{
 "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": 281,
   "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": 282,
   "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\": np.nan, \"n\": 0, \"rimossi\": []}\n",
    "\n",
    "  # caso 1\n",
    "  if len(valori_raw) == 1:\n",
    "    return {\"media\": valori_raw[0], \"u\": err_strumentale, \"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",
    "  s = np.sqrt(s**2 + err_strumentale**2)\n",
    "\n",
    "  return {\"media\": media, \"u\": s, \"n\": n, \"rimossi\": rimossi}\n",
    "\n",
    "# Calcola la statistica con Criterio di Chauvenet\n",
    "def calcola_stats(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",
    "    df.at[i, prefix]         = ans[\"media\"]\n",
    "    df.at[i, f\"u{prefix}\"]   = ans[\"u\"]\n",
    "    df.at[i, f\"n{prefix}\"]   = int(ans[\"n\"])\n",
    "    df.at[i, f\"out{prefix}\"] = str(ans[\"rimossi\"])\n",
    "\n",
    "  return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "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": 284,
   "id": "e649eddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dati, analisi statistica medie e ricerca outlier\n",
    "\n",
    "dfCalibro = pd.read_csv(r'molla2Calibro.csv')\n",
    "\n",
    "# Costanti strumentali\n",
    "ERR_CALIBRO = 0.02 / np.sqrt(12)\n",
    "ERR_BILANCIA = 0.01 / np.sqrt(12)\n",
    "\n",
    "# Chauvenet e poi statistica di base\n",
    "dfCalibro = calcola_stats(dfCalibro, \"dx\", ERR_CALIBRO)\n",
    "dfCalibro = calcola_stats(dfCalibro, \"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": 285,
   "id": "42ff21d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f6f02e6c078c4e3aaa5bdfeeee71d1cb",
       "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=[49.25, …"
      ]
     },
     "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": 286,
   "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": 287,
   "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  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": 288,
   "id": "cd68b201",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AC      = 3.2009 ± 0.0034\n",
      "BC      = 1161.4138 ± 0.4687\n",
      "cov_ABC = 0.001385\n",
      "chi²    = 4.3252\n",
      "P(chi², ∞)= 0.1150\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": 289,
   "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 = 10\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\"Fit: $A={AC:.3f}\\\\pm{uAC:.3f}$\\n\"\n",
    "          f\"     $B={BC:.2f}\\\\pm{uBC:.2f}$\\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": 290,
   "id": "f98a3612",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui\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": 291,
   "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": 292,
   "id": "5643144a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Probabilità chi quadro con uR invece di sigmaY_equiv\n",
      "P(chi², ∞)= 0.1902\n"
     ]
    }
   ],
   "source": [
    "# Chi2 alternativo\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": 293,
   "id": "d6195b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfSonar = pd.read_csv(r'molla2Sonar.csv')\n",
    "\n",
    "ERR_OMEGA = 0.000001\n",
    "ERR_C = 0.000001\n",
    "ERR_CRONOMETRO = 0.01\n",
    "\n",
    "# Bilancia trattata esattamente come prima\n",
    "dfSonar = calcola_stats(dfSonar, \"m\",  ERR_BILANCIA)\n",
    "dfSonar = calcola_stats(dfSonar, \"w\",  ERR_OMEGA)\n",
    "dfSonar = calcola_stats(dfSonar, \"c\",  ERR_C)\n",
    "dfSonar = calcola_stats(dfSonar, \"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": 294,
   "id": "02e2d183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b823e2edf7a14e0fb65cecae3774c242",
       "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": 295,
   "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": 296,
   "id": "75653d7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAHqCAYAAACZcdjsAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAArndJREFUeJzs3Xd8U9X/x/FXWjqgkwJdUIaIbAFBAdmKIENFcCAoQ4bI8IsgAoIMWQICDkD0+5OhggNEVFQUBQQUEZla2TKkpYuupDNN7u+P+01MaAsptL1p7+f5ePShubm5+SR959KTc885BkVRFIQQQgghhBDiJnhoXYAQQgghhBCi7JOGhRBCCCGEEOKmScNCCCGEEEIIcdOkYSGEEEIIIYS4adKwEEIIIYQQQtw0aVgIIYQQQgghbpo0LIQQQgghhBA3TRoWQgghhBBCiJsmDQshhBBCCCHETZOGhRAl6Pz58xgMBtauXXvDx+jcuTOdO3cu1mOKkrNr1y4MBgO7du2ybxsyZAi1a9fWrKaCWK1WmjRpwrx587QupdyqXbs2Q4YM0boM4WauPqdradu2bfj7+5OYmKh1KaKckIaFEDdh7dq1GAyGAn+mTJlS4GO++eYbZs2aVbqFCrvOnTtjMBioV69egfdv377d/jvctGlTKVfnms2bN/P4449zyy23UKlSJerXr8/EiRNJTU11+RgfffQR//zzD2PHji25Qt1cZmYms2bNcmoEFtUvv/zCrFmzivTeC+Eu7r//fm699VYWLFigdSminKigdQFClAevvPIKderUcdrWpEkTatWqRVZWFl5eXvbt33zzDStWrLjhxkVBxxRF4+vry5kzZ/jtt9+46667nO5bv349vr6+ZGdna1Td9Y0cOZLIyEiefPJJatasyR9//MHy5cv55ptvOHToEBUrVrzuMRYvXkz//v0JCgoqhYrdU2ZmJrNnzwa44W+Qf/nlF2bPns2QIUMIDg52uu/kyZN4eMj3d8LZ999/r3UJTp555hleeOEFZs+eTUBAgNbliDJOGhZCFIMePXrQqlWrAu/z9fUt1ucyGAzFfszilpmZSaVKlbQuo1B169YlLy+Pjz76yKlhkZ2dzeeff06vXr347LPPNKzw2jZt2pTvD+GWLVsyePBg1q9fz/Dhw6/5+MOHD3P06FGWLFlSglWWPKvVSm5urtt+Hnx8fLQuQbjgWjnKyMjAz8+vWJ/P29u7WI93s/r168e4cePYuHEjTz/9tNbliDJOvkoRogRdPR5iyJAhrFixAsDpsqmbOabtuP7+/sTExNCnTx/8/f2pVq0aL7zwAhaLxenxVquV119/ncaNG+Pr60tYWBjPPPMMKSkpTvt98cUX9OrVi8jISHx8fKhbty5z5szJd7zOnTvTpEkTDh48SMeOHalUqRIvvfQSADk5OcycOZNbb70VHx8foqKiePHFF8nJySnSay4JTzzxBJ988glWq9W+7auvviIzM5PHHnuswMccPnyYHj16EBgYiL+/P/feey+//vrrDT3/a6+9xt13302VKlWoWLEiLVu2dPnSq4K+XX/44YcBOH78+HUfv2XLFry9venYsaPTdqPRyPjx46lduzY+Pj6EhoZy3333cejQIaf9Nm7cSMuWLalYsSJVq1blySefJCYmxmmfomTS1ffCYDAwduxY1q9fT+PGjfHx8WHbtm2Fvs7ff/+d7t27U7VqVSpWrEidOnXsfzidP3+eatWqATB79mz7Z9HWk3js2DGGDBnCLbfcgq+vL+Hh4Tz99NNcuXLFfvxZs2YxadIkAOrUqWM/xvnz54GCx1ikpqby/PPP29/jGjVqMGjQIJKSkuz7JCQkMGzYMMLCwvD19aVZs2asW7eu0Nfp6mu2cfUcULt2bXr37s3evXu566678PX15ZZbbuH999932q+wy0Ed3wtX3s9ryc7OZtasWdx22234+voSERFB3759OXv2rH2fjIwMJk6cSFRUFD4+PtSvX5/XXnsNRVHy1VtQjmyXtv7000+MHj2a0NBQatSoYX/ct99+S4cOHfDz8yMgIIBevXoRHR1tv982vqqgH8cxVgWNsXDld24797/22mu8++671K1bFx8fH+68804OHDiQ7z07ceIEjzzyCCEhIfj6+tKqVSu+/PLLfPuFhoZy++2388UXX7j0uxDiWqTHQohikJaW5vSHAUDVqlXz7ffMM88QGxvL9u3b+eCDD4q1BovFQvfu3WndujWvvfYaP/zwA0uWLKFu3bo8++yzTjWsXbuWoUOH8txzz3Hu3DmWL1/O4cOH+fnnn+2XWK1duxZ/f38mTJiAv78/O3bsYMaMGaSnp7N48WKn575y5Qo9evSgf//+PPnkk4SFhWG1WnnwwQfZu3cvI0eOpGHDhvzxxx8sW7aMU6dOsWXLlmJ9/UU1YMAA+/X199xzDwAbNmzg3nvvJTQ0NN/+0dHRdOjQgcDAQF588UW8vLx455136Ny5Mz/99BOtW7cu0vO/8cYbPPjggwwcOJDc3Fw+/vhjHn30UbZu3UqvXr2K/Hri4uKAgnN3tV9++YUmTZrku5xu1KhRbNq0ibFjx9KoUSOuXLnC3r17OX78OHfccQeAPTt33nknCxYsID4+njfeeIOff/6Zw4cPO10O5Gomi/Je7Nixg08//ZSxY8dStWrVQgfFJyQk0K1bN6pVq8aUKVMIDg7m/PnzbN68GYBq1arx9ttv8+yzz/Lwww/Tt29fAG6//XZAHWvz999/M3ToUMLDw4mOjubdd98lOjqaX3/9FYPBQN++fTl16hQfffQRy5Yts7/3tgbL1UwmEx06dOD48eM8/fTT3HHHHSQlJfHll19y6dIlqlatSlZWFp07d+bMmTOMHTuWOnXqsHHjRoYMGUJqair/+c9/Cv29Xu8127h6DgA4c+YMjzzyCMOGDWPw4MGsXr2aIUOG0LJlSxo3bgxQ4Lls+vTpJCQk4O/v7/L7WRiLxULv3r358ccf6d+/P//5z38wGo1s376dP//8k7p166IoCg8++CA7d+5k2LBhNG/enO+++45JkyYRExPDsmXLnI5ZUI6OHDkCwOjRo6lWrRozZswgIyPD/hoHDx5M9+7dWbhwIZmZmbz99tu0b9+ew4cPU7t2bRo2bJjvvUhNTWXChAkFnlNsivo737BhA0ajkWeeeQaDwcCiRYvo27cvf//9t/13Fx0dTbt27ahevTpTpkzBz8+PTz/9lD59+vDZZ5/Zv4iwadmypebnZFFOKEKIG7ZmzRoFKPBHURTl3LlzCqCsWbPG/pgxY8YoRfnoderUSenUqZP9dkHHHDx4sAIor7zyitNjW7RoobRs2dJ+e8+ePQqgrF+/3mm/bdu25duemZmZr5ZnnnlGqVSpkpKdne1UH6CsWrXKad8PPvhA8fDwUPbs2eO0fdWqVQqg/Pzzz9d/8SWgU6dOSuPGjRVFUZRWrVopw4YNUxRFUVJSUhRvb29l3bp1ys6dOxVA2bhxo/1xffr0Uby9vZWzZ8/at8XGxioBAQFKx44d7dtsj925c6d92+DBg5VatWo51XH1+5ubm6s0adJEueeee27odQ0bNkzx9PRUTp06dd19a9SoofTr1y/f9qCgIGXMmDGFPi43N1cJDQ1VmjRpomRlZdm3b926VQGUGTNm2Le5mklFcf29ABQPDw8lOjr6uq/x888/VwDlwIEDhe6TmJioAMrMmTPz3VdQ/j/66CMFUHbv3m3ftnjxYgVQzp07l2//WrVqKYMHD7bfnjFjhgIomzdvzrev1WpVFEVRXn/9dQVQPvzwQ/t9ubm5Stu2bRV/f38lPT290NfjymsuyjmgVq1a+V5vQkKC4uPjo0ycOLHQ51i0aJECKO+//759m6vvZ0FWr16tAMrSpUvz3Wd737Zs2aIAyty5c53uf+SRRxSDwaCcOXPGvq2wHNnO5+3bt1fy8vLs241GoxIcHKyMGDHCaf+4uDglKCgo33bH2nr37q34+/s7PdfV53RXf+e2c3+VKlWU5ORk+75ffPGFAihfffWVfdu9996rNG3a1OlcbbValbvvvlupV69evlrnz5+vAEp8fHyBr0UIV8mlUEIUgxUrVrB9+3anHy2MGjXK6XaHDh34+++/7bc3btxIUFAQ9913H0lJSfafli1b4u/vz86dO+37Og4ANhqNJCUl0aFDBzIzMzlx4oTT8/j4+DB06FCnbRs3bqRhw4Y0aNDA6blsvQOOz6WVAQMGsHnzZnJzc9m0aROenp75vskD9RvT77//nj59+nDLLbfYt0dERDBgwAD27t1Lenp6kZ7b8f1NSUkhLS2NDh065LvsyBUbNmzgvffeY+LEiYXOduXoypUrVK5cOd/24OBg9u/fT2xsbIGP+/3330lISGD06NFO16P36tWLBg0a8PXXX+d7zPUyCUV7Lzp16kSjRo2u/QL/91oAtm7ditlsvu7+V3OsKTs7m6SkJNq0aQNwQ78jgM8++4xmzZoVmDHbN/bffPMN4eHhPPHEE/b7vLy8eO655zCZTPz000+FHt+V11yUcwBAo0aN6NChg/12tWrVqF+/fr7foc3OnTuZOnUq48aN46mnnrJvv5n387PPPqNq1aqMGzcu332O75unpyfPPfec0/0TJ05EURS+/fZbp+3XytGIESPw9PS0396+fTupqak88cQTTu+Zp6cnrVu3LvRcNmfOHLZu3cratWuvmdmi/s4ff/xxp8+v7fdj+50kJyezY8cOHnvsMfu5OykpiStXrtC9e3dOnz6d79JF2/Gu7nkXoqjkUighisFdd91V6ODt0uLr65vvEozKlSs7XTd9+vRp0tLSCu2WT0hIsP9/dHQ006dPZ8eOHfn+aE5LS3O6Xb169XwDEk+fPs3x48cLvSzE8bmuZjKZMJlMhd5/Lf7+/vbLL66nf//+vPDCC3z77besX7+e3r17FzgrSmJiIpmZmdSvXz/ffQ0bNsRqtfLPP//YLw1xxdatW5k7dy5HjhxxGnNS1DE3e/bsYdiwYXTv3r1Ia1IoV113DrBo0SIGDx5MVFQULVu2pGfPngwaNMjemLpw4QJAge9DgwYN2Lt3r9M2VzIJRXsvrp59rTCdOnWiX79+zJ49m2XLltG5c2f69OnDgAEDXBpUnZyczOzZs/n444/zZfXq/Lvq7Nmz9OvX75r7XLhwgXr16uWbTaphw4b2+wvjymsuyjkAoGbNmvn2Keh3CHDp0iUef/xx2rVrx9KlS53uu5n38+zZs9SvX58KFQr/k+XChQtERkbm+/wW9r5dK0dX33f69GkA+5ciVwsMDMy3bdu2bcyePZupU6cW++/86t+JrVFg+52cOXMGRVF4+eWXefnllwt8zoSEBKpXr26/bTsfFPX8I8TVpGEhRDnh+A1bYaxWK6Ghoaxfv77A+21/BKamptKpUycCAwN55ZVXqFu3Lr6+vhw6dIjJkyc7DXgGCpze1Gq10rRp03x/YNhERUUVWudrr71mnwa0qGbOnOnyVL4RERF07tyZJUuW8PPPP5faTFB79uzhwQcfpGPHjqxcuZKIiAi8vLxYs2YNGzZscPk4R48e5cEHH6RJkyZs2rTpmn94OapSpUqBfxg+9thjdOjQgc8//5zvv/+exYsXs3DhQjZv3kyPHj1crsvGlUwW9b1wZSpdwL4Oya+//spXX33Fd999x9NPP82SJUv49ddfr9v4fOyxx/jll1+YNGkSzZs3x9/fH6vVyv33358v/+7Cldfs6jnAprDf4dUN09zcXB555BF8fHz49NNP82XR3d7Pa+Xo6vts9X3wwQeEh4fn2//q13ru3DkGDhzIfffdx9y5c4uhWmfX+53Y6n3hhRfo3r17gfveeuutTrdt5wNXxmgJcS3SsBCilGn5jVDdunX54YcfaNeu3TX/Yd21axdXrlxh8+bNTjMHnTt3rkjPdfToUe69994iv+ZBgwbRvn37Ij3GxvFSJVcMGDCA4cOHExwcTM+ePQvcp1q1alSqVImTJ0/mu+/EiRN4eHhcs6F0tc8++wxfX1++++47p2/P16xZ4/Ixzp49y/33309oaCjffPONy700oPYuFPa7jIiIYPTo0YwePZqEhATuuOMO5s2bR48ePahVqxagrs9w9be3J0+etN9fFMXxXlxLmzZtaNOmDfPmzWPDhg0MHDiQjz/+mOHDhxeay5SUFH788Udmz57NjBkz7Ntt31w7Kkq269aty59//nnNfWrVqsWxY8ewWq1O32DbLj905T2+1mt29RxQVM899xxHjhxh9+7dhIWFOd1XlPezIHXr1mX//v2YzeZC1++pVasWP/zwA0aj0anXoijv27WeH9TZk7p27XrNfbOysujbty/BwcF89NFHLq1jUhy/c0e2c6CXl9d167U5d+4cVatWLbSHWQhXyRgLIUqZbU50LVbqfeyxx7BYLMyZMyfffXl5efaabN+IOX4rmZuby8qVK4v0XDExMfz3v//Nd19WVpZ9tpWC3HLLLXTt2vWGforasHjkkUeYOXMmK1euLHR+eU9PT7p168YXX3xhnz4TID4+ng0bNtC+ffsCL4cojKenJwaDwWna1fPnz7s8K0tcXBzdunXDw8OD7777rsh/DLRt25Y///zT6bIji8WS75KU0NBQIiMj7fu1atWK0NBQVq1a5fTYb7/9luPHj9/QbFY3+14UJiUlJd+36s2bNwew125ba+Xqz2JB+Qd4/fXX8z1PUT7P/fr14+jRo3z++ef57rM9V8+ePYmLi+OTTz6x35eXl8dbb72Fv78/nTp1KvT4rrxmV88BRbFmzRreeecdVqxYkW/BSSja+1mQfv36kZSUxPLly/Pd5/i+WSyWfPssW7YMg8FwQz1uNt27dycwMJD58+cXOHYlMTHR/v+jRo3i1KlTfP755wWOYyrIzfzOCxIaGkrnzp155513uHz58jXrtTl48CBt27Yt0vMIURDpsRCilLVs2RJQv+Hr3r07np6e9O/fv1Seu1OnTjzzzDMsWLCAI0eO0K1bN7y8vDh9+jQbN27kjTfe4JFHHuHuu++mcuXKDB48mOeeew6DwcAHH3xQ4HX5hXnqqaf49NNPGTVqFDt37qRdu3ZYLBZOnDjBp59+ynfffaf5uBSAoKAgly6dmjt3Ltu3b6d9+/aMHj2aChUq8M4775CTk8OiRYuK9Jy9evVi6dKl3H///QwYMICEhARWrFjBrbfeyrFjx677+Pvvv5+///6bF198kb179zqNbQgLC+O+++675uMfeugh5syZw08//US3bt0AdYB+jRo1eOSRR2jWrBn+/v788MMPHDhwwL6QnpeXFwsXLmTo0KF06tSJJ554wj7dbO3atXn++eeL9D4Ux3tRmHXr1rFy5Uoefvhh6tati9Fo5L///S+BgYH2nqmKFSvSqFEjPvnkE2677TZCQkJo0qQJTZo0oWPHjixatAiz2Uz16tX5/vvvC+zlsX2ep02bRv/+/fHy8uKBBx4ocFG1SZMmsWnTJh599FGefvppWrZsSXJyMl9++SWrVq2iWbNmjBw5knfeeYchQ4Zw8OBBateuzaZNm/j55595/fXXr7kysiuv2dVzgKuSkpIYPXo0jRo1wsfHhw8//NDp/ocffpjAwECX38+CDBo0iPfff58JEybw22+/0aFDBzIyMvjhhx8YPXo0Dz30EA888ABdunRh2rRpnD9/nmbNmvH999/zxRdfMH78eHuvw40IDAzk7bff5qmnnuKOO+6gf//+VKtWjYsXL/L111/Trl07li9fztdff837779Pv379OHbsmFN+/f396dOnT4HHv5nfeWFWrFhB+/btadq0KSNGjOCWW24hPj6effv2cenSJY4ePWrfNyEhgWPHjjFmzJgiP48Q+WgxFZUQ5YVtesLCpncsaGrYvLw8Zdy4cUq1atUUg8Fw3alnXZ1u1s/PL99jZ86cWeDx3333XaVly5ZKxYoVlYCAAKVp06bKiy++qMTGxtr3+fnnn5U2bdooFStWVCIjI5UXX3xR+e677/JNpeo4fevVcnNzlYULFyqNGzdWfHx8lMqVKystW7ZUZs+eraSlpV3zdZeUa9VrU9B0s4qiKIcOHVK6d++u+Pv7K5UqVVK6dOmi/PLLLwU+9nrTzb733ntKvXr1FB8fH6VBgwbKmjVrCv19XY1CpjgGnLJyLbfffrt9ql1FUZScnBxl0qRJSrNmzZSAgADFz89PadasmbJy5cp8j/3kk0+UFi1aKD4+PkpISIgycOBA5dKlS077FCWTrr4XwDWnw3V06NAh5YknnlBq1qyp+Pj4KKGhoUrv3r2V33//3Wm/X375RWnZsqXi7e3tNPXspUuXlIcfflgJDg5WgoKClEcffVSJjY0tcHraOXPmKNWrV1c8PDycpp69erpZRVGUK1euKGPHjlWqV6+ueHt7KzVq1FAGDx6sJCUl2feJj49Xhg4dqlStWlXx9vZWmjZt6vR5v9nXrCiunQNq1aql9OrVK99jHc9JtvNRYT+296Io72dBMjMzlWnTpil16tRRvLy8lPDwcOWRRx5xmv7ZaDQqzz//vBIZGal4eXkp9erVUxYvXmyfktamsBxd73y+c+dOpXv37kpQUJDi6+ur1K1bVxkyZIj9/b3W9OOOn/+rz+mK4trv3PZeL168OF9tBb2PZ8+eVQYNGqSEh4crXl5eSvXq1ZXevXsrmzZtctrv7bffVipVqnTNqYyFcJVBUYrwFaQQQohy4YMPPmDMmDFcvHjRaVE7IYS+tGjRgs6dO+dbRFCIGyFjLIQQQocGDhxIzZo1WbFihdalCCE0sm3bNk6fPs3UqVO1LkWUE9JjIYQQQgghhLhp0mMhhBBCCCGEuGnSsBBCCCGEEELcNGlYCCGEEEIIIW6aNCyEEEIIIYQQN03ThsXu3bt54IEHiIyMxGAw5FtpddasWTRo0AA/Pz8qV65M165d2b9/v9M+ycnJDBw4kMDAQIKDgxk2bBgmk8lpn2PHjtGhQwd8fX2Jiooq8mJWoK7umZ6eXqQFwoQQQgghhNALTRsWGRkZNGvWrNDpDm+77TaWL1/OH3/8wd69e6lduzbdunVzWo5+4MCBREdHs337drZu3cru3bsZOXKk/f709HS6detGrVq1OHjwIIsXL2bWrFm8++67RarVaDQSFBSE0Wi8sRcryrS0tDStSxBuQrIgHEkehI1kQTjSax7cZrpZg8HA559/XuiS96A2EoKCgvjhhx+49957OX78OI0aNeLAgQO0atUKUOdk7tmzJ5cuXSIyMpK3336badOmERcXh7e3NwBTpkxhy5YtnDhxwuX6bM+dlpZGYGDgTb1WUfYYjUYCAgK0LkO4AcmCcCR5EDaSBeFIr3koM2MscnNzeffddwkKCqJZs2YA7Nu3j+DgYHujAqBr1654eHjYL5nat28fHTt2tDcqALp3787JkydJSUkp9PlycnJIT093+hH6lZ2drXUJwk1IFoQjyYOwkSwIR3rNg9s3LLZu3Yq/vz++vr4sW7aM7du3U7VqVQDi4uIIDQ112r9ChQqEhIQQFxdn3ycsLMxpH9tt2z4FWbBgAUFBQfafqKgoQA1KbGwsVquVmJgYAGJiYsjNzSUhIQGTyURqairJyclkZWURFxdHXl6e075ms5m4uDgyMzNJTk4mJSWFjIwM4uPjMZvNTvtaLBZiY2PJysriypUrpKWlYTQaSUxMJCcnx2lfRVGIiYkhJyeHxMREe4MoKSmJrKwsl+vOzMwssO68vLx8dZtMpgLrtlqt163bVu/VdRuNRtLS0rhy5UqhdZvNZuLj4zGZTKSkpBSp7tTUVEwmEwkJCeTm5hZad1JSkv09NJvN+eq2PeZadVsslgLrzsjIyFf31e+hre6srCyX687Oznaq2/Z+x8bGFlh3QkJCvrovX7583bqvl9nLly871W00GotUd1JS0nU/a0aj0emzVpx1O2Y2ISHB6bOWkZGBoijExsYW+FmTc4S+zhEGg8HpsybnCP2eIzIyMpw+a3KO0Pc5wtvbu8DPWlk9R7jK7S+FysjI4PLlyyQlJfHf//6XHTt2sH//fkJDQ5k/fz7r1q3j5MmTTo8JDQ1l9uzZPPvss3Tr1o06derwzjvv2O//66+/aNy4MX/99RcNGzYssJ6cnBynNzI9PZ2oqCi5FEqnYmJiqF69utZlCDcgWRCOJA/CRrIgHOk1D27fY+Hn58ett95KmzZteO+996hQoQLvvfceAOHh4SQkJDjtn5eXR3JyMuHh4fZ94uPjnfax3bbtUxAfHx8CAwOdfoR+6fHkIAomWRCOJA/CRrIgHOk1DxW0LqCorFarvSehbdu2pKamcvDgQVq2bAnAjh07sFqttG7d2r7PtGnTMJvNeHl5AbB9+3bq169P5cqVi60ui8WC2WwutuMJ9xIfH5/vkjo98fLywtPTU+sy3IJev4USBZM8CBvJgnCk1zxoeimUyWTizJkzALRo0YKlS5fSpUsXQkJCqFKlCvPmzePBBx8kIiKCpKQkVqxYwYYNGzh48CCNGzcGoEePHsTHx7Nq1SrMZjNDhw6lVatWbNiwAVCn+6pfvz7dunVj8uTJ/Pnnnzz99NMsW7bMaVra67nWrFAmk4lLly7JGhei3DIYDNSoUQN/f3+tS9GcoigYDAatyxBuQvIgbCQLwpFe86Bpw2LXrl106dIl3/bBgwezatUqBgwYwP79+0lKSqJKlSrceeedTJ8+nTvvvNO+b3JyMmPHjuWrr77Cw8ODfv368eabbzr9AXTs2DHGjBnDgQMHqFq1KuPGjWPy5MlFqrWwhoXFYuH06dNUqlSJatWq6TJEepCbm+s0s5ieKIpCYmIimZmZ1KtXT/c9F3r9FkoUTPIgbCQLwpFe8+A2g7fdXWENi+zsbM6dO0ft2rWpWLGihhWKkmS1WvHwcPshSSUmKyuL8+fPU6dOHXx9fbUuR1M5OTn4+PhoXYZwE5IHYSNZEI70mgf9/qVUzKSnonyzWCxal6Apyfe/ZE0b4UjyIGwkC8KRXvMgDQshXKDn3grhTI/fQInCSR6EjWRBONJrHuSvJSGEEEIIIcRNk4aFAKBx48Zs3bpV6zLcltVq1boE4SZyc3O1LkG4EcmDsJEsCEd6zYM0LHSkc+fO+Pj44O/vb/9ZuXIlANHR0fTu3RuA2rVrs2XLlht+nocffhiDwUBcXFxxlA3AuHHjiIqKIjAwkOrVqzN+/PgCP7Q5OTmMGDGCOnXqEBAQQIMGDVi9erXTPsuXL6dVq1b4+PjkW+kd4OzZs/To0YPKlStTvXp1Fi1aVKwzIZnNZsaOHUvlypUJCQlh3Lhx5OXl3fD+17rf1fdNuM7Pz0/rEoQbkTwIG8mCcKTXPEjDQmcWLlyIyWSy/4wePbpYj799+3a+++47fH19OXLkSLEdd/To0Zw4cYL09HSOHj3K0aNHWbRoUb798vLyiIiI4IcffiA9PZ21a9cyceJEvv/+e/s+kZGRTJ8+nREjRuR7vMVi4cEHH+SOO+4gISGBHTt2sHz5cj788MPr1rhr1y46d+583f3mzp3L3r17+euvv4iOjmbPnj3Mnz//hve/1v2uvm/CdSkpKVqXINyI5EHYSBaEI73mQRoWAvi3l+LRRx/l4sWLPPHEE/j7+zNq1CiXj2E2m/nPf/7DxIkTadq0abE2LBo2bGhv/SuKgoeHB6dPn863n5+fH6+88gp169bFYDDQpk0bunTpwt69e+379O3blz59+lC1atV8jz958iQnT55k5syZeHl5Ub9+fYYNG5av1+NmrF69munTpxMREUFERATTpk3jvffeu+H9r3W/q++bcF14eLjWJQg3InkQNpIF4UiveZCGhXCyceNGatasyUcffYTJZGLVqlUAvPrqq/ZLpQrz1ltvkZqayuTJk6/ZsBg9ejTBwcGF/jg2Ahy9+uqr+Pv7ExoaytGjRxk3btx1X092dja//fYbt99++3X3hX/HUjgu72K1Wjl27JhLj7+elJQULl26RPPmze3bmjdvzsWLF0lLSyvy/q4c70beN1G4y5cva12CcCOSB2EjWRCObiYPRqORj7/ewZwvjzHuo8PM+fIYH3+9A6PRWIwVlgxpWOjM1KlTnf6Iz8jIcOlxU6ZMuebg7vj4eGbPns3cuXPx9/e/ZsNi5cqVpKamFvrTvn37QmswmUz89ddfjBo16rrfBiiKwvDhw6lXrx59+/Z16XXWr1+f2rVrM2PGDHJycoiOjmb16tWFzkft2Ejq3bs3e/fuvWYjyWQyARAcHGzfZvv/gk4Y19vfleMV9X0T16bHlVRF4SQPwkayIBzdaB6MRiP/9+OfTN2TyZp9//D1sVjW7PuHqXsy+b8f/3T7xoU0LHRmwYIFTn/EF