{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "f34c5b88",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4545/438762488.py:5: DeprecationWarning: `scipy.odr` is deprecated as of version 1.17.0 and will be removed in SciPy 1.19.0. Please use `https://pypi.org/project/odrpack/` instead.\n",
      "  from scipy.odr import ODR, Model, RealData\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sc\n",
    "from scipy.stats import chi2\n",
    "from scipy.odr import ODR, Model, RealData\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\n",
    "\n",
    "m_mol = 29.89\n",
    "um_mol = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "08efb2be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'dinamica1.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.max() - valori.min() )  /  2\n",
    "      sigma = max(sigma, err_arbitrario)  if prefix == \"w\" else sigma  # Line cursed per non duplicare il codice\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",
    "\n",
    "df = calcola_stats(df, \"w\", err_arbitrario=0.0002)\n",
    "df = calcola_stats(df, \"m\", err_arbitrario=0.01)\n",
    "df = calcola_stats(df, \"c\", err_arbitrario=0.01)\n",
    "df = calcola_stats(df, \"a\", err_arbitrario=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "5494409f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m1</th>\n",
       "      <th>a1</th>\n",
       "      <th>ua1</th>\n",
       "      <th>w1</th>\n",
       "      <th>uw1</th>\n",
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       "      <th>uc1</th>\n",
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       "      <th>uc</th>\n",
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       "      <th>ua</th>\n",
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       "      <th>0</th>\n",
       "      <td>108.610000</td>\n",
       "      <td>9.210</td>\n",
       "      <td>0.029</td>\n",
       "      <td>14.2459</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>268.151</td>\n",
       "      <td>0.020</td>\n",
       "      <td>10.690</td>\n",
       "      <td>0.040</td>\n",
       "      <td>14.2434</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>268.326</td>\n",
       "      <td>0.026</td>\n",
       "      <td>14.24465</td>\n",
       "      <td>0.00125</td>\n",
       "      <td>108.610000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>268.2385</td>\n",
       "      <td>0.0875</td>\n",
       "      <td>9.9500</td>\n",
       "      <td>0.7400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>128.636667</td>\n",
       "      <td>10.037</td>\n",
       "      <td>0.025</td>\n",
       "      <td>13.1939</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>259.605</td>\n",
       "      <td>0.018</td>\n",
       "      <td>9.744</td>\n",
       "      <td>0.027</td>\n",
       "      <td>13.1913</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>259.745</td>\n",
       "      <td>0.020</td>\n",
       "      <td>13.19260</td>\n",
       "      <td>0.00130</td>\n",
       "      <td>128.636667</td>\n",
       "      <td>0.01</td>\n",
       "      <td>259.6750</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>9.8905</td>\n",
       "      <td>0.1465</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>148.380000</td>\n",
       "      <td>9.930</td>\n",
       "      <td>0.030</td>\n",
       "      <td>12.3469</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>251.525</td>\n",
       "      <td>0.021</td>\n",
       "      <td>11.410</td>\n",
       "      <td>0.030</td>\n",
       "      <td>12.3461</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>251.542</td>\n",
       "      <td>0.023</td>\n",
       "      <td>12.34650</td>\n",
       "      <td>0.00040</td>\n",
       "      <td>148.380000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>251.5335</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>10.6700</td>\n",
       "      <td>0.7400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>168.530000</td>\n",
       "      <td>11.340</td>\n",
       "      <td>0.030</td>\n",
       "      <td>11.6345</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>243.211</td>\n",
       "      <td>0.021</td>\n",
       "      <td>11.500</td>\n",
       "      <td>0.030</td>\n",
       "      <td>11.6344</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>243.130</td>\n",
       "      <td>0.021</td>\n",
       "      <td>11.63445</td>\n",
       "      <td>0.00020</td>\n",
       "      <td>168.530000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>243.1705</td>\n",
       "      <td>0.0405</td>\n",
       "      <td>11.4200</td>\n",
       "      <td>0.0800</td>\n",
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      ],
      "text/plain": [
