284 lines
6.8 KiB
Plaintext
284 lines
6.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9c25e271",
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"metadata": {},
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"outputs": [],
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"source": [
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"import importlib\n",
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"from statlib import *\n",
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"\n",
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"g = 9.806\n",
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"ug = 0.004"
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]
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},
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{
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"cell_type": "markdown",
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"id": "233e717f",
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"metadata": {},
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"source": [
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"# Calcolo w0"
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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": null,
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"id": "e1efad4a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# k e m varie configurazioni\n",
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"\n",
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"# k [N/m]\n",
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"k1 = 23.631\n",
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"uk1 = 0.017\n",
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"\n",
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"k2 = 3.2053\n",
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"uk2 = 0.0013\n",
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"\n",
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"# m [g]\n",
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"m_CD = [63.13, 83.04, 103.02, 122.83, 142.77, 162.44, 182.33]\n",
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"m_rete = [58.96, 78.86, 98.85, 118.66, 138.61, 158.26, 178.16]"
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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": null,
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"id": "81d935ac",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Creazione Data\n",
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"\n",
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"df = pd.DataFrame()\n",
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"df[\"m_CD\"] = m_CD\n",
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"df[\"um_CD\"] = 0.01/np.sqrt(12)\n",
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"df[\"m_rete\"] = m_rete\n",
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"df[\"um_rete\"] = 0.01/np.sqrt(12)\n",
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"df[\"k1\"] = k1\n",
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"df[\"uk1\"] = uk1\n",
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"df[\"k2\"] = k2\n",
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"df[\"uk2\"] = uk2\n",
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"\n",
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"param_sistema = Data(df)\n",
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"param_sistema.analisi_stat = df"
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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": null,
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"id": "7b2c2af7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Calcolo w0 varie cnfigurazioni\n",
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"\n",
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"m_CD, m_rete, k1, k2 = sp.symbols('m_CD, m_rete, k1, k2', positive=True)\n",
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"\n",
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"w0_1_CD = sp.sqrt(k1/(m_CD/1000))\n",
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"w0_1_rete = sp.sqrt(k1/(m_rete/1000))\n",
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"w0_2_CD = sp.sqrt(k2/(m_CD/1000))\n",
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"w0_2_rete = sp.sqrt(k2/(m_rete/1000))\n",
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"\n",
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"param_sistema = param_sistema.calc_var(w0_1_CD, \"w0_1_CD\")\n",
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"param_sistema = param_sistema.calc_var(w0_1_rete, \"w0_1_rete\")\n",
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"param_sistema = param_sistema.calc_var(w0_2_CD, \"w0_2_CD\")\n",
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"param_sistema = param_sistema.calc_var(w0_2_rete, \"w0_2_rete\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ecf23a81",
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"metadata": {},
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"source": [
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"# Creazione classi dati\n",
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"\n",
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"Selezione picchi delle oscillazioni"
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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": null,
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"id": "c040f35e",
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"metadata": {},
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"outputs": [],
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"source": [
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"config = [molla1_leggera_rete_inizio, molla1_leggera_rete_fine, molla1_leggera_rete_mid,\n",
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" molla1_pesante_rete_inizio, molla1_pesante_rete_fine, molla1_pesante_rete_mid,\n",
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" molla1_leggera_CD_inizio, molla1_leggera_CD_fine, molla1_leggera_CD_mid,\n",
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" molla1_pesante_CD_inizio, molla1_pesante_CD_fine, molla1_pesante_CD_mid,\n",
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" molla2_leggera_rete_inizio, molla2_leggera_rete_fine, molla2_leggera_rete_mid,\n",
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" molla2_pesante_rete_inizio, molla2_pesante_rete_fine, molla2_pesante_rete_mid,\n",
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" molla2_leggera_CD_inizio, molla2_leggera_CD_fine, molla2_leggera_CD_mid,\n",
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" molla2_pesante_CD_inizio, molla2_pesante_CD_fine, molla2_pesante_CD_mid]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3b522bfe",
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"metadata": {},
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"source": [
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"# Rgressione lineare"
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]
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},
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{
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"cell_type": "markdown",
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"id": "30f903b5",
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"metadata": {},
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"source": [
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"## Attrito proporzionale a v"
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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": null,
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"id": "64e483e1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Calcolo simbolico x e y\n",
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"\n",
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"# x = n-1\n",
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"n = sp.Symbol('n', positive=True)\n",
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"x = n-1\n",
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"\n",
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"# y = ln(h1/hn)\n",
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"h1, hn = sp.symbols('h1 hn', positive=True)\n",
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"y = sp.ln(h1/hn)"
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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": null,
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"id": "b0df03fc",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Calcolo numerico x e y\n",
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"\n",
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"for data in config:\n",
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" data = data.calc_var(x, \"x\")\n",
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" data = data.calc_var(y, \"y\")"
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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": null,
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"id": "497ed583",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Regressione\n",
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"\n",
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"for data in config:\n",
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" data = data.reg_lin(stampa_param=True, plot_regressione=True, calc_residui=True,\n",
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" x_label=\"\", y_label=\"\", r_label=\"\",\n",
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" titolo_reg=\"\", titolo_residui=\"\",\n",
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" cd_A=4, cd_B=4, scala_barre=1)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "453d5b53",
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"metadata": {},
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"source": [
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"## Attrito proporzionale a v^2"
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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": null,
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"id": "cdb8d336",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Calcolo simbolico x e y\n",
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"\n",
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"# x = n-1\n",
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"n = sp.Symbol('n', positive=True)\n",
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"x = n-1\n",
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"\n",
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"# y = 1/hn - 1/h1\n",
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"h1, hn = sp.symbols('h1 hn', positive=True)\n",
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"y = 1/hn - 1/h1"
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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": null,
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"id": "84ad3d64",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Calcolo numerico x e y\n",
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"\n",
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"for data in config:\n",
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" data = data.calc_var(x, \"x\")\n",
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" data = data.calc_var(y, \"y\")"
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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": null,
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"id": "8fee50b3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Regressione\n",
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"\n",
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"for data in config:\n",
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" data = data.reg_lin(stampa_param=True, plot_regressione=True, calc_residui=True,\n",
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" x_label=\"\", y_label=\"\", r_label=\"\",\n",
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" titolo_reg=\"\", titolo_residui=\"\",\n",
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" cd_A=4, cd_B=4, scala_barre=1)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dc3ce36f",
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"metadata": {},
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"source": [
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"# Stima errore campionamento"
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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": null,
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"id": "513736c3",
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"metadata": {},
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"outputs": [],
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"source": [
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"A_max =\n",
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"w0_max =\n",
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"fc = 50 # [Hz]\n",
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"\n",
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"err_picco_max = A_max * (1 - np.cos(w0_max / (2*fc)))\n",
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"print(f\"errore massimo individuazione picco > {err_picco_max}\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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