def plot_delay_spectrum():
x1, y1 = np.loadtxt("data/pm1_delay.txt", unpack=True)
x2, y2 = np.loadtxt("data/pm2_delay.txt", unpack=True)
time = np.hstack((-x1, x2))
count = np.hstack((y1, y2))
error_count = np.sqrt(count + 1)
plt.clf()
plt.errorbar(time, count, yerr=error_count, fmt='s', color="black")
func = lambda x, offset, mu, sigma, A: offset + GAUSS(x, mu, sigma, A)
fit = Fit(func)
fit.offset = 0
fit.mu = -45
fit.sigma = 60
fit.fit(time, count, error_count)
fit.plot(box="tr", units={"mu": "ns", "sigma": "ns"}, color="red")
print(fit)
plt.xlabel(r"$\Delta T / \textrm{ns}$")
plt.ylabel("Count")
plt.savefig("out/delay_fit." + SAVETYPE)
plt.clf()
fit.plot_residual(time,
count,
error_count,
fmt="s",
box="br",
color="black")
plt.xlabel(r"$\Delta T / \textrm{ns}$")
plt.ylabel("Count")
plt.savefig("out/delay_residual." + SAVETYPE)
def plot_energy_spectra():
mus = []
widths = []
energies = []
#leer = TkaFile("data/EnergiespektrumLeer.TKA")
with LatexTable("out/calib_peaks.tex") as peaktable, LatexTable(
"out/rates.tex") as ratetable:
peaktable.header("El.",
"Höhe",
r"Channel $x_\textrm{max}$",
r"Breite $\Delta x$",
r'$\chi^2/\textrm{ndf}$',
"Energie / keV",
lineafter=1)
ratetable.header("El.", "Events", "Messzeit", "Rate")
for filename, meta in energy_spectra.items():
peaktable.hline()
tka = TkaFile("data/" + filename)
x = np.arange(len(tka))
#y = tka.data - leer.data
y = tka.data
#errors = np.sqrt(tka.data + leer.data + 2)
errors = np.sqrt(tka.data + 1)
plt.clf()
plt.plot(x, y, ',', color="black")
for i, (mu0, sigma0, energy,
sigma_energy) in enumerate(meta["peaks"], 1):
fit = local_fit(x, y, errors, mu=mu0, sigma=sigma0)
fit.plot(fit.mu - 5 * fit.sigma,
fit.mu + 5 * fit.sigma,
1000,
zorder=10000,
color="red")
mus.append((fit.mu, fit.error_mu))
if meta["element"] in ("Na", "Cs"):
E_gamma = 0.09863 * fit.mu - 19.056
m_electron = 511 # keV
E_compton = E_gamma / (1 + m_electron / (2 * E_gamma))
c_compton = (E_compton + 19.056) / 0.09863
plt.axvline(c_compton, color="red", linestyle="--")
peaktable.row((meta["element"], len(meta["peaks"])),
formatQuantityLatex(fit.A, fit.error_A),
formatQuantityLatex(fit.mu, fit.error_mu),
formatQuantityLatex(fit.sigma, fit.error_sigma),
"%.2f" % fit.chi2ndf,
formatQuantityLatex(energy, sigma_energy))
widths.append((fit.sigma, fit.error_sigma))
energies.append((energy, sigma_energy))
#print(meta["element"], i, fit)
plt.xlabel("Kanal")
plt.ylabel("Count")
plt.xlim(0, 2**14)
plt.title(meta["title"])
plt.savefig("out/" + clean_filename(filename) + "_all." + SAVETYPE)
N = ufloat(y.sum(), errors.sum())
t = ufloat(tka.real_time, 1 / math.sqrt(12))
m = N / t # Hz/Bq
r = ufloat(91.5, 0.01) # mm
A = ufloat(*meta["activity"]) * 1000 # Bq
I_g = 1
r = ufloat(15, 5)
F_D = ufloat(7.45, 0.05) * ufloat(80.75, 0.05) # mm^2
efficiency = 4 * math.pi * r**2 * m / (F_D * A * I_g)
print("Efficiency:", meta["element"], efficiency)
