Lab1/mollaDefinitiva/molla-1/molla1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bdf377b8",
   "metadata": {},
   "source": [
    "Tutto quello che c'è da sapere sulla molla 1, possibilmente con la massima chiarezza."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "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": 261,
   "id": "1e2fabea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Funzioni criterio di Chauvenet\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": 262,
   "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",
    "# 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": 263,
   "id": "e649eddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dati, analisi statistica medie e ricerca outlier\n",
    "\n",
    "dfCalibro = pd.read_csv(r'molla1Calibro.csv')\n",
    "\n",
    "# Costanti strumentali\n",
    "ERR_CALIBRO = 0.02 / np.sqrt(24)\n",
    "ERR_BILANCIA = 0.01 / np.sqrt(12)\n",
    "\n",
    "# Chauvenet e poi statistica di base\n",
    "dfCalibro = 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": 264,
   "id": "42ff21d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "960aa9065c084071b0b24391d679b814",
       "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(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": 265,
   "id": "0500f913",
   "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": 266,
   "id": "5d7ca0a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residuo:\n",
      "  r = x + B/A - y/A\n",
      "\n",
      "Formula LaTeX:\n",
      "  r       = x + \\frac{B}{A} - \\frac{y}{A}\n",
      "  sigma_r = \\sqrt{\\frac{A^{4} \\sigma_{x}^{2} + A^{2} \\left(\\sigma_{B}^{2} + \\sigma_{y}^{2}\\right) - 2 A cov_{AB} \\left(B - y\\right) + \\sigma_{A}^{2} \\left(B - y\\right)^{2}}{A^{4}}}\n"
     ]
    }
   ],
   "source": [
    "# Calcolo simbolico residui\n",
    "\n",
    "A, B, x, y = sp.symbols('A B x y', real=True)\n",
    "sigma_A, sigma_B, sigma_x, sigma_y = sp.symbols('sigma_A sigma_B sigma_x sigma_y', positive=True)\n",
    "cov_AB = sp.symbols('cov_AB', real=True)\n",
    "\n",
    "# Residuo: r = x - (y/A - B/A)\n",
    "r = x - (y / A - B / A)\n",
    "\n",
    "# Propagazione errore (con covarianza A,B)\n",
    "dr_dA  = sp.diff(r, A)\n",
    "dr_dB  = sp.diff(r, B)\n",
    "dr_dx  = sp.diff(r, x)\n",
    "dr_dy  = sp.diff(r, y)\n",
    "\n",
    "sigma_r  = sp.sqrt(\n",
    "  (dr_dA * sigma_A)**2 +\n",
    "  (dr_dB * sigma_B)**2 +\n",
    "  (dr_dx * sigma_x)**2 +\n",
    "  (dr_dy * sigma_y)**2 +\n",
    "  2 * dr_dA * dr_dB * cov_AB\n",
    ")\n",
    "\n",
    "# Funzioni numeriche\n",
    "r_fn      = sp.lambdify((x , y , A , B ), r, 'numpy')\n",
    "sigma_r_fn = sp.lambdify(\n",
    "  (x , y , A , B , sigma_x , sigma_y , sigma_A , sigma_B , cov_AB ),\n",
    "  sigma_r , 'numpy'\n",
    ")\n",
    "\n",
    "print(\"Residuo:\")\n",
    "print(f\"  r = {r }\")\n",
    "print(f\"\\nFormula LaTeX:\")\n",
    "print(f\"  r       = {sp.latex(r)}\")\n",
    "print(f\"  sigma_r = {sp.latex(sp.simplify(sigma_r))}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "cd68b201",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AC      = 23.9432 ± 0.0422\n",
      "BC      = 2719.8356 ± 2.8402\n",
      "cov_ABC = 0.118646\n",
      "chi²    = 56.2014\n",
      "P(chi², ∞)= 0.0000\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": 268,
   "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 lineare\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 1 (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": 269,
   "id": "f98a3612",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui\n",
    "\n",
    "dfCalibro[\"r\"] = r_fn(\n",
    "  -dfCalibro[\"dx\"],\n",
    "  dfCalibro[\"F\"],\n",
    "  AC,\n",
    "  BC\n",
    ")\n",
    "\n",
    "dfCalibro[\"ur\"] = sigma_r_fn(\n",
    "  -dfCalibro[\"dx\"], dfCalibro[\"F\"],\n",
    "  AC, BC,\n",
    "  dfCalibro[\"udx\"], dfCalibro[\"uF\"],\n",
    "  uAC, uBC, covABC\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "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 1 (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": "markdown",
   "id": "4af43cc4",
   "metadata": {},
   "source": [
    "### Rimozione outlier\n",
    "Il punto 2 sembra molto fuori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "a345ce91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indice outlier residui: None\n"
     ]
    }
   ],
   "source": [
    "# Outlier residui\n",
    "outR = trova_outlier(dfCalibro['r'], dfCalibro['ur'])\n",
    "print(f\"Indice outlier residui: {outR}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "4d8e0c76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit completo:   chi²=56.20,  P=0.0000\n",
      "Senza punto 1:  chi²=1.811,  P=0.4044\n",
      "\n",
      "A:  23.9432 ± 0.0422  to  23.8834 ± 0.0429\n",
      "B:  2719.8356 ± 2.8402  to  2718.1321 ± 2.8464\n"
     ]
    }
   ],
   "source": [
    "# Regressione lineare\n",
    "\n",
    "mask = dfCalibro.index != 1\n",
    "dfCalibro_senza1 = dfCalibro.loc[mask]\n",
    "dfCalibro_senza1 = dfCalibro_senza1.copy()\n",
    "\n",
    "AC2, BC2, uAC2, uBC2, covABC2, chiC2, PC2 = reg_lin(-dfCalibro_senza1[\"dx\"], dfCalibro_senza1[\"F\"], dfCalibro_senza1[\"udx\"], dfCalibro_senza1[\"uF\"])\n",
    "\n",
    "print(f\"Fit completo:   chi²={chiC:.2f},  P={PC:.4f}\")\n",
    "print(f\"Senza punto 1:  chi²={chiC2:.3f},  P={PC2:.4f}\")\n",
    "print()\n",
    "print(f\"A:  {AC:.4f} ± {uAC:.4f}  to  {AC2:.4f} ± {uAC2:.4f}\")\n",
    "print(f\"B:  {BC:.4f} ± {uBC:.4f}  to  {BC2:.4f} ± {uBC2:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "d75d9912",
   "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_senza1[\"dx\"]\n",
    "y_data =  dfCalibro_senza1[\"F\"]\n",
    "\n",
    "x_fit = np.linspace(x_data.min(), x_data.max(), 300)\n",
    "y_fit = AC2 * x_fit + BC2\n",
    "\n",
    "ax.errorbar(\n",
    "    x_data, y_data,\n",
    "    xerr=SCALA * dfCalibro_senza1[\"udx\"], yerr=SCALA * dfCalibro_senza1[\"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: $A={AC2:.3f}\\\\pm{uAC2:.3f}$\\n\"\n",
    "          f\"     $B={BC2:.2f}\\\\pm{uBC2:.2f}$\\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 1 (calibro tolto dato 1)\")\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": 274,
   "id": "26c5dc3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui\n",
    "\n",
    "dfCalibro_senza1[\"r\"] = r_fn(\n",
    "  -dfCalibro_senza1[\"dx\"],\n",
    "   dfCalibro_senza1[\"F\"],\n",
    "   AC2, BC2\n",
    ")\n",
    "\n",
    "dfCalibro_senza1[\"ur\"] = sigma_r_fn(\n",
    "  -dfCalibro_senza1[\"dx\"], dfCalibro_senza1[\"F\"],\n",
    "   AC2, BC2,\n",
    "   dfCalibro_senza1[\"udx\"], dfCalibro_senza1[\"uF\"],\n",
    "   uAC2, uBC2, covABC2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "278a5713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot residui\n",
    "\n",
    "# WARNING: non intuitivo, ma molto molto figo\n",
    "fig, axes = plt.subplots(1, 2, figsize=(13, 5), sharey=True)\n",
    "\n",
    "for ax, (r_col, ur_col, f_col, uf_col, df_plot, titolo) in zip(axes, [\n",
    "  (\"r\",  \"ur\",  \"F\", \"uF\", dfCalibro, \"Fit completo (tutti i punti)\"),\n",
    "  (\"r\", \"ur\", \"F\", \"uF\",   dfCalibro_senza1,  \"Fit senza punto 1\"),\n",
