def test_table():
x = array([1., 2., 3.])
y = array([ufloat(2, 0.1), ufloat(4, 0.5), ufloat(2, 0.04)])
f = open('test.tex', 'w')
f.write(table(['x', 'y'], [x, y]))
f.close()
(open('build/theta_D_theo.tex', 'w', 'utf-8').
write(r'$\Theta_\mathrm{{D}} = \SI{{{:.3f}}}{{\kelvin}}$'.
format(theta_D)))
f = open('build/Fit.txt', 'w', 'utf-8')
f.write("Temperatur\n")
for i in range(len(popt_temp)):
f.write(str(popt_temp[i])+"\n")
f.write('\nAlpha\n')
for i in range(len(popt_alpha)):
f.write(str(popt_alpha[i])+"\n")
f.close()
f = open('build/Berechnung_Alpha.tex', 'w', 'utf-8')
f.write(table([r'$T/\si{\kelvin}$', r'$\alpha \cdot 10^{-6}/\si{1\per\kelvin}$',
r'$T/\si{\kelvin}$', r'$\alpha \cdot 10^{-6}/\si{1\per\kelvin}$',],
[Talpha[:12], alpha_plot[:12],
Talpha[12:23], alpha_plot[12:23]]))
f.close()
f = open('build/Berechnung_Temperatur.tex', 'w', 'utf-8')
f.write(table([r'$T/\si{\kelvin}$', r'$R/\si{\ohm}$',
r'$T/\si{\kelvin}$', r'$R/\si{\ohm}$',],
[Tt[:12], Rt[:12],
Tt[12:24], Rt[12:24]]))
f.close()
f = open('build/Theta.tex', 'w', 'utf-8')
f.write(table([r'$T/\si{\kelvin}$', r'$C_V/\si{\joule\per\mol\per\kelvin}$',
r'$\frac{\Theta_\mathrm{{D}}}{T}/\si{}$', r'$\Theta_\mathrm{{D}}/\si{\kelvin}$',],
[T_Cv[:11],Cv[:11],
r'$\Theta_\mathrm{{D}} = \SI{{{:.3f}}}{{\kelvin}}$'.format(theta_D)))
f = open('build/Fit.txt', 'w', 'utf-8')
f.write("Temperatur\n")
for i in range(len(popt_temp)):
f.write(str(popt_temp[i]) + "\n")
f.write('\nAlpha\n')
for i in range(len(popt_alpha)):
f.write(str(popt_alpha[i]) + "\n")
f.close()
f = open('build/Berechnung_Alpha.tex', 'w', 'utf-8')
f.write(
table([
r'$T/\si{\kelvin}$',
r'$\alpha \cdot 10^{-6}/\si{1\per\kelvin}$',
r'$T/\si{\kelvin}$',
r'$\alpha \cdot 10^{-6}/\si{1\per\kelvin}$',
], [Talpha[:12], alpha_plot[:12], Talpha[12:23], alpha_plot[12:23]]))
f.close()
f = open('build/Berechnung_Temperatur.tex', 'w', 'utf-8')
f.write(
table([
r'$T/\si{\kelvin}$',
r'$R/\si{\ohm}$',
r'$T/\si{\kelvin}$',
r'$R/\si{\ohm}$',
], [Tt[:12], Rt[:12], Tt[12:24], Rt[12:24]]))
f.close()
f = open('build/Theta.tex', 'w', 'utf-8')
(open('build/polarisation_I0.tex', 'w',
'utf-8').write(r'$I_0 = \SI{{{:L}}}{{\milli\ampere}}$'.format(I0)))
(open('build/polarisation_phi.tex', 'w', 'utf-8').write(
r'$\varphi = \SI{{{:L}}}{{\degree}}$'.format(phi * 180 / np.pi)))
plt.plot(xs, ys, 'r-', label='Fit')
theta = theta[:-1]
I = I[:-1]
plt.errorbar(theta, I, yerr=0.05 * I, fmt='bo', label='Messpunkte')
plt.xlabel(r'$\theta/^\circ$')
plt.ylabel(r'$I/\mathrm{mA}$')
plt.xlim(-10, 360)
plt.legend(loc='best')
plt.savefig('build/polarisation_lin.pdf')
plt.clf()
f = open('build/polarisation_tab.tex', 'w', 'utf-8')
f.write(
table([
r'$\theta/\si{\degree}$', r'$I/\si{\milli\ampere}$',
r'$\theta/\si{\degree}$', r'$I/\si{\milli\ampere}$',
