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Molto lavoro per negare il modello, ora si inizia con LaTeX perché altrimenti non finiamo

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This commit is contained in:
Jacopo Basso Ricci
2026-04-08 10:11:20 +02:00
parent 84e88fd6e6
commit 1c8f6fb418
13 changed files with 14051 additions and 378 deletions
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278
.gitignore vendored
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@@ -2,3 +2,281 @@
Funzioni\ matlab.txt Funzioni\ matlab.txt
prova* prova*
test* test*
## Core latex/pdflatex auxiliary files:
*.aux
*.lof
*.log
*.lot
*.fls
*.out
*.toc
*.fmt
*.fot
*.cb
*.cb2
.*.lb
## Intermediate documents:
*.dvi
*.xdv
*-converted-to.*
## Bibliography auxiliary files (bibtex/biblatex/biber):
*.bbl
*.bcf
*.blg
*-blx.aux
*-blx.bib
*.run.xml
## Build tool auxiliary files:
*.fdb_latexmk
*.synctex
*.synctex(busy)
*.synctex.gz
*.synctex.gz(busy)
*.pdfsync
## Build tool directories for auxiliary files
# latexrun
latex.out/
## Auxiliary and intermediate files from other packages:
# algorithms
*.alg
*.loa
# achemso
acs-*.bib
# amsthm
*.thm
# beamer
*.nav
*.pre
*.snm
*.vrb
# changes
*.soc
# comment
*.cut
# cprotect
*.cpt
# elsarticle (documentclass of Elsevier journals)
*.spl
# endnotes
*.ent
# fixme
*.lox
# feynmf/feynmp
*.mf
*.mp
*.t[1-9]
*.t[1-9][0-9]
*.tfm
#(r)(e)ledmac/(r)(e)ledpar
*.end
*.?end
*.[1-9]
*.[1-9][0-9]
*.[1-9][0-9][0-9]
*.[1-9]R
*.[1-9][0-9]R
*.[1-9][0-9][0-9]R
*.eledsec[1-9]
*.eledsec[1-9]R
*.eledsec[1-9][0-9]
*.eledsec[1-9][0-9]R
*.eledsec[1-9][0-9][0-9]
*.eledsec[1-9][0-9][0-9]R
# glossaries
*.acn
*.acr
*.glg
*.glo
*.gls
*.glsdefs
*.lzo
*.lzs
# uncomment this for glossaries-extra (will ignore makeindex's style files!)
# *.ist
# gnuplottex
*-gnuplottex-*
# gregoriotex
*.gaux
*.glog
*.gtex
# htlatex
*.4ct
*.4tc
*.idv
*.lg
*.trc
*.xref
# hyperref
*.brf
# knitr
*-concordance.tex
# TODO Uncomment the next line if you use knitr and want to ignore its generated tikz files
# *.tikz
*-tikzDictionary
# listings
*.lol
# luatexja-ruby
*.ltjruby
# makeidx
*.idx
*.ilg
*.ind
# minitoc
*.maf
*.mlf
*.mlt
*.mtc[0-9]*
*.slf[0-9]*
*.slt[0-9]*
*.stc[0-9]*
# minted
_minted*
*.pyg
# morewrites
*.mw
# newpax
*.newpax
# nomencl
*.nlg
*.nlo
*.nls
# pax
*.pax
# pdfpcnotes
*.pdfpc
# sagetex
*.sagetex.sage
*.sagetex.py
*.sagetex.scmd
# scrwfile
*.wrt
# sympy
*.sout
*.sympy
sympy-plots-for-*.tex/
# pdfcomment
*.upa
*.upb
# pythontex
*.pytxcode
pythontex-files-*/
# tcolorbox
*.listing
# thmtools
*.loe
# TikZ & PGF
*.dpth
*.md5
*.auxlock
# todonotes
*.tdo
# vhistory
*.hst
*.ver
# easy-todo
*.lod
# xcolor
*.xcp
# xmpincl
*.xmpi
# xindy
*.xdy
# xypic precompiled matrices and outlines
*.xyc
*.xyd
# endfloat
*.ttt
*.fff
# Latexian
TSWLatexianTemp*
## Editors:
# WinEdt
*.bak
*.sav
# Texpad
.texpadtmp
# LyX
*.lyx~
# Kile
*.backup
# gummi
.*.swp
# KBibTeX
*~[0-9]*
# TeXnicCenter
*.tps
# auto folder when using emacs and auctex
./auto/*
*.el
# expex forward references with \gathertags
*-tags.tex
# standalone packages
*.sta
# Makeindex log files
*.lpz
# xwatermark package
*.xwm

46
molla/compatibilita/comp1.ipynb
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@@ -8,7 +8,7 @@
"## Valori statici (calibro)\n", "## Valori statici (calibro)\n",
"N = 10\n", "N = 10\n",
"\n", "\n",
"A = 23.97 +- 0.16 " "A = 23.98765 ± 0.18498"
] ]
}, },
{ {
@@ -19,7 +19,7 @@
"## Valori statici (sonar)\n", "## Valori statici (sonar)\n",
"N = 6\n", "N = 6\n",
"\n", "\n",
"A = 23.46 +- 0.23\n" "A = 23.41058 ± 0.22237"
] ]
