1232 lines
203 KiB
Plaintext
1232 lines
203 KiB
Plaintext
{
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"cells": [
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{
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||
"cell_type": "code",
|
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"execution_count": 181,
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||
"id": "f34c5b88",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"import numpy as np\n",
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"import pandas as pd\n",
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||
"import scipy as sc\n",
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"from scipy.stats import chi2\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
|
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"import matplotlib as mpl\n",
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"import statsmodels.api as sm\n",
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"\n",
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"\n",
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"g = 9.806\n",
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"ug = 0.001\n",
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"\n",
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"m_mol = 29.89\n",
|
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"um_mol = 0.01"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 182,
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"id": "08efb2be",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv(r'dinamica1.csv')\n",
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"\n",
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"def calcola_stats(df, prefix, err_arbitrario):\n",
|
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" cols = [col for col in df.columns if col.startswith(prefix)]\n",
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"\n",
|
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" def riga_stats(row):\n",
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" valori = row[cols].dropna()\n",
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" n = len(valori)\n",
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"\n",
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" if n == 0:\n",
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" return pd.Series({prefix: np.nan, f\"u{prefix}\": np.nan})\n",
|
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" elif n == 1:\n",
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" return pd.Series({prefix: valori.iloc[0], f\"u{prefix}\": err_arbitrario})\n",
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" else:\n",
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" media = valori.mean()\n",
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" sigma = ( valori.max() - valori.min() ) / 2\n",
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" sigma = max(sigma, err_arbitrario) if prefix == \"w\" else sigma # Line cursed per non duplicare il codice\n",
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" return pd.Series({prefix: media, f\"u{prefix}\": sigma})\n",
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"\n",
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" stats = df.apply(riga_stats, axis=1)\n",
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" df[prefix] = stats[prefix]\n",
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" df[f\"u{prefix}\"] = stats[f\"u{prefix}\"]\n",
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"\n",
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" return df\n",
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"\n",
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"\n",
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"df = calcola_stats(df, \"w\", err_arbitrario=0.025)\n",
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"df = calcola_stats(df, \"m\", err_arbitrario=0.01)\n",
|
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"df = calcola_stats(df, \"c\", err_arbitrario=0.01)\n",
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"df = calcola_stats(df, \"a\", err_arbitrario=0.01)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 183,