9fgoilTpnDLLbcwZMgQAJo2bcrp06fJzMwsluM7atiwIc2aNbM/V0EURWH06NGcPHmSLVu2uLwOhZeXF1988QWHDx+mevXqDBw4kKFDh1KlSpUC93dsJG3dupX27dtfs5Hk7+8P4NQ7Yfv/gICAfMe/3v5FOZ4r75u4vpiYGK1LEG5E8iBsJAvC0Y3m4evdB3jz1ysoGLAq2H8U4M1fr/DN7gPFW2gxk4aFyKeoi8H99ttvrFu3jtOnTxMZGUl4eDj9+/cv9BKiUaNGOc1MdfXPnj17rvucZrO50LECiqIwZswY9u/fz/fff09QUFCRXk/jxo35/vvvSUpK4siRI+Tk5NCxY8ciHaMwlStXpkaNGk69OUeOHCEqKqrAOq+3f1GPd633TbimWrVqWpcg3IjkQdhIFoSjG83DaUtVDAZDAfcYMBgMnLLkHx/qTqRhIfIJCwvj7NmzLu2rKArjxo3j8ccf58yZMxw5coQjR47wxx9/UL169QIvh1q1apXTzFRX/3To0MFpf5PJxJo1a0hNTUVRFP744w/mzp1L9+7dC6xp7Nix/Pzzz2zfvp3KlSvnuz8vL4/s7Gzy8vKwWq1kZ2c7TcF67NgxMjIyyM3NZfPmzaxevZopU6Zc973o3Lkzu3btuu5+Q4cOZd68ecTFxREXF8f8+fMZPnz4De9f2P1Ffd+Ea1JTU7UuQbgRyYOwkSwIRzeah4QMCziM8yzwfjcmDQuRz0svvcTy5csJDg62T0c7f/58evTokW/ftWvXcvHiRVasWEF4eLjTT8uWLYtlZiiDwcCGDRuoW7cuAQEBPPTQQ/Tq1YvXX3/dvs+oUaMYNWoUFy5cYOXKlZw8eZJatWrZe0EcZ7eaO3cuFStWZN68eXz11VdUrFiRbt262e//9NNPqVmzJpUrV+a1115jy5YtToOjHd1I78vLL79M27ZtadiwIQ0bNqRdu3a89NJL+V6Lq/sXdr8r75soukqVKmldgnAjkgdhI1kQjm4oD6dPE/nDVgzXWJQ31K/41tUqCQZFuUazSNilp6cTFBREWloagYGB9u3Z2dmcO3eOOnXq4Ovrq2GFoiTl5eVRoUIFrcvQjOT8X6mpqU6D5YW+SR6EjWRBOCpSHlJTYc4ceOstzvlX497hq7AaDOB0SZSCAXi1QyUe73VP8RdcTKTHQgghisB6jW+ShP5IHoSNZEE4cikPeXnw9ttQrx4sXQpmM1F3NmFCPQWDATwcfgzAc22q0LPjnSVe+83Q71ewQhRBwQOphB7pvcdGOJM8CBvJgnB03Tx8/z1MmADR0erthg1h6VIq3H8/g41Gqu0+wClLVRIyLIT6eXKbZxI9OzYpcAZJdyINCyFcYLVa8fR07+saRelIT0+Xa6mFneRB2EgWhKNC83DyJEycCF9/rd4OCYFXXoFnnoH/XXIdEBDg1pc7XYs0LIRwgZ7HVwhnVau691R/onRJHoSNZEE4ypeH5GS1AbFihXoJVIUKMG4cvPwyFDCDZVklYyyEcIHZbNa6BOEm4uPjtS5BuBHJg7CRLAhH9jyYzbB8uTqO4o031EbFAw+ol0AtXVquGhUgDQtxg8xmM76+vvj5+eHv74+fnx933XUXR48evelj5+TkMGLECOrUqUNAQAANGjRg9erV+Z5/7NixVK5cmZCQEMaNG0deXl6+Y2VlZXHrrbc6zczgyvGvNnLkSLy9vZ2mkt23b98NH68oli9fTqtWrfDx8aFPnz7X3f967824ceOIiooiMDCQ6tWrM378eKd1PMS1Va9eXesShBuRPAgbyYJwVL16dfj2W7j9drVnIjkZmjRRx1Z8+SXcdpvWJZYIaViIGxIdHU1ubi7x8fGYTCaSkpKoWrUqU6dOvelj5+XlERERwQ8//EB6ejpr165l4sSJfP/99/Z95s6dy969e/nrr7+Ijo5mz549zJ8/P9+xZsyYQa1atYp8/KtZrVZGjx7ttJBf27Ztb/h4ALt27aJz587XfT8iIyOZPn06I0aMuO6+cP33ZvTo0Zw4cYL09HSOHj3K0aNHWbRokUvHFhATE6N1CcKNSB6EjWRB2P31F9ldukDPnnDiBFStCqtWweHDcN99WldXoqRhIW7IoUOHqFOnDv7+/gBUrFiRZs2aFcslQ35+frzyyivUrVsXg8FAmzZt6NKlC3v37rXvs3r1aqZPn05ERAQRERFMmzaN9957z+k4Bw8eZNu2bUyePLnIx7+ah0fhH5UbOV5R9O3blz59+rh8/e713puGDRvi5+cHqCune3h4cPr06WKpVQ/CwsK0LkG4EcmDsJEsCK5cUXsnbr8d3127wMsLXngBTp92GpxdnknDorgpCmRklN6PRusbHjp0iNtvv/1/L1nh999/Z/369YwfPz7fvqNHjyY4OLjQn+v9AZ6dnc1vv/1mf76UlBQuXbrktBp28+bNuXjxImlpaYDaizBixAhWrFiBt7d3kY5fEKvVyvvvv09ISAiNGzdmyZIlhc5R7crxSoor7w3Aq6++ir+/P6GhoRw9epRx48aVeq1lVVJSktYlCDcieRA2kgUdy82F11+HW29Vx1NYLGTffz/89RcsXgw6Wjix/DedSltmJvzvW/xSYTLB/759Lk2HDh3i8OHDBAcHk5OTQ25uLq+++iq9evXKt+/KlStZuXLlDT2PoigMHz6cevXq0bdvXwBMJhOA07gJ2/8bjUaCgoJYvHgxLVq0oGPHjuzatatIxy/IuHHjWLJkCSEhIRw4cIDHHnsMDw8Pnn/++SIdb/To0WzYsAFQGz/Z2dlOr2Pr1q20b9/+Wm/JNbny3gBMmTKFKVOmcPz4cdavX094ePgNP6feBAYGal2CcCOSB2EjWdAhRVGnjZ04EU6dUrc1awbLlmFt3Rp0OP2w9FiIIrNYLBw9epRNmzaRmppKZmYmP/74I5MnT+aPP/4otudRFIXRo0dz8uRJtmzZYr8cyXb5leM38Lb/DwgI4MyZM6xatYrFixff0PEL0qJFC6pVq4anpydt2rRhypQpfPLJJ0U+3sqVK0lNTSU1NdXeiLDdTk1NvalGBVz/vblaw4YNadasGUOGDLmp59WT7OxsrUsQbkTyIGwkCzrz55/Qvbs6w9OpUxAaCv/9Lxw8CF266DYP0mNR3CpVUnsRSvP5StmJEyfIzMzkzjvVZeUNBgMdOnTA29ubc+fO0bRpU6f9R40axYcffljo8b799ls6dOjgtE1RFMaMGcP+/fv58ccf7d+0A1SuXJkaNWpw5MgR6tatC8CRI0eIiooiKCiIzz//nPj4eG7734wLZrMZo9FI1apV+frrr2nduvU1j++KqxsNN3u84nK996YgZrNZxlgUgazCLhxJHoSNZEEnEhNh5kx45x2wWsHbG55/Hl56CRx6rfSaB2lYFDeDQZNLk0rToUOHqFWrFqGhoYC6uuTcuXMJDAykY8eO+fZftWoVq1atKtJzjB07lp9//pkdO3ZQuYA5nocOHcq8efNo164dAPPnz2f48OEAPPbYY3Tt2tW+7759+xg+fDhHjhyx13y9419t06ZN9OzZk4CAAA4ePMirr77KmDFjXK63IJ07d77mZVo2eXl59h+r1Up2djYeHh6Fjh251ntjMpnYuHEjDz/8MEFBQfz555/MnTuX7t27u1SzAC8vL61LEG5E8iBsJAvlXG4uvPUWzJkDtqsCHnkEFi6EW27Jt7tu86AIl6SlpSmAkpaW5rQ9KytL+euvv5SsrCyNKit948ePVzw8PBQ/Pz8lMDBQqVGjhjJw4EDl5MmTxXL88+fPK4Di4+Oj+Pn52X+eeeYZ+z65ubnK6NGjleDgYCU4OFgZO3asYjabCzzezp07laCgoCId/5lnnnG63b59eyUoKEjx8/NTbrvtNmXhwoWKxWJx+XiOx3Xc5+qf3bt353vMzJkzFcDpp1OnToXWeq33xmQyKV27dlVCQkIUPz8/pU6dOsoLL7ygZGRkXPN3osecFyYuLk7rEoQbkTwIG8lCOWW1KsqWLYpy662Koo6qUJQWLRTlp5+u+TC95sGgKBpNK1TGpKenExQURFpamtMArezsbM6dO0edOnXw9fXVsEJRkqxW6zXHYJR3kvN/mc1m/X4TJfKRPAgbyUI5dPSoepnTzp3q7fBwmD8fBg0CT89rPlSvedDvX0pCFEFBq3oLfUpISNC6BOFGJA/CRrJQjsTHw8iR0KKF2qjw8YFp09RB2kOHXrdRAfrNg4yxEMIF11sLQ+hH9erVtS5BuBHJg7CRLJQDOTnwxhswdy4Yjeq2xx+HV1+F2rWLdCi95kF6LIRwQW5urtYlCDcRExOjdQnCjUgehI1koQxTFPjsM2jYECZPVhsVrVrB3r3w8cdFblSAfvMgPRZCuECP10mKgkVERGhdgnAjkgdhI1koow4fhvHjYfdu9XZkJCxYAE8+CTcxtlKveZAeCyFcYDabtS5BuIm4uDitSxBuRPIgbCQLZczlyzBsGLRsqTYqfH1hxgx1HMWgQTfVqAD95kF6LIRwQYUK8lERKlfXKRH6IHkQNpKFMiI7G5YtU2d3si1oPGCA2ktRs2axPY1e86Bpj8Xu3bt54IEHiIyMxGAwsGXLFvt9ZrOZyZMn07RpU/z8/IiMjGTQoEHExsY6HSM5OZmBAwcSGBhIcHAww4YNw3TVytfHjh2jQ4cO+Pr6EhUVxaJFi0rj5YlyxGq1al2CcBOZmZlalyDciORB2EgW3JyiwKefQoMG6irZJhO0bg2//ALr1xdrowL0mwdNGxYZGRk0a9aMFStW5LsvMzOTQ4cO8fLLL3Po0CE2b97MyZMnefDBB532GzhwINHR0Wzfvp2tW7eye/duRo4cab8/PT2dbt26UatWLQ4ePMjixYuZNWsW7777bom/PiFE+SO9V8KR5EHYSBbc2O+/Q4cO6gxPFy5AjRpqY+KXX6Bt2xJ5Sr3mQdNX3aNHD3r06FHgfUFBQWzfvt1p2/Lly7nrrru4ePEiNWvW5Pjx42zbto0DBw7QqlUrAN566y169uzJa6+9RmRkJOvXryc3N5fVq1fj7e1N48aNOXLkCEuXLnVqgAhxLQaDQesShJvQ80KJIj/Jg7CRLLihmBi1d+L999XblSqpsz698IL6/yVIr3koU686LS0Ng8FAcHAwAPv27SM4ONjeqADo2rUrHh4e7N+/375Px44dndYh6N69OydPniQlJaVU6xdll1wKJWyys7O1LkG4EcmDsJEsuJHMTJgzB2677d9GxaBB6sDsGTNKvFEB+s1Dmemnyc7OZvLkyTzxxBMEBgYC6oj70NBQp/0qVKhASEiIfTR+XFwcderUcdonLCzMfl9hg2tycnLIycmx305PTy+21yLKHk8XVtkU+mA7/wgBkgfxL8mCG1AUdd2JyZPhn3/UbXffrQ7WvuuuUi1Fr3koEz0WZrOZxx57DEVRePvtt0vlORcsWEBQUJD9JyoqClAbOLGxsVitVvviJ3l5eVitVsxmMxaLhby8PKdtiqLYF1jLzc0tcF+LxYLZbMZqtTrta3us1Wot0r6241+9b1FquXpfRVGKtW7bPgXV7fgealW343uYk5NTpFpupO6CarFtL63f/bX2tVgsZGZmkpKSQkpKChkZGcTHx2M2m+2fhZiYGCwWC5cvXyYrK4vk5GRSU1MxGo0kJCSQm5vrtK/VaiU2Npbs7GySkpJIT08nPT2dpKSkAj9rMTEx5ObmkpCQgNFoJDU1leTkZLKysrh8+TIWi8VpX7PZTHx8PBkZGUWq+8qVK6SlpdnrzsnJse976tQpFEUhNjaWnJwcEhMTi1S3yWRyqjsuLo68vLx8dcfFxZGZmUlycvJ1646Njc1Xd2JiolPdMTExKIpCTExMgXVnZWW5XHdmZmaBdefl5eWr22QyFVi37Xd/rbpt9V5dt9FoJC0tjStXrhRat+13bzKZSElJKVLdqampmEyma2Y2KyuLpKQkLl68SHp6eoF12x5zrbqvl1nHuq9+D211O37Wrlf31Z81W92xsbEF1m37rDnWXZyftfJ0jjh16pTTZ03OEaV7jkj46iusbduqMzz98w/WGjUw/ve/ZG3fTmyNGqV+joiNjS3ws1ZWzxGuMiiKori8dwkyGAx8/vnn9OnTx2m7rVHx999/s2PHDqpUqWK/b/Xq1UycONHpkqa8vDx8fX3ZuHEjDz/8MIMGDSI9Pd1pxqmdO3dyzz33kJycXKQei6ioKNLS0pxaodnZ2Zw7d446derg6+t7k++CcFeKouh6nIXk/F96z4JwJnkQNpIFjfzzD0ydqg7GBvDzU29PmAAVK2pWll7z4NY9FrZGxenTp/nhhx+cGhUAbdu2JTU1lYMHD9q37dixA6vVSuvWre377N6922mBs+3bt1O/fv1rzjHs4+NDYGCg04/QL1kgT9hcPeW10DfJg7CRLJSyjAyYNQvq11cbFQYDDB0Kp0/DtGmaNipAv3nQtGFhMpk4cuQIR44cAeDcuXMcOXKEixcvYjabeeSRR/j9999Zv349FouFuLg44uLi7JdoNGzYkPvvv58RI0bw22+/8fPPPzN27Fj69+9PZGQkAAMGDMDb25thw4YRHR3NJ598whtvvMGECRO0etmiDPLy8tK6BOEmbOcWIUDyIP4lWSglVit8+KHaoJg9G7Ky1KlkDxyA1ashIkLrCgH95kHThsXvv/9OixYtaNGiBQATJkygRYsWzJgxg5iYGL788ksuXbpE8+bNiYiIsP/88ssv9mOsX7+eBg0acO+999KzZ0/at2/vtEZFUFAQ33//PefOnaNly5ZMnDiRGTNmyFSzokikx0LY6PVbKFEwyYOwkSyUgl9+gTZt4Kmn1Klka9eGTZvgp5+gZUutq3Oi1zy4zRgLd5eenk5QUJCMsdApq9Wq2zmpQXLuKCcnBx8fH63LEG5C8iBsJAsl6MIFmDJFnfEJwN8fpk+H//wH3PTfJL3mQb9/KQlRBBaLResShJuQqaeFI8mDsJEslACTSW1ANGigNioMBhg+XB1HMXmy2zYqQL95KDPrWAihJT33Vghneu+xEc4kD8JGslCMrFZ1YbuXXoLLl9VtnTur61E0b65lZS7Tax6kYSGEC+SKQWEjq7ALR5IHYSNZKCZ79sDzz4Ntxs+6deG11+Chh9QeizJCr3mQr2GFEKII8vLytC5BuBHJg7CRLNykc+fgscegY0e1UREYCIsXQ3Q09OlTphoVoN88SI+FRoxGIyaTqdD7/f39CQgIKMWKxLXIpVDCplKlSlqXINyI5EHYSBZuUHo6LFigXuaUkwMeHjBiBLzyCoSGal3dDdNrHqRhoZGDBw/y008/FXp/p06d6Ny5c+kVJK4pLy8Pb29vrcsQbiAlJYWKGi+8JNyH5EHYSBaKyGKBtWvVxezi49Vt996rNjCaNtW0tOKg1zxIw0IjLVu2pH79+gAkJSWxefNm+vbtS9WqVQG1x6IsGDVqFEFBQSxcuPCa+9x+++2MHj2aXbt20adPH1JTU0uvyGJQ3hfIMxqNNG/enP3799szKAoWHh6udQnCjUgehI1koQh27VLHUfxvgWTq1YMlS6B37zJ3yVNh9JoHub6jFBmNRnbt2oXRaCQgIICIiAiyvQL59GQOu3Lr8OnJHLK9AomIiLBfBuX4mJvRuXNnfHx8CAgIICgoiCZNmjBx4kQSExNdPsasWbPo06eP07ZVq1Zds1Fx5swZvv76a4YPH36jpbuFsrBA3ssvv0zTpk2pUKEC48ePz3d/bGwsPXv2xM/Pj5o1a/Lf//7Xfl9AQACDBg1i3rx5pVhx2XTZNkOJEEgexL8kCy44exb69oUuXdRGRXCw2kPx55/wwAPlplEB+s2DNCxKkclk4qeffrKPrfj093+4d8kuNhyM57wlhA0H47l3yS42/v5PoY+5GQsXLsRoNJKamsqnn35KTEwMLVu2JN7WBVkCVq1axeOPP15ilxEV9Ad/SQyYcqy/oOcsasMjLy+v2GeauvXWW1m0aBEPPvhggfc/8cQThIeHk5CQwMaNG5k0aZLT5XiDBw9mzZo1ZGZmFmtd5U316tW1LkG4EcmDsJEsXENaGrz4IjRqBJ9/Dp6eMGaMuh7F+PFQDi811msepGGhkXNJGUz57BhWBSwKKBiwKGBVYPJnxziflFFiz20wGGjUqBEffvghgYGBLFmyBFAbMQ899BChoaEEBQXRsWNHjh49CsCWLVuYP38+W7duxd/f336p1pAhQwr8dtzmyy+/5J577sm3/a233iIiIoLw8HBmzpxp/yP74sWL3HfffVSrVo3KlSvTq1cvzp8/b3/ckCFDGDZsGI899hiBgYGsWrWKzp078+KLL9KtWzf8/Pz49ttvMZlMjB07lpo1axIaGsqgQYNIS0srtM6zZ8/ywAMPUK1aNWrVqsXcuXPtU8WtXbuWZs2aMXPmTMLDw+nfvz+zZs2id+/ePPvss4SEhDBlyhTMZjNTp06lZs2aVKtWjccff9ypR8hgMLB8+XKaNGmCn58fJpPpms97tUmTJtGpUyf7/Zs2bbI3FEBtGPTo0cNpZXjH17d3714WLFiAn58frVu3ZuDAgaxevdq+T+3atalSpco1x/4IiImJ0boE4UYkD8JGslAAiwXeeUe91GnxYsjNhW7d4OhRWL4cyvGlt3rNgzQsNJCUlMTqXce5Voffe7uOc/nyZZKSkkqsjgoVKtCnTx/7H5JWq5UBAwZw7tw54uPjadGiBY899hiKotCnTx9eeuklevfujclkcqkHJTMzk9OnT9OgQQOn7UajkUOHDnH27Fl27drF6tWref/99+01TJgwgX/++YcLFy5QqVIlRowY4fT4jz76iGHDhpGamsqwYcMA9Y//uXPnYjKZ6Nq1K08//TTJyckcO3aMc+fOYTabGTt2bKF13nvvvdx7773ExMSwZ88ePv74Y9asWWPfJzo6mgoVKnDx4kU++OADALZt20br1q1JSEhgzpw5LFiwgK1bt7J3717OnTuHwWBg4MCBTs+1YcMGvv/+e9LT0/H09Lzu8zqaN28eGRkZzJ07lwsXLjBy5Ejef/99Ql2YNePYsWNEREQQFhZm39a8eXOOHTvmtF+jRo04YrvmVRTIlfdb6IfkQdhIFq7y44/QogWMGgWJiVC/Pnz9NWzbBo0ba11didNrHqRhoYHNmzez9/BfWAu5FEZRFPYe/ot3332XzZs3l2gt1atXJzk5GYDAwEAef/xx/Pz88PX1Zfbs2Zw6dYrY2NgbOnZKSor9uI6sVisLFy6kUqVKNGjQgLFjx9r/WK9duzY9evTA19eXwMBApk2bxp49e5y+xe/WrRvdu3fHw8PDPp3bgAEDuOuuuzAYDJhMJj777DNWrFhBcHAwfn5+vPLKK3zyySdYLJZ8dX799ddUrlyZ8ePH4+3tTc2aNfnPf/7Dhg0b7PsEBQUxbdo0vL297c/ZpEkThgwZQoUKFahUqRIffPAB06dPp2bNmvj7+7N06VK2b9/u9P69+OKLREZG4uPj49LzOvL29uajjz5i2bJl9OzZk2HDhtGtWzeXfhcmk4ng4GCnbcHBwfnG7gQGBtp/b6Jgts+LECB5EP+SLPzP6dPqYnZdu8Iff0DlyvDmm+r/9+xZrsZRXIte8yCzQmmgb9++5J3M4eLBeCwFtC0MBgPtWzRiZLv77DNGlZSYmBhCQkIAyMrKYuLEiXzzzTckJyfb125ISkq6oWsFK1euDEB6errTTEO+vr5OLflatWrZuwwTExP5z3/+w549e+yXLuXk5GA0GgkKCgKgZs2a+Z7Lcdv58+exWq3UqVPHaR8PDw/i4uLyvZbz58/z559/Ov3hbbVaiYqKst+uXr16vrUsrq7j0qVL1K5d237b1oC4dOkSkZGRBdZ5vee9Wr169ejcuTNbt25lx44dhe53NX9//3yXgqWlpeVbKyU9PZ0mTZq4fFw98vPz07oE4UYkD8JG91lITYU5c+Ctt8Bs/nccxcyZ8L+/M/REr3mQHgsNVK1alac7N+RaQ3eHdW5IREREiU79mZeXxxdffGFfL2PJkiUcPHiQvXv3kp6ebh/bYBv/UNRF4ipVqkS9evU4ceKE0/bs7Gz7uABQx1XY/tifOnUqmZmZHDp0iPT0dHbv3u1UQ2F1OG6LiorCw8OD2NhYUlNT7T/Z2dkFNpCioqJo2bKl077p6elER0fb9zEU8A3L1XXUqFHDaTxIXFwcOTk51KhRo9A6r/e8V9u0aRO//vorvXr1YsyYMYXud7Xbb7+d2NhYp/f9yJEjNL1qrvC//vqL5s2bu3xcPSoLM4SJ0iN5EDa6zUJeHqxcCbfeCkuXqo2Knj3VHoo33tBlowL0mwdpWGikTlU/Fva7HQ8DeBrAgIKHATwMsLDf7dSuWrIt3RMnTjB48GDS0tKYMGECoH5b7evrS+XKlTGZTLz00ktOjwkLC+PChQtFmnXpgQceYOfOnU7bPDw8mDp1KllZWZw8eZIVK1bYxyKkp6dTqVIlgoODuXLlCrNnzy7yawsPD6dPnz6MHTvWPkYlLi6Ozz//vMD9e/fuTXx8PCtXriQ7OxuLxcLJkyfZtWtXkZ73ySefZP78+fzzzz+YTCYmTJhA165d7b0VN/u8Fy9e5JlnnmHdunW8//77HD58mHfffdd+v9lsth/HYrGQnZ1tP7HVrVuXdu3a8dJLL5GZmclvv/3G+vXr7WNUAC5cuEBSUhIdO3Ys0uvWm+KezUuUbZIHYaPLLHz/PTRrpvZMXLmizvq0bZs6lqJhQ62r05Qu84A0LEqVv78/nTp1ss+o9GirKHZM7MyAlmHU9kxmYMswdkzszKOtogp9zM2YPHmyfR2Lvn37Eh4ezu+//24f0DthwgQ8PT0JCwujSZMmtG3b1unxjz76KIGBgVSrVi3f9fqFeeaZZ/j444+dWu4BAQE0b96cW265hY4dOzJo0CAGDx4MwOzZszlz5gyVK1emXbt29OjR44Ze69q1awkODubOO+8kMDCQDh06cPDgwQL39ff354cffuDHH3+0z4w0YMAA4uLi7PsU1GNxtalTp9K9e3fatm1L7dq1MZvNfPjhh4Xu78rz2lgsFgYOHMjQoUPp1q0bgYGBfPTRR7z44oscP34cgBEjRlCxYkU+/PBDli9fTsWKFZ0Gvn/00UfExMRQrVo1+vXrx6JFi+jUqZP9/vfff58hQ4botvvWVb6+vlqXINyI5EHY6CoLJ06oi9l17w5//QVVqsCKFepsT927a12dW9BVHhwYFL02qYooPT2doKAg0tLSnAYjZ2dnc+7cOerUqXPDIbp8+TLvvvsuI0eOJCIiorhKdhvPPPMMzZs359lnn9W6lBtmNpvL9erbRqORFi1asG/fPqpVq5bv/uLIeXkRFxen2xVVRX6SB2GjiywkJ8Ps2eqlT3l5UKECPPccTJ+uDtIWdrrIQwFk8LZGjEajfcpW2+U6jlPL+vv75xtYW1a98847Wpdw0ypUKN8flYCAAM6cOaN1GWVCSY57EmWP5EHYlOssmM2wapU6ENs2c+CDD6prU9x2m7a1ualynYdrKN9/LbmxgwcP5luIzHH2p06dOtkHVQvtmc3mEls9XJQt8fHxul1RVeQneRA25TYL334LEyaolz8BNGkCy5ap08mKQpXbPFyHNCw00rJlS+rXr1/o/cUxpkIUH2lUCBs9/kMhCid5EDblLgt//aU2KL77Tr1drZo6neywYeolUOKayl0eXCTJ0EhAQEC5udRJD3Jzc6VxIQB17Re9/oMh8pM8CJtyk4WkJJg1S730yWIBLy8YPx6mTYP/rSclrq/c5KGIpGFRTGQMfPlWngduu0Ly/S/bLGpCgORB/KvMZyE3Vx2UPXu2utgdwMMPw6JF6hoVokjKfB5ukEw3e5M8PT0B9RttUX4VZe2O8siWb1ve9cxxkgUhJA/CpsxmQVHgq6/UsRPPP682Kpo1g507YfNmaVTcoDKbh5skPRY3qUKFClSqVInExES8vLyKvDq1KBtsi87pkdVqJTExkUqVKpX72bFc4TjdtBCSB2FTJrPwxx/qOIofflBvh4bC/PkwZAjIF0k3pUzmoRjIXwk3yWAwEBERwblz57hw4YLW5YgSYrVadd1o9PDwoGbNmi4tFFjeZWdnU6lSJa3LEG5C8iBsylQWEhNhxgx4912wWsHbW21gTJ0KOv2DuLiVqTwUI2lYFANvb2/q1asnl0OVY+np6br99gHUjOu5YeVI3gfhSPIgbMpEFnJy4K231Nmd0tPVbY88oo6jqFNH29rKmTKRhxIgDYti4uHhofsVicuzvLw8+f0KoPwvliiKRvIgbNw6C4oCX3wBL7wAZ8+q2+64Q12PomNHbWsrp9w6DyVIn80pIYooMzNT6xKEm5AsCEeSB2Hjtlk4ehTuvVed4ensWQgPhzVr4MABaVSUILfNQwnTZ3NKiCIKDg7WugThJiQLwpHkQdi4XRbi42H6dHjvPbXHwtcXJk6EKVNAFuEtcW6Xh1IiPRZCuCAxMVHrEoSbkCwIR5IHYeM2WcjOhoULoV49+L//UxsV/fvDiRMwd640KkqJ2+ShlBkUWfnKJenp6QQFBZGWlqbrQbxCCCGEcEOKoq47MWkSnDunbrvzTnUcRbt22tYmdEN6LIRwQUxMjNYlCDchWRCOJA/CRtMsHDoEnTurMzydOwfVq8MHH8Cvv0qjQiN6PTdIj4WLpMdC3/S+joX4l2RBOJI8CBtNsnD5MkybBmvXqj0WFSvCiy+qvRZ+fqVbi3Ci13OD/l6xEDcgLi5O6xKEm5AsCEeSB2FTqlnIylJXyK5XT53hSVFg4EA4eRJmzZJGhRvQ67lBZoUSwgWVK1fWugThJiQLwpHkQdiUShYUBT79VO2VuHhR3da6Nbz+OrRpU/LPL1ym13OD9FgI4YKMjAytSxBuQrIgHEkehE2JZ+HAAejQQZ3h6eJFiIqC9eth3z5pVLghvZ4bNG1Y7N69mwceeIDIyEgMBgNbtmxxun/z5s1069aNKlWqYDAYOHLkSL5jZGdnM2bMGKpUqYK/vz/9+vUjPj7eaZ+LFy/Sq1cvKlWqRGhoKJMmTSIvL68EX5kob7y9vbUuQbgJyYJwJHkQNiWWhZgYGDwY7roLfv4ZKlWCV15Rp48dMAAMhpJ5XnFT9Hpu0LRhkZGRQbNmzVixYkWh97dv356FCxcWeoznn3+er776io0bN/LTTz8RGxtL37597fdbLBZ69epFbm4uv/zyC+vWrWPt2rXMmDGj2F+PEEIIIUSxyMxUGxC33Qbvv69uGzQITp2Cl19WGxhCuBm3mRXKYDDw+eef06dPn3z3nT9/njp16nD48GGaN29u356Wlka1atXYsGEDjzzyCAAnTpygYcOG7Nu3jzZt2vDtt9/Su3dvYmNjCQsLA2DVqlVMnjyZxMREl1uUMiuUviUmJlKtWjWtyxBuQLIgHEkehE2xZUFR4KOPYPJkuHRJ3daunboexZ133vzxRanQ67mhTI+xOHjwIGazma5du9q3NWjQgJo1a7Jv3z4A9u3bR9OmTe2NCoDu3buTnp5OdHR0ocfOyckhPT3d6UfolzQmhY1kQTiSPAibYsnCr7/C3XerMzxdugS1asEnn8CePdKoKGP0em4o0w2LuLg4vL29CQ4OdtoeFhZmn+YrLi7OqVFhu992X2EWLFhAUFCQ/ScqKgpQx3TExsZitVrti5/ExMSQm5tLQkICJpOJ1NRUkpOTycrKIi4ujry8PKd9zWYzcXFxZGZmkpycTEpKChkZGcTHx2M2m532tVgsxMbGkpWVxZUrV0hLS8NoNJKYmEhOTo7TvoqiEBMTQ05ODomJifYGUVJSEllZWS7XnZmZWWDdeXl5+eo2mUwF1m21Wq9bt63eq+s2Go2kpaVx5cqVQus2m83Ex8djMplISUkpUt2pqamYTCYSEhLIzc0ttO6kpCT7e/j333/nq9v2mGvVbbFYCqw7IyMjX91Xv4e2urOyslyuOzs726lu2/sdGxtbYN0JCQn56r58+fJ1675eZi9fvuxUt9FoLFLdSUlJ1/2sGY1Gp89acdbtmNmEhASnz9qpU6dQFIXY2NgCP2tyjtDXOeLixYtOnzU5R+j3HHHq1Cmnz1pRzhFxBw5geeIJaNsWfv0Vxc+PzGnTyDp0iLiOHckroG45R7j3OSI2NrbAz1pZPUe4qkxfCrVhwwaGDh2a7wXfdddddOnShYULFzJy5EguXLjAd999Z78/MzMTPz8/vvnmG3r06FFgPTk5OU7HTU9PJyoqSi6F0ilFUTDIADmBZEE4kzwImxvKQkYGLFoEixera1MYDDB0KMydCxERJVOoKBV6PTeU6R6L8PBwcnNzSU1NddoeHx9PeHi4fZ+rZ4my3bbtUxAfHx8CAwOdfoR+xcbGal2CcBOSBeFI8iBsipQFqxU++EAdmP3KK2qjomNH+P13eO89aVSUA3o9N5TphkXLli3x8vLixx9/tG87efIkFy9epG3btgC0bduWP/74g4SEBPs+27dvJzAwkEaNGpV6zaJsql69utYlCDchWRCOJA/CxuUs/PKLuu7EoEEQGwt16sCmTbBrF9xxR4nWKEqPXs8Nmq68bTKZOHPmjP32uXPnOHLkCCEhIdSsWZPk5GQuXrxob/WdPHkSUHsawsPDCQoKYtiwYUyYMIGQkBACAwMZN24cbdu2pc3/Fovp1q0bjRo14qmnnmLRokXExcUxffp0xowZg4+PT+m/aFEmxcTE6PYkIZxJFoQjyYOwuW4WLlxQZ3r65BP1dkAATJ8Ozz0Hvr6lU6QoNXo9N2g6xmLXrl106dIl3/bBgwezdu1a1q5dy9ChQ/PdP3PmTGbNmgWog6knTpzIRx99RE5ODt27d2flypVOlzlduHCBZ599ll27duHn58fgwYN59dVXqVDB9XaVTDerbzk5OdIQFYBkQTiTPAibQrNgMsGrr8Jrr0FOjjqOYvhwmDMHrppcRpQfej03uM3gbXcnDQt90+t81CI/yYJwJHkQNvmyYLXCunXw0ktgm4WySxdYuhQcJqIR5ZNezw2aXgolRFnhK93U4n8kC8KR5EHYOGVh9254/nk4dEi9XbcuLFkCDz6o9liIck+v54YyPXhbiNJitVq1LkG4CcmCcCR5EDZWqxXOnYNHH4VOndRGRWCgeglUdDQ89JA0KnREr+cG6bEQwgV5eXlalyDchGRBOJI8CADS0/GeMQNWrYLcXPDwgJEj1alkdXg5jNDvuUEaFkK4oFKlSlqXINyEZEE4kjzonMUCa9bAtGlUtE1r37WrOo6iaVNtaxOa0uu5QS6FEsIFKSkpWpcg3IRkQTiSPOjYzp3QsiWMGAEJCeTdcgt89RV8/700KoRuzw0yK5SLZFYofbNYLHh6empdhnADkgXhSPJQPhmNRg4ePEjVOo34+ngyl1KyCPXzpJ5nEr2rh+A/ezZs2aLuHBwMM2dieeYZPCtW1LJs4Ub0em6QS6GEcEFcXJwuF7oR+UkWhCPJQ/mUkJDA//34Jz+bM/AwgO0bWMVqxeO7hTx29Hvw9IRnn4VZs6BKFeJ0uiCaKJhezw3SY+Ei6bEQQggh9OG9T79i7iEDClfN4qQoeCgKm39bQfOVS6FRI20KFMJNyRgLIVwQExOjdQnCTUgWhCPJQ/l0xlK14DsMBvAw8NWERfkaFZIF4UiveZCGhRAuCA0N1boE4SYkC8KR5KF8upJoBGshF3QYDCRkWPJtliwIR3rNgzQshHBBcnKy1iUINyFZEI4kD+VMSgpMmEDdzz/Cg8KvFA/1yz8oV7IgHOk1D9KwEMIF/v7+Wpcg3IRkQTiSPJQTeXmwYgXUqwfLlvHY0e+xGgyQbxiqgqIo3OaZlO8QkgXhSK95kIaFEC7Izc3VugThJiQLwpHkoRz47jto1gzGjoUrV6BxY3yWzqOd9wUMBgOe6rAKPAxgAJ5rU4WeHe/MdxjJgnCk1zzIdLNCuEAmTxM2kgXhSPJQhp04ARMnwjffqLerVIE5c2DECAKyshh+8CBT6zRiq8M6Frd5JtGzYxMCAgLyHU6yIBzpNQ/SsBDCBb6+vlqXINyEZEE4kjyUQVeuwOzZsHIlWCzg5QXjxsHLL6uL3QEBAQF07twZgMa1XBuEK1kQjvSaB7kUSggXpKena12CcBOSBeFI8lCGmM3w5pvqOIq33lIbFQ89BNHRsGSJvVFxoyQLwpFe8yA9FkK4oEqVKlqXINyEZEE4kjyUAYqiXu40cSKcPKlua9oUli2De+8ttqeRLAhHes2D9FgI4YKEhAStSxBuQrIgHEke3Fx0NNx/P/TurTYqqlWDd96Bw4eLtVEBkgXhTK95MCh6HV1SROnp6QQFBZGWlkZgYKDW5QghhBCiMElJMHOm2oiwWMDbG8aPh5degqAgrasTotySHgshXBATE6N1CcJNSBaEI8mDm8nNVS9xuvXWfwdn9+0Lf/0FCxeWaKNCsiAc6TUP0mPhIumx0Le8vDwqVJAhSUKyIJxJHtyEosDWreo4itOn1W3Nm6uNjP/N7lTSJAvCkV7zID0WQrggKSn/KqtCnyQLwpHkwQ0cOwb33QcPPqg2KsLC4P/+D37/vdQaFSBZEM70mgf9NaWEuAFBck2u+B/JgnAkedBQQgLMmAH//S9YreDjAxMmwNSpUMACdiVNsiAc6TUP0mMhhAuysrK0LkG4CcmCcCR50EBODixerK5H8c47aqPi0Ufh+HGYP1+TRgVIFoQzveZBeiyEcIGHh7TBhUqyIBxJHkqRosCWLfDCC/D33+q2li3VcRQdOmhaGkgWhDO95kGfr1qIItLjACxRMMmCcCR5KCVHjsA996gzPP39N0REwNq18NtvbtGoAMmCcKbXPEjDQggXZGZmal2CcBOSBeFI8lDC4uJg+HC44w7YtQt8fWH6dDh1CgYPBjf6VliyIBzpNQ/6bE4JUUTBwcFalyDchGRBOJI8lJDsbHj9dZg3D0wmddsTT8Crr0LNmpqWVhjJgnCk1zy4T1NfCDeWmJiodQnCTUgWhCPJQzFTFNi0CRo2VGd3Mpngrrvg559hwwa3bVSAZEE402seZIE8F8kCeUIIIUQJOngQnn8e9uxRb1evrvZQDBjgVpc8CSEKJ59UIVwQExOjdQnCTUgWhCPJQzGIjYWhQ+HOO9VGRcWKMGsWnDwJTz5ZZhoVkgXhSK95kB4LF0mPhb5ZrVbdTh0nnEkWhCPJw03IyoKlS2HBAsjIULc9+aR6u0YNbWu7AZIF4UivedDfKxbiBsTFxWldgnATkgXhSPJwAxQFPv4YGjRQZ3jKyIA2beDXX+GDD8pkowIkC8KZXvMgs0IJ4YKQkBCtSxBuQrIgHEkeiui339RxFL/8ot6OioJFi+Dxx8Fg0La2myRZEI70mgfpsRDCBSbbdIdC9yQLwpHkwUWXLsGgQdC6tdqoqFQJ5syBEyegf/8y36gAyYJwptc8aNqw2L17Nw888ACRkZEYDAa2bNnidL+iKMyYMYOIiAgqVqxI165dOX36tNM+ycnJDBw4kMDAQIKDgxk2bFi+X+axY8fo0KEDvr6+REVFsWjRopJ+aaKc8fb21roE4SYkC8KR5OE6MjNh9myoX1+9zAnUhe1On1Yvg6pUSdv6ipFkQTjSax40bVhkZGTQrFkzVqxYUeD9ixYt4s0332TVqlXs378fPz8/unfvTnZ2tn2fgQMHEh0dzfbt29m6dSu7d+9m5MiR9vvT09Pp1q0btWrV4uDBgyxevJhZs2bx7rvvlvjrE0IIIXTJaoX169UGxaxZagOjfXs4cADWroXISK0rFEKUALeZFcpgMPD555/Tp08fQO2tiIyMZOLEibzwwgsApKWlERYWxtq1a+nfvz/Hjx+nUaNGHDhwgFatWgGwbds2evbsyaVLl4iMjOTtt99m2rRpxMXF2VuPU6ZMYcuWLZw4ccLl+mRWKH1LTEykWrVqWpch3IBkQTiSPBTg119h/HjYv1+9XasWLF4MjzxSLi55KoxkQTjSax7cdozFuXPniIuLo2vXrvZtQUFBtG7dmn379gGwb98+goOD7Y0KgK5du+Lh4cH+/53Q9u3bR8eOHZ26pLp3787JkydJSUkppVcjyjppTAobyYJwJHlwcPGiuphd27Zqo8LfH+bPV8dRPPpouW5UgGRBONNrHty2YWGbpissLMxpe1hYmP2+uLg4QkNDne6vUKECISEhTvsUdAzH5yhITk4O6enpTj9Cv65cuaJ1CcJNSBaEI8kDYDLBjBnqZU8ffaQ2IIYNU8dRTJ0Kvr5aV1gqJAvCkV7z4LYNC60tWLCAoKAg+09UVBQA2dnZxMbGYrVa7asqxsTEkJubS0JCAiaTidTUVJKTk8nKyiIuLo68vDynfc1mM3FxcWRmZpKcnExKSgoZGRnEx8djNpud9rVYLMTGxpKVlcWVK1dIS0vDaDSSmJhITk6O076KohATE0NOTg6JiYn2BlFSUhJZWVku152ZmVlg3Xl5efnqNplMBdZttVqvW7et3qvrNhqNpKWlceXKlULrNpvNxMfHYzKZSElJKVLdqampmEwmEhISyM3NLbTupKQk+3vo5eWVr27bY65Vt8ViKbDujIyMfHVf/R7a6s7KynK57uzsbKe6be93bGxsgXUnJCTkq/vy5cvXrft6mb18+bJT3UajsUh1JyUlXfezZjQanT5rxVm3Y2YTEhKcPmtWqxVFUYiNjS3wsybnCH2dI/z8/Jw+a7o6R/zzD7z/PpZbb1VneMrOxtK+Pek7d5KyeDEZAQG6OkdYrVanz5qcI/R9jqhcuXKBn7Wyeo5wlduOsfj777+pW7cuhw8fpnnz5vb9OnXqRPPmzXnjjTdYvXo1EydOdLqkKS8vD19fXzZu3MjDDz/MoEGDSE9Pd5pxaufOndxzzz0kJydTuXLlAuvJyclxeiPT09OJioqSMRY6FRMTQ/Xq1bUuQ7gByYJwpNs8/PyzOo7i99/V23XqwGuvwcMPl/tLngqj2yyIAuk1D27bY1GnTh3Cw8P58ccf7dvS09PZv38/bdu2BaBt27akpqZy8OBB+z47duzAarXSunVr+z67d+/GbDbb99m+fTv169cvtFEB4OPjQ2BgoNOP0C89nhxEwSQLwpHu8nD+vLqYXfv2aqMiIEBd4O74cejbV7eNCtBhFsQ16TUPmjYsTCYTR44c4ciRI4A6YPvIkSNcvHgRg8HA+PHjmTt3Ll9++SV//PEHgwYNIjIy0t6r0bBhQ+6//35GjBjBb7/9xs8//8zYsWPp378/kf+bym7AgAF4e3szbNgwoqOj+eSTT3jjjTeYMGGCRq9alEW27kIhJAvCkW7yYDTCtGnQoAF8+il4eMDIkeo4ikmTwMdH6wo1p5ssCJfoNQ+aXgq1a9cuunTpkm/74MGDWbt2LYqiMHPmTN59911SU1Np3749K1eu5LbbbrPvm5yczNixY/nqq6/w8PCgX79+vPnmm/j7+9v3OXbsGGPGjOHAgQNUrVqVcePGMXny5CLVKtPN6ltOTg4+8g+nQLIgnJX7PFitsG4dvPQS2CY86dIFli2DZs20rc3NlPssiCLRax7cZoyFu5OGhb4lJCTkm4FM6JNkQTgq13n46Sd4/nk4fFi9feut6jiKBx/U9SVPhSnXWRBFptc8uO0YCyHcScWKFbUuQbgJyYJwVC7z8Pff6mJ2nTurjYqgIFiyBKKj4aGHpFFRiHKZBXHD9JqHCloXIERZYJtGUAjJgnBUrvKQng7z5sHrr0NurjqO4plnYPZs0OEKwkVVrrIgbppe8yANCyFckJeXp3UJwk1IFoSjcpEHiwVWr4bp0yEhQd12332wdCk0aaJtbWVIuciCKDZ6zYM0LIRwQaVKlbQuQbgJyYJwVObzsGOHOo7i2DH19m23qQ2Knj3lkqciKvNZEMVKr3mQMRZCuCA1NVXrEoSbkCwIR2U2D6dPQ58+cO+9aqMiOFi9BOrPP6FXL2lU3IAymwVRIvSaB5kVykUyK5S+WSwWPD09tS5DuAHJgnBU5vKQmgpz58Kbb4LZDJ6eMHo0zJwJVapoXV2ZVuayIEqUXvMgPRZCuCDONn+70D3JgnBUZvKQlwerVkG9euoMT2Yz9OgBf/yhNjKkUXHTykwWRKnQax6kx8JF0mMhhBCiTNq+HSZMUC9zAmjYUG1c9OihbV1CiHJHeiyEcEFMTIzWJQg3IVkQjtw6DydPwgMPQLduaqMiJASWL4ejR6VRUQLcOgui1Ok1D9Jj4SLpsdA3s9mMl5eX1mUINyBZEI7cMg8pKfDKK2ojIi8PKlSAsWNhxgyoXFnr6sott8yC0Ixe8yA9FkK4IDk5WesShJuQLAhHbpUHs1ltTNx6qzrDU14e9O6t9lYsWyaNihLmVlkQmtNrHmQdCyFc4O/vr3UJwk1IFoQjt8nDtm3qOIrjx9XbjRurjYn77tO2Lh1xmywIt6DXPEiPhRAuyM3N1boE4SYkC8KR5nk4flxdzK5HD/X/q1aFt9+GI0ekUVHKNM+CcCt6zYP0WAghhBBlzZUrMHs2rFwJFgt4ecFzz8H06epid0IIoQFpWAjhAm9vb61LEG5CsiAclXoezGa1MTF7tjpIG9QVtBctUteoEJqRc4NwpNc8yKVQQrjAZDJpXYJwE5IF4ajU8qAo8PXX0LQpjB+vNipuvx1+/BE+/1waFW5Azg3CkV7zIA0LIVwQEhKidQnCTUgWhKNSycOff0L37uoMTydPQmgovPsuHDoE99xT8s8vXCLnBuFIr3mQhoUQLkhISNC6BOEmJAvCUYnmITERRo+GZs3U1bO9vWHyZDh9GkaMAE/PkntuUWRybhCO9JoHWSDPRbJAnhBCiFKRm6uuR/HKK5CWpm7r108dR3HLLdrWJoQQ1yA9FkK4ICYmRusShJuQLAhHxZoHRYEvvlDXoJg4UW1UtGgBu3bBpk3SqHBzcm4QjvSaB+mxcJH0WOibxWLBUy47EEgWhLNiy8OxY/D887Bjh3o7LAzmz4fBg+WSpzJCzg3CkV7zID0WQrhAr9dKivwkC8LRTechIQGeeUbtmdixA3x84KWX1HEUTz8tjYoyRM4NwpFe8yDrWAjhgmBZcEr8j2RBOLrhPOTkwJtvwpw5YDSq2x57DBYuhNq1i6s8UYrk3CAc6TUP0mMhhAuysrK0LkG4CcmCcFTkPCgKbN4MjRrBiy+qjYpWrWDPHvjkE2lUlGFybhCO9JoH6bEQwgUeHtIGFyrJgnBUpDwcPqyOo/jpJ/V2RAS8+io8+SRIrso8OTcIR3rNgzQshHCBHgdgiYJJFoQjl/IQFwfTpsGaNWqPha8vTJqk9lj4+5d8kaJUyLlBONJrHvTZnBKiiPTapSnykywIR9fMQ3Y2LFgA9erB6tVqo2LAAHX17FdekUZFOSPnBuFIr3mQHgshXKDXQVgiP8mCcFRgHhRFXXfixRfh/Hl1W+vWsGwZtG1bmuWJUiTnBuFIr3lwqWHx5ptvFvnAQ4cOJSAgoMiPE8IdJSYmUr16da3LEG5AsiAc5cvD77+r4yj27lVvV6+uzvT0xBMyjqKck3ODcKTXPLi0QJ6Hhwc1atRw+Xqxf/75h1OnTnFLOVolVBbIE0IIUajYWHX9iXXr1NuVKqk9Fi+8AH5+2tYmhBClxOVLoX7//XdCQ0Nd2ld6KkR5ExMTo8tvHkR+kgXhKObMGap//LE6u1NGhrrxqafUVbNr1NC2OFGq5NwgHOk1Dy41LGbOnIl/EQaZvfTSS4SEhNxwUUK4m4iICK1LEG5CsiAAdRzFJ58Q+eKL8M8/6ra2beH11+GuuzQtTWhDzg3CkV7z4NKlUEIuhdK72NhYIiMjtS5DuAHJgmD/fnUcxb596u2aNdVxFI8/DgaDtrUJzci5QTjSax5kVighXCA9cMJGsqBjly7B1Knw4YfqbT8/zJMm4fXii1Cxora1Cc3JuUE40mseXG5YdOnSBcN1vokxGAz8+OOPN12UEO7GZDLh6+urdRnCDUgWdCgjAxYvhkWLwDY3/ZAhMG8ead7eVJVGhUDODcKZXvPgcsOiefPmhd5nNBrZsGEDOTk5xVGTEG7H29tb6xKEm5As6IjVChs2wJQpEBOjbmvfXh1H0bIlAN7p6drVJ9yKnBuEI73mweVJtZctW5bvZ/HixdSuXZsvv/yS6tWrs379+mIv0Gg0Mn78eGrVqkXFihW5++67OXDggP1+RVGYMWMGERERVKxYka5du3L69GmnYyQnJzNw4EACAwMJDg5m2LBhmEymYq9VCCFEObFvnzoY+6mn1EZF7dqwcSPs3m1vVAghhHB2w6v1rF+/nvr167Nw4UJmzZrF8ePH6d+/f3HWBsDw4cPZvn07H3zwAX/88QfdunWja9euxPzv26NFixbx5ptvsmrVKvbv34+fnx/du3cnOzvbfoyBAwcSHR3N9u3b2bp1K7t372bkyJHFXqsov3Jzc7UuQbgJyUI5d/EiDBgAd98Nv/0G/v6wYAEcPw6PPJJvcLbkQdhIFoQjveahyLNCbdu2jSlTpnDu3DleeOEFJkyYgF8JLf6TlZVFQEAAX3zxBb169bJvb9myJT169GDOnDlERkYyceJEXnjhBQDS0tIICwtj7dq19O/fn+PHj9OoUSMOHDhAq1at7K+hZ8+eXLp0yeUR+zIrlL5lZ2fr8lpJkZ9koZwymdSZnV57DbKz1QbE00/D3LkQHl7owyQPwkayIBzpNQ8u91j89ttvdOnShYcffpguXbpw9uxZXn755RJrVADk5eVhsVjy/WIqVqzI3r17OXfuHHFxcXTt2tV+X1BQEK1bt2bf/6YB3LdvH8HBwfZGBUDXrl3x8PBg//79hT53Tk4O6enpTj9Cv5KTk7UuQbgJyUI5Y7Wqq2XfdpvaiMjOhk6d4NAh+L//u2ajAiQP4l+SBeFIr3lwuWHRpk0bfvvtN0aNGkWdOnXYsGEDb775Zr6f4hQQEEDbtm2ZM2cOsbGxWCwWPvzwQ/bt28fly5eJi4sDICwszOlxYWFh9vvi4uLyrRheoUIFQkJC7PsUZMGCBQQFBdl/oqKiALUFGhsbi9VqtV+OFRMTQ25uLgkJCZhMJlJTU0lOTiYrK4u4uDjy8vKc9jWbzcTFxZGZmUlycjIpKSlkZGQQHx+P2Wx22tdisRAbG0tWVhZXrlwhLS0No9FIYmIiOTk5TvsqikJMTAw5OTkkJibaG0RJSUlkZWW5XHdmZmaBdefl5eWr22QyFVi31Wq9bt22eq+u22g0kpaWxpUrVwqt22w2Ex8fj8lkIiUlpUh1p6amYjKZSEhIIDc3t9C6k5KS7O9hhQoV8tVte8y16rZYLAXWnZGRka/uq99DW91ZWVku152dne1Ut+39jo2NLbDuhISEfHVfvnz5unVfL7OXL192qttoNBap7qSkpOt+1oxGo9NnrTjrdsxsQkKC02fNYrGgKAqxsbEFftbkHFGGzhF795LbooU6w9Ply1jr1MG4bh2pn3+O6dZbXTpHVKpUyemzJucI/Z4jLBaL02dNzhHl4BxRSN2ufNaCg4ML/KyV1XOEq1y+FKp27douTTf7999/u/zkrjh79ixPP/00u3fvxtPTkzvuuIPbbruNgwcP8t5779GuXTtiY2OdVjh87LHHMBgMfPLJJ8yfP59169Zx8uRJp+OGhoYye/Zsnn322QKfNycnx+mNTE9PJyoqSi6F0qmYmBiqV6+udRnCDUgWyoFz52DyZHUwNkBgILz8MowbBz4+RTqU5EHYSBaEI73mweXpZs+fP1+CZRSubt26/PTTT2RkZJCenk5ERASPP/44t9xyC+H/66KOj493aljEx8fbp8cNDw8nISHB6Zh5eXkkJyfbH18QHx8ffIr4D4wov/R4chAFkyyUYUajOhB76VLIyQEPDxgxAl55Ba7q2XaV5EHYSBaEI73m4YZnhSptfn5+REREkJKSwnfffcdDDz1EnTp1CA8Pd1qULz09nf3799O2bVsA2rZtS2pqKgcPHrTvs2PHDqxWK61bty711yHKJlt3oRCShTLIYoH33oN69dSGRU4O3HsvHD4Mq1bdcKMCJA/iX5IF4UiveSjyrFAABw4cYOfOnSQkJGC1Wp3uW7p0abEVB/Ddd9+hKAr169fnzJkzTJo0CV9fX/bs2YOXlxcLFy7k1VdfZd26ddSpU4eXX36ZY8eO8ddff9kHfffo0YP4+HhWrVqF2Wxm6NChtGrVig0bNrhch8wKpW+5ubm6XexGOJMslDE//QTjx8ORI+rtW2+FJUvggQfyTR17IyQPwkayIBzpNQ8uXwplM3/+fKZPn079+vUJCwtzGndxvTEYNyItLY2pU6dy6dIlQkJC6NevH/PmzcPLywuAF198kYyMDEaOHElqairt27dn27ZtTjNJrV+/nrFjx3Lvvffi4eFBv379in2guSjfUlNT800CIPRJslBGnD0LL74Imzert4OCYOZMGDMGivEfe8mDsJEsCEd6zUOReyzCwsJYuHAhQ4YMKaGS3JP0WOib0WgkICBA6zKEG5AsuLn0dJg3D15/HXJz1XEUo0bB7NlQtWqxP53kQdhIFoQjveahyD0WHh4etGvXriRqEcJt2aYRFEKy4KZs4yimT4fERHVbt27qZU9NmpTg00oehEqyIBzpNQ9FHrz9/PPPs2LFipKoRQi3dfVYIqFfkgU3tGMH3HEHPPOM2qioXx++/hq2bSvRRgVIHsS/JAvCkV7zUORLoaxWK7169eLUqVM0atTIPtbBZrPtetZyRi6F0resrCwqVqyodRnCDUgWtPPnhQTe3naEXC9/vM0mRjesTOOl8+CLL9QdKleGWbPg2Wfhqn+bSorkQdhIFoQjveahyJdCPffcc+zcuZMuXbpQpUqVEhmwLYS7SU1N1eUJQuQnWSh9RqORj/dfYP4PFwAFSMFgtfLt2TgWnjXxiKcnhjFj1MHZISGlWpvkQdhIFoQjveahyD0WAQEBfPzxx/Tq1aukanJL0mOhbxaLBU9PT63LEG5AslD6Pv56B1P3ZKJw1RdZioIBhTduTeXBEU9pUpvkQdhIFoQjveahyGMsQkJCqFu3bknUIoTbiouL07oE4SYkC6XvtKVqwf9YGQwYPDw4GtastEuykzwIG8mCcKTXPBS5YTFr1ixmzpxJZmZmSdQjhFuqXr261iUINyFZKGUnT5LwzQ6UawyETMjQbvYVyYOwkSwIR3rNQ5EbFm+++SbffvstYWFhNG3alDvuuMPpR4jyKCYmRusShJuQLJSS5GR1xewmTahx/MjVF0E5CfXT7nIDyYOwkSwIR3rNQ5EHb/fp06cEyhDCvelx9UxRMMlCCTOb4Z131IHYyckA3FchiVUGD9SB245NDAVFgds8k7SoFJA8iH9JFoQjveahyIO39UoGb+tbfHw8YWFhWpch3IBkoQR9+y1MmAAnTqi3mzSBpUsxtmnDJ79dYN5226xQBvt/p91Xi8fvqqXZCreSB2EjWRCO9JqHIvdYCKFH/v7+Wpcg3IRkoQT89RdMnKguaAdQtSrMnQvDhkGFCgQAw+9tQttbQ1npuI7F/c1pXEvbbwUlD8JGsiAc6TUPLjUsQkJCOHXqFFWrVnXpoDVr1mTPnj3UqlXrpooTwl3k5ubi5+endRnCDUgWitGVK+qCdm+/DRaLuqjdf/4D06ZBcHC+3RvXCmXFM91KvcxrkTwIG8mCcKTXPLjUsEhNTeXbb78lKCjIpYNeuXIFi0W7WTqEEEK4sdxcWLkSZs+G1FR1W58+sHgx3HqrlpUJIYS4CS5fCjV48OCSrEMIt+bt7a11CcJNSBZugqLA11+rlz2dOqVua9YMli2DLl20re0GSR6EjWRBONJrHlxqWFivMX+4EHpgMpl02aUp8pMs3KA//1QHZm/frt4ODYV582DoUCjDq9NKHoSNZEE40mseiryOhRB6FBISonUJwk1IFoooMRFGj1Z7JrZvB29vmDwZTp+G4cPLdKMCJA/iX5IF4UiveZCGhRAuSEhI0LoE4SYkCy7KzYUlS6BePXVwttUKjzwCx4/Dq69COZm2W/IgbCQLwpFe8yDrWLhI1rEQQggXKAp8+SW88AKcOaNua9ECXn8dOnbUtDQhhBAlS3oshHBBTEyM1iUINyFZuIajR+Hee9UZns6cgfBwWLMGfv+93DYqJA/CRrIgHOk1D9Jj4SLpsdA3i8WCZxm/FlwUD8lCAeLj4eWX4f/+T+2x8PFReywmTwaNVsQuLZIHYSNZEI70mgfpsRDCBXq9VlLkJ1lwkJMDixap4yj++1+1UfH443DihLpydjlvVIDkQfxLsiAc6TUPLq9jIYSeBRewCrDQJ8kCagNi82aYNAnOnVO3tWqljqNo107T0kqb5EHYSBaEI73mQXoshHBBZmam1iUIN6H7LBw6BJ07qzM8nTsHkZGwbh3s36+7RgVIHsS/JAvCkV7zID0WQrigQgX5qAiVbrNw+TJMn64OxlYU8PWFF19Uf3S4CJSNbvMg8pEsCEd6zYPLPRZ///03Ms5b6JWHh3TuCZXuspCVBfPnw223werVaqNiwAA4eRJmz9Z1owJ0mAdRKMmCcKTXPLj8quvVq0diYqL99uOPP058fHyJFCWEu8nKytK6BOEmdJMFRYFPP4WGDWHaNDCZoHVr2LcP1q+HmjW1rtAt6CYP4rokC8KRXvPgcsPi6t6Kb775hoyMjGIvSAh3FBQUpHUJwk3oIgu//w4dOqgzPF24ADVqqI2JX36BNm20rs6t6CIPwiWSBeFIr3nQZz+NEEWUlJSkdQnCTZTrLMTEwODBcOed8PPPUKmSernTyZPq5U867dq/lnKdB1EkkgXhSK95cHlkicFgwGAw5NsmhB5Ur15d6xKEmyiXWcjMhCVL4NVX1f8HGDRIHVtRHl9vMSqXeRA3RLIgHOk1Dy43LBRFYciQIfj4+ACQnZ3NqFGj8Ltq4N7mzZuLt0Ih3EBMTIxuTxLCWbnKgqLAxx+rK2T/84+67e671fUo7rxT09LKinKVB3FTJAvCkV7zYFBcnOpp6NChLh1wzZo1N1WQu0pPTycoKIi0tDQCAwO1LkeUMkVRpIdOAOUoC/v3w/jx8Ouv6u2aNdVVtB97DMrD6ysl5SYP4qZJFoQjvebB5YaF3knDQt9iY2OJjIzUugzhBsp8Fv75B6ZOVQdjgzpd7NSpMGECVKyobW1lUJnPgyg2kgXhSK950OfqHUIUUZUqVbQuQbiJMpuFjAxYvFjtlcjKUnslhgyBefMgIkLr6sqsMpsHUewkC8KRXvMgU3wI4YL09HStSxBuosxlwWqFDz6A+vXVGZ6ystSpZA8cUBe8k0bFTSlzeRAlRrIgHOk1D9JjIYQLbJMWCFGmsvDLL+o4igMH1Nu1a8Nrr0HfvjKOopiUqTyIEiVZEI70mge37rGwWCy8/PLL1KlTh4oVK1K3bl3mzJnjtFifoijMmDGDiIgIKlasSNeuXTl9+rTTcZKTkxk4cCCBgYEEBwczbNgwTCZTab8cIYQoHRcuwBNPQLt2aqMiIECdSvb4cejXTxoVQgghSoRbNywWLlzI22+/zfLlyzl+/DgLFy5k0aJFvPXWW/Z9Fi1axJtvvsmqVavYv38/fn5+dO/enezsbPs+AwcOJDo6mu3bt7N161Z2797NyJEjtXhJoozKzc3VugThJtw6CyYTTJ8ODRqo08gaDDB8OJw6pU4p6+urdYXljlvnQZQqyYJwpNc8uPWsUL179yYsLIz33nvPvq1fv35UrFiRDz/8EEVRiIyMZOLEibzwwgsApKWlERYWxtq1a+nfvz/Hjx+nUaNGHDhwgFatWgGwbds2evbsyaVLl1wesS+zQulbdnY2vvJHmcBNs2C1wvvvw0svweXL6rbOnWHZMmjeXMvKyj23zIPQhGRBONJrHty6x+Luu+/mxx9/5NSpUwAcPXqUvXv30qNHDwDOnTtHXFwcXbt2tT8mKCiI1q1bs2/fPgD27dtHcHCwvVEB0LVrVzw8PNi/f38pvhpRliUnJ2tdgnATbpeFPXvUxeyGDlUbFXXrwuefw44d0qgoBW6XB6EZyYJwpNc8uPXg7SlTppCenk6DBg3w9PTEYrEwb948Bg4cCEBcXBwAYWFhTo8LCwuz3xcXF0doaKjT/RUqVCAkJMS+T0FycnLIycmx39br6H6hCg8P17oE4SbcJgvnzqmXN23cqN4ODISXX4Zx40Cngwa14DZ5EJqTLAhHes2DW/dYfPrpp6xfv54NGzZw6NAh1q1bx2uvvca6detK/LkXLFhAUFCQ/ScqKgpQu7ZiY2OxWq3ExMQA6rLtubm5JCQkYDKZSE1NJTk5maysLOLi4sjLy3Pa12w2ExcXR2ZmJsnJyaSkpJCRkUF8fDxms9lpX4vFQmxsLFlZWVy5coW0tDSMRiOJiYnk5OQ47asoCjExMeTk5JCYmEh6ejrp6ekkJSWRlZXlct2ZmZkF1p2Xl5evbpPJVGDdVqv1unXb6r26bqPRSFpaGleuXCm0brPZTHx8PCaTiZSUlCLVnZqaislkIiEhgdzc3ELrTkpKsr+HJ0+ezFe37THXqttisRRYd0ZGRr66r34PbXVnZWW5XHd2drZT3bb3OzY2tsC6ExIS8tV9+fLl69Z9vcxevnzZqW6j0VikupOSkq77WTMajU6fteKs2zGzCQkJTp+16OhoFEUhNja2wM9aiZ8jrlzBOHYsNGwIGzeieHiQ8dRTZB07xpWhQ0nLzpZzRCmeI86ePev0WZNzhH7PEdHR0U6fNc3OEfJ3hFucIy5cuFDgZ62sniNc5dZjLKKiopgyZQpjxoyxb5s7dy4ffvghJ06c4O+//6Zu3bocPnyY5g5d/p06daJ58+a88cYbrF69mokTJ5KSkmK/Py8vD19fXzZu3MjDDz9c4HMX1GMRFRUlYyyEENqwWGDtWpg2DeLj1W333quOo2jaVNPShBBCCHDzHovMzEw8PJxL9PT0xGq1AlCnTh3Cw8P58ccf7fenp6ezf/9+2rZtC0Dbtm1JTU3l4MGD9n127NiB1WqldevWhT63j48PgYGBTj9Cv2yteiE0ycKuXdCqlTrDU3w81KsHX34J27dLo0Jjcm4QNpIF4UiveXDrMRYPPPAA8+bNo2bNmjRu3JjDhw+zdOlSnn76aQAMBgPjx49n7ty51KtXjzp16vDyyy8TGRlJnz59AGjYsCH3338/I0aMYNWqVZjNZsaOHUv//v1dnhFKiGrVqmldgnATpZqFs2dh0iR1MDZAcDDMnAmjR4O3d+nVIQol5wZhI1kQjvSaB7e+FMpoNPLyyy/z+eefk5CQQGRkJE888QQzZszA+3//qCqKwsyZM3n33XdJTU2lffv2rFy5kttuu81+nOTkZMaOHctXX32Fh4cH/fr1480338Tf39/lWmS6WX1LSEjINwmA0KdSyUJaGsybB2+8Abm54OkJo0bBrFlQtWrJPrcoEjk3CBvJgnCk1zy4dcPCnUjDQt9MJlORGqKi/CrRLFgs8H//p87ulJiobuvWDZYuhcaNS+Y5xU2Rc4OwkSwIR3rNg1tfCiWEu8jLy9O6BOEmSiwLP/4Izz8Pf/yh3m7QAJYsgR491BW0hVuSc4OwkSwIR3rNg1sP3hbCXdgmDBCi2LNw+jQ89BB07ao2KipXhjffhGPHoGdPaVS4OTk3CBvJgnCk1zxIj4UQLqhYsaLWJQg3UWxZSEmBOXNg+XIwm6FCBXVQ9syZEBJSPM8hSpycG4SNZEE40msepMdCCBekpaVpXYJwEzedhbw8WLlSnTJ22TK1UdGrl9pb8cYb0qgoY+TcIGwkC8KRXvMgg7ddJIO39S0vL48KFaSDT9xkFr7/Xh1H8ddf6u1GjdSB2d27F1+BolTJuUHYSBaEI73mQXoshHBBvG2lY6F7N5SFEyegd2+1AfHXX1ClCqxYAUePSqOijJNzg7CRLAhHes2D9Fi4SHoshBBFlpwMs2erlz7l5anjKJ57DqZPVwdpCyGEEOWI9FgI4YKYmBitSxBuwqUsmM3w1ltw663qDE95efDggxAdrU4hK42KckPODcJGsiAc6TUP0mPhIumx0Dez2YyXl5fWZQg3cN0sfPstTJigXv4E0KSJOki7a9fSKVCUKjk3CBvJgnCk1zxIj4UQLrhy5YrWJQg3UWgWoqPh/vvVtSdOnIBq1WDVKjh8WBoV5ZicG4SNZEE40mse9DdcXYgbIL1UwiZfFpKSYNYstRFhsYCXF4wfD9OmQVCQFiWKUiTnBmEjWRCO9JoH6bEQwgXZ2dlalyDchD0LubnqJU716qkzPFks8PDD6qxPixZJo0In5NwgbCQLwpFe8yA9FkK4wGAwaF2CcBMGgK++gokT4fRpdWOzZvD669C5s3aFCU3IuUHYSBaEI73mQRoWQrjA29tb6xKEO/jjDwL/8x/YuVO9HRYG8+bBkCHg6alpaUIbcm4QNpIF4UiveZBLoYRwgclk0roEoaWEBBg1Cpo3x3PnTvD2hilT4NQpGDZMGhU6JucGYSNZEI70mgfpsRDCBSEhIVqXILSQk6OuRzFnDqSnA2Dt1w+PxYuhTh2NixPuQM4NwkayIBzpNQ/SYyGECxISErQuQZQmRYEtW6BxY5g0SW1U3HEH7N7N5TfekEaFsJNzg7CRLAhHes2DLJDnIlkgTwidOHoUnn/+33EU4eGwYAEMGgQe8l2MEEIIURj5V1IIF8TExGhdgihp8fEwYgS0aKE2Knx91bUoTp9WB2f/r1EhWRCOJA/CRrIgHOk1D9Jj4SLpsdA3i8WCpwzQLZ+ys+GNN9TZnYxGdVv//vDqq1CrVr7dJQvCkeRB2EgWhCO95kF6LIRwQXx8vNYliOKmKLBpEzRqpM7wZDTCnXfC3r3w0UcFNipAsiCcSR6EjWRBONJrHmRWKCFcULlyZa1LEMXp0CEYPx727FFvV6+u9lAMGHDdcRSSBeFI8iBsJAvCkV7zID0WQrggMzNT6xJEcbh8GZ5+Glq1UhsVFSvCzJlw8iQ8+aRLg7MlC8KR5EHYSBaEI73mQXoshHBBhQryUSnTsrJg6VJ1dqeMDHXbwIHq7aioIh1KsiAcSR6EjWRBONJrHvT5qoUoIg+ZZrRsUhT49FN48UW4eFHd1qYNvP46tG59Q4eULAhHkgdhI1kQjvSaB32+aiGKKDs7W+sSRFEdOADt26szPF28qPZMbNgAv/xyw40KkCwIZ5IHYSNZEI70mgfpsRDCBTLFcBkSEwMvvQTvv6/erlRJnfVp4kT1/2+SZEE4kjwIG8mCcKTXPEiPhRAuSEpK0roEcT2ZmfDKK3Dbbf82KgYPhlOn4OWXi6VRAZIF4UzyIGwkC8KRXvMgC+S5SBbIE8JNKYq67sTkyXDpkrqtXTtYtkxdl0IIIYQQpUJ6LIRwQUxMjNYliIL8+ivcfbc6w9OlS+qidp98ok4lW0KNCsmCcCR5EDaSBeFIr3mQHgsXSY+FvimKgsFg0LoMYfPPP+q4iQ0b1Nt+fuq4iuefV9emKEGSBeFI8iBsJAvCkV7zID0WQrggNjZW6xIEqGtQzJwJ9eurjQqDQV3w7vRptWFRwo0KkCwIZ5IHYSNZEI70mgeZFUoIF1StWlXrEvTNaoX169VeCtvJumNHdRzFHXeUaimSBeFI8iBsJAvCkV7zID0WQrggPT1d6xL06+ef1XUnBg1SGxV16sCmTbBrV6k3KkCyIJxJHoSNZEE40msepGEhhAt8fHy0LkF/LlxQF7dr3x5+/x0CAmDhQvjrL+jXT70MSgOSBeFI8iBsJAvCkV7zIJdCCSHci9EIr74KS5ZATo7agBg+HObMgbAwrasTQgghRCHcvseidu3aGAyGfD9jxowB1CXTx4wZQ5UqVfD396dfv37Ex8c7HePixYv06tWLSpUqERoayqRJk8jLy9Pi5YgyKjc3V+sSyj+rFdasURe4mz9fbVR06QKHD8O777pNo0KyIBxJHoSNZEE40mse3L5hceDAAS5fvmz/2b59OwCPPvooAM8//zxfffUVGzdu5KeffiI2Npa+ffvaH2+xWOjVqxe5ubn88ssvrFu3jrVr1zJjxgxNXo8om/z8/LQuoXzbvVtdd+LppyEuDurWhS1b4McfoVkzratzIlkQjiQPwkayIBzpNQ9lbh2L8ePHs3XrVk6fPk16ejrVqlVjw4YNPPLIIwCcOHGChg0bsm/fPtq0acO3335L7969iY2NJex/33iuWrWKyZMnk5iYiLe3t0vPK+tY6FtsbCyRkZFal1H+/P03vPgifPaZejswEGbMgLFjwU2vT5UsCEeSB2EjWRCO9JoHt++xcJSbm8uHH37I008/jcFg4ODBg5jNZrp27Wrfp0GDBtSsWZN9+/YBsG/fPpo2bWpvVAB0796d9PR0oqOjC32unJwc0tPTnX6EfoWHh2tdQvmSnq5OHduwodqo8PCAZ5+FM2dg4kS3bVSAZEE4kzwIG8mCcKTXPJSphsWWLVtITU1lyJAhAMTFxeHt7U1wcLDTfmFhYcTFxdn3Cbvq2mzbbds+BVmwYAFBQUH2n6ioKEAd0xEbG4vVarUv1x4TE0Nubi4JCQmYTCZSU1NJTk4mKyuLuLg48vLynPY1m83ExcWRmZlJcnIyKSkpZGRkEB8fj9lsdtrXYrEQGxtLVlYWV65cIS0tDaPRSGJiIjk5OU77KopCTEwMOTk5JCYm2htESUlJZGVluVx3ZmZmgXXn5eXlq9tkMhVYt9VqvW7dtnqvrttoNJKWlsaVK1cKrdtsNhMfH4/JZCIlJaVIdaempmIymUhISCA3N7fQupOSkuzv4cmTJ/PVbXvMteq2WCwF1p2RkZGv7qvfQ1vdWVlZLtednZ3tVLft/Y6NjS2w7oSEhHx1X758+bp1Xy+zly9fdqrbaDSqdWdlkbJ4MdSrp87wlJtLTocO5Pz2G0mvvEK6j489s9f7rBmNRqfPWnHW7ZjZhIQEp89adHQ0iqIQGxtb4GdNzhH6OkecPXvW6bMm54ibPEe4WLc7niNsX1baMiDnCH2fIy5cuFDgZ62sniNcVaYuherevTve3t589dVXAGzYsIGhQ4fme8F33XUXXbp0YeHChYwcOZILFy7w3Xff2e/PzMzEz8+Pb775hh49ehT4XDk5OU7HTU9PJyoqSi6FEuJG7dwJzz8PR4+qt2+7TZ35qVcvzaaOFUIIIUTxKTM9FhcuXOCHH35g+PDh9m3h4eHk5uaSmprqtG98fLy9Cyo8PDzfLFG229fqpvLx8SEwMNDpR+iXrVUvbsCZM/Dww3DPPWqjIjhYXTH7jz+gd+8y16iQLAhHkgdhI1kQjvSahzLTsFizZg2hoaH06tXLvq1ly5Z4eXnx448/2redPHmSixcv0rZtWwDatm3LH3/8QUJCgn2f7du3ExgYSKNGjUrvBYgyrVq1alqXUPakpcGkSdCokTrDk6enOij7zBkYPx5cnDjB3UgWhCPJg7CRLAhHes1DmWhYWK1W1qxZw+DBg6lQ4d81/YKCghg2bBgTJkxg586dHDx4kKFDh9K2bVvatGkDQLdu3WjUqBFPPfUUR48e5bvvvmP69OmMGTNGt6siiqK7uldMXENeHqxapY6jeO01MJvh/vvh2DF46y2oUkXrCm+KZEE4kjwIG8mCcKTXPJSJlbd/+OEHLl68yNNPP53vvmXLluHh4UG/fv3Iycmhe/furFy50n6/p6cnW7du5dlnn6Vt27b4+fkxePBgXnnlldJ8CaKMq1SpktYllA0//KCOo/jzT/V2gwawdCkUMpapLJIsCEeSB2EjWRCO9JqHMjV4W0uyjoW+paam5pt9TI+MRiNf7z7AaUtVEjIs1KhckV4NQ8jY/R0tP/qICt9+q+4YEgKzZ8Mzz4CXl7ZFFzPJgnAkeRA2kgXhSK95KBM9FkJozWq1al2CW/h4/wXm78nEw/APCmAA3tl5hgXfvk/rP36AChVgzBh1kbuQEK3LLRGSBeFI8iBsJAvCkV7zIA0LIVzg6+urdQma+/NCAvN/uICCActV/ZxT73+OptUDaPT6AqhfX5sCS4lkQTiSPAgbyYJwpNc8lInB20JoTVZeh7e3HQEKuHLSYEAxwLKeT5f7RgVIFoQzyYOwkSwIR3rNgzQshHBB1apVtS5Bc+ZsBUNhQ7IMBnIq+JVuQRqRLAhHkgdhI1kQjvSaB2lYCOGCqxdZ1JUrV+C556j7+ceFNywAn7yMUixKO7rOgshH8iBsJAvCkV7zIA0LIVxQvXp1rUsofWYzvPmmuh7FW2/x2LHvUQwGKKRx8VS7uqVcoDZ0mQVRKMmDsJEsCEd6zYM0LIRwQUxMjNYllB5Fga+/hqZN4T//gZQUaNqUOhvf56VudTAYwNMAHgbw9DDgYYB2XuepFxGsdeWlQldZENcleRA2kgXhSK95kFmhhHBBWFiY1iWUjuhomDABvv9evV2tGsydC8OGgacnjxuNBGQncMphHYveDUNIPOeHv7+/trWXEt1kQbhE8iBsJAvCkV7zIAvkuUgWyNO3uLg4wsPDtS6j5CQlwcyZ8M47YLGAtzeMHw8vvQRBQVpX51bKfRZEkUgehI1kQTjSax6kx0IIF5TbxmRuLqxYoa6SnZambuvbFxYtgrr6GDNRVOU2C+KGSB6EjWRBONJrHmSMhRAuyM7O1rqE4qUo8OWX0KSJeulTWho0bw47d8Jnn0mj4hrKXRbETZE8CBvJgnCk1zxIw0IIFxgMBq1LKD7HjsF998FDD8Hp0xAWBu+9B7//Dp07a12d2ytXWRA3TfIgbCQLwpFe8yCXQgnhAi8vL61LuHkJCfDyy/B//wdWK/j4qL0VU6dCQIDW1ZUZ5SILothIHoSNZEE40msepMdCCBdkZJThxd9ycmDxYnU9inffVRsVjz0GJ07A/PnSqCiiMp0FUewkD8JGsiAc6TUP0mMhhAtCQkK0LqHoFAW2bIEXXoC//1a3tWwJy5ZBhw6allaWlcksiBIjeRA2kgXhSK95kB4LIVyQkJCgdQlFc+QI3HOPOsPT339DRASsXQu//SaNiptU5rIgSpTkQdhIFoQjveZB1rFwkaxjIcqEuDiYPh1Wr1Z7LHx91R6LyZNBJwvYCSGEEEIb0mMhhAtiYmK0LuHasrPh1VfVcRTvvac2Kp54Ak6ehDlzpFFRjNw+C6JUSR6EjWRBONJrHqTHwkXSY6FvVqsVDw83bIcrirruxKRJcP68uu2uu9RxFHffrWlp5ZXbZkFoQvIgbCQLwpFe86C/VyzEDYiLi9O6hPwOHoROneDRR9VGRfXq8MEHsG+fNCpKkFtmQWhG8iBsJAvCkV7zILNCCeGCypUra13Cv2JjYdo0WLdO7bGoWFEdQ/HCC+Dnp3V15Z5bZUFoTvIgbCQLwpFe8yA9FkK4IDMzU+sSICsL5s6F225TZ3hSFHjySTh1CmbOlEZFKXGLLAi3IXkQNpIF4UiveZAeCyFcUKGChh8VRYFPPlF7JS5eVLe1aQOvvw6tW2tXl05pmgXhdiQPwkayIBzpNQ/6fNVCFJFmA7B++w2efx5++UW9HRUFixbB44+DwaBNTTqnx8F4onCSB2EjWRCO9JoHfb5qIYooOzu7dJ/w0iUYNEjtkfjlF6hUSZ029uRJ6N9fGhUaKvUsCLcmeRA2kgXhSK95kB4LIVxQalMMZ2bC4sVqr4Tt+szBg2H+fIiMLJ0axDXJdNPCkeRB2EgWhCO95kF6LIRwQVJSUsk+gdUK69dD/fowa5baqGjfHg4cUAdqS6PCbZR4FkSZInkQNpIF4UiveZAF8lwkC+Tpm6IoGErq8qNff4Xx42H/fvV2rVpqr8Ujj8glT26oRLMgyhzJg7CRLAhHes2D9FgI4YLY2NjiP+jFizBgALRtqzYq/P3VS55OnFAXvdPhCaksKJEsiDJL8iBsJAvCkV7zID0WLpIeC30r1m8eTCZ1DMXixZCdrTYgnn5aXaMiPLx4nkOUGL1+CyUKJnkQNpIF4UiveZAeCyFcUCzfPFit6mrZ9eurMzxlZ0OnTnDwIPzf/0mjoozQ67dQomCSB2EjWRCO9JoHmRVKCBdUrVr15g6wd6+6HsXvv6u369SB116Dhx+WS57KmJvOgihXJA/CRrIgHOk1D9JjIYQL0tPTb+yB58+ri9l16KA2KgIC1Mugjh+Hvn2lUVEG3XAWRLkkeRA2kgXhSK95kB4LIVzg6+tbtAcYjfDqq7BkCeTkgIcHDB8Or7wCYWElU6QoFUXOgijXJA/CRrIgHOk1D9KwEMIFVqvVtR0tFnUcxbRpEBenbrvnHli6FJo1K7kCRalxOQtCFyQPwkayIBzpNQ9ufylUTEwMTz75JFWqVKFixYo0bdqU323XqaOOup8xYwYRERFUrFiRrl27cvr0aadjJCcnM3DgQAIDAwkODmbYsGGYTKbSfimiDMvLy7v+Tj/9BHfeCcOGqY2KW2+FL76AH36QRkU54lIWhG5IHoSNZEE40mse3LphkZKSQrt27fDy8uLbb7/lr7/+YsmSJVSuXNm+z6JFi3jzzTdZtWoV+/fvx8/Pj+7du5OdnW3fZ+DAgURHR7N9+3a2bt3K7t27GTlypBYvSZRRlSpVKvzOv/9WF7Pr3BkOH4agIPUSqOhoePBBGUdRzlwzC0J3JA/CRrIgHOk1D269jsWUKVP4+eef2bNnT4H3K4pCZGQkEydO5IUXXgAgLS2NsLAw1q5dS//+/Tl+/DiNGjXiwIEDtGrVCoBt27bRs2dPLl26RGRkpEu1yDoW+hYbG5s/K+npMG8evP465Oaq4yhGjYJZs6BaNS3KFKWgwCwI3ZI8CBvJgnCk1zy4dY/Fl19+SatWrXj00UcJDQ2lRYsW/Pe//7Xff+7cOeLi4ujatat9W1BQEK1bt2bfvn0A7Nu3j+DgYHujAvj/9u48Pqr63v/4exJI2ANhSQADggoE1AcVNYZilQsCiloq6pWLUHzwA6GCF0UFUQuXB6LiLr0srthWbOXRujxkUYTWazXovSyyFKiglDKTyUL2hWxzfn+cnukZksBAljOT83o+HvOQOXOG+Uzynu/DD9/5fo9Gjx6tmJgYff311833ZhDVku3XmKipkV57TbrkEnOHp8pK6YYbpG+/lf77v2kqWrhkrjcCG/IAC1mAnVvzENGNxffff6/Vq1frkksu0SeffKLZs2fr/vvv19tvvy1J8v9zcWzSabvsJCUlBR/z+/3q0aNHyOOtWrVSYmJi8Jy6VFRUqKioKOQG98rMzDT/sH27dMUV0syZUna2NGCA9PHH0iefSJde6myRaBbBLAAiD/gXsgA7t+YhohuLQCCgK664QsuXL9ePfvQjzZw5UzNmzNCaNWua/LWfeuopJSQkBG8pKSmSpFOnTsnn8ykQCMjr9UoyF5hXVlYqOztbJSUlKigoUF5ensrLy+X3+1VdXR1yblVVlfx+v8rKypSXl6f8/HyVlpYqKytLVVVVIefW1NTI5/OpvLxcJ0+eVGFhoYqLi5WTk6OKioqQcw3DkNfrVUVFhXJycoINUW5ursrLy8Ouu6ysrM66q6ura9VdUlJSZ92BQOCsdVv1nl53cXGxCgsLdfLkyXrrrqqqUlZWlkpKSpSfn39OdRcUFKikpETZ2dmqrKyst+7c3Nzgz7DtiROqufVWadQoae9eBRISpJdekveTT1QxerRycnPrrLumpqbOuktLS2vVffrP0Kq7vLw87LpPnToVUrf18/b5fMGfs/WciooKZWdn16o7MzPzrHWfLbOZmZkhdRcXF59T3bm5uWf9rBUXF4d81hqzbntms7OzQz5rkvk1TJ/PV+dnjTHCXWNEx44dQz5r9nrtn7X66maMaDljhMXKAGOEu8eIxMTEOj9r0TpGhCui11j07dtXN9xwg15//fXgsdWrV2vZsmXyer36/vvvddFFF2n37t0aOnRo8JzrrrtOQ4cO1csvv6w333xT8+fPV35+fvDx6upqtWnTRhs2bNDPfvazOl+7oqIi5AdZVFSklJQU1li4TUGBtGyZjFdekaeqSoqNlX7xC2nxYqlrV6ergwO8Xq969+7tdBmIEOQBFrIAO7fmIaJnLH784x/r8OHDIcf+9re/qW/fvpKkfv36KTk5Wdu2bQs+XlRUpK+//lrp6emSpPT0dBUUFGjnzp3Bc7Zv365AIKC0tLR6Xzs+Pl6dOnUKucFFqqulNWvMdRTPP282FTfeKO3bJ73yCk2Fi53+1Uq4G3mAhSzAzq15iOjG4oEHHtCOHTu0fPlyHTlyROvXr9err76q++67T5Lk8Xg0b948LVu2TB999JH27dunqVOnqlevXpowYYIkKTU1VePGjdOMGTP0zTff6Msvv9ScOXN01113uXK1PsKwdas0dKg0e7aUmyulpir/nXekTZuk1FSnq4PD8vLynC4BEYQ8wEIWYOfWPET0lbevuuoqvf/++3r00Ue1dOlS9evXTy+99JImT54cPOeRRx5RaWmpZs6cqYKCAo0YMUJbtmwJuZT6O++8ozlz5mjUqFGKiYnRxIkT9corrzjxlhDJDh+WHnrIXIwtSYmJ0tKl0syZan0O3y9Ey9a+fXunS0AEIQ+wkAXYuTUPEb3GIpJwHYsWLD/fbCB+9SvzK1CtWklz5ki//KX0z4sx5ufnh1yYEe5FFmBHHmAhC7Bzax4iesYCaFJVVdLateZCbGvK8uabpeeekwYODDmV/hsWsgA78gALWYCdW/NAYwF32rJFevBB6eBB8/6ll0ovvGBe6K4O9q/Wwd3IAuzIAyxkAXZuzUNEL94GGt3Bg9JNN5k7PB08KHXrJq1eLe3eXW9TIYkLJCKILMCOPMBCFmDn1jywxiJMrLGIcidPSv/1X9KqVVJNjdS6tXT//dLjj0udO5/16dXV1WrVigk+kAWEIg+wkAXYuTUPzFigZauqkl5+2bwexcqVZlMxYYL017+aaynCaCokKSsrq0nLRPQgC7AjD7CQBdi5NQ/MWISJGYsoYxjmdSfmzze3kZWkyy+XXnxR+rd/c7Y2AACAFogZC7Q8+/dLY8eaOzwdPiz16CG9+qq0a9d5NxVer7eRi0S0IguwIw+wkAXYuTUPzFiEiRmLKJCTY24du3atFAhIcXHSAw9IixZJDfydufW7kqiNLMCOPMBCFmDn1jwwY4HoV1lpbhV7ySXmDk+BgDRxornr09NPN7ipkKTc3NxGKBQtAVmAHXmAhSzAzq15cF8rhZbDMKSPPpIeekg6csQ89qMfmesorruuUV+KWSpYyALsyAMsZAF2bs0DMxaITnv3SqNHmzs8HTkiJSdLb7wh/e//NnpTIUmnTp1q9L8T0YkswI48wEIWYOfWPDBjgeiSnS098YT0+uvmV57i482dnxYulDp2bLKXjYmhB4eJLMCOPMBCFmDn1jzQWCA6VFSY16NYtkwqLjaP3Xmn9Mwz0oUXNvnLu3EBFupGFmBHHmAhC7Bzax7c2U4hehiG9Mc/SoMHSwsWmE3FlVdKX3wh/f73zdJUSFJZWVmzvA4iH1mAHXmAhSzAzq15cGc7heiwe7e5Xeznn5v3e/WSnnpKuvtuqZmnGDuHeYVutHxkAXbkARayADu35oEZC0Qev1+aPl0aNsxsKtq0MddVHD4sTZ3a7E2FJOXk5DT7ayIykQXYkQdYyALs3JoHLpAXJi6Q1wxOnTK3il2+XCopMY/9x3+YsxR9+jhbGwAAAM6IGQs4zzCkDRuk1FTzKtklJVJamvTVV9I770REU+H1ep0uARGCLMCOPMBCFmDn1jwwYxEmZiyayP/9n7mO4i9/Me/37m3u9DRpkiNfeapPIBBw7dZxCEUWYEceYCELsHNrHtz3jhEZfD5p2jTpqqvMpqJdO2nJEnMdxeTJEdVUSJLf73e6BEQIsgA78gALWYCdW/PArlBoXuXl0vPPm+smrK3Ypkwx11VccIGztZ1Bly5dnC4BEYIswI48wEIWYOfWPETWPwuj5TIM6d13pYEDzR2eysqk9HTp66+lX/86opsKSSotLXW6BEQIsgA78gALWYCdW/PAjAWa3tdfm+soMjLM+336SCtWmFfO9nicrS1McXFxTpeACEEWYEceYCELsHNrHmgs0HROnJAefVT67W/N++3bm/cffFBq29bZ2gAAANCoaCzQ+EpLpWefNWclysvNY9OmSU8+aV49OwpVVFQ4XQIiBFmAHXmAhSzAzq15oLFA4wkEpPXrpYULJWv/5muvNS96N2yYs7U1EFsMw0IWYEceYCELsHNrHli8jcaRkWEuxp4yxWwqLrzQvOjd559HfVMhSbm5uU6XgAhBFmBHHmAhC7Bzax64QF6YuEBePY4fN2co3n3XvN+hg/TYY9K8eVKbNo6W1pgMw5AnShaao2mRBdiRB1jIAuzcmgdmLHB+SkrMbWMHDjSbCo9Hmj5d+u47s9FoQU2FJPl8PqdLQIQgC7AjD7CQBdi5NQ/MWISJGYt/CgSk3/zG3N0pM9M8dt110ksvSUOHOlkZAAAAHMSMBcL3l79IV19t7vCUmSn17y/98Y/Sn/7U4psKr7UYHa5HFmBHHmAhC7Bzax5oLHB2P/xgXszu2mulnTulTp3M7WT/+lfpZz+LmovcNUS3bt2cLgERgizAjjzAQhZg59Y80FigfsXF0qJFUmqqucNTTIx0773mOoqHHpLi452usNkUFRU5XQIiBFmAHXmAhSzAzq154DoWqK2mRlq3ztzdKSvLPDZqlPTCC9LllztamlPatLDF6Dh/ZAF25AEWsgA7t+aBxgKh/vxn6YEHpD17zPuXXCI995x0yy2u+MpTfQKBgNMlIEKQBdiRB1jIAuzcmge+CgXT0aPSxInSyJFmU5GQYM5Q7N8v3Xqrq5sKSaqurna6BEQIsgA78gALWYCdW/MQ8Y3FkiVL5PF4Qm6DBg0KPn7q1Cndd9996tq1qzp06KCJEycqy/r6zj8dP35c48ePV7t27dSjRw89/PDDrv2F11JUJC1YIA0ebO7wFBsr/eIX0pEj5sxFXJzTFUaEdu3aOV0CIgRZgB15gIUswM6teYiKr0INGTJEn332WfB+q1b/KvuBBx7Qxo0btWHDBiUkJGjOnDm67bbb9OWXX0qSampqNH78eCUnJ+urr75SZmampk6dqtatW2v58uXN/l4iRk2N9MYb0uOPSzk55rExY8xZiiFDnK0tAuXn56tt27ZOl4EIQBZgRx5gIQuwc2seIv4CeUuWLNEHH3ygPdZ3/m0KCwvVvXt3rV+/Xrfffrsk6dChQ0pNTVVGRoauueYabd68WTfffLN8Pp+SkpIkSWvWrNGCBQuUk5OjuDD/Rb5FXSBv+3ZzNmLvXvP+wIFmQ3Hjja7/ylN9ampqFBsb63QZiABkAXbkARayADu35iHivwolSd9995169eql/v37a/LkyTp+/LgkaefOnaqqqtLo0aOD5w4aNEh9+vRRRkaGJCkjI0OXXXZZsKmQpLFjx6qoqEgHDhyo9zUrKipUVFQUcot6330nTZhg7vC0d6/UpYv08svSvn3STTfRVJyB3+93ugRECLIAO/IAC1mAnVvzEPGNRVpamtatW6ctW7Zo9erV+uGHH3TttdequLhYfr9fcXFx6ty5c8hzkpKSgr9Qv98f0lRYj1uP1eepp55SQkJC8JaSkiLJXNPh8/kUCASCV1X0er2qrKxUdna2SkpKVFBQoLy8PJWXl8vv96u6ujrk3KqqKvn9fpWVlSkvL0/5+fkqLS1VVlaWqqqqQs6tqamRz+dTeXm5Tp48qcLCQhUXFysnJ0cVFRUh5xqGIa/Xq4qKCuXk5AQbopNHj6rqP/9TxpAh0ocfyoiNle6/X77/+R9Vzpql7Pz8kLrLysrqrLu6urpW3SUlJXXWHQgEzlq3Ve/pdRcXF6uwsFAnT55UeXl5nT/vqqoqZWVlqaSkRPn5+edUd0FBgUpKSpSdna3Kysp6687NzQ3+DOPi4mrVbT3nTHXX1NTUWXdpaWmtuk//GVp1l5eXh133qVOnQuq2ft4+n6/OurOzs2vVnZmZeda6z5bZzMzMkLqLi4vPqe7c3NyzftaKi4tDPmuNWbc9s9nZ2SGfNUkyDEM+n6/WZy2cuiNxjMjNza33s1ZX3YwR/xojOnbsGPJZY4xw7xhhsTLAGOHuMSIxMbHOz1q0jhHhivivQp2uoKBAffv21QsvvKC2bdvqnnvuqfWGr776ao0cOVLPPPOMZs6cqb///e/65JNPgo+XlZWpffv22rRpk2688cY6X6eioiLk7y0qKlJKSkp0fRWqulp67TXpl7+UcnPNYzfdZG4fm5rqbG1Rxuv1qnfv3k6XgQhAFmBHHmAhC7Bzax4ifsbidJ07d9aAAQN05MgRJScnq7KyUgUFBSHnZGVlKTk5WZKUnJxca5co6751Tl3i4+PVqVOnkFtU+fRTaehQc4en3Fyzkdi8Wdq4kabiPPTo0cPpEhAhyALsyAMsZAF2bs1D1DUWJSUlOnr0qHr27Klhw4apdevW2rZtW/Dxw4cP6/jx40pPT5ckpaena9++fcrOzg6es3XrVnXq1EmDBw9u9vqb3KFD0s03S2PHSgcOSF27Sr/6lbmmYtw4p6uLWnl5eU6XgAhBFmBHHmAhC7Bzax4ifrvZhx56SLfccov69u0rn8+nxYsXKzY2VpMmTVJCQoKmT5+uBx98UImJierUqZPmzp2r9PR0XXPNNZKkMWPGaPDgwZoyZYpWrFghv9+vxx9/XPfdd5/i4+MdfnfhKy4u1s6dO9Wt32BtPJinE/nluqBLW915ZYq6xQf07Z/+pKu3bFHca6+ZX4Fq1UqaO1d64glzkTYapEOHDk6XgAhBFmBHHmAhC7Bzax4ivrE4ceKEJk2apJMnT6p79+4aMWKEduzYoe7du0uSXnzxRcXExGjixImqqKjQ2LFjtWrVquDzY2Nj9fHHH2v27NlKT09X+/bt9fOf/1xLly516i2dl5KSEr2+bb++qi6Vx+ORYRjyeDxa+/lRLYn7u25/9iHFlZebJ99yi7mOYsAAZ4tuQSorK9W+fXuny0AEIAuwIw+wkAXYuTUPUbd42ylOX8fi810HNe29ozJ02pawhqEYw9D21+5V7+QEtV65UrJtv4vGkZeXp8TERKfLQAQgC7AjD7CQBdi5NQ9Rt8bCrX775dG6H/B45JGhJ2+brdxPP6WpaCJt2rRxugRECLIAO/IAC1mAnVvzQGMRJSpa1T+dFoiJ0ZHkvua6CjSJFnGBRDQKsgA78gALWYCdW/NAYxEl4qtLz/h4B09lM1XiTl27dnW6BEQIsgA78gALWYCdW/NAYxEl7v7xRWd41KMBsbnNVosb2bcrhruRBdiRB1jIAuzcmgcaiygxoGdn/bj1McV4pNgYT8h/H7uhrzrFhH+5dZw7N149E3UjC7AjD7CQBdi5NQ98KT9KdOjQQf9v1KV6tN9gfWy7jsW/X5mirvEB7Yy9zrV7JjcHr9fr2kECocgC7MgDLGQBdm7NA9vNhsnp7WbhrOrqarVicTxEFhCKPMBCFmDn1jzwVSggDLm5rGGBiSzAjjzAQhZg59Y80FgAYUhISHC6BEQIsgA78gALWYCdW/NAYwGEoby83OkSECHIAuzIAyxkAXZuzQONBRCGmBg+KjCRBdiRB1jIAuzcmgd3vmvgHLlxARbqRhZgRx5gIQuwc2seaCyAMJSVlTldAiIEWYAdeYCFLMDOrXmgsQDC0LlzZ6dLQIQgC7AjD7CQBdi5NQ80FkAYcnJynC4BEYIswI48wEIWYOfWPHCBvDBxgTwAAACgfsxYAGHwer1Ol4AIQRZgRx5gIQuwc2semLEIEzMW7hYIBFy7dRxCkQXYkQdYyALs3JoH971j4Dz4/X6nS0CEIAuwIw+wkAXYuTUP7txk9zxYEztFRUUOVwIntGrVit89JJEFhCIPsJAF2LXEPHTs2FEej+eM59BYhKm4uFiSlJKS4nAlAAAAQPMKZzkAayzCFAgE5PP5wurW0LIUFRUpJSVF//jHP1hf43JkAXbkARayALuWmgdmLBpRTEyMLrjgAqfLgIM6derUogYInD+yADvyAAtZgJ0b88DibQAAAAANRmMBAAAAoMFoLICziI+P1+LFixUfH+90KXAYWYAdeYCFLMDOzXlg8TYAAACABmPGAgAAAECD0VgAAAAAaDAaCwAAAAANRmMBAAAAoMFoLIDTVFRUaOjQofJ4PNqzZ0/w+LFjx+TxeGrdduzYEfL8DRs2aNCgQWrTpo0uu+wybdq0qZnfARpTfXmQpL179+raa69VmzZtlJKSohUrVtR6PnmIfrfeeqv69OmjNm3aqGfPnpoyZYp8Pl/wccYGdzlbHiTGBjc4duyYpk+frn79+qlt27a66KKLtHjxYlVWVoac47axgcYCOM0jjzyiXr161fv4Z599pszMzOBt2LBhwce++uorTZo0SdOnT9fu3bs1YcIETZgwQfv372+O0tEE6stDUVGRxowZo759+2rnzp169tlntWTJEr366qvBc8hDyzBy5Ei99957Onz4sP7whz/o6NGjuv3222udx9jgDmfLA2ODOxw6dEiBQEBr167VgQMH9OKLL2rNmjVatGhRrXNdNTYYAII2bdpkDBo0yDhw4IAhydi9e3fwsR9++KHWsdPdeeedxvjx40OOpaWlGffee28TVYymdKY8rFq1yujSpYtRUVERPLZgwQJj4MCBwfvkoWX68MMPDY/HY1RWVhqGwdjgdqfngbHBvVasWGH069cveN+NYwMzFsA/ZWVlacaMGfrNb36jdu3a1Xverbfeqh49emjEiBH66KOPQh7LyMjQ6NGjQ46NHTtWGRkZTVIzms7Z8pCRkaGf/OQniouLCx4bO3asDh8+rPz8/OA55KFlycvL0zvvvKPhw4erdevWIY8xNrhPXXlgbHCvwsJCJSYm1jruprGBxgKQZBiGpk2bplmzZunKK6+s85wOHTro+eef14YNG7Rx40aNGDFCEyZMCBkk/H6/kpKSQp6XlJQkv9/fpPWjcYWTh/p+19ZjZzqHPESfBQsWqH379uratauOHz+uDz/8MPgYY4P7nCkPjA3udOTIEa1cuVL33ntv8JgbxwYaC7RoCxcurHPhlP126NAhrVy5UsXFxXr00Ufr/bu6deumBx98UGlpabrqqqv09NNP6+6779azzz7bjO8IDdGYeUB0CzcLlocffli7d+/Wp59+qtjYWE2dOlWGYUhibGgJGjMPiG7nmgVJ8nq9GjdunO644w7NmDEjeNyNY0MrpwsAmtL8+fM1bdq0M57Tv39/bd++XRkZGYqPjw957Morr9TkyZP19ttv1/nctLQ0bd26NXg/OTlZWVlZIedkZWUpOTn5/N4AGlVj5qG+37Wk4O+bPESucLNg6datm7p166YBAwYoNTVVKSkp2rFjh9LT0+t8LmNDdGnMPDA2RLdzzYLP59PIkSM1fPjwkAX69WnpYwONBVq07t27q3v37mc975VXXtGyZcuC930+n8aOHavf//73SktLq/d5e/bsUc+ePYP309PTtW3bNs2bNy94bOvWrfX+zweaV2PmIT09XY899piqqqqC363eunWrBg4cqC5dugTPIQ+RKdws1CUQCEgytyKuD2NDdGnMPDA2RLdzyYLX69XIkSM1bNgwvfXWW4qJOfsXgVr82ODs2nEgMtW1k8O6deuM9evXGwcPHjQOHjxoPPnkk0ZMTIzx5ptvBs/58ssvjVatWhnPPfeccfDgQWPx4sVG69atjX379jnwLtBY6spDQUGBkZSUZEyZMsXYv3+/8bvf/c5o166dsXbt2uA55CH67dixw1i5cqWxe/du49ixY8a2bduM4cOHGxdddJFx6tQpwzAYG9wknDwwNrjDiRMnjIsvvtgYNWqUceLECSMzMzN4s7hxbKCxAOpQX2ORmppqtGvXzujUqZNx9dVXGxs2bKj13Pfee88YMGCAERcXZwwZMsTYuHFjM1aOplDfloHffvutMWLECCM+Pt7o3bu38fTTT9d6LnmIbnv37jVGjhxpJCYmGvHx8caFF15ozJo1yzhx4kTwHMYG9wgnD4bB2OAGb731liGpzpvFjWODxzBYbQQAAACgYdgVCgAAAECD0VgAAAAAaDAaCwAAAAANRmMBAAAAoMFoLAAAAAA0GI0FAAAAgAajsQAAAADQYDQWAAAAABqMxgIAAABAg9FYAAAi0vXXXy+PxyOPx6M9e/Y4UsO0adOCNXzwwQeO1AAA0YLGAgAQsWbMmKHMzExdeumljrz+yy+/rMzMTEdeGwCiTSunCwAAoD7t2rVTcnKyY6+fkJCghIQEx14fAKIJMxYAgEb37rvvqm3btiH/2n/PPffo8ssvV2FhYYP+7uuvv15z587VvHnz1KVLFyUlJem1115TaWmp7rnnHnXs2FEXX3yxNm/e3KDnAADODY0FAKDR3XXXXRowYICWL18uSVq8eLE+++wzbd68uVFmAN5++21169ZN33zzjebOnavZs2frjjvu0PDhw7Vr1y6NGTNGU6ZMUVlZWYOeAwAIn8cwDMPpIgAALc/HH3+s22+/XU888YSef/55ffHFFxoyZEjYz7/++us1dOhQvfTSS7WO19TU6IsvvpAk1dTUKCEhQbfddpt+/etfS5L8fr969uypjIwMXXPNNef1HDuPx6P3339fEyZMOM+fBgC0fMxYAADCsnDhwuAOSfXdDh06FDz/5ptv1uDBg7V06VK9//7759RUnM3ll18e/HNsbKy6du2qyy67LHgsKSlJkpSdnd2g5wAAwsfibQBAWObPn69p06ad8Zz+/fsH/7xlyxYdOnRINTU1wf9pbyytW7cOue/xeEKOeTweSVIgEGjQcwAA4aOxAACEpXv37urevXtY5+7atUt33nmn3njjDa1bt05PPPGENmzY0MQVAgCcRGMBAGhUx44d0/jx47Vo0SJNmjRJ/fv3V3p6unbt2qUrrrjC6fIAAE2ENRYAgEaTl5encePG6ac//akWLlwoSUpLS9ONN96oRYsWOVwdAKApMWMBAGg0iYmJIQu4LRs3bmy01/jzn/9c69ixY8dqHbNveng+zwEAnBtmLAAAEWvVqlXq0KGD9u3b58jrz5o1Sx06dHDktQEg2nAdCwBARPJ6vSovL5ck9enTR3Fxcc1eQ3Z2toqKiiRJPXv2VPv27Zu9BgCIFjQWAAAAABqMr0IBAAAAaDAaCwAAAAANRmMBAAAAoMFoLAAAAAA0GI0FAAAAgAajsQAAAADQYDQWAAAAABqMxgIAAABAg9FYAAAAAGgwGgsAAAAADfb/Afbgs1HU7EKJAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot regressione\n",
    "\n",
    "SCALA = 10\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\"Fit: $A={AS:.3f}\\\\pm{uAS:.3f}$\\n\"\n",
    "          f\"     $B={BS:.2f}\\\\pm{uBS:.2f}$\\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": 297,
   "id": "09445aac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui\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": 298,
   "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": 299,
   "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": 300,
   "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": 301,
   "id": "ac4860fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43c6ba91a5734cbdbd447d5ad48dd212",
       "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": 302,
   "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": 303,
   "id": "d5646b8c",
   "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 = 10\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\"Fit: $A={AS2:.3f}\\\\pm{uAS2:.3f}$\\n\"\n",
    "          f\"     $B={BS2:.2f}\\\\pm{uBS2:.2f}$\\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": 304,
   "id": "e35a9456",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui\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": 305,
   "id": "8de2a48e",
   "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",