r'$\theta/\si{\degree}$', r'$I/\si{\milli\ampere}$',
r'$\theta/\si{\degree}$', r'$I/\si{\milli\ampere}$'
], [
theta[:9], I[:9], theta[9:18], I[9:18], theta[18:27], I[18:27],
theta[27:36], I[27:36]
]))
f.close()
period2E = (tTwo2E - tOne2E) / number2
with open('i_raw.tex', 'w') as f:
f.write(
table([
r'$V_\text{87} / \si{\volt}$',
r'$t_\text{1,87} / \si{\micro\second}$',
r'$t_\text{2,87} / \si{\micro\second}$',
r'$T_\text{87} / \si{\micro\second}$',
r'#Perioden$_\text{87}$',
r'$V_\text{85} / \si{\volt}$',
r'$t_\text{1,85} / \si{\micro\second}$',
r'$t_\text{2,85} / \si{\micro\second}$',
r'#Perioden$_\text{85}$',
r'$T_\text{85} / \si{\micro\second}$'
],[
V1,
tOne1E*10**6,
tTwo1E*10**6,
number1,
period1E*10**6,
V2,
tOne2E*10**6,
tTwo2E*10**6,
number2,
period2E*10**6
]
)
)
poptE1, pcovE1 = curve_fit(
func_exp2, expX1[600:-1000], expY1[600:-1000],
sweep2Raw = unp.uarray(sweep2Raw, DELTA_I)
hor0Raw = unp.uarray(hor0Raw, DELTA_I)
hor1Raw = unp.uarray(hor1Raw, DELTA_I)
hor2Raw = unp.uarray(hor2Raw, DELTA_I)
with open('c_data_raw.tex', 'w') as f:
f.write(
table(
[
r'$\nu / \si{\kilo\hertz}$',
r'$I_\text{0,sweep} / \si{\milli\ampere}$',
r'$I_\text{0,hor} / \si{\milli\ampere}$',
r'$I_\text{87,sweep} / \si{\milli\ampere}$',
r'$I_\text{87,hor} / \si{\milli\ampere}$',
r'$I_\text{85,sweep} / \si{\milli\ampere}$',
r'$I_\text{85,hor} / \si{\milli\ampere}$'
],[
nuArr*1e-3,
sweep0Raw*1e3,
hor0Raw*1e3,
sweep1Raw*1e3,
hor0Raw*1e3,
sweep2Raw*1e3,
hor0Raw*1e3
]
)
)
sweep0B = _calc_b(sweep0Raw, N_SWEEP, R_SWEEP)
sweep1B = _calc_b(sweep1Raw, N_SWEEP, R_SWEEP)
sweep2B = _calc_b(sweep2Raw, N_SWEEP, R_SWEEP)
U_e = unp.uarray(U_e_raw,dU_e)
U_a = unp.uarray(U_a_raw,dU_a)
U_a_m = I * R_N
print(U_a_m)
with open('build/c_U_a.tex', 'w') as f:
f.write(r'\num{{{:L}}}'.format( U_a_m ))
R_e = np.array(U_e / I)
V = np.array(R_N / R_e)
with open('build/c_table.tex', 'w', 'utf-8') as f:
f.write(table([r'$\nu/\si{\kilo\hertz}$', r'$U_e/\si{\milli\volt}$', r'$U_a/\si{\volt}$',
r'$R_e/\si{\ohm}$', r'$V$'],
[nu/1e3, U_e*1e3, U_a, R_e, V]))
def func(x,a,b):
return a*x+b
popt_1, pcov_1 = curve_fit(func,np.log(nu[:5]),np.log(unp.nominal_values(R_e[:5])))
popt_2, pcov_2 = curve_fit(func,np.log(nu[:5]),np.log(unp.nominal_values(V[:5])))
popt_3, pcov_3 = curve_fit(func,np.log(nu[:9]),np.log(unp.nominal_values(R_e[:9])))
popt_4, pcov_4 = curve_fit(func,np.log(nu[:9]),np.log(unp.nominal_values(V[:9])))
print(str(ufloat(popt_1[0],pcov_1[0][0])) + ' ' + str(ufloat(popt_1[1],pcov_1[1][1])))
print(str(ufloat(popt_2[0],pcov_2[0][0])) + ' ' + str(ufloat(popt_2[1],pcov_2[1][1])))
print(str(ufloat(popt_3[0],pcov_3[0][0])) + ' ' + str(ufloat(popt_3[1],pcov_3[1][1])))
print(str(ufloat(popt_4[0],pcov_4[0][0])) + ' ' + str(ufloat(popt_4[1],pcov_4[1][1])))
(open('build/polarisation_I0.tex', 'w', 'utf-8').