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"id": "5494409f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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" <thead>\n",
|
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|
||
" <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>a2</th>\n",
|
||
" <th>ua2</th>\n",
|
||
" <th>w2</th>\n",
|
||
" <th>uw2</th>\n",
|
||
" <th>c2</th>\n",
|
||
" <th>uc2</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",
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||
" <th>ua</th>\n",
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||
" </tr>\n",
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||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>108.610000</td>\n",
|
||
" <td>9.210</td>\n",
|
||
" <td>0.029</td>\n",
|
||
" <td>14.2459</td>\n",
|
||
" <td>0.0008</td>\n",
|
||
" <td>268.151</td>\n",
|
||
" <td>0.020</td>\n",
|
||
" <td>10.690</td>\n",
|
||
" <td>0.040</td>\n",
|
||
" <td>14.2434</td>\n",
|
||
" <td>0.0009</td>\n",
|
||
" <td>268.326</td>\n",
|
||
" <td>0.026</td>\n",
|
||
" <td>14.24465</td>\n",
|
||
" <td>0.025</td>\n",
|
||
" <td>108.610000</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" <td>268.2385</td>\n",
|
||
" <td>0.0875</td>\n",
|
||
" <td>9.9500</td>\n",
|
||
" <td>0.7400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>128.636667</td>\n",
|
||
" <td>10.037</td>\n",
|
||
" <td>0.025</td>\n",
|
||
" <td>13.1939</td>\n",
|
||
" <td>0.0007</td>\n",
|
||
" <td>259.605</td>\n",
|
||
" <td>0.018</td>\n",
|
||
" <td>9.744</td>\n",
|
||
" <td>0.027</td>\n",
|
||
" <td>13.1913</td>\n",
|
||
" <td>0.0009</td>\n",
|
||
" <td>259.745</td>\n",
|
||
" <td>0.020</td>\n",
|
||
" <td>13.19260</td>\n",
|
||
" <td>0.025</td>\n",
|
||
" <td>128.636667</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" <td>259.6750</td>\n",
|
||
" <td>0.0700</td>\n",
|
||
" <td>9.8905</td>\n",
|
||
" <td>0.1465</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>148.380000</td>\n",
|
||
" <td>9.930</td>\n",
|
||
" <td>0.030</td>\n",
|
||
" <td>12.3469</td>\n",
|
||
" <td>0.0007</td>\n",
|
||
" <td>251.525</td>\n",
|
||
" <td>0.021</td>\n",
|
||
" <td>11.410</td>\n",
|
||
" <td>0.030</td>\n",
|
||
" <td>12.3461</td>\n",
|
||
" <td>0.0006</td>\n",
|
||
" <td>251.542</td>\n",
|
||
" <td>0.023</td>\n",
|
||
" <td>12.34650</td>\n",
|
||
" <td>0.025</td>\n",
|
||
" <td>148.380000</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" <td>251.5335</td>\n",
|
||
" <td>0.0085</td>\n",
|
||
" <td>10.6700</td>\n",
|
||
" <td>0.7400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>168.530000</td>\n",
|
||
" <td>11.340</td>\n",
|
||
" <td>0.030</td>\n",
|
||
" <td>11.6345</td>\n",
|
||
" <td>0.0005</td>\n",
|
||
" <td>243.211</td>\n",
|
||
" <td>0.021</td>\n",
|
||
" <td>11.500</td>\n",
|
||
" <td>0.030</td>\n",
|
||
" <td>11.6344</td>\n",
|
||
" <td>0.0006</td>\n",
|
||
" <td>243.130</td>\n",
|
||
" <td>0.021</td>\n",
|
||
" <td>11.63445</td>\n",
|
||
" <td>0.025</td>\n",
|
||
" <td>168.530000</td>\n",
|
||
" <td>0.01</td>\n",
|
||
" <td>243.1705</td>\n",
|
||
" <td>0.0405</td>\n",
|
||
" <td>11.4200</td>\n",
|
||
" <td>0.0800</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
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"</table>\n",
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"</div>"
|
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],
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"text/plain": [
|
||
" m1 a1 ua1 w1 uw1 c1 uc1 a2 ua2 \\\n",
|
||
"0 108.610000 9.210 0.029 14.2459 0.0008 268.151 0.020 10.690 0.040 \n",
|
||
"1 128.636667 10.037 0.025 13.1939 0.0007 259.605 0.018 9.744 0.027 \n",
|
||
"2 148.380000 9.930 0.030 12.3469 0.0007 251.525 0.021 11.410 0.030 \n",
|
||
"3 168.530000 11.340 0.030 11.6345 0.0005 243.211 0.021 11.500 0.030 \n",
|
||
"\n",
|
||
" w2 uw2 c2 uc2 w uw m um \\\n",
|
||
"0 14.2434 0.0009 268.326 0.026 14.24465 0.025 108.610000 0.01 \n",
|
||
"1 13.1913 0.0009 259.745 0.020 13.19260 0.025 128.636667 0.01 \n",
|
||
"2 12.3461 0.0006 251.542 0.023 12.34650 0.025 148.380000 0.01 \n",
|
||
"3 11.6344 0.0006 243.130 0.021 11.63445 0.025 168.530000 0.01 \n",
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||
"\n",
|
||
" c uc a ua \n",
|
||
"0 268.2385 0.0875 9.9500 0.7400 \n",
|
||
"1 259.6750 0.0700 9.8905 0.1465 \n",
|
||