    "    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": 306,
   "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 = pd.DataFrame([[15.43, 15.43, 15.43]], columns=[\"m1\", \"m2\", \"m3\"])\n",
    "# dfMasse = pd.DataFrame([[29.90, 29.89, 29.89]], columns=[\"m1\", \"m2\", \"m3\"])\n",
    "dfMasse = calcola_stats(dfMasse, \"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": 307,
   "id": "a1e69bd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meq = 1000/(alp*m_molla + m_osc)\n",
      "sigma_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",
      "Meq = \\frac{1000}{alp m_{molla} + m_{osc}}\n",
      "sigma_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(\"Meq =\", Meq)\n",
    "print(\"sigma_Meq =\", sigma_Meq.simplify())\n",
    "print(\"\\nFormula LaTeX:\")\n",
    "print(f\"Meq = {sp.latex(Meq)}\")\n",
    "print(f\"sigma_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": 308,
   "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": 309,
   "id": "455271a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45efe82629a04163b5008c384f0ccb3e",
       "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['Meq'] = Meq_fn(dfSonar['m'], dfSonar['alp'], massaMolla)\n",
    "dfSonar['uMeq']= 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": 310,
   "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    = 15.1284\n",
      "P(chi², ∞)= 0.0017\n"
     ]
    }
   ],
   "source": [
    "# Regressione lineare\n",
    "\n",
    "AD, BD, uAD, uBD, covABD, chiD, PD = reg_lin( dfSonar[\"Meq\"], dfSonar[\"Omega2\"], dfSonar[\"uMeq\"], 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": 311,
   "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 = 10\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "x_data =  dfSonar[\"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[\"uMeq\"], 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\"Fit: $A={AD:.3f}\\\\pm{uAD:.3f}$\\n\"\n",
    "          f\"     $B={BD:.2f}\\\\pm{uBD:.2f}$\\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\"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": 312,
   "id": "96f12fe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui\n",
    "\n",
    "dfSonar[\"R\"] = r_fn(\n",
    "  dfSonar[\"Meq\"],\n",
    "  dfSonar[\"Omega2\"],\n",
    "  AD,\n",
    "  BD\n",
    ")\n",
    "\n",
    "dfSonar[\"uR\"] = sigma_r_fn(\n",
    "  dfSonar[\"Meq\"], dfSonar[\"Omega2\"],\n",
    "  AD, BD,\n",
    "  dfSonar[\"uMeq\"], dfSonar[\"uOmega2\"],\n",
    "  uAD, uBD, covABD\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "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": 314,
   "id": "97921cfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wt = 40*pi/w\n",
      "w2t = 1600*pi**2/w**2\n",
      "sigma_w2t = 3200*pi**2*sigma_t/w**3\n",
      "\n",
      "Formula LaTeX:\n",
      "w2t = \\frac{1600 \\pi^{2}}{w^{2}}\n",
      "sigma_w2t = \\frac{3200 \\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/20)\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": 315,
   "id": "0b3f8a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f32452ef1d234e4d81610e7348cc60e4",
       "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": 316,
   "id": "0421b1cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "ACr      = 3.4239 ± 0.0469\n",
      "BCr      = 1.3116 ± 0.5445\n",
      "cov_ABCr = -0.024903\n",
      "chi²    = 3.1950\n",
      "P(chi², ∞)= 0.3625\n"
     ]
    }
   ],
   "source": [
    "# Regressione lineare\n",
    "\n",
    "ACr, BCr, uACr, uBCr, covABCr, chiCr, PCr = reg_lin( dfSonar[\"Meq\"], dfSonar[\"Omega2t\"], dfSonar[\"uMeq\"], 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": 317,
   "id": "739c2c88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAHpCAYAAAAf5apCAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAApupJREFUeJzs3Xl4E2XXBvA7Ld1XoKUtUHYEZBEFQQQBZRcUBMVd8EMRBRRxAdwARRAVQWWTV8QNVEDEDVcUBMUFEFAQBCyL6UbpkqRNm7SZ748xoaEptNP0TNrcv+vKJZlMJqd3JzGnz8wzBkVRFBAREREREVVBgN4FEBERERFRzcfGgoiIiIiIqoyNBRERERERVRkbCyIiIiIiqjI2FkREREREVGVsLIiIiIiIqMrYWBARERERUZWxsSAiIiIioipjY0FERERERFXGxoKIdHXs2DEYDAa8+eabmrfRt29f9O3b16vbpOqzZcsWGAwGbNmyxbVs7NixaNasmW41eeJwONChQwc8++yzepdC/1m+fDmaNGmCoqIivUshIg/YWBBRtXrzzTdhMBg83qZPn+7xOZs2bcKsWbNkCyWXvn37wmAwoHXr1h4f/+abb1y/w/Xr1wtXVzEbNmzAjTfeiBYtWiA8PBxt2rTBQw89hNzc3Apv47333sPJkycxadKk6iu0FqrO9+/YsWNhs9nw2muvVcv2iahq6uhdABH5h6effhrNmzd3W9ahQwc0bdoUVqsVQUFBruWbNm3CkiVLNH858bRNqpzQ0FAcOXIEv/76K7p16+b22OrVqxEaGorCwkKdqju/8ePHo2HDhrjtttvQpEkT/PHHH1i8eDE2bdqE3bt3Iyws7LzbeOGFF3DTTTchJiZGoOLao6rv33MJDQ3FmDFj8NJLL2Hy5MkwGAxefw0i0o6NBRGJGDJkCLp27erxsdDQUK++lsFg8Po2va2goADh4eF6l1Guli1bori4GO+9955bY1FYWIiPPvoIQ4cOxYcffqhjhee2fv16t8PjAKBLly4YM2YMVq9ejbvuuuucz//999+xd+9eLFiwwCv15OfnIyIiwivbqk2Ki4vhcDgQHBxc4eeMHj0azz//PL7//ntcddVV1VgdEVUWD4UiIl2dfT7E2LFjsWTJEgBwO2yqKtt0bjcyMhJGoxEjRoxAZGQk4uPj8fDDD6OkpMTt+Q6HA4sWLUL79u0RGhqKhIQE3HPPPcjJyXFb7+OPP8bQoUPRsGFDhISEoGXLlnjmmWfKbK9v377o0KEDdu3ahd69eyM8PByPPfYYAKCoqAgzZ85Eq1atEBISguTkZDz66KM+cQz5zTffjA8++AAOh8O17NNPP0VBQQFGjx7t8Tm///47hgwZgujoaERGRqJfv374+eefNb3+iy++iMsvvxz169dHWFgYunTpUuFDr85uKgDguuuuAwD89ddf533+xo0bERwcjN69e5d5zGg0Yty4ca7fe/PmzXHvvffCZrMBOHP439atW3HfffehQYMGaNy4sev5S5cuRfv27RESEoKGDRti4sSJZQ7Rcu4zBw4cwJVXXonw8HA0atQIzz//fJl6MjMzMW7cOCQkJCA0NBQXXXQR3nrrLbd1nO+JF198EUuWLHEdIjZw4ECcPHkSiqLgmWeeQePGjREWFobhw4cjOzu7zGt98cUXuOKKKxAREYGoqCgMHToU+/fvdz1+rvdv6RoWLVqEli1bIiQkBAcOHAAAfPfdd65tx8bGYvjw4R5/V126dEG9evXw8ccfe/zdEZF+OGJBRCLy8vKQlZXltiwuLq7Mevfccw9SU1PxzTff4J133vFqDSUlJRg0aBC6d++OF198Ed9++y0WLFiAli1b4t5773Wr4c0338Sdd96J+++/HykpKVi8eDF+//13/Pjjj65DrN58801ERkZi6tSpiIyMxHfffYennnoKJpMJL7zwgttrnz59GkOGDMFNN92E2267DQkJCXA4HLj22muxfft2jB8/Hu3atcMff/yBhQsX4u+//8bGjRu9+vNX1i233IJZs2Zhy5Ytrr8Mr1mzBv369UODBg3KrL9//35cccUViI6OxqOPPoqgoCC89tpr6Nu3L7Zu3Yru3btX6vVffvllXHvttbj11lths9nw/vvv44YbbsBnn32GoUOHVvrnSU9PB+B5vzvbTz/9hA4dOpQ5nC41NRXdunVDbm4uxo8fj7Zt28JoNGL9+vUoKChw+8v7fffdh/j4eDz11FPIz88HAMyaNQuzZ89G//79ce+99+LQoUNYtmwZfvvtN7d9CwBycnIwePBgjBw5EqNHj8b69esxbdo0dOzYEUOGDAEAWK1W9O3bF0eOHMGkSZPQvHlzrFu3DmPHjkVubi4eeOABt/pXr14Nm82GyZMnIzs7G88//zxGjx6Nq666Clu2bMG0adNw5MgRvPrqq3j44YfxxhtvuJ77zjvvYMyYMRg0aBDmz5+PgoICLFu2DL169cLvv/+OZs2aVej9u2rVKhQWFmL8+PEICQlBvXr18O2332LIkCFo0aIFZs2aBavVildffRU9e/bE7t27y5zYf8kll+DHH3887++RiIQpRETVaNWqVQoAjzdFUZSUlBQFgLJq1SrXcyZOnKhU5uOpT58+Sp8+fVz3PW1zzJgxCgDl6aefdnvuxRdfrHTp0sV1f9u2bQoAZfXq1W7rffnll2WWFxQUlKnlnnvuUcLDw5XCwkK3+gAoy5cvd1v3nXfeUQICApRt27a5LV++fLkCQPnxxx/P/8NXgz59+ijt27dXFEVRunbtqowbN05RFEXJyclRgoODlbfeekv5/vvvFQDKunXrXM8bMWKEEhwcrBw9etS1LDU1VYmKilJ69+7tWuZ87vfff+9aNmbMGKVp06ZudZydr81mUzp06KBcddVVmn6ucePGKYGBgcrff/993nUbN26sjBo1qszyO+64QwkICFB+++23Mo85HA5FUc7s87169VKKi4tdj2dmZirBwcHKwIEDlZKSEtfyxYsXKwCUN954w7XMuc+8/fbbrmVFRUVKYmKiW12LFi1SACjvvvuua5nNZlN69OihREZGKiaTSVGUM++J+Ph4JTc317XujBkzFADKRRddpNjtdtfym2++WQkODnbtx2azWYmNjVXuvvtut585PT1diYmJcVte3vvXWUN0dLSSmZnp9ljnzp2VBg0aKKdPn3Yt27t3rxIQEKDccccdZbY1fvx4JSwsrMxyItIXD4UiIhFLlizBN99843bTw4QJE9zuX3HFFfjnn39c99etW4eYmBgMGDAAWVlZrluXLl0QGRmJ77//3rVu6ROAzWYzsrKycMUVV6CgoAAHDx50e52QkBDceeedbsvWrVuHdu3aoW3btm6v5RwdKP1aernllluwYcMG2Gw2rF+/HoGBga5DikorKSnB119/jREjRqBFixau5UlJSbjllluwfft2mEymSr126XxzcnKQl5eHK664Art37670z7FmzRqsXLkSDz30ULmzXZV2+vRp1K1b122Zw+HAxo0bcc0113g8X+jsQ/buvvtuBAYGuu5/++23sNlsmDJlCgICAtzWi46Oxueff+72/MjISNx2222u+8HBwejWrZvb/rpp0yYkJibi5ptvdi0LCgrC/fffD4vFgq1bt7pt84YbbnA7Gd05inTbbbehTp06bsttNhuMRiMAdSaw3Nxc3HzzzW77amBgILp3716pfXXUqFGIj4933U9LS8OePXswduxY1KtXz7W8U6dOGDBgADZt2lRmG3Xr1oXVakVBQUGFX5eIqh8PhSIiEd26dSv35G0poaGhbl9oAPULSulzJw4fPoy8vDyPh/oA6vHsTvv378cTTzyB7777rsyX5ry8PLf7jRo1KnOC6uHDh/HXX3+VqcnTa53NYrHAYrGU+/i5REZGIjIyskLr3nTTTXj44YfxxRdfYPXq1Rg2bBiioqLKrHfq1CkUFBSgTZs2ZR5r164dHA4HTp48ifbt21e4zs8++wxz5szBnj173M45qew5N9u2bcO4ceMwaNCgSl2TQlEUt/unTp2CyWRChw4dKvT8s2dBO378OACUySg4OBgtWrRwPe7UuHHjMj9r3bp1sW/fPrdttm7d2q1RAdTMS7+mU5MmTdzuO5uM5ORkj8ud743Dhw8DQLknS0dHR3tc7klFcwHUn+Orr74qc/K783fDWaGIfAsbCyLyG6X/elweh8OBBg0aYPXq1R4fdzYBubm56NOnD6Kjo/H000+jZcuWCA0Nxe7duzFt2jS3E54BeJze1OFwoGPHjnjppZc8vtbZX/ZKe/HFFzF79uzz/jyezJw5s8JTgSYlJaFv375YsGABfvzxR7GZoLZt24Zrr70WvXv3xtKlS5GUlISgoCCsWrUKa9asqfB29u7di2uvvRYdOnTA+vXr3f4qfy7169cvc7J+ZVVkSttzKW9/Pbvh8cY2z/dazv35nXfeQWJiYpn1KporUPVcALXhCQ8P98q2iMh72FgQkc/R86+QLVu2xLfffouePXue80vLli1bcPr0aWzYsMFt5qCUlJRKvdbevXvRr1+/Sv/Md9xxB3r16lWp5ziVPlSpIm655RbcddddiI2NxdVXX+1xnfj4eISHh+PQoUNlHjt48CACAgLO2Sid7cMPP0RoaCi++uorhISEuJavWrWqwts4evQoBg8ejAYNGmDTpk0VHqUBgLZt25b5XcbHxyM6Ohp//vlnhbdTWtOmTQEAhw4dcvsd2Gw2pKSkoH///pq2uW/fPjgcDrdRC+eheM7XrKqWLVsCABo0aHDeOiu7L5fO5WwHDx5EXFxcmal6U1JSXKMyROQ72FgQkc9xfonIzc1FbGys6GuPHj0aS5cuxTPPPIO5c+e6PVZcXAyLxYLY2FjXX3hL//XYZrNh6dKllXqtTZs24X//+x/Gjx/v9pjVaoXD4Sj32gctWrSodIOg1fXXX4+TJ0+iTZs25V5vIDAwEAMHDsTHH3+MY8eOuWbxycjIwJo1a9CrV69KHS4TGBgIg8HgNnXvsWPHKjxTVnp6OgYOHIiAgAB89dVX5R5uVp4ePXrgueeeQ1FRkauxCQgIwIgRI/Duu+9i586dZQ7tUxTlnF+q+/fvj+DgYLzyyisYPHiwa92VK1ciLy9P00xXV199Nb7++mt88MEHrvMsiouL8eqrryIyMhJ9+vSp9DY9GTRoEKKjozF37lxceeWVZWbLOnXqlCvjyr5/k5KS0LlzZ7z11luYMWOG6zl//vknvv76a7fzTJx2796NW2+9tWo/FBF5HRsLIvI5Xbp0AQDcf//9GDRoEAIDA3HTTTeJvHafPn1wzz33YN68edizZw8GDhyIoKAgHD58GOvWrcPLL7+M66+/Hpdffjnq1q2LMWPG4P7774fBYMA777xTqcNUbr/9dqxduxYTJkzA999/j549e6KkpAQHDx7E2rVr8dVXX+l+XgqgHm9fkUOn5syZg2+++Qa9evXCfffdhzp16uC1115DUVGRx+svnMvQoUPx0ksvYfDgwbjllluQmZmJJUuWoFWrVm7nGJRn8ODB+Oeff/Doo49i+/bt2L59u+uxhIQEDBgw4JzPHz58OJ555hls3boVAwcOdC2fO3cuvv76a/Tp08c1RXBaWhrWrVuH7du3n/OLdHx8PGbMmIHZs2dj8ODBuPbaa3Ho0CEsXboUl156qccv0Oczfvx4vPbaaxg7dix27dqFZs2aYf369fjxxx+xaNEij+fDaBEdHY1ly5bh9ttvxyWXXIKbbroJ8fHxOHHiBD7//HP07NkTixcvBqDt/fvCCy9gyJAh6NGjB8aNG+eabtbTvrdr1y5kZ2dj+PDhXvnZiMiLdJyRioj8gHPqTU/TcyqK56lhi4uLlcmTJyvx8fGKwWA479SzFZ1uNiIiosxzZ86c6XH7K1asULp06aKEhYUpUVFRSseOHZVHH31USU1Nda3z448/KpdddpkSFhamNGzYUHn00UeVr776qsxUqqWnbz2bzWZT5s+fr7Rv314JCQlR6tatq3Tp0kWZPXu2kpeXd86fu7qcq14nT9PNKoqi7N69Wxk0aJASGRmphIeHK1deeaXy008/eXzu+aabXblypdK6dWslJCREadu2rbJq1apyf19nQzlTHANw21fOpVOnTq6pdks7fvy4cscddyjx8fFKSEiI0qJFC2XixIlKUVGRoijn3+cXL16stG3bVgkKClISEhKUe++9V8nJyXFbp7zfgaecMjIylDvvvFOJi4tTgoODlY4dO7rt+4py5j3xwgsvuC0v7/dY3s/w/fffK4MGDVJiYmKU0NBQpWXLlsrYsWOVnTt3utYp7/1bXg1O3377rdKzZ08lLCxMiY6OVq655hrlwIEDZdabNm2a0qRJE9f0vkTkOwyKUoWzwIiIiGqpd955BxMnTsSJEyfED8kjz4qKitCsWTNMnz69zMX/iEh/vI4FERGRB7feeiuaNGmCJUuW6F0K/WfVqlUICgoqcz0aIvINHLEgIiIiIqIq44gFERERERFVGRsLIiIiIiKqMjYWRERERERUZX7bWCiKApPJVKk554mIiIiIyDO/bSzMZjNiYmJgNpv1LoWIiIiIqMbz28aC9GE0GvUuwa8wb3nMXB4zl8fM5TFzWcxbG7+dbtZkMiEmJgZ5eXmIjo7Wuxy/oSgKDAaD3mX4DeYtj5nLY+bymLk8Zi6LeWvDEQsSlZqaqncJfoV5y2Pm8pi5PGYuj5nLYt7asLEgUXFxcXqX4FeYtzxmLo+Zy2Pm8pi5LOatDRsLEmUymfQuwa8wb3nMXB4zl8fM5TFzWcxbmzp6F+CrFEVBcXExSkpK9C6lVjEYDCgsLNS7DL9RXt6BgYGoU6cOjx+tBiEhIXqX4HeYuTxmLo+Zy2Le2rCx8MBmsyEtLQ0FBQV6l1LrOBwOnDp1Su8y/Ma58g4PD0dSUhKCg4OFqyIiIqLaiI3FWRwOB1JSUhAYGIiGDRsiODiYf9X1IrvdjqCgIL3L8Bue8lYUBTabDadOnUJKSgpat26NgAAeFektNptN7xL8DjOXx8zlMXNZzFsbNhZnsdlscDgcSE5ORnh4uN7l1DrBwcH8EiuovLzDwsIQFBSE48ePw2azITQ0VIfqaqeIiAi9S/A7zFweM5fHzGUxb234Da8c/PJbPYqLi/Uuwa+cK2/u49UjJydH7xL8DjOXx8zlMXNZzFsbfrMgUTwMShbzlpeYmKh3CX6Hmctj5vKYuSzmrQ0bCxJlt9v1LsGvMG95aWlpepfgd5i5PGYuj5nLYt7a+GRj0axZMxgMhjK3iRMnAgAKCwsxceJE1K9fH5GRkRg1ahQyMjJ0rpoqgjMQyWLe8ho1aqR3CX6Hmctj5vKYuSzmrY1PNha//fYb0tLSXLdvvvkGAHDDDTcAAB588EF8+umnWLduHbZu3YrU1FSMHDlSz5J9Tvv27fHZZ5/pXUYZnGVBFvOWZzQa9S7B7zBzecxcHjOXxby18cnGIj4+HomJia7bZ599hpYtW6JPnz7Iy8vDypUr8dJLL+Gqq65Cly5dsGrVKvz000/4+eefy91mUVERTCaT262m69u3L0JCQhAZGem6LV26FACwf/9+DBs2DIA6ArRx40bNr3PdddfBYDAgPT29yjXXqaNORDZ58mQkJycjOjoajRo1wpQpUyr0JdhqtaJVq1aIjY11LSsqKsLdd9+N5s2bIyoqCm3btsUbb7xR4edXld1ux6RJk1C3bl3Uq1cPkydPPudJ0xVdv7xaS/++IyMjERQUhE6dOnl8LWfeJCc+Pl7vEvwOM5fHzOUxc1nMWxufbCxKs9lsePfdd/F///d/MBgM2LVrF+x2O/r37+9ap23btmjSpAl27NhR7nbmzZuHmJgY1y05ORmAelhVamoqHA6HqzstLi6Gw+GA3W5HSUkJiouL3ZY5rwPgrM/TuiUlJbDb7XA4HG7rOp/rcDgqta5z+6XXVRQFzz33HLKzs2GxWJCdnY0JEyaUqcX5M51dt6Io5637m2++wVdffYXQ0FD89ttv56zbuX1PdTufU1xcDJvNhnvvvRf79u2DyWTCb7/9hr1792LevHkeayld9+OPP46mTZsCgGvdwsJCJCQk4IsvvkBubi7+97//4aGHHsLnn39eppYnnngCTZo0AYAydZ/9+9y8eTP69Onj+t2Xt+7s2bOxfft27Nu3D3v37sW2bdvwzDPPePx92u12PPPMM9i2bRv27duHffv24YcffsCzzz5bZt2zf1ZnHiaTCdnZ2TCbzcjOzka7du1www03nDPv8n5Gh8OBvLw85OTkID8/HxkZGbDb7a73gtFoRElJCdLS0mC1WpGdnY3c3FyYzWZkZmbCZrO5retwOJCamorCwkJkZWW5mvisrCyP7zWj0QibzYbMzEyYzWbk5uYiOzsbVqsVaWlpKCkpcVvXbrcjIyMD+fn5yMnJqXDdp0+fRl5enqvuoqIit3UVRUFqaiqKiopw6tSpStVtsVjc6v7nn39QXFxcpu709HQUFBQgOzv7vHWnpqaWqfvUqVMe6zYajR7rtlqtFa67oKAA6enpZeouLi4uU7fFYvFYt/N3f666nfWeXbfZbEZeXh5Onz5dbt3O373FYkFOTo5b3adPnz5n3bm5ubBYLOfcZ61Wq9s+66lu53POVff59tnSdZ+dobPu0u+189V99nvNWXdqaqrHup3vtdJ1a3mvHTlyhJ8RGj8jPL3XKvIZkZuby88IjZ8R56vb03vNmTc/I6yVO99E8XEffPCBEhgYqBiNRkVRFGX16tVKcHBwmfUuvfRS5dFHHy13O4WFhUpeXp7rdvLkSQWAkpeX57ae1WpVDhw4oFitVu/+INWgT58+ysKFCz0+1rRpU+Wjjz5Srr/+esVgMCihoaFKRESEcs8991R4+zabTWnXrp3yxBNPKJdeeqkyb968KtdcXFxcZllmZqZy1VVXKXfcccc5n7tz506lQ4cOyldffaXExMScc93rrrtOefLJJzU/X1EU5fvvv1f69Olz3vUaN26srFu3znV/7dq1SpMmTaq0fkVr/eWXX9zeH2fzlLdTTdrXaxKz2ax3CX6Hmctj5vKYuSzmrY3Pj1isXLkSQ4YMQcOGDau0nZCQEERHR7vd/MG6devQpEkTvPfee7BYLFi+fDkA4LnnnnMdKlWeV199Fbm5uZg2bRo6duyIPXv2eFzvvvvuQ2xsbLm37du3u9ZVFMX17+eeew6RkZFo0KAB9u7di8mTJ5dbS3FxMe6++24sWbLkvCckFxYW4tdff3U7PKgyz6+MnJwc/Pvvv+jcubNrWefOnXHixAnk5eVpWr8ytZ7v/VE6b5LBa7XIY+bymLk8Zi5Lj7zNZrPbOcZn38xms3hNleXTB2AfP34c3377LTZs2OBalpiYCJvNhtzcXLdjzzMyMvxyzuEZM2Zg1qxZrvtGo7FCV4ucPn36OR/PyMjA7NmzsXDhQkRGRqJjx46upuRsS5cudZ3bURnTp0/H9OnT8ddff2H16tXn/P298MILuPjii9G7d29s2bKl3PUURcFdd92F1q1bu53QX9Hn33fffVizZg0AuA6zKr2fffbZZ+jVq5frvsViAQC3dZz/NpvNiImJcdt+RdavaK35+fl4//338fbbb5e7DslzOBx6l+B3mLk8Zi6PmcvSI+9du3Zh69at5T7ep08f9O3bV64gDXy6sVi1ahUaNGiAoUOHupZ16dIFQUFB2Lx5M0aNGgUAOHToEE6cOIEePXroVapu5s2bhylTpnh9u9OnT0eLFi0wduxYAEDHjh1x+PBhFBQUIDw8XPN2DQZDmWXt2rXDRRddhLFjx+Lbb78t8/iRI0ewfPly/P777+fctqIouO+++3Do0CF8++23ritLV/T5gHuTtGXLFsyaNeucX+4jIyMBAHl5eYiLi3P9GwCioqIqvX5lal23bh3Cw8Pd3h9n85Q3Va/Q0FC9S/A7zFweM5fHzGXpkXeXLl3Qpk0bAEBWVhY2bNiAkSNHur4vOL9D+DKfbSwcDgdWrVqFMWPGuM1sExMTg3HjxmHq1KmoV68eoqOjMXnyZPTo0QOXXXaZjhX7LucX7Ir69ddf8dZbbyE8PNx1iE1JSQkcDgf27dtXJucJEybg3XffLXd7X3zxBa644goA6u81MDCwzDp2ux2HDx/2+Pzt27cjIyMDF1xwgWtds9mMuLg4fP755+jevTsURcHEiRPxyy+/YPPmzW4jBRV5vlZ169ZF48aNsWfPHrRs2RIAsGfPHiQnJ5cZrajI+h999FGFa3399dfLvD/OVl7eVH1MJlOVmm+qPGYuj5nLY+ay9Mg7KiqqzB8l4+LikJSUJFpHVfhsY/Htt9/ixIkT+L//+78yjy1cuBABAQEYNWoUioqKMGjQIE2H4viLhIQEHD16tELrKoqCyZMn48Ybb8TChQvdHuvatSv27NlTprFYvnx5uYdJna1OnTqwWCxYt24drrvuOsTExODPP//EnDlzMGjQII/PGT16tNssYDt27MBdd92FPXv2oEGDBgCASZMm4ccff8R3332HunXrVvr5nvTt2/ecoxVOd955J5599ln07NkTADB37lzcddddmtavaK2HDh3CTz/9hFWrVp2zNk43K8/5lyWSw8zlMXN5zFwW89bGZ0/eHjhwIBRFcf3ltrTQ0FAsWbIE2dnZyM/Px4YNG/zy/IqKeuyxx7B48WLExsbivvvuA6B+mR0yZEiZdd98802cOHECS5YscbuWSGJiIrp06VLuCdwVZbfbYTAYsGbNGrRs2RJRUVEYPnw4hg4dikWLFrnWmzBhAiZMmAAACA8PR+PGjV23+Ph4GAwGNG7cGMHBwTh+/DiWLl2KQ4cOoWnTpq7rO1T0+aVNmDChzHUiSt+2bdtW5md68skn0aNHD7Rr1w7t2rVDz5498dhjj3n8Wc63fkVrXblyJa644gq0bt36vHmTrIyMDL1L8DvMXB4zl8fMZTFvbQyKn04bYzKZEBMTg7y8PLcZogoLC5GSkoLmzZvzeEaq1bivExER+aa0tDSsWLEC48ePr1GHQvnsiAXVThW5ujZ5D/OW57zYEMlh5vKYuTxmLot5a8PGgkQFBQXpXYJfYd7yEhIS9C7B7zBzecxcHjOXxby1YWNBoniBH1nMW15WVpbeJfgdZi6Pmctj5rKYtzZsLEhUZae+paph3vJKn7NFMpi5PGYuj5nLYt7a8FsHifLTuQJ0w7zlFRYW6l2C32Hm8pi5PGYui3lrw8aCAKjTkoaGhiIiIgKRkZGIiIhAt27dsHfvXq9sf/HixejatSsiIiIwYsSI864/efJkJCcnIzo6Go0aNcKUKVPcTkR2bi8kJKRC26sMu92OSZMmoW7duqhXrx4mT558zkOKxo4di+DgYLdpaXfs2FFmPavVilatWiE2Ntar9ZJv4dXO5TFzecxcHjOXxby1YWNBAID9+/fDZrMhIyMDFosFWVlZiIuLw4wZM7yy/YYNG+KJJ54454XjSrvvvvtw8OBBmEwm7N27F3v37sXzzz9fZnt33313perYsmUL+vbte8515syZg+3bt+PAgQPYv38/tm3bhrlz5563XovF4rr16NGjzDpPPfUUmjZtWql6q4ofjPJ4wrw8Zi6Pmctj5rKYtzZsLM5HUYD8fLmbToeu7N69G82bN0dkZCQAICwsDBdddJHXLrA2cuRIjBgxAvXq1avQ+u3atUNERAQA9XCegIAAHD58uMz2quPKmG+88QaeeOIJJCUlISkpCY8//jhWrlxZpW3u2rULX375JaZNm+alKivG4XCIvh4B+fn5epfgd5i5PGYuj5nLYt7asLE4n4ICIDJS7lZQoMuPuXv3bnTq1AmA+kV+586dWL16NaZMmVJm3fvuuw+xsbHl3rZv317u61TmZOLnnnsOkZGRaNCgAfbu3YvJkydX+ueqrJycHPz777/o3Lmza1nnzp1x4sQJ5OXllfu8t99+G/Xq1UP79u2xYMECty/0xcXFuPvuu7FkyZIyV/quboGBgaKvR6hw80zew8zlMXN5zFwW89aGjQUBUBuLL7/8ErGxsQgPD0f37t0xefJkDB06tMy6S5cuRW5ubrm3Xr16lfs6lfkL+vTp02GxWHDgwAFMmDABiYmJmn620o3QsGHDsH379nIbIYvFAgBu50E4/202mz1u//7778ehQ4dw6tQprFy5Ei+//DJefvll1+MvvPACLr74YvTu3VtT/VXB6WblZWZm6l2C32Hm8pi5PGYui3lrw8bifMLDAYtF7hYeLv4jlpSUYO/evVi/fj1yc3NRUFCAzZs3Y9q0afjjjz+8+lpa/oLerl07XHTRRRg7dqym1yzdCH322Wfo1atXuY2Q81Cw0qMTzn9HRUV53P4ll1yC+Ph4BAYG4rLLLsP06dPxwQcfAACOHDmC5cuX44UXXtBUe1VJj5AQ0KhRI71L8DvMXB4zl8fMZTFvbdhYnI/BAEREyN10ONn24MGDKCgowKWXXvrfj2zAFVdcgeDgYKSkpJRZf8KECW4zIJ1927ZtW7mvVVJSoqlGu93udo5Fdalbty4aN26MPXv2uJbt2bMHycnJiImJqdA2Sh/utX37dmRkZOCCCy5AXFwchg8fDpPJhLi4OPzyyy/eLr+M0jNpkQyj0ah3CX6Hmctj5vKYuSzmrQ0bC8Lu3bvRtGlTNGjQAABgMpkwY8YMREdHezx8Z/ny5W4zIJ19u+KKK8o8p7i4GIWFhXA4HHA4HCgsLCz3S6/FYsGqVauQm5sLRVHwxx9/YM6cORg0aFCZ7RUXF593e6X17dsXW7ZsOec6d955J5599lmkp6cjPT0dc+fOPedsVmvXroXJZHKdm/Lcc89h1KhRAIDRo0fjyJEj2LNnD/bs2YPXX38dUVFR2LNnDy6++OLz1ltVnNVCXlJSkt4l+B1mLo+Zy2Pmspi3NmwsCLt378bJkycRGRmJmJgYtG/fHqmpqa5zEbxhzpw5CAsLw9y5c/Hpp58iLCwMAwcOdD0+YcIETJgwAYA6YrJmzRq0bNkSUVFRGD58OIYOHYpFixaV2d6zzz7rcXulVXaE5cknn0SPHj3Qrl07tGvXDj179sRjjz3msVZAvaZGkyZNEBUVhVtvvRX33XcfHnroIQBAeHg4Gjdu7LrFx8fDYDCgcePGIocpeWtWL6q49PR0vUvwO8xcHjOXx8xlMW9tDIqfXprXZDIhJiYGeXl5bpdtLywsREpKCpo3b47Q0FAdK6ydHA5HpWaGoqo5V97c16uH1WpFWFiY3mX4FWYuj5nLY+ay9M47LS0NK1aswPjx42vU6Am/4ZEoXldBFvOWV6DTlNH+jJnLY+bymLks5q0NGwsiIi+qU6eO3iX4HWYuj5nLY+aymLc2bCxIlEGHWa/8GfOWx0P95DFzecxcHjOXxby1YWokiofmyGLe8goLC/Uuwe8wc3nMXB4zl8W8tWFjQaK0XCCPtGPe8kpPBkEymLk8Zi6Pmcti3tqwsSBRxcXFepfgV5i3vKysLL1L8DvMXB4zl8fMZemZd0pWPl7Zcgxbiprj8bW/4s/jmbrVUllsLEgUL9gmi3nLa9iwod4l+B1mLo+Zy2PmsvTKe+3Ok+i3YAvW7snC8eJYfJ8agGuX/4Z1O0/qUk9lsbEgUbxgmyzmLS81NVXvEvwOM5fHzOUxc1l65J1yIhPT1++FQwFKDAFwBARCMQTAoQDTPtyHY1n54jVVFhsLEsW/oMti3vL4V0V5zFweM5fHzGWJ5p2fD7zwAtbeMxOGciZdMRgM+KAGjFqwsSBR/Au6LOYtj39VlMfM5TFzecxclkje+fnAiy8CzZsDjz6Kf4OioMDzNPGKouDfHGv111RFvPoHieIFZ2Qxb3lxcXF6l+B3mLk8Zi6Pmcuq1rwLCoBly4Dnnwcy/zsxu2VLNL7iUhiyAwFFKfMUg8GAxnXDqq8mL+GIBYkqKSnRuwS/wrzlmUwmvUvwO8xcHjOXx8xlVUveBQXASy+pIxQPP6w2FS1aAKtWAQcPYvSdV0NB2aYCUEcsbuya7P2avIyNBYnilSxlMW95oaGhepfgd5i5PGYuj5nL8mreViuwcKHaRDz0kNpQNG8OvPEGcPAgMHYsUKcOmsdFYP6oTggwAAEGwAAFBigIMADzR3VCs7gI79VUTXicBIlSPAzvUfVh3vJ4tXN5zFweM5fHzGV5JW+rFXjtNWD+fCA9XV3WrBnwxBPAHXcAHiZYuaFrMi5tVg+vfbMPP/95GM0bxOChET3QvmmDqtcjgI0FEZEX8aKE8pi5PGYuj5nLqlLeViuwYgXw3HNnGoqmTdWGYswYjw1Fac3iInB/32YI/ftrjB89HklJNaOpANhYkDAemiOLecsLDw/XuwS/w8zlMXN5zFyWprwLC880FGlp6rKmTYHHH1cbiuBg7xbpg/itg0TxLy6ymLe8nJwcvUvwO8xcHjOXx8xlVSrvwkLg1VeBli2BBx5Qm4omTdTDoP7+G7j7br9oKgCOWJAwXrBNFvOWl5iYqHcJfoeZy2Pm8pi5rArlXVgIvP46MG8e4LzuRXKyOkJx551+00yUxhELEsULtsli3vLSnMPfJIaZy2Pm8pi5rHPmXVQELFkCtGoFTJ6sNhXJyeq1KQ4fBu65xy+bCoAjFiQs2E/faHph3vIaNWqkdwl+h5nLY+bymLksj3kXFQErV6ojFP/+qy5r3Bh47DHg//4PCAmRLdIHsbHwIrPZDIvFUu7jkZGRiIqKEqzI99hsNn7ZFcS85RmNRn4BEMbM5TFzecxcllveRUXqRezmzgVOnlSXNWqkNhTjxrGhKIWNhRft2rULW7duLffxPn36oG/fvnIF+aA6dbjLSWLe8ho0qDnTAtYWzFweM5fHzGU1aNAAsNnUhuLZZ880FA0bnmkoeNHCMvitw4u6dOmCNm3aAACysrKwYcMGjBw5EnFxcQDUEQt/V1JSwilQBTFvednZ2UhISNC7DL/CzOUxc3nMXJDNBusrryDo1VeBEyfUZQ0bAjNmAHfdxYbiHPiNowrMZjO2bNkCs9kMAIiKikJSUhIKg6Kx9lARttiaY+2hIhQGRSMpKQlRUVFlnuNrJkyYgGnTpp13naVLlwIAtmzZgtjY2Apvn19yZZjNZrRs2RLZ2dl6l+J3IiIi9C7B7zBzecxcHjMXYLMB//sfcMEFiH7kEbWpSEoCXnkFOHoUmDSJTcV58FteFVgsFmzdutXtvIq1O0+i34ItWLMrA8dK6mHNrgz0W7AF63aeLPc5WvTt2xchISGIiopCTEwMOnTogIceeginTp2q8DZmzZqFESNGuC1bvnw55s+fX+5zjhw5gs8//xx33XWXproVRdH0PH/05JNPomPHjqhTpw6mTJlS5vHU1FRcffXViIiIQJMmTfC///3P9VhUVBTuuOMOzJ07V7BiAjgTlx6YuTxmLo+ZVyO7XZ02tk0bYPx44PhxOBISgJdfVhuKyZPZUFQQGwsvSsnKx/QP98GhACUKoMCAEgVwKMC0D/fhWFa+V19v/vz5MJvNyM3Nxdq1a2E0GtGlSxdkZGR49XVKW758OW688cZqOyHY0wdndV/kzdNrVvYDvLi42OtNU6tWrfD888/j2muv9fj4zTffjMTERGRmZmLdunV45JFH3M7xGTNmDN566y0UFBR4tS46NzbP8pi5PGYuj5lXA7tdneXpggvUi9gdOwYkJgKLFiF31y7g/vuBsDC9q6xR2Fh4QVZWFtLS0vDGlr9gOMd6K7f8haysLK+/vsFgwIUXXoh3330X0dHRWLBgAQB1dGT48OFo0KABYmJi0Lt3b+zduxcAsHHjRsydOxefffYZIiMjXed/jB071uNfx50++eQTXHXVVWWWv/rqq0hKSkJiYiJmzpzp+gA8ceIEBgwYgPj4eNStWxfXXnstjh075nre2LFjMW7cOIwePRrR0dFYvnw5+vbti0cffRQDBw5EREQEvvjiC1gsFkyaNAlNmjRBgwYNcMcddyAvL6/cOo8ePYprrrkG8fHxaNq0KebMmQOHwwEAePPNN9G5c2fMnDkTiYmJuOmmmzBr1iwMGzYM9957L+rVq4fp06fDbrdjxowZaNKkCeLj43HjjTe6jQgZDAYsXrwYHTp0QEREBCwWyzlf92yPPPII+vTp43p8/fr1rkYBUBuDIUOGIDo62uPPt337dsybNw8RERHo3r07br31VrzxxhuudZo1a4b69eufc0IB8r5Q/lVLHDOXx8zlMXMvstuBN95QRyjuukttKBISgIULgX/+AR54AKF16+pdZY3kk42F0WjEbbfdhvr16yMsLAwdO3bEzp07XY8rioKnnnoKSUlJCAsLQ//+/XH48GHd6t2wYQNWrFiB7b8fgKOcvygoioLtvx/Ahg0bqq2OOnXqYMSIEa4vkg6HA7fccgtSUlKQkZGBiy++GKNHj4aiKBgxYgQee+wxDBs2DBaLpUKHZhUUFODw4cNo27at23Kz2Yzdu3fj6NGj2LJlC9544w28/fbbrhqmTp2KkydP4vjx4wgLC8Pdd9/t9vz33nsP48aNQ25uLsaNGwdA/fI/Z84cWCwW9O/fH//3f/+H7Oxs7Nu3DykpKbDb7Zg0aVK5dfbr1w/9+vWD0WjEtm3b8P7772PVqlWudf7880/UqVMHJ06cwDvvvAMA+PLLL9G9e3dkZmbimWeewbx58/DZZ59h+/btSElJgcFgwK233ur2WmvWrMHXX38Nk8mEwMDA875uac8++yzy8/MxZ84cHD9+HOPHj8fbb79doZk/9u3bh6SkJLcT+Tp37ox9+/a5rde2bVvs2bPnvNsj7zGZTHqX4HeYuTxmLo+Ze4Hdrs7y1LatOqtTSgrQoAGwYIHaUEyZ4hqhYN7a+FxjkZOTg549eyIoKAhffPEFDhw4gAULFqBuqc7x+eefxyuvvILly5fjl19+QUREBAYNGoTCwkJdah45ciTGjx+PXhdfiACD5zELg8GAXhdfiJEjR1ZrLY0aNXKdsBsdHY0bb7wRERERCA0NxezZs/H3338j1XnZ+UrKyclxbbc0h8OB+fPnIzw8HG3btsWkSZNcX9abNWuGIUOGIDQ0FNHR0XjiiSewbds2t7/iDxw4EIMGDUJAQADCw8MBALfccgu6desGg8EAi8WCDz/8EEuWLEFsbCwiIiLw9NNP44MPPkBJSUmZOj///HPUrVsXU6ZMQXBwMJo0aYIHHngAa9asca0TExODxx9/HMHBwa7X7NChA8aOHYs6deogPDwc77zzDp544gk0adIEkZGReOmll/DNN9+45ffoo4+iYcOGCAkJqdDrlhYcHIz33nsPCxcuxNVXX41x48Zh4MCBFfpdWCyWMifNx8bGlpkUIDY21vV7IxnOWeBIDjOXx8zlMfMqKC4G3nwTaNdOvZDdP/+oDcWLL6rNxdSpwH/fBZyYtzY+N93s/PnzkZyc7PZX3ubNm7v+rSgKFi1ahCeeeALDhw8HALz99ttISEjAxo0bcdNNN4nXHBcXh6SkJPxf32is3lX++Q3j+rZDiL16O2Cj0Yh69eoBAKxWKx566CFs2rQJ2dnZrhmZsrKyNF1kx9ncmUwmtzdcaGio21/ZmzZtCqPRCAA4deoUHnjgAWzbts116FJRURHMZjNiYmIAAE2aNCnzWqWXHTt2DA6Hw20/ANQZptLT08v8LMeOHcOff/7p9sXb4XAgOTnZdb9Ro0ZlZqg6u45///0XzZo1c913NhD//vsvGjZs6LHO873u2Vq3bo2+ffvis88+w3fffVfuemeLjIwscyhYXl5emQsw5ubmokOHDhXeLlVdRkYGL2IljJnLY+bymLkGxcXA6tXAM8+oJ2EDQHw88OijwL33AueYaYt5a+NzIxaffPIJunbtihtuuAENGjTAxRdf7DbbTUpKCtLT09G/f3/XspiYGHTv3h07duwod7tFRUUwmUxuN29rHheB+aM6IcAABBoAAxQEGIAAAzB/VCc0i6veqeKKi4vx8ccfuy7Ct2DBAuzatQvbt2+HyWRyndvgPP+hslO/hoeHo3Xr1jh48KDb8sLCQtd5AYB6XoXzzThjxgwUFBRg9+7dMJlM+OGHH9xqKK+O0suSk5MREBCA1NRU5Obmum6FhYUe3/TJycno0qWL27omkwn79++v8GsCQOPGjd3OB0lPT0dRUREaN25cbp3ne92zrV+/Hj///DOGDh2KiRMnlrve2Tp16oTU1FS33Pfs2YOOHTu6rXfw4EF07ty5wtulquP/iOQxc3nMXB4zr4TiYuDtt9URirFj1aYiLg54/nl1hOLhh8/ZVADMWyufayz++ecfLFu2DK1bt8ZXX32Fe++9F/fffz/eeustAOqXOwBlLhKTkJDgesyTefPmISYmxnVz/hW5sLAQqampcDgcrr+yFxcXw+FwwG63o6SkBMXFxW7LFEWBzWZDZGQkevXqhfDwcNe613VOwtdTeuHmixugWWA2brmkAb66vydu6JoMm82GiIgI13Oc2y0pKYHdbofD4YDNZgMA2Gw21+uUrsW5rrNO5zoHDx7E7bffjry8PEyePBklJSXIzc1FSEgIoqOjkZOTgxkzZrjysNlsaNCgAY4dO4aioiJXLYqiwOFwlFvLsGHDsHnzZlfdxcXFCAgIwLRp01BQUIA//vgDS5YswY033giHw4Hc3FyEhYUhMjISGRkZmDlzJgC4tgeoF3Er/TMqiuL2syYkJODaa6/FpEmTkJqaCkVRcPLkSXz44YceMxw4cCAyMjLwyiuvwGq1wmq14q+//sK3337rei3nz+n82ZyHVJXO+5ZbbsGzzz6LlJQUmEwmTJkyBf3793cbrSld9+DBg5GRkYHFixfDbDbDbrfjjz/+wJYtW1yv46yxuLgY//zzD+655x6sXLkSb775Jnbv3o0VK1a41s3Pz0dBQQHsdjuKi4td952jID179sT06dNRUFCAH3/8EatXr8aYMWNcv5ejR48iKysLl112mVvepX9Gh8OBvLw85OTkID8/HxkZGbDb7a73gtFoRElJCdLS0mC1WpGdnY3c3FyYzWZkZmbCZrO5retwOJCamorCwkJkZWW5mvisrCyP7zWj0QibzYbMzEzXDGfZ2dmwWq1IS0tDSUmJ27p2ux0ZGRnIz89HTk5Ohes+ffo08vLyXHUXFRW5rasoClJTU1FUVIRTp05Vqm6LxeJW9/79+1FcXFym7vT0dBQUFCA7O/u8daemppap+9SpUx7rNhqNHuu2Wq0VrrugoADp6ell6i4uLi5Tt8Vi8Vi383d/rrqd9Z5dt9lsRl5eHk6fPl1u3c7fvcViQU5OjlvdJ06cOGfdubm5sFgs59xnrVar2z7rqW7nc85V9/n22dJ1n52hs+7S77Xz1X32e81Zt/Oz8uy6ne+10nVrea/98ccf/IzQ+Bnh6b1Wkc8I542fEef4jDAaUfi//6GkbVtgzBjgyBE46tVDybx5SP3xR+CRR2DMza3QZ4TzptdnhHOyH+cfEPX+jKgwxccEBQUpPXr0cFs2efJk5bLLLlMURVF+/PFHBYCSmprqts4NN9ygjB49utztFhYWKnl5ea7byZMnFQBKXl6e23pWq1U5cOCAYrVaq/RzpKamKrNmzSpTp7f06dNHCQ4OViIjI5Xo6GilXbt2ytSpU5WMjAzXOmlpacqVV16pREREKE2bNlXefvttBYDy+++/K4qiKKdPn1Z69+6txMbGKjExMYqiKMqYMWOUBx54oNzXPXTokNK4cWPFZrMpiqIo33//vRITE6O88sorSmJiotKgQQPliSeeUEpKShRFUZQDBw4ol156qRIREaG0adNGWb58uQJAycnJKff1+vTpoyxcuNBtmclkUh588EGlWbNmSlRUlNKqVSvl8ccfL7fOI0eOKCNHjlQSEhKUmJgY5ZJLLlHee+89RVEUZdWqVcpFF13ktv7MmTOV4cOHuy0rKipSHnnkEaVRo0ZK/fr1leuvv15JT093PV46y4q8bmnFxcVKr169lIceesi17JdfflFiYmKUAwcOuLIB4HYbM2aMa/1///1XGTx4sBIeHq40btxYWbFihdtrPP3008rkyZPLzchb+zq5s9vtepfgd5i5PGYuj5mfQ3GxorzzjqJccIGiAOqtfn1Fee45RTGbNW1S77yr+3tkdTEoim9NjNy0aVMMGDAAr7/+umvZsmXLMGfOHBiNRvzzzz9o2bIlfv/9d7dDPPr06YPOnTvj5ZdfrtDrmEwmxMTEIC8vz+1k5MLCQqSkpKB58+aVntrNbDa7ZlfKysrChg0bMHLkSNdfuCMjI8scA18T3XPPPejcuTPuvffeSj/XbrcjKCioGqqi0sxmMy6++GL88MMPrvNBzlaVfZ3Kl56ejsTERL3L8CvMXB4zl8fMPSgpAd5/Xz2H4tAhdVn9+uqhTpMmAf9Npa+F3nmnpaVhxYoVGD9+PJKSknSro7J87uTtnj174pBz5/jP33//jaZNmwJQT+ROTEzE5s2bXY2FyWTCL7/8oumLrjft2rWrzDUDSk8v26dPH9f5DzXZa6+9pvm5lT2vg7SJiorCkSNHPM6aRdXL03VHqHoxc3nMXB4zL6WkBPjgA+Dpp880FPXqnWkovPBHXOatjc81Fg8++CAuv/xyzJ07F6NHj8avv/6KFStWYMWKFQDUaVunTJmCOXPmoHXr1mjevDmefPJJNGzYECNGjNC19i5duqBNmzblPh5Zhc65tvCxAbJaj3nLKywsdE1hTDKYuTxmLo+ZQ20o1q5VGwrnRDJ166oNxeTJXmkonJi3Nj7XWFx66aX46KOPMGPGDDz99NNo3rw5Fi1a5HZhskcffRT5+fkYP348cnNz0atXL3z55Ze6H84RFRVVKw51IiLtOConj5nLY+by/DpzhwNYtw6YPRv46y91Wd26wEMPqQ1FNYwu+HXeVeBzjQUADBs2DMOGDSv3cYPBgKeffhpPP/10tdXAv/RWD0M5FxCk6nGuvLmPV486dXzyY7VWY+bymLk8v8zc4QDWr1cbigMH1GWxsWcaiv+uh1Ud/DJvL2BqZ3GeWFxQUICw/y7rTt7jcDgQGBiodxl+41x5FxQUAABPpveygoICHvYojJnLY+by/CpzhwP48EO1oXBeCyo2Vr1C9v33V2tD4aRH3mdPAlT6v0DNmASIjcVZAgMDERsb65o3ODw8nH9l9yKHw8ETigV5yltRFBQUFCAzMxOxsbFs9Lys9JXXSQYzl8fM5flF5g4HsGGD2lD8+ae6LCbmTEMhmIEeedeGSYDYWHjgnF6s9FWNyTuKi4s5vCjoXHnHxsZy6sJqcOrUKV6xVRgzl8fM5dXqzB0O4KOP1Ibiv4svIiYGePBB4IEHRBsKJz3yrg2TAPncdSyklHcdi9JKX+WaqDYJCgriSAUREenL4QA2blQbin371GXR0WpDMWWKLg0FVQ3/dHwOgYGB/PLlZUajsfb+xcUHMW95zFweM5fHzOXVqswdDuDjj9WGYu9edVl0tNpMTJmizviks1qVtyCOWJxjxIK8z+FwcAo3QcxbHjOXx8zlMXN5tSJzRTnTUOzZoy6LilIPd3rwQfUidz6iVuStAyZGotLT0/Uuwa8wb3nMXB4zl8fM5dXozJ0NxSWXANddpzYVkZHA448Dx44BzzzjU00FUMPz1hEPhSJRdX1geNOfMG95zFweM5fHzOXVyMwVBfj0U2DWLOD339VlkZHqDE9TpwL16+ta3rnUyLx9AEcsSFR+fr7eJfgV5i2Pmctj5vKYubwalbmzoejaFRg+XG0qIiOBGTPUEYpnn/XppgKoYXn7EI5YkKjg4GC9S/ArzFseM5fHzOUxc3k1InNFAT7/XB2h2LVLXRYRoV4l+6GHgLg4XcurjBqRtw9iY0FERERE2ikKsGmT2lDs3Kkui4gAJk0CHn64RjUUVDVsLEhUUVGR3iX4FeYtj5nLY+bymLk8n8xcUYAvvlAbit9+U5eFh59pKOLjdS2vKnwy7xqAjQWJ4tS+spi3PGYuj5nLY+byfCpzRQG+/FJtKH79VV0WHg5MnAg88kiNbiicfCrvGoQnb5OorKwsvUvwK8xbHjOXx8zlMXN5PpG5s6Ho0QO4+mq1qQgLU0cnUlKA55+vFU0F4CN510C8QB4vkCdKURQYDAa9y/AbzFseM5fHzOUxc3m6Zq4owNdfqyMUP/+sLgsLA+67Tx2hSEjQp65qxH1cG45YkKjU1FS9S/ArzFseM5fHzOUxc3m6ZK4owDffAD17AoMHq01FaKh6DYp//gFefLFWNhUA93GtOGLBEQsiIiKiMxQF2LxZHaH48Ud1WWgocO+9wKOPAomJupZHvosjFiTKaDTqXYJfYd7ymLk8Zi6PmcsTydzZUPTuDQwYoDYVoaHAlCnqCMVLL/lNU8F9XBvOCkWi4jiXtSjmLY+Zy2Pm8pi5vGrNXFGA779XRyi2bVOXhYQAEyYA06YBSUnV99o+ivu4NhyxIFEmk0nvEvwK85bHzOUxc3nMXF61Zf7990DfvkC/fmpTERIC3H+/OkKxaJFfNhUA93GtOGJBokJDQ/Uuwa8wb3nMXB4zl8fM5Xk98y1b1BGKrVvV+yEhwPjx6ghFo0befa0aiPu4NmwsSJTD4dC7BL/CvOUxc3nMXB4zl+e1zLduVRuKLVvU+8HBwN13AzNmsKEohfu4NmwsSFRxcbHeJfgV5i2Pmctj5vKYubwqZ/7DD2pD8f336v3gYOCuu9SGonHjKtdX23Af14aNBYkKDw/XuwS/wrzlMXN5zFweM5enOfNt29SG4rvv1PtBQWcaiuRkr9VX23Af14Ynb5OonJwcvUvwK8xbHjOXx8zlMXN5lc78xx+B/v3VqWO/+05tKCZMAI4cAZYuZVNxHtzHteEF8niBPFElJSUIDAzUuwy/wbzlMXN5zFweM5dX4cx/+gmYORP49lv1flAQ8H//Bzz2GNCkSfUWWYtwH9eGIxYkKj09Xe8S/ArzlsfM5TFzecxc3nkz37EDGDgQ6NlTbSrq1FFneTp8GFi+nE1FJXEf14YjFhyxICIioppqxw71HIqvv1bv16kD3HmnOkLRrJmelZEf4ogFiTIajXqX4FeYtzxmLo+Zy2Pm8spk/vPPwODBwOWXq01FnTrqSdmHDwMrVrCpqCLu49pwxIIjFqLsdjuCgoL0LsNvMG95zFweM5fHzOW5Mv/lF3WE4ssv1QcCA4GxY4HHHweaN9ezxFqF+7g2HLEgUdnZ2XqX4FeYtzxmLo+Zy2Pm8kzffgtcfTVw2WVqUxEYqJ6U/fffwOuvs6nwMu7j2vA6FiQqMjJS7xL8CvOWx8zlMXN5zFzQb78Bs2ah/qZN6v3AQOCOO9QRipYt9a2tFuM+rg1HLEiUzWbTuwS/wrzlMXN5zFweMxewcydwzTVAt27Apk1QnIc8HTwIvPEGm4pqxn1cG45YkCg/PaVHN8xbHjOXx8zlMfNqtGsXMHs28Omn6v2AAOD225E3aRJiu3bVtzY/wn1cGzYWJCo0NFTvEvwK85bHzOUxc3nMvBrs3q02FJ98ot4PCABuuw144gmgdWsEFxToW5+f4T6uDQ+FIlEmk0nvEvwK85bHzOUxc3nM3It+/x0YMQLo0kVtKv4bocBffwFvvQW0bg2AmUtj3tpwullONyuK07fJYt7ymLk8Zi6PmXvBnj3qCMXGjer9gADgllvUEYo2bcqszsxlMW9tOGJBojIzM/Uuwa8wb3nMXB4zl8fMq2DvXmDkSODii9WmwmBQG4r9+4F33vHYVADMXBrz1oYjFhyxICIiouq2b586QrFhg3rfYABuugl48kmgXTt9ayPyEo5YkCij0ah3CX6Fectj5vKYuTxmXgl//AFcfz1w0UVqU+FsKPbvB9asqXBTwcxlMW9tOGLBEQtRxcXFqFOHk5FJYd7ymLk8Zi6PmVfAn3+qIxTr16v3DQZg9GjgqaeACy+s9OaYuSzmrQ1HLEhUVlaW3iX4FeYtj5nLY+bymPk57N+vNhAdO6pNhbOh+OMP4P33NTUVADOXxry1YStGomJiYvQuwa8wb3nMXB4zl8fMPdi/H3j6aWDdOsB5MMgNN6gjFB06VHnzzFwW89bGJ0csZs2aBYPB4HZr27at6/HCwkJMnDgR9evXR2RkJEaNGoWMjAwdK6aKslqtepfgV5i3PGYuj5nLY+alHDignjPRsSOwdq3aVFx/vXqy9tq1XmkqAGYujXlr45ONBQC0b98eaWlprtv27dtdjz344IP49NNPsW7dOmzduhWpqakYOXKkjtVSRQUE+OwuVysxb3nMXB4zl8fMoV7A7uab1cbhgw/UhmLUKLWhWLdObTS8iJnLYt7a+OyhUHXq1EFiYmKZ5Xl5eVi5ciXWrFmDq666CgCwatUqtGvXDj///DMuu+wy6VKpEngilCzmLY+Zy2Pm8vw684MH1UOe3n//zCFPI0eqhzxddFG1vaxfZ64D5q2Nz7Zjhw8fRsOGDdGiRQvceuutOHHiBABg165dsNvt6N+/v2vdtm3bokmTJtixY0e52ysqKoLJZHK7kbyCggK9S/ArzFseM5fHzOX5ZeaHDgG33qqefP3ee2pTcd11wO+/Ax9+WK1NBeCnmeuIeWvjk41F9+7d8eabb+LLL7/EsmXLkJKSgiuuuAJmsxnp6ekIDg5GbGys23MSEhKQnp5e7jbnzZuHmJgY1y05ORmAer5GamoqHA6Ha85io9EIm82GzMxMWCwW5ObmIjs7G1arFenp6SguLnZb1263Iz09HQUFBcjOzkZOTg7y8/ORkZEBu93utm5JSQlSU1NhtVpx+vRp5OXlwWw249SpUygqKnJbV1EUGI1GFBUV4dSpU66GKCsrC1artcJ1FxQUeKy7uLi4TN0Wi8Vj3Q6H47x1O+s9u26z2Yy8vDycPn0aoaGhHuu22+3IyMiAxWJBTk5OperOzc2FxWJBZmYmbDZbuXVnZWW5MvRUt/M5nup25l1SUuKx7vz8/DJ1n52hs26r1VrhugsLCz3WnZqa6rHuzMxMt7pDQkKQlpZ23rrPt8+mpaW51W02mytVd1ZW1nnfa2az2e295s26S++zmZmZHt9rqampHt9rlf2MKCoq4mdEFT4jyqv7XJ8RkZGR/IzQ+Bmh9b3mPP7cHz4jMrZtg3LbbVAuvFC97oSiwD5sGCw//ICsFStQ2LatyPeI2NhYfkZo/IzQ8j3CmTc/I9T3WkXViOtY5ObmomnTpnjppZcQFhaGO++8E0VFRW7rdOvWDVdeeSXmz5/vcRtFRUVuzzGZTEhOTuZ1LIQZjUY0atRI7zL8BvOWx8zlMXN5fpH5338Dc+YAq1cDDoe6bPhwYOZM4OKLxcvxi8x9CPPWpkYcQBYbG4sLLrgAR44cwYABA2Cz2ZCbm+s2apGRkeHxnAynkJAQhISECFRL58I3qSzmLY+Zy2Pm8vTM3Gw2w2KxlPt4ZGQkoqKitL/A4cNqQ/Huu2caimuvVRuKSy7Rvt0q4n4ui3lrUyMaC4vFgqNHj+L2229Hly5dEBQUhM2bN2PUqFEAgEOHDuHEiRPo0aOHzpXS+fAvALKYtzxmLo+Zy9Mz8127dmHr1q3lPt6nTx/07du38hs+cuRMQ1FSoi675hq1oejSRVuxXsT9XBbz1sYnD4V6+OGHcc0116Bp06ZITU3FzJkzsWfPHhw4cADx8fG49957sWnTJrz55puIjo7G5MmTAQA//fRThV/DZDIhJiaGh0IJczgcnMJNEPOWx8zlMXN5emZeesQiKysLGzZswMiRIxEXFwdAw4jF0aNqQ/HOO2caimHD1Iaia1dvl68Z93NZzFsbnxyx+Pfff3HzzTfj9OnTiI+PR69evfDzzz8jPj4eALBw4UIEBARg1KhRKCoqwqBBg7B06VKdq6aKSE9PR8OGDfUuw28wb3nMXB4zl6dn5lFRUWUah7i4OCQlJVVuQ0ePAs8+C7z99pmGYuhQtaG49FIvVes93M9lMW9tfHLEQgJHLPRRWFiI0NBQvcvwG8xbHjOXx8zl+UrmaWlpWLFiBcaPH1/xxuKff9SG4q23zjQUQ4YAs2YB3bpVW61V5SuZ+wvmrQ3HeEjUuU64I+9j3vKYuTxmLq9GZp6SAtx1F9CmDfDGG2pTMXgw8PPPwKZNPt1UADU08xqMeWvjk4dCUe0VHBysdwl+hXnLY+bymLm8GpX5sWPqCMWbbwLFxeqyQYPUEYrLLtOxsMqpUZnXAsxbGzYWREREVPscP642FKtWnWkoBg5UGwrOIklULdhYkKizL2xI1Yt5y2Pm8pi5PJ/O/PhxYO5ctaGw29VlAwaoDcXll+taWlX4dOa1EPPWho0FieKJ8rKYtzxmLo+Zy/OFzP88nomXNv6KFFtzFG45hvHtLWi+dIF6/oSzoejfX20oevbUtVZv8IXM/Qnz1oYnb5Oo06dP612CX2He8pi5PGYuT+/M1+48iWuX/4bv0wJxrLge1u7JQr93D2Ldj0fUpqJfP2DbNuCbb2pFUwHon7m/Yd7acLpZTjcrSlEUGAwGvcvwG8xbHjOXx8zl6Zl5SlY++i3YAsfZ314UBQFQ8N2V0Wg2uI8utVUn7ueymLc2HLEgUampqXqX4FeYtzxmLo+Zy9Mz87XfHYDB4Sj7gMEAQ2AgPkCCfFECuJ/LYt7a8BwLEtWoUSO9S/ArzFseM5fHzOXpkrnRCMybh3//jYRyweWAhz8mK4qCf3Os8rUJ4H4ui3lrwxELEmU0GvUuwa8wb3nMXB4zlyeaeWoqMHky0KIFsGQJGuekl3uIisFgQOO6YXK1CeJ+Lot5a8PGgkTFxcXpXYJfYd7ymLk8Zi5PJPPUVOCBB9SGYvFiwGYDrrgCox+5HUqA568viqLgxq7J1V+bDrify2Le2rCxIFF5eXl6l+BXmLc8Zi6Pmcur1szT0oApU4CWLYFXXgGKioBevYDNm4GtW9F8WD/MH9UJAQbAAAUGKAgwAAEGYP6oTmgWF1F9temI+7ks5q0Nz7EgUWFhtXOI2lcxb3nMXB4zl1ctmaelAc8/DyxfDhQWqst69gRmzwauugoodfjTDV2TcWF8CBZs3IGUzDxc1qE17hlQe5sKgPu5NOatDUcsSJTD00weVG2YtzxmLo+Zy/Nq5unpwNSp6iFPixapTcXll6vXoNi2Tb0mhYdzKto3bYBnR3dD3+AU3N+3Wa1uKgDu59KYtzYcsSBRxcXFepfgV5i3PGYuj5nL80rmGRnqCMWyZYD1v5mcevRQRyj69/fYTPgz7ueymLc2bCxIVHh4uN4l+BXmLY+Zy2Pm8qqUeUYG8MILwNKlZxqK7t3VhmLgQDYU5eB+Lot5a8NDoUhUbm6u3iX4FeYtj5nLY+byNGWemQk88gjQvDmwYIHaVHTvDnzxBbBjBzBoEJuKc+B+Lot5a8MRCxLVoEEDvUvwK8xbHjOXx8zlVSrzU6fUEYolS4CCAnVZt27ArFnA4MFsJiqI+7ks5q0NRyxIVHp6ut4l+BXmLY+Zy2Pm8iqU+alTwLRpQLNmamNRUABceinw+efAzz8DQ4ZoairMZjPS0tKQlpaGrKwsAEBWVpZrmdlsrvQ2awLu57KYtzYGRVEUvYvQg8lkQkxMDPLy8hAdHa13OURERLVDVhbw4ovqRe3y89VlXbuqIxRXX13lEYotW7Zg69at5T7ep08f9O3bt0qvQUTa8FAoEmU0GtGoUSO9y/AbzFseM5fHzOV5zDwrSz134tVXzzQUXbqoDcXQoV475KlLly5o06ZNuY9HRkZ65XV8DfdzWcxbG45YcMRClN1uR1BQkN5l+A3mLY+Zy2Pm8twyP336TENhsajLLrlEbSiGDeM5FF7C/VwW89aG51iQqOzsbL1L8CvMWx4zl8fM5WVnZwPZ2cDjj6vnUMybpzYVF18MfPwxsHMncM01bCq8iPu5LOatDQ+FIlG1dYjaVzFvecxcHjMXlp2NugsWAMuXA84TpTt3Vkcorr2WzUQ14X4ui3lrwxELEmWz2fQuwa8wb3nMXB4zF5KTAzz5JNCsGYJfeEFtKi66CPjoI2D3bmD4cDYV1Yj7uSzmrQ1HLIiIiKh8OTnAwoXAyy8DJhMAoLh9e9R55hm1mQjg3yiJSMXGgkQFBwfrXYJfYd7ymLk8Zl5NcnOBRYvUW16euqxjR2DWLBQNGIA6UVE6Fud/uJ/LYt7a8M8MJMrinDGERDBvecxcHjP3stxcYPZs9aTs2bPVpqJDB2D9emDPHmDkSFicV9AmMdzPZTFvbThiQaLq1aundwl+hXnLY+bymLmX5OWphzstXKg2F4DaUMycCYwc6XbIEzOXx8xlMW9tOGJBojIzM/Uuwa8wb3nMXB4zr6K8POCZZ9QRipkz1aaifXtg7Vpg717g+uvLnEfBzOUxc1nMWxteII8XyCMiIn9kMgGvvAK89JJ6gjYAXHih2lx4aCaIiM6Hnxokymg06l2CX2He8pi5PGZeSSYT8Oyz6gjFk0+qTUW7dsB77wH79gGjR5+3qWDm8pi5LOatDUcsOGIhqqSkBIGBgXqX4TeYtzxmLo+ZV5DZDLz6KrBggXrVbABo2xZ46im1mahEhsxcHjOXxby14YgFieIxi7KYtzxmLo+Zn4fZDMybp45QPP642lS0aQOsXg38+Sdw882VaioAZq4HZi6LeWvDWaFIVGxsrN4l+BXmLY+Zy2Pm5bBYgMWLgRdfBE6fVpddcIE6QnHTTZVuJkpj5vKYuSzmrQ1HLEiU1WrVuwS/wrzlMXN5zPwsFgswf746QjFjhtpUtG4NvPMOcOAAcOutVWoqAGauB2Yui3lrwxELEhXAWUZEMW95zFweM/9