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{
"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": 94,
"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": 95,
"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": 96,
"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": 96,
"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": 97,
"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": 98,
"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": 99,
"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": 100,
"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": 101,
"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": 102,
"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: 09:46:35 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": 103,
"id": "8d795186",
"metadata": {},
"outputs": [
{
"data": {
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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": 104,
"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": 105,
"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": 106,
"id": "b49ec5a3",
"metadata": {},
"outputs": [
{
"data": {
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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": 107,
"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": 108,
"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": 109,
"id": "fc824066",
"metadata": {},
"outputs": [
{
"data": {
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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": 110,
"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": 111,
"id": "95dde99f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"res_T = 0.010000\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 0.016412\n",
"1 0.019158\n",
"2 0.021501\n",
"3 0.023587\n",
"4 0.025521\n",
"dtype: float64\n"
]
}
],
"source": [
"Meq = df.m + ( m_mol * 0.33333 )\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 # riso bilancia\n",
"res_T = 0.01 # 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": 112,
"id": "8c9f5b44",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 3.189871\n",
"1 3.202362\n",
"2 3.215330\n",
"3 3.229732\n",
"4 3.245788\n",
"dtype: float64\n",
"0 0.077772\n",
"1 0.066869\n",
"2 0.059828\n",
"3 0.054789\n",
"4 0.050908\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": 113,
"id": "0d899f17",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"K-medio: 3.2221352803714884\n",
"sigmaC: 0.026847776957366426\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": 114,
"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": 115,
"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: 09:46:36 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 0.0219 0.004 5.124 0.014 0.008 0.035\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. 344.\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 = 0.02190 ± 0.00427\n",
"R² = 0.99996\n",
"Kdols = 3.28476 ± 0.01190\n",
"KBdols = 1.80283 ± 0.35181\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": 116,
"id": "a0ba5c8d",
"metadata": {},
"outputs": [
{
"data": {
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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": 117,
"id": "3c7c05e6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chi² = 0.05333\n",
"GdL = 3\n",
"Chi² rid = 0.01778\n",
"P(0, chi²)= 0.00322\n",
"\n",
"\n",
"############################################################\n",
"capiamo quale dato sta contribuendo maggiormente\n",
"0 0.022300\n",
"1 0.003403\n",
"2 0.013819\n",
"3 0.003505\n",
"4 0.010305\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": 118,
"id": "698e3c48",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ax + B : \n",
"AdC = 0.01204 ± 0.00033\n",
"BdC = 0.02009 ± 0.02973\n",
"cov_ABdC = -0.000009\n",
"P(0,chi²)= 0.00290\n",
"KdC = 3.27952 ± 0.09031\n",
"KBdC = 1.96545 ± 2.90895\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": 119,
"id": "a0ab8534",
"metadata": {},
"outputs": [
{
"data": {
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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": 120,
"id": "12bb0479",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chi² = 0.04962\n",
"GdL = 3\n",
"Chi² rid = 0.01654\n",
"P(0, chi²)= 0.00290\n",
"\n",
"\n",
"############################################################\n",
"capiamo quale dato sta contribuendo maggiormente\n",
"0 0.010533\n",
"1 0.006123\n",
"2 0.013854\n",
"3 0.001878\n",
"4 0.017229\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": 121,
"id": "385db415",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AdY = 0.0120379 ± 0.0003315\n",
"BdY = 0.0200862 ± 0.0297283\n",
"cov_ABdY = -9.375764600499696e-06\n",
"chi² = 0.04962\n",
"chi² rid = 0.01654\n",
"P(0, chi²)= 0.00290\n",
"KdY = 3.27952 ± 0.09031\n",
"KBdY = 1.96545 ± 2.90895\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": 122,
"id": "698bc57c",
"metadata": {},
"outputs": [
{
"data": {
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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": 123,
"id": "fc32f9e6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RISULTATI senza ERRORE STRUMENTALE INCLUSO:\n",
"Media pesata K = 3.22214 ± 0.02685\n",
"\n",
"RISULTATI REGRESSIONE OLS:\n",
"Adols = 0.0120187 ± 0.00004\n",
