Lab1/mollaDefinitiva/molla-1/molla1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bdf377b8",
   "metadata": {},
   "source": [
    "Tutto quello che c'è da sapere sulla molla 1, possibilmente con la massima chiarezza."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3a34cc3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Heavy lifting\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "# Mostrare i dati\n",
    "import ipysheet\n",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Calcolo simbolico\n",
    "import sympy as sp\n",
    "\n",
    "\n",
    "G = 9.806\n",
    "uG = 0.004"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eaece9c",
   "metadata": {},
   "source": [
    "# Calibro"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "960e68a5",
   "metadata": {},
   "source": [
    "## Import dati e ricerca outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1e2fabea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Funzioni criterio di Chauvenet\n",
    "\n",
    "# Probabilità\n",
    "def p_t_student(valori, err_strumentale):\n",
    "  n   = len(valori)\n",
    "  GdL = n - 1\n",
    "  if n <= 2:\n",
    "    return np.ones(n)\n",
    "\n",
    "  media = valori.mean()\n",
    "  s     = valori.std(ddof=1)\n",
    "  if s == 0:\n",
    "    return np.ones(n)\n",
    "\n",
    "  s = np.sqrt(s**2 + err_strumentale**2)\n",
    "  return 2 * (1 - stats.t.cdf(np.abs(valori - media) / s, df=GdL))\n",
    "\n",
    "# Indice del peggiore outlier o None\n",
    "def trova_outlier(valori, err_strumentale):\n",
    "  n       = len(valori)\n",
    "  soglia  = 1 / (2 * n)\n",
    "  p       = p_t_student(valori, err_strumentale)\n",
    "  idx_min = np.argmin(p)\n",
    "\n",
    "  if p[idx_min] < soglia:\n",
    "      return idx_min\n",
    "  return None\n",
    "\n",
    "# Rimuove outlier\n",
    "def rimuovi_outlier(valori, err_strumentale):\n",
    "  rimossi  = []\n",
    "  campione = valori.copy()\n",
    "\n",
    "  while len(campione) > 2:\n",
    "    idx = trova_outlier(campione, err_strumentale)\n",
    "\n",
    "    if idx is None: # nessun outlier: stop\n",
    "      break\n",
    "\n",
    "    rimossi.append(campione[idx])\n",
    "    campione = np.delete(campione, idx)\n",
    "\n",
    "  return campione, rimossi\n",
    "\n",
    "# return media u rimossi []\n",
    "def stats_riga(valori_raw, err_strumentale):\n",
    "  # caso 0\n",
    "  if len(valori_raw) == 0:\n",
    "    return {\"media\": np.nan, \"u_strum\": err_strumentale, \"u_stat\": np.nan, \"u\": np.nan, \"n\": 0, \"rimossi\": []}\n",
    "\n",
    "  # caso 1\n",
    "  if len(valori_raw) == 1:\n",
    "    return {\"media\": valori_raw[0], \"u_strum\": err_strumentale, \"u_stat\": np.nan, \"u\": np.nan, \"n\": 1, \"rimossi\": []}\n",
    "\n",
    "  # caso n>1\n",
    "  puliti, rimossi = rimuovi_outlier(valori_raw, err_strumentale)\n",
    "\n",
    "  media = puliti.mean()\n",
    "\n",
    "  s = puliti.std(ddof=1)\n",
    "  n = len(puliti)\n",
    "  s = s / np.sqrt(n)\n",
    "  u = np.sqrt(s**2 + err_strumentale**2)\n",
    "\n",
    "  return {\"media\": media, \"u_strum\": err_strumentale, \"u_stat\": s, \"u\": u, \"n\": n, \"rimossi\": rimossi}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5f29a38d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accessor: aggiunta metodi ai df\n",
    "\n",
    "# Nasconde specificamente l'avviso di sovrascrittura dell'accessor\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", message=\"registration of accessor\")\n",
    "\n",
    "@pd.api.extensions.register_dataframe_accessor(\"acc\")\n",
    "\n",
    "class Miei_Metodi:\n",
    "\n",
    "    def __init__(self, pandas_obj):\n",
    "        self._df = pandas_obj\n",
    "    \n",
    "    # Calcola la statistica con Criterio di Chauvenet\n",
    "    def calcola_stats(self, df, prefix, err_strumentale):\n",
    "        cols = [col for col in df.columns if col.startswith(prefix)]\n",
    "\n",
    "        for i, row in df.iterrows():\n",
    "            valori = row[cols].dropna().values.astype(float)\n",
    "            ans    = stats_riga(valori, err_strumentale)\n",
    "\n",
    "            self._df.at[i, prefix]              = ans[\"media\"]\n",
    "            self._df.at[i, f\"u{prefix}_strum\"]  = ans[\"u_strum\"]\n",
    "            self._df.at[i, f\"u{prefix}_stat\"]   = ans[\"u_stat\"]\n",
    "            self._df.at[i, f\"u{prefix}\"]        = ans[\"u\"]\n",
    "            self._df.at[i, f\"n{prefix}\"]        = int(ans[\"n\"])\n",
    "            self._df.at[i, f\"out{prefix}\"]      = str(ans[\"rimossi\"])\n",
    "\n",
    "        return self._df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a5ff5ff5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F  = g*m\n",
      "sigma_F = sqrt(g**2*sigma_m**2 + m**2*sigma_g**2)\n",
      "\n",
      "Formula LaTeX:\n",
      "F = g m\n",
      "sigma_F = \\sqrt{g^{2} \\sigma_{m}^{2} + m^{2} \\sigma_{g}^{2}}\n"
     ]
    }
   ],
   "source": [
    "# Calcolo simbolico F\n",
    "# simboli\n",
    "m, x, g = sp.symbols('m x g', positive=True)\n",
    "sigma_m, sigma_x, sigma_g = sp.symbols('sigma_m sigma_x sigma_g', positive=True)\n",
    "\n",
    "F = m * g\n",
    "\n",
    "# derivate parziali\n",
    "dF_dm = sp.diff(F, m)\n",
    "dF_dg = sp.diff(F, g)\n",
    "\n",
    "sigma_F = sp.sqrt((dF_dm * sigma_m)**2 + (dF_dg * sigma_g)**2)\n",
    "\n",
    "print(\"F  =\", F)\n",
    "print(\"sigma_F =\", sigma_F)\n",
    "print(\"\\nFormula LaTeX:\")\n",
    "print(f\"F = {sp.latex(F)}\")\n",
    "print(f\"sigma_F = {sp.latex(sigma_F.simplify())}\")\n",
    "\n",
    "# funzioni numeriche\n",
    "F_fn  = sp.lambdify((m, g),  F,  'numpy')\n",
    "uF_fn = sp.lambdify((m, sigma_m, g, sigma_g), sigma_F, 'numpy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e649eddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dati, analisi statistica medie e ricerca outlier\n",