"2 251.5335 0.0085 10.6700 0.7400 \n",
|
||
"3 243.1705 0.0405 11.4200 0.0800 "
|
||
]
|
||
},
|
||
"execution_count": 183,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"pd.set_option('display.max_columns', None)\n",
|
||
"pd.set_option('display.width', None)\n",
|
||
"\n",
|
||
"df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 184,
|
||
"id": "976d5531",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]\n",
|
||
"6\n",
|
||
"############################################################\n",
|
||
"[ 8.5635 16.705 25.068 8.1415 16.5045 8.363 ]\n",
|
||
"[0.11205467 0.08791189 0.09641836 0.07051418 0.08087181 0.04138236]\n",
|
||
"############################################################\n",
|
||
"[-20.02666667 -39.77 -59.92 -19.74333333 -39.89333333\n",
|
||
" -20.15 ]\n",
|
||
"[0.01414214 0.01414214 0.01414214 0.01414214 0.01414214 0.01414214]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"perm = [(x,i) for x in range(0,len(df)) for i in range(x+1,len(df))]\n",
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"\n",
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"este = np.array([df.c[x] - df.c[j] for x,j in perm])\n",
|
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"ueste = np.array([np.sqrt(df.uc[x]**2 + df.uc[j]**2) for x,j in perm])\n",
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"\n",
|
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"masse = np.array([df.m[x] - df.m[j] for x,j in perm])\n",
|
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"umasse = np.array([np.sqrt(df.um[x]**2 + df.um[j]**2) for x,j in perm])\n",
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"\n",
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"\n",
|
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"print(perm)\n",
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"print(len(perm))\n",
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"\n",
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"print(\"#\"* 60)\n",
|
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"print(este)\n",
|
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"print(ueste)\n",
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"\n",
|
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"print(\"#\"* 60)\n",
|
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"print(masse)\n",
|
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"print(umasse)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 185,
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||
"id": "2ad19283",
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||
"metadata": {},
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"outputs": [
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{
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||
"name": "stdout",
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||
"output_type": "stream",
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"text": [
|
||
"[22.93238668 23.34538282 23.439266 23.77978587 23.70226463 23.6267966 ]\n",
|
||
"[0.30051945 0.12316079 0.090355 0.20667602 0.11646939 0.11810642]\n"
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||
]
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||
}
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||
],
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||
"source": [
|
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"F = masse * g\n",
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"uF = np.sqrt((g * umasse)**2 + (masse * ug)**2)\n",
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"\n",
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"K = - F / este\n",
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"uK = np.sqrt((1/este)**2 * uF**2 + (F / este**2)**2 * ueste**2 )\n",
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"\n",
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"\n",
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"print(K)\n",
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"print(uK)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 186,
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||
"id": "5f59d6c9",
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||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
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||