       "           m1      a1    ua1       w1     uw1       c1    uc1      a2    ua2  \\\n",
       "0  108.610000   9.210  0.029  14.2459  0.0008  268.151  0.020  10.690  0.040   \n",
       "1  128.636667  10.037  0.025  13.1939  0.0007  259.605  0.018   9.744  0.027   \n",
       "2  148.380000   9.930  0.030  12.3469  0.0007  251.525  0.021  11.410  0.030   \n",
       "3  168.530000  11.340  0.030  11.6345  0.0005  243.211  0.021  11.500  0.030   \n",
       "\n",
       "        w2     uw2       c2    uc2         w       uw           m    um  \\\n",
       "0  14.2434  0.0009  268.326  0.026  14.24465  0.00125  108.610000  0.01   \n",
       "1  13.1913  0.0009  259.745  0.020  13.19260  0.00130  128.636667  0.01   \n",
       "2  12.3461  0.0006  251.542  0.023  12.34650  0.00040  148.380000  0.01   \n",
       "3  11.6344  0.0006  243.130  0.021  11.63445  0.00020  168.530000  0.01   \n",
       "\n",
       "          c      uc        a      ua  \n",
       "0  268.2385  0.0875   9.9500  0.7400  \n",
       "1  259.6750  0.0700   9.8905  0.1465  \n",
       "2  251.5335  0.0085  10.6700  0.7400  \n",
       "3  243.1705  0.0405  11.4200  0.0800  "
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.width', None)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "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",
      "[ 8.5635 16.705  25.068   8.1415 16.5045  8.363 ]\n",
      "[0.11205467 0.08791189 0.09641836 0.07051418 0.08087181 0.04138236]\n",
      "############################################################\n",
      "[-20.02666667 -39.77       -59.92       -19.74333333 -39.89333333\n",
      " -20.15      ]\n",
      "[0.01414214 0.01414214 0.01414214 0.01414214 0.01414214 0.01414214]\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.c[x] - df.c[j] for x,j in perm])\n",
    "ueste = np.array([np.sqrt(df.uc[x]**2 + df.uc[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": 309,
   "id": "2ad19283",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[22.93238668 23.34538282 23.439266   23.77978587 23.70226463 23.6267966 ]\n",
      "[0.30051945 0.12316079 0.090355   0.20667602 0.11646939 0.11810642]\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": 310,
   "id": "5f59d6c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-medio: 23.518009860547757\n",
      "sigmaC:  0.052106970693817985\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": 311,
   "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:                 1.044e+04\n",
      "Date:                Thu, 02 Apr 2026   Prob (F-statistic):           5.50e-08\n",
      "Time:                        19:05:38   Log-Likelihood:                -14.806\n",
      "No. Observations:                   6   AIC:                             33.61\n",
      "Df Residuals:                       4   BIC:                             33.20\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.1355      3.495      0.039      0.971      -9.568       9.839\n",
      "este          23.4628      0.230    102.161      0.000      22.825      24.100\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   0.555\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.395\n",
      "Skew:                          -0.285   Prob(JB):                        0.821\n",
      "Kurtosis:                       1.880   Cond. No.                         37.4\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) = 23.46278 ± 0.22966\n",
      "intercetta  = 0.13546 ± 3.49489\n",
      "R² = 0.99962\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}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "8d795186",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi^2 = 12.06747\n",
      "Gradi di libertà = 4\n",
      "Chi^2 ridotto = 3.01687\n",
      "Probabilità P(0 → chi^2) = 0.98314\n"
     ]
    }
   ],
   "source": [
    "## X^2 sui dai fatti così\n",
    "F_fit = intercetta + pente * data[\"este\"]\n",
    "sigma = np.sqrt(data[\"uF\"]**2 + (pente * 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}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "1d42b009",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": 314,
   "id": "986ff4a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi^2 = 12.06747\n",
      "Gradi di libertà = 4\n",
      "Chi^2 ridotto = 3.01687\n",
      "Probabilità P(0 → chi^2) = 0.98314\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": 315,
   "id": "ef0817f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.156242\n",
       "1    1.028093\n",
       "2    0.102207\n",
       "3    2.169242\n",
       "4    4.023666\n",
       "5    1.588019\n",
       "dtype: float64"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((data[\"F\"] - F_fit) / sigma)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "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>8.5635</td>\n",