write(r'$I_0 = \SI{{{:L}}}{{\milli\ampere}}$'.
format(I0)))
(open('build/polarisation_phi.tex', 'w', 'utf-8').
write(r'$\varphi = \SI{{{:L}}}{{\degree}}$'.
format(phi * 180 / np.pi)))
plt.plot(xs, ys, 'r-', label='Fit')
theta = theta[:-1]
I = I[:-1]
plt.errorbar(theta, I, yerr=0.05 * I, fmt='bo', label='Messpunkte')
plt.xlabel(r'$\theta/^\circ$')
plt.ylabel(r'$I/\mathrm{mA}$')
plt.xlim(-10, 360)
plt.legend(loc='best')
plt.savefig('build/polarisation_lin.pdf')
plt.clf()
f = open('build/polarisation_tab.tex', 'w', 'utf-8')
f.write(table([r'$\theta/\si{\degree}$', r'$I/\si{\milli\ampere}$',
r'$\theta/\si{\degree}$', r'$I/\si{\milli\ampere}$',
r'$\theta/\si{\degree}$', r'$I/\si{\milli\ampere}$',
r'$\theta/\si{\degree}$', r'$I/\si{\milli\ampere}$'],
[theta[:9], I[:9],
theta[9:18], I[9:18],
theta[18:27], I[18:27],
theta[27:36], I[27:36]]))
f.close()
nuRaw, sweep0Raw, hor0Raw, sweep1Raw, hor1Raw, sweep2Raw, \
hor2Raw = np.genfromtxt('data/aufg_c.dat', unpack=True)
nuArr = unp.uarray(nuRaw * 10**3, DELTA_NU)
sweep0Raw = unp.uarray(sweep0Raw, DELTA_I)
sweep1Raw = unp.uarray(sweep1Raw, DELTA_I)
sweep2Raw = unp.uarray(sweep2Raw, DELTA_I)
hor0Raw = unp.uarray(hor0Raw, DELTA_I)
hor1Raw = unp.uarray(hor1Raw, DELTA_I)
hor2Raw = unp.uarray(hor2Raw, DELTA_I)
with open('build/tex/c_data_raw_0.tex', 'w') as f:
f.write(
table([
r'$\nu / \si{\kilo\hertz}$',
r'$I_\text{0,sweep} / \si{\milli\ampere}$',
r'$I_\text{0,hor} / \si{\milli\ampere}$'
], [nuArr * 1e-3, sweep0Raw * 1e3, hor0Raw * 1e3]))
with open('build/tex/c_data_raw_87.tex', 'w') as f:
f.write(
table([
r'$\nu / \si{\kilo\hertz}$',
r'$I_\text{87,sweep} / \si{\milli\ampere}$',
r'$I_\text{87,hor} / \si{\milli\ampere}$'
], [
nuArr * 1e-3,
sweep1Raw * 1e3,
hor1Raw * 1e3,
]))
tTwo2E = unp.uarray(tTwo2, DELTA_T)
period1 = (tTwo1 - tOne1) / number1
period2 = (tTwo2 - tOne2) / number2
period1E = (tTwo1E - tOne1E) / number1
period2E = (tTwo2E - tOne2E) / number2
with open('i_raw.tex', 'w') as f:
f.write(
table([
r'$V_\text{87} / \si{\volt}$',
r'$t_\text{1,87} / \si{\micro\second}$',
r'$t_\text{2,87} / \si{\micro\second}$',
r'$T_\text{87} / \si{\micro\second}$', r'#Perioden$_\text{87}$',
r'$V_\text{85} / \si{\volt}$',
r'$t_\text{1,85} / \si{\micro\second}$',
r'$t_\text{2,85} / \si{\micro\second}$', r'#Perioden$_\text{85}$',
r'$T_\text{85} / \si{\micro\second}$'
], [
