{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f34c5b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sc\n",
    "from scipy.stats import chi2\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "g = 9.806\n",
    "ug = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08efb2be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ndf[\"K\"] = df.m * g / df.Dx\\ndf[\"uK\"] = np.sqrt( (df.m * g / df.Dx**2)**2 * df.uDx**2 + (g/df.Dx)**2 * df.um**2 + (df.m / df.Dx)**2 * ug**2)\\n\\ndf[\"F\"] = df.m * g\\ndf[\"uF\"] = np.sqrt( df.m**2 * ug**2 + g**2 * df.um**2)\\nmedia_K, sigma_K = mediaPesata(df[\"K\"], df[\"uK\"])\\n\\n\\n#chi 2\\nchi2_val = np.sum((df[\"K\"] - media_K)**2 / df[\"uK\"]**2)   # formula corretta\\ndof      = len(df[\"K\"]) - 1                                # -1 perché stimi solo la media\\nchi2_rid = chi2_val / dof\\np_value  = 1 - sc.stats.chi2.cdf(chi2_val, dof)\\n\\nprint(\"#\"*60)\\nprint(\"Valori di K\")\\nprint(\"media pesata K:\", media_K)\\nprint(\"sigma K:\", sigma_K)\\nprint(f\"Chi2           : {chi2_val:.4f}\")\\nprint(f\"DOF            : {dof}\")\\nprint(f\"Chi2 ridotto   : {chi2_rid:.4f}   (ideale ~ 1)\")\\nprint(f\"p-value        : {p_value:.4f}\")\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'statica2.csv')\n",
    "\n",
    "def calcola_Dx_stats(df, err_arbitrario_DX):\n",
    "  dx_cols = [col for col in df.columns if col.startswith('Dx')]\n",
    "  \n",
    "  def riga_stats(row):\n",
    "    valori = row[dx_cols].dropna()\n",
    "    n = len(valori)\n",
    "      \n",
    "    if n == 0:\n",
    "      return pd.Series({'Dx': np.nan, 'uDx': np.nan})\n",
    "    elif n == 1:\n",
    "      return pd.Series({'Dx': valori.iloc[0], 'uDx': err_arbitrario_DX})\n",
    "    else:\n",
    "      media = valori.mean()\n",
    "      sigma = valori.std(ddof=1)  # sigma sperimentale S\n",
    "      return pd.Series({'Dx': media, 'uDx': sigma})\n",
    "  \n",
    "  stats = df.apply(riga_stats, axis=1)\n",
    "  df['Dx'] = stats['Dx']\n",
    "  df['uDx'] = stats['uDx']\n",
    "  \n",
    "  return df\n",
    "\n",
    "def calcola_m_stats(df, err_arbitrario_m):\n",
    "  m_cols = [col for col in df.columns if col.startswith('m')]\n",
    "  \n",
    "  def riga_stats(row):\n",
    "    valori = row[m_cols].dropna()\n",
    "    n = len(valori)\n",
    "      \n",
    "    if n == 0:\n",
    "      return pd.Series({'m': np.nan, 'um': np.nan})\n",
    "    elif n == 1:\n",
    "      return pd.Series({'m': valori.iloc[0], 'um': err_arbitrario_m})\n",
    "    else:\n",
    "      media = valori.mean()\n",
    "      sigma = valori.std(ddof=1)  # sigma sperimentale S\n",
    "      return pd.Series({'m': media, 'um': sigma})\n",
    "  \n",
    "  stats = df.apply(riga_stats, axis=1)\n",
    "  df['m'] = stats['m']\n",
    "  df['um'] = stats['um']\n",
    "  \n",
    "  return df\n",
    "\n",
    "def mediaPesata(x, ux, dim = 0):\n",
    "  x_arr = x.to_numpy() if isinstance(x, (pd.Series, pd.DataFrame)) else np.asarray(x)\n",
    "  sigma_arr = ux.to_numpy() if isinstance(ux, (pd.Series, pd.DataFrame)) else np.asarray(ux)\n",
    "\n",
    "  w = 1.0 / (sigma_arr ** 2)\n",
    "\n",
    "  num = np.nansum(w * x_arr, axis=dim)\n",
    "  den = np.nansum(w, axis=dim)\n",
    "\n",
    "  media = num / den\n",
    "  sigma = np.sqrt(1.0 / den)\n",
    "\n",
    "  return media, sigma\n",
    "\n",
    "df = calcola_Dx_stats(df, err_arbitrario_DX=0.01)\n",