    "]):\n",
    "  ax.errorbar(\n",
    "    df_plot[f_col], df_plot[r_col],\n",
    "    xerr=df_plot[uf_col], yerr=df_plot[ur_col],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=\"Residui\"\n",
    "  )\n",
    "  ax.axhline(0, color='red', linestyle='--', linewidth=1)\n",
    "  sns.despine(ax=ax)\n",
    "  ax.set_xlabel(\"F  [mN]\")\n",
    "  ax.set_title(titolo)\n",
    "  ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "axes[0].set_ylabel(r\"Residuo $x_i - \\left(\\frac{y_i}{A} - \\frac{B}{A}\\right)$  [mm]\")\n",
    "fig.suptitle(\"Confronto residui — Molla 1 (calibro)\", y=1.02)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e0cfa7",
   "metadata": {},
   "source": [
    "# Sonar statico"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "988414a6",
   "metadata": {},
   "source": [
    "## Sonar senza correzione $v_{suono}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e347644",
   "metadata": {},
   "source": [
    "### Import dei dati, medie e incertezze\n",
    "- masse trattate come prima\n",
    "- $\\omega$, c trattati con semidispersione"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "d6195b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfSonar = pd.read_csv(r'molla1Sonar.csv')\n",
    "\n",
    "# Bilancia trattata esattamente come prima\n",
    "dfSonar = calcola_stats(dfSonar, \"m\",  ERR_BILANCIA)\n",
    "\n",
    "# Semidispersioni su w e uw\n",
    "dfSonar['w']  = (dfSonar['w1'] + dfSonar['w2']) / 2\n",
    "dfSonar['uw'] = np.maximum((dfSonar['w1'] - dfSonar['w2']).abs() / 2,\n",
    "                            np.sqrt(dfSonar['uw1']**2 + dfSonar['uw2']**2))\n",
    "\n",
    "# Semidispersione su c e uc\n",
    "dfSonar['c']  = (dfSonar['c1'] + dfSonar['c2']) / 2\n",
    "dfSonar['uc'] = np.maximum((dfSonar['c1'] - dfSonar['c2']).abs() / 2,\n",
    "                            np.sqrt(dfSonar['uc1']**2 + dfSonar['uc2']**2))\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": 277,
   "id": "02e2d183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd3b5e1a62994ef4a2b2d1ed69eca9ad",
       "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=[108.61,…"
      ]
     },
     "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": "4c49606b",
   "metadata": {},
   "source": [
    "### Regressione lineare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "06f9ccef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AS      = 23.5225 ± 0.0853\n",
      "BS      = 7371.9487 ± 21.4728\n",
      "cov_ABS = 1.830281\n",
      "chi²    = 3.8872\n",
      "P(chi², ∞)= 0.1432\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": 279,
   "id": "75653d7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot regressione\n",
    "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 1 (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": 280,
   "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": 281,
   "id": "1ecf9ab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NgIAAfPnll7jqqqsAnH85eMuWLRg7dizy8vKQnp6O9PR0ZGRkYMiQITh69KjtKFKhoaE4ePAgjh49qvn6Kw8ZN3XqVLvTp02b5pwbeInr9vLysv0rFgB4enri4YcfttuuNveBVv7+/rb/z8rKQk5ODvr27Yu9e/dq+n5fX19MmjTJ7rTPPvsMbdu2RZs2bWwzpqen429/+xsAYOvWrQCA7777DsD5Q3de6OLbfaH777/f7s9r166F1WrF2LFj7a4rOjoarVq1sl1XSEgIAGDDhg3Vvie48lWAL774wuFL3o4kJydj//79mDhxIsLDw22nd+zYETfccIPDQxFefBv69u2LjIwM5Obm1uo26Wn8+PE4duwYfv31V9t/q3sb1DfffIPo6GjccccdttO8vb0xdepU5OfnY/v27bW67gt/ZisfB3379kVhYaHd27NcISMjw+5V1EqhoaE4d+4cfv31V4ffV1FRge+//x4jRoxA8+bNbac3btwY48ePx48//mjrX2nKlCl2b/vo27cvKioqcPr0adtptbkvHD1WHfH394ePjw+2bduGrKysS25/MV9fX3h4nP91oaKiAhkZGba3FWp9XrnYmjVrYDKZMGfOnCrnVd5HlY+16dOn250/Y8YMAMDXX39d7eVruc0bN25EdnY27rjjDrvHpaenJ3r06OHwcenosX7ixAmHl5+SkoLRo0ejc+fOWLp0qe30utyf69atg9VqxezZs22XUanyftu0aRNKS0sxbdo0u23uu+8+BAcHV7nfavo5GjVqFCIjI21/vty/s7Zu3YpZs2bh4YcfrvFAGbVt3q5dO9urYwAQGRmJ1q1b2zX57LPP0LdvX4SFhdl1HjRoECoqKrBjxw67y6x8PkhPT692Tqoe3wpFLvPmm2/i6quvRk5ODt59913s2LHDbue9Y8eOQSmFp59+utrDu1ksFsTExGDevHm49dZbcfXVV6N9+/YYOnQo7r77bnTs2LHa6z99+jQ8PDzQokULu9Nbt27tnBtYg9OnT6Nx48ZVXu69+Lprcx9otX79ejz33HPYv3+/3XtStR4rPCYmBj4+PnanHT16FIcOHbL7C+biGYH/3efx8fF257ds2bLa67t426NHj0IphVatWjncvvIl/Pj4eEyfPh0LFy7ERx99hL59++KWW27BXXfdZVt0jBs3Dv/+978xefJkPP744xg4cCBGjhyJ0aNHV/lLuVLlL3mOfk7atm2LDRs2oKCgAAEBAbbTmzZtardd5V9MWVlZCA4O1nybHCktLUVmZma159fEx8fHbnFUky5duqBNmzb4+OOPERoaiujoaNvC8WKnT59Gq1atqtyHbdu2tZ1fGwcPHsRTTz2FLVu2VPll/ML9ZVxFOXgv9WOPPYZNmzahe/fuaNmyJQYPHozx48ejd+/eAIC0tDQUFhZW+3NitVpx9uxZ29sxgJp/TirV5r5w9Fh1xNfXFwsWLMCMGTPQqFEjXH/99bjppptwzz33aNo/pXJfiKVLl+LkyZN2+xQ0bNjwkt/vyPHjx9GkSZMafz4rn08ufv6Ijo5GaGhojT9nWm5z5T9UVfdzHhwcbPfnyv1ALhQWFuZw4VJeXo6xY8eioqICa9eutfu7ry735/Hjx+Hh4YF27dpVu011z2E+Pj5o3rx5lfutpp+ji5+fL+fvrHPnzmHcuHHo3bs3Fi5cWP2NQ+2bX/yYAqo2OXr0KA4cOHDJv78qVT4fGOXzNaThwoJcpnv37rb3yY4YMQJ9+vTB+PHjcfjwYQQGBtr+BXnmzJkYMmSIw8uofHLp168fjh8/ji+++ALff/89/v3vf2PRokVYtmxZlfezXo7qnkC07DxaF7W5D7T44YcfcMstt6Bfv35YunQpGjduDG9vb6xcubLKTqfVufBfTC+cs0OHDtX+pRAbG6t5xktdn9VqhclkwrfffuvwSE0XLtZeffVVTJw40fZzMXXqVMyfPx8///wzrrrqKvj7+2PHjh3YunUrvv76a3z33XdYtWoV/va3v+H777932pGgqrucyr+ganObLrZz587LPgxqQkKC3QcqXsr48ePx1ltvISgoCOPGjat28eVM2dnZSEhIQHBwMObNm4cWLVrAz88Pe/fuxWOPPab5labL1bBhQ4e/GLZt2xaHDx/G+vXr8d1332HNmjVYunQpZs+ejblz517WdV3q56S294Wjx2p1pk2bhptvvhnr1q3Dhg0b8PTTT2P+/PnYsmVLlf1LLvbCCy/g6aefxt///nc8++yzCA8Ph4eHB6ZNm+byPsDl/4J3qdtcOfsHH3zgcIHl5WX/K1Jtni8effRR7Nq1C5s2bbK9Sl9J7/vzYjX9HDl6fga0/51VWlqK0aNHw9fXF59++mmV+7Q6Wptf6jFVOfMNN9yAf/3rXw63vfrqq+3+XPl8EBERoWkGsseFBdULT09PzJ8/HwMGDMCSJUvw+OOP294+4O3t7fCILBcLDw/HpEmTMGnSJOTn56Nfv3545plnql1YNGvWDFarFcePH7f7lxtHx1IPCwtzeMSm2v7L64XXvXnzZuTn59v90njxddf2PriUNWvWwM/PDxs2bLD7F7KVK1fW6XJbtGiB3377DQMHDqzxCb/yPj958qTdv84fO3asVtellEJ8fHyVJ3xHOnTogA4dOuCpp57Czp070bt3byxbtgzPPfccAMDDwwMDBw7EwIEDsXDhQrzwwgt48sknsXXrVof3ebNmzQA4/jn566+/EBERYfdqhStu04U6depkd/Sz2nD0Fp+ajB8/HrNnz0ZycrLDHS8rNWvWDAcOHIDVarVbfFS+VafyPtRi27ZtyMjIwNq1a9GvXz/b6SdPnqzV7JerTZs2+Oijj5CTk2N7patSQEAAxo0bh3HjxqG0tBQjR47E888/j1mzZiEyMhINGjSo9ufEw8Oj1gtuV98XLVq0wIwZMzBjxgwcPXoUnTt3xquvvooPP/wQQPW/zK1evRoDBgzAO++8Y3d6dna23S9ftVkAtGjRAhs2bEBmZma1r1pUPp8cPXrU9moYAKSmpiI7O1vTz1lNt7ny1eyoqCinPP9W+uSTT7B48WIsXrwYCQkJVc7Xen9Wd3usViv+/PNPdO7c2eE2Fz6HXfg2vdLSUpw8ebJOt7W2f2dNnToV+/fvx44dO2o8uEAlZzS/WIsWLZCfn6/5dlc+3i68ftKO+1hQvenfvz+6d++OxYsXo7i4GFFRUejfvz+WL1+O5OTkKttfeBjGjIwMu/MCAwPRsmXLGg85OGzYMADA66+/bnf6xUeAAM4/8eTk5ODAgQO205KTky951JHq3HjjjSgvL8dbb71lO62iogJvvPGG3Xa1uQ+08PT0hMlksnul5dSpU3ZHbbocY8eOhdlsdvjBgEVFRbbPn6j8F6wL308MoMrtrsnIkSPh6emJuXPnVnmLilLK9rOQm5uL8vJyu/M7dOgADw8P28+Fo7cQVf5lXN3PTuPGjdG5c2e8//77dovNP/74A99//z1uvPFGzbeltrfJkbCwMAwaNOiyvrp27VqrOVu0aIHFixdj/vz56N69e7Xb3XjjjUhJSbE7mlF5eTneeOMNBAYGOvxlqjqV/+J44f1SWlpa5WfIVXr27AmlFBITE+1Ov7iJj48P2rVrB6UUysrK4OnpicGDB+OLL76wO3xpamoqPv74Y/Tp06fKW2kuxVX3RWFhYZUPJm3RogWCgoLsHgcBAQEO/4HF09Ozys/tZ599VuW99JUL7uoOq32hUaNGQSnl8NWfyuuqfKxd/Jxd+cppTUeb03KbhwwZguDgYLzwwgsoKyurchm1ff4Fzj9PTJ48GXfddVe1R0fTen86MmLECHh4eGDevHlVXt2ovMxBgwbBx8cHr7/+ut31vPPOO8jJyanTkRFr83fWypUrsXz5crz55ps1Pp9cqC7NqzN27Fjs2rULGzZsqHJednZ2lb9HEhMTERISYvc2RtKOr1hQvXr00UcxZswYvPfee7j//vvx5ptvok+fPujQoQPuu+8+NG/eHKmpqdi1axfOnTtnO653u3bt0L9/f3Tt2hXh4eHYs2cPVq9ejYceeqja6+rcuTPuuOMOLF26FDk5OejVqxc2b97s8F/Pb7/9djz22GO47bbbMHXqVNshB6+++urL2jnx5ptvRu/evfH444/j1KlTaNeuHdauXevw/eJa7wMthg8fjoULF2Lo0KEYP348LBYL3nzzTbRs2dJu0VRbd999Nz799FPcf//92Lp1K3r37o2Kigr89ddf+PTTT7FhwwZ069YNXbt2xahRo7B48WJkZGTYDjd75MgRANr+RbNFixZ47rnnMGvWLJw6dQojRoxAUFAQTp48ic8//xxTpkzBzJkzsWXLFjz00EMYM2YMrr76apSXl+ODDz6Ap6cnRo0aBeD8p7/v2LEDw4cPR7NmzWCxWLB06VJcddVVdp+ZcbGXX34Zw4YNQ8+ePXHvvffaDjcbEhJi91kcWmm9TUag5VCxU6ZMwfLlyzFx4kQkJiYiLi4Oq1evxk8//YTFixcjKChI8/X16tULYWFhmDBhAqZOnQqTyYQPPvigVseQX7JkCbKzs5GUlAQA+Oqrr2yfV/Pwww9XeSXiQn369EHDhg2xadMmu/faDx48GNHR0ejduzcaNWqEQ4cOYcmSJRg+fLjt9j333HO2z0n5xz/+AS8vLyxfvhwlJSV46aWXNM/vzPvCkSNHjmDgwIEYO3Ys2rVrBy8vL3z++edITU3F7bffbtuua9eueOutt/Dcc8+hZcuWiIqKwt/+9jfcdNNNmDdvHiZNmoRevXrh999/x0cffWT3r+HA+Z/z0NBQLFu2DEFBQQgICECPHj2qvE8fAAYMGIC7774br7/+Oo4ePYqhQ4fCarXihx9+wIABA/DQQw+hU6dOmDBhAlasWGF7m9gvv/yC999/HyNGjKjxLYJabnNwcDDeeust3H333bj22mtx++23IzIyEmfOnMHXX3+N3r17Y8mSJbW6ryt3gu7Xr5/tlaBKvXr1QvPmzTXfn460bNkSTz75JJ599ln07dsXI0eOhK+vL3799Vc0adIE8+fPR2RkJGbNmoW5c+di6NChuOWWW3D48GEsXboU1113He66665a3aaLafk7Kz09Hf/4xz/Qrl07+Pr6VrkvbrvtNoev/NaleXUeffRRfPnll7jppptsh6ItKCjA77//jtWrV+PUqVN2rxRt3LgRN998M/exuFz1c/ApupJUHlrx119/rXJeRUWFatGihWrRooUqLy9XSil1/Phxdc8996jo6Gjl7e2tYmJi1E033aRWr15t+77nnntOde/eXYWGhip/f3/Vpk0b9fzzz6vS0lLbNo4ODVtUVKSmTp2qGjZsqAICAtTNN9+szp49W+UQmEop9f3336v27dsrHx8f1bp1a/Xhhx9e9uFmlVIqIyND3X333So4OFiFhISou+++23aI1IsPx6jlPtB6uNl33nlHtWrVSvn6+qo2bdqolStXOrwdjiQkJFR7iM3S0lK1YMECdc011yhfX18VFhamunbtqubOnatycnJs2xUUFKgHH3xQhYeHq8DAQDVixAh1+PBhBUC9+OKLtu0uPNSpI2vWrFF9+vRRAQEBKiAgQLVp00Y9+OCD6vDhw0oppU6cOKH+/ve/qxYtWig/Pz8VHh6uBgwYoDZt2mS7jM2bN6tbb71VNWnSRPn4+KgmTZqoO+64w+6wg44ON6uUUps2bVK9e/dW/v7+Kjg4WN18883qzz//tNumutvg6JCqWm5TfbtUg0q46HCzSimVmpqqJk2apCIiIpSPj4/q0KGDw8OMXvxYc3Tf/PTTT+r6669X/v7+qkmTJupf//qX2rBhQ5Wf9+pUHnbS0dfFDRyZOnWq3aF2lVJq+fLlql+/fqphw4bK19dXtWjRQj366KN2P+tKKbV37141ZMgQFRgYqBo0aKAGDBigdu7cabdNdc+Jjh7TWu+Lmh6rF0tPT1cPPvigatOmjQoICFAhISGqR48e6tNPP7XbLiUlRQ0fPlwFBQUpALZDzxYXF6sZM2aoxo0bK39/f9W7d2+1a9culZCQUOXwtF988YVq166d8vLysntcOXquKi8vVy+//LJq06aN8vHxUZGRkWrYsGEqMTHRtk1ZWZmaO3euio+PV97e3io2NlbNmjXrkoch1nqblTrfYciQISokJET5+fmpFi1aqIkTJ6o9e/bYtpkwYYIKCAio8r0XP7fW9LNYeV/U5v6szrvvvqu6dOliey5OSEhQGzdutNtmyZIlqk2bNsrb21s1atRIPfDAAyorK8tum+p+jiqfF6s7XO+l/s6q/P5LPS4d/d2ktXmzZs0cHobe0f2Yl5enZs2apVq2bKl8fHxURESE6tWrl3rllVfsfo84dOiQwv8/XD5dHpNS/GhBInKt/fv3o0uXLvjwww9x55136j0OkZ0TJ06gTZs2+PbbbzFw4EC9xyEinUybNg07duxAYmIiX7G4TNzHgoicqqioqMppixcvhoeHh90OqURG0bx5c9x777148cUX9R6FiHSSkZGBf//733juuee4qKgDvmJBRE41d+5cJCYmYsCAAfDy8sK3336Lb7/91va+fCIiInJPXFgQkVNt3LgRc+fOxZ9//on8/Hw0bdoUd999N5588knNxzAnIiIiebiwICIiIiKiOuM+FkREREREVGdcWBARERERUZ257cLizTffRFxcHPz8/NCjRw/88ssv1W779ttvo2/fvggLC7N9ym1N2zuilEJubm6dP8iIiIiIiEgit1xYrFq1CtOnT8ecOXOwd+9edOrUCUOGDIHFYnG4/bZt23DHHXdg69at2LVrF2JjYzF48GCYzWbN15mXl4eQkBDk5eU562a4jdLSUr1HII3YShb2koW95GArWdjLONxy5+0ePXrguuuuw5IlSwAAVqsVsbGxePjhh/H4449f8vsrKioQFhaGJUuW4J577tF0nbm5uQgJCUFOTg6Cg4PrNL+7KSoqgr+/v95jkAZsJQt7ycJecrCVLOxlHG73ikVpaSkSExMxaNAg22keHh4YNGgQdu3apekyCgsLUVZWhvDwcFeNeUUpLCzUewTSiK1kYS9Z2EsOtpKFvYzD7Q4qn56ejoqKCjRq1Mju9EaNGuGvv/7SdBmPPfYYmjRpYrc4uVhJSQlKSkpsf87Nzb28ga8A/OwCOdhKFvaShb3kYCtZ2Ms43O4Vi7p68cUX8cknn+Dzzz+Hn59ftdvNnz8fISEhtq/Y2FgAQHFxMZKSkmC1Wm37aJjNZpSWlsJisSA/Px/Z2dnIzMxEUVERUlJSUF5ebrdtWVkZUlJSUFhYiMzMTGRlZaGgoACpqakoKyuz27aiogJJSUkoKipCRkYGcnJykJeXh7S0NJSUlNhtq5SC2WxGSUkJ0tLSkJubi9zcXKSnp6OoqEjz3IWFhQ7nLi8vrzJ3fn4+srKyqsxttVovOXflvBfPnZeXh5ycHGRkZFQ7d1lZGVJTU23XX5u5s7OzkZ+fD4vFgtLS0mrnTk9Pt92Hjuau/J6a5q6oqHA4d0FBQZW5L74PK+cuKirSPHdxcbHDuZOSkqCUQlpamt3cFoulytzJycmXnPtSP7PJycl2c+fl5dVq7vT09Es+1vLy8uwea86c+8KfWYvF4vCxlpSU5PCx5szniJKSErd4jnA0tzs+R1ReruTniIvndtfnCA8PD7d4jnCX3yMu9RyRm5vrFs8RRv49Qiu328eitLQUDRo0wOrVqzFixAjb6RMmTEB2dja++OKLar/3lVdewXPPPYdNmzahW7duNV6Po1csYmNjuY+FA2lpaYiMjNR7DNKArWRhL1nYSw62koW9jMPtXjvy8fFB165dsXnzZtvCwmq1YvPmzXjooYeq/b6XXnoJzz//PDZs2HDJRQUA+Pr6wtfX11ljuzUutORgK1nYSxb2koOt6ldFRQXKysou+/t9fX1RXFzsxImuLN7e3vD09HTKZbndwgIApk+fjgkTJqBbt27o3r07Fi9ejIKCAkyaNAkAcM899yAmJgbz588HACxYsACzZ8/Gxx9/jLi4OKSkpAAAAgMDERgYqNvtcBfp6emIiYnRewzSgK1kYS9Z2EsOtqofSimkpKQgOzu7TpdTXl7O/SzqKDQ0FNHR0TCZTHW6HLesMG7cOKSlpWH27NlISUlB586d8d1339l26D5z5gw8PP63e8lbb72F0tJSjB492u5y5syZg2eeeaY+R3dLfHKWg61kYS9Z2EsOtqoflYuKqKgoNGjQoM6/1FLtKaVQWFho+6y3xo0b1+ny3G4fC73wcyyqZzab+SQtBFvJwl6ysJccbOV6FRUVOHLkCKKiotCwYcM6XVZpaSl8fHycNNmVKSMjAxaLBVdffXWd3hbFo0KRyzVp0kTvEUgjtpKFvWRhLznYyvUq96lo0KBBnS/L29u7zpdxpavsUJd9XQA3fSsUGUtSUhL/5UcItpKFvWRhLznYqv5c7tuf8vLykJ+fD+D8L8MXLy4CAwMRFBRU5/muFM56GxoXFuRyEREReo9AGrGVLOwlC3vJwVbGl5iYiO3bt1d7fkJCAvr3719/AxEALiyoHuTm5vL40kKwlSzsJQt7ycFWxte1a1e0bt0awPmdwL/88kuMHDnStiiUelTPU6dOIT4+Hvv27UPnzp0dbrNt2zYMGDAAWVlZCA0N1XS5EydORHZ2NtatW+e0WR3hwoJcjp/3IQdbycJesrCXHGxlfEFBQba3OlVUVAA4/0pTXY9qpMXEiRPx/vvvAwC8vLxw1VVXYcyYMZg3bx78/PzqdNmxsbFITk52+qtmr732GurjeE3ceZuIiIiIDC8vLw/btm1DXl6e7bST6QVYtjMZ20rjsfQnM06mF1zye5xh6NChSE5OxokTJ7Bo0SIsX74cc+bMqfPlenp6Ijo62umfyxESEqL51Y264MKCXK60tFTvEUgjtpKFvWRhLznYypjy8/Oxfft2207bn+45i4GvbsN/91lwqiIcHyemYuCr2/DZnrPVfo+z+Pr6Ijo6GrGxsRgxYgQGDRqEjRs3AgCsVivmz5+P+Ph4+Pv7o1OnTli9erXte7OysnDnnXciMjIS/v7+aNWqFVauXAng/FuhTCYT9u/fb9v+m2++wdVXXw1/f38MGDAAp06dspvlmWeeqfK2qcWLFyMuLs7254kTJ2LEiBHOvAsc4luhyOUCAgL0HoE0YitZ2EsW9pKDrYzvZHoBHl9zAFbbu3tMqPj////YmgO4Li4ccRH10/GPP/7Azp070axZMwDA/Pnz8eGHH2LZsmVo1aoVduzYgbvuuguRkZFISEjA008/jT///BPffvstIiIicOzYMRQVFTm87LNnz2LkyJF48MEHMWXKFOzZswczZsyol9t1ObiwIJfLysqCv7+/3mOQBldqqwsPW+iIUQ9beKX2koq95GArY0tPT8enh82o6QCp72w7hH/0jkF6erpLZli/fj0CAwNRXl6OkpISeHh4YMmSJSgpKcELL7yATZs2oWfPngCA5s2b48cff8Ty5cuRkJCAM2fOoEuXLujWrRsA2L2ycLG33noLLVq0wKuvvgoAaN26NX7//XcsWLDAJberrriwIJeLjo7WewTS6EptJfWwhVdqL6nYSw62Mra1a9fix9J4WFU44GB5oZTCj/v+hNfBr102w4ABA/DWW2+hoKAAixYtgpeXF0aNGoWDBw+isLAQN9xwg932paWl6NKlCwDggQcewKhRo7B3714MHjwYI0aMQK9evRxez6FDh9CjRw+70yoXLEbEhQW5XHJyMj9oSIgrtdWFhy1MT0/H2rVrRRy28ErtJRV7ycFWxjZy5EiUHy7BmcRU29ufLmQymdCnSztM6X2D7Tnd2QICAtCyZUsAwLvvvotOnTrhnXfeQfv27QEAX3/9dZWfocqjjQ0bNgynT5/GN998g40bN2LgwIF48MEH8corr1zWLB4eHlWO+FTXT9C+XFxYkMvxyVmOK7XVhYctrFRfhy2siyu1l1TsJQdbGVtERAT+3jgYHyWmVrvNvf3bonE97WPh4eGBJ554AtOnT8eRI0fg6+uLM2fOICEhodrviYyMxIQJEzBhwgT07dsXjz76qMOFRdu2bfHll1/anfbzzz9XuayUlBQopWyfoH3hzt/1iUeFIpczm816j0AasZUs7CULe8nBVsYXHxGABaM6wsMEeJoAExQ8TICHCVgwqmO97bhdacyYMfD09MTy5csxc+ZMPPLII3j//fdx/Phx7N27F2+88Ybtsy9mz56NL774AseOHcPBgwexfv16tG3b1uHl3n///Th69CgeffRRHD58GB9//DHee+89u2369++PtLQ0vPTSSzh+/DjefPNNfPvtt66+yQ5xYUEux08vlYOtZGEvWdhLDrYypsDAQCQkJNjenjqmWyy2zOiPO66NQpxnJu7s2ghbZvTHmG6x1X6Pq3h5eeGhhx7CSy+9hFmzZuHpp5/G/Pnz0bZtWwwdOhRff/014uPjAQA+Pj6YNWsWOnbsiH79+sHT0xOffPKJw8tt2rQp1qxZg3Xr1qFTp05YtmwZXnjhBbtt2rZti6VLl+LNN99Ep06d8Msvv2DmzJkuvb3VMan6+Bi+K0Bubi5CQkKQk5OD4OBgvccxFIvFgqioKL3HIA3Y6vx7q1esWIEpU6YY/q1Q7CULe8nBVq5XXFyMkydPIj4+vs6fVn327Fm8++67Ip63jcpZPbiPBblcgwYN9B6BNGIrWdhLFvaSg62M78LDhGdkZACA3aFljXqYcHfHhQW5XHl5ud4jkEZsJQt7ycJecrCV8Tk6TPiFR38y6mHC3R0XFuRyVqtV7xFII7aShb1kYS852Mr4LjxMeHl5Oby87H+lNephwt0dFxbkcnV97yTVH7aShb1kYS852Mr4LjxMeEVFBTw9PXWeiAAeFYrqQW5urt4jkEZsJQt7ycJecrCVLHyFyTi4sCCXq/z0YjI+tpKFvWRhLznYqv44Y1Fw8dugqPactThjCXK51NRUfoqpEGwlC3vJwl5ysJXr+fj4wMPDA0lJSYiMjISPj4/tU6Nrq7S0FD4+Pk6e8MqglEJpaSnS0tLg4eFR5/uRCwtyOT45y8FWsrCXLOwlB1u5noeHB+Lj45GcnIykpCS9x7niNWjQAE2bNoWHR93ezMSFBbmc2Wzmk7QQbCULe8nCXnKwVf3w8fFB06ZNUV5ejoqKisu+nNTUVDRq1MiJk11ZPD094eXlddmvGF2ICwtyOT7Y5WArWdhLFvaSg63qj8lkgre3N7y9vS/7MmJiYrifhUFw521yuQs/CZOMja1kYS9Z2EsOtpKFvYyDCwtyueDgYL1HII3YShb2koW95GArWdjLOLiwIJcrLi7WewTSiK1kYS9Z2EsOtpKFvYyDCwtyOWfsDET1g61kYS9Z2EsOtpKFvYyDCwtyubrskEX1i61kYS9Z2EsOtpKFvYyDCwtyuYKCAr1HII3YShb2koW95GArWdjLOLiwIJcLDw/XewTSiK1kYS9Z2EsOtpKFvYyDCwtyOYvFovcIpBFbycJesrCXHGwlC3sZBxcW5HL89FI52EoW9pKFveRgK1nYyzi4sCCXM5vNeo9AGrGVLOwlC3vJwVaysJdxcGFBLte4cWO9RyCN2EoW9pKFveRgK1nYyzi4sCCXS0lJ0XsE0oitZGEvWdhLDraShb2MgwsLcrmwsDC