    "\n",
    "dfCalibro_raw = pd.read_csv(r'molla1Calibro.csv')\n",
    "\n",
    "# Costanti strumentali\n",
    "ERR_CALIBRO = 0.02 / np.sqrt(24)\n",
    "ERR_BILANCIA = 0.01 / np.sqrt(12)\n",
    "\n",
    "# Chauvenet e poi statistica di base\n",
    "dfCalibro = pd.DataFrame()\n",
    "dfCalibro = dfCalibro.acc.calcola_stats(dfCalibro_raw, \"dx\", ERR_CALIBRO)\n",
    "dfCalibro = dfCalibro.acc.calcola_stats(dfCalibro_raw, \"m\",  ERR_BILANCIA)\n",
    "\n",
    "# Aggiunta delle forze\n",
    "dfCalibro[\"F\"]  = F_fn(dfCalibro[\"m\"],  G)\n",
    "dfCalibro[\"uF\"] = uF_fn(dfCalibro[\"m\"], dfCalibro[\"um\"], G, uG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "42ff21d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a459321191844ca5a43de4699ff8c72b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, row_end=4, row_start=0, squeeze_row=False, type='numeric', val…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheet = ipysheet.from_dataframe(dfCalibro)\n",
    "\n",
    "display(sheet)\n",
    "# dfCalibro.head(2).to_json(orient=\"records\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c58a6e26",
   "metadata": {},
   "source": [
    "## Regressione lineare\n",
    "Regressione e residui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0500f913",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Funzioni regressione\n",
    "\n",
    "def sigma_y_equiv(x, y, sigma_x, sigma_y):\n",
    "  # Stima iniziale di A con sola sigma_y\n",
    "  sy2 = sigma_y**2\n",
    "  Sw   = np.sum(1 / sy2)\n",
    "  Sx   = np.sum(x / sy2)\n",
    "  Sxx  = np.sum(x**2 / sy2)\n",
    "  Sy   = np.sum(y / sy2)\n",
    "  Sxy  = np.sum(x * y / sy2)\n",
    "  delta = Sxx * Sw - Sx**2\n",
    "  A_est = (Sxy * Sw - Sx * Sy) / delta\n",
    "\n",
    "  # Propagazione\n",
    "  sigma_eq = np.sqrt(sigma_y**2 + A_est**2 * sigma_x**2)\n",
    "  return sigma_eq\n",
    "\n",
    "\n",
    "def reg_lin(x, y, sigma_x, sigma_y):\n",
    "  x = np.asarray(x, dtype=float)\n",
    "  y = np.asarray(y, dtype=float)\n",
    "  sigma_x = np.asarray(sigma_x, dtype=float)\n",
    "  sigma_y = np.asarray(sigma_y, dtype=float)\n",
    "\n",
    "  # Propagazione errore x → y\n",
    "  sigma_y_eq = sigma_y_equiv(x, y, sigma_x, sigma_y)\n",
    "\n",
    "  # Somme pesate\n",
    "  w    = 1.0 / sigma_y_eq**2\n",
    "  Sw   = np.sum(w)\n",
    "  Sx   = np.sum(w * x)\n",
    "  Sxx  = np.sum(w * x**2)\n",
    "  Sy   = np.sum(w * y)\n",
    "  Sxy  = np.sum(w * x * y)\n",
    "  delta = Sxx * Sw - Sx**2\n",
    "\n",
    "  # Parametri\n",
    "  A      = (Sxy * Sw - Sx * Sy)  / delta\n",
    "  B      = (Sxx * Sy - Sxy * Sx) / delta\n",
    "  sigma_A = np.sqrt(Sw  / delta)\n",
    "  sigma_B = np.sqrt(Sxx / delta)\n",
    "  cov_AB  = -Sx / delta\n",
    "\n",
    "  # Chi quadro\n",
    "  x2  = np.sum((y - A * x - B)**2 / sigma_y_eq**2)\n",
    "  dof = len(x) - 2\n",
    "  P = stats.chi2.sf(x2, dof)\n",
    "\n",
    "  return A, B, sigma_A, sigma_B, cov_AB, x2, P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5d7ca0a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residuo:\n",
      "  r = x + B/A - y/A\n",
      "\n",
      "Formula LaTeX:\n",
      "  r       = x + \\frac{B}{A} - \\frac{y}{A}\n",
      "  sigma_r = \\sqrt{\\frac{A^{4} \\sigma_{x}^{2} + A^{2} \\left(\\sigma_{B}^{2} + \\sigma_{y}^{2}\\right) - 2 A cov_{AB} \\left(B - y\\right) + \\sigma_{A}^{2} \\left(B - y\\right)^{2}}{A^{4}}}\n"
     ]
    }
   ],
   "source": [
    "# Calcolo simbolico residui\n",
    "\n",
    "A, B, x, y = sp.symbols('A B x y', real=True)\n",
    "sigma_A, sigma_B, sigma_x, sigma_y = sp.symbols('sigma_A sigma_B sigma_x sigma_y', positive=True)\n",
    "cov_AB = sp.symbols('cov_AB', real=True)\n",
    "\n",
    "# Residuo: r = x - (y/A - B/A)\n",
    "r = x - (y / A - B / A)\n",
    "\n",
    "# Propagazione errore (con covarianza A,B)\n",
    "dr_dA  = sp.diff(r, A)\n",
    "dr_dB  = sp.diff(r, B)\n",
    "dr_dx  = sp.diff(r, x)\n",
    "dr_dy  = sp.diff(r, y)\n",
    "\n",
    "sigma_r  = sp.sqrt(\n",
    "  (dr_dA * sigma_A)**2 +\n",
    "  (dr_dB * sigma_B)**2 +\n",
    "  (dr_dx * sigma_x)**2 +\n",
    "  (dr_dy * sigma_y)**2 +\n",
    "  2 * dr_dA * dr_dB * cov_AB\n",
    ")\n",
    "\n",
    "# Funzioni numeriche\n",
    "r_fn      = sp.lambdify((x , y , A , B ), r, 'numpy')\n",
    "sigma_r_fn = sp.lambdify(\n",
    "  (x , y , A , B , sigma_x , sigma_y , sigma_A , sigma_B , cov_AB ),\n",
    "  sigma_r , 'numpy'\n",
    ")\n",
    "\n",
    "print(\"Residuo:\")\n",
    "print(f\"  r = {r }\")\n",
    "print(f\"\\nFormula LaTeX:\")\n",
    "print(f\"  r       = {sp.latex(r)}\")\n",
    "print(f\"  sigma_r = {sp.latex(sp.simplify(sigma_r))}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cd68b201",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AC      = 23.9432 ± 0.0422\n",
      "BC      = 2719.8356 ± 2.8402\n",
      "cov_ABC = 0.118646\n",
      "chi²    = 56.2014\n",
      "P(chi², ∞)= 0.0000\n"
     ]
    }
   ],
   "source": [
    "# Regressione lineare\n",
    "\n",
    "AC, BC, uAC, uBC, covABC, chiC, PC = reg_lin(-dfCalibro[\"dx\"], dfCalibro[\"F\"], dfCalibro[\"udx\"], dfCalibro[\"uF\"])\n",
    "print(\"Ax + B : \")\n",
    "print(f\"AC      = {AC:.4f} ± {uAC:.4f}\")\n",
    "print(f\"BC      = {BC:.4f} ± {uBC:.4f}\")\n",
    "print(f\"cov_ABC = {covABC:.6f}\")\n",
    "print(f\"chi²    = {chiC:.4f}\")\n",
    "print(f\"P(chi², ∞)= {PC:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "68eb66dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot regressione lineare\n",
    "\n",
    "SCALA = 50\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "x_data = -dfCalibro[\"dx\"]\n",
    "y_data =  dfCalibro[\"F\"]\n",
    "\n",
    "x_fit = np.linspace(x_data.min(), x_data.max(), 300)\n",
    "y_fit = AC * x_fit + BC\n",
    "\n",
    "ax.errorbar(\n",
    "    x_data, y_data,\n",