       "      <td>0.112055</td>\n",
       "      <td>196.381493</td>\n",
       "      <td>0.140116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.7050</td>\n",
       "      <td>0.087912</td>\n",
       "      <td>389.984620</td>\n",
       "      <td>0.144268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.0680</td>\n",
       "      <td>0.096418</td>\n",
       "      <td>587.575520</td>\n",
       "      <td>0.151069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.1415</td>\n",
       "      <td>0.070514</td>\n",
       "      <td>193.603127</td>\n",
       "      <td>0.140076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.5045</td>\n",
       "      <td>0.080872</td>\n",
       "      <td>391.194027</td>\n",
       "      <td>0.144302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.3630</td>\n",
       "      <td>0.041382</td>\n",
       "      <td>197.590900</td>\n",
       "      <td>0.140134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      este     ueste           F        uF\n",
       "0   8.5635  0.112055  196.381493  0.140116\n",
       "1  16.7050  0.087912  389.984620  0.144268\n",
       "2  25.0680  0.096418  587.575520  0.151069\n",
       "3   8.1415  0.070514  193.603127  0.140076\n",
       "4  16.5045  0.080872  391.194027  0.144302\n",
       "5   8.3630  0.041382  197.590900  0.140134"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "2d4b7144",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "A      = 23.39301 +- 0.12446\n",
      "B      = 1.80115 +- 1.59592\n",
      "cov_AB = -0.180473\n",
      "p-value chi² = 0.9614234\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:.5f} +- {sA:.5f}\")\n",
    "print(f\"B      = {B:.5f} +- {sB:.5f}\")\n",
    "print(f\"cov_AB = {covAB:.6f}\")\n",
    "print(f\"p-value chi² = {chi:.7f}\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "32e9948f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi^2 = 10.17614\n",
      "Gradi di libertà = 4\n",
      "Chi^2 ridotto = 2.54404\n",
      "Probabilità P(0 → chi^2) = 0.96244\n"
     ]
    }
   ],
   "source": [
    "F_fit = B + A * data[\"este\"]\n",
    "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": 319,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "95dde99f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.194561\n",
      "1    0.226829\n",
      "2    0.258983\n",
      "3    0.291654\n",
      "Name: w, dtype: float64\n",
      "0    0.000034\n",
      "1    0.000045\n",
      "2    0.000017\n",
      "3    0.000010\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "## Dinamica davvero\n",
    "\n",
    "Meq = df.m + ( m_mol / 3 )\n",
    "uMeq = np.sqrt( df.um**2 + um_mol**2 / 9 )\n",
    "\n",
    "T2 = ( 2 * np.pi / df.w )**2\n",
    "uT2 = np.sqrt( (8*np.pi**2 / df.w**3)**2 * df.uw**2 ) \n",
    "print(T2)\n",
    "print(uT2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "1bb433f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   tmp2   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 4.713e+05\n",
      "Date:                Thu, 02 Apr 2026   Prob (F-statistic):           2.12e-06\n",
      "Time:                        19:05:39   Log-Likelihood:                 32.343\n",
      "No. Observations:                   4   AIC:                            -60.69\n",
      "Df Residuals:                       2   BIC:                            -61.91\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0023      0.000      6.364      0.024       0.001       0.004\n",
      "masse          0.0016   2.36e-06    686.488      0.000       0.002       0.002\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   2.752\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.628\n",
      "Skew:                          -0.880   Prob(JB):                        0.730\n",
      "Kurtosis:                       2.180   Cond. No.                     1.01e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.01e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "\n",
      "RISULTATI REGRESSIONE:\n",
      "B) = 0.0016212 ± 0.0000024\n",
      "intercetta  = 0.00226 ± 0.00035\n",
      "R² = 1.00000\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; 4 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "dfDin = pd.DataFrame({\n",
    "    \"masse\": Meq,\n",
    "    \"umasse\": uMeq,\n",
    "    \"tmp2\": T2,\n",
    "    \"utmp2\": uT2\n",
    "})\n",
    "\n",
    "\n",
    "X = sm.add_constant(dfDin[\"masse\"])\n",
    "model = sm.OLS(dfDin[\"tmp2\"], X).fit()\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "# Estrazione parametri\n",
    "intercetta = model.params[\"const\"]\n",
    "pente = model.params[\"masse\"]\n",
    "u_intercetta = model.bse[\"const\"]\n",
    "u_pente = model.bse[\"masse\"]\n",