    "df = calcola_m_stats(df, err_arbitrario_m=0.0028867513)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5494409f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m1</th>\n",
       "      <th>Dx1</th>\n",
       "      <th>Dx2</th>\n",
       "      <th>Dx3</th>\n",
       "      <th>Dx4</th>\n",
       "      <th>Dx5</th>\n",
       "      <th>Dx6</th>\n",
       "      <th>Dx</th>\n",
       "      <th>uDx</th>\n",
       "      <th>m</th>\n",
       "      <th>um</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49.246667</td>\n",
       "      <td>212.10</td>\n",
       "      <td>211.64</td>\n",
       "      <td>212.00</td>\n",
       "      <td>212.18</td>\n",
       "      <td>212.52</td>\n",
       "      <td>212.04</td>\n",
       "      <td>212.080000</td>\n",
       "      <td>0.284816</td>\n",
       "      <td>49.246667</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69.276667</td>\n",
       "      <td>150.92</td>\n",
       "      <td>150.26</td>\n",
       "      <td>150.02</td>\n",
       "      <td>150.16</td>\n",
       "      <td>150.40</td>\n",
       "      <td>150.18</td>\n",
       "      <td>150.323333</td>\n",
       "      <td>0.317847</td>\n",
       "      <td>69.276667</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88.966667</td>\n",
       "      <td>90.34</td>\n",
       "      <td>90.34</td>\n",
       "      <td>90.38</td>\n",
       "      <td>90.52</td>\n",
       "      <td>90.26</td>\n",
       "      <td>90.28</td>\n",
       "      <td>90.353333</td>\n",
       "      <td>0.092664</td>\n",
       "      <td>88.966667</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108.610000</td>\n",
       "      <td>29.82</td>\n",
       "      <td>30.18</td>\n",
       "      <td>30.10</td>\n",
       "      <td>30.20</td>\n",
       "      <td>30.10</td>\n",
       "      <td>30.40</td>\n",
       "      <td>30.133333</td>\n",
       "      <td>0.188750</td>\n",
       "      <td>108.610000</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           m1     Dx1     Dx2     Dx3     Dx4     Dx5     Dx6          Dx  \\\n",
       "0   49.246667  212.10  211.64  212.00  212.18  212.52  212.04  212.080000   \n",
       "1   69.276667  150.92  150.26  150.02  150.16  150.40  150.18  150.323333   \n",
       "2   88.966667   90.34   90.34   90.38   90.52   90.26   90.28   90.353333   \n",
       "3  108.610000   29.82   30.18   30.10   30.20   30.10   30.40   30.133333   \n",
       "\n",
       "        uDx           m        um  \n",
       "0  0.284816   49.246667  0.002887  \n",
       "1  0.317847   69.276667  0.002887  \n",
       "2  0.092664   88.966667  0.002887  \n",
       "3  0.188750  108.610000  0.002887  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "976d5531",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]\n",
      "6\n",
      "############################################################\n",
      "[ 61.75666667 121.72666667 181.94666667  59.97       120.19\n",
      "  60.22      ]\n",
      "[0.42678644 0.29951071 0.34168211 0.33107904 0.36966652 0.21026967]\n",
      "############################################################\n",
      "[-20.03       -39.72       -59.36333333 -19.69       -39.33333333\n",
      " -19.64333333]\n",
      "[0.00408248 0.00408248 0.00408248 0.00408248 0.00408248 0.00408248]\n"
     ]
    }
   ],
   "source": [
    "perm = [(x,i) for x in range(0,len(df)) for i in range(x+1,len(df))]\n",
    "\n",
    "este = np.array([df.Dx[x] - df.Dx[j] for x,j in perm])\n",