    "    xerr=SCALA * dfCalibro[\"udx\"], yerr=SCALA * dfCalibro[\"uF\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=f\"Dati (barre errore x{SCALA})\"\n",
    ")\n",
    "ax.plot(\n",
    "    x_fit, y_fit,\n",
    "    color='red', linewidth=1.5,\n",
    "    label=f\"$A={AC:.3f}\\\\pm{uAC:.3f}$\\n\"\n",
    "          f\"$B={BC:.1f}\\\\pm{uBC:.1f}$\\n\"\n",
    "          f\"$P(chi², ∞)= {PC:.4f}$\\n\"\n",
    ")\n",
    "\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(r\"$- x$  [mm]\")\n",
    "ax.set_ylabel(\"F  [mN]\")\n",
    "ax.set_title(\"Fit lineare — Molla 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": 52,
   "id": "f98a3612",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui\n",
    "\n",
    "dfCalibro[\"r\"] = r_fn(\n",
    "  -dfCalibro[\"dx\"],\n",
    "  dfCalibro[\"F\"],\n",
    "  AC,\n",
    "  BC\n",
    ")\n",
    "\n",
    "dfCalibro[\"ur\"] = sigma_r_fn(\n",
    "  -dfCalibro[\"dx\"], dfCalibro[\"F\"],\n",
    "  AC, BC,\n",
    "  dfCalibro[\"udx\"], dfCalibro[\"uF\"],\n",
    "  uAC, uBC, covABC\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "831a4ac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot residui\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "# Residui con barre d'errore\n",
    "ax.errorbar(\n",
    "    dfCalibro[\"F\"], dfCalibro[\"r\"],\n",
    "    xerr=dfCalibro[\"uF\"], yerr=dfCalibro[\"ur\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=\"Residui\"\n",
    ")\n",
    "\n",
    "# Linea dello zero\n",
    "ax.axhline(0, color='red', linestyle='--', linewidth=1)\n",
    "\n",
    "# Estetica\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(\"F  [mN]\")\n",
    "ax.set_ylabel(r\"Residuo $x_i - \\left(\\frac{y_i}{A} - \\frac{B}{A}\\right)$  [mm]\")\n",
    "ax.set_title(\"Residui della regressione — Molla 1 (calibro)\")\n",
    "ax.legend()\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4af43cc4",
   "metadata": {},
   "source": [
    "### Rimozione outlier\n",
    "Il punto 2 sembra molto fuori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a345ce91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indice outlier residui: None\n"
     ]
    }
   ],
   "source": [
    "# Outlier residui\n",
    "outR = trova_outlier(dfCalibro['r'], dfCalibro['ur'])\n",
    "print(f\"Indice outlier residui: {outR}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4d8e0c76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit completo:   chi²=56.20,  P=0.0000\n",
      "Senza punto 1:  chi²=1.811,  P=0.4044\n",
      "\n",
      "A:  23.9432 ± 0.0422  to  23.8834 ± 0.0429\n",
      "B:  2719.8356 ± 2.8402  to  2718.1321 ± 2.8464\n"
     ]
    }
   ],
   "source": [
    "# Regressione lineare\n",
    "\n",
    "mask = dfCalibro.index != 1\n",
    "dfCalibro_senza1 = dfCalibro.loc[mask]\n",
    "dfCalibro_senza1 = dfCalibro_senza1.copy()\n",
    "\n",
    "AC2, BC2, uAC2, uBC2, covABC2, chiC2, PC2 = reg_lin(-dfCalibro_senza1[\"dx\"], dfCalibro_senza1[\"F\"], dfCalibro_senza1[\"udx\"], dfCalibro_senza1[\"uF\"])\n",
    "\n",
    "print(f\"Fit completo:   chi²={chiC:.2f},  P={PC:.4f}\")\n",
    "print(f\"Senza punto 1:  chi²={chiC2:.3f},  P={PC2:.4f}\")\n",
    "print()\n",
    "print(f\"A:  {AC:.4f} ± {uAC:.4f}  to  {AC2:.4f} ± {uAC2:.4f}\")\n",
    "print(f\"B:  {BC:.4f} ± {uBC:.4f}  to  {BC2:.4f} ± {uBC2:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d75d9912",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot regressione\n",
    "\n",
    "SCALA = 10\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "x_data = -dfCalibro_senza1[\"dx\"]\n",
    "y_data =  dfCalibro_senza1[\"F\"]\n",
    "\n",
    "x_fit = np.linspace(x_data.min(), x_data.max(), 300)\n",
    "y_fit = AC2 * x_fit + BC2\n",
    "\n",
    "ax.errorbar(\n",
    "    x_data, y_data,\n",
    "    xerr=SCALA * dfCalibro_senza1[\"udx\"], yerr=SCALA * dfCalibro_senza1[\"uF\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=f\"Dati (barre errore x{SCALA})\"\n",
    ")\n",
    "ax.plot(\n",
    "    x_fit, y_fit,\n",
    "    color='red', linewidth=1.5,\n",
    "    label=f\"Fit: $A={AC2:.3f}\\\\pm{uAC2:.3f}$\\n\"\n",
    "          f\"     $B={BC2:.2f}\\\\pm{uBC2:.2f}$\\n\"\n",
    ")\n",
    "\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(r\"$- x$  [mm]\")\n",
    "ax.set_ylabel(\"F  [mN]\")\n",
    "ax.set_title(\"Fit lineare — Molla 1 (calibro tolto dato 1)\")\n",
    "ax.legend(fontsize=9)\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "26c5dc3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui\n",
    "\n",
    "dfCalibro_senza1[\"r\"] = r_fn(\n",
    "  -dfCalibro_senza1[\"dx\"],\n",
    "   dfCalibro_senza1[\"F\"],\n",
    "   AC2, BC2\n",
    ")\n",
    "\n",
    "dfCalibro_senza1[\"ur\"] = sigma_r_fn(\n",
    "  -dfCalibro_senza1[\"dx\"], dfCalibro_senza1[\"F\"],\n",
    "   AC2, BC2,\n",
    "   dfCalibro_senza1[\"udx\"], dfCalibro_senza1[\"uF\"],\n",
    "   uAC2, uBC2, covABC2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "278a5713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot residui\n",
    "\n",
    "# WARNING: non intuitivo, ma molto molto figo\n",
    "fig, axes = plt.subplots(1, 2, figsize=(13, 5), sharey=True)\n",
    "\n",
    "for ax, (r_col, ur_col, f_col, uf_col, df_plot, titolo) in zip(axes, [\n",
    "  (\"r\",  \"ur\",  \"F\", \"uF\", dfCalibro, \"Fit completo (tutti i punti)\"),\n",
    "  (\"r\", \"ur\", \"F\", \"uF\",   dfCalibro_senza1,  \"Fit senza punto 1\"),\n",
    "]):\n",
    "  ax.errorbar(\n",
    "    df_plot[f_col], df_plot[r_col],\n",
    "    xerr=df_plot[uf_col], yerr=df_plot[ur_col],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=\"Residui\"\n",
    "  )\n",
    "  ax.axhline(0, color='red', linestyle='--', linewidth=1)\n",
    "  sns.despine(ax=ax)\n",
    "  ax.set_xlabel(\"F  [mN]\")\n",
    "  ax.set_title(titolo)\n",