"output_type": "stream",
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||
"text": [
|
||
"K-medio: 23.518009860547757\n",
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"sigmaC: 0.052106970693817985\n"
|
||
]
|
||
}
|
||
],
|
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"source": [
|
||
"def mediaPesata(x, ux, dim = 0):\n",
|
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" x_arr = x.to_numpy() if isinstance(x, (pd.Series, pd.DataFrame)) else np.asarray(x)\n",
|
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" sigma_arr = ux.to_numpy() if isinstance(ux, (pd.Series, pd.DataFrame)) else np.asarray(ux)\n",
|
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"\n",
|
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" w = 1.0 / (sigma_arr ** 2)\n",
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"\n",
|
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" num = np.nansum(w * x_arr, axis=dim)\n",
|
||
" den = np.nansum(w, axis=dim)\n",
|
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"\n",
|
||
" media = num / den\n",
|
||
" sigma = np.sqrt(1.0 / den)\n",
|
||
"\n",
|
||
" return media, sigma\n",
|
||
"\n",
|
||
"media, uA = mediaPesata(K, uK)\n",
|
||
"print(\"K-medio:\", media)\n",
|
||
"print(\"sigmaC: \", uA)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 187,
|
||
"id": "aefe7756",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" OLS Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: F R-squared: 1.000\n",
|
||
"Model: OLS Adj. R-squared: 1.000\n",
|
||
"Method: Least Squares F-statistic: 1.044e+04\n",
|
||
"Date: Thu, 02 Apr 2026 Prob (F-statistic): 5.50e-08\n",
|
||
"Time: 17:16:40 Log-Likelihood: -14.806\n",
|
||
"No. Observations: 6 AIC: 33.61\n",
|
||
"Df Residuals: 4 BIC: 33.20\n",
|
||
"Df Model: 1 \n",
|
||
"Covariance Type: nonrobust \n",
|
||
"==============================================================================\n",
|
||
" coef std err t P>|t| [0.025 0.975]\n",
|
||
"------------------------------------------------------------------------------\n",
|
||
"const 0.1355 3.495 0.039 0.971 -9.568 9.839\n",
|
||
"este 23.4628 0.230 102.161 0.000 22.825 24.100\n",
|
||
"==============================================================================\n",
|
||
"Omnibus: nan Durbin-Watson: 0.555\n",
|
||
"Prob(Omnibus): nan Jarque-Bera (JB): 0.395\n",
|
||
"Skew: -0.285 Prob(JB): 0.821\n",
|
||
"Kurtosis: 1.880 Cond. No. 37.4\n",
|
||
"==============================================================================\n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
|
||
"\n",
|
||
"RISULTATI REGRESSIONE:\n",
|
||
"k (pendenza) = 23.46278 ± 0.22966\n",
|
||
"intercetta = 0.13546 ± 3.49489\n",
|
||
"R² = 0.99962\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/home/jack/uni/lab/.venv/lib/python3.13/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
|
||
" warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"data = pd.DataFrame({\n",
|
||
" \"este\": este,\n",
|
||
" \"ueste\": ueste,\n",
|
||
" \"F\": - F,\n",
|
||
" \"uF\": uF\n",
|
||
"})\n",
|
||
"\n",
|
||
"\n",
|
||
"X = sm.add_constant(data[\"este\"])\n",
|
||
"model = sm.OLS(data[\"F\"], X).fit()\n",
|
||
"\n",
|
||
"print(model.summary())\n",
|
||
"\n",
|
||
"# Estrazione parametri\n",
|
||
"intercetta = model.params[\"const\"]\n",
|
||
"pente = model.params[\"este\"]\n",
|
||
"u_intercetta = model.bse[\"const\"]\n",
|
||
"u_pente = model.bse[\"este\"]\n",
|
||
"R2 = model.rsquared\n",
|
||
"\n",
|
||
"print(\"\\nRISULTATI REGRESSIONE:\")\n",
|
||
"print(f\"k (pendenza) = {pente:.5f} ± {u_pente:.5f}\")\n",
|
||
"print(f\"intercetta = {intercetta:.5f} ± {u_intercetta:.5f}\")\n",
|
||
"print(f\"R² = {R2:.5f}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 188,
|
||
"id": "8d795186",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Chi^2 = 12.06747\n",
|
||
"Gradi di libertà = 4\n",
|
||
"Chi^2 ridotto = 3.01687\n",
|
||
"Probabilità P(0 → chi^2) = 0.98314\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"## X^2 sui dai fatti così\n",
|
||
"F_fit = intercetta + pente * data[\"este\"]\n",
|
||
"sigma = np.sqrt(data[\"uF\"]**2 + (pente * data[\"ueste\"])**2)\n",
|
||
"\n",
|
||
"\n",
|
||
"chi2_val = np.sum( ((data[\"F\"] - F_fit) / sigma)**2 )\n",
|
||
"\n",
|
||