Pfj6wZAnwwgtAVpa6rHVr9QTtm28G6njvf/nMXB4zl8W8tWFjQaJ4IpQs5i2Pmcvz+8zz84GlS9WG4tQpdVmrVmpDccstXm0onPw+cx0wc1nMWxtNnzaffPJJpZ8zYMAAhIWFaXk5qkWsViuioqL0LsNvMG95zFye32aenw8sWwY8//yZhqJlS7WhuPXWamkonPw2cx0xc1nMWxtN081WdnjIYDDg8OHDaNGiRWVfqtpwull92Gw2BAcH612G32De8pi5PL/LvKDgTEPhnLmmRQu1objttmptKJz8LnMfwMxlMW9tNB9Alp6eDofDUaFbeHi4N2umGuyU869qJIJ5y2Pm8vwm84IC9SrZzZsDDz+sNhXNmwNvvAEcPAiMHSvSVAB+lLkPYeaymLc2mj6BxowZU6nDmm677TaOChAAoFGjRnqX4FeYtzxmLq/WZ261AsuXqzM9ZWSoy5o3B554Arj9diAoSLykWp+5D2Lmspi3NrzyNg+FEmU0GvlmFcS85TFzebU2c6sVWLECeO45ID1dXdasmdpQ3HGHLg2FU63N3Icxc1nMWxs2FmwsRDkcDk7hJoh5y2Pm8mpd5lYr8L//qQ1FWpq6rGnTMw2FDxz3XesyrwGYuSzmrU2lE7NarTAajWWW79+/3ysFne25556DwWDAlClTXMsKCwsxceJE1K9fH5GRkRg1ahQynMPD5NPSnX91IxHMWx4zl1drMi8sBF59VZ3Z6YEH1KaiSRN11OLvv4G77vKJpgKoRZnXIMxcFvPWplKNxfr169G6dWsMHToUnTp1wi+//OJ67Pbbb/d6cb/99htee+01dOrUyW35gw8+iE8//RTr1q3D1q1bkZqaipEjR3r99cn76tWrp3cJfoV5y2Pm8mp85oWFwOLFakNx//1nGorXXgMOHwbuvttnGgqnGp95DcTMZTFvbSp18vacOXOwa9cuJCQkYNeuXRgzZgwee+wx3HLLLfD2EVUWiwW33nor/ve//2HOnDmu5Xl5eVi5ciXWrFmDq666CgCwatUqtGvXDj///DMuu+wyj9srKipCUVGR677JZPJqvVQxFosFoaGhepfhN5i3PGYuz5uZm81mWCyWch+PjIz03tz2hYXAypXAvHmA80iA5GTg8ceBO+/0uWaiNO7n8pi5LOatTaVGLOx2OxISEgAAXbp0wQ8//IDXXnsNTz/9NAwGg1cLmzhxIoYOHYr+/fu7Ld+1axfsdrvb8rZt26JJkybYsWNHudubN28eYmJiXLfk5GQA6mFVqampcDgcrkO8jEYjbDYbMjMzYbFYkJubi+zsbFitVqSnp6O4uNhtXbvdjvT0dBQUFCA7Oxs5OTnIz89HRkYG7Ha727olJSVITU2F1WrF6dOnkZeXB7PZjFOnTqGoqMhtXUVRYDQaUVRUhFOnTsFkMsFkMiErKwtWq7XCdRcUFHisu7i4uEzdFovFY90Oh+O8dTvrPbtus9mMvLw8nD59GoqieKzbbrcjIyMDFosFOTk5lao7NzcXFosFmZmZsNls5dadlZXlytBT3c7neKrbmXdJSYnHuvPz88vUfXaGzrqtVmuF6y4sLPRYd2pqqse6MzMz3ep2OBxIS0s7b93n22fT0tLc6jabzZWqOysr67zvNbPZ7PZe82bdpffZzMxMj++11NRUj++1yn5GmEwmfkZU4TOivLrP9RkRGBjotc+In376CStWrCj39v3331f9MyInB3nPPQelVStg0iTAaERxUhJKXn0V6du2wXrHHcj+7/dT3Z8RWt9rOTk5/IzQ+Bmh9XtEcHAwPyM0fkZo+R7hzNvfv0c432sVVamTt6+88kq8/PLLbocm2Ww2jBkzBuvWrUNxcXGFX/hc3n//fTz77LP47bffEBoair59+6Jz585YtGgR1qxZgzvvvNNt9AEAunXrhiuvvBLz58/3uE1PIxbJyck8eVuYyWRi3oKYtzxmLs+bmZcescjKysKGDRswcuRIxMXFAajiiEVRkXrNiblzgX//VZc1agQ89hgwbhwQEuKNH0EE93N5zFwW89amUodCvfPOO6hz1sV3goOD8d5772HSpEleKejkyZN44IEH8M0333h1CCokJAQhNehDu7ay2Wx6l+BXmLc8Zi7Pm5lHRUWVaRzi4uKQlJSkfaM225mG4uRJdVmjRsCMGeoJ2TXw/03cz+Uxc1nMW5tKHQrVuHFjJCYmuu6XPmO+Z8+eXilo165dyMzMxCWXXII6deqgTp062Lp1K1555RXUqVMHCQkJsNlsyM3NdXteRkaGW23kmyIjI/Uuwa8wb3nMXJ7PZm6zqSdgt24N3Huv2lQ0bKjO/HTkCDBxYo1sKgAfzrwWY+aymLc2VZqgd+DAgd6qw6Vfv374448/sGfPHteta9euuPXWW13/DgoKwubNm13POXToEE6cOIEePXp4vR7yruzsbL1L8CvMWx4zl+dzmdts6hSxrVsDEyYAJ04ASUnAK68AR4+q51XU8JNCfS5zP8DMZTFvbSp1KNTZquPaelFRUejQoYPbsoiICNSvX9+1fNy4cZg6dSrq1auH6OhoTJ48GT169Ch3RijyHRxVksW85TFzeT6Tud0OvPkm8OyzwPHj6rKkJGD6dGD8+BrfTJTmM5n7EWYui3lrU6URC2/PBFVRCxcuxLBhwzBq1Cj07t0biYmJ2LBhgy61UOVUZmYBqjrmLY+Zy9M9c7sdeP114IIL1Abi+HEgMRFYtEgdobj//lrVVAA+kLkfYuaymLc2lZoV6mydOnXCvn37vFmPGJPJhJiYGM4KRUREHqWlpWHFihUYP36855O37Xbg7beBOXOAY8fUZQkJ6gjFPfcAYWGi9RIR6a1KIxZEleWcL5lkMG95zFxedWSekpWPBd8cwRZbcyz45ghSsvLPPGi3q7M8tWmjzup07JjaULz0EvDPP8CUKbW+qeB+Lo+Zy2Le2lRpxOLiiy/G77//7s16xHDEQh82mw3BPnw12dqGectj5vK8nfnanScx/UN1NF5RFNdhv/Ov64Ab9n+njlD884+6coMGwLRp6kna4eFeq8HXcT+Xx8xlMW9tqjRiUVObCtLP2dMEU/Vi3vKYuTxvZp6SlY/pH+6DQwEcCqDA8N+/FUz7cB+OTX1cbSri44EXX1T/PXWqXzUVAPdzPTBzWcxbmyrNCgUAVqsViqIg/L8P1ePHj+Ojjz7ChRdeWC3T0VLNFlbLDw/wNcxbHjOX583M1+48qY5QlBnMN8CgOPDBZSMwrV8L9boUERFee92ahvu5PGYui3lrU+VzLIYPH463334bgNrdde/eHQsWLMDw4cOxbNmyKhdItUtJSYneJfgV5i2PmcvzZub/5ljLnUpdCQjAv7fcCTz8sF83FQD3cz0wc1nMW5sqNxa7d+/GFVdcAQBYv349EhIScPz4cbz99tt45ZVXqlwg1S4Oh0PvEvwK85bHzOV5LfOSEjQ2HoWhnC8UhoAANI6P8s5r1XDcz+Uxc1nMW5sqNxYFBQWIilI/aL/++muMHDkSAQEBuOyyy3DceYEgov9waFEW85bHzOVVOfOSEmD1auDCCzF67gNQ4OlQKPVE7hu7JlfttWoJ7ufymLks5q1NlRuLVq1aYePGjTh58iS++uor13kVmZmZnG2JyuDJULKYtzxmLk9z5iUlwJo1QPv2wG23AX//jeaGQsyPMCIgwIAAA2CAggADEGAA5o/qhGZx/n0IlBP3c3nMXBbz1qZK080C6uFPt9xyC0pKStCvXz98/fXXAIB58+bhhx9+wBdffOGVQr2N083qo6SkBIGBgXqX4TeYtzxmLq/SmZeUAGvXAk8/DRw8qC6rVw946CFg8mQgKgrHsvKxeNNu7DqUgi5tmmPS1ZewqSiF+7k8Zi6LeWtT5RGL66+/HidOnMDOnTvx5Zdfupb369cPCxcurOrmqZZJT0/XuwS/wrzlMXN5Fc68pAR4/32gY0fgllvUpqJuXfW6FCkpwGOPAf8d2tssLgIPDWiFvsEpeGhAKzYVZ+F+Lo+Zy2Le2lR5ulkASExMRGJiotuybt26eWPTVMs0atRI7xL8CvOWx8zlnTdzhwNYt04doThwQF0WG6uOUNx/P8BR60rjfi6Pmcti3tpoGrHYt29fpc6W379/P4qLi7W8FNUyRqNR7xL8CvOWx8zllZu5w6Ee8tSxI3DTTWpTERurNhjHjgFPPMGmQiPu5/KYuSzmrY2mxuLiiy/G6dOnK7x+jx49cOLECS0vRbVMgwYN9C7BrzBvecxcXpnMnSMUnToBN96oNhQxMcDs2WpD8eST6n3SjPu5PGYui3lro+lQKEVR8OSTT7qutn0+NptNy8tQLZSdnY2EhAS9y/AbzFseM5fnytzhADZsUBuIP/9UH4yJAR58EHjgAXW0gryC+7k8Zi6LeWujqbHo3bs3Dh06VOH1e/TowfmACQAQGRmpdwl+hXnLY+byIsPDgQ8/VBuKP/5QF0ZHqw3FlClsKKoB93N5zFwW89ZGU2OxZcsWL5dB/sJmsyEigrOrSGHe8pi5IIcD2LgRIU89Bezfry6LjlabiSlT1BmfKslsNsNisQAAsrKy3P4LqF82nBeF9Wfcz+Uxc1nMWxuvzApFREQkRlGAjRvVEYq9e9X/kUVFnWko6tXTvOldu3Zh69atbss2bNjg+nefPn3Qt29fzdsnIqrN2FiQqODgYL1L8CvMWx4zr0aKAnz8sdpQ7NmjLouKgu3eexE8bVqVGgqnLl26oE2bNuU+zsMjVNzP5TFzWcxbGzYWJMpisXBoURDzlsfMq4GiAJ98ojYUv/+uLouMVE/IfvBB5BQXI8ELTQUAREVF8VCnCuB+Lo+Zy2Le2rCxIFH1vPQ/f6oY5i2PmXuRogCffQbMmgXs3q0ui4xUL2o3dSpQvz4AoJ7drl+Nfor7uTxmLot5a6PpOhZEWmVmZupdgl9h3vKYuRc4G4pLLwWuvVZtKiIigBkzgJQU4NlnXU0FwMz1wMzlMXNZzFsbg6Ioit5F6MFkMiEmJgZ5eXmI5pVXiYj0pyjApk3qCMXOneqyiAhg8mTgoYeAuDhdyyMionPjiAWJMhqNepfgV5i3PGaugbOh6N4dGDZMbSoiIoBp09QRinnzztlUMHN5zFweM5fFvLXhiAVHLESVlJQgMDBQ7zL8BvOWV9MyL33dBk+q9boNigJ8+aU6QvHrr+qy8HBg4kTgkUeA+PgKbaamZV4bMHN5zFwW89aGJ2+TqMzMTCQlJeldht9g3vJqWuaerttQWrVct0FRgK++UhuKX35Rl4WFnWkoGjSo1OZqWua1ATOXx8xlMW9tKt1YWK1WZGdno1GjRm7L9+/fj/bt23utMKqdYmNj9S7BrzBveTUt89LXbcjKysKGDRswcuRIxP136JFXr9ugKMDXX6sNxc8/q8vCwoD77lMbioQETZutaZnXBsxcHjOXxby1qdQ5FuvXr0fr1q0xdOhQdOrUCb84/9IE4Pbbb/d6cVT7FBQU6F2CX2He8mpa5lFRUUhKSkJSUpKrmYiLi3Mt88phUM6GomdPYPBgtakIC1OnjE1JAV58UXNTAdS8zGsDZi6Pmcti3tpUqrGYM2cOdu3ahT179mDVqlUYN24c1qxZAwDw01M1qJLq1OHRd5KYtzxmXoqiAN98A/TqBQwaBOzYAYSGAg8+CPzzD7BgQZUaCidmLo+Zy2Pmspi3NpVKzW63I+G//wl06dIFP/zwA6677jocOXIEBoOhWgqk2iUggBORSWLe8pg51IZi82b1kKcff1SXhYYCEyaoMz0lJnr15Zi5PGYuj5nLYt7aVCq1Bg0aYN++fa779erVwzfffIO//vrLbTlReaxWq94l+BXmLc+vM3c2FL17AwMGqE1FSAjwwAPqCMXChV5vKgA/z1wnzFweM5fFvLWpVGPxzjvvoMFZs3UEBwfjvffeO+esIkROMTExepfgV5i3PL/MXFGA774D+vQB+vcHtm9XG4r771cbikWLgGqcXcUvM9cZM5fHzGUxb20q1Vg0btwYieX8talnz55eKYhqt6ysLL1L8CvMW57fZb5lC9C3L9CvH7Btm9pQTJ4MHD0KvPwy0LBhtZfgd5n7AGYuj5nLYt7aVPnMlOeffx579uxBeno6wsLCcOGFF2LkyJHo0aOHN+qjWubsaYqpejFveX6T+datwMyZ6n8BIDgYGD8emD4dEM7AbzL3IcxcHjOXxby1qfKZKa+++iqysrJch0i9//776NWrFwYPHoy8vLwqF0i1i9Fo1LsEv8K85dX6zH/4AbjySnWUYutWtaGYOFEdoXj1VfGmAvCDzH0QM5fHzGUxb20MSjXME/vzzz/j3nvvRfv27fHuu+96e/NeYTKZEBMTg7y8PERHR+tdjt9QFIUziAli3vJqauYpWflYsmk3dh1KQZc2zTHx6kvQPC7izArbtqkjFN9/r94PDgbuuksdoUhO1qfo/9TUzGsyZi6Pmcti3tpUS2MBAHv27EHv3r1hMpmqY/NVxsZCH6mpqWgocMw1qZi3vJqY+dqdJzH9Q3Vmv9L/M50/qhNuKDyuNhTffaeuHBSkNhQzZujeUDjVxMxrOmYuj5nLYt7aePXqH6tWrUJUVBRCQ0OxceNG1K9f35ubp1qA+4Qs5i2vpmWekpWP6R/ug8P1JyYDnH9umrZuDy5dcQ+a5aapDcW4cWpD0aSJXuV6VNMyrw2YuTxmLot5a+PVq3/88ssvuOeeezB8+HBkZmbik08+8ebmqRbw1RGs2op5y6tpma/debLc4X6DouCDiwcD99wDHD4MLFvmc00FUPMyrw2YuTxmLot5a+PVxmL58uXIysrCZ599hn/++Qe7d+/25uapFggJCdG7BL/CvOXVtMz/zbGivCNiFYMB/97yf8Dy5UDTpsKVVVxNy7w2YObymLks5q1NlRuL3r1745dffnHdNxgMGDJkCN59913MmDGjqpsnIqJq1LggGwaHw+NjhsBANG4cJ1wRERHVVFU+x6J9+/bo2bMnunXrhlGjRqFjx46IjIzEe++9x8uhUxk2m03vEvwK85ZXYzL/5Rdg1iyM/mUfXrtrOQAFOOuQKEVRcGNX3zhB+1xqTOa1CDOXx8xlMW9tqtxYLFu2DJMmTcILL7yAp59+GmazGYA6cjF37twqF0i1S2RkpN4l+BXmLc/nM//1V2DWLOCLLwAAzQMDMd+yG9NiugIoOytUs9JTzvoon8+8FmLm8pi5LOatjVenmy0pKcHRo0eRm5uLpk2bIiEhwVub9jpON6sPTt8mi3nL89nMf/0VmD0b2LRJvR8YCNxxB/DEE0CLFjiWlY/Fpa5jMenqS2pEUwH4cOa1GDOXx8xlMW9tvHrydmBgIC644AJ069atSk3FsmXL0KlTJ0RHRyM6Oho9evTAF//9dQ0ACgsLMXHiRNSvXx+RkZEYNWoUMjIyvPEjUDVLTEzUuwS/wrzl+VzmO3cCw4YB3burTUVgIDB2LHDoEPDGG0CLFgCAZnEReGhAK/QNTsFDA1rVmKYC8MHM/QAzl8fMZTFvbbzaWHhL48aN8dxzz2HXrl3YuXMnrrrqKgwfPhz79+8HADz44IP49NNPsW7dOmzduhWpqakYOXKkzlVTRaSlpeldgl9h3tXLbDYjLS3N7fbHH3+4/u08NFQXu3YB11wDXHop8PnnQEAAMGYMcPAgsGoV0LKlfrV5GfdzecxcHjOXxby1qbYrb3tbvXr18MILL+D6669HfHw81qxZg+uvvx4AcPDgQbRr1w47duzAZZddVqHt8VAoIqqqLVu2YOvWreU+3qdPH/Tt21euIADYvVs9h+LTT9X7AQHAbbephzy1bn3Op6alpWHFihUYP348kpKSqr9WIiKqVbx65e3qUFJSgnXr1iE/Px89evTArl27YLfb0b9/f9c6bdu2RZMmTc7ZWBQVFaGoqMh1nxc+0YfRaESjRo30LsNvMO/q1aVLF7Rp0wYAkJWVhQ0bNqBv37644IILAAif/Pf772pD4bwwaUAAcOutakPxXz21FfdzecxcHjOXxby18clDoQDgjz/+QGRkJEJCQjBhwgR89NFHuPDCC5Geno7g4GDExsa6rZ+QkID09PRytzdv3jzExMS4bsnJ6hSKhYWFSE1NhcPhgNFoBKDuTDabDZmZmbBYLMjNzUV2djasVivS09NRXFzstq7dbkd6ejoKCgqQnZ2NnJwc5OfnIyMjA3a73W3dkpISpKamwmq14vTp08jLy4PZbMapU6dQVFTktq6iKDAajSgqKsKpU6dgMplgMpmQlZUFq9Va4boLCgo81l1cXFymbovF4rFuh8Nx3rqd9Z5dt9lsRl5eHk6fPo2oqCiPddvtdmRkZMBisSAnJ6dSdefm5sJisSAzMxM2m63curOyslwZeqrb+RxPdTvzLikp8Vh3fn5+mbrPztBZt9VqrXDdhYWFHutOTU31WHdmZqZb3ZGRkUhLSztv3efbZ9PS0tzqNpvNlao7KyvrvO81s9ns9l7zZt2l99nMzEyP77XU1FSP77Vz1W21WhEVFYWwsDAEBQUBAKKjoxEfHw+Hw4GoqKhq/4zI/PprlFx7LXDJJcAnn0AJCEDhDTegcPdupD73HBytWlXqM8JJr8+I8j7bzvUZUbduXX5GaPyM0Ppec+4r/Iw4f93e+h4RHx/P7xEaPyO0fI9w5s3PCGulDgvz2UOhbDYbTpw4gby8PKxfvx6vv/46tm7dij179uDOO+90G30AgG7duuHKK6/E/PnzPW7P04hFcnIyD4USlpmZiQYNGuhdht9g3nKchxFdf/31aN++ffW/4J496ixPGzeq9w0G4JZb1BGKtm01bbKmHgrF/VweM5fHzGUxb2189lCo4OBgtGrVCoB6uMFvv/2Gl19+GTfeeCNsNhtyc3PdRi0yMjLOeQZ/SEgIL8/uA8LDw/Uuwa8wb3mhoaHV+wJ796oNxUcfqfcNBuDmm4Enn9TcUNR03M/lMXN5zFwW89bGZw+FOpvD4UBRURG6dOmCoKAgbN682fXYoUOHcOLECfTo0UPHCqkiSh9qQdWPecsrKSmpng3v2weMGgV07qw2Fc6GYv9+YPVqv20qAO7nemDm8pi5LOatjU+OWMyYMQNDhgxBkyZNYDabsWbNGmzZsgVfffUVYmJiMG7cOEydOhX16tVDdHQ0Jk+ejB49elR4RijSj8Ph0LsEv8K85Xk98z/+UEcoPvxQvW8wADfeqI5QXHihd1+rhuJ+Lo+Zy2Pmspi3Nj7ZWGRmZuKOO+5AWloaYmJi0KlTJ3z11VcYMGAAAGDhwoUICAjAqFGjUFRUhEGDBmHp0qU6V00VERYWpncJfoV5y/PaIZd//gk8/TSwbp1632AARo9WGwovnsNhNpthsVgAqDNblf4voM5sFRUV5bXXqw7cz+Uxc3nMXBbz1sYnG4uVK1ee8/HQ0FAsWbIES5YsEaqIvCUvL49vVkHMW57zS7pm+/efaSicc2s4G4oOHape4Fl27dpV5locGzZscP1bl2txVBL3c3nMXB4zl8W8tfHJxoJqr7i4OL1L8CvMW97ZU2FX2IEDakOxdu2ZhuL664GZM6uloXAqfS0OT0SvxaER93N5zFweM5fFvLWpMSdvU+2QkZGhdwl+hXnLy87OrtwTDhxQT8Lu0AH44AO1qRg1Sj1Ze926am0qACAqKgpJSUnl3nz9MCiA+7kemLk8Zi6LeWvDEQsSxatYymLe8io87/lffwHPPAO8//6ZEYqRI9URik6dqq/AWoj7uTxmLo+Zy2Le2nDEgkQ5r/BIMpi3vMzMzHOvcPAgcOut6gnY772nNhXXXade8O7DD9lUaMD9XB4zl8fMZTFvbThiQaJ4FUtZzFtevXr1PD9w6JA6QvHee4BzGsMRI9QRis6dpcqrlbify2Pm8pi5LOatDUcsSNTp06f1LsGvMG95ubm57gv+/hu4/Xb1mhOrV6tNxfDhwO7d6oXu2FRUGfdzecxcHjOXxby14YgFiYqOjta7BL/CvOW5ZlE6fFgdoXA2EwBw7bXqCMUll+hXYC3E/VweM5fHzGUxb204YkGiCgsL9S7BrzBveY6//wbGjAHatgXeeUdtKq65Bti5E/j4YzYV1YD7uTxmLo+Zy2Le2nDEgkQZDAa9S/ArzFtOYEoKhn/0EVo88wxQUqIuHDZMHaHo2lXf4mo57ufymLk8Zi6LeWvDxoJEBQcH612CX2HeAo4eRcq8RVh71IJ/mwxE45jOGF3fjuYzpgCXXqp3dX6B+7k8Zi6Pmcti3tqwsSBRFosFERERepfhN5h3NfrnH2DOHKzdbcT0gZNgqKfAAQMMAQa8ZjBgviERN+hdo5/gfi6Pmctj5rKYtzY8x4JElTsVJ1UL5l0NUlKAceOACy5AysavMH3gJDgCAlASEAglIAAOGOBQgGkf7sOxrHy9q/UL3M/lMXN5zFwW89aGjQWJOu/Fw8irmLcXpaQAd90FXHAB8MYbQEkJ1g6/B4ZAzx+jBoMBH+w8KVykf+J+Lo+Zy2Pmspi3NmwsSFSjRo30LsGvMG8vOHYMuPtutaFYuRIoLgYGDQJ++gn/DrwGSjlPUxQF/+ZYJSv1W9zP5TFzecxcFvPWho0FiTIajXqX4FeYdxUcPw7ccw/QujXw+utqQzFwIPDjj8CXXwI9eqBx3bByZw4xGAxoXDdMuGj/xP1cHjOXx8xlMW9t2FiQqMTERL1L8CvMW4MTJ4AJE9SGYsUKtaEYMADYvh346ivg8stdq47umgxF8TxmoSgKbuyaLFW1X+N+Lo+Zy2Pmspi3NmwsSFRGRobeJfgV5l0JJ04A994LtGoFvPYaYLcD/foB27YBX38N9OxZ5inN4yIwf1QnBBiAAANggIIAqP+eP6oTmsVxRhEJ3M/lMXN5zFwW89bGoJT357ZazmQyISYmBnl5ebxsuyCr1YqwMB4eIoV5V8DJk8C8eerhTna7uuyqq4BZs4ArrqjQJo5l5WPxpt3YdSgFnVsl44FrurGpEMT9XB4zl8fMZTFvbThiQaIKCgr0LsGvMO9z+PdfYOJEdYRi2TK1qbjySmDrVmDz5go3FQDQLC4CDw1ohb7BKRh/WSKbCmHcz+Uxc3nMXBbz1oYXyCNRdepwl5PEvD3491/gueeA//0PsNnUZX37qiMUffpUefPMXB4zl8fM5TFzWcxbG6ZGogICOEgmiXmXYjSqDcWKFWcait69gdmz1cbCS8qbJYqqD/dzecxcHjOXxby1YWokqrCwUO8S/ArzBpCaCtx/P9CyJbB4sdpUXHEF8N136mFPXmwqAMDmbFpIDPdzecxcHjOXxby14YgFieKJ8rL8Ou+0NHWE4rXXgKIidVmvXuoIxZVXAtU0shARwfMrpPn1fq4TZi6Pmcti3tpwxIJEZWVl6V2CX/HLvNPSgClTgBYtgFdeUZuKnj2Bb78FfvhBnfGpGg9Xys3NrbZtk2d+uZ/rjJnLY+aymLc2HLEgUY0aNdK7BL/iV3mnpwPz5wPLlwPOIezLL1dHKPr1q9ZmorQGDRqIvA6d4Vf7uY9g5vKYuSzmrQ1HLEiU0WjUuwS/4hd5p6cDU6cCzZsDixapTUWPHupF7bZvB/r3F2sqACAzM1PstUjlF/u5j2Hm8pi5LOatDUcsSFTDhg31LsGv1Oq8MzKA559Xr0FhtarLLrtMHaEYMEC0mSgtPj5el9f1Z7V6P/dRzFweM5fFvLXhiAWJSk1N1bsEv1Ir887MBB5+WB2heOkltano3h348kvgp5+AgQN1ayoA4NSpU7q9tr+qlfu5j2Pm8pi5LOatDUcsSFRcXJzeJfiVWpV3ZibwwgvA0qWA84qo3bqpIxSDBunaTJQWGxurdwl+p1bt5zUEM5fHzGUxb204YkGiTCaT3iX4lVqR96lTwKOPqiMUL76oNhWXXgps2gT8/DMweLDPNBUAkJ+fr3cJfqdW7Oc1DDOXx8xlMW9tOGJBokJCQvQuwa/U6LyzstRGYvFiwPllvWtXdYRiyBCfaiZKCwoK0rsEv1Oj9/MaipnLY+aymLc2bCyIyLdkZQELFgCvvureUMyaBVx9tc82FERERP6OjQWJstlsepfgV2pU3qdPn2koLBZ12SWXqCMUQ4fWmIaiuLhY7xL8To3az2sJZi6Pmcti3tqwsSBRERERepfgV2pE3qdPq7M7vfLKmYbi4ovVEYprrqkxDYVTaGio3iX4nRqxn9cyzFweM5fFvLXhydskKicnR+8S/IpP552dDTzxhHpS9ty5alPRuTOwcSOwaxdw7bU1rqkAALPZrHcJfsen9/NaipnLY+aymLc2HLEgUYmJiXqX4Fd8Mu/sbGDhQuDllwHnl/CLLlJHKIYPr1HNhNlshuW/UZasrCwAgKIoSEtLAwBERkYiKipKt/r8hU/u57UcM5fHzGUxb23YWJCotLQ0NGrUSO8y/IZP5Z2Tc6ahcE7j16nTmYYioOYNoO7atQtbt251W7Zx40bXv/v06YO+ffvKFuWHfGo/9xPMXB4zl8W8tTEoiqLoXYQeTCYTYmJikJeXh+joaL3LIaq9cnPVhmLRIveGYuZMYMSIGtlQOJUesfCEIxZERORPau7/0alGMhqNepfgV3TNOzdXHY1o1gx4+mm1qejYEVi/Hvj9d2DkyBrdVABAVFQUkpKS3G4Oh8P1bzYVMvi5Io+Zy2Pmspi3Nhyx4IiFKJvNhuDgYL3L8Bu65J2Xp45OLFyo/hsAOnRQRyhqQTNxPtzH5TFzecxcHjOXxby1qd3/hyefk5ubq3cJfkU077w8dWSiWTN1pCIvD2jfHli7Fti7F7j++lrfVADcx/XAzOUxc3nMXBbz1oYnb5Oo8PBwvUvwKyJ5m0zqCdkvvaQe/gQAF16ojlD4STNRGvdxecxcHjOXx8xlMW9t2FiQKF6VWFa15m0yqRe1e+kldcYnAGjX7kxDERhYfa/tw7iPy2Pm8pi5PGYui3lrw8aCRDkcDr1L8CvVkrfJBLz6KrBgwZmGom1btaG44Qa/bSicuI/LY+bymLk8Zi6LeWvjk8cozJs3D5deeimioqLQoEEDjBgxAocOHXJbp7CwEBMnTkT9+vURGRmJUaNGISMjQ6eKqaJCQ0P1LsGveDVvs1m9Qnbz5uoVs3Ny1IZizRrgzz+Bm27y+6YC4D6uB2Yuj5nLY+aymLc2PtlYbN26FRMnTsTPP/+Mb775Bna7HQMHDkR+fr5rnQcffBCffvop1q1bh61btyI1NRUjR47UsWqqCJPzOgYkwit5m83AvHnqSdmPP65eObtNG2D1arWhuPlmNhSlcB+Xx8zlMXN5zFwW89amRkw3e+rUKTRo0ABbt25F7969kZeXh/j4eKxZswbXX389AODgwYNo164dduzYgcsuu6zMNoqKilBUVOS6bzKZkJyczOlmhRUXF6NOHR6BJ6VKeVsswJIlwAsvAKdPq8suuAB46imOTpwD93F5zFweM5fHzGUxb218csTibHn/zYVfr149AMCuXbtgt9vRv39/1zpt27ZFkyZNsGPHDo/bmDdvHmJiYly35ORkAOohVampqXA4HK6LoRiNRthsNmRmZsJisSA3NxfZ2dmwWq1IT09HcXGx27p2ux3p6ekoKChAdnY2cnJykJ+fj4yMDNjtdrd1S0pKkJqaCqvVitOnTyMvLw9msxmnTp1CUVGR27qKosBoNKKoqAinTp2CyWSCyWRCVlYWrFZrhesuKCjwWHdxcXGZui0Wi8e6HQ7Heet21nt23WazGXl5eTh9+jROnjzpsW673Y6MjAxYLBbk5ORUqu7c3FxYLBZkZmbCZrOVW3dWVpYrQ091O5/jqW5n3iUlJR7rzs/PL1P32Rk667ZarRWuu7Cw0GPdqampHuvOzMwsk3daWtp563bbZ3NykPf44+ohT9OnA6dPo7hFC9hWrkT2tm3IHToU5oKCStWdlZV13vea2Wx2e69Vum4P77W0tLQy+2xmZqbH91pqaqrH91plPyMOHTrEz4gqfEaUV/e5PiNSU1P5GaHxM0Lre+2vv/7y+F5z1u18r/EzwnvfIzIyMvgZofEzQsv3CGfe/IxQ32sV5fMjFg6HA9deey1yc3Oxfft2AMCaNWtw5513uo1AAEC3bt1w5ZVXYv78+WW2wxELovPIzweWLgWefx7IylKXtWoFPPkkcMstAP9yQ0REROfg8yMWEydOxJ9//on333+/StsJCQlBdHS0243kOTtjklGhvPPzgRdfVEcoHn1UbSpatgTefBP46y/gjjvYVFQC93F5zFweM5fHzGUxb218+tvCpEmT8Nlnn+GHH35A48aNXcsTExNhs9mQm5uL2NhY1/KMjAwkJibqUClVVEJCgt4l+JVz5l1QACxbpo5QZGaqy1q0UEcobruNzYRG3MflMXN5zFweM5fFvLXxyRELRVEwadIkfPTRR/juu+/QvHlzt8e7dOmCoKAgbN682bXs0KFDOHHiBHr06CFdLlVClvMQGxLhMe+CAvWids2bAw8/rDYVLVoAb7wBHDwIjB3LpqIKuI/LY+bymLk8Zi6LeWvjk98eJk6ciDVr1uDjjz9GVFQU0tPTAQAxMTEICwtDTEwMxo0bh6lTp6JevXqIjo7G5MmT0aNHD48zQpHv4CFostzyLigAXnsNmD8fcF7zxXlNittvB4KC9CmyluE+Lo+Zy2Pm8pi5LOatjU82FsuWLQMA9O3b1235qlWrMHbsWADAwoULERAQgFGjRqGoqAiDBg3C0qVLhSulyiosLER4eLjeZfiNwsJChBsMZxqK/5p0NGumNhR33MGGwsu4j8tj5vKYuTxmLot5a+OTjUVFJqoKDQ3FkiVLsGTJEoGKyFsMBoPeJfgPqxWhr70GvPLKmYaiaVO1oRgzhg1FNeE+Lo+Zy2Pm8pi5LOatjU82FlR7BfHLbPUrLARWrACeew7hzrmnmzQ501AEB+tbXy3HfVweM5fHzOUxc1nMWxufPHmbaq/8/Hy9S6i9CguBV19Vp4p94AEgLQ0ljRqph0EdPgzcfTebCgHcx+Uxc3nMXB4zl8W8teGIBYlyXj2dvKiwEHj9dWDePCA1VV2WnAw8/jgct92GwIgIfevzM9zH5TFzecxcHjOXxby14YgFicp0Xi+Bqq6oCFiyRL069uTJalPRuLF6bYrDh4F77kFmbq7eVfod7uPymLk8Zi6Pmcti3toYlIqcKV0LmUwmxMTEIC8vj1OKUc1SVASsXKmOUPz7r7qscWPgsceA//s/ICRE3/qIiIjIL3HEgkQZjUa9S6i5iorU0YhWrYCJE9WmolEjddTiyBHg3nvLNBXMWx4zl8fM5TFzecxcFvPWhiMWHLEQ5XA4EBDAfrZSbDb1qthz5wInT6rLGjZURyjGjQNCQ8t9KvOWx8zlMXN5zFweM5fFvLVhYiTKeRV1qgCbTZ02tnVrdTTi5Em1oXj1VeDoUXXU4hxNBcC89cDM5TFzecxcHjOXxby14axQJKpu3bp6l+D7bDbgrbeAZ58Fjh9XlyUlAdOnA+PHn7eZKI15y2Pm8pi5PGYuj5nLYt7acMSCRBUUFOhdgu+y29VpY9u0URuI48eBxERg0SJ1hOL++yvVVADMWw/MXB4zl8fM5TFzWcxbG45YkKg6dbjLlWG3A2+/DcyZAxw7pi5LSFBHKO65BwgL07xp5i2Pmctj5vKYuTxmLot5a8PUSBRPhCrFbgfeeUdtKFJS1GUJCcC0aWpDER5e5Zdg3vKYuTxmLo+Zy2Pmspi3NkyNRBUWFupdgv7sdmDVKqBtW3VWp5QUoEEDYMEC4J9/gAcf9EpTATBvPTBzecxcHjOXx8xlMW9tOGJBovx6at/iYuDdd9URiqNH1WUNGgCPPqrO+uSlZqI0v85bJ8xcHjOXx8zlMXNZzFsbjliQqKysLL1LkFdcrM7y1LYtcOedalMRHw+88II6QvHQQ9XSVAB+mrfOmLk8Zi6Pmctj5rKYtza8QB4vkCdKURQYDAa9y5BRXAysWQM884x6ZWwAiItTRyjuuw+IiKj2Evwqbx/BzOUxc3nMXB4zl8W8teGIBYlKTU3Vu4TqV1ysnpR94YXAmDFqUxEXB8yfr55P8cgjIk0F4Cd5+xhmLo+Zy2Pm8pi5LOatDUcsOGIhqlb/BaCkBHjvPXWE4u+/1WX166uNxMSJQGSkeEm1Om8fxczlMXN5zFweM5fFvLXhiAWJqpV/ASgpAVavBtq3B26/XW0q6tUD5s1TRyimTdOlqQBqad4+jpnLY+bymLk8Zi6LeWvDWaFIVFxcnN4leE9JCfDBB8DTTwOHDqnL6tUDHn4YmDQJiIrStz7UsrxrCGYuj5nLY+bymLks5q0NRyxIlMlk0ruEqnMe8tShA3DrrWpTUbcu8Oyz6gjFjBk+0VQAtSTvGoaZy2Pm8pi5PGYui3lrwxELEhUaGqp3CdqVlADr1qkjFH/9pS6rW1edLnbyZMAHz9Wp0XnXUMxcHjOXx8zlMXNZzFsbNhYkyuFw6F1C5TkcZxqKAwfUZbGxZxqKmBhdyzuXGpl3DcfM5TFzecxcHjOXxby1YWNBooqLi/UuoeIcDuDDD4HZs4H9+9VlsbHA1KnA/ff7dEPhVKPyriWYuTxmLo+Zy2Pmspi3NmwsSFR4NV1h2qscDmDDBrWh+PNPdVlMDPDgg8ADD6jNRQ1RI/KuZZi5PGYuj5nLY+aymLc2PHmbROXk5OhdQvmcIxSdOwM33KA2FdHRwMyZwLFj6n9rUFMB+HjetRQzl8fM5TFzecxcFvPWhhfI4wXyRDkcDgQE+Fg/63AAGzeqIxT79qnLoqOBKVPUW926OhZXNT6Zdy3HzOUxc3nMXB4zl8W8tWFiJCotLU3vEs5wOICPPgIuuQQYNUptKqKigCefVEcoZs+u0U0F4GN5+wlmLo+Zy2Pm8pi5LOatDUcsOGLhfxQF+PhjtXHYs0ddFhWlnj/x4IPqRe6IiIiIqFI4YkGijEajfi/ubCguuQS47jq1qYiMBB5/XB2heOaZWtdU6Jq3n2Lm8pi5PGYuj5nLYt7acMSCIxai7HY7goKCZF9UUYBPPwVmzQJ+/11dFhmpThk7dSpQv75sPYJ0ydvPMXN5zFweM5fHzGUxb204YkGisrOz5V7M2VB07QoMH642FZGRwIwZ6gjFs8/W6qYCEM6bADBzPTBzecxcHjOXxby14XUsSFRERET1v4iiAJ9/ro5Q7NrlfGH1KtkPPQTExVV/DT5CJG9yw8zlMXN5zFweM5fFvLVhY0Gi7HZ79W1cUYBNm9SGYudOdVlEBDBpktpQxMdX32v7qGrNmzxi5vKYuTxmLo+Zy2Le2rCxIFHVckqPogBffKE2FL/9pi4LD1cbiocf9suGwslPT6HSFTOXx8zlMXN5zFwW89aGjQWJCg0N9d7GFAX48ku1ofj1V3VZeDgwcaLaUDRo4L3XqqG8mjdVCDOXx8zlMXN5zFwW89aGJ2+TKJPJVPWNOBuKHj2Aq69Wm4qwMLWZSEkBnn+eTcV/vJI3VQozl8fM5TFzecxcFvPWhtPNcrpZUcXFxahTR+NAmaIAX3+tjlD8/LO6LCwMuO8+4JFHgIQEr9VZW1Qpb9KEmctj5vKYuTxmLot5a8MRCxKVkZFR+Sc5G4qePYHBg9WmIjRUvQbFP/8AL77IpqIcmvKmKmHm8pi5PGYuj5nLYt7acMSCIxa+S1GAzZuBmTOBn35Sl4WGAvfeCzz6KJCYqG99REREROTCEQsSZTQaz7+Ss6Ho3RsYMEBtKkJCgAceUEcoXnqJTUUFVShv8ipmLo+Zy2Pm8pi5LOatDUcsOGIh6pzHLCoK8P336jkU27apy0JCgHvuAaZNAxo2FKuztuAxovKYuTxmLo+Zy2Pmspi3NhyxIFFZWVmeH/j+e6BvX6BfP7WpCAlRr5T9zz/Ayy+zqdCo3Lyp2jBzecxcHjOXx8xlMW9tfLKx+OGHH3DNNdegYcOGMBgM2Lhxo9vjiqLgqaeeQlJSEsLCwtC/f38cPnxYn2KpUsqMDm3ZojYUV10F/PADEBysXtju6FHglVfYUFQRR+PkMXN5zFweM5fHzGUxb218srHIz8/HRRddhCVLlnh8/Pnnn8crr7yC5cuX45dffkFERAQGDRqEwsJC4Uqpsly/o61bgSuvVG9bt6oNxcSJakPx6qtAo0b6FlpL8D0hj5nLY+bymLk8Zi6LeWvjkwePDRkyBEOGDPH4mKIoWLRoEZ544gkMHz4cAPD2228jISEBGzduxE033SRZKlVS0I4dwIIF6qFPgNpQ3HUXMGMG0LixvsXVQgEBPvm3g1qNmctj5vKYuTxmLot5a1PjUktJSUF6ejr69+/vWhYTE4Pu3btjx44d5T6vqKgIJpPJ7UaCtm0D+vVD1LBhalMRFKROG3vkCLBkCZuKasITz+Qxc3nMXB4zl8fMZTFvbWpcY5Geng4ASDjrgmgJCQmuxzyZN28eYmJiXLfk5GQA6lBXamoqHA6Ha2oxo9EIm82GzMxMWCwW5ObmIjs7G1arFenp6SguLnZb1263Iz09HQUFBcjOzkZOTg7y8/ORkZEBu93utm5JSQlSU1NhtVpx+vRp5OXlwWw249SpUygqKnJbV1EUGI1GFBUV4dSpU66GKCsrC1artcJ1FxQUeKy7uLi4TN0Wi8Vj3Q6H47x1O+stXXfOp5+i+Mor1aljv/sOSlAQ8m+/HY6//4bx8ceB5GRXhhkZGbBYLMjJyalU3bm5ubBYLMjMzITNZiu37qysLFeGnup2PseZt9lsRl5eHk6fPu3Ku6SkpMzvPiMjA/n5+WXqPjtDZ91Wq7XCdRcWFnqsOzU11WPdmZmZbnXn5OQgLS3tvHWfb59NS0tzq9tsNleq7qysrPO+18xms9t7zZt1l95nMzMzPb7XUlNTPb7XKvsZ8e+///IzohKfEeW9186u+1yfEWazmZ8RGj8jtL7XTpw4wc8IjZ8RWr9HFBQU8DNC42eElu8Rzrz5GaG+1yrK56ebNRgM+OijjzBixAgAwE8//YSePXsiNTUVSUlJrvVGjx4Ng8GADz74wON2ioqKUFRU5LpvMpmQnJzM6Wary08/qRe2+/Zb9X5QEPB//wfbQw8huHVrfWvzIzabDcHBwXqX4VeYuTxmLo+Zy2Pmspi3NjVuxCLxvwujnX2p9YyMDNdjnoSEhCA6OtrtRtVgxw5g4ECgZ0+1qahTBxg/Hjh8GFi+HKfCw/Wu0K+cOnVK7xL8DjOXx8zlMXN5zFwW89amxjUWzZs3R2JiIjZv3uxaZjKZ8Msvv6BHjx46VubnduwABg0CLr8c+OYbtaG4+261oXjtNaBpUwBAI872JIp5y2Pm8pi5PGYuj5nLYt7a+GRjYbFYsGfPHuzZsweAesL2nj17cOLECRgMBkyZMgVz5szBJ598gj/++AN33HEHGjZs6DpcigT9/DMweLDaUHz9tdpQ3HUX8PffwIoVQLNmbqs7j+UjGcxbHjOXx8zlMXN5zFwW89bGJ8+x2LJlC6688soyy8eMGYM333wTiqJg5syZWLFiBXJzc9GrVy8sXboUF1xwQYVfw2QyISYmhudYaPXLL8CsWcCXX6r3AwOBsWOBxx4DWrQo92kOh4NTuAli3vKYuTxmLo+Zy2Pmspi3Nj7ZWEhgY6HRr7+qDcUXX6j3AwOBMWOAxx8/Z0PhlJqaioa8mrYY5i2Pmctj5vKYuTxmLot5a8NJeqlifvtNbSg2bVLvBwYCd9yhNhQtW1Z4M3Xr1q2e+sgj5i2Pmctj5vKYuTxmLot5a8MxHjq3nTuBYcOAbt3UpsJ5yNPBg8Abb1SqqQCA/Pz86qmTPGLe8pi5PGYuj5nLY+aymLc2HLEgz3btAmbPBj79VL0fEADcdhvw5JNAq1aaN8s5oWUxb3nMXB4zl8fM5TFzWcxbGzYW5G73brWh+OQT9X5AAHDrrWpDwQvbEREREVE52FiQ6vff1Ybi44/V+wEBwC23qA1FJWbbOp/SVz+n6se85TFzecxcHjOXx8xlMW9t2Fj4uz171IZi40b1fkAAcPPNakPRpo3XX44zcMli3vKYuTxmLo+Zy2Pmspi3Njx521/t3QuMHAlcfLHaVBgM6gjF/v3Au+9WS1MBAFlZWdWyXfKMectj5vKYuTxmLo+Zy2Le2vA6Fv52HYt9+9QRig0b1PsGA3DTTeoIRbt21f7yiqLAYDBU++uQinnLY+bymLk8Zi6Pmcti3tpwxMJf7NsHXH89cNFFalPhbCj27wfWrBFpKgD1gjMkh3nLY+bymLk8Zi6Pmcti3tpwxKK2j1j88Qfw9NPA+vXqfYMBGD0aeOop4MIL9a2NiIiIiGoNjljUVvv3qw1Ep05nmorRo9VG4/33dWsqjEajLq/rr5i3PGYuj5nLY+bymLks5q0NZ4WqbfbvV0co1q0DnINRN9ygjlB06KBvbQDi4uL0LsGvMG95zFweM5fHzOUxc1nMWxuOWNQWBw6o50x07AisXas2Fddfr55bsXatTzQVgHoIGslh3vKYuTxmLo+Zy2Pmspi3NhyxqOn++ksdofjggzMjFKNGqSMUnTrpW5sHoaGhepfgV5i3PGYuj5nLY+bymLks5q0NG4ua6uBBtaF4//0zDcXIkWpDcdFF+tZ2Dg6HQ+8S/ArzlsfM5TFzecxcHjOXxby1YWNR0xw6pDYU7713pqG47jq1oejcWdfSKqK4uFjvEvwK85bHzOUxc3nMXB4zl8W8tWFjUVMcOgQ884zaUDi76BEj1Ibi4ot1La0ywsPD9S7BrzBvecxcHjOXx8zlMXNZzFsbnrzt6/7+G7j9dnV62NWr1abi2muB3buBjz6qUU0FAOTk5Ohdgl9h3vKYuTxmLo+Zy2Pmspi3NrxAnq9eIO/wYWDOHODdd8+MUFxzDTBzJtCli761VUFJSQkCAwP1LsNvMG95zFweM5fHzOUxc1nMWxuOWPiaI0eAsWOBdu2At99Wm4phw4CdO4FPPqnRTQUApKen612CX2He8pi5PGYuj5nLY+aymLc2HLHwlRGLo0fVEYp33gFKStRlQ4cCs2YBXbvqWhoRERER0flwxEJvR48C//d/QJs2wJtvqk3F1VcDv/4KfPZZrWsqjEaj3iX4FeYtj5nLY+bymLk8Zi6LeWvDEQu9Riz++Qd49lngrbfOjFAMGaKOUHTrJl+PELvdjqCgIL3L8BvMWx4zl8fM5TFzecxcFvPWhiMW0lJSgLvuUkco3nhDbSoGDwZ+/hnYtKlWNxUAkJ2drXcJfoV5y2Pm8pi5PGYuj5nLYt7a8DoWUo4dU0co3nwTcF50ZdAgdYTisst0LExWZGSk3iX4FeYtj5nLY+bymLk8Zi6LeWvDEYvqduwYMH480Lo18PrralMxcCDw00/Al1/6VVMBADabTe8S/ArzlsfM5TFzecxcHjOXxby14YhFdTl+HJg7F1i1CrDb1WUDBqjXoejZU9/adOSnp/TohnnLY+bymLk8Zi6Pmcti3tqwsfC2EyfUhuKNN840FP37qw1Fr1761uYDQkND9S7BrzBvecxcHjOXx8zlMXNZzFsbHgrlLSdPAvfeC7RqBbz2mtpU9OsH/PAD8M03bCr+YzKZ9C7BrzBvecxcHjOXx8zlMXNZzFsbTjdb1elmT54E5s1Tz59wjlBcdZU6QtG7t3eKrUU4fZss5i2Pmctj5vKYuTxmLot5a8MRC63+/ReYOFEdoVi2TG0qrrwS2LIF2LyZTUU5MjMz9S7BrzBvecxcHjOXx8zlMXNZzFsbjlhUdsTCaFRHKP73P8A5Y0CfPuq0sX37VkepREREREQ+jyMWFWU0ApMnAy1aAEuWqE1F797A99+roxRsKirEaDTqXYJfYd7ymLk8Zi6Pmctj5rKYtzYcsTjfiEVqKvDcc8CKFUBRkbrsiiuA2bPVZsJgEKm3tiguLkadOpyMTArzlsfM5TFzecxcHjOXxby14YhFedLSgClTgJYtgVdfVZuKnj2Bb78Ftm5Vz6dgU1FpWVlZepfgV5i3PGYuj5nLY+bymLks5q0NW7GzpaUBzz8PLF8OFBaqyy6/XB2h6NePzUQVxcTE6F2CX2He8pi5PGYuj5nLY+aymLc2HLFwSk8Hpk5Vz6FYtEhtKnr0AL7+Gti+Xb3IHZuKKrNarXqX4FeYtzxmLo+Zy2Pm8pi5LOatDUcsMjPV0YhlywDnTnTZZeqyAQPYTHhZQAB7WUnMWx4zl8fM5TFzecxcFvPWho1Fx45nDnnq3l1tKAYOZENRTXgilCzmLY+Zy2Pm8pi5PGYui3lrw3assFBtKL74AtixAxg0iE1FNSooKNC7BL/CvOUxc3nMXB4zl8fMZTFvbdiOrV8PjBzJZkJIbGys3iX4FeYtj5nLY+bymLk8Zi6LeWvDEQueRyHq1KlTepfgV5i3PGYuj5nLY+bymLks5q0NL5B3vgvkERERERHReXHEgkQZjUa9S/ArzFseM5fHzOUxc3nMXBbz1qZGNxZLlixBs2bNEBoaiu7du+PXX3/VuyQ6j6SkJL1L8CvMWx4zl8fM5TFzecxcFvPWpsY2Fh988AGmTp2KmTNnYvfu3bjoooswaNAgZGZm6l0anUN6erreJfgV5i2Pmctj5vKYuTxmLot5a1Njz7Ho3r07Lr30UixevBgA4HA4kJycjMmTJ2P69Oll1i8qKkJRUZHrfl5eHpo0aYKTJ0/yHAtBhYWFCA0N1bsMv8G85TFzecxcHjOXx8xlMe+yoqKiYDjPhEc1crpZm82GXbt2YcaMGa5lAQEB6N+/P3bs2OHxOfPmzcPs2bPLLE9OTq62OomIiIiIaoOKTHhUIxuLrKwslJSUICEhwW15QkICDh486PE5M2bMwNSpU133c3Nz0bRpU5w4cQIxMTHVWi+pTCYTkpOTOUokhHnLY+bymLk8Zi6Pmcti3p5FRUWdd50a2VhoERISgpCQkDLLY2JiuNMIi46OZuaCmLc8Zi6Pmctj5vKYuSzmXXk18uTtuLg4BAYGIiMjw215RkYGEhMTdaqKiIiIiMh/1cjGIjg4GF26dMHmzZtdyxwOBzZv3owePXroWBkRERERkX+qsYdCTZ06FWPGjEHXrl3RrVs3LFq0CPn5+bjzzjsr9PyQkBDMnDnT4+FRVD2YuSzmLY+Zy2Pm8pi5PGYui3lrV2OnmwWAxYsX44UXXkB6ejo6d+6MV155Bd27d9e7LCIi+v/27jwoyvqPA/j7UbmUK0XuQ7TU8sBjgsFqvEgwUrDDYzAlydLBxjs6xqicifKsHANnEtAxJXVUGs0DyCUlrxEsNYcUGcwJdHSSOESI/fz+6OczrezBugvI7vs1szPs8/083/3w9TPfpw/P7kZERHanUzcWRERERET0aOiUn7EgIiIiIqJHCxsLIiIiIiKyGBsLIiIiIiKyGBsLIiIiIiKymE03Fhs3bkSfPn3g7OyMiIgInD592mj8rl27MHDgQDg7O2PIkCH44Ycf2inTzi8tLQ1PP/003Nzc4O3tjfj4eJSWlho9Jzs7G4qi6DycnZ3bKePO76OPPmqxfgMHDjR6Dmv84fXp06fFeiuKguTkZL3xrG/z/fTTT5g0aRL8/f2hKAr27dunMy4i+PDDD+Hn5wcXFxdERUXh8uXLJuc191pgT4yteVNTE1JSUjBkyBD06NED/v7+mDVrFv7880+jcz7M3mRPTNV5YmJii/WLiYkxOS/r3DBTa65vb1cUBatXrzY4J+tcP5ttLL777jssWbIEqampKC4uRlhYGKKjo3Hz5k298T///DNmzJiBpKQklJSUID4+HvHx8bhw4UI7Z945FRYWIjk5GSdPnkReXh6ampowYcIE1NXVGT3P3d0dlZWV6qOioqKdMrYNgwYN0lm/48ePG4xljVvmzJkzOmudl5cHAHj11VcNnsP6Nk9dXR3CwsKwceNGveOrVq3CV199hYyMDJw6dQo9evRAdHQ0GhoaDM5p7rXA3hhb8/r6ehQXF2PFihUoLi7Gnj17UFpaismTJ5uc15y9yd6YqnMAiImJ0Vm/HTt2GJ2TdW6cqTX/71pXVlYiMzMTiqLg5ZdfNjov61wPsVHh4eGSnJysPm9ubhZ/f39JS0vTGz916lSJjY3VORYRESFvvfVWm+Zpq27evCkApLCw0GBMVlaWeHh4tF9SNiY1NVXCwsJaHc8at66FCxdKv379RKvV6h1nfVsGgOzdu1d9rtVqxdfXV1avXq0eu3Pnjjg5OcmOHTsMzmPutcCePbjm+pw+fVoASEVFhcEYc/cme6ZvzWfPni1xcXFmzcM6b73W1HlcXJyMGzfOaAzrXD+bvGPR2NiIs2fPIioqSj3WpUsXREVF4cSJE3rPOXHihE48AERHRxuMJ+Oqq6sBAD179jQaV1tbi5CQEAQFBSEuLg4XL15sj/RsxuXLl+Hv74++ffsiISEB165dMxjLGreexsZGbNu2DXPmzIGiKAbjWN/WU15ejqqqKp0a9vDwQEREhMEafphrARlXXV0NRVHg6elpNM6cvYla0mg08Pb2xoABAzB//nzcvn3bYCzr3Lpu3LiBAwcOICkpyWQs67wlm2wsbt26hebmZvj4+Ogc9/HxQVVVld5zqqqqzIonw7RaLRYtWoRnnnkGgwcPNhg3YMAAZGZmIjc3F9u2bYNWq8WoUaNw/fr1dsy284qIiEB2djYOHTqE9PR0lJeX47nnnkNNTY3eeNa49ezbtw937txBYmKiwRjWt3Xdr1NzavhhrgVkWENDA1JSUjBjxgy4u7sbjDN3byJdMTEx2Lp1KwoKCvD555+jsLAQEydORHNzs9541rl1bdmyBW5ubnjppZeMxrHO9evW0QmQ7UlOTsaFCxdMvtcwMjISkZGR6vNRo0bhySefxKZNm7By5cq2TrPTmzhxovrz0KFDERERgZCQEOzcubNVf2mhh7d582ZMnDgR/v7+BmNY32RLmpqaMHXqVIgI0tPTjcZyb7LM9OnT1Z+HDBmCoUOHol+/ftBoNBg/fnwHZmYfMjMzkZCQYPLLNljn+tnkHQsvLy907doVN27c0Dl+48YN+Pr66j3H19fXrHjSb8GCBdi/fz+OHj2KwMBAs851cHDA8OHDceXKlTbKzrZ5enqif//+BtePNW4dFRUVyM/PxxtvvGHWeaxvy9yvU3Nq+GGuBdTS/aaioqICeXl5Ru9W6GNqbyLj+vbtCy8vL4Prxzq3nmPHjqG0tNTs/R1gnd9nk42Fo6MjRo4ciYKCAvWYVqtFQUGBzl8Q/ysyMlInHgDy8vIMxpMuEcGCBQuwd+9e/PjjjwgNDTV7jubmZpw/fx5+fn5tkKHtq62tRVlZmcH1Y41bR1ZWFry9vREbG2vWeaxvy4SGhsLX11enhv/++2+cOnXKYA0/zLWAdN1vKi5fvoz8/Hz06tXL7DlM7U1k3PXr13H79m2D68c6t57Nmzdj5MiRCAsLM/tc1vn/dfSnx9tKTk6OODk5SXZ2tvz222/y5ptviqenp1RVVYmIyGuvvSbvvvuuGl9UVCTdunWTNWvWyKVLlyQ1NVUcHBzk/PnzHfUrdCrz588XDw8P0Wg0UllZqT7q6+vVmAfX/OOPP5bDhw9LWVmZnD17VqZPny7Ozs5y8eLFjvgVOp2lS5eKRqOR8vJyKSoqkqioKPHy8pKbN2+KCGu8LTQ3N0twcLCkpKS0GGN9W66mpkZKSkqkpKREAMi6deukpKRE/Qaizz77TDw9PSU3N1d+/fVXiYuLk9DQULl79646x7hx42TDhg3qc1PXAntnbM0bGxtl8uTJEhgYKOfOndPZ2+/du6fO8eCam9qb7J2xNa+pqZFly5bJiRMnpLy8XPLz82XEiBHyxBNPSENDgzoH69w8pvYWEZHq6mrp3r27pKen652Ddd46NttYiIhs2LBBgoODxdHRUcLDw+XkyZPq2OjRo2X27Nk68Tt37pT+/fuLo6OjDBo0SA4cONDOGXdeAPQ+srKy1JgH13zRokXqv4+Pj4+88MILUlxc3P7Jd1LTpk0TPz8/cXR0lICAAJk2bZpcuXJFHWeNW9/hw4cFgJSWlrYYY31b7ujRo3r3kfvrqtVqZcWKFeLj4yNOTk4yfvz4Fv8WISEhkpqaqnPM2LXA3hlb8/LycoN7+9GjR9U5HlxzU3uTvTO25vX19TJhwgTp3bu3ODg4SEhIiMydO7dFg8A6N4+pvUVEZNOmTeLi4iJ37tzROwfrvHUUEZE2vSVCREREREQ2zyY/Y0FERERERO2LjQUREREREVmMjQUREREREVmMjQUREREREVmMjQUREREREVmMjQUREREREVmMjQUREREREVmMjQUREREREVmMjQUREREREVmMjQUREdm9KVOm4LHHHsMrr7zS0akQEXVabCyIiMjuLVy4EFu3bu3oNIiIOjU2FkRE9EgYM2YMFEWBoig4d+5cu7+2m5ub3rHExEQ1r3379rVrXkREnQkbCyIiO3P/P5TnzZvXYiw5ORmKoiAxMbH9EwMwd+5cVFZWYvDgwQD+zTU+Pl4nZvfu3XB2dsbatWvbJacvv/wSlZWV7fJaRESdWbeOToCIiNpfUFAQcnJysH79eri4uAAAGhoasH37dgQHB3dYXt27d4evr6/B8W+++QbJycnIyMjA66+/3up5hw0bhn/++afF8SNHjsDf39/ouR4eHvDw8Gj1axER2SvesSAiskMjRoxAUFAQ9uzZox7bs2cPgoODMXz4cPWYVqtFWloaQkND4eLigrCwMOzevVtnrrq6OsyaNQuurq7w8/PD2rVrMWbMGCxatMiqOa9atQpvv/02cnJydJqKmpoaJCQkoEePHvDz88P69etbvP65c+dw4cKFFg9TTQUREbUeGwsiIjs1Z84cZGVlqc8zMzNb3AVIS0vD1q1bkZGRgYsXL2Lx4sWYOXMmCgsL1Zjly5ejsLAQubm5OHLkCDQaDYqLi62aa0pKClauXIn9+/djypQpOmNLlixBUVERvv/+e+Tl5eHYsWNWf30iIjKNb4UiIrJTM2fOxHvvvYeKigoAQFFREXJycqDRaAAA9+7dw6effor8/HxERkYCAPr27Yvjx49j06ZNGD16NGpra7F582Zs27YN48ePBwBs2bIFgYGBVsvz4MGDyM3NRUFBAcaNG6czVlNTgy1btmD79u3q62dlZZl9JyIqKgq//PIL6urqEBgYiF27dqm/MxERtQ4bCyIiO9W7d2/ExsYiOzsbIoLY2Fh4eXmp41euXEF9fT2ef/55nfMaGxvVt0uVlZWhsbERERER6njPnj0xYMAAq+U5dOhQ3Lp1C6mpqQgPD4erq6s6dvXqVTQ1NSE8PFw95uHhYfbr5+fnWy1fIiJ7xcaCiMiOzZkzBwsWLAAAbNy4UWestrYWAHDgwAEEBATojDk5ObVPggACAgKwe/dujB07FjExMTh48KDBr4YlIqKOw89YEBHZsZiYGDQ2NqKpqQnR0dE6Y0899RScnJxw7do1PP744zqPoKAgAEC/fv3g4OCAU6dOqef99ddf+P33362aZ0hICAoLC1FVVYWYmBjU1NQA+PetWQ4ODjhz5owaW11dbfXXJyIi03jHgojIjnXt2hWXLl1Sf/4vNzc3LFu2DIsXL4ZWq8Wzzz6L6upqFBUVwd3dHbNnz4arqyuSkpKwfPly9OrVC97e3vjggw/QpYv1/24VFBQEjUaDsWPHIjo6GocOHVLzWL58OXr27Alvb2+kpqaiS5cuUBTF6jkQEZFhbCyIiOycu7u7wbGVK1eid+/eSEtLw9WrV+Hp6YkRI0bg/fffV2NWr16N2tpaTJo0CW5ubli6dCmqq6vbJNfAwECd5uLw4cNYt24d5s2bhxdffBHu7u5455138Mcff8DZ2blNciAiIv0UEZGOToKIiGzLmDFjMGzYMHzxxRdteo4+dXV1CAgIwNq1a5GUlGTRXP+lKAr27t3b4v8ETkRE/+JnLIiI6JHx9ddfw9XVFefPn2/1OSUlJdixYwfKyspQXFyMhIQEAEBcXJxVcpo3b57ON1EREZF+fCsUERE9Er799lvcvXsXABAcHGzWuWvWrEFpaSkcHR0xcuRIHDt2TOercy3xySefYNmyZQAAPz8/q8xJRGSL+FYoIiIiIiKyGN8KRUREREREFmNjQUREREREFmNjQUREREREFmNjQUREREREFmNjQUREREREFmNjQUREREREFmNjQUREREREFmNjQUREREREFmNjQUREREREFmNjQUREREREFmNjQUREREREFmNjQUREREREFvsfGoNyQThxduIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SCALA = 10\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "x_data =  dfSonar[\"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[\"uMeq\"], 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\"Fit: $A={ACr:.3f}\\\\pm{uACr:.3f}$\\n\"\n",
    "          f\"     $B={BCr:.2f}\\\\pm{uBCr:.2f}$\\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\"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": 318,
   "id": "2fb7140a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Residui\n",
    "\n",
    "dfSonar[\"Rc\"] = r_fn(\n",
    "  dfSonar[\"Meq\"],\n",
    "  dfSonar[\"Omega2t\"],\n",
    "  ACr,\n",
    "  BCr\n",
    ")\n",
    "\n",
    "dfSonar[\"uRc\"] = sigma_r_fn(\n",
    "  dfSonar[\"Meq\"], dfSonar[\"Omega2t\"],\n",
    "  ACr, BCr,\n",
    "  dfSonar[\"uMeq\"], dfSonar[\"uOmega2t\"],\n",
    "  uACr, uBCr, covABCr\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "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": 320,
   "id": "e87e24b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calibro\n",
      "AC      = 3.2009 ± 0.0034\n",
      "BC      = 1161.4138 ± 0.4687\n",
      "cov_ABC = 0.001385\n",
      "chi²    = 4.3252\n",
      "GdL     = 4\n",
      "P(chi², ∞)= 0.1150\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    = 15.1284\n",
      "GdL     = 5\n",
      "P(chi², ∞)= 0.0017\n",
      "\n",
      "Cronometro\n",
      "ACr      = 3.4239 ± 0.0469\n",
      "BCr      = 1.3116 ± 0.5445\n",
      "cov_ABCr = -0.024903\n",
      "chi²    = 3.1950\n",
      "P(chi², ∞)= 0.3625\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": 321,
   "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 - 4 * sigma\n",
    "    massimoLocale = mu + 4 * 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": 322,
   "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 = 5.84\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": 323,
   "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": 324,
   "id": "57de948b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compatibilità: Calibro vs Sonar statico\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ           6.8052     0.0065\n",
      "2σ           3.4026     0.0424\n",
      "3σ           2.2684     0.1081\n",
      "\n",
      "\n",
      "Compatibilità: Calibro vs Sonar statico corretto\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ           6.4288     0.0076\n",
      "2σ           3.2144     0.0488\n",
      "3σ           2.1429     0.1215\n",
      "\n",
      "\n",
      "Compatibilità: Calibro corretto vs Sonar statico corretto\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ           4.5101     0.0458\n",
      "2σ           2.2551     0.1528\n",
      "3σ           1.5034     0.2716\n",
      "\n",
      "\n",
      "Compatibilità: Calibro vs Sonar dinamico\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ          27.2492     0.0001\n",
      "2σ          13.6246     0.0009\n",
      "3σ           9.0831     0.0028\n",
      "\n",
      "\n",
      "Compatibilità: Calibro corretto vs Sonar dinamico\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ          49.5399     0.0004\n",
      "2σ          24.7699     0.0016\n",
      "3σ          16.5133     0.0036\n",
      "\n",
      "\n",
      "Compatibilità: Sonar Dinamico vs Sonar statico\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ          42.0850     0.0000\n",
      "2σ          21.0425     0.0000\n",
      "3σ          14.0283     0.0001\n",
      "\n",
      "\n",
      "Compatibilità: Sonar Dinamico vs cronometro\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ          10.1203     0.0005\n",
      "2σ           5.0601     0.0072\n",
      "3σ           3.3734     0.0280\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 corretto 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(AC2, uAC2*n, len(dfCalibro_senza1)-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: Calibro corretto 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(AC2, uAC2*n, len(dfCalibro_senza1)-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)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}