def table(name, data):
j=0
i=0
f = np.zeros(len(data))
for i in range(len(data)):
if(type(data[i][0]) == type(dummy) or type(data[i][0]) == type(dummyarray[1]) or type(data) == type(udummyarray)):
f[i] = True
else:
f[i] = False
print(f)
#Runden
for i in range(len(data)):
if(f[i]):
for j in range(data[0]):
sdevs(data[i][j]) = rts(sdevs(data[i][j]))
noms(data[i][j]) = round((noms(data[i][j]), -int(m.floor(m.log10(abs(x))))))
output = open(name, 'w')
output.write(r'\begin{table}[h]' + '\n' + r'\centering' + '\n' + r'\caption{CAPTION}' + '\n' +r'\sisetup{%uncertainty-seperator = {\,},'+'\n'+r'table-number-alignment = center,'+'\n'+'table-unit-alignment = center,'+'\n'+'%table-figures-integer = 1,'+'\n'+'%table-figures-decimal = 1,'+'\n'+'table-auto-round = true'+'\n'+'}'+'\n'+ r'\begin{tabular}{ ')
for i in range(len(data)):
if(f[i]):
output.write(r'S[table-format= 3.1]'+'\n'+' @{\,$\pm{}$\,} '+'\n' + r' S[table-format= 3.1] ')
else:
output.write(r' S[table-format= 3.1] '+'\n')
output.write(r'}' + '\n' + r'\toprule' + '\n')
for i in range(len(data)):
if(i < (len(data)-1)):
if(f[i]):
output.write(r'\multicolumn{2}{c}{TITLE}'+'\n'+'&')
else:
output.write(r'{$\text{Title}$}'+'\n'+'&')
else:
if(f[i]):
output.write(r'\multicolumn{2}{c}{TITLE} \\'+'\n')
else:
output.write(r'{$\text{Title}$} \\'+'\n')
output.write(r' \midrule' + '\n')
#Tabelle
for j in range(len(data[0])):
i = 0
while i <= len(data)-1:
if(f[i]):
if(i == len(data)-1):
output.write(str(data[i][j].n) + '&' + str(data[i][j].s) + r'\\' + '\n')
else:
output.write(str(data[i][j].n)+ '&' + str(data[i][j].s) + '&')
i = i+1
else:
if(i == len(data)-1):
output.write(str(data[i][j]) + r'\\' + '\n')
else:
output.write(str(data[i][j]) + '&')
i = i+1
#Tabelle Ende
output.write(r'\bottomrule' + '\n' + r'\end{tabular}' + '\n' + r'\label{tab:LABEL}' + '\n' + r'\end{table}')
output.close()
Z.append(np.sqrt(2 * np.pi) * hoehe[i] * sigma[i])
Q.append(Z[i] / (omega_4pi * A_jetzt * W[i] / 100 * 4676))
#Potenzfunktion für Fit
def potenz(x, a, b, c, e):
return a * (x - b)**e + c
#Durchführung des Exponential-Fits und Ausgabe der Parameter
#print('Daten für den Exponentialfit:')
#print(noms(Q), noms(E_det))
params2, covariance2 = curve_fit(potenz,
noms(E_det),
noms(Q),
sigma=sdevs(Q),
p0=[1, 0.1, 0, 0.5])
errors2 = np.sqrt(np.diag(covariance2))
#Zusammenfassen der Fit-Parameter
a = ufloat(params2[0], errors2[0])
b = ufloat(params2[1], errors2[1])
c = ufloat(params2[2], errors2[2])
e = ufloat(params2[3], errors2[3])
#-----------------------Teilaufgabe b) {Untersuchung des Cs-Spektrums}
data_b = np.genfromtxt('data/Cs137.txt', unpack=True)
x_plot = np.linspace(0, len(data_b), len(data_b))
#Finde Peaks in Spektrum und ordne sie der Energie zu