"N = len(data)\n",
|
||
"nu = N - 2\n",
|
||
"\n",
|
||
"chi2_red = chi2_val / nu\n",
|
||
"P = chi2.cdf(chi2_val, df=nu)\n",
|
||
"\n",
|
||
"\n",
|
||
"print(f\"Chi^2 = {chi2_val:.5f}\")\n",
|
||
"print(f\"Gradi di libertà = {nu}\")\n",
|
||
"print(f\"Chi^2 ridotto = {chi2_red:.5f}\")\n",
|
||
"print(f\"Probabilità P(0 → chi^2) = {P:.5f}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 189,
|
||
"id": "1d42b009",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"sns.set_theme(style=\"whitegrid\")\n",
|
||
"\n",
|
||
"plt.figure(figsize=(10,6))\n",
|
||
"\n",
|
||
"# Seaborn scatter\n",
|
||
"sns.scatterplot(\n",
|
||
" data=data,\n",
|
||
" x=\"este\",\n",
|
||
" y=\"F\",\n",
|
||
" s=7\n",
|
||
")\n",
|
||
"\n",
|
||
"# Barre d’errore\n",
|
||
"plt.errorbar(\n",
|
||
" data[\"este\"],\n",
|
||
" data[\"F\"],\n",
|
||
" xerr=data[\"ueste\"],\n",
|
||
" yerr=data[\"uF\"],\n",
|
||
" fmt=\"none\",\n",
|
||
" ecolor=\"gray\",\n",
|
||
" elinewidth=1,\n",
|
||
" capsize=3,\n",
|
||
" alpha=0.7\n",
|
||
")\n",
|
||
"\n",
|
||
"# Linea di regressione\n",
|
||
"xfit = np.linspace(0, data[\"este\"].max(), 200)\n",
|
||
"yfit = intercetta + pente * xfit\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.plot(\n",
|
||
" xfit,\n",
|
||
" yfit,\n",
|
||
" color=\"crimson\",\n",
|
||
" linewidth=1,\n",
|
||
" zorder=10,\n",
|
||
" label=\"Fit lineare\"\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.xlim(left=0)\n",
|
||
"plt.ylim(bottom=0)\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.xlabel(\"Δx (mm)\")\n",
|
||
"plt.ylabel(\"Forza F (mN)\")\n",
|
||
"plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
|
||
"plt.legend()\n",
|
||
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
|
||
"\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 190,
|
||
"id": "986ff4a6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Chi^2 = 12.06747\n",
|
||
"Gradi di libertà = 4\n",
|
||
"Chi^2 ridotto = 3.01687\n",
|
||
"Probabilità P(0 → chi^2) = 0.98314\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"F_fit = intercetta + pente * data[\"este\"]\n",
|
||
"sigma = np.sqrt(data[\"uF\"]**2 + (pente * data[\"ueste\"])**2)\n",
|
||
"\n",
|
||
"chi2_val = np.sum( ((data[\"F\"] - F_fit) / sigma)**2 )\n",
|
||
"\n",
|
||
"N = len(data)\n",
|
||
"nu = N - 2\n",
|
||
"\n",
|
||
"chi2_red = chi2_val / nu\n",
|
||
"P = chi2.cdf(chi2_val, df=nu)\n",
|
||
"\n",
|
||
"\n",
|
||
"print(f\"Chi^2 = {chi2_val:.5f}\")\n",
|
||
"print(f\"Gradi di libertà = {nu}\")\n",
|
||
"print(f\"Chi^2 ridotto = {chi2_red:.5f}\")\n",
|
||
"print(f\"Probabilità P(0 → chi^2) = {P:.5f}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 191,
|
||
"id": "ef0817f4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0 3.156242\n",
|
||
"1 1.028093\n",
|
||
"2 0.102207\n",
|
||
"3 2.169242\n",
|
||
"4 4.023666\n",
|
||
"5 1.588019\n",
|
||
"dtype: float64"
|
||
]
|
||
},
|
||
"execution_count": 191,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"((data[\"F\"] - F_fit) / sigma)**2"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 192,
|
||
"id": "cebe6742",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>este</th>\n",
|
||
" <th>ueste</th>\n",
|
||
" <th>F</th>\n",
|
||
" <th>uF</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>8.5635</td>\n",
|
||
" <td>0.112055</td>\n",
|
||
" <td>196.381493</td>\n",
|
||
" <td>0.140116</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>16.7050</td>\n",
|
||
" <td>0.087912</td>\n",
|
||
" <td>389.984620</td>\n",
|
||
" <td>0.144268</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>25.0680</td>\n",
|
||
" <td>0.096418</td>\n",
|
||
" <td>587.575520</td>\n",
|
||
" <td>0.151069</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>8.1415</td>\n",
|
||
" <td>0.070514</td>\n",
|
||
" <td>193.603127</td>\n",
|
||
" <td>0.140076</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>16.5045</td>\n",
|
||
" <td>0.080872</td>\n",
|
||
" <td>391.194027</td>\n",
|
||
" <td>0.144302</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>8.3630</td>\n",
|
||
" <td>0.041382</td>\n",
|
||
" <td>197.590900</td>\n",
|
||
" <td>0.140134</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" este ueste F uF\n",
|
||
"0 8.5635 0.112055 196.381493 0.140116\n",
|
||
"1 16.7050 0.087912 389.984620 0.144268\n",
|
||
