{
 "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": 323,
   "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": "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": 324,
   "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": 325,
   "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": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "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": 327,
   "id": "22ee2969",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u_strum_m    = 0.004082\n",
      "u_strum_Dx   = 0.122474\n"
     ]
    }
   ],
   "source": [
    "res_b = 0.01\n",
    "res_c = 0.3\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  = u_strum_Dx\n",
    "umasse_strum = u_strum_m\n",
    "\n",
    "# Worst-case scalare: prendi il massimo anche di ueste_strum\n",
    "uF_strum  = np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 )\n",
    "uDx_strum = 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": "code",
   "execution_count": 328,
   "id": "2ad19283",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.06306119 0.07970365 0.10131313 0.12595571 0.06330605 0.07980326\n",
      " 0.10191206 0.06312361 0.07992295 0.06338217]\n",
      "[0.14669697 0.16585134 0.16012495 0.24097026 0.15958279 0.15362291\n",
      " 0.23669953 0.17200775 0.24902476 0.24524817]\n"
     ]
    }
   ],
   "source": [
    "F = masse * g\n",
    "uF = np.sqrt((g * umasse)**2 + (masse * ug)**2)\n",
    "\n",
    "uF = np.sqrt(uF**2 + uF_strum**2)\n",
    "ueste = np.sqrt(uDx_strum**2 + ueste**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": 329,
   "id": "5f59d6c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-medio: 23.960003307113187\n",
      "sigmaC:  0.08168429733984035\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": 330,
   "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": 331,
   "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:                        16:05:09   Log-Likelihood:                -27.237\n",
      "No. Observations:                  10   AIC:                             58.47\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      2.914      0.034      0.973      -6.621       6.821\n",
      "x             23.9663      0.160    149.602      0.000      23.597      24.336\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  = 23.96627 ± 0.16020\n",
      "Bols  = 0.09992 ± 2.91442\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": 332,
   "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": 333,
   "id": "986ff4a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 8.98934\n",
      "GdL       = 8\n",
      "Chi² rid  = 1.12367\n",
      "P(0, chi²)= 0.65680\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    4.133515\n",
      "1    0.519142\n",
      "2    0.225526\n",
      "3    0.189276\n",
      "4    1.196827\n",
      "5    2.013893\n",
      "6    0.639176\n",
      "7    0.052148\n",
      "8    0.001775\n",
      "9    0.018061\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": 334,
   "id": "2d4b7144",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AC      = 23.9876 ± 0.1850\n",
      "BC      = -0.4229 ± 3.1072\n",
      "cov_ABC = -0.515395\n",
      "P(0, chi²)= 0.6507\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": 335,
   "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": 336,
   "id": "32e9948f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 8.93858\n",
      "GdL       = 8\n",
      "Chi² rid  = 1.11732\n",
      "P(0, chi²)= 0.65250\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    3.737859\n",
      "1    0.457413\n",
      "2    0.224745\n",
      "3    0.216224\n",
      "4    1.403759\n",
      "5    2.150882\n",
      "6    0.638812\n",
      "7    0.098109\n",
      "8    0.005076\n",
      "9    0.005697\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": 337,
   "id": "bfb895c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AY        = 24.0003646 ± 0.1848548\n",
      "BY        = -0.6143995 ± 3.1050788\n",
      "cov_ABY   = -0.5147053957702236\n",
      "chi²      = 8.93384\n",
      "chi² rid  = 1.11673\n",
      "P(0, chi²)= 0.65209\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": 338,
   "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": 339,
   "id": "caf23dbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI senza ERRORE STRUMENTALE INCLUSO:\n",
      "Media pesata K  = 23.96000 ± 0.08168\n",
      "\n",
      "RISULTATI REGRESSIONE OLS:\n",
      "Aols  = 23.96627 ± 0.16020\n",
      "Bols  = 0.09992 ± 2.91442\n",
      "P(0, chi²)= 0.65680\n",
      "\n",
      "RISULTATI REGRESSIONE Carpi:\n",
      "Ac  = 23.98765 ± 0.18498\n",
      "Bc  = -0.42288 ± 3.10716\n",
      "P(0, chi²)= 0.65250\n",
      "\n",
      "RISULTATI REGRESSIONE York:\n",
      "Ay  = 24.00036 ± 0.18485\n",
      "By  = -0.61440 ± 3.10508\n",
      "P(0, chi²)= 0.65209\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": "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)"
   ]
  }
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