    "  ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "axes[0].set_ylabel(r\"Residuo $x_i - \\left(\\frac{y_i}{A} - \\frac{B}{A}\\right)$  [mm]\")\n",
    "fig.suptitle(\"Confronto residui — Molla 1 (calibro)\", y=1.02)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e0cfa7",
   "metadata": {},
   "source": [
    "# Sonar statico"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "988414a6",
   "metadata": {},
   "source": [
    "## Sonar senza correzione $v_{suono}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e347644",
   "metadata": {},
   "source": [
    "### Import dei dati, medie e incertezze\n",
    "- masse trattate come prima\n",
    "- incertezza $\\omega$ e c da max{ semidispersione, errore software } (errore di risoluzione trascurabile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d6195b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfSonar_raw = pd.read_csv(r'molla1Sonar.csv')\n",
    "\n",
    "# Bilancia trattata esattamente come prima\n",
    "dfSonar = pd.DataFrame()\n",
    "dfSonar = dfSonar.acc.calcola_stats(dfSonar_raw, \"m\",  ERR_BILANCIA)\n",
    "\n",
    "# Semidispersioni su w e uw\n",
    "dfSonar['w']  = (dfSonar_raw['w1'] + dfSonar_raw['w2']) / 2\n",
    "dfSonar['uw'] = np.maximum((dfSonar_raw['w1'] - dfSonar_raw['w2']).abs() / 2,\n",
    "                            np.sqrt(dfSonar_raw['uw1']**2 + dfSonar_raw['uw2']**2))\n",
    "\n",
    "# Semidispersione su c e uc\n",
    "dfSonar['c']  = (dfSonar_raw['c1'] + dfSonar_raw['c2']) / 2\n",
    "dfSonar['uc'] = np.maximum((dfSonar_raw['c1'] - dfSonar_raw['c2']).abs() / 2,\n",
    "                            np.sqrt(dfSonar_raw['uc1']**2 + dfSonar_raw['uc2']**2))\n",
    "\n",
    "# Aggiunta delle forze\n",
    "dfSonar[\"F\"]  = F_fn(dfSonar[\"m\"],  G)\n",
    "dfSonar[\"uF\"] = uF_fn(dfSonar[\"m\"], dfSonar[\"um\"], G, uG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "02e2d183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b5e85ac7e5114d4e81aa5bfa0b9d6d68",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, row_start=0, squeeze_row=False, type='numeric', value=[108.61,…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheet = ipysheet.from_dataframe(dfSonar)\n",
    "\n",
    "display(sheet)\n",
    "# dfSonar.head(4).to_json(orient=\"records\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c49606b",
   "metadata": {},
   "source": [
    "### Regressione lineare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "06f9ccef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AS      = 23.5225 ± 0.0853\n",
      "BS      = 7371.9487 ± 21.4728\n",
      "cov_ABS = 1.830281\n",
      "chi²    = 3.8872\n",
      "P(chi², ∞)= 0.1432\n"
     ]
    }
   ],
   "source": [
    "# Regressione lineare\n",
    "\n",
    "AS, BS, uAS, uBS, covABS, chiS, PS = reg_lin(-dfSonar[\"c\"], dfSonar[\"F\"], dfSonar[\"uc\"], dfSonar[\"uF\"])\n",
    "print(\"Ax + B : \")\n",
    "print(f\"AS      = {AS:.4f} ± {uAS:.4f}\")\n",
    "print(f\"BS      = {BS:.4f} ± {uBS:.4f}\")\n",
    "print(f\"cov_ABS = {covABS:.6f}\")\n",
    "print(f\"chi²    = {chiS:.4f}\")\n",
    "print(f\"P(chi², ∞)= {PS:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "75653d7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot regressione\n",
    "\n",
    "SCALA = 20\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "x_data = -dfSonar[\"c\"]\n",
    "y_data =  dfSonar[\"F\"]\n",
    "\n",
    "x_fit = np.linspace(x_data.min(), x_data.max(), 300)\n",
    "y_fit = AS * x_fit + BS\n",
    "\n",
    "ax.errorbar(\n",
    "    x_data, y_data,\n",
    "    xerr=SCALA * dfSonar[\"uc\"], yerr=SCALA * dfSonar[\"uF\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=f\"Dati (barre errore x{SCALA})\"\n",
    ")\n",
    "ax.plot(\n",
    "    x_fit, y_fit,\n",
    "    color='red', linewidth=1.5,\n",
    "    label=f\"$A={AS:.3f}\\\\pm{uAS:.3f}$\\n\"\n",
    "          f\"$B={BS:.0f}\\\\pm{uBS:.0f}$\\n\"\n",
    "          f\"$P(chi², ∞)= {PS:.4f}$\\n\"\n",
    ")\n",
    "\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(r\"$- x$  [mm]\")\n",
    "ax.set_ylabel(\"F  [mN]\")\n",
    "ax.set_title(\"Fit lineare — Molla 1 (sonar statico senza correzione)\")\n",
    "ax.legend(fontsize=9)\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "09445aac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui\n",
    "\n",
    "dfSonar[\"r\"] = r_fn(\n",
    "  -dfSonar[\"c\"],\n",
    "  dfSonar[\"F\"],\n",
    "  AS,\n",
    "  BS\n",
    ")\n",
    "\n",
    "dfSonar[\"ur\"] = sigma_r_fn(\n",
    "  -dfSonar[\"c\"], dfSonar[\"F\"],\n",
    "  AS, BS,\n",
    "  dfSonar[\"uc\"], dfSonar[\"uF\"],\n",
    "  uAS, uBS, covABS\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1ecf9ab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Residui\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "# Residui con barre d'errore\n",
    "ax.errorbar(\n",
    "    dfSonar[\"F\"], dfSonar[\"r\"],\n",
    "    xerr=dfSonar[\"uF\"], yerr=dfSonar[\"ur\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=\"Residui\"\n",
    ")\n",
    "\n",
    "# Linea dello zero\n",
    "ax.axhline(0, color='red', linestyle='--', linewidth=1)\n",
    "\n",
    "# Estetica\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(\"F  [mN]\")\n",
    "ax.set_ylabel(r\"Residuo $x_i - \\left(\\frac{y_i}{A} - \\frac{B}{A}\\right)$  [mm]\")\n",
    "ax.set_title(\"Residui della regressione — Molla 1 (sonar statico senza correzione)\")\n",
    "ax.legend()\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29d0b0ab",
   "metadata": {},
   "source": [
    "## Sonar con correzione $v_{suono}$\n",
    "- La velocità del suono dipende dalla temperatura -> anche la distanza misurata dal sonar\n",
    "- Formula di Cramer 1993\n",
    "- !! Io (Giulia) ho usato un'altra formula, non capisco il perchè di quella di Jacopo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac818d04",
   "metadata": {},
   "source": [
