Lab1/mollaDefinitiva/molla-2/molla2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bdf377b8",
   "metadata": {},
   "source": [
    "# Analisi completa della molla 1\n",
    "Tutto quello che c'è da sapere sulla molla 1, possibilmente con la massima chiarezza."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3a34cc3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Heavy lifting\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "# Mostrare i dati\n",
    "import ipysheet\n",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Calcolo simbolico\n",
    "import sympy as sp\n",
    "\n",
    "\n",
    "G = 9.806\n",
    "uG = 0.004"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eaece9c",
   "metadata": {},
   "source": [
    "# Analisi dati del calibro"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "960e68a5",
   "metadata": {},
   "source": [
    "## Import dei dati calibro (criterio di Chauvenet)\n",
    "- Primo test di implementazione 1\n",
    "- Scrittura con logica chiara   2\n",
    "- Codice pulito per bene        3 (sono abbastanza soddisfatto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1e2fabea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Blocco funzioni per 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": 3,
   "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": [
    "# Blocco funzioni calcolo simbolico la forza\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": 4,
   "id": "e649eddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfCalibro = pd.read_csv(r'molla2Calibro.csv')\n",
    "\n",
    "# Costanti strumentali\n",
    "ERR_CALIBRO = 0.02 / np.sqrt(12)\n",
    "ERR_BILANCIA = 0.01 / np.sqrt(12)\n",
    "\n",
    "# Chauvenet e poi statistica di base\n",
    "dfCalibro = calcola_stats(dfCalibro, \"dx\", ERR_CALIBRO)\n",
    "dfCalibro = calcola_stats(dfCalibro, \"m\",  ERR_BILANCIA)\n",
    "\n",
    "# Aggiunta delle forze\n",
    "dfCalibro[\"F\"]  = F_fn(dfCalibro[\"m\"],  G)\n",
    "dfCalibro[\"uF\"] = uF_fn(dfCalibro[\"m\"], dfCalibro[\"um\"], G, uG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "42ff21d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e6d22eda4344bf19ac6192fc7bab556",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, row_start=0, squeeze_row=False, type='numeric', value=[49.25, …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheet = ipysheet.from_dataframe(dfCalibro)\n",
    "\n",
    "display(sheet)\n",
    "# dfCalibro.head(2).to_json(orient=\"records\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c58a6e26",
   "metadata": {},
   "source": [
    "## Regressione lineare Carpi dati del calibro\n",
    "chi² Come persone normali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dfab4331",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Blocco di funzione regressione Carpi\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": 7,
   "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": [
    "# Blocco calcolo simbolico residui\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": 8,
   "id": "cd68b201",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AC      = 3.2009 ± 0.0034\n",
      "BC      = 1161.4138 ± 0.4687\n",
      "cov_ABC = 0.001385\n",
      "chi²    = 4.3252\n",
      "P(chi², ∞)= 0.1150\n"
     ]
    }
   ],
   "source": [
    "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": 9,
   "id": "f98a3612",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui sul dfCalibro\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": "markdown",
   "id": "5de53d0e",
   "metadata": {},
   "source": [
    "### Grafici calibro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "68eb66dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit\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": 11,
   "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": [
    "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": "c5e02e3d",
   "metadata": {},
   "source": [
    "### Sezione non espressamente richiesta\n",
    "Dal grafico dei residui risulta chiaro che tutti i punti sono \"sotto\" mentre uno solo è \"sopra\".\n",
    "Per diletto personale ho voluto vedere cosa sarebbe successo escludendo quel punto.\n",
    "\n",
    "La sezione riguarda solo **Esclusione del punto 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "17cbeaea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit completo:   chi²=4.33,  P=0.1150\n",
      "Senza punto 1:  chi²=0.007,  P=0.9328\n",
      "\n",
      "A:  3.2009 ± 0.0034  to  3.1995 ± 0.0034\n",
      "B:  1161.4138 ± 0.4687  to  1161.4675 ± 0.4694\n"
     ]
    }
   ],
   "source": [
    "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": 13,
   "id": "0bc01605",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataFrame  CALIBRO\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4fea2f4e82b4b9ab4505c40a7691071",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, row_start=0, squeeze_row=False, type='numeric', value=[49.25, …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "dataFrame  CALIBRO TOLTO DATO 1\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9372f3c98be34baebe1f3042dcea187f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, row_end=2, row_start=0, squeeze_row=False, type='numeric', val…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ricalcolo Residui\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",
    ")\n",
    "\n",
    "print(\"dataFrame  CALIBRO\")\n",
    "sheet = ipysheet.from_dataframe(dfCalibro)\n",
    "display(sheet)\n",
    "\n",
    "print(\"\\ndataFrame  CALIBRO TOLTO DATO 1\")\n",
    "sheet = ipysheet.from_dataframe(dfCalibro_senza1)\n",
    "display(sheet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d2c1a550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafico ringraziate Cloude per l'idea super compatta di questo codice\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": "code",
   "execution_count": 15,
   "id": "8f79348f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit senza un dato\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": "markdown",
   "id": "b952c206",
   "metadata": {},
   "source": [
    "# Analisi dati sonar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e347644",
   "metadata": {},
   "source": [
    "## Import dei dati sonar (semidispersioni)\n",
    "- masse trattate come prima\n",
    "- $\\omega$, c trattati con semidispersione"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d6195b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfSonar = pd.read_csv(r'molla2Sonar.csv')\n",
    "\n",
    "ERR_OMEGA = 0.000001\n",
    "ERR_C = 0.000001\n",
    "ERR_CRONOMETRO = 0.01\n",
    "\n",
    "# Bilancia trattata esattamente come prima\n",
    "dfSonar = calcola_stats(dfSonar, \"m\",  ERR_BILANCIA)\n",
    "dfSonar = calcola_stats(dfSonar, \"w\",  ERR_OMEGA)\n",
    "dfSonar = calcola_stats(dfSonar, \"c\",  ERR_C)\n",
    "dfSonar = calcola_stats(dfSonar, \"t\",  ERR_CRONOMETRO)\n",
    "\n",
    "# Aggiunta delle forze\n",
    "dfSonar[\"F\"]  = F_fn(dfSonar[\"m\"],  G)\n",
    "dfSonar[\"uF\"] = uF_fn(dfSonar[\"m\"], dfSonar[\"um\"], G, uG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "02e2d183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "01c761139d6e49809ecf4f1cdca97cb5",
       "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"
    },
    {
     "data": {
      "text/plain": [
       "'[{\"m1\":49.25,\"m2\":49.24,\"m3\":49.25,\"a1\":9.7171,\"ua1\":0.016,\"w1\":7.6565,\"uw1\":0.0004,\"c1\":484.455,\"uc1\":0.011,\"t1\":15.62,\"a2\":8.911,\"ua2\":0.015,\"w2\":7.6569,\"uw2\":0.0004,\"c2\":484.516,\"uc2\":0.011,\"t2\":15.58,\"a3\":10.446,\"ua3\":0.027,\"w3\":7.6603,\"uw3\":0.0005,\"c3\":485.082,\"uc3\":0.019,\"t3\":15.76,\"a4\":8.377,\"ua4\":0.016,\"w4\":7.6582,\"uw4\":0.0004,\"c4\":484.752,\"uc4\":0.011,\"t4\":15.87,\"alp\":0.333333,\"ualp\":0.000001,\"m\":49.2466666667,\"um\":0.0044095855,\"nm\":3.0,\"outm\":\"[]\",\"w\":7.657975,\"uw\":0.000855741,\"nw\":4.0,\"outw\":\"[]\",\"c\":484.70125,\"uc\":0.1421568236,\"nc\":4.0,\"outc\":\"[]\",\"t\":15.7075,\"ut\":0.0672526332,\"nt\":4.0,\"outt\":\"[]\",\"F\":482.9128133333,\"uF\":0.2016766686},{\"m1\":69.28,\"m2\":69.28,\"m3\":69.27,\"a1\":9.86,\"ua1\":0.016,\"w1\":6.55968,\"uw1\":0.00029,\"c1\":423.352,\"uc1\":0.011,\"t1\":18.31,\"a2\":10.39,\"ua2\":0.012,\"w2\":6.55891,\"uw2\":0.00022,\"c2\":423.154,\"uc2\":0.009,\"t2\":18.27,\"a3\":10.491,\"ua3\":0.013,\"w3\":6.56002,\"uw3\":0.00024,\"c3\":423.697,\"uc3\":0.01,\"t3\":18.34,\"a4\":10.968,\"ua4\":0.019,\"w4\":6.56,\"uw4\":0.0003,\"c4\":423.465,\"uc4\":0.014,\"t4\":18.16,\"alp\":0.333333,\"ualp\":0.000001,\"m\":69.2766666667,\"um\":0.0044095855,\"nm\":3.0,\"outm\":\"[]\",\"w\":6.5596525,\"uw\":0.0002594685,\"nw\":4.0,\"outw\":\"[]\",\"c\":423.417,\"uc\":0.1133203424,\"nc\":4.0,\"outc\":\"[]\",\"t\":18.27,\"ut\":0.040620192,\"nt\":4.0,\"outt\":\"[]\",\"F\":679.3269933333,\"uF\":0.2804600444},{\"m1\":88.97,\"m2\":88.97,\"m3\":88.96,\"a1\":11.584,\"ua1\":0.014,\"w1\":5.84417,\"uw1\":0.0002,\"c1\":363.229,\"uc1\":0.01,\"t1\":20.27,\"a2\":10.1763,\"ua2\":0.017,\"w2\":5.84585,\"uw2\":0.00028,\"c2\":363.354,\"uc2\":0.012,\"t2\":20.44,\"a3\":12.044,\"ua3\":0.018,\"w3\":5.845,\"uw3\":0.00026,\"c3\":363.183,\"uc3\":0.013,\"t3\":20.54,\"a4\":11.224,\"ua4\":0.016,\"w4\":5.84513,\"uw4\":0.00025,\"c4\":363.233,\"uc4\":0.011,\"t4\":20.49,\"alp\":0.333333,\"ualp\":0.000001,\"m\":88.9666666667,\"um\":0.0044095855,\"nm\":3.0,\"outm\":\"[]\",\"w\":5.8450375,\"uw\":0.0003443214,\"nw\":4.0,\"outw\":\"[]\",\"c\":363.24975,\"uc\":0.0365544685,\"nc\":4.0,\"outc\":\"[]\",\"t\":20.435,\"ut\":0.0595119036,\"nt\":4.0,\"outt\":\"[]\",\"F\":872.4071333333,\"uF\":0.358484053},{\"m1\":108.61,\"m2\":108.61,\"m3\":108.61,\"a1\":11.542,\"ua1\":0.026,\"w1\":5.3278,\"uw1\":0.0003,\"c1\":303.5502,\"uc1\":0.019,\"t1\":22.49,\"a2\":8.424,\"ua2\":0.017,\"w2\":5.3282,\"uw2\":0.0003,\"c2\":303.581,\"uc2\":0.012,\"t2\":22.27,\"a3\":10.501,\"ua3\":0.022,\"w3\":5.3296,\"uw3\":0.0003,\"c3\":303.842,\"uc3\":0.016,\"t3\":22.55,\"a4\":9.959,\"ua4\":0.014,\"w4\":5.32822,\"uw4\":0.0002,\"c4\":303.445,\"uc4\":0.01,\"t4\":22.15,\"alp\":0.333333,\"ualp\":0.000001,\"m\":108.61,\"um\":0.0028867513,\"nm\":3.0,\"outm\":\"[]\",\"w\":5.328455,\"uw\":0.0003937334,\"nw\":4.0,\"outw\":\"[]\",\"c\":303.60455,\"uc\":0.0843342704,\"nc\":4.0,\"outc\":\"[]\",\"t\":22.365,\"ut\":0.0941187194,\"nt\":4.0,\"outt\":\"[]\",\"F\":1065.02966,\"uF\":0.4353612606}]'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet = ipysheet.from_dataframe(dfSonar)\n",
