{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a80529e4",
   "metadata": {},
   "source": [
    "# Analisi dei dati con il sonar\n",
    "Minimo Indice:\n",
    "- Analisi dei dati statici\n",
    "- Analisi dei dati dinamici\n",
    "  - Sonar\n",
    "  - Cronometro"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32702b8f",
   "metadata": {},
   "source": [
    "## Import delle librerie e set di variabili gloabali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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\n",
    "\n",
    "m_mol = 15.43\n",
    "um_mol = 0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd0b8b1d",
   "metadata": {},
   "source": [
    "## Lettura dei dati e calcolo delle deviazioni standard campionarie\n",
    "- Lettura del CSV\n",
    "- Creazione del data frame\n",
    "- Deviazioni standard\n",
    "\n",
    "ATTENZIONE: Linea cursed ~17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "08efb2be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'dinamica2.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",
    "\n",
    "df = calcola_stats(df, \"w\", err_arbitrario=0.0002)\n",
    "df = calcola_stats(df, \"m\", err_arbitrario=0.0028867513)\n",
    "df = calcola_stats(df, \"c\", err_arbitrario=0.01)\n",
    "df = calcola_stats(df, \"a\", err_arbitrario=0.01)\n",
    "df = calcola_stats(df, \"t\", err_arbitrario=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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>a1</th>\n",
       "      <th>ua1</th>\n",
       "      <th>w1</th>\n",
       "      <th>uw1</th>\n",
       "      <th>c1</th>\n",
       "      <th>uc1</th>\n",
       "      <th>t1</th>\n",
       "      <th>a2</th>\n",
       "      <th>ua2</th>\n",
       "      <th>w2</th>\n",
       "      <th>uw2</th>\n",
       "      <th>c2</th>\n",
       "      <th>uc2</th>\n",
       "      <th>t2</th>\n",
       "      <th>a3</th>\n",
       "      <th>ua3</th>\n",
       "      <th>w3</th>\n",
       "      <th>uw3</th>\n",
       "      <th>c3</th>\n",
       "      <th>uc3</th>\n",
       "      <th>t3</th>\n",
       "      <th>a4</th>\n",
       "      <th>ua4</th>\n",
       "      <th>w4</th>\n",
       "      <th>uw4</th>\n",
       "      <th>c4</th>\n",
       "      <th>uc4</th>\n",
       "      <th>t4</th>\n",
       "      <th>w</th>\n",
       "      <th>uw</th>\n",
       "      <th>m</th>\n",
       "      <th>um</th>\n",
       "      <th>c</th>\n",
       "      <th>uc</th>\n",
       "      <th>a</th>\n",
       "      <th>ua</th>\n",
       "      <th>t</th>\n",
       "      <th>ut</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49.25</td>\n",
       "      <td>9.7171</td>\n",
       "      <td>0.016</td>\n",
       "      <td>7.65650</td>\n",
       "      <td>0.00040</td>\n",
       "      <td>484.4550</td>\n",
       "      <td>0.011</td>\n",
       "      <td>15.62</td>\n",
       "      <td>8.9110</td>\n",
       "      <td>0.015</td>\n",
       "      <td>7.65690</td>\n",
       "      <td>0.00040</td>\n",
       "      <td>484.516</td>\n",
       "      <td>0.011</td>\n",
       "      <td>15.58</td>\n",
       "      <td>10.446</td>\n",
       "      <td>0.027</td>\n",
       "      <td>7.66030</td>\n",
       "      <td>0.00050</td>\n",
       "      <td>485.082</td>\n",
       "      <td>0.019</td>\n",
       "      <td>15.76</td>\n",
       "      <td>8.377</td>\n",
       "      <td>0.016</td>\n",
       "      <td>7.65820</td>\n",
       "      <td>0.00040</td>\n",
       "      <td>484.752</td>\n",
       "      <td>0.011</td>\n",
       "      <td>15.87</td>\n",
       "      <td>7.657975</td>\n",
       "      <td>0.001711</td>\n",
       "      <td>49.25</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>484.70125</td>\n",
       "      <td>0.284314</td>\n",
       "      <td>9.362775</td>\n",
       "      <td>0.908254</td>\n",
       "      <td>15.7075</td>\n",
       "      <td>0.133010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>69.28</td>\n",
       "      <td>9.8600</td>\n",
       "      <td>0.016</td>\n",
       "      <td>6.55968</td>\n",
       "      <td>0.00029</td>\n",
       "      <td>423.3520</td>\n",
       "      <td>0.011</td>\n",
       "      <td>18.31</td>\n",
       "      <td>10.3900</td>\n",
       "      <td>0.012</td>\n",
       "      <td>6.55891</td>\n",
       "      <td>0.00022</td>\n",
       "      <td>423.154</td>\n",
       "      <td>0.009</td>\n",
       "      <td>18.27</td>\n",
       "      <td>10.491</td>\n",
       "      <td>0.013</td>\n",
       "      <td>6.56002</td>\n",
       "      <td>0.00024</td>\n",
       "      <td>423.697</td>\n",
       "      <td>0.010</td>\n",
       "      <td>18.34</td>\n",
       "      <td>10.968</td>\n",
       "      <td>0.019</td>\n",
       "      <td>6.56000</td>\n",
       "      <td>0.00030</td>\n",
       "      <td>423.465</td>\n",
       "      <td>0.014</td>\n",
       "      <td>18.16</td>\n",
       "      <td>6.559652</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>69.28</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>423.41700</td>\n",
       "      <td>0.226641</td>\n",
       "      <td>10.427250</td>\n",
       "      <td>0.454472</td>\n",
       "      <td>18.2700</td>\n",
       "      <td>0.078740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88.97</td>\n",
       "      <td>11.5840</td>\n",
       "      <td>0.014</td>\n",
       "      <td>5.84417</td>\n",
       "      <td>0.00020</td>\n",
       "      <td>363.2290</td>\n",
       "      <td>0.010</td>\n",
       "      <td>20.27</td>\n",
       "      <td>10.1763</td>\n",
       "      <td>0.017</td>\n",
       "      <td>5.84585</td>\n",
       "      <td>0.00028</td>\n",
       "      <td>363.354</td>\n",
       "      <td>0.012</td>\n",
       "      <td>20.44</td>\n",
       "      <td>12.044</td>\n",
       "      <td>0.018</td>\n",
       "      <td>5.84500</td>\n",
       "      <td>0.00026</td>\n",
       "      <td>363.183</td>\n",
       "      <td>0.013</td>\n",
       "      <td>20.54</td>\n",
       "      <td>11.224</td>\n",
       "      <td>0.016</td>\n",
       "      <td>5.84513</td>\n",
       "      <td>0.00025</td>\n",
       "      <td>363.233</td>\n",
       "      <td>0.011</td>\n",
       "      <td>20.49</td>\n",
       "      <td>5.845038</td>\n",
       "      <td>0.000689</td>\n",
       "      <td>88.97</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>363.24975</td>\n",
