Lab1/mollaDefinitiva/molla-1/molla1.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": 210,
   "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": "960e68a5",
   "metadata": {},
   "source": [
    "## Import dei dati (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": 211,
   "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": 212,
   "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": 213,
   "id": "e649eddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfCalibro = pd.read_csv(r'molla1Calibro.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": 214,
   "id": "42ff21d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e9d979d582d48549847d52a98f7dae6",
       "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 Carpi dati del calibro\n",
    "chi² Come persone normali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "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": 216,
   "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": 217,
   "id": "cd68b201",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AC      = 23.9437 ± 0.0424\n",
      "BC      = 2719.8777 ± 2.8546\n",
      "cov_ABC = 0.119914\n",
      "chi²    = 55.1017\n",
      "P(chi², ∞)= 0.0000\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": 218,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "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": 220,
   "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": 221,
   "id": "17cbeaea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit completo:   chi²=55.10,  P=0.0000\n",
      "Senza punto 1:  chi²=1.794,  P=0.4078\n",
      "\n",
      "A:  23.9437 ± 0.0424  to  23.8835 ± 0.0432\n",
      "B:  2719.8777 ± 2.8546  to  2718.1378 ± 2.8613\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": 222,
   "id": "0bc01605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2cd706cd740242dc8c4f0d77578ff0cf",
       "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05f2a07809a448b9a2b8c385f180ec43",
       "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=[88.97, …"
      ]
     },
     "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",
    "sheet = ipysheet.from_dataframe(dfCalibro)\n",
    "display(sheet)\n",
    "sheet = ipysheet.from_dataframe(dfCalibro_senza1)\n",
    "display(sheet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "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()"
   ]
  }
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