    "\n",
    "display(sheet)\n",
    "dfSonar.head(4).to_json(orient=\"records\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e12f76c",
   "metadata": {},
   "source": [
    "## Regressione lineare Carpi dati del sonar Caso statico\n",
    "chi² Come persone normali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "06f9ccef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AS      = 3.2192 ± 0.0027\n",
      "BS      = 2042.5203 ± 1.0326\n",
      "cov_ABS = 0.002760\n",
      "chi²    = 9.0319\n",
      "P(chi², ∞)= 0.0289\n"
     ]
    }
   ],
   "source": [
    "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": 19,
   "id": "09445aac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui sul dfSonar\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": "markdown",
   "id": "f6641e08",
   "metadata": {},
   "source": [
    "### Grafici sonar valori statici"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "# Fit\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)\")\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": 21,
   "id": "1ecf9ab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafico Residui\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)\")\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": "4eebafaf",
   "metadata": {},
   "source": [
    "### Sezione non espressamente richiesta\n",
    "La velocità del suono è chiaramente un fattore moltiplicativo che viene ignorato.\n",
    "\n",
    "Formula di Cramer 1993"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4088453c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c = 0.00301841231512225*c_raw*v0/sqrt(0.00366099212886692*T + 1)\n",
      "sigma_c = v0*sqrt(3.05277451303515e-11*c_raw**2*sigma_T**2 + 9.11081290408163e-6*sigma_c_raw**2*(0.00366099212886692*T + 1)**2)/(0.00366099212886692*T + 1)**(3/2)\n",
      "\n",
      "Formula LaTeX:\n",
      "c = \\frac{0.00301841231512225 c_{raw} v_{0}}{\\sqrt{0.00366099212886692 T + 1}}\n",
      "sigma_c = \\frac{v_{0} \\sqrt{3.05277451303515 \\cdot 10^{-11} c_{raw}^{2} \\sigma_{T}^{2} + 9.11081290408163 \\cdot 10^{-6} \\sigma_{c raw}^{2} \\left(0.00366099212886692 T + 1\\right)^{2}}}{\\left(0.00366099212886692 T + 1\\right)^{\\frac{3}{2}}}\n"
     ]
    }
   ],
   "source": [
    "# propagazione dell'errore con la temperatura\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",
    "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": 23,
   "id": "1584a5d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "Temperatura = 22.6\n",
    "uTemperatura = 0.7 / 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ac4860fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "171eb89119374f73bb0b77330b08f1c1",
       "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"
    },
    {
     "data": {
      "text/plain": [
       "'[{\"m1\":49.25,\"m2\":49.24,\"m3\":49.25,\"a1\":9.7171,\"ua1\":0.016,\"w1\":7.6565,\"uw1\":0.0004,\"c1\":484.455,\"uc1\":0.011,\"t1\":15.62,\"a2\":8.911,\"ua2\":0.015,\"w2\":7.6569,\"uw2\":0.0004,\"c2\":484.516,\"uc2\":0.011,\"t2\":15.58,\"a3\":10.446,\"ua3\":0.027,\"w3\":7.6603,\"uw3\":0.0005,\"c3\":485.082,\"uc3\":0.019,\"t3\":15.76,\"a4\":8.377,\"ua4\":0.016,\"w4\":7.6582,\"uw4\":0.0004,\"c4\":484.752,\"uc4\":0.011,\"t4\":15.87,\"alp\":0.333333,\"ualp\":0.000001,\"m\":49.2466666667,\"um\":0.0044095855,\"nm\":3.0,\"outm\":\"[]\",\"w\":7.657975,\"uw\":0.000855741,\"nw\":4.0,\"outw\":\"[]\",\"c\":484.70125,\"uc\":0.1421568236,\"nc\":4.0,\"outc\":\"[]\",\"t\":15.7075,\"ut\":0.0672526332,\"nt\":4.0,\"outt\":\"[]\",\"F\":482.9128133333,\"uF\":0.2016766686,\"r\":-0.2364148992,\"ur\":0.1942155847},{\"m1\":69.28,\"m2\":69.28,\"m3\":69.27,\"a1\":9.86,\"ua1\":0.016,\"w1\":6.55968,\"uw1\":0.00029,\"c1\":423.352,\"uc1\":0.011,\"t1\":18.31,\"a2\":10.39,\"ua2\":0.012,\"w2\":6.55891,\"uw2\":0.00022,\"c2\":423.154,\"uc2\":0.009,\"t2\":18.27,\"a3\":10.491,\"ua3\":0.013,\"w3\":6.56002,\"uw3\":0.00024,\"c3\":423.697,\"uc3\":0.01,\"t3\":18.34,\"a4\":10.968,\"ua4\":0.019,\"w4\":6.56,\"uw4\":0.0003,\"c4\":423.465,\"uc4\":0.014,\"t4\":18.16,\"alp\":0.333333,\"ualp\":0.000001,\"m\":69.2766666667,\"um\":0.0044095855,\"nm\":3.0,\"outm\":\"[]\",\"w\":6.5596525,\"uw\":0.0002594685,\"nw\":4.0,\"outw\":\"[]\",\"c\":423.417,\"uc\":0.1133203424,\"nc\":4.0,\"outc\":\"[]\",\"t\":18.27,\"ut\":0.040620192,\"nt\":4.0,\"outt\":\"[]\",\"F\":679.3269933333,\"uF\":0.2804600444,\"r\":0.0351995054,\"ur\":0.1633888866},{\"m1\":88.97,\"m2\":88.97,\"m3\":88.96,\"a1\":11.584,\"ua1\":0.014,\"w1\":5.84417,\"uw1\":0.0002,\"c1\":363.229,\"uc1\":0.01,\"t1\":20.27,\"a2\":10.1763,\"ua2\":0.017,\"w2\":5.84585,\"uw2\":0.00028,\"c2\":363.354,\"uc2\":0.012,\"t2\":20.44,\"a3\":12.044,\"ua3\":0.018,\"w3\":5.845,\"uw3\":0.00026,\"c3\":363.183,\"uc3\":0.013,\"t3\":20.54,\"a4\":11.224,\"ua4\":0.016,\"w4\":5.84513,\"uw4\":0.00025,\"c4\":363.233,\"uc4\":0.011,\"t4\":20.49,\"alp\":0.333333,\"ualp\":0.000001,\"m\":88.9666666667,\"um\":0.0044095855,\"nm\":3.0,\"outm\":\"[]\",\"w\":5.8450375,\"uw\":0.0003443214,\"nw\":4.0,\"outw\":\"[]\",\"c\":363.24975,\"uc\":0.0365544685,\"nc\":4.0,\"outc\":\"[]\",\"t\":20.435,\"ut\":0.0595119036,\"nt\":4.0,\"outt\":\"[]\",\"F\":872.4071333333,\"uF\":0.358484053,\"r\":0.2254752232,\"ur\":0.1342349411},{\"m1\":108.61,\"m2\":108.61,\"m3\":108.61,\"a1\":11.542,\"ua1\":0.026,\"w1\":5.3278,\"uw1\":0.0003,\"c1\":303.5502,\"uc1\":0.019,\"t1\":22.49,\"a2\":8.424,\"ua2\":0.017,\"w2\":5.3282,\"uw2\":0.0003,\"c2\":303.581,\"uc2\":0.012,\"t2\":22.27,\"a3\":10.501,\"ua3\":0.022,\"w3\":5.3296,\"uw3\":0.0003,\"c3\":303.842,\"uc3\":0.016,\"t3\":22.55,\"a4\":9.959,\"ua4\":0.014,\"w4\":5.32822,\"uw4\":0.0002,\"c4\":303.445,\"uc4\":0.01,\"t4\":22.15,\"alp\":0.333333,\"ualp\":0.000001,\"m\":108.61,\"um\":0.0028867513,\"nm\":3.0,\"outm\":\"[]\",\"w\":5.328455,\"uw\":0.0003937334,\"nw\":4.0,\"outw\":\"[]\",\"c\":303.60455,\"uc\":0.0843342704,\"nc\":4.0,\"outc\":\"[]\",\"t\":22.365,\"ut\":0.0941187194,\"nt\":4.0,\"outt\":\"[]\",\"F\":1065.02966,\"uF\":0.4353612606,\"r\":0.035850533,\"ur\":0.1812705598}]'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet = ipysheet.from_dataframe(dfSonarCorrezione)\n",
    "\n",
    "display(sheet)\n",
    "dfSonar.head(4).to_json(orient=\"records\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8917049c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit completo:   chi²=9.03,  P=0.0289\n",
      "Temperatura :  chi²=5.062,  P=0.1673\n",
      "\n",
      "A:  3.2192 ± 0.0027  to  3.2369 ± 0.0035\n",
      "B:  2042.5203 ± 1.0326  to  2043.0827 ± 1.2689\n"
     ]
    }
   ],
   "source": [
    "AS2, BS2, uAS2, uBS2, covABS2, chiS2, PS2 = reg_lin(-dfSonarCorrezione[\"c\"], dfSonarCorrezione[\"F\"], dfSonarCorrezione[\"uc\"], dfSonarCorrezione[\"uF\"])\n",
    "\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": 26,
   "id": "e35a9456",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui sul dfSonar\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": 27,
   "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": [
    "# Fit\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 Correzione della temperatura)\")\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": 28,
   "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": [
    "# Grafico Residui\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)\")\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": "c9c20bd0",
   "metadata": {},
   "source": [
    "## Analisi dati sonar dinamica\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": 29,
   "id": "af6143df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Massa molla = 29.8933 ± 0.0044\n"
     ]
    }
   ],
   "source": [
    "# Calcolo della massa della molla per bene\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": 30,
   "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": [
    "#massa della molla equivalente\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": 31,
   "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": [
    "# w in 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": 32,
   "id": "455271a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a984db0b92847f6b999c2f27771fa72",
       "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"
    },
    {
     "data": {
      "text/plain": [
