Lab1/molla/statica/mollaStatica1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a8dfc0e",
   "metadata": {},
   "source": [
    "# Analisi della molla statica 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ff20b69",
   "metadata": {},
   "source": [
    "## Import delle librerie e set di variabili gloabali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 = 9.807\n",
    "ug = 0.001"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d2ffea",
   "metadata": {},
   "source": [
    "## Lettura dei dati e calcolo delle deviazioni standard campionarie\n",
    "- Lettura del CSV\n",
    "- Creazione del data frame\n",
    "- Deviazioni standard\n",
    "- Permutazioni e calcolo dei Delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "08efb2be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'statica1.csv')\n",
    "\n",
    "def calcola_stats(df, prefix, err_arbitrario):\n",
    "  cols = [col for col in df.columns if col.startswith(prefix)]\n",
    "\n",
    "  def riga_stats(row):\n",
    "    valori = row[cols].dropna()\n",
    "    n = len(valori)\n",
    "\n",
    "    if n == 0:\n",
    "      return pd.Series({prefix: np.nan, f\"u{prefix}\": np.nan})\n",
    "    elif n == 1:\n",
    "      return pd.Series({prefix: valori.iloc[0], f\"u{prefix}\": err_arbitrario})\n",
    "    else:\n",
    "      media = valori.mean()\n",
    "      sigma = valori.std(ddof=1)\n",
    "      return pd.Series({prefix: media, f\"u{prefix}\": sigma})\n",
    "\n",
    "  stats = df.apply(riga_stats, axis=1)\n",
    "  df[prefix] = stats[prefix]\n",
    "  df[f\"u{prefix}\"] = stats[f\"u{prefix}\"]\n",
    "\n",
    "  return df\n",
    "\n",
    "df = calcola_stats(df, \"Dx\", err_arbitrario=0.01)\n",
    "df = calcola_stats(df, \"m\", err_arbitrario=0.0028867513)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>88.97</td>\n",
       "      <td>77.26</td>\n",
       "      <td>77.28</td>\n",
       "      <td>77.26</td>\n",
       "      <td>77.28</td>\n",
       "      <td>77.24</td>\n",
       "      <td>77.42</td>\n",
       "      <td>77.290000</td>\n",
       "      <td>0.065422</td>\n",
       "      <td>88.97</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108.61</td>\n",
       "      <td>68.92</td>\n",
       "      <td>68.94</td>\n",
       "      <td>69.00</td>\n",
       "      <td>68.98</td>\n",
       "      <td>68.90</td>\n",
       "      <td>69.02</td>\n",
       "      <td>68.960000</td>\n",
       "      <td>0.047329</td>\n",
       "      <td>108.61</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>128.64</td>\n",
       "      <td>60.88</td>\n",
       "      <td>61.00</td>\n",
       "      <td>60.82</td>\n",
       "      <td>60.94</td>\n",
       "      <td>61.08</td>\n",
       "      <td>60.94</td>\n",
       "      <td>60.943333</td>\n",
       "      <td>0.090701</td>\n",
       "      <td>128.64</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>148.38</td>\n",
       "      <td>52.96</td>\n",
       "      <td>52.96</td>\n",
       "      <td>52.78</td>\n",
       "      <td>52.88</td>\n",
       "      <td>52.88</td>\n",
       "      <td>53.00</td>\n",
       "      <td>52.910000</td>\n",
       "      <td>0.079750</td>\n",
       "      <td>148.38</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168.53</td>\n",
       "      <td>44.42</td>\n",
       "      <td>44.68</td>\n",
       "      <td>44.48</td>\n",
       "      <td>44.80</td>\n",
       "      <td>44.92</td>\n",
       "      <td>44.52</td>\n",
       "      <td>44.636667</td>\n",
       "      <td>0.196943</td>\n",
       "      <td>168.53</td>\n",
       "      <td>0.002887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       m1    Dx1    Dx2    Dx3    Dx4    Dx5    Dx6         Dx       uDx  \\\n",
       "0   88.97  77.26  77.28  77.26  77.28  77.24  77.42  77.290000  0.065422   \n",