}, },
{ {
@@ -30,12 +30,12 @@
"## Valori dinamici (sonar)\n", "## Valori dinamici (sonar)\n",
"N = 4\n", "N = 4\n",
"\n", "\n",
"K nostro: 24.35 +- 0.25\n" "KC= 24.3546921 ± 0.03539\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 1,
"id": "b349ba73", "id": "b349ba73",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -46,7 +46,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 2,
"id": "a68eb302", "id": "a68eb302",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -54,22 +54,22 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"t = 1.751\n", "t = 1.951\n",
"p-value (two-tailed) = 10.1827 %\n" "p-value (two-tailed) = 7.1395 %\n"
] ]
} }
], ],
"source": [ "source": [
"# Valori statici (calibro)\n", "# Valori statici (calibro)\n",
"Nd = 10\n", "Nd = 10\n",
"Ad = 23.97\n", "Ad = 24.00\n",
"uAd = 0.16 * 3\n", "uAd = 0.18 * 3\n",
"\n", "\n",
"\n", "\n",
"#Valori statici (sonar)\n", "#Valori statici (sonar)\n",
"Ns = 6\n", "Ns = 6\n",
"As = 23.46\n", "As = 23.41\n",
"uAs = 0.23 * 3\n", "uAs = 0.22 * 3\n",
"\n", "\n",
"#Nomi coerenti con Cannelli\n", "#Nomi coerenti con Cannelli\n",
"GdL = Nd + Ns - 2\n", "GdL = Nd + Ns - 2\n",
@@ -88,7 +88,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 6,
"id": "2746d086", "id": "2746d086",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -96,21 +96,21 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"t = -1.147\n", "t = -0.972\n",
"p-value (two-tailed) = 27.3612 %\n" "p-value (two-tailed) = 35.0452 %\n"
] ]
} }
], ],
"source": [ "source": [
"# Valori statici (calibro)\n", "# Valori statici (calibro)\n",
"Nd = 10\n", "Nd = 10\n",
"Ad = 23.97\n", "Ad = 24.00\n",
"uAd = 0.16 * 3\n", "uAd = 0.18 *3\n",
"\n", "\n",
"#Valori dinamici (sonar)\n", "#Valori dinamici (sonar)\n",
"Ns = 4\n", "Ns = 4\n",
"As = 24.35\n", "As = 24.35\n",
"uAs = 0.25 * 3\n", "uAs = 0.26 *3\n",
"\n", "\n",
"#Nomi coerenti con Cannelli\n", "#Nomi coerenti con Cannelli\n",
"GdL = Nd + Ns - 2\n", "GdL = Nd + Ns - 2\n",
@@ -129,7 +129,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 4,
"id": "be0c6cc6", "id": "be0c6cc6",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -137,21 +137,21 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"t = -1.934\n", "t = -2.775\n",
"p-value (two-tailed) = 8.9234 %\n" "p-value (two-tailed) = 2.4092 %\n"
] ]
} }
], ],
"source": [ "source": [
"# Valori statici (sonar)\n", "# Valori statici (sonar)\n",
"Nd = 6\n", "Nd = 6\n",
"Ad = 23.46\n", "Ad = 23.41\n",
"uAd = 0.23 * 3\n", "uAd = 0.22 * 3\n",
"\n", "\n",
"#Valori dinamici (sonar)\n", "#Valori dinamici (sonar)\n",
"Ns = 4\n", "Ns = 4\n",
"As = 24.35\n", "As = 24.35\n",
"uAs = 0.25 * 3\n", "uAs = 0.03 * 3\n",
"\n", "\n",
"#Nomi coerenti con Cannelli\n", "#Nomi coerenti con Cannelli\n",
"GdL = Nd + Ns - 2\n", "GdL = Nd + Ns - 2\n",

120
molla/compatibilita/comp2.ipynb
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@@ -2,29 +2,25 @@
"cells": [ "cells": [
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "ba4e56bc", "id": "3b66972b",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Valori dinamici\n", "# Molla statica 1 Calibro\n",
"N = 10\n", "Ac = 3.20021 ± 0.00923\n",
"\n", "\n",
"A = 3.21951 +- 0.00470 " "# Molla statica 1 Sonar\n",
] "Ac = 3.21962 ± 0.00633\n",
},
{
"cell_type": "markdown",
"id": "aaf30c1f",
"metadata": {},
"source": [
"## Valori statici\n",
"N = 6\n",
"\n", "\n",
"A = 3.2002 +- 0.0092\n" "# Molla dinamica 1 Sonar\n",
"KdC = 3.2792872 ± 0.00924\n",
"\n",
"# Molla dinamica 1 Cronometro\n",
"KdtC = 3.66092 ± 0.10078\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 2,