"Bdols = 0.0218980 ± 0.00427\n",
"P(0,chi²)= 0.0032238\n",
"Kdols = 3.2847627 ± 0.01190\n",
"KBdols = 1.8028281 ± 0.35181\n",
"\n",
"RISULTATI REGRESSIONE Carpi:\n",
"AdC = 0.0120379 ± 0.00033\n",
"BdC = 0.0200862 ± 0.02973\n",
"P(0,chi²)= 0.0028961\n",
"KdC = 3.2795186 ± 0.09031\n",
"KBdC = 1.96545 ± 2.90895\n",
"\n",
"RISULTATI REGRESSIONE York:\n",
"Ady = 0.01204 ± 0.00033\n",
"Bdy = 0.02009 ± 0.02973\n",
"P(0,chi²)= 0.00290\n",
"KdY = 3.27952 ± 0.09031\n",
"KBdY = 1.96545 ± 2.90895\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": 124,
"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": 125,
"id": "99bc83a7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 3481.375388\n",
"1 3520.881843\n",
"2 3558.945703\n",
"3 3591.251814\n",
"4 3515.262640\n",
"dtype: float64\n",
"0 58.964242\n",
"1 30.353010\n",
"2 40.870817\n",
"3 60.111122\n",
"4 110.836851\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": 126,
"id": "ae55d12a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"K-medio (t): 3.5339477952923186\n",
"sigmaC (t): 0.02071746981839289\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": 127,
"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": 128,
"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: 09:46:38 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 0.0107 0.021 0.506 0.648 -0.057 0.078\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. 344.\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 = 0.01070 ± 0.02113\n",
"R² = 0.99886\n",
"Kdtols = 3.57541 ± 0.06970\n",
"KBdtols = 3.69001 ± 7.28846\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": 129,
"id": "61e40e4c",
"metadata": {},
"outputs": [
{
"data": {
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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": 130,
"id": "354687ec",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chi² = 1.29711\n",
"GdL = 3\n",
"Chi² rid = 0.43237\n",
"P(0, chi²)= 0.27018\n",
"\n",
"\n",
"############################################################\n",
"capiamo quale dato sta contribuendo maggiormente\n",
"0 0.279595\n",
"1 0.079492\n",
"2 0.241430\n",
"3 0.601905\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": 131,
"id": "8d6cd1df",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ax + B (Carpi, t):\n",
"AtC = 0.01078 ± 0.00030\n",
"BtC = 0.03084 ± 0.02321\n",
"cov_ABtC = -0.000007\n",
"P(0,chi²)= 0.08857\n",
"KdtC = 3.66092 ± 0.10078\n",
"KBdtC = 1.28006 ± 0.96321\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": 132,
"id": "592c257c",
"metadata": {},
"outputs": [
{
"data": {
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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": 133,
"id": "12f276cc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chi² = 0.53370\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 0.003196\n",
"1 0.022509\n",
"2 0.021597\n",
"3 0.113279\n",
"4 0.373115\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": 134,
"id": "d04ee20e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AtY = 0.0107837 ± 0.0002969\n",
"BtY = 0.0308411 ± 0.0232070\n",
"cov_ABtY = -6.719692180359672e-06\n",
"chi² = 0.53370\n",
"chi² rid = 0.17790\n",
"P(0, chi²)= 0.08857\n",
"KdtY = 3.66092 ± 0.10078\n",
"KBdtY = 1.28006 ± 0.96321\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": 135,
"id": "e795e732",
"metadata": {},
"outputs": [
{
"data": {
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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": 136,
"id": "46ef07bd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RISULTATI senza ERRORE STRUMENTALE (cronometro):\n",
"Media pesata K = 3.53395 ± 0.02072\n",
"\n",
"RISULTATI REGRESSIONE OLS:\n",
"Atols = 0.01104 ± 0.00022\n",
"Btols = 0.01070 ± 0.02113\n",
"P(0,chi²)= 0.27018\n",
"Kdtols = 3.57541 ± 0.06970\n",
"KBdtols = 3.69001 ± 7.28846\n",
"\n",
"RISULTATI REGRESSIONE Carpi:\n",
"AtC = 0.01078 ± 0.00030\n",
"BtC = 0.03084 ± 0.02321\n",
"P(0,chi²)= 0.08857\n",
"KdtC = 3.66092 ± 0.10078\n",
"KBdtC = 1.28006 ± 0.96321\n",
"\n",
"RISULTATI REGRESSIONE York:\n",
"AtY = 0.01078 ± 0.00030\n",
"BtY = 0.03084 ± 0.02321\n",
"P(0,chi²)= 0.08857\n",
"KdtY = 3.66092 ± 0.10078\n",
"KBdtY = 1.28006 ± 0.96321\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": 137,
"id": "dfcd391e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"res_t = 0.000000\n",
"res_t2 = 0.000002\n",
"res_uAdt = 0.000002\n",
"uKdt_strum = 0.000681\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": 138,
"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": 139,
"id": "0b1e4a1d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RISULTATI CON ERRORE STRUMENTALE (cronometro):\n",
"Media pesata K = 3.53395 ± 0.02073\n",
"\n",
"RISULTATI OLS:\n",
"Atols = 0.01104 ± 0.00022\n",
"Btols = 0.01070 ± 0.02113\n",
"Kdtols = 3.57541 ± 0.06970\n",
"Chi² = 1.29702 | rid = 0.43234 | P = 0.27016\n",
"\n",
"RISULTATI Carpi:\n",
"AtC = 0.01078 ± 0.00030\n",
"BtC = 0.03084 ± 0.02321\n",
"KdtC = 3.66092 ± 0.10078\n",
"Chi² = 0.53368 | rid = 0.17789 | P = 0.08857\n",
"\n",
"RISULTATI York:\n",
"AtY = 0.01078 ± 0.00030\n",
"BtY = 0.03084 ± 0.02321\n",
"KdtY = 3.66092 ± 0.10078\n",
"Chi² = 0.53368 | rid = 0.17789 | 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
}
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