def f3(korr=False):
i = len(data)-3
x = np.arange(0, (len(data[i]))*10, 10)
y = np.array(data[i])
ey = np.ones(len(x))*e_s
ex = np.ones(len(x))*e_m
ax1, ax2, a,ea,b,eb,chiq,corr = bib.pltmitres(x,y,ey, ex=ex,yeinheit="mm", xl="m", yl="s", regData=True, title="kalib3")
#Korrektur?
if korr:
deltas = []
for j in range(len(x)-1):
deltas.append((y[j+1]-y[j]))
deltas = np.array(deltas)
#1. Fall: Nehme +0.07 als 10 an
ax1, ax2, a,ea,b,eb,chiq,corr = bib.pltmitres(x[:-1],deltas,ey[:-1],yeinheit="mm", xl="m", yl="δ", regData=True)
korrektur = np.round((deltas-0.07)/0.01)
korrektur = np.append(np.zeros(1), korrektur)
x2=np.array(x)
for j in range(len(korrektur)):
for k in range(j,len(x2)):
x2[k] = x2[k]+korrektur[j]
ax1, ax2, a,ea,b,eb,chiq,corr=bib.pltmitres(x2,y,ey,yeinheit="mm", xl="m", yl="s", regData=True)
print("a=", a)
k=l/(2*a)
k*=10**-9 * 10**3
ek = l*0.5*ea/a**2 * 10**-9 * 10**3
print("k=", k)
print("ek=", ek)
print("ek/k", ek/k)
return k, ek
def f12(korr=False):
i = len(data) - 1
#N=20
N = 0
x = np.arange(10 * N, (N + len(data[i][N:])) * 10, 10) #12
y1 = np.array(data[i][N:]) #12
i = len(data) - 2
y2 = np.array(data[i][N:]) #12
y = 0.5 * (y1 + y2)
ey = np.ones(len(y)) * e_s
ex = np.ones(len(y)) * e_m
ax1, ax2, a, ea, b, eb, chiq, corr = bib.pltmitres(x,
y,
ey,
ex=ex,
yeinheit="mm",
xl="m",
yl="s",
regData=True,
title="kalib12")
print("a=", a)
k = l / (2 * a)
k *= 10**-9 * 10**3
ek = l * 0.5 * ea / a**2 * 10**-9 * 10**3
print("k=", k)
print("ek=", ek)
print("ek/k", ek / k)
return k, ek
def kalib_2():
i = len(data) - 2
intervalle = [0, 60, 100, 200, 300]
K = []
eK = []
for I in range(len(intervalle) - 1):
j = int(intervalle[I] / 10)
k = int(intervalle[I + 1] / 10)
x = np.arange(intervalle[I], (j + len(data[i][j:k])) * 10, 10) #12
y = np.array(data[i][j:k]) #12
ey = np.ones(len(x)) * e_s
ex = np.ones(len(x)) * e_m
ax1, ax2, a, ea, b, eb, chiq, corr = bib.pltmitres(x,
y,
ey,
ex=ex,
yeinheit="mm",
xl="m",
yl="s",
regData=True,
title="kalib2")
print("a=", a)
k = l / (2 * a)
k *= 10**-9 * 10**3
ek = l * 0.5 * ea / a**2 * 10**-9 * 10**3
print("k=", k)
print("ek=", ek)
print("ek/k", ek / k)
K.append(k)
eK.append(ek)
return np.array(K), np.array(eK)
def f2(korr=False):
i = len(data) - 2
x = np.arange(0, (0 + len(data[i][0:])) * 10, 10) #12
y = np.array(data[i][0:]) #12
ey = np.ones(len(x)) * e_s
ex = np.ones(len(x)) * e_m
ax1, ax2, a, ea, b, eb, chiq, corr = bib.pltmitres(x,
y,
ey,
ex=ex,
yeinheit="mm",
xl="m",
yl="s",
regData=True,
title="kalib2")
print("a=", a)
k = l / (2 * a)
k *= 10**-9 * 10**3
ek = l * 0.5 * ea / a**2 * 10**-9 * 10**3
print("k=", k)
print("ek=", ek)
print("ek/k", ek / k)
return k, ek
for line in file:
d = np.array(line.split(" "))