       "'[{\"m1\":49.25,\"m2\":49.24,\"m3\":49.25,\"a1\":9.7171,\"ua1\":0.016,\"w1\":7.6565,\"uw1\":0.0004,\"c1\":484.455,\"uc1\":0.011,\"t1\":15.62,\"a2\":8.911,\"ua2\":0.015,\"w2\":7.6569,\"uw2\":0.0004,\"c2\":484.516,\"uc2\":0.011,\"t2\":15.58,\"a3\":10.446,\"ua3\":0.027,\"w3\":7.6603,\"uw3\":0.0005,\"c3\":485.082,\"uc3\":0.019,\"t3\":15.76,\"a4\":8.377,\"ua4\":0.016,\"w4\":7.6582,\"uw4\":0.0004,\"c4\":484.752,\"uc4\":0.011,\"t4\":15.87,\"alp\":0.333333,\"ualp\":0.000001,\"m\":49.2466666667,\"um\":0.0044095855,\"nm\":3.0,\"outm\":\"[]\",\"w\":7.657975,\"uw\":0.000855741,\"nw\":4.0,\"outw\":\"[]\",\"c\":484.70125,\"uc\":0.1421568236,\"nc\":4.0,\"outc\":\"[]\",\"t\":15.7075,\"ut\":0.0672526332,\"nt\":4.0,\"outt\":\"[]\",\"F\":482.9128133333,\"uF\":0.2016766686,\"r\":-0.2364148992,\"ur\":0.1942155847,\"Meq\":16.8887249288,\"uMeq\":0.0013258035,\"Omega2\":58.6445811006,\"uOmega2\":0.0131064864},{\"m1\":69.28,\"m2\":69.28,\"m3\":69.27,\"a1\":9.86,\"ua1\":0.016,\"w1\":6.55968,\"uw1\":0.00029,\"c1\":423.352,\"uc1\":0.011,\"t1\":18.31,\"a2\":10.39,\"ua2\":0.012,\"w2\":6.55891,\"uw2\":0.00022,\"c2\":423.154,\"uc2\":0.009,\"t2\":18.27,\"a3\":10.491,\"ua3\":0.013,\"w3\":6.56002,\"uw3\":0.00024,\"c3\":423.697,\"uc3\":0.01,\"t3\":18.34,\"a4\":10.968,\"ua4\":0.019,\"w4\":6.56,\"uw4\":0.0003,\"c4\":423.465,\"uc4\":0.014,\"t4\":18.16,\"alp\":0.333333,\"ualp\":0.000001,\"m\":69.2766666667,\"um\":0.0044095855,\"nm\":3.0,\"outm\":\"[]\",\"w\":6.5596525,\"uw\":0.0002594685,\"nw\":4.0,\"outw\":\"[]\",\"c\":423.417,\"uc\":0.1133203424,\"nc\":4.0,\"outc\":\"[]\",\"t\":18.27,\"ut\":0.040620192,\"nt\":4.0,\"outt\":\"[]\",\"F\":679.3269933333,\"uF\":0.2804600444,\"r\":0.0351995054,\"ur\":0.1633888866,\"Meq\":12.619713577,\"uMeq\":0.0007402603,\"Omega2\":43.0290409208,\"uOmega2\":0.0034040467},{\"m1\":88.97,\"m2\":88.97,\"m3\":88.96,\"a1\":11.584,\"ua1\":0.014,\"w1\":5.84417,\"uw1\":0.0002,\"c1\":363.229,\"uc1\":0.01,\"t1\":20.27,\"a2\":10.1763,\"ua2\":0.017,\"w2\":5.84585,\"uw2\":0.00028,\"c2\":363.354,\"uc2\":0.012,\"t2\":20.44,\"a3\":12.044,\"ua3\":0.018,\"w3\":5.845,\"uw3\":0.00026,\"c3\":363.183,\"uc3\":0.013,\"t3\":20.54,\"a4\":11.224,\"ua4\":0.016,\"w4\":5.84513,\"uw4\":0.00025,\"c4\":363.233,\"uc4\":0.011,\"t4\":20.49,\"alp\":0.333333,\"ualp\":0.000001,\"m\":88.9666666667,\"um\":0.0044095855,\"nm\":3.0,\"outm\":\"[]\",\"w\":5.8450375,\"uw\":0.0003443214,\"nw\":4.0,\"outw\":\"[]\",\"c\":363.24975,\"uc\":0.0365544685,\"nc\":4.0,\"outc\":\"[]\",\"t\":20.435,\"ut\":0.0595119036,\"nt\":4.0,\"outt\":\"[]\",\"F\":872.4071333333,\"uF\":0.358484053,\"r\":0.2254752232,\"ur\":0.1342349411,\"Meq\":10.1080447747,\"uMeq\":0.0004749192,\"Omega2\":34.1644633764,\"uOmega2\":0.0040251434},{\"m1\":108.61,\"m2\":108.61,\"m3\":108.61,\"a1\":11.542,\"ua1\":0.026,\"w1\":5.3278,\"uw1\":0.0003,\"c1\":303.5502,\"uc1\":0.019,\"t1\":22.49,\"a2\":8.424,\"ua2\":0.017,\"w2\":5.3282,\"uw2\":0.0003,\"c2\":303.581,\"uc2\":0.012,\"t2\":22.27,\"a3\":10.501,\"ua3\":0.022,\"w3\":5.3296,\"uw3\":0.0003,\"c3\":303.842,\"uc3\":0.016,\"t3\":22.55,\"a4\":9.959,\"ua4\":0.014,\"w4\":5.32822,\"uw4\":0.0002,\"c4\":303.445,\"uc4\":0.01,\"t4\":22.15,\"alp\":0.333333,\"ualp\":0.000001,\"m\":108.61,\"um\":0.0028867513,\"nm\":3.0,\"outm\":\"[]\",\"w\":5.328455,\"uw\":0.0003937334,\"nw\":4.0,\"outw\":\"[]\",\"c\":303.60455,\"uc\":0.0843342704,\"nc\":4.0,\"outc\":\"[]\",\"t\":22.365,\"ut\":0.0941187194,\"nt\":4.0,\"outt\":\"[]\",\"F\":1065.02966,\"uF\":0.4353612606,\"r\":0.035850533,\"ur\":0.1812705598,\"Meq\":8.4335211413,\"uMeq\":0.000230411,\"Omega2\":28.392432687,\"uOmega2\":0.0041959815}]'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": "5dda09cd",
   "metadata": {},
   "source": [
    "## Regressione lineare Carpi dati del sonar Caso Dinamico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "02119d66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AD      = 3.5059 ± 0.0010\n",
      "BD      = -1.1685 ± 0.0099\n",
      "cov_ABD = -0.000009\n",
      "chi²    = 3294.6334\n",
      "P(chi², ∞)= 0.0000\n"
     ]
    }
   ],
   "source": [
    "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": 34,
   "id": "96f12fe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui sul dfSonar caso dinamico\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": 35,
   "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": [
    "# Fit\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": 36,
   "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": [
    "# Grafico Residui\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": "d35498c5",
   "metadata": {},
   "source": [
    "# Analisi dati Cronometro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "97921cfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wt = 40*pi/w\n",
      "w2t = 1600*pi**2/w**2\n",
      "sigma_w2t = 3200*pi**2*sigma_t/w**3\n",
      "\n",
      "Formula LaTeX:\n",
      "w2t = \\frac{1600 \\pi^{2}}{w^{2}}\n",
      "sigma_w2t = \\frac{3200 \\pi^{2} \\sigma_{t}}{w^{3}}\n"
     ]
    }
   ],
   "source": [
    "# t/20 to w\n",
    "# simboli\n",
    "t = sp.symbols('w', positive=True)\n",
    "sigma_t = sp.symbols('sigma_t', positive=True)\n",
    "\n",
    "# omega^2\n",
    "wt = (2 * sp.pi) / (t/20)\n",
    "w2t = wt**2\n",
    "\n",
    "# derivata parziale\n",
    "dw2t_dt = sp.diff(w2t, t)\n",
    "\n",
    "sigma_w2t = sp.sqrt((dw2t_dt * sigma_t)**2)\n",
    "\n",
    "print(\"wt =\", wt)\n",
    "print(\"w2t =\", w2t)\n",
    "print(\"sigma_w2t =\", sigma_w2t.simplify())\n",
    "print(\"\\nFormula LaTeX:\")\n",
    "print(f\"w2t = {sp.latex(w2t)}\")\n",
    "print(f\"sigma_w2t = {sp.latex(sigma_w2t.simplify())}\")\n",
    "\n",
    "# funzioni numeriche\n",
    "w2t_fn  = sp.lambdify((t), w2t, 'numpy')\n",
    "uw2t_fn = sp.lambdify((t, sigma_t), sigma_w2t, 'numpy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0b3f8a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e929528924a64990a32d4a2965d3f44f",
       "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"
    },
    {
     "data": {
      "text/plain": [
       "'[{\"m1\":49.25,\"m2\":49.24,\"m3\":49.25,\"a1\":9.7171,\"ua1\":0.016,\"w1\":7.6565,\"uw1\":0.0004,\"c1\":484.455,\"uc1\":0.011,\"t1\":15.62,\"a2\":8.911,\"ua2\":0.015,\"w2\":7.6569,\"uw2\":0.0004,\"c2\":484.516,\"uc2\":0.011,\"t2\":15.58,\"a3\":10.446,\"ua3\":0.027,\"w3\":7.6603,\"uw3\":0.0005,\"c3\":485.082,\"uc3\":0.019,\"t3\":15.76,\"a4\":8.377,\"ua4\":0.016,\"w4\":7.6582,\"uw4\":0.0004,\"c4\":484.752,\"uc4\":0.011,\"t4\":15.87,\"alp\":0.333333,\"ualp\":0.000001,\"m\":49.2466666667,\"um\":0.0044095855,\"nm\":3.0,\"outm\":\"[]\",\"w\":7.657975,\"uw\":0.000855741,\"nw\":4.0,\"outw\":\"[]\",\"c\":484.70125,\"uc\":0.1421568236,\"nc\":4.0,\"outc\":\"[]\",\"t\":15.7075,\"ut\":0.0672526332,\"nt\":4.0,\"outt\":\"[]\",\"F\":482.9128133333,\"uF\":0.2016766686,\"r\":-0.2364148992,\"ur\":0.1942155847,\"Meq\":16.8887249288,\"uMeq\":0.0013258035,\"Omega2\":58.6445811006,\"uOmega2\":0.0131064864,\"R\":-0.1718035617,\"uR\":0.0044735623,\"Omega2t\":64.0037752139,\"uOmega2t\":0.5480722477},{\"m1\":69.28,\"m2\":69.28,\"m3\":69.27,\"a1\":9.86,\"ua1\":0.016,\"w1\":6.55968,\"uw1\":0.00029,\"c1\":423.352,\"uc1\":0.011,\"t1\":18.31,\"a2\":10.39,\"ua2\":0.012,\"w2\":6.55891,\"uw2\":0.00022,\"c2\":423.154,\"uc2\":0.009,\"t2\":18.27,\"a3\":10.491,\"ua3\":0.013,\"w3\":6.56002,\"uw3\":0.00024,\"c3\":423.697,\"uc3\":0.01,\"t3\":18.34,\"a4\":10.968,\"ua4\":0.019,\"w4\":6.56,\"uw4\":0.0003,\"c4\":423.465,\"uc4\":0.014,\"t4\":18.16,\"alp\":0.333333,\"ualp\":0.000001,\"m\":69.2766666667,\"um\":0.0044095855,\"nm\":3.0,\"outm\":\"[]\",\"w\":6.5596525,\"uw\":0.0002594685,\"nw\":4.0,\"outw\":\"[]\",\"c\":423.417,\"uc\":0.1133203424,\"nc\":4.0,\"outc\":\"[]\",\"t\":18.27,\"ut\":0.040620192,\"nt\":4.0,\"outt\":\"[]\",\"F\":679.3269933333,\"uF\":0.2804600444,\"r\":0.0351995054,\"ur\":0.1633888866,\"Meq\":12.619713577,\"uMeq\":0.0007402603,\"Omega2\":43.0290409208,\"uOmega2\":0.0034040467,\"R\":0.013214804,\"uR\":0.0015581397,\"Omega2t\":47.3088763774,\"uOmega2t\":0.2103662444},{\"m1\":88.97,\"m2\":88.97,\"m3\":88.96,\"a1\":11.584,\"ua1\":0.014,\"w1\":5.84417,\"uw1\":0.0002,\"c1\":363.229,\"uc1\":0.01,\"t1\":20.27,\"a2\":10.1763,\"ua2\":0.017,\"w2\":5.84585,\"uw2\":0.00028,\"c2\":363.354,\"uc2\":0.012,\"t2\":20.44,\"a3\":12.044,\"ua3\":0.018,\"w3\":5.845,\"uw3\":0.00026,\"c3\":363.183,\"uc3\":0.013,\"t3\":20.54,\"a4\":11.224,\"ua4\":0.016,\"w4\":5.84513,\"uw4\":0.00025,\"c4\":363.233,\"uc4\":0.011,\"t4\":20.49,\"alp\":0.333333,\"ualp\":0.000001,\"m\":88.9666666667,\"um\":0.0044095855,\"nm\":3.0,\"outm\":\"[]\",\"w\":5.8450375,\"uw\":0.0003443214,\"nw\":4.0,\"outw\":\"[]\",\"c\":363.24975,\"uc\":0.0365544685,\"nc\":4.0,\"outc\":\"[]\",\"t\":20.435,\"ut\":0.0595119036,\"nt\":4.0,\"outt\":\"[]\",\"F\":872.4071333333,\"uF\":0.358484053,\"r\":0.2254752232,\"ur\":0.1342349411,\"Meq\":10.1080447747,\"uMeq\":0.0004749192,\"Omega2\":34.1644633764,\"uOmega2\":0.0040251434,\"R\":0.0299946727,\"uR\":0.0013924172,\"Omega2t\":37.8155519739,\"uOmega2t\":0.2202569594},{\"m1\":108.61,\"m2\":108.61,\"m3\":108.61,\"a1\":11.542,\"ua1\":0.026,\"w1\":5.3278,\"uw1\":0.0003,\"c1\":303.5502,\"uc1\":0.019,\"t1\":22.49,\"a2\":8.424,\"ua2\":0.017,\"w2\":5.3282,\"uw2\":0.0003,\"c2\":303.581,\"uc2\":0.012,\"t2\":22.27,\"a3\":10.501,\"ua3\":0.022,\"w3\":5.3296,\"uw3\":0.0003,\"c3\":303.842,\"uc3\":0.016,\"t3\":22.55,\"a4\":9.959,\"ua4\":0.014,\"w4\":5.32822,\"uw4\":0.0002,\"c4\":303.445,\"uc4\":0.01,\"t4\":22.15,\"alp\":0.333333,\"ualp\":0.000001,\"m\":108.61,\"um\":0.0028867513,\"nm\":3.0,\"outm\":\"[]\",\"w\":5.328455,\"uw\":0.0003937334,\"nw\":4.0,\"outw\":\"[]\",\"c\":303.60455,\"uc\":0.0843342704,\"nc\":4.0,\"outc\":\"[]\",\"t\":22.365,\"ut\":0.0941187194,\"nt\":4.0,\"outt\":\"[]\",\"F\":1065.02966,\"uF\":0.4353612606,\"r\":0.035850533,\"ur\":0.1812705598,\"Meq\":8.4335211413,\"uMeq\":0.000230411,\"Omega2\":28.392432687,\"uOmega2\":0.0041959815,\"R\":0.0018307505,\"uR\":0.0014336937,\"Omega2t\":31.5705336508,\"uOmega2t\":0.2657168072}]'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfSonar['Omega2t'] = w2t_fn(dfSonar['t'])\n",
    "dfSonar['uOmega2t']= uw2t_fn(dfSonar['t'], dfSonar['ut'])\n",
    "\n",
    "sheet = ipysheet.from_dataframe(dfSonar)\n",
    "display(sheet)\n",
    "dfSonar.head(4).to_json(orient=\"records\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0421b1cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "ACr      = 3.8380 ± 0.0526\n",
      "BCr      = -1.0200 ± 0.5757\n",
      "cov_ABCr = -0.029589\n",
      "chi²    = 2.0359\n",
      "P(chi², ∞)= 0.5650\n"
     ]
    }
   ],
   "source": [
    "ACr, BCr, uACr, uBCr, covABCr, chiCr, PCr = reg_lin( dfSonar[\"Meq\"], dfSonar[\"Omega2t\"], dfSonar[\"uMeq\"], dfSonar[\"uOmega2t\"])\n",
    "\n",
    "print(\"Ax + B : \")\n",
    "print(f\"ACr      = {ACr:.4f} ± {uACr:.4f}\")\n",
    "print(f\"BCr      = {BCr:.4f} ± {uBCr:.4f}\")\n",
    "print(f\"cov_ABCr = {covABCr:.6f}\")\n",
    "print(f\"chi²    = {chiCr:.4f}\")\n",
    "print(f\"P(chi², ∞)= {PCr:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2fb7140a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui sul dfSonar caso dinamico\n",
    "dfSonar[\"Rc\"] = r_fn(\n",