"2 25.0680 0.096418 587.575520 0.151069\n",
|
||
"3 8.1415 0.070514 193.603127 0.140076\n",
|
||
"4 16.5045 0.080872 391.194027 0.144302\n",
|
||
"5 8.3630 0.041382 197.590900 0.140134"
|
||
]
|
||
},
|
||
"execution_count": 192,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 193,
|
||
"id": "2d4b7144",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Ax + B : \n",
|
||
"A = 23.39301 +- 0.12446\n",
|
||
"B = 1.80115 +- 1.59592\n",
|
||
"cov_AB = -0.180473\n",
|
||
"p-value chi² = 0.9614234\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"def sigma_y_equiv(x, y, sigma_x, sigma_y):\n",
|
||
" # Stima iniziale di A con sola sigma_y\n",
|
||
" sy2 = sigma_y**2\n",
|
||
" Sw = np.sum(1 / sy2)\n",
|
||
" Sx = np.sum(x / sy2)\n",
|
||
" Sxx = np.sum(x**2 / sy2)\n",
|
||
" Sy = np.sum(y / sy2)\n",
|
||
" Sxy = np.sum(x * y / sy2)\n",
|
||
" delta = Sxx * Sw - Sx**2\n",
|
||
" A_est = (Sxy * Sw - Sx * Sy) / delta\n",
|
||
"\n",
|
||
" # Propagazione\n",
|
||
" sigma_eq = np.sqrt(sigma_y**2 + A_est**2 * sigma_x**2)\n",
|
||
" return sigma_eq\n",
|
||
"\n",
|
||
"\n",
|
||
"def reg_lin(x, y, sigma_x, sigma_y):\n",
|
||
" x = np.asarray(x, dtype=float)\n",
|
||
" y = np.asarray(y, dtype=float)\n",
|
||
" sigma_x = np.asarray(sigma_x, dtype=float)\n",
|
||
" sigma_y = np.asarray(sigma_y, dtype=float)\n",
|
||
"\n",
|
||
" # Propagazione errore x → y\n",
|
||
" sigma_y_eq = sigma_y_equiv(x, y, sigma_x, sigma_y)\n",
|
||
"\n",
|
||
" # Somme pesate\n",
|
||
" w = 1.0 / sigma_y_eq**2\n",
|
||
" Sw = np.sum(w)\n",
|
||
" Sx = np.sum(w * x)\n",
|
||
" Sxx = np.sum(w * x**2)\n",
|
||
" Sy = np.sum(w * y)\n",
|
||
" Sxy = np.sum(w * x * y)\n",
|
||
" delta = Sxx * Sw - Sx**2\n",
|
||
"\n",
|
||
" # Parametri\n",
|
||
" A = (Sxy * Sw - Sx * Sy) / delta\n",
|
||
" B = (Sxx * Sy - Sxy * Sx) / delta\n",
|
||
" sigma_A = np.sqrt(Sw / delta)\n",
|
||
" sigma_B = np.sqrt(Sxx / delta)\n",
|
||
" cov_AB = -Sx / delta\n",
|
||
"\n",
|
||
" # Chi quadro\n",
|
||
" x2 = np.sum((y - A * x - B)**2 / sigma_y_eq**2)\n",
|
||
" dof = len(x) - 2\n",
|
||
" chi = sc.stats.chi2.cdf(x2, dof) # P(X² > x2)\n",
|
||
"\n",
|
||
" return A, B, sigma_A, sigma_B, cov_AB, chi\n",
|
||
"\n",
|
||
"\n",
|
||
"A, B, sA, sB, covAB, chi = reg_lin(data[\"este\"], data[\"F\"], data[\"ueste\"], data[\"uF\"])\n",
|
||
"print(\"Ax + B : \")\n",
|
||
"print(f\"A = {A:.5f} +- {sA:.5f}\")\n",
|
||
"print(f\"B = {B:.5f} +- {sB:.5f}\")\n",
|
||
"print(f\"cov_AB = {covAB:.6f}\")\n",
|
||
"print(f\"p-value chi² = {chi:.7f}\")\n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 194,
|
||
"id": "32e9948f",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Chi^2 = 10.17614\n",
|
||
"Gradi di libertà = 4\n",
|
||
"Chi^2 ridotto = 2.54404\n",
|
||
"Probabilità P(0 → chi^2) = 0.96244\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"F_fit = B + A * data[\"este\"]\n",
|
||
"sigma = np.sqrt(data[\"uF\"]**2 + (A * data[\"ueste\"])**2)\n",
|
||
"\n",
|
||
"\n",
|
||
"chi2_val = np.sum( ((data[\"F\"] - F_fit) / sigma)**2 )\n",
|
||
"\n",
|
||
"N = len(data)\n",
|
||
"nu = N - 2\n",
|
||
"\n",
|
||
"chi2_red = chi2_val / nu\n",
|
||
"P = chi2.cdf(chi2_val, df=nu)\n",
|
||
"\n",
|
||
"\n",
|
||
"print(f\"Chi^2 = {chi2_val:.5f}\")\n",
|
||
"print(f\"Gradi di libertà = {nu}\")\n",
|
||
"print(f\"Chi^2 ridotto = {chi2_red:.5f}\")\n",
|
||
"print(f\"Probabilità P(0 → chi^2) = {P:.5f}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 195,
|
||
"id": "e2407a04",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"sns.set_theme(style=\"whitegrid\")\n",
|
||
"\n",
|
||
"plt.figure(figsize=(10,6))\n",
|
||
"\n",
|
||
"# Seaborn scatter\n",
|
||
"sns.scatterplot(\n",
|
||
" data=data,\n",
|
||
" x=\"este\",\n",
|
||
" y=\"F\",\n",
|
||
" s=7\n",
|
||
")\n",
|
||
"\n",
|
||
"# Barre d’errore\n",
|
||
"plt.errorbar(\n",
|
||
" data[\"este\"],\n",
|
||
" data[\"F\"],\n",
|
||
" xerr=data[\"ueste\"],\n",
|
||
" yerr=data[\"uF\"],\n",
|
||
" fmt=\"none\",\n",
|
||
" ecolor=\"gray\",\n",
|
||
" elinewidth=1,\n",
|
||
" capsize=3,\n",
|
||
" alpha=0.7\n",
|
||
")\n",
|
||
"\n",
|
||
"# Linea di regressione\n",
|
||
"xfit = np.linspace(0, data[\"este\"].max(), 200)\n",
|
||
"yfit = B + A * xfit\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.plot(\n",
|
||
" xfit,\n",
|
||
" yfit,\n",
|
||
" color=\"crimson\",\n",
|