       "1  108.61  68.92  68.94  69.00  68.98  68.90  69.02  68.960000  0.047329   \n",
       "2  128.64  60.88  61.00  60.82  60.94  61.08  60.94  60.943333  0.090701   \n",
       "3  148.38  52.96  52.96  52.78  52.88  52.88  53.00  52.910000  0.079750   \n",
       "4  168.53  44.42  44.68  44.48  44.80  44.92  44.52  44.636667  0.196943   \n",
       "\n",
       "        m        um  \n",
       "0   88.97  0.002887  \n",
       "1  108.61  0.002887  \n",
       "2  128.64  0.002887  \n",
       "3  148.38  0.002887  \n",
       "4  168.53  0.002887  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "976d5531",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 1), (0, 2), (0, 3), (0, 4), (1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 4)]\n",
      "10\n",
      "############################################################\n",
      "[ 8.33       16.34666667 24.38       32.65333333  8.01666667 16.05\n",
      " 24.32333333  8.03333333 16.30666667  8.27333333]\n",
      "[0.08074652 0.11183321 0.10315038 0.2075251  0.10230673 0.09273618\n",
      " 0.20255041 0.12077527 0.21682558 0.21247745]\n",
      "############################################################\n",
      "[-19.64 -39.67 -59.41 -79.56 -20.03 -39.77 -59.92 -19.74 -39.89 -20.15]\n",
      "[0.00408248 0.00408248 0.00408248 0.00408248 0.00408248 0.00408248\n",
      " 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": "markdown",
   "id": "5b3b6776",
   "metadata": {},
   "source": [
    "## Calcolo dei K e media pesata\n",
    "- Calcolo del K\n",
    "- Calcolo della media pesata (sui K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2ad19283",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.04459466 0.05636189 0.07164148 0.08906597 0.04476779 0.05643232\n",
      " 0.07206497 0.04463879 0.05651695 0.04482161]\n",
      "[0.08074652 0.11183321 0.10315038 0.2075251  0.10230673 0.09273618\n",
      " 0.20255041 0.12077527 0.21682558 0.21247745]\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(uF)\n",
    "print(ueste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5f59d6c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-medio: 23.951129031636384\n",
      "sigmaC:  0.057316734630371916\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": "markdown",
   "id": "02fc45a9",
   "metadata": {},
   "source": [
    "## Analisi con regressioni e grafici\n",
    "Ogni blocco ha:\n",
    "- Regressione\n",
    "- grafico\n",
    "- chi^2\n",
    "\n",
    "Nota: Ax + B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7e75ec05",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "data = pd.DataFrame({\n",
    "    \"x\": este,\n",
    "    \"ux\": ueste,\n",
    "    \"y\": - F,\n",
    "    \"uy\": uF\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "313237da",
   "metadata": {},
   "source": [
    "### Regressione lineare \"OLS\"\n",
    "\n",
    "Non tiene conto dei nostri errori, un risultato puro e semplice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aefe7756",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 2.238e+04\n",
      "Date:                Tue, 07 Apr 2026   Prob (F-statistic):           4.46e-15\n",
      "Time:                        15:51:49   Log-Likelihood:                -27.238\n",
      "No. Observations:                  10   AIC:                             58.48\n",
      "Df Residuals:                       8   BIC:                             59.08\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0999      2.915      0.034      0.973      -6.621       6.821\n",
      "x             23.9687      0.160    149.602      0.000      23.599      24.338\n",
      "==============================================================================\n",
      "Omnibus:                        0.134   Durbin-Watson:                   0.595\n",
      "Prob(Omnibus):                  0.935   Jarque-Bera (JB):                0.285\n",