    "### Correzione distanza sonar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4088453c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c = 0.049862877088008*c_raw*v0/sqrt(T + 273.15)\n",
      "sigma_c = v0*sqrt(0.000621576627873448*c_raw**2*sigma_T**2 + 0.00248630651149379*sigma_c_raw**2*(T + 273.15)**2)/(T + 273.15)**(3/2)\n",
      "\n",
      "Formula LaTeX:\n",
      "c = \\frac{0.049862877088008 c_{raw} v_{0}}{\\sqrt{T + 273.15}}\n",
      "sigma_c = \\frac{v_{0} \\sqrt{0.000621576627873448 c_{raw}^{2} \\sigma_{T}^{2} + 0.00248630651149379 \\sigma_{c raw}^{2} \\left(T + 273.15\\right)^{2}}}{\\left(T + 273.15\\right)^{\\frac{3}{2}}}\n"
     ]
    }
   ],
   "source": [
    "# Funzioni c\n",
    "\n",
    "# simboli\n",
    "v0, T, c_raw = sp.symbols('v0 T c_raw', positive=True)\n",
    "sigma_T, sigma_c_raw = sp.symbols('sigma_T sigma_c_raw', positive=True)\n",
    "\n",
    "# velocità corretta e posizione\n",
    "# v = 331.3 * sp.sqrt(1 + T / 273.15)\n",
    "v = 20.055 * sp.sqrt(T + 273.15)\n",
    "c = c_raw * v0 / v\n",
    "\n",
    "# derivate parziali\n",
    "dc_dcraw = sp.diff(c, c_raw)\n",
    "dc_dT    = sp.diff(c, T)\n",
    "\n",
    "sigma_c = sp.sqrt((dc_dcraw * sigma_c_raw)**2 + (dc_dT * sigma_T)**2)\n",
    "\n",
    "print(\"c =\", c)\n",
    "print(\"sigma_c =\", sigma_c.simplify())\n",
    "print(\"\\nFormula LaTeX:\")\n",
    "print(f\"c = {sp.latex(c)}\")\n",
    "print(f\"sigma_c = {sp.latex(sigma_c.simplify())}\")\n",
    "\n",
    "# funzioni numeriche\n",
    "c_fn  = sp.lambdify((c_raw, T, v0), c, 'numpy')\n",
    "uc_fn = sp.lambdify((c_raw, sigma_c_raw, T, sigma_T, v0), sigma_c, 'numpy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1584a5d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico c\n",
    "\n",
    "Temperatura = (23.6+21.2) / 2\n",
    "Temperatura1 = 21.2\n",
    "Temperatura2 = 23.6\n",
    "uTemperatura = (23.6-21.2) / np.sqrt(12)\n",
    "Velocita_impostata = 343.0\n",
    "\n",
    "dfSonarCorrezione = dfSonar.copy()\n",
    "\n",
    "dfSonarCorrezione['c']  =  c_fn(dfSonarCorrezione['c'], Temperatura, Velocita_impostata)\n",
    "dfSonarCorrezione['uc'] = uc_fn(dfSonarCorrezione['c'], dfSonarCorrezione['uc'],\n",
    "                      Temperatura, uTemperatura,\n",
    "                      Velocita_impostata)\n",
    "dfSonarCorrezione['cA']  =  c_fn(dfSonarCorrezione['c'], Temperatura1, Velocita_impostata)\n",
    "dfSonarCorrezione['cB']  =  c_fn(dfSonarCorrezione['c'], Temperatura2, Velocita_impostata)\n",
    "dfSonarCorrezione['vecchioC'] = dfSonar['c']\n",
    "dfSonarCorrezione['vecchioUc'] = dfSonar['uc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ac4860fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2edff6df7c984567a1c9326fc1324973",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, row_start=0, squeeze_row=False, type='numeric', value=[108.61,…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sheet = ipysheet.from_dataframe(dfSonarCorrezione)\n",
    "\n",
    "display(sheet)\n",
    "# dfSonar.head(4).to_json(orient=\"records\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d8c8b79",
   "metadata": {},
   "source": [
    "### Regressione lineare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8917049c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit completo:   chi²=3.89,  P=0.1432\n",
      "Temperatura :  chi²=0.227,  P=0.8927\n",
      "\n",
      "A:  23.5225 ± 0.0853  to  23.6041 ± 0.3869\n",
      "B:  7371.9487 ± 21.4728  to  7361.9067 ± 98.1235\n"
     ]
    }
   ],
   "source": [
    "# Regressione lineare\n",
    "\n",
    "AS2, BS2, uAS2, uBS2, covABS2, chiS2, PS2 = reg_lin(-dfSonarCorrezione[\"c\"], dfSonarCorrezione[\"F\"], dfSonarCorrezione[\"uc\"], dfSonarCorrezione[\"uF\"])\n",
    "\n",
    "print(f\"Fit completo:   chi²={chiS:.2f},  P={PS:.4f}\")\n",
    "print(f\"Temperatura :  chi²={chiS2:.3f},  P={PS2:.4f}\")\n",
    "print()\n",
    "print(f\"A:  {AS:.4f} ± {uAS:.4f}  to  {AS2:.4f} ± {uAS2:.4f}\")\n",
    "print(f\"B:  {BS:.4f} ± {uBS:.4f}  to  {BS2:.4f} ± {uBS2:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d5646b8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot regressione\n",
    "\n",
    "SCALA = 10\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "x_data = -dfSonarCorrezione[\"c\"]\n",
    "y_data =  dfSonarCorrezione[\"F\"]\n",
    "\n",
    "x_fit = np.linspace(x_data.min(), x_data.max(), 300)\n",
    "y_fit = AS2 * x_fit + BS2\n",
    "\n",
    "ax.errorbar(\n",
    "    x_data, y_data,\n",
    "    xerr=SCALA * dfSonarCorrezione[\"uc\"], yerr=SCALA * dfSonarCorrezione[\"uF\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=f\"Dati (barre errore x{SCALA})\"\n",
    ")\n",
    "ax.plot(\n",
    "    x_fit, y_fit,\n",
    "    color='red', linewidth=1.5,\n",
    "    label=f\"$A={AS2:.3f}\\\\pm{uAS:.3f}$\\n\"\n",
    "          f\"$B={BS2:.0f}\\\\pm{uBS:.0f}$\\n\"\n",
    "          f\"$P(chi², ∞)= {PS2:.4f}$\\n\"\n",
    ")\n",
    "\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(r\"$- x$  [mm]\")\n",
    "ax.set_ylabel(\"F  [mN]\")\n",
    "ax.set_title(\"Fit lineare — Molla 1 (sonar statico con correzione)\")\n",
    "ax.legend(fontsize=9)\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e35a9456",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui\n",
    "\n",
    "dfSonarCorrezione[\"r\"] = r_fn(\n",
    "  -dfSonarCorrezione[\"c\"],\n",
    "  dfSonarCorrezione[\"F\"],\n",
    "  AS2,\n",
    "  BS2\n",
    ")\n",
    "\n",
    "dfSonarCorrezione[\"ur\"] = sigma_r_fn(\n",
    "  -dfSonarCorrezione[\"c\"], dfSonarCorrezione[\"F\"],\n",
    "  AS2, BS2,\n",
    "  dfSonarCorrezione[\"uc\"], dfSonarCorrezione[\"uF\"],\n",
    "  uAS2, uBS2, covABS2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8de2a48e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot residui\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "# Residui con barre d'errore\n",
    "ax.errorbar(\n",