||
" linewidth=1,\n",
|
||
" zorder=10,\n",
|
||
" label=\"Fit lineare\"\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.xlim(left=0)\n",
|
||
"plt.ylim(bottom=0)\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.xlabel(\"Δx (mm)\")\n",
|
||
"plt.ylabel(\"Forza F (mN)\")\n",
|
||
"plt.title(\"Legge di Hooke — punti permutati con errorbar\")\n",
|
||
"plt.legend()\n",
|
||
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
|
||
"\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 196,
|
||
"id": "95dde99f",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"0 0.194561\n",
|
||
"1 0.226829\n",
|
||
"2 0.258983\n",
|
||
"3 0.291654\n",
|
||
"Name: w, dtype: float64\n",
|
||
"0 0.000683\n",
|
||
"1 0.000860\n",
|
||
"2 0.001049\n",
|
||
"3 0.001253\n",
|
||
"dtype: float64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"## Dinamica davvero\n",
|
||
"\n",
|
||
"Meq = df.m + ( m_mol / 3 )\n",
|
||
"uMeq = np.sqrt( df.um**2 + um_mol**2 / 9 )\n",
|
||
"\n",
|
||
"T2 = ( 2 * np.pi / df.w )**2\n",
|
||
"uT2 = np.sqrt( (8*np.pi**2 / df.w**3)**2 * df.uw**2 ) \n",
|
||
"print(T2)\n",
|
||
"print(uT2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 197,
|
||
"id": "1bb433f0",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" OLS Regression Results \n",
|
||
"==============================================================================\n",
|
||
"Dep. Variable: tmp2 R-squared: 1.000\n",
|
||
"Model: OLS Adj. R-squared: 1.000\n",
|
||
"Method: Least Squares F-statistic: 4.713e+05\n",
|
||
"Date: Thu, 02 Apr 2026 Prob (F-statistic): 2.12e-06\n",
|
||
"Time: 17:16:41 Log-Likelihood: 32.343\n",
|
||
"No. Observations: 4 AIC: -60.69\n",
|
||
"Df Residuals: 2 BIC: -61.91\n",
|
||
"Df Model: 1 \n",
|
||
"Covariance Type: nonrobust \n",
|
||
"==============================================================================\n",
|
||
" coef std err t P>|t| [0.025 0.975]\n",
|
||
"------------------------------------------------------------------------------\n",
|
||
"const 0.0023 0.000 6.364 0.024 0.001 0.004\n",
|
||
"masse 0.0016 2.36e-06 686.488 0.000 0.002 0.002\n",
|
||
"==============================================================================\n",
|
||
"Omnibus: nan Durbin-Watson: 2.752\n",
|
||
"Prob(Omnibus): nan Jarque-Bera (JB): 0.628\n",
|
||
"Skew: -0.880 Prob(JB): 0.730\n",
|
||
"Kurtosis: 2.180 Cond. No. 1.01e+03\n",
|
||
"==============================================================================\n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
|
||
"[2] The condition number is large, 1.01e+03. This might indicate that there are\n",
|
||
"strong multicollinearity or other numerical problems.\n",
|
||
"\n",
|
||
"RISULTATI REGRESSIONE:\n",
|
||
"B) = 0.0016212 ± 0.0000024\n",
|
||
"intercetta = 0.00226 ± 0.00035\n",
|
||
"R² = 1.00000\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/home/jack/uni/lab/.venv/lib/python3.13/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 4 samples were given.\n",
|
||
" warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"dfDin = pd.DataFrame({\n",
|
||
" \"masse\": Meq,\n",
|
||
" \"umasse\": uMeq,\n",
|
||
" \"tmp2\": T2,\n",
|
||
" \"utmp2\": uT2\n",
|
||
"})\n",
|
||
"\n",
|
||
"\n",
|
||
"X = sm.add_constant(dfDin[\"masse\"])\n",
|
||
"model = sm.OLS(dfDin[\"tmp2\"], X).fit()\n",
|
||
"\n",
|
||
"print(model.summary())\n",
|
||
"\n",
|
||
"# Estrazione parametri\n",
|
||
"intercetta = model.params[\"const\"]\n",
|
||
"pente = model.params[\"masse\"]\n",
|
||
"u_intercetta = model.bse[\"const\"]\n",
|
||
"u_pente = model.bse[\"masse\"]\n",
|
||
"R2 = model.rsquared\n",
|
||
"\n",
|
||
"print(\"\\nRISULTATI REGRESSIONE:\")\n",
|
||
"print(f\"B) = {pente:.7f} ± {u_pente:.7f}\")\n",
|
||
"print(f\"intercetta = {intercetta:.5f} ± {u_intercetta:.5f}\")\n",
|
||
"print(f\"R² = {R2:.5f}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 198,
|
||
"id": "67d16b84",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"24351.577653480002\n",
|
||
"0.0016211852129718464\n",
|
||
"Forse K 24.351577653480003\n",
|
||
"Forse uK 16717.076316471204\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"Kstrano = (4 * np.pi**2) / pente\n",
|
||
"uKstrano = (4 * np.pi**2) / u_pente\n",
|
||
"print(Kstrano)\n",
|
||
"print(pente)\n",
|
||
"\n",
|
||
"K = Kstrano / 1000\n",
|
||