       "      <td>0.073109</td>\n",
       "      <td>11.257075</td>\n",
       "      <td>0.794837</td>\n",
       "      <td>20.4350</td>\n",
       "      <td>0.117331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108.61</td>\n",
       "      <td>11.5420</td>\n",
       "      <td>0.026</td>\n",
       "      <td>5.32780</td>\n",
       "      <td>0.00030</td>\n",
       "      <td>303.5502</td>\n",
       "      <td>0.019</td>\n",
       "      <td>22.49</td>\n",
       "      <td>8.4240</td>\n",
       "      <td>0.017</td>\n",
       "      <td>5.32820</td>\n",
       "      <td>0.00030</td>\n",
       "      <td>303.581</td>\n",
       "      <td>0.012</td>\n",
       "      <td>22.27</td>\n",
       "      <td>10.501</td>\n",
       "      <td>0.022</td>\n",
       "      <td>5.32960</td>\n",
       "      <td>0.00030</td>\n",
       "      <td>303.842</td>\n",
       "      <td>0.016</td>\n",
       "      <td>22.55</td>\n",
       "      <td>9.959</td>\n",
       "      <td>0.014</td>\n",
       "      <td>5.32822</td>\n",
       "      <td>0.00020</td>\n",
       "      <td>303.445</td>\n",
       "      <td>0.010</td>\n",
       "      <td>22.15</td>\n",
       "      <td>5.328455</td>\n",
       "      <td>0.000787</td>\n",
       "      <td>108.61</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>303.60455</td>\n",
       "      <td>0.168669</td>\n",
       "      <td>10.106500</td>\n",
       "      <td>1.299853</td>\n",
       "      <td>22.3650</td>\n",
       "      <td>0.187172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>128.64</td>\n",
       "      <td>11.5740</td>\n",
       "      <td>0.020</td>\n",
       "      <td>4.92663</td>\n",
       "      <td>0.00023</td>\n",
       "      <td>242.9620</td>\n",
       "      <td>0.014</td>\n",
       "      <td>24.33</td>\n",
       "      <td>11.5920</td>\n",
       "      <td>0.023</td>\n",
       "      <td>4.92537</td>\n",
       "      <td>0.00029</td>\n",
       "      <td>242.876</td>\n",
       "      <td>0.017</td>\n",
       "      <td>24.38</td>\n",
       "      <td>10.264</td>\n",
       "      <td>0.023</td>\n",
       "      <td>4.92430</td>\n",
       "      <td>0.00030</td>\n",
       "      <td>242.789</td>\n",
       "      <td>0.017</td>\n",
       "      <td>25.09</td>\n",
       "      <td>9.118</td>\n",
       "      <td>0.021</td>\n",
       "      <td>4.92610</td>\n",
       "      <td>0.00030</td>\n",
       "      <td>243.115</td>\n",
       "      <td>0.015</td>\n",
       "      <td>24.26</td>\n",
       "      <td>4.925600</td>\n",
       "      <td>0.001009</td>\n",
       "      <td>128.64</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>242.93550</td>\n",
       "      <td>0.138954</td>\n",
       "      <td>10.637000</td>\n",
       "      <td>1.188344</td>\n",
       "      <td>24.5150</td>\n",
       "      <td>0.386480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       m1       a1    ua1       w1      uw1        c1    uc1     t1       a2  \\\n",
       "0   49.25   9.7171  0.016  7.65650  0.00040  484.4550  0.011  15.62   8.9110   \n",
       "1   69.28   9.8600  0.016  6.55968  0.00029  423.3520  0.011  18.31  10.3900   \n",
       "2   88.97  11.5840  0.014  5.84417  0.00020  363.2290  0.010  20.27  10.1763   \n",
       "3  108.61  11.5420  0.026  5.32780  0.00030  303.5502  0.019  22.49   8.4240   \n",
       "4  128.64  11.5740  0.020  4.92663  0.00023  242.9620  0.014  24.33  11.5920   \n",
       "\n",
       "     ua2       w2      uw2       c2    uc2     t2      a3    ua3       w3  \\\n",
       "0  0.015  7.65690  0.00040  484.516  0.011  15.58  10.446  0.027  7.66030   \n",
       "1  0.012  6.55891  0.00022  423.154  0.009  18.27  10.491  0.013  6.56002   \n",
       "2  0.017  5.84585  0.00028  363.354  0.012  20.44  12.044  0.018  5.84500   \n",
       "3  0.017  5.32820  0.00030  303.581  0.012  22.27  10.501  0.022  5.32960   \n",
       "4  0.023  4.92537  0.00029  242.876  0.017  24.38  10.264  0.023  4.92430   \n",
       "\n",
       "       uw3       c3    uc3     t3      a4    ua4       w4      uw4       c4  \\\n",
       "0  0.00050  485.082  0.019  15.76   8.377  0.016  7.65820  0.00040  484.752   \n",
       "1  0.00024  423.697  0.010  18.34  10.968  0.019  6.56000  0.00030  423.465   \n",
       "2  0.00026  363.183  0.013  20.54  11.224  0.016  5.84513  0.00025  363.233   \n",
       "3  0.00030  303.842  0.016  22.55   9.959  0.014  5.32822  0.00020  303.445   \n",
       "4  0.00030  242.789  0.017  25.09   9.118  0.021  4.92610  0.00030  243.115   \n",
       "\n",
       "     uc4     t4         w        uw       m        um          c        uc  \\\n",
       "0  0.011  15.87  7.657975  0.001711   49.25  0.002887  484.70125  0.284314   \n",
       "1  0.014  18.16  6.559652  0.000519   69.28  0.002887  423.41700  0.226641   \n",
       "2  0.011  20.49  5.845038  0.000689   88.97  0.002887  363.24975  0.073109   \n",
       "3  0.010  22.15  5.328455  0.000787  108.61  0.002887  303.60455  0.168669   \n",
       "4  0.015  24.26  4.925600  0.001009  128.64  0.002887  242.93550  0.138954   \n",
       "\n",
       "           a        ua        t        ut  \n",
       "0   9.362775  0.908254  15.7075  0.133010  \n",
       "1  10.427250  0.454472  18.2700  0.078740  \n",
       "2  11.257075  0.794837  20.4350  0.117331  \n",
       "3  10.106500  1.299853  22.3650  0.187172  \n",
       "4  10.637000  1.188344  24.5150  0.386480  "
      ]
     },
     "execution_count": 142,
     "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": "markdown",
   "id": "9b8d8cb4",
   "metadata": {},
   "source": [
    "# Analisi statica"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd1b1164",
   "metadata": {},
   "source": [
    "## Permutazioni"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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",
      "[ 61.28425 121.4515  181.0967  241.76575  60.16725 119.81245 180.4815\n",
      "  59.6452  120.31425  60.66905]\n",
      "[0.36359352 0.29356288 0.33058029 0.31645313 0.23814054 0.28251562\n",
      " 0.26584645 0.18383143 0.15701353 0.21853469]\n",
      "############################################################\n",
      "[-20.03 -39.72 -59.36 -79.39 -19.69 -39.33 -59.36 -19.64 -39.67 -20.03]\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.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": "markdown",
   "id": "d37fbd7c",
   "metadata": {},
   "source": [
    "## Calcolo dei K e media pesata\n",
