffc5vd/kfialdDiKiIgIwKFDh9ChQwd89NFHlXqPLpGWTp8+jZYtW+Lbb79F37599R4OEVXBoUOHcOONN+LgwYM1clA5lcdJBRFpIj8/v9ynj4wbNw4ffvghzpw545YDH4nc7aGHHsLJkyfFB8UTkT5GjhwJm82GdevW6T2UqwYnFUSkiblz5+LAgQO4+eab4ePjg2+//Rbffvut/X34REREZFycVBCRJrZu3Yq5c+fir7/+Qk5ODq699lrcf//9ePbZZ+Hjw8+MICIiMjJOKoiIiIiIqFp4ngoiIiIiIqoWTiqIiIiIiKhaOKnQiFIKWVlZ1T5ZERERERHRlYaTCo1kZ2cjNDQU2dnZeg+lSoqKivQegiGxmwy7ybGdDLvJsJsc28mwm4yW3TipIJesVqveQzAkdpNhNzm2k2E3GXaTYzsZdpPRshsnFeRSXl6e3kMwJHaTYTc5tpNhNxl2k2M7GXaT0bIbJxXkEs8fIMNuMuwmx3Yy7CbDbnJsJ8NuMlp246SCXPLy4kNEgt1k2E2O7WTYTYbd5NhOht1ktOzGnxC5VFBQoPcQDIndZNhNju1k2E2G3eTYTobdZLTsxteSyKWQkBC9h2BI7CbDbnJsJ8NuMuwm52ntrFYriouLa3w7fn5+nFg4UatWLXh7e1d4vZaPN04qyKWUlBQ0bNhQ72EYDrvJsJsc28mwmwy7yXlKO6UULBYLMjIyNNleSUkJj6uoQFhYGKKjo2Eymcpdp+XjzaR4NjZNZGVlITQ0FJmZmR73VwoiIiK6uiQkJCAjIwNRUVGoXbu2019oqWYppZCXl4ekpCSEhYWhfv36uo6HUz5yKT4+3iP+oqI1dpNhNzm2k2E3GXaT84R2VqvVPqGoU6eOJtssKiqCr6+vJtsykoCAAABAUlISoqKiyr0VSsvHG1+p0IhRX6lQSvGvDwLsJsNucmwnw24y7CbnCe0KCgoQFxeHxo0b23+prWme0K2m5Ofn48yZM4iNjYW/v7/DdVp246c/kUtms1nvIRgSu8mwmxzbybCbDLvJeVK76vyymp2djYSEhAq/srOzHZbX4mBwo3L1c9Dy8ca3P5FLkZGReg/BkNhNht3k2E6G3WTYTY7tSh04cAC7du2q8PrevXujT58+9u95kLaMlo83j32lYtmyZWjcuDH8/f3RpUsX7Nu3r8Jl+/TpA5PJVO5r8ODB9mXGjRtX7vqBAwdqcVd0lZWVpfcQDIndZNhNju1k2E2G3eTYrlTHjh0xefJkTJ48GUOHDgUADB061H5Zx44dHZa3Wq16DLNKzpw5A5PJhEOHDlW4zM6dO2Eymar0qVnjxo3DkCFDRGPS8vHmkdO+tWvXYtq0aVixYgW6dOmCpUuXYsCAATh27BiioqLKLb9x40YUFRXZv09NTcUNN9yA4cOHOyw3cOBArF692v69n59fzd2JK8TVcB9rArvJsJsc28mwmwy7ybFdqeDgYAQHBztcFhkZWeEnGLnrzNDjxo3DmjVrAJS++nHNNddg+PDhmDdvXrnjEaoqJiYGCQkJbn914PXXX4f0EGgtH28e+UrF4sWLMWnSJIwfPx6tW7fGihUrULt2bbz33ntOl4+IiEB0dLT9a+vWrahdu3a5SYWfn5/DcuHh4VrcHSIiIiJDy87Oxs6dO8sdKxGXkovlP8djZ1Eslv8cj7iU3ErdrjoGDhyIhIQEnD59GkuWLMHKlSsxZ86caq/X29sb0dHRbn+rVmhoKMLCwty6zprgcZOKoqIiHDhwAP369bNf5uXlhX79+mHPnj2VWse7776LkSNHIjAw0OHynTt3IioqCi1atMBDDz2E1NTUCtdRWFiIrKwshy8juvgVHKo8dpNhNzm2k2E3GXaTu1rb5eTkYNeuXcjJybFftm7/efR9bSc+OZCIM9YIfHIgEX1f24n1+8+Xu507JxVlfySOiYnBkCFD0K9fP2zduhUAYLPZsGDBAsTGxiIgIAA33HADPv/8c/tt09PTMXr0aNStWxcBAQFo3ry5/V0szt7+9M033+C6665DQEAAbr75Zpw5c8ZhLM8//zzat2/vcNnSpUvRuHFj+/fVefuTlo83j5tUpKSkwGq1ol69eg6X16tXDxaL5bK337dvH/78809MnDjR4fKBAwfigw8+wLZt27Bw4ULs2rULgwYNqvA9fgsWLEBoaKj9KyYmBkDpx7CZzWbYbDbEx8cDKP0M4aKiIiQlJSEnJwcZGRlIS0tDfn4+LBYLSkpKHJYtLi6GxWJBXl4e0tLSkJ6ejtzcXCQmJqK4uNhhWavVCrPZjPz8fKSmpiIzMxPZ2dlITk5GYWGhw7JKKcTHx6OwsBDJycnIysqCzWZDSkoK8vPzKz3uvLw8p+MuKSkpN+6cnByn47bZbJcdd9l4Lx13dnY2MjMzkZqaWuG4i4uLkZiYiJycHKSnp1dp3BkZGcjJyUFSUhKKioqcjtvb2xspKSn2CaWzcZfdxtW4rVar03Hn5uaWG/elDcvGnZ+fX+lxFxQUOB232Wx2Ou6kpKRy405ISLjsuCt6zGZnZ8NqtSIhIcFh3NnZ2VUad0pKymWfa9nZ2Q7PteqMu+y5Vjbuix+zSUlJTp9rZrPZ4blW2XFXtI8o+x+u1vuIsnEbdR/h7e2tyz4iPz/f0PsIm82myz7i0ueaEfcR3t7euuwj3Pl7RG5uLqxWK2w2m/2X1qKiIiilUFRUBJvNhuLiYlitVlitVhQXF8NmswGAfZm4lFzM3HAYNgVYFaBgglUBNgXM2HAYJy2ZUErZP/XJZrPZv7darSgpKUFJSYnD+isay8XLlq2nbJnffvsNu3fvRq1atWCz2fDCCy/ggw8+wLJly/D777/jX//6F+677z7s3LkTRUVFmDVrFo4cOYL//ve/OHz4MP79738jPDzcvv6L7+P58+cxdOhQ3Hbbbfj111/xwAMPYObMmQBgH7dSyv7WprIxlZSUAID9viqlYLPZ7PexbP1lt7HZbLBarcjNzS23j9DqI38BDzxPhdlsRsOGDbF792507drVfvlTTz2FXbt2Ye/evS5v/89//hN79uzB4cOHXS53+vRpNG3aFN9//z369u1b7vrCwkIUFhbav8/KykJMTIzhzlNhNpvRoEEDvYdhOOwmw25yntYuOzvb4S+alwoKCir3fmwJT+umFXaT84R2ZeepcHZehIokJCRg1apVGDp0KCIjI7H853h8ciARVie/hXqZgNEd6+Hh7g2RkpKCjRs3Yvz48bj22murPfZx48bho48+gr+/P0pKSlBYWAgvLy+sW7cOt912GyIiIvD99987/A45ceJE5OXl4ZNPPsEdd9yByMhIp2+pLztXxG+//Yb27dvjmWeewZdffokjR47Yl5k5cyYWLlyI9PR0hIWF4fnnn8emTZscXt1YunQpli5dan9VY9y4ccjIyMCmTZuc3idXPw8tH28ed6B2ZGQkvL29kZiY6HB5YmIioqOjXd42NzcXn332GebNm3fZ7TRp0gSRkZE4efKk00mFn5+fRxyMdblm5By7ybCbnKe1q+rHTUp5WjetsJvc1d5u48aNAICfimJhUxEAyp9jQSmFn377Cz5H/mu/zJ3HKdx888146623kJubiyVLlsDHxwfDhg3DkSNHkJeXh1tuucVh+aKiInTo0AEA8NBDD2HYsGE4ePAg+vfvjyFDhqBbt25Ot3P06FF06dLF4bKLJyta0PLx5nGTCl9fX3Ts2BHbtm2zv//MZrNh27ZtmDJlisvbrl+/HoWFhbjvvvsuu50LFy4gNTW1wk8p8BQJCQmand7dk7CbDLvJeVq7jh07okWLFgBg/0tl2V84gdJXKtzB07pphd3krvZ2Zc/jkp/jca6CVypMJhN6dGiNyd1vsT//y94S5A6BgYFo1qwZAOC9997DDTfcgHfffRdt2rQBAPz3v/8t9zMq+0PxoEGDcPbsWXzzzTfYunUr+vbti0ceeQSvvvqqaCxeXl7lPtnJnSf60/Lx5nGTCgCYNm0axo4di06dOqFz585YunQpcnNzMX78eADAmDFj0LBhQyxYsMDhdu+++y6GDBmCOnXqOFyek5ODuXPnYtiwYYiOjsapU6fw1FNPoVmzZhgwYIBm90sPV/OOrzrYTYbd5DytXVU/blLK07pphd3krvZ2Zc/jB/qE4OMDiRUuN6FPK9SP/N8H5tSqVatGxuPl5YVnnnkG06ZNw/Hjx+Hn54dz586hd+/eFd6mbt26GDt2LMaOHYuePXviySefdDqpaNWqFb766iuHy3755Zdy67JYLFBK2c+M7eo8F1Wl5ePN4w7UBoB77rkHr776KmbPno327dvj0KFD2Lx5s/3g7XPnziEhIcHhNseOHcNPP/2ECRMmlFuft7c3Dh8+jDvuuAPXXXcdJkyYgI4dO+LHH3/0iLc4uVJ2sBZVDbvJsJsc28mwmwy7ybFdqdjIQCwc1g5eJsDbBJig4GUqPZ5i4bB2aBzp+Amc7vzr/aWGDx8Ob29vrFy5Ek888QQef/xxrFmzBqdOncLBgwfx5ptv2s9tMXv2bHz55Zc4efIkjhw5gq+//hqtWrVyut4HH3wQJ06cwJNPPoljx47hk08+wfvvv++wTJ8+fZCcnIxFixbh1KlTWLZsGb799lu33TctH28e+UoFAEyZMqXCtzvt3Lmz3GUtWrSo8MQiAQEB2LJlizuHZxh169bVewiGxG4y7CbHdjLsJsNucldru6CgIPTu3dvhrYvDO8Xg/xpH4N2dR/HTb3+hR4fWmNCnlcOEoux2oaGhNTY2Hx8fTJkyBYsWLUJcXBzq1q2LBQsW4PTp0wgLC8ONN96IZ555BkDp2+yffvppnDlzBgEBAejZsyc+++wzp+u99tprsWHDBjz++ON488030blzZ7z00kt44IEH7Mu0atUKy5cvx0svvYT58+dj2LBheOKJJ7Bq1Sq33DctH28e9+lPV6qsrCyEhoYa7tOfkpKSnJ6FnFxjNxl2k/PkdmWfGjN58mS3v/3Jk7vVJHaT84R2kk9/cqUyz/Hi4uIaewuU0bn6eWj5ePPYVyrIPWrXrq33EAyJ3WTYTY7tZNhNht3k2K7UxR8bnZKS4vBfoPzHRnt5eeQ79muclo83TirIJXd+2sLVhN1k2E2O7WTYTYbd5NiulLOPjS77uFmg/MdG8401Mlo+3jipIJfKzoBJVcNuMuwmx3Yy7CbDbnJsV+rij412xl0fG3210/LxxkkFueSO90pejdhNht3k2E6G3WTYTY7tSjn72GhXyj5ulapGy8cb36BGLmVlZek9BENiNxl2k2M7GXaTYTc5tpPhKzwyWj7eOKkgl8rOXktVw24y7CbHdjLsJsNucp7UTtO31vjwzTUVcfVz0PLxxp8QuZSYmHjVn/1Tgt1k2E2O7WTYTYbd5Dyhna+vL7y8vGA2m1G3bl34+vrW+NuTioqK4OvrW6PbMBqlFIqKipCcnAwvLy+nfbR8vHFSQS4ZfcenF3aTYTc5tpNhNxl2k/OEdl5eXoiNjUVCQgLMZrPew7nq1a5dG9dee63Tj93V8vHGSQW5FB8f7xE7QK2xmwy7ybGdDLvJsJucp7Tz9fXFtddei5KSElit1hrfXmJiIurVq1fj2zEab29v+Pj4VPhKkZaPN04qyCU+gWXYTYbd5NhOht1k2E3Ok9qZTCbUqlVLkzNdN2zYkMdVCGj5eOOB2uTSxWe3pMpjNxl2k2M7GXaTYTc5tpNhNxktu3FSQS6FhIToPQRDYjcZdpNjOxl2k2E3ObaTYTcZLbtxUkEuFRQU6D0EQ2I3GXaTYzsZdpNhNzm2k2E3GS27cVJBLvEMljLsJsNucmwnw24y7CbHdjLsJqNlN04qyCUtDr7yROwmw25ybCfDbjLsJsd2Muwmo2U3TirIpdzcXL2HYEjsJsNucmwnw24y7CbHdjLsJqNlN04qyKWIiAi9h2BI7CbDbnJsJ8NuMuwmx3Yy7CajZTdOKsilpKQkvYdgSOwmw25ybCfDbjLsJsd2Muwmo2U3TirIJU8466ce2E2G3eTYTobdZNhNju1k2E1Gy26cVJBL8fHxeg/BkNhNht3k2E6G3WTYTY7tZNhNRstunFSQS/Xr19d7CIbEbjLsJsd2Muwmw25ybCfDbjJaduOkglyyWCx6D8GQ2E2G3eTYTobdZNhNju1k2E1Gy24+mm2JDCk8PFzvIRjS1dItOzsbOTk5FV4fFBSE4ODgSq/vaulWE9hOht1k2E2O7WTYTUbLbpxUkEt5eXkICAjQexiGc7V0O3DgAHbt2lXh9b1790afPn0qvb6rpVtNYDsZdpNhNzm2k2E3GS27cVJBLvn48CEicbV069ixI1q0aAEASElJwcaNGzF06FBERkYCKH2loiqulm41ge1k2E2G3eTYTobdZLTsxp8QueTlxcNuJK6WbsHBweXe3hQZGSk+MOxq6VYT2E6G3WTYTY7tZNhNRstu/AmRSwUFBXoPwZDYTYbd5NhOht1k2E2O7WTYTUbLbpxUkEshISF6D8GQ2E2G3eTYTobdZNhNju1k2E1Gy26cVJBLKSkpeg/BkNhNht3k2E6G3WTYTY7tZNhNRstunFSQSw0aNNB7CIbEbjLsJsd2Muwmw25ybCfDbjJaduOkglwym816D8GQ2E2G3eTYTobdZIzcLTs7GwkJCRV+ZWdn1+j2jdxOT+wmo2U3fvoTucS/DMiwmwy7ybGdDLvJGLmbu8+vU1VGbqcndpPRshsnFeSS2WxGw4YN9R6G4bCbDLvJsZ0Mu8kYuZu7z69TVUZupyd2k9GyGycV5FLZTpaqht1k2E2O7WTYTcbI3dx9fp2qMnI7PbGbjJbdeEwFuZSVlaX3EAyJ3WTYTY7tZNhNht3k2E6G3WS07MZJBbnk7++v9xAMid1k2E2O7WTYTYbd5NhOht1ktOzGSQW5ZLPZ9B6CIbGbDLvJsZ0Mu8mwmxzbybCbjJbdOKkgl0pKSvQegiGxmwy7ybGdDLvJsJsc28mwm4yW3TipIJdq166t9xAMid1k2E2O7WTYTYbd5NhOht1ktOzGSQW5lJ6ervcQDIndZNhNju1k2E2G3eTYTobdZLTs5rGTimXLlqFx48bw9/dHly5dsG/fvgqXff/992EymRy+Lj2wRSmF2bNno379+ggICEC/fv1w4sSJmr4buouOjtZ7CIbEbjLsJsd2Muwmw25ybCfDbjJadvPIScXatWsxbdo0zJkzBwcPHsQNN9yAAQMGICkpqcLbhISEICEhwf519uxZh+sXLVqEN954AytWrMDevXsRGBiIAQMGoKCgoKbvjq4SEhL0HoIhsZsMu8mxnQy7ybCbHNvJsJuMlt08clKxePFiTJo0CePHj0fr1q2xYsUK1K5dG++9916FtzGZTIiOjrZ/1atXz36dUgpLly7Fc889hzvvvBPt2rXDBx98ALPZjE2bNmlwj/TDs1fKsJsMu8mxnQy7ybCbHNvJsJuMlt08blJRVFSEAwcOoF+/fvbLvLy80K9fP+zZs6fC2+Xk5KBRo0aIiYnBnXfeiSNHjtivi4uLg8VicVhnaGgounTpUuE6CwsLkZWV5fBlRPHx8XoPwZDYTYbd5NhOht1k2E2O7WTYTUbLbh43qUhJSYHVanV4pQEA6tWrB4vF4vQ2LVq0wHvvvYcvv/wSH330EWw2G7p164YLFy4AgP12VVnnggULEBoaav+KiYkBABQUFMBsNsNms9l/0PHx8SgqKkJSUhJycnKQkZGBtLQ05Ofnw2KxoKSkxGHZ4uJiWCwW5OXlIS0tDenp6cjNzUViYiKKi4sdlrVarTCbzcjPz0dqaioyMzORnZ2N5ORkFBYWOiyrlEJ8fDwKCwuRnJyMrKws+Pv7IyUlBfn5+ZUed15entNxl5SUlBt3Tk6O03HbbLbLjrtsvJeOOzs7G5mZmUhNTa1w3MXFxUhMTEROTg7S09OrNO6MjAzk5OQgKSkJRUVFTscdEhKClJQU+4TS2bjLbuNq3Far1em4c3Nzy4370oZl487Pz6/0uAsKCpyO22w2Ox13UlKSfdwZGRkOz0FX467oMVtSUgKr1YqEhASHcWdnZ1dp3CkpKZd9rmVnZzs81xISEsTjLnuulY374sdsUlKS0+ea2Wx2eK5VdtwV7SPKPjZQ631E2bhrch+RmpoKAPa3sLpzHxESEqLLPiI/P9/Q+wh/f/8q7yPKxu3O51p19xHJycnlHls1vY8IDg7WZR+h1+8R7tpHlO3nrpbfI9y1j6hTpw60YlJKKc22pgGz2YyGDRti9+7d6Nq1q/3yp556Crt27cLevXsvu47i4mK0atUK9957L+bPn4/du3eje/fuMJvNqF+/vn25ESNGwGQyYe3ateXWUVhYiMLCQvv3WVlZiImJQWZmJkJCQqp5L7WTmJhYbjJFl3c1dktISMCqVaswefJkh+dJVVyN3dzFk9u547FVEU/uVpM8pVtNPrYq4inttMZuMlp287hXKiIjI+Ht7Y3ExESHyxMTEyt9BHytWrXQoUMHnDx5EsD/jpyvyjr9/PwQEhLi8GVEgYGBeg/BkNhNht3k2E6G3WTYTY7tZNhNRstuHjep8PX1RceOHbFt2zb7ZTabDdu2bXN45cIVq9WKP/74w/5Xi9jYWERHRzusMysrC3v37q30Oo2quLhY7yEYErvJsJsc28mwmwy7ybGdDLvJaNnNR7MtaWjatGkYO3YsOnXqhM6dO2Pp0qXIzc3F+PHjAQBjxoxBw4YNsWDBAgDAvHnzcNNNN6FZs2bIyMjAK6+8grNnz2LixIkASj8Z6rHHHsMLL7yA5s2bIzY2FrNmzUKDBg0wZMgQve6mJjzs3XGaYTcZdpNjOxl2k2E3ObaTYTcZLbt55KTinnvuQXJyMmbPng2LxYL27dtj8+bN9veUnTt3Dl5e/3uRJj09HZMmTYLFYkF4eDg6duyI3bt3o3Xr1vZlnnrqKeTm5mLy5MnIyMhAjx49sHnz5nInyfM0nn7/agq7ybCbHNvJsJsMu8mxnQy7yWjZzSMnFQAwZcoUTJkyxel1O3fudPh+yZIlWLJkicv1mUwmzJs3D/PmzXPXEA0hKysLtWvX1nsYhsNuMuwmx3Yy7CbDbnJsJ8NuMlp287hjKsi9IiMj9R6CIbGbDLvJsZ0Mu8mwmxzbybCbjJbdOKkgly79xCuqHHaTYTc5tpNhNxl2k2M7GXaT0bIbJxXkkpand/ck7CbDbnJsJ8NuMuwmx3Yy7CajZTdOKsglLU/v7knYTYbd5NhOht1k2E2O7WTYTUbLbpxUkEs8e6UMu8mwmxzbybCbDLvJsZ0Mu8lo2Y2TCnIpJSVF7yEYErvJsJsc28mwmwy7ybGdDLvJaNmNkwpyKSQkRO8hGBK7ybCbHNvJsJsMu8mxnQy7yWjZjZMKcqmgoEDvIRgSu8mwmxzbybCbDLvJsZ0Mu8lo2Y2TCnLp4jOPU+Wxmwy7ybGdDLvJsJsc28mwm4yW3fgTIpd8fDz2pOs1it1k2E2O7WTYTYbd5NhOht1ktOzGSQW5lJeXp/cQDIndZNhNju1k2E2G3eTYTobdZLTsxkkFuRQWFqb3EAyJ3WTYTY7tZNhNht3k2E6G3WS07MZJBbmUnJys9xAMid1k2E2O7WTYTYbd5NhOht1ktOzGSQW5pOXp3T0Ju8mwmxzbybCbDLvJsZ0Mu8lo2Y2TCg+VnZ2NhISECr+ys7MrtR4tT+/uSdhNht3k2E6G3WTYTY7tZNhNRstuPJTeQx04cAC7du2q8PrevXujT58+l11P/fr13Tiqqwe7ybCbHNvJsJsMu8mxnQy7yWjZjZMKD9WxY0e0aNECQOkp2jdu3IihQ4ciMjISABAUFFSp9VgsFjRo0KDGxump2E2G3eTYTobdZNhNju1k2E1Gy26cVHio4OBgBAcHO1wWGRlZ5RlreHi4O4d11WA3GXaTYzsZdpNhNzm2k2E3GS278ZgKcik3N1fvIRgSu8mwmxzbybCbDLvJsZ0Mu8lo2Y2TCnLJ19dX7yEYErvJsJsc28mwmwy7ybGdDLvJaNmNkwoiIiIiIqoWTirIpcLCQr2HYEjsJsNucmwnw24y7CbHdjLsJqNlN04qyKWQkBC9h2BI7CbDbnJsJ8NuMuwmx3Yy7CajZTdOKsillJQUvYdgSOwmw25ybCfDbjLsJsd2Muwmo2U3TirIJX4mtAy7ybCbHNvJsJsMu8mxnQy7yWjZjZMKcslsNus9BENiNxl2k2M7GXaTYTc5tpNhNxktu3FSQS41bNhQ7yEYErvJsJsc28mwmwy7ybGdDLvJaNmNkwpyKT4+Xu8hGBK7ybCbHNvJsJsMu8mxnQy7yWjZjZMKcikyMlLvIRgSu8mwmxzbybCbDLvJsZ0Mu8lo2Y2TCnIpKytL7yEYErvJsJsc28mwmwy7ybGdDLvJaNmNkwpyyd/fX+8hGBK7ybCbHNvJsJsMu8mxnQy7yWjZjZMKcslms+k9BENiNxl2k2M7GXaTYTc5tpNhNxktu3FSQS6VlJToPQRDYjcZdpNjOxl2k2E3ObaTYTcZLbtxUkEu1a5dW+8hGBK7ybCbHNvJsJsMu8mxnQy7yWjZjZMKcik9PV3vIRgSu8mwmxzbybCbDLvJsZ0Mu8lo2Y2TCnKpXr16eg/BkNhNht3k2E6G3WTYTY7tZNhNRstuPtIbfvXVV1W+zS233IKAgADpJkkHFouFZ7EUYDcZdpNjOxl2k2E3ObaTMVq37Oxs5OTkVHh9UFAQgoODa3wcWnYTTyqGDBlSpeVNJhNOnDiBJk2aSDdJOjDSE/hKwm4y7CbHdjLsJsNucmwnY7RuBw4cwK5duyq8vnfv3ujTp0+Nj0PLbuJJBVA6+4mKiqrUslrMxsj94uPjDfdEvhKwmwy7ybGdDLvJsJsc28kYrVvHjh3RokULAEBKSgo2btyIoUOH2s9wHRQUpMk4tOwmnlSMHTu2Sm9luu+++xASEiLdHOmkspNGcsRuMuwm56nt4lJysWrnGfxSFIuz6/Zh2pCuaNPIfffVU7vVNHaTYzsZo3ULDg4u9wf1yMhI1K9fX9NxaNlNfKD26tWrq/Tqw1tvvWWfnZFxpKWl6T0EQ2I3GXaT88R26/afR9/XdmLt4TScsUZgR4I37ljxK9bvP++2bXhiNy2wmxzbybCbjJbdPPbTn5YtW4bGjRvD398fXbp0wb59+ypc9u2330bPnj0RHh6O8PBw9OvXr9zy48aNg8lkcvgaOHBgTd8N3Wn18pynYTcZdpPztHZxKbmYueEwbAqwKUDBBAUTbAqYseEwzqTkumU7ntZNK+wmx3Yy7CajZbdqHVNxsYKCAhw+fBhJSUnlTgl+xx13uGszlbJ27VpMmzYNK1asQJcuXbB06VIMGDAAx44dc/oy0M6dO3HvvfeiW7du8Pf3x8KFC9G/f38cOXLE4X1oAwcOxOrVq+3f+/n5aXJ/9FRUVITAwEC9h2E47CbDbnKe1m7d/vMwmUyAUuWuM5lMWLv/PGYMbFnt7XhaN62wmxzbybCbjJbd3DKp2Lx5M8aMGYOUlJRy15lMJlitVndsptIWL16MSZMmYfz48QCAFStW4L///S/ee+89zJw5s9zyH3/8scP377zzDjZs2IBt27ZhzJgx9sv9/PwQHR1ds4O/wign/0Ony2M3GXaT87R2F9LzK7xPSilcSM93y3Y8rZtW2E2O7WTYTUbLbm55+9Ojjz6K4cOHIyEhATabzeFL6wlFUVERDhw4gH79+tkv8/LyQr9+/bBnz55KrSMvLw/FxcWIiIhwuHznzp2IiopCixYt8NBDDyE1NbXCdRQWFiIrK8vhy4j8/f31HoIhsZsMu8l5WrtrwgNKX6lwwmQy4Zpw95zzyNO6aYXd5NhOht1ktOzmlklFYmIipk2bdkWc7TAlJQVWq7XcWOrVqweLxVKpdcyYMQMNGjRwmJgMHDgQH3zwAbZt24aFCxdi165dGDRoUIWTpgULFiA0NNT+FRMTA6D0bWJmsxk2mw3x8fEASj/uq6ioCElJScjJyUFGRgbS0tKQn58Pi8WCkpISh2WLi4thsViQl5eHtLQ0pKenIzc3F4mJiSguLnZY1mq1Ijk5GQCQkZGBzMxMZGdnIzk5GYWFhQ7LKqUQHx+PwsJCJCcnIysrCxaLBSkpKcjPz6/0uPPy8pyOu6SkpNy4c3JynI7bZrPBbDYjPz8fqampTsddNt5Lx52dnY3MzEykpqZWOO7i4mIkJiYiJycH6enpVRp3RkYGcnJykJSUhKKiIqfjTklJQUpKin1C6WzcZbdxNW6r1ep03Lm5ueXGfWnDsnHn5+dXetwFBQVOx202m52OOykpyT7ujIwMh+egq3FX9JiNi4uD1WpFQkKCw7izs7OrNO6UlJTLPteys7MdnmsJCQnicZc918rGffFjNikpyelzzWw2OzzXKjvuivYRcXFx4n3E5Z5rrvYRZeN29z5iQLNgl69UDO/Y0C37iJSUFF32Efn5+YbeR1gslirvI8rG7c7nWnX3EWX/f0xKStJsH5GcnKzLPqI6v0dcCfuIuLg4Q/8eAZT+Dqj1PqLs/81aMCk3vC7ywAMPoHv37pgwYYI7xlQtZrMZDRs2xO7du9G1a1f75U899RR27dqFvXv3urz9yy+/jEWLFmHnzp1o165dhcudPn0aTZs2xffff4++ffuWu76wsBCFhYX277OyshATE4PMzEzNP1o3ISEBq1atwuTJk6v8UWbFxcWoVatWDY3Mc12N3arzOCtzNXZzF09st37/eczYcBjA/17CN5lMWDisHYZ3inHLNjyxmxY8pZs79ltV5SnttGbkbno8zspo2c0tx1T8+9//xvDhw/Hjjz+ibdu25QY/depUd2ymUiIjI+Ht7Y3ExESHyxMTEy97PMSrr76Kl19+Gd9//73LCQUANGnSBJGRkTh58qTTSYWfn59HHMidlJRkqJPNXCnYTYbd5Dyx3fBOMfi/xhFYufUwfvnzBGKjQjF9SFdc78bzVHhiNy2wmxzbybCbjJbd3DKp+PTTT/Hdd9/B398fO3fudHgfrMlk0nRS4evri44dO2Lbtm0YMmQIAMBms2Hbtm2YMmVKhbdbtGgRXnzxRWzZsgWdOnW67HYuXLiA1NRUzWecWuMTWIbdZNhNzlPbNY4MxNQ+jeF//DtMHjEZ9eu790ROntqtprGbHNvJsJuMlt3cckzFs88+i7lz5yIzMxNnzpxBXFyc/ev06dPu2ESVTJs2DW+//TbWrFmDo0eP4qGHHkJubq7906DGjBmDp59+2r78woULMWvWLLz33nto3LgxLBYLLBYLcnJyAAA5OTl48skn8csvv+DMmTPYtm0b7rzzTjRr1gwDBgzQ/P5pqew9e1Q17CbDbnJsJ8NuMuwmx3Yy7CajZTe3vFJRVFSEe+65B15eV8a59O655x4kJydj9uzZsFgsaN++PTZv3mw/ePvcuXMOY33rrbdQVFSEu+++22E9c+bMwfPPPw9vb28cPnwYa9asQUZGBho0aID+/ftj/vz5HvEWJ1euhIPvjYjdZNhNju1k2E2G3eTYTobdZLTs5pZJxdixY7F27Vo888wz7lidW0yZMqXCtzvt3LnT4fszZ864XFdAQAC2bNnippEZS0pKylV3bg53YDcZdpNjOxl2k2E3ObaTYTcZLbu5ZVJhtVqxaNEibNmyBe3atSt3oPbixYvdsRnSQWhoqN5DMCR2k2E3ObaTYTcZdpNjOxl2k9Gym1smFX/88Qc6dOgAAPjzzz8drqvo5EVkDPn5+QgIcM9Jpq4m7CbDbnJsJ8NuMuwmx3Yy7CajZTe3TCp27NjhjtXQFehKOU7GaNhNht3k2E6G3WTYTY7tZNhNRstubplUAKVnCTx8+DCSkpJgs9nsl5tMJtx+++3u2gxpzMfHbQ+Rqwq7ybCbHNvJsJsMu8mxnQy7yWjZzS1b2rx5M+6//36kpqaWu85kMsFqtbpjM6SDvLw8BAUF6T0Mw2E3GXaTYzsZdpNhNzm2k2E3GS27ueU1kUcffRQjRoxAQkICbDabwxcnFMYWFham9xAMid1k2E2O7WTYTYbd5NhOht1ktOzmlklFYmIipk2bxs8Q9kDJycl6D8GQ2E2G3eTYTobdZNhNju1k2E1Gy25umVTcfffd5c79QJ5By9O7exJ2k2E3ObaTYTcZdpNjOxl2k9Gym1uOqfj3v/+N4cOH48cff0Tbtm3Lnadi6tSp7tgM6SA+Pp5PZAF2k2E3ObaTYTcZdpNjOxl2k9Gym1smFZ9++im+++47+Pv7Y+fOnQ7npjCZTJxUGFj9+vX1HoIhsZsMu8mxnQy7ybCbHNvJsJuMlt3c8vanZ599FnPnzkVmZibOnDmDuLg4+9fp06fdsQnSicVi0XsIhsRuMuwmx3Yy7CbDbnJsJ8NuMlp2c8ukoqioCPfccw9PTOKBIiIi9B6CIbGbDLvJsZ0Mu8mwmxzbybCbjJbd3DILGDt2LNauXeuOVdEVJicnR+8hGBK7ybCbHNvJsJsMu8mxnQy7yWjZzS3HVFitVixatAhbtmxBu3btyh2ovXjxYndshnTg6+ur9xAMid1k2E2O7WTYTYbd5NhOht1ktOzmlknFH3/8gQ4dOgAA/vzzT4frLj5om4iIiIiIPI9bJhU7duxwx2roClRYWKj3EAyJ3WTYTY7tZNhNxhO6xaXkYtXOM/ilKBZn1+3DtCFd0aZRVI1v1xPa6YHdZLTsxiOryaWQkBC9h2BI7CbDbnJsJ8NuMkbvtm7/efR9bSfWHk7DGWsEdiR4444Vv2L9/vM1vm2jt9MLu8lo2U08qTh8+DBsNlullz9y5AhKSkqkmyOdpKam6j0EQ2I3GXaTYzsZdpMxcre4lFzM3HAYNgXYFKBggoIJNgXM2HAYZ1Jya3T7Rm6nJ3aT0bKbeFLRoUOHKg20a9euOHfunHRzpBOebEaG3WTYTY7tZNhNxsjd1u0/X+HxniaTCWtr+NUKI7fTE7vJaNlNfEyFUgqzZs1C7dq1K7V8UVGRdFOkI7PZrNnp3T0Ju8mwmxzbybCbjJG7XUjPh1LK6XVKKVxIz6/R7Ru5nZ7YTUbLbuJJRa9evXDs2LFKL9+1a1cEBARIN0c64RNYht1k2E2O7WTYTcbI3a4JDyh9pcLJxMJkMuGa8Jr9XcXI7fTEbjJadhNPKnbu3OnGYdCVKj4+nk9kAXaTYTc5tpNhNxkjdxvRKQYrd51yep1SCvd0iqnR7Ru5nZ7YTUbLbvz0J3IpMjJS7yEYErvJsJsc28mwm4yRu8VGBmLhsHbwMgFeJvz/w7QVvEzAwmHt0DgysEa3b+R2emI3GS27cVJBLmVmZuo9BENiNxl2k2M7GXaTMXq34Z1isH16H9zTLgKNvdNwc30r/vPg/2F4Db9KARi/nV7YTUbLbpxUkEs8DkaG3WTYTY7tZNhNxhO6NY4MxNQ+jdHHNw4vjuiM6zU48R3gGe30wG4yWnbjpIJcqsq5SOh/2E2G3eTYTobdZNhNju1k2E1Gy26cVJBLPGGhDLvJsJsc28mwmwy7ybGdDLvJaNmNkwpyqbLnISFH7CbDbnJsJ8NuMuwmx3Yy7CajZTdOKsiljIwMvYdgSOwmw25ybCfDbjLsJsd2Muwmo2U3TirIpagobQ5c8zTsJsNucmwnw24y7CbHdjLsJqNlN04qyCWLxaL3EAyJ3WTYTY7tZNhNht3k2E6G3WS07MZJBbnEs1fKsJsMu8mxnQy7ybCbHNvJsJuMlt18qnPjwsJCbN++Hdu3b8eFCxeQlJSE/Px81K1bF1FRUWjfvj1uvfVWxMbGumu8pDEtT+/uSdhNht3k2E6G3WTYTY7tZNhNRstuoklFfHw85s6di23btqFFixZo06YNbrjhBoSEhMDPzw9ZWVnIzs7G/v378e6778LHxwczZszAsGHD3D1+qmF8D6MMu8mwmxzbybCbDLvJsZ0Mu8lo2a3Kk4o33ngDFy5cwOTJk7Fq1apK3SYrKwtr167Fgw8+iGeeeQbXXnttlQdK+khLS0O9evX0HobhsJsMu8mxnQy7ybCbHNvJsJuMlt2qNKn4+uuvcfvtt1f57UwhISGYNGkSJk6ciHXr1iEwMBB16tSp0jpIH0FBQXoPwZDYTYbd5NhOht1k2E2O7WTYTUbLblU6UPu2226r1vERJpMJ99xzDycUBlJUVKT3EAyJ3WTYTY7tZNhNht3k2E6G3WS07FYjn/7k7e1dE6slIiIiIqIrUI1MKpRSNbFa0oGvr6/eQzCkq61bXEouXtt6EjuLYvHa1pOIS8kVredq6+ZObCfDbjLsJsd2Muwmo2W3Kk8q8vLyLruMyWQSDYauPDk5OXoPwZCupm7r9p9H39d2YuPRLJyxRmDj0Sz0fW0n1u8/X+V1XU3d3I3tZNhNht3k2E6G3WS07FalScWUKVPs5584efIkHn74Ydx999146623KrxNYmIiduzYgWXLluGRRx5Bnz59ePS+gUREROg9BEO6WrrFpeRi5obDsCnApgAFk/3fMzYcxpkqvmJxtXSrCWwnw24y7CbHdjLsJqNltypNKr799lukpKTgrbfeQo8ePXDNNddgzJgx+OGHHzBr1iz7ckop9OzZE/Xq1UOvXr2wZMkSnD17FuvXr8eiRYvw999/u/2OXGrZsmVo3Lgx/P390aVLF+zbt8/l8uvXr0fLli3h7++Ptm3b4ptvvnG4XimF2bNno379+ggICEC/fv1w4sSJmrwLV4SkpCS9h2BIV0u3dfvPV/jKpMlkwtoqvlpxtXSrCWwnw24y7CbHdjLsJqNltypNKkJDQ+Hv74+uXbsiNDQUzzzzDO644w589NFH+Pbbbx2WbdCgAZo2bYpVq1bhq6++wqJFixAYGIjOnTsjPDzcrXfiUmvXrsW0adMwZ84cHDx4EDfccAMGDBhQYdjdu3fj3nvvxYQJE/Dbb79hyJAhGDJkCP7880/7MosWLcIbb7yBFStWYO/evQgMDMSAAQNQUFBQo/dFbzx7pczV0u1Cen6Fx1AppXAhPb9K67tautUEtpNhNxl2k2M7GXaT0bJblSYVycnJ2LRpE+Li4hAYGGi/3Nvb2+EXC5PJhLVr12LlypVYunQp+vfvj71792p2rMXixYsxadIkjB8/Hq1bt8aKFStQu3ZtvPfee06Xf/311zFw4EA8+eSTaNWqFebPn48bb7wR//73vwGU/nK0dOlSPPfcc7jzzjvRrl07fPDBBzCbzdi0aZMm90kv8fHxeg/BkK6WbteEB7h8peKa8IAqre9q6VYT2E6G3WTYTY7tZNhNRstuVTr53bRp0/Cf//wHCxYswOnTp9GtWze0aNECLVq0QGpqarnl27Ztiy+++AL79+/H7NmzkZiYiL1796JLly5uuwOXKioqwoEDB/D000/bL/Py8kK/fv2wZ88ep7fZs2cPpk2b5nDZgAED7BOGuLg4WCwW9OvXz359aGgounTpgj179mDkyJHl1llYWIjCwkL791lZWaX/OHQIuPhEJOHhQGwsUFAA/PVX+cHdeGPpf48dA3IveX9648ZARASQnAycv+RtJsHBQPPmgNUKn8OHEW02w+fwYSAhofT6tm2BWrWAU6eAzEzH2zZsCNSrB6SnI9psBhIT/3ddQADQqlXpv3/7Dbj0r9StWpUuc/YscOljol690nVnZwOXvnWsVq3SMQHAH38AxcWO1zdvXnqf4uMdxwMAdeoAjRoB+fnA0aOO15lMQIcOpf8+erR0mYvFxpb+DBITS9d9sdBQoGnT0rH88QfKueEGwNu79L5kZztcFd2gQek/0tKAM2ccbxcYCLRoUfrvgwfLr7d1a8DfH4iLA9LTHa+rX7/0KysLOHnS8To/P+D660v/ffgwUFLieP1115U+9i5cAC591S4yErj2WiAvD7j07YleXkD79qX//uuv0sfq/zcioAQrK3qlwqZwT9nhU0VFwEWv/Nm1b1+6/uPHgZwcRFut//v5Xntt6bhSUoBz5xxvFxRUen9sttLn1KXatAF8fYHTp4GMDMfrGjQAoqNLLz992vE6f//S/kDpem02x+tbtgRq1y4dT0qK43VRUcA11wA5OaX352I+PkC7dqX/PnIEuGjfAABo1gwICSl9fpY9R8tUch8RnZlZ/rlRyX0Efv+9/HoruY9AXJzjdTWwj/BJTkbUxffNjfuIaJutdJuApvsIxMQAdesadh/h8Fx1sY8AADRpAoSFARYLYDY7XhcWVnp9JfcRDtywj/A+e7b8/x9reB8RHRRU/ueqwT6iOr9HXAn7iGir9X/rNuDvEeUeZ4Am+4jolJT/7eNqmqqG06dPq6+++kq99NJLatSoUfbLTSaT0+V/+ukn9Y9//EP17du3Opt1KT4+XgFQu3fvdrj8ySefVJ07d3Z6m1q1aqlPPvnE4bJly5apqKgopZRSP//8swKgzGazwzLDhw9XI0aMcLrOOXPmKADlvjJLnz7/+xo9Wl24cEEVHjniePn//0pISFDFxcWq8MYby11X8M47KjU1VeUuWlT+tv37qwsXLqiStDSn603+6y9VUFCg8m65pdx1xQsXqqSkJJX7/vvlrrN16KAuXLiglFLK5utb7vqMn39Wubm5KnfUqHLXWZ96SiUkJKj8b78tv96GDe3rLYmOLnd99n/+o7KyslTu1Knlb/vAA6UNDx4sf52vr4qPj1dWq1UVtmlT7vq8Dz5QaWlpKnf+/PKdbr9dXbhwQRWbzU4bJp08qQoLC1V+797lrkuZN08lJyervFWryo/pppvs99XZetP27VN5eXkqd+jQcteVPPecslgsKn/TpvLrbdr0fw0jIspdn7Vli8rOzla5//xn+ds+9JCKj49XBbt3l78uOFjFx8crm82miq67rtz1Hy37XMXO/FrFPvWVin3yS9XkyS9V7JNfqXVt+ip1993qwoULquj0aaf31XL2rCoqKlIFN91U7rrCZctKH9+vv17+tr17qwsXLihrXp7T9ab+/rvKz89XeYMHl398z5+vEhMTVe5nn5W/r61b2xtag4LKXZ+5Y4fKyclRuePGlX98/+tfymw2q4IdO8qvNzLSvt7iRo3KXZ+zYYPKzMxUuU8+Wf7+VHIfkduuXbnrPGkfkRkcrA4dOuQx+4jCxYuvmn1E7mefqYyMDJXz3HPl74/O+4h0Z4/vGt5HmDdsKL9eDfYR/D1Cv33Ehd9+c9pQs32ERkyl49HWjh07cPPNN9fIus1mMxo2bIjdu3eja9eu9sufeuop7Nq1C3v37i13G19fX6xZswb33nuv/bLly5dj7ty5SExMxO7du9G9e3eYzWbUr1/fvsyIESPsb/W6lLNXKmJiYpC5axdCNH6lIvn777Fx40YMHToUdevWLb2+kn9hKDh6FP7+/v+7jq9U/I+LvzDkR0Yi4NprDftXSAeV+CvkmZJaWL7hJ/xx7AxuiInCgzc2RONgnyr/FbKgoOB/jze+UlGqkvuIgt9/h3/ZX/LKGOivkAAq3EckJyfj8y+/xJBZs0r3wW7cRxQUFsK/7P8VfKWi0vsIh+eqgV+pSPrlF3yxerXj/x9reB+Rn5yMgEufj3yl4n8q2EcUFBTAv1EjQ/4ekXDuHL568UXHxxmgyT6iICEB/t26lV+mBugyqahJRUVFqF27Nj7//HMMGTLEfvnYsWORkZGBL7/8stxtrr32WkybNg2PPfaY/bI5c+Zg06ZN+P3333H69Gk0bdoUv/32G9qX7TgB9O7dG+3bt8frr79+2XFlZWUhNDQUmZmZCAkJqc5drLKEhASsWrUKkydPdpgUVUZaWho/xk3gauxWncdZmauxm7t4cjt3PLYq4sndapKndKvJx1ZFPKWd1ozcTY/HWRktu9XIGbX15Ovri44dO2Lbtm32y2w2G7Zt2+bwysXFunbt6rA8AGzdutW+fGxsLKKjox2WycrKwt69eytcp6fw8vK4h4gm2E2G3eTYTobdZNhNju1k2E1Gy25VOlC7Mo4fP44mTZrAx8ftq660adOmYezYsejUqRM6d+6MpUuXIjc3F+PHjwcAjBkzBg0bNsSCBQsAAP/617/Qu3dvvPbaaxg8eDA+++wz7N+/H6tWrQJQ+ik2jz32GF544QU0b94csbGxmDVrFho0aODwaogn8vb21nsIhsRuMuwmx3Yy7CbDbnJsJ8NuMlp2c/tv/q1atcLRo0dx3XXXuXvVlXbPPfcgOTkZs2fPhsViQfv27bF582b7mbzPnTvnMHPr1q0bPvnkEzz33HN45pln0Lx5c2zatAlt2rSxL/PUU08hNzcXkydPRkZGBnr06IHNmzc7Hm/ggfLz8xEcHKz3MAyH3WTYTY7tZNhNht3k2E6G3WS07Ob2ScWVcojGlClTMGXKFKfX7dy5s9xlw4cPx/Dhwytcn8lkwrx58zBv3jx3DdEQwsLC9B6CIbGbDLvJsZ0Mu8mwmxzbybCbjJbd+AY1cik5OVnvIRgSu8mwmxzbybCbDLvJsZ0Mu8lo2Y2TCnJJy9O7exJ2k2E3ObaTYTcZdpNjOxl2k9GyGycV5JKWp3f3JOwmw25ybCfDbjLsJsd2Muwmo2U3TirIJa0/T9lTsJsMu8mxnQy7ybCbHNvJsJuMlt04qSCXLBaL3kMwJHaTYTc5tpNhNxl2k2M7GXaT0bIbJxXkklHPXqk3dpNhNzm2k2E3GXaTYzsZdpPRspvbJxUzZsxAnTp13L1a0klOTo7eQzAkdpNhNzm2k2E3GXaTYzsZdpPRspvbz1NRdpZq8gy+vr56D8GQ2E2G3eTYTobdZNhNju1k2E1Gy258+xMREREREVULJxXkUlFRkd5DMCR2k2E3ObaTYTcZdpNjOxl2k9GyGycV5FJQUJDeQzAkdpNhNzm2k2E3GXaTYzsZdpPRsluNTCq8vb1rYrWkg7S0NL2HYEjsJsNucmwnw24y7CbHdjLsJqNltxqZVCilamK1pIPo6Gi9h2BI7CbDbnJsJ8NuMuwmx3Yy7CajZTfRpOKFF17AwoUL8e233zo9/bfJZKr2wOjKkJCQoPcQDIndZNhNju1k2E2G3eTYTobdZLTsJvpI2eeeew6nT5/G4cOH8e677yI+Ph4rV650umxiYiL++usv+9eRI0dw9OhRJCYmVmvgpI2GDRvqPQRDYjcZdpNjOxl2k2E3ObaTYTcZLbtV6ZWKBx98EElJSQCAJk2aYMiQIZg9e3a5CYVSCj179kS9evXQq1cvLFmyBGfPnsX69euxaNEi/P333+67B1SjnL0SRZfHbjLsJsd2Muwmw25ybCfDbjJadqvSKxWDBg3Crbfeittuuw1PPvkkAgMDK1y2QYMGsFqtWLBgAXr37g0AWL9+PTp37ly9EZOm6tatq/cQDIndZNhNju1k2E2G3eTYTobdZLTsVqVXKu68807s3bsX9erVQ7du3bBixQrYbLZyy5lMJqxduxYrV67E0qVL0b9/f+zdu5fHWhhQRkaG3kMwJHaTYTc5tpNhNxl2k2M7GXaT0bJblQ/U9vb2xuDBg/H444/jueeeQ+vWrfGf//zH6bJt27bFF198gZdeeglz585FYmIi9u7dW+1Bk3YCAgL0HoIhsZsMu8mxnQy7ybCbHNvJsJuMlt2qNKkYOHAgGjVqhFGjRuHw4cN488038fHHH2PTpk147LHHKrxdp06d8M033+C7777DM888g379+lV33KQRq9Wq9xAMid1k2E2O7WTYTYbd5NhOxqjd4lJy8cbOM9hZFItn1+3Dn2eTNN2+lt2qdEzFyy+/jLZt25Y7ud27776Lli1b2r+v6DwV3bt3x7Zt27Bjxw7BUEkPzt7eRpfHbjLsJsd2Muwmw25ybCdjxG7r9p/HzA2HAQBKReBMArBzxa9YOKwdhneK0WQMWnar0isVqampKCkpcXrdN998Y/+3qzuwb98+tGvXriqbJR3x5UYZdpNhNzm2k2E3GXaTYzsZo3WLS8nFzA2HYVOATQEKJiiYYFPAjA2HcSYlV5NxXLFvf+rWrRvmzJmD999/H7m5jjGaNGni8rYHDhzAk08+icLCQtSpU6fqIyVd8MAoGXaTYTc5tpNhNxl2k2M7GaN1W7f/fIUfUGQymbB2/3lNxqFltyq9/SkgIAAvv/wytmzZgltvvRVBQUFo37492rZti4iICISEhMDX1xfZ2dnIysrCmTNncOjQIRw6dAj9+/fHM888g/Dw8Jq6L1QDoqKi9B6CIbGbDLvJsZ0Mu8mwmxzbyRit24X0/AoPB1BK4UJ6vibj0LKb6IzaAwYMwIABA3Dw4EF8++23ePvtt3HhwgUkJSUhPz8fkZGRiIqKwg033IBBgwbhtddeQ1hYmJuHTlqwWCw8i6UAu8mwmxzbybCbDLvJsZ2M0bpdEx5Q+kqFk4mFyWTCNeHavC1Jy26iSUWZG2+8ETfeeCOeffZZd42HrjBGegJfSdhNht3k2E6G3WTYTY7tZIzWbUSnGKzcdcrpdUop3KPRgdpadqvyeSro6qLl6d09CbvJsJsc28mwmwy7ybGdjNG6xUYGYuGwdvAyAV4m/P/DtBW8TMDCYe3QODJQk3Fo2a1ar1SQ5zPaexivFOwmw25ybCfDbjLsJsd2MkbsNrxTDP6vcQRWbj2MX/48gdioUEwf0hXXN9LuvmjZja9UkEtpaWl6D8GQ2E2G3eTYTobdZNhNju1kjNqtcWQgpvZpjD6+cXhxRGdNJxSAtt04qSCXgoKC9B6CIbGbDLvJsZ0Mu8mwmxzbybCbjJbdOKkgl4qKivQegiGxmwy7ybGdDLvJsJsc28mwm4yW3dw+qTh+/HiFZ90mIiIiIiLP4/ZJRatWrXD69Gl3r5Z04uvrq/cQDIndZNhNju1k2E2G3eTYTobdZLTs5vZJRUVnDyRjysnJ0XsIhsRuMuwmx3Yy7CbDbnJsJ8NuMlp24zEV5FJERITeQzAkdpNhNzm2k2E3GXaTYzsZdpPRshsnFeRSUlKS3kMwJHaTYTc5tpNhNxl2k2M7GXaT0bIbJxXkkpand/ck7CbDbnJsJ8NuMuwmx3Yy7CajZTdOKsglLU/v7knYTYbd5NhOht1k2E2O7WTYTUbLbj6abYkMKTo6Wu8hGBK7ybCbnKe1y87Oth9gmJKS4vBfoPSETsHBwdXejqd10wq7ybGdDLvJaNmNkwpyKSkpCfXr19d7GIbDbjLsJudp7Q4cOIBdu3Y5XLZx40b7v3v37o0+ffpUezue1k0r7CbHdjLsJqNlN7dPKmbMmIE6deq4e7Wkk7CwML2HYEjsJsNucp7WrmPHjmjRokWF1wcFBbllO57WTSvsJsd2Muwmo2U3tx9TsWDBAl0nFWlpaRg9ejRCQkIQFhaGCRMmuPyM3rS0NDz66KNo0aIFAgICcO2112Lq1KnIzMx0WM5kMpX7+uyzz2r67uguLy9P7yEYErvJsJucp7ULDg5G/fr1K/xyx1ufAM/rphV2k2M7GXaT0bKbx739afTo0UhISMDWrVtRXFyM8ePHY/Lkyfjkk0+cLm82m2E2m/Hqq6+idevWOHv2LB588EGYzWZ8/vnnDsuuXr0aAwcOtH9/NcyafXw87iGiCXaTYTc5tpNhNxl2k2M7GXaT0bKbR/2Ejh49is2bN+PXX39Fp06dAABvvvkmbr31Vrz66qto0KBBudu0adMGGzZssH/ftGlTvPjii7jvvvtQUlLi8MMICwu76g4U8vLiB4RJsJsMu8mxnQy7ybCbHNvJsJuMlt086ie0Z88ehIWF2ScUANCvXz94eXlh7969lV5PZmYmQkJCys3uHnnkEURGRqJz58547733oJSqcB2FhYXIyspy+DKi/Px8vYdgSOwmw25ybCfDbjLsJsd2Muwmo2U3t08qlixZAgA4cuQIrFaru1fvksViQVRUlMNlPj4+iIiIgMViqdQ6UlJSMH/+fEyePNnh8nnz5mHdunXYunUrhg0bhocffhhvvvlmhetZsGABQkND7V8xMTEAgIKCApjNZthsNvtnB8fHx6OoqAhJSUnIyclBRkYG0tLSkJ+fD4vFgpKSEodli4uLYbFYkJeXh7S0NKSnpyM3NxeJiYkoLi52WNZqtSI5ORkAkJGRgczMTGRnZyM5ORmFhYUOyyqlEB8fj8LCQiQnJyMrKwteXl5ISUlBfn5+pcedl5fndNwlJSXlxp2Tk+N03DabDWazGfn5+UhNTXU67rLxXjru7OxsZGZmIjU1tcJxFxcXIzExETk5OUhPT6/SuDMyMpCTk4OkpCQUFRU5Hbefnx9SUlLsE0pn4y67jatxW61Wp+POzc0tN+5LG5aNOz8/v9LjLigocDpus9nsdNxJSUn2cWdkZNifQ5cbd0WP2by8PFitViQkJDiMOzs7u0rjTklJuexzLTs72+G5lpCQIB532XOtbNwXP2aTkpKcPtfMZrPDc62y465oH1H2vlnJPuJyzzVX+4iycRt1H+Hn56fLPiI/P9/Q+wgvL68q7yPKxu3O51p19xFl/38sO+uwFvsIPz8/XfYR1fk94krYR+Tl5Rn69wig9HdArfcR7jr+rDJMytWf2wV27NiBm2++GXfeeSf+/vtvBAQE4Prrr0fbtm3Rpk0b3HbbbVVe58yZM7Fw4UKXyxw9ehQbN27EmjVrcOzYMYfroqKiMHfuXDz00EMu15GVlYVbbrkFERER+Oqrr1CrVq0Kl509ezZWr16N8+fPO72+sLAQhYWFDuuOiYmxvwqipYSEBKxatQqTJ0+u8seKxcfH8yyWAldjt+o8zspcjd3che1k2E3GU7q5Y79VVZ7STmtG7qbH46yMlt3cckxFdna2fSZ08803AwC+/PJLAEBOTg6OHDmCP/74A99//71oUjF9+nSMGzfO5TJNmjRBdHS0/a8NZUpKSpCWlnbZYyGys7MxcOBABAcH44svvnA5oQCALl26YP78+SgsLISfn1+56/38/JxebjRGfQLrjd1k2E2O7WTYTYbd5NhOht1ktOzmlklFz549sXnzZqe/uAcFBaFLly7o0qWLeP1169ZF3bp1L7tc165dkZGRgQMHDqBjx44AgO3bt8Nms7ncflZWFgYMGAA/Pz989dVX8Pf3v+y2Dh06hPDwcI+YOLhi5L8M6IndZNhNju1k2E2G3eTYTobdZLTs5pZjKjp06IAuXbrg77//drj80KFDuPXWW92xiUpp1aoVBg4ciEmTJmHfvn34+eefMWXKFIwcOdL+yU/x8fFo2bIl9u3bB6B0QtG/f3/k5ubi3XffRVZWFiwWCywWi/2YkP/85z9455138Oeff+LkyZN466238NJLL+HRRx/V7L7pxdknZtHlsZsMu8mxnQy7ybCbHNvJsJuMlt3cMqlYvXo1xo0bhx49euCnn37C8ePHMWLECHTs2BHe3t7u2ESlffzxx2jZsiX69u2LW2+9FT169MCqVavs1xcXF+PYsWP2gxoPHjyIvXv34o8//kCzZs0cTq5UdrxErVq1sGzZMnTt2hXt27fHypUrsXjxYsyZM0fT+6aHhIQEvYdgSOwmw25ybCfDbjLsJsd2Muwmo2U3t52nYu7cufDz88Mtt9wCq9WKvn37Ys+ePejcubO7NlEpERERFZ7oDgAaN27s8FGwffr0cfnRsAAwcOBAh5PeXU30PDu6kbGbDLvJsZ0Mu8mwmxzbybCbjJbd3PJKRWJiIv71r3/hhRdeQOvWrVGrVi2MGzdO8wkFuZ9Rz6+hN3aTYTc5tpNhNxl2k2M7GXaT0bKbWyYVsbGx+OGHH7B+/XocOHAAGzZswOTJk/HKK6+4Y/WkI08/EL2msJsMu8mxnQy7ybCbHNvJsJuMlt3c8van9957DyNHjrR/P3DgQOzYsQO33XYbzpw5g2XLlrljM0REREREdAVyyysVF08oytx4443YvXs3tm/f7o5NkFBcSi5e23oSO4ti8drWk4hLya3S7YuKimpoZJ6N3WTYTY7tZNhNht3k2E6G3WS07Oa2A7Wdady4MXbv3l2TmyAX1u0/j5kbDgMAlIrA2aNZ2Hh0JxYOa4fhnWIqtY6goKCaHKLHYjcZdpNjOxl2kzFyt+zsbOTk5AAAUlJSHP4LlN63shP61gQjt9MTu8lo2a1GJxUAEB4eXtObICfiUnIxc8Nh2OwfbGVC2YdczdhwGP/XOAKNIwMvu560tDR+NrQAu8mwmxzbybCbjJG7HThwALt27XK4bOPGjfZ/9+7dG3369Kmx7Ru5nZ7YTUbLbjU+qSB9rNt/HiaTCXDycbkmkwlr95/HjIEtL7seZ2dJp8tjNxl2k2M7GXaTMXK3jh07okWLFhVeX9N/2TVyOz2xm4yW3dxyTAVdeS6k51d4/g2lFC6k51dqPTzZjAy7ybCbHNvJsJuMkbsFBwc7nOj20q+afOsTYOx2emI3GS27cVLhoa4JDyh9pcIJk8mEa8IDKrWehg0bunNYVw12k2E3ObaTYTcZdpNjOxl2k9Gym9smFRkZGXjttdcwceJETJw4EUuWLEFmZqa7Vk9VNKJTjMtXKu6p5IHa8fHx7hzWVYPdZNhNju1k2E2G3eTYTobdZLTs5pZJxf79+9G0aVMsWbIEaWlpSEtLw+LFi9G0aVMcPHjQHZugKoqNDMTCYe3gZQK8TIAJyv7vhcPaVeogbQCoW7duDY/UM7GbDLvJsZ0Mu8mwmxzbybCbjJbd3DKpePzxx3HHHXfgzJkz2LhxIzZu3Ii4uDjcdttteOyxx9yxCRIY3ikG26f3wdBWIWjsnYahrUKwfXqfSn+cLFD6ChRVHbvJsJsc28mwmwy7ybGdDLvJaNnNba9UzJgxAz4+//swKR8fHzz11FPYv3+/OzZBQo0jAzH9lmbo4xuH6bc0q/QrFGVq165dQyPzbOwmw25ybCfDbjLsJsd2Muwmo2U3t0wqQkJCcO7cuXKXnz9/vsY/RYFqVklJid5DMCR2k2E3ObaTYTcZdpNjOxl2k9Gym1smFffccw8mTJiAtWvX4vz58zh//jw+++wzTJw4Effee687NkE6sdlseg/BkNhNht3k2E6G3WTYTY7tZNhNRstubjn53auvvgqTyYQxY8agpKQESin4+vrioYcewssvv+yOTZBOAgIq99Gz5IjdZNhNju1k2E2G3eTYTobdZLTs5pZXKnx9ffH6668jPT0dhw4dwu+//460tDQsWbIEfn5+7tgE6YQfCyzDbjLsJsd2Muwmw25ybCfDbjJadnPLKxXz5s1zef3s2bPdsRnSQWRkpN5DMCR2k2E3ObaTYTcZdpNjOxl2k9Gym1smFV988YXD98XFxYiLi4OPjw+aNm3KSYWBJSYm8iyWAuwmw25ybCfDbjLsJsd2Muwmo2U3t0wqfvvtt3KXZWVlYdy4cbjrrrvcsQnSCZ/AMuwmw25ybCfDbjLsJsd2Muwmo2U3txxT4UxISAjmzp2LWbNm1dQmSANant7dk7CbDLvJsZ0Mu8mwmxzbybCbjJbdamxSAZQeHMIDa4wtKipK7yEYErvJsJsc28mwmwy7ybGdDLvJaNnNLW9/euONNxy+V0ohISEBH374IQYNGuSOTZBOUlNTER0drfcwDIfdZNhNju1k2E2G3eTYTobdZLTs5pZJxZIlSxy+9/LyQt26dTF27Fg8/fTT7tgE6SQkJETvIRgSu8mwmxzbybCbDLvJsZ0Mu8lo2c0tk4q4uDh3rIauQAUFBahdu7bewzAcdpNhNzm2k2E3GXaTYzsZdpPRsluNHlNBxmcymfQegiGxmwy7ybGdDLvJsJsc28mwm4yW3cSvVEybNq3Syy5evFi6GdKZr6+v3kMwJHaTYTc5tpNhNxl2k2M7GXaT0bKbeFJx6bkpDh48iJKSErRo0QIAcPz4cXh7e6Njx47VGyHpKicnB4GBgXoPw3DYTYbd5NhOht1k2E2O7WTYTUbLbuJJxY4dO+z/Xrx4MYKDg7FmzRqEh4cDANLT0zF+/Hj07Nmz+qMk3UREROg9BENiNxl2k2M7GXaTYTc5tpNhNxktu7nlmIrXXnsNCxYssE8oACA8PBwvvPACXnvtNXdsgnSSlJSk9xAMid1k2E2O7WTYTYbd5NhOht1ktOzmlklFVlYWkpOTy12enJyM7Oxsd2yCdKLl6d09CbvJsJsc28mwmwy7ybGdDLvJaNnNLZOKu+66C+PHj8fGjRtx4cIFXLhwARs2bMCECRMwdOhQd2yCdKLl6d09CbvJsJsc28mwmwy7ybGdDLvJaNnNLeepWLFiBZ544gmMGjUKxcXFpSv28cGECRPwyiuvuGMTpBOevVKG3WTYTY7tZNhNht3k2E6G3WS07OaWVypq166N5cuXIzU1Fb/99ht+++03pKWlYfny5TxS3+ASExP1HoIhsZsMu8mxnQy7ybCbHNvJsJuMlt3c8kpFmcDAQLRr186dqySdXXzwPVUeu8mwmxzbybCbDLvJsZ0Mu8lo2a1aJ7+bP38+AgMDL3siPJ78zrjy8vIQEBCg9zAMh91k2E2O7WTYTYbd5NhOht1ktOxWrZPflR0/cemJ8C7G06obm4+PW1/Mumqwmwy7ybGdDLvJsJsc28mwm4yW3dxy8ruL/02excvLLYfdXHXYTYbd5NhOht1k2E2O7WTYTUbLbm7ZUn5+PvLy8uzfnz17FkuXLsV3333njtWTjgoKCvQegiGxmwy7ybGdDLvJsJsc28mwm4yW3dwyqbjzzjvxwQcfAAAyMjLQuXNnvPbaa7jzzjvx1ltvuWMTpJOQkBC9h2BI7CbDbnJsJ8NuMuwmx3Yy7CajZTe3TCoOHjyInj17AgA+//xzREdH4+zZs/jggw/wxhtvuGMTpJOUlBS9h2BI7CbDbnJsJ8NuMuwmx3Yy7CajZTe3TCry8vIQHBwMAPjuu+8wdOhQeHl54aabbsLZs2fdsYlKS0tLw+jRoxESEoKwsDBMmDABOTk5Lm/Tp08fmEwmh68HH3zQYZlz585h8ODBqF27NqKiovDkk0+ipKSkJu/KFUHL07t7EnaTYTc5tpNhNxl2k2M7GXaT0bKbWyYVzZo1w6ZNm3D+/Hls2bIF/fv3BwAkJSVp/nLV6NGjceTIEWzduhVff/01fvjhB0yePPmyt5s0aRISEhLsX4sWLbJfZ7VaMXjwYBQVFWH37t1Ys2YN3n//fcyePbsm78oVQcvTu3sSdpNhNzm2k2E3GXaTYzsZdpPRsptbJhWzZ8/GE088gcaNG6NLly7o2rUrgNJXLTp06OCOTVTK0aNHsXnzZrzzzjvo0qULevTogTfffBOfffYZzGazy9vWrl0b0dHR9q+LJ0Pfffcd/vrrL3z00Udo3749Bg0ahPnz52PZsmUoKiqq6bulqwYNGug9BENiNxl2k2M7GXaTYTc5tpNhNxktu7llUnH33Xfj3Llz2L9/PzZv3my/vG/fvliyZIk7NlEpe/bsQVhYGDp16mS/rF+/fvDy8sLevXtd3vbjjz9GZGQk2rRpg6efftrh06z27NmDtm3bol69evbLBgwYgKysLBw5csTp+goLC5GVleXwZUSXm4yRc+wmw25ybCfDbjLsJsd2Muwmo2U3t314bXR0NDp06ODwebidO3dGy5Yt3bWJy7JYLIiKinK4zMfHBxEREbBYLBXebtSoUfjoo4+wY8cOPP300/jwww9x3333Oaz34gkFAPv3Fa13wYIFCA0NtX/FxMQAKP1oL7PZDJvNZn9JKj4+HkVFRUhKSkJOTg4yMjKQlpaG/Px8WCwWlJSUOCxbXFwMi8WCvLw8pKWlIT09Hbm5uUhMTERxcbHDslarFcnJyQBKP5krMzMT2dnZSE5ORmFhocOySinEx8ejsLAQycnJyMrKgp+fH1JSUpCfn1/pcefl5Tkdd0lJSblx5+TkOB23zWaD2WxGfn4+UlNTnY67bLyXjjs7OxuZmZlITU2tcNzFxcVITExETk4O0tPTqzTujIwM5OTkICkpCUVFRU7HHRQUhJSUFPuE0tm4y27jatxWq9XpuHNzc8uN+9KGZePOz8+v9LgLCgqcjttsNjsdd1JSkn3cGRkZAEoPCrvcuCt6zBYXF8NqtSIhIcFh3NnZ2VUad0pKymWfa9nZ2Q7PtYSEBPG4y55rZeO++DGblJTk9LlmNpsdnmuVHXdF+4iyk5FK9hGXe6652keUjduo+4igoCBd9hH5+fmG3kf4+flVeR9RNm53PteMuI8ICgrSZR9Rnd8jroR9RHFxsaF/jwBKfwfUeh8REREBzSg3+eGHH9To0aPVTTfdpC5cuKCUUuqDDz5QP/74Y7XXPWPGDAXA5dfRo0fViy++qK677rpyt69bt65avnx5pbe3bds2BUCdPHlSKaXUpEmTVP/+/R2Wyc3NVQDUN99843QdBQUFKjMz0/51/vx5BUBlZmZW4Z67h9lsVs8//7wym81Vvm1SUlINjMjzXY3dqvM4K3M1dnMXtpNhNxl2k2M7GSN3c8f/H6W07OaWc3dv2LAB999/P0aPHo3ffvsNhYWFAIDMzEy89NJL+Oabb6q1/unTp2PcuHEul2nSpAmio6ORlJTkcHlJSQnS0tIQHR1d6e116dIFAHDy5Ek0bdoU0dHR2Ldvn8MyiYmJAFDhev38/ODn51fpbV6pPOE+6IHdZNhNju1k2E2G3eTYTobdZLTs5pZJxQsvvIAVK1ZgzJgx+Oyzz+yXd+/eHS+88EK111+3bl3UrVv3sst17doVGRkZOHDgADp27AgA2L59O2w2m32iUBmHDh0CANSvX9++3hdffBFJSUn2t1dt3boVISEhaN26dRXvDRERERGRZ3HLMRXHjh1Dr169yl0eGhpqf5+1Flq1aoWBAwdi0qRJ2LdvH37++WdMmTIFI0eOtB/9Hh8fj5YtW9pfeTh16hTmz5+PAwcO4MyZM/jqq68wZswY9OrVC+3atQMA9O/fH61bt8b999+P33//HVu2bMFzzz2HRx55xONnzp7+6VY1hd1k2E2O7WTYTYbd5NhOht1ktOzmlklFdHQ0Tp48We7yn376CU2aNHHHJirt448/RsuWLdG3b1/ceuut6NGjB1atWmW/vri4GMeOHbN/upOvry++//579O/fHy1btsT06dMxbNgw/Oc//7HfxtvbG19//TW8vb3RtWtX3HfffRgzZgzmzZun6X3TQ2BgoN5DMCR2k2E3ObaTYTcZdpNjOxl2k9Gym1ve/jRp0iT861//wnvvvQeTyQSz2Yw9e/bgiSeewKxZs9yxiUqLiIjAJ598UuH1jRs3hlLK/n1MTAx27dp12fU2atSo