"id": "b349ba73", "id": "b349ba73",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -35,7 +31,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 3,
"id": "a68eb302", "id": "a68eb302",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -43,21 +39,103 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"t = 5.774\n", "t = 3.807\n",
"p-value (two-tailed) = 0.0048 %\n" "p-value (two-tailed) = 0.1924 %\n"
] ]
} }
], ],
"source": [ "source": [
"# Valori dimanici (sonar)\n", "# Valori statici (sonar)\n",
"Nd = 10\n", "Nd = 10\n",
"Ad = 3.220\n", "Ad = 3.220\n",
"uAd = 0.005\n", "uAd = 0.006\n",
"\n", "\n",
"#Valori statici (calibro)\n", "#Valori statici (calibro)\n",
"Ns = 6\n", "Ns = 6\n",
"As = 3.200\n", "As = 3.200\n",
"uAs = 0.009\n", "uAs = 0.015\n",
"\n",
"#Nomi coerenti con Cannelli\n",
"GdL = Nd + Ns - 2\n",
"\n",
"s2 = ( (Nd - 1) * uAd**2 + (Ns - 1) * uAs**2 ) / GdL\n",
"\n",
"sigma2 = ( s2 / Nd ) + ( s2 / Ns )\n",
"\n",
"t = ( Ad - As ) / np.sqrt( sigma2 )\n",
"\n",
"\n",
"p_value = 2 * (1 - student_t.cdf(abs(t), df=GdL))\n",
"print(f\"t = {t:.3f}\")\n",
"print(f\"p-value (two-tailed) = {p_value * 100:.4f} %\")\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "459b7f56",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"t = -5.278\n",
"p-value (two-tailed) = 0.0117 %\n"
]
}
],
"source": [
"# Valori statici (sonar)\n",
"Nd = 10\n",
"Ad = 3.220\n",
"uAd = 0.006 * 3\n",
"\n",
"#Valori dinamici (sonar)\n",
"Ns = 6\n",
"As = 3.279\n",
"uAs = 0.009 * 3\n",
"\n",
"#Nomi coerenti con Cannelli\n",
"GdL = Nd + Ns - 2\n",
"\n",
"s2 = ( (Nd - 1) * uAd**2 + (Ns - 1) * uAs**2 ) / GdL\n",
"\n",
"sigma2 = ( s2 / Nd ) + ( s2 / Ns )\n",
"\n",
"t = ( Ad - As ) / np.sqrt( sigma2 )\n",
"\n",
"\n",
"p_value = 2 * (1 - student_t.cdf(abs(t), df=GdL))\n",
"print(f\"t = {t:.3f}\")\n",
"print(f\"p-value (two-tailed) = {p_value * 100:.4f} %\")\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5d82905f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"t = 0.701\n",
"p-value (two-tailed) = 49.4616 %\n"
]
}
],
"source": [
"# Valori dinamici (sonar)\n",
"Nd = 10\n",
"Ad = 3.279\n",
"uAd = 0.09 *3\n",
"\n",
"#Valori statici (calibro)\n",
"Ns = 6\n",
"As = 3.200\n",
"uAs = 0.015 * 3\n",
"\n", "\n",
"#Nomi coerenti con Cannelli\n", "#Nomi coerenti con Cannelli\n",
"GdL = Nd + Ns - 2\n", "GdL = Nd + Ns - 2\n",

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molla/statica/mollaStatica1.ipynb
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@@ -18,7 +18,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 58, "execution_count": 20,
"id": "f34c5b88", "id": "f34c5b88",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -51,7 +51,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 59, "execution_count": 21,
"id": "08efb2be", "id": "08efb2be",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -86,7 +86,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 60, "execution_count": 22,
"id": "5494409f", "id": "5494409f",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -215,7 +215,7 @@
"4 168.53 0.002887 " "4 168.53 0.002887 "
] ]
}, },
"execution_count": 60, "execution_count": 22,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@@ -226,7 +226,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 61, "execution_count": 23,
"id": "976d5531", "id": "976d5531",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -282,7 +282,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 62, "execution_count": 24,