    "- Calcolo del K\n",
    "- Calcolo della media pesata (sui K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "2ad19283",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.20529679 3.20732177 3.21454516 3.2203806  3.20938434 3.21927571\n",
      " 3.22550245 3.22925365 3.23356286 3.23779934]\n",
      "[0.01903074 0.00776638 0.00588125 0.00423125 0.01272429 0.00760543\n",
      " 0.00476765 0.00998087 0.00424581 0.01168613]\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": 145,
   "id": "5f59d6c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-medio: 3.22307834012753\n",
      "sigmaC:  0.0020227911977321114\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": "21baebb4",
   "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": 146,
   "id": "1d4c0ffa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u_strum_m    = 0.004082\n",
      "u_strum_Dx   = 0.244949\n"
     ]
    }
   ],
   "source": [
    "res_b = 0.01\n",
    "res_c = 0.6\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": 147,
   "id": "5be377eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "data = pd.DataFrame({\n",
    "    \"x\": este,\n",
    "    \"ux\": np.sqrt(ueste**2 + uDx_strum**2),\n",
    "    \"y\": - F,\n",
    "    \"uy\": np.sqrt(uF**2 + uF_strum**2)\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ec60a92",
   "metadata": {},
   "source": [
    "### Regressione lineare \"OLS\"\n",
    "\n",
    "Non tiene conto dei nostri errori, un risultato puro e semplice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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:                 3.114e+05\n",
      "Date:                Wed, 08 Apr 2026   Prob (F-statistic):           1.19e-19\n",
      "Time:                        09:36:10   Log-Likelihood:                -14.036\n",
      "No. Observations:                  10   AIC:                             32.07\n",
      "Df Residuals:                       8   BIC:                             32.68\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0101      0.779      0.013      0.990      -1.785       1.805\n",
      "x              3.2201      0.006    558.014      0.000       3.207       3.233\n",
      "==============================================================================\n",
      "Omnibus:                        0.921   Durbin-Watson:                   0.565\n",
      "Prob(Omnibus):                  0.631   Jarque-Bera (JB):                0.614\n",
      "Skew:                           0.062   Prob(JB):                        0.736\n",
      "Kurtosis:                       1.792   Cond. No.                         302.\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  = 3.22008 ± 0.00577\n",
      "Bols  = 0.01011 ± 0.77853\n",
      "R² = 0.99997\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": 149,
   "id": "8d795186",
   "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": 150,
   "id": "1d42b009",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 7.89493\n",
      "GdL       = 8\n",
      "Chi² rid  = 0.98687\n",
      "P(0, chi²)= 0.55620\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    0.420110\n",
      "1    1.597434\n",
      "2    0.580057\n",
      "3    0.002382\n",
      "4    0.351718\n",
      "5    0.007741\n",
      "6    0.687794\n",
      "7    0.295535\n",
      "8    2.940543\n",
      "9    1.011619\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": "9f26f65e",
   "metadata": {},
   "source": [
    "### Regressione lineare Carpi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "6a910226",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AC      = 3.2196 +- 0.0063\n",
      "BC      = 0.2465 +- 0.8111\n",
      "cov_ABC = -0.004592\n",
      "P(0, chi²)= 0.5298\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": 152,
   "id": "b49ec5a3",
   "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": 153,
   "id": "538ddb98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 7.63303\n",
      "GdL       = 8\n",
      "Chi² rid  = 0.95413\n",
      "P(0, chi²)= 0.52989\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    0.633484\n",
      "1    1.991195\n",
      "2    0.770664\n",
      "3    0.002415\n",
      "4    0.613056\n",
      "5    0.057088\n",
      "6    0.485976\n",
      "7    0.110084\n",
      "8    2.315219\n",
      "9    0.653852\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": "4ec42f61",
   "metadata": {},
   "source": [
    "### Regressione lineare pesata Yorkfit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "c7f54cf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AY        = 3.2197184 ± 0.0063280\n",
      "BY        = 0.2357100 ± 0.8110331\n",
      "cov_ABY   = -0.0045912041981810425\n",
      "chi²      = 7.63281\n",
      "chi² rid  = 0.95410\n",
      "P(0, chi²)= 0.52987\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": 155,
   "id": "fc824066",
   "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": "15a6fec5",
   "metadata": {},
   "source": [
    "## Raccolta finale dei dati caso statico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "1203e1a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI senza ERRORE STRUMENTALE INCLUSO:\n",
      "Media pesata K  = 3.22308 ± 0.00202\n",
      "\n",
      "RISULTATI REGRESSIONE OLS:\n",
      "Aols  = 3.22008 ± 0.00577\n",
      "Bols  = 0.01011 ± 0.77853\n",
      "P(0, chi²)= 0.55620\n",
      "\n",
      "RISULTATI REGRESSIONE Carpi:\n",
      "Ac  = 3.21962 ± 0.00633\n",
      "Bc  = 0.24654 ± 0.81108\n",
      "P(0, chi²)= 0.52989\n",
      "\n",
      "RISULTATI REGRESSIONE York:\n",
      "Ay  = 3.21972 ± 0.00633\n",
      "By  = 0.23571 ± 0.81103\n",
      "P(0, chi²)= 0.52987\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": "fb210b61",
   "metadata": {},
   "source": [
    "# Dinamica Sonar\n",
    "Qui specifico la formula perché non è così ovvia:\n",
    "- $M_{eq} = M + \\frac m 3$\n",
    "- $\\omega = \\frac{2\\pi}{T} \\quad \\Rightarrow \\quad T = \\frac{2\\pi}{\\omega}$\n",
    "- A: coefficiente della regressione lineare $A = \\frac{T^2}{M_{eq}}$\n",
    "- $K = \\frac{4 \\pi^2}{A}$\n",
    "\n",
    "Nota: nelle formule comparirà un $/1000$, questo serve a riportare il valore in unità di misura standard: N/m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "95dde99f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "res_T    = 0.001000\n",
      "0    0.673181\n",
      "1    0.917483\n",
      "2    1.155540\n",
      "3    1.390456\n",