2seGGFF6ejoCAgL0HobhsJsMu8mxnQy7ybCbHNvJsJuMlt3cMqmYOXMmbDYb+vbti7y8PPTq1Qt+fn544okn8Oijj7pjE6STqhzgTv/DbjLsJsd2Muwmw25ybCfDbjJadnPL259MJhOeffZZpKWl4c8//8Qvv/yC5ORkzJ8/H/n5+e7YBOkkISFB7yEYErvJsJsc28mwmwy7ybGdDLvJaNnNbSe/A0qPT2jdujU6d+6MWrVqYfHixYiNjXXnJkhjDRs21HsIhsRuMuwmx3Yy7CbDbnJsJ8NuMlp2q9akorCwEE8//TQ6deqEbt26YdOmTQCA1atXIzY2FkuWLMHjjz/ujnGSTsrO2EhVw24y7CbHdjLsJsNucmwnw24yWnar1jEVs2fPxsqVK9GvXz/s3r0bw4cPx/jx4/HLL79g8eLFGD58OLy9vd01VtJBZc4PQuWxmwy7ybGdDLvJsJsc28mwm4yW3ar1SsX69evxwQcf4PPPP8d3330Hq9WKkpIS/P777xg5ciQnFB5Ay/OMeBJ2k2E3ObaTYTcZdpNjOxl2k9GyW7UmFRcuXLCfubpNmzbw8/PD448/DpPJ5JbBkf5q166t9xAMid1k2E2O7WTYTYbd5NhOht1ktOxWrbc/Wa1W+Pr6/m9lPj4ICgqq9qDoylFSUqL3EAzpaumWnZ2NnJwcAEBKSorDfwEgKCgIwcHBlV7f1dKtJrCdDLvJsJsc28mwm4yW3ao1qVBKYdy4cfDz8wMAFBQU4MEHHyx39r6NGzdWZzOkI5vNpvcQDOlq6XbgwIFyJ4+8+Pneu3dv9OnTp9Lru1q61QS2k2E3GXaTYzsZdpPRslu1JhVjx451+P6+++6r1mDoyuPv76/3EAzpaunWsWNHtGjRosLrq/rK5dXSrSawnQy7ybCbHNvJGK2bu1/Jl9KyW7UmFatXr3bXOOgKlZWVxfcxClwt3YKDg926U7xautUEtpNhNxl2k2M7GaN1c/cr+VJadqvWpII8X2RkpN5DMCR2k2E3ObaTYTcZdpNjOxmjdXP3K/lSWnZz6xm1yfMkJibqPQRDYjcZdpNjOxl2k2E3ObaTMVq34OBg1K9fv8IvLd76BGjbjZMKcknL07t7EnaTYTc5tpNhNxl2k2M7GXaT0bIbJxXkkpand/ck7CbDbnJsJ8NuMuwmx3Yy7CajZTdOKsilevXq6T0EQ2I3GXaTYzsZdpNhNzm2k2E3GS27cVJBLl388WdUeewmw25ybCfDbjLsJsd2Muwmo2U3TirIpZCQEL2HYEjsJsNucmwnw24y7CbHdjLsJqNlN04qyKWCggK9h2BI7CbDbnJsJ8NuMuwmx3Yy7CajZTdOKsglk8mk9xAMid1k2E2O7WTYTYbd5NhOht1ktOzGSQW5VKtWLb2HYEjsJsNucmwnw24y7CbHdjLsJqNlN04qyKXc3Fy9h2BI7CbDbnJsJ8NuMuwmx3Yy7CajZTdOKsiliIgIvYdgSOwmw25ybCfDbjLsJsd2Muwmo2U3TirIpaSkJL2HYEjsJsNucmwnw24y7CbHdjLsJqNlN04qyCUtT+/uSdhNht3k2E6G3WTYTY7tZNhNRstunFSQS1qe3t2TsJsMu8mxnQy7ybCbHNvJsJuMlt04qSCX6tevr/cQDIndZNhNju1k2E2G3eTYTobdZLTsxkkFuWSxWPQegiGxmwy7ybGdDLvJsJsc28mwm4yW3TipIJfCw8P1HoIhsZsMu8mxnQy7ybCbHNvJsJuMlt04qSCX8vLy9B6CIbGbDLvJsZ0Mu8mwmxzbybCbjJbdOKkgl3x8fPQegiGxmwy7ybGdDLvJsJsc28mwm4yW3TipIJe8vPgQkWA3GXaTYzsZdpNhNzm2k2E3GS278SdELhUUFOg9BENiNxl2k2M7GXaTYTc5tpNhNxktu3FSQS6FhIToPQRDYjcZdpNjOxl2k2E3ObaTYTcZLbtxUkEupaSk6D0EQ2I3GXaTYzsZdpNhNzm2k2E3GS27cVJBLjVo0EDvIRgSu8mwmxzbybCbDLvJsZ0Mu8lo2Y2TCnLJbDbrPQRDYjcZdpNjOxl2k2E3ObaTYTcZLbtxUkEu8S8DMuwmw25ybCfDbjLsJsd2Muwmw1cq6IrBvwzIsJsMu8mxnQy7ybCbHNvJsJsMX6mgK0ZkZKTeQzAkdpNhNzm2k2E3GXaTYzsZdpPRshsnFeRSVlaW3kMwJHaTYTc5tpNhNxl2k2M7GXaT0bKbx00q0tLSMHr0aISEhCAsLAwTJkxATk5OhcufOXMGJpPJ6df69evtyzm7/rPPPtPiLunK399f7yEYErvJsJsc28mwmwy7ybGdDLvJaNnNR7MtaWT06NFISEjA1q1bUVxcjPHjx2Py5Mn45JNPnC4fExODhIQEh8tWrVqFV155BYMGDXK4fPXq1Rg4cKD9+7CwMLeP/0pjs9n0HoIhsZsMu8mxnQy7ybCbHNvJsJuMlt08alJx9OhRbN68Gb/++is6deoEAHjzzTdx66234tVXX3V6BLy3tzeio6MdLvviiy8wYsQIBAUFOVweFhZWbllPV1JSovcQDIndZNhNju1k2E2G3eTYTobdZLTs5lFvf9qzZw/CwsLsEwoA6NevH7y8vLB3795KrePAgQM4dOgQJkyYUO66Rx55BJGRkejcuTPee+89KKUqXE9hYSGysrIcvoyodu3aeg/BkNhNht3k2E6G3WTYTY7tZNhNRstuHjWpsFgsiIqKcrjMx8cHERERsFgslVrHu+++i1atWqFbt24Ol8+bNw/r1q3D1q1bMWzYMDz88MN48803K1zPggULEBoaav+KiYkBABQUFMBsNsNmsyE+Ph4AEB8fj6KiIiQlJSEnJwcZGRlIS0tDfn4+LBYLSkpKHJYtLi6GxWJBXl4e0tLSkJ6ejtzcXCQmJqK4uNhhWavViuTkZABARkYGMjMzkZ2djeTkZBQWFjosq5RCfHw8CgsLkZycjKysLMTHxyMlJQX5+fmVHndeXp7TcZeUlJQbd05OjtNx22w2mM1m5OfnIzU11em4y8Z76bizs7ORmZmJ1NTUCsddXFyMxMRE5OTkID09vUrjzsjIQE5ODpKSklBUVOR03ImJiUhJSbFPKJ2Nu+w2rsZttVqdjjs3N7fcuC9tWDbu/Pz8So+7oKDA6bjNZrPTcSclJZUbd0JCwmXHXdFj9uTJk7BarUhISHAYd3Z2dpXGnZKSctnnWnZ2tsNzrTrjLnuulY374sdsUlKS0+ea2Wx2eK5VdtwV7SNOnjwp3kdc7rnmah9RNm6j7iMSExN12Ufk5+cbeh8RHx+vyz7i0ueaEfcRiYmJuuwjqvN7xJWwjzh58uRV9XuEu/YRqamp0IpJufpz+xVi5syZWLhwoctljh49io0bN2LNmjU4duyYw3VRUVGYO3cuHnroIZfryM/PR/369TFr1ixMnz7d5bKzZ8/G6tWrcf78eafXFxYWorCw0P59VlYWYmJikJmZiZCQEJfrdreEhASsWrUKkydPRv369at0W5vNBi8vj5p7aoLdZNhNju1k2E2G3eTYTobdZLTsZoifzvTp03H06FGXX02aNEF0dDSSkpIcbltSUoK0tLRKHQvx+eefIy8vD2PGjLnssl26dMGFCxccJg4X8/PzQ0hIiMOXEV16EDtVDrvJsJsc28mwmwy7ybGdDLvJaNnNEAdq161bF3Xr1r3scl27dkVGRgYOHDiAjh07AgC2b98Om82GLl26XPb27777Lu64445KbevQoUMIDw+Hn5/f5e+AgTVs2FDvIRgSu8mwmxzbybCbDLvJsZ0Mu8lo2c0Qr1RUVqtWrTBw4EBMmjQJ+/btw88//4wpU6Zg5MiR9k9+io+PR8uWLbFv3z6H2548eRI//PADJk6cWG69//nPf/DOO+/gzz//xMmTJ/HWW2/hpZdewqOPPqrJ/dJT2Xv2qGrYTYbd5NhOht1k2E2O7WTYTUbLboZ4paIqPv74Y0yZMgV9+/aFl5cXhg0bhjfeeMN+fXFxMY4dO4a8vDyH27333nu45ppr0L9//3LrrFWrFpYtW4bHH38cSik0a9YMixcvxqRJk2r8/ujt0gPfqXLYTYbd5NhOht1k2E2O7WTYTUbLboY4UNsTZGVlITQ01HAHaicmJqJevXo1NDLPxW4y7CbHdjLsJsNucmwnw24yWnbzqLc/kfsFBgbqPQRDYjcZdpNjOxl2k2E3ObaTYTcZLbtxUkEuFRcX6z0EQ2I3GXaTYzsZdpNhNzm2k2E3GS27cVJBLvHdcTLsJsNucmwnw24y7CbHdjLsJqNlN04qyCV/f3+9h2BI7CbDbnJsJ8NuMuwmx3Yy7CajZTdOKsilrKwsvYdgSOwmw25ybCfDbjLsJsd2Muwmo2U3TirIpcjISL2HYEjsJsNucmwnw24y7CbHdjLsJqNlN04qyKXExES9h2BI7CbDbnJsJ8NuMuwmx3Yy7CajZTdOKsglLU/v7knYTYbd5NhOht1k2E2O7WTYTUbLbpxUkEtant7dk7CbDLvJsZ0Mu8mwmxzbybCbjJbdOKkgl3j2Shl2k2E3ObaTYTcZdpNjOxl2k9GyGycV5FJKSoreQzAkdpNhNzm2k2E3GXaTYzsZdpPRshsnFeRSSEiI3kMwJHaTYTc5tpNhNxl2k2M7GXaT0bIbJxXkUkFBgd5DMCR2k2E3ObaTYTcZdpNjOxl2k9GyGycV5JKXFx8iEuwmw25ybCfDbjLsJsd2Muwmo2U3/oTIJR8fH72HYEjsJsNucmwnw24y7CbHdjLsJqNlN04qyKW8vDy9h2BI7CbDbnJsJ8NuMuwmx3Yy7CajZTdOKsilsLAwvYdgSOwmw25ybCfDbjLsJsd2Muwmo2U3TirIpeTkZL2HYEjsJsNucmwnw24y7CbHdjLsJqNlN04qyCUtT+/uSdhNht3k2E6G3WTYTY7tZNhNRstunFSQS1qe3t2TsJsMu8mxnQy7ybCbHNvJsJuMlt04qSCX6tevr/cQDIndZNhNju1k2E2G3eTYTobdZLTsxkkFuWSxWPQegiGxmwy7ybGdDLvJsJsc28mwm4yW3TipIJfCw8P1HoIhsZsMu8mxnQy7ybCbHNvJsJuMlt04qSCXcnNz9R6CIbGbDLvJsZ0Mu8mwmxzbybCbjJbdOKkgl3x9ffUegiGxmwy7ybGdDLvJsJsc28mwm4yW3TipICIiIiKiauGkglwqLCzUewiGxG4y7CbHdjLsJsNucmwnw24yWnbjpIJcCgkJ0XsIhsRuMuwmx3Yy7CbDbnJsJ8NuMlp246SCXEpJSdF7CIbEbjLsJsd2Muwmw25ybCfDbjJaduOkglxq0KCB3kMwJHaTYTc5tpNhNxl2k2M7GXaT0bIbJxXkktls1nsIhsRuMuwmx3Yy7CbDbnJsJ8NuMlp246SCXGrYsKHeQzAkdpNhNzm2k2E3GXaTYzsZdpPRshsnFeRSfHy83kMwJHaTYTc5tpNhNxl2k2M7GXaT0bIbJxXkUmRkpN5DMCR2k2E3ObaTYTcZdpNjOxl2k9GyGycV5FJWVpbeQzAkdpNhNzm2k2E3GXaTYzsZdpPRshsnFeSSv7+/3kMwJHaTYTc5tpNhNxl2k2M7GXaT0bIbJxXkks1m03sIhsRuMuwmx3Yy7CbDbnJsJ8NuMlp246SCXCopKdF7CIbEbjLsJsd2Muwmw25ybCfDbjJaduOkglyqXbu23kMwJHaTYTc5tpNhNxl2k2M7GXaT0bIbJxXkUnp6ut5DMCR2k2E3ObaTYTcZdpNjOxl2k9GyGycV5FK9evX0HoIhsZsMu8mxnQy7ybCbHNvJsJuMlt08blLx4osvolu3bqhduzbCwsIqdRulFGbPno369esjICAA/fr1w4kTJxyWSUtLw+jRoxESEoKwsDBMmDABOTk5NXAPriwWi0XvIRgSu8mwmxzbybCbDLvJsZ0Mu8lo2c3jJhVFRUUYPnw4HnrooUrfZtGiRXjjjTewYsUK7N27F4GBgRgwYAAKCgrsy4wePRpHjhzB1q1b8fXXX+OHH37A5MmTa+IuXFG0PL27J2E3GXaTYzsZdpNhNzm2k2E3GS27edykYu7cuXj88cfRtm3bSi2vlMLSpUvx3HPP4c4770S7du3wwQcfwGw2Y9OmTQCAo0ePYvPmzXjnnXfQpUsX9OjRA2+++SY+++wzmM3mGrw3+tPy9O6ehN1k2E2O7WTYTYbd5NhOht1ktOzmcZOKqoqLi4PFYkG/fv3sl4WGhqJLly7Ys2cPAGDPnj0ICwtDp06d7Mv069cPXl5e2Lt3r9P1FhYWIisry+HLiKKiovQegiGxmwy7ybGdDLvJsJsc28mwm4yW3a76SUXZe80uPZClXr169ussFku5H4qPjw8iIiIqfK/aggULEBoaav+KiYkBABQUFMBsNsNms9lnj/Hx8SgqKkJSUhJycnKQkZGBtLQ05Ofnw2KxoKSkxGHZ4uJiWCwW5OXlIS0tDenp6cjNzUViYiKKi4sdlrVarUhOTgYAZGRkIDMzE9nZ2UhOTkZhYaHDskopxMfHo7CwEMnJycjKysKFCxeQkpKC/Pz8So87Ly/P6bhLSkrKjTsnJ8fpuG02G8xmM/Lz85Gamup03GXjvXTc2dnZyMzMRGpqaoXjLi4uRmJiInJycpCenl6lcWdkZCAnJwdJSUkoKipyOm6LxYKUlBT7hNLZuMtu42rcVqvV6bhzc3PLjfvShmXjzs/Pr/S4CwoKnI7bbDY7HXdSUlK5cSckJFx23BU9Zo8fPw6r1YqEhASHcWdnZ1dp3CkpKZd9rmVnZzs816oz7rLnWtm4L37MJiUlOX2umc1mh+daZcdd0T7i+PHj4n3E5Z5rrvYRZeM26j7CYrHoso/Iz8839D7iwoULuuwjLn2uGXEfkZCQoMs+ojq/R1wJ+4jjx49fVb9HuGsfkZKSAq2YlFJKs60JzZw5EwsXLnS5zNGjR9GyZUv79++//z4ee+wxZGRkuLzd7t270b17d5jNZtSvX99++YgRI2AymbB27Vq89NJLWLNmDY4dO+Zw26ioKMydO9fp8RuFhYUoLCy0f5+VlYWYmBhkZmYiJCTE5ZjcLSEhAatWrcLkyZMd7mNl5ObmIjAwsIZG5rnYTYbd5NhOht1k2E2O7WTYTUbLbj6abKWapk+fjnHjxrlcpkmTJqJ1R0dHAwASExMdfuFOTExE+/bt7cskJSU53K6kpARpaWn221/Kz88Pfn5+ojFdSYqKivgkFmA3GXaTYzsZdpNhNzm2k2E3GS27GWJSUbduXdStW7dG1h0bG4vo6Ghs27bNPonIysrC3r177a9AdO3aFRkZGThw4AA6duwIANi+fTtsNhu6dOlSI+O6UhjghawrErvJsJsc28mwmwy7ybGdDLvJaNnNEJOKqjh37hzS0tJw7tw5WK1WHDp0CADQrFkzBAUFAQBatmyJBQsW4K677oLJZMJjjz2GF154Ac2bN0dsbCxmzZqFBg0aYMiQIQCAVq1aYeDAgZg0aRJWrFiB4uJiTJkyBSNHjkSDBg10uqeuZWdn28+jUfZ+uovfVxcUFITg4ODLrsff379mBujh2E2G3eTYTobdZNhNju1k2E1Gy24eN6mYPXs21qxZY/++Q4cOAIAdO3agT58+AIBjx44hMzPTvsxTTz2F3NxcTJ48GRkZGejRowc2b97s8IP4+OOPMWXKFPTt2xdeXl4YNmwY3njjDW3ulMCBAwewa9cuh8s2btxo/3fv3r3tPVzJyspC7dq13T08j8duMuwmx3Yy7CbDbnJsJ8NuMlp2M8SB2p4gKysLoaGhmh2offErFc5U9pWK4uJi1KpVy51Duyqwmwy7ybGdDLvJsJsc28mwm4yW3a76j5T1VMHBwahfv36FX5WZUAAod4A6VQ67ybCbHNvJsJsMu8mxnQy7yWjZja9UaETrVyqIiIiIiLTCVyrIJS1P7+5J2E2G3eTYTobdZNhNju1k2E1Gy258pUIjRn2loqSkBD4+Hnc8f41jNxl2k2M7GXaTYTc5tpNhNxktu/GVCnJJy9O7exJ2k2E3ObaTYTcZdpNjOxl2k9GyGycV5FJoaKjeQzAkdpNhNzm2k2E3GXaTYzsZdpPRshsnFeRSfn6+3kMwJHaTYTc5tpNhNxl2k2M7GXaT0bIbJxXkkpcXHyIS7CbDbnJsJ8NuMuwmx3Yy7CajZTf+hMglHhQlw24y7CbHdjLsJsNucmwnw24yWnbjpIJcysvL03sIhsRuMuwmx3Yy7CbDbnJsJ8NuMlp246SCXAoLC9N7CIbEbjLsJsd2Muwmw25ybCfDbjJaduOkglxKTk7WewiGxG4y7CbHdjLsJsNucmwnw24yWnbjye80YtST3xERERERXQ5fqSCXtDy9uydhNxl2k2M7GXaTYTc5tpNhNxktu/GVCo0Y9ZUKm83Gj3ETYDcZdpNjOxl2k2E3ObaTYTcZLbvxp0MuWSwWvYdgSOwmw25ybCfDbjLsJsd2Muwmo2U3fuivRspeEMrKytJ5JFXj4+NjuDFfCdhNht3k2E6G3WTYTY7tZNhNpqxbcHAwTCZTzW6rRtdOdtnZ2QCAmJgYnUdCRERERFcTLd5+z2MqNGKz2WA2mzWZKbpLVlYWYmJicP78eUMdB6I3dpNhNzm2k2E3GXaTYzsZdpO5uFvDhg35SoWn8PLywjXXXKP3MERCQkL4JBZgNxl2k2M7GXaTYTc5tpNhN5mQkBBN/qDNA7WJiIiIiKhaOKkgIiIiIqJq4aSCKuTn54c5c+bAz89P76EYCrvJsJsc28mwmwy7ybGdDLvJaN2NB2oTEREREVG18JUKIiIiIiKqFk4qiIiIiIioWjipICIiIiKiauGk4irz/PPPw2QyOXy1bNnSfn1BQQEeeeQR1KlTB0FBQRg2bBgSExMd1nHu3DkMHjwYtWvXRlRUFJ588kmUlJRofVc0Fx8fj/vuuw916tRBQEAA2rZti/3799uvV0ph9uzZqF+/PgICAtCvXz+cOHHCYR1paWkYPXo0QkJCEBYWhgkTJiAnJ0fru6KZxo0bl3u8mUwmPPLIIwD4eKuI1WrFrFmzEBsbi4CAADRt2hTz58/HxYfA8fFWsezsbDz22GNo1KgRAgIC0K1bN/z666/269kO+OGHH3D77bejQYMGMJlM2LRpk8P17mp0+PBh9OzZE/7+/oiJicGiRYtq+q7VuMu127hxI/r37486derAZDLh0KFD5dZxNe77XHUrLi7GjBkz0LZtWwQGBqJBgwYYM2YMzGazwzquxsfc5R5vzz//PFq2bInAwECEh4ejX79+2Lt3r8MymnVTdFWZM2eOuv7661VCQoL9Kzk52X79gw8+qGJiYtS2bdvU/v371U033aS6detmv76kpES1adNG9evXT/3222/qm2++UZGRkerpp5/W4+5oJi0tTTVq1EiNGzdO7d27V50+fVpt2bJFnTx50r7Myy+/rEJDQ9WmTZvU77//ru644w4VGxur8vPz7csMHDhQ3XDDDeqXX35RP/74o2rWrJm699579bhLmkhKSnJ4rG3dulUBUDt27FBK8fFWkRdffFHVqVNHff311youLk6tX79eBQUFqddff92+DB9vFRsxYoRq3bq12rVrlzpx4oSaM2eOCgkJURcuXFBKsZ1SSn3zzTfq2WefVRs3blQA1BdffOFwvTsaZWZmqnr16qnRo0erP//8U3366acqICBArVy5Uqu7WSMu1+6DDz5Qc+fOVW+//bYCoH777bdy67ga932uumVkZKh+/fqptWvXqr///lvt2bNHde7cWXXs2NFhHVfjY+5yj7ePP/5Ybd26VZ06dUr9+eefasKECSokJEQlJSXZl9GqGycVV5k5c+aoG264wel1GRkZqlatWmr9+vX2y44ePaoAqD179iilSh/cXl5eymKx2Jd56623VEhIiCosLKzRsetpxowZqkePHhVeb7PZVHR0tHrllVfsl2VkZCg/Pz/16aefKqWU+uuvvxQA9euvv9qX+fbbb5XJZFLx8fE1N/gryL/+9S/VtGlTZbPZ+HhzYfDgweqBBx5wuGzo0KFq9OjRSik+3lzJy8tT3t7e6uuvv3a4/MYbb1TPPvss2zlx6S8q7mq0fPlyFR4e7vBcnTFjhmrRokUN3yPtOPslr0xcXJzTSQX3fa67ldm3b58CoM6ePauU4mNOqcp1y8zMVADU999/r5TSthvf/nQVOnHiBBo0aIAmTZpg9OjROHfuHADgwIEDKC4uRr9+/ezLtmzZEtdeey327NkDANizZw/atm2LevXq2ZcZMGAAsrKycOTIEW3viIa++uordOrUCcOHD0dUVBQ6dOiAt99+2359XFwcLBaLQ7vQ0FB06dLFoV1YWBg6depkX6Zfv37w8vIq91KlJyoqKsJHH32EBx54ACaTiY83F7p164Zt27bh+PHjAIDff/8dP/30EwYNGgSAjzdXSkpKYLVa4e/v73B5QEAAfvrpJ7arBHc12rNnD3r16gVfX1/7MgMGDMCxY8eQnp6u0b258nDfVzmZmZkwmUwICwsDwMdcZRQVFWHVqlUIDQ3FDTfcAEDbbpxUXGW6dOmC999/H5s3b8Zbb72FuLg49OzZE9nZ2bBYLPD19bU/gcvUq1cPFosFAGCxWBx2cmXXl13nqU6fPo233noLzZs3x5YtW/DQQw9h6tSpWLNmDYD/3XdnbS5uFxUV5XC9j48PIiIiPLpdmU2bNiEjIwPjxo0DAD7eXJg5cyZGjhyJli1bolatWujQoQMee+wxjB49GgAfb64EBweja9eumD9/PsxmM6xWKz766CPs2bMHCQkJbFcJ7mp0tT5/L4f7vssrKCjAjBkzcO+99yIkJAQAH3OufP311wgKCoK/vz+WLFmCrVu3IjIyEoC23XyqcyfIeMr+0gkA7dq1Q5cuXdCoUSOsW7cOAQEBOo7symaz2dCpUye89NJLAIAOHTrgzz//xIoVKzB27FidR2cM7777LgYNGoQGDRroPZQr3rp16/Dxxx//v/buJSbK643j+G8cYBSmFSPCDBAEvOCtaasLnLSLkppGY7wlKmFhFGOMsQup2hrTekkMrXFhWGnSRlm0Grtp03jD+yXeIJpBZQOC1wWpUUqElBbqPF38wxvnDyjmxaEO309yFu/tnPM+ObzMM2fmjA4ePKipU6eqtrZWZWVlyszMZLz1w48//qiVK1cqKytLXq9X06dPV0lJiW7cuDHYXQPwCl1dXVq6dKnMTHv37h3s7rwVioqKVFtbqydPnuiHH37Q0qVLVV1d3SOZeNOYqRjiUlNTNXHiRDU2NioQCKizs1Otra1R5/z+++8KBAKSpEAg0GOFiu7t7nPiUTAY1JQpU6L2TZ482fnoWPe99xabF2P3+PHjqOP//POPWlpa4jp2kvTgwQOdPn1aq1atcvYx3vr25ZdfOrMV7733npYtW6YvvvhC3333nSTG26uMGzdOFy5cUHt7ux49eqSamhp1dXUpPz+f2PXDQMVoqP79vgrPvr51JxQPHjzQqVOnnFkKiTH3MikpKRo/frxmzpypffv2KSEhQfv27ZMU27iRVAxx7e3tampqUjAY1IwZM5SYmKgzZ844x+vr6/Xw4UOFQiFJUigU0u3bt6MGaPcf/v+/6I4nH330kerr66P2NTQ0aOzYsZKkvLw8BQKBqNg9e/ZM1dXVUbFrbW2Nerf07NmzikQiKiwsjMFdDJ7Kykqlp6dr7ty5zj7GW9/+/PNPDRsW/Xj2er2KRCKSGG/9lZKSomAwqD/++EMnTpzQggULiF0/DFSMQqGQLl68qK6uLuecU6dOqaCgQKNGjYrR3fz38OzrXXdCcefOHZ0+fVqjR4+OOs6Y679IJKK///5bUozj9lpf68Zbb8OGDXb+/Hm7d++eXb582WbNmmVpaWnO0mNr1qyxnJwcO3v2rF2/ft1CoZCFQiHn+u5l7j777DOrra21qqoqGzNmzFu9zF1/1NTUWEJCgpWXl9udO3fswIEDlpycbD/99JNzzs6dOy01NdV+++03u3Xrli1YsKDXJRg//PBDq66utkuXLtmECRPiapnK3jx//txycnJs06ZNPY4x3nq3fPlyy8rKcpaU/eWXXywtLc2++uor5xzGW9+qqqrs+PHjdvfuXTt58qS9//77VlhYaJ2dnWZG7MzM2traLBwOWzgcNkm2e/duC4fDzko7AxGj1tZWy8jIsGXLllldXZ0dOnTIkpOT3+rlPc1eHbunT59aOBy2o0ePmiQ7dOiQhcNha25uduoYis++l8Wts7PT5s+fb9nZ2VZbWxu1FPmLKxINxTH3sri1t7fb5s2b7erVq3b//n27fv26lZaWms/ns7q6OqeOWMWNpGKIKS4utmAwaElJSZaVlWXFxcVRv7XQ0dFha9eutVGjRllycrItWrQo6kFoZnb//n2bM2eOjRgxwtLS0mzDhg3W1dUV61uJucOHD9u0adPM5/PZpEmT7Pvvv486HolEbMuWLZaRkWE+n88+/fRTq6+vjzrn6dOnVlJSYn6/3959910rLS21tra2WN5GzJ04ccIk9YiFGeOtL8+ePbN169ZZTk6ODR8+3PLz8+3rr7+O+ufKeOvbzz//bPn5+ZaUlGSBQMA+//xza21tdY4TO7Nz586ZpB5l+fLlZjZwMbp586Z9/PHH5vP5LCsry3bu3BmrW3xjXhW7ysrKXo9v27bNqWMoPvteFrfu5Xd7K92/a2Q2NMfcy+LW0dFhixYtsszMTEtKSrJgMGjz58+3mpqaqDpiFTeP2Qs/0QoAAAAAr4nvVAAAAABwhaQCAAAAgCskFQAAAABcIakAAAAA4ApJBQAAAABXSCoAAAAAuEJSAQAAAMAVkgoAAAAArpBUAAAGncfjkcfjUWpqasza3L59u9NuRUVFzNoFgHhEUgEAcGXFihXOi/MXS2Nj42vVU1lZqYaGBtf9yc3Nlcfj0bVr16L2l5WV6ZNPPnG2N27cqObmZmVnZ7tuEwCGOpIKAIBrs2fPVnNzc1TJy8t7rTpSU1OVnp4+IP0ZPny4Nm3a9NJz/H6/AoGAvF7vgLQJAEMZSQUAwDWfz6dAIBBV3L5Y3759uz744APt379fOTk58vv9Wrt2rZ4/f65du3YpEAgoPT1d5eXlPa5dvXq1rl27pmPHjrnqAwCgfxIGuwMAAPSlqalJx48fV1VVlZqamrR48WLdvXtXEydO1IULF3TlyhWtXLlSs2bNUmFhoXNdXl6e1qxZo82bN2v27NkaNoz30ADgTeIpCwBw7ciRI/L7/U5ZsmTJgNQbiUS0f/9+TZkyRfPmzVNRUZHq6+tVUVGhgoIClZaWqqCgQOfOnetx7TfffKN79+7pwIEDA9IXAEDfmKkAALhWVFSkvXv3OtspKSkDUm9ubq7eeecdZzsjI0Nerzdq5iEjI0OPHz/uce2YMWO0ceNGbd26VcXFxQPSHwBA75ipAAC4lpKSovHjxzslGAwOSL2JiYlR2x6Pp9d9kUik1+vXr1+vjo4O7dmzZ0D6AwDoHUkFACBu+f1+bdmyReXl5Wpraxvs7gBA3CKpAADEtdWrV2vkyJE6ePDgYHcFAOIWSQUAIK4lJiZqx44d+uuvvwa7KwAQtzxmZoPdCQDA0ObxePTrr79q4cKFMW87NzdXZWVlKisri3nbABAvmKkAAPwnlJSUKDs7O2btffvtt/L7/Xr48GHM2gSAeMVMBQBg0DU2NkqSvF6v8vLyYtJmS0uLWlpaJP1v+dmRI0fGpF0AiEckFQAAAABc4eNPAAAAAFwhqQAAAADgCkkFAAAAAFdIKgAAAAC4QlIBAAAAwBWSCgAAAACukFQAAAAAcIWkAgAAAIArJBUAAAAAXPkXJHkEhYEiBMoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot residui\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "# Residui con barre d'errore\n",
    "ax.errorbar(\n",
    "    dfSonarCorrezione[\"F\"], dfSonarCorrezione[\"r\"],\n",
    "    xerr=dfSonarCorrezione[\"uF\"], yerr=dfSonarCorrezione[\"ur\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=\"Residui\"\n",
    ")\n",
    "\n",
    "# Linea dello zero\n",
    "ax.axhline(0, color='red', linestyle='--', linewidth=1)\n",
    "\n",
    "# Estetica\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(\"F  [mN]\")\n",
    "ax.set_ylabel(r\"Residuo $x_i - \\left(\\frac{y_i}{A} - \\frac{B}{A}\\right)$  [mm]\")\n",
    "ax.set_title(\"Residui della regressione — Molla 2 (sonar statico con correzione)\")\n",
    "ax.legend()\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b575ad7d",
   "metadata": {},
   "source": [
    "# Sonar dinamico\n",
    "Ancora non abbiamo il parametro $\\alpha$, quindi useremo $\\alpha = \\frac 1 3$. Lo imposto nel csv così è più facile da cambiare con il metodo della molla\n",
    "- !! Succede qualcosa di strano con Meq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "af6143df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Massa molla = 15.4300 ± 0.0029\n"
     ]
    }
   ],
   "source": [
    "# Calcolo numerico massa molla\n",
    "\n",
    "dfMasse_raw = pd.DataFrame([[15.43, 15.43, 15.43]], columns=[\"m1\", \"m2\", \"m3\"])\n",
    "\n",
    "dfMasse = pd.DataFrame()\n",
    "dfMasse = dfMasse.acc.calcola_stats(dfMasse_raw, \"m\",  ERR_BILANCIA)\n",
    "\n",
    "massaMolla  = dfMasse.m[0]\n",
    "umassaMolla = dfMasse.um[0]\n",