    "R2 = model.rsquared\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE:\")\n",
    "print(f\"B) = {pente:.7f} ± {u_pente:.7f}\")\n",
    "print(f\"intercetta  = {intercetta:.5f} ± {u_intercetta:.5f}\")\n",
    "print(f\"R² = {R2:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "67d16b84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K :  24.351577653480003\n",
      "uK : 1.1291301601259716e-05\n"
     ]
    }
   ],
   "source": [
    "\n",
    "K = (2*np.pi)**2 / pente / 1000\n",
    "uK = np.sqrt( (4*np.pi / pente**2)**2 * u_pente**2 ) / 1e6\n",
    "\n",
    "print(\"K : \", K)\n",
    "print(\"uK :\", uK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "a0ba5c8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "# Seaborn scatter\n",
    "sns.scatterplot(\n",
    "    data=dfDin,\n",
    "    x=\"masse\",\n",
    "    y=\"tmp2\",\n",
    "    s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "    dfDin[\"masse\"],\n",
    "    dfDin[\"tmp2\"],\n",
    "    xerr=dfDin[\"umasse\"],\n",
    "    yerr=dfDin[\"utmp2\"],\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, dfDin[\"masse\"].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(\"Meq (g)\")\n",
    "plt.ylabel(\"T^2 (s^2)\")\n",
    "plt.title(\"Legge di Stocazzo — 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": 324,
   "id": "859f2337",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "A      = 0.0016212 +- 0.0000007\n",
      "B      = 0.0022701 +- 0.0001084\n",
      "cov_AB = -0.000000\n",
      "p-value chi² = 0.9964\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(dfDin[\"masse\"], dfDin[\"tmp2\"], dfDin[\"umasse\"], dfDin[\"utmp2\"])\n",
    "print(\"Ax + B : \")\n",
    "print(f\"A      = {A:.7f} +- {sA:.7f}\")\n",
    "print(f\"B      = {B:.7f} +- {sB:.7f}\")\n",
    "print(f\"cov_AB = {covAB:.6f}\")\n",
    "print(f\"p-value chi² = {chi:.4f}\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "fef04a85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K nostro:  24.351020780404994\n",
      "uK nostro: 0.24912686265195058\n"
     ]
    }
   ],
   "source": [
    "K = (2*np.pi)**2 / A / 1000\n",
    "uK = np.sqrt( (4*np.pi / A**2)**2 * uA**2 ) / 1e6\n",
    "\n",
    "print(\"K nostro: \", K)\n",
    "print(\"uK nostro:\", uK)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "5b191469",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi^2 = 11.23106\n",
      "Gradi di libertà = 2\n",
      "Chi^2 ridotto = 5.61553\n",
      "Probabilità P(0 → chi^2) = 0.99636\n"
     ]
    }
   ],
   "source": [
    "F_fit = B + A * dfDin[\"masse\"]\n",
    "sigma = np.sqrt(dfDin[\"utmp2\"]**2 + (A * dfDin[\"umasse\"])**2)\n",
    "\n",
    "\n",
    "chi2_val = np.sum( ((dfDin[\"tmp2\"] - F_fit) / sigma)**2 )\n",
    "\n",
    "N = len(dfDin)\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": 327,
   "id": "9ac14621",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== REGRESSIONE T^2 vs Meq (ODR/York) ===\n",
      "A = 0.0016212 ± 0.0000016\n",
      "B = 0.0022701 ± 0.0002570\n",
      "cov(A,B) = -7.179292197278527e-11\n",
      "chi² = 11.23106\n",
      "chi² ridotto = 5.61553\n",
      "\n",
      "=== COSTANTE ELASTICA DINAMICA DA ODR ===\n",
      "k = 24.351014 ± 0.023754 N/m\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# --- DATI DELLA TUA ANALISI ---\n",
    "x = Meq.to_numpy()      # masse equivalenti\n",
    "sx = uMeq.to_numpy()    # errori masse equivalenti\n",
    "y = T2.to_numpy()       # T^2\n",
    "sy = uT2.to_numpy()     # errori di T^2\n",
    "\n",
    "# --- MODELLO LINEARE ---\n",
    "def f(B, x):\n",
    "    return B[0] * x + B[1]   # A*x + B\n",
    "\n",
    "# Pacchetto \"RealData\" con errori anche su x\n",
    "dati = RealData(x, y, sx=sx, sy=sy)\n",
    "\n",
    "# Modello lineare\n",
    "modello = Model(f)\n",
    "\n",
    "# Esecuzione ODR (metodo di York)\n",
    "odr = ODR(dati, modello, beta0=[0.01, 0])   # stima iniziale: A≈0.01, B≈0\n",
    "out = odr.run()\n",
    "\n",
    "A, B = out.beta\n",
    "sA, sB = out.sd_beta\n",
    "cov_AB = out.cov_beta[0, 1]\n",
    "chi2 = out.sum_square\n",
    "chi2_red = out.res_var\n",
    "\n",
    "print(\"=== REGRESSIONE T^2 vs Meq (ODR/York) ===\")\n",
    "print(f\"A = {A:.7f} ± {sA:.7f}\")\n",
    "print(f\"B = {B:.7f} ± {sB:.7f}\")\n",
    "print(f\"cov(A,B) = {cov_AB}\")\n",
    "print(f\"chi² = {chi2:.5f}\")\n",
    "print(f\"chi² ridotto = {chi2_red:.5f}\")\n",
    "\n",
    "# --- COSTANTE ELASTICA ---\n",
    "K = (2*np.pi)**2 / A / 1000     # k in N/m → convertito a N/mm?\n",
    "uK = (2*np.pi)**2 / (A**2) * sA / 1000\n",
    "\n",
    "print(\"\\n=== COSTANTE ELASTICA DINAMICA DA ODR ===\")\n",
    "print(f\"k = {K:.6f} ± {uK:.6f} N/m\")\n"
   ]
  }
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