V1, tOne1E * 10**6, tTwo1E * 10**6, number1, period1E * 10**6, V2,
tOne2E * 10**6, tTwo2E * 10**6, number2, period2E * 10**6
]))
poptE1, pcovE1 = curve_fit(func_exp2,
expX1[600:-1000],
expY1[600:-1000],
p0=(-1000000, 110, 9.3))
print(poptE1)
print(expY1[700:-1])
print(np.sqrt(np.diag(pcovE1)))
# with open('i_fit_em_87.tex', 'w') as f:
nuArr = unp.uarray(nuRaw * 10**3, DELTA_NU)
sweep0Raw = unp.uarray(sweep0Raw, DELTA_I)
sweep1Raw = unp.uarray(sweep1Raw, DELTA_I)
sweep2Raw = unp.uarray(sweep2Raw, DELTA_I)
hor0Raw = unp.uarray(hor0Raw, DELTA_I)
hor1Raw = unp.uarray(hor1Raw, DELTA_I)
hor2Raw = unp.uarray(hor2Raw, DELTA_I)
with open('build/tex/c_data_raw_0.tex', 'w') as f:
f.write(
table(
[
r'$\nu / \si{\kilo\hertz}$',
r'$I_\text{0,sweep} / \si{\milli\ampere}$',
r'$I_\text{0,hor} / \si{\milli\ampere}$'
], [
nuArr*1e-3,
sweep0Raw*1e3,
hor0Raw*1e3
]
)
)
with open('build/tex/c_data_raw_87.tex', 'w') as f:
f.write(
table(
[
r'$\nu / \si{\kilo\hertz}$',
r'$I_\text{87,sweep} / \si{\milli\ampere}$',
r'$I_\text{87,hor} / \si{\milli\ampere}$'
], [
popt, pcov = curve_fit(func, val_x[0:6], val_log[0:6])
popt1, pcov1 = curve_fit(func2, val_x, val)
print(ufloat(popt[0], pcov[0][0]))
print(ufloat(popt[1], pcov[1][1]))
x = np.linspace(val_x[0] - 0.0015, val_x[5] + 0.00001, 1e4)
x1 = np.linspace(val_x[0], val_x[len(val_x) - 1], 1e4)
plt.plot(x, func(x, *popt), 'g-', label=r'Regression $f_h(x)$')
plt.plot(val_x, val_log, 'rx', label=r'Messpunkte')
plt.grid()
plt.xlabel(r'$t/\mathrm{s}$')
plt.ylabel(r'$\log{U/\mathrm{V}}$')
plt.legend(loc="best")
plt.savefig('build/h_plot.pdf')
with open('build/h_table.tex', 'w', 'utf-8') as f:
f.write(
table([
r'$t/\si{\second}$', r'$U/\si{\volt}$', r'$t/\si{\second}$',
r'$U/\si{\volt}$'
], [val_x[0:19], val[0:19], val_x[19:38], val[19:38]]))
val = val * 1e3
val_x = val_x * 1e3
print(val)
popt1, pcov1 = curve_fit(func2,val_x,val)
print(ufloat(popt[0],pcov[0][0]))
print(ufloat(popt[1],pcov[1][1]))
x = np.linspace(val_x[0]-0.0015,val_x[5]+0.00001,1e4)
x1 = np.linspace(val_x[0],val_x[len(val_x)-1],1e4)
plt.plot(x,func(x,*popt),'g-',label=r'Regression $f_h(x)$')
plt.plot(val_x,val_log,'rx',label=r'Messpunkte')
plt.grid()
plt.xlabel(r'$t/\mathrm{s}$')
plt.ylabel(r'$\log{U/\mathrm{V}}$')
plt.legend(loc="best")
plt.savefig('build/h_plot.pdf')
with open('build/h_table.tex', 'w', 'utf-8') as f:
f.write(
table(
