return a*x+b
def logaritmico(x, a, b):
f = a*np.log(b*x)
# f[f<0]=0
return f
def dlogaritmico(x, a, b):
return a/x
#2c-1
Vin, tmin, tmax = np.genfromtxt("dati/1c.txt", unpack=True)
#tmin, tmax = tmin*10**-6, tmax*10**-6
dVin, dtmax = mz.dVosc(Vin), mz.dtosc(tmax)
tmax, dtmax = tmin, mz.dtosc(tmin)
popt, pcov, dpopt, chi2, pvalue = mz.curve_fitdx(logaritmico, Vin, tmax, dVin, dtmax, dlogaritmico)
print(popt,dpopt, chi2/2)
t = np.linspace(0.2, 10, 2000)
pl.errorbar(Vin, tmax, dtmax, dVin, fmt='.', label="Misure")
pl.plot(t, logaritmico(t, *popt), label="Fit")
pl.xlabel("Vin [V]")
pl.ylabel("Durata segnale in uscita [us]")
pl.legend()
pl.savefig("dati/1c.png")
pl.close()
#2c-2
Vdiscr, Vpot = np.genfromtxt("dati/1c-2.txt", unpack=True)
import menzalib as l3
import numpy as np
Vout, f = np.loadtxt("2a.txt", unpack=True)
Vin = 1.14
dVin = l3.dVosc(Vin)
dVout = l3.dVosc(Vout)
Av = Vout / Vin
dAv = l3.drapp(Vout, dVout, Vin, dVin)
out = open("output.txt", "w")
for i in range(len(Vout)):
s = "$%0.1f \pm %0.1f$& $ %0.2f \pm %0.3f$& $%0.3f\pm %0.4f$ \\\ \n" % (
f[i], f[i] * 0.01, Vout[i], dVout[i], Av[i], dAv[i])
out.write(s)
out.close()
def costante(x, a, b):
return a
#Punto 1.a
R1 = 1.187e3
dR1 = mz.dRdig(R1)
R2 = 12.16e3
dR2 = mz.dRdig(R2)
Aexp = R2 / R1
dAexp = mz.drapp(R2, dR2, R1, dR1)
#punto 1.c
Vin, Vout = np.genfromtxt('dati/1c.txt', unpack='True')
dVin = mz.dVosc(Vin)
dVout = mz.dVosc(Vout)
A = Vout / Vin
dA = mz.drapp(Vout, dVout, Vin, dVin)
tab1 = [mz.ne_tex(Vin, dVin), mz.ne_tex(Vout, dVout), mz.ne_tex(A, dA)]
mz.stampa_matrice_latex(tab1)
popt, pcov = mz.curve_fitdx(lineare, Vin, Vout, dx=dVin, dy=dVout)
chi2, pval = mz.chi2_pval(lineare,
Vin,
Vout,
dVout,
popt,
dx=dVin,
df=costante)
dR1 = mz.dRdig(R1)
dC1 = mz.dCdig(C1)
#errore frequenza di taglio (punto 1a)
Ft1, dFt1 = frequenza_di_taglio(R1, C1, dR1, dC1)
#Attenzuazione e errore (punto 1c e 1d)
A1 = ([
attenuazione_passa_basso(2e3, Ft1, dFt1),
attenuazione_passa_basso(2e4, Ft1, dFt1)
])
#qua faccio il plot del diagramma di bode
Vout, freq = np.genfromtxt('dati/2.txt', unpack=True)
Vin = 12.5
dVin = mz.dVosc(12.5)
A = Vout / Vin
dA = errore_rapporto(Vout, mz.dVosc(Vout), Vin, dVin)
pl.errorbar(freq, 20 * np.log10(A), yerr=dA, fmt='.', markersize=3)
#qua faccio il punto 2.b.ii
#la retta obliqua
popt1, pcov1 = curve_fit(lineare, np.log10(freq[7:]), np.log10(A[7:]), (1, 1),
dA[7:] / (A[7:] * np.log(10)))
x = np.linspace(3.5, 6.5, 10)
pl.plot(10**x, 20 * (lineare(x, *popt1)))
#la retta parallela
popt2, pcov2 = curve_fit(lineare, np.log10(freq[:4]), np.log10(A[:4]), (1, 1),
dA[:4] / (A[:4] * np.log(10)))
x = np.linspace(1, 4, 10)
pl.plot(10**x, 20 * (lineare(x, *popt2)))
#l'intersezione delle due rette
from numpy.random import normal as nm
def ordina_2_vett(v1, v2):
if len(v1) != len(v2): return 0
s1 = np.sort(v1)
s2 = np.zeros(len(s1))
for i in range(0, len(v1)):
for j in range(0, len(v1)):
if (s1[i] == v1[j]):
s2[i] = v2[j]
break
return s1, s2
Vt, dVt = [0.260, mz.dVosc(0.260)] #voltaggio in ingresso
f, df, deltaf, ddeltaf, V = np.genfromtxt(
'dati/1.txt', unpack='True') #aggiusta l'errore sullo sfasamento
ddeltaf = np.sqrt((0.02 * deltaf)**2 + ddeltaf**2)
#calcolo la fase
fase = 360 * f / deltaf
dfase = 360 * mz.drapp(f, df, deltaf, ddeltaf)
#metto i dati alla frequenza di taglio
f = np.append(f, 1598.34) #frequenza di tagli
df = np.append(df, 0.01)
V = np.append(V, 182) #ampiezza di taglio
fase = np.append(fase, 0)
dfase = np.append(dfase, 0.08) #aggiusta l'errore sullo sfasamento
s2 = np.zeros(len(s1))
for i in range(0, len(v1)):
for j in range(0, len(v1)):
if (s1[i] == v1[j]):
s2[i] = v2[j]
break
return s1, s2
f, Vout = ordina_2_vett(f, Vout)
t1 = 3
t2 = 6
t3 = -5
t4 = -3
dVout = mz.dVosc(Vout)
Av = Vout / Vin
dAv = mz.drapp(Vout, dVout, Vin, mz.dVosc(Vin))
popt1, pcov1 = curve_fit(lineare,
np.log10(f[:t1]),
20 * np.log10(Av[:t1]),
sigma=20 * mz.dlog10(Av[:t1], dAv[:t1]))
popt2, pcov2 = curve_fit(lineare,
np.log10(f[t2:t3]),
20 * np.log10(Av[t2:t3]),
sigma=20 * mz.dlog10(Av[t2:t3], dAv[t2:t3]))
popt3, pcov3 = curve_fit(lineare,
np.log10(f[t4:]),
20 * np.log10(Av[t4:]),
sigma=20 * mz.dlog10(Av[t4:], dAv[t4:]))