      "4    1.627202\n",
      "Name: w, dtype: float64\n",
      "0    0.001668\n",
      "1    0.001921\n",
      "2    0.002167\n",
      "3    0.002394\n",
      "4    0.002637\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Meq = df.m + ( m_mol * 0.438 )\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",
    "\n",
    "res_b = 0.01 / np.sqrt(6) # riso bilancia\n",
    "res_T = 0.001           # riso T\n",
    "\n",
    "T = 2 * np.pi / df.w\n",
    "res_T2 = 2 * T * res_T\n",
    "\n",
    "res_uAd = np.sqrt(  (1 / Meq)**2 * res_T2**2 + (T2 / Meq**2)**2 * res_b**2)\n",
    "\n",
    "\n",
    "print(f\"res_T    = {res_T:.6f}\")\n",
    "# print(f\"res_T2   = {res_T2:.6f}\")\n",
    "# print(f\"res_b    = {res_b:.6f}\")\n",
    "# print(f\"res_uAd  = {res_uAd:.6f}\")\n",
    "\n",
    "uT2 = np.sqrt(uT2**2 + res_T2**2)\n",
    "uMeq = np.sqrt(uMeq**2 + res_b**2)\n",
    "\n",
    "\n",
    "print(T2)\n",
    "print(uT2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bd743d4",
   "metadata": {},
   "source": [
    "## Calcolo dei K e media pesata\n",
    "- Calcolo della Meq (massa equivalente)\n",
    "- Calcolo della media pesata sui K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "8c9f5b44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    3.284586\n",
      "1    3.271857\n",
      "2    3.270507\n",
      "3    3.275588\n",
      "4    3.284972\n",
      "dtype: float64\n",
      "0    0.008148\n",
      "1    0.006856\n",
      "2    0.006137\n",
      "3    0.005642\n",
      "4    0.005325\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Ad = T2 / Meq\n",
    "uAd = np.sqrt( (1/Meq)**2 * uT2**2 + (T2/Meq**2)**2 * uMeq**2 )\n",
    "\n",
    "Kd = 4 * np.pi**2 / Ad\n",
    "uKd = np.sqrt( (4 * np.pi**2 / Ad**2)**2 * uAd**2 )\n",
    "\n",
    "Kd /= 1000\n",
    "uKd /= 1000\n",
    "\n",
    "print(Kd)\n",
    "print(uKd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "0d899f17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-medio: 3.277534061953767\n",
      "sigmaC:  0.0027781383643383675\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",
    "KdM, uKdM = mediaPesata(Kd, uKd)\n",
    "print(\"K-medio:\", KdM)\n",
    "print(\"sigmaC: \", uKdM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6741544e",
   "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": 160,
   "id": "4b4c2682",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "datad = pd.DataFrame({\n",
    "    \"x\": Meq,\n",
    "    \"ux\": uMeq,\n",
    "    \"y\": T2,\n",
    "    \"uy\": uT2\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad203fbd",
   "metadata": {},
   "source": [
    "### Regressione lineare \"OLS\"\n",
    "\n",
    "Non tiene conto dei nostri errori, un risultato puro e semplice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "90d191d5",
   "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:                 7.624e+04\n",
      "Date:                Wed, 08 Apr 2026   Prob (F-statistic):           1.05e-07\n",
      "Time:                        09:36:11   Log-Likelihood:                 23.705\n",
      "No. Observations:                   5   AIC:                            -43.41\n",
      "Df Residuals:                       3   BIC:                            -44.19\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0025      0.004      0.573      0.607      -0.011       0.016\n",
      "x              0.0120   4.35e-05    276.124      0.000       0.012       0.012\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   1.430\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.670\n",
      "Skew:                          -0.251   Prob(JB):                        0.716\n",
      "Kurtosis:                       1.279   Cond. No.                         355.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "RISULTATI REGRESSIONE:\n",
      "Adols  = 0.01202 ± 0.00004\n",
      "Bdols  = 0.00249 ± 0.00434\n",
      "R²     = 0.99996\n",
      "Kdols  = 3.28476 ± 0.01190\n",
      "KBdols = 15.87245 ± 27.70024\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; 5 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "X = sm.add_constant(datad[\"x\"])\n",
    "model = sm.OLS(datad[\"y\"], X).fit()\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "# Estrazione parametri\n",
    "Bdols = model.params[\"const\"]\n",
    "Adols = model.params[\"x\"]\n",
    "uBdols = model.bse[\"const\"]\n",
    "uAdols = model.bse[\"x\"]\n",
    "R2 = model.rsquared\n",
    "\n",
    "# Calcolo del K dai parametri\n",
    "Kdols = (2*np.pi)**2 / Adols / 1000\n",
    "uKdols = np.sqrt( (4*np.pi**2 / Adols**2)**2 * uAdols**2 ) / 1000\n",
    "KBdols = (2*np.pi)**2 / Bdols / 1000\n",
    "uKBdols = np.sqrt( (4*np.pi**2 / Bdols**2)**2 * uBdols**2 ) / 1000\n",
    "\n",
    "\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE:\")\n",
    "print(f\"Adols  = {Adols:.5f} ± {uAdols:.5f}\")\n",
    "print(f\"Bdols  = {Bdols:.5f} ± {uBdols:.5f}\")\n",
    "print(f\"R²     = {R2:.5f}\")\n",
    "print(f\"Kdols  = {Kdols:.5f} ± {uKdols:.5f}\")\n",
    "print(f\"KBdols = {KBdols:.5f} ± {uKBdols:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "a0ba5c8d",
   "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=datad,\n",
    "  x=\"x\",\n",
    "  y=\"y\",\n",
    "  s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "  datad[\"x\"],\n",
    "  datad[\"y\"],\n",
    "  xerr=datad[\"ux\"],\n",
    "  yerr=datad[\"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, datad[\"x\"].max(), 200)\n",
    "yfit = Bdols + Adols * xfit\n",
    "\n",
    "plt.plot(\n",
    "  xfit, yfit,\n",
    "  color=\"crimson\", linewidth=1, zorder=10, label=\"Fit lineare OLS\"\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 ??\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "3c7c05e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 5.15565\n",
      "GdL       = 3\n",
      "Chi² rid  = 1.71855\n",
      "P(0, chi²)= 0.83925\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    2.154276\n",
      "1    0.337895\n",
      "2    1.358887\n",
      "3    0.340009\n",
      "4    0.964582\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "F_fit = Bdols + Adols * datad[\"x\"]\n",
    "sigma = np.sqrt(datad[\"uy\"]**2 + (Adols * datad[\"ux\"])**2)\n",
    "\n",
    "chi2_val = np.sum( ((datad[\"y\"] - F_fit) / sigma)**2 )\n",
    "\n",
    "N = len(datad)\n",
    "nu = N - 2\n",
    "\n",
    "chi2_red = chi2_val / nu\n",
    "Pdols = 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)= {Pdols:.5f}\")\n",