9RyCN2EoW9pKFveRgK1nYyzi4sCCXKyws1HsE0oitZGEvWdhLDraShb2MgwsLcjkvLy+9RyCN2EoW9pKFveRgK1nYyzi4sCCX8/Dgj5kUbCULe8nCXnKwlSzsZRwsQS5XXFys9wikEVvJwl6ysJccbCULexkHFxbkcsHBwXqPQBqxlSzsJQt7ycFWsrCXcXBhQS6Xnp6u9wikEVvJwl6ysJccbCULexkHFxbkck2aNNF7BNKIrWRhL1nYSw62koW9jIMLC3K5pKQkvUcgjdhKFvaShb3kYCtZ2Ms4uLAgl+O/JMjBVrKwlyzsJQdbycJexsGFBbkc/yVBDraShb1kYS852EoW9jIOLizI5SIiIvQegTRiK1nYSxb2koOtZGEv4+DCglwuNzdX7xFII7aShb1kYS852EoW9jIOLizI5fz8/PQegTRiK1nYSxb2koOtZGEv4+DCglzOarXqPQJpxFaysJcs7CUHW8nCXsbBhQW5XHl5ud4jkEZsJQt7ycJecrCVLOxlHFxYkMs1aNBA7xFII7aShb1kYS852EoW9jIOLizI5bKysvQegTRiK1nYSxb2koOtZGEv4+DCglwuOjpa7xFII7aShb1kYS852EoW9jIOLizI5ZKTk/UegTRiK1nYSxb2koOtZGEv4+DCglwuJiZG7xFII7aShb1kYS852EoW9jIOLizI5cxms94jkEZsJQt7ycJecrCVLOxlHFxYkMtFRUXpPQJpxFaysJcs7CUHW8nCXsbBhQW5XGZmpt4jkEZsJQt7ycJecrCVLOxlHFxYkMsFBAToPQJpxFaysJcs7CUHW8nCXsbBhQW5XFlZmd4jkEZsJQt7ycJecrCVLOxlHFxYkMsppfQegTRiK1nYSxb2koOtZGEv4+DCglzOz89P7xFII7aShb1kYS852EoW9jIOLizI5XJzc/UegTRiK1nYSxb2koOtZGEv4+DCglwuIiJC7xFII7aShb1kYS852EoW9jIOLizI5VJTU/UegTRiK1nYSxb2koOtZGEv4+DCglwuJiZG7xFII7aShb1kYS852EoW9jIOLizI5cxms94jkEZsJQt7ycJecrCVLOxlHFxYkMs1atRI7xFII7aShb1kYS852EoW9jIOLizI5dLT0/UegTRiK1nYSxb2koOtZGEv4+DCglwuODhY7xFII7aShb1kYS852EoW9jIOLizI5YqLi/UegTRiK1nYSxb2koOtZGEv4+DCglzOw4M/ZlKwlSzsJQt7ycFWsrCXcbAEuZyXl5feI5BGbCULe8nCXnKwlSzsZRxcWJDLFRYW6j0CacRWsrCXLOwlB1vJwl7GwYUFuVxoaKjeI5BGbCULe8nCXnKwlSzsZRxcWJDLpaWl6T0CacRWsrCXLOwlB1vJwl7G4bYLizfffBNxcXHw8/NDjx498Msvv1S77cGDBzFq1CjExcXBZDJh8eLF9TfoFSAmJkbvEUgjtpKFvWRhLznYShb2Mg63XFisWrUK06dPx5w5c7B371506tQJQ4YMgcVicbh9YWEhmjdvjhdffBHR0dH1PK37M5vNeo9AGl3prU6mF+D1baewrTQeT376C/447fg5wyiu9F7SsJccbCULexmHSSml9B7C2Xr06IHrrrsOS5YsAQBYrVbExsbi4YcfxuOPP17j98bFxWHatGmYNm1ara4zNzcXISEhyMnJ4Qe1XMRqtfJQcEJcya0+3XMWj685AACofFo0mUxYMKojxnSL1XO0al3JvSRiLznYShb2Mg63q1BaWorExEQMGjTIdpqHhwcGDRqEXbt26TjZlSslJUXvEUijK7XVyfQCPL7mAKwKsCpAwQQFE6wKeGzNAZxKL9B7RIeu1F5SsZccbCULexmH2y0s0tPTUVFRgUaNGtmd3qhRI6f+4JWUlCA3N9fuixwLCwvTewTS6Ept9emeszCZTA7PM5lMWLXnbD1PpM2V2ksq9pKDrWRhL+Nwu4VFfZk/fz5CQkJsX7Gx598qUVxcjKSkJFitVtt7/sxmM0pLS2GxWJCfn4/s7GxkZmaiqKgIKSkpKC8vt9u2rKwMKSkpKCwsRGZmJrKyslBQUIDU1FSUlZXZbVtRUYGkpCQUFRUhIyMDOTk5yMvLQ1paGkpKSuy2VUrBbDajpKQEaWlptgVReno6ioqKNM9dWFjocO7y8vIqc+fn5+PcuXNV5rZarZecu3Lei+fOy8tDTk4OMjIyqp27rKwMqampyM/PR1ZWVq3mzs7ORn5+PiwWC0pLS6udOz093XYfOpq78ntqmruiosLh3AUFBVXmvvg+rJy7qKhI89zFxcUO505KSoJSCqdOnbKb22KxVJk7OTn5knNf6mc2OTnZbu68vLxazZ2enn7Jx1peXp7dY62muU+l5aG6d4UqpXDUnGE394U/sxaLxeFjLSkpyeFjzZnPERkZGW7xHOFobnd8jsjKyhL/HHHx3O76HFFQUFCnuY3yHOEuv0dc6jkiKSnJLZ4jjPx7hFZut49FaWkpGjRogNWrV2PEiBG20ydMmIDs7Gx88cUXNX6/1n0sSkpKUFJSYvtzbm4uYmNjuY+FA7m5ubxPhLhSWy347i+s2HECFdaqT4eeHiZM6dccjw1to8NkNbtSe0nFXnKwlSzsZRxu94qFj48Punbtis2bN9tOs1qt2Lx5M3r27Om06/H19UVwcLDdFxHJNLZbbI2vWIwz6M7bRERERuJ2CwsAmD59Ot5++228//77OHToEB544AEUFBRg0qRJAIB77rkHs2bNsm1fWlqK/fv3Y//+/baXfvbv349jx47pdRPcyoWv7JCxXamt4iMCsGBUR3iYAA8T/v+u2woeJmDBqI6IiwjQe0SHrtReUrGXHGwlC3sZh9u9FarSkiVL8PLLLyMlJQWdO3fG66+/jh49egAA+vfvj7i4OLz33nsAgFOnTiE+Pr7KZSQkJGDbtm2aro+Hm61eSUkJfH199R6DNLjSW51KL8DyjQfw8x9HER8VghkjeuKaZlF6j1WtK72XNOwlB1vJwl7G4bYLi/rGhUX1zGYzPxVTCLYCkpOTsWLFCkyZMgWNGzfWe5wasZcs7CUHW8nCXsbhlm+FImNp0qSJ3iOQRmwlC3vJwl5ysJUs7GUcXFiQyyUlJek9AmnEVrKwlyzsJQdbycJexsGFBbkcX56Ug61kYS9Z2EsOtpKFvYyDCwtyudp8sArpi61kYS9Z2EsOtpKFvYyDCwtyuYiICL1HII3YShb2koW95GArWdjLOLiwIJfLzc3VewTSiK1kYS9Z2EsOtpKFvYyDCwtyOT8/P71HII3YShb2koW95GArWdjLOLiwIJezWq16j0AasZUs7CULe8nBVrKwl3FwYUEuV15ervcIpBFbycJesrCXHGwlC3sZBxcW5HINGjTQewTSiK1kYS9Z2EsOtpKFvYyDCwtyuaysLL1HII3YShb2koW95GArWdjLOLiwIJdr1KiR3iOQRmwlC3vJwl5ysJUs7GUcXpf7jV9++WWtv+eGG26Av7//5V4lCZWSksJPxRSCrWRhL1nYSw62koW9jOOyFxYjRoyo1fYmkwlHjx5F8+bNL/cqSSg+2OVgK1nYSxb2koOtZGEv46jTW6FSUlJgtVo1fXHHmiuX2WzWewTSiK1kYS9Z2EsOtpKFvYzjshcWEyZMqNXbmu666y4EBwdf7tWRYFFRUXqPQBqxlSzsJQt7ycFWsrCXcVz2wmLlypUICgrSvP1bb72FiIiIy706EiwzM1PvEUgjtpKFvWRhLznYShb2Mg4eFYpcLjAwUO8RSCO2koW9ZGEvOdhKFvYyjsveeftixcXFOHDgACwWS5WPVr/lllucdTUkUGlpKQICAvQegzRgK1nYSxb2koOtZGEv43DKwuK7777DPffcg/T09CrnmUwmVFRUOONqSCillN4jkEZsJQt7ycJecrCVLOxlHE55K9TDDz+MMWPGIDk5ucrRoLioID8/P71HII3YShb2koW95GArWdjLOJyysEhNTcX06dP5yYfkUG5urt4jkEZsJQt7ycJecrCVLOxlHE5ZWIwePRrbtm1zxkWRG2rYsKHeI5BGbCULe8nCXnKwlSzsZRxO2cdiyZIlGDNmDH744Qd06NAB3t7edudPnTrVGVdDQlksFn4qphBsJQt7ycJecrCVLOxlHE5ZWPz3v//F999/Dz8/P2zbtg0mk8l2nslk4sLiCscHuxxsJQt7ycJecrCVLOxlHE55K9STTz6JuXPnIicnB6dOncLJkydtXydOnHDGVZBgZrNZ7xFII7aShb1kYS852EoW9jIOpywsSktLMW7cOHh48PP2qCru1C8HW8nCXrKwlxxsJQt7GYdTVgITJkzAqlWrnHFR5IYcfb4JGRNbycJesrCXHGwlC3sZh1P2saioqMBLL72EDRs2oGPHjlV23l64cKEzroaECgkJ0XsE0oitZGEvWdhLDraShb2MwykLi99//x1dunQBAPzxxx925124IzddmYqKiuDv76/3GKQBW8nCXrK4c6+8vDzk5+dXe35gYCCCgoLqcaK6cedW7oi9jMMpC4utW7c642LITXHfGznYShb2ksWdeyUmJmL79u3Vnp+QkID+/fvX30B15M6t3BF7GYdTFhYAUFxcjAMHDsBiscBqtdpON5lMuPnmm511NSSQl5fTfszIxdhKFvaSxZ17de3aFa1btwZw/v3ua9euxciRIxEREQHg/CsWkrhzK3fEXsbhlBLfffcd7r77bmRkZFQ5z2QyoaKiwhlXQ0IVFhaK+0vlSsVWsrCXLO7cKygoqMpbnSIiItC4cWOdJqobd27ljtjLOJzy2tHDDz+MsWPHIjk5GVar1e6LiwoKDQ3VewTSiK1kYS9Z2EsOtpKFvYzDKQuL1NRUTJ8+nccRJofS0tL0HoE0YitZ2EsW9pKDrWRhL+NwysJi9OjR2LZtmzMuitxQTEyM3iOQRmwlC3vJwl5ysJUs7GUcTtnHYsmSJRgzZgx++OEHdOjQocrnWEydOtUZV0NCmc1mPuiFYCtZ2EsW9pKDrWRhL+NwysLiv//9L77//nv4+flh27Ztdp9dYTKZuLC4wkndee9KxFaysJcs7CUHW8nCXsbhlLdCPfnkk5g7dy5ycnJw6tQpnDx50vZ14sQJZ1wFCZaSkqL3CKQRW8nCXrKwlxxsJQt7GYdTFhalpaUYN24cP6CEHAoPD9d7BNKIrWRhL1nYSw62koW9jMMpK4EJEyZg1apVzrgockP5+fl6j0AasZUs7CULe8nBVrKwl3E4ZR+LiooKvPTSS9iwYQM6duxYZefthQsXOuNqSCgfHx+9RyCN2EoW9pKFveRgK1nYyzicsrD4/fff0aVLFwDAH3/8YXfehTtyExERERGRe3LKwmLr1q3OuBhyUyUlJXqPQBqxlSzsJQt7ycFWsrCXcXBva3K54OBgvUcgjdhKFvaShb3kYCtZ2Ms4LnthceDAAVitVs3bHzx4EOXl5Zd7dSRYRkaG3iOQRmwlC3vJwl5ysJUs7GUcl72w6NKlS61C9uzZE2fOnLncqyPB+ME1crCVLOwlC3vJwVaysJdxXPY+FkopPP3002jQoIGm7UtLSy/3qki4pKQkxMTE6D0GacBWsrCXLOwlB1vJwl7GcdkLi379+uHw4cOat+/Zsyf8/f0v9+pIMD7Y5WArWdhLFvaSg61kccdeeXl5NX4+R2BgIIKCgupxIm0ue2Gxbds2J45B7sxsNrvlg94dsZUs7CULe8nBVrK4Y6/ExERs37692vMTEhLQv3//+htII6ccbpaoJhEREXqPQBqxlSzsJQt7ycFWsrhjr65du6J169YAgPT0dKxduxYjR4603dbAwEA9x6sWDzdLLpeTk6P3CKQRW8nCXrKwlxxsJYs79goKCkLjxo3RuHFj22IiIiLCdpoR3wYFcGFB9YD71sjBVrKwlyzsJQdbycJexsGFBblcbT7vhPTFVrKwlyzsJQdbycJexsGFBbkcPxhRDraShb1kYS852EoW9jIOLizI5bR+1gnpj61kYS9Z2EsOtpKFvYyDCwtyuezsbL1HII3YShb2koW95GArWdjLOLiwIJeLiorSewTSiK1kYS9Z2EsOtpKFvYyDCwtyuZSUFL1HII3YShb2koW95GArWdjLOLiwIJdzt0/DdGdsJQt7ycJecrCVLOxlHHX65O2SkhJs2bIFW7Zswblz52CxWFBUVITIyEhERUWhc+fOuPHGGxEfH++seUkgs9nMB70QbCULe8nCXnKwlSzsZRyXtbAwm82YO3cuNm/ejNatW6N9+/bo1KkTgoOD4evri9zcXOTl5WHPnj1455134OXlhcceewyjRo1y9vwkAN/7KAdbycJesrCXHGwlC3sZR60XFq+//jrOnTuHKVOmYMWKFZq+Jzc3F6tWrcL999+PJ554Ak2bNq31oCRXZmYmGjVqpPcYpAFbycJesrCXHGwlC3sZR60WFuvXr8fNN99c67c2BQcH47777sPkyZPx6aefIiAgAA0bNqzVZZBcgYGBeo9AGrGVLOwlC3vJwVaysJdx1Grn7ZtuuqlO+0uYTCaMGzeOi4orTGlpqd4jkEZsJQt7ycJecrCVLOxlHC45KpSnp6crLpaIiIiIiAzKJQsLpZQrLpaE8vHx0XsE0oitZGEvWdhLDraShb2Mo9YLi8LCwktuYzKZLmsYck/5+fl6j0AasZUs7CULe8nBVrKwl3HUaufthx56CO+99x5atmyJ1atXY+HChbBYLBg4cCAeeOABh9+TmpqKP//80/Z18OBBHDp0CKmpqU65AWR84eHheo9AGl2prfLy8mx/MaWnp9v9Fzi/Y2BQUJAus9XkSu0lFXvJwVaysJdx1Gph8e233yI9PR379u1Dnz59MHXqVAwdOhT//e9/kZSUhGeffRbA+bdC9e3bF0eOHEFoaChat26NNm3a4LPPPsP69evRqlUrl9wYMiaLxcIPrhHiSm2VmJiI7du32522du1a2/8nJCSgf//+9TzVpV2pvaRiLznYShb2Mg6TqsUOEddeey327t0LAGjdujUOHz4MAKioqECPHj2wZ88eAICHhwfGjBmDs2fPYv78+UhISAAAxMfH4+TJk86+DYaQm5uLkJAQ5OTkIDg4WO9xiKgWLnzFwhGjvmJBZDR/nLZg4bpdOGnJwfXtW2HKDR0RHxGg91hEoiUnJ2PFihWYMmUKGjdurPc4NarVPhZpaWlYt24dTp48iYCA/z1ReHp62u2wbTKZsGrVKixfvhyLFy/G4MGDsXv3bu57cYUym816j0AaXamtgoKC0Lhx42q/jLqouFJ7SeXuvT7dcxa3LPsVW5M9caoiHKsOZGLgq9vw2Z6zeo9Wa+7eyt2wl3HU6q1Q06dPx1dffYX58+fjxIkT6NWrF1q3bo3WrVsjIyOjyvYdOnTA559/jj179mD27NlITU3F7t270aNHD6fdADK+6OhovUcgjdhKFvaSxZ17nUwvwONrDsCqAOD8PyJW/nvjY2sO4Lq4cMQJeuXCnVu5I/YyjlotLB555BG7P588eRJ//PEH/vjjD/Tu3dt2+sXvrurWrRu++eYb/PTTT3jiiSdgMpmwadOmOoxNklgsFsO/dEfnsZUs7CWLO/f6dM/Z8+9KcPDuapPJhFV7zuKxoW10mOzyuHMrd8RexlGrhcXF4uPjER8fj5tvvtnudKvV6nD73r17Y/Pmzdi6dWtdrpaECQ0N1XsE0oitZGEvWdy517msomo/w0ophXNZRfU8Ud24cyt3xF7G4ZIPyLuUAQMG6HG1pJOiIll/oVzJ2EoW9pLFnXtdFeZf7X6UJpMJV4X51/NEdePOrdwRexmHLgsLurJ4ePDHTAq2koW9ZHHnXmO7xdb4isW4brH1PFHduHMrd8RexuH0EkeOHEF5ebmzL5YE8/T01HsE0oitZGEvWdy5V3xEABaM6ggPE2CCggkKHibAwwQsGNVR1I7bgHu3ckfsZRxOX1i0bdsWJ06ccPbFkmB8iVIOtpKFvWRx915jusXiq/uvw4DGFYjzzMS4juHYMqM/xgh7tQJw/1buhr2Mw+kLi1p83p5Lvfnmm4iLi4Ofnx969OiBX375pcbtP/vsM7Rp0wZ+fn7o0KEDvvnmm3qa1P1xpyo52EoW9pLlSuh1TbMoPD+2O/r7nMTU/nHiXqmodCW0cifsZRxu+aa0VatWYfr06ZgzZw727t2LTp06YciQIbBYLA6337lzJ+644w7ce++92LdvH0aMGIERI0bgjz/+qOfJ3VNaWpreI5BGbCULe8nCXnKwlSzsZRxuubBYuHAh7rvvPkyaNAnt2rXDsmXL0KBBA7z77rsOt3/ttdcwdOhQPProo2jbti2effZZXHvttViyZEk9T+6eYmJi9B6BNGIrWdhLFvaSg61kYS/jqNPnWBhRaWkpEhMTMWvWLNtpHh4eGDRoEHbt2uXwe3bt2oXp06fbnTZkyBCsW7eu2uspKSlBSUmJ7c+5ubnn/2f/fiAw8H8bhoUB8fFAcTHw559VL+jaa8//9/BhoKDA/ry4OCA8HEhLA86etT8vKAho1QqoqAB++63q5XboAHh7A8ePAzk59ufFxACNGgFZWcDJk/bn+fsDbdue//99+6p+2FHbtue3OX0auPjT1hs1On/ZeXnA0aO2ky0WC6JiYs7PBAC//w6Uldl/b6tW52+T2Qykptqf17Ah0KwZUFQEHDpkf57JBHTpcv7/Dx06v82F4uPPN0hNPX/ZFwoJAVq0OD/L77+jik6dAE/P87clL8/+vNhYIDISyMwETp2yPy8gAGjd+vz/791b9XLbtQP8/M7f91lZ9uc1bnz+KzcXOHbM/jxfX+Caa87//4EDwMUHSbj66vM/e+fOARe/OhcRATRtChQWAn/9ZX+ehwfQuTMAIHXrVjQKCbE/v3lzIDQUSEkBkpLszwsNPX9+aSng6BW+zp3PX/6RI0B+vv15TZuenys9HThzxv68wMDzt8dqPf+Yulj79oCPD3DiBJCdbX9ekyZAdPT50y/e38vP7/z9D5y/3Is/c6dNG6BBg/PzpKfbnxcVBVx11fnbceSI/XleXkDHjuf//+BB4ILnBgBAy5ZAcDCQnHz+60J1eI5I8vFBk/btxT9HADg/i5s/R1gsFkRFRYl+jsCff57/Wb3QRc8RXmlpiE5KgteBA+fvb4HPEWazGTFpaeKfI9zl9wgANT5HWCwWRPXuLf45wuai5wi7x1Rysj7PEZU/Z5einMxkMqnDhw87+2I1M5vNCoDauXOn3emPPvqo6t69u8Pv8fb2Vh9//LHdaW+++aaKioqq9nrmzJmjAFT5yjn/EPrf1513qnPnzqmSgwftT///X8nJyaqsrEyVXHttlfOK//1vlZGRoQpeeqnq9w4erM6dO6fKMzMdXm7an3+q4uJiVXjDDVXOK1uwQFksFlXw3ntVzrN26aLOnTunlFLK6uNT5fzsn35SBQUFqmD8+CrnVfzrXyo5OVkVfftt1cuNibFdbnl0dJXz8776SuXm5qqCqVOrfu/f/37+Pty7t+p5Pj7KbDariooKVdK+fZXzC//zH5WZmakKnn226v10883q3LlzqiwpyeF9aDl2TJWUlKiihIQq55UsXKjS0tJU4YoVVWe6/nrbbXV0uZm//KIKCwtVwciRVc4rf+oplZKSoorWrat6uS1a/O8+DA+vcn7uhg0qLy9PFfzf/1X93gceUGazWRXv3Fn1vKAgZTabldVqVaWtWlU5v+CTT1R2drbKf+qpqrdn9Gh17tw5VXrihMPbmnL6tCotLVXF119f9T58883zP9+vvVb1exMS1Llz51RFYaHDy8347TdVVFSkCocPr/rz/eyzKjU1VRV88knV29qune0+rAgMrHJ+ztatKj8/XxVMnFj15/uf/1RJSUmqeOvWqpcbEWG73LJmzaqcn79mjcrJyVEFjz5a9fbU4Tmi6O23+RwBPkfYLrc+niOuvrrK+e74HFFRUeEWzxH8PYLPEXaXW4fnCK1M52d2Hg8PD/z111+4+uqrnXmxmiUlJSEmJgY7d+5Ez549baf/61//wvbt27F79+4q3+Pj44P3338fd9xxh+20pUuXYu7cuUi9eNX7/zl6xSI2NhY527cjmK9Y2P1LQ1paGiKbNHH7f420EfyvkZZt2xAVHGx/Pl+xOM+A/xqZ7OuLxtdcI/45AsAV8YpFWloaIiMjRT9HaHnFIi0tDWvXrsXIkSMR2aqVyOeIpKQkNLFYxD9HuMvvEQBqfI5IS0tDZK9e4p8jbC56jrB7TEVGGvoVC7dbWJSWlqJBgwZYvXo1RowYYTt9woQJyM7OxhdffFHle5o2bYrp06dj2rRpttPmzJmDdevW4TdHDzYHcnNzERISgpycHARf/IvZFa64uBh+fn56j0EasJUs7CXLldIrOTkZK1aswJQpU9C4cWO9x7ksV0ord+HuvSQ9ppy+8/Zjjz2Ghg0bOvtiNfPx8UHXrl2xefNm22lWqxWbN2+2ewXjQj179rTbHgA2btxY7fZGkZeXh+Tk5Gq/8i5eHesk/+J/hSLDYitZ2EsW9pKDrWRhL+Nw+s7b8+fPd/ZF1tr06dMxYcIEdOvWDd27d8fixYtRUFCASZMmAQDuuecexMTE2Gb95z//iYSEBLz66qsYPnw4PvnkE+zZswcrVqzQ82ZcUmJiIrZv317t+QkJCejfv3/9DVQNHx8fvUcgjdhKFvaShb3kYCtZ2Ms43O6oUAAwbtw4pKWlYfbs2UhJSUHnzp3x3XffoVGjRgCAM2fOwMPjfy/W9OrVCx9//DGeeuopPPHEE2jVqhXWrVuH9u3b63UTNOnatSta///34KWnp9vefxcREQEACLxwXw8iIiIiIhdyy4UFADz00EN46KGHHJ63bdu2KqeNGTMGY8aMcfFUzhUUFISgoCC70yIiIgz3/rvS0lK9RyCN2EoW9pKFveRgK1nYyzjc8gPyyFj4yokcbCULe8nCXnKwlSzsZRwuWVh4enq64mJJqMzMTL1HII3YShb2koW95GArWdjLOFyysHDyEWxJuOjoaL1HII3YShb2koW95GArWdjLOC5rYfHcc89hwYIF+Pbbb2G++MNCAJhMpjoPRu4j+eIP+yHDYitZ2EsW9pKDrWRhL+O4rJ23n3rqKZw4cQIHDhzAO++8A7PZjOXLlzvcNjU1FX/++aft6+DBgzh06FC1n2hN7icmJkbvEUgjtpKFvWRhLznYShb2Mo5aLSzuv/9+zJs3D1FRUWjevDmaN29u9+nWlZRS6Nu3L44cOYLQ0FC0bt0abdq0wWeffYb169ejVatWzpqfBDCbzXzQC8FWsrCXLOwlB1vJwl7GUauFxbBhw3DjjTfipptuwqOPPoqAgIBqt23SpAkqKiowf/58JCQkAAA+++wzdO/evW4TkziRkZF6j0AasZUs7CULe8nBVrKwl3HUah+LW2+9Fbt370ajRo3Qq1cvLFu2DFartcp2JpMJq1atwvLly7F48WIMHjwYu3fv5r4XV6js7Gy9RyCN2EoW9pKFveRgK1nYyzhqvfO2p6cnhg8fjkceeQRPPfUU2rVrh6+++srhth06dMDnn3+OF154AXPnzkVqaip2795d56FJFn9/f71HII3YShb2koW95GArWdjLOGr1VqihQ4fi0KFDiI2NRffu3fHGG2/g6quvxtKlS7F582YsXrzY4fd169YN33zzDX766Sc88cQTMJlM2LRpkzPmJwEqKir0HoE0YitZ2EsW9pKDrWRhL+Oo1cLixRdfRIcOHap8AN4777yDNm3a2P5c3edY9O7dG5s3b8bWrVsvY1SSytHb5ciY2EoW9pKFveRgK1nYyzhq9VaojIwMlJeXOzzvm2++sf1/TYF/+eUXdOzYsTZXS8LxJUo52EoW9pKFveRgK1nYyzhqtbDo1asX5syZg/feew8FBQV25zVv3rzG701MTMSjjz6KkpISNGzYsPaTkljcqUoOtpKFvWRhLznYShb2Mo5avRXK398fL774IjZs2IAbb7wRgYGB6Ny5Mzp06IDw8HAEBwfDx8cHeXl5yM3NxalTp7B//37s378fgwcPxhNPPIGwsDBX3RYyqKioKL1HII3YShb2koW95GArWdy51x+nLVi47hecLI1H8bZTmHJDMOIjqv+4B71d1idvDxkyBEOGDMHevXvx7bff4u2338a5c+dgsVhQVFSEiIgIREVFoVOnThg2bBheffVVhIaGOnl0kiIlJYUfXCMEW8nCXrKwlxxsJYu79vp0z1k8vuYAlPIEEI7TBzKx6sA2LBjVEWO6xeo9nkOXtbCodO211+Laa6/Fk08+6ax5yA2544PdXbGVLOwlC3vJwVayuGOvk+kFeHzNAVgVAJz/HLjKYyM9tuYArosLR5wBX7mo9edYENWW2WzWewTSiK1kYS9Z2EsOtpLFHXt9uudstR8sbTKZsGrP2XqeSBsuLMjl3Pm9j+6GrWRhL1nYSw62ksUde53LKqr24xuUUjiXVVTPE2nDhQW5XGZmpt4jkEZsJQt7ycJecrCVLO7Y66ow/xpfsbgqzJiH2OXCglwuMDBQ7xFII7aShb1kYS852EoWd+w1tltsja9YjDPozttcWJDLlZaW6j0CacRWsrCXLOwlB1vJ4o694iMCsGBUR3iYABMUTFDwMAEeJmDBqI6G3HEbqONRoRw5cuQImjdvDi8vp180EREREdEVYUy3WLSL9MWr63bhpCUH17dvhf+7wbiLCsAFr1i0bdsWJ06ccPbFkmA+Pj56j0AasZUs7CULe8nBVrK4c69rmkXh+bHd0d/nJKb2jzP0ogJwwcKiuveD0ZUrPz9f7xFII7aShb1kYS852EoW9jIO7mNBLhceHq73CKQRW8nCXrKwlxxsJQt7GQcXFuRyFotF7xFII7aShb1kYS852EoW9jIO7mFNLhcTE6P3CKQRW8nCXrK4c6+8vDzb21HS09Pt/gucPxxoUFCQLrNdDndu5Y7Yyzi4sCCXM5vNfNALwVaysJcs7twrMTER27dvtztt7dq1tv9PSEhA//7963mqy+fOrdwRexkHFxbkctHR0XqPQBqxlSzsJYs79+ratStat25d7fnSPsDMnVu5I/YyDi4syOUsFgsaN26s9xikAVvJwl6yuHOvoKAgUW91uhR3buWO2Ms4nL7z9mOPPYaGDRs6+2JJsNDQUL1HII3YShb2koW95GArWdjLOJy+sJg/fz4XFmSnsLBQ7xFII7aShb1kYS852EoW9jIOHm6WXM7Li++4k4KtZGEvWdhLDraShb2MgwsLcjkPD/6YScFWsrCXLOwlB1vJwl7GwRLkckVFRXqPQBqxlSzsJQt7ycFWsrCXcTh9YbFo0SIAwMGDB1FRUeHsiyeBQkJC9B6BNGIrWdhLFvaSg61kYS/jcPrConPnzgCAJ554Au3atUPnzp1x55134sUXX8T69eudfXUkwIWfvkrGxlaysJcs7CUHW8nCXsbhlL1d8vLybMevHjBgAADgiy++AADk5+fj4MGD+P3337Fp0ybcdNNNzrhKEoSfhikHW8nCXrKwlxxsJQt7GYdTXrHo27cvUlJSHJ4XGBiIHj16YPLkyVi8eLEzro6EMZvNeo9AGrGVLOwlC3vJwVaysJdxOGVh0aVLF/To0QN//fWX3en79+/HjTfe6IyrIMGaNGmi9wikEVvJwl6ysJccbCULexmHUxYWK1euxMSJE9GnTx/8+OOPOHLkCMaOHYuuXbvC09PTGVdBgiUnJ+s9AmnEVrKwlyzsJQdbycJexuG0TxSZO3cufH19ccMNN6CiogIDBw7Erl270L17d2ddBQnFT2KXg61kYS9Z2EsOtpKFvYzDKa9YpKam4p///Ceee+45tGvXDt7e3pg4cSIXFQQAyM3N1XsE0oitZGEvWdhLDraShb2MwykLi/j4eOzYsQOfffYZEhMTsWbNGkyZMgUvv/yyMy6ehPP19dV7BNKIrWRhL1nYSw62koW9jMMpb4V69913cfvtt9v+PHToUGzduhU33XQTTp06hTfffNMZV0NERERERAbllFcsLlxUVLr22muxc+dObNmyxRlXQYKVlpbqPQJpxFaysJcs7CUHW8nCXsbh9E/evlBcXBx27tzpyqsgAQIDA/UegTRiK1nYSxb2koOtZGEv43DpwgIAwsLCXH0VZHCZmZl6j0AasZUs7CULe8nBVrKwl3G4fGFBFB0drfcIpBFbycJesrCXHGwlC3sZBxcW5HL84Bo52EoW9pKFveRgK1nYyzi4sCCXi4mJ0XsE0oitZGEvWdhLDraShb2Mw2kLi+zsbLz66quYPHkyJk+ejEWLFiEnJ8dZF0+Cmc1mvUcgjdhKFvaShb3kYCtZ2Ms4nLKw2LNnD1q0aIFFixYhMzMTmZmZWLhwIVq0aIG9e/c64ypIsMjISL1HII3YShb2koW95GArWdjLOJyysHjkkUdwyy234NSpU1i7di3Wrl2LkydP4qabbsK0adOccRUkWHZ2tt4jkEZsJQt7ycJecrCVLOxlHE755O09e/bg7bffhpfX/y7Oy8sL//rXv9CtWzdnXAUJ1qBBA71HII3YShb2koW95GArWdjLOJzyikVwcDDOnDlT5fSzZ88iKCjIGVdBgpWXl+s9AmnEVrKwlyzsJQdbycJexuGUhcW4ceNw7733YtWqVTh79izOnj2LTz75BJMnT8Ydd9zhjKsgwaxWq94jkEZsJQt7ycJecrCVLOxlHE55K9Qrr7wCk8mEe+65B+Xl5VBKwcfHBw888ABefPFFZ1wFCebv76/3CKQRW8nCXrKwlxxsJQt7GYdTXrHw8fHBa6+9hqysLOzfvx+//fYbMjMzsWjRIvj6+jrjKkgwHnZYDraShb1kYS852EoW9jIOp7xiMW/evBrPnz17tjOuhoSKiIjQewTSiK1kYS9Z2EsOtpKFvYzDKQuLzz//3O7PZWVlOHnyJLy8vNCiRQsuLK5wqamp/FRMIdhKFvaShb3kYCtZ2Ms4nLKw2LdvX5XTcnNzMXHiRNx2223OuAoSjA92OdhKFvaShb3kYCtZ2Ms4nLKPhSPBwcGYO3cunn76aVddBQlhNpv1HoE0YitZ2EsW9pKDrWRhL+Nw2cICOL8zDXeooaioKL1HII3YShb2koW95GArWdjLOJzyVqjXX3/d7s9KKSQnJ+ODDz7AsGHDnHEVJFhGRgaio6P1HoM0YCtZ2EsW9pKDrWRhL+NwysJi0aJFdn/28PBAZGQkJkyYgFmzZjnjKkiw4OBgvUcgjdhKFvaShb3kYCtZ2Ms4nLKwOHnypDMuhtxUcXExGjRooPcYpAFbycJesrCXHGwlC3sZh0v3sSACAJPJpPcIpBFbycJesrCXHGwlC3sZx2W/YjF9+nTN2y5cuPByr4bcgI+Pj94jkEZsJQt7ycJecrCVLOxlHJe9sLj4syv27t2L8vJytG7dGgBw5MgReHp6omvXrnWbkMTLz89HQECA3mOQBmwlC3vJwl5ysJUs7GUcl72w2Lp1q+3/Fy5ciKCgILz//vsICwsDAGRlZWHSpEno27dv3ack0cLDw/UegTRiK1nYSxb2koOtZGEv43DKPhavvvoq5s+fb1tUAEBYWBiee+45vPrqq864ChLMYrHoPQJpxFaysJcs7CUHW8nCXsbhlIVFbm4u0tLSqpyelpaGvLw8Z1wFCRYTE6P3CKQRW8nCXrKwlxxsJQt7GYdTFha33XYbJk2ahLVr1+LcuXM4d+4c1qxZg3vvvRcjR450xlWQYGazWe8RSCO2koW9ZGEvOdhKFvYyDqd8jsWyZcswc+ZMjB8/HmVlZecv2MsL9957L15++WVnXAUJxk/DlIOtZGEvWdhLDraSxR175eXlIT8/HwCQnp5u918ACAwMRFBQkC6z1cQpC4sGDRpg6dKlePnll3H8+HEAQIsWLbiHPgEAUlNT0aRJE73HIA3YShb2koW95GArWdyxV2JiIrZv32532tq1a23/n5CQgP79+9fzVJfmlIVFpYCAAHTs2NGZF0lu4MKd+snY2EoW9pKFveRgK1ncsVfXrl1tH+HgSGBgYD1Oo12dPiDv2WefRUBAwCU/LI8fkHdlKywshL+/v95jkAZsJQt7ycJecrCVLO7YKygoyJBvdbqUOn1AXuX+FBd/WN6F+DHr5OXl1BfGyIXYShb2koW95GArWdjLOJzyAXkX/r/eMjMz8fDDD+Orr76Ch4cHRo0ahddee63Gl4xWrFiBjz/+GHv37kVeXh6ysrIQGhpaf0O7OQ8Ppxx8jOoBW8nCXrKwlxxsJQt7GYdTShQVFaGwsND259OnT2Px4sX4/vvvnXHxtXLnnXfi4MGD2LhxI9avX48dO3ZgypQpNX5PYWEhhg4diieeeKKepryyFBcX6z0CacRWsrCXLOwlB1vJwl7G4ZTXjm699VaMHDkS999/P7Kzs9G9e3f4+PggPT0dCxcuxAMPPOCMq7mkQ4cO4bvvvsOvv/6Kbt26AQDeeOMN3HjjjXjllVeqPWLAtGnTAADbtm2rlzmvNMHBwXqPQBqxlSzsJQt7ycFWsrCXcTjlFYu9e/eib9++AIDVq1cjOjoap0+fxn/+8x+8/vrrzrgKTXbt2oXQ0FDbogIABg0aBA8PD+zevdup11VSUoLc3Fy7L3LswuMuk7GxlSzsJQt7ycFWsrCXcThlYVFYWGjbc/3777/HyJEj4eHhgeuvvx6nT592xlVokpKSgqioKLvTvLy8EB4ejpSUFKde1/z58xESEmL7io2NBXD+5bikpCRYrVbbJ0GazWaUlpbCYrEgPz8f2dnZyMzMRFFREVJSUlBeXm63bVlZGVJSUlBYWIjMzExkZWWhoKAAqampKCsrs9u2oqICSUlJtpcB8/PzkZeXh7S0NJSUlNhtq5SC2WxGSUkJ0tLSbAui9PR0FBUVaZ67sLDQ4dzl5eVV5s7Pz4eXl1eVua1WK5KSklBUVISMjAzk5ORUmbty3ovnzsvLQ05ODjIyMqqdu6ysDKmpqcjPz0dWVlat5s7OzkZ+fj4sFgtKS0urnTs9Pd12Hzqau/J7apq7oqLC4dwFBQVV5r74Pqycu6ioSPPcxcXFDudOSkqCUsr28105t8ViqTJ3cnLyJee+1M9scnKy3dx5eXm1mjs9Pf2Sj7W8vDy7x5oz577wZ9ZisTh8rCUlJTl8rDnzOSIsLEzzc0RNjzW9nyMcze2OzxERERHinyMunttdnyNiYmLc4jmiNr9HSH6O8PPzc4vnCCP/HqGVSV3428Rl6tixIyZPnozbbrsN7du3x3fffYeePXsiMTERw4cPr/Mv9Y8//jgWLFhQ4zaHDh3C2rVr8f777+Pw4cN250VFRWHu3LmXfEvWtm3bMGDAAE07b5eUlKCkpMT259zcXMTGxiInJ0eXl+SSk5OxYsUKTJkyBY0bN673669J5ZM0GR9bycJesrCXHGwlC3sZh1P2sZg9ezbGjx+PRx55BAMHDkTPnj0BnH/1okuXLnW+/BkzZmDixIk1btO8eXNER0fDYrHYnV5eXo7MzEynf9y7r68vfH19nXqZ7srdPg3TnbGVLOwlC3vJwVaysJdxOGVhMXr0aPTp0wfJycno1KmT7fSBAwfitttuq/PlR0ZGIjIy8pLb9ezZE9nZ2UhMTETXrl0BAFu2bIHVakWPHj3qPAddnqSkJP5LghBsJQt7ycJecrCVLOxlHE478G90dDS6dOlidyzh7t27o02bNs66iktq27Ythg4divvuuw+//PILfvrpJzz00EO4/fbbbatZs9mMNm3a4JdffrF9X0pKCvbv349jx44BAH7//Xfs378fmZmZ9Ta7O4uIiNB7BNKIrWRhL1nYSw62koW9jMNpC4sffvgBd911F3r27GnbyeODDz7Ajz/+6Kyr0OSjjz5CmzZtMHDgQNx4443o06cPVqxYYTu/rKwMhw8ftvvcjWXLlqFLly647777AAD9+vVDly5d8OWXX9br7O6KR8ySg61kYS9Z2EsOtpKFvYzDKQuLNWvWYMiQIfD398e+fftsOzXn5OTghRdecMZVaBYeHo6PP/7Ytuf8u+++a/ep23FxcVBKoX///rbTnnnmGSilqnxdar8O0ob7osjBVrKwlyzsJQdbycJexuGUhcVzzz2HZcuW4e2334a3t7ft9N69e2Pv3r3OuAoiIiIiIjIwpywsDh8+jH79+lU5PSQkBNnZ2c64ChKstLRU7xFII7aShb1kYS852EoW9jIOpywsoqOjbTs+X+jHH39E8+bNnXEVJFhAQIDeI5BGbCULe8nCXnKwlSzsZRxOWVjcd999+Oc//4ndu3fDZDIhKSkJH330EWbOnHnJD6WjuvvjtAVPfvoLtpXG4/Vtp3AyvUDvkexkZWXpPQJpxFaysJcs7CUHW8nCXsbhlE/eVkrhhRdewPz5821HW/L19cXMmTPx7LPP1nlICXJzcxESElLvn7z96Z6zeHzNAVRmNJlMAIAFozpiTLfYepujJlar1e4wxGRcbCULe8nCXnKwlSzsZRxOWVhUKi0txbFjx5Cfn4927dohMDAQRUVF8Pf3d9ZVGJYeC4uT6QUY+Oo2WB0U9DABW2b0R1yE/i8Pms1mfnCNEGwlC3vJwl5ysJUs7GUcTl3e+fj4oF27dujevTu8vb2xcOFCxMfHO/Mq6AKf7jlre4XiYiaTCav2nK3niRzjg10OtpKFvWRhLznYShb2Mo46LSxKSkowa9YsdOvWDb169cK6desAACtXrkR8fDwWLVqERx55xBlzkgPnsopQ3QtOSimcyyqq54kcq/zARDI+tpKFvWRhLznYShb2Mg6vunzz7NmzsXz5cgwaNAg7d+7EmDFjMGnSJPz8889YuHAhxowZA09PT2fNShe5Ksz//CsWDhYXJpMJV4UZ4y1okZGReo9AGrGVLOwlC3vJwVaysJdx1OkVi88++wz/+c9/sHr1anz//feoqKhAeXk5fvvtN9x+++1cVLjY2G6xNb5iMc4gO2/zs0zkYCtZ2EsW9pKDrWRhL+Oo08Li3Llz6Nq1KwCgffv28PX1xSOPPFLt+/7JueIjArBgVEd4mAATFExQ8DCd33F7waiOhthxGwAaNGig9wikEVvJwl6ysJccbCULexlHnd4KVVFRAR8fn/9dmJcXAgMD6zwUaTemWyzaRfri1XW7cNKSg+vbt8L/3WCcRQUAlJeX6z0CacRWsrCXLOwlB1vJwl7GUaeFhVIKEydOhK+vLwCguLgY999/f5VPQFy7dm1droYu4ZpmUXh+bHesWLECU/rfgMYGWlQA548vTTKwlSzsJQt7ycFWsrCXcdRpYTFhwgS7P9911111Gobck5+fn94jkEZsJQt7ycJecrCVLOxlHHVaWKxcudJZc5Aby83N5fsfhWArWdhLFvaSg61kYS/j4Oefk8tFREToPQJpxFaysJcs7CUHW8nCXsbBhQW5XGpqqt4jkEZsJQt7ycJecrCVLOxlHFxYkMvFxMToPQJpxFaysJcs7CUHW8nCXsbBhQW5nNls1nsE0oitZGEvWdhLDraShb2MgwsLcrlGjRrpPQJpxFaysJcs7CUHW8nCXsbBhQW5XHp6ut4jkEZsJQt7ycJecrCVLOxlHFxYkMsFBwfrPQJpxFaysJcs7CUHW8nCXsbBhQW5XHFxsd4jkEZsJQt7ycJecrCVLOxlHFxYkMuZTCa9RyCN2EoW9pKFveRgK1nYyzi4sCCX8/b21nsE0oitZGEvWdhLDraShb2MgwsLcrmCggK9RyCN2EoW9pKFveRgK1nYyzi4sCCXCw8P13sE0oitZGEvWdhLDraShb2MgwsLcjmLxaL3CKQRW8nCXrKwlxxsJQt7GQcXFuRyMTExeo9AGrGVLOwlC3vJwVaysJdxcGFBLmc2m/UegTRiK1nYSxb2koOtZGEv4+DCglyucePGeo9AGrGVLOwlC3vJwVaysJdxcGFBLpeSkqL3CKQRW8nCXrKwlxxsJQt7GQcXFuRyYWFheo9AGrGVLOwlC3vJwVaysJdxcGFBLldYWKj3CKQRW8nCXrKwlxxsJQt7GQcXFuRyXl5eeo9AGrGVLOwlC3vJwVaysJdxcGFBLufhwR8zKdhKFvaShb3kYCtZ2Ms4WIJcrri4WO8RSCO2koW9ZGEvOdhKFvYyDi4syOWCg4P1HoE0YitZ2EsW9pKDrWRhL+PgwoJcLj09Xe8RSCO2koW9ZGEvOdhKFvYyDi4syOWaNGmi9wikEVvJwl6ysJccbCULexkHFxbkcklJSXqPQBqxlSzsJQt7ycFWsrCXcXBhQS7Hf0mQg61kYS9Z2EsOtpKFvYyDCwtyOf5LghxsJQt7ycJecrCVLOxlHFxYkMtFREToPQJpxFaysJcs7CUHW8nCXsbBhQW5XG5urt4jkEZsJQt7ycJecrCVLOxlHFxYkMv5+fnpPQJpxFaysJcs7CUHW8nCXsbBhQW5nNVq1XsE0oitZGEvWdhLDraShb2MgwsLcrny8nK9RyCN2EoW9pKFveRgK1nYyzi4sCCXa9Cggd4jkEZsJQt7ycJecrCVLOxlHFxYkMtlZWXpPQJpxFaysJcs7CUHW8nCXsbBhQW5XHR0tN4jkEZsJQt7ycJecrCVLOxlHFxYkMslJyfrPQJpxFaysJcs7CUHW8nCXsbBhQW5XExMjN4jkEZsJQt7ycJecrCVLOxlHFxYkMuZzWa9RyCN2EoW9pKFveRgK1nYyzi4sCCXi4qK0nsE0oitZGEvWdhLDraShb2MgwsLcrnMzEy9RyCN2EoW9pKFveRgK1nYyzi4sCCXCwgI0HsE0oitZGEvWdhLDraShb2MgwsLcrmysjK9RyCN2EoW9pKFveRgK1nYyzi4sCCXU0rpPQJpxFaysJcs7CUHW8nCXsbBhQW5nJ+fn94jkEZsJQt7ycJecrCVLOxlHFxYkMvl5ubqPQJpxFaysJcs7CUHW8nCXsbBhQW5XEREhN4jkEZsJQt7ycJecrCVLOxlHFxYkMulpqbqPQJpxFaysJcs7CUHW8nCXsbBhQW5XExMjN4jkEZsJQt7ycJecrCVLOxlHFxYkMuZzWa9RyCN2EoW9pKFveRgK1nYyzi4sCCXa9Sokd4jkEZsJQt7ycJecrCVLOxlHFxYkMulp6frPQJpxFaysJcs7CUHW8nCXsbBhQW5XHBwsN4jkEZsJQt7ycJecrCVLOxlHFxYkMsVFxfrPQJpxFaysJcs7CUHW8nCXsbBhQW5nIcHf8ykYCtZ2EsW9pKDrWRhL+NgCXI5Ly8vvUcgjdhKFvaShb3kYCtZ2Ms4uLAglyssLNR7BNKIrWRhL1nYSw62koW9jIMLC3K50NBQvUcgjdhKFvaShb3kYCtZ2Ms4uLAgl0tLS9N7BNKIrWRhL1nYSw62koW9jIMLC3K5mJgYvUcgjdhKFvaShb3kYCtZ2Ms4uLAglzObzXqPQBqxlSzsJQt7ycFWsrCXcXBhQS7XuHFjvUcgjdhKFvaShb3kYCtZ2Ms4uLAgl0tJSdF7BNKIrWRhL1nYSw62koW9jIMLC3K5sLAwvUcgjdhKFvaShb3kYCtZ2Ms43G5hkZmZiTvvvBPBwcEIDQ3Fvffei/z8/Bq3f/jhh9G6dWv4+/ujadOmmDp1KnJycupxavdWUFCg9wikEVvJwl6ysJccbCULexmH2y0s7rzzThw8eBAbN27E+vXrsWPHDkyZMqXa7ZOSkpCUlIRXXnkFf/zxB9577z189913uPfee+txavfm4+Oj9wikEVvJwl6ysJccbCULexmHW30G+qFDh/Ddd9/h119/Rbdu3QAAb7zxBm688Ua88soraNKkSZXvad++PdasWWP7c4sWLfD888/jrrvuQnl5OT8mnoiIiIhIA7d6xWLXrl0IDQ21LSoAYNCgQfDw8MDu3bs1X05OTg6Cg4NrXFSUlJQgNzfX7oscKykp0XsE0oitZGEvWdhLDraShb2Mw60WFikpKYiKirI7zcvLC+Hh4ZqPGJCeno5nn322xrdPAcD8+fMREhJi+