data.append(d.astype(np.float))
data = np.array(data)
for i in range(1, len(data)):
if i != 2:
s = data[i][:] #13:
m = np.arange(0, len(s) * 10, 10) #13
delta, edelta_sys, edelta = stodelta(s, np.ones(len(s)) * e_s)
em = np.ones(len(delta)) * e_m
ax1, ax2, a, ea, b, eb, chiq, corr = bib.pltmitres(m,
delta,
edelta,
ex=em,
xl="m",
yl="mm",
regData=True,
title="grun" +
str(i))
print("lambda", i, "=", 2 * a * 10**6)
print("e_lambda", i, "=", 2 * ea * 10**6)
#Verschiebemethode
ap, eap, bp, ebp, chiq, corr = anal.lineare_regression_xy(
m + e_m, delta + edelta_sys, em, edelta)
am, eam, bm, ebm, chiq, corr = anal.lineare_regression_xy(
m - e_m, delta - edelta_sys, em, edelta)
e_l_sys = np.max((np.abs(a - ap), np.abs(a - am))) * 2 * 10**6
print("e_lambda_sys", i, "=", e_l_sys)
else:
sigma_winkel1 = np.array(sigma_winkel1)
sigma_winkel2 = np.array(sigma_winkel2)
sigma_winkel = np.array(np.sqrt(sigma_winkel1**2 + sigma_winkel2**2))
dmin = np.array(dmin)
e_dmin = sigma_winkel / 2
n = np.sin(bib.DegToRad(dmin + e) * 0.5) / np.sin(bib.DegToRad(e / 2))
e_n = (np.cos(bib.DegToRad(dmin + e) * 0.5) /
np.sin(bib.DegToRad(e / 2))) * 0.5 * bib.DegToRad(e_dmin)
ax1, ax2, c, ec, d, ed, chiq, corr = bib.pltmitres(lambdas**-2,
n,
e_n,
yl="n",
xl="$\lambda$",
xeinheit="$nm^{-2}$",
ratios=[4, 1],
regData=True,
markersize=10)
ax1.set_ylim(top=1.9)
p, cov = curve_fit(lton, lambdas**-2, n, sigma=e_n)
a = np.arange(
min(lambdas**-2) * 0.95,
max(lambdas**-2) * 1.05,
max(lambdas**-2) * 0.01)
b = lton(a, *p)
#ax1.plot(a,b, color="b", label="fit")
#ax2.plot(a,b-c*a-d, color="b")
plt.show()
plt.close()
(R * C))**2) * eC**2 / 4)
return eQ_1
x = R_1 #R**-1
ex = eR_1
y = np.array([R_L * np.sqrt(C / L)]) #Q**-1
y = np.append(y, Q_p(R_1[1:]**-1)**-1)
ey = np.array([
y[0] * np.sqrt((eR_L / R_L)**2 + 0.25 * (eC / C)**2 + 0.25 * (eL / L)**2)
])
ey = np.append(ey, eQ_p_1(R_o, eR_o))
ax1, ax2, a, ea, b, eb, chiq, corr = bib.pltmitres(x,
y,
ey,
ex,
regData=True,
xl="$R^{-1} [\Omega^{-1}]$",
yl="$Q^{-1}$")
ax1.legend(
title=
"Lineare Regression\n{} = ({:.6f} ± {:.6f}){} $\cdot$ {}+({:.6f}±{:.6f}){}"
.format("$Q^{-1}$", a, ea, "/$\Omega^{-1}$", "$R^{-1}$", b, eb, "", loc=1))
ax1.set_ylim(top=1.5)
plt.show()
plt.close()
print("Aus den Messgrößen erhält man:")
for i in range(len(x)):
print("Q=", y[i]**-1, "+-", ey[i] * y[i]**-2, "für R^-1=", R_1[i])
#Plot der LSG:
import numpy as np
import praktikum_addons.bib as bib
import matplotlib.pyplot as plt
x, y = bib.c_open("../WegaufKal3.lab", ["R_B1", "s"])
ex = np.ones(len(x)) * 0.005
ey = np.ones(len(y)) * 0.1 / np.sqrt(12)
ax1, ax2 = bib.pltmitres(x,
y,
ex,
ey,
yl="s",
xl="R",
yeinheit="cm",
xeinheit="$k\Omega$",
ratios=[5, 1])
ax1.set_ylim(top=120)
plt.show()