    "ueste = np.array([np.sqrt(df.uDx[x]**2 + df.uDx[j]**2) for x,j in perm])\n",
    "\n",
    "masse = np.array([df.m[x] - df.m[j] for x,j in perm])\n",
    "umasse = np.array([np.sqrt(df.um[x]**2 + df.um[j]**2) for x,j in perm])\n",
    "\n",
    "\n",
    "print(perm)\n",
    "print(len(perm))\n",
    "\n",
    "print(\"#\"* 60)\n",
    "print(este)\n",
    "print(ueste)\n",
    "\n",
    "print(\"#\"* 60)\n",
    "print(masse)\n",
    "print(umasse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2ad19283",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.18045307 3.19974522 3.19938176 3.21961214 3.2091078  3.19864707]\n",
      "[0.02199135 0.00788665 0.00602107 0.01779022 0.00988124 0.01119321]\n"
     ]
    }
   ],
   "source": [
    "F = masse * g\n",
    "uF = np.sqrt((g * umasse)**2 + (masse * ug)**2)\n",
    "\n",
    "K = - F / este\n",
    "uK = np.sqrt((1/este)**2 * uF**2   +   (F / este**2)**2 * ueste**2  )\n",
    "\n",
    "\n",
    "print(K)\n",
    "print(uK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5f59d6c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-medio: 3.201234977342435\n",
      "sigmaC:  0.003860115909468001\n"
     ]
    }
   ],
   "source": [
    "def mediaPesata(x, ux, dim = 0):\n",
    "  x_arr = x.to_numpy() if isinstance(x, (pd.Series, pd.DataFrame)) else np.asarray(x)\n",
    "  sigma_arr = ux.to_numpy() if isinstance(ux, (pd.Series, pd.DataFrame)) else np.asarray(ux)\n",
    "\n",
    "  w = 1.0 / (sigma_arr ** 2)\n",
    "\n",
    "  num = np.nansum(w * x_arr, axis=dim)\n",
    "  den = np.nansum(w, axis=dim)\n",
    "\n",
    "  media = num / den\n",
    "  sigma = np.sqrt(1.0 / den)\n",
    "\n",
    "  return media, sigma\n",
    "\n",
    "media, uA = mediaPesata(K, uK)\n",
    "print(\"K-medio:\", media)\n",
    "print(\"sigmaC: \", uA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aefe7756",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      F   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 1.277e+05\n",
      "Date:                Thu, 02 Apr 2026   Prob (F-statistic):           3.68e-10\n",
      "Time:                        14:04:37   Log-Likelihood:                -7.2416\n",
      "No. Observations:                   6   AIC:                             18.48\n",
      "Df Residuals:                       4   BIC:                             18.07\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0265      0.991      0.027      0.980      -2.724       2.777\n",
      "este           3.2011      0.009    357.408      0.000       3.176       3.226\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   1.155\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.275\n",
      "Skew:                          -0.058   Prob(JB):                        0.871\n",
      "Kurtosis:                       1.957   Cond. No.                         271.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "RISULTATI REGRESSIONE:\n",
      "k (pendenza) = 3.20112 ± 0.00896\n",
      "intercetta  = 0.02655 ± 0.99066\n",
      "R² = 0.99997\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jack/uni/lab/.venv/lib/python3.13/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "data = pd.DataFrame({\n",
    "    \"este\": este,\n",
    "    \"ueste\": ueste,\n",
    "    \"F\": - F,\n",
    "    \"uF\": uF\n",
    "})\n",
    "\n",
    "\n",
    "X = sm.add_constant(data[\"este\"])\n",
    "model = sm.OLS(data[\"F\"], X).fit()\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "# Estrazione parametri\n",