      "Skew:                          -0.202   Prob(JB):                        0.867\n",
      "Kurtosis:                       2.277   Cond. No.                         40.8\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "RISULTATI REGRESSIONE:\n",
      "Aols  = 23.96871 ± 0.16022\n",
      "Bols  = 0.09993 ± 2.91472\n",
      "R² = 0.99964\n"
     ]
    }
   ],
   "source": [
    "X = sm.add_constant(data[\"x\"])\n",
    "model = sm.OLS(data[\"y\"], X).fit()\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "# Estrazione parametri\n",
    "Bols = model.params[\"const\"]\n",
    "Aols = model.params[\"x\"]\n",
    "uBols = model.bse[\"const\"]\n",
    "uAols = model.bse[\"x\"]\n",
    "R2 = model.rsquared\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE:\")\n",
    "print(f\"Aols  = {Aols:.5f} ± {uAols:.5f}\")\n",
    "print(f\"Bols  = {Bols:.5f} ± {uBols:.5f}\")\n",
    "print(f\"R² = {R2:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot( data=data,\n",
    "  x=\"x\",\n",
    "  y=\"y\",\n",
    "  s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "  data[\"x\"],\n",
    "  data[\"y\"],\n",
    "  xerr=data[\"ux\"],\n",
    "  yerr=data[\"uy\"],\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[\"x\"].max(), 200)\n",
    "yfit = Bols + Aols * xfit\n",
    "\n",
    "plt.plot(\n",
    "  xfit,\n",
    "  yfit,\n",
    "  color=\"crimson\",\n",
    "  linewidth=1,\n",
    "  zorder=10,\n",
    "  label=\"Fit lineare OLS\"\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "986ff4a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 25.02317\n",
      "GdL       = 8\n",
      "Chi² rid  = 3.12790\n",
      "P(0, chi²)= 0.99846\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    13.640283\n",
      "1     1.141735\n",
      "2     0.543390\n",
      "3     0.255239\n",
      "4     2.911852\n",
      "5     5.525531\n",
      "6     0.872959\n",
      "7     0.105774\n",
      "8     0.002341\n",
      "9     0.024062\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "F_fit = Bols + Aols * data[\"x\"]\n",
    "sigma = np.sqrt(data[\"uy\"]**2 + (Aols * data[\"ux\"])**2)\n",
    "\n",
    "chi2_val = np.sum( ((data[\"y\"] - F_fit) / sigma)**2 )\n",
    "\n",
    "N = len(data)\n",
    "nu = N - 2\n",
    "\n",
    "chi2_red = chi2_val / nu\n",
    "Pols = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "\n",
    "print(f\"Chi²      = {chi2_val:.5f}\")\n",
    "print(f\"GdL       = {nu}\")\n",
    "print(f\"Chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {Pols:.5f}\")\n",
    "print(\"\\n\\n\" + \"#\"*60)\n",
    "print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
    "print(((data[\"y\"] - F_fit) / sigma)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6015e6fd",
   "metadata": {},
   "source": [
    "### Regressione lineare Carpi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2d4b7144",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AC      = 24.0474 ± 0.1316\n",
      "BC      = -1.5698 ± 2.0811\n",
      "cov_ABC = -0.246370\n",
      "P(0, chi²)= 0.9979\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",
    "AC, BC, uAC, uBC, covABC, chiC = reg_lin(data[\"x\"], data[\"y\"], data[\"ux\"], data[\"uy\"])\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\"P(0, chi²)= {chiC:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot(\n",
    "  data=data,\n",
    "  x=\"x\",\n",
    "  y=\"y\",\n",
    "  s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "  data[\"x\"],\n",
    "  data[\"y\"],\n",
    "  xerr=data[\"ux\"],\n",
    "  yerr=data[\"uy\"],\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[\"x\"].max(), 200)\n",
    "yfit = BC + AC * xfit\n",
    "\n",
    "\n",
    "plt.plot(\n",
    "  xfit,\n",
    "  yfit,\n",
    "  color=\"crimson\",\n",
    "  linewidth=1,\n",
    "  zorder=10,\n",