"id": "2ad19283", "id": "2ad19283",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -290,10 +290,10 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[23.12238655 23.79957321 23.89802584 23.89480155 24.50322786 24.30058505\n", "[0.04459466 0.05636189 0.07164148 0.08906597 0.04476779 0.05643232\n",
" 24.15933014 24.09836266 23.99026349 23.88530016]\n", " 0.07206497 0.04463879 0.05651695 0.04482161]\n",
"[0.22419984 0.16285763 0.10115386 0.15188559 0.31275403 0.14045171\n", "[0.08074652 0.11183321 0.10315038 0.2075251 0.10230673 0.09273618\n",
" 0.2012065 0.36234381 0.31901124 0.61345112]\n" " 0.20255041 0.12077527 0.21682558 0.21247745]\n"
] ]
} }
], ],
@@ -305,13 +305,13 @@
"uK = np.sqrt((1/este)**2 * uF**2 + (F / este**2)**2 * ueste**2 )\n", "uK = np.sqrt((1/este)**2 * uF**2 + (F / este**2)**2 * ueste**2 )\n",
"\n", "\n",
"\n", "\n",
"print(K)\n", "print(uF)\n",
"print(uK)" "print(ueste)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 63, "execution_count": 25,
"id": "5f59d6c9", "id": "5f59d6c9",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -360,7 +360,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 64, "execution_count": 26,
"id": "7e75ec05", "id": "7e75ec05",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -387,7 +387,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 65, "execution_count": 27,
"id": "aefe7756", "id": "aefe7756",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -400,8 +400,8 @@
"Dep. Variable: y R-squared: 1.000\n", "Dep. Variable: y R-squared: 1.000\n",
"Model: OLS Adj. R-squared: 1.000\n", "Model: OLS Adj. R-squared: 1.000\n",
"Method: Least Squares F-statistic: 2.238e+04\n", "Method: Least Squares F-statistic: 2.238e+04\n",
"Date: Sat, 04 Apr 2026 Prob (F-statistic): 4.46e-15\n", "Date: Tue, 07 Apr 2026 Prob (F-statistic): 4.46e-15\n",
"Time: 19:37:55 Log-Likelihood: -27.238\n", "Time: 15:51:49 Log-Likelihood: -27.238\n",
"No. Observations: 10 AIC: 58.48\n", "No. Observations: 10 AIC: 58.48\n",
"Df Residuals: 8 BIC: 59.08\n", "Df Residuals: 8 BIC: 59.08\n",
"Df Model: 1 \n", "Df Model: 1 \n",
@@ -449,7 +449,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 66, "execution_count": 28,
"id": "1d42b009", "id": "1d42b009",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -515,7 +515,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 67, "execution_count": 29,
"id": "986ff4a6", "id": "986ff4a6",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -577,7 +577,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 68, "execution_count": 30,
"id": "2d4b7144", "id": "2d4b7144",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -653,7 +653,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 69, "execution_count": 31,
"id": "e2407a04", "id": "e2407a04",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -722,7 +722,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 70, "execution_count": 32,
"id": "32e9948f", "id": "32e9948f",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -785,7 +785,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 71, "execution_count": 33,
"id": "bfb895c6", "id": "bfb895c6",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -868,7 +868,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 72, "execution_count": 34,
"id": "202de438", "id": "202de438",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -942,7 +942,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 73, "execution_count": 35,
"id": "caf23dbe", "id": "caf23dbe",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -1000,7 +1000,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 74, "execution_count": 36,