ratetable.row(meta["element"], formatUFloatLatex(N),
formatUFloatLatex(t, unit="s"),
formatUFloatLatex(m, unit="1/s"))
mus, error_mus = np.array(mus).T
widths, error_widths = np.array(widths).T
energies, error_energies = np.array(energies).T
# Kalibration
plt.clf()
fit = Fit(LINEAR)
fit.slope = 0.1
fit.offset = -20
for i in range(5):
errors = fit.combine_errors(mus, error_mus, error_energies)
fit.fit(mus, energies, errors)
plt.errorbar(mus,
energies,
xerr=error_mus,
yerr=error_energies,
fmt=',',
color="black")
fit.plot(box='tl',
units={
"slope": "eV / Channel",
"offset": "eV"
},
factors={
"slope": 1000,
"offset": 1000
},
color="red")
plt.xlabel("Kanal")
plt.ylabel("Energie / keV")
plt.savefig("out/calib_fit." + SAVETYPE)
plt.clf()
errs = fit.combine_errors(mus, xerr=error_mus, yerr=error_energies)
fit.plot_residual(mus, energies, errs, box='tl', fmt=',', color="black")
plt.xlabel("Kanal")
plt.ylabel("Energie / keV")
plt.savefig("out/calibresiduum." + SAVETYPE)
s = formatQuantityLatex(fit.slope * 1000,
fit.error_slope * 1000,
unit="eV / Kanal",
math=False)
o = formatQuantityLatex(fit.offset * 1000,
fit.error_offset * 1000,
unit="eV",
math=False)
with open('out/calib.tex', 'w') as file:
file.write(
r'Die in diesem Versuch verwendeten Einstellungen f\"uhren zu einer Einteilung von \[ '
+ s + r' \] wobei der erste Kanal die Energie \[ ' + o +
r' \] besitzt.')
# Energieauflösung
error_widths = np.sqrt(
np.power(fit.error_slope * widths, 2) +
np.power(error_widths * fit.slope, 2))
widths = widths * fit.slope
error_energies = np.sqrt(
np.power(fit.error_offset, 2) + np.power(mus * fit.error_slope, 2) +
np.power(fit.slope * error_mus, 2))
energies = fit.slope * mus + fit.offset
X = energies
Y = widths
SX = error_energies
SY = error_widths
func = lambda E, a, b: np.sqrt(np.power(a * E, 2) + np.power(b, 2) * E)
fit = Fit(func)
fit.a = 0.01
fit.b = 0.01
for _ in range(10):
err = fit.combine_errors(X, SX, SY)
fit.fit(X, Y, err)
plt.clf()
plt.errorbar(X, Y, xerr=SX, yerr=SY, fmt=',', color="black")
fit.plot(box='br', units={'b': r'\sqrt{\textrm{keV}}'}, color="red")
plt.xlabel(r"$ E $ / keV")
plt.ylabel(r"$ \Delta E / keV$")
plt.title(
r'Energieauflösung: Fit zu $ \Delta E = \sqrt{\left(a E\right)^2 + b^2 E} $'
)
plt.savefig("out/energyresolution_fit." + SAVETYPE)
print("a =", formatQuantity(fit.a, fit.error_a))
print("b =", formatQuantity(fit.b, fit.error_b))
plt.clf()
err = fit.combine_errors(X, SX, SY)
fit.plot_residual(X, Y, err, box='tr', fmt=",", color="black")
plt.xlabel(r"$ E $ / keV")
plt.ylabel(r"$ \Delta E / keV$")
plt.title("Energieauflösung: Residuen")
plt.savefig("out/energyresolution_residual." + SAVETYPE)
with LatexTable("out/energyresolution.tex") as table:
table.header("Parameter", "Wert")
table.row("$a$", formatQuantityLatex(fit.a, fit.error_a))
table.row("$b$",
formatQuantityLatex(fit.b, fit.error_b, unit="\sqrt{keV}"))