    "  label=\"Fit lineare Carpi\"\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": 32,
   "id": "32e9948f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 24.13715\n",
      "GdL       = 8\n",
      "Chi² rid  = 3.01714\n",
      "P(0, chi²)= 0.99783\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    9.978674\n",
      "1    0.850746\n",
      "2    0.696840\n",
      "3    0.467438\n",
      "4    4.507635\n",
      "5    6.377901\n",
      "6    0.776558\n",
      "7    0.464361\n",
      "8    0.014989\n",
      "9    0.002006\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "F_fit = BC + AC * data[\"x\"]\n",
    "sigma = np.sqrt(data[\"uy\"]**2 + (AC * data[\"ux\"])**2)\n",
    "\n",
    "\n",
    "chi2_val = np.sum( ((data[\"y\"] - F_fit) / sigma)**2 )\n",
    "\n",
    "N = len(data)\n",
    "nu = N - 2\n",
    "\n",
    "chi2_red = chi2_val / nu\n",
    "PC = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "\n",
    "print(f\"Chi²      = {chi2_val:.5f}\")\n",
    "print(f\"GdL       = {nu}\")\n",
    "print(f\"Chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {PC:.5f}\")\n",
    "print(\"\\n\\n\" + \"#\"*60)\n",
    "print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
    "print(((data[\"y\"] - F_fit) / sigma)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad1c81fc",
   "metadata": {},
   "source": [
    "### Regressione lineare pesata Yorkfit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bfb895c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AY        = 24.0647990 ± 0.1318513\n",
      "BY        = -1.8175805 ± 2.0850560\n",
      "cov_ABY   = -0.2473126752648217\n",
      "chi²      = 24.11969\n",
      "chi² rid  = 3.01496\n",
      "P(0, chi²)= 0.99781\n"
     ]
    }
   ],
   "source": [
    "def york_fit(x, y, sx, sy, max_iter=50, tol=1e-15):\n",
    "  # Pesi iniziali\n",
    "  wx = 1 / sx**2\n",
    "  wy = 1 / sy**2\n",
    "\n",
    "  # Stima iniziale della pendenza (OLS y|x)\n",
    "  A = np.cov(x, y, aweights=wy)[0,1] / np.cov(x, x, aweights=wy)[0,1]\n",
    "\n",
    "  for _ in range(max_iter):\n",
    "    A_old = A\n",
    "\n",
    "    # 1) Pesi combinati\n",
    "    Wi = (wx * wy) / (A**2 * wy + wx)\n",
    "\n",
    "    # 2) x e y pesati (eq. 10)\n",
    "    X_bar = np.sum(Wi * x) / np.sum(Wi)\n",
    "    Y_bar = np.sum(Wi * y) / np.sum(Wi)\n",
    "\n",
    "    # 3) Variabili centrate\n",
    "    Ui = x - X_bar\n",
    "    Vi = y - Y_bar\n",
    "\n",
    "    # 4) beta\n",
    "    beta_i = Wi * (Ui / wy + A * Vi / wx)\n",
    "\n",
    "    # 5) Aggiornamento pendenza\n",
    "    A = np.sum(Wi * beta_i * Vi) / np.sum(Wi * beta_i * Ui)\n",
    "\n",
    "    # Convergenza\n",
    "    if abs(A - A_old) < tol:\n",
    "      break\n",
    "\n",
    "  # 6) Intercetta\n",
    "  B = Y_bar - A * X_bar\n",
    "\n",
    "  # S = somma Wi (xi - x)**2\n",
    "  S = np.sum(Wi * Ui**2)\n",
    "\n",
    "  sA = np.sqrt(1 / S)\n",
    "  sB = np.sqrt(1 / np.sum(Wi) + X_bar**2 * sA**2)\n",
    "\n",
    "  # cov(A,B)\n",
    "  cov_AB = -X_bar * sA**2\n",
    "\n",
    "  # CHI QUADRATO\n",
    "  chi2 = np.sum(Wi * (Vi - A * Ui)**2)\n",
    "  dof = len(x) - 2\n",
    "  chi2_red = chi2 / dof\n",
    "\n",
    "  return A, B, sA, sB, cov_AB, chi2, chi2_red\n",
    "\n",
    "AY, BY, uAY, uBY, cov_ABY, chi2_val, chi2_red = york_fit(data.x, data.y, data.ux, data.uy)\n",
    "PY = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "\n",
    "print(f\"AY        = {AY:.7f} ± {uAY:.7f}\")\n",
    "print(f\"BY        = {BY:.7f} ± {uBY:.7f}\")\n",
    "print(f\"cov_ABY   = {cov_ABY}\")\n",
    "print(f\"chi²      = {chi2_val:.5f}\")\n",
    "print(f\"chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {PY:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "202de438",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot( data=data,\n",
    "  x=\"x\",\n",
    "  y=\"y\",\n",