    "    dfSonarCorrezione[\"F\"], dfSonarCorrezione[\"r\"],\n",
    "    xerr=dfSonarCorrezione[\"uF\"], yerr=dfSonarCorrezione[\"ur\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=\"Residui\"\n",
    ")\n",
    "\n",
    "# Linea dello zero\n",
    "ax.axhline(0, color='red', linestyle='--', linewidth=1)\n",
    "\n",
    "# Estetica\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(\"F  [mN]\")\n",
    "ax.set_ylabel(r\"Residuo $x_i - \\left(\\frac{y_i}{A} - \\frac{B}{A}\\right)$  [mm]\")\n",
    "ax.set_title(\"Residui della regressione — Molla 1 (sonar statico con correzione)\")\n",
    "ax.legend()\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6ca9f7e",
   "metadata": {},
   "source": [
    "# Sonar dinamico\n",
    "Ancora non abbiamo il parametro $\\alpha$, quindi useremo $\\alpha = \\frac 1 3$. Lo imposto nel csv così è più facile da cambiare con il metodo della molla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "af6143df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Massa molla = 29.8933 ± 0.0044\n"
     ]
    }
   ],
   "source": [
    "# Calcolo numerico massa molla\n",
    "\n",
    "dfMasse_raw = pd.DataFrame([[29.90, 29.89, 29.89]], columns=[\"m1\", \"m2\", \"m3\"])\n",
    "\n",
    "dfMasse = pd.DataFrame()\n",
    "dfMasse = dfMasse.acc.calcola_stats(dfMasse_raw, \"m\",  ERR_BILANCIA)\n",
    "\n",
    "massaMolla  = dfMasse.m[0]\n",
    "umassaMolla = dfMasse.um[0]\n",
    "print(f\"Massa molla = {massaMolla:.4f} ± {umassaMolla:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a1e69bd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/Meq = 1000/(alp*m_molla + m_osc)\n",
      "sigma_1/Meq = 1000*sqrt(alp**2*sigma_m_molla**2 + m_molla**2*sigma_alp**2 + sigma_m_osc**2)/(alp*m_molla + m_osc)**2\n",
      "\n",
      "Formula LaTeX:\n",
      "1/Meq = \\frac{1000}{alp m_{molla} + m_{osc}}\n",
      "sigma_1/Meq = \\frac{1000 \\sqrt{alp^{2} \\sigma_{m molla}^{2} + m_{molla}^{2} \\sigma_{alp}^{2} + \\sigma_{m osc}^{2}}}{\\left(alp m_{molla} + m_{osc}\\right)^{2}}\n"
     ]
    }
   ],
   "source": [
    "# Calcolo simbolico Meq\n",
    "\n",
    "# simboli\n",
    "m_osc, alp, m_molla = sp.symbols('m_osc alp m_molla', positive=True)\n",
    "sigma_m_osc, sigma_alp, sigma_m_molla = sp.symbols('sigma_m_osc sigma_alp sigma_m_molla', positive=True)\n",
    "\n",
    "# massa equivalente\n",
    "Meq = 1 / (m_osc + alp * m_molla) * 1000\n",
    "\n",
    "# derivate parziali\n",
    "dMeq_dm_osc   = sp.diff(Meq, m_osc)\n",
    "dMeq_dalp     = sp.diff(Meq, alp)\n",
    "dMeq_dm_molla = sp.diff(Meq, m_molla)\n",
    "\n",
    "sigma_Meq = sp.sqrt(\n",
    "    (dMeq_dm_osc   * sigma_m_osc  )**2 +\n",
    "    (dMeq_dalp     * sigma_alp    )**2 +\n",
    "    (dMeq_dm_molla * sigma_m_molla)**2\n",
    ")\n",
    "\n",
    "print(\"1/Meq =\", Meq)\n",
    "print(\"sigma_1/Meq =\", sigma_Meq.simplify())\n",
    "print(\"\\nFormula LaTeX:\")\n",
    "print(f\"1/Meq = {sp.latex(Meq)}\")\n",
    "print(f\"sigma_1/Meq = {sp.latex(sigma_Meq.simplify())}\")\n",
    "\n",
    "# funzioni numeriche\n",
    "Meq_fn  = sp.lambdify((m_osc, alp, m_molla), Meq, 'numpy')\n",
    "uMeq_fn = sp.lambdify((m_osc, sigma_m_osc, alp, sigma_alp, m_molla, sigma_m_molla), sigma_Meq, 'numpy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "56679fc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w2 = w**2\n",
      "sigma_w2 = 2*sigma_w*w\n",
      "\n",
      "Formula LaTeX:\n",
      "w2 = w^{2}\n",
      "sigma_w2 = 2 \\sigma_{w} w\n"
     ]
    }
   ],
   "source": [
    "# Calcolo simbolico w^2\n",
    "\n",
    "# simboli\n",
    "w = sp.symbols('w', positive=True)\n",
    "sigma_w = sp.symbols('sigma_w', positive=True)\n",
    "\n",
    "# omega^2\n",
    "w2 = w**2\n",
    "\n",
    "# derivata parziale\n",
    "dw2_dw = sp.diff(w2, w)\n",
    "\n",
    "sigma_w2 = sp.sqrt((dw2_dw * sigma_w)**2)\n",
    "\n",
    "print(\"w2 =\", w2)\n",
    "print(\"sigma_w2 =\", sigma_w2.simplify())\n",
    "print(\"\\nFormula LaTeX:\")\n",
    "print(f\"w2 = {sp.latex(w2)}\")\n",
    "print(f\"sigma_w2 = {sp.latex(sigma_w2.simplify())}\")\n",
    "\n",
    "# funzioni numeriche\n",
    "w2_fn  = sp.lambdify((w), w2, 'numpy')\n",
    "uw2_fn = sp.lambdify((w, sigma_w), sigma_w2, 'numpy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "455271a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d526367a60e4b03992bdae29ec743d8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, row_start=0, squeeze_row=False, type='numeric', value=[108.61,…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calcolo numerico Meq e w^2\n",
    "\n",
    "dfSonar['alp'] = 1/3\n",
    "dfSonar['ualp'] = 0.000001\n",
    "\n",
    "dfSonar['1/Meq'] = Meq_fn(dfSonar['m'], dfSonar['alp'], massaMolla)\n",
    "dfSonar['u1/Meq']= uMeq_fn(dfSonar['m'], dfSonar['um'], dfSonar['alp'], dfSonar['ualp'], massaMolla, umassaMolla)\n",
    "\n",
    "dfSonar['Omega2'] = w2_fn(dfSonar['w'])\n",
    "dfSonar['uOmega2']= uw2_fn(dfSonar['w'], dfSonar['uw'])\n",
    "\n",
    "\n",
    "sheet = ipysheet.from_dataframe(dfSonar)\n",
    "display(sheet)\n",
    "# dfSonar.head(4).to_json(orient=\"records\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56713ba2",
   "metadata": {},
   "source": [
    "## Regressione lineare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "02119d66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AD      = 23.8854 ± 0.0133\n",
      "BD      = 1.5710 ± 0.0856\n",
      "cov_ABD = -0.001129\n",
      "chi²    = 27.0309\n",
      "P(chi², ∞)= 0.0000\n"
     ]
    }
   ],
   "source": [
    "# Regressione lineare\n",
    "\n",
    "AD, BD, uAD, uBD, covABD, chiD, PD = reg_lin( dfSonar[\"1/Meq\"], dfSonar[\"Omega2\"], dfSonar[\"u1/Meq\"], dfSonar[\"uOmega2\"])\n",
    "\n",
    "print(\"Ax + B : \")\n",
    "print(f\"AD      = {AD:.4f} ± {uAD:.4f}\")\n",
    "print(f\"BD      = {BD:.4f} ± {uBD:.4f}\")\n",
    "print(f\"cov_ABD = {covABD:.6f}\")\n",