    "print(f\"Massa molla = {massaMolla:.4f} ± {umassaMolla:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a1e69bd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/Meq = 1000/(alp*m_molla + m_osc)\n",
      "sigma_1/Meq = 1000*sqrt(alp**2*sigma_m_molla**2 + m_molla**2*sigma_alp**2 + sigma_m_osc**2)/(alp*m_molla + m_osc)**2\n",
      "\n",
      "Formula LaTeX:\n",
      "1/Meq = \\frac{1000}{alp m_{molla} + m_{osc}}\n",
      "sigma_1/Meq = \\frac{1000 \\sqrt{alp^{2} \\sigma_{m molla}^{2} + m_{molla}^{2} \\sigma_{alp}^{2} + \\sigma_{m osc}^{2}}}{\\left(alp m_{molla} + m_{osc}\\right)^{2}}\n"
     ]
    }
   ],
   "source": [
    "# Calcolo simbolico Meq\n",
    "\n",
    "# simboli\n",
    "m_osc, alp, m_molla = sp.symbols('m_osc alp m_molla', positive=True)\n",
    "sigma_m_osc, sigma_alp, sigma_m_molla = sp.symbols('sigma_m_osc sigma_alp sigma_m_molla', positive=True)\n",
    "\n",
    "# massa equivalente\n",
    "Meq = 1 / (m_osc + alp * m_molla) * 1000\n",
    "\n",
    "# derivate parziali\n",
    "dMeq_dm_osc   = sp.diff(Meq, m_osc)\n",
    "dMeq_dalp     = sp.diff(Meq, alp)\n",
    "dMeq_dm_molla = sp.diff(Meq, m_molla)\n",
    "\n",
    "sigma_Meq = sp.sqrt(\n",
    "    (dMeq_dm_osc   * sigma_m_osc  )**2 +\n",
    "    (dMeq_dalp     * sigma_alp    )**2 +\n",
    "    (dMeq_dm_molla * sigma_m_molla)**2\n",
    ")\n",
    "\n",
    "print(\"1/Meq =\", Meq)\n",
    "print(\"sigma_1/Meq =\", sigma_Meq.simplify())\n",
    "print(\"\\nFormula LaTeX:\")\n",
    "print(f\"1/Meq = {sp.latex(Meq)}\")\n",
    "print(f\"sigma_1/Meq = {sp.latex(sigma_Meq.simplify())}\")\n",
    "\n",
    "# funzioni numeriche\n",
    "Meq_fn  = sp.lambdify((m_osc, alp, m_molla), Meq, 'numpy')\n",
    "uMeq_fn = sp.lambdify((m_osc, sigma_m_osc, alp, sigma_alp, m_molla, sigma_m_molla), sigma_Meq, 'numpy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "56679fc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w2 = w**2\n",
      "sigma_w2 = 2*sigma_w*w\n",
      "\n",
      "Formula LaTeX:\n",
      "w2 = w^{2}\n",
      "sigma_w2 = 2 \\sigma_{w} w\n"
     ]
    }
   ],
   "source": [
    "# Calcolo simbolico w^2\n",
    "\n",
    "# simboli\n",
    "w = sp.symbols('w', positive=True)\n",
    "sigma_w = sp.symbols('sigma_w', positive=True)\n",
    "\n",
    "# omega^2\n",
    "w2 = w**2\n",
    "\n",
    "# derivata parziale\n",
    "dw2_dw = sp.diff(w2, w)\n",
    "\n",
    "sigma_w2 = sp.sqrt((dw2_dw * sigma_w)**2)\n",
    "\n",
    "print(\"w2 =\", w2)\n",
    "print(\"sigma_w2 =\", sigma_w2.simplify())\n",
    "print(\"\\nFormula LaTeX:\")\n",
    "print(f\"w2 = {sp.latex(w2)}\")\n",
    "print(f\"sigma_w2 = {sp.latex(sigma_w2.simplify())}\")\n",
    "\n",
    "# funzioni numeriche\n",
    "w2_fn  = sp.lambdify((w), w2, 'numpy')\n",
    "uw2_fn = sp.lambdify((w, sigma_w), sigma_w2, 'numpy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "455271a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "371558377daf421fbae4ee1e04dd9b3a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, row_end=4, row_start=0, squeeze_row=False, type='numeric', val…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calcolo numerico Meq e w^2\n",
    "\n",
    "dfSonar['alp'] = 1/3\n",
    "dfSonar['ualp'] = 0.000000000001\n",
    "\n",
    "dfSonar['1/Meq'] = Meq_fn(dfSonar['m'], dfSonar['alp'], massaMolla)\n",
    "dfSonar['u1/Meq']= uMeq_fn(dfSonar['m'], dfSonar['um'], dfSonar['alp'], dfSonar['ualp'], massaMolla, umassaMolla)\n",
    "\n",
    "dfSonar['Omega2'] = w2_fn(dfSonar['w'])\n",
    "dfSonar['uOmega2']= uw2_fn(dfSonar['w'], dfSonar['uw'])\n",
    "\n",
    "\n",
    "sheet = ipysheet.from_dataframe(dfSonar)\n",
    "display(sheet)\n",
    "# dfSonar.head(4).to_json(orient=\"records\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a9d21f",
   "metadata": {},
   "source": [
    "## Regressione lineare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "02119d66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AD      = 3.1496 ± 0.0009\n",
      "BD      = 0.7065 ± 0.0094\n",
      "cov_ABD = -0.000008\n",
      "chi²    = 15.1289\n",
      "P(chi², ∞)= 0.0017\n"
     ]
    }
   ],
   "source": [
    "# Regressione lineare\n",
    "\n",
    "AD, BD, uAD, uBD, covABD, chiD, PD = reg_lin( dfSonar[\"1/Meq\"], dfSonar[\"Omega2\"], dfSonar[\"u1/Meq\"], dfSonar[\"uOmega2\"])\n",
    "\n",
    "print(\"Ax + B : \")\n",
    "print(f\"AD      = {AD:.4f} ± {uAD:.4f}\")\n",
    "print(f\"BD      = {BD:.4f} ± {uBD:.4f}\")\n",
    "print(f\"cov_ABD = {covABD:.6f}\")\n",
    "print(f\"chi²    = {chiD:.4f}\")\n",
    "print(f\"P(chi², ∞)= {PD:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "de571f32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot regressione\n",
    "\n",
    "SCALA = 1000\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "x_data =  dfSonar[\"1/Meq\"]\n",
    "y_data =  dfSonar[\"Omega2\"]\n",
    "\n",
    "x_fit = np.linspace(0, x_data.max(), 300)\n",
    "y_fit = AD * x_fit + BD\n",
    "\n",
    "ax.errorbar(\n",
    "    x_data, y_data,\n",
    "    xerr=SCALA * dfSonar[\"u1/Meq\"], yerr=SCALA * dfSonar[\"uOmega2\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=f\"Dati (barre errore x{SCALA})\"\n",
    ")\n",
    "ax.plot(\n",
    "    x_fit, y_fit,\n",
    "    color='red', linewidth=1.5,\n",
    "    label=f\"$A={AD:.5f}\\\\pm{uAD:.5f}$\\n\"\n",
    "          f\"$B={BD:.4f}\\\\pm{uBD:.4f}$\\n\"\n",
    "          f\"P(chi², ∞)= {PD:.4f}\\n\"\n",
    ")\n",
    "\n",
    "\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(r\"1/Meq  [Kg$^{-1}$]\")\n",
    "ax.set_ylabel(r\"$\\omega^2$  [s$^{-2}$]\")\n",
    "ax.set_title(\"Fit lineare — Molla 2 (sonar dinamico)\")\n",
    "ax.legend(fontsize=9)\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "96f12fe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui\n",
    "\n",
    "dfSonar[\"R\"] = r_fn(\n",
    "  dfSonar[\"1/Meq\"],\n",
    "  dfSonar[\"Omega2\"],\n",
    "  AD,\n",
    "  BD\n",
    ")\n",
    "\n",
    "dfSonar[\"uR\"] = sigma_r_fn(\n",
    "  dfSonar[\"1/Meq\"], dfSonar[\"Omega2\"],\n",
    "  AD, BD,\n",
    "  dfSonar[\"u1/Meq\"], dfSonar[\"uOmega2\"],\n",
    "  uAD, uBD, covABD\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c87033e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot residui\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "# Residui con barre d'errore\n",
    "ax.errorbar(\n",
    "    dfSonar[\"Omega2\"], dfSonar[\"R\"],\n",
    "    xerr=dfSonar[\"uOmega2\"], yerr=dfSonar[\"uR\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=\"Residui\"\n",
    ")\n",
    "\n",
    "# Linea dello zero\n",
    "ax.axhline(0, color='red', linestyle='--', linewidth=1)\n",
    "\n",
    "# Estetica\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(r\"$\\omega^2$  [s$^{-2}$]\")\n",
    "ax.set_ylabel(r\"Residuo $x_i - \\left(\\frac{y_i}{A} - \\frac{B}{A}\\right)$  [Kg$^{-1}$]\")\n",
    "ax.set_title(\"Residui della regressione — Molla 2 (sonar dinamico)\")\n",
    "ax.legend()\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d35498c5",
   "metadata": {},
   "source": [
    "# Cronometro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "97921cfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wt = 38*pi/w\n",
      "w2t = 1444*pi**2/w**2\n",
      "sigma_w2t = 2888*pi**2*sigma_t/w**3\n",
      "\n",
      "Formula LaTeX:\n",
      "w2t = \\frac{1444 \\pi^{2}}{w^{2}}\n",
      "sigma_w2t = \\frac{2888 \\pi^{2} \\sigma_{t}}{w^{3}}\n"
     ]
    }
   ],
   "source": [
    "# Calcolo w^2 da t/20\n",
    "\n",
    "# simboli\n",
    "t = sp.symbols('w', positive=True)\n",
    "sigma_t = sp.symbols('sigma_t', positive=True)\n",
    "\n",
    "# omega^2\n",
    "wt = (2 * sp.pi) / (t/19)\n",
    "w2t = wt**2\n",
    "\n",
    "# derivata parziale\n",
    "dw2t_dt = sp.diff(w2t, t)\n",
    "\n",
    "sigma_w2t = sp.sqrt((dw2t_dt * sigma_t)**2)\n",
    "\n",
    "print(\"wt =\", wt)\n",
    "print(\"w2t =\", w2t)\n",
    "print(\"sigma_w2t =\", sigma_w2t.simplify())\n",
    "print(\"\\nFormula LaTeX:\")\n",
    "print(f\"w2t = {sp.latex(w2t)}\")\n",
    "print(f\"sigma_w2t = {sp.latex(sigma_w2t.simplify())}\")\n",
    "\n",
    "# funzioni numeriche\n",
    "w2t_fn  = sp.lambdify((t), w2t, 'numpy')\n",
    "uw2t_fn = sp.lambdify((t, sigma_t), sigma_w2t, 'numpy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0b3f8a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "57acfdfd8a2242a9bb2338407fefd332",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, row_end=4, row_start=0, squeeze_row=False, type='numeric', val…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfSonar['Omega2t'] = w2t_fn(dfSonar['t'])\n",
    "dfSonar['uOmega2t']= uw2t_fn(dfSonar['t'], dfSonar['ut'])\n",
    "\n",
    "sheet = ipysheet.from_dataframe(dfSonar)\n",
    "display(sheet)\n",
    "# dfSonar.head(4).to_json(orient=\"records\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0421b1cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "ACr      = 3.0896 ± 0.0419\n",
      "BCr      = 1.1894 ± 0.4871\n",
      "cov_ABCr = -0.019876\n",
      "chi²    = 3.2171\n",
      "P(chi², ∞)= 0.3593\n"
     ]
    }
   ],
   "source": [
    "# Regressione lineare\n",
    "\n",
    "ACr, BCr, uACr, uBCr, covABCr, chiCr, PCr = reg_lin( dfSonar[\"1/Meq\"], dfSonar[\"Omega2t\"], dfSonar[\"u1/Meq\"], dfSonar[\"uOmega2t\"])\n",
    "\n",
    "print(\"Ax + B : \")\n",
    "print(f\"ACr      = {ACr:.4f} ± {uACr:.4f}\")\n",
    "print(f\"BCr      = {BCr:.4f} ± {uBCr:.4f}\")\n",
    "print(f\"cov_ABCr = {covABCr:.6f}\")\n",
    "print(f\"chi²    = {chiCr:.4f}\")\n",
    "print(f\"P(chi², ∞)= {PCr:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "739c2c88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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06dOnRpfL+Pv7Q6FQuEzde+HChWrPlJWdnY0hQ4bAz88Pu3fvrvRys8qkpaXh9ddfh8VicTY2fn5+GDVqFD799FP8+uuvFS7tEwThmh+qBw0ahMDAQLz33nu4/fbbneuuWrUKer2+VjNd3XHHHfj222/x2WefOcdZlJSU4P3330d4eDj69etX4226M3ToUERGRuK1117DbbfdVmG2rNzcXGfGNX3/JiUloVu3bvj444/x9NNPO5/z+++/49tvv3UZZ1Lq2LFjuO+++6T9UIwxj+PGgjHmdVJTUwEA//nPfzB06FD4+/tj/PjxJK/dr18/PPjgg1i0aBFOnDiBIUOGICAgAOfOncPmzZvxv//9D3fffTduueUWxMTEYMqUKfjPf/4DhUKBtWvX1ugylUmTJmHTpk146KGHsH//fvTu3Rt2ux1nzpzBpk2bsHv3btnHpQDi9fbVuXRq4cKF2LNnD/r06YNZs2ahUaNG+PDDD2GxWNzef+Fahg8fjnfeeQe33347JkyYAI1Gg6VLl+K6665zGWNQmdtvvx3//PMPnnzySRw+fBiHDx92PpaQkIDBgwdf8/kjR47EK6+8goMHD2LIkCHO5a+99hq+/fZb9OvXzzlFsFqtxubNm3H48OFrfpBu0qQJnn76aSxYsAC333477rrrLpw9exbLli1Djx493H6ArsrMmTPx4YcfYurUqcjMzETLli2xZcsW/PDDD1iyZInb8TC1ERkZieXLl2PSpEm48cYbMX78eDRp0gSXLl3C119/jd69e+ODDz4AULv375tvvolhw4YhLS0NM2bMcE43627fy8zMRH5+PkaOHOmRn40x5kEyzkjFGPMBpVNvupueUxDcTw1bUlIizJ07V2jSpImgUCiqnHq2utPNhoWFVXjuiy++6Hb7K1euFFJTU4WQkBAhIiJC6Ny5s/Dkk08KKpXKuc4PP/wg3HzzzUJISIjQtGlT4cknnxR2795dYSrV8tO3Xs1qtQqLFy8WOnbsKAQFBQkxMTFCamqqsGDBAkGv11/z564r16q3lLvpZgVBEI4dOyYMHTpUCA8PF0JDQ4XbbrtN+PHHH90+t6rpZletWiW0bdtWCAoKEtq3by9kZGRU+vu6GiqZ4hiAy75yLV26dHFOtVvexYsXhcmTJwtNmjQRgoKChNatWwuzZ88WLBaLIAhV7/MffPCB0L59eyEgIEBISEgQHn74YaGgoMBlncp+B+5yysnJEaZNmybExcUJgYGBQufOnV32fUEoe0+8+eabLssr+z1W9jPs379fGDp0qBAVFSUEBwcLbdq0EaZOnSr8+uuvznUqe/9WVkOpvXv3Cr179xZCQkKEyMhIYcSIEcIff/xRYb358+cLzZs3d07vyxjzHgpBkDAKjDHGGGug1q5di9mzZ+PSpUvkl+Qx9ywWC1q2bImnnnqqws3/GGPy4/tYMMYYY27cd999aN68OZYuXSp3KexfGRkZCAgIqHA/GsaYd+AzFowxxhhjjDHJ+IwFY4wxxhhjTDJuLBhjjDHGGGOScWPBGGOMMcYYk8xnGwtBEGAwGGo05zxjjDHGGGPMPZ9tLIxGI6KiomA0GuUuhTHGGGOMsXrPZxsLRk+pVMpdgs/hzGlx3vQ4c1qcNz3OnBbnLY3PTjdrMBgQFRUFvV6PyMhIucvxCYIgQKFQyF2GT+HMaXHe9DhzWpw3Pc6cFuctDZ+xYGRUKpXcJfgczpwW502PM6fFedPjzGlx3tJwY8HIxMXFyV2Cz+HMaXHe9DhzWpw3Pc6cFuctDTcWjIzBYJC7BJ/DmdPivOlx5rQ4b3qcOS3OW5pGchfgrQRBQElJCex2u9ylNBgKhQLFxcVyl+FTKsvc398fjRo14utIPSwoKEjuEnwOZ06L86bHmdPivKXhxsINq9UKtVqNoqIiuUtpUBwOB3Jzc+Uuw6dcK/PQ0FAkJSUhMDCQuCrGGGOMNUTcWFzF4XAgKysL/v7+aNq0KQIDA/mvuh5is9kQEBAgdxk+xV3mgiDAarUiNzcXWVlZaNu2Lfz8+KpIT7BarXKX4HM4c1qcNz3OnBbnLQ03FlexWq1wOBxISUlBaGio3OU0KIGBgfwBllhlmYeEhCAgIAAXL16E1WpFcHCwDNU1PGFhYXKX4HM4c1qcNz3OnBbnLQ1/yqsEfwD2vJKSErlL8DnXypz3cc8rKCiQuwSfw5nT4rzpcea0OG9p+JMFI8OXQdHjzGklJibKXYLP4cxpcd70OHNanLc03FgwMjabTe4SfA5nTkutVstdgs/hzGlx3vQ4c1qctzTcWDAyPPsQPc6cVnJystwl+BzOnBbnTY8zp8V5S8ONRQMxevRoKBQKZGdne2ybc+fORUpKCiIjI5GcnIx58+Zdc7YEm82GOXPmICYmBrGxsZg7d67LNf5//vknhg0bhpiYGCQnJ+ONN96o0fOlqOm2q7u+2WzGddddh+joaOcyi8WCBx54AK1atUJERATat2+P1atXe+TnqCme3YKWUqmUuwSfw5nT4rzpcea0OG9puLFoAPbs2YPdu3cjODgYJ06c8Nh2Z82ahTNnzsBgMODkyZM4efJkhWagvIULF+Lw4cP4448/cPr0aRw6dAivvfYaAMBut+Puu+/GjTfeCI1Gg++++w4ffPAB1q9fX63nV+bAgQPo379/lT9LTbdd3fVfeOEFtGjRwmVZSUkJkpKSsHfvXhgMBqxZswaPP/44vv322yrr9LRGjXjiN0pNmjSRuwSfw5nT4rzpcea0OG+JBB+l1+sFAIJer3dZbjabhT/++EMwm80yVVYzVqtV6NChg/Dcc88JPXr0EBYtWlQnr6PRaIQBAwYIkydPrnSdZs2aCZs3b3Z+v2nTJqF58+aCIAjC6dOnBX9/f8FisTgff+mll4R+/fpV6/mV2b9/v8s2alNbbdf/9ddfhU6dOgm7d+8WoqKirvn6o0ePFp5//vkq6/Q0q9Va6WP1bV+vD3JycuQuwedw5rQ4b3qcOS3OWxo+Y1HPvf/++9DpdJg/fz46d+5c6RmLWbNmITo6utKvw4cPu33e66+/jvDwcMTHx+PkyZOYO3eu2/UKCgpw5coVdOvWzbmsW7duuHTpEvR6PRwOBwDx5mylHA4HTp06Va3nS1HTbVdn/ZKSEjzwwANYunRpleMYiouL8fPPP6NLly6Sfo7a4CllafG9b+hx5rQ4b3qcOS3OWxr+1FGP5eTkYMGCBVi4cCHCw8Ov2VgsW7YMOp2u0q8+ffq4fd5TTz0Fk8mEP/74Aw899FCl07CZTCYAcBlrUPpvo9GIdu3aoWXLlnjhhRdgsVhw+vRprF69GgaDoVrPL698k3TnnXfi8OHD12ySarLt6q7/5ptvonv37ujbt6/bPEoJgoD7778fbdu2xZgxY665bl0o38ixusf3aqHHmdPivOlx5rTkyNtoNEKtVlf65e6zirfiC7DrsaeeegqtW7fG1KlTAQCdO3fGuXPnUFRU5PGOu0OHDujatSumTp2KvXv3Vng8PDwcAKDX6xEXF+f8NwBEREQgICAAW7duxX//+18kJyejWbNmmDZtGj788MNqPb+8ZcuWYdmyZQDEMRYvvfQSDhw4UGntNdl2ddY/f/48VqxYgePHj1f6moD4oX7WrFk4e/Ys9u7dy2cPfEDpmTlGhzOnxXnT48xpyZF3ZmYmDh48WOnj/fr1q9Z4Um/AjUU99fPPP+Pjjz9GaGgomjZtCkAcIF16edHNN9/ssv5DDz2ETz/9tNLt7dq1C7feeus1X9Nms+HcuXNuH4uJiUGzZs1w4sQJtGnTBgBw4sQJpKSkICoqCgDQqVMnlwHM8+fPR79+/ar9/Nqq6barWn/79u3IycnB9ddf78zFaDQiLi4OX3/9NXr16gVBEDB79mwcPXoU+/btk/wz1JZCoZDldX1VcHCw3CX4HM6cFudNjzOnJUfeqampaNeuHQBAq9Vi27ZtGDNmjPOPm6V/8KwXZB3hIaP6PHjb4XAIPXv2FMaPHy+o1WqXr+TkZGH58uWSX8NoNAqrV68WCgoKBIfDIZw6dUro0KGD8MADD1T6nOeff17o3r27s5bu3bsLCxYscD7+66+/CiaTSbBYLMLWrVuFuLg44eTJk9V+vhQ13fa11i8sLBQuX77s/Nq0aZMQGRkpXL582Tk4fdasWUKXLl0ErVbrkfpriwdv01Kr1XKX4HM4c1qcNz3OnJbceatUKuGll14SVCqVrHXUFp+xqIfWrFmDS5cuYdeuXYiNjXV5LDU11SNTzioUCqxfvx5PPPEELBYL4uPjkZ6ejgULFjjXeeihhwAAK1asAAA8//zzyMvLQ4cOHQAAEydOxDPPPONcf9u2bVixYgWKi4vRtWtX7Nixw2VAc1XPL/+6NT37UtW2a/KzhIaGulxq1qRJEygUCjRr1gwAcPHiRSxbtgxBQUEuU9FOnDjRuX0qPN0srdK/LjE6nDktzpseZ06L85ZGIQi+ObrTYDAgKioKer0ekZGRzuXFxcXIyspCq1at+PSjh1mtVr4TNLFrZc77uucplUq+aysxzpwW502PM6cld95qtRorV67EzJkzkZSUJFsdtcWjSRkZbirocea0+H/+9DhzWpw3Pc6cFuctDTcWjIzVapW7BJ/DmdNSKpVyl+BzOHNanDc9zpwW5y2N1zUWdrsdzz//PFq1aoWQkBC0adMGr7zyist8/IIg4IUXXkBSUhJCQkIwaNCgSmcrYt4jICBA7hJ8DmdOKyEhQe4SfA5nTovzpseZ0+K8pfG6xmLx4sVYvnw5PvjgA/z5559YvHgx3njjDbz//vvOdd544w289957WLFiBY4ePYqwsDAMHToUxcXFMlbOqsI3+aHHmdPSarVyl+BzOHNanDc9zpwW5y2N100Z8+OPP2LkyJEYPnw4AKBly5bYsGEDfv75ZwDi2YolS5bgueeew8iRIwEAn3zyCRISErBjxw6MHz/e7XYtFgssFovz+9I7PjM6fIM4epw5rfITQTAanDktzpseZ06L85bG6z513HLLLdi3bx/++usvAMDJkydx+PBhDBs2DACQlZWF7OxsDBo0yPmcqKgo9OrVCz/99FOl2120aBGioqKcXykpKQDEmXFUKhUcDofzurqSkhI4HA7YbDbY7XaUlJS4LBMEwXntutVqdbuu3W6HzWaDw+FwWbf0uQ6Ho0brlm6//LqFhYUIDg5GWFgYwsPDERYWhp49e+LXX391W8vVdQuCUGnd77//PlJTUxEUFIS77rqryrqXLFmCm266CUFBQRg1alSFus+ePYs77rgDMTExSE5OxuLFi6udYXXqLi4uxqxZsxATE4PY2FjMnTsXRUVFVWao1+tx3XXXITo62rn9c+fO4fbbb3fW+vrrrzt/N1J/96XLpf7ua1LLtdYtzaCgoACFhYXIycmBzWZzvheUSiXsdjvUajXMZjPy8/Oh0+lgNBqh0WhgtVpd1nU4HFCpVCguLoZWq4XBYIDBYIBWq3X7XlMqlbBardBoNDAajdDpdMjPz4fZbIZarYbdbndZ12azIScnB4WFhSgoKKh23Xl5edDr9c66LRaLy7qCIEClUsFisSA3N7dGdZtMJmfder0e2dnZKCkpqVB3dnY2ioqKkJ+fX2XdKpWqQt25ublu61YqlW7rNpvN1a67qKjIbd0lJSUV6jaZTG7rLv3dX6vu0nqvrttoNEKv1yMvL6/Sukt/9yaTCQUFBc66S+u8Vt06nQ4mk+ma+6zZbHbZZ93VXfqca9Vd1T5bPu+rMyytu/x7raq6r36vldatUqnc1l36Xitfd03eayqVio8REo4RZrO5xscIk8nExwgJx4jq1F3+vWY0GmU9Ruj1egDimRNvOkZUG8G9MmrEbrcL8+fPFxQKhdCoUSNBoVAIr732mvPxH374QQBQ4cYhY8eOFcaNG1fpdouLiwW9Xu/8unz5cr29QV6p48ePCwqFQjAajYIgCEJRUZEwbNgwYdiwYZK3vXXrVmH79u3C7NmzhZEjR0pav6SkRLjhhhuEp556SrBarcKZM2eElJQUYd26dVVud//+/UK/fv2qXO+FF14QunbtKqhUKkGlUgldu3at1s31nnjiCWHAgAFCVFSUS63PPPNMjWv1RjabrdLH6tO+Xl/k5+fLXYLP4cxpcd70OHNacudd32+Q53VnLDZt2oR169Zh/fr1OHbsGD7++GO89dZb+PjjjyVtNygoCJGRkS5f9d2xY8fQqlUr563eQ0JC0LVrV9hsNsnbHjNmDEaNGlXtG8Vca/2zZ8/i7NmzeOGFFxAQEIB27dphxowZWLlypeQ6S61evRrPPfcckpKSkJSUhGeffRarVq265nMyMzPxzTffYP78+RVqffHFF+usVkoKhULuEnwKD5anx5nT4rzpcea0OG9pvK6x+O9//4unnnoK48ePR+fOnTFp0iQ8+uijWLRoEQAgMTERAJCTk+PyvJycHOdjvuLYsWPOO1cLgoBff/0V69atw7x58yqsO2vWLERHR1f6dfjw4Tqr0+FwABBn/Cq/7NSpUx7ZfkFBAa5cuYJu3bo5l3Xr1g2XLl1ynlK8WklJCR544AEsXbrU5V4PpbUK5WYh82St1Ep/HkajsLBQ7hJ8DmdOi/Omx5nT4ryl8brGoqioqMKAU39/f+cHpFatWiExMRH79u1zPm4wGHD06FGkpaWR1iq3Y8eO4ZtvvkF0dDRCQ0PRq1cvzJ071znwvbxly5ZBp9NV+tWnT586q7Ndu3Zo2bIlXn75ZVgsFpw+fRqrV6+udAB9+SbozjvvxOHDh6/ZBJlMJgBwjpMo/2+j0ej2Nd588010794dffv2dVvrCy+8UK1avZ2/v7/cJfiU2NhYuUvwOZw5Lc6bHmdOi/OWxusaixEjRuDVV1/F119/jQsXLmD79u145513MHr0aADipR3z5s3DwoUL8cUXX+C3337D5MmT0bRpU4waNUre4gnZ7XacPHkSW7ZsgU6nQ1FREfbt24f58+fjt99+k7s8FwEBAfj8889x7NgxJCcn47777sO0adPQuHFjt+uXb4K++uor9OnT55pNUOmlYOXPTpT+OyIiosL2z58/jxUrVuDNN9+stNbjx49Xq1Zvx9PN0tJoNHKX4HM4c1qcNz3OnBbnLY3XNRbvv/8+7r77bsyaNQsdOnTAE088gQcffBCvvPKKc50nn3wSc+fOxcyZM9GjRw+YTCZ88803CA4OlrFyWmfOnEFRURF69OgBQGy4br31VgQGBiIrK6vC+g899BDCw8Mr/Tp06FCd1tuxY0fs3bsXWq0WJ06cgMViQb9+/Tyy7ZiYGDRr1gwnTpxwLjtx4gRSUlIQFRVVYf3Dhw8jJycH119/PeLi4jBy5EgYDAbExcXh6NGj6NixI7799ts6qZVa+cu8WN1LTk6WuwSfw5nT4rzpcea0OG9pvO4+FhEREViyZAmWLFlS6ToKhQIvv/wyXn75ZbrCvMyxY8fQokULxMfHAxAvB1u4cCEiIyMrXN4DACtWrMCKFSuqvf3S6UpLp9ktLi6Gn59fpR9Uq1r/1KlTSElJQVhYGL766iusXr3a5XK2yvTv3x8HDhyocr1p06bh1VdfRe/evQEAr732Gu6//363644bN85luuKffvoJ999/P06cOIH4+HicOnUKbdq0QUBAQI1q9UZWq5WbC0JKpZL/p0SMM6fFedPjzGlx3tJ4XWPBqufYsWO4fPkywsPD4e/vj8jISPTr1885HkGqhQsXYsGCBc7vQ0JC0K9fP+eH/IceeggAnM1KVetv2rQJy5cvR3FxMbp27YodO3Y4B55f7aGHHsKnn35aaW27du3Crbfe6rLs+eefR15eHjp06AAAmDhxIp555hmXbZbWGxoaitDQUOdjTZo0gUKhQLNmzWpcq7fj2S1oJSUlyV2Cz+HMaXHe9DhzWpy3NAqh/PQ3PsRgMCAqKgp6vd5l6tni4mJkZWWhVatWPnVpFQX+6zm9a2XO+7rnqVQqNG3aVO4yfApnTovzpseZ05I7b7VajZUrV2LmzJn1ssnhMxZVEQSgqIju9UJDgQZ674FGjXh3o8aZ04qJiZG7BJ/DmdPivOlx5rQ4b2n4U0dVioqAf2cdImEyAWFhdK9HyOFwVJhKmNUtzpxWUVERQkJC5C7Dp3DmtDhvepw5Lc5bGv7EwRhjHsJniOhx5rQ4b3qcOS3OWxpOryqhoeJZBMrXa6AUDfQSL2/GmdPis0P0OHNanDc9zpwW5y0Np1cVhUK8NInqy0MfBO12O7p06YLff/+9ynVfeumla95c8KGHHsL8+fMl11R69/RreeCBB/DRRx9Jfi0mqk7mzHOKi4vlLsHncOa0OG96nDktzlsabizqqf79+yMoKAjh4eGIjY1F//79kZmZ6Xz8k08+Qdu2bdGpUyfJr7VixQosXrwYAPD2229j/PjxuOWWW2rcAPj7+1e5zrPPPosXX3wRFoulVrVe7c8//0Tv3r0RGhqK66+/Hl988UWl6165cgW33HILGjdujKioKHTr1g3bt293Pn7hwgUoFAqXGwuOGDHCZRtLlixB69atER4ejgEDBuD8+fPOx3bt2oXOnTsjJiYGsbGxGDx4cJ3fJb06mTPPKT/DHKPBmdPivOlx5rQ4b2m4sajHFi9eDJPJBJVKhe7du2PkyJHOx5YuXYpp06Z5/DUfe+wxbNy4ETt27KjRDfcA8SZ6VWnZsiWuv/56bNmypbYlOtlsNowYMQIDBw5Efn4+3nnnHUyYMMHlw355MTExWLNmDXJzc6HX67Fs2TJMnDixwp3Mr1y5ApPJBJPJhC+//NK5fMOGDXj77bexc+dOFBQU4JZbbsGIESNgt9sBAN26dcO3336LgoICaDQaDB8+HKNHj5b8c15LdTJnnqPVauUuwedw5rQ4b3qcOS3OWxpuLBqA4OBgzJgxA0qlEnl5eVCpVDh+/Dj69evnst6GDRvQtWtXREZGokWLFlizZo3zMbvdjjlz5iA6OhrNmzfHZ5995nxs6tSpmDdvHgDxmn273Y5HH320xnc+r+7N2gYOHHjNMwvV9f333yMvLw/PP/88goODceedd6Jfv35Yu3at2/XDwsJw/fXXw8/PD4IgwM/PD3a7HRcuXKjW623fvh3Tpk1D+/btERAQgBdffBF///03Dh06BEC86U7pnNSCIMDf3x8XLlyAzWaT/LNWhm+QR4vnmqfHmdPivOlx5rQ4b2m4sWgAioqK8H//939o0aIFGjdujBMnTiA5ORkRERHOdb788kvMmTMH7777LnQ6HX755Rd07drV+fju3bvRt29f5OXlYeHChbj//vthNBorvJbZbMakSZMwZswYDB8+vNo1/vbbb+jRowfat2+P/fv3Y9euXWjfvj3S0tJw+vRpl3VvuOEGnDhxwvn9rFmzEB0dXenX4cOH3b7mqVOn0LFjR5cP1926dcOpU6euWWuXLl0QFBSEtLQ09O7du8Jdvjt16oTExETcddddOHPmjHO5w+HA1febFATB5fUuXbqE6OhoBAcH45FHHsHTTz9dpx/+67JpYRWpVCq5S/A5nDktzpseZ06L85aGG4t67Omnn0Z0dDRat26NM2fOOP/KX1BQUOEawWXLluGRRx7BgAED4Ofnh/j4eHTv3t35+I033ohx48bB398fkyZNgtVqxV9//VXhNWfNmoWjR48iIyMDkyZNqnatb775Jnbt2oV9+/Zh3rx5eOWVV/DDDz/giy++cI7fKBUZGYmCggKX2nU6XaVfffr0cfuaJpMJ0dHRLsuio6PdNkzlnTp1ynmZ07Bhw5zjFOLi4nD06FFkZWXhzJkzaNu2LQYPHgyDwQAAGD58ODIyMnD69GlYLBY8//zzsNvtzscBoHnz5s6633vvPdx0003VzrA2+IwFLf5LFz3OnBbnTY8zp8V5S8ONRT22aNEi6HQ6ZGdn45tvvkGXLl0AiGMFyn+YBYCLFy+ibdu2lW4rMTHR+W+FQoGQkBC3H8AzMjLw999/46uvvqr0kiJ3goKCEBUVheTkZNx555247bbb0LhxYzRp0qTCAGODweCRO1+Gh4dDr9e7LNPr9S5ncioTGBiIO++8E/v378e6deuc2+vZsycCAgIQHR2Nt956CzabDT/++CMA8ZKxhx9+GCNHjkSzZs1gt9txww03oHHjxhW2HxERgVmzZmHatGkVxnB4Ep+xoMV/6aLHmdPivOlx5rQ4b2m4sWiAunXrBqVSCVO5+2+0aNGi0kHLVE6fPo0///wT33//PXbu3IlffvkFp06dqtBY/PHHH+jWrZvz+4ceeshlJqarv0rHMFytS5cuOH36tMuH6xMnTqBz587Vrtlms+HcuXNuH1MoFC73iVAoFHj22Wdx/vx55Obm4qmnnsI///yDvn37un2+IAgoLi6u9hiO2uAb/dCKi4uTuwSfw5nT4rzpcea0OG9puLFogJo2bYpu3brh4MGDzmUPPvgg/ve//+HgwYNwOBzQaDQ4fvy4x16zf//+eOmllyp9/OWXX8aCBQswZ84cLF26FOvWrcPTTz+N5557DgsWLHBZ97vvvsOdd97p/H7FihXOWZjcfV09BqJU3759ERsbi1dffRUWiwU7d+7EgQMHMHnyZLfrHzx4ED/99BOsViusVivWrFmD/fv3Y/DgwQCAo0eP4s8//4TdbofJZML8+fOhUCiQlpYGANDpdDh79iwEQYBKpcL06dMxatQodOzYEQCwceNGnD9/Hg6HAzqdDo888gjCwsJw4403VjvnmiqdkYrRuPpMIat7nDktzpseZ06L85aGG4sGavbs2cjIyHB+P2rUKLzzzjuYPXs2oqKi0KNHD4/eQ+HSpUvo3bt3pY8nJSVh69at2LdvH7p06YIbbrgBe/fuxRdffIHk5GTnehcvXsSZM2cwduxYyTUFBATgiy++wJ49exAdHY1HHnkE69atw3XXXedcp/wZj8LCQjz44INo3LgxEhISsHz5cmzcuNE5huOff/7BnXfeicjISLRq1QqnT5/Gt99+i6ioKABiYzF69GiEh4fjxhtvxHXXXefyO7hw4QIGDx6MiIgIXH/99bhw4QL27NnjfH5d4DuI0goODpa7BJ/DmdPivOlx5rQ4b2kUwtXT2PgIg8GAqKgo6PV6l4HOxcXFyMrKQqtWrer1zmW329G9e3ds2LDB+RfzunLx4kWMHz8eP/300zXXKykpqfLSnJkzZ6JHjx544IEHPFmiz7pW5g1lX/cmer2+ThtFVhFnTovzpseZ05I7b7VajZUrV2LmzJnOKerrE74Au4Hy9/evclpVT2nRokWVTUV1rVy50iPbYUwOfENCepw5Lc6bHmdOi/OWhq+TYGT4shx6nDmt0NBQuUvwOZw5Lc6bHmdOi/OWhj91MDL8VwB6nDmt8vdfYTQ4c1qcNz3OnBbnLQ03FowM36yNHmdOq/z9YBgNzpwW502PM6fFeUvDjQUjwzdro8eZ01Kr1XKX4HM4c1qcNz3OnBbnLQ03FoxMYGCg3CX4HM6cVvmpkxkNzpwW502PM6fFeUvDjQUjY7Va5S7B53DmtJRKpdwl+BzOnBbnTY8zp8V5S8ONBSNT1T0smOdx5rTi4+PlLsHncOa0OG96nDktzlsabiwYGbvdLncJPoczp5Wfny93CT6HM6fFedPjzGlx3tJwY8HI8D0V6HHmtMLCwuQuwedw5rQ4b3qcOS3OWxr+1MHICIIgdwk+hzOnxbNw0ePMaXHe9DhzWpy3NNxYMMaYh3AjR48zp8V50+PMaXHe0nBjwcgoFAq5S/A5nDmt4OBguUvwOZw5Lc6bHmdOi/OWhhsLRsbhcMhdgs/hzGkZDAa5S/A5nDktzpseZ06L85aGGwtGhqc+pceZ04qLi5O7BJ/DmdPivOlx5rQ4b2m4sWBkeEAUPc6cVk5Ojtwl+BzOnBbnTY8zp8V5S8ONBSMTGBgodwk+hzOnlZycLHcJPoczp8V50+PMaXHe0nBjwchYrVa5S/A5nDktpVIpdwk+hzOnxXnT48xpcd7SeGVj0bJlSygUigpfs2fPBgAUFxdj9uzZaNy4McLDw5Gens6nruqBgIAAuUvwOZw5rYSEBLlL8DmcOS3Omx5nTovzlsYrG4tffvkFarXa+bVnzx4AwNixYwEAjz76KL788kts3rwZBw8ehEqlwpgxY+QsmVVDSUmJ3CX4HM6cllarlbsEn8OZ0+K86XHmtDhvabxyypgmTZq4fP/666+jTZs26NevH/R6PVatWoX169djwIABAICMjAx06NABR44cwc033+x2mxaLBRaLxfl9XUwnZjQaYTKZKn08PDwcERERHn/d+sLPzyv72AaNM6cVGRkpdwk+hzOnxXnT48xpcd7SeP2nDqvVik8//RTTp0+HQqFAZmYmbDYbBg0a5Fynffv2aN68OX766adKt7No0SJERUU5v1JSUgCIl1WpVCo4HA7ndXUlJSVwOByw2Wyw2+0oKSlxWSYIgvPadavV6lz+yy+/YOXKlZV+/fzzz87nOhwO53btdjtsNhscDofLdsuvW1rL1etWVkv5ukufc/W6giBUuu61aqls3dJ13NVdPkO56paSYW3qdldL6XLK3/211nU4HNDr9SgoKEBhYSFycnJgs9mc7wWlUgm73Q61Wg2z2Yz8/HzodDoYjUZoNBpYrVaXdR0OB1QqFYqLi6HVamEwGGAwGKDVat2+15RKJaxWKzQaDYxGI3Q6HfLz82E2m6FWq2G3213WtdlsyMnJQWFhIQoKCqpdd15eHvR6vbNui8Xisq4gCFCpVLBYLMjNza1R3SaTyVm3Xq9HdnY2SkpKKtSdnZ2NoqIi5OfnV1m3SqWqUHdubq7bupVKpdu6zWZztesuKipyW3dJSUmFuk0mk9u6S3/316q7tN6r6zYajdDr9cjLy6u07tLfvclkQkFBgbPu0jqvVbdOp4PJZLrmPms2m132WXd1lz7nWnVXtc+Wz/vqDEvrLv9eq6ruq99rpXWrVCq3dZe+18rXXZP3mkql4mOEhGOE2Wyu8THCZDLxMULCMaI6dZd/rxmNRlmPEXq9HoB45sSbjhHVpRC8/N7lmzZtwoQJE3Dp0iU0bdoU69evx7Rp01zOPgBAz549cdttt2Hx4sVut+PujEVKSgr0er1Ld1pcXIysrCy0atWqxndfLH/GQqvVYtu2bRgzZoxzTmRfP2NRUlLC91Ugdq3MpezrzD2dTofo6Gi5y/ApnDktzpseZ05L7rzVajVWrlyJmTNnIikpSbY6asvrz1isWrUKw4YNQ9OmTSVtJygoCJGRkS5fUhmNRhw4cABGoxEAEBERgaSkJBQHRGLTWQsOWFth01kLigMikZSUhIiIiArP8TYPPfQQ5s+fX+U6y5YtAwAcOHCg2m9AhUIhtTxWTXa7HZ07d8aZM2fkLsWncONMjzOnxXnT48xpcd7SeHVjcfHiRezduxf333+/c1liYiKsVit0Op3Lujk5OUhMTCStz2Qy4eDBgy7jKjb9ehkD3z6A9Zk5uGCPxfrMHAx8+wA2/3q50ufURv/+/REUFISIiAhERUWhU6dOePzxx5Gbm1vtbbz00ksYNWqUy7IVK1ZUetYHAM6fP4+vv/7a5XdSXQ6Ho8bP8WWlv+Pw8HDnl0qlcj5uMBgwYcIEREZGIiEhAa+88orzMX9/fzzxxBN49tln5SjdZxUVFcldgs/hzGlx3vQ4c1qctzRe3VhkZGQgPj4ew4cPdy5LTU1FQEAA9u3b51x29uxZXLp0CWlpaXKU6ZSlLcRTW0/BIQB2ARCggF0AHAIwf+spXNAWevT1Fi9e7LzmdNOmTVAqlUhNTa3TqXdXrFiBe+65p1Y3XvP3969yHXd3iq7rmY3cvWZN71hdUlKCuriqcPHixTCZTM6v8mfu5s6di/z8fFy6dAmHDh3CRx99hE8++cT5+N133439+/fj0qVLHq+LuceXK9DjzGlx3vQ4c1qctzRe21g4HA5kZGRgypQpLqeloqKiMGPGDDz22GPYv38/MjMzMW3aNKSlpVU6I1Rd02q1UKvVWH3gT1zrYp9VB/6sk2nMFAoFbrjhBnz66aeIjIzE22+/DUA8OzJy5EjEx8cjKioKffv2xcmTJwEAO3bswGuvvYavvvrK+ddwAJg6dSrmzZtX6Wt98cUXztm4ynv//feRlJSExMREvPjii84P2ZcuXcLgwYPRpEkTNG7cGMOHD8eFCxecz5s6dSpmzJiBcePGITIyEitWrED//v3x5JNPYsiQIQgLC8OuXbtgMpkwZ84cNG/eHPHx8Zg8ebJzgJM7f//9N0aMGIEmTZqgRYsWWLhwofOMyZo1a9CtWze8+OKLSExMxPjx4/HSSy/hzjvvxMMPP4zY2Fg89dRTsNlsePrpp9G8eXM0adIE99xzj8sZIYVCgQ8++ACdOnVCWFgYTCbTNV/3av/973/Rr18/5+NbtmxBYmIiNBpNpT9XqaKiImzcuBELFy5EdHQ0rr/+esydOxerVq1yrhMWFobU1FR8/fXXVW6PeUZNzhgyz+DMaXHe9DhzWpy3NF7bWOzduxeXLl3C9OnTKzz27rvv4s4770R6ejr69u2LxMREbNu2TYYqRdu2bcPKlStx+PgfcFTyV2tBEHD4+B91WmejRo0watQoHDx4EIDYnE2YMAFZWVnIyclB9+7dMW7cOAiCgFGjRuGZZ57BnXfe6fxreFWKiopw7tw5tG/f3mW50WjEsWPH8Pfff+PAgQNYvXq18y/nDocDjz32GC5fvoyLFy8iNDQUDzzwgMvzN2zYgBkzZkCn02HGjBkAxA//CxcuhMlkwqBBgzB9+nTk5+fj1KlTyMrKgs1mw5w5cyqtc+DAgRg4cCCUSiUOHTqEjRs3IiMjw7nO77//jkaNGuHSpUtYu3YtAOCbb75Br169oNFo8Morr2DRokX46quvcPjwYWRlZUGhUOC+++5zea3169fj22+/hcFggL+/f5WvW96rr76KwsJCLFy4EBcvXsTMmTPxySefID4+3rnOwoULERsbi+7du7ucjTh79iysViu6devmXNatWzecOnXK5TU6deqEEydOuH195nnJyclyl+BzOHNanDc9zpwW5y2N1zYWQ4YMgSAIuP766ys8FhwcjKVLlyI/Px+FhYXYtm0b+fiK8saMGYOZM2eiT/cb4FfJAGWFQoE+3W+o8xv5JScnIz8/H4A4F/M999yDsLAwBAcHY8GCBfjrr79crtOviYKCAud2y3M4HFi8eDFCQ0PRvn17zJkzx/lhvWXLlhg2bBiCg4MRHByMZ599FocOHXL5K/6QIUMwdOhQ+Pn5ITQ0FAAwYcIE9OzZEwqFAiaTCVu3bsXSpUsRHR2NsLAwvPzyy/jss89gt9sr1Pn1118jJiYG8+bNQ2BgIJo3b45HHnkE69evd64TFRWFZ599FoGBgc7X7NSpE6ZOnYpGjRohNDQUa9euxXPPPYfmzZsjPDwc77zzDvbs2eOS35NPPommTZsiKCioWq9bXmBgIDZs2IB3330Xd9xxB2bMmIEhQ4Y4H1+0aBH+/vtv5OTk4PXXX8fcuXOxfft2AOLZqLCwMJezedHR0RUmBQgLC3P+3ljdK522j9HhzGlx3vQ4c1qctzQ89N0D4uLikJSUhOn9I7Eus/LxDTP6d0CQzfM35itPqVQiNjYWAGA2m/H4449j586dyM/Pd94sTavV1qojj4mJASAOGi6dQhcQG73yf2Vv0aKF842Zm5uLRx55BIcOHXJeumSxWGA0GhEVFQUAaN68eYXXKr/swoULcDgcaNWqlcs6fn5+yM7OrvCzXLhwAb///rvLdZIOh8N57xJAbMCuvnnc1XVcuXIFLVu2dH5f2kBcuXLFOdbh6jqret2rtW3bFv3798dXX32F7777zuWx8mOGhg4digcffBCfffYZRo8ejfDwcBQVFblMJ6vX6ytMZ2wymZy/N1b36uPUgPUdZ06L86bHmdPivKXx2jMW9VGruDAsTu8CPwXgrwAUEOCnAPwUwOL0LmgZF1anr19SUoLPP/8c/fv3BwC8/fbbyMzMxOHDh2EwGJxjG0rHP9T0rsyhoaFo27ZthSlMi4uLXcYFXLp0yflh/+mnn0ZRURGOHTsGrVaL77//3qWGyuoovywlJQV+fn5QqVTQ6XTOr+LiYrcNUkpKClJTU13WNRgMOH36dLVfEwCaNWvmMh4kOzsbFosFzZo1q7TOql73alu2bMGRI0cwfPhwzJ49u9L1rn6tdu3aISAgwDlmBgBOnDiBzp07uzzn9OnTLpdLsbqVnZ0tdwk+hzOnxXnT48xpcd7ScGMhQXh4OPr16+cc+AwAY29KwXeP98eE1AS09M/HfakJ+O7x/hh7U0qlz/GEM2fOYMqUKdDr9XjssccAiGcWgoODERMTA5PJhGeeecblOQkJCbh48WKNZl0aMWIE9u/f77LMz88PTz/9NMxmM86ePYulS5c6xyIYDAaEhoYiOjoaer0eCxYsqPHPlpiYiFGjRmHOnDnOwe/Z2dnOy4KudueddyInJwfLli1DcXEx7HY7zp49iwMHDtTodSdOnIjXXnsNly9fhslkwmOPPYZBgwZVek+Vmr7upUuX8OCDD+Ljjz/GJ598guPHj2PlypUAxBv07Ny5E0VFRbDb7di3bx9WrFiB9PR0AGKTd8899+D555+HXq/HuXPn8P7777tMA1xUVITMzEzccccdNfq5We3x2SF6nDktzpseZ06L85aGGwsJIiIi0L9//wqXn7SMC8Os3snoH5iFWb2TXc5UVPac2pg/f77zPhZjxoxBYmIifv31VyQkJAAAHnvsMfj7+yMhIQGdOnWqMB3v2LFjERkZiSZNmlR7erUHH3wQGzdudJmONSIiAt26dUPr1q3Rt29fTJ48GVOmTAEALFiwAOfPn0dMTAz69OmDYcOG1epnXbNmDaKjo9GjRw9ERkbi1ltvRWZmptt1w8PDsXfvXuzbtw8tW7ZE48aNMWHChBr/FeLpp5/G0KFDkZaWhpYtW8Jms+HTTz+tdP2avK7dbsd9992HadOmYciQIYiMjMSGDRvw5JNP4s8//4TNZsOCBQuQmJiImJgYPProo3jnnXcwduxY5zY++OADREVFoVmzZujduzdmzJiByZMnOx/funUr+vXrhxYtWtTo52a1V1jo2SmlWdU4c1qcNz3OnBbnLY1CqIvJ9+sBg8GAqKgo6PV6l8HIxcXFyMrKQqtWrRAcHFyjbRqNRufsSlqtFtu2bcOYMWOc4xHCw8M90lDI7cEHH0S3bt3w8MMP1+h5dru9WveyYNI5HA5069YN69atq3B5VCkp+zpzz2AwVJjcgNUtzpwW502PM6cld95qtRorV67EzJkz6+V4Dx687UGZmZnOqV5LlZ9etl+/fs7xD/XZhx9+KHcJrAp+fn44deqU21mzGGOMMcbqAjcWHpSamop27dpV+rinx1XUNw6Hg89YEOPMaVksFrlL8DmcOS3Omx5nTovzloYbCw+KiIhoEJc61RX+gEuPM6fFlyvQ48xpcd70OHNanLc0PHi7Ej469KRO1WT2KeYZ18qc93HPK521jNHhzGlx3vQ4c1qctzTcWFwlICAAgDhVJ/Os0mwZnWtlXrqP8+/FcyqbipjVHc6cFudNjzOnxXlLw5dCXcXf3x/R0dHOG76FhoZCoVDIXFXDYLVaERgYKHcZPsVd5oIgoKioCBqNBtHR0Xy5lAepVKpa3dWe1R5nTovzpseZ0+K8peHGwo3ExEQAcLmbNGMNTXR0tHNfZ57B/zOix5nT4rzpcea0OG9puLFwQ6FQICkpCfHx8S43gmPS5OTkOG/ex2hUlnlAQACfqagDSqWS/6dEjDOnxXnT48xpcd7ScGNxDf7+/vzhy4MSExMRFBQkdxk+hTOnVXozTEaHM6fFedPjzGlx3tLw4G1GxmAwyF2Cz+HMaXHe9DhzWpw3Pc6cFuctDTcWjExwcLDcJfgczpwW502PM6fFedPjzGlx3tJwY8HIOBwOuUvwOZw5Lc6bHmdOi/Omx5nT4ryl4caCkeEb5NHjzGlx3vQ4c1qcNz3OnBbnLQ03FoxMaGio3CX4HM6cFudNjzOnxXnT48xpcd7ScGPByBQUFMhdgs/hzGlx3vQ4c1qcNz3OnBbnLQ03FowM38OCHmdOi/Omx5nT4rzpcea0OG9puLFgZLKzs+Uuwedw5rQ4b3qcOS3Omx5nTovzloYbC0aG72RJjzOnxXnT48xpcd70OHNanLc03FgwMkqlUu4SfA5nTovzpseZ0+K86XHmtDhvabixYGTi4+PlLsHncOa0OG96nDktzpseZ06L85aGGwtGJj8/X+4SfA5nTovzpseZ0+K86XHmtDhvabixYGTCw8PlLsHncOa0OG96nDktzpseZ06L85aGGwtGxmq1yl2Cz+HMaXHe9DhzWpw3Pc6cFuctDTcWjIwgCHKX4HM4c1qcNz3OnBbnTY8zp8V5S8ONBSMTHBwsdwk+hzOnxXnT48xpcd70OHNanLc03FgwMgaDQe4SfA5nTovzpseZ0+K86XHmtDhvabixYGQaN24sdwk+hzOnxXnT48xpcd70OHNanLc03FgwMhqNRu4SfA5nTovzpseZ0+K86XHmtDhvabixYGSSk5PlLsHncOa0OG96nDktzpseZ06L85aGGwtGRqlUyl2Cz+HMaXHe9DhzWpw3Pc6cFuctjVc2FkqlEhMnTkTjxo0REhKCzp0749dff3U+LggCXnjhBSQlJSEkJASDBg3CuXPnZKyYVUdCQoLcJfgczpwW502PM6fFedPjzGlx3tJ4XWNRUFCA3r17IyAgALt27cIff/yBt99+GzExMc513njjDbz33ntYsWIFjh49irCwMAwdOhTFxcUyVs6qotVq5S7B53DmtDhvepw5Lc6bHmdOi/OWppHcBVxt8eLFSElJQUZGhnNZq1atnP8WBAFLlizBc889h5EjRwIAPvnkEyQkJGDHjh0YP3682+1aLBZYLBbn9zydGL2oqCi5S/A5nDktzpseZ06L86bHmdPivKXxujMWX3zxBW666SaMHTsW8fHx6N69Oz766CPn41lZWcjOzsagQYOcy6KiotCrVy/89NNPlW530aJFiIqKcn6lpKQAAIqLi6FSqeBwOJzX1SmVSlitVmg0GphMJuh0OuTn58NsNiM7OxslJSUu69psNmRnZ6OoqAj5+fkoKChAYWEhcnJyYLPZXNa12+1QqVQwm83Iy8uDXq+H0WhEbm4uLBaLy7qCIECpVMJisSA3NxcGgwEGgwFarRZms7nadRcVFbmtu6SkpELdJpPJbd0Oh6PKukvrvbpuo9EIvV6P7OzsSuu22WzIycmByWRCQUFBjerW6XQwmUzQaDSwWq2V1q3Vap0Zuqu79DlX152Xl+es2263u627sLCwQt1XZ1hat9lsrnbdxcXFbutWqVRu69ZoNC5163Q6qNXqKuuuap9Vq9UudRuNxhrVrdVqq3yvGY1Gl/eaJ+suv89qNBq37zWVSuX2vVaTY4ROp+NjhIRjRPn3WnWPEVeuXOFjhIRjRE3fa6WP8TGidseI2nyOMBqNfIyQcIyo6ecIg8Eg6zFCr9cDEM+ceNMxoroUgpfdu7z0joePPfYYxo4di19++QWPPPIIVqxYgSlTpuDHH39E7969oVKpkJSU5HzeuHHjoFAo8Nlnn7ndrrszFikpKdDr9YiMjKzbH4oBAHQ6HaKjo+Uuw6dw5rQ4b3qcOS3Omx5nTkvuvNVqNVauXImZM2e6fM6tL7zuUiiHw4GbbroJr732GgCge/fu+P33352NRW0FBQUhKCjIU2WyWmjUyOt2twaPM6fFedPjzGlx3vQ4c1qctzRedylUUlISbrjhBpdlHTp0wKVLlwAAiYmJAICcnByXdXJycpyPMe9UVFQkdwk+hzOnxXnT48xpcd70OHNanLc0XtdY9O7dG2fPnnVZ9tdff6FFixYAxIHciYmJ2Ldvn/Nxg8GAo0ePIi0tjbRWVjN8KpceZ06L86bHmdPivOlx5rQ4b2m8rrF49NFHceTIEbz22ms4f/481q9fj5UrV2L27NkAAIVCgXnz5mHhwoX44osv8Ntvv2Hy5Mlo2rQpRo0aJW/x7Jpyc3PlLsHncOa0OG96nDktzpseZ06L85bG6y4k69GjB7Zv346nn34aL7/8Mlq1aoUlS5bgvvvuc67z5JNPorCwEDNnzoROp0OfPn3wzTffOAd+M++UnJwsdwk+hzOnxXnT48xpcd70OHNanLc0XjcrFBWDwYCoqCieFYqQUqnkNywxzpwW502PM6fFedPjzGnJkbfRaERmZibiWt2Az365hMPH/0Cf7jfgnh7Noc36A6mpqYiIiCCtqba4seDGgozD4YCfn9ddfdegcea0OG96nDktzpseZ05LjrzVajX+u2wrfrS1ggICHA4Bfv5+EADc0igLb85KrzdTz/KeyshkZ2fLXYLP4cxpcd70OHNanDc9zpwWed4lJcj5ai9+sLaAA4AdCgh+frALgEMAfrC1xGVdMW1NEnBjwcjExsbKXYLP4cxpcd70OHNanDc9zpwWWd5nzgDz5wMpKdj96T74XeMCoi9P59HU5AHcWDAyJpNJ7hJ8DmdOi/Omx5nT4rzpcea06jRvkwlYvRro0wfo0AF44w0gOxuX41PgUCgqfVq2wVp3NXmY180KxRquwMBAuUvwOZw5Lc6bHmdOi/Omx5nT8njeggD8+KPYUHz2GVBYKC738wPuuAOYPh0x1qbA8cqnuU2MrD/7ADcWjDHGGGOMeZJaDaxdKzYU5W/8fP31wPTpwKRJQNOmAIARf2Th02s0FiM6Nq7raj2GGwtGxmKxyF2Cz+HMaXHe9DhzWpw3Pc6clqS8bTZg505g1Srxv3a7uDw0FLjnHrGh6N0buOqyp5SYYPQOuIAfS1pBAcAhCFD8u84tjS4gJTq19jUR48aCkeFpfelx5rQ4b3qcOS3Omx5nTqtWef/5p3hm4pNPAI2mbPktt4jNxLhxwDXuQxEeHo77B3bC061uwMZy97EY36M5crPCEB4eXoufRB7cWDAyeXl5aPrvaT9GgzOnxXnT48xpcd70OHNa1c7bYAA2bRIbip9+KluekABMngxMmyYO0K6GiIgI9O/fHwAwK9CORqe/xszeg5GUFA+0iK/FTyEfbiwYmfpyc5eGhDOnxXnT48xpcd70OHNa18xbEIDDh8VLnTZvBoqKxOX+/sDw4cCMGcCwYUBAAE2xXoinm2VkVCqV3CX4HM6cFudNjzOnxXnT48xpuc1bpQIWLRIHXvftC3z8sdhUtGsnThl75Qrw+efAXXf5dFMB8BkLRig5OVnuEnwOZ06L86bHmdPivOlx5rSceVutwFdfiZc67doFOBzi8vDwsoHYaWkVBmL7Oj5jwcgolUq5S/A5nDktzpseZ06L86bHmdPK+e474PHHgWbNgPR04OuvxaaiTx+xyVCrgf/7P3FgNjcVFfAZC0YmLi5O7hJ8DmdOi/Omx5nT4rzpceYE9Hrx5nWrViHh55/LlicmAlOmiGcnrr9evvrqET5jwcjo9Xq5S/A5nDktzpseZ06L86bHmdcRQQAOHhRncEpKAh58EPj5ZwiNGgGjRgFffglcvgy8/jo3FTXAZywYmZCQELlL8DmcOS3Omx5nTovzpseZe9iVK+Lg64wM4O+/y5Z36ADMmIHCUaMQ3qaNfPXVc9xYMDKO0oFPjAxnTovzpseZ0+K86XHmHmCxiGcgVq8Gdu8uG4gdEQGMHy9e6tSrF6BQwM5niCThxoKRKSkpkbsEn8OZ0+K86XHmtDhvepy5BL/9JjYTa9cCeXlly/v2FZuJu+8GwsJcnsJ5S8ONBSMTGhoqdwk+hzOnxXnT48xpcd70OPMa0umAjRvFm9j9+mvZ8qZNxYHY06YBbdtW+nTOWxpuLBgZnU7H14oS48xpcd70OHNanDc9zrwaHA5xIPaqVcDWrUBxsbg8IEC8ad306cCQIUCjqj/2ct7ScGPByMTHx8tdgs/hzGlx3vQ4c1qcNz3O/BouXSobiJ2VVba8Y0dgxgxg4kSgSZMabZLzloanm2VksrOz5S7B53DmtDhvepw5Lc6bHmd+FYsF2LQJuP12oGVL4IUXxKYiMlKcMvboUXFsxaOP1ripADhvqfiMBSOTnJwsdwk+hzOnxXnT48xpcd70OPN/nTwpDsT+9FMgP79sef/+4qVO6emAB8ZHcN7S8BkLRkapVMpdgs/hzGlx3vQ4c1qcNz2fzrygAFi6FEhNBbp1A957T2wqmjUDnnsOOH8e2L8fmDTJI00F4ON5ewCfsWBk+LpFepw5Lc6bHmdOi/Om53OZOxzAd9+JZye2bRMvfQLEgdijRolnJwYPBvz96+TlfS5vD+MzFoxMfvlTl4wEZ06L86bHmdPivOn5TOYXLwILFgCtW4uNw4YNYlPRuTOwZAmgUpWNraijpgLwobzrCJ+xYGTCw8PlLsHncOa0OG96nDktzpteg868uBjYsUM8O7F3LyAI4vKoKGDCBHFmpxtvBBQKspIadN4EuLFgZKxWK8KuusMlq1ucOS3Omx5nTovzptcgMz9+XLznxLp14g3tSg0cKF7qNHo0INO9JBpk3oS4sWCMMcaY1zMajTCZTJU+Hh4ejoiICMKKWI3k54uNxOrVwIkTZctTUsS7YU+dCrRqJVd1zEO4sWBkAgMD5S7B53DmtDhvepw5LTnzzszMxMGDByt9vF+/fujfvz9dQUTq9T5utwP79onNxPbtgNUqLg8MFM9KTJ8unqWowzETNVWv8/YC3FgwMiaTiU8vEuPMaXHe9DhzWnLmnZqainbt2gEAtFottm3bhjFjxiAuLg5Aw702vl7u41lZwJo14telS2XLu3YVx03cdx8QGytXdddUL/P2ItxYMDKxXnoQacg4c1qcNz3OnJaceUdERFS41CkuLg5JSUkyVUSj3uzjZrN4VmLVKnG62FLR0WIjMWMG0L27bOVVV73J20vxdLOMjEajkbsEn8OZ0+K86XHmtDhvel6duSAAmZnArFlAUpLYQHz3nTiL06BB4pSxajXwwQf1oqkAvDzveoDPWDAyycnJcpfgczhzWpw3Pc6cFudNzysz12rLBmKfOlW2vEULcSD2lClAy5aylSeFV+Zdj/AZC0ZGqVTKXYLP4cxpcd70OHNanDc9r8ncbge++QYYNw5ITgbmzRObiqAg4N57gT17gH/+AV58sd42FYAX5V1PeWVj8dJLL0GhULh8tW/f3vl4cXExZs+ejcaNGyM8PBzp6enIycmRsWJWHYmJiXKX4HM4c1qcNz3OnBbnTU/2zP/+G3juObFZGDYM2LxZnN3pxhvFS5zUamD9evHSJz+v/FhZI7LnXc957R7QsWNHqNVq59fhw4edjz366KP48ssvsXnzZhw8eBAqlQpjxoyRsVpWHXzdIj3OnBbnTY8zp8V505Ml86IiYO1a4LbbgOuuA159FbhyRZzJ6T//EW9wl5kJzJ4NxMTQ11eHeB+XxmvHWDRq1Mht16jX67Fq1SqsX78eAwYMAABkZGSgQ4cOOHLkCG6++Wa327NYLLBYLM7vDQZD3RTOKhUdHS13CT6HM6fFedPjzGlx3vTIMhcE4JdfxHETGzYApZ+TFApgyBDxnhN33QUEB9PUIxPex6Xx2jMW586dQ9OmTdG6dWvcd999uPTvPMiZmZmw2WwYNGiQc9327dujefPm+Omnnyrd3qJFixAVFeX8SklJASBeVqVSqeBwOJzX1SmVSlitVmg0GphMJuh0OuTn58NsNiM7OxslJSUu69psNmRnZ6OoqAj5+fkoKChAYWEhcnJyYLPZXNa12+1QqVQwm83Iy8uDXq+H0WhEbm4uLBaLy7qCIECpVMJisSA3NxcGgwEGgwFarRZms7nadRcVFbmtu6SkpELdJpPJbd0Oh6PKukvrvbpuo9EIvV6P7OzsSuu22WzIycmByWRCQUFBjerW6XQwmUzQaDSwWq2V1q3Vap0Zuqu79DlX152Xl+es2263u627sLCwQt1XZ1hat9lsrnbdxcXFbutWqVRu69ZoNC5163Q6qNXqKuuuap9Vq9UudRuNxhrVrdVqq3yvGY1Gl/eaJ+suv89qNBq37zWVSuX2vVaTY4ROp+NjhIRjRPn3WnWPEVeuXOFjhIRjRE3fa6WPyX2MKC4uBoB6d4yozecIo9FYt8eInBzoXnoJ6NIF6NUL+PBDwGCAo2VLmJ95BroTJ1C0bRuy+/ZFSaNG9e4YUdPPEQaDQdZjhF6vByDeq8WbjhHVpRAEQaj22kR27doFk8mEdu3aQa1WY8GCBVAqlfj999/x5ZdfYtq0aS5nHwCgZ8+euO2227B48WK323R3xiIlJQV6vR6RkZF1+vMwkU6n478EEOPMaXHe9DhzWt6St1qtxsqVKzFz5swGfx+LOsm8pAT49lvxnhNffgnYbOLy4GAgPV2850S/fg1izERNyb2P1/d92ysvhRo2bJjz3126dEGvXr3QokULbNq0CSEhIbXaZlBQEIKCgjxVIqsFf39/uUvwOZw5Lc6bHmdOi/Om59HMz58HMjLEO2KrVGXLb7pJbCbGjxdvaOfDeB+XplaNxRdffFHj5wwePLjWTUF0dDSuv/56nD9/HoMHD4bVaq3QUebk5PBIfi9nNpsr3DWV1S3OnBbnTY8zp8V505OceWEhsGWLOHbi++/LljduDEyaJN53oksX6YU2ELyPS1OrxmLUqFE1Wl+hUODcuXNo3bp1bV4OJpMJf//9NyZNmoTU1FQEBARg3759SE9PBwCcPXsWly5dQlpaWq22z2h4w+lzX8OZ0+K86XHmtDhverXKXBCAo0fFZmLjRsBoFJf7+QFDh4oDsUeMEO9BwVzwPi5NrS+ey87OhsPhqNZXaGhojbb9xBNP4ODBg7hw4QJ+/PFHjB49Gv7+/rj33nsRFRWFGTNm4LHHHsP+/fuRmZmJadOmIS0trdIZoZh3yM3NlbsEn8OZ0+K86XHmtDhvejXKXKMB3n4b6NQJSEsDPvpIbCpatwYWLgQuXgR27gTuvpubikrwPi5Nrc5YTJkypUaXNU2cOLFGA6SvXLmCe++9F3l5eWjSpAn69OmDI0eOoEmTJgCAd999F35+fkhPT4fFYsHQoUOxbNmyGv8cjFZycrLcJfgczpwW502PM6fFedOrMvOSEvGO2KtXiwOxS0rE5SEhYgMxYwZw660+ORC7Nngfl6ZWjUVGRkaN1l++fHmN1t+4ceM1Hw8ODsbSpUuxdOnSGm2XyUupVPIblhhnTovzpseZ0+K86VWa+V9/ic3Exx8D2dlly3v1Ei91uuceICqKrtAGgvdxabxyVijWMNXHadPqO86cFudNjzOnxXnTc8ncZAI2bxYbisOHy5bHxQGTJ4sDsTt1oi+yAeF9XJoanxczm83Om2mUd/r0aY8UxBqu7PJ/UWEkOHNanDc9zpwW500vW60GfvwRuP9+IClJPBtx+LB4adPw4cDWrYBSWTa2gknC+7g0NTpjsWXLFsybNw9xcXFwOBz46KOP0KtXLwDApEmTcOzYsTopkjUMsbGxcpfgczhzWpw3Pc6cFudNKDsbWLsWiatWAWfPli2/7jqxuZgyBWjaVL76Gijex6WpUWOxcOFCZGZmIiEhAZmZmZgyZQqeeeYZTJgwAV54A2/mZUwmE4KDg+Uuw6dw5rQ4b3qcOS3Ou47ZbOKsTatXA19/Ddjt4qUloaHA2LHiQOw+fQCFQu5KGyzex6WpUWNhs9mQkJAAAEhNTcX333+P0aNH4/z581DwTs6qEBgYKHcJPoczp8V50+PMaXHedeTMGbGZ+OQTICenbHlaGsz33ouQKVOAGsyuyWqP93FpatRYxMfH49SpU+jy7x0aY2NjsWfPHkyZMgWnTp2qkwIZY4wxxhocoxHYtElsKH78sWx5fHzZQOwbboDNYEAINxWsnqhRY7F27Vo0auT6lMDAQGzYsAFz5szxaGGs4bFarXKX4HM4c1qcNz3OnBbnLZEgAD/8IDYTmzYBhYXicn9/4I47xEud7rgDCAhwPoUzp8V5S1OjxqJZs2Yu32dnZyMxMREA0Lt3b89VxRqk8PBwuUvwOZw5Lc6bHmdOi/OuJbVavN/E6tXAuXNly6+/XmwmJk0SZ3xygzOnxXlLI+k2jEOGDPFUHcwH5Ofny12Cz+HMaXHe9DhzWpx3DdhswPbtwIgRQEoK8PTTYlMRFlY2ZeyZM8CTT1baVACcOTXOWxpJN8jjmaBYTZSe3WJ0OHNanDc9zpwW510Nf/whnplYuxbQaMqW9+4tNhRjxwIREdXeHGdOi/OWRtIZC54JitWEWq2WuwSfw5nT4rzpcea0OO9KGAzARx8BaWlAx47izeo0GiAhQTwjceaMeIZi+vQaNRUAZ06N85ZG0hkLxmoiOTlZ7hJ8DmdOi/Omx5nT4rzLEQTg0CFg1Spg82bAbBaX+/sDd94pjp24/XaXgdi1wZnT4rylkXTGgrGaUCqVcpfgczhzWpw3Pc6cFucNQKkEXntNHHjdr5947wmzGWjfHnjzTeDKFWDHDnFshcSmQnw5zpwS5y2NpDMW/v7+nqqD+YAmTZrIXYLP4cxpcd70OHNaPpu31Qp8+aU4duKbbwCHQ1weHg6MHy9e4nTzzXVyR2yfzVwmnLc0ks5YHD9+3FN1MB+g0+nkLsHncOa0OG96nDktn8v799+Bxx4DkpOBu+8Gdu4Um4pbbwUyMoDs7LKxFXU07tTnMpcZ5y2N5DEWZrMZgiAgNDQUAHDx4kVs374dN9xwA09Hy1yEhITIXYLP4cxpcd70OHNaPpG3Xg9s3CiOnfjll7LlSUnAlCniHbGvv56sHJ/I3Itw3tJIbixGjhyJMWPG4KGHHoJOp0OvXr0QEBAArVaLd955Bw8//LAn6mQNgN1ul7sEn8OZ0+K86XHmtBps3g4HcPCgeKnTli1AcbG4vFEj4K67xEudhg4VvyfWYDP3Upy3NJIHbx87dgy33norAGDLli1ISEjAxYsX8cknn+C9996TXCBrOByl16QyMpw5Lc6bHmdOq8HlffkysHAh0LYtMGAA8OmnYlNxww3ilLFKJbB1KzB8uCxNBdAAM/dynLc0kt8lRUVFiPh3TuZvv/0WY8aMgZ+fH26++WZcvHhRcoGs4eDTi/Q4c1qcNz3OnFaDyNtiAb74Qjw7sXu3OG0sIN5f4t57xbMTPXvW2ZiJmmoQmdcjnLc0khuL6667Djt27MDo0aOxe/duPProowAAjUaDyMhIyQWyhkOn0/EblhhnTovzpufJzI1GI0wmU6WPh4eHO/+Q5qvq9T5+6pTYTHz6KZCXV7a8Xz+xmUhPB8LC5KuvEvU683qI85ZGcmPxwgsvYMKECXj00UcxcOBApKWlARDPXnTv3l1ygazhiI+Pl7sEn8OZ0+K86Xky88zMTBw8eLDSx/v164f+/ft77PXqo3q3j+t0wIYN4kDszMyy5cnJwNSp4td118lUXPXUu8zrOc5bGsmNxd13340+ffpArVaja9euzuUDBw7E6NGjpW6eNSDZ2dl8R0tinDktzpueJzNPTU1Fu3btAABarRbbtm3DmDFjEBcXB0A8Y+Hr6sU+7nAA+/eLZye2bSsbiB0QIA7EnjEDGDJEvEN2PVAvMm9AOG9pPDISKTExEYmJiS7Levbs6YlNswaE36j0OHNanDc9T2YeERFR4VKnuLg4JCUleew16ju593Gj0Yivv/8FJw0hOGJthStbMvHIXT3RqUU8cOkSsGaNeH+JCxfKntSpk9hM3HcfUA9vfiZ35r6G85amVrNCnTp1qkaj5k+fPo2SkpLavBRrQJRKpdwl+BzOnBbnTY8zpyV33huPXsTTh4rw2al8XLDHYq8SuGv5z9g8/hGgZUvgxRfFpiIyEnjoIeDnn8WxFfPm1cumApA/c1/DeUtTq8aie/fuyCs/8KkKaWlpuHTpUm1eijUgfN0iPc6cFudNjzOnJWfeWdpCLNp3EQIUcAiAAIXz3/ObD8KFqETgttvEwdlqNbB8OdCjh9fM7lRbvI/T4rylqdWlUIIg4Pnnn3febbsqVqu1Ni/DGpj8/HwkJCTIXYZP4cxpcd70OHNacua96dBfUAgCgKsaBYUCCgXw2fJtmD/+Zllqq0u8j9PivKWpVWPRt29fnD17ttrrp6Wl8dRdjAc+yoAzrzvupiY1m81Qq9UAeGpSKryP0yLP2+EA9u0DVq/GleKWENqmAX4VB10LCgWuCEG0tRHhfZwW5y1NrRqLAwcOeLgM5gusVivCvHCO8IaMM687PDWpd+B9nBZZ3hculA3E/vdS6mZ9p1x9rsJJoVCgWUzD/AMm7+O0OG9p5Lk/PWOM1XPupiYdMmQIWrZsCYD/6sVYjZnNwI4d4j0n9u0rWx4VBdx3H8aNm4IPv8n9907Zri2GIAi456YU0nIZYxVxY8HIBAYGyl2Cz+HM6467qUkTEhJ4alJivI/T8njeggAcOybec2L9evGGdqUGDhSniR01CggJQSsAz5T8jlf3XIBCITYTgHi2YnF6F7SMa5h/ZeZ9nBbnLQ03FoyMyWTi04vEOHNaRUVFcpfgc3gfp+WxvPPygHXrxIbi5Mmy5c2bA9OmiXfE/vfsX3n39GyBiGINThhCcOT3c7guMRbz7uqBji0a7kw+vI/T4ryl4caCkYmNjZW7BJ/DmdOKioqSuwSfw/s4LUl52+3A3r1iM7FjB1A6Y2RQEDB6NDB9uniWwq/ymfAjIiJwz/AB6KtWI/ivbzHz7sFISmq4TQXA+zg1zluaWt3HgrHa0Gg0cpfgczhzWvn5+XKX4HN4H6dVq7z/+Qd44QWgVSvg9tuBTZvEpqJ7d+D99wGVCtiwARg8+JpNha/ifZwW5y0Nn7FgZJKTk+Uuwedw5rT4xkr0eB+nVe28zWZg61bx7MT+/WXLY2KA++4Tz0507143RTYwvI/T4ryl4T8NMDJKpVLuEnwOZ06L/9JFj/dxWtfMWxCAX34BHn4YSEoCJk0SmwqFAhgyBNi4UTw78f773FTUAO/jtDhvaby+sXj99dehUCgwb94857Li4mLMnj0bjRs3Rnh4ONLT05GTkyNfkaxaEhMT5S7B53DmtBo3bix3CTViNBqhVqsr/TIajXKXWCXex2m5zVurBZYsAbp2BXr2BFasAPR6cfD1ggVAVhawezdwzz1AcDB1yfUe7+O0OG9pvPpSqF9++QUffvghunTp4rL80Ucfxddff43NmzcjKioKc+bMwZgxY/DDDz/IVCmrDo1Gw1NxEuPMaRUUFKBZs2Zyl1FtDeEmf7yP03LmbbcD334rXur0+eeAzSauEBQEpKeLlzrddhuPmfAA3sdpcd7S1LixMJvNyM/Pr3AN2unTp9GxY0ePFWYymXDffffho48+wsKFC53L9Xo9Vq1ahfXr12PAgAEAgIyMDHTo0AFHjhzBzTff7HZ7FosFFovF+b3BYPBYrax6oqOj5S7B53DmtOrbTfHc3eRvzJgxiIuLA1A/fh7ex2nF5OUBH3wAfPwxUP6SkdRUsZm4915xHAXzGN7HaXHe0tToTwlbtmxB27ZtMXz4cHTp0gVHjx51PjZp0iSPFjZ79mwMHz4cgwYNclmemZkJm83msrx9+/Zo3rw5fvrpp0q3t2jRIkRFRTm/UlLEO3QWFxdDpVLB4XA4r6tTKpWwWq3QaDQwmUzQ6XTIz8+H2WxGdnY2SkpKXNa12WzIzs5GUVER8vPzUVBQgMLCQuTk5MBms7msa7fboVKpYDabkZeXB71eD6PRiNzcXFgsFpd1BUGAUqmExWJBbm4uDAYDDAYDtFotzGZztesuKipyW3dJSUmFuk0mk9u6HQ5HlXWX1nt13UajEXq9Hmq1utK6bTYbcnJyYDKZUFBQUKO6dTodTCYTNBoNrFZrpXVrtVpnhu7qLn3O1XXn5eU567bb7W7rLiwsrFD31RmW1l3anFen7uLiYrd1q1Qqt3VrNBqXugsKCqBWq6usu6p9tvR3V1q30WisUd1arbbK95rRaHR5r3my7vL7rEajcfteU6lUbt9r1TlGlF4yZDQa69Uxwm63IygoyGWaXIfDgaSkJDgcDoSEhJAeI8q/16p7jLhy5UqdHCMKCgoAiH+9bMjHiGq915RKFC5fDvuttyK4c2fgtdfEpiI2FqYZM2DPzIT6yy9hnjYN+YJAcowoLi4GgHpzjJDyOcJgMPDnCAnHiJp+jrg6b+rPEXq9HoD4xx5vOkZUm1ADXbt2FbKzswVBEIRff/1V6Nixo7Bu3TpBEAShW7duNdnUNW3YsEHo1KmTYDabBUEQhH79+gmPPPKIIAiCsG7dOiEwMLDCc3r06CE8+eSTlW6zuLhY0Ov1zq/Lly8LAAS9Xu+xutm16XQ6uUvwOZw5DZVKJbz00kvCX3/9JXcptVb6M6hUKrlLqZG62sfrax4e43AIwpEjgjBzpiBERAiCODRbcCgUgnD77YKwaZMgFBfLVp4v/X74OE5L7rzr+75do0uhbDYbEhISAIin0L///nuMHj0a58+fh0KhqMmmKnX58mU88sgj2LNnD4I9OMgrKCgIQUFBHtseqzk/vtaWHGdOy1PHQVZ9vI97mEYDfPqpOHbi9Omy5a1aAdOnozA9HeEdOshXnw/ifZwW5y1NjdKLj4/HqVOnnN/HxsZiz549+PPPP12WS5GZmQmNRoMbb7wRjRo1QqNGjXDw4EG89957aNSoERISEmC1WqHT6Vyel5OTwyP5vZzZbJa7BJ/DmdMqP46L0eB93ANKSoCvvwbGjAGSk4HHHxebiuBgYOJEccrY8+eB555DUT2b+awh4H2cFuctTY3OWKxduxaNGrk+JTAwEBs2bMCcOXM8UtDAgQPx22+/uSybNm0a2rdvj/nz5yMlJQUBAQHYt28f0tPTAQBnz57FpUuXkJaW5pEaWN0ofx03o8GZ06oPg50bGt7HJTh3Tjwz8fHHQPlrqHv0AGbMAMaPB67Kl/Omx5nT4rylqVFjca1pFHv37i25GACIiIhAp06dXJaFhYWhcePGzuUzZszAY489htjYWERGRmLu3LlIS0urdEYo5h20Wi3f0ZIYZ05Lp9OhRYsWcpfhU3gfryGTCdiyRWwoDh0qWx4XJ97Qbto0oHPnSp/OedPjzGlx3tJIvo/FG2+8gRMnTiA7OxshISG44YYbMGbMmDo9e/Duu+/Cz88P6enpsFgsGDp0KJYtW1Znr8c8g9+o9DhzWvHx8XKX4HN4H68GQQCOHBGbiY0bxeYCEO8xcfvt4jSxI0YAgYFVborzpseZ0+K8pZE8QuX999+HVqt1/g9148aN6NOnD26//XbnlFlSHThwAEuWLHF+HxwcjKVLlyI/Px+FhYXYtm0bj6+oB0qnNGN0OHNaGo1G7hJ8Du/j15CTA7z1FtCxI3DLLcD//Z/YVLRpA7z6KnDpkji2Ij29Wk0FwHnLgTOnxXlLI/mMxeXLlyssO3LkCB5++GHMnj0bn376qdSXYA1E06ZN5S7B53DmtJo0aSJ3CT6H9/GrlJQAu3YBq1aJTUNJibg8JAQYO1YcO3HrrUAtZzDjvOlx5rQ4b2nqZE6tm2++GRkZGfjiiy/qYvOsnqrRDVaYR3DmtLRardwl+Bzex/915gwwfz6QkgLcdRfw+ediU9GrF7ByJZCdLQ7S7tu31k0FwHnLgTOnxXlLI/mMRXkZGRmIiIhAcHAwduzYgcY8LR0rh/cHepw5LZ5NhJ5P7+NGI7B5szh24ocfypY3aQJMniwOxO7Y0aMv6dN5y4Qzp8V5S+PRxuLo0aPYvHkzdDodhg8fzmcsmAuDwcCXihDjzGkVFhbKXYLP8bl9XBCAH38UL3XatAko3ef8/IA77hAHYg8fXu0xEzXlc3l7Ac6cFuctjUcvhVqxYgW0Wi2++uor/PPPPzh27JgnN8/qOb7zOT3OnFZAQIDcJfgcn9nH1WrgjTeADh2APn2AjAyxqWjbFli0CLh8GfjyS2D06DprKgAfytuLcOa0OG9pJJ+x6Nu3L95880306tULAKBQKDBs2DAkJSXhjjvuwJQpUyQXyRhjjPkcm00cgL16NbBzJ2C3i8tDQ4Fx48SB2L17SxozwRhjniS5sejYsSN69+6Nnj17Ij09HZ07d0Z4eDg2bNjAt0VnLqxWq9wl+BzOnFZJ6Qw8jEyD3Mf//FNsJj75BCg/hfEtt4iXOo0bB0REyFJag8zby3HmtDhvaSQ3FsuXL8ecOXPw5ptv4uWXX4bRaAQgnrl47bXXJBfIGo7w8HC5S/A5nDmtkJAQuUvwOQ1mHzcYxDETq1cDP/1UtjwhoWwgdocO8tX3rwaTdz3CmdPivKXxyODtjh07Ys2aNVi1ahX+/vtv6HQ6tGjRAgkJCZ7YPGsg8vPzeX5oYpw5LYPBIHcJPqde7+OCABw+LA7E3rwZKCoSl/v7iwOwp08XB2R70didep13PcWZ0+K8pfHorFD+/v64/vrrPblJ1oDw3dHpcea0eJpCevVyH1cqxcucVq8Gzp8vW96unThuYtIkwEt/rnqZdz3HmdPivKWpkxvkMeYO33SGHmdOi2+QR6/e7ONWK7BtG3DnnUDz5sAzz4hNRXi42Ez88IM4tuK///XapgKoR3k3IJw5Lc5bGo+esWDsWpKTk+Uuwedw5rTi4+PlLsHneP0+fvq0eGZi7VogN7dseZ8+4qVOY8eKzUU94fV5N0CcOS3OWxo+Y8HIKJVKuUvwOZw5LU35GXwYCa/cx/V6YOVKoFcvoFMn4J13xKYiMRGYPx84cwY4dEgckF2PmgrAS/Nu4DhzWpy3NHzGgpHhO1nS48xpxcTEyF2Cz/GafVwQgIMHxbMTW7YApdOtN2okXv40YwZw++3i9/WY1+TtQzhzWpy3NHzGgpHR6XRyl+BzOHNapdNtMzqy7+NXrgCvvireAfu228RLnsxmcWrYt94SH9++XWwu6nlTAXhB3j6IM6fFeUtT/49yrN4IDQ2VuwSfw5nTCg4OlrsEn1MX+/jvFzX43xeZOG9thStbMvHIXT3RqUW58TMWC/Dll+LZid27AYdDXB4RAYwfL46d6NWrQd4Rm48p9DhzWpy3NNxYMDJ8V2J6nDktu90udwk+x9P7+KZfL+OpracgCAAQiwtKYN+KX7A4vQvGBunKBmLn5ZU9qW9fsZm4+24gLMyj9XgbPqbQ48xpcd7ScGPByDhK/6rHyHDmtDhvep7MPEtbiKe2noJDAICysw2CIGD+5hPosfJBtNT9OxVl06bAlCniAOy2bT1Wg7fjfZweZ06L85aGGwtGJiQkRO4SfA5nTisoKEjuEnyOJ/fxTb9ehkKhwL+nK8pRQCE48Fm3oZgfaxAHYg8Z0iDGTNQUH1Pocea0OG9pfO+oyGSj1+v5DUuMM6dlMpnkLsHneHIfv3JFC8HhQPmzFaUEPz9cmTwTmJbmkdeqr/iYQo8zp8V5S8OzQjEycXFxcpfgczhzWtHR0XKX4HMk7+MWC7BpE3D77Wj26f9BUcllEAo/PzRL4umE+ZhCjzOnxXlLw40FI5OTkyN3CT6HM6eVn58vdwk+p9b7+MmTwH/+I46VuOceYPdujDu1B4JCAeDqS6HEcRb33JQirdgGQM5jitFohFqthlqthlarBQBotVrnsoY63TMfx2lx3tLwpVCMTHJystwl+BzOnFZ8fHzVKzGPqtE+XlAArF8vzux07Fj5jQBTp6LVtGlYXBCI+VtPQSg3zkKhUGBxehe0jGvYMz5Vh5zHlMzMTBw8eNBl2bZt25z/7tevH/r3709cVd3j4zgtOfI2Go3OS2nLN82lwsPDERERQV5XbXBjwcgolUo+QBLjzGlpNBokJSXJXYZPqXIfdziA774Tm4lt28RLnwAgIAAYNUqcJnbwYMDfHwAwFsANTYKw5IufcT47H9clxmLeXT3QsQU3jYC8x5TU1FS0a9eu0sfDw8MJq6HDx3FacuTdkJpmbiwYGf5rLj3OnFZsbKzcJficSvfxixeBNWuAjAzx36U6dxZndbrvPqCSa6k7tojHy3enYuXKlZh592AkJfH7qJScx5SIiIh681dbT+LjOC058m5ITTM3FoxMXl4eEhMT5S7Dp3DmtHQ6HVJS+Dp8Si77eHExsGMHsGoVsG9f2bSxUVHAhAliQ3HjjQ3yjthU+JhCjzOnJUfeDalp5saCkYmMjJS7BJ/DmdOqT39VaigiIyOB48fFZmLdOkCnK3twwACxmRg9GuDpIz2Cjyn0OHNanLc03FgwMsXFxQgNDZW7DJ/CmdOylF6/z+peXh6wfj0CP/oI+O23suUpKeLdsKdOBVq1kq28hoqPKfQ4c1qctzTcWDAyCr78gBxnTovzrmN2u3iJ0+rVwPbtgNUq/k8sMFA8KzF9OjBwoHMgNvM83sfpcea0OG9puLFgZAIDA+Uuwedw5rQCAgLkLqFhysoSB2KvWQNculS2vGtXWCZORND06QAPnCfBxxR6nDktzlsabiwYGZPJhLAwngeeEmdOq6ioSO4SGg6zWTwrsWqVOF1sqehocUan6dOBG2+ELicHCdxUkOFjCj3OnBbnLQ03FowMT8VJjzOnFRUVJXcJ9ZsgAJmZ4qVO69cDer24XKEQL3GaMUO890RwsPMpvI/T4rzpcea0OG9p/OQugPkOjUYjdwk+hzOnlZ+fL3cJ9ZNWC/zvf0C3bkCPHsDy5WJT0aIF8NJLwD//AHv2AOPHuzQVAO/j1Dhvepw5Lc5bGj5jwcjwnUPpcea0+EZWNWC3i83C6tXA558DVqu4PCgIGDNGvNRpwADA79p//+J9nBbnTY8zp8V5S8NnLBgZpVIpdwk+hzOnxX/pqoa//waeew5o2RIYNgzYvFlsKm68EfjgA0CtFi+DGjSoyqYC4H2cGudNjzOnxXlL45WNxfLly9GlSxdERkYiMjISaWlp2LVrl/Px4uJizJ49G40bN0Z4eDjS09ORk5MjY8WsOvjOofQ4c1qNGzeWuwTvVFQErF0L3HYbcN11wKuvAleuiDM5/ec/4g3uMjOB2bOBmJgabZr3cVqcNz3OnBbnLY1XNhbNmjXD66+/jszMTPz6668YMGAARo4cidOnTwMAHn30UXz55ZfYvHkzDh48CJVKhTFjxshcNasKN3/0OHNaPMaiHEEAfv4ZeOghICkJmDwZOHBAHIg9ZAjw2WeAUlk2tqKWeB+nxXnT48xpcd7SeOUYixEjRrh8/+qrr2L58uU4cuQImjVrhlWrVmH9+vUYMGAAACAjIwMdOnTAkSNHcPPNN7vdpsVicbkrrsFgqLsfgLkVU8O/RDLpOHNaERERcpcgv9xc4NNPxbETv/9etrxlS3HcxJQpQPPmHns53sdpcd70OHNanLc0XnnGojy73Y6NGzeisLAQaWlpyMzMhM1mw6BBg5zrtG/fHs2bN8dPP/1U6XYWLVqEqKgo51dKSgoA8bIqlUoFh8PhvK5OqVTCarVCo9HAZDJBp9MhPz8fZrMZ2dnZKCkpcVnXZrMhOzsbRUVFyM/PR0FBAQoLC5GTkwObzeayrt1uh0qlgtlsRl5eHvR6PYxGI3Jzc2GxWFzWFQQBSqUSFosFubm5MBgMMBgM0Gq1MJvN1a67qKjIbd0lJSUV6jaZTG7rdjgcVdZdWu/VdRuNRuj1eqjV6krrttlsyMnJgclkQkFBQY3q1ul0MJlM0Gg0sFqtldat1WqdGbqru/Q5V9edl5fnrNtut7utu7CwsELdV2dYWrfZbK523cXFxW7rVqlUbuvWaDQudRcUFECtVldZd1X7bOnvrrRuo9FYo7q1Wm2V7zWj0ejyXvNk3eX3WY1G4/a9plKp3L7XqnOMMBqNAACj0VhvjxFarRZA2TiRGh0jLl6E46uvYB4+HEJyMvDYY8Dvv0MIDkZxejqsu3ZB+f33EJ57Dkp//0qPEeXfa9U9Rly5cqVOjhEFBQXOPBryMaKm77UrV67wMaIWxwgpnyMMBoNXHCO84XNEbY4RNf0ccXXevv45ovS9Vl0KQRCEaq9N6LfffkNaWhqKi4sRHh6O9evX44477sD69esxbdo0l7MPANCzZ0/cdtttWLx4sdvtuTtjkZKSAr1ej8jIyDr9WZhIr9fzPP/EOHMaarUaK1euxIQJE9C2bVu5y6mV0p9h5syZSEpKqt6Tzp8Xz0x8/DGgUpUtv+km8Z4T48eLN7SrQ3W1j9cqDx/AxxR6nDktzlsar7wUCgDatWuHEydOQK/XY8uWLZgyZQoOHjxY6+0FBQUhKCjIgxWymvKrxgwvzLM4c1oKhULuEupeYSGwZYvYUHz/fdnyxo2BSZOAadOALl3IyuF9nBbnTY8zp8V5S+O1jUVgYCCuu+46AEBqaip++eUX/O9//8M999wDq9UKnU6H6HJ/CcvJyeGR/F6uuLiYr0EnxpnTspbei6GeydIWYume88i0toJxz3nMviMSreLCylYQBODoUbGZ2LgR+PfSL/j5AUOHimMnRowQ70FBjPdxWpw3Pc6cFuctjdc2FldzOBywWCxITU1FQEAA9u3bh/T0dADA2bNncenSJaSlpclcJbsWvuSMHmdOKywsrOqVvMymXy/jqa2nAACCEIuLfxqw7c8DWJzeBWNTAssGYv/xR9mTWrcuG4jdrJlMlYt4H6fFedPjzGlx3tJ4ZWPx9NNPY9iwYWjevDmMRiPWr1+PAwcOYPfu3YiKisKMGTPw2GOPITY2FpGRkZg7dy7S0tIqnRGKeQetVst3tCTGmdPS6XRo0aKF3GVUW5a2EE9tPQWHc6SdAuKoOwHzN59Aj9Wz0FJ7RXwoJAS4+25x7MStt1br5nUUeB+nxXnT48xpcd7SeGVjodFoMHnyZKjVakRFRaFLly7YvXs3Bg8eDAB499134efnh/T0dFgsFgwdOhTLli2TuWpWFX6j0uPMacXHx8tdQo1s+vWyOC6kwhweCigEBz67YSDmF/8pNhP33AN44YBG3sdpcd70OHNanLc0XtlYrFq16pqPBwcHY+nSpVi6dClRRcwTlEolv2GJcea0NBpNvZpB6IrGAMHhAFBx0LmgUODK+CnAw7fRF1YDvI/T4rzpcea0OG9pvLKxYA1T06ZN5S7B53DmtJo0aSJ3CVUTBOCnn4DVq9HsogKK7iMAP/8Kqyn8/dGslfc3SbyP0+K86XHmtDhvabzjIlnmE1Tl57lnJDhzWrm5uXKXULnsbOCNN4AOHYDevYFVqzAucycEhQJAxdsZCYKAe25Koa+zhngfp8V50+PMaXHe0vAZC0YmLi5O7hJ8DmdOK7qObwZXYzYbsHOnOKvT118Ddru4PDQUGDsWrWbMwOKQlpjvnBVKcN6LY3F6F7SM8/5Zrngfp8V50+PMaXHe0nBjwcgYDIb6calIA8KZ0yosLJS7BNGZM2Iz8cknQE5O2fK0NHGa2HHjgH+nVBwLoEfLWHyw8xgyz2YhtV0rzLnjxnrRVAC8j1PjvOlx5rQ4b2m4sWBk+M7n9DhzWgEBAfK9uNEIbNoErFoljqEoFR8PTJ4s3hH7hhvcPrVlXBgeH3wdVmZ9h5mDByOpnjQVAO/j1Dhvepw5Lc5bGm4sGGOsvhIE4IcfxLMTmzYBpWdM/P2BO+4Qz04MHw7I2fAwxhjzGdxYMDJWq1XuEnwOZ06rpKSE5oXUauDjj8WG4ty5suXXXy82E5MnA/Vo2lspeB+nxXnT48xpcd7ScGPByISF1Z/LKxoKzpxWcHBw3W3cahUHYK9eDezaVTYQOyxMvHnd9OnALbcAior3pGjIeB+nxXnT48xpcd7ScGPByBQUFCAkJETuMnwKZ07LaDR6fqN//FE2ELv8dLa33CLeEXvsWCAiwvOvW0/wPk6L86bHmdPivKXhxoKRSUxMlLsEn8OZ02rcuLFnNmQwAJ99JjYUR46ULU9IAKZMEQdit2/vmdeq53gfp8V50+PMaXHe0vAN8hgZtVotdwk+hzOnpdVqa/9kQQC+/15sHBITgZkzxabC3x8YORL4/HPg8mVg8WJuKsrhfZwW502PM6fFeUvDZywYmeTkZLlL8DmcOa34+PiaP0mpFAdiZ2QA58+XLW/fXrzUaeJEsdFgbvE+TovzpseZ0+K8peEzFoyMUqmUuwSfw5nT0mg01VvRagW2bhWngm3eHHj2WbGpCA8H7r8f+PFHcWzFE09wU1EF3sdpcd70OHNanLc0fMaCkeE7WdLjzGnFxMRce4XffxfHTaxdC5S/bOrWW8VZne6+W2wuWLXxPk6L86bHmdPivKXhMxaMjE6nk7sEn8OZ03I7K5ReD3z4IdCzJ9C5M/Duu2JTkZQEPPUUcPasOLZi6lRuKmqB93FanDc9zpwW5y0Nn7FgZEJDQ+Uuwedw5rSc97FwOICDB8WzE1u2AMXF4vJGjYARI8SxE0OHit8zSXgfp8V50+PMaXHe0vD/1RgZsrsSMyfOnJbiyhVgwwZxIPY//5Q9cMMNZQOxazPAm1WK93FanDc9zpwW5y0NNxaMjMPhkLsEn8OZ170sVQEyVu/Dpb/8sHl/Bsad2oNWBSrxpnX33iuOnejZ0+fuiE3Fk/u40WiEyWQCUDZ1cPkphMPDwxHhwzcjBPiYIgfOnBbnLQ03FoyM8zIRRoYzr0OnTmHTml14qtENUCASQnJXHEruig97pWNxchHGzrgTCAuTu8oGz5P7eGZmJg4ePOiybNu2bc5/9+vXD/379/fY69VHfEyhx5nT4ryl4caCkTEYDHztIjHO3MN0OmD9emD1amT9o8ZT96+Aw6/iHBjz1eHoYQZacl9R5zy5j6empqJdu3aVPh7Og+v5mCIDzpwW5y0NNxaMTFxcnNwl+BzO3AMcDmD/fnEg9rZtzoHYm26bVunVTQqFAp/9ehnzb+c7ZNc1T+7jERERPn+pU1X4mEKPM6fFeUvD080yMjk5OXKX4HM4cwkuXQJefhlo0wYYNEg8U1FcDHTqBLz7Lq5MegCCm7MVACAIAq4UmIkL9k28j9PivOlx5rQ4b2n4jAUjk5ycLHcJPoczr6HiYuDzz4FVq4C9ewFBEJdHRgITJogDsW+6CVAo0OybM1CcKyhbpxyFQoFmMSHExfsm3sdpcd70OHNanLc0fMaCkVEqlXKX4HM482o6cQKYOxdo2hQYPx7Ys0dsGG67TbxLtloNLF8O9OjhnN1p3E0pENw0FYB4xuKem1IIfwDfxfs4Lc6bHmdOi/OWhs9YMDIJCQlyl+BzOPNryM93DsTG8eNly5s1E++CPW0a0Lp1pU9vFReGxeldMH/rKQBwNhkKhQKL07ugZRyP3KbA+zgtzpseZ06L85aGGwtGRqvVIjExUe4yfApnfhWHA9i3T2wmtm8HLBZxeWAgMGqUeKnToEGAv3+1Njf2phT0aBmLD/ecwpHfzyElNgzz774VHVvwTfCo8D5Oi/Omx5nT4ryl4caCkYmMjJS7BJ/Dmf/rwgVgzRrxjtiXLpUt79JFvCP2ffcBjRvXatMt48Lwn/4tEfzXt5g8ZjJacVNBivdxWpw3Pc6cFuctDTcWjExxcTHPDU3MpzM3m4EdO8SB2Pv2lS2PihIbienTgRtv9OgdsS2lZ0AYGZ/ex2XAedPjzGlx3tJwY8HIKDz4AY5Vj89lLgjAsWPipU7r14s3tCs1cKB4dmLUKCCkbmZs8rm8vQBnTovzpseZ0+K8peHGgpEJCAiQuwSf4zOZ5+UB69aJDcXJk2XLmzcXB2FPnQq0bFnnZTRqxIdUaj6zj3sJzpseZ06L85aG/y/IyBQWFiI8PFzuMnxKg87cbhfvNbF6tXjJk9UqLg8MBMaMES91GjgQqOQmdnXBbOab4lFr0Pu4F+K86XHmtDhvabixYGRiY2PlLsHnNMjM//lHHIi9Zg1w+XLZ8u7dxWZiwgRApp87KipKltf1ZQ1yH/dinDc9zpwW5y0N3yCPkdFoNHKX4HMaTOZmM/Dpp8CAAUCbNsArr4hNRUwMMGeOOK7i2DHx3zL+TyE/P1+21/ZVDWYfryc4b3qcOS3OWxo+Y8HIJCcny12Cz6nXmQsC8Ouv4qVOGzYAer24XKEQ7zUxYwYwciQQHCxvneXEx/NUs9Tq9T5eD3He9DhzWpy3NHzGgpFRKpVyl+Bz6mXmWi2wZAnQtSvQsyewYoXYVLRsCSxYAGRlAd9+C9xzj1c1FQD/pUsO9XIfr8c4b3qcOS3OWxqvbCwWLVqEHj16ICIiAvHx8Rg1ahTOnj3rsk5xcTFmz56Nxo0bIzw8HOnp6cjJyZGpYlYdSUlJcpfgc+pN5nY7sGsXMHYs0LQp8OijwG+/AUFB4piJvXuBv/8GXngBaNFC7morFRcXJ3cJPqfe7OMNBOdNjzOnxXlL45WNxcGDBzF79mwcOXIEe/bsgc1mw5AhQ1BYWOhc59FHH8WXX36JzZs34+DBg1CpVBgzZoyMVbOqZGdny12Cz/H6zM+fB559VmwW7rgD2LIFsNmA1FRg6VJArRankSWe3am28vLy5C7B53j9Pt7AcN70OHNanLc0XjnG4ptvvnH5fs2aNYiPj0dmZib69u0LvV6PVatWYf369RgwYAAAICMjAx06dMCRI0dw8803V9imxWJxuSuuwWCo2x+CVRATEyN3CT7HKzMvLAS2bhXHThw8WLY8NhaYOFGc2alrV/nqkyAiIkLuEnyOV+7jDRjnTY8zp8V5S+P9fwIEoP930GbpFGCZmZmw2WwYNGiQc5327dujefPm+Omnn9xuY9GiRYiKinJ+paSkABAvqVKpVHA4HM7r6pRKJaxWKzQaDUwmE3Q6HfLz82E2m5GdnY2SkhKXdW02G7Kzs1FUVIT8/HwUFBSgsLAQOTk5sNlsLuva7XaoVCqYzWbk5eVBr9fDaDQiNzcXFovFZV1BEKBUKmGxWJCbmwuDwQCDwQCtVguz2VztuouKitzWXVJSUqFuk8nktm6Hw1Fl3aX1Xl230WiEXq+HWq2utG6bzYacnByYTCYUFBTUqG6dTgeTyQSNRgOr1Vpp3Vqt1pmhu7pLn3N13Xl5ec667Xa727oLCwsr1H11hqV1m83matddXFzstm6VSuW2bo1G41J3QUEB1Gp1lXVXtc+W/u5K6zYajTWqW5ubC8v336Nw4kQISUnAlCnAwYMQFAo4hgyB/qOPYDx7FroFC5CfkgKz2ezRusvvsxqNxu17TaVSuX2vVecYYTQaAQBGo7HeHiO0Wi2AsnEichwjyr/XqnuMuHLlCh8jJBwjavpeu3LlSt0cI6rxXjMajS7/P65PxwgpnyMMBoNXHCO84XNEbY4RNf0ccXXefIwQ32vVpRAEQaj22jJwOBy46667oNPpcPjwYQDA+vXrMW3aNJczEADQs2dP3HbbbVi8eHGF7bg7Y5GSkgK9Xo/IyMi6/SEYALFB5Hn+acmeuUYjThO7ejVw+nTZ8latxDMTU6YA/zb59ZlarcbKlSsxYcIEtG3bVu5yaqX0Z5g5c2a9usZY9n3cx3De9DhzWpy3NF55KVR5s2fPxu+//+5sKmorKCgIQUFBHqqK1YZfPbhGvqGRJfOSEmD3bmDVKuDLL8XvAXEGp7vvFqeJ7du3XoyZqCmFQiF3CT6Hjyu0OG96nDktzlsar24s5syZg6+++grff/89mjVr5lyemJgIq9UKnU6H6Oho5/KcnBwkJibKUCmrjuLiYr4GnRhp5ufOiWcmPv5YHHRdqkcPsZkYPx5o4H8Fslqtcpfgc/i4QovzpseZ0+K8pfHKxkIQBMydOxfbt2/HgQMH0KpVK5fHU1NTERAQgH379iE9PR0AcPbsWVy6dAlpaWlylMyqgS85o1fnmZtM4kxOq1cDhw6VLY+LAyZNAqZNAzp3rtsavEhYWJjcJfgcPq7Q4rzpcea0OG9pvLKxmD17NtavX4/PP/8cERERzqm/oqKiEBISgqioKMyYMQOPPfYYYmNjERkZiblz5yItLc3tjFDMO2i1Wr6jJbE6yVwQgCNHxGZi40axuQDES5tuv10cOzFiBBAY6NnXrQd0Oh1aePF9NhoiPq7Q4rzpcea0OG9pvLKxWL58OQCgf//+LsszMjIwdepUAMC7774LPz8/pKenw2KxYOjQoVi2bBlxpawmmjZtKncJPsejmefkAJ98IjYUZ86ULW/TRmwmJk8Gyl2y6IuaNGkidwk+h48rtDhvepw5Lc5bGq9sLKozUVVwcDCWLl2KpUuXElTEPEGlUvFfAYhJzrykBNi5U2wmvvpKvEM2AISEiHfJnj5dHIjNg5YBALm5ufw/JWJ8XKHFedPjzGlx3tJ4ZWPBGib+wEWv1pmfOQNkZIhnKMrfhbRXL3Eg9j33AHwdagV8xoIeH1docd70OHNanLc0PKcWI6NSqeQuwefUKHOjUTwz0acP0KED8MYbYlPRpAnw+OPA77+LYyseeICbikrk5ubKXYLP4eMKLc6bHmdOi/OWhs9YMDJxcXFyl+BzqsxcEIAffxTvObFpE1BYKC738wPuuEO81Gn4cJ8ciF0b5ae/ZjT4uEKL86bHmdPivKXhxoKRMRgMfKkIsUozV6vLBmL/9VfZ8rZtywZi8+ngGissbcwYGT6u0OK86XHmtDhvabixYGSCg4PlLsHnuGRuswFffy02Ezt3lg3EDg0Fxo0Tx0707s0DsSUI5DM75Pi4QovzpseZ0+K8peHGgpFxOBxyl+BzHA4H8OefYjPxySeARlP2YFqa2EyMGwfwXUY9ojoz2jHP4uMKLc6bHmdOi/OWhhsLRqakpETuEnyHwQBs2oTQDz8Efv21bHl8PDBlinhH7A4d5KuvgeJ9nB5nTovzpseZ0+K8peHGgpEJDQ2Vu4SGTRCAw4fFgdibNwNFRQgAAH9/cQD29OnigOyAALkrbbDq2yl0o9EI0793TtdqtS7/BYDw8HBEePnZLD6u0OK86XHmtDhvabixYGQKCgoQEhIidxkNj1JZNhD7/Pmy5e3aQT92LKJmzwYSE+Wrz4cYjUa5S6iRzMxMHDx40GXZtm3bnP/u168f+vfvT1xVzfBxhRbnTY8zp8V5S8ONBSOTyB9uPcdqFe+EvXo1sGsXUHpNaFiYePO6GTOAtDRECII4dSwj0bhxY7lLqJHU1FS0a9eu0sfDw8MJq6kdPq7Q4rzpcea0OG9puLFgZNRqNZKTk+Uuo347fVpsJtauBcrfjK1PH/FSp7FjgXIfBtUqFWdOSKvV1qu8IyIivP5Sp6rwcYUW502PM6fFeUvDjQUjw2/UWtLrgc8+E8dO/Pxz2fLExLKB2JX81ZkzpxUfHy93CT6H93FanDc9zpwW5y0NXyPByCiVSrlLqD8EAThwQLxRXVIS8OCDYlPRqBEwahTw5ZfA5cvA669X2lQAnDk1TfnpfBkJ3sdpcd70OHNanLc0fMaCkeG/5lbDlSvAxx8DGRnA33+XLe/QQRw3MXEikJBQ7c1x5rRiY2PlLsHn8D5Oi/Omx5nT4ryl4TMWjEx+fr7cJXgniwXYsgUYNgxo0QJ47jmxqYiIAB54APjpJ3FsxeOP16ipADhzanq9Xu4SfA7v47Q4b3qcOS3OWxo+Y8HIhIWFyV2Cd/ntt7KB2Hl5Zcv79hUHYt99tzjLkwScOS2eopAe7+O0OG96nDktzlsabiwYGZvNJncJ8tPpgA0bxIai/B2xk5KAqVPFgdht23rs5ThzWnzHVnq8j9PivOlx5rQ4b2m4sWBkBEGQuwR5OBziQOzVq4GtW4HiYnF5o0bAXXeJYyeGDBG/9zCfzVwmnDc9zpwW502PM6fFeUvDjQUjExwcLHcJtC5dKhuInZVVtrxjx7KB2E2a1GkJPpe5zIKCguQuwefwPk6L86bHmdPivKXhxoKRMRgMCA0NlbuMumWxAJ9/Lp6d+PZbcdpYAIiMBO69Vxw70aMHoFCQlOMTmXsRk8kkdwk+h/dxWpw3Pc6cFuctDTcWjExcXJzcJdSdkyfFG9itWweUn1Gif3+xmUhPB2Q4UDXozL1QdHS03CX4HN7HaXHe9DhzWpy3NDzdLCOTk5MjdwmeVVAALF0KpKYC3boB778vNhXJycCzzwLnzwP79wOTJsnSVAANMHMvx9MU0uN9nBbnTY8zp8V5S8NnLBiZ5ORkuUuQzuEAvvtOvNRp2zbx0icACAgQ74g9fToweDDg7y9rmaUaROb1CN9YiR7v47Q4b3qcOS3OWxo+Y8HIKJVKuUuovYsXgQULgNatxcZhwwaxqejcGViyBFCpgE2bgNtv95qmAqjnmddDGo1G7hJ8Du/jtDhvepw5Lc5bGj5jwcgk1PCu0bIrLga2bxfPTuzbVzYQOyoKmDBBPDuRmko2ELs26l3m9VxsbKzcJfgc3sdpcd70OHNanLc0fMaCkdFqtXKXUD3HjgFz5og3rZswAdi7V2wqBgwAPv0UUKuBZcuAm27y6qYCqEeZNxA6nU7uEnwO7+O0OG96nDktzlsaPmPByERGRspdQuXy8oD168WzEydOlC1PSRHvhj11KtCqlVzV1ZpXZ94AhYeHy12Cz+F9nBbnTY8zp8V5S8ONBSNTXFzsXXND2+3iJU6rV4uXPFmt4vLAQGD0aPFSp4EDvWrMRE15XeYNnKV0MD8jw/s4Lc6bHmdOi/OWhhsLRsbPz0uuvMvKAtasEe+Iffly2fKuXcU7Yk+YADRuLFt5nuQ1mfsIzpseZ06L86bHmdPivKXhxoKRadRIxt3NbBbPSqxaJU4XWyo6GrjvPvHsxI03ylZeXZE1cx/kX4/PbtVXvI/T4rzpcea0OG9pOD1GpqioiPYadEEAMjPFS53Wrwf0+rLHBg0Sz06MGgUEB9PVRIw8cx9XXFwsdwk+h/dxWpw3Pc6cFuctDTcWjEx0dDTNC2m1wLp1YkNx6lTZ8hYtxIHYU6YALVvS1CIzsswZACAiIkLuEnwO7+O0OG96nDktzlsavpCMkcnNza27jdvtwDffAGPHAk2bAvPmiU1FUBBw773Anj3AP/8AL77oM00FUMeZswoKCgrkLsHn8D5Oi/Omx5nT4ryl4TMWjExycrLnN/r33+Ig7I8/Bq5cKVt+443iuIkJE4CYGM+/bj1RJ5mzSsXHx8tdgs/hfZwW502PM6fFeUvDZywYGaVS6ZkNFRUBa9cCt90GXHcd8OqrYlMREwPMnQscPy6OrZg926ebCsCDmbNq0Wg0cpfgc3gfp8V50+PMaXHe0nhlY/H9999jxIgRaNq0KRQKBXbs2OHyuCAIeOGFF5CUlISQkBAMGjQI586dk6dYVm1JSUm1f7IgAD//DDz0kHhH7MmTgQMHxDtfDxkCfPYZoFIB770HdOvmqZLrPUmZsxqLi4uTuwSfw/s4Lc6bHmdOi/OWxisbi8LCQnTt2hVLly51+/gbb7yB9957DytWrMDRo0cRFhaGoUOH8owsXi47O7vmT8rNBd59F+jcGejVC/jwQ8BgEMdJvPwycOECsHs3MG5cg57dqbZqlTmrtby8PLlL8Dm8j9PivOlx5rQ4b2m8cozFsGHDMGzYMLePCYKAJUuW4LnnnsPIkSMBAJ988gkSEhKwY8cOjB8/3u3zLBaLy11xDQaD5wtn1xRT3cuSSkqAb78V7znx5ZeAzSYuDw4G0tPFsRP9+wN8E5sqVTtz5hE8KxQ93sdpcd70OHNanLc09e6TWVZWFrKzszFo0CDnsqioKPTq1Qs//fRTpc9btGgRoqKinF8pKSkAxHnnVSoVHA6H87o6pVIJq9UKjUYDk8kEnU6H/Px8mM1mZGdno6SkxGVdm82G7OxsFBUVIT8/HwUFBSgsLEROTg5sNpvLuna7HSqVCmazGXl5edDr9TAajcjNzYXFYnFZVxAEKJVKWCwW5ObmwmAwwGAwQKvVwmw2V7vuoqIit3WXlJRUqNtkMrmt2+FwVFl3ab1X1200GqHX653Pd1e3zWaD9qefYH3iCTiaNweGDwe2bQNsNgipqdC99hqgVkO5eDFK+vZFtkbjrFun08FkMkGj0cBqtVZat1ardWboru7S51xdd15enrNuu91eoe6cnBwUFhaioKDAJe+rMyzN22w2V7vu4uJit3WrVCq3dWs0Gpe68/PzoVarq6y7qn1WrVa71G00GmtUt1arrfK9ZjQaXd5rnqy7/D6r0WjcvtdUKpXb91p1jhFGoxGA+AcLPkbU/hhR/r3m7hiRk5MDk8nk8l67fPlylXXzMaLyY0RN32uXL1/mY0QtjhFSPkfo9Xo+Rkg4RlSn7vLvtavz5mOE+F6rLoUgCEK115aBQqHA9u3bMWrUKADAjz/+iN69e0OlUrlcBzdu3DgoFAp89tlnbrfj7oxFSkoK9Ho9IiMj6/RnYCKDwVAx68JCYMsW8Z4T339ftrxxY2DiRPHsRJcutIU2IG4zZx6nVquxcuVKTJgwAW3btpW7HJ/C+zgtzpseZ06L85bGKy+FqgtBQUEICgqSuwwGiAOxjx4Vm4mNG4F//9oLPz9g6FCxmRgxQrwHBWOMMcYYqxfqXWORmJgIAMjJyXE5Y5GTk4NuPBuQV7NevizexG71auCPP8oeaN1abCamTAGaNZOvwAao/Fk6VvdspeOBGBnex2lx3vQ4c1qctzT1rrFo1aoVEhMTsW/fPmcjYTAYcPToUTz88MPyFscqKikBdu0CVq9G46++Er8HgJAQ4O67xYaib18eiF1H+HQurbCwMLlL8Dm8j9PivOlx5rQ4b2m8srEwmUw4f/688/usrCycOHECsbGxaN68OebNm4eFCxeibdu2/9/enUdFdd5vAH9GZVGWIQTZZBFN1SYqLhWCNccNBWMUshn9YQUlSc3RnBiMGttYkmNPaLWaRa3xtAp6rPtRsZpoBYUkxGgUTNQY4kJckCWYsINDmff3x61DRmYG4cI7MvN8zuEc55333nnn8XXGL/e+9yIkJATLli2Dv7+/YR0GPQDy85vuiP2/S7dpACAsDEhMBF54AdBqrTpEe1BWVsa7iEpUXl6O4OBgaw/DrnCOy8W85WPmcjFvdR7IwuL06dMYO3as4XFSUhIAID4+HmlpaVi8eDFqamrw8ssvo7y8HKNGjcLhw4fhzPsYWFd1NbB7t3Kq0+efN7V7eQGzZkEkJEAzaJD1xmeH/P39rT0Eu9KzZ09rD8HucI7LxbzlY+ZyMW91HvirQnWUyspKaLVaXhVKLSGAEyeUYmLnTqW4AJRTmyZNUk51euopwNERhYWF/C2AZMxcjrtXhYqNjUVoaKi1h2NXOMflYt7yMXO5mLc6D+QRC+oEiouBLVuUgiI/v6n9kUeUYmLWLOCef5j8hyofM5fL29vb2kOwO5zjcjFv+Zi5XMxbHa6YpfvX0ACkpwMxMcrVm5YsUYqKHj2UKzp9+inw/ffA0qXNigoAhpuwkDzMXK7S0lJrD8HucI7LxbzlY+ZyMW91eMSCWvbdd8qRiS1bgJKSpvbHH1cWYk+bBtzH6WReXl4dOEgyhZnL5eHhYe0h2B3OcbmYt3zMXC7mrQ4LCzKtqgrYtQvYuFFZQ3GXt7dymtPs2cCjj7Zql5WVlVzcKhkzl6umpsbaQ7A7nONyMW/5mLlczFsdFhbURAggJ0cpJnbtAmprlfauXYEnn1TWTkyeDDg4tGn3vGqXfMxcLkdHR2sPwe5wjsvFvOVj5nIxb3VYWBBQVKTcb2LTJuDSpab2fv2aFmL/4i7nbaXX61Xvg1qHmctlpxfZsyrOcbmYt3zMXC7mrQ4LC3ul0wGHDinFxCefAI2NSruLi7JmIjERGDkS0Gja7SX/e/eu2yQNM5eLecvHzOVi3vIxc7mYtzosLOzNt982LcT+8cem9pEjlWLi+ecBN7cOeekePXp0yH7JPGYuFw+hy8c5Lhfzlo+Zy8W81WFhYQ8qK5Wb123cCJw82dTu46NcJnb2bGDAgA4fxs8//4zu3bt3+OtQE2YuV1VVlbWHYHc4x+Vi3vIxc7mYtzosLGyVEMBnnynFxO7dQF2d0t61q3In7DlzlDtjt3Ehdlv4+PhIey1SMHO5PD09rT0Eu8M5Lhfzlo+Zy8W81eEN8mxNYSHw7rvKwuvRo5VTnurqlCMSK1YAN28C+/cDU6dKLSoAoLi4WOrrETOX7fbt29Yegt3hHJeLecvHzOVi3urwiIUt0OmAf/9bWTtx+DBw94oGrq7A9OnK0YnHH2/Xhdht0cvE3bipYzFzuby9va09BLvDOS4X85aPmcvFvNXhEYvO7Px54PXXgV69gOeeAz7+WCkqnngCSE1VLiP7j38AERFWLyoAoLCw0NpDsDvMXK7S0lJrD8HucI7LxbzlY+ZyMW91eMSis6moAHbsUNZOfPVVU7ufX9NC7H79rDc+C/jbXPmYuVxcYyEf57hczFs+Zi4X81aHhUVnoNcD2dnKqU579gD19Up7t27AlCnKqU7R0crjB9hPP/3ERVGSMfOOU1VVherqagBAWVkZAOCHH35At//9O3R1dYVbB126mZpwjsvFvOVj5nIxb3Ue7P+J2rsbN5Q7YqemAlevNrU/+qhyz4mZM4FOVFm7urpaewh2h5l3nDNnziA7O9uo7dixYzh27BgAYPTo0RgzZowVRmZfOMflYt7yMXO5mLc6LCweNHfuAAcOKEcnjhxRLhsLKDetmzFDOToRFvZArJloLZ1OBxcXF2sPw64w844zfPhw9O/f36itsrIS7u7uAPjlJAvnuFzMWz5mLhfzVoeFxYPim2+UdRNbtwI//dTUPnq0Ukw8+yzQySe6uFskkTTMvOO4ubk1O9XJycmJ6ywk4xyXi3nLx8zlYt7qsLCwpp9/BrZvV45OnDnT1O7vDyQkKAuxH3nEasNrb87OztYegt1h5nIxb/mYuVzMWz5mLhfzVoeFhWx6PXD8uFJM7N3btBDbwUG5aV1iIjBxonKHbBtTWVmJHj16WHsYdoWZy8W85WPmcjFv+Zi5XMxbHY2w02M+lZWV0Gq1qKioMJwT3aGuXwfS0pSF2D/80NQ+cKBSTMTFAT17dvw4rKihoQEOku/2be+YuVzMWz5mLhfzlo+Zy8W81eEN8jpSfT2wc6dyBKJ3byA5WSkq3N2BuXOBU6eUtRULFth8UQHw5mHWwMzlYt7yMXO5mLd8zFwu5q0Oj1h0xBGLvDzlVKd//UtZR3HX2LHKQuxnngF4mI2IiIiIbAiPWLSXn34C1q4Fhg1TftauVYqKgADgrbeAK1eAY8eUe0/YaVFRWFho7SHYHWYuF/OWj5nLxbzlY+ZyMW91eMRCzRELvR7IzFSOTuzbp9yDAlAWYsfGKmsnIiNtciF2W/z3v/813JWY5GDmcjFv+Zi5XMxbPmYuF/NWh0cs2uKHH4C33wZCQpT1Ezt2KEXF4MHABx8At24Bu3YBUVEsKn6hrKzM2kOwO8xcLuYtHzOXi3nLx8zlYt7qsCS7X3V1wP79yk3sMjOb2rVa5YpOc+Yop0B1wjtiy6LVaq09BLvDzOVi3vIxc7mYt3zMXC7mrQ4LC0uEAHJzlVOdtm0Dysubnhs/XjnVKTYW6N7dWiPsVOrq6tCdWUnFzOVi3vIxc7mYt3zMXC7mrQ4LC1Nu31au6LRpE/D1103tQUHK3bATEpTLx1KrdOnCM+9kY+ZyMW/5mLlczFs+Zi4X81aHhcVdjY1ARoZyqlN6OqDTKe2OjsrlYefMAcaN45oJFbgYSj5mLhfzlo+Zy8W85WPmcjFvdZheQQGwZ49yV+ybN5vahw5Vion/+z/A09Nqw7MltbW1cHV1tfYw7Aozl4t5y8fM5WLe8jFzuZi3OrzcLADDxWYfeqhpIfbQoVYcnW3S6XRwdHS09jDsCjOXi3nLx8zlYt7yMXO5mLc6PJEMACZMALZvVy4Tu2YNi4oO8uOPP1p7CHaHmcvFvOVj5nIxb/mYuVzMWx0esTh3Du4DB1p7OEREREREnRqPWAQFWXsEdqOwsNDaQ7A7zFwu5i0fM5eLecvHzOVi3up06sJi3bp16N27N5ydnREeHo5Tp05Ze0hkgZ+fn7WHYHeYuVzMWz5mLhfzlo+Zy8W81em0hcXOnTuRlJSE5ORk5ObmIjQ0FFFRUSgtLbX20MiM4uJiaw/B7jBzuZi3fMxcLuYtHzOXi3mr02nXWISHh2PEiBFYu3YtAECv1yMwMBCvvvoq3nzzzWb979y5gzt37hgeV1RUICgoCDdu3IC7u3uz/tT+6uvr4ezsbO1h2BVmLhfzlo+Zy8W85WPmcjFv89zc3KDRaCz26ZT3sdDpdDhz5gyWLl1qaOvSpQsiIyNx4sQJk9ukpKTgnXfeadYeGBjYYeMkIiIiIrIFFRUVLf4yvlMWFmVlZWhsbISPj49Ru4+PD7777juT2yxduhRJSUmGx+Xl5QgODsb169eh1Wo7dLykXIUrMDCQR4gkYuZyMW/5mLlczFs+Zi4X87bMzc2txT6dsrBoCycnJzg5OTVr12q1nDwSubu7M2/JmLlczFs+Zi4X85aPmcvFvNuuUy7e9vLyQteuXVFSUmLUXlJSAl9fXyuNioiIiIjIfnXKwsLR0RHDhw9HZmamoU2v1yMzMxMRERFWHBkRERERkX3qtKdCJSUlIT4+Hr/5zW8QFhaG999/HzU1NZg9e/Z9be/k5ITk5GSTp0dR+2Pe8jFzuZi3fMxcLuYtHzOXi3mr12kvNwsAa9euxcqVK1FcXIwhQ4bgww8/RHh4uLWHRURERERkdzp1YUFERERERA+GTrnGgoiIiIiIHiwsLIiIiIiISDUWFkREREREpBoLCyIiIiIiUs2mC4t169ahd+/ecHZ2Rnh4OE6dOmWx/+7duzFgwAA4Oztj0KBB+PjjjyWNtHNLSUnBiBEj4ObmBm9vb8TGxiI/P9/iNmlpadBoNEY/zs7Okkbc+b399tvN8hswYIDFbTi/2653797N8tZoNJg3b57J/pzfrffpp59iypQp8Pf3h0ajwf79+42eF0LgT3/6E/z8/NC9e3dERkbi0qVLLe63td8D9sJS3g0NDViyZAkGDRoEFxcX+Pv7Y9asWbh165bFfbblc8metDTHExISmuUXHR3d4n45x01rKW9Tn+kajQYrV640u0/O8ZbZbGGxc+dOJCUlITk5Gbm5uQgNDUVUVBRKS0tN9v/iiy8wY8YMJCYmIi8vD7GxsYiNjcX58+clj7zzyc7Oxrx58/Dll1/i6NGjaGhowMSJE1FTU2NxO3d3dxQVFRl+rl27JmnEtuGxxx4zyu/zzz8325fzW52vvvrKKOujR48CAJ5//nmz23B+t05NTQ1CQ0Oxbt06k8+vWLECH374IT766COcPHkSLi4uiIqKQn19vdl9tvZ7wJ5Yyru2tha5ublYtmwZcnNzsXfvXuTn52Pq1Kkt7rc1n0v2pqU5DgDR0dFG+W3fvt3iPjnHzWsp71/mXFRUhE2bNkGj0eDZZ5+1uF/O8RYIGxUWFibmzZtneNzY2Cj8/f1FSkqKyf7Tpk0TkydPNmoLDw8Xv//97zt0nLaotLRUABDZ2dlm+6SmpgqtVitvUDYmOTlZhIaG3nd/zu/29dprr4m+ffsKvV5v8nnOb3UAiH379hke6/V64evrK1auXGloKy8vF05OTmL79u1m99Pa7wF7dW/eppw6dUoAENeuXTPbp7WfS/bMVObx8fEiJiamVfvhHL8/9zPHY2JixLhx4yz24RxvmU0esdDpdDhz5gwiIyMNbV26dEFkZCROnDhhcpsTJ04Y9QeAqKgos/3JvIqKCgCAp6enxX7V1dUIDg5GYGAgYmJicOHCBRnDsxmXLl2Cv78/+vTpg7i4OFy/ft1sX87v9qPT6bB161bMmTMHGo3GbD/O7/ZTUFCA4uJiozms1WoRHh5udg635XuAzKuoqIBGo4GHh4fFfq35XKLmsrKy4O3tjf79++OVV17B7du3zfblHG8/JSUlOHToEBITE1vsyzlumU0WFmVlZWhsbISPj49Ru4+PD4qLi01uU1xc3Kr+ZJper8eCBQvw29/+FgMHDjTbr3///ti0aRPS09OxdetW6PV6jBw5Ejdv3pQ42s4rPDwcaWlpOHz4MNavX4+CggI88cQTqKqqMtmf87v97N+/H+Xl5UhISDDbh/O7fd2dp62Zw235HiDT6uvrsWTJEsyYMQPu7u5m+7X2c4mMRUdHY8uWLcjMzMRf//pXZGdnY9KkSWhsbDTZn3O8/WzevBlubm545plnLPbjHG9ZN2sPgGzLvHnzcP78+RbPOYyIiEBERITh8ciRI/HrX/8aGzZswPLlyzt6mJ3epEmTDH8ePHgwwsPDERwcjF27dt3Xb1yo7TZu3IhJkybB39/fbB/Ob7IVDQ0NmDZtGoQQWL9+vcW+/FxSZ/r06YY/Dxo0CIMHD0bfvn2RlZWF8ePHW3Fktm/Tpk2Ii4tr8SIbnOMts8kjFl5eXujatStKSkqM2ktKSuDr62tyG19f31b1p+bmz5+PgwcP4vjx4wgICGjVtg4ODhg6dCguX77cQaOzbR4eHujXr5/Z/Di/28e1a9eQkZGBF198sVXbcX6rc3eetmYOt+V7gIzdLSquXbuGo0ePWjxaYUpLn0tkWZ8+feDl5WU2P87x9vHZZ58hPz+/1Z/rAOe4KTZZWDg6OmL48OHIzMw0tOn1emRmZhr9FvGXIiIijPoDwNGjR832pyZCCMyfPx/79u3DsWPHEBIS0up9NDY24ty5c/Dz8+uAEdq+6upqXLlyxWx+nN/tIzU1Fd7e3pg8eXKrtuP8VickJAS+vr5Gc7iyshInT540O4fb8j1ATe4WFZcuXUJGRgYefvjhVu+jpc8lsuzmzZu4ffu22fw4x9vHxo0bMXz4cISGhrZ6W85xE6y9eryj7NixQzg5OYm0tDTx7bffipdffll4eHiI4uJiIYQQv/vd78Sbb75p6J+TkyO6desm/va3v4mLFy+K5ORk4eDgIM6dO2ett9BpvPLKK0Kr1YqsrCxRVFRk+KmtrTX0uTfvd955Rxw5ckRcuXJFnDlzRkyfPl04OzuLCxcuWOMtdDoLFy4UWVlZoqCgQOTk5IjIyEjh5eUlSktLhRCc3x2hsbFRBAUFiSVLljR7jvNbvaqqKpGXlyfy8vIEALF69WqRl5dnuArRX/7yF+Hh4SHS09PFN998I2JiYkRISIioq6sz7GPcuHFizZo1hsctfQ/YM0t563Q6MXXqVBEQECDOnj1r9Ll+584dwz7uzbulzyV7Zynzqqoq8cYbb4gTJ06IgoICkZGRIYYNGyZ+9atfifr6esM+OMfvX0ufKUIIUVFRIXr06CHWr19vch+c461ns4WFEEKsWbNGBAUFCUdHRxEWFia+/PJLw3OjR48W8fHxRv137dol+vXrJxwdHcVjjz0mDh06JHnEnRMAkz+pqamGPvfmvWDBAsPfjY+Pj3jyySdFbm6u/MF3Ui+88ILw8/MTjo6OolevXuKFF14Qly9fNjzP+d3+jhw5IgCI/Pz8Zs9xfqt3/Phxk58jd3PV6/Vi2bJlwsfHRzg5OYnx48c3+7sIDg4WycnJRm2WvgfsmaW8CwoKzH6uHz9+3LCPe/Nu6XPJ3lnKvLa2VkycOFH07NlTODg4iODgYPHSSy81KxA4x+9fS58pQgixYcMG0b17d1FeXm5yH5zjracRQogOPSRCREREREQ2zybXWBARERERkVwsLIiIiIiISDUWFkREREREpBoLCyIiIiIiUo2FBRERERERqcbCgoiIiIiIVGNhQUREREREqrGwICIiIiIi1VhYEBERERGRaiwsiIiILHj66afx0EMP4bnnnrP2UIiIHmgsLIiIiCx47bXXsGXLFmsPg4jogcfCgoiIHmhjxoyBRqOBRqPB2bNnrfL6bm5uzdoTEhIM49q/f7/0cRERPWhYWBAR2aBPP/0UU6ZMgb+/v8X/+M6ePRtvvfUWgKb/KM+dO7dZv3nz5kGj0SAhIaEDR23eSy+9hKKiIgwcONDQlpCQgNjYWKN+e/bsgbOzM1atWtXhY/rggw9QVFTU4a9DRNRZsLAgIrJBNTU1CA0Nxbp168z2aWxsxMGDBzF16lRDW2BgIHbs2IG6ujpDW319PbZt24agoKAOHbMlPXr0gK+vL7p162a2zz//+U/ExcVh/fr1WLhw4X3ve8iQIRg4cGCzn1u3blncTqvVwtfX975fh4jI1rGwICKyQZMmTcKf//xnPP3002b7fPHFF3BwcMCIESMMbcOGDUNgYCD27t1raNu7dy+CgoIwdOhQo+31ej1SUlIQEhKC7t27IzQ0FHv27DHqU1NTg1mzZsHV1RV+fn5YtWoVxowZgwULFrTPG/2fFStW4NVXX8WOHTswe/ZsQ3tVVRXi4uLg4uICPz8/vPfee81e/+zZszh//nyzH39//3YdIxGRrWNhQURkpw4cOIApU6ZAo9EYtc+ZMwepqamGx5s2bTL6z/pdKSkp2LJlCz766CNcuHABr7/+OmbOnIns7GxDn0WLFiE7Oxvp6en4z3/+g6ysLOTm5rbr+1iyZAmWL1+OgwcPNiukkpKSkJOTgwMHDuDo0aP47LPP2v31iYhIYf6YMhER2bT09HS89957zdpnzpyJpUuX4tq1awCAnJwc7NixA1lZWYY+d+7cwbvvvouMjAxEREQAAPr06YPPP/8cGzZswOjRo1FdXY2NGzdi69atGD9+PABg8+bNCAgIaLf38MknnyA9PR2ZmZkYN26c0XNVVVXYvHkztm3bZnj91NTUVh+JiIyMxNdff42amhoEBARg9+7dhvdMRERNWFgQEdmhixcv4tatW4b/cP9Sz549MXnyZKSlpUEIgcmTJ8PLy8uoz+XLl1FbW4sJEyYYtet0OsMpU1euXIFOp0N4eLjheU9PT/Tv37/d3sfgwYNRVlaG5ORkhIWFwdXV1fDc1atX0dDQgLCwMEObVqtt9etnZGS023iJiGwZCwsiIjt04MABTJgwAc7OziafnzNnDubPnw8AJheAV1dXAwAOHTqEXr16GT3n5OTUzqM1r1evXtizZw/Gjh2L6OhofPLJJyYvDUtERB2PayyIiOxQeno6YmJizD4fHR0NnU6HhoYGREVFNXv+0UcfhZOTE65fv45HHnnE6CcwMBAA0LdvXzg4OODkyZOG7X7++Wd8//337fpegoODkZ2djeLiYkRHR6OqqgqAcmqWg4MDvvrqK0PfioqKdn99IiJS8IgFEZENqq6uxuXLlw2PCwoKcPbsWXh6esLZ2RmnT5/GgQMHzG7ftWtXXLx40fDne7m5ueGNN97A66+/Dr1ej1GjRqGiogI5OTlwd3dHfHw8XF1dkZiYiEWLFuHhhx+Gt7c3/vjHP6JLl/b/nVZgYCCysrIwduxYREVF4fDhw4ZxLFq0CJ6envD29kZycjK6dOnSbME6ERGpx8KCiMgGnT59GmPHjjU8TkpKAgDEx8fjiSeeQFhYWLN1E/dyd3e3+Pzy5cvRs2dPpKSk4OrVq/Dw8MCwYcPwhz/8wdBn5cqVqK6uxpQpU+Dm5oaFCxeioqJCxTszLyAgwKi4OHLkCFavXo25c+fiqaeegru7OxYvXowbN26YPQWMiIjaTiOEENYeBBERyTN16lSMGjUKixcvtsrrjxkzBkOGDMH777/fIf0tqampQa9evbBq1SokJiaq3h8AaDQa7Nu3r9ldwImI7A3XWBAR2ZlRo0ZhxowZ1h5Gq/z973+Hq6srzp0716rt8vLysH37dly5cgW5ubmIi4sDAIvrS+7X3Llzja5CRURk73jEgoiIpGrtEYjCwkLU1dUBAIKCguDo6Hjfr5WXl4cXX3wR+fn5cHR0xPDhw7F69WoMGjSoLUM3UlpaisrKSgCAn58fXFxcVO+TiKgzY2FBRERERESq8VQoIiIiIiJSjYUFERERERGpxsKCiIiIiIhUY2FBRERERESqsbAgIiIiIiLVWFgQEREREZFqLCyIiIiIiEg1FhZERERERKQaCwsiIiIiIlKNhQUREREREanGwoKIiIiIiFRjYUFERERERKr9P1I68CIOPZZvAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SCALA = 50\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "x_data =  dfSonar[\"1/Meq\"]\n",
    "y_data =  dfSonar[\"Omega2t\"]\n",
    "\n",
    "x_fit = np.linspace(0, x_data.max(), 300)\n",
    "y_fit = ACr * x_fit + BCr\n",
    "\n",
    "ax.errorbar(\n",
    "    x_data, y_data,\n",
    "    xerr=SCALA * dfSonar[\"u1/Meq\"], yerr=SCALA * dfSonar[\"uOmega2t\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=f\"Dati (barre errore x{SCALA})\"\n",
    ")\n",
    "ax.plot(\n",
    "    x_fit, y_fit,\n",
    "    color='red', linewidth=1.5,\n",
    "    label=f\"$A={ACr:.3f}\\\\pm{uACr:.3f}$\\n\"\n",
    "          f\"$B={BCr:.2f}\\\\pm{uBCr:.2f}$\\n\"\n",
    "          f\"P(chi², ∞)= {PCr:.4f}\\n\"\n",
    ")\n",
    "\n",
    "\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(r\"1/Meq  [Kg$^{-1}$]\")\n",
    "ax.set_ylabel(r\"$\\omega^2$  [s$^{-2}$]\")\n",
    "ax.set_title(\"Fit lineare — Molla 2 (cronometro)\")\n",
    "ax.legend(fontsize=9)\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2fb7140a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Residui\n",
    "\n",
    "dfSonar[\"Rc\"] = r_fn(\n",
    "  dfSonar[\"1/Meq\"],\n",
    "  dfSonar[\"Omega2t\"],\n",
    "  ACr,\n",
    "  BCr\n",
    ")\n",
    "\n",
    "dfSonar[\"uRc\"] = sigma_r_fn(\n",
    "  dfSonar[\"1/Meq\"], dfSonar[\"Omega2t\"],\n",
    "  ACr, BCr,\n",
    "  dfSonar[\"u1/Meq\"], dfSonar[\"uOmega2t\"],\n",
    "  uACr, uBCr, covABCr\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b44b497b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "# Residui con barre d'errore\n",
    "ax.errorbar(\n",
    "    dfSonar[\"Omega2t\"], dfSonar[\"Rc\"],\n",
    "    xerr=dfSonar[\"uOmega2t\"], yerr=dfSonar[\"uRc\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=\"Residui\"\n",
    ")\n",
    "\n",
    "# Linea dello zero\n",
    "ax.axhline(0, color='red', linestyle='--', linewidth=1)\n",
    "\n",
    "# Estetica\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(r\"$\\omega^2$  [s$^{-2}$]\")\n",
    "ax.set_ylabel(r\"Residuo $x_i - \\left(\\frac{y_i}{A} - \\frac{B}{A}\\right)$  [Kg$^{-1}$]\")\n",
    "ax.set_title(\"Residui della regressione — Molla 2 (cronometro)\")\n",
    "ax.legend()\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "255e0632",
   "metadata": {},
   "source": [
    "# Raccolta finale dati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e87e24b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calibro\n",
      "AC      = 3.2009 ± 0.0033\n",
      "BC      = 1161.4139 ± 0.4685\n",
      "cov_ABC = 0.001384\n",
      "chi²    = 4.3285\n",
      "GdL     = 4\n",
      "P(chi², ∞)= 0.1148\n",
      "\n",
      "Sonar statico senza correzione\n",
      "AS      = 3.2192 ± 0.0027\n",
      "BS      = 2042.5203 ± 1.0326\n",
      "cov_ABS = 0.002760\n",
      "chi²    = 9.0319\n",
      "GdL     = 5\n",
      "P(chi², ∞)= 0.0289\n",
      "\n",
      "Sonar statico con correzione\n",
      "AS      = 3.2390 ± 0.0076\n",
      "BS      = 2043.7514 ± 2.5703\n",
      "cov_ABS = 0.018946\n",
      "chi²    = 0.9520\n",
      "GdL     = 5\n",
      "P(chi², ∞)= 0.8129\n",
      "\n",
      "Sonar dinamico\n",
      "AD      = 3.1496 ± 0.0009\n",
      "BD      = 0.7065 ± 0.0094\n",
      "cov_ABD = -0.000008\n",
      "chi²    = 15.1289\n",
      "GdL     = 5\n",
      "P(chi², ∞)= 0.0017\n",
      "\n",
      "Cronometro\n",
      "ACr      = 3.0896 ± 0.0419\n",
      "BCr      = 1.1894 ± 0.4871\n",
      "cov_ABCr = -0.019876\n",
      "chi²    = 3.2171\n",
      "P(chi², ∞)= 0.3593\n"
     ]
    }
   ],
   "source": [
    "# Stampa regressioni\n",
    "\n",
    "print(\"Calibro\")\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\"chi²    = {chiC:.4f}\")\n",
    "print(f\"GdL     = {len(dfCalibro)}\")\n",
    "print(f\"P(chi², ∞)= {PC:.4f}\")\n",
    "\n",
    "print(\"\\nSonar statico senza correzione\")\n",
    "print(f\"AS      = {AS:.4f} ± {uAS:.4f}\")\n",
    "print(f\"BS      = {BS:.4f} ± {uBS:.4f}\")\n",
    "print(f\"cov_ABS = {covABS:.6f}\")\n",
    "print(f\"chi²    = {chiS:.4f}\")\n",
    "print(f\"GdL     = {len(dfSonar)}\")\n",
    "print(f\"P(chi², ∞)= {PS:.4f}\")\n",
    "\n",
    "print(\"\\nSonar statico con correzione\")\n",
    "print(f\"AS      = {AS2:.4f} ± {uAS2:.4f}\")\n",
    "print(f\"BS      = {BS2:.4f} ± {uBS2:.4f}\")\n",
    "print(f\"cov_ABS = {covABS2:.6f}\")\n",
    "print(f\"chi²    = {chiS2:.4f}\")\n",
    "print(f\"GdL     = {len(dfSonar)}\")\n",
    "print(f\"P(chi², ∞)= {PS2:.4f}\")\n",
    "\n",
    "print(\"\\nSonar dinamico\")\n",
    "print(f\"AD      = {AD:.4f} ± {uAD:.4f}\")\n",
    "print(f\"BD      = {BD:.4f} ± {uBD:.4f}\")\n",
    "print(f\"cov_ABD = {covABD:.6f}\")\n",
    "print(f\"chi²    = {chiD:.4f}\")\n",
    "print(f\"GdL     = {len(dfSonar)}\")\n",
    "print(f\"P(chi², ∞)= {PD:.4f}\")\n",
    "\n",
    "print(\"\\nCronometro\")\n",
    "print(f\"ACr      = {ACr:.4f} ± {uACr:.4f}\")\n",
    "print(f\"BCr      = {BCr:.4f} ± {uBCr:.4f}\")\n",
    "print(f\"cov_ABCr = {covABCr:.6f}\")\n",
    "print(f\"chi²    = {chiCr:.4f}\")\n",
    "print(f\"P(chi², ∞)= {PCr:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9fc6bca",
   "metadata": {},
   "source": [
    "# Compatibilità"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0bc96952",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot gaussiane\n",
    "\n",
    "# Parametri gaussiane\n",
    "# (media, deviazione standard, colore, nome)\n",
    "gaussiane = [\n",
    "    (AC, uAC, 1, 'calibro'),\n",
    "    (AS2, uAS2, 2, 'sonar statico'),\n",
    "    (AD, uAD, 4, 'sonar dinamico'),\n",
    "    (ACr, uACr, 8, 'cronometro')\n",
    "]\n",
    "\n",
    "# Creazione figura\n",
    "plt.figure(figsize=(12, 7))\n",
    "\n",
    "# Creazione asse x\n",
    "xMin = float('inf')\n",
    "xMax = float('-inf')\n",
    "for mu, sigma, _, _ in gaussiane:\n",
    "    minimoLocale = mu - 3 * sigma\n",
    "    massimoLocale = mu + 3 * sigma\n",
    "    \n",
    "    if minimoLocale < xMin:\n",
    "        xMin = minimoLocale\n",
    "    \n",
    "    if massimoLocale > xMax:\n",
    "        xMax = massimoLocale\n",
    "x = np.linspace(xMin, xMax, 1000)\n",
    "\n",
    "# Ciclo gaussiane\n",
    "for mu, sigma, colore, etichetta in gaussiane:\n",
    "    \n",
    "    y = stats.norm.pdf(x, mu, sigma)\n",
    "    plt.plot(x, y, color=sns.color_palette()[colore], linewidth=1, label=etichetta)\n",
    "    \n",
    "    puntiLinee = [mu - sigma, mu, mu + sigma]\n",
    "    for px in puntiLinee:\n",
    "        py = stats.norm.pdf(px, mu, sigma) \n",
    "        plt.vlines(x=px, ymin=0, ymax=py, colors=sns.color_palette()[colore], linestyles='dashed', linewidth=1)\n",
    "        \n",
    "# Dettagli estetici finali\n",
    "plt.ylim(bottom=0)\n",
    "plt.title('Confronto dati')\n",
    "plt.xlabel('k')\n",
    "plt.legend()\n",
    "\n",
    "# Mostriamo il grafico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "da18d444",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compatibilità calibro e sonar statico:\n",
      "k = 4.59\n",
      "\n",
      "Compatibilità cronometro e sonar dinamico:\n",
      "k = 1.43\n",
      "\n",
      "Dati statici e dinamici sono visibilmente incompatibili\n"
     ]
    }
   ],
   "source": [
    "# Fattori di compatibilità\n",
    "\n",
    "def print_k(x1, u1, x2, u2):\n",
    "    k = abs(x1-x2) / np.sqrt(u1**2 + u2**2)\n",
    "    print(f\"k = {k:.2f}\\n\")\n",
    "\n",
    "print(\"Compatibilità calibro e sonar statico:\")\n",
    "print_k(AC, uAC, AS2, uAS2)\n",
    "\n",
    "print(\"Compatibilità cronometro e sonar dinamico:\")\n",
    "print_k(ACr, uACr, AD, uAD)\n",
    "\n",
    "print(\"Dati statici e dinamici sono visibilmente incompatibili\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5b70e361",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADin = 3.14960 ± 0.00087\n"
     ]
    }
   ],
   "source": [
    "# Media pesata dati dinamici\n",
    "\n",
    "ADin = ( ACr/(uACr**2) + AD/(uAD**2) ) / ( 1/(uACr**2) + 1/(uAD**2) )\n",
    "uADin = 1 / np.sqrt( 1/(uACr**2) + 1/(uAD**2) )\n",
    "\n",
    "print(f\"ADin = {ADin:.5f} ± {uADin:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d4af85a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compatibilità con t di Student\n",
    "\n",
    "def p_t_student_compatibilita(x, ux, gx, y, uy, gy):\n",
    "  GdL = gx + gy - 2\n",
    "  s2 = ( (gx - 1) * ux**2  +  (gy - 1) * uy**2 ) / GdL\n",
    "  sigma2 = ( s2 / gx )  + ( s2 / gy )\n",
    "  t = abs(x - y) / np.sqrt( sigma2 )\n",
    "\n",
    "  return {\"p\": 2 * (1 - stats.t.cdf(abs(t), df=GdL)), \"t\": t}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "57de948b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compatibilità: Calibro vs Sonar statico\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ           6.8065     0.0065\n",
      "2σ           3.4033     0.0424\n",
      "3σ           2.2688     0.1080\n",
      "\n",
      "\n",
      "Compatibilità: Calibro vs Sonar statico corretto\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ           6.4291     0.0076\n",
      "2σ           3.2145     0.0488\n",
      "3σ           2.1430     0.1215\n",
      "\n",
      "\n",
      "Compatibilità: Calibro vs Sonar dinamico\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ          27.2592     0.0001\n",
      "2σ          13.6296     0.0009\n",
      "3σ           9.0864     0.0028\n",
      "\n",
      "\n",
      "Compatibilità: Sonar Dinamico vs Sonar statico\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ          42.0848     0.0000\n",
      "2σ          21.0424     0.0000\n",
      "3σ          14.0283     0.0001\n",
      "\n",
      "\n",
      "Compatibilità: Sonar Dinamico vs cronometro\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ           2.4807     0.0682\n",
      "2σ           1.2404     0.2826\n",
      "3σ           0.8269     0.4548\n"
     ]
    }
   ],
   "source": [
    "print(\"Compatibilità: Calibro vs Sonar statico\")\n",
    "print(f\"{'Sigma':<10} {'t':>8} {'p-value':>10}\")\n",
    "print(\"-\" * 30)\n",
    "for n in [1, 2, 3]:\n",
    "    res = p_t_student_compatibilita(AC, uAC*n, len(dfCalibro)-2,\n",
    "                                    AS,  uAS*n, len(dfSonar)-2)\n",
    "    print(f\"{n}σ         {res['t']:>8.4f} {res['p']:>10.4f}\")\n",
    "\n",
    "print(\"\\n\\nCompatibilità: Calibro vs Sonar statico corretto\")\n",
    "print(f\"{'Sigma':<10} {'t':>8} {'p-value':>10}\")\n",
    "print(\"-\" * 30)\n",
    "for n in [1, 2, 3]:\n",
    "    res = p_t_student_compatibilita(AC, uAC*n, len(dfCalibro)-2,\n",
    "                                    AS2,  uAS2*n, len(dfSonar)-2)\n",
    "    print(f\"{n}σ         {res['t']:>8.4f} {res['p']:>10.4f}\")\n",
    "\n",
    "print(\"\\n\\nCompatibilità: Calibro vs Sonar dinamico\")\n",
    "print(f\"{'Sigma':<10} {'t':>8} {'p-value':>10}\")\n",
    "print(\"-\" * 30)\n",
    "for n in [1, 2, 3]:\n",
    "    res = p_t_student_compatibilita(AC, uAC*n, len(dfCalibro)-2,\n",
    "                                    AD,  uAD*n,  len(dfSonar)-2)\n",
    "    print(f\"{n}σ         {res['t']:>8.4f} {res['p']:>10.4f}\")\n",
    "\n",
    "print(\"\\n\\nCompatibilità: Sonar Dinamico vs Sonar statico\")\n",
    "print(f\"{'Sigma':<10} {'t':>8} {'p-value':>10}\")\n",
    "print(\"-\" * 30)\n",
    "for n in [1, 2, 3]:\n",
    "    res = p_t_student_compatibilita(AD,  uAD*n,  len(dfSonar)-2,\n",
    "                                    AS, uAS*n, len(dfSonar)-2)\n",
    "    print(f\"{n}σ         {res['t']:>8.4f} {res['p']:>10.4f}\")\n",
    "\n",
    "print(\"\\n\\nCompatibilità: Sonar Dinamico vs cronometro\")\n",
    "print(f\"{'Sigma':<10} {'t':>8} {'p-value':>10}\")\n",
    "print(\"-\" * 30)\n",
    "for n in [1, 2, 3]:\n",
    "    res = p_t_student_compatibilita(AD,  uAD*n,  len(dfSonar)-2,\n",
    "                                    ACr, uACr*n, len(dfSonar)-2)\n",
    "    print(f\"{n}σ         {res['t']:>8.4f} {res['p']:>10.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b66ccc91",
   "metadata": {},
   "source": [
    "# Note finali\n",
    "SI VEDE LA RISONANZA E LA DEFORMAZIONE DELLE MASSE EQUIVALENTI (NON È LINEARE)"
   ]
  }
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