    "  s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "  data[\"x\"],\n",
    "  data[\"y\"],\n",
    "  xerr=data[\"ux\"],\n",
    "  yerr=data[\"uy\"],\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[\"x\"].max(), 200)\n",
    "yfit = BY + AY * xfit\n",
    "\n",
    "plt.plot(\n",
    "  xfit,\n",
    "  yfit,\n",
    "  color=\"crimson\",\n",
    "  linewidth=1,\n",
    "  zorder=10,\n",
    "  label=\"Fit lineare York\"\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()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63442336",
   "metadata": {},
   "source": [
    "## Raccolta finale dei dati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "caf23dbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI senza ERRORE STRUMENTALE INCLUSO:\n",
      "Media pesata K  = 23.95113 ± 0.05732\n",
      "\n",
      "RISULTATI REGRESSIONE OLS:\n",
      "Aols  = 23.96871 ± 0.16022\n",
      "Bols  = 0.09993 ± 2.91472\n",
      "P(0, chi²)= 0.99846\n",
      "\n",
      "RISULTATI REGRESSIONE Carpi:\n",
      "Ac  = 24.04738 ± 0.13160\n",
      "Bc  = -1.56983 ± 2.08108\n",
      "P(0, chi²)= 0.99783\n",
      "\n",
      "RISULTATI REGRESSIONE York:\n",
      "Ay  = 24.06480 ± 0.13185\n",
      "By  = -1.81758 ± 2.08506\n",
      "P(0, chi²)= 0.99781\n"
     ]
    }
   ],
   "source": [
    "print(\"RISULTATI senza ERRORE STRUMENTALE INCLUSO:\")\n",
    "print(f\"Media pesata K  = {media:.5f} ± {uA:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE OLS:\")\n",
    "print(f\"Aols  = {Aols:.5f} ± {uAols:.5f}\")\n",
    "print(f\"Bols  = {Bols:.5f} ± {uBols:.5f}\")\n",
    "print(f\"P(0, chi²)= {Pols:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
    "print(f\"Ac  = {AC:.5f} ± {uAC:.5f}\")\n",
    "print(f\"Bc  = {BC:.5f} ± {uBC:.5f}\")\n",
    "print(f\"P(0, chi²)= {PC:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE York:\")\n",
    "print(f\"Ay  = {AY:.5f} ± {uAY:.5f}\")\n",
    "print(f\"By  = {BY:.5f} ± {uBY:.5f}\")\n",
    "print(f\"P(0, chi²)= {PY:.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7692f792",
   "metadata": {},
   "source": [
    "## Aggiunta degli errori strumentali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8f5c8bb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u_strum_m    = 0.004082\n",
      "u_strum_Dx   = 0.020412\n",
      "uF_strum     = 0.058091\n",
      "uK_strum     = 0.273764\n"
     ]
    }
   ],
   "source": [
    "res_b = 0.01\n",
    "res_c = 0.05\n",
    "\n",
    "# Incertezza strumentale su una differenza: res/sqrt(12) * sqrt(2) = res/sqrt(6)\n",
    "u_strum_m  = res_b / np.sqrt(6)\n",
    "u_strum_Dx = res_c / np.sqrt(6)\n",
    "\n",
    "# Massimo tra campionario e strumentale, punto per punto\n",
    "ueste_strum  = np.maximum(ueste,  u_strum_Dx)\n",
    "umasse_strum = np.maximum(umasse, u_strum_m)\n",
    "\n",
    "# Worst-case scalare: prendi il massimo anche di ueste_strum\n",
    "uF_strum  = np.average(np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 ))\n",
    "uDx_strum = np.average(ueste_strum)\n",
    "\n",
    "uK_strum  = np.average(np.sqrt( (1/este)**2 * uF_strum**2 + (F/este**2)**2 * uDx_strum**2 ))\n",
    "\n",
    "print(f\"u_strum_m    = {u_strum_m:.6f}\")\n",
    "print(f\"u_strum_Dx   = {u_strum_Dx:.6f}\")\n",
    "print(f\"uF_strum     = {uF_strum:.6f}\")\n",
    "print(f\"uK_strum     = {uK_strum:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8706126d",
   "metadata": {},
   "source": [
    "### Propapazione dell'errore strumentale max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a1dc24c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Media pesata\n",
    "uA_fin   = np.sqrt(uA**2      + uK_strum**2)\n",
    "\n",
    "# OLS\n",
    "uAols_fin = np.sqrt(uAols**2  + uK_strum**2)\n",
    "uBols_fin = np.sqrt(uBols**2  + uK_strum**2)\n",
    "\n",
    "# Carpi\n",
    "uAC_fin  = np.sqrt(uAC**2     + uK_strum**2)\n",