"uK = uKstrano / 1000\n",
|
||
"print(\"Forse K \", K)\n",
|
||
"print(\"Forse uK \", uK)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 199,
|
||
"id": "a0ba5c8d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sns.set_theme(style=\"whitegrid\")\n",
|
||
"\n",
|
||
"plt.figure(figsize=(10,6))\n",
|
||
"\n",
|
||
"# Seaborn scatter\n",
|
||
"sns.scatterplot(\n",
|
||
" data=dfDin,\n",
|
||
" x=\"masse\",\n",
|
||
" y=\"tmp2\",\n",
|
||
" s=7\n",
|
||
")\n",
|
||
"\n",
|
||
"# Barre d’errore\n",
|
||
"plt.errorbar(\n",
|
||
" dfDin[\"masse\"],\n",
|
||
" dfDin[\"tmp2\"],\n",
|
||
" xerr=dfDin[\"umasse\"],\n",
|
||
" yerr=dfDin[\"utmp2\"],\n",
|
||
" fmt=\"none\",\n",
|
||
" ecolor=\"gray\",\n",
|
||
" elinewidth=1,\n",
|
||
" capsize=3,\n",
|
||
" alpha=0.7\n",
|
||
")\n",
|
||
"\n",
|
||
"# Linea di regressione\n",
|
||
"xfit = np.linspace(0, dfDin[\"masse\"].max(), 200)\n",
|
||
"yfit = intercetta + pente * xfit\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.plot(\n",
|
||
" xfit,\n",
|
||
" yfit,\n",
|
||
" color=\"crimson\",\n",
|
||
" linewidth=1,\n",
|
||
" zorder=10,\n",
|
||
" label=\"Fit lineare\"\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.xlim(left=0)\n",
|
||
"plt.ylim(bottom=0)\n",
|
||
"\n",
|
||
"\n",
|
||
"plt.xlabel(\"Meq (g)\")\n",
|
||
"plt.ylabel(\"T^2 (s^2)\")\n",
|
||
"plt.title(\"Legge di Stocazzo — punti permutati con errorbar\")\n",
|
||
"plt.legend()\n",
|
||
"plt.grid(True, linestyle=\"--\", alpha=0.1)\n",
|
||
"\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 200,
|
||
"id": "859f2337",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Ax + B : \n",
|
||
"A = 0.0016202 +- 0.0000212\n",
|
||
"B = 0.0023963 +- 0.0029727\n",
|
||
"cov_AB = -0.000000\n",
|
||
"p-value chi² = 0.0156\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"def sigma_y_equiv(x, y, sigma_x, sigma_y):\n",
|
||
" # Stima iniziale di A con sola sigma_y\n",
|
||
" sy2 = sigma_y**2\n",
|
||
" Sw = np.sum(1 / sy2)\n",
|
||
" Sx = np.sum(x / sy2)\n",
|
||
" Sxx = np.sum(x**2 / sy2)\n",
|
||
" Sy = np.sum(y / sy2)\n",
|
||
" Sxy = np.sum(x * y / sy2)\n",
|
||
" delta = Sxx * Sw - Sx**2\n",
|
||
" A_est = (Sxy * Sw - Sx * Sy) / delta\n",
|
||
"\n",
|
||
" # Propagazione\n",
|
||
" sigma_eq = np.sqrt(sigma_y**2 + A_est**2 * sigma_x**2)\n",
|
||
" return sigma_eq\n",
|
||
"\n",
|
||
"\n",
|
||
"def reg_lin(x, y, sigma_x, sigma_y):\n",
|
||
" x = np.asarray(x, dtype=float)\n",
|
||
" y = np.asarray(y, dtype=float)\n",
|
||
" sigma_x = np.asarray(sigma_x, dtype=float)\n",
|
||
" sigma_y = np.asarray(sigma_y, dtype=float)\n",
|
||
"\n",
|
||
" # Propagazione errore x → y\n",
|
||
" sigma_y_eq = sigma_y_equiv(x, y, sigma_x, sigma_y)\n",
|
||
"\n",
|
||
" # Somme pesate\n",
|
||
" w = 1.0 / sigma_y_eq**2\n",
|
||
" Sw = np.sum(w)\n",
|
||
" Sx = np.sum(w * x)\n",
|
||
" Sxx = np.sum(w * x**2)\n",
|
||
" Sy = np.sum(w * y)\n",
|
||
" Sxy = np.sum(w * x * y)\n",
|
||
" delta = Sxx * Sw - Sx**2\n",
|
||
"\n",
|
||
" # Parametri\n",
|
||
" A = (Sxy * Sw - Sx * Sy) / delta\n",
|
||
" B = (Sxx * Sy - Sxy * Sx) / delta\n",
|
||
" sigma_A = np.sqrt(Sw / delta)\n",
|
||
" sigma_B = np.sqrt(Sxx / delta)\n",
|
||
" cov_AB = -Sx / delta\n",
|
||
"\n",
|
||
" # Chi quadro\n",
|
||
" x2 = np.sum((y - A * x - B)**2 / sigma_y_eq**2)\n",
|
||
" dof = len(x) - 2\n",
|
||
" chi = sc.stats.chi2.cdf(x2, dof) # P(X² > x2)\n",
|
||
"\n",
|
||
" return A, B, sigma_A, sigma_B, cov_AB, chi\n",
|
||
"\n",
|
||
"\n",
|
||
"A, B, sA, sB, covAB, chi = reg_lin(dfDin[\"masse\"], dfDin[\"tmp2\"], dfDin[\"umasse\"], dfDin[\"utmp2\"])\n",
|
||
"print(\"Ax + B : \")\n",
|
||
"print(f\"A = {A:.7f} +- {sA:.7f}\")\n",
|
||
"print(f\"B = {B:.7f} +- {sB:.7f}\")\n",
|
||
"print(f\"cov_AB = {covAB:.6f}\")\n",
|
||
"print(f\"p-value chi² = {chi:.4f}\")\n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 203,
|
||
"id": "fef04a85",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"K nostro: 24.36602987010158\n",
|
||
"uK nostro: 0.2494340628966531\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"K = (2*np.pi)**2 / A / 1000\n",
|
||
"uK = np.sqrt( (4*np.pi / A**2)**2 * uA**2 ) / 1e6\n",
|
||
"\n",
|
||
"print(\"K nostro: \", K)\n",
|
||
"print(\"uK nostro:\", uK)\n"
|
||
]
|
||
}
|
||
],
|
||
"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
|
||
}
|