4qNjQUAFBcXIykpCVar1XZcZbPZjNLSUlgsFuTn5yM7OxuZmZkoKipCSkoKysvL7bYtKytDSkoKCgsLkZmZiaysLBQUFCA1NRVlZWV221ZUVCApKQnFxcUAgPz8fOTl5SEtLQ0lJSV22yqlYDabUVJSgrS0NNuCKD09HUVFRZrnLiwsdDh3eXl5lbnz8/NRUlJSZW6r1YqkpCQUFRUhIyMDOTk5VeaunPfiufPy8pCTk4OMjIxq5y4rK0Nqairy8/ORlZVVq7mzs7ORn58Pi8WC0tLSaudOT0+33YeO5q78nprmrqiocDh3QUFBlbkvvg8r5y4qKtI8d3FxscO5k5KSoJSyvU+1cm6LxVJl7uTk5EvOfamf2eTkZLu58/LyajV3enr6JR9reXl5do81Z8594c+sxWJx+FhLSkpy+Fhz5nOEl5eX5ueImh5rej9HOJrbHZ8jfH19xT9HXDy3uz5HBAcHu8VzRG1+j5D8HFFeXu4WzxFG/j1CK5NSSmneWiePP/44FixYUOM2hw4dwtq1a/H+++/j8OHDdudFRUVh7ty5eOCBB2q8jNzcXNxwww0IDw/Hl19+CW9v72q3LSkpsVsh5+bmIjY21vZqR31LTk7GihUrMGXKFMMdz9lsNvNTMYVgK1nYSxb2koOtZGEv4xCxA8GMGTMwceLEGrdp3rw5oqOjYbFY7E4vLy9HZmYmoqOja/z+vLw8DB06FEFBQfj8889rXFQAgK+vL3x9fTXNf6VztG8LGRNbycJesrCXHGwlC3sZh4iFRWRkJCIjIy+5Xc+ePZGdnY3ExER07doVALBlyxZYrVb06NGj2u/Lzc3FkCFD4Ovriy+//BJ+fn5Om53OH3mL/5IgA1vJwl6ysJccbCULexmHW+1j0bZtWwwdOhT33XcffvnlF/z000946KGHcPvtt9tWs2azGW3atMEvv/wC4PyiYvDgwSgoKMA777yD3NxcpKSkICUlBRUVFXreHLfBB7scbCULe8nCXnKwlSzsZRxutbAAgI8++ght2rTBwIEDceONN6JPnz5YsWKF7fyysjIcPnwYhYWFAIC9e/di9+7d+P3339GyZUs0btzY9nX27Fm9boZbqc1OP6QvtpKFvWRhLznYShb2Mg4Rb4WqjfDwcHz88cfVnh8XF4cL91fv378/BOy/LlpERITeI5BGbCULe8nCXnKwlSzsZRxu94oFGQ8/40MOtpKFvWRhLznYShb2Mg4uLMjluDO8HGwlC3vJwl5ysJUs7GUcXFiQy1mtVr1HII3YShb2koW95GArWdjLOLiwIJcrLy/XewTSiK1kYS9Z2EsOtpKFvYyDCwtyuQYNGug9AmnEVrKwlyzsJQdbycJexsGFBblcVlaW3iOQRmwlC3vJwl5ysJUs7GUcXFiQyzVq1EjvEUgjtpKFvWRhLznYShb2Mg4uLMjlUlJS9B6BNGIrWdhLFvaSg61kYS/j4MKCXC4mJkbvEUgjtpKFvWRhLznYShb2Mg4uLMjlzGaz3iOQRmwlC3vJwl5ysJUs7GUcXFiQy0VFRek9AmnEVrKwlyzsJQdbycJexsGFBblcZmam3iOQRmwlC3vJwl5ysJUs7GUcXFiQywUGBuo9AmnEVrKwlyzsJQdbycJexsGFBblcaWmp3iOQRmwlC3vJwl5ysJUs7GUcXFiQyyml9B6BNGIrWdhLFvaSg61kYS/j8NJ7ALp8eXl5yM/PBwCkp6fb/Rc4/9JgUFCQLrNdyM/PT+8RSCO2koW9ZGEvOdhKFvYyDi4sBEtMTMT27dvtTlu7dq3t/xMSEtC/f/96nqqq3NxcNGjQQO8xSAO2koW9ZGEvOdhKFvYyDpPi60dOkZubi5CQEOTk5CA4OLhervPCVywcMcorFmVlZfD29tZ7DNKArWRhL1nYSw62koW9jIOvWAgWFBRkiIXDpVgsFn4qphBsJQt7ycJecrCVLOxlHHzFwkn0eMWCiIiIiMgoeFQocjmz2az3CKQRW8nCXrKwlxxsJQt7GQdfsXASvmJRvfLycnh58V13ErCVLOwlC3vJwVaysJdx8BULcrkLD4FLxsZWsrCXLOwlB1vJwl7GwYUFuVxISIjeI5BGbCULe8nCXnKwlSzsZRxcWJDLFRUV6T0CacRWsrCXLOwlB1vJwl7GwYUFuZyHB3/MpGArWdhLFvaSg61kYS/jYAlyOe5QJQdbycJesrCXHGwlC3sZBxcW5HKFhYV6j0AasZUs7CULe8nBVrKwl3FwYUEuFxoaqvcIpBFbycJesrCXHGwlC3sZBxcW5HJpaWl6j0AasZUs7CULe8nBVrKwl3HwA/KchB+QR0RERERXMr5iQS5nNpv1HoE0YitZ2EsW9pKDrWRhL+PgKxZOwlcsqme1WnkoOCHYShb2koW95GArWdjLOFiBXC4lJUXvEUgjtpKFvWRhLznYShb2Mg4e+NdJKl/4yc3N1XkS4/Hy8uL9IgRbycJesrCXHGwlC3vVj6CgIJhMphq34cLCSfLy8gAAsbGxOk9CRERERORcWt7uz30snMRqtSIpKUnTau5Kkpubi9jYWJw9e5b7nhgcW8nCXrKwlxxsJQt71R++YlGPPDw8cNVVV+k9hmEFBwfzAS8EW8nCXrKwlxxsJQt7GQN33iYiIiIiojrjwoKIiIiIiOqMCwtyKV9fX8yZMwe+vr56j0KXwFaysJcs7CUHW8nCXsbCnbeJiIiIiKjO+IoFERERERHVGRcWRERERERUZ1xYEBERERFRnXFhQTXasWMHbr75ZjRp0gQmkwnr1q2zO3/t2rUYPHgwGjZsCJPJhP3791e5jOLiYjz44INo2LAhAgMDMWrUKKSmptptc+bMGQwfPhwNGjRAVFQUHn30UZSXl7vwlrmnmnqVlZXhscceQ4cOHRAQEIAmTZrgnnvuQVJSkt1lZGZm4s4770RwcDBCQ0Nx7733Ij8/326bAwcOoG/fvvDz80NsbCxeeuml+rh5budSj69nnnkGbdq0QUBAAMLCwjBo0CDs3r3bbhv2qj+X6nWh+++/HyaTCYsXL7Y7nb3qx6VaTZw4ESaTye5r6NChdtuwVf3R8tg6dOgQbrnlFoSEhCAgIADXXXcdzpw5Yzufv2sYAxcWVKOCggJ06tQJb775ZrXn9+nTBwsWLKj2Mh555BF89dVX+Oyzz7B9+3YkJSVh5MiRtvMrKiowfPhwlJaWYufOnXj//ffx3nvvYfbs2U6/Pe6upl6FhYXYu3cvnn76aezduxdr167F4cOHccstt9htd+edd+LgwYPYuHEj1q9fjx07dmDKlCm283NzczF48GA0a9YMiYmJePnll/HMM89gxYoVLr997uZSj6+rr74aS5Yswe+//44ff/wRcXFxGDx4MNLS0mzbsFf9uVSvSp9//jl+/vlnNGnSpMp57FU/tLQaOnQokpOTbV///e9/7c5nq/pzqV7Hjx9Hnz590KZNG2zbtg0HDhzA008/DT8/P9s2/F3DIBSRRgDU559/7vC8kydPKgBq3759dqdnZ2crb29v9dlnn9lOO3TokAKgdu3apZRS6ptvvlEeHh4qJSXFts1bb72lgoODVUlJidNvx5Wipl6VfvnlFwVAnT59Wiml1J9//qkAqF9//dW2zbfffqtMJpMym81KKaWWLl2qwsLC7No89thjqnXr1s6/EVcQLb1ycnIUALVp0yalFHvpqbpe586dUzExMeqPP/5QzZo1U4sWLbKdx176cNRqwoQJ6tZbb632e9hKP456jRs3Tt11113Vfg9/1zAOvmJBLpWYmIiysjIMGjTIdlqbNm3QtGlT7Nq1CwCwa9cudOjQAY0aNbJtM2TIEOTm5uLgwYP1PvOVJCcnByaTCaGhoQDOtwgNDUW3bt1s2wwaNAgeHh62t+Ds2rUL/fr1g4+Pj22bIUOG4PDhw8jKyqrX+a8kpaWlWLFiBUJCQtCpUycA7GU0VqsVd999Nx599FFcc801Vc5nL2PZtm0boqKi0Lp1azzwwAPIyMiwncdWxmG1WvH111/j6quvxpAhQxAVFYUePXrYvV2Kv2sYBxcW5FIpKSnw8fGx/eJaqVGjRkhJSbFtc+EDvfL8yvPINYqLi/HYY4/hjjvuQHBwMIDz93dUVJTddl5eXggPD2cvnaxfvx6BgYHw8/PDokWLsHHjRkRERABgL6NZsGABvLy8MHXqVIfns5dxDB06FP/5z3+wefNmLFiwANu3b8ewYcNQUVEBgK2MxGKxID8/Hy+++CKGDh2K77//HrfddhtGjhyJ7du3A+DvGkbipfcARFT/ysrKMHbsWCil8NZbb+k9DtVgwIAB2L9/P9LT0/H2229j7Nix2L17d5VfekhfiYmJeO2117B3716YTCa9x6FLuP32223/36FDB3Ts2BEtWrTAtm3bMHDgQB0no4tZrVYAwK233opHHnkEANC5c2fs3LkTy5YtQ0JCgp7j0UX4igW5VHR0NEpLS5GdnW13empqKqKjo23bXHzkhso/V25DzlO5qDh9+jQ2btxoe7UCOH9/WywWu+3Ly8uRmZnJXjoJCAhAy5Ytcf311+Odd96Bl5cX3nnnHQDsZSQ//PADLBYLmjZtCi8vL3h5eeH06dOYMWMG4uLiALCXkTVv3hwRERE4duwYALYykoiICHh5eaFdu3Z2p7dt29Z2VCj+rmEcXFiQS3Xt2hXe3t7YvHmz7bTDhw/jzJkz6NmzJwCgZ8+e+P333+2exCt/4b34iYTqpnJRcfToUWzatAkNGza0O79nz57Izs5GYmKi7bQtW7bAarWiR48etm127NiBsrIy2zYbN25E69atERYWVj835ApmtVpRUlICgL2M5O6778aBAwewf/9+21eTJk3w6KOPYsOGDQDYy8jOnTuHjIwMNG7cGABbGYmPjw+uu+46HD582O70I0eOoFmzZgD4u4ah6L33OBlbXl6e2rdvn9q3b58CoBYuXKj27dtnO4pQRkaG2rdvn/r6668VAPXJJ5+offv2qeTkZNtl3H///app06Zqy5Ytas+ePapnz56qZ8+etvPLy8tV+/bt1eDBg9X+/fvVd999pyIjI9WsWbPq/fZKV1Ov0tJSdcstt6irrrpK7d+/XyUnJ9u+LjwixtChQ1WXLl3U7t271Y8//qhatWql7rjjDtv52dnZqlGjRuruu+9Wf/zxh/rkk09UgwYN1PLly/W4yaLV1Cs/P1/NmjVL7dq1S506dUrt2bNHTZo0Sfn6+qo//vjDdhnsVX8u9Xx4sYuPCqUUe9WXmlrl5eWpmTNnql27dqmTJ0+qTZs2qWuvvVa1atVKFRcX2y6DrerPpR5ba9euVd7e3mrFihXq6NGj6o033lCenp7qhx9+sF0Gf9cwBi4sqEZbt25VAKp8TZgwQSml1MqVKx2eP2fOHNtlFBUVqX/84x8qLCxMNWjQQN122212Cw+llDp16pQaNmyY8vf3VxEREWrGjBmqrKysHm+pe6ipV+UhgR19bd261XYZGRkZ6o477lCBgYEqODhYTZo0SeXl5dldz2+//ab69OmjfH19VUxMjHrxxRfr+Za6h5p6FRUVqdtuu001adJE+fj4qMaNG6tbbrlF/fLLL3aXwV7151LPhxdztLBgr/pRU6vCwkI1ePBgFRkZqby9vVWzZs3UfffdZ3cYUqXYqj5peWy98847qmXLlsrPz0916tRJrVu3zu4y+LuGMZiUUsr5r4MQEREREdGVhPtYEBERERFRnXFhQUREREREdcaFBRERERER1RkXFkREREREVGdcWBARERERUZ1xYUFERERERHXGhQUREREREdUZFxZERERERFRnXFgQEZEhmUwmmEwmhIaG1tt1PvPMM7brXbx4cb1dLxGRO+DCgoiInG7ixIm2X9Av/Dp27FitLmflypU4cuRIneeJi4uDyWTCzz//bHf6tGnT0L9/f9ufZ86cieTkZFx11VV1vk4ioisNFxZEROQSQ4cORXJyst1XfHx8rS4jNDQUUVFRTpnHz88Pjz32WI3bBAYGIjo6Gp6enk65TiKiKwkXFkRE5BK+vr6Ijo62+6rrL+zPPPMMOnfujHfffRdNmzZFYGAg/vGPf6CiogIvvfQSoqOjERUVheeff77K906ZMgU///wzvvnmmzrNQEREjnnpPQAREVFtHD9+HN9++y2+++47HD9+HKNHj8aJEydw9dVXY/v27di5cyf+/ve/Y9CgQejRo4ft++Lj43H//fdj1qxZGDp0KDw8+G9rRETOxGdVIiJyifXr1yMwMND2NWbMGKdcrtVqxbvvvot27drh5ptvxoABA3D48GEsXrwYrVu3xqRJk9C6dWts3bq1yvc+9dRTOHnyJD766COnzEJERP/DVyyIiMglBgwYgLfeesv254CAAKdcblxcHIKCgmx/btSoETw9Pe1egWjUqBEsFkuV742MjMTMmTMxe/ZsjBs3zinzEBHReXzFgoiIXCIgIAAtW7a0fTVu3Ngpl+vt7W33Z5PJ5PA0q9Xq8PunT5+OoqIiLF261CnzEBHReVxYEBHRFSUwMBBPP/00nn/+eeTl5ek9DhGR2+DCgoiIrjhTpkxBSEgIPv74Y71HISJyG1xYEBHRFcfb2xvPPvssiouL9R6FiMhtmJRSSu8hiIiILmYymfD5559jxIgR9X7dcXFxmDZtGqZNm1bv101EJBVfsSAiIsO64447cNVVV9Xb9b3wwgsIDAzEmTNn6u06iYjcBV+xICIiQzp27BgAwNPTE/Hx8fVynZmZmcjMzARw/tC0ISEh9XK9RETugAsLIiIiIiKqM74VioiIiIiI6owLCyIiIiIiqjMuLIiIiIiIqM64sCAiIiIiojrjwoKIiIiIiOqMCwsiIiIiIqozLiyIiIiIiKjOuLAgIiIiIqI648KCiIiIiIjq7P8BFWIJwz1IRHsAAAAASUVORK5CYII=",
      "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 1 (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": "29d0b0ab",
   "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": "markdown",
   "id": "ac818d04",
   "metadata": {},
   "source": [
    "### Correzione distanza sonar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "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": 283,