    "uBC_fin  = np.sqrt(uBC**2     + uK_strum**2)\n",
    "\n",
    "# York\n",
    "uAY_fin  = np.sqrt(uAY**2     + uK_strum**2)\n",
    "uBY_fin  = np.sqrt(uBY**2     + uK_strum**2)\n",
    "\n",
    "\n",
    "# Nuovo uy e ux per ricalcolare i chi^2\n",
    "uy_fin = np.sqrt(data[\"uy\"]**2 + uF_strum**2)\n",
    "ux_fin = np.sqrt(data[\"ux\"]**2 + uDx_strum**2)\n",
    "\n",
    "# OLS\n",
    "F_fit_ols = Bols + Aols * data[\"x\"]\n",
    "sigma_ols = np.sqrt(uy_fin**2 + (Aols * ux_fin)**2)\n",
    "chi2_ols  = np.sum(((data[\"y\"] - F_fit_ols) / sigma_ols)**2)\n",
    "GdL       = len(data) - 2\n",
    "\n",
    "# Carpi\n",
    "F_fit_c   = BC + AC * data[\"x\"]\n",
    "sigma_c   = np.sqrt(uy_fin**2 + (AC * ux_fin)**2)\n",
    "chi2_c    = np.sum(((data[\"y\"] - F_fit_c) / sigma_c)**2)\n",
    "\n",
    "# York\n",
    "F_fit_y   = BY + AY * data[\"x\"]\n",
    "sigma_y_   = np.sqrt(uy_fin**2 + (AY * ux_fin)**2)\n",
    "chi2_y     = np.sum(((data[\"y\"] - F_fit_y) / sigma_y_)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e57c7d8",
   "metadata": {},
   "source": [
    "## Risultati della propagazione dell'errore strumentale massimo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c8e264fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI CON ERRORE STRUMENTALE INCLUSO:\n",
      "Media pesata K  = 23.95113 ± 0.27970\n",
      "\n",
      "RISULTATI REGRESSIONE OLS:\n",
      "Aols  = 23.96871 ± 0.31720\n",
      "Bols  = 0.09993 ± 2.92755\n",
      "Chi² OLS   = 7.21389  |  rid = 0.90174  |  P = 0.48626\n",
      "\n",
      "RISULTATI REGRESSIONE Carpi:\n",
      "AC  = 24.04738 ± 0.30375\n",
      "BC  = -1.56983 ± 2.09901\n",
      "Chi² Carpi = 7.28743  |  rid = 0.91093  |  P = 0.49404\n",
      "\n",
      "RISULTATI REGRESSIONE York:\n",
      "AY  = 24.06480 ± 0.30386\n",
      "BY  = -1.81758 ± 2.10295\n",
      "Chi² York  = 7.33375  |  rid = 0.91672  |  P = 0.49891\n"
     ]
    }
   ],
   "source": [
    "print(\"RISULTATI CON ERRORE STRUMENTALE INCLUSO:\")\n",
    "print(f\"Media pesata K  = {media:.5f} ± {uA_fin:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE OLS:\")\n",
    "print(f\"Aols  = {Aols:.5f} ± {uAols_fin:.5f}\")\n",
    "print(f\"Bols  = {Bols:.5f} ± {uBols_fin:.5f}\")\n",
    "print(f\"Chi² OLS   = {chi2_ols:.5f}  |  rid = {chi2_ols/GdL:.5f}\" f\"  |  P = {chi2.cdf(chi2_ols, df=GdL):.5f}\")\n",
    "\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
    "print(f\"AC  = {AC:.5f} ± {uAC_fin:.5f}\")\n",
    "print(f\"BC  = {BC:.5f} ± {uBC_fin:.5f}\")\n",
    "print(f\"Chi² Carpi = {chi2_c:.5f}  |  rid = {chi2_c/GdL:.5f}\" f\"  |  P = {chi2.cdf(chi2_c, df=GdL):.5f}\")\n",
    "\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE York:\")\n",
    "print(f\"AY  = {AY:.5f} ± {uAY_fin:.5f}\")\n",
    "print(f\"BY  = {BY:.5f} ± {uBY_fin:.5f}\")\n",
    "print(f\"Chi² York  = {chi2_y:.5f}  |  rid = {chi2_y/GdL:.5f}\" f\"  |  P = {chi2.cdf(chi2_y, df=GdL):.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e38b0bd",
   "metadata": {},
   "source": [
    "## Minima interpretazione (Mio commento -> Non necessario)\n",
    "Sovrastimando l'errore in modo molto marcato ovviamente il Chi² viene ridotto.\n",
    "Il Chi² che stiamo usando serve per stimare quale sia la probabilità di ottenere un Chi² minore o uguale a quello osservato.\n",
    "Sapendo che il valore buono è ~50% stiamo un po' allargando le distribuzioni in modo un po' esagerato.\n",
    "\n",
    "In generale con il Chi² vale:\n",
    "- \\~50%: Errori ottimamente stimati\n",
    "- \\<5% : Fit troppo grande (errori sovrastimati)\n",
    "- \\>95%: Fit troppo piccolo (errori sottostimati)\n",
    "\n",
    "In generale mi sembra che nei paper se è presente un Chi² si riporta la probabilità complementare (probabilità di ottenere gli stessi risultati o peggio)"
   ]
  }
 ],
 "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
}