"id": "8f5c8bb7", "id": "8f5c8bb7",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -1010,8 +1010,8 @@
"text": [ "text": [
"u_strum_m = 0.004082\n", "u_strum_m = 0.004082\n",
"u_strum_Dx = 0.020412\n", "u_strum_Dx = 0.020412\n",
"uF_strum = 0.089066\n", "uF_strum = 0.058091\n",
"uK_strum = 0.662828\n" "uK_strum = 0.273764\n"
] ]
} }
], ],
@@ -1028,10 +1028,10 @@
"umasse_strum = np.maximum(umasse, u_strum_m)\n", "umasse_strum = np.maximum(umasse, u_strum_m)\n",
"\n", "\n",
"# Worst-case scalare: prendi il massimo anche di ueste_strum\n", "# Worst-case scalare: prendi il massimo anche di ueste_strum\n",
"uF_strum = np.max(np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 ))\n", "uF_strum = np.average(np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 ))\n",
"uDx_strum = np.max(ueste_strum)\n", "uDx_strum = np.average(ueste_strum)\n",
"\n", "\n",
"uK_strum = np.max(np.sqrt( (1/este)**2 * uF_strum**2 + (F/este**2)**2 * uDx_strum**2 ))\n", "uK_strum = np.average(np.sqrt( (1/este)**2 * uF_strum**2 + (F/este**2)**2 * uDx_strum**2 ))\n",
"\n", "\n",
"print(f\"u_strum_m = {u_strum_m:.6f}\")\n", "print(f\"u_strum_m = {u_strum_m:.6f}\")\n",
"print(f\"u_strum_Dx = {u_strum_Dx:.6f}\")\n", "print(f\"u_strum_Dx = {u_strum_Dx:.6f}\")\n",
@@ -1049,7 +1049,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 75, "execution_count": 37,
"id": "a1dc24c9", "id": "a1dc24c9",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -1096,12 +1096,12 @@
"id": "2e57c7d8", "id": "2e57c7d8",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Risutltati della propagazione dell'errore strumentale massimo" "## Risultati della propagazione dell'errore strumentale massimo"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 76, "execution_count": 38,
"id": "c8e264fa", "id": "c8e264fa",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -1110,22 +1110,22 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"RISULTATI CON ERRORE STRUMENTALE INCLUSO:\n", "RISULTATI CON ERRORE STRUMENTALE INCLUSO:\n",
"Media pesata K = 23.95113 ± 0.66530\n", "Media pesata K = 23.95113 ± 0.27970\n",
"\n", "\n",
"RISULTATI REGRESSIONE OLS:\n", "RISULTATI REGRESSIONE OLS:\n",
"Aols = 23.96871 ± 0.68192\n", "Aols = 23.96871 ± 0.31720\n",
"Bols = 0.09993 ± 2.98913\n", "Bols = 0.09993 ± 2.92755\n",
"Chi² OLS = 3.95368 | rid = 0.49421 | P = 0.13872\n", "Chi² OLS = 7.21389 | rid = 0.90174 | P = 0.48626\n",
"\n", "\n",
"RISULTATI REGRESSIONE Carpi:\n", "RISULTATI REGRESSIONE Carpi:\n",
"AC = 24.04738 ± 0.67577\n", "AC = 24.04738 ± 0.30375\n",
"BC = -1.56983 ± 2.18409\n", "BC = -1.56983 ± 2.09901\n",
"Chi² Carpi = 4.03420 | rid = 0.50428 | P = 0.14598\n", "Chi² Carpi = 7.28743 | rid = 0.91093 | P = 0.49404\n",
"\n", "\n",
"RISULTATI REGRESSIONE York:\n", "RISULTATI REGRESSIONE York:\n",
"AY = 24.06480 ± 0.67582\n", "AY = 24.06480 ± 0.30386\n",
"BY = -1.81758 ± 2.18788\n", "BY = -1.81758 ± 2.10295\n",
"Chi² York = 4.06701 | rid = 0.50838 | P = 0.14897\n" "Chi² York = 7.33375 | rid = 0.91672 | P = 0.49891\n"
] ]
} }
], ],
@@ -1163,8 +1163,8 @@
"\n", "\n",
"In generale con il Chi² vale:\n", "In generale con il Chi² vale:\n",
"- \\~50%: Errori ottimamente stimati\n", "- \\~50%: Errori ottimamente stimati\n",
"- \\<5% : Fit troppo grande\n", "- \\<5% : Fit troppo grande (errori sovrastimati)\n",
"- \\>95%: Fit troppo piccolo\n", "- \\>95%: Fit troppo piccolo (errori sottostimati)\n",
"\n", "\n",