   "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": 284,
   "id": "ac4860fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4424407c97a64e1aa8e7a26386338341",
       "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=[108.61,…"
      ]
     },
     "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": "6d8c8b79",
   "metadata": {},
   "source": [
    "### Regressione lineare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "8917049c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit completo:   chi²=3.89,  P=0.1432\n",
      "Temperatura :  chi²=0.227,  P=0.8927\n",
      "\n",
      "A:  23.5225 ± 0.0853  to  23.6041 ± 0.3869\n",
      "B:  7371.9487 ± 21.4728  to  7361.9067 ± 98.1235\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": 286,
   "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 1 (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": 287,
   "id": "e35a9456",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui\n",
    "\n",
    "dfSonarCorrezione[\"r\"] = r_fn(\n",
    "  -dfSonarCorrezione[\"c\"],\n",
    "  dfSonarCorrezione[\"F\"],\n",
    "  AS2,\n",
    "  BS2\n",
    ")\n",
    "\n",
    "dfSonarCorrezione[\"ur\"] = sigma_r_fn(\n",
    "  -dfSonarCorrezione[\"c\"], dfSonarCorrezione[\"F\"],\n",
    "  AS2, BS2,\n",
    "  dfSonarCorrezione[\"uc\"], dfSonarCorrezione[\"uF\"],\n",
    "  uAS2, uBS2, covABS2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "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 1 (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": "c6ca9f7e",
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "af6143df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Massa molla = 29.8933 ± 0.0044\n"
     ]
    }
   ],
   "source": [
    "# Calcolo numerico massa molla\n",
    "\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": 290,
   "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": 291,
   "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",
    "# 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": 292,
   "id": "455271a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c33f1b841294fafbac15037ed80ccfc",
       "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=[108.61,…"
      ]
     },
     "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": "56713ba2",
   "metadata": {},
   "source": [
    "## Regressione lineare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "02119d66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AD      = 23.8854 ± 0.0133\n",
      "BD      = 1.5710 ± 0.0856\n",
      "cov_ABD = -0.001129\n",
      "chi²    = 27.0310\n",
      "P(chi², ∞)= 0.0000\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": 294,
   "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 lineare\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 1 (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": 295,
   "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": 296,
   "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 1 (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": "c085b1c5",
   "metadata": {},
   "source": [
    "# Raccolta finale dati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "e87e24b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calibro\n",
      "AC      = 23.9432 ± 0.0422\n",
      "BC      = 2719.8356 ± 2.8402\n",
      "cov_ABC = 0.118646\n",
      "chi²    = 56.2014\n",
      "GdL     = 5\n",
      "P(chi², ∞)= 0.0000\n",
      "\n",
      "Sonar statico senza correzione\n",
      "AS      = 23.5225 ± 0.0853\n",
      "BS      = 7371.9487 ± 21.4728\n",
      "cov_ABS = 1.830281\n",
      "chi²    = 3.8872\n",
      "GdL     = 4\n",
      "P(chi², ∞)= 0.1432\n",
      "\n",
      "Sonar statico con correzione\n",
      "AS      = 23.6041 ± 0.3869\n",
      "BS      = 7361.9067 ± 98.1235\n",
      "cov_ABS = 37.934456\n",
      "chi²    = 0.2270\n",
      "GdL     = 4\n",
      "P(chi², ∞)= 0.8927\n",
      "\n",
      "Sonar dinamico\n",
      "AD      = 23.8854 ± 0.0133\n",
      "BD      = 1.5710 ± 0.0856\n",
      "cov_ABD = -0.001129\n",
      "chi²    = 27.0310\n",
      "GdL     = 4\n",
      "P(chi², ∞)= 0.0000\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}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12a4cb87",
   "metadata": {},
   "source": [
    "# Compatibilità"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "dc913446",
   "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",
    "]\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": 299,
   "id": "45a291fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compatibilità calibro e sonar statico:\n",
      "k = 0.87\n",
      "\n",
      "Compatibilità calibro e sonar dinamico:\n",
      "k = 1.31\n",
      "\n",
      "Compatibilità sonar statico e sonar dinamico:\n",
      "k = 0.73\n",
      "\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 calibro e sonar dinamico:\")\n",
    "print_k(AC, uAC, AD, uAD)\n",
    "\n",
    "print(\"Compatibilità sonar statico e sonar dinamico:\")\n",
    "print_k(AS2, uAS2, AD, uAD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "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": 301,
   "id": "57de948b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compatibilità: Calibro vs Sonar statico (T considerata)\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ           1.9804     0.0881\n",
      "2σ           0.9902     0.3551\n",
      "3σ           0.6601     0.5303\n",
      "\n",
      "\n",
      "Compatibilità: Calibro vs Sonar dinamico\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ           2.6076     0.0350\n",
      "2σ           1.3038     0.2335\n",
      "3σ           0.8692     0.4135\n",
      "\n",
      "\n",
      "Compatibilità: Sonar Dinamico vs Sonar statico (T considerata)\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ           1.4534     0.1963\n",
      "2σ           0.7267     0.4948\n",
      "3σ           0.4845     0.6452\n"
     ]
    }
   ],
   "source": [
    "print(\"Compatibilità: Calibro vs Sonar statico (T considerata)\")\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),\n",
    "                                    AS2,  uAS2*n, len(dfSonar))\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),\n",
    "                                    AD,  uAD*n,  len(dfSonar))\n",
    "    print(f\"{n}σ         {res['t']:>8.4f} {res['p']:>10.4f}\")\n",
    "\n",
    "print(\"\\n\\nCompatibilità: Sonar Dinamico vs Sonar statico (T considerata)\")\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),\n",
    "                                    AS2, uAS2*n, len(dfSonar))\n",
    "    print(f\"{n}σ         {res['t']:>8.4f} {res['p']:>10.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b66ccc91",
   "metadata": {},
   "source": [
    "# Note finali\n",
    "Chiaramente i dati presi con il sonar statico soffrono di altri errori che dovremmo discutere come introdurre, sicuramente soffrono della precisione con cui abbiamo puntato il sonar.\n",
    "\n",
    "La temperatura sfortunatamente è stata presa con troppa poca attenzione e questo fa perdere di integrità hai nostri dati, ma altro per ora non mi viene su 2 piedi.\n",
    "\n",
    "Abbiamo almeno una buona basa su cui poter ragionare."
   ]
  }
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