peaks_2 = find_peaks(data_b, height=60, distance=20)
indexes_2 = peaks_2[0]
#E=E[1:]
#E_det=E_det[1:]
#Potenzfunktion für Fit
def potenz(x, a, b, c, e):
return a * (x - b)**e + c
#Durchführung des Exponential-Fits und Ausgabe der Parameter
#print('Daten für den Exponentialfit:')
#print(noms(Q), noms(E_det))
params2, covariance2 = curve_fit(potenz,
noms(E_det),
noms(Q),
sigma=sdevs(Q),
p0=[1, 0.1, 0, 0.5])
errors2 = np.sqrt(np.diag(covariance2))
#Zusammenfassen der Fit-Parameter
a = ufloat(params2[0], errors2[0])
b = ufloat(params2[1], errors2[1])
c = ufloat(params2[2], errors2[2])
e = ufloat(params2[3], errors2[3])
#Ausgabe der Fit-Parameter
print('\nKalibrationswerte Potenzfunktion:')
print(f'Steigung a = {a}')
print(f'Verschiebung b = {b}')
print(f'Verschiebung c = {c}')
print(f'Exponent e = {e}')
#Plotten des vom Detektor aufgenommenen Spektrums + logarithmische y-Achse evtl mit Enegrie
x=np.linspace(1,8192,8192)
plt.bar(lin(x,*params), data, label='Messdaten')
plt.xlim(0, 1600)
plt.xlabel(r'Energie $E$')
plt.ylabel(r'Zählergebnis $N$')
plt.legend(loc='best')
plt.yscale('log')
plt.savefig('build/Eu_log_Energie.pdf')
plt.clf()
#Plotten der Eichung/Kalibrierung am Eu-Spektrum
x=np.linspace(250,3700,3450)
plt.plot(x, lin(x,*params),'r-',label='Fit')
plt.errorbar(noms(index_f), noms(E), yerr=sdevs(E), xerr=sdevs(index_f), fillstyle= None, fmt=' x', label='Daten')
plt.ylim(0,1500)
plt.xlim(0, 4000)
plt.xlabel(r'Kanalnummer $\mu_\mathrm{i}$')
plt.grid()
plt.ylabel(r'$E_\gamma\:/\: \mathrm{keV}$')
plt.legend()
plt.savefig('build/kalibration.pdf')
plt.clf()
#--------------------Detektoreffizenz
print('\n-------------Detektoreffizentz----------------')
#Berechnung der Aktivität am Messtag
A=ufloat(4130,60) #Aktivität Europium-Quelle am 01.10.2000
lamda = ufloat(1.6244e-9, 0.0019e-9) #Zerfallskonstante Europium in Tagen in s⁻¹
dt = 18*365.25 + 194 #Zeitintervall in Tagen
perr = np.sqrt(np.diag(covs))
param = unp.uarray(params, perr)
# print(param)
alpha = param[2] * unp.log(param[0] * T + param[1])
# print(alpha)
# Cp bestimmen
Cp = M / m * E / dT
# Cv bestimmen
Cv = Cp - 9 * (alpha[1:]) ** 2 * V0 * T[1:] * K
# Plot
plt.errorbar(noms(T[1:]), noms(Cp), xerr=sdevs(T[1:]), yerr=sdevs(Cp), fmt="bx", label=r"$C_p$")
plt.errorbar(noms(T[1:]), noms(Cv), xerr=sdevs(T[1:]), yerr=sdevs(Cv), fmt="mx", label=r"$C_V$")
plt.grid()
plt.xlabel(r"$T\:/\:\si\kelvin$")
plt.ylabel(r"$C_\text{V / p}$ $/$ $\si{\joule\per\mole\per\kelvin}$")
plt.legend(loc="best", numpoints=1)
# plt.show()
plt.savefig("pc/cvcp.pdf")
plt.savefig("pc/plot.pdf")
plt.clf()
TT = T[1:]
Cvv = Cv
Td = unp.uarray(np.zeros(8), 0)
Cvd = unp.uarray(np.zeros(8), 0)
for i in range(len(TT)):