"In generale mi sembra che nei paper se è presente un Chi² si riporta la probabilità complementare (probabilità di ottenere gli stessi risultati o peggio)" "In generale mi sembra che nei paper se è presente un Chi² si riporta la probabilità complementare (probabilità di ottenere gli stessi risultati o peggio)"
] ]

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74
molla/statica/mollaStatica2.ipynb
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@@ -18,7 +18,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 82, "execution_count": 105,
"id": "f34c5b88", "id": "f34c5b88",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -51,7 +51,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 83, "execution_count": 106,
"id": "08efb2be", "id": "08efb2be",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -86,7 +86,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 84, "execution_count": 107,
"id": "5494409f", "id": "5494409f",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -199,7 +199,7 @@
"3 0.188750 108.610000 0.002887 " "3 0.188750 108.610000 0.002887 "
] ]
}, },
"execution_count": 84, "execution_count": 107,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@@ -210,7 +210,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 85, "execution_count": 108,
"id": "976d5531", "id": "976d5531",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -265,7 +265,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 86, "execution_count": 109,
"id": "2ad19283", "id": "2ad19283",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -292,7 +292,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 87, "execution_count": 110,
"id": "5f59d6c9", "id": "5f59d6c9",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -341,7 +341,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 88, "execution_count": 111,
"id": "7e75ec05", "id": "7e75ec05",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -368,7 +368,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 89, "execution_count": 112,
"id": "aefe7756", "id": "aefe7756",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -381,8 +381,8 @@
"Dep. Variable: y R-squared: 1.000\n", "Dep. Variable: y R-squared: 1.000\n",
"Model: OLS Adj. R-squared: 1.000\n", "Model: OLS Adj. R-squared: 1.000\n",
"Method: Least Squares F-statistic: 1.277e+05\n", "Method: Least Squares F-statistic: 1.277e+05\n",
"Date: Sat, 04 Apr 2026 Prob (F-statistic): 3.68e-10\n", "Date: Mon, 06 Apr 2026 Prob (F-statistic): 3.68e-10\n",
"Time: 19:38:07 Log-Likelihood: -7.2422\n", "Time: 10:04:08 Log-Likelihood: -7.2422\n",
"No. Observations: 6 AIC: 18.48\n", "No. Observations: 6 AIC: 18.48\n",
"Df Residuals: 4 BIC: 18.07\n", "Df Residuals: 4 BIC: 18.07\n",
"Df Model: 1 \n", "Df Model: 1 \n",
@@ -438,7 +438,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 90, "execution_count": 113,
"id": "1d42b009", "id": "1d42b009",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -504,7 +504,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 91, "execution_count": 114,
"id": "986ff4a6", "id": "986ff4a6",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -562,7 +562,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 92, "execution_count": 115,
"id": "2d4b7144", "id": "2d4b7144",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -638,7 +638,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 93, "execution_count": 116,
"id": "e2407a04", "id": "e2407a04",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -707,7 +707,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 94, "execution_count": 117,