    "print(\"\\n\\n\" + \"#\"*60)\n",
    "print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
    "print(((datad[\"y\"] - F_fit) / sigma)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1296a66",
   "metadata": {},
   "source": [
    "### Regressione lineare Carpi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "698e3c48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B : \n",
      "AdC      = 0.01204 ± 0.00003\n",
      "BdC      = 0.00060 ± 0.00308\n",
      "cov_ABdC = -0.000000\n",
      "P(0,chi²)= 0.81144\n",
      "KdC      = 3.27929 ± 0.00924\n",
      "KBdC     = 65.55634 ± 335.64841\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",
    "AdC, BdC, uAdC, uBdC, covABdC, chidC = reg_lin(datad[\"x\"], datad[\"y\"], datad[\"ux\"], datad[\"uy\"])\n",
    "\n",
    "# Calcolo del K dai parametri\n",
    "KdC = (2*np.pi)**2 / AdC / 1000\n",
    "uKdC = np.sqrt( (4*np.pi**2 / AdC**2)**2 * uAdC**2 ) / 1000\n",
    "KBdC = (2*np.pi)**2 / BdC / 1000\n",
    "uKBdC = np.sqrt( (4*np.pi**2 / BdC**2)**2 * uBdC**2 ) / 1000\n",
    "\n",
    "print(\"Ax + B : \")\n",
    "print(f\"AdC      = {AdC:.5f} ± {uAdC:.5f}\")\n",
    "print(f\"BdC      = {BdC:.5f} ± {uBdC:.5f}\")\n",
    "print(f\"cov_ABdC = {covABdC:.6f}\")\n",
    "print(f\"P(0,chi²)= {chidC:.5f}\")\n",
    "print(f\"KdC      = {KdC:.5f} ± {uKdC:.5f}\")\n",
    "print(f\"KBdC     = {KBdC:.5f} ± {uKBdC:.5f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "a0ab8534",
   "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=datad,\n",
    "  x=\"x\",\n",
    "  y=\"y\",\n",
    "  s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "  datad[\"x\"],\n",
    "  datad[\"y\"],\n",
    "  xerr=datad[\"ux\"],\n",
    "  yerr=datad[\"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, datad[\"x\"].max(), 200)\n",
    "yfit = BdC + AdC * 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(\"Meq (g)\")\n",
    "plt.ylabel(\"T^2 (s^2)\")\n",
    "plt.title(\"Legge di ??\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "12bb0479",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 4.78098\n",
      "GdL       = 3\n",
      "Chi² rid  = 1.59366\n",
      "P(0, chi²)= 0.81144\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    1.024112\n",
      "1    0.589929\n",
      "2    1.320412\n",
      "3    0.162804\n",
      "4    1.683719\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "F_fit = BdC + AdC * datad[\"x\"]\n",
    "sigma = np.sqrt(datad[\"uy\"]**2 + (AdC * datad[\"ux\"])**2)\n",
    "\n",
    "\n",
    "chi2_val = np.sum( ((datad[\"y\"] - F_fit) / sigma)**2 )\n",
    "\n",
    "N = len(datad)\n",
    "nu = N - 2\n",
    "\n",
    "chi2_red = chi2_val / nu\n",
    "PdC = 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)= {PdC:.5f}\")\n",
    "print(\"\\n\\n\" + \"#\"*60)\n",
    "print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
    "print(((datad[\"y\"] - F_fit) / sigma)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3bf75d6",
   "metadata": {},
   "source": [
    "### Regressione lineare pesata Yorkfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "385db415",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdY       = 0.0120387 ± 0.0000339\n",
      "BdY       = 0.0006022 ± 0.0030833\n",
      "cov_ABdY  = -9.968105610528536e-08\n",
      "chi²      = 4.78098\n",
      "chi² rid  = 1.59366\n",
      "P(0, chi²)= 0.81144\n",
      "KdY       = 3.27929 ± 0.00924\n",
      "KBdY      = 65.56142 ± 335.70050\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",
    "AdY, BdY, uAdY, uBdY, cov_ABdY, chi2_val, chi2_red = york_fit(datad.x, datad.y, datad.ux, datad.uy)\n",
    "PdY = chi2.cdf(chi2_val, df=nu)\n",
    "\n",
    "# Calcolo del K dai parametri\n",
    "KdY = (2*np.pi)**2 / AdY / 1000\n",
    "uKdY = np.sqrt( (4*np.pi**2 / AdY**2)**2 * uAdY**2 ) / 1000\n",
    "KBdY = (2*np.pi)**2 / BdY / 1000\n",
    "uKBdY = np.sqrt( (4*np.pi**2 / BdY**2)**2 * uBdY**2 ) / 1000\n",
    "\n",
    "\n",
    "print(f\"AdY       = {AdY:.7f} ± {uAdY:.7f}\")\n",
    "print(f\"BdY       = {BdY:.7f} ± {uBdY:.7f}\")\n",
    "print(f\"cov_ABdY  = {cov_ABdY}\")\n",
    "print(f\"chi²      = {chi2_val:.5f}\")\n",
    "print(f\"chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {PdY:.5f}\")\n",
    "print(f\"KdY       = {KdY:.5f} ± {uKdY:.5f}\")\n",
    "print(f\"KBdY      = {KBdY:.5f} ± {uKBdY:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "698bc57c",
   "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=datad,\n",
    "  x=\"x\",\n",
    "  y=\"y\",\n",
    "  s=7\n",
    ")\n",
    "\n",
    "# Barre d’errore\n",
    "plt.errorbar(\n",
    "  datad[\"x\"],\n",
    "  datad[\"y\"],\n",
    "  xerr=datad[\"ux\"],\n",
    "  yerr=datad[\"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, datad[\"x\"].max(), 200)\n",
    "yfit = BdY + AdY * xfit\n",
    "\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",
    "\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 ??\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e612bc6",
   "metadata": {},
   "source": [
    "## Raccolta finale di dati dinamici"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "fc32f9e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI senza ERRORE STRUMENTALE INCLUSO:\n",
      "Media pesata K  = 3.27753 ± 0.00278\n",
      "\n",
      "RISULTATI REGRESSIONE OLS:\n",
      "Adols    = 0.0120187 ± 0.00004\n",
      "Bdols    = 0.0024872 ± 0.00434\n",
      "P(0,chi²)= 0.8392518\n",
      "Kdols    = 3.2847627 ± 0.01190\n",
      "KBdols   = 15.8724517 ± 27.70024\n",
      "\n",
      "RISULTATI REGRESSIONE Carpi:\n",
      "AdC      = 0.0120387 ± 0.00003\n",
      "BdC      = 0.0006022 ± 0.00308\n",
      "P(0,chi²)= 0.8114442\n",
      "KdC      = 3.2792872 ± 0.00924\n",
      "KBdC     = 65.55634 ± 335.64841\n",
      "\n",
      "RISULTATI REGRESSIONE York:\n",
      "Ady      = 0.01204 ± 0.00003\n",
      "Bdy      = 0.00060 ± 0.00308\n",
      "P(0,chi²)= 0.81144\n",
      "KdY      = 3.27929 ± 0.00924\n",
      "KBdY     = 65.56142 ± 335.70050\n"
     ]
    }
   ],
   "source": [
    "print(\"RISULTATI senza ERRORE STRUMENTALE INCLUSO:\")\n",
    "print(f\"Media pesata K  = {KdM:.5f} ± {uKdM:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE OLS:\")\n",
    "print(f\"Adols    = {Adols:.7f} ± {uAdols:.5f}\")\n",
    "print(f\"Bdols    = {Bdols:.7f} ± {uBdols:.5f}\")\n",
    "print(f\"P(0,chi²)= {Pdols:.7f}\")\n",
    "print(f\"Kdols    = {Kdols:.7f} ± {uKdols:.5f}\")\n",