    "  dfSonar[\"Meq\"],\n",
    "  dfSonar[\"Omega2t\"],\n",
    "  ACr,\n",
    "  BCr\n",
    ")\n",
    "\n",
    "dfSonar[\"uRc\"] = sigma_r_fn(\n",
    "  dfSonar[\"Meq\"], dfSonar[\"Omega2t\"],\n",
    "  ACr, BCr,\n",
    "  dfSonar[\"uMeq\"], dfSonar[\"uOmega2t\"],\n",
    "  uACr, uBCr, covABCr\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "739c2c88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit\n",
    "SCALA = 10\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "x_data =  dfSonar[\"Meq\"]\n",
    "y_data =  dfSonar[\"Omega2t\"]\n",
    "\n",
    "x_fit = np.linspace(0, x_data.max(), 300)\n",
    "y_fit = ACr * x_fit + BCr\n",
    "\n",
    "ax.errorbar(\n",
    "    x_data, y_data,\n",
    "    xerr=SCALA * dfSonar[\"uMeq\"], yerr=SCALA * dfSonar[\"uOmega2t\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=f\"Dati (barre errore x{SCALA})\"\n",
    ")\n",
    "ax.plot(\n",
    "    x_fit, y_fit,\n",
    "    color='red', linewidth=1.5,\n",
    "    label=f\"Fit: $A={ACr:.3f}\\\\pm{uACr:.3f}$\\n\"\n",
    "          f\"     $B={BCr:.2f}\\\\pm{uBCr:.2f}$\\n\"\n",
    ")\n",
    "\n",
    "\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(r\"Meq  [Kg$^{-1}$]\")\n",
    "ax.set_ylabel(r\"$\\omega^2$  [s$^{-2}$]\")\n",
    "ax.set_title(\"Fit lineare — Molla 1 (cronometro)\")\n",
    "ax.legend(fontsize=9)\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b44b497b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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h1VdfxQ033ICZM2di5MiRmDBhAi655JJKj+x14MABABXvJz169MD333+PnJwcNG/e3HH5sT+oAf/8MJSeno6goKBqf00VKSwsRFpaWqXXV8XX19fpB9uq9O/fH927d8f777+PkJAQhIeHOxaOxztw4AC6detWrmHZ22DKGlbXtm3bcP/992PFihWOH6TLHPt5mRNJTEzEeeedh+DgYHz88cfV/kC+9f9vwTvW3r17ERUVBR+fE/+Icfw+XBf7UNm/reP/LYWHhyMkJKRc8+Mfr+zfRPv27Su8PD09HQD3Oen4LsDRNmWLmWMdu+8ceySvsu9NdZ+/RBi0sBAx0MCBAzFgwAAAwPjx4zF06FBMmjQJO3fuREBAgOM3yHfddVe532qVKfs/0eHDh2Pv3r34/PPP8cMPP+DVV1/F008/jUWLFuGGG25wedbK/k+rOh8edUVNGlTHzz//jAsvvBDDhw/Hiy++iIiICDRp0gRvvPEG3n///Wo9xrG/PT52zt69e2PBggUV3uf4H1Zq4vjtlZaWwmaz4dtvv63wB8NjF2tPPfUUpkyZ4tgvpk+fjnnz5uG3337DSSedhKZNm+Knn37CypUr8fXXX+O7777DRx99hLPOOgs//PAD7UhQlT1O2Q9FNfmajvfrr7/W+jCoI0aMcDqh4olMmjQJL730EgIDA3HZZZfVy2GVMzIyMGLECAQFBeGhhx5Cly5d4O/vj40bN+Kee+6p9itNmZmZGDt2LDIyMvDzzz+jbdu21bpfy5YtHT9U11ZF/2Zq6kT7UJnq/oBd2eNVZ18FOM9JjC5l35uwsDCXH0ukurSwEDGct7c35s2bhzPPPBPPP/88Zs6cic6dOwM4+tvaio7IcrzQ0FBce+21uPbaa5GdnY3hw4djzpw5lS4sOnbsiNLSUsdvHsvs3Lmz3G1btGhR4RGbavqb12O3vXz5cmRnZzv90Hj8tmva4EQ++eQT+Pv74/vvv3d6deiNN95w6XG7dOmCP/74AyNHjqzyB5uy5vv27XP67fyePXtqtC3LshAZGYmTTz75hLfv3bs3evfujfvvvx+//vorhgwZgkWLFuGRRx4BAHh5eWHkyJEYOXIkFixYgEcffRT33XcfVq5cWWHzjh07Aqh4P/nrr78QFhbm9JvmuviajtW3b1+no5/VREVv8anKpEmTMHv2bCQkJOCdd96p9HYdO3bEli1bUFpa6rT4KHvbUlnD6li1ahWOHDmCpUuXYvjw4Y7L9+3bV+3HyM/PxwUXXIBdu3Zh2bJl6NmzZ7Xv2717d7z33nvIzMx0/PYeOPo9W7duHYqKimp8eOm62IfK/m3t3r3b6QPSSUlJyMjIqFHzqtTkOak2ryJ07Nix0i5l1x9r37598PLyqvG/GxFX6DMWIg1ATEwMBg4ciIULFyI/Px+tW7dGTEwMXn75ZSQkJJS7fUpKiuO/jxw54nRdQEAAunbtWu4wi8caO3YsAODZZ591unzhwoXlbtulSxdkZmZiy5YtjssSEhLKHWmlus4991wUFxfjpZdeclxWUlKC5557zul2NWlQHd7e3rDZbE6vtOzfv9/pqE21cemllyIuLg6vvPJKuevy8vIc558o+w3niy++6HSb47/uqkyYMAHe3t6YO3duud/WWpbl2BeysrJQXFzsdH3v3r3h5eXl2C8qegtRv379AKDSfSciIgL9+vXDW2+95bTY3Lp1K3744Qece+651f5aavo1VaRFixYYNWpUrf5ER0fXaM4uXbpg4cKFmDdvHgYOHFjp7c4991wkJibio48+clxWXFyM5557DgEBARgxYkS1t1n2G/RjuxQWFpbbhypTUlKCyy67DGvXrsWSJUucPldQHYMHD4ZlWYiNjXW6/OKLL0Zqaiqef/75cvep6K1Tx6qLfajsPsc/f5W9isg62l1NnpPKFkeVHUa7Iueeey7Wr1+PtWvXOi7LycnB4sWL0alTp3KLwtjYWJxyyilOiz6RuqZXLEQaiP/85z+YOHEi3nzzTdx000144YUXMHToUPTu3Rs33ngjOnfujKSkJKxduxaHDx/GH3/8AeDoBxFjYmIQHR2N0NBQbNiwAR9//DGmTZtW6bb69euHK664Ai+++CIyMzNxxhlnYPny5RX+9vzyyy/HPffcg4suugjTp09Hbm4uXnrpJZx88snV/tDzsS644AIMGTIEM2fOxP79+9GzZ08sXbq0wveLV7dBdZx33nlYsGABxowZg0mTJiE5ORkvvPACunbt6rRoqqmrr74a//vf/3DTTTdh5cqVGDJkCEpKSvDXX3/hf//7n+NY9dHR0bj44ouxcOFCHDlyxHG42V27dgGo3m84u3TpgkceeQT33nsv9u/fj/HjxyMwMBD79u3Dp59+iqlTp+Kuu+7CihUrMG3aNEycOBEnn3wyiouL8c4778Db2xsXX3wxgKNnf//pp59w3nnnoWPHjkhOTsaLL76Ik046yenDo8d74oknMHbsWAwePBjXX3+941ChwcHBTufiqK7qfk0mqM6hYqdOnYqXX34ZU6ZMQWxsLDp16oSPP/4Ya9aswcKFCxEYGFjt7Z1xxhlo0aIFJk+ejOnTp8Nms+Gdd9454Q/vZe6880588cUXuOCCC5CWloZ3333X6fqrrrqqyvsPHToULVu2xLJly5w+U3LNNdfg7bffxowZM7B+/XoMGzYMOTk5WLZsGW655RaMGzeuysdl70N9+/bF5MmTsXjxYsfbx9avX4+33noL48ePp541vLrPSf369YO3tzcee+wxZGZmws/PD2eddRZat25d6WPPnDkTH3zwAcaOHYvp06cjNDQUb731Fvbt24dPPvnE6RWwoqIirF69utzBIETqXP0ehEpEqlJ2OMnff/+93HUlJSVWly5drC5duljFxcWWZVnW3r17rWuuucYKDw+3mjRpYrVr1846//zzrY8//thxv0ceecQaOHCgFRISYjVt2tTq3r279d///tcqLCx03KaiQ8Pm5eVZ06dPt1q2bGk1b97cuuCCC6xDhw6VOwSmZVnWDz/8YPXq1cvy9fW1oqKirHfffbfWh5u1LMs6cuSIdfXVV1tBQUFWcHCwdfXVVzsOkXrsoU2r26C6h5t97bXXrG7dull+fn5W9+7drTfeeKPCr6MiI0aMsE455ZQKryssLLQee+wx65RTTrH8/PysFi1aWNHR0dbcuXOtzMxMx+1ycnKsW2+91QoNDbUCAgKs8ePHWzt37rQAWPPnz3fc7thDnVbkk08+sYYOHWo1b97cat68udW9e3fr1ltvtXbu3GlZlmX9/fff1nXXXWd16dLF8vf3t0JDQ60zzzzTWrZsmeMxli9fbo0bN85q27at5evra7Vt29a64oorrF27djluU9HhZi3LspYtW2YNGTLEatq0qRUUFGRdcMEF1vbt251uU9nXUNEhVavzNdW3E30PyuC4w81almUlJSVZ1157rRUWFmb5+vpavXv3Ltew7L4nOtzsmjVrrNNPP91q2rSp1bZtW+vuu+92HB61okOZHqvsEMmV/amO6dOnOx1qt0xubq513333WZGRkVaTJk2s8PBw65JLLrH27t1rWdY/+84TTzxR4eOy96GioiJr7ty5jnnat29v3XvvveUOyduxY8cKD89d0fexsq+hOs9JlmVZr7zyitW5c2fL29vb6ftV2Qxlj33JJZdYISEhlr+/vzVw4EDrq6++Kne7b7/91gJg7d69u8LHEakrNsuq5q82RESk3m3evBn9+/fHu+++iyuvvNLd44g4+fvvv9G9e3d8++23GDlypLvHkf83fvx42Gy2Wr8lVaS2tLAQETFEXl5euaPBTJkyBe+88w7279/v0hGkROrKzTffjD179tT6g/LCtWPHDvTu3RubN292OvysSH3QwkJExBBz585FbGwszjzzTPj4+ODbb7/Ft99+63hfvoiIiMm0sBARMcSPP/6IuXPnYvv27cjOzkaHDh1w9dVX47777qvWycZERETcSQsLERERERFxmc5jISIiIiIiLtPCQkREREREXKaFBYllWcjKyqr2iYlERERERDyJFhYkdrsdwcHBsNvt7h5FRERERKTeaWFhmLi4OHeP4BHUkUMdOdSRQx051JFDHTnUkcOUjjoqFElWVhaCg4ORmZmJoKAgd48jIiIiIlKv9IqFYUxZcTZ06sihjhzqyKGOHOrIoY4c6shhSke9YkHCesXCsizYbDbiZI2TOnKoI4c6cqgjhzpyqCOHOnKY0lGvWBgmPj7e3SN4BHXkUEcOdeRQRw515FBHDnXkMKWjXrEgYb1iUVBQAD8/P+JkjZM6cqgjhzpyqCOHOnKoI0dBQQF8fHxQVFTk7lEatMLCQvj6+tbqvk2aNIG3tzdlDh/KowhNVlYWWrVq5e4xGjx15FBHDnXkUEcOdeRQR9dZloVDhw5pUUFQUlLi0uIgJCQE4eHhLr+dSgsLw+i3HxzqyKGOHOrIoY4c6sihjq5LTExEQUEBwsPD0axZMyM+I9BQ1XZhYVkWcnNzkZycDACIiIhwaQ4tLERERESkXpWUlCAjIwOtW7dGy5Yt3T1Og+fKKxZNmzYFACQnJ6N169YuvfKhD28bprCw0N0jeAR15FBHDnXkUEcOdeRQR9eUvf1Jr/xwlJaWunT/Zs2aAYDLb0vTKxaGad68ubtH8AjqyKGOHOrIoY4c6sihjhw+PrX/UdRutyM7O7vS6wMCAhAYGFjrx29IXP3wNettaFpYGCY9Pd3xkpTUnjpyqCOHOnKoI4c6cqgjR3Fxca3vGxsbi9WrV1d6/YgRIxATE1Prx29IiouLa31UKCYtLAwTHh7u7hE8gjpyqCOHOnKoI4c6cqgjR5MmTWp93+joaERFRQEAUlNTsXTpUkyYMAFhYWEAjr5i0dDs378fkZGR2LRpE/r161fhbVatWoUzzzwT6enpCAkJAXDijlOmTEFGRgY+++wz7sDH0WcsDJOQkODuETyCOnKoI4c6cqgjhzpyqCOHK+/pDwwMREREBCIiIhyLibCwMMdldfU2qClTpsBms8Fms6FJkyaIjIzE3Xffjfz8fJcfu3379khISECvXr1qdL8TdXzmmWfw5ptvujBZ9WhhYZh27dq5ewSPoI4c6sihjhzqyKGOHOrIUZO379jtdqxatQp2u93p8n2pOXhxTRxWFUbixTVx2Jeac8L7uGrMmDFISEjA33//jaeffhovv/wyHnzwQZcf19vbG+Hh4TX+7MmJOgYHBzte3ahLWlgYJi4uzt0jeAR15FBHDnXkUEcOdeRQR46aHF0rOzsbq1evdvrA9v82HMLIp1bh/dgk7C8JxfuxSRj51Cos2XCo0vsw+Pn5ITw8HO3bt8f48eMxatQo/PjjjwCOHqFp3rx5iIyMRNOmTdG3b198/PHHjvump6fjyiuvRKtWrdC0aVN069YNb7zxBoCjb4Wy2WzYvHmz4/bffPMNTj75ZDRt2hRnnnkm9u/f7zTLnDlz0LdvX6fLFi5ciE6dOjn+PmXKFIwfP57aoCL6jIVhGGfx1FESOB1FHVnUkUMdOdSRQx05XDkq1L7UHMz8ZAtKrbJLbCj5//++55MtOK1TKOrjYLZbt27Fr7/+io4dOwIA5s2bh3fffReLFi1Ct27d8NNPP+Gqq65Cq1atMGLECDzwwAPYvn07vv32W4SFhWHPnj3Iy8ur8LEPHTqECRMm4NZbb8XUqVOxYcMG3HnnneVuZ8rJBbWwMEzZyWJcoaMkcDqKOrKoI4c6cqgjhzpylJSU1Pg+qampAIDX18Shqh+nX1u1A5dG1c3S4quvvkJAQACKi4tRUFAALy8vPP/88ygoKMCjjz6KZcuWYfDgwQCAzp0745dffsHLL7+MESNG4ODBg+jfvz8GDBgAAE6vLBzvpZdeQpcuXfDUU08BAKKiovDnn3/isccec7qdZVkV3b3eaWFhmLITlLjCE4+SUFOMjqKOLOrIoY4c6sihjhxeXjV/V/7SpUsBAL8URqLUCgUqWF5YloVfNm2Hz7Z9ro5YoTPPPBMvvfQScnJy8PTTT8PHxwcXX3wxtm3bhtzcXJx99tlOty8sLET//v0BADfffDMuvvhibNy4Eeeccw7Gjx+PM844o8Lt7NixA4MGDXK6rGzBciy9YiEVcuV4zmUCAwPLvdWp7CgJjQWjo6gjizpyqCOHOnKoI0dtftNe9svS4jVxOBib5Hj707FsNhuG9u+JCVH9HQsRpubNm6Nr164AgNdffx19+/bFa6+95jia09dff13uA/5lZxkfO3YsDhw4gG+++QY//vgjRo4ciVtvvRVPPvlkrWbx8vIqd+ZtV8+gXVv68LZhXD0luxyljhzqyKGOHOrIoY4c6ug+Zb8svS6mB6pallwf08Pxbo265OXlhVmzZuH+++9Hz5494efnh4MHD6Jr165Of9q3b++4T6tWrTB58mS8++67WLhwIRYvXlzhY/fo0QPr1693uuy3335z+nurVq2QlJTktEg79sPf9UkLC8P4+/u7ewSPoI4c6sihjhzqyKGOHOrI4cpbeCLDmuOxi/vAywZ42wAbLHjZAC8b8NjFfdAprDlx0qpNnDgR3t7eePnll3HXXXfhjjvuwFtvvYW9e/di48aNeO655/DWW28BAGbPno3PP/8ce/bswbZt2/DVV1+hR48eFT7uTTfdhN27d+M///kPdu7ciffff7/c+ShiYmKQkpKCxx9/HHv37sULL7yAb7/9tq6/5AppYWGYrKwsd4/gEdSRQx051JFDHTnUkUMdOWryyk9AQABGjBjh9FnRiQPaY8WdMZgU3QadvNNwZXQbrLgzBhMHtK/0PnXBx8cH06ZNw+OPP457770XDzzwAObNm4cePXpgzJgx+PrrrxEZGQng6Dkn7r33XvTp0wfDhw+Ht7c3Pvzwwwoft0OHDvjkk0/w2WefoW/fvli0aBEeffRRp9v06NEDzz33HF544QX07dsX69evx1133VWnX29lbJYpHyNv4LKyshAcHIzMzEwEBQXV+nGKi4tdOvTa8RISErB48WJMnTq10X3GgtmxsVJHDnXkUEcOdeRQR9fk5+dj37596NSpE5o2bery4zXWn3fKWJbl0qs/Zd+PyMhIl16N0ysWhklKSnL3CB5BHTnUkUMdOdSRQx051JHDlQ8Z2+12JCQkICEhwXEI2tTUVMdl7LNtm8xdH9Y+npbahjn+CAJSO+rIoY4c6sihjhzqyKGOHL6+vrW+b0Xn7Tr2CFCN4bxdZVzpyKSFhWHi4uL0ZEWgjhzqyKGOHOrIoY4c6shRWFhY67feHHveroo0hvN2lSksLDRicaGFhWHatGnj7hE8gjpyqCOHOnKoI4c6cqgjR5MmTWp934rO29VYudKRSZ+xMEzZewTFNerIoY4c6sihjhzqyKGOHDrRIIcpHbWwMIwrR5SSf6gjhzpyqCOHOnKoI4c6ikm8vFz7kZ51wke9Fcow+fn5aNasmbvHaPDUkUMdOdSRQx051JFDHV3j6+sLLy8vJCQkoLS0FL6+vi4dLrWxq+3hjy3LQmFhIVJSUuDl5eXy5zS0sDCM/lFxqCOHOnKoI4c6cqgjhzq6xsvLC5GRkdi/fz/i4+PdPU6DV1pa6tKrFs2aNUOHDh1cfuVDCwvDmPLhm4ZOHTnUkUMdOdSRQx051NF1vr6+iIiIgL+/P0pKStw9ToOWk5OD5s2b1+q+3t7e8PHxoSyWtbAwTE5OTqM6PFpdUUcOdeRQRw515FBHDnXkyM3NRWBgoBZqLsrMzHTpjNksHvvh7RdeeAGdOnWCv78/Bg0ahPXr11d621deeQXDhg1DixYt0KJFC4waNarK29el0NBQt2zX06gjhzpyqCOHOnKoI4c6cqgjhykdPXJh8dFHH2HGjBl48MEHsXHjRvTt2xejR49GcnJyhbdftWoVrrjiCqxcuRJr165F+/btcc455yAuLq6eJ0elM0rNqCOHOnKoI4c6cqgjhzpyqCOHKR1tlmVZ7h6CbdCgQTjttNPw/PPPAzj6gZb27dvj3//+N2bOnHnC+5eUlKBFixZ4/vnncc0111Rrm1lZWQgODkZmZqZRh6BLSEjA4sWLMXXqVERERLh7HBERERHxUB73ikVhYSFiY2MxatQox2VeXl4YNWoU1q5dW63HyM3NRVFRUZUvKxUUFCArK8vpD4M7XiXxROrIoY4c6sihjhzqyKGOHOrIYUpHj1tYpKamoqSkBG3atHG6vE2bNkhMTKzWY9xzzz1o27at0+LkePPmzUNwcLDjT/v27QEcPa51fHw8SktLHd/kuLg4FBYWIjk5GdnZ2cjIyEBaWhry8vKQmJiI4uJix21LS0tRVFSExMRE5ObmIi0tDenp6cjJyUFSUhKKioqcHrekpATx8fHIy8vDkSNHkJmZCbvdjpSUFBQUFDheGktOToZlWYiLi0NBQQFSUlIcC6LU1FTk5eVVe+7c3Nxyc8fFxaG4uLjc3NnZ2RXOXVpaWuXccXFxjnmPn9tutyMzMxNHjhypdO6wsDAkJSUhOzsb6enpNZo7IyMD2dnZSE5ORmFhYaVzp6amOhpWNHfZfaqau6SkxOm2RUVFSEpKQk5OTrm5j29YNndeXl61587Pz69w7vj4+Arn9vHxKTd3QkLCCec+0T6bkJDgNLfdbq/R3KmpqSf8t2a3253+rTHnPnafTU5Odnzvy25rWRbi4+Md3/uAgIBqz32i54iyuVnPEcfPbfJzREBAAO05oux73xifI3x8fGjPEWX/1hrjc0RYWBjtOaImc3vac0TZidlM+zmioT1HRERE1OnPEdXlcW+Fio+PR7t27fDrr79i8ODBjsvvvvturF69GuvWravy/vPnz8fjjz+OVatWoU+fPpXerqCgAAUFBY6/Z2VloX379i6/FSo+Ph5t27at9f2P11jfCsXu2FipI4c6cqgjhzpyqCOHOnKY0tHjDjcbFhYGb29vJCUlOV2elJSE8PDwKu/75JNPYv78+Vi2bFmViwoA8PPzg5+fn8vzHq9Fixb0x2yM1JFDHTnUkUMdOdSRQx051JHDlI4e91YoX19fREdHY/ny5Y7LSktLsXz5cqdXMI73+OOP4+GHH8Z3332HAQMG1MeoFcrNzXXbtj2JOnKoI4c6cqgjhzpyqCOHOnKY0tHjXrEAgBkzZmDy5MkYMGAABg4ciIULFyInJwfXXnstAOCaa65Bu3btMG/ePADAY489htmzZ+P9999Hp06dHJ/FCAgIqPeT3/j4eOS3pN6pI4c6cqgjhzpyqCOHOnKoI4cpHc2Yguyyyy5DSkoKZs+ejcTERPTr1w/fffed4wPdBw8ehJfXPy/WvPTSSygsLMQll1zi9DgPPvgg5syZU5+jO80ltaeOHOrIoY4c6sihjhzqyKGOHKZ09MiFBQBMmzYN06ZNq/C6VatWOf19//79dT9QNeXn5yMwMNDdYzR46sihjhzqyKGOHOrIoY4c6shhSkczljfiYNLJ9RoydeRQRw515FBHDnXkUEcOdeQwpaMWFoZJTU119wgeQR051JFDHTnUkUMdOdSRQx05TOmohYVhTDgGsSdQRw515FBHDnXkUEcOdeRQRw5TOmphYZj4+Hh3j+AR1JFDHTnUkUMdOdSRQx051JHDlI5aWBjGlBVnQ6eOHOrIoY4c6sihjhzqyKGOHKZ01MLCMKasOBs6deRQRw515FBHDnXkUEcOdeQwpaMWFoYJCwtz9wgeQR051JFDHTnUkUMdOdSRQx05TOmohYVhsrKy3D2CR1BHDnXkUEcOdeRQRw515FBHDlM6amFhGH9/f3eP4BHUkUMdOdSRQx051JFDHTnUkcOUjlpYGKa0tNTdI3gEdeRQRw515FBHDnXkUEcOdeQwpaMWFoYpLi529wgeQR051JFDHTnUkUMdOdSRQx05TOmohYVhmjVr5u4RPII6cqgjhzpyqCOHOnKoI4c6cpjSUQsLw6Snp7t7BI+gjhzqyKGOHOrIoY4c6sihjhymdNTCwjDh4eHuHsEjqCOHOnKoI4c6cqgjhzpyqCOHKR21sDBMQkKCu0fwCOrIoY4c6sihjhzqyKGOHOrIYUpHLSwM065dO3eP4BHUkUMdOdSRQx051JFDHTnUkcOUjlpYGCYuLs7dI3gEdeRQRw515FBHDnXkUEcOdeQwpaMWFoZp3bq1u0fwCOrIoY4c6sihjhzqyKGOHOrIYUpHLSwMk5aW5u4RPII6cqgjhzpyqCOHOnKoI4c6cpjSUQsLwzRv3tzdI3gEdeRQRw515FBHDnXkUEcOdeQwpaMWFoYpKipy9wgeQR051JFDHTnUkUMdOdSRQx05TOmohYVhLMty9wgeQR051JFDHTnUkUMdOdSRQx05TOmohYVh/P393T2CR1BHDnXkUEcOdeRQRw515FBHDlM6amFhmKysLHeP4BHUkUMdOdSRQx051JFDHTnUkcOUjlpYGCYsLMzdI3gEdeRQRw515FBHDnXkUEcOdeQwpaMWFoZJSkpy9wgeQR051JFDHTnUkUMdOdSRQx05TOmohYVhTDkle0OnjhzqyKGOHOrIoY4c6sihjhymdNTCwjCmnJK9oVNHDnXkUEcOdeRQRw515FBHDlM6amFhmDZt2rh7BI+gjhzqyKGOHOrIoY4c6sihjhymdNTCwjCpqanuHsEjqCOHOnKoI4c6cqgjhzpyqCOHKR21sDBMUFCQu0fwCOrIoY4c6sihjhzqyKGOHOrIYUpHLSwMk5+f7+4RPII6cqgjhzpyqCOHOnKoI4c6cpjSUQsLw3h56VvCoI4c6sihjhzqyKGOHOrIoY4cpnQ0Ywpx8PHxcfcIHkEdOdSRQx051JFDHTnUkUMdOUzpqIWFYXJzc909gkdQRw515FBHDnXkUEcOdeRQRw5TOmphYZiQkBB3j+AR1JFDHTnUkUMdOdSRQx051JHDlI5aWBgmJSXF3SN4BHXkUEcOdeRQRw515FBHDnXkMKWjFhaGMeWU7A2dOnKoI4c6cqgjhzpyqCOHOnKY0lELC8OYckr2hk4dOdSRQx051JFDHTnUkUMdOUzpqIWFYSIiItw9gkdQRw515FBHDnXkUEcOdeRQRw5TOmphYZjExER3j+AR1JFDHTnUkUMdOdSRQx051JHDlI5aWBimRYsW7h7BI6gjhzpyqCOHOnKoI4c6cqgjhykdtbAwTE5OjrtH8AjqyKGOHOrIoY4c6sihjhzqyGFKRy0sDOPr6+vuETyCOnKoI4c6cqgjhzpyqCOHOnKY0lELCxERERERcZkWFoYpKChw9wgeQR051JFDHTnUkUMdOdSRQx05TOmohYVhgoKC3D2CR1BHDnXkUEcOdeRQRw515FBHDlM6amFhmNTUVHeP4BHUkUMdOdSRQx051JFDHTnUkcOUjlpYGKZt27buHsEjqCOHOnKoI4c6cqgjhzpyqCOHKR21sDBMfHy8u0fwCOrIoY4c6sihjhzqyKGOHOrIYUpHLSwM065dO3eP4BHUkUMdOdSRQx051JFDHTnUkcOUjlpYGCYuLs7dI3gEdeRQRw515FBHDnXkUEcOdeQwpaMWFoYJCwtz9wgeQR051JFDHTnUkUMdOdSRQx05TOmohYVhsrKy3D2CR1BHDnXkUEcOdeRQRw515FBHDlM6amFhGH9/f3eP4BHUkUMdOdSRQx051JFDHTnUkcOUjlpYGKa0tNTdI3gEdeRQRw515FBHDnXkUEcOdeQwpaMWFoYpLi529wgeQR051JFDHTnUkUMdOdSRQx05TOmohYVhmjVr5u4RPII6cqgjhzpyqCOHOnKoI4c6cpjSUQsLw6Snp7t7BI+gjhzqyKGOHOrIoY4c6sihjhymdNTCwjBt2rRx9wgeQR051JFDHTnUkUMdOdSRQx05TOmohYVhEhMT3T2CR1BHDnXkUEcOdeRQRw515GiMHe12OxISEir9Y7fba/yYpnT0qe0dv/jiixrf5+yzz0bTpk1ru8lGwZRTsjd06sihjhzqyKGOHOrIoY4cjbFjbGwsVq9eXen1I0aMQExMTI0e05SOtV5YjB8/vka3t9ls2L17Nzp37lzbTTYKcXFxxuwcDZk6cqgjhzpyqCOHOnKoI0dj7BgdHY2oqCgAQGpqKpYuXYoJEyY4zp4dEBBQ48c0pWOtFxbA0ZddWrduXa3bBgYGurKpRqO6PaVq6sihjhzqyKGOHOrIoY4cjbFjYGBguZ+Lw8LCEBERUevHNKVjrT9jMXny5Bq9remqq65CUFBQbTfXaKSlpbl7BI+gjhzqyKGOHOrIoY4c6sihjhymdKz1KxZvvPFGjW7/0ksv1XZTjUptXv6S8tSRQx051JFDHTnUkUMdOdSRw5SO1KNCrVu3jvlwjVJhYaG7R/AI6sihjhzqyKGOHOrIoY4c6shhSkfqwmLixInMh2uULMty9wgeQR051JFDHTnUkUMdOdSRQx05TOlY47dCXXrppRVeblmWMe/vasj8/f1rfB+73Y7Y2FhER0c7fRhoX2oOXl8Th18KI1G8Jg7XxQQhMqz5Ce/nCWrTUcpTRw515FBHDnXkUEcOdeQwpWONFxbLli3DO++8U+69XJZl4aeffqIN1lhlZWWhWbNmNbpPdnY2Vq9ejaioKMcC4X8bDmHmJ1tgA1BqheJgbBLei03CYxf3wcQB7Su9n6eoTUcpTx051JFDHTnUkUMdOdSRw5SONX4rVExMDAIDAzFixAinPzExMejTp09dzFgrL7zwAjp16gR/f38MGjQI69evr/S227Ztw8UXX4xOnTrBZrNh4cKF9TfocVq2bOnyY+xLzcHMT7ag1AJKLMCCDSUWUGoB93yyBftTcwiTmo3RUdSRRR051JFDHTnUkUMdOUzpWOOFxdKlSzF8+PAKr/vxxx9dHojho48+wowZM/Dggw9i48aN6Nu3L0aPHo3k5OQKb5+bm4vOnTtj/vz5CA8Pr+dpnVU2Y3WkpqYiISEBr6/aAVsVt3tt1Q4kJCQgNTW11tsynSsd5R/qyKGOHOrIoY4c6sihjhymdHTpBHnA0ZPkufuH8eMtWLAAN954I6699loAwKJFi/D111/j9ddfx8yZM8vd/rTTTsNpp50GABVeX59cOWvi0qVLAQC/FEai1AoFKlheWJaFXzZth8+2r2u9nYbAhLNPegJ15FBHDnXkUEcOdeRQRw5TOrp8VKhzzjmHMQdNYWEhYmNjMWrUKMdlXl5eGDVqFNauXUvbTkFBAbKyspz+MMTFxdX6vhMmTMDUqVMxtH9PeNkqfs3CZrNhaP+emDp1KiZMmFDrbZnOlY7yD3XkUEcOdeRQRw515FBHDlM6urywMOXwVmVSU1NRUlKCNm3aOF3epk0bJCYm0rYzb948BAcHO/60b3/0A9H5+fmIj49HaWmp45scFxeHwsJCJCcnIzs7GxkZGUhLS0NeXh4SExNRXFzsuG1JSQmKioqQmJiI3NxcpKWlIT09HTk5OUhKSkJRUZHT45aUlCAlJQUA4OPjg2bNmuGy0zqgqu/K1Wd0gY+Pj+MIAhkZGcjLy6v23Lm5ueXmjouLQ3Fxcbm5s7OzK5y7tLQU8fHxyMvLw5EjR5CZmQm73Y6UlBQUFBQgLi4OlmU5/W9BQQFSUlJgt9uRmZmJI0eOVDp3aGgokpKSkJ2djfT09BrNnZGRgezsbCQnJ6OwsLDSuVNTUx2LyormLrtPVXOXlJQ43baoqAhJSUnIyckpN/fxDcvmzsvLq/bc+fn5Fc4dHx9f4dze3t7l5k5ISDjh3CfaZxMSEpzmttvtNZo7NTX1hP/W7Ha707815tzH7rPJycmO733ZbS3LQnx8vON736xZs2rPfaLniLK5a/IccaJ/a8fOfew+e2xvE54jmjVrRnuOKPveN8bnCC8vL9pzRNm/tcb4HBEaGkp7jqjJ3J72HFFSUkJ7jmD+HFFfzxHZ2dkAgPT0dJeeI9q0aVOnP0dUl81ycWXQp08fbNmyxZWHoIqPj0e7du3w66+/YvDgwY7L7777bqxevfqEJ/Hr1KkTbr/9dtx+++1V3q6goAAFBQWOv2dlZaF9+/bIzMxEUFBQreevzVvLEhISsHjxYkydOhUREREAgCUbDuEex1GhLNj+/xWMY48KVdH9PIWJb9FriNSRQx051JFDHTnUkaOxd2T9LGZKR5c/Y2GasLAweHt7IykpyenypKQkanA/Pz/4+fnRHq9McHBwje8TEBCAESNGOB0CeOKA9jitUyheW7UDv2zajqH9e+L6mB7odMx5LCq6n6eoTUcpTx051JHDlI52u93xW8aKBAQEGH0Ib1M6NnTqyKGOHKZ09LiFha+vL6Kjo7F8+XKMHz8eAFBaWorly5dj2rRp7h2uGvLy8tC0adMa3ScwMBAxMTHlLu8U1hy3DGkHn21fY+qQsxFxzKKiqvt5gtp0lPLUkUMdOUzpGBsbi9WrV1d6fdkh2E1lSseGTh051JHDlI4uLyy8vb0Zc1DNmDEDkydPxoABAzBw4EAsXLgQOTk5jqNEXXPNNWjXrh3mzZsH4OgHvrdv3+7477i4OGzevBkBAQHo2rVrvc7u5eXyx14E6siijhzqyGFKx+joaERFRQE4+rm+pUuXYsKECQgLCwMA418FNqVjQ6eOHOrIYUpHlxcWmzZtYsxBddlllyElJQWzZ89GYmIi+vXrh++++87xge6DBw86fQPi4+PRv39/x9+ffPJJPPnkkxgxYgRWrVpVr7P7+Hjci0huoY4c6sihjhymdAwMDCz3VqewsLAG81k1Uzo2dOrIoY4cpnQ0Y4o6MG3atErf+nT8YqFTp07GHN0qNzfX+N92NQTqyKGOHOrIoY4c6sihjhzqyGFKR8rCYsaMGRVebrPZ4O/vj65du2LcuHEIDQ1lbM6jhYSEuHsEj6COHOrIoY4c6sihjhzqyKGOHKZ0pCwsNm3ahI0bN6KkpMTxvtNdu3bB29sb3bt3x4svvog777wTv/zyC3r27MnYpMdKSUkx5uyJDZk6cqgjhzpyqCOHOnKoI4c6cpjSkfJJj3HjxmHUqFGIj49HbGwsYmNjcfjwYZx99tm44oorEBcXh+HDh+OOO+5gbM6jmbBTeAJ15FBHDnXkUEcOdeRQRw515DClI2Vh8cQTT+Dhhx92OjFccHAw5syZg8cffxzNmjXD7NmzERsby9icR6vJ2Q2lcurIoY4c6sihjhzqyKGOHOrIYUpHysIiMzMTycnJ5S4vO907cPS9X4WFhYzNebSGclQR06kjhzpyqCOHOnKoI4c6cqgjhykdaW+Fuu666/Dpp5/i8OHDOHz4MD799FNcf/31jpPUrV+/HieffDJjcx4tMTHR3SN4BHXkUEcOdeRQRw515FBHDnXkMKUj5cPbL7/8Mu644w5cfvnlKC4uPvrAPj6YPHkyFixYAADo3r07Xn31VcbmPJqOnMWhjhzqyKGOHOrIoY4c6sihjhymdKQsLAICAvDKK6/g6aefxt9//w0A6Ny5MwICApCdnQ0A6NevH2NTHi87Oxv+/v7uHqPBU0cOdeRQRw515FBHDnXkUEcOUzq69Faop59+2unvAQEB6NOnD/r06YOAgADY7XaMHj3apQEbG19fX3eP4BHUkUMdOdSRQx051JFDHTnUkcOUji4tLGbNmoW33367wuuys7MxZswYHDlyxJVNiIiIiIhIA+DSwuKdd97Bv/71L3zxxRdOl5ctKlJSUrBy5UqXBmxsCgoK3D2CR1BHDnXkUEcOdeRQRw515FBHDlM6uvQZi0suuQQZGRm44oor8PXXXyMmJgY5OTkYO3YskpKSsHr1amMOf9VQHHsuEKk9deRQRw515FBHDnXkUEcOdeQwpaPLh5u94YYb8OCDD2LcuHFYtWoVxo4di/j4eKxcuRJt27ZlzNio6K1jHOrIoY4c6sihjhzqyKGOHOrIYUpHylGh7r77bqSlpWHkyJHo1KkTVq1ahZNOOonx0I2OXuHhUEcOdeRQRw515FBHDnXkUEcOUzq6tLCYMGGC09+bNGmCsLAw3HbbbU6XL1261JXNNCrx8fFo166du8do8NSRQx051JFDHTnUkUMdOdSRw5SOLi0sgoODnf5+xRVXuDSMwIidwhOoI4c6cqgjhzpyqCOHOnKoI4cpHV1aWLzxxhusOeT/xcXFGbNzNGTqyKGOHOrIoY4c6sihjhzqyGFKR5c/vC1cYWFh7h7BI6gjhzpyqCOHOnKoI4c6cqgjhykda72w2LJlC0pLS6t9+23btqG4uLi2m2s0MjMz3T2CR1BHDnXkUEcOdeRQRw515FBHDlM61nph0b9//xod2mrw4ME4ePBgbTfXaDRt2tTdI3gEdeRQRw515FBHDnXkUEcOdeQwpWOtP2NhWRYeeOABNGvWrFq3LywsrO2mGpWavAoklVNHDnXkUEcOdeRQRw515FBHDlM61nphMXz4cOzcubPatx88eLAxqymT6e1iHOrIoY4c6sihjhzqyKGOHOrIYUrHWi8sVq1aRRxDylT3FSCpmjpyqCOHOnKoI4c6cqgjhzpymNJRR4UyTEZGhrtH8AjqyKGOHOrIoY4c6sihjhzqyGFKRy0sDNO6dWt3j+AR1JFDHTnUkUMdOdSRQx051JHDlI5aWBgmMTHR3SN4BHXkUEcOdeRQRw515FBHDnXkMKWjFhaGMeGsiZ5AHTnUkUMdOdSRQx051JFDHTlM6aiFhWHi4uLcPYJHUEcOdeRQRw515FBHDnXkUEcOUzrW+qhQUjdMeY9cQ6eOHA2lo91uR3Z2dqXXBwQEIDAwsB4nctZQOppOHTnUkUMdOdSRw5SOWlgYJi0tDW3atHH3GA2eOnI0lI6xsbFYvXp1pdePGDECMTEx9TfQcRpKR9OpI4c6cqgjhzpymNJRCwvDBAQEuHsEj6COHA2lY3R0NKKiogAAqampWLp0KSZMmICwsDAA7v863L19T6GOHOrIoY4c6shhSkd9xsIwhYWF7h7BI6gjR0PpGBgYiIiICERERDgWE2FhYY7L3Pk2KKDhdDSdOnKoI4c6cqgjhykdtbAQERERERGXufRWqIKCAqxYsQIrVqzA4cOHkZycjLy8PLRq1QqtW7dGv379cO655yIyMpI1r8fz9fV19wgeQR051JFDHTnUkUMdOdSRQx05TOlYq4VFXFwc5s6di+XLlyMqKgq9evVC3759ERQUBD8/P2RlZcFut2PDhg147bXX4OPjg3vuuQcXX3wxe36Pk52djebNm7t7jAZPHTnUkUMdOdSRQx051JFDHTlM6VjjhcWzzz6Lw4cPY+rUqVi8eHG17pOVlYWPPvoIN910E2bNmoUOHTrUeNDGIjQ01N0jeAR15FBHDnXkUEcOdeRQRw515DClY40+Y/HVV1/hggsuwOOPP44BAwZU+35BQUG48cYb8dJLL2Ht2rU4cuRIjQdtLJKTk909gkdQRw515FBHDnXkUEcOdeRQRw5TOtboFYvzzz/fpY3ZbDZcdtllLj2GpzPllOwNnTpyqCOHOnKoI4c6cqgjhzpymNKxTo4K5e3tXRcP2yiYckr2hk4dOdSRQx051JFDHTnUkUMdOUzpWCcLC8uy6uJhG4Xw8HB3j+AR1JFDHTnUkUMdOdSRQx051JHDlI41Xljk5uae8DY2m61Ww4g575Fr6NSRQx051JFDHTnUkUMdOdSRw5SONfqMxbRp0/Dmm2+ia9eu+Pjjj7FgwQIkJydj5MiRuPnmmyu8T1JSErZv3+74s23bNuzYsQNJSUmUL8DThISEuHsEj6COHOrIoY4c6sihjhzqyKGOHKZ0rNHC4ttvv0Vqaio2bdqEoUOHYvr06RgzZgw++OADxMfH4+GHHwZw9K1Qw4YNw65duxASEoKoqCh0794dS5YswVdffYVu3brVyRfjCfLy8tC0aVN3j9HgqSOHOnKoI4c6cqgjhzpyqCOHKR1rtLAIDg6Gv78/Bg8ejODgYMyaNQsAcN5552HQoEGOhQUAtG3bFiUlJZg3bx5GjBgBAFiyZAkGDhxIHN/zeHnVycdeGh115FBHDnXkUEcOdeRQRw515DClY42mSElJwWeffYZ9+/Y5nd3P29vb6QPbNpsNH330EV5++WUsXLgQ55xzDtatW6fPXlSDjqjFoY4c6sihjhzqyKGOHOrIoY4cpnSs0SsWM2bMwJdffol58+bh77//xhlnnIGoqChERUVVeNK73r1749NPP8WGDRswe/ZsJCUlYd26dRg0aBDtC/A0eXl5CAwMdPcYDZ46cqgjhzpyqCOHOnKoI4c6cpjSsUYLizvuuMPp7/v27cPWrVuxdetWDBkyxHH58YebHTBgAL755husWbMGs2bNgs1mw7Jly1wY23OZ8uGbhk4dOdSRQx051JFDHTnUkUMdOUzp6NIbsiIjI3HBBRfg3nvvxXvvvee4vLS0tMLbDxkyBMuXL8d9993nymY9WkpKirtH8AjqyKGOHOrIoY4c6sihjhzqyGFKR7d80uPMM890x2YbBFNOyd7QqSOHOnKoI4cJHe12O1atWgW73Y59qTl4cU0cVhVG4sU1cdh6INlxnclM6OgJ1JFDHTlM6WjGR8jFwZRTsjd06sihjhzqyGFCx+zsbKxevRofrjuAkU+twvuxSdhfEor3Y5Nw4aLf8eryrcjOznb3mFUyoaMnUEcOdeQwpSN9YbFr1y4UFxezH7bRiIiIcPcIHkEdOdSRQx05TOmYWeqHecsPoNQCSizAgg0lFlBqAWuKOuFQRr67R6ySKR0bOnXkUEcOUzrSFxY9evTA33//zX7YRiMxMdHdI3gEdeRQRw515DCl4+6SsCqv/3Jb+aMkmsSUjg2dOnKoI4cpHekLi+OPCCU1Exoa6u4RPII6cqgjhzpymNIx2/Kt8v/rErMK63GamjOlY0OnjhzqyGFKxxodblbqXnZ2Nvz9/d09RoNXVx3tdnuV758OCAgw4jjSLNofOdSRw5SOAbZC2Gw2VLa2CA/yrd+BasiUjg2dOnKoI4cpHbWwMIyvr9n/h9RQ1FXH2NhYrF69utLrR4wYgZiYmDrZtjtof+RQRw5TOnbzTsW2ksrfz3zBKS3rcZqaM6VjQ6eOHOrIYUpHLSxEaiA6OhpRUVEAgNTUVCxduhQTJkxAWNjR91wHBAS4czwRqQfBXgWYNaojHl12ADYApZYFm80GADjDZz/ah0S7d0ARETfRwsIwhYVmvze3oairjoGBgeXe6hQWFmbM0RjYtD9yqCOHCR0DAgIwYsQIREd3xKi+kXht1Q78smk7hvbvictP64CUfc2N/wWDCR09gTpyqCOHKR21sDCM6f+H1FCoI4c6cqgjhwkdAwMDHW93DAwEbhnSDj7bvsbUIWcjIqI10LG1ewesBhM6egJ15FBHDlM66gR5hklLS3P3CB5BHTnUkUMdOdSRQx051JFDHTlM6UhfWNxzzz1o2dLsD66ZLDw83N0jeAR15FBHDnXkUEcOdeRQRw515DClI31hMW/ePC0sXJCQkODuETyCOnKoI4c6cqgjhzpyqCOHOnKY0lFvhTJMu3bt3D2CR1BHDnXkUEcOdeRQRw515FBHDlM6amFhmLi4OHeP4BHUkUMdOdSRQx051JFDHTnUkcOUjlpYGKZVq1buHsEjqCOHOnKoI4c6cqgjhzpyqCOHKR3rZGHh7e1dFw/bKGRkZLh7BI+gjhzqyKGOHOrIoY4c6sihjhymdKyThYVlWXXxsI1C06ZN3T2CR1BHDnXkUEcOdeRQRw515FBHDlM61uoEeY888giaNGmCPn36oE+fPuU+MGKz2SjDNUYlJSXuHsEjqCOHOnKoI4c6cqgjhzpyqCOHKR1rtbC4//778ffff2PLli147bXXEBcXh5dffrnC2yYlJWH79u2OP9u2bcOOHTuQlJTk0uCeqrS01N0jeAR15FBHDnXkUEcOdeRQRw515DClY40WFjfddBMeeughtG7dGp07d0bnzp0xfvz4crezLAvDhg3Drl27EBISgqioKHTv3h1LlizBV199hW7durHm9zimvJTV0KkjhzpyqCOHOnKoI4c6cqgjhykda/QZi7Fjx+Lcc8/FnDlzkJOTU+Vt27Ztiy5dumDx4sX44osv8Pjjj6N58+YYOHAgWrRo4dLQ1fHCCy+gU6dO8Pf3x6BBg7B+/foqb79kyRJ0794d/v7+6N27N7755ps6n7Eipnz4pqFTRw515FBHDnXkUEcOdeRQRw5TOtZoYTFu3DisW7cObdq0wRlnnIFFixZV+NKLzWbDRx99hJdffhkLFy7EOeecg3Xr1tXbZy8++ugjzJgxAw8++CA2btyIvn37YvTo0UhOTq7w9r/++iuuuOIKXH/99di0aRPGjx+P8ePHY+vWrfUy77Fat25d79v0ROrIoY4c6sihjhzqyKGOHOrIYUrHGh8VytvbG+eddx7uuOMO3H///ejZsye+/PLLCm/bu3dvfPrpp3j00Ucxd+5cJCUlYd26dS4PfSILFizAjTfeiGuvvRY9e/bEokWL0KxZM7z++usV3v6ZZ57BmDFj8J///Ac9evTAww8/jFNPPRXPP/98nc96vMTExHrfpidSRw515FBHDnXkUEcOdeRQRw5TOtZoYTFmzBh07NgRkyZNwpYtW/Dcc8/hvffew2effYbbb7+90vsNGDAA33zzDX744QfMmjULo0aNcnXuShUWFiI2NtZpG15eXhg1ahTWrl1b4X3Wrl1bbqbRo0dXevu6ZMop2Rs6deRQRw515FBHDnXkUEcOdeQwpWONPrw9f/589O7du9wJ8F577TV0797d8ffKzmMxZMgQLF++HCtXrqzFqNWTmpqKkpIStGnTxunyNm3a4K+//qrwPomJiRXevqrVX0FBAQoKChx/z8rKOvofmzcDAQH/3LBFCyAyEsjPB7ZvL/9Ap5569H937gRycpCcnPzPy1mdOgGhoUBKCnDokPP9AgOBbt2AkhLgjz/KP27v3kCTJvDevx/h8fHw2bIFSEg4el27dkCbNkB6OrBvn/P9mjYFevQ4+t+bNgHHfy979Dh6mwMHgCNHnK9r0+boY9vtwO7dztc1aXJ0JgD480+gqMj5+m7djn5NcXHA8UcMa9kS6NgRyMsDduxwvs5mA/r3P/rfO3YcvQ3wT8fIyKPfg6Sko499rOBgoEuXo7P8+SfK6dsX8PY++rXY7c7XtW8PAPDPzXVuCwDNmwNRUUf/e+PG8o/bsyfg73+0fXq683UREUf/ZGUBe/Y4X+fnB5xyytH/3rIFKC52vv7kk4/ue4cPA8e/7S8sDOjQAcjNBY7/d+DlBfTrd/S/t28/uq/+v+TkZLQ+/XQgJARITATi453vGxICdO4MFBYCFb11sF+/o4+/axeQne18XYcOR+dKTQUOHnS+LiDg6NdTWnr039TxevUCfH2Bv/8Gjntfqdf/Pz/ZMjOdvy/A0e49ex79782bjz7+sbp3B5o1OzpPaqrzda1bAyeddPTr2LXL+TofH6BPn6P/vW0bcMxzAwDEN2uGtt27H53n+Jlq+BzhhPAcgb17gcxM5+sMfY6Iy8pCO4DyHOHg6nMEgNAjR8o/D7RvD7RqBaSlAfv3O9/Pzc8RCb//jojjT2Rby+cIAEefAxrQcwTatgXCw49e/vffztfV4Dkieft257efuPAcga5dgaCgRvkc4fRzj0E/RzjU5c8RrVrBlp5e/ue0WjxHODrW1c8RZfvZiVg1sGzZMis/P7/C6/bu3Vutx1i3bp2Vmppak83WSFxcnAXA+vXXX50u/89//mMNHDiwwvs0adLEev/9950ue+GFF6zWrVtXup0HH3zQAlDuT+bRf0L//LnySuvw4cNWwbZtzpf//5+EhASrqKjIKjj11HLX5b/6qnXkyBEr5/HHy9/3nHOsw4cPW8VpaRU+bsr27VZ+fr6VMWJEueuKHnvMSk5OtnLefLPcdaX9+1uHDx+2LMuySn19y12fsWaNlZOTY+VMmlTuupK777YSEhKsvG+/Lf+47do5Hrc4PLzc9fYvv7SysrKsnOnTy9/3uuuONty4sfx1vr5WXFycVVJSYhX06lXu+ty337bS0tKsnIcfLt/pggusw4cPW0Xx8RU2TN6zxyooKLDyKmhYsGCBtX37duuTiy4qP9Pppzu+1ooeN239eis3N9fKmTCh3HXF999vJSYmWnmffVb+cbt0+adhaGi567O+/96y2+1Wzr/+Vf6+N99sxcXFWfm//lr+usBAKy4uziotLbUKTz653PU5H35oZWRkWNn331/+67nkEuvw4cNW4d9/V/i1Jh44YBUWFlr5p59evuELLxzdv595pvx9R4ywDh8+bJXk5lb4uEf++MPKy8uzcs87r9x16Xffbc2ZM8eKW7So/Nfas6ejYUlAQLnrM1eutLKzs62cKVPK79+33WbFx8db+StXln/csDDH4xZ17Fj+e7NkiZWZmWnl/Oc/5b8eNz9H5J59drnrTH2OSEtLM+454sCBA9buLl3K798LFlgpKSlW7uLF5Wdy83OE/cYby9+3ET1HFD38sJWUlGTlfPhh+a/VTc8R2Z98oucINL6fI1JSUqyEJ54oP5OBP0dUl+3ozNWTl5eHuXPnonv37pg4cSKaN29e3bsiNjYWH374IS688EIMGzas2verqcLCQjRr1gwff/yx06FwJ0+ejIyMDHz++efl7tOhQwfMmDHD6e1cDz74ID777DP8UdEqHhW/YtG+fXtkrl6NIBdesThy5Ahatmx59DLCbxqS167Fp2++iQkTJqBVq1ZHrzP0t5HM3zQ4OtbhbxoSiovx9sKFuO6ss/5pC7j9t5HMVyyOHDmClqed1qB+G5nk7Y1Fn32Gf112GcJzc53v56ZXLJKDgtC6a9dG+dtIJy4+RyTl5qJNcbFRv41MSE7Gx/Pm4fLzznN+HjD4FYuUTZvQ6vjvm16xOKoGzxFHdu785/+vAb1iUaaGzxFOP/cY9HOEQx2/YpG4fTs+f+YZ55/TavEc4ejo5lcsarSwKPP999/j0UcfRUBAAPr164fevXsjNDQUQUFB8PX1hd1uR1ZWFvbv34/Nmzdj8+bNOOecc3D33XfXy6FmBw0ahIEDB+K5554DcPSkIR06dMC0adMwc+bMcre/7LLLkJub6/Qh9DPOOAN9+vTBokWLqrXNrKwsBAcHIzMzE0FBQbWePScnp0YLthNJSEjA4sWLMXXqVERERNAe13TsjhVpDG3royObid+XhtjRRCZ2NHF/OxETOzZE6sjR2DuynkNM6VirM2+PHj0ao0ePxsaNG/Htt9/ilVdeweHDh5GcnIy8vDyEhYWhdevW6Nu3L8aOHYunnnoKISEh5NErN2PGDEyePBkDBgzAwIEDsXDhQuTk5ODaa68FAFxzzTVo164d5s2bBwC47bbbMGLECDz11FM477zz8OGHH2LDhg1YvHhxvc1cprCw0Igdo6FTRw515FBHDnXkUEcOdeRQRw5TOtZqYVHm1FNPxamnnor77ruPNQ/FZZddhpSUFMyePRuJiYno168fvvvuO8cHtA8ePAgvr38OiHXGGWfg/fffx/33349Zs2ahW7du+Oyzz9CrVy93fQkiUgN2ux2xsbEIi+yJj36Pwy+FkSheE4frYoIQ5leK2NhYREdHIzAw0N2jioiIeCyXFhYmmzZtGqZNm1bhdatWrSp32cSJEzFx4sQ6nurEfH193T2CR1BHjobSMTs7G68u34pfi3NgA1BqheJgbBLei03CrFEdEffLakRFRbltYdFQOppOHTnUkUMdOdSRw5SONT5BntSt7OM/vCa1oo4cDaXjofR8rCnqhFILKLEACzaUWECpBTy67ACySv3cOl9D6Wg6deRQRw515FBHDlM6amFhmNDQUHeP4BHUkaOhdPxy+5Eqr99VElZPk1SsoXQ0nTpyqCOHOnKoI4cpHbWwMEzy8Yf3klpRR46G0jExq7DS6yzLQrbl3peIG0pH06kjhzpyqCOHOnKY0lELC8OYckr2hk4dORpKx/CgyhcONpsNAbbKFx71oaF0NJ06cqgjhzpyqCOHKR3pC4tdu3ah+PgTbki1xR1/8hWpFXXkaCgdL+jZssrrT/ZOrfL6utZQOppOHTnUkUMdOdSRw5SO9IVFjx498PfxZ7KUagsPD3f3CB5BHTkaSsf2LfwxpMl+eNkAbxtggwUvG+BlA2aN6oggr4ITP0gdaigdTaeOHOrIoY4c6shhSkf6wqIWJ/KWY5jyHrmGTh05GkrHgIAA3DCyF7686TRMim6DTt5puDK6DVbcGYPLBnbEiBEjEBAQ4Lb5GkpH06kjhzpyqCOHOnKY0tFjz2PRUNXnGco9mTpyNJSOgYGBiImJAQDc4lsCn21fY+qQsxERdvQspGXXuUtD6Wg6deRQRw515FBHDlM66sPbhsnNzXX3CB5BHTnUkUMdOdSRQx051JFDHTlM6ahXLAzj4+P6t8RutztOlJKamur0v8DRt4246wzE9YXRUdSRRR051JFDHTnUkUMdOUzpaMYU4uDl5fqLSLGxsVi9erXTZUuXLnX894gRI9z+1pC6xugo6siijhzqyKGOHOrIoY4cpnTUwsIweXl5Lr+aEB0djaioqEqvd+eHWOsLo6OoI4s6cqgjhzpyqCOHOnKY0lELC8MEBwe7/BiBgYFG7FzuxOgo6siijhzqyKGOHOrIoY4cpnSkv25yzz33oGXLqk9WJZU79rMQUnvqyKGOHOrIoY4c6sihjhzqyGFKR/orFvPmzWM/ZKNiyinZGzp15FBHDnXkUEcOdeRQRw515DCloxmf9BAHU07J3tCpI4c6cqgjhzpyqCOHOnKoI4cpHbWwMEzbtm3dPYJHUEcOdeRQRw515FBHDnXkUEcOUzpqYWGYhIQEd4/gEdSRQx051JFDHTnUkUMdOdSRw5SO9IXF008/DQDYtm0bSkpK2A/v8fTBdw515FBHDnXkUEcOdeRQRw515DClI31h0a9fPwDArFmz0LNnT/Tr1w9XXnkl5s+fj6+++oq9OY+TlZXl7hE8gjpyqCOHOnKoI4c6cqgjhzpymNKRclQou93uOG/CmWeeCQD4/PPPAQDZ2dnYtm0b/vzzTyxbtgznn38+Y5Mey8/Pz90jeAR15FBHDnXkUEcOdeRQRw515DClI2VhMWzYMHz33XcIDw8vd11AQAAGDRqEQYMGMTYlIiIiIiIGorwVqn///hg0aBD++usvp8s3b96Mc889l7GJRqOwsNDdI3gEdeRQRw515FBHDnXkUEcOdeQwpSNlYfHGG29gypQpGDp0KH755Rfs2rULl156KaKjo+Ht7c3YRKMREBDg7hE8gjpyqCOHOnKoI4c6cqgjhzpymNKR9uHtuXPnYsaMGTj77LPRq1cv2O12rF27Fl9++SVrE41CWlqau0fwCOrIoY4c6sihjhzqyKGOHOrIYUpHysIiKSkJt912Gx555BH07NkTTZo0wZQpUzBw4EDGwzcqFX1ORWpOHTnUkUMdOdSRQx051JFDHTlM6UhZWERGRuKnn37CkiVLEBsbi08++QRTp07FE088wXj4RsWUE5w0dOrIoY4c6shhSke73Y6EhAQkJCQgNTUVAJCamuq4zG63u3nCqpnSsaFTRw515DClI+WoUK+//jouv/xyx9/HjBmDlStX4vzzz8f+/fvxwgsvMDbTKLRr187dI3gEdeRQRw515DClY2xsLFavXu102dKlSx3/PWLECMTExNTzVNVnSseGTh051JHDlI6UhcWxi4oyp556Kn799VeMHTuWsYlGIy4uzpidoyFTRw515FBHDlM6RkdHIyoqqtLrTfkQZWVM6djQqSOHOnKY0pGysKhMp06d8Ouvv9blJjxOq1at3D2CR1BHDnXkUEcOUzoGBgY6TgrbEJnSsaFTRw515DClI+2oUJVp0aJFXW/Co2RkZLh7BI+gjhzqyKGOHOrIoY4c6sihjhymdKzzhYXUTLNmzdw9gkdQRw515FBHDnXkUEcOdeRQRw5TOmphYZji4mJ3j+AR1JFDHTnUkUMdOdSRQx051JHDlI5aWBimtLTU3SN4BHXkUEcOdeRQRw515FBHDk/uaLfbsWrVqkoPQb0vNQcvronDqsJIvLgmDvtSc2r1OIA5HWkf3s7IyMBrr72GHTt2AABOOeUUXHfddQgODmZtolFo2rSpu0fwCOrIoY4c6sihjhzqyKGOHJ7cMTs7G6tXr0ZUVFS5Az78b8MhzPxkC2wASq1QHIxNwnuxSXjs4j6YOKB9tR+njCkdKa9YbNiwAV26dMHTTz+NtLQ0pKWlYcGCBejSpQs2btzI2ESjkZmZ6e4RPII6cqgjhzpyqCOHOnKoI0dj7LgvNQczP9mCUgsosQALNpRYQKkF3PPJFuyv5JWLqpjSkfKKxR133IELL7wQr7zyCnx8jj5kcXExbrjhBtx+++346aefGJtpFMLCwtw9gkdQRw515FBHDnXkUEcOdeRoDB1TU1Od/v76mjjYqrj9a6t24JYh/5yT4vj7V8SUjpSFxYYNG5wWFQDg4+ODu+++GwMGDGBsotFISkoy4gQnDV1ddLTb7YiNjUV0dDRSC7zw+po4/FIYieI1cbguJghhfqWO6xvyMe6Ppf2RQx051JFDHTnUkaMxdFy6dKnT338pjESpFQpUsLywLAu/bNoOn21f12gbpnSkLCyCgoJw8OBBdO/e3enyQ4cOecwPWPXFhJ3CE9RFx7L3OO4uDsO85QfKvS9y1qiOiPul6vdANjTaHznUkUMdOdSRQx05GkPHCRMmOL2iULwmDgdjk1Bilb+tzWbD0P49MXXI2Y7LUlNTyy1OjmdKR8pnLC677DJcf/31+Oijj3Do0CEcOnQIH374IW644QZcccUVjE00GnFxce4ewSPUVcfMUj/MW36gwvdFPrrsALJK/epku+6i/ZFDHTnUkUMdOdSRozF0DAsLQ0REhOPPdTE9UMGawuH6mB5Ot6/O25xM6Uh5xeLJJ5+EzWbDNddcg+LiYliWBV9fX9x8882YP38+YxONRuvWrd09gkeoq467S6r+x73rBNc3NNofOdSRQx051JFDHTkaY8fIsOZ47OI+uMdxVCgLNtvRt0U9dnEfdAprXuPHNKUj5RULX19fPPPMM0hPT8fmzZvxxx9/IC0tDU8//TT8/DzrN7h17ciRI+4ewSPUVcdsyxeWVfHvGSzLQrblWyfbdRftjxzqyKGOHOrIoY4cntwxICAAI0aMQEBAQLnrJg5ojxV3xmBSdBt08k7DldFtsOLOmHKHmj3R45QxpSPlFYuHHnqoyutnz57N2EyjEBQU5O4RPEJddQywFcJms6GitYXNZkOArbBOtusu2h851JFDHTnUkUMdOTy5Y2BgIGJiYiq9vlNYc9wypB18tn2NqUPORkQlr1Sc6HEAczpSFhaffvqp09+Lioqwb98++Pj4oEuXLlpY1EB+fj6aNWvm7jEavLrq2M07FdtKIiq9/mTvEx8SriHR/sihjhzqyKGOHOrIoY4cpnSkLCw2bdpU7rKsrCxMmTIFF110EWMTjUbZe+zENXXVMdirALNGdcSjyw6Ue1/k0aNCbaiT7bqL9kcOdeRQRw515FBHDnXkMKUjZWFRkaCgIMydOxcXXHABrr766rrajMfx9fWs9+i7S110LHuPY3R0R4zqG4nXVu3AL5u2Y2j/nrg+pgda+pUi1rvq90A2NNofOdSRQx051JFDHTnUkcOUjpQPb1cmMzPTmFOMNxTZ2dnuHsEj1EXHsvc4BgYGOt4XGeO7D7cMaYdOYc2drvcU2h851JFDHTnUkUMdOdSRw5SOlFcsnn32Wae/W5aFhIQEvPPOOxg7dixjE41GaGiou0fwCOrIoY4c6sihjhzqyKGOHOrIYUpHysLi6aefdvq7l5cXWrVqhcmTJ+Pee+9lbKLRSE5ONubsiQ2ZOnKoI4c6cqgjhzpyqCOHOnKY0pGysNi3bx/jYQTmnJK9oVNHDnXkUEcOdeRQRw515FBHDlM61ulnLKTmTDkle0OnjhzqyKGOHOrIoY4c6sihjhymdKz1KxYzZsyo9m0XLFhQ2800OuHh4e4ewSOoI4c6cqgjhzpyqCOHOnKoI4cpHWu9sDj+3BUbN25EcXExoqKiAAC7du2Ct7c3oqOjXZuwkUlKSkLbtm3dPUaDp44c6sihjhzqyKGOHOrIoY4cpnSs9cJi5cqVjv9esGABAgMD8dZbb6FFixYAgPT0dFx77bUYNmyY61M2ImX9xDXqyKGOHOrIoY4c6sihjhzqyGFKR8pnLJ566inMmzfP6Ytq0aIFHnnkETz11FOMTTQaubm57h7BI6gjhzpyqCOHOnKoI4c6cqgjhykdKUeFysrKQkpKSrnLU1JSYLfbGZtoNHx86uxk6I2KOnI0lI52u91xcqDU1FSn/wWOnjXdnScubCgdTaeOHOrIoY4c6shhSkfKFBdddBGuvfZaPPXUUxg4cCAAYN26dfjPf/6DCRMmMDbRaHh56UBdDOrI0VA6xsbGYvXq1U6XLV261PHfI0aMQExMTD1P9Y+G0tF06sihjhzqyKGOHKZ0pCwsFi1ahLvuuguTJk1CUVHR0Qf28cH111+PJ554grGJRiM/P9+tv1n1FOrI0VA6RkdHOw4cUZGAgIB6nKa8htLRdOrIoY4c6sihjhymdKQsLJo1a4YXX3wRTzzxBPbu3QsA6NKlC5o3b854+EYlKCjI3SN4BHXkaCgdAwMDjXhCrUxD6Wg6deRQRw515FBHDlM6Ul83ad68Ofr06YM+ffpoUVFLx74vXGpPHTnUkUMdOdSRQx051JFDHTlM6ejSCfIefvhhNG/e/IQny9MJ8qrPlFOyN3TqyKGOHOrIoY4c6sihjhzqyGFKR5dOkFf2eYrjT5Z3LJvNVttNNEpxcXHG7BwNmTpyqCOHOnKoI4c6cqgjhzpymNKRcoK8Y/9bXGPCWRM9gTpyqCOHOnKoI4c6cqgjhzpymNKR8hmLvLw8pxNzHDhwAAsXLsQPP/zAePhGJT4+3t0jeAR15FBHDnXkUEcOdeRQRw515DClI2VhMW7cOLz99tsAgIyMDAwcOBBPPfUUxo0bh5deeomxiUYjLCzM3SN4BHXkUEcOdeRQRw515FBHDnXkMKUjZWGxceNGDBs2DADw8ccfIzw8HAcOHMDbb7+NZ599lrGJRiMrK8vdI3gEdeRQRw515FBHDnXkUEcOdeQwpSNlYZGbm+s4hvwPP/yACRMmwMvLC6effjoOHDjA2ESj4efn5+4RPII6cqgjhzpyqCOHOnKoI4c6cpjSkbKw6Nq1Kz777DMcOnQI33//Pc455xwAQHJysjEn7BARERERkbpDWVjMnj0bd911Fzp16oRBgwZh8ODBAI6+etG/f3/GJhqNwsJCd4/gEdSRQx051JFDHTnUkUMdOdSRw5SOtT7c7LEuueQSDB06FAkJCejbt6/j8pEjR+Kiiy5ibKLR0BnLOdSRQx051JFDHTnUkUMdOdSRw5SOlFcsACA8PBz9+/eHl9c/Dzlw4EB0796dtYlqSUtLw5VXXomgoCCEhITg+uuvR3Z2dpX3Wbx4MWJiYhAUFASbzYaMjIz6GbYC6enpbtu2J1FHDnXkUEcOdeRQRw515FBHDlM60hYWP//8M6666ioMHjwYcXFxAIB33nkHv/zyC2sT1XLllVdi27Zt+PHHH/HVV1/hp59+wtSpU6u8T25uLsaMGYNZs2bV05SVCw8Pd/cIHkEdOdSRQx051JFDHTnUkUMdOUzpSFlYfPLJJxg9ejSaNm2KTZs2oaCgAACQmZmJRx99lLGJatmxYwe+++47vPrqqxg0aBCGDh2K5557Dh9++GGVJw65/fbbMXPmTJx++un1NmtlEhIS3D2CR1BHDnXkUEcOdeRQRw515FBHDlM6UhYWjzzyCBYtWoRXXnkFTZo0cVw+ZMgQbNy4kbGJalm7di1CQkIwYMAAx2WjRo2Cl5cX1q1bR91WQUEBsrKynP4wtGvXjvI4jZ06cqgjhzpyqCOHOnKoI4c6cpjSkbKw2LlzJ4YPH17u8uDg4Hr9vEJiYiJat27tdJmPjw9CQ0ORmJhI3da8efMQHBzs+NO+fXsAQH5+PuLj41FaWup4S1hcXBwKCwuRnJyM7OxsZGRkIC0tDXl5eUhMTERxcbHjtn/++SeKioqQmJiI3NxcpKWlIT09HTk5OUhKSkJRUZHT45aUlCA+Ph55eXk4cuQIMjMzYbfbkZKSgoKCAqfbWpaFuLg4FBQUICUlxbEgSk1NRV5eXrXnzs3NLTd3XFwciouLy82dnZ1d4dylpaUnnLts3uPnttvtyMzMxJEjRyqd+8CBA0hKSkJ2djbS09NrNHdGRgays7ORnJyMwsLCSucu27ezs7MrnLvsPlXNXVJS4nTboqIiJCUlIScnp9zcxzcsmzsvL6/ac+fn5yM1NdXxvS+bOz4+vsK5d+zYUW7uhISEE859on02ISHBaW673V6juVNTU0/4b81utzv9W2POfew+m5ycXOG/tfj4eMf3fs+ePdWe+0TPEWVzN8bniD179tCeI8q+93X5HFHRvzUTniO2b99Oe44o+7fWGJ8jDh48SHuOqMncnvYc8eeff9KeI5g/R9TXc0TZ54DT09Ndeo4o+1NXP0dUl82yLKvat65E586dsXjxYowaNQqBgYH4448/0LlzZ7z99tuYP38+tm/f7tLjz5w5E4899liVt9mxYweWLl2Kt956Czt37nS6rnXr1pg7dy5uvvnmKh9j1apVOPPMM5Geno6QkJAqb1tQUOB4yxdw9IyH7du3R2Zmpkvn7igsLISvr2+t7y9H1UfHhIQELF68GFOnTkVERESdbstdtD9yqCOHOnKoI4c6cjT2jqyfJUzpSDnc7I033ojbbrsNr7/+Omw2G+Lj47F27VrcddddeOCBB1x+/DvvvBNTpkyp8jadO3dGeHg4kpOTnS4vLi5GWloa/UMtfn5+dXKWw4yMjHKvukjNqSOHOnKoI4c6cqgjhzpyqCOHKR0pC4uZM2eitLQUI0eORG5uLoYPHw4/Pz/cdddd+Pe//+3y47dq1QqtWrU64e0GDx6MjIwMxMbGIjo6GgCwYsUKlJaWYtCgQS7PUR+aNWvm7hE8gjpyqCOHOnKoI4c6cqgjhzpymNKR8hkLm82G++67D2lpadi6dSt+++03pKSk4OGHH0ZeXh5jE9XSo0cPjBkzBjfeeCPWr1+PNWvWYNq0abj88svRtm1bAEffJ9a9e3esX7/ecb/ExERs3rwZe/bsAXD0cw6bN29GWlpavc1epri4uN636YnUkUMdOdSRQx051JFDHTnUkcOUjrTzWACAr68vevbsiYEDB6JJkyZYsGABIiMjmZs4offeew/du3fHyJEjce6552Lo0KFYvHix4/qioiLs3LkTubm5jssWLVqE/v3748YbbwQADB8+HP3798cXX3xRr7MDQGlpab1v0xOpI4c6cqgjhzpyqCOHOnKoI4cpHV16K1RBQQHmzJmDH3/8Eb6+vrj77rsxfvx4vPHGG7jvvvvg7e2NO+64gzVrtYSGhuL999+v9PpOnTrh+M+rz5kzB3PmzKnjyarH39/f3SN4BHXkUEcOdeRQRw515FBHDnXkMKWjS69YzJ49Gy+99BI6deqE/fv3Y+LEiZg6dSqefvppLFiwAPv378c999zDmrVRYJ0Po7FTRw515FBHDnXkUEcOdeRQRw5TOrr0isWSJUvw9ttv48ILL8TWrVvRp08fFBcX448//oDNZmPN2KiEhYW5ewSPoI4c6sihjhzqyKGOHOrIoY4cpnR06RWLw4cPO46+1KtXL/j5+eGOO+7QosIFSUlJ7h7BI6gjhzpyqCOHOnKoI4c6cqgjhykdXVpYlJSUOJ2Mw8fHBwEBAS4P1ZiZckr2hk4dOdSRQx051JFDHTnUkUMdOUzp6NJboSzLwpQpUxwnisvPz8dNN92E5s2bO91u6dKlrmymUYmLizNm52jI1JFDHTnUkUMdOdSRQx051JHDlI4uLSwmT57s9PerrrrKpWEEaNOmjbtH8AjqyKGOHOrIoY4c6sihjhzqyGFKR5cWFm+88QZrDvl/qampCA8Pd/cYDZ46cqgjhzpyqCOHOnKoI4c6cpjSkXqCPHFdUFCQu0fwCOrIoY4c6sihjhzqyKGOHOrIYUpHLSwMk5+f7+4RPII6cqgjhzpyqCOHOnKoI4c6cpjSUQsLw+hQvRzqyKGOHOrIoY4c6sihjhzqyGFKRy0sDNOkSRN3j+AR1JFDHTnUkUMdOdSRQx051JHDlI5aWBgmJyfH3SN4BHXkUEcOdeRQRw515FBHDnXkMKWjFhaGCQ0NdfcIHkEdOdSRQx051JFDHTnUkUMdOUzpqIWFYZKTk909gkdQRw515FBHDnXkUEcOdeRQRw5TOmphYRgTzproCdSRQx051JFDHTnUkUMdOdSRw5SOWlgYJi4uzt0jeAR15FBHDnXkUEcOdeRQRw515DCloxYWhomIiHD3CB5BHTnUkUMdOdSRQx051JFDHTlM6aiFhWESExPdPYJHUEcOdeRQRw515FBHDnXkUEcOUzpqYWGYFi1auHsEj6COHOrIoY4c6sihjhzqyKGOHKZ01MLCMLm5ue4ewSOoI4c6cqgjhzpyqCOHOnKoI4cpHbWwMIyPj4+7R/AI6sihjhzqyKGOHOrIoY4c6shhSkctLAzj5aVvCYM6cqgjhzpyqCOHOnKoI4c6cpjS0YwpxCE/P9/dI3gEdeRQRw515FBHDnXkUEcOdeQwpaMWFoYJCgpy9wgeQR051JFDHTnUkUMdOdSRQx05TOmohYVhUlNT3T2CR1BHDnXkUEcOdeRQRw515FBHDlM6mvFJD3Fo27atu0fwCHXV0W63Izs7G8A//4iP/cccEBCAwMDAOtm2O2h/5FBHDnXkUEcOdeRQRw5TOmphYZj4+Hi0a9fO3WM0eHXVMTY2FqtXr3a6bOnSpY7/HjFiBGJiYujbdRftjxzqyKGOHOrIoY4c6shhSkctLAxjyoqzoaurjtHR0YiKiqr0+oCAgDrZrrtof+RQRw515FBHDnXkUEcOUzrqMxaGiY+Pd/cIHqGuOgYGBiIiIqLSP570NihA+yOLOnKoI4c6cqgjhzpymNJRCwvDhIWFuXsEj6COHOrIoY4c6sihjhzqyKGOHKZ01MLCMFlZWe4ewSOoI4c6cqgjhzpyqCOHOnKoI4cpHbWwMIy/v7+7R/AI6sihjhzqyKGOHOrIoY4c6shhSkctLAxTWlrq7hE8gjpyqCOHOnKoI4c6cqgjhzpymNJRCwvDFBcXu3sEj6COHOrIoY4c6sihjhzqyKGOHKZ01MLCMM2aNXP3CB5BHTnUkUMdOdSRQx051JFDHTlM6aiFhWHS09PdPYJHUEcOdeRQRw515FBHDnXkUEcOUzpqYWGY8PBwd4/gEdSRQx051JFDHTnUkUMdOdSRw5SOWlgYJiEhwd0jeAR15FBHDnXkUEcOdeRQRw515DCloxYWhmnXrp27R/AI6sihjhzqyKGOHOrIoY4c6shhSkctLAwTFxfn7hE8gjpyqCOHOnKoI4c6cqgjhzpymNJRCwvDtG7d2t0jeAR15FBHDnXkUEcOdeRQRw515DCloxYWhklLS3P3CB5BHTnUkUMdOdSRQx051JFDHTlM6aiFhWGaN2/u7hE8gjpyqCOHOnKoI4c6cqgjhzpymNJRCwvDFBUVuXsEj6COHOrIoY4c6sihjhzqyKGOHKZ01MLCMJZluXsEj6COHOrIoY4c6sihjhzqyKGOHKZ01MLCMP7+/u4ewSOoI4c6cqgjhzpyqCOHOnKoI4cpHbWwMExWVpa7R/AI6sihjhzqyKGOHOrIoY4c6shhSkctLAwTFhbm7hE8gjpyqCOHOnKoI4c6cqgjhzpymNJRCwvDJCUluXsEj6COHOrIoY4c6sihjhzqyKGOHKZ01MLCMKackr2hU0cOdeRQRw515FBHDnXkUEcOUzpqYWEYU07J3tCpI4c6cqgjhzpyqCOHOnKoI4cpHbWwMEybNm3cPYJHUEcOdeRQRw515FBHDnXkUEcOUzpqYWGY1NRUd4/gEdSRQx051JFDHTnUkUMdOdSRw5SOWlgYJigoyN0jeAR15FBHDnXkUEcOdeRQRw515DCloxYWhsnPz3f3CB5BHTnUkUMdOdSRQx051JFDHTlM6aiFhWG8vPQtYVBHDnXkUEcOdeRQRw515FBHDlM6mjGFOPj4+Lh7BI+gjhzqyKGOHOrIoY4c6sihjhymdNTCwjC5ubnuHsEjqCOHOnKoI4c6cqgjhzpyqCOHKR21sDBMSEiIu0fwCOrIoY4c6sihjhzqyKGOHOrIYUpHLSwMk5KS4u4RPII6cqgjhzpyqCOHOnKoI4c6cpjSUQsLw5hySvaGTh051JFDHTnUkUMdOdSRQx05TOmohYVhTDkle0OnjhzqyKGOHOrIoY4c6sihjhymdNTCwjARERHuHsEjqCOHOnKoI4c6cqgjhzpyqCOHKR21sDBMYmKiu0fwCOrIoY4c6sihjhzqyKGOHOrIYUpHLSwM06JFC3eP4BHUkUMdOdSRQx051JFDHTnUkcOUjlpYGCYnJ8fdI3gEdeRQRw515FBHDnXkUEcOdeQwpaMWFobx9fV19wgeQR051JFDHTnUkUMdOdSRQx05TOmohYWIiIiIiLhMCwvDFBQUuHsEj6COHOrIoY4c6sihjhzqyKGOHKZ01MLCMEFBQe4ewSOoI4c6cqgjhzpyqCOHOnKoI4cpHT1uYZGWloYrr7wSQUFBCAkJwfXXX4/s7Owqb//vf/8bUVFRaNq0KTp06IDp06cjMzOzHqf+R2pqqlu262nUkUMdOdSRQx051JFDHTnUkcOUjh63sLjyyiuxbds2/Pjjj/jqq6/w008/YerUqZXePj4+HvHx8XjyySexdetWvPnmm/juu+9w/fXX1+PU/2jbtq1btutp1JFDHTnUkUMdOdSRQx051JHDlI4etbDYsWMHvvvuO7z66qsYNGgQhg4diueeew4ffvgh4uPjK7xPr1698Mknn+CCCy5Aly5dcNZZZ+G///0vvvzySxQXF9fzV4BK55SaUUcOdeRQRw515FBHDnXkUEcOUzp61MJi7dq1CAkJwYABAxyXjRo1Cl5eXli3bl21HyczMxNBQUHw8fGp9DYFBQXIyspy+sPQrl07yuM0durIoY4c6sihjhzqyKGOHOrIYUpHj1pYJCYmonXr1k6X+fj4IDQ0tNqnOk9NTcXDDz9c5dunAGDevHkIDg52/Gnfvj0AID8/H/Hx8SgtLUVcXBwAIC4uDoWFhUhOTkZ2djYyMjKQlpaGvLw8JCYmori42HHbP//8E0VFRUhMTERubi7S0tKQnp6OnJwcJCUloaioyOlxS0pKEB8fj7y8PBw5cgSZmZmw2+1ISUlBQUGB020ty0JcXBwKCgqQkpLiWBClpqYiLy+v2nPn5uaWmzsuLg7FxcXl5s7Ozq5w7tLS0hPOXTbv8XPb7XZkZmbiyJEjlc594MABJCUlITs7G+np6TWaOyMjA9nZ2UhOTkZhYWGlc6empjoaVjR32X2qmrukpMTptkVFRUhKSkJOTk65uY9vWDZ3Xl5etefOz8+vcO74+PgK596xY0e5uRMSEk4494n22YSEBKe57XZ7jeZOTU094b81u93u9G+NOfex+2xycnKF/9bi4+Md3/s9e/ZUe+4TPUeUzd0YnyP27NlDe44o+943xueI7du3054jyv6tNcbniIMHD9KeI2oyt6c9R/z555+05wjmzxH19RxR9jng9PR0l54jyv7U1c8R1WWzLMuq9q3dZObMmXjssceqvM2OHTuwdOlSvPXWW9i5c6fTda1bt8bcuXNx8803V/kYWVlZOPvssxEaGoovvvgCTZo0qfS2BQUFTof2ysrKQvv27R2vdtRWQUEB/Pz8an1/OUodOdSRQx051JFDHTnUkaOxd0xISMDixYsxdepURERE1PpxTOlY+Xt9DHLnnXdiypQpVd6mc+fOCA8PR3JystPlxcXFSEtLQ3h4eJX3t9vtGDNmDAIDA/Hpp59WuagAAD8/vzr5BmZlZaFVq1b0x21s1JFDHTnUkUMdOdSRQx051JHDlI4NYmHRqlWrasUaPHgwMjIyEBsbi+joaADAihUrUFpaikGDBlV6v6ysLIwePRp+fn744osv4O/vT5u9pty5bU+ijhzqyKGOHOrIoY4c6sihjhymdPSoz1j06NEDY8aMwY033oj169djzZo1mDZtGi6//HLHYbji4uLQvXt3rF+/HsDRRcU555yDnJwcvPbaa8jKykJiYiISExNRUlJS719DaWlpvW/TE6kjhzpyqCOHOnKoI4c6cqgjhykdG8QrFjXx3nvvYdq0aRg5ciS8vLxw8cUX49lnn3VcX1RUhJ07dyI3NxcAsHHjRscRo7p27er0WPv27UOnTp3qbXYAbjnErSdSRw515FBHDnXkUEcOdeRQRw5TOnrcwiI0NBTvv/9+pdd36tQJx35ePSYmBiZ9fr1Zs2buHsEjqCOHOnKoI4c6cqgjhzpyqCOHKR096q1QniA9Pd3dI3gEdeRQRw515FBHDnXkUEcOdeQwpaMWFoZp06aNu0fwCOrIoY4c6sihjhzqyKGOHOrIYUpHLSwMU90T+UnV1JFDHTnUkUMdOdSRQx051JHDlI5aWBjGlFOyN3TqyKGOHOrIoY4c6sihjhzqyGFKRy0sDFOT06ZL5dSRQx051JFDHTnUkUMdOdSRw5SOWlgYpnXr1u4ewSOoI4c6cqgjhzpyqCOHOnKoI4cpHbWwMExaWpq7R/AI6sihjhzqyKGOHOrIoY4c6shhSkctLAwTEBDg7hE8gjpyqCOHOnKoI4c6cqgjhzpymNJRCwvDFBYWunsEj6COHOrIoY4c6sihjhzqyKGOHKZ01MLCMCadBbwhU0cOdeRQRw515FBHDnXkUEcOUzpqYWEYf39/d4/gEdSRQx051JFDHTnUkUMdOdSRw5SOWlgYJisry90jeAR15FBHDnXkUEcOdeRQR47G2NFutyMhIQEJCQlITU0FAKSmpjous9vtNX5MUzraLFNeO2ngsrKyEBwcjMzMTAQFBdX6cYqKitCkSRPiZI2TOnKoI4c6cqgjhzpyqCNHY+y4atUqrF69utLrR4wYgZiYmBo9pikdfdw9gDhLTk425uyJDZk6cqgjhzpyqCOHOnKoI0dj7BgdHY2oqKhKr6/NEZ5M6ahXLEhYr1iIiIiIiDRE+oyFYUw5JXtDp44c6sihjhzqyKGOHOrIoY4cpnTUKxYkrFcsiouL4eOjd6i5Sh051JFDHTnUkUMdOdSRQx05TOmoVywMU3Z0AHGNOnKoI4c6cqgjhzpyqCOHOnKY0lELC8MEBwe7ewSPoI4c6sihjhzqyKGOHOrIoY4cpnTUwsIweXl57h7BI6gjhzpyqCOHOnKoI4c6cqgjhykdtbAwjJeXviUM6sihjhzqyKGOHOrIoY4c6shhSkczphAHEz544wnUkUMdOdSRQx051JFDHTnUkcOUjlpYGCY3N9fdI3gEdeRQRw515FBHDnXkUEcOdeQwpaMWFoYJCQlx9wgeQR051JFDHTnUkUMdOdSRQx05TOmohYVhUlJS3D2CR1BHDnXkUEcOdeRQRw515FBHDlM66gR5JKwT5ImIiIiINER6xcIwppySvaFTRw515FBHDnXkUEcOdeRQRw5TOuoVCxLWKxalpaXGHDKsIVNHDnXkUEcOdeRQRw515FBHDlM6un8CcZKYmOjuETyCOnKoI4c6cqgjhzpyqCOHOnKY0tGMg956gLIXfrKyslx6HB8fH5cfQ9SRRR051JFDHTnUkUMdOdSRoz46BgYGwmazVT1HnU7QiNjtdgBA+/bt3TyJiIiIiAhXdd7ur89YkJSWliI+Pr5aq7nKZGVloX379jh06JCOLOUCdeRQRw515FBHDnXkUEcOdeSor456xaIeeXl54aSTTqI8VlBQkP6BEagjhzpyqCOHOnKoI4c6cqgjhwkd9eFtERERERFxmRYWIiIiIiLiMi0sDOLn54cHH3wQfn5+7h6lQVNHDnXkUEcOdeRQRw515FBHDpM66sPbIiIiIiLiMr1iISIiIiIiLtPCQkREREREXKaFhYiIiIiIuEwLCxERERERcZkWFm4wb948nHbaaQgMDETr1q0xfvx47Ny50+k2MTExsNlsTn9uuukmN01sppdeegl9+vRxnBBm8ODB+Pbbbx3X5+fn49Zbb0XLli0REBCAiy++GElJSW6c2Dwnaqj9sHbmz58Pm82G22+/3XGZ9seaq6ij9skTmzNnTrlG3bt3d1yvfbF6TtRR+2L1xcXF4aqrrkLLli3RtGlT9O7dGxs2bHBcb1kWZs+ejYiICDRt2hSjRo3C7t273TixmU7UccqUKeX2yTFjxtTrjFpYuMHq1atx66234rfffsOPP/6IoqIinHPOOcjJyXG63Y033oiEhATHn8cff9xNE5vppJNOwvz58xEbG4sNGzbgrLPOwrhx47Bt2zYAwB133IEvv/wSS5YswerVqxEfH48JEya4eWqznKghoP2wpn7//Xe8/PLL6NOnj9Pl2h9rprKOgPbJ6jjllFOcGv3yyy+O67QvVl9VHQHti9WRnp6OIUOGoEmTJvj222+xfft2PPXUU2jRooXjNo8//jieffZZLFq0COvWrUPz5s0xevRo5Ofnu3Fys1SnIwCMGTPGaZ/84IMP6ndQS9wuOTnZAmCtXr3acdmIESOs2267zX1DNVAtWrSwXn31VSsjI8Nq0qSJtWTJEsd1O3bssABYa9eudeOE5itraFnaD2vKbrdb3bp1s3788Uendtofa6ayjpalfbI6HnzwQatv374VXqd9sfqq6mhZ2her65577rGGDh1a6fWlpaVWeHi49cQTTzguy8jIsPz8/KwPPvigPkZsEE7U0bIsa/Lkyda4cePqZ6BK6BULA2RmZgIAQkNDnS5/7733EBYWhl69euHee+9Fbm6uO8ZrEEpKSvDhhx8iJycHgwcPRmxsLIqKijBq1CjHbbp3744OHTpg7dq1bpzUXMc3LKP9sPpuvfVWnHfeeU77HQDtjzVUWccy2idPbPfu3Wjbti06d+6MK6+8EgcPHgSgfbGmKutYRvviiX3xxRcYMGAAJk6ciNatW6N///545ZVXHNfv27cPiYmJTvtkcHAwBg0apH3yGCfqWGbVqlVo3bo1oqKicPPNN+PIkSP1OqdPvW5NyiktLcXtt9+OIUOGoFevXo7LJ02ahI4dO6Jt27bYsmUL7rnnHuzcuRNLly5147Tm+fPPPzF48GDk5+cjICAAn376KXr27InNmzfD19cXISEhTrdv06YNEhMT3TOsoSprCGg/rIkPP/wQGzduxO+//17uusTERO2P1VRVR0D7ZHUMGjQIb775JqKiopCQkIC5c+di2LBh2Lp1q/bFGqiqY2BgoPbFavr777/x0ksvYcaMGZg1axZ+//13TJ8+Hb6+vpg8ebJjv2vTpo3T/bRPOjtRR+Do26AmTJiAyMhI7N27F7NmzcLYsWOxdu1aeHt718+gbn29RKybbrrJ6tixo3Xo0KEqb7d8+XILgLVnz556mqxhKCgosHbv3m1t2LDBmjlzphUWFmZt27bNeu+99yxfX99ytz/ttNOsu+++2w2TmquyhhXRflixgwcPWq1bt7b++OMPx2XHvk1C+2P1nKhjRbRPnlh6eroVFBRkvfrqq9oXXXBsx4poX6xYkyZNrMGDBztd9u9//9s6/fTTLcuyrDVr1lgArPj4eKfbTJw40br00kvrbU7TnahjRfbu3WsBsJYtW1bX4znorVBuNG3aNHz11VdYuXIlTjrppCpvO2jQIADAnj176mO0BsPX1xddu3ZFdHQ05s2bh759++KZZ55BeHg4CgsLkZGR4XT7pKQkhIeHu2dYQ1XWsCLaDysWGxuL5ORknHrqqfDx8YGPjw9Wr16NZ599Fj4+PmjTpo32x2o4UceSkpJy99E+eWIhISE4+eSTsWfPHj03uuDYjhXRvlixiIgIx6vgZXr06OF4W1nZfnf8kcm0Tzo7UceKdO7cGWFhYfW6T2ph4QaWZWHatGn49NNPsWLFCkRGRp7wPps3bwZwdMeSypWWlqKgoADR0dFo0qQJli9f7rhu586dOHjwoNPnB6S8soYV0X5YsZEjR+LPP//E5s2bHX8GDBiAK6+80vHf2h9P7EQdK3opX/vkiWVnZ2Pv3r2IiIjQc6MLju1YEe2LFRsyZEi5Q+rv2rULHTt2BABERkYiPDzcaZ/MysrCunXrtE8e40QdK3L48GEcOXKkfvfJenttRBxuvvlmKzg42Fq1apWVkJDg+JObm2tZlmXt2bPHeuihh6wNGzZY+/btsz7//HOrc+fO1vDhw908uVlmzpxprV692tq3b5+1ZcsWa+bMmZbNZrN++OEHy7KOvs2sQ4cO1ooVK6wNGzZYgwcPLvcyYmNXVUPth645/i082h9r59iO2ier584777RWrVpl7du3z1qzZo01atQoKywszEpOTrYsS/tidVXVUfti9a1fv97y8fGx/vvf/1q7d++23nvvPatZs2bWu+++67jN/PnzrZCQEOvzzz+3tmzZYo0bN86KjIy08vLy3Di5WU7U0W63W3fddZe1du1aa9++fdayZcusU0891erWrZuVn59fb3NqYeEGACr888Ybb1iWdfR9xsOHD7dCQ0MtPz8/q2vXrtZ//vMfKzMz072DG+a6666zOnbsaPn6+lqtWrWyRo4c6VhUWJZl5eXlWbfccovVokULq1mzZtZFF11kJSQkuHFi81TVUPuha45fWGh/rJ1jO2qfrJ7LLrvMioiIsHx9fa127dpZl112mdP7/rUvVk9VHbUv1syXX35p9erVy/Lz87O6d+9uLV682On60tJS64EHHrDatGlj+fn5WSNHjrR27tzppmnNVVXH3Nxc65xzzrFatWplNWnSxOrYsaN14403WomJifU6o82yLKv+Xh8RERERERFPpM9YiIiIiIiIy7SwEBERERERl2lhISIiIiIiLtPCQkREREREXKaFhYiIiIiIuEwLCxERERERcZkWFiIiIiIi4jItLERERERExGVaWIiIiIiIiMu0sBARkXp16NAhxMTEoGfPnujTpw+WLFmieUREPIDNsizL3UOIiEjjkZCQgKSkJPTr1w+JiYmIjo7Grl270Lx5c80jItKA6RULERGpVxEREejXrx8AIDw8HGFhYUhLS6v2/WNiYmCz2WCz2bB58+Y6n2fKlCmO7X322Wcub09ExFNpYSEiIm4TGxuLkpIStG/fvkb3u/HGG5GQkIBevXrV+TzPPPMMEhISqNsREfFEPu4eQEREGqe0tDRcc801eOWVV2p832bNmiE8PLzat+/Xrx+Ki4vLXf7DDz+gbdu2Vc4THByM4ODgGs8oItLY6BULERGhuPnmmzF06NAKrzvppJMwf/58x98LCgowfvx4zJw5E2eccQZl+x9//DF69+6Npk2bomXLlhg1ahRycnIAAJs3b8bWrVvL/SlbVNTFPCIijY1esRAREZdt27YNixcvxs8//1zh9T169HB8HsKyLEyZMgVnnXUWrr76asr2ExIScMUVV+Dxxx/HRRddBLvdjp9//hnVOT5JXcwjItIYaWEhIiIue+KJJ3DaaadV+tv+0NBQJCYmAgDWrFmDjz76CH369HF8GPqdd95B7969a739hIQEFBcXY8KECejYsSMAVPvx6mIeEZHGSAsLERFxSXFxMZYuXYoHHnjAcdm//vUvDBw4ENdffz0AwG63o2nTpgCAoUOHorS0lDpD3759MXLkSPTu3RujR4/GOeecg0suuQQtWrQ44X3rYh4RkcZIn7EQERGX7N27F3a73fEb/tLSUixZsgSBgYGO22zZsgU9e/assxm8vb3x448/4ttvv0XPnj3x3HPPISoqCvv27auzbYqIiDMtLERExCUZGRkAgICAAADA999/j/T0dPj7+wMAfvvtN8TFxeGiiy6q0zlsNhuGDBmCuXPnYtOmTfD19cWnn35ap9sUEZF/6K1QIiLiko4dO8Jms+GDDz5A8+bNcdddd+G8887D559/jvbt2+Omm27CqFGjKj1iFMO6deuwfPlynHPOOWjdujXWrVuHlJQU9OjRo862KSIizrSwEBERl4SHh+O///0v5s+fj08++QSPPvoooqOjMW7cOHz00Ue44IIL8OKLL9bpDEFBQfjpp5+wcOFCZGVloWPHjnjqqacwduzYOt2uiIj8QwsLERFx2b333ot7773X6bL9+/fX2/Z79OiB7777rt62JyIi5ekzFiIi0uC8+OKLCAgIwJ9//lnn27rpppscnx8REZHK2azqnD1IRETEEHFxccjLywMAdOjQAb6+vnW6veTkZGRlZQEAIiIi0Lx58zrdnohIQ6WFhYiIiIiIuExvhRIREREREZdpYSEiIiIiIi7TwkJERERERFymhYWIiIiIiLhMCwsREREREXGZFhYiIiIiIuIyLSxERERERMRlWliIiIiIiIjLtLAQERERERGXaWEhIiIiIiIu08JCRERERERc9n9192O08ED6AAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafico Residui\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "# Residui con barre d'errore\n",
    "ax.errorbar(\n",
    "    dfSonar[\"Omega2t\"], dfSonar[\"Rc\"],\n",
    "    xerr=dfSonar[\"uOmega2t\"], yerr=dfSonar[\"uRc\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=\"Residui\"\n",
    ")\n",
    "\n",
    "# Linea dello zero\n",
    "ax.axhline(0, color='red', linestyle='--', linewidth=1)\n",
    "\n",
    "# Estetica\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(r\"$\\omega^2$  [s$^{-2}$]\")\n",
    "ax.set_ylabel(r\"Residuo $x_i - \\left(\\frac{y_i}{A} - \\frac{B}{A}\\right)$  [Kg$^{-1}$]\")\n",
    "ax.set_title(\"Residui della regressione — Molla 2 (cronometro)\")\n",
    "ax.legend()\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5ca1727",
   "metadata": {},
   "source": [
    "# Raccolta finale di tutti i dati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e87e24b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calibro Con tutti i dati\n",
      "AC      = 3.2009 ± 0.0034\n",
      "BC      = 1161.4138 ± 0.4687\n",
      "cov_ABC = 0.001385\n",
      "chi²    = 4.3252\n",
      "GdL     = 4\n",
      "P(chi², ∞)= 0.1150\n",
      "\n",
      "Calibro tolto il dato 1\n",
      "AC      = 3.1995 ± 0.0034\n",
      "BC      = 1161.4675 ± 0.4694\n",
      "cov_ABC = 0.001368\n",
      "chi²    = 0.0071\n",
      "GdL     = 3\n",
      "P(chi², ∞)= 0.9328\n",
      "\n",
      "Sonar caso statico (temperatura non considerata)\n",
      "AS      = 3.2192 ± 0.0027\n",
      "BS      = 2042.5203 ± 1.0326\n",
      "cov_ABS = 0.002760\n",
      "chi²    = 9.0319\n",
      "GdL     = 5\n",
      "P(chi², ∞)= 0.0289\n",
      "\n",
      "Sonar caso statico (temperatura considerata)\n",
      "AS      = 3.2369 ± 0.0035\n",
      "BS      = 2043.0827 ± 1.2689\n",
      "cov_ABS = 0.004333\n",
      "chi²    = 5.0621\n",
      "GdL     = 5\n",
      "P(chi², ∞)= 0.1673\n",
      "\n",
      "Sonar caso Dinamico\n",
      "AD      = 3.5059 ± 0.0010\n",
      "BD      = -1.1685 ± 0.0099\n",
      "cov_ABD = -0.000009\n",
      "chi²    = 3294.6334\n",
      "GdL     = 5\n",
      "P(chi², ∞)= 0.0000\n",
      "\n",
      "Cronometro\n",
      "ACr      = 3.8380 ± 0.0526\n",
      "BCr      = -1.0200 ± 0.5757\n",
      "cov_ABCr = -0.029589\n",
      "chi²    = 2.0359\n",
      "P(chi², ∞)= 0.5650\n"
     ]
    }
   ],
   "source": [
    "print(\"Calibro Con tutti i dati\")\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(\"\\nCalibro tolto il dato 1\")\n",
    "print(f\"AC      = {AC2:.4f} ± {uAC2:.4f}\")\n",
    "print(f\"BC      = {BC2:.4f} ± {uBC2:.4f}\")\n",
    "print(f\"cov_ABC = {covABC2:.6f}\")\n",
    "print(f\"chi²    = {chiC2:.4f}\")\n",
    "print(f\"GdL     = {len(dfCalibro_senza1)}\")\n",
    "print(f\"P(chi², ∞)= {PC2:.4f}\")\n",
    "\n",
    "print(\"\\nSonar caso statico (temperatura non considerata)\")\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 caso statico (temperatura considerata)\")\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 caso Dinamico\")\n",
    "print(f\"AD      = {AD:.4f} ± {uAD:.4f}\")\n",
    "print(f\"BD      = {BD:.4f} ± {uBD:.4f}\")\n",
    "print(f\"cov_ABD = {covABD:.6f}\")\n",
    "print(f\"chi²    = {chiD:.4f}\")\n",
    "print(f\"GdL     = {len(dfSonar)}\")\n",
    "print(f\"P(chi², ∞)= {PD:.4f}\")\n",
    "\n",
    "print(\"\\nCronometro\")\n",
    "print(f\"ACr      = {ACr:.4f} ± {uACr:.4f}\")\n",
    "print(f\"BCr      = {BCr:.4f} ± {uBCr:.4f}\")\n",
    "print(f\"cov_ABCr = {covABCr:.6f}\")\n",
    "print(f\"chi²    = {chiCr:.4f}\")\n",
    "print(f\"P(chi², ∞)= {PCr:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d4af85a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 45,
   "id": "57de948b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compatibilità: Calibro (tolto dato 1) vs Sonar statico (T considerata)\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ           9.2578     0.0115\n",
      "2σ           4.6289     0.0436\n",
      "3σ           3.0859     0.0909\n",
      "\n",
      "\n",
      "Compatibilità: Calibro (tolto dato 1) vs Sonar dinamico\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ         273.1973     0.0000\n",
      "2σ         136.5986     0.0001\n",
      "3σ          91.0658     0.0001\n",
      "\n",
      "\n",
      "Compatibilità: Sonar Dinamico vs Sonar statico (T considerata)\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ         128.2851     0.0000\n",
      "2σ          64.1425     0.0000\n",
      "3σ          42.7617     0.0000\n"
     ]
    }
   ],
   "source": [
    "# Ringraziate Cloude per questo incredibile print\n",
    "print(\"Compatibilità: Calibro (tolto dato 1) 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(AC2, uAC2*n, len(dfCalibro_senza1)-2,\n",
    "                                    AS2,  uAS2*n, len(dfSonar)-2)\n",
    "    print(f\"{n}σ         {res['t']:>8.4f} {res['p']:>10.4f}\")\n",
    "\n",
    "print(\"\\n\\nCompatibilità: Calibro (tolto dato 1) vs Sonar dinamico\")\n",
    "print(f\"{'Sigma':<10} {'t':>8} {'p-value':>10}\")\n",
    "print(\"-\" * 30)\n",
    "for n in [1, 2, 3]:\n",
    "    res = p_t_student_compatibilita(AC2, uAC2*n, len(dfCalibro_senza1)-2,\n",
    "                                    AD,  uAD*n,  len(dfSonar)-2)\n",
    "    print(f\"{n}σ         {res['t']:>8.4f} {res['p']:>10.4f}\")\n",
    "\n",
    "print(\"\\n\\nCompatibilità: Sonar Dinamico vs Sonar statico (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)-2,\n",
    "                                    AS2, uAS2*n, len(dfSonar)-2)\n",
    "    print(f\"{n}σ         {res['t']:>8.4f} {res['p']:>10.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b66ccc91",
   "metadata": {},
   "source": [
    "# Note finali\n",
    "SI VEDE LA RISONANZA E LA DEFORMAZIONE DELLE MASSE EQUIVALENTI (NON È LINEARE)"
   ]
  }
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