    "print(f\"chi²    = {chiD:.4f}\")\n",
    "print(f\"P(chi², ∞)= {PD:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "de571f32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot regressione lineare\n",
    "\n",
    "SCALA = 1000\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "x_data =  dfSonar[\"1/Meq\"]\n",
    "y_data =  dfSonar[\"Omega2\"]\n",
    "\n",
    "x_fit = np.linspace(0, x_data.max(), 300)\n",
    "y_fit = AD * x_fit + BD\n",
    "\n",
    "ax.errorbar(\n",
    "    x_data, y_data,\n",
    "    xerr=SCALA * dfSonar[\"u1/Meq\"], yerr=SCALA * dfSonar[\"uOmega2\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=f\"Dati (barre errore x{SCALA})\"\n",
    ")\n",
    "ax.plot(\n",
    "    x_fit, y_fit,\n",
    "    color='red', linewidth=1.5,\n",
    "    label=f\"$A={AD:.3f}\\\\pm{uAD:.3f}$\\n\"\n",
    "          f\"$B={BD:.3f}\\\\pm{uBD:.3f}$\\n\"\n",
    "          f\"P(chi², ∞)= {PD:.4f}\\n\"\n",
    ")\n",
    "\n",
    "\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(r\"1/Meq  [Kg$^{-1}$]\")\n",
    "ax.set_ylabel(r\"$\\omega^2$  [s$^{-2}$]\")\n",
    "ax.set_title(\"Fit lineare — Molla 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": 78,
   "id": "96f12fe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcolo numerico residui\n",
    "\n",
    "dfSonar[\"R\"] = r_fn(\n",
    "  dfSonar[\"1/Meq\"],\n",
    "  dfSonar[\"Omega2\"],\n",
    "  AD,\n",
    "  BD\n",
    ")\n",
    "\n",
    "dfSonar[\"uR\"] = sigma_r_fn(\n",
    "  dfSonar[\"1/Meq\"], dfSonar[\"Omega2\"],\n",
    "  AD, BD,\n",
    "  dfSonar[\"u1/Meq\"], dfSonar[\"uOmega2\"],\n",
    "  uAD, uBD, covABD\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c87033e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot residui\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "# Residui con barre d'errore\n",
    "ax.errorbar(\n",
    "    dfSonar[\"Omega2\"], dfSonar[\"R\"],\n",
    "    xerr=dfSonar[\"uOmega2\"], yerr=dfSonar[\"uR\"],\n",
    "    fmt='o', color=sns.color_palette()[0],\n",
    "    ecolor='gray', elinewidth=1, capsize=3,\n",
    "    markersize=5, label=\"Residui\"\n",
    ")\n",
    "\n",
    "# Linea dello zero\n",
    "ax.axhline(0, color='red', linestyle='--', linewidth=1)\n",
    "\n",
    "# Estetica\n",
    "sns.despine(ax=ax)\n",
    "ax.set_xlabel(r\"$\\omega^2$  [s$^{-2}$]\")\n",
    "ax.set_ylabel(r\"Residuo $x_i - \\left(\\frac{y_i}{A} - \\frac{B}{A}\\right)$  [Kg$^{-1}$]\")\n",
    "ax.set_title(\"Residui della regressione — Molla 1 (sonar dinamico)\")\n",
    "ax.legend()\n",
    "ax.grid(True, linestyle=':', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c085b1c5",
   "metadata": {},
   "source": [
    "# Raccolta finale dati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e87e24b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calibro\n",
      "AC      = 23.9432 ± 0.0422\n",
      "BC      = 2719.8356 ± 2.8402\n",
      "cov_ABC = 0.118646\n",
      "chi²    = 56.2014\n",
      "GdL     = 5\n",
      "P(chi², ∞)= 0.0000\n",
      "\n",
      "Sonar statico senza correzione\n",
      "AS      = 23.5225 ± 0.0853\n",
      "BS      = 7371.9487 ± 21.4728\n",
      "cov_ABS = 1.830281\n",
      "chi²    = 3.8872\n",
      "GdL     = 4\n",
      "P(chi², ∞)= 0.1432\n",
      "\n",
      "Sonar statico con correzione\n",
      "AS      = 23.6041 ± 0.3869\n",
      "BS      = 7361.9067 ± 98.1235\n",
      "cov_ABS = 37.934456\n",
      "chi²    = 0.2270\n",
      "GdL     = 4\n",
      "P(chi², ∞)= 0.8927\n",
      "\n",
      "Sonar dinamico\n",
      "AD      = 23.8854 ± 0.0133\n",
      "BD      = 1.5710 ± 0.0856\n",
      "cov_ABD = -0.001129\n",
      "chi²    = 27.0309\n",
      "GdL     = 4\n",
      "P(chi², ∞)= 0.0000\n"
     ]
    }
   ],
   "source": [
    "# Stampa regressioni\n",
    "\n",
    "print(\"Calibro\")\n",
    "print(f\"AC      = {AC:.4f} ± {uAC:.4f}\")\n",
    "print(f\"BC      = {BC:.4f} ± {uBC:.4f}\")\n",
    "print(f\"cov_ABC = {covABC:.6f}\")\n",
    "print(f\"chi²    = {chiC:.4f}\")\n",
    "print(f\"GdL     = {len(dfCalibro)}\")\n",
    "print(f\"P(chi², ∞)= {PC:.4f}\")\n",
    "\n",
    "print(\"\\nSonar statico senza correzione\")\n",
    "print(f\"AS      = {AS:.4f} ± {uAS:.4f}\")\n",
    "print(f\"BS      = {BS:.4f} ± {uBS:.4f}\")\n",
    "print(f\"cov_ABS = {covABS:.6f}\")\n",
    "print(f\"chi²    = {chiS:.4f}\")\n",
    "print(f\"GdL     = {len(dfSonar)}\")\n",
    "print(f\"P(chi², ∞)= {PS:.4f}\")\n",
    "\n",
    "print(\"\\nSonar statico con correzione\")\n",
    "print(f\"AS      = {AS2:.4f} ± {uAS2:.4f}\")\n",
    "print(f\"BS      = {BS2:.4f} ± {uBS2:.4f}\")\n",
    "print(f\"cov_ABS = {covABS2:.6f}\")\n",
    "print(f\"chi²    = {chiS2:.4f}\")\n",
    "print(f\"GdL     = {len(dfSonar)}\")\n",
    "print(f\"P(chi², ∞)= {PS2:.4f}\")\n",
    "\n",
    "print(\"\\nSonar dinamico\")\n",
    "print(f\"AD      = {AD:.4f} ± {uAD:.4f}\")\n",
    "print(f\"BD      = {BD:.4f} ± {uBD:.4f}\")\n",
    "print(f\"cov_ABD = {covABD:.6f}\")\n",
    "print(f\"chi²    = {chiD:.4f}\")\n",
    "print(f\"GdL     = {len(dfSonar)}\")\n",
    "print(f\"P(chi², ∞)= {PD:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12a4cb87",
   "metadata": {},
   "source": [
    "# Compatibilità"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "dc913446",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot gaussiane\n",
    "\n",
    "# Parametri gaussiane\n",
    "# (media, deviazione standard, colore, nome)\n",
    "gaussiane = [\n",
    "    (AC, uAC, 1, 'calibro'),\n",
    "    (AS2, uAS2, 2, 'sonar statico'),\n",
    "    (AD, uAD, 4, 'sonar dinamico')\n",
    "]\n",
    "\n",
    "# Creazione figura\n",
    "plt.figure(figsize=(12, 7))\n",
    "\n",
    "# Creazione asse x\n",
    "xMin = float('inf')\n",
    "xMax = float('-inf')\n",
    "for mu, sigma, _, _ in gaussiane:\n",
    "    minimoLocale = mu - 3 * sigma\n",
    "    massimoLocale = mu + 3 * sigma\n",