"id": "32e9948f", "id": "32e9948f",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -766,7 +766,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 95, "execution_count": 118,
"id": "bfb895c6", "id": "bfb895c6",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -849,7 +849,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 96, "execution_count": 119,
"id": "202de438", "id": "202de438",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -923,7 +923,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 97, "execution_count": 120,
"id": "caf23dbe", "id": "caf23dbe",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -981,7 +981,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 98, "execution_count": 121,
"id": "8f5c8bb7", "id": "8f5c8bb7",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -991,8 +991,8 @@
"text": [ "text": [
"u_strum_m = 0.004082\n", "u_strum_m = 0.004082\n",
"u_strum_Dx = 0.020412\n", "u_strum_Dx = 0.020412\n",
"uF_strum = 0.071603\n", "uF_strum = 0.053018\n",
"uK_strum = 0.022946\n" "uK_strum = 0.012599\n"
] ]
} }
], ],
@@ -1009,10 +1009,10 @@
"umasse_strum = np.maximum(umasse, u_strum_m)\n", "umasse_strum = np.maximum(umasse, u_strum_m)\n",
"\n", "\n",
"# Worst-case scalare: prendi il massimo anche di ueste_strum\n", "# Worst-case scalare: prendi il massimo anche di ueste_strum\n",
"uF_strum = np.max(np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 ))\n", "uF_strum = np.average(np.sqrt( (g * umasse_strum)**2 + (masse * ug)**2 ))\n",
"uDx_strum = np.max(ueste_strum)\n", "uDx_strum = np.average(ueste_strum)\n",
"\n", "\n",
"uK_strum = np.max(np.sqrt( (1/este)**2 * uF_strum**2 + (F/este**2)**2 * uDx_strum**2 ))\n", "uK_strum = np.average(np.sqrt( (1/este)**2 * uF_strum**2 + (F/este**2)**2 * uDx_strum**2 ))\n",
"\n", "\n",
"print(f\"u_strum_m = {u_strum_m:.6f}\")\n", "print(f\"u_strum_m = {u_strum_m:.6f}\")\n",
"print(f\"u_strum_Dx = {u_strum_Dx:.6f}\")\n", "print(f\"u_strum_Dx = {u_strum_Dx:.6f}\")\n",
@@ -1030,7 +1030,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 99, "execution_count": 122,
"id": "a1dc24c9", "id": "a1dc24c9",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -1082,7 +1082,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 100, "execution_count": 123,
"id": "c8e264fa", "id": "c8e264fa",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -1091,22 +1091,22 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"RISULTATI CON ERRORE STRUMENTALE INCLUSO:\n", "RISULTATI CON ERRORE STRUMENTALE INCLUSO:\n",
"Media pesata K = 3.20156 ± 0.02327\n", "Media pesata K = 3.20156 ± 0.01318\n",
"\n", "\n",
"RISULTATI REGRESSIONE OLS:\n", "RISULTATI REGRESSIONE OLS:\n",
"Aols = 3.20145 ± 0.02463\n", "Aols = 3.20145 ± 0.01546\n",
"Bols = 0.02655 ± 0.99102\n", "Bols = 0.02655 ± 0.99084\n",
"Chi² OLS = 1.17582 | rid = 0.29396 | P = 0.11794\n", "Chi² OLS = 1.52520 | rid = 0.38130 | P = 0.17783\n",
"\n", "\n",
"RISULTATI REGRESSIONE Carpi:\n", "RISULTATI REGRESSIONE Carpi:\n",
"AC = 3.20054 ± 0.02472\n", "AC = 3.20054 ± 0.01559\n",
"BC = 0.11948 ± 0.95263\n", "BC = 0.11948 ± 0.95244\n",
"Chi² Carpi = 1.17417 | rid = 0.29354 | P = 0.11767\n", "Chi² Carpi = 1.52143 | rid = 0.38036 | P = 0.17716\n",
"\n", "\n",
"RISULTATI REGRESSIONE York:\n", "RISULTATI REGRESSIONE York:\n",
"AY = 3.20061 ± 0.02472\n", "AY = 3.20061 ± 0.01559\n",
"BY = 0.11263 ± 0.95193\n", "BY = 0.11263 ± 0.95173\n",
"Chi² York = 1.17400 | rid = 0.29350 | P = 0.11764\n" "Chi² York = 1.52133 | rid = 0.38033 | P = 0.17714\n"
] ]
} }
], ],

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