    "intercetta = model.params[\"const\"]\n",
    "pente = model.params[\"este\"]\n",
    "u_intercetta = model.bse[\"const\"]\n",
    "u_pente = model.bse[\"este\"]\n",
    "R2 = model.rsquared\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE:\")\n",
    "print(f\"k (pendenza) = {pente:.5f} ± {u_pente:.5f}\")\n",
    "print(f\"intercetta  = {intercetta:.5f} ± {u_intercetta:.5f}\")\n",
    "print(f\"R² = {R2:.5f}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d42b009",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot(\n",
    "    data=data,\n",
    "    x=\"este\",\n",
    "    y=\"F\",\n",
    "    s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "    data[\"este\"],\n",
    "    data[\"F\"],\n",
    "    xerr=data[\"ueste\"],\n",
    "    yerr=data[\"uF\"],\n",
    "    fmt=\"none\",\n",
    "    ecolor=\"gray\",\n",
    "    elinewidth=1,\n",
    "    capsize=3,\n",
    "    alpha=0.7\n",
    ")\n",
    "\n",
    "# Linea di regressione\n",
    "xfit = np.linspace(0, data[\"este\"].max(), 200)\n",
    "yfit = intercetta + pente * xfit\n",
    "\n",
    "\n",
    "plt.plot(\n",
    "    xfit,\n",
    "    yfit,\n",
    "    color=\"crimson\",\n",
    "    linewidth=1,\n",
    "    zorder=10,\n",
    "    label=\"Fit lineare\"\n",
    ")\n",
    "\n",
    "\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "\n",
    "\n",
    "plt.xlabel(\"Δx (mm)\")\n",
    "plt.ylabel(\"Forza F (mN)\")\n",
    "plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "986ff4a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi^2 = 2.77690\n",
      "Gradi di libertà = 4\n",
      "Chi^2 ridotto = 0.69422\n",
      "Probabilità P(0 → chi^2) = 0.40417\n"
     ]
    }
   ],
   "source": [
    "F_fit = intercetta + pente * data[\"este\"]\n",
    "sigma = np.sqrt(data[\"uF\"]**2 + (pente * data[\"ueste\"])**2)\n",
    "\n",
    "chi2_val = np.sum( ((data[\"F\"] - F_fit) / sigma)**2 )\n",
    "\n",
    "N = len(data)\n",
    "nu = N - 2\n",
    "\n",
    "chi2_red = chi2_val / nu\n",
    "P = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "\n",
    "print(f\"Chi^2 = {chi2_val:.5f}\")\n",
    "print(f\"Gradi di libertà = {nu}\")\n",
    "print(f\"Chi^2 ridotto = {chi2_red:.5f}\")\n",
    "print(f\"Probabilità P(0 → chi^2) = {P:.5f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef0817f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.908593\n",
       "1    0.040832\n",
       "2    0.097969\n",
       "3    1.041093\n",
       "4    0.620683\n",
       "5    0.067728\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((data[\"F\"] - F_fit) / sigma)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cebe6742",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>este</th>\n",
       "      <th>ueste</th>\n",
       "      <th>F</th>\n",
       "      <th>uF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61.756667</td>\n",
       "      <td>0.426786</td>\n",
       "      <td>196.414180</td>\n",
       "      <td>0.044764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>121.726667</td>\n",
       "      <td>0.299511</td>\n",
       "      <td>389.494320</td>\n",
       "      <td>0.056394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>181.946667</td>\n",
       "      <td>0.341682</td>\n",
       "      <td>582.116847</td>\n",
       "      <td>0.071601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59.970000</td>\n",
       "      <td>0.331079</td>\n",
       "      <td>193.080140</td>\n",
