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Molla statica fatta benegit status

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Jacopo Basso Ricci
2026-04-01 11:28:01 +02:00
parent 39acd5e4aa
commit 8c77baa938
7 changed files with 2691 additions and 13 deletions
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.gitignore vendored Normal file
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# L'inzio di questo file è specifico per ogniuno di noi
Funzioni\ matlab.txt
prova*
test*

39
funzioni.ipynb
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@@ -15,12 +15,12 @@
"metadata": {},
"outputs": [],
"source": [
"pip install matplotlib numpy pandas scipy"
"# pip install matplotlib numpy pandas scipy"
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"id": "e31f5b72",
"metadata": {},
"outputs": [],
@@ -79,10 +79,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"id": "3d561eb0",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1.5 3. ]\n",
"a 0.50000\n",
"b 0.57735\n",
"dtype: float64\n"
]
}
],
"source": [
"def media(x, dim = 0):\n",
" uA = np.nanstd(x, axis=dim, ddof=1)\n",
@@ -250,18 +261,19 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"id": "78c30380",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A = 1.9900 +- 0.0892\n",
"B = 0.0500 +- 0.2959\n",
"cov_AB = -0.023880\n",
"p-value chi² = 0.7187\n"
"ename": "NameError",
"evalue": "name 'pd' is not defined",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 49\u001b[39m\n\u001b[32m 45\u001b[39m chi = sc.stats.chi2.cdf(x2, dof) \u001b[38;5;66;03m# P(X² > x2)\u001b[39;00m\n\u001b[32m 47\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m A, B, sigma_A, sigma_B, cov_AB, chi\n\u001b[32m---> \u001b[39m\u001b[32m49\u001b[39m df = \u001b[43mpd\u001b[49m.DataFrame({\n\u001b[32m 50\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mx\u001b[39m\u001b[33m\"\u001b[39m:[\u001b[32m1.0\u001b[39m, \u001b[32m2.0\u001b[39m, \u001b[32m3.0\u001b[39m, \u001b[32m4.0\u001b[39m, \u001b[32m5.0\u001b[39m],\n\u001b[32m 51\u001b[39m \u001b[33m\"\u001b[39m\u001b[33my\u001b[39m\u001b[33m\"\u001b[39m:[\u001b[32m2.1\u001b[39m, \u001b[32m3.9\u001b[39m, \u001b[32m6.2\u001b[39m, \u001b[32m7.8\u001b[39m, \u001b[32m10.1\u001b[39m],\n\u001b[32m 52\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mux\u001b[39m\u001b[33m\"\u001b[39m:[\u001b[32m0.1\u001b[39m, \u001b[32m0.1\u001b[39m, \u001b[32m0.1\u001b[39m, \u001b[32m0.1\u001b[39m, \u001b[32m0.1\u001b[39m],\n\u001b[32m 53\u001b[39m \u001b[33m\"\u001b[39m\u001b[33muy\u001b[39m\u001b[33m\"\u001b[39m:[\u001b[32m0.2\u001b[39m, \u001b[32m0.2\u001b[39m, \u001b[32m0.2\u001b[39m, \u001b[32m0.2\u001b[39m, \u001b[32m0.2\u001b[39m]\n\u001b[32m 54\u001b[39m })\n\u001b[32m 56\u001b[39m A, B, sA, sB, covAB, chi = reg_lin(df[\u001b[33m\"\u001b[39m\u001b[33mx\u001b[39m\u001b[33m\"\u001b[39m], df[\u001b[33m\"\u001b[39m\u001b[33my\u001b[39m\u001b[33m\"\u001b[39m], df[\u001b[33m\"\u001b[39m\u001b[33mux\u001b[39m\u001b[33m\"\u001b[39m], df[\u001b[33m\"\u001b[39m\u001b[33muy\u001b[39m\u001b[33m\"\u001b[39m])\n\u001b[32m 57\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mAx + B : \u001b[39m\u001b[33m\"\u001b[39m)\n",
"\u001b[31mNameError\u001b[39m: name 'pd' is not defined"
]
}
],
@@ -310,7 +322,7 @@
" # 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.sf(x2, dof) # P(X² > x2)\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",
@@ -322,6 +334,7 @@
" })\n",
"\n",
"A, B, sA, sB, covAB, chi = reg_lin(df[\"x\"], df[\"y\"], df[\"ux\"], df[\"uy\"])\n",
"print(\"Ax + B : \")\n",
"print(f\"A = {A:.4f} +- {sA:.4f}\")\n",
"print(f\"B = {B:.4f} +- {sB:.4f}\")\n",
"print(f\"cov_AB = {covAB:.6f}\")\n",

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molla/statica1.csv Normal file
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m1,Dx1,Dx2,Dx3,Dx4,Dx5,Dx6
88.970,77.26,77.28,77.26,77.28,77.24,77.42
108.61,68.92,68.94,69.00,68.98,68.90,69.02
128.64,60.88,61.00,60.82,60.94,61.08,60.94
148.38,52.96,52.96,52.78,52.88,52.88,53.00
168.53,44.42,44.68,44.48,44.80,44.92,44.52
1 m1 Dx1 Dx2 Dx3 Dx4 Dx5 Dx6
2 88.970 77.26 77.28 77.26 77.28 77.24 77.42
3 108.61 68.92 68.94 69.00 68.98 68.90 69.02
4 128.64 60.88 61.00 60.82 60.94 61.08 60.94
5 148.38 52.96 52.96 52.78 52.88 52.88 53.00
6 168.53 44.42 44.68 44.48 44.80 44.92 44.52

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m1,Dx1,Dx2,Dx3,Dx4,Dx5,Dx6
49.2466666666667,212.10,211.64,212.00,212.18,212.52,212.04
69.2766666666667,150.92,150.26,150.02,150.16,150.40,150.18
88.9666666666667,90.34,90.34,90.38,90.52,90.26,90.28
108.61,29.82,30.18,30.10,30.20,30.10,30.40
1 m1 Dx1 Dx2 Dx3 Dx4 Dx5 Dx6
2 49.2466666666667 212.10 211.64 212.00 212.18 212.52 212.04
3 69.2766666666667 150.92 150.26 150.02 150.16 150.40 150.18
4 88.9666666666667 90.34 90.34 90.38 90.52 90.26 90.28
5 108.61 29.82 30.18 30.10 30.20 30.10 30.40