    "    \n",
    "    if minimoLocale < xMin:\n",
    "        xMin = minimoLocale\n",
    "    \n",
    "    if massimoLocale > xMax:\n",
    "        xMax = massimoLocale\n",
    "x = np.linspace(xMin, xMax, 1000)\n",
    "\n",
    "# Ciclo gaussiane\n",
    "for mu, sigma, colore, etichetta in gaussiane:\n",
    "    \n",
    "    y = stats.norm.pdf(x, mu, sigma)\n",
    "    plt.plot(x, y, color=sns.color_palette()[colore], linewidth=1, label=etichetta)\n",
    "    \n",
    "    puntiLinee = [mu - sigma, mu, mu + sigma]\n",
    "    for px in puntiLinee:\n",
    "        py = stats.norm.pdf(px, mu, sigma) \n",
    "        plt.vlines(x=px, ymin=0, ymax=py, colors=sns.color_palette()[colore], linestyles='dashed', linewidth=1)\n",
    "        \n",
    "# Dettagli estetici finali\n",
    "plt.ylim(bottom=0)\n",
    "plt.title('Confronto dati')\n",
    "plt.xlabel('k')\n",
    "plt.legend()\n",
    "\n",
    "# Mostriamo il grafico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "45a291fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compatibilità calibro e sonar statico:\n",
      "k = 0.87\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Fattori di compatibilità dati statici\n",
    "\n",
    "def print_k(x1, u1, x2, u2):\n",
    "    k = abs(x1-x2) / np.sqrt(u1**2 + u2**2)\n",
    "    print(f\"k = {k:.2f}\\n\")\n",
    "\n",
    "print(\"Compatibilità calibro e sonar statico:\")\n",
    "print_k(AC, uAC, AS2, uAS2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "44d63a1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AStat = 23.939 ± 0.042\n"
     ]
    }
   ],
   "source": [
    "# Media pesata dati statici\n",
    "\n",
    "AStat = ( AC/(uAC**2) + AS2/(uAS2**2) ) / ( 1/(uAC**2) + 1/(uAS2**2) )\n",
    "uAStat = 1 / np.sqrt( 1/(uAC**2) + 1/(uAS2**2) )\n",
    "\n",
    "print(f\"AStat = {AStat:.3f} ± {uAStat:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "27079e36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compatibilità k statico e sonar dinamico:\n",
      "k = 1.22\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Fattori di compatiilità dati statici e dinamici\n",
    "\n",
    "print(\"Compatibilità k statico e sonar dinamico:\")\n",
    "print_k(AStat, uAStat, AD, uAD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "fd47805e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 23.890 ± 0.013\n"
     ]
    }
   ],
   "source": [
    "# Media pesata dati statici e dinamici\n",
    "\n",
    "kBest = ( AStat/(uAStat**2) + AD/(uAD**2) ) / ( 1/(uAStat**2) + 1/(uAD**2) )\n",
    "ukBest = 1 / np.sqrt( 1/(uAStat**2) + 1/(uAD**2) )\n",
    "\n",
    "print(f\"k = {kBest:.3f} ± {ukBest:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d4af85a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compatibilità con t di Student\n",
    "\n",
    "def p_t_student_compatibilita(x, ux, gx, y, uy, gy):\n",
    "  GdL = gx + gy - 2\n",
    "  s2 = ( (gx - 1) * ux**2  +  (gy - 1) * uy**2 ) / GdL\n",
    "  sigma2 = ( s2 / gx )  + ( s2 / gy )\n",
    "  t = abs(x - y) / np.sqrt( sigma2 )\n",
    "\n",
    "  return {\"p\": 2 * (1 - stats.t.cdf(abs(t), df=GdL)), \"t\": t}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "57de948b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compatibilità: Calibro vs Sonar statico (T considerata)\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ           1.9804     0.0881\n",
      "2σ           0.9902     0.3551\n",
      "3σ           0.6601     0.5303\n",
      "\n",
      "\n",
      "Compatibilità: Calibro vs Sonar dinamico\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ           2.6075     0.0350\n",
      "2σ           1.3037     0.2336\n",
      "3σ           0.8692     0.4136\n",
      "\n",
      "\n",
      "Compatibilità: Sonar Dinamico vs Sonar statico (T considerata)\n",
      "Sigma             t    p-value\n",
      "------------------------------\n",
      "1σ           1.4534     0.1963\n",
      "2σ           0.7267     0.4948\n",
      "3σ           0.4845     0.6452\n"
     ]
    }
   ],
   "source": [
    "print(\"Compatibilità: Calibro vs Sonar statico (T considerata)\")\n",
    "print(f\"{'Sigma':<10} {'t':>8} {'p-value':>10}\")\n",
    "print(\"-\" * 30)\n",
    "for n in [1, 2, 3]:\n",
    "    res = p_t_student_compatibilita(AC, uAC*n, len(dfCalibro),\n",
    "                                    AS2,  uAS2*n, len(dfSonar))\n",
    "    print(f\"{n}σ         {res['t']:>8.4f} {res['p']:>10.4f}\")\n",
    "\n",
    "print(\"\\n\\nCompatibilità: Calibro vs Sonar dinamico\")\n",
    "print(f\"{'Sigma':<10} {'t':>8} {'p-value':>10}\")\n",
    "print(\"-\" * 30)\n",
    "for n in [1, 2, 3]:\n",
    "    res = p_t_student_compatibilita(AC, uAC*n, len(dfCalibro),\n",
    "                                    AD,  uAD*n,  len(dfSonar))\n",
    "    print(f\"{n}σ         {res['t']:>8.4f} {res['p']:>10.4f}\")\n",
    "\n",
    "print(\"\\n\\nCompatibilità: Sonar Dinamico vs Sonar statico (T considerata)\")\n",
    "print(f\"{'Sigma':<10} {'t':>8} {'p-value':>10}\")\n",
    "print(\"-\" * 30)\n",
    "for n in [1, 2, 3]:\n",
    "    res = p_t_student_compatibilita(AD,  uAD*n,  len(dfSonar),\n",
    "                                    AS2, uAS2*n, len(dfSonar))\n",
    "    print(f\"{n}σ         {res['t']:>8.4f} {res['p']:>10.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b66ccc91",
   "metadata": {},
   "source": [
    "# Note finali\n",
    "Chiaramente i dati presi con il sonar statico soffrono di altri errori che dovremmo discutere come introdurre, sicuramente soffrono della precisione con cui abbiamo puntato il sonar.\n",
    "\n",
    "La temperatura sfortunatamente è stata presa con troppa poca attenzione e questo fa perdere di integrità hai nostri dati, ma altro per ora non mi viene su 2 piedi.\n",
    "\n",
    "Abbiamo almeno una buona basa su cui poter ragionare."
   ]
  }
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