    "print(f\"KBdols   = {KBdols:.7f} ± {uKBdols:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
    "print(f\"AdC      = {AdC:.7f} ± {uAdC:.5f}\")\n",
    "print(f\"BdC      = {BdC:.7f} ± {uBdC:.5f}\")\n",
    "print(f\"P(0,chi²)= {PdC:.7f}\")\n",
    "print(f\"KdC      = {KdC:.7f} ± {uKdC:.5f}\")\n",
    "print(f\"KBdC     = {KBdC:.5f} ± {uKBdC:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE York:\")\n",
    "print(f\"Ady      = {AdY:.5f} ± {uAdY:.5f}\")\n",
    "print(f\"Bdy      = {BdY:.5f} ± {uBdY:.5f}\")\n",
    "print(f\"P(0,chi²)= {PdY:.5f}\")\n",
    "print(f\"KdY      = {KdY:.5f} ± {uKdY:.5f}\")\n",
    "print(f\"KBdY     = {KBdY:.5f} ± {uKBdY:.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f4edfa1",
   "metadata": {},
   "source": [
    "# Dinamica Cronometro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "0f407f81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.616814\n",
      "1    0.834482\n",
      "2    1.043973\n",
      "3    1.250483\n",
      "4    1.502463\n",
      "Name: t, dtype: float64\n",
      "0    0.010446\n",
      "1    0.007193\n",
      "2    0.011988\n",
      "3    0.020931\n",
      "4    0.047373\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "T2t  = (df.t / 20)**2\n",
    "uT2t = 2 * (df.t / 20) * (df.ut / 20)\n",
    "\n",
    "print(T2t)\n",
    "print(uT2t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d097869",
   "metadata": {},
   "source": [
    "## Calcolo dei K e media pesata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "99bc83a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    3584.745203\n",
      "1    3597.288427\n",
      "2    3620.020017\n",
      "3    3642.240060\n",
      "4    3557.699582\n",
      "dtype: float64\n",
      "0     60.711968\n",
      "1     31.008503\n",
      "2     41.570688\n",
      "3     60.963865\n",
      "4    112.174630\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "Adt  = T2t / Meq\n",
    "uAdt = np.sqrt( (1/Meq)**2 * uT2t**2 + (T2t/Meq**2)**2 * uMeq**2 )\n",
    "\n",
    "Kdt  = 4 * np.pi**2 / Adt\n",
    "uKdt = (4 * np.pi**2 / Adt**2) * uAdt\n",
    "\n",
    "Kdt\n",
    "uKdt\n",
    "\n",
    "print(Kdt)\n",
    "print(uKdt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "ae55d12a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-medio (t): 3.60564201903545\n",
      "sigmaC  (t): 0.021135882837478397\n"
     ]
    }
   ],
   "source": [
    "KdtM, uKdtM = mediaPesata(Kdt, uKdt)\n",
    "KdtM /= 1e3\n",
    "uKdtM /= 1e3\n",
    "print(\"K-medio (t):\", KdtM)\n",
    "print(\"sigmaC  (t):\", uKdtM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "634a06e9",
   "metadata": {},
   "source": [
    "## Analisi con regressioni e grafici"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "75e94b0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "datat = pd.DataFrame({\n",
    "    \"x\":  Meq,\n",
    "    \"ux\": uMeq,\n",
    "    \"y\":  T2t,\n",
    "    \"uy\": uT2t\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7761130",
   "metadata": {},
   "source": [
    "### Regressione lineare \"OLS\"\n",
    "Non tiene conto dei nostri errori, un risultato puro e semplice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "896388e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.999\n",
      "Model:                            OLS   Adj. R-squared:                  0.998\n",
      "Method:                 Least Squares   F-statistic:                     2631.\n",
      "Date:                Wed, 08 Apr 2026   Prob (F-statistic):           1.63e-05\n",
      "Time:                        09:36:12   Log-Likelihood:                 15.713\n",
      "No. Observations:                   5   AIC:                            -27.43\n",
      "Df Residuals:                       3   BIC:                            -28.21\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.0071      0.021     -0.332      0.761      -0.075       0.061\n",
      "x              0.0110      0.000     51.298      0.000       0.010       0.012\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   2.074\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.262\n",
      "Skew:                          -0.213   Prob(JB):                        0.877\n",
      "Kurtosis:                       1.963   Cond. No.                         355.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "RISULTATI REGRESSIONE OLS (t):\n",
      "Atols   = 0.01104 ± 0.00022\n",
      "Btols   = -0.00713 ± 0.02147\n",
      "R²      = 0.99886\n",
      "Kdtols  = 3.57541 ± 0.06970\n",
      "KBdtols = -5.53371 ± 16.64994\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; 5 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "X = sm.add_constant(datat[\"x\"])\n",
    "model = sm.OLS(datat[\"y\"], X).fit()\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "Btols  = model.params[\"const\"]\n",
    "Atols  = model.params[\"x\"]\n",
    "uBtols = model.bse[\"const\"]\n",
    "uAtols = model.bse[\"x\"]\n",
    "R2t    = model.rsquared\n",
    "\n",
    "Kdtols  = 4 * np.pi**2 / Atols  / 1000\n",
    "uKdtols = (4 * np.pi**2 / Atols**2)  * uAtols  / 1000\n",
    "KBdtols  = 4 * np.pi**2 / Btols  / 1000\n",
    "uKBdtols = (4 * np.pi**2 / Btols**2) * uBtols  / 1000\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE OLS (t):\")\n",
    "print(f\"Atols   = {Atols:.5f} ± {uAtols:.5f}\")\n",
    "print(f\"Btols   = {Btols:.5f} ± {uBtols:.5f}\")\n",
    "print(f\"R²      = {R2t:.5f}\")\n",
    "print(f\"Kdtols  = {Kdtols:.5f} ± {uKdtols:.5f}\")\n",
    "print(f\"KBdtols = {KBdtols:.5f} ± {uKBdtols:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "61e40e4c",
   "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",
    "sns.scatterplot(data=datat, x=\"x\", y=\"y\", s=7)\n",
    "\n",
    "plt.errorbar(\n",
    "    datat[\"x\"], datat[\"y\"],\n",
    "    xerr=datat[\"ux\"], yerr=datat[\"uy\"],\n",
    "    fmt=\"none\", ecolor=\"gray\", elinewidth=1, capsize=3, alpha=0.7\n",
    ")\n",
    "\n",
    "xfit = np.linspace(0, datat[\"x\"].max(), 200)\n",
    "yfit = Btols + Atols * xfit\n",
    "\n",
    "plt.plot(xfit, yfit, color=\"crimson\", linewidth=1, zorder=10, label=\"Fit OLS t\")\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "plt.xlabel(\"Meq (g)\")\n",
    "plt.ylabel(\"T² (s²)\")\n",
    "plt.title(\"Legge di ?? — cronometro\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "354687ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 1.29718\n",
      "GdL       = 3\n",
      "Chi² rid  = 0.43239\n",