[r'$t/\si{\second}$', r'$U/\si{\volt}$', r'$t/\si{\second}$', r'$U/\si{\volt}$'],
[val_x[0:19], val[0:19], val_x[19:38], val[19:38]]
)
)
val = val*1e3
val_x = val_x*1e3
print(val)
I_cleaned -= I_min
print(I_min)
if name == 'set1':
I1_cleaned = I_cleaned
else:
I2_cleaned = I_cleaned
with open('{}data-{}.tex'.format(texdir, name), 'w') as f:
f.write(
table(
3 * [
r'$T/\si{\kelvin}$', r'$I/\si{\pico\ampere}$',
r'$I_\mathrm{cl}/\si{\pico\ampere}$'
], [
np.around(T[:23], 1),
np.around(I[:23], 2),
np.around(I_cleaned[:23], 2),
np.around(T[23:46], 1),
np.around(I[23:46], 2),
np.around(I_cleaned[23:46], 2),
np.around(T[46:], 1),
np.around(I[46:], 2),
np.around(I_cleaned[46:], 2),
]))
xs = np.linspace(240, 340)
plt.plot(xs, fit_function(xs, *var), label='fit')
plt.plot(T[~selection],
I[~selection],
'b.',
label='Data (ignored for fit)')
plt.plot(T[selection], I[selection], 'g.', label='Data (used for fit)')
plt.plot(T, I_cleaned + I_min, 'r.', label='Cleaned Data')
print(R_1_4)
# Import Data
nu_1, U_1, dU_1 = np.genfromtxt("data/data_a_1.txt",unpack=True)
nu_2, U_2, dU_2 = np.genfromtxt("data/data_a_2.txt",unpack=True)
nu_3, U_3, dU_3 = np.genfromtxt("data/data_a_3.txt",unpack=True)
nu_4, U_4, dU_4 = np.genfromtxt("data/data_a_4.txt",unpack=True)
print(len(nu_1))
print(len(nu_2))
print(len(nu_3))
print(len(nu_4))
with open('build/a_data_1.tex', 'w') as f:
f.write(table([r'$\nu / \si{\kilo\hertz}$', r'$U / \si{\volt}$',
r'$\nu / \si{\kilo\hertz}$', r'$U / \si{\volt}$'],
[nu_1[0:7]/1e3, unp.uarray(U_1[0:7], dU_1[0:7]),
nu_1[7:14]/1e3, unp.uarray(U_1[7:14], dU_1[7:14])]
))
with open('build/a_data_2.tex', 'w') as f:
f.write(table([r'$\nu / \si{\kilo\hertz}$', r'$U / \si{\volt}$',
r'$\nu / \si{\kilo\hertz}$', r'$U / \si{\volt}$'],
[nu_2[0:9]/1e3, unp.uarray(U_2[0:9], dU_2[0:9]),
nu_2[9:18]/1e3, unp.uarray(U_2[9:18], dU_2[9:18])]
))
with open('build/a_data_3.tex', 'w') as f:
f.write(table([r'$\nu / \si{\kilo\hertz}$', r'$U / \si{\milli\volt}$',
r'$\nu / \si{\kilo\hertz}$', r'$U / \si{\milli\volt}$'],
[nu_3[0:10]/1e3, unp.uarray(U_3[0:10], dU_3[0:10])*1e3,
nu_3[10:20]/1e3, unp.uarray(U_3[10:20], dU_3[10:20])*1e3]
))
with open('build/a_data_4.tex', 'w') as f:
return s / n * unp.sin(alpha)
lambdas_left = unp.uarray([lam(x, n=n+1).n for n, x in enumerate(left)],
[lam(x, n=n+1).s for n, x in enumerate(left)])
lambdas_right = unp.uarray([lam(x, n=n+1).n for n, x in enumerate(right)],