       "      <td>0.044613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120.190000</td>\n",
       "      <td>0.369667</td>\n",
       "      <td>385.702667</td>\n",
       "      <td>0.056123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60.220000</td>\n",
       "      <td>0.210270</td>\n",
       "      <td>192.622527</td>\n",
       "      <td>0.044592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         este     ueste           F        uF\n",
       "0   61.756667  0.426786  196.414180  0.044764\n",
       "1  121.726667  0.299511  389.494320  0.056394\n",
       "2  181.946667  0.341682  582.116847  0.071601\n",
       "3   59.970000  0.331079  193.080140  0.044613\n",
       "4  120.190000  0.369667  385.702667  0.056123\n",
       "5   60.220000  0.210270  192.622527  0.044592"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2d4b7144",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "A      = 3.2002 +- 0.0092\n",
      "B      = 0.1195 +- 0.9523\n",
      "cov_AB = -0.007941\n",
      "p-value chi² = 0.4020\n"
     ]
    }
   ],
   "source": [
    "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",
    "  chi = sc.stats.chi2.cdf(x2, dof)  # P(X² > x2)\n",
    "\n",
    "  return A, B, sigma_A, sigma_B, cov_AB, chi\n",
    "\n",
    "\n",
    "A, B, sA, sB, covAB, chi = reg_lin(data[\"este\"], data[\"F\"], data[\"ueste\"], data[\"uF\"])\n",
    "print(\"Ax + B : \")\n",
    "print(f\"A      = {A:.4f} +- {sA:.4f}\")\n",
    "print(f\"B      = {B:.4f} +- {sB:.4f}\")\n",
    "print(f\"cov_AB = {covAB:.6f}\")\n",
    "print(f\"p-value chi² = {chi:.4f}\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32e9948f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi^2 = 2.76835\n",
      "Gradi di libertà = 4\n",
      "Chi^2 ridotto = 0.69209\n",
      "Probabilità P(0 → chi^2) = 0.40269\n"
     ]
    }
   ],
   "source": [
    "F_fit = B + A * data[\"este\"]\n",
    "sigma = sigma = np.sqrt(data[\"uF\"]**2 + (A * data[\"ueste\"])**2)\n",
    "\n",
    "\n",
    "chi2_val = np.sum( ((data[\"F\"] - F_fit) / sigma)**2 )\n",
    "\n",
    "N = len(data)\n",
    "nu = N - 2\n",
    "\n",
    "chi2_red = chi2_val / nu\n",
    "P = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "\n",
    "print(f\"Chi^2 = {chi2_val:.5f}\")\n",
    "print(f\"Gradi di libertà = {nu}\")\n",
    "print(f\"Chi^2 ridotto = {chi2_red:.5f}\")\n",
    "print(f\"Probabilità P(0 → chi^2) = {P:.5f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e2407a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot(\n",
    "    data=data,\n",
    "    x=\"este\",\n",
    "    y=\"F\",\n",
    "    s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "    data[\"este\"],\n",
    "    data[\"F\"],\n",
    "    xerr=data[\"ueste\"],\n",
    "    yerr=data[\"uF\"],\n",
    "    fmt=\"none\",\n",
    "    ecolor=\"gray\",\n",
    "    elinewidth=1,\n",
    "    capsize=3,\n",
    "    alpha=0.7\n",
    ")\n",
    "\n",
    "# Linea di regressione\n",
    "xfit = np.linspace(0, data[\"este\"].max(), 200)\n",
    "yfit = B + A * xfit\n",
    "\n",
    "\n",
    "plt.plot(\n",
    "    xfit,\n",
    "    yfit,\n",
    "    color=\"crimson\",\n",
    "    linewidth=1,\n",
    "    zorder=10,\n",
    "    label=\"Fit lineare\"\n",
    ")\n",
    "\n",
    "\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "\n",
    "\n",
    "plt.xlabel(\"Δx (mm)\")\n",
    "plt.ylabel(\"Forza F (mN)\")\n",
    "plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}