      "P(0, chi²)= 0.27020\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    0.279621\n",
      "1    0.079508\n",
      "2    0.241447\n",
      "3    0.601919\n",
      "4    0.094687\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "F_fit  = Btols + Atols * datat[\"x\"]\n",
    "sigma  = np.sqrt(datat[\"uy\"]**2 + (Atols * datat[\"ux\"])**2)\n",
    "\n",
    "chi2_val = np.sum(((datat[\"y\"] - F_fit) / sigma)**2)\n",
    "\n",
    "N   = len(datat)\n",
    "nut = N - 2\n",
    "\n",
    "chi2_red = chi2_val / nut\n",
    "Ptols = chi2.cdf(chi2_val, df=nut)\n",
    "\n",
    "print(f\"Chi²      = {chi2_val:.5f}\")\n",
    "print(f\"GdL       = {nut}\")\n",
    "print(f\"Chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {Ptols:.5f}\")\n",
    "print(\"\\n\\n\" + \"#\"*60)\n",
    "print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
    "print(((datat[\"y\"] - F_fit) / sigma)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28f2d34",
   "metadata": {},
   "source": [
    "### Regressione lineare Carpi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "8d6cd1df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ax + B (Carpi, t):\n",
      "AtC      = 0.01078 ± 0.00030\n",
      "BtC      = 0.01342 ± 0.02367\n",
      "cov_ABtC = -0.000007\n",
      "P(0,chi²)= 0.08857\n",
      "KdtC     = 3.66092 ± 0.10078\n",
      "KBdtC    = 2.94068 ± 5.18574\n"
     ]
    }
   ],
   "source": [
    "AtC, BtC, uAtC, uBtC, covABtC, chitC = reg_lin(datat[\"x\"], datat[\"y\"], datat[\"ux\"], datat[\"uy\"])\n",
    "\n",
    "KdtC  = 4 * np.pi**2 / AtC  / 1000\n",
    "uKdtC = (4 * np.pi**2 / AtC**2)  * uAtC  / 1000\n",
    "KBdtC  = 4 * np.pi**2 / BtC  / 1000\n",
    "uKBdtC = (4 * np.pi**2 / BtC**2) * uBtC  / 1000\n",
    "\n",
    "print(\"Ax + B (Carpi, t):\")\n",
    "print(f\"AtC      = {AtC:.5f} ± {uAtC:.5f}\")\n",
    "print(f\"BtC      = {BtC:.5f} ± {uBtC:.5f}\")\n",
    "print(f\"cov_ABtC = {covABtC:.6f}\")\n",
    "print(f\"P(0,chi²)= {chitC:.5f}\")\n",
    "print(f\"KdtC     = {KdtC:.5f} ± {uKdtC:.5f}\")\n",
    "print(f\"KBdtC    = {KBdtC:.5f} ± {uKBdtC:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "592c257c",
   "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",
    "sns.scatterplot(data=datat, x=\"x\", y=\"y\", s=7)\n",
    "\n",
    "plt.errorbar(\n",
    "    datat[\"x\"], datat[\"y\"],\n",
    "    xerr=datat[\"ux\"], yerr=datat[\"uy\"],\n",
    "    fmt=\"none\", ecolor=\"gray\", elinewidth=1, capsize=3, alpha=0.7\n",
    ")\n",
    "\n",
    "xfit = np.linspace(0, datat[\"x\"].max(), 200)\n",
    "yfit = BtC + AtC * xfit\n",
    "\n",
    "plt.plot(xfit, yfit, color=\"crimson\", linewidth=1, zorder=10, label=\"Fit Carpi (t)\")\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "plt.xlabel(\"Meq (g)\")\n",
    "plt.ylabel(\"T² (s²)\")\n",
    "plt.title(\"Legge di Hooke dinamica — cronometro\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "12f276cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi²      = 0.53371\n",
      "GdL       = 3\n",
      "Chi² rid  = 0.17790\n",
      "P(0, chi²)= 0.08857\n",
      "\n",
      "\n",
      "############################################################\n",
      "capiamo quale dato sta contribuendo maggiormente\n",
      "0    0.003197\n",
      "1    0.022511\n",
      "2    0.021599\n",
      "3    0.113281\n",
      "4    0.373118\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "F_fit  = BtC + AtC * datat[\"x\"]\n",
    "sigma  = np.sqrt(datat[\"uy\"]**2 + (AtC * datat[\"ux\"])**2)\n",
    "chi2_val = np.sum(((datat[\"y\"] - F_fit) / sigma)**2)\n",
    "chi2_red = chi2_val / nut\n",
    "PtC = chi2.cdf(chi2_val, df=nut)\n",
    "\n",
    "print(f\"Chi²      = {chi2_val:.5f}\")\n",
    "print(f\"GdL       = {nut}\")\n",
    "print(f\"Chi² rid  = {chi2_red:.5f}\")\n",
    "print(f\"P(0, chi²)= {PtC:.5f}\")\n",
    "print(\"\\n\\n\" + \"#\"*60)\n",
    "print(\"capiamo quale dato sta contribuendo maggiormente\")\n",
    "print(((datat[\"y\"] - F_fit) / sigma)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0823cca1",
   "metadata": {},
   "source": [
    "### Regressione lineare pesata Yorkfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "d04ee20e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AtY       = 0.0107837 ± 0.0002969\n",
      "BtY       = 0.0134249 ± 0.0236741\n",
      "cov_ABtY  = -6.86161269517289e-06\n",
      "chi²      = 0.53371\n",
      "chi² rid  = 0.17790\n",
      "P(0, chi²)= 0.08857\n",
      "KdtY      = 3.66092 ± 0.10078\n",
      "KBdtY     = 2.94068 ± 5.18574\n"
     ]
    }
   ],
   "source": [
    "AtY, BtY, uAtY, uBtY, cov_ABtY, chi2_val_t, chi2_red_t = york_fit(datat.x, datat.y, datat.ux, datat.uy)\n",
    "PtY = chi2.cdf(chi2_val_t, df=nut)\n",
    "\n",
    "KdtY  = 4 * np.pi**2 / AtY  / 1000\n",
    "uKdtY = (4 * np.pi**2 / AtY**2)  * uAtY  / 1000\n",
    "KBdtY  = 4 * np.pi**2 / BtY  / 1000\n",
    "uKBdtY = (4 * np.pi**2 / BtY**2) * uBtY  / 1000\n",
    "\n",
    "print(f\"AtY       = {AtY:.7f} ± {uAtY:.7f}\")\n",
    "print(f\"BtY       = {BtY:.7f} ± {uBtY:.7f}\")\n",
    "print(f\"cov_ABtY  = {cov_ABtY}\")\n",
    "print(f\"chi²      = {chi2_val_t:.5f}\")\n",
    "print(f\"chi² rid  = {chi2_red_t:.5f}\")\n",
    "print(f\"P(0, chi²)= {PtY:.5f}\")\n",
    "print(f\"KdtY      = {KdtY:.5f} ± {uKdtY:.5f}\")\n",
    "print(f\"KBdtY     = {KBdtY:.5f} ± {uKBdtY:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "e795e732",
   "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",
    "sns.scatterplot(data=datat, x=\"x\", y=\"y\", s=7)\n",
    "\n",
    "plt.errorbar(\n",
    "    datat[\"x\"], datat[\"y\"],\n",
    "    xerr=datat[\"ux\"], yerr=datat[\"uy\"],\n",
    "    fmt=\"none\", ecolor=\"gray\", elinewidth=1, capsize=3, alpha=0.7\n",
    ")\n",
    "\n",
    "xfit = np.linspace(0, datat[\"x\"].max(), 200)\n",
    "yfit = BtY + AtY * xfit\n",
    "\n",
    "plt.plot(xfit, yfit, color=\"crimson\", linewidth=1, zorder=10, label=\"Fit York (t)\")\n",
    "plt.xlim(left=0)\n",
    "plt.ylim(bottom=0)\n",
    "plt.xlabel(\"Meq (g)\")\n",
    "plt.ylabel(\"T² (s²)\")\n",
    "plt.title(\"Legge di Hooke dinamica — cronometro\")\n",
    "plt.legend()\n",
    "plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "038b2684",
   "metadata": {},
   "source": [
    "## Raccolta finale di dati dinamici col cronometro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "46ef07bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI senza ERRORE STRUMENTALE (cronometro):\n",