[lam(x, n=n+1).s for n, x in enumerate(right)])
lambdas = np.append(np.abs(lambdas_left), np.abs(lambdas_right))
(open('build/lambda_slot.tex', 'w', 'utf-8')
.write(r'$\overline{{\lambda}}_\text{{Spalt}}'
r'= \SI{{{:.0f}+-{:.0f}}}{{\nano\meter}}$'
.format(unp.nominal_values(lambdas).mean() * 1e6,
unp.std_devs(lambdas).mean() * 1e6)))
f = open('build/lambdas_slot_mm_l.tex', 'w', 'utf-8')
f.write(table([r'$x/\si{\milli\meter}$', r'$\lambda/\si{\nano\meter}$'],
[left, lambdas_left*1e6]))
f.close()
f = open('build/lambdas_slot_mm_r.tex', 'w', 'utf-8')
f.write(table([r'$x/\si{\milli\meter}$', r'$\lambda/\si{\nano\meter}$'],
[right, lambdas_right*1e6]))
f.close()
# grid
s = ufloat(0.01, 0.0)
d = ufloat(980, 5)
xs1 = 10 * np.genfromtxt('data/grid1.txt', unpack=True)
xs2_right = np.genfromtxt('data/grid2_right.txt', unpack=True)
xs2_left = np.genfromtxt('data/grid2_left.txt', unpack=True)
U_e = unp.uarray(U_e_raw, dU_e)
U_a = unp.uarray(U_a_raw, dU_a)
U_a_m = I * R_N
print(U_a_m)
with open('build/c_U_a.tex', 'w') as f:
f.write(r'\num{{{:L}}}'.format(U_a_m))
R_e = np.array(U_e / I)
V = np.array(R_N / R_e)
with open('build/c_table.tex', 'w', 'utf-8') as f:
f.write(
table([
r'$\nu/\si{\kilo\hertz}$', r'$U_e/\si{\milli\volt}$',
r'$U_a/\si{\volt}$', r'$R_e/\si{\ohm}$', r'$V$'
], [nu / 1e3, U_e * 1e3, U_a, R_e, V]))
def func(x, a, b):
return a * x + b
popt_1, pcov_1 = curve_fit(func, np.log(nu[:5]),
np.log(unp.nominal_values(R_e[:5])))
popt_2, pcov_2 = curve_fit(func, np.log(nu[:5]),
np.log(unp.nominal_values(V[:5])))
popt_3, pcov_3 = curve_fit(func, np.log(nu[:9]),
np.log(unp.nominal_values(R_e[:9])))
popt_4, pcov_4 = curve_fit(func, np.log(nu[:9]),
np.log(unp.nominal_values(V[:9])))
tOne1E = unp.uarray(tOne1, DELTA_T)
tTwo1E = unp.uarray(tTwo1, DELTA_T)
tOne2E = unp.uarray(tOne2, DELTA_T)
tTwo2E = unp.uarray(tTwo2, DELTA_T)
period1 = (tTwo1 - tOne1) / number1
period2 = (tTwo2 - tOne2) / number2
period1E = (tTwo1E - tOne1E) / number1
period2E = (tTwo2E - tOne2E) / number2
with open('build/tex/i_exp_raw_87.tex', 'w') as f:
f.write(
table([
r'$t_\text{87} / \si{\milli\second}$',
r'$U_\text{87} / \si{\volt}$'
], [
expX1E * 1e3,
expY1E
])
)
with open('build/tex/i_exp_raw_85.tex', 'w') as f:
f.write(
table([
r'$t_\text{85} / \si{\milli\second}$',
r'$U_\text{85} / \si{\volt}$'
], [
expX2E * 1e3,
expY2E
])
)