      "Media pesata K  = 3.60564 ± 0.02114\n",
      "\n",
      "RISULTATI REGRESSIONE OLS:\n",
      "Atols    = 0.01104 ± 0.00022\n",
      "Btols    = -0.00713 ± 0.02147\n",
      "P(0,chi²)= 0.27020\n",
      "Kdtols   = 3.57541 ± 0.06970\n",
      "KBdtols  = -5.53371 ± 16.64994\n",
      "\n",
      "RISULTATI REGRESSIONE Carpi:\n",
      "AtC      = 0.01078 ± 0.00030\n",
      "BtC      = 0.01342 ± 0.02367\n",
      "P(0,chi²)= 0.08857\n",
      "KdtC     = 3.66092 ± 0.10078\n",
      "KBdtC    = 2.94068 ± 5.18574\n",
      "\n",
      "RISULTATI REGRESSIONE York:\n",
      "AtY      = 0.01078 ± 0.00030\n",
      "BtY      = 0.01342 ± 0.02367\n",
      "P(0,chi²)= 0.08857\n",
      "KdtY     = 3.66092 ± 0.10078\n",
      "KBdtY    = 2.94068 ± 5.18574\n"
     ]
    }
   ],
   "source": [
    "print(\"RISULTATI senza ERRORE STRUMENTALE (cronometro):\")\n",
    "print(f\"Media pesata K  = {KdtM:.5f} ± {uKdtM:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE OLS:\")\n",
    "print(f\"Atols    = {Atols:.5f} ± {uAtols:.5f}\")\n",
    "print(f\"Btols    = {Btols:.5f} ± {uBtols:.5f}\")\n",
    "print(f\"P(0,chi²)= {Ptols:.5f}\")\n",
    "print(f\"Kdtols   = {Kdtols:.5f} ± {uKdtols:.5f}\")\n",
    "print(f\"KBdtols  = {KBdtols:.5f} ± {uKBdtols:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE Carpi:\")\n",
    "print(f\"AtC      = {AtC:.5f} ± {uAtC:.5f}\")\n",
    "print(f\"BtC      = {BtC:.5f} ± {uBtC:.5f}\")\n",
    "print(f\"P(0,chi²)= {PtC:.5f}\")\n",
    "print(f\"KdtC     = {KdtC:.5f} ± {uKdtC:.5f}\")\n",
    "print(f\"KBdtC    = {KBdtC:.5f} ± {uKBdtC:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI REGRESSIONE York:\")\n",
    "print(f\"AtY      = {AtY:.5f} ± {uAtY:.5f}\")\n",
    "print(f\"BtY      = {BtY:.5f} ± {uBtY:.5f}\")\n",
    "print(f\"P(0,chi²)= {PtY:.5f}\")\n",
    "print(f\"KdtY     = {KdtY:.5f} ± {uKdtY:.5f}\")\n",
    "print(f\"KBdtY    = {KBdtY:.5f} ± {uKBdtY:.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70201336",
   "metadata": {},
   "source": [
    "## Aggiungi gli errori strumentali"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "dfcd391e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "res_t     = 0.000000\n",
      "res_t2   = 0.000002\n",
      "res_uAdt      = 0.000001\n",
      "uKdt_strum    = 0.000270\n"
     ]
    }
   ],
   "source": [
    "res_t  = 0.000001 / 20  # risoluzione cronometro\n",
    "res_t2 = np.max(2 * df.t * res_t)\n",
    "\n",
    "res_uAdt = np.max(np.sqrt( (1 / Meq)**2* res_t2**2 +(T2t / Meq**2)**2  * res_b**2))\n",
    "uKdt_strum = np.max(np.abs(4 * np.pi**2 / Adt**2) * res_uAdt) / 1000\n",
    "\n",
    "print(f\"res_t     = {res_t:.6f}\")\n",
    "print(f\"res_t2   = {res_t2:.6f}\")\n",
    "print(f\"res_uAdt      = {res_uAdt:.6f}\")\n",
    "print(f\"uKdt_strum    = {uKdt_strum:.6f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "b52fea3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "uKdtM_fin   = np.sqrt(uKdtM**2   + uKdt_strum**2)\n",
    "\n",
    "uAtols_fin  = np.sqrt(uAtols**2  + res_uAdt**2)\n",
    "uBtols_fin  = np.sqrt(uBtols**2  + res_uAdt**2)\n",
    "uKdtols_fin = np.sqrt(uKdtols**2 + uKdt_strum**2)\n",
    "\n",
    "uAtC_fin    = np.sqrt(uAtC**2    + res_uAdt**2)\n",
    "uBtC_fin    = np.sqrt(uBtC**2    + res_uAdt**2)\n",
    "uKdtC_fin   = np.sqrt(uKdtC**2  + uKdt_strum**2)\n",
    "\n",
    "uAtY_fin    = np.sqrt(uAtY**2    + res_uAdt**2)\n",
    "uBtY_fin    = np.sqrt(uBtY**2    + res_uAdt**2)\n",
    "uKdtY_fin   = np.sqrt(uKdtY**2  + uKdt_strum**2)\n",
    "\n",
    "uy_fin_t = np.sqrt(datat[\"uy\"]**2 + res_t2**2)\n",
    "ux_fin_t = np.sqrt(datat[\"ux\"]**2 + res_b**2)\n",
    "\n",
    "GdLt = len(datat) - 2\n",
    "\n",
    "F_fit_tols = Btols + Atols * datat[\"x\"]\n",
    "sigma_tols  = np.sqrt(uy_fin_t**2 + (Atols * ux_fin_t)**2)\n",
    "chi2_tols   = np.sum(((datat[\"y\"] - F_fit_tols) / sigma_tols)**2)\n",
    "\n",
    "F_fit_tC   = BtC + AtC * datat[\"x\"]\n",
    "sigma_tC    = np.sqrt(uy_fin_t**2 + (AtC * ux_fin_t)**2)\n",
    "chi2_tC     = np.sum(((datat[\"y\"] - F_fit_tC) / sigma_tC)**2)\n",
    "\n",
    "F_fit_tY   = BtY + AtY * datat[\"x\"]\n",
    "sigma_tY    = np.sqrt(uy_fin_t**2 + (AtY * ux_fin_t)**2)\n",
    "chi2_tY     = np.sum(((datat[\"y\"] - F_fit_tY) / sigma_tY)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "858bbff7",
   "metadata": {},
   "source": [
    "## Risultati della propagazione dell'errore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "0b1e4a1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RISULTATI CON ERRORE STRUMENTALE (cronometro):\n",
      "Media pesata K  = 3.60564 ± 0.02114\n",
      "\n",
      "RISULTATI OLS:\n",
      "Atols  = 0.01104 ± 0.00022\n",
      "Btols  = -0.00713 ± 0.02147\n",
      "Kdtols = 3.57541 ± 0.06970\n",
      "Chi²   = 1.29717 | rid = 0.43239 | P = 0.27019\n",
      "\n",
      "RISULTATI Carpi:\n",
      "AtC    = 0.01078 ± 0.00030\n",
      "BtC    = 0.01342 ± 0.02367\n",
      "KdtC   = 3.66092 ± 0.10078\n",
      "Chi²   = 0.53370 | rid = 0.17790 | P = 0.08857\n",
      "\n",
      "RISULTATI York:\n",
      "AtY    = 0.01078 ± 0.00030\n",
      "BtY    = 0.01342 ± 0.02367\n",
      "KdtY   = 3.66092 ± 0.10078\n",
      "Chi²   = 0.53370 | rid = 0.17790 | P = 0.08857\n"
     ]
    }
   ],
   "source": [
    "print(\"RISULTATI CON ERRORE STRUMENTALE (cronometro):\")\n",
    "print(f\"Media pesata K  = {KdtM:.5f} ± {uKdtM_fin:.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI OLS:\")\n",
    "print(f\"Atols  = {Atols:.5f} ± {uAtols_fin:.5f}\")\n",
    "print(f\"Btols  = {Btols:.5f} ± {uBtols_fin:.5f}\")\n",
    "print(f\"Kdtols = {Kdtols:.5f} ± {uKdtols_fin:.5f}\")\n",
    "print(f\"Chi²   = {chi2_tols:.5f} | rid = {chi2_tols/GdLt:.5f} | P = {chi2.cdf(chi2_tols, GdLt):.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI Carpi:\")\n",
    "print(f\"AtC    = {AtC:.5f} ± {uAtC_fin:.5f}\")\n",
    "print(f\"BtC    = {BtC:.5f} ± {uBtC_fin:.5f}\")\n",
    "print(f\"KdtC   = {KdtC:.5f} ± {uKdtC_fin:.5f}\")\n",
    "print(f\"Chi²   = {chi2_tC:.5f} | rid = {chi2_tC/GdLt:.5f} | P = {chi2.cdf(chi2_tC, GdLt):.5f}\")\n",
    "\n",
    "print(\"\\nRISULTATI York:\")\n",
    "print(f\"AtY    = {AtY:.5f} ± {uAtY_fin:.5f}\")\n",
    "print(f\"BtY    = {BtY:.5f} ± {uBtY_fin:.5f}\")\n",
    "print(f\"KdtY   = {KdtY:.5f} ± {uKdtY_fin:.5f}\")\n",
    "print(f\"Chi²   = {chi2_tY:.5f} | rid = {chi2_tY/GdLt:.5f} | P = {chi2.cdf(chi2_tY, GdLt):.5f}\")"